diff --git a/.gitignore b/.gitignore
index 1352d0d..0942912 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,3 +1,6 @@
 course/build
 .DS_Store
 __pycache__
+node_modules/
+dist
+qpycourse
diff --git a/.vscode/settings.json b/.vscode/settings.json
deleted file mode 100644
index a7d0fc7..0000000
--- a/.vscode/settings.json
+++ /dev/null
@@ -1,3 +0,0 @@
-{
-    "esbonio.sphinx.confDir": ""
-}
\ No newline at end of file
diff --git a/CLAUDE.md b/CLAUDE.md
new file mode 100644
index 0000000..8e81e2f
--- /dev/null
+++ b/CLAUDE.md
@@ -0,0 +1,76 @@
+# CLAUDE.md
+
+This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
+
+## Project Overview
+
+A React + TypeScript single-page application for QPython+ course navigation, built with Vite.
+
+## Tech Stack
+
+- React 18 + TypeScript
+- Vite (build tool)
+- Tailwind CSS (styling)
+- React Router 6 (routing)
+- Zustand (state management - available if needed)
+- Lucide React (icons)
+
+## Development
+
+```bash
+npm install     # Install dependencies
+npm run dev     # Start dev server
+npm run build   # Build for production (outputs to dist/)
+npm run preview # Preview production build
+```
+
+## Architecture
+
+```
+src/
+├── components/      # Reusable UI components
+├── context/         # React context (LanguageContext)
+├── i18n/            # Translation files (translations.json)
+├── lib/             # Utilities (cn helper)
+├── pages/           # Route pages (CoursesPage)
+├── types/           # TypeScript types
+├── App.tsx          # Root component with routes
+├── main.tsx         # Entry point
+public/
+├── assets/          # Images
+├── data/
+│   └── courses.json # Course data (edit this to add courses)
+```
+
+## Key Files
+
+- `public/data/courses.json` - Course data file. Edit this to add/modify courses.
+- `src/i18n/translations.json` - UI translations (zh/en)
+- `src/context/LanguageContext.tsx` - Language switching logic
+- `src/pages/CoursesPage.tsx` - Main courses listing page
+
+## Adding Courses
+
+Edit `public/data/courses.json`:
+
+```json
+{
+  "courses": [
+    {
+      "id": "unique-id",
+      "title": "Course Title",
+      "description": "Course description",
+      "image": "/assets/course-image.png",
+      "url": "https://example.com",
+      "tags": ["python", "beginner"],
+      "difficulty": "beginner"
+    }
+  ]
+}
+```
+
+## Notes
+
+- Language auto-detects from browser (`navigator.language`)
+- Courses open in new tab via standard `<a>` (no app-specific navigation)
+- Images go in `public/assets/`
diff --git a/LICENSE b/LICENSE
deleted file mode 100644
index 8dada3e..0000000
--- a/LICENSE
+++ /dev/null
@@ -1,201 +0,0 @@
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diff --git a/README.md b/README.md
index 9ecf55e..64aff72 100644
--- a/README.md
+++ b/README.md
@@ -1,65 +1,2 @@
-QPython Courses
-======================
-
-![Course](http://dl.qpy.io/assets/banners/activity-qpython-edu.png "QPython EDU")
-
-
-About
--------
-QPython courses is a social Python learning project, based on QPython community, we hope more and more people could join us wo help write or translate and share QPython learning materials which could help other friends learn Python with more fun.
-
-How to contribute
-------------------
-* We extend sphinx to organize the courses,  you need to learn some [sphinx's syntax](https://www.sphinx-doc.org/)
-* Fork & clone our project on github, [project address](https://github.com/qpython-android/course)
-* Add new course or improve the current course or translate to other language
-* Send pull reuqest to us
-
-
-Course structure
------------------
-
-    Course root
-        |
-        - LICENSE
-        |
-        - README.md
-        |
-        - RST_UP_README.md (Extended rst syntax introduction )
-        |
-        - docs (Auto generated html pages from course)
-        |
-        - course (courses source and the files that were used to generate the website)
-            |
-            - CNAME  analytics.py analyticscode.txt favicon.ico Makefile  replace.py media  (You could ignore these files, they are used when generated the html files)
-            |
-            - build.sh (the shellscript generate the docs)
-            |
-            - source (courses source)
-                |
-                - _static conf.py qtheme _templates (you could ignore them, they are the website's template or other style files)
-                |
-                - *.rst (real courses)
-                |
-                - qpython-quick-start (The qpython quick start chapter)
-                |
-                - kivy-qpython (The kivy chapter)
-                |
-                - qpython-webapp (The webapp chapter)
-                |
-                - data-analytics (The data analytics chapter)
-
-
-Other
----------------------
-... To be continued
-
-
-
-Thank you for contributing
-
-
-
-License
-----------------
-<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="" style="border-width:0" src="https://i.creativecommons.org/l/by-sa/4.0/88x31.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" href="http://purl.org/dc/dcmitype/Text" property="dct:title" rel="dct:type">This tutorial </span> is contributed by <a xmlns:cc="http://creativecommons.org/ns#" href="http://plus.google.com/u/1/communities/111340957575273631204" property="cc:attributionName" rel="cc:attributionURL">QPython course community</a>，and licensed under a  <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">CC BY-SA 4.0</a>.
+# About
+Please visit https://www.qpython.com for QPython courses
diff --git a/RST_EXT_README.md b/RST_EXT_README.md
deleted file mode 100644
index a2286eb..0000000
--- a/RST_EXT_README.md
+++ /dev/null
@@ -1,32 +0,0 @@
-RST 自定义补丁说明
-
-rst文件新增三种组件模式
-1.普通接控制台运行按钮
-    <button></button>
-    以html为模板，书写内容即可。eg:<button>Run ...</button>
-
-2.跳转按钮
-    <jumpbutton data-url=""></jumpbutton>
-    eg:<jumpbutton data-url="http\://www.baidu.com">Watch Tutorial</jumpbutton>
-    注意: 链接里的 : 需要转义
-
-3.代码缩进采用正则的开头结尾符号^$，内部直接填写数字即可(js端脚本都有处理处理)
-    eg: class TestApp(App):
-            ^4$def build(self):
-                ^8$# display a button with the text : Hello QPython 
-                ^8$return Button(text='Hello QPython')
-    注意:请使用不解析代码语法形式
-        ..  code-block :: none
-
-4.图片没有target的话不会居中显示，如需居中请使用有链接的(有target)形式，无需链接要求请按照eg,不跳转即可
-    eg:    .. image:: http://img4.duitang.com/uploads/item/201408/30/20140830185456_Eijik.jpeg
-           :target: javascript:void(0)
-           :alt: MathiasLuo is a a"ndroid geek developer
-
-5.视频显示，跳转链接。以图片的形式，将target按eg添加data-video: 视频跳转观看链接
-    eg:     .. image:: http://img4.duitang.com/uploads/item/201408/30/20140830185456_Eijik.jpeg
-            :target: data-video: "http://www.quseit.cn"
-            :alt: MathiasLuo is a a"ndroid geek developer
-
-6.运行代码按钮必须存在有标题之后，且同一个标题下不能有多个按钮！
-
diff --git a/course/CNAME b/course/CNAME
deleted file mode 100644
index 576df4f..0000000
--- a/course/CNAME
+++ /dev/null
@@ -1 +0,0 @@
-edu.qpython.org
diff --git a/course/Makefile b/course/Makefile
deleted file mode 100644
index 37eab44..0000000
--- a/course/Makefile
+++ /dev/null
@@ -1,225 +0,0 @@
-# Makefile for Sphinx documentation
-#
-
-# You can set these variables from the command line.
-SPHINXOPTS    =
-SPHINXBUILD   = sphinx-build
-PAPER         =
-BUILDDIR      = build
-
-# Internal variables.
-PAPEROPT_a4     = -D latex_paper_size=a4
-PAPEROPT_letter = -D latex_paper_size=letter
-ALLSPHINXOPTS   = -d $(BUILDDIR)/doctrees $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) source
-# the i18n builder cannot share the environment and doctrees with the others
-I18NSPHINXOPTS  = $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) source
-
-.PHONY: help
-help:
-	@echo "Please use \`make <target>' where <target> is one of"
-	@echo "  html       to make standalone HTML files"
-	@echo "  dirhtml    to make HTML files named index.html in directories"
-	@echo "  singlehtml to make a single large HTML file"
-	@echo "  pickle     to make pickle files"
-	@echo "  json       to make JSON files"
-	@echo "  htmlhelp   to make HTML files and a HTML help project"
-	@echo "  qthelp     to make HTML files and a qthelp project"
-	@echo "  applehelp  to make an Apple Help Book"
-	@echo "  devhelp    to make HTML files and a Devhelp project"
-	@echo "  epub       to make an epub"
-	@echo "  epub3      to make an epub3"
-	@echo "  latex      to make LaTeX files, you can set PAPER=a4 or PAPER=letter"
-	@echo "  latexpdf   to make LaTeX files and run them through pdflatex"
-	@echo "  latexpdfja to make LaTeX files and run them through platex/dvipdfmx"
-	@echo "  text       to make text files"
-	@echo "  man        to make manual pages"
-	@echo "  texinfo    to make Texinfo files"
-	@echo "  info       to make Texinfo files and run them through makeinfo"
-	@echo "  gettext    to make PO message catalogs"
-	@echo "  changes    to make an overview of all changed/added/deprecated items"
-	@echo "  xml        to make Docutils-native XML files"
-	@echo "  pseudoxml  to make pseudoxml-XML files for display purposes"
-	@echo "  linkcheck  to check all external links for integrity"
-	@echo "  doctest    to run all doctests embedded in the documentation (if enabled)"
-	@echo "  coverage   to run coverage check of the documentation (if enabled)"
-	@echo "  dummy      to check syntax errors of document sources"
-
-.PHONY: clean
-clean:
-	rm -rf $(BUILDDIR)/*
-
-.PHONY: html
-html:
-	$(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html
-	@echo
-	@echo "Build finished. The HTML pages are in $(BUILDDIR)/html"
-
-.PHONY: dirhtml
-dirhtml:
-	$(SPHINXBUILD) -b dirhtml $(ALLSPHINXOPTS) $(BUILDDIR)/dirhtml
-	@echo
-	@echo "Build finished. The HTML pages are in $(BUILDDIR)/dirhtml."
-
-.PHONY: singlehtml
-singlehtml:
-	$(SPHINXBUILD) -b singlehtml $(ALLSPHINXOPTS) $(BUILDDIR)/singlehtml
-	@echo
-	@echo "Build finished. The HTML page is in $(BUILDDIR)/singlehtml."
-
-.PHONY: pickle
-pickle:
-	$(SPHINXBUILD) -b pickle $(ALLSPHINXOPTS) $(BUILDDIR)/pickle
-	@echo
-	@echo "Build finished; now you can process the pickle files."
-
-.PHONY: json
-json:
-	$(SPHINXBUILD) -b json $(ALLSPHINXOPTS) $(BUILDDIR)/json
-	@echo
-	@echo "Build finished; now you can process the JSON files."
-
-.PHONY: htmlhelp
-htmlhelp:
-	$(SPHINXBUILD) -b htmlhelp $(ALLSPHINXOPTS) $(BUILDDIR)/htmlhelp
-	@echo
-	@echo "Build finished; now you can run HTML Help Workshop with the" \
-	      ".hhp project file in $(BUILDDIR)/htmlhelp."
-
-.PHONY: qthelp
-qthelp:
-	$(SPHINXBUILD) -b qthelp $(ALLSPHINXOPTS) $(BUILDDIR)/qthelp
-	@echo
-	@echo "Build finished; now you can run "qcollectiongenerator" with the" \
-	      ".qhcp project file in $(BUILDDIR)/qthelp, like this:"
-	@echo "# qcollectiongenerator $(BUILDDIR)/qthelp/QPython.qhcp"
-	@echo "To view the help file:"
-	@echo "# assistant -collectionFile $(BUILDDIR)/qthelp/QPython.qhc"
-
-.PHONY: applehelp
-applehelp:
-	$(SPHINXBUILD) -b applehelp $(ALLSPHINXOPTS) $(BUILDDIR)/applehelp
-	@echo
-	@echo "Build finished. The help book is in $(BUILDDIR)/applehelp."
-	@echo "N.B. You won't be able to view it unless you put it in" \
-	      "~/Library/Documentation/Help or install it in your application" \
-	      "bundle."
-
-.PHONY: devhelp
-devhelp:
-	$(SPHINXBUILD) -b devhelp $(ALLSPHINXOPTS) $(BUILDDIR)/devhelp
-	@echo
-	@echo "Build finished."
-	@echo "To view the help file:"
-	@echo "# mkdir -p $$HOME/.local/share/devhelp/QPython"
-	@echo "# ln -s $(BUILDDIR)/devhelp $$HOME/.local/share/devhelp/QPython"
-	@echo "# devhelp"
-
-.PHONY: epub
-epub:
-	$(SPHINXBUILD) -b epub $(ALLSPHINXOPTS) $(BUILDDIR)/epub
-	@echo
-	@echo "Build finished. The epub file is in $(BUILDDIR)/epub."
-
-.PHONY: epub3
-epub3:
-	$(SPHINXBUILD) -b epub3 $(ALLSPHINXOPTS) $(BUILDDIR)/epub3
-	@echo
-	@echo "Build finished. The epub3 file is in $(BUILDDIR)/epub3."
-
-.PHONY: latex
-latex:
-	$(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex
-	@echo
-	@echo "Build finished; the LaTeX files are in $(BUILDDIR)/latex."
-	@echo "Run \`make' in that directory to run these through (pdf)latex" \
-	      "(use \`make latexpdf' here to do that automatically)."
-
-.PHONY: latexpdf
-latexpdf:
-	$(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex
-	@echo "Running LaTeX files through pdflatex..."
-	$(MAKE) -C $(BUILDDIR)/latex all-pdf
-	@echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex."
-
-.PHONY: latexpdfja
-latexpdfja:
-	$(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex
-	@echo "Running LaTeX files through platex and dvipdfmx..."
-	$(MAKE) -C $(BUILDDIR)/latex all-pdf-ja
-	@echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex."
-
-.PHONY: text
-text:
-	$(SPHINXBUILD) -b text $(ALLSPHINXOPTS) $(BUILDDIR)/text
-	@echo
-	@echo "Build finished. The text files are in $(BUILDDIR)/text."
-
-.PHONY: man
-man:
-	$(SPHINXBUILD) -b man $(ALLSPHINXOPTS) $(BUILDDIR)/man
-	@echo
-	@echo "Build finished. The manual pages are in $(BUILDDIR)/man."
-
-.PHONY: texinfo
-texinfo:
-	$(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo
-	@echo
-	@echo "Build finished. The Texinfo files are in $(BUILDDIR)/texinfo."
-	@echo "Run \`make' in that directory to run these through makeinfo" \
-	      "(use \`make info' here to do that automatically)."
-
-.PHONY: info
-info:
-	$(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo
-	@echo "Running Texinfo files through makeinfo..."
-	make -C $(BUILDDIR)/texinfo info
-	@echo "makeinfo finished; the Info files are in $(BUILDDIR)/texinfo."
-
-.PHONY: gettext
-gettext:
-	$(SPHINXBUILD) -b gettext $(I18NSPHINXOPTS) $(BUILDDIR)/locale
-	@echo
-	@echo "Build finished. The message catalogs are in $(BUILDDIR)/locale."
-
-.PHONY: changes
-changes:
-	$(SPHINXBUILD) -b changes $(ALLSPHINXOPTS) $(BUILDDIR)/changes
-	@echo
-	@echo "The overview file is in $(BUILDDIR)/changes."
-
-.PHONY: linkcheck
-linkcheck:
-	$(SPHINXBUILD) -b linkcheck $(ALLSPHINXOPTS) $(BUILDDIR)/linkcheck
-	@echo
-	@echo "Link check complete; look for any errors in the above output " \
-	      "or in $(BUILDDIR)/linkcheck/output.txt."
-
-.PHONY: doctest
-doctest:
-	$(SPHINXBUILD) -b doctest $(ALLSPHINXOPTS) $(BUILDDIR)/doctest
-	@echo "Testing of doctests in the sources finished, look at the " \
-	      "results in $(BUILDDIR)/doctest/output.txt."
-
-.PHONY: coverage
-coverage:
-	$(SPHINXBUILD) -b coverage $(ALLSPHINXOPTS) $(BUILDDIR)/coverage
-	@echo "Testing of coverage in the sources finished, look at the " \
-	      "results in $(BUILDDIR)/coverage/python.txt."
-
-.PHONY: xml
-xml:
-	$(SPHINXBUILD) -b xml $(ALLSPHINXOPTS) $(BUILDDIR)/xml
-	@echo
-	@echo "Build finished. The XML files are in $(BUILDDIR)/xml."
-
-.PHONY: pseudoxml
-pseudoxml:
-	$(SPHINXBUILD) -b pseudoxml $(ALLSPHINXOPTS) $(BUILDDIR)/pseudoxml
-	@echo
-	@echo "Build finished. The pseudo-XML files are in $(BUILDDIR)/pseudoxml."
-
-.PHONY: dummy
-dummy:
-	$(SPHINXBUILD) -b dummy $(ALLSPHINXOPTS) $(BUILDDIR)/dummy
-	@echo
-	@echo "Build finished. Dummy builder generates no files."
diff --git a/course/analytics.py b/course/analytics.py
deleted file mode 100644
index 7f3aef6..0000000
--- a/course/analytics.py
+++ /dev/null
@@ -1,13 +0,0 @@
-#!/usr/bin/env python2
-#author: River
-import sys,os
-with open(sys.argv[1], 'r') as f, open('/tmp/qpydoc.tmp', 'w') as g, open(os.path.dirname(os.path.abspath(__file__))+'/analyticscode.txt','r') as e:
-    pth = sys.argv[1][1:]
-    print(pth)
-    extra = "".join(e.readlines()).replace("{{PTH}}",pth)
-    g.write('\n'.join(
-        filter(lambda s: len(s),
-               map(lambda s:
-                       ('',extra+"<hr/>")[s=='<div role="contentinfo">']+s,
-                   map(str.strip, f.readlines())))))
-os.rename('/tmp/qpydoc.tmp', sys.argv[1])
diff --git a/course/analyticscode.txt b/course/analyticscode.txt
deleted file mode 100644
index 90a31b0..0000000
--- a/course/analyticscode.txt
+++ /dev/null
@@ -1,20 +0,0 @@
-<script>(function(d, s, id) {
-  var js, fjs = d.getElementsByTagName(s)[0];
-  if (d.getElementById(id)) return;
-  js = d.createElement(s); js.id = id;
-  js.src = "//connect.facebook.net/en_US/sdk.js#xfbml=1&version=v2.8&appId=1680815145503311";
-  fjs.parentNode.insertBefore(js, fjs);
-}(document, 'script', 'facebook-jssdk'));</script>
-
-<div class="fb-comments" data-href="http://qpython.org{{PTH}}" data-numposts="10"></div>
-
-<script>
-  (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
-  (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
-  m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
-  })(window,document,'script','https://www.google-analytics.com/analytics.js','ga');
-
-  ga('create', 'UA-36070037-1', 'auto');
-  ga('send', 'pageview');
-
-</script>
diff --git a/course/append.html b/course/append.html
deleted file mode 100644
index 58ac3fc..0000000
--- a/course/append.html
+++ /dev/null
@@ -1,6 +0,0 @@
-<a href="https://edu.qpython.org/"> 📌 go home|回首页<a>
-        <hr />
-
-        <script src="https://utteranc.es/client.js" repo="qpython-android/comments" issue-term="url" label="💬"
-            theme="preferred-color-scheme" crossorigin="anonymous" async>
-            </script>
\ No newline at end of file
diff --git a/course/append_ann.py b/course/append_ann.py
deleted file mode 100644
index 11ee81f..0000000
--- a/course/append_ann.py
+++ /dev/null
@@ -1,152 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-'''主要行为:
-    检验已经追加 _ann
-    如果没有 就追加
-    已经有, 就替换
-
-
-::...
-替换区
-...::
-</body>
-
-'''
-
-import os
-import sys
-import time
-import shutil
-from pprint import pprint as pp
-
-
-VERSION = "append_ann.py v220628.2142"
-class Borg():
-    '''base http://blog.youxu.info/2010/04/29/borg
-        - 单例式配置收集类
-    '''
-    __collective_mind = {}
-    def __init__(self):
-        self.__dict__ = self.__collective_mind
-
-    #PAGE = 'data'
-    AIM = 'index.html'
-    INC = 'append.html'
-
-    TAG0 = '::...'
-    TAG1 = '...::'
-    TAGb = '</body>'
-
-
-
-# init all cfg. var
-CF = Borg()
-
-
-def append(troot,sroot,bname):
-    #print(troot,sroot,bname)
-    #print(CF.AIM)
-    _inc = '%s/%s'%(troot, CF.INC)
-    #_pages = '%s/%s'%(sroot, CF.PAGE)
-    #print(_inc, _pages)
-    
-    _inc_htm = open(_inc,'r').read()
-    #print(_inc_htm)
-    _aim_pages = _page_walker(sroot)
-    pp(_aim_pages)
-    print('\n append append.html into all index.html in', bname)
-    _replace_inc(_aim_pages,sroot,_inc_htm)
-    return None
-
-
-def _replace_inc(subs,sroot,_inc):
-    #print(len(subs),subs[0])
-    #print('subs[0]', subs[0], _aim)
-    for p in subs:
-        _aim = '{}/{}/{}'.format(sroot
-                , p
-                , CF.AIM)
-        #print(_aim)
-        if os.path.exists(_aim):
-            _htm = open(_aim,'r').readlines()
-            print(_aim)
-            _exp = ''
-            isTAG0 = 0
-            isTAG1 = 0
-            noTAG = 0
-            for l in _htm:
-                if CF.TAG0 == l[:5]:
-                    #print('got>>>',CF.TAG0)
-                    isTAG0 = 1
-                    _exp += l
-                elif CF.TAG1 == l[:5]:
-                    #print('got<<<',CF.TAG1)
-                    isTAG1 = 1
-                    _exp += _inc
-                    _exp += l
-                elif CF.TAGb in l:
-                    #print('TAGb ~> ',l)
-                    if isTAG0 or isTAG1:
-                        pass
-                    else:
-                        noTAG = 1
-                        _exp += '\n%s\n'%CF.TAG0
-                        _exp += _inc
-                        _exp += '\n%s\n'%CF.TAG1
-                        _exp += l
-                else:
-                    if isTAG0:
-                        pass
-                    else:
-                        _exp += l
-
-            #print(_exp)
-            open(_aim,'w').write(_exp)
-
-
-        else:
-            continue
-    
-    return None
-
-
-
-def _page_walker(sroot):
-    #_pages = '%s/%s'%(sroot, CF.PAGE)
-    print(sroot)
-    _subs = os.listdir(sroot)
-    #print(len(_subs),_subs[0])
-    
-    return _subs
-
-if __name__ == "__main__":
-    #cli(obj={})
-
-    if 2 != len(sys.argv):
-        print("""替换所有 ScrapBook 页面缀文
-    usage::
-    $ python /path/2/append_ann.py /path/2/docs/
-            |                       +- 最终页面目录入口
-            +- 指出脚本自身
-    by  %s 
-        """ % VERSION)
-    else:
-        TPATH = os.path.dirname(os.path.abspath(sys.argv[0]))
-        MYBOOK = os.path.abspath(sys.argv[1])
-        REPO_NAME = os.path.basename(MYBOOK)
-        #print(TPATH,MYBOOK,REPO_NAME)
-        append(TPATH,MYBOOK,REPO_NAME)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/course/build.sh b/course/build.sh
deleted file mode 100755
index 766aa9c..0000000
--- a/course/build.sh
+++ /dev/null
@@ -1,11 +0,0 @@
-#!/bin/bash
-rm -fr ../docs
-make html
-
-#mv static static
-#mv sources sources
-
-cd build/html && find . -name "*.html" -exec python2 ../../analytics.py {} \; && find . -name "*.html" -exec sed -i -e 's/_static/static/g;s/_images/images/g' {} \; && python ../../replace.py && find . -name "*-e" -exec rm {} \;&& mv _static static && [ -d _images ] &&  mv _images  images
-
-cd ../.. && mv build/html  ../docs && cp -r comments ../docs && cp -r index ../docs && cp CNAME ../docs  && cp favicon.ico  ../docs && cp index.html ../docs && ls ../docs && mv ../docs/_sources ../docs/sources && cp courses.html courses-cn.html ../docs && cp media/qpyio.jpg ../docs/static/
-
diff --git a/course/comments/AIPY.html b/course/comments/AIPY.html
deleted file mode 100755
index 39354ee..0000000
--- a/course/comments/AIPY.html
+++ /dev/null
@@ -1,46 +0,0 @@
-<!DOCTYPE html>
-<html>
-
-<head>
-<title>QPython - Course</title>
-<meta charset="utf-8">
-<meta name="viewport" content="width=device-width, initial-scale=1,maximum-scale=1, user-scalable=no">
-<link href="http://cdn.bootcss.com/bootstrap/3.3.7/css/bootstrap.min.css" rel="stylesheet">
-</head>
-
-<body>
-
-<div style='text-align:center'>
-
-<div class="fb-box-box" >
-<div class="fb-comments" data-href="http://edu.qpython.org/comments/AIPY.html" data-numposts="5" data-width='400'></div>
-</div>
-<div id="fb-root"></div>
-
-</div>
-
-<div style='display:none'>
-<script type="text/javascript">
-(function(d, s, id) {
-var js, fjs = d.getElementsByTagName(s)[0];
-if (d.getElementById(id)) return;
-js = d.createElement(s); js.id = id;
-js.src = "//connect.facebook.net/en_US/sdk.js#xfbml=1&version=v2.9&appId=240696906043445";
-fjs.parentNode.insertBefore(js, fjs);
-}(document, 'script', 'facebook-jssdk'));
-
-</script>
-<script>
-(function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
-(i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
-m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
-})(window,document,'script','https://www.google-analytics.com/analytics.js','ga');
-
-ga('create', 'UA-103260045-3', 'auto');
-ga('send', 'pageview');
-
-</script>
-</div>
-</body>
-</html>
-
diff --git a/course/comments/_static.html b/course/comments/_static.html
deleted file mode 100755
index ea9c43f..0000000
--- a/course/comments/_static.html
+++ /dev/null
@@ -1,46 +0,0 @@
-<!DOCTYPE html>
-<html>
-
-<head>
-<title>QPython - Course</title>
-<meta charset="utf-8">
-<meta name="viewport" content="width=device-width, initial-scale=1,maximum-scale=1, user-scalable=no">
-<link href="http://cdn.bootcss.com/bootstrap/3.3.7/css/bootstrap.min.css" rel="stylesheet">
-</head>
-
-<body>
-
-<div style='text-align:center'>
-
-<div class="fb-box-box" >
-<div class="fb-comments" data-href="http://edu.qpython.org/comments/_static.html" data-numposts="5" data-width='400'></div>
-</div>
-<div id="fb-root"></div>
-
-</div>
-
-<div style='display:none'>
-<script type="text/javascript">
-(function(d, s, id) {
-var js, fjs = d.getElementsByTagName(s)[0];
-if (d.getElementById(id)) return;
-js = d.createElement(s); js.id = id;
-js.src = "//connect.facebook.net/en_US/sdk.js#xfbml=1&version=v2.9&appId=240696906043445";
-fjs.parentNode.insertBefore(js, fjs);
-}(document, 'script', 'facebook-jssdk'));
-
-</script>
-<script>
-(function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
-(i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
-m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
-})(window,document,'script','https://www.google-analytics.com/analytics.js','ga');
-
-ga('create', 'UA-103260045-3', 'auto');
-ga('send', 'pageview');
-
-</script>
-</div>
-</body>
-</html>
-
diff --git a/course/comments/aipy-keras.html b/course/comments/aipy-keras.html
deleted file mode 100755
index f344853..0000000
--- a/course/comments/aipy-keras.html
+++ /dev/null
@@ -1,46 +0,0 @@
-<!DOCTYPE html>
-<html>
-
-<head>
-<title>QPython - Course</title>
-<meta charset="utf-8">
-<meta name="viewport" content="width=device-width, initial-scale=1,maximum-scale=1, user-scalable=no">
-<link href="http://cdn.bootcss.com/bootstrap/3.3.7/css/bootstrap.min.css" rel="stylesheet">
-</head>
-
-<body>
-
-<div style='text-align:center'>
-
-<div class="fb-box-box" >
-<div class="fb-comments" data-href="http://edu.qpython.org/comments/aipy-keras.html" data-numposts="5" data-width='400'></div>
-</div>
-<div id="fb-root"></div>
-
-</div>
-
-<div style='display:none'>
-<script type="text/javascript">
-(function(d, s, id) {
-var js, fjs = d.getElementsByTagName(s)[0];
-if (d.getElementById(id)) return;
-js = d.createElement(s); js.id = id;
-js.src = "//connect.facebook.net/en_US/sdk.js#xfbml=1&version=v2.9&appId=240696906043445";
-fjs.parentNode.insertBefore(js, fjs);
-}(document, 'script', 'facebook-jssdk'));
-
-</script>
-<script>
-(function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
-(i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
-m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
-})(window,document,'script','https://www.google-analytics.com/analytics.js','ga');
-
-ga('create', 'UA-103260045-3', 'auto');
-ga('send', 'pageview');
-
-</script>
-</div>
-</body>
-</html>
-
diff --git a/course/comments/aipy-numpy.html b/course/comments/aipy-numpy.html
deleted file mode 100755
index 98a66e9..0000000
--- a/course/comments/aipy-numpy.html
+++ /dev/null
@@ -1,46 +0,0 @@
-<!DOCTYPE html>
-<html>
-
-<head>
-<title>QPython - Course</title>
-<meta charset="utf-8">
-<meta name="viewport" content="width=device-width, initial-scale=1,maximum-scale=1, user-scalable=no">
-<link href="http://cdn.bootcss.com/bootstrap/3.3.7/css/bootstrap.min.css" rel="stylesheet">
-</head>
-
-<body>
-
-<div style='text-align:center'>
-
-<div class="fb-box-box" >
-<div class="fb-comments" data-href="http://edu.qpython.org/comments/aipy-numpy.html" data-numposts="5" data-width='400'></div>
-</div>
-<div id="fb-root"></div>
-
-</div>
-
-<div style='display:none'>
-<script type="text/javascript">
-(function(d, s, id) {
-var js, fjs = d.getElementsByTagName(s)[0];
-if (d.getElementById(id)) return;
-js = d.createElement(s); js.id = id;
-js.src = "//connect.facebook.net/en_US/sdk.js#xfbml=1&version=v2.9&appId=240696906043445";
-fjs.parentNode.insertBefore(js, fjs);
-}(document, 'script', 'facebook-jssdk'));
-
-</script>
-<script>
-(function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
-(i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
-m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
-})(window,document,'script','https://www.google-analytics.com/analytics.js','ga');
-
-ga('create', 'UA-103260045-3', 'auto');
-ga('send', 'pageview');
-
-</script>
-</div>
-</body>
-</html>
-
diff --git a/course/comments/aipy-pandas.html b/course/comments/aipy-pandas.html
deleted file mode 100755
index 6ebade0..0000000
--- a/course/comments/aipy-pandas.html
+++ /dev/null
@@ -1,46 +0,0 @@
-<!DOCTYPE html>
-<html>
-
-<head>
-<title>QPython - Course</title>
-<meta charset="utf-8">
-<meta name="viewport" content="width=device-width, initial-scale=1,maximum-scale=1, user-scalable=no">
-<link href="http://cdn.bootcss.com/bootstrap/3.3.7/css/bootstrap.min.css" rel="stylesheet">
-</head>
-
-<body>
-
-<div style='text-align:center'>
-
-<div class="fb-box-box" >
-<div class="fb-comments" data-href="http://edu.qpython.org/comments/aipy-pandas.html" data-numposts="5" data-width='400'></div>
-</div>
-<div id="fb-root"></div>
-
-</div>
-
-<div style='display:none'>
-<script type="text/javascript">
-(function(d, s, id) {
-var js, fjs = d.getElementsByTagName(s)[0];
-if (d.getElementById(id)) return;
-js = d.createElement(s); js.id = id;
-js.src = "//connect.facebook.net/en_US/sdk.js#xfbml=1&version=v2.9&appId=240696906043445";
-fjs.parentNode.insertBefore(js, fjs);
-}(document, 'script', 'facebook-jssdk'));
-
-</script>
-<script>
-(function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
-(i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
-m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
-})(window,document,'script','https://www.google-analytics.com/analytics.js','ga');
-
-ga('create', 'UA-103260045-3', 'auto');
-ga('send', 'pageview');
-
-</script>
-</div>
-</body>
-</html>
-
diff --git a/course/comments/aipy-scikit-learn.html b/course/comments/aipy-scikit-learn.html
deleted file mode 100755
index f008704..0000000
--- a/course/comments/aipy-scikit-learn.html
+++ /dev/null
@@ -1,46 +0,0 @@
-<!DOCTYPE html>
-<html>
-
-<head>
-<title>QPython - Course</title>
-<meta charset="utf-8">
-<meta name="viewport" content="width=device-width, initial-scale=1,maximum-scale=1, user-scalable=no">
-<link href="http://cdn.bootcss.com/bootstrap/3.3.7/css/bootstrap.min.css" rel="stylesheet">
-</head>
-
-<body>
-
-<div style='text-align:center'>
-
-<div class="fb-box-box" >
-<div class="fb-comments" data-href="http://edu.qpython.org/comments/aipy-scikit-learn.html" data-numposts="5" data-width='400'></div>
-</div>
-<div id="fb-root"></div>
-
-</div>
-
-<div style='display:none'>
-<script type="text/javascript">
-(function(d, s, id) {
-var js, fjs = d.getElementsByTagName(s)[0];
-if (d.getElementById(id)) return;
-js = d.createElement(s); js.id = id;
-js.src = "//connect.facebook.net/en_US/sdk.js#xfbml=1&version=v2.9&appId=240696906043445";
-fjs.parentNode.insertBefore(js, fjs);
-}(document, 'script', 'facebook-jssdk'));
-
-</script>
-<script>
-(function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
-(i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
-m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
-})(window,document,'script','https://www.google-analytics.com/analytics.js','ga');
-
-ga('create', 'UA-103260045-3', 'auto');
-ga('send', 'pageview');
-
-</script>
-</div>
-</body>
-</html>
-
diff --git a/course/comments/aipy-scipy.html b/course/comments/aipy-scipy.html
deleted file mode 100755
index 51340eb..0000000
--- a/course/comments/aipy-scipy.html
+++ /dev/null
@@ -1,46 +0,0 @@
-<!DOCTYPE html>
-<html>
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diff --git a/course/comments/wego.html b/course/comments/wego.html
deleted file mode 100755
index fb6aaf0..0000000
--- a/course/comments/wego.html
+++ /dev/null
@@ -1,46 +0,0 @@
-<!DOCTYPE html>
-<html>
-
-<head>
-<title>QPython - Course</title>
-<meta charset="utf-8">
-<meta name="viewport" content="width=device-width, initial-scale=1,maximum-scale=1, user-scalable=no">
-<link href="http://cdn.bootcss.com/bootstrap/3.3.7/css/bootstrap.min.css" rel="stylesheet">
-</head>
-
-<body>
-
-<div style='text-align:center'>
-
-<div class="fb-box-box" >
-<div class="fb-comments" data-href="http://edu.qpython.org/comments/wego.html" data-numposts="5" data-width='400'></div>
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-<div id="fb-root"></div>
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-ga('create', 'UA-103260045-3', 'auto');
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diff --git a/course/courses-cn.html b/course/courses-cn.html
deleted file mode 100644
index a0f0408..0000000
--- a/course/courses-cn.html
+++ /dev/null
@@ -1,20 +0,0 @@
-<!DOCTYPE html>
-<html>
-<head>
-  <meta charset='utf-8'>
-  <meta name='viewport' content='width=device-width'>
-  <title>QPython Courses</title>
-  <style> body { font-family: 'Helvetica Neue', Helvetica, Arial, sans-serif; padding:1em; } </style>
-</head>
-<body>
-<h2>QPython Courses</h2>
-<p>请下载视频号图片，并用微信打开后关注视频号，即可获得最新的QPY课程。
-</p>
-  <div style='text-align:center'>
-<img src='static/qpyio.jpg' onclick="milib.download('https://edu.qpython.org/static/qpyio.jpg','image/jpg')" style='width:100%'/>
-</div>
-  <div style='text-align:center'>
-<a href='#' onclick="milib.download('https://edu.qpython.org/static/qpyio.jpg','image/jpg')">下载</a>
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-</body>
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diff --git a/course/courses.html b/course/courses.html
deleted file mode 100644
index a0f0408..0000000
--- a/course/courses.html
+++ /dev/null
@@ -1,20 +0,0 @@
-<!DOCTYPE html>
-<html>
-<head>
-  <meta charset='utf-8'>
-  <meta name='viewport' content='width=device-width'>
-  <title>QPython Courses</title>
-  <style> body { font-family: 'Helvetica Neue', Helvetica, Arial, sans-serif; padding:1em; } </style>
-</head>
-<body>
-<h2>QPython Courses</h2>
-<p>请下载视频号图片，并用微信打开后关注视频号，即可获得最新的QPY课程。
-</p>
-  <div style='text-align:center'>
-<img src='static/qpyio.jpg' onclick="milib.download('https://edu.qpython.org/static/qpyio.jpg','image/jpg')" style='width:100%'/>
-</div>
-  <div style='text-align:center'>
-<a href='#' onclick="milib.download('https://edu.qpython.org/static/qpyio.jpg','image/jpg')">下载</a>
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diff --git a/course/favicon.ico b/course/favicon.ico
deleted file mode 100644
index 5650a96..0000000
Binary files a/course/favicon.ico and /dev/null differ
diff --git a/course/gen_comments.sh b/course/gen_comments.sh
deleted file mode 100644
index eab8dc6..0000000
--- a/course/gen_comments.sh
+++ /dev/null
@@ -1,15 +0,0 @@
-#!/bin/bash
-commentFolder=./comments
-tamplate=./comments/data-analytics-index.html 
-for i in ./source/* ; do
-    if [ -d "$i" ]; then
-        basename=$(basename "$i").html
-        file=$commentFolder/$basename
-        echo $file
-        touch $file 
-        chmod +x $file        
-        while read line; do
-            echo ${line/data-analytics-index.html/$basename} >> $file
-        done < $tamplate
-    fi
-done
diff --git a/course/index.html b/course/index.html
deleted file mode 100755
index 7fcba70..0000000
--- a/course/index.html
+++ /dev/null
@@ -1,237 +0,0 @@
-<!DOCTYPE html>
-<html>
-
-<head>
-    <title>QPython - Course</title>
-    <meta charset="utf-8">
-    <meta name="viewport" content="width=device-width, initial-scale=1,maximum-scale=1, user-scalable=no">
-    <link href="//cdn.bootcss.com/bootstrap/3.3.7/css/bootstrap.min.css" rel="stylesheet">
-    <script src="//cdn.bootcss.com/jquery/3.2.1/jquery.min.js"></script>
-    <link rel="stylesheet" type="text/css" href="//www.qpython.org/static/qpython_theme.css">
-    <link rel="stylesheet" type="text/css" href="static/course_web.css">
-    <script src="//cdn.bootcss.com/jquery/3.2.1/jquery.min.js"></script>
-    <script src="//cdn.bootcss.com/bootstrap/3.3.7/js/bootstrap.min.js"></script>
-    <script src="//cdn.bootcss.com/vue/2.5.9/vue.min.js"></script>
-</head>
-
-<body>
-    <div id="app">
-        <section v-if="!new_page">
-            <header>
-                <div class="col-sm-offset-2 col-sm-8 index-header">
-
-                    <div class="navbar-header">
-                        <button type="button" class="navbar-toggle" data-toggle="collapse"
-                            data-target="#example-navbar-collapse">
-                            <span class="sr-only">切换导航</span>
-                            <span class="icon-bar"></span>
-                            <span class="icon-bar"></span>
-                            <span class="icon-bar"></span>
-                        </button>
-                        <a href="/index.html" class="logo"><img src="http://www.qpython.org/static/img_logo.png"></a>
-                    </div>
-
-                    <ul class="header-title collapse navbar-collapse" id="example-navbar-collapse">
-                        <li>
-                            <a href="http://www.qpython.org/index.html">Home</a>
-                        </li>
-                        <li>
-                            <a href="http://www.qpython.org/document.html">Guide</a>
-                        </li>
-                        <li class="header-phone-selected">
-                            <a href="http://edu.qpython.org/" class="header-selected">Course</a>
-                        </li>
-                        <li>
-                            <a href="http://www.aipy.org">AIPY</a>
-                        </li>
-
-                        <li>
-                            <a target="_blank" href="https://github.com/qpython-android/qpython">Github</a>
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-                    <div class="search-box hidden-xs">
-                        <!-- <form action="/search.html">
-                    <input type="search" name="q" placeholder="keyword">
-                    <input type="hidden" name="check_keywords" value="yes" />
-                                <input type="hidden" name="area" value="default" />
-                    <button><img src="http://www.qpython.org/static/ic_search.png"></button>
-                </form> -->
-                    </div>
-                </div>
-                <div class="head-more hidden-xs">
-                    <div class="head-title-tit">Learning Python Efficiently on mobile. </div>
-                    <div class="index-search-box">
-                        <form action="/search.html">
-                            <input type="text" id="index_search" name="q" placeholder="search" autocomplete="off">
-                            <input type="hidden" name="check_keywords" value="yes" />
-                            <input type="hidden" name="area" value="default" />
-                            <input type="hidden" name="form" value="web" />
-                            <button><img src="http://www.qpython.org/static/ic_search.png"></button>
-                            <div class="search-history" style="display: none;">
-                                <div class="s-headline">
-                                    <span>最近搜索</span>
-                                    <span id="clear_history">清除全部</span>
-                                </div>
-                                <ul class="history-list">
-
-                                </ul>
-                            </div>
-                        </form>
-                    </div>
-                    <div class="hot-search">
-                        <div class="hot-name">Hot： </div>
-                        <div><a href="javascript:void(0)">WebApp</a></div>
-                        <div><a href="javascript:void(0)">SL4A</a></div>
-                        <div><a href="javascript:void(0)">Numpy</a></div>
-                        <div><a href="javascript:void(0)">Scipy</a></div>
-                        <div><a href="javascript:void(0)">Scikit-learn</a></div>
-                    </div>
-                    <div class="head-more-other">
-                    </div>
-                </div>
-            </header>
-            <main class="doc-content clearfix sc-doc-con">
-                <div class="sc-con">
-                    <div class="sc-body">
-                        <div class="sc-tag">
-                            <div @click="toggle_tag('new')" class="sc-la" :class="{active: tag=='new'}">{{is_zh? '最新' :
-                                'Latest'}}</div>
-                            <div @click="toggle_tag('featured')" class="sc-re" :class="{active: tag=='featured'}">
-                                {{is_zh? '推荐' : 'Recommend'}}</div>
-                        </div>
-                        <ol class="sc-content clearfix">
-                            <li class="sc-item" v-for="v,k in content[tag]"
-                                v-if="k < c_page[tag]*page_size && k >= (c_page[tag]-1)*page_size">
-                                <div v-if="v.open == 1">
-                                    <div class="hidden-xs">
-                                        <a :href="v.link+'?form=web'" :title="v.title">
-                                            <div class="sc-img">
-                                                <img :src="v.logo" alt="picture">
-                                                <span class="sc-lv">lv {{v.level}}</span>
-                                            </div>
-                                        </a>
-                                        <div class="sc-i-content" @click="v.is_open = !v.is_open"
-                                            title="click open course list">
-                                            {{v.title}}
-                                        </div>
-                                    </div>
-
-                                    <div class="visible-xs" @click="course_desc(v)">
-                                        <a href="javascript:void(0);">
-                                            <div class="sc-img">
-                                                <img :src="v.logo" alt="picture">
-                                                <span class="sc-lv">lv {{v.level}}</span>
-                                            </div>
-
-                                            <div class="sc-i-content">
-                                                {{v.title}}
-                                            </div>
-                                        </a>
-                                    </div>
-
-                                    <div class="sc-info">
-                                        <div class="sc-info-box">
-                                            <span>{{v.rdate}}</span>
-                                            <span class="sc-click-num">{{v.downloads}}</span>
-                                        </div>
-                                    </div>
-                                    <transition-group name="list" tag="div">
-                                        <div class="sc-course-item hidden-xs" v-if="v.is_open === true"
-                                            :key="v.is_open">
-                                            <a v-for="v1 in v.courses" :href="v1.link+'?form=web'"
-                                                title="click to enter">{{ v1.title }}</a>
-                                        </div>
-                                    </transition-group>
-                                </div>
-                                <div v-else="">
-                                    <a :href="v.link+'?form=web'" :title="v.title">
-                                        <div class="sc-img">
-                                            <img :src="v.logo" alt="picture">
-                                            <span class="sc-lv">lv {{v.level}}</span>
-                                        </div>
-                                        <div class="sc-i-content">
-                                            {{v.title}}
-                                        </div>
-                                    </a>
-                                    <div class="sc-info">
-                                        <div class="sc-info-box">
-                                            <span>{{v.rdate}}</span>
-                                            <span class="sc-click-num">{{v.downloads}}</span>
-                                        </div>
-                                    </div>
-                                </div>
-                            </li>
-                        </ol>
-                    </div>
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-                        <li v-if=" c_page[tag] > 1 " @click="change_page(c_page[tag]-1)">&laquo;</li>
-                        <li class="active">{{c_page[tag]}}</li>
-                        <li v-if="is_has_next() === true" @click="change_page(c_page[tag]+1)">&raquo;</li>
-                    </ul>
-                </div>
-            </main>
-
-            <div class="foo"></div>
-
-            <footer class="clearfix fixfooter">
-                <div class="col-sm-offset-2 col-sm-6 footer-div1">
-                    <div class="footer-block-item">
-                        <span class="footer-item1">Built with Sphinx using a theme provid by QPython.</span>
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-            <div class="desc-title">
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-                <div>{{new_page_data.title}}</div>
-            </div>
-            <div style="height: 50px;"></div>
-            <a :href="new_page_data.link+'?form=web'" :title="new_page_data.title">
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-                    <span class="sc-lv">lv {{new_page_data.level}}</span>
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-            <div class="sc-i-content">
-                {{new_page_data.title}}
-            </div>
-            <div class="sc-info">
-                <div class="sc-info-box">
-                    <span>{{new_page_data.rdate}}</span>
-                    <span class="sc-click-num">{{new_page_data.downloads}}</span>
-                </div>
-            </div>
-            <div class="sc-course-item">
-                <a v-for="v1 in new_page_data.courses" :href="v1.link+'?form=web'" title="click to enter">{{ v1.title
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-            </div>
-        </section>
-    </div>
-    <hr />
-
-    <script src="https://utteranc.es/client.js" repo="qpython-android/comments" issue-term="url" label="💬"
-        theme="preferred-color-scheme" crossorigin="anonymous" async>
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-
-</body>
-
-</html>
-<script src="static/course_web.js"></script>
-<script>
-    (function (i, s, o, g, r, a, m) {
-        i['GoogleAnalyticsObject'] = r; i[r] = i[r] || function () {
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-    })(window, document, 'script', 'https://www.google-analytics.com/analytics.js', 'ga');
-
-    ga('create', 'UA-103260045-3', 'auto');
-    ga('send', 'pageview');
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\ No newline at end of file
diff --git a/course/index/default.json b/course/index/default.json
deleted file mode 100644
index 66d8cae..0000000
--- a/course/index/default.json
+++ /dev/null
@@ -1,247 +0,0 @@
-[
-   
-    {
-        "src":"http://kivy.org",
-        "rdate":"2017-06-15",
-        "link":"http://edu.qpython.org/kivy-qpython/index.html",
-        "smodule":"kivy-qpython",
-        "description":"Kivy is a multi-touch GUI development frameworks for Python. After completing this course, you will learn how to use kivy to quickly develop innovative cross-platform graphics programs.",
-        "title":"Kivy on QPython",
-        "ver":"1.0.0",
-        "downloads": "5,000+",
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-        "courses":[
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-                "rdate":"2017-06-15",
-                "link":"http://edu.qpython.org/kivy-qpython/index.html",
-                "smodule":"qpython-webapp-principle",
-                "description":"Kivy on QPython",
-                "title":"QPython Kivy Principle",
-                "ver":"1.0.0",
-                "downloads": "10,000+"
-            },
-            {
-                "link":"http://edu.qpython.org/kivy-qpython/your-first-kivyapp.html",
-                "smodule":"kivy-qpython-your-first-kivyapp",
-                "description":"How to develop your first KivyApp",
-                "title":"Your first KivyApp",
-                "ver":"1.0.0",
-                "downloads": "10,000+"
-            }
-        ]
-    }, {
-        "src":"http://qpython.org",
-        "rdate":"2017-07-15",
-        "link":"http://edu.qpython.org/qsl4a-develop/index.html",
-        "smodule":"qsl4a-develop",
-        "description":"SL4A lets developers use scripting languages to interact with Android devices. In QPython, QSL4 lets you use the Python language to call APIs on Android, get the hardware status, and take full control of the phone. Isn't it great? Come and learn.",
-        "title":"QSL4A - QPython's Android APIs",
-        "ver":"1.0.0",
-        "downloads": "3,000+",
-        "cat":"new",
-        "level": 3,
-        "auth_avatar":"",
-        "auth":"RH",
-        "auth_desc":"Senior Engineer of QPython Team",
-        "logo": "http://edu.qpython.org/static/course-sl4a.png",
-        "open": 1,
-        "crowdfunding": 1,
-        "courses":[
-            {
-                "rdate":"2017-06-15",
-                "link":"http://edu.qpython.org/qsl4a-develop/index.html",
-                "smodule":"qsl4a-develop-index",
-                "description":"QSL4A is the bridge of calling Android APIs with Python",
-                "title":"QSL4A Introduction",
-                "ver":"1.0.0",
-                "downloads": "10,000+"
-            }
-
-        ]
-    }, {
-        "src":"http://qpython.org",
-        "rdate":"2017-06-15",
-        "author":"River",
-        "link":"http://edu.qpython.org/qpython-webapp/index.html",
-        "smodule":"qpython-webapp",
-        "description":"As the most widely used web development language in today's application, Python has a web development library that includes bottle, django, tornado, and flask to meet your different development needs. This course will not only teach you how to use these development frameworks to develop web applications but also lets you master how to develop super WebApp with QPython.",
-        "title":"QPython WebApp",
-        "ver":"1.0.0",
-        "downloads": "10,000+",
-        "cat":"new",
-        "level": 2,
-        "auth_avatar":"https://avatars1.githubusercontent.com/u/3059527?s=400&v=4",
-        "auth":"River",
-        "auth_desc":"Original Author of QPython, CTO of some startup",
-        "open": 1,
-        "logo": "http://edu.qpython.org/static/course-qpython-webapp.png",
-        "courses":[
-            {
-                "rdate":"2017-06-15",
-                "link":"http://edu.qpython.org/qpython-webapp/index.html",
-                "smodule":"qpython-webapp-principle",
-                "description":"Why QPython WebApp",
-                "title":"QPython WebApp Principle",
-                "ver":"1.0.0",
-                "downloads": "10,000+"
-            },
-            {
-                "link":"http://edu.qpython.org/qpython-webapp/your-first-webapp.html",
-                "smodule":"qpython-webapp-your-first-webapp",
-                "description":"How to develop your first WebApp",
-                "title":"Your first WebApp",
-                "ver":"1.0.0",
-                "downloads": "10,000+"
-            },
-            {
-                "link":"http://edu.qpython.org/qpython-webapp/web-frameworks.html",
-                "smodule":"qpython-webapp-web-frameworks",
-                "description":"Develop WebApp with your familiar web framework",
-                "title":"Web Frameworks on QPython",
-                "ver":"1.0.0",
-                "downloads": "10,000+"
-            }
-        ]
-    },
-    
-   {
-        "src":"http://qpython.org",
-        "rdate":"2017-06-15",
-        "link":"http://edu.qpython.org/qpython-quick-start/index.html",
-        "smodule":"qpython-quick-start",
-        "description":"What is QPython? How to use it? Why use it? This is probably the most frequently asked question for new users. To this end, we introduced the \"User's Guide\" to introduce QPython core functions and modules, and QPython is also constantly upgrading. The course will also be updated according to the new features, so stay tuned.",
-        "title":"QPython Quick Start",
-        "ver":"1.0.0",
-        "downloads": "10,000+",
-        "cat":"new",
-        "level": 1,
-        "auth_avatar":"https://avatars1.githubusercontent.com/u/3059527?s=400&v=4",
-        "auth":"River",
-        "auth_desc":"Original Author of QPython, CTO of some startup",
-        "logo": "http://edu.qpython.org/static/course-qpython-quick-start.png",
-        "open": 1,
-        "courses":[
-            {
-                "rdate":"2017-06-15",
-                "link":"http://edu.qpython.org/qpython-quick-start/principle.html",
-                "smodule":"qpython-quick-start-principles",
-                "description":"Why QPython",
-                "title":"QPython principle",
-                "downloads": "10,000+"
-            },
-            {
-                "rdate":"2017-06-15",
-                "link":"http://edu.qpython.org/qpython-quick-start/terminal-howto.html",
-                "smodule":"qpython-quick-start-terminal-howto",
-                "description":"Explore to use QPython's Terminal",
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diff --git a/course/index/zh.json b/course/index/zh.json
deleted file mode 100644
index f30fa20..0000000
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-                "link":"https://edu.qpython.org/qpython-webapp/cn/index.html",
-                "smodule":"qpython-webapp-principle",
-                "description":"为什么要使用QPython WebApp",
-                "title":"QPython上的WebApp",
-                "ver":"1.0.0",
-                "downloads": "10,000+"
-            },
-            {
-                "link":"https://edu.qpython.org/qpython-webapp/cn/your-first-webapp.html",
-                "smodule":"qpython-webapp-your-first-webapp",
-                "description":"如何开发你的第一个WebApp",
-                "title":"你的第一个WebApp",
-                "ver":"1.0.0",
-                "downloads": "10,000+"
-            },
-            {
-                "link":"https://edu.qpython.org/qpython-webapp/cn/web-frameworks.html",
-                "smodule":"qpython-webapp-web-frameworks",
-                "description":"在QPython中使用你熟悉的Web开发框架来开发WebApp",
-                "title":"QPython支持的Web开发框架",
-                "ver":"1.0.0",
-                "downloads": "10,000+"
-            }
-        ]
-    },{
-        "src":"http://qpython.org",
-        "rdate":"2017-06-15",
-        "link":"https://edu.qpython.org/qpython-quick-start/cn/index.html",
-        "smodule":"qpython-quick-start",
-        "description":"QPython是什么? 怎么用? 为什么要使用它? 这大概是新朋友们最常问的问题。为此我们推出了该\"用户指南\"来介绍QPython核心功能及模块，同时QPython也在不断升级换代，该课程也会根据推出的特性进行同步更新，敬请持续关注。",
-        "title":"QPython快速开始",
-        "ver":"1.0.0",
-        "downloads": "10,000+",
-        "cat":"new",
-        "level": 1,
-        "auth_avatar":"https://avatars1.githubusercontent.com/u/3059527?s=400&v=4",
-        "auth":"River",
-        "auth_desc":"QPython 最初作者，某创业公司CTO",
-        "logo": "https://edu.qpython.org/static/course-qpython-quick-start.png",
-        "open": 1,
-        "crowdfunding": 0,
-        "funding_process": 69,
-        "courses":[
-            {
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-                "description":"为何要使用QPython",
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-                "downloads": "10,000+"
-            },
-            {
-                "rdate":"2017-06-15",
-                "link":"https://edu.qpython.org/qpython-quick-start/cn/terminal-howto.html",
-                "smodule":"qpython-quick-start-terminal-howto",
-                "description":"使用QPython的终端来探索",
-                "title":"终端使用帮助",
-                "downloads": "10,000+"
-            },
-            {
-                "rdate":"2017-06-15",
-                "link":"https://edu.qpython.org/qpython-quick-start/cn/editor-howto.html",
-                "smodule":"qpython-quick-start-editor-howto",
-                "description":"使用QPython的编辑器来进行开发",
-                "title":"编辑器使用帮助",
-                "downloads": "10,000+"
-            },
-            {
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-                "smodule":"qpython-quick-start-qpypi-howto",
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-                "title":"QPYPI使用帮助",
-                "downloads": "10,000+"
-            },
-            {
-                "rdate":"2017-06-15",
-                "link":"https://edu.qpython.org/qpython-quick-start/cn/community-howto.html",
-                "smodule":"qpython-quick-start-community-howto",
-                "description":"如何加入QPython的社区",
-                "title":"关于社区",
-                "downloads": "10,000+"
-            }
-        ]
-    }, {
-        "src":"http://qpython.org",
-        "rdate":"2017-06-15",
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-        "smodule":"qpython-quick-start",
-        "description":"QPython是什么? 怎么用? 为什么要使用它? 这大概是新朋友们最常问的问题。为此我们推出了该\"用户指南\"来介绍QPython核心功能及模块，同时QPython也在不断升级换代，该课程也会根据推出的特性进行同步更新，敬请持续关注。",
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-        "logo": "https://edu.qpython.org/static/course-qpython-quick-start.png",
-        "open": 1,
-        "crowdfunding": 0,
-        "funding_process": 69,
-        "courses":[
-            {
-                "rdate":"2017-06-15",
-                "link":"https://edu.qpython.org/qpython-quick-start/cn/principle.html",
-                "smodule":"qpython-quick-start-principles",
-                "description":"为何要使用QPython",
-                "title":"随时随地学习编程",
-                "downloads": "10,000+"
-            },
-            {
-                "rdate":"2017-06-15",
-                "link":"https://edu.qpython.org/qpython-quick-start/cn/terminal-howto.html",
-                "smodule":"qpython-quick-start-terminal-howto",
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-                "link":"https://edu.qpython.org/qpython-quick-start/cn/qpypi-howto.html",
-                "smodule":"qpython-quick-start-qpypi-howto",
-                "description":"在QPython上如何安装包和快捷工具",
-                "title":"QPYPI使用帮助",
-                "downloads": "10,000+"
-            },
-            {
-                "rdate":"2017-06-15",
-                "link":"https://edu.qpython.org/qpython-quick-start/cn/community-howto.html",
-                "smodule":"qpython-quick-start-community-howto",
-                "description":"如何加入QPython的社区",
-                "title":"关于社区",
-                "downloads": "10,000+"
-            }
-        ]
-    }
-]
diff --git a/course/media/logo32x32.png b/course/media/logo32x32.png
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index 0528050..0000000
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diff --git a/course/replace.py b/course/replace.py
deleted file mode 100644
index 3e06c03..0000000
--- a/course/replace.py
+++ /dev/null
@@ -1,48 +0,0 @@
-#!/usr/bin/python
-#-*- coding: UTF-8 -*- 
-import os
-import re
-import glob
-
-path = './'   #相对路径
-
-filelist = glob.glob(path+"*.html")+glob.glob(path+"*/*.html")+glob.glob(path+"*/*/*.html")
-
-
-def call_repl_jumpbutton(matchobj):
-    return '''
-        <button data-url="%s" class="button">%s</button>
-    ''' % (matchobj.group(1), matchobj.group(2))
-
-def call_repl_video(matchobj):
-    return '''
-        <a class="reference external image-reference video" target="_blank" href="%s"
-    ''' % matchobj.group(1)
-
-def call_repl_space(matchobj):
-    num = matchobj.group(1)
-    space_str = ''
-    for x in range(int(num)):
-        space_str += ' '
-    return space_str
-
-for x in filelist:
-    _temp= ''
-    with open(x, 'r') as f:
-        _temp = f.read()
-
-    #处理
-    _temp=  _temp.replace('_static', 'static').replace('_images', 'images').replace('<p>&lt;button&gt;', '<button class="button">').replace('&lt;/button&gt;</p>', '</button>').replace('&#8221;','"')
-    #jumpbutton
-    reg = r'<p>&lt;jumpbutton\s+data-url=["”](.+)["”](?=&gt;)&gt;(.+)&lt;/jumpbutton&gt;</p>'
-    _temp = re.sub(reg, call_repl_jumpbutton, _temp)
-
-    #video
-    reg = r'<a\s+class="reference\s+external\s+image-reference"\s+href="data-video:&quot;([^&]*)(?=&quot;")&quot;"'
-    _temp = re.sub(reg, call_repl_video, _temp)
-
-    reg = r'\^(\d{1,2})\$'
-    _temp = re.sub(reg, call_repl_space, _temp)
-
-    with open(x, 'w') as f:
-        f.write(_temp)
diff --git a/course/source/AIPY/index.rst b/course/source/AIPY/index.rst
deleted file mode 100644
index a313434..0000000
--- a/course/source/AIPY/index.rst
+++ /dev/null
@@ -1,98 +0,0 @@
-AIPY - Learn AI programming on Mobile
---------------------------------------------------------
-.. image:: http://www.aipy.org/img/img_logo.png
-    :alt: AIPY Logo
-    :target: http://www.aipy.org
-
-AIPY  is a high-level AI learning app, based on related libraries like Numpy, Scipy, theano, keras, etc.... It was developed with a focus on helping you learn and practise AI related programming well and fast.
-
-Now, AIPY is released as a QPython plugin, after being installed, you can see the AIPY category in QPYPI, where you could find Numpy, Scipy, Pandas, Matplotlib, Scikit-learn, Theano, Lasange, Keras.
-
-By installing these libraries, you could start mathematics, scientific, data  analytics, deeplearning etc. with QPython.
-
-
-How to use
---------------
-You should install AIPY application first, then please make sure you have the latest QPython(version >=2.2.3-1) installed.
-
-If you have completed the previous step, then you could launch QPython and enter the QPYPI, you will see the AIPY category, from where you can install the related libraries. They are numpy, scipy, pandas, matplotlib, scikit-learn, theano, Lasagne, keras etc.
-
-.. image:: http://edu.qpython.org/static/qpypi-aipy.png
-    :alt: AIPY libraires
-
-*You can install AIPY libraries from QPython's QPYPI*
-
-
-Keras
------------
-Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.
-
-It runs on top of theano in QPython now, we will try to add tensorflow and other deeplearning frmaework in the future.
-
-Lasange
------------
-Lasagne is a lightweight library to build and train neural networks in Theano.
-
-
-Theano
--------
-Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently.
-
-
-**Besides these high-level AI related libraries, QPython supports the following libraries also.**
-
-
-Numpy
------------
-NumPy is the fundamental package for scientific computing with Python. It contains the following things:
-
-- 1 A powerful N-dimensional array object
-- 2 Sophisticated (broadcasting) functions
-- 3 Tools for integrating C/C++ and Fortran code
-- 4 Useful linear algebra, Fourier transform, and random number capabilities
-
-
-Scipy
--------
-SciPy is a Python-based ecosystem of open-source software for mathematics, science, and engineering
-
-
-Pandas
---------
-Pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Additionally, it has the broader goal of becoming the most powerful and flexible open source data analysis / manipulation tool available in any language. It is already well on its way toward this goal.
-
-
-Matplotlib
-------------
-Matplotlib is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms.
-
-In QPython you could use the webagg as backends with tornado, please install tornado from QPYPI and make sure your script contains the below declarations.
-
-::
-
-    #qpy:webapp:QPyMatplot
-    #qpy://127.0.0.1:8988/
-
-    import matplotlib
-    matplotlib.use('webagg')
-    ...
-
-
-Scikit-learn
-------------
-Scikit-learn is a Machine Learning framework in Python.
-
-It has the following features:
-
-- 1 Simple and efficient tools for data mining and data analysis
-- 2 Accessible to everybody, and reusable in various contexts
-
-
-
-**It's AIPY's first release, we will keep developing, and bring more amazing features. Please give us any feedback to support@qpython.org.**
-
-.. image:: http://edu.qpython.org/static/aipy.png
-    :alt: Get AIPY Application
-    :target: http://www.aipy.org
-
-*Get AIPY Application http://www.aipy.org*
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diff --git a/course/source/_static/editor-toolbar-1.png b/course/source/_static/editor-toolbar-1.png
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diff --git a/course/source/_static/format_800_width.sh b/course/source/_static/format_800_width.sh
deleted file mode 100755
index a9f8295..0000000
--- a/course/source/_static/format_800_width.sh
+++ /dev/null
@@ -1,26 +0,0 @@
-#!/bin/bash
-
-{
-	F_NAME="${1}"
-
-	if test -f "${F_NAME}"
-	then
-		convert "${F_NAME}" -resize 800 "${F_NAME}"
-		echo 'resize finish'
-	elif test -d "${F_NAME}"
-	then
-		find $1 -type f -name "*.png" | while read png; do
-			convert "$png" -resize 800 "$png"
-		done
-		echo 'resize finish'
-	else                                   
-	   echo "${F_NAME} is not valid"
-	fi
-} || {
-	echo 'please install imagemagick by'
-	echo '1. sudo apt-get install imagemagick'
-	echo '2. brew install imagemagick'
-	echo 'or any package manager you have'
-}
-exit 0
-
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diff --git a/course/source/_static/terminal-menu.png b/course/source/_static/terminal-menu.png
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diff --git a/course/source/activity-edu.rst b/course/source/activity-edu.rst
deleted file mode 100644
index 5fc4e69..0000000
--- a/course/source/activity-edu.rst
+++ /dev/null
@@ -1,8 +0,0 @@
-QPython Course
-===============================
-QPython courses is a social Python learning project, based on QPython community, we hope more and more people could join us wo help write or translate and share QPython learning materials which could help other friends learn Python with more fun.
-
-
-How to contribute
------------------
-Please visit `QPython course <https://github.com/qpython-android/course>`_
diff --git a/course/source/activity-qpypi.rst b/course/source/activity-qpypi.rst
deleted file mode 100644
index e2b189b..0000000
--- a/course/source/activity-qpypi.rst
+++ /dev/null
@@ -1,4 +0,0 @@
-What package do you want
-=========================
-What pakcage do you need, pelase file a ticket in `QPYPI <https://github.com/qpython-android/QPYPI/issues>`_
-
diff --git a/course/source/aipy-keras/index.rst b/course/source/aipy-keras/index.rst
deleted file mode 100644
index 431de56..0000000
--- a/course/source/aipy-keras/index.rst
+++ /dev/null
@@ -1,19 +0,0 @@
-Keras Programming
-=======================
-The courses are still in the planning. Welcome to reward us. 
-
-1. **Primary course**
-
-If the total number of people who reward us is more than 100, we promise to complete the primary course within 1 week, so that, after finishing your study, you can master the basic concepts and write simple scripts.

-
-2. **Intermediate course**
-
-If the total number of people who reward us is more than 500, we will launch the intermediate course within 2 weeks, so that, after finishing your study, you will be familiar with the knowledge and framework of the course and apply it to practical development.
-
-3. **Advanced course**
-
-If the total number of people who reward us is more than 2,000, we will launch the advanced course in a month, so that, after finishing your study, you will have a better understanding of the principles of the framework and be able to do advanced development.
-
-
-**The primary course is free to all, and the intermediate and advanced courses are only open to those who have rewarded us.**
-
diff --git a/course/source/aipy-numpy/cn/10sample.rst b/course/source/aipy-numpy/cn/10sample.rst
deleted file mode 100644
index f62e824..0000000
--- a/course/source/aipy-numpy/cn/10sample.rst
+++ /dev/null
@@ -1,1269 +0,0 @@
-
-各种绘图实例
-=============
-
-简单绘图
-----------
-
-plot 函数：
-
-
-
-::
-
-    %matplotlib inline
-
-    import numpy as np
-    import matplotlib.pyplot as plt
-
-    t = np.arange(0.0, 2.0, 0.01)
-    s = np.sin(2*np.pi*t)
-    plt.plot(t, s)
-
-    plt.xlabel('time (s)')
-    plt.ylabel('voltage (mV)')
-    plt.title('About as simple as it gets, folks')
-    plt.grid(True)
-    plt.show()
-
-
-<button>Run ...</button>
-
-
-.. image:: 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-子图
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-
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-*subplot* 函数：
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-::
-
-    import numpy as np
-    import matplotlib.mlab as mlab
-
-    x1 = np.linspace(0.0, 5.0)
-    x2 = np.linspace(0.0, 2.0)
-
-    y1 = np.cos(2 * np.pi * x1) * np.exp(-x1)
-    y2 = np.cos(2 * np.pi * x2)
-
-    plt.subplot(2, 1, 1)
-    plt.plot(x1, y1, 'yo-')
-    plt.title('A tale of 2 subplots')
-    plt.ylabel('Damped oscillation')
-
-    plt.subplot(2, 1, 2)
-    plt.plot(x2, y2, 'r.-')
-    plt.xlabel('time (s)')
-    plt.ylabel('Undamped')
-
-    plt.show()
-
-
-<button>Run ...</button>
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-.. image:: 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hG9AjHp9FEVW8KWB4iIstjJVCsqV79Ur744hZycw+TlpZcT+Ht2tVl5MhK/OpXx01O48Y1oXv3gcf8FpMnD8eZpCrRpcv5nHzy39i//wqqVv2pP0IbhpFShJOnsQC4T1XnecuXAk+rats4yBcW4eZpgHMmjxnTnSpVziAtrXbShKXu3buIlSuvZ9++J5k583XyFUOnTgOLlH/r1lf5+uvfs3v3I7zzzlsWjmsYxjFiladxJzBGRKp7y7uA3iUVLhHIjz7q02cv4CZLyRCWmpOzj9Wru9O06b857bSudOjQN+zP1q7djY8+Wsxbbw2kX7/cY+uT4XsbhpF4FDvTODbQUxqqmnDhtuHONAYNupabbnqv0PrJk6/l2WffiYVoUWHNmr5AOc4++5WIPp+s39swjNgSq5kGkJjKoqSEij5KtLDUwAS+I0d2ct55O/jNb76KeH/J8r0Nw0h8wlYaqYBqxRBbEschHiyBb+zY+px11ocRm5KS4XsbhpEchBNymzIEC0sdN64JnToN9EmiwgRL4OvV6wemTBke8T6Dfe+xYxsm1Pc2DCM5CDnTEJEuuO56QpAue6o6KYZyxYTAsNTs7B84fHgLPXs+m1DO4FiYkgqG4x46tJF27WpwxRXXRbzPRKKoelxWwNEwoktR5qlf4pTFaUA7YLa3/grgYyDplAa4G+iVV15PdvYuFiz4CZdcklgVb2NlSsr/3gB5eUdZsuRCtm17jdq1u5Vqv34TqlDjihWL+OKLcVbA0TCiTEjzlKr2UdW+QAVcefQuqtoFONdbl9Skp5/MSSedyb59i/0W5QQ6dx7E2LENTlgXbRNauXIVOOusV1i37l6OHt0etf36weTJzwQt1Dht2pNB15fGzGcYRniO8AbAloDlrcBPYiNOfMlvAVu9eqi6TfHnyiuvZ+PGnzFhQgVOOqk+UOmEjO9oUa3aRdSu3ZN16/6P5s3HR3XfsaCgqaljx5tp2nQ1+/YFLwlTsWLwEGzVfUH3Z6YrwwiPcJTGB8C7IjIB59+4FXg/plLFiRo1Mtm48d80bPiQ36IcIydnL2ec8Qm33LKSihXrxfRYZ5zxGIsWtWDHjrepVesXMT1WaQhmgnrllQ/o2PEmqlZtC8wr9JmcnKpAYf/Qnj2fMnZsB7KyVtGr1/fH1qeS6coUohFLwikjIsCNwGXeqrmqOjnWgpWEkpQRCSQ7eycLFjTkkkt2Uq5cegwkKzkbN/6b3buzOPfcN+NyvF275vDqq11Zvfo8ypXLTcibTFHJiZ06DQzSY6QJP/1pz0I+jXHjmnDLLY8yceJgunf/Ouj+kiXZsWTNuJrQrduzFhxgFCImyX2qqiKyFNinqu+LSGURydD8eX4pEJEOwFAgDXhZVZ8MMmYY0BE4CPRR1c9Ke9x80tNrHvNrVK/ufyktVWXjxudo1uyFuB3zs88OsnDhUfr2nX1sXaI9defmbg6x5XDQQo355rzZsy8Kun769JeAwkojWZIdi+rSGCxkO78ZF1Bkd8dkVihFyR7pNiM4xSoNEbkD+A1QE2gC1AeeB64qzYFFJA14Drga2AgsEpGpqro6YMx1wJmq2lRELvaO26Y0xy1I9ertPb+G/0pj9+7ZiJSnevXL43bMt94aRt++J+r/ROn4p6r88MOz7Nv3ZYgRLqIsMDIskFDrQ0WoHTy4nuzsncyb90lC3EhC3dBCKYYxY/rgnq0Ks3fvHEaP/oi+fQ8U+tykSc8AoRUKkBDnA4KfEyha9ki2FadE46lsYqEQ87dFQjg+jbuB1sACAFX9SkROi+hoJ9IaWKeqGwBE5DWgE7A6YMwNwGjvuAu9hlC1VXVrFI4POL/Gpk3P07DhH6O1y4jZuPE56tUbgLMIxodEKjFy4kWezoUXwjnnbKN79+cZP/5vhUxN3btHFlHmkh2/LpB134jMzHMYMaIxS5ak07v3jmPb/Jh5hWrtu2fPfA4fXhH0M5Uq1UakGvBJoW3O93MAKBwtuHfvbEaPnk/fvif+z3v0+JoXXniEjIy9cQ1dLpnp7WsOHarGr39dWIlOnPhX8vJygyrYN9/8C1AuollZUduKUjaR3OCLmlVGKmPgtuERBBOGozSOqOqR/BuZiJQnSLJfBNQDvg9Y/gG4OIwx9XERXFGhRo3LWbOmF3l52b76NQ4f/o7du+dy9tlj43rcRCkxEuzHMXJkFerW/S8dOtxMhQq1g5qaIiGYSatHD7e/u+9uR+/eJ950/Zh5hWrtO2bMi5QvX4sTAxod6en16dRpIOPHF/bxdO9+X8gny+rVryEvbxewqNC27ds/5847s09YF3g+ov0UHOommZ29kylTngt6k3/iieAPWQcPrkAkeFbB4cOf47pXF2bfvg8ZM+YTrxr2icd6/fWHgfIlVjZF5Q2F+gyENje++eaj5OXlBN02YcI9qObSo8eGQtvGjbsDVaVXr1Am3+IJR2l8KCIPA5VF5Brgt8C0iI94nHAVT8ErIujnhgwZcux9ZmYmmZmZYe08Pb0mlSo18d2vsWnTC9Su3Yvy5avG9bjBnrpHj65Fr17xLTES7Mfxq18dYPLkl7n66ptDmpoiJdT+ypcPlYJ0CIi+WSLY/tq2PZejR9cGHZ+R8VM6dbovhGII3owrcH3B/7X73KCQCqVixXQgu9D6Q4c+Z+LEAcycOZlevTYdWx/pU/DYsWvYv/9Opk+fEPRGOGpUP8qXD/6Ak5eXgWsueiIZGZfiAmQKB1FUrXpJyG1VqlyI6n5gWaFtOTkbycsrfD4gf8Y2j759TzQR9ujxNY8//jceeaSw8h0zpg+qefTuvbPQtlGjOiOSE/RYhw9/ibPwF0Ykh3LlgivLbdvS2LTpILm5QTeHRThK40GgH7AS6A/MAF6O/JDH2IjLAcmnAW4mUdSY+t66QgQqjZLi8jU+9E1p5OYeZvPml2nZ8qO4H7vgTSY3N4dWrVZw8cVNiv5glEkUM1momdeePUv43//u5Z13pkVkqgnXFj9y5IesWlWBnJzqIfZUqVjFUJSPp6jPBVMoNWtWAwrHnohUYebMt05QGOBudqNH90Ikjdtv31Fo25gxfVHNoXfvXSds69XrO0aPfpry5YPfkmrUaOf9bwrf5GvWbML48XuDKtFQ36vobQ+GVKKVK7cqQhG1I5QJsGLF8gRTvpUq1fHM0TsLbatevTVQGZf1UPBYbUPKUanS2d629YW2nXFGcxo10mPRiKNHFxpSLOFET+WKyGhgIe4pf01E8a2FWQw0FZFGwCZc/kfBmhZTgQHAayLSBtgdTX9GPjVqtGfTphE0bPhgtHcdFtu3v0HVqhdQuXIzX45f8CazceMLrF7dnQsu+IRy5UKZr6LL0aOhgvHiayYLNvMaN64JN93Ul8mT/1noZheOqSaUyWX//jT69y84uzrC5Mnt6d59UMjZBIRWDMVRUoXiZA0mx7+YMuVpgj3DVa78E1RzgB2FtlWqdJr3hLyr0LZq1VqEVAxQic6dBwb939xxx+NBZQ/8npFsK7myCW0CzMmpTP5sNZD09HreDX5loW0iGZ65cX0UFWLwbSUhnOip64EXgG+8VY1FpL+qzojoiB6qmiMiA4B3cSG3r6jqahHp720foaozROQ6EVmHU+Hht6wrAc6v0Tvufo38m8yBAx9ToUJTunad7nvEEkDduv3ZufMd1q//E02aPB3z423b9ibnnvs1Y8fWo1ev4zeh0ji7I6Wop/FZs94HCmegHz26npkzRzBp0tMlsks/8USoKj5Hip0VxIKiFFEwOULdIMuXr+PdCL8otC09vb63LZgzP7RiCMf0Fkr2or5XpLOyorYFk/+663oyfnzhvKHibvClkSO8be8GPS9FEU5y35fA9aq6zltuAsxQ1bNKfLQYEWlyXyCLFp1Hs2YjqF49qhG9ISkuCctvjh7dweLF57N9+53MmjUvZqGFmzePYv36h2nRYiaffppfAj68/ufxJlSS4dixdcjJ2V0o+ghg1KgKiGTTp0/h6/NvfzuZhx4q/MSdLEmGwSO8mtC9+7NAYdNbONvyZ2aJfB0URyj5i/pefn3nSJL7wlEai1T1ooBlAT4NXOc30VAaa9feQ4UKp8fNRJUMLVinTHmciRMfLdBbvHSKLdCEc+jQFs4/fyd9+syjcuWEeQYJSVE3ySlTnubGGwvPQiZNaoNqFbp0mVVo2wsvtCwUzhp480wGIr0RJrtiSBVi1e51iYjMAN7wlrsCi0XkJkjOvhrBcPka8fNrJIrjtyhmzfroBIUBpQs/DX7TbcjZZ6/jyisTX2kUZSoIZaoRqe6ZXDZEZItPdCIx/RS3zUhswlEalYBtQHtvebu37pfecooojfj6NRIlP6Iooq3YgucefJsQ2efhEupmF8qBXhpbvGEkIuFET/WJgxy+k55+CpUqNWL//qVUq1YwxzD6dOo0kFdemUO/fsfD8Pxw/BZFtBVbUTWkkp1Iw2ANI9kIJ3qqMTAQaBQwXlX1hhjK5Qv5/TXioTRatarG2rW1mTz5XBLVNBHs6fnll8txww0/Ydast5kyZXhY2b433NCPBg1msX//VyGOlDizq9JgisEoC4TjCF+BS+b7HMjPu1dVDd79xgei4QgH2L59Eps3v0SLFjOjIFXRrFrVk4yMC2nQ4P9ifqzSUNBh2bHjzXz77d/5+OOtJxS/y3eQQ+HImFdeKc8111xB3bq/5vXXH0pqx69hpBKxip76VFVbl0qyGBMtpXH06A4WLmzs9dcIx90TGdnZP7JgQRPatPmG9PSaMTtOrBg48Bq6dCmcpTpxYlvy8vK45ZaFhbblR4VZ1IxhJA6xip4aLiJDcFkgxzyjqrq0ZOIlPh99tJAxY5TXX29NuXKnxqzc8ZYtY6lV65dJqTAAypULXnvn4MHlIQvE5fstzIRjGMlNOErjXKAXcAXHzVN4yylDfjhonz77ya+1E4sS0KrK5s0v0qzZiKjtM96EcpBnZFwWsh5OqvgtDKOsE+qxMJCuwBmq2l5Vr8h/xVqweBOq1IMzpUSPPXs+ApTq1S+N6n7jiXOQn1jQcNy4JnTqNLDIbYZhJD/hzDRWAicTxR4WiUi8ku02b36R00+/I66NlqJNaevhGIaRvISjNE4G1ojIIo77NFIu5DYeyXbZ2TvZsWMaZ545NGr79AvL9jWMskk4SmNwzKVIAIJn9DaOarLd1q1jOeWU60lPPyVq+zQMw4gn4WSEZ8VBDt8paHLZu3cFN9xwa9SemFWVTZtepFmz/0Rlf4ZhGH4QTkZ4W2AYcA5QEdf7Yr+qVouxbHEn0Kyydet4tmyJoK1VCPbu/RjVXKpXvzxq+zQMw4g34URPPQd0B9biDPz9gJR/XK5Vqwv793/GoUPfFD84DDZtepG6dZPbAW4YhhFW2rOqrhWRNFXNBUaJyDJc7/CUJS2tErVr92Lz5pdp3PhvEe9n9uzpTJ78T/btm0tGxnfceONZ5iQ2DCNpCUdpHBCRisByEXkK2AKUicfl00+/g+XLr6BRo0cjKpdeuH9EFuPHfw9YOWzDMJKTcMxTt3vjBgAHgfpAl1gKlShUqXI2J53UjB9/nBrR5+OVMGgYhhEvwome2iAip3rvh0TjoCJSE3gdaAhsAG5R1d1Bxm0A9gK5QLYfhRPr1u3Ppk0jOPXUkuvJZOjOZxiGURJCzjTEMUREdgBfAV+JyA4RGSyl9+Y+CLyvqs2AWYT2jyiQqaot/aq0W6vWTRE7xLOzQykNq8NkGEZyUpR56l7gEuAiVT1ZVU8GWnvr7i3lcW8A8uNZRwOdixjrq//kuEP8pRJ9Li/vKOedt5nRo087Yb3VYTIMI5kJ2U/Di5C6RlW3F1h/Km6WcH7EBxXZ5SkhvFnLzvzlAuO+AfbgzFMjVDXonTta/TRCceDAGpYty6Rt2+8oV65CWJ/ZsOFx9u5dwPbtdzF16nNY/wjDMBKNaPfTKF9QYQCo6nYRCScp8H2gTpBNDxfYn4pIqDv+Jaq6OV9RicgaVZ0XbOCQIUOOvc/MzCQzM7M4EcOmSpWzqVz5LH78cVpYvo0DB1axceMwWrWik1cnAAAgAElEQVRaSosWDbjqql9ETRbDMIxIycrKIisrq1T7KGqm8ZmqtizptrAOKrIG56vYIiKnA3NU9exiPjMYl4n+zyDbYjrTAJg06T6mTRtJtWo/K9QTOxDVXJYuvYQ6dfpQr96dMZXJMAyjNEQy0yjKp9FCRPYFewE/K52oTAV6e+97A28VHCAilUUkw3tfBfg5rkx73Jk9ezozZkyid++d3Hjjh9x003u8+uo9zJ49vdDYH34YRrlylahb9w4fJKXUTxGphJ2L49i5OI6di9IRUmmoapqqZoR4lbaB9hPANSLyFXClt4yI1BWR/DtxHWCe51tZCLytqsFawsWct94aRs+eJ0ZPBeZbzJ49nUGDrmXgwIt5+OH72bKlexFtT2OL/SCOY+fiOHYujmPnonSU9uYfEaq6E7g6yPpNwPXe+2+AiJ3t0SRUvsWBA4uYNOk+ZsyYdIJSGT/+KSpWrGcOb8MwUg5/HoeTjFANmtLT6/L226OKnIUYhmGkEiEd4clEEdFXhmEYRhGU1BGeEkrDMAzDiA9mnjIMwzDCxpSGYRiGETamNAzDMIywSWqlISIdRGSNiKwVkQf8lsdPRGSkiGwVEV8SIBMFEWkgInNE5AsR+VxEBvktk1+ISCURWSgiy0RklYj83W+Z/EZE0kTkMxGZ5rcsfiIiG0RkhXcuPi3RZ5PVES4iacCXuHyPjcAioJuqrvZVMJ8QkcuA/cAYVS1txn7SIiJ1gDqqukxEqgJLgM5l+LqorKoHvXpxHwF/UNWP/JbLL0Tkd0ArIENVb/BbHr8QkfVAKy9nrkQk80yjNbBOVTeoajbwGtDJZ5l8wyvkuMtvOfxGVbeo6jLv/X5gNVDXX6n8Q1UPem8rAGlAiW8SqYKI1AeuA16mjLSsLoaIzkEyK416wPcByz946wwDABFpBLTElaEpk4hIOa8Uz1ZcYdBVfsvkI88A9wF5fguSACjwgYgsFpHflOSDyaw0ktOuZsQFzzQ1EbjHm3GUSVQ1z+t9Ux+4XEQyfRbJF0TkF8A2Vf0Mm2WAazvREugI3O2Zt8MimZXGRqBBwHID3GzDKOOISDrwP2CcqhaqoFwWUdU9wHTgQr9l8Yl2wA2eLf9V4EoRGeOzTL6hqpu9v9uByThzf1gks9JYDDQVkUYiUgG4FVdy3SjDeJ0gXwFWqepQv+XxExGpJSI1vPcnAdcAn/krlT+o6kOq2kBVzwBuA2ar6u1+y+UHpW07kbRKQ1VzgAHAu8Aq4PWyGiEDICKvAh8DzUTkexHp67dMPnEJ0BO4wgsn/ExEOvgtlE+cDswOaC8wTVVn+SxTolCWzdu1KUXbiaQNuTUMwzDiT9LONAzDMIz4Y0rDMAzDCBtflUY4pS9EZJhXJmS5iLSMp3yGYRjGifg90xgFhHRSish1wJmq2hS4A3g+XoIZhmEYhfFVaYRR+uIGYLQ3diFQQ0Rqx0M2wzAMozB+zzSKI1ipkPo+yWIYhlHmKe+3AGFQMOW/UIyw9Qg3DMOIjJL2CE/0mUbBUiH1vXWF0KZN0UGD0DZt0CpV0EsuQVu2dH87dkR37UJV7VXMa/Dgwb7LkFSvvDx01iz0yivRhg3RM89EcU82evnlDO7VC/3gA/c655zj2y66CD161H/5k+xl12cJXjk5aLt26Mkno+XLo2edhd55JzphAvr992jHjhHdlBNdaUwFbgcQkTbAblXdGnTkp5/Cs8/CJ5/Ali0wZAhs2gTz58PMmXDHHfGT2kh9VGH6dGjXDu66C3r1grVroWlTt/3CC2HKFGjcGK66yr0aNXLbmjWDChXgrLNgxAg4csS3r2GkKN9+C1dcAV98Abt2QU4OtGgBzz8P3bpB/fowYUJEu/Y75Da/9MVZXumLX4lIfxHpD6CqM4BvRGQdMAL4bcid1ahx/H3VqnD11XDBBW65fHn3g83OjtE3McoU110HGRlw223uYWTVKujTB9LT3Q+xa1d4//0Tr0k4vm3hQvjoIxg71imWmjWdsunYEXbv9uUrGSnEa6/BRRfBL34Bbdu6dRdeCC++eOK4gtdnuPg+hYrCy32NIOzapdq1q+rq1ao//7nqxRerrlsXfKyhqqpz5szxW4TEZvRo1fR0VTfXcNdXEYR1Pi+44Pj+br45OnKmKHZ9FsGePaq9eqk2a6a6eLFbl38P3LUr6Ee8e2eJ7reJbp4qHTVqwBtvwNlnOxNVt27Qpg2MHu1+okYhMjMz/RYhcRk2DP70J3cNQfCntwKEdT5re1HkVatCuXI2Iy4Cuz5D8PHHcP75ULkyLF0KrVq59fn3wEhnFUFIiYKFIqJhf48VK6B9e2dTbtnSTeWieEKNFEQVHnsMxo93Zqfq1Z1Z6sUXo3Pt7N7t9jd0KPz6186c+sYbUKlS6fdtpD5t28KSJfCzn8GsWSW6JkUELWH0VNlTGgCXXeZsyuBszG+8ERvBjOQnLw/uvRfmzoV33jk+K4gVR49C794umGPKFKhWLbbHM5Kb//4X7rzzeDBFCe9nkSiN1DZPhSIjw/096SQXyWIYwcjJgb593XR/zpzYKwxwM+Bx45xJ9aqrYMeO2B/TSE7eew8efBBae033wjCXRoOyqTTyo1iWLnU/0DFltuujEYp+/aBOHZgxA15/Pb4mzLQ0+M9/4Jpr4Mwznfnhuusssso4zrJl0LMnTJwIU6eGjtiLAWXTPBXIqlUunnn8eBemaxiqblaxfbtb9tOE2bgxrF/vvxxG4vDddy4/aOhQuPnmUu3KzFOR0Lw5vPkmdO8Oy5f7LY2RCDz/PBw+7N7HacofkrPPdn9r1nSJgEbZZtcul8/z+9+XWmFEiikNgMsvh+HDXTLM998XP95IXZYscdUEsrLiOuUPyYQJcNNNzlT25pv+yWH4z5EjcOON8POfu+AMnzDzVCD/+IeLRvjoIwvDLYvs3u3i25980renuJB8+SVceqlzfra0XmRljrw86NHD5fC88YbL54kCZp4qLb//vbNnN25sJR3KGqouUur66xNPYYCrUzV8uJv97NnjtzRGvGnd2tU627cP9u71VRRTGoGIQK1azm74zjtW5LAsMXQobNwITz/ttyShue02Z5ro188qGpQlli51/tZ9+9xM0+f7kimNglSp4v6mpcHDD/srixEfPvkEnnjCTfsrVvRbmqL5179cNNVzz/ktiREPjhxxyZ7Nm7tlvwMzMJ9GYfJLOpx7rnOGzpoVNfuhkYDs2OH8GMOHww03+C1NeHzzjat/9fbbxxO7jNTk4YddefNRo6B//+iVrvGwMiLRJDfXOR579IABA6K7byMx+M1v4H//cz/CpUuTK/hh8mS4/XbXI6F6dRdllUzyG8Xz6afuQWb58phVIzClEW2+/BIuuQQWLHCZuUZq0bQprFvn3idj4lz9+s4PA8kpvxGaw4ddlNyQIXDrrTE7jEVPRZuzznKlsPv0cTMPI3XYvNl1N4OEsBNHRL6du1mz5JTfCM2f/+yq1sZQYUSKrzMNEekADAXSgJdV9ckC2zOBKcA33qr/qepfguwnNjMNcPHRmZkuqcbHhBojynTrBnXrumTOKNuJ48bu3fDLX7rZxqpVVko9Vfj4Y+jSxbVxOPXUmB4qqcxTIpIGfAlcDWwEFgHdVHV1wJhM4HeqWqSHMqZKA+Drr+Hii12/8bPOit1xjPjw/vvOqfj5565pTbLTpYvzbQwe7LckRmk5eNA1U3riCVcJIMYkm3mqNbBOVTeoajbwGtApyLgSfaGY0KQJPPqoC30zM1Vyc/gw/Pa3LmQ1FRQGuByT4cNh7Vq/JTFKy8MPu/7ecVAYkeKn0qgHBBZ6+sFbF4gC7URkuYjMEJHmcZOuIHfd5apLNmtmZaqTmSeegPPOc//DVKFBA3joIbj7bkv6S2ZuuAH+/W/YujWh7y9+Ko1wru6lQANVPQ8YDrwVW5GKoFw59+P85hvXb9yyxZOPr75yP8qhQ/2WJPoMGgTbtrneH0bykZsLs2e72lKzZiX0/aV8qA0iMi1gUTnRTKTF+RnCYCPQIGC5AW62EXiQfQHvZ4rIf0SkpqruLLizIUOGHHufmZkZmwb0p5zi/taqZdEqyYaqexJ/6CEXqppqlC/vSrrffLOrm1a9ut8SGSXhv/+F9HT3PobRfFlZWWRlZZVqHyEd4Z4TGuBGoA4wDqc4ugFbVfX/SnVgkfI4R/hVwCbgUwo7wmsD21RVRaQ18IaqNgqyr9g6wvPZvdsVtfv4Y1c87MILY39MIzq8+qqrXrt4sbvBpir9+7uWscOH+y2JES5797oAm1dfdR0b4xjNF5PoKRFZoqqtilsXCSLSkeMht6+o6t9FpD+Aqo4QkbuBu4Ac4CAukmpBkP3ER2nk8/LL7slg3jxX5NBIbHbvdjkNkya58hupzM6d7ru+/bY91CQL99/vytmMHBn3Q8dKaawGfqGqX3vLjYHpqnpOxJJGmbgrjdxcF+Fw//2u8qiR2AwYADk58MILfksSH8aMgWHDYOFCV3jTSFzWrnU94D//3DXaijOxUhodgBcBr1ExjYA7VPXdSISMBXFXGuBmGT17wurVqRO6mYp06QLTpkH79q7zXTIm8ZUUVZe4mJHhyt9YXarEpVMn1+/7gQd8OXzMkvtEpBKQn9W2RlWPRCBfzPBFaYBL8W/e3JKqEhVV11s7P3yxLNVnatXKFWGEsvW9k4n333eh/F984VtJ/pgk94lIFeA+YICqLgd+IiK/iFDG1OKpp5wZwPqKJybvvutCGCF560tFSn5V1NNPL1vfO1nIyXFlif7xj8Tv4VKAcPI0RgFHgXbe8ibgrzGTKJlo2NCFcfo0tTSKIDfX+ZxGjHBP2u+/X7ZMNBMmuATGQ4dcaQojsRgxwvkwOgUrgpHYhB09JSKfqWpLb91yL+EuIfDNPAVw4ACcfbZLqmrXrvjxRnwYNcpFo8ydW7Yj3B54wEVUvfSS35IY+ezc6e4Zs2a5SrY+EqvaU0dE5KSAgzQBEsqn4StVqsDf/w733OMq4hr+c/AgPPKIm/qXZYUB8Mc/wtSpzm5uJAZDhrgkTJ8VRqSEozSGAO8A9UVkAjAbMHtMIN27u94MzZtbXapEYOhQN+u7+GK/JfGfGjWc4rj/fr8lMQBuucVl7n/1VdLeJ8KNnqoFXIzLCF+gqjtiLVhJ8NU8lU/LlrBsmXtv0Sr+sW2bU94LF7rqxAYcPQrnnONMVFde6bc0ZZtateDHH937BLhPxCp6SoD2uL4XVwKXRSZeinP66e5vgwYWreInjz3m+rqbwjhOhQrwt7/BffeZCdVPFi6E/fvd+ySO5gvHEf480AR4FTfTuAX4RlV/G3vxwiMhZhq7d7u8jaVLXdOmatX8lacs8tVXrqf76tXuic44jqoroTJokFOqRnxRdbO8G2+Ejz5KmG6RscoIXwM0V9U8b7kcsEpVz45Y0iiTEEojn9tvhzPOcE2bjPjSpQu0bm0h0KGYO9ddn2vWWGvYePPuuy5Y5vPPE6pgZqyip9YBPwlY/om3zgjGY4+5rnBbt/otSdli/nxYtMg9SRvBufxy14DKKuDGl7w8ePBB+OtfE0phREo4M425wEW40uWKa9O6CNhLdPpqlJqEmmmAe6JQddniRuxRdYlSJ58MjRtbraWiWLPG9aBu1cr13LBzFXteew3+9S/n00iwEPBYmacyi9isqvphSQ4YCxJOaWzb5qJVFi1yNzEjtkyZ4sKe8zOfEyAqJaE5/XTYssW9t3MVW44eddF8L76YkJFrMStY6O28GgGd/oJ1z/OLhFMa4Hwa69bB2LF+S5La5OY6k0ulSrBkiYtKKWslQ0rKFVdAVpY7b1lZdq5iyfPPw1tvOZ9GAhKrkNv+IrIFWAks8V6LIxOxDPG737mb14oVfkuS2kyY4CLV3n+/bNaYioTJk6FZM7jgAjtXseTAAXj8cVcxIoUIxzy1DmiTaAl9gSTkTAOcT+Pdd11rWCP6HD3qaviMGuX6ZRjhs2OHO3eWBBk7/vpXWLnS+TQSlFhFT30DHIpMpKIRkQ4iskZE1opI0DhJERnmbV8uIi1jIUfM6N8fVq1yoY5G9HnpJffEbAqj5NSq5SLN/vxnvyVJTX78EZ55xs00UoxwZhoXAP8FPsGVSAfnAC9VbKOIpAFf4jLNN+Iisrqp6uqAMdfh+nhcJyIXA8+qaqEmzwk70wDn0/jDH9xTXZUqFq0SLQ4cgKZNXS/sCy7wW5rkZN8+dw7few9atPBbmtSiRQs3mzv//IT+zccqemoxMBfn08jDZYWrqo6OVFBvv22BwarawVt+ELfjJwLGvADMUdXXveU1QHtV3VpgX4mrNHJznc3dInuiyxNPuOx7O5elY+hQV6J72jS/JUkdvv/eJfjm5rrlBP7NR6I0wsk0SVPV30UoU1HUAwJb3v2AK4pY3Jj6QPJkzqWlufDb/MieJK03k1Ds2gX//Kcrx2CUjjvvdGaU+fNdCRaj9Dz2mGvQ9s03KfmbD0dpzBSR/sBUAvpoRCHkNtypQUEtGPRzQ4YMOfY+MzOTzMzMiISKCe+/78wA/fol7DQ1qXj6adfx7Kyzih9rFE2lSq7H/UMPufDbBEs+Szq+/NKF2C5a5MrRJ0iNqXyysrLIysoq1T7CMU9tIMiNWlXPKNWBRdoAQwLMU38E8lT1yYAxLwBZqvqat5x85ql85s6F3r3dRVWhgt/SJC9btsC557oy9A0a+C1NapCT4xoCDR0K117rtzTJzS23OB/bgw/6LUlYxCR6SlUbqeoZBV+Ri3mMxUBTEWkkIhWAW3GzmUCmArfDMSWzu6DCSBouv9yZqVJsqhp3/vIXp3xNYUSP8uVdlM9DD1np9NKwZIkz86V4/bNwmzD9FGgOHCuNqapjSn1wkY7AUCANeEVV/+6ZwlDVEd6Y54AOwAGgr6ouDbKfxJ9pAHz2mevst3YtVK3qtzTJx/r1zka8Zg2ceqrf0qQWeXlw0UXuCblrV7+lSU6uvRY6d4a77vJbkrCJVfTUEFwTpnOB6UBH4CNVvTlCOaNO0igNgNtuc6aAhx/2W5Lko2lTl9B37rkJHcaYtLz7rjOvnH++hYeXlDlz4Ne/dr1cksj8HCul8TlwHrBUVc8TkdrAeFW9OnJRo0tSKY21a6FtW+fbOOUUv6VJHlascJVZc3LccgKHMSYtqq5S8J49btnOcXiout/0oEGucGYSEauM8EOqmgvkiEh1YBtgBuVIadrUNQt68snixxrH+eMf3bmDlAxjTAhEXEVWcM5cO8fhMWUKHDrkrAhlgHCUxiIRORl4Cee8/gz4OKZSpTp//jO88gps3Oi3JMlBVpab9s+ZY0UJY82MGVC/vvO92TkuntxcZ2r+29+gXDi30+Qn7NLoACJyBlBNVZfHTqSSk1TmqXzuv9+ZAUaM8FuSxEYVLr4Y7r0XunXzW5qyQb4Jdc0a67VeHKNHuxpo8+YlZY5LVH0aItKKIhLwgkUx+UVSKo2dO12xvU8+OW52MQozcaJ7ilu8uMw8ySUEd9/tHLrPPOO3JInLkSMuwXTcOLj0Ur+liYhoK40snNI4CWgF5DeGaAEsVtW2kYsaXZJSaYArnTxypMs5qFzZolUKkp3tIqX+/W+45hq/pSlb5CdRLl7s6igZhWnXzs3G2rRJ2t9urKKnJuEKC670ln8KPKqqXSKWNMokrdLYvx9q1nQ3R7BolYI8/zxMmuR8GEb8GTzY1U+y7pOF2bPH5Qol+W83Vkpjlao2L26dnySt0gCXs/H559amtCD79zuz3fTpVvrcL/JLp7/zjsvdMI7z4IPOLLVxY1L/dmMVcrtCRF4WkUwRuUJEXgISyhGe1GRludLp99yTlBddzHjmGdfL2hSGf2RkwJ/+lDR1lOLGhg3O+f3ee2Uymi+cmcZJwF3AZd6qucDzqno4xrKFTVLPNMBddHfd5br8JVE2aczYvt3V6fr0U2jc2G9pyjZHj7rcjREj4Kqr/JYmMejWzTVVGzzYb0lKTUzMU8lA0isNgOuvh6uvdqGlZZ1Bg1z44rPP+i2JAfD6664c/aefWgTbggVw882uokOVKn5LU2pi5dO4FBgMNOJ4/w1V1YR5BEwJpbFqFWRmumiMmjX9lsY/vv7a5WWsXm1FCROFvDxo3Rruuw9uvdVvafxD1TWquuMO6NPHb2miQqx8Gq8A/wIuBS7yXq1LLp5RJM2buyeYxx7zWxJ/ueYaVwG4d2/YvdtvaQxws4vatd3/5Npry+7/5c034fBhuP12vyXxlXBmGgtVtWAb1oQiJWYaANu2OeXx8ccu8a+sMWcOdOzokqYgacMYU5LMTPjwQ/e+LP5fDh92fraRI12ARooQq5nGHBF5WkTaikgr72UhLbHgtNOcCeCBB/yWJP5kZ8OAAccL5llRwsSicmX3Ny0NHnnEX1n8YPhwaNEipRRGpIQz05gTbL2qJszZS5mZBhx/ovnvf6F9e7+liR/PPOPyAV57Dfr3T7jeymWe3budLb9xY+d3evNNvyWKH/nRfPPnp1xf+miXEfl9gVUK7MA1YPomMhGP7bsm8DrQENgA3KKqhQylXn/yvUAukK2qQX0pKaU0wEWrPPWUa05fFqJVtmyBn/40JX+UKcehQ242+NJLLtqvLDBggJthpWA0X7TNUxlA1YBXBq4G1UwRKW250QeB91W1GTDLWw6GApmq2jKUwkhJbrnF5WuMG+e3JPHhgQegXz9TGMnASSfB0KEwcKDL4Uh1Vq92/ps//9lvSRKGEudpeLOEWaraMuKDiqwB2qvqVhGpA2Sp6tlBxq0HLlTVH4vZX2rNNAA6dXLmmssvd6aAVDXVzJ/vwjhXr3YZyEbio+r6bVx1FfzhD35LEztUXTHRihXdA02SFiUsilg5wk9AVXeW9DNBqK2qW733W4HaoQ4HfCAii0XkN1E4bvKwZ497kvvgA2dLTkVyc93U/+mnTWEkE/mJl088AZs2+S1N7Bg3zvlyvvkGZs5M3d9hCSlf/JATEZErgF1hjHsfqBNk08OBC6qqIhJqmnCJqm4WkVOB90VkjarOCzZwyJAhx95nZmaSmZlZnIiJTX60Snp6yiQSFeLFF13drTLSJjOlaNYMfvMb10wsFc2o27a5WdR557kQ+BSJ5svKyiIrK6tU+yjKEb4yyOqTgc3A7aq6OuKDOvNUpqpuEZHTgTnBzFMFPjMY2K+q/wyyLfXMU/nRKjfcAH/5CyxbBpUq+S1V9NixwzlUZ81ylX6N5GP/fhdVNH68M6OmEj16QN26rpXrHXekbDRftKOnGhVYpcCPqro/IulO3PdT3r6eFJEHgRqq+mCBMZWBNFXdJyJVgPdwfTzeC7K/1FMagdx8syuQ9pe/+C1J9LjjDjebGjrUb0mM0vDGG66Z2JIlUL7EhovEZPp0V/9s5crjM/4UJWkKFnrO9DeAnxAQcisidYGXVPV6EWkMTPI+Uh4Yr6p/D7G/1FYamze7afIHH7gEo2TnxhvdD7N9+9R28pcFVKFePXdzbdYs+Z3F+/a58O+RI8tEVd+kURrRJuWVBsDLL7sp8iefuJjxZOXgQahVy8X7Q9ksSZFqXHSRawsLyf//HDTImd1GjvRbkrgQl+gpwyf69XOF/IYN81uS0nH//cefRFPEuVjmya9GXLmy6+eerHzyCUycCP/4h9+SJDSmNJIFEXeD/etfYf16v6WJjLffdq9PPimTHc9SlgkT3P/zkkuSN2v6yBH3YPbss2W7NUEYmHkq2XjqKRdx9M47TpEkC1u3uj7Tb7wBl11W/Hgj+cj/H7/+evJFUz36KHz2GUyenFy/q1JiPo2yQE4O1Knj/AKNGyeH41HVdSa84ILUigAzCjN9Otx9twsRT/TrMp+bb4YpU9zDzKRJySN3FDClUVa48EIX4gjJ4Xh87jkYM8aVDElP91saI9YMGAA//ugeaBL9qX3vXvcQVkYDM8wRXlY47TT3t2JFePJJf2Upji++cFP/8eNNYZQVnn4ali93//NEJi8PevZ0s3awwIwwsZlGMpKfLX7qqbB2LcyYkZiJVYcPu37f99wDv/qV39IY8WTZMte699NP4Ywz/JYmOI884roRTpzoZkcpmvVdFGaeKmvk5Lhqoz/7GfyzUHUV/7n3Xvj+e5fAl+hmCiP6/Otf7oY8d27iPdS8+aarLbVo0fGZexnEzFNljfLlXae7KVOczyCRuPpq+M9/3Kxozx6/pTH84P/+D777zs00OnZ010IisHw5/Pa3LlKqDCuMSDGlkezUrOmUxu9/70wBicCMGW7af/SoCw+2ktJlk3LloGFD+OEHFyKeCNfBjh3QubPr+X3BBX5Lk5SY0kgFzj3XlRnp0sXVqfKTDz5wpdwvusgtm3OxbFO9uvtbsaKr6eQn2dkuOuq226wcfykwpZEqdOrknuRuusllt/rBvHnQrZuzY8+YYVnfxvFs8aVLYfRo94TvF7/7nSt1YrlCpcIc4alEXh40aeJiz1u1cvHm8bphL1jgen9MmOD8GYZRkG+/dZnif/qTa+AUL/Ly3Ix3zRpX6sQqKx/DHOFlnXLlXE/jnTvdE36PHvE57tKlTmGMGmUKwwhNw4bOx/XoozB2bHyOeeAA3HILrFvnEvhSuX1ynDClkWpUrer+NmjgylV/9FFsj/f55y7s94UXXKkQwyiKM890DzQPPBD7zOvvvoNLL3W/ibZt3TrzsZUaUxqpRr4NecUKF4bbpYtzkseCDh1cBEqdOnDllbE5hpF6nHOOi6bq08cpkQ4doh+OO38+tGnjMr5HjXJFFM3HFh1UNe4voCvwBZALXFDEuA7AGmAt8EAR49QIwZdfqjZrpjpokGp2dnT2uX69aqdOqpUqqbpyhKpdu0Zn30bZ4aKLjl8/V1wRvf2OHKl66qmqM2ZEb58pinfvLNH926+ZxkrgRmBuqAEikgY8h1MczYFuInJOfMRLIQkdCi0AAAalSURBVJo1g4UL4csvnU25bVtnTgrxZJeVlRV6X4cPw2OPuSn+RRcdL39tU/6QFHk+yzr5NZ8aN3ZO6t69XXn1IijyfPbpA/Xru5Ig06a5hEIj6viiNFR1jap+Vcyw1sA6Vd2gqtnAa0Cn2EuXgtSo4ZofpaW5KKeZM+HnP3dtLQsQ8kf59tsuH2T5cldh9+GHbcofBqY0iiDflLpkiXuoqV3b5XIMG+ZK5ASh0PnMyXHXc/fuzrm+caNrKZyIZXVShET2adQDvg9Y/sFbZ0RC+fLHk6saN4aTT3ZPZT17wrvvHv+RZme7H/B778FLL7nwyHr13I+7Zk145RU3YwGnKOIZ1mukFoHXT0aGazA2dy5MneqKcTZoAM2bw6uvOh9dfjkaVRexd++97hoeMgTatYMrrnDbbeYbU2JWRUxE3gfqBNn0kKpOC2MXlngRbSZMcOGG+dU8t21zs4VHHnHJgbm5TnlMmOAUS6NGTkFUqwabNrlorDvuKFP9Bow4c845buZ63nmwcqVb97vfuQeWb7911+c//uH+1q/vzFD51Qd69jzx+jZigq/JfSIyB/i9qi4Nsq0NMERVO3jLfwTyVLVQAwkRMQVjGIYRAVrC5L5EqFccSuDFQFMRaQRsAm4FugUbWNIvbRiGYUSGLz4NEblRRL4H2gDTRWSmt76uiEwHUNUcYADwLrAKeF1VV/shr2EYhuFIidpThmEYRnxI5OipExCRDiKyRkTWisgDIcYM87YvF5GW8ZYxmSjufIpIpojsEZHPvNef/JAzGRCRkSKyVURWFjHGrs0wKe582rUZPiLSQETmiMgXIvK5iAwKMS7867Ok2YB+vIA0YB3QCEgHlgHnFBhzHTDDe38xsMBvuRP1Feb5zASm+i1rMryAy4CWwMoQ2+3ajO75tGsz/HNZBzjfe18V+LK0985kmWmEk+h3AzAaQFUXAjVEpHZ8xUwawk2ctACDMFDVecCuIobYtVkCwjifYNdmWKjqFlVd5r3fD6wG6hYYVqLrM1mURjiJfsHG1I+xXMlKOOdTgXbedHWGiDSPm3Sph12b0cWuzQjwIlFbAgsLbCrR9ZkIIbfhEK63vuDTh3n5gxPOeVkKNFDVgyLSEXgLaBZbsVIauzajh12bJUREqgITgXu8GUehIQWWQ16fyTLT2Ag0CFhugNOGRY2p760zClPs+VTVfap60Hs/E0gXkZrxEzGlsGsziti1WTJEJB34HzBOVd8KMqRE12eyKI1jiX4iUgGX6De1wJipwO1wLJt8t6oWXTKz7FLs+RSR2iIi3vvWuPDsnfEXNSWwazOK2LUZPt55egVYpapDQwwr0fWZFOYpVc0RkfxEvzTgFVVdLSL9ve0jVHWGiFwnIuuAA0BfH0VOaMI5n8DNwF0ikgMcBG7zTeAER0ReBdoDtbyk1cG4qDS7NiOguPOJXZsl4RKgJ7BCRD7z1j0E/AQiuz4tuc8wDMMIm2QxTxmGYRgJgCkNwzAMI2xMaRiGYRhhY0rDMAzDCBtTGoZhGEbYmNIwDMMwwsaUhmEUQESqi8hdAct1ReTNGB3rFyIypIjtLUTklVgc2zAiwfI0DKMAXmG3aar6szgcaw5wW1EZuCKSBdyiqttiLY9hFIfNNAyjME8ATbwGP0+KSMP8hkAi0kdE3hKR90RkvYgMEJE/iMhSEflERE72xjURkZkislhE5orIWQUPIiINgAr5CkNEuorIShFZJiIfBgydCXSN/dc2jOIxpWEYhXkA+FpVW6rqAxSuAHoucCNwEfBXYK+qXgB8glfDB3gRGKiqFwL3Af8JcpxLcBVb83kE+Lmqng/8MmD9p8DlpftKhhEdkqL2lGHEmeIa/MxR1QPAARHZDUzz1q8EWohIFaAd8KZXVw+gQpD9/ATYHLA8HxgtIm8AkwLWb8Z1WTQM3zGlYRgl50jA+7yA5Tzcb6ocsEtVw+kFfkyrqOpdXtXW64ElItLKq94qWP8NI0Ew85RhFGYfkBHB5wRcvwdgvYjcDK48tYi0CDL+W1wPZ7xxTVT1U1UdDGznePe0072xhuE7pjQMowCq+iMw33NKP4l7ys9/0g98T5D3+cs9gH4isgz4HNeHuSDzgQsClp8SkRWe032+qq7w1rcG5pbmOxlGtLCQW8PwERGZDfRQ1c1FjMnCQm6NBMFmGobhL/8A7gy10TNrrTOFYSQKNtMwDMMwwsZmGoZhGEbYmNIwDMMwwsaUhmEYhhE2pjQMwzCMsDGlYRiGYYSNKQ3DMAwjbP4fLgxre1MOTEUAAAAASUVORK5CYII=
-
-
-直方图
---------------
-
-hist 函数：
-
-
-
-
-::
-
-    import numpy as np
-    import matplotlib.mlab as mlab
-    import matplotlib.pyplot as plt
-
-    # example data
-    mu = 100 # mean of distribution
-    sigma = 15 # standard deviation of distribution
-    x = mu + sigma * np.random.randn(10000)
-
-    num_bins = 50
-    # the histogram of the data
-    n, bins, patches = plt.hist(x, num_bins, normed=1, facecolor='green',     alpha=0.5)
-    # add a 'best fit' line
-    y = mlab.normpdf(bins, mu, sigma)
-    plt.plot(bins, y, 'r--')
-    plt.xlabel('Smarts')
-    plt.ylabel('Probability')
-     plt.title(r'Histogram of IQ: $\mu=100$, $\sigma=15$')
-
-    # Tweak spacing to prevent clipping of ylabel
-    plt.subplots_adjust(left=0.15)
-    plt.show()
-
-
-<button>Run ...</button>
-
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-.. image:: 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4kXFY79eXZLDnnW2xr1jjqUMpU1gHt2Q3P5Qd/ncr0aEJyWcaHm1xWm3d+V/IH50UdRqUszDuBb20vpsEWn0bcpZ8nCZfVtrTMZUuLQ6IOo1L21KtDr+5Hs/OQhlGH4rKAJwnnMlBxLX/ruqrh/2nOOecS8iThnHMuIT+7yWWfLVtg4cKoo3AuI3hPwmWfcePgwQejjiIljvhsPV3eXBB1GK4G8yThss/jj8O110YdRWqY8d1Rr1Fr776oI3E1lCcJl10WLIDVq+G73406kpRYf2wLtrQ4hA7vfRx1KK6G8iThsssTT8A110BOTtSRpMysi0/hlP/7MOowXA3lScJlj9274emnYfDgqCNJqUV5nWm9eBWHrNsSdSiuBvIk4bLHnj3w8MPQoUPUkaTU7vp1mP+dLpz86pyoQ3E1kJ8C62q8ocOG7n+DnvemAFAwu6DMezJkoreuyWN33dowdU3UobgaxpOEq/ES3aBnRsGMqg8mTXb4PE4uTXy4yTnnXEKeJJxzziWU1iQhqbekJZKWSrotQZ2Hw/VzJXULy9pImi5poaQFkm6Oqd9M0jRJH0t6TVJuOtvgMl/Dol1+sZlzByhtSUJSDvAI0Bs4ARgoqVNcnT5AezPrAFwPjApX7QZ+YWadgR7AjZK+Fa4bBkwzs47AG+GycwkNmDGb018qiDqMKlF7z16YNi3qMFwNks6exOnAMjNbbma7gXFAv7g6fYGxAGY2E8iV1MLM1prZR2H5NmAx0Cp+m/Bn/zS2wWW67ds5ddkKFvTqHHUkVUIYDBgAa/wsJ5ca6UwSrYCVMcur+OaDvrw6rWMrSGoLdANmhkUtzGxd+Hwd0CI14boa6V//4pOWh7PtsCZRR1IldteuDZdeCs89F3UoroZI5ymwlmQ9JdpOUmPgeeDnYY9i/4pmJinh6wwfPvzr53l5eeTl5SUZkqsxnnuO977VLuooqtaVV8KvfgW33BJ1JC4i+fn55Ofnp2Rf6UwSq4E2McttCHoK5dVpHZYhqQ7wAvC0mU2MqbNOUkszWyvpSGB9ogBik4TLQhs3wttvM+fKi2kZdSxV6dxzYf364J4ZnbNjmM3tL/5L8YgRIw54X+lMErOADuFw0RpgADAwrs4kYAgwTlIPoNDM1kkSMAZYZGYPlbHNNcB94c+JOFeWDRvgttsoWp89M6QWzCxg8C3XcUXLXPYOHsgLZ3cHYO6suZx06kn71c2tn8tDI+PfXs7tL21Jwsz2SBoCTAVygDFmtljSDeH60WY2WVIfScuA7cCPws3PBgYB8ySVTEhzu5m9CowEJki6DlgOXJGuNrgMd/zxcMcdMHRw1JFUmWIV07Z/W5ad1pRD1hbS9pS2QHB1efxV58snLq/y+FzmSeu0HGY2BZgSVzY6bnlIGdvNIMFBdTPbBJyfwjCdq3E2tWrGplbNog7D1QB+xbVzzrmEPEk455xLyJOEc865hDxJuJrnT3/yi8li1N61O+oQXAbzJOFqFjMYPRqOOy7qSKqF2sV7+MUPHqLpnr1Rh+IylCcJV7PMng379sFpp0UdSbWwp25tPu96NBds2h51KC5DeZJwNcuzz8IPfwiKn+0ley3I68z3NpSa1ca5pHiScDXH3r0wfjwMjL+wP7t9fGZHum3bRcMtO6IOxWWgCpOEpL6SPJm46m/hQmjdGjp1qrhuFtndoC5v5zag01uLog7FZaBkPvwHAMsk/THmxj/OVT8nngjvvht1FNXSi4c3od7O4qjDcBmowmk5zOxKSYcQTM73ZDg19xPAc2a2Nd0BOlcZQ++8hcKiwv3KCmYXlJq3KNu8dWgj2gw4K+owXAZKau4mM9si6XmgATAUuBS4VdLDZvZwOgN0rjIKiwpLJYQZBTOiCca5GiCZYxL9JL0E5AN1gNPM7CLgROCX6Q3POedclJLpSVwGPGhmb8cWmtkOST9OT1jOOeeqg2QOXK+LTxCS7gMws9fTEpVzlfHBB/Dvf0cdhXM1UjJJ4oIyyvqkOhDnDtiDD8K8eVFHkREab9xK7z9Pqbiic6GESULSTyXNB46XND/msRzwd6SrHnbsgMmT4fLLo44kI+zIbUTXNxdw6JrNUYfiMkR5xySeJbir3EjgNqBknoOtZrYx3YE5l5QpU+DUU+GII6KOJCPsy6nF4m93onP+QuY0bB11OC4DlDfcZGa2HLgR2Ap8FT5Mkt8X0VUP48fDgAFRR5FRFpzXhc7TF0YdhssQ5fUkngO+B8wGrIz17dISkXNJGDpsKDu2fslD/5rIrw+txfaFwYFrv3CuYiu6Hk3jzdtouXlL1KG4DJAwSZjZ98KfbassGueSVFhUSPt+7fjnsT/g8FOO5fCw3C+cq5jl1GJRzxM4ZdnnUYfiMkDCJCGpe3kbmtmHqQ/HueTtqVubz045NuowMtL0a3uxdOoa/ivqQFy1V95w0wOUPcxUoleKY3HOVZGixvXZm+OTO7uKlTfclFeFcTjnnKuGyhtuOs/M3pR0OWX0KMzsxbRG5pxzLnLlDTedC7wJXELZw06eJFw0ioqou3tP1FE4lxXKG266K/w5uMqicS4Z48fz42kzmPxf7aOOJPNt3gwffwxnnBF1JK6aSmaq8OaS/ixpjqQPJf0/SYdVRXDOlWn8eGYfd3TUUdQMn30W3BPcyjtHxWWzZE5vGAesJ5gy/PvAl8D4ZHYuqbekJZKWSrotQZ2Hw/VzJXWLKX9c0rpw/qjY+sMlrQqT1hxJvZOJxdUQGzfCv//NR+3aRB1JzdCtG+TkwKxZUUfiqqlkkkRLM7vbzD4zs0/N7B6gRUUbScoBHgF6AycAAyV1iqvTB2hvZh2A64FRMaufCLeNZ8ADZtYtfLyaRBtcTfHii3DhheyqWyfqSGoGKZjWZHxS3/tcFkomSbwmaaCkWuFjAPBaEtudDiwzs+VmtpugR9Ivrk5fYCyAmc0EciW1DJffARJNVakE5a6m87maUm/AAJgwwYecXJnKOwV2G9+c1TQUeCp8XgvYDtxSwb5bAStjllcB8UfHyqrTClhbwb5vknQ1MAu4xcwKK6jvMtTQYUMpLAr+vDLjx6s/48npL1Ew90OfoylVunSBRo3g/ffhzDOjjsZVM+Wd3dT4IPed7NeS+F5BRduNAn4fPr8b+BNwXVkVhw8f/vXzvLw88vLykgzJVReFRYX7JYNpl7ajFVD84fuRxVTjSHD//dDMJ3euKfLz88nPz0/JvpK5xzWSDgU6APVLyuJvaVqG1UDs0cU2BD2F8uq0DssSMrP1MXE9BrycqG5sknDOlePii6OOwKVQ/JfiESNGHPC+kjkF9r+BtwmOQ4wApgLDk9j3LKCDpLaS6gIDgElxdSYBV4ev0wMoNLN1FcRzZMzipcD8RHWdc84dnGQOXP+c4CD0cjPrBXQDKpyI3sz2AEMIksoiYLyZLZZ0g6QbwjqTgU8lLQNGAz8r2V7Sc8C7QEdJKyX9KFx1n6R5kuYSXBX+iyTb6pxzrpKSGW4qMrOdkpBU38yWSDo+mZ2b2RSCW6DGlo2OWx6SYNuBCcqvTua1nXPlK5hZwOChg0uV59bP5aGRD1V9QK5aSiZJrAyPSUwEpknaDCxPa1TOxcj9YjPnPDuD/7vlkqhDqVGKVVzmGWIrXvqs6oNx1VaFScLMLg2fDpeUDzQF/AI2V2U65y/EavmlMVWh67R59PT7X7sYSd11RNIpkn4OnAisMrPi9Ibl3De6vLmQhb26RB1GVljZ9WhOW7oCiv0t7gLJnN30O+BJoBnQHHhC0m/THJdzALTcvIXGm7exoqtP6FcVClvmsvbQpjBtWtShuGoimZ7EIOA0M7vLzH4H9ACuSm9YzgVO/3g5C889AfNbbVaZ948/Fp59NuowXDWRzDtvNdAgZrk+pS+Kcy4tTv5sJQvO86GmqlTQ4Rh45RXYvj3qUFw1UN7cTX8On24BFkoqmdTvAqAg3YE5BzDy8t606tQ66jCyytaGDaB//+BmRN26VbyBq9HKO7tpNsE8SrMITn8tmVMpn+TnZXLuoBTXqe1nNkXhySejjsBVE+VN8PdkyXNJ9YCO4eKScOpv55xzNVyF10lIyiO458OKsOhoSdeY2VvpDMw551z0krni+gHgu2b2HwBJHQluINQ9nYE555yLXjJnN9UuSRAAZvYxSU4x7twBKS4OTsH0O6U5F7lkksRsSY9JypPUK7yHg9813aXPq6/CX/8a3AzHRev112HcuKijcBFKJkn8BFgM3AzcBCwEfprOoFyWe+YZuPLKqKNwALVqBXetc1mr3GEjSbWBuWb2LYLbhDqXXlu2BD2JUaOijiRrxU4hrn37eOA/i/nj1Zey86hjfArxLFRukjCzPZL+I+kYM1tRXl3nUuLFF+G88/x+yxGKn0J8yRcncqE28WTRIdEF5SKTzHBTM4Irrt+U9HL4iL8NqXOp8fTTMGhQ1FG4GPO/05Wuby7wEwmyVDJnKf0m/Bl7FNH/W1x63HJL0JNw1caa448Cg2PWb4o6FBeB8uZuakBw0Lo9MA943K+0dmnXp0/UEbh4Ek8+NJgV72yIOhIXgfJ6EmOBYuAdoA9wAvDzqgjKZZ+hw4ZSWFRYqrxgdkGZt9h0VWtr8yagjVGH4SJQXpLoZGZdASSNAT6ompBcNiosKiwzGcwomFH1wTjnvlbeges9JU/MbE859ZxzztVQ5fUkTpS0NWa5QcyymVnTNMblssm+fTQq2hV1FM65MiTsSZhZjpk1iXnUjnnuCcKlzjvv8OuXXqu4novemjUwc2bUUbgq5DcOdtEbO5aZHdtFHYVLxuLFcMMNUUfhqpAnCRetr76Cl17i3986LupIXDJ69YLNm+Gjj6KOxFURTxIuWuPHQ14eXzVqEHUkLhm1asE11/jtTbOI3xfCVan46yF+O/4VJp12IgWz/+PXQ1RzJRP/HV74Fb+dMIVf7N3I3pwccuvn+sR/NVhaexKSektaImmppNsS1Hk4XD9XUreY8sclrZM0P65+M0nTJH0s6TVJuelsg0utkush2vZvy3Hfa8OXZx3L1p+fTfHe4qhDcxUomfiv0eAT2dShBRccESyXdRGkqznSliQk5QCPAL0JrtYeKKlTXJ0+QHsz6wBcD8TOD/1EuG28YcA0M+sIvBEuuwy0t04Or950EftyfNQz00z7yQWsP7ZF1GG4KpDOd+fpwDIzWx7O+TQO6BdXpy/B9B+Y2UwgV1LLcPkdYHMZ+/16m/Bn/zTE7pwrx+pOrdjUyqdzzwbpTBKtgJUxy6vCssrWidfCzNaFz9cB/nXGOefSJJ0HrpOdTjz+RsZJT0NuZiYpYf3hw4d//TwvL4+8vLxkd+2ccxkrPz+f/Pz8lOwrnUliNdAmZrkNQU+hvDqtw7LyrJPU0szWSjoSWJ+oYmyScNWIGSj+u4FzLlXivxSPGDHigPeVzuGmWUAHSW0l1QUGAPF3tJsEXA0gqQdQGDOUlMgk4Jrw+TXAxNSF7KrCuf94m9Nf9KkdaopDtu+MOgSXRmlLEuHMsUOAqcAiYLyZLZZ0g6QbwjqTgU8lLQNGAz8r2V7Sc8C7QEdJKyX9KFw1ErhA0sfAeeGyyxDat49uU+bwedejow7FpUDDLTv4n6cmwpYtUYfi0iStF9OZ2RRgSlzZ6LjlIQm2HZigfBNwfqpidFWr88ov2NG0IWs7HBl1KC4FdhzSkIVHH8UZ//gH3HRT1OG4NPAT1F2V6rlwKXP6dKu4ossYb5x0PDz6aHCsydU4niRc1dmwgS4r1jD//K5RR+JS6OOjWkBODkyfHnUoLg08Sbiqs2gR75zQnqLG9aOOxKWSBDfeCH/5S9SRuDTwCf5c1enZk+fOPZ22UcfhUm/QoGDad1fjeE/COXfwmjSB28qcw9NlOO9JOOcOSskU4vF8CvGawZOEc+6glEwhHm/5xOVVHotLPU8SLi3iby5UomB2gd9cyLkM4sckXFqU3FyoS8/m/O7tt2nX9xja9m/rNxfKBmbU3el/55rCexIurU6dNIuNbZphtXxCv2xxxgszOfzzDXx8bJeoQ3Ep4D0JlzZuRM63AAARQklEQVQ5xXs4ddIsZl52RtShuCq0sFdnOk9fSINd3puoCTxJuLTp8uYC1h7Xkg3HHB51KK4KbTusCctOb8+5Cz6OOhSXAj7c5NLDjB4vzuSN686LOhIXgX8PPJvLfjaG/x5yFbtr5+y3zk+NzSyeJFxaNN1RxLZmjfnktPZRh+IisLZ9SxY2qkvfupuZ3ffU/db5qbGZxYebXFp81agBz4y80g9YZ7GH2xzKjkMaRh2GO0ieJJxzaTG/cX0Wn3tC1GG4g+RJwjnnXEKeJJxzziXkScKl1r59UUfgnEshTxIudT75BE491ROFKyWneA9Nv/T7TWQiTxLuoA0dNpTBQwfzdt/eTKyzj8G/vJaC2QVRh+Wqkc5vLaL/yIlRh+EOgCcJd9AKiwrpdlpTTl21miW/udAn8nOlLOjVmWarN9Fq8eqoQ3GV5EnCpUTPp9+h4NLTKWrSIOpQXDW0r3YO715xJuc8OyPqUFwleZJwB+2Iwq84/t3/8P73e0QdiqvG5vTpTpuFKzn6y01Rh+IqwZOEO2gNdu1m2g0XUNS4ftShuGpsd/06vHVVT654ZxaYRR2OS5LP3eQO2ooWh6E+baMOw2WA2ZecwuLPd+N3msgc3pNwzlWZfbVzWHBMK5DP6ZUpPEk455xLKK1JQlJvSUskLZV0W4I6D4fr50rqVtG2koZLWiVpTvjonc42OOdcNktbkpCUAzwC9AZOAAZK6hRXpw/Q3sw6ANcDo5LY1oAHzKxb+Hg1XW1w5Zg9G7ZsiToK51yapbMncTqwzMyWm9luYBzQL65OX2AsgJnNBHIltUxiWx/QjNLWrdCvHyxcGHUkLtPddx+sWBF1FK4c6UwSrYCVMcurwrJk6hxVwbY3hcNTYyTlpi5kl5R77oHvfAfOOivqSFym274d7rgj6ihcOdKZJJI9EbqyvYJRQDvgZOAL4E+V3N4djCVLYMyY4Bugcwfr1lth+nQo8Lm+qqt0XiexGmgTs9yGoEdQXp3WYZ06ibY1s/UlhZIeA15OFMDw4cO/fp6Xl0deXl4lwnelmMHNN8Odd0LLllFH4zJUwcwCBg8d/PVyzxPacu7ll3Lc56v81NgUyc/PJz8/PyX7SmeSmAV0kNQWWAMMAAbG1ZkEDAHGSeoBFJrZOkkbE20r6Ugz+yLc/lJgfqIAYpOEO3gPXDOQKz78gLuOb8nemDd5wewC2vZvG1lcLrMUq3i//5eVlxyNrnwU/vY3uOGG6AKrQeK/FI8YMeKA95W2JGFmeyQNAaYCOcAYM1ss6YZw/Wgzmyypj6RlwHbgR+VtG+76PkknEwxnfQb4f1UVmdesPkVP/ZQ2DeruVz6jwCdtcwfOcmrx2AXncO/773uSqIbSOi2HmU0BpsSVjY5bHpLstmH51amM0VVOcVyCcC4V1hyWCw89EXUYrgw+d5MrZeiwoRQWFZYq92El57KPJwlXSmFRYZnJwIeVnMs+PneTK9ehq33uf+eymScJl1DX1+cz8Dfj0D6f+99Vsc8+gwkToo7C4cNNLoFD12ym9yOv8tT9g7Bafu66S6/4aycO37KV345/hcEj76JpzzNK1c+tn8tDIx+qwgizlycJV0rO3n1cfs8LvHPlOaztcGTU4bgsEH/tBMDbh+7i93+bxsQ/XMaeenX2W7d84vKqCy7L+XCTK6X/zI/Y2aQBMy/3e1a76My65BQ+rV+Xfn+c5Lc7jZAnCbe/wkJO/nQlE2/r58NMLloStx93OIeu2cy5/3g76miylicJt7/cXH53ZV+2N2scdSTOsSunFuPuGcC2QxtFHUrW8iThSjGfZM1VI9sOa8LsvqdGHUbW8iThnHMuIU8S2c4PCDrnyuFJIpvt3Qv/9V8weXLUkThXKS02b4Fdu6IOIyt4kshWZvCzn0FhYXArUucySO8PF8HFF8O2bVGHUuN5kshG+/bBr38NH3wAL74I9epFHZFzlfKPXmfA0UfDd78LmzdHHU6N5ldcZ5udO+Hqq2HtWu749mms+d3Npar4lOCuurNateDvf4dbboFevWDqVGjRIuqwaiRPEtnmiy+geXN46inWDPuJTwnuMlLBzAIG//JaMKNffejRqSO/+2FfGjU+zOd0SjFPEtnm2GNh1Kioo3DuoMTO9TT30nas/WQdrY5r4XM6pYEniSzgd5pzNd2644KhpvjZZEv4rLEHzpNETbZ3L0h+pzmXNcqaTRZ81tiD4Wc31VSLF8O3v+03bnFZq92Hn9Fmwcqow8h43pOoQYYOG8pXOzZx0eyF9P5wIS/1OJnp702h4MMPfFjJZZ3axXu4/J4X+KDfaXzepE3U4WQs70nUIE1Wfso9r75O9+JCxjzxEz69qzfHXNqO4r3FUYfmXJVb2qMDo0dfz9HzP+f3z74ML7/s09AcAO9JZKBEB6IHvTqdgh+fx0e9TwafydU5th7elKfuH0TugzMYOmwYTJwIY8ZEHVZG8SSRgRIdiP7JzCMYdFG3qg/IuepM4qNj28BLr8GqVVFHk3E8SWSgxjuLyl7hvQfnylQws4DBt1xXqtxPja2YJ4lM8cUXwZlKzzzDb5b+h79d0ZF9OX5IyblkJDo1dsLtE/jxkKv43fhX+PDYo3mnc3s2Nm3sySOGJ4lq7qlLetNt7jzard/InGPb8N7x7fhH3aO43BOEcwetWMW0/v5xvNJtAN1fmc3dL0xmTcejeLdxc/jsM2jXLuoQI5fWTxpJvSUtkbRU0m0J6jwcrp8rqVtF20pqJmmapI8lvSYpN51tiFrdwg0suvZsHvzXr3j90UFs//nZ7Ny3O+qwnKtR1h3Xgik39+GBCb9gzkUnc+y6DXDvvVGHVS2krSchKQd4BDgfWA18IGmSmS2OqdMHaG9mHSSdAYwCelSw7TBgmpn9MUwew8JHZtm5M7jgbf58mD+f0SuW8V6r0vmugB1ckdf56+XlHy2vwiCrzs4tO6MOIS2Wf7Sctie3jTqMlKup7Vq2eDV7zuvCXdMWcXqj3RAzxcfXQ1BvvQULFkD37nDSSdCwYXQBV4F0DjedDiwzs+UAksYB/YDFMXX6AmMBzGympFxJLYF25WzbFzg33H4skE8GJYmnLunNBTPeo9nW7aw9tCmrD8tldbNcXinaQvcbrypVP37qjBqbJL7yJJFJanq7yjqGMeH2CQweOpiOq9dy1pJPOWb9Jo7aVMiWpk044owz4aab4KKLogk8jdKZJFoBsdfErwLOSKJOK+CocrZtYWbrwufrgJROIj9//nx279qFioupVVyMdu2iVnExf3nuCXYe0bRU/a6bi7jliDawYUPw2Lgx+HnZZTByZKn6cw5vzFcPDmJj62bsq53zdfmiO56meyob4pxLqZLEUUxb8ukBQM7uvez8xzzuveLHpY5flFzPdPm/P6TL52v4qmF9tjRswJ5GTTnvkv7Qvz907VqqfpMdO8nZt4/dtWtTVKc2TRpFO/15OpNEspc2JnPepsran5mZpJReQrng1l8w4NU32J1Ti905OeyunUNxTi1OPKQ+6566qVT9924ay/hDlrGtQT22NqjHtta5bOvQghn5k2k/dG2p+gVLF3HF1V1SGbJzLiJ76+QwcdlS1rzxAryx/7qC2QVccfcVLOnZnLXrttBo8zYab97OxxNmsu3lf/Lhopksb9G8VP0L/zKVLtMXUHvXHmZf3J3HcnKIlJml5QH0AF6NWb4duC2uzl+BH8QsLyHoGSTcNqzTMnx+JLAkweubP/zhD3/4I3gc6Gd5OnsSs4AOktoCa4ABwMC4OpOAIcA4ST2AQjNbJ2ljOdtOAq4B7gt/Tizrxc3MryxzzrmDlLYkYWZ7JA0BpgI5wBgzWyzphnD9aDObLKmPpGXAduBH5W0b7nokMEHSdcBy4Ip0tcE557KdzGdFdM45l0CNuWxXUo6kOZJeDpcz/qK78JTg5yUtlrRI0hk1pF23S1ooab6kZyXVy8R2SXpc0jpJ82PKErYjbPfS8CLR70YTdcUStOv+8P9wrqQXJR0Ssy5j2xWz7hZJ+yQ1iynL6HZJuin8my2QdF9MeaXaVWOSBPBzYBHBQRr45qK7jgTnHWTMtRQx/h8w2cw6AScSHLTP6HaFx5n+G+huZl0JhhN/QGa26wmgd1xZme2QdALBsbUTwm0elVRd339ltes1oLOZnQR8THAySU1oF5LaABcAK2LKMrpdknoRXFN2opl1Af43LK90u6proytFUmugD/AY35xS+/WFeuHP/hGEdsDCb2rfNrPHIThOY2ZbyPB2AV8Bu4GGkmoDDQlOTsi4dpnZO8DmuOJE7egHPGdmu8OLRJcRXHBa7ZTVLjObZmb7wsWZQOvweUa3K/QAcGtcWaa366fAH8xsd1jny7C80u2qEUkCeBD4NbAvpiytF91VgXbAl5KekPShpL9LakSGt8vMNgF/Aj4nSA6FZjaNDG9XjETtOIrgotASJReOZqJrgcnh84xul6R+wCozmxe3KqPbBXQAekp6X1K+pFPD8kq3K+OThKSLgfVmNocEF+ZZcHQ+047Q1wa6A4+aWXeCs7/2G4LJxHZJOg4YCrQl+IdtLGlQbJ1MbFdZkmhHxrVR0p1AsZk9W061jGiXpIbAHcBdscXlbJIR7QrVBg41sx4EX6AnlFO33HZlfJIAzgL6SvoMeA44T9JTwLpwHigkHQmsjzDGA7GK4BvOB+Hy8wRJY22Gt+tU4F0z22hme4AXgTPJ/HaVSPR/txpoE1OvdViWMSQNJhjWvTKmOJPbdRzBl5W54edHa2C2pBZkdrsg+Px4ESD8DNknqTkH0K6MTxJmdoeZtTGzdgQHQN80s6v45qI7KOeiu+rKzNYCKyV1DIvOBxYCL5PB7SI4+N5DUgNJImjXIjK/XSUS/d9NAn4gqa6kdgTDAQURxHdAJPUm+Ebaz8xib42Yse0ys/lm1sLM2oWfH6sITqhYRwa3KzQROA8g/Aypa2YbOJB2pWtajigeBLPDTgqfNwNeJzgT4zUgN+r4DqA9JwEfAHMJvhUcUkPadStBwptPcHC3Tia2i6DnugYoJpiQ8kfltYNgaGMZQaK8MOr4K9Gua4GlBGf/zAkfj2Zwu3aV/L3i1n8KNKsJ7QrfU0+F77HZQN6BtssvpnPOOZdQxg83OeecSx9PEs455xLyJOGccy4hTxLOOecS8iThnHMuIU8SzjnnEvIk4Vw5JN0ZTrU8V8FU9Cmf5E3SHanep3Op4tdJOJeApDMJJiM818x2h/caqGdmX6TwNWoBW8ysSar26VwqeU/CucRaAhvsm+mWN5nZF5KWS7o37FnMktQ9vMHQMoW355XUWNLrkmZLmiepb1jeVtJ/JI2VtIBgevsG4b6ektRQ0iuSPlJwUya/Pa+LlPcknEsgnJp9BsE9L14HxpvZ2+FkcCPNbLSkBwjmnzoTaAAsMLOWknKAhma2NZxY7T0z6xDedOkT4EwzKwhfZ2tJT0LS5QRTJVwfLjc1s6+qst3OxfKehHMJmNl24BTgeuBLYHw4EyoEE6VBMDfOe2a23YIJ1HZJakrw3vqDpLnANOAoSUeE26woSRBlmAdcIGmkpHM8Qbio1Y46AOeqMwvuxvYW8FZ4D+HB4apd4c99BBPhEbNcB7gMaE4wq+jesPdRP6yzvZzXWyqpG/A94B5Jb5jZ3alqj3OV5T0J5xKQ1FFSh5iibsDy+GoJNm9KcDOsveH9ho8p56V2h7dyLbkHRZGZPUNwX+LuBxS8cyniPQnnEmsM/FlSLrCHYLrsG4CLY+rE332uZPkZ4GVJ84BZwOK4OrH+BsyTNJtgeuf7JZX0UH6auuY4V3l+4No551xCPtzknHMuIU8SzjnnEvIk4ZxzLiFPEs455xLyJOGccy4hTxLOOecS8iThnHMuIU8SzjnnEvr/P/hclE1xHEoAAAAASUVORK5CYII=
-
-
-路径图
-----------
-
-
-matplotlib.path 包：
-
-
-
-
-
-::
-
-    import matplotlib.path as mpath
-    import matplotlib.patches as mpatches
-    import matplotlib.pyplot as plt
-
-    fig, ax = plt.subplots()
-
-    Path = mpath.Path
-    path_data = [
-        ^4$(Path.MOVETO, (1.58, -2.57)),
-        ^4$(Path.CURVE4, (0.35, -1.1)),
-        ^4$(Path.CURVE4, (-1.75, 2.0)),
-        ^4$(Path.CURVE4, (0.375, 2.0)),
-        ^4$(Path.LINETO, (0.85, 1.15)),
-        ^4$(Path.CURVE4, (2.2, 3.2)),
-        ^4$(Path.CURVE4, (3, 0.05)),
-        ^4$(Path.CURVE4, (2.0, -0.5)),
-        ^4$(Path.CLOSEPOLY, (1.58, -2.57)),
-        ^4$ ]
-    codes, verts = zip(*path_data)
-    path = mpath.Path(verts, codes)
-    patch = mpatches.PathPatch(path, facecolor='r', alpha=0.5)
-    ax.add_patch(patch)
-
-    # plot control points and connecting lines
-    x, y = zip(*path.vertices)
-    line, = ax.plot(x, y, 'go-')
-
-    ax.grid()
-    ax.axis('equal')
-    plt.show()
-
-
-<button>Run ...</button>
-
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-.. image:: 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
-
-
-三维绘图
----------
-
-
-导入 Axex3D：
-
-
-
-
-
-::
-
-
-    from mpl_toolkits.mplot3d import Axes3D
-    from matplotlib import cm
-    from matplotlib.ticker import LinearLocator, FormatStrFormatter
-    import matplotlib.pyplot as plt
-    import numpy as np
-
-    fig = plt.figure()
-    ax = fig.gca(projection='3d')
-    X = np.arange(-5, 5, 0.25)
-    Y = np.arange(-5, 5, 0.25)
-    X, Y = np.meshgrid(X, Y)
-    R = np.sqrt(X**2 + Y**2)
-    Z = np.sin(R)
-    surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1,        cmap=cm.coolwarm,
-             ^4$linewidth=0, antialiased=False)
-    ax.set_zlim(-1.01, 1.01)
-
-    ax.zaxis.set_major_locator(LinearLocator(10))
-    ax.zaxis.set_major_formatter(FormatStrFormatter('%.02f'))
-
-    fig.colorbar(surf, shrink=0.5, aspect=5)
-
-    plt.show()
-
-
-
-<button>Run ...</button>
-
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-.. image::data:image/png;base64,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-流向图
--------------
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-主要函数：plt.streamplot
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-::
-
-    import numpy as np
-    import matplotlib.pyplot as plt
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-    Y, X = np.mgrid[-3:3:100j, -3:3:100j]
-    U = -1 - X**2 + Y
-    V = 1 + X - Y**2
-    speed = np.sqrt(U*U + V*V)
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-    plt.streamplot(X, Y, U, V, color=U, linewidth=2, cmap=plt.cm.autumn)
-    plt.colorbar()
-
-    f, (ax1, ax2) = plt.subplots(ncols=2)
-    ax1.streamplot(X, Y, U, V, density=[0.5, 1])
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-    lw = 5*speed/speed.max()
-    ax2.streamplot(X, Y, U, V, density=0.6, color='k', linewidth=lw)
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-    plt.show()
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-<button>Run ...</button>
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-.. image:: 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
-
-
-
-
-椭圆
-------------
-
-
-Ellipse 对象：
-
-
-
-
-::
-
-
-    from pylab import figure, show, rand
-    from matplotlib.patches import Ellipse
-
-    NUM = 250
-
-    ells = [Ellipse(xy=rand(2)*10, width=rand(), height=rand(),     angle=rand()*360)
-            for i in range(NUM)]
-
-    fig = figure()
-    ax = fig.add_subplot(111, aspect='equal')
-    for e in ells:
-        ^4$ax.add_artist(e)
-        ^4$e.set_clip_box(ax.bbox)
-        ^4$e.set_alpha(rand())
-        ^4$e.set_facecolor(rand(3))
-
-    ax.set_xlim(0, 10)
-    ax.set_ylim(0, 10)
-
-    show()
-
-
-<button>Run ...</button>
-
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-.. image:: 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
-
-
-
-条状图
----------
-
-
-bar 函数：
-
-
-
-
-::
-
-    import numpy as np
-    import matplotlib.pyplot as plt
-
-
-    n_groups = 5
-
-    means_men = (20, 35, 30, 35, 27)
-    std_men = (2, 3, 4, 1, 2)
-
-    means_women = (25, 32, 34, 20, 25)
-    std_women = (3, 5, 2, 3, 3)
-
-    fig, ax = plt.subplots()
-
-    index = np.arange(n_groups)
-    bar_width = 0.35
-
-    opacity = 0.4
-    error_config = {'ecolor': '0.3'}
-
-    rects1 = plt.bar(index, means_men, bar_width,
-                     alpha=opacity,
-                     color='b',
-                     yerr=std_men,
-                     error_kw=error_config,
-                     label='Men')
-
-    rects2 = plt.bar(index + bar_width, means_women, bar_width,
-                     alpha=opacity,
-                     color='r',
-                     yerr=std_women,
-                     error_kw=error_config,
-                     label='Women')
-
-    plt.xlabel('Group')
-    plt.ylabel('Scores')
-    plt.title('Scores by group and gender')
-    plt.xticks(index + bar_width, ('A', 'B', 'C', 'D', 'E'))
-    plt.legend()
-
-    plt.tight_layout()
-    plt.show()
-
-
-<button>Run ...</button>
-
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-.. image:: 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ihh6iqquKcc85h0qRJVFVVMW3aNG677Ta+9KUvMXXqVLp3787NN9/MCSecsF322UnKbAu2yyN+tuJX+IN1dSUv09pf41mrrnYyyspOO+3EkUceyeOPP86QIUN44oknGDp0KH379uWJJ57g0EMPbZh2+umnc/DBB3Pvvffy8ssvM3LkSPbZZx9GjBgBwIMPPsjMmTOZNm0a5557LiNHjuSCCy5gyZIl3HHHHVxwwQW88cYbAJx99tn07t2b119/nfXr1/PVr36V/v37M25c0inmj3/8I+eccw4rV67klltuYezYsSxevHi77LPfJ2VmVkGGDx/OE088AcCTTz7JsGHDGDp06CbThg8fzlNPPcW1117LTjvtxBe/+EXOO+88ZsyY0bCeYcOGMXLkSDp16sQpp5zCypUrGT9+PJ06deL000/nzTffZO3atSxbtoyHHnqIn/zkJ3Tt2pXdd9+dSy65hHvuuadhXQMHDmTs2LFI4qyzzmLp0qW8995722V/XZMyM6sgw4YN46abbmLVqlUsX76cffbZh913350xY8awatUqXnjhBQ444AB69OjBzjvv3LDcgAEDeOaZZxrG99hjj4bhrl27Ul1d3fCuqfoXKa5fv5533nmHDRs2bPKm340bNzJgwICG8d69ezcMd+vWrWHZ4m1sLScpM7MKctRRR7FmzRpuvfVWjj76aAB23XVX+vbty5QpU+jXrx99+/bl/fffZ/369eyyyy4AvPXWW+y5554lb69///507tyZlStXUlXV9o1vbu4zM6sgXbt25fDDD+eGG27YpAffMccc0zBtzz335Mtf/jJXXHEFH3/8Mc8//zy33347o0ePLnl7ffr04fjjj+fSSy9l3bp1bNy4kddff72heTFrrkmZmbWke/dsO6aU+P744cOHM3fuXI455piGaUOHDuWmm25qSFx333033/rWt+jbty+77bYbV111Fcceeyzw2avmi21pfMaMGYwfP54DDzyQdevWMWjQIMaPH9/qdW0LJykzsxbkoWt/sWuuuYZrrrlmk2mnnnoqp556asN4v379mDWryceicuWVV24yftxxxzX05APYYYcd+PTTTxvGd911V26++WZuvvnmzdY1ZswYxowZs8m04mW3lZv7zMwst5ykzMwst5ykzMwst3xNqoPZHo+seXnuw0yZUPpF5Ep9zE9bqVuxgrqVKwE4qEcP7lqwAICanj2pqa4uZ2hmZeMk1cFsj0fWdP7oww71mJ+2UlNd7WRk1oib+8zMLLdckzIza8L2vNfHtp6TlJlZIxFR7hAs5eY+MzPLLScpMzPLLScpMzPLrcySlKQukp6WNF/SS5Imp9N7SJot6VVJj0gq7cmKZmbWYWSWpCLiI2BERBwCHAyMkHQMMB6YHRGDgd+l42ZmZpvJtLkvIj5IB3cCOgGrgFHA9HT6dOCkLGMwM7PKlWmSklQlaT6wDHgsIl4EekXEsrTIMqBXljGYmVnlyvQ+qYjYCBwi6XPAw5JGNJofkpq9IWHixIkNw4VCgUKhkFGk1tFcd90UVq8ufbkJE6aUvMySufNKfiSUWXtXW1tLbW1ti+Xa5GbeiFgj6dfAYcAySb0j4l1JfYD3mluuOEmZbU+rV8PAgeNKWqau7sGSlwF4o/b+kpcxa+8aVzwmTZrUZLkse/dV1/fck9QVGAnMA2YC9a9xHAP4f7CZmTUpy5pUH2C6pCqSZHhnRPxO0jzgF5LGAm8Cp2UYg5mZVbDMklRE1AGHNjH9feC4rLZrZmbthx8wa2ZWRnV1ddTV1TUM19Qk73urqalpGO7InKTMzMqoOBmNGjWKyZMnlzmifPGz+8zMLLecpMzMLLecpMzMLLecpMzMLLfccaIE7oVjZpWqUs9fTlIlcC8cM6tUlXr+cnOfmZnllpOUmZnllpOUmZnllpOUmZnlljtOWGbqVqygbuVKAA7q0YO7FiwAoKZnT2qqq8sZmlmmtvalmlD6izW7d4fLLiv9PWeVwknKMlNTXe1kZB3S1rxUE7buxZqLFpX+tuhK4uY+MzPLLScpMzPLLScpMzPLLV+TMtuCFSvqWLkyeZRMjx4HsWDBXQD07FlDdXV+HyVj1l44SZltQXW1k5FZOWXa3Cepv6THJL0o6QVJF6fTJ0p6R9K89HNClnGYmVllyromtQH4x4iYL2kX4FlJs4EAboiIGzLevpmZVbBMk1REvAu8mw6vl/Qy0C+drSy3bWZmla/NevdJ2gsYAsxNJ10k6TlJUyV1b6s4zMyscrRJx4m0qe9e4HtpjeqnwFXp7B8B1wNjGy83ceLEhuFCoUChUNjusU257jq29vklUyZMKG2B7t0Zd9llW7UtM2uftrUH6ctzH2bKhEUlb7fk8xds13NYbW0ttbW1LZbLPElJ2hG4D/hZRNwPEBHvFc2/DZjV1LLFSSozq1czbuDAkhd7sK6u5OWmLCr9i2Rm7du29iDt/NGHJZ+Ltub8Bdv3HNa44jFp0qQmy2Xdu0/AVOCliLixaHqfomJfA+qyjMPMzCpT1jWpo4HRwPOS5qXTfgB8Q9IhJL38FgIXZByHmZlVoKx79z1J07W1h7LcrpmZtQ9+dp+ZmeWWk5SZmeWWk5SZmeWWk5SZmeWWk5SZmeWWk5SZmeWW3ydlZhWprq6Ourq6huGamuSpDTU1NQ3DVvmcpMysIhUno1GjRjF58uQyR2RZcHOfmZnlVotJStK+krqkwyMkXexXa5iZWVtoTU3qPuATSfsCtwD9gbsyjcrMzIzWJamNEfEJcDLwbxFxGdCnhWXMzMy2WWs6TvxF0jeBs4AT02k7ZhdSadqyh0/dihXUrVwJwEE9enDXggXJtnr2pKa6ertuy8xse6rU81drktS5JK/SuDoiFkraG7gz27Bary17+NRUV+f6j2lm1pxKPX+1mKQi4kVJ44EB6fhC4NqsAzMzM2tN775RwDzgt+n4EEkzsw7MzMysNR0nJgJHAqsAImIeMCjDmMzMzIDWJakNEbG60bSNWQRjZmZWrDUdJ16UdAawg6T9gIuBp7INy8zMrHU1qQuBg4CPgbuBtcAlrVm5pP6SHpP0oqQXJF2cTu8habakVyU94idYmJlZU7ZYk5K0A/DriBgB/GAr1r8B+MeImC9pF+BZSbOBc4DZEfG/JV0OjE8/ZmZmDbZYk0qfNLFxa2s6EfFuRMxPh9cDLwP9gFHA9LTYdOCkrVm/mZm1b625JvVnoC6tAf05nRYRcXEpG5K0FzAEeBroFRHL0lnLgF6lrMvMzDqG1iSp/0w/kY6raLhV0qa++4DvRcQ6SQ3zIiIkNbm+oUNPbBgeOHAwAwfu3+K2JkyYUkpoLJk7j3EDB5a0jJll5+W5DzNlwqKSl5syYULpG+venXGXXVb6crbNamtrqa2tbbFca544MU1SZ2BwOumViNjQ2kAk7UiSoO6MiPvTycsk9Y6IdyX1Ad5ratkzz5zV2s0AUFf3IAMHjitpmTdq72+5kJm1mc4ffVjyD8cH6+q26sfmlEWlJ0PbPgqFAoVCoWF80qRJTZZrzRMnCsCrwE3p50+ShrcmCCVVpqnASxFxY9GsmcCYdHgM4ExhZmabaU1z3w3A8RGxAEDSYOAe4NBWLHs0MBp4XtK8dNoVwI+BX0gaC7wJnFZi3GZm1gG0JkntUJ+gACLi1bRreosi4kmar60d15p1mJlZx9WaZPOspNuAn5F0mjgDeCbTqMzMzGhdkvo28F2SxyEBzAFuziwiMzOzVGuSVCfgxoi4HkBSJ6BzplGZmZnRumf3PQp0LRrvBvxXNuGYmZl9pjVJqnP6SCMAImIdSaIyMzPLVGuS1J8lHVY/Iulw4MPsQjIzM0u05prUJST3NC1Nx3sDX88upNKsWFHHypV1APTocRALFtwFQM+eNVRX15QzNDMz20bNJilJRwBvR8T/k/R5YBxwMvAw8EYbxdei6monIzOz9mpLzX23kLzoEOAoYALJY5FWAaU9xdXMzGwrbKm5ryoi3k+HTwduiYj7gPskPZd9aGZm1tFtqSbVKX2COSSPMHqsaF6rHotkZma2LbaUbO4GHpe0AviA5EkTSNoPWN0GsZmZWQfXbJKKiKslPUrSm++RiNiYzhJwUVsEZ2bWnLoVK6hbuRKAg3r04K4FyXOwa3r2pKa6upyh2Xa0xWa7iPhDE9NezS4cM7PWqamudjLqAFpzM6+ZmVlZOEmZmVluOUmZmVluOUmZmVluOUmZmVluZZqkJN0uaZmkuqJpEyW9I2le+jkhyxjMzKxyZV2TugNonIQCuCEihqSf32Ycg5mZVahMk1REzCF5IG1jynK7ZmbWPpTrmtRFkp6TNFVS9zLFYGZmOVeOB8X+FLgqHf4RcD0wtqmCs2ZNbBgePLjA/vsXMg7NzMzaQm1tLbW1tS2Wa/MkFRHv1Q9Lug2Y1VzZE0+c2BYhmZlZGysUChQKhYbxSZMmNVmuzZv7JPUpGv0aUNdcWTMz69gyrUlJuhsYDlRLehu4EihIOoSkl99C4IIsYzAzs8qVaZKKiG80Mfn2LLdpZmbth584YWZmueUkZWZmueUkZWZmueUkZWZmueUkZWZmueUkZWZmueUkZWZmueUkZWZmueUkZWZmueUkZWZmueUkZWZmueUkZWZmueUkZWZmueUkZWZmueUkZWZmueUkZWZmueUkZWZmueUkZWZmueUkZWZmuZVpkpJ0u6RlkuqKpvWQNFvSq5IekdQ9yxjMzKxyZV2TugM4odG08cDsiBgM/C4dNzMz20ymSSoi5gCrGk0eBUxPh6cDJ2UZg5mZVa5yXJPqFRHL0uFlQK8yxGBmZhVgh3JuPCJCUjQ3f9asiQ3DgwcX2H//QhtEZWZmWautraW2trbFcuVIUssk9Y6IdyX1Ad5rruCJJ05su6jMzKzNFAoFCoVCw/ikSZOaLFeO5r6ZwJh0eAxwfxliMDOzCpB1F/S7gaeA/SW9Lekc4MfASEmvAsem42ZmZpvJtLkvIr7RzKzjstyumZm1D37ihJmZ5ZaTlJmZ5ZaTlJmZ5ZaTlJmZ5ZaTlJmZ5ZaTlJmZ5ZaTlJmZ5ZaTlJmZ5ZaTlJmZ5ZaTlJmZ5ZaTlJmZ5ZaTlJmZ5ZaTlJmZ5ZaTlJmZ5ZaTlJmZ5ZaTlJmZ5ZaTlJmZ5ZaTlJmZ5ZaTlJmZ5dYO5dqwpDeBtcCnwIaIOKJcsZiZWT6VLUkBARQi4v0yxmBmZjlW7uY+lXn7ZmaWY+VMUgH8l6RnJJ1fxjjMzCynytncd3RELJW0OzBb0isRMaeM8ZiZWc6ULUlFxNL03+WSfgUcAWySpGbNmtgwPHhwgf33L7RhhGZmlpXa2lpqa2tbLFeWJCWpG9ApItZJ2hk4HpjUuNyJJ05s69DMzKwNFAoFCoVCw/ikSZulAKB8NalewK8k1cfwHxHxSJliMTOznCpLkoqIhcAh5di2mZlVjnJ3QTczM2uWk5SZmeWWk5SZmeWWk5SZmeWWk5SZmeWWk5SZmeWWk5SZmeWWk5SZmeWWk5SZmeWWk5SZmeWWk5SZmeWWk5SZmeWWk5SZmeWWk5SZmeWWk5SZmeWWk5SZmeWWk5SZmeWWk5SZmeWWk5SZmeWWk5SZmeVW2ZKUpBMkvSLpT5IuL1ccy9asLNemK5aPWWl8vErj41Wa9n68ypKkJHUC/h04ATgQ+Iakz5cjlvfWvF+OzVY0H7PS+HiVxserNO39eJWrJnUE8FpEvBkRG4B7gL8vUyxmZpZT5UpS/YC3i8bfSaeZmZk1UES0/UalfwBOiIjz0/HRwJERcVFRmbYPzMzMyiYi1HjaDuUIBFgM9C8a709Sm2rQVLBmZtaxlKu57xlgP0l7SdoJOB2YWaZYzMwsp8pSk4qITyRdCDwMdAKmRsTL5YjFzMzyqyzXpMzMzFqjQz9xQtJJkjZK2r/cseSdpE8lzZM0X9Kzkr5U7pjyTlJvSfdIek3SM5J+LWm/cseVR0XfrxfS79ilknxdeguKjln955/KHVMWOnRNStLPga7Af0fExDKHk2uS1kXEX6XDxwM/iIhCeaPKr/RqLi5/AAADZklEQVQE+xRwR0RMSacdDOwaEU+WNbgcavT92h24C/i9/182r/iYtWcdtiYlaRfgSOBCko4b1nqfA9r3be7bbgTwl/oEBRARzztBtSwilgPjSP5vWgdXri7oefD3wG8j4i1JyyUdGhH/Xe6gcqyrpHlAF6APcGyZ48m7LwDPljuIShURCyV1krR7mrRsc/X/J+tdExG/LFs0GenISeobwE/S4V+m405SzfswIoYASDoKmEFyIramddx2dGsrDf8n27MOmaQk9SBpjvlC+mSLTiQnlcvKGliFiIi5kqolVUfEinLHk1MvAqeUO4hKJWkQ8KlrUdZRr0mdAsyIiL0iYu+IGAAslDS03IFVAkkHkCT29v2OgG0QEY8CnSWdXz9N0sGSjiljWBUh7Tjxf4F/K3csVn4dsiYFfB34caNp96XT57R9OBWhuP1bwFnRkbuGts7XgBvT96V9BCwELilvSLlV//3aEfiEpDn5J1tepMNrfE3qoYj4QdmiyUiH7oJuZmb51lGb+8zMrAI4SZmZWW45SZmZWW45SZmZWW45SZmZWW45SZmZWW45SZllRFIvSXdJej19VcdTkk4qd1xmlcRJyiwD6as67gdqI2KfiDic5GbxPRuV66g31Ju1im/mNcuApL8B/qWpd25JOhs4GdiZ5IfiycAdwN7AB8C4iKiTNBFYFxHXp8u9APxtusxvgWeAQ0meE3hWRHyY7V6ZtT3XpMyycRBbfqr+EOAfImIEcBXwbER8EfgBySOBYPMnqRePDwZuiogDgbXAd7ZL1GY54yRllo1NEoykm9LXov8xnTc7Ilans48G7gSIiMeAnpJaeuPq2xHxh3T4Z4AfXGvtkpOUWTZeJGmKAyAivgv8DbB7OunPjcqriXV8wqb/R7sUDRcnQeH3V1k75SRlloH0VR1dJH2raPLOzRSfA5wBIKkALI+IdcCbpIlO0qEk16zqDUhfPgnwTfz0fmun3HHCLCOSepO8buJIYDlJ7emnQDfgsIi4OC23G3A7MCgtMy4iXpDUBXgA6Ac8DRwFfIXkx+VDJB0nDiOptZ0ZER+13d6ZtQ0nKbMKI2kvYFZE1JQ5FLPMubnPrDL516V1CK5JmZlZbrkmZWZmueUkZWZmueUkZWZmueUkZWZmueUkZWZmufX/AYMHWp/GzUEOAAAAAElFTkSuQmCC
-
-
-饼状图
-----------
-
-
-pie 函数：
-
-
-
-
-::
-
-    import matplotlib.pyplot as plt
-
-
-    # The slices will be ordered and plotted counter-clockwise.
-    labels = 'Frogs', 'Hogs', 'Dogs', 'Logs'
-    sizes = [15, 30, 45, 10]
-    colors = ['yellowgreen', 'gold', 'lightskyblue', 'lightcoral']
-    explode = (0, 0.1, 0, 0) # only "explode" the 2nd slice (i.e. 'Hogs')
-
-    plt.pie(sizes, explode=explode, labels=labels, colors=colors,
-            ^4$autopct='%1.1f%%', shadow=True, startangle=90)
-    # Set aspect ratio to be equal so that pie is drawn as a circle.
-    plt.axis('equal')
-
-    plt.show()
-
-
-<button>Run ...</button>
-
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-.. image:: 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
-
-
-
-图像中的表格
---------------
-
-
-table 函数：
-
-
-
-
-
-::
-
-    import numpy as np
-    import matplotlib.pyplot as plt
-
-
-    data = [[  66386,  174296,   75131,  577908,   32015],
-            ^4$[  58230,  381139,   78045,   99308,  160454],
-            ^4$[  89135,   80552,  152558,  497981,  603535],
-            ^4$[  78415,   81858,  150656,  193263,   69638],
-            ^4$[ 139361,  331509,  343164,  781380,   52269]]
-
-    columns = ('Freeze', 'Wind', 'Flood', 'Quake', 'Hail')
-    rows = ['%d year' % x for x in (100, 50, 20, 10, 5)]
-
-    values = np.arange(0, 2500, 500)
-    value_increment = 1000
-
-    # Get some pastel shades for the colors
-    colors = plt.cm.BuPu(np.linspace(0, 0.5, len(columns)))
-    n_rows = len(data)
-
-    index = np.arange(len(columns)) + 0.3
-    bar_width = 0.4
-
-    # Initialize the vertical-offset for the stacked bar chart.
-    y_offset = np.array([0.0] * len(columns))
-
-    # Plot bars and create text labels for the table
-    cell_text = []
-    for row in range(n_rows):
-        ^4$plt.bar(index, data[row], bar_width, bottom=y_offset,     color=colors[row])
-        ^4$y_offset = y_offset + data[row]
-        ^4$cell_text.append(['%1.1f' % (x/1000.0) for x in y_offset])
-    # Reverse colors and text labels to display the last value at the     top.
-    colors = colors[::-1]
-    cell_text.reverse()
-
-    # Add a table at the bottom of the axes
-    the_table = plt.table(cellText=cell_text,
-                          ^4$rowLabels=rows,
-                          ^4$rowColours=colors,
-                          ^4$colLabels=columns,
-                          ^4$loc='bottom')
-
-    # Adjust layout to make room for the table:
-    plt.subplots_adjust(left=0.2, bottom=0.2)
-
-    plt.ylabel("Loss in ${0}'s".format(value_increment))
-    plt.yticks(values * value_increment, ['%d' % val for val in values])
-    plt.xticks([])
-    plt.title('Loss by Disaster')
-
-    plt.show()
-
-
-<button>Run ...</button>
-
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-.. image:: 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
-
-
-
-散点图
-------------
-
-scatter 函数：
-
-
-
-
-::
-
-    import numpy as np
-    import matplotlib.pyplot as plt
-    import matplotlib.cbook as cbook
-
-    # Load a numpy record array from yahoo csv data with fields date,
-    # open, close, volume, adj_close from the mpl-data/example directory.
-    # The record array stores python datetime.date as an object array in
-    # the date column
-    datafile = cbook.get_sample_data('goog.npy')
-    price_data = np.load(datafile).view(np.recarray)
-    price_data = price_data[-250:] # get the most recent 250 trading days
-
-    delta1 = np.diff(price_data.adj_close)/price_data.adj_close[:-1]
-
-    # Marker size in units of points^2
-    volume = (15 * price_data.volume[:-2] / price_data.volume[0])**2
-    close = 0.003 * price_data.close[:-2] / 0.003 * price_data.open[:-2]
-
-    fig, ax = plt.subplots()
-    ax.scatter(delta1[:-1], delta1[1:], c=close, s=volume, alpha=0.5)
-
-    ax.set_xlabel(r'$\Delta_i$', fontsize=20)
-    ax.set_ylabel(r'$\Delta_{i+1}$', fontsize=20)
-    ax.set_title('Volume and percent change')
-
-    ax.grid(True)
-    fig.tight_layout()
-
-    plt.show()
-
-
-<button>Run ...</button>
-
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-.. image:: 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-
-
-设置按钮
-----------
-
-
-matplotlib.widgets 模块：
-
-
-
-
-
-::
-
-    import numpy as np
-    import matplotlib.pyplot as plt
-    from matplotlib.widgets import Slider, Button, RadioButtons
-
-    fig, ax = plt.subplots()
-    plt.subplots_adjust(left=0.25, bottom=0.25)
-    t = np.arange(0.0, 1.0, 0.001)
-    a0 = 5
-    f0 = 3
-    s = a0*np.sin(2*np.pi*f0*t)
-    l, = plt.plot(t,s, lw=2, color='red')
-    plt.axis([0, 1, -10, 10])
-
-    axcolor = 'lightgoldenrodyellow'
-    axfreq = plt.axes([0.25, 0.1, 0.65, 0.03], axisbg=axcolor)
-    axamp  = plt.axes([0.25, 0.15, 0.65, 0.03], axisbg=axcolor)
-
-    sfreq = Slider(axfreq, 'Freq', 0.1, 30.0, valinit=f0)
-    samp = Slider(axamp, 'Amp', 0.1, 10.0, valinit=a0)
-
-    def update(val):
-        ^4$amp = samp.val
-        ^4$freq = sfreq.val
-        ^4$l.set_ydata(amp*np.sin(2*np.pi*freq*t))
-        ^4$fig.canvas.draw_idle()
-    sfreq.on_changed(update)
-    samp.on_changed(update)
-
-    resetax = plt.axes([0.8, 0.025, 0.1, 0.04])
-    button = Button(resetax, 'Reset', color=axcolor, hovercolor='0.975')
-    def reset(event):
-        ^4$sfreq.reset()
-        ^4$samp.reset()
-    button.on_clicked(reset)
-
-    rax = plt.axes([0.025, 0.5, 0.15, 0.15], axisbg=axcolor)
-    radio = RadioButtons(rax, ('red', 'blue', 'green'), active=0)
-    def colorfunc(label):
-        ^4$l.set_color(label)
-        ^4$fig.canvas.draw_idle()
-    radio.on_clicked(colorfunc)
-
-    plt.show()
-
-<button>Run ...</button>
-
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-.. image:: 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
-
-
-
-填充曲线
-----------------
-
-fill 函数：
-
-
-
-::
-
-    import numpy as np
-    import matplotlib.pyplot as plt
-
-
-    x = np.linspace(0, 1)
-    y = np.sin(4 * np.pi * x) * np.exp(-5 * x)
-
-    plt.fill(x, y, 'r')
-    plt.grid(True)
-    plt.show()
-
-
-<button>Run ...</button>
-
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-.. image:: 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
-
-
-
-时间刻度
------------
-
-
-
-
-::
-
-    """
-    Show how to make date plots in matplotlib using date tick locators and
-    formatters.  See major_minor_demo1.py for more information on
-    controlling major and minor ticks
-
-    All matplotlib date plotting is done by converting date instances into
-    days since the 0001-01-01 UTC.  The conversion, tick locating and
-    formatting is done behind the scenes so this is most transparent to
-    you.  The dates module provides several converter functions date2num
-    and num2date
- 
-    """
-    import datetime
-    import numpy as np
-    import matplotlib.pyplot as plt
-    import matplotlib.dates as mdates
-    import matplotlib.cbook as cbook
-
-    years    = mdates.YearLocator()   # every year
-    months   = mdates.MonthLocator()  # every month
-    yearsFmt = mdates.DateFormatter('%Y')
-
-    # load a numpy record array from yahoo csv data with fields date,
-    # open, close, volume, adj_close from the mpl-data/example directory.
-    # The record array stores python datetime.date as an object array in
-    # the date column
-    datafile = cbook.get_sample_data('goog.npy')
-    r = np.load(datafile).view(np.recarray)
-
-    fig, ax = plt.subplots()
-    ax.plot(r.date, r.adj_close)
-
-
-    # format the ticks
-    ax.xaxis.set_major_locator(years)
-    ax.xaxis.set_major_formatter(yearsFmt)
-    ax.xaxis.set_minor_locator(months)
-
-    datemin = datetime.date(r.date.min().year, 1, 1)
-    datemax = datetime.date(r.date.max().year+1, 1, 1)
-    ax.set_xlim(datemin, datemax)
-
-    # format the coords message box
-    def price(x): return '$%1.2f'%x
-    ax.format_xdata = mdates.DateFormatter('%Y-%m-%d')
-    ax.format_ydata = price
-    ax.grid(True)
-
-    # rotates and right aligns the x labels, and moves the bottom of the
-    # axes up to make room for them
-    fig.autofmt_xdate()
-
-    plt.show()
-
-
-
-<button>Run ...</button>
-
-
-.. image:: 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
-
-
-金融数据
---------------
-
-
-
-::
-
-    import datetime
-    import numpy as np
-    import matplotlib.colors as colors
-    import matplotlib.finance as finance
-    import matplotlib.dates as mdates
-    import matplotlib.ticker as mticker
-    import matplotlib.mlab as mlab
-    import matplotlib.pyplot as plt
-    import matplotlib.font_manager as font_manager
-
-
-    startdate = datetime.date(2006,1,1)
-    today = enddate = datetime.date.today()
-    ticker = 'SPY'
-
-
-    fh = finance.fetch_historical_yahoo(ticker, startdate, enddate)
-    # a numpy record array with fields: date, open, high, low, close, volume, adj_close)
-
-    r = mlab.csv2rec(fh); fh.close()
-    r.sort()
-
-
-    def moving_average(x, n, type='simple'):
-        ^4$"""
-        ^4$compute an n period moving average.
-
-        ^4$type is 'simple' | 'exponential'
-
-        ^4$"""
-        ^4$x = np.asarray(x)
-        ^4$if type=='simple':
-            ^4$^4$weights = np.ones(n)
-        ^4$else:
-            ^4$^4$weights = np.exp(np.linspace(-1., 0., n))
-
-        ^4$weights /= weights.sum()
-
-
-        ^4$a =  np.convolve(x, weights, mode='full')[:len(x)]
-        ^4$a[:n] = a[n]
-        ^4$return a
-
-    def relative_strength(prices, n=14):
-        ^4$"""
-        ^4$compute the n period relative strength indicator
-        ^4$http://stockcharts.com/school/doku.php?    id=chart_school:glossary_r#relativestrengthindex
-        ^4$http://www.investopedia.com/terms/r/rsi.asp
-        ^4$"""
-
-        ^4$deltas = np.diff(prices)
-        ^4$seed = deltas[:n+1]
-        ^4$up = seed[seed>=0].sum()/n
-        ^4$down = -seed[seed<0].sum()/n
-        ^4$rs = up/down
-        ^4$rsi = np.zeros_like(prices)
-        ^4$rsi[:n] = 100. - 100./(1.+rs)
-
-        ^4$for i in range(n, len(prices)):
-            ^4$^4$delta = deltas[i-1] # cause the diff is 1 shorter
-
-            ^4$^4$if delta>0:
-                ^4$^4$^4$upval = delta
-                ^4$^4$^4$downval = 0.
-            ^4$^4$else:
-                ^4$^4$^4$upval = 0.
-                ^4$^4$^4$downval = -delta
-
-            ^4$^4$up = (up*(n-1) + upval)/n
-            ^4$^4$down = (down*(n-1) + downval)/n
-
-            ^4$^4$rs = up/down
-            ^4$^4$rsi[i] = 100. - 100./(1.+rs)
-
-        ^4$return rsi
-
-    def moving_average_convergence(x, nslow=26, nfast=12):
-        ^4$"""
-        ^4$compute the MACD (Moving Average Convergence/Divergence) using a fast and slow exponential moving avg'
-        ^4$return value is emaslow, emafast, macd which are len(x) arrays
-       """
-        ^4$emaslow = moving_average(x, nslow, type='exponential')
-        ^4$emafast = moving_average(x, nfast, type='exponential')
-        ^4$return emaslow, emafast, emafast - emaslow
-
-
-    plt.rc('axes', grid=True)
-    plt.rc('grid', color='0.75', linestyle='-', linewidth=0.5)
-
-    textsize = 9
-    left, width = 0.1, 0.8
-    rect1 = [left, 0.7, width, 0.2]
-    rect2 = [left, 0.3, width, 0.4]
-    rect3 = [left, 0.1, width, 0.2]
-
-
-    fig = plt.figure(facecolor='white')
-    axescolor  = '#f6f6f6'  # the axes background color
-
-    ax1 = fig.add_axes(rect1, axisbg=axescolor)  #left, bottom, width, height
-    ax2 = fig.add_axes(rect2, axisbg=axescolor, sharex=ax1)
-    ax2t = ax2.twinx()
-    ax3  = fig.add_axes(rect3, axisbg=axescolor, sharex=ax1)
-
-
-
-    ### plot the relative strength indicator
-    prices = r.adj_close
-    rsi = relative_strength(prices)
-    fillcolor = 'darkgoldenrod'
-
-    ax1.plot(r.date, rsi, color=fillcolor)
-    ax1.axhline(70, color=fillcolor)
-    ax1.axhline(30, color=fillcolor)
-    ax1.fill_between(r.date, rsi, 70, where=(rsi>=70),     facecolor=fillcolor, edgecolor=fillcolor)
-    ax1.fill_between(r.date, rsi, 30, where=(rsi<=30),     facecolor=fillcolor, edgecolor=fillcolor)
-    ax1.text(0.6, 0.9, '>70 = overbought', va='top',     transform=ax1.transAxes, fontsize=textsize)
-    ax1.text(0.6, 0.1, '<30 = oversold', transform=ax1.transAxes,     fontsize=textsize)
-    ax1.set_ylim(0, 100)
-    ax1.set_yticks([30,70])
-    ax1.text(0.025, 0.95, 'RSI (14)', va='top', transform=ax1.transAxes,     fontsize=textsize)
-    ax1.set_title('%s daily'%ticker)
-
-    ### plot the price and volume data
-    dx = r.adj_close - r.close
-    low = r.low + dx
-    high = r.high + dx
-
-    deltas = np.zeros_like(prices)
-    deltas[1:] = np.diff(prices)
-    up = deltas>0
-    ax2.vlines(r.date[up], low[up], high[up], color='black',     label='_nolegend_')
-    ax2.vlines(r.date[~up], low[~up], high[~up], color='black',     label='_nolegend_')
-    ma20 = moving_average(prices, 20, type='simple')
-    ma200 = moving_average(prices, 200, type='simple')
-
-    linema20, = ax2.plot(r.date, ma20, color='blue', lw=2, label='MA (20)')
-    linema200, = ax2.plot(r.date, ma200, color='red', lw=2, label='MA (200)')
-
-
-    last = r[-1]
-    s = '%s O:%1.2f H:%1.2f L:%1.2f C:%1.2f, V:%1.1fM Chg:%+1.2f' % (
-        ^4$today.strftime('%d-%b-%Y'),
-        ^4$last.open, last.high,
-        ^4$last.low, last.close,
-        ^4$last.volume*1e-6,
-        ^4$last.close-last.open )
-    t4 = ax2.text(0.3, 0.9, s, transform=ax2.transAxes, fontsize=textsize)
-
-    props = font_manager.FontProperties(size=10)
-    leg = ax2.legend(loc='center left', shadow=True, fancybox=True,     prop=props)
-    leg.get_frame().set_alpha(0.5)
-
-
-    volume = (r.close*r.volume)/1e6  # dollar volume in millions
-    vmax = volume.max()
-    poly = ax2t.fill_between(r.date, volume, 0, label='Volume',     facecolor=fillcolor, edgecolor=fillcolor)
-    ax2t.set_ylim(0, 5*vmax)
-    ax2t.set_yticks([])
-
-
-    ### compute the MACD indicator
-    fillcolor = 'darkslategrey'
-    nslow = 26
-    nfast = 12
-    nema = 9
-    emaslow, emafast, macd = moving_average_convergence(prices, nslow=nslow, nfast=nfast)
-    ema9 = moving_average(macd, nema, type='exponential')
-    ax3.plot(r.date, macd, color='black', lw=2)
-    ax3.plot(r.date, ema9, color='blue', lw=1)
-    ax3.fill_between(r.date, macd-ema9, 0, alpha=0.5,     facecolor=fillcolor, edgecolor=fillcolor)
-
-
-    ax3.text(0.025, 0.95, 'MACD (%d, %d, %d)'%(nfast, nslow, nema),     va='top',
-             ^4$transform=ax3.transAxes, fontsize=textsize)
-
-    #ax3.set_yticks([])
-    # turn off upper axis tick labels, rotate the lower ones, etc
-    for ax in ax1, ax2, ax2t, ax3:
-        ^4$if ax!=ax3:
-            ^4$^4$for label in ax.get_xticklabels():
-                ^4$^4$^4$label.set_visible(False)
-        ^4$else:
-            ^4$^4$for label in ax.get_xticklabels():
-                ^4$^4$^4$label.set_rotation(30)
-                ^4$^4$^4$label.set_horizontalalignment('right')
-
-        ^4$ax.fmt_xdata = mdates.DateFormatter('%Y-%m-%d')
-
-
-
-    class MyLocator(mticker.MaxNLocator):
-        ^4$def __init__(self, *args, **kwargs):
-            ^4$^4$mticker.MaxNLocator.__init__(self, *args, **kwargs)
-
-        ^4$def __call__(self, *args, **kwargs):
-            ^4$^4$return mticker.MaxNLocator.__call__(self, *args, **kwargs)
-
-    # at most 5 ticks, pruning the upper and lower so they don't overlap
-    # with other ticks
-    #ax2.yaxis.set_major_locator(mticker.MaxNLocator(5, prune='both'))
-    #ax3.yaxis.set_major_locator(mticker.MaxNLocator(5, prune='both'))
-
-    ax2.yaxis.set_major_locator(MyLocator(5, prune='both'))
-    ax3.yaxis.set_major_locator(MyLocator(5, prune='both'))
-
-    plt.show()
-
-
-
-<button>Run ...</button>
-
-
-.. image:: 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
-
-
-
-basemap 画地图
---------------------
-
-需要安装 basemap 包：
-
-
-
-
-
-::
-
-    import matplotlib.pyplot as plt
-    import numpy as np
-
-    try:
-        ^4$from mpl_toolkits.basemap import Basemap
-        ^4$have_basemap = True
-    except ImportError:
-        ^4$have_basemap = False
-
-
-    def plotmap():
-        ^4$# create figure
-        ^4$fig = plt.figure(figsize=(8,8))
-        ^4$# set up orthographic map projection with
-        ^4$# perspective of satellite looking down at 50N, 100W.
-        ^4$# use low resolution coastlines.
-        ^4$map = Basemap(projection='ortho',lat_0=50,lon_0=-100,resolution='l')
-        ^4$# lat/lon coordinates of five cities.
-        ^4$lats=[40.02,32.73,38.55,48.25,17.29]
-        ^4$lons=[-105.16,-117.16,-77.00,-114.21,-88.10]
-        ^4$cities=['Boulder, CO','San Diego, CA',
-        ^4$^4$'Washington, DC','Whitefish, MT','Belize City, Belize']
-        ^4$# compute the native map projection coordinates for cities.
-        ^4$xc,yc = map(lons,lats)
-        ^4$# make up some data on a regular lat/lon grid.
-        ^4$nlats = 73; nlons = 145; delta = 2.*np.pi/(nlons-1)
-        ^4$lats = (0.5*np.pi-delta*np.indices((nlats,nlons))[0,:,:])
-        ^4$lons = (delta*np.indices((nlats,nlons))[1,:,:])
-        ^4$wave = 0.75*(np.sin(2.*lats)**8*np.cos(4.*lons))
-        ^4$mean = 0.5*np.cos(2.*lats)*((np.sin(2.*lats))**2 + 2.)
-        ^4$# compute native map projection coordinates of lat/lon grid.
-        ^4$# (convert lons and lats to degrees first)
-        ^4$x, y = map(lons*180./np.pi, lats*180./np.pi)
-        ^4$# draw map boundary
-        ^4$map.drawmapboundary(color="0.9")
-        ^4$# draw graticule (latitude and longitude grid lines)
-        ^4$map.drawmeridians(np.arange(0,360,30),color="0.9")
-        ^4$map.drawparallels(np.arange(-90,90,30),color="0.9")
-        ^4$# plot filled circles at the locations of the cities.
-        ^4$map.plot(xc,yc,'wo')
-        ^4$# plot the names of five cities.
-        ^4$for name,xpt,ypt in zip(cities,xc,yc):
-        ^4$^4$plt.text(xpt+100000,ypt+100000,name,fontsize=9,color='w')
-        ^4$# contour data over the map.
-        ^4$cs = map.contour(x,y,wave+mean,15,linewidths=1.5)
-        ^4$# draw blue marble image in background.
-        ^4$# (downsample the image by 50% for speed)
-        ^4$map.bluemarble(scale=0.5)
-
-    def plotempty():
-        ^4$# create figure
-        ^4$fig = plt.figure(figsize=(8,8))
-        ^4$fig.text(0.5, 0.5, "Sorry, could not import Basemap",
-        ^4$^4$horizontalalignment='center')
-
-    if have_basemap:
-        ^4$plotmap()
-    else:
-        ^4$plotempty()
-    plt.show()
-
-
-<button>Run ...</button>
-
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-.. image:: 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
-
-
-对数图
------------
-
-loglog, semilogx, semilogy, errorbar 函数：
-
-
-
-
-::
-
-    import numpy as np
-    import matplotlib.pyplot as plt
-
-    plt.subplots_adjust(hspace=0.4)
-    t = np.arange(0.01, 20.0, 0.01)
-
-    # log y axis
-    plt.subplot(221)
-    plt.semilogy(t, np.exp(-t/5.0))
-    plt.title('semilogy')
-    plt.grid(True)
-
-    # log x axis
-    plt.subplot(222)
-    plt.semilogx(t, np.sin(2*np.pi*t))
-    plt.title('semilogx')
-    plt.grid(True)
-
-    # log x and y axis
-    plt.subplot(223)
-    plt.loglog(t, 20*np.exp(-t/10.0), basex=2)
-    plt.grid(True)
-    plt.title('loglog base 4 on x')
-
-    # with errorbars: clip non-positive values
-    ax = plt.subplot(224)
-    ax.set_xscale("log", nonposx='clip')
-    ax.set_yscale("log", nonposy='clip')
-
-    x = 10.0**np.linspace(0.0, 2.0, 20)
-    y = x**2.0
-    plt.errorbar(x, y, xerr=0.1*x, yerr=5.0+0.75*y)
-    ax.set_ylim(ymin=0.1)
-    ax.set_title('Errorbars go negative')
-
-
-    plt.show()
-
-
-<button>Run ...</button>
-
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-.. image:: 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
-
-
-极坐标
----------
-
-设置 polar=True：
-
-
-
-
-::
-
-    import numpy as np
-    import matplotlib.pyplot as plt
-
-
-    r = np.arange(0, 3.0, 0.01)
-    theta = 2 * np.pi * r
-
-    ax = plt.subplot(111, polar=True)
-    ax.plot(theta, r, color='r', linewidth=3)
-    ax.set_rmax(2.0)
-    ax.grid(True)
-
-    ax.set_title("A line plot on a polar axis", va='bottom')
-    plt.show()
-
-
-<button>Run ...</button>
-
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-.. image:: 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
-
-
-
-标注
----------
-
-legend 函数：
-
-
-
-
-::
-
-    import numpy as np
-    import matplotlib.pyplot as plt
-
-    # Make some fake data.
-    a = b = np.arange(0,3, .02)
-    c = np.exp(a)
-    d = c[::-1]
-
-    # Create plots with pre-defined labels.
-    plt.plot(a, c, 'k--', label='Model length')
-    plt.plot(a, d, 'k:', label='Data length')
-    plt.plot(a, c+d, 'k', label='Total message length')
-
-    legend = plt.legend(loc='upper center', shadow=True, fontsize='x-large')
-
-    # Put a nicer background color on the legend.
-    legend.get_frame().set_facecolor('#00FFCC')
-
-    plt.show()
-
-
-<button>Run ...</button>
-
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-.. image:: 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
-
-
-
-数学公式
---------------
-
-
-
-
-::
-
-    from __future__ import print_function
-    import matplotlib.pyplot as plt
-    import os
-    import sys
-    import re
-    import gc
-
-    # Selection of features following "Writing mathematical expressions" tutorial
-    mathtext_titles = {
-        ^4$0: "Header demo",
-        ^4$1: "Subscripts and superscripts",
-        ^4$2: "Fractions, binomials and stacked numbers",
-        ^4$3: "Radicals",
-        ^4$4: "Fonts",
-        ^4$5: "Accents",
-        ^4$6: "Greek, Hebrew",
-        ^4$7: "Delimiters, functions and Symbols"}
-    n_lines = len(mathtext_titles)
-
-    # Randomly picked examples
-    mathext_demos = {
-        ^4$0: r"$W^{3\beta}_{\delta_1 \rho_1 \sigma_2} = "
-        ^4$r"U^{3\beta}_{\delta_1 \rho_1} + \frac{1}{8 \pi 2} "
-        ^4$r"\int^{\alpha_2}_{\alpha_2} d \alpha^\prime_2 \left[\frac{ "
-        ^4$r"U^{2\beta}_{\delta_1 \rho_1} - \alpha^\prime_2U^{1\beta}_"
-        ^4$r"{\rho_1 \sigma_2} }{U^{0\beta}_{\rho_1 \sigma_2}}\right]$",
-
-        ^4$1: r"$\alpha_i > \beta_i,\ "
-        ^4$r"\alpha_{i+1}^j = {\rm sin}(2\pi f_j t_i) e^{-5 t_i/\tau},\ "
-        ^4$r"\ldots$",
-
-        ^4$2: r"$\frac{3}{4},\ \binom{3}{4},\ \stackrel{3}{4},\ "
-        ^4$r"\left(\frac{5 - \frac{1}{x}}{4}\right),\ \ldots$",
-
-        ^4$3: r"$\sqrt{2},\ \sqrt[3]{x},\ \ldots$",
-
-        ^4$4: r"$\mathrm{Roman}\ , \ \mathit{Italic}\ , \ \mathtt{Typewriter} \ "
-        ^4$r"\mathrm{or}\ \mathcal{CALLIGRAPHY}$",
-
-        ^4$5: r"$\acute a,\ \bar a,\ \breve a,\ \dot a,\ \ddot a, \ \grave a, \ "
-        ^4$r"\hat a,\ \tilde a,\ \vec a,\ \widehat{xyz},\ \widetilde{xyz},\ "
-        ^4$r"\ldots$",
-
-        ^4$6: r"$\alpha,\ \beta,\ \chi,\ \delta,\ \lambda,\ \mu,\ "
-        ^4$r"\Delta,\ \Gamma,\ \Omega,\ \Phi,\ \Pi,\ \Upsilon,\ \nabla,\ "
-        ^4$r"\aleph,\ \beth,\ \daleth,\ \gimel,\ \ldots$",
-
-        ^4$7: r"$\coprod,\ \int,\ \oint,\ \prod,\ \sum,\ "
-        ^4$r"\log,\ \sin,\ \approx,\ \oplus,\ \star,\ \varpropto,\ "
-        ^4$r"\infty,\ \partial,\ \Re,\ \leftrightsquigarrow, \ \ldots$"}
-
-
-    def doall():
-        ^4$# Colors used in mpl online documentation.
-        ^4$mpl_blue_rvb = (191./255., 209./256., 212./255.)
-        ^4$mpl_orange_rvb = (202/255., 121/256., 0./255.)
-        ^4$mpl_grey_rvb = (51./255., 51./255., 51./255.)
-
-        ^4$# Creating figure and axis.
-        ^4$plt.figure(figsize=(6, 7))
-        ^4$plt.axes([0.01, 0.01, 0.98, 0.90], axisbg="white", frameon=True)
-        ^4$plt.gca().set_xlim(0., 1.)
-        ^4$plt.gca().set_ylim(0., 1.)
-        ^4$plt.gca().set_title("Matplotlib's math rendering engine",
-            ^4$^4$            color=mpl_grey_rvb, fontsize=14, weight='bold')
-        ^4$plt.gca().set_xticklabels("", visible=False)
-        ^4$plt.gca().set_yticklabels("", visible=False)
-
-        ^4$# Gap between lines in axes coords
-        ^4$line_axesfrac = (1. / (n_lines))
-
-        ^4$# Plotting header demonstration formula
-        ^4$full_demo = mathext_demos[0]
-        ^4$plt.annotate(full_demo,
-            ^4$^4$         xy=(0.5, 1. - 0.59*line_axesfrac),
-            ^4$^4$         xycoords='data', color=mpl_orange_rvb, ha='center',
-            ^4$^4$         fontsize=20)
-
-        ^4$# Plotting features demonstration formulae
-        ^4$for i_line in range(1, n_lines):
-            ^4$^4$baseline = 1. - (i_line)*line_axesfrac
-            ^4$^4$baseline_next = baseline - line_axesfrac*1.
-            ^4$^4$title = mathtext_titles[i_line] + ":"
-            ^4$^4$fill_color = ['white', mpl_blue_rvb][i_line % 2]
-            ^4$^4$plt.fill_between([0., 1.], [baseline, baseline],
-            ^4$^4$                 [baseline_next, baseline_next],
-            ^4$^4$                 color=fill_color, alpha=0.5)
-            ^4$^4$plt.annotate(title,
-            ^4$^4$             xy=(0.07, baseline - 0.3*line_axesfrac),
-            ^4$^4$             xycoords='data', color=mpl_grey_rvb, weight='bold')
-            ^4$^4$demo = mathext_demos[i_line]
-            ^4$^4$plt.annotate(demo,
-            ^4$^4$             xy=(0.05, baseline - 0.75*line_axesfrac),
-            ^4$^4$             xycoords='data', color=mpl_grey_rvb,
-            ^4$^4$             fontsize=16)
-
-        ^4$for i in range(n_lines):
-            ^4$^4$s = mathext_demos[i]
-            ^4$^4$print(i, s)
-        ^4$plt.show()
-
-    if '--latex' in sys.argv:
-        ^4$# Run: python mathtext_examples.py --latex
-        ^4$# Need amsmath and amssymb packages.
-        ^4$fd = open("mathtext_examples.ltx", "w")
-        ^4$fd.write("\\documentclass{article}\n")
-        ^4$fd.write("\\usepackage{amsmath, amssymb}\n")
-        ^4$fd.write("\\begin{document}\n")
-        ^4$fd.write("\\begin{enumerate}\n")
-
-        ^4$for i in range(n_lines):
-            ^4$^4$s = mathext_demos[i]
-            ^4$^4$s = re.sub(r"(?<!\\)\$", "$$", s)
-            ^4$^4$fd.write("\\item %s\n" % s)
-
-        ^4$fd.write("\\end{enumerate}\n")
-        ^4$fd.write("\\end{document}\n")
-        ^4$fd.close()
-
-        ^4$os.system("pdflatex mathtext_examples.ltx")
-    else:
-        ^4$doall()
-    0 $W^{3\beta}_{\delta_1 \rho_1 \sigma_2} = U^{3\beta}_{\delta_1 \rho_1} + \frac{1}{8 \pi 2} \int^{\alpha_2}_{\alpha_2} d \alpha^\prime_2 \left[\frac{ U^{2\beta}_    {\delta_1 \rho_1} - \alpha^\prime_2U^{1\beta}_{\rho_1 \sigma_2} }{U^{0\beta}_{\rho_1 \sigma_2}}\right]$
-    1 $\alpha_i > \beta_i,\ \alpha_{i+1}^j = {\rm sin}(2\pi f_j t_i) e^{-5 t_i/\tau},\ \ldots$
-    2 $\frac{3}{4},\ \binom{3}{4},\ \stackrel{3}{4},\ \left(\frac{5 - \frac{1}{x}}{4}\right),\ \ldots$
-    3 $\sqrt{2},\ \sqrt[3]{x},\ \ldots$
-    4 $\mathrm{Roman}\ , \ \mathit{Italic}\ , \ \mathtt{Typewriter} \ \mathrm{or}\ \mathcal{CALLIGRAPHY}$
-    5 $\acute a,\ \bar a,\ \breve a,\ \dot a,\ \ddot a, \ \grave a, \ \hat a,\ \tilde a,\ \vec a,\ \widehat{xyz},\ \widetilde{xyz},\ \ldots$
-    6 $\alpha,\ \beta,\ \chi,\ \delta,\ \lambda,\ \mu,\ \Delta,\ \Gamma,\ \Omega,\ \Phi,\ \Pi,\ \Upsilon,\ \nabla,\ \aleph,\ \beth,\ \daleth,\ \gimel,\ \ldots$
-    7 $\coprod,\ \int,\ \oint,\ \prod,\ \sum,\ \log,\ \sin,\ \approx,\ \oplus,\ \star,\ \varpropto,\ \infty,\ \partial,\ \Re,\ \leftrightsquigarrow, \ \ldots$
-
-
-
-<button>Run ...</button>
-
-
-.. image:: 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
-
-
-
-作者 & 更新时间
-------------------------------------
-作者:`李金  <lijinwithyou@gmail.com>`
-
-提交: 2017/12/14
-
-
-
-
-
-
diff --git a/course/source/aipy-numpy/cn/1pyplot.rst b/course/source/aipy-numpy/cn/1pyplot.rst
deleted file mode 100644
index 70585a5..0000000
--- a/course/source/aipy-numpy/cn/1pyplot.rst
+++ /dev/null
@@ -1,547 +0,0 @@
-
-Pyplot 教程
-================
-
-Matplotlib 简介
-------------------
-
-**matplotlib** 是一个 **Python** 的 2D 图形包。
-
-在线文档：http://matplotlib.org ，提供了 Examples, FAQ, API, Gallery，其中 Gallery 是很有用的一个部分，因为它提供了各种画图方式的可视化，方便用户根据需求进行选择。
-
-
-使用 Pyplot
------------------------
-
-导入相关的包：
-
-
-
-
-::
-
-    import numpy as np
-    import matplotlib.pyplot as plt
-
-
-matplotlib.pyplot 包含一系列类似 **MATLAB** 中绘图函数的相关函数。每个 matplotlib.pyplot 中的函数对当前的图像进行一些修改，例如：产生新的图像，在图像中产生新的绘图区域，在绘图区域中画线，给绘图加上标记，等等…… matplotlib.pyplot 会自动记住当前的图像和绘图区域，因此这些函数会直接作用在当前的图像上。
-
-
-下文中，以 plt 作为 matplotlib.pyplot 的省略。
-
-
-plt.show() 函数
-----------------------
-
-默认情况下，matplotlib.pyplot 不会直接显示图像，只有调用 plt.show() 函数时，图像才会显示出来。plt.show() 默认是在新窗口打开一幅图像，并且提供了对图像进行操作的按钮。
-
-
-不过在 ipython 命令行中，我们可以使用 magic 命令将它插入 notebook 中，并且不需要调用 plt.show() 也可以显示：
-
-
-- %matplotlib notebook
-- %matplotlib inline
-
-
-不过在实际写程序中，我们还是需要调用 plt.show() 函数将图像显示出来。
-
-
-这里我们使图像输出在 notebook 中：
-
-
-
-
-::
-
-    %matplotlib inline
-
-
-plt.plot() 函数
-------------------
-
-**例子**
-
-plt.plot() 函数可以用来绘图：
-
-
-
-
-::
-
-    plt.plot([1,2,3,4])
-    plt.ylabel('some numbers')
-
-    plt.show()
-
-
-.. image:: data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAYYAAAEACAYAAAC3adEgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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%0A
-
-
-**基本用法**
-
-plot 函数基本的用法有以下四种：
-
-默认参数
-
-- plt.plot(x,y)
-
-指定参数
-
-- plt.plot(x,y, format_str)
-
-默认参数，x 为 0~N-1
-
-- plt.plot(y)
-
-指定参数，x 为 0~N-1
-
-- plt.plot(y, format_str)
-
-
-因此，在上面的例子中，我们没有给定 x 的值，所以其默认值为 [0,1,2,3]。
-
-传入 x 和 y：
-
-
-
-
-::
-
-    plt.plot([1,2,3,4], [1,4,9,16])
-
-
-结果:
-::
-
-    [<matplotlib.lines.Line2D at 0xa48a550>]
-
-
-字符参数
--------------
-
-和 **MATLAB** 中类似，我们还可以用字符来指定绘图的格式：
-
-
-表示颜色的字符参数有：
-
-
-+------------+------------------------------+
-| 字符        | 颜色                         |                                           
-+============+==============================+
-|‘b’         |  蓝色，blue                   |                           
-+------------+------------------------------+
-|‘g’         |绿色，green                    |                               
-+------------+------------------------------+
-|‘r’         |红色，red                      |                             
-+------------+------------------------------+
-|‘c’         |青色，cyan                     |                                   
-+------------+------------------------------+
-|‘m’	     |品红，magenta                  |                              
-+------------+------------------------------+
-|‘y’	     |黄色，yellow                   |                                                     
-+------------+------------------------------+
-|‘k’	     |黑色，black                    |                                                       
-+------------+------------------------------+
-|‘w’	     |白色，white                    |                                               
-+------------+------------------------------+
-
-
-表示类型的字符参数有：
-
-
-
-+------------+------------------------------+-------------------------------+-----------------------------+
-| 字符        | 类型                         |字符                            |类型                         |
-+============+==============================+===============================+=============================+
-|'-'         | 实线                          | '--'                          |虚线                         |    
-+------------+------------------------------+-------------------------------+-----------------------------+
-|'-.'        |虚点线                         |':'                            |点线                         |
-+------------+------------------------------+-------------------------------+-----------------------------+
-|'.'         |点                            |','                            |像素点                       |
-+------------+------------------------------+-------------------------------+-----------------------------+
-|'o'         |圆点                           |'v'                            |下三角点                     |         
-+------------+------------------------------+-------------------------------+-----------------------------+
-|'^'	     |上三角点                       |'<'                            |左三角点                     |
-+------------+------------------------------+-------------------------------+-----------------------------+
-|'>'	     |右三角点                       |'1'                            |下三叉点                     |
-+------------+------------------------------+-------------------------------+-----------------------------+
-|'2'	     |上三叉点                       |'3'                            |左三叉点                     |
-+------------+------------------------------+-------------------------------+-----------------------------+
-|'4'	     |右三叉点                       |'s'                            |正方点                       |
-+------------+------------------------------+-------------------------------+-----------------------------+
-|'p'	     |五角点                         |'*'                            |星形点                       |
-+------------+------------------------------+-------------------------------+-----------------------------+
-|'h'	     |六边形点                       |'H'                            |六边形点2                    |
-+------------+------------------------------+-------------------------------+-----------------------------+
-|'+'	     |加号点                         |'*'                            |乘号点                       |
-+------------+------------------------------+-------------------------------+-----------------------------+
-|'D'	     |实心菱形点                     |'d'                            |瘦菱形点                     |
-+------------+------------------------------+-------------------------------+-----------------------------+
-|'_'	     |横线点                         |                               |                             |
-+------------+------------------------------+-------------------------------+-----------------------------+
-
-	
-例如我们要画出红色圆点：
-
-
-
-
-::
-
-    plt.plot([1,2,3,4], [1,4,9,16], 'ro')
-    plt.show()
-
-.. image:: data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAXMAAAEACAYAAABBDJb9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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%0ADHNJaoBhLkkNMMwlqQGGuSQ14P8AGTGlG2xI8vsAAAAASUVORK5CYII=%0A
-
-
-可以看出，有两个点在图像的边缘，因此，我们需要改变轴的显示范围。
-
-
-显示范围
------------
-
-与 **MATLAB** 类似，这里可以使用 axis 函数指定坐标轴显示的范围：
-
-plt.axis([xmin, xmax, ymin, ymax])
-
-
-
-
-::
-
-    plt.plot([1,2,3,4], [1,4,9,16], 'ro')
-    # 指定 x 轴显示区域为 0-6，y 轴为 0-20
-    plt.axis([0,6,0,20])
-    plt.show()
-
-
-.. image:: data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAW4AAAEACAYAAACTXJylAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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%0A
-
-
-传入 Numpy 数组
----------------------
-
-之前我们传给 plot 的参数都是列表，事实上，向 plot 中传入 numpy 数组是更常用的做法。事实上，如果传入的是列表，matplotlib 会在内部将它转化成数组再进行处理：
-
-
-
-
-
-::
-
-    import numpy as np
-    import matplotlib.pyplot as plt
-
-    # evenly sampled time at 200ms intervals
-    t = np.arange(0., 5., 0.2)
-
-    # red dashes, blue squares and green triangles
-    plt.plot(t, t, 'r--', 
-            ^4$^4$t, t**2, 'bs', 
-            ^4$^4$t, t**3, 'g^')
-
-    plt.show()
-
-
-
-<button>Run ...</button>
-
-
-.. image:: data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAXQAAAEACAYAAACj0I2EAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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%0A
-
-
-
-传入多组数据
---------------
-
-事实上，在上面的例子中，我们不仅仅向 plot 函数传入了数组，还传入了多组 (x,y,format_str) 参数，它们在同一张图上显示。
-这意味着我们不需要使用多个 plot 函数来画多组数组，只需要可以将这些组合放到一个 plot 函数中去即可。
-
-
-线条属性
--------------
-
-之前提到，我们可以用字符串来控制线条的属性，事实上还可以通过关键词来改变线条的性质，例如 linwidth 可以改变线条的宽度，color 可以改变线条的颜色：
-
-
-
-
-::
-
-    x = np.linspace(-np.pi,np.pi)
-    y = np.sin(x)
-
-    plt.plot(x, y, linewidth=2.0, color='r')
-
-    plt.show()
-
-
-<button>Run ...</button>
-
-
-.. image:: 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pnB+ef79bBhcWPJESV6kUyMGePJ/vDDYf/9Y0cjtXXOOX6A+2OPwbJlsaPJOiV6kUxsuHZekqtJ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%0AfxeZmr/ZF8WeAAAAAElFTkSuQmCC%0A
-
-
-
-使用 plt.plot() 的返回值来设置线条属性
----------------------------------------------
-
-plot 函数返回一个 Line2D 对象组成的列表，每个对象代表输入的一对组合，例如：
-
-
-- line1, line2 为两个 Line2D 对象
-
-  line1, line2 = plt.plot(x1, y1, x2, y2)
-
-
-- 返回 3 个 Line2D 对象组成的列表
-  lines = plt.plot(x1, y1, x2, y2, x3, y3)
-
-
-我们可以使用这个返回值来对线条属性进行设置：
-
-
-
-
-
-
-::
-
-    # 加逗号 line 中得到的是 line2D 对象，不加逗号得到的是只有一个     line2D 对象的列表
-    line, = plt.plot(x, y, 'r-')
-
-    # 将抗锯齿关闭
-    line.set_antialiased(False)
-
-    plt.show()
-
-<button>Run ...</button>
-
-
-.. image:: 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E/cgcFw1HzA2n5Q0k0R8YWK7x2QtBMRB6eXb5F0OiJur9iWcgDAFiJi6WS2dKLfwKIbeUTS5bYv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%0AJyfj1DY0XlkAAAAASUVORK5CYII=%0A
-
-
-plt.setp() 修改线条性质
---------------------------
-
-
-更方便的做法是使用 plt 的 setp 函数：
-
-
-
-
-
-::
-
-    lines = plt.plot(x, y)
-
-    # 使用键值对
-    plt.setp(lines, color='r', linewidth=2.0)
-
-    # 或者使用 MATLAB 风格的字符串对
-    plt.setp(lines, 'color', 'r', 'linewidth', 2.0)
-
-    plt.show()
-
-
-<button>Run ...</button>
-
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-.. image:: 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pnB+ef79bBhcWPJESV6kUyMGePJ/vDDYf/9Y0cjtXXOOX6A+2OPwbJlsaPJOiV6kUxsuHZekqtJ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%0AfxeZmr/ZF8WeAAAAAElFTkSuQmCC%0A
-
-
-可以设置的属性有很多，可以使用 plt.setp(lines) 查看 lines 可以设置的属性，各属性的含义可参考 matplotlib 的文档。
-
-
-
-
-::
-
-    plt.setp(lines)
-
-
-::
-
-      agg_filter: unknown
-      alpha: float (0.0 transparent through 1.0 opaque)         
-      animated: [True | False]         
-      antialiased or aa: [True | False]         
-      axes: an :class:`~matplotlib.axes.Axes` instance         
-      clip_box: a :class:`matplotlib.transforms.Bbox` instance         
-      clip_on: [True | False]         
-      clip_path: [ (:class:`~matplotlib.path.Path`,               :class:`~matplotlib.transforms.Transform`) |                    :class:`~matplotlib.patches.Patch` |       None ]         
-      color or c: any matplotlib color         
-      contains: a callable function         
-      dash_capstyle: ['butt' | 'round' | 'projecting']         
-      dash_joinstyle: ['miter' | 'round' | 'bevel']         
-      dashes: sequence of on/off ink in points         
-      drawstyle: ['default' | 'steps' | 'steps-pre' | 'steps-mid' |                   'steps-post']         
-      figure: a :class:`matplotlib.figure.Figure` instance         
-      fillstyle: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none']         
-      gid: an id string         
-      label: string or anything printable with '%s' conversion.         
-      linestyle or ls: [``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` |                   ``' '`` | ``''``]
-      linewidth or lw: float value in points         
-      lod: [True | False]         
-      marker: :mod:`A valid marker style <matplotlib.markers>`
-      markeredgecolor or mec: any matplotlib color         
-      markeredgewidth or mew: float value in points         
-      markerfacecolor or mfc: any matplotlib color         
-      markerfacecoloralt or mfcalt: any matplotlib color         
-      markersize or ms: float         
-      markevery: [None | int | length-2 tuple of int | slice |             list/array of int | float | length-2 tuple of float]
-      path_effects: unknown
-      picker: float distance in points or callable pick function         ``fn(artist, event)``         
-      pickradius: float distance in points         
-      rasterized: [True | False | None]         
-      sketch_params: unknown
-      snap: unknown
-      solid_capstyle: ['butt' | 'round' |  'projecting']         
-      solid_joinstyle: ['miter' | 'round' | 'bevel']         
-      transform: a :class:`matplotlib.transforms.Transform` instance         
-      url: a url string         
-      visible: [True | False]         
-      xdata: 1D array         
-      ydata: 1D array         
-      zorder: any number 
-
-        
-子图
---------
-
-
-figure() 函数会产生一个指定编号为 num 的图：
-
-     plt.figure(num)
-
-
-
-这里，figure(1) 其实是可以省略的，因为默认情况下 plt 会自动产生一幅图像。
-
-
-使用 subplot 可以在一副图中生成多个子图，其参数为：
-
-
-     plt.subplot(numrows, numcols, fignum)
-
-当 numrows * numcols < 10 时，中间的逗号可以省略，因此 plt.subplot(211) 就相当于 plt.subplot(2,1,1)。
-
-
-
-
-
-
-::
-
-    def f(t):
-        ^4$return np.exp(-t) * np.cos(2*np.pi*t)
-
-    t1 = np.arange(0.0, 5.0, 0.1)
-    t2 = np.arange(0.0, 5.0, 0.02)
-
-    plt.figure(1)
-    plt.subplot(211)
-    plt.plot(t1, f(t1), 'bo', t2, f(t2), 'k')
-
-    plt.subplot(212)
-    plt.plot(t2, np.cos(2*np.pi*t2), 'r--')
-    plt.show()
-
-<button>Run ...</button>
-
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-.. image:: 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swLI/hK/1L69r2Azp1vYc6cXUBTTAGXIb7KgXTiqj2HlStv5OKLP2bZskxgErAOc9M+FajG5s1b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%0ASXD+P5ePy54P9ccKAAAAAElFTkSuQmCC%0A
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swLI/hK/1L69r2Azp1vYc6cXUBTTAGXIb7KgXTiqj2HlStv5OKLP2bZskxgErAOc9M+FajG5s1b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%0ASXD+P5ePy54P9ccKAAAAAElFTkSuQmCC%0A
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-
-图形上加上文字
-------------------
-
-plt.hist() 可以用来画直方图。
-
-
-
-
-
-::
-
-    mu, sigma = 100, 15
-    x = mu + sigma * np.random.randn(10000)
-
-    # the histogram of the data
-    n, bins, patches = plt.hist(x, 50, normed=1, facecolor='g', alpha=0.75)
-
-
-    plt.xlabel('Smarts')
-    plt.ylabel('Probability')
-    plt.title('Histogram of IQ')
-    plt.text(60, .025, r'$\mu=100,\ \sigma=15$')
-    plt.axis([40, 160, 0, 0.03])
-    plt.grid(True)
-    plt.show()
-
-
-<button>Run ...</button>
-
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-.. image:: 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DrQ8MFbZmWQ9uUmiaodoE5N6vGNh/tQUpZ6fO1wg2JmZh3hLq8SSr0PN/X4OnkfSqP7YEZz3CX1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%0A60kAAAAASUVORK5CYII=%0A
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-对于这幅图形，我们使用 xlabel ，ylabel，title，text 方法设置了文字，其中：
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-- xlabel ：x 轴标注
-- ylabel ：y 轴标注
-- title ：图形标题
-- text ：在指定位置放入文字
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-输入特殊符号支持使用 Tex 语法，用 $<some Tex code>$ 隔开。
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-除了使用 text 在指定位置标上文字之外，还可以使用 annotate 函数进行注释，annotate 主要有两个参数：
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-- xy ：注释位置
-- xytext ：注释文字位置
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-::
-
-    ax = plt.subplot(111)
-
-    t = np.arange(0.0, 5.0, 0.01)
-    s = np.cos(2*np.pi*t)
-    line, = plt.plot(t, s, lw=2)
-
-    plt.annotate('local max', xy=(2, 1), xytext=(3, 1.5),
-                ^4$arrowprops=dict(facecolor='black', shrink=0.05),
-                ^4$)
-
-    plt.ylim(-2,2)
-    plt.show()
-
-
-
-<button>Run ...</button>
-
-
-
-.. image:: 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7jnHjnnc881HdmpU8yjR8v748a5a5+T3HWXnPMFFzAfOiTvVVQwDx8u70+Y4K59TnLrrXLO/fuL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%0ARVGUgPP/Qde6gvF4TtQAAAAASUVORK5CYII=%0A
-
-
-
-作者 & 更新时间
-------------------------------------
-作者:`李金  <lijinwithyou@gmail.com>`
-
-提交: 2017/12/14
-
-
-
diff --git a/course/source/aipy-numpy/cn/2pyplot-style.rst b/course/source/aipy-numpy/cn/2pyplot-style.rst
deleted file mode 100644
index 90aee83..0000000
--- a/course/source/aipy-numpy/cn/2pyplot-style.rst
+++ /dev/null
@@ -1,175 +0,0 @@
-
-使用 style 来配置 pyplot 风格
----------------------------------
-
-
-
-
-::
-
-    import matplotlib.pyplot as plt
-    import numpy as np
-
-    %matplotlib inline
-
-
-style 是 pyplot 的一个子模块，方便进行风格转换， pyplot 有很多的预设风格，可以使用 plt.style.available 来查看：
-
-
-
-
-::
-
-    plt.style.available
-
-
-结果:
-::
-
-    [u'dark_background', u'bmh', u'grayscale', u'ggplot', 
-    u'fivethirtyeight']
-
-
-
-
-::
-
-    x = np.linspace(0, 2 * np.pi)
-    y = np.sin(x)
-
-    plt.plot(x, y)
-
-    plt.show()
-
-
-<button>Run ...</button>
-
-
-.. image:: 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tkX6/3ohwyB4cPhvfcChpJYWLUKataERYvg8MNDpxHJrtgO3Tjnb8Lee2/oJBIHhx7qZ98MGBA6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%0Agg==%0A
-
-
-例如，我们可以模仿 R 语言中常用的 ggplot 风格：
-
-
-
-
-::
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-    plt.style.use('ggplot')
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-    plt.plot(x, y)
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-    plt.show()
-
-
-<button>Run ...</button>
-
-
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-.. image:: 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H7NOwX9Yh0kkZuxBTIRgSTdBnv5fNMpRK4VOIN/zWJI+67cniEISIeewOFvoIe/NZ1C5EoBMfi1%0A4AJ0zSJuzxAkpHp1SO9B0BV810/kjcAY/J+tBq6/oXjVBwUF6TUQun0TNDfHdAqR67h+8KsqdMV8%0AWHy3H1QkMgqS0J0XdBF5wfWDHxlpgGUVr/agoCL9boOuXQwtuGA6hchVXD/47RULIH2HQkRMp1AV%0Ak2uvAxo3g36+1nQKkau4evDr0UPAN/+GdOplOoUMsZJ4QRdRRbl78K9cWHzBVg1esBW0WrQt/v+M%0ANLMdRC7i2sGv+aeLL9jqPdB0ChkkIpB+vKCLqCLcO/g3LIe0ToBE1zWdQoZJp17F9+X97qDpFCJX%0AcOXg16Ii6KqFkL5DTaeQA0j1GpBet0JXLjCdQuQKrhz8SNsMeOpBmjQ3XUIOIb0GQrdsgOafNp1C%0A5HiuHPz2ivmw+t1mOoMcRGrXgbTuAN2wzHQKkeO5bvDrN/8Gsk8AbTubTiGHkb5DoKsXQYuKTKcQ%0AOZr7Bv/KBZDEIZAQ3mGLLiVNmgPRHhRs22g6hcjRXDX4NScLumMLpHuS6RRyKOl7G84v/th0BpGj%0AuWvwr1kM6dgLElHLdAo5lLTrDPvYd9ADX5tOIXIs1wx+LbgAXbcUkjjEdAo5mFSrhtD+w6CruLST%0AqDTuGfxb1gONm0KujTWdQg5Xo+8QaOpmaN4p0ylEjuSKwa+q0JULYPGCLSoHK7I25JZu0LVLTKcQ%0AOZIrBj/2ZQDnzgLx7U2XkEtI4pDivfoLC0ynEDmOKwa/rlxYvITTckUuOYDENgEaNIJu22Q6hchx%0AHD9JNfsEdHcapGtf0ynkMla/ody/h+gKnD/41y6GdOkDqRluOoXcpnUHIO8U9Os9pkuIHMXRg18v%0AnIeuXwbpM9h0CrmQWCGQPoOhKxeaTiFyFGcP/i3rgSbNIQ1iTKeQS0n3ftBdW6E52aZTiBzD2YN/%0A5QJYvGCLfCDhtSAde0LXcWkn0X9U8/UJ0tLSMGvWLNi2jcTERAwfPvyyY9555x2kpaUhNDQUY8eO%0ARVxcXPmevOAC0LKtr4kU5KTPYNivvgAdOAJSvbrpHCLjfHrHb9s2Zs6ciXHjxuHVV1/Fxo0bcejQ%0AoUuO2b59O44dO4Y33ngDjzzyCGbMmFHu5+cSTqoMEtMYiGkM3bbBdAqRI/g0VTMzM9GwYUPUr18f%0A1apVQ7du3bB169ZLjtm6dSt69eoFAGjevDny8/ORk5NTrueXLn18ySMqYfUdCl2xAKpqOoXIOJ8G%0Af3Z2NurW/e/Nzj0eD7Kzs696TN26dS87pjQSxiWcVEla3QKcOQ1waSdVMj1zGpqZYTqjQnw+x18e%0A5XmXlZ6ejvT09JLHycnJiIyM9GeWX9WoUcO1/W5uB0rvP3frHShavxQRbTsYqCq/QP35u0VF+8+t%0AW4yir/ciol1HP1ZVTEpKSsmf4+PjER8ff8nXfRr8Ho8HWVlZJY+zsrLg8XgqfExpcXl5eb7kGRUZ%0AGenafje3A6X3a0J32B+/h9yD30Ci617hO50hUH/+blGRfrWLYC/+BNbDTzvm7zkyMhLJyclXPcan%0AUz3NmjXD0aNHcfz4cRQWFmLTpk1ISEi45JiEhASsW7cOALB3715EREQgOjral5cl8krx0s4e0LVL%0ATadQoNixFYisDWl6k+mSCvHpHX9ISAhGjx6NiRMnliznjI2NxfLlywEASUlJaN++PVJTU/HEE08g%0ALCwMY8aMqZRwIm9In8GwX/k9dBCXdpLv7FULXXlzKJ/P8bdr1w7t2rW75K8lJV16T9yHHnrI15ch%0AqhQS0xiIbQLdtgHSmavGyHt6+ABw5AAkoZvplArjInkKOlYil3aS73TVQkjPWyHV3PebIwc/BZ9W%0A7bm0k3yi+aehW9dDet1qOsUrHPwUdP67ayf36ifv6IblkNYdILXrmE7xCgc/BSXp1g+ango9mVX2%0AwUQXUbsIuvpTSKJ77wHOwU9BScIjIJ16QtcsNp1CbvPlFqB2HUhcc9MlXuPgp6AliUOg65dCCy6Y%0ATiEXsVcucOUSzotx8FPQkoaxwPU3QL9YZzqFXEIPfwscPQy5pavpFJ9w8FNQs/oO4dJOKjddtRDS%0A251LOC/GwU/BrWU7oPACsDe97GMpqGl+HnTrBkjPAaZTfMbBT0FNLAuSOBT2Ki7tpKvT9csgrTtC%0Aoty5hPNiHPwU9KRLH2DPLuiJY6ZTyKG0sLB4CWe/20ynVAoOfgp6ElYT0jURunqR6RRyKE3dDNSt%0AD7m+memUSsHBT4TiXTt10wro+XOmU8iBdOV8WAHybh/g4CcCAMg1DYEbWkI/W206hRxG9+8FcrKB%0Atp1Mp1QaDn6iH1h9h0JXLeTSTrqErii+YEusENMplYaDn+g/bmoFWBawO810CTmEnsyC7toG6Z5U%0A9sEuwsFP9AMRgfQdCpu7dtIPdM0iSOfekPAI0ymVioOf6CLSqRfwzb+h3x0ynUKG6fnzxWv3Xb4v%0Az5Vw8BNdRGqEQnoNhK6YbzqFDNPP1wBxN0IaxJhOqXQc/EQ/In0GQreuh+blmk4hQ1QVuiKwlnBe%0AjIOf6Eckqg6kXWfouiWmU8iUjLTiD/pvbm26xC84+ImuQPoNg65eBC0oMJ1CBtgrFkD6DoWImE7x%0ACw5+oiuQ2CZAzHXQLetNp1AVKzpyAPjm38Uf9AcoDn6iUlhJw6Ar5vGCriBzfsknkB4DIDVCTaf4%0ADQc/UWni2wMFBcCenaZLqIpoXi4KNq6EJA42neJXHPxEpRDLgvS7DTaXdgYNXbsI1Tv0gNR2/577%0AV8PBT3QV0rkP8PUe6NHDplPIz7TgAnT1IoQOHmE6xe84+ImuQkJDIT0GQLmNQ8DTzWuA629AyHVx%0AplP8joOfqAzSZxD0i3XQ/DzTKeQnatvQZXNhJQ0znVIlOPiJyiDRHkibDtB1S02nkL/s2gbUqBGw%0AF2z9GAc/UTlI0vDivfp5QVdAspfNhfS/PWAv2PoxDn6icpDr4oCY66FfrDWdQpVMv80Evv8Ocks3%0A0ylVhoOfqJysW++ALvkEatumU6gS6bK5xdszVKtmOqXKcPATldfNrYHQMGDHFtMlVEk063toeiqk%0AxwDTKVWKg5+onEQEMuAO2Es+Np1ClURXzod06wepGW46pUpx8BNVgNzSBcjNgWbuNp1CPtIzp6Eb%0AV0L6Bt4dtsrCwU9UAWKFQPoPh73kE9Mp5CNdvwzS6haI5xrTKVWOg5+ogqRrX2D/XuiRA6ZTyEta%0AUABdMR/S/3bTKUZw8BNVkNQIhfQZDF06x3QKeUk/Wwlc1xTSuKnpFCM4+Im8IH0GQdM+h2afMJ1C%0AFaRFRdAln8AadJfpFGM4+Im8IBGRkK6J3LzNhXTrBqBOXcgNLU2nGMPBT+Ql6TcMunEF9Mxp0ylU%0ATmrb0MWzYQ0M3nf7AAc/kdek7jWQVgnQtUtMp1B57dgChIQU310tiHHwE/lABtwOXbkAeuG86RQq%0Ag6rCXvQRrEEjgmYzttJw8BP5QGKbAHE3QtcvN51CZflqB3A2H2jX2XSJcRz8RD6yhoyELvkYWnDB%0AdApdhb14NuTWuyBWiOkU4zj4iXwk198AXBcH3bjCdAqVQvfvBY4dgXTqZTrFEbzeh/T06dN47bXX%0AcOLECVxzzTV46qmnEBERcdlxjz32GGrWrAnLshASEoJJkyb5FEzkRNaQkbCnvwjtngSpVt10Dv2I%0AvWh28Y1Wgmjr5avx+qcwd+5ctG7dGsOGDcPcuXMxd+5c3HvvvVc8dvz48ahVq5bXkUROJ01vAq69%0ADrppFaRncG3x63R6+ADw9VeQn//GdIpjeH2qZ+vWrejVq/jXpt69e2PLltL3KFdVb1+GyDWsIXdD%0AF30ELSw0nUIX0SWzi2+0EhpqOsUxvB78p06dQnR0NACgdu3aOHXq1BWPExFMmDABzz33HFas4DlQ%0AClxyQwug/rXQz9eYTqEf6HeHoLu2Q3oPMp3iKFc91TNhwgTk5ORc9tfvueeeSx5fbU3shAkTUKdO%0AHeTm5mLChAlo1KgRWrRocdlx6enpSE9PL3mcnJyMyMjIMv8GnKpGjRqu7XdzO2C2vzD5QZyZ9hJq%0AJd0GCfFu9Qh//pUn/2+zUWPwCIQ1aFju73FSv7dSUlJK/hwfH4/4+PhLvn7Vwf/CCy+U+rXatWsj%0AJycH0dHROHnyJGrXrn3F4+rUqQMAiIqKQseOHZGZmXnFwX+luLy8vKvlOVpkZKRr+93cDhjuj20K%0Au7YHuSs/hdWlj1dPwZ9/5dDD38LeuQ1F9zyKggr0OKXfW5GRkUhOTr7qMV6f6klISMCaNWsAAGvX%0ArkWHDh0uO+b8+fM4e/YsAODcuXPYsWMHGjdu7O1LErmCNWQk9NMUqF1kOiWo2fM/gAy4AxJW03SK%0A43i9qmf48OF47bXXsHr16pLlnACQnZ2N6dOn4/nnn0dOTg7+8pe/AABs20b37t3Rpk2byikncqqb%0AWwORtaFbNnDduCF6YB+wbw9k9K9NpziSqIOX3Bw5csR0gtfc/Ouim9sBZ/Tr7lTY/5wBa/wbFb5S%0A1An9vnBCf9GbEyAt28LqO7TC3+uEfl/ExMSUeQyv3CXyhxZtgfAI6OfrTJcEHf16D3BwP6+nuAoO%0AfiI/EBFYd9wPnfc+tKDAdE5Qsed9ABk0AlK9hukUx+LgJ/ITuTEeiGkMXbvYdErQ0H/vBo4dhnTv%0AZzrF0Tj4ifzIuuNnxVfznj1jOiUo2PPehwwZyf2SysDBT+RHEtsEEt8eumyu6ZSApxlfAidPQLok%0Amk5xPA5+Ij+TYaOgqz+F5p40nRKwVLX43f7Qu72+YjqYcPAT+ZnUawDp3Bu6MKXsg8k7qZ8B585C%0AOvY0XeIKHPxEVUAGJ0O3rIN+f9R0SsDRggLYs2fBSn6Id9cqJw5+oiogkbUhiUOhc983nRJwdPVC%0AoGEspGVb0ymuwcFPVEUkaRh0zw7oga9NpwQMzcuFLp4Na8SDplNchYOfqIpIWE3IoBGw57xnOiVg%0A6IIPIR16QK69znSKq3DwE1Uh6TkAOHYEujvNdIrr6XeHoFvWQ4aOMp3iOhz8RFVIqlWHlTwa9od/%0AhRZyKwdf2LP/Bhl4JyQyynSK63DwE1W1Np2Aeg2gKxeYLnEt3Z0GfHcQ0meI6RRX4uAnqmIiAuvu%0Ah6FLPoaezDKd4zpqF8H+6B1Ydz4Aqc6tGbzBwU9kgDSIgfS8FTr7b6ZTXEc3rgRqhgPtu5hOcS0O%0AfiJDZNAIaGYGdM9O0ymuoWfyofM+KL5YS8R0jmtx8BMZIqFhsJIfgv3BdGhhoekcV9A570FaJ0Ca%0ANDed4moc/EQmte8CRHugqz81XeJ4mrkbmvY55M4HTKe4Hgc/kUEiAuueR4r37M/JNp3jWFpQAPu9%0AKbDufhgSUct0jutx8BMZJg1jId2ToB/PMp3iWLr4I6D+tUD7rqZTAgIHP5EDyOBk6J5d0D27TKc4%0Ajh45AF29CNaoX/AD3UrCwU/kABJWE9a9v4A9azJv03gRtW3Y770FuW0UxFPPdE7A4OAncghp0xFy%0AUyuc/ftU0ymOoeuWAACk162GSwILBz+Rg8jIn6Nw5zboji2mU4zT7BPFa/Z/9jjE4qiqTPxpEjmI%0A1AxH+JhnYf99CjQv13SOMaoK+8PpkD6DIDGNTecEHA5+Ioep1rItpGNP2O9PhaqazjFCN68Bjh2B%0ADBxhOiUgcfATOZAMvw84chD6+VrTKVVOvzsITZkJ65FnuAmbn3DwEzmQVK8B66FfQ1NmQrNPmM6p%0AMnr+POzpL0Hu+BkktonpnIDFwU/kUHJ9M0jikOIlnrZtOqdK6D//CrkuDtI9yXRKQOPgJ3IwGXgX%0AcO5sUNy0xf5sNTRzN+TeMbxQy884+IkcTEJCYD38NHTpJ9D0VNM5flNyXv/R30LCaprOCXgc/EQO%0AJ9c0hPXIM7Bnvgo9esh0TqW79Lx+nOmcoMDBT+QCcuNPILf/FPZbE6H5p03nVCr9518hsU14Xr8K%0AcfATuYTVoz/kJ+1h//UlaFGR6ZxKYa9ZXHxe/76xPK9fhTj4iVxERowGINCP3jGd4jN7ywbown/B%0AeuIFntevYhz8RC4iISGwHn0Gums77HVLTed4TXdth344HdYv/wCpH2M6J+hw8BO5jITXgvX476Fz%0A/+HKG7Xrvq9gz3wV1tjnIdfxw1wTOPiJXEgaNoL18NOwp78EzfjSdE656aFvYE+ZCGv0U5AbWprO%0ACVoc/EQsiRx2AAAIN0lEQVQuJS3awHr0Wdj/9xfol1+YzimTfn8U9uTxkJE/h7S6xXROUOPgJ3Ix%0AueknsJ74H9jvvQXbwRu6aU427Nf+BzI4GVanXqZzgh4HP5HLSVxzWL+eAJ09C/YPd6xyEv02E/ak%0AZyA9BsDqPch0DoGDnyggSKPrYT0zEbpoNuylc0znlLA3r4H9+nhYyQ/BGnin6Rz6AQc/UYCQ+jGw%0Afvtn6IZlsGf/DVpYYKxFi4pgp8yEzv8A1tMTIbd0NdZCl+PgJwog4qkH65lJ0CMHYU98Gnrg6ypv%0AsPNOwZ48Hnr4W1i/ewXS6Poqb6Cr4+AnCjASFV18NWzSbbBf/wPseR9U2bt//TYTp383BtK4Kawn%0A/wCJiKyS16WKqebtN3722Wf46KOPcPjwYUyaNAlNmza94nFpaWmYNWsWbNtGYmIihg8f7nUsEZWP%0AiEC69oW2aAv7H1OhE38D64FfQq5v5pfX06OHoPM/hO7ZifD7H8f51h398jpUObx+x9+4cWM8/fTT%0AaNmy9IswbNvGzJkzMW7cOLz66qvYuHEjDh0KvG1liZxK6tSF9fjvIQNuhz15POzZs6BZxyvt+TXr%0AOOxZk2G/+BzQ6HpYE6ejRre+lfb85B9ev+Nv1KhRmcdkZmaiYcOGqF+/PgCgW7du2Lp1K2JjY719%0AWSKqIBGBdO4DvbkNdFEK7D8+BTRqAumaCGnf1asN0jT7e+iSj6FfrIf0Gghr4jRIeC0/1JM/eD34%0AyyM7Oxt169YteezxeJCZmenPlySiUki0BzLqF9ARDwE7voC9aRX0XzMgbTpBOvYE6jUAakUC4bUg%0A1n9PBmhREXD4G+i+PcC+DOjXe4D8PEjXfrD+dwokKtrg3xV546qDf8KECcjJybnsr99zzz1ISEjw%0AWxQR+Y9Urw7c0g0ht3SD5p6Ebl4L+9MU4FQ2kJ8HnDsLhEcAEVFAWE3g6GHAUw/S9CbgplawBo0A%0AGsZe8h8HcperDv4XXnjBpyf3eDzIysoqeZyVlQWPx3PFY9PT05Genl7yODk5GTEx7t6uNTLSvSsa%0A3NwOsL/cYmKAm+OBB8ZW6tPy529WSkpKyZ/j4+MRHx9/ydf9+p/sZs2a4ejRozh+/DgKCwuxadOm%0AUn9TiI+PR3Jycsn/Lg53Izf3u7kdYL9p7DcrJSXlkln646EP+DD4v/jiC4wZMwZ79+7FpEmT8Kc/%0A/QlA8Xn9SZMmAQBCQkIwevRoTJw4EU899RS6du3KD3aJiAzz+sPdjh07omPHy9fqejwePP/88yWP%0A27Vrh3bt2nn7MkREVMkc++nMlX49cRM397u5HWC/aew3qzz9oqpaBS1EROQQjn3HT0RE/sHBT0QU%0AZPx65a433Lyp29SpU5GamoqoqCi88sorpnMq7MSJE5gyZQpOnToFEUHfvn0xaJB77ph04cIFjB8/%0AHgUFBSgsLESHDh0watQo01kVYts2nnvuOXg8Hjz33HOmcyrsscceQ82aNWFZFkJCQkpW+LlBfn4+%0Apk2bVrKf2JgxY3DjjTcariqfI0eO4PXXXy95fOzYMYwcObL0f3/VQYqKivTxxx/XY8eOaUFBgT79%0A9NN68OBB01nltnv3bv3666/117/+tekUr5w8eVL379+vqqpnz57VJ5980lU/f1XVc+fOqapqYWGh%0Ajhs3TjMyMgwXVcyCBQt08uTJ+uc//9l0ilfGjh2reXl5pjO88uabb+rKlStVtfifn/z8fMNF3ikq%0AKtKHH35Yv//++1KPcdSpnos3datWrVrJpm5u0aJFC0RERJjO8Fp0dDSaNGkCAAgLC0OjRo1w8uRJ%0As1EVFBoaCgAoLCyEbduoVcs9G4dlZWUhNTUViYmJUBevuXBj+5kzZ/DVV18hMTERQPE1SOHh4Yar%0AvLNz5040aNAA9erVK/UYR53q4aZuznH8+HF88803aN68uemUCrFtG88++yyOHTuG/v37u+qCwXff%0AfRf33Xcfzp49azrFayKCCRMmwLIs9OvXD/369TOdVC7Hjx9HVFQUpk6dim+//RZxcXF48MEHS95I%0AuMnGjRvRvXv3qx7jqHf85Aznzp3Dq6++igceeABhYWGmcyrEsiy8/PLLmDZtGjIyMi7Z/8nJtm3b%0AhqioKMTFxbnyHfN/TJgwAS+99BLGjRuHpUuXIiMjw3RSuRQVFWH//v3o378/XnzxRYSFhWHu3Lmm%0AsyqssLAQ27ZtQ5cuXa56nKMGf0U2dSP/KCwsxCuvvIIePXpc8cpstwgPD0e7du2wb98+0ynlsmfP%0AHmzbtg2PPfYYJk+ejPT0dLz11lumsyqsTp06AICoqCh07NjRNb+x161bFx6PBzfccAMAoHPnzti/%0Af7/hqopLTU1F06ZNERUVddXjHDX4K7KpG1U+VcW0adPQqFEjDB482HROheXm5iI/Px9A8QqfnTt3%0AIi4uznBV+YwaNQpvv/02pkyZgl/96leIj4/H448/bjqrQs6fP19ymurcuXPYsWMHGjdubLiqfKKj%0Ao1GvXj0cOXIEALBjxw5XnSb8j40bN6Jbt25lHueoc/wXb+r2n+Wcbvrhv/7668jIyEBeXh7GjBmD%0A5ORk9OnTx3RWue3Zswfr169H48aN8dvf/hZA8UBq27at4bLyycnJwZQpU2DbNlQVPXv2RKtWrUxn%0AeUVETCdU2KlTp/Dyyy8DKP6spXv37mjTpo3hqvJ78MEH8eabb6KwsBANGjTA2LGVu1W1v507dw47%0Ad+7Eo48+Wuax3LKBiCjIOOpUDxER+R8HPxFRkOHgJyIKMhz8RERBhoOfiCjIcPATEQUZDn4ioiDD%0AwU9EFGT+Pxvu78Bmq8eQAAAAAElFTkSuQmCC%0A
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-有时候，我们不希望改变全局的风格，只是想暂时改变一下分隔，则可以使用 context 将风格改变限制在某一个代码块内：
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-::
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-    with plt.style.context(('dark_background')):
-        ^4$plt.plot(x, y, 'r-o')
-        ^4$plt.show()
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-<button>Run ...</button>
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-.. image:: 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/7Ro0dTUVFBVVUVM2fOjHrO7NmzqaqqYsOGDQwcOLDZj90BeCvRACWQwrt2jgXuxfxd0sauSIKJ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%0ASUVORK5CYII=%0A
-
-
-在代码块外绘图则仍然是全局的风格。
-
-
-
-
-
-::
-
-    with plt.style.context(('dark_background')):
-        ^4$pass
-    plt.plot(x, y, 'r-o')
-    plt.show()
-
-<button>Run ...</button>
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-.. image:: 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ydPQmu3c3gbkRcCr/BLEqLWr0f1rFmik5APfX3yJA6qVMjJy3Mc4/A2Is8E3Bp/aEEB1FevwnL3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%0AYeEnIgoyLPxEREHm/wEVP/FL2qENYwAAAABJRU5ErkJggg==%0A
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-还可以混搭使用多种风格，不过最右边的一种风格会将最左边的覆盖：
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-::
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-    plt.style.use(['dark_background', 'ggplot'])
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-    plt.plot(x, y, 'r-o')
-    plt.show()
-
-<button>Run ...</button>
-
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-.. image:: 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ydPQmu3c3gbkRcCr/BLEqLWr0f1rFmik5APfX3yJA6qVMjJy3Mc4/A2Is8E3Bp/aEEB1FevwnL3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%0AYeEnIgoyLPxEREHm/wEVP/FL2qENYwAAAABJRU5ErkJggg==%0A
-
-事实上，我们还可以自定义风格文件。
-
-
-自定义文件需要放在 matplotlib 的配置文件夹 mpl_configdir 的子文件夹 mpl_configdir/stylelib/ 下，以 .mplstyle 结尾。
-mpl_configdir 的位置可以这样查看：
-
-
-
-
-
-::
-
-    import matplotlib
-    matplotlib.get_configdir()
-
-
-结果:
-::
-
-    u'c:/Users/Jin\\.matplotlib'
-
-
-里面的内容以 属性：值 的形式保存：
-
-
-axes.titlesize : 24
-
-axes.labelsize : 20
-
-lines.linewidth : 3
-
-lines.markersize : 10
-
-xtick.labelsize : 16
-
-ytick.labelsize : 16
-
-
-假设我们将其保存为 mpl_configdir/stylelib/presentation.mplstyle，那么使用这个风格的时候只需要调用：
-
-
-     plt.style.use('presentation')
-
-
-作者 & 更新时间
-------------------------------------
-作者:`李金  <lijinwithyou@gmail.com>`
-
-提交: 2017/12/14
\ No newline at end of file
diff --git a/course/source/aipy-numpy/cn/3text-base.rst b/course/source/aipy-numpy/cn/3text-base.rst
deleted file mode 100644
index d6b7c27..0000000
--- a/course/source/aipy-numpy/cn/3text-base.rst
+++ /dev/null
@@ -1,396 +0,0 @@
-
-处理文本（基础）
-=====================
-
-
-
-::
-
-    import matplotlib.pyplot as plt
-    import numpy as np
-    %matplotlib inline
-
-
-matplotlib 对文本的支持十分完善，包括数学公式，Unicode 文字，栅格和向量化输出，文字换行，文字旋转等一系列操作。
-
-
-基础文本函数
-----------------
-
-在 matplotlib.pyplot 中，基础的文本函数如下：
-
-
-- text() 在 Axes 对象的任意位置添加文本
-- xlabel() 添加 x 轴标题
-- ylabel() 添加 y 轴标题
-- title() 给 Axes 对象添加标题
-- figtext() 在 Figure 对象的任意位置添加文本
-- suptitle() 给 Figure 对象添加标题
-- anotate() 给 Axes 对象添加注释（可选择是否添加箭头标记）
-
-
-
-
-
-::
-
-    # -*- coding: utf-8 -*-
-    import matplotlib.pyplot as plt
-    %matplotlib inline
-
-    # plt.figure() 返回一个 Figure() 对象
-    fig = plt.figure(figsize=(12, 9))
-
-    # 设置这个 Figure 对象的标题
-    # 事实上，如果我们直接调用 plt.suptitle() 函数，它会自动找到当前    的 Figure 对象
-    fig.suptitle('bold figure suptitle', fontsize=14,     fontweight='bold')
-
-    # Axes 对象表示 Figure 对象中的子图
-    # 这里只有一幅图像，所以使用 add_subplot(111)
-    ax = fig.add_subplot(111)
-    fig.subplots_adjust(top=0.85)
-
-    # 可以直接使用 set_xxx 的方法来设置标题
-    ax.set_title('axes title')
-    # 也可以直接调用 title()，因为会自动定位到当前的 Axes 对象
-    # plt.title('axes title')
-
-    ax.set_xlabel('xlabel')
-    ax.set_ylabel('ylabel')
-
-    # 添加文本，斜体加文本框
-    ax.text(3, 8, 'boxed italics text in data coords',     style='italic',
-             ^4$bbox={'facecolor':'red', 'alpha':0.5, 'pad':10})
-
-    # 数学公式，用 $$ 输入 Tex 公式
-    ax.text(2, 6, r'an equation: $E=mc^2$', fontsize=15)
-
-    # Unicode 支持
-    ax.text(3, 2, unicode('unicode: Institut f\374r Festk    \366rperphysik', 'latin-1'))
-
-    # 颜色，对齐方式
-    ax.text(0.95, 0.01, 'colored text in axes coords',
-            ^4$verticalalignment='bottom', horizontalalignment='right',
-            ^4$transform=ax.transAxes,
-            ^4$color='green', fontsize=15)
-
-    # 注释文本和箭头
-    ax.plot([2], [1], 'o')
-    ax.annotate('annotate', xy=(2, 1), xytext=(3, 4),
-                ^4$arrowprops=dict(facecolor='black', shrink=0.05))
-
-    # 设置显示范围
-    ax.axis([0, 10, 0, 10])
-
-    plt.show()
-
-
-<button>Run ...</button>
-
-
-.. image:: 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wAAAOAR4RkAAADwiPAMAAAAeER4BgAAADwiPAMAAAAeEZ4BAAAAjwjPAAAAgEeEZwAAAMAjwjMA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%0A
-
-
-文本属性和布局
-----------------------
-
-我们可以通过下列关键词，在文本函数中设置文本的属性：
-
-+--------------------+--------------------------------------------------------------------------------+
-| 关键词             | 值                                                                             |
-+====================+================================================================================+
-|alpha	             | float                                                                          |    
-+--------------------+--------------------------------------------------------------------------------+
-|backgroundcolor     |any matplotlib color                                                            |
-+--------------------+--------------------------------------------------------------------------------+
-|bbox	             |rectangle prop dict plus key 'pad' which is a pad in points                     |
-+--------------------+--------------------------------------------------------------------------------+
-|clip_box	         |a matplotlib.transform.Bbox instance                                            |         
-+--------------------+--------------------------------------------------------------------------------+
-|clip_on	         |[True ， False]                                                                 |
-+--------------------+--------------------------------------------------------------------------------+
-|clip_path	         |a Path instance and a Transform instance, a Patch                               |
-+--------------------+--------------------------------------------------------------------------------+
-|color	             |any matplotlib color                                                            |
-+--------------------+--------------------------------------------------------------------------------+
-| family	         |[ 'serif' , 'sans-serif' , 'cursive' , 'fantasy' , 'monospace' ]                |
-+--------------------+--------------------------------------------------------------------------------+
-| fontproperties     |a matplotlib.font_manager.FontProperties instance                               |
-+--------------------+--------------------------------------------------------------------------------+
-|horizontalalignment |[ 'center' , 'right' , 'left' ]                                                 |
-+--------------------+--------------------------------------------------------------------------------+
-|label	             |any string                                                                      |
-+--------------------+--------------------------------------------------------------------------------+
-|linespacing	     |float                                                                           |
-+--------------------+--------------------------------------------------------------------------------+
-|multialignment      |['left' , 'right' , 'center' ]                                                  |
-+--------------------+--------------------------------------------------------------------------------+
-|name or fontname    |string e.g., ['Sans' , 'Courier' , 'Helvetica' ...]                             |
-+--------------------+--------------------------------------------------------------------------------+
-|picker	             |[None,float,boolean,callable]                                                   |
-+--------------------+--------------------------------------------------------------------------------+
-|position	         |(x,y)                                                                           |
-+--------------------+--------------------------------------------------------------------------------+
-|rotation	         |[ angle in degrees 'vertical' , 'horizontal'                                    |
-+--------------------+--------------------------------------------------------------------------------+
-|size or fontsize    |[ size in points , relative size, e.g., 'smaller', 'x-large' ]                  |
-+--------------------+--------------------------------------------------------------------------------+
-|style or fontstyle  |[ 'normal' , 'italic' , 'oblique']                                              |
-+--------------------+--------------------------------------------------------------------------------+
-|text	 	         |string or anything printable with '%s' conversion                               |
-+--------------------+--------------------------------------------------------------------------------+
-|transform	         |a matplotlib.transform transformation instance                                  |
-+--------------------+--------------------------------------------------------------------------------+
-|variant	         |[ 'normal' , 'small-caps' ]                                                     |
-+--------------------+--------------------------------------------------------------------------------+
-|verticalalignment   |[ 'center' , 'top' , 'bottom' , 'baseline' ]                                    |
-+--------------------+--------------------------------------------------------------------------------+
-|visible	 n       |[True , False]                                                                  |
-+--------------------+--------------------------------------------------------------------------------+
-|weight or fontweight|[ 'normal' , 'bold' , 'heavy' , 'light' , 'ultrabold' , 'ultralight']           |
-+--------------------+--------------------------------------------------------------------------------+
-|x		             |float                                                                           |
-+--------------------+--------------------------------------------------------------------------------+
-|y	                 |float                                                                           |
-+--------------------+--------------------------------------------------------------------------------+
-|zorder		         |any number                                                                      |
-+--------------------+--------------------------------------------------------------------------------+
-
-
-
-其中 va, ha, multialignment 可以用来控制布局。
-
-
-
-- horizontalalignment or ha ：x 位置参数表示的位置
-- verticalalignment or va：y 位置参数表示的位置
-- multialignment：多行位置控制
-
-
-
-
-
-::
-
-    import matplotlib.pyplot as plt
-    import matplotlib.patches as patches
-
-    # build a rectangle in axes coords
-    left, width = .25, .5
-    bottom, height = .25, .5
-    right = left + width
-    top = bottom + height
-
-    fig = plt.figure(figsize=(10,7))
-    ax = fig.add_axes([0,0,1,1])
-
-    # axes coordinates are 0,0 is bottom left and 1,1 is upper right
-    p = patches.Rectangle(
-        ^4$(left, bottom), width, height,
-        ^4$fill=False, transform=ax.transAxes, clip_on=False
-        ^4$)
-
-    ax.add_patch(p)
-
-    ax.text(left, bottom, 'left top',
-            ^4$horizontalalignment='left',
-            ^4$verticalalignment='top',
-            ^4$transform=ax.transAxes,
-            ^4$size='xx-large')
-
-    x.text(left, bottom, 'left bottom',
-            ^4$horizontalalignment='left',
-            ^4$verticalalignment='bottom',
-            ^4$transform=ax.transAxes,
-            ^4$size='xx-large')
-
-    ax.text(right, top, 'right bottom',
-            ^4$horizontalalignment='right',
-            ^4$verticalalignment='bottom',
-            ^4$transform=ax.transAxes,
-            ^4$size='xx-large')
-
-    ax.text(right, top, 'right top',
-            ^4$horizontalalignment='right',
-            ^4$verticalalignment='top',
-            ^4$transform=ax.transAxes,
-            ^4$size='xx-large')
-
-    ax.text(right, bottom, 'center top',
-            ^4$horizontalalignment='center',
-            ^4$verticalalignment='top',
-            ^4$transform=ax.transAxes,
-            ^4$size='xx-large')
-
-    ax.text(left, 0.5*(bottom+top), 'right center',
-            ^4$horizontalalignment='right',
-            ^4$verticalalignment='center',
-            ^4$rotation='vertical',
-            ^4$transform=ax.transAxes,
-            ^4$size='xx-large')
-
-    ax.text(left, 0.5*(bottom+top), 'left center',
-            ^4$horizontalalignment='left',
-            ^4$verticalalignment='center',
-            ^4$rotation='vertical',
-            ^4$transform=ax.transAxes,
-            ^4$size='xx-large')
-
-    ax.text(0.5*(left+right), 0.5*(bottom+top), 'middle',
-            ^4$horizontalalignment='center',
-            ^4$verticalalignment='center',
-            ^4$fontsize=20, color='red',
-            ^4$transform=ax.transAxes)
-
-    ax.text(right, 0.5*(bottom+top), 'centered',
-            ^4$horizontalalignment='center',
-            ^4$verticalalignment='center',
-            ^4$rotation='vertical',
-            ^4$transform=ax.transAxes,
-            ^4$size='xx-large')
-
-    ax.text(left, top, 'rotated\nwith newlines',
-            ^4$horizontalalignment='center',
-            ^4$verticalalignment='center',
-            ^4$rotation=45,
-            ^4$transform=ax.transAxes,
-            ^4$size='xx-large')
-
-    ax.set_axis_off()
-    plt.show()
-
-<button>Run ...</button>
-
-
-.. image:: 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PZVdeUs7rlGtUWsNq6qf1TVUd3Y+n1pIf/+3R8VVNVbu30vAN6V5JVVdWa3z3Avjc4VMzxuLlVk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%0A/g8NfX/IA8ubvAAAAABJRU5ErkJggg==%0A
-
-
-注释文本
---------------
-
-
-text() 函数在 Axes 对象的指定位置添加文本，而 annotate() 则是对某一点添加注释文本，需要考虑两个位置：一是注释点的坐标 xy ，二是注释文本的位置坐标 xytext：
-
-
-
-
-
-::
-
-    fig = plt.figure()
-    ax = fig.add_subplot(111)
-
-    t = np.arange(0.0, 5.0, 0.01)
-    s = np.cos(2*np.pi*t)
-    line, = ax.plot(t, s, lw=2)
-
-    ax.annotate('local max', xy=(2, 1), xytext=(3, 1.5),
-                ^4$arrowprops=dict(facecolor='black', shrink=0.05),
-                ^4$)
-
-    ax.set_ylim(-2,2)
-    plt.show()
-
-
-<button>Run ...</button>
-
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-.. image:: 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7jnHjnnc881HdmpU8yjR8v748a5a5+T3HWXnPMFFzAfOiTvVVQwDx8u70+Y4K59TnLrrXLO/fuL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%0ARVGUgPP/Qde6gvF4TtQAAAAASUVORK5CYII=%0A
-
-
-
-在上面的例子中，两个左边使用的都是原始数据的坐标系，不过我们还可以通过 xycoords 和 textcoords 来设置坐标系（默认是 'data'）：
-
-
-
-
-+--------------------+--------------------------------------------------------------------------------+
-| 参数               | 坐标系                                                                         |
-+====================+================================================================================+
-|‘figure points’   | points from the lower left corner of the figure                                |    
-+--------------------+--------------------------------------------------------------------------------+
-|‘figure pixels’   |pixels from the lower left corner of the figure                                 |
-+--------------------+--------------------------------------------------------------------------------+
-|‘figure fraction   |0,0 is lower left of figure and 1,1 is upper right                              |
-+--------------------+--------------------------------------------------------------------------------+
-|‘axes points       |points from lower left corner of axes                                           |         
-+--------------------+--------------------------------------------------------------------------------+
-|‘axes pixels’     |pixels from lower left corner of axes                                           |
-+--------------------+--------------------------------------------------------------------------------+
-|clip_path	         |a Path instance and a Transform instance, a Patch                               |
-+--------------------+--------------------------------------------------------------------------------+
-|‘axes fraction’   |0,0 is lower left of axes and 1,1 is upper right                                |
-+--------------------+--------------------------------------------------------------------------------+
-
-
-
-
-
-::
-
-    fig = plt.figure()
-    ax = fig.add_subplot(111)
-
-    t = np.arange(0.0, 5.0, 0.01)
-    s = np.cos(2*np.pi*t)
-    line, = ax.plot(t, s, lw=2)
-
-    ax.annotate('local max', xy=(3, 1),  xycoords='data',
-                ^4$xytext=(0.8, 0.95), textcoords='axes fraction',
-                ^4$arrowprops=dict(facecolor='black', shrink=0.05),
-                ^4$horizontalalignment='right',     verticalalignment='top',
-                ^4$)
-
-    ax.set_ylim(-2,2)
-    plt.show()
-
-
-<button>Run ...</button>
-
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-.. image:: 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meesUoi71FRwdyxY8u1kdpa5osvljIvv+ysfU5y7Bhzerqc54YN0ctUV0tIB2B+6y1n7XOSgwfN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%0A/wdcAmhbrixx1QAAAABJRU5ErkJggg==%0A
-
-
-极坐标系注释文本
----------------------
-
-
-产生极坐标系需要在 subplot 的参数中设置 polar=True：
-
-
-
-
-::
-
-    fig = plt.figure()
-    ax = fig.add_subplot(111, polar=True)
-    r = np.arange(0,1,0.001)
-    theta = 2*2*np.pi*r
-    line, = ax.plot(theta, r, color='#ee8d18', lw=3)
-
-    ind = 800
-    thisr, thistheta = r[ind], theta[ind]
-    ax.plot([thistheta], [thisr], 'o')
-    ax.annotate('a polar annotation',
-                ^4$xy=(thistheta, thisr),  # theta, radius
-                ^4$xytext=(0.05, 0.05),    # fraction, fraction
-                ^4$textcoords='figure fraction',
-                ^4$arrowprops=dict(facecolor='black', shrink=0.05),
-                ^4$horizontalalignment='left',
-                ^4$verticalalignment='bottom',
-                ^4$)
-    plt.show()
-
-<button>Run ...</button>
-
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-.. image:: 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/0TQQiZpFKpVv/+++/ybt26RU2O8vJy6HQ6iMXiqMnABY1hTbUhtm/fjt69e7sUXTTIycnBkCFD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%0A
-
-
-
-作者 & 更新时间
-------------------------------------
-作者:`李金  <lijinwithyou@gmail.com>`
-
-提交: 2017/12/14
-
diff --git a/course/source/aipy-numpy/cn/4text-math.rst b/course/source/aipy-numpy/cn/4text-math.rst
deleted file mode 100644
index 8f2b94d..0000000
--- a/course/source/aipy-numpy/cn/4text-math.rst
+++ /dev/null
@@ -1,221 +0,0 @@
-
-处理文本（数学表达式）
-===========================
-
-
-在字符串中使用一对 $$ 符号可以利用 Tex 语法打出数学表达式，而且并不需要预先安装 Tex。在使用时我们通常加上 r 标记表示它是一个原始字符串（raw string）
-
-
-
-
-::
-
-    import matplotlib.pyplot as plt
-    import numpy as np
-    %matplotlib inline
-
-
-
-
-::
-
-    # plain text
-    plt.title('alpha > beta')
-
-    plt.show()
-
-<button>Run ...</button>
-
-
-.. image:: data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAXcAAAEKCAYAAADpfBXhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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%0A
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-::
-
-    # math text
-    plt.title(r'$\alpha > \beta$')
-
-    plt.show()
-
-
-<button>Run ...</button>
-
-
-.. image:: 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WD8JupNwFJVrfWfZc9l06zF6xndKwGj66vvSHK4qm5bzBQXYpp1OAg8XlVPAU8l+SZwAdAt7tOs%0AxR8CnwKoqn9L8u/AKxndf/N8M3M3531Z5uhNT0lOZXTT0+ofztuA98PRO2CPe9NTAxPXIslLgVuB%0A91bVgS2Y46JMXIuqenlVvayqXsbouvtHmoUdpvv5+Efgj5JsS3IaozfPfrDgeS7CNGuxD7gEYHx9%0A+ZXADxc6y5PHzN2c65l7edPTUdOsBfBJ4MXADeMz1sNVdeFWzXmzTLkW7U3587EvyR3Ag8DTwE1V%0A1S7uU35PfBq4JckDjE5EP1ZVP92ySW+iJF8C3grsSHIQuI7RJbp1d9ObmCSpIf83e5LUkHGXpIaM%0AuyQ1ZNwlqSHjLkkNGXdJasi4S1JDxl2SGvp/zy4/4DuVo0MAAAAASUVORK5CYII=%0A
-
-
-上下标
--------------
-
-
-使用 _ 和 ^ 表示上下标：
-
-
-$\alpha_i &gt; \beta_i$：
-
-r'$\alpha_i > \beta_i$'
-
-$\sum\limits_{i=0}^\infty x_i$：
-
-r'$\sum_{i=0}^\infty x_i$'
-
-注：
-
-- 希腊字母和特殊符号可以用 '\ + 对应的名字' 来显示
-- {} 中的内容属于一个部分；要打出花括号是需要使用 \{\}
-
-
-分数，二项式系数，stacked numbers
------------------------------------------
-
-
-$\frac{3}{4}, \binom{3}{4}, \stackrel{3}{4}$：
-
-r'$\frac{3}{4}, \binom{3}{4}, \stackrel{3}{4}$'
-
-
-$\frac{5 - \frac{1}{x}}{4}$：
-
-r'$\frac{5 - \frac{1}{x}}{4}$'
-
-
-在 Tex 语言中，括号始终是默认的大小，如果要使括号大小与括号内部的大小对应，可以使用 \left 和 \right 选项：
-
-$(\frac{5 - \frac{1}{x}}{4})$
-
-r'$(\frac{5 - \frac{1}{x}}{4})$'
-
-$\left(\frac{5 - \frac{1}{x}}{4}\right)$：
-
-r'$\left(\frac{5 - \frac{1}{x}}{4}\right)$'
-
-
-
-根号
------------
-
-
-$\sqrt{2}$：
-
-r'$\sqrt{2}$'
-
-$\sqrt[3]{x}$：
-
-r'$\sqrt[3]{x}$'
-
-
-特殊字体
----------------
-
-
-默认显示的字体是斜体，不过可以使用以下方法显示不同的字体：
-
-
-
-+--------------------------+--------------------------------------------------------------------------+
-| 命令	                 |显示                                                                      |
-+==========================+==========================================================================+
-|\mathrm{Roman}	           | $\mathrm{Roman}$                                                         |    
-+--------------------------+--------------------------------------------------------------------------+
-| \mathit{Italic}	       |$\mathit{Italic}$                                                         |
-+--------------------------+--------------------------------------------------------------------------+
-| \mathtt{Typewriter}	   |$\mathtt{Typewriter}$                                                     |
-+--------------------------+--------------------------------------------------------------------------+
-| \mathcal{CALLIGRAPHY}	   |$\mathcal{CALLIGRAPHY}$                                                   |         
-+--------------------------+--------------------------------------------------------------------------+
-| \mathbb{blackboard}	   |$\mathbb{blackboard}$                                                     |
-+--------------------------+--------------------------------------------------------------------------+
-|\mathfrak{Fraktur}	       |$\mathfrak{Fraktur}$                                                      |
-+--------------------------+--------------------------------------------------------------------------+
-| \mathsf{sansserif}	   |$\mathsf{sansserif}$                                                      |
-+--------------------------+--------------------------------------------------------------------------+
-
-
-
-$s(t) = \mathcal{A}\ \sin(2 \omega t)$：
-s(t) = \mathcal{A}\ \sin(2 \omega t)
-
-
-
-
-- Tex 语法默认忽略空格，要打出空格使用 '\ '
-- \sin 默认显示为 Roman 字体
-
-
-
-音调
-------------
-
-+--------------------------+--------------------------------------------------------------------------+
-|命令	                     |结果                                                                      |
-+==========================+==========================================================================+
-|\acute a	               | $\acute a$                                                               |    
-+--------------------------+--------------------------------------------------------------------------+
-|\breve a		           |$\breve a$                                                                |
-+--------------------------+--------------------------------------------------------------------------+
-|\ddot a		           |$\ddot a$                                                                 |
-+--------------------------+--------------------------------------------------------------------------+
-|\dot a		               |$\dot a$                                                                  |         
-+--------------------------+--------------------------------------------------------------------------+
-|\tilde a		           |$\tilde a$                                                                |
-+--------------------------+--------------------------------------------------------------------------+
-|\4vec a		           |$\vec a$                                                                  |
-+--------------------------+--------------------------------------------------------------------------+
-|\overline{abc}		       |$\overline{abc}$                                                          |
-+--------------------------+--------------------------------------------------------------------------+
-|\widehat{xyz}		       |$\widehat{xyz}$                                                           |
-+--------------------------+--------------------------------------------------------------------------+
-|\widetilde{xyz}	       |$\widetilde{xyz}$                                                         |
-+--------------------------+--------------------------------------------------------------------------+
-
-
-
-
-特殊字符表
---------------
-
-
-参见：http://matplotlib.org/users/mathtext.html#symbols
-
-
-
-**例子**
-
-
-
-
-::
-
-    import numpy as np
-    import matplotlib.pyplot as plt
-    t = np.arange(0.0, 2.0, 0.01)
-    s = np.sin(2*np.pi*t)
-
-    plt.plot(t,s)
-    plt.title(r'$\alpha_i > \beta_i$', fontsize=20)
-    plt.text(1, -0.6, r'$\sum_{i=0}^\infty x_i$', fontsize=20)
-    plt.text(0.6, 0.6, r'$\mathcal{A}\ \mathrm{sin}(2 \omega t)$',
-             ^4$fontsize=20)
-    plt.xlabel('time (s)')
-    plt.ylabel('volts (mV)')
-    plt.show()
-
-
-
-<button>Run ...</button>
-
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-.. image:: 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k2ZnsOWc2PHjqVJLj3N06dPZ/Xq1Zx88smZj/3xxx9s3ryZRo0aZT526NAh+vTpk+fvcscdd1C3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%0AoFnSMMYYE7T/Bxi7FV50SWh9AAAAAElFTkSuQmCC%0A
-
-
-作者 & 更新时间
-------------------------------------
-作者:`李金  <lijinwithyou@gmail.com>`
-
-提交: 2017/12/14
-
-
diff --git a/course/source/aipy-numpy/cn/5image-base.rst b/course/source/aipy-numpy/cn/5image-base.rst
deleted file mode 100644
index 101a260..0000000
--- a/course/source/aipy-numpy/cn/5image-base.rst
+++ /dev/null
@@ -1,228 +0,0 @@
-
-图像基础
------------------
-
-
-导入相应的包：
-
-
-
-
-::
-
-    import matplotlib.pyplot as plt
-    import matplotlib.image as mpimg
-    import numpy as np
-    %matplotlib inline
-
-<button>Run ...</button>
-
-.. image:: https://github.com/riverfor/notes-python/raw/9e0c173527920683229678ba0e4482a74d74618a/06-matplotlib/stinkbug.png
-
-
-
-导入图像
-------------
-
-
-我们首先导入上面的图像，注意 matplotlib 默认只支持 PNG 格式的图像，我们可以使用 mpimg.imread 方法读入这幅图像：
-
-
-
-
-::
-
-    img = mpimg.imread('stinkbug.png')
-
-
-
-
-::
-
-    img.shape
-
-
-结果:
-::
-
-    (375L, 500L, 3L)
-
-这是一个 375 x 500 x 3 的 RGB 图像，并且每个像素使用 uint8 分别表示 RGB 三个通道的值。不过在处理的时候，matplotlib 将它们的值归一化到 0.0~1.0 之间：
-
-
-
-
-::
-
-    img.dtype
-
-
-结果:
-::
-
-    dtype('float32')
-
-
-显示图像
--------------------
-
-使用 plt.imshow() 可以显示图像：
-
-
-
-
-::
-
-    imgplot = plt.imshow(img)
-
-
-.. image:: 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s58mYDr0pYCJBteClktY00A/HSSJOfJuT8FcF+SJP+3c+5PLhz/Kc8ogSo/L0kMwHLni2VCMYBA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%0AdJpO02l6yukUWE/TaTpNp+kpp/8PTYoQ8rA9sPAAAAAASUVORK5CYII=%0A
-
-
-
-伪彩色图像
------------------
-
-
-从单通道模拟彩色图像：
-
-
-
-
-::
-
-
-    lum_img = img[:,:,0]
-    imgplot = plt.imshow(lum_img)
-
-
-<button>Run ...</button>
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-.. image:: 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qjn8jet3KgxHzwsbhuw0plEMJxgAdevF+3A60lORn/T5CePso79cIigq+E5ljvU4GXqlXXTwrfR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%0A2+yGbQPWzTbbbLMbtv8GmbHHpC2s7ygAAAAASUVORK5CYII=%0A
-
-
-改变 colormap
-------------------
-
-
-
-::
-
-    imgplot = plt.imshow(lum_img)
-    imgplot.set_cmap('hot')
-
-
-
-.. image:: 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HXomfFqrFQ20WfW4Qs857Ial3zbVWPX0JeLoWNFyYe36/FoAQJ3I9N4paH0tjw9Y/w+7r5/4OkJ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%0A5bbcltslbsvAutyW23Jbbpe4/X9nGwcxiuGaUwAAAABJRU5ErkJggg==%0A
-
-
-
-
-::
-
-    imgplot = plt.imshow(lum_img)
-    imgplot.set_cmap('spectral')
-
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-.. image:: 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HUB4GvrGEqX2Nsha5Isvi5GPXjANFE+n35fBdbrUfrIAOO+qNWhgaXGwXEFiaubIhkwGqQFrD6Y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%0A
-
-显示色度条：
-
-
-
-
-::
-
-    imgplot = plt.imshow(lum_img)
-    imgplot.set_cmap('spectral')
-    plt.colorbar()
-    plt.show()
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-.. image:: 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pNdyCo1YsSOEONVbx4yV5ZnyNAFlk3YatDrKHW1CFklwHuUQcYrKokU1wCA188BecpOkFIVRbCS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%0ARK5CYII=%0A
-
-
-限制显示范围
--------------------
-
-
-先查看直方图：
-
-
-
-
-::
-
-    plt.hist(lum_img.flatten(), 256, range=(0.0,1.0), fc='k', ec='k')
-    plt.show()
-
-
-.. image:: data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAYYAAAEACAYAAAC3adEgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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%0A
-
-
-将显示范围设为 0.0-0.7：
-
-
-
-
-::
-
-    imgplot = plt.imshow(lum_img)
-    imgplot.set_clim(0.0,0.7)
-
-
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-.. image:: 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XfOgoy5pEL9HlMvBgdPYu4k31egjNtKIe1FSeFHa2+Eex4bLkxCNG0ZF26X32X+4rxTUTIIZCUn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%0AbtWA5gcAAAAASUVORK5CYII=%0A
-
-
-
-resize 操作
------------------
-
-
-
-
-::
-
-    from PIL import Image
-    img = Image.open('stinkbug.png')
-    rsize = img.resize((img.size[0]/10,img.size[1]/10))
-    rsizeArr = np.asarray(rsize) 
-    imgplot = plt.imshow(rsizeArr)
-
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-.. image:: 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3fv3sUrr7yCo6MjrK2tYXNzEzs7OxPwjOizCEA5RGZpso8hEV9ljzq3xs6i1F1rGWcClj1AQd73%0A/JZaOhHQbJ0pF21Q9xDOHG/cuDEBxePjY7z2ta/FzZs3sbu7i83NzcnizqIMhloZsz1rd154dej1%0A5yEmc2+pLXMv91gpX2tBOJrXzL/Bo0kE8KKmU8l536vDXEeA5LKysoKNjQ1sb2/j5s2byDljZWUF%0AJycnePTRR3Hz5k3s7OxMwPKqAqWUMdp16FY1bxxFF2VmXa4hgFmKZ61ZyHw1cPTClWSmPstZ+S1q%0AGaUVttWEv0qidSL6JWbJfZRnZ2fY3d2dHBsbG5e2DUXyichQkPFk1tZBS1k8kPSAQErJ9B4iHsB5%0AOzZqd3OUdLAAU16zQHJhVsNrVklLCziaCR4BSO+apof3/zqAZ6nsHCw5y+T7LDc3N6fM8Jb8h7hF%0A5ABZdIkAmwaK2nkUKC3h8XqBZk17tfgwPTY41mIXl7l9Cre3WCb3GGb4dQRPKSsrK1hfX0dKaQKU%0AtEWIP72ztrY2ZYbX+s68QRNZkIiks0hSMl21/x5gltKdlUnu6WDJ0DbzALPFHC/Jwm4dqvVXWuFK%0ADCbacUv51saZp0Q6BzFLAkztvnV+VfzCiwywEihLZKNmcXKRpLYNooyyFL+lny4sWEYXh6REFoui%0A6Yyd9yIPVpKaSSrCYlo6duRei0k3a+HP25+dnU3OAVxi6vTrMWt50HUeRsbR0vHu9xIv7ZpxME+w%0An6nPMvo7VKLgVGsyAmUd5wGYvUB3yMRQGnS9nPfyXm1a8xTa6H94eIijoyMcHR3h8PAQAKZ8wJub%0Am1hZWZm8Qk+rAwmSdE071/6PIUMWkiILRDVgH62XGpmZz7I13hgMc4hEwHNegGnpI2XMSWkMptLD%0AtB/qG+shtE/14OAA9+/fnxwAJrsL+M4Db+FMsskWoOzVViXWOEa6Q2XhFnjGktrV9TGlFzD20jXq%0AjxyStrYqy+9rg3Bo2YYs1PWYtGvz1eJysNzb28Pdu3fx6quvAgBOTk4mQLm2tobt7e1L7g1+rtV1%0AjX5jAdwY6UTSH5tVAjMEyxZTvJVVchkKQhG/2KIB5lAprWxHTHBrEPcqXxQ8rRXm0sAZwzUin7e/%0Ac+cOXn755ck9YpRbW1tTz9tb6cvzqPvDA9h5AWWt/3kevssrySxrpRWEtE51nQFTA5YoYNI17Zen%0APVb5Iqa6NvmOberxPDmzJLD867/+a+ScJ1u1tra2Jua4rL8akBviPxwqQ4CS/4+u/A9hmDW6zhUs%0AvUEzb4ZZAxKeXj2Boaep7pmYGpDUmr3chKSBT5+loP/00mBazOCLGi1S69vsDRzW5MCvESDS8/a0%0Ad/XRRx/F7u4utre3sb6+fqkeImx+qM496mMoUPYMb7kwrPOSzHU13Lo/lkTBptcgGgPYxwZMutci%0AViekLTN8u8zZ2dmlz1Osra0NAktNBy6af9WSmnoumbqUDrFHejkJmd4AcPPmTTzyyCPY3t7GxsaG%0AWg/zMD1rpDfweRPEPKQIlimlnwXwdwB8Nef8TRfXXgvglwA8DuBFAN+Rc75jxLfSNe/XsMoWxuiF%0AnzdQSj3GXgwac/LgzPL09BTHx8eT4/T0FJubm9jY2MDGxgYAuEBZwwBKYWsXQWQfrDFxJXhznyQt%0A5gAPV8M9sBxSFi1sT1Y5FkPsCZBDd1VEmOXPAfhnAH6eXftBAM/knH88pfQDF/9/UFNOno9hrnoV%0AMGRFMyoWU+4hJZO5Rkrp9MhH65AcLOlbPgSY9C5MYl2e3rWAqUlLvck68twUlp8xpTR5xp4+PUyP%0AkQLT+yzJDO8F+rVS8hlGdZg3E/SkBTiLYJlz/u2U0pvF5fcBeMfF+UcBPAsFLC1pWQGf12q4lFJa%0AY7gSZsUkx2LVHCwPDg6wv7+Po6OjiRm6srKCtbW1qZcIW2kNZQe9RQKm5xcjZil9lwCmntwpuSN6%0A+xa989b+skhtxGVI/2n1Wb4+5/zSxflLAF4fidQD/FqkJ2D2Bt9Z5zsP/WnLDD2xcv/+/cmTKwQQ%0Am5ubl8ByXrsCaoVAkoMlCQdKWsBaX183TXZ5Lgf3ooIQl0XSsefkOniBJ+ecU0qqNp/61Kcm57dv%0A38aTTz45NLu5S8mHWLsw0LJC38Mf2gt4I/oQc1xfX8fm5iZOT0+nzO6TkxPs7+/j7OwMR0dHWF1d%0AnTqIdfE8F1Wig7PFJzmW/847j+gVuT6WRBaJPHn++efx3HPPhfJqBcuXUkrfkHP+SkrpDQC+qgV6%0A73vfayZgAUzkfF4yZHXUSqPUmC1+xogM9f1F0yJGRUBJYbmpeXJygrOzMxwcHGB1dXWy8EO/QBxc%0A5iVaP23d5TGrxZYWkPTSnxVQRvP3dinQ761bt3Dr1q3J/aefftrMtxUsPw7gAwB+7OL3Y6UI8wa6%0AoVLTgSIzci3wRhYsxvKXcmnJg5jl5ubm5P/6+rq6nYjexL6zs4PT01PknCcrxosqQ1fqI3F7rB5r%0AeVuAWZv+2EBZq9cY+kS2Dv0iHizmvC6l9CUA/wjAPwHwyymlD+Ji69BQRYawS211slZ6r2CX0m8F%0ATS9OLzN9qB5SCCzpnN6TeXh4iIODg8kjgAcHBzg6OpqslOecJ0zT08OTsSfplhX6Wj9aTzDVxkoE%0AMHst5vD26FWuKFAOLUNkNfz9xq13leJ6wHaR9uDOLJ3iLYA5C9ZrAVlE31qgjdZriwugJj4JvUl9%0AdXV18h0f2kJDYHn//n28+uqr2NvbmwLKjY2Nyf8WmZf7Zpam+JA+X/Jb9sy7dmdDC8uNAKU0xaNl%0AmLt9Y3XmWfkuF51RenG8tLx6qh0IQ+tIe4zx7OwMh4eHSClNFnju3LmDO3cePNtAQEmfsxgii+Dv%0Ajkot8A01NyMLPWPlHc0nmu/YLoK5gyWX0gCPmOm1aQ/1+UU7lMcoh/ovh7DWmny1PGpEWgFra2vY%0A2NjA1tYWdnZ2Jo8A3rhxAzs7O9ja2pp8PXKsslmilVn7rTVZvfuzBspeadTkFTXDewFiz/ItFFhK%0A0Sq3BiS9+Fb4saTEAi3xwpcAZCiz1NJprSNttZX8kXwxZ2NjY/Jd8t3d3aknWrjZ1OL3a3ErSJ1T%0ASs31buXhXa9NZ0jcUj+cFTiPCZStJjiw4GAJXO6sEZNc+id6+0mHSMRU18JHQHaRmaVm3hFY7u7u%0ATv7TSyZu3LiB3d1dbG1tYW1trUvZWny5Vv1z8I5aNi33eoSPxJklw/RkERklycKDJYkcbEMZZkRq%0AWWA0jUiaLb7OMTpILwDW2ojM8Jzz5FlpepnE9vb25PvknFlazKBlV4F3X/slVskP/uagWfjfatMZ%0AEleWp/S/d/6l+z0BNFKOKwOWJNwEqwHJFkY5dkdoYZS1zHSo1JiY0XSkGc5XvWmlfH19ffIrmaXW%0A9j0tBp6mlm5KaQLeBJoac47mE71em85Y8YZKrfuk9/VWU3zhwVIbrBpgWnFqzXaeT1SnUvgh6cyL%0AUVo6DclLAhw9xsif7Mk5T14ILF8MTC8N7sVopMh+ww9eBu63lEzzKgDl0DacFTgvmkk+c7AsMYDS%0AYJBAWTt4IoyU36tdvRvKbiKMctY+155l01ia5g+U1wkoNVO8l57n5+eTp4j4E0V8Iz0dvA9dFdN7%0Alqb9WHmPDYiejAqW1gCXg0Z28lrgi/g8WtnRrDqYBo5DVrNrV35bfau1+UZEgiH5BS225+lZU66z%0As7PJy4npSaLj42Osrq5O/KdbW1vIOU+9bzKik6Zb6fpQAJgnOC6iHkNlZsxSA8QagLDCtcRZRBlS%0AH1Z6JNEJhYdvlRYQtoRAkpu4HEhbwMmrCwJLeucm/W5sbGBnZ2fyvRximqurqyZQ1iwOWgy5RRYN%0AmK4C+41apjNnliWgKyldswrOw1v5WfnW+Ah7LyzUsuBaFhnVI5J2JK0hC2ulPtLKIjWR3/W+d+8e%0A9vb2ph655G9QkgCu6VEzSUXC15apR/gWiTL/sfKIiFyYK8lMmWVptm0dVBEfqJZfJO3eYS2ZB5uL%0A1l9UejJ8CxCjcaL68LicWe7t7eHu3bu4e/cutra2AGAKKOkrlTzfoaDVk2EOSWeoFTYWUM7bPJ8p%0As+Q+xtZV6RL7stJdZBMciLkWIvXDw0VZdM3s6omVzhB26ZU5yqijk4EGli+//DK2t7envutNn7Ad%0A8o7NWsY5Rh5e+F59Yog+s0orKjN7o6rs/EMZnuWzqr2+qBJlGYvYAVsGqTZQrcHLD75yrq2sW/pY%0A5jO9mZ2eWafN8fTYJW1nqi1T5Lqnb61cpb4+a+F9pGYymAmzLDGcGp9bdB9i6Xok36suLT7hWUiU%0AOUufknVwcOXWSyRPni//4uLJycnER7m5uTn1rLr1MuJW89MCyllbRLLeWvOu2crXKjV59NJnJj5L%0AaX6X/IgRs2koaNbmOwtp9a9G0qxl8vOuC0BvH41VWsxUS0/WBT8nU3t7e3vyuVraV0lvb+fPqkfq%0AtAZAS+ya6mBM8dxkLemUrkXFcse0AqaciCPpzO0JnshMPyvQlGHGllkDUUu5agZMC8BGdNLS40DJ%0AwZJ/GbKVxXG/ZEoPP1dLn8Tg5viQlfchZngNOPSM2yK9GJ2XRi+GOffVcInctbOWNZtYeQy5PkuZ%0AtXnVKhE9SyyuJq2SaEDJ3/5DgBkZQBo40TPq3BznX6Kkb3prb0HS0o3eWxQzfBElOqm2AGYtmM/V%0ADG/xj/QCx3mDpue3W6QBErEAImEj+ZB4jFKa4fTMuPRf1uZLzJJe3sHv8dV3ayFpKEjK/9IM79En%0AZuFL7JlnTbxZlG1hXqTRCyRqQWhe4GTluUhASWLVUa8FgZp8yT/JTW/tuXFPzs7Opp4D58+Ay2+W%0A87e0t7ozrpO0lKeVzY0FgK36LJTPcl7A1ZLvmCBRK/Nkyb1Av9RpORDytw9JoOThrHbVngE/Ojqa%0AvBaOvlm+ubk5MfdbpeQna/Xdzlp6glbN5FMbNurf5OlG6zfyKdyfBfB3AHw15/xNF9d+CMB3Afja%0ARbAP5Zw/GcmwpFwPwGxdcGhd/JH3WvQfk1X3kJo6bdWhBjBLCxjSjOV5nJ2d4ejoaPLsNx30DPjO%0Azs7Ugo+WTq9yWSxn3gDZm/1p4WpcGVHQLNWhZg31ZJY/B+CfAfh5di0D+HDO+cOhXAwZ2zSuTafX%0A7Fnb4VtZ7ZAVz9b8S3XaYxHHyk+a2R679/ysxCz39/dx7949vPrqq7h37x62tramngGnt7j36BdD%0ATPlZSu/+UwNMkTqKgFupD8p8ujHLnPNvp5TerOUZyqGc/sx8ib3TbGXJQ3QYY7D1qJcxmZBkidLk%0AthZeLJ2IWe7v7+PVV1/FK6+8gjt37kwxyo2NjcnG9J5iMawWH1pvKeXb0k8sv3YENC3dWuuoRRcu%0AQx53/N6U0v+dUvpISuk1A9Ixle3RacYCFzlwW2UI8+3FerS0a8LOymSkRR3+gl466Br5MCm8Vq7T%0A09MpZvnKK6/ga1/7Gl5++WXcvXsXe3t7ODw8nLDM3jJvE7tWhvZ3izDU1MNQv7Hc0aDdL+XRusDz%0A0wB+5OL8RwH8BIAPykBPP/305PzWrVu4ffs2gH5+yavQ6UoLWVelHFxa9e1ZVrmIw9PmeRDA8kFO%0AoAo8eGqHngWnvZT0/HftYK4BEc93NpRdltwktQtOFiOr1clKG6hfwBm6OEbX//RP/xTPP/98MR2g%0AESxzzl9lmf4MgF/Twr3nPe9pSV7mNVPTuUVK6UUXj+a1MNQznTGFgyLpyzenS3OcXqNG4EjnJycn%0AAID19fXJS31XV1exs7ODRx99FDdu3Jh8VZLSqh2YrYses5ASYALjWA+RfC0ZaxHsrW99K9761rdO%0A/n/yk/Y6dRNYppTekHP+y4u/3w7g8y3pRGVRVgg1ieokAdNinDVpzwMoh/qXh7YlB0nODjXABB5+%0AV4eb7Kenp0gpTT69Sx9N498s55/g1fStXWRoWQ0fc7EuutgC9N82NNTfWOtr7CWRrUO/COAdAF6X%0AUvoSgP8BwDtTSm/Dg1XxFwB8dyCdgar2XSVvlVpfi8y3BJgyvMy7p7Q46y3pufMgOknwz9FqzJJW%0AvslHeXJyMnkXJYElvVXoxo0bk0cc6fAea2xhkdGwWh5DZKgJXDsRRvRpSU/qWzOB9QD/yGr4+5XL%0AP1ub0TxXt3v7yoakpQEmMHvW3BMoSXpOZh5ISf+kdZC/8vT0FCcnJzg6OsLR0dHkPr31nICWf72R%0AfJiR8tQv8Nx1AAAgAElEQVQO2lmuhvN6qjGBe6w2DxUL4Ia6Clr76Mye4FlEE7pFxgKDWYLmGEBJ%0AMiZgakDJfZjyAHAJLA8PDydvEdKe1pEHT6uWOdUupPQWq/5a4nLprXsErLU6HctVYMncv8Ezq3Tm%0AnX80HS/9q7hyDjx8PFH+8rLwc/lWIb4yLbexyGv8+uHhIQ4PD3FwcDB1bG1tTd4wRMC5u7uLlKYf%0An+RbhyyfqCceaErg6umT83TrAdQeSNWmX7Ighl7vKQvzIo1ZSMRHuCjmphcHWFymrjFm8hvyhRby%0AG1IcftBr0Phq9Nra2hSQ8Y+F0Uq3XP0moKTP2tJBq98bGxuTl/yura1N0qIVc74YRFuKtBdsROuk%0ABCwln9y8xXIhAH02jUfy65k+STStKw+WLQsLXmVH/SBjAmYk7UUcUJYuBD785RUnJydTTI0zSTKR%0A+XUCJwJEakMOxnQQ0GlgeXh4CACTb+xQWmtra5PvgtOCEPk4+avb6Fnx1dXV5vopAUuND7RFIszV%0AY3xaXI8p16SvhYsCZhRER1vguQpS2yg1leWBUs9VxFbQWwTTvMTWCSy5SUwgpB3EGldXVyfsksCM%0A0uS/GhjTVxrlcXh4iJWVlcmLfan++Hd1+Fce9/f3sbq6OrWRfXV1FTln98NlNYs8Jd9byeytybsU%0ANyoR4O/VL3sxzKGTzpVc4KkBiHmbzNcdMCP5EvgcHh5if38f9+/fx8HBweT9kQSS3LwloCRQIiZH%0ApjIfrASWtIBDoChBks7X19dx48YNnJycTDFLMvM5WN6/f38CjgAmwNlSL5HBWoo31Fc6RCJm+Fi+%0Aw6GA2UOnK7fAM0bepdk8ms7YEnURzEqi/l3OLOl5bAIheZBvkLb1cDADMPExcpbJwZIATvopOVhu%0AbW1Nnv3mPsvT01MAl78fTvstuV4tg08u4kQsIt7nrPAW8+zpO9T0kmHmBZgl8eLUpLcwZnir73Fo%0A+LFX0OYNsFJqdSn5rmrSkAs5wMOVbP7BMfI70kHmNX9ZhkyP+y4JOIk5Ag+fAacPjvEN53LhiefB%0A8+khJVCR+QwJP5aMnX5Pia5NRORKvym9dpEjmn4PgBgSB7D3Gbbm0UP31jRo5Zk+MQtgslCihSfg%0Ao9eopZSmTF8Ovuvr6xNWSj5EAl96jHFtbW2S9/n5OW7evIlHHnkEm5ubAIDj42Pcu3dvAsw550nc%0AGzduTOIT0NashNfW1VAZA8hKafL7MqzFQFvKqulh5d2LTXKZKVjKStLArtU3KMVKo5R+T6CslZay%0Aa+F76NoClJYufJsO+SDpP3/JBX+9Gq1Ip/RgBfz4+HiypYjMdX5oG8iJTXK/6MrKyuRt6Jubm0gp%0A4fj4GHt7exPzmwMtuQD4Z3CHfmpC1k9Nu9cyxzGZZg2Iymu1LqUhgF2TjiczZ5YRRtljy0F0FXsM%0AVqaVryW9WmDvOYC1/63Mkl6BBmDyqYbNzc1LL7cgM5gzS/JHkq9RfhuHA6hklpQPbUWiX2KH5Acl%0AgKR4nFlubGxMfJU8bk8Z0r+ii0a1K+QtgBMFrJo0a+MM1duTuZjhQxhlDYusSdeSXj4+Lj1Wycdi%0Auj2BkuLx73Gvr69PPt8gt/rwPAg4aWFoZWVlavWathQRePEnfcgPSoC3u7s7dRAokn+SFnPkqjzp%0ALBefxjLDx5QIYMqFnJJJbV2fhU+zBMxRHa7MAk+vxY+S77I1nzGAsofUgGfNolYEKCPMQ8bhK9x8%0AQYdWqDnISbOcm+e0cr2+vj61Sk7p8zwpLL2G7dFHH8VrXvMaPProoxNwpPzpPy0AEUBKFkvHmF98%0A7BG+JZ1WM9gDyijrrZWSLmMAJbBAq+E9pOfKc0+glJ20J9OdpY+1xDysvAlguBD4kfktn//mq9L0%0AS0yUVsk1E54f3AeqAR3lT+z2/Px88tgjMeIxzO55rSZHTFRtMowwx1Z22bqoK//XAmWLXCuwrAWO%0AWQJrKUyJLfYESW1ARFwZ/LdWtMHIN4Hz7TvEPgng+Gcezs/PJ1uDaKV8b29vsq/y8PAQx8fHOD09%0AnYQh3yR/5pseZTw6Opp6YUaPwdZrwM4jnVqmOMQErpV5mPtc5gqWHhgMSWvI9aH5Rs3eIQs3vXyY%0AtSuxwPAOKgeffAEGLfCQ8Bf8kr7kxwQesEMOluT/JEAlVsifIuJ50eGV0Vuga62PMUG5ZZW6Nq9Z%0AgqRMu3Te6mooydzAssZsHZJObRztfrSShwLzWCDp+Rmji2MeqMrrkXy471J+sZH7B8l05sySmCGd%0A379/f/IIJTHLk5OTKXObAPb+/fuTdLleln49QGFWDKg3SA5d0PHys/pyqb5bgVLe430gKnMBSw8E%0AeOetWUSI3htrEaYnMx4CkmMuMg3xCWsDTK5KkxnOzW6+Qs3NcG6O88cbiVmSaU3h6JlwevWb3KfJ%0AX6QxVObhk4yCRM29UthWZutJlCjVAqUWf+HBskbBVv9YTyBp8Uf2WsDpDZIt9Sk7VY9FNMkqpVlM%0AunKfJYFZznkCqiT7+/uXfJbkmySGydnp5uYmtra2pg4PLKW/tRQuej0qvSao0vXSPSvcrM1vDRyt%0A857kYe4LPK2FGctHWavPVQFKHraH37HVz8k7umYWSTbJtwZJgKX/HBy535Pnwc0uerJHPmdeehO6%0ABZpjgaSXb8vCS8v1UtghPtteQObp0zOfuYMll6gfY1aLObULUGMCZU/pDZhRs4kfBGjEHPnbfOQG%0AcDpobyUd9LZ1/iijfJO5fAUcvVyYs0r+ZA53AVjt0MLMon24VIfetVaf5RBTvFW8yUj7HzW1Zd/u%0AiQkuWKaUHgPw8wD+BoAM4J/nnP9pSum1AH4JwOMAXgTwHTnnO7WZ17C9kinUUinReyU9e7Lj1kHU%0Awohn4VvjJjf/5aveHCwJ/DSQ45+ioLQBmOE1vyQ9eskff6Rv8cjnyKN1OsTcHTIxzhooxxLN1VMj%0AVl/ubU2WmOUJgO/POX8upXQDwL9OKT0D4L8G8EzO+cdTSj8A4Acvjinp0SFIei7m1IBfC6C1Lk6V%0ArvX2xbSAgaZPiYHJxRxuRnNmCeASs5OmMd/mw8FSA0wOjPRsN/88hDysxxl7MK9aE54kwm5nsbij%0AhW8F2girHJLP0PFtiQuWOeevAPjKxfleSumPAbwRwPsAvOMi2EcBPAsFLFk6o5mWswLKWjO8xkS1%0A0vfyHLNOPdHyLZnk1mIOmdW0F5LYIKUlf7X9kJS3ZoYTWJLZTb/8zUXa75jMspZAaOZ7yUfXkm7v%0A8Ja0+rpr7o0lYZ9lSunNAL4ZwO8DeH3O+aWLWy8BeH1txj0WRnoBZQmoepnLVtze6Y8tFmBaYSWz%0A5Isw3K9Ijxry9CzAyTlPNqxr3/Hhbwra2trC9vb25OC+Sc3fOabPkk8qQ/2WrQstiwiUUb+3l3Yr%0AA+1lhlNiNwD8CoDvyznfEyZKTimpWj7zzDOT81u3buHWrVshpYI6dbleCjsUyEqNGE3fAo3o4JMm%0AsZVmCcg1BlmqDwmU3qdwuY7y4OBIC0EALpnqPF1ukhN4yu+Sc2Yq07DqMXq9dG/RZWzd5103zz33%0AHJ5//vlQ2CJYppTW8QAofyHn/LGLyy+llL4h5/yVlNIbAHxVi/vud7+bp+PmMy+zsjbvVgbcMvNH%0A4pVMYB5OvpiCszvvsMBI09ECem3zuZWfBZa8XujFHJyZUt6UF/88xfHx8RSD1VglpctfwBEVr61m%0A0a9r+lgLC1s00cpQ6/rKOePJJ5/Ek08+Obn2iU98wgxfWg1PAD4C4As5559ktz4O4AMAfuzi92NK%0AdJ6Oqqjn+2qRIXGjLK1VR2rcoT4bDZgifjACEL5Zm3+wSzIsCST8vyyTpgu/z32W/Nve1kIOpSNB%0Akn45UHKwpDASLDlQ0qq63JZE6RJQ8nyjEgWhUh+IujiiE1UkbosMIQGebl7cqF6lvPn1Xmb42wH8%0AfQB/mFL67MW1DwH4JwB+OaX0QVxsHYoqXZJaB7gnXqeLAngpzSH61EjJDLfC8Hv8+9r0WCAHSwIU%0AzsBoMzgBigb61kTAQY4zS2J7HIDJvOYDUAKlxiw5K+R50WOTBJgUJqU0tfLN/aSUFl+tr53ghvZb%0Ayy3TCnoeMPUCTCt9S3r5TGvyjrioSlJaDf8dANaX5N8VysFPvzuDIynNzq3p93QXRNKJmOGRtHLO%0AE+A4PDycHBrLIhA5OzubvLCX8rDMcI1lkvBVcMkstf2RFlhyXyTXQ74lXTJL6Uag+/yjacRQOUjO%0AWrw2jJj5JdAcCzB7piOlBQjHaru5P8EzJmAOyXveEllIiLJK4KEZTqzy4OAA+/v7OD09VT8ERm8j%0AJ9HMcK6H5cbgZrj0IxJrJaDkZZGmN2ee0nSXzJK/vUgu3HCXANefTHDJKntJTVvx8C3mZy0LXVTA%0AbE2rpq5r8pg7WAJtgFkDdCXzVFauZEuzBtQIUPL/ERbNWR0B5uHh4QQsyTSVYKytEPP/8pD3pc+S%0AL/LQPQJNy0dJ5wSY9AvgEsjJg7+FXdYT93mOxSqjZrVk61rYGp9orzKMxRhLMnaeLekvxNcdvet0%0ADyj7ICOdSkur5Mus0bWH1DIAWW7t4J9hkJ9eIOF7DfnbxQGowCMZnrY4RGlon70l3clvKh9nLNUR%0Ayd7e3uT1bPT6Nkpbslqus/RPlqQ3iI4JCLXpD/GLAv3qpjWdWbTfQjBLkhIIRRoz2kk8gIywyh6A%0A6aXRYjJJFsefx+bfrZGPHJIeBCJkkgOYYmiUvwWW8kkYMo85MEu95MsxLIbFy0dx6Zy/y5JW+LlJ%0ASmDJfZxra2tVQDmGzMtysaQFMIfU3SwArmfbjgqWFiMcM44XP+rv83xwVnpRqSnXUMDkICU/8KV9%0A0Es+LigBju/LlAyTb/ymg0xs60uNmu6kCzfp6Vd+xIx/ypa+pyOZJdedrtEquawDafbPQhYFKEk8%0Av6fsd4vIAksutyEyE2bZMnt6Pp1SQ9aaDRazLF2bhbQAplx95gsr3AyXrIpMZ2KGlA6xUv7dbgmY%0A9J1tLV2NWcr9jPywfKHya458AuD7RzkQ5/zwDUcEnvQoJNe1diC1TOpjiedSagWIsdwEY5jzYwIk%0Al4Uywz2JVnKNE9xKW94rMcwaaUmjxrUgWSVf1JE+S87A5GLH6urqpc830IKQ9FNSPOtlutLsl2a4%0A9iYiDZD5p3D5ubawI81w7htdW1tTmSWFt+q2V3t6sgjgS1Lr0irp3gJqPcFz6AQ3M7DsDTqUDok0%0AH6INV/KRLgrDLAk3JSVgaos7EiS4Gc4fB6Q0OTvVwJJvbpcLPNb3vTWwtHyYBJa0mZ6fa/Ug+wP9%0A0v5KqYP0h/L686wQSnusPjG2T7A1vue6qHFZ9QLJGiBuBc0rwyw18RZHhsSfpZQGWktnkmApV781%0AkJRP8NCmdApL161nuuUTPycnJ8g5T/yIBHD8ExA8bWKnAKbAi5vQJycnl3SS251kPfCDrnH3hNwo%0AT2yKuzG0NxTJV7hZA7B2YHr9oaYvtPoWS2nx/xEGF9V5DB9lb7nSYKlJK1DOEjhbBoR3Xw4MyS4J%0ADOTqLwdKDpj8jeIUjvx8EizpvgQO8nHSOd+MTvpwdsofR+RgJn2v3NSXm9mlWKvvfPLgPl3awyk3%0A0Gub9uUClGwL6zoXrw+09McWAIoyMg98a8o8FCRnCY5SZgaWPcCoF6B5QBkxs3qbXUM7tRZWAwbO%0A0kg4M5SPO9J9Akq+eCLBMuc8ZWZrprf85cDD8z4+Pp4wQL7AxP2P3AVgCV8BB6ACJT/InUC60VNG%0A/C3rvE7koqLWXhGrocZsjYTrCUgRkJT/o+VpZb8l0fp3KVxE5uKzrJUxHeiLYIp7UuOLoWsSLDVm%0ASe0h2SUHS/ovwZaE1935+TmOjo4moEnPoB8fH7uvhuOmP30Lh4CS0qUFKpkvf/uQV0dyNVxOIPzT%0AumdnZ1hZWZl60cfGxsbUJEGuBq0tWkFzKJPsYdaWQLIWwCNmeNTnGc1Tu95rjF8pZumlG2nIElBa%0AphWlP2tTveUe3beAwfNZcjOcng0n8KL0LCFA4qb34eEhDg4O1FVwABPQ4eyVTH0CQgLLo6Mj1W8o%0An1WXFgBfGfeAknyomnuBb8hfWVmZ7CHV+kTEJG3pXx4YRnyINSvVHpBZ/4F6M7x03uLLH1MW3mfp%0AVZhmPluVWOoss2aYVgceApIyrHaQaAs0FhBZg5H/p4UQAJMV+MPDQ+zv718KS2W3NsVbq/DyY2IU%0Ah86lyGfCJZBKneRkwHXjLxiRTwlp/VBLS4KwjN/CrCJtU0rbA8oaVlkL+FaevD6GuCh4Wj1kLmBZ%0A2xkijvDePg+twazznvnWXNeEAES+OYhMy5OTk0mavCz8kE/tWJvJedzT01Ps7e1hb28P+/v7ExNc%0AWz3nq8nczCZGur+/P9kWRMDE8+JAZdUBgEk4Yq0ExltbW9jZ2Zk6tre31TcqAZjyrdITQ9xvKctG%0AIK/t/eRuDvnNH9I9Ysb2Eo89cumhS6/yWGN+bMKz8MwSKAPm2HlY4Yb4WLR0I9ciQqBA5iJfDOHm%0AOc9DAqa2ACI3cvPj9PQU+/v7U89p08q3/AiYZI9kupO5TRvgCSy5z7HkDtDqj0CNJpDt7e0JQNKx%0As7NjgiVfrT8/P5/4YbWdBPQWJO0pI/J/8gO4/Hgtv+aVq4b1LYqUALNmLA0dd7XgPSpY9kT6nsDk%0A5RFhkaX/UR1bTe5SZyNmSf+56UoshxgfpaexSmJ8co+k3IpDQMdfKszBjtcJBxZuivMXXfD8iFla%0AQOlNMpzxcRbIwXJnZwe7u7vY2dkJ+fVoEqGy8FV8/iYjuYGePm1BX5qkyUrbs6mBZ0Skm6UUxrru%0AubKs+tbys8Lz8snfHjKWr/NKMEsukQ5Rk1ZrGmOY4TztyDVNuO9PnhOo8QUUnj4HJPI7Ess7PDyc%0A+hSF9pINDgwUlhgYPZNNwCXf+iNBmh+aGc7/W/UjwYwOziY5aHI/IheLZct9qWRan52dXZo4Dg8P%0Asbq6it3d3akFro2NDRNQWtp/0WSI6R0dX7VstFWfmYPlGGyzZ9o1Psqx/ZZDzHDqFPxt5ASA/FML%0AlA8HIvolZkkLNQcHBzg8PLy0Dcjat0hAxzePc78d5Wm9EUn6SXmdkA+QA6ast/X19SmfJR3SX0mA%0AaW1wp8Uq+ZYjyoO/bYmeO6c30fODnooCHgIllZUvVlE9RayLHqZ4TZ+TekXyHcP3WvJZjzG5XDlm%0A6cmYbI/SBy6vqraYESX2OGQ2tkwiYooWUHLATClNfG78UxQcNLRtSRJIef7Sv0dgKs196Uflwp8d%0Ap3StuuMb3tfX17G5uYnNzc0pXyU/rJX1lZWVqUUo8q2SPtKHy7dN3b9/H/fv38fe3t5kkYl0kY9v%0A8v41tC/Xmsa9JJqvZYpHxKoXyxXWq7zXCixbpMQYrfvA5S0OtY2upTmWaH5JGqjyhRIkHGTIdOTm%0As9xsbq2ay5cC04qx1IsfUiQ4Wve58F0Bm5ubE3/h1tYWNjc3J0xQrkbLdPni0Obm5pTu/Llx4OHr%0A6CiMfO2dXP2m9iBXBf8ttSf/9cJoZeJhtD4bcQXJCWrsRdhSXkPdaiUpfTf8MQA/D+BvAMgA/nnO%0A+Z+mlH4IwHcB+NpF0A/lnD9ZymxM1jck/dZK1kC1R8cYCzglUHrbgjgj49uP6GkWCxzlYgyvI/5r%0AMVoLLGV8/muFI4CmlWcOlhsbG1hfX59ambdAhT9hxAFI28IkJx3uo+WThQRLyoeAstQHSlaItwCp%0A3bfSttKP9NF5MNiasVzrwigxyxMA359z/lxK6QaAf51SegYPgPPDOecPh3IxpNdM1NtXKf9HZs0e%0AZsTYwkGJg6XcDkQ6ElgCD1kaPemigaN1aHlROpYLQEqpzjQw4FuoiFnu7OxMgFJjllp68u1G/DFQ%0APgFJxi31sICSl52AcigYeX3WKmsLaNbmTflp1pgFfprOXj5yTEYmhsHMMuf8FQBfuTjfSyn9MYA3%0AXtweNOJLlXmR56B0aiVqSngNFNFJlqumvFbcSDw5sAmwtPc6Apj41wgsiFV6prP8PT8/v/SKNvL9%0Aeenwslm+KE14HP7oJjfDCSQ507PSoHqQC1T0JA/fS6ktSNUyS60eSu1q1YEGGlqdlphjC0NstdY8%0AHWqZ5JA61CTss0wpvRnANwP4PQBvB/C9KaXvBPAHAP5BzvlONK2KPN2CjM3QSjOXpUstw+zdqF58%0AySw5gGrMkrMqDRC1mZnXxfn5OQ4ODnBwcDABA9qn6IGlZAUe8+Nh+Dn3WXIznIOWfL7cAmj+YhGq%0AM1owo3ICmGLpUg8LLEuTRq14bEzWEw/P9bHueWNOY3Feu9VKC2DWpF2SEFhemOD/B4Dvu2CYPw3g%0ARy5u/yiAnwDwQRnv6aefnpzfunULt2/fjmQXkrGAsrXiazp3xLyvybcmDc9s1AY5DSx+8LS0cvFf%0Aet0Z+fhoJVkblBazjIimJ/dZcmYpny+X5dLSBR6++IOEv6yYTwCaCZ5zLjJLijMULGX/svqINIOl%0AaKAZsbBKgFmjQyn9aD5a+JwznnvuOXzxi18MxSmCZUppHcCvAPjfcs4fu8joq+z+zwD4NS3uu9/9%0AbgozUdBiU7KgpYorzZ5Dwks9x2Sw0fSHskoSGsCcUfH/HnBoaXl6eWAqWaB1eGlzM1muuK+treHG%0AjRvY3d2dLOjwp3gsPbRfrzzac/j865H8l57g2dzcnLyOTk5IJYlYWrV9qlff4ulqk6rsL56pH+1X%0A1uTtMWcut2/fxu3btyfXn3nmGTPf0mp4AvARAF/IOf8ku/6GnPNfXvz9dgCf99KxKqvFdLXSFnpX%0AhdfijgmSXmeqFc0clenSPXlwsOTXaoBLA0yv7qx0rTy8vClf7lMlFrmxsTHZcE5gqW0TKgGkJxws%0AOZukzefyoBV1uRI/pL/KsLWA6wHJkPy0eyX22qJHC560MvcSs3w7gL8P4A9TSp+9uPYPAbw/pfQ2%0AABnACwC+21IK0B3O/D+/1kOGgI/VyLWDKMKMrc7Uqy5kOhIoPWapgaQHgJ5QWSMM0gJnr075Rm/a%0AGiR/aVGnhkmWJKWHLwKmMtKTUtK9QY998le9yc9TRMDFu051xH+1ePL+ELPf67MtaQ6ZHKJxoy4F%0AKaXV8N8BoO2O/URIK6GcB5JWpXsdI5pvTRwZv5Zp8s7fApiRvLT70bJxoJRg6bG+WrH8UDUMNpov%0Amd0Elru7u9jd3Z0wTDqkr1L7jebNWS35LkvPvFO5+Us3vHduWnl696PjgtqmlwneaokNBdQI4A1l%0ArySjPsHTApK1swOXaAf3xJqVKb6XRw0bkjr1MsWsPDVQkma4BppDpOSz1ACT3+dxrLLlnCfMkvZR%0APvLII3jkkUcmpi5/0YXcgB4FZ6tOqQx8lZxASB5aPlZZ5URd0kXWjzXJW5NYDymZ5b3y6H0vKjN5%0A3NEDSbrWwwQd08/YKtGOWetv8tilN/FIoPJMcJluq2jgUGKYNWlLM3x3dxc3b96cevOSx+BaLA5e%0AFi0NaepJ4NS2atXkbd3zwEoDUAvQW6Xkq2whOa3p9MqXZFSw5E5vElmZHpOLSMkv2pLeUJ2kRACz%0AxFgtf1OtaINVe6uPjBPVVRO+ACOfbsn54RM+9N1uqSuJ9u7I1dVV3Lhxw1zI6cGQeZl79QuLNIzF%0A6umalm8JVHrlXRO+Nn1NtHFD5y0yEzO8ZBqQ1ACBZZrUduoaIIiY+VaYWrZUI5bLQP732E0Ns/DC%0AaWxX215DbU17FPlr4zSdcs4TcKRN5vTIIoGltyXHAqASMyyVL1Innlj50zVpNkcmXs/UtshFqQw1%0AFlIPGcNNMHQCmimz9EDSu++JB5I9/TO1pkJrw3hg28tV4R1DRWtD/ow2/aeneYhRHh8fmy8k5syX%0Att9sbW1NVrvp5b2cWXITv0VqJ1rJiK1wVvolYK9tfwmC0cmgRay0rHK3MMkh7dirrDNjlpHCeqDJ%0AC2398nDazFmj91CfXW0De8yiJo6li/zVmKWXd40esuzELOmcGCK9SFe+0EKyX64nvflIrnp7m72t%0AuvLuRcQDyFI8CXyRSbJGIoAt8/XGXFSXIfdr7tWOrR6AOROwpPNSASP3rU4w1JdksYSe0gKetSaY%0AJSWgHMIuPcbDzXB6BRmBI/9shbZBm4MlfzqGVr53d3fxyCOPTG30pi1C3puEStaNJ5bLowVY5MRe%0AYpUtYBU12XleEZ219EuTa8RdVNJ51hMbl5mY4YDOGi3TIOK7lP4szfSuZV5jypg+SymeWSRZmzRz%0AvfRKs7vWDgCmttfQvfX19cknKwg8+eOIUk/6oBmZ4cQsH3300akX61pvErLEAlSvrHLgSTDzJnWe%0AhgaUJT2iYrWZVa6hbh4tP5meHJMlcPXqbKhpbuXpycy+7jjU5LEamSpNpt8KTtbsXqtrCyOUjVjy%0AM5UGqEybf9KBgCXnhwsnWkf2XBxSb/m/ZFrSRvKdnZ2p7/XI91/S+c2bN3Hz5k3cuHED29vb2Nzc%0AvPQGIQ62Vp1Yk6ulK6+HknUj64ynY21WB6afovIeg6y1LjTGq+lYqgcPdCMmfiSdSPyxiEREZrLP%0AshXIak13a5amtLy4Q/TU8hjSKCXQ0hh4yRSmg3/mgIOlBJrIAIiApAWwcm8kPeFCK+baW9x3d3cn%0AL8fY3t6e8k9ysJT1JM/5tdIEEDGttXyseFQ2esEG/4AZtQnF0epR1q/XTrVAFJl0vTqNxquVoQBZ%0Ayj+q29yYZQ/ArGE01gxrDf4eQBlhJZae0YHpMVitfixm6QFNaYYvWRBafAJs+r5PSmnqP2dfdM6f%0A9+bMksqlWQWeOVoz2Wh1UgMWdJ9vlaIDwKVPVmgTVg2A14hk2pHw2nk0L0+GjMFIngvLLFuZm+bb%0AsGbZSNolMNGYqKePpwd18poZWqYl2Y5l5nlxNF09M5w/K87LoJXfA8SayZDAkQPlzs6OufgkF3KI%0AWW5p2c4AAB+YSURBVFJ+ljXBfz0TXKtDq695JrdnxRCzpMWt4+PjS7rx3QOa1E7CESY6BPRKzFzW%0Av9WftPhDx2UvoARm+HVHq/A1gBlNR7vnmSv8sNhCpIFlx/B0sAaUx3BkPG2QWwOfyhcxw2XaUkft%0At3RPKyd9soJ/Q5ve3q4BnVzEoUOrHzlA+bk2EXlAaYGwlFpmSQtcPD8+ibVIDXBqOnvjRN63ANMb%0AG5Ixe5O8R3y0fD3pAZpzYZbeORero0bSiZoVEix5PnQ/Mgis/54eHgPxwkog04BSS5PAkoPO+fm5%0A+akDSycLFCNgKcGbfzUx2ok1QJOMXg5M7b9s76j1MEQ4WNK32CkfmsT4p3S1MnN9S8AY1d2b5OX1%0A0oTtxbXysMBWTuDRMnnEp1UW/rvhNYPWCmOZCa0zkkbz5QDVOkoNq9b+Rxo75zz1HRjy+52cnODw%0A8BBHR0eTlWcCK2t/o5xIIqxTgqEsq1Y2r3wWS7Hie+DI/1uTiwTTUh+J9CGpK7URX+DhbJMmM771%0ATuYXmZgiE7RW99Z44fe8duPxpUvF2qYmxxQftxL4rPw0MK0lOp7M3AwfItrsU9OhZfwSWGmdwWoM%0AyYysgeixLU0HeW6FJyHmwr86KBcUTk5OkHOeevMQ75TevssSk7GYugWcGqDxNrLqrAS63rlM1xps%0Akfay6sPSmU9g5L/MOU+1E01gGlhS2tRukZ0A8lqJJcqyW1YXL5MmkkB4E5b8X2KRVrwoztQAKsnM%0AwNLqeEMkyg55eHke7WCl/9pMqzWiBviWRBtRloE+CsYP+alWi1nyAa2Bn2WWyfsyjlYn2gDSBqkE%0ANq2OvF/tmgXEpfbWBnDNANX2WcqXiVB7EOuUQu0m/bbyg2paOTwiIAFIy1cLa/3nDNR7tNaS2nAa%0A+Fpl4fEWDixbgTLSMVvTLpkw0cqsYS8l3UvMzfsPPGSW5A/b39/HwcEBTk9Pp8BLshL+xAsHUx6+%0AVDcWuFoM02IdXvyaQe+FIV1ku5Ta3GLIWtmsCZmAg2+4l0AJwAVLeosT/acXk3iiTVLafVk+baLk%0AQGgx0hKz1OpQ0yXCGi2WWiJFNeC90MzSYnpWI1pxSFrAKgKYJfZC+ViNZ7E1GT7CSolZHh8f4+Dg%0AAHt7e9jb25t8kpZ//8X7jjX/DAKAydM1GhPkZaQ4GtjJ+rC2CGkALeu2xIw88LTMthKY8PCW1VBq%0AG8kqCTA1U5o+rytFmujENCNigRbXX5aTfilfCUwWq9TyK9VrqY29iVem55ELrR+XZOEXeEg8Zqax%0At0gFe3m1/NeA0mOWmplKYbVOVQJKYJpZ7u/vY29vD6+++irOzs4mb+cBHj5epz29w5kl5ScHCmdI%0AHCQIULVN4hJoZBp0Lr/AKPd/enUUaR+vDkssyJoES+0imRgHSnqiR/Zl/iw9F6pjCs8/muaVrwRa%0AFgBqpESmqV2Xv16beTpqJEGOGSm835TGfEknkrmApceuZDiLAfCB7VFsq2NredbMPBEWozWwxWSt%0ATqIBrtVpgYcmNC+zZHzeR7Msps7r2dKTD2JPV562BpYWQ61hHVrdyDqU7eKlr7WhVl/aIOZ1CDx8%0ATR29l5NYPzF+/so6TbTnxyOTh9enNZ3li5ol2Ev/t1Y/Vp1qdcjHtKa7BZDaOLPEG2clKX03fAvA%0AvwKwCWADwK/mnD+UUnotgF8C8DiAFwF8R875jqaYLJBWQAtMJFBYaXvnXr6y0aKzZEkfTTSGpaWv%0ANaDWuSwd+B49DozEQOSGbu0bPFqZZFt4wC7rVqtDCZAaWHpgJvXzwmvl8QaYNSClXt4kAzwERw0s%0A6TMbBDbULtrH1aRO/JV3nHVrnwepmexlGElIZDtpX67kdcXdNjKfErjyvqBNXNYk5mEJ/bYAJVD+%0AFO5hSumpnPN+SmkNwO+klP42gPcBeCbn/OMppR8A8IMXR5XIGdkCMg30+OCU1/l5BKTlPQuwtPS9%0AsmmHJdrMLTuHVi/ylzNLypODpQaY2hM8Ui+vfiRQppSmFoi0clnXJEvh9WnVfaSurbay+gA/t9LP%0AOV8CLK+OOFiur69PwtODAVqbWDrzN0VxULNAwSqv1b95G3LdeVm435VcARwoeb3IX2uykguLWrtp%0AoM7PrbHmAWUEMItmeM55/+J0A8AqgFfwACzfcXH9owCehQKW2iDTOqfGumQca6CUZkyt0lqAzPvV%0AJNKAUlcNPHg4bQaW4eTTH5xZaiDpsUutbkszs9aWEsTlr1ZmS7T7NDjlrxXeA84SQGp5UD1rdSXL%0ATWH5m+NzzpfaQb4yTyuvBFRtMpITpyyn1Y9kWA0kJbOUfZ5vZdLGrDYp8jD8utdmcqLWGLk2ifAw%0AXcAypbQC4N8AuAXgp3POf5RSen3O+aWLIC8BeL2XBq8cD6Tkfw6UUcCsAUtuNpRmZA28vBlbXtfK%0AJtO2mIjU0dKJd2aKIwegxSo9ZmnlZYGlPNdet8ZBRA5sq44t4eClbYXS6lxrAw6E3rnsjxxMtLbg%0AB+lLZjQxTN4GVlvwepV6yXqU9euxY62NJav08qCdElw/2QYeWGrtVCIxEtg1sPSAUitzSSLM8hzA%0A21JKjwL4VErpKXE/p5TU3EqVQqKxSw0oZYNRHq1gCTyc2S09vfzkYLHS4I1ogbAGlLyjaw2qxeMv%0AouAMBMAlkKQBawGmpZsFnFY7yxf5yscxtTJrdW+JXKziZebtJP/LfCRg8YMAgfdF3rZW36Fy84lA%0A6mcxWn6/JJS2NjFR2Szm7U18FrPk57T1iY8Ba8KiPL0yUdpWGfl/qS/P13NLjAKWTLG7KaVfB/C3%0AALyUUvqGnPNXUkpvAPBVLc6zzz47OX/iiSdw69atqYJpLEayPEu0mV2m5aUh8/ZmMZkmcNkBzQEm%0ApTS1L9Fa3YuUz2J6Wtl4ZyEwXF9fn2xu5p9v0LZVWKzfAnF5zwIhbYDJwWANYK2uouccgKRo+mvm%0ApBWHfnkcvomcA6ScUKx6jgCkNj6syUVjXLyPcOao5cPLyM9lWaQLwWP3Xn3K/CXJ0ES6kLR6sNLP%0AOeOFF17Aiy++6OpJUloNfx2A05zznZTSNoB3A/hhAB8H8AEAP3bx+zEt/lNPPTVRTM6cXGkqkMeg%0AHB0vgZaXFqWnAU1ptpOAoS1G8DS4OSzLUQI9YJr1WpOBFoeA8uzsbOqNPuvr6+qWE6m/5Z7QTGd5%0AaIzRAz6NSZVAwAIgb3Wal0/6ECNtodW9BbRyUrDKXDK3NbHqQAMH3pYas+RjReopgUe2K0+fAybv%0AXx5YyjGq1ZEGktp4kxO710elvOUtb8ETTzwx+c8JnpQSs3wDgI+mB37LFQC/kHP+zZTSZwH8ckrp%0Ag7jYOqRF1liLdo+EswEPNC3WxdOIgK4GFF54usYHAx8gEvjJfOP3PdZSYjTWfx6Pg6UcAPKDXha7%0A5mXi+ZU6IjcB+bkFWhbz8cBSG7jShaAxNvmfgJIDplYfsn5lO3DQlROlBpZyUtIWhvhvKV/ZJlb9%0Aev5XrZ41ZqmNK9JfWyz0VvMt/zQfJxJwtQlRqzMtbUsPb9xJKW0d+jyAv6lcfxnAu0qJe4NRu3+R%0AduiaBCYJpPLc+pW68TwtcLeYjQREuQ1CmxWterDqxhp4JBwsZT3QPW2llXdO6pAc6OSg1BZn+NMo%0A3DcpGQ1nrvyat92E6pO+8sjBLgKS/OAulFL9exOpBEuur1ZXEiglGHgDnF/jk7U8l2BigWQJLGW9%0A030PjOWhkQM5oXjkhtePrDP6lZMGd/HIOuPpyjEUkZl9sEwCTxQoI2mX0igBpdSpxPI0RmGBrTd4%0Ao2XyyiXTBi6/fYaDpZyxZTjZQWW5LTObgIy/Fo5Ak7MOWtiQg8DaW8j140BJ55SWNyFp4CQZlNcO%0AWh+jOiXftBygGvO2AIaDv1Z2q+2zYPMUTtatxbo9sLQmRguMra1osv6sSUeWUxIA6+CTNB+/JSzh%0AYaMy0w+WaaDpAVkJ9Uv3tZnFy8djGjJdPoN5HbHEciQ4yPrRZl+NWfL0OFhSR5bmsCaafrzMFlhS%0AXWjvzqRH+YjtUj4SuLmLQLaZ1I8vpFDdeIAgV7m53rKMWp1oIpmXbG8eRmNrEmwoHAc9K19Z7wQY%0A0rLhdWu1rQaW3lYhQAdja5FH1qFGKLS618aknHSlm8vyq2p5e9csGRUsucJRBJeNqAGEHBwyvGda%0AaIyCKpze8mLNdlzH0iHLI9PiensD1tLfS4ODAon8r3UmPvA4UMt6l8yK7km2SfnKJ4d422nbmax6%0A0Oqe8pD9Qpr9FsPSwlnPzcv6ktdLwGqlyUWbpLR21tpD1o+ni1YWEs6+rX4t05NAy5meVk/WWKUw%0Ask9LHzt3w2iTUKmMLTITMzxCibWCSPYC4NKMpS0KRA45m1N+ET058FhgqZVZAyjtvjXba9t2KD0P%0AMDmbonhy8FMenLVJsCSGIhkHr1MOOPTCYY1xSJZQYpal+tYmUckouekmdaXB7fneNNCsGYwyfSu8%0ATNOasDX/rtRZMlopGnhSPOlPlXUgAVJL22onbWeFVwdafrzPesRgKEiSzIRZesIHupxRgelVRR6H%0AfrnZGTmks11rcI8t8IHI/2sDygIor648nfk5T5P/WnpqICvByPK7AdOmF9eNrnMA4luXOPOUenGg%0AtJilLBMH8whoasDHQZNcFFxPyU4tMNbaj+smz71+IuNowtuYtysHNQ2Eo+OQ5yGBUjOp6Z4kGbxv%0AaPVIda8BpjcZ8bRl/twlJsEyUu6IzB0sAd+noc0ekllKsORxNfYj7/Hw1iCh/CJAKfPwAFMrrweS%0AWhk5YFKavCy8LayZXKsTrl9KD18wa+lBbcEHgrUySelpYOmBumQnJcDUQFPWKZ9ALWDTBj4vj6an%0A9+uJ1o4aGFMd8j6nsdbSOJTjj4M470MyvDXpWmDJrUDZp3l82Qb8nhyv8tyr5+ikZMncwdJqKIrP%0AV7uoAXihaZBZQMk7Gw0ubUaja9wHpHU8OpdvWZEHH4jR+tCA0gJMGd4DYckuLSCX+mmdXerKzwko%0ArTLIOuHskr+5ndeVnGjIXKY6Lk1cGrO09Jf1xgeexk6tOrX08fLV2s0TOQny+DI/Tzggy8legiQX%0ADyipXbRJSiMSVj145EMSIBlP07UVJEnmvsCjNRS/RxVzeno6GZA8Xc4seRw651s7JAOjsPyZZbnF%0AhesuZy0PLGkwA5ed0KU6s0BGmwC065auvD5lnhoY00TE65pPVtqvdk0+G04Tk+W3tNKjeuULUNb2%0ALQny/JBl19qAC03KMh1r8HJ9JBPVGLcFFLydNZHEQWt/q4xafnKSkXWiATCdy/El/cKy/r1JP0qg%0ApOltYYhXh7KuPBkVLHlh6Feey87izZgkspPKwS87DDfXrFmNp8cbm/syo4AndZEdUGtUKRpTkenK%0ABRMLCHiH5p1ZA1oeRuvoGlhqdcPDrKysTC2gkJXgvflIdnKePgdKy2Ug60j2PRKNPclyWfXA65TO%0AuS5aX7f+a6JN7lrY0oKG1MP6L+NqfVcjQCVWqDE9K66U0kSspV0aVxbzLMlMwBLQG1AyQ27aarM0%0Ar3g+w1jMjucrwVUzxWXDSLBvFSqnHBwRwKRwctbldSdNRAvM6B6fCCx2KMHSG1heB+V6E1BSXVgf%0ATZN1JNORizGyrDwNr3418JT/PbDkdSoBUNaB9kt5lUBTAyiue4Q1lQCTp2VNzKSLxYYtHyU/90Qj%0AJRIPvEmBx5HnGujWjuuZgaUmKaVLPiq6Lv/L1Ti5J9BaeZMzs3dYYbxyaMzEGgDeuWxga7bm+Vpg%0AyetTzsKSMXszvNbZNVCIDAIOvgTSklla4MXriOdPaUn9vUmW14UMowkHS2syLoEk/6/1cwus5T2t%0AT3j1Jf/XlFlLR3M98Hua20PmZ9WVNla5Ph5YynKUwFILF5GZgaXVmakC+P463igUToIlZ5YAphZ6%0AKJw2SDi79IDSA0ieJl9k0MrKdamd0axBzzukfIejBEAJlhIwPVC32EekDDI93qZygUdjrpo+nPXS%0AuewrvA2tQaSBhjWQ5UQsJ6USIFuDXqsnrQ4tYJHn0QnL0lX7lenL8UF1L5m+Vk+Wjta4kPVUAkyu%0At+wvVtlqZaZgqQ1eWXAqqHTca2BJQClBhc458+A6WQsnXE861/yhslHkqqzVmKWBwcui1Ys2aLWn%0ATbQOwsGSgFLmX2IcnnjllIxEToIWUHoTniybtrshUt9W/5L6S115mWTZS4yypJMl1liJtJkGlhIA%0AtTx4meiXL5oCl5/d5hO4VwaZjwVsmn5ef/PiLTxYyoFizfzWzKINJD5AJIhQOOnY95ilVrFywPFO%0AQ+Gkz8zS2WtQS3hecuaWq8h0rqWvgaXG/CRwWJOI1l4ltiNZghZOpqHpq6XDXQp8C1ikTmVemvB6%0A53VViltqW1lmnp8Wn1+3wM8qr6YvnzS1iZXnK3WSgCkncb4WIdO16kVO9BqT1OpDlleeW5OBFc+S%0Aue2z1MCjxAa0dOmcFg7kIoL8lIG1CizZW4nt8HNLZ21CKInVqT2dvPw8AKI60hzzlJ7srBrYSL01%0A3fhAkJOOFl7TWUuHh+HmYMmN4k1kmkhGxX2vNQNa1oEnVhivj8i8LNCT4Swyw9vKOpf5lJhdaRKR%0Ah7dQ7IlFglrZ5UyeDSeRM5TGGnjleAOOpyEXe0hWVlamXhWmvc6KdzxvQYPrXwOYUlc6t+qHp6k5%0AyGWapc5UAgYL5Gp09X65rvLcA08tnlYemQ7f3yfjStYvfWqajjw815PvoZV7LK364OBSYlkyneh1%0AT7T6kDtD6FwD4lK7cXCSz2tb55qOEiityTpSVmsSaJGZgaXFRuQAlnsiLUbBKyOl6beccBYgX0hr%0AdWy+FaTm0MrE9eLnkQ5Tk5cES01/i21adeyVx7oXqQtZ7ghQWiLz5QDGwcirb2uCsxiQ1R5yQGtx%0ApK5aXqXye21YAlSv/uVWOrnPlsfT8tHAX+60iArXSQKcFo6XUUvLOhYSLOVMzs8thiAHvDaYtQGX%0AUpo85SPBUj49ojVALUhq23SsOpCsolRPsqNK3yuPw01CLb5V716baPnIyUqLy/OT53LAynrhdeXV%0Aj1YnMj3evtavlY+Mq7UNj18KK/uyLJMGmLIetAlFA09LPODywBJ4WM8eyFjjV973dPSYYGl8yXx4%0AOpbbTYtTkpma4doiAoksjNY5LaYgK5oAhM908pANWAIVDSgt8NZmwhJQkvB0pRluxeXl4SBesxFY%0A+5V6yzJYAKsBCo8rgYWH0con07aASEvfu6blZQ1U2T800dKVenIwqmFeFmDWDHapIy+jBEu5g0HW%0AsZe21kc8fS2dImCppcPHvQaYWh+PyEzNcAIavmqtVQwHOzmzeYxBxpWVVhoA1gGUt49oHUWyhdq6%0AsmZNnrbsADk/fBKKDmtzsOwsEcCUOnpgyf+XJhSrfNp/OXHJ9DTgs/5bv7zfSB2tsnqgLt08Wj/R%0ABq6crLw6K4lWDxqRsLbLeflbk5I3png8/r8GMC3wlW3IyyX7URQwZ8osuWgDITKLyAGsdWCt4ay0%0AtIEnG1X7jXbUEihoOpWut8zO/LqWbrQ8HjvggO0NCJ5OpLyeLvI80vE18JagVMM4eP5av9HCWn1M%0Ai9cy4XLR+ob1PyJSH02/FkbI40WZZeSeNtlok3NJSt8N3wLwrwBsAtgA8Ks55w+llH4IwHcB+NpF%0A0A/lnD+pxJ/8ajOKxVhkHKtjyVnf6rARVhTtuDLfSOOWgEOKNlg9xqHpJ2dXeV2LI+u1tZNq6Wmg%0AxMui9ZFS57bK4eko87dAVj4ZpEnNZKptZeKMs6SnJlqbeWFLkxYnDdZkJicVnrc2VrzJ1fs/VCyL%0AoMSQPSl9CvcwpfRUznk/pbQG4HdSSn8bQAbw4Zzzh734JSVKrMsCQBne+rV00syD0rPJmr5RaYnn%0ANahsdMuUkQO0NFt7JhHP14uj3YtMiF5ZtXRLYBKxUHha2v2opeP9WvVlTeZykNcAppaPF0+WQxtv%0AJdDUiIA3/kqTmUU2PGLgSQkoa6Rohuec9y9ONwCsAniF9IhmolW210l5nBrmV6oICyhrWQ0vA/1G%0AzQZLr9b/Ejjkr/Z8vjbrRnTUBn5kQMs8KT1ZxzX135InT09OANZk5E0q0UnaYnSaPloe3mTllVO7%0A77FYznStMcHrxQIgq39ZddqThHh17ekZkSJYppRWAPwbALcA/HTO+Y9SSv8FgO9NKX0ngD8A8A9y%0AzneiSmrKypnYm+W8tLWZjq7L/6XZ09K9xMC8sJ6eNWARHdCA/fanUtlKg9wKY0kEZLxzi61FRetj%0A1qCPMierLNbkxePKfYwRwIxIDQuzAK/mLVA1EgX2GnYpw2ttyX9bwTrCLM8BvC2l9CiAT6WU3gng%0ApwH8yEWQHwXwEwA+KOO2UN4SQFrXLnQNpe+xy5JoAMU7uscsvc6vlSeikwduvJwyH3nulS0iVO4a%0Apqr9RnWs0S8y2XA9LHPSGpAl5iL7hNbnKIx8umwIg7buWX3P0k/2Sat+WvNvEa2Pau3j1XXtJBRe%0ADc85300p/TqAfz/n/CxdTyn9DIBf0+L87u/+LoXBY489hscff/xSoTSpGXAaG9DCyYFRA5AksoJL%0A+Vk6R695M7uVtmUWaQNCO+dha8w775qXn5Uu10Ge13Tw2vCtcYDLusoj0o6liaAHgMo0vP7nTTQR%0AaZl8vT4UmUiidZ1zxgsvvIAXX3wxpFdpNfx1AE5zzndSStsA3g3gh1NK35Bz/spFsG8H8Hkt/tvf%0A/nZK55KSNVIaJF6DtDIPGV8CiAeUtXlZzNlKy2ND3qxfYkIkPWZ+LV9PNJZXAsrIoPBAwTPHSozd%0A0pmu1zIqDVy5RIGilA9Pj4evZestojHAyHgpjXv5K/Oz8s854/HHH8fjjz8+ufZbv/Vbpi4lZvkG%0AAB9ND/yWKwB+Ief8mymln08pvQ1ABvACgO8upKMqTxJhHTUsp4X6a/lqaUZYl5WWBlIagEVYpQaY%0All6eydjKomT6sjyl8BFAjABETbvWgKv1a5VF6s5/W+u3BTBrGJ+MJ/tlreWlSakeLasnSoysMJoO%0A/H9L+5S2Dn0ewN9Urn9nJHEPNGSjaw3VwkC186honZ2fe+aLZ+Zq5ZPnEiy1NDV95bnWOT2g1Mo1%0ApKN6OnIpAaZ23qKHZxl4cSyGZ6UZZTdaOjzPkpQmxYhYfbU0oVv5tIzTmklMG3cafnh1ok1mpYlQ%0Aylye4LFmzJZZLEK7h8yOJXbpgaQlvcBSS6s0Y2r5eJ1Ni+91tCHtqAGa1oalbztJfUt68TDWEdG5%0ANGC9yZaHrQG9UlhZf1rfsiaoiMUn84mGqR2bVt157FvmVWs1SLG/ot5R/vzP/xxAfWckKcXTzlup%0AtsxXi2/pzk2XP/uzP5v6L80a65523xIexvqejQXM1kCo7UAkzz//vKqfJ1a9Sl34vdqDvxfAekeA%0ANri8ODlnPPfcc6H0S5O5NQasia5mIrXaMNIHtXy0NvbKZ4XxJhRPSpOdlqYcw/LlGpEXOJPMBCy/%0A9KUvXbrWCl7RzqgNhJY8tfxLklLCF7/4xan/8n7kl4f3GGyp01vAaIFxLWDmnKfK20NK7UrnpYOH%0A4/FLA7uU7vPPPx+erEvl866R1LA8K60I2Hj59GzjUjtEgTFSL1bba23myUzA0pKh4LWUpSxlKbOS%0AuYLlVZQh/s+rIl8PZVzKUmoljcXuUkpL2riUpSzlyknOWWULo4HlUpaylKVcJ1ma4UtZylKWEpAl%0AWC5lKUtZSkBGB8uU0ntTSn+SUnoupfQDY+c3a0kp/WxK6aWU0ufZtdemlJ5JKf1pSunplNJr5qlj%0Ab0kpPZZS+nRK6Y9SSv9PSum/u7h+LcudUtpKKf1+SulzKaUvpJT+8cX1a1lekpTSakrpsymlX7v4%0Af63LW5JRwTKltArgfwbwXgD/DoD3p5T+7THznIP8HB6Uj8sPAngm5/xWAL958f86yQmA7885/7sA%0A/gMA/81Fu17LcuecDwE8lXN+G4B/D8BT6cEXA65leZl8H4AvAKCFjeteXlfGZpbfCuD5nPOLOecT%0AAP8CwN8dOc+ZSs75t/Hw7fEk7wPw0YvzjwL4z2eq1MiSc/5KzvlzF+d7AP4YwBtxjcud9S8GXNvy%0AppS+EcB/CuBngMlXEa5teSMyNli+EQB/fOcvLq5dd3l9zvmli/OXALx+nsqMKSmlNwP4ZgC/j2tc%0A7pTSSkrpc3hQrk/nnP8I17i8AP4nAP89AP4g/nUub1HGBsuv+31J+cHerGtZDymlGwB+BcD35Zzv%0A8XvXrdw55/MLM/wbAfxHKaWnxP1rU96U0n8G4Ks5588C+re2rlN5ozI2WH4ZwGPs/2N4wC6vu7yU%0AUvoGAEgpvQHAV+esT3dJKa3jAVD+Qs75YxeXr325c853Afw6gL+F61ve/xDA+1JKLwD4RQD/cUrp%0AF3B9yxuSscHyDwA8mVJ6c0ppA8DfA/DxkfNcBPk4gA9cnH8AwMecsFdO0oPnIT8C4As5559kt65l%0AuVNKr6OV3/TwiwGfxTUtb875H+acH8s5PwHgvwTwf+ac/ytc0/JGZfQneFJK/wmAn8QDp/hHcs7/%0AeNQMZywppV8E8A4Ar8MDP84/AvCrAH4ZwJsAvAjgO7Ly9curKhcrwb8F4A/x0BT7EID/C9ew3Cml%0Ab8KDBQ3+xYD/MaX0WlzD8nJJKb0DD77e+r6vh/J6snzccSlLWcpSArJ8gmcpS1nKUgKyBMulLGUp%0ASwnIEiyXspSlLCUgS7BcylKWspSALMFyKUtZylICsgTLpSxlKUsJyBIsl7KUpSwlIEuwXMpSlrKU%0AgPz/QCjzUOP0/xAAAAAASUVORK5CYII=%0A
-
-上面我们将这个图像使用 PIL 的 Image 对象导入，并将其 resize 为原来的 1/100，可以看到很多细节都丢失了。
-
-
-在画图时，由于画面的大小与实际像素的大小可能不一致，所以不一致的地方会进行插值处理，尝试一下不同的插值方法：
-
-
-
-
-::
-
-    imgplot = plt.imshow(rsizeArr)
-    imgplot.set_interpolation('nearest')
-
-.. image:: data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAUsAAAD9CAYAAAA1U1VCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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%0AhP8H0Tz8GZcurRgAAAAASUVORK5CYII=%0A
-
-
-
-
-
-::
-
-    imgplot = plt.imshow(rsizeArr)
-    imgplot.set_interpolation('bicubic')
-
-.. image:: 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WhYBGKR44iT2tqmNdRIKzT3m1TltWvXTn6TG84Vp6YwvfJ77doCpp5LLy4K3O97qscKW41wQ6Vd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%0AHVnusMMOOySwI8sddthhhwT+Pzl/C8KJDU7hAAAAAElFTkSuQmCC%0A
-
-
-
-作者 & 更新时间
-------------------------------------
-作者:`李金  <lijinwithyou@gmail.com>`
-
-提交: 2017/12/14
-
diff --git a/course/source/aipy-numpy/cn/6annotation.rst b/course/source/aipy-numpy/cn/6annotation.rst
deleted file mode 100644
index 7ac6f1c..0000000
--- a/course/source/aipy-numpy/cn/6annotation.rst
+++ /dev/null
@@ -1,517 +0,0 @@
-
-注释
-=========
-
-
-使用文本框进行注释
------------------------
-
-先看一个简单的例子：
-
-
-
-
-::
-
-    import numpy.random
-    import matplotlib.pyplot as plt
-    %matplotlib inline
-
-    fig = plt.figure(1, figsize=(5,5))
-    fig.clf()
-
-    ax = fig.add_subplot(111)
-    ax.set_aspect(1)
-
-    x1 = -1 + numpy.random.randn(100)
-    y1 = -1 + numpy.random.randn(100)
-    x2 = 1. + numpy.random.randn(100)
-    y2 = 1. + numpy.random.randn(100)
-
-    ax.scatter(x1, y1, color="r")
-    ax.scatter(x2, y2, color="g")
-
-    # 加上两个文本框
-    bbox_props = dict(boxstyle="round", fc="w", ec="0.5", alpha=0.9)
-    ax.text(-2, -2, "Sample A", ha="center", va="center", size=20,
-            ^4$bbox=bbox_props)
-    ax.text(2, 2, "Sample B", ha="center", va="center", size=20,
-            ^4$bbox=bbox_props)
-
-    # 加上一个箭头文本框
-    bbox_props = dict(boxstyle="rarrow", fc=(0.8,0.9,0.9), ec="b", lw=2)
-    t = ax.text(0, 0, "Direction", ha="center", va="center", rotation=45,
-                ^4$size=15,
-                ^4$bbox=bbox_props)
-
-    bb = t.get_bbox_patch()
-    bb.set_boxstyle("rarrow", pad=0.6)
-
-    ax.set_xlim(-4, 4)
-    ax.set_ylim(-4, 4)
-
-    plt.show()
-
-
-
-.. image:: data:image/png;base64,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
-
-
-
-*text()* 函数接受 *bbox* 参数来绘制文本框。
-
-
-::
-
-    bbox_props = dict(boxstyle="rarrow,pad=0.3", fc="cyan", ec="b", lw=2)
-
-    t = ax.text(0, 0, "Direction", ha="center", va="center", rotation=45,
-
-                ^4$size=15,
-
-                ^4$bbox=bbox_props)
-
-
-
-可以这样来获取这个文本框，并对其参数进行修改：
-
-::
-
-    bb = t.get_bbox_patch()
-
-    bb.set_boxstyle("rarrow", pad=0.6)
-
-
-可用的文本框风格有：
-
-::
-
-    class	name	    attrs
-
-    LArrow	larrow	    pad=0.3
-
-    RArrow	rarrow	    pad=0.3
-
-    Round	round	    pad=0.3,rounding_size=None
-
-    Round4	round4	    pad=0.3,rounding_size=None
-
-    Roundtooth	roundtooth  pad=0.3,tooth_size=None
-
-    Sawtooth	sawtooth    pad=0.3,tooth_size=None
-
-    Square	square	    pad=0.3
-
-
-
-
-
-::
-
-    import matplotlib.patches as mpatch
-    import matplotlib.pyplot as plt
-
-    styles = mpatch.BoxStyle.get_styles()
-
-    figheight = (len(styles)+.5)
-    fig1 = plt.figure(figsize=(4/1.5, figheight/1.5))
-    fontsize = 0.3 * 72
-    ax = fig1.add_subplot(111)
-
-    for i, (stylename, styleclass) in enumerate(styles.items()):
-        ^4$ax.text(0.5, (float(len(styles)) - 0.5 - i)/figheight, stylename,
-                  ^4$^4$ha="center",
-                  ^4$^4$size=fontsize,
-                  ^4$^4$transform=fig1.transFigure,
-                  ^4$^4$bbox=dict(boxstyle=stylename, fc="w", ec="k"))
-
-    # 去掉轴的显示
-    ax.spines['right'].set_color('none')
-    ax.spines['top'].set_color('none')
-    ax.spines['left'].set_color('none')
-    ax.spines['bottom'].set_color('none')
-    plt.xticks([])
-    plt.yticks([])
-
-    plt.show()
-
-
-.. image:: 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6cqVJOEs7OzpBIJBqzabVq1QoSiQSXL19GUlKSOCWShL+/v5iPWtNUuWnTJnTt2hWOjo6wtrZGQEAAxo4dW+qUeubMGQwaNAienp6wsrLS+p1e1BageC38rbfegre3N6ysrFC3bl1Mnz5d47o1UJyDMCYmBmvXrkXbtm212lxplOb3qSjHUnp6Ops3b662xvwi+aOHDx9OiUTCxo0bs2fPnuzfv7+YisPc3FzNSX38+HEKgsBOnTqplJ86dYqCIGjMWpCRkUEzMzN6e3uTLHaaJyQkiDbGxsZy6NCh4j8l/XQTJ06kIAi0sLBgREQEBw4cyPr161MQBNauXZtHjx5Vuy/r1q0TU3aEhIRw0KBBbNOmDQVBoJmZGRcuXCh+9lm2KB3hixcvpiAIjI6OZlBQEN3d3dm/f3927dpVTOExatQoNVuOHz9OZ2fnynR2K9F96o0LFy5QEATu2bNHLHuR/NH79+/n9evX1T63fft2Wlpa0tHRUcUhLZfL6eDgQBsbGxWn71dffUVBENikSRMKgsB///1XrNu0aRMFQeBrr72mco1nJQ/asmWLKDrl0hr5v00YgiDQ19dXXI4jydu3b7NmzZoaN3UoNy5YWFjwzJkzL2SLUoyCILBv374q1zx69KiYMyctLU0sl8lkrF+/PidOnKixzwpGt2J8+PAhmzRpwilTpqiMjM+bP/pZDBw4UGPuPmUC9L1794plPXr0oK2tLVetWkVBEPjTTz+JdW+99ZbG/M7PEkCnTp0oCAI///xztTqZTMa6detSEAT+9ttvYvm0adNKzW+o/KGOGDHihWxRitHBwUHjqkzPnj0pCILaDqFTp07RxcVFYyauCkar3irdtfP48WN07doVr7zyCmbMmKHyJtiyZUsAwLhx45CYmIjCwsJS+8rMzMTy5csxadIkjBw5EgkJCUhISMDZs2cBFO/jK0l4eDgAiM+eMpkMBw4cQNu2bREZGQmJRKLyXJqYmAhBEMR2z4NMJsOhQ4cgCAKGDBmiVm9mZobXXnsNALB//36xXJkTW1MbABg2bJjK516U0NBQjW60+vXrA4DavspmzZph27ZtGDVqFLZv316ma5aXSnXtFBUVITIyEi1atMDs2bPVXBLPmz8aADZs2IBhw4YhMzNTpY+SgdOzsrJU6pSi2rt3L2bOnInjx48jOzsb4eHhcHBwQLNmzUQx3r9/H2fPnkVgYOALOeYfPXqEwsJCWFlZaUy0DgD+/v4AgNu3b4tlyo3ByjptbbRtIH4W2r5DzZo1ARRvtH2aFi1aYNOmTejcuTN27tyJdu3alenaZaXS36ZjY2Oxb98+jTuclfmjjx8/jqlTp6Jt27b4+++/MXPmTAQFBeHnn38GULzbeeDAgcjKysJHH32Ef/75R8x0L5fLMXnyZADq7o5GjRrB2dkZJ0+eRHZ2tii8iIgI8d9KEe7btw8AXmhU1GfKsrZMEr///juCgoLQuHHjSrCqdCp9mp44cSKGDh2K8PBw3Lt3T+NnQkND8cknn2Dv3r1IT0/H//3f/0GhUGD8+PHIysrCtm3bUFBQgL59+2L69OkICgpSSRj+9PRckk6dOqGoqAj79+9HYmIi7O3t0aJFCwD/E2ViYqIo1BcVY506dWBpaYnCwkLcvHlT42f+/fdfAICnp6dYpvxvbUcXNLWpTEhi8uTJSExMxB9//AEHB4cquW5JqmQ5cPLkyYiPj0dERAQePHhQ6mdL5o8uKCjApUuXxPVSTVPPw4cPsXv3bq39KcW1fft2HD58GB06dBBHjXbt2sHCwgJ79+4t9XnR0tISADSehTE3N4dUKgVJLF26VK1eLpdj2bJlAICwsDCxXPnfmtoAwOLFi9XaPMuW8vDpp59i+/bt2LNnT5WvvCipsgNZISEhuHv3rsp0vWDBAo0HsVJSUnDt2jVIJBJ4e3sjKCgIALB27Vrcv39f/FxOTg5GjBih9hxZEqW4Fi9ejPz8fHE0BIofE1q3bo3du3cjNTUVwcHBGpcsPT09QRLnzp3TeI23334bAPCf//wHycnJYrlCocBHH32E1NRU+Pr6IjY2VqwbOXIkatSogT179oiPI0o2b96M3377DRYWFhg/fvwL2VIW5HI5Tp06hbp16+pkRBQp7VW7ot7lt2/fTmdnZxUfHPn8+aOLiorYrFkz0WXRu3dv9unTh05OTnRzc+OwYcMoCAKnTZum8fo+Pj6i7+1pv93UqVPFuvHjx2ts/80331AQBNrb27Nv374cPnw4R4wYobLjWun0Njc3Z0REBOPj40Wnt6Ojo0an9/r162llZUVBENi8eXMOHDiQbdu2FZ3e33///QvbonTtDB06VON3+fTTTzXeq/z8fEZGRjI+Pl7rBuYKQnd+xl27dtHZ2ZmHDx9Wq3uR/NFZWVl85513WL9+fdrY2NDb25sjRozg7du3OXXqVEokEq1iHDJkCAVBoIuLi1rdn3/+SUEQKJFItPrYFAoFZ86cyaCgIHEVQ5Ovb+PGjezSpQtr165NKysr+vn5ccyYMaXmkz59+jQHDhxId3d3WllZ0cXFhTExMfzrr7/KZMuvv/5aqhhLu1e5ubns3LkzX3vtNZ3s9K5UMRYWFtLf359Tpkwpb1cmqogrV67QzMyM27dvr6xL6G5kPHv2rMpJOxP6y7179xgUFKR2gK2C0e1yoPIM8rp16yqqSxMVzIMHD9ioUSN+8sknlX0p3R7IAoDjx4+jffv22Lt3L6RSaUV1a6ICkMvlaNWqFVq0aIGFCxdW9kZi3Z6Bkcvl+PrrrxEWFobQ0NCquKSJF8DMzAwTJkzAli1bcOnSJZ3ZUenHDhQKBYYPH467d+9i69atsLa2ruxLmigDr776KoqKitC5c2fs27dPZV9AVVGpI6NCocCoUaNw9epVbN68WWUJ7+jRozh48KBam4KCAsyfPx8KhUKtbtOmTRqX/h49eiSuWDzN4sWLNR4wunLlCjZt2qTR5vnz52vcSHDw4EEcOXJErTwvLw8LFizQuJN93bp1GiN/3b9/X+vqyy+//ILHjx+rlZ8/fx7btm1TK5fL5fjuu+807npKSkpSccQrycnJwcKFC1VsHjp0KD7++GNERERojVZWqZT2QFneJ9WioiL269eP3bt3V9ngevToUTo7O9PJyUklJEh+fj579uxJe3t7jho1SsXXtW7dOjo6OtLb25uXL18Wyx89esRmzZrRwcFBLSTInDlz6ODgwGbNmqk4qC9fvkxvb286OjqqvFTJ5XKOGjWK9vb2aiFBDh48SCcnJzo7O6s4sJWhWuzt7fnGG2+o7NdcuXIla9euTT8/P169elUsV4ZqcXBw4DfffKNi82effUZ7e3u2bNlSJXrEhQsX6OHhQUdHR27ZskXF5oSEBNrb2zMmJkYlwltSUhKdnJzo4uKislFZGarF3t6eEydOVLF5xYoVdHNzU4uuVoHo7m26sLBQRZDHjx+ni4sLt2zZwp07d4rBkgoKCti7d2/GxMTw0aNHKsGSNmzYQFdXV548eZI//PADfXx8eOXKFfE4w8SJE3njxg0GBgZy7ty5JMm5c+cyMDCQN27c4MSJE8VgSampqfTx8eEPP/zAkydP0tXVlRs2bKBCoeCYMWPYtm1bPnr0iDExMezduzcLCgp46NAhOjs7c+fOndyyZYsYLEkZxGrAgAF8+PAhX375ZY4fP54KhYKrV6+mm5sbz5w5w2+++Yb+/v5MS0sTg1hNmTKFV69epZ+fH7/77juSxUGsGjRowFu3bvHNN9/kyy+/zIyMDDGI1a+//sqjR4+KUcPkcjmHDRvGsLAwpqens2fPnmLIQWUQq8TERK5fv56urq48deqUGMTqtdde44MHDxgSEsJJkyZRoVCIQkxJSamIP702dOvaUQqyY8eOdHFx4aZNm8Q6ZRi58PBwRkVFidvkMzMz2aZNG/bo0YMuLi48ceKE2Ob777+nj48Pmzdvzrffflv8ZSvDyEVHRzMgIEBclVAoFHznnXfYvHlz+vj4qJwtSU5OpouLC3v06MHWrVszMzOTZHHQqujoaIaHh9PZ2VnFCbx582a6uLgwLCxMDO9Hko8fP2aLFi3Ys2dPlfB+JPn1118zICBAJbwfSV69epW+vr6Mjo5m/fr1xfB+CoWCr7/+Olu2bEkvLy/+8ssvYl9Hjhyhi4sLu3XrJob3I4tnlu7du7NLly50dnZWOeKxdu1aurq6sn379mJ4P7J4F37Tpk3Zu3fvqhAiqWsxksWCjI+PF0O2lWTbtm3s37+/ynkNsliQkZGRamvaJLlgwQJOmDBBZYohyWvXrjEiIkJtCU6hUPDtt9/m/Pnz1fo6ceIEIyMjmZGRoVJeUFDA/v37azyktHHjRg4cOFBlWiSLz+507dpVLfApSc6bN08chUqSmprK8PBwtcCnSkGWPBqh5PDhw+zevbsoRCV5eXns06ePGGeyJGvWrOFrr72mtvb84MEDdu7cuSqESOqDn9GEif+iWz+jCRPPg0mMJvQGkxhN6A0mMZrQG0xiNKE3mMRoQm8widGE3mASowm9wSRGE3qDSYwm9AaTGE3oDSYxmtAbTGI0oTeU+wzM48ePcfv2bVhbWyMwMFAsJ4lLly5BJpPB19cXNWrUEOsePXqEu3fvws7ODn5+fiptLly4AIVCAX9/f9ja2op1Dx48wP3791GzZk34+PiI5QqFQozMX7duXVhZWYl1d+/exaNHj+Dg4AAvLy+xXCaTicm+69evDwsLC7Hu9u3bePz4MRwdHeHu7i6WFxYWikceGjZsqJJF6saNG8jKyoKTkxNcXV3F8vz8fKSmpkIQBDRs2FAlTN21a9eQnZ0NFxcXlfg+OTk5SEtLg0QiQcOGDVVO6l29ehW5ublwd3eHo6OjWP7kyRNcv34d5ubmaNCggcrf58qVKygoKICnpydq1aollmdmZuLmzZti4PmSXLp0CUVFRfD29oa9vb1Ynp6ejjt37sDGxgYBAQGocErbX/asjWnHjh2jh4cHg4KCWKdOHU6YMIEFBQV88uQJBw0aRFdXVzZo0ICBgYHitvdt27bR1dWVQUFBdHR05IcffigmaIyJiaGHhwfr1q3L4OBgMY/z77//TicnJwYHB7N27dr87LPPKJfLef/+fb7yyiv08vJiQEAAQ0JCxCMJS5YsYZ06dcQ28+bNo0Kh4K1btxgWFkYfHx/6+fmxTZs2vHbtGhUKBRcsWEBHR0cGBwfT0dGRP/74IxUKBdPS0vjyyy/T39+f3t7e7NSpE+/cuUOFQsE5c+aIberUqSOGSr548SKbNm3KwMBAenp6skePHnz48CFlMhmnTZsmtnF2dhazoZ45c4YNGjRgvXr16O7uzn79+jEjI4OFhYV8//336ejoyKCgILq6unLHjh0kiwPD+/v7s0GDBnRxceGQIUOYnZ3N/Px8vvHGG3RycmJQUBA9PT3FTLV//vknvby8GBQURCcnJ44dO5Z5eXnMycnhsGHD6OLiwoYNG9LPz49HjhwhWZyI3t3dXfxblyNKSMVvrpXL5fT29ubvv/9OsvgsSq9evWhlZUVLS0sOHz5cTN+7atUq1qpVi3Z2dvTy8hIzrN69e5edO3emtbU1LS0tOX78eObn51OhUHDRokWsUaMG7ezsGBAQIG6wvXHjBtu1a0cbGxtaWlrygw8+YGFhIRUKBb/99lva2trS1taWDRs2FDeLpqamskWLFrSxsaGVlRVnzJhBmUxGuVzO2bNn09ramra2tmzatKmYr/r8+fNs1KgRbW1taWNjwzlz5lChUFAmk/GTTz6hlZUVbWxs2KpVK/F8y99//8169erR1taWdnZ2XLhwIRUKBQsLC/nuu+/S0tKS1tbWDAsLE3d0HzlyhH5+frSzs6O9vb0YazsvL49jx44V20RGRorpiJOSkujh4UE7OzvWrl1b/BtkZ2fztddeo6WlJa2srBgTEyOeo9mxYwddXFxoZ2enktUgIyOD/fr1E/9ugwYNEjfsrl+/no6OjrSzs6O7uzsTExNJFm/GrVevHrdu3fpCKvwvFb+5ViZ/6Nz6AAAgAElEQVSTwdraWiVOIEnk5OQAgMq0DBRPWTKZDJaWlmKMQaB4ms3NzYUgCLCzs9PYxsrKSmUqlcvlyMvL09gmLy8Pcrkc1tbWMDc3V2sjkUhUpn8AyM3NhUKhUGsjk8mQn58PMzMzlZONJdvY2NioTNlFRUUoKCjQ2CYnJwckYWtrqzJlK9uYm5urHeXNzs4GALU2hYWFKCwshIWFhcqjSck2dnZ2KtO8tjYl/25PtykoKEBRUZHa3+31119HcHAwXn/9dbwgFZ9VVS6Xw8nJCbt27cLLL7/8ogaZMGCKiooglUrx9ttvIz4+/kWbV/xObzMzM/z888+Ij49XC+xuwriZNm0a6tSpg7i4uArtt1yunb59+8LOzq7UmNomjI+dO3fi008/rfAE6eXqTSaTITs7W68yjlYV+pT2t6yUJTc1UJzuRFPEi/JSLjFOmzYN9erVQ7NmzSrKHoNC049Qn0RaWbmpJ02ahDfffLPCH8/KLEaZTIZZs2Zh6dKlFT5cGzr6NFNUhi2xsbFo3LgxVqxYUaH9lktFJA06oXll8gwvRZVSGba4urpqTRVcVsr1Nt22bVv85z//Ue/0vzmQAWDhwoUIDQ1FjRo1VPKLKBQK/Prrr2jfvj1q1aoFGxsbNGzYEJMmTdIYNexZzzdTp06FRCLBtGnTtJbfvn0bQ4cOhZubG6ytrfHSSy9h/vz5Wr/jvXv3MHr0aLi7u8PGxgZBQUGYNWuWxhws1Sk39dWrV7F+/fqKz0ldmkf8Wa70a9eusU6dOippckmKEfjHjh2rkn+5Xbt2JIvDdvTv35+CINDGxobdu3fngAED6OXlRUEQxMBOJSlrSgll+bBhw+jm5saAgADGx8ezU6dONDMz05oN9ebNm/T19aUgCPT09OSAAQMYGRkprmz4+flREAQxjEp1yk3dvXt3jWmZn5PKi7XTvHlzlaBMJMW8KnXq1NGYvvfbb78VczCnpqaK5QUFBRw0aBAFQWCrVq1U2pRXjMo8LyXj3Kxdu5aCILBmzZri0qUSZXrgqKgoldB4586do6urq9b0G8aem5okQ0NDNcY/ek4qR4xXr15lnTp1VGIPkv8T4+zZszW28/f3pyAIXLFihVpdRkYGa9WqRUEQVGI3lleM/v7+akGaSPKll16iIAjcv3+/WJaWlkZBEGhtbS2uIZdE+WMqixiNITd1z549OWvWLI19Pgda9VbmZ0aSiI+Px+TJk1W2gSkRBAHR0dFq5Tdv3kRaWhqsrKwwYMAAtXoHBwf06dMHgGp+5vLSqVMnlfVtJcotVyXTyClzPHfo0EFj2t7BgweXyQZjyU393XffYe7cuRoj4paHMotRLpfj2LFjYt48Tfj6+qqVKfMn+/j4aHU7aMrPXF5eJP+y0kZNPzKg+AdTcp/f86LMTW1paWnQual9fX3Rt29fjSGly0O5XDsSiURr2l4AartJKhNNMcBLYvKFaqcs9+b27dsqO5wqxI6yNjQ3N8eUKVPw6quvvpC/Sbnj+vr161oFpCnXsnL7knJ71NPcuHHjuW14XhvT0tI01mdkZJRp9cFYclOvXLkS58+fx8CBAyu033INFx9//DHS0tJw+vTp527j6ekJf39/FBQUYNWqVWr1mZmZ2LBhAwRBUMm1rLzRyuMCJSksLERSUtKLfwEttG/fHkDxM9jTz0sAsHz5cq1tq0Nu6rlz5+Lbb78Vp/GKolxiNDMzg52d3Qt7+JXPmZMnTxZ/1UCxqN544w1kZmbi5ZdfVnGqtmzZEnZ2dkhJScH69etV2kyYMAHXrl0rz1dRwdfXF7169UJBQQFef/11lWemCxcuYMaMGVrbGntuaqD45bUsz8zP1XEp/5TKqlWrWLduXWZlZamUK90e2nja6d2tWzfGxcWpOL1L+h+VfPbZZ6KTNywsjNHR0fTy8qK7u7vWnNPaXD5KlOl/n3ZfPO307t+/P7t160Zra2uNTm8l1SE39SeffMLOnTuXNQ1wxfsZZTIZHRwc1Bze5LPFSBYLcvHixWzXrh3t7e1pbW3N+vXr87333tPo81Iyf/58BgcH09ramq6urnzttdd469YtrXmUn5WLOiEhgRKJRE2MZPEZnZEjR9Ld3Z02NjZs0KABZ8yYwaKiIvr5+Wn04VWH3NRFRUVs1aoVly9frtXWUqiaMzAmqg+VcQamzM+MEokEHh4e2Lp1a1m7MGGgZGdnY8+ePVr9sGWlXKk3Dh48iH79+iElJcW0lawaMWrUKMhkMixatKgszSsn9Ua7du3g4eGhF7uaTVQdJ0+exNixYyu833KJMScnB3fv3q3SlRYTusfKykrFJVdRlGuaHjZsmLhJ1kT14cCBA4iLi0NycrLWNfZSqPhD/MroEFlZWWrRI0wYP4MHD0br1q0r9G263CvdJUN7XLp0CVu2bAEAxMfHi78auVyOpUuXIj09HYGBgSpby1JSUvDHH39AEAQMHjxYfBEqKirCr7/+iqysLAQFBaF79+5im+TkZCQlJcHMzAxDhgwRjzMUFBRg0aJFyM3NRbNmzRARESG2OXz4MA4dOgQLCwsMHTpUXMrKzc3FokWLUFBQgJdffllcCgT+lzjc2toaw4YNE8OVZGVlYfHixZDJZGjXrh1atWoltvnjjz+QkpICOzs7DBs2TFySS09Px5IlS6BQKBAeHo6QkBCxzdatW3Hx4kU4ODggISFB3IBw7949LF++HCTRrVs3BAcHAyj2Da9fvx5paWlwcnLC4MGDxc0ON27cwJo1awAAUVFRYoQxkli9ejVu3boFd3d3xMfHi7umUlNTsXHjRgDFh62UUd4UCgV+++03PHjwQNypo2xT0ZskAMBs6tSppdVrrRQEARcvXsSqVavg6emJP/74A4MGDYK7uzvu3LmD9957D56enkhPT8fIkSNx7NgxODg44Pvvv0dycjLq1KmDLVu2YNiwYfDy8sK1a9cwefJk+Pr64u7duxgyZAjOnz+PGjVqYN68ebh8+TLs7e3x+++/Y+zYsfDx8cGlS5fwySefwN/fHzdu3MDAgQNx48YNWFtbY9asWWL4tqVLl+Ldd9+Fr68vzpw5g88++wwBAQFITU1FbGws0tPTYWFhgalTpyIrKwsWFhb44Ycf8Mknn8DHxwdHjx7F3LlzERgYiAsXLqBPnz4oLCwESUyZMgUymQyCIODrr7/G7Nmz4eXlhf3792PhwoUICAjAmTNnEB0dDTMzMxQVFeGDDz6Aubk55HI5Pv/8cyxcuBAeHh7YtWsXlixZAj8/P5w4cQIxMTGws7NDbm4uJk6ciJo1ayI/Px8ff/wxVqxYAVdXV2zYsAHr1q2Dt7c3Dh06hH79+sHR0RGZmZmYOHEi6tSpg+zsbLzzzjvYunUrnJycsHLlSuzcuRMeHh5ITEzEgAED4OLiggcPHuC9996Dm5sbMjIyMG7cOOzfvx+1a9fGokWL8Ndff8HFxQU7d+7Ejz/+iC+//FIlzN5zMk1rTWke8We50vPy8jh+/HhKpVJ27txZJa3tvn37GBYWRqlUyk8//VTMyfzkyROOHj2aUqmUkZGRPHfunNhm+/btbN++PaVSKWfNmiUuNz1+/JgJCQmUSqXs2bOnylLhunXrKJVKKZVK+c0334jHCh48eMD4+HhKpVJGR0fz+vXrYpulS5eKbX766Sexze3bt9mvXz9KpVL279+fd+/eJVm8kvH999+LbUruUE9LS2NUVBSlUikHDRokLrUpFArOnTuXUqmU7dq1U0ltfOnSJXbv3p1SqZTDhg0Tc1zL5XLOnDmTUqmU7du3565du8Q2KSkp7Nq1K6VSKceOHcvs7GySxashU6ZMoVQqZVhYmMqO9RMnTjAiIoJSqZRvv/22eHwiPz+f7777LqVSKcPDw1WWHw8ePMiOHTtSKpVy8uTJ4u743NxcvvHGG5RKpezSpQtPnz79LHloo+JXYEyYKCOmFL8m9B+TGE3oDSYxmtAbTGI0oTeYxGhCbzCJ0YTeYBKjCb3BJEYTeoNJjCb0BpMYTegNJjGa0BtMYjShN5jEaEJvqPgdklWIQqHA7du3kZmZWeHBzvUZOzs7uLm5qeVNNHQMUozJyclYsmQJ1q1bB4VCgdq1a1fKzmN9hP9NOvngwQOEhYUhNjYWAwcO1BgI1eAobbNjWXdPViYbNmygi4sLZ8yYwQsXLujaHJ2RmZnJ3377jVKplH379tUYIlpPMY7Ntfv27cOAAQOwY8cONG/eXNfm6AUFBQXo27cv6tSpgyVLlujanOeh4k8H6oK+ffuiR48eYgxrE8Xk5ubCw8MDFy9ehKurq67NeRaGv9NbGd9FU9D66o6trS26d++uErfSEDEYMR4/fhyNGzeGo6Ojrk3RSyIjI8uc7UBfMBgxpqenw8XFRddm6C0uLi5IT0/XtRnlwmDEWFRUJB6Ir66UlrLX0tISRUVFOrCq4jAYMZooRp/SB1c0JjGa0BtMYjShNxiNGKtjjmtjw6gWdAVBwLhx4/Dzzz+jQ4cOiIqKEh/2+d/Em7///jusra3RqVMn2Nvb4+DBg5gzZw5Wr16NxMREBAYGauz3WdfVxPXr1xEaGgpbW1uEh4fj7t27OHDgAN58801kZWVh8uTJKp+/desWpFIprl+/Dg8PD0RHRyMjIwPTpk3D8ePHIQjCC+fcMShKWyus8lXLUli5ciXj4uK01lfnHNckuXfvXnbq1Enr/dEjKj7Fr74yadIkNG3aVK187ty5AIBZs2YhICBALLe0tMT8+fPh4OCAY8eO4a+//qowW/z8/DBnzhyVkbNv374IDg5GdnY2Tpw4IZZfu3YNmzdvhpWVFRYsWKASmjooKAgfffRRhdmlrxiVGE05rg0boxIjYMpxbcgYnRhNOa4Nl2pxt6pzjmtDolqI0dhzXBsL1UKMgHHnuDYWqo0YX3/9dcTGxuLGjRto1KgRunfvjgEDBiAwMBDLly+Ht7e32uhja2srOqb79++Pjh07IiYmBoGBgdi4caPWlZmysmDBAvj4+GDjxo0IDAxEXFwcunfvjpCQELRt2xa+vr5G7fSuNmIUBAGrVq3CokWLEBoair/++gubNm2Cra0tJk6ciJMnT6r4H5V8+OGH+O6779CgQQMcPXoUhw8fRnh4OE6cOKH17VwQhFJXbbTVe3p64ujRoxgxYgQUCgW2bNmCf//9F1OmTBFzuxjzrh2DOQOzatUqbNy4UeMznwkgMTERM2fORGJioq5NeRaGfwbGhPFjEqMJvcGgxGjMD+/lxRjujcGI0c7ODjk5Obo2Q2/Jzs42+Oy2BiNGb29vXLx40ShGgMrg8uXL4iqOoWIwYmzatCmKioqQkpKia1P0krVr1xp8gAODEaMgCIiNjcV3331nGh2f4vjx47hy5Qo6deqka1PKhcGIEQDef/99HDt2DB9++OEzd8xUF06cOIGePXvi559/NviweAbj9Fby8OFD9O7dGzdu3EC/fv3QqVMnODo6Vqv4jE+ePMGFCxewdu1anD17FosXL0ZUVJSuTXtejCMKWUnOnTuH33//HceOHUNGRka1ilxbs2ZN+Pj4ICYmBl26dKnSPZwVgPGJ0YTBYloONKH/mMRoQm8widGE3mCUYqxOLzPGhNGJMT8/H2PHjtW1GSbKgFE55/Lz8xETE4OzZ8/q2hQTZcBoRkalEPPz83VtiokyYhRiVArR3t4eixYt0rU5JsqIwYuxpBCXL19ebZYFjRGDFqNJiMaFwYrRJETjwyDFaBKicWJwf8XnEWJhYSHOnDmjA+v0By8vL4PLJmZQu3aeR4h3795F165ddWCd/nDjxg189dVX+prwU+uuHYMZGZ93anZzc6v2o6KeivCZGMQzo+kZsXqg92I0CbH6oNdiNAmxeqG3YjQJsfqhl2I0CbF6ondifBEhlswXaMLw0au/ZFlGRGOO5Frd0BsxmqZmE3ohRpMQTQB6IMbKEOLu3bsxbtw4NGnSBI6OjrC2tkZAQADGjh0rpvx9mo4dO0IikWD//v3Ys2cPunbtCkdHR0gkEpw5cwZpaWmQSCTw9/eHTCbD559/jpdeegk2NjYICQkR+yksLMTXX3+NFi1aoGbNmrCzs0PTpk0xc+ZMtfiSq1evhkQiwYgRI9TsCQ0NhUQiQefOndXq4uLiIJFIsHv37nLeKT2jtJSrlZ3rNS8vj5GRkezfvz+LiopeuL0y5e3TBAYG0tbWli1btmS/fv0YFRVFX19fMQXwxYsX1dqEhYVREASOGTOGEomEzZs356BBg9ihQwempKTw6tWrFASBPj4+7NGjB21sbNitWzcOGDCAffr0IUnm5uayffv2FASBDg4OjI6OZmxsLJ2cnCgIAps0acKHDx+K17x//z4lEgkDAgJUbHn06BElEgkFQaCtra1Kul+FQkFnZ2daWVkxLy9P430ZOnQof/nllxe+n1WEVr3pTIzlFSKpXYybN29mVlaWSplcLhfzQEdGRqq1UYpREAQuWbJErV4pRkEQGBAQoDHn87vvvktBEBgSEsIHDx6I5VlZWQwPD6cgCGo5sxs1akRBEHj16lWxbN26daJ4BUFgYmKiWHf69GkKgsAOHTpovS8mMb4AFSFEUrsYS8PT05Pm5ubMzs5WKVeKsVu3bhrblRTj6tWr1epzc3NpZ2dHiUTCQ4cOqdVfuXKF5ubmNDc35/Xr18Xyt956i4IgqIjn9ddfpyAI3LhxIwVB4JQpU8S6efPmURAETp06Vet3NFQxVvkzY1W9rFy7dg0LFizAhAkTMHz4cCQkJCAhIQEymQxyuRxXrlzR2O5Z0V8FQdAYfi45ORm5ubkIDAxEmzZt1OoDAwPRoUMHyOVy/Pnnn2J5eHg4AKjkb0lMTERgYCCioqLg6OioVleynTFRpa+tJNGvXz+Ym5tXqhA/+ugjfPHFF2oBRQVBEKPeastQqilfdUlcXFw0hqBT5otW5q3WhL+/P/bt26eS0zosLAwSiQT79u0DUJwQ/cKFCxg5ciSA4herTZs2ITs7GzY2Nti/fz9sbW01Ct7QqdKRURAEDBgwAMnJybh06VKlXGPt2rX4/PPPUbNmTSxevBhpaWkoKCiAQqGAXC5H69atAWhPVWFjY1Nq/8+qf1EcHBwQEhKCO3fu4Pz58+LIFxERIf5bLpcjKSkJJ06cwJMnTyCVSo3S/VXl3+jVV18FAHTu3Bl79uxBcHBwhfa/du1aAMBnn32GIUOGqNVrm57LizLTQMmMrU+jKac1UCy45ORkJCYm4uTJkwD+Nw0rRZmYmAhnZ2eVOmNDJ37GV199FV9++SU6d+6Mc+fOVWjf6enpAKAxDcXevXvx8OHDSllCDA0Nha2tLVJTU3Ho0CG1+tTUVPz5558wMzMTc0srKfncuG/fPjRq1AhOTk4AgPr168PT0xN79+416udFQIdO78oSZFBQEADgp59+gkwmE8vT0tLEgFDapujyYG1tjTFjxgAA3njjDTx8+FCse/LkCUaPHg25XI5+/fqp/VDat28PCwsL7NixA2lpaeJoqCQ8PBxnz57FwYMH4eDggBYtWlS4/fqATldgKkOQ48ePh729PbZt24Z69eqhf//+iIyMRHBwMDw9PVWSm1c0M2fORPv27fH333+jbt26iI6ORmxsLAICApCYmIjGjRtj/vz5au1sbGzQqlUrMU7Q0yNfeHg4SKKgoAAdOnQw2s0hOl8OrGhBBgYGIjk5Gf369YNMJsO2bdtw/fp1fPDBB9i1axcsLCzKlCP6ebC2tsaePXswd+5c1K1bF3v37sX27dvh7u6O6dOn4/Dhw1qPjyoFaG5ujrCwMJU65UgpCILRTtEAdLscWJJly5bR3d2d//zzT1Ve1igxVKe33vgHKvst24T+ozdiBEyCrO7olRgBkyCrM3onRsAkyOqKXooRMAmyOqK3YgRMgqxu6LUYAd0KUiaT4cKFC0hOTkZycjJOnjyJhw8fIj8/H3l5eZDL5bC2toaNjQ3s7OwQHByM0NBQhIaGonnz5rC3t68yW40BvRcjUHWCJIkDBw5g3bp1SE5OxunTp+Hh4SEKLCYmBu7u7rC2toa1tTXMzMyQn5+P/Px8ZGVl4ezZszhx4gTWrVuHM2fOiG07duyIgQMHombNmpVit7FgEGIEnl+QBQUFOHXq1Av1nZOTgx07dmDjxo2QSCQYOnQoZs6ciZCQENSqVeu5+wkNDRV3CslkMpw/fx7JycnYsGED3n//fXTp0gXR0dEIDAx8IftelPv371dq/5WFwYgReD5B3r9/Hx06dEDz5s2f2V9ubi4eP36MR48eITw8HD/99BM6duxYIWu/5ubmaNy4MRo3boyEhATcvHkTCxcuxPjx42FhYYGaNWuidu3alRYRQ7ndzKAobXlGF2tFz0NpS4fXr1+nl5dXqe3v3r3LPn360MPDg1OnTuWtW7cqy1Q1CgsLuWbNGnbs2JE+Pj7csWNHlV1bT9CvA1kVgTZBliZGhULBFStW0MXFhZMnT1Y5AqoLdu/eTV9fXw4bNowZGRk6taUKMT4xkpoFqU2Md+/eZUxMDIODg3ns2LGqNLNUsrKyOHr0aHp7e1eXUdI4xUiqC1KTGFeuXKk3o6E2So6SmZmZujanMjFeMZKqgiwpRoVCwU8//ZR169bVq9FQG1lZWRw2bBibNm3Ku3fv6tqcysK4xUj+T5A7d+6kl5cX5XI5x48fz2bNmvHevXu6Nu+5Uf6A6tWrpzFqhRFg/GIkiwVpb29PT09Pjho1ilKplI8fP9a1WWXi66+/pq+vrzEKsnqIkSSXLl3KOnXq8OWXX1aLt2NozJs3j/Xr1ze2KVv/d3pXFLdv34azszN27Nhh8MtvEyZMQEZGBl555RUcOHDA6Ne6DSpd27M4fPgw+vTpg+TkZHh4eOjanAqBJEaMGAFBEPDzzz/r2pyKQPvyVmnDpg6G8DKTm5vLBg0a8Pfff9e1KRVOZmYmfX19jcUPqVVvRjMyvvfee7h+/TpWr16ta1MqhT179mDYsGFISUmBg4ODrs0pD1pHRqMQo3J6PnPmjGFuEHhOxowZA5lMZujTtfFO08Y8PT+NkUzXxjtNT506FefOncOaNWt0bUqVsGfPHgwfPhyXL1+GpaWlrs0pC8Y5TRcUFMDHxwcHDhxAgwYNdG1OldGpUyeMHTsW/fv317UpZUGrGHUea6c8rFu3Dk2aNKlWQgSAcePGYcGCBbo2o8IxaDEuWLAA48aN07UZVU50dDQuXbqEf/75R9emVCgGK8bTp0/j2rVr6NWrl65NqXIsLCwwYsQILFy4UNemVCgG+8w4ZswYeHp64uOPP9a1KTrh5s2baNKkCa5du2Zoy57G9QKTnZ0Nb29vnDt3Du7u7ro2R2f06dMHkZGRGDVqlK5NeRGM6wXmxIkTCAoKKrcQqypf9dSpUyGRSDBt2rQK7bdXr15ISkqq0D51iUGKMTk5GaGhoRXSV1WGJK7oa4WGhiI5OblC+9QlBrmFLDk5GV27di13PxcuXKgAa3RHcHAwbt68iaysLKPYXlatR8b69eujfv36FWCRbjA3N0eTJk1eOIKGvmJwYszKysKtW7fEFBuaePLkCWbNmoWWLVvCwcEBdnZ2qFevHoYMGYLDhw+Ln9P2zFiyfOHChQgNDUWNGjVQu3Ztlc/99ddfiIuLg5eXF6ytreHm5gapVIrZs2eLmQueh7/++guxsbHw8PCApaUl3N3dERcXh9OnTz+zrVFN1aUtXFf5EvpzkJSUxDZt2mit//fff1m3bl0KgsDatWuzV69eHDBgAFu3bk0rKysOHTpU/Ky2rKzK8rFjx9LCwoIREREcOHAg27VrJ35m+vTpYpbV5s2bc+DAgezWrRt9fX0pkUhUzq4oUwtPmzZN7VpffPEFBUGgubk5W7duzbi4OLZo0YKCINDKyopbtmwp9X4sXryYAwcOLPUzeobxnIFZsGABR44cqbFOLpezadOmFASBgwYNUkvj+/DhQx48eFD8/9LEqEyU/vfff6vVr127loIg0NHRUSUXtJKkpCSVs8/axLh161YKgkA/Pz+eOnVKpW7Lli20sLBgrVq1mJ6ervH7kuSJEyfYtGlTrfV6iPGI8csvv+S7776rsW79+vUUBIFBQUGUyWTP7OtZYpw9e7bGdsqk5MuWLXsum7WJsWXLlpRIJExKStLYbvz48RQEgd98843Wvi9evMh69eo9lx16gla9GdwzY15entbMpjt37gQADB48GGZmZuW6jiAIGnNP37lzBykpKbCzs0N8fHyZ+3/48CFOnDgBJycntSRESpQ5Bo8ePaq1H2tra+Tl5ZXZDn3C4Fw7+fn5sLOz01h3/fp1AKiwXTyack8rr+Hv718uwV+9ehUA8ODBg2c63h88eKC1ztra+oVelvQZgxOjmZkZ5HK5xrqKdiprSnJeUddQfgdHR0f07t271M82bNiw1H7KOwvoCwYnRhsbGzx58kRjnY+PDwDg4sWLlXZ95TWuXr0KmUxW5iTkyn7s7OywaNGiMtuTn59f4QnZdYXBPTPa2NhonZZeeeUVAMCyZctU0vtWJG5ubmjcuDFycnLKdRLRw8MDjRo1wo0bN3Ds2LEy95Ofnw9ra+syt9cnDE6MTk5OuHPnjsa6qKgoNGnSBBcuXMCwYcOQk5OjUv/w4UP89ddf5bZBuW1t/PjxGjcqJCUlISsr65n9TJ8+HQAQHx+PAwcOqNUXFhZiy5YtpY70d+7cEROlGzylvWrr5MX/GZw9e5Z169bVWp+amsqAgADR6d2zZ0/GxcWxVatWL+z0Lo1PPvlExekdHx/PyMhI+vj4UBCE53Z6f/nllzQzM6MgCHzppZcYHR3NAQMGsH379qxRowYFQeCuXbu02vHll19y/PjxpdqqZxiPn1Emk9HOzq7UsMOZmZmcNm0amzZtSjs7O9aoUYP169fn0KFDefToUfFz5UzK9TwAABTWSURBVBEjWezc7tOnD93c3GhlZUU3Nze2a9eOc+bMUQlKOnXqVEokEo1iJMmTJ08yISGB/v7+tLGxYa1atRgUFMS4uDiuWLGCOTk5Wm2Ii4vjkiVLnmmrHmFcR1WlUilmzpyJTp066doUnVOvXj1s3LgRL730kq5NeV6Ma3OtUW0OKAcZGRm4c+dOqa4fQ8IgxdiiRQuTGAGcPHkSzZo1Mxo/o0GKMTQ0FEeOHMEzHjGMnqNHj1bYjnd9wCDFGBwcDCsrKxw8eFDXpugMkliyZAn69u2ra1MqDIMUoyAIRhtV4XnZt28fzM3Nxc0UxoBBvk0DxQ/v/v7+OH/+PNzc3HRtTpXTr18/hIeHG2JEDeM6N61k1KhR8PX1xZQpU3RtSpVy69YtNGrUCNeuXTPEg1jG5dpRMm7cOHz//feVtg6tr/z000+Ij483RCGWikGLsVmzZvD29samTZt0bUqVkZ+fj59++gljx47VtSkVjkFP0wDwxx9/YNSoUUhJSTG0mDNlYvLkybh8+TLWrl2ra1PKinE+MyoZMWIEzM3N8f333+valErl2LFj6NWrF86cOQNXV1ddm1NWjDemN0lmZGTQ29ubu3fv1rUplUZeXh6Dg4O5cuVKXZtSXoxro4Qmdu3ahdGjRxvtdD158mRcunQJa9eurdL4QJWAcU/TSox1uj527Bh69+6N06dPG/L0rMS4p2klGRkZ9PHx4YoVK3RtSoVx9+5d1q1b15i+k/Gcmy4NBwcHbNu2DW+//Ta2bduma3PKjTKJ5auvvordu3drPRVpNJSmVJ38biqAI0eO0NnZmfv27dO1KWUmKyuLbdu25VtvvUWFQkEzMzMOHjz4uSJl6DnVY2RU0qpVK6xZswb9+/c3yBHy0aNHiIiIQOPGjTF37lzxheXGjRsYOnSo8Y6QpSlVJ7+bCuTIkSN0dXU1qOetW7duMTg4mO+//z4VCoVYbmZmxszMTIaHhxv6CGk8B7JelJSUFPr4+HDUqFEqkcH0kTVr1tDV1ZVffPGFWp2ZmRmLioqYk5Nj6IKsvmIki9+yR4wYQV9fX710jN+/f5+xsbFs0KABDx8+rPEzSjGSNHRBVm8xKtm5cye9vb31apRUjobvvfcec3NztX6upBhJgxakSYxKMjIyOHz4cPr6+nLt2rUqf+Cq5Pz5888cDUvytBhJgxWk4S0H3rt3D/fu3au0/v/66y/88MMPuHPnDmJjY9GnT59KDxMik8mQlJSE1atX48qVK+jfvz+GDh36XLFyQkJCUFBQoBZoKjc3F7169YKnpycWL15sCCcFDW85cObMmfjmm28q/UiBXC5HYWEhioqKYGFhAUtLywr/g5JEYWEhCgsLIZFIYGlpCQsLixfu5+TJkxqjnhmYIA1TjPn5+Zg5c6auTDAoDEiQxnnswMT/sLW1xZYtW3Dr1i2DdYybxGhEGLogTWI0MgxZkCYxGiGGKkiTGI0UQxSkSYxGjKEJ0iRGI8eQBGkSYzXAUARpEmM1wRAEaZBifJ580Lt378a4cePQpEkTODo6wtraGgEBARg7dqyYcu1pOnbsCIlEgv3792PPnj3o2rUrHB0dIZFIcObMGaSlpUEikcDf3x8ymQyff/45XnrpJdjY2CAkJETsp7CwEF9//TVatGiBmjVrws7ODk2bNsXMmTPV0oGsXr0aEokEI0aMULMnNDQUEokEnTt3VquLi4uDRCLB7t27n/u+6b0gS9tFUeX7OUowY8YMTpkyRWPd8+SDDgwMpK2tLVu2bMl+/foxKiqKvr6+YureixcvqvUbFhZGQRA4ZswYSiQSNm/enIMGDWKHDh2YkpLCq1evUhAE+vj4sEePHrSxsWG3bt04YMAA9unThySZm5vL9u3bUxAEOjg4MDo6mrGxsXRycqIgCGzSpAkfPnwoXvP+/fuUSCQMCAhQseXRo0eUSCQUBIG2trYq2RMUCgWdnZ1pZWXFvLy8F763Ot7tY3hbyJ4lxtLyQZPk5s2bmZWVpVIml8vFnCyRkZFqbZRiFARBYzoLpRgFQWBAQIBKrhcl7777LgVBYEhICB88eCCWZ2VlMTw8nIIgMC4uTqVNo0aNKAgCr169KpatW7dOFK8gCCp5rU+fPk1BENihQweN3/150KEgjVOM2vJBPwtPT0+am5urJUdXirFbt24a25UU4+rVq9Xqc3NzaWdnR4lEwkOHDqnVX7lyhebm5jQ3N+f169fF8rfeeouCIPCXX34Ry15//XUKgsCNGzdSEASVezFv3jwKgsCpU6e+8HcviY4EaXynA7Xlgy7JtWvXsGDBAkyYMAHDhw9HQkICEhISIJPJIJfLceXKFY3tntWvIAiIiopSK09OTkZubi4CAwPRpk0btfrAwEB06NABcrkcf/75p1geHh4OAEhMTBTLEhMTERgYiKioKDg6OqrVlWxXVvTtGdLgsqqWRFM+aCUfffQRvvjiCygUCpVyQRCKpwRAa36/0voFABcXF43pf2/dugWgOBe1Nvz9/bFv3z7cvn1bLAsLC4NEIsG+ffsAFOcDvHDhAkaOHAmg+MVq06ZNyM7Oho2NDfbv3w9bW1uNgn9RlIKMiIjA2LFj8eOPP5a7z7JisCMjoDkfNACsXbsWn3/+OWrWrInFixcjLS0NBQUFUCgUkMvlaN26NQCIonyaZ6XMreiUug4ODggJCcGdO3dw/vx5ceSLiIgQ/y2Xy5GUlIQTJ07gyZMnkEqlZU4v/DR///03UlNTdZ45waBHRm0oA2l+9tlnGDJkiFq9tum5vHh5eQEA/v33X62fUdZ5enqqlEdERCA5ORmJiYk4efIkgP9Nw0pRJiYmwtnZWaWuvBw6dAjR0dFYtmyZmCJZVxj0yKiN9PR0AP8TR0n27t2Lhw8fVkpYudDQUNja2iI1NRWHDh1Sq///9s42JoprjeP/M7PLzr6DpkGhssAqiOgltVoSGqiQNFGSBltfalOblMaIqdZW09hqm9xim/ihXnNvWmnaWOVDbdTWglVqNFJblFiqNfXKbdEbdFmQvlBbZl9ZdmfO/aC7l3VnAXVxZ9b5JRPgzJ7dZ+HHmXNmZp+nu7sbp06dAsuyMSUzRs4bT548idmzZ0c+k1NQUIDs7Gy0trYmbL4IyEtEIEVlLCoqAnAjEfvI5PMOhyOSCzveIfpu4DgOa9asAQCsW7cOf/zxR2Sf2+1GXV0dBEHA0qVLY/5RysvLodVqcfToUTgcjshoGKaqqgqdnZ04ffo0rFYr5s2bd1exyk1EIEVlXL9+PSwWC1paWjBjxgwsX74cCxcuxKxZs5CdnY2ysrIJe+133nkH5eXl+PHHHzF9+nQsXrwYy5YtQ35+Pr7++mvMmTMHO3fujOmn1+tRWlqKoaEhALEjX1VVFSilCAQCqKiouKuRXY4iAikqo91uxw8//IClS5ciFAqhpaUFTqcTr7/+Oo4dOwatViv5xySE3PXhm+M4nDhxAjt27MD06dPR2tqKr776ClOnTsXWrVtx5swZTJo0SbJvWECNRoPHHnssal94pCSE3NUhWq4iAuqnA+8rZCKi+unA+x2ZiDgqqoz3AUoQEVBlTHmUIiKgypjSKElEQJUxZVGaiIAqY0qiRBEBVcaUQ6kiAqqMKYWSRQRkftdOX18fvvvuu2SHIRtKS0vjXiFSuoiAjK/A7NmzBx9++GGyXl52dHR0IBgMSt7DqDARlZcsVCUajUaDoaGhGBkVJiKgXg5MTRQo4qioMiqUVBMRUGVUJKkoIqDKqDhSVURAlVFRpLKIgLqaVgwajQYZGRn45JNPlC6iempH6ZhMJhw8eFDpIgKqjMrnxIkTkqnxFIgqo4psiCvjWNemE/9JdxWVOKiraRXZoMqoIhtUGVVkg6zvZ0w0hBAtABMA882v4S0Z9XB9ADwA3De/egD46UQkAVIIY62mZQUhhAUwBYDt5jYVgJnjuPS0tLQMjUZjJYRYAZgppSZRFI2hUMgQCoX0wWBQRylldDrdsF6vDxkMBtFoNIpmszlheQ7HC6UUXq8XXq+XeL1exu/3s36/XysIAqvVaoe1Wq2fZVk/y7I+hmHCovKCIPDBYHDQ7/f/JYqiG8B1AD0AnACclFLfPX0jCUbWMhJC5mk0mkUWi2VRMBic7vP5JplMpuGsrKxhu93O2Gw2XUZGhtZsNhOTyQSTyQSz2Yzw97f+rNPpJiQVXqIIhULwer3weDxwu93weDyR7dafeZ4P9ff3D3d3dwd7e3uZgYEBg1ar9XMc1x8MBts8Hs9xAEcopf5kv6/xIlsZOY7baDKZtq5cuVJbWVmZVlxcjAcffBAcxyU7NFkiiiJ+//13XLlyBe3t7bSpqclz8eLFPo/HM08pI6YsZSSEZOh0uv5Lly5xY+XXVpGGUorq6mr/8ePHtwiC8M9kxzMe5LqaXvL444+HVBHvHEIINmzYoLdYLHXJjmW8yFLGjIyM1S+88IIp2XEonZsJRm2EEHuyYxkPspOREEK8Xu/fFixYkOxQ7jm5ublgGCZubcORrFy5MlJDsaWlRfIxGo0GZWVlIQDzExzqhCA7GQFk6nQ6cWRRyvuJ8az2Dx8+jE8//TSSaXe0PiUlJQaWZQsTGeNEIUcZC/Pz84eTHYRcGRwcxJo1a1BSUoKysrIxE+XPnDmTtVgsc+9ReHeFHGUsKC4u1iY7CLmyceNGDAwM4OOPPwbLjn3hqLCwEACKJjywBCA7GTmOK54zZ05MCarx1JgWRRGNjY0oLy9Heno69Ho9Zs6ciU2bNuH69esxr9XY2AiGYVBbWysZy1tvvQWGYVBfXx+3vb+/H7W1tZgyZQo4jkNxcbFkNYMwv/32G+rq6jB16lTo9XoUFRVh27ZtUSVC4nHs2DE0Njbi5Zdfxty54xvsCgoK4PV6c4icz/bfRHbXpo1G48y8vDzJXxwhBC+++CJ27dqFiooK1NTURCb7lFI888wz+Oyzz8BxHCorK2GxWHD69Gls374d+/fvjxSHlHre0Yi33+l0RgoRVVVV4ddff0VbWxteeukluFwubN68Oerx165dw6OPPgqn04msrCwsXrwYg4ODqK+vx9mzZ6PqGt6K2+3G6tWrkZ+fj7fffnvUeEcyadIksCxLAGQA+HPcHZPBaCVXk7FNnjz5xMGDB2Pqwo5VY/q9996jhBBqs9lod3d3pD0QCNBnn32WEkJoaWlpVJ89e/ZQQgitra2NeT5KaaQ2dX19vWQ7IYSuX7+eiqIY2ff5559TQgg1m83U6/VG9aupqaGEEFpTUxNVzPynn36imZmZkaLuUnWs6+rqKCGEtra2RtrCJYlbWlok4w9jMpn8AB6gMvj7jrbJ7jA9Fps2bUJJSUlM+44dOwAA27ZtQ35+fqQ9LS0NO3fuhNVqxffff4/29vaExZKbm4vt27dHjZxLlizBrFmz4PF4cO7cuUh7T08PvvzyS+h0OjQ0NEQV4SwqKsKbb74Z93VOnjyJjz76CM8//3zCagbKEUXJGK/GdF9fHxwOB3Q6HVasWBGz32q14qmnngIAfPvttwmLp7KyElpt7Frr5qIBv/zyS6Stra0NAFBRUYGsrKyYPs8995zka/h8PqxatQqZmZmRf7hURXZzxrGQukQYrvOck5MTd34XrgE9ss7z3TJt2jTJdrPZDAAIBAIxMebm5kr2sVqtsFgscLvdUe2bN2/G1atXsX//fqSnp0v2pTK8v+BOUJyM8WpMTwS3Fk6/lfDqfiI5dOgQWJZFQ0NDzCr9woULAG4I++6772LRokV47bXXJjymiUJxMkoRrlDqdDohiqKkJFJ1ntPS0gAAHo9H8nl7e3sTHqPD4ZDcPzg4CJfLFTOyE0IgimLkMD+S8IjY2dkJQkjUXFmJKGrOGI/s7Gzk5eUhEAhg3759Mft5nkdTUxMIIVEFIsNiXrp0KabP8PAwvvnmm4TFGK4v3dbWFjWXDLN3717JflevXoUgCJJbRUUFAODIkSMQBAG7d+9OWLzJICVkBIANGzYAuHHICo+CwA2p1q1bB57n8cgjj0SV950/fz6MRiMuXryIL774IqrPK6+8gp6enoTFZ7PZ8MQTTyAQCGDt2rVR88murq7bOneYqshORlEUeZ7nb7vf2rVrsWzZMvT29mL27Nmorq7GihUrYLfbsXfvXkybNi1m9DEYDJET08uXL8eCBQvw5JNPwm63o7m5Oe6VmTuloaEBOTk5aG5uht1ux9NPP43q6mo89NBDKCsrg81mS/hiRBAEDA0NaQB4E/rEE4DsZOR5/kJXV5dwu/0IIdi3bx92796Nhx9+GO3t7Th06BAMBgNeffVVnD9/XnJOtWXLFrz//vsoLCxER0cHzpw5g6qqKpw7dy7u6nysO2Xi7c/OzkZHRwdWrVoFURRx+PBhXLlyBW+88QYOHDgQ6Xs773msxzudTuh0OhdVwkcPkn3W/dYNwPKFCxfyo15SUBk3R48epZMnTz5LZfC3HWuT3cgI4HJXV1eyY0gZLl++jEAg8O9kxzEe5Cjjf/v6+gyCcNtHahUJOjs7Ax6PR5XxTqCUenU6nWvkiljlzjl//nwAQOy5KxkiOxkBgGGYlqamptEvf6iMycDAADo7O9MAnEp2LONBljK63e7du3btkv2pCLlz4MABynHcMUqpIn6XspQRQFt/f//gBx98QNW5453R3d2N+vp6P8/z/0p2LONFlhklAIAQUmSxWJqDwaBt/vz5gblz5+rz8vK0NpsN4S09PV3WuXMmmkAggN7eXvT09MDpdMLhcNCff/7Zd+rUKTo4OEgYhvm71+v9R7LjHC+ylTEMIeQBAOUAZphMphk6na5AEIQcn8+XKYqixmAwBPV6vWAwGESTyUTNZjOsViuxWCys1WplrVar1mKxsLcmhAonhTIajZL3JE4klNJIgiep5E5ut5vyPB/keT7E87zgcrlEt9sNt9sNr9fL+Hw+1u/3a4LBIGswGP5MS0u7Jopit8vl6hIEwQGgA8B/KKWKmnfLXsbRIIQYEZ1nUSr3olmj0Vg4jsvQarXpDMNYAFgBmERRNAmCYKSU3vPpCsuyfoZhvAzDuCmlbkqpKxQK8cPDw38NDQ39hf/nbAxvbqk2SmnKzGMULaNKavE/bWPmvsbEThUAAAAASUVORK5CYII=
-
-
-各个风格的文本框如上图所示。
-
-
-
-使用箭头进行注释
-----------------------
-
-
-
-
-::
-
-    plt.figure(1, figsize=(3,3))
-    ax = plt.subplot(111)
-
-    ax.annotate("",
-                ^4$xy=(0.2, 0.2), xycoords='data',
-                ^4$xytext=(0.8, 0.8), textcoords='data',
-                ^4$arrowprops=dict(arrowstyle="->",
-                                ^4$^4$connectionstyle="arc3"), 
-                ^4$)
-
-    plt.show()
-
-
-<button>Run ...</button>
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-.. image:: data:image/png;base64,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-之前介绍了 *annotate* 中 *xy*, *xycoords*, *xytext*, *textcoords* 参数的含义，通常我们把 *xy* 设在 *data* 坐标系，把 *xytext* 设在 *offset* 即以注释点为原点的参考系。
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-箭头显示是可选的，用 *arrowprops* 参数来指定，接受一个字典作为参数。
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-不同类型的绘制箭头方式：
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-::
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-    import matplotlib.pyplot as plt
-    import matplotlib.patches as mpatches
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-    x1, y1 = 0.3, 0.3
-    x2, y2 = 0.7, 0.7
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-    fig = plt.figure(1, figsize=(8,3))
-    fig.clf()
-    from mpl_toolkits.axes_grid.axes_grid import AxesGrid
-    from mpl_toolkits.axes_grid.anchored_artists import AnchoredText
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-    #from matplotlib.font_manager import FontProperties
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-    def add_at(ax, t, loc=2):
-        ^4$fp = dict(size=10)
-        ^4$_at = AnchoredText(t, loc=loc, prop=fp)
-        ^4$ax.add_artist(_at)
-        ^4$return _at
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-
-    grid = AxesGrid(fig, 111, (1, 4), label_mode="1", share_all=True)
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-    grid[0].set_autoscale_on(False)
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-    ax = grid[0]
-    ax.plot([x1, x2], [y1, y2], ".")
-    el = mpatches.Ellipse((x1, y1), 0.3, 0.4, angle=30, alpha=0.2)
-    ax.add_artist(el)
-    ax.annotate("",
-                ^4$xy=(x1, y1), xycoords='data',
-                ^4$xytext=(x2, y2), textcoords='data',
-                ^4$arrowprops=dict(arrowstyle="-", #linestyle="dashed",
-                                ^4$^4$color="0.5",
-                                ^4$^4$patchB=None,
-                                ^4$^4$shrinkB=0,
-                                ^4$^4$connectionstyle="arc3,rad=0.3",
-                                ^4$^4$),
-                ^4$)
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-    add_at(ax, "connect", loc=2)
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-    ax = grid[1]
-    ax.plot([x1, x2], [y1, y2], ".")
-    el = mpatches.Ellipse((x1, y1), 0.3, 0.4, angle=30, alpha=0.2)
-    ax.add_artist(el)
-    ax.annotate("",
-                ^4$xy=(x1, y1), xycoords='data',
-                ^4$xytext=(x2, y2), textcoords='data',
-                ^4$arrowprops=dict(arrowstyle="-", #linestyle="dashed",
-                                ^4$^4$color="0.5",
-                                ^4$^4$patchB=el,
-                                ^4$^4$shrinkB=0,
-                                ^4$^4$connectionstyle="arc3,rad=0.3",
-                                ^4$^4$),
-                ^4$)
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-    add_at(ax, "clip", loc=2)
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-
-    ax = grid[2]
-    ax.plot([x1, x2], [y1, y2], ".")
-    el = mpatches.Ellipse((x1, y1), 0.3, 0.4, angle=30, alpha=0.2)
-    ax.add_artist(el)
-    ax.annotate("",
-                ^4$xy=(x1, y1), xycoords='data',
-                ^4$xytext=(x2, y2), textcoords='data',
-                ^4$arrowprops=dict(arrowstyle="-", #linestyle="dashed",
-                                ^4$^4$color="0.5",
-                                ^4$^4$patchB=el,
-                                ^4$^4$shrinkB=5,
-                                ^4$^4$connectionstyle="arc3,rad=0.3",
-                                ^4$^4$),
-                ^4$)
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-    add_at(ax, "shrink", loc=2)
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-
-    ax = grid[3]
-    ax.plot([x1, x2], [y1, y2], ".")
-    el = mpatches.Ellipse((x1, y1), 0.3, 0.4, angle=30, alpha=0.2)
-    ax.add_artist(el)
-    ax.annotate("",
-                ^4$xy=(x1, y1), xycoords='data',
-                ^4$xytext=(x2, y2), textcoords='data',
-                ^4$arrowprops=dict(arrowstyle="fancy", #linestyle="dashed",
-                                ^4$^4$color="0.5",
-                                ^4$^4$patchB=el,
-                                ^4$^4$shrinkB=5,
-                                ^4$^4$connectionstyle="arc3,rad=0.3",
-                                ^4$^4$),
-                ^4$)
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-    add_at(ax, "mutate", loc=2)
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-    grid[0].set_xlim(0, 1)
-    grid[0].set_ylim(0, 1)
-    grid[0].axis["bottom"].toggle(ticklabels=False)
-    grid[0].axis["left"].toggle(ticklabels=False)
-    fig.subplots_adjust(left=0.05, right=0.95, bottom=0.05, top=0.95)
-
-    plt.draw()
-    plt.show()
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-.. image:: 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-
-
-
-字典中，connectionstyle 参数控制路径的风格：
-
-
-
-::
-
-    Name	Attr
-    angle	angleA=90,angleB=0,rad=0.0
-    angle3	angleA=90,angleB=0
-    arc	        angleA=0,angleB=0,armA=None,armB=None,rad=0.0
-    arc3	rad=0.0
-    bar	        armA=0.0,armB=0.0,fraction=0.3,angle=None
-
-
-
-
-
-::
-
-    import matplotlib.pyplot as plt
-    import matplotlib.patches as mpatches
-
-    fig = plt.figure(1, figsize=(8,5))
-    fig.clf()
-    from mpl_toolkits.axes_grid.axes_grid import AxesGrid
-    from mpl_toolkits.axes_grid.anchored_artists import AnchoredText
-
-    #from matplotlib.font_manager import FontProperties
-
-    def add_at(ax, t, loc=2):
-        ^4$fp = dict(size=8)
-        ^4$_at = AnchoredText(t, loc=loc, prop=fp)
-        ^4$ax.add_artist(_at)
-        ^4$return _at
-
-
-    grid = AxesGrid(fig, 111, (3, 5), label_mode="1", share_all=True)
-
-    grid[0].set_autoscale_on(False)
-
-
-    x1, y1 = 0.3, 0.3
-    x2, y2 = 0.7, 0.7
-
-
-    def demo_con_style(ax, connectionstyle, label=None):
-
-        ^4$if label is None:
-            ^4$^4$label = connectionstyle
-
-        ^4$x1, y1 = 0.3, 0.2
-        ^4$x2, y2 = 0.8, 0.6
-
-        ^4$ax.plot([x1, x2], [y1, y2], ".")
-        ^4$ax.annotate("",
-                    ^4$^4$xy=(x1, y1), xycoords='data',
-                    ^4$^4$xytext=(x2, y2), textcoords='data',
-                    ^4$^4$arrowprops=dict(arrowstyle="->", #linestyle="dashed",
-                                    ^4$^4$^4$color="0.5",
-                                    ^4$^4$^4$shrinkA=5, shrinkB=5,
-                                    ^4$^4$^4$patchA=None,
-                                    ^4$^4$^4$patchB=None,
-                                    ^4$^4$^4$connectionstyle=connectionstyle,
-                                   ^4$^4$^4$),
-                    ^4$^4$)
-
-        ^4$add_at(ax, label, loc=2)
-
-    column = grid.axes_column[0]
-
-    demo_con_style(column[0], "angle3,angleA=90,angleB=0",
-                   ^4$label="angle3,\nangleA=90,\nangleB=0")
-    demo_con_style(column[1], "angle3,angleA=0,angleB=90",
-                   ^4$label="angle3,\nangleA=0,\nangleB=90")
-
-
-
-    column = grid.axes_column[1]
-
-    demo_con_style(column[0], "arc3,rad=0.")
-    demo_con_style(column[1], "arc3,rad=0.3")
-    demo_con_style(column[2], "arc3,rad=-0.3")
-
-
-
-    column = grid.axes_column[2]
-
-    demo_con_style(column[0], "angle,angleA=-90,angleB=180,rad=0",
-                   ^4$label="angle,\nangleA=-90,\nangleB=180,\nrad=0")
-    demo_con_style(column[1], "angle,angleA=-90,angleB=180,rad=5",
-                   ^4$label="angle,\nangleA=-90,\nangleB=180,\nrad=5")
-    demo_con_style(column[2], "angle,angleA=-90,angleB=10,rad=5",
-                   ^4$label="angle,\nangleA=-90,\nangleB=10,\nrad=0")
-
-
-    column = grid.axes_column[3]
-
-    demo_con_style(column[0], "arc,angleA=-    90,angleB=0,armA=30,armB=30,rad=0",
-                       ^4$label="arc,\nangleA=-90,\nangleB=0,\narmA=30,\narmB=30,\nrad=0")
-    demo_con_style(column[1], "arc,angleA=-90,angleB=0,armA=30,armB=30,rad=5",
-                   ^4$label="arc,\nangleA=-90,\nangleB=0,\narmA=30,\narmB=30,\nrad=5")
-    demo_con_style(column[2], "arc,angleA=-90,angleB=0,armA=0,armB=40,rad=0",
-                   ^4$label="arc,\nangleA=-90,\nangleB=0,\narmA=0,\narmB=40,\nrad=0")
-
-
-    column = grid.axes_column[4]
-
-    demo_con_style(column[0], "bar,fraction=0.3",
-                   ^4$label="bar,\nfraction=0.3")
-    demo_con_style(column[1], "bar,fraction=-0.3",
-                   ^4$label="bar,\nfraction=-0.3")
-    demo_con_style(column[2], "bar,angle=180,fraction=-0.2",
-                   ^4$label="bar,\nangle=180,\nfraction=-0.2")
-
-
-    #demo_con_style(column[1], "arc3,rad=0.3")
-    #demo_con_style(column[2], "arc3,rad=-0.3")
-
-
-    grid[0].set_xlim(0, 1)
-    grid[0].set_ylim(0, 1)
-    grid.axes_llc.axis["bottom"].toggle(ticklabels=False)
-    grid.axes_llc.axis["left"].toggle(ticklabels=False)
-    fig.subplots_adjust(left=0.05, right=0.95, bottom=0.05, top=0.95)
-
-    plt.draw()
-    plt.show()
-
-
-.. image:: data:image/png;base64,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
-
-
-*arrowstyle* 参数控制小箭头的风格：
-
-
-::
-
-    Name	Attrs
-    -	        None
-    ->	        head_length=0.4,head_width=0.2
-    -[	        widthB=1.0,lengthB=0.2,angleB=None
-    |-|	        widthA=1.0,widthB=1.0
-    -|>	        head_length=0.4,head_width=0.2
-    <-	        head_length=0.4,head_width=0.2
-    <->	        head_length=0.4,head_width=0.2
-    <|-	        head_length=0.4,head_width=0.2
-    <|-|>	head_length=0.4,head_width=0.2
-    fancy	head_length=0.4,head_width=0.4,tail_width=0.4
-    simple	head_length=0.5,head_width=0.5,tail_width=0.2
-    wedge	tail_width=0.3,shrink_factor=0.5
-
-
-
-
-
-::
-
-    import matplotlib.patches as mpatches
-    import matplotlib.pyplot as plt
-
-    styles = mpatches.ArrowStyle.get_styles()
-
-    ncol=2
-    nrow = (len(styles)+1) // ncol
-    figheight = (nrow+0.5)
-    fig1 = plt.figure(1, (4.*ncol/1.5, figheight/1.5))
-    fontsize = 0.2 * 70
-
-
-    ax = fig1.add_axes([0, 0, 1, 1], frameon=False, aspect=1.)
-
-    ax.set_xlim(0, 4*ncol)
-    ax.set_ylim(0, figheight)
-
-    def to_texstring(s):
-        ^4$s = s.replace("<", r"$<$")
-        ^4$s = s.replace(">", r"$>$")
-        ^4$s = s.replace("|", r"$|$")
-        ^4$return s
-
-    for i, (stylename, styleclass) in enumerate(sorted(styles.items())):
-        ^4$x = 3.2 + (i//nrow)*4
-        ^4$y = (figheight - 0.7 - i%nrow) # /figheight
-        ^4$p = mpatches.Circle((x, y), 0.2, fc="w")
-        ^4$ax.add_patch(p)
-
-        ^4$ax.annotate(to_texstring(stylename), (x, y),
-                    ^4$^4$(x-1.2, y),
-                    ^4$^4$#xycoords="figure fraction", textcoords="figure     fraction",
-                    ^4$^4$ha="right", va="center",
-                    ^4$^4$size=fontsize,
-                    ^4$^4$arrowprops=dict(arrowstyle=stylename,
-                                    ^4$^4$^4$patchB=p,
-                                    ^4$^4$^4$shrinkA=5,
-                                    ^4$^4$^4$shrinkB=5,
-                                    ^4$^4$^4$fc="w", ec="k",
-                                    ^4$^4$^4$connectionstyle="arc3,rad=-0.05",
-                                    ^4$^4$^4$),
-                    ^4$^4$bbox=dict(boxstyle="square", fc="w"))
-
-    ax.xaxis.set_visible(False)
-    ax.yaxis.set_visible(False)
-
-
-
-    plt.draw()
-    plt.show()
-
-
-
-
-.. image:: 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O5VkRqu7lMMkBBQQFcLle8nm4EQBz3v9Gn5431AZ5WPdTnKScxMdGTk5PjKS4uFk3Tzlk/t9sta9as0bKzs2tdLtc+ABlG180R0R446/d98uRJOXjwoBw8eFDq6uqkM+z/6Bk8OB4mnlY9dEqpZJPJNNPpdD6QkpKSNmXKFHNGRobN6/VqZWVlvqKiIovNZit2u90rAOzusSeWekRvPK16a/HU/wyOMDE49Gs+MeJVAC5D0zmGfGg6jUqRiBw3sjbqPr09OM6Ih/5ncISJwUEUGgZH/ODHcSMgng/uEUUSt5X4wD0OIooqSikzgH8GMBDAZAAnROTnhhZFZ+EeBxFFGzOAsWg6zfkOALuMLYfa4h4HERHpwi8AEhGRLgwOIiLShcFBRES6MDiIiEgXBgcREenC4CAiIl0YHEREpAuDg4iIdGFwEBGRLgwOIiLShcFBRES6MDiIiEgXBgcREenC4CAiIl0YHEREpAuDg4iIdOF/AKSIUEo5AQwCUAPALSI1BpdE1GOa+38I/t7/VQaX1K0YHBQpDwEoANAIIFEpNVZEDhlcE8UApdRoAJcB6AugWkT+YHBJXXE/gHlo+ne3dqXURBH50uCaug3/dSxFhFJqLYC/isgzRtdCsUUptQLARQDcAH4qIikGl6SbUuopAF+LyFNG19ITuMdBkZKMpt10Il1E5AEAUEqZANyulLKJiM/gsvTqVf3Pg+MUKS70og2HIk9ENACnAFxgdC1d0KuCg3scPUgpFfb7giKiIlFLN0gGUGt0ERQdwuz1o0q13+ZR3v8MDuoe4RxT6mhjihIh73EopdLNZvPPXS7XWLPZnC4iXp/PV+7xeF4D8HbzK0+KcZE+fhrl/R9ycMRD/zM4KFI63eNQSo1JTk5elJiYmDtt2jRt6tSp9pSUFPh8PpSXl2P9+vW3HDt2rN5qtT4ZCAReEJHTPVQ7Ubg6DY646n8R4eih0fR0d13z/IavRwfrVg4gs4OfqaSkpEdTU1PrCgsLgxUVFe2un6ZpUlJSIvn5+fV2u/0UgNFGrxdHl/uhvV9xWKK8/48CGNDBz+Ku/w0voDeNOA8ODwBXez9zOBxrhg8f7jl+/HjI67p161bNbrfXAhhj9LpxdKkf2v29hiPK+78GQHJ7P4vH/je8gN404jU4AJgBBAGY2v4sISFhQXZ2dl1lZaXu9X399dfFbrdXAsgyeh05dPdE+7/UMERx/5t6W//z47hRJjs7GyaTCWazGSdPnjS6nFA5AXikzUE9pZRDKbVsx44d9tTUVN0LzcvLw9y5c11Op/NXkSqUjDFr1iyYTCaYTCa89tpr7U6zcePGlmnmzp3bwxWGxQmgrjf1P4Mjyiil8Otf/xrl5eW44IKY+Th7RwfGfzpx4kRt6NChXV7w/PnzLYFAYEbzuYAoRq1Zswbl5eUAzv/pKLvdjhMnTuDxxx/vqdIioaMD43Hb/wyOKORyudC3b99o//hha+d8FFcppZKTkx9auHBhWA2flZWFSZMmaUqp6WFVSIZyuVzo169fp9MppdC3b1+4XK4eqCpizgmOeO9/fhw3RimlzAD6AEhtHn0AWNH0O43UMAPwAWhoHvWtrre+PQqAo02JwywWS+bUqVPDXtd58+Y59+7dWwBgfdgLo5imlErE33s+FUAKgEQ09Wp7/RvOfYK/9/j5Lkej6e2q1uK6/xkcMUYpdQhAGppe5dcCqGoe1Wj6Ix8IcQTb3PYCqGtznwbABiCpefRrdb31aG/ruHDgwIF+kyn8ndohQ4ZA07S+YS+I4kEN/t7zZ0Yjzt/b7d3feJ5pz9xnQlN/25svHQAyWt0+c/njduqM6/5ncMSeH6JpY3GLSNDoYgBAKXUzgJ+1udvucLTdCekah8OBQCCQFJGFUaxLkOaPMkULpdRPAPxzm7vjuv95jMNAmzdvhsvlahnvvfdep/OIyEERqYyW0GjW3sHBGrfbHZGF19TUwGq11kVkYWQ4EcGll17a0vfXX3+9nnmjKjSa9br+5x6HgXJzczF+/PiW25mZmQZWE5b2zlN15PDhwwmNjY1ITEwMa+GlpaVn3qKjOKCUQlFREfx+PwAgKSmqXkx3RXvBEdf9zz0OAzmdTgwZMqRlhNtgBjrn47gictRqtX68bdu2sBe+cuXK2qqqql7xD3J6i6ysrJa+79+/v9HlhOuc4Ij3/mdwUCS0e2bc6urq5StWrAjrVOulpaU4cOCAH8D2cJZD1I3a/R5HPPc/g4MioaMvAL516NAhb3FxcZcWKiJ47LHHGgOBwNMiEgirQqLu09EXAOO2/xkcUSg6j/+dV7t7HCISrKurm5GXl9ewf/9+3QtdsmSJf+fOnUe8Xu+qSBRJ1E3aDY547n8GRxRavHgxXC4XTp06ZXQpoerwf3GIyNsNDQ1zx40b11BSUhLSwjRNw6JFi3zLly8/6fF4rhUR/mfBGFdQUACXy3XesyEopVBXVweXy4X777+/B6sLW4f/iyNu+9/osyz2poEQzhh65MgROXjwoBw8eFCCweBZP0P0nh10F4BrOpkmJzEx0ZOTk+MpLi4WTdPOWXe32y1r1qzRsrOza10u1z4AGUavG0eXe+Ks3+3Jkydb+rqurk7aU1tb2zLNqVOnzvl5FPf/ewAmdjJNXPW/al4p6gFKKQnn+VZKQaLwfy4rpT4GcIeIfNzJdMkmk2mm0+l8ICUlJW3KlCnmjIwMm9fr1crKynxFRUUWm81W7Ha7VwDYHdaTRYYKt9c7WGa09v9nAGaIyGedTBc3/c/g6EFxHBwHAFwvIl+GOL0CcBWAy9B0riEfgEoARSJyvNsKpR7Ty4LjawCTReTrEKeP+f5ncPSgOA6OEwC+KyInjK6FokMvC45KAP9PRCqNrqWn8JvjPSyGTpWuRzKAwUqpJAAnRKTB6ILIeHHa62dp3ntIBjBUKXUawHERqTe4rG7HPQ4Km1LqTQCD0PSx3CIRucvgkigGKaWyAeQA6AvgLjS9/bnXyJo6o5QyAfh3AAPQFCBvisi9xlbV/RgcRBQVlFKTANwC4CSAbwG8JCJRdXI/asLgICIiXfgFQCIi0oXBQUREujA4iIhIFwYHERHpwuAgIiJdGBxERKQLg4OIiHRhcBARkS4MDiIi0oXBQUREujA4iIhIFwYHERHpwuAgIiJdGBxERKQLg4OIiHRhcBARkS4MDiIi0oXBQUREujA4iIhIF4vRBcQapZSh/6RdRJSRj09ExODoAhFjskMpZgYRGY/BQdTLGL3XHA7ucUcHBgf1OKVUutls/rnL5RprNpvTRcTr8/nKPR7PawDeFhHN6BrjnVF7zeGIlz3ueOh/FYsNZCSllBj5VlUsv+JSSo1JTk5e5PP5cqdNm6ZNnTrVnpKSAp/Ph/Lycqxfv7722LFj9Y2NjU8GAoEXROS00TXHIyN7OBzs/ygiIhw6RtNTZozmxzb8OdA7AKikpKRHU1NT6woLC4MVFRXtrp+maVJSUiL5+fn1drv9FIDRRtcej8PIHg4H+z96huEFxNpgcOgfDodjzfDhwz3Hjx8PeV23bt2q2e32WgBjjK4/3gaDg/0f7jC8gFgbeja6V155RY4dOxby9J2JxQ0nISFhQXZ2dl1lZaXu9X399dfFbrdXAsgyej3iaXTUw8FgUO644w5JT08XpZS8++675/v19Dj2f/QMfgGwG/l8Pvj9fqPLiBil1CSlVKaO6R1KqWU7duywp6am6n68vLw8zJ071+V0On+le2bS7T//8z+xceNGvPXWWzhx4gTGjx9vdElRRyl1mVLq/4U4bdz2Pz9VZYDVq1fDZrNh1qxZSEpKMrqckLlcrk1er7dfUlLSssbGxuUi0tjJLD+dOHGiNnTo0C4/5vz58y3PPPPMDKXUQhHxdHlB1KmvvvoK/fv3x9ixY40upUMul+s5TdN8gUDAGwgEvJqm+QAEAPjbjLb3dTSND0AtgGoAbhE57yu9hISE271e74KUlJRdbrf7bhH563kmj9v+Z3AYYMGCBfjLX/6CxYsXIz09HXfeeScyMjKMLqtTmqa5duzYkbB8+fKH3n333bvNZvPdmqa9JiLnfERHKaWSk5MfWrhwoTOcx8zKysKkSZO0HTt2TAewPpxlUcdmzZqFTZs2AQBMJhMGDRqE5557DkuXLsXnn38OpRR+8IMf4KmnnsLw4cMBAF9//TWGDBmCV199Fc8++yz+/Oc/Izs7G6tXr8aUKVNalr1//348+OCD2LNnD4LBIEaOHIn169ejsrISU6ZMwTfffIN+/fq1TL948WJs374dpaWl59S5dOnSO/1+P86MQCAAn8+neb1ezefzaT6fT/P7/ZrP55PWw+/3i9/vb3kXoNVQHo/H5PF4LA0NDQk2m81vs9nqrFarx2w2u5VS1Zqmnfb7/acaGhpOBgIBMZlMcuedd07613/9149dLtfLHo/nIRGpal1nvPc/P46rk56PMr744ouYPHkyBg0a1OE0R48exXPPPYdAIIA5c+bg4osvPt9jA8AvANjRFPpth7mD+/UOAKhvHnVnrttstrsqKirMycnJ2LVrF+644w7vt99+e6y2tnaOiBS3qfWStLS0fRUVFXaTKbx3RLdv346ZM2d+UllZ+f2wFkQA2u/hmpoarFq1Chs2bMBHH30Ek8mEPXv2QCmFUaNGoaGhAUuWLMG+ffvwxRdfwGq1tgTHJZdcgieeeALDhw/HkiVLsH37dhw5cgQOhwPHjx/HqFGjcNVVV2Hx4sVIS0vD3r17MWLECIwaNQrf+c538Itf/AIPPPAAAEDTNAwaNAgPPvgg7rnnnrZ1ozv/XokIPB4PqqurOxynT5/2u93u4G9+85vEpKQk/PKXv/Ru2bKltq6uLkdESlrVGtf9zz2OCNi8eTMKCgpabhcVFWHChAkhzTtgwAAsXboUbrcb69atw9GjR3Hbbbdh9OjRHc0yGYAHTbvZwebL1qOxnfv0DhOAJDQF1Jnh8Pv9ZqfTCRGBzWbDsGHDzGVlZdlKqVwAZwUHgAsHDhzoD3ejAYAhQ4ZA07S+YS+IOpScnAyn0wmz2Yy+fZue6ptuuumsaTZs2IA+ffrgww8/xJVXXtly//3334/rr78eALBs2TJs2rQJpaWluPLKK7F27Vq4XC5s27YNFkvTn5shQ4a0zHv77bfj97//fUtwvP3226ioqMCMGTM6rVlE4PV6UVNTA7fbjZqamnavV1dXBzweT7B5b0T8fr/4fD4JBAIteyLN11v2Yvx+PzRNg8VigcVigdlsbrnucrmsSUlJcLlcuPTSS22apvWx2+0/BlDSqry47n8GRwTk5uaedSAxMzPk48ctHA4HBg4ciCNHjqCqqqrD6UTk510qMkxKqRSr1frEpk2brIWFhbXHjx+vra+vfzwYDG4UkZp2ZrE7HI6IPLbD4UAgEIidg0Fx4uDBg3jkkUewd+9eVFRUQNM0aJqGsrKys4Jj1KhRLdf79+8PADh58iQA4JNPPsHEiRNbQqOtmTNnYvHixfjggw8wbtw4bNiwATfeeCM6Opg8YsQId2Vlpbmurs7S0NBgA6DZbLZGq9VaZzabPSaTqUYpVaNpWpXf7z/t9XpP+3y+KjTtNZ85rhFoc7294Qcg+PtevLl55CQlJd21YsWK4IYNG7x+v7+koaHhPhFp+75aXPc/gyMCnE4nnM6uvZXp8XiwceNGfPnll8jPz8fatWsjXF3E2Hw+n+W+++57r7q6ehk6PzVCjdvtjsgD19TUwGq11kVkYRSynJwcDBw4EOvXr8dFF10Es9mMESNGwOfznTWd1WptuX7mtCCaprXcPt/bSxdccAGmTZuG3//+9xg6dCjefPNNbN++vcPp//rXv/4DgFMA3ABqRMTb5RXsgsTExLVKqdq1a9e+UldX9wcR+aiDSeO6/xkcBvn222/x/PPPo6amBrfddhvmzZtndEnnJSInlVJpVVVV1SHOcuTw4cMJjY2NSExMDOuxS0tLoZQ6FNZCSJfTp0/jb3/7G5577jlMmjQJALBv3z4EAgFdy/ne976Hl19+GX6//6yAaW2tlab/AAAgAElEQVTOnDn4yU9+gsGDB6N///5nHVhvS0Q+0FVAhHm93qsBfCsiwU4mjev+5/c4DPD000/jueeew5w5c7B8+XJccsklRpcUEhEJNTQgIketVuvH27ZtC/txV65cWVtVVfVU2AuikKWmpiIjIwPr16/HV199hXfffRcFBQUdvuXUkblz58Lj8eCWW27BRx99hK+++gpbtmw56xNTP/zhD5Geno7f/OY3mDVrVoTXJLJE5HgIoRH3/c/gMMD8+fPx61//+qyPIMaj6urq5StWrKgNZxmlpaU4cOCAH0DH719QRCilWt5qMplM2Lp1Kz777DOMHDkS99xzD5YuXYqEhIRz5jmfzMxM7NmzBz6fD9dccw2+//3vY+3atefsfcyaNQt+vx+zZ8+O7EoZKK773+ivrsfagI5TjmzcuFG+/vrrkKfvDGLslAsAzA6Ho+Kdd97p0vpqmiY33nhjQ0JCwq+NXpd4Gnp6uKcUFBTI1KlTzzsN+z96Bvc4qNuISLCurm5GXl5ew/79+3XPv2TJEv/OnTuPeL3eVd1QHkUBt9uN999/Hy+99BLuvfdeo8uJqLjuf6OTK9YGdLxa27JlS68/yaGIwGKxzOrTp0/9Bx98ENJ6BoNBWbhwodfhcBwFkGl0/fE29PRwd5s0aZLY7XaZP39+p9Oy/6NnGF5ArA0jN7pY3XCaSkdOYmKiJycnx1NcXCyapp2zfm63W9asWaNlZ2fXulyufQAyjK47Hkc0BYce7P/oGTzliE78D4Bdp5RKNplMM51O5wMpKSlpU6ZMMWdkZNi8Xq9WVlbmKyoqsthstmK3270CwG7Dnug4x/8AaIx46n8Gh04MjvCppo/iXAXgMgApaDpDaSWAIhE5bmRtvQGDw1jx0P8MDp0YHBTrGBwULn5znKgX6uz7F0Tnw+Ag6mX4qp3CxeDoAr5aI6LejMc4iIhIF35znIiIdGFwEBGRLgwOIiLShcFBRES6MDiIiEgXBgcREenC4CAiIl0YHEREpAuDg4iIdGFwEBGRLgwOIiLShcFBRES6MDiIiEgXBgcREenC4CAiIl0YHEREpAuDg4iIdGFwEBGRLgwOIiLShcFBRES6MDiIiEgXi9EFxDKllPT0Y4qI6unHpNhjRG/2BPZ/dGBwhEmk57ZPpeJjm1FKpZvN5p+7XK6xZrM5XUS8Pp+v3OPxvAbgbRHRjK4xHvRkb/YE9n/0UPHWXD1JKSU9HRyx/IpLKTUmOTl5kc/ny502bZo2depUe0pKCnw+H8rLy7F+/fraY8eO1Tc2Nj4ZCAReEJHTRtccq3q6N3sC+z+KiAhHF0fT09dzmh/P8PXWOwCopKSkR1NTU+sKCwuDFRUV7a6fpmlSUlIi+fn59Xa7/RSA0UbXHqujp3uzJ7D/o2cYXkAsDwZHaMPhcKwZPny45/jx4yGv69atWzW73V4LYIzR9cfiYHBEz4jH/je8gFgeDI7OR0JCwoLs7Oy6yspK3ev7+uuvi91urwSQZfR6xNrQ05u7du0SpZScPn065Hm6atKkSTJv3rwuzWt0/wNI1TtPvPY/P45L3UYp5VBKLduxY4c9NTVV9/x5eXmYO3euy+l0/qobyqNmEyZMwIkTJ5CWltbtj6WUismD3EqpNKXUqT59+vxJKTUwxHnitv95cDwMPDh+fkqp26dMmfLkzp07nV1dxjfffINhw4bVNzY29hMRTyTri2fRenD8mmuuwciRI/H000/rnrc5cAYCsAKwNV9a27l9vp+duR0E4AXQ2Dy8nVz2S01NLb7rrrusTz75pF8p9Ux9ff0SEak9T71x2//8OK4Bqqur8fzzz6Nfv36YOXOm0eV0C6WUSk5OfmjhwoVd3mgAICsrC5MmTdJ27NgxHcD6CJXXK+3ZswcPPvggPv/8c5jNZlxyySXYsGEDKioqcO211+LUqVNIS0vDxo0bcc8992Dbtm2499578c0332DKlCl46aWX8F//9V945JFHcPLkSeTl5WHdunVISEgAAEyePBnf+c53YLPZ8NJLLwEAbr/9djz++OMd7mX4fD488sgjeOWVV1BZWYlLL70US5cuxdSpU9udPjU1db/FYtGsVqs0D1itVrHZbGi+joSEBNhsNnVmJCQkKJvNZmq+bkpISDAFg0Gpr68PNjQ0aK0GGhsbpbGxEQ0NDfD5fKqxsVH5fD7l8/nMl19+ufz2t7+1FBQUWBYtWjRv+/btcywWy/3BYPBFafMR2njvfwZHDyorK8O6devg9/tRUFCAIUOG6F6GUuqHAFIBpABIAmBuNSxtbodzHwA0AKgHUNd82dGoA/BXEfmmVanDLBZLZkd/APSYN2+ec+/evQWIog0n1gQCAeTm5mLOnDnYsmUL/H4/9u3bB7PZ3O70Xq8Xq1atwpYtW+D1enHzzTfjpptugt1ux5/+9CecOnUKN910E7773e/i3nvvbZlv8+bNmD17Nj744AOUlpZizpw56N+/P+677752H2f27Nk4fPgwtmzZggEDBuCtt97CDTfcgA8//BCjRo06Z/rKykp7ZJ4RAE17H7plZWVh69atSSUlJUl33nnnmsOHDz+olJohIh+3miyu+5/B0QM+/fRTbNy4ERkZGbj//vuRnp4ezuIeAlDVPBrQtMt9ZgRaXXrbub/t6Oj+YPNjJQGwtxnJAPq3c/87AB5vVeeFAwcO9JtM4R9GGzJkCDRN6xv2gnqxmpoauN1u5OTkYPDgwQCAYcOGAQBOnDhxzvSBQABr167F0KFDAQDTp0/Hk08+iZMnT7YcC8nNzcU777xzVnBkZmZi9erVLcv/8ssvsWrVqnaD4+DBg/i3f/s3fP3118jKygIA3H333di5cyfWrVuHtWvXnjPPG2+8Ab/fD5/PB7/ff871M7d9Pp/4fL5gY2Oj5vP5tObbWmNjo/j9fjGbzbDb7abExERTUlKS2W63m5OSksyJiYlISEhAe5eXXnrpWS/2kpOTcfnll1s+//zzoYmJif8AoHVwxHX/Mzi60Z49e7BlyxaMHDkShYWFSExMbHe6zZs3o6CgoOV2UVERJkyY0O60IjKlW4qNPLvD4YjIghwOBwKBQFJEFtZLpaWlYdasWfjRj36E6667Dtdddx1+8pOftPzBbishIaElNACgb9++uPDCC886gN63b1988cUXLbeVUhg3btxZyxk3bhweeeQReDweOJ1nv2uzb98+iAhGjBhx1v1erxfXXXddu3XNmTPnXRHxiogPgE/TNO+ZEQwGvYFAoCEQCHhFxA/AB8DfarS+bQaQACDxzKVSKsFqtTosFovDYrHYTSaT3WQyJSmlkrxe74Crr746680333S8/fbbWLZsmWffvn1BTdOeCQQCa/1+f3mbUuO6/xkc3ai8vBwigsGDB3cYGkDTK7fx48e33M7MzOyJ8rpbjdvtjsyCampgtVrrIrKwXmzDhg249957UVRUhP/4j//A4sWL8cYbb8Bms50zrcVy9p8GpRSsVus592na2WfH0HNAXtM0KKXw0UcfnbPspKT2/05WVFRMDvkBIkgpdevf/va33w8ePNhTXV19ora2domIbBURbwezxHX/Mzi6UX5+PvLz8/Hmm2/i7rvvxtixYzF9+vRzNkqn03nOq7E4cOTw4cMJjY2N5w3NUJSWlkIpdShCdfVqo0aNwqhRo/Dggw/ixz/+MV588UXccccdEVm2iKCkpOSs+z744ANcdNFF7fb39773PYgIysvLMXny5IjU0I3KTp48ubO2tnYFgHdD+MhaXPc/v8fRA2644QasXbsWw4cPx6JFi/Dkk0+itrbDT/HFBRE5arVaP962bVvYy1q5cmVtVVXVUxEoq9f6+uuv8fDDD+P999/HkSNHsGvXLnz22We49NJLde0ldOb48eO499578be//Q2vvvoqnnjiibOOb7T6Mh2GDRuGn/3sZ5g1axZee+01HDp0CB999BGeeOIJvP766xGrKRJE5N2amprrRWR3KJ9zjvf+Z3D0oCuuuAJPPfUUcnNz8bvf/Q4vv/yy0SV1q+rq6uUrVqwIKyFLS0tx4MABP4DtESqrV7Lb7Thw4AD+6Z/+CZdccglmzZqFGTNm4KGHHgJw7pln27vd2X1KKcyYMQPBYBDjxo3DHXfcgdtvv/2sg+dt5/nDH/6A2bNn48EHH8R3vvMd3HDDDXjvvfeQnZ0dqVU3TFz3v9FfXY/lAZ5ypLPnx+xwOCreeeedLq2vpmly4403NiQkJPza6HWJtdHTvSkiMnnyZLnnnnu6bfns/+gZ3OOgbiMiwbq6uhl5eXkN+/fv1z3/kiVL/Dt37jzi9XpXdUN5FGGt/mAS4rv/GRzUrUTk7YaGhrnjxo1raHvgtCOapmHRokW+5cuXn/R4PNfKeU7rQNEjVs9D1Z3itv+N3uWJ5QG+VaXnucpJTEz05OTkeIqLi0XTtHPWz+12y5o1a7Ts7Oxal8u1D0CG0XXH6ujp3uwJ7P/oGTzJYRh4kkN9lFLJJpNpptPpfCAlJSVtypQp5oyMDJvX69XKysp8RUVFFpvNVux2u1cA2N2jT26cidaTHIaD/R89GBxhYHB0jWp6P+MqAJeh6ZxbPgCVAIpE5LiRtcULBkf0iof+Z3CEgcFB0YrBQd2J3xwPEw8GUrRib1J34R4HERHpwo/jEhGRLgwOIiLShcFBRES6MDiIiEgXBgcREenC4CAiIl0YHEREpAuDg4iIdGFwEBGRLgwOIiLShcFBRES6MDiIiEgXBgcREenC4CAiIl0YHEREpAuDg4iIdGFwEBGRLgwOIiLShcFBRES6MDiIiEgXBgcREenC4CAiIl0YHEREpIvF6AKihVJKjK4hFCKijK6Bol+s9LNe7P/owOBoRSS6tzWl4mObUUqlm83mn7tcrrFmszldRLw+n6/c4/G8BuBtEdGMrjEeRHs/68X+jx4q3pqrq5RSEu3PhVIqpl9xKaXGJCcnL/L5fLnTpk3Tpk6dak9JSYHP50N5eTnWr19fe+zYsfrGxsYnA4HACyJy2uiaY1Us9LNe7P8oIiIcTRuYRLvmGg1/rvQOACopKenR1NTUusLCwmBFRUW766dpmpSUlEh+fn693W4/BWC00bXH6oiFftaL/R89g3sczWLhFVqsvuJyOp1rsrKyZhcXFzv69+8f0jx//OMfZfbs2XX19fXXiMhH3Vxi3ImFftaL/R89+Kkq6laJiYkLLrjggl/8+c9/DnmjAYBbbrlFbd682Wm323copbK6sUSKkHnz5uGaa64xuoyoEq/9z+CgkCmlpiilBuuY3qGUWrZjxw57amqq7sfLy8vD3LlzXU6n81e6ZyZDxMsB7EiI5/5ncJAe2QA+VEotVUo5Q5j+pxMnTtSGDh3a5QecP3++JRAIzAjx8chg3f32mFJqvFJqjFJqlFJquFJqiFIqSynVTymV2vzH2qqiI8Hitv8ZHGGqrq7GihUrsGnTppDnWb16NZ599lk0NDR0Y2WRJyIvABiNpgDZr5SaoZRqt4eUUio5OfmhhQsXhtXwWVlZmDRpkqaUmh7Ocnq7oqIiJCcnQ9OaPun51VdfwWQy4a677mqZ5le/+hV++MMfAgC++OILXH/99UhOTka/fv0wffp0fPvtty3TBoNBLFq0CGlpaUhLS8N9992HYDB41mPW1dVh5syZcLlcyMzMxBNPPIGcnBzMnj27ZRqfz4eHHnoIWVlZcDgcuOKKK7Bjx44O12PEiBH/NXTo0P8ePHjw/1x00UV7+/btW5qamrrf5XIdTkpKKrfZbFUmk6kRgGY2mwM2m82blJRUZ7fba1wu1+nU1NSy9PT0v2RkZLyXnp7+78nJyRusVmuhUuohpdSdSql/Vkr9Q3NAjVBKZTaHka4givf+5/c4uqisrAzr1q2D3+9HQUEBhgwZEvK8CxYswF/+8hcsXrwY6enpuPPOO5GRkRHSvEqpf0XT783calg6uN6VnwUB1AOoazNa3/clgCoASwE8qpSaJyJFbUodZrFYMqdOnRry89KRefPmOffu3VsAYH3YC+ulJk6ciMbGRnz00Ue44oorsHv3bmRkZGD37t0t0+zevRs//vGPUV5ejquvvhpz5szBqlWr4Pf78S//8i/Izc3F+++/D6UUVq5ciRdeeAEvvPACRo0ahWeeeQavvPIKLr/88pblLVy4EHv27MEbb7yB/v37Y8mSJXjvvfdw0003tUwze/ZsHD58GFu2bMGAAQPw1ltv4YYbbsCHH36IUaNGnbMen3/+eZ/zraeIwOfzoaamBlVVVeaqqipzZWWlrbKyEpWVlaiqqkqrrKzMqqqqwpn7mu9HZWUl/H5/h8tumx1KqWMichTAUQArReT9Vj+O6/7np6qahfoplE8//RQbN25ERkYG7rrrLqSnp4f1uEePHsVzzz2HQCCAOXPm4OKLLz5fjQAwD0AATX/gg+e53tWfmQHYATjajLb3DQcwBsBFANaKyH1tap303e9+998/+eST827oofjiiy9w5ZVXHquurh4Q7rJ6i/b6efz48cjNzcXDDz+MGTNmYNiwYSgsLMThw4fhcrmQlpaG4uJiFBUV4c9//jP++7//u2XeqqoqpKenY+/evRgzZgwyMzNxzz334Je//CWApj/Yw4cPx0UXXYTi4mJ4PB6kp6fjpZdewi233AIAqK+vx4ABA5CXl4cNGzbg4MGDGDZsGL7++mtkZf39+G9eXh4uuugirF27tu06YcaMGfU1NTXBmpoarba2FrW1tairqzPV19ebGxsbLV6v16qU0qxWq9disTRYLJYGk8lUp5TyAKhXSvkBBKXpS3Ytl83XgwA0EQmKiCYigebrQU3TLJqmXahpWmYwGMwMBoN92zzl/yoid7eqNa77n3scIdqzZw+2bNmCkSNHorCwEImJie1Ot3nzZhQUFLTcLioqwoQJEzpc7oABA7B06VK43W6sW7cOR48exW233YbRo0e3O72IrG33Bz1AKeUAcCuAGc13FQJ4WUTq2pnc7nA4IvK4DocDgUAgKSIL68UmT56M3bt34+GHH8aePXuwYMEC7Nq1C7t27UJGRgYsFgt+8IMf4Le//S327NkDl8t11vxKKRw8eBBDhw7FiRMnMH78+LN+NnbsWHzzzTcAgIMHD8Lv9+OKK65omcZut+Oyyy5rub1v3z6ICEaMGHHW43i9Xlx33XXtrsPLL798H4AaAB407f162ow6EfF1+UmKnLjufwZHiMrLyyEiGDx4cIehAQC5ublnbVCZmZkhLd/hcGDgwIE4cuQIqqqqwq430pRSiQAOAngfwHwAuzrZRatxu90ReeyamhpYrdb2wol0mDx5Mp555hns378fNTU1uPzyyzF58mTs2rULffv2xZVXXgmr1QpN05CTk4MnnnjinGX07dv3nGMZZ4Syx956Gk3ToJTCRx99BKvVetZ0SUnt/50Ukah5u6YTcd3/DI4Q5efnIz8/H2+++SbuvvtujB07FtOnT4fFcvZT6HQ64XSGfjzM4/Fg48aN+PLLL5Gfn3/O7nm0EJFGpdRgEQn1iP6Rw4cPJzQ2Np43aENRWloKpdShsBZCmDBhArxeL5YvX46rrroKJpMJkydPxu23344LL7wQ//iP/wgAuPzyy/HHP/4RAwcOPKe/z+jfvz/ef/99TJ48GUBTIOzduxcXXXQRAODiiy+G1WrF3r17kZ2dDaDprar/+7//w5lPGX3ve9+DiKC8vLxlOXEkvvvf6K+uR8uAzlM0lJSUyIIFC2TVqlVSU1Oja14RkRMnTsiSJUvkgQcekP3794c0D2LslAspKSnvbdq0Sfdz09Z3v/vdGgB5Rq9PLI2O+nncuHFitVpl5cqVIiLS0NAgiYmJYrFY5H//939FROT48ePSt29fuemmm6SkpEQOHjwoO3fulDvuuENqa2tFROTxxx+XlJQUefXVV2X//v0yf/58SU5OlsmTJ7c8VkFBgWRnZ8s777wjn3/+ufz0pz+VPn36yC9+8YuWaWbMmCGDBg2SV199VQ4ePCgffvihrFixQv70pz+dUzv73/h1OjMMLyBaht7gOOPgwYPyy1/+Ul566aWQ51m9erU8+uijcuLECV2PFWsbDoBpI0eO1J+qrXz66aficDhOA7AYvT6xNDrq54cfflhMJpN8/PHHLfdNnjxZnE6n+P3+lvsOHDggP/nJTyQ1NVWSkpLkkksukfnz54vP5xMRkUAgIPfdd5+kpKRISkqKzJ8/X+666y655pprWpbh8Xjk1ltvFYfDIRdeeKEUFhbKddddJ3Pnzm2Zxu/3y6OPPipDhgwRm80mF154oeTm5sq+ffvOqZ39b/w6tayb0QVEy+hqcPSkGNxwzA6Ho+Kdd97p0vpqmiY33nhjQ0JCwq+NXpdYG9HYz42NjdKvXz9ZtWpVl+Zn/0fPMLyAaBnRuKG1FWsbTlPJ+JHL5ar/61//qnt9H3vsMZ/T6dwPwGX0esTaiIZ+/uSTT2Tz5s1y4MAB2bdvn+Tn54vT6ZRjx451aXns/+gZhhcQLSMaNrTOxOKGIyKwWCyz+vTpU//BBx+EtJ7BYFAWLlzodTgcRwFkGl1/LI5o6OdPPvlExowZIy6XS1JTU+Xaa69t9y2oULH/o2cYXkC0jGjY0DoTqxtOU+nISUxM9OTk5HiKi4tF07Rz1s/tdsuaNWu07OzsWpfLtQ9AhtF1x+qIhX7Wi/0fPYPfHG8WC/+/IFb/H8EZSqlkk8k00+l0PpCSkpI2ZcoUc0ZGhs3r9WplZWW+oqIii81mK3a73SsA7I76X0gUi4V+1ov9Hz0YHM1iYUOL9Q3njOYTxl0F4DIAKQB8ACoBFInIcSNrixex0M96sf+jB4OjWSxsaPGy4VD3i4V+1ov9Hz34zfFWouMU/kSRwX6m7sI9DiIi0oX/yImIiHRhcBARkS4MDiIi0oXBQUREujA4iIhIFwYHERHpwuAgIiJdGBxERKQLg4OIiHRhcBARkS4MDiIi0oXBQUREujA4iIhIFwYHERHpwuAgIiJdGBxERKQLg4OIiHRhcBARkS4MDiKiMCml7Eoph9F19BQGBxFFBdXkFqPr6KL7AMw3uoiewuAgomihAGxWStmMLqQLMgB4jS6ip1iMLiCWKKXE6BpERBldA1FnwtxWvEq13+ZR3P/JAGqMLqKnMDh0EjEuOzramIiiUaS3lSjv/14VHHyriogofL0qOLjHQT1OKZVuNpt/7nK5xprN5nQR8fp8vnKPx/MagLdFRDO6RiKdQg6OeOh/Bgf1GKXUmOTk5EWJiYm506ZN06ZOnWpPSUmBz+dDeXk51q9ff8uxY8fqrVbrk4FA4AUROW10zUQh6jQ44qr/RYQjxNH0dBmn+fENfx70DgAqKSnp0dTU1LrCwsJgRUVFu+unaZqUlJRIfn5+vd1uPwVgtNG1c3T5d97erzgs0dz/AL4BMLCDn8Vd/xteQCwNBkfXhsPhWDN8+HDP8ePHQ17XrVu3ana7vRbAGKPr59A/emFwuAGktPezeOx/wwuIpRHpjeGVV16RY8eOhTx9NG84HY2EhIQF2dnZdZWVlfqeHBF5/fXXxW63VwLIMno9OPSNjraVznr+fD+P1v5H04eMggDMbX8Wr/3PT1UZyOfzwe/3G11Gt1FKOZRSy3bs2GFPTU3VPX9eXh7mzp3rcjqdv+qG8sgAnfV8jG4TDgANIhJsfWc89z+Do4dUV1djxYoV2LRpk9Gl9KSfTpw4URs6dGiXFzB//nxLIBCYoZRyRrAuokjq6MB43PY/g6OblZWVYfHixVi2bBluvvlmzJw50+iSeoRSSiUnJz+0cOHCsBo+KysLkyZN0pRS0yNVG1GEnRMc8d7//DhuN/n000+xceNGZGRk4P7770d6enpElquUuhNAHzT97szNo/X19kZnP29vGh+AOgD1rS7r27nvzOUXInKkVanDLBZL5tSpU8Ne53nz5jn37t1bAGB92AujmNZ8HqtUAGnNl6kAEtDUv62HOQL3CYAGNPX3+S5HAnC1KTWu+5/BEWF79uzBli1bMHLkSBQWFiIxMTGk+TZv3oyCgoKW20VFRZgwYUJ7k45B0yc4/Gg6IBdA08nVgucZgS783AbAjqb3b9teprZz/zsAVrSq88KBAwf6Tabwd2qHDBkCTdP6hr0giho6+r2tOgBVbUYDmnr4TB8H2hlt728MYVoTgCQ09XcSmno9o9XtM5c/bqfOuO5/BkeElZeXQ0QwePDgkEMDAHJzczF+/PiW25mZme1OJyJzwi6yZ9gdjsj8ewKHw4FAIJAUkYVRVAi139thk+aPMkULpdTNAH7W5u647n8GR4Tl5+cjPz8fb775Ju6++26MHTsW06dPh8Vy/qfa6XTC6Yyq41/hqnG73ZFZUE0NrFZrXUQWRlGhq/0ebaHRrL2D43Hd/zw43k1uuOEGrF27FsOHD8eiRYvw5JNPora21uiyetKRw4cPJzQ2Noa9oNLSUiilDkWgJqLu4MK5wRHX/c/g6GZXXHEFnnrqKeTm5uJ3v/sdXn75ZaNL6hEictRqtX68bdu2sJe1cuXK2qqqqqciUBZRdzhnjyPe+5/B0UOGDBmCZcuWYcaMGUaX0mOqq6uXr1ixIqzdrNLSUhw4cMAPYHuEyiKKtHa/xxHP/c/goO701qFDh7zFxcVdmllE8NhjjzUGAoGnRSQQ4dqIIqWjLwDGbf8zOKjbiEiwrq5uRl5eXsP+/ft1z79kyRL/zp07j3i93lXdUB5RpLQbHPHc/wwOAyUkJMBqtRpdRrcSkbcbGhrmjhs3rqGkpCSkeTRNw6JFi3zLly8/6fF4rhWRXvWpgnjWWc/H6DbR4f/iiNv+N/osi7E0wNOqh/Pc5SQmJnpycnI8xcXFomnaOevndrtlzZo1WnZ2dq3L5doHIMPoujm6/Ps+5/cbrmjtfwB7AFzdyTRx1f+qeaUoBL88D8UAAAF7SURBVEopMfL5UkpBRJRhBYRJKZVsMplmOp3OB1JSUtKmTJlizsjIsHm9Xq2srMxXVFRksdlsxW63ewWA3YY+2RSW7thWorX/lVKfApglIp92Ml3c9D+DQwcGR2QopRSAqwBcBiAFTefFqgRQJCLHjayNIqOXBcchAFNEJKTvWsRD/zM4dGBwEIWmlwXHKQDDReSU0bX0FB4cJyLqoua9h2QA0XcAuxsxOIiIwrNeRLxGF9GT+FaVDkopw5+saNxVJ2qru7YV9n90YHAQEZEufKuKiIh0YXAQEZEuDA4iItKFwUFERLowOIiISBcGBxER6cLgICIiXRgcRESkC4ODiIh0YXAQEZEuDA4iItKFwUFERLowOIiISBcGBxER6cLgICIiXRgcRESkC4ODiIh0YXAQEZEuDA4iItKFwUFERLowOIiISBcGBxER6cLgICIiXRgcRESkC4ODiIh0YXAQEZEuDA4iItKFwUFERLowOIiISBcGBxER6fL/AT5KlOR8hRt9AAAAAElFTkSuQmCC
-
-
-
-作者 & 更新时间
-------------------------------------
-作者:`李金  <lijinwithyou@gmail.com>`
-
-提交: 2017/12/14
\ No newline at end of file
diff --git a/course/source/aipy-numpy/cn/7tag.rst b/course/source/aipy-numpy/cn/7tag.rst
deleted file mode 100644
index 305e2f9..0000000
--- a/course/source/aipy-numpy/cn/7tag.rst
+++ /dev/null
@@ -1,317 +0,0 @@
-
-标签
-============
-
-
-
-::
-
-    import numpy as np
-    import matplotlib as mpl
-    import matplotlib.pyplot as plt
-
-    %matplotlib inline
-
-
-legend() 函数被用来添加图像的标签，其主要相关的属性有：
-
-
-- legend entry - 一个 legend 包含一个或多个 entry，一个 entry 对应一个 key 和一个 label
-- legend key - marker 的标记
-- Nlegend label - key 的说明
-- legend handle - 一个 entry 在图上对应的对象
-
-
-
-使用 legend
---------------
-
-调用 *legend()* 会自动获取当前的 *Axes* 对象，并且得到这些 *handles* 和 *labels*，相当于：
-
-    handles, labels = ax.get_legend_handles_labels()
-
-    ax.legend(handles, labels)
-
-
-我们可以在函数中指定 *handles * 的参数：
-
-
-
-
-::
-
-    line_up, = plt.plot([1,2,3], label='Line 2')
-    line_down, = plt.plot([3,2,1], label='Line 1')
-    plt.legend(handles=[line_up, line_down])
-    plt.show()
-
-
-.. image:: data:image/png;base64,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-可以将 *labels* 作为参数输入 *legend* 函数：
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-::
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-    line_up, = plt.plot([1,2,3])
-    line_down, = plt.plot([3,2,1])
-    plt.legend([line_up, line_down], ['Line Up', 'Line Down'])
-    plt.show()
-
-.. image:: data:image/png;base64,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
-
-
-产生特殊形状的 marker key
----------------------------------
-
-
-有时我们可以产生一些特殊形状的 marker：
-
-
-块状：
-
-
-
-
-::
-
-    import matplotlib.patches as mpatches
-
-    red_patch = mpatches.Patch(color='red', label='The red data')
-    plt.legend(handles=[red_patch])
-
-    plt.show()
-
-.. image:: data:image/png;base64,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
-
-
-点线组合：
-
-
-
-
-::
-
-    import matplotlib.lines as mlines
-    import matplotlib.pyplot as plt
-
-    blue_line = mlines.Line2D([], [], color='blue', marker='*',
-                          markersize=15, label='Blue stars')
-    plt.legend(handles=[blue_line])
-
-    plt.show()
-
-.. image:: data:image/png;base64,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-指定 legend 的位置
--------------------------
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-*bbox_to_anchor* 关键词可以指定 *legend* 放置的位置，例如放到图像的右上角：
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-
-
-::
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-    plt.plot([1,2,3], label="test1")
-    plt.plot([3,2,1], label="test2")
-    plt.legend(bbox_to_anchor=(1, 1),
-               ^4$bbox_transform=plt.gcf().transFigure)
-
-    plt.show()
-
-
-.. image:: data:image/png;base64,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
-
-
-更复杂的用法：
-
-
-
-
-::
-
-    plt.subplot(211)
-    plt.plot([1,2,3], label="test1")
-    plt.plot([3,2,1], label="test2")
-    # Place a legend above this legend, expanding itself to
-    # fully use the given bounding box.
-    plt.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3,
-               ^4$ncol=2, mode="expand", borderaxespad=0.)
-
-    plt.subplot(223)
-    plt.plot([1,2,3], label="test1")
-    plt.plot([3,2,1], label="test2")
-    # Place a legend to the right of this smaller figure.
-    plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
-
-    plt.show()
-
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-.. image:: data:image/png;base64,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
-
-
-同一个 Axes 中的多个 legend
------------------------------------
-
-
-可以这样添加多个 legend：
-
-
-
-
-
-::
-
-    line1, = plt.plot([1,2,3], label="Line 1", linestyle='--')
-    line2, = plt.plot([3,2,1], label="Line 2", linewidth=4)
-
-    # Create a legend for the first line.
-    first_legend = plt.legend(handles=[line1], loc=1)
-
-    # Add the legend manually to the current Axes.
-    ax = plt.gca().add_artist(first_legend)
-
-    # Create another legend for the second line.
-    plt.legend(handles=[line2], loc=4)
-
-    plt.show()
-
-
-
-.. image:: data:image/png;base64,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
-
-
-
-其中 *loc* 参数可以取 0-10 或者 字符串，表示放置的位置：
-
-
-::
-
-    loc string	    loc code
-    'best'	        0
-    'upper right'	1
-    'upper left'	2
-    'lower left'	3
-    'lower right'	4
-    'right'	        5
-    'center left'	6
-    'center right'	7
-    'lower center'	8
-    'upper center'	9
-    'center'	        10
-
-
-
-更多用法
--------------
-
-
-多个 *handle* 可以通过括号组合在一个 *entry* 中：
-
-
-
-
-::
-
-    from numpy.random import randn
-
-    z = randn(10)
-
-    red_dot, = plt.plot(z, "ro", markersize=15)
-    # Put a white cross over some of the data.
-    white_cross, = plt.plot(z[:5], "w+", markeredgewidth=3, markersize=15)
-
-    plt.legend([red_dot, (red_dot, white_cross)], ["Attr A", "Attr A+B"])
-
-    plt.show()
-
-
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-.. image:: 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
-
-
-自定义 *handle*：
-
-
-
-
-::
-
-    import matplotlib.pyplot as plt
-    import matplotlib.patches as mpatches
-
-    class AnyObject(object):
-        ^4$pass
-
-    class AnyObjectHandler(object):
-        ^4$def legend_artist(self, legend, orig_handle, fontsize, handlebox):
-            ^4$^4$x0, y0 = handlebox.xdescent, handlebox.ydescent
-            ^4$^4$width, height = handlebox.width, handlebox.height
-            ^4$^4$patch = mpatches.Rectangle([x0, y0], width, height, facecolor='red',
-                                       ^4$^4$^4$edgecolor='black', hatch='xx', lw=3,
-                                       ^4$^4$^4$transform=handlebox.get_transform())
-            ^4$^4$handlebox.add_artist(patch)
-            ^4$^4$return patch
-
-    plt.legend([AnyObject()], ['My first handler'],
-               ^4$handler_map={AnyObject: AnyObjectHandler()})
-
-    plt.show()
-
-
-.. image:: data:image/png;base64,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-椭圆：
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-::
-
-    from matplotlib.legend_handler import HandlerPatch
-    import matplotlib.pyplot as plt
-    import matplotlib.patches as mpatches
-
-
-    class HandlerEllipse(HandlerPatch):
-        ^4$def create_artists(self, legend, orig_handle,
-                           ^4$^4$^4$xdescent, ydescent, width, height, fontsize,     trans):
-            ^4$^4$center = 0.5 * width - 0.5 * xdescent, 0.5 * height - 0.5 *     ydescent
-            ^4$^4$p = mpatches.Ellipse(xy=center, width=width + xdescent,
-                                  ^4$^4$^4$height=height + ydescent)
-            ^4$^4$self.update_prop(p, orig_handle, legend)
-            ^4$^4$p.set_transform(trans)
-            ^4$^4$return [p]
-
-
-    c = mpatches.Circle((0.5, 0.5), 0.25, facecolor="green",
-                    ^4$edgecolor="red", linewidth=3)
-    plt.gca().add_patch(c)
-
-    plt.legend([c], ["An ellipse, not a rectangle"],
-                ^4$handler_map={mpatches.Circle: HandlerEllipse()})
-
-    plt.show()
-
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-.. image:: data:image/png;base64,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
-
-
-作者 & 更新时间
-------------------------------------
-作者:`李金  <lijinwithyou@gmail.com>`
-
-提交: 2017/12/14
-
-
diff --git a/course/source/aipy-numpy/cn/8figures-subplots-axes-ticks.rst b/course/source/aipy-numpy/cn/8figures-subplots-axes-ticks.rst
deleted file mode 100644
index 2214b67..0000000
--- a/course/source/aipy-numpy/cn/8figures-subplots-axes-ticks.rst
+++ /dev/null
@@ -1,180 +0,0 @@
-
-figures, subplots, axes 和 ticks 对象
-==============================================
-
-figures, axes 和 ticks 的关系
-----------------------------------------
-
-
-这些对象的关系可以用下面的图来表示：
-
-
-示例图像：
-
-
-<img src="./artists_figure.png" width = "600" height = "400" alt="图1" align=left />
-
-
-具体结构：
-<img src="./artists_tree.png" width = "600" height = "400" alt="图2" align=left />
-
-
-figure 对象
----------------
-
-
-*figure* 对象是最外层的绘图单位，默认是以 *1* 开始编号（**MATLAB** 风格，*Figure 1*, *Figure 2*,*...*），可以用 *plt.figure()* 产生一幅图像，除了默认参数外，可以指定的参数有：
-
-
-- num - 编号
-- figsize - 图像大小
-- dpi - 分辨率
-- facecolor - 背景色
-- edgecolor - 边界颜色
-- frameon - 边框
-
-
-这些属性也可以通过 *Figure* 对象的 *set_xxx* 方法来改变。
-
-
-subplot 和 axes 对象
---------------------------
-
-subplot
--------------
-
-
-*subplot* 主要是使用网格排列子图：
-
-
-
-
-::
-
-    %pylab inline
-
-    subplot(2,1,1)
-    xticks([]), yticks([])
-    text(0.5,0.5, 'subplot(2,1,1)',ha='center',va='center',size=24,alpha=.5)
-
-    subplot(2,1,2)
-    xticks([]), yticks([])
-    text(0.5,0.5, 'subplot(2,1,2)',ha='center',va='center',size=24,alpha=.5)
-
-    show()
-
-
-Populating the interactive namespace from numpy and matplotlib
-
-
-.. image:: 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-.. image:: data:image/png;base64,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
-
-
-
-更高级的可以用 *gridspec* 来绘图：
-
-
-
-
-::
-
-    import matplotlib.gridspec as gridspec
-
-    G = gridspec.GridSpec(3, 3)
-
-    axes_1 = subplot(G[0, :])
-    xticks([]), yticks([])
-    text(0.5,0.5, 'Axes 1',ha='center',va='center',size=24,alpha=.5)
-
-    axes_2 = subplot(G[1,:-1])
-    xticks([]), yticks([])
-    text(0.5,0.5, 'Axes 2',ha='center',va='center',size=24,alpha=.5)
-
-    axes_3 = subplot(G[1:, -1])
-    xticks([]), yticks([])
-    text(0.5,0.5, 'Axes 3',ha='center',va='center',size=24,alpha=.5)
-
-    axes_4 = subplot(G[-1,0])
-    xticks([]), yticks([])
-    text(0.5,0.5, 'Axes 4',ha='center',va='center',size=24,alpha=.5)
-
-    axes_5 = subplot(G[-1,-2])
-    xticks([]), yticks([])
-    text(0.5,0.5, 'Axes 5',ha='center',va='center',size=24,alpha=.5)
-
-    show()
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-.. image:: 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
-
-
-
-axes 对象
--------------
-
-
-*subplot* 返回的是 *Axes* 对象，但是 *Axes* 对象相对于 *subplot* 返回的对象来说要更自由一点。*Axes* 对象可以放置在图像中的任意位置：
-
-
-
-
-::
-
-    axes([0.1,0.1,.8,.8])
-    xticks([]), yticks([])
-    text(0.6,0.6, 'axes    ([0.1,0.1,.8,.8])',ha='center',va='center',size=20,alpha=.5)
-
-    axes([0.2,0.2,.3,.3])
-    xticks([]), yticks([])
-    text(0.5,0.5, 'axes    ([0.2,0.2,.3,.3])',ha='center',va='center',size=16,alpha=.5)
-
-    show()
-
-.. image:: 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-    axes([0.4,0.4,.5,.5])
-    xticks([]), yticks([])
-    text(0.1,0.1, 'axes    ([0.4,0.4,.5,.5])',ha='left',va='center',size=16,alpha=.5)
-
-    show()
-
-
-.. image:: 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
-
-
-后面的 Axes 对象会覆盖前面的内容。
-
-
-ticks 对象
----------------
-
-
-*ticks* 用来注释轴的内容，我们可以通过控制它的属性来决定在哪里显示轴、轴的内容是什么等等。
-
-
-作者 & 更新时间
-------------------------------------
-作者:`李金  <lijinwithyou@gmail.com>`
-
-提交: 2017/12/14
diff --git a/course/source/aipy-numpy/cn/9dont-trust-default.rst b/course/source/aipy-numpy/cn/9dont-trust-default.rst
deleted file mode 100644
index 4db7c74..0000000
--- a/course/source/aipy-numpy/cn/9dont-trust-default.rst
+++ /dev/null
@@ -1,641 +0,0 @@
-
-不要迷信默认设置
----------------------
-
-
-导入相关的包：
-
-
-
-
-::
-
-    import numpy as np
-    import matplotlib.pyplot as plt
-
-
-生成三角函数：
-
-
-
-
-::
-
-    x = np.linspace(-np.pi, np.pi)
-    c, s = np.cos(x), np.sin(x)
-
-
-默认绘图
-------------
-
-
-
-
-::
-
-    %matplotlib inline
-
-    # 画图
-    p = plt.plot(x,c)
-    p = plt.plot(x,s)
-
-    # 在脚本中需要加上这句才会显示图像
-    plt.show()
-
-
-<button>Run ...</button>
-
-
-.. image::data:image/png;base64,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-默认效果如图所示，我们可以修改默认的属性来得到更漂亮的结果。
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-图
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-图像以 *Figure #* 为窗口标题，并且数字从 *1* 开始，*figure()* 函数的主要参数如下：
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-
-::
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-    参数	        默认值	                 描述
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-    num	           1	                  图号
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-    figsize        figure.figsize         图大小（宽，高）（单位英寸）
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-    dpi	           figure.dpi	          分辨率（每英寸所打印的点数）
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-    facecolor	   figure.facecolor       背景颜色
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-    edgecolor	   figure.edgecolor       边界颜色
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-    frameon        True                   是否显示图框架
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-
-::
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-    # 设置图像大小
-    f = plt.figure(figsize=(10,6), dpi=80)
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-    # 画图
-    p = plt.plot(x,c)
-    p = plt.plot(x,s)
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-    # 在脚本中需要加上这句才会显示图像
-    plt.show()
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-设置线条颜色，粗细，类型
---------------------------------
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-首先，我们使用 *figure()* 函数来创建一幅新图像，并且指定它的大小，使得长宽比更合适。
-然后，我们使用 *color*, *linewidth*, *linestyle* 参数，指定曲线的颜色，粗细，类型：
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-::
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-    # 设置图像大小
-    f = plt.figure(figsize=(10,6), dpi=80)
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-    # 画图，指定颜色，线宽，类型
-    p = plt.plot(x, c, color="blue", linewidth=2.5, linestyle="-")
-    p = plt.plot(x, s, color="red",  linewidth=2.5, linestyle="-")
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-    # 在脚本中需要加上这句才会显示图像
-    # plt.show()
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-<button>Run ...</button>
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-.. image:: 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
-
-
-
-也可以像 **Matlab** 中一样使用格式字符来修改参数：
-
-
-
-表示颜色的字符参数有：
-
-
-::
-
-    字符	颜色
-   ‘b’	蓝色，blue
-   ‘g’	绿色，green
-   ‘r’	红色，red
-   ‘c’	青色，cyan
-   ‘m’	品红，magenta
-   ‘y’	黄色，yellow
-   ‘k’	黑色，black
-   ‘w’	白色，white
-
-
-
-表示类型的字符参数有：
-
-+------------+----------+---------------+----------------+
-|   字符     | 	  类型| 	        字符| 	       类型| 
-+------------+----------+---------------+----------------+
-|    '-'     |	    实线|	          '--'|	           虚线|
-+------------+----------+---------------+----------------+
-|   '-.'     |	  虚点线|	          ','|            点线|
-+------------+----------+---------------+----------------+
-|    '.'     |	     点 |	        ','|	      像素点|
-+------------+----------+---------------+----------------+
-|    'o'     |	    圆点|	           'v'|	       下三角点|
-+------------+----------+---------------+----------------+
-|    '^'     |  上三角点|	         '<'|        左三角点|
-+------------+----------+---------------+----------------+
-|    '>'     |  右三角点|	         '1'|	     下三叉点|
-+------------+----------+---------------+----------------+
-|    '2'     |	上三叉点|	         '3'|        左三叉点|
-+------------+----------+---------------+----------------+
-|    '4'     |  右三叉点|	         's'|	       正方点|
-+------------+----------+---------------+----------------+
-|    'p'     |	  五角点|	          '*'|	        星形点|
-+------------+----------+---------------+----------------+
-|    'h'     | 六边形点1|	         'H'|       六边形点2|
-+------------+----------+---------------+----------------+
-|    '+'     |    加号点|	          'x'|	        乘号点|
-+------------+----------+---------------+----------------+
-|    'D'     | 实心菱形点|	        'd'|	    瘦菱形点|
-+------------+----------+---------------+----------------+
-|   '_'	     |  横线点  |               |                |
-+------------+----------+---------------+----------------+
-
-
-		
-
-
-::
-
-    # 设置图像大小
-    f = plt.figure(figsize=(10,6), dpi=80)
-
-    # 画图，指定颜色，线宽，类型
-    p = plt.plot(x, c, 'b-', 
-             ^4$x, s, 'r-', linewidth=2.5)
-
-    # 在脚本中需要加上这句才会显示图像
-    # plt.show()
-
-<button>Run ...</button>
-
-
-.. image:: data:image/png;base64,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
-
-
-
-
-设置横轴纵轴的显示区域
-------------------------------
-
-
-我们希望将坐标轴的显示区域放大一些，这样可以看到所有的点，可以使用 *plt* 中的 *xlim* 和 *ylim* 来设置：
-
-
-
-
-::
-
-    # 设置图像大小
-    p = plt.figure(figsize=(10,6), dpi=80)
-
-    # 画图，指定颜色，线宽，类型
-    p = plt.plot(x, c, 'b-', 
-             ^4$x, s, 'r-', linewidth=2.5)
-
-    ####################################################################
-
-    # 设置显示范围
-    p = plt.xlim(x.min() * 1.1, x.max() * 1.1)
-    p = plt.ylim(c.min() * 1.1, c.max() * 1.1)
-
-    ####################################################################
-
-   # 在脚本中需要加上这句才会显示图像
-   # plt.show()
-
-
-
-<button>Run ...</button>
-
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-.. image:: 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-
-
-设置刻度
-------------
-
-
-对于三教函数来说，我们希望将 x 轴的刻度设为与 $\pi$ 有关的点，可以使用 plt 中的 xticks 和 yticks 函数，将需要的刻度传入：
-
-
-
-
-
-::
-
-    # 设置图像大小
-    f = plt.figure(figsize=(10,6), dpi=80)
-
-    # 画图，指定颜色，线宽，类型
-    p = plt.plot(x, c, 'b-', 
-             ^4$x, s, 'r-', linewidth=2.5)
-
-    # 设置显示范围
-    plt.xlim(x.min() * 1.1, x.max() * 1.1)
-    plt.ylim(c.min() * 1.1, c.max() * 1.1)
-
-    ####################################################################
-
-    # 设置刻度
-    p = plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi])
-    p = plt.yticks([-1, 0, 1])
-
-    #####################################################################
-
-    # 在脚本中需要加上这句才会显示图像
-    # plt.show()
-
-
-<button>Run ...</button>
-
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-.. image:: 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
-
-
-
-设定 x 轴 y 轴标题
---------------------------
-
-
-我们想让刻度的位置显示的是含有 $\pi$ 的标识而不是浮点数，可以在 xticks 中传入第二组参数，这组参数代表对应刻度的显示标识。这里，我们使用 latex 的语法来显示特殊符号（使用 $$ 包围的部分）：
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-
-
-
-::
-
-    # 设置图像大小
-    f = plt.figure(figsize=(10,6), dpi=80)
-
-    # 画图，指定颜色，线宽，类型
-    p = plt.plot(x, c, 'b-', 
-             ^4$x, s, 'r-', linewidth=2.5)
-
-    # 设置显示范围
-    plt.xlim(x.min() * 1.1, x.max() * 1.1)
-    plt.ylim(c.min() * 1.1, c.max() * 1.1)
-
-    # 设置刻度及其标识
-    p = plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi], 
-                   ^4$['$-\pi$', '$-\pi/2$', '$0$', '$\pi/2$', '$\pi$'],     fontsize ='xx-large')
-    p = plt.yticks([-1, 0, 1], 
-                   ^4$['$-1$', '$0$', '$+1$'], fontsize ='xx-large')
-
-    # 在脚本中需要加上这句才会显示图像
-    # plt.show()
-
-<button>Run ...</button>
-
-
-.. image::data:image/png;base64,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
-
-
-
-移动坐标轴的位置
-----------------------
-
-
-现在坐标轴的位置是在边界上，而且有上下左右四条，我们现在想将下面和左边的两条移动到中间，并将右边和上面的两条去掉：
-
-
-
-
-::
-
-    # 设置图像大小
-    f = plt.figure(figsize=(10,6), dpi=80)
-
-    # 画图，指定颜色，线宽，类型
-    plt.plot(x, c, 'b-', 
-             ^4$x, s, 'r-', linewidth=2.5)
-
-    # 设置显示范围
-    plt.xlim(x.min() * 1.1, x.max() * 1.1)
-    plt.ylim(c.min() * 1.1, c.max() * 1.1)
-
-    # 得到轴的句柄
-    ax = plt.gca()
-    # ax.spines参数表示四个坐标轴线
-    # 将右边和上边的颜色设为透明
-    ax.spines['right'].set_color('none')
-    ax.spines['top'].set_color('none')
-
-    ####################################################################
-
-    # 将 x 轴的刻度设置在下面的坐标轴上
-    ax.xaxis.set_ticks_position('bottom')
-    # 设置位置
-    ax.spines['bottom'].set_position(('data',0))
-
-    # 将 y 轴的刻度设置在左边的坐标轴上
-    ax.yaxis.set_ticks_position('left')
-    # 设置位置
-    ax.spines['left'].set_position(('data',0))
-
-    #####################################################################
-
-    # 设置刻度及其标识
-    p = plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi], 
-               ^4$['$-\pi$', '$-\pi/2$', '$0$', '$\pi/2$', '$\pi$'],     fontsize ='xx-large')
-    p = plt.yticks([-1, 0, 1], 
-               ^4$['$-1$', '$0$', '$+1$'], fontsize ='xx-large')
-
-    # 在脚本中需要加上这句才会显示图像
-    # plt.show()
-
-
-<button>Run ...</button>
-
-
-.. image::data:image/png;base64,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
-
-
-加入图例
---------------
-
-
-使用 legend() 加入图例：
-
-
-
-
-
-::
-
-    # 设置图像大小
-    plt.figure(figsize=(10,6), dpi=80)
-
-    # 画图，指定颜色，线宽，类型
-    plt.plot(x, c, 'b-', 
-             ^4$x, s, 'r-', linewidth=2.5)
-
-    # 设置显示范围
-    plt.xlim(x.min() * 1.1, x.max() * 1.1)
-    plt.ylim(c.min() * 1.1, c.max() * 1.1)
-
-    # 得到画图的句柄
-    ax = plt.gca()
-
-    # ax.spines参数表示四个坐标轴线
-    # 将右边和上边的颜色设为透明
-    ax.spines['right'].set_color('none')
-    ax.spines['top'].set_color('none')
-
-    # 将 x 轴的刻度设置在下面的坐标轴上
-    ax.xaxis.set_ticks_position('bottom')
-    # 设置位置
-    ax.spines['bottom'].set_position(('data',0))
-
-    # 将 y 轴的刻度设置在左边的坐标轴上
-    ax.yaxis.set_ticks_position('left')
-    # 设置位置
-    ax.spines['left'].set_position(('data',0))
-
-    # 设置刻度及其标识
-    plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi], 
-               ^4$['$-\pi$', '$-\pi/2$', '$0$', '$\pi/2$', '$\pi$'],     fontsize ='xx-large')
-    plt.yticks([-1, 0, 1], 
-               ^4$['$-1$', '$0$', '$+1$'], fontsize ='xx-large')
-
-    #####################################################################
-
-    # 加入图例，frameon表示去掉图例周围的边框
-    l = plt.legend(['cosine', 'sine'], loc='upper left', frameon=False)
-    ####################################################################
-
-    # 在脚本中需要加上这句才会显示图像
-    # plt.show()
-
-
-<button>Run ...</button>
-
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-.. image:: 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
-
-
-注释特殊点
-----------------
-
-
-我们可以使用 *anotate* 函数来注释特殊的点，假设我们要显示的点是 $2\pi/3$：
-
-
-
-
-::
-
-    # 设置图像大小
-    plt.figure(figsize=(10,6), dpi=80)
-
-    # 画图，指定颜色，线宽，类型
-    plt.plot(x, c, 'b-', 
-             ^4$x, s, 'r-', linewidth=2.5)
-
-    # 设置显示范围
-    plt.xlim(x.min() * 1.1, x.max() * 1.1)
-    plt.ylim(c.min() * 1.1, c.max() * 1.1)
-
-    # 得到画图的句柄
-     ax = plt.gca()
-
-    # ax.spines参数表示四个坐标轴线
-    # 将右边和上边的颜色设为透明
-    ax.spines['right'].set_color('none')
-    ax.spines['top'].set_color('none')
-
-    # 将 x 轴的刻度设置在下面的坐标轴上
-    ax.xaxis.set_ticks_position('bottom')
-    # 设置位置
-    ax.spines['bottom'].set_position(('data',0))
-
-    # 将 y 轴的刻度设置在左边的坐标轴上
-    ax.yaxis.set_ticks_position('left')
-    # 设置位置
-    ax.spines['left'].set_position(('data',0))
-
-    # 设置刻度及其标识
-    plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi], 
-               ^4$['$-\pi$', '$-\pi/2$', '$0$', '$\pi/2$', '$\pi$'],     fontsize ='xx-large')
-    plt.yticks([-1, 0, 1], 
-               ^4$['$-1$', '$0$', '$+1$'], fontsize ='xx-large')
-
-    # 加入图例，frameon表示图例周围是否需要边框
-    l = plt.legend(['cosine', 'sine'], loc='upper left', frameon=False)
-
-    ###################################################################
-
-    # 数据点
-    t = 2 * np.pi / 3
-
-    # 蓝色虚线
-    plt.plot([t,t],[0,np.cos(t)], color ='blue', linewidth=2.5,     linestyle="--")
-
-    # 该点处的 cos 值
-    plt.scatter([t,],[np.cos(t),], 50, color ='blue')
-
-    # 在对应的点显示文本
-    plt.annotate(r'$\sin(\frac{2\pi}{3})=\frac{\sqrt{3}}{2}$', # 文本
-                 ^4$xy=(t, np.sin(t)), # 数据点坐标位置
-                 ^4$xycoords='data',   # 坐标相对于数据
-                 ^4$xytext=(+10, +30), # 文本位置坐标
-                 ^4$textcoords='offset points', # 坐标相对于数据点的坐标
-                 ^4$fontsize=16,       # 文本大小
-                 ^4$arrowprops=dict(arrowstyle="->",     connectionstyle="arc3,rad=.2")) # 箭头
-
-    # 红色虚线
-    p = plt.plot([t,t],[0,np.sin(t)], color ='red', linewidth=2.5,     linestyle="--")
-
-    # 该点处的 sin 值
-    p = plt.scatter([t,],[np.sin(t),], 50, color ='red')
-
-    # 显示文本
-    p = plt.annotate(r'$\cos(\frac{2\pi}{3})=-\frac{1}{2}$',
-                 ^4$xy=(t, np.cos(t)), xycoords='data',
-                 ^4$xytext=(-90, -50), textcoords='offset points',     fontsize=16,
-                 ^4$arrowprops=dict(arrowstyle="->",     connectionstyle="arc3,rad=.2"))
-
-
-    ##################################################################
-
-    # 在脚本中需要加上这句才会显示图像
-    # plt.show()
-
-
-<button>Run ...</button>
-
-
-.. image:: 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
-
-
-最后调整
--------------
-
-
-调整刻度值的大小，并让其显示在曲线上方。
-
-
-
-
-::
-
-    # 设置图像大小
-    plt.figure(figsize=(10,6), dpi=80)
-
-    # 画图，指定颜色，线宽，类型
-    plt.plot(x, c, 'b-', 
-             ^4$x, s, 'r-', linewidth=2.5)
-
-    # 设置显示范围
-    plt.xlim(x.min() * 1.1, x.max() * 1.1)
-    plt.ylim(c.min() * 1.1, c.max() * 1.1)
-
-    # 得到画图的句柄
-    ax = plt.gca()
-
-    # ax.spines参数表示四个坐标轴线
-    # 将右边和上边的颜色设为透明
-    ax.spines['right'].set_color('none')
-    ax.spines['top'].set_color('none')
-
-    # 将 x 轴的刻度设置在下面的坐标轴上
-    ax.xaxis.set_ticks_position('bottom')
-    # 设置位置
-    ax.spines['bottom'].set_position(('data',0))
-
-    # 将 y 轴的刻度设置在左边的坐标轴上
-    ax.yaxis.set_ticks_position('left')
-    # 设置位置
-    ax.spines['left'].set_position(('data',0))
-
-    # 设置刻度及其标识
-    plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi], 
-               ^4$['$-\pi$', '$-\pi/2$', '$0$', '$\pi/2$', '$\pi$'],     fontsize ='xx-large')
-    plt.yticks([-1, 0, 1], 
-               ^4$['$-1$', '$0$', '$+1$'], fontsize ='xx-large')
-
-    # 加入图例，frameon表示图例周围是否需要边框
-    l = plt.legend(['cosine', 'sine'], loc='upper left', frameon=False)
-
-    # 数据点
-    t = 2 * np.pi / 3
-
-    # 蓝色虚线
-    plt.plot([t,t],[0,np.cos(t)], color ='blue', linewidth=2.5,     linestyle="--")
-
-    # 该点处的 cos 值
-    plt.scatter([t,],[np.cos(t),], 50, color ='blue')
-
-    # 在对应的点显示文本
-    plt.annotate(r'$\sin(\frac{2\pi}{3})=\frac{\sqrt{3}}{2}$', # 文本
-                 ^4$xy=(t, np.sin(t)), # 数据点坐标位置
-                 ^4$xycoords='data',   # 坐标相对于数据
-                 ^4$xytext=(+10, +30), # 文本位置坐标
-                 ^4$textcoords='offset points', # 坐标相对于数据点的坐标
-                 ^4$fontsize=16,       # 文本大小
-                 ^4$arrowprops=dict(arrowstyle="->",     connectionstyle="arc3,rad=.2")) # 箭头
-
-    # 红色虚线
-    p = plt.plot([t,t],[0,np.sin(t)], color ='red', linewidth=2.5,     linestyle="--")
-
-    # 该点处的 sin 值
-    p = plt.scatter([t,],[np.sin(t),], 50, color ='red')
-
-    # 显示文本
-    p = plt.annotate(r'$\cos(\frac{2\pi}{3})=-\frac{1}{2}$',
-                 ^4$xy=(t, np.cos(t)), xycoords='data',
-                 ^4$xytext=(-90, -50), textcoords='offset points',     fontsize=16,
-                 ^4$arrowprops=dict(arrowstyle="->",     connectionstyle="arc3,rad=.2"))
-
-
-    ####################################################################
-
-    for label in ax.get_xticklabels() + ax.get_yticklabels():
-        ^4$label.set_fontsize(16)
-        ^4$label.set_bbox(dict(facecolor='white', edgecolor='None',     alpha=0.65 ))
-
-    ####################################################################
-
-    # 在脚本中需要加上这句才会显示图像
-    # plt.show()
-
-<button>Run ...</button>
-
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-.. image:: 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
-
-
-
-
-
-作者 & 更新时间
-------------------------------------
-作者:`李金  <lijinwithyou@gmail.com>`
-
-提交: 2017/12/14
-
diff --git a/course/source/aipy-numpy/index.rst b/course/source/aipy-numpy/index.rst
deleted file mode 100644
index 5bd2d46..0000000
--- a/course/source/aipy-numpy/index.rst
+++ /dev/null
@@ -1,19 +0,0 @@
-Numpy Programming
-=======================
-The courses are still in the planning. Welcome to reward us. 
-
-1. **Primary course**
-
-If the total number of people who reward us is more than 100, we promise to complete the primary course within 1 week, so that, after finishing your study, you can master the basic concepts and write simple scripts.

-
-2. **Intermediate course**
-
-If the total number of people who reward us is more than 500, we will launch the intermediate course within 2 weeks, so that, after finishing your study, you will be familiar with the knowledge and framework of the course and apply it to practical development.
-
-3. **Advanced course**
-
-If the total number of people who reward us is more than 2,000, we will launch the advanced course in a month, so that, after finishing your study, you will have a better understanding of the principles of the framework and be able to do advanced development.
-
-
-**The primary course is free to all, and the intermediate and advanced courses are only open to those who have rewarded us.**
-
diff --git a/course/source/aipy-pandas/index.rst b/course/source/aipy-pandas/index.rst
deleted file mode 100644
index 96bdd68..0000000
--- a/course/source/aipy-pandas/index.rst
+++ /dev/null
@@ -1,19 +0,0 @@
-Pandas Programming
-=======================
-The courses are still in the planning. Welcome to reward us. 
-
-1. **Primary course**
-
-If the total number of people who reward us is more than 100, we promise to complete the primary course within 1 week, so that, after finishing your study, you can master the basic concepts and write simple scripts.

-
-2. **Intermediate course**
-
-If the total number of people who reward us is more than 500, we will launch the intermediate course within 2 weeks, so that, after finishing your study, you will be familiar with the knowledge and framework of the course and apply it to practical development.
-
-3. **Advanced course**
-
-If the total number of people who reward us is more than 2,000, we will launch the advanced course in a month, so that, after finishing your study, you will have a better understanding of the principles of the framework and be able to do advanced development.
-
-
-**The primary course is free to all, and the intermediate and advanced courses are only open to those who have rewarded us.**
-
diff --git a/course/source/aipy-scikit-learn/index.rst b/course/source/aipy-scikit-learn/index.rst
deleted file mode 100644
index c0f29bc..0000000
--- a/course/source/aipy-scikit-learn/index.rst
+++ /dev/null
@@ -1,19 +0,0 @@
-Scikit Learn Programming
-============================
-The courses are still in the planning. Welcome to reward us. 
-
-1. **Primary course**
-
-If the total number of people who reward us is more than 100, we promise to complete the primary course within 1 week, so that, after finishing your study, you can master the basic concepts and write simple scripts.

-
-2. **Intermediate course**
-
-If the total number of people who reward us is more than 500, we will launch the intermediate course within 2 weeks, so that, after finishing your study, you will be familiar with the knowledge and framework of the course and apply it to practical development.
-
-3. **Advanced course**
-
-If the total number of people who reward us is more than 2,000, we will launch the advanced course in a month, so that, after finishing your study, you will have a better understanding of the principles of the framework and be able to do advanced development.
-
-
-**The primary course is free to all, and the intermediate and advanced courses are only open to those who have rewarded us.**
-
diff --git a/course/source/aipy-scipy/cn/10linear-algebra.rst b/course/source/aipy-scipy/cn/10linear-algebra.rst
deleted file mode 100644
index 086a061..0000000
--- a/course/source/aipy-scipy/cn/10linear-algebra.rst
+++ /dev/null
@@ -1,1163 +0,0 @@
-线性代数
---------------
-
-numpy 和 scipy 中，负责进行线性代数部分计算的模块叫做 linalg。
-
-
-
-
-::
-
-    import numpy as np
-    import numpy.linalg
-    import scipy as sp
-    import scipy.linalg
-    import matplotlib.pyplot as plt
-    from scipy import linalg
-
-
-%matplotlib inline
-
-numpy.linalg VS scipy.linalg
-------------------------------
-
-一方面scipy.linalg 包含 numpy.linalg 中的所有函数，同时还包含了很多 numpy.linalg 中没有的函数。
-
-另一方面，scipy.linalg 能够保证这些函数使用 BLAS/LAPACK 加速，而 numpy.linalg 中这些加速是可选的。
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-因此，在使用时，我们一般使用 scipy.linalg 而不是 numpy.linalg。
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-我们可以简单看看两个模块的差异：
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-
-
-::
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-    print "number of items in numpy.linalg:", len(dir(numpy.linalg))
-    print "number of items in scipy.linalg:", len(dir(scipy.linalg))
-
-
-结果:
-::
-
-    number of items in numpy.linalg: 36
-
-    number of items in scipy.linalg: 115
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-
-
-numpy.matrix VS 2D numpy.ndarray
------------------------------------
-
-
-线性代数的基本操作对象是矩阵，而矩阵的表示方法主要有两种：numpy.matrix 和 2D numpy.ndarray。
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-
-numpy.matrix
----------------------
-
-
-numpy.matrix 是一个矩阵类，提供了一些方便的矩阵操作：
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-- 支持类似 MATLAB 创建矩阵的语法
-- 矩阵乘法默认用 * 号
-- .I 表示逆，.T 表示转置
-
-
-可以用 mat 或者 matrix 来产生矩阵：
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-
-
-
-::
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-    A = np.mat("[1, 2; 3, 4]")
-    print repr(A)
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-    A = np.matrix("[1, 2; 3, 4]")
-    print repr(A)
-
-
-结果:
-::
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-    matrix([[1, 2],
-
-        [3, 4]])
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-    matrix([[1, 2],
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-            [3, 4]])
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-
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-转置和逆：
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-
-
-
-::
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-    print repr(A.I)
-    print repr(A.T)
-
-
-结果:
-::
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-    matrix([[-2. ,  1. ],
-
-        [ 1.5, -0.5]])
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-    matrix([[1, 3],
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-            [2, 4]])
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-矩阵乘法：
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-::
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-    b = np.mat('[5; 6]')
-    print repr(A * b)
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-
-结果:
-::
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-    matrix([[17],
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-        [39]])
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-2 维 numpy.ndarray
--------------------------
-
-
-虽然 numpy.matrix 有着上面的好处，但是一般不建议使用，而是用 2 维 numpy.ndarray 对象替代，这样可以避免一些不必要的困惑。
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-
-我们可以使用 array 复现上面的操作：
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-::
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-    A = np.array([[1,2], [3,4]])
-    print repr(A)
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-结果:
-::
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-    array([[1, 2],
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-       [3, 4]])
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-逆和转置：
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-
-
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-::
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-    print repr(linalg.inv(A))
-    print repr(A.T)
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-
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-结果:
-::
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-    array([[-2. ,  1. ],
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-       [ 1.5, -0.5]])
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-    array([[1, 3],
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-           [2, 4]])
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-矩阵乘法：
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-
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-::
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-    b = np.array([5, 6])
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-    print repr(A.dot(b))
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-结果:
-::
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-    array([17, 39])
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-
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-普通乘法：
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-
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-::
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-    print repr(A * b)
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-结果:
-::
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-    array([[ 5, 12],
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-       [15, 24]])
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-scipy.linalg 的操作可以作用到两种类型的对象上，没有区别。
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-
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-基本操作
------------
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-**求逆**
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-矩阵 $\mathbf{A}$ 的逆 $\mathbf{B}$ 满足：$\mathbf{BA}=\mathbf{AB}=I$，记作 $\mathbf{B} = \mathbf{A}^{-1}$。
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-
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-事实上，我们已经见过求逆的操作，linalg.inv 可以求一个可逆矩阵的逆：
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-::
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-    A = np.array([[1,2],[3,4]])
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-    print linalg.inv(A)
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-    print A.dot(scipy.linalg.inv(A))
-
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-结果:
-::
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-    [[-2.   1. ]
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-    [ 1.5 -0.5]]
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-    [[  1.00000000e+00   0.00000000e+00]
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-    [  8.88178420e-16   1.00000000e+00]]
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-
-求解线性方程组
----------------------
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-例如，下列方程组 $$
-
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-\begin{eqnarray*} 
-x + 3y + 5z &amp; = &amp; 10 \\
-2x + 5y + z &amp; = &amp; 8  \\
-2x + 3y + 8z &amp; = &amp; 3
-\end{eqnarray*}
-
-
-
-$$ 的解为： $$
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-\begin{split}\left[\begin{array}{c} x\\ y\\ z\end{array}\right]=\left[\begin{array}{ccc} 1 &amp; 3 &amp; 5\\ 2 &amp; 5 &amp; 1\\ 2 &amp; 3 &amp; 8\end{array}\right]^{-1}\left[\begin{array}{c} 10\\ 8\\ 3\end{array}\right]=\frac{1}{25}\left[\begin{array}{c} -232\\ 129\\ 19\end{array}\right]=\left[\begin{array}{c} -9.28\\ 5.16\\ 0.76\end{array}\right].\end{split}
-$$
-
-
-
-我们可以使用 linalg.solve 求解方程组，也可以先求逆再相乘，两者中 solve 比较快。
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-
-
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-::
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-
-    import time
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-    A = np.array([[1, 3, 5],
-                  [2, 5, 1],
-                 [2, 3, 8]])
-    b = np.array([10, 8, 3])
-
-    tic = time.time()
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-    for i in xrange(1000):
-       x = linalg.inv(A).dot(b)
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-    print x
-    print A.dot(x)-b
-    print "inv and dot: {} s".format(time.time() - tic)
-
-    tic = time.time()
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-    for i in xrange(1000):
-        x = linalg.solve(A, b)
-
-    print x
-    print A.dot(x)-b
-    print "solve: {} s".format(time.time() - tic)
-
-
-
-结果:
-::
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-    [-9.28  5.16  0.76]
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-    [  0.00000000e+00  -1.77635684e-15  -8.88178420e-16]
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-    inv and dot: 0.0353579521179 s
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-    [-9.28  5.16  0.76]
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-    [  0.00000000e+00  -1.77635684e-15  -1.77635684e-15]
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-    solve: 0.0284671783447 s
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-
-计算行列式
------------------
-
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-方阵的行列式为
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-$$
-\left|\mathbf{A}\right|=\sum_{j}\left(-1\right)^{i+j}a_{ij}M_{ij}.
-$$
-
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-其中 $a_{ij}$ 表示 $\mathbf{A}$ 的第 $i$ 行 第 $j$ 列的元素，$M_{ij}$ 表示矩阵 $\mathbf{A}$ 去掉第 $i$ 行 第 $j$ 列的新矩阵的行列式。
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-
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-例如，矩阵 $$
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-\begin{split}\mathbf{A=}\left[\begin{array}{ccc} 1 &amp; 3 &amp; 5\\ 2 &amp; 5 &amp; 1\\ 2 &amp; 3 &amp; 8\end{array}\right]\end{split}
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-
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-$$ 的行列式是： $$
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-\begin{eqnarray*} \left|\mathbf{A}\right| &amp; = &amp; 1\left|\begin{array}{cc} 5 &amp; 1\\ 3 &amp; 8\end{array}\right|-3\left|\begin{array}{cc} 2 &amp; 1\\ 2 &amp; 8\end{array}\right|+5\left|\begin{array}{cc} 2 &amp; 5\\ 2 &amp; 3\end{array}\right|\\  &amp; = &amp; 1\left(5\cdot8-3\cdot1\right)-3\left(2\cdot8-2\cdot1\right)+5\left(2\cdot3-2\cdot5\right)=-25.\end{eqnarray*}
-$$
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-
-
-可以用 linalg.det 计算行列式：
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-
-
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-::
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-    A = np.array([[1, 3, 5],
-                  [2, 5, 1],
-                  [2, 3, 8]])
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-    print linalg.det(A)
-
-
-结果:
-::
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-    -25.0
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-
-
-计算矩阵或向量的模
-----------------------
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-矩阵的模定义如下： $$
-
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-\begin{split}\left\Vert \mathbf{A}\right\Vert =\left\{ \begin{array}{cc} \max_{i}\sum_{j}\left|a_{ij}\right| &amp; \textrm{ord}=\textrm{inf}\\ \min_{i}\sum_{j}\left|a_{ij}\right| &amp; \textrm{ord}=-\textrm{inf}\\ \max_{j}\sum_{i}\left|a_{ij}\right| &amp; \textrm{ord}=1\\ \min_{j}\sum_{i}\left|a_{ij}\right| &amp; \textrm{ord}=-1\\ \max\sigma_{i} &amp; \textrm{ord}=2\\ \min\sigma_{i} &amp; \textrm{ord}=-2\\ \sqrt{\textrm{trace}\left(\mathbf{A}^{H}\mathbf{A}\right)} &amp; \textrm{ord}=\textrm{'fro'}\end{array}\right.\end{split}
-
-
-$$ 其中，$\sigma_i$ 是矩阵的奇异值。
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-
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-向量的模定义如下：
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-$$
-\begin{split}\left\Vert \mathbf{x}\right\Vert =\left\{ \begin{array}{cc} \max\left|x_{i}\right| &amp; \textrm{ord}=\textrm{inf}\\ \min\left|x_{i}\right| &amp; \textrm{ord}=-\textrm{inf}\\ \left(\sum_{i}\left|x_{i}\right|^{\textrm{ord}}\right)^{1/\textrm{ord}} &amp; \left|\textrm{ord}\right|&lt;\infty.\end{array}\right.\end{split}
-$$
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-
-
-linalg.norm 可以计算向量或者矩阵的模：
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-
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-::
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-    A = np.array([[1, 2],
-                  [3, 4]])
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-    print linalg.norm(A)
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-    print linalg.norm(A,'fro') # frobenius norm 默认值
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-    print linalg.norm(A,1) # L1 norm 最大列和
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-    print linalg.norm(A,-1) # L -1 norm 最小列和
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-    print linalg.norm(A,np.inf) # L inf norm 最大行和
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-结果:
-::
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-    5.47722557505
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-    5.47722557505
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-    6
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-    4
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-    7
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-最小二乘解和伪逆
--------------------------
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-**问题描述**
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-
-
-所谓最小二乘问题的定义如下：
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-假设 $y_i$ 与 $\mathbf{x_i}$ 的关系可以用一组系数 $c_j$ 和对应的模型函数 $f_j(\mathbf{x_i})$ 的模型表示：
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-$$
-y_{i}=\sum_{j}c_{j}f_{j}\left(\mathbf{x}_{i}\right)+\epsilon_{i}
-$$
-
-
-其中 $\epsilon_i$ 表示数据的不确定性。最小二乘就是要优化这样一个关于 $c_j$ 的问题：
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-$$
-J\left(\mathbf{c}\right)=\sum_{i}\left|y_{i}-\sum_{j}c_{j}f_{j}\left(x_{i}\right)\right|^{2}
-$$
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-其理论解满足：
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-$$
-\frac{\partial J}{\partial c_{n}^{*}}=0=\sum_{i}\left(y_{i}-\sum_{j}c_{j}f_{j}\left(x_{i}\right)\right)\left(-f_{n}^{*}\left(x_{i}\right)\right)
-$$
-
-
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-改写为：
-
-$$
-\begin{eqnarray*} \sum_{j}c_{j}\sum_{i}f_{j}\left(x_{i}\right)f_{n}^{*}\left(x_{i}\right) &amp; = &amp; \sum_{i}y_{i}f_{n}^{*}\left(x_{i}\right)\\ \mathbf{A}^{H}\mathbf{Ac} &amp; = &amp; \mathbf{A}^{H}\mathbf{y}\end{eqnarray*}
-$$
-
-
-其中：
-
-$$
-\left\{ \mathbf{A}\right\} _{ij}=f_{j}\left(x_{i}\right).
-$$
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-当 $\mathbf{A^HA}$ 可逆时，我们有：
-
-$$
-\mathbf{c}=\left(\mathbf{A}^{H}\mathbf{A}\right)^{-1}\mathbf{A}^{H}\mathbf{y}=\mathbf{A}^{\dagger}\mathbf{y}
-$$
-
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-矩阵 $\mathbf{A}^{\dagger}$ 叫做 $\mathbf{A}$ 的伪逆。
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-
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-**问题求解**
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-注意到，我们的模型可以写为：
-
-$$
-\mathbf{y}=\mathbf{Ac}+\boldsymbol{\epsilon}.
-$$
-
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-在给定 $\mathbf{y}$ 和 $\mathbf{A}$ 的情况下，我们可以使用 linalg.lstsq 求解 $\mathbf c$。
-
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-在给定 $\mathbf{A}$ 的情况下，我们可以使用 linalg.pinv 或者 linalg.pinv2 求解 $\mathbf{A}^{\dagger}$。
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-
-
-**例子**
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-
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-假设我们的数据满足：
-
-$$
-\begin{align}
-y_{i} &amp; =c_{1}e^{-x_{i}}+c_{2}x_{i} \\
-z_{i} &amp; = y_i + \epsilon_i
-\end{align}
-$$
-
-
-其中 $x_i = \frac{i}{10},\ i = 1,\dots,10$，$c_1 = 5, c_2 = 2$，产生数据
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-
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-
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-::
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-    c1, c2 = 5.0, 2.0
-    i = np.r_[1:11]
-    xi = 0.1*i
-    yi = c1*np.exp(-xi) + c2*xi
-    zi = yi + 0.05 * np.max(yi) * np.random.randn(len(yi))
-
-
-
-构造矩阵 $\mathbf A$：
-
-
-
-
-
-
-::
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-    A = np.c_[np.exp(-xi)[:, np.newaxis], xi[:, np.newaxis]]
-    print A
-
-
-结果:
-::
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-    [[ 0.90483742  0.1       ]
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-     [ 0.81873075  0.2       ]
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-     [ 0.74081822  0.3       ]
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-     [ 0.67032005  0.4       ]
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-     [ 0.60653066  0.5       ]
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-     [ 0.54881164  0.6       ]
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-     [ 0.4965853   0.7       ]
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-     [ 0.44932896  0.8       ]
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-     [ 0.40656966  0.9       ]
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-     [ 0.36787944  1.        ]]
-
-
-求解最小二乘问题：
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-
-
-
-::
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-    c, resid, rank, sigma = linalg.lstsq(A, zi)
-
-    print c
-
-
-结果:
-::
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-    [ 4.87016856  2.19081311]
-
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-其中 c 的形状与 zi 一致，为最小二乘解，resid 为 zi - A c 每一列差值的二范数，rank 为矩阵 A 的秩，sigma 为矩阵 A 的奇异值。
-查看拟合效果：
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-
-
-
-
-::
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-    xi2 = np.r_[0.1:1.0:100j]
-    yi2 = c[0]*np.exp(-xi2) + c[1]*xi2
-
-    plt.plot(xi,zi,'x',xi2,yi2)
-    plt.axis([0,1.1,3.0,5.5])
-    plt.xlabel('$x_i$')
-    plt.title('Data fitting with linalg.lstsq')
-    plt.show()
-
-
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-.. image:: 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2VsWBcXB73R885r2jLdnTkfzeHGV2/k6B5Hc8859zCm75jkFCwiUof6hlAaG+A/cPcLzexqAHd/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%0AgIuIRNT/AXU6OlSJKzNiAAAAAElFTkSuQmCC%0A
-
-
-
-广义逆
---------------
-
-linalg.pinv 或 linalg.pinv2 可以用来求广义逆，其区别在于前者使用求最小二乘解的算法，后者使用求奇异值的算法求解。
-
-
-矩阵分解
------------------------
-特征值和特征向量
------------------------
-
-问题描述
-------------
-
-对于给定的 $N \times N$ 矩阵 $\mathbf A$，特征值和特征向量问题相当与寻找标量 $\lambda$ 和对应的向量 $\mathbf v$ 使得： $$
-\mathbf{Av} = \lambda \mathbf{v}
-$$
-
-
-
-矩阵的 $N$ 个特征值（可能相同）可以通过计算特征方程的根得到： $$
-\left|\mathbf{A} - \lambda \mathbf{I}\right| = 0
-$$
-
-
-
-然后利用这些特征值求（归一化的）特征向量。
-
-
-
-问题求解
-----------------
-
-
-- linalg.eig(A)
-    - 返回矩阵的特征值与特征向量
-- linalg.eigvals(A)
-    - 返回矩阵的特征值
-- linalg.eig(A, B)
-    - 求解 $\mathbf{Av} = \lambda\mathbf{Bv}$ 的问题
-
-
-**例子**
-
-
-矩阵为 $$
-\begin{split}\mathbf{A}=\left[\begin{array}{ccc} 1 &amp; 5 &amp; 2\\ 2 &amp; 4 &amp; 1\\ 3 &amp; 6 &amp; 2\end{array}\right].\end{split}
-$$
-
-
-
-特征多项式为： $$
-\begin{eqnarray*} \left|\mathbf{A}-\lambda\mathbf{I}\right| &amp; = &amp; \left(1-\lambda\right)\left[\left(4-\lambda\right)\left(2-\lambda\right)-6\right]-\\  &amp;  &amp; 5\left[2\left(2-\lambda\right)-3\right]+2\left[12-3\left(4-\lambda\right)\right]\\  &amp; = &amp; -\lambda^{3}+7\lambda^{2}+8\lambda-3.\end{eqnarray*}
-$$
-
-
-特征根为： $$
-\begin{eqnarray*} \lambda_{1} &amp; = &amp; 7.9579\\ \lambda_{2} &amp; = &amp; -1.2577\\ \lambda_{3} &amp; = &amp; 0.2997.\end{eqnarray*}
-$$
-
-
-
-
-
-::
-
-    A = np.array([[1, 5, 2], 
-                  [2, 4, 1],
-                  [3, 6, 2]])
-
-
- 
-    la, v = linalg.eig(A)
-
-    print la
-
-
-    # 验证是否归一化
-    print np.sum(abs(v**2),axis=0)
-
-    # 第一个特征值
-    l1 = la[0]
-    # 对应的特征向量
-    v1 = v[:, 0].T
-
-    # 验证是否为特征值和特征向量对
-    print linalg.norm(A.dot(v1)-l1*v1)
-
-
-
-结果:
-::
-
-    [ 7.95791620+0.j -1.25766471+0.j  0.29974850+0.j]
-
-    [ 1.  1.  1.]
-
-    3.23301824835e-15
-
-
-奇异值分解
-----------------
-
-
-**问题描述**
-
-
-$M \times N$ 矩阵 $\mathbf A$ 的奇异值分解为： $$
-\mathbf{A=U}\boldsymbol{\Sigma}\mathbf{V}^{H}
-$$
-
-
-其中 $\boldsymbol{\Sigma}, (M \times N)$ 只有对角线上的元素不为 0，$\mathbf U, (M \times M)$ 和 $\mathbf V, (N \times N)$ 为正交矩阵。
-
-
-其具体原理可以查看维基百科： https://en.wikipedia.org/wiki/Singular_value_decomposition
-
-
-
-**问题求解**
-
-
--mU,s,Vh = linalg.svd(A)
-    - 返回 $U$ 矩阵，奇异值 $s$，$V^H$ 矩阵
--mSig = linalg.diagsvd(s,M,N)
-    - 从奇异值恢复 $\boldsymbol{\Sigma}$ 矩阵
-
-
-
-**例子**
-
-
-奇异值分解：
-
-
-
-
-::
-
-    A = np.array([[1,2,3],[4,5,6]])
-
-    U, s, Vh = linalg.svd(A)
-
-
-$\boldsymbol{\Sigma}$ 矩阵：
-
-
-
-
-::
-
-    M, N = A.shape
-    Sig = linalg.diagsvd(s,M,N)
-
-    print Sig
-
-
-结果:
-::
-
-    [[ 9.508032    0.          0.        ]
-
-
-    [ 0.          0.77286964  0.        ]]
-
-
-检查正确性：
-
-
-
-
-::
-
-    print A
-    print U.dot(Sig.dot(Vh))
-
-
-结果:
-::
-
-    [[1 2 3]
-
-     [4 5 6]]
-
-    [[ 1.  2.  3.]
-
-     [ 4.  5.  6.]]
-
-
-**LU 分解**
-
-
-$M \times N$ 矩阵 $\mathbf A$ 的 LU 分解为： $$
-\mathbf{A}=\mathbf{P}\,\mathbf{L}\,\mathbf{U}
-$$
-
-
-
-$\mathbf P$ 是 $M \times M$ 的单位矩阵的一个排列，$\mathbf L$ 是下三角阵，$\mathbf U$ 是上三角阵。
-
-
-可以使用 linalg.lu 进行 LU 分解的求解：
-
-
-具体原理可以查看维基百科： https://en.wikipedia.org/wiki/LU_decomposition
-
-
-
-
-
-::
-
-    A = np.array([[1,2,3],[4,5,6]])
-
-    P, L, U = linalg.lu(A)
-
-    print P
-    print L
-    print U
-
-
-
-
-print P.dot(L).dot(U)
-
-结果:
-::
-
-    [[ 0.  1.]
-
-     [ 1.  0.]]
-
-    [[ 1.    0.  ]
-
-     [ 0.25  1.  ]]
-
-    [[ 4.    5.    6.  ]
-
-     [ 0.    0.75  1.5 ]]
-
-    [[ 1.  2.  3.]
-
-     [ 4.  5.  6.]]
-
-
-Cholesky 分解
-------------------
-
-
-Cholesky 分解是一种特殊的 LU 分解，此时要求 $\mathbf A$ 为 Hermitian 正定矩阵 （$\mathbf A = \mathbf{A^H}$）。
-此时有： $$
-\begin{eqnarray*} \mathbf{A} &amp; = &amp; \mathbf{U}^{H}\mathbf{U}\\ \mathbf{A} &amp; = &amp; \mathbf{L}\mathbf{L}^{H}\end{eqnarray*}
-$$ 即 $$
-\mathbf{L}=\mathbf{U}^{H}.
-$$
-
-
-可以用 linalg.cholesky 求解。
-
-
-
-**QR 分解**
-
-$M×N$ 矩阵 $\mathbf A$ 的 QR 分解为： $$
-\mathbf{A=QR}
-$$
-$\mathbf R$ 为上三角形矩阵，$\mathbf Q$ 是正交矩阵。
-
-
-维基链接： https://en.wikipedia.org/wiki/QR_decomposition
-
-
-
-可以用 linalg.qr 求解。
-
-
-**Schur 分解**
-
-
-对于 $N\times N$ 方阵 $\mathbf A$, Schur 分解要求找到满足下式的矩阵： $$
-\mathbf{A=ZTZ^H}
-$$
-其中 $\mathbf Z$ 是正交矩阵，$\mathbf T$ 是一个上三角矩阵。
-
-
-维基链接： https://en.wikipedia.org/wiki/Schur_decomposition
-
-
-
-
-::
-
-    A = np.mat('[1 3 2; 1 4 5; 2 3 6]')
-
-    print A
-
-    T, Z = linalg.schur(A)
-
-    print T, Z
-
-    print Z.dot(T).dot(Z.T)
-
-
-结果:
-::
-
-    [[1 3 2]
-
-     [1 4 5]
-
-     [2 3 6]]
-
-    [[ 9.90012467  1.78947961 -0.65498528]
-
-     [ 0.          0.54993766 -1.57754789]
-
-     [ 0.          0.51260928  0.54993766]] [[ 0.36702395 -0.85002495 -0.37782404]
-
-     [ 0.63681656 -0.06646488  0.76814522]
-
-     [ 0.67805463  0.52253231 -0.51691576]]
-
-    [[ 1.  3.  2.]
-
-     [ 1.  4.  5.]
-
-     [ 2.  3.  6.]]
-
-
-
-
-矩阵函数
---------------
-
-
-考虑函数 $f(x)$ 的泰勒展开： $$
-f\left(x\right)=\sum_{k=0}^{\infty}\frac{f^{\left(k\right)}\left(0\right)}{k!}x^{k}
-$$
-
-
-对于方阵，矩阵函数可以定义如下： $$
-f\left(\mathbf{A}\right)=\sum_{k=0}^{\infty}\frac{f^{\left(k\right)}\left(0\right)}{k!}\mathbf{A}^{k}
-$$
-
-
-这也是计算矩阵函数的最好的方式。
-
-
-
-指数和对数函数
-------------------
-
-指数
-----------
-
-指数可以定义如下： $$
-e^{\mathbf{A}}=\sum_{k=0}^{\infty}\frac{1}{k!}\mathbf{A}^{k}
-$$
-
-
-
-linalg.expm3 使用的是泰勒展开的方法计算结果：
-
-
-
-
-
-::
-
-    A = np.array([[1, 2], [3, 4]])
-
-    print linalg.expm3(A)
-
-
-结果:
-::
-
-    [[  51.96890355   74.73648784]
-
-    [ 112.10473176  164.07363531]]
-
-另一种方法先计算 A 的特征值分解：
-$$
-\mathbf{A}=\mathbf{V}\boldsymbol{\Lambda}\mathbf{V}^{-1}
-$$
-
-
-然后有（正交矩阵和对角阵的性质）：
-$$
-e^{\mathbf{A}}=\mathbf{V}e^{\boldsymbol{\Lambda}}\mathbf{V}^{-1}
-$$
-
-
-linalg.expm2 使用的就是这种方法：
-
-
-
-
-
-::
-
-    print linalg.expm2(A)
-
-
-结果:
-::
-
-    [[  51.9689562    74.73656457]
-
-    [ 112.10484685  164.07380305]]
-
-
-最优的方法是用 Padé 近似 实现，Padé 近似往往比截断的泰勒级数准确，而且当泰勒级数不收敛时，Padé 近似往往仍可行，所以多用于在计算机数学中。
-
-
-linalg.expm 使用的就是这种方法：
-
-
-
-
-::
-
-    print linalg.expm(A)
-
-
-结果:
-::
-
-    [[  51.9689562    74.73656457]
-
-
-    [ 112.10484685  164.07380305]]
-
-
-
-对数
------------
-
-
-指数的逆运算，可以用 linalg.logm 实现：
-
-
-
-
-::
-
-    print A
-    print linalg.logm(linalg.expm(A))
-
-
-结果:
-::
-
-    [[1 2]
-
-     [3 4]]
-
-    [[ 1.  2.]
-
-     [ 3.  4.]]
-
-
-三角函数
--------------------
-
-
-根据欧拉公式，其定义为： $$
-\begin{eqnarray*} \sin\left(\mathbf{A}\right) &amp; = &amp; \frac{e^{j\mathbf{A}}-e^{-j\mathbf{A}}}{2j}\\ \cos\left(\mathbf{A}\right) &amp; = &amp; \frac{e^{j\mathbf{A}}+e^{-j\mathbf{A}}}{2}.\end{eqnarray*}
-$$
-
-
-正切函数定义为： $$
-\tan\left(x\right)=\frac{\sin\left(x\right)}{\cos\left(x\right)}=\left[\cos\left(x\right)\right]^{-1}\sin\left(x\right)
-$$
-
-
-
-因此矩阵的正切函数定义为： $$
-\left[\cos\left(\mathbf{A}\right)\right]^{-1}\sin\left(\mathbf{A}\right).
-$$
-
-
-
-具体实现：
-
-
-- linalg.sinm
-- linalg.cosm
-- linalg.tanm
-
-
-
-双曲三角函数
----------------------
-
-
-$$\begin{eqnarray*} \sinh\left(\mathbf{A}\right) &amp; = &amp; \frac{e^{\mathbf{A}}-e^{-\mathbf{A}}}{2}\\ \cosh\left(\mathbf{A}\right) &amp; = &amp; \frac{e^{\mathbf{A}}+e^{-\mathbf{A}}}{2}\\ \tanh\left(\mathbf{A}\right) &amp; = &amp; \left[\cosh\left(\mathbf{A}\right)\right]^{-1}\sinh\left(\mathbf{A}\right).\end{eqnarray*}$$
-
-
-
-具体实现：
-
-
-- linalg.sinhm
-- linalg.coshm
-- linalg.tanhm
-
-
-
-特殊矩阵
-------------------
-
-
-Scipy 提供了一些特殊矩阵的实现，具体可以参考：
-
-
-http://docs.scipy.org/doc/scipy/reference/tutorial/linalg.html#special-matrices
-
-
-
-
-作者 & 更新时间
-------------------------------------
-作者:`李金  <lijinwithyou@gmail.com>`
-
-提交: 2017/12/14
\ No newline at end of file
diff --git a/course/source/aipy-scipy/cn/11sparse-matrix.rst b/course/source/aipy-scipy/cn/11sparse-matrix.rst
deleted file mode 100644
index 32ccde8..0000000
--- a/course/source/aipy-scipy/cn/11sparse-matrix.rst
+++ /dev/null
@@ -1,71 +0,0 @@
-
-稀疏矩阵的线性代数
-------------------------
-
-
-对于稀疏矩阵来说，其线性代数操作可以使用 scipy.sparse.linalg 实现：
-
-
-
-
-
-::
-
-
-    import scipy.sparse.linalg
-
-
-
-矩阵操作
--------------
-
-
-- scipy.sparse.linalg.inv
-     - 稀疏矩阵求逆
-- scipy.sparse.linalg.expm
-     - 求稀疏矩阵的指数函数
-
-
-
-矩阵范数
---------------
-
-
-- scipy.sparse.linalg.norm
-    - 稀疏矩阵求范数
-
-
-
-线性方程组求解
-------------------------
-
-
-提供了一系列求解方法： http://docs.scipy.org/doc/scipy/reference/sparse.linalg.html#solving
--linear-problems
-
-
-主要使用的是迭代方法求解。
-
-
-
-特征值分解和奇异值分解
---------------------------------
-
-
-对于特别大的矩阵，原来的方法可能需要太大的内存，考虑使用这两个方法替代：
-
-
-
-- scipy.sparse.linalg.eigs
-      - 返回前 k 大的特征值和特征向量
-- scipy.sparse.linalg.svds
-      - 返回前 k 大的奇异值和奇异向量
-
-
-
-
-作者 & 更新时间
-------------------------------------
-作者:`李金  <lijinwithyou@gmail.com>`
-
-提交: 2017/12/14
diff --git a/course/source/aipy-scipy/cn/1synopsis.rst b/course/source/aipy-scipy/cn/1synopsis.rst
deleted file mode 100644
index b2a89c0..0000000
--- a/course/source/aipy-scipy/cn/1synopsis.rst
+++ /dev/null
@@ -1,252 +0,0 @@
-
-SCIentific PYthon 简介
-===========================
-
-
-**Ipython** 提供了一个很好的解释器界面。
-
-
-**Matplotlib** 提供了一个类似 **Matlab** 的画图工具。
-
-
-**Numpy** 提供了 ndarray 对象，可以进行快速的向量化计算。
-
-
-**Scipy** 是 **Python** 中进行科学计算的一个第三方库，以 **Numpy** 为基础。
-
-
-**Pandas** 是处理时间序列数据的第三方库，提供一个类似 **R** 语言的环境。
-
-
-**StatsModels** 是一个统计库，着重于统计模型。
-
-
-**Scikits** 以 **Scipy** 为基础，提供如 **scikits-learn** 机器学习和**scikits-image 图像处理** 等高级用法。
-
-
-
-Scipy
---------------
-
-
-**Scipy** 由不同科学计算领域的子模块组成：
-
-
-+------------+------------------------------------------------------------------------------------------+
-| 子模块      | 描述                                                                                     |
-+============+==========================================================================================+
-|cluster     | 聚类算法                                                                                  |    
-+------------+------------------------------------------------------------------------------------------+
-| constants  |物理数学常数                                                                              |
-+------------+------------------------------------------------------------------------------------------+
-| fftpack    |快速傅里叶变换                                                                            |
-+------------+------------------------------------------------------------------------------------------+
-|integrate   |积分和常微分方程求解                                                                      |         
-+------------+------------------------------------------------------------------------------------------+
-|interpolate |插值                                                                                      |
-+------------+------------------------------------------------------------------------------------------+
-|io	         |输入输出                                                                                  |
-+------------+------------------------------------------------------------------------------------------+
-|linalg	     |线性代数                                                                                  |
-+------------+------------------------------------------------------------------------------------------+
-|odr	     |正交距离回归                                                                              |
-+------------+------------------------------------------------------------------------------------------+
-|optimize    |优化和求根                                                                                |
-+------------+------------------------------------------------------------------------------------------+
-|signal	     |信号处理                                                                                  |
-+------------+------------------------------------------------------------------------------------------+
-|sparse	     |稀疏矩阵                                                                                  |
-+------------+------------------------------------------------------------------------------------------+
-|spatial     |空间数据结构和算法                                                                        |
-+------------+------------------------------------------------------------------------------------------+
-|special     |特殊方程                                                                                  |
-+------------+------------------------------------------------------------------------------------------+
-|stats	     |统计分布和函数                                                                            |
-+------------+------------------------------------------------------------------------------------------+
-|weave	     |C/C++ 积分                                                                                |
-+------------+------------------------------------------------------------------------------------------+
-
-
-
-在使用 **Scipy** 之前，为了方便，假定这些基础的模块已经被导入：
-
-
-
-
-::
-
-    import numpy as np
-    import scipy as sp
-    import matplotlib as mpl
-    import matplotlib.pyplot as plt
-
-
-使用 **Scipy** 中的子模块时，需要分别导入：
-
-
-
-
-::
-
-    from scipy import linalg, optimize
-
-
-对于一些常用的函数，这些在子模块中的函数可以在 **scipy** 命名空间中调用。另一方面，由于 **Scipy** 以 **Numpy** 为基础，因此很多基础的 Numpy 函数可以在scipy 命名空间中直接调用。
-
-
-我们可以使用 numpy 中的 info 函数来查看函数的文档：
-
-
-
-
-::
-
-    np.info(optimize.fmin)
-
-
-::
-
-     fmin(func, x0, args=(), xtol=0.0001, ftol=0.0001, maxiter=None,     maxfun=None,
-          ^4$full_output=0, disp=1, retall=0, callback=None)
-
-
-
-    Minimize a function using the downhill simplex algorithm.
-
-
-
-    This algorithm only uses function values, not derivatives or second
-    derivatives.
-
-
-
-    Parameters
-
-    func : callable func(x,*args)
-        The objective function to be minimized.
-    x0 : ndarray
-        Initial guess.
-    args : tuple, optional
-        Extra arguments passed to func, i.e. ``f(x,*args)``.
-    callback : callable, optional
-        Called after each iteration, as callback(xk), where xk is the
-        current parameter vector.
-    xtol : float, optional
-        Relative error in xopt acceptable for convergence.
-    ftol : number, optional
-        Relative error in func(xopt) acceptable for convergence.
-    maxiter : int, optional
-        Maximum number of iterations to perform.
-    maxfun : number, optional
-        Maximum number of function evaluations to make.
-    full_output : bool, optional
-        Set to True if fopt and warnflag outputs are desired.
-    disp : bool, optional
-        Set to True to print convergence messages.
-    retall : bool, optional
-        Set to True to return list of solutions at each iteration.
-
-    Returns
-
-    xopt : ndarray
-        Parameter that minimizes function.
-    fopt : float
-        Value of function at minimum: ``fopt = func(xopt)``.
-    iter : int
-        Number of iterations performed.
-    funcalls : int
-        Number of function calls made.
-    warnflag : int
-        1 : Maximum number of function evaluations made.
-        2 : Maximum number of iterations reached.
-    allvecs : list
-        Solution at each iteration.
-
-    See also
-
-    minimize: Interface to minimization algorithms for multivariate
-        functions. See the 'Nelder-Mead' `method` in particular.
-
-    Notes
-
-    Uses a Nelder-Mead simplex algorithm to find the minimum of     function of
-    one or more variables.
-
-    This algorithm has a long history of successful use in     applications.
-    But it will usually be slower than an algorithm that uses first or
-    second derivative information. In practice it can have poor
-    performance in high-dimensional problems and is not robust to
-    minimizing complicated functions. Additionally, there currently is     no
-    complete theory describing when the algorithm will successfully
-    converge to the minimum, or how fast it will if it does.
-
-    References
-
-    .. [1] Nelder, J.A. and Mead, R. (1965), "A simplex method for     function
-           minimization", The Computer Journal, 7, pp. 308-313
-
-    .. [2] Wright, M.H. (1996), "Direct Search Methods: Once Scorned,     Now
-           Respectable", in Numerical Analysis 1995, Proceedings of the
-           1995 Dundee Biennial Conference in Numerical Analysis, D.F.
-           Griffiths and G.A. Watson (Eds.), Addison Wesley Longman,
-           Harlow, UK, pp. 191-208.
-
-
-
-可以用 lookfor 来查询特定关键词相关的函数：
-
-
-
-
-
-::
-
-    np.lookfor("resize array")
-
-
-
-Search results for 'resize array'
-
-numpy.chararray.resize
-    Change shape and size of array in-place.
-numpy.ma.resize
-    Return a new masked array with the specified size and shape.
-numpy.oldnumeric.ma.resize
-    The original array's total size can be any size.
-numpy.resize
-    Return a new array with the specified shape.
-numpy.chararray
-    chararray(shape, itemsize=1, unicode=False, buffer=None, offset=0,
-numpy.memmap
-    Create a memory-map to an array stored in a *binary* file on disk.
-numpy.ma.mvoid.resize
-     warning
-
-
-
-
-还可以指定查找的模块：
-
-
-
-
-
-::
-
-    np.lookfor("remove path", module="os")
-
-
-Search results for 'remove path'
-
-os.removedirs
-    removedirs(path)
-os.walk
-    Directory tree generator.
-
-
-
-作者 & 更新时间
-------------------------------------
-作者:`李金  <lijinwithyou@gmail.com>`
-
-提交: 2017/12/14
\ No newline at end of file
diff --git a/course/source/aipy-scipy/cn/2interpolation.rst b/course/source/aipy-scipy/cn/2interpolation.rst
deleted file mode 100644
index 46b1d2c..0000000
--- a/course/source/aipy-scipy/cn/2interpolation.rst
+++ /dev/null
@@ -1,581 +0,0 @@
-
-插值
-=========
-
-
-
-::
-
-    import numpy as np
-    import matplotlib.pyplot as plt
-    %matplotlib inline
-
-
-设置 **Numpy** 浮点数显示格式：
-
-
-
-
-::
-
-    np.set_printoptions(precision=2, suppress=True)
-
-
-从文本中读入数据，数据来自 http://kinetics.nist.gov/janaf/html/C-067.txt ，保存为结构体数组：
-
-
-
-
-::
-
-    data = np.genfromtxt("JANAF_CH4.txt", 
-                      ^4$delimiter="\t", # TAB 分隔
-                      ^4$skiprows=1,     # 忽略首行
-                      ^4$names=True,     # 读入属性
-                      ^4$missing_values="INFINITE",  # 缺失值
-                      ^4$filling_values=np.inf)      # 填充缺失值
-
-显示部分数据：
-
-
-
-
-::
-
-    for row in data[:7]:
-        ^4$print "{}\t{}".format(row['TK'], row['Cp'])
-    print "...\t..."
-
-0.0	0.0
-
-100.0	33.258
-
-200.0	33.473
-
-250.0	34.216
-
-298.15	35.639
-
-300.0	35.708
-
-350.0	37.874
-
-...	...
-
-
-
-绘图：
-
-
-
-
-::
-
-    p = plt.plot(data['TK'], data['Cp'], 'kx')
-    t = plt.title("JANAF data for Methane $CH_4$")
-    a = plt.axis([0, 6000, 30, 120])
-    x = plt.xlabel("Temperature (K)")
-    y = plt.ylabel(r"$C_p$ ($\frac{kJ}{kg K}$)")
-
-.. image:: 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dddw/L1LC2zMcorQrXLC7dYCq2RcZPRrjWz9XCLxbpBtdYJwOzZszdomQyuPcm2TpYvX75By2TR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%0AZmaWKwcWMzPLlQOLmZnl6n8BKYzjzfpoiFsAAAAASUVORK5CYII=%0A
-
-
-插值
---------
-
-
-假设我们要对这组数据进行插值。
-
-
-先导入一维插值函数 interp1d：
-
-
-interp1d(x, y)
-
-
-
-
-::
-
-    from scipy.interpolate import interp1d
-
-
-
-
-::
-    ch4_cp = interp1d(data['TK'], data['Cp'])
-
-
-interp1d 的返回值可以像函数一样接受输入，并返回插值的结果。
-单个输入值，注意返回的是数组：
-
-
-
-
-::
-
-    ch4_cp(382.2)
-
-
-
-结果:
-::
-
-    array(39.565144000000004)
-
-
-输入数组，返回的是对应的数组：
-
-
-
-
-::
-
-    ch4_cp([32.2,323.2])
-
-
-结果:
-::
-
-    array([ 10.71,  36.71])
-
-
-默认情况下，输入值要在插值允许的范围内，否则插值会报错：
-
-
-
-
-::
-
-    ch4_cp(8752)
-
-报错: 
-::
-
-    ValueError                                Traceback (most recent call last)
-    <ipython-input-10-5d727af9aa33> in <module>()
-    ----> 1 ch4_cp(8752)
-
-    d:\Miniconda\lib\site-packages\scipy\interpolate\polyint.pyc in __call__(self, x)
-         77         """
-         78         x, x_shape = self._prepare_x(x)
-    ---> 79         y = self._evaluate(x)
-         80         return self._finish_y(y, x_shape)
-         81 
-
-    d:\Miniconda\lib\site-packages\scipy\interpolate\interpolate.pyc in _evaluate(self, x_new)
-        496         #    The behavior is set by the bounds_error variable.
-        497         x_new = asarray(x_new)
-    --> 498         out_of_bounds = self._check_bounds(x_new)
-        499         y_new = self._call(self, x_new)
-        500         if len(y_new) > 0:
-
-    d:\Miniconda\lib\site-packages\scipy\interpolate\interpolate.pyc in _check_bounds(self, x_new)
-        526                 "range.")
-        527         if self.bounds_error and above_bounds.any():
-    --> 528             raise ValueError("A value in x_new is above the interpolation "
-        529                 "range.")
-        530 
-
-
-    ValueError: A value in x_new is above the interpolation range.
-
-
-但我们可以通过参数设置允许超出范围的值存在：
-
-
-
-
-::
-
-    ch4_cp = interp1d(data['TK'], data['Cp'], 
-                      ^4$bounds_error=False)
-
-
-不过由于超出范围，所以插值的输出是非法值：
-
-
-
-
-::
-
-    ch4_cp(8752)
-
-
-
-结果:
-::
-
-    array(nan)
-
-
-可以使用指定值替代这些非法值：
-
-
-
-
-::
-
-    ch4_cp = interp1d(data['TK'], data['Cp'], 
-                      ^4$bounds_error=False, fill_value=-999.25)
-
-
-
-
-::
-
-    ch4_cp(8752)
-
-
-
-结果:
-::
-
-    array(-999.25)
-
-
-线性插值
---------------
-
-
-interp1d 默认的插值方法是线性，关于线性插值的定义，请参见：
-
-
-
-- 维基百科-线性插值： https://zh.wikipedia.org/wiki/%E7%BA%BF%E6%80%A7%E6%8F%92%E5%80%BC
-- 百度百科-线性插值： http://baike.baidu.com/view/4685624.htm
-
-
-
-其基本思想是，已知相邻两点 $x_1,x_2$ 对应的值 $y_1,y_2$ ，那么对于 $(x_1,x_2)$ 之间的某一点 $x$ ，线性插值对应的值 $y$ 满足：点 $(x,y)$ 在 $(x_1,y_1),(x_2,y_2)$ 所形成的线段上。
-
-
-应用线性插值：
-
-
-
-
-::
-
-    T = np.arange(100,355,5)
-    plt.plot(T, ch4_cp(T), "+k")
-    p = plt.plot(data['TK'][1:7], data['Cp'][1:7], 'ro', markersize=8)
-
-
-.. image:: data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAXUAAAEACAYAAABMEua6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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%0ASSViqEtSiRjqklQihroklcj/A8xgms4BB0nlAAAAAElFTkSuQmCC%0A
-
-
-
-其中红色的圆点为原来的数据点，黑色的十字点为对应的插值点，可以明显看到，相邻的数据点的插值在一条直线上。
-
-
-多项式插值
---------------
-
-
-我们可以通过 kind 参数来调节使用的插值方法，来得到不同的结果：
-
-
-- nearest 最近邻插值
-- zero 0阶插值
-- linear 线性插值
-- quadratic 二次插值
-- cubic 三次插值
-- 4,5,6,7 更高阶插值
-
-
-最近邻插值：
-
-
-
-
-::
-
-    cp_ch4 = interp1d(data['TK'], data['Cp'], kind="nearest")
-    p = plt.plot(T, cp_ch4(T), "k+")
-    p = plt.plot(data['TK'][1:7], data['Cp'][1:7], 'ro', markersize=8)
-
-
-.. image:: data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAXUAAAEACAYAAABMEua6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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%0AiKEuSQUx1CWpIIa6JBXk/wGLsxItFlRd2gAAAABJRU5ErkJggg==%0A
-
-0阶插值：
-
-
-
-
-::
-
-    cp_ch4 = interp1d(data['TK'], data['Cp'], kind="zero")
-    p = plt.plot(T, cp_ch4(T), "k+")
-    p = plt.plot(data['TK'][1:7], data['Cp'][1:7], 'ro', markersize=8)
-
-
-.. image:: data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAXUAAAEACAYAAABMEua6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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%0AxFCXpIIY6pJUEENdkgpiqEtSQf4fzDOJu6Be93AAAAAASUVORK5CYII=%0A
-
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-二次插值：
-
-
-
-
-::
-
-    cp_ch4 = interp1d(data['TK'], data['Cp'], kind="quadratic")
-    p = plt.plot(T, cp_ch4(T), "k+")
-    p = plt.plot(data['TK'][1:7], data['Cp'][1:7], 'ro', markersize=8)
-
-
-
-三次插值：
-
-
-
-
-::
-
-    cp_ch4 = interp1d(data['TK'], data['Cp'], kind="cubic")
-    p = plt.plot(T, cp_ch4(T), "k+")
-    p = plt.plot(data['TK'][1:7], data['Cp'][1:7], 'ro', markersize=8)
-
-
-.. image:: data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAXUAAAEACAYAAABMEua6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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%0AAElFTkSuQmCC%0A
-
-
-
-事实上，我们可以使用更高阶的多项式插值，只要将 kind 设为对应的数字即可：
-
-
-
-四次多项式插值：
-
-
-
-
-::
-
-    cp_ch4 = interp1d(data['TK'], data['Cp'], kind=4)
-    p = plt.plot(T, cp_ch4(T), "k+")
-    p = plt.plot(data['TK'][1:7], data['Cp'][1:7], 'ro', markersize=8)
-
-
-
-可以参见：
-
-
-- 维基百科-多项式插值：https://zh.wikipedia.org/wiki/%E5%A4%9A%E9%A1%B9%E5%BC%8F%E6%8F%92%E5%80%BC
-- 百度百科-插值法：http://baike.baidu.com/view/754506.htm
-
-
-对于二维乃至更高维度的多项式插值：
-
-
-
-
-::
-
-    from scipy.interpolate import interp2d, interpnd
-
-
-其使用方法与一维类似。
-
-
-径向基函数
----------------
-
-关于径向基函数，可以参阅：
-
-
-- 维基百科-Radial basis fucntion：https://en.wikipedia.org/wiki/Radial_basis_function
-
-
-径向基函数，简单来说就是点 $x$ 处的函数值只依赖于 $x$ 与某点 $c$ 的距离：
-
-$$\Phi(x,c) = \Phi(\|x-c\|)$$
-
-
-
-
-::
-
-    x = np.linspace(-3,3,100)
-
-
-常用的径向基（RBF）函数有：
-
-高斯函数：
-
-
-
-
-::
-
-    plt.plot(x, np.exp(-1 * x **2))
-    t = plt.title("Gaussian")
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-.. image:: 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zNbaWarzOzavRxTamZLzexlMyvLeEopeCrk9WOm7hVJX62F3MxKgLuA/sCxwDAzO6baMQcC/w8Y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%0AwKmQi4gUOBVyEZEC9/8BloffFHVXiTcAAAAASUVORK5CYII=%0A
-
-Multiquadric 函数：
-
-
-
-
-::
-
-    plt.plot(x, np.sqrt(1 + x **2))
-    t = plt.title("Multiquadric")
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-.. image:: 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Ei+3Xdf+HQ+YQKssUbsaKqXSWll9Qwu0hgYCTxYNYkDuPv3lR4/a2ZDzKy5u8+t2rZ///4/Pi4r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%0AJJwSuYhIwv0/gG56R7Cwt98AAAAASUVORK5CYII=%0A
-
-
-Inverse Multiquadric 函数：
-
-
-
-
-::
-
-    plt.plot(x, 1. / np.sqrt(1 + x **2))
-    t = plt.title("Inverse Multiquadric")
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-.. image:: 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bFkCey6K0yaBDvuGDtN5nvoIXjhBXj11dhJJDa1yCVjPPhg2I9TRbxizjwz7GE6ZUrsJJIN1CKX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%0A9/9saTRo/aYs3AAAAABJRU5ErkJggg==%0A
-
-
-径向基函数插值
-----------------
-
-对于径向基函数，其插值的公式为：
-
-$$
-f(x) = \sum_j n_j \Phi(\|x-x_j\|)
-$$
-
-
-我们通过数据点 $x_j$ 来计算出 $n_j$ 的值，来计算 $x$ 处的插值结果。
-
-
-
-
-::
-
-    from scipy.interpolate.rbf import Rbf
-
-
-使用 multiquadric 核的：
-
-
-
-
-::
-
-    cp_rbf = Rbf(data['TK'], data['Cp'], function = "multiquadric")
-    plt.plot(data['TK'], data['Cp'], 'k+')
-    p = plt.plot(data['TK'], cp_rbf(data['TK']), 'r-')
-
-
-.. image:: 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yFCEycePFaze5Hkoit3E9QLDz/MI+vWwZQp8Le/wfXXE+zXT59+JhKndOWuHNnu3dC/P73eeANO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%0A
-
-
-使用 gaussian 核：
-
-
-
-
-::
-
-    cp_rbf = Rbf(data['TK'], data['Cp'], function = "gaussian")
-    plt.plot(data['TK'], data['Cp'], 'k+')
-    p = plt.plot(data['TK'], cp_rbf(data['TK']), 'r-')
-
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-.. image:: 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ptxPPPM7rQePeDQQ+G55/zywRFEs1RCpGadhtu2cc2LL9Lk2mt5b/NmTo5wZ12wciX7DhoE//kP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%0AwgAAAABJRU5ErkJggg==%0A
-
-
-使用 nverse_multiquadric 核：
-
-
-
-
-::
-
-    cp_rbf = Rbf(data['TK'], data['Cp'], function =     "inverse_multiquadric")
-     plt.plot(data['TK'], data['Cp'], 'k+')
-     p = plt.plot(data['TK'], cp_rbf(data['TK']), 'r-')
-
-不同的 RBF 核的结果也不同。
-
-
-高维 RBF 插值
---------------------
-
-
-
-
-::
-
-    from mpl_toolkits.mplot3d import Axes3D
-
-三维数据点：
-
-
-
-
-::
-
-    x, y = np.mgrid[-np.pi/2:np.pi/2:5j, -np.pi/2:np.pi/2:5j]
-    z = np.cos(np.sqrt(x**2 + y**2))
-
-
-
-
-::
-
-    fig = plt.figure(figsize=(12,6))
-    ax = fig.gca(projection="3d")
-    ax.scatter(x,y,z)
-
-
-
-结果:
-::
-
-    <mpl_toolkits.mplot3d.art3d.Path3DCollection at 0x176b4da0>
-
-
-.. image:: 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A4O4plnnpnx/w8++CDOPvtsHHTQQQCA973vfbj66qtD70cYsFhlYkP0oVYqFWSzWWSzWUcfar1e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%0AIbFKEARBEARBqJb/D6nRM2cdX17QAAAAAElFTkSuQmCC%0A
-
-3维 RBF 插值：
-
-
-
-
-::
-
-    zz = Rbf(x, y, z)
-
-
-
-
-::
-
-    xx, yy = np.mgrid[-np.pi/2:np.pi/2:50j, -np.pi/2:np.pi/2:50j]
-    fig = plt.figure(figsize=(12,6))
-    ax = fig.gca(projection="3d")
-    ax.plot_surface(xx,yy,zz(xx,yy),rstride=1, cstride=1, cmap=plt.cm.jet)
-
-
-结果:
-::
-
-    
-<mpl_toolkits.mplot3d.art3d.Poly3DCollection at 0x176e5c50>
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-.. image:: 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NV0C9SFx5oZmjoFpKJ1ra2tSR9qaWlpUtypGqOFHmdn36+sCqnT/IVsOuAE6Ty09fX1ycQHu5ue%0AVXDaCfFMST7aF7n1kotfNl1meqoIUb51fV7khhvEakdYzxW3dP9S5ySQDLCowIlVzK5evZp4PM6Y%0AMWOcPShbEVqsalyLuojEYrGM9VCtUTzlQy0vLy+6sOvMxVztQ6aSWYW8WeQbWU09/vl6aDNtPxvR%0AUqg6otpL615yPS+graanVcy6wS/bHcSgm8nm+GUbyU8Vs6ndv/IVs4lEImmLSRWzr7/+OoFAQIvV%0ADGixqnEV1gzOjnyoqR2ZqqqqMmaipovsOUWuwsauZFamfSi0cMp17Epcw5YdsezWXcjjnqsvMpv2%0Ak8pb6zbcOCY3Yj0vVNS/tLQ0+Xsx/bLdHbeLaSfGl68tJdtWtkqsqm1Zf27atImddtqpU+Pf2tFi%0AVdPl5OpDVR7OXEs1FVrsZbP+TFaFrrwZZLNtu7GXl5d3WO6rq/crnxuQNfoWDoe3iMB1deKXW4WD%0A20WNopB+2XzErNuPmx6fM61soS3C+vnnn/PVV18xfPhw1q9fzwEHHOD4mM8++2xefvll+vXrx6JF%0Ai2yXueSSS3jllVcoKyvj0UcfZffdd3d8HE6gxaqmy7D6UJubmwkEAgQCAVsfqp2HM9eLUzGmdNOt%0A39oZy+fzEQwGc7YqdFVkVXXEKkS5r64m0w3IMIxk9QgVGUmNpljfrxO/3EuuYsZJv2x391F3h0i+%0AG8R0R+eMqnSibADr169n9uzZfPfddyxbtoyHHnqI4cOHJ/+NGDGC3XffnUMPPTTvMZ111llcfPHF%0AnH766bavz549m6+//pqlS5cyf/58LrjgAj744IO8t1dItFjVFJV0PlTr/+18qLl2ZLKj2JFV6zS/%0AYUirwo6sCrms30lSL/ROjj2XcbvhpqOw3nw6292puwsWTXvy9VGnE7Pq3HHzQ44bx2TF7eMDieKr%0AGsRTp05l6tSpAEybNo0HH3yQhoYGli9fzrJly1i+fDkLFizolFj94Q9/yPLly9O+/sILL3DGGWcA%0AMHHiROrq6li3bh3bbbdd3tssFFqsagqOdZo/FosBW/pQPR4P8XiclpaWdn2oOyPuUimGWLV6abtD%0AZywrVptFNBot6tjdfmzscEqwuCHBZ2un2NHBXH3UQHIGqbNJgU7jpgfIdHT3MTY0NDBw4EB8Ph/j%0Axo0r2phWr17NkCFDkr8PHjyYVatWabGq2XbI1oeqEh/UhVplwRdKIBXipqX2U7VuVclSTk+VF0ps%0Aq4cI9a+7NBxwO/kkfqUm+KTr1tMdpmbdhlvEjJ1fViV/qe+49dzINZFnW3zQ6c5iVX2Xu+p6m3ot%0Acetx1GJV4yhWgWqd2k/1oSofpPKh+v3+ZOH4QuH0l1BlxKtoiJriqaqqcnQ7CifFqnpIUDVRvV4v%0AgUCAyspKR9ZvpRhe4e5GPgk+qbVlm5ubtxArXR2V7Q6iwe04cW446ZftDp9pdx9jV41/0KBBrFy5%0AMvn7qlWrGDRoUNHHkQ1arGo6TTofaupN05pklFpLtKWlpV3bzULghGiyy4hXkeBoNEo4HHZotM5j%0A95CgktWUaC0Gbr+puIFMFgPVNjgUCm1hMdBll+xx88OSddYpG5z2y24N9hM3f76QeXzKctUVHHPM%0AMdxzzz2cdNJJfPDBB1RXV7vSAgBarGryJBsfKtjXEq2srEyazBXFiL7luw27hC+7af5i+DrzGb+1%0AJqrH47FNVit08lY261bLdcebZbHJxWKQTdmlbSHxa2vdr1Q6Ojegred9OvuJ9VxILbnktuOYq9jv%0ACqzHNZWamhp69+5dkO2efPLJvPPOO9TU1DBkyBBuuOGGZGvY8847jyOOOILZs2czevRoysvLeeSR%0ARwoyDifQYlWTNbn6UJWHM5tEnWIUYM9VkNkVvu/Kov2QfQQh9TPItSZtIdGCtLDkMo28rUTe3Ewx%0Avw/W8yKbLk7Wfy0tLa72y7r5vMz0GW/cuJE+ffoUZLszZ87scJl77rmnINt2Gi1WNR2Siw9VCaRc%0A63EWK7LakdXATuRlU/herb/Q1QYyrT/bCHA+69ZsPWSaRs418paa+OVW9ANSdtg96ESjUeLxOCUl%0AJVn5ZdM95BQqat8dPttM19aamhr69u1bxNF0T7RY1dhi9aE2Nzfj9/uThfitFwarOFJTzJnabqaj%0AK20AnRXaHa3fKdKJ7dSaqNm0ni0mqcelu033b0siPtvIWyax0tLS4lhyz7aA278L1vE57Zd1IjHQ%0A7cdPkSmyqsVqx2ixqkli50MF2kVU1O9WH6oTU8xdIVatNVGh4/72bkCNP1OiVz6fQbEiq+qYA52+%0ASRUDt46rq+goKtvU1JRz4pe2GLibXMRgrn7ZTB3htqbzI9MxrKmpcW0GvpvQYlWTvGCk86F6PPbF%0A7p3saV9MG4DyocbjcUe9nMWyATQ3N7dr29odWp9GIpGk5021G0y9SRmGkdwvt/jgNLmTa23ZbOqH%0AKnGc73ng5uibm8fmJLn6ZbM9P6wP8G49jh2J1d12263II+p+aLG6jZKtD1VFWePxOH6/n1AolHNP%0A+2wopNBT0/zhcDgpukOhULLnu1OodTl90VSR7HA4TCKRwOfzOT7N7/TxV8dclclSNXRViS/rMVI3%0AItU7W92sOqob2d2jLdsauSR+WaNuhZxC1nSMYRhFmW3K5/ywniNNTU1F98tmS6Z7wqZNmwqWYLU1%0AocXqNoTVh5qpHmrq9Lh6Eq6oqCjKGJ26qFj3w+PxEAgEiMViBSl8r3DKj5kq9vx+P8FgkFgsVtDG%0ACZ0ltUyWmhIOBoNpawlaz0HVHMJKuhtURwk/nY3G5UssFmP58uV8/PHHPPvssyxYsICGhgai0Wi7%0A8lHWsft8PsrLy+nZsydjxoxh0KBBHHrooRx88MFblHnbWsnVD5nNFHJq9M2NuDkiCO4ZX7rzw9qe%0Au9h+2WzJJPg3btxIv379CrbtrYVt4yq4DZPOh5r65bT2hU+dHlftUAuJGk9nL4wqm99uml8JKTeT%0AmrBmbZwQjUbbfYZO0pkbeqp/NtVaYbWX5Du2jqaWs+3m45QHrra2luuvv57nnnuO2tpa1FoMwAuk%0ApsGpC60H8FmWNQwDDINoIkFdXR11dXWsWLYMH/Dnhx/G7qhVVVWxzz77cOmll7LffvvlNf5CUGhR%0Ak+t5kFpbFuQ657aom6ZzWK1r2fhlc3nYccovqyOrnUeL1a2Ujnyo0CYylBi1djSyLlesyES+27GL%0AQqbbj0KTzz6kE9hd0TghF5SwVj7TfCooWMln/3KNxtmVYcrkkQTxlF1xxRW8/NJLSfEYQESnEqV+%0A8//qE2s1lzHMv0WBOCJkrbfTBFCOiNgG2sSsYb5fPSIa5t99QH19Pa+99hqvvfZau33dbbfdmDFj%0ABhMmTMj+AG4ldHQeqM5yfr8/Y9StKxJ73BK5TIfbxwcdX9sz2Qugc37qbHz1mY6hesDXZEaL1a2I%0AXHyo1lqcdh2NrHg8HdcndYJcxUqmKGSm9Rfy4pvtPiiBraLZ6QR2schl3FZhnW2ZLOu6iym684nK%0Afvzxx/zkJz9h7dq1eKBddNOLXDQ9iPj0mL8b5r+o+bvP8prX8t4o0AsYAZQBy4Fl5rJey3uqzdcb%0AgHVAyFxHzFymJ7DZXFbNFSxesIApkycnx9u7d2+effZZdt999xyP2taH1WaSSjqhks1DTTZCRVNY%0AnPDU5uuXzbYrnOr+lW69mo7RYrWbY40Yqd7udhdQNQVu9fdkm6RTjO5SkJ2ISWdXyMbX54bIql1X%0ArGzLZXVVZDX1ASdXYe3WG7n1O3LFFVfwl7/8JflaCCgBwubvPkRAVgCbgGZErA4GqoCN5r8EMAYY%0AhojPjxCxOQIYbS6zAfjQMg4lTscCg8zX55ivRc2xhIDx5s8vgbVA0Hw9aG5LRV9j5jo3b9zIAQcc%0AgN/828EHH8yDDz64TU45ZhI0nREqTngh3R651ONzpr5sOBxOrmPWrFlUV1czdOjQgnUVfPXVV7ns%0AssuIx+Occ845/OIXv2j3ek1NDaeddhpr164lFotx1VVXceaZZzo+DqfQYrUboi6esViMlpaW5BSC%0A3TR/6vRyPrU4u9oGkK1dIZdtFHpaL/X31Jqo2XbFslLIz8Fu3akPON2hDm22rFu3jqOPPpovv/wS%0AP20XQiXs1JFQgtALNAL1iGDc3vxbC/CVudz2iIhdB3xuvq6E4zqgDpnar0VE7WRE2K4FVgMLgbm0%0AWQpGAUeYy38JLDLHoNLUyhBxuwSJtvZHRO/X5piVu1lFbd944w1GjxxJAhg5ciRvv/02PXv27MRR%0A3DbIV6hkk/jlZhGo0GK1YzLN4CQSCZqbm5P36UQiwaJFi/jyyy9ZsWIFK1asYLvttmPEiBHt/h19%0A9NH0798/r/HE43Euuugi3njjDQYNGsRee+3FMcccw9ixY5PL3HPPPey+++7ceuut1NTUsOOOO3La%0Aaae5NqHTnaPS2JLqQzUMqblZUlKSXMbOv9nZMk3FmD63bkdh9UR6vd4Op/nz2YbTWI+P+hw60xWr%0A2NgJ6840GwD3eG0TiQTTpk3j9ddft/WbBhHxV4YIUAPYy/z9c0Rw+pFoqgeJgG5ExOseiFAMAe+Z%0Ayx0L7IRM128EnkME60BgPfA2MB+oRERvPbALMAH4ztzmXUiEF2S6v7+5zCLLeA4G+gKvAwvMMSTM%0Afz7a7Achc9xhYOW33zJi2DASwNixY/n3v/+dtlpDtrhBNHQF2VhNUpN6UhO/lNXKzjfblbjhe9sR%0A3WGMqRaU2267DYCvv/6aGTNmcPvtt7Ns2bLkv/fff5/99tsvb7H6n//8h9GjRzN8+HAATjrpJJ5/%0A/vl2YnXAgAF8+umngPjge/fu7VqhClqsup5MPlT1JVXLqCiqU8JOUawLprpgh8PhZHesUChEZWWl%0AY1+iYggnawH8XOwWHVHIsatzq66uLnn+FKKebiYKtX+33XYbt9xyCz5k6j6ICLk+SKQyggjTnsAa%0A2oReKRK13IwI1gm0Cdv/muuZADQhkdGPkWhmwlzfi8AbiGDcZC5/DiKIQcTpbHMbqpjat+b6dgdO%0AAZ4yxzQcEbCbgVXACeb43gVeQYRoBLEf7AF8YG5zFLAD8B9EMJcjtoVay/H5+osv6N27NwCnnnoq%0A999/fy6Ht1vQVUI6G4uBdXo4XeKXnQ+ymJHZrhbNHeHm8WU69zZs2EC/fv0YOHAgAwcOZNKkSY5s%0Ac/Xq1QwZMiT5++DBg5k/f367Zc4991wmT57MwIEDaWho4Omnn3Zk24VCi1UXkq0PVd3Y6+vrCyLs%0ArCghWYh+8+oCrabOgsGgo92xrBRCEKXaFKwF8AtxEXXqxptqEwEKdv4Um40bNzJhwgQ2btyIHxF2%0ALbRN85fRNi2/MxItnYsIy4sRkbcIeASJZh6NiM31SIS0PyI8VeXhFxAReSywN2IH2Ai8jCRRjUIi%0AoQ8ikdJKRCjHEFE6DomcfotEVJ9GBGgcOAg4BBHAXyHC825zrBEk0rovYiP4yFzvFETg/sscW8jc%0A9xba7AEJ2pK6gub6nnjiCZ544gkqKipYvHgx1dXVOR55TS6o77Hf79/ie2ctt5Qp8SuTkO3sdaI7%0ARMvdPsZM49u4cSN9+/Z1fJvZHI9bbrmF3XbbjTlz5vDNN99wyCGHsHDhwoLWIe8M3f+utJWgpoSy%0AKTelEoxisVgyCz4UChX0C1uIJCuVDa+iwT6fL+nnLBROilU7m4K6WXR2StUOpz5fO3tCMBhMRlWd%0ARj3oWH8vFG+++SbHHXdcMotfRVO9iDAdiURUP0IE6y7A98CbtGXt34WIwABygdwIPIaISVVaajVw%0APW3lpLxI1PITRLQOQJKoNgLnIQlWIFPwc4G3kKoAG4F/INP4uyCieSkS5T0eWIlESd9BIrK7mO/x%0AAZPMZZeY4z0B+DHwPvC8ub2YOa6xiPj2AdOQqOsb5hiGIUJXHa8A0NjYyOihQ4kA11133RbJGZrC%0AY43I5puhnq6CgRssBk7QHSwAmcRqTU1NQRIeBw0axMqVK5O/r1y5ksGDB7dbZt68eVx33XUAjBo1%0AihEjRrBkyRLXlr7TYrWLSVcPNbXclLXMkdX/2NDQULBsQitOiTyVzd/a2rrFNLlqh1pIOrsfdqWb%0ArNHI5uZmp4ZqS74JYqnHPduSU92F888/nyeffDLp71S+TRVJ9SDZ+GuQSGccibauRsTlD4D9kCny%0AOea/CwAfrnhZAAAgAElEQVTVsfstZEr+JCRyCjJdfxsSKT0OmVrfAKxAxHDMHMffETG6A1CDREaP%0ANbeXMMezGPGcYo71IGBHpALAVMQC8ABiLQggU/1Hm9teiYjtW2mLxgaB/RGBut7c198jtoFnzd9P%0AA74x/1ZmjleV44I2T+9tN9/MrTffzIGTJ/Pcc8+l+QTcjZujb/mOrRCJX6mRWTcfN6DdfriVjiKr%0AhSgtN2HCBJYuXcry5csZOHAgTz31FDNnzmy3zJgxY3jjjTeYNGkS69atY8mSJYwcOdLxsTiFFqtd%0AQCYfqpXUOqLBYHCLbOyuztTPhmyTdoqxL/lswy5pLV01gmLsQ7brTx13R8lShRq73XrtLuD5bP+I%0AI47gvX//OzmVHUPE3CgkergZEV0DkAhqGPg5sCciKO9AkqBGA5+Zf1uLTKH/GxFy3yNT+KOQqOZc%0ARCQ+Y673fNrqoNYgkdCdgTPM5VcgGfovm2MpAz6lLTFrmPmaHzgdEZfvIZHPfojI/RBJ4LrSfP01%0A4BpznMchkdig+VP5Ywcg0dklwCzgCmBXJCL7HvAkEk2tQITxInNMB5j7GEfEexSxD8x76y2qqqoY%0AM2YMH3zwwRbXK7cLm22NXBK/1D0ptQi++k6qe1BXtjHurmS6phXKBuD3+7nnnns47LDDiMfjTJ8+%0AnbFjx/LAAw8AcN5553Httddy1llnseuuu5JIJLjjjjvo1auX42NxCk8HNwf3x9i7Cel8qNaf0D4C%0ApvyboVAobfS0qakJn8/XriJAIWhsbCQQCBAKhTpeGPvmAx1VJVD7XUjPTFNTE16vl9LS0g6XtSat%0AqYeFUCiUcapcRYcLZWWoq6ujsrIyY0TUWstV2USCwWCHU/zZrDsflFiuqBCHZywWIxaLbTEe63Hu%0AiAkTJrD8q6+IIOLPoG3qPkFb/dErkanzG5Ho5Xbmsg20RVzLEcG23nz/RNosAZ8gHtAJ5s9mRAxa%0A31+GRCoDiNAdBlxCW4mpGPA7872XIdHXT5HM/QZzW15EfFo7hK8H7kcEcACJsp4I9DBfXw08bv5M%0AAIchU/xRJBL8T/MYHGuOcSaS1OUzj8MViAieBQxBIskvAfOQiHJvJGJbjVggWhHRqqLWPfr0YfHi%0AxUn7SywWIxqNZvXdKjbNzc3J66jbaGpqorS01FUl4ZRgVS2erZ2/nKgt6xTqHlNWVlaU7eVDpuva%0Aeeedxw033MD222/fBSNzLbYnj46sFpBcfKipkcdsE4zcFlntTPOBVG9jIehoP+xq01r73Hd2/Z0l%0A3fpTz6Fcx51p3W5i1113ZfmyZUlRCiK+okh09HjgVSQzvwK4FxGZZUjEcDRtkckrkCgjiE+1DrgF%0AKS0FMu3fCFyLeF1BxN41iHg9D7ECrEPE50vmcuvMdVchiVQ1SMTzV+Y4tkMirz9EROxYJAJ7M2IX%0A2BtJmHoUiQRfjwjdl4BfIsldxyBRX1UZoAWJzv4HOBU4HKnjejciUkEiyBchloL7gN8AZyFJX4+a%0A697TXOZ+8/j2RyLDqnWsIgrU1dQwuF8/SquqWLBwYfKBWXm4uyJrPR066psbVouACjRYsfPKWiOz%0AdhaDQnT86g6fq/IO21GoyOrWiBarBUBNv3bkQ7WLPOZaLqhYAkO1jLPDqdqcXWUDyCcKnIliCj5r%0Akpdba7laj3k8HiccDrerKZlNsseUKVP48MMPqUBEk8pir0aikxVIBPP35muTkAip8njehwjBd5Fk%0AqeOQae5Z5t+WI1Pyz5rrX2H+bSKSoV+LRE/vRUTfeeZ2KhGrwSvAUcD/mONtQqbf70cE6nrgfxHx%0AtxeS2PQwcCQS+fQggvV9xLs62xzfZbQlZ+2ECN8ngT+bfzvW3C7mul43t1lq7kcMEaRhRLT+Fok2%0A/wmpFPAQEqk9ELENvI9YIYKI8F5jrmuiuT+15j7XI5HsGBCpr2fEiBGMHj2auXPn4vF40vZV31oT%0AfTpDdxBcqWRjMbCK2dTask6dD93h2GUaY1NTk2uz792GFqsOYY2gxuNxNm/eTHV19RZPkdapZaDT%0AiS5er5doNOrIPmQiNeqZTuB1RigVo62rdT8KkXRUjEQ36zS/SvJyIlmqUA8L6oZVX19PPB5Pzhik%0AJnyoMSgh6/F4uO666/jTn/6UnBeKI37RQ5Go4le0RQDXIkLqUWRq/2IkW34UcDkiKqPmOl6hfb3R%0ASUiE1uOBbwyJjv4AEWVvI4K4CRFoC4BLERFaQZvQPdayzwFkin4kEon1IGJvARIhVZYFFRUOItPu%0AY5FSU/shF+e7EEE+BYmWfoRk7v+Pue2/IwL1eKQ5wFGI3WEJIqZ7I2WxhpnreNw8FuPN8Y5FotBP%0Am+t7ALEp3IEI3PvM8T6DRKZHIeW8Rpv7sAqxB5QgBc4HDBjA1KlTeeqpp9p9/tYIXGot0WJF4TS5%0Ak48YzDfxK9VikCnxqzPjKzbpxmit2KDpGO1ZdQDDMJKCR52UdXV1yWQou372mXyouVAMnye0+Q7L%0Aysq2aMEZDAYd8YIZhkFtbW1BTd7hcDiZLKCiwKFQyLGaqNFolJaWFqqqqhwYbRsqWt/Y2IhhGMlx%0AO1mLtr6+Pmk/cQKV3KU8W+Xl5QQCgWR5ndSZhkgkQiKRwO/388ILL3DWWWehzirV9tRARGKT+bcr%0AEOF2MRIVHGq+VodEBPdE2qAGEJF1gbm8B7EL3IkIs3Hmdj5Fpv2vRqbiQcTwxeb6bkIE7vdI0pTK%0A9K8xl6tESmOtQSKV15njVHwM/BE4ExF7r5vvHWiO8x1kWl9FW1vMvz1HWzTzl7RVJIgiiVhP0ubV%0AHW6OvwL4m7mNnWizJrwN/ME8Jj2BGYiw/S0iPi839/1eJPp6FPAjJDJci1QZeN18f7W5rwFz2z7a%0AKjD8IouSV3aJPlZR61QUrrGxkfLyctcJG8MwaGpqcuXYIDcfuROkOx+sP62fvdIvahbMjQ836TzJ%0AhmEwdepU3nvvvS4amWux/QC1WHUIFSlV1NbWUlpamkw6UG1PnS50XyhxZMUwpK1rJBIB5MIQDAYd%0AL3qvxGrPnj0dv+BYp8sBSktLC1KbNhaL0dTURI8ePTpeOAusUVRFSUlJQRLqGhoakg8f+aIe3Kyl%0AyXw+H62trclzVE0Jpl68I5EIS5YsYdKkScnkpAAwyAM1hvg2x5r/nkF8n03IlL0HEaaTEMH5LhJF%0AHIYIrOMR0XWBud5PgKsQ4ad8q6vN109HxJni1+ZrdyNCGUQ0no9ETq+lrf3qEuAec5kWRMwNRabZ%0Ag4hIPB+JcirWIJaAheb+7o6IWfXIFkOEYj0ihL9BPLMXIhFkEN/pv8xtVCAJXrta1v+geVyqzeMx%0ABRGdt5vL32oeq9mImB5i7tcztDUVUNUBmhBxOxw59pjbDZv/lFhVNW5ffvllfvjDH5IP6URLrt5I%0At4tVlXzoNootVjsiVchaZxUzJX51leUk08NIOBzmxBNP5K233irqmLoBOsGqkHi93qQFQN2ow+Ew%0AJSUljrU9TbfdQiQlqUieigirL3qPHj0K9oW3lkpxYhuplRVUC9rW1taCVU9wYirdzgNcXl6O3++n%0AqanJlTdcqyXE7/e3SxDM1qayy/jxrPn+e0K0Rem8HlhqiEh60AOPGuJDNYCEB7yGCLxnEPH0LJLB%0AfhEScVyCRFw95t9nI9PWys/5W7UuJHIaRUTu35GLYysiTEcjns/tkSjlDciYfkFb5YB+iF+0Apk+%0A9yDT/u8jYjGOiN04bd2jQITwIsRL2gtpEnAh0iDgWHO7ap1VyDT/nxEv6q6ISK5FLAPDkMSwGxDB%0AeQ2S0DXeHEvYXNeRiLifaK53OiJgr6atesG5yPT+CeZ7ZprjORP4GRJdvgexXXwKnOGDmXHZ77h5%0A3OLAkUceSUVFOatWrc75OpiNNzI1ycfOGwkkH5C0VzZ73DZNnWoxiMfjyWYykF9t2WJYTuzWW1NT%0Ak2xzrOkYLVYdIhwOt+sHr2pxFvqJ1GmfYTpPLUjkrRiezM7sjxLZ1pqoVuEUi8Vcm/Fu1xEr1QNc%0AyCS0XNdt5/lNrQOczXpvuOEG/vj73yc7KEUQ4TjKB4YBnxkQ9MCZhoinX3jhDA88Z8AvDClu/3tE%0AxG1CBNIjSETzG0TAnYFEFSsRv+l+HrjaEGHlQQRYpQduMSRy2IhUDJgF/BRY74FlwHuGJEsFEeH4%0AG0QI7o9k439Bm2AFidpuj9Q1PQ4RqH8H/mKOaxdkiv9S2qKtuyMC9BlESHoRkajmTsaZ+zsfqSCQ%0AMLczkrZarUchyVM/M8fqRaK6eyLWgCsRoXu7+f/DEYH8FiL+T0Y8u79CfMGPAEcgdoi3gL8iwv9n%0ASBT6KOD6OEzwyLb+a8BAD6w3I+LRxiaqq6v58Y9/zF/+8hfb8yBXrOIiXYcnq4h1i3BJHaObRXN3%0AG59TDzfprCedHZ+VmpoaXQkgB7RYdQiv19suA76pqangZZigTQh05qKSTbkmaxJMIclXjKU2UFBR%0A1FyFU2fJdf12x97aEcttpD4M5Fv5AWD16tWMHTsWL3IhUm1RS4BzS2FmGOoMmOSHHwXgphY423z9%0A6LgkNiWQiOnOHthoiHiabi5zE9Ld6SHaptTPQKbSrzHa6qD+FolM3mOImK0y3/cM4jk9BJKGqFmI%0AgJyBZO+rRgJPIlPlVUjk9xjEy9oMXOKBgww4BxHGZyOJX48jQjVormcf2kRuOWJl2AMRog8jEdNp%0AiOhdgXhtD0CE7AwPHG/AKUgt1l5Iaax3zHW1IEI+gERkD0FKYh2FiOT3gB4e2N+MXEcQAbs3Em09%0A0DxOr5nHdar570BzXC+a+/au+UARBhqNtmYJCXPbs2bNYtasWcyZM4c99tiDQqKEi9frpbW1tV35%0ApWyEixvqiGoyk8u1NpuHm0yJgEBGIZsuiSrduaLLVuWGO++I3ZBQKNSuVWihRZF1O/lMndtFIDOV%0Aa3JCFGdDLsctncjOJPTcIlbVsVfT5uk6YuW7/nzItO5U72y6h4Fst3PggQeyYMECQESMxyOircmA%0AUg/8X4sIt/k9oDUBUxpEDN0P9DYkgeoqD5zjhVID9kyIcPoZbclTTyBlqiJI8fvHkQSnn9FmJfgM%0AiRD+AhGW5ebyF3jgGMMUqiZfINPet9LWhnUKkmh1LuITbQHmeOAZQ8bfAgw0JHKqPlkPkti0AJju%0Age0NeMwDJxkwAomM/h8SKb0DuUj/yNynB5Dp+BbgVA/81IwO/9WQiOcMxAoxxtznnyPi9kWkNeyT%0A5roHI2J1OlIZoRcw2xzzaUgE9WBE6D9srvOXwCBz/FHzfR7gHJ9Eo5+Pw69KxEbwxzBMCsLqOHwX%0Ab7NaRM33HHzggfTpvx1ffbXU7hQpONlGZbOZTrYTLdleI7tb5NKNODW+VIuBFXVdtApZFa3PdE6o%0A99kdx5qaGvr06ePI2LcFtFh1iNQTUXlYi7XtbAWMXVembERHsS5YHe1Lqpc2F6GXzfqdwu7i5FSp%0ArEKO37puuyiq8s7mcj5Yj/nrr7/Oj445hhBm9ycP7FICKyKwKQH7lMBOQXi0Hnb1wxEN8vdxPris%0AFCb7Ye/NcLQHLvGKTeCYhEzNJxBRtxFJRgI4CbnIeRHBNAJ4zSPbbjIkE387JPP9TtrKSAUMiUou%0ARATaMMRL+hMkAqoII/7YkxFvJ8DphojJ6Ug0c4P52o6IEBwB/MwDkw042xSb+xtSrP9JpNuWFynP%0ApS7QfiQK2h+JdJYh4nIsYkHwIMJ5ornP75vbmWy+dgwSab0VicTuiojlfZGI6TWI2L8Z6YQ12zwe%0A/2P+XoNEvmvNYzQnBAN9cEorvJiAF0JwtB9+GoZDAvB8JZzQCOP9cGAQ/hqGiSUwPwwtpq1j7dp1%0A9Kiq4q677+aMM85IfwJ1knwEV67TyarCxdYWlXW7WC3W+KwPNh1FZe3sJ6pz4ueff87MmTMZPnw4%0ANTU1DBw4kMbGRscT7F599VUuu+wy4vE455xzjm1Vjjlz5nD55ZcTjUbp06cPc+bMcXQMTqOrAThE%0APB4nFoslf09tMVlINm/enBQRdthFIPMpnVVbW2vrSXSSdG1d7SJ7HbU+taOQFQcUmzZtorq6Olk3%0ANlXwdaZUlvJFF6K9YHNzc9JCkU+71nTE43EaGhrYY4/dWbfme+JI9BRDkqRiBpR44KWBIlKfbYSI%0AAfuXwvIoGAn4pBoqPLDPJlhgiNha7YV1CfG57uSDMV7Y0YA/xOAwD/zKKxHMuAG7JMSTeb45pgQi%0A1PZDBJzifiR6+TAi0FYA33jhpYREDL1IEf2ByBjmAv08cLfRljAF4ul8BGl5uh0S5XzeA6+Zy/mQ%0AaGVqkbazPbKdowyzoYAHzjJEhC9Eykpdikz1P4mI7P5IlLQfcKlHhPIjBtzqgXmGTPufa66/HvHn%0Arja3/TQixg1EjN+ACN67kajzleZxKAHmBGCIF34Vg8ficGsQzvLDTVG4Owq/CohgPT4sY5hVCRc3%0Aw/IEXFgGdzTBSD+si0Ndoi3SGvBAsKyM779fm+Esyp9EIkFLS0vBWiCnkksFA5DvR0lJSVG9stni%0AxlawVtxa5UFh7eSYSCRYsWIFL730EsuXL2fRokWsXbuW9evXU1FRwYgRIxg5ciQTJ07ksssuy3ub%0A8XicHXfckTfeeINBgwax1157MXPmTMaOHZtcpq6ujkmTJvHaa68xePBgt0V5bT9MLVYdQj1ZK4pR%0AUkphV3Io3TR/Z0pndSSKnaCpqQmfz0dJSckWWfFOlczatGlTQcVqbW0tlZWVybE7IfgU4XCYeDzu%0A+I3XMAwaGxuTkQAnawHPmzePKZMnYwDlXhFqMSQqOqYUPmuGwaaIAbiyB1zRAx7cDL+pg3NDMC8G%0AX8RF4OxqRuv6eeBXTfD3EBxsnpLTwzA/Ae96TUEMHBqTaOlTtAnKSxBLwKu0+Sq/QqKODyIiVnGn%0A+d43kUjqQiT7/W+I+KtCBN9kpETWN8g0+n2I8LOiGhUM8sAnBoz2wIWGJDNdA3yOCNyeSCWCWUiW%0AP8hU+xWITUCxGbjfA08ZclyHIaWm1Df0HaREV5m5nd8h4vlJ4BYPvGhIl6xzzOVXIQllKxEheaIP%0Arg/CuVFYEIeZAZjohVfi8rf9fPCPILxtwMlhGOyBnb0wKy7HNYJZ1cH8P8iDSdiAco9Et0s8cqNp%0ANeD+++/n1FNPxUlU17RiidVMpEbgYrFYsjFCptJLXRWVdbMYzFQWyi1kKv116aWXctlllzF+/HjW%0ArVvHt99+y7fffgvAaaedlvc233//fW644QZeffVVAG677TYArrnmmuQy9913H2vXruXGG2/MezsF%0AxPbD1DaAAlGs6ebUbWWbaNTZ7RQKj0daNTY1NTnWGctuG4WYPrIa8hsaGmwT1ZzajlNYKxCoG2Nl%0AZaVj4x05cgQbvv8eD+JN9ZuR1Gm9Jfv+kRr5295V8GodHFsOJ1fASevg/VYRp+8YMK4UFjfCv6ph%0AvyDEEjBkI5zrbxOqz8fEN/mmD0IGrDVgRkIE4F3AB+aYPkIy95+iTajGkOSr02gvVBcimft/QyKq%0APZDp+SqkfNSLyPT428DrHviTIcJ4EBJ9tfI4ksE/GxhiTvv/DRGTHkSczkaEKubYTjXHcyyy3r95%0AYIQhU/qY45luSOKTDxGZj9ImPg9AErWmG1IvdhBS0N8L/J8BRyMe3ucRf+8cJJo8yiOdvIZ5ob8X%0AXgjCjBgcG4GLfXBtAOZ54ZgIDGmRsSYQ28OrCbihJ/T0wDW1cEkvmFQKJ6+Gwyqhpx+erIVefojH%0ARLgGzdPtggsu4KqrrmLVqlWONBpRuEXM2PkiVWQVtozKqqlkp72y2eDWqimpuOWztSNT6a9NmzbR%0At29fPB4P/fv3p3///uy77762y+bC6tWrGTJkSPL3wYMHM3/+/HbLLF26lGg0ykEHHURDQwOXXnop%0AP/nJTzq97UKixapD2HlWi1ENQKGieNkmGuVDIcVqqp+zpKTEkRaidji9H9ZkKSVMKyoqHOsEZcWJ%0AC7MS1SpKqyoQqAQSJ7bx+eefs+eee+L1QMKQqV6ASELEysu10JSAi/rBtf3hkCVQn4BXW+DxBgh5%0A4IqecGW1CJnhy+GX5SJUAQ6vE5HTaMDUFlhlwAZDhO+UuERAwSwx5ZGsfg8QNUQUBpAoqNdcxkAu%0Ahv/2wvSElJvaGSlN9TMP7Gk5XRqR5gE/py3Rak/gKgMO9YiALDPEJ9ob8ZT+EElsegCpfwpSWuta%0A09JwNTKdPw34MTLd7ze3dRLieb0esSdcYa7jDmRbJyJ+2KeRSPEVSOT0QaQ+7DOIP/UKHzxpiOf2%0AiYS8NgXxt57vgUlmdPaxUjgmCHNjcEIz/CsOs0NwZQD29cK0MPwzLoJ9LTDID9/HYFYfmFwKP62F%0AGXXwXH94ewAcsRbmN8P7w2HqSujjhzsGws/XwJE94a3N8rl4zM+mqamJnj178swzz3DIIdYUt62f%0AbLyyqWJWfW+hMFFZt4pBt/tpIfMYN23aVJCp92yOSTQa5eOPP+bNN9+kubmZffbZh7333pvtt9/e%0A8fE4hRarBaLQ2fPWaX5VtL+srMzxDllWnBZ5SjRFIpGknzMYDBbMk6lwYj8Mo61Tk2o4oMT15s2b%0AHRrplnRm7NYoqopYW6s/WD3XneGQQw5h7ty5lHmhOQGlXpjYAz7aDHjA54X6KBzbQ4TLyEUiXA+u%0AgrP7wE1rYJAXbuglVQImficid24ERkVhbVSis8P8sC4E+wTgrw2wrw9+XwG9PNDTCzvVwlQ/3G2Z%0A/d2/QaKuL5fK703AUxG4Mgz3hETwfpWALwx4NC5T148CL3thh4REKv8KjPfAuSkfw63I+1/ySRmo%0AegNeNeT9LyYkKWoN7RsCrESm/+/0ieh8xYCbDPGPHmHAex4pX3WzKSQvMiSZ6w5EwHoRsfu0ub7D%0AEWF8MyKWhyD+1KcCcIAPLjPg+gQcmoDzkCoIS4BPDRjrgyUJeCEGR/nhh374tAJObIEdW+A5M9O/%0ArxdWJmRf5vWH3YLwQAP8eCP8sgoe7Ql3++HItXBjT1g0GA5fC0ethDeGwk/Xwo1r4eEhcNFq2KEE%0AGhPwfRQCCbED+IDjjz+eESNGsHDhws6cjq6OEOZyf8iUra7WlU9UVq0vdRxuF4NuHx9kPvdUa2mn%0AGTRoECtXrkz+vnLlSgYPHtxumSFDhtCnTx9KS0spLS1l//33Z+HChVqsbgtYS0il/u7kF8qucLzP%0A5yORSHSbBgR2+6CsCspjW0jy3Q87H7C14YB1/W4hNbmuowoEnfl8o9EolZWVSSGW8IhvdHIfmLMR%0ABpTA73aEm76G1a3wUj08t1m8q++NgT3L4ecrYWUr/KgaJq6ELyLinRwbgnEVcHwQrvoeHu0ndgGA%0Au+ognIDHe0Ifc+OXNkA0AXdYnnn+3AILI/BZpYhgAH8CrmuFm4PwY8vV8Jko/DcOn1TAZgM+isPc%0AONwYFQHbywvT4yIwD0Hat/4F+KcpVEEiutM88OcE7OWHKR6YEZVC/Ach0dQTvXCCB04233OkR0Tq%0A22algIQh9UytZ1RvJKr7ogeqPbA4IRHb88zXyxGxugHx2VZ6ZSwg/tDbfHCUB86MSAS2Dik5dV05%0ALI7BkQ0wvgneLod+XnijDC4Ow2QzXD29Emb0hqs2wf7r4PFecF4l7BSAozfAB63wQh8YH4RjN8Cr%0AzTC9QiwBeyyDah+sj8NZK2W/5jdBhVceWMq9bVF4rwHLli2jqqqKFStW0LOnMkjkjpu+k4XCyais%0AErDFKFeYL24dVyp2YyzkA9SECRNYunQpy5cvZ+DAgTz11FPMnDmz3TI/+tGPuOiii5L34vnz53PF%0AFVcUbExOoMVqAXFK3Nm1DbUWjldCpNBYkwByxU402RW/L5YvNpdt5FpjtJD7kO2686nj2pkL/+23%0A3871v/kNAAEftMYhkYCgD15ZD0NK4ObR8NPPoT4Gx/eDswbAKZ/BjYNEoBz2FbzfKFPBL0Zg7x7w%0A+QZ4bhgcXinb2XMpHFQKJ5nR0jUxuK4WZla1CdUFUXi4Beb0aEuy2piAy5vhgTLxYCqOa4EdPPBT%0Ay2nYmIBLYjCjFEab9/49/XBUAl6JwWPlEvl7MQbXtMLFhpn45YGxBu2U5Z1x+NaABSXQxwOXB+CN%0AONyVgMlR8CTgJ9727/F4YE5CrBB/KIHfROBZA65JSNH/MHCYFw4OwOMhGcf5LeJnfSghloBfIV7V%0Aj3vA4xGxSpzig9/5wOuFfTwwxQcvxcUGMc7c/3F++KwapjfCuAb4cyl8b8CTEdgjBItaoSYhNoq7%0A+8CeQThlI1wWgZt7wsIBcOgGGP497BOS4zKvFd4PwyE9oU8A/l4Dvx4K2wXg6mVw6nZQF4M5tVAd%0AgA0Rqa0b8spPnwdGDBvGiSefzAMPPJD3OepGill6KZeorIrIAskWz+ksBmr9xaY7iNV0Y0wkEgVr%0A+ev3+7nnnns47LDDiMfjTJ8+nbFjxya/O+eddx5jxozh8MMPZ5dddsHr9XLuuecybtw4x8fiJLoa%0AgINYn1IB6uvrk5G3XEk3RW4nOIpVeSDXTPR8KhIUY1/SlceyklqJIJeSU9msP19isRhNTU306NHD%0AdsxKVKdm9GeDepiorKzMaUyDBw+kftMmIgko9cGgMljbAiU+GF8Nc9ZB3yCsj0CZF/49AXavhB3n%0Awcow9AxCTUQE68X94ZqB0MMP238CB1bAQ2am0owNcNN6+GIwLI/BB2G4qU78juP90OCBhriIKb8H%0AKvwStU0kxA/rAwYHoCwBPQyp9zkvAX8MwPF+qDbv44eF5f2vlrVFYAF2aYCJQXjUcvobBhzYABsS%0AMv6lcRjuhRMT4g09zoB/lsAPUz6CGRH4fQyOD8ETYRgB3OmFvc0yWRck4K0K2MMnpbcej8I1Yemw%0AlTBgqB9eK2tLTGo04Fdh+EtEkqjWA//tAaNNEfrfmNQ9BXjcB7+JwecemL8dvBmGi2thWgAeLhcx%0ACzoAZcgAACAASURBVHB7C/xvswjTvw+AYyrg6wgcskYioe8PkJ/zw+JL3cUPB4Xg7iaJPscN+NNI%0AOLonHLEEVkXgw13gnXo4cyn8cqgk1h33OZw+UD6v+76DS0bBH7+BUj80RaHFvKT6AU/Az8aNm3I6%0AP6PRaLskJjeRKVu8q4nFYkSj0WRlFrsyXPl2dnICN3+ukLlawcaNG7n44ot58cUXu2h0rkZXAyg2%0A+UTYVMF7uylyJ7eTD9luJ9/GA7lsozNk2oadRSHXSgTFrAShOqlYo6h21oRCsGLFCsbsuCM+04ca%0A8EBlENa0wEU7Qm0r/H059AnBz7aHGV/Ab0fCP9bB5I9l6v7wvnDGQBEpmyNw0xCJpF29HJrjMK0S%0Arv4e5jbB4lYRj4O+g0qfiDYfcFov2M4HPX0wsw6MGNzfv22czzfAE/Xwp34iWjfFYXUM/lIPu4Tg%0Atjhc3iJT5H5DaoQe6od/RMW7WeaFG8OwyYC7U6zUz0dECH7WC4b7pFvTU2F4qAV+Z8j0ezzlVPgs%0AAbfG4LkecHAQbqqAO5vhhBYYkIA1CbivTIQqyPE4MyhickIDLEO6YjUa4s8FqUH7h1KJZP41AlV+%0AaT872tzmnn5Y3AMuaYEpLeKf/W6wiM2zKuAHQThqA4xtgLmV8FUc7myBPUuk1u3tm+HwMhgdhIVD%0AYNo6GLUK3uoPY4NwXDk80gAfRuEPw2F6P7hsBVy4DAYH4e2xcOa3MG4BzNkJXt8JjlgMx/SGebvB%0AlE9hjyqYsSNc9iWcMwJmroShFbCmGZpiEqVvjsaorqri5VdeYdKkSY6ez11Bpmxxt5BtVDZTZ6dC%0ARGW7Q2QV7PfPZXVNuwVarDpIvhUB7Kb5c8mEd4NYtWs8kE/Zpq4Qq9laFHKh0DYAu25YnW3YkMux%0Av+mmm7j5ppsAmX6JJ8DvhcYIDCiFR7+Bxij8785w5Y6w44tS6P/ab6HcL9O7c38AE3rA09/DvDr4%0AZDw8tgEeWQcLwhIxnbYSdquEpVGYUg03D4bhIakEMOITeGwwHGUG4b9thWvWwb+GwD6mqGxOwCnf%0Aw7194URLwPi4NbBzCbzfXwRw3JBtTFwNZ1fB+gT8MgxntUAPr4jcY4NSF7TS07bu6c0wo0KEKsBQ%0AH1xdDh/GoTQh2fHT6qFHXLL6r/HDMa1wQakIVYDtvPC7Cri2FEbXShLTH6KSxLWL5RS8txU2eODd%0AfnDzZti+Ea4MwrVmYGlmBB6LwL8GwButMLkWTg7Cg2USLY0CH0VhaABqYnD4enijH5R4pWvYZwPg%0AnE0wyoxW/7I3/KafLHvUKhixAuYNgmFBmD0Art0EP1gtJ8CgELw2Fv6wDn69Gg7tAXcNh+1L4Mgl%0AcN9weGIU/GoV7LMIntoBPtoV9lsECxvhikHwv8vhswaYVA33fgM/6Cm/V/rlnNkUkbGGEzB16lSm%0ATZvGww8/nMsprsmBbMWg8spm29nJKmYh/6is28VqpvHV1NTQt2/fIo+oe6PFagHpSNylTjOXlZXl%0AVfC+WGWy7ESeNapnl2GezzYKvS9qG/n4OrNdfyFQxzuRSLB58+ZOnTOdYeDAAdRuqqUsAC1Rma4d%0AVgUbmyXLf1NE6qAeM1g8iP3+KclOZ46E80fD1Dnwi5EiVN/eAGd+ZloGFkkSVk0YTuwHNw6HgSF4%0AaA180gAPj4DepqPmkMViETjK4hY5agWcVNkmVAGmrREhdqpFqL7XAq81w4JBbdP8Pg9cvQnGl4qw%0AVX+vj8P4FTCuDL4FhtVJBYOdDViVkAL456bMQr7eCi+3wif9YYcA3FoF/2iGO+vh/lbAgAttZi6n%0AN8BAP7zRH26ugx82wF4+KSX1VRxuicDLfWHfELzcD15pgemb4K9NcIlfEsX+th3sWyr/jimFH6+D%0AYZvh6Qq4sAkiXlg8VFrY/s/3MHSNrHOvkPh7e/lkDs7raSvi38cPc4fBRetgl5Uwsz+MDsC/mkQ8%0AtiTg9L5wcA84qAouWQG7fgYv7yi2jmFBOOVr+CoMF/aH/zTCsV+aNx+PPMTc/B3sVAWNcZi3Gbav%0Akrq6CQPWtYotQ9lMfEht21n/eJp//vPZDm0BbhY1W/vYMkVl1b0kl6is9f9uj0pnOn4bN27UkdUc%0A0WLVQewiq6mJT6k1OYPBIOXl5Y586Qp94csU1XOqJqoaf6H2RYlU5cdyIiKZitPR4dQEL6AgbW87%0AGnd9fT39+vUDRKBG4lDih1PGwD+/hpYY/GYv+GAtPLcM/rUWnvkOAl744FDxrx77rgjZ+XWw3Tti%0AFdipEq4aLVUD7lsGf14Bd42GMh80xuDqb+EBi1B9ugY+bYSlO7SNbcY6KWl151DxqHq9MKcJ3m6E%0ARcPaxGciASeth2t6wg4Wm+C7LfBWC3w6tL1P9ZF6KW/14mCo8kny1TtNcNtGWNIipacm1cPFQTgx%0AJFHR0xrh5h4iVEHsBT8pl6n3M2rh4HIYVwsTgvBAOYz1w2Mt8EYUFg6G/n5JXrqyB1xdJ1PzCeDW%0AajjAInKnlsI3A+C8WrimCcb44Uelba/vFoLPh8DPa+HAOrEKrBsuEfB+Xnh3MNy0CQ5cD1dUwqdx%0AmB+BhWNgfQyOXAbzW+HVQfIZPtAfJpSIyE0YcFQveH9H+LBRpvS/CsNjo+He4TAyCId9IZ/bCb3h%0AvO3g1jVw5xoYUQEnDYZZq+GXY+HyHeDwd2FlC3x0GPz0I5i3AV49BKa9A4MrYGSVnFM+jxnJj5vG%0AtliMqqoqPv30UwYPHtxO3GjcjfqMconKqqYrKqARj8eJx+O2VoOuPgcy3cM2bNigI6s5ohOsHCQe%0Aj7erVakSi8rKytqJu1wTX7Khtra2IAJGoabKVWZoZ3vcZ2LTpk1UV1c7ti+piV7qYuZkpyYrTrRE%0ATZfg5fP5qKuro1ev1K7ynSdT8tajjz7K+eefT8gPGOJRNUxRGIlD0Avzjoer3oN5a2FIJVy3J1w2%0AVwTJ0YNg+nxYUCci43+GQa8APLQUvpoiEdXvmmHcW/DiznCQWaXowE/gu1Y4tQ8sboZlrfB1q/hj%0AvV6JtoXjEnlrSbS/YJV7JWpX4pXIbcgrCTvNCRF6u4ckYWrfEhi7Bi7sAb+0VEeqi8GwFfDXgXCs%0AJTIbScDAr+Hm/jCxFJ7YDI/XijXBl4D+Plg0QKKTisYEDFsLt/WFc6vhmwjcVANP1cNILyyLSzmu%0AEyq2/FxGrRKfrdeAGdVwhmWZ2gSM/x5+UAFLIlAXh+f6wF6mqI0bcPR6+DwKLXEY4od3TK+q4tUm%0AOGKNtGT9bpx8LiB1T6cug81xeH+oRF5P+R4+aBGbwDG94G/mA8NXLXDQZzA8CHPHyWfztw1w9rcS%0ACa0OwAlD4KmVsHs1zN4X3q+Fqe/BWcPhjl3g+Pfhw03w38Pg+s/h2ZXw/BQ4fx7ghcOHwkOLYf8h%0A8PZ3ct55zYirzwNnn3MuN9544xY1RdU9ztruuKuFjKK5udnxe4FTuDn5yzAMWlpakvefVN9sR1HZ%0AYnz+mRLA7rzzTvbaay+OPvrogo+jG2L74Wix6iDqyQ/avkwqGlZIcQewefNmysvLHS8ynJpwFI/H%0A6dmzZ0G/7HV1dVRWVnb6Ap6a6KUsCtFolGg0SkWFjTJwgHA4TCwWy2v91iiqdczWuoe1tbUF+Qzi%0A8TgNDQ1UV1e3+/uUKVN477338AIlAQj5oTUmV5Sh1bB8E+zaBxbXStT07v3hrLEw5Tn4aB30LYWV%0AjZIg8+vxcMU4WW7gLLhjHJwzTLYz5k2IxmH7UvgmCmvDIkj6lcDICtihTGwDFT74vzFQ7RcRdNan%0AIlre3KVtzNd8CzPXwce7SYWATTH4shnO/QYuGyhR2C/C8G2LVCmo8EEfH+wcgEPKRDSetA7K/DB7%0AUPvjdMIqWJGAD0a0CVLDgN9vgF+vF+Fc5YdTQ3BDDxHLB2yQkl7/Gtw+crsmCruYgnDPUniiN4yy%0AaINzN8C/WuGzHeGfm+HiVVIJ4Pk+4o+dUiPNBz4eIwLy5vVw5zo4pRwe6C0e1FfD8NU4mdY/6Vv4%0AbxM80x8OLJPappNXQS0y5rURmLc9DDXH0JqA6Svhxc1S4qp/Cby3lxyzAz+GIUGYu7NEazdE4ZDP%0AoSEGF24Hv10DPUOwPgzTh8Efd5Pku/3ekaS7Dw4QT+qB78LU/vDYRJj+Eby4GuYdCo9+C/cuhWcO%0AgusXwupmOHcnuPVjOHQEvLZMjmkkLnYUkGLnn332WbuInLVuc1dmr9uhxWr+ZDp21qisNTKb6pW1%0AS/xy6hyIRCLJmcdUrrnmGk4//XQmTpzY6e1shehqAIXGrqsUQHV1dcEvgE5OPadL+PJ6vdTW1jqy%0AjUx0Zl/UzcnaSjS19Wyhk7hy/axTo6iZ2uUWOyJUXl6GYV7cS4Mwqg+s2AiVIbj3aPjpPyWKVh+H%0AkE+iXwcPgR/8AxZvgoHlcOmu8Ny30BiGq3YSgfejt6FfCJY0wq7vwDeN0pVqpx6wQ184tSdc/gn8%0Aeke4epSMZUkDPLESPtgbdjEjnfNr4T/1sGhC25hrInDvanhurFgHegdgOPCzb+CInnDzsLZl17TC%0ADh/DQ6MkMvpeI/ypHi7fICJzWABu3ADn94R+fmkbOrsJ/juqfeQ0bsCdG+GmwfCzvvBMrYjXe9fA%0AQI9k+C8d1V6oAtxVK8dt8Vi4ZT2MXwMHlcDjfaQ+6ZONMH8HEdM/6QVHV8HP18L4tTDEB7UGLDfL%0AIwY8cP12EgU+YQX0+U4eChbvImWhAF7bHv5vPRy5Gs6uhP+0Qr0XPttN7hAXLofxX8HMIXBED4lG%0An9ULZtVBqwduHSqitsoPC34AhyyQ4/fxbtA3AHeNkAjrdSvh9p3hku3h080iSGui8MRe8NFk+X3n%0At2DBZPjwIBGwR8yFP+wKi+pgwquwd1+xPxz9JvQvhRVNcNvH0Pf/2TvvsCjO9+t/ZukdFaVZQFTA%0Agg3sBXvD3jCWiN0Ya2JMNLElsX4tUbHFFhsxFtTYNXbF3hW7KDZUEBCWBZad949nl10QrGDI7/Vc%0AFxewO7vzTNnZM+c597nNYccdqOwE556CpakYu0otuvXY2dkRExOT/vnRtRDWka73rV7PTUUur3tW%0A/6ue0Ld5ZTNbDAw7feXUOfDZs5qz+Kys5iDUajXR0dEZiozi4uI+qvPKu+LVq1fp6/0QvGsmam7b%0ADeDD8mkze4HfVOiV21mu75pX+rb2p9kht46BrnArX758xMbG4uLshEYGU2NAI5Q7Y4Xwq/5UHybu%0ABzszmNcMjkfC7BPglR/CY4QtYF5d+NIL9j+EVtvgYgCcjYZpV+HmK/F+fgWhiStMuwiL/KCLmxjL%0AyPPC73qjvvBLApQ7BLXtYb6XfszFj0CgA0xy1z9W9wLYmcBWT/1jO2Kg0w24XRmcDD4iVS+AmwWs%0AM1hWo4HC5+CLgiLvdXM0XEmA/MbCQ9vaHlZn7F5Ij0g4q4JLZcSUtA4nE6DBTUHEPczgt4LQUCu4%0AX1VBlfuwpyTU1J4q15NgeCQcfiUI8KzCMDCL77TJUfDzU7A1gS3FoGomx0nIS+jzQFzAJ7jCSKeM%0Azx9PgFrXRSzXI1+xv3T4PQqG3YURBaGkGQx8CFO8oLgldD4PA11hurYroyoNOl0VU/qtC0DIc/jS%0ATairO57ACX8oaSNuRmodgvL2sKO6sGI0OSo6mU31ht8fwD/PRGveQhbgYCludgK9wNkKZp2Ddt7w%0AMgkO3Qcna3gUL9ZvpLWEmChAadD87sKFCxQvXvy9FMLsFLm3tSzVPf6+xDMxMRELC4s8SQqTkpIw%0AMTHJlZagOYGEhIQsM0w/FjmlyqpUKoyMjLL8Hmvbti2hoaHvnWn9/wmyPKB57xPyH4aRkRF2dnZY%0AWFikRzbpTvzcxocmAqSlpaFUKomLi0OpVGJsbIydnR02NjZZEqd/OwfVELIso1KpiIuL49WrV0iS%0AhK2tLba2tpiZmb3xrju3ldU3pUAkJycTHx9PfHw8sixjY2Pz1jF/SqxZswZnJ0FUrcwgVQ0mxlDA%0ASlwwTIxg5E5AhuNBcPMFzD8NxkbgXxy8HaCWiyCqKWnQcacguJV2wOCzgqgOLwevvoQjAXAuGsrk%0Ag0Ct4vlMBQtuw/IKeqK67AE8VMIkD/04p9wRHtSfiuof2/8SzrwSRT46aDTQ5y5MKJaRqG6PgWtJ%0AQg00xMj72rakxeGnYnC2EryoAVVsxLZvewUFwoWf82ACXEqCjfGwtnhGogow+jFUs4eH1aF1IRGZ%0A5REBa2KFT3RAIT1RBfCygJ2loJiFuDH46SmEZCp2v5cMk6LgtxIw0Bnq34Gg+2I7AS4mQd9IWOwN%0AW8rDpKdQ+4aIfAJRHDX1KRQxg6r5wfMiXE3Uv39fR/inrGjC0DcS1paHwW7QohAcrgbLnkDLC2J9%0A5kYw3UOQz1XP4HdfmF8ZVlYRpNXvoPCheljD2fpwKwGqHhRNI8rawoNECDoPlhawqyUUsQVHGzgV%0ACBOqw/qb0MwdFjaE0HD4sjx0KgMvVbCkHViZQjkXvTVFd75IQIUKFZgwYcJ7qZc60mFsbJxeW2Bh%0AYYGVlRVWVlZYWFikTz0bpqEolUoSExNRKpXp9q/U1FTUanU60ckKeVlZzcvI7et3VueApaUlVlZW%0AWFpapp8DkiSl282SkpJITEwkMTGRpKSk9OIvtVpNWlpaBjuKSqX6qJqG7LBr1y68vLwoWbIkU6dO%0AzXa506dPY2xszKZNm3J8DLmFz8pqDkPnU9Ehp4uFsoNSqUSSJCwsLN66bFaZqDo/7dvwMV253hVv%0A6gD1rgrwm5CdNzOnkFWh0oeqqFkhpzy9mSHLMi1btuTQgX2kaiv9TYxFhuq3jWHmHqH2tasAmy9A%0Aj/Kw/Ra8UELXsvC/xvD3TRi4DVY3ht8uwrEnolFAv3LQyRNWXYNNN+FGR0EsLkRDja2iqMZbu7uq%0A7Ib4FGheCG4mwAMV3FcK8iYjFNlktVBvE7VhG5L2x1gSYyxoKnrQO2g7Z91VwW/uggyWsAAXEyh6%0ADka6wHAX/T54lgLFz8KOclDH4PSISYFipyC0ItTLD4djYGUUbHwiSJuTCRwoJQigDttiIfAehPuK%0AdrMgFMeFT2BchLARLC4KXxTIeBymP4UpUXCjGoQ+h29uQXEzoaA6m0Clm+BhCVvKieUvJUCn62Jf%0ALHUWKm9nZ/hNW/wUlQwdLov0gq3usDJW2BRu1BHK7OibEBwBwe7Qw1G85pdImPpI+IVNgHM1hX8X%0ARNW+/wmwMoIfikHf66KArrw9/HoN/qoGzZzFspPDYdJ12FAFmjjBqRiocUgcJ58CMK0mzLkEx5/A%0AlS5CIa29CSxM4FQnmHcRRofB1jbwIgmCdsHiADj2EEKuwPxW0H8LtCgNu6+LG6ZXyaBKFZYLCShR%0AshRhYWG5es2C11uWZlblsppaVqlU79ww5VMjL/tpZVl0h8qtuoMPhaEqq6vz0D3eo0cPzp49i5ub%0AG0qlknbt2uHh4UHx4sUpXrw4rq6uH3UepKWl4enpyb59+3B1dcXPz4+QkBC8vb1fW65Ro0ZYWloS%0AFBRE+/btP2qbcwGfC6w+BTKT1dwiFpmRlJSELMtYWlpm+byO5KWkpKTnir4vyYOPtxu8CxITEzEy%0AMspQRZk5vsnMzAwzM7MP+nAbTnfnBnRk1dbW9rVmAzlx8c+NYjpZlilQID+pyUloZK2CaiwuAF38%0A4M/TUNoZQnpDg5nwIlEQgwKWgnhcGQAxSigxT5BKtQbqFIeDdyCsC5QvBI8ToNRS2NkUajsJkuf+%0Al+ga5WgBj1LgmVI8XtQa3O3B0x4ORAIyzK8GdqZiynrIKXieBMcbivFrZJhxXfzsrwsPkwTBvfUK%0A5t2GyvkgOhliU0TBlTJNbFuHglDPBvyswccKGlwFR3OJjd4ZL33+F4SKt71Sxv02+S7Mvg+V7eHA%0AC3A3h+8LwRf5weUK/FgUhmQq0LqaCFXOwWB3WHgfHM3g98JQxwbuq6BMOGwoB021JDYmFUbcEp2/%0ACpuAErhfRd8WFUSO7fhImH5fZKU+rZtxnRoZJkWIH40MN+pCMYNLxaan8OVF6OIgCPf0R7DfH7xs%0AoV0YXHoJJ6vrXxObCqUOQbwapleEwVpivPQODDkLiytDV61S/vtdGHZBqLgnYqCms9gmZSpc7CSS%0AJb7YBwcj4WKguEmqswkkBZwNhN+vwMijsL6luEnpugPmNoOrz2HpeVjQBgZugYal4NAdyG8NkS8h%0AKUUo4WkakBGWrH8L2U0t66INDe0FmW0G/1YMU162KGg0GpKSknJFncwpZN5/Go2GqKgo7ty5w9ix%0AYwkICODu3bvcuXOHu3fvEhMTw7p162jduvUHrS8sLIwJEyawa9cuAKZMmQKIYi5DzJ49G1NTU06f%0APk1AQMB/hqzmTTPKfxiZp4B1AfS5TVYVCkWGaQYdsiJ5H+N3/JQ2gKwKj3Qk7WMu3rm9DbovodjY%0A2BxtNpAb0F30CxQogEISRMbMWNsiVAYjI1h2DBysYWpbqDkdlCkwpinUKg5N58OKVtB0DYQ9hCJ2%0AMKEhtCkNleZBUBlBVAHabAYPGwi+CkHHIDJOEJWaLlDTFSoVgn57YUhZGK0lhU+VsOwa/NMYqmnf%0A5/4r+OcxHG+kL1hKS4Mp4YIklbETPwABR6FWIdhbW7/N8Snguh2GloB7iTD3GTyLhLhUQZRKyjIz%0AIqGvsygkOvhSZIlez9TdM0ENU+7BqkrQykmkFyyNFLmw/R+AqQQ9HV/f562uQp+iMMUTxnjAtHvQ%0A7DZ4mkN0CnRy1BNVEFFSK0pDOSv48a4g6ydfQXWDhDET7Q1CPhOxT73C4HBlKKS9p1RIUN5a/DYz%0AgqArsMdX3FgAtHMCLyuoESZ8oAfrga82HW1nLRh0AXyOwvbKorNU36ugkaC+C/x8DVq5QDFr6O0B%0A+U2h2wl4ngxDS4riLgk4Fg1j/WCMn7BvNNoKZf+ES4GwtiF8uR981sGFTnC0PfhvAp/VsDEA6heG%0AdlugXEFxjPpvAztziE+GXhvBxRY2XYLyLhD+DIrkh8ex4lw1N4HkVBlbW1uePHnyrxCcrAp+dOqg%0AlZXVa2RWN22c19IL8gr+C/aJzGNUKBQ4Oztja2uLnZ0d48aNy7C8bnb0Q/Ho0SOKFCmS/n/hwoU5%0AefLka8ts2bKF/fv3c/r06Ty/Dw3xmazmMgxz/nIThgQst0he5vXkFgwr+nVT5tbW1nm6ElfnRdUl%0AKAA51ighMz72GBhaKR4/foyPjw9GCqFAWZlDYQd4HA3qNKjlDQcug70lNJkjlKpDw6ByEXAeI8jO%0Al1uFb1AhwZ5e4JYP5h6H56/guyow5gisuQFRiWBrBqVt4Iey8M1uWNAQumhnqaafAkmGb8rrx9pp%0ALzR21RNVgA4HoV0RKG8gjPc/I1S/9gYqZng87H8G5xtl3P6up6FKAfilXMbHi+0U09f5TWFZJIyO%0AgIJmEJcMXZyhSCaHTaeLUMkOWmoJqZM5jCkJbRzB7wiUsgWXE9DQHhaWEn7Z8RGQJMNkbUGXjTH8%0AXBIGF4WGp+Bpmpi2T1ILn68O0alCFZ1QBpJlaHgZ2hWAPzyFwro7BoIfwomGYj/0PgMlw2CxJ3R2%0AgqsJ8MVV+K0yNHeBgCOiOO2oHxTVqqUn4kRTA+/80OEEnGkgtslIgoUVwdMKmpwBZzNIVcD1jpDf%0ADAYeg4p7BMH1yQdti8A2U2h5GGbfFirs3HrgYAGBO0WUWb+y8E9raLwFSq+FK4GwsgH0PgDl18Gc%0AWuBhDxtuQ6UQcLQS59y5h9DVV5xvP/wNfWuL827lCfB1h+tPBDG+/Uz4rY0VwhJgYSqUdGdnZw4d%0AOkTFihXf8inJfRgWbBkG5Ge1XHbtSnMzvSAvE8K8PDZ4s6f2xYsXWSYBZDcr+q54l/0xbNgwpkyZ%0A8knraXIKeU/f/48jq4KkT9UKVaPRkJiYSGxsLCqVClNTU+zt7bGyssoxZS+3yKph4ZFOBX6XYqkP%0Age7L4WO3Q0f6EhISiI2NJTU1FQsLi3Svam6p6R86dp2Kqium27p1K5Ur+WBspCeq5YvDkxgxlbp3%0ALBwLF2TIszDYW8HXdeHaE8g3SmRbjmgE93+Fhy9hTD1BVG88h+92giyB53LY9RhikuDX+hD1DYS0%0AgaMPxDR/oLayP0UNk05BcG2h/AGEPYUzz+A3P/02/PMYwuNgegX9Y4+V8NcDoaoaniadT0APN/A0%0AKGAKj4d/oiDY4PUAs2+K7ZldESb5wNUW8LQtVHMAhRGsi4KCB6HlOVH5fjoODr2E38u/HkfV4awI%0Auj/dEA7VA7UpFD8FNS7A/yJhlY/ozmUIVZrIfF3gC7ESuByH3x6I52QZgq5LuFvDd17wkzecbABn%0Ak8D1FGx6Dp2vwa8+UNYObEzgr+oQXAl634A2F6HheejqLpRPZwsIawBNXaDcMdj6FLY/g8FX4a+6%0AcLI51HUC791wxqC4q6ebyEeNVEHvUuBgLojiwprwdWmo9Q8cjBLLKiSh9j5Phi6eQmFvWRw2toAR%0Ax2DeJUHG97YGdzvwDoF7ceBiIc6VXgfghQa294TSTmBrCYcHwW9tIeQclHeFkC9h9UmoUwIG14Ob%0ATyFkEFiYQUMfcQ6bmwjympQi7CVmJlDPv+4bi0/yGiRJwsjIKL3gx9zc/LWiLxMTk/SiL51QkZiY%0ASEJCAkqlEpVKle7zN4xpyg7/JRKTl5HV91Z0dHSudK9ydXUlMjIy/f/IyEgKF84YW3L27FkCAwNx%0Ad3dn48aNfPXVV2zdujXHx5Ib+Kys5jJyW1nVZaKqVCo0Gg0mJia5puhBzpJvw2panY/W3Nw8vSVq%0AblonPoasZtVu1rBIwlDh/rfv/jMXpJmYmGBtbU2PHj3YsmUTaWlgaiJ8o4kqOH1DfKEPDYCmv0BB%0AW1gzBP44CLsTYfUZiEkQZOTQN+DnBsPWCWKSmgYlZsL9GHDPDz82ghbeMPMQRL+CwVrS+TAe0bxf%0AAQAAIABJREFU1l+DA530RK/XHihhDzWd4PQzeJAAg44I7+r0axCtEq1Zjz0ThVUdwyQUyChkuBQr%0AfLN/3Ic9UVDAFCKVIpd1XTVB9nTrCTwB3YsJL6YOag1MDBdE0dzglLNQiDilFbUgoAgcfAp/3IZG%0A58TzRSxEFqshFkdAlAoma1XbyvlgWy3hna22XwTz/3xbqJSFDZTaFuclOrpBLw+ZoOKw6SEMOA1z%0AH0O3QnA4RuZeC/3yZe3gUmP49YZE4FUZBzMYbJCUANCtGFTJD2V3axsylNE/Z2oEi32hWj7oclZ4%0AXhdUh+baWcTVteGXS+B/CJb6QiNHqHkAClrDmqbQ/G94roK5NcS+nVhZRE4FHIYAFxHs/2NNaOEB%0AddYCMixoAE3dYHMAtNkmkiJGVIRl9cDnTygTAsXzwYbusDUctl0D3yJwoC/UWgi+s+HMMPG6gEWw%0AeyCs7Ao9VsPKnqCuCd3mw9pB0HUBNKsEx66Dixk8jYF4pX77p039lb1797Jv377sPzy5jJy4PrxP%0AnqihvUD3eFbxS4bv9W9fv7JDXri2vglvGl92yurHwtfXl1u3bhEREYGLiwvr1q0jJCQkwzJ3795N%0A/zsoKIiWLVvSqlWrHB9LbuAzWc1hfAplVXf3nJKSkk5ALCwsSExM/OiphLdB18XqY5AV2TP00WYu%0AUsstvM86siJ9lpaWWVor8kIDiKw6YekItbe3NxER95BlsLIAVQqYmUJ+C3iVCOam8M1yoVD9Mxb+%0APAZrjoKdFYzpAHO2gX8pQVTXn4XFR8U6/7wKrSrCwoOwtRd4OUK8CuYehfXthYUAoON68HUSBVfj%0Aj8PRJ3A0EpLTwG0NWGt9lkmp4OkAESmQz1L4E40UMNwPNLKMWgP34yEsGjp7iuKkc9FiuYh4oTBW%0A2ifIqqsF5DOFK3HCR/koCVzMBdEacA6KWEKnohn3Ye9TUMIG2hQVyzVyET9zr8GES1DcQaLkARl3%0ASxjlAYGu8P11mFdJVNkb4mKsSDE4GgDTL4PnEWjsAMvLwopH8EQlM7u87vhC+yLQ1BmGnoUpEeBj%0AJywDhjBWgLEkY28ufMYldsFBf/20PsCq+6KLVOMi4L0DQmoIG4AODZ0EcZUUsPE+BJUQSrokwU/l%0AwdMWgo4KEl8iH4S1F88f7wD+ocKnu76+eK++nrDiJmx5BCOqwA/VxePHu0GtNaIb1rJG0LAobG8F%0ALbbAlrtw5jmUcQJLM4iIhmaloHVp6K6BcrMg/Bs41A9qzIcac+H4YHGuNF0I+7+GZV2gxwpR/JcG%0AdJ0Pa76CL+ZDSz84dBWKOcKDZ6BMFoWBSclw5swpnJwcefo06o2fpf8q3tdeoCOzhqprUlJStjaD%0AfxP/dbKaG8qqsbEx8+bNo0mTJqSlpdG7d2+8vb1ZtGgRAP3798/xdX5KfE4DyGHoctV00JGbnIjY%0AyNz61LAVpyznXhtOQ7xr4H1mZEX2sms/m9uh/fDuFfVva3+aHXKzeUJWaQmQ9T42NzdPzwMEsLa2%0ARKHQkJIC5mbgUhCePIev2sMf20GpgsZV4cAZ6FIL9l0R/tUv68KsnrBwD0z4C772h+Vh8Cwe6nrB%0A7M5Q2gXKjIOGHvCbtqC1xRJ4lQST/OFgBITehItPxBRwPgtRCHP7Bfi5wrLWUEj7MXH5n8SP1WW+%0Aqiz+12jAaQ5MrQtBBm1VfVZI1C8sM9tf/9i88zDxBET2FSQuIhaOPIbB+8HJEhKTRTKAqUJkfZ6L%0AhakVYGAJfcHRg0Tw3g5Hm0NFg2IntQYc18GcmtC1lCgAW3od5l6BOBVYGEFEc7A1CMtI04DTNpjo%0ACwO1/tzLMTD8lMSJpzKpaRBSU/hwMx5PaHQAYjWQKktEKWGtr0x9rUf2/EuodQD2toSKDkKJXn8H%0AZpSHfsWF3aH1cTjaGioUhMXhMPwYfF0CplaEhFSotAe8HGC+PzQIBYUGTrfQ3zAkpkLlvyEiEbqU%0AhOUN9eOLiBcxUx62oiVqk90QrYZf6kG/bTCzHvTXWkNvREPNNdCkqFBm9z0Q/lWlGrpVhMUdRKV/%0AoyXwKE4QVCMFdA6BY/cgfIQo+qsWDM62cGgQDN4Mf5yCfjVg53W4+wIqFIYLD8WNUZXicOA61C8D%0A5yKgSCG4HyW+1NRp4lzXITY29pNXvavV6nTrUF6DWq1GpVJhbm6eZYoBvB6M/ynTC/J6K9g3Hdt5%0A8+ZRokQJOnbs+C+M7D+Bz9FVnwIajSZDVb5uiv5DiVfmTFQdWcqKZH2K7lLvSyQNp/nflexllVOa%0A03hTXmzmArWsSN/bkJuRZZnJ6rsQao1Gg5WVJUYKoaIZK8DYWFTRf9Uelm0TCtu6iTB6EZwKF1/4%0ATvkhJQVuzYGnL8F7uFCmCtmJ4qtNYXB3ChS0gVVhMGQtRIyBx/HwxxmYcVAkDOS3hGIOcO859KsK%0AkwPEuC48ghpz4PoQKKrNNZ18GIJPwr2v9GrsuMOw8jLc7idIDMDuu9BhM9zvK1RhsZ3guBBm1IUe%0ABtPeiy7Cj8cgsp+oJtdo4MRTCNROR6dqhJJby1GitbPM4jtQOh+EZIqA6ncMjr+AS50ytlu9HQc+%0A68HHAa68gDoOsLCyUDn7nIETcRIX28jpY9eh0ma4His8rNMrQFBx/XPr7kP/0/CgO1gZw8zLMP60%0AmJJf6Qc+e6CZGyyoo3/N5nvQYz9UzCcybMdWyViwdvoZtNgpukrZKCAiGa51E2ppQgq03Q6XnsOJ%0A5uBqCQ33iuzZ0PbgvwYqOcD2AH1s1jOliJmKTBDNIE70EefWrtvQfj1MqgNDta1w77yEGqtFC9eX%0AKTCyHvh7QLPfYVJTGFpLHIP6i0UM2tXhgARtV8H5x3D6a9hwGYZsFTchRgqws4DYJKhWChzsYdsp%0AaOYHsYlw8joUKQgPn4vZA7UG8tvAywSwtQK1WiisGu233KNHjz5pR6G8TFZ11+2sZuqyaldqSGYN%0Ai76yI7MfC5VKhUKhyLNkVecNziwoAIwfP56WLVvi7+//6Qf230CWJ8jnAqtcxod4VnVkSVe4k5KS%0Agrm5Ofb29ulTz1khr3SXyqpLk7W1NXZ2dpibm7+VTP9b25GWlpZegKRrNWhvb4+1tfV7Jynk5jbo%0ArCW6cyQuLo60tDSsrKywtbV9bR/HxsZiZSW+dExMSSesKSlCvZu7HpJU8Pc0GPs7nL0BnevD4bkQ%0A9RJm9IA208BrOJRwhm1j4FYw7LkAE9sIopqqhmEh4GQD3tOgym8w7yg0Lg33J8HzGTC8vlAZxxhU%0A53dbC3199URVrYbpR2F2Qz1RTVHDnDPwWwMykL0Be+H7KnqiCoKQ2ppBV4McbI0GxobBtDqCqIIg%0AWwXMRND80W4QPRROfQklHWWm3BAe06NRMO48hMeK1zxLgrX34Pe6GYkqQKd/INBL4kRnONoJzKzA%0Ac5ewIax9ACtqv05Udz+EG7FwowfMqAMjLoD3TrgaBy+SBVGdUUOotEYKGFkeLnSAhylQeLtIBQiu%0AlfE927jDlU5wKhrUEjTPZG3wKwRXOwlP76HnouBJd6pYm8Ku1tChJJTfCvV3w70EuNAHvAvCud5w%0AMx6qbBDED4QyGpsiMmjjU8XxBWhaAv4OhNGHYUqYeCwxVRzTlymCpI5tDHU8hGVk9C5YdEI0A9jX%0AF2zMoMJcsZ9H1YWYRHCfClMPQ9dawpLS0hce/Q69G8ClCJjTFyZ2g33n4LcB0MwX4pSwbaqYRWhX%0AX5z/lmYQlyAIrLmZ/pvR1dWViIgIPhXy8lT2m8amI5+6VqK6oi9dhycrK6sMcX267zPDDk+6oi+d%0AlU2XcPA+yKv7Dt68/6Kjo3PFs/p/HZ/Jag7jYzyrhmRJqVSmt2/NrvVpVuv+FCQvu+3RKaKxsbEk%0AJydjZmaWnkbwPgH2nzrLNSUlhVevXhEfH49Go8lArPPaBVFXfKZr8ahrj2ttbZ1l4sPZs2dxcnJC%0AlsHMTJBBIyMo4gJGxuDoIAqqfL2g0XA4cwPmD4dVY+DLSYAMPedDVJIgDlt/gLpl4IfVgvjVLglD%0A/gS7wUKhdHaAWV9CyBDhe1zZEwrnE4Txmw0wqTlYa7s87bgmirHG+evHO3QXuFhDixLwPBHuvoTA%0AzaIZQHF7uB4Nt2Jgxil4qYShBiH9KjXMvwhz6mUktePDRPelHqUz7suuu6FrGeHFBChbEIIbg6W5%0AxOCqMLo2/P0U/LaB23oxHV7LBao7ZXyf7ffh5kuYWl2csxUKQmgAhPeA+0nCq/r1SbhlkEmv0UDP%0AIzC+OhSxgS9LQ0QQNCgGfnug7A4R3dQ7Y/MZStrDzOpiSjwuFXof1LdZ1WHtbUE8+1eGKqGwPDzj%0A8xdeQIwKWpSCGuvh4EP9c0YKCK4HVZzgbDSMrKYn+K42cCYIZCMJ7z8lLr2AKusF4bz3AxSylfBe%0AKKFMEcvXd4ddX8AvYdBiPVRfDQHl4eJoOBkJPdaK5RqUgk09YcQ2WH5GEN8D/SEuCezHQ7Plorq/%0AfDGwsYSVAyFsAuy9BF8vhTlB0LQiVBgKfRvBVy2g9jcwpTeUc4OekyD0F9h1XMwiWJpDjYqixWtK%0AKhhOrvj4+BAaGspnfDgypxdk1bLW1NQ0Pb1Adz3LLr0gq5a1eZnow9vJaqFChbJ87jOyx+cCq1yG%0ATlnN7uTNqvWptbX1e005G64rt2OyMivFmYulTE1NPzqNwJBI5uYFSUf4ciPLNScJd2YvqkKhwMTE%0ABCsrqzeOd+XKlQwY0A8QBFUGChWAcqXh4DEY1gtOX4SnL+DyPbC1FtOmjf2gXE+4/Qja+cOEXtD0%0AGxjeEtwd4cYjCN4h1LX6/wPvokKd+/s78NcSwqKDYExzKKD1oE7eJVS1vtXEVG94FPRaB6UKwDd7%0AJe6+hAcxMs8SRWcjm/+J5Y0lMW4zE6gTIv7WaETygCyD7VywNAF7c6HcKVMF+YpKFGSvmA3MOQ8r%0Am2YksIcfQvgL2J6pecuG6/D0lcyY6kKhHVhZbOePB2HuGTj8GGpslRheRqa1m7BO9D8sptsLZpox%0AvfxCEPgzQTD7NJQPhWoFYbU/TL8kCPQwgyl6OzOY5w/lCsDww5ASD6H3oK27fpkkNXyxH4b4iha3%0ArTeARwjsbwnutoKI/nwWdneBWkWgTmHovhX2PII19UXMV8e98Et9GFYN5pyGFltF29NB2rGsCIeT%0AT2F6AIzaKZTTsVoFN58FHO0m0+wviRobobEn/NVNPLevt0yLPyS8FsCV/mBrDjWKQJMSsPMWtCgL%0ACwPFsse/garToc9fsKQTNPGCdT2g80p48BL+DhdtU20swcMJto6EBBXUHAfVxsGJCXB4LNQYBw42%0A8McgaDMdfIbCjfliqr/aUDgXDIGTYfBvsH48dBwPY/vC1D+gti8cPg3mFpCohKQkMbaePb/k9OnT%0ATJo0KdvPVk4gLxOu3Brb29ILgAyWAsOCL0N7QVpaWvp75ESmbE5Dl7SQFWJjY8mfP/8nHtF/H589%0Aq7mAzNXsMTExGQqfMkc2fWyveB0SEhLSC5dyC7pCLmtr6/QpnA9t3fomZN5nOQHDGwO1Wo2xsTFW%0AVla54ivNiba02XlRU1JS0qf9s0O/fv1YuXIlxsba6m4FpKkhfz5ITIBfR8L+Y7DnKAS1h8a1oPNQ%0AqFkWwq5pg+BHQrcmMCMEpq2G1UMheLfEzrMyBWzhh84wMADaTIBkFez9Qax73m6YuBEeTBJT+Mfu%0AQOASQXRS0+BZgvAcKiQoU1gUWZUsBLuvgqkx7PkarLVWr06LxfIHh+i3bdZ+mLYPHowXxPVeDFx+%0ADD3XQFsfePoKHsUreJkoE50go5ahkqMgbtWdwdcRmoRCJ0/4tU7G/VZ4gcS3VWWG+WV8vNzvUL8E%0ATPCHH/+BDVdFdqdXPohMFKqoaabTyHWZxHA/mW+rif/vvIRRhxRsv6FBI8PWVtCkWMbXqNTgsQL6%0A+ImuTN/sAt9CsK2xUEuHHYOtDyXuDpTTlx+6F9ZcgXGVYf5VMQW/oJn+PW/FQNN1YCxJmEsyTnaw%0Au5v++d23ocMGCCwJPUtD41AI6QqtSsPxCGi6DAK9YXFzsfzTBKi0TESeKVPhwlAxVhDHu80q4UU+%0A1Qv67oDzURJzusj0WgE/NBLdzwBuREH1GdC5PCzoIJTUpr/DxUdQuQTs/Qnik6DyKKjkBlu+FbFp%0Afj9CcSfY+z2cug31f4Ffu8DAxtDwZ3ieAMcmQ5vJcD0SpvWFoQvAMT+0rQ1zNsIvA2HiEqhfHfYe%0Ag0IFIeqFIKy6S3e1atXYs2dPlp+vnIDueyI3r9cfirw4NkOPrEqlwtjY+LXmCNl5ZeHT2gZ0NrLM%0AM4qyLNO8eXOOHDmSp8h1HsPnAqtPhcxkVVdsI0lSOkHVXQh00yE5geyqxHMKOvKUlJSUnkZgZmaW%0AKwVdOVkslpaWlu6P0t0Y6OK3civq60NvHN4lNUGlUr2RrPr7+3PixAlMtX48dZpQKRVGoqDK2UFE%0A+CQmwpSR0K8zOFQRxK9yWfE7KQFOL4HHz8G7q74LkE9JOBsOlxZCCVe49QjKD4Bzk8DLFV7EQ6kR%0A4GwniMv9aEFALc0hsAbUKwONfKDkYBjXGgbUE2OOSYCi38L+YVBFqyS+SAC30dqOWVrvpUYDTj9K%0AzGgl092AUAaugCfxcMiA1CpTwOlHCO4o1LoDt0Rno6hXQvGs5yYR6CXT0A3c7IT6OeUkPPg6I/Hc%0AeQc6hcL9EaJQTIftN6DDOvF3/cIwrqqYPgeYcAJ+vwZ3v3qdxFZcBvfjhL/z24rwU1X9c2NOSKy+%0AAfdHiOvHo3j4crPEmYcyA71gzhU4FQRlMiXf7LwD7TeKG4DoYUKJNkRiiiCYD+LgQA+olil54Npz%0AaLBKRI195w/jDHzF4VFQd5GIG1vTCqqsgGIFxU1F0BrYdhnOfA3u2tQEdRq0Ww37b0MhG7gwToT6%0AH7sFTWbB2Gbwnfb9rz2BGjOhlhuceABO+aBXAxi7DnZ8D3XKwIPn4Ps9tKgAywfCk5dQeQzU9oJ1%0AQ2D/FQiYDq394P5zCLsppvatTMWNWooa7G0gMUl4q2VZqOUm2girimXg0nUo7AJPn0FyivicADg5%0AOXHz5k1yA3m5oj0vklVDJCQkvJZtnbngy7DwCz5ty1qlUomZmdlr3+2yLNOsWTOOHTuWo+v7P4bP%0ABVafCoYnvo60JiYmphfCWFpaYmdnh4WFRY6qerlhAzD0dMbFxaHRaJAkCWtraywsLHIteeBj82kz%0AF3lJkpShI9anaIP7Pu+fubvUm7yob7IYFC5cmBMnTmCs7RGfzwHMzcHEDFoEQGoqRMWIu1AvDzHN%0Amt8P7Gzg78WwcCKcvQI/94Em34B7J/HFP6EvRO+B2FcQ1FQQVYDOv4KfB2w8BeVHgdMAQcLcXGBk%0AJ4hcKUjyX8NhTi9oWxWW/CP8rL1r68fdaznUKCGlE1WAoJVQu5SUTlQBJu8Bc2OZLyrrH4tJFAHy%0AM9pk3Bf910FZV4nufjCmMewbBBETRKODr+pCMSeZqecVlF4KTvNg3DH4oszrV8qBeyV+qJORqAKE%0AhoOXk8Sd0WBiDQ02gW8IhN6GmRdgUbPXier+CLgZDVdGwJousPAaFF4G++7DrZcw66zMXx31x9bV%0AFvZ2l5nXAqZeEOpqsSxCMqxNxD4t4QhuiyRuRGd8/uRjePQK+teGhmtg+YWMz3vkg/zmQoFffUki%0AMVn/nLcjnBsiUguKzQdrS0FUFQpY0Q06+0lUmitILYgOVOHPREc0pVpfjFWzJOwYBhN3wuz92vU6%0AiA5U+28Lf+mVWTAiQCilAVPhwj0oWhCOTITQszByNTjng2PjYc9Fcc4FzgETY9h0CvIXgj3zwNUB%0AalaCR/vAqzgULAhX/gYrK2jXAiZ/Dxqgmh9cvikI9oOHYpvMzERSBsDTp09xcHB4a8en/2vI6xYF%0A4LVrokKhwNjYOP0GX1f0ZW1tjZWVVYab/rS0NFJSUjIUfSUlJaWLSWq1+oOKvgzHmNX+S0tLy9Vm%0AN/+X8dmzmktQq9XpU84gQpl16mpuIScbEBhmumb2dKrV6jyROpAVdKqkYUesrOwJuV3E9S7HOSsV%0AVVeM9qbXZzd2W1tbUlJEdYuREZhbwqt4cCgA330P330L5cvBoH4wYIgI/+/7k4iiCp0PvuWgZH3x%0Af7sfoWoF8fehBVDWA7YchogncGgqnL0FM0Phwl0xZR+XAm0bQMQ6+HOUiA8C6DYdyhYBf22MlEYD%0Ak0IlZnaWMdERgljYewXCvtNv0+NY+Oc6nPrW0B8NM/fDwk4Z/adBIVDTQ4FvUf25H5cEoZdh31cZ%0A99PKUyJndVproRSDBrUa2i+Bw3dg1TWJxedlWpWEHuVERmtCkszw6hn3dYwS1l2F3X1lXOxgc5BQ%0Ackf+DV12CfVO56s1PJQ9t8P39cS0uYst3B0FM45A6+0ie7ZWMaiaSfWUJIiMA0db8HKWKBoss6YV%0ANNN2rFKmQuAWGFIHfm4B322DystFokKfChCtFMrw6Mbip34J+GIlnHsCc7V2gQG7FCTIMk+nybRd%0ADCVmSpwdJKdP77vYQllniYO3ZFQaCbVGxlTbPCC4g4y1mUS1+TLru0L/UJFpGv49dA6GMuMlro6X%0AyW8NdUrB30MgYI4Y18aLEspUmbXfQrdZMHsbDAuAoc0h5hX4j4ezU8DTFQ6Mgzpj4Vkc3IoS6vid%0AKGhSHdZPgkWb4bt5MOVrOLIEKnWDYdNg7wLw7Qq9f4Ijq8CvExR2gkE9YMk6WDQTBn4Lvn5w5rSw%0AAphrixFBqIz29vY8fvw4R9W5N/ka/23k5bHp8L7pLEZGRtk2R8gcv5WamvpWe8GbMmWzI6sxMTGf%0A/aofiM9kNRegVCpJSkrCzMwMW1tbkpKS3jv66EPwsQQsq0zXrIqlPoUq+T7r0Kmo2XXEygqfgqxm%0A9/6GXlQgXQH4mC8HkVwg/jYxhZRkkDVCKSrqBt8Mh2aNYflCKFJKqKUBAbB/v1CgrCzAsxFERkH3%0AtjBmEHQYBJ0bCaIKMHCaRGEHGZ8B8EolyOPg9jBriFhPnyng4QxNtbmaMfGw+TjsH6cf58QNYGki%0A00XbBvVlIrQLhlKF4Gk83DkPCcnw6y4Rg3UiAs4+EKR5w3nxmqL54F60aP2ZkAz7bkDYsIw3ab1D%0AoEpRqOaWcT/9sENiYktZS1T1OHxPYmWQTEsfmWO3YdpuUZikVEM5Rwh/DpUMOj912wg13aGGgRJs%0AaQqj6sOyU9CjOvTaKeF0GGY1kGnsDtNPCII10sAna2YMo+uBs7XIDz0eCVMOw/cGy9yPhV8Owd8D%0AoF4pmeAj0CEUWpUQ0/KjDkmYm8HkVuJ8m9laxr84dF0lsmgTUqFYfkFUAVqVg+PDoNF8uBAFQRVg%0A41UN18aLG4+dg2R6r5EoO1viUF+Zcs4wZjccj5AJnwYdgsHrV4krP8hYmgrCOq2VjKwRmajeLnB4%0AjFjXukHQYY5M2QkS1ybI2FtCPS8Y3Rx+3gaerjJ3F2gL9Kwg4Gewt4Ke9WB8J6GaVx0DV2aIGKoC%0ANrD+FFQoBU+2w+U70GQorN4FA9vBo2dQpy9c+QsOLoJqQVDEGQ4vhYqBMPl32LcM/L8U6mpAfRg1%0AAWb9At/8BL36wLIlYG0LxIMqSX8cXFxciI6OThcFdMqbIanJzWzRzxDIadX3bUVfmcmsLjbwTfaC%0A7MYZHR1NgQIFXlvPZ7wdnz2ruYCUlJT06XIQ5FWSpFwPf/6Qzk+Zi73epVjqUxRyvW0dmVXJ9y3y%0A+thmDW9DUlISsiyne2Lfp4PX22B4nHXeVSMTkNMEgTQzB2MT0KjFtK4qCezs4Jex8O0YcCwIW/6C%0A02fhq6FQoTRcvC4IYfAE+LI97D4M7QdA+Do4fB5+WgxPXoB3cRj8hYj96TceHoYKK0F8Ari2gZ0/%0AQy2titpqAigTYUE/uBoJVx7AtC3CT2mkED5VCeEdtLIAYxMFJpIGWRbqWXEn0EgK4TFUyzyJlrGz%0AgFSNRHKqjCpFRDhJgG8xKOOioJyjBicb6P0nHBkClQxUyvlHYMJuiPxZ+Gh1GLoe/rktcflHOYMK%0AOmknzDsoyNepu+BsKzG8qkzVwlBrKZwfAZ6ZEmhqBksUdYCQXjJqNYzcBMuOgbs93IuDFR2hbdmM%0Ar1GligzRYQ3ApzB8+Qfks1CwrYuGkgWg0QqQTWDfYP1rrj+FNr+LfvexSXD+O/B0zPi+d16A/1zh%0A/b32A7hninZ89goaBIvOT8FdoGcN/XOyDGP/htn/QP8qsOgkhI0XKrkqBVrMgBuP4cposLeEWCVU%0AmQ4aI4iKhf0/gJ+2wYE6Ddr9BmfuwfWJsOEcDF4LgfXgz0Owcii0rymW/fsUBE6HNUOgTRUxjrYz%0AJPZfktFoIKgl1KsM3SfA5qnQqCpsPgRdx8HWaVDfD4J+ldgVJnM7FC7ehCaDIfh7qOoDVbvD0O5Q%0AuzK0GQwrZsCydXDjHgzsDT9Phy+6w5pV4FJU4sE9mZTkjPFgUVFRr13Ls1LnDP82VON0pCYlJQUT%0AE5MsG5P828iuQCgv4E0NCz41sjvmOiIrSRJJSUlMmDABd3d3TExMuHfvHrNmzcqRrpaG2LVrF8OG%0ADSMtLY0+ffowatSoDM+vWbOGadOmIcsyNjY2LFiwAB8fn2ze7V/F5wKrT4XMLVczE5fcwvt0fsoc%0AOfU+xVK5Xcj1pnV8zLgNkdstXXVFUBYWFunEGIQC+i7tWt8E3XG2tLQUGbzaXWRsAmgkPH1MeBqp%0A5mW0hvY9zdn8hwprG3jxQlRwH9gliklKVRBfwi2bCxX2ymW4uluoXK7VhKE9LgGsLCFBCUsmQGdt%0AJXfhBvBtIAzrJP5vPwaiX8KSIXA8HHafhY3HRI6ltaVQy5QpQkkc0RlKu0HFEtDtV7C3ktgwRn+p%0Aafg92JnDxh/02zxmJaw/Bjfm6afVn74E9wEwvx88eAGX7sPdZxK3HsrpXYnKuEjUcZeU0BCRAAAg%0AAElEQVSp5gaDNklMaSnTy2BKX5UCjqNhQz9oZJDDqtFAwVESC7rJdKoipoT/txsWH4DIWDEtvrMP%0AlDbIXD39AOrOh5sTRbas4TpKT4AncVC/lILglhrcDGYCJ+6DJWfgwWTxf4IKvt2sYNVxDf7F4MgD%0AePgL2Ga6101MBscfhFo7uy18VTvj8w9jwWsSeBeGO1ESe/rL+Br4f5NSoOwUUKaJ7Qv7DkpkIt/f%0Ah8Lc/dDbH+b00D+eqoYOwQpO3tQQNgLaLZOQTOHM/2SmboTJG2D/9+BrQFjbzJE4el0mVQ3rf4Tm%0AVWHdQeg1E7aMgYYVxLKrD8KA+bDlO7gQIYqt8tsJH+mtv8TvBaHw3Vw4tkgU/c3fCKOCIWwxeLlB%0Ai5EStyNlbmyANbtgwGTo3Rou3YHjF8DNFV68FM0wynjC+StQ2BmcHOHWXWjTDkJDwcFRIuqxuPFI%0A1rZnVRjB1SvhuLq68i7IrtuTrsgT/t3WpVlBqVRm2ynx30ZeIqtZQTc+CwsLZFkmPj6eNWvWcO/e%0APW7cuMGNGzeIi4vDxsYGDw8PPDw88Pb2ZvTo0R+1Tk9PT/bt24erqyt+fn6EhITg7a0Pag4LC6N0%0A6dLY2dmxa9cuxo8fz4kTJ3Jik3MaWZ7wee9M/D8IhUKRoQVrbuFtU9tZtRHVdcTKK92ZslpHVqrk%0Ah4w7u/fPaejU6tTU1HT15F28qO+DuLg4nJwdMTaF1GSdN1LCvZQRz56k8SpOJmR/PoZ0iUUDVKpp%0ASviFVOrXkVn7FyxcCu5FYf0aKJAfSvnAruVw9AwMHi+IZ4Vy8Mf38Pce2LkPOmqnkeetFd2vBraB%0AO48g9DBsPy4IScXBUDA/xL2CGhUgZIKIDNJowKEpzB8BrbWZnQ+fw/ErcD7YwKsaDcevwflZ+m3V%0AaGDhblgyMKP/s3cwNKigIKiBXvZ6ES9TrD+ETRZK8cYTMoeuiql5ZbLM6K1w+K6CgNIa6pWEUVug%0ApKNEQ++M58KYLZDfCjpoLQ3GxvB9C2hYGmpPhmJO4DsbqhaTGNtIxt8DuoeIwi1DogqieUFUAmz7%0ABib9raH0TBhUHcY1hDgVTD0E2wbpl7c2h4WBGjpXhPqzhHL5NP51sjrjgIS9lcyiPtBlHuy4LrG1%0At4xCIc6HrqskqpSEf0bL/BIq4z8P5raDIG2U1rBQ0CgkHgbLjFgFladIbBsgU7uUdl8mwLLjUM8H%0Alh4GLxf4qqF4zsQYNg3W0GOxhM9kmUL5ZW7MFjc6P3QULUzrT4UD30Nld5FS4GIno06DfPZQv6J4%0An87+QpVtOxn2TYSqntDNH249hpZThK968wyoXRFq9YUqveHMchjYFh69kKj7lczVtSLs/9FzqDMQ%0ALq+FUV1lmo8AO39BxPPZw9Kt4FcJun8Bf66HToHiHPkzBBq3kLh4Vub6bZEEEBIiot5AxjYfvIoD%0ASSGhUspo0qB0GW+2bvmbunUz9ePNAtlNMyuVSkxMTNLD8Q1J7NummT+FvSCvWhfycvEX6Men+7G3%0At2fQIPEBX7JkCQUKFKBbt248efKEO3fucPfuXV6+fPlR6zx16hQlSpTAzc0NgMDAQLZs2ZKBrFav%0Arr9Lr1q1Kg8fPsz8Nnkan8lqLuBTF/PokF0agOE0vy6v08rK6oPVvZws5HrbOnQVmrpxf6y30/D9%0Ac/qY6FRflUqVXqBga2ub44UK9+7do1z5cigkUKeITlQaDSQnydy/rQYZRk+35uvAOGJjZBZvtOFh%0ARBo7N6UQGgsKEwkjSWbVUvD2hAYtwMYKBoyVePBIRpZh2Sz4oh0oldAmCDbOFEQkSQXj5kNBe3Dr%0ACPGJ2rzUkrDgR6hSDh4/gxItIHiEIKoAYxaCg51Eq5r6fd5rCjT1k/Ason8saCY08VXgWVh/fo0P%0AgXxW0LqKfh88i4WDV+HElIznYZ/5ULOMgvLu4vEyWiXRMQhm9RGWhVUHNAzfqiAqWoOJMbQpL3Mz%0ACjy1KqlaDQuPSqzsI5P50AWtUNC/EczuriEmAYb+IdNmuYhJepkEY5rxGgKXQdPyEg3KyjQoC6fv%0AQpdgiWWnZTwcoEJRiXqer5+Le8PBzQEC/KDSNPi+EfyoVbbvPIepe2R2/wC1vODyVAj4HxT7ReLw%0AIJl/bsLlxzIPgwXB/6kd+BSFrvPg5H1oWRbWnoVLM2SMjOC3nuBeEJoGw+KuEOgLbRZAcWfYNg72%0AnheEMlYJo1uJMSgkMDKSkBQyMfHw+KWo3AcY00lMy9WbIhTW3/ZK7Lwgc34FBE2RKD8Iri6QMTaG%0A/s0hNlGi0TiZ41Ph9hOYuQU8isLDKPB0EwVP+4KhcjcI+AZ2zIKf+8g8jJKo9KXM7Q0wpCOs3QOl%0AOgoyXbUS3IqAar6w8Q8YOV5i+WqZDWugcgUYPQ4OHQeFscTmTTLbD5vSom4KTdqY8eShhhOHUjEy%0AgZgXkJwEltYy5paCsMoaaNmqJUuXLKVjx46vH/R3hGEOaHbFP+8Skp/TPtm8TAjz8tjg7d2rvLy8%0AUCgUuLq64urqSp06dbJc9n3w6NEjihTRe54KFy7MyZMns11+6dKlNG/e/KPX+ynxmazmAjKfqJ+i%0As5QhdCQsq85YOTGto+sgkhvQqb+64HtdCsGHdPR6E3KKrGZX0a8jrjlNVA8cOECz5oIRaTRgbiVh%0AbCKRkqRBYST+NjKS+XVkAgoJpi+xxtEZBnRUYmMnMWqSJasXqahQP41yZWDGHDh6HPLlk2jUQiYl%0ABfbthEBtDFSfb8GzuESqWqbTN7BlP5iagpsbBHWEmn5Qqi6smgSltYVYQT9C0+oS3u5y+jgXbZZY%0APkrvCX38Ao5ehnPz9MfgaYx47MxM/WdFo4HgnRKL+2ckjr0XQJ1yCsoV0y8b8wr2XoSjv2b8rP22%0ATaQjdKsnlLT2NQA0dJsBp27B1WiJypNlHKwlulWRufUMCueTCSif4W04GA73ojT89KP4P781rBok%0AyG3BAcLi4DtF4n/tZNpUECTx3AM4HQHhU/Xb6Vccbs+QGfUnzNsDXi6CLJcy8JzeeyH8ogfGQ9VS%0A0K4KdJoFf52H/YOh5xoF/mU01PISyxcrCGd/lRmyAnymCVVw5SCRb6tDa18ImwiNJsGqMzD5C3A3%0AWOewFjJFHaD7XJh/CG6/kLi/VIy7UUXYPR6ajhdFcdO7wLTtsPWMhvDlMH4lVBwG52frCeuPnUCT%0ABv6/grmpzKVV4OIAu/8nU+dr8B0C5+aJm6BRHWVeJkpU/04ol3NGQ6920O9nBX49NNzYAPa2cPh3%0AqNgV+k2CxaNhyfcyDb6Goq1FdrBHcYkyhWReJcKBzRDxACo3gp8mwdSxMncjJPxqy4Sfhzv3oJE/%0AnL0kExkp0al5Cpv2mtK8VjJfjTLneZQGdZoCC2sF4edTSFPLJCfLWFhKJCllkKF37948fPiQ4cOH%0Akxt4WxV75lzRzFXsb1Jl/6vI62T1TcitAqv32R8HDhxg2bJl/7ms189k9RPgUymruhM2MTGR1NTU%0AHOuMldV6cnp7Mqu/RkZGKBSKN3Zp+hh87DZk9s5mruhPTU3N8X20fv16uvfojoSY5jW3UlCiggV3%0ALykp6GpC7QAbNi2KwdxKQUEXYxwdNfyzI4VhPVIoXcGIkL22nDqSyt3rafTsBB5lIS4evugG8xfK%0AqFTgUQw2LhZEa89BCN0ByakyPcdCjepgYQVLpkCHFmJMTbpD45oSpT3Etj6MgiPn4Nwf+m0fsxAc%0AbGVa1RTELuoltB8LpQrD7cdw6Z4gGtPWQyF7OHoNTt0U6tiWE6BJkylWUITDF7QVXs39lyBsckZS%0A2nchVPeSqFg8436fHKpgSg8Nxgbf90oVbDkFOydCrTLCk7h0j8yiHXDzEdhbwJy90L2GIKUAfVcp%0AGN5cQwGbjMdl2SFBhiNX8//YO+voKq62i/9mruTGE6IkIcGCuxd39+IS3IsVd3cKxaW4FIoUKxBc%0AChR3dwgWCCF2k5srM98fw81NILTQEl7e9+tei7XIzNyZM2dmzuzZ53n2w6QNSib94K0yP3wLA7dA%0ApwoQ9E5ykyzDvusiDUpLxCVAgfEwqBoMqa4kf3VbByVzKEQVoEIeuDMbOiwUyDRaBlkifGHKfWrV%0AsKCDkgx25THsvwqNSqTcJk8GyBskcOKWzI+hIiHlJNyS5Xk0LA4PXipxorWLyuiSuSaUygVHJ0GF%0A4XDuIZy+B/ung78XLP4eVCqRwt/LnJshE+ilnOPjCBGVSsIkKab8oCTTHZwFJbpAmX5wfCbExsPp%0A6zKqtz6nDSop9+DCYRLPXwkUaClzezP4eyuEtXgbcHeB2ESRMzckHB0hZxBcOCgTFwdFq0K1prBv%0AI+z5BSo2hODM8PMimbK1oEwV+OMQPHgkUOYbmVPnZKpXgb6dTazboaVJTQNjZzsye3wCHj4q/AJV%0ACBoVLx6bSIyXUGvA/Daya9SoUdy/f585c+bwKfinpMs6Tn5o3++SWeuYZE3+/bNqT18zIfya2wZ/%0A3r6IiAh8fHxSXfdP4O/vT1hYWNLfYWFhBAQEvLfd5cuX6dSpE6Ghobi7u7+3/mvGvwlWaQSrLRHY%0ASpR+7vKhViQnTlYV9XMXHEiOz5Wc9G4MrVarTcqQt56Ps7PzX+/ob+JTS7p+Ska/yWQiPj7+o5Ld%0APgazZs1i0OBBCIKSDKXViWjsBExGCU9fNS2+92De4HBKVneidogL/b99ioOjgJ29QFy0xO5zrmTO%0AJlLI9w2vI8DDE0pW1nJ4p5GbdwVcXQVCWkncuAztm8LspfDqNQQFwpL5UKIYjBgLGzfB7SOKGvbk%0AOWQrC+c3KMbrAJU7A2YY0xFuPITLd2H5bySponHxYKdVppCdHUBtJ6JRC4DE83CZzAEgI2CRRcxm%0AmRevJFzelmk1GJV/MkoEfskckCcQcmcA/3TQ6kc4Mh4KZ7H129xdMH4zhC0lydcVoN0suPEUTs5I%0A2c/d5ioVkFpVggXbBZ5FyFTJq6JIBgvT98KTuUo1JiskCdJ/JzIxRKJDNduyIStgwW9KstSOfkr1%0ApeTYchba/wThPytK9fFr0GSyiFaQ6V5WZtwueLyAFEQSIDYB/DorCU51CisVnJIrzptPQfvFAnun%0AyNQfDRncBY6NkpPcD1YdhV6r4M4qaD8NTl6Hk+MVyzFQMvlz9IG2tWD1HqXa2LoBKdvw6wloOg0K%0AZ4OTc23LZRm6zxbZdFjm7A8ys3eKrDwkc3mjzA9rRFZukzm/TCbw7bs6IgqKdRII9JJ5HimgtRf4%0AY4NE64EC56/K3PlN6ZtEI5Rrq3xgXFyrkNje02HRr4qH8J71SmJU/gpQvhSsXQDPwyF/efi2NiyY%0ABtt2Q4tuELoRcueAguWgUEFYsxTKVwezDFt3CHxTVKZwCYF6jdX07mRi1monBnbSU662Ayf2GfDL%0AYset8wYcXFTERVtIjH8bV6qGShWqsHnzZj4Wer0+TQurfAip2TG9W+0JSBIMvoaEr+T4mit/wZ+3%0Ar379+uzYseOzJ4eZzWayZ8/OgQMH8PPzo1ixYu8lWD1+/JiKFSuyZs0aSpQo8Sd7+4/jXzeAL4l3%0AS65+7lr3qREnrVZLQkICDg4OaWqH8imuA6khtYID76q/aZ2tDx9f0vXvOBD80z5Kjv79+zN37lxU%0AGgGLSUZQAZKSlaxSQVBOHQ+vGyhZ3YlRy3ypk/kekgR9p3ixbvYbylVVU7uRhu9axhLzRqbHUAe6%0AD3bgm6A39Okt0eM72LdXplljhWgFZRZp1ErF7EkmTh6GXDkURTR9ZsXqp97bRKvKLUAww8B2cOIS%0A7DkJ568qpMXVRcQ9HcTrJbDA5EGQOxvkyqr4t1rMsDOZMtiwJ+jjYc8i27IZq2DGSni0WzlPUBK3%0A/KrAxJ7wMhIu34GHz0Xuh0lY3paVzZdJoFwumeLB0HkBTGwNHara9htvAN+2sHM0lElmI2Uwgk9L%0AgS1j5KQEoHtPYfAS+O0k2GtgXBNoVw4c3jqqTdkOs/fCoxWkUG4BMrQBT1e4EwYV84jMaSUR5KkQ%0AzYzfQ4+6MLSpbXtJgr6LYfEuCPaHUxPA/h3ntj4rBHZfFtg5WaLWEEhMFDk6XCLQU/EhzdQLxneA%0A7vXgdTTUGQ4PnsOJUQpZz9kflvSHphWU4/VbKLI8VGbrAJmyOaHCGBE0MkfmyTx4BqW7Q55Agd0j%0AlTCMqDjI3VPx5j18Firmh/UjbO2TZegxW2TtfgkBOLMOgoOU5V0niGw5IHN1pYz321jmw+ehQi/w%0A84KwYwrxTkyEym1FoqLh0iYJUVSue7Hm4OGiJG/dDoP2rWHBMoWsli4Bd+8rU/79usPIfnD1BnxT%0AE8YNhj5dYOZCGD0dju2CP85A1+8hY5ByvGfPFRIsispzZVJqa6CzBy8fgadhMmVr2PPHfgOFyjty%0A4ageRzc1cW/MmC0yFqNCWIOzZOfMmTN/9jgnIS4uDkdHx/84+UsOK4m1lgt9l9BaY/H/EwlfVhgM%0ABlQq1Vdp+QV/3r4aNWrw+++/p0k/7d69O8m6qkOHDgwZMoRFi5QBtUuXLnTs2JEtW7YQGKgE8ms0%0AGk6fPv3Z2/EZ8C9Z/ZJ4l6xGRUXh7Oz8j9XO5Iby1qSj5FZIsbGxScvSChaLhdjYWNzc3D76N6kV%0AHEitdrIVn5PsfQh/dk3+qS/q3+mj1NCyZUs2/7oZlVZAkAERRFHA3kmFMcGMZBGUl6wAbYekY8n4%0ASDy8RdacDOR4qJ7RHcIpWFzN9ctmBARm/+xClbp2LJyq56fpeiZMFJgxAx49kMmSTWT1dh0ZgkQa%0AVErAy8nChtVKOwYNh5274PR2OHISNu+CddsVEpLOQyRDgMSz51AwD2xfpbz0JQl8cinerU3ehg1E%0AxYD/N3BsDRR8axUVpwffMnB4ORTJbTv39OUVUtouWRnVkOHw4Cn8vty2LD4BfCrB3gWKarv5gBKK%0AcOU26A1K6EDNIlCzMJTPC/2WwbUnIqdmpAwj6DYXTt4ROb9ASuE6sOkItJ8OozrArA0ib6IlulUV%0A6F1VJt9gmN8Dmr6TFL5sLwxYBk82KVPcTUbB6RvQo7KSLDb3gMizNe/Hsc/fCaPXviXDFtjUH4q8%0AVYpvPoVCA+HUQsibBQyJCjHceFBiQVs4cEPg1D2Za8n6xmyBPvMFVu+VCfIELw+BA9NTDutztgoM%0A+UmmSl44flvg8WYZq2Pc8wgo00PA0wmOTZKpMVbkTSKcXSdx/wl8EwJl88LGkbb9rdoL3X4ErQ5u%0AbAbftyEQkgSth4scOi1zfbXM43Ao3xMqlYEDx6FdQ/jhrXtPbBx800TA0w0OL1Pa+8NKGDQDfHzg%0A7lmljPCshTBqMlw8CJmC4MRpqNIYlsyE5g3hwFGoGwILp4FeD/1HK33i4irgHyRy44qFui0daNjW%0Agc61XxPS14XS1expVymcbuM8uX3RyKEtMWTJY8fNCwbFIUAG34waXj83JxFWBDAmKB+Sfr7+3Lhx%0A471rmxyyLKPX6786sgq20s+phWClZsGVWmGEtIyT/Zo9YOHD7ZNlmRo1anDs2LGv7pp/ZfiXrH5J%0AJLceAcVqyGpf9Kn40HR5aklHX8ID1RrW8DFl45KXP7W262OM+z8X2fszpHZNPpePqyRJREdH/6O4%0AoGrVqnHk6BFUGgFRFHDy1JIQZSJrERfehCcS8dBA3d4B7F745K2tjoRKhHm7/PEJUNEk/2PMZpmi%0AFR3QOap49TCB7afdiYm2UCooktgY8PIVqVhXx7bV8Ry+6ECWbCIP70uUzR3P+ROQJTNcvQ7lqir2%0ATXo9pEsHCYlQvBhsWKeQhlevIHtOOL1HmWYF+GE+/LhI4OFROUkZbd4bnr+Aw6ts59lmMNwLEzm2%0A0va8LP0VBv8Iz/Yq1bYADAbwrgS/zYayhW2/7zwWLt6C02tT9l9AVRjUAdK5wM+74OItkVevJbQa%0AqFcCRjSHHG8TaBON4N0Sfh0NlQql3E/GFtCjMQxopfy97xT0nw03HiiK79WFtml0K3xbCYxuK9O1%0Anm3ZmRvQbKzIg+cS39WGWV1TWnHFxEOGEFg4AJpXgV4zYelv0LeWwKjGMuXHiHh5SmydkPJYGw5C%0Ah6mK+n1tGWT24z10ngE/H4CBTWFkyPvrZ2yE4cuhWSVY9o7dY2QMVOwt8PyVjAw83K0UhQB4+BS+%0AaQMlcsCWMXD0MtQYAmtnwNb9InuOylzZKOP59jGwWODb/iJnrsjExst0aAYzR8Ol61DmWxjRAwZ0%0AUrZ9+RoK14eiecDRXmTHYZmhw2TGT4BBvWHo25ymXoNFNm6Vuf2HjIsLbNoBbXsq8aqiCCHfQdgz%0AcPMQadhKy8VTJl48h/3X3AndYqRfm1jWHPbCZJRpUyWCySs9kGUY2u41Cw8GMWfwS149txAy2Jtp%0APZ5SvrknB9e+QjIrtXTNRhm1BlQaFSaTBckEzs7OPH369P2OfgsrWf3cxvCfA3/Xx/RjCiP8mXvB%0Ax8Kq+qZVmNs/xYfaJ0kSNWvW/K9LbPoP4F+y+iXxLln9O4rnx0yXv4svUS3rr2JwP1VFTQ2fg+z9%0AFWJiYrC3t0etVn+26lJWfAqhTw3FihXj8uXLiGoQVCKWRAm1nUg6Py1aexUvHyQwYW8+Fva+w5Ob%0ACRSt7k74gwT8MoC7h5oda2Jw81Cx+FAgTi4q6gffZekON04fNTJ/Ujz2TjBiljv1WjnStFQ4mTLJ%0ALFyjfODUKqUHE5QrAz+vl3kVoZSf7NpHTbsuKsxmKJQ5kZPHIXt2pb2Nm4JRD6G/2M7BN4/I1IES%0AIQ2Vv+Pjwac47PkJSr6dZjcYwLs0/DYXyhax/TZDVZGBIRI9m9uWfTcJjl8WubDO9lyZTOBZAX79%0AASoVt237Syh0mwjPDiq2R1Z0HAkHToOLI9x7DB4uAi0rwO0nMvfC4fxC3lNVO86AZ7+lzKw3myFd%0AdQjwUghbg9IC41vJZPJVEsVmbYeHG1LGyQIMXiywfLdMYiLkzSSwsq9M5rdEt98S+O0M3Fpn2/7s%0ATagzSESNREw8PN+ash3WtuRqA08jIGN6kRM/Srgm40DhkRDcBno0h/m/QINSsGKgbX2iEXJ3EMie%0AVebYOWhVFeb1S3mM7ceg6Ujw9oBbWyD5t/Dj51AiBLL7w/nbSqnegZ0VYtq8r8iJczLXf5Vxedum%0AM1ehZBtwdoKXF5SPIICjp6BmCCwcB63ekvwjp6B8K3B1gUsXldjU4yegTj1YNhuaNFAU23otRW7f%0AhRvHJVQqhaxu2gEIUKaKBp8AFdt/MXLstitqtUD1wtFkzKpixU43Zo/T89PMBPbc8OaPg0aGd3rD%0AmuO+HNoez8qZsaw+k4letcLwzagld3EnNs55RdMhGVgz9jFZi7lx90w0hlgLgggaOxFJBrNBQqPR%0A8Pr1a1LD/yJZ/TN8qDCC9f/w8YUR9Ho9Op3uqyWrH4pFjomJoX379uzZs+c/1LL/GvxbFOBL4u96%0AraZG9FxcXD76wfxSHqjW80l+nslVVLVa/dEq6oeOkdxLMC0gyzKJiYnEx8cnqaify8c1+TE+tf3Z%0AsmXj8ePHCCoQVSole1cFskUi+pUJQTbSoG8A83rc4dmdBIatz47GTmRYzWs8f6DCyUNCoxGYvjmA%0ATDl0tC35AEEU6Fg3mnTpVag0ArM3eFCyko7bV41cv2Bi6ToHHj+UWPRjIudOyTg4gFFQ890YByYN%0AjGXBSg1Vair3YNPaRipWEsmeXbnPoqLgwEH4fbvtHBauUNrbvI5tWc8xEOQn4Octc+Oe4tk6YRG4%0AOSsZ4lduKxW2jpyF6BiJDsmm/81mWLsLfn7HAWDoHAjwEahYLOWzNXiuyJCOMjo7OcU+Nu2HDTOh%0A6ltngmWbZRZtgJv3wdVBURhDqio+sgD9fxIZ1k56jyAOngf+3gLXtsrcfwJth8nk7goNS8LuczCv%0A7/tENTwS5myS2bsQCuaARv1l8naHwU0EmpeTWbATjs5L+ZsiOeD+LxLpaoJZgj5zYPE7CU8LtgnE%0AJMDzQzKthkDWNgK7J8oUefsh0W22SJ5gmUl9ZNrVhwodoHQfgcPTFZ/ToctELMCOBcp1KRei+Nhu%0AHGdrd9sJCgk98IdIrkZwdaOUpK4Gpoc98yFfYwjwVYgqKKrzzzMkGvQQyddE4PpmiUfPoWo36N4B%0A/jgrULyewJkdSlxq2eKwdg60eFtYwt0FGvSAsuUEzp6V2bYNunSGUiXhp0XQoauSAFi8MGxYJlGy%0AukCxauDqLHL2skymHCoiwmXmrnNCqxV49shCjSIxHLvtwi/7nalaIJrJQ+IYNNGROzfMNCz6kgP3%0AfLl3w4l2lV6y66YvD26a6FT+EUuOBhFS7CFBOXQUr+bM1llPqd3Nl9AlL8lR2p1bx6OwWCSMeuX+%0AVOsEJMmCm7sbUW+ieBdfc0Z7WrTtQ4URrMd7V5W1FkZILU72XUeDr60fP9R/aWVb9f8F/yqraQSz%0A2ZzCi/TPFE9rfKTRaEwiep9S5z45rOpgWn+xW+M9BUH4LNPmqeFzJ6VBylhUa19b1dXPPej9nfYH%0ABAQQ8TpC+UMEjZ0KOycNibFGBAQc0mnQv05EY6fCYpJo1M+fGp286ZznIsjQaXomjqx/hbOjhcnr%0A/Vk4+iXrZr0hIFhH/9k+7Fj2hvCHRjYeVwIJ6xYKx5RgwclZ4OYVC6JGoHwNHbPXKcUMJg2M5vCO%0ABE5et0MQBF5HSOQLSuT3Q5DnbXJS67bw4gmsmgO378Ht+zBysqIAurtBRCRERinT+ZKkkBi1SkAU%0AZQxGcNQpSTOS9La4gQlki+IA4OwAbq4CiUaZiEgY0RmyBUFwIGQJgMx1BFaMkalb3taHu49B00GK%0AquqUTBwaOAN2HBG4/pucQj3tOwn2nYROTWHeKnj8DMoVECmYWWLhztRVVa8aAmsmy9RK5ud95xGU%0Ab6ckfnWtJzKuvYRbMjOLTtPg/F04t9627Og5aD5Y5FWkRJ4scH7Z+/fEpDWwYB+E5DIAACAASURB%0AVKvAziUydbsI2KkFDs+U8PWAl28gS3NYPQnqV1JiiCf+JDBpiczUThDkAy0mwL1QkqbiI95AtS4C%0A0TECc7+TaDQGTm2E3FmV9Q+fQplWAln9YP9MmUp9RCwqmd83KIpw3c4it+7B5Q0SLk6Kglqlm8ir%0AWJmISJkyhWDDbFv7jUao1Unk7iOZmDiZb+vBopmKbVrxygKBfrBvre1V89M6+H4MWCTo3ldk5Dg1%0A+/dItG5iZt0aqPY2WW7GTIGp0+DiURnPdNB7qMCq9TKevgL7r3ug0wm0qBRDfJzE7nMuJCTI1C0R%0Ag7uHwIaDLlw6a6ZRuRgmLXaiVmMd35ZRSOWmk970ahLJxTMmloR60a32K9QagY4jvBjd/jndJ/ly%0AYGM0ej1kyOnA1d9j8Ayy59XjRPRRJox6C6JGRLZICCoByaSU3EyOP4sL/U/DZDJhNpvTdHbuU/Cu%0AImv1r07rwgh/t60fUszPnDnDtm3bmDVr1hdt038h/g0D+JKwWCyYzeakvxMSEpBlOcXUijVZypqM%0A9TmI3pewfJJlmejoaFQqVVLZ1n86bZ4aPjZb/2OQWixq8vjftMCntt/Dw4OExAQks4SgFlBrRPzy%0AuRNxV3nRNRiXj3V9zqHWiGTI58abx7FUbu3F5pnPcfdSs+BiQcIfGehb8jIt+qRj8+I3mE0ydTqk%0Ao/+P6YmONFM38BZrDnnh4iYwZ2wsO9fH4+kjUqWJCzVaONO+7BP23/TCP1CFxSJRxOslC1ZqqF5H%0AUVVbNTCgfyMzYRycOweHj4mE7pIwJICDIzi7qpBlibgYmZDOajJmgizZBXZutXD8gMS5S7YXyMTx%0AEuvWwvUrtqn3Yyegbj14fFMhtXfuKgS4ex8oXgTi4uD5c4iNhTfRCsktUwgqFFFiG4vkgnIdBZpV%0AlxmTrISpJIFXWYGl42XqV0653KOkwJofZGpVVJY9fgrDZ8CmXYo6OqwtdK5PEvHsOxP2nRO48mtK%0A0ms2g1c5RYFcvlEk7JnEuA7QvT6EvYQ8bRSimjNzyut+7jqUaqOM0D0awYROSqIYKGQ0c2PYPB+q%0AlVEcE7qOFNlxQGJxf9h+XOTOCzj1c0rFeffv0LS/cn4ju8LADimPaUiEZgNF9p+QaFEHFo9Nuf7l%0AaygXIhATI2M0Q9gJ29S/0QgNu4lcugFXNkhMWSGyfLvMvQsyLyOgeBVFuV4z3ba/sOeQqQI4OcHr%0Auza7rfCXULgClC0G697aYC1ZD71HAio4e02Nf4Cy8erlEoO/t3B4v0yePAox79VbZMtWGY1aRuek%0AZsh0B74PiaXPaAc6fe9IdJREzYJvKFhUzYINTrx8IVE1fzS1m2gYP8eJnZsT6RMSx8JfXXnyQGJY%0At1jcPEWMBpnEBBmVFnQ6EZNJRrLIiCoRi1kGWcaYCL6Z7Yh+ZcYtvRZZFpAFgTdPDMo2gMUoIWre%0AJ6xfc317k8mExWJJ07yHv4t3E9M+Jk72SxZG+LOPkN27d3P79m2GDx/+2Y/7P4Z/yeqXhNWE2Qqr%0A4uno6JgiWepzE73P7e+ZHMmdCCRJQqvVfvZp8+T4pw4Kf5XRn9bJaJ/SfhcXF4wmI4iC8iIXRERR%0ARmOvRjJKdN1YikVNj+OVyZH2S4sypdxB1HYqUAmYE8wM35iDotXdaR10mpdhJtx91JRp6sO+pc/Z%0A/jgbrunU9Kn5kHuXEwjMrOLKWROiWqB+O2cGzVKML1uXCiM4GKatUObApw+LIXRjPEcu2HHymMS+%0AXRKrFpsxJoKrG3j4qIiLk0nnIbB2jwtePsp9UCrLG9p0UtFnsDIPLkkS2bwTmTVHoEFD2z2eKYPE%0A1MnQvJmtH4qWgIplYfpE27L5i2HyD/Dgus3CCsA/K3RqD4YEOH4SHj8WeRUhYZGgWkloVgMqFlOm%0ApscthGXbBO7tSVkJa8QsWB8qcPtASuK5bS+EDIBpI2DqPJFnLyRa1RDp01SiZCdYNxVqlEl5DftN%0Ag93H4dpehXxv3g09R4EIBPqAnQ4OLX3/2pduC/4BMLwn1G4roFXBhrEyBbNBx8kiFx/C2S0pyeja%0AbdBlpGIBdnsnZPB9f7/dxgms2i6TM4vIiVUS74bL95kisnK7hAyELoIS73jBHjkNVTuCjxfc3p8y%0ATtVkgiY9RY6dkUgwwMn9tsS6u/fhm2rQoDL8NEEJ9/imiYDOVSA+AezVcCJUSroODx5B0UrQqj5k%0AzCAw8geZZZt1bN8osX+XmXM3RJyclI3Hj5RYutDChbMynp4waozAnLkyTi4CZ194IIoixw8a6VAn%0AmkW/ulCumh2P71uoVSiSLv119B7uwPXLZhqUiqHXMB1qjcCPYxMwGmVc3EUy5Xfi8u+x1OrkQ4sh%0A/nTMd5kS9T1p1D8DPQufo/HwzKi1ImuH36Vy10zsX/wQJAlToozaTiQxzoJfLmciHsZjMUvIFhDU%0AAhaTBBJJhPVrJqvJxZOvDZ+iSP9VnOxfFUb4O+/jP7uua9cqGaBdu3b95P3+P0OqHf9l3Yj/H8NK%0AnKKjo5OsLVxdXXFycvrbcZ2pwTo98rlgdSKIi4sjOjoai8WCo6MjGo0GjUaTpobWf7fKlCRJGAwG%0AYmJikghpan39JSqL/dX+rf1pNBoRtUo8KYKA8Nb63mK0EFjInbn1f8feVcPwk5WY0+AYkixQuk0Q%0AmYu4k72oM57+dnTOd4GoV2baTMjEmmclOftbJG2GeOHgJPLLnAjO7I8jIV7GM6sz47cFI1lkOg1X%0AYqge3DJy60ICfcY4Icsy1y4YWTNPT2QEZElnoFtbC+tXWwjOo+WPl76cjgxgxxUf9LEyw6c5JBHV%0AE4eNvAqXaN/dxioXz7FgZydQN1lm/IplCvlq3Mi27OZNuHMH+vVK2UdTZ8LwQSmJ6vI1irI2bABM%0AHge/74NHtyRy5YQ6tcElPYxaLBJcG/wrwQ+roFFVpVKVFZIEC9bDpP4piSpAv8kig7pD51Zw9w+J%0Ao1vhWphEgdZKmILPO6FnRiMs3SLwwzCbSvxtDXh2GkoVh0t3IdEM95+k/N2BU3DlDiyfDnlzwoM/%0AZCqVkyndHbr/ILBun8QvP74fg968jhL/K6igWleRyHfCIq/dhVXbZfZsBp0TZK0t8uylbf2Zq/DT%0AJonje2BYP4EqHWH7Qdt6QyK0GybQvAVkzCySu4ZIfLxtvUYDUwZKRMeCWgvpvW3rsmaG33fC5j3Q%0AYzQ07iWiNwqEHhXZvk/Fqyio1SzZR0sQHNquTP8Pmyqzfo+OCtXUTF+kIWc+FeWL25Jwho0RqFJd%0AxTdlBMpXFFi9VmT1YS/cPNS0rx0LQKmKWkbNdKJ7k1ge3DETmFnFsp2uzJ1kYPeWRGKjZfyDVMwc%0Am8BPs820HJKekrXTobHXMmVnNsZvycZvi1/y6EYC0/fn4tDacC4djmLUtjysH32PgByOlG7iy/G1%0AYQzcUQJJEmgyvRBqrQqds5qX9/SYDRZUahFEGYtRQvXWhNfF1QWLxfJVx6z+r8BKPtVqdZJQYW9v%0Aj4ODA46Ojtjb2ycl/sqynEQ09Xo9er2e+Ph4EhISksQOs9mcRHY/hL+qXuXl5ZVWp/s/j3/JahrB%0ASoQSExOJiYlJCgNwcnLC1dUVnU6XJkTvcxEw6xdsdHQ08fHxqNXqFITvc5Pi1PAp52L9GLCSarPZ%0AjIODA66urh+sEpPWZPVDg5Y1iS4mJgZHJycssoTaQQOSjKhSIYoCokbEPp0dFqPMg3ORqNQCdYbl%0AZGCW3cRFmhjxezkqdM7EjUMvkWXo/c0lXj5KpP2ULDQbGsSB1eFEhiciCFDT7xbzBodTtJo72yIK%0AMXhZFhYOeErT7u6k81LUz5FtnxOcS8PCKXqK+rykUcnXIIq0/D4doWGZ2fkgExazwNCZrqTzVH4z%0Aa2Qs3ulVlKlsM78e2Sue9t21uLjYzn3ONJmhw0Clsi2bPAkGD7JlggN81xsa1hdJn0wl3LpDmfoP%0AaZGyD8dPhsH9bLZWALfuKP9mzoCVK+DmdYmIl1C+ElhkWLEN3ItDoz4iW/fD+AXg6AANq6fc977f%0AIfyVxHftbcuK5IcjWxST+Hx5oExbqNQRzl1T1vebDoF+MtXf8VsFOHlJZGBfcPaAvI1gzEIlA1+S%0AoOcUgXZNwSrEiCIsnARHNsHP+2Q0Wt4j0gBrtyuxsc+uQY6cEFxH4PgFZZ0sQ7sRAnVqQKkScHCb%0AROUKkKehwB+XwGiCFoMEQppDzmzQ/zuZedOgxQBY/NbNYeB0EUEtsGAu7NgmkTkr5KwmEq3wQRIT%0AoX5Xgeq1RcpVUpO/nEhUMsKcIxsc2QnLN8PvZyQOnVEUq3TpBHYfUnHpOrTsYtt+7yEBlRok4PFD%0A5ZlUqQRW/KpFawe1KirLBEGgfRd48Vzmxi2ZAw+9KVjCjhX7Pbl42sS4fkoDm3e2p1kHexqViSY2%0ARqJoKS3NOuro2TKO1jVjCMzrTPMB6dHHStRo68Hw1UE4OIkMqHqHolVc6TQhgBH1buHkpmLEumws%0A7nMPjU6k04ysTG18iW8HZ8Q3sz2r+lyh6/JC/Dr0Is1mFkKyyJTsmguVnQplaFEunsVoQVSLCKKA%0Aezp3DAbD+xf1K8HXTKQ/V9usRNZq3m9nZ5dUMtvJyQlHR8cUs3AWiwWj0UhCQkISmbUSWaPRmERk%0ArYptanj9+vW/ZPUf4F+ymkaQJImoqCgSExOxs7NLSkZKayPj5HE8nworiYqNjSU6OhpJknBycsLF%0AxeU9cv0lVMmPOUZyFTUuLu6DKurf3f8/wbv7t1gsxMfHExUVhcFgwNvbG0RQ2akwJ5qxc7VD0IjY%0Ap9NRqH0eEqONBH6THq8srqjUAuv7XcYQY6Te0JwEFXBjarVjmI0yMTEiFbpnxd5ZTc2ufsTHmlny%0A/T0MeomtS2KpPygzsizQY2YGRFHkyvFYntyJp90gdy6fTGBYSDg3zify9JHEzdsq+i/KiIOziqHz%0AvOk83AMPbzUzB7wiMKuGIqXtkvr9l5/iGTBOl9THN6+aeXjXwnf9bPfJ1g1m4vUWWrSy9Uvobono%0AKGjXxrbs5Us4dxaGDUipIg4ZJdK3Z8op6NB9EPkGOrRJsSnde0ODBiJ+yTxP1Wo4fBTGTxJ4+FQk%0A9ABIThLdxotMWQoebnDwhEIcreg5VqRPJwGXd8K+h0wEfz+BYweUkARXHyjbDsq1g1U7YMaw94nl%0A2m0QEyvRv4dS6jN0MyzbIZCtrsDQOUpM6vRUQthiYpUYrDJlIH8dWLfDti5OD30mwMQRMq6usGmF%0AxJDvoWpXmLIU1v4G95/CyvnK9hoNLJmtbFOlM9TvDQaTwNyptn22bgobV8D306B5P1i2WWLbNmWq%0AXqeDXzdJ5M0PuaoJRERC73EiCSaBVRtElv8skL+ISL6yIsnziM5fBpVaKWW6ZIGtg9P7Cew+rCb0%0AAPQeAguWCYyZKrNqrxuzf3alXxcjxw4rEriDg8Cm/ToePpTp0MrMrxstNKhhplVvF9y9NfRtrjBk%0AX38Vy/d5snaRgQ3LEwAYNt2BvIU01CgYxbel37BxRSLBhZywc9DQf1Eg7Uenp2RNN7p+cweVGqaH%0AZuXuJT3zBzymUW8fyn3rwXffXKVwVVdaDfdnZI0rlGrgScXWvgwpc4aBG/Ohj0zk7Nbn1OqXlfV9%0AztFqXhFO/nSD0t/lRlAJpC/kg9ZJAwJIZultYJ2An79f0hib1uPop+L/A1n9KwiCkERktVptEpF1%0AdHTE0dExhdONNeQvISEhibgmJCRgMBi4ePEiW7du5dKlS2lGVkNDQ8mRIwfBwcFMmTIl1W169epF%0AcHAw+fPn58KFC5+9DV8C/8aspiESEhKSCN4/9d38FHxqYo91+sNoNCZVxbKzs/vTQSG1hLHPjbi4%0AuCQLrOSwTtkYDIZ/5Iua1s4JsbGxSb64yQs66HS6pJgrUacCWUC2WFDbaUCWyNciJ1fW3aBMnwI4%0ABzixs9/vOHrYk6W8H/f2P6bPryVY2PosEWHxdFlTnIL1/enru51OP2Qm5pWJ1aMeoFKL9Fmdh+J1%0AvRlW7ix+gSqGrc4EQJucVzAazJhNEB8nIckypWq7M2ptRgA2zQnn58kvCH2cCZVKGYzLetxj5s/p%0AKFdDyRBePjOGZTPiOPXQDVmGZ2ESbevEolFLNG2t4sUzeBomc2CPBZUAvunBaBRITITXr2VkCby9%0AldrvWq1S7tJshLq1IUOA4qcZEwMTp8G544pSZ720eYuLNKgtM3aEbXh6+Qqy5oXTf0BwsO0abN0G%0AnbvB/UcCOp3t3lixTGLkCChZVuTEYRkRmU7NIF8O6DAYnpxTnAyskCTwzANLF0CdmrblUVFQpAyE%0Ah0Pl0iKzRkhkDrStDygJ/XtB72QqIsCQsTBnkaJs7l8LbslCzCUJ8lQRqFBZZsZ0+GUD9OwFtSuI%0ALBorMWmRwMY9ArfOpCT2h49Bw9aKajtnCrRvxXuYtwQGjILG9WxkNjn2H4bqjSFfPjj5jne52Qxt%0A24scPChhTIQTV9QEBSljjMkk0/JbiRtXZK4ek7h5B8rXhTk/O+HuIdKyeiyTpom062yL5bh6WaJq%0AWTMWEyzf5UrJisqH0Io5CUwbHse+0/Zkza7s/8E9ibJ545FlGL/CmxpNnXn2yESjgk9o0dWRfhOV%0ADjywPYG+LSJZs9eV3AU0jO8Xzy9L9TilU7Pxfl60OoFBte/z/KGRVVezYzZB15K3cXJRMftQMDfP%0A6fmu3G0G/JSR8o3S0avsLRAFZh/Lxfjmd7h6IpaJBwowpt41LCaZ5mOyMLvdVWp+n4Vnt/TcPxdN%0A+S7B7J56nUItgzn/812cA1yIDovBZDAjJaa8ZtevX8fNze0/XsI0Ob7mcqZfc/IXKH1nJbqSJLF3%0A715WrFjBgwcPePToEW5ubgQHB5M1a1ayZMlC1qxZKV68OJkzZ/7rnacCi8VC9uzZ2b9/P/7+/hQt%0AWpR169aRM2fOpG127drF3Llz2bVrF6dOnaJ3796cPHnyc51yWuDfBKsvjeQlV//KSP9z4mMSez5U%0AFetjlV+DwYDZbE5Tiyy9Xo8oikkWKp+rupQVaemcIEkSMTExSR6BOp0uibjqdDrlcRQFRK0aEaUy%0AkCCAWqsiMcaIfyFv8jXJwv4xpynQNJhaU0owJeta3P11vHqoR1QJNBiTh2p9s7Gy+zmOLL6Ho6sG%0AnYsW/ZtEus7PSbkW6Xl6K46+BU+y7HIeHl1PYPWEF9y/EkdAdidqfedPwarp6Jr9JCsv5yQgq/IC%0AaJjhCt1GudOwo0IA5o2MYO8vsey+5s2DW2aunTcyrncUWq0yVRvxUkJrB8jg7afGwVWNm6eIxQwX%0Aj8fTa3w6HJ1FHJxEXr8w8+PQSGZvSIckCcTrJaIjJaYNjqZuCwcMCTKvnluIioSHd4zYaZQpZ8kC%0A/v4Cvt4yp87C/B+hWmXwS6/0W+NWoDeI7Niakgzkzi/SoiUMGpLy+uQIhp4DVXT6Trnft/xiZtYU%0AM9evyHilg0VToVZlW9b6iClKedkbF1Kqp0YjpM8qMGORmlWLLZw5IdG2EYzrC5tDYegPEHYlpTIM%0AsHQNDJsg4JNe5NljC6tmQs23bgTrt8N3IwUePZCTwiRehEPNmiIxURKvo+DgdsVf9F107y+wcr1M%0ABn+Rk3skkheAk2UoU1PErIab12XqVpNZ9Q5h7dRH5MBxiI6SqV5VZvk7CWGPwyB3XiVZ7Nx1Nb7p%0Abc+e0SjTvIHM7esW9HqZpp10DJ2sfJQdCjXSuVEc85eINGysjEsH9kq0bGTGbIHJi5xp1MZmlTRp%0AYDwblsdz7JoOTy+Bkf3MrF1qJNEoM3aJN7VbKs/sldMG2ld8xriFbtRrpRxr2Q9xzBkbg04n4+Bq%0AR58FQYxqco8GXT3pPMGf+DgLHQrdJGNuHZO2ZCEy3ESbfDeo3MyN3rMCObAhkskdHvPDvmw8u5fI%0AhJD7uPtoMRkk9DFmVFoRO3sVZpOEZJHQ6NSYEiwYDYpy6uihRZZAVAsElvDh8ZkIVHYqLEYZQ3Qi%0A5nhb4i3AtWvX8PPz+8sSpl/KmulrLmf6NSd/wZ8T/erVq/Pzzz/z4MED7t27x927d7l37x61a9cm%0AJCSVsnIfgT/++IMxY8YQGhoKwOTJkwEYPHhw0jZdu3alQoUKNG3aFIAcOXJw5MgRfHx8/tYxvwD+%0ALQrwn4R1kJEkKc0rb1inJlI7TmpVsZycnD550PsSMavW/jKbzSlUVAcHh8/invC5wwCscbNWIm9V%0AqZOrz0lEVQaVnQbZZAaNCq2TFlOcEY2TDmO8icQ4E7sGnSB9bg8a/1Se6fnWEx9txNXPkeJdcnPl%0AlztU7J6Fk+sfc3z5A9x87ak/IT+v7sdxZs19yjRTAj9ntrqGg7OKHiVvgCBiMlmo1SOQ9tMUU81R%0ANS5SspZ7ElENXRWBMcFCnRAXXoebOXM4nnWzo0CAfI5P0TmIqDUiJpNA/a6e5CnhSL7Sjoxr+xgV%0AEj9uz5B0ri0K36dJFzfa9rNVIWtV6gn1WjtSqa6tT8b3eUOmbBom/GSbdXj6yEy1HC/47YY3fhlU%0APHlk5vQRI5MHxODtB2Mmy/QeoKiyuXMJXL4sM2a0RGIiWN9jJ/6A588kunQTSD7+7dopER0NLdvb%0A0uMbNFUTnFOkcrFEytZU07avGa0GBnaHdk1h/iqFIL97yw0aDgFBIg2aqGjYVM31qxJdW5jIWEbC%0Azg7GDXufqCYmwtBxMGSCiradNcz7wUSz78zUriQyc6TE92NhQH85RTyvrw+cPyeRv5ASq3r56vtk%0A9eZtWLVeZvtRe34YZyFbcTMHt0jkyaWsX/8r3Lgjc/mZPc+eQL1yBirVl9n3q+KQcOQ4rP9V4tA1%0AZyxmmbql4vm2iczmDcozIknQpp1IibJqvP3VlC5s4Ph58PFVCKtWK7B6IwT7K8p5/7E28lmhupaZ%0Ayx3p0U6Pm6uApze0bmKm/zR30mdQ0695BD5+ImWqKBdv8BR7noVZqFwkkTIVRPbuklh9LjN3LhsY%0AGfKcDFk15C+uI28xHRNXeTM05BVBwWryFNYSGSFhMkogqlh3NQ9arcjUXdnoW/Em2Qo5UP5bd2bu%0Ay0rbAjdYPu4Z7Ub4MWNPVrqVvkVQLnt0DiIaO4HeFW7i5KYlWylPHpx9Q/nuwVTpm4OxBXdTvE1W%0AynTJzqRCO6gyojBaBw3b+p+k7qyy7Bp8QnECMMnc2vcUi8GCc3oHEqON6NI5kAiYEs1KIDWQO19e%0ALp47n6q6llo2u8ViSVMi+zWHAfy3wvqeCQwMJCgoiPLly3+W/T59+pQMGWxjbkBAAKdOnfrLbZ48%0AefI1k9VU8W/Mahri71ax+qd4l0gmT/SyWqe4uLjg4uLyl9P9H0Jan4uVpBqNxk+ORf1YpFUymtXp%0A4V23BIWoCiCDqFMjatSoHbT4V8mOKdZIgR7fIJstqDQqRAcdGp2aysMLMzn4ZyLuRFFryjf0udyU%0ACytvUbJVIBNKHWJJ29MEFfFg2pMGFG+ZkcNzb9N2ejBPbuqZ0ugyj67F4OJtT6tZ+RiwswRmg0TD%0AAco8ddRLI1ePvqH9GGXQio0ys3DwM5xcReoEP6R60APGd3uFLAqEjAlkzb1CbHtTHCd3Le2G+9Jt%0Aoh9l6rpipxM5fzCWzqM8k8716QMjD64badvfNr8d8cLMjfOJdBlsU7IlSWLbmgR6jXZJ0aejur+h%0AXA0dfhmUD66AIDWlq2iJjZVZd9iTY0/Sc1Xvw7JQD17HCWh0MHGKgJcvlKuoYtp06NwV2rYHN7eU%0A98rQISp69NPg4JByed8uFhq11vHjckeuRDjz/Tgds1YIeOeF+AQoXjTldTebYdV6gdFTVEn3Y648%0AIkcv29E4RMRghEk/Cuw/nPJ3i1aAnU6kbWdFfenRT8PpO3ZcfSgQ9I1iht+n9/v32bnzEBYG05Y4%0AMnCMQEg3kYQE2/ruA0TKVVVRoIiKlVs0hHTV8E0NgU3bISoaegyAEVO16HQimbOKHDiv49lrkfxl%0ARSIjoWVn6NhbS4YgkYxZVOw+48jFywLVa4lIEsyeI3DnLiz5zYXpyx0pW01HqcIS4S9savbkseDo%0ArCI4nx3VC8WlqKZXu7EdY350pFUTC7UrmWnc2YkW3V2oUMeBITPS0eXbWK5fVlRHQRCYvsKJhASJ%0AbRtMrLuYkcCsWio1dKHzKC+6Vn9B+BMlrrVKQye6jkhHhxqRfFvsFb+uMjD7RF6CcjrSu9wt5boU%0Ad6LfooxMaPuIhzcS8A2yY+pvWVk7OZw/dkVj5yCSJY89c/o+Yf6gF5RoEUSOst44etoz6HB5uq4r%0AwZFFd4l7lUif3RU4Ov8WYRde02VLBXYNO4N3DlcKtcxG6PCTdNhVB9lsocyosjj5OOGY3gljgoTZ%0AKKF/HovKXo3aTo2ofSsmWCQKFCzAmTNn3rvm1illtVqdFEJkb2+fFDtpb2+fNB5aZ8veTQIyGAxJ%0AsZRWJ4L/VnztRPqv2pcWlcE+Bu9e86+5Dz+Ef8nqF8SXKIVqPY5V5dPr9UmJXjqdDjc3NxwcHP6x%0AupsWZPXdjH6rOvxnGf2f45h/93epWXolT0az9pEkSbYYK1lG5aBBRkAymnAJ9uLp/luUnlaDh/vu%0AYIhJpMzkagiAxkHN+g6HiAnXU6J9HioOLMj6NgeIi0zkwLx7uGRJh0ot0uxHRWLbNOgCpkQLu+c9%0ApX/RU5zfF0GZVhmZfKUipZpnYGnXS1TrFICbt6Iozutyk8BsOk6FxtC5+G3qeF8iLsaCXx5XWkwM%0AZn1MeeydtXSaFMS3vdLjFWDHleMxvHpioEFXm3fT7H5PyZxbR67CNiVtYrdwytZ2xjfAJg+O6x5B%0A8QoOBGWxLVs9V4/WTqBiHZv8GBcncfqIkZ4jUoaYjOwRQ8mKDgRmVn4viiIFiqsJfyozbbUHpyL8%0ACb3pS4EKDixbq+LZc1i7Grp1kTm4X8Zkkjl1UuLpEwude6a8/8MeSVy9vpq3LQAAIABJREFUaKbP%0AME3Svlt3tuPkAxccnAU8fURyFoKOPUQePVZ+M3QUpPcXqFTt/fty9w6BET86U7+tjoZtoH4rkSdP%0AIT4eRk+BUVNTHt/HV2TnMQ2iGqJjoVdfkeQJ47IMfb8XqVZfw7etdRy66cbxcyKFKwjcfwg798KF%0AyzLz19i9bb/AkHEaZv6kpW0PKFsT/APVtO5om5709hEJPaXD1VtFliKgdRAZPMF2Df0ziOw+48jj%0ApwLFvhEYM05mznpndDoRURSYvsKRMlUUwvrqpcS+3RJLFphZut+bJXu9EdUidYrrU4x5dZtpsXcU%0ASDBAk862j5bGnZ1o+70rzSrE8PyJGUmSGdxRj0qtwsNfy/jO4UnbhvR3p9K3rjQv8YyEBGXfRcrZ%0AkaC3cP+WiRV38hNc0InxO7LxMiyRqR0fAFC1lSf1u/nQs/xd4uPM5CvlRNPvfRje+D5t8l1D0jlQ%0AOiQjZjM0Gp+LXpuKY4w3s6jFaQrV86dGvxzMqHII72An2i4pxpr2J3APcKTexCIsrbuXqkPz45nJ%0Amc1dDtNifXUODT9M5WkVMOtN5AopgMZRg87LEWOcEXO8CVEUEHRv7wMZKlWuxKFDh967lz6E5NZM%0A1jCu1IisVbwwmUwYDIYU1kypEdmvmRB+zW2DD7fPZDKlSViFv78/YWFhSX+HhYUREBDwp9s8efIE%0Af3//z96WtMa/ZDUN8e5N+yWmzq3kKD4+ntjYWARBSFJRrTGTnwOfk6zKspxqRr+9vX2aJhj8nf0m%0Ab+ufebha928ymZQwAJWIoBIRNCpkWfFRlWV4feEJbpnScWnWH0TdiqDhztZIkkTEzZeoHe0oMbg0%0AoiBQaUQh9ow+w9Ut98lcxo/+t1tjjDWRs2J6ggq5c+m3pxxZeBdRLaLxcaXnwRpYTDL1hyvZRo8u%0AR/PkegyNBgfy5JaelUPvcS40grDbBnYsjyG4cnq8MjrRcEBGRm7LT4WW6Tm7MwJ9tJGqITbFdH7v%0AxzTo4oWTq/KSlSSJgxui6Tratk1cjJkLv+vpOsKmlhoMEn/sS+C7kSnNvJdMi+O7kc6Ioq3fpvSP%0AInseLbkLalL8/tg+Iz1HpCwBuWRGPI7OImWrK2Q3QyY1/Se54hOgpkJdZ6at8+ZBuJa2bWX8fGSa%0ANYbylUWc3glT7tPZROVaWjJkTDkkrlyQiEYrcPCuN1vPenL5joo8RSGkEyxfKzBm6vvhKMsXmTCZ%0AZL4N0dF/nDPHH3nyKl5NzhJQvxW4p1PxbfP3X1wLZkr4+mkIvebFnn0ihYoKXHtrjRW6B27dlpm2%0AVCHw3r4iR247kyW/hoLloH1P6NpfnWSeb0WDZhqmzLfj7iPwTv/+8+rkJDBishpDIkRFyTx5lPJj%0A2stHZOvvDty8KaO1FyhR3nZNRFHgh5WOlK5iR4kCEu1amOk7xY0sObU4OomsOORFTCw0qagHQJJk%0AujfV4+qhpVF3T1qWfsXrlzbj2x6jnKnSwJG6xWIY0D6OI3uNLLuUkzmHs3P1tIHpfV8AynM1dKE3%0AgcF2tCz+nO2rY+hY+RkN+2cgcwFX+ldS1FRXDw3T9uXk4C+RbJmvkN3Ok/3JVsiRjkVuM7blQ9bP%0ACMcjyBGndPYMCC1Jm/kFyFzEnXElj2LnqGLQ3lJc3v2MPT/eou7InGQt4cGkb/ZTtGkg5bsGM7P8%0AHkp3CqZAvSDmlNlB+61ViHsRx5XNd6k0rCg7O++m7sraXF1yjqIDSmOKSSRTw/yoHbTKVL5ZRu2g%0ARdCIIIrUq1+PLVu2vHedPhUfQ2S1Wm2Sx6g1f0Gv1yeNcVYia01q+hoU2f9WshoZGYmHh0cqv/hn%0AKFKkCHfu3OHhw4cYjUZ++eUX6tatm2KbunXrsmrVKgBOnjyJm5vbf10IAPybYJWmsH6tWvFuwtDn%0AQnJDY2tGv0ql+luxqB8LqzXX33U3eLfN1qz/5LGo1iktFxeXv9jb34MkSURHR+Pu7v6X21orYX2o%0AralBr9crA5RGhSCIyEYTooMW0U6NbLRgn94d/cNwVHZaZIuF7A1z4xjowsW5J8ndIj8Vf6zGsuxz%0A8c3tyotrkegjEshcNoAOoXWJfBTDjJxraDAhP8eXP+DF7Wg8M7sw+GI91Fo1M0v9RmAOezovVcoS%0ADS18mLgIA3b2Kl6FGRDVAgG5XBj5e2nUapG7Z94wvvzvrAgrjXM6hZB0y3WKKi3S0Wq48hUedieB%0AzvkvseFOTrz9FXV2xcQX7FwaydY7mYmLloiKsPBD3xfcv57I8HmemIxgMsr8uiSaGxeMjF/sjp1O%0AwE4Hty6bmDo4mv130uPjJybNPBT1DGfWzy6Uq25TW8f2ieb0URO/nfdM0ccl/CPoM86Jxu1tJDgq%0AUqJMhudsOpueLDlt5Grvr3q+bxKBi5sKySLRvI2a1p1EfNJD3gyJ7D7tRPbcKRXPggF6ug91oFV3%0A2/4f3DHTqlIEES9kmrfRMHS8Cm8f232QO8BI71EONO+U8jnfu81Ar+YxeHgJrN6iJX8hG7F8EymT%0AN9DAgi3ulKmiQ5IkhnSMYecvCYwdLTB3PtRtacfA8e9X7unRPJa9/8feWUdHcf3v/7UedyMJEhII%0AGiC4hgSCuxR3CsUJFCvuTiktpTgUd3d31wQSCBHirpvNZvX3x36SsIU60H5/p885HHJm7ty5M3Nn%0A9rlved7HVIz7RsaE6cZzUq/X06xmAXZuEsKDNTg5wZnbEsRiw7m1Wj1+Pkoq1TNFKhNzdl8ux66b%0A4F25mEwvn6Vi1xY11g5ihHodpx5ZFR0PhsSq2iXSUCrhSqwrNnbFx6anaPmiThIVq4ioXF3Mzg0F%0AHHzjhbmliNn9EnhyVc7p1y6YmRWPp7V3AslxGna/qoJLaYOlOPyZgpGNXjF+mSPdRxi+OfIcLW3L%0AvEGp0DFlT2UadnYkN0PNSJ+H1Aq0ZvJWTwDun81kTrfXLDvrTZX6FuxalMjWuXGY2UhY8Lg5tq4m%0ALPK/iVatY87dpuTnqple/RJla9syem9dQi4m822nO0w814RS1WyYXeMiJavbMHxvQ75teQ15uoqg%0A661YWucUMispzaZVZ3uPiwTOqUP8k3Ri7qfQaHpDLky8SN3pftydfw3XQG8Sr0WAUIAmVwkI0Gm1%0ACMQi9AUaVq9ezeDB7wj9fiYUljM1MTH5YMIX8MEY2b9T9enPQKFQIJVK/5XJX78sBfsuQkJC2Lp1%0AKxs2bPjo5z1z5gzjx49Hq9UyZMgQpk2bxvr16wEYPtwgQzJ69GjOnj2Lubk5W7duxdfX96OP4yPi%0APzWAzw2tVovmnZI5H1vu6dey41UqVZFb+lPhr6obFMbP/pGM/sIwhk9ROrZwLL91DYW6s0ql8k+r%0AD+h0OsNzlooRioTo8lUITSXYNvAm6/Zryk9uR8TqM2g1Wkp1rUX0nrtYl7IjKyINWy97hoaN4vqM%0AS9xbcguZpZSK/avzYutjxj7ogVNFW1b77CExJB0zWxlV+1Tk+Y6XDD4QQMVAN5LDs1la7QjLggOI%0ACc7m1IpIop5k4FTGirqDyuL3lRdT3Y8w8VhdKvkZyN/MuteoWNeS4WsMltiwe9lMD3jM/riaWNqK%0A0Wr1BPm9QJWnpd0QW+LC1USHFvD8di7qAh16HYjEAqQmArRaPRZWYsQSIQj0IIDMFDW2DmIQGFQC%0AdFo98hw1YpGBzGo0YGFlsKLnZOroM9KM8pXFlCknpoyXkA41M1m21YrmHYoJ7Lkj+UwelMOd5BLI%0AZMXPb2LfdJISBGy/bKxpOKh5MtYOYpbtdeX6KTmbFmYQ/lyJUAhOJYRceGyBmXlxP8f3q5g0XMm9%0ARGdk78he6XQ66rikM2iqDef2yIl4qWTUBAljJos4sk/L/Ola7sbaI5Uaz6nvFyo4sE1FbX8Zp3fl%0AMnSUlKnzhJiYCJg9Scv5s3rOBBuT8RsXlIzrkYVGred2tA129sZkOjNDR73SWQybbc+2JZk08BPx%0A447ieNwDO9VMH6/hUkIp8uV6hrdKIiddw6VHUmxshKz/Ts13iw37BQIB38/MYvf3Wew5a0qt+mJe%0APNPSvkEeG6+VplR5KcP8Y9GptJx5UkxYv52jYNeGAirXNyf0voJTL50xtyx+R5LjNXTxTUSerWPb%0AQy+8qhieoUajJ6hNDMlvVZx44YRYLGTrylx+nJ+NQ0kZVtYifrxZvqifu2ezmdE1klVH3ajTzJwl%0Ao1I4ty8HrVZPh9FuDFhoSFCKDctjXJ3HDFlYki5jDKK7B79N5Od5cTi6SclM09JlUVX2THhGpxne%0AtP3am9z0AqZVvYhvxxIMXudLcoScGTUu0W1BJVqOLcfpleEcXxTGotBWxL/MYXngNTxq2aPIUpMY%0Alg1CkJoYCJReDyKpEE2BFk2BFvQgszUp+gX2aOdN5KnX2Pm4khmWilpegFapRigWo1OqQCoGtYb5%0A8+YzbtwHgpc/IX6LcBWGCHyIxP4WkS30jn0MIqtQKIqqTv3bUHjvPqSQc+3aNe7cucPChQv/gZH9%0An8N/ZPVzo1AsuBAfQ+7p9+rdA0UWwE8hyfQu/qie6x+xon4IWq2W3NxcbN7V3/nIyMjIeI+sFmq4%0AFo5VJpP9qaQutVptuPcyCag1oNMjMpchNJeiy1Ph8aU/sbtuI5KJaHpmPJcaL0Gj0uLWqiqJ54Lp%0AcqwnacEpXJt2EedarnQ81ofjHffgVNqERkHVOPLVVRJDUqk1rBotV/hxZtwVEu/GM/lRBwQCAcvr%0AHif9TTZ6nR6xiQSNRke1Nu4M3F4fgH1BD3hzJYnFT/wQCAQkR8iZXPUyG17Xx8HdBLVKxzjf+4iF%0AerxrWxF2P5eYV3mIpUKs7KRY2MmwdjNBq9YR+SCTeRdr4uptjpmFmAOLI7m0OYHN4bWL7tepn+LZ%0APTeaXXF1iqpYxYQpGFnjCXve1sTWSYI8W0NMWD7T2oZStaEVWo2O5CgN8kw12RkqNGrwqiShThMp%0AvvXFVK0lYXinbDr0NWX0O+EGGo2O2vaJrD3uRB2/YmKbk6XDzy2OXfdL4VW5WPZGnqulqUMENvZi%0A5NkaegySMjxIShlPEfW8cunxpQVfTTFe9K1fJufntUpOR5VBKBTw6IaC+V+mkJ6kRiKFoLkW9Bth%0AbFXNzdFR1z2d5XudadLGnFfPCxjfKRm9Vsui1WKG91Wx87I9NepKjY5T5utp4J6MmaUItVLHluMW%0A1KhTbC2eOTaf29d07H9WiqwMDQPqxSGTaNl/ToaVtYAapRWMX2pPty8NC74CpY7JPdN4ekfB1oMS%0AerZWsmK/C03aFF/j9pXZrJ2Twfq9JsyZWEDVRhbM2uQKQF6uluEBsWjytZx5asXzh1p6N8ti7TUv%0AylUzY0qnaN6+zOdkqDMmJv/TSH2tpqtvInq9gE7DbJnwbXHVBqVCx5eNo5GJdfQbb86sLzNZfKEy%0AbuXMGFnjKdWbmDN7l0dR+2M/pbF2chy+Tcx48aCAxQ8akJlYwJyA+wRtKY9fD4N78/GFDOZ1CmHx%0AqQpU87Pi+I8prJ0QhVAi5If0jkhlYl5eTmZ1+5tMOt2Qin6OxIZkM6feVQb+WJ3G/UsTfCGZ1Z3v%0AMOVcI2TmYpa1voUiRw16PVaulmTFyfEZUJUKHctzqMdR/Fe2wKWmK7ubbKXFz1+Q/Sadh8uu03TT%0AF1wZdsAgVScQUJCTjy5fg8zODJ1Gi9TREmVijqFoAEBhUReNjpEjRxbJEX0O/Bbh+r3jgA+S2HeJ%0A7N/Vks3Ly/tk+Qt/F4WJth8yEh06dIjMzEyCgoL+gZH9n8N/ZPVz45dk9e/oeup0uiKLJPCbVr5P%0A7T4vxO/puf4ZK+qH8Gfc9H8VhYRbIBAUPR+tVls01j+7gi8oKDBYgkVCQ1q3RAyF7j21BpGpFIFY%0AjF6tJuDyJG52X4cmV0nDnwfx+oer5EclITGTkv46FfsKTvR9PIKUZ0nsrvMT7jWcSHqRjkAswqeH%0ANx3WN0el1LDCZR1DDgYgFAs5O+8p0feScalkT8CsOrjVdGS518/MfNYO53JW6HQ6JjkdZNiW6tTq%0AYCANCwJuokgvoE57B56czyDiaTZiiRBHD0tcKlji7efIy0vJqLI1zLjSoOhaJ1W6TNM+znSfXkwm%0Ahpa8Qb/5pQgcWExIBnvep/N4FzqNcS3aNrlZMPYuMqbv8izadut4Bkv6veFQci2kJsVzpEepR7Qe%0A4oSphYinl7OIDS0gM0WJqgAq15DRtqcJ9QJkVPCRsGp6DheOKTn5ooTRD+DkvqkkxgvYfMU4sWDp%0A+BQeXFPz85NyPLuZyw9fJ/DmeT4VqooJC9ZyL8kJSyvj+VrPNY2xS+xp39/4/Zo/PJnTu7JxcBaz%0AdJMFDfyLieeaBQoO/azm5Gvj5IcVk1LZuzYHW3shl8OdjCy4ABuW57HjRyUnojxZPTmJA2uzmLbY%0AnEFjpERH6Aj0yWbn/ZKU+5+1UqfTMa5DIs9vK2jYVERYqIhjocbn1On0rPg6k/3rsyjrLWH/45L8%0AEoc35bJoXCpmZkLOJ3sZvbMKuY6vmsWSn6MhJ0tNi34OjF5muK+qAh0T2kSTFlvA8RAntBro5JOE%0AV11rvphSkqBGzxk4zZ5B05yK+svO0NDfN5L0RBVTdlegcVeDdTn+TT6jaz/ji3EODJlj6F+j1tO3%0A0gtS4lSsftkEZw+Dl+rWvkR+HBrMypvVKVvN8H09/n08P8+MolI9C0Lv5zFoe31OzHuBibmYqVcN%0AdXEvfBfO0TkvWBoaiI2LCQ8Ox7N+wENm3fLDpZwlqzvfIexmGgKBgBLVnclNVmBXzo4+p7pxZ+UD%0Ari+6w8hXw4m/l8ChHkfofX0Q6S/TOD/yFL0ej+bOtPOkPkui2d4+HG38I3W+/4LnC88hkEnQ5qsp%0ASMlCq1AjdbBAm6cyED21DoFUjF6pAmDgwIGsWbPmvWf0KfBbhOvv4NessYWW2j9KZOVy+Qetvv8G%0AFBpkPuQ5Xb9+PSVKlKBPnz7/wMj+z+GDD/fftzz5/wgfkq76M2oAv5Zx/nvZ8Z9LIuvXzvOuCoFa%0ArcbU1PQvZfR/ruvIz883Ko37VxUTFApFcciCXo/ARAZ6PUITGdIS9ghEQvQIQK/DqWE5rrX9HlW6%0AnOYXgpDZW5B0NYyc2GxMvFwRikUErG1HQZaSw622G95eKwvaHeuPVqWh6ex6AJwacwmNSsv+UXfY%0A2PkSsc8yqNypHKMf9KBSew8OD7tClVbuOJczEKuzi19gaiXGsbQZx5a85pua13hzL4OsVA0Pryrw%0AalcWj3ouVG/rzoKQlow+2AD/EZ68vpZWlKwFEPUki5RoOa1GFBOhO0eSUcjV+PUqDt4Pvp5JZnIB%0ALQcVb8vL0RB6N5fe096piwpsmhZH57GuRkT18aUs5JlaenztSo+Jriw+VYmdkTUoW82S+h3s8Wpk%0Az96tGvo2TaO6VQK7fpTj21BGVnrxe6bR6Lh8QsmIOcaLHp1Ox8kduXw5z0CcqjWyZONdb47EVCH8%0AlUF7tHvDDC4eV6LTGebhng15aHV6WvV6f8F5+7ySYQvdaNTFhqEds/myUy4JsVpyc3T8tCyPyavf%0AX3T1GWuLTgd6gYhWVdMIfV68uM3J1vH9/FwmrjaMb/wyF749UZJVc/MZ2lnBjFH5+DY2LSKqYLBe%0AfX/SjZa9Lbl0VoOXz/uxfUKhgIYtTRAIIeKVmsvH8t5rU62hDPQgl2s5syvHaJ+ZhZD1l0uSlakl%0ANwe+WlT8HKUyIStOlsHKQULXGinMGJwBIjGTd5SnrI85C09XYuvCNI5tzii+zgwtOZkaBGIhz65k%0AF2138zJl0ZlK7Fmewtkd6Wg0emZ2i6KgQEhlf2cWtHpU9D1t2KMEHSaUZVrzYHIyDCSvRqANGrWO%0AZ9dzmP+qHdXbuzPulB/xL7PZPeEpAM3HelGjvRtz618zWOW7uNF8hCcLmlxnhPMJ4sIUOFdxwrKE%0AFYOufcGgS92IvR3HjSV3qDehFp7NyrC9wQ48W5elwaT6HGy5C88O5ak2uAaH/TYSsKETQrGAh7PO%0AE7C1Bw/GHaDe2h4oEzJx/6IOInNTLH3LolGo0CpVCISG74NeqTIsdMUitm3bRt++HyhH9gnwqRKY%0Afq986bveK61W+6sSXGD4ffm/VqY2PT0dBweHD+77D38M/5HVz4g/qgbwS91OsVj8pzRGP7dEFhRn%0AyWdnZyOXyxEKhVhbW2NpafmXVQgKj/kUElkqlYrc3Nyilf3f1Z3Nz88vTjYTgMBEhsjFDqFMgn0v%0Af7RZuYhtLXHq6YcmV0nyrTfotBrKdKsFej2XWn2HqZMVLW9PRatU496wDKlPk9hQagUFOQX0fDCK%0ALheGcHvKWWoN8UFmJeXGsgcE7w7D1M6UCn2rM+T5V2jyNTSfXQsARZaSiKtxtJtTFY1ax4tzCZxb%0A/oLU6DzmNrnJzX2p5OToKNvAlfkJvQm62Y6ACVWIfZxGm2+8i67t5MJQrBylVA4o/tj+PDYE/35u%0ARclYAHtmRtIlqCRSWfFnZdPEKNp9VQJTi2Liv35iFOV9LfCoUmyBeBuqIDEyn85jjLNU1018S4ev%0AnDE1Lz4+M0VF+JM8hiwtw1eryrI+uAaHshrQcZwbWr2Qmxc0+LnH07VmEttX5zB/VCbObmJqNjF2%0Aze/4NgtTcyEN2xpbSNMSVGhUen5+XY3qLeyYPCiHgPJpHN+Tz9qF+QyfZYdEYjxHLhzKJSdLQ8dh%0A9oxZ7s7ByMqkZ4sIqJDB0A45OJaQGrnaC/HT3Cy8a1qwP7oKPv7WdKufzvpleeh0ejYsU+DsLqNp%0Ax2JiXCfAnKMRZQkL1XH3egHDZ384wTE+UoeHjwX3rqgY1jLJ6HugKtAza2gq3Se6MmG9J5N7p3Bg%0AQzFJ1On0TO+fSp229nyztyILRyRzeFOmUf8Pr+aRr9Dj5m3BgBoRaDTF/ZuYCll93oMCNVw8qmDZ%0A9SpFi9QqjayZvq8CK8clcf14DvJsLaObv6VGG2eW3GvA+e0pHFwVV9RXxXpWTN5ZnhUjYpkQGE7o%0AIyWLnvsz7mAtRFIR81s8Kmr7xRxPKvvZE1TnGfdPpzGu9mNqdPPAvboDazveBMDKyYSgc025tiGS%0Au/tiEAgEDNjoi5mNhGUtbnFhbQSXNkQiEAsxd7QgKHoYAy93ByHs63YS65JW9D7akesL7vD2Riwd%0AtrVGIIKDXY/QeGZDXOu4sqfhVvyWN8emrC3HWm+n07lBJN6OIut1Kj5jGnF7wHb89g4h4ocLVJjR%0AEcWreFz7+SM0kSK0tkRgJgORwBBCpNeDSMTx48dp27btB5/1/3X8npasiYmJkWa1RqN5j8gWhpj9%0Ak0T298iqo6PjB/f9hz+G/8jqJ8Sfsay+S6AKNUYtLCyMdDv/KD6HRFbhedRq9Uexov4aPqZ19d1F%0AQH5+fpF0y98N2FcoFMahClIp+nwl2uQMzKp6kHHoJmJ7ayqfmE3q/uuYlHLEc04v9AUaEAo412Q5%0AApGQdi/nIrE0IenSS+Jvv+XO/GuIzWTU/toPx2quJN6PJeV5InqdjuWu67ky7w723o6Mih1Po5lN%0AODviFN4ty+BY3jCWw8OvYGEn48KKUILs9vFT9+voBQIGn+nA7MxhjLzXDUVqPi1n+hQN/fCEe5Sq%0Abkvp6sXXc21DFJ1nli+az9kpSiIfZdB5UumiNm9DckmMzKPtyGIrW0qMkugQOZ3HF2/T6XTcPpxB%0An+nFIQEAa0ZH07iLPbZOxeQ3MUpJ7Kt8ugYZW2B/GBdN5YY2uHkZk8+re9LoO6cMW6PrsTOhPj5t%0AnNm1XsmxHXko5Dr2r8smK71YnWPHqmyGzHExks0CWDEqgRZ9HXF0lTJqZWmOpNagWX8nZo3KISFG%0AjZmFsMjSWjT+qZn0n+qMialhzts4iPnhihcLDpThyX0VCrmWJ7eVRsfER6s5uTuHqVtKIxQKmbKh%0ADMvPlGPjSgXdGmSwdbWcGRvfl5ixtBZhZinCtoSUES0TuHpcbrT/wVUFj28qWHq2Ihue+hAXo6dT%0A1UQUeYZvz7YV2QiEIgbOKUVgX0dmHyzP0gkZrF9gsHYe2pRLXLSWybvK06CjPTMOVGDF+BT2rTXs%0Az87QMqt/Ir3nebL4mi8CiYjBtSOMvm0psWpSE9RY2MtY3PON0fjqtrVjzI9ezOwTz8iAaEztTAja%0AU51SVSyZerwm22fGcvNQWlH7hp3sKVvNnJB7cr4+VRcLGykyMzHTztcn+nkuW8a9BAzfirE7q1JQ%0AoGVepxd0WFqLAdsbM+JEAKnRefw8wiC6X7qGHQM312Xb0EfEh2YjkYloN70CL6+ksHdqCG3XBTI+%0A8kt0Gj2HBpxBaiah/9kuRFyK4daqh3j4lSRwQSP2dTqCSq6i95nuRF+J5u639+m8twPqvAJODzxG%0A56M9yI3J5P7CK7Q72IfHiy/h3KgMjr7uPJ50hDoruxI64wBVl/cicedVXAc1Q5+Xj4mHK0KZFCQi%0AQyiRVgsCATdu3MDf3/+9+fAx8W+ThnqXyBbmOPxbiyL8Hll1cnL64L7/8MfwH1n9xPil7iYYWwq1%0AWi0KhYKsrKwiAmVjY4O5uflfLin6qSyShSi0ohZq830MK+qv4e+S1cIP2C8XAdbW1kXxs3+nf7lc%0Ajl2hfp5QADIpaLSAAH1+AfLnUegLCnAZ2JznAd9gVcOT+i++J3bVUbRKNYlXw5FYmOK7uDOaXCVn%0AGyxFIBRSdmgTfNf1Q6tUUePrRigz8znZ6Wf0Wh1vriTit7M/IomIpkv8EQgEKDIUxFx5S+Dc2iQ8%0ATeXIiKuEnoxCXaAjLVNAz7O9MHe0oOXceni3NJCjczPvYVvSAs/GhtKsOp2OZ4fe0mFmxaLru7sv%0ABrVSQ/0e7ui0etLj8vm+10PsXE0IvprB4WVRbJsczqzAR8hMRawe/IZZrV8wrVkwo30fIRTB9yOi%0AWNwznFVDIvjaLwSFXENSdAE3j2bw8m4uUS/kvLybS+9vjAnsd6P7+dp8AAAgAElEQVSiqNfWFgfX%0A4thPjUbHvdNZ9JlpHHsacjObzGQVrb40EFsrOykD5pel88SSyCzE+A8txdaV2QS6RTKiZTyrp6ai%0AzNfRso+xaz41QUXYIwW9pxaTRKFQyMBZ7lg5yPAJsGXFxDS6VI7h1jmDJuXNM3mkJavoMvJ9HcU3%0AzwpwcDOlYU9nhrVIZO7wdOQ5BlL34+xsKtayoLR3sRu/ehNLDsZWJTXNMHezM7Tv9Xn9pJy4CDWb%0AwmoxfHVZpvZO4sfZGeh0erRaPQu+SqXVICcsbMQ4uEpZ96AKdu6mtCkXz5Nb+WxYlMHk7cWxwnVb%0A27LsfCU2L8vhm4EpLP86ndHrPJFKDT8PdVrbMedoJb6bksqOleksGp6Mk4c5ncaXwsxSzKIrNVBr%0ABAypayCsBUodUzpFU7tTCZY+aUJ8hJK5XcKMriGwvxOVGlgRFapk1LaqRdur+tszYlMVlg0MJ+xB%0ALgBbpsYQ+0pJrS88WdL6HkqFQWHFtoQJ31xowKUt8VzcHINer+fwoigU2Rok5hLSIgzHW9ibMOZ8%0AIHd3RnFja4ThmnqUxn9keZb5X2fLkIdsGviAqn0ro9MJkFlKMbGS0f9cF14eDufR5mBsy1jT61B7%0ALs26TcydBOqNq0G5lh5sbbQb61KW9DjcmWuzrnF7xV2cqjoRujeEDeXXkJ+u4MXWRxwK3IS2QMPZ%0ALtuJvfyajJB4ni04i1AiJHTWQdy+qEPS7us4tKuNOikdsaMNIgcbBGbFxUQAHj16RNWqxffrY+Pf%0ARlbfxS/H9qmKInys8b2LjIyM/8IA/ib+S7D6xFCpVEYvQGZmJlZWVkUZ51qttuhF+5jacYXn+ZgS%0AH7/UGgUQiUQfTYrrQ8jOzi4i7n8G7yakCQSCooSpX35M5HJ5kaLCn0VWVhYuLi4gFILeUIscmQSB%0ATGqozykSIRAJEIhFaHMUCKVi6j3+lsdt51PwNgX34S0R21mQsv0SFcYG8HTWMfRAu5cLsCjjwMmy%0AU6jY1weRRMT9JVfRafW0Pj0ct4By3JlwlOSLoQx59iUCgYADHfYSdyMGC0czcpLyEEhE2JW1YciD%0AQQBEnI/kULdDzEwcgtTcYL2c77yZL9bWo0Y3Q4LU2UVPub/5FdNvBxAfkk1cSDbH5r1Er9MjkYnI%0ATStALBMiEIClowkycxkSCzEiUxFv7yRTs4cHJlYSQxuhgGtrw2jQ3xORVIgyV41KoSHkdAJ2JU0R%0Ai4QUyA3bcjML0GnA0k6Mi4cJHlXMcK8gY+e8eOYe8qZ2y2I1iK2zYrmyP53Nob5Gz3JU7WdUbGDF%0AiO+K42oBBnrcp+3YknQMMliBU2Py2T37Dbf2JyEUCPhirBNdRtrh5G4gxJM6RCEQiVlwxNOon2fX%0Ac5jUJpyf4xthailk65Q3XNiYgEcFGRkpGlr1s2foXGMraH6elvZuLwnaWoGGnR1JjMpnXvsQspKU%0AjFlgy/IJ6WwProSbp4nRccmxKnp7v6BjUElOroml42Bbxi93RCI1yIJ1KheJX18nBs4rA8Cbp3Km%0At3hB5VoyGrY2ZeOCLPYn+hp5NrRaPd+Pecv57cmUqmjKTw99+CWiQhSMqP0cUysxB5Lrvrf/+bVs%0AZrR9gV6vZ0t0I6wdixcR8kw1kxo8wsZOSPlqJtw6m8fqN/4IhULSYhRMrXWD+u1tmbjZ8HxuHEpj%0AxcBwfLuW5vmpeNaENsLKobi/I0ujOLIkgg4jXTj2QyJT7rbBubwla1pdITcpjyXP/Iqu7/GpJNb0%0AeED1QAdeXM9k1LX2aNV61jQ+Tt+NDajdyyBp9exYDFv7XGfy9WaU8bUn/FYqy/wuIjYVMSJkKLal%0ArXmy5Tlngy4zOrg/NqWsCD32hoN9TjP0di9K+DhyY9kDbi59wLjwQYgkIlaX24xQKkKdp0FToEGr%0ABcf6ZbHydiFi+23q7RmBTqnm4Zdb8XuwkIhlJ0k68xSvxX0IHb0Jka0FFKhR5+ajz1chtjFHjx6x%0AjSWa7DwEZqZoM3PRF6gMVlYAoRBXFxfCwowXAB8DhQUAiiru/Yug0WiKvHd/B+/Kb30o8aswqevX%0AZLh+DUqlsigu95do1aoVN2/e/NcuBP5l+E8N4J+AWq0uco9ptVpycgzJCoXu549tiSzEXyV5v8Rv%0AZfR/bN3YDyEnJ6dohfxHxvp7sl6/hFwuL5LS+jNQKBSGGFWJBNRqw/8SEeIKZdFFxiIwkWIWWB/5%0AwQtISzigy87FtnElMq6/RJuvpPq+Sdg28+FGiYFoFAWYOFqhFwjw7Fefaku68mbzDe4P3YbYVIK5%0Aux0apZpyPapTZ3kHdBoNO51m0mFHR8yczLm3/A5vTr3G0s2aCkPrUm18Q7aVWECnnR3wam0gXRur%0AbaJy+9K0XGBIzLqz7jmX5z9g2vPOvH2QStStFK6ueUFBnhqRRIiFvSlCEzHZ8bkEzq6Ley0n3Gs7%0AcfPbpwQfeMPUF92K7umeodfIDM8h6FrLovtzePJDws4nMPNpcZxd6KUE1nW+zqqkzsjMDPNSp9MR%0A5HSUgZvrYGot4c2tFGKeZBF2ORG9Vo+6QIeJuQiv6uZU87Pk8PdJDF1ahlaDXYr6TY1TMqjcYza+%0AqoNTqeLnGHw9k1ltQtia6IeZZfF78PpBNtObPuSrjT6cXBFJXGgOtZtZ02WUHTO6R7PmRkXK1zCO%0ALx3q+xKfZg4MWl5MYlVKDQs6BfPiRia1/K2ZtM4V55LFhGv3qlQO/pDBlkhj4ndoZQy7ZkdhbiPm%0A5+BKWNkav6OLh8QQFaZi2a1aJEYomBHwBCtrWHXMjUfXFKyZmsbuhNpGZDQvV8OEBsFEheQxdHFJ%0Aek81VgAAeHYtm2ltw9Dp4JsdnjTpamzpeXQxi1mdXyMxE1GpjiXzTlQ02p+bqWGA50Py87T0neNB%0A92keRvtz0tUE1X5ARoKSlS+b4lK2WP4oPiyX6fVv0W6YEy0HOzO61jN6r61Nvb4ebOx1h8i7KXz/%0AquE7WqV6lnR4zPOLaYw47k/lQIPVXSlXs6jWGUp4mjL5lGEu67R6ZtW/TkxwNqOudaBMHYO79emB%0ASPYMvsakO21wq2KwoJ+e94wra0LxH1GOc6tCqTHcl7BDryjjX5LO2wxz9dTw84SfiWBc5BDEYiGX%0AZtzi4cZgxkcMRmouYUerwyQ8TkZdoEFiJkOZW0CZL2rScHM/rnfbRFZoEm1fzCZk3ile/XiN1pHL%0ACJ1zjLe77+IftoI7TRcgsrXEY2onHndeSqWDM3g99Duk5UuiTkhDFZ+KTq5EaGOBPi8foZMduoxc%0A9Hod/E8hALEYa3NzozKaHwOFxpW/snj/1PgcRPpDWrJ/tChCoWf0l7+5er2e1q1b/0dW/zj+UwP4%0AJ1BI9nJycoqIqpmZ2d9K5vkj+Lvu8z+S0f85svX/yDn+TAnUD/X/ZyGXyw1EVSAwuPylUsPfQiG6%0AyHgQibCfOxL5oYvYdG2GRdemqDNySTv/FKRi7BtVwtavCvdqTkSbr8LtyxaU/2kkmtx8Kk5pRdSu%0Auzwauxtzd1tq7RhJje1fUZAux2dKAAD3ppxEnafiypTL7G62k8grb3GsUZI+r6dQc3JTHi2+gqmd%0AKZ6tDBal5OfJpIdn0HBcNfLS83m2P5yz0+8iT1cy3W0Pu7+8w93d0egFAobe7cc3iomMjx+Npasl%0AdQZVJmBaLcoHlsLMxoQHm0MJ/KZ60X3T6XSEHH1Li2mVje7R/R2RtP7GeNvhKU/x/6pcEVEFuLw2%0AHKmpkGrt3ajQ1Jl206sy8mBjhGIxg7Y34EdFD4YfaIK9jwsnt2WiUupZ81UEQys/ZuPkKB5dyGT1%0AsAh8W9gbEVWA9UERtBxe0oioAmwe95qAgaVp1NudJY+b8F14M3RmZkzvFo1GoycmTIlGUzznol4o%0AeBuqoNNEYwIoNRGTk6qlYa+SZOaK6FkhjM1zklHmG1zh2xYk0W9hmffmT4POjqg1esztTOhV7gX3%0AzhUnN8VHFnBxTzrjtlUCoISnGRuj6mPvaUmPalGsCEqm79xS78WDm1uKadTVAXNrEXuWJBByyziD%0AX6vVs2pYFE2HlGHYZl8WD4jk8PeJRftVBTqWD46k1XhPFj3y583zfCYHvDCKQ103Ngr70pZMvtiU%0AvQtjOLgs+r1ry8vSIJKJ2T4u1Gi7WwVLZl2qx/Efkwlq+JwaXUrRoH9ZhEIBQ3bUxb6MJVNq3y86%0AX8TDbIKvpGPvZc2+scVZ/yYWEoIuNiP8XiY7JwWj0+r5sd9j0mILqNSpPNu7XUKjMoQJVO9eFr/x%0AVVkdcB5FjoHk+Y2pgE6n48yKl/S+1JsWq5rT+3xPQg+95tEmg0JAy++bYe5szs+BhwDwn9cAFx9H%0AfvLdya52x4i+EYdWB3bVS9EzaQmBx4bz9sBj0h9E03DHAHRqDbf6bKHqrLbY+5biWuNFVFnaHcty%0ATtxruZi6pyaR8yyS9KsvKDe7B6/6LKPSvqkoHoThMKQtIlMTLDo1NXj+hUK0yRnoVQWg04OpicGT%0Ao9GQLc/76HGQ/7YM+8+NQgJaaCEtDC0oVC4wMzMzynEolKvKy8sr8uYplUoKCgo4f/48t27dIikp%0A6ZOHV2RkZBAYGEj58uVp0aIFWVlZ77WJjY3F39+fypUrU6VKlc8mh/ax8B9Z/cRQKBSoVCpMTEyw%0AsbH5QxbCj4G/ogjwZzP6P4fqwG+R1V8SajMzsz+d3PVnCXdOTs7/Yo8EIJaAiRQEQkAPWh16VQHS%0ASmVJGbcUq1b1sRnVjcx1hzGpUIZSu+agy5Zj36YmN8oOQxGdTK2rC6n04wjejN+Ms583Fxst5f6w%0A7YjNZQS+WYVb19o8+2orVUY2AqGAR3PPEvrTLUwdLHDuXIvOictBo6POvMCiMb786R5N5jY2PB+N%0AjmP9TmBiI+Mnv8MscN3C0dHXURdoab21E+Ozp/FV3EQkplIafF0Xt9quCIVC8tIUJDxOovHE6kX9%0ABh96g0qhpvoXZYu2XV8TgsxcTMUWxTGk93dFoNVoqdGlVNG2zLg8El5mETC2uCIRwMVvX9NqSiWj%0AJKfrm94gFAqo3t4NoVBIxQAXen1bE7FUTIspVVmW1I06gyvw5I6KxX1eE3w9m8QIBSfXxZMWb9Ah%0ATn6bT+xLBR2CShmdLz1BSeTTbNp9XWwVtHc3ZdzeGggkQmp1deeHCXF0L/mUo+tSKFDqWDUihqa9%0AS2DrYmxtig2VExMqp8d8b+Zca8C0c/U4+XM2Xcq8ZMmwWMwsJfj3ej9BavfcGMrXtWf586a0n1KO%0AGd2iWPplLPl5WjbNTMK7rjVu5Yq9FUKhkBlHfKjb0QlVAcS/LkCrMZ6zmSkq9i+PJehoPdp8XY7J%0ALUI5vyO1aP+ZLSnkZuvo920VGvZyZ8KRumz6Jo4NU6IB2LcsEb1AxBfzK2Hvbsr8+01IilUT1NBA%0AWJ9cyuLm0TTGnWiId2NHJpxuzJ55bzm88i1g+HasGRyGo5cVc1+0J/xBNt/3fWI0xrK+NlRsYo9S%0AoaNCQPF9EUtFjDnZBI0O5jV/RGpMPvNbPaTx2KqMv9sJgUjIdy0uF7W3dTdn/IXmXPzpLXMa3SD4%0ASjqjn/Wl2/ZALF0t+KHp6aK2rebWpExdZ1bUO0PSqywWVT+BtYcdVqVsuLvsHgAO3vZ03t2Rc0FX%0ASHyajFgqotfJriQHp3J+2nXSwzMN1e5icoh9nka36Hm0vz+Z9KfxhK6/iVvzClSf3opLbdehU2tp%0AfnY08aee83rjDRrtG0pBhpxHw7dS/8gY8qJTeb34GPVOTubt6hOYV3bHobkPrwauosKuSSTO3Yb7%0AipEoLtzFdlgXEImQ1K+BwNQUVGooUBkk8aRS0GlRqlQfXYf632r9+6fjaT9EZN+V4AKKknYBLl26%0AxIwZM6hfvz6PHj2iRo0adO/enWnTprF582auXbtWpJv+d7FkyRICAwN5/fo1zZo1+2AhCYlEwrff%0AfsuLFy+4e/cua9euJTQ09AO9/TvxH1n9xLCwsDAie59LO/TPJA79VV3Uz6E68Mv7VWip/hCh/jNV%0Apn6t/9+CVqs1WDJEYjAxAb0WdAA6wzaJGL1Wh/LmE4RiESJPN6L8R2Beryrln24nZfpP6NUaImbt%0AARMZzm1rY9OgIpFLD5EXlUzy1VeYBfgitbGk8sIvEMkkpN97Q9aLOOSxWewuOYfnq69h4mxNp9il%0A1JjfiedzTmDpboO7v8E9HbLhHlqVBomZmGP9TrDMdiXp4RlYlnWk3PDGDEqfj5mrLbXH1adybx/E%0AJhJSgpPIjMqg1ohiYnpm3EXKNnLDwas4XvTC7Pv4B1VFLC2Og77x3UtaTqtiRDbPLgwhcEIlRO/U%0Aj9875gFVW7ph515Mwl7fTCEnJZ+GA43dyWeXhdFyckWEouLj40KySH8rp+mI8ljYmdBiYmUm32hF%0A3QFe2JSywqtlaQ6uSmSw1z2GV3rAzDbB+AQ44OBubG3dMCqMaoHOOHkYh64cWfQGK0dThu2sx7eJ%0AHWk3uyq7lqbQ2eUxoffltB9nnNAF8MPw1zTq5Y5tCcM5KjayZ01kM9pM9OLq4Wxk5iISIvKNjkmO%0Azuf6gWS+2mKIGe0wyYsVIU15clNBT68XXD+SwbhtFd87lyJXw73jaXRdXJXLe9L4umkI2WnFmqw/%0Az4zDvZI1lZs60mVGBUbursV3I6PYNC0WeZaGDZPf0mdlsYSUT6ATM6824sTGNL5pH8buJXGM2FFc%0AL9zG2YR5d5sgz9Uz0jeYJX1f0XKid9Hz827iSNCpRuyaHc3R1TFc35tM8LVMxpwNwNbdnMm3WvL4%0ATCqbRgYX9Xl1WyyvbmXQ9cfG7Bz1kJDzCUX7TC0lfH05gNiwPIJ8blKmgSvtFtVBZi5hxMU2xAVn%0AsXPEvaL2Javb4u3nwtvn2XTa1BwLJzPEUhH9T3UgIzqXvV9eBwyasv32+htCNnyO49bUk/6PvuKL%0Ac32JuhzN7eV3ACjfvhz1J9ZlZ4sDFMhVWDib03VvB26tfMTaattRyyxofWU8qsx8Es6HYeXpiP/e%0AQdyfeIT05/H4TA3Eub4H5xqvwsrLCb+9Q3g88SC5UWk0OzuO2H33STz5jCZnJ/J2y1XyY9Op+u0A%0AnvdejdeCnoikIpI3nKX01B7ETfiBUj+MI3vdAewm9kP75CXShjURWFqATIZekW8oOiKTgliCVqv9%0AaAVg/mlC+Fv4N4+tcFxisbiIyC5dupQrV65w6dIlOnXqxKZNm+jatSvm5ubcuHGD6dOnk5f3vsbx%0AX8Hx48cZMGAAAAMGDODo0aPvtXFxcaF6dcM33sLCgooVK5KQkPBeu38r/iOrnxi/fLmEQuFn10D9%0AED6GLurnCgPQ6XRotdoiQq1SqT6aRNYfvQa9Xm9YPYulIJWBMh9kpmBmCiYmCNu2Aq0WaeO6CG2t%0AQCIhY9VuBAIouWkqSXM2oQh9i7lvBUqfXIk2MxeP2T14PXErkfP2YePvQ93YHZh5u6PXaSg9sDG5%0ArxO53W4lApGAzLe51L06F7FMSvUFHRH8bx5Fb7tDnfmB6DQ6ok6+5NaUU+Rn5nNm1AUy5SJsqpSk%0AZDNvOt0cQ7VxTVAk5JD5KhnfsXWKru3SmLNU610ZcwcDEdFqdEScjqTptGLykvwinfSILBp8VUyk%0AXl+OR56moG6/4jjOuOfppEXn0GR4caKTRqUh7HISLScXa7cCHJj4lCZDvTCxKPY2RD1IJzM+j8ZD%0Ayhq13TvuMTW7l8bC3ti6eWd7FO3nVafbyjrMCe/GivTeVO9XnqRIJcFX0xhT5Q6nfoghO1WFSqnh%0A2aUMOk83Tp4CuLAulg6zKhbN+4CvvFgW3Q5Hb2vEMiFT/R5z7LtY1AWGdzctTkn4wxw6ffN+X9ZO%0AJphYybApa8tInwfsXRiDWmU4bs/8WDxr2uLiWRzP6VjajFWh/kjMpej0eq5sT0arNZ6TR1fGYu1s%0ASstx3iyLaoNaL+HLyo95/TCXuNf5XNyZxMhdxc+rdkdX5t7149TmVAZVeoaNqxmN+hhXqipb04aF%0AD5ry6GI2UlMx3o2MNVst7aXMudWY7AwNilwd7acbk+gKfk6MP9GIHTMi+W5IKD1+qIOFnYG4O3la%0AMul6C27sjmfXtJckvJKzeXQwPbY0pe5Ab7p824Afu90i5mmxfquVkwklq9uhUelx9S1WVrByMWPk%0A5bbc2xXFxdUGmapjM54RcSeN2mPrcKDfeXKSDD/65vamDL7Yhcd7I7i13tD2xYm35KYYCJ6tt6Ff%0A6zK2dD7Sk+tzb/L2egwAjWc1wrVWCbY23EXo0dcc7HEcc1drhDIp9df3xLm+B0229eP2iH1kvUqm%0AZJvKVJ0QwLnAtWjz1TTZPQBVbj43BmzDvU0VqkxszpUW3yEQCynbrx6PRmzn5ZxjSCxNeDRgHa/m%0AHkKdk8+Nal+jypSTdvo+qUfvoNdoSZy1Bdvu/uSsO4DNwPZoHoUg8vZAYGdj+Oao1KDRgVoFMhMQ%0AirCyskKlUhXFdv7/5tL/N5PV37rXaWlpuLm5UbNmTXr27MmMGTPYtm0bN2/eLNbm/ptITk7G2dng%0ArXB2diY5Ofk320dHR/PkyRPq1n0/kfLfio+Xfv4fPoh/wnX+W+f5ZUb/uzp1f+Ucn/KDqNfr0Wq1%0ARWOWyWQfXeHgj1yDTqfDzMwcRBJDbKqqwEBYxRJQ5iPu3wfNjt2YTR6J+nEwugIVJg1row0Nx7pt%0AfRLGfEv2lYc4zxhIiXnDCK/RH5MSdjxsOgOdTofE2hyfcwsRSsTEL9pHqQGNedjnJ+JOPAQE+D1f%0AiaW3K5FrTiMQCSj9hUH0/8XSs6gVBcScesWF/vsQiIToNDraPZ+NbRU3NEoVh5wm0uTC8KJruTHq%0ACBW6VcbCxSA0r8hQkPggng7rmxe1ub7gNlILCVILCSFHIsiKyeXayidIzMTs7H0VRWYBiswCMuPl%0A6DQ6prjsQ6fVo9eDVq1Dr9PzTdmjSExESM3EKHNVKOUaLq0O58mReGxcTZCaiYh5lkGvNb5GP0L7%0AJjyhYX9PzKyLE5UUOSoi7qbxzf3WRs/lzs8GGaJqnYpd/VIzMblJSkpUtmf0zfZcWfGc49+Hs/Xr%0A19g4S7Gwl1KmhrVRPzf3xFOg1FC3hzGZUyo0JL6UE3SlFakRORyZ/Ih9C6IYsNiTO4dTqdHaGRdP%0A40QsnVbP/lmvaD6xIi0mVib8ZjJbet3k/NZEBi324MqeJJY+afLeHIsPyyUjQcnQA/7sGXqbR2fT%0AmXqwCvauMnIz1Bxe8ZYxxxoCIJGJ+eZWAHsnPWOiXzDOpU2o0MQB1/LGVbVKVrZi6oX6zKh9DRMb%0AGbnpKiztpUZtkiPyEEtESK1lzKp/k3l3Ghkt/tJiFOSmq3AoZ8tMn0vMf9bMyLJeoakjparZEvkg%0AHdX/JKUK4VrZhqCLgaz0P8/lTbFU7eRBtS4GK3q9LyuQk5TP8oDLzH7SEofSFpxZGkrk/XR6HO3K%0Avs5HcCpnRa2+hrCREpXtGHK0BZvanyP+WRaPDscy8PYAHCo5II/PY13d/UwM74dYKsa5kj299rVh%0AT/dTJIVkcm/rawK398DMyZwjrbZSoo4bZZp7UtrfgyYLmrG/0yFGhH2JhZMFrde34gfPHznU6wS+%0ASzpTaVwAt4fs5Ezj1XQNn4lHtxqk3o7irP8aukXPpcac1qTeieZ0k+/o8HASLc+O5Hjt5dyz3Y8m%0AS0l+ai4nqs7F1NkGqZsDCRdf4jyuK6rweLIvPcbj+HKSZq5Hm5uP3YR2ZKzdh6SEI+qsHNJ2X4QC%0AFVm7zhgq4aVlIBALoKQ7+pg4gzqAXggFSkNYgAYcHBxITk7+y5nt/3ZC+G8dWyE+NL60tLSPIlsV%0AGBhIUlLSe9sXLlz43hh+6z7J5XK6devGd999h4WFxa+2+7fhP8vqZ8bnsqy+66L/Ldf531Ej+FRk%0A9V3tWa1Wi1Ao/MslUH8Pf+QaAlu2ApEItGpDnqJEYohTVSvB3ALNlp+RNamH5m0c6vPXsFw0BVFr%0AP9SxiWT8fJqcOyFIbCxxmTmYtE3HyHv2BnVWHg4LRyKSSvFYNgShREzEN1vJT8zgzXfnkGdpMPN0%0Ax2NYIJbehkzoqGXH8ZnbHlWWgpcrzhO84DRCiYiU6DzqHJ+EqbsDlYJaYFvF4LJ+Mu0INuUcca5r%0AkG1SZilIuh1FnSkNAMO8ODvkGGYOpoQefs3B7sf5wXsTt5bdIy9dyZY2Jzkx8S63N4UjT8mnZMsK%0AmPt6ULp3LapOaIIe6HGhH/0eDmfwi9EMeDwckVRE70v96HWlP213daHx0hZodUIq9ahIgbkVr5/l%0Ac2NnAvu+fopYJmKZ/yVGmO1jeoVTfNf2OtEP03H2tiAzQVF0/w9MekJpX3tcK9sYPZczi14SOKmK%0AUbgBwMO90TSfXh2piZiWM3yZ+qoH0yN7kpmiIT9XyzDn8+yf/ZqMBINI/8E54bSdXMGIhAHsm/gM%0A10q2lK7lQK0eZZn/tjvtFtZix8y3PL+aSbn6Nu/NnftHElEp9TQPMlghyzVyZuHbzlRqX4YVA8Kw%0AtJdi7/a+9M6+GeF4NXbGp10p5sd0AzMzRlS6y/2TaRxYHIOjhwWVm7kYHdNzeTU6z69KYpQSW1dT%0AtJr3vyuH54Tj2bgEMlszvvG9SkpUsdtRo9axcdhTGo2twsSHXVAWCJnicw3N/6zAOp2enwY8oUqn%0Asoy+1RmZrQkzfC6iURVrv97ZFUNCaC4997ZlX9Aj7u6MNDp/mVr2VO9QigKFlrJNjccfOKM6vj28%0AWFj3Io8Ox3Jifgi9TnXHq4UHXXa2Y/9Xt4i4WZwEVj7AjbpDvLm//y0t17bCsbIjAoGAtptaY1HC%0Ago1NDxe1rdDGAw8/d+5sDqPFrh6U61oFt8YeNFnelqPdDpATZ0hqqzmuLp6ty7Gt/k5ibsawudZW%0ArMs7oxMKEJkZLP51f+yB2MqUC+03AFBrWUcsPew5F/A9AvqtywYAACAASURBVKGQpgcGoUjK5nyH%0An3i+8Dw6jZZX62+SHJFDxT1TEdtZYdmuPr6hm7Gs7on88lM8ds7A1MuNtBW7KHt6FbrMHARmJrgs%0AH48uMwfHc5sRmsqQ9e+CwM4WvVqLOjYRfWYO+tdvEHiVNZRjFf3P3qTHIJ8nEODs7IyJiUmR1mhh%0AQtC7aikf0hp9V7nmP/w5/BaRTktL+yjVqy5cuEBwcPB7/zp06ICzs3MRkU1MTPzVxDu1Wk3Xrl3p%0A27cvnTp1+ttj+pz4j6x+YnzIsvo53DOFltVP4Tp/9xyFMh9/F+9W8MrJySkqgWpmZva7+nZ/B7/3%0APEaOGsWtGzdAqwETM9CoAcH/yKsecgzC45pXUaj2HMVsYHfErZuimL4coZU55kunINRqcVkykqTJ%0Aa4kfvxqLFnUol3AabUomIjMpNk2q8HrQKuK+O4Zlo6pUD99JyZXDUUYm4Dm1IwAx266Qn5hJ/LHn%0AHC45leCl5xGaSmmZsoEGF6YjsTFHHpGM91hDhRudTsfbXfeoOas48er6yMOY2JkStu8Fe5psY5XF%0AQqIuRKJHSPCxGJRWttg1rYBQJmFI5kIGpMynV+R0nBt54FzTnda7e9J4aWtqTmhM3NUoPAPLUdq/%0ALPbejth42PJozT1K1HCljH8ZXGqUwKNZWYQSIXqdno7b2tNhSzv6nOvFwPsDEYhEdD/2BVMVUxgW%0AMpxakxoSE5aLxELKuW/fMM3rBKNtDrDc/zIP9sdQMdAFVX6x5e7t43TSY3JpONRYV/XmplcIhAKq%0AdChttP3FiRjMbGXMSB5K543NuXMsnTGel5jrf4fUmDz8hhuHHeh0Ou7vj6PNbGM90sbDvPFu4YqZ%0AgxlHFr1hRv3bRD81EB+9Xs/eGa9pNMzL6P0SCoW0nFwJrU6PQCZhXPkrvLyeXrQ/PiyXx6eT6LvZ%0AYDkVS8WMvdiC9otqsqxXCMfXvKXfOl9+Cb1ez8MD8ZQLLMXTs2ksCLiNPFNVtD/8bgbPLyTTf28z%0Axt7qSMl6LnxT8yqRjwyZwufWRKHVCGg9ryZmtjLG3myP1MaMiRWvoJRruLr5LWlxSnpsb4bMXMLw%0Ai+0xdTBjepULqJQaspOV/DzqMW3XNKVKFy967GrFzuH3eHTobdEYXl5M5OnxGAJXNuPI+Hs8PxZV%0AtE8gENBlbQNK1XFiY7/bNJnTEPe6hoVZxc7labbQj43tzpMeZVA2iLqTzL0tr3Dz8+TixMsoswyL%0ADZFURI9T3cmMyeXQ4Avo9XouzLzD29tJlPAvz42gM+g0hrnjM7Iu5bpWZXfDreg0WgQCAa02t6dA%0AXsAO/914DGlCu5C5NNnzJQ+CDpH+PA6RTEKzUyNJuRfN08XnEIpFBBz9kqzXKdybeJi4sy8RiIUk%0AXHpFwrNkqt/6lpKjO6KKTMKxS318zi4gdfsF0o/cosKR2Sgj4oj7ZiNexxaSHxJJ6g8H8Ti5gsyV%0AO5CUL41luyZk9pyAw+HvUR08g9ncIJBKEA4Zgl4oBjNT9KGvoKAApBKDl0etMug8C4QgEGBjY4NO%0Ap/vNzPYPEdnC8LAPEdl/OrTg32xZ/acLAnTo0IH/x95Zh0d1b1//M5qJG/EQLCSEhEBwCe5upTgU%0Ap2gLxb20eCkOxV0DFGtwh+CuEUggSnxio2feP4ZMmNLetrfAvff9dT0PD3D0e3TW2Xvttbds2QLA%0Ali1bfpOIGgwGBgwYQPny5fnqq68+6ng+Bv4hq58YHzuyWhhFzc/PR6fTffTuUn+XfP9WC9TCDl4S%0AieSjk/t/tf0xY8awccMGwGAkqnJLkFlArWZgMCBq1BqRlaWxyKHgbZWuqxOZFZojdnHGJeYCupsP%0A0avUJH69lLRtxxHLpBQPmw9SKZnLdiOxs+JGwGDehN9B5mBL4LnFWHi7EDt4McX71Edqb0XsT6d4%0ANHwjUjtLVHIbqj75CZmdFeWmdkQiN0Z/Ho3YjG/vWiiKGVPBz1eewyAYPyQujTjI9tLf8/LQIwwG%0AEVHnk7AKDaR4rzpYeTjSMXYOra5NIHRDb9IjYqk4si4SiyIf1Jf7H1B1Yv2ia6bT8epUNNUn1Cqa%0AJghEhj2j5sSiaQCXZ1yk+ihjH/dCRCy4hpWzJSXqG9P3TmUcqdgvGHWWhg472jMibgTjc8fx+bFu%0A5Btk6PVwflUUox32MKdaOIdn3Gdr/whq9vbF2tFcw3pq3hMaja9oVpwFcG7BQxpMrIJYIqZCZ19G%0A3+vO+Ji+vLivRCwRMyf0HDfDXiO8NV7/Zf5zLO3lBLY0L6zS6QQeHIqn69amTEocgHXpYkyrc4WV%0Afe5zYUs8ylQ1bWZWfO9eOjn/KZ5BLkyK7k2V/oHMbXWdjcMfo87XmaKqjt7mkoIGwwKo8nkZxBIx%0AO0beIyM+32z+49MpJDxV0ntvMybE9CQ/X8zEiudJiszFYDCwafhDKnUtg42LMZLbd08Tagwqz+wG%0Al7m49RVhM5/SZW1dE7G2sJHx5emWFPN3ZIz/WbaOeUiHFfWQvo1cy61kDDnVFht3G6ZWOM3Gfrfw%0AqOBCSC9jFDmwgy+dNzZlU98IHoUnkJehZn23S4ROqU21oSG0XdOc7T3Pm0VLBZ1AZlwOIomExzuf%0Am70ba4yuQqW+FVhS6wjx99NY2yqcyuMb0P5oH9xr+LCx+hbT8lbOVvQ63Z0HYVFsbHqQqyvu0/ry%0AaJodGIDM3pKDzbeYtttwdTsUrjbsabINbb6Go71+RtAZEFvKTdHU4u0qUf6rJpxuuhxtnhprb0ca%0AHRzC/e9OkHQxCoWzNf4Da/F4+QWuDNuLS99m+K0eiTouFamzLSXmfoGilDsPGk/BJrgUfj+NJHrA%0AYvR5Ksof+443qw6Se+sZfsfmkbZkN7rsPLwWjSKx6wScZw9FbG1J7oINOM4fQ96I6dhuWIBh+3ak%0AX40yRlE79wGZHFQqY3crmcz4QW0Q3rqTgKOj4+9Wm/+eRVNhO9O/GpH9FET2f5WspqWlffRWqxMn%0ATuTUqVP4+flx9uxZJk6cCEBiYiKtWxv9g69cucL27ds5d+4cISEhhISEcPz48Y86rg+Jf8jqR8an%0Aiqy+W9FfaJVV+OL5EFHU38O/czx/1AL11y31/hNkNSwsjFWrVhn/o7AGnc6Y9g8JheunYeAYDJGP%0AMajUMGkuIk0BBrGE/PlrQSzC8eAa9MmpqPYcQSyTYjFuKGJLBW6zhyKSSYlrNgJtRg6CQYrXhU2I%0ARVB8zgBEUgn5z1+TcycKrbKAU26DeDZlNwaJmBrx2wncP5X8yATUqVmUGGT0XS1IzCDzzgsCxjcl%0A495rHsw6yr3JP6POLuDyqMOkROdgEVASS3dHWr5eQsNLUwie04XU8IdUmNzcdL6zo1LIikohcHht%0A03l4uv46IqmYkq2KiqNuzLmAjZsNXnWKtKIPNt5FLBVRppWvaVrmy0zSI9Op8qV5VPDO6nvUnljL%0A7DrfXH4LuY2c0k2NmkaxWIxPneLkxudR/7tGjEgey7CXI/FpG8jtY29IfZHD9e0xrO18npu7X5Cf%0ApeZFxBuykvOoOcC8kCv6YiLKlHyq9TcvEDIIoFUJDLvfl9Jt/dg6/B5jih/j7JpozqyIodX04Pee%0A3yPT7mDvbUPp+l7IFVJ67GzOmKe9iYvWsHrAPdzK2b/XSiUnTcXFtc/ptLouAC2/q8GYe125eyqD%0AUb7nzKKq7yIrMZ8bu2IYfPlzFO72TA06wePTxsIJQTCwc/R9qvYrh1whRa6QMvrWZ5Rs4M2UqufZ%0AOf4xb17m0WW1+Xbbza9Bux9qsX7wPaxdFJRvZW7tJVNIGXC4GVJrGXodlAo1T93LLKUMOtEGCwcF%0AT86l0H1/a7P5wV39abe8IWu6XGJF2/PYl3Sk7tsPmOBegTT6rh5rW58g6XGG8XyOv0Fupo6BsWNR%0A5WnZ1eaA2faa/dgIz2oeLK1zhOLN/Kk5vTEisZiWe7ohUsjY2WyPadli5YpR/rMA4q4lUmNxB5yD%0APJBYSGkRPpjUB8lcGm+0tJLIpbQ72ofUJ6msLrGE5AdpNI9aQN1jX/No7nGSzxrtfIK/bYtjsDcn%0A6v0IgHt9Pyp/147T7deyv9z3PFsXQbHOdTEYRHgNbYVnn0Z49GjAg3rjAQPlD04l7/lrXkzejFvP%0ARrj3aMjjemOxrlSa0kuG8bLnd8iLu1LihxG87j4N2w51cWhbj/hGQ/E6thT19fvoM5RYt29EwZjZ%0AWP8wDf3iH5FOHI/o2F5o1Rms7cDFA8RSI2E1GEDQG7X1gIuLC+np6fxZGAwGk6b1r0RkPwWR/V8l%0AqxkZGR9EBvCv4OTkxOnTp4mMjOTkyZM4OBglU56enhw7dgyA0NBQBEHg3r173L17l7t379KiRYuP%0AOq4PiX/I6ifAb5GvD/XwvqtFFYlE2NnZmaKonwJ/hUy+G0XNz89HJpOZoqi/12nrP0FW4+Li6NWr%0At/E/Ysnb1L8BNCq4cRY8iiM6tg8S4mDPaTh5CEOBGnG1Ooj9/LHu1BzttXukVeuAxLcUjnERiORy%0ADFoNYkc7npdoR971x7itnYb3gzAKLt8FMbj0bEL+45c8afA1CAayn6dS4sB8ZK7OlBj3GVJrY4Qs%0A9psN+I5qidRagaDVceOzJYjEYsJrzuNk/UVEbrqGAWiavI6Gr9ZQ4/gU8h7GUW5KG9O9mHj0Hmpl%0AAaW6VzMd941ReynTMRgrt6JCnQeLLlBlXD2zSOWT9beoMamOOdlccJWa42qZLXd61An82/ph414k%0A4n9xJpb8jHyCepg3DLi19Da1x9c022b89QRyknII/sIYqbT1tCN0ej2KVXDFrUpxul/qj9rSloMT%0A7zPBfQ8rWp3Gu7ILeq155uLwNzeoOTgICxvzZ+LQyAv4NS2FUxkHms2txzdJQwmdWouDM56SmZSP%0AMkVlJj0QBIGIjS9oMrO62TgdfWxptSgUqUJK6st8ZpY/QvSVN6b5ZxY/o1gZB3yqFXmLFvN1YHxk%0ADyTWFggGA5fWRL6nOQ3/9gEewS54VXbji/AONJhWk6Xtr3Do26fcCotH+UZNm4W1zdbpsbUJTWbV%0A4PiyF5So7Y5U/v5z5VPdBYNYhDKlgDPz7783/9XNVLIT8/FrV44fKoaREWfeYECn0ZP+Qomluy1r%0A6+43mfAXokq/8oT0CeDV/QwazTMvJqv5VVVqjKzK8npHubruKdc2PKPzmS+wdLKiy7n+JNxK5sjQ%0AE0UrGAxocrUYDAbyk4v0tlJLGR1P9iflYSrHRxmXv7H0Jk/3P8N3cAMixhwmN94od7Byt6Pl8SHc%0AX3mdyL0PAFCl5YMB1EoNAXM6Y+Fkg0tdf4Lnf86Fzj+Rn5yNSCym7r7B5KcouTpkB+qMPDJuv0av%0A1pGXnk/1xB0E7pqAS7ta3Kk7AUEQ8F06CJmzLY9bz0Tu4kCFo7OIX/ozGafuUHrpEKTOdjxrPQ33%0AQS0p1qEOT2t8CQ5WiO2siQzujaBWo36ZQFzNvshKeKD8fg2CWIwuJQ31+l0o2jbBsGYN0r69EV88%0AgSikOiJBD5bWIFUYHUrenrdClCpVisjIyPeu86/xZ96zf2Sa/7GI7H+7s8Gn0Kz+X8c/ZPUT40Ok%0Azn8dRS3Uor5bgPQhSfG/wp+xyNJqteTm5pKdnY1er8fa2ho7OztT9PfvbP/v4t3zpNPpSExMxL/c%0AO0RK9jbNLJYYfwzEYkiKx5AYBz0Hw5LZcOsqzFuO0H84wpNHqCPuoJy4AEQi7A9vADtbVN8tRZ+Z%0AQ9KoH8DTC0VJb+wGdAJAuWATju3rENlmCg+qfok2M5eyNzbge3szYmsFqrgkPEe2AyDnXgx50QnY%0AVvTh3oC1HHMcSPb9OGxrl6fsxrHUygxD6mBNmVGtsXA2ks7ko7fRKvMp0aOm6bAeT9lP+ZENkSqM%0AaU9dvoY3l2OoOK4o3Z9y8xU5CVkE9je6Dwg6PVH7H1GQnkfx+iXJSVCifJ3Ny5PRZL/Kwr9zOYS3%0AhEun0RF3IY4a3xRZZAGcn3ieKkMqI7Mqsqt6deU1OW9yCe5bwWzZ02PPEty3EhZ2Ral+QRCIPhxN%0A9Yl1cK/sSdvtnRkc+zV9bg9BlaMl43UBM7x2sLppOHf2xJAanUXiw3RCvzZPzWtUOqLPxFN3ShFZ%0AF4vF1BgWgsLRCv9O5bi4JprJ3vs4v+IpWrWeC6ueI5KKCOporm8FODPrJmXblmVk/EhKtvFnSfMz%0AbOp9ldQXOZxZ9pT2y96PnKZFZ5OdkMdnBz/jyqYY5lU9RuoLowY643UeEVuj6LShSHNcd2wVBl3o%0AwukV0azucY1awwJNKfp3IZNLsLBX8OJSMj9/fQ1BMPcp3j/sKn7t/Ol5vi+n5t7j0JhrpmdM0Avs%0AGXCRwN4VaL+7I+U+K8+PVfaTFlPUZevYuGvYeNrzxZNRWLjYsrzSbnTvEO2c5Dzu7XhG8cZ+7P3s%0AMKlPzSN7DWeH4te2LAe/vkbNbxvjWNao57P1sqPLuX483PmUS/OvGe+BCRdJfZ5J52fTyYhK59TA%0A/abtWLvb0vFUf+5tesjPvQ5zbuoF6h8bRZUfuuDTMYTDtZeZiLRLleLUW9+VUwMP8HjTLfbUWo17%0Al1qEbBjMrf6byIkxRqx9hzfGs3VFToYuRBAE5A5WND4xmpht19lXYgqpT9Op8mA1cidbnvf5wbjO%0ATyMRWch51GkOYrmMCkemkX3zOXFzd2NX3R/fRYN4+vlcNG+y8PqqPRmn7nDdvSspO8+izVQSO3gx%0AuLoiyCzIOncf2YBe6HILUOUJiKpXJ293OGJXV7TPX1Cw6xD6xGS0e8IQ1CqIjcJg72CMriKAXTGw%0AsDJJAQpRtWo1Ll269N698lv4OwW3H5vI/i9GVnNycj6YD+7/ZfxjXfUJ8GvCVahb/Svp+cICJJVK%0AhSAIf2jj9C4p/pgP+O+RycKor1qtNvWaLiyW+hDb/1Ao3LZSqSQ/P5/SZcsZK/4NBrCwhpIVIe4+%0AVGoEjy+BRoASARB9D9HhPRiyMxF/1gOhQ1dElUtgEIvQ+4UgsorFqm5FRNaWZNfqgE6Zh7xbB6xW%0AfEeOTzWcd89DJBLx5qsFaJLSSNt+Gnnz+shrVMTa1Q6rEGMqO2nkYryHtkbmYEPuw5c8bDMDQaXl%0A/rBNKKoFYlW/MuLUDCqenANA3vPX5D2Pp8TxSaZjjJq8C7+RTZEojJHFnOgUlJHJ+A8fbrxOablE%0ADN+NRCEl/nQUzzbcJDc2k9cXohE0ejaWmI+2QIug1SOWSRFLYGOF1aYOznqtHrFIxBq/VcZ/S8Qg%0AAkEr8MuQ49h52mJXwg4LRwuSH6RQ45vqFGQUYOlkjAKdHXeOkP6VkL8T+czPyCfpbjKtN7U1u163%0Alt9EZiOjVDNzf9OI7y5Rsqkf7cO/ICc+m2uzTnPom5soE5TYuluTk5KPQ/GiiPGJKRE4+zriXd3D%0AbDvxN5PIfK2kV0Q/LB0tebjtASemnOPYrPsgFtF0Vo339LApTzJ4cTmBUa9HGIupfmxKrbE1COsQ%0AxjT/Q9h7WFOmwftNBU7Nuo13TS/KtvBlZNwI9nc5wPfBh+i+uhZR51PwCnHDLdDZbB3vqm40+b42%0AR7+6wI2NzwjpXha3gCKvRnWulvCp12m8sjWeNbzYWXsjadFK+uxthNxSyuMjcSQ/zWTk2X5IFVL6%0A3hzA9tqbyU1V0W1TPa6tf0ZehobmK1siEolotqoFUoWUJdX2M/JKRwqy1NzeGUnvhyOQWcrofLIP%0A+xptYkWlnYy41wOJRMT+/qcpVsGT1oe+4Or4X9gYupMhd/vi4GP8wTYIBtKepiOWS7m3/AaVhtcw%0ARYCLBbrR8WgvDrTaStbLLB7ueErbWxOwKe5Ey7MjOVx9EU4BrlQZa5RUuAR7UHV8PW7Ov0Dg5Fa4%0AhRoL7qqv6cmpegv5peEq2l0ZBYBv9yrEHXzE2eGHKDupI+WmGT8Ws2++5EK9ebR8uRCJXEqV9f04%0AXfVbLrRbReieQTyZdwKRRIxea8B/90SsfL0ICp/N7ZARJK4Lx3NQS4KOzeJ2xWG8Wn4Yn5HtCD4w%0AmfutZ2FftwKWfp4YxCKulRmAzMEGi9ohqO88xuHAauTB5UgNboWse3tsO7cko2ILJP6+yMO2kN+h%0AF9JlSzBs3oJw/Sbs/AW6toSeI+HnzcaOeYmvjZZ6BXkQVA2iH4OtIyiNMgs0hU0pDLRu3Zrly5eb%0AzON/jY8dFCj8Hfr1b1ZhsKDQbqvQV/vX9lsAGo3mD+23/hMolE/81nTgo0rx/q9A9Ac36H937P1/%0ABL+2BFEqlSZ/0z/Cr31RLSws/rQvalZWFra2th/c7uld5OXlIZFIUCgUvzlehUKBVCr9t18qBoOB%0AzMxMHB0dP+iLSa/Xm77iDQYDNjY29B/0JWF7dgIGkFuBpS2ocqFma3gSAeo8mLoTpnc0PhmBNeD5%0ALdhxGEYPhNQU2HrQqG/9oiPWXw0gb/kmEImx3boMefvm5A6fjOj6DVyWTSB9/BLyH0ahaBaK09YF%0ACHkFvCnVEL+bG1GUL4XqaSzPK/bGZ0IX0sIuU/DqDYJOoOTOWTh0boggCDx1bYX/5q8p1sZo7vyg%0A6WSs3W2ptG0EADnPE7hUaTytYxchkknIuhPH7eHbUCdnYePjTM6LVAx6ASRiLN3skRWzQ+bhiNzN%0AgYRt56n00yDsgryx9HRCr9Fx1n8MLaMXYOVlbPGoUeZzxOMrWtyahn2AJ4IgoMnM45fA6fgOqIPC%0AzY7cmFTyXmWQfD4SiQQkEjGqrAIkcjH2Pg5kxGRQfWRV/Dv44VbJDbm1nEP9jqCMy6XH2V5m121V%0A6RVUG1uLysOLIraCXs9yl0W0PtCL4g2Kop46jY41jt/iHORB1tNk7D1tqDu2IpW6+zG/zHbarG5M%0AYCdzN4F1tXfhGuJFs5XmWq7wL4/xePtDrJ0UtF9Rn4A2JU33464eJ1Gma+hxorvZOtp8LYtcfkQq%0AFeNd2ZVuWxri6GMkzBmxShaU382Qx4NwLFXULvPx3ieED/4FTb6OoRFd8a5i3q5Vp9Ez32cD1ac2%0A5M2dJCL3PqTXrqYEti0JwIkZN7m96wUDI0cCoFKq2F51HQpLGPRLC5bU/JngwVUJnVaUns9NzmVz%0AlXV4BjoSez2ZFuvaUv7zouyCwWDgwpTz3Fl1C0t7GSU7BNJoaZFWVZOrZm/9jYj1OkK/CeHIyAv0%0AjZuM3E5hXHfIQV4cesSwx/2wLmbFxe8juL78Lp2iZnCy+SoMBSp63h5i9oN+a/EVrkw7Q9D4JlSZ%0AUbSvpPORnGy9hlZ7ulG6TQBv7iSwr/5aijUOIv3ic9o+moGVp1Gvp0rL4WiFbynZPpC6az7nxf77%0AnO+zAwsfV2SWMhrcMX7gCTo9VxvMBkGg0dUpAOS/TudEhWmIJCLkrk74n/mBpAV7yAi7QPUXGxDL%0A5aQducbT7vMJufYjNkElyThxm8edv6PyxXlYlfXkQfvvyL7+HLFCjqxedbQPI5FXroDT3qXkLlhL%0AzoL1FIs+h/bWAzI7DMX+3G7IziG7wyCsToQhXLmOev4yZBFX0XXsjODpg6FlR0Qzx2FYvAdGfwZ9%0Ap8C2+cZIqqYANBqwtAKpJRTkGDNBGg0YdBgTqQYmT55kKsB5F4UyrcLWof8NeDfrpdVqkUqlCILw%0Ab/vIfiwUFBQgk8nek7MZDAZatmzJlStXPtlY/j/Ab164f+j+J8Bf7WL1rhY1JyfnPS3qn30IP0UD%0AgsJ9fMgWqL/e/oeKrhZGp5VKJUql0nReRSIRq1evIWzfXqNvoVhifPErU43FCnfPgTINZh+E73sZ%0AC65+DEf8+jl4ekO31pCWAgtWQq16iMcOApUa1eYDUCUUacniyNo1Q1Cp0O48gPZ1CgmtRlCQq0Ns%0AZYnz7iWIra3JGjIduwaVsQgoSe6V+0Q3GIZBEEjZcwWLPp1QNKyBY+s6OHQ22lOlLd2DxNoC51bG%0AVLZOmUf21SeUntAOQaMjIyKSm23mg1jEyeBpHPH4imu915EXl45Dy+o4D21HpZvLKLNmJBJrBXUi%0AV1LzxgKqHJoEgkCxmv6U6FMPx8qlUbg78HjMNjyaVTARVYCHE/fhVMkH+wCj5ZBYLCbtagyCWkvw%0AjLYEjGpMtaXdqLd/KGIR1N03lE4pi+lWsJJmEZPRWVpi4WpH5Ml49nU6yEKHxSwtsYLnByJxKudE%0AZkyG6dq/uvyKvDd5BPU1T+lfm38FhbMV3vXN27ZGTDuFg68Lna+Ppl/GbEr2qs6ZOfeYWWwd+ZkF%0AuAWZRy2Vibkk3n1D9W/e7+oSfzGBSmPq49uvBrv7nGZV7f3E33lD5qscHv0cQ8s17xcq3NtwHxtX%0AW/onTUErtWRB4G4iVj9GEAycnn0HjyoeZkQVIPDz8pRt64/EUsr2DkdJup9qNv/2hseIZVJCRtSk%0A+caO1F/Siu3dT3Fq1i1yUvI5v+guzda2MS2vsFPQ/9lwJI42fF92N3q9yIyoAti42zD4+XBe309H%0AEKB4qHlzBJFIRIM5DSlez4ec1ALK961kNl9uY0GXc/3QI+Hg4DPUWdIWuZ3CtG79NR3walCGnypt%0A4eWFOC7NiaDRoSHIbRU0C/8Sdb6OA823mbanVqq4syQCW38Pniy5QE5ckYzAo4EftVd+TniPPcSd%0AjORA042UGtqEWge/xqtDVY7XnG9K/SuK2dL41FdEbb/NhQG7ON9nBwHrR1L92gLU6bnc+WINAGKp%0AhOqHxpL7MpU7o7cDkHY5CkGrQ6NU4bPuG+SexfBZNBR5cVceNp0KQLG2NSk+qj0Pm05Gr9Lg1LwK%0A3qPac7fRFC669aLgdSby4HJIvD1xPbgC1xPrKTh+gdx1e7AeNwiLWiFk1uuGRZNQbCd9SU7rL5DW%0ArITN5OGoOvRC2r8Hsnq10LVtj2TvLrgZAWlvEHXoinj6IJi7GbbMgeELQKuG9iOMNlZIIF9pLAjV%0A642uAVIFxt7QBubMmUv//v3fu1//GwuY3iWfYrH4HO4ZnQAAIABJREFUN6UFcrn8T0kLdDrdR3Mt%0A+L1zp9FoPln9yP/v+IesfgL8WUeA39Ki/h0z/HcbA3wM6PV6tFqt6WXwoX1cC/Eh7bFUKhUWFhZm%0A5/XgwYNMmDzdSFL1OpDIQf5Oxyp1LgTWgsntQJ0Pm27Bz2sRUpMQZ2VBSENEXiWgTgP4vAVCWioM%0AGYv+fCSi+9exXDQd/ZNIlMFNMGi0iJo0RhH9CElmJnazRiOSyRCUOWhOXUJSwp3n/l2JaTHGaGcT%0AsROvyF+wH9kT1YWbuEz9wnRcGUv24DO1m7H1qlbHk27zQCzi4cC1hNv14Ubb+eS/TsexexN8Nk+l%0Act4JHD5vjG2FUgTunoT38HbYlC9B4vwwSn3THrFUYjpfqT/fxHd8EekRdDpSTz+i7DhzUpaw/zYB%0A482nPZp+iHLDGyF5p7jn6Y+nkTtY49bAKG8Qi8XYl/cgPzaDWuv60Or+DDqlLKZL5hKcQsshiMS8%0APBvP+oprWeKymJ+7HuCXAUcI7BmM3Mbcrur+mjtUm9Tgvefs2bZ7hEwyOiaIpVKqTm1Kj5dTsXSx%0Aw9LDjpWVtrGjzc+8vmbsj310+BlKNyuDw68IZMq9ZDJfZhA8sg41ZzajX9JULEq7sabuAdbU249r%0AkMt7pFOv1XPpu8tUm94IuZWcjqf603x7N8Kn32R5zQPc2fWc1uvMO3IBZL/K5sn+p3S+8RXebYNY%0AXXsPN356aNR+F+g4OfUqtb8r6jYWPLAqn18ayKUVj1hUYQ9OfsXwaWBO2sViMR1/7oYgQEG2ioTr%0ACe/tN+tFJppcLV7NAllfcT2ZLzPN5me/yib2zEtKdKnKvkZbSLmXZDbfwk6BXQkHDMCTtbfMPpJF%0AYjFNtnfDMciDHa32U3ZgbVxrlARAbm9Jq/OjSX30hvC+B4yNKvocQOZgS5Pbs/DpWoOjtRajyVWZ%0Atlf2i5qUH1qPI5124FjLjwoLeyASiaj0Uz8UXo6cafCDaVnHIC/8RzYkevddSk7tikf3esjsral8%0AciYJ+68Tu/aMcRzOttQ6PpGXGy5xue0Sbg3ZTMnNk/GZO5io9tPQZigRSSX4HZpN7pNXvJi8GYAS%0As3tj5V+cB/XHE7/kIPErj2CQSZH6eOEWdRKXE+vRZylJHzQNWRkfiu1YSPbXc9A+icZhxyKEbCVZ%0AAydiNelL5JWDUNbvimLScGTVK1HQuCMWG5eDToMwYyayndth6VyE1p0wuLkj2r4Mcb8xiNZNhaHf%0Aw7G10HowIokYSlc2ypj0WtDkv21mYgliOWAgLCyMJk2K7qP/RRQSWalU+rsa2XeJrFar/WhE9vfI%0AalpaGs7Ozr+xxj/4q/iHrP4H8G5ktTCKqlQq/1YU9bfwMfSe7463MDopl8s/io9rIf6OPVZhYVeh%0APZadnZ2ZPdazZ88YNnIMIICFjfEF71UB5ApjlLVcA1AXwKNrxihr7/GwZAycPwANOyNsewgPLmIo%0AXwFCg+DebZi2GCbPg+mjENvbotsaRnb1VuiT32BxYDeKDavRHzqGUJCPdb9O6F4n8abW5wj5KnLC%0AryPq0x1Z/drYtqyLoloQAGljFmBVwRerykayl33kMurkdASVhkctp3PJrhNZFx4h9yuBtEU9fJ/v%0Ax6ZLM2wr+1N6/QQcWtZALJWSufssxSd9bjpPOQ9ekB+XgvfAxqZp8T+dQqyQ4dq8yBA/av5hFG52%0AFKtTlDZ/ucVYsOHZpijSmfcqnexnSZQdVlSoBRC18gLlJzQzuz8iV11AainDo2mRpZTcRkHGtViC%0AZ3ek5bM5dFauosauoSi1FuQk5vBo6z3W+a/g0rSzJN1KICY8ElV2Af49zKOtz3bfR6/WUbqzual/%0A8tWXqDLz6fRoKl1efItaYcOWZvtZWWkrMadfUWuSeXU9wMmRJwjoVQXLYsb0qFQhp/mOHrQ5MYDs%0AxDxSHqcSseAaem1RZ6dHu54glkoJ7FfVNK1M+/L0i59IZlIBiES8vhT/3n196dsruIR44+jvRv1V%0An9FsX1/CJ15hd9fjXFx4Gws7SwL7hpit4xbiSeezX5CXqaYgW2XqzvQuImZfwqGsKxXGN2Nn421E%0AHS2qDjcYDBwfGk7x1kE0DBtA6W5V2VRlI6mPi6K6J4cdx6Vmaepu6UvQ2KbsbbDJjLBGH3rKq/Mv%0AaXX/W3KTcznSfKPZ/iUyCbbFHUAkIuVCjBmZtfKwp9WF0UQffsbeBhuIvxRHvYuTjAR0ZW/sg304%0AUnWRaR1BL5B2+xUiiYSc58mm6WKZlFpHx5LzKpOr/bcCEH/sAc+XncW+RXXifjiEJsNYwGbt703w%0A7nE8HLOdzFvGrlvWvm7Y+nmQfOYJpfd/j3OXhriN7Ypdg0o8qTPa2BrZ1RH/I98Tv/QQ6SduIZJI%0A8BzZluwbz3k5cyeOm+biEXkCQZlLxqjvENvZ4PLLT+TtOkbe3nCs2jbEfngPMpr2BQs5zr+sR7X7%0AKKp9v2C3exn6lDRyhk3BesN8hIRE8vuNQNqrC7qDh9AfOIg4KAjR0O4YOvXAcO8agjILUYVqiI+s%0AR9S8B6JL+6ByE0QZ8eDkCa6+RkmTVg06tVEWIFGASMqNGzcICCh69v4bI6uF+Ktj+9RE9vfGl56e%0A/o8TwAfCP2T1E+C3Iqt6vf49X9QP3VL0Q8oA3m2BqlarTeO1sLD445X/Jv4KWS3swKJUKsnLy0Mq%0AlWJvb/+b9lgajYaOXbobi6n0GmME1c4NspOMkYhhe+H5JXDyhuqdQNAh2r8GLh9CXLURzA2DiZ0g%0ALxfRxbNQsxXY2EC3/pCUAEf3oE9KQRX1Bhq3R1rOH0k9Y1W4/vt5KNo2IqvnNySXbYIuNgGb7Sux%0AjbmOYlR/dOevYj99KPA2Mrz/JK7T+1HwKIbkGeuJ7T4NRCLiV4SjKlEKmyHdkLo5UfrWVlxnDkZa%0A3I3c/Wdwm9zTdLypW8IxGASc2xW5Arz4eh1e3UKROxUVH8UtPoLvuDaI3omOv1p7Dv8JrczJ5rxw%0Ayo1tZlZwdOfr3Xg1DzLpBgHeXImm4E02pXqap9efLzlDwDfNzPaTev0FeclZlP7CSBrFYjGeTQOR%0AWskpVtOfdpmr8BnWjMiT8expup0D7XZjV8KRN3cTze6Rm9+eo9LYBkhk5s/S1bFH8e9fG7mtAit3%0AOxqFDaLbm3moDTIMBgNHeh/i2YGnGN5W0Ocm55J0O4lK481T5wC3Zp2lVJeqNPp5GNeX32GV30/E%0AnovFIBi4OP0iFUe/T3w12WoK0guoOKsdpyeeY3fLveS9MVoyZb9W8nDnIxqsL/qYKNGqPN2jJpHw%0AOJPTMyIIHvm+RAHg1rwruNUug12AN5srriHpVlH0VPkqizurbxK6pTeVZ7SixtLP+bnbAe6vv2e8%0Ajoeek/Y0jbpbeyMSiaix7DPKfVmXLXU2k3gzgZjj0by6FEf9/YMBCJ7W0oywqrNVnBzwM0HfdsC2%0ArBtNLk8k7VkaxzpuNY3h1clInu++R92I2WjUBo43XmE2fns/N2r82Jmkm4kU71MHuYPxw0AsEVNr%0A/wgMUinHmxjXuTXuZzKepFDn5VrE1pZcaTLftB0LZ1tCT08kLuwW17/czqWuaym5bDgBYdNwaFCR%0AmzUnmN6JLq2rUnpCZyJazicnKplLtWagyRdw7tuGuH7zEN4W85TcOhlBqyOmp7EHu23N8pRcOJSn%0A3ebzpOtcnn2xGMt+nRG0ekQKCySO9rgeW0PuhgPkHT6LPLAszutmkzFwGrq4BOzmfIWsdHEyGvdF%0AFlgWx7WzUQ6aRN6yzSg8XFGt301WyVBcHJzwinlFw6cvadqmNa2ylfQIDqZSQAChV09RMqA8imM7%0A4MpJhJjHSJ7fwZCTBYkx4ORq/PDOTwc3fyNhFctArwa9CkQSEFuQkJCAm5tRF/3/E1n9V/jQRFan%0A0/3uvtLS0j5696r/K/jHDeAT4tcV/QqF4l9W9P9d/F0ZwLsPqk6nQy6XvzfeT6WL/aPjeLewSyaT%0AYWVl9YeFXR06deHli1gQGYzpMUEH2W8APbQYAys/B99qMGwLjC0HBhGGCk3g2n6EEQsQLRqO4clN%0AaNETw+SNiNp7YZi+EHb8BHMmgoMzrD2Mwbc8olruSHcbu+ioZ89DFxePfl8G1K4PbTsje/EIi27G%0A1qp5X81EEVIOi5AADAYD6SPnoMvM4fXAueiVeYg93THoBLxizyB1N361J5VoRLFp/UzHm/nTQUQy%0AKQ6tiohpytwd+HzT2ZTu1+Xmo7z+lMCl80zLZN2MpiAhHc8uNVAlZ6HLVZF28SkFyZnYlHEl/YYx%0ACpUfn0FOTAqu9f0pSM5GZqtAbCEh5cwzGh4dbnae747fT9n+dZHZKEzT0m/FkpeURZl+5oTuzjf7%0AKdOnNjJbS9M0QRBICn9IzT3DkCrk+I1uht/oZuS9SueY73hwcOBQqy1I5BLK962CWw0vsmIzKD+k%0Aptm285OVpN5PoN6ufmbTpQo5quRcqm8eQPaDBMKHhHN+wlkazG/E4+2P8GlUFgdf8x8cVVY+iVdj%0AaX1jAo6BnnSM+547Uw6xp10YxQKc0eRpqfxN3ffuuTsLL2Hv50bQN03xGxLK2ZYrWeW/hnZb2hJ9%0A9AUulbxxDDAvqrIsZoNf72rcnn+Wq9PPYlfCAb/ORQVQ6U/eEPnzEzo8nYmNjxO3JhxkV4PNtN7S%0AEf/O5bkw4Qwu1UpQrLKxAYD/gFpYe9tzqssGsuOyuLfhLkGTmiF96xYhEomo8n1bZHYKdjTegUwh%0AIWBsUywcrEz7DJ5mlDDsbbCJ4qE+WHo6UW600WbL0sOBplcmcqL6d5zsvZv6y9tzsucuyk7rhH0F%0AH+qcn8qFKlM402UDjfcNAECTo+L2lCM41A/ixdqLeLQIxr250cpMam1B/TPjOFVpOofr/EDWwySq%0A316M3MmOkBMzuVZxNHeHbyZk5RcA2JXzJHDO5zwavxuXfs1wH2Acq+/WcdyvOoL77eYQctSoOy05%0AtQtZ155zrtJErKsE4Ht+JSJBoOBBFJGNxlDu8gok1pb4HV/A48qDSPnpMG5D2qHw90afpyL16A1c%0An59E5u1BXs0Q0nuMQ/7sGPKQ8jgtn0JGnwlYPDmGdffWaC/e4k293rjHnMR5/1ISyjQnI7QbRMVh%0AZ2dHg8cJtBk6ikqVKuHr6/unpVS5ubncuHGDx4+fcPGmN+fOnEGv06MvyIXyDSD6OngGQfJz0IqM%0AZBWD8X0nllNQUICdvT3paWl/an//CXwqIv1uwdZvjaGwsKvw73eLp/Pz8xGJRGRnZ7Nt2zZKly5N%0Aamoqtra2723rQyEjI4OuXbsSFxdHyZIl2bt3r6khwK+h1+upWrUq3t7eHDly5KON6WPhHzeATwC9%0AXk92drapQl4ikaDVarG3t/+o+1WpVCZf07+CwoIptVqNSCRCoVD8boq/UGf7MY8lNzfX9AX8LgrJ%0Av1qtRq/Xo1AosLCw+FMv+YGDBrF9xz4jUUVs/FskMlbVit9W1soVMO0sfN8MrOxg3H7EK/si2Nog%0ASk3EoMxE3Lw7wqR1sGwc7F2KyNUDg1oN+Tmw8xwEV4OpQ5E+u450whi0M+egfx0PofVh5UawskIc%0AWALrrUuQt2qMoNOhdAvGafE49Emp5KwNQ5uaidSvFBajByHv1Zm8pl1RlPbEccN3AOSHXyS92xjK%0AJYcjtjQSwmjfjriN7ozbyM4A5N2L4mntYdR4sQldZi4FMUnEzd1D/r0XuDYPQZWQgSo5k4KkTNDq%0AEMkkRpsqmQS9RodEIXurQX1rIaPMQywzfgwIWh2CRofhrdemtY8TVh4O2Pg4I3e3JXrdJaos+gzP%0AlkFYl3BGLBFzsv4iHMp5UP2nosivJiufMK/xtLwzAzv/os5JTxefJHLZGVq9XGh2D17puBwQU+3g%0AGARBIGHfNWKXHifr9gvECim15rambM/KWNgbie+JLlvQFRhocnSI2b3wfP0Vbk/7hfbxixBLjBKd%0ARzMPE7PqLFqVhhozmhLyTX2zfZ/stYucpAKanRltti1VWi5hJadg0AvUX9yGCkOqmSLHqqwC1nvN%0ApcmJUbiHFnX6erryPHcnHUSn0tEpYhSuVcwLnDQ5KrZ6z6bmtoFoswu4NWwblYZUJ3R+U8QSMT+3%0A2YHaIKXJsaKPhJgdN7g2ZAeBvYJ5vO0+naNmYO1p/iOWfu814Q2Wggh6pi8wi3AX4myXDbw69oiG%0AB4bg3SLwvfnXhu0ieus16uwbhldLc5/cnJg3nKzxHXIbOVJHO+rfLfooyotN5UKVyZTpUZnayz/n%0AfLdNpD56Q81HK4hfd5LIsRtpdHUyDkFF5+L13htc77MWly51qLhtTNF+HsVxo9Z4ghd2o/TQJuTG%0ApHC22nTkQb6oH8YQ8ngdFp5G3aD69RvuVhyKz1dt8Z3ejbzIBG7WmYhWpcW+RS1K7jNGT3WpmTwL%0A6kWxXk3w+cF4XrOORRDddSbFujQgbd8FrCYMRb3zMLKAMjgdWGlcpvc4NBF3cIsMRywWk9FvMqqL%0At3CPOo5IpyOlelcMegF5di7OdvY0D63HkMGD8fHxMdk2FRKzX1e3F/7/j0ibwWAgMjKSzVu2cuDY%0AcZJeRoFMAfYekJ9lfLfpVMauV4JgjLIatACkpqZ+kmzZX4VKpTL5t/63odBZxtLSEkEQSElJYdWq%0AVcTExBAZGcmrV69wcnLC19eXsmXL4uvrS/Xq1T+IZnj8+PEUK1aM8ePHM3/+fDIzM5k3b95vLrt4%0A8WJu375NTk4Ohw8f/tv7/oj4zRv8H7L6CSAIAkql0pRa0Ov15OTk/O4X0IdCIZH7M192hZWUKpXK%0AFEW1sLD43c5ShfgUx/Jreyy9Xm8i04XT/4rrQEREBA0btwBEYOlojDDoVeDbAiKPgJUTaHOhdFWI%0AMpqTs/QJ3DsOP30JCisIaQV3j8H+F5CVCv2rg1wOHUdB7CPEQi7ClhNQUIColhsGjRaxtTVCUC24%0AcwHR/WhE1jYIC79Heng3dk8vQoGKnO5foj162phO9C2D3i8Azp/BMfGusRArI5OsEtXwuL0fWTmj%0ATVNKxQ7YtamN6/dfGs/XlfvENR1BubM/oopJpOBOFG82HkOfnYdILEJia4XU3hZ1WhaKEH8sgsog%0AL+WFxNOF5EGz8bu5Ccsg47a1bzJ4UqIjgU93YFHS6EkqqFQ8cGlL4MUlWIcUaVjvluiBy+BWKMr5%0AoIp8jSomkYwjEYgFPXJLCzRZuegKNFh5OKBKzaFUzxr4dArBqUoJLN3siBi8lZxnb2h8cYLZ9TpS%0AeiJlx7XE98tGpmmCTsch55HUDJ+Ic20/03RdropjLoPxGdqcjCM3yU9Ix7dzRQKG1uSX1htoeuxL%0A3EPNPVrDfL+lzIhGlPuqqdn021/v4uX2G4h0Whz9XKi7vB3u1X3QaXRscJ1No8Nf4l7P3Poq6Xwk%0A5zqsIWTdQB4M34R9CQeabf8cJ38Xrn97hme7HtH+6Yz37skr/bbyct8drFytaXmoP84Vivxfb39/%0Ammebb9M6ai4A2c+SON9oIY5lHKg+pT5HPttN59jvURSzMdtm2q04TjRcjIWTNV2iZyL+lSSiIDWH%0APSWnI7aQ4d3Inwa7vjBbJvdVBvsDvsN7SDNerz1Fg139Kd62SAOs1+j4OWAW2NmheplCs4hJ2AeY%0A+9ZGrTnP3W/2Urx/fSouM49oKx+95mLtGXjUK03K5RfUilyDhavxPRIzbQevV4fT/NFsLN0dUKfn%0AciJwCoqaQShP3yZ411hc2xbZl6WG3+ZBl/lU2z6U+yO2YVm/MiW3z+B1/7nknL1N5ZhNiN++y5RX%0AHvOo2UTKzu3Ni5m7sezQBKepg3gV8jkeswfhMqorAPl3nhNddyild07DqX0omoRUHlcdjC4rF6er%0A+5GHBKJ7+Zq0iq2xmzMG2xG9MRSoeFO5A7KA0rgcWIZBpSa5ymeIPV2xLlWcvH3HqVK5MnNnzqJS%0AJXNXhUL82nP01//+V1ZNv/UOfPnyJdNmzOTC5WtkpyWBtQuoc4yEFZFRHiBoTMtnZGT84Xv/U+O/%0Anayq1WqsrKzem/fdd9/RuHFjypcvT1RUFNHR0URFReHi4sK4ceP+9r7LlSvHhQsXcHNzIzk5mQYN%0AGvDs2bP3louPj+eLL75gypQpLF68+L89svqPddV/CmKxGEtLy/e6S31s/Jn9/DstUP/qPv4uCqUG%0AGo2GnJwclEolBoMBOzs77Ozs/lJhl1KppHvvAbx1/jdO1BdAk7kQ/Quici2hRG1j9OHFHZDKEXcY%0AC3EPYNPX4BMAS54gfnUPUcchiLfNhb5VwNUHwhKh8yi4eRJh3Fz4ZR/U8caAGAZMRjiTijg+BvGX%0AoxFZ2xiNpLetR9qxJQVDJpDpFoz2/DXE3XsgeRqN+EIE4gf3sBw/DNHbl3TeVzOwrBViIqqaqFjU%0AkS+x7Vif7L2nSB65iNgWoxAK1ES2nEDCzG2k3opFr9LgvPsHvHNv45V5E+tZI5FYW1Li/Do8Vk/B%0AefwXFFy+h03VABNRBUgYswz7+iEmogqQOGMjlr5eZkQ1++J9NOnZeHz9GcU618N7Uk9Krx2LRDBQ%0AdssEQl7tpIbyMNWS9iD1L4HU1Yk3j9O5NmQnB0tOYo/z18TuuY2iuCPpt2IRdMZipdQrUeS/UVKi%0At7lc4Ml3R7D0csKpljlZfDRuB46Vy1D+x36ERq+i1s2FZGYaONJ8Ldo8NcqoN+jVWtPyyVeMGtky%0AA0LNtiMIAnE7bhD80yAaJa1DUq4kPzday7F2Wzg3ZD/WxZ1wq+vLr/Fg5jHc21XGu0sNWsSvQFrS%0Ag52Vl3Nt+mluL7pE5R86v7dOQYqSF3tuEXp5Jk5NKrG/5jIer44wZg6UKu7MP0vFH7ualrcv50Hr%0AF/PQ6GUcaLmVYnXKvEdUATTZBSAWoxdEHG+yAo2ywGz+nSnHsAsoTqPoFby58YqTLVahyy8iLddG%0AhmFfw4+Axf0IXDWY8903Erf/rmn+40Wn0WsM1Lj9A8W/bMmpOvNQRqUU7T8rn/tTDuD6RTNebbpE%0A1ELzH0i7oOKErB9EwunnFPs81ERUAUp/2wOXVlU5U2022jwVEZ1XIC/pid/Pcyi58mse9vyRnEdx%0ApuVdWlbBd1ZPbvRcg8TbnVI7ZiISifD+aRxSd2ceNxxftN86gXhP6ErUpG0o2jTAfeMs5KW98Qj7%0AgaTJP5F37REAVpX98V41jpd95pC+5yyPKvRDFByErE4NcgZNBkBaqjgOe5ahnLgQzb0niCwVOP+y%0AjoLTEShX7USfmY2iXGl0V+/Sx6E4T+7cJWzHToKDzQv/3sW7msrCoIGlpSXW1tYmTWXhx3lhKrqg%0AoMCkqSwoKECtVqPVatHpdJQoUYJtWzYTG/WE9evX4+1ibySq0rdSG0EDIimFqkAnJyeUSuXvju8/%0Agf9VPW16ejqurq54e3vTsGFDBg0axIIFCz4IUQVISUkxaY7d3NxISUn5zeW+/vprFi5c+D/dnOB/%0Ad+T/Y3j3Zv5Pt0L9rRaohZXyf6YF6m/t42MdiyAIJj1qofFyIZn+q1pfg8HA5937kJz4toBKJAGN%0AEoqVg5PfIPJtgiGgIzwPR1SmHtQZabR+ib0Piz4DRw9Y9AAenUNIiMJw8CcMl8JBKoPx640R1/n9%0AwMYW0ejuMHUYaLWwaD8MmQ53LiMkv8Yw8EsMBQUYRg5CeJOKetUWVE+SMXQdhsjaGsnSFYjt7BBu%0A3kCfmIjFYKMxvqDToTt2GtspQ9C9TiJ380FS6vXBoNLwot4QUiasIeN+PAatHsd74ThlPsI+6iLS%0AgDJYVgnC5vOWiN9Gp3PnrcNpbC9E75zDvANnKTa+KC0vCAK54RG4fNPN7DxmbT+Jx/iuZtNeT1yH%0A+8BWSKyKdKmpm46DVIJji6KKeHkxewoexlJy2QgCI1ZS8fU+quaF49yvFQaRmIwnaZxv/iP77IZz%0AtsFCIvpuwLtdCFJr89Rk7IbL+E5sZ3avCoJA0v4blJzYwTTNNsiHKsemILW3wblNdW5PP85O98nc%0AnRVOQWoON8cexHdgfTONLEDMuosgleDevhpShZzKW0bQMHYVuWoJUXseYO3jaCSD7yDjfjypt2Kp%0AuNzYIUgsl1Jj32hCT0/m7qprIAJrr/czEI/nn8KunBcOlUpSae0gqoZ9zbUp4Rxvv5nbs09h5e6A%0AdxvzKJxUISd4fmckljJSLkURG3bHbL7BYODmqH149G1EaNRK8rM0HK62kLyELOM1fJpM9PbrVNr9%0AFXInG+o9+5GceCW/1F+KOiufxHORJJ6NpFLYNwB49WlA0LovudhnCy/23CLnZRoPvg+n/I6xiMVi%0AyszphWe/JpysOYfcWKP28c6oXSiKu+G3YhgVjs3i2awDxG44VzRGvUD0/CMoypckeecl0k4UHYNI%0AJCJgwwgs/bw4Vmo82c+S8Tu/FIBifVvgPqoztxtOM1X3GwSBrEuPEcmkqF+nIqhUb6+BjNJH55Mf%0AnUTM8OUA5D99ReKPBxAX90R17haCxkjQrZvWwnnaYF62/gZdhtFRwalvK6yqluNF3zlIRw7ENnwn%0A1nvXoEt6Q/awaQAoWjbAduwg0pv3R8jPR1qqOE5bF5A1bhHp5dvS3cuPmKfPmDVt+t8utvk1kVUo%0AFGZE9t0sk16vR6PRmIisWq2mbdu23L0Zwfnz5/Hz8wMMRqJq0AFvJQGAt3dxIiMj/+VYPiX+18nq%0A30HTpk2pUKHCe39+ncr/vcj60aNHcXV1JSQk5JMEyT4W/iGr/wF8SKP7f4VfNx/4vUp5Gxubf7vL%0AVOE6H/pYdDqdiUwbDAakUum/RabfxbjxEzl//sJbnepbfapeC4m3QCzFIJHDz0MhpBuGfkfg6jJQ%0A52FIjAWZBQxaBa8ewbphYOcMfZZhKF4RcfnqEBwKV47AzZOINBoMNbtAve6IfXyhhtEWSrxgBOLm%0ArREtWYghqBSEH4PPBmCIyMCw+QySU2FIxo6nMlNPAAAgAElEQVQzEUjDlIlYDeiB2N4OQ0EBeV+M%0ARlDmkN57Aon+rciatRYhKwf5+lVYvnmF/PEdxG5uKBrWRlaxPGAkcNr94dhMHGg6D5pHUWjiEnEY%0A2NE0LWvTIRCLsGtVFMFMXxmG2FqBbeMispl1+DK6fBVOnYuq47UZSvLuxeA2sogkAiQv3IvX2M5m%0AWsg3209jEAw4tikqfhKLxSiPXsd7ai+C7q6jcvphgm6vRSjvR35iFonH7nPYdSS3B20m6Zf7xB+8%0AhTanAK+utcz292r9OZBIcG1V2XwcB66hL9BQYe9Ear3egv/mMUSHPWR38Wmk3X2NV/v307FP55/A%0Ad1IHo2flW1gUs8OtQzXENpYoX+UQVnIqkesvY3j7jD2cHY5LvQDk9ubpQIfKJREEsKrkyy+1F3J/%0A9i+myLEqLZenP12kwuoBpuXdW/4/9s46PIrrffufmdW4EIIFd4oFd2uhuBR3L5TiXtydFi1eIDjF%0Airu7Q3AnBCLEZbNZmXn/GHaTJdDSQmm/76/3deVimTNH5szs2Xue8zz3U5wvn8whJiiea7OPk6tX%0A9TTjk2WZ64M2k6F1Vb5Y1pfTnQO4NnqXfSzPt17DEBZHwR87otZrKXdtFro8WdjhP42owJec77MF%0An5rFcM2j+AarnfVUvj0biySyq+xsTnddh1/PWg4qEZlbV6bI6j6c6bKWw3UX4ln5C7yrKH6sgiCQ%0Ad1YnMrWpyoFSk3gScIag7VcptHucMgdVilBo0w8E9l3Ny60XAEUOzfAqlkIXl5B9Tl9uNJtB3PUn%0AKc+FRk3WvvWxxCehzpEFUZ/ywpJ5YlfcqvlzsfRgJIuFJ2M3EHXmHtkf7UGTy48HlfvYz9Wk9yLv%0AwR8JW32YoInruFlpILqW9ckQuAfRLyOvvvw25V4N7YxztVI8rtATSZIIHb6IxEv3IGs2pFMXlXF5%0AeuC2Zw1JAdtJ2rIXAOexfdEUzk9ktfYkn7yIafhsihQtwvH9B5k5eQpeXik6vH8X8RIEAZVK5UBk%0AbRHuLi4udrcuQRAoVKgQJ48e4MSJE/iXKqushUggW7FluipVqjTr1q375OP8K/hfJatRUVEf/YJy%0A6NAhAgMD0/w1bNjQvv0PEBIS8k5ifPbsWXbu3EnOnDlp3bo1R48epUOHDh81pn8C/5HVz4Q/m8Xq%0AU/Vps6LaZLIsFgvOzs54eHig1+s/ybbAp0o+kDpzV0JCAiqVyj7ODwks+D1cvHiRhYuWg9pTsSRk%0AbQDIkLEc6DwAAQK3gloHFfvChCyKNuE385C9cyNkyY94bS8MLwNOnrDgJZRsANf3In3VDrFPZRjX%0AEgqURV4fBu3HIxxfi9R3mhK4tTMA6eFtpD074MQZxV1ApYYxCxRf1+N7scZGIbRWLJvSiyCsgYHI%0Anm4k1m5DlE9hTDuPQMlyWEbMQL4XgbV6bdSFC6Ft3Vx5niwWpMNH0A/rab/u5OUbQaPGqW6K7mlM%0A/6l4tqiFyjslKC56+mrSD2rtYGmNmvsrGYa0cZj30LEryNSrEaI2xXfs+eDFuJcrhD53Fvsxw+1n%0AGINC8e3ytcN9eDl1E5n6N3Xox3DrKUlBoaTvkiKS71wwO7LRhHu5IhSL3ku2lSOJeGXkcrfVnG22%0AEI2XC6E7r2AxJNvrPJ61l5xDGzm0DfB47Gay9U8Zs2+j8pQO/BnPqkUQXXQcrzeXs62WEHtP0Q0N%0APXqXpPA4snZOSxKfzNhNrrFtKB24kLwLenF1xC52Fp3M081XCNobSPHFndPUCVp7BrWznpInplP8%0A6BTuLT7DLv+pxNwN4c6PR3DLlQHvso4uBVpPVzI1K4vG243AUdt5+PMxh+9Y2JG7xD4IpcDCHmRq%0AW5XSZ2dwf8lpjjVagik2iUsDt5BtUEO7n6YoipTcMwrflpXYVW424RefUuxNSl4bRLWaCpemImu1%0AJL1OwLepo5oCQKZm5ck5uCEJwTGkb+IooyUIAvnmdce3SXku9VpHxl710fulaEymq1eGfMv7c7Xj%0AYh4vPMD9yTvItW0SolpN+m71yDykNVdqjCEpSNF3TQ6J4lanuXgN6kDys1Ce9Zzl0FfOtSMRvT04%0AU7A3z+bsItPRFah9vMi0cx6m8BietptgP9+pSG6yzunLiykbUJUtjtfiiQgaDd47F5P88AXh/abb%0A2/VdMxmrLHM3exMiVuxCf/wgTgd+w3zjLoljlTGoixbCZfF0YrsMx/I8GEEUcV89k+TrdzA27cui%0AMeM5ffAwhQoVcpijf8qyZSOyGo3Ggcj6+/tz/PBe9u/bi5uHjVBL2Nykvvvue/r374/RaLS7Fvxd%0AWaB+D/9mi+DvkVWLxfK3+tk2bNiQ1asVlZnVq1fTuHHjNOdMmTKFFy9e8PTpUzZu3EiNGjUICAhI%0Ac96/Hf+R1c+ED81i9algi5QHJUDJlgLV1dX1o1Ogvo2PvZbUGq7vyoT1sfJYcXFxNGvZERkNWGIg%0A+zcQvBuKDVT8taxmhLxNEJw8IIs/zCsP5iQYcBEK1YW7+5AfX0W+fAC0TtBprpLWcG4LJWHAvD5I%0AoqeyvvdZDGo1/DIMfDOD0YDQojhM+g4KlYUtz5GWXUI8sQWh+1DQKdvm4uyhqL/7HuLisC5dgqVa%0AFZAkzGt3YUz3BfSbBhoNbD4AjVuCKCLu/BX1kP726zT/OA/RxxtN1RSSYZy1BPchXe3WTclgIPnC%0AdTwHtbOfY7h2j+SnL3Gp4k/SjYckngskYtkOkp6FoMmUjrhDl4g7cpnobScw3HmKa6XCGJ+FYo6K%0Aw2oyE7PrHBmHpGiDAjwf+DO+zaqg8XZP6efhS5KehODbvZ7juYMW4dOsmsO5kiQRs/McvkNaIooi%0AXvUrkH/PdPJdWAJqNdpyxbg1cD1703XnUtM5PJq7D8PLCPy61HBoO+H+SxIehZDlu7oOxyWTibjz%0ADyiweypF764iJtLC/pITON1kIVd6rydnr1qoU0ltAYQfuI4xMo5MnZUo3kztqlM+dC3OlYtxutNq%0AtF4uqJzfUqywStwbt40sgxQrtkfZApR7sQptkdzsKjWN2z8dodDctFYOc3wSD2bsJG/AUPJvG8eN%0AUds512oplsRkZFnm2qDNZGxbzS435VYkBxUeLSH6cRRbco5CMkvkGtYkTbuF5nZF7e2GJclCxP7r%0AacotMQYMzyNwr1+ZyzUnEnE00KHcakgmaPFB3BtW4f7AVbwMOOpQLggCol4LKhWv1xzDHJPgUJ6h%0AdTVyTenE7cHrcatbDrfyKQoDGUd3wLt5dS6WHYo5OoGbzWag9y+I76TvyXZ0CRHrjxAye5P9fFGn%0AJcu0HhifhaPxL4S+iOK/rPJyJ8vhJcTsOkPo7A3KdUXFETo5ADFzBiwXA5GiFHcIVfp0pNu/gtgV%0A24nbtN/etjqDD5aoOITveiAWyIeYMQP6LWsx/rgE06ETAOjaNMGp3TdEV25F8oVrGL5sT5369bly%0A9hwNGzRMM7dvz9O/BYIgUKFCBR7cu0O/fjZ1C9t6LvHLL79Qu7aSpc4WTJSYmEhCQoKD5ujfRWRt%0A7f2b5iw13kdWPwfBHj58OIcOHSJfvnwcPXqU4cOHA/Dq1Svq1av3zjr/1nn8I/ynBvCZYMuCYUN8%0AfLzdef5TwiajYZPJslgsuLm5/a3RnXFxcXan/w+FzeJrk9fS6XTodLp3+qGazWYMBsNfkseSZZmG%0ATVpy6MAh5dVMUCuR/1mqKCoAYReh7jq4sxqeHQCdu0IEizVE+vIHmF1KEdKuPQVeP0AIOoY89iTC%0Ahh+QT61ByFsR+ds1sLwTorsz0phtkJQI7bKAIQHB3Ru5aG04/ytsfgTps8C1kzCkLpx6CW4ecPEk%0AdKqBmCsXUlAQQoasyOGvYPUJKKxswYsNCyE3bYncT1mMWLsC4ccJOD+8YbckGgsUx2lcP5y6KP6k%0Apks3iKnUFN9di5CiYrA8fUlCwG9YHj5Hny8b5ogYpNgEZKsVQatB1GkR1CoEtRpzXCIqZz1qZ72y%0A6MpgjopB0KhRqVVIJjOSyYycbAYBtBm90WVOjy6rL+qsPoT/so9sY9qR7puK6HNkRFCruF1vFGp3%0AV/JsGGW/P5LRxOX0TdIoC4Qt2UnwhLUUe/GrgxvBw29GgySQfYciz5J0+wlhk1YSv+s0ssVCtu61%0A8OtZE7cvFE3Ry7Unok7nSaF1gx2ei0cjVhOx6zJFbi63L96mVxE8aj2JhMv38alSkC/md8ElT4qE%0A1sniQ/GuV4Zckx3JpSkillN+nXDO44fpRSjF57UnW4fKCIJA8JaLXO+1mgqha9LsYtztMpeQDcdJ%0AVzo3pTb1RZ8pZav4wZTfeL7yJP4PV9n7uFN5AIIpmbx9v+TWuF1UCVuF+FbecYvByDHvdohqkbLH%0AJ+BRytFi+2r9Ke72X0nm+QN40XUahaa0JkffFIv27e9/IfzMI/JfX0XEoh0ED11I8Q398a2vPIcP%0AfljHq60Xyf/gV2J3nuJ56zEUWtyTzO0VK3Ts5YdcrjqSnJcDiBi1mKRLtyl9ZxFq1xTXiCeDlvNy%0A5UFkSabwteXoc6YE78lWK4+bjCbuzE0EjZpcQfvs15h47BLB9fvZo/PNETHcKtwZ1ZeVSN59lHRj%0AvsV7UEd7W4ZjF3lZvw+5NowldNxKLFoXXM7sxNCsO9KdB/jc3We/J4ZNe4jpPpIsR5YR0XsK5qgk%0A5AlTsHbrjH7PVtRllOs3L1uJeexkPO4cQ8zoi2wyEZunAmJUDIsWLKRF8+b8HmRZJjExEVfXtAFx%0A/zQMBgM6nQ6LxUKTb5px+tQJh3JPT0+CgoKAFMWC1EoFqZULUuuVvq1c8GfJ0r95zgB7LMXbv7GS%0AJFGvXj1Onz79D43sfxb/qQH8k3iXG8CnevN6VwpUW8rWzxH992csq6nVB4xGIzqd7g8zd32M5Xb8%0A+AkcOnhYEf2XAcms+GW9vgHhlxDKDofXgfD8EGKBFlB5NpjikZy9YWpBSDbA0AdQujNcDUDOkAd6%0A50A+tQ6xZGPkoYcU0vvgFFLrkQjbfoKWviDJ0HE28rJQiAxC/KqVQlQBcX5/hBbd4eIJxO51oUtN%0A8PZFKvsN/BaKXLImYv6idqLKvZtIL58id0jxrVMtmo12YG87UTXt2IXlVShSRDQJnQcTXaw2MRWb%0AgiAQ2W4YMSN/JnbLaSzB4YhNm2HuOQhh+RrEC1cQ9E7oj+zD6eUT9M8forlxCQFwPbYV16eXcHt2%0AGZenFxG0Otx/W4FXxE3Sxd0lvfERmvy5cR3xPS4rZkHrpiRmzErYjnOg1RKycDfXS/bmrFN9LmZt%0AQ+yJm8gqkejd5zC9UoJwgkb/glNePweiChA6eysZB7d0IKqSxULc0aukG9Lafszpi1z4LRqCJEOG%0AxaOIuBHCubI/cKboQIIWHyDq9D2yDvkmzXMRuuoomUc4ujhoM/sgujrhVr0USRYNJ4oO5ma3JRhf%0ARRF/9yXx91+RpW+DNG29nL8Hlzx+FLy1Br+Fg7g5aAMnyo8n7t4r7o7eQsZutdJ8D62JRsK3niHn%0AurGY1E4cLjiIkJ2XAUV+68H038g2O+V+a308KHp7OU7li3B14CbcKxRIQ1QBXi7chz6zD+n6tOBC%0AtTGEbr+Q0meymXsDV+E7qhPpWn5F7r2zuDt6E/d/2IAsy8Tfe8nzVUfJtmEcAD7fNSbrwoFca/kT%0ArzadJvHBK57N203WjRMB8GhYmezrx3On52JerT2GZLFyq91PuLevh75gTrJsnIyuUC6uFOuD1ajs%0A8sSev0vw4t1kOrUG9w6NuFv2O8wRMfYxCioVvgOaYU00IqbzVHYp3sClemkyLhnJk3aTSbh0j8eN%0AR6HKlR2vdXPw2rGEyDGLSNyXQgycq5fBd+ZAnrQeT3KsEeeT2xEEAec185FUKqIbprjLOLesh2v3%0AFgTX6IY5wQpnLqL6ujbqIcMwNW2LFKOMUd2tE+o6NYmv/A1SfAKWjv3JmS49Rw4c/EOiCv8bvpc6%0AnY69e3YRGBiIWp1igIiJScDdXTEY2IinzbXgQ7JAGY1GB8WC1FmgbET398b1b8X7xhcTE/O3y1P+%0AX8K/S0zt/xA+ReanD9Eb/ZzSUu+DTcPV5vOk1WrtQV0fgr9K7O/evcv0WfPAowbEHUXI3BI5bMsb%0A4X8nJYr/8W6IvIVQpDtSlZ8QVmRGNiUinl+NrPOAij2R3TPCwiqQFIf4+ApSzclwYBjSN4ooP8s6%0AKG0N+xJcvBWx7d6roGwTiAyGRxeRxixXzj29C+lRIDy5jbh7I1Le8sp4fj4DmXOCJCEc24w0ZXXK%0AhUzrg/hNayQvb+X/509hDXqGKj4BU6tOWC5eRoqKRvD0wLh6D9bs+eHL9vBwIuw/jzVPfqXejk0I%0Aowegmv8zwpu5t4wZhSpvblTFUgTdTWMmoSlaCHWhFP3S5B+XInq5o6mS4qdouf0A8/NgvAd2RfT2%0AhDrVAHjtVx7XBeNxaqNk5JKiY4npNgzOXSP+aRTx38/H/DoKQatFliU8a5Qg/uwtXErlR9RqSLz+%0ACGNwOD6dUyx+ACFT16HJ4I1LBUfZn1dDFuBS+gs8OzXEs1NDJKORiCkruT9iPVajiZDlB1ENaIRz%0AbsWCF7rpJNZkM97Nqjq0YzEYiT15kzwnF+Hsnx/j/ecEd5zA0bx90fq4k7FFZXQZvBzqWJOSCZqz%0Ag+zrFO3UdO1q49WsGs/aTOCI/0hEjYoSox3VFABeLt2PxscTr2+q4fVNNcIXbedKu5/J2qIcOr90%0A6NJ7kq6ho1yXKIqka16FyF0XiDpxm0cj1pB7Uls7obfEGXg8aTN+AWPxbFQF/Re5uNl+OkljmpNj%0ASCOC5u9D1OnI0FchVW5VipPv/FIeVe6FMSSapKAI3L4sjVPBHPY+03Wog+jiRGDHSegzeeH6ZRmc%0ASxSwl3s0qqIQ1jZjCd96Dkt8Mtl/VmSiBI0avx0zCarVl6v+ffC/9BN3W07DrUdLdF/kQTtnGFJ4%0AFLf9u1P4/hrUznqscYk8bjMRp55tMe0+xqvGg/Db+VNKf+3qYXnyins1BiC6OpPuuUJOddXL4z5n%0ADCGthpHt0jq0+ZRrMN18CCoVVoMRLBZQqxGcnXDdt45Y/5rEjp+Px9g+SLHxGA+eQUZAUqfoSwt9%0A+iFevoSpRj20l08hiiKahT9hrPQVcdnL0KRBA34+fMSuAf2/jLdJV/bs2YmMjKBLl65s3boFsABK%0AoGtMTMx7DSEfmgXKRlBtWaDepyH7b/ZXhfeT1cjIyP9SrX5C/GdZ/Uz4VJZVmy+qTW8U+F290c8R%0AyPW+a3mf+sCHarja8FcIt9FopFmLjsiabBB3DHIORQ7fC2ig+FIwR4AxDjn6Gaj1yP79YWUuZEME%0AFO6OVG4ysmRCLtgAYWElCLoENUYjDXuBELgRsVxrcEsPW0bCw7OI7hmgw2rksp0Q3H2gtELUWNID%0AsWQNCDyL2MkfRrWAjLlgzG6kgBBAQiz3tUJUATb9BE4uUPkNUYuJgsBLSEVKwIxxiHUrQKu64OaO%0AZct+TGJGpO6TQBSRt9/Euv0GzNkMr54hFi0BNqIKiPOmoerZy05UAYStv6Ia4BhoI2/fiXaQY5Yn%0A8+IA9EN6ODxfiYMm4dK0jkJUbfO+9xjWBAP6prVT+vXyQLoUiNvUoXif2YL387OkT7iDfmgPJAkS%0Ag2O533gMF93rc7t8bx60nIDHlyVRuTtmXotYtof0w9qlkauK23EKr6Ep27+iXo/vhO9Ao8Nr3PdE%0AXgvmQpHe3Kg1mqgj13k2bgOZBzVH1Dg+gy+GLcWpYE6c/ZU50+fPTp7zK8i+YybJEXGE7ThH8M97%0A7JH8ACErD6PxcsezfkWH/nNtm4Iub1ZkQcWl4n2Jv/Y4ZczJZp5N2kSGCSkKAL7fNaHgrbWEnXjA%0AvfFbSN8rrQVXlmWeD12OZ6/mZD8fQPCKI1z7epzdL/T5rN/QZvLBs5Gi1ODdrja5ji7g8bTtBHaY%0Az6MJm8k8f4BDm04Fc5D/1hpCD9wg8sJDsv3yQ5p+vZpWI+OwdhhDY3Cu5p+m3KNRFTL/2JfXB67j%0A1qm+A0kR9Tqy7ZuD7OrC+ZxdkNVafH58Q2ZFEZ81U9AUyM0d/+5IFgvPu89E9E2Px5wxeB9fT+K5%0AQEL6THfoT1MkN7IkIcmikoHpDZy7t8Sla0uCq3TBGpdAzNx1xG7cj+r4WcRceUmokqJxq8qaGbed%0Aq0mYsQzDr3uJqNoWC05w5gnS6wjMfRU1AUEQEBYtRTJbMHVWLLHS9ZtoIqPp0qo1Kxb+/KeI6r/V%0ASvi+9VUQBFau/IUnT2wqDRZAxNPT8y9psf4ZDVkbkTWZTHZXAJuG7IdYZD8X3ndPX79+/R9Z/YT4%0Aj6z+Q/izltW3t8+1Wu0fbp/b+vnceq62FKwxMTGYzeZPpj7wZ65j0OARPH54H4wPQOMJz+eBNQGq%0AXYBbA0EWofgcBFFSFAECioEhAhrsgC8XI5z7QfFfXVgF+dUdxIJ1oNYECLmJ/OIyktYZBvjBgbmI%0ARRsgjbsPRRsgnFyI3GaykrL1yXUIPIp0bh/ikpFIfuUUZYBxe6BETTAa4MYRpA4pPpzilnnIPUbB%0A4zuwYgY0LAjJRsTZkxCOnEAq+KViuQ24grTuOoxZDmf2IFapAxneRONLEsKhbUjfDUqZkCcPkZ4/%0AReiYEq1u3bMbyZiEumGKI75581ZkqwVtw1opxy5cwxL2Gn27lIAdyWTCdPYyTgMco98TR/+E63dt%0AEVL5YicfPo0lJg6nlvVTrlMUsWzeh0u/rnhc2o1X+A287h3DXKUKxufhxJ0J5KpnPZ60HE/kpqNE%0AbT+JOSYer9aOWaailv2GoNXgUtvRChmzehcyMl4/dCPTmbVkDTqM0TcLN5tOJfHBS0R3Z+RUpFOS%0AJCI3Hsd3VKc0z1JUwF6cyvmTbul4nk7czPn8PYg8dA3ZauXZpE34DG2dpk7ipbskPw0hx8vDqKuW%0A43KloTwZEYCUbCYk4AgqFyfStXVUStBmy4D3d00Q3ZwJGhNA6PJ9Ds981G9nMUfG4TO5N/rCecj5%0AdDeGSCPnivQj+tRtns3eTqZFQx3adCnzBXlvrSd8/3VkUcDtHWRTk94TwcUJWaPhacPhWOMNDuWS%0AMZnXP29H16gmoWOWEz5no0O5LMvEbTmOmDMrkXM2EbvtmEO56OKE77TvscYnIXu4O5QJGg2+O+eD%0Auxs387Qlav9FPA6vBUDllwnvY+uIXb2byNlrADA9CSak4xhUU6Yg5s5NdPnmDuuoy6wf0JQowvPC%0ATQkfMQ9x3SbEHDkQ167HEhZJQoeU9LiaCqVwnjGKqE7DMRskpF0XwdMbAvYgb9+KdZ0yDsHFBdXm%0ArVj2Hybp296ILTuy9uefmT1z5r+SeH4M3nc9Pj4+xMXFMWrUaBS1APDz8+PMmTOftO93EVlbCu0/%0AmwzhcxDZ32s/MjKS9OnTv7f8P/w5/EdWPxP+imXV9mYZHx9PbGwskiTZxft1Ot0HLZSf0w3A5jcb%0AHx9vVx9wc3P7aPWBP6tLu379BlasWAVqb5BlMEaClAx+bRGOl1cyVtUKhNADyKZEeHURnDIjZioN%0AOevCrsbICWGIGjeotw2kZKTaM8BshIAGYDUhXt8HteYAMlJDxYePwz+BWgsunojjv4JhpcHNF4Yf%0ARZr1AmJDEEvUhMxvgl5WDEbMVQgKloJkI6wYhxQaBLOGQNsKCNvXgcEAU3cjbQ1FXngWwl8glqwK%0Afm+yTJlMcOkoUrdUJGXLCmS1GmqkkCFh3BDUdeshpH7TnzEN7XfdEVL5PlpnzkHft6uD9TVp6CSc%0AO3yD6JYS4GAYNwdNDj80JVPcByyhrzHfeYhTrxSlAYCEEbNx7dEGIZVOpiU4hOT7j9H3am8/ps6R%0AFUwmtEUL4RlxF+fda4nDhRdDl/Go2VhEZz1Ra/ZjSeXjGDF7A95DOqTJax89dSWegzvZfXrVPl5k%0AWDsNrf8XaArm4eXk9VzN2pKwJbuQkk2ELd4JWg3uqSykoPjJxu8+i8eoHri1rEOWl8fQtajHrWZT%0AuVisL7JFwqdn2qj7sPErcf6qPCpXZzIsHYPfmQBC1p/kfKHveDJqLemHt01TRzImEzJpNT6Lx+K7%0AfibPhizjYaspWBOSkCWJZ0OX496juf2FT3R2IvvVDehrVeRyjVGofb1wq14yTbtIMlaDEdE3PffK%0AdLf7C9sQuXIvUkIyGcPOY0owc798T4c5Dp+xAUGvJ93a2aTbtZSQUUsJnbHGXh7720kSL93B69wO%0A3FbMILjDeOJ2nUrp3mTm1bdTUbdogjU6gZC63zn0Lzrp8Vk5CdOLcIRsWVClT2cv0xTOj9fOZbwe%0As5jYDfsJrt8PoXoNNJ07od64AUusgdim39vPF0QR5/H9sLx6DX7ZEcsr91Pw8ES9bSfJOw+SNFdx%0AyZFNJsxb9oBGixwbp7gJAOQrBHNWYx0+FOmWks1KyJkLVbNmqPceZP/27dSs6fjS9KH4N1tWP2Rc%0AQ4cO4dq1lCxmderUZcKECb9T4+Mhy7LdNeCvJEMwGAwORNZqtX4yImubt3fNXURExH+W1U+I/8jq%0AP4Tfs6x+bArU1Pi73QCsVqt9qyY5ORm9Xo+np6dddupT4UPJanh4ON179AWv70CKB7UXOBcBqwGC%0A1iBLRig+Bx4tgtA9iNlbQ+WDkBSEVKgLrC0OQUeg2hyktoEIl8YjFm0Oz87AlCwQFwbNNyL1eQS3%0ANyEWqQMZC4DJAAenIkeHIMxpg6TyBa0eem2EAlXBmAB3jiC1GqMMVJIQTm9CylkE1fBGUMcb1s+C%0AvKWh/zpYG4NcoQWCd0Yo98YlwGJBuLQfqeOwlAtePhEhY1YoluJLKv4yE3oOAJvF3WRCvnAaevVW%0AfMbi4pCuXsX64B5C0cJYT53Bcugo5nUbsTx8hODrg2n3YUz7jpK8+zCWqzdQVSmL9UkQUmQ0ssWC%0AKWArTkNSAoAA4gdOQl+tHKqsme3HpPvOnSIAACAASURBVIgozLfuo/++veO5AybjVKsKqswZHI6b%0ANu5CO1jZbtVWKoPbhkW4nN+j+Bo2rE/4zE3c9mvE48o9CZ34C8kvX+PR2VEmyHjzAclBIbh1c0xt%0AKsUlYLwYiMfm+fiEXEI/qi/BkzdwJVNzXo5fg+/QtmlIb9iklagypENfrbQyt6JIuqkD8As6QuKT%0AUMxxiYSNXo6UlKL3anwQROzRK/guHmk/pi9eAL8n+yBHNiwJSVhDY5DNFoe+IlbsQe3mgluberg0%0ArE6W+3uIu/WCa4W/JXjaJixRCfhMdCR6AD6TeiGrVCSHRhM+dXWa70n46KVoixUi/b2DCDmzc8+/%0AM0m3lW1da4KB4KE/4zp9KCqdjnTXdyJ7enKvdHdML19jCg4nZPpa3FfPUK6jejl89q0gbOIqQib9%0AotT/djr6Mf0R3d3Qt2qI28KJvGg9ivgD5wCInLQS2SyjX/ojzkd3YLx2n7DWKS9XsiQR9d1EVKVL%0AI4XHENPZMRWlrlo5PH6ZTki3CZhjk1CvXgWA4OGOZtdvJJ+4QOwPyvikqBhiGveE2k3hdQSWsaPt%0A7Qi5c6NetQbDqBmYjp3B0LY3locv4OALxCw5EVqkkj37uhFC135YmzZGio9HmDoZ31MnOXv4MHny%0A5PnbSdC/Gblz5yYmJoZKlaoCMrNmzaJWrVp/WO+v4o+I9J9JhmCL8zAYDPZ7+C4N2Q+9h3+Uveo/%0Ay+qnw3/SVZ8RyckpP2iSJBEbG2vPbPJ2EJJGo0Gv16NSqT7qTdzW3qeU/bCN1Wg0YrFYUKvVSJL0%0Al6SlPhSxsbF/SNZlWaZu/eacvKLGGnsE9HnAdyi86AoaH9BmBTkEUdAgJT5DyN4OueRyhKPFkWPv%0AKakG1e6IXtmRWp6DsMuwqQJoXRDUemRJRvRvh1RrNsS9gvl5od8BhLsHkQ/OUup/PR4q94dt3yOG%0AXUUa+yYae0U3xIi7SBMPwaXdsGECBN1F9PFDylcNSjWFRS1heRB4KAuc2C0rUteJUKeT0kbAJIRD%0AAcjb7ivuBIBY1w9p0DRo1A6sVrhwHLp9DSOnQEIcTi9fwI0rJN27jZOvL8nR0ai1WvRubohqFb4Z%0AM6LT6RVLvSwTGxuNb8aMmC0WrBYrRmMS4aFh6J2dSIyLwxAXT1JsHAgCbjmyos3uB5l9MWfxJWHZ%0ARpz7dcKpUzNUWTMhiCIxHQfDq3A8D6WIUEuSxOt0JXDfsQxtak3YX/cQ33MEXq+uIaSSQYtv1wci%0AY3DatV6pHx5B8sz5mH9ZByYT7nUq4d6rGS5flkFQqQiq0QN1rmz4LB/v8HyEfzsO891neJ3a7HA8%0AbuRMDHNXIWrUZBrfDe8ejRF1irX5dpaGeM0YhFvb+g51YhZuIGbKcty2L8XQpg9yYiLZlw/Do14F%0AgjpPwfAsnCzHVjg+n5LE87wNEKtXwLr/OBpvN3JumYg+XzYkk5lAvyZ4TemPe7dmb417LPErd+BS%0AqzzZ9sxL89yH9ZpK4uUHOC2cSHzt9njUKoPfypGIeh3G+8954N8R31t7UedS5Lyie47BuP43cu+a%0ATsKhy0RuPkH6B4cc2oyq3x3z5Zs4FcqJySLic3K9Q3ny2atEfN0FfZ4sWJMseN5z3Po3LttI/MDx%0AZJzVl9CBc3A+uA11acUFQXrynISKdXBr+TXpfx5F7IL1RI9bhHDzHkJwMOaa1XHp0x73SSluLEnb%0A9hPbYRCyqEF39hRitqz2MunqNZLrNcBj/liMq7ZgjpOQtl2CwCvQuhqqH39E1SIlyE1avhTLhHEI%0AeifknfeVrf+YSGhcBGo1gCkL35woIXZqALevkTNTRg7u2GEnH28HCaWWbwLSBAnZ/mwShv+2gCwb%0AiXN2dv7jk1NhwYIFjBgxAlBiJ4KDgz/52JKTkxEEAe071C8+Bn/1Hqb+PbZYLJjNZpycnNK0P2LE%0ACNq0aUP58uXTlP2H38U7Cc9/agCfEamtg7bPkiTZrZKyLNvfCj+VVfJTugHYtvpti4dOp8PV1RWr%0A1UpiYuIn6eN9+JDrWLFiJceO7Aa0IGrAvRG86A7eDSDjQLhdCQQ1klMhELXIhafBrdHIUTcRvYsh%0AFV8Cp2ogVZsPIedgex3QuEDhAchZvoS9XyNVfBOAsrU1yBLMqYXglRvUzsh1J0P5HooF9OYmpO8U%0AQXJMyXBlC7JXJmjtg+iWDskQD52WIFVT0qAK02sgVG6J9IaocmGnck6NlB9ZcfdSpJ5vCNjLp7B/%0AA1LYS1wObUFcOYOkp49wcnPDLU9eyjy6Sd6sWclRpSwZWzTCw8ODHDly4O3t/ae0fVMv4JIkYbVa%0AsVqtREVF8fr1a8LDwwkLCyMkNIRDRYpiOXaVZ8s2Ex0Vg2vuHCS/eImuThWSD5xEXeILVOnTYfhx%0AOaKXp4OyAIBx4jyc+nZ1IKqSJGHZfwyn9UtT5sHXB93oQZiWr0GzYT3xa9eR2HYUMjKenRthuBCI%0A34JRDm1LkoRh2xHcA2bxNswHz6Dr3hGhRFHCRk0idOJKMk3pieCkRTKacG3xdZo68bNW4TSyN9oy%0AxdE+OkXC1IU8bTMB1zIFiTtzk+zXNqWpk7j3FFJsAu5LlYChhNZ9uOvfBb8ZvRB0GkSNNg1RBXCu%0AXYnEXw9hOH2N0K4T8P15uJ1Mm1+EEr1qJ16XdqP+Ih9e944RV64Rj8p/S879PxE6ZAG6auXsRBXA%0Aa/EE4nJl5VHdIciyjM+h1Wn69N69jIh63Yg/dh6PeWPSlOsqlMBr2SSiOg9H1y6tG4S+eytkUzIh%0A/SejqljWTlQBxFzZcTmynfiqDZBMZhI37kX1yxpEvR7y5EG9ZQeJTRqg8suIS8+2WINeEttpCPLY%0AOYg3rmD+shaaa5cQ37yAiyX80S5fSmzX7gguLsgnFC1QipSE2auxDuqIkDc/ov+bMSQmggyyqAXn%0ANy/xnulg6QFoUx5KloOmyk6AOrMfXkGPOLhjB15eXlitVgfZpnfFC7xNfGy7T6l3uIxG40frj35K%0A/NXfiN69e9OhQwf8/PyIi4vD3d39LwVe/ROwWWTfvoe2ufi9e2i7dzYrrNVqTXMPIyIi/lbLalRU%0AFC1btuT58+fkyJGDzZs3v1MqKyYmhm7dunH79m0EQeCXX36hXLm02en+7fjPsvoZkfpht1gsdk1U%0Am06dbaviU8IW7PQxVk+bFdVm8X17rFarlfj4+L9VUy4+Ph6dTvfet+s7d+5QqnQlJKsGBAuIzmB9%0ADbpsUGAf3KoAKmfIswHxSWukLE0QYi4hx9yFvH2g8BSE07XAGobgng3p2WHFUtr+OejTIf5aBLlg%0AfeSi7RCOjER+fAgy+EP1BRBxC070g7GvlHSte0Yi3N2K3Hc74onlSEcWK1vyeWvB1+Mg/B5s+Rbm%0AhYBGB4kxMCALzL4MWQsCIPYrgly1CXKXCRDxCg6shaXDcCleDvOTe7i4uJA7X36y+/pQr05t8uXL%0AR968eT+bcHbqRTr1gm6zUiQlJfH06VMOHTpETGICFwNvcu/GTUQXZ4xJSajKlcBlTF/UJQojaDRY%0Anr4gqtCXeD05j5ghZYFPmrMM49wVuD646PDdMHw/BPnWIzQH9tqPWbbtwDJ0CHJsHC6VSuA+tAtO%0ANcsrFt4F64mZvpL0z085bPVbgkN4nfdL3G6dRsyqBKglr1iLddJMzFGxuDarhW/AFEcVhH0nCWs1%0AFJ+QywjOKRYVKS6e6MJfIUXGkH5KHzx6p6SvlWWZFyVaIVQpj9vccfY6yXuPkthhAJboOLwn9sZr%0AhKNrhSxJBBdoAE0bo/u2I0k1GqF21eG3Zy6abJkI7Toew71gPM5sSxmHxUJ8zbZYbt5BTjbj++w4%0Aah/vNPcwsnYXko5fwGv6UFz6dXQokyWJ10UbYHb1Rg68ideicbh0SCGlsiwTUaElJpyQbwXi1Lcj%0AbpMdt++TFqwiYeQsZIuEy9FtqP0dJccsF6+SWL0h5C+A9uRZhzLp6GEsHdvjuWYWhqmLMbtlQl61%0AG6xWxK6NEV48Rn3pnP2l3rp9B6ZevZXv7P5AyJxCzoWfp8CKH1Gdv4h8+iTWvn1gzkGEeYNALSCv%0AS9X34e3wQ3vYchzt+mXkf3CDPVt+xc3NTRnXWy5VqeWWAIfPb8Om5CJJkn03KrVl712yTbbPfzeR%0A/T0L4YdAlmWyZMlKQkIcIPDiRdAn22kzGo12Pdd/Gm8nQzCbzfZ7J0kSAQEB7Nixg1y5cvHq1Su6%0AdOlC8eLFyZ0795+2Wv8Rhg4dio+PD0OHDmX69OlER0czbdq0NOd17NiRqlWr0qVLl0/CBz4D3vmw%0A/0dWPyNMJhNGoxGj0Wh/4F1cXD759kZq/FUiaVtYbWO1ZZh610L8tkvD34GEhAQ7UU49RovFQkJC%0AAvkLlCQhKQ+SZADLXdDkBusj8KwDUXtA7QL+T+DFaAidByo9aPyABKjzFGKuw4kqIAgI6asjJD5C%0ALtAWudR4CD4Ku2si+JVFDr0BggYxe3WkRtsBEFfkQqrSF6r0h6Q4mJoDLIoIupChKPLre9B0Efgr%0AmaXEmQWRK7VHbqBsn7G0I2L8C6SJR5WAsNsnYcxX6ErWQPXyAXJCLIWKFiN/Nj9aNG9GsWLF8PX1%0A/dvm+mNhW8htVtjUkbnPnz9n165dBIWFcvriBUKePce1bAniwsIRfdLhenCDneABxOatiLpfD3Q9%0AHVUH4rMURr1gPqo6KRJZkiRhzpMfadw0OHUc1eE9CM46PAZ3Jm7OWpwGdcOlt2P2qegmPZElFfpf%0Af3E4brlyg8QqDRDdXdD4+ZLu51E4VSwBwMui36BqVBuXiYMc6khx8URmLoMwaAjC4oVoMqUjw7op%0A6L7Ig+HkFV416Iv362tpxPyTVv1KfC9l2z7Dhpk4f50S5JW44wivu43FOfi23f/c2LQj1jPnyTB7%0AIKF9puN14wDqvDnT3IfInBWxvgwj3bYFONV3TENrvv2Q8DLfIMxbBAP74tqzNW7TBttJUeKaHcQN%0AnIZw+wEcPIClRzc8Zw7B9U3wnGHTHmK+G4t07THCvTvIzeo5EFbry1CiClRHnrse4e5NWDob51M7%0AURdI0e41/bQI4/QFyAYDqjlzHbbqAaRNG7EM6o/g6oJ8/kVKgoAkA0KTyohermj37Ua6f5/k6l/B%0A8CWI104gn9mLfOQB2IiXLCMOaIt86QRybAyMXAVftYCYCGhXFKo3hHGL7f2KC8YgBcylUIF87Nq8%0ACQ8PD7sl1WZNS70zlprAvA0b0bTNq8Visa+nqfEu/dH3Edm3rbGfgsja/DU/1j1hwIABrFihuL9c%0AunSJ/Pnz/0GNP8b7MkT9G2Bz7dPpdMoLXEQEt27d4vHjx6xdu5YsWbLw6NEjnj59io+PD3nz5mXC%0AhAlUqlTpo/suUKAAJ06cIEOGDISGhlKtWjXu3bvncE5sbCz+/v6ppMf+J/AfWf2nER0dbU8tqtFo%0AiI+P/9NpSv8s/iyRTJ1oQK1W28f6ewuiLMtER0fj5eX1t1kAEhMT7YkPbK4TRqMRQRD48af5zFtw%0AlqQkL7DuRXAfD8YAZNNjEF0AI+ReAYbb8GoaglM25FwbEB7URi4+FyE5HDlwFDhnhrK/QcIDuNYR%0A2gdD9F3YXQtkM2T+GkpMht2loO1F8PkCHmyHg52h+z7ESyuQLq1VFAHKDoSKw+HGajg1Dka/AJVa%0A0WtdVA3mBIOLlxJo1ccHuWJznKxGuHEErWAle/bsdGzVgooVK1KoUKHflSf7t8FmcU1OTsZqtaLV%0Aau0awKmJrCRJREZGcuHCBVavX8+dx4+Ijo5C91UVrHVrIKTzIr75t7gH3URIpUSQvHI9yWOmo7t3%0A24HYWlYHYJk0Fa49QFCpFCvY6uWIC2YhhYbh0q0lLqN7o8qipFCVTCbCfUrhvHcT6jIlHK4hsVpD%0A5PzFsE6aBSMGIWzbgEvV0rj0aEZYiyH4PD+D6OsY6WuYtQTj4o2IF68hWSxI3/dE3rsL7wHtMZ64%0AgiVnDjwCfnKoI0sSUbkrI7frDDo98rTJeHZvitf0gaBRE1ywITSoh9PkkQ71kucuIXnMVMQM6fF+%0AejrN98509AyxTXogDxuLMHk0HlMH49o3hahH1uxEstYT9Zr1SA/uI9X/Gue61fD4ZQpysonQbFUR%0ARk9A1UGxuNosnW5jv8etV1tCc1RHGjwasbNiCZavX3UgrHH1O5McaUbedBQAYcYoWLcU1/P7EHNm%0Ax/rgMQlla8Ki3ZAQB4Pbol4VgPhVSpS9dOkiliYNlaQZB65CtlwpFxj5GuqUQlW+NPLVa0jFqsO4%0AVWCxIPb+GuIjkHZdVSTkAB7dhTpFlSxyvz1PaefJbehaFkbMhW+6giyjnTmIdKd2cf7YETw8PBye%0AVxt5tJFGG4G1/aX+DrxNZG3qLrao9reJ7O9ZZN/lV2lr80N8K/8In4qsAhw5coQmTRQr/Ny5c+nc%0AufMf1Ph92NLA/hvXwPf508qyTJ06dTh9+rQ9sOvFixc8fPiQggUL4ufn99F9e3l5ER0dbe/P29vb%0A/n8brl+/To8ePShUqBA3btygZMmSzJ0795NbeT8x/iOr/zTe9lv6o63tT4EPIZK2RTQ5ORmLxWK3%0Aov6ZxSEqKupvJasGg8FuuTCZTHYr661bt6hUqTpWyReIBLeZYDwG5j2Irq2RLKHAfUQEJONT8KwJ%0ABfbCo24QuQ5B54NsNYDVCF8/BZ0v4uFcSBnKIBpfI4WcBVTQ7DE4Z4AjDRG1MlLjXWAxworckPga%0ANDoE37IQeRO5xlTwVwTfxYU5kaoOgkqK8L6woAJCruJIFTsh3tyF/upWksOeUbnGV9StXpmKFSuS%0AJ0+eTxJc97mR+jkCPuhFJ3VdSZIICgriwMED/Hb4MGcOHwY3NzTD+6H5pj6in6IykFi4EkKHDqjf%0ACLfbYCpRGql9V4SefR0br1sdyccPVWgQ0sNbOH9TG6eR35O0aivJ+07hcvmIw+lSaDjx+csiHL+I%0AkEMhSFJ0FML3XZFPHEFdIA+eZ7YiptIMlU0mIjOVRpgyE1XzFiltXbuG3L4lUlQU7vsC0FV31INN%0A3r6f+G9/QLz7WLGcPnwIzRuhcnfCtXtTYiYswfnl7bTpWh8+IaFUDdDpcKpRAde1c+wuCbIsE1O8%0ANuaSVRCn/oh09iRi55a4tGmI+/zRmE5fJqJBD1Q37yG6K9cghYUhf1kFbdG8aL7Ii+G344gXrzn0%0AKZ09g6VVczR5syElWpBPOZbbCKumelksJy4gn34CHp62G4w4bgDyzo24XjxAUvMuWHzzwrwtSvmW%0AFTC5P+odOxBLlkaOj8dSrjRyrfaIyUnIhzchHw1UgqFsePoQahYHdy84GJpyPCEOoV1J5PyFYMkO%0AiI9DaOCPnPkLeHgZqjaGYYtSzj+1C8a0hpXHUJ/eS9ajWzh5YB/e3mldJ2zz+zaBtf3/bcJou282%0AMqhWq+3GCdtvwdu/wTYC+nuZoN4eyx8FCb3LtSA1bML7f8af/fdgtVrx9vZGlmUGDx7MmDFp/Z4/%0AFImJifb18N+G97ko2Mjqx+rQ1qxZk9DQ0DTHJ0+eTMeOHR3Iqbe3N1FRUQ7nXb58mfLly3P27FlK%0Aly5N//79cXd3/9vlxj4S/wVY/dN4W0bqc2SXSq1R+vYC9XbAlF6vx9XV9S8RJNu1fOoFJXUWE0mS%0AcHJysm/LGY1GmnzTBqtVBF6DOjeCYTGy9TF4LkDSFIfwskpQlb4mCMGQ42eIvwSRa0DlhJyuO2L0%0AeuRczZF1vnB3PFL8UzC+RvKpj+jsh5ynPbJzBjBGQMgRpIZbEU7/gHxlAUgWKD4MSo5EfrIVzvaF%0AIm90Rh/uRUqMhDJdwJIMd3YjB11GeHmNLM+O0bheber3+JFixYo5+C5/SsmvzwGbpdtkMqFSqXBy%0AcvrTRNsW7JAzZ0569uhJzx49SUxM5Pjx42zYsYMDU35CzJcHY7WKWF68RN/eUQpLun4D66tXCK0d%0At/ml1+Fw5xbs34g1a0549gjDmJ4YSjUCjQbd0LeILZA0aDSqilWQc6RY8kQvb6T5y8A/P1aDlcgc%0AFXGdMxZ9+28QRBHjhp0Ier0DUQUQ/f2x+pdCvnaDuPpdcOnXBadx/d+kmpUxjJwFbTqkaKfmzYt0%0A9RbmLp2IHDgTdf2v3/k8mMdNRyhVEXnhRkxNKxFdoh4e+1ejypEV067DWF+Ewl5FzkmsUAXp8HkM%0ADapjefwcy8swaNHGTlQBxAwZkM5fwVS9IkmHz6LauDlNn2KFiqjmLcDcqyc0aJxG91AoXgICNmNu%0AVg/KVk0hqsoNRhr3E2KSgXj/Ggg6Lay9klLerCtCTBTWpk3h0BH4aRaCixdyv+lIkoT4+iVCvTJI%0Ax+6A7eX+zFHQO0F8HOzfALXfJGdwdUdedATa+MP0YYi3r4LODXncbxB0B/qXh/z+0PiNf3DlBggd%0ARyB3+wofHx8OHzn0XqKqXMr7A3NSE0aLxWIngbZ6tuNvE9rUdW1t2T5brdY0/acmsn8mSMjWbur6%0AoijaA4Q+lQ6sSqUiNjaWVq1aMWvWLE6ePMnhw4f/cnv/1pf2982X0Wj8JFbqQ4cOvbfMtv2fMWNG%0AQkJC3uka5ufnh5+fH6VLK9J7zZo1e6df6/8C/rd+Ff8/w+cQ7H+7Hxv5S0hIcEg04OHh8cGJBv6o%0Aj08Bm9ZsTEwMRqPRrqGXWr+1ceMWhIa8Br4DTGB5imx5gKgrCaos8LoqaPKB71lE6T6Cb0fE0Jlw%0AqzI4F4bir8CpAJIpFDlDA8Tz9eHhTPCpA1VfQub2SMYw5EL9lUEda6aQzt+aIDw8iKDxRCg+EMpO%0ABLUe8cpYhEojlCArQDw2DPxK4Ly9O7qJGSl8+0c6d2zPxbOnuXLmOCOHD6VYsWLKuaksMP+WNIJ/%0ABKvVisFgICEhwe5/bZMX+xQ/Li4uLtSrV4+1y5bx8slTVg4bQcWb99GoVGg7d8Xy6xZkg5JtyTxy%0AFKqmrRA83vLNHj0UsUwVyPrGpzNHHuSAw8jDZiEnJZM85UeS6rXGej0QAMlsxnrgOFLfwWkHNHIw%0AYplqyPsfIQ+dQ+KgycSUrIf58k2Sxs9B7tojTRX56ROsx47ClnPIm85gCPiN6CJfY75xB9PBk1hD%0AwxFHjnaoI4oiYtt24OSE9dhpkr8bjGw02sut9x+RvOcg8qwV4OmF9VAg1pyFiSpeF9PRMyQOmIDU%0AoTtiKh8/MWt2rKdvYnzwAsuzYFT9HNOuAkp0fRF/cHJG/mEYcnh42jlY+QvkLwH79yP98I42DuxV%0AMqnduARzJzkWCgLS98PAmIRskZQMbqnnqtsQaPktlppfYtm9C2nhIduEIE1eD96ZERtVUFKs3r0J%0Ak4bCsA0wdA1M+hYCL6Q0likbLDwAq+cjBV5Dmn1OcQnIURhGbII5A+FGitVLzpILJ42G3zZtIGPG%0AjGmv+wOQ2jBgSweq0+ns6bBdXV3tL6O2FzyDwWBPR22TApQkCVEU0Wq1aDQa+782lwPAIULd9meT%0AxUrt52qz+tmyQdn0R52cnNBqtahUKodgobezQX1sWtONGzeyceNGLl68SKtWrf64wjvwb02kAO8f%0A2+dICNCwYUNWr1aUPFavXk3jxo3TnJMxY0ayZs3KgwcPADh8+DBffPHF3zquvwv/uQF8RtgWBBuM%0ARiNWqxUXF5ffqfXxiIuLs/t6ppbI0mq1n8yKFxcX99H+t29rzdpS7qnV6jR6sWfPnqV27VaYTIeA%0Ayig7B32BGaAtBqYrIHpCpmeQuA5ieoKgB3UmsIZAsVugz41wLYuSKMBqBFUGEJKgWhCIGsTT+ZDz%0AtEP2KoJwfRxy7EPIUBOKz4ekEDhVA9oHgT4dBB2EQ02h3wsIPofTvbUk3dxCsVJl6dSqKQ0bNsDX%0A19d+HSqVyv7Dldoa8y7fuNR+cbbP/9TinfoeSZJkd2P5nONJSkpi9+7dLF67lmuXL6OqVxfDth0I%0AB04i5CtgP0+yWBAK5UBetA3KVXNoQ6xZELlBJ+RmPWF8V4Sz+9FULofskw7r5VvIxx3VBySTCeGL%0AnMiLd0GpyspBiwVGdYX9mxHUKlTXbiF6OVrkpH59sN5/irzxxJsDEozsjrB7I4KbC3K9xmhmznao%0AI8sy1qqVkPxrQKdBiJ2rIrhocNoWgCp3DoytumOKSkYO2OtQj8Wz4aexCHod8p3gNN9t2WyG0l8g%0Aq3QI5kTUv+1ByJcS8CRdv4alQV3Y8QBxTAfk5/dQ792PkC27Un7wANYe3ZH3BMOrZ9C9CjRohDh7%0AgdL+3dvIdavBsotKEoy+NaH3cPh+mO3CEJtXRxLdlLEF3UHaexdSW6AiwqBadiWhxt4gcE2VnjU+%0AFqFjGcicBZ4/QvavB32V7Xxhy0zYMAV54w2FqAIc2Pj/2DvrMKnK941/3rOzs013d5d0KI0ICCpS%0AkiKCIIg0SHeJ0gjIlxBBUrokpEE6VLq7Ntiu8/7+OHtmz8zOJrvLrr+9r4sLmPfMiZk577nf57mf%0A+4EJX0K4Ct8fghJVLLsSG3+A1ZOQv/0N96/hPqYd+3dso0yZMiQExvtCT6fHVwIT0xwQX32sLWLT%0Ax+rpbJPJZFcbazwXe9rY2Aq9nj17RnBwMPny5Yt2m+g+G39/f9zc3FIkYfX397fbAOf8+fOsXbuW%0A+fPnJ9mxPT09adOmDffv37eyrnr8+DHdu3dnx44dAFy8eJEvv/ySkJAQChcuzLJly9LcANIQM2zJ%0AalIY9ttCr5ZXVdXSaCApLLLeRH9r6zxgj0jrXrQeHh74+/tTpkw1njxpBKwA8gEHEUoFpOqLEOWR%0AXIZMP0P4a/DpCw4ekG4mSsB0yFgbNddIuNUZfPaDR23Iswxxqxqy+HTI3Rkeb4CLbcApI0I4IFVn%0AlEzFUd/Voj3Kn5Ugz3uoNWdpD+ENZZHBXjgpYRQoUIAvO7WlZctPyJYtm1WxkR4piYtcIjprKD2N%0AZ/vw0lPvSTGp699RcHCwVdTncQIEwAAAIABJREFUbT9AHj9+zOIlS/hp6TLIlg3/Lt3h07YIN3fU%0A2TMQq39F7o9sogDAvxegVS3Y+xDSR5BLb08Y1w2O70aULAML/ocoWNjyFnXSaMTuPchtl633BfBR%0ABXj2CNQwHL7/AaXlp1qE7dkzQiuWg02noZhNNOO3RTChH6JYMUzLVyIKRFbzqwf/JLxrF+Sh51rK%0AW1URg9rCsV04jRxE0LjpcOgqZM9lvc+wMKhWAHx9cGjZBnX6bCvPWrl8CWLmdNSdj2D8l3BgPaY1%0A61Cq19AIyfsNCM9ZAib/CoAY1BrOHsC0bRcULEhY5QrIFj2he0Qk+M5V6PYuNG6MmL0I8UFt1GzF%0AYMIabfziMejXGPqPhq8GwLoViIlDkOsfgRAogxuDz1PU7X9rlf5SovRohnzpjXDPBI+uoG64Epn2%0AB3jxBD4uAk4usM7QNlZKlHm94OQ21M034OFN6FoTuv8P8eIubJuG/PlfyJQjcvsfuiLP78MpPJjf%0AV/1CnTp1Yvil2YdRp617Tyfm/BpffazRsQCwW+Rlu39FUQgJCcFkMmEymd5qoZct9AxbUgd0EoKY%0AiPTevXu5dOkSY8eOfTsnl7qRpll927D9QSeVZtVILPRJzcXFJcEeenFBQmQAts4DemTW3uRm3P+g%0AQSN58uQBsAhwivi7GlL1B35Cym1georiOwE15B8wlYDsf0PwbtTgqwj5HpwrCMIEeZdD5o7wdCIo%0AzpC1GeLWBOSNKeCcC3J/h8zaEc7kQS01WTsZn39Qva9AvSWIC9Nwvb0cGeZFt27t6f5FVwoXLmwl%0AtwAwm824urrGW8dpz67F9mGhL4KSIhqr99kODQ3FZDLh6uqaoixkcuXKxdjRoxk9ciR//vknMxcv%0A5sSUcciWbQjdsRX123FRyeWE/ijNOqCmN0RBM2SCRq3gzGEwZUTWq47Sqh3q0JGIrNkRa1Yhh8+J%0Auq9Lp+H+Ldj5DHb/ijp4ICxfijJnHnL5MpTCJVBtiSqgbF+LWudTCPYntHYtTNO+R7Rrr/3OJ4xD%0ANvkskqQpCvLH9fD7MoLG9YaMmSGzHeuyjStRhIK6/gayRw1EyybIX9YhMmZC+vsjp4xFDpqrbTt6%0ACeQpTFjrlpjm/wQmR9Rbt+Cno5bdyRnrYWJPQps0wqFZcwQOyO4GyULBErD8BHxRC9moFjx6BHNP%0ARo6XrwU/7oQBTSHAD36ehRywCJy0SKo6bSfi2zooraqg/n4Wtq1Gnj+BXHoPaTKjDK+P0qUa6qqz%0AkVX9pw9orhpBwbB6ErSPcEkQAvXr+ShP7yI6vIP094V3u0DNtto98fgKom9V1KU3tc9VCNRO4+DA%0AKkZPmBBvomq7eEuITjsuiKs+Vp8D9IyHUc9qj8ga32ucO3S7OePxbfWx0X0ecTHRT0mNEBIL9q7h%0A1atXSS4D+P+GtMhqMkKf4HQktkGvqqqWPse6zZOjoyOBgYEWwppU8Pf3t0zaMSGhzgP6Z7V79266%0AdOmFECWAR0iZA7gNhAM7gcxARcCEEK2RbISse8BcFZ7mhrCXKE7FUEU5BOeRJa6BBPFvNqRLXvC/%0ADoo7qEFQ7RE4uMPVDijiEWrtg6CGIg7VQnpfxNnZlRYtPqJHt05Ur17dUkBhLDYym81JEsmODrFF%0AY+09wGyjsdFZT6WWwq+HDx+ycMkS5i74CXPF6gR89Z0mAxACXntDjTyw5qxGtgxQPiqBbNwV2X4o%0A3L+GMrkj6t1/EbXrIs+cgcOPIr0+9ff0aoEa7gDTNM9dggIQI1ojz/2peeb+vB1qWnuc8s95aFcb%0Atj7W0tz7NyCmd8ehZk1o157wPl8jDz6zTo8D3PwX2lQG9/SIfPmQP/8OWbNrY8HBUKMgdBsPLXtC%0ASAjK17WRrx7Aum2I7Zvht9Wom25a73PPWpjYTWta0WkQfGWtnwVgWj9YvwA+/BxGLo46fvU8dKyk%0AFS0tPxt1/Oyf0L+JZhu1+pb1mJ8PondNpLsr3LkGvX6Ceh20sYDXiAHVIEcu5ML98PA2tCsPXy6G%0ALPlg8vvw7WJo0CFyf4F+0C4HODjC/ww2PmGhKBPqggxFnXsKgvxxHVKHfm2bM2KodTODmGAkqbqU%0AJyUt3sB+ww7jH50k6gRT17Tqc0BiyApszyU6aYGRtNqTFiS0DWxyIKao7/z58ylUqBBt2rSx8840%0AxAK7D8vU8fT5jyApIqs6+fP19cXHxwcppUXQr2sJk6OQy5h6sgedSPv4+BAYGIjZbCZDhgy4urrG%0AKSUuhMDPz4/evQcjxACkrISU3sB9wBVFaQ7cBWoApYG/QDxEcW0I4c/hcW4ID4QMq1EzXEKE7kXm%0Amg2hj+BmdWSoF0qYgNx7UEweiHzDNaIaFgBe21AL9sLhykhc9uUnl7sPUyeN5/6d6yxbsoDq1atb%0AWs7aFhsld6pcj8aazWacnZ1xc3PDw8PDboFHcHAwfn5+vH79Gl9fX0txha+vLwEBATg4OODh4YGz%0As3OqIaqgRVtHDhvGjb8vM+HjpuQe1wv3jyvD1t9g3LcoZatGIar8cwb1yQNk84giqXzFUReehmm7%0AkIcOQnAQbP1V05zquH8L9dg+GGKwQXJ2Rf6wAxp3BikQY7+BKxetDqXMGQtVGkbqMRu0Qq6/g3rv%0AGWFdOiIr14lKVAFl7kioUBd+vQfSBRqVh3MRkcw1S1AcnTWiCmA2oy45iaz0PvKDOqhzfkA12jXp%0AaNwW2n6jkdvXntbXpx/39SvIWgB2/QYLx0Yd37gQkbs4PHsMQ6O2XiUoQCOPz5/Ajv9Zj7mnR84+%0ABNf/0QoT6xmIp2s65JSDyDvXYGgbxOCWUKYB1PoMiteC3r/C7K/g8mHLW8SmWQizCyhm+Lln5L5M%0AjqiDtyM9n8Hkdjh/34EPKpZg+BA7hXR2oM9fvr6+ljoDvZgwpUEnfsbCKn0e0FPWesbN0dERVVUt%0A971e6KVHV/UFt22hl/5M0SOxMRV66Yvi6Aq9HB0dLc+P0NBQAgMDLXORrgFOiYWnMRV+pUVWEx8p%0A7077j8NIHPV/J6Ta0dYY38nJKVrbKT3il5SI7hh60YHujZrQanEhBKNGTSQsrAFSBgG/IsRHSFkH%0A+BYpzwNrgfTAQeA6Uj2CDMoKAYeAcEi/EFzagfeXSAd3lNcbUG+3BEyQay1q+lbwehNq6EvI2Qdk%0AOFzvAmG+uP7zNW3btqHXsi2WakpjlAUSlupPLsTk2ahHL0JDQy3bSCkt31tcorEpAfp1hIWF4ejo%0ASObMmenZ8yt69OjOnj17mDhzNhfOnkVt3lkjn06RhFBM74f4oDOqh22nN6H96TARMX0I/G8Gcvwi%0AqFQL5edpyBIVkZltqsdDgmH/Oui/FHn+D2hdC9G1H/Kb0fDwDurR/bDRJsKYLgNq/7nwdV04sQ8x%0AZyTy67GRkdyb/6Ie3QMrb4HZjPzhICwdBe0bIwaNQ86ZjNp/TtQPZeQyePUUzh1EPLiBrN7Ietzv%0ANaz/CTpMQv4+BeXlE9SJK0HXul45h7rvd5h3BTyfwJhG4P8aBv6ojV+7gLrzV5hxCUyO8F11xOCP%0AkN9v0cYD/WHiF/DJKMhdCma304qnGhlI6fFt2muYYHp7GLI6cixjdph2BPqURwoFRp+JHKv6CcJz%0AMnJ0c5h7Gl4+Qq6dCn3/BEdXmFEDcpWAZhFuHu4ZkSMPwJBy5CtWlCVb/4z1N6wv6vRW025ubinS%0A7zM26POw3pTGns7SVh+rk8Po9LG6vjU6fayt9ZatfyxEygNsoe9Dtyw02m69SaFXYiKm5/bLly/J%0AmjWr3bE0JAxpMoBkhm1jAC8vL4tvaFwQFhZGUFCQZfKMi6DfWJyUVDAWi9lqZp2dnd/YP/Tw4cM0%0AafIxqpodKR9rKX45EygEhCFEa2A7kukgGwLvgAgC2RZwRzjuQ2b+B0KvgGcVIBzhWBUpcyNMV5D5%0AL2hFH3eKoGZtg3DMhIvnT2RMZ6Jr5zZ8++23llTU2071JxaMJFVP9RsfxPbSifYiJvaKvJILehV2%0ASEhInCQLu3btYs6SpZw+f4GgTgM1N4CgAGicD5ZegjxFrLZXvqoCJd5F7T5TK2Ba1BcOrkSp2QD1%0AyB/w83EoVsH6IFuWoPxvHOrKB9r/b55DmfAJ0uwIefJDKMi51o0IAJQ+DVBds8MnQxATm0KuPMhZ%0Av0OOPCjftkT1DYLJNg4A5/bDuJZal6ftT6wIOADPH0HrotDxe8Sa4YgWXVH7/6Cl/QEx7zvEvs2o%0A867A65coAytC/sKoc7aBixuiYzVklmLQXyu64s5FGFkP6n8Mo5YgOlZG5igDfX/Rxl8+0AhrqYrI%0AGdtQZvaDo7tRf4hoA3nqd5jfCUashDot4eVj6FgCPv8ZClaG8VWhbnvoNTfyGq6ehOENQJXQehy0%0AsE7bK78ORB7+BRkeBg2HwfsR7gPXDsDC5tBvLVT8UHvt2G9kWj+E/bu2UbBgwWgXXrYLn9TofwxR%0ASWpCnDvspfFjkxfZ+sfaI7FGsmdLZPXP2l6zAuO52J4XxL0Rwpsipq5fHTt2ZPHixWTPnj1Rj/n/%0ABGluACkBtmTV29sbDw+PGFfrttXyus4zrpOnnlpJZzABT2zo56fbTBk1s286Sfj7+1OoUCl8fHzQ%0ANKn+wEw0q6r0wCrgF4Q4AOJTpDoXrfDqCpAORG5INxsl/DCq36+g5AO3PaDkRvjmQObZCG4NwPsX%0AeNIFk6MLTZq2YEC/nlStWhWI1HHqvoPxqepPSbC1nkqoHtVelbL+77hqY9/0OhLaLQu0NoRjpk7n%0A6ImTBGXOiZIuG+qMPdYbPbkHnUvA4uuQNW/k697PoWdJCAtGfD4C2X4gOEYUQ4WHIz4pgPy4P3w6%0AIPI9qgozPodjG6BxRxg0L/I9AFfPQa/asOQhuGeAsDDE5ObIq8eh91iYMxJ+uQmZc1qfY6AftM4F%0Azq6IzNmQP+6CbLktw8rEL5A3ryInHYcntxCjayFKVkSdth58veGTYjB+PxSvrr0hJAhlUCWkWUG2%0A6Y2YOwL5vyfWFfkPr8F370H23IgXD5GLnlhreV89hO+qaxZSNy/CxNOQp1Tk+PE1sOhLGLcOZeNs%0ApF8wctjBiH3/DRNrwcf9oOM4CPRDfFUCWb49lG4Gi5pBj0XwriEyGx4GPXNCcCB876m1O9Zxcjms%0A7wsTjkNoMC7TP2DP1t8pVapUlIWXTmj037JunZfaSOqb2Ggl5Fix6WPtuRUYs4rG1L7x2ahfhxCR%0A7Uyjyw4Z32NPG6vPS/aisQklsjF1/WrWrBn79+9P0lbq/2GkuQGkBESnW7VHeuJTLR/bMZNK56NP%0AKLqhta5zTEwtV/PmrfDxCQemASMBN6AbGiFdA2QFViNlEIItgCuIaUAukM1A+sHrr1FlKcAE7pvA%0AoTD4d0M4F0fKMNxeNkYGnqXhRy2ZMH4MOXLkQFEUywJBtxzTNVepLYqqL3hCQkIsk/+bPMBiq1I2%0APrxsnQqiI7JxgW1UO6FV2BUqVGDLmtX8/fffdO3Vm1u3LhCycY6mWTVHPHxm9Uap2gzVSFQBFJMm%0AI+g8B7FhHGxejBy5FCrVg8NbICQIPuln8x4FxdUdNUtBlOO7kR3KISevhyJlteGfR6GWb6QRVQCT%0ACTl6F+xeBHP6Q+bckC5zlOsQm+YgMmRH/eEafP8hdCwLM7ZDuZpw/zrq3jXwg9bwgJyFkXNuIr6r%0AguhcFfIWgYIVkDpRBTA7o866jBhZGyb1QrYaYU1UAfIUhwn74NvyyLxlohSdkTkPTD4BvQpBhuzW%0ARBWgZjsICYQxrVEdTDDzkWHfZWDwHzCtAXhkQrlzEczpkB9p3bjo+Ass7gwZckIZrXBN2TQBFBMy%0AeznEjOqoQ85EugdU/xzx4iaMq4eTqzOL5sykUqVKVqdjvDeMBEt3wbBHtlJiJXtyklQdMcmLYnII%0AiG4uMJlMlm31uUr3ftWfYdG5FQCW/cXknqD/bTwX2wisrbTAHmKSAeitddOQeEiLrCYzdPG5Dlt/%0A0oRWy8cEVVXx8fEhY8aMb3z+OnRNoz4xms1mgoODE/UYoJn/N23alqCgDUBX4DGKUgMp7yBEPVS1%0ADDACcAYmA1cQYhdSHkGIMUi5EqGUR6pLEaI3wikLqss6UH3ANy+KCCdf/sIMHdybtm3bYI5ogxkW%0AFmYR9dumqt6EbCU3jJo7vfDqbZ1vfKOxxgeGUbKgp2UTM6p98eJFhowex7l/rxLQeSzU/hRa5oJp%0Ah6BIReuNv++A8uo56rC9WsR03QjYNw+lemPknX+RlZpBj++t3+P9Ajrnh1FHIX8F+LkbnFqH6DoS%0AWbMZdK8Bi+9Cehud24Mr0L8ieGRGZMyMnLANskcYq/u/hnZ5oM8aqNhUe23DONg+Hfr9iHJiN9LH%0AHznqD+t9qiqMrg03T8N3m6FSkyifh9g4Dbl+khalnHwI8lnbbylLvkWe3o0MCUbkLIgcuz+SIALs%0Ano9YOw4pzIhCFZBDt1sf4PUL6FtIk1Z8dwCK1LAe/2c/zGoBSBhxHTLkiTy3oz8htw2F8cfB7xVM%0AawafH4ZMhRGLK0POEshe2yL3FR6KMjIPLRrWZtXKFZaXbbMMtmly2/S3bfRQX7DZk8G8DSlMUESX%0As6Ty0k5MxDQX6DCZTJbPN77+sRB/xwJ70VhbImsks3oQyTZ6KqWkSZMmHD16NEV/BykYaTKAlIDw%0A8HDCwsIs//f397foHnXyJ4SwGOMnxo9dSomXlxcZM2Z84/2Fh4cTFBRkMZHWJ0YpZaIT4qCgIEqX%0ArsyjRx8ixFqk9EJL/3sCo1CUXKjqYzSieh5wBUoB5YFzgEBRWqCqq9EkAZXA4yiKuhtHdTZZs2Tg%0ApwU/UK9ePavq1uhS/dE9uIwFCCmhEMnWeiqla+6iSyXqRRU69IpiPXKSFJ/r8ePHGTRqLH9fvoya%0AKRfyp3+tvVVDQxHtsyIHbIWStSNf93kOU+rB8zvQdSp81MeKvIllwxEntqNOvhT5nqtHUea3RvXz%0AgpLvwviovdOV6a2RPq+RfXciFnyEvH4ERv4G1ZoiVoxF7F8TqQfVcWEPzG0LocEw74YW6bTd7/hG%0AqPeuQbA3DN8M5Qz2Wj4voEch6LYBLm2Bs6tg7J5IqcCDKzCwMgw+Be5ZET/WggxZkZOOatfs/Qx6%0AF4X2KyB/VcTMalDoHeSQSAKpzGqNfPIAyrRB7h8Lww9rJF6H3ysYXASCA6DbJijd1Pr8d41BHpmH%0AFBKq9IO6oyPO/SEsfAeqfAZttIIz8+YBVA3/h11bNloIj5HcJSQCaRs1tKfnjm7xlVgwBjb0ItuU%0ATlLtwVbS4+zsbJFjvIk+NjrngOj0sdGdmz1trLEwWp+Ljh49ipubG4ULF+bzzz/n8OHD0e73TeDp%0A6Unbtm25d+8eBQydq2wxZcoUfv31VxRFoWzZsixbtsyuZCEFIo2spgQYyaqUWgcM/WY0thdNbHh6%0AeiaYrMYl2puYhFjH4MEjmDt3HtrP0AEh+iLlB0AzQEVRuiLlZqAHUvYE6gHXUJTiqOpXwHDgGpAX%0ARamKKq/j5KTQpEkThg3tS9myZa2uT9cg6TrOuF6HcXK0JbH2CpGSqmWq7aQf3+tIKTBeh/596HIZ%0Ae9FYew+vN7lmKSVLlixh+twF+Lhlx7/rD1AsolXn8u8QJ7Yhp0btZKVMro8aEIrwvAbZ8iIHr4T8%0ApbQIaPtc0H+LZr1kxKOr8F1ZLYLZcwHU6xy530fXoV8FmHQdMkUQzgPzYeNQRLPuyJ1LYMBmKGuz%0AT0CMr4u8cRqlcAXUYdvAw9AA4Z/DMPlDmPgQji+BHaOg10Ko10m7jvk94Po51MERlfc7xsL+GTBs%0AI7zzPsqIOqimzNAjwlvW7xViVm2EizPqlL9Q5nRAPr6P7HdCG/d+CDOrIwpXQg7eAud2wNzPYOBt%0AcM+CcmA88siPyDF/Qc7i2jnMbQlPH6JW7Am7v4Wv/4CChuirlDC+ELx+BoOfgrNBj//sMiypCc0n%0AQPpcZPtjKGdPHCFjxozJQu7szQXRVdUnRFbwXySpeoAmpmdfdPNsYuhj9f0byWtsRNbf39+yyJFS%0AMmbMGI4ePcrt27cJCwujfPnyFCtWzPKnaNGilCtX7o0XLEOGDCFLliwMGTKEadOm4eXlxdSpU622%0AuXv3LvXr1+fKlSs4OTnRtm1bmjZtSpcuXd7o2MmENM1qSoD+w9ajqPpNFh9HgIQgJm1sdNBTyMbJ%0AJCZ7rMTEtm3bmDt3PkLkRMpMgDdSPgHeR2uvughV3YbWDMANKAEEAktR1RYoyrtAb1Q1EGfnLwgJ%0A/ZcO7dsyetRQ8uTJY7k+o/4xoZO+cYVuO9naTq7GtKNtlMA4ucbnHGyvIzWkAe0hPtdh78GlS2ze%0AVK4hhKB79+588cUX/LLyV0aO/5jg0nUI7DQFZe8y1M5zo3ayun8Z9eYpGPsQaXaHXzvCN5URnw4E%0AkxmRMSeqLVEFlG2TUQu9C7V6IZZ8hfhrM+o3S8E9I8qaMcgiNZGZDJHR+r2heF3kjLqaA0C+slEv%0A4Npx5O2zMPIectmHMLAcjN4LeUqClIilfZGV24NLOmgwALIUhoUdUZ7fQa3RCvXgr/CdIQLcbCy4%0AZ4OpLaF+F+TdyzDRoDN1z4wceBxm14M+RVFfv4CRhuYDGfJA/5PIH6shpjZF3j4DdUeBu+ZDqdYf%0AjRLsB+NrIsefgzunkP8cQH59G1wzIQK9kAubwIATkL2k9h0dXQBBvpC3DmJRRdQ+V7XOVgDZy8Jn%0AW2B1C5ycHNm0Zwdubm74+vqiKEnXbUpHbHpuI3nVdbJxkRXo2tqk7pqV1LAlqS4uLnEK0MRlnjX+%0AiYs+VieoRiIbV32s7fc8ZcoUAK5cucKsWbP4+uuvuX79OtevX2fNmjXcvXuX06dPv/Hnt3XrVg4d%0AOgRAly5dqFu3bhSymi5dOhwdHS1+2QEBAeTOndve7lIN0iKryYyQkBA8PT0tKXQ90uru7p6kx/Xx%0A8YmzibWtN6qzs3OcJkUvLy/SpUv3xlrC0NBQihUrx9On7yJlLeAbtGIqgFC0oqrcQAPAH0XJgqpK%0AFKUpqvojsAvojJNTA0ym8/Tu3YNvvulFpkyZLNenF0/Ys2xKDthLfcdkC2UvGpvUOs7kgm1L1ze9%0AjrhGtuIajfXz8+P7H2cxZ948QkJCYcFTcLXuOqfMa4vq/Rp67op88d5plF9aob68D5+Og5ajrXf8%0A6gEMLA5DL0LWouDvibKwIarPQ/j8e/ipF0y4Alny25yQJwzJBxkLg/9jGLIdilbTLx4xqjoyQyn4%0AbJn22rpucHEdDFoPIYGIBV8iJz2zLoy6fw4xvxFSqlC4Nny1JeoHcWoVrO4G2YrD8ItRx/094bsc%0AYPaAiU+sq/IBvB7A1DIayR7jZT0mJcrW3sjL65FhIdDwR3inW+Tn++d3yHOLkUMvQtBr+KEqNF0P%0AeeshNtRBiHDU7qcipRfBfjguLEOPz1owcuTwFNttSkdssgKdUAmhGfnrRvopVdpjDzpJDQoKQlGU%0AWCOpiXncuEa67eljbYms/v6wsDCrxjv6n2PHjnHo0CGmTZuWJNeTMWNGvLy8LOeWKVMmy/+NWLx4%0AMQMHDsTFxYXGjRuzcuXKJDmfJEBaZDUlwGQyRYmiGluwJhVicwQwrtr1YoP4RnuNN/ibYOrUGfj4%0AZEXKBkAftNapHYHdCFEDVT0DfAY4AF+jqiWBwajqcOA0Dg79cXRMz4gR9ejefSUeHh5RKn7fdlV/%0AbFW0ttX0xmisMa1lNCpPTREWW12t2WzG3d09UR6+cYlsxTUaqygK7u7ujBs9ks/atGLwiDGcGF6a%0AwNbToeZnWoT15X3UM1vhOxvtaP4qqPWHw+bBsH06SoAXapvJYNZaEitbpyDzlEdmLapt75YJdeA5%0A2DUOFvTUUv8Zo0ZDxJ7piEwFUftchD9GwMT6iA7fIxv1gsv7kI+vQbdDkW9o8z/IUxm+bwUOJmT9%0AQVEr+PNVRLZZACs6axHLYH9wsm4jKTzvIl2ygPdzxE/NkL12WI8f/xnhng3pnh8xtTTqkMsRZv8R%0A8HkMahg4uMHqttB+rfFLQ20xH67ugqBnUNy6E5ZadzJK4CuYURnp7AGFW0JBrTBMfrwHfquCWN0c%0A2XEHSInzrq/4oH4Nxo4dnSoWcfZ+s/qcpZM7ndipqmrpMpUYsoKkhpGkOjg44OrqmqyLhrhGuu3N%0AtfYCBXrzFD2IYySzr169YtGiRXh4eFiypglBo0aNePr0aZTXJ02aFOXa7H3Pt27dYtasWdy9e5f0%0A6dPTunVrVq1aRYcOHaJsm1qQFllNZugTkA6953369OljeNebw8/PzxJ9M0Kf+BLDG/X169cWe62E%0A4tq1a1St+h7BwaWB02hFUwuBU8BshMiK1mZVoEVYC0VEVMvh5haEs/NN+vbtTs+eX+Hu7m6VWlYU%0AJVXru/RoN2Ahp0bBv71IbEp6aMGb64OT8rxiK57TP9dTp07Re+BQnkh3AjrMQzm8FHntFLL/Seud%0AquEwNh/UHAaFG6KsaYGUwcg+ayB7YehXEPqdhNzlrN/36g5MKgmuGRGZcyN7b4JMEfZZvi9hSH7o%0AsgcKvKu9dmMvYl0bxDtNkPcuIQs0gE9mR73IXaPg0A+IKp8h2y3UWqAazlVMKInM0xjlwV6kkxPy%0Am/2WVD3ej2FcMfh0K2Qqhvj1XciSB9nvsBbN9HoI40vAR1sgV02UTU3A/z7qsL/B7ArhoYgppZC5%0AGkOlQbCmGhRrCO1WRZ7DxTWITb0gRx2E51nUXlfAbMg4SRUWlQWv29DTExxdIsf8HsGqilCiBSJP%0AFfL+O5tTRw8kaSOUpILxXo8p05DS3QpsSWpKjmzbwnZhGxYWZlUYrSgK165dY/Xq1RQpUoQ8efJw%0A5MgRzp49S79+/fjkk0+SrJipRIkSHDx4kBw5cvDkyRPq1avH1avWC+W1a9eyd+9elixZAsDKlSs5%0AefIk8+fPT5JzSmSkFVgKTQZ5AAAgAElEQVSlFOgFMJA0tlL2oLsOODs7W1XD6qkMfZX4JrC14Yov%0AVFWlfPkq3LhxE8gA+ACDgGzAMEAixGcI8SdQNSKSuhiYT5Ys+RkzZjAdO3bAbDaniFR/YkDXDYeG%0AhlomfHtRVHvpQ3uFB7YPr+SCrd4utSwaoovGhoaGsmr1b0yYOh1/39fQfTuUsGllen4dYuM3yAFP%0AItPTewbD2QWQpQCK4oQ66FyUYyq/fYF8fAP52Z+IjR8hHx6BL1fCOx+hrB8MF/eg9rlk/Sa/57Cw%0ACgS8ggEXNR2qEaGBMD4PVB6GcnEOZC2I+tVWcI2oIj65ArFpMLK3Fs0Rq2uD331kv0OQpSDK8vbI%0AJ3eRnY5r2we8QKyqg3A2ow4+g7K0FdLLG9nmoDYeFoSyuTn43EAd9jfiyFzE4fmoXe9HOAbcgjXV%0AoWRTaLNCO/8ZxaDKTCjaEWVvc/C7gdrzHzBFRGfvHIC1LSB9WRTVG7XTZc3zVofnVfitGmYTnDx6%0AkOLFi8fvy37LMC6s31QOk1C3gsQgssZ7PbWRVCP056Re6GmULaiqyt27d9m4cSNnzpzhxo0bPH/+%0AnODgYIoUKWIpqmrRogXVq1eP5Ujxw5AhQ8icOTNDhw5l6tSpeHt7R9GsXrx4kQ4dOnD69GmcnZ35%0A/PPPqVq1Kr17907Uc0kipJHVlAI9qgRJU0VvDwEBAYC2IjTefE5OTol23Oiit3FFv379WbhwGdAa%0AIc4BKuCElJfRiqqmAheB2cBk3NxW4+R0n65dOzBq1EhMJpMlaqen+nVtV2pCYlpPxSdimFjV9EYY%0Aybbu85paH1y2ZNvBwYFnz57x1dffcOz0eYI+nAGV2mvSACkRk0sgS7SBBhOsd/bwL1hWB9wyQ7fN%0AkL9K5JjnfZhcHLpegMwRZOv8z3BwIEqV1qh/rYGu+yCfjS+pqiJmFkWGKxD8ArpugiL1LMPiwFTE%0A8cWo3W5rRHJNLWSoJ7LvfkiXA0bmg/cmwzs9Ive5qQ3c3w8fT4MN/aD7VUhnKPYK8kGsbYQMeAIB%0A3vDlHXDNEjkeHoKy9RPki4vIQE/4eDfkMdh9ed2ANTWgzEcoAS+Rns+RH0Y4CIQFoexuBGGvUL+6%0ABCF+ML8YFO4LJfoj9r8HZkdk2xORC4EQP5zXvkPfbq0YM3pUPL/htwfbeySpZQtJ5VbwXyepxuv3%0A9/dn8eLFbN68mZ49e9K5c2ccHR3x9fXl5s2blsKqqlWr0rhx40Q9P09PT9q0acP9+/etrKseP35M%0A9+7d2bFDk+dMnz6dFStWoCgKFStWZMmSJamlo1YaWU0psG256uXllaRuAOHh4fj7+1u8Q5OqWtzf%0A399SpRofSCm5desWVarUIjBwAOCNRkhNCJEXKR8APwAFUZQuqKovWbJkZ/jwAbRr1xaz2WyJeBmF%0A+yk9ameL5EyRx8VuK6HV9BBZxJbae6vbRruiI9unT5+m29ff8oTMBHy8ADzvIn75DDnoeWSVegTE%0An2MQf29AzdUQri5B1BuAbDwaHBxR1vZA3v87MoKpw/serKyqRUh7n4fMNpHTS2sQ279Bfv4MLvwA%0Ap8cimkxEvtcPAr1hYj5ougYKN4t8z7Y2cH8vlGmKcvskao9bUT+A/YPg9CzIXx8++yPqeJAPzMkO%0ADk7Q4x442/g9hofCojwa2ez+KOq45zWNsIYHQbuH4Gyw2Ar1R+ysg3BQkZmKIJ7dQH3/vDYW7InY%0AWxUyF0N+shMAp/2f06RYCIsXzLZbmJjS5gMjSU0J90hCZQWAReuZmkkqYMk4Smm/A1hgYCBLly5l%0A3bp1fPHFF3Tr1i3BmcQ0RIu0AquUitiKnxICo15Ib/3m6OiYpBqu+BZY6dHDoKAgevX6ltDQusDf%0AwFYgF9ARIX5DiPqoajguLqMIDw9hxowZdOqkeUIaW4jq9lyBgYFR7KBS6gMLrAlRcqXI42u3FVOn%0AKSOJte0IlBpb00Lkb1Mn23oRW3SoUqUK504cZsFPi5gwpRaB0ows2ToKUSXYD3l8JrLxOijwAZTs%0Agtj1EVz8HdlyDurpX6GLHXsbsysE+0HW6jCvArRaAaVbRpxsGGL3EGS5gVqUseJgyFYNdn+Mcv8U%0A0j0rIl1+VCNRBWi+Dg4OgnNzUasOtH9hOauAoys8OAJX1kHJNlbD4uIihEtWZMaKsKw0svN5cMsW%0AucGNTYjwEMjeCH4pjex82ZqQumYFpJZEOT8BasyMHHN0QzbZj9xcGV5sQ7a4FznmlAlZ/zD8UQl2%0Ad4b875Pl9Ql+mnvIsni1194zOfyOY4MtSU2swsI3RWxFn/YKP43WTnoRkzFwkFLnXFvERlKDg4NZ%0AsWIFq1atolOnThw5cgRnZ+cY9piGxEYaWX0LsL159UKZxEj92PNGNZvNFuKalNCvIybY6mWdnJzY%0At28fx44dRfNL9QSyoLVQPYuq3sfJKR2urtMYPXooXbp0Rghhlep3dXW1TLC2Wq2wsLBkTXvHB7pl%0Ak24RFhshSi7EVj1r+8DSyYDxvYmhgU5uvKlDgclkou83vWn16Sd8+FFL7j/8k8C7h6FAZOpbnPkJ%0A4ZYNtcAH2gvZKqJ2ugf7OsHCJpA+L2QpFWXfysmpkL4YatP9cH05bPgc5c4B1A9+hEurtQhmxSGR%0Ab8hTG9n+KmysgfS5h/zod7vnrIT5obrmhjNzEU4eyOrDIj1kQwNhb18oNwFcc8GOroiAZ8hK32jj%0Afk+QR8Yja2+EHA1QTnwOK8oiO56GdPkg2Af29UKWmgwFu6Ocbge/lEF2vGSRCygHvwG3gqgVVsCR%0A98DBDFUNlj9hgRD4HBRXxMkuyLoGazDXXNDgCPxRFdOdzWz48w/SpTM0BzAgNocNe3NCYs8LxgVQ%0AYrpfJAeMRFZP9xulPXqgQC9G0v8NpOjCT31xHR4ebre4OCQkhFWrVrF8+XLatm3LoUOHcHV1fYtn%0A/P8XaTKAtwA9UqXjTQuT9H0GBQVZVuu2HnbJ4TqgRwvsecYaGyEAli5Yjx49olSp8hFV7pXR2qT2%0ABdKhKNMwmxXGjBnBl19+gaIoCY4+xjftnZRdpvQUuU6I9Mk+tcE2IqxP9LafsW36MKWlZm01aokl%0Av9i8eQu9+w0isHBzgutN01LlM3JBnZ+gWFvrjQOewbJ84OiOkqMcaovfwD2HNub/HBYUgKYHIVtV%0A7TWfWyh76iNdMyB9n0KlUVC+T5RzEPu7IW9uAkVAyx2Qy1Ds4XUDVpSHxucg1A9xuDGiWAvUDxaD%0AgyPi+ETEhaWoLW5r2z89jDjcHCr1QdaeiLL1M+SrB8j3j0V8kCrK6d7Ie+uRnx1DOT8b7h1BbXBZ%0AG1fDUE63R746iux0CV6ch20tocE1cMkF3ufhSB0o2w8qjde8V//4AOkfgCy9Fk5Vhuy1odaayGtQ%0AQ3H+sxqdPq7GrJk/xPs7srcAi00OE9/iRFuSmph1AsmJ+GpS7VlDxdY6NbncCmIjqWFhYaxZs4Yl%0AS5bwySef0KdPn1TpLJFKkaZZTSkwtlwF60r9+MDow6enL6LTPYWHh+Pr62u3h3BiQZ/IjDe1PlEH%0ABwdbGiHoJFNKyaeftmPPnquEhzdHUdYgpRtmcw5Mpou0bfspEyaMt0SGk8r4PjoSm5hEy+jzCgnr%0AR55SYJsij0lrF1vzg7cZdTF+J0IkTdtKb29vBg4byZadewnM1xDl9gHUzveibKccHQD3D6LWP4o4%0A3BTpfRGar4SiH6IcGAC3/0T96Lz1m1QVtlYBn+vQZAPktynk8LkFq8tCg4tw7xe4NRMaLoDSnbVj%0Abm6BDAhB1tmtbR/wGGV/NchcGLXxYlhWEepuhRz1I/fp9Q9ifx3IWRn54Ci0uAmuOSLHpUQ5PwT1%0Axs8QHgINL4JHUcN4OMqZjsgXB7UGBAW+gRIjI8c9T8GxBlB+GLjlRpwcgKx5F0zpIPAOnKoK+VtC%0AlUUAOP79HTWyXGDntg1JsrB8k0Kk8PBwiwzrv0RSEyNrEtdFQmJLuYzfiR4gsm20smHDBhYuXEjT%0Apk3p169fkttKpiEK0shqSoEtWQ0ICEBvOxfX9+sEMK7eqKqq4u3tbenilBQIDQ0lMDAQDw+PKKl+%0AI8nUHwKHDx+mZcsOBAb2AG4CGzGZnOjd+2v69++Li4sLqqq+teij7YRqnFjjSrSM0ceYrKdSOozR%0AR11+8abRx+hIbEx2W4kRdbH3nSR1Qcjhw4dp1b4LIS4FCW26HVwMVfOBL2F5fqh3ALJEdKK6Ph8u%0Af4co0RL57zpodgiyVrHeaVgQrMkDGevDy50oFQeiVhmjdYgClN2tkK+9ke/t07Z/tAXOdkIp9yVq%0A0U9hwwfw4R1wzmK1T2V/NVTvK5CpHDQ5E/Vi/B/AtlIgFfj0HphtFsBqOPyeF4JeQd2DkNnGuUCG%0Aw753wO8GvH8PnLNZj786DsfeB1QouQxyGKLQfv/CmVpQpAfkakL6C+25cPY42bLZ7CMJEZdCJH07%0APU2enP6miYWkIKlxPW50DiZAlCh3XLJgsZFUVVXZsmUL8+bNo379+gwcODBJn5VpiBFpBVYpBdFp%0AVmOCPW/U+LQ2NU6gSTlhhoeH4+PjY4lUubu7W6KoxkkoKCiIL7/sRWBgHRwc/kKIE9SsWZ+FC+eS%0AMWPGZCs0igkxFRzYRgOMPb71iVO/XpPJlGL0qPGFXqhnlG8kVkRYCGGXJCaV7tjY1jW5NcK1a9fm%0A7vV/GDF6PCtXlyWw5nwoohVIiQs/IDwKoepEFaBYb8j1IXJvJU3H6WAn63J1EYrJDbXyOvC5gDzd%0AGOXxUdQP1oPvA9Q7u+ADQ4V/7o8g3Vk4UhvOzYd8ba2JKoDJGbXKCthbDV7fAa/LkLGs9TaPdiIc%0AXMGjGmwthWx6RtOP6rixECFVZIFJcLgx1NoC2SJttPA6B363EBkaw4FyyPqXrAlrphqI9GWRXmch%0A5IX1sd1LQcX9cLYujvd+ZvnqpclKVCH6eUGfn3W7OV3HqS/wUlKRV0ywJanJPXcZNfNGq6X4dpzS%0Avx9dcqXXN9iS1J07dzJ79mxq1arFtm3byJIlS5RzSsPbR1pk9S1Av8l0JETrmZDJLakssvRVq265%0A5OHhYZXqN7ajA20yGjNmPD/+OAsnJ1caNarPmDHDyZUrV6rucW9MK+skFbCKuESni01JDytImRHh%0A2CJa0dlt2RZNvW2N8IkTJ+j0RU+83CsSVGkS/FYBau+A7HWsNwx6DlvyQ8ZG4LMfqn0PJXppBVBh%0AAbA6N5RZCLkjIo9hASh/1UUNeoBwz4V0LAA1NkY9gQcb4HQncM4ODQ6Ae6HIMSlR9tdCFfnAMSs8%0AWw51N0POBtp4sBdsKghFf4JsbVBu9EC+2IL84BikL64VQ20pAkWWQZZPEU9+Qt4ZAtXXQs6moIYi%0A9pZButaHgvNRbndG+uyzJqx3/4f4Zygy53J40A6Kz4bc3azO0fxPc2qUDGXn9k2J9K0kDHp2IKZK%0ActttU5p+03h+byOSmhiwlwULCwuzPHP0+33KlCkULlyYwoUL8+LFC37++WcqV67MsGHDyJEjR0yH%0ASEPyIU0GkFJg23JV150aK1mNBNBW65lQeHt74+7unigpT2PETU/1m81mXr9+benGpRNUYzRXCBFR%0AVFWaatVqMWHCaEqWLJkiSERCYVzhRxcRTsnaTSN0YpfaOn/ZI7H63xAZxTUuFN7mIiEgIICRY8az%0AeNFCcMuLbBbV41Q5PwCe/Ila8Ty82oW41gGRoxZq7V8Q1xYj/l2MWs+ON+rZdvB8O5SZCkVsiq7U%0AMMSeosjMXSDwX/D+A2pvg2zvaeOPtiNOdkJWf6JFc+/PgnsjoNpCKNQJ5XRveHwYtVJE0ZSUiDvD%0AkI8WQcM9KNdnI1/dRpYztJ59thxu9YEqKxD+N+DGPGT5iE5WUkW53QXps1cjrDIE9pWEnMsgQyt4%0AvQMetIGSiyFnRF/zJ7+SL2Ay504fibenc2LBtijvTTIOsc0NiVHkFdvxUytJtYWetTMWs+mvBwYG%0AMnfuXC5dusStW7e4desWZrOZ4sWLU7x4cYoVK0aFChVo3rz5W76K//dII6spBbZkVa/UT5cunZU3%0AqpOTE87Ozok2Kb1+/RoXF5c36mKhR3qDgoKsrLH0KKqXl5dVgZUtIdAnjcuXL1OmTBnMZnOqLjQy%0AWk8lNCIcnS42Ou1mYkc4/0sOBcZFlBACR0dHTCaTXTKQnC4Q0WHt2rUMGz6O1+nqEFR2DpgjijmC%0AnsPWAlD+IKSPcAAI9Ua53BA15IFm6VRhJeT4yPYDQDlWDTXEFULOo+Rrh1p+LigRTiN3/of4ZySy%0A0iONLN6fAg8nQuV5UKAjYnsRZJauUGhs5D5fbIGrHRGFOyNvLoNKZ8GtpNVhxYPvkXfGASpUuglO%0AuazGebEOrn8BhEOJXZC+ruGcIwkrHkUgxBmZf1/kuM8meNgRyvwCHpVxvlCZ/X9soUKFCgn6zN8E%0ARjkWkOQNSOJa5JUQKz4jSU2OzllJCXsk1TZYcOzYMaZNm0ahQoUYOXIk+fPn59WrV1y/fp1r165x%0A/fp1hBBMmjTpLV5JGkgjqykLxparoaGh+Pr6WiaaxChesYc3sciKKdJrTM8GBAQQFhYWZSIFjZQb%0AK8hT48SYnMQupkKDxEgbJqUeNbmRENlCbC4QyZWW9fPzY+DgEWzc+geB7yzVPEsvDITHB7Soqi0u%0ANILXxxHFRyMLDbYUVAHwfA/iXDtk2ScQ8gzl1ntIl+zImtu0ivqdeSHfdMhpSKu/2g43OkD60gj/%0Ae8iqDyJbmOrwPQcX6gCOUOtpJPnVoYbByQIQ/AyKLYfsHazHpYQLlcHvMhSaB9l72IyrcOV98D0O%0ARf8Bc0Hrce818OhLzOkKMbRvK4YNHRSHTzbxYLsIett6+rhKYqLTxv6XSKptJsg4F0spOXXqFFOn%0ATiVHjhyMHj2awoULx7DHpMeDBw/o3Lkzz58/RwhBjx496Nu3b5Tt+vbty65du3B1dWX58uW88847%0Ab+Fs3wrSCqxSGozeqABubm5J2rs3LoVcRthL9RuLuvRolTHVrxsm62P6+42LIj3V/LZT3vGBPWJn%0AK9ZPbMSl0EB/SMWnU48tsUut7Wkhal/1+BSDJLT5QWIXybi7u7Pop9l83GIXX37VGf/HLQi9uQLK%0A/xl14zAfeH0SsoyFm9NRXu5DfWcNmDNrKfl/+yEzfgGKMzjnRy15G3HrffijDORsiuKYAdVIVAEy%0AfwjmA3CpFtKtJMhAtAYdBgQ/AkwIxxKIv4qjVjoL5shqafF4HkKqqNnXwo1OEOYJub+JfP+LNYig%0AO8jM6+BOB1CDIadhPPQZ+J0ChwqIWzWQRS+ByVA4laEdwn8PmZwOM3hQ/wR9zgmBLUlNKfdKTMWf%0A9rxN7TXvcHR0tJrL3/Y1xQfG+95eFzApJefPn2fKlCmkT5+eOXPmUKxYsRRxjY6OjsycOZMKFSrg%0A5+dHpUqVaNSoESVLRmYrdu7cyc2bN7lx4wZ//fUXvXr14uTJkzHs9b+PNLL6luDr62shgK6urnh7%0Aeyf56lZR4tYOVSczxlR/dFX9QJRJ0zjBK4piZa1lSwJsq+iTOuUdX9gSOxcXl7d+TsYHlb1WqTF1%0A6tG3MZLU1Jju16PbSdENKDYiENPnG19PXn1/wcHB1KpVi7OnjtC2fVfOmVxQlagLV+XhD2DOg5pt%0AMDJTL3jQEP4sCVW2QNAjCH4BRQ0doBQTsugBuN8PHixEzdEjyj4BlFe/Ic0FEaHByLNVkeX3Rqby%0A1RDE9V5Ij0HITIMRL9ojTpVEVjwOroUh+Cny9ihktt/A7UNwSA93P4bQV1BgLIR6wa2vkR4zwPVj%0AULbCvY8gPBDyDNGkC3e6IR0rIDMcRPh2gpvlkUUuRhLWoH9wDt7G1t27LE0okrJA0ZgiVxQlRdz3%0AcYXtIkyXbulZMX3hG5dq+ret7bZFXEjq33//zZQpU3B0dGTatGmULl06xZw/QI4cOSzFXO7u7pQs%0AWZLHjx9bkdWtW7fSpUsXAKpVq4a3tzfPnj0je/bsb+WcUwLSyOpbgpubFrnQb6K4Esk3gU4Wo4Mx%0A1a9b+xhT/UaPUX1/tobKRg2nq6trFDIVF7si25Z9b6OK3kiGUlIr1Nhg+/kaq5XDw8Mt5FR/GAcG%0ABtrVvqW0hxTY93p1cXFJ1nOMq92WbTTLHgHQt9ELdFxdXUmXLh2HDuzit9/W0Ld/Y4JzDCI89yAQ%0ADhDqiXr/R8i7VTuoyR1Z8CQ8HQEnG4JiRmYdCErU81MUiXTIhXy2AoUQ1IJzQSfDQfdQHy2A7IeR%0A5oqIF03gdAWosBfcyyMezUKgIDOPAEDNug7FcwCcqYQsvwvl8RykUxmk24fa/lwbQK598Oh9CH2F%0AIvzBVADVPSKi69wAsuyEh81ADQTXkkifE8gs90AoqB6/oPh2Qtwoj1r0Mjikx/VlZyZOGE2xYsWS%0ANNptW2xkbw5LLbAlqTHNYXHJJkRHZJMDxqBBdCT1ypUrTJkyBVVVGTt2LOXLl09R85c93L17l/Pn%0Az1OtWjWr1x89ekTevHkt/8+TJw8PHz5MI6tpSH6YTCarlqt6ij4pCZH+gDRCj4Iai7qM9lbGCUzf%0Ah5HE2BIIs9mMh4dHvKNcsaW8o5tEbQnsm0gKUgIZSizo36uujTabzbi5uUW5FnuSAr0dcErxhUwN%0A2troJAUQ1ZM3KCjI6n7S7bUAy+f82WftqFmzBu07duf61d0EFPwF8XgBwqkAqkd96wPkmAQ4wstp%0ACP9jyDBvMBmM+oPvoT7/GbIeByUjvHwXxe8iasmtYM6Kcm8QqlM1cK4MgMy+B172h3PvQtF5yDsT%0AkNnWGy5WQc08C2HKC+cboQoV8t20PifnapD7GDyqgyoDIMc1m/HakHUvPH4fRCjSfS4oEW4owgHV%0AYyUKHVFulENkbss7ZbLQo0e3OFlCxRTtjm4h9v+VpOqIi6zAOD8kVpFXfK4lOpJ68+ZNpk6dip+f%0AH6NGjaJKlSopam6IDn5+frRq1YrZs2dHa1tpRGq4pqRE6rwb/4NIjsiq8Rj2Uv3Gqn57qf7oJndF%0AUZKsqj+mSdSWBNiSrLhWedtey9sunngTxPdaYpMU2Opi3yTl/SbXklIkGAmB/rmEhYURGhpquRb9%0Afowum5A5c2Z279zIrNnzmTWnIkGBAcgCe6IeQA0Gr4XgPAERuBr5b2kosgNctWp55cl3SHNlpFn7%0Av5r1FsKzPpwrAwWmoL7cBbltyGaWmfC6NFzvCcID3D6IcliZrh94zYGwR+C3HjJ8a72BuTjCIQMy%0A1A/h3ROZZaf1uFN1hEs9ZMAeCLtiPSYcUD1+RVFbwqulrFh2LsbfcHTR7rhEC/VtTCbT/zuSGhfE%0Apu22R2Sjk3XFdY6wvRZ7Mp87d+4wbdo0Xrx4wciRI6lZs2aqmRtCQ0P59NNP6dixIx9//HGU8dy5%0Ac/PgwQPL/x8+fEju3LmT8xRTHFLnXfkfgO1NFVuKPrGOqaoq/v7+VitVnQAkVqo/uaBPfrawN4Ha%0AI1l6pFlP9afmB9WbFBpFh5geUtGlvAG7D6j4RFqS4lreFuJyLbEV0A0a+C0VypemR8++BPv9j2Dn%0ACuBgiMR4/YwiHFHdBqEyCPz6wtVakG8WuFZH9dwC2Q1kUDEjsxwFr2/hxlfgVBVMdgzRnSqDKgAn%0AlIdVUHMds3YBeL0QhVBUx93w8hMIfQRZp1uGhc+PCDUYqdyCoPcQL95FZj4c6TQQuBsZ9CewHQJa%0AgQyGDPMMJxCGs+kWEyeNI2fOnPH+7GNa6Br9hPXfqj43pgbtphFJRVJjQ1wWuvofI4m1lcUYP2sg%0A1mt58OAB06dP5/79+4wYMYI6deqkyO8lOkgp6datG6VKlaJfv352t2nRogXz5s2jXbt2nDx5kgwZ%0AMvy/lgBAmnXVW4NOknQEBAQghEgSk2s9jRoYGEh4eDjOzs5W/q32oqjGv/8rPpz6dYaGhlrIlU7S%0AU4tu0xY6EU8J30t0djrRRbttdW86gTDam6W235iOpLgWPz8/evcZxI49fxGY7TdwqQhqAFzNAy5z%0AwLlj5MbB2xEBHZEo4PguZN0adYeBW+FVR5DhiIz9kenHR9pgSYnyrAZqSEFwXIAS2hSpPEHmPgOm%0ALBD+Eu4VAoflYGoJ6jkIaQjuTSD7Kgi9D/dLgtgKSgOQLxGyDsLRjJrlNBAETwtD+LcghoP8G6gN%0ALq0gw2IAHAO/o06Vf9m8aXWi3YO2iwdb2yZ70Vhbg/7oZAXJDVuSmlosqIwk1tZ2CyIj5eHh4Rw/%0AfpzixYuTN29enj9/zowZM7h69SrDhw+nYcOGKXpujg5Hjx6ldu3alCtXznL+kydP5v79+wB89dVX%0AAPTp04fdu3fj5ubGsmXLqFix4ls752RGms9qSoJOmnQEBgaiqqql8CqxjqG3ahVC8wYMCAggU6ZM%0AVnKA2FL9ISEhCCFStYF/TLpHe+ksW5JlTxv7tj4He9rapPDlTUzE5mmqb2MymSxds1L6QsEekmPx%0AsHbtOvr0HUxQuu+QajDi1RLU9HY6WQVvBd/W4JAPsv0BJoN3qQxDPC2MDO8KohVC1Ec4V0XNshYU%0AN/DfjHjZFen4RLPBkiEo4Z2R4fuQuQ+i+P4IfpdQHc9E7lO9BSG1ES7FQThCUDhSGMz9pQ9CNkI4%0AvEY610EEHEZVDRFfeRV4F1yag2svPEJbcPHCiUSJKNlWkSdk8ZBSfHlTK0m1B1upj+4BrqoqT58+%0ApUePHty8edPillOhQgXq1Klj6TpVvHhxMmTIEMtR0pDKkEZWUxJsyao+kdoTWscX+gNTT9XrFkVS%0Aah2m0qdPbyFoEH2qX48+GMnD/7V37uFRlGcb/83kfOBsgBDAcEyCUA4qKJYKLUFRQCuWg/WTT4Fy%0AkFO1FbAqYBXCWQS0WAQELSLwISkkqYgmKhiCqFVJIkGMJiARREAIIcnufH+EGXYns9ndZA+z4f1d%0AF5dmZ5K8M9mduX3vZi8AACAASURBVOd9n+e+Aw299VRoaKhb9aiOZllsa7J81SFb32pr1cY+wNDa%0AzKiL3t8PCkb44+GhsLCQP4z8X3K/OgTR6yH8If2gkM7fhFLRE6SzwDvQbAtEDK7afuFl5PN/x6oU%0AVy3LW88jy7eiyJUozXfByf7AVAiZZfczZescrBXLQamEsHyQr9f93hK4/GtQikEqgqDrdNtLkZRk%0AFOvnwIcg6WaLlAKgL0HBlax79UXuv394nc6TJ0SqM1yZjfXEdcL2WlbfRKpRxOvp06dZsWIF2dnZ%0ATJs2jQ4dOnD06FG+/vpr7V/z5s1JT0/301EIvIQQq2ZC/bCqqB9c26hSd3+ebVd/eHi43YVZnRU4%0Af/68Xd2mvn4z0Jf6Vbydce9oudtolqWuzUf6pUtVcAci7ghuR0uF/nhQcHQs/nQpKC8vZ+LEqaT+%0A+30uBb8Oof2vbrz8b6SLY1CsJ4FQ4CWQnkBu9DjWqMfgh3hgJcg2pQNWKzAcrLuR5GYoYT9U/6WK%0AFS53BuU7CF4MIbqaO6UULrcHJRhZDsHK5yA3stlegWTtgqJcQJJkFOW/INkL2uCg6XTu/DEHD2bW%0A+tzYlmH481rmidlYvUj1ZAS3r3FFpP7888+sXLmSDz74gBkzZnD//ff7/XgfeeQRdu/eTfPmzfny%0Ayy+rbc/MzOSee+6hffv2AAwfPpynnnrK18OsLwixaib0YrWyspKLFy/SqFGjGr6rOrZL/erN37ar%0AX91HXeq3/dpisWj/VIKCgjQvTn/YFNUFoxmukJAQn17onM2yuGO1VZ9qOPU33LoIbnceFLyxHGu2%0AGe49e/bw0JiJlCoTqAx5GgDpbGcUy2jgOZs9P0cKugNFkZClhlilI9V/mHIKLG0BBULXQNAY++2V%0Am5Aq/4yibARGgvwQhK3WNsuWWWDZjtXyGbI8GvgEKwdBbl01LmU+kvISVmsusjwORclCUQ6C1PbK%0A78+hQYNhfPFFNs2bN8ddzCJSnWF0nTCq71b3sXXDCERsJ1OCgoK0z4wt58+fZ/Xq1bzzzjtMmzaN%0AUaNGmeZ4P/zwQ6Kjo3nooYccitVly5aRmmpQGy5wFxG3aibUm6ftUrw7bgD6pf7o6Gjtw++sq1+W%0AZU2kWq1WQkJCtBkhW4sXVfSZYRarJnxlo+UKdbHasj2navdseHh4wHq9QnXB7Ymkqbp68ta2pMC2%0AVtBMXpzJycl8eugjRo4aS96RDyitvBOUC8Czuj17oFj2AwlYsUDQVyB1tdtDlv4Gches1r9Bxf8i%0A8xVWeWFV45XyC1ROR1EWAMnAB2C9A6n8GErwbqAAa/mLQCYQhtW6FVl+FEnpjmLNAikcxfI8Cm8D%0AIVit65HlqUBPFCUbaEtk5MOsWJHitlC1Db5QvZ7N/Jlx1alAnTywWq1cuHDB8GHMjKUxKrarD5Ik%0AGX5mLly4wJo1a0hNTWXy5Mns27fPFJ8rW/r160dhYWGN+3jbzedax1zviGsYWZa1m6qji47RUr/e%0AwN+oYaqmrn5n4kE/i6UKLL0NlP4C6gv0tbVmEQ+OqMlqy9YSDK46MahCz+gGZVbU47FtNPJkHKoj%0AahIAehHrLObX9hwHgpVWbGws77/3b55/fhELF85CYTJQ/TzI8nMoyk0oSjew3ALyepD/ULVRycVq%0AeQM4BHQAZT9K5UBk+SuswVuRrfNAao5VeeTKT+sK5IB1EHJlDxQaoEiDQOl5ZXsQVuvLSFILsPYF%0AKR6k/qD8Wh0NVusqZLkBcDOyPIxbb+3AiBF/cPm4bRva1BQwM4o2V7BdfQgJCakWruJKgIdZJhX0%0AIjUiIqLatbm0tJS1a9eyfft2xo0bx759+7QGq0BDkiT2799P9+7diYuLY8mSJXTp0sXfw6pXmPfO%0Afg2gn1l1hH6pPzw83LCT3ZWufsCti7qrs1iq16ZRPKonl2L9JYS8hb4BTBVCet9bW69Cb5/j2qLe%0AoNTULDOJh5qM4/UPY7bnWN1Hra8zc0NbUFAQzzwzm5tu6s748dO5cKExlZXzAFVY52K1vgUcBNoB%0AfcE6Flk+gFVZiMyjWEkGOlzZPwHFmofEr5HKe2C1ngCydL81DkX5GMUyEPgU0C+RSijKPOAkKFuA%0Ax6ptt1oXIEnlKMobvPTSJy5dE+ubSK0p717FFV9Tf6RM6cehF6l6wVxWVsaGDRvYvHkzY8aM4aOP%0APiIsLMyj4/A1vXr1oqioiMjISNLT07n33ns5csSgzEZQa0TNqh+xNVIHOHv2LA0aNNBmbSorKykr%0AK9MuYurNEuyfss3U1W/09O+J7m59M4tajxaoN6i6NIA5O8e+ttoyWw1nXVE/dxaLRat5tj3fRn6b%0AZqvvLikpYeTIRzh8WKK09F9AS2R5EFZrKGATncoRZPlOrErTqg5+CgG9I4mVKnF7GngJ0NWx8gvQ%0ACWiJJP2AouylatZV5TSQCNwB7AJSgPE22yuIjOzH888/wh//+ECNM4VqKYb6MBSoVnrgW6eCmmz5%0APDEbq67aqYmIRteA8vJyNm7cyKZNmxg9ejSTJk3yiq+4tygsLGTo0KGGNat62rVrx6FDh2jatKkP%0ARlbvEDWrZkN/QVBrRvVNQpGRkV5d6vf0MdX09G8rrtQx1nTzt50VDoTZrZow+tvol/pcwdVz7Gi5%0A21PLhPpZYbOXYdSEUXNeVFSU4bkxqos1SkhzN2LSk8TExLB791s899xCXnmlJ2Vl07Fas4EC3Z6d%0AsVq/BOKpukd8C3TT7fMOknQRRVkITKdq9nSJtlWW5wLNsFp3ACuAfsAWYNCV7Y8D12O1Pg/cCUwB%0ASoCqbumgoGX06NGC8ePH2V3HbJtA1c+MSlBQkGG9dyBcF1ydSfUErszGOlpVcGU21lakAobX54qK%0ACjZv3syrr77K/fffz/vvv+8Ri0YzUVJSQvPmzZEkiZycHBRFEULVwwTmnaUeos6KXbhwQRNlvlrq%0A9xXOlmJtM+jLysrsZoyDg4MDWqTqLY689bdxZ7nbWe2xo5u/7Yx9SEiIKWs4XaU29lOunGN/LcXq%0AhdDzz89l4MDbue++0VRWdqP6rCnAZmQ5Gqu1P/Ab4FXgvivbKpCkR1GU/wGGAO2BR5CkXBRlF1Wl%0ABWupmq2VUJQZQCvgD8BSoBNWayqQceXn9QNeAx4BfgQmERa2inXr9tmdB/V8qecRsKt7dNREZ+YZ%0Ab1+KVFdwVuLlrDYW0B4gbO9XKpWVlWzdupU1a9YwdOhQ9u7dS8OGDX17kB5i9OjRZGVlcfr0adq0%0AacO8efM0n/QJEyawbds2Xn75Za134s033/TziOsfogzAj6gdrOpSv7p8oi6NuLvUr1qCmKlT3x30%0As1shISHVbk5mae5yBf3Moxn/Nka1x7ZOErazhOrfR60TNKstkCv4snTB3aXY2ggsZzZnRUVFjBjx%0AMAUFDbh06VWg2ZUt54HOVM1yDqFqmf5ZZHkSVuvfkaQVSNILWK3vc7VhqwRJeuTKSlADoDlVM6q2%0AZFI1CxsCjAL+otteADyILEssWvQ0kyb9ye582dY9uvq3cWQZV5sHMk/ii+V+X2G78qe+d9X39tKl%0AS/nwww/p1KkTYWFhfPTRRwwcOJC5c+cSExPj76ELAgfhs2o2SktL+eWXXwgLCyMsLEyr9wkPD682%0Ai2r7X6Mmo/ogHFyNdXUksMzSeGTb2a/enAJx5tF2FraiosLOmsXMM1g1YfQA4c/SBWem8c4Elt6y%0AKSwszOHfoKKigpkzn2Hjxre5dOl14CZk+Ulg15XZT5WjSNJYJOkGrNaDwHKgv+6nXQL+F8gDtlEl%0AePX8/cq2gVTNstojSS8RG7ubr7/+VBM9tRGpztDPeDt6IPN0jXd9EqlwtZbbqF5YURRKSkrYunUr%0AH374IaWlpQQFBXHs2DGOHz9OfHw8iYmJPPHEE/Tt29fPRyIwOUKsmg21g15d6ldnWFXhaVQfpF/q%0Ary8NBp5oAPNWc5erv7s+P0DYCgdH59iRDZQZZpP17zWzP0A4m/FWxZ2iKISEhLj12Xn77Z386U/T%0AKC2diKIspWpZvqtur1LgLqqap1ZRtXxvywXgt1TVuhYAL+r2KQSGAY8jSa8A16Mor3N1draYiIjh%0A7N//Hp06dbKb5VZTjXzxnnEkYl2xNKvpZ14rIhWqjvedd95h+fLl9O7dm5kzZ9r55JaVlWkxqT17%0A9tRSnryJs8QpgGnTppGenk5kZCQbNmygZ8+ehvsJfI4Qq2bDtrNVXV6xbYixrXNTL6a2EXX+FgC1%0AQS/qfHUxdzQTW9c6t9rUPJqZupQu6Otibf/fXzPe9SkFzLaZRVEU7eHBSGA5a6I7evQo/fvfzblz%0AVqzWfwP6ruw84I9ULeG/CUwGJmpbZXk+kInVup6q0oHVwONUOQUoyPIDWK1hwDzgLJL0xJWx/h8Q%0AQWTkeP785/48/vgMLXrT37PctrjyXjYqP7Kt5Q7k9xrY24MZ1aRarVbef/99lixZwq9+9SuefPJJ%0AYmNj/TjiqzhLnEpLS2PVqlWkpaVx4MABpk+fTnZ2th9GKjBAiFWzkZWVxXvvvUdiYiKJiYm0a9dO%0AuyBYLBb2799PXFwczZo10y56tiLWU9nzvkDvwWkW66nazhKqDxrl5eVer3n0Bd6cefTHjLftjdbZ%0A8rjZcfWByFlJgf59fOnSJf70p2m8++4XlJYuB65XfxKSNApFiQFmA18AzwC3UCVKj1HVgLWaqoYr%0AqAoSeBoYCtyCJM1FUd4Awq9sv4Qsz0FRjqMo42jXbidZWemEh4ebSqQ6w9F7WW00sm1a8oTjhj+w%0ALS2xje9WURSFDz/8kEWLFtGpUyf+9re/0bZtWz+O2JiarKYmTpzIgAEDGDlyJACJiYlkZWXRokUL%0AXw9TUB1hXWU2unTpwvnz58nNzSUjI4Nvv/1WEwznzp1DlmXmzJnDXXfdpd1sjS6WRpGSenHlr4ul%0AoxpBs1y8bW8uttTUPa8iy7KWcR8I9ZpGqLP5apa6NzqU3bUzq63Vlm2DnvpAZDZHDHfQ13A6s22r%0A6b1sZLelKAovv7ycDRs28ve/P0hZ2Tyqlvb/A3xHlR8qwK+ANUjSLCTpLhSlAYrSm6tCFeBGqjxY%0AHwdSUZQJXBWqABFYrQuQ5UUoyjJeeeXfNG7c2NSlGEbYvpdlWdaaQdWHb7jaDKp33PB3Lb0z9CJV%0A/9lRFIXs7GwWLlxI69atefXVV2nXrp0fR1x7jh8/Tps2bbSvW7duTXFxsRCrJkaIVT8SExPD0KFD%0AGTp0KN999x2rV69m3bp19OrViwceeABJksjMzGTt2rWUl5fTvHlzEhISSEhIICkpic6dOxMeHq5d%0AUPSzKbZ2I96s1zTC1vQ+EO2NbG/8wcHB2vGoNya1O972Am+0PGi2GxIYe4pGRET4ZYyu2pnVZLWl%0AlsnYNuYEcimGrVNBUFCQYQqQO9gKLD1Wq5UpUyZz8803MnLk//LLL59TWfl/KMoDgG30ZQsU5SVg%0ANoqSD7xg8JvikeVbsFr3IsvbsFoHAFE224MIDZW55577A7rJxpkFlaOHBUcTDP5uVnRFpB46dIiU%0AlBSaNWvG6tWr6dSpk0/G5k30q8qBer24VhBi1SS8/vrrWCwWcnJyDAvQrVYrJSUl5ObmcvjwYV57%0A7TUKCgooKyujcePGJCYmaiI2ISHBztDcVTP+uopYT5nemwWj0gVHM3Wuznj76mGhpuMJhPpaV2YJ%0A1QZFW7N4WZaprKy0e2+b7WHBEbalJb4KWVDfh7fddhuffbaf3/1uKMeOWVCUZAdjLKYqjnUGVXZU%0AA2225mO1vkeVDdZ2JGkMirICiLuy/SANGnzNypX/8t4BeZHa+qQ6W1nQP5QZBUx4o9zLtp7bkUj9%0A4osvWLBgARERESxZsoSkpKSA+Cw5Iy4ujqKiIu3r4uJi4uLiavgOgb8RNasBjqIo/PTTTxw+fJjc%0A3Fxyc3PJz8+ntLSU6OhoEhIStJrYxMREGjVqZCdindVruuL9WN9cCjztwemt5i53fr+tCDKj36s7%0AOGoCc+ZlalarLdvj8bdTQUVFBbNmPc1rr/0fly49BXTUtknSq0hSFlbrfOAgVeEBw6hqvrIgSWNR%0AlOuBhwErsvwWVusHwFwgiYiIibz55ssMHDhQ/2tNja1I9ZXLh1HphqfqvG1FqlE9t6Io5OXlMX/+%0AfGRZ5plnnqFbt26m+Ky4Q001q7YNVtnZ2cyYMUM0WJkH0WB1LaEoCufOnePw4cPk5eWRm5tLXl4e%0A58+fJzw8XJuJVWdjmzVr5raIlSSJyspKKisr7W6ygXZRUzGy0vLmzJa3LaACza7JGbU9HrNabZnZ%0A4mj79u1MnDid0tLxQDJwHJgAzATUOsXvgKVIUkcU5TYk6Q0UZQlX7alAkt5DUbYQFJTInXd25LXX%0AXnHLBsqfWK1WysrKNFFnFiu6msIPHNlt2doj1iRSjxw5QkpKCmVlZTz99NPceOONAXk9t02catGi%0ARbXEKYApU6aQkZFBVFQU69evp1evXv4csuAqQqwKqi5IFy5c0ASsKmLPnDlDaGgonTp10gRsYmIi%0AzZs31y7Q6jL/t99+S2xsrLZUpSiK3ZKVmfw1XcG2ycgMosHINsddo/j6YtcE3jsef1lt6We2zCKC%0A9OTm5jJs2Ah++qk7lZXfXWkunKHb6xyStAxFOQk8SHU/VoB3CA7eSX7+FzRp0sSpDZS/SzfMKlKd%0A4Sj8QF8mExISwunTp7lw4QLt27cnNDSUb775hpSUFH7++WeefvppbrnlloC4dgvqJUKsChyjKAqX%0ALl3iyJEjWklBXl4eJSUlBAcHEx8fD8Dnn3/OuXPneO+992jRooUmVh1dJB2JK39f/I2ajMxgpVUT%0AzpK7bN0ibEWdmY/JEUYhC76yn/LWEqw7aVNm4ezZs9xzz0g++SQHeBao3i0tyy9htX6FJMkoyuNc%0AnXkFsBAZOZ9ly2byP//zoN33GdV5+7N0I1BFqiPUmfvy8nKtKRSq3odvv/02zz33HD/88AMxMTFc%0AvnyZ5ORkBg4cqJWMNWnSxM9HILhGEWJV4D6nT5/m5ZdfZvXq1TRr1owBAwZQUlLCiRMnkGWZ+Csx%0Aempt7PXXX2+37FSTuHJUE+vNG7g+mclZtKvZsa2vBbSyBfXGb5bmLlfR20+Zrf7ZUaqUozpv1bRf%0AFd2B8FCkx2KxMGfO3/nHPzZw6dIEqhqsVPKAFcAMJOlTFCUL+B/g1wDI8n/o1auYzMwMt465ptIN%0ATzceBcpMt6u4Ul5y/PhxFi9ezDfffMODDz5IdHQ0R44cIT8/X/s3adIkFi1a5KejEFzDCLEqcA9F%0AUbj55pvp1q0b06dPp0ePHnbbLBYL33zzjV1zV1FREVarlTZt2tg1drVr184urtMVk3hPLgs6asoJ%0AJNFgi6tNYM6au1xtovPF8XgjF95XODKKV6+vaie4Lx/MPE1qaipjx07m0qV7UZTfAJVI0mwUJQm4%0A48peucA24HZgEBERz5Gd/QEdO3Z09GPdwtEqjup/bPRQ5ug97azRKNBwRaSePHmSpUuX8tVXXzF7%0A9mwGDRpkKMzVpszw8PBq27xBRkYGM2bMwGKxMG7cOGbOnGm3PTMzk3vuuUdzyhk+fDhPPfWUT8Ym%0A8DlCrArcR73wuYp6M/nuu+80m63c3Fy+/fZbKisradWqlTYLm5SURIcOHexmmhzVEBqJWFdmCPX1%0AjrbLYYGIp5qmzNJ0pPcUrQ8PEbb2YGqTnqMSGbPVaxphmwb2/fffM3z4A5w6FU95eYMr7gB/xrap%0ACn4ANiDLMrNnz+DJJ2d5fYy2D8DOHswALegjLCysXohU9UHckUg9deoUL7zwAgcPHmTmzJncfffd%0Appk9tlgsJCQk8O677xIXF8fNN9/M5s2bSUpK0vbJzMxk2bJlpKam+nGkAh8hEqwE7uOOUIWr/pjt%0A27enffv2DBkyRNtmtVopLi4mLy+Pw4cPk5WVxdGjR+0CD9SZWH3ggX6G0DbwwNHSq5qG5E/Te0+h%0AF911TZqqTXKXJ0s39HZNgRYaocdZ2pQrRvE1vad9XbphG3ihlmNERkbStWtXDh78iJEjH+KDD1JR%0AlBHYC1WAWCCZhg3385e/POaT8dYUfKCe54qKCs37WD2Pqie0p0oKfImtJV1wcLDhNeHMmTOsWLGC%0Affv28dhjj7F06VLTiFSVnJwcOnbsqPVFjBo1ip07d9qJVahu4i+4thBiVeAzZFmmbdu2tG3bljvu%0AuEN73WqtW+CBesMvLy/XbkZwVZCpQsJM3pquYNRk1KBBA6+O31bE2j6oGNUf255rV2cI9UuV9UGk%0A1iZtyplRvO1Mt1EErbdmvV2pGW7YsCG7dm3nL3+ZyaZNb3HpUmOgtc1PuURExAds375ViyD1J+p7%0ATr/cX9N72sy13q6I1HPnzrFq1Sr27t3L9OnTSUlJMe3nzCj69MCBA3b7SJLE/v376d69O3FxcSxZ%0AsoQuXbr4eqgCPyLEqsDvyLJMbGwssbGx/O53v9Ne1wcevPXWW4aBBy1btuSjjz5i06ZN7Nq1i6Sk%0AJGRZtrsRqbNejhphzHATUjFKmvJ3xr2jmStXk7skSdLqOENDQ+s8M+xv9EELnhTdklRzBK3trLen%0AGhZVkVpWVgY4D/YICgpi+fIl/Pa3tzN27EQuXvwt0OvK977H738/lFtuuaX2J8ED6GtS9Q96Nc3G%0A6utijR4YjGzNvIlepBq953755Rf+8Y9/sHv3bqZMmcK8efO8noJWV1w5b7169aKoqIjIyEjS09O5%0A9957OXLkiA9GJzALomZVEHCogQe7du1izZo1HDx4kD59+hAeHk5lZaVLgQfOPEz94RXr6eQsf6MK%0AV/VGrz5AmK25yx305QtmCFpw1WrLqNbbE41teXl5DBlyH2fOtKW8PJFGjd7m8OHP/WZ95M3GKWfX%0AD0citi6/3/a64Og9d/HiRf75z3+yY8cOJkyYwJgxY9wu4fIX2dnZzJ07l4yMDAAWLFiALMvVmqxs%0AadeuHYcOHaJp06a+GqbAd4gGK0H9Ydq0abz11ltMnjyZSZMmERMTU+fAA395xRo5FZh9NqQmnC0l%0Am6W5yx1c6bQ2I47e02qQh/rfkJAQQkJCan2uf/75Z0aOfJD9+z9k3bq1jBgxwgtHUzP+7O73hjev%0AvsQkPDy8mki9dOkS69evZ8uWLTz88MOMGzfOFKUX7lBZWUlCQgJ79+6lVatW9O7du1qDVUlJCc2b%0AN0eSJHJychgxYgSFhYX+G7TAmwix6mv++te/smvXLkJDQ+nQoQPr16+nUaNG1fZzZtshqM6RI0do%0A27atS9YqzgIP2rdvb2ezFRcXZydiveUVW9+cCuo6S+dOopSvGmHqmwen+je6dOkSslyVZqR/jxut%0AMLjycGaxWHjvvfcYOHCgTx8uzG5BVZt4VPVz5EikXr58mY0bN/L666/z4IMPMmHCBJ/ZTHmD9PR0%0A7R44duxYZs+ezZo1a4CqeNTVq1fz8ssvExwcTGRkJMuWLfN7mYnAawix6mv27NnD7373O2RZZtas%0AKvuWlJQUu31cse0QeAd15qKgoECbic3NzeX48ePI8tXAA7WswCjwwF2vWLh6c1XrN+uDAPKm/ZSj%0ABwZ3m7vcwdauyYwCyF2M/kbO6mLNbrVlK1IDMWzB6OGssrKymjfv/v37KS8vJzExkdjYWN58803W%0Ar1/PiBEjmDx5MlFRUX4+EoHAowjrKl+TnJys/X+fPn3Yvn17tX1cte0QeB519q9r16507dpVe90o%0A8GD79u0OAw/UfG1HXrG2lkQqwcHB2oxJIN1gbfGV/VRtm7vctX/Suy+YobGtruhFamRkZI0lJjVZ%0AmpnFass2tjaQbelsZ7DVv1NQUJC2wqKe6/z8fHbt2sWRI0c4c+YMzZo1o2/fvpSWlrJ79247qz+B%0AoL4ixKqPWLduHaNHj672uiu2HQLfonZjq01a9913H2AceJCens63336LxWIhNja2WuCBxWJh06ZN%0AnDp1ij//+c9a7aYqrFQfS3/7arqDrU2YJzxfa4ur9k+udHOrwtvWU9SM595VXOkcd4e6Wm15onNe%0AL1Lrw9/ItmxG/yChfv5jYmIoKytjwoQJPPLII/z444/k5eWRn5/Pli1byMvL46mnnuKBBx7w49EI%0ABN5FiNU6kpyczMmTJ6u9Pn/+fIYOHQrA888/T2hoqOHFJJAvttca7gQeZGRksG/fPk6fPk23bt3o%0A3bs3aWlpDgMPbGesarrZ+7NrXl8baGb7KWf2T6qNlmpnpn5PUFAQFosFwDTNXe7gj7AFd6y2ahMw%0AUd9FakRERLXzZ7VaSU1NZeXKlQwYMID09HSt871t27bcdNNN/hi6hit9FtOmTSM9PZ3IyEg2bNhA%0Az549/TBSQX1BiNU6smfPnhq3b9iwgbS0NPbu3Wu4PS4ujqKiIu3roqIiWrdubbivwLyogQdRUVHs%0A3LmTtLQ07r//fmbMmEHTpk3tAg+OHDnC5cuX3Qo88JdXrNHSeKAuuwKaSFLFkzqjpYZHeMPD1BfY%0AuhWYJRHM1YCJmrx51b9DfRGpqpetUcoZVP0d09PTeeGFF+jbty+pqanExMT4cdTVsVgsTJkyxa7P%0AYtiwYXala2lpaRw9epSCggIOHDjApEmTyM7O9uOoBYGOEKteJCMjg8WLF5OVleWwnuimm26ioKCA%0AwsJCWrVqxZYtW9i8ebNXx7V161bmzp1Lfn4+Bw8epFevXob7xcfH07BhQ+1mk5OT49Vx1QciIiJo%0A0aIFeXl5tGzZUnu9toEH6r9GjRo59IpVm4E86RVrZD9VH8SCs7QpbyV3eQu9pZaZZ7tVnJnx276f%0AVdGqHqNZVhncwVWRunfvXpYuXUqvXr3Yvn273fXDTLjSZ5GamsqYMWOAqn6Ns2fPUlJSQosWLfwx%0AZEE9QIhVLzJ16lTKy8u1Rqtbb72Vl156iRMnTjB+/Hh2795NcHAwq1at4o477tBsO7zdXNWtWzfN%0APLomJEkiBH7HKwAAFudJREFUMzNTGC+7QWRkJHPmzHG6nyRJXHfdddx+++3cfvvt2utq4MHhw4fJ%0Ay8tj165dLF68mPPnzxMeHq7NxKoi1lHggXrTd9cr1hMm8WajLmlT7jR3+bLhKBBFqjP0y/22TYs1%0ArTLU1mrL2+gf+IxEqqIoZGVlsXjxYpKSkvjXv/5l+pU1V/osjPYpLi4WYlVQa4RY9SIFBQWGr7dq%0A1Yrdu3drXw8ePJjBgwf7algkJia6vK8TazOBh5EkicaNG3Pbbbdx2223aa/rAw/effddVq5cWWPg%0AQVhYmPa9RrODejsi9eaqejsGukj15tK4p5q73J0dDKS6YVdxpSa1JpeCmh7QvJEo5QxXRer+/ftZ%0AuHAh8fHxrF+/XpupNDvu+CbX5vsEAiOEWBU4RJIkBg4cSFBQEBMmTGD8+PH+HtI1iyRJNGjQgN69%0Ae9O7d2/tdX3gwb59+1i7dm2NgQe2IrakpIRTp07Rtm1bu8740tJSh+UEZr/p+HvW0ZXmLv1yt7Py%0AjfooUm29bGtbZuKO1VZdbM3cOSbV4UOf3KaO6+DBg6SkpNCiRQvWrFlDhw4d6vQ7fY0rfRb6fYqL%0Ai4mLi/PZGAX1DyFW6ymuuBQ4Y9++fcTGxnLq1CmSk5NJTEykX79+nh6qoA6oDUI9evSgR48e2uv6%0AwIPPPvuMN954Qws8aNasGRcvXuTgwYNMnDiR2bNn283+6L1ijRpgjLLm/YnZBZ0rs4NGzV3qPsHB%0AwYZ1toGGJ0SqMzxpa+bK+XZFpH7++ecsWLCAhg0b8sILL5CQkBCQf0dX+iyGDRvGqlWrGDVqFNnZ%0A2TRu3FiUAAjqhBCr9RRnLgWuEBsbC0BMTAy///3vycnJEWI1QHAUePDJJ5+wcOFC9u7dy6BBg5gx%0AYwZHjhxhyJAhLgUe6G/0/jKGt0WfNtWgQYOAEgFGXfOq+LFYLISEhGgz3upMbE0paWY9dl+IVFeo%0ArdWWUc23KnYtFgvh4eGGIvXw4cMsWLCA4OBgUlJSuOGGG0z7N3IFR30WtvGod911F2lpaXTs2JGo%0AqCjWr1/v51ELAh0Rt3oNM2DAAJYsWcKNN95YbVtpaSkWi4UGDRpw8eJFBg0axJw5cxg0aJDXxuOq%0AS4ErHn+C6nz//ff069ePGTNmMH78eKKjo7VtRoEHubm5NQYeOBKxjvLPPdnFbWSpFWhxm0boBZ2j%0AYzI6z/5+aHCEq8dkVoxqvtX/h6vi98cff+TLL78kMTGR+Ph4jh49SkpKCpWVlcyZM4fu3bsH1HEL%0ABH7C8EMixOo1yI4dO5g2bRqnT5+mUaNG9OzZk/T0dDuXgmPHjmnJTZWVlfzxj39k9uzZXh1Xfn4+%0AsiwzYcIEzcJFj8ViISEhwc7jb/PmzSKe1kUsFovbTUZq4EFubq727+jRo5SXl9O8eXM7dwJngQd1%0AFbFGllr62axAQxVCNS0ju/uz9OfaHwETgS5SjdA3gwUHB2vv8U8++YT58+dTUFDA6dOnCQkJoU+f%0APvTt25cuXbqQlJQkYlEFAucIsSoIDAYMGOBQrH788cfMmzePjIwMAFJSUgCYNWuWT8coqBKxJSUl%0AdjOx7gYeGAlZR1ZEqvgB6oVbgS+Ft/6hwdn5rktdrK1INVoaD0RqstVSKSwsZOHChZSUlPD444/T%0AtGlT8vPzyc/PJy8vj7y8PJo1a8YHH3zgp6MQCAICw4uFqFkVBBSuePwJfIMsy8TGxno18EC12FIf%0AqtWGGXW7Gfw03cXWJB7wyexwbZu73Enu0ovUQA+RAPumPUd1tsXFxSxatIjCwkL+9re/0b9/f20f%0AfYmVP60Az5w5w8iRI/nuu++Ij4/nrbfeonHjxtX2E2EwAjMixKrAp9TVpSDQb37XAp4IPGjTpg3b%0At29n06ZN/Oc//6FJkyYEBQXV6BVr5jhUqB64YIbZ4bpGoqoxteq2iIiIeidSHTXtnTx5ksWLF5OX%0Al8eTTz5JcnKy0+P253lJSUkhOTmZJ554goULF5KSkqKtTNkiwmAEZkSIVYFPqatLgSsefwJz4krg%0AQU5ODs8++yyffPIJPXv2JCEhgfnz5zsNPHDVZssfHfNmFKnOcBaJapsipSiKdiyqwDNLc5e7WK1W%0AysrKahSpP/74I8uXL+fTTz9l1qxZrF69OiBm91NTU8nKygJgzJgx9O/f31CsggiDEZgPIVYFpsTR%0AxdIVjz9BYKEGHrz33nssXryYe++9l1dffZXOnTu7HXhgKxr87RWritSysjJkWa4XHqlwVdCp6Uxq%0ACYN+JtaTyV3exhWR+tNPP7FixQo+/vhjHn/8cZYvXx4QIlWlpKRE8zpt0aIFJSUlhvuJMBiBGREN%0AVgLT4IpLAUB6erpmXTV27FivuxSAqPfyBe+++y6dO3embdu2Ne5nG3iglhTk5uZqgQfx8fF2IlZN%0A53LkFetp2yd1fJcvXyYoKEjrGg9k6uJY4MvmLnexTTsLDQ0lNDS0mgA9e/YsK1euJCsrixkzZjB8%0A+HCPxfZ6GkdlVs8//zxjxozh559/1l5r2rQpZ86cqbbvDz/8YBcGs3LlSuGvLfAlwg1AIKgtTzzx%0ABNddd51W7/Xzzz8bLqG1a9eOQ4cOiXovP6A2Lh07dkxr7srNzeX7779HURTDwANbYVRXr1ghUt3/%0A2Y78Yr1dh6yP5A0LC6smUs+fP89LL73Ef/7zH6ZOncro0aNNK1JdITExkczMTFq2bMkPP/zAgAED%0AyM/Pr/F75s2bR3R0NI8//riPRikQCLEqENSaxMREsrKyaNGiBSdPnqR///6GF/p27drxySef0KxZ%0AMz+MUmCEJwIPnHnFqqIuODhYiFQP/G5HDw51rUN2RaReuHCBV155hdTUVCZNmsSDDz5o13wWqDzx%0AxBM0a9aMmTNnkpKSwtmzZ6s9cPsjDEYg0CHEqkBQW5o0aaItoSmKQtOmTe2W1FTat29Po0aNRL1X%0AgGAbeKCWFBgFHiQlJdGpUye7wIPTp0/zxRdfcOONN2qiVRVX/l7eri1mD12obXKXWkNbXl7uUKRe%0AunSJtWvXsm3bNsaNG8fDDz9MaGion47U85w5c4YRI0bw/fff25Uy+TsMRiDQIcSqQFATot5LoFJT%0A4EFERAQWi4XPP/+cIUOGsGTJEqKjo2sMPLBd3jbKmPd3o47ZRaozairhUJu/ZFkmNDSUy5cvI8uy%0AFjdcVlbGa6+9xr/+9S8eeugh/vSnP2luEwKBwOcIsSoQ1BZR7yU4fvw4ixYtYuPGjfTv359f//rX%0AFBYWuhV44Ki5y19esYEuUh2hKAqXL1/m8uXLBAcH28Wi7ty5kylTphATE0Pr1q05duwYv/nNb5g0%0AaRI9evSgSZMm/h6+QHAtI8SqQFBbzFbvlZGRoTkijBs3jpkzZ1bbZ9q0aaSnpxMZGcmGDRvo2bOn%0Ax8dxraAoCrfeeit9+/blL3/5C61ataq2XQ08yM3N1eI1jQIPEhMTadasWTURa1QX6y2v2PouUsvL%0Ay7X6YX1TVEVFBW+88Qbbtm3jhhtuICYmhm+++Ub7m0VFRTFkyBDWrl3rp6MQCK5phFgVCGqLmeq9%0ALBYLCQkJvPvuu8TFxXHzzTezefNmkpKStH3S0tJYtWoVaWlpHDhwgOnTp5Odne3xsVxLWCwWt7vB%0AbQMPVHeCvLw8fvrpJ8LCwujUqVO1wIOavGJrErGu2GzVZ5GqOjE4EqmVlZVs27aNNWvWcPfddzN9%0A+nQaNWpU7eccP36cn376ie7du/vyEDS2bt3K3Llzyc/P5+DBg/Tq1ctwP1ceWAWCAESIVYGgPvDx%0Axx8zb948MjIyALQZ3lmzZmn7TJw4kQEDBjBy5EjA3s1A4H8URbELPFBFrBp40KFDB7uZWH3ggbte%0AsZIkaRGiqpm/2VO0XMEVkWqxWHj77bdZvXo1ycnJPPbYY6Ze6s/Pz0eWZSZMmMDSpUsNxaorD6wC%0AQYBieFEKbH8VgeAa5Pjx47Rp00b7unXr1hw4cMDpPsXFxUKsmgRJkoiMjKRHjx706NFDe10fePDZ%0AZ5/xxhtv1Bh4YDszapQipYpYgKCgILv6TTOlSLmD3tM2Kiqqmki1Wq3s3r2bFStW0K9fP3bt2sV1%0A113npxG7TmJiotN9cnJy6NixI/Hx8QCMGjWKnTt3CrEqqLcIsSoQBBiuigv9qkkgipJrDUmSCAsL%0Ao2vXrnTt2lV73SjwYPv27Q4DD+Lj48nIyGDlypWsW7eO2NhYO2utiooKLl++HBBRqLa4KlL37NnD%0AsmXLuPnmm9mxY0e9e0hz5YFVIKhPCLEqEAQYcXFxFBUVaV8XFRXRunXrGvcpLi4mLi7OZ2MUeBZJ%0AkggJCSEhIYGEhAStNlofePDVV1+xZs0aDh06RExMDH369GHTpk0kJSVpgQdhYWEObbbUelazecUq%0AikJFRQVlZWUEBQURGRlZLXjBarWSmZnJkiVL6Nq1K1u2bKnWCGcWHNnkzZ8/n6FDhzr9fjM+SAgE%0A3kSIVYEgwLjpppsoKCigsLCQVq1asWXLFjZv3my3z7Bhw1i1ahWjRo0iOzubxo0b17vZJQGaoGzf%0Avj3Hjh1jy5YtAGzcuJEhQ4Zw4sQJzSs2KyvL5cADIxHrD69YVaRevnxZK53Qi1RFUfjoo49YtGgR%0AHTp04LXXXuP666/3+Fg8yZ49e+r0/a48sAoE9QkhVgWCACM4OJhVq1Zxxx13YLFYGDt2LElJSaxZ%0AswaACRMmcNddd5GWlkbHjh2Jiopi/fr1Ph2js07lzMxM7rnnHtq3bw/A8OHDeeqpp3w6xvpGRUUF%0Ac+fOZdiwYZrobNu2LW3btuXOO+/U9tMHHmzYsEELPGjcuLFms5WUlERCQgJRUVE1esVWVFR43CtW%0AL1IjIiIMReqBAwdISUkhLi6Of/7zn9r7qb7gqAHalQdWgaA+IdwABAKBR3GlUzkzM5Nly5aRmprq%0Ax5EKbFEUhZ9++kmric3NzXU78MDIZgswrIs1ErF6kRoeHl6t9EBRFD799FNSUlJo0qQJzzzzDJ07%0Ad/bdifIyO3bsYNq0aZw+fZpGjRrRs2dP0tPT7WzyANLT07UHwrFjx4pYVEF9QVhXCQQC7+OKtVZm%0AZiZLly7l3//+t1/GKHAdfeCBKmJdCTwA17xiZVnWhKoqUvXWWoqi8OWXX7JgwQLCw8OZM2cOSUlJ%0Aon5TIKhfCOsqgUDgfVzpVJYkif3799O9e3fi4uJYsmQJXbp08fVQBS4gSRKNGzfmtttu47bbbtNe%0A1wcevPvuu6xcuZIzZ84QGhrqNPBAdTg4deoUUVFR2u+yWq2UlZWRkpJCREQESUlJREZG8vrrryPL%0AMs8++yy/+tWvhEgVCK4hhFgVCAQexRUR0atXL4qKioiMjCQ9PZ17772XI0eO+GB0Ak8hSRINGjSg%0Ad+/e9O7dW3tdH3iwb98+1q5daxd40LlzZywWC1u3biUmJoZt27ZpM6lqTWzXrl05cOAAL730El9/%0A/TWlpaV06NCB5557ji5dutClSxduv/12WrZs6cezIBAIfIEQqwKBwKO40qncoEED7f8HDx7M5MmT%0AOXPmDE2bNvXZOAXeoabAg8uXL/P666+zePFizp49y29/+1u+//57hgwZYhd4EB0dzfvvv8+ZM2dY%0AtmwZt9xyC2VlZRw5ckQrRXjrrbeIiYnxm1h1NRY1Pj6ehg0bEhQUREhICDk5OT4eqUAQ+AixKhAI%0APIorncolJSU0b94cSZLIyclBURSfCdVHHnmE3bt307x5c7788kvDfaZNm0Z6ejqRkZFs2LCBnj17%0A+mRs9RlJkhgzZgyff/45c+bMYeTIkQQFBRkGHmzbto0XX3yRfv36aTP1ERERdO/ene7du/v5SKro%0A1q0bO3bsYMKECTXuJ0kSmZmZ4kFMIKgDQqwKBAKP4oq11rZt23j55ZcJDg4mMjKSN99802fje/jh%0Ah5k6dSoPPfSQ4fa0tDSOHj1KQUEBBw4cYNKkSWRnZ/tsfPWZuXPn0qlTJzsbKqPAg0CwMXMlFlXF%0ASSOzQCBwgnADEAgE1xyFhYUMHTrUcGZ14sSJDBgwgJEjRwJVoiQrK0uEKggMGTBgAEuXLnVYBtC+%0AfXsaNWpEUFAQEyZMYPz48T4eoUAQUAg3AIFAIHCGkZtBcXGxEKvXIHWNRQXYt28fsbGxnDp1iuTk%0AZBITE+nXr5+nhyoQ1GuEWBUIBAId+hUnYZN0bVLXWFSA2NhYAGJiYvj9739PTk6OEKsCgZt4PsxZ%0AIBAIAhi9m0FxcTFxcXF+HJHA7DgqpystLeWXX34B4OLFi7zzzjt069bNl0MTCOoFQqwKBAKBDcOG%0ADWPjxo0AZGdn07hxY1ECIKjGjh07aNOmDdnZ2dx9990MHjwYgBMnTnD33XcDcPLkSfr160ePHj3o%0A06cPQ4YMYdCgQf4ctkAQkIgGK4FAcE0xevRosrKyOH36NC1atGDevHlUVFQAaDZEU6ZMISMjg6io%0AKNavX++wecYbOLPWyszM5J577qF9+/YADB8+PCC65wUCgcAFDGuuhFgVCAQCE/Hhhx8SHR3NQw89%0A5FCsLlu2jNTUVD+MTiAQCLyKoVgVZQACgUBgIvr160eTJk1q3Ef4dgoEgmsJIVYFAoEggJAkif37%0A99O9e3fuuusucnNz/T0kgUAg8CpCrAoEAkEA0atXL4qKivjvf//L1KlTuffee/09JFPz17/+laSk%0AJLp37859993HuXPnDPfLyMggMTGRTp06sXDhQh+PUiAQ1IQQqwKBQBBANGjQgMjISAAGDx5MRUUF%0AZ86c8fOozMugQYM4fPgw//3vf+ncuTMLFiyoto/FYtGa6nJzc9m8eTN5eXl+GK1AIDBCiFWBQCAI%0AIEpKSrSa1ZycHBRFoWnTpn4elXlJTk5GlqtudX369KG4uLjaPjk5OXTs2JH4+HhCQkIYNWoUO3fu%0A9PVQBQKBA4RYFQgEAhMxevRo+vbty9dff02bNm1Yt24da9asYc2aNQBs27aNbt260aNHD2bMmMGb%0Ab77p8zEWFRUxYMAAbrjhBrp27cqLL75ouN+0adPo1KkT3bt357PPPvPxKKuzbt067rrrrmqvG0Xs%0AHj9+3JdDEwgENSDiVgUCgcBEbN68ucbtjz76KI8++qiPRmNMSEgIy5cvp0ePHly4cIEbb7yR5ORk%0AkpKStH3S0tI4evQoBQUFHDhwgEmTJpGdne2V8SQnJ3Py5Mlqr8+fP5+hQ4cC8PzzzxMaGsoDDzxQ%0AbT8RpysQmBshVgUCgUDgFi1btqRly5YAREdHk5SUxIkTJ+zEampqKmPGjAGqlt/Pnj1LSUmJV9LA%0A9uzZU+P2DRs2kJaWxt69ew236yN2i4qKaN26tUfHKBAIao8oAxAIBAJBrSksLOSzzz6jT58+dq8b%0ALa0b1Yt6m4yMDBYvXszOnTsJDw833Oemm26ioKCAwsJCysvL2bJlC8OGDfPxSAUCgSOEWBUIBAJB%0Arbhw4QL3338/K1asIDo6utp2fXiBP5bbp06dyoULF0hOTqZnz55MnjwZgBMnTnD33XcDEBwczKpV%0Aq7jjjjvo0qULI0eOtJslFggE/kXErQoEAoHAbSoqKhgyZAiDBw9mxowZ1bZPnDiR/v37M2rUKAAS%0AExPJysryShmAQCCoN4i4VYFAIBDUHUVRGDt2LF26dDEUqgDDhg1j48aNAGRnZ9O4cWMhVAUCQa1w%0ANrMqEAgEAoEdkiT9GvgA+IKrK3BPAm0BFEVZc2W/VcCdwEXgYUVRPvX9aAUCQaAjxKpAIBAIBAKB%0AwLSIMgCBQCAQCAQCgWkRYlUgEAgEAoFAYFqEWBUIBAKBQCAQmBYhVgUCgUAgEAgEpkWIVYFAIBAI%0ABAKBafl/CyJH4aoVOLIAAAAASUVORK5CYII=%0A
-
-
-作者 & 更新时间
-------------------------------------
-作者:`李金  <lijinwithyou@gmail.com>`
-
-提交: 2017/12/14
-
diff --git a/course/source/aipy-scipy/cn/3csv.rst b/course/source/aipy-scipy/cn/3csv.rst
deleted file mode 100644
index a2c153a..0000000
--- a/course/source/aipy-scipy/cn/3csv.rst
+++ /dev/null
@@ -1,248 +0,0 @@
-CSV 文件和 csv 模块
-=======================
-标准库中有自带的 csv (逗号分隔值) 模块处理 csv 格式的文件：
-
-
-::
-
-  import csv
-
-
-
-读 csv 文件
-----------------------------------------------------------
-假设我们有这样的一个文件：
-
-
-%% file data.csv
-
-::
-
-	"alpha 1",  100, -1.443
-	"beat  3",   12, -0.0934
-	"gamma 3a", 192, -0.6621
-	"delta 2a",  15, -4.515
-
-
-打开这个文件，并产生一个文件 reader：
-
-
-
-::
-
-	fp = open("data.csv")
-	r = csv.reader(fp)
-
-可以按行迭代数据：
-
-
-::
-
-	for row in r:
-	^4$print row
-	    
-	fp.close()
-
-
-结果:
-::
-    
-    ['alpha 1', '  100', ' -1.443']
-	['beat  3', '   12', ' -0.0934']
-	['gamma 3a', ' 192', ' -0.6621']
-	['delta 2a', '  15', ' -4.515']
-
-
-默认数据内容都被当作字符串处理，不过可以自己进行处理：
-
-
-
-::
-
-	data = []
-
-	with open('data.csv') as fp:
-		^4$r = csv.reader(fp)
-		^4$for row in r:
-
-	data.append([row[0], int(row[1]), float(row[2])])
-	    
-	data
-
-
-结果:
-::
-    
-	[['alpha 1', 100, -1.443],
-	['beat  3', 12, -0.0934],
-	['gamma 3a', 192, -0.6621],
-	['delta 2a', 15, -4.515]]
-
-
-
-::
-
-	import os
-	os.remove('data.csv')
-
-
-
-写 csv 文件
--------------------------------------------------------------
-可以使用 *csv.writer* 写入文件，不过相应地，传入的应该是以写方式打开的文件，不过一般要用 *'wb'* 即二进制写入方式，防止出现换行不正确的问题：
-
-
-::
-
-	data = [('one', 1, 1.5), ('two', 2, 8.0)]
-	with open('out.csv', 'wb') as fp:
-	w = csv.writer(fp)
-	w.writerows(data)
-
-
-显示结果：
-
-::
-
-	!cat 'out.csv'
-
-
-结果:
-::
-
-	one,1,1.5
-	two,2,8.0
-
-
-
-
-
-更换分隔符
-----------------------------------------------------
-默认情况下，*csv* 模块默认 *csv* 文件都是由 *excel* 产生的，实际中可能会遇到这样的问题：
-
-
-::
-
-	data = [('one, \"real\" string', 1, 1.5), ('two', 2, 8.0)]
-	with open('out.csv', 'wb') as fp:
-	w = csv.writer(fp)
-	w.writerows(data)
-
-
-
-
-::
-
-	!cat 'out.csv'
-
-
-结果:
-::
-
-	"one, ""real"" string",1,1.5
-	two,2,8.0
-
-
-可以修改分隔符来处理这组数据：
-
-
-
-::
-
-	data = [('one, \"real\" string', 1, 1.5), ('two', 2, 8.0)]
-	with open('out.psv', 'wb') as fp:
-	w = csv.writer(fp, delimiter="|")
-	w.writerows(data)
-
-
-
-
-::
-
-	!cat 'out.psv'
-
-
-结果:
-::
-
-	"one, ""real"" string" | 1 | 1.5
-	two|2|8.0
-
-
-
-
-::
-
-	import os
-	os.remove('out.psv')
-	os.remove('out.csv')
-
-其他选项
--------------------------------------------------------------------------
-
-*numpy.loadtxt()* 和 *pandas.read_csv()* 可以用来读写包含很多数值数据的 *csv* 文件：
-
-
-
-::
-
-	%%file trades.csv
-	Order,Date,Stock,Quantity,Price
-	A0001,2013-12-01,AAPL,1000,203.4
-	A0002,2013-12-01,MSFT,1500,167.5
-	A0003,2013-12-02,GOOG,1500,167.5
-
-
-Writing trades.csv
-
-
-使用 pandas 进行处理，生成一个 DataFrame 对象：
-
-
-
-::
-
-	import pandas
-	df = pandas.read_csv('trades.csv', index_col=0)
-	print df
-
-
-             
-
-结果:
-::
-
-    Order  Date Stock  Quantity  Price                                 
-	A0001  2013-12-01  AAPL      1000  203.4
-	A0002  2013-12-01  MSFT      1500  167.5
-	A0003  2013-12-02  GOOG      1500  167.5
-
-
-通过名字进行索引：
-
-
-
-::
-
-	df['Quantity'] * df['Price']
-
-结果:
-::
-    
-	Order
-	A0001    203400
-	A0002    251250
-	A0003    251250
-	dtype: float64
-
-
-::
-
-	import os
-	os.remove('trades.csv')
-
-
-作者 & 更新时间
-------------------------------------
-作者:李金  lijinwithyou@gmail.com
\ No newline at end of file
diff --git a/course/source/aipy-scipy/cn/4odd.rst b/course/source/aipy-scipy/cn/4odd.rst
deleted file mode 100644
index 0a3bffa..0000000
--- a/course/source/aipy-scipy/cn/4odd.rst
+++ /dev/null
@@ -1,860 +0,0 @@
-
-概率统计方法
-=============
-
-简介
----------
-
-**Python** 中常用的统计工具有 **Numpy**, **Pandas**, **PyMC**, **StatsModels** 等。
-
-
-**Scipy** 中的子库 scipy.stats 中包含很多统计上的方法。
-
-
-导入 numpy 和 matplotlib：
-
-
-
-
-::
-
-    %pylab inline
-
-Populating the interactive namespace from numpy and matplotlib
-
-
-
-::
-
-    heights = array([1.46, 1.79, 2.01, 1.75, 1.56, 1.69, 1.88, 1.76, 1.88, 1.78])
-
-
-Numpy 自带简单的统计方法：
-
-
-
-
-::
-
-    print 'mean, ', heights.mean()
-    print 'min, ', heights.min()
-    print 'max, ', heights.max()
-    print 'standard deviation, ', heights.std()
-
-
-结果:
-::
-
-    mean,  1.756
-
-    min,  1.46
-
-    max,  2.01
-
-    standard deviation,  0.150811140172
-
-
-导入 **Scipy** 的统计模块：
-
-
-
-
-::
-
-    import scipy.stats.stats as st
-
-
-其他统计量：
-
-
-
-
-::
-
-    print 'median, ', st.nanmedian(heights)    # 忽略nan值之后的中位数
-    print 'mode, ', st.mode(heights)           # 众数及其出现次数
-    print 'skewness, ', st.skew(heights)       # 偏度
-    print 'kurtosis, ', st.kurtosis(heights)   # 峰度
-    print 'and so many more...'
-
-
-
-结果:
-::
-
-    median,  1.77
-
-    mode,  (array([ 1.88]), array([ 2.]))
-
-    skewness,  -0.393524456473
-
-    kurtosis,  -0.330672097724
-
-    and so many more...
-
-
-
-概率分布
------------------
-
-
-常见的连续概率分布有：
-
-
-- 均匀分布
-- 正态分布
-- 学生t分布
-- F分布
-- Gamma分布
-- ...
-
-
-
-离散概率分布：
-
-
-- 伯努利分布
-- 几何分布
-- ...
-
-
-这些都可以在 scipy.stats 中找到。
-
-
-连续分布
-=========
-
-
-正态分布
-------------
-
-以正态分布为例，先导入正态分布：
-
-
-
-
-::
-
-    from scipy.stats import norm
-
-
-它包含四类常用的函数：
-
-
-- norm.cdf 返回对应的累计分布函数值
-- norm.pdf 返回对应的概率密度函数值
-- norm.rvs 产生指定参数的随机变量
-- norm.fit 返回给定数据下，各参数的最大似然估计（MLE）值
-
-
-从正态分布产生500个随机点：
-
-
-
-
-::
-
-    x_norm = norm.rvs(size=500)
-    type(x_norm)
-
-
-
-
-结果:
-::
-
-    numpy.ndarray
-
-
-直方图：
-
-
-
-::
-
-    h = hist(x_norm)
-    print 'counts, ', h[0]
-    print 'bin centers', h[1]
-
-
-
-
-结果:
-::
-
-    counts,  [   7.   21.   42.   97.  120.   91.   64.   38.   17.    3.]
-    bin centers [-2.68067801 -2.13266147 -1.58464494 -1.0366284  -0.48861186  0.05940467
-    0.60742121  1.15543774  1.70345428  2.25147082  2.79948735]
-
-
-.. image:: 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W/Xywj98TDvPA34Th91TEtVzVfV8W71LuCcPuuZtKo6VFWH+65jgpp+Q1xVfRb4ft91TEtVHa2q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%0AkhphoEtSI/4P1qxllG6H6EYAAAAASUVORK5CYII=%0A
-
-归一化直方图（用出现频率代替次数），将划分区间变为 20（默认 10）：
-
-
-
-
-::
-
-    h = hist(x_norm, normed=True, bins=20)
-
-
-.. image:: data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAXIAAAEACAYAAACuzv3DAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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%0AAAAASUVORK5CYII=%0A
-
-
-在这组数据下，正态分布参数的最大似然估计值为：
-
-
-
-
-::
-
-    x_mean, x_std = norm.fit(x_norm)
-
-    print 'mean, ', x_mean
-    print 'x_std, ', x_std
-
-
-
-
-结果:
-::
-
-    mean,  -0.0426135499965
-
-    x_std,  0.950754110144
-
-
-将真实的概率密度函数与直方图进行比较：
-
-
-
-
-::
-
-    h = hist(x_norm, normed=True, bins=20)
-
-    x = linspace(-3,3,50)
-    p = plot(x, norm.pdf(x), 'r-')
-
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-.. image:: 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ZFLxhIl8jEMZBXNuJjh9bWut20KPYfUNsy+01tYrAnwFnA5MEHnWNHRw5clZ7rxd7rzIocwJ+xQ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%0AKZGLiETc/wfoqVYxTpVDEwAAAABJRU5ErkJggg==%0A
-
-
-导入积分函数：
-
-
-
-
-::
-
-    from scipy.integrate import trapz
-
-
-通过积分，计算落在某个区间的概率大小：
-
-
-
-
-::
-
-    x1 = linspace(-2,2,108)
-    p = trapz(norm.pdf(x1), x1) 
-    print '{:.2%} of the values lie between -2 and 2'.format(p)
-
-    fill_between(x1, norm.pdf(x1), color = 'red')
-    plot(x, norm.pdf(x), 'k-')
-
-
-
-结果:
-::
-
-    95.45% of the values lie between -2 and 2
-
-
-
-
-
-[<matplotlib.lines.Line2D at 0x15cbb8d0>]
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-.. image:: 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PM7bYAyPh+z//Uv11FMKbMO3oTMU5ddxqa5cxmTm+s6iilgDPBl9epM2b7ddRQTInaapCk1OVlZ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%0AFXhjjIlSVuCNMSZK/T8+DStxCxquAQAAAABJRU5ErkJggg==%0A
-
-
-
-默认情况，正态分布的参数为均值0，标准差1，即标准正态分布。
-
-
-可以通过 loc 和 scale 来调整这些参数，一种方法是调用相关函数时进行输入：
-
-
-
-
-::
-
-    p = plot(x, norm.pdf(x, loc=0, scale=1))
-    p = plot(x, norm.pdf(x, loc=0.5, scale=2))
-    p = plot(x, norm.pdf(x, loc=-0.5, scale=.5))
-
-.. image:: data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAXIAAAEACAYAAACuzv3DAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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%0AAAAAAElFTkSuQmCC%0A
-
-
-另一种则是将 loc, scale 作为参数直接输给 norm 生成相应的分布：
-
-
-
-
-::
-
-    p = plot(x, norm(loc=0, scale=1).pdf(x))
-    p = plot(x, norm(loc=0.5, scale=2).pdf(x))
-    p = plot(x, norm(loc=-0.5, scale=.5).pdf(x))
-
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-.. image:: 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sSYCrFEbi4UjfVx0FZ4//5aVjImjFgiNxeKxvp4oYED9YPMmDBiidyczzlN5NHYIgdL5CYslZvI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%0AAAAAAElFTkSuQmCC%0A
-
-
-其他连续分布
-----------------
-
-
-
-
-::
-
-    from scipy.stats import lognorm, t, dweibull
-
-
-支持与 norm 类似的操作，如概率密度函数等。
-
-
-不同参数的对数正态分布：
-
-
-
-
-::
-
-    x = linspace(0.01, 3, 100)
-
-    plot(x, lognorm.pdf(x, 1), label='s=1')
-    plot(x, lognorm.pdf(x, 2), label='s=2')
-    plot(x, lognorm.pdf(x, .1), label='s=0.1')
-
-    legend()
-
-
-
-
-结果:
-::
-
-    <matplotlib.legend.Legend at 0x15781c88>
-
-
-.. image::data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAXcAAAEACAYAAABI5zaHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz%0AAAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xl8lNW9+PHPN3tCViCGQEKDLLILiqjgErQWtS2tSltB%0AXy61gnWr7W1r688qVXvVXq/7Uq5bsS1a21rFaov3AkGogiIG2cMiOwQIgQTIMknO74+TyTIkzCSz%0APLN836/Xec3zzJx55gyPfufk+5xzHjHGoJRSKrrEOd0ApZRSgafBXSmlopAGd6WUikIa3JVSKgpp%0AcFdKqSikwV0ppaKQT8FdROJF5HMRebeT158WkU0iskpExga2iUoppbrK1577j4B1wAmD4kXkcmCQ%0AMWYwMAN4IXDNU0op1R1eg7uIFACXAy8B0kGVKcAcAGPMciBbRPIC2UillFJd40vP/QngZ0BTJ6/3%0AA3a22d8FFPjZLqWUUn44aXAXkW8A+40xn9Nxr72lqse+rmmglFIOSvDy+gRgSnNePQXIFJHXjDHX%0AtamzGyhss1/Q/Fw7IqIBXymlusEYc7LOdYdO2nM3xtxjjCk0xgwArgYWegR2gHnAdQAicg5w2BhT%0A3snxorbcf//9jrchoOX22zHp6Zh+/TA33cT911/vfJv03On3i8Hv111dHedumoP4TBGZ2Ryw3we2%0AishmYDZwa7dbo8LHmjXw1luwaBEMGwZvvgl+/IemlAotn4O7MWaxMWZK8/ZsY8zsNq/dbowZZIw5%0A3RizMhgNVSFkDKxeDaNGweDB8JOf2OfLO/yDTCkVhnSGaoAUFxc73YTA2b/fPuY1j2gVoXjIENub%0Aj0JRde46oN8vNok/OZ0ufZCICdVnKT8tWAAPPACLF7c+d8cdMHAg3HWXc+1SKgaJCKYbF1S9jZZR%0AsWjNGhg5sv1zI0fCp5860x4VcUS6HIsU+HUB1ZMGd3WiNWvgjDPaPzdyJLz6qjPtURFJ/1LvmkD/%0AIGrOXZ2oo577iBGwdi00dTZRWSkVTjS4q/aMsUF8xIj2z2dn27JjhzPtUkp1iQZ31d6OHZCRAT17%0AnvjayJF2iKRSKuxpcFftdZSScRs5MmqHQyoVbTS4q/Y0uCvVqTVr1jB58mRyc3OJiwvv8BnerVOh%0At3atBnelOpGUlMTVV1/Nyy+/7HRTvNLgrto7Wc992DAoKwOXK7RtUiqAHn30UQoKCsjMzGTo0KEs%0AXLjQ5/cOGTKEG2+8keHDhwexhYGhwV21amyEDRtsEO9IWhoUFMDmzaFtl1IBsnHjRp577jlWrFhB%0AVVUVH3zwAUVFRcydO5ecnJwOS8+ePdm1a5fTTe8yncSkWm3ZAn36QHp653XcqZnOfgCU8kGg5ut0%0AdZ5UfHw8dXV1rF27ll69etG/f38ATj31VKZPnx6YRoUJ7bmrVidLybhp3l0FgDGBKV01aNAgnnzy%0ASWbNmkVeXh7Tpk1j7969gf+CYUCDu2qlwV3FgGnTprFkyRK2b9+OiHD33Xczd+5cMjIyOiyZmZkR%0AmZbR4K5aaXBXUa6srIyFCxdSV1dHcnIyKSkpxMfHM336dKqrqzssVVVVFBQUtByjtraW+vp6AOrq%0A6qirq3Pq65yUBnfVas2aE5cd8DRkiJ3FWlMTmjYpFUB1dXX88pe/JDc3l/z8fA4ePMjDDz/s8/u3%0AbdtGWloaI0eORERITU1lWJhef9L13FWrjAzYvRsyM09eb9QomDPnxJUjlWrWvAa5082IKJ39m3V3%0APXftuSvr2DFoaLAB3puiIl1ATKkw5zW4i0iKiCwXkVIRWSciJ/wNIyLFInJERD5vLvcGp7kqaMrL%0A7TBIX8ao9esHe/YEv01KqW7zOs7dGFMrIpOMMcdFJAFYKiLnGWOWelRtuYG2ikD79tng7ot+/Wz6%0ARikVtnxKyxhjjjdvJgHxwKEOqul9tSLZvn2tN8T2RoO7UmHPp+AuInEiUgqUA4uMMes8qhhggois%0AEpH3RST8F15Q7bnTMr7Q4K5U2PO1595kjBkDFAAXiEixR5WVQKEx5nTgGeDtgLZSBZ+mZZSKKl1a%0AW8YYc0RE3gPGASVtnq9us/1PEXleRHoaY9qlb2bNmtWyXVxcTHFxcfdarQJv3z4YM8a3uhrclQqa%0AkpISSkpK/D6O13HuItIbaDDGHBaRVGA+8GtjzII2dfKA/cYYIyLjgTeNMUUex9Fx7uHs29+G66+H%0AK67wXtcYu0LkgQMnX2RMxSwd5951gR7n7kvPPR+YIyJx2DTOH4wxC0RkJoAxZjYwFfihiDQAx4Gr%0Au9oQ5bCuXFAVae29n3ZacNullOoWrzl3Y8xqY8wZxpgxxpjRxpj/an5+dnNgxxjznDFmZHOdCcaY%0AZcFuuAqwruTcQce6q5g0Z84cxo0bR1ZWFoWFhdx99900NjY63awO6QxVZdMs5eW+99xB8+4qJtXU%0A1PDUU09RUVHB8uXLWbBgAY899pjTzeqQBncFVVWQkAA9evj+Hg3uKkL5c5u9W265hYkTJ5KQkEDf%0Avn255ppr+Pe//x3E1naf3olJdT0lAza4b90anPYoFSRtb7PXp08fduzYQUNDA3PnzuW2227r8D0i%0AwhdffNFu2V+3xYsXM9LbMtkO0eCuujaBya1fP1iyJDjtUVFPfh2YCe3m/q6NyAnkbfZeeeUVVq5c%0AySuvvNKl94WKBnfVtZEybpqWUX7oalAOlLa32Vu7di2TJ0/m8ccfJz8/v0vHefvtt7nnnntYsGAB%0APXv2DFJr/aM5d9X9nrsGdxWB/L3N3r/+9S9mzJjBP/7xD0Z4u7mNg7TnrrqXc8/Ptz8KjY0QHx+c%0AdikVYGVlZezatYuJEye23GbPGMP06dN9SsssXLiQa665hnfeeYdx48aFoMXdpz131b20TFISZGfb%0AWapKRQh/b7P30EMPUV1dzWWXXdbSs//6178exBZ3n/bcVffSMtCamunOe5VywKhRo1i+fHm339+V%0AYZNO05676l5aBjTvrlQY0+CuupeWAQ3uSoUxDe6xrqkJ9u/X4K5UlNHgHusqK+2yvcnJXX+vBnel%0AwpYG91jX3ZQMaHBXKoxpcI913R0pAxrclQpjGtxjXXdHyoCu6a5UGNPgHuv8Scvk5EBdHRw/Htg2%0AKaX8psE91vmTlhGBvn01NaNUGNLgHuv8ScuABncVc5544gny8/PJysripptuor6+vtO6M2bMYOjQ%0AocTHxzNnzpwQttJLcBeRFBFZLiKlIrJORDpchEFEnhaRTSKySkTGBqepKij8ScuAXlRVMWX+/Pk8%0A+uijLFy4kO3bt7N161buv//+TuuPGTOG559/njPOOAORwKxh76uTBndjTC0wyRgzBhgNTBKR89rW%0AEZHLgUHGmMHADOCFYDVWBYE/aRnQ4K4ijj+32ZszZw4/+MEPGDZsGNnZ2dx33338/ve/77T+rbfe%0AykUXXURKSkoAWt41XtMyxhj31bIkIB445FFlCjCnue5yIFtE/OgKqpDyNy2jwV1FkLa32auqquKD%0ADz6gqKiIuXPnkpOT02lxr+e+bt06Tj/99JbjjR49mvLyciorK536Sp3yuiqkiMQBK4GBwAvGmHUe%0AVfoBO9vs7wIKgPJANVIFSWMjVFRAbm73j5GfD8uWBa5NKjYEKkVhQnubvaNHj5KVldWyn5mZCUB1%0AdTU5OTldakuweQ3uxpgmYIyIZAHzRaTYGFPiUc3zTHX4Lz5r1qyW7eLiYoqLi7vSVhVoBw/a4YwJ%0Afqz8nJdnUztKdUUXg3Kg+HubvfT0dKqqqlr2jxw5AkBGRkbA2lhSUkJJSYnfx/H5/2pjzBEReQ8Y%0AB7T95N1AYZv9gubnTtA2uKsw4G9KBjS4q4gzbdo0pk2bRnV1NTNnzuTuu+/m0ksvZebMmR3WFxHW%0ArVtHQUEBI0aMoLS0lKlTpwKwatUq8vLyAtpr9+z4/vrXv+7WcbyNluktItnN26nAJcDnHtXmAdc1%0A1zkHOGyM0f/bI0F5OZxyin/HyMuzPxJKRYCysjIWLlxIXV1dy2324uPjmT59OtXV1R2WqqoqCgoK%0AALjuuut4+eWXWb9+PZWVlTz44IPceOONnX6ey+WitraWpqYm6uvrqa2txYTorxZvF1TzgYUiUgos%0AB941xiwQkZkiMhPAGPM+sFVENgOzgVuD2mIVOBUV0Lu3f8fIyYFjx+xMVaXCnL+32Zs8eTI///nP%0AmTRpEkVFRQwcOLBdz/ryyy/nkUceadm/5JJLSEtLY9myZcyYMYO0tDSWLFkS0O/UGQnVr4iImFB9%0AlvLRs8/C+vXw3HP+HaegAD7+GAoLvddVMUFEQtZDjRad/Zs1P9/lK9A6QzWWHTwIvXr5fxzNuysV%0AdjS4x7KKisAFd827KxVWNLjHskDk3MGOuNGeu1JhRYN7LAtkz12Du1JhRYN7LNPgrlTU0uAeywJ5%0AQVVz7kqFFT/mnauIF6icu/bcVQdCvcStak+De6yqr7cTjwKxJoZeUFUedIy78zQtE6sqKqBnz8Cs%0Azqc9d6XCjgb3WBWoi6lglyA4elSXIFAqjGhwj1WBupgKEBdn14Tfvz8wx1NK+U2De6wK1MVUN827%0AKxVWNLjHqkCmZUDz7kqFGQ3usSoYwV3HuisVNjS4x6pA5txBe+5KhRkN7rEq0D13zbkrFVY0uMeq%0AQF9Q1Z67UmFFg3us0py7UlFNg3us0tEySkU1r8FdRApFZJGIrBWRNSJyZwd1ikXkiIh83lzuDU5z%0AVcDoBVWlopovC4e5gB8bY0pFJB34TET+1xiz3qPeYmPMlMA3UQVcYyMcOWKXDQiUnj3tEgT19ZCU%0AFLjjKqW6xWvP3RizzxhT2rx9FFgP9O2gqq7vGSkOH4bMTEgI4KKgugSBUmGlSzl3ESkCxgLLPV4y%0AwAQRWSUi74vI8MA0TwVFoPPtbnpRVamw4XPXrTkl81fgR809+LZWAoXGmOMichnwNjDE8xizZs1q%0A2S4uLqa4uLgbTVZ+C3S+3U3z7kr5raSkhJKSEr+PI74sqi8iicA/gH8aY570of6XwJnGmENtnjO6%0AgH+YePdd+N3v4L33AnvcG2+E88+H738/sMdVKoaJCMaYLqe9fRktI8DLwLrOAruI5DXXQ0TGY380%0ADnVUV4WBQE9gctOeu1Jhw5e0zETgWuALEfm8+bl7gP4AxpjZwFTghyLSABwHrg5CW1WgBDPnvm1b%0A4I+rlOoyr8HdGLMULz18Y8xzwHOBapQKsmAG9+We19qVUk7QGaqxSC+oKhX1NLjHomDl3HVlSKXC%0Ahgb3WKTj3JWKehrcY1Gwgrt7CYK6usAfWynVJRrcY1GwgntcHJxyii5BoFQY0OAea4wJ3gVVsHn3%0AvXuDc2yllM80uMeao0ftqo0pKcE5fp8+mndXKgxocI81wUrJuGlwVyosaHCPNcEO7vn5GtyVCgMa%0A3GNNMPPtoD13pcKEBvdYE6wJTG4a3JUKCxrcY43m3JWKCRrcY00ogrsOhVTKcRrcY02oeu56Yxal%0AHKXBPdYE+4Jqjx6QmAhVVcH7DKWUVxrcY01FBeTmBvczNO+ulOM0uMeagweDO1oGNLgrFQY0uMca%0ADe5KxQQN7rEmVMFdR8wo5SivwV1ECkVkkYisFZE1InJnJ/WeFpFNIrJKRMYGvqnKb8eP21EsaWnB%0A/RztuSvlOF967i7gx8aYEcA5wG0iMqxtBRG5HBhkjBkMzABeCHhLlf9C0WsHDe5KhQGvwd0Ys88Y%0AU9q8fRRYD/T1qDYFmNNcZzmQLSJ5AW6r8leogrsuHqaU47qUcxeRImAssNzjpX7Azjb7u4ACfxqm%0AgkB77krFjARfK4pIOvBX4EfNPfgTqnjsnzBFcdasWS3bxcXFFBcX+/rxKhA0uCsV9kpKSigpKfH7%0AOGJ8mCYuIonAP4B/GmOe7OD13wElxpg3mvc3ABcaY8rb1DG+fJYKoqefhk2b4Jlngvs5DQ2Qmgq1%0AtRAfH9zPUirKiQjGGM/Os1e+jJYR4GVgXUeBvdk84Lrm+ucAh9sGdhUmgr3cr1tCAvTsqTfKVspB%0AvqRlJgLXAl+IyOfNz90D9Acwxsw2xrwvIpeLyGbgGHBjUFqr/HPwIAwfHprPcqdm8vND83lKqXa8%0ABndjzFJ8G1Vze0BapIInVDl30Ly7Ug7TGaqxJNgrQralwyGVcpQG91iiPXelYoYG91iiwV2pmKHB%0APVYYE9q0jC4eppSjNLjHiqNH7R2SUlND83nac1fKURrcY0UoUzKgwV0ph2lwjxUa3JWKKRrcY0Wo%0AZqe6ZWWBywXHjoXuM5VSLTS4x4pQ99xFbO+9XFehUMoJGtxjRaiDO2hqRikHaXCPFaEcBummwyGV%0AcowG91jhRM+9Xz/YtSu0n6mUAjS4xw4ngnthoQZ3pRyiwT1WOBHcCwpg507v9ZRSAafBPVZoz12p%0AmKLBPVZoz12pmOLTPVQD8kF6D1XnGANJSXZCUVJS6D63rg4yMqCmRu+lqlQ3Be0eqioKVFVBWlpo%0AAztAcjLk5OhEJqUcoME9FjiRknHTvLtSjvAa3EXkFREpF5HVnbxeLCJHROTz5nJv4Jup/OLEBCY3%0Azbsr5QivN8gGXgWeAV47SZ3FxpgpgWmSCjjtuSsVc7z23I0xS4BKL9W6nOxXIeRkcNeeu1KOCETO%0A3QATRGSViLwvIsMDcEwVSNpzVyrm+JKW8WYlUGiMOS4ilwFvA0M6qjhr1qyW7eLiYoqLiwPw8cor%0A7bkrFTFKSkooKSnx+zg+jXMXkSLgXWPMKB/qfgmcaYw55PG8jnN3ys03w1lnwYwZof/sL7+E4mLY%0Avj30n61UFHBsnLuI5ImING+Px/5gHPLyNhVKTvbc+/Wzy/42Njrz+UrFKK9pGRF5HbgQ6C0iO4H7%0AgUQAY8xsYCrwQxFpAI4DVwevuapbnAzuSUnQs6edyNS3rzNtUCoGeQ3uxphpXl5/DnjOlw/bcHAD%0AQ3sP9bFpKmBCff9UT4WFNu+uwV2pkAnpDNXzXz2fl1a+hObeQ8zJnjvYi6o6YkapkAppcF98w2Ke%0A+eQZvvvX71JZ423ovAqIpiaorLSpEae4e+5KqZAJaXAfnjuc5T9YTt/0vox7cRx1DXWh/PjYdOAA%0AZGdDQiBGvXaT9tyVCrmQLxyWkpDCU5c9Rd+Mviz4ckGoPz727N0L+fnOtkF77kqFnGOrQl4x9Ar+%0Avv7vTn187Nizx/kLmdpzVyrkHA3u72x8h8YmHf8cVNpzVyomORbcB+QMoCCzgKU7ljrVhNgQDsG9%0Ab1/Yt08nMikVQo7erOOKoVfw9w2amgmqcAjuSUl2Pfl9+5xth1IxxNHgfuWwK3lr/Vs67j2YwiG4%0Ag+bdlQoxR4P78NzhpCam8tnez5xsRnQLl+CueXelQsrR4C4iOmom2MIluGvPXamQcvwG2VcOu5K3%0ANrzldDOikzE2zx0OwV177kqFlOPBfVzfcRytP8r6A+udbkr0qayElBRITXW6JXrTDqVCzPHgHidx%0ATB02lT+t/pPTTYk+4TCBya2oyN64QykVEo4Hd4Cbz7yZVz5/BVejy+mmRJdwybcDDB4MmzbZVJFS%0AKujCIrgPzx3OoJ6DmLdxntNNiS7hFNx79YK4OLv8sFIq6MIiuAPcMu4WZn822+lmRJdwCu4irb13%0ApVTQhU1wv2rYVZTuK2Xzoc1ONyV6hFNwBw3uSoVQ2AT35IRkrj/9el787EWnmxI9wjG4l5U53Qql%0AYoLX4C4ir4hIuYisPkmdp0Vkk4isEpGx3W3MjDNn8Grpq3oTj0AJx+CuPXelQsKXnvurwKWdvSgi%0AlwODjDGDgRnAC91tzOBegxmdN1oXEwsUDe5KxSyvwd0YswQ42Q1PpwBzmusuB7JFJK+7Dbpl3C08%0AvfxpXUzMX8aEZ3DfvFmHQyoVAoHIufcD2k493AUUdPdgVwy9gsO1h5m/Zb7fDYtp1dX2MSPD2Xa0%0AlZ1tZ8zq0r9KBV2g7posHvsdds1mzZrVsl1cXExxcfEJdeLj4nlg0gPcu/BeJg+cjIjnoZVP3L32%0AcPv3c6dmwukvCqXCSElJCSUlJX4fR3xJf4hIEfCuMWZUB6/9DigxxrzRvL8BuNAYU+5Rz/iaamky%0ATYz7n3Hce8G9XDnsSp/eozyUlMB998GHHzrdkvauvx4uuABuusnpligVEUQEY0yXe2mBSMvMA65r%0AbsQ5wGHPwN7lRkkcD130EL9a9Cu9x2p3hVu+3U0vqioVEr4MhXwd+Ag4TUR2isj3RWSmiMwEMMa8%0AD2wVkc3AbODWQDTsskGXkZ2SzetrXg/E4WKPBnelYprXnLsxZpoPdW4PTHNaiQi/ueg33DTvJr47%0A4rskxScF+iOimwZ3pWJa2MxQ7UhxUTHDc4fz6NJHnW5K5Ann4L55MzQ1Od0SpaJaWAd3gOcvf56n%0AP3matfvXOt2UyBKuwT0jAzIzYfdup1uiVFQL++BemFXIQ5Me4qZ5N+nF1a4I1+AOmppRKgTCPriD%0AvZlHSkIKTy1/yummRI5wuguTpyFDNLgrFWQREdzjJI6XprzEfy75T10S2Bc1NVBbCzk5TrekY9pz%0AVyroIiK4AwzqOYhfXfArrv7r1dS4apxuTnjbuxf69Am/2aluGtyVCrqICe4Ad559J4N7DeaW927R%0AhcVOJpzz7aDBXakQiKjgLiK89M2XKN1XyrOfPOt0c8JXuAf3QYPgyy+hUS+QKxUsERXcAXok9eDt%0A773Nb5b8hsXbFjvdnPD05Zfwla843YrOpaVBXh5s2eJ0S5SKWhEX3AEG5AzgD1f8ge/99Xus2b/G%0A6eaEn7IyOO00p1txcmeeCStWON0KpaJWRAZ3gEsGXsLjkx9n8h8nU1ah9+Vsp6zMDjcMZ2edpcFd%0AqSCK2OAOMH3UdB6c9CBffe2rbDu8zenmhI9I6LmPG6fBXakg8mk994B8UBfWc++qZz95lieWPcHC%0A6xbylewwzjWHQlWVnbxUXR2+QyEBKiuhf384fBji451ujVJhq7vruQfqTkyOun387RhjmPjKRN6d%0A9i5j88eetH5Dgx1QsmMH7N8PdXW2uFyQnAzp6dCjB/TsaQed5OVBQqT8S5WV2aGG4RzYwU6w6tMH%0ANmyAESOcbo1SUSdSQpZXd5x9B30z+jL5j5P5wxV/YPKgyS2vbd4MS5bA0qXw73/D1q3Qu7cdUJKX%0AZ2/rmZwMiYl2YuexY3D0KFRU2B+BgwchNxcGDrSj+AYPhuHDYdQoGDAA4sIpuRUJ+Xa3s86CTz/V%0A4K5UEERNcAe4avhV9Envw1VvXsXd4x8g8Yub+f3vhd27YdIkmDgR7rgDhg2zwdxX7p7+li127s2m%0ATfDSS7B6tf0BGDnSDv444wybSh4xwsGefiQFd3fe/YYbnG6JUlEnqoI7wKDkiUzatpifbr+K/olL%0AefKBF/jG5B5+pXUTEqCw0BbPe3ofOQJffAGffWZvW/rYY7BrF4wdC+ecA+eeCxMm2L8QQmLjRvj6%0A10P0YX4aNw7efNPpVigVlaLigirYtbKeeAIefxyuuw7u+vkx7lt2G5/u+ZS/fOcvDM8dHrTP9nT4%0AsM02LFsGH39sS+/e9i+H886D88+3neugpMXPPBNeeAHGjw/CwQPs6FH7q1dZCUl6py2lOtLdC6o+%0ABXcRuRR4EogHXjLGPOrxejHwDrC1+am/GWMe8qgTtOBeWgpTp8KYMfDIIzYv7vbq56/ys//9Gfde%0AcC93jL+D+LjQj8xoaoL1623Of8kSW2prbZC/4AK48EKbv/c7d2+MvRHGjh3huyKkpxEj4I9/tH/q%0AKKVOELTgLiLxwEbgq8Bu4FNgmjFmfZs6xcBPjDFTTnKcoAT3116D//gPeOYZuPrqjutsqtjED979%0AAa5GFy9PeZlhucMC3o6u2rHDBvkPP4TFi6G83PbqL7zQlrFju5G337PH/sLt3x+UNgfFDTfYP2lu%0AvtnpligVlrob3H3pK44HNhtjthljXMAbwLc6akNXP9wfLhfcdhs89BAsWtR5YAcY3Gswi65fxLWj%0Ar+X8V8/n/kX3c6z+WOga24H+/eGaa2D2bDsacP16m07atg2+/33o1Qsuu8z+JfLxx/b7ehUJk5c8%0AjRtnc1hKqYDyJbj3A3a22d/V/FxbBpggIqtE5H0RCWqCu7HRdvi2bLFxYeRI7++JkzhuPetWVs5c%0ASdmhMoY+N5S5q+eGzdLBffrAd74Dzz5rR+Fs2QIzZsC+fXDrrXbM/Ve/Cg8+aHv6NR0taR9JI2Xc%0AdBkCpYLCl7TMVcClxpibm/evBc42xtzRpk4G0GiMOS4ilwFPGWOGeBwnIGkZY2zQ27IF3nsPUlO7%0Ad5ylO5Zy17/uIj4ungcnPcglp16ChPHEn8pKm7P/8ENb1qyxQy/PP9+WCRMg68Gf2gH5d9/tdHN9%0AV1Nj/0w5dMhOOFBKtRPMGaq7gcI2+4XY3nsLY0x1m+1/isjzItLTGHOobb1Zs2a1bBcXF1PsOa7Q%0AC2Pgxz+2ge2DD7of2AHO638en9z8CW+ufZM7/3knvdN688CkB5hUNCksg3xODnzzm7aAHWjy8cc2%0Ab//b39q/YN6L30jZxIlkFtk0dkGBo032TWqqTSWtWgVnn+10a5RyXElJCSUlJX4fx5eeewL2gurF%0AwB7gE068oJoH7DfGGBEZD7xpjCnyOI7fPfdHHoE//xkWLgzsYJDGpkZeX/M6Dyx+gJzUHH567k+5%0AYtgVJMRFzjSA+npoHHwab3z378zbPJylS+2y6RMn2l79hAkwenSYLqMwY4bNrd15p9MtUSrsBHso%0A5GW0DoV82RjzsIjMBDDGzBaR24AfAg3AcezImWUex/AruC9ZYnPSn30G/Twz/gHS2NTIu2Xv8thH%0Aj7Gneg+3j7+dG8bcQM/UnsH5wEByuSAjw86qSk7GGDuT9t//tuXjj+0InXHj7MSqc8+1HeVTTnG6%0A4cDf/gb/8z8wf77TLVEq7AQ1uAeCP8G9osIODXzhhdBNvly2axnPf/o875a9y5TTpjDjjBlMKJwQ%0AlikbwEbyyZPtwjmdqKy0E6vcZflye6H27LPtnKfx4+2/c1paCNsNNsfUty/s3AlZWSH+cKXCW9QG%0Ad2Pg298Mv5XCAAAQcElEQVS2E5P++7+D0DAvKo5XMGfVHF5c+SINTQ1cN/o6rh19LQNyBoS+MSfz%0Aj3/YoTb/+pfPb2lqsqsVfPKJDfSffALr1tkBN2edZcuZZ9oJVkGfQPqNb8C11558TKtSMShqg/sz%0Az8CcOfDRR87OUDfGsGLPCl5b9Rp/XvtnTs05le8M/w5Th08NjzXkH38ctm+Hp57y6zC1tXatnE8+%0AsSmwFSvsyKRhw1oXRzvjDBvw/bmgfYIXX7QXU15/PYAHVSryRWVw37rV9h6XL2+/pIDTXI0uFm1b%0AxF/W/oW/b/g7A3IGMGXIFKacNoXReaOdSd3ccouNuLfdFvBDHz9uB7OsXNlaNm6EU0+1E2LHjIHT%0AT7cXbLu9QNq+ffYXpLxc15lRqo2oDO7f+pbNB99zT5AaFQCuRhdLdyxl3sZ5zCubh6vRxeSBk5k8%0AaDIXD7iYnNQQrfFy3nnw61/DxReH5OPq620Kp7TUllWrbElKskF+1KjWMmyYj3n8CRNg1iz42teC%0A3XylIkbUBff334cf/ciOae/K2utOMsaw4eAG5m+Zz/wt81m6YynDeg/jogEXMaloEhP7TyQ9KT3w%0AH+y+td7+/Q5cDW1ljF3uePVqW1atgrVr7cTZggK7Rpi7DB9uh7e3S+389rd2/YXnn3fqKygVdqIq%0AuNfV2WHPTz9t11eJVLUNtSzbtYxFXy5i0bZFfLb3M4bnDue8wvM4r/95nFt4Ln0z+vr/Qe+8Yy9O%0A/N//+X+sIHC57N2w1qyxvf1162zQ37LF/iYNHWp792dnb2TK0xdTtXoHvU+JC/s7BSoVClEV3B9+%0A2A7Ve+edIDcqxGpcNazYs4KlO5aydOdSlu9aTmpiKucUnMNZfc9iXN9xnJF/Btkp2V078G23QVER%0A/OxnQWl3sDQ02Osq69bZxdM2boRZbwzlxoQ/UJp4FkOG2JE7gwfbMmSIvfaSmel0y5UKnagJ7rt2%0A2Qt0n35q708azYwxbKncwrJdy1ixZwUr9qygdF8pfdL7MDZ/LGPyxjCmzxhG5Y2iMLOw8wu1gwbB%0AW2/ZZHek+8UvMPEJHLzrIcrKbMB339qwrMz29tPT7VceOLC1nHqqLXl54X9vcKW6ImqC+80327sW%0APfxwCBoVhhqbGtlYsZFV+1ZRuq+U0vJSVpev5pjrGCNPGcnI3JEMyx3G8NzhDOs9jIIDdcj559u1%0A3KMhqi1fbse7b9hAR/dGNMbez3bzZlu2brUBf8sW+PJLO7JnwABbiopaH7/yFVt69YqOfyYVO6Ii%0AuG/aZAdMlJVFzo2EQqXieAWr969m3YF1LWX9wfV878NDXLw/nbk//RqDew62pddgBuYMpHda7/Cd%0AUdsZY+ztqX74Q5g+vctvr662QX7rVnttdts2u799uy0ul11L/ytfsY/9+7feH7ew0F741cUpVTiJ%0AiuB+zTX2wtq994akSVHB9c2vs+Nr5/DRBUVsOrSJzYc2tzw2NjVyas6pDOw5kKKsIgbkDKAou4ii%0A7CL6Z/UnMzlMk9cLFtjrCGvXdth790dVlV1jZ8cOG+x37LCrHrgf9+yxOf2CAlv69Wtf+va1jzk5%0A+heACo2ID+5r1tibUWzaZNe/Uj5wuez67WVlHa4AVllTydbKrWyp3MK2w9vYdngbXx7+ku2Ht7P9%0AyHaS4pPon9WfwsxCCjILWh4LMgvol9mPfhn9yEh24GS4e++33GJ/8UOoqQkOHLCBfvduew1o925b%0A9uxp3a6rg/x8G+zz823p06f10V1ycyExMaRfQUWZiA/uV1xhbzrxk5+EpDnRYckSOxlg5couv9UY%0Aw6GaQ+w4soOdVTvZeWQnO6t2srt6N7uqdrG7aje7q3cTJ3H0zehLfno++Rn55Kfn0ye9D33S+5DX%0AI4+89DzyeuSR2yM3sEskL1hgb0G1bl3Ae++BcPy4zf3v2WMf9+61k2z37rWTbN37FRV2LbS8PFtO%0AOaV1OzfXllNOad3OytK/CFR7ER3cP/3UBvdNmwK8Xkm0+9Wv7HjCIF19NsZQXV/Nnuo97K7azb6j%0A+9h7dC97q/dSfqzclqP28VDNIbKSszilxynk9si1j2m55Kbl0jutN73TetMrrRe9Unu1bPdI7NH5%0ANQEHe++B1NhoA3x5uS3799vi3j5wwG4fPGi3a2vtgILcXPvoLr16tT56lsxM/UGIZhEb3I2xs82v%0Ausr+f6y6YPx4ePRRmDTJ6ZbQ2NTIoZpDlB8r58CxAxw4foD9x/Zz8PjBdqWipsI+Hq+g0TTSM7Vn%0Au5KTkmNLag6jVu/n4sf+xpJ/zia7Ry+yU7LJSskiOyX75D8MEay21gb6igob7A8csNsVFa3Pty2H%0ADtm/IrKz7fLNPXva6wE5Oe23PUt2ti3p6frDEO4iNrjPn29vwLNmjeYmu2TXLjuPf//+yFmfwUON%0Aq4ZDNYeoqKmgsqaSytpKDtUc4lDNIQ7XHqby+CFm/r+/s7F/Kk98uw+Haw9zuPYwR+qOUNdQR1ZK%0AFpnJmWQlZ5GVkkVWst33LBlJGfYxOYOMpIyWx/SkdDKSMyLqjlsdcblskK+stI/uUlnZvhw+3Pro%0A3q6ttT1/d7DPyuq8ZGbakpVlr4u59zMywvQOX1EiIoN7Y6NdRva+++DKK0PSjOgxdapdoOWBB5xu%0ASXBVVMA558AvfgE33dTytKvRxZG6IxypPdLyWFVX1VKO1B2huq7a7tdXUV1XTXV9dcvj0fqjLduJ%0AcYmkJ6WfUHok9bCPiT1sSepBWmJap9ueJTUhlZSElLD+C8PlsiOI3AH/yJHWcviwfc29X13dul9V%0A1bpfVWUXjHMH+pOV9PSOS48e7bfT0vQvCreIDO6vvQa/+529DZyeyC547z246y67OlcsDMreuNHm%0A319/HS66KKCHNsZQ21Brg319Ncfqj3HMdYyj9Uc5Wn+0Zb+zx+Ou4xxzHaPGVdPyfE1DTcu+q9FF%0ASkKKDfaJqaQmpLbbdj+mJKS07Lu3UxJS7HZi63ZyfHLrdkLrtvs193PJ8ckkxCWE5IfFGJsacgf7%0A6ur25ejR9tvu/WPHWvePHm2/X1dnA3yPHicW9/NpaZ1vp6Z6f0xNhbi4oP/z+C3ignttrV0VcO5c%0AexNn5aNjx+yqai++aMeOxoqSEvje92DRIvsXS4RobGqktqGW467jHHcdbwn8no+1DbXtnqtrqGvZ%0Ar2uso7ahtqVOXUMddY11La+567qfr22opa6hjibTRHJCckvQ93xMik86YTspPsnuxyWRFN9a3HXc%0AJTEusd1+UnwSifGJJ7zufi4xLvGE7baPnj9CjY32B+PYsdbSdt+93fa5mhq7X1PTft/9XNvtmhob%0AgxITWwO9u6SktH/0fM6zJCd3vp+c3HFxv+bLj0vQgruIXErrzbFfMsY82kGdp4HLsDfHvsEY83kH%0AddoF9//6L9tjf/vtrjY5xt19t823/+lPTrck9P70J/sXy6xZdgZrJHS7HNTY1NgS7Osb61uCv/ux%0A7XPu7frG+pbift79mqvJdcK2u7TddzW62j3v3u9o29XkoqGpgXiJPyHgt31MiEto91xCXMIJ2wlx%0ACS312j62PB/ffj9eEqApAdNoS1ND+9Loan101cfT5EqgoT4BV33zc3XxNNQnUF8b3/x8PPW19nlX%0AfQL1NQnU18Xjqounrta+VlcbT31NArU1dj8hPu6EwJ+U1H77o4+CENxFJB7YCHwV2A18Ckwzxqxv%0AU+dy4HZjzOUicjbwlDHmnA6O1RLcS0vhkkvgww/tjNRoUFJSQnFxcXA/5LPP7BrIq1f7ccuj7gnJ%0A9/PFhg3w/e/bK3gvv2yXi/RT2Hy3IAn372eMoaGpoV3wb7vfdtv9Y9B2e+VHKxl61lAamhpaivt9%0A7vc2NjW2bHvWazSN7d7Tsu/xvsamRhpNY7v3uF9377d9zv28+33u19rWBYiXeOIlnrh2jwnEEU+c%0AJHDwl3u6Fdy9XeMeD2w2xmwDEJE3gG8B69vUmQLMaT5Jy0UkW0TyjDHlHR3www/ttcAXXoiewA5B%0A/h+oqgp+8xsbzF54IeSBHcIoQAwdaidvPfusHQp67rl2DZpvfavbU5vD5rsFSbh/PxGxvfD4ROjG%0AiLllf1zGlddF5oiMJtPUEvA9H90/BgW/LOjWsb0F937Azjb7u4CzfahTAJwQ3N9913a6Xn89ttLF%0A3VJdDevX24XtH3kEJk+2Pfb8fKdb5rz4eDsz96abYN48e+HmttvsWtGjR9sydGjrVFCd5aPCVJzE%0AERcfR2J3ftW88Bbcfb3a6vl/TofvS576TcrGQ85TwFM+HjlSbNxo0yYn0zYFZowtTU32sb6+9UpP%0ARYUdhHzaaXYs+zvv2DuFq/bS022vffp0+++1ciV88YW9mDNnTuu00Lq61jF2PXrYZGZiYmvZvh2W%0ALrU5fJHWAidudyacfzx8+W8zkkX79+smbzn3c4BZxphLm/d/CTS1vagqIr8DSowxbzTvbwAu9EzL%0AiEhohuUopVSUCUbOfQUwWESKgD3A94BpHnXmAbcDbzT/GBzuKN/encYppZTqnpMGd2NMg4jcDszH%0ADoV82RizXkRmNr8+2xjzvohcLiKbgWPAjUFvtVJKqZMK2SQmpZRSoRPwWSAicqmIbBCRTSJydyd1%0Anm5+fZWIjA10G4LJ2/cTkWIROSIinzeXiLmvlIi8IiLlIrL6JHUi8tx5+26RfN4ARKRQRBaJyFoR%0AWSMid3ZSL1LPn9fvF8nnUERSRGS5iJSKyDoR6XAd7y6dP2NMwAo2dbMZKMKOWC0FhnnUuRx4v3n7%0AbGBZINsQzOLj9ysG5jnd1m5+v/OBscDqTl6P5HPn7btF7Hlrbn8fYEzzdjp28mE0/b/ny/eL9HOY%0A1vyYACwDzvPn/AW6594y6ckY4wLck57aajfpCcgWkdDPyukeX74fnDg0NCIYY5YAlSepErHnzofv%0ABhF63gCMMfuMMaXN20exEw37elSL5PPny/eDyD6Hx5s3k7AdyUMeVbp0/gId3Dua0NTPhzrdm4IV%0Aer58PwNMaP6z6X0RiZxVrryL5HPnTdSct+bRbWOB5R4vRcX5O8n3i+hzKCJxIlKKnQC6yBizzqNK%0Al85foJfYD+ikpzDkSztXAoXGmOMichnwNjAkuM0KqUg9d95ExXkTkXTgr8CPmnu4J1Tx2I+o8+fl%0A+0X0OTTGNAFjRCQLmC8ixcaYEo9qPp+/QPfcdwOFbfYLsb8uJ6tT0PxcJPD6/Ywx1e4/r4wx/wQS%0ARaRn6JoYVJF87k4qGs6biCQCfwP+aIzpaL3ViD5/3r5fNJxDAGPMEeA9YJzHS106f4EO7i2TnkQk%0ACTvpaZ5HnXnAddAyA7bDSU9hyuv3E5E8aV6cWkTGY4ebeubOIlUkn7uTivTz1tz2l4F1xpgnO6kW%0AsefPl+8XyedQRHqLSHbzdipwCeC5dHqXzl9A0zImyic9+fL9gKnAD0WkAbu+/dWONbiLROR14EKg%0At4jsBO6neZ2+SD933r4bEXzemk0ErgW+EBF3ULgH6A+Rf/7w4fsR2ecwH5gjInHYTvcfjDEL/Imd%0AOolJKaWikN7KRimlopAGd6WUikIa3JVSKgppcFdKqSikwV0ppaKQBnellIpCGtyVUioKaXBXSqko%0A9P8BfIyZcbomsyAAAAAASUVORK5CYII=%0A
-
-
-
-不同的韦氏分布：
-
-
-
-
-::
-
-    x = linspace(0.01, 3, 100)
-
-    plot(x, dweibull.pdf(x, 1), label='s=1, constant failure rate')
-    plot(x, dweibull.pdf(x, 2), label='s>1, increasing failure rate')
-    plot(x, dweibull.pdf(x, .1), label='0<s<1, decreasing failure rate')
-
-    legend()
-
-
-
-
-结果:
-::
-
-    <matplotlib.legend.Legend at 0xaa9bc50>
-
-
-.. image:: 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O3atZMZM2aISMEp+QYMGCBNmzaVHTt2iN1ul1OnTknjxo1l3rx5YrfbZdOmTRIaGio7duwQEZGY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%0AAAAAAElFTkSuQmCC%0A
-
-
-不同自由度的学生 t 分布：
-
-
-
-
-::
-
-    x = linspace(-3, 3, 100)
-
-    plot(x, t.pdf(x, 1), label='df=1')
-    plot(x, t.pdf(x, 2), label='df=2')
-    plot(x, t.pdf(x, 100), label='df=100')
-    plot(x[::5], norm.pdf(x[::5]), 'kx', label='normal')
-
-    legend()
-
-
-
-
-结果:
-::
-
-    <matplotlib.legend.Legend at 0x164582e8>
-
-.. image::data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAXgAAAEACAYAAAC57G0KAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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%0A
-
-
-离散分布
-------------------
-
-导入离散分布：
-
-
-
-
-::
-
-    from scipy.stats import binom, poisson, randint
-
-
-离散分布没有概率密度函数，但是有概率质量函数。
-
-
-离散均匀分布的概率质量函数（PMF）：
-
-
-
-
-::
-
-    high = 10
-    low = -10
-
-    x = arange(low, high+1, 0.5)
-    p = stem(x, randint(low, high).pmf(x))  # 杆状图
-
-
-.. image::data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAXsAAAEACAYAAABS29YJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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%0A
-
-
-二项分布：
-
-
-
-
-::
-
-    num_trials = 60
-    x = arange(num_trials)
-
-    plot(x, binom(num_trials, 0.5).pmf(x), 'o-', label='p=0.5')
-    plot(x, binom(num_trials, 0.2).pmf(x), 'o-', label='p=0.2')
-
-    legend()
-
-
-
-
-结果:
-::
-
-    <matplotlib.legend.Legend at 0x1738a198>
-
-.. image:: 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eQhH+8PFZGPRaRaRH7Rmn1jlbuEE4ppDUb0GsHnBz+n3tQH/djKqabG9x+81dXBG0arVLg1m+xF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%0AaLJXSqk48P8B4O96y1bc/nYAAAAASUVORK5CYII=%0A
-
-泊松分布：
-
-
-
-
-::
-
-    x = arange(0,21)
-
-    plot(x, poisson(1).pmf(x), 'o-', label=r'$\lambda$=1')
-    plot(x, poisson(4).pmf(x), 'o-', label=r'$\lambda$=4')
-    plot(x, poisson(9).pmf(x), 'o-', label=r'$\lambda$=9')
-
-    legend()
-
-
-
-
-结果:
-::
-
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A65P3TXdOwIX37p6yiUUso3yjuy7w7sEJGdIpIDLAQGFd5BRFaIyBH7w5XAecX68It6pTrXXikV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%0Agg==%0A
-
-
-自定义离散分布
-------------------
-
-
-导入要用的函数：
-
-
-
-
-::
-
-    from scipy.stats import rv_discrete
-
-
-一个不均匀的骰子对应的离散值及其概率：
-
-
-
-
-::
-
-    xk = [1, 2, 3, 4, 5, 6]
-    pk = [.3, .35, .25, .05, .025, .025]
-
-
-定义离散分布：
-
-
-
-
-::
-
-    loaded = rv_discrete(values=(xk, pk))
-
-
-此时， loaded 可以当作一个离散分布的模块来使用。
-
-
-产生两个服从该分布的随机变量：
-
-
-
-
-::
-
-    loaded.rvs(size=2)
-
-
-
-
-结果:
-::
-
-    array([3, 1])
-
-
-产生100个随机变量，将直方图与概率质量函数进行比较：
-
-
-
-
-::
-
-    samples = loaded.rvs(size=100)
-    bins = linspace(.5,6.5,7)
-
-    hist(samples, bins=bins, normed=True)
-    stem(xk, loaded.pmf(xk), markerfmt='ro', linefmt='r-')
-
-
-
-
-<Container object of 3 artists>
-
-.. image::data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAXgAAAEACAYAAAC57G0KAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz%0AAAALEgAACxIB0t1+/AAAFktJREFUeJzt3X9sXWd9x/H3B7tJl7SsDBhlqVGR2y2tBGqBhmpdy4WF%0AxG2gqUAiRCAkfi1/EDvb2BRGV7CFKkAaGnOqVaEEVFhHKkpTZXOD027ctTDWxl2b8sNGiSFSEkoX%0AWgZLq4Sk/e6Pe+Jeu/Y999o+PrlPPi/JyjnPOc+5X6fpx4+f80sRgZmZpeclZRdgZmbFcMCbmSXK%0AAW9mligHvJlZohzwZmaJcsCbmSUqN+Al9Ugak7RP0uYG+10h6aSkd7fa18zM5l/DgJfUAdwC9ACX%0AAuslXTLDfp8Hvt1qXzMzK0beCH4FsD8iDkTECWA7sHaa/XqBu4Ajs+hrZmYFyAv4ZcDBuvVDWdsE%0AScuoBfetWdOpW2Nz+5qZWXHyAr6Z5xh8EfhE1J55oOyr2b5mZlaQzpzth4GuuvUuaiPxem8EtksC%0AeAVwraQTTfZFkn8QmJnNQkQob4cZv6j9ABgHLgQWAY8BlzTY/6vAu1rpWyuhfX36058uu4Q5cf3l%0Aauf627n2iPavP8vOhhnecAQfESclbQSGgQ5gW0SMStqQbd/aat+GP23MzGze5E3REBG7gF1T2qYN%0A9oj4YF5fMzNbGL6TdY4qlUrZJcyJ6y9XO9ffzrVD+9ffDEXJL/yQFGXXYGbWbiTlnmT1CN7MLFEO%0AeDOzRDngzcwS5YA3M0uUA97MLFEOeDOzRDngzcwS5YA3M0uUA97MLFEOeDOzROU+bMzKlT1nv635%0AURRm5XDAt4V2Dsj2/wFl1q48RWNmligHvJlZohzwZmaJcsCbmSXKAW9mlqjcgJfUI2lM0j5Jm6fZ%0AvlbSXkmPSnpE0tvqth2Q9Hi27eH5Lt7MzGbW8JV9kjqAnwArgcPAHmB9RIzW7bM0Ip7Jll8H7IiI%0Ai7L1nwFvjIinG3yGX9nXQO06+Hb++5GvgzcrQDOv7Mu7Dn4FsD8iDmQH3A6sBSYC/lS4Z84Bfjm1%0AjmYLtoVzDkMsZ5ClHOcZFjNGH0dZU3ZZZjaP8gJ+GXCwbv0Q8OapO0m6Afgs8GpgVd2mAO6X9Byw%0ANSJum1u5Nh/OYYjr2MSdjE+0rWOce8Ehb5aQvDn4pn63joh7IuIS4J3A1+s2XRURlwPXAh+TdPXs%0AyrT5tJzBSeEOcCfjLGdLSRWZWRHyRvCHga669S5qo/hpRcSDkjolvTwinoqIJ7L2I5J2UJvyeXBq%0Av/7+/onlSqVCpVJp+huw1i3l+Aztxxa4EjNrVrVapVqtttQn7yRrJ7WTrH8K/Bx4mBefZO0GfhoR%0AIekNwDcjolvSEqAjIv5P0lJgNzAQEbunfIZPsjZQxEnWN7GaPex+UfsVrGaEb8/rZ/kkq1kx5nyS%0ANSJOStoIDAMdwLaIGJW0Idu+FXg38AFJJ4CjwHuz7ucDd2dPQ+wE7pga7laOMfpYx/ikaZr30M0Y%0AvSVWZWbzreEIfkEK8Ai+oaIuk6xdRbOFPQxzBasZo7egE6wewZsVoZkRvAP+NFf0dfCBUKHX2Tvg%0AzYrQTMD7UQVmZolywJuZJcoBb2aWKAe8mVmiHPBmZolywJuZJcoBb2aWKAe8mVmiHPBmZolywJuZ%0AJcoBb2aWKAe8mVmiHPBmZolywJuZJcoBb2aWKAe8mVmiHPBmZolywJuZJSo34CX1SBqTtE/S5mm2%0Ar5W0V9Kjkh6R9LZm+5qZWXEavpNVUgfwE2AlcBjYA6yPiNG6fZZGxDPZ8uuAHRFxUTN9sz5t+U7W%0AB4aG2D04SOfx45xcvJhVfX1cs2b+X1rtd7Ka2XSaeSdrZ84xVgD7I+JAdsDtwFpgIqRPhXvmHOCX%0AzfZtVw8MDTG8aRM3j49PtN2YLRcR8mZms5E3RbMMOFi3fihrm0TSDZJGgV1AXyt929HuwcFJ4Q5w%0A8/g4923ZUlJFZmYvljeCb+p364i4B7hH0tXA1yUtb6WI/v7+ieVKpUKlUmml+4LrPH582vaOY8cW%0AuBIzO1NUq1Wq1WpLffIC/jDQVbfeRW0kPq2IeFBSJ/B72X5N9a0P+HZwcvHiadufO/vsBa7EzM4U%0AUwe/AwMDuX3ypmhGgIslXShpEbAO2Fm/g6Ru1c4EIukNABHxVDN929Wqvj5u7O6e1PbJ7m7e3ttb%0AUkVmZi/WcAQfESclbQSGgQ5gW0SMStqQbd8KvBv4gKQTwFHgvY36FvetLJxTJ1Jv2rKFzwwPc9Pq%0A1fT09voEq5mdVhpeJrkgBbTpZZITJCiwfl8maWbTmY/LJNvayMgI3/rWjiLzl88Bn/jEjYUcWw3/%0A05mZNZZ0wO/du5cvfOHbnDjxrsI+43PA5z+/pJBjd3QMFXJcMzszJB3wAGeddRknThQzwq75W6CY%0A43d0/JLnnvt+Icc2s/T5YWNmZolywJuZJcoBb2aWKAe8mVmiHPBmZolywJuZJcoBb2aWKAe8mVmi%0AHPBmZolywJuZJcoBb2aWKAe8mVmiHPBmZolywJuZJcoBb2aWKAe8mVmicgNeUo+kMUn7JG2eZvv7%0AJO2V9Lik70l6fd22A1n7o5Ienu/izcxsZg3f6CSpA7gFWAkcBvZI2hkRo3W7/RS4JiJ+LakH+BJw%0AZbYtgEpEPD3/pduZ7IGhIXYPDtJ5/DgnFy9mVV8f16xZU3ZZZqeVvFf2rQD2R8QBAEnbgbXARMBH%0ARP075R4CLphyDL862ubVA0NDDG/axM3j4xNtN2bLDnmzF+RN0SwDDtatH8raZvJh4N669QDulzQi%0A6aOzK9Fsst2Dg5PCHeDm8XHu27KlpIrMTk95I/ho9kCS3gp8CLiqrvmqiHhC0iuB+ySNRcSDU/v2%0A9/dPLFcqFSqVSrMfa2egzuPHp23vOHZsgSsxWzjVapVqtdpSn7yAPwx01a13URvFT5KdWL0N6ImI%0AX51qj4gnsj+PSNpBbcqnYcCb5Tm5ePG07c+dffYCV2K2cKYOfgcGBnL75E3RjAAXS7pQ0iJgHbCz%0AfgdJrwHuBt4fEfvr2pdIOjdbXgqsAn7Q1Hdi1sCqvj5u7O6e1PbJ7m7e3ttbUkVmp6eGI/iIOClp%0AIzAMdADbImJU0oZs+1bgU8DLgFslAZyIiBXA+cDdWVsncEdE7C7sO7EzxqkTqTdt2cJnhoe5afVq%0Aenp7fYLVbApFND3NXkwBUhRVw7Zt2+jr+0+efXZbIccHCISaP1XRkkWL/oLf/vaLtHAqpGVF1l8j%0ACv03JkHJ/4bNyiCJiGh4laLvZDUzS1TeSVazOcum6QoRBR8fKPY3ELMCOeBtARQ7BVT88c3ak6do%0AzMwS5YA3M0uUA97MLFEOeDOzRDngzcwS5YA3M0uUA97MLFEOeDOzRDngzcwS5YA3M0uUA97MLFEO%0AeDOzRDngzcwS5YA3M0uUA97MLFEOeDOzROUGvKQeSWOS9knaPM3290naK+lxSd+T9Ppm+5qZWXEa%0ABrykDuAWoAe4FFgv6ZIpu/0UuCYiXg98BvhSC33NzKwgeSP4FcD+iDgQESeA7cDa+h0i4vsR8ets%0A9SHggmb7mplZcfICfhlwsG79UNY2kw8D986yr5mZzaO8l243/TZjSW8FPgRc1Wrf/v7+ieVKpUKl%0AUmm2q5nZGaFarVKtVlvqkxfwh4GuuvUuaiPxSbITq7cBPRHxq1b6wuSANzOzF5s6+B0YGMjtkzdF%0AMwJcLOlCSYuAdcDO+h0kvQa4G3h/ROxvpa+ZmRWn4Qg+Ik5K2ggMAx3AtogYlbQh274V+BTwMuBW%0ASQAnImLFTH0L/F7MzKxO3hQNEbEL2DWlbWvd8keAjzTb18zMFobvZDUzS5QD3swsUQ54M7NEOeDN%0AzBLlgDczS5QD3swsUQ54M7NEOeDNzBLlgDczS5QD3swsUQ54M7NEOeDNzBLlgDczS5QD3swsUQ54%0AM7NEOeDNzBLlgDczS5QD3swsUQ54M7NE5Qa8pB5JY5L2Sdo8zfblkr4v6Zikj0/ZdkDS45IelfTw%0AfBZuZmaNNXzptqQO4BZgJXAY2CNpZ0SM1u32FNAL3DDNIQKoRMTT81SvmZk1KW8EvwLYHxEHIuIE%0AsB1YW79DRByJiBHgxAzH0NzLNDOzVuUF/DLgYN36oaytWQHcL2lE0kdbLc7MzGav4RQNtYCei6si%0A4glJrwTukzQWEQ9O3am/v39iuVKpUKlU5vixZmZpqVarVKvVlvrkBfxhoKtuvYvaKL4pEfFE9ucR%0ASTuoTfk0DHgzM3uxqYPfgYGB3D55UzQjwMWSLpS0CFgH7Jxh30lz7ZKWSDo3W14KrAJ+kFuRmZnN%0Ai4Yj+Ig4KWkjMAx0ANsiYlTShmz7VknnA3uAlwLPS9oEXAr8PnC3pFOfc0dE7C7uWzEzs3p5UzRE%0AxC5g15S2rXXLv2DyNM4pR4HL5lqgmZnNju9kNTNLlAPezCxRDngzs0Q54M3MEuWANzNLlAPezCxR%0ADngzs0Q54M3MEuWANzNLlAPezCxRDngzs0Q54M3MEuWANzNLlAPezCxRDngzs0Q54M3MEuWANzNL%0AlAPezCxRDngzs0TlBrykHkljkvZJ2jzN9uWSvi/pmKSPt9LXzMyK0zDgJXUAtwA9wKXAekmXTNnt%0AKaAX+LtZ9DUzs4LkjeBXAPsj4kBEnAC2A2vrd4iIIxExApxota+ZmRUnL+CXAQfr1g9lbc2YS18z%0AM5ujzpztMYdjN923v79/YrlSqVCpVObwsWZm6alWq1Sr1Zb65AX8YaCrbr2L2ki8GU33rQ94MzN7%0AsamD34GBgdw+eVM0I8DFki6UtAhYB+ycYV/Noa+Zmc2zhiP4iDgpaSMwDHQA2yJiVNKGbPtWSecD%0Ae4CXAs9L2gRcGhFHp+tb5DdjZmYvyJuiISJ2AbumtG2tW/4Fk6diGvY1M7OF4TtZzcwS5YA3M0uU%0AA97MLFEOeDOzRDngzcwS5YA3M0uUA97MLFEOeDOzRDngzcwS5YA3M0uUA97MLFEOeDOzRDngzcwS%0A5YA3M0uUA97MLFEOeDOzRDngzcwS5YA3M0tUbsBL6pE0JmmfpM0z7DOYbd8r6fK69gOSHpf0qKSH%0A57NwMzNrrOE7WSV1ALcAK4HDwB5JO+tfni3pOuCiiLhY0puBW4Ers80BVCLi6UKqNzOzGeW9dHsF%0AsD8iDgBI2g6sBUbr9rkeuB0gIh6SdJ6kV0XEk9l2zW/JZu3vgaEhdg8O0nn8OCcXL2ZVXx/XrFlT%0AdlmWmLyAXwYcrFs/BLy5iX2WAU9SG8HfL+k5YGtE3Da3cs3a3wNDQwxv2sTN4+MTbTdmyw55m095%0Ac/DR5HFmGqX/SURcDlwLfEzS1U1XZpao3YODk8Id4Obxce7bsqWkiixVeSP4w0BX3XoXtRF6o30u%0AyNqIiJ9nfx6RtIPalM+DUz+kv79/YrlSqVCpVJoq3mwhSPM7y/iWGdofHB6e98+KaHaMZqe7arVK%0AtVptrVNEzPhF7QfAOHAhsAh4DLhkyj7XAfdmy1cC/5UtLwHOzZaXAt8DVk3zGVGUL3/5y7FkyYcC%0AorCvgMKOvWjRnwcFHr/o+mtfrn/q15tYNe2GN7F63mu3dGX/fWn01XAEHxEnJW0EhoEOYFtEjEra%0AkG3fGhH3SrpO0n7gGeCDWffzgbuzEUkncEdE7G7tx49ZesboYx3j3MkL0zTvoZsxekusylKUN0VD%0AROwCdk1p2zplfeM0/X4KXDbXAs1Sc5Q13AtcwRb2MMwVrGaMXo7iE6w2v3ID3szm31HWMMIaQIzw%0A7bLLsUT5UQVmZolywJuZJcoBb2aWKAe8mVmiHPBmZolywJuZJcqXSZpZSxbqSZjz/diGU84BllO7%0Avf4ZYAw4WsgnZfdal8gBb2ZNW/gnYc5vQJ7DENexadJdxOvo5l7+oYAbzcp/UrqnaMysae3+JMzl%0ADE4Kd4A7GWc57VF/qxzwZta0zuPHp23vOHZsgSuZnaVMX/9S2qP+VjngzaxpJxcvnrb9ubPPXuBK%0AZucZpq//Gdqj/lY54M2saav6+rixu3tS2ye7u3l7b3s8CbP2JM/J9af8JE+fZDVLWBFXopwD7Ab2%0AAFcAY+PjfPYd75j3zynCmfYkTwe8WdLm/zK9o8AIUHsSZpGXARZzFcqZ9CRPT9GYmSXKAW9mligH%0AvJlZohzwZmaJyg14ST2SxiTtk7R5hn0Gs+17JV3eSl8zMytGw4CX1AHcAvQAlwLrJV0yZZ/rgIsi%0A4mLgz4Bbm+2bgmrZBcxRtewC5qhadgFzVC27gDmoll3AHFXLLmAB5I3gVwD7I+JARJwAtgNrp+xz%0APXA7QEQ8BJwn6fwm+7a9atkFzFG17ALmqFp2AXNULbuAOaiWXcAcVcsuYAHkBfwy4GDd+qGsrZl9%0A/qCJvmZmVpC8G52avYuh/OdizuD553fz0pe+s7gP+A2FHf+3v/1hIcc1szNDXsAfBrrq1ruojcQb%0A7XNBts9ZTfQFinuw/ynHjk37sfNiAOA3/1rY8WuK+/sZKPj4FHx815+nnWsv9vgLUX/R2ZYnL+BH%0AgIslXQj8HFgHrJ+yz05gI7Bd0pXA/0bEk5KeaqIvEXHajv7NzNpZw4CPiJOSNgLDQAewLSJGJW3I%0Atm+NiHslXSdpP7U3YH2wUd8ivxkzM3uByn5noJmZFaPUO1nb+UYoSV+R9KSkH5Rdy2xI6pL0HUk/%0AkvRDSX1l19QsSWdLekjSY5J+LOmzZdc0G5I6JD0q6V/KrqVVkg5Iejyr/+Gy62mVpPMk3SVpNPs3%0AdGXZNTVL0h9lf++nvn490/+/pY3gsxuhfgKspHaidg+wvl2mcSRdTe3JqV+LiNeVXU+rsnsVzo+I%0AxySdAzwC3NBGf/9LIuJZSZ3Ad4G/iojvll1XKyT9JfBG4NyIuL7seloh6WfAGyPi6bJrmQ1JtwP/%0AERFfyf4NLY2IX5ddV6skvYRafq6IiINTt5c5gm/rG6Ei4kHgV2XXMVsR8YuIeCxbPgqMUrt3oS1E%0AxLPZ4iJq53jaKmgkXQBcB3yZ0/gy4xxtWbek3wWujoivQO18YTuGe2YlMD5duEO5Ad/MTVS2ALIr%0AnS4HHiq3kuZJeomkx4Ange9ExI/LrqlFfw/8NfB82YXMUgD3SxqR9NGyi2nRa4Ejkr4q6b8l3SZp%0ASdlFzdJ7gX+eaWOZAe+zu6eBbHrmLmBTNpJvCxHxfERcRu2+i2skVUouqWmS3gH8T0Q8SpuOgoGr%0AIuJy4FrgY9mUZbvoBN4A/GNEvIHa1X+fKLek1klaBLwT+OZM+5QZ8M3cRGUFknQW8C3gnyLinrLr%0AmY3sV+sh4E1l19KCPwauz+axvwG8TdLXSq6pJRHxRPbnEWAHtSnXdnEIOBQRe7L1u6gFfru5Fngk%0A+28wrTIDfuImquwn0TpqN03ZAlDtFrttwI8j4otl19MKSa+QdF62/DvA24FHy62qeRHxyYjoiojX%0AUvsV+98j4gNl19UsSUsknZstLwVWAW1zNVlE/AI4KOkPs6aVwI9KLGm21lMbIMyotJdut/uNUJK+%0AAbwFeLmkg8CnIuKrJZfViquA9wOPSzoVjn8TEe3wFuJXA7dnVxC8BPh6RPxbyTXNRbtNV74K2JHd%0Aht8J3BERu8stqWW9wB3Z4HKc7AbNdpH9YF0JNDz/4RudzMwS5Vf2mZklygFvZpYoB7yZWaIc8GZm%0AiXLAm5klygFvZpYoB7yZWaIc8GZmifp/J4pqDI9mKMcAAAAASUVORK5CYII=%0A
-
-
-假设检验
--------------
-
-
-导入相关的函数：
-
-
-- 正态分布
-- 独立双样本 t 检验，配对样本 t 检验，单样本 t 检验
-- 学生 t 分布
-
-
-t 检验的相关内容请参考：
-
-
-- 百度百科-t 检验：http://baike.baidu.com/view/557340.htm
-- 维基百科-学生 t 检验：https://en.wikipedia.org/wiki/Student%27s_t-test
-
-
-
-
-::
-
-    from scipy.stats import norm
-    from scipy.stats import ttest_ind, ttest_rel, ttest_1samp
-    from scipy.stats import t
-
-
-独立样本 t 检验
---------------------
-
-
-两组参数不同的正态分布：
-
-
-
-
-::
-
-    n1 = norm(loc=0.3, scale=1.0)
-    n2 = norm(loc=0, scale=1.0)
-
-
-从分布中产生两组随机样本：
-
-
-
-
-::
-
-    n1_samples = n1.rvs(size=100)
-    n2_samples = n2.rvs(size=100)
-
-
-将两组样本混合在一起：
-
-
-
-
-::
-
-    samples = hstack((n1_samples, n2_samples))
-
-
-最大似然参数估计：
-
-
-
-
-::
-
-    loc, scale = norm.fit(samples)
-    n = norm(loc=loc, scale=scale)
-
-
-比较：
-
-
-
-
-
-::
-
-    x = linspace(-3,3,100)
-
-    hist([samples, n1_samples, n2_samples], normed=True)
-    plot(x, n.pdf(x), 'b-')
-    plot(x, n1.pdf(x), 'g-')
-    plot(x, n2.pdf(x), 'r-')
-
-
-
-
-[<matplotlib.lines.Line2D at 0x17ca7278>]
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-.. image:: 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FsNuew8JnduGDzYTH4KCoKaeWrSrXQ3GZYoAEnkIjbYv9/c0Vu9GlKGHl3SuzfUrAmVK9s+xcT3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%0AIYRwcpLIhRDCyUkiF0IIJ/d/XdaGnd6OVMIAAAAASUVORK5CYII=%0A
-
-
-
-独立双样本 t 检验的目的在于判断两组样本之间是否有显著差异：
-
-
-
-
-::
-
-    t_val, p = ttest_ind(n1_samples, n2_samples)
-
-    print 't = {}'.format(t_val)
-    print 'p-value = {}'.format(p)
-
-
-
-结果:
-::
-
-    t = 0.868384594123
-
-    p-value = 0.386235148899
-
-
-p 值小，说明这两个样本有显著性差异。
-
-
-
-配对样本 t 检验
----------------------
-
-
-配对样本指的是两组样本之间的元素一一对应，例如，假设我们有一组病人的数据：
-
-
-
-
-::
-
-    pop_size = 35
-
-    pre_treat = norm(loc=0, scale=1)
-    n0 = pre_treat.rvs(size=pop_size)
-
-
-经过某种治疗后，对这组病人得到一组新的数据：
-
-
-
-
-
-::
-
-    effect = norm(loc=0.05, scale=0.2)
-    eff = effect.rvs(size=pop_size)
-
-    n1 = n0 + eff
-
-
-新数据的最大似然估计：
-
-
-
-
-::
-
-    loc, scale = norm.fit(n1)
-    post_treat = norm(loc=loc, scale=scale)
-
-
-画图：
-
-
-
-
-
-::
-
-    fig = figure(figsize=(10,4))
-
-    ax1 = fig.add_subplot(1,2,1)
-    h = ax1.hist([n0, n1], normed=True)
-    p = ax1.plot(x, pre_treat.pdf(x), 'b-')
-    p = ax1.plot(x, post_treat.pdf(x), 'g-')
-
-    ax2 = fig.add_subplot(1,2,2)
-    h = ax2.hist(eff, normed=True)
-
-
-.. image:: 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NqqK49+8WjS91WZFf2DqXfv3uGFEREJQbzCaiLQOPq48hLgcmCHm0Hd/aBfX5vZc0Qea96pqJJs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%0A
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NqqK49+8WjS91WZFf2DqXfv3uGFEREJQbzCaiLQOPq48hLgcmCHm0Hd/aBfX5vZc0Qea96pqJJs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%0A
-
-
-
-独立 t 检验：
-
-
-
-
-::
-
-    t_val, p = ttest_ind(n0, n1)
-
-    print 't = {}'.format(t_val)
-    print 'p-value = {}'.format(p)
-
-
-
-
-
-
-结果:
-::
-
-    t = -0.347904839913
-
-
-    p-value = 0.728986322039
-
-
-高 p 值说明两组样本之间没有显著性差异。
-
-
-
-配对 t 检验：
-
-
-
-
-::
-
-    t_val, p = ttest_rel(n0, n1)
-
-    print 't = {}'.format(t_val)
-    print 'p-value = {}'.format(p)
-
-
-
-结果:
-::
-
-    t = -1.89564459709
-
-
-    p-value = 0.0665336223673
-
-
-配对 t 检验的结果说明，配对样本之间存在显著性差异，说明治疗时有效的，符合我们的预期。
-
-
-p 值计算原理
--------------
-
-
-p 值对应的部分是下图中的红色区域，边界范围由 t 值决定。
-
-
-
-
-::
-
-    my_t = t(pop_size) # 传入参数为自由度，这里自由度为50
-
-    p = plot(x, my_t.pdf(x), 'b-')
-    lower_x = x[x<= -abs(t_val)]
-    upper_x = x[x>= abs(t_val)]
-
-
-
-    p = fill_between(lower_x, my_t.pdf(lower_x), color='red')
-
-    p = fill_between(upper_x, my_t.pdf(upper_x), color='red')
-
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-.. image:: 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wBV/UJVt4YfzgIKfvvYQvU08P77UK2aFfdEVakSXH89DB7sO4kpTZEKfDVgdb7Ha8LPHcitQP7r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%0AMSZFWYE3xpgU9f8B8tTCMYTHdwAAAABJRU5ErkJggg==%0A
-
-
-作者 & 更新时间
-------------------------------------
-作者:`李金  <lijinwithyou@gmail.com>`
-
-提交: 2017/12/14
-
-
-
-
-
diff --git a/course/source/aipy-scipy/cn/5curve-fitting.rst b/course/source/aipy-scipy/cn/5curve-fitting.rst
deleted file mode 100644
index ceaf084..0000000
--- a/course/source/aipy-scipy/cn/5curve-fitting.rst
+++ /dev/null
@@ -1,558 +0,0 @@
-
-曲线拟合
--------------
-
-导入基础包：
-
-::
-
-    import numpy as np
-    import matplotlib as mpl
-    import matplotlib.pyplot as plt
-
-
-多项式拟合
------------------------
-
-导入线多项式拟合工具：
-
-
-
-::
-
-     from numpy import polyfit, poly1d
-
-产生数据：
-
-::
-
-    x = np.linspace(-5, 5, 100)
-    y = 4 * x + 1.5
-    noise_y = y + np.random.randn(y.shape[-1]) * 2.5
-
-
-画出数据：
-
-::
-
-    %matplotlib inline
-
-    p = plt.plot(x, noise_y, 'rx')
-    p = plt.plot(x, y, 'b:')
-
-.. image:: 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1/BtekpsIuUI07QzgTacMANb+cKuOnXJ0xwX7V8U3Ddzk73ceOU2pC8FNhFyhU3zRKz5/722+7P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%0A/wEcY+GYZJlNlgAAAABJRU5ErkJggg==%0A
-
-
-
-进行线性拟合，polyfit 是多项式拟合函数，线性拟合即一阶多项式：
-
-
-
-
-::
-
-    coeff = polyfit(x, noise_y, 1)
-    print coeff
-
-
-
-结果:
-::
-
-    [ 3.93921315  1.59379469]
-
-
-一阶多项式 *y = a_1 x + a_0* 拟合，返回两个系数 *[a_1, a_0]*。
-
-
-画出拟合曲线：
-
-
-
-
-::
-
-    p = plt.plot(x, noise_y, 'rx')
-    p = plt.plot(x, coeff[0] * x + coeff[1], 'k-')
-    p = plt.plot(x, y, 'b--')
-
-.. image:: 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nnqHQcy1e3QoaqA5v7rS+3f6K8qclKbVXhTN6/YXLTP/fcX76n7W07oOgXusmXsxZcAcNpeolIk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%0ArkJggg==%0A
-
-
-
-还可以用 poly1d 生成一个以传入的 coeff 为参数的多项式函数：
-
-
-
-
-::
-
-    f = poly1d(coeff)
-    p = plt.plot(x, noise_y, 'rx')
-    p = plt.plot(x, f(x))
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-.. image:: 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JJ9OcS2X7JKgV2kpUnWC07Qw371VbPjjzfr0cOL2Vu3Ro7V1Jx9EImOPWeOeuw5psAu0pIkC+Bx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%0AkFFgFxEJGQV2EZGQ+X/DyfQNy7jRVwAAAABJRU5ErkJggg==%0A
-
-
-
-
-::
-
-    f
-
-
-
-结果:
-::
-
-    poly1d([ 3.93921315,  1.59379469])
-
-
-显示 f：
-
-
-
-
-::
-
-    print f
-
-
- 
-
-结果:
-::
-
-    3.939 x + 1.594
-
-
-
-还可以对它进行数学操作生成新的多项式：
-
-
-
-
-::
-
-    print f + 2 * f ** 2
-
-
-
-
-结果:
-::
-
-    2
-
-    31.03 x + 29.05 x + 6.674
-
-
-
-多项式拟合正弦函数
----------------------
-
-正弦函数：
-
-
-
-
-::
-
-    x = np.linspace(-np.pi,np.pi,100)
-    y = np.sin(x)
-
-
-用一阶到九阶多项式拟合，类似泰勒展开：
-
-
-
-
-::
-
-    y1 = poly1d(polyfit(x,y,1))
-    y3 = poly1d(polyfit(x,y,3))
-    y5 = poly1d(polyfit(x,y,5))
-    y7 = poly1d(polyfit(x,y,7))
-    y9 = poly1d(polyfit(x,y,9))
-
-
-
-
-::
-
-    x = np.linspace(-3 * np.pi,3 * np.pi,100)
-
-    p = plt.plot(x, np.sin(x), 'k')
-    p = plt.plot(x, y1(x))
-    p = plt.plot(x, y3(x))
-    p = plt.plot(x, y5(x))
-    p = plt.plot(x, y7(x))
-    p = plt.plot(x, y9(x))
-
-    a = plt.axis([-3 * np.pi, 3 * np.pi, -1.25, 1.25])
-
-
-.. image:: 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tBkGUf58P6tVE1uJeWSDF7If4E7Ft6BXueYgbr4ynRqErp59zdbuXbGtWw7tY3OvtH/OD//PHR3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%0AiBJ3hUKh0CFK3BUKhUKH/AdKQZTK2jwwogAAAABJRU5ErkJggg==%0A
-
-黑色为原始的图形，可以看到，随着多项式拟合的阶数的增加，曲线与拟合数据的吻合程度在逐渐增大。
-
-
-最小二乘拟合
---------------------
-
-
-导入相关的模块：
-
-
-
-
-::
-
-    from scipy.linalg import lstsq
-    from scipy.stats import linregress
-
-
-
-
-::
-
-    x = np.linspace(0,5,100)
-    y = 0.5 * x + np.random.randn(x.shape[-1]) * 0.35
-
-    plt.plot(x,y,'x')
-
-
-结果:
-
-    
-.. image:: data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAXkAAAEACAYAAABWLgY0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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%0AAAAASUVORK5CYII=%0A
-
-
-一般来书，当我们使用一个 N-1 阶的多项式拟合这 M 个点时，有这样的关系存在：
-
-
-*XC = Y*
-
-
-Scipy.linalg.lstsq 最小二乘解
-----------------------------------
-
-
-要得到 C ，可以使用 scipy.linalg.lstsq 求最小二乘解。
-
-
-这里，我们使用 1 阶多项式即 N = 2，先将 x 扩展成 X：
-
-
-
-
-::
-
-    X = np.hstack((x[:,np.newaxis], np.ones((x.shape[-1],1))))
-    X[1:5]
-
-
-
-结果:
-::
-
-    array([[ 0.05050505,  1.        ],
-               [ 0.1010101 ,  1.        ],
-               [ 0.15151515,  1.        ],
-               [ 0.2020202 ,  1.        ]])
-
-
-求解：
-
-
-
-
-
-::
-
-    C, resid, rank, s = lstsq(X, y)
-    C, resid, rank, s
-
-
-
-结果:
-::
-
-    (array([ 0.50432002,  0.0415695 ]),
-
-        12.182942535066523,
-
-        2,
-
-        array([ 30.23732043,   4.82146667]))
-
-画图：
-
-
-
-
-::
-
-    p = plt.plot(x, y, 'rx')
-    p = plt.plot(x, C[0] * x + C[1], 'k--')
-    print "sum squared residual = {:.3f}".format(resid)
-    print "rank of the X matrix = {}".format(rank)
-    print "singular values of X = {}".format(s)
-
-
-
-
-结果:
-::
-
-    sum squared residual = 12.183
-
-    rank of the X matrix = 2
-
-    singular values of X = [ 30.23732043   4.82146667]
-
-
-.. image:: data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAXkAAAEACAYAAABWLgY0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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%0ATkSuQmCC%0A
-
-
-Scipy.stats.linregress 线性回归
---------------------------------------
-
-对于上面的问题，还可以使用线性回归进行求解：
-
-
-
-
-::
-
-    slope, intercept, r_value, p_value, stderr = linregress(x, y)
-    slope, intercept
-
-
-
-结果:
-::
-
-    (0.50432001884393252, 0.041569499438028901)
-
-
-
-
-
-::
-
-    p = plt.plot(x, y, 'rx')
-    p = plt.plot(x, slope * x + intercept, 'k--')
-    print "R-value = {:.3f}".format(r_value)
-    print "p-value (probability there is no correlation) = {:.3e}".format(p_value)
-    print "Root mean squared error of the fit = {:.3f}".format(np.sqrt(stderr))
-
-
-
-结果:
-::
-
-    R-value = 0.903
-
-    p-value (probability there is no correlation) = 8.225e-38
-
-    Root mean squared error of the fit = 0.156
-
-.. image:: 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5XyNV9vEoM8kVPcbE1mk2rJIY2zf9IkHXDuuXphcbEuXrBAj9xwQ9vPmeuAJKt5eeCYTRjkiZzi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%0A
-
-
-
-可以看到，两者求解的结果是一致的，但是出发的角度是不同的。
-
-
-
-更高级的拟合
-----------------
-
-
-
-
-::
-
-    from scipy.optimize import leastsq
-
-
-先定义这个非线性函数： *y = a e^{-b sin( f x + \phi)}*
-
-
-
-
-::
-
-    def function(x, a , b, f, phi):
-        """a function of x with four parameters"""
-        result = a * np.exp(-b * np.sin(f * x + phi))
-        return result
-
-画出原始曲线：
-
-
-
-
-::
-
-    x = np.linspace(0, 2 * np.pi, 50)
-    actual_parameters = [3, 2, 1.25, np.pi / 4]
-    y = function(x, *actual_parameters)
-    p = plt.plot(x,y)
-
-
-.. image:: data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAW4AAAEACAYAAACTXJylAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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%0ALSKSMApuEZGEUXCLiCSMgltEJGH+Px9Ir5f410BLAAAAAElFTkSuQmCC%0A
-
-
-加入噪声：
-
-
-
-
-::
-
-    from scipy.stats import norm
-    y_noisy = y + 0.8 * norm.rvs(size=len(x))
-    p = plt.plot(x, y, 'k-')
-    p = plt.plot(x, y_noisy, 'rx')
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-.. image:: 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AkjRoxAn+bNgYgIIDkZCA83OiyPU1WEh4dj9erV1lkqjiyNk1mRV128eBHbt2/Hky1a5C4IjBkz%0A/LLEICLo1q0bVqxYYXQoRLmYwKnE1q1bh44PPYQqf/tb7oLAmDLFb+vE3bt3x8qVK40OgygXSyhU%0AYkOHDkWvChXQY9q0G2veNptjoWA/qxNfuXIFd955J44dO8ZFHsjrWAMnr7Hb7QgNDcW3336LevXq%0AGR2Oz/Tq1Qt9+/bFwIEDjQ6F/Bxr4OQ1u3btQnBwcJlK3gDQrVs3llHINJjAqUT8evRlMaKjoxEf%0AH4/r168bHQoREzi5IC7upouSm5YuxaAyWAcODQ1F/fr1sW3bNqNDIWICJxe0aXNDz5JTiYkYcvgw%0AGg4danBgxmB3QjILJnC6taCgvO6BKSmwvfgidvTogfLBwUZHZgjWwcksmMDJNUFBjqHyERF4JyAA%0AHZ56yuiIDNOsWTNcvHgRhw4dMjoUKuOYwMk1NhswYwYuJyWh1ZYt6NyqldERGaZcuXKIjo5GnL/M%0AuEiWxQROt5ZvVr4NR49ieYsWCPLTIfOu6t69O+vgZDgO5KFbyzcr38iRIxEZGYnXhg3zy9GWrsrI%0AyECtWrVw4sQJBPnDzItkOhyJSR6lqqhTpw42bNiABg0aGB2O4bp27Yrnn38e/fv3NzoU8kMciUke%0AtXPnTgQGBuLee+81OhRjOfvF9+rVC1999ZVjmz+tQkSW4VICF5EPReRXEdmfb1sNEVknIodEZK2I%0A8DzSzy1ZsgRPP/00RIptFPg/Z7/4pzp0wJo1a5B5+rT/rEJEluJSCUVEHgVwCcC/VPUB57bpAM6p%0A6nQReR1AdVV9o5DXsoTiB3IWNFi5ciUeeOABo8MxnvPC7nN79uDtKlVQf/Fi/1iFiEzDYyUUVd0C%0AIL3A5h4AFjpvLwTQy+0IyTJ27tyJSpUq4f777zc6FHNw9otftH07Zpcvz+RNhihNDfxOVf3VeftX%0AAHd6IB4yqSVLlqBfv34sn+Rw9otP27ULTdaudZRRiHysvCfeRFVVRIqsk0yaNCn3dlRUFKKiojyx%0AW/IRVcWSJUs4fDxHvn7xNYOCsLJ1azw2dCjLKFQqCQkJSEhIcOs1LncjFJFwACvy1cB/AhClqqki%0AEgpgk6o2LOR1rIFb3I4dOzBkyBAkJSWxBQ7c0C8eAObNm4dvV6/GR3/4Q5ntF0+e5+1uhF8DeN55%0A+3kAy0rxXmRiLJ8UEB19Q0u7d+/e+GrjRmQ+9piBQVFZ5Go3wk8BbAfQQEROiMgLAP4G4AkROQSg%0Ag/M++Zmc8km/fv2MDsW0QkJC8PDDD2PVqlVGh0JljEs1cFUdUMRDHT0YC5kQe5+45umnn8aSJUvQ%0At29fo0OhMoQjMalYLJ+4pnfv3o5BPZmZRodCZQgTOOUpsHSaqiL+s88wtFYtA4OyhpCQELRo0YJl%0AFPIpJnDKU2DptN0bNmD8pUsIG1BUBY3y69evH5YsWWJ0GFSGcDZCulFOH+fYWGzr1QvfPPkk3pw+%0A3eioLOHs2bOoX78+zpw5g0qVKhkdDlkcZyMk9+VbOm3c2bPoMXiw0RFZBsso5GtM4HQj5xDxH7/6%0ACi9mZqJR7dpGR2QpLKOQL7GEQnnyDRH/03//N2qUK4c3MzIcK9JziLhLWEYhT2EJhdyzbRswZQq0%0AWjUsWbLEUT6ZMsWxnVzCMgr5EhM45XEOEd+6dSsCAwPRqFEjR8ub83u4pX///li0aJHRYVAZwBIK%0A3WTQoEF48MEHMWbMGKNDsaSLFy+ibt26SEpKQmhoqNHhkEVxUWNy2/nz51GvXj0cPXoUNWvWNDoc%0AyxoxYgTCwsIwYcIEo0Mhi2INnNz28ccfo1u3bkzepRQTE4P58+fDbrcbHQr5MSZwyqWq+OCDDzBi%0AxAijQ7GmfFMRNG/eHDVr1sSmpUu5Wj15DRM45dq2bRvsdjseffRRo0OxpgJTEbw0cCCuxcZytXry%0AGiZwyjVv3jyMGDGCMw+WVFCQo9vlhAlASgqeS0rCyLQ0nLl82ejIyE/xIiYB4MVLj0pJASIigORk%0AjPjrXxEeHo4333zT6KjIYngRk1z28ccfIzo6msm7tJxTESA5GZgxA6MGDODFTPIaJnCCquaWT6gU%0A8k1FgPBwYMoUNPviC9StWhXr1q0zOjryQ0zghG3btiE7Oxvt2rUzOhRrc05FkDtvjLMm/sajj2Le%0AvHnGxkZ+iTVwwuDBg9G0aVOMHTvW6FD80m+//YawsDCOzCS3cCQm3VLOxcsjR44gODjY6HD81ogR%0AIxAREYHx48cbHQpZBC9i0i19/PHH6Nq1K5O3l40YMYIXM8njmMDLMF689J3mzZsjKCgI69evNzoU%0A8iNM4GXY5s2bkZWVhfbt2xsdit8TEcTExGDOnDlGh0J+hAm8LHLO2TF58mSMGzfOMfLSZuOcHV42%0AaNAg7Ny5E3v27DE6FPITTOBlUZs2OPXCC0g7ehSDBw/O67/MOTu8qlKlShg3bhzefvtto0MhP8EE%0AXhYFBSHm7Fl8es89qHDqVN7gE6576R35ZimMiYnBzp07sfebb3jGQ6XGBF4Gbdq0CT//+isiP/jA%0AMWdHbCyTtzflm6WwYsWKeOvll3FyyBCe8VCplTqBi0iKiOwTkR9FZKcngiLvUVVMnDgR//Xaayj/%0A7ru5c3bktBDJCwrMUjgsORmxV69i15EjRkdGFlfqgTwikgyguaqeL+JxDuQxkfXr1+ONkSOx84kn%0AUG7qVEdyyT+HB1vi3pNvlsI5q1YhLi4OcSyjUBF8OZCHE0hbQE7re0avXnnJG8hrIW7bZmyA/qzA%0ALIXD+vTB/v37sWPHDqMjIwvzRAv8GIALALIBfKCq8ws8zha4ScTHx+PVV1/FgQMHEBAQYHQ4ZUfB%0AMxzn/Q/vuQefr12LNWvWGB0hmZArLfDyHthPG1U9IyIhANaJyE+quiX/EyZNmpR7OyoqClFRUR7Y%0ALbkjp/U9ceJEJm9fK2KWwkEJCZj800/49ttv0bp1a2NjJMMlJCQgISHBrdd4dDIrEZkI4JKqzsq3%0AjS1wE1i9ejViY2Oxd+9eJnATmT9/PpYsWYK1a9caHQqZjNdr4CJSSUSqOG9XBvAkgP2leU/yPFXF%0AW2+9xda3CQ0ZMgRHjhzB1q1bjQ6FLKi0FzHvBLBFRPYA2AFgpaqyKWEyy5cvx9WrV9GnTx+jQ6EC%0AKlSogD//+c+YMGECeKZK7uJ84H7OZrPhgQcewMKFC9GhQwejw6FCZGVloVWrVoiJicHw4cONDodM%0Aggs6EIYOHYrbb7+ds+CZ3IEDB/DYY49h9+7dqFu3rtHhkAlwQYcy7vtJk7B7wwZMnz49byNnHTSl%0A+++/H2PGjMEf/vAHllLIZUzgfspms2HI/PmIa9YMgVlZORs566CJjRs3Dunp6Zg/f/6tn0wEllD8%0A1gsvvIBKlSrhvZw5OGJjHSMBOVze1BITExEVFYVdu3YhLCzM6HDIQKyBl1FxcXEYPXo09u3bh8DA%0AwBvm4EB4uNHh0S1MnToVGzduxNq1ax2LbVCZxBp4GZSeno6YmBgsWLDAkbwLzMHBWQfNLzY2Fjab%0ADfPmzTM6FDI5tsD9zJAhQ1C5cmW89957Rc7BwTKK+SUmJqJ9+/bYtWsXwnnWVCaxBV7GrFixAps3%0Ab8a0adMcG4qYg4OzDppEvpV6cjl7CTVq1AivvfYahg0bhuzsbGPiI9NjC9xP7Nq1C127dsWyZcvw%0AyCOPGB0OueIWZ0hZWVno3Lkz6tevj7lz57IeXsbwImYZcejQIbRv3x4ffPABevToYXQ45I6cpJ2/%0Al9C2bY6unkFBuHjxIqKiotC3Y0eMb9cOiI42OmLyEZZQyoDTp0+jU6dOmDJlCpO3FQUFOZJ3/rVJ%0A862hWaVKFaxZvBh1P/gA8xITjY6WTIYJ3MLS09PRqVMnxMTEYOjQoUaHQyVRWC+hAmtohsyejbbf%0AfIPJ//u/+Pzzz42OmMxEVb3649iFH1m5UjU9/cZt6emO7T6UmZmpbdu21VdffVXtdrtP900ekp6u%0A+sc/5h1PBe8nJ6sCjn9Vde/evRoSEqLr1q0zJFzyLWfuLDa/sgXurnyntwAMGZ6elZWF/v37Iyws%0ADLNmzeLFLasqrpdQIS3zxo0b44svvsCzzz6LXbt2ubaPYnq6kB+4VYYv7Q/8rQWumtdSSk6+scXk%0AA5mZmfrMM89o586d9erVqz7bL/nQLVrmy5Yt01q1aul3331X6vci84ILLXAm8JIqcHrrC8eOHdNm%0AzZrpM888o5cuXcp7wCRlHfKQ4v6ezseWL1+uISEh+v7776v9/Pni/9YGNjio5JjAvcWAD8Tq1av1%0Ajjvu0NmzZ99c82Yrq+zI97f9+eeftVXDhrq+YUPNPH26+NcZ0OCwDJM2gMyVwE3wC/EIHyfL7Oxs%0AnTx5st511126efPmW8fFVpb/y/e3vjp8uL7Qu7c++OCDmlxUci7s2DBp0jKESRtA5kngJvmFeIQP%0AD/z09HTt3r27PvLII3rq1Klbv4CtrLIj39/abrfru+++q3feeaeuWbPmxucVlZxSUkyZtAxjwgaQ%0AeRK4SX4hVpGdna2ffPKJhoWF6UsvveTaxUoTHoDkJUW0qLeuXKmhoaEaExOjqampju0TJxbd4DDb%0AMWP0WYHJGkDmSeAm+YVYQUJCgr4aGantmzTRTZs25T1Q3IFs0lNA8oJbtKjPHzumY8aM0Yjq1XVn%0AixZ66eTJ4t/PTEnLyOPYbF9maqYEbpJfiJkdPHhQe/TooWFhYfr5vHlqHzXq5gN58eLCWyjFtbLI%0AvxTXSs2XhGwDB+oLvXvrXXfdpf/4xz80Kyvr5vcyYdIyJCaTNoDMk8BL+gsx+pTKE27xf9i7d6+O%0AGDFCg4ODdcaMGXr58uW85xQ8kE16oJGJFGhRf/fdd9q2bVu9//77ddGiRZqZmel4nqeOJRe6PBb6%0AmBv/B68zaZ4xTwJXLdkvxMiE5ak/aiH/h8vDhun8GTO0efPmWqdOHf3LX/6i586du/m1hR3IZmw1%0AkTkUcWzY7XZdvny5Pvnkk1qjRg0dNWqUHnr3XUf/8YKv9+RntCSfX/aYyWWuBF5SRiUsT355pKdr%0AVkyMbv7XvzQ+MlLrVq2q/fv3192TJ2tWwcTtygUmM9UtyRxcPF5/+eUXnTx5skZERGjjxo119uzZ%0AevpWfchd3Xdhx6o7n1/2mLmBfyRwVeMSVim+PLKzs3XPnj06c+ZM7dKlizaqXFkV0I8mTtS0tLQb%0A39+dA5YtcCqMm63U7Oxs3bhxoz733HMaFBSkjRo10ldeeUW//vprvXDhgvv7L+4z6urn18X6fomP%0Ae4u15E2TwP/zn/+U/H/hqVMqL9fjUlNTde3atTpz5kzt37+/hoSEaGRkpI4cOVKXL1yol4cOdb2F%0AUlSsixeXyZYIeVdWVpbu2LFD//rXv+rjjz+ugYGB2rp1a3399dd10aJFun//fr127VrRb+BuC9zb%0AtfESfH6uXLmi+/bt02XLlhX/3j5kmgRerVo1DQ4O1vbt2+uoUaP0//7v/zQ+Pl6TkpL0t99+K/p/%0A4MlTKg/U4347flz379+vcXFxOmfOHB0zZox27NhR77jjDu0fGKjRbdroyy+/rB999JH+8ssvridd%0AT7RQiDwkMzNT169fr5MnT9Z+/fppgwYNtGLFitqkSRMdNGiQ/u1vf9NPP/1Ut2/frqcSEwvvMVVc%0ADbw0n99SNHSunz2rJw8c0DNPPaXL/+d/9LvmzXVgt27aoEEDve2227RBgwbap0+fwnvsGMCVBF7q%0AJdVEpDOA2QACAPxDVacVeFztdjvOnDmDpKQkJCYmIikpCYcPH8apU6dw4sQJVKhQAXfffTfq1KmD%0A0NBQ1KhRAzVq1EDTU6dwpXlzVKlTB0FBQahUqRIqX7+OagcOIKBdOwROnYpyr78OmTkzb1rOuLjc%0A5ahy2WzAtm3QRx6BvvkmMl98ETJrFv7z8su4GBCAjIwMZGRkwGaz4dy5c0hLS8Olkyfx+MaN+KBu%0AXRw+exa2lBSMz8jAgogI1KhXD2FhYahXrx4aN26Mxo0b465KlSB//vPN6xu2awd06lRoPIiOLnxJ%0ALa4YTyaTmZmJpKQk7Nu3D0lJSTh+/Dh++eUXRB46hLUZGahSpw7q1KmDkJAQ3B0YiGaZmahatSqu%0ANG+OqnXrIjAwEJUrV0aV7GxUT0pC+fbtUXXaNMi4cSg3a1bxx32+tUKzq1TB5TNnEPDWWzg3dCgC%0A58zBiZH7YoWwAAAIX0lEQVQjka6KSydPot6CBdjQoQPS09PRLj4ei+vUweM//IC3AgJw5Nw5hISE%0A4OGQECzbuxfvjB6N2m3a4Pe//z3uvfde3Hbbbb79pd6C19fEFJEAAD8D6AjgFIDvAQxQ1YP5nqPF%0A7UNVYbPZcPLkSZw8eRKnT59Geno6zp8/n/vv+fPnYbPZkJmZicuXL+f+G5KRgSPZ2YgsXx4nAgIQ%0AEBCAGuXK4a1r1/DXSpWQrorK16/jL1euYAKA83Y7IkRwTBUP1ayJ81WrorPdjkPBwdBq1RAUFITg%0A4GDUrlwZHQ8cQEqvXqgWFoZatWohLCwMNQMCINu3F70uobvJ+BaL2hJZwZUrV3DixAmcOHEitwFU%0A8N9Lly7lNpRyfoIvXcLhrCzUE8HJ8uVRPt+PqsJutyM7OxtPXr+Orao4b7fDbrejYsWKuPO229Au%0AIAAHqlVDrM2GLyIi8MK5c1jZujVur1ULNWrUQJgqBr31FnZ/8QVCHn4YoaGhqJCRYZkGkysJvLTl%0AkdYA1uS7/waANwo8xzvnFzmnRYcPa1ZMjGaePq0XL17UCxcuaHpysl4eOlQv7N2rV4cP18tnzuj1%0A69cd3aa83bfanQuuLIlQWeX8nNmPHdPsUaP0SmqqXrp0SS9cuKDnzp3TtLQ0tdls+ttvv2lGRoZe%0AvnxZr127VvjqU652t/X0Z93L4O0aOIC+AObnuz8QwN/V2wnclT9EwT9qca/xVM8O9hAhf+XJxoaH%0Au+i6nKiLGsls0gaTLxJ4H0MS+K0OppJc+S5tV0WLfbsTucWTx7cXB8lZMVEXxZUEXtoaeCsAk1S1%0As/P+eAB2zXchU0R04sSJua+JiopCVFRUifd5SyWpK3viQmIxF0+LrJkTWYnZLrj72WcuISEBCQkJ%0Auffffvttr1/ELA/HRczHAZwGsBNuXsT0OHf/qLyQSOS6lBQgIsKx2HJ4uNHR+DVXLmKWalV6Vc0C%0A8BKAeABJAD7Ln7wNER19c+INCir6G7m4lcGJypriVrG32Rwt7+Rkx78Fn0c+V+p+4LfcQf4WuJ+d%0A8hD5naLOSMeNA6ZP55mqD3m9Be62Nm0cf/Scb+6cg+DSpaK/9YnId3LOQCdMcJRLcpL0gQM8UzUh%0A37bAgcIvhACsQxOZCWvdhjNfCxxwJOTYWMfBERvruF/Utz6TN5HvsdZtGb5P4EUdHIUldiLyrfxn%0Av+HheQ0rJnFT8m0CL+7g4Lc+kfHYK8tSzNELJT4e2LyZNXAiIievz0boYhC3HsjD7oVERDewTgIn%0AIqIbmLMXChEReQQTOBGRRTGBExFZFBM4EZFFMYETEVkUEzgRkUUxgRMRWRQTOBGRRTGBExFZFBM4%0AEZFFMYETEVkUEzgRkUUxgRMRWRQTOBGRRTGBExFZFBM4EZFFMYETEVkUEzgRkUUxgRMRWRQTOBGR%0ARZU4gYvIJBE5KSI/On86ezIwIiIqXmla4ArgHVVt5vxZ46mgzCQhIcHoEErFyvFbOXaA8RvN6vG7%0AorQllGKXvPcHVj8IrBy/lWMHGL/RrB6/K0qbwEeLyF4RWSAiQR6JiIiIXFJsAheRdSKyv5CfHgDm%0AAogA0BTAGQCzfBAvERE5iaqW/k1EwgGsUNUHCnms9DsgIiqDVLXYMnX5kr6xiISq6hnn3d4A9pck%0AACIiKpkSJ3AA00SkKRy9UZIBxHgmJCIicoVHSihEROR7XhuJKSKdReQnETksIq97az/eIiIfisiv%0AIlJoacjMRKSOiGwSkUQROSAiLxsdkztE5HYR2SEie0QkSUSmGh1TSYhIgHOQ2wqjY3GXiKSIyD5n%0A/DuNjscdIhIkIl+IyEHn8dPK6JhcJSIN8g2O/FFELhT3+fVKC1xEAgD8DKAjgFMAvgcwQFUPenxn%0AXiIijwK4BOBfhV2cNTMRqQWglqruEZFAALsB9LLY77+SqmaKSHkAWwH8SVW3Gh2XO0RkLIDmAKqo%0Aag+j43GHiCQDaK6q542OxV0ishDAN6r6ofP4qayqF4yOy10iUg6O/NlCVU8U9hxvtcBbADiiqimq%0Aeh3AYgA9vbQvr1DVLQDSjY6jJFQ1VVX3OG9fAnAQwF3GRuUeVc103vwdgAAAlkokInI3gK4A/gHr%0ADnizXNwiUg3Ao6r6IQCoapYVk7dTRwBHi0regPcSeG0A+Xd60rmNfMzZxbMZgB3GRuIeESknInsA%0A/Apgk6omGR2Tm94FEAvAbnQgJaQA1ovILhEZbnQwbogAcFZEPhKRH0RkvohUMjqoEnoGwCfFPcFb%0ACZxXRk3AWT75AsArzpa4ZaiqXVWbArgbQDsRiTI4JJeJSDcA/1HVH2HBVqxTG1VtBqALgBedJUUr%0AKA/gQQBzVPVBABkA3jA2JPeJyO8AdAewpLjneSuBnwJQJ9/9OnC0wslHRKQCgC8B/FtVlxkdT0k5%0AT3/jADxkdCxueARAD2cd+VMAHUTkXwbH5JacMR6qehbAUjjKolZwEsBJVf3eef8LOBK61XQBsNv5%0A+y+StxL4LgCRIhLu/CbpD+BrL+2LChARAbAAQJKqzjY6HneJSHDO3DoiUhHAEwB+NDYq16nqm6pa%0AR1Uj4DgN3qiqg42Oy1UiUklEqjhvVwbwJIoYqGc2qpoK4ISI3Ovc1BFAooEhldQAOL78i1WagTxF%0AUtUsEXkJQDwcF6AWWKkHBACIyKcA2gOoKSInALylqh8ZHJar2gAYCGCfiOQkvvEWmvI3FMBC51X4%0AcgA+VtUNBsdUGlYrKd4JYKmjHYDyABap6lpjQ3LLaACLnI3HowBeMDgetzi/NDsCuOW1Bw7kISKy%0AKC6pRkRkUUzgREQWxQRORGRRTOBERBbFBE5EZFFM4EREFsUETkRkUUzgREQW9f/dYlve7WDutgAA%0AAABJRU5ErkJggg==%0A
-
-
-Scipy.optimize.leastsq
-----------------------------
-
-
-定义误差函数，将要优化的参数放在前面：
-
-
-
-
-::
-
-    def f_err(p, y, x):
-        ^4$return y - function(x, *p)
-
-
-将这个函数作为参数传入 leastsq 函数，第二个参数为初始值：
-
-
-
-
-::
-
-    c, ret_val = leastsq(f_err, [1, 1, 1, 1], args=(y_noisy, x))
-    c, ret_val
-
-
-
-结果:
-::
-
-    (array([ 3.03199715,  1.97689384,  1.30083191,  0.6393337 ]), 1)
-
-
-ret_val 是 1~4 时，表示成功找到最小二乘解：
-
-
-
-
-::
-
-    p = plt.plot(x, y_noisy, 'rx')
-    p = plt.plot(x, function(x, *c), 'k--')
-
-
-.. image::data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAXAAAAEACAYAAACqOy3+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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%0AA5MAAAAASUVORK5CYII=%0A
-
-
-Scipy.optimize.curve_fit
---------------------------
-
-
-更高级的做法：
-
-
-
-
-::
-
-    from scipy.optimize import curve_fit
-
-
-不需要定义误差函数，直接传入 function 作为参数：
-
-
-
-
-::
-
-    p_est, err_est = curve_fit(function, x, y_noisy)
-
-
-
-
-::
-
-    print p_est
-    p = plt.plot(x, y_noisy, "rx")
-    p = plt.plot(x, function(x, *p_est), "k--")
-
-
-结果:
-::
-
-    [ 3.03199711  1.97689385  1.3008319   0.63933373]
-
-.. image::data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAXAAAAEACAYAAACqOy3+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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%0AA5MAAAAASUVORK5CYII=%0A
-
-
-这里第一个返回的是函数的参数，第二个返回值为各个参数的协方差矩阵：
-
-
-
-
-::
-
-    print err_est
-
-
-结果:
-::
-
-    [[ 0.08483704 -0.02782318  0.00967093 -0.03029038]
-
-     [-0.02782318  0.00933216 -0.00305158  0.00955794]
-
-     [ 0.00967093 -0.00305158  0.0014972  -0.00468919]
-
-     [-0.03029038  0.00955794 -0.00468919  0.01484297]]
-
-协方差矩阵的对角线为各个参数的方差：
-
-
-
-
-::
-
-    print "normalized relative errors for each parameter"
-    print "   a\t  b\t f\tphi"
-    print np.sqrt(err_est.diagonal()) / p_est
-
-
-
-结果:
-::
-
-    normalized relative errors for each parameter
-    a	          b	          f	          phi
-    [ 0.09606473  0.0488661   0.02974528  0.19056043]
-
-
-
-作者 & 更新时间
-------------------------------------
-作者:`李金  <lijinwithyou@gmail.com>`
-
-提交: 2017/12/14
\ No newline at end of file
diff --git a/course/source/aipy-scipy/cn/6minfunc.rst b/course/source/aipy-scipy/cn/6minfunc.rst
deleted file mode 100644
index 21f2a08..0000000
--- a/course/source/aipy-scipy/cn/6minfunc.rst
+++ /dev/null
@@ -1,963 +0,0 @@
-最小化函数
-===============
-
-minimize 函数
-------------------
-
-
-
-::
-
-    %pylab inline
-    set_printoptions(precision=3, suppress=True)
-
-
-Populating the interactive namespace from numpy and matplotlib
-
-
-已知斜抛运动的水平飞行距离公式：
-
-
-*d = 2 \frac{v_0^2}{g} \sin(\theta) \cos (\theta)*
-
-
-- *d* 水平飞行距离
-- *v_0* 初速度大小
-- *g* 重力加速度
-- *\theta* 抛出角度
-
-
-希望找到使 *d* 最大的角度 *\theta*。
-
-
-定义距离函数：
-
-
-
-
-::
-
-    def dist(theta, v0):
-        ^4$"""calculate the distance travelled by a projectile launched
-        ^4$at theta degrees with v0 (m/s) initial velocity.
-        ^4$"""
-        ^4$g = 9.8
-        ^4$theta_rad = pi * theta / 180
-        ^4$return 2 * v0 ** 2 / g * sin(theta_rad) * cos(theta_rad)
-    theta = linspace(0,90,90)
-    p = plot(theta, dist(theta, 1.))
-    xl = xlabel(r'launch angle $\theta (^{\circ})$')
-    yl = ylabel('horizontal distance traveled')
-
-
-.. image:: 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7t3qlF0MVCyyJ3rrw+jnl57LbQ7i2TL/ffDLbfAW2/BxhsnHU39EFeJ8jPc/dNKF/ptjSKTgvbo%0Ao/DAA+E/sxKFZFuPHvDpp2HS3iuvhKVaJf9kcmcx0d13q7RtgrvvHmtkGdCdRfzGjQtDZF99FXba%0AKelopFiVl0P37tCwIfzjH2Ein8Qnq3cW0WzrHQgT5Y4m6qsgDJ1V7q8HPvsMjjkmlJpWopA4NWgQ%0A5uzsv38ob37llUlHJJVV1wz1O+APwPrR7wo/EEpxSBFbtAi6doXeveGww5KORuqDtdeG556DvfaC%0A1q3hqKOSjkhSZdIM1cHdx+UonhpRM1Q8ysvDENmNNgqdj2oSkFwaPz58QRk9GnbdNeloilMsVWfz%0ANVFIfK6+OgyRvftuJQrJvT32gEGDQof3118nHY1UyGQ0lNQjTz4ZRj+99x40apR0NFJf/fGPMG1a%0AmN398suhrpQkq0ZrcOcbNUNl19SpYRnMUaNgt93S7y8Sp/Ly0G+29dbw178mHU1xiaUZysyam9kQ%0AM3sxer6Dmf2ptkFKfvrmm9ChOHCgEoXkhwYNwjDaUaPCSClJViYlyh8GXgI2j55/RGZVZ6VArFwZ%0Axrh37RoKBIrki6ZNQ+WA3r1D06gkJ6NlVd39cWAlQFSfSWtdFZGrrw6L0txyS9KRiPzaDjuEUXnd%0AusG8eUlHU39l0sG9yMw2qngSVYf9Lr6QJJeGDw+3+hMmhNmzIvnoyCPh3XfDHfCoUSpkmYRM5lns%0ATlj0aEdgOrAJcIy7vx9/eNVTB3fdfPJJWOVu+HBo3z7paESqt3IlHHpo+Ld6/fVJR1PYYqk6G514%0ADWA7QsmPWZVLhSdFyaL2liwJiaJHDzj//PT7i+SDuXPDPIy774bDD086msIVV4nyC4BH3f3b6PkG%0AQHd3v7vWkWaJkkXtVKxNsWyZirZJ4XnrrdAs9dZb8FvVv66VWIbOAj0qEgVA9PismgYn+ePhh0P7%0A7333KVFI4dl7b7jqqlDkcunSpKOpPzJJFg3M7Of9onW5NZ+yQE2bBpddBk88obUppHBdeGG4q7j0%0A0qQjqT8ySRajgGFmdqCZHQQMA16MNyyJw6JFcOyxMGBAGI4oUqjMYMgQGDkyfPGR+GXSZ1FCaHY6%0AMNo0GnjA3VfGHFta6rPInHtY67ikBB56KOloRLJjwoRQoXbcONh226SjKRyxjYbKV0oWmXvoIbjt%0AttBXoeYnKSaDBsGDD4aEoSVZMxPXaKh9gWuAlqyaxOfunvg4BCWLzMyYAR07wmuvqflJio97aF7d%0AbDMVHMxUXMniA+AiYCJRyQ8Ad59fmyCzSckivaVLoV076NkTzjwz6WhE4vHtt9C2bUgWf/hD+v3r%0Au7iSxTvuvledIouJkkV6F14YFpB5/HENk5Xi9uaboX7UhAnQokXS0eS3uJLFTUAJ8DTwU8V2d59Y%0AmyCzScmiesOHhzuKyZND9U6RYnfddTBmTFiSVfWjVi+uZFEG/Gond+9Uo+hioGSxel98EdaleOYZ%0A6NAh6WhEcmPlyrCA16GHwpVXJh1N/tJoKAHCf5iDD4ZOnUL5cZH6ZM4c2H13eO45Fchcndoki4yK%0AUpvZ4cAOwM8D09y9X83Ck1wZMCCsT6FvVlIfbbEF3HMPnHQSTJoEjRsnHVFxyKQZajCwNnAAcD9w%0ALPCOuye+tKruLH5t4sRwCz5+PGy1VdLRiCTnT38Kw2offDDpSPJPXIUEO7j7KcA37n4t0J5QrjyT%0AgDqb2Swz+8jMLl/NPndFr79vZm1Ttjc1syfNbKaZzYgWXZJq/PhjWBZ14EAlCpE774SxY+Gpp5KO%0ApDhkkiyWRL9/NLMWhCVVm6c7KCoTMgjoTGjC6m5mrSvt0wXY1t1bEUqK3JPy8p3AC+7eGtgZmJlB%0ArPXaZZeFseZaR1sE1lsPHn0UzjsvDPiQuskkWTwfrWFxKzABmA08lsFx7YCP3X12tFjSMOCISvt0%0ABYYCuPs7QFMz29TM1gf2c/cHo9dWuLuWcq3GCy/AiBFhURgRCfbaKyzuddppUF6edDSFLZNkcYu7%0Af+vuTxFKfmwP9M/guBbA5ynP50Tb0u2zBbA1MM/MHjKziWZ2v5mtk8E166X588OKd488ovkUIpVd%0AeWWouPy3vyUdSWHLZDTUOGA3AHdfCiw1s4kV26qRac9z5U4Wj+LaDbjA3d8zs4HAFcD/VT64b9++%0APz8uLS2ltLQ0w8sWB3c499ywkH3HjklHI5J/GjYMX6Q6dAhDyrffPumIcq+srIyysrI6nWO1o6HM%0AbDNgc+BR4ATCh7oDTYB73b3atzzqkO7r7p2j532Acne/OWWfe4Eydx8WPZ8FdIyu9Za7bx1t3xe4%0Awt0Pr3SNej8a6tFH4YYbQokDVdwUWb177gnVl8eNCwmkPsv2aKhDgNsITUUDoscDgIuBTEbwjwda%0AmVlLM2sE/BEYXmmf4cApUfDtgYXu/rW7fwV8bma/i/Y7CJie2R+p/pgzB/7857COthKFSPXOOQc2%0A3BBuvDHpSApTJvMsukX9FTU/udlhwEBCbakh7n6jmZ0N4O6Do30qRkwtBk6vqDllZrsADwCNgE+i%0A176rdP56e2dRXh7mU5SWhvWIRSS9ijI4L7wQZnnXV3HVhroIeBD4gfDh3Rbo4+6jahtottTnZHH3%0A3aEd9o03dEstUhOPPRYKDk6cWH/vyONKFlPcfWczOxQ4B7ga+Lu7t632wByor8ni00/DkMA33oDt%0AMpoeKSIVKhZL2nZbuOmmpKNJRlwzuCtO+HtCkphW48gka8rL4fTToU8fJQqR2jALd+ZDh8Lbbycd%0ATeHIJFlMMLOXgC7AKDNrAmh6S0IGDQoJo1evpCMRKVzNmoX/S6edBkuWpN1dyKwZqgGhn+ITd19o%0AZhsBLdx9Si4CrE59a4b68EPYZx94661wCy0iddO9O2y+eajUXJ9ktc/CzFq7+0wzqzz5zgDXSnm5%0AtXIl7L8/HH98WCpVROpuwQJo0wb+9S/Yd9+ko8mdbCeL+929h1bKyw8DB4ZV78aMgQaZNB6KSEae%0AfRYuvzwsP7z22klHkxtaKa9IffJJGP309ttqfhKJw/HHw29+A7fcknQkuZHtO4tuVFPfyd2frll4%0A2VcfkkV5eVhTuGtXuPjipKMRKU7z5oXmqOeeC1/Mil22l1X9AyFZNAM6AK9G2zsRigsmnizqg8GD%0A4aefNPpJJE6bbBKaes84I0zWW3PNpCPKP5mMhhoNnOLuX0bPNwOGuvshOYivWsV+Z/HZZ6Ekweuv%0AQ+vW6fcXkdpzh6OPhh13hOuvTzqaeMU1g3sW0LriUzkaSjsjXdXZXCjmZOEOnTuHsuNXZlK2UUTq%0A7MsvYZddYNSosOpksYprBvfLhMl4p5nZ6cALwOjaBCiZe+QRmDsXevdOOhKR+mOzzeDmm+HMM2HF%0AiqSjyS8ZjYYys6OB/aKnY939mVijylCx3ll8/TXsvDOMHBkqZIpI7rjDIYeEhZIuuyzpaOKhobNF%0A4vjjYautwjccEcm9Tz+Fdu1CtYRWrZKOJvuULIrAiBFhiOyUKfVngpBIPrr99vD/8dVXQ/HBYhJX%0An4XkyHffwXnnwf33K1GIJK1XL1i8GB54IOlI8oPuLPLIeeeFTrX77ks6EhEBmDo1TIqdMiV0fheL%0AbM/gnlrNce7uO9fkQnEopmQxblxYkGX6dGjaNOloRKTCVVfBRx+FYoPFItvJomV1B7r77JpcKA7F%0AkiyWLQujnq65JiQMEckfS5aE0Ym33w5/+EPS0WSHOrgLVP/+YdTFiBHF15EmUgxeeSWUApk+HdZb%0AL+lo6i6uGdx7A3cBOwCNgBJgkbs3qW2g2VIMyeLDD6FDB5gwIQyXFZH8dNppsMEGcMcdSUdSd3El%0AiwnA8cC/gD2AU4Dt3P2K2gaaLYWeLNxD59kRR8BFFyUdjYhUZ/582Gmn0AKw555JR1M3sQ2ddfeP%0AgBJ3X+nuDwGdaxOg/NLQofDDD1r5TqQQbLwx3HornHVW/SwFkkmyWGxmawLvm9ktZnYxYWlVqYP5%0A8+GKK8Iw2ZKSpKMRkUycdBJstBH89a9JR5J7mTRDbQXMJfRX/BloAtzt7h/HH171CrkZ6owzoEmT%0AUENfRApHRT/jpEmw5ZZJR1M7cfVZ9HL3O9NtS0KhJouxY+HEE2HGDGjcOOloRKSmrr02rNn9TF6U%0AVK25uPosTqti2+k1uYissmwZnHMO3HmnEoVIobriivBlb/jwpCPJneom5XUHTiCUJn895aXGwEp3%0APzD+8KpXiHcW/fvD22+Hf2SaUyFSuMaMCcNpC3HuRbZncG8FbA3cBFzOqk7tH4D33T3teAAz6wwM%0AJMzNeMDdf1V028zuAg4DfgROc/dJKa+VAOOBOe7+q7mThZYsPvkkLAavORUixeGUU6BZM7jttqQj%0AqZm8msEdfdB/ABwEfAG8B3R395kp+3QBLnD3Lma2F3Cnu7dPef1iYHegsbt3reIaBZMs3KFLF+jU%0AqXgXVBGpb+bODXMvXnkF2rRJOprMxdJnYWbdzOwjM/vezH6Ifr7P4NztgI/dfba7LweGAUdU2qcr%0AMBTA3d8BmprZptF1twC6AA9QBEN1n3kG/vtfTb4TKSbNmkG/fnDuuVBennQ08cqkg/sWoKu7N3H3%0AxtFPJqU+WgCfpzyfE23LdJ87gN5Awf8VLFoUksTdd0OjRklHIyLZ1KNHGLgydGjSkcSrYQb7fJXa%0AdFQDmbYPVb5rMDM7HJjr7pPMrLS6g/v27fvz49LSUkpLq909EddeG5qfOnZMOhIRybaSErjnntDM%0A3LVrmLSXb8rKyigrK6vTOTKZZ3En0Bx4FlgWbXZ3fzrNce2Bvu7eOXreByhP7eQ2s3uBMncfFj2f%0ABZQCPYGTgRXAWoSJgE+5+ymVrpH3fRYVi6dMmwabbpp0NCISlwsvhJ9+KozFy+KalPdw9PAXO7p7%0AtXMtzKwhoYP7QOB/wLtU38HdHhiY2sEd7dMRuLQQR0O5w/77wwknhDZNESleCxfCDjvA009D+/bp%0A909SbZJF2mYodz+tNsG4+wozuwAYRRg6O8TdZ5rZ2dHrg939BTPrYmYfA4tZ/WS//M0I1fj732Hp%0A0lB4TESKW9OmodDgeefBe+8VX823TO4stiSsZ7FvtGks0Mvd58QcW1r5fGexcCG0bh0m3xV6OWMR%0AyYw7lJbCccfB+ecnHc3qxdUM9TLwKPCPaNOJwInufnCtosyifE4WPXuG9svBg5OORERyadq0MKBl%0A+vQwtDYfxZUs3nf3XdJtS0K+JotJk6Bz51A7Jh9HRohIvC69FBYsgIceSjqSqsVVSHCBmZ1sZiVm%0A1tDMTgLm1y7E4ldeHm4/+/dXohCpr665BkaPhjffTDqS7MkkWZwBHAd8BXwJHIuqzq7W0KGwcmVY%0Ar0JE6qfGjUO9qPPOK55V9TLq4Hb3zytta+7uX8UaWQbyrRnq229Dp/a//w277550NCKSJHc48EA4%0A+mi44IKko/mluPosVgBPAme4+4/Rtknu3rbWkWZJviWLnj3DtP977006EhHJB9Onh9FR+dbZHVef%0AxVTCehZvmtm2tYqsHpgyBYYNC30VIiIAO+4IJ58MffokHUndZZIscPe/ARcAI8zsVzOp6zv30Knd%0Ar586tUXkl/r2hZEj4Z13ko6kbjJKFgDu/iZwAGEhpO1ji6gA/fOfsHhxqD4pIpKqSRO4+ebwhXLl%0AyqSjqb1M+iw2c/cvU543BDq4+9i4g0snH/osvv8+dGo/8QR06JBoKCKSp9xhv/3g1FPz40tltpdV%0APdnd/25ml1Txsrv77bUJMpvyIVn07g3z5sHDDycahojkucmT4dBDYeZM2HDDZGPJdgf3OtHv9ar4%0AaVyrCIvMrFlhhuZNNyUdiYjku113hW7dwoS9QlRtM1S0jnavfLiLqEqSdxbucNhhcPDBcElV914i%0AIpUsWBCarV9+GXbeObk4sj501t1XAt3rFFWRGjECPvssLHgiIpKJjTYKo6N69gxfOAtJJh3cdwBr%0AAI8T1pwAwN0nxhtaekndWSxdGsZP33MPHHJIzi8vIgVs5cpQ4eHKK0Mp8yTENYO7jCoWH3L3TjWK%0ALgZJJYv+/WH8eHjmmZxfWkSKwNixcNJJobN73XVzf/1YkkU+SyJZzJkDu+wSVsL67W9zemkRKSLd%0Au8O228J11+X+2nHdWTQFrgH2jzaVAf3c/bvaBJlNSSSLE06AbbZJ5i9YRIpHxRfP8eNh661ze+24%0AksXThPrWZsoMAAAQZElEQVRQQwEDTgZ2dvejaxtotuQ6WbzxRvg2MGtWMreOIlJcrr8+LJb21FO5%0Ava5WyovRypVhLe3evUPCEBGpqyVLYIcdYMgQOOCA3F03rqqzS8xsv5SL7Av8WNPgCt2DD4a7ieOP%0ATzoSESkWa68NAwZAr175v0hSJncWuwKPAOtHm74FTnX392OOLa1c3VksXAjbbx8qR7ZNfBUPESkm%0A7nDQQXDUUblbJCnW0VBm1gTA3b+vRWyxyFWyuPhiWLQI7rsv9kuJSD00bRp06hSG0m68cfzXi6vP%0AYi2gG9ASKCF0cru796tlnFmTi2Qxa1aoFplvK12JSHG58EIoL4e//S3+a8WVLEYBC4EJwM/V2N19%0AQG2CzKZcJIsuXUL9pz//OdbLiEg99803obn7lVegTZt4rxVXspjm7jvVKbKYxJ0sXnghJImpU6FR%0Ao9guIyICwKBBoTLEyy+D1eijvGbiGg01zswSrI+YjGXLQl/FHXcoUYhIbpxzDnz9NTz3XNKR/Fp1%0Aix9NjR6WAK2A/wA/Rdvc3RNPIHHeWdxxB4weHe4uRERy5eWX4eyzYcYMWHPNeK6R7ZXyWlZ3oLvP%0AzjCozsBAQtJ5wN1vrmKfu4DDCPM3TnP3SWa2JWHIbjNCIcP73P2uSsfFkizmzg1VZV9/PbQhiojk%0A0hFHhGWaL788nvPnXSHBaPGkD4CDgC+A94Du7j4zZZ8uwAXu3sXM9gLudPf2ZtYcaO7uk81sPUIH%0A+5GVjo0lWZxzDqy1FgwcmPVTi4ik9fHH0L59GFLbvHn2zx9Xn0VdtAM+dvfZ7r4cGAYcUWmfroS6%0AU7j7O0BTM9vU3b9y98nR9kXATGDzmONlypTQwVSoSx+KSOHbdls44wy46qqkI1kl7mTRAvg85fmc%0AaFu6fbZI3SFqEmsLvJP1CFO4w0UXhUSxwQZxXklEpHpXXRX6TCcmvsxc0DDm82faRlT5dujn46Im%0AqCcJa4Evqnxg3759f35cWlpKaWlpjYOs8NxzMG8enHVWrU8hIpIV668P/fqFL7CvvVa3obRlZWWU%0AlZXVKZ64+yzaA33dvXP0vA9QntrJbWb3AmXuPix6Pgvo6O5fm9kawPPASHf/VQ9CNvssfvpp1VKp%0ABx+clVOKiNRJxRKsf/kLHHNM9s6bj30W44FWZtbSzBoBfwSGV9pnOHAK/JxcFkaJwoAhwIyqEkW2%0A3XVXKBWsRCEi+aKkJAy06d0bli5NNpbYl1U1s8NYNXR2iLvfaGZnA7j74GifQUBnYDFwurtPjEqh%0AjwWmsKpZqo+7v5hy7qzcWcydGxLFW29Bq1Z1Pp2ISFZ16xbuMK68Mjvny7uhs3HLVrI4++ywVsXt%0At2chKBGRLPv0U2jXLpQe2myzup9PyaIW3n8fDjkEPvgAmjbNUmAiIll2+eUwf35YVa+ulCxqqGLR%0AkW7d4LzzshiYiEiWff89bLcd/PvfsNtudTtXPnZw57URI+CrrzRUVkTyX5MmcO21ocBpEt/x622y%0AWLYMLrkk9FM0jHu2iYhIFvzpT2Hdi2eeyf21622y+NvfwsinQw9NOhIRkcyUlISK2L17h7lhuVQv%0A+yzmz4fWrWHs2PBbRKSQdO0alnvu3bt2x6uDO0MXXBB+DxqU5YBERHLgww9hn31g+nRo1qzmxytZ%0AZGDGDOjYEWbOhI03jikwEZGYXXRRaIq6556aH6tkkYEuXcJw2YsvjikoEZEc+OabsDjbq6/CTjvV%0A7FgNnU1j1Cj46KNVzVAiIoVqww1DgcFLLsnNUNp6kyxWrAh3E7feCo0aJR2NiEjdnXsuzJ4NI0fG%0Af616kyweeCB0BB1ReZ0+EZECtcYacNtt4e5i+fJ4r1Uv+iy++y5Mkx85Etq2zUFgIiI54h6WVjjq%0AKDj//MyOUQf3amSzAJeISL6ZMiUkjEwLoipZVOE//4E99gilfTffPEeBiYjkWI8eIVHcemv6fZUs%0AqnDccdCmDVx9dY6CEhFJwFdfhSG077wD22xT/b5KFpW8+SZ07w6zZsE66+QwMBGRBNxwA0ycCE8+%0AWf1+ShYpysth773hwgvhpJNyHJiISAKWLAkT9f7xj1A7anU0KS/FsGEhYZxwQtKRiIjkxtprw403%0Ahjll5eXZPXdRJoslS6BPn7BWRYOi/BOKiFTt+OPD594//5nd8xZlM1Sm7XYiIsXozTdD0vjgg6r7%0Aa9VnQc1GBIiIFKvqRoIqWRDW027SJEyBFxGprz79FPbcs+o5ZvU+WdR0FqOISDG77DJYsODX1Svq%0AdbJwh0MOCYUCVYJcRAQWLgx18UaNgl13XbW9Xg+dHTkSPv8czj476UhERPJD06ZwzTVhKG1d7wuK%0AIlksXx5K9N56ayjZKyIiwVlnhYE/zz9ft/MURbK4/35o0QIOPzzpSERE8kvDhmHAT+/edVvzItZk%0AYWadzWyWmX1kZpevZp+7otffN7O2NTkWwloV/frBgAFgNWqBExGpHw47DH7zGxg8uPbniC1ZmFkJ%0AMAjoDOwAdDez1pX26QJs6+6tgLOAezI9tsINN4Q7il12ietPUnNlZWVJh/Ariikziilz+RiXYqqa%0AWfhCfd11odO7NuK8s2gHfOzus919OTAMqLyoaVdgKIC7vwM0NbPmGR4LhCFh110X1x+hdvLhH0dl%0Aiikziilz+RiXYlq9Nm3CaNH+/Wt3fMPshvMLLYDPU57PAfbKYJ8WwOYZHAvARRfBZpvVOVYRkaLX%0Ar1+ocFEbcd5ZZDpQq049DRdfXJejRUTqj+bNa/+ZGdukPDNrD/R1987R8z5AubvfnLLPvUCZuw+L%0Ans8COgJbpzs22l64MwpFRBJU00l5cTZDjQdamVlL4H/AH4HulfYZDlwADIuSy0J3/9rMFmRwbI3/%0AsCIiUjuxJQt3X2FmFwCjgBJgiLvPNLOzo9cHu/sLZtbFzD4GFgOnV3dsXLGKiEj1Cro2lIiI5EbB%0AzuDOdNJezDE8aGZfm9nUlG0bmtloM/vQzF4ys5zWvzWzLc1sjJlNN7NpZtYz6bjMbC0ze8fMJpvZ%0ADDO7MemYUmIrMbNJZjYij2KabWZTorjezYe4zKypmT1pZjOjv8O9Ev43tV30/lT8fGdmPfPgfeoT%0A/d+bamb/NLM18yCmXlE808ysV7StxjEVZLKoyaS9mD0UxZDqCmC0u/8OeCV6nkvLgT+7+45Ae+D8%0A6L1JLC53Xwp0cvddgZ2BTma2b5IxpegFzGDV6L18iMmBUndv6+7t8iSuO4EX3L014e9wVpIxufsH%0A0fvTFtgd+BF4JsmYoj7WHsBu7t6G0IR+fMIx7QScCewJ7AIcbmbb1Comdy+4H2Bv4MWU51cAVyQU%0AS0tgasrzWcCm0ePmwKyE36tngYPyJS5gHeA9YMekYwK2AF4GOgEj8uXvD/gPsFGlbYnFBawPfFrF%0A9sTfq+jahwCvJx0TsCHwAbABoT94BHBwwjEdAzyQ8vwvwGW1iakg7yxY/WS+fLCpu38dPf4a2DSp%0AQKJvOm2Bd0g4LjNrYGaTo2uPcffpSccE3AH0BspTtiUdE4Q7i5fNbLyZ9ciDuLYG5pnZQ2Y20czu%0AN7N1E44p1fHAY9HjxGJy92+AAcB/CaM4F7r76CRjAqYB+0XNTusAXQhfkmocU6Emi4LolfeQthOJ%0A1czWA54Cern7D0nH5e7lHpqhtgD2N7NOScZkZocDc919EquZGJrg398+HppXDiM0I+6XcFwNgd2A%0Au919N8LIxV80WyT1XplZI+APwBOVX0vg39Q2wEWE1obNgfXM7KQkY3L3WcDNwEvASGAysLI2MRVq%0AsvgC2DLl+ZaEu4t88LWF+laY2WbA3FwHYGZrEBLF39392XyJC8DdvwP+TWhnTjKmDkBXM/sP4Vvp%0AAWb294RjAsDdv4x+zyO0w7dLOK45wBx3fy96/iQheXyV9HtFSKgTovcKkn2f9gDGufsCd18BPE1o%0AMk/0fXL3B919D3fvCHwLfEgt3qdCTRY/T/iLvln8kTDBLx8MB06NHp9K6DPIGTMzYAgww90H5kNc%0AZrZxxWgLM1ub0I47KcmY3P1Kd9/S3bcmNGO86u4nJxkTgJmtY2aNo8frEtrjpyYZl7t/BXxuZr+L%0ANh0ETCe0ySf2XkW6s6oJCpL9+5sFtDeztaP/hwcRBk8k+j6ZWbPo92+Ao4F/Upv3KVcdLTF03BxG%0A6Ez6GOiTUAyPEdomlxH6UE4ndHK9TMjeLwFNcxzTvoQ2+MmED+RJhBFbicUFtAEmRjFNAXpH2xN9%0Ar1Li6wgMz4eYCP0Dk6OfaRX/tvMgrl0IAxPeJ3xjXj8PYloXmA80TtmWdEyXERLpVEJF7TXyIKax%0AUUyTCaMSa/U+aVKeiIikVajNUCIikkNKFiIikpaShYiIpKVkISIiaSlZiIhIWkoWIiKSlpKFiIik%0AFeeyqiKSB6KyDm2An9x9bNLxSGHSnYVIkTKzNaOHO3iofrosqjyKma2VXGRSiJQspOCY2aIcXael%0ApayCmKNr1ujPZsHZZtYjqnpasf1woHH0dJaZHQo0dPcfo21bmNlB2Yla6gMlCylExVyjpqZ/tl6E%0A9UrGEBa6qagi2sTd5wO4+//cfZS7v/HzRdw/BnaICjuKpKVkIQXLzJ6NFgiaVrFIUOW7ATO71Myu%0ASXltppndFx0zqqI5xsxOMbP3LawT/kjKZUqq2r9SHM+sJo4qrxW9frWFNeRfN7PHzOySKs57koW1%0AyyeZ2b1m1qDS62sAh7v7ZGArQnE/CAUtn8ngLfw3oWqrSFpKFlLITnf3PQjrC/c0sw2q2KfyN/Vt%0AgUHuvhOwEOhmZjsCV7FqnfBeKfu3qrx/Fdc4YzVx/OpaAGa2J6FU9M6E6sm7V44zWjf9OKCDh4WQ%0AyoETK133AOAHMzsVOJdVq0c2c/clVcT5C+7+CaHjWyQtjYaSQtbLzI6MHm9B+GBPt4jLf9x9SvR4%0AAmFVsw2Af3lYFhN3/zbN/tXFsWVKHKs7dh/gWXdfRuh0HlHFOQ8kJJHxYWkE1ga+qrTP3sAQd3/e%0AzI4F3oq216TzWp8BkhH9Q5GCZGYdCR+o7d19qZmNIXxIruCXd8yV2+R/Snm8MuX1KpdWrWb/ijhK%0AVxNHdcd6peut7tpD3f3K1bwGsBnwaTTqabOoOQrCGgqZWqcG+0o9pmYoKVTrA99GH9DbA+2j7V8D%0AzaIF6tcEDs/gXK8Cx5rZhgAVvzPUZDVxVOdN4A9mtqaFtdJ/v5qYjjGzTSpiilY6S7WAkJCOBm5P%0A2b6SzJXXYF+px3RnIYXIgReBc8xsBmHFxLcA3H25mfUD3iWs1T6DX/YHVO7DcHefYWb9gdfMbCVh%0AVb8zVrd/pedVxhHtV+Wx7j7ezIYTVg38mrCq2neV9plhZn8BXoo6tpcD5wH/TTnfY4REscjd70nZ%0A/iMZiJb+/CGTfUW0Up5IAsxsXXdfHE2Sew3okdKMVNdzX0roy/g2zX67ANu7++PZuK4UNzVDiSTj%0APjObROj4fjJbiSJyP3BsBvsdCDyRxetKEdOdhUgRMrP9gM/c/b+reX1Hwozu93MbmRQqJQsREUlL%0AzVAiIpKWkoWIiKSlZCEiImkpWYiISFpKFiIikpaShYiIpKVkISIiaSlZiIhIWv8Pck/yIdKtOMoA%0AAAAASUVORK5CYII=%0A
-
-因为 Scipy 提供的是最小化方法，所以最大化距离就相当于最小化距离的负数：
-
-
-
-
-::
-
-    def neg_dist(theta, v0):
-        ^4$return -1 * dist(theta, v0)
-
-
-导入 scipy.optimize.minimize：
-
-
-
-
-::
-
-    from scipy.optimize import minimize
-    result = minimize(neg_dist, 40, args=(1,))
-    print "optimal angle = {:.1f} degrees".format(result.x[0])
-
-
-结果:
-::
-
-    optimal angle = 45.0 degrees
-
-
-minimize 接受三个参数：第一个是要优化的函数，第二个是初始猜测值，第三个则是优化函数的附加参数，默认 minimize 将优化函数的第一个参数作为优化变量，所以第三个参数输入的附加参数从优化函数的第二个参数开始。
-
-
-
-查看返回结果：
-
-
-
-
-::
-
-    print result
-
-
-结果:
-::
-
-    status: 0
-
-    success: True
-
-    njev: 18
-
-    nfev: 54
-
-    hess_inv: array([[ 8110.515]])
-
-    fun: -0.10204079220645729
-
-    x: array([ 45.02])
-
-    message: 'Optimization terminated successfully.'
-
-    jac: array([ 0.])
-
-
-Rosenbrock 函数
----------------------
-
-Rosenbrock 函数是一个用来测试优化函数效果的一个非凸函数：
-
-
-$f(x)=\sum\limits_{i=1}^{N-1}{100\left(x_{i+1}^2 - x_i\right) ^2 + \left(1-x_{i}\right)^2 }$
-
-
-导入该函数：
-
-
-
-
-::
-
-    from scipy.optimize import rosen
-    from mpl_toolkits.mplot3d import Axes3D
-
-
-使用 N = 2 的 Rosenbrock 函数：
-
-
-
-
-::
-
-    x, y = meshgrid(np.linspace(-2,2,25), np.linspace(-0.5,3.5,25))
-    z = rosen([x,y])
-
-
-图像和最低点 (1,1)：
-
-
-
-
-::
-
-    fig = figure(figsize=(12,5.5))
-    ax = fig.gca(projection="3d")
-    ax.azim = 70; ax.elev = 48
-    ax.set_xlabel("X"); ax.set_ylabel("Y")
-    ax.set_zlim((0,1000))
-    p = ax.plot_surface(x,y,z,rstride=1, cstride=1, cmap=cm.jet)
-    rosen_min = ax.plot([1],[1],[0],"ro")
-
-
-.. image:: 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jCsm96iI2mN6LcquDEQxuoNzLPPPo8E1Ii8WGhls8OJM6DY0lixYkVlqicQVHmuRvW8q6GoFZ4v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%0Abxj8dmObAAAAAElFTkSuQmCC%0A
-
-
-传入初始值：
-
-
-
-
-::
-
-    x0 = [1.3, 1.6, -0.5, -1.8, 0.8]
-    result = minimize(rosen, x0)
-    print result.x
-
-
-结果:
-::
-
-    [ 1.  1.  1.  1.  1.]
-
-
-随机给定初始值：
-
-
-
-
-::
-
-    x0 = np.random.randn(10)
-    result = minimize(rosen, x0)
-    print x0
-    print result.x
-
-
-结果:
-::
-
-    [ 0.815 -2.086  0.297  1.079 -0.528  0.461 -0.13  -0.715  0.734  0.621]
-
-
-    [-0.993  0.997  0.998  0.999  0.999  0.999  0.998  0.997  0.994  0.988]
-
-
-对于 N > 3，函数的最小值为 *(x_1,x_2, ..., x_N) = (1,1,...,1)*，不过有一个局部极小值点 *(x_1,x_2, ..., x_N) = (-1,1,...,1)*，所以随机初始值如果选的不好的话，有可能返回的结果是局部极小值点：
-
-
-
-优化方法
----------------
-
-BFGS 算法
------------------
-
-
-minimize 函数默认根据问题是否有界或者有约束，使用 'BFGS', 'L-BFGS-B', 'SLSQP' 中的一种。
-
-
-可以查看帮助来得到更多的信息：
-
-
-
-
-::
-
-    info(minimize)
-
-
-结果:
-::
-
-    minimize(fun, x0, args=(), method=None, jac=None, hess=None, 
-
-    hessp=None,bounds=None, constraints=(), tol=None, callback=None, 
-
-    options=None)
-
-
-
-Minimization of scalar function of one or more variables.
-
-
-
-Parameters
-
-fun : callable
-
-    Objective function.
-
-x0 : ndarray
-
-    Initial guess.
-
-args : tuple, optional
-
-    Extra arguments passed to the objective function and its
-
-    derivatives (Jacobian, Hessian).
-
-method : str or callable, optional
-
-    Type of solver.  Should be one of
-
-        - 'Nelder-Mead'
-
-        - 'Powell'
-
-        - 'CG'
-
-        - 'BFGS'
-
-        - 'Newton-CG'
-
-        - 'Anneal (deprecated as of scipy version 0.14.0)'
-
-        - 'L-BFGS-B'
-
-        - 'TNC'
-
-        - 'COBYLA'
-
-        - 'SLSQP'
-
-        - 'dogleg'
-
-        - 'trust-ncg'
-
-        - custom - a callable object (added in version 0.14.0)
-
-
-    If not given, chosen to be one of ``BFGS``, ``L-BFGS-B``, ``SLSQP``,
-
-    depending if the problem has constraints or bounds.
-
-jac : bool or callable, optional
-
-    Jacobian (gradient) of objective function. Only for CG, BFGS,
-
-    Newton-CG, L-BFGS-B, TNC, SLSQP, dogleg, trust-ncg.
-
-    If `jac` is a Boolean and is True, `fun` is assumed to return the
-
-    gradient along with the objective function. If False, the
-
-    gradient will be estimated numerically.
-
-    `jac` can also be a callable returning the gradient of the
-
-    objective. In this case, it must accept the same arguments as 
-
-`fun`.
-
-hess, hessp : callable, optional
-
-    Hessian (matrix of second-order derivatives) of objective function
- 
-or
-
-    Hessian of objective function times an arbitrary vector p.  Only 
-
-for
-
-    Newton-CG, dogleg, trust-ncg.
-
-    Only one of `hessp` or `hess` needs to be given.  If `hess` is
-
-    provided, then `hessp` will be ignored.  If neither `hess` nor
-
-    `hessp` is provided, then the Hessian product will be approximated
-
-    using finite differences on `jac`. `hessp` must compute the Hessian
-
-    times an arbitrary vector.
-
-bounds : sequence, optional
-
-    Bounds for variables (only for L-BFGS-B, TNC and SLSQP).
-
-    ``(min, max)`` pairs for each element in ``x``, defining
-
-    the bounds on that parameter. Use None for one of ``min`` or
-
-    ``max`` when there is no bound in that direction.
-
-constraints : dict or sequence of dict, optional
-
-    Constraints definition (only for COBYLA and SLSQP).
-
-    Each constraint is defined in a dictionary with fields:
-
-        type : str
-
-            Constraint type: 'eq' for equality, 'ineq' for inequality.
-
-        fun : callable
-
-            The function defining the constraint.
-
-        jac : callable, optional
-
-            The Jacobian of `fun` (only for SLSQP).
-
-        args : sequence, optional
-
-            Extra arguments to be passed to the function and Jacobian.
-
-    Equality constraint means that the constraint function result is to
-
-    be zero whereas inequality means that it is to be non-negative.
-
-    Note that COBYLA only supports inequality constraints.
-
-tol : float, optional
-
-    Tolerance for termination. For detailed control, use solver-
-
-specific
-
-    options.
-
-options : dict, optional
-
-    A dictionary of solver options. All methods accept the following
-
-    generic options:
-
-        maxiter : int
-
-            Maximum number of iterations to perform.
-
-        disp : bool
-
-            Set to True to print convergence messages.
-
- 
-
-callback : callable, optional
-
-    Called after each iteration, as ``callback(xk)``, where ``xk`` is
-
- the
-
-    current parameter vector.
-
-
-Returns
-
-res : OptimizeResult
-
-    The optimization result represented as a ``OptimizeResult`` object.
-
-    Important attributes are: ``x`` the solution array, ``success`` a
-
-    Boolean flag indicating if the optimizer exited successfully and
-
-    ``message`` which describes the cause of the termination. See
-
-    `OptimizeResult` for a description of other attributes.
-
-
-
-See also
-
-minimize_scalar : Interface to minimization algorithms for scalar
-
-    univariate functions
-
-show_options : Additional options accepted by the solvers
-
-
-Notes
-
-This section describes the available solvers that can be selected by
-
- the
-
-'method' parameter. The default method is *BFGS*.
-
-
-**Unconstrained minimization**
-
-
-Method *Nelder-Mead* uses the Simplex algorithm [1]_, [2]_. This
-
-algorithm has been successful in many applications but other algorithms
-
-using the first and/or second derivatives information might be 
-
-preferred
-
-for their better performances and robustness in general.
-
-
-Method *Powell* is a modification of Powell's method [3]_, [4]_ which
-
-is a conjugate direction method. It performs sequential one-dimensional
-
-minimizations along each vector of the directions set (`direc` field in
-
-`options` and `info`), which is updated at each iteration of the main
-
-minimization loop. The function need not be differentiable, and no
-
-derivatives are taken.
-
-Method *CG* uses a nonlinear conjugate gradient algorithm by Polak and
-
-Ribiere, a variant of the Fletcher-Reeves method described in [5]_ pp.
-
-120-122. Only the first derivatives are used.
-
-
-Method *BFGS* uses the quasi-Newton method of Broyden, Fletcher,
-
-Goldfarb, and Shanno (BFGS) [5]_ pp. 136. It uses the first derivatives
-
-only. BFGS has proven good performance even for non-smooth
-
-optimizations. This method also returns an approximation of the Hessian
-
-inverse, stored as `hess_inv` in the OptimizeResult object.
-
-
-Method *Newton-CG* uses a Newton-CG algorithm [5]_ pp. 168 (also known
-
-as the truncated Newton method). It uses a CG method to the compute the
-
-
-search direction. See also *TNC* method for a box-constrained
-
-minimization with a similar algorithm.
-
-Method *Anneal* uses simulated annealing, which is a probabilistic
-
-metaheuristic algorithm for global optimization. It uses no derivative
-
-information from the function being optimized.
-
-Method *dogleg* uses the dog-leg trust-region algorithm [5]_
-
-for unconstrained minimization. This algorithm requires the gradient
-
-and Hessian; furthermore the Hessian is required to be positive 
-
-definite.
-
-Method *trust-ncg* uses the Newton conjugate gradient trust-region
-
-algorithm [5]_ for unconstrained minimization. This algorithm requires
-
-the gradient and either the Hessian or a function that computes the
-
-product of the Hessian with a given vector.
-
-**Constrained minimization**
-
-Method *L-BFGS-B* uses the L-BFGS-B algorithm [6]_, [7]_ for bound
-
-constrained minimization.
-
-Method *TNC* uses a truncated Newton algorithm [5]_, [8]_ to minimize a
-
-function with variables subject to bounds. This algorithm uses
-
-gradient information; it is also called Newton Conjugate-Gradient. It
-
-differs from the *Newton-CG* method described above as it wraps a C
-
-implementation and allows each variable to be given upper and lower
-
-bounds.
-
-Method *COBYLA* uses the Constrained Optimization BY Linear
-
-Approximation (COBYLA) method [9]_, [10]_, [11]_. The algorithm is
-
-based on linear approximations to the objective function and each
-
-constraint. The method wraps a FORTRAN implementation of the algorithm.
-
-Method *SLSQP* uses Sequential Least SQuares Programming to minimize a
-
-function of several variables with any combination of bounds, equality
-
-and inequality constraints. The method wraps the SLSQP Optimization
-
-subroutine originally implemented by Dieter Kraft [12]_. Note that the
-
-wrapper handles infinite values in bounds by converting them into large
-
-floating values.
-
-**Custom minimizers**
-
-It may be useful to pass a custom minimization method, for example
-
-when using a frontend to this method such as 
-
-`scipy.optimize.basinhopping`
-
-or a different library.  You can simply pass a callable as the
-
- ``method``
-
-parameter.
-
-The callable is called as ``method(fun, x0, args, **kwargs, **options)``
-where ``kwargs`` corresponds to any other parameters passed to `minimize`
-(such as `callback`, `hess`, etc.), except the `options` dict, which has
-its contents also passed as `method` parameters pair by pair.  Also, if
-
-`jac` has been passed as a bool type, `jac` and `fun` are mangled so that
-`fun` returns just the function values and `jac` is converted to a function
-returning the Jacobian.  The method shall return an ``OptimizeResult``
-object.
-
-The provided `method` callable must be able to accept (and possibly ignore)
-arbitrary parameters; the set of parameters accepted by `minimize` may
-
-expand in future versions and then these parameters will be passed to
-
-the method.  You can find an example in the scipy.optimize tutorial.
-
-
-
-References
-
-.. [1] Nelder, J A, and R Mead. 1965. A Simplex Method for Function
-
-    Minimization. The Computer Journal 7: 308-13.
-
-.. [2] Wright M H. 1996. Direct search methods: Once scorned, now
-
-    respectable, in Numerical Analysis 1995: Proceedings of the 1995
-
-    Dundee Biennial Conference in Numerical Analysis (Eds. D F
-
-    Griffiths and G A Watson). Addison Wesley Longman, Harlow, UK.
-
-    191-208.
-.. [3] Powell, M J D. 1964. An efficient method for finding the minimum of
-   a function of several variables without calculating derivatives. The
-
-   Computer Journal 7: 155-162.
-
-.. [4] Press W, S A Teukolsky, W T Vetterling and B P Flannery.
-
-   Numerical Recipes (any edition), Cambridge University Press.
-
-.. [5] Nocedal, J, and S J Wright. 2006. Numerical Optimization.
-
-   Springer New York.
-
-.. [6] Byrd, R H and P Lu and J. Nocedal. 1995. A Limited Memory
-
-   Algorithm for Bound Constrained Optimization. SIAM Journal on
-
-   Scientific and Statistical Computing 16 (5): 1190-1208.
-
-.. [7] Zhu, C and R H Byrd and J Nocedal. 1997. L-BFGS-B: Algorithm
-
-   778: L-BFGS-B, FORTRAN routines for large scale bound constrained
-
-   optimization. ACM Transactions on Mathematical Software 23 (4):
-
-   550-560.
-
-.. [8] Nash, S G. Newton-Type Minimization Via the Lanczos Method.
-
-   1984. SIAM Journal of Numerical Analysis 21: 770-778.
-
-.. [9] Powell, M J D. A direct search optimization method that models
-
-   the objective and constraint functions by linear interpolation.
-
-   1994. Advances in Optimization and Numerical Analysis, eds. S. Gomez
-
-   and J-P Hennart, Kluwer Academic (Dordrecht), 51-67.
-
-.. [10] Powell M J D. Direct search algorithms for optimization
-
-   calculations. 1998. Acta Numerica 7: 287-336.
-
-.. [11] Powell M J D. A view of algorithms for optimization without
-
-   derivatives. 2007.Cambridge University Technical Report DAMTP
-
-   2007/NA03
-
-.. [12] Kraft, D. A software package for sequential quadratic
-
-   programming. 1988. Tech. Rep. DFVLR-FB 88-28, DLR German Aerospace
-
-   Center -- Institute for Flight Mechanics, Koln, Germany.
-
-Examples
-
-Let us consider the problem of minimizing the Rosenbrock function. This
-
-function (and its respective derivatives) is implemented in `rosen`
-
-(resp. `rosen_der`, `rosen_hess`) in the `scipy.optimize`.
-
->>> from scipy.optimize import minimize, rosen, rosen_der
-
-A simple application of the *Nelder-Mead* method is:
-
-
-::
-
-
-    x0 = [1.3, 0.7, 0.8, 1.9, 1.2]
-    res = minimize(rosen, x0, method='Nelder-Mead')
-    res.x
-    [ 1.  1.  1.  1.  1.]
-
-
-
-Now using the *BFGS* algorithm, using the first derivative and a few
-options:
-
-::
-
-    res = minimize(rosen, x0, method='BFGS', jac=rosen_der,
-                   options={'gtol': 1e-6, 'disp': True})
-    Optimization terminated successfully.
-             Current function value: 0.000000
-             Iterations: 52
-             Function evaluations: 64
-             Gradient evaluations: 64
-     res.x
-
-
-     [ 1.  1.  1.  1.  1.]
-
-::
-
-    print res.message
-    Optimization terminated successfully.
-    res.hess
-
-结果:
-::
-
-    [[ 0.00749589  0.01255155  0.02396251  0.04750988  0.09495377]
-
-     [ 0.01255155  0.02510441  0.04794055  0.09502834  0.18996269]
-
-     [ 0.02396251  0.04794055  0.09631614  0.19092151  0.38165151]
-
-     [ 0.04750988  0.09502834  0.19092151  0.38341252  0.7664427 ]
-
-     [ 0.09495377  0.18996269  0.38165151  0.7664427   1.53713523]]
-
-
-
-Next, consider a minimization problem with several constraints (namely
-Example 16.4 from [5]_). The objective function is:
-
-
-
-::
-
-    fun = lambda x: (x[0] - 1)**2 + (x[1] - 2.5)**2
-
-    There are three constraints defined as:
-
-    cons = ({'type': 'ineq', 'fun': lambda x:  x[0] - 2 * x[1] + 2},
-            {'type': 'ineq', 'fun': lambda x: -x[0] - 2 * x[1] + 6},
-            {'type': 'ineq', 'fun': lambda x: -x[0] + 2 * x[1] + 2})
-
-    And variables must be positive, hence the following bounds:
-
-    bnds = ((0, None), (0, None))
-
-    The optimization problem is solved using the SLSQP method as:
-
-    res = minimize(fun, (2, 0), method='SLSQP', bounds=bnds,
-                   constraints=cons)
-
-    It should converge to the theoretical solution (1.4 ,1.7).
-
-
-默认没有约束时，使用的是 BFGS 方法。
-
-
-利用 callback 参数查看迭代的历史：
-
-
-
-
-::
-
-    x0 = [-1.5, 4.5]
-    xi = [x0]
-    result = minimize(rosen, x0, callback=xi.append)
-    xi = np.asarray(xi)
-    print xi.shape
-    print result.x
-    print "in {} function evaluations.".format(result.nfev)
-
-
-结果:
-::
-
-    (37L, 2L)
-
-
-    [ 1.  1.]
-
-
-    in 200 function evaluations.
-
-
-绘图显示轨迹：
-
-
-
-
-
-
-::
-
-    x, y = meshgrid(np.linspace(-2.3,1.75,25), np.linspace(-0.5,4.5,25))
-    z = rosen([x,y])
-    fig = figure(figsize=(12,5.5))
-    ax = fig.gca(projection="3d"); ax.azim = 70; ax.elev = 75
-    ax.set_xlabel("X"); ax.set_ylabel("Y"); ax.set_zlim((0,1000))
-    p = ax.plot_surface(x,y,z,rstride=1, cstride=1, cmap=cm.jet)
-    intermed = ax.plot(xi[:,0], xi[:,1], rosen(xi.T), "g-o")
-    rosen_min = ax.plot([1],[1],[0],"ro")
-
-.. image:: 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IWlO2wc269qekaPk/ArrqPlR+o6Z+1YlcqRnemMinuOyA2Xidl7Rt2FBAIPR4hVgUAg+A+TX5Sq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%0AYII=%0A
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-
-
-BFGS 需要计算函数的 Jacobian 矩阵：
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-
-给定 *\left[y_1,y_2,y_3\right] = f(x_0, x_1, x_2)*
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-**J=\left[ \begin{matrix} \frac{\partial y_1}{\partial x_0} &amp; \frac{\partial y_1}{\partial x_1} &amp; \frac{\partial y_1}{\partial x_2} \\\ \frac{\partial y_2}{\partial x_0} &amp; \frac{\partial y_2}{\partial x_1} &amp; \frac{\partial y_2}{\partial x_2} \\\ \frac{\partial y_3}{\partial x_0} &amp; \frac{\partial y_3}{\partial x_1} &amp; \frac{\partial y_3}{\partial x_2} \end{matrix} \right]**
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-
-在我们的例子中
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-**J= \left[ \begin{matrix}\frac{\partial rosen}{\partial x_0} &amp; \frac{\partial rosen}{\partial x_1} \end{matrix} \right] **
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-
-导入 rosen 函数的 Jacobian 函数 rosen_der：
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-
-
-
-::
-
-    from scipy.optimize import rosen_der
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-此时，我们将 Jacobian 矩阵作为参数传入：
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-
-
-::
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-    xi = [x0]
-    result = minimize(rosen, x0, jac=rosen_der, callback=xi.append)
-    xi = np.asarray(xi)
-    print xi.shape
-    print "in {} function evaluations and {} jacobian     evaluations.".format(result.nfev, result.njev)
-
-
-
-结果:
-::
-
-    (38L, 2L)
-
-    in 49 function evaluations and 49 jacobian evaluations.
-
-
-可以看到，函数计算的开销大约减少了一半，迭代路径与上面的基本吻合：
-
-
-
-
-
-::
-
-    x, y = meshgrid(np.linspace(-2.3,1.75,25), np.linspace(-0.5,4.5,25))
-    z = rosen([x,y])
-    fig = figure(figsize=(12,5.5))
-    ax = fig.gca(projection="3d"); ax.azim = 70; ax.elev = 75
-    ax.set_xlabel("X"); ax.set_ylabel("Y"); ax.set_zlim((0,1000))
-    p = ax.plot_surface(x,y,z,rstride=1, cstride=1, cmap=cm.jet)
-    intermed = ax.plot(xi[:,0], xi[:,1], rosen(xi.T), "g-o")
-    rosen_min = ax.plot([1],[1],[0],"ro")
-
-
-.. image:: data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqsAAAFBCAYAAABQLOaIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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%0ACOGxJFgVQgghhBAe6/8D6bfN7+5DVO4AAAAASUVORK5CYII=%0A
-
-Nelder-Mead Simplex 算法
------------------------------
-
-
-改变 minimize 使用的算法，使用 Nelder–Mead 单纯形算法：
-
-
-
-
-
-::
-
-    xi = [x0]
-    result = minimize(rosen, x0, method="nelder-mead", callback =     xi.append)
-    xi = np.asarray(xi)
-    print xi.shape
-    print "Solved the Nelder-Mead Simplex method with {} function evaluations.".format(result.nfev)
-
-
-结果:
-::
-
-    (120L, 2L)
-
-    Solved the Nelder-Mead Simplex method with 226 function evaluations.
-
-
-
-::
-
-    x, y = meshgrid(np.linspace(-1.9,1.75,25), np.linspace(-0.5,4.5,25))
-    z = rosen([x,y])
-    fig = figure(figsize=(12,5.5))
-    ax = fig.gca(projection="3d"); ax.azim = 70; ax.elev = 75
-    ax.set_xlabel("X"); ax.set_ylabel("Y"); ax.set_zlim((0,1000))
-    p = ax.plot_surface(x,y,z,rstride=1, cstride=1, cmap=cm.jet)
-    intermed = ax.plot(xi[:,0], xi[:,1], rosen(xi.T), "g-o")
-    rosen_min = ax.plot([1],[1],[0],"ro")
-
-
-
-.. image:: 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ukXxbu3OBkz8Dz3fdCcG29JF8U3NijOvl3LcTgcREZmbMtqNBozlLQym81CsAquS8RdLxAIcpVA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%0AEEIIIYTP+v/YLseLtZ7EnQAAAABJRU5ErkJggg==%0A
-
-Powell 算法
-------------------
-
-
-使用 Powell 算法
-
-
-
-
-
-::
-
-    xi = [x0]
-    result = minimize(rosen, x0, method="powell", callback=xi.append)
-    xi = np.asarray(xi)
-    print xi.shape
-    print "Solved Powell's method with {} function evaluations.".format(result.nfev)
-
-结果:
-::
-
-    (31L, 2L)
-
-
-    Solved Powell's method with 855 function evaluations.
-
-
-
-
-::
-
-    x, y = meshgrid(np.linspace(-2.3,1.75,25), np.linspace(-0.5,4.5,25))
-    z = rosen([x,y])
-    fig = figure(figsize=(12,5.5))
-    ax = fig.gca(projection="3d"); ax.azim = 70; ax.elev = 75
-    ax.set_xlabel("X"); ax.set_ylabel("Y"); ax.set_zlim((0,1000))
-    p = ax.plot_surface(x,y,z,rstride=1, cstride=1, cmap=cm.jet)
-    intermed = ax.plot(xi[:,0], xi[:,1], rosen(xi.T), "g-o")
-    rosen_min = ax.plot([1],[1],[0],"ro")
-
-
-.. image:: 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IWlO2wc269qekaPk/ArrqPlR+o6Z+1YlcqRnemMinuOyA2Xidl7Rt2FBAIPR4hVgUAg+A+TX5Sq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%0AQgghhBBeS4JVIYQQQgjhtf4/QWwBBPzDxt8AAAAASUVORK5CYII=%0A
-
-
-作者 & 更新时间
-------------------------------------
-作者:`李金  <lijinwithyou@gmail.com>`
-
-提交: 2017/12/14
-
diff --git a/course/source/aipy-scipy/cn/7integral.rst b/course/source/aipy-scipy/cn/7integral.rst
deleted file mode 100644
index cc5019f..0000000
--- a/course/source/aipy-scipy/cn/7integral.rst
+++ /dev/null
@@ -1,876 +0,0 @@
-
-积分
-=======
-符号积分
------------------
-积分与求导的关系：
-
-**\frac{d}{dx} F(x) = f(x)
-\Rightarrow F(x) = \int f(x) dx**
-
-
-符号运算可以用 sympy 模块完成。
-
-先导入 init_printing 模块方便其显示：
-
-
-
-
-::
-
-    from sympy import init_printing
-    init_printing()
-
-
-
-
-::
-
-    from sympy import symbols, integrate
-    import sympy
-
-
-产生 x 和 y 两个符号变量，并进行运算：
-
-
-
-
-::
-
-    x, y = symbols('x y')
-    sympy.sqrt(x ** 2 + y ** 2)
-
-
-
-结果:
-::
-
-    sqrt{x^{2} + y^{2}}
-
-
-对于生成的符号变量 z，我们将其中的 x 利用 subs 方法替换为 3：
-
-
-
-
-::
-
-    z = sympy.sqrt(x ** 2 + y ** 2)
-    z.subs(x, 3)
-
-
-
-结果:
-::
-
-    sqrt{y^{2} + 9}
-
-
-再替换 y：
-
-
-
-
-::
-
-    z.subs(x, 3).subs(y, 4)
-
-
-结果:
-::
-
-    5
-
-
-还可以从 sympy.abc 中导入现成的符号变量：
-
-
-
-
-::
-
-    from sympy.abc import theta
-    y = sympy.sin(theta) ** 2
-    y
-
-
-结果:
-::
-
-    sin^{2}{\left (\theta \right )}
-
-对 y 进行积分：
-
-
-
-
-::
-
-    Y = integrate(y)
-    Y
-
-
-
-结果:
-::
-
-    frac{\theta}{2} - \frac{1}{2} \sin{\left (\theta \right )} \cos{\left (\theta \right )}
-
-
-计算 *Y(\pi) - Y(0)* ：
-
-
-
-
-::
-
-    import numpy as np
-    np.set_printoptions(precision=3)
-
-    Y.subs(theta, np.pi) - Y.subs(theta, 0)
-
-
-结果:
-::
-
-    1.5707963267949
-
-计算 int_0^\pi y d\theta ：
-
-
-
-
-::
-
-    integrate(y, (theta, 0, sympy.pi))
-
-
-结果:
-::
-
-     frac{\pi}{2} 
-
-
-显示的是字符表达式，查看具体数值可以使用 evalf() 方法，或者传入 numpy.pi，而不是 sympy.pi ：
-
-
-
-
-::
-
-    integrate(y, (theta, 0, sympy.pi)).evalf()
-
-
-结果:
-::
-
-     1.5707963267949 
-
-
-
-::
-
-    integrate(y, (theta, 0, np.pi))
-
-
-结果:
-::
-
-     1.5707963267949 
-
-
-根据牛顿莱布尼兹公式，这两个数值应该相等。
-产生不定积分对象：
-
-
-
-
-::
-
-    Y_indef = sympy.Integral(y)
-    Y_indef
-
-
-
-结果:
-::
-
-     int \sin^{2}{\left (\theta \right )}\, d\theta 
-
-
-
-
-::
-
-    print type(Y_indef)
-
-
-<class 'sympy.integrals.integrals.Integral'>
-
-
-定积分：
-
-
-
-
-::
-
-    Y_def = sympy.Integral(y, (theta, 0, sympy.pi))
-    Y_def
-
-
-
-
-
-结果:
-::
-
-     int_{0}^{\pi} \sin^{2}{\left (\theta \right )}\, d\theta 
-
-产生函数 *Y(x) = \int_0^x sin^2(\theta) d\theta*，并将其向量化：
-
-
-
-
-::
-
-    Y_raw = lambda x: integrate(y, (theta, 0, x))
-    Y = np.vectorize(Y_raw)
-
-
-
-
-::
-
-    %matplotlib inline
-    import matplotlib.pyplot as plt
-
-    x = np.linspace(0, 2 * np.pi)
-    p = plt.plot(x, Y(x))
-    t = plt.title(r'$Y(x) = \int_0^x sin^2(\theta) d\theta$')
-
-
-.. 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caV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%0Agg==%0A
-
-数值积分
------------
-
-数值积分：
-
- F(x) = \lim_{n \rightarrow \infty} \sum_{i=0}^{n-1} f(x_i)(x_{i+1}-x_i) 
-\Rightarrow F(x) = \int_{x_0}^{x_n} f(x) dx 
-
-
-导入贝塞尔函数：
-
-
-
-
-::
-
-    from scipy.special import jv
-
-
-
-
-::
-
-    def f(x):
-        ^4$return jv(2.5, x)
-
-
-
-
-::
-
-    x = np.linspace(0, 10)
-    p = plt.plot(x, f(x), 'k-')
-
-
-.. image::data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAX0AAAEACAYAAABfxaZOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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%0AUn7k/wDY8//eOSbGSgAAAABJRU5ErkJggg==%0A
-
-quad 函数
---------------
-
-Quadrature 积分的原理参见：
-
-
-http://en.wikipedia.org/wiki/Numerical_integration#Quadrature_rules_based_on_interpolating_functions
-
-
-quad 返回一个 (积分值，误差) 组成的元组：
-
-
-
-
-::
-
-    from scipy.integrate import quad
-    interval = [0, 6.5]
-    value, max_err = quad(f, *interval)
-
-
-积分值：
-
-
-
-
-::
-
-    print value
-
-结果:
-::
-
-    1.28474297234
-
-
-最大误差：
-
-
-
-
-::
-
-    print max_err
-
-
-结果:
-::
-
-    2.34181853668e-09
-
-
-积分区间图示，蓝色为正，红色为负：
-
-
-
-
-
-::
-
-    print "integral = {:.9f}".format(value)
-    print "upper bound on error: {:.2e}".format(max_err)
-    x = np.linspace(0, 10, 100)
-    p = plt.plot(x, f(x), 'k-')
-    x = np.linspace(0, 6.5, 45)
-    p = plt.fill_between(x, f(x), where=f(x)>0, color="blue")
-    p = plt.fill_between(x, f(x), where=f(x)<0, color="red",     interpolate=True)
-
-
-结果:
-::
-
-    integral = 1.284742972
-
-
-    upper bound on error: 2.34e-09
-
-
-
-.. image:: 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ufZQ86jy2rgMjZvXkvZsie5DmPikKpy1113sWnTJiZOnBjWMf35QFURqSIiJwC3AH97orSIVCKz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%0AR6zoG2NMHLGib4wxceT/AZhppPNX9o1QAAAAAElFTkSuQmCC%0A
-
-积分到无穷
---------------
-
-
-
-
-::
-
-    from numpy import inf
-    interval = [0., inf]
-
-    def g(x):
-        ^4$return np.exp(-x ** 1/2)
-
-
-
-
-
-
-::
-
-    value, max_err = quad(g, *interval)
-    x = np.linspace(0, 10, 50)
-    fig = plt.figure(figsize=(10,3))
-    p = plt.plot(x, g(x), 'k-')
-    p = plt.fill_between(x, g(x))
-    plt.annotate(r"$\int_0^{\infty}e^{-x^1/2}dx = $" + "{}".format    (value), (4, 0.6),
-             ^4$fontsize=16)
-    print "upper bound on error: {:.1e}".format(max_err)
-
-
-
-结果:
-::
-    
-    upper bound on error: 7.2e-11
-
-
-.. image:: 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QMmTJiA7/vFeruefPJJrr/+epKTkxk6dChPP/00EyZM4L777mP06NHFAlVBQQHXXHMN+/btK/Ha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%0ATkSuQmCC%0A
-
-
-双重积分
----------------
-
-
-假设我们要进行如下的积分：
-
-
-  I_n = \int \limits_0^{\infty} \int \limits_1^{\infty} \frac{e^{-xt}}{t^n}dt dx = \frac{1}{n} 
-
-
-
-
-
-::
-
-    def h(x, t, n):
-        ^4$"""core function, takes x, t, n"""
-        ^4$return np.exp(-x * t) / (t ** n)
-
-
-一种方式是调用两次 quad 函数，不过这里 quad 的返回值不能向量化，所以使用了修饰符 vectorize 将其向量化：
-
-
-
-
-
-::
-
-    from numpy import vectorize
-    @vectorize
-    def int_h_dx(t, n):
-        ^4$"""Time integrand of h(x)."""
-        ^4$return quad(h, 0, np.inf, args=(t, n))[0]
-
-
-
-
-
-::
-
-    @vectorize
-    def I_n(n):
-        ^4$return quad(int_h_dx, 1, np.inf, args=(n))
-
-
-
-
-::
-
-    I_n([0.5, 1.0, 2.0, 5])
-
-
-
-结果:
-::
-
-    (array([ 1.97,  1.  ,  0.5 ,  0.2 ]),
-
-
-    array([  9.804e-13,   1.110e-14,   5.551e-15,   2.220e-15]))
-
-
-或者直接调用 dblquad 函数，并将积分参数传入，传入方式有多种，后传入的先进行积分：
-
-
-
-
-
-::
-
-    from scipy.integrate import dblquad
-    @vectorize
-    def I(n):
-        ^4$"""Same as I_n, but using the built-in dblquad"""
-        ^4$x_lower = 0
-        ^4$x_upper = np.inf
-        ^4$return dblquad(h,
-                       ^4$^4$lambda t_lower: 1, lambda t_upper: np.inf,
-                       ^4$^4$x_lower, x_upper, args=(n,))
-
-
-
-
-::
-
-    I_n([0.5, 1.0, 2.0, 5])
-
-
-
-结果:
-::
-
-    (array([ 1.97,  1.  ,  0.5 ,  0.2 ]),
-
-    array([  9.804e-13,   1.110e-14,   5.551e-15,   2.220e-15]))
-
-
-
-采样点积分
-------------------------
-
-**trapz** 方法 和 **simps** 方法
-
-
-
-
-
-::
-
-    from scipy.integrate import trapz, simps
-
-
-sin 函数， 100 个采样点和 5 个采样点：
-
-
-
-
-::
-
-    x_s = np.linspace(0, np.pi, 5)
-    y_s = np.sin(x_s)
-    x = np.linspace(0, np.pi, 100)
-    y = np.sin(x)
-
-
-
-
-::
-
-    p = plt.plot(x, y, 'k:')
-    p = plt.plot(x_s, y_s, 'k+-')
-    p = plt.fill_between(x_s, y_s, color="gray")
-
-
-.. image:: 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wCxwFFgrdY6USn1tFLq6aLN3gH6KKUOAFuBV7TW6dYMLf5Pz549adOmTanfy8vLY+jQoWzYsIGI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%0AQmCC%0A
-
-
-采用 trapezoidal 方法 和 simpson 方法 对这些采样点进行积分（函数积分为 2）：
-
-
-
-
-::
-
-    result_s = trapz(y_s, x_s)
-    result_s_s = simps(y_s, x_s)
-    result = trapz(y, x)
-    print "Trapezoidal Integration over 5 points : {:.3f}".format(result_s)
-    print "Simpson Integration over 5 points : {:.3f}".format(result_s_s)
-    print "Trapezoidal Integration over 100 points : {:.3f}".format(result)
-
-
-
-Trapezoidal Integration over 5 points : 1.896
-
-
-Simpson Integration over 5 points : 2.005
-
-
-Trapezoidal Integration over 100 points : 2.000
-
-
-使用 ufunc 进行积分
----------------------------
-
-
-Numpy 中有很多 ufunc 对象：
-
-
-
-
-::
-
-    type(np.add)
-
-
-
-结果:
-::
-
-    numpy.ufunc
-
-
-
-
-::
-
-    np.info(np.add.accumulate)
-
-
-
-    accumulate(array, axis=0, dtype=None, out=None)
-
-
-Accumulate the result of applying the operator to all elements.
-
-
-For a one-dimensional array, accumulate produces results equivalent 
-
-to::
-
-
-  r = np.empty(len(A))
-
-  t = op.identity        # op = the ufunc being applied to A's  
-
-elements
-
-  for i in range(len(A)):
-
-      t = op(t, A[i])
-
-      r[i] = t
-
-  return r
-
-
-For example, add.accumulate() is equivalent to np.cumsum().
-
-
-For a multi-dimensional array, accumulate is applied along only one
-
-axis (axis zero by default; see Examples below) so repeated use is
-
-necessary if one wants to accumulate over multiple axes.
-
-
-Parameters
-
-array : array_like
-
-    The array to act on.
-
-axis : int, optional
-
-    The axis along which to apply the accumulation; default is zero.
-
-dtype : data-type code, optional
-
-    The data-type used to represent the intermediate results. Defaults
-
-    to the data-type of the output array if such is provided, or the
-
-    the data-type of the input array if no output array is provided.
-
-out : ndarray, optional
-
-    A location into which the result is stored. If not provided a
-
-    freshly-allocated array is returned.
-
-
-Returns
-
-r : ndarray
-
-    The accumulated values. If `out` was supplied, `r` is a reference 
-
-to
-
-    `out`.
-
-
-Examples
-
-
-1-D array examples:
-
-
-
-::
-
-
-    np.add.accumulate([2, 3, 5])
-    array([ 2,  5, 10])
-    np.multiply.accumulate([2, 3, 5])
-    array([ 2,  6, 30])
-
-
-
-2-D array examples:
-
-
-
-::
-
-    I = np.eye(2)
-    I
-    array([[ 1.,  0.],
-           ^4$[ 0.,  1.]])
-
-
-
-
-Accumulate along axis 0 (rows), down columns:
-
-
-::
-
-    np.add.accumulate(I, 0)
-    array([[ 1.,  0.],
-           ^4$[ 1.,  1.]])
-    np.add.accumulate(I) # no axis specified = axis zero
-    array([[ 1.,  0.],
-           ^4$[ 1.,  1.]])
-
-Accumulate along axis 1 (columns), through rows:
-
-
-
-::
-
-    np.add.accumulate(I, 1)
-    array([[ 1.,  1.],
-           ^4$[ 0.,  1.]])
-
-
-np.add.accumulate 相当于 cumsum ：
-
-
-
-
-::
-
-    result_np = np.add.accumulate(y) * (x[1] - x[0]) - (x[1] - x[0]) / 2
-
-
-
-
-::
-
-    p = plt.plot(x, - np.cos(x) + np.cos(0), 'rx')
-    p = plt.plot(x, result_np)
-
-
-速度比较
-------------
-
-
-计算积分： int_0^x sin \theta d\theta 
-
-
-
-
-
-::
-
-    import sympy
-    from sympy.abc import x, theta
-    sympy_x = x
-
-
-
-
-
-::
-
-    x = np.linspace(0, 20 * np.pi, 1e+4)
-    y = np.sin(x)
-    sympy_y = vectorize(lambda x: sympy.integrate(sympy.sin(theta), (theta, 0, x)))
-
-
-numpy 方法：
-
-
-
-
-::
-
-    %timeit np.add.accumulate(y) * (x[1] - x[0])
-    y0 = np.add.accumulate(y) * (x[1] - x[0])
-    print y0[-1]
-
-
-The slowest run took 4.32 times longer than the fastest. This could 
-
-mean that an intermediate result is being cached 
-
-10000 loops, best of 3: 56.2 μs per loop
-
--2.34138044756e-17
-
-
-
-quad 方法：
-
-
-
-
-::
-
-    %timeit quad(np.sin, 0, 20 * np.pi)
-    y2 = quad(np.sin, 0, 20 * np.pi, full_output=True)
-    print "result = ", y2[0]
-    print "number of evaluations", y2[-1]['neval']
-
-
-
-10000 loops, best of 3: 40.5 μs per loop
-
-result =  3.43781337153e-15
-
-number of evaluations 21
-
-
-trapz 方法：
-
-
-
-
-::
-
-    %timeit trapz(y, x)
-    y1 = trapz(y, x)
-    print y1
-
-
-10000 loops, best of 3: 105 μs per loop
-
-
--4.4408920985e-16
-
-
-simps 方法：
-
-
-
-
-::
-
-    %timeit simps(y, x)
-    y3 = simps(y, x)
-    print y3
-
-
-1000 loops, best of 3: 801 μs per loop
-
-
-3.28428554968e-16
-
-
-sympy 积分方法：
-
-
-
-
-::
-
-    %timeit sympy_y(20 * np.pi)
-    y4 = sympy_y(20 * np.pi)
-    print y4
-
-
-100 loops, best of 3: 6.86 ms per loop
-
-
-0
-
-作者 & 更新时间
-------------------------------------
-作者:`李金  <lijinwithyou@gmail.com>`
-
-提交: 2017/12/14
\ No newline at end of file
diff --git a/course/source/aipy-scipy/cn/8differential-equation.rst b/course/source/aipy-scipy/cn/8differential-equation.rst
deleted file mode 100644
index cefebcb..0000000
--- a/course/source/aipy-scipy/cn/8differential-equation.rst
+++ /dev/null
@@ -1,162 +0,0 @@
-
-解微分方程
----------------
-
-
-
-
-::
-
-    %pylab inline
-
-
-Populating the interactive namespace from numpy and matplotlib
-
-
-积分求解
--------------
-
-简单的例子
------------------------
-
-frac{dy}{dt} = sin(t)
- 
-
-
-
-::
-
-    def dy_dt(y, t):
-        ^4$return np.sin(t)
-
-
-积分求解：
-
-
-
-
-::
-
-    from scipy.integrate import odeint
-
-    t = np.linspace(0, 2*pi, 100)
-
-    result = odeint(dy_dt, 0, t)
-
-
-
-
-::
-
-    fig = figure(figsize=(12,4))
-    p = plot(t, result, "rx", label=r"$\int_{0}^{x}sin(t) dt $")
-    p = plot(t, -cos(t) + cos(0), label=r"$cos(0) - cos(t)$")
-    p = plot(t, dy_dt(0, t), "g-", label=r"$\frac{dy}{dt}(t)$")
-    l = legend(loc="upper right")
-    xl = xlabel("t")
-
-
-
-.. image:: data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAsgAAAEPCAYAAABfrjLnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz%0AAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xd8zPcfwPHXN4m9zt5KrR9F7NEYQa1qa7VUjVKbqhkr%0A1EoQlFaNqk0RatbeMWJFbFLzVBKbXCJLxn1+f0QjicRoxl0u7+fjcQ++3/vc997uzn3f9/m+P5+P%0AppRCCCGEEEIIEcXK1AEIIYQQQghhTiRBFkIIIYQQIgZJkIUQQgghhIhBEmQhhBBCCCFikARZCCGE%0AEEKIGCRBFkIIIYQQIoZEJciaphXVNO2QpmlXNE27rGnaDwm0m6Np2g1N0y5omlYlMc8phBBCCCFE%0AcrJJ5OPDgSFKqfOapmUFPDVN26eU8vq3gaZpnwKllFKlNU2rBSwAaifyeYUQQgghhEgWiepBVko9%0AUEqdf/n3QMALKBSn2RfAipdtTgE6TdPyJ+Z5hRBCCCGESC5JVoOsaVpxoApwKs5dhQHvGNs+QJGk%0Ael4hhBBCCCGSUpIkyC/LKzYAg172JL/WJM62rG8thBBCCCHMUmJrkNE0LR2wEfhDKbUlnia+QNEY%0A20Ve7ot7HEmahRBCCCFEslNKxe28jSWxs1howBLgqlLq5wSa/QV0fdm+NmBQSj2Mr6FSSm4muI0f%0AP97kMaTlm7z+73Dbvh3l5xd7n58fytUV1b8/ys+PkBDF6QMB/NZgDb2+DaVaNUWmjJGU5AYt7IMY%0ANEgxb55i3z7FnaN3icAKpde/ev39/KKOpddHHzNJ9yuF0usxkJ0zf/myZo1iwgRFp06KGpVCyY6B%0AooXCadVKMXGiYtva5/h+OxrjszjHdXWN/7XYvt3079N73uSzL69/Wr7J62+627tIbImFHdAZaKhp%0A2rmXtxaapvXRNK3Py6R3J3Bb07SbwEKgfyKfUwiR1tjZgaMjGAxR2wYDj4dOZXP4Zww2zqJq6QBy%0A5TTSq70/p4q0pVL1DMxxfs7jLsO4qbdhZ3kHfp5goH9/+KS6gQ/WTsNafwtmzIDQ0KjjOjqCszMU%0ALx71p6Mj/PNP0uw3GKJuM2aQQ3+Barud6djCwPjx8MdcA6frDsXvlh+HGk6mU+sgQkLg12VZsd3u%0ARMHCVnzZIog5n+/j/NfTiPyk2WuvBY6OUa+REEKIpGHqLD5GNq+EaYwfP97UIaRp8vrHsH27Un5+%0Asff5+Sm1fbt6pjeodU0Xq76d/FX5nPdU9mxG1aKFUlOnKuW+4Z4KIYNSev2rx/Tv/+pY/27fufPa%0A/vHVqyvl6hr/844fnzT7XV3fOZ6Y20ajUneO3lV/8I3q9XWAKltWKZ1Oqc+ahanpdTapCzt9lLFf%0A/+jXKKHXzlzJZ9+05PU3LXn9TedlzvnmvPRtDVLqJgmy6Rw6dMjUIaRp8vrHECNBNBqVunDUX02t%0AvVnVqxOusmVT6tOGQWoWg5XnNl8VHh7nMXr9q+QyoWQxngT20LZtyZ9Evkc8sZLaeP5tDx4o9eef%0ASg3o4q8+5KYqXCBc9eql1OY/AlVAzyEJJtvmSD77piWvv2nJ628675Iga+odazGSm6ZpylxiEUIk%0Asx07okoCdLpX+wwGIo4cx81Ynz8dz7PjWW0yBPvR8uvsfNomAw1sDWRycgQHh6jSCGfnqMf9W9Kg%0A08UulYh57NQo7r8l5jaAoyNquAM3xq5gZ4UR7DyUiRMnFLV012jTKy/t9DMpMHskuLvH+1rj7g4t%0AW5rm3yaEECakaRrqLYP0JEEWQqS8GMleRFYdh3cEsn7MeTY/+pgPilvxVeNntHapTenbe9FKFE84%0AWaxfH5o1s8zkL4EfEezZA0eOxJs4B9ro2L/mERv67GVH9o7YVrHmq5bBtLs6OSpZtrQfEUIkg6j5%0AB4SliC+3lARZCGFaCSR56pg7pzPWZ/ng82x88DEfWPnw1YB8fNU1EyVyvkzgYvYUSy/oKwklzv++%0ARi9fu9Cps9nTwJk/d2Zlx3Yjtllu0nlgLtrfmkr2GePkNRUiAS+TJ1OHIZJAQu+lJMhCCNOK01t5%0A/29//vjuIMuefUF4pDXdWvnR8adqfKg/GDXrw5vKCqS3883e8NqFZtSxe+UjVvRx51C2VnzR2oru%0AXwXSYNcorKY4yWstRAySIFsOSZCFEGYr4omBbV3WsySsM+7u0K69Nd16Z8DuIwPaWOkpTjLv2LP8%0AeNICVpeewDLXTDz3j+TbfLv57udKFF09TZJjIZAE2ZJIgiyEMK14krOH1/1ZNPkBC93KUrxAKL3O%0A9KbdlclkKf+B9BSnpARea+XkzDm9jqU/B7B2VTj2zTMywCELDYN3oNWVHyki7ZIE2XIkJkFO7EIh%0AQggR3UOp/AycOAGdvgrjf5XScde6ONvXBHC05jC66ieRZd70V8lWzGRYp3vVgyySVgKvtXbcnaof%0AGpibbTT/XA6kSfBf/DAgko+GN2feF3t47i0LkQgh0i7pQRZCJFpEBPy5PIiZY54SkKUg/Qtvpdsf%0ATcipU9JTbK7i6VlWYxw5/KkL8xan58DucL7tFMGQ8BkUmzNc3i+RZkgPsuWQEgshRPKLp4wiyNfA%0A0sm+zNrzEcWKgUPXh3zasyBW+ttRg+7eVBcrl+tN6y3vjc8Jb3752JWluqF8+pk1DrWOUKlzJXkv%0AhcVLzQmyv78/9vb2ZMuWDVdXV7y8vDhw4AD58+enfPnyNGnSxNQhpigpsRBCJL9/B3oZDDx+DONH%0AhlKitDVu90qzdi0c3mrgs7OTopLjGTOikqeWLV/vedTpJKEyB296bwwGivwxjRn6r7jVbiQVSobQ%0A3LkuLWx9ObQtEKWQ0gshzJCLiwuNGzfGYDBw69YtGjduzNOnT+nbty+NGjUydXipivQgCyHe2YNr%0A/szocIZld+z5qvAJhq2oRJnq2WXQnSVJ4L188aMzf6xPz4wfA9AVz8mEIotptrITWk55f4VlSa09%0AyEajkSJFirB3714KFy6MtbU1ISEhzJ07FwcHBwCyZ89u4ihTlvQgCyGSzo4dUUlRDA+u+TOszW3K%0A18lBRJUaXPYvysIdRaKSY5BBd5Ykgfcywxl3egzMzFXPUIae78Kwa72o00LH7kmnUX6xPy8YDFGf%0AIyFEijl16hTW1tZUqFCBnDlz4uTkhJubG1myZOHgwYNpLjlOLOlBFkLEFqMH8UGojhlOoSxbHEmX%0AbjaMHBRKobljYs9dLD3Eace/nw0HB4zTZ7KhhgsTp2ckm99dJszNS7N2WdH85eqBSN1Saw/ylClT%0AuHjxIq6urqYOxWxID7IQIunodBhGTGF0o1N8VC6SiCMnuHwugl+mhUQlx87OUQPwnJ2ja5JFGhCz%0A9KJ4caymONH+zAguuT9n6NR8DOv9nLo1Qjnafakkx0KYwOHDh6lRo4apw7AYkiALkVbFU0oRct/A%0AjO+8KFMjB49Lf8wFwwf88lcJCpXLIWUUaV0C77/VCXfad8/CRY8X9PXsRRePH/iss45L8468/uNJ%0ASi+ESBaRkZGcOHGCKlWqmDoUiyEJshBpVYxZKSIiYOncYMqWMXLicUkObwtgcZ5RFNEfkxkpRJS3%0AzHphPWsGXfSTufbZMJrUDeaTiXX5tvbf/HPRP6qtzHohRLI5f/48gYGB2NramuT59Xp9vPvv379P%0AcHBwCkeTNCRBFiKtetkDuLPzGmzLh7Fiqi/rNqZj06pgyq0cLaUU4t3EKb3IMG0ig7wduHEmgA++%0AqEzVmtYM6+mPYbiTlF4IkUxOnDhB/vz5yZ07d4o/9+3btzl58mS89+XNm5fp06encERJQxJkISxd%0APKUUGAxcXXCYFh11DLnaC5cbbXE7lo46TbNJKYV4Pwl8XrJfcmfS9IxcOerH8yXrKLvFhQVrdUT8%0AtVNKL4RIYqdOnaJChQomee6FCxfSsWPHeO+zsbGhZcuWrFy5MoWjSjxJkIWwdDFKKQCe3vZnYP3z%0ANBhXj2b1Q7jUdBif6eeizZRSCvEfvKX0osDyafyub8qextNZvyacKqOasb/ryldJspReCJFoJ0+e%0ATNIEedSoUezdu/et7S5cuECRIkXe2KZGjRrs378/wfsPHTpEoUKF8Pb2fu84k5MkyEJYupc9euGj%0AxjFn/FPKVbDCWLMOXqeeM9hnOOmnTZJSCpH04pReVF7Yj4MVBzNpTCh9Lg3giyp3uXHIR6aEE6lD%0AAlfi3uvKR1IcIx7Pnj3j1q1bVKxYMVHHiWnatGk0bdr0re22b98e7wp9jRs3JiIiIno7b9683Lx5%0AM95j1KtXj/z581O0aNF4H2sqkiALkQYcuaijqttPbJt0loObA5i3OAN5/j4mpRQi+cRTeqFNcaZN%0ATjeu/m2NXYci1GmUkbGaM8HpJTkWZi7Olbj/dOUjKY4RD09PT4AkTZDflYeHB+XLl4+1z9fXF6UU%0ANjY20ftsbW2j44zL09Mzenq6+B5rKpIgC2EpElgBr0sjHzp1NDK++Er23i5Nhb+mSCmFSH5v+Hxl%0ACDEw8vk4LpwI4daem5T/n5EtY8/IinzCfP3bgeDoCHfu/LcrH0lxjHh4enpiZWX13iUWAQEBzJ07%0Al507dzJr1iwgqjd63bp1tG/fPvrY8+fPZ+zYsWzZsoWNGzfy3XffRR8jODgYTXu13sa+ffsYMmQI%0ABQoUYNWqVdH7c+bMiY+PT/T29evXGTduHLt372bq1Kk0btw4wceajFLKLG5RoQgh/jM/P6X691fK%0Az0+Fhyv1y7QglSdjgBrRx6Ce9xoSdX+cdkKkuLifPz8/tf+LX9T/SoapT4tdUjfP+sffTogU8sZ8%0ARK9XCqL+/K+S4hgxtG/fXpUrV+69H7dixQo1YsQI5efnp3r06KGUUmrfvn3Kz89PVa9eXSml1K5d%0Au9SBAwdU69atlVJKGY1G9eGHH0Yfo1GjRq8dt2PHjurMmTOx9u3bt09NmTJFKaVUYGCgsrW1VX4v%0A/283bNhQPXr0KMHHJkZC7+XL/W/MS6UHWQhL8bJ34mSPRVSvFMaWX7054qZw+fwYWaf/KKUUwjzE%0AU3rReEVXLszcR4PvSlLrYysmDvHjxajxUpsszIvBEDUvvF7/an54UxwjjgsXLlCtWrX3flyLFi14%0A8uQJFStWjH78J598wvLly+nWrRsAzZs3Z9++fXTp0gWImk4u5mp9cUshlFKcO3futXj8/f3JlSsX%0AAJs2baJixYrodDpCQ0MJDAwkb968CT7WVCRBFsJCBATA92N1tDk6hJFe33LgaAbK1coupRTCvCTw%0AeUzf+lNGjM/EuQN+nPv5MJX3z+DIRUmOhZmIM+j0Pw1qTopjxBESEsLNmzffO6k8deoUjo6OLFmy%0ABE9PTw4fPhx939q1a+ncuTM7XpY3HTp0iMaNGwOwcuVKevXqxe7duwEoUKAAgYGB0Y+9evUq5cqV%0AA8DV1TV6//379ylVqhQAT548iV7QZN++fdSuXZvdu3fj5eUV72NNRRJkIVKbeGqNt64J5KOSIYQG%0AvODK56PpqJ/6ato2IVILg4Giq6exRV+ZKaWX801HI72b38Xvjv9r7aQ2WaSopJgfPhnmmPfy8sJo%0ANMbq1X0X+fLlo1q1avz111+sWbOGn376Kfq+Dz/8kO3bt1OrVi2Cg4PR6XTkyJEDgCxZsvDo0aPo%0A3uAGDRpw+vTp6Mfmzp2bHDlysHbtWho0aBC9//z589i9HIzYsWNHfHx82LVrF0+ePCFdunQ8f/6c%0AXLlyxftYk3lbDUZK3ZAaZCHeTYzaTF9fpdp+/kKV0T1Qh9bef622U2o4RaoRT22yocdQ1b9LgCqY%0A2U+tWxqojMZ42gmRxFJTPrJy5UplY2OjgoODTfL8fn5+ytHR8Y1tQkJC1JAhQ1IootgSei9JiRpk%0ATdOWapr2UNO0Swncb69pmr+maede3sYm9jmFSNN0OpSTM7+33oltxUg+ur+fC14ZsM/mKdO2idQr%0Ant61HDPHMa/DETZssmbScH+++CQY30HTpTZZiJe8vLywtbUlU6ZMJnl+nU5Hnjx5ePLkSYJtXF1d%0A6dOnTwpGlTS0qEQ6EQfQtHpAILBSKfXaJHyaptkDQ5VSX7zlOCqxsQiRFty5Az17QsDjFyy9WI0K%0A+u1R9WxCWLCw63eYWnYZ83L/yLTp1nTvDjFmlxIiyWiaRmrJRz7//HNKlSrF7NmzTRaDUorFixfT%0Aq1ev1+7z9vbm7NmztGrVygSRJfxevtz/xm+QRPcgK6WOAn5vaSZfY0K8rzi1xkYjzJsRTHXbMJrW%0AC+H4x8OjkuMkGgkthNkyGEj/ywzG67uzr7ELc3+JoEX1R9y9JLXJIm27dOlSdG2vqWiaFm9yDFC0%0AaFGTJceJlRKD9BTwsaZpFzRN26lpWvm3PkIIEWvVpVu3oFH9CP746QHHNj1mxKPh2EydLEtEC8sX%0AZ+S/7cL+nKozhHqN0lGtphW//xyMUiTZqmRCpBbPnj3j7t27Jk+QLVWiSywANE0rDmxLoMQiGxCp%0AlArWNK0F8ItSqkw87aTEQog4jM8MzGu1l4lX2jGm3BYG/dUY65PuUUlAzBpMgyGqhlOmbhOWZseO%0ABD/vl3PWo3trP3Rl8rHkQ2eKzRkutcki0VJLicXevXsZNGgQXl5epg7FbCWmxCLZF7tWSj2P8fdd%0AmqbN1zQtl1LqWdy2EyZMiP67vb099vb2yR2eEGbL2xu6d9cRFPQFx/3KUWb1XsidwPzFMq+xsFRv%0A+LxXAE4cf8aM0pOo/rcTMxpb07Wr1CaLtMHNzY0mTZqYOoxUwc3NDTc3t/d6TEr0IOcHHimllKZp%0ANYH1Sqni8bSTHmSRNsXpIVMKVv4WhMNoG4b8YMTh8QhsRg6LqjWW0ftCvPJvWYWDAxdGrqHL5ZGU%0AzP6YhSszka90jtjt5AqLeEeppQe5XLlyLFiwQDoT38Ckg/Q0TVsLHAfKaprmrWnad5qm9dE07d85%0APb4ELmmadh74Gfg6sc8phEWJUWv86BG0/TyMn8Ya2Lf2KaOfSq2xEPGKpzbZo95Q/lclE7aVYfMf%0AQbHbSZ2msACHDh2iY8eOLFmyhKxZs0Ynx8+ePcPFxYVly5bh6elp2iAtRJL0ICcF6UEWaZrBwNZv%0A1tH3TA+6FTnAhF21yHBGao2FSNAbapOP29Sna/tQ7D7JxJyc48kxc5xceRHvzJx7kH19fbG3tydD%0Ahgxs2rSJMmWihnTNnj0bOzs7qlatyrfffsvq1atNHKl5SEwPsiTIQphYUBAMGQL7d4ezytseO/1q%0AmddYiEQKvPIPwyvsYk+RHqxam466dU0dkUgtzDlBTsjAgQMZMWIERYsWpUWLFuzatcvUIZkFk5ZY%0ACCHeUZx5jQE8Dj6nSplAwgLDON9sVFRyLPMaC5E4BgNZ50/nN31zfqm4hC/bGRk7FsK37nz9/5bM%0AnSwsgNFoxNraGohK/kTiSYIsREqJUWscGQnOY0P47DOFs2MIy3MOIfuMcVJrLERixalN/mLN15z/%0A1BHPk+HYTWrKjQE/v/q/JfXJwkKULVuWhw8fEhoaSvbs2U0djkWQEgshUpLBwJ2BP9H5b0fS3/+H%0AlXsKUOTOMak1FiKpJFCbrI65M+9OSyaMNzK10lp6LrVDmykzw4jXpcYSi6dPn7J06VJy5MhBxYoV%0AqVOnjqlDMgtSgyxEKrFuHQwcEInD01EMuzUAqw+LmzokIdKUK1fgmy/DKP33Xyw6X5OctsVMHZIw%0AM6kxQRbxkxpkIcxcUBD06AFjx0Sys+FMHPQDsPpJao2FSGkfFTZwqr4Dhb5tSuW6WTm26/nbHySE%0ASHMkQRYiqcUZjHf+PFSrEknETT1nGzlQfVEfqTUWwhRe1hxndJnInOXZmft7er5sp5jU/hKRT2Xw%0AnhDiFUmQhUhqLwfjKT8Dc+ZAk0+MjPtgFSv6nybbjB9f1TvqdFFJsru7aeMVIq1wd49Vc/x5x6x4%0AnlG4Xc1Ho0qP8b7sH9VOBu8JkeZJDbIQyeDpbX+6NbrLwxxlWFtxCiXnDpGBQEKYqchIcJkQwi8/%0AhfP7zyG0ujBJBu+lYVKDbDlkkJ4QZuTYMfjmG+jQ3B/nRXlJr78uC38IkQqc2HSfr9uF0aZ7TlwW%0AZCdDBlNHJExBEmTLIYP0hDCFOLXGRiNMGRfCl5+HsmBGIDPSjYlKjmXhDyHMn8FAnQNOnDunccdN%0Aj13tCG4tPiQLiwiRRkmCLMR/FWPhj4cPofkn4exe4suZXU9oeWRk9EIFMhhPCDMXY3GRXJWLsdnz%0AA7pm3UydUfVZ32GjLCwiRBokJRZCJIbBwMFvV9Dl1AC+K7KP8bvrYHPKXRb+ECI1SWBxkTPLL/P1%0AnDo0yXyc2RuLkXHOdKlNTgOkxMJySA2yECYQGRl1rvxtXgQrHzXnE/1iqTUWwsL4+0PvTkFc33Gd%0APw/lpZR9EVOHJJKZJMiWQ2qQhUhhjx5B8+ZwcG8Eni3GRSXHUmsshMXJoQy4FhtBr0nFqNMiB38u%0ADzJ1SEKIFCAJshBvE2cw3pEjULWKkZpZr7K/4hAK/jxSao2FsEQva461Kc70H5eb3btg1KBgBja/%0AwYuHMnhPpG56vf6N99+/f5/g4OAUiiZ+CcWYErFJgizE27wcjGd8ZmDaNGj/lZHFVRfg/PUlbKZO%0AloU/hLBUcRYWqWafDc+L6fF5lom6Ff3RXwiIaieD90Qqc/v2bU6ePPnGNnnz5mX69OkpFNHr3hRj%0ASsQmNchCvIOnt/3p2tAbQ95SuJafTNE5DjJQR4g0Sin4ZVoIUyZHsOjnYFlYxMKkhRrkkSNH4uLi%0AEr29ZcsWrl69ipWVFYULF6ZLly4AeHh44OXlRdeuXU0eY1zvEpvUIAuRjE6fhmqNclCuaVHcPLNR%0AdFIvOREKkYZpGgwenYm/VgfyQ59QRkZOISKrfCeI1OHChQsUKfJqsKm/vz+TJ09mzJgxjBo1ivnz%0A5/PkyRMAatSowf79+00eY3ySOzZJkIX4V5xaY6Vg3oxgPmv6glmTg5iZfgzp9DdkMJ4QAgwGau93%0AwtNT4/xOXxo3iOD+qv2ysIgwe9u3b6dRo0bR20eOHKF8+fLR27a2thw6dCh6O2/evNy8edOkMf6r%0AcePGRERERG8nZ2ySIAvxrxgLfwQGwjdfhrHI5RnHtz6h7ckRsvCHECJKjIVF8lQtxs5zhWgcvpvq%0ADva4dVsuC4sIs+bh4RErIfbx8UEX46qoTqfjxo0b0du2trZ4enqaNEYAX19flFLY2NikSGySIAvx%0Ar5eD7K72+5UalcPI4uXBiUtZKRV4PnZ9oQzGEyJtizN4zzq3jh/31mX59550PPkD01q4Ybx9JzqJ%0AlpIsy6JpSXP7r3x9fZk0aRK7du2ievXqhIWF4evry+TJk9mxYwcTJkzg1q1bBAQEMHfuXHbu3Mms%0AWbOiHx8cHIwWIwCDwUDGjBmjt9OnT09gYGD0ds6cOfHx8UmWGIF444wb4759+xgyZAgFChRg1apV%0AiYrtXdm8vYkQaceanToG7R3D9Gc96a4fDwV18a9+p0tgvxDC8iXwndBkbC08ukH7Vs1xL7mPledH%0AklOSY4tjyvF7QUFBtGnThl27dpE7d27q169PeHh4rH1WVlbMnDmTOnXq4O3tTefOndm0aVP0MSIj%0AI2MdM1u2bDx9+jR6OyQkhPz580dvZ8qUibCwMACmT59OSEhIvLF9++23FC9e/L1iXLBgAVu2bHkt%0AzrgxNmnShGXLljFs2DCqVasWb2xJTRJkIYCwMBg2DHbtiGT/Jy7YuoyPqjWW3h8hxHsoktWAW40f%0AGWHrRPX6L9iw7TlV6mczdVjCQqxbt47q1auTO3duALJkycLSpUtj7bt69SqZM2emRYsWHD58mIoV%0AKzJmzJjoY8QsUQAoWbIkZ86cid5+8uQJVatWjd729/cnV65cAIwYMSJJYwRo3rx5dJyOjo7xxqiU%0A4ty5c7GS47ixJTUpsRBpT5zBeN7eUN8ugrsnfDnTaCS2C/tLrbEQ4v29rDlOP20SPy/NzpTZmWna%0ADJb0OS2D90SSiIiIoFSpUtHbJ0+eJDg4OHpfSEgIGzdupFWrVjg6OrJkyRI8PT05fPhw9GMKFCgQ%0Aq4Sifv36sep4z549S+PGjaO379+/H+s5kyrGoUOHcurUKcaOHRsdp5ubW7wxXr16lXLlygHg6ur6%0An2N7HzIPskh7Ygyw2X9GR5fORgaX+AuHH15g1aJZ7B5jgyGq3lDKKYQQb7NjR9SAvBjfIV6nAmjX%0ATlEn2xXmHixPpoK6WN9BcoXK/JjzPMjPnz/H2dkZOzs7wsPDKVCgABUqVMDFxYU6depw/vx52rZt%0AS6ZMmdi7dy8FCxbk9u3bfPXVVxQuXBiApUuXUrx48VizRKxatYp//vkHo9FIyZIl6dSpU/R9PXv2%0AZO7cubHqlJMixvLly6PX6+ONM26MDx48YPTo0TRt2hR7e3sKFiz4TrElZh5kSZBFmmR8ZmBqiyPM%0Au/Mpa+rMxX55NzlRCSGSRWAg9OwaxrWjD9m4UePDdVMlOTZj5pwgJwWDwcDMmTNxcnJ6a9vQ0FDG%0AjBkTa5BfSniXGN8lNlkoRIj34OcHX3TVscvYlDOPimH/c2s5UQkhkk3WrLB2Y3q6989M7Qbp2V71%0AR/nOESaj0+nIkydP9GIgb+Lq6kqfPn1SIKrY3iXG5I5NEmSRppw7B9WqQelioRyq5kAh/XFZ+EMI%0Akew0fwM/PPmRzX9G0ndQesY5hBJnoL4QKWbQoEFs3rz5jW28vb3JmTMnZcuWTaGoYntTjCkRW6JL%0ALDRNWwq0BB4ppSom0GYO0AIIBroppc7F00ZKLETSiacWcNm8YEaMsWbu7Ag6eI54dYlT6gGFEMkp%0AznfMw+v+fN3wIelyZ2fN5kzkKZkjdlsZ92BSll5ikZaYusRiGdA8oTs1TfsUKKWUKg30BhYkwXMK%0A8WYxVsULDYXe3V4wfWwAh/e8oEN+N1n4QwiRcuIsLJK/TA72XchHleJ+VKscicfB51HtZOU9IcxG%0AkgzS0zStOLAtvh5kTdN+Aw4ppda93P4baKCUehinnfQgi6RlMHBn4E98dXEsxYOusPTQh2QrKj3E%0AQgjzsfneHY4tAAAgAElEQVSPIPr0NjJ57At6+4xHmyJXskxNepAtR2J6kFNioZDCgHeMbR+gCPAw%0A/uZCJEwpRWBYIIZQQ6yb/wt/Al4EEGGMIMIYQaQxEq9rkax/nI56uobUcGrAivuFyfwkM5lsMpE5%0AXWaypM9Cnsx5yJclH3kz5yWddTpT//OEEGlMm85Z+KiQD+0a+3GinQvz02cls6mDEkKk2Ep6cbP0%0AeH+aTZgwIfrv9vb22NvbJ19EwiwppXgQ+ACvJ17ceHoDnwAffJ774BPgg7e/Nz4BPigUuTLlIkeG%0AHOgy6qJv2dJnI511Oqw0azxOWXPxnDVtSpzlgyZVeXx0N8F1qhFsFUlIeAjB4cEEhQfxJPgJj4Ie%0A8ST4CdnSZyNflnzkz5qfD3J8QMmcJfkw54eUzBX1Z/4s+WOtDS+EEIlmMFBm41ROXhlB79ZefFyr%0AKhu3WFOypKkDE8JyuLm5RS9C8q5SqsTCTSnl+nJbSiwEAH4hfpy5d4bzD87j9cQr6vbYi3TW6SiX%0Apxylc5WmWI5iFMlehKI5ilIkexGKZC9C9gzZXx0kzmC8Z8+gc4dwnnsbWF97FgV/HvlOA/GMyohf%0AiB+Pgh7xIPABdwx3uO13m1t+t6L/DAkPoXze8tjmt8W2gC22+W2plL8SOTLmeO14QgjxVnG+l5Sf%0AgXmt9jL54hcsXhDB5x2zxm4rg/dShJRYWA6TLxTylgT5U+B7pdSnmqbVBn5WStWOp50kyBYsJDwE%0Az/ueePh64HEv6vYg8AFVC1alSoEqlM9bnnJ5ylEubznyZM7z7geOcYI5e1vHl20jaa1zw8XhKela%0ANk3SVfH8Q/25/OgyFx5e4MKDC1x4eIHLjy6TN0teahauiV1RO+yK2mFbwBYbq5S6OCOESLXimW0H%0Ag4ETv56hvUs1vu2biYkuGbF+LjPtpCRJkC2HSRNkTdPWAg2APETVFY8H0gEopRa+bDOXqJkugoDu%0ASqmz8RxHEmQL8iLiBad9T3NQf5BDdw5x5t4ZyuUtR41CNahZuCY1CtXgf3n+h7WVdeKfzGBgadvt%0AjLzQkXnVl9N+XbsUO4lEGiO5+ewmJ31O4u7tjru3O97+3tQoXAO7onY0KtGIj4t+THrr9CkSjxDC%0AMjy64c/XDR9gXbwYa8pMIO+s0ZIcpxBJkC2HyXuQk4IkyKmbUoprT6+x7do29t7ey0mfk5TNXZaG%0AxRvSsERD6hWrR7YM2ZL8eUND4fvv4fjhMDbetKWcfhcUL57kz/M+noU847j3cY7dPcYB/QGuP71O%0Agw8a0KxkM5qVakapXKVMGp8QInWIuHmHcaXXsqaQA+s32VCrlqkjShskQbYckiALkwiPDOfo3aNs%0Au7aN7Te2ExIewmdlPqNFqRY0KN4AXcYk7O2I51Kk/kIAX7ZTlK6UicV5RpF1zA9Rq+KZ2WXIx0GP%0A2X97P3tu7WHvrb1kSpeJz0p/Rrvy7bArapc0vehCCMvyb/mYgwNb+++hl0cvJnx1lX7ORdByJl3p%0AmHhdakmQHR0dqVGjBq1btzZ1KGZLEmSRYsIiw9h3ax+uV1zZfn07pXKV4vMyn/N5mc+pXKBy8s3y%0AEGcwy871gXTvZmSMQwQ/PB73au5QM18VTynFpUeX2Pr3VjZ6beRB4ANa/6817cq1w764vUw1J4R4%0A/XvMYODm9z/TznMMlYznWXioDJkLmf/3XWqVWhLk0aNHM3z4cHLnzp1gG71eT4kSJV7bf//+fXLk%0AyEHmzJY9qaAkyCJZRRojOfzPYVwvu7LJaxP/y/M/vq7wNW3LtaVQtkIpF4jBQOTosUxM58TSpYp1%0AG2ywizwS7yCX1NKjcvPZTTZ5bWKT1yZuPLtBm/+1oattV+oWq4uVlhQLXQohUp0EBu8FHzhBvw2N%0AObf3MRs3KEpvmCrJcTIw9wR57ty5lCxZknnz5jFhwgQ2btzI1KlTmTRpEsOGDSNLliwA3L59m1On%0ATtGxY8fXjhEREYGTk1Os6XUtkSTIIllceXSFJeeWsPbyWgplK8TXH31N+4/a84HuA5PE8/gxdGob%0AQvixk7ieLkn+GsVMEkdy8fb3xvWyKysurCAoPIgulbrQpVIXSucuberQhBBmQin4feoTxjka+W0B%0AtO2bz9QhWRxzTpDXr1+PtbU1TZs2ZfTo0Tg6OjJ58mTmz59Pz549Wbx4cXTbkSNH4uLikuCxPDw8%0A8PLyomvXrikRukkkJkGWLioRS2BYIEvOLqHOkjo0/aMpmWwy4fatG569PXGwczBZcnzyJFSrYqRa%0A0BH23ShB/uUuUT3FFqRojqI42Dlwqd8lNrXfxPMXz6m7rC4fL/mYRZ6LCAwLNHWIQggT0/wN9PEd%0Az86tEQwdYc3wgaGEh5s6KpFS3NzcsLe35/jx49SpU4fw8HBy585NeHg4Njavphe9cOECRYoUeeOx%0AatSowf79+5M75FRLEmQBwGnf0/T8qydFZxdl2/VtONZz5J/B/+Dc2JmyecqmXCA7dsRKfJWCX6cH%0A80WzUOZW+p2pB2thU6p41GVFR0eLS5Ih6pdtlYJVmN18Nj5DfBhTbww7buyg2OxifL/ze648umLq%0AEIUQphCj5rj6F4XwPG/D1b9u0ajiY+55+b/edscO08Qpkk2zZs3Yt28fV65c4cGDB+h0OiIjI5kx%0AYwZVqlSJbrd9+3YaNWr02uMbN25MRERE9HbevHm5efNmisSe2kiJRRoWFhnGhqsb+OXULzwOekzv%0Aar3pVrkbBbIWMF1QMU4AgTY6enYN49rRh2wYe4GS39ZNtbXGScHb35tFZxex+OxiSuUqRb/q/Whb%0Ari0ZbDKYOjQhREqIpzbZ+MzAlH7ezN9elDXrbLD/LKsM3kskcy6xSIijoyP9+/encOHCALRu3ZrN%0AmzfHGjjv6+tLly5dOHjwYPS+lStXkiFDBjp06JDiMacEqUEW7+Vh4EMWei7ktzO/UT5veX6o9QMt%0AS7c0n+nGDAau9vuVdh4jsUvvwa8HPiJTQfmS/1d4ZDhbr21lvsd8/n7yN9/X/J6+1fuSK1MuU4cm%0AhDCRfZsD6do5kh8GGBkZOA6rKU6SHP9HqSlBXrZsGVmzZkUpRfv27aP3N23alL1790Zv79u3j0WL%0AFmFjY0OLFi3o0qULANu2beP69esMGzYsxWNPCZIgi3dy9fFVprtPZ+u1rbQv356BtQZSIV8FU4f1%0Amj/+gCGDIpn+rCfd9eNNvvCHObv48CKzTsxi67WtdK7YmcG1B1MyV0lThyWEMAGfE950+PguORtV%0AYeWfmcklv5n/k9SUICekcePGHDhwINa+b775hmHDhlGtWrXoffv378fDw4PRo0endIgpIjEJss2b%0A7hSW4ZTPKaYem8oJnxP8UPMHbv1wyyx7G0NDYdAgcDsYycEm06g4bbxZLvxhTirlr8Ty1su59/we%0Av576lVqLa2Ff3B6Hjx2oVUSW3RIizTAYKPLHNNyuOzDqqxNUrdyQ9RusqFnT1IFZHm1i0sz3r8a/%0AfxJuZfVuQ8eaNm0a+7mU4ty5c7GSYwB/f39yyS+p+CmlzOIWFYpIKkajUe25uUfZL7dXH8z+QP16%0A6lcVFBZk6rBe2b5dKT+/6M2bN5WqXDFcta/9j/LvOfTVfX5+SvXvH6utSNjzF8/VLyd/UcVmF1NN%0AVjZRR+4cMXVIQojkFvd70s9PbWq+UOXNHqLmuAQpozFO2+3bTRJmapGa8hFHR0e1ZcsWNWvWLPXg%0AwYPo/V27dlXPnz+P3r58+bJq06aNUkqptWvXRu//9ddf1f79+1Mu4BSW0Hv5cv8b81KZxcLCKKXY%0AcX0HNRbVYMieIXxX+TtuDLzB9zW/J3M6M1oxx84uehaKTZugTm0jPXUbcR10guwzxr3qMdbponqQ%0A3d1NG28qkTV9Vn6o9QM3Bt6g/Uft6ba1Gw1XNOSQ/lCqv2QohEiAu3vsK206HW3WtufEpP0sm/GE%0ADm3DCAjg1eA9OzuThiuSTmRkJHXr1uX69evkz58/en+DBg04ffp09Hbu3LnJkSMHa9eupUGDBtH7%0Az58/j518HuIlNcgWQinFvtv7+PHQjwSFBzGhwQTalGtj1quxhT0yMLLJWTY/rc/62rOoubi3lFIk%0AsQhjBGsurcHpiBP5suRjfIPxfPLhJ8m3JLgQwqyEPjAwuPElDgTX5s+aM6m8sJ98z75FaqhBjrma%0A3qhRo3B2dmb8+PHUrl0bAIPBwMyZM3FyckrwGKGhoYwZM4ZZs2alVNgpThYKSeMO6g9Sb1k9Bu0e%0AxJDaQ7jQ9wLtyrcz6+RYr4e6n+m4na82Z33zUXNme/nSTgY2VjZ0te2K1wAvBtQYwMBdA2m0shEn%0AfU6aOjQhRArIWEDHbzuKMvHOtzTZP4LfXHWYee4n3mL9+vUULFiQunXrUrx4cYoWLYq9vX10cgyg%0A0+nIkycPT548SfA4rq6u9OnTJyVCTpXMN4MSb3Xa9zSNVjSi7/a+9Kvej8v9LtOhQgfzSozjLPwB%0AsGV1ELWqvKBj6xC2lHYgl/5s1GA8C1z0w1xYW1nTsWJHLve/TOeKnWn/Z3taubbi8qPLpg5NCJGc%0ADAaYMYNv9FM41syJBXMj6djgHgF3Da+3k4VFUoW4q+m5u7tjZ2fH3bt3Y7UbNGgQmzdvjvcY3t7e%0A5MyZk7JlU3AhsFRGSixSoVvPbjHm4Bjc77ozwX4C3Sp3w8bKTCckiTFhfVhmHSMGvWDr6ue4rgyn%0A1j6nV3VzMrF9igqNCGWBxwKmuU+jacmmTLSfyIc5PzR1WEKIpBT3e9VgIGTkBAY/n8zBnSGs/ysT%0AVepnk+/fOMy9xGLr1q2EhIRw7949jEYjBQoUIEOGDNSoUYPiMi1qLDIPchrxOOgxk49MZs2lNQyt%0AM5TBtQeb18C7hBgM6AfOosNFRwoGXGPZwQ/IdfXYaytCpbWV8cxBwIsAZp+Yza+nf6Vb5W6MrT8W%0AXUY5QQphEeJZee/f79k19+wZ9IORSY5h9L33I9oUSY7/Ze4Jsnh3kiBbuJDwEGafnM2sE7P4puI3%0AjKs/jrxZ8po6rHf2558woF8ko58OZ/DtQWglips4IhHXg8AH/HjoR7Ze28q4+uPoU60P6azTmTos%0AIUQyun7Qh/aNn1C6RWkWrcki+fFLkiBbDhmkZ6GUUmy8upHy88vjed+Tkz1PMqfFnFSTHAcHQ58+%0AMHpkJDsbzmSIfhDaTKk1NkcFshbg989/Z1+XfWy9tpWKCyqy/fp2OUkIYakMBspsnMrJv3NSwMeD%0AKraRnJSxu0JEkx5kM3Xx4UUG7x7Mk+An/NL8FxqWaGjqkN4szqW8K1egw5eRVMrlw2/l57ya21hq%0A3cyeUopdN3cxbO8wCmcrzJwWcyift7ypwxJCJJV4apO3dFxHnxPfMnSwwuHHTEQv2JYGS9+kB9ly%0ASImFBXka/JRxh8ax0WsjExpMoFe1XuY7AC+ml1+4ysmZJRt1jB5lxKXSGrr3TofWvJnUGqdC4ZHh%0ALDizgMlHJtO1UlfG248ne4bspg5LCJFYCdQm3119lG+cypOlfDFWrklH/gxps0NDEmTLIQmyBTAq%0AI4s8FzHu0Dg6fNSBiQ0nkitT6lof3e+OP30/uYmXTUXWVZlGuQU/pKkvVUv1MPAhow+MZs+tPbh8%0A4kKnip1koREhLFTEEwMTm59giXcTltX8jWarOqe573FJkC2HJMip3Nn7Z+m3ox82VjYsaLmASvkr%0AmTqk93b0KHTuDK0aBuCyIj+Z9F4g081YlJM+JxmwcwCZ02Vmbou52BawNXVIQojkcOcObiW60bXQ%0Afr7sYMPUqZAhg6mDSjmSIFsOGaSXSvmH+vPDrh9osboFfar14Wj3o+afHMdZ+CM8HMY5hNK+VSjz%0ApwcyJ8voqORYFv6wOLWL1OZ0z9N0rtiZpn80Zfje4QSGBZo6LCFEUnq5sIi9fjnnm4/mn5th1Crn%0Aj9epgNfbycIiwoJJgmwCSinWXlpL+fnlCY0I5Wr/q3xX5TvzWgEvIXZ2UTVpBgO3b0N9uwg81t3m%0A3J7HtDwyMqpWrXjxqD9fthOWw9rKmj7V+3C532UeBT2iwvwKbL++3dRhCSGSQszBe8WLk+snRzYU%0AGcKA3hHUt9dYODs4apnqf9vZ2Zk64mSjaZrcLOCWqM+AuVxGSCslFncMd+i7vS/3A+/zW8vfqFO0%0AjqlDem/Kz8Dqr7Yw9HwXxvxvMz/89QlWJ9xl4Y80aP/t/fTb0Y/KBSrzS/NfKJStkKlDEkL8V29Y%0AWOTvvPX4pqWBYlXysKjIRPLOGp3mapOF5ZAaZDMSaYzk19O/4nTEiWF1hjH84+GpciGGZ8+gb1+4%0Acj6M1TdqUlm/RWqN07iQ8BCcjzrz25nfmGg/kX41+qWOqyFCiPfy4todxv1vPX/kG8qipTbS9yFS%0ALalBNhOXHl7i46Ufs+XvLRzvcZzR9Uabf3Icp9YYYM/GQCqVCaFI3lA8GzpEJcdSa5zmZUqXCadG%0AThzudpg1l9dQf1l9rj25ZuqwhBBJyWAgw5wZTNe3Z22dXxnQz0i/fhC0cffr5wCpTxYWINEJsqZp%0AzTVN+1vTtBuapo2M5357TdP8NU079/I2NrHPmVq8iHjBuIPjaLSyET2r9OTgtwcpk7uMqcN6NzFq%0AjYODYWDvF/TqFsaKOQHMYhgZXSZKrbGI5aN8H3Gk2xHaf9Qeu6V2uBxzIcIYYeqwhBCJFac2ucHy%0A7lxo6kCwIYzKI5pwqueiV+eANFCfLNKGRJVYaJpmDVwDPgF8AQ+go1LKK0Ybe2CoUuqLtxzLokos%0APHw96La1G2Vyl2Hep/NSZ22mwcCZXgvpfHYoVa3PM29vGXJeOSa1xuKt9H56em3rhSHUwNJWS81/%0AdhYhRMLeUJu8IaQlA/ob6fvBbsauKU+6n2ekuYVFROqT7DXImqbVAcYrpZq/3B4FoJSaFqONPTBM%0AKfX5W45lEQnyi4gXTDw8kSXnlvBzs5/5usLXiR5JaQphYTB5MixcEMkvTzvTUT9Vao3Fe1FKsfTc%0AUkYdGEX/6v1xrO9Ieuv0pg5LCJHE7t2DHt8E8/Dw36zYlZ+KzQubOiQh3iglapALA94xtn1e7otJ%0AAR9rmnZB07SdmqaVT+Rzmi0PXw+q/l6Vv5/8zcW+F+lYsaP5J8fx1BqfP/qcGmUDOO8RzoXPxkYl%0Ax1JrLN6Tpmn0qNqD833O43nfk5qLanLx4UVThyWESGKFMhvYWd6BAdOK0ahNdqaMCyHir51SmyxS%0ANZtEPv5dunzPAkWVUsGaprUAtgDxFuJOmDAh+u/29vbY29snMryUkap7jf+tNXZ2JjyLjqnjQ/h1%0ANsx0CqPrrdFoU15eKvu31lgunYn3VDh7YbZ13Mby88tpvLIxQ2sPxcHOARurxH79CCFM7mXNsTbF%0AmR46HZ+08KdHy3/Ysq0xK8q5UG7BD1HnjJh1zEKkMDc3N9zc3N7rMYktsagNTIhRYjEaMCqlXN7w%0AGD1QTSn1LM7+VFlicfHhRbps7kIJXQl+++w3CmQtYOqQ3p/BwOW+c+l21YE8hlss3lWYInek1lgk%0Avbv+d+m+tTtBYUGsaL2CsnnKmjokIURixFOfrPwMLBzrzbh1HzGyzBaGrKqK9SypTRbmIyVqkG2I%0AGqTXGLgHnOb1QXr5gUdKKaVpWk1gvVKqeDzHSlUJcqQxkp9O/MSM4zOY0WQG39p+m3p6jWMIC4Op%0AU2HunEimPOtLz9uOaCWKmzgqYcmMysgCjwVMODyBsfXGMrDWQJk3WQgLdPs29OgUQvDJiyzZXYQK%0AzaQ2WZiHZK9BVkpFAN8De4CrwDqllJemaX00TevzstmXwCVN084DPwNfJ+Y5zYHeT0/DFQ3ZeWMn%0AHr086Fa5m/knx/HUGp/a/5yqpZ9z5kQ45z77kV56R7SZUmsskpeVZsWAmgM4/t1x1l9dT5NVTfAJ%0A8DF1WEKIJPZhLgMHqjjQw7kkDVtnZ8KoUF5s2SW1ySJVkJX03kPMUfmj7EYxpM6Q1NPzFaP+Kyid%0AjnEjQlm7/AWzp4XRwWvCq1rjmHVicilMJLMIYwQux1yYc3oOv7b4lfYftTd1SEKIpBDnXOJ71Z9+%0ALf/hdvr/saTiL9Ra3EvOOcJkZKnpJPQk+Am9tvVC76fnj7Z/UCFfBVOH9P4MBvZ3XUnv8/2wy3SO%0A2TvLkudvqTUWpnfm3hk6bepErcK1+LXFr+TImMPUIQkhEiOB2uT1024zeEVlvs7vxuQ1Jck6f7ok%0AxyLFSYKcRPbd2kf3rd3pWKEjTo2cyGCTwdQhvbeHD2HoUHA/HMF83y/4VD9f5jUWZiUoLAiHfQ7s%0AurmLla1XUu+DeqYOSQiRDJ4+haG9A3Hb9JQ5CzPSqnd+U4ck0piUmAfZor2IeMGwPcPovrU7y1sv%0AZ0bTGeafHMepNTYa4bdZwVQs+4KieUO58qlDVHIs8xoLM5MlfRbmt5zP3BZz6bChA44HHAmPDDd1%0AWEKIJJbb2sCKAiNZviYDI0cqWrcM5+6yA1KbLMyKJMgJuPr4KrUW10Jv0HOh7wU++fATU4f0bv6d%0A19hg4MIF+LhWBKtm3OfAuqdMCx9Glunjo3qO/53XWJJkYWZalmnJ+b7nOffgHPWW1eO2321ThySE%0ASCoxao4bdizAhb8zUu3JbqoOs2fmpwcJf2yI3c7OzrTxijRLSiziUEqx0HMh4w6NY2rjqfSo0sP8%0AZ6iII+CugYlfeLLKx54pFV35bmNLrE64S62xSFWMysicU3NwPurMz81+plOlTqYOSQiRWPHUJmMw%0AcPPPc/RfW5eHFx8yb4E1dd2cpDZZJBupQX5PfiF+9NrWi1t+t3Bt52reixjE8yVjfGZgldMdRrtW%0Apnnd50z7syT59Kel1likaufun6Pjxo7ULFyTeZ/OI1uGbKYOSQiRDJSC9fMeMXzgCxq0yonLvKwU%0APh9/Qi2dOyIxpAb5PbjfdafKwioUyV6Ekz1OmndyDLFKKQDOHHqOXflnzD9SgS2rnrM076io5Fhq%0AjUUqV6VgFTx7e5LBOgNVFlbBw9fD1CEJIZKB5m+gg9dE/r5i5APvY9hWMuLi0YgXo8a/Oo9J6YVI%0AIWm+BznSGMnUY1OZe3oui79YzGdlPkvxGP4zg4FHQ6YyJngsO7YbmTojHV2/DsNqXIw5JWWOSWFB%0ANlzdQP8d/RlVdxRDag9JdeVPQogExD1XGQzc+n42Q56O5e8bVvz8v4V8OvfTqE4fOZ+JRJISi7e4%0A9/wenTZ1QinF6rarKZw99SyDGRoKv/wCM6dH0vXZz/x44UtyVPogwfouuRwlLIXeT8/XG78mb+a8%0ALG+9nDyZ85g6JCFEYr3h3LXLqiWDB4RTQn+AGbsqUrF56jlXC/MkJRZvsOfmHqr9Xo2GxRtyoOsB%0A802O45m2bfXvQZQtFsypY2G4N3fiJ307ciycHtWuZcvXf1nrdJIcC4tRImcJjnY/Srk85ai6sCpH%0A/jli6pCEEIn1hnNXizoGLjUdRsvxNfikbTZ6dn3BvZX7ZVo4kazSXA9yhDGCcQfHseriKla3XU2D%0A4g2S/TkTJcZlp8MXdAwfEoHm68NPczNSz22ylFKING3XjV1039qdATUGMKbeGKytrE0dkhAiKcU5%0Atxn+8Wdqm1Ms1jdmYKndDN9sR9Yicg4U70dKLOLwCfCh48aOZEmXhVVtVpE3S95kfb6kcsk9gLGd%0A73AxohxTy62ivWtbmbZNiJd8A3zpvLkz1po1q9uuJn9WWZVLCIuRQOnFP5vP4rirLod2BjNudCTf%0AeU8k/bRJkhyLdyIJcgw7b+zku63fMbj2YEbYjcBKM6PqkgS+AG6sP8d4t4YcPAgjez2jn1MhMur/%0AlmnbhIgj0hjJxMMTWXJuCX+0+YOGJRqaOiQhRAo489c9HFtd4kbRRkxwSkcn3Q6s60vnkXgzqUEG%0AwiPDGblvJH2392VD+w2MqjvKvJJjeG3Ktn8u+tOjzlU+HtOAjz6Cm2cMDHk2Lio5lmnbhHiNtZU1%0AkxpOYlmrZXyz6RsmH55MpDHS1GEJIZKTwUD1Pc7s0ZdlebW5LFoQQQWH5vzZYQPGZzItnEgci+5B%0A9g3w5euNX5M1fVZWtVll3qPdDQZ8Bs3AhZGsWWdFv+9tGOaYkZza61PfSJ2VEAm79/weHTd2JIN1%0ABv5o+wf5suQzdUhCiKQWz7Rwaowjexu74OiUkUif+0yckp7Pzk3GaoqTnC9FLGm6B3nvrb1UX1Sd%0AFqVasOObHeaRHMeZkQKIWmJz0SF6OeiotHUSGVb+jtexZzjNzEjOnERdFoqZDOt0Udvu7ikevhCp%0AQaFshTjQ9QA1CtWQWS6EsFTxnBu1Kc40y3gYj7M2/OiUgQm9fal8cBauu3VE/hX/+VdmvRAJsbge%0A5EhjJJMOT2LxucWsbrsa++L2iQ8uqcT5xXv5eABTu11jz9Nq9O8RxqCnP5J7XH+ZCF2IJLL75m66%0AbenG0DpDGf7xcPMrrxJCJL2X51o13IFdA3fi/Lg3j59qjCq2ls6un5E+n1yNTevS3CC9h4EP6bSp%0AE0ZlZE27NRTIWiCJoks6ys/A8R5LmBnUlxNHIxnskI7+PV6Q3UXKKIRIDt7+3rTf0J68mfOyovUK%0AcmbKaeqQhBDJJYHSi8OfuuD8UwaunXnOsKGK7+45k23Gj3KOTaPSVIJ89J+jdNzYkW6VuzHRfqJp%0A50ONZ1aK8McGNszQM9utCs8ehTPon6H0uDqczOVk9TshkltYZBgj941ky7Ut/PnVn1QvVN3UIQkh%0AksNbzqent9xjZptjHNC1o9t31gz86CDF21aV828akyZqkJVS/HT8J77880sWfb4Ip0ZOpl8sIMas%0AFM+ewbTxIZQooVh4vCKOgwO51mIIA/XDyDxXVr8TIiWkt07P7OazmdFkBi1Wt2CBxwLMpXNACJGE%0A3nQ+NRiouc+Z9fqanP18AlpYKNWG2/NV9dsc3/McpZBZL0S0VN2D7B/qT/et3fEJ8OHPr/7kA90H%0AyQ7+jfQAAB/xSURBVBRdAhL4paqOueORqT6/D7zIRt/afFHQg8G/f0SVSpEyI4UQJnb96XW++vMr%0AKuSrwO+f/U6W9FlMHZIQIrnFU3qBoyPPRzmzbE16fpkSRM4PstMn9wa+XtmSbBdlMS5LZtE9yBce%0AXKDa79UomLUgR7sfTfnkGF6bv9j/HwPzW+2myujmdOydjVKfl+fvgEKs2F2AKvWzyYwUQpiBMrnL%0AcKLHCdJZpaPW4lpce3LN1CEJIZJbAuffbBfd+WFkZq6fDcLpUit2pm/NB7Y6+m74hLO9f3s184X0%0ALKc5qTJBXn5+OZ+s+oRJDScxr+U8MthkSN4nTGB6NtzdMU525kj3ZfTo8JziZdPjlqstM2dbc8PD%0AwKjAseTXn3q1uIeUUghhFjKny8yyVssYVGsQ9ZbVY8PVDaYOSQiRnN5SemE9awbN9b+xudQILrv7%0AU7RUBtqeHEH1Mv785vyUp8OmvOrQkuni0gallFncokJ5s5DwENXrr16q7K9l1eWHl9/aPsn4+SnV%0Av3/Uny+3L3WYrEYNDlHFiilVoewL5YKDenD6nwTbx9oWQpiNM75nVImfS6jBuwarsIgwU4cjhEhJ%0AbzhfR0QotXv5fdUeV5U9W6T64gul1i0NVMG9B8n5PZV7mXO+MS9NNT3Iej89dkvtMIQa8OjlwUf5%0APkr6J3lDTzHOzugHzmL6qGfYlgqkxdExGNNnZNvqAC41HsIIfX/yL3eJ1V5KKYQwf9UKVeNM7zPc%0AeHaDhisa4hvga+qQhBAp5Q3na+vnBpqdnsw6fS28OzjQtnkQi9dmodC6WXSr/Td7Vz4gbNSP0rNs%0Aqd6WQafUjTf0IO+4vkPlm5FPzT4xWxmNxsT+cEhYnF+Cxmd+6uxXU9SPI0JUpUpK5c0doXryuzq0%0A9r6KjHy9vfySFCL1ijRGKucjzqrgzILq4O2Dpg5HCGFKbzi/37un1KyxT1UtTqicOSJUx45RPcv+%0APYdKPpBK8A49yGY9i0WkMZKJhyey9NxSXL90pW6xuknzZG+YJzG4ih1H+/zBzjxd2bIhgnR5c9C6%0ArTWtGz+nzl+jsR45/NVKd+4yylUIS7P/9n66bO7C4FqDGWE3Ak1740BnIYQletN8yv8O0Hdw4N6E%0A39lmO5YtezPj7q6om/MqX/QuQBOvOZScO0TyBDOVqhcKeRL8hE6bOhEWGcbadmv/26p4CX3A9+yB%0AI0fA2ZmIrDo83Z6z3/EQ+9O3wONcOqqWD6WZx2Ra7+lP+SaF0fzjnx5GpmcTwjJ5+3vz1Z9fUTBb%0AQZa3Wk6OjDlMHZIQwhwkMF0czs4EWOn+396dx1VZ5n0c/1yioiAqoikuaJqaK4pLGklqpeaoLeQ2%0AZZup2WI9ZU9ZllozTjkz5fhULimWS7mbaY2aFpWauaG4gAtK7huKWyIC1/OHVGTgClzA+b5fr/Pi%0AnMPtzdfzOuWX6/zu+2bhpMPMf3YRS2/oSTHfwtwZdo47D0yh7YcPULZGqd+3DwuD9u1VnB3JldO8%0AGWM6GGNijTHbjTEvZ7HNqPTvbzDGNL7cPlfvW02TcU0ILh/M172+vnw5zmp2+PTpP5yGjcRETgx8%0AiyXef+Fv/v+iU6M9lAtIpU/3kyQ0bcdLg4pwMDaR75u9yGu7+lBv3vAL5VgzxSIepUqpKnz/2PdU%0A8qtE04+aEn0o2nUkEckLLtEHSqYl0i1mGJN3tWJf+HMs+PQk9Rp7M9k8TI26RWlUN5mnbotmUoMR%0AbKtxN/bV1zI/jVxWnUbzzLnrcjMYl7oBXsAOoBpQBFgP1Llom47AV+n3bwFWZrEvm5aWZsd8/64t%0AN8THzl496Y8DI8ePWztkyJ/neY4ft3batExnhY7tSrTff3nSfhj2me3d7aStV2af9fVNs61aWfvS%0AS9bOHn3IHqC8tbt2/eHPaYZIRH41ZcMUW3ZEWTt5w2TXUUQkr7pMf0jetsv+yC32vdcTbLdu1gYF%0AWVvGP9XeHbTJDn3+mJ3TYazdtuaETUm5xL6mTcu8A2XVjbJ6fsGC7P7b5ztcwQzy9RbklsDCDI9f%0AAV65aJsxQPcMj2OB8pnsyz4yvaetN7iM3brhm8zfHPHxf3r+l77P2dhVJ+yi2afs2Nun2hd6J9p2%0AVTbbioGp1s/P2hYtrO3d7aT9P562a77YZ5N/PYvTr/vctev3fS5YoDeTiPxJ9MFoW3NUTfvUgqds%0A0vkk13FEJK+5VH/IrG9Ya/fts3bOmEP2FYbbTm3P2GrVrPXxsTYkxNqHe5yz77ScY2e8f8iuCn/b%0AHtqWaNOOZVGcM+lGl3xei345f5CeMeYBoL21tk/644eAW6y1z2bYZj7wD2vtivTHS4CXrbVrL9qX%0A7fxCAwbfu4jCvoGcO3qKcx9O4ETHniRM+5qE1uEcO1uchAPnSPhhC3v96vLztnMkpvlRpYqhalWo%0AGnCKWjP+Rv0JL1D/jvIEBfH7/PBLL/1+cB1oplhErsqJpBM8Ou9RDpw6wMyuM6lSqorrSCKS111i%0AZhn4Uz855VWaLVtg0ybY8uMJ4icsIb5+J+L3e5OUBEGVU6l6divlmgQRsHMVAZ1aElCpOGW8zxAw%0AL4LivR7Ae/okij7XH+9yJSmadJLEUa9T7+Xn8R71L/WcdDl+kJ4xJhzocAUF+W1r7fL0x0uA/7XW%0ArrtoX9a/1GC8inhRuDD4+7embIkWlFr9NQHhbQioWoKAAAgIgDIph6n0zL1UWzmdCs2qUKgQv7/p%0ArqQIazheRK6BtZYRy0cw8qeRTLlvCndUv8N1JBHJy67gZAGZLtRl0mlOeZXm559h9+pDHHn8f0l4%0A7T2OUYaEBC7c9p0lacU6khs25Zz1JjkZEgMWceS2Rxg3pRy9l8+HatWcvRQuRUZGEhkZ+dvjYcOG%0AXbYgX++IRQv+OGIxiAurwxePWPTI8DjLEYtMPwq46COJTJ+/2nkdjUyIyHVYunOprfCvCnb498Nt%0Aalqq6zgikt9cyUhGZqMRV9iNUo8l2GGRw2zFfwba756798/bezhyYQa5MBDHhYP0inL5g/RacImD%0A9K55nkZFWERy2Z4Te2yL8S3sPZ/dY4+f1T86IpJNsirPWZyQ4OJulHBgp+04KMiGftjU7nvmEc0g%0AZ+JKCvJ1nwfZGHM3MJILZ7SYYK39hzGmX/rq9Nj0bd4HOgBngMfsReMV6dtcyJyYCCNHwvPP//kj%0Aiaye12iEiDiQnJrMi4teZGHcQmZ3m03D8g1dRxKRgiqrcY0M3SjqQBThM8K5p1oHRkQFUOT5F9WZ%0AMpGvLxQiIpJfTI2eyvOLnufddu/SK7iX6zgi4oEioiJ4ecnLfNDxA7rV6+Y6Tp6mgiwikks2Hd5E%0A+Ixw2lZry8gOI/Eu7O06koh4gKSUJJ796ll+2P0Dc7rPoW65uq4j5Xm5ciU9ERGB+jfUZ3Wf1Rw6%0Ac4iwj8PYfWK360giUsDtOr6L0IhQTiafZHWf1SrH2UgFWUQkm5T0LsnsbrPpWrcrzT9qzuK4xa4j%0AiUgB9eW2L2kxoQUPN3yYaeHT8PP2cx2pQNGIhYhIDvgu/jv+Ouev9GvSj8FhgylktB4hItcvNS2V%0AYd8NY+L6iUwLn0ZoUKjrSPmOZpBFRBw6cOoA3Wd1x7eoL1Pum0KAT4DrSCKSjx05c4QH5zzI+bTz%0ATAufRvkS5V1Hypc0gywi4lCgXyBLH15K/XL1aTKuCav3rXYdSUTyqR/3/EiTcU0ICQzh615fqxzn%0AMK0gi4jkgrkxc+m3oB/DWg/jyaZPYsylr3IqIgIXLug26qdRDF82nPGdx9O5dmfXkfI9jViIiOQh%0A2xO2Ez4jnAblGzC201hKFC3hOpKI5GEnz53kiS+eIO54HDO7zqS6f3XXkQoEjViIiOQhNQNqsvKJ%0AlXh7edP8o+bEHIlxHUlE8qhNhzfR7KNmlC5WmuWPL1c5zmUqyCIiuciniA8R90Qw8NaBhH0cxqcb%0AP3UdSUTymEkbJtHmkza8eturjOs8jmKFi7mO5HE0YiEi4siGgxt4YOYD3FX9Lt5r/56uvifi4c6e%0AP8uz/32WZbuXMbPrTBqUb+A6UoGkEQsRkTwsuEIwa/qs4fCZw4RGhLLz+E7XkUTEke0J22kxoQVn%0Azp9hdZ/VKseOqSCLiDhUqlgpZnadSa+GvWgxvgVzY+a6jiQiuWzm5pmERoTSv2l/Pr3/U10VLw/Q%0AiIWISB6xat8qus/qzj2172HEXSMo6lXUdSQRyUHnUs4xcPFAvtrxFTO7ziQkMMR1JI+gEQsRkXyk%0AeaXmrOu7jvjEeFpNbEV8YrzrSCKSQ+KOxREaEcq+U/tY23etynEeo4IsIpKH+Bf3Z273uXSv151b%0Axt/CvNh5riOJSDabuXkmLSe05JHgR5jdbTali5V2HUkuohELEZE8auXelfSY1YN7b76Xd+58R2e5%0AEMnnklKSeGHRCyyKW8SMB2bQpGIT15E8kkYsRETysRaVW7Cu3zp+PvEzt0bcyo5jO1xHEpFrtD1h%0AOy0ntOToL0dZ13edynEep4IsIpKHlSlehjnd5vBYo8doOaEln238zHUkEblKU6OncmvErfRr0o/p%0AD0ynVLFSriPJZWjEQkQkn4g6EEX3Wd0JqxrGqLtH4VPEx3UkEbmE08mneearZ1i5dyXTH5hOcIVg%0A15EEjViIiBQojQMbs7bvWpJSkmj2UTM2HtroOpKIZCHqQBRNxjXBy3ixtu9aleN8RgVZRCQf8fP2%0AY/J9k3np1pdoO6ktH6z6AH36JpJ3WGsZuXIk7aa0Y+jtQ5lwzwR8i/q6jiVXSSMWIiL51LaEbfx1%0A9l+p6FeRiHsiKOtT1nUkEY925MwRHv/icQ6dPsRn4Z9Ro0wN15EkExqxEBEpwGoF1GJF7xXUDqhN%0AozGNWLpzqetIIh5rcdxiGo1tRN2ydVn2+DKV43xOK8giIgXA4rjFPDbvMXo17MWbbd7UZapFcklS%0AShKDlgxiVswsPrn3E9re2NZ1JLkMrSCLiHiIdjXaEdUvik2HN3HrhFuJPRrrOpJIgbf58GZuGX8L%0Au0/uZn2/9SrHBYgKsohIAXGD7w3M7zmfJ0KeoNXEVoxePVoH8InkAGst7696n9s/vp0BzQcwq+ss%0AAnwCXMeSbKQRCxGRAmjr0a08NPchbvC9gYguEZQvUd51JJECYf+p/fT+ojdHfznK1PunUiuglutI%0AcpVydMTCGFPGGPO1MWabMWaxMaZ0FtvFG2OijTFRxphV1/rzRETkytUuW5sVj68gpEIIjcY24out%0AX7iOJJLvzdg8g8ZjG9O8YnNWPL5C5bgAu+YVZGPMCOCotXaEMeZlwN9a+0om2+0Cmlhrj11mf1pB%0AFhHJAct3L+fhzx+mddXWvNfhPUp6l3QdSSRfOX72OM/89xnW7F/D5Psm07xSc9eR5Drk9EF6XYBP%0A0u9/Atx7qSzX8XNEROQ6hAaFsr7feop4FaHh6IZ8s+sb15FE8o0lO5cQPCaYMsXKENUvSuXYQ1zP%0ACvJxa61/+n0DHPv18UXb7QROAKnAWGvtR1nsTyvIIiI5bOGOhfSZ34d7a9/L23e+rSt8iWThdPJp%0AXlnyCp/Hfk7EPRG0q9HOdSTJJte9gpw+Y7wxk1uXjNulN9us2m2otbYxcDfwtDGm1dX8JUREJPt0%0AuKkD0U9Gk3gukUZjG7FizwrXkUTynMj4SBqObsip5FNs7L9R5dgDFb7UN621d2X1PWPMIWNMBWvt%0AQWNMIHA4i30cSP96xBgzF2gO/JDZtkOHDv3tfuvWrWnduvXl8ouIyFXyL+7P5PsmMydmDuEzwnmw%0AwYO82eZNfIr4uI4m4tTp5NMMWjKIubFzGdNpDJ1qdXIdSbJBZGQkkZGRV/VnrvcgvQRr7TvGmFeA%0A0hcfpGeM8QG8rLWnjDG+wGJgmLV2cSb704iFiEguO3LmCAMWDmDN/jVM6DKBsKphriOJOPFd/Hc8%0A/sXj3BZ0GyPbj8S/+J+mRqWAuJIRi+spyGWAGUAQEA90s9YmGmMqAh9Za/9ijKkOzEn/I4WBqdba%0Af2SxPxVkERFH5sXO46mvnvptNtnP2891JJFccfLcSQYtGcS8rfMY/ZfRdK7d2XUkyWE5WpCzmwqy%0AiIhbx88eZ+DigSzZtYRxncbR/qb2riOJ5Kj5W+fz9FdP075Ge0bcNUKrxh5CBVlERK7a4rjF9J3f%0Al7CqYfy73b8p51vOdSSRbHXo9CEGLBzAugPrGNdpHG1ubOM6kuSinD4PsoiIFEDtarRj01ObKOdT%0Ajvqj6zMxaiJawJCCwFrLxKiJNBjdgOqlqxP9ZLTKsWRKK8giIpKldQfW0W9BP3yL+DK201hql63t%0AOpLINYk5EsNTXz3FqXOnGN9lPI0qNHIdSRzRCrKIiFyXkMAQVvZeyf117ic0IpShkUNJSklyHUvk%0Aiv1y/hcGLRlE2MdhhNcJ56cnflI5lstSQRYRkUvyKuTFgFsGsP7J9Ww4tIEGoxvw3+3/dR1L5LK+%0A2PoFdT+oy88nfib6yWieaf4MXoW8XMeSfEAjFiIiclW+2v4Vzy18jnrl6vFe+/e40f9G15FE/iA+%0AMZ7nFj5H7NFYPuz4IXdUv8N1JMlDNGIhIiLZrmPNjmzqv4nmlZrT9KOmDI0cytnzZ13HEuFM8hne%0A+PYNmoxrQrOKzYh+MlrlWK6JCrKIiFw178LevNrqVaL6RbH5yGbqfliXuTFzdbYLccJay6cbP+Xm%0AD25mx7EdrO+3nsFhg/Eu7O06muRTGrEQEZHrtmTnEp5f+Dxlfcrybvt3CQkMcR1JPMTa/WsZsHAA%0ASSlJ/KfDf7gt6DbXkSSP04VCREQk16SkpRARFcGQyCG0r9Gev7f9O5VKVnIdSwqovSf38vq3r7Nw%0Ax0L+1uZvPNroUR2AJ1dEM8giIpJrChcqTN8mfdn6zFYq+lWk4ZiGDI0cypnkM66jSQFyIukEg5YM%0AInhMMIElAol9OpbeIb1VjiVbqSCLiEi2KuldkuF3DGdd33VsTdhKzf+ryQerPiA5Ndl1NMnHzqWc%0A4z8r/0Ot92tx+MxhNjy5geF3DKdUsVKuo0kBpBELERHJUWv3r+W1b15jW8I23mzzJj3r99Rqn1yx%0A1LRUpm+ezuBvBlOnXB3evuNtGpRv4DqW5GOaQRYRkTzju/jvGLR0EKeSTzG87XA61eqEMZf8N0o8%0AWJpNY07MHIZEDsGvqB/D7xhO2xvbuo4lBYAKsoiI5CnWWuZvm89r37yGbxFf3rj9De6+6W4VZfmN%0AtZZ5W+cxJHIIRb2K8mbrN+lwUwe9RyTbqCCLiEielJqWyuyY2bz1/Vt4e3nzetjrdKndRSXIg1lr%0A+XL7lwyJHEJqWipvtnmTzrU66z0h2U4FWURE8rQ0m8bnsZ/z1vdvYa1lcNhg7q9zP4WMjiH3FClp%0AKczYPIO3l72NMYY3wt7gvjr36T0gOUYFWURE8gVrLQu2LeCt79/idPJpXmz5Ig82fJBihYu5jiY5%0AJCkliYlRE/nnin9SuWRlBt02SKMUkitUkEVEJF+x1rJ011L+/eO/WX9wPU83e5r+TfsT4BPgOppk%0Ak4RfEhi3dhyjVo2iacWmvBL6CqFBoa5jiQdRQRYRkXxr8+HNvPvju8yJnUPP+j35nxb/Q82Amq5j%0AyTXaeGgjo34axayYWXSp3YWBLQfqdG3ihAqyiIjkewdPH+T9Ve8zdu1YQgJD6N+0P51qdaJwocKu%0Ao8llpKalMn/bfEb9NIrYo7H0b9qffk37cYPvDa6jiQdTQRYRkQIjKSWJWVtmMXrNaHaf2E2fkD48%0AEfIEFf0quo4mF9lzYg8fr/+YCVETCPQLZEDzAYTXDaeoV1HX0URUkEVEpGDacHADY9aMYfrm6bS5%0AsQ2PBj9Kh5s6UMSriOtoHis5NZkF2xYwft14Vu5dSY/6PejduDdNKjZxHU3kD1SQRUSkQDt57iTT%0ANk1j0oZJbD+2nZ71e/Jw8MM0rtBYZ0PIBdZa1h9cz9SNU5kcPZk6ZevQu3FvwuuG41PEx3U8kUyp%0AIIuIiMfYcWwHU6KnMGnDJHyK+NCrYS+61utKdf/qrqMVOLFHY5m2aRrTNk3jfNp5etTrwSONHqFW%0AQC3X0UQuSwVZREQ8jrWW5XuWMyV6CnNj5xJYIpDwOuGE1w2nTtk6Wlm+BtZatiZs5fPYz5m2aRpH%0AfjlC93rd6VG/B80qNtNrKvmKCrKIiHi01LRUlu9Zzuwts5kTOwffIr7cX+d+OtbsSIvKLXQmjEs4%0An3qeH3b/wPyt81mwfQFJKUl0rtWZ7vW6c1vQbXgV8nIdUeSaqCCLiIikS7NprNm/hrkxc1kYt5D4%0AxHjaVGtD+xrtaX9Te6qVruY6olPWWnYe38m38d+yZOcSFsct5qYyN9G5Vmc61+5McPlgrRRLgZCj%0ABdkY0xUYCtwMNLPWrstiuw7ASMALGG+tfSeL7VSQRUQk1xw8fZCv475mUdwiFsctxr+4P2FBYYQG%0AhRJaJZSbytxU4Avhz4k/8238txduu74lJS2FNje2oW21tnSs2ZFAv0DXEUWyXU4X5JuBNGAs8GJm%0ABdkY4wVsBe4E9gGrgZ7W2phMtlVBdiQyMpLWrVu7juGx9Pq7pdffnbz02qfZNDYc3MCy3ctYvmc5%0Ay/csJzk1mdAqF8pySGAIwRWCKVO8jOuo1yzhlwTW7F/D6v2rWb1/Ncu+W4ZXdS9aV2tNm2ptaHtj%0AW2oF1CrwvxTkFXnp/e9prqQgX/PwlbU29tcfcgnNgR3W2vj0bacB9wB/Ksjijv4jdUuvv1t6/d3J%0AS699IVOIxoGNaRzYmGdveRaA3Sd2s3z3clbsWcHc2LlEH4qmVLFSBJcPvnCrEEztgNpU96+On7ef%0A47/B706dO0Xs0VhijsYQcySGmKMxbDy8kSNnjhASGEKzis14sMGDVIuqxsiBI1WIHclL73/5s5w+%0AOqESsCfD473ALTn8M0VERK5bUKkgghoE0bNBT+DCKnN8YjwbDm5gw6ENfLbpM7YnbGfn8Z2UKFqC%0AGmVqUN2/OjX8a1DJrxI3+N7wh1tJ75LXVUbTbBonz50kMSmRI2eOsPfkXvae3Muek3t+u7/z+E6O%0AJx2ndkBtbi57M3XK1uGhhg9Rt1xdagfU/sOBdVuKb1E5FsnCJQuyMeZroEIm33rVWjv/CvavmQkR%0AESkQCplCVPevTnX/6txX577fnrfWcvD0QXYe30nc8TjijsWx9sBaDp85/IfbudRzlPIuRfEixSle%0AuDg+RXwoXuTC10KmEKlpqaTa1D98PZtylsSkRBKTEjmdfJoSRUtQulhpAooHUKVUFaqUrELlkpUJ%0ALh9M5ZKVqVq6KkGlgihkCjl8pUTyv+s+i4Ux5luynkFuAQy11nZIfzwISMvsQD1jjMq0iIiIiOS4%0AHJtBvkhWP2QNUNMYUw3YD3QHema24eWCioiIiIjkhmv+DMYYc58xZg/QAvjSGPPf9OcrGmO+BLDW%0ApgDPAIuALcD0zM5gISIiIiKSV+SZC4WIiIiIiOQFzqf4jTEdjDGxxpjtxpiXXefxJMaYCGPMIWPM%0ARtdZPJExpoox5ltjzGZjzCZjzADXmTyFMaaYMeYnY8x6Y8wWY8w/XGfyRMYYL2NMlDHmSg76lmxk%0AjIk3xkSnv/6rXOfxJMaY0saYWcaYmPT//7RwnclTGGNqp7/nf72dyOrfXqcryFdzIRHJfsaYVsBp%0AYJK1toHrPJ7GGFMBqGCtXW+MKQGsBe7V+z93GGN8rLW/GGMKA8uAgdbaZa5zeRJjzAtAE8DPWtvF%0AdR5PYozZBTSx1h5zncXTGGM+Ab6z1kak///H11p7wnUuT2OMKcSF7tncWrvn4u+7XkH+7UIi1trz%0AwK8XEpFcYK39ATjuOoenstYetNauT79/mgsX0KnoNpXnsNb+kn63KOAFqCjkImNMZaAjMJ6sD/SW%0AnKXXPZcZY0oBray1EXDhWC2VY2fuBOIyK8fgviBndiGRSo6yiDiTfqaXxsBPbpN4DmNMIWPMeuAQ%0A8K21dovrTB7mPeAlIM11EA9lgSXGmDXGmD6uw3iQG4EjxpiJxph1xpiPjDE+rkN5qB7Ap1l903VB%0A1hGC4vHSxytmAc+lryRLLrDWpllrGwGVgTBjTGvHkTyGMaYTcNhaG4VWMV0JtdY2Bu4Gnk4fuZOc%0AVxgIAT601oYAZ4BX3EbyPMaYokBnYGZW27guyPuAKhkeV+HCKrKIRzDGFAFmA1OstZ+7zuOJ0j/e%0A/BJo6jqLB7kV6JI+B/sZ0NYYM8lxJo9irT2Q/vUIMJcLI4+S8/YCe621q9Mfz+JCYZbcdTewNv39%0AnynXBfm3C4mkt/nuwBeOM4nkCmOMASYAW6y1I13n8STGmLLGmNLp94sDdwFRblN5Dmvtq9baKtba%0AG7nwMec31tqHXefyFMYYH2OMX/p9X6AdoLMZ5QJr7UFgjzGmVvpTdwKbHUbyVD258Mt5lrLrSnrX%0AxFqbYoz59UIiXsAEHcGfe4wxnwG3AwHpF315w1o70XEsTxIKPAREG2N+LWeDrLULHWbyFIHAJ+lH%0AMRcCJltrlzrO5Mk0bpe7ygNzL/yOTmFgqrV2sdtIHuVZYGr6wmAc8JjjPB4l/ZfCO4FLzt7rQiEi%0AIiIiIhm4HrEQEREREclTVJBFRERERDJQQRYRERERyUAFWUREREQkAxVkEREREZEMVJBFRERERDJQ%0AQRYRyYeMMaWMMf1d5xARKYhUkEVE8id/4CnXIURECiIVZBGR/OltoIYxJsoY847rMCIiBYmupCci%0Akg8ZY6oCC6y1DVxnEREpaLSCLCKSPxnXAURECioVZBERERGRDFSQRUTyp1OAn+sQIiIFkQqyiEg+%0AZK1NAJYbYzbqID0Rkeylg/RERERERDLQCrKIiIiISAYqyCIiIiIiGaggi4iIiIhkoIIsIiIiIpKB%0ACrKIiIiISAYqyCIiIiIiGaggi4iIiIhkoIIsIiIiIpLB/wMvF4e3Wv7SRgAAAABJRU5ErkJggg==%0A
-
-
-高阶微分方程
-------------------
-
-
-抛物运动（竖直方向）：
-
-
-**
-frac{d^2x}{dt^2} = g - \frac{D}{m}\frac{dx}{dt}
-**
-
-
-
-改写成如下形式：
-
-
-**
-y = \left[x, \frac{dx}{dt}\right] ****begin{aligned}
-\frac{dy_0}{dt} &amp;= y_1 \\\
-\frac{dy_1}{dt} &amp;= -g - \frac{D}{m} y_1 \\\
-\end{aligned}
-**
-
-
-
-
-
-::
-
-    def dy_dt(y, t):
-        ^4$"""Governing equations for projectile motion with drag.
-        ^4$y[0] = position
-        ^4$y[1] = velocity
-        ^4$g = gravity (m/s2)
-        ^4$D = drag (1/s) = force/velocity
-        ^4$m = mass (kg)
-        ^4$"""
-        ^4$g = -9.8
-        ^4$D = 0.1
-        ^4$m = 0.15
-        ^4$dy1 = g - (D/m) * y[1]
-        ^4$dy0 = y[1] if y[0] >= 0 else 0.
-        ^4$return [dy0, dy1]
-
-
-
-
-
-::
-
-    position_0 = 0.
-    velocity_0 = 100
-    t = linspace(0, 12, 100)
-    y = odeint(dy_dt, [position_0, velocity_0], t)
-
-
-
-
-
-::
-
-    p = plot(t, y[:,0])
-    yl = ylabel("Height (m)")
-    xl = xlabel("Time (s)")
-
-
-
-.. image:: 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QtiRyT5bqNdTGbW3t3nbvLNKZyTCZnuYlqzJkxrXbw4lDwQyTf33RdWXd92W2hViID2g0iL++6D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%0ASUoJQkREklKCEBGRpP4fPzntqp99LaMAAAAASUVORK5CYII=%0A
-
-
-
-
-::
-
-    y, infodict = odeint(dy_dt, [position_0, velocity_0], t,     full_output=True, printmessg=True, )
-    print sorted(infodict.keys())
-    print "cumulative number of function evaluations at each calculated     point:", infodict['nfe']
-    print "cumulative number of time steps", infodict['nst']
-    Integration successful.
-    ['hu', 'imxer', 'leniw', 'lenrw', 'message', 'mused', 'nfe', 'nje',     'nqu', 'nst', 'tcur', 'tolsf', 'tsw']
-    cumulative number of function evaluations at each calculated point:     [ 45  49  51  53  55  59  61  61  63  65  67  67  69  71  73  73  75  77
-    77  79  79  81  81  83  85  85  87  87  89  89  91  91  93  95  95  97
-    97  99  99 101 101 103 103 105 107 107 109 109 111 111 113 113 115 115
-    117 117 119 119 121 121 123 123 123 125 125 127 127 129 129 131 131 131
-    133 133 135 135 135 137 137 139 139 139 141 141 143 143 143 145 145 147
-    147 149 149 149 154 158 274 280 280]
-    cumulative number of time steps [ 20  22  23  24  25  27  28  28      29  30  31  31  32  33  34  34  35  36
-    36  37  37  38  38  39  40  40  41  41  42  42  43  43  44  45  45  46
-    46  47  47  48  48  49  49  50  51  51  52  52  53  53  54  54  55  55
-    56  56  57  57  58  58  59  59  59  60  60  61  61  62  62  63  63  63
-    64  64  65  65  65  66  66  67  67  67  68  68  69  69  69  70  70  71
-    71  72  72  72  73  75 130 133 133]
-
-
-
-作者 & 更新时间
-------------------------------------
-作者:`李金  <lijinwithyou@gmail.com>`
-
-提交: 2017/12/14
\ No newline at end of file
diff --git a/course/source/aipy-scipy/cn/9sparse.rst b/course/source/aipy-scipy/cn/9sparse.rst
deleted file mode 100644
index b60e5a9..0000000
--- a/course/source/aipy-scipy/cn/9sparse.rst
+++ /dev/null
@@ -1,406 +0,0 @@
-稀疏矩阵
--------------------
-
-
-Scipy 提供了稀疏矩阵的支持（scipy.sparse）。
-
-
-稀疏矩阵主要使用 位置 + 值 的方法来存储矩阵的非零元素，根据存储和使用方式的不同，有如下几种类型的稀疏矩阵：
-
-	
-	
-+----------------------------------------------------+---------------------------------------------------+
-|Type                                                | Description                                       |
-+====================================================+===================================================+
-|bsr_matrix(arg1[, shape, dtype, copy, blocksize])   | Block Sparse Row matrix                           |    
-+----------------------------------------------------+---------------------------------------------------+
-| coo_matrix(arg1[, shape, dtype, copy])             |A sparse matrix in COOrdinate format.              |
-+----------------------------------------------------+---------------------------------------------------+
-| csc_matrix(arg1[, shape, dtype, copy])             |Compressed Sparse Column matrix                    |
-+----------------------------------------------------+---------------------------------------------------+
-| csr_matrix(arg1[, shape, dtype, copy])             |Compressed Sparse Row matrix                       |         
-+----------------------------------------------------+---------------------------------------------------+
-| dia_matrix(arg1[, shape, dtype, copy])             |Sparse matrix with DIAgonal storage                |
-+----------------------------------------------------+---------------------------------------------------+
-|dok_matrix(arg1[, shape, dtype, copy])              |Dictionary Of Keys based sparse matrix.            |
-+----------------------------------------------------+---------------------------------------------------+
-| lil_matrix(arg1[, shape, dtype, copy])             |Row-based linked list sparse matrix                |
-+----------------------------------------------------+---------------------------------------------------+
-
-
-在这些存储格式中：
-
-- COO 格式在构建矩阵时比较高效
-- CSC 和 CSR 格式在乘法计算时比较高效
-
-
-构建稀疏矩阵
---------------
-
-
-
-::
-
-    from scipy.sparse import *
-    import numpy as np
-
-
-创建一个空的稀疏矩阵：
-
-
-
-
-::
-
-    coo_matrix((2,3))
-
-
-
-结果:
-::
-
-    <2x3 sparse matrix of type '<type 'numpy.float64'>'
-    with 0 stored elements in COOrdinate format>
-
-
-也可以使用一个已有的矩阵或数组或列表中创建新矩阵：
-
-
-
-::
-
-    A = coo_matrix([[1,2,0],[0,0,3],[4,0,5]])
-    print A
-
-
-结果:
-::
-
-    (0, 0)	1
-
-    (0, 1)	2
-
-    (1, 2)	3
-
-    (2, 0)	4
-
-    (2, 2)	5
-
-不同格式的稀疏矩阵可以相互转化：
-
-
-
-
-::
-
-    type(A)
-
-
-
-结果:
-::
-
-    scipy.sparse.coo.coo_matrix
-
-
-
-
-::
-
-    B = A.tocsr()
-
-
-    type(B)
-
-
-
-结果:
-::
-
-    scipy.sparse.csr.csr_matrix
-
-
-可以转化为普通矩阵：
-
-
-
-
-::
-
-    C = A.todense()
-    C
-
-
-
-结果:
-::
-
-    matrix([[1, 2, 0],
-
-               [0, 0, 3],
-
-               [4, 0, 5]])
-
-与向量的乘法：
-
-
-
-
-::
-
-    v = np.array([1,0,-1])
-    A.dot(v)
-
-
-
-结果:
-::
-
-    array([ 1, -3, -1])
-
-
-还可以传入一个 (data, (row, col)) 的元组来构建稀疏矩阵：
-
-
-
-
-::
-
-    I = np.array([0,3,1,0])
-    J = np.array([0,3,1,2])
-    V = np.array([4,5,7,9])
-    A = coo_matrix((V,(I,J)),shape=(4,4))
-
-
-
-
-
-::
-
-  print A
-
-结果:
-::
-
-    (0, 0)	4
-
-    (3, 3)	5
-
-    (1, 1)	7
-
-    (0, 2)	9
-
-COO 格式的稀疏矩阵在构建的时候只是简单的将坐标和值加到后面，对于重复的坐标不进行处理：
-
-
-
-::
-
-    I = np.array([0,0,1,3,1,0,0])
-    J = np.array([0,2,1,3,1,0,0])
-    V = np.array([1,1,1,1,1,1,1])
-    B = coo_matrix((V,(I,J)),shape=(4,4))
-
-
-    print B
-
-
-结果:
-::
-
-  (0, 0)	1
-
-  (0, 2)	1
-
-  (1, 1)	1
-
-  (3, 3)	1
-
-  (1, 1)	1
-
-  (0, 0)	1
-
-  (0, 0)	1
-
-
-转换成 CSR 格式会自动将相同坐标的值合并：
-
-
-
-
-::
-
-    C = B.tocsr()
-    print C
-
-
-结果:
-::
-
-  (0, 0)	3
-
-  (0, 2)	1
-
-  (1, 1)	2
-
-  (3, 3)	1
-
-求解微分方程
---------------
-
-
-
-
-
-::
-
-    from scipy.sparse import lil_matrix
-    from scipy.sparse.linalg import spsolve
-    from numpy.linalg import solve, norm
-    from numpy.random import rand
-
-
-构建 1000 x 1000 的稀疏矩阵：
-
-
-
-
-::
-
-    A = lil_matrix((1000, 1000))
-    A[0, :100] = rand(100)
-    A[1, 100:200] = A[0, :100]
-    A.setdiag(rand(1000))
-
-
-转化为 CSR 之后，用 spsolve 求解 $Ax=b$：
-
-
-
-
-
-::
-
-    A = A.tocsr()
-    b = rand(1000)
-    x = spsolve(A, b)
-
-
-转化成正常数组之后求解：
-
-
-
-
-::
-
-    x_ = solve(A.toarray(), b)
-
-
-查看误差：
-
-
-
-
-::
-
-    err = norm(x-x_)
-    err
-
-
-结果:
-::
-
-    6.4310987107687431e-13
-
-sparse.find 函数
---------------------
-
-
-返回一个三元组，表示稀疏矩阵中非零元素的 (row, col, value)：
-
-
-
-
-::
-
-    from scipy import sparse
-
-    row, col, val = sparse.find(C)
-    print row, col, val
-
-
-结果:
-::
-
-    [0 0 1 3] [0 2 1 3] [3 1 2 1]
-
-
-sparse.issparse 函数
----------------------------
-
-
-查看一个对象是否为稀疏矩阵：
-
-
-
-
-::
-
-    sparse.issparse(B)
-
-
-结果:
-::
-
-    True
-
-或者
-
-
-
-
-::
-
-    sparse.isspmatrix(B.todense())
-
-
-结果:
-::
-
-    False
-
-还可以查询是否为指定格式的稀疏矩阵：
-
-
-
-
-::
-
-    sparse.isspmatrix_coo(B)
-
-
-结果:
-::
-
-    True
-
-
-
-
-::
-
-    sparse.isspmatrix_csr(B)
-
-
-结果:
-::
-
-    False
-
-
-
-作者 & 更新时间
-------------------------------------
-作者:`李金  <lijinwithyou@gmail.com>`
-
-提交: 2017/12/14
diff --git a/course/source/aipy-scipy/index.rst b/course/source/aipy-scipy/index.rst
deleted file mode 100644
index 3d4bdc7..0000000
--- a/course/source/aipy-scipy/index.rst
+++ /dev/null
@@ -1,19 +0,0 @@
-Scipy Programming
-=======================
-The courses are still in the planning. Welcome to reward us. 
-
-1. **Primary course**
-
-If the total number of people who reward us is more than 100, we promise to complete the primary course within 1 week, so that, after finishing your study, you can master the basic concepts and write simple scripts.

-
-2. **Intermediate course**
-
-If the total number of people who reward us is more than 500, we will launch the intermediate course within 2 weeks, so that, after finishing your study, you will be familiar with the knowledge and framework of the course and apply it to practical development.
-
-3. **Advanced course**
-
-If the total number of people who reward us is more than 2,000, we will launch the advanced course in a month, so that, after finishing your study, you will have a better understanding of the principles of the framework and be able to do advanced development.
-
-
-**The primary course is free to all, and the intermediate and advanced courses are only open to those who have rewarded us.**
-
diff --git a/course/source/aipy-tensorflow/index.rst b/course/source/aipy-tensorflow/index.rst
deleted file mode 100644
index 1bcbd65..0000000
--- a/course/source/aipy-tensorflow/index.rst
+++ /dev/null
@@ -1,19 +0,0 @@
-Tensorflow Programming
-=======================
-The courses are still in the planning. Welcome to reward us. 
-
-1. **Primary course**
-
-If the total number of people who reward us is more than 100, we promise to complete the primary course within 1 week, so that, after finishing your study, you can master the basic concepts and write simple scripts.

-
-2. **Intermediate course**
-
-If the total number of people who reward us is more than 500, we will launch the intermediate course within 2 weeks, so that, after finishing your study, you will be familiar with the knowledge and framework of the course and apply it to practical development.
-
-3. **Advanced course**
-
-If the total number of people who reward us is more than 2,000, we will launch the advanced course in a month, so that, after finishing your study, you will have a better understanding of the principles of the framework and be able to do advanced development.
-
-
-**The primary course is free to all, and the intermediate and advanced courses are only open to those who have rewarded us.**
-
diff --git a/course/source/aipy-theano/index.rst b/course/source/aipy-theano/index.rst
deleted file mode 100644
index 1490255..0000000
--- a/course/source/aipy-theano/index.rst
+++ /dev/null
@@ -1,19 +0,0 @@
-Theano Programming
-=======================
-The courses are still in the planning. Welcome to reward us. 
-
-1. **Primary course**
-
-If the total number of people who reward us is more than 100, we promise to complete the primary course within 1 week, so that, after finishing your study, you can master the basic concepts and write simple scripts.

-
-2. **Intermediate course**
-
-If the total number of people who reward us is more than 500, we will launch the intermediate course within 2 weeks, so that, after finishing your study, you will be familiar with the knowledge and framework of the course and apply it to practical development.
-
-3. **Advanced course**
-
-If the total number of people who reward us is more than 2,000, we will launch the advanced course in a month, so that, after finishing your study, you will have a better understanding of the principles of the framework and be able to do advanced development.
-
-
-**The primary course is free to all, and the intermediate and advanced courses are only open to those who have rewarded us.**
-
diff --git a/course/source/ar/index.rst b/course/source/ar/index.rst
deleted file mode 100644
index aa27495..0000000
--- a/course/source/ar/index.rst
+++ /dev/null
@@ -1,19 +0,0 @@
-AR Programming with Python
-=============================
-The courses are still in the planning. Welcome to reward us. 
-
-1. **Primary course**
-
-If the total number of people who reward us is more than 100, we promise to complete the primary course within 1 week, so that, after finishing your study, you can master the basic concepts and write simple scripts.

-
-2. **Intermediate course**
-
-If the total number of people who reward us is more than 500, we will launch the intermediate course within 2 weeks, so that, after finishing your study, you will be familiar with the knowledge and framework of the course and apply it to practical development.
-
-3. **Advanced course**
-
-If the total number of people who reward us is more than 2,000, we will launch the advanced course in a month, so that, after finishing your study, you will have a better understanding of the principles of the framework and be able to do advanced development.
-
-
-**The primary course is free to all, and the intermediate and advanced courses are only open to those who have rewarded us.**
-
diff --git a/course/source/blockchain/index.rst b/course/source/blockchain/index.rst
deleted file mode 100644
index 2995cc5..0000000
--- a/course/source/blockchain/index.rst
+++ /dev/null
@@ -1,19 +0,0 @@
-Blockchain with Python
-=======================
-The courses are still in the planning. Welcome to reward us. 
-
-1. **Primary course**
-
-If the total number of people who reward us is more than 100, we promise to complete the primary course within 1 week, so that, after finishing your study, you can master the basic concepts and write simple scripts.

-
-2. **Intermediate course**
-
-If the total number of people who reward us is more than 500, we will launch the intermediate course within 2 weeks, so that, after finishing your study, you will be familiar with the knowledge and framework of the course and apply it to practical development.
-
-3. **Advanced course**
-
-If the total number of people who reward us is more than 2,000, we will launch the advanced course in a month, so that, after finishing your study, you will have a better understanding of the principles of the framework and be able to do advanced development.
-
-
-**The primary course is free to all, and the intermediate and advanced courses are only open to those who have rewarded us.**
-
diff --git a/course/source/bottle-web/index.rst b/course/source/bottle-web/index.rst
deleted file mode 100644
index d358d7b..0000000
--- a/course/source/bottle-web/index.rst
+++ /dev/null
@@ -1,19 +0,0 @@
-Bottle Web
-=======================
-The courses are still in the planning. Welcome to reward us. 
-
-1. **Primary course**
-
-If the total number of people who reward us is more than 100, we promise to complete the primary course within 1 week, so that, after finishing your study, you can master the basic concepts and write simple scripts.
-
-2. **Intermediate course**
-
-If the total number of people who reward us is more than 500, we will launch the intermediate course within 2 weeks, so that, after finishing your study, you will be familiar with the knowledge and framework of the course and apply it to practical development.
-
-3. **Advanced course**
-
-If the total number of people who reward us is more than 2,000, we will launch the advanced course in a month, so that, after finishing your study, you will have a better understanding of the principles of the framework and be able to do advanced development.
-
-
-**The primary course is free to all, and the intermediate and advanced courses are only open to those who have rewarded us.**
-
diff --git a/course/source/bottle-web/tutorial.rst b/course/source/bottle-web/tutorial.rst
deleted file mode 100644
index e13ab34..0000000
--- a/course/source/bottle-web/tutorial.rst
+++ /dev/null
@@ -1,1073 +0,0 @@
-pache Server:
-.. _Apache: http://www.apache.org/
-.. _cherrypy: http://www.cherrypy.org/
-.. _decorator: http://docs.python.org/glossary.html#term-decorator
-.. _flup: http://trac.saddi.com/flup
-.. _http_code: http://www.w3.org/Protocols/rfc2616/rfc2616-sec10.html
-.. _http_method: http://www.w3.org/Protocols/rfc2616/rfc2616-sec9.html
-.. _json: http://de.wikipedia.org/wiki/JavaScript_Object_Notation
-.. _lighttpd: http://www.lighttpd.net/
-.. _mako: http://www.makotemplates.org/
-.. _mod_wsgi: http://code.google.com/p/modwsgi/
-.. _Paste: http://pythonpaste.org/
-.. _Pound: http://www.apsis.ch/pound/
-.. _`WSGI Specification`: http://www.wsgi.org/
-.. _issue: http://github.com/bottlepy/bottle/issues
-.. _Python: http://python.org/
-.. _SimpleCookie: http://docs.python.org/library/cookie.html#morsel-objects
-.. _testing: http://github.com/bottlepy/bottle/raw/master/bottle.py
-
-========
-Tutorial
-========
-
-This tutorial introduces you to the concepts and features of the Bottle web framework and covers basic and advanced topics alike. You can read it from start to end, or use it as a reference later on. The automatically generated :doc:`api` may be interesting for you, too. It covers more details, but explains less than this tutorial. Solutions for the most common questions can be found in our :doc:`recipes` collection or on the :doc:`faq` page. If you need any help, join our `mailing list <mailto:bottlepy@googlegroups.com>`_ or visit us in our `IRC channel <http://webchat.freenode.net/?channels=bottlepy>`_.
-
-.. _installation:
-
-Installation
-==============================================================================
-
-Bottle does not depend on any external libraries. You can just download `bottle.py </bottle.py>`_ into your project directory and start coding:
-
-.. code-block:: bash
-
-    $ wget https://bottlepy.org/bottle.py
-
-This will get you the latest development snapshot that includes all the new features. If you prefer a more stable environment, you should stick with the stable releases. These are available on `PyPI <http://pypi.python.org/pypi/bottle>`_ and can be installed via :command:`pip` (recommended), :command:`easy_install` or your package manager:
-
-.. code-block:: bash
-
-    $ sudo pip install bottle              # recommended
-    $ sudo easy_install bottle             # alternative without pip
-    $ sudo apt-get install python-bottle   # works for debian, ubuntu, ...
-
-Either way, you'll need Python 2.7 or newer (including 3.2+) to run bottle applications. If you do not have permissions to install packages system-wide or simply don't want to, create a `virtualenv <http://pypi.python.org/pypi/virtualenv>`_ first:
-
-.. code-block:: bash
-
-    $ virtualenv develop              # Create virtual environment
-    $ source develop/bin/activate     # Change default python to virtual one
-    (develop)$ pip install -U bottle  # Install bottle to virtual environment
-
-Or, if virtualenv is not installed on your system:
-
-.. code-block:: bash
-
-    $ wget https://raw.github.com/pypa/virtualenv/master/virtualenv.py
-    $ python virtualenv.py develop    # Create virtual environment
-    $ source develop/bin/activate     # Change default python to virtual one
-    (develop)$ pip install -U bottle  # Install bottle to virtual environment
-
-
-
-Quickstart: "Hello World"
-==============================================================================
-
-This tutorial assumes you have Bottle either :ref:`installed <installation>` or copied into your project directory. Let's start with a very basic "Hello World" example::
-
-    from bottle import route, run
-
-    @route('/hello')
-    def hello():
-        return "Hello World!"
-
-    run(host='localhost', port=8080, debug=True)
-
-This is it. Run this script, visit http://localhost:8080/hello and you will see "Hello World!" in your browser. Here is how it works:
-
-The :func:`route` decorator binds a piece of code to an URL path. In this case, we link the ``/hello`` path to the ``hello()`` function. This is called a `route` (hence the decorator name) and is the most important concept of this framework. You can define as many routes as you want. Whenever a browser requests a URL, the associated function is called and the return value is sent back to the browser. It's as simple as that.
-
-The :func:`run` call in the last line starts a built-in development server. It runs on ``localhost`` port ``8080`` and serves requests until you hit :kbd:`Control-c`. You can switch the server backend later, but for now a development server is all we need. It requires no setup at all and is an incredibly painless way to get your application up and running for local tests.
-
-The :ref:`tutorial-debugging` is very helpful during early development, but should be switched off for public applications. Keep that in mind.
-
-This is just a demonstration of the basic concept of how applications are built with Bottle. Continue reading and you'll see what else is possible.
-
-.. _tutorial-default:
-
-The Default Application
-------------------------------------------------------------------------------
-
-For the sake of simplicity, most examples in this tutorial use a module-level :func:`route` decorator to define routes. This adds routes to a global "default application", an instance of :class:`Bottle` that is automatically created the first time you call :func:`route`. Several other module-level decorators and functions relate to this default application, but if you prefer a more object oriented approach and don't mind the extra typing, you can create a separate application object and use that instead of the global one::
-
-    from bottle import Bottle, run
-
-    app = Bottle()
-
-    @app.route('/hello')
-    def hello():
-        return "Hello World!"
-
-    run(app, host='localhost', port=8080)
-
-The object-oriented approach is further described in the :ref:`default-app` section. Just keep in mind that you have a choice.
-
-
-
-
-.. _tutorial-routing:
-
-Request Routing
-==============================================================================
-
-In the last chapter we built a very simple web application with only a single route. Here is the routing part of the "Hello World" example again::
-
-    @route('/hello')
-    def hello():
-        return "Hello World!"
-
-The :func:`route` decorator links an URL path to a callback function, and adds a new route to the :ref:`default application <tutorial-default>`. An application with just one route is kind of boring, though. Let's add some more (don't forget ``from bottle import template``)::
-
-    @route('/')
-    @route('/hello/<name>')
-    def greet(name='Stranger'):
-        return template('Hello {{name}}, how are you?', name=name)
-
-This example demonstrates two things: You can bind more than one route to a single callback, and you can add wildcards to URLs and access them via keyword arguments.
-
-
-
-.. _tutorial-dynamic-routes:
-
-Dynamic Routes
--------------------------------------------------------------------------------
-
-Routes that contain wildcards are called `dynamic routes` (as opposed to `static routes`) and match more than one URL at the same time. A simple wildcard consists of a name enclosed in angle brackets (e.g. ``<name>``) and accepts one or more characters up to the next slash (``/``). For example, the route ``/hello/<name>`` accepts requests for ``/hello/alice`` as well as ``/hello/bob``, but not for ``/hello``, ``/hello/`` or ``/hello/mr/smith``.
-
-Each wildcard passes the covered part of the URL as a keyword argument to the request callback. You can use them right away and implement RESTful, nice-looking and meaningful URLs with ease. Here are some other examples along with the URLs they'd match::
-
-    @route('/wiki/<pagename>')            # matches /wiki/Learning_Python
-    def show_wiki_page(pagename):
-        ...
-
-    @route('/<action>/<user>')            # matches /follow/defnull
-    def user_api(action, user):
-        ...
-
-Filters can be used to define more specific wildcards, and/or transform the covered part of the URL before it is passed to the callback. A filtered wildcard is declared as ``<name:filter>`` or ``<name:filter:config>``. The syntax for the optional config part depends on the filter used.
-
-The following filters are implemented by default and more may be added:
-
-* **:int** matches (signed) digits only and converts the value to integer.
-* **:float** similar to :int but for decimal numbers.
-* **:path** matches all characters including the slash character in a non-greedy way and can be used to match more than one path segment.
-* **:re** allows you to specify a custom regular expression in the config field. The matched value is not modified.
-
-Let's have a look at some practical examples::
-
-    @route('/object/<id:int>')
-    def callback(id):
-        assert isinstance(id, int)
-
-    @route('/show/<name:re:[a-z]+>')
-    def callback(name):
-        assert name.isalpha()
-
-    @route('/static/<path:path>')
-    def callback(path):
-        return static_file(path, ...)
-
-You can add your own filters as well. See :doc:`routing` for details.
-
-
-HTTP Request Methods
-------------------------------------------------------------------------------
-
-.. __: http_method_
-
-The HTTP protocol defines several `request methods`__ (sometimes referred to as "verbs") for different tasks. GET is the default for all routes with no other method specified. These routes will match GET requests only. To handle other methods such as POST, PUT, DELETE or PATCH, add a ``method`` keyword argument to the :func:`route` decorator or use one of the five alternative decorators: :func:`get`, :func:`post`, :func:`put`, :func:`delete` or :func:`patch`.
-
-The POST method is commonly used for HTML form submission. This example shows how to handle a login form using POST::
-
-    from bottle import get, post, request # or route
-
-    @get('/login') # or @route('/login')
-    def login():
-        return '''
-            <form action="/login" method="post">
-                Username: <input name="username" type="text" />
-                Password: <input name="password" type="password" />
-                <input value="Login" type="submit" />
-            </form>
-        '''
-
-    @post('/login') # or @route('/login', method='POST')
-    def do_login():
-        username = request.forms.get('username')
-        password = request.forms.get('password')
-        if check_login(username, password):
-            return "<p>Your login information was correct.</p>"
-        else:
-            return "<p>Login failed.</p>"
-
-In this example the ``/login`` URL is linked to two distinct callbacks, one for GET requests and another for POST requests. The first one displays a HTML form to the user. The second callback is invoked on a form submission and checks the login credentials the user entered into the form. The use of :attr:`Request.forms` is further described in the :ref:`tutorial-request` section.
-
-.. rubric:: Special Methods: HEAD and ANY
-
-The HEAD method is used to ask for the response identical to the one that would correspond to a GET request, but without the response body. This is useful for retrieving meta-information about a resource without having to download the entire document. Bottle handles these requests automatically by falling back to the corresponding GET route and cutting off the request body, if present. You don't have to specify any HEAD routes yourself.
-
-Additionally, the non-standard ANY method works as a low priority fallback: Routes that listen to ANY will match requests regardless of their HTTP method but only if no other more specific route is defined. This is helpful for *proxy-routes* that redirect requests to more specific sub-applications.
-
-To sum it up: HEAD requests fall back to GET routes and all requests fall back to ANY routes, but only if there is no matching route for the original request method. It's as simple as that.
-
-
-
-Routing Static Files
-------------------------------------------------------------------------------
-
-Static files such as images or CSS files are not served automatically. You have to add a route and a callback to control which files get served and where to find them::
-
-  from bottle import static_file
-  @route('/static/<filename>')
-  def server_static(filename):
-      return static_file(filename, root='/path/to/your/static/files')
-
-The :func:`static_file` function is a helper to serve files in a safe and convenient way (see :ref:`tutorial-static-files`). This example is limited to files directly within the ``/path/to/your/static/files`` directory because the ``<filename>`` wildcard won't match a path with a slash in it. To serve files in subdirectories, change the wildcard to use the `path` filter::
-
-  @route('/static/<filepath:path>')
-  def server_static(filepath):
-      return static_file(filepath, root='/path/to/your/static/files')
-
-Be careful when specifying a relative root-path such as ``root='./static/files'``. The working directory (``./``) and the project directory are not always the same.
-
-
-
-
-.. _tutorial-errorhandling:
-
-Error Pages
-------------------------------------------------------------------------------
-
-If anything goes wrong, Bottle displays an informative but fairly plain error page. You can override the default for a specific HTTP status code with the :func:`error` decorator::
-
-  from bottle import error
-  @error(404)
-  def error404(error):
-      return 'Nothing here, sorry'
-
-From now on, `404 File not Found` errors will display a custom error page to the user. The only parameter passed to the error-handler is an instance of :exc:`HTTPError`. Apart from that, an error-handler is quite similar to a regular request callback. You can read from :data:`request`, write to :data:`response` and return any supported data-type except for :exc:`HTTPError` instances.
-
-Error handlers are used only if your application returns or raises an :exc:`HTTPError` exception (:func:`abort` does just that). Changing :attr:`Request.status` or returning :exc:`HTTPResponse` won't trigger the error handler.
-
-
-
-
-
-
-.. _tutorial-output:
-
-Generating content
-==============================================================================
-
-In pure WSGI, the range of types you may return from your application is very limited. Applications must return an iterable yielding byte strings. You may return a string (because strings are iterable) but this causes most servers to transmit your content char by char. Unicode strings are not allowed at all. This is not very practical.
-
-Bottle is much more flexible and supports a wide range of types. It even adds a ``Content-Length`` header if possible and encodes unicode automatically, so you don't have to. What follows is a list of data types you may return from your application callbacks and a short description of how these are handled by the framework:
-
-Dictionaries
-    As mentioned above, Python dictionaries (or subclasses thereof) are automatically transformed into JSON strings and returned to the browser with the ``Content-Type`` header set to ``application/json``. This makes it easy to implement json-based APIs. Data formats other than json are supported too. See the :ref:`tutorial-output-filter` to learn more.
-
-Empty Strings, ``False``, ``None`` or other non-true values:
-    These produce an empty output with the ``Content-Length`` header set to 0.
-
-Unicode strings
-    Unicode strings (or iterables yielding unicode strings) are automatically encoded with the codec specified in the ``Content-Type`` header (utf8 by default) and then treated as normal byte strings (see below).
-
-Byte strings
-    Bottle returns strings as a whole (instead of iterating over each char) and adds a ``Content-Length`` header based on the string length. Lists of byte strings are joined first. Other iterables yielding byte strings are not joined because they may grow too big to fit into memory. The ``Content-Length`` header is not set in this case.
-
-Instances of :exc:`HTTPError` or :exc:`HTTPResponse`
-    Returning these has the same effect as when raising them as an exception. In case of an :exc:`HTTPError`, the error handler is applied. See :ref:`tutorial-errorhandling` for details.
-
-File objects
-    Everything that has a ``.read()`` method is treated as a file or file-like object and passed to the ``wsgi.file_wrapper`` callable defined by the WSGI server framework. Some WSGI server implementations can make use of optimized system calls (sendfile) to transmit files more efficiently. In other cases this just iterates over chunks that fit into memory. Optional headers such as ``Content-Length`` or ``Content-Type`` are *not* set automatically. Use :func:`send_file` if possible. See :ref:`tutorial-static-files` for details.
-
-Iterables and generators
-    You are allowed to use ``yield`` within your callbacks or return an iterable, as long as the iterable yields byte strings, unicode strings, :exc:`HTTPError` or :exc:`HTTPResponse` instances. Nested iterables are not supported, sorry. Please note that the HTTP status code and the headers are sent to the browser as soon as the iterable yields its first non-empty value. Changing these later has no effect.
-
-The ordering of this list is significant. You may for example return a subclass of :class:`str` with a ``read()`` method. It is still treated as a string instead of a file, because strings are handled first.
-
-.. rubric:: Changing the Default Encoding
-
-Bottle uses the `charset` parameter of the ``Content-Type`` header to decide how to encode unicode strings. This header defaults to ``text/html; charset=UTF8`` and can be changed using the :attr:`Response.content_type` attribute or by setting the :attr:`Response.charset` attribute directly. (The :class:`Response` object is described in the section :ref:`tutorial-response`.)
-
-::
-
-    from bottle import response
-    @route('/iso')
-    def get_iso():
-        response.charset = 'ISO-8859-15'
-        return u'This will be sent with ISO-8859-15 encoding.'
-
-    @route('/latin9')
-    def get_latin():
-        response.content_type = 'text/html; charset=latin9'
-        return u'ISO-8859-15 is also known as latin9.'
-
-In some rare cases the Python encoding names differ from the names supported by the HTTP specification. Then, you have to do both: first set the :attr:`Response.content_type` header (which is sent to the client unchanged) and then set the :attr:`Response.charset` attribute (which is used to encode unicode).
-
-.. _tutorial-static-files:
-
-Static Files
---------------------------------------------------------------------------------
-
-You can directly return file objects, but :func:`static_file` is the recommended way to serve static files. It automatically guesses a mime-type, adds a ``Last-Modified`` header, restricts paths to a ``root`` directory for security reasons and generates appropriate error responses (403 on permission errors, 404 on missing files). It even supports the ``If-Modified-Since`` header and eventually generates a ``304 Not Modified`` response. You can pass a custom MIME type to disable guessing.
-
-::
-
-    from bottle import static_file
-    @route('/images/<filename:re:.*\.png>')
-    def send_image(filename):
-        return static_file(filename, root='/path/to/image/files', mimetype='image/png')
-
-    @route('/static/<filename:path>')
-    def send_static(filename):
-        return static_file(filename, root='/path/to/static/files')
-
-You can raise the return value of :func:`static_file` as an exception if you really need to.
-
-.. rubric:: Forced Download
-
-Most browsers try to open downloaded files if the MIME type is known and assigned to an application (e.g. PDF files). If this is not what you want, you can force a download dialog and even suggest a filename to the user::
-
-    @route('/download/<filename:path>')
-    def download(filename):
-        return static_file(filename, root='/path/to/static/files', download=filename)
-
-If the ``download`` parameter is just ``True``, the original filename is used.
-
-.. _tutorial-error:
-
-HTTP Errors and Redirects
---------------------------------------------------------------------------------
-
-The :func:`abort` function is a shortcut for generating HTTP error pages.
-
-::
-
-    from bottle import route, abort
-    @route('/restricted')
-    def restricted():
-        abort(401, "Sorry, access denied.")
-
-To redirect a client to a different URL, you can send a ``303 See Other`` response with the ``Location`` header set to the new URL. :func:`redirect` does that for you::
-
-    from bottle import redirect
-    @route('/wrong/url')
-    def wrong():
-        redirect("/right/url")
-
-You may provide a different HTTP status code as a second parameter.
-
-.. note::
-    Both functions will interrupt your callback code by raising an :exc:`HTTPError` exception.
-
-.. rubric:: Other Exceptions
-
-All exceptions other than :exc:`HTTPResponse` or :exc:`HTTPError` will result in a ``500 Internal Server Error`` response, so they won't crash your WSGI server. You can turn off this behavior to handle exceptions in your middleware by setting ``bottle.app().catchall`` to ``False``.
-
-
-.. _tutorial-response:
-
-The :class:`Response` Object
---------------------------------------------------------------------------------
-
-Response metadata such as the HTTP status code, response headers and cookies are stored in an object called :data:`response` up to the point where they are transmitted to the browser. You can manipulate these metadata directly or use the predefined helper methods to do so. The full API and feature list is described in the API section (see :class:`Response`), but the most common use cases and features are covered here, too.
-
-.. rubric:: Status Code
-
-The `HTTP status code <http_code>`_ controls the behavior of the browser and defaults to ``200 OK``. In most scenarios you won't need to set the :attr:`Response.status` attribute manually, but use the :func:`abort` helper or return an :exc:`HTTPResponse` instance with the appropriate status code. Any integer is allowed, but codes other than the ones defined by the `HTTP specification <http_code>`_ will only confuse the browser and break standards.
-
-.. rubric:: Response Header
-
-Response headers such as ``Cache-Control`` or ``Location`` are defined via :meth:`Response.set_header`. This method takes two parameters, a header name and a value. The name part is case-insensitive::
-
-  @route('/wiki/<page>')
-  def wiki(page):
-      response.set_header('Content-Language', 'en')
-      ...
-
-Most headers are unique, meaning that only one header per name is send to the client. Some special headers however are allowed to appear more than once in a response. To add an additional header, use :meth:`Response.add_header` instead of :meth:`Response.set_header`::
-
-    response.set_header('Set-Cookie', 'name=value')
-    response.add_header('Set-Cookie', 'name2=value2')
-
-Please note that this is just an example. If you want to work with cookies, read :ref:`ahead <tutorial-cookies>`.
-
-
-.. _tutorial-cookies:
-
-Cookies
--------------------------------------------------------------------------------
-
-A cookie is a named piece of text stored in the user's browser profile. You can access previously defined cookies via :meth:`Request.get_cookie` and set new cookies with :meth:`Response.set_cookie`::
-
-    @route('/hello')
-    def hello_again():
-        if request.get_cookie("visited"):
-            return "Welcome back! Nice to see you again"
-        else:
-            response.set_cookie("visited", "yes")
-            return "Hello there! Nice to meet you"
-
-The :meth:`Response.set_cookie` method accepts a number of additional keyword arguments that control the cookies lifetime and behavior. Some of the most common settings are described here:
-
-* **max_age:**    Maximum age in seconds. (default: ``None``)
-* **expires:**    A datetime object or UNIX timestamp. (default: ``None``)
-* **domain:**     The domain that is allowed to read the cookie. (default: current domain)
-* **path:**       Limit the cookie to a given path (default: ``/``)
-* **secure:**     Limit the cookie to HTTPS connections (default: off).
-* **httponly:**   Prevent client-side javascript to read this cookie (default: off, requires Python 2.7 or newer).
-* **same_site:**  Disables third-party use for a cookie. Allowed attributes: `lax` and `strict`. In strict mode the cookie will never be sent. In lax mode the cookie is only sent with a top-level GET request.
-
-If neither `expires` nor `max_age` is set, the cookie expires at the end of the browser session or as soon as the browser window is closed. There are some other gotchas you should consider when using cookies:
-
-* Cookies are limited to 4 KB of text in most browsers.
-* Some users configure their browsers to not accept cookies at all. Most search engines ignore cookies too. Make sure that your application still works without cookies.
-* Cookies are stored at client side and are not encrypted in any way. Whatever you store in a cookie, the user can read it. Worse than that, an attacker might be able to steal a user's cookies through `XSS <http://en.wikipedia.org/wiki/HTTP_cookie#Cookie_theft_and_session_hijacking>`_ vulnerabilities on your side. Some viruses are known to read the browser cookies, too. Thus, never store confidential information in cookies.
-* Cookies are easily forged by malicious clients. Do not trust cookies.
-
-.. _tutorial-signed-cookies:
-
-.. rubric:: Signed Cookies
-
-As mentioned above, cookies are easily forged by malicious clients. Bottle can cryptographically sign your cookies to prevent this kind of manipulation. All you have to do is to provide a signature key via the `secret` keyword argument whenever you read or set a cookie and keep that key a secret. As a result, :meth:`Request.get_cookie` will return ``None`` if the cookie is not signed or the signature keys don't match::
-
-    @route('/login')
-    def do_login():
-        username = request.forms.get('username')
-        password = request.forms.get('password')
-        if check_login(username, password):
-            response.set_cookie("account", username, secret='some-secret-key')
-            return template("<p>Welcome {{name}}! You are now logged in.</p>", name=username)
-        else:
-            return "<p>Login failed.</p>"
-
-    @route('/restricted')
-    def restricted_area():
-        username = request.get_cookie("account", secret='some-secret-key')
-        if username:
-            return template("Hello {{name}}. Welcome back.", name=username)
-        else:
-            return "You are not logged in. Access denied."
-
-In addition, Bottle automatically pickles and unpickles any data stored to signed cookies. This allows you to store any pickle-able object (not only strings) to cookies, as long as the pickled data does not exceed the 4 KB limit.
-
-.. warning:: Signed cookies are not encrypted (the client can still see the content) and not copy-protected (the client can restore an old cookie). The main intention is to make pickling and unpickling safe and prevent manipulation, not to store secret information at client side.
-
-
-
-
-
-
-
-
-
-.. _tutorial-request:
-
-Request Data
-==============================================================================
-
-Cookies, HTTP header, HTML ``<form>`` fields and other request data is available through the global :data:`request` object. This special object always refers to the *current* request, even in multi-threaded environments where multiple client connections are handled at the same time::
-
-  from bottle import request, route, template
-
-  @route('/hello')
-  def hello():
-      name = request.cookies.username or 'Guest'
-      return template('Hello {{name}}', name=name)
-
-The :data:`request` object is a subclass of :class:`BaseRequest` and has a very rich API to access data. We only cover the most commonly used features here, but it should be enough to get started.
-
-
-
-Introducing :class:`FormsDict`
---------------------------------------------------------------------------------
-
-Bottle uses a special type of dictionary to store form data and cookies. :class:`FormsDict` behaves like a normal dictionary, but has some additional features to make your life easier.
-
-**Attribute access**: All values in the dictionary are also accessible as attributes. These virtual attributes return unicode strings, even if the value is missing or unicode decoding fails. In that case, the string is empty, but still present::
-
-  name = request.cookies.name
-
-  # is a shortcut for:
-
-  name = request.cookies.getunicode('name') # encoding='utf-8' (default)
-
-  # which basically does this:
-
-  try:
-      name = request.cookies.get('name', '').decode('utf-8')
-  except UnicodeError:
-      name = u''
-
-**Multiple values per key:** :class:`FormsDict` is a subclass of :class:`MultiDict` and can store more than one value per key. The standard dictionary access methods will only return a single value, but the :meth:`~MultiDict.getall` method returns a (possibly empty) list of all values for a specific key::
-
-  for choice in request.forms.getall('multiple_choice'):
-      do_something(choice)
-
-**WTForms support:** Some libraries (e.g. `WTForms <http://wtforms.simplecodes.com/>`_) want all-unicode dictionaries as input. :meth:`FormsDict.decode` does that for you. It decodes all values and returns a copy of itself, while preserving multiple values per key and all the other features.
-
-.. note::
-
-    In **Python 2** all keys and values are byte-strings. If you need unicode, you can call :meth:`FormsDict.getunicode` or fetch values via attribute access. Both methods try to decode the string (default: utf8) and return an empty string if that fails. No need to catch :exc:`UnicodeError`::
-
-      >>> request.query['city']
-      'G\xc3\xb6ttingen'  # A utf8 byte string
-      >>> request.query.city
-      u'Göttingen'        # The same string as unicode
-
-    In **Python 3** all strings are unicode, but HTTP is a byte-based wire protocol. The server has to decode the byte strings somehow before they are passed to the application. To be on the safe side, WSGI suggests ISO-8859-1 (aka latin1), a reversible single-byte codec that can be re-encoded with a different encoding later. Bottle does that for :meth:`FormsDict.getunicode` and attribute access, but not for the dict-access methods. These return the unchanged values as provided by the server implementation, which is probably not what you want.
-
-      >>> request.query['city']
-      'Göttingen' # An utf8 string provisionally decoded as ISO-8859-1 by the server
-      >>> request.query.city
-      'Göttingen'  # The same string correctly re-encoded as utf8 by bottle
-
-    If you need the whole dictionary with correctly decoded values (e.g. for WTForms), you can call :meth:`FormsDict.decode` to get a re-encoded copy.
-
-
-Cookies
---------------------------------------------------------------------------------
-
-Cookies are small pieces of text stored in the clients browser and sent back to the server with each request. They are useful to keep some state around for more than one request (HTTP itself is stateless), but should not be used for security related stuff. They can be easily forged by the client.
-
-All cookies sent by the client are available through :attr:`BaseRequest.cookies` (a :class:`FormsDict`). This example shows a simple cookie-based view counter::
-
-  from bottle import route, request, response
-  @route('/counter')
-  def counter():
-      count = int( request.cookies.get('counter', '0') )
-      count += 1
-      response.set_cookie('counter', str(count))
-      return 'You visited this page %d times' % count
-
-The :meth:`BaseRequest.get_cookie` method is a different way do access cookies. It supports decoding :ref:`signed cookies <tutorial-signed-cookies>` as described in a separate section.
-
-HTTP Headers
---------------------------------------------------------------------------------
-
-All HTTP headers sent by the client (e.g. ``Referer``, ``Agent`` or ``Accept-Language``) are stored in a :class:`WSGIHeaderDict` and accessible through the :attr:`BaseRequest.headers` attribute. A :class:`WSGIHeaderDict` is basically a dictionary with case-insensitive keys::
-
-  from bottle import route, request
-  @route('/is_ajax')
-  def is_ajax():
-      if request.headers.get('X-Requested-With') == 'XMLHttpRequest':
-          return 'This is an AJAX request'
-      else:
-          return 'This is a normal request'
-
-
-Query Variables
---------------------------------------------------------------------------------
-
-The query string (as in ``/forum?id=1&page=5``) is commonly used to transmit a small number of key/value pairs to the server. You can use the :attr:`BaseRequest.query` attribute (a :class:`FormsDict`) to access these values and the :attr:`BaseRequest.query_string` attribute to get the whole string.
-
-::
-
-  from bottle import route, request, response, template
-  @route('/forum')
-  def display_forum():
-      forum_id = request.query.id
-      page = request.query.page or '1'
-      return template('Forum ID: {{id}} (page {{page}})', id=forum_id, page=page)
-
-
-HTML `<form>` Handling
-----------------------
-
-Let us start from the beginning. In HTML, a typical ``<form>`` looks something like this:
-
-.. code-block:: html
-
-    <form action="/login" method="post">
-        Username: <input name="username" type="text" />
-        Password: <input name="password" type="password" />
-        <input value="Login" type="submit" />
-    </form>
-
-The ``action`` attribute specifies the URL that will receive the form data. ``method`` defines the HTTP method to use (``GET`` or ``POST``). With ``method="get"`` the form values are appended to the URL and available through :attr:`BaseRequest.query` as described above. This is considered insecure and has other limitations, so we use ``method="post"`` here. If in doubt, use ``POST`` forms.
-
-Form fields transmitted via ``POST`` are stored in :attr:`BaseRequest.forms` as a :class:`FormsDict`. The server side code may look like this::
-
-    from bottle import route, request
-
-    @route('/login')
-    def login():
-        return '''
-            <form action="/login" method="post">
-                Username: <input name="username" type="text" />
-                Password: <input name="password" type="password" />
-                <input value="Login" type="submit" />
-            </form>
-        '''
-
-    @route('/login', method='POST')
-    def do_login():
-        username = request.forms.get('username')
-        password = request.forms.get('password')
-        if check_login(username, password):
-            return "<p>Your login information was correct.</p>"
-        else:
-            return "<p>Login failed.</p>"
-
-There are several other attributes used to access form data. Some of them combine values from different sources for easier access. The following table should give you a decent overview.
-
-==============================   ===============   ================   ============
-Attribute                        GET Form fields   POST Form fields   File Uploads
-==============================   ===============   ================   ============
-:attr:`BaseRequest.query`        yes               no                 no
-:attr:`BaseRequest.forms`        no                yes                no
-:attr:`BaseRequest.files`        no                no                 yes
-:attr:`BaseRequest.params`       yes               yes                no
-:attr:`BaseRequest.GET`          yes               no                 no
-:attr:`BaseRequest.POST`         no                yes                yes
-==============================   ===============   ================   ============
-
-
-File uploads
-------------
-
-To support file uploads, we have to change the ``<form>`` tag a bit. First, we tell the browser to encode the form data in a different way by adding an ``enctype="multipart/form-data"`` attribute to the ``<form>`` tag. Then, we add ``<input type="file" />`` tags to allow the user to select a file. Here is an example:
-
-.. code-block:: html
-
-    <form action="/upload" method="post" enctype="multipart/form-data">
-      Category:      <input type="text" name="category" />
-      Select a file: <input type="file" name="upload" />
-      <input type="submit" value="Start upload" />
-    </form>
-
-Bottle stores file uploads in :attr:`BaseRequest.files` as :class:`FileUpload` instances, along with some metadata about the upload. Let us assume you just want to save the file to disk::
-
-    @route('/upload', method='POST')
-    def do_upload():
-        category   = request.forms.get('category')
-        upload     = request.files.get('upload')
-        name, ext = os.path.splitext(upload.filename)
-        if ext not in ('.png','.jpg','.jpeg'):
-            return 'File extension not allowed.'
-
-        save_path = get_save_path_for_category(category)
-        upload.save(save_path) # appends upload.filename automatically
-        return 'OK'
-
-:attr:`FileUpload.filename` contains the name of the file on the clients file system, but is cleaned up and normalized to prevent bugs caused by unsupported characters or path segments in the filename. If you need the unmodified name as sent by the client, have a look at :attr:`FileUpload.raw_filename`.
-
-The :attr:`FileUpload.save` method is highly recommended if you want to store the file to disk. It prevents some common errors (e.g. it does not overwrite existing files unless you tell it to) and stores the file in a memory efficient way. You can access the file object directly via :attr:`FileUpload.file`. Just be careful.
-
-
-JSON Content
---------------------
-
-Some JavaScript or REST clients send ``application/json`` content to the server. The :attr:`BaseRequest.json` attribute contains the parsed data structure, if available.
-
-
-The raw request body
---------------------
-
-You can access the raw body data as a file-like object via :attr:`BaseRequest.body`. This is a :class:`BytesIO` buffer or a temporary file depending on the content length and :attr:`BaseRequest.MEMFILE_MAX` setting. In both cases the body is completely buffered before you can access the attribute. If you expect huge amounts of data and want to get direct unbuffered access to the stream, have a look at ``request['wsgi.input']``.
-
-
-
-WSGI Environment
---------------------------------------------------------------------------------
-
-Each :class:`BaseRequest` instance wraps a WSGI environment dictionary. The original is stored in :attr:`BaseRequest.environ`, but the request object itself behaves like a dictionary, too. Most of the interesting data is exposed through special methods or attributes, but if you want to access `WSGI environ variables <WSGI specification>`_ directly, you can do so::
-
-  @route('/my_ip')
-  def show_ip():
-      ip = request.environ.get('REMOTE_ADDR')
-      # or ip = request.get('REMOTE_ADDR')
-      # or ip = request['REMOTE_ADDR']
-      return template("Your IP is: {{ip}}", ip=ip)
-
-
-
-
-
-
-
-.. _tutorial-templates:
-
-Templates
-================================================================================
-
-Bottle comes with a fast and powerful built-in template engine called :doc:`stpl`. To render a template you can use the :func:`template` function or the :func:`view` decorator. All you have to do is to provide the name of the template and the variables you want to pass to the template as keyword arguments. Here’s a simple example of how to render a template::
-
-    @route('/hello')
-    @route('/hello/<name>')
-    def hello(name='World'):
-        return template('hello_template', name=name)
-
-This will load the template file ``hello_template.tpl`` and render it with the ``name`` variable set. Bottle will look for templates in the ``./views/`` folder or any folder specified in the ``bottle.TEMPLATE_PATH`` list.
-
-The :func:`view` decorator allows you to return a dictionary with the template variables instead of calling :func:`template`::
-
-    @route('/hello')
-    @route('/hello/<name>')
-    @view('hello_template')
-    def hello(name='World'):
-        return dict(name=name)
-
-.. rubric:: Syntax
-
-.. highlight:: html+django
-
-The template syntax is a very thin layer around the Python language. Its main purpose is to ensure correct indentation of blocks, so you can format your template without worrying about indentation. Follow the link for a full syntax description: :doc:`stpl`
-
-Here is an example template::
-
-    %if name == 'World':
-        <h1>Hello {{name}}!</h1>
-        <p>This is a test.</p>
-    %else:
-        <h1>Hello {{name.title()}}!</h1>
-        <p>How are you?</p>
-    %end
-
-.. rubric:: Caching
-
-Templates are cached in memory after compilation. Modifications made to the template files will have no affect until you clear the template cache. Call ``bottle.TEMPLATES.clear()`` to do so. Caching is disabled in debug mode.
-
-.. highlight:: python
-
-
-
-
-.. _plugins:
-
-Plugins
-================================================================================
-
-.. versionadded:: 0.9
-
-Bottle's core features cover most common use-cases, but as a micro-framework it has its limits. This is where "Plugins" come into play. Plugins add missing functionality to the framework, integrate third party libraries, or just automate some repetitive work.
-
-We have a growing :doc:`/plugins/index` and most plugins are designed to be portable and re-usable across applications. The chances are high that your problem has already been solved and a ready-to-use plugin exists. If not, the :doc:`/plugindev` may help you.
-
-The effects and APIs of plugins are manifold and depend on the specific plugin. The ``SQLitePlugin`` plugin for example detects callbacks that require a ``db`` keyword argument and creates a fresh database connection object every time the callback is called. This makes it very convenient to use a database::
-
-    from bottle import route, install, template
-    from bottle_sqlite import SQLitePlugin
-
-    install(SQLitePlugin(dbfile='/tmp/test.db'))
-
-    @route('/show/<post_id:int>')
-    def show(db, post_id):
-        c = db.execute('SELECT title, content FROM posts WHERE id = ?', (post_id,))
-        row = c.fetchone()
-        return template('show_post', title=row['title'], text=row['content'])
-
-    @route('/contact')
-    def contact_page():
-        ''' This callback does not need a db connection. Because the 'db'
-            keyword argument is missing, the sqlite plugin ignores this callback
-            completely. '''
-        return template('contact')
-
-Other plugin may populate the thread-safe :data:`local` object, change details of the :data:`request` object, filter the data returned by the callback or bypass the callback completely. An "auth" plugin for example could check for a valid session and return a login page instead of calling the original callback. What happens exactly depends on the plugin.
-
-
-Application-wide Installation
---------------------------------------------------------------------------------
-
-Plugins can be installed application-wide or just to some specific routes that need additional functionality. Most plugins can safely be installed to all routes and are smart enough to not add overhead to callbacks that do not need their functionality.
-
-Let us take the ``SQLitePlugin`` plugin for example. It only affects route callbacks that need a database connection. Other routes are left alone. Because of this, we can install the plugin application-wide with no additional overhead.
-
-To install a plugin, just call :func:`install` with the plugin as first argument::
-
-    from bottle_sqlite import SQLitePlugin
-    install(SQLitePlugin(dbfile='/tmp/test.db'))
-
-The plugin is not applied to the route callbacks yet. This is delayed to make sure no routes are missed. You can install plugins first and add routes later, if you want to. The order of installed plugins is significant, though. If a plugin requires a database connection, you need to install the database plugin first.
-
-
-.. rubric:: Uninstall Plugins
-
-You can use a name, class or instance to :func:`uninstall` a previously installed plugin::
-
-    sqlite_plugin = SQLitePlugin(dbfile='/tmp/test.db')
-    install(sqlite_plugin)
-
-    uninstall(sqlite_plugin) # uninstall a specific plugin
-    uninstall(SQLitePlugin)  # uninstall all plugins of that type
-    uninstall('sqlite')      # uninstall all plugins with that name
-    uninstall(True)          # uninstall all plugins at once
-
-Plugins can be installed and removed at any time, even at runtime while serving requests. This enables some neat tricks (installing slow debugging or profiling plugins only when needed) but should not be overused. Each time the list of plugins changes, the route cache is flushed and all plugins are re-applied.
-
-.. note::
-    The module-level :func:`install` and :func:`uninstall` functions affect the :ref:`default-app`. To manage plugins for a specific application, use the corresponding methods on the :class:`Bottle` application object.
-
-
-Route-specific Installation
---------------------------------------------------------------------------------
-
-The ``apply`` parameter of the :func:`route` decorator comes in handy if you want to install plugins to only a small number of routes::
-
-    sqlite_plugin = SQLitePlugin(dbfile='/tmp/test.db')
-
-    @route('/create', apply=[sqlite_plugin])
-    def create(db):
-        db.execute('INSERT INTO ...')
-
-
-Blacklisting Plugins
---------------------------------------------------------------------------------
-
-You may want to explicitly disable a plugin for a number of routes. The :func:`route` decorator has a ``skip`` parameter for this purpose::
-
-    sqlite_plugin = SQLitePlugin(dbfile='/tmp/test1.db')
-    install(sqlite_plugin)
-
-    dbfile1 = '/tmp/test1.db'
-    dbfile2 = '/tmp/test2.db'
-
-    @route('/open/<db>', skip=[sqlite_plugin])
-    def open_db(db):
-        # The 'db' keyword argument is not touched by the plugin this time.
-
-        # The plugin handle can be used for runtime configuration, too.
-        if db == 'test1':
-            sqlite_plugin.dbfile = dbfile1
-        elif db == 'test2':
-            sqlite_plugin.dbfile = dbfile2
-        else:
-            abort(404, "No such database.")
-
-        return "Database File switched to: " + sqlite_plugin.dbfile
-
-The ``skip`` parameter accepts a single value or a list of values. You can use a name, class or instance to identify the plugin that is to be skipped. Set ``skip=True`` to skip all plugins at once.
-
-Plugins and Sub-Applications
---------------------------------------------------------------------------------
-
-Most plugins are specific to the application they were installed to. Consequently, they should not affect sub-applications mounted with :meth:`Bottle.mount`. Here is an example::
-
-    root = Bottle()
-    root.mount('/blog', apps.blog)
-
-    @root.route('/contact', template='contact')
-    def contact():
-        return {'email': 'contact@example.com'}
-
-    root.install(plugins.WTForms())
-
-Whenever you mount an application, Bottle creates a proxy-route on the main-application that forwards all requests to the sub-application. Plugins are disabled for this kind of proxy-route by default. As a result, our (fictional) `WTForms` plugin affects the ``/contact`` route, but does not affect the routes of the ``/blog`` sub-application.
-
-This behavior is intended as a sane default, but can be overridden. The following example re-activates all plugins for a specific proxy-route::
-
-    root.mount('/blog', apps.blog, skip=None)
-
-But there is a snag: The plugin sees the whole sub-application as a single route, namely the proxy-route mentioned above. In order to affect each individual route of the sub-application, you have to install the plugin to the mounted application explicitly.
-
-
-
-Development
-================================================================================
-
-So you have learned the basics and want to write your own application? Here are
-some tips that might help you being more productive.
-
-.. _default-app:
-
-Default Application
---------------------------------------------------------------------------------
-
-Bottle maintains a global stack of :class:`Bottle` instances and uses the top of the stack as a default for some of the module-level functions and decorators. The :func:`route` decorator, for example, is a shortcut for calling :meth:`Bottle.route` on the default application::
-
-    @route('/')
-    def hello():
-        return 'Hello World'
-
-    run()
-
-This is very convenient for small applications and saves you some typing, but also means that, as soon as your module is imported, routes are installed to the global default application. To avoid this kind of import side-effects, Bottle offers a second, more explicit way to build applications::
-
-    app = Bottle()
-
-    @app.route('/')
-    def hello():
-        return 'Hello World'
-
-    app.run()
-
-Separating the application object improves re-usability a lot, too. Other developers can safely import the ``app`` object from your module and use :meth:`Bottle.mount` to merge applications together.
-
-
-.. versionadded:: 0.13
-
-Starting with bottle-0.13 you can use :class:`Bottle` instances as context managers::
-
-    app = Bottle()
-
-    with app:
-
-        # Our application object is now the default
-        # for all shortcut functions and decorators
-
-        assert my_app is default_app()
-
-        @route('/')
-        def hello():
-            return 'Hello World'
-
-        # Also useful to capture routes defined in other modules
-        import some_package.more_routes
-
-
-
-
-.. _tutorial-debugging:
-
-
-Debug Mode
---------------------------------------------------------------------------------
-
-During early development, the debug mode can be very helpful.
-
-.. highlight:: python
-
-::
-
-    bottle.debug(True)
-
-In this mode, Bottle is much more verbose and provides helpful debugging information whenever an error occurs. It also disables some optimisations that might get in your way and adds some checks that warn you about possible misconfiguration.
-
-Here is an incomplete list of things that change in debug mode:
-
-* The default error page shows a traceback.
-* Templates are not cached.
-* Plugins are applied immediately.
-
-Just make sure not to use the debug mode on a production server.
-
-Auto Reloading
---------------------------------------------------------------------------------
-
-During development, you have to restart the server a lot to test your
-recent changes. The auto reloader can do this for you. Every time you
-edit a module file, the reloader restarts the server process and loads
-the newest version of your code.
-
-::
-
-    from bottle import run
-    run(reloader=True)
-
-How it works: the main process will not start a server, but spawn a new
-child process using the same command line arguments used to start the
-main process. All module-level code is executed at least twice! Be
-careful.
-
-The child process will have ``os.environ['BOTTLE_CHILD']`` set to ``True``
-and start as a normal non-reloading app server. As soon as any of the
-loaded modules changes, the child process is terminated and re-spawned by
-the main process. Changes in template files will not trigger a reload.
-Please use debug mode to deactivate template caching.
-
-The reloading depends on the ability to stop the child process. If you are
-running on Windows or any other operating system not supporting
-``signal.SIGINT`` (which raises ``KeyboardInterrupt`` in Python),
-``signal.SIGTERM`` is used to kill the child. Note that exit handlers and
-finally clauses, etc., are not executed after a ``SIGTERM``.
-
-
-Command Line Interface
---------------------------------------------------------------------------------
-
-.. versionadded: 0.10
-
-Starting with version 0.10 you can use bottle as a command-line tool:
-
-.. code-block:: console
-
-    $ python -m bottle
-
-    Usage: bottle.py [options] package.module:app
-
-    Options:
-      -h, --help            show this help message and exit
-      --version             show version number.
-      -b ADDRESS, --bind=ADDRESS
-                            bind socket to ADDRESS.
-      -s SERVER, --server=SERVER
-                            use SERVER as backend.
-      -p PLUGIN, --plugin=PLUGIN
-                            install additional plugin/s.
-      -c FILE, --conf=FILE  load config values from FILE.
-      -C NAME=VALUE, --param=NAME=VALUE
-                            override config values.
-      --debug               start server in debug mode.
-      --reload              auto-reload on file changes.
-
-
-The `ADDRESS` field takes an IP address or an IP:PORT pair and defaults to ``localhost:8080``. The other parameters should be self-explanatory.
-
-Both plugins and applications are specified via import expressions. These consist of an import path (e.g. ``package.module``) and an expression to be evaluated in the namespace of that module, separated by a colon. See :func:`load` for details. Here are some examples:
-
-.. code-block:: console
-
-    # Grab the 'app' object from the 'myapp.controller' module and
-    # start a paste server on port 80 on all interfaces.
-    python -m bottle -server paste -bind 0.0.0.0:80 myapp.controller:app
-
-    # Start a self-reloading development server and serve the global
-    # default application. The routes are defined in 'test.py'
-    python -m bottle --debug --reload test
-
-    # Install a custom debug plugin with some parameters
-    python -m bottle --debug --reload --plugin 'utils:DebugPlugin(exc=True)'' test
-
-    # Serve an application that is created with 'myapp.controller.make_app()'
-    # on demand.
-    python -m bottle 'myapp.controller:make_app()''
-
-
-Deployment
-================================================================================
-
-Bottle runs on the built-in `wsgiref WSGIServer <http://docs.python.org/library/wsgiref.html#module-wsgiref.simple_server>`_  by default. This non-threading HTTP server is perfectly fine for development, but may become a performance bottleneck when server load increases.
-
-The easiest way to increase performance is to install a multi-threaded server library like paste_ or cherrypy_ and tell Bottle to use that instead of the single-threaded server::
-
-    bottle.run(server='paste')
-
-This, and many other deployment options are described in a separate article: :doc:`deployment`
-
-
-
-
-.. _tutorial-glossary:
-
-Glossary
-========
-
-.. glossary::
-
-   callback
-      Programmer code that is to be called when some external action happens.
-      In the context of web frameworks, the mapping between URL paths and
-      application code is often achieved by specifying a callback function
-      for each URL.
-
-   decorator
-      A function returning another function, usually applied as a function transformation using the ``@decorator`` syntax. See `python documentation for function definition  <http://docs.python.org/reference/compound_stmts.html#function>`_ for more about decorators.
-
-   environ
-      A structure where information about all documents under the root is
-      saved, and used for cross-referencing.  The environment is pickled
-      after the parsing stage, so that successive runs only need to read
-      and parse new and changed documents.
-
-   handler function
-      A function to handle some specific event or situation. In a web
-      framework, the application is developed by attaching a handler function
-      as callback for each specific URL comprising the application.
-
-   source directory
-      The directory which, including its subdirectories, contains all
-      source files for one Sphinx project.
-
diff --git a/course/source/conf.py b/course/source/conf.py
deleted file mode 100644
index 2b6408b..0000000
--- a/course/source/conf.py
+++ /dev/null
@@ -1,342 +0,0 @@
-# -*- coding: utf-8 -*-
-#
-# QPython documentation build configuration file, created by
-# sphinx-quickstart on Fri Apr  7 15:07:35 2017.
-#
-# This file is execfile()d with the current directory set to its
-# containing dir.
-#
-# Note that not all possible configuration values are present in this
-# autogenerated file.
-#
-# All configuration values have a default; values that are commented out
-# serve to show the default.
-
-# If extensions (or modules to document with autodoc) are in another directory,
-# add these directories to sys.path here. If the directory is relative to the
-# documentation root, use os.path.abspath to make it absolute, like shown here.
-#
-import os
-import sys
-from zlib import Z_HUFFMAN_ONLY
-sys.path.insert(0, os.path.abspath('.'))
-sys.path.append(os.path.abspath('sphinxext'))
-extensions = ['mathjax']
-try:
-    import qtheme
-except:
-    print('please install qtheme')
-
-    sys.exit(1)
-
-# -- General configuration ------------------------------------------------
-
-# If your documentation needs a minimal Sphinx version, state it here.
-#
-# needs_sphinx = '1.0'
-
-# Add any Sphinx extension module names here, as strings. They can be
-# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
-# ones.
-extensions = []
-
-# Add any paths that contain templates here, relative to this directory.
-templates_path = ['_templates']
-
-# The suffix(es) of source filenames.
-# You can specify multiple suffix as a list of string:
-#
-# source_suffix = ['.rst', '.md']
-source_suffix = '.rst'
-
-# The encoding of source files.
-#
-# source_encoding = 'utf-8-sig'
-
-# The master toctree document.
-master_doc = 'sample'
-
-# General information about the project.
-project = u'QPython'
-copyright = u'2017, QPython'
-author = u'QPython'
-
-# The version info for the project you're documenting, acts as replacement for
-# |version| and |release|, also used in various other places throughout the
-# built documents.
-#
-# The short X.Y version.
-version = u'0.9'
-# The full version, including alpha/beta/rc tags.
-release = u'0.9'
-
-# The language for content autogenerated by Sphinx. Refer to documentation
-# for a list of supported languages.
-#
-# This is also used if you do content translation via gettext catalogs.
-# Usually you set "language" from the command line for these cases.
-#language = None
-language = 'en'
-
-# There are two options for replacing |today|: either, you set today to some
-# non-false value, then it is used:
-#
-# today = ''
-#
-# Else, today_fmt is used as the format for a strftime call.
-#
-# today_fmt = '%B %d, %Y'
-
-# List of patterns, relative to source directory, that match files and
-# directories to ignore when looking for source files.
-# This patterns also effect to html_static_path and html_extra_path
-exclude_patterns = []
-
-# The reST default role (used for this markup: `text`) to use for all
-# documents.
-#
-# default_role = None
-
-# If true, '()' will be appended to :func: etc. cross-reference text.
-#
-# add_function_parentheses = True
-
-# If true, the current module name will be prepended to all description
-# unit titles (such as .. function::).
-#
-# add_module_names = True
-
-# If true, sectionauthor and moduleauthor directives will be shown in the
-# output. They are ignored by default.
-#
-# show_authors = False
-
-# The name of the Pygments (syntax highlighting) style to use.
-pygments_style = 'sphinx'
-
-# A list of ignored prefixes for module index sorting.
-# modindex_common_prefix = []
-
-# If true, keep warnings as "system message" paragraphs in the built documents.
-# keep_warnings = False
-
-# If true, `todo` and `todoList` produce output, else they produce nothing.
-todo_include_todos = False
-
-
-# -- Options for HTML output ----------------------------------------------
-
-# The theme to use for HTML and HTML Help pages.  See the documentation for
-# a list of builtin themes.
-#
-html_theme = 'qtheme'
-
-# Theme options are theme-specific and customize the look and feel of a theme
-# further.  For a list of options available for each theme, see the
-# documentation.
-#
-# html_theme_options = {}
-
-# Add any paths that contain custom themes here, relative to this directory.
-html_theme_path = [qtheme.theme_path]
-
-# The name for this set of Sphinx documents.
-# "<project> v<release> documentation" by default.
-#
-# html_title = u'QPython v0.9'
-
-# A shorter title for the navigation bar.  Default is the same as html_title.
-#
-# html_short_title = None
-
-# The name of an image file (relative to this directory) to place at the top
-# of the sidebar.
-#
-# html_logo = None
-
-# The name of an image file (relative to this directory) to use as a favicon of
-# the docs.  This file should be a Windows icon file (.ico) being 16x16 or 32x32
-# pixels large.
-#
-# html_favicon = None
-
-# Add any paths that contain custom static files (such as style sheets) here,
-# relative to this directory. They are copied after the builtin static files,
-# so a file named "default.css" will overwrite the builtin "default.css".
-html_static_path = ['_static']
-
-# Add any extra paths that contain custom files (such as robots.txt or
-# .htaccess) here, relative to this directory. These files are copied
-# directly to the root of the documentation.
-#
-# html_extra_path = []
-
-# If not None, a 'Last updated on:' timestamp is inserted at every page
-# bottom, using the given strftime format.
-# The empty string is equivalent to '%b %d, %Y'.
-#
-# html_last_updated_fmt = None
-
-# If true, SmartyPants will be used to convert quotes and dashes to
-# typographically correct entities.
-#
-# html_use_smartypants = True
-
-# Custom sidebar templates, maps document names to template names.
-#
-#html_sidebars = {}
-
-# Additional templates that should be rendered to pages, maps page names to
-# template names.
-#
-# html_additional_pages = {}
-
-# If false, no module index is generated.
-#
-# html_domain_indices = True
-
-# If false, no index is generated.
-#
-# html_use_index = True
-
-# If true, the index is split into individual pages for each letter.
-#
-# html_split_index = False
-
-# If true, links to the reST sources are added to the pages.
-#
-# html_show_sourcelink = True
-
-# If true, "Created using Sphinx" is shown in the HTML footer. Default is True.
-#
-# html_show_sphinx = True
-
-# If true, "(C) Copyright ..." is shown in the HTML footer. Default is True.
-#
-# html_show_copyright = True
-
-# If true, an OpenSearch description file will be output, and all pages will
-# contain a <link> tag referring to it.  The value of this option must be the
-# base URL from which the finished HTML is served.
-#
-# html_use_opensearch = ''
-
-# This is the file name suffix for HTML files (e.g. ".xhtml").
-# html_file_suffix = None
-
-# Language to be used for generating the HTML full-text search index.
-# Sphinx supports the following languages:
-#   'da', 'de', 'en', 'es', 'fi', 'fr', 'hu', 'it', 'ja'
-#   'nl', 'no', 'pt', 'ro', 'ru', 'sv', 'tr', 'zh'
-#
-# html_search_language = 'en'
-
-# A dictionary with options for the search language support, empty by default.
-# 'ja' uses this config value.
-# 'zh' user can custom change `jieba` dictionary path.
-#
-# html_search_options = {'type': 'default'}
-
-# The name of a javascript file (relative to the configuration directory) that
-# implements a search results scorer. If empty, the default will be used.
-#
-# html_search_scorer = 'scorer.js'
-
-# Output file base name for HTML help builder.
-htmlhelp_basename = 'QPythondoc'
-
-# -- Options for LaTeX output ---------------------------------------------
-
-latex_elements = {
-     # The paper size ('letterpaper' or 'a4paper').
-     #
-     # 'papersize': 'letterpaper',
-
-     # The font size ('10pt', '11pt' or '12pt').
-     #
-     # 'pointsize': '10pt',
-
-     # Additional stuff for the LaTeX preamble.
-     #
-     # 'preamble': '',
-
-     # Latex figure (float) alignment
-     #
-     # 'figure_align': 'htbp',
-}
-
-# Grouping the document tree into LaTeX files. List of tuples
-# (source start file, target name, title,
-#  author, documentclass [howto, manual, or own class]).
-latex_documents = [
-    (master_doc, 'QPython.tex', u'QPython User Guide',
-     u'River', 'manual'),
-]
-
-# The name of an image file (relative to this directory) to place at the top of
-# the title page.
-#
-# latex_logo = None
-
-# For "manual" documents, if this is true, then toplevel headings are parts,
-# not chapters.
-#
-# latex_use_parts = False
-
-# If true, show page references after internal links.
-#
-# latex_show_pagerefs = False
-
-# If true, show URL addresses after external links.
-#
-# latex_show_urls = False
-
-# Documents to append as an appendix to all manuals.
-#
-# latex_appendices = []
-
-# If false, no module index is generated.
-#
-# latex_domain_indices = True
-
-
-# -- Options for manual page output ---------------------------------------
-
-# One entry per manual page. List of tuples
-# (source start file, name, description, authors, manual section).
-man_pages = [
-    (master_doc, 'qpython', u'QPython Courses',
-     [author], 1)
-]
-
-# If true, show URL addresses after external links.
-#
-# man_show_urls = False
-
-
-# -- Options for Texinfo output -------------------------------------------
-
-# Grouping the document tree into Texinfo files. List of tuples
-# (source start file, target name, title, author,
-#  dir menu entry, description, category)
-texinfo_documents = [
-    (master_doc, 'QPython', u'QPython Courses',
-     author, 'QPython', 'One line description of courses.',
-     'Miscellaneous'),
-]
-
-# Documents to append as an appendix to all manuals.
-#
-# texinfo_appendices = []
-
-# If false, no module index is generated.
-#
-# texinfo_domain_indices = True
-
-# How to display URL addresses: 'footnote', 'no', or 'inline'.
-#
-# texinfo_show_urls = 'footnote'
-
-# If true, do not generate a @detailmenu in the "Top" node's menu.
-#
-# texinfo_no_detailmenu = False
diff --git a/course/source/data-analytics/index.rst b/course/source/data-analytics/index.rst
deleted file mode 100644
index dd04a53..0000000
--- a/course/source/data-analytics/index.rst
+++ /dev/null
@@ -1,19 +0,0 @@
-Data Analytics
-=======================
-The courses are still in the planning. Welcome to reward us. 
-
-1. **Primary course**
-
-If the total number of people who reward us is more than 100, we promise to complete the primary course within 1 week, so that, after finishing your study, you can master the basic concepts and write simple scripts.

-
-2. **Intermediate course**
-
-If the total number of people who reward us is more than 500, we will launch the intermediate course within 2 weeks, so that, after finishing your study, you will be familiar with the knowledge and framework of the course and apply it to practical development.
-
-3. **Advanced course**
-
-If the total number of people who reward us is more than 2,000, we will launch the advanced course in a month, so that, after finishing your study, you will have a better understanding of the principles of the framework and be able to do advanced development.
-
-
-**The primary course is free to all, and the intermediate and advanced courses are only open to those who have rewarded us.**
-
diff --git a/course/source/dive-into-python/cn/actionscope.rst b/course/source/dive-into-python/cn/actionscope.rst
deleted file mode 100644
index f18db1f..0000000
--- a/course/source/dive-into-python/cn/actionscope.rst
+++ /dev/null
@@ -1,399 +0,0 @@
-作用域
--------
-
-
-在函数中，*Python* 从命名空间中寻找变量的顺序如下：
-
-
-- local function scope
-- enclosing scope
-- global scope
-- builtin scope
-
-
-例子：
-
-local 作用域
----------------
-
-
- 
-
-::
-
-
-    def foo(a,b): 
-       ^4$c = 1 
-       ^4$d = a + b + c
-
-
-
-这里所有的变量都在 *local* 作用域。
-
-
-
-global 作用域
----------------
-
- 
-
-::
-
-
-     c = 1
-     def foo(a,b): 
-       ^4$d = a + b + c
-
-
-这里的 *c* 就在 *global* 作用域。
-
-
-
-
-global 关键词
------------------
-
-
-使用 *global* 关键词可以在 *local* 作用域中修改 *global* 作用域的值。
-
-
-
- 
-
-::
-
-   c = 1
-   def foo(): 
-      ^4$global c 
-      ^4$c = 2
-    
-   print c
-   foo()
-   print c
-
-结果:
-
-::
-
-  1
-  2
-
-
-
-其作用是将 *c* 指向 *global* 中的 *c*
-
-
-
-如果不加关键词，那么 *local* 作用域的 *c* 不会影响 *globa* 作用域中的值：
-
-
-
- 
-
-::
-
-   c = 1
-   def foo(): 
-       ^4$c = 2
-    
-   print c
-   foo()
-   print c
-
-结果:
-
-::
-
-  1
-  1
-
-
-
-built-in 作用域
------------------
-
-::
-
-
-
-    def list_length(a): 
-       ^4$return len(a)
-
-    a = [1,2,3]
-    print list_length(a)
-
-
-
-结果:
-
-::
-
-  3
-
-
-这里函数 *len *就是在* built-in* 作用域中：
-
-
-
-
- 
-
-::
-
-    import __builtin__
-
-    __builtin__.len
-
-
-
-
-class 中的作用域
-----------------------
-
-
-+------------+------------+
-|Global      |	MyClass   |
-+============+============+
-|var = 0     |            | 
-+------------+------------+
-|MyClass     |    var = 1 |
-+------------+------------+
-|access_class|access_class|
-+------------+------------+
-
- 
-
-
-::
-
-    # global
-    var = 0
-
-    class MyClass(object): 
-       ^4$# class variable 
-       ^4$var = 1
-     
-       ^4$def access_class_c(self): 
-       ^4$^4$    print 'class var:', self.var
-     
-       ^4$def write_class_c(self): 
-       ^4$ ^4$    MyClass.var = 2 
-       ^4$ ^4$    print 'class var:', self.var 
-       ^4$def access_global_c(self): 
-       ^4$ ^4$    print 'global var:', var
-     
-       ^4$def write_instance_c(self): 
-       ^4$ ^4$    self.var = 3 
-       ^4$ ^4$    print 'instance var:', self.var
-
-
-
-::
-
-  Global	 MyClass	obj
-  var = 0 
-  MyClass 
-  [access_class] 
-  obj	         var = 1
-  access_class	
-
-
- 
-
-::
-
-
-   obj = MyClass()
-
-
-
-查询 *self.var* 时，由于 *obj* 不存在 *var*，所以跳到 **MyClass** 中：
-
-::
-
-   Global	 MyClass	obj
-   var = 0 
-   MyClass 
-   [access_class 
-   self] 
-   obj	         var = 1
-   access_class	
-
-
- 
-
-::
-
-   obj.access_class_c()
-
-
-class var: 1
-
-
-查询 *var* 直接跳到 *global* 作用域：
-
-::
-
-   Global	MyClass	obj
-   var = 0 
-   MyClass 
-   [access_class 
-   self] 
-   obj	       var = 1
-   access_class	
-
-
-
- 
-
-::
-
-
-    obj.access_global_c()
-
-
-global var: 0
-
-
-修改类中的 *MyClass.var*：
-
-
-::
-
-   Global	MyClass	obj
-   var = 0 
-   MyClass 
-   [access_class 
-   self] 
-   obj	      var = 2
-   access_class	
-
-
-
- 
-
-::
-
-   obj.write_class_c()
-   class var: 2
-
-修改实例中的 *var* 时，会直接在 *obj* 域中创建一个：
-
-
-::
-
-  Global	MyClass	obj
-  var = 0 
-  MyClass 
-  [access_class 
-  self] 
-  obj	var = 2
-  access_class	var = 3
-
-
-
-
- 
-
-::
-
-   obj.write_instance_c()
-
-
-instance var: 3
-
-
- 
-
-::
-
-    MyClass.var
-
-
-结果:
-
-::
-
-  2
-
-
-
-*MyClass* 中的 *var* 并没有改变。
-
-
-
-词法作用域
--------------
-
-
-
-对于嵌套函数：
-
-
-
- 
-
-::
-
-    def outer(): 
-      ^4$a = 1 
-      ^4$def inner():
- 
-       ^4$ ^4$    print "a =", a
- 
-       ^4$ ^4$inner()
-    
-    outer() 
-
-
-结果:
-
-::
-
-  a = 1
-
-
-
-
-如果里面的函数没有找到变量，那么会向外一层寻找变量，如果再找不到，则到 global 作用域。
-
-返回的是函数的情况：
-
-
-
- 
-
-::
-
-    def outer(): 
-       ^4$a = 1 
-       ^4$def inner(): 
-       ^4$ ^4$   return a 
-       ^4$return inner
-    
-    func = outer()
-
-    print 'a (1):', func()
-
-
-
-结果:
-
-::
-
-  a (1): 1
-
-
-*func()* 函数中调用的* a *要从它定义的地方开始寻找，而不是在 *func* 所在的作用域寻找。
-
-
-
-
-
-作者 & 更新时间
-------------------------------------
-作者:李金  lijinwithyou@gmail.com
-
-提交: 2017/12/6
-
-   
\ No newline at end of file
diff --git a/course/source/dive-into-python/cn/builder.rst b/course/source/dive-into-python/cn/builder.rst
deleted file mode 100644
index 1fcbf3c..0000000
--- a/course/source/dive-into-python/cn/builder.rst
+++ /dev/null
@@ -1,280 +0,0 @@
-生成器 &  迭代器
-================
-
-*while* 循环通常有这样的形式：
-
-
-::
-
-    <do setup>
-    result = []
-    while True:
-        ^4$<generate value>
-        ^4$result.append(value)
-        ^4$if <done>:
-             ^8$break
-
-
-
-使用迭代器实现这样的循环：
-
-
-::
-
-
-    class GenericIterator(object):
-        ^4$def __init__(self, ...):
-            ^4$<do setup>
-            ^4$# 需要额外储存状态
-            ^4$<store state>
-        ^4$def next(self): 
-            ^4$<load state>
-            ^4$<generate value>
-            ^4$if <done>:
-                ^8$raise StopIteration()
-            ^4$<store state>
-            ^4$return value
-
-
-更简单的，可以使用生成器：
-
-
-::
-
-
-    def generator(...):
-        ^4$<do setup>
-        ^4$while True:
-            ^8$<generate value>
-            ^8$# yield 说明这个函数可以返回多个值！
-            ^8$yield value
-            ^8$if <done>:
-                ^12$break
-
-生成器使用 *yield* 关键字将值输出，而迭代器则通过 *next* 的 *return* 将值返回；与迭代器不同的是，生成器会自动记录当前的状态，而迭代器则需要进行额外的操作来记录当前的状态。
-
-对于之前的 *collatz* 猜想，简单循环的实现如下：
-
-::
-
-
-    def collatz(n):
-        ^4$sequence = []
-        ^4$while n != 1:
-            ^8$if n % 2 == 0:
-                ^12$n /= 2
-            ^8$else:
-                ^12$n = 3*n + 1
-            ^8$sequence.append(n)
-        ^4$return sequence
-
-    for x in collatz(7):
-        ^4$print x,
-
-
-<button>Run ...</button>
-
-
-运行的结果会是：
-
-::
-
-    22 11 34 17 52 26 13 40 20 10 5 16 8 4 2 1
-
-
-迭代器的版本如下：
-
-::
-
-    class Collatz(object):
-        ^4$def __init__(self, start):
-            ^8$self.value = start
-
-        ^4$def __iter__(self):
-            ^8$return self
-
-        ^4$def next(self):
-            ^8$if self.value == 1:
-                ^12$raise StopIteration()
-            ^8$elif self.value % 2 == 0:
-                ^12$self.value = self.value/2
-            ^8$else:
-                ^12$self.value = 3*self.value + 1
-            ^8$return self.value
-
-    for x in Collatz(7):
-        ^4$print x,
-
-<button>Run ...</button>
-
-
-
-生成器的版本如下：
-
-::
-
-    def collatz(n):
-        ^4$while n != 1:
-            ^8$if n % 2 == 0:
-                ^12$n /= 2
-            ^8$else:
-                ^12$n = 3*n + 1
-            ^8$yield n
-
-    for x in collatz(7):
-        ^4$print x,
-
-<button>Run ...</button>
-
-
-
-事实上，生成器也是一种迭代器：
-
-::
-
-    x = collatz(7)
-    print x
-
-<button>Run ...</button>
-
-
-输出如下
-::
-
-    <generator object collatz at 0x0000000003B63750>
-
-
-它支持 *next* 方法，返回下一个 *yield* 的值：
-
-::
-
-    x = collatz(7)
-    print x.next()
-    print x.next()
-
-<button>Run ...</button>
-
-
-*__iter__* 方法返回的是它本身：
-
-
-::
-
-    x = collatz(7)
-    print x.__iter__()
-
-输出结果：
-::
-
-    <generator object collatz at 0x0000000003B63750>
-
-
-之前的二叉树迭代器可以改写为更简单的生成器模式来进行中序遍历：
-
-::
-
-    class BinaryTree(object):
-        ^4$def __init__(self, value, left=None, right=None):
-            ^8$self.value = value
-            ^8$self.left = left
-            ^8$self.right = right
-
-        ^4$def __iter__(self):
-            ^8$# 将迭代器设为生成器方法
-            ^8$return self.inorder()
-
-        ^4$def inorder(self):
-            ^4$# traverse the left branch
-            ^4$if self.left is not None:
-                ^8$for value in self.left:
-                    ^12$yield value
-
-            ^4$# yield node's value
-            ^4$yield self.value
-
-            ^4$# traverse the right branch
-            ^4$if self.right is not None:
-                ^8$for value in self.right:
-                    ^12$yield value
-
-
-非递归的实现：
-
-::
-
-    def inorder(self):
-        ^4$node = self
-        ^4$stack = []
-        ^4$while len(stack) > 0 or node is not None:
-            ^8$while node is not None:
-                ^12$stack.append(node)
-                ^12$node = node.left
-            ^8$node = stack.pop()
-            ^8$yield node.value
-            ^8$node = node.right
-
-<button>Run ...</button>
-
-
-
-::
-
-    class BinaryTree(object):
-        ^4$def __init__(self, value, left=None, right=None):
-            ^8$self.value = value
-            ^8$self.left = left
-            ^8$self.right = right
-
-        ^4$def __iter__(self):
-            ^8$# 将迭代器设为生成器方法
-            ^8$return self.inorder()
-
-        ^4$def inorder(self):
-            ^4$# traverse the left branch
-            ^4$if self.left is not None:
-                ^8$for value in self.left:
-                    ^12$yield value
-
-            ^4$# yield node's value
-            ^4$yield self.value
-
-            ^4$# traverse the right branch
-            ^4$if self.right is not None:
-                ^8$for value in self.right:
-                    ^12$yield value
-
-
-    tree = BinaryTree(
-        ^4$left=BinaryTree(
-            ^8$left=BinaryTree(1),
-            ^8$value=2,
-            ^8$right=BinaryTree(
-                ^12$left=BinaryTree(3),
-                ^12$value=4,
-                ^12$right=BinaryTree(5)
-            ^8$),
-        ^4$),
-        ^4$value=6,
-        ^4$right=BinaryTree(
-            ^8$value=7,
-            ^8$right=BinaryTree(8)
-        ^4$)
-    )
-    for value in tree:
-        ^4$print value,
-
-<button>Run ...</button>
-
-
-正确应输出：
-
-::
-
-    1 2 3 4 5 6 7 8
-
-
-作者 & 更新时间
-------------------------------------
-作者:李金  lijinwithyou@gmail.com
-
-提交: 2017/12/6
diff --git a/course/source/dive-into-python/cn/csv.rst b/course/source/dive-into-python/cn/csv.rst
deleted file mode 100644
index abcd72b..0000000
--- a/course/source/dive-into-python/cn/csv.rst
+++ /dev/null
@@ -1,178 +0,0 @@
-CSV 文件和 csv 模块
-=======================
-标准库中有自带的 csv (逗号分隔值) 模块处理 csv 格式的文件：
-
-::
-
-    import csv
-    dir(csv)
-
-读 csv 文件
-----------------------------------------------------------
-假设我们有这样的一个文件：
-
-::
-
-    #file data.csv, 内容如下
-
-    "alpha 1",  100, -1.443
-    "beat  3",   12, -0.0934
-    "gamma 3a", 192, -0.6621
-    "delta 2a",  15, -4.515
-
-
-打开这个文件，并产生一个文件 reader：
-
-::
-
-    fp = open("data.csv")
-    r = csv.reader(fp)
-
-
-
-可以按行迭代数据：
-
-::
-
-    fp = open("data.csv")
-    r = csv.reader(fp)
-
-
-    for row in r:
-        ^4$print row
-
-    fp.close()
-
-<button>Run ...</button>
-
-
-::
-
-    #如运行正确，内容将会如下
-
-    ['alpha 1', '  100', ' -1.443']
-    ['beat  3', '   12', ' -0.0934']
-    ['gamma 3a', ' 192', ' -0.6621']
-    ['delta 2a', '  15', ' -4.515']
-
-
-默认数据内容都被当作字符串处理，不过可以自己进行处理：
-
-::
-
-    data = []
-
-    with open('data.csv') as fp:
-        ^4$r = csv.reader(fp)
-        ^4$for row in r:
-            ^8$data.append([row[0], int(row[1]), float(row[2])])
-
-    print(data)
-
-<button>Run ...</button>
-
-
-
-写 csv 文件
--------------------------------------------------------------
-可以使用 *csv.writer* 写入文件，不过相应地，传入的应该是以写方式打开的文件，不过一般要用 *'wb'* 即二进制写入方式，防止出现换行不正确的问题：
-In [7]:
-
-::
-
-    data = [('one', 1, 1.5), ('two', 2, 8.0)]
-    with open('out.csv', 'wb') as fp:
-        ^4$w = csv.writer(fp)
-        ^4$w.writerows(data)
-
-<button>Run ...</button>
-
-
-如果运行正确，结果将会如下：
-
-::
-
-    one,1,1.5
-    two,2,8.0
-
-
-更换分隔符
-----------------------------------------------------
-默认情况下，*csv* 模块默认 *csv* 文件都是由 *excel* 产生的，实际中可能会遇到这样的问题：
-
-::
-
-    data = [('one, \"real\" string', 1, 1.5), ('two', 2, 8.0)]
-    with open('out.csv', 'wb') as fp:
-        ^4$w = csv.writer(fp)
-        ^4$w.writerows(data)
-
-<button>Run ...</button>
-
-
-可以修改分隔符来处理这组数据：
-
-::
-
-    data = [('one, \"real\" string', 1, 1.5), ('two', 2, 8.0)]
-    with open('out.psv', 'wb') as fp:
-        ^4$w = csv.writer(fp, delimiter="|")
-        ^4$w.writerows(data)
-
-
-<button>Run ...</button>
-
-
-其他选项
--------------------------------------------------------------------------
-
-*numpy.loadtxt()* 和 *pandas.read_csv()* 可以用来读写包含很多数值数据的*csv*文件：
-
-
-::
-    #有文件 trades.csv,内容如下
-
-    Order,Date,Stock,Quantity,Price
-    A0001,2013-12-01,AAPL,1000,203.4
-    A0002,2013-12-01,MSFT,1500,167.5
-    A0003,2013-12-02,GOOG,1500,167.5
-
-
-使用 pandas 进行处理，生成一个 DataFrame 对象：
-
-::
-
-    import pandas
-    df = pandas.read_csv('trades.csv', index_col=0)
-    print df
-
-
-::
-                 Date Stock  Quantity  Price
-    Order                                   
-    A0001  2013-12-01  AAPL      1000  203.4
-    A0002  2013-12-01  MSFT      1500  167.5
-    A0003  2013-12-02  GOOG      1500  167.5
-
-
-通过名字进行索引：
-
-::
-
-    df['Quantity'] * df['Price']
-
-
-将会输出如下内容：
-
-::
-
-    Order
-    A0001    203400
-    A0002    251250
-    A0003    251250
-    dtype: float64
-
-
-作者 & 更新时间
-------------------------------------
-作者:`李金  <lijinwithyou@gmail.com>`
diff --git a/course/source/dive-into-python/cn/datetime.rst b/course/source/dive-into-python/cn/datetime.rst
deleted file mode 100644
index 166da4f..0000000
--- a/course/source/dive-into-python/cn/datetime.rst
+++ /dev/null
@@ -1,185 +0,0 @@
-datetime 模块
-==============================
-
-datetime 提供了基础时间和日期的处理。
-
-::
-
-    import datetime as dt
-    dir(dt)
-
-<button>Run ...</button>
-
-
-date 对象
-----------------------------------------------------------
-可以使用 date(year, month, day) 产生一个 date 对象：
-
-::
-
-    import datetime as dt
-    d1 = dt.date(2007, 9, 25)
-    d2 = dt.date(2008, 9, 25)
-
-
-可以格式化 date 对象的输出：
-
-::
-
-    import datetime as dt
-    d1 = dt.date(2007, 9, 25)
-    d2 = dt.date(2008, 9, 25)
-
-
-    print d1
-    print d1.strftime('%A, %m/%d/%y')
-    print d1.strftime('%a, %m-%d-%Y')
-
-<button>Run ...</button>
-
-
-可以看两个日期相差多久：
-
-::
-
-    import datetime as dt
-    d1 = dt.date(2007, 9, 25)
-    d2 = dt.date(2008, 9, 25)
-
-    print d2 - d1
-
-<button>Run ...</button>
-
-返回的是一个 timedelta 对象：
-::
-
-    import datetime as dt
-    d1 = dt.date(2007, 9, 25)
-    d2 = dt.date(2008, 9, 25)
-
-    d = d2 - d1
-    print d.days
-    print d.seconds
-
-
-查看今天的日期：
-
-
-::
-
-    import datetime as dt
-    print dt.date.today()
-
-<button>Run ...</button>
-
-
-time 对象
-------------------------------------------------------------------------
-可以使用 time(hour, min, sec, us) 产生一个 time 对象：
-
-::
-
-    import datetime as dt
-    t1 = dt.time(15, 38)
-    t2 = dt.time(18)
-
-<button>Run ...</button>
-
-改变显示格式：
-
-::
-
-    import datetime as dt
-    t1 = dt.time(15, 38)
-    print t1
-    print t1.strftime('%I:%M, %p')
-    print t1.strftime('%H:%M:%S, %p')
-
-<button>Run ...</button>
-
-因为没有具体的日期信息，所以 time 对象不支持减法操作。
-
-datetime 对象
--------------------------------------------------------------------------
-可以使用 datetime(year, month, day, hr, min, sec, us) 来创建一个 datetime 对象。
-
-
-
-获得当前时间：
-
-::
-
-    import datetime as dt
-    d1 = dt.datetime.now()
-    print d1
-
-<button>Run ...</button>
-
-
-给当前的时间加上 30 天，timedelta 的参数是 timedelta(day, hr, min, sec, us)：
-
-::
-
-    import datetime as dt
-    d1 = dt.datetime.now()
-    d2 = d1 + dt.timedelta(30)
-    print d2
-
-<button>Run ...</button>
-
-
-除此之外，我们还可以通过一些指定格式的字符串来创建 datetime 对象：
-
-::
-
-    import datetime as dt
-    print dt.datetime.strptime('2/10/01', '%m/%d/%y')
-
-<button>Run ...</button>
-
-
-+------------+------------------------------------------------------------------------------------------+
-| 子表达式   | 匹配内容                                                                                 |
-+============+==========================================================================================+
-|%a          | 星期英文缩写                                                                             |    
-+------------+------------------------------------------------------------------------------------------+
-| %A         |星期英文                                                                                  |
-+------------+------------------------------------------------------------------------------------------+
-| %w         |一星期的第几天，[0(sun),6]                                                                |
-+------------+------------------------------------------------------------------------------------------+
-|%b          | 月份英文缩写                                                                             |         
-+------------+------------------------------------------------------------------------------------------+
-| %B         | 月份英文                                                                                 |
-+------------+------------------------------------------------------------------------------------------+
-|%d          |日期，[01,31]                                                                             |
-+------------+------------------------------------------------------------------------------------------+
-| %H         |小时，[00,23]                                                                             |
-+------------+------------------------------------------------------------------------------------------+
-| %I         |小时，[01,12]                                                                             |
-+------------+------------------------------------------------------------------------------------------+
-|%j          |      一年的第几天，[001,366]                                                             |
-+------------+------------------------------------------------------------------------------------------+
-|%m          |	月份，[01,12]                                                                           |
-+------------+------------------------------------------------------------------------------------------+
-|%M 	     | 分钟，[00,59]                                                                            |
-+------------+------------------------------------------------------------------------------------------+
-|%p 	     | AM 和 PM                                                                                 |
-+------------+------------------------------------------------------------------------------------------+
-|%S 	     |秒钟，[00,61] （大概是有闰秒的存在）                                                      |
-+------------+------------------------------------------------------------------------------------------+
-|%U          |       一年中的第几个星期，星期日为第一天，[00,53]                                        |
-+------------+------------------------------------------------------------------------------------------+
-|%W          |	一年中的第几个星期，星期一为第一天，[00,53]                                             |
-+------------+------------------------------------------------------------------------------------------+
-|%y  	     |没有世纪的年份                                                                            |
-+------------+------------------------------------------------------------------------------------------+
-|%Y  	     |完整的年份                                                                                |
-+------------+------------------------------------------------------------------------------------------+
-
-
-
-作者 & 更新时间
-------------------------------------
-作者:`李金  <lijinwithyou@gmail.com>`
-
-提交: 2017/12/6
diff --git a/course/source/dive-into-python/cn/dynamiccompiling.rst b/course/source/dive-into-python/cn/dynamiccompiling.rst
deleted file mode 100644
index 7559e48..0000000
--- a/course/source/dive-into-python/cn/dynamiccompiling.rst
+++ /dev/null
@@ -1,301 +0,0 @@
-动态编译
-================
-
-
-标准编程语言
----------------
-
-
-对于 **C** 语言，代码一般要先编译，再执行。
-
-::
-
-   .c -> .exe
-
-
-解释器语言
---------------
-
-
-::
-
-    shell 脚本
-
-       .sh -> interpreter
-
-
-Byte Code 编译
-------------------
-
-
-**Python**, **Java** 等语言先将代码编译为 byte code（不是机器码），然后再处理：
-
-
-::
-
-    .py -> .pyc -> interpreter
-
-
-
-eval 函数
---------------
-
-::
- 
-    eval(statement, glob, local)
-
-
-
-使用 *eval* 函数动态执行代码，返回执行的值：
-
-
-
-
-::
- 
-   a = 1
-
-   eval("a+1")
-
-
-
-结果:
-::
-
-    2
-
-
-
-可以接收明明空间参数：
-
-
-
-
-
-::
-
-
-   local = dict(a=2)
-   glob = {}
-   eval("a+1", glob, local)
-
-
-
-结果:
-::
-
-    3
-
-
-
-这里 *local* 中的 *a* 先被找到。
-
-
-
-exec 函数
---------------
-
-::
-
-   exec(statement, glob, local)
-
-
-
-使用 *exec* 可以添加修改原有的变量。
-
-
-
-
-
-
-::
-
-   a = 1
-
-   exec("b = a+1")
-
-   print b
-
-结果:
-::
-
-  2
-
-
-
-
-::
-
-
-   local = dict(a=2)
-   glob = {}
-   exec("b = a+1", glob, local)
-
-   print local
-
-
-
-结果:
-::
-
-  {'a': 2, 'b': 3}
-
-
-执行之后，*b* 在 *local* 命名空间中。
-
-
-
-警告
---------
-
-
-动态执行的时候要注意，不要执行不信任的用户输入，因为它们拥有 *Python* 的全部权限。
-
-
-
-compile 函数生成 byte code
-------------------------------
-
-
-     compile(str, filename, mode)
-
-
-
-
-
-::
-
-
-   a = 1
-   c = compile("a+2", "", 'eval')
-
-   eval(c)
-
-
-
-结果:
-::
-
-    3
-
-
-
-
-
-
-::
-
-
-   a = 1
-   c = compile("b=a+2", "", 'exec')
-
-   exec(c)
-   b
-
-
-结果:
-::
-
-    3
-
-
-
-abstract syntax trees
------------------------
-
-
-
-
-
-::
-
-    import ast
-
-
-
-
-
-::
-
-
-    tree = ast.parse("a+2", "", "eval")
-
-    ast.dump(tree)
-
-
-
-结果:
-::
-
-    "Expression(body=BinOp(left=Name(id='a', ctx=Load()), op=Add(), right=Num(n=2)))"
-
-
-
-改变常数的值：
-
-
-
-
-
-
-::
-
-
-    tree.body.right.n = 3
-
-    ast.dump(tree)
-
-
-
-结果:
-::
-
-    "Expression(body=BinOp(left=Name(id='a', ctx=Load()), op=Add(), right=Num(n=3)))"
-
-
-
-
-
-
-
-::
-
-   a = 1
-   c = compile(tree, '', 'eval')
-
-   eval(c)
-
-
-
-结果:
-::
-
-    4
-
-
-
-
-安全的使用方法 *literal_eval* ，只支持基本值的操作：
-
-
-
-
-
-::
-
-
-   ast.literal_eval("[10.0, 2, True, 'foo']")
-
-
-结果:
-::
-
-    [10.0, 2, True, 'foo']
-
-
-
-作者 & 更新时间
-------------------------------------
-作者:李金  lijinwithyou@gmail.com
-
-提交: 2017/12/6
diff --git a/course/source/dive-into-python/cn/index.rst b/course/source/dive-into-python/cn/index.rst
deleted file mode 100644
index d971f49..0000000
--- a/course/source/dive-into-python/cn/index.rst
+++ /dev/null
@@ -1,19 +0,0 @@
-Dive into Python
-=======================
-The courses are still in the planning. Welcome to reward us. 
-
-1. **Primary course**
-
-If the total number of people who reward us is more than 100, we promise to complete the primary course within 1 week, so that, after finishing your study, you can master the basic concepts and write simple scripts.
-
-2. **Intermediate course**
-
-If the total number of people who reward us is more than 500, we will launch the intermediate course within 2 weeks, so that, after finishing your study, you will be familiar with the knowledge and framework of the course and apply it to practical development.
-
-3. **Advanced course**
-
-If the total number of people who reward us is more than 2,000, we will launch the advanced course in a month, so that, after finishing your study, you will have a better understanding of the principles of the framework and be able to do advanced development.
-
-
-**The primary course is free to all, and the intermediate and advanced courses are only open to those who have rewarded us.**
-
diff --git a/course/source/dive-into-python/cn/modifier.rst b/course/source/dive-into-python/cn/modifier.rst
deleted file mode 100644
index ff5642b..0000000
--- a/course/source/dive-into-python/cn/modifier.rst
+++ /dev/null
@@ -1,462 +0,0 @@
-修饰符
-================
-
-
-函数是一种对象
-----------------
-
-
-
-在 *Python* 中，函数是也是一种对象。
-
-
-
-::
-
-    def foo(x):
-       print x
-    
-    print(type(foo))
-
-结果:
-
-::
-
-    type 'function'
-
-
-
-查看函数拥有的方法：
-
-
-
-
-::
-
-   dir(foo)
-   Out[2]:
-   ['__call__',
-    '__class__',
-    '__closure__',
-    '__code__',
-    '__defaults__',
-    '__delattr__',
-    '__dict__',
-    '__doc__',
-    '__format__',
-    '__get__',
-    '__getattribute__',
-    '__globals__',
-    '__hash__',
-    '__init__',
-    '__module__',
-    '__name__',
-    '__new__',
-    '__reduce__',
-    '__reduce_ex__',
-    '__repr__',
-    '__setattr__',
-    '__sizeof__',
-    '__str__',
-    '__subclasshook__',
-    'func_closure',
-    'func_code',
-    'func_defaults',
-    'func_dict',
-    'func_doc',
-    'func_globals',
-    'func_name']
-
-
-
-在这些方法中，*__call__* 是最重要的一种方法：
-
-
-
-
-::
-
-   foo.__call__(42)
-
-结果:
-::
-
-    42
-
-
-相当于：
-
-::
-
-
-   foo(42)
-
-结果:
-::
-
-    42
-
-
-因为函数是对象，所以函数可以作为参数传入另一个函数：
-
-
-::
-
-   def bar(f, x):
-       x += 1
-       f(x)
-
-
-::
-
-   bar(foo, 4)
-
-结果:
-::
-
-    5
-
-
-修饰符
---------
-
-
-修饰符是这样的一种函数，它接受一个函数作为输入，通常输出也是一个函数：
-
-
-
-
-::
-
-    def dec(f):
-        print 'I am decorating function', id(f)
-        return f
-
-
-将 *len* 函数作为参数传入这个修饰符函数：
-
-
-
-::
-
-
-   declen = dec(len)
-
-
-结果:
-::
-
-    I am decorating function 33716168
-
-
-使用这个新生成的函数：
-
-
-::
-
-
-    declen([10,20,30])
-
-
-
-结果:
-::
-
-    3
-
-
-
-上面的例子中，我们仅仅返回了函数的本身，也可以利用这个函数生成一个新的函数，看一个新的例子：
-
-
-
-
-::
-
-
-    def loud(f):
-        def new_func(*args, **kw):
-            print 'calling with', args, kw
-            rtn = f(*args, **kw)
-            print 'return value is', rtn
-            return rtn
-        return new_func
-
-
-<button>Run ...</button>
-
-
-
-
-::
-
-    loudlen = loud(len)
-
-    loudlen([10, 20, 30])
-    calling with ([10, 20, 30],) {}
-    return value is 3
-
-<button>Run ...</button>
-
-
-结果:
-::
-
-    3
-
-
-用 @ 来使用修饰符
----------------------
-
-
-
-*Python* 使用 @ 符号来将某个函数替换为修饰符之后的函数：
-
-
-
-例如这个函数：
-
-
-
-::
-
-    def foo(x):
-        print x
-    
-    foo = dec(foo)
-
-
-
-结果:
-::
-
-    I am decorating function 64021672
-
-
-可以替换为：
-
-
-
-
-::
-
-
-    @dec
-    def foo(x):
-        print x
-
-
-结果:
-::
-
-    I am decorating function 64021112
-
-
-
-事实上，如果修饰符返回的是一个函数，那么可以链式的使用修饰符：
-
-
-
-    @dec1
-
-
-    @dec2
-
-    def foo(x):
-
-        print x
-
-
-
-
-
-使用修饰符 *loud* 来定义这个函数：
-
-
-
-
-
-
-::
-
-
-    @loud
-    def foo(x):
-       print x
-
-
-
-
-::
-
-    foo(42)
-    calling with (42,) {}
-    42
-    return value is None
-
-
-例子
-----------
-
-
-
-定义两个修饰器函数，一个将原来的函数值加一，另一个乘二：
-
-
-
-
-
-
-::
-
-    def plus_one(f):
-        def new_func(x):
-            return f(x) + 1
-        return new_func
-
-    def times_two(f):
-        def new_func(x):
-            return f(x) * 2
-        return new_func
-
-<button>Run ...</button>
-
-
-定义函数，先乘二再加一：
-
-
-
-
-::
-
-
-
-    @plus_one
-    @times_two
-    def foo(x):
-       return int(x)
-
-
-
-
-::
-
-   foo(13)
-
-
-
-
-结果:
-::
-
-    27
-
-
-
-
-修饰器工厂
--------------
-
-
-*decorators factories* 是返回修饰器的函数，例如：
-
-
-
-
-
-::
-
-
-    def super_dec(x, y, z):
-        def dec(f):
-            def new_func(*args, **kw):
-                ^12$print x + y + z
-                ^12$return f(*args, **kw)
-            return new_func
-        return dec
-
-
-
-它的作用在于产生一个可以接受参数的修饰器，例如我们想将 *loud* 输出的内容写入一个文件去，可以这样做：
-
-
-
-
-
-::
-
-    def super_loud(filename):
-        fp = open(filename, 'w')
-        def loud(f):
-            def new_func(*args, **kw):
-                ^12$fp.write('calling with' + str(args) + str(kw))
-                ^12$# 确保内容被写入
-                ^12$fp.flush()
-                ^12$fp.close()
-                ^12$rtn = f(*args, **kw)
-                ^12$return rtn
-            return new_func
-        return loud
-
-
-
-
-可以这样使用这个修饰器工厂：
-
-
-
-
-
-::
-
-
-    @super_loud('test.txt')
-    def foo(x):
-        print x
-
-
-
-
-
-调用 *foo* 就会在文件中写入内容：
-
-
-
-
-::
-    
-    foo(12)
-
-
-结果:
-::
-
-    12
-
-
-
-查看文件内容：
-
-
-
-::
-
-    with open('test.txt') as fp:
-        print fp.read()
-    calling with(12,){}
-
-
-
-
-
-::
-
-    import os
-    os.remove('test.txt')
-
-
-作者 & 更新时间
-------------------------------------
-作者:李金  lijinwithyou@gmail.com
-
-提交: 2017/12/6
diff --git a/course/source/dive-into-python/cn/operator.rst b/course/source/dive-into-python/cn/operator.rst
deleted file mode 100644
index b83f39b..0000000
--- a/course/source/dive-into-python/cn/operator.rst
+++ /dev/null
@@ -1,240 +0,0 @@
-operator, functools, itertools, toolz, fn, funcy 模块
-================================================================
-
-
-operator 模块
----------------
-
-::
-
-    import operator as op
-
-
-*operator* 模块提供了各种操作符（*+,*,[]*）的函数版本方便使用：
-
-
-
-加法：
-
-
-
-::
-
-
-    print reduce(op.add, range(10))
-
-结果:
-
-::
-
-  45
-
-
-乘法：
-
-
-
-
-
-::
-
-
-    print reduce(op.mul, range(1,10))
-
-
-
-结果:
-
-::
-
-  362880
-
-
-
-
-::
-
-    my_list = [('a', 1), ('bb', 4), ('ccc', 2), ('dddd', 3)]
-
-   # 标准排序
-    print sorted(my_list)
-
-   # 使用元素的第二个元素排序
-    print sorted(my_list, key=op.itemgetter(1))
-
-   # 使用第一个元素的长度进行排序：
-    print sorted(my_list, key=lambda x: len(x[0]))
-
-
-
-结果:
-
-::
-
-  [('a', 1), ('bb', 4), ('ccc', 2), ('dddd', 3)]
-  [('a', 1), ('ccc', 2), ('dddd', 3), ('bb', 4)]
-  [('a', 1), ('bb', 4), ('ccc', 2), ('dddd', 3)]
-
-
-
-functools 模块
-----------------
-
-
-
-*functools* 包含很多跟函数相关的工具，比如之前看到的 *wraps* 函数，不过最常用的是 *partial* 函数，这个函数允许我们使用一个函数中生成一个新函数，这个函数使用原来的函数，不过某些参数被指定了：
-
-
-
-
-
-::
-
-    from functools import partial
-
-    # 将 reduce 的第一个参数指定为加法，得到的是类似求和的函数
-    sum_ = partial(reduce, op.add)
-
-    # 将 reduce 的第一个参数指定为乘法，得到的是类似求连乘的函数
-    prod_ = partial(reduce, op.mul)
-
-    print sum_([1,2,3,4])
-    print prod_([1,2,3,4])
-
-结果:
-
-::
-
-  10
-  24
-
-
-*partial* 函数还可以按照键值对传入固定参数。
-
-
-
-
-
-itertools 模块
-------------------
-
-
-
-*itertools* 包含很多与迭代器对象相关的工具，其中比较常用的是排列组合生成器* permutations *和* combinations*，还有在数据分析中常用的 *groupby* 生成器：
-
-
-
-
-
-::
-
-    from itertools import cycle, groupby, islice, permutations, combinations
-
-
-
-*cycle* 返回一个无限的迭代器，按照顺序重复输出输入迭代器中的内容，*islice* 则返回一个迭代器中的一段内容：
-
-
-
-
-
-
-::
-
-    print list(islice(cycle('abcd'), 0, 10))
-
-
-
-结果:
-
-::
-
-  ['a', 'b', 'c', 'd', 'a', 'b', 'c', 'd', 'a', 'b']
-
-
-
-*groupby* 返回一个字典，按照指定的 *key* 对一组数据进行分组，字典的键是 *key*，值是一个迭代器：
-
-
-
-
-
-::
-
-    animals = sorted(['pig', 'cow', 'giraffe', 'elephant',
-                  'dog', 'cat', 'hippo', 'lion', 'tiger'], key=len)
-
-    # 按照长度进行分组
-    for k, g in groupby(animals, key=len):
-        ^4$print k, list(g)
-    print
-
-
-
-
-结果:
-
-::
-  
-  3 ['pig', 'cow', 'dog', 'cat']
-  4 ['lion']
-  5 ['hippo', 'tiger']
-  7 ['giraffe']
-  8 ['elephant']
-
-
-
-
-排列：
-
-
-
-
-
-::
-
-
-    print [''.join(p) for p in permutations('abc')]
-
-
-结果:
-
-::
-
-  ['abc', 'acb', 'bac', 'bca', 'cab', 'cba']
-
-
-
-组合：
-
-
-
-
-
-::
-
-    print [list(c) for c in combinations([1,2,3,4], r=2)]
-
-
-结果:
-
-::
-
-  [[1, 2], [1, 3], [1, 4], [2, 3], [2, 4], [3, 4]]
-
-
-
-toolz, fn 和 funcy 模块
---------------------------
-
-
-这三个模块的作用是方便我们在编程的时候使用函数式编程的风格。
-
-
-
-作者 & 更新时间
-------------------------------------
-作者:李金  lijinwithyou@gmail.com
-
-提交: 2017/12/6
-
diff --git a/course/source/dive-into-python/cn/orm.rst b/course/source/dive-into-python/cn/orm.rst
deleted file mode 100644
index 8b30c5c..0000000
--- a/course/source/dive-into-python/cn/orm.rst
+++ /dev/null
@@ -1,140 +0,0 @@
-对象关系映射
-=================================
-
-数据库中的记录可以与一个 Python 对象对应。
-
-
-例如对于上一节中的数据库：
-
-
-+------------+--------------------------------+-------------------+-----------------------+----------------------+
-| Order      | Date                           |          Stock    |             Quantity  |        Price         |
-+============+================================+==================================================================+
-|A0001       |2013-12-01                      |         AAPL      |              1000     |        203.4         |    
-+------------+--------------------------------+-------------------+-----------------------+----------------------+
-|A0002       |2013-12-01                      |         MSFT      |              1500     |        167.5         |
-+------------+--------------------------------+-------------------+-----------------------+----------------------+
-|A0003       |2013-12-02                      |           GOOG    |              150      |        167.5         | 
-+------------+--------------------------------+-------------------+-----------------------+----------------------+
-
-
-
-可以用一个类来描述：
-
-
-+------------+--------------------------------+
-| Attr.      |  Method                        |          
-+============+================================+
-|Order id    |  Cost                          |      
-+------------+--------------------------------+
-|Stock       |                                |       
-+------------+--------------------------------+
-|Quant.      |                                |           
-+------------+--------------------------------+
-|Price       |                                |
-+------------+--------------------------------+
-
-可以使用 sqlalchemy 来实现这种对应：
-
-::
-
-    from sqlalchemy.ext.declarative import declarative_base
-    from sqlalchemy import Column, Date, Float, Integer, String
-
-    Base = declarative_base()
-
-    class Order(Base):
-        ^4$__tablename__ = 'orders'
-        ^4$order_id = Column(String, primary_key=True)
-        ^4$date = Column(Date)
-        ^4$symbol = Column(String)
-        ^4$quantity = Column(Integer)
-        ^4$price = Column(Float)
-
-        ^4$def get_cost(self):
-            ^8$return self.quantity*self.price
-
-
-生成一个 Order 对象：
-
-::
-
-    import datetime
-    order = Order(order_id='A0004', date=datetime.date.today(), symbol='MSFT', quantity=-1000, price=187.54)
-
-
-调用方法：
-
-::
-
-    order.get_cost()
-
-
-使用上一节生成的数据库产生一个 session：
-
-::
-
-    from sqlalchemy import create_engine
-    from sqlalchemy.orm import sessionmaker
-
-    engine = create_engine("sqlite:///my_database.sqlite")   # 相当于 connection
-    Session = sessionmaker(bind=engine) # 相当于 cursor
-    session = Session()
-
-
-使用这个 session 向数据库中添加刚才生成的对象：
-
-::
-
-    session.add(order)
-    session.commit()
-
-显示是否添加成功：
-
-::
-
-    for row in engine.execute("SELECT * FROM orders"):
-        ^4$print row
-
-
-成功则会输出以下内容
-::
-
-    (u'A0001', u'2013-12-01', u'AAPL', 1000, 203.4)
-    (u'A0002', u'2013-12-01', u'MSFT', 1500, 167.5)
-    (u'A0003', u'2013-12-02', u'GOOG', 1500, 167.5)
-    (u'A0004', u'2015-09-10', u'MSFT', -1000, 187.54)
-
-使用 filter 进行查询，返回的是 Order 对象的列表：
-
-::
-
-    for order in session.query(Order).filter(Order.symbol=="AAPL"):
-        ^4$print order.order_id, order.date, order.get_cost()
-
-成功则会输出以下内容
-
-
-::
-
-    A0001 2013-12-01 203400.0
-
-返回列表的第一个：
-
-::
-
-    order_2 = session.query(Order).filter(Order.order_id=='A0002').first()
-
-::
-
-    order_2.symbol
-
-
-输出： u'MSFT'
-
-
-作者 & 更新时间
-------------------------------------
-作者:`李金  <lijinwithyou@gmail.com>`
-
-提交: 2017/12/6
diff --git a/course/source/dive-into-python/cn/os.rst b/course/source/dive-into-python/cn/os.rst
deleted file mode 100644
index bbf12df..0000000
--- a/course/source/dive-into-python/cn/os.rst
+++ /dev/null
@@ -1,149 +0,0 @@
-与操作系统进行交互：os 模块
-======================================================
-os 模块提供了对系统文件进行操作的方法：
-
-::
-
-    import os
-    dir(os)
-
-<button>Run ...</button>
-
-
-文件路径操作
-----------------------------------------------------------
-- *os.remove(path)* 或 *os.unlink(path)* ：删除指定路径的文件。路径可以是全名，也可以是当前工作目录下的路径。
-- *os.removedirs*：删除文件，并删除中间路径中的空文件夹
-- *os.chdir(path)*：将当前工作目录改变为指定的路径
-- *os.getcwd()*：返回当前的工作目录
-- *os.curdir*：表示当前目录的符号
-- *os.rename(old, new)*：重命名文件
-- *os.renames(old, new)*：重命名文件，如果中间路径的文件夹不存在，则创建文件夹
-- *os.listdir(path)*：返回给定目录下的所有文件夹和文件名，不包括 *'.'* 和 *'..'* 以及子文件夹下的目录。（*'.'* 和* '..'* 分别指当前目录和父目录）
-- *os.mkdir(name)*：产生新文件夹
-- *os.makedirs(name)*：产生新文件夹，如果中间路径的文件夹不存在，则创建文件夹
-
-获得当前目录：
-::
-
-    import os
-    os.getcwd()
-
-<button>Run ...</button>
-
-
-获得当前目录下的文件：
-
-::
-
-    import os
-    os.listdir(os.curdir)
-
-
-
-写文件：
-
-::
-
-    import os
-    f = open("test.file", "w")
-    f.close()
-    print "test.file" in os.listdir(os.curdir)
-
-
-<button>Run ...</button>
-
-重命名文件：
-
-::
-
-    import os
-    os.rename("test.file", "test.new.file")
-    print "test.file" in os.listdir(os.curdir)
-    print "test.new.file" in os.listdir(os.curdir)
-
-<button>Run ...</button>
-
-删除文件：
-
-::
-
-    import os
-    os.remove("test.new.file")
-
-<button>Run ...</button>
-
-
-系统常量
--------------------------------------------------------------
-当前操作系统的换行符：
-
-::
-
-    import os
-    os.linesep
-
-<button>Run ...</button>
-
-
-当前操作系统的路径分隔符：
-
-::
-
-    import os
-    os.sep
-
-<button>Run ...</button>
-
-当前操作系统的环境变量中的分隔符（*';'* 或 *':'*）：
-
-
-::
-
-    import os
-    os.pathsep
-
-<button>Run ...</button>
-
-
-其他
-----------------------------------------------------
-*os.environ* 是一个存储所有环境变量的值的字典，可以修改。
-
-::
-
-    import os
-    os.environ["USER"]
-
-<button>Run ...</button>
-
-
-*os.urandom(len)* 返回指定长度的随机字节。
-
-os.path 模块
-------------------
-不同的操作系统使用不同的路径规范，这样当我们在不同的操作系统下进行操作时，可能会带来一定的麻烦，而 *os.path* 模块则帮我们解决了这个问题。
-
-- os.path.isfile(path) ：检测一个路径是否为普通文件
-- os.path.isdir(path)：检测一个路径是否为文件夹
-- os.path.exists(path)：检测路径是否存在
-- os.path.isabs(path)：检测路径是否为绝对路径
-
-split 和 join
-----------------
-- os.path.split(path)：拆分一个路径为 (head, tail) 两部分
-- os.path.join(a, * p)：使用系统的路径分隔符，将各个部分合成一个路径
-
-其他
-----------------------
-- os.path.abspath()：返回路径的绝对路径
-- os.path.dirname(path)：返回路径中的文件夹部分
-- os.path.basename(path)：返回路径中的文件部分
-- os.path.splitext(path)：将路径与扩展名分开
-- os.path.expanduser(path)：展开 '~' 和 '~user'
-
-作者 & 更新时间
-------------------------------------
-作者:`李金  <lijinwithyou@gmail.com>`
-
-提交: 2017/12/6
diff --git a/course/source/dive-into-python/cn/parameterpassing.rst b/course/source/dive-into-python/cn/parameterpassing.rst
deleted file mode 100644
index d6c8b5c..0000000
--- a/course/source/dive-into-python/cn/parameterpassing.rst
+++ /dev/null
@@ -1,552 +0,0 @@
-函数进阶
-========
-本章内容包括参数传递，高阶函数，lambda 匿名函数，global 变量，递归
-
-
-
-函数是基本类型
-----------------
-
-在 Python 中，函数是一种基本类型的对象，这意味着
-
-- 可以将函数作为参数传给另一个函数
-- 将函数作为字典的值储存
-- 将函数作为另一个函数的返回值
-
-::
-
-    def square(x):
-        ^4$"""Square of x."""
-        ^4$return x*x
-
-    def cube(x):
-        ^4$"""Cube of x."""
-        ^4$return x*x*x
-
-<button>Run ...</button>
-
-
-作为字典的值：
-
-
-::
-
-    funcs = {
-        'square': square,
-        'cube': cube,
-    }
-
-
-例子：
-
-
-::
-    def square(x):
-        ^4$"""Square of x."""
-        ^4$return x*x
-
-    def cube(x):
-        ^4$"""Cube of x."""
-        ^4$return x*x*x
-
-    funcs = {
-        'square': square,
-        'cube': cube,
-    }
-
-
-
-    x = 2
-
-    print square(x)
-    print cube(x)
-
-
-    func in sorted(funcs):
-        ^4$print func, funcs[func](x)
-
-<button>Run ...</button>
-
-
-函数参数
-================
-
-引用传递
-------------
-
-
-Python 中的函数传递方式是 call by reference 即引用传递，例如，对于这样的用法：
-
-::
-
-    x = [10, 11, 12]
-    f(x)
-
-
-
-传递给函数 f 的是一个指向 x 所包含内容的引用，如果我们修改了这个引用所指向内容的值（例如 x[0]=999），那么外面的 x 的值也会被改变。不过如果我们在函数中赋给 x 一个新的值（例如另一个列表），那么在函数外面的 x 的值不会改变：
-
-::
-
-    def mod_f(x):
-        ^4$x[0] = 999
-        ^4$return x
-
-    x = [1, 2, 3]
-
-    print x
-    print mod_f(x)
-    print x
-
-<button>Run ...</button>
-
-
-::
-
-    def no_mod_f(x):
-        ^4$x = [4, 5, 6]
-        ^4$return x
-
-    x = [1,2,3]
-
-    print x
-    print no_mod_f(x)
-    print x
-
-<button>Run ...</button>
-
-
-默认参数是可变的
-------------------
-
-函数可以传递默认参数，默认参数的绑定发生在函数定义的时候，以后每次调用默认参数时都会使用同一个引用。
-
-这样的机制会导致这种情况的发生：
-
-::
-
-    def f(x = []):
-        ^4$x.append(1)
-        ^4$return x
-
-
-理论上说，我们希望调用 f() 时返回的是 [1]， 但事实上：
-
-
-::
-    def f(x = []):
-        ^4$x.append(1)
-        ^4$return x
-
-    print f()
-    print f()
-    print f()
-    print f(x = [9,9,9])
-    print f()
-    print f()
-
-<button>Run ...</button>
-
-将会得到以下结果
-::
-
-    [1]
-    [1, 1]
-    [1, 1, 1]
-    [9, 9, 9, 1]
-    [1, 1, 1, 1]
-    [1, 1, 1, 1, 1]
-
-
-而我们希望看到的应该是这样：
-
-
-::
-
-    def f(x = None):
-        if x is None:
-            x = []
-        x.append(1)
-        return x
-
-    print f()
-    print f()
-    print f()
-    print f(x = [9,9,9])
-    print f()
-    print f()
-
-<button>Run ...</button>
-
-
-输出应该是
-::
-
-    [1]
-
-    [1]
-
-    [1]
-
-    [9, 9, 9, 1]
-
-    [1]
-
-    [1]
-
-
-
-高阶函数
---------------
-
-
-以函数作为参数，或者返回一个函数的函数是高阶函数，常用的例子有 *map* 和 *filter* 函数：
-
-*map(f, sq)* 函数将 *f* 作用到*sq* 的每个元素上去，并返回结果组成的列表，相当于：
-
-
-*[f(s) for s in sq]*
-
-
-::
-
-    map(square, range(5))
-
-
-输入：
-::
-
-    [0, 1, 4, 9, 16]
-
-
-*filter(f, sq)* 函数的作用相当于，对于 *sq* 的每个元素 *s*，返回所有 *f(s)* 为 *True* 的 *s* 组成的列表，相当于：
-
-*[s for s in sq if f(s)]*
-
-
-::
-
-    def is_even(x):
-        return x % 2 == 0
-
-    filter(is_even, range(5))
-
-<button>Run ...</button>
-
-
-
-输出：
-
-::
-
-    [0, 2, 4]
-
-
-一起使用：
-
-
-::
-
-    map(square, filter(is_even, range(5)))
-
-
-
-输出：
-
-::
-
-    [0, 4, 16]
-
-
-*reduce(f, sq)* 函数接受一个二元操作函数 *f(x,y)*，并对于序列 *sq* 每次合并两个元素：
-
-
-::
-
-    def my_add(x, y):
-        ^4$return x + y
-
-    reduce(my_add, [1,2,3,4,5])
-
-
-Out[12]:
-15
-
-
-传入加法函数，相当于对序列求和。
-
-返回一个函数：
-
-In [13]:
-
-::
-    def make_logger(target):
-        def logger(data):
-            with open(target, 'a') as f:
-                f.write(data + '\n')
-
-        return logger
-
-    foo_logger = make_logger('foo.txt')
-    foo_logger('Hello')
-    foo_logger('World')
-
-<button>Run ...</button>
-
-
-In [14]:
-
-::
-
-   !cat foo.txt
-   Hello
-   World
-
-<button>Run ...</button>
-
-
-In [15]:
-
-::
-
-   import os
-   os.remove('foo.txt')
-
-<button>Run ...</button>
-
-
-匿名函数
-----------
-
-
-在使用 *map*， *filter*，*reduce* 等函数的时候，为了方便，对一些简单的函数，我们通常使用匿名函数的方式进行处理，其基本形式是：
-
-lambda <variables>: <expression>
-
-例如，我们可以将这个：
-
-In [16]:
-
-
-::
-
-   print map(square, range(5))
-   [0, 1, 4, 9, 16]
-
-<button>Run ...</button>
-
-用匿名函数替换为：
-
-In [17]:
-
-::
-
-    print map(lambda x: x * x, range(5))
-    [0, 1, 4, 9, 16]
-
-
-<button>Run ...</button>
-
-
-匿名函数虽然写起来比较方便（省去了定义函数的烦恼），但是有时候会比较难于阅读：
-
-In [18]:
-
-::
-
-
-   s1 = reduce(lambda x, y: x+y, map(lambda x: x**2, range(1,10)))
-   print(s1)
-   285
-
-<button>Run ...</button>
-
-
-当然，更简单地，我们可以写成这样：
-
-In [19]:
-
-::
-
-   s2 = sum(x**2 for x in range(1, 10))
-   print s2
-   285
-
-<button>Run ...</button>
-
-
-
-global 变量
----------------
-
-
-一般来说，函数中是可以直接使用全局变量的值的：
-
-In [20]:
-
-::
-
-   x = 15
-
-   def print_x():
-       print x
-    
-   print_x()
-   15
-
-<button>Run ...</button>
-
-
-但是要在函数中修改全局变量的值，需要加上 *global* 关键字：
-
-In [21]:
-
-::
-
-
-   x = 15
-
-   def print_newx():
-       global x
-       x = 18
-       print x
-    
-   print_newx()
-
-   print x
-   18
-   18
-
-<button>Run ...</button>
-
-
-如果不加上这句 global 那么全局变量的值不会改变：
-
-In [22]:
-
-::
-
-
-   x = 15
-
-   def print_newx():
-       x = 18
-       print x
-    
-   print_newx()
-
-   print x
-   18
-   15
-
-<button>Run ...</button>
-
-
-递归
---------
-
-
-递归是指函数在执行的过程中调用了本身，一般用于分治法，不过在 *Python* 中这样的用法十分地小，所以一般不怎么使用：
-
-Fibocacci 数列：
-
-In [23]:
-
-
-::
-
-
-   def fib1(n):
-       """Fib with recursion."""
-
-       # base case
-       if n==0 or n==1:
-           return 1
-       # recurssive caae
-       else:
-           return fib1(n-1) + fib1(n-2)
-
-   print [fib1(i) for i in range(10)]
-   [1, 1, 2, 3, 5, 8, 13, 21, 34, 55]
-
-
-<button>Run ...</button>
-
-
-一个更高效的非递归版本：
-
-In [24]:
-
-::
-
-
-    def fib2(n):
-         """Fib without recursion."""
-        a, b = 0, 1
-        for i in range(1, n+1):
-           a, b = b, a+b
-        return b
-
-    print [fib2(i) for i in range(10)]
-    [1, 1, 2, 3, 5, 8, 13, 21, 34, 55]
-
-<button>Run ...</button>
-
-
-速度比较：
-
-In [25]:
-
-::
-
-
-
-   %timeit fib1(20)
-   %timeit fib2(20)
-   100 loops, best of 3: 5.35 ms per loop
-   100000 loops, best of 3: 2.2 μs per loop
-
-<button>Run ...</button>
-
-对于第一个递归函数来说，调用 *fib(n+2)* 的时候计算 *fib(n+1)*, *fib(n)*，调用 *fib(n+1)* 的时候也计算了一次 *fib(n)*，这样造成了重复计算。
-
-使用缓存机制的递归版本，这里利用了默认参数可变的性质，构造了一个缓存：
-
-In [26]:
-
-::
-
-
-    def fib3(n, cache={0: 1, 1: 1}):
-         """Fib with recursion and caching."""
-
-        try:
-            return cache[n]
-        except KeyError:
-            cache[n] = fib3(n-1) + fib3(n-2)
-            return cache[n]
-
-    print [fib3(i) for i in range(10)]
-
-    %timeit fib1(20)
-    %timeit fib2(20)
-    %timeit fib3(20)
-
-
-
-<button>Run ...</button>
-
-
-    [1, 1, 2, 3, 5, 8, 13, 21, 34, 55]
-    100 loops, best of 3: 5.37 ms per loop
-    100000 loops, best of 3: 2.19 μs per loop
-    The slowest run took 150.16 times longer than the fastest. This could mean that an intermediate result is being cached 
-    1000000 loops, best of 3: 230 ns per loop
-
-
-作者 & 更新时间
-------------------------------------
-作者:李金  lijinwithyou@gmail.com
-
-提交: 2017/12/6
-
diff --git a/course/source/dive-into-python/cn/re.rst b/course/source/dive-into-python/cn/re.rst
deleted file mode 100644
index 305869a..0000000
--- a/course/source/dive-into-python/cn/re.rst
+++ /dev/null
@@ -1,240 +0,0 @@
-正则表达式和 re 模块
-==============================
-正则表达式
--------------
-正则表达式是用来匹配字符串或者子串的一种模式，匹配的字符串可以很具体，也可以很一般化。
-
-Python 标准库提供了 re 模块。
-
-::
-
-    import re
-    dir(re)
-
-<button>Run ...</button>
-
-
-
-re.match & re.search
-----------------------------------------------------------
-在 re 模块中， re.match 和 re.search 是常用的两个方法：
-re.match(pattern, string[, flags])
-re.search(pattern, string[, flags])
-
-
-两者都寻找第一个匹配成功的部分，成功则返回一个 match 对象，不成功则返回 None，不同之处在于 re.match 只匹配字符串的开头部分，而 re.search 匹配的则是整个字符串中的子串。
-
-re.findall & re.finditer
------------------------------------------------------------------
-re.findall(pattern, string) 返回所有匹配的对象,
-re.finditer 则返回一个迭代器。
-
-re.split
------------
-re.split(pattern, string[, maxsplit]) 按照 pattern 指定的内容对字符串进行分割。
-
-re.sub
-------------------------------------------------------------------------
-re.sub(pattern, repl, string[, count]) 将 pattern 匹配的内容进行替换。
-
-re.compile
-------------------------------------------------------------------------
-re.compile(pattern) 生成一个 pattern 对象，这个对象有匹配，替换，分割字符串的方法。
-
-正则表达式规则
--------------------------------------------------------------------------
-
-正则表达式由一些普通字符和一些元字符（metacharacters）组成。普通字符包括大小写的字母和数字，而元字符则具有特殊的含义：
-
-
-+------------+------------------------------------------------------------------------------------------+
-| 子表达式   | 匹配内容                                                                                 |
-+============+==========================================================================================+
-|.           | 匹配除了换行符之外的内容                                                                 |    
-+------------+------------------------------------------------------------------------------------------+
-| \w         |匹配所有字母和数字字符                                                                    |
-+------------+------------------------------------------------------------------------------------------+
-| \d         |匹配所有数字，相当于 [0-9]                                                                |
-+------------+------------------------------------------------------------------------------------------+
-| \s         |        匹配空白，相当于 [\t\n\t\f\v]                                                     |         
-+------------+------------------------------------------------------------------------------------------+
-| \W,\D,\S   |   匹配对应小写字母形式的补                                                               |
-+------------+------------------------------------------------------------------------------------------+
-|[...]       |表示可以匹配的集合支持范围表示如 a-z, 0-9 等                                              |
-+------------+------------------------------------------------------------------------------------------+
-| (...)      |表示作为一个整体进行匹配                                                                  |
-+------------+------------------------------------------------------------------------------------------+
-| ^          |表示匹配后面的子表达式的补                                                                |
-+------------+------------------------------------------------------------------------------------------+
-|*           |      表示匹配前面的子表达式 0 次或更多次                                                 |
-+------------+------------------------------------------------------------------------------------------+
-|?           |	表示匹配前面的子表达式 1 次或更多次                                                     |
-+------------+------------------------------------------------------------------------------------------+
-|{m} 	     | 表示匹配前面的子表达式 m 次                                                              |
-+------------+------------------------------------------------------------------------------------------+
-|{m,} 	     |表示匹配前面的子表达式至少 m 次                                                           |
-+------------+------------------------------------------------------------------------------------------+
-|{m,n} 	     |表示匹配前面的子表达式至少 m 次，至多 n 次                                                |
-+------------+------------------------------------------------------------------------------------------+
-
-例如：
-- ca*t 匹配： ct, cat, caaaat, ...
-- ab\d|ac\d 匹配： ab1, ac9, ...
-- ([^a-q]bd) 匹配： rbd, 5bd, ...
-
-例子
---------------------------------------------------------------------
-
-假设我们要匹配这样的字符串：
-
-::
-
-    string = 'hello world'
-    pattern = 'hello (\w+)'
-
-    match = re.match(pattern, string)
-    print match
-
-<button>Run ...</button>
-
-
-一旦找到了符合条件的部分，我们便可以使用 group 方法查看匹配的部分：
-::
-
-    string = 'hello world'
-    pattern = 'hello (\w+)'
-
-    match = re.match(pattern, string)
-
-    if match is not None:
-        ^4$print match.group(0)
-
-<button>Run ...</button>
-
-
-获得第二个匹配的部分
-
-::
-
-    string = 'hello world'
-    pattern = 'hello (\w+)'
-
-    match = re.match(pattern, string)
-
-    if match is not None:
-        ^4$print match.group(1)
-
-<button>Run ...</button>
-
-
-我们可以改变 string 的内容：
-
-::
-
-    string = 'hello there'
-    pattern = 'hello (\w+)'
-
-    = re.match(pattern, string)
-    if match is not None:
-        ^4$print match.group(0)
-
-    print match.group(1)
-
-<button>Run ...</button>
-
-通常，match.group(0) 匹配整个返回的内容，之后的 1,2,3,... 返回规则中每个括号（按照括号的位置排序）匹配的部分。
-
-如果某个 pattern 需要反复使用，那么我们可以将它预先编译：
-
-::
-
-    pattern1 = re.compile('hello (\w+)')
-
-    match = pattern1.match(string)
-    if match is not None:
-        ^4$print match.group(1)
-
-<button>Run ...</button>
-
-
-由于元字符的存在，所以对于一些特殊字符，我们需要使用 '\' 进行逃逸字符的处理，使用表达式 '\\' 来匹配 '\' 。
-但事实上，Python 本身对逃逸字符也是这样处理的：
-
-::
-
-    pattern = '\\'
-    print pattern
-
-<button>Run ...</button>
-
-
-因为逃逸字符的问题，我们需要使用四个 '\\\\' 来匹配一个单独的 '\'：
-
-::
-
-    pattern = '\\\\'
-    path = "C:\\foo\\bar\\baz.txt"
-    print re.split(pattern, path)
-
-<button>Run ...</button>
-
-
-
-这样看起来十分麻烦，好在 Python 提供了 raw string 来忽略对逃逸字符串的处理，从而可以这样进行匹配：
-
-
-::
-
-    pattern = r'\\'
-    path = r"C:\foo\bar\baz.txt"
-    print re.split(pattern, path)
-
-<button>Run ...</button>
-
-
-如果规则太多复杂，正则表达式不一定是个好选择。
-
-Numpy 的 fromregex()
-----------------------------------------------------------
-
-::
-
-    # 有 test.dat 文件，内容如下
-
-    1312 foo
-    1534    bar
-    444  qux
-
-<button>Run ...</button>
-
-numpy.fromregex(file, pattern, dtype) 中，dtype 中的内容与 pattern 的括号一一对应：
-
-::
-
-    pattern = "(\d+)\s+(...)"
-    dt = [('num', 'int64'), ('key', 'S3')]
-
-    from numpy import fromregex
-    output = fromregex('test.dat', pattern, dt)
-    print output
-
-<button>Run ...</button>
-
-
-显示 num 项：
-::
-
-    pattern = "(\d+)\s+(...)"
-    dt = [('num', 'int64'), ('key', 'S3')]
-
-    from numpy import fromregex
-    output = fromregex('test.dat', pattern, dt)
-    print output['num']
-
-<button>Run ...</button>
-
-作者 & 更新时间
-------------------------------------
-作者:`李金  <lijinwithyou@gmail.com>`
-
-提交: 2017/12/6
diff --git a/course/source/dive-into-python/cn/sql.rst b/course/source/dive-into-python/cn/sql.rst
deleted file mode 100644
index de089a8..0000000
--- a/course/source/dive-into-python/cn/sql.rst
+++ /dev/null
@@ -1,141 +0,0 @@
-SQL 数据库
-===============================
-Python 提供了一系列标准的数据库的 API，这里我们介绍 sqlite 数据库的用法，其他的数据库的用法大同小异：
-
-::
-
-    import sqlite3 as db
-
-
-首先我们要建立或者连接到一个数据库上：
-
-::
-
-    import sqlite3 as db
-    connection = db.connect("my_database.sqlite")
-
-不同的数据库有着不同的连接方法，例如 cx-oracle 数据库的链接方式为：
-
-
-::
-
-    connection = db.connect(username, password, host, port,  'XE')
-
-
-一旦建立连接，我们可以利用它的 cursor() 来执行 SQL 语句：
-
-::
-
-    import sqlite3 as db
-    connection = db.connect("my_database.sqlite")
-    cursor = connection.cursor()
-    cursor.execute("""CREATE TABLE IF NOT EXISTS orders(
-          order_id TEXT PRIMARY KEY,
-          date TEXT,
-          symbol TEXT,
-          quantity INTEGER,
-          price NUMBER)""")
-
-    cursor.execute("""INSERT INTO orders VALUES ('A0001', '2013-12-01', 'AAPL', 1000, 203.4)""")
-    connection.commit()
-
-<button>Run ...</button>
-
-
-
-不过为了安全起见，一般不将数据内容写入字符串再传入，而是使用这样的方式：
-
-::
-
-    import sqlite3 as db
-    connection = db.connect("my_database.sqlite")
-    cursor = connection.cursor()
-    orders = [
-        ("A0002","2013-12-01","MSFT",1500,167.5),
-        ("A0003","2013-12-02","GOOG",1500,167.5)
-    ]
-    cursor.executemany("""INSERT INTO orders VALUES
-        (?, ?, ?, ?, ?)""", orders)
-    connection.commit()
-
-<button>Run ...</button>
-
-cx-oracle 数据库使用不同的方式：
-
-::
-
-    cursor.executemany("""INSERT INTO orders VALUES
-        (:order_id, :date, :symbol, :quantity, :price)""",
-    orders)
-
-
-查看支持的数据库格式：
-
-::
-
-    import sqlite3 as db
-    print(db.paramstyle)
-
-
-<button>Run ...</button>
-
-在 query 语句执行之后，我们需要进行 commit，否则数据库将不会接受这些变化，如果想撤销某个 commit，可以使用 rollback() 方法撤销到上一次 commit() 的结果：
-
-::
-    try:
-        ^4$... # perform some operations
-    except:
-        ^4$connection.rollback()
-        ^4$raise
-    else:
-        ^4$connection.commit()
-
-
-使用 SELECT 语句对数据库进行查询：
-
-::
-
-    import sqlite3 as db
-    connection = db.connect("my_database.sqlite")
-    cursor = connection.cursor()
-
-    stock = 'MSFT'
-    cursor.execute("""SELECT *
-        FROM orders
-        WHERE symbol=?
-        ORDER BY quantity""", (stock,))
-    for row in cursor:
-        print row
-
-<button>Run ...</button>
-
-cursor.fetchone() 返回下一条内容， cursor.fetchall() 返回所有查询到的内容组成的列表（可能非常大）：
-
-::
-
-    import sqlite3 as db
-    connection = db.connect("my_database.sqlite")
-    cursor = connection.cursor()
-    stock = 'AAPL'
-    cursor.execute("""SELECT *
-    FROM orders
-    WHERE symbol=?
-    ORDER BY quantity""", (stock,))
-    cursor.fetchall()
-
-<button>Run ...</button>
-
-
-读写完毕数据库之后需要关闭数据库：
-
-::
-
-    cursor.close()
-    connection.close()
-
-
-作者 & 更新时间
-------------------------------------
-作者:`李金  <lijinwithyou@gmail.com>`
-
-提交: 2017/12/6
diff --git a/course/source/dive-into-python/cn/sys.rst b/course/source/dive-into-python/cn/sys.rst
deleted file mode 100644
index 94169d7..0000000
--- a/course/source/dive-into-python/cn/sys.rst
+++ /dev/null
@@ -1,145 +0,0 @@
-sys 模块简介
-=======================
-
-通过以下方式引入sys模块
-::
-
-    import sys
-
-
-命令行参数
-----------------------------------------------------------
-*sys.argv* 显示传入的参数：
-
-假设print_args.py的内容如下：
-
-::
-
-    import sys
-    print sys.argv
-
-
-用以下方法运行这个程序
-
-::
-
-    python print_args.py 1 foo
-
-将会得到以下的输出
-
-::
-
-    ['print_args.py', '1', 'foo']
-
-
-第一个参数 （*sys.args[0]*） 表示的始终是执行的文件名，然后依次显示传入的参数。
-
-可以用os.remove删除文件：
-
-::
-
-    import os
-    os.remove('print_args.py')
-
-
-异常消息
--------------------------------------------------------------
-*sys.exc_info() *可以显示 *Exception* 的信息，返回一个 *(type, value, traceback)* 组成的三元组，可以与 *try/catch* 块一起使用：
-
-::
-
-    try:
-        ^4$x = 1/0
-
-    except Exception:
-        ^4$print sys.exc_info()
-
-
-运行上述代码，将会得到：
-
-::
-
-    (<type 'exceptions.ZeroDivisionError'>, ZeroDivisionError('integer division or modulo by zero',), <traceback object at 0x0000000003C6FA08>)
-
-*sys.exc_clear()* 用于清除所有的异常消息。
-
-
-
-标准输入输出流
-----------------------------------------------------
-
-以下属性代表python的标准输入输出流操作句柄
-
-- sys.stdin
-- ys.stdout
-- sys.stderr
-
-退出Python
-------------------------------------------------------
-*sys.exit(arg=0)* 用于退出 Python。*0* 或者 *None* 表示正常退出，其他值表示异常。
-
-Python Path
---------------------------------------------------------
-*sys.path* 表示 Python 搜索模块的路径和查找顺序：
-
-::
-
-    import sys
-    print sys.path
-
-<button>Run ...</button>
-
-在程序中可以通过sys.append, sys.insert 等方式添加，修改新的路径。
-
-
-操作系统信息
-----------------------------------------------------------------
-*sys.platform* 显示当前操作系统信息：
-
-- Windows: win32
-- Mac OSX: darwin
-- Linux: linux2
-
-::
-
-    import sys
-    sys.platform
-
-<button>Run ...</button>
-
-'win32'
-返回*Windows* 操作系统的版本：
-
-::
-
-    import sys
-    sys.getwindowsversion()
-
-
-将会输出
-::
-
-    sys.getwindowsversion(major=6, minor=2, build=9200, platform=2, service_pack='')
-
-标准库中有 *planform* 模块提供更详细的信息。
-
-Python 版本信息
-------------------------------------------------------------
-::
-
-    import sys
-    sys.version
-
-::
-
-    import sys
-    sys.version_info
-
-
-
-作者 & 更新时间
-------------------------------------
-作者:`李金  <lijinwithyou@gmail.com>`
-
-提交: 2017/12/6
-
diff --git a/course/source/dive-into-python/cn/the-use-of-modifier.rst b/course/source/dive-into-python/cn/the-use-of-modifier.rst
deleted file mode 100644
index 8bb9fb6..0000000
--- a/course/source/dive-into-python/cn/the-use-of-modifier.rst
+++ /dev/null
@@ -1,361 +0,0 @@
-修饰符的使用
-================
-
-
-
-@classmethod 修饰符
---------------------
-
-
-在 *Python* 标准库中，有很多自带的修饰符，例如 *classmethod* 将一个对象方法转换了类方法：
-
-
- 
-
-::
-
-    class Foo(object):
-        @classmethod
-        ^4$def bar(cls, x):
-            ^8$print 'the input is', x
-        
-        ^4$def __init__(self):
-            ^8$pass
-
-
-
-类方法可以通过 **类名.方法** 来调用：
-
-
-
- 
-
-::
-
-   Foo.bar(12)
-
-
-结果:
-::
-    12
-
-
-@property 修饰符
------------------
-
-
-有时候，我们希望像 **Java** 一样支持 *getters* 和 *setters* 的方法，这时候就可以使用 *property* 修饰符：
-
-
-
- 
-
-::
-
-
-    class Foo(object):
-        ^4$def __init__(self, data):
-            ^8$self.data = data
-    
-        ^4$@property
-        ^4$def x(self):
-            ^8$return self.data
-
-
-
-此时可以使用* .x* 这个属性查看数据（不需要加上括号）：
-
-
-
- 
-
-::
-
-   foo = Foo(23)
-   foo.x
-
-结果: 
-::
-
-    23
-
-
-这样做的好处在于，这个属性是只读的：
-
-::
-
-
-   foo.x = 1
-
-
-
-如果想让它变成可读写，可以加上一个修饰符 *@x.setter*：
-
-
-
-
- 
-
-
-::
-
-    class Foo(object):
-    def __init__(self, data):
-        ^4$self.data = data
-    
-    @property
-    def x(self):
-        ^4$return self.data
-    
-    @x.setter
-    def x(self, value):
-        ^4$self.data = value
-
-
-
- 
-
-
-::
-
-    foo = Foo(23)
-    print foo.x
-
-结果:
-::
-
-    23
-
-
-可以通过属性改变它的值：
-
-
-
- 
-
-::
-
-
-   foo.x = 1
-   print foo.x
-
-
-结果:
-::
-
-    1
-
-
-Numpy 的 @vectorize 修饰符
----------------------------
-
-*numpy* 的 *vectorize* 函数讲一个函数转换为 *ufunc*，事实上它也是一个修饰符：
-
-
-
- 
-
-
-::
-     
-    from numpy import vectorize, arange
-
-    @vectorize
-    def f(x):
-        ^4$if x <= 0:
-            ^8$return x
-        ^4$else:
-            ^8$return 0
-
-    f(arange(-10.0,10.0))
-
-
-
-
-结果: 
-::
-
-    
-    array([-10.,  -9.,  -8.,  -7.,  -6.,  -5.,  -4.,  -3.,  -2.,  -1.,   0.,0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.])
-
-
-
-注册一个函数
-------------------
-
-
-来看这样的一个例子，定义一个类：
-
-
-
- 
-
-::
-
-
-    class Registry(object):
-    def __init__(self):
-        ^4$self._data = {}
-    def register(self, f, name=None):
-        ^4$if name == None:
-            ^8$name = f.__name__
-        ^4$self._data[name] = f
-        ^4$setattr(self, name, f)
-
-
-*register* 方法接受一个函数，将这个函数名作为属性注册到对象中。
-
-
-
-
-产生该类的一个对象：
-
-
-
- 
-
-::
-
-    registry = Registry()
-
-
-
-使用该对象的 *register* 方法作为修饰符：
-
-
-
- 
-
-::
-
-    @registry.register
-    def greeting():
-        ^4$print "hello world"
-
-
-这样这个函数就被注册到 *registry* 这个对象中去了：
-
-
-
- 
-
-::
-
-
-    registry._data
-
-
-结果: 
-::
-
-    {'greeting': <function __main__.greeting>}
-
-
-
- 
-
-::
-
-
-    registry.greeting
-
-
-结果: 
-::
-
-    <function __main__.greeting>
-
-
-
-*flask* ，一个常用的网络应用，处理 url 的机制跟这个类似。
-
-
-
-
-使用 @wraps
-----------------
-
-
-一个通常的问题在于：
-
-
-
- 
-
-::
-
-     def logging_call(f):
-        ^4$def wrapper(*a, **kw):
-            ^8$print 'calling {}'.format(f.__name__)
-            ^8$return f(*a, **kw)
-        ^4$return wrapper
-
-    @logging_call
-    def square(x):
-        '''
-       square function.
-        '''
-       return x ** 2
-
-    print square.__doc__, square.__name__
-
-
-
-None wrapper
-
-
-
-我们使用修饰符之后，*square* 的 *metadata* 完全丢失了，返回的函数名与函数的 *docstring* 都不对。
-
-一个解决的方法是从 *functools* 模块导入 *wraps* 修饰符来修饰我们的修饰符：
-
-
-
- 
-
-::
-
-     import functools
-
-    def logging_call(f):
-        @functools.wraps(f)
-        ^4$def wrapper(*a, **kw):
-            ^8$print 'calling {}'.format(f.__name__)
-            ^8$return f(*a, **kw)
-        ^4$return wrapper
-
-    @logging_call
-    def square(x):
-       '''
-       square function.
-       '''
-       return x ** 2
-
-    print square.__doc__, square.__name__
- 
-
-<button>Run ...</button>
-
-
-    square function.
-     square
-
-
-
-现在这个问题解决了，所以在自定义修饰符方法的时候为了避免出现不必要的麻烦，尽量使用* wraps* 来修饰修饰符！
-
-Class 修饰符
----------------
-
-
-
-与函数修饰符类似，类修饰符是这样一类函数，接受一个类作为参数，通常返回一个新的类。
-
-
-
-作者 & 更新时间
-------------------------------------
-作者:李金  lijinwithyou@gmail.com
-
-提交: 2017/12/6
-
diff --git a/course/source/dive-into-python/cn/with.rst b/course/source/dive-into-python/cn/with.rst
deleted file mode 100644
index 0a9b610..0000000
--- a/course/source/dive-into-python/cn/with.rst
+++ /dev/null
@@ -1,504 +0,0 @@
-with 语句和上下文管理器
-================================
-
-
-::
-
-    # create/aquire some resource
-    ...
-    try:
-        # do something with the resource
-        ...
-    finally:
-        # destroy/release the resource
-        ...
-
-
-处理文件，线程，数据库，网络编程等等资源的时候，我们经常需要使用上面这样的代码形式，以确保资源的正常使用和释放。
-
-好在Python 提供了 with 语句帮我们自动进行这样的处理，例如之前在打开文件时我们使用：
-
-
-
-::
-
-    with open('my_file', 'w') as fp:
-        ^4$# do stuff with fp
-        ^4$data = fp.write("Hello world")
-
-<button>Run ...</button>
-
-
-这等效于下面的代码，但是要更简便：
-
-::
-
-    fp = open('my_file', 'w')
-    try:
-        ^4$# do stuff with f
-        ^4$data = fp.write("Hello world")
-    finally:
-        ^4$fp.close()
-
-<button>Run ...</button>
-
-上下文管理器
----------------
-
-
-其基本用法如下：
-
-::
-
-    with <expression>:
-         ^4$<block>
-
-
-*<expression>* 执行的结果应当返回一个实现了上下文管理器的对象，即实现这样两个方法，*__enter__* 和 *__exit__*：
-
-
-::
-
-    with open('my_file', 'w') as fp:
-        ^4$# do stuff with fp
-        ^4$data = fp.write("Hello world")
-
-        ^4$print fp.__enter__
-        ^4$print fp.__exit__
-
-输出如下：
-::
-
-    <built-in method __enter__ of file object at 0x0000000003C1D540>
-    <built-in method __exit__ of file object at 0x0000000003C1D540>
-
-
-
-*__enter__ *方法在 *<block>* 执行前执行，而 *__exit__ *在 *<block>* 执行结束后执行：
-
-
-比如可以这样定义一个简单的上下文管理器：
-
-
-
-::
-
-    class ContextManager(object):
-        ^4$def __enter__(self):
-            ^8$print "Entering"
-
-        ^4$def __exit__(self, exc_type, exc_value, traceback):
-            ^4$print "Exiting"
-
-
-
-    with ContextManager():
-        ^4$print "  Inside the with statement"
-
-
-<button>Run ...</button>
-
-
-即使 *<block>* 中执行的内容出错，*__exit__* 也会被执行：
-
-
-
-::
-
-    class ContextManager(object):
-        ^4$def __enter__(self):
-            ^8$print "Entering"
-
-        ^4$def __exit__(self, exc_type, exc_value, traceback):
-            ^4$print "Exiting"
-
-
-    with ContextManager():
-        ^4$print 1/0
-
-<button>Run ...</button>
-
-输出会是：
-
-::
-
-    Entering
-    Exiting
-
-    ZeroDivisionError Traceback (most recent call last)
-
-    ZeroDivisionError: integer division or modulo by zero
-
-
-*__enter__* 的返回值
----------------------
-
-如果在 *__enter__ *方法下添加了返回值，那么我们可以使用 *as* 把这个返回值传给某个参数：
-
-
-
-::
-
-    class ContextManager(object):
-        ^4$def __enter__(self):
-            ^8$print "Entering"
-            ^8$return "my value"
-
-        ^4$def __exit__(self, exc_type, exc_value, traceback):
-            ^8$print "Exiting"
-
-
-    #将 *__enter__*返回的值传给 *value* 变量：
-    with ContextManager() as value:
-        ^4$print value
-
-<button>Run ...</button>
-
-输出应该是：
-
-::
-
-    Entering
-    my value
-    Exiting
-
-
-一个通常的做法是将 *__enter__* 的返回值设为这个上下文管理器对象本身，文件对象就是这样做的：
-
-
-
-::
-
-
-    fp = open('my_file', 'r')
-    print fp.__enter__()
-    fp.close()
-    import os
-    os.remove('my_file')
-
-
-
-<button>Run ...</button>
-
-
-实现方法非常简单：
-
-
-::
-
-    class ContextManager(object):
-        ^4$def __enter__(self):
-            ^8$print "Entering"
-            ^8$return self
-
-        ^4$def __exit__(self, exc_type, exc_value, traceback):
-            ^8$print "Exiting"
-
-<button>Run ...</button>
-
-
-
-::
-
-    with ContextManager() as value:
-        ^4$print value
-
-
-输出是：
-::
-
-    Entering
-    <__main__.ContextManager object at 0x0000000003D48828>
-    Exiting
-
-
-错误处理
--------------
-
-上下文管理器对象将错误处理交给 *__exit__* 进行，可以将错误类型，错误值和 *traceback* 等内容作为参数传递给 *__exit__* 函数：
-
-
-::
-
-    class ContextManager(object):
-        ^4$def __enter__(self):
-            ^8$print "Entering"
-
-        ^4$def __exit__(self, exc_type, exc_value, traceback):
-            ^8$print "Exiting"
-            ^4$if exc_type is not None:
-                ^8$print "  Exception:", exc_value
-
-    #如果没有错误，这些值都将是 *None*, 当有错误发生的时候：
-
-    with ContextManager():
-        ^4$print 1/0
-
-
-<button>Run ...</button>
-
-输出是：
-::
-
-    Entering
-    Exiting
-    Exception: integer division or modulo by zero
-
-    ZeroDivisionError Traceback (most recent call last)
-    ZeroDivisionError: integer division or modulo by zero
-
-
-在这个例子中，我们只是简单的显示了错误的值，并没有对错误进行处理，所以错误被向上抛出了，如果不想让错误抛出，只需要将 *__exit__* 的返回值设为* True*：
-
-
-
-::
-
-    class ContextManager(object):
-        ^4$def __enter__(self):
-            ^8$print "Entering"
-
-        ^4$def __exit__(self, exc_type, exc_value, traceback):
-            ^8$print "Exiting"
-            ^4$if exc_type is not None:
-                ^8$print " Exception suppresed:", exc_value
-                ^8$return True
-
-    with ContextManager():
-        ^4$print 1/0
-
-
-<button>Run ...</button>
-
-
-输出是：
-::
-
-    Entering
-    Exiting
-    Exception suppresed: integer division or modulo by zero
-
-
-在这种情况下，错误就不会被向上抛出。
-
-
-数据库的例子
--------------
-
-对于数据库的 *transaction* 来说，如果没有错误，我们就将其 *commit* 进行保存，如果有错误，那么我们将其回滚到上一次成功的状态。
-
-
-::
-
-    class Transaction(object):
-        ^4$def __init__(self, connection):
-            ^8$self.connection = connection
-
-        ^4$def __enter__(self):
-            ^8$return self.connection.cursor()
-
-        ^4$def __exit__(self, exc_type, exc_value, traceback):
-            ^8$if exc_value is None:
-                ^12$# transaction was OK, so commit
-                ^12$self.connection.commit()
-            ^8$else:
-                ^12$# transaction had a problem, so rollback
-                self.connection.rollback()
-
-    #建立一个数据库，保存一个地址表：
-
-    import sqlite3 as db
-    connection = db.connect(":memory:")
-
-    with Transaction(connection) as cursor:
-        ^4$cursor.execute("""CREATE TABLE IF NOT EXISTS addresses (
-            ^8$address_id INTEGER PRIMARY KEY,
-            ^8$street_address TEXT,
-            ^8$city TEXT,
-            ^8$state TEXT,
-            ^8$country TEXT,
-            ^8$postal_code TEXT
-        ^4$)""")
-
-
-    #插入数据：
-
-    with Transaction(connection) as cursor:
-        ^4$cursor.executemany("""INSERT OR REPLACE INTO addresses VALUES (?, ?, ?, ?, ?, ?)""", [
-            ^8$(0, '515 Congress Ave', 'Austin', 'Texas', 'USA', '78701'),
-            ^8$(1, '245 Park Avenue', 'New York', 'New York', 'USA', '10167'),
-            ^8$(2, '21 J.J. Thompson Ave.', 'Cambridge', None, 'UK', 'CB3 0FA'),
-            ^8$(3, 'Supreme Business Park', 'Hiranandani Gardens, Powai, Mumbai', 'Maharashtra', 'India', '400076'),
-        ^4$])
-
-    #假设插入数据之后出现了问题：
-    with Transaction(connection) as cursor:
-        ^4$cursor.execute("""INSERT OR REPLACE INTO addresses VALUES (?, ?, ?, ?, ?, ?)""",
-            ^8$(4, '2100 Pennsylvania Ave', 'Washington', 'DC', 'USA', '78701'),
-        ^4$)
-        ^4$raise Exception("out of addresses")
-
-<button>Run ...</button>
-
-
-输出为：
-
-::
-
-    Exception Traceback (most recent call last)
-    ...
-    Exception: out of addresses
-
-
-最新的一次插入将不会被保存，而是返回上一次 commit 成功的状态：
-
-
-
-如果继续执行：
-
-::
-
-    cursor.execute("SELECT * FROM addresses")
-    for row in cursor:
-        ^4$print row
-
-
-输出为：
-::
-
-    (0, u'515 Congress Ave', u'Austin', u'Texas', u'USA', u'78701')
-
-    (1, u'245 Park Avenue', u'New York', u'New York', u'USA', u'10167')
-
-    (2, u'21 J.J. Thompson Ave.', u'Cambridge', None, u'UK', u'CB3 0FA')
-
-    (3, u'Supreme Business Park', u'Hiranandani Gardens, Powai, Mumbai', u'Maharashtra', u'India', u'400076')
-
-
-contextlib 模块
-----------------
-
-很多的上下文管理器有很多相似的地方，为了防止写入很多重复的模式，可以使用 *contextlib* 模块来进行处理。
-
-最简单的处理方式是使用 *closing* 函数确保对象的 *close()* 方法始终被调用：
-
-::
-
-    from contextlib import closing
-    import urllib
-
-    with closing(urllib.urlopen('http://www.qpython.com')) as url:
-        ^4$html = url.read()
-
-    print html[:100]
-
-
-<button>Run ...</button>
-
-
-另一个有用的方法是使用修饰符 *@contextlib*：
-
-
-::
-
-    from contextlib import contextmanager
-
-    @contextmanager
-    def my_contextmanager():
-        ^4$print "Enter"
-        ^4$yield
-        ^4$print "Exit"
-
-    with my_contextmanager():
-        ^4$print "  Inside the with statement"
-
-<button>Run ...</button>
-
-输出应是：
-::
-
-    Enter
-    Inside the with statement
-    Exit
-
-
-*yield *之前的部分可以看成是* __enter__* 的部分，*yield* 的值可以看成是 *__enter__* 返回的值，*yield *之后的部分可以看成是* __exit__* 的部分。
-
-使用 *yield* 的值：
-
-
-
-::
-
-
-    @contextmanager
-    def my_contextmanager():
-        print "Enter"
-        yield "my value"
-        print "Exit"
-
-    with my_contextmanager() as value:
-        print value
-
-
-输出是：
-::
-
-    Enter
-    my value
-    Exit
-
-
-错误处理可以用 *try* 块来完成：
-
-::
-
-    @contextmanager
-    def my_contextmanager():
-        ^4$print "Enter"
-        ^4$try:
-            ^8$yield
-        except Exception as exc:
-            ^8$print "   Error:", exc
-        finally:
-            ^4$print "Exit"
-
-
-    with my_contextmanager():
-        ^4$print 1/0
-
-<button>Run ...</button>
-
-输出为：
-::
-
-    Enter
-    Error: integer division or modulo by zero
-    Exit
-
-
-对于之前的数据库 *transaction* 我们可以这样定义：
-
-
-::
-
-    @contextmanager
-    def transaction(connection):
-        ^4$cursor = connection.cursor()
-        ^4$try:
-            ^8$yield cursor
-        ^4$except:
-            ^8$connection.rollback()
-            ^8$raise
-        ^4$else:
-            ^8$connection.commit()
-
-<button>Run ...</button>
-
-
-作者 & 更新时间
-------------------------------------
-作者:`李金  <lijinwithyou@gmail.com>`
-
-提交: 2017/12/6
-
diff --git a/course/source/dive-into-python/index.rst b/course/source/dive-into-python/index.rst
deleted file mode 100644
index d971f49..0000000
--- a/course/source/dive-into-python/index.rst
+++ /dev/null
@@ -1,19 +0,0 @@
-Dive into Python
-=======================
-The courses are still in the planning. Welcome to reward us. 
-
-1. **Primary course**
-
-If the total number of people who reward us is more than 100, we promise to complete the primary course within 1 week, so that, after finishing your study, you can master the basic concepts and write simple scripts.
-
-2. **Intermediate course**
-
-If the total number of people who reward us is more than 500, we will launch the intermediate course within 2 weeks, so that, after finishing your study, you will be familiar with the knowledge and framework of the course and apply it to practical development.
-
-3. **Advanced course**
-
-If the total number of people who reward us is more than 2,000, we will launch the advanced course in a month, so that, after finishing your study, you will have a better understanding of the principles of the framework and be able to do advanced development.
-
-
-**The primary course is free to all, and the intermediate and advanced courses are only open to those who have rewarded us.**
-
diff --git a/course/source/django/index.rst b/course/source/django/index.rst
deleted file mode 100644
index 49feed4..0000000
--- a/course/source/django/index.rst
+++ /dev/null
@@ -1,19 +0,0 @@
-Django Programming
-=======================
-The courses are still in the planning. Welcome to reward us. 
-
-1. **Primary course**
-
-If the total number of people who reward us is more than 100, we promise to complete the primary course within 1 week, so that, after finishing your study, you can master the basic concepts and write simple scripts.
-
-2. **Intermediate course**
-
-If the total number of people who reward us is more than 500, we will launch the intermediate course within 2 weeks, so that, after finishing your study, you will be familiar with the knowledge and framework of the course and apply it to practical development.
-
-3. **Advanced course**
-
-If the total number of people who reward us is more than 2,000, we will launch the advanced course in a month, so that, after finishing your study, you will have a better understanding of the principles of the framework and be able to do advanced development.
-
-
-**The primary course is free to all, and the intermediate and advanced courses are only open to those who have rewarded us.**
-
diff --git a/course/source/featured/featured_2017_0821.rst b/course/source/featured/featured_2017_0821.rst
deleted file mode 100644
index e09e226..0000000
--- a/course/source/featured/featured_2017_0821.rst
+++ /dev/null
@@ -1,13 +0,0 @@
-QPython Daily Featured
-========================
-
-How to help with translate QPython to other languages?
-------------------------------------------------------
-Thank you very much for being willing to contribute, pelase see `QPython localization project <https://github.com/qpython-android/localization>`_ for more detail.  by *Railson Oliveira ( from facebook )*
-
-Run Kivy app don't show log information
-----------------------------------------
-How to avoid the qpython miss important log in kivy project ? Just see  `an example of changing qpython log behavior <https://github.com/qpython-android/qpython/issues/24>`_ by  *yingshaoxo ( from github )*
-
-
-*If you have any question, please leave a message to us, we will answer in next featured.*
diff --git a/course/source/featured/featured_2017_0822.rst b/course/source/featured/featured_2017_0822.rst
deleted file mode 100644
index ffec3a6..0000000
--- a/course/source/featured/featured_2017_0822.rst
+++ /dev/null
@@ -1,10 +0,0 @@
-QPython Daily Featured
-========================
-
-How to copy and paste text in terminal?
-----------------------------------------
-*By Neelu from Facebook*
-
-By long touching on the terminal, the menu shows, you can copy text through "Select text"  and paste text through "Paste".
-
-.. image:: http://edu.qpython.org/static/terminal-howto-select-text.png
diff --git a/course/source/featured/featured_2017_0923.rst b/course/source/featured/featured_2017_0923.rst
deleted file mode 100644
index 3a9274a..0000000
--- a/course/source/featured/featured_2017_0923.rst
+++ /dev/null
@@ -1,23 +0,0 @@
-QPython Featured
-========================
-
-How to close the push notification
-------------------------------------------------------
-Last time, someone asked how to close the push notification. The fact is QPython didn't support that function, so we stopped sending push notifications. But now QPython supports closing push notifications. Besides, QPython supports closing log notifications too. You can find these options in your QPython's setting page.
-
-
-We don't recommend to close notifications because you may lost some amazing featured contents.
-
-What's new recently
---------------------
-We published a stable version (v2.0.5) a few days ago, which supports Russian language and fixes some bugs like FTP issue.
-
-We published a BETA version (v2.0.6) yesterday which fixes sqlite3 error (get it from https://github.com/qpython-android/qpython/releases) and allows users to call QPython API with service.
-
-
-The QPython community
--------------------------
-More and more talent people join the QPython community (https://www.facebook.com/groups/qpython)  recently.  And we hope that we could learn from each other.
-
-
-*If you want to talk to us, please leave a message to us through twitter ( @qpython ), and we will reply you as soon as possible.*
diff --git a/course/source/featured/featured_2017_1013.rst b/course/source/featured/featured_2017_1013.rst
deleted file mode 100644
index b256bd9..0000000
--- a/course/source/featured/featured_2017_1013.rst
+++ /dev/null
@@ -1,18 +0,0 @@
-Hello
-========
-
-
-What's new
------------------------------------------------
-
-We are working on the AIPY branch which offers data analysis and deeplearning features. It mainly includes numpy, scipy, matplitlib, pandas, theano, scikit-learn and keras etc. Please follow us on facebook (http://www.facebook.com/qpython) to get it's progress.
-
-A Chinese qpython users group is going to launching a QPython Programming contest, if you want to join this contest, please follow wechat GONGZHONGHAO qpython to get the detail information.
-
-
-Saving scripts to cloud
------------------------
-Newest QPython (2.0.6) has support saving script to cloud (require google login), Would you like give a try ?
-
-
-*If you want to talk to us, please leave a message to us through twitter ( @qpython ), and we will reply you as soon as possible.*
diff --git a/course/source/featured/featured_2018_0116.rst b/course/source/featured/featured_2018_0116.rst
deleted file mode 100644
index 7a0c174..0000000
--- a/course/source/featured/featured_2018_0116.rst
+++ /dev/null
@@ -1,52 +0,0 @@
-QPY Programming Challenge 0x00
-================================================
-
-.. image:: http://edu.qpython.org/static/qpyprogrammingchanlleage-0x00.png
-    :alt: QPython - QPY Programming Challenge 0x00
-
-
-Did you learn the word "Pythonic" when you first write Python?
-
-Have you read the P2P transmission of 15 lines, neural network of 11 lines or block chain python code of 50 lines?
-
-Even if you have written countless Bugs, do you still remember the heart that you wanted to write Simple and elegant code at first?
-
-Why not try to put down the impetuous and try to revisit Pythonic?
-
-
-QPY Programming Challenge
------------------------------------------------
-Since 2018, we plan to start QPython Challenge once a month.
-
-
-
-How to submit
------------------------
-You can send your entries to us via email (support@qpython.org)，note QPyC20181 participate in this event and your nickname.
-
-You can submit multiple times, we will take the final manuscript as your final work. The event at the end of the month, the highest percentage of votes is the champion.
-
-
-Evaluation methods
------------------------
-At the end of the month, we will issue a document on QPython community to list the entries, Each contest will voted the user selected the most Pythonic code winner, Domestic winners receive QPython T-Shirt or themed souvenirs.
-
-The 0x00 issue of the challenge
---------------------------------
-#The first topic
-The title of Challenge 2 is already in the article, please find it.
-
-
-# The second topic
-100 * 100 a chessboard, the first piece at the position [0, 0],Every second piece random walk horizontal or vertical step, At the same time there are n traps on the board, traps arranged randomly. Known traps abscissa i(nt[]) coordx and ordinate (int[]) coordy,how many steps can a player move without falling into the trap?
-
-
-Take out QPython in your pocket and write out the Pythonic code comments on the subject two subject names and send them to us, Compared to other QPythoners, see who is more Pythonic?
-
-
-Join the QPython community
-----------------------------
-
-* `QPython@Facebook <https://www.facebook.com/groups/qpython>`_
-* `QPython@G+ <https://plus.google.com/u/2/communities/111759148772865961493>`_
-
diff --git a/course/source/featured/featured_2018_0301_notebook.rst b/course/source/featured/featured_2018_0301_notebook.rst
deleted file mode 100644
index adeb72e..0000000
--- a/course/source/featured/featured_2018_0301_notebook.rst
+++ /dev/null
@@ -1,74 +0,0 @@
-Share a live documents with QPyNotebook
-========================================
-
-Do you want some application which helps you create and share documents that contain live Python code, equations, visualizations and narrative text?
-
-Now, QPyNotebook for QPython is published, which is developed based on the great opensource project jupyter notebook, it will help you do the following jobs easily like data cleaning and transformation, numerical simulation, statistical modeling, data visualization, machine learning and much more.
-
-Before Use
------------
-**1. Make sure you have installed the newest QPython (>=2.2.0)**
-
-**2. Download QPyNotebook**
-
-- Click the QPyNotebook App button to download QPyNotebook, the button will take you to the Google Play App Store.
-
-- If you could not connect to Google you can download directly from our `Github page <https://github.com/qpython-android/notebook/releases/>`_ *(please check the "Trust unknow source" in system's setting if you install it with APK downloaded from github)*
-
-.. image:: http://edu.qpython.org/static/notebook-setting.png
-    :alt: Enable QPyNotebook in QPython Setting
-
-
-
-**3. Download related libraries**
-
-Click the QPyNotebook Libraries button and there'll be a terminal window to download those libraries. It may take a while, and after downloading finish, the terminal window will be closed automatically.
-
-.. image:: http://edu.qpython.org/static/notebook-lib.png
-    :alt: Enable QPyNotebook in QPython Setting
-
-
-
-.. image:: http://edu.qpython.org/static/notebook-download.png
-    :alt: Download libraries
-
-
-**Now all set!**
-
-.. image:: http://edu.qpython.org/static/notebook-downloaded.png
-    :alt: all set
-
-
-How to use
------------------------
-Open the Explorer from QPython dashboard, find some *.ipynb files (For example notebooks/Welcome.ipynb) and open it, then you will start the QPyNotebook.
-
-- You could start the QPyNotebook from QPython's explorer*
-
-.. image:: http://edu.qpython.org/static/notebook-explorer.png
-    :alt: You could start QPyNotebook from QPython's explorer
-
-- Or from QPyNotebook App
-
-.. image:: http://edu.qpython.org/static/notebook-app.png
-    :alt: Start QPyNotebook
-
-*QPyNotebook's live documentation*
-
-.. image:: http://edu.qpython.org/static/notebook-page.png
-    :alt: Start to access Welcome.ipynb
-
-
-Feedback
------------------------
-Feel free to give us any feedback please.
-
-* `@qpython <http://twitter.com/qpython>`_
-* `support@qpython.org <support@qpython.org>`_
-
-Join the community
-----------------------------
-
-* `QPython@G+ <https://plus.google.com/u/2/communities/111759148772865961493>`_
-* `QPython@Facebook <https://www.facebook.com/groups/qpython>`_
-
diff --git a/course/source/featured/index.rst b/course/source/featured/index.rst
deleted file mode 100644
index 0a0ec80..0000000
--- a/course/source/featured/index.rst
+++ /dev/null
@@ -1,8 +0,0 @@
-QPython Featured
-========================
-
-How to help with translate QPython to other language?
-------------------------------------------------------
-
-
-.. image:: http://dl.qpy.io/assets/banners/course-qpython-quick-start.png
diff --git a/course/source/featured/tpl.rst b/course/source/featured/tpl.rst
deleted file mode 100644
index bb656e9..0000000
--- a/course/source/featured/tpl.rst
+++ /dev/null
@@ -1,8 +0,0 @@
-QPython Featured
-========================
-
-Question
-------------------------------------------------------
-Answer
-
-.. image:: http://dl.qpy.io/assets/banners/course-qpython-quick-start.png
diff --git a/course/source/full-text-search/index.rst b/course/source/full-text-search/index.rst
deleted file mode 100644
index 54e949b..0000000
--- a/course/source/full-text-search/index.rst
+++ /dev/null
@@ -1,19 +0,0 @@
-Full Text Search with Python
-=============================
-The courses are still in the planning. Welcome to reward us. 
-
-1. **Primary course**
-
-If the total number of people who reward us is more than 100, we promise to complete the primary course within 1 week, so that, after finishing your study, you can master the basic concepts and write simple scripts.

-
-2. **Intermediate course**
-
-If the total number of people who reward us is more than 500, we will launch the intermediate course within 2 weeks, so that, after finishing your study, you will be familiar with the knowledge and framework of the course and apply it to practical development.
-
-3. **Advanced course**
-
-If the total number of people who reward us is more than 2,000, we will launch the advanced course in a month, so that, after finishing your study, you will have a better understanding of the principles of the framework and be able to do advanced development.
-
-
-**The primary course is free to all, and the intermediate and advanced courses are only open to those who have rewarded us.**
-
diff --git a/course/source/interesting-3rd-packages/index.rst b/course/source/interesting-3rd-packages/index.rst
deleted file mode 100644
index 0654a8b..0000000
--- a/course/source/interesting-3rd-packages/index.rst
+++ /dev/null
@@ -1,19 +0,0 @@
-Python Advanced
-=======================
-The courses are still in the planning. Welcome to reward us. 
-
-1. **Primary course**
-
-If the total number of people who reward us is more than 100, we promise to complete the primary course within 1 week, so that, after finishing your study, you can master the basic concepts and write simple scripts.

-
-2. **Intermediate course**
-
-If the total number of people who reward us is more than 500, we will launch the intermediate course within 2 weeks, so that, after finishing your study, you will be familiar with the knowledge and framework of the course and apply it to practical development.
-
-3. **Advanced course**
-
-If the total number of people who reward us is more than 2,000, we will launch the advanced course in a month, so that, after finishing your study, you will have a better understanding of the principles of the framework and be able to do advanced development.
-
-
-**The primary course is free to all, and the intermediate and advanced courses are only open to those who have rewarded us.**
-
diff --git a/course/source/kivy-qpython/cn/index.rst b/course/source/kivy-qpython/cn/index.rst
deleted file mode 100644
index bd4b423..0000000
--- a/course/source/kivy-qpython/cn/index.rst
+++ /dev/null
@@ -1,53 +0,0 @@
-QPython的KivyApp
-==========================
-Kivy是Python上一个支持多点触摸的跨平台图形界面开发框架，使用Kivy可以方便地开发包括游戏在内的图形界面程序。QPython能较好地支持Kivy。
-
-Kivy的安装
-----------
-在使用Kivy之前，您需要先安装Kivy。您可以在QPYPI中安装及升级kivy相关的库，QPYPI能自动安装依赖库。如果Kivy有更新，也可以通过QPYPI来进行升级操作。
-
-.. image:: http://edu.qpython.org/static/qpypi-kivy.png
-    :alt: 在QPYPI中管理kivy库
-
-
-KivyApp声明
------------
-在QPython中，通过头部声明来区分程序类型，当头部有以下定义时，我们知道它是一个KivyApp程序。
-
-::
-
-    #qpy:kivy
-
-此外，如果想要让你的Kivy程序按照全屏模式运行，您需要增加下面声明。
-
-::
-
-    #qpy:fullscreen
-
-
-KivyApp运行流程
---------------
-在程序被识别为KivyApp运行后，QPython会启动OpenGL引擎，新建一个SDL视图，并在其中完成渲染，输出画面。
-
-
-KivyApp退出流程
---------------
-按退出退出时，QPython会结束Kivy程序，进行清理，结束OpenGL引擎，同时退出SDL视图。 
-
-例子
---------
-让我们来看一个最简单的KivyApp示例，这个是安装完QPython后默认带的示例程序。
-
-::
-
-    #qpy:kivy
-    from kivy.app import App
-    from kivy.uix.button import Button
-
-    class TestApp(App):
-        ^4$def build(self):
-            ^8$return Button(text='Hello World')
-
-    TestApp().run()
-
-<button>Run ...</button>
diff --git a/course/source/kivy-qpython/cn/your-first-kivyapp.rst b/course/source/kivy-qpython/cn/your-first-kivyapp.rst
deleted file mode 100644
index 8c97eca..0000000
--- a/course/source/kivy-qpython/cn/your-first-kivyapp.rst
+++ /dev/null
@@ -1,309 +0,0 @@
-Kivy App 教程>>一个简单的绘画应用程序
-============================================
-
-贡献者
-------------------------------------------------------
-作者: `Kivy <https://kivy.org/>`_
-
-
-
-在接下来的教程中, 您将需要创建一个新的小部件作为引导。 这将在编写Kivy应用程序时提供了丰富并且重要的知识，它可以根据您想要达到的目的让您创建全新的用户界面与自定义元素。 
-
-基本事项
----------------------
-在创建应用程序时，你必须问自己三个重要的问题：
-
-- 我的应用程序要处理什么数据？
-- 我该如何直观地表示这些数据？
-- 用户如何与这些数据交互？
-
-
-举个例子，如果您想写一个非常简单的线画应用程序，让用户用手指在屏幕上画画， 这就是用户和您的应用程序的交互方式。 当用户这么做时, 应用程序将会记住用户手指的位置， 以便稍后您可以在这些位置进行画线。所以手指所在的点将是您的数据，你在它们之间画的线将是您的视觉表现。
-
-
-在Kivy上, 应用程序的用户界面由小部件组成。 您在屏幕上所看的的东西都是由这个小部件绘制而成。 通常情况下，你会想要重写之前在不同背景下的写的代码，这就是为什么小部件典型地代表了一个回答上述三个问题的具体实例。 小部件封装数据，定义用户与数据的交互并绘制其可视化表示。 您可以通过嵌套小部件来构建从简单到复杂的用户界面。 这里有很多内置的小部件，如按钮，滑块和其他常见的东西。在很多情况下， 你可能你需要一个超出Kivy附带范围的自定义小部件 (例如：一个医学可视化部件)。
-
-
-所以，在设计小部件的时候请记住这三个问题。 尝试以最小及可重用的方式写它们 (即一个小部件完全是它应该做的，只不过是其他事情而已。 如果您需要更多，可以编写更多的小部件或编写更小的小部件的其他小部件。 我们试图坚持单一责任原则).
-
-
-
-绘制小部件
-------------
-
-我们确信您从童年开始就梦想创造属于自己的多点触控画图程序。而我们，可以助您圆梦 。在下面的章节中，您将会学习如何使用Kivy来编写这样的程序。 在此之前，请确保您已经阅读和理解创建一个应用程序。准备好了吗？让我们开始吧！ 
-
-
-**初始结构**
-
-
-先来写一个我们需要的基础代码框架。顺便提一句，本节中使用的所有不同的代码片段也可以在Kivy附带的examples / guide / firstwidget目录中找到，所以您可以不用每次都要复制和粘贴 。下面是我们所需要的一个基础代码框架 ：
-
-
-::
-
-    #qpy:kivy
-    from kivy.app import App
-    from kivy.uix.widget import Widget
-
-
-    class MyPaintWidget(Widget):
-        ^4$pass
-
-
-    class MyPaintApp(App):
-        ^4$def build(self):
-            ^8$return MyPaintWidget()
-
-
-    if __name__ == '__main__':
-        ^4$MyPaintApp().run()
-
-<button>Run ...</button>
-
-
-说实话，这很简单。保存为paint.py. 如果你运行它，你应该只会看见黑屏。这时你所看到，并不是使用像Button这样的内置小部件（请参阅创建应用程序），我们将继续编写小部件来完成绘图。我们通过一个从小部件(line 5-6)继承的类来实现这一点，虽然这个类暂时还没什么用，但我们还能像正常的Kivy部件一样对待它（line 11）。if __name__ ...结构（第14行）是一种Python机制，可防止在从文件导入时执行if语句中的代码， 即：如果您编写导入绘图，它不会执行一些计划之外的事情，但可以很好地提供文件中定义的类。
-
-*注意: 您可能想知道为什么您必须单独导入App和Widget， 而不是从kivy导入 *. 尽管时间较短，但这会使你的命名造成污染，并导致应用程序的启动速度变慢。它也可能引入歧义类和变量命名，所以通常在Python社区中被人们所诟病。 我们这样做的方式更快，更全面。*
-
-
-**添加行为**
-
-现在就让我们开始添加一些实际行为，即：即使其对用户输入做出反应。
-
-像这样改编代码：
-
-
-::
-    from kivy.app import App
-    from kivy.uix.widget import Widget
-
-
-    class MyPaintWidget(Widget):
-        ^4$def on_touch_down(self, touch):
-            ^8$print(touch)
-
-
-    class MyPaintApp(App):
-        ^4$def build(self):
-            ^8$return MyPaintWidget()
-
-
-    if __name__ == '__main__':
-        ^4$MyPaintApp().run()
-
-
-<button>Run ...</button>
-
-这只是为了显示对用户输入做出反应的容易程度。当发生MotionEvent（即触摸，点击等）时，我们将会把关于触摸对象的信息打印6给控制台。 在屏幕上您不会看到任何东西，但是如果您观察从中运行程序的命令行，你会看到每一个触摸的消息。这也表明一个小部件不必具有可视化表示。
-
-这些用户体验并不会让人无所适从。接下来让我们在绘制窗口中添加一些实际的代码：
-
-
-::
-
-    #qpy:kivy
-    from kivy.app import App
-    from kivy.uix.widget import Widget
-    from kivy.graphics import Color, Ellipse
-
-    class MyPaintWidget(Widget):
-
-        ^4$def on_touch_down(self, touch):
-            ^8$with self.canvas:
-                ^12$Color(1, 1, 0)
-                ^12$d = 30.
-                ^12$Ellipse(pos=(touch.x - d / 2, touch.y - d / 2), size=(d, d))
-
-
-    class MyPaintApp(App):
-
-        ^4$def build(self):
-            ^8$return MyPaintWidget()
-
-
-    if __name__ == '__main__':
-        ^4$MyPaintApp().run()
-
-<button>Run ...</button>
-
-.. image:: http://edu.qpython.org/static/kivy-guide-3.jpg
-    :alt: screenshot of pong game
-
-
-如果您通过这些修改运行代码，您会看到每次触摸时，你会触摸到一个小的黄色圆圈。 
-
-它是如何工作的？
-
-- Line 9: 我们使用Python的语句和小部件的Canvas对象 这就像是小部件可以在屏幕上绘制事物来表示自己的一个区域。通过使用with语句，所有正确缩进的连续绘图命令都将修改此画布。 with声明还确保在我们绘制之后，内部状态可以被妥善地清理。
-- Line 10: 您可能已经猜到了：这将连续绘制操作的颜色设置为黄色（默认颜色格式为RGB，所以（1,1,0）为黄色）。直到另一个颜色设置。 然后将该颜色当作是您的画笔， 您可以使用它在画布上绘画，直到将画笔切换成另一种颜色。
-- Line 11: 我们需要先确定即将绘制的圆的直径。 使用一个可取的变量，因为我们需要参考该值多次，我们并不希望在哪些地方会改变它，假如我们想圆更大或更小
-- Line 12: 想要绘制一个圆，我们只需绘制一个宽度和高度相等的椭圆。 由于我们希望在用户触摸的位置绘制圆，我们会将触摸的位置传递给椭圆。 请注意，我们需要在x和y方向上（即向左和向下）将椭圆移动到-d / 2，因为位置指定了椭圆边界框的左下角，我们希望它以我们的触摸为中心。
-
-
-那很简单，不是吗？ 它变得更好了！ 更新代码如下所示：
-
-::
-
-    #qpy:kivy
-    from kivy.app import App
-    from kivy.uix.widget import Widget
-    from kivy.graphics import Color, Ellipse, Line
-
-    class MyPaintWidget(Widget):
-
-        ^4$def on_touch_down(self, touch):
-            ^8$with self.canvas:
-                ^12$Color(1, 1, 0)
-                ^12$d = 30.
-                ^12$Ellipse(pos=(touch.x - d / 2, touch.y - d / 2), size=(d, d))
-                ^12$touch.ud['line'] = Line(points=(touch.x, touch.y))
-
-        ^4$def on_touch_move(self, touch):
-            ^8$touch.ud['line'].points += [touch.x, touch.y]
-
-    class MyPaintApp(App):
-
-        ^4$def build(self):
-            ^8$return MyPaintWidget()
-
-    if __name__ == '__main__':
-        ^4$MyPaintApp().run()
-
-<button>Run ...</button>
-
-.. image:: http://edu.qpython.org/static/kivy-guide-4.jpg
-    :alt: screenshot of pong game
-
-
-
-发生了以下的变化：
-
-- Line 3: 我们现在不仅导入了椭圆绘图指令，还导入了线绘图指令如果您查看Line的文档，您将看到它接受的点参数必须是二维点坐标列表，比如 (x1, y1, x2, y2, ..., xN, yN).
-- Line 13: 这是它变得有趣的地方。 touch.ud是一个Python字典（键入<dict>），它允许我们存储触摸的自定义属性。
-- Line 13:我们利用我们导入的Line指令，并设置一个Line来绘制。由于这是在on_touch_down中完成的，所以每次新的触摸都会有一个新的线。 通过在with块内创建行， 画布会感应到该线，并将绘制它。 我们只是想稍后修改这行，所以我们在touch.ud字典下面存储了一个对它的引用，这个字典在任意选择的，但是恰当的命名键'line'下面。 通过我们创建初始触摸位置的线，这是我们的线开始的地方。
-- Lines 15: 我们添加一个新的方法到我们的小部件。这与on_touch_down方法类似，但不是在发生新的触摸时被调用，当现有的触摸（已经调用on_touch_down）移动，即其位置改变时，他的方法被调用。 请注意，这是具有更新属性的相同MotionEvent对象。 这会让人觉得十分方便，你很快就会明白为什么会有这样的感觉了。
-- Line 16: 请记住：这是我们在on_touch_down中获得的触摸对象，因此我们可以简单地访问我们存储在touch.ud字典中的数据！早些时候我们为这条线设置了触摸点，我们现在在当前位置添加新的触摸点。我们需要扩展这条线，因为这发生在on_touch_move上，这只是在触摸移动时才被调用，这正是我们要更新这一行的原因。为了使我们不必将这些触摸点记录到在线簿记中，在touch.ud中存储该行使我们在使用时可以更方便一些。
-
-到目前为止一切还不错，虽然还不算漂亮。这让它看上去有点像意大利面。如何让触摸点有自己的颜色？让我们接着往下看：
-
-::
-
-    #qpy:kivy
-    from random import random
-    from kivy.app import App
-    from kivy.uix.widget import Widget
-    from kivy.graphics import Color, Ellipse, Line
-
-    class MyPaintWidget(Widget):
-
-        ^4$def on_touch_down(self, touch):
-            ^8$color = (random(), random(), random())
-            ^8$with self.canvas:
-                ^12$Color(*color)
-                ^12$d = 30.
-                ^12$Ellipse(pos=(touch.x - d / 2, touch.y - d / 2), size=(d, d))
-                ^12$touch.ud['line'] = Line(points=(touch.x, touch.y))
-
-        ^4$def on_touch_move(self, touch):
-            ^8$touch.ud['line'].points += [touch.x, touch.y]
-
-
-    class MyPaintApp(App):
-
-        ^4$def build(self):
-            ^8$return MyPaintWidget()
-
-
-    if __name__ == '__main__':
-        ^4$MyPaintApp().run()
-
-<button>Run ...</button>
-
-.. image:: http://edu.qpython.org/static/kivy-guide-5.jpg
-    :alt: Screenshot of pong game
-
-Here are the changes:
-
-- Line 1: 我们导入Python的random（）函数，它会给我们在[0.，1）范围内的随机值。
-- Line 10: 在这种情况下，我们只需创建一个包含3个随机浮点值的新元组，它们将代表随机的RGB颜色。这样，每一个新的触摸点都将有属于自己的颜色。 不要被使用元组困惑。我们只是将这个元组绑定在颜色上作为这个方法的一个快捷方式。
-- Line 12: 和上面一样，我们设置画布的颜色。这一次，我们使用我们生成的随机值，并使用Python的元组解压缩语法将它们提供给颜色类（因为Color类需要三个单独的颜色分量，而不是一个。如果我们要直接传递元组，1值被传递，而不管元组本身包含3个值的事实）。
-
-这看起来好多了！假如你有更多 的技能和耐心，您甚至可以创建一个漂亮的小绘图！
-
-
-*注意：由于默认情况下，颜色指令采用RGB模式，我们正在向它提供一个带有三个随机浮点值的元组，如果运气不好，很可能会得到黑色。 这并不是好的情况，因为默认情况下，背景颜色也很暗， 这样的话，你会很难看到你自己画的线。有一个很好的技巧来防止这种情况：而不是创建一个具有三个随机值的元组，像这样创建一个元组：（random（），1.，1）。然后，将它传递给颜色指令时，将模式设置为HSV颜色空间：颜色（* color，mode ='hsv'）。这样可能出现的颜色将会变少，但是你得到的颜色将永远是同样明亮的：只有色调会改变。
-
-
-**奖励积分**
-
-
-在这一点上，我们可以说我们已经完成了。小部件做了它应该做的事：跟踪触摸并画线。在一条线开始的位置绘制圆圈。
-
-但是，如果用户想要开始一个新的绘图呢？使用当前的代码，清除窗口的唯一方法是重新启动整个应用程序。不过，我们可以做得更好。现在让我们添加一个清除按钮，删除迄今为止绘制的所有线条和圆圈。 有两种选择：
-
-- 我们可以创建按钮作为我们的小部件的附件。这意味着如果您创建多个小部件，则每个小部件都有自己的按钮。如果你不小心，这也将允许用户在按钮上绘制，这可能不是你想要的得到的结果。
-- 如果你不小心，这也将允许用户在按钮上绘制，这可能不是你想要的。或者我们只在我们的应用程序类中设置按钮一次，当它被按下时，我们清除这个小部件。
-
-就我们这个简单的例子来说，这并不重要。对于较大的应用程序，您应该考虑一下谁在你的应用程序中做什么。 我们将在这里介绍第二个选项，以便您了解如何在应用程序类的build（）方法中构建应用程序的构件树。我们也将更改为HSV色彩空间（参见前面的注释）：
-
-::
-
-    #qpy:kivy
-    from random import random
-    from kivy.app import App
-    from kivy.uix.widget import Widget
-    from kivy.uix.button import Button
-    from kivy.graphics import Color, Ellipse, Line
-
-    class MyPaintWidget(Widget):
-
-        ^4$def on_touch_down(self, touch):
-            ^8$color = (random(), 1, 1)
-            ^8$with self.canvas:
-                ^12$Color(*color, mode='hsv')
-                ^12$d = 30.
-                ^12$Ellipse(pos=(touch.x - d / 2, touch.y - d / 2), size=(d, d))
-                ^12$touch.ud['line'] = Line(points=(touch.x, touch.y))
-
-        ^4$def on_touch_move(self, touch):
-            ^8$touch.ud['line'].points += [touch.x, touch.y]
-
-
-    class MyPaintApp(App):
-
-        ^4$def build(self):
-            ^8$parent = Widget()
-            ^8$self.painter = MyPaintWidget()
-            ^8$clearbtn = Button(text='Clear')
-            ^8$clearbtn.bind(on_release=self.clear_canvas)
-            ^8$parent.add_widget(self.painter)
-            ^8$parent.add_widget(clearbtn)
-            ^8$return parent
-
-        ^4$def clear_canvas(self, obj):
-            ^8$self.painter.canvas.clear()
-
-
-    if __name__ == '__main__':
-        ^4$MyPaintApp().run()
-
-<button>Run ...</button>
-
-.. image:: http://edu.qpython.org/static/kivy-guide-6.jpg
-    :alt: screenshot of pong game
-
-
-将会发生以下的事情：
-
-- Line 4: 我们添加了一个导入语句，以便能够使用Button类。
-- Line 25: 我们创建一个虚拟的Widget（）对象作为我们的绘画控件和我们即将添加的按钮的父对象。 这只是一个比较简陋的方法来设置一个小部件树层次结构。 我们也可以使用布局或做其他一些丰富的东西。再一次：这个小部件除了保存我们现在添加的两个小部件以外，完全没有任何功能。
-- Line 26:我们像往常一样创建我们的MyPaintWidget（），只是这次我们不直接返回它，而是将它绑定到一个变量名。
-- Line 27: 我们创建一个按钮小部件。 它上面会有一个标签，显示文本“清除”。
-- Line 28: 然后，我们将按钮的on_release事件（当按钮被按下然后释放时触发）绑定到第33行和第34行上定义的回调函数clear_canvas。
-- Line 29 & 30: 我们通过制作虚拟父窗口小部件的画家和clearbtn子元素来设置窗口小部件层次结构。 这意味着画家和clearbtn在通常的计算机科学树术语中是兄弟姐妹。
-- Line 33 & 34: 到目前为止，按钮什么也没做。 它在那里，你可以看见它，也可以按它，但什么都不会发生。我们在这里改变：我们创建了一个小的抛弃函数，当按钮被按下时，它将成为我们的回调函数。该功能只是清除画家的画布内容，使其再次变黑。
-
-*注意：Kivy Widget类在设计上保持简单。没有一般属性，如背景颜色和边框颜色。相反，这些示例和文档说明了如何轻松地处理这些简单的事情，就像我们在这里完成的那样，为画布设置颜色并绘制形状。从一个简单的开始，你可以逐渐使它成为更精细的定制。从Widget派生的更高级别的内置小部件（如Button）确实具有诸如background_color之类的便利属性，但这些属性因widget而异。如果您需要添加更多功能，请使用API文档查看小部件提供的内容以及子类。*
-
-恭喜！ 你已经写了你的第一个Kivy部件。很明显，这只是一个快速介绍。还会有更多的东西等着你去发现。 不过我们建议您短暂休息一下，让刚刚学到的东西沉淀一下。 不如先画一些不错的图片让自己放松一下？如果您觉得您已经理解了所有内容，并准备好学习更多，请继续往下阅读。
diff --git a/course/source/kivy-qpython/index.rst b/course/source/kivy-qpython/index.rst
deleted file mode 100644
index c8829d1..0000000
--- a/course/source/kivy-qpython/index.rst
+++ /dev/null
@@ -1,54 +0,0 @@
-QPython's KivyApp
-==========================
-Kivy is a support multi-touch cross-platform graphical interface development framework in python.Use Kivy can easily develop GUI applications including games.QPython can support Kivy better.
-
-Kivy's installation
-----------
-Before using Kivy, you need to have Kivy installed.You can install and upgrade kivy related libraries in QPYPI,QPYPI can automatically install dependent libraries.If Kivy needs to be updated, You can also upgrade operated by QPYPI.
-
-.. image:: http://edu.qpython.org/static/qpypi-kivy.png
-    :alt: Manage kivy libraries in QPYPI
-
-
-KivyApp'sstatement
------------
-In QPython, the program type distinguished by head declarations, When the header has the following definition, we know it is a KivyApp program.
-
-::
-
-    #qpy:kivy
-
-In addition, If you want to make your Kivy program run in full-screen mode, You need to add the following statement.
-
-::
-
-    #qpy:fullscreen
-
-
-KivyApp's Run Process
---------------
-After the program is identified as KivyApp operation, QPython will start the OpenGL engine at the same time, Create a new SDL view, And in which to complete the rendering, output screen.
-
-
-Exit
------------------------------
-Press quit exiting, QPython will finish the Kivy program, clean up, end OpenGL engine, and exit SDL view.
-
-E.g
---------
-Let's look at a simple example about KivyApp,This is the default sample program installed after installing QPython.
-
-::
-
-    #qpy:kivy
-    from kivy.app import App
-    from kivy.uix.button import Button
-
-    class TestApp(App):
-        ^4$def build(self):
-            ^8$return Button(text='Hello World')
-
-    TestApp().run()
-
-<button>Run ...</button>
-
diff --git a/course/source/kivy-qpython/your-first-kivyapp.rst b/course/source/kivy-qpython/your-first-kivyapp.rst
deleted file mode 100644
index 5af8812..0000000
--- a/course/source/kivy-qpython/your-first-kivyapp.rst
+++ /dev/null
@@ -1,315 +0,0 @@
-Kivy App Tutorials >> A Simple Paint App
-============================================
-
-
-In the following tutorial, you will be guided through the creation of your first widget. This provides powerful and important knowledge when programming Kivy applications, as it lets you create completely new user interfaces with custom elements for your specific purpose.
-
-Basic Considerations
----------------------
-When creating an application, you have to ask yourself three important questions:
-
-- What data does my application process?
-- How do I visually represent that data?
-- How does the user interact with that data?
-
-
-If you want to write a very simple line drawing application for example, you most likely want the user to just draw on the screen with his/her fingers. That’s how the user interacts with your application. While doing so, your application would memorize the positions where the user’s finger were, so that you can later draw lines between those positions. So the points where the fingers were would be your data and the lines that you draw between them would be your visual representation.
-
-
-In Kivy, an application’s user interface is composed of Widgets. Everything that you see on the screen is somehow drawn by a widget. Often you would like to be able to reuse code that you already wrote in a different context, which is why widgets typically represent one specific instance that answers the three questions above. A widget encapsulates data, defines the user’s interaction with that data and draws its visual representation. You can build anything from simple to complex user interfaces by nesting widgets. There are many widgets built in, such as buttons, sliders and other common stuff. In many cases, however, you need a custom widget that is beyond the scope of what is shipped with Kivy (e.g. a medical visualization widget).
-
-
-So keep these three questions in mind when you design your widgets. Try to write them in a minimal and reusable manner (i.e. a widget does exactly what its supposed to do and nothing more. If you need more, write more widgets or compose other widgets of smaller widgets. We try to adhere to the Single Responsibility Principle).
-
-
-
-Paint Widget
-------------
-
-We’re sure one of your childhood dreams has always been creating your own multitouch paint program. Allow us to help you achieve that. In the following sections you will successively learn how to write a program like that using Kivy. Make sure that you have read and understood Create an application. You have? Great! Let’s get started!
-
-
-**Initial Structure**
-
-
-Let’s start by writing the very basic code structure that we need. By the way, all the different pieces of code that are used in this section are also available in the examples/guide/firstwidget directory that comes with Kivy, so you don’t need to copy & paste it all the time. Here is the basic code skeleton that we will need:
-
-
-::
-
-    #qpy:kivy
-    from kivy.app import App
-    from kivy.uix.widget import Widget
-
-
-    class MyPaintWidget(Widget):
-        ^4$pass
-
-
-    class MyPaintApp(App):
-        ^4$def build(self):
-            ^8$return MyPaintWidget()
-
-
-    if __name__ == '__main__':
-        ^4$MyPaintApp().run()
-
-<button>Run ...</button>
-
-
-This is actually really simple. Save it as paint.py. If you run it, you should only see a black screen. As you can see, instead of using a built-in widget such as a Button (see Create an application), we are going to write our own widget to do the drawing. We do that by creating a class that inherits from Widget (line 5-6) and although that class does nothing yet, we can still treat it like a normal Kivy widget (line 11). The if __name__ ... construct (line 14) is a Python mechanism that prevents you from executing the code in the if-statement when importing from the file, i.e. if you write import paint, it won’t do something unexpected but just nicely provide the classes defined in the file.
-
-
-*Note: You may be wondering why you have to import App and Widget separately, instead of doing something like from kivy import *. While shorter, this would have the disadvantage of polluting your namespace and make the start of the application potentially much slower. It can also introduce ambiguity into class and variable naming, so is generally frowned upon in the Python community. The way we do it is faster and cleaner.*
-
-
-**Adding Behaviour**
-
-Let’s now add some actual behaviour to the widget, i.e. make it react to user input. Change the code like so:
-
-
-::
-
-    #qpy:kivy
-    from kivy.app import App
-    from kivy.uix.widget import Widget
-
-
-    class MyPaintWidget(Widget):
-        ^4$def on_touch_down(self, touch):
-            ^8$print(touch)
-
-
-    class MyPaintApp(App):
-        ^4$def build(self):
-            ^8$return MyPaintWidget()
-
-
-    if __name__ == '__main__':
-        ^4$MyPaintApp().run()
-
-<button>Run ...</button>
-
-
-This is just to show how easy it is to react to user input. When a MotionEvent (i.e. a touch, click, etc.) occurs, we simply print the information about the touch object to the console. You won’t see anything on the screen, but if you observe the command-line from which you are running the program, you will see a message for every touch. This also demonstrates that a widget does not have to have a visual representation.
-
-Now that’s not really an overwhelming user experience. Let’s add some code that actually draws something into our window:
-
-
-::
-
-    #qpy:kivy
-    from kivy.app import App
-    from kivy.uix.widget import Widget
-    from kivy.graphics import Color, Ellipse
-
-    class MyPaintWidget(Widget):
-
-        ^4$def on_touch_down(self, touch):
-            ^8$with self.canvas:
-                ^12$Color(1, 1, 0)
-                ^12$d = 30.
-                ^12$Ellipse(pos=(touch.x - d / 2, touch.y - d / 2), size=(d, d))
-
-
-    class MyPaintApp(App):
-
-        ^4$def build(self):
-            ^8$return MyPaintWidget()
-
-
-    if __name__ == '__main__':
-        ^4$MyPaintApp().run()
-
-<button>Run ...</button>
-
-
-.. image:: http://edu.qpython.org/static/kivy-guide-3.jpg
-    :alt: screenshot of pong game
-    :align: center
-
-
-If you run your code with these modifications, you will see that every time you touch, there will be a small yellow circle drawn where you touched. How does it work?
-
-- Line 9: We use Python’s with statement with the widget’s Canvas object. This is like an area in which the widget can draw things to represent itself on the screen. By using the with statement with it, all successive drawing commands that are properly indented will modify this canvas. The with statement also makes sure that after our drawing, internal state can be cleaned up properly.
-- Line 10: You might have guessed it already: This sets the Color for successive drawing operations to yellow (default color format is RGB, so (1, 1, 0) is yellow). This is true until another Color is set. Think of this as dipping your brushes in that color, which you can then use to draw on a canvas until you dip the brushes into another color.
-- Line 11: We specify the diameter for the circle that we are about to draw. Using a variable for that is preferable since we need to refer to that value multiple times and we don’t want to have to change it in several places if we want the circle bigger or smaller.
-- Line 12: To draw a circle, we simply draw an Ellipse with equal width and height. Since we want the circle to be drawn where the user touches, we pass the touch’s position to the ellipse. Note that we need to shift the ellipse by -d/2 in the x and y directions (i.e. left and downwards) because the position specifies the bottom left corner of the ellipse’s bounding box, and we want it to be centered around our touch.
-
-
-That was easy, wasn’t it? It gets better! Update the code to look like this:
-
-::
-
-    #qpy:kivy
-    from kivy.app import App
-    from kivy.uix.widget import Widget
-    from kivy.graphics import Color, Ellipse, Line
-
-    class MyPaintWidget(Widget):
-
-        ^4$def on_touch_down(self, touch):
-            ^8$with self.canvas:
-                ^12$Color(1, 1, 0)
-                ^12$d = 30.
-                ^12$Ellipse(pos=(touch.x - d / 2, touch.y - d / 2), size=(d, d))
-                ^12$touch.ud['line'] = Line(points=(touch.x, touch.y))
-
-        ^4$def on_touch_move(self, touch):
-            ^8$touch.ud['line'].points += [touch.x, touch.y]
-
-    class MyPaintApp(App):
-
-        ^4$def build(self):
-            ^8$return MyPaintWidget()
-
-    if __name__ == '__main__':
-        ^4$MyPaintApp().run()
-
-<button>Run ...</button>
-
-
-.. image:: http://edu.qpython.org/static/kivy-guide-4.jpg
-    :alt: screenshot of pong game
-    :align: center
-
-
-
-This is what has changed:
-
-- Line 3: We now not only import the Ellipse drawing instruction, but also the Line drawing instruction. If you look at the documentation for Line, you will see that it accepts a points argument that has to be a list of 2D point coordinates, like (x1, y1, x2, y2, ..., xN, yN).
-- Line 13: This is where it gets interesting. touch.ud is a Python dictionary (type <dict>) that allows us to store custom attributes for a touch.
-- Line 13: We make use of the Line instruction that we imported and set a Line up for drawing. Since this is done in on_touch_down, there will be a new line for every new touch. By creating the line inside the with block, the canvas automatically knows about the line and will draw it. We just want to modify the line later, so we store a reference to it in the touch.ud dictionary under the arbitrarily chosen but aptly named key ‘line’. We pass the line that we’re creating the initial touch position because that’s where our line will begin.
-- Lines 15: We add a new method to our widget. This is similar to the on_touch_down method, but instead of being called when a new touch occurs, this method is being called when an existing touch (for which on_touch_down was already called) moves, i.e. its position changes. Note that this is the same MotionEvent object with updated attributes. This is something we found incredibly handy and you will shortly see why.
-- Line 16: Remember: This is the same touch object that we got in on_touch_down, so we can simply access the data we stored away in the touch.ud dictionary! To the line we set up for this touch earlier, we now add the current position of the touch as a new point. We know that we need to extend the line because this happens in on_touch_move, which is only called when the touch has moved, which is exactly why we want to update the line. Storing the line in the touch.ud makes it a whole lot easier for us as we don’t have to maintain our own touch-to-line bookkeeping.
-
-So far so good. This isn’t exactly beautiful yet, though. It looks a bit like spaghetti bolognese. How about giving each touch its own color? Great, let’s do it:
-
-::
-
-    #qpy:kivy
-    from random import random
-    from kivy.app import App
-    from kivy.uix.widget import Widget
-    from kivy.graphics import Color, Ellipse, Line
-
-    class MyPaintWidget(Widget):
-
-        ^4$def on_touch_down(self, touch):
-            ^8$color = (random(), random(), random())
-            ^8$with self.canvas:
-                ^12$Color(*color)
-                ^12$d = 30.
-                ^12$Ellipse(pos=(touch.x - d / 2, touch.y - d / 2), size=(d, d))
-                ^12$touch.ud['line'] = Line(points=(touch.x, touch.y))
-
-        ^4$def on_touch_move(self, touch):
-            ^8$touch.ud['line'].points += [touch.x, touch.y]
-
-
-    class MyPaintApp(App):
-
-        ^4$def build(self):
-            ^8$return MyPaintWidget()
-
-
-    if __name__ == '__main__':
-        ^4$MyPaintApp().run()
-
-<button>Run ...</button>
-
-
-.. image:: http://edu.qpython.org/static/kivy-guide-5.jpg
-    :alt: Screenshot of pong game
-    :align: center
-
-Here are the changes:
-
-- Line 1: We import Python’s random() function that will give us random values in the range of [0., 1.).
-- Line 10: In this case we simply create a new tuple of 3 random float values that will represent a random RGB color. Since we do this in on_touch_down, every new touch will get its own color. Don’t get confused by the use of tuples. We’re just binding the tuple to color for use as a shortcut within this method because we’re lazy.
-- Line 12: As before, we set the color for the canvas. Only this time we use the random values we generated and feed them to the color class using Python’s tuple unpacking syntax (since the Color class expects three individual color components instead of just 1. If we were to pass the tuple directly, that would be just 1 value being passed, regardless of the fact that the tuple itself contains 3 values).
-
-This looks a lot nicer already! With a lot of skill and patience, you might even be able to create a nice little drawing!
-
-
-*Note: Since by default the Color instructions assume RGB mode and we’re feeding a tuple with three random float values to it, it might very well happen that we end up with a lot of dark or even black colors if we are unlucky. That would be bad because by default the background color is dark as well, so you wouldn’t be able to (easily) see the lines you draw. There is a nice trick to prevent this: Instead of creating a tuple with three random values, create a tuple like this: (random(), 1., 1.). Then, when passing it to the color instruction, set the mode to HSV color space: Color(*color, mode='hsv'). This way you will have a smaller number of possible colors, but the colors that you get will always be equally bright: only the hue changes.*
-
-
-**Bonus Points**
-
-
-At this point, we could say we are done. The widget does what it’s supposed to do: it traces the touches and draws lines. It even draws circles at the positions where a line begins.
-
-But what if the user wants to start a new drawing? With the current code, the only way to clear the window would be to restart the entire application. Luckily, we can do better. Let us add a Clear button that erases all the lines and circles that have been drawn so far. There are two options now:
-
-- We could either create the button as a child of our widget. That would imply that if you create more than one widget, every widget gets its own button. If you’re not careful, this will also allow users to draw on top of the button, which might not be what you want.
-- Or we set up the button only once, initially, in our app class and when it’s pressed we clear the widget.
-
-For our simple example, it doesn’t really matter that much. For larger applications you should give some thought to who does what in your app. We’ll go with the second option here so that you see how you can build up your application’s widget tree in your app class’s build() method. We’ll also change to the HSV color space (see preceding note):
-
-::
-
-    #qpy:kivy
-    from random import random
-    from kivy.app import App
-    from kivy.uix.widget import Widget
-    from kivy.uix.button import Button
-    from kivy.graphics import Color, Ellipse, Line
-
-    class MyPaintWidget(Widget):
-
-        ^4$def on_touch_down(self, touch):
-            ^8$color = (random(), 1, 1)
-            ^8$with self.canvas:
-                ^12$Color(*color, mode='hsv')
-                ^12$d = 30.
-                ^12$Ellipse(pos=(touch.x - d / 2, touch.y - d / 2), size=(d, d))
-                ^12$touch.ud['line'] = Line(points=(touch.x, touch.y))
-
-        ^4$def on_touch_move(self, touch):
-            ^8$touch.ud['line'].points += [touch.x, touch.y]
-
-
-    class MyPaintApp(App):
-
-        ^4$def build(self):
-            ^8$parent = Widget()
-            ^8$self.painter = MyPaintWidget()
-            ^8$clearbtn = Button(text='Clear')
-            ^8$clearbtn.bind(on_release=self.clear_canvas)
-            ^8$parent.add_widget(self.painter)
-            ^8$parent.add_widget(clearbtn)
-            ^8$return parent
-
-        ^4$def clear_canvas(self, obj):
-            ^8$self.painter.canvas.clear()
-
-    if __name__ == '__main__':
-        ^4$MyPaintApp().run()
-
-<button>Run ...</button>
-
-
-.. image:: http://edu.qpython.org/static/kivy-guide-6.jpg
-    :alt: screenshot of pong game
-    :align: center
-
-
-Here’s what happens:
-
-- Line 4: We added an import statement to be able to use the Button class.
-- Line 25: We create a dummy Widget() object as a parent for both our painting widget and the button we’re about to add. This is just a poor-man’s approach to setting up a widget tree hierarchy. We could just as well use a layout or do some other fancy stuff. Again: this widget does absolutely nothing except holding the two widgets we will now add to it as children.
-- Line 26: We create our MyPaintWidget() as usual, only this time we don’t return it directly but bind it to a variable name.
-- Line 27: We create a button widget. It will have a label on it that displays the text ‘Clear’.
-- Line 28: We then bind the button’s on_release event (which is fired when the button is pressed and then released) to the callback function clear_canvas defined on below on Lines 33 & 34.
-- Line 29 & 30: We set up the widget hierarchy by making both the painter and the clearbtn children of the dummy parent widget. That means painter and clearbtn are now siblings in the usual computer science tree terminology.
-- Line 33 & 34: Up to now, the button did nothing. It was there, visible, and you could press it, but nothing would happen. We change that here: we create a small, throw-away function that is going to be our callback function when the button is pressed. The function just clears the painter’s canvas’ contents, making it black again.
-
-*Note: The Kivy Widget class, by design, is kept simple. There are no general properties such as background color and border color. Instead, the examples and documentation illustrate how to easily handle such simple things yourself, as we have done here, setting the color for the canvas, and drawing the shape. From a simple start, you can move to more elaborate customization. Higher-level built-in widgets, deriving from Widget, such as Button, do have convenience properties such as background_color, but these vary by widget. Use the API docs to see what is offered by a widget, and subclass if you need to add more functionality.*
-
-Congratulations! You’ve written your first Kivy widget. Obviously this was just a quick introduction. There is much more to discover. We suggest taking a short break to let what you just learned sink in. Maybe draw some nice pictures to relax? If you feel like you’ve understood everything and are ready for more, we encourage you to read on.
-
-Chapter Author, contributor
-------------------------------------------------------
-Author: `Kivy <https://kivy.org/>`_
-
diff --git a/course/source/opencv/index.rst b/course/source/opencv/index.rst
deleted file mode 100644
index 7bb2868..0000000
--- a/course/source/opencv/index.rst
+++ /dev/null
@@ -1,19 +0,0 @@
-OpenCV Programming
-=======================
-The courses are still in the planning. Welcome to reward us. 
-
-1. **Primary course**
-
-If the total number of people who reward us is more than 100, we promise to complete the primary course within 1 week, so that, after finishing your study, you can master the basic concepts and write simple scripts.

-
-2. **Intermediate course**
-
-If the total number of people who reward us is more than 500, we will launch the intermediate course within 2 weeks, so that, after finishing your study, you will be familiar with the knowledge and framework of the course and apply it to practical development.
-
-3. **Advanced course**
-
-If the total number of people who reward us is more than 2,000, we will launch the advanced course in a month, so that, after finishing your study, you will have a better understanding of the principles of the framework and be able to do advanced development.
-
-
-**The primary course is free to all, and the intermediate and advanced courses are only open to those who have rewarded us.**
-
diff --git a/course/source/python-base/cn/index.rst b/course/source/python-base/cn/index.rst
deleted file mode 100644
index a4ce056..0000000
--- a/course/source/python-base/cn/index.rst
+++ /dev/null
@@ -1,16 +0,0 @@
-学习 Python 的由来
-==============================
-
-
-第一次接触 Python 时，是在刚毕业不久，那时公司在做一个网盘客户端，需要调研一些 GUI 框架。由于当时 Python 很火（当然，现在也一样），便尝试了一下 PyQt（Python 语言和 Qt 库的融合）。
-
-很长时间里，我对 Python 的认知停留在“Life is short, You need Python ”上，就像“PHP 是世界上最好的语言”一样。直到去年的一次“机缘巧合”，要做一个邮件收发组件，我发现 Python 简直太不可思议了，它提供了很多方便的模块（例如：email、smtplib、poplib），使用起来十分简单，能够让我在很短的时间里出色地完成任务。
-
-在此以后，我几乎每天都要面对 Python，很高兴，我又迈出了新的一步！开始认真研究，包括它自带的一些文档、教程，里面的很多内容确实很好，但大部分都是概念相关的，不够全面，我觉得不太适合新手。所以，是时候该做笔记了，学习 -> 编写 -> 改进 -> 学习… 这是一个漫长的过程，希望在多次的改进和重写后，它能成为了有用的 Python 学习指南。
-
-
-作者 & 更新时间
-------------------------------------
-作者: `一去丶二三里 <http://blog.csdn.net/liang19890820>`_
-
-提交: 2017/12/6
diff --git a/course/source/python-base/cn/python-base-annotation.rst b/course/source/python-base/cn/python-base-annotation.rst
deleted file mode 100644
index d0a7b50..0000000
--- a/course/source/python-base/cn/python-base-annotation.rst
+++ /dev/null
@@ -1,95 +0,0 @@
-简述
-================
-
-对于任何编程语言来说，都可以在代码中包含注释。这不仅有助于解释代码以提高可读性，也便于日后自己参考或者他人阅读，有时对跟踪问题也非常有用。
-
-添加注释很有必要，好的开发人员基本都会大量地使用注释。
-
-
-注释风格
----------
-关键字是预先保留的标识符，每个关键字都有特殊的含义。编程语言众多，但每种语言都有相应的关键字，Python 也不例外，它自带了一个 *keyword* 模块，用于检测关键字。
-
-在 Python 中，注释的风格与其他语言相比有很大的不同，但使用起来也很简单，主要包含两种：
-  - 单行注释
-  - 多行注释
-
-单行注释适用于简短、快速的注释（或者用于调试），而多行注释常用于描述一段内容，或屏蔽整个代码块。
-
-**PS**： 注释是写给其他开发人员（包括自己）看的，Python 解释器会忽略注释。
-
-单行注释
--------------
-# 被用作单行注释符号，**使用 # 时，其右侧的任何数据都会被忽略，被当做是注释。**
-
-例如，在代码的上面或下面添加注释：
-
-::
-
-
-    #print("这是单行注释")
-
-    print("Hello, World!")
-
-    #print("这也是单行注释")
-
-这将产生以下结果：
-
-::
-
-    Hello, World!
-
-也可以在代码的后面添加注释：
-
-::
-
-
-    print("Hello, World!")  #这是单行注释
-
-总之，# 号右侧的内容是不会被执行的。
-
-
-
-多行注释
-----------------
-
-
-相比单行注释，多行注释略有不同，需要在想要注释的部分之前和之后添加 三个单引号（'''）或三个双引号（"""）。
-
-例如，描述一段内容，或屏蔽整个代码块：
-
-::
-
-   '''
-   这在多行注释中
-   这也在多行注释中
-   这仍然在多行注释中
-   '''
-
-   """
-   if True:
-       print("Hello")
-   else:
-       print("Python")
-   """
-
-   print("Hello, World!")
-
-除此之外，还有一种方式，使用多个单行注释来表示多行注释：
-
-::
-
-   #print("这是注释")
-
-   print("Hello, World!")
-
-
-这种方式适用于简短的代码注释。大多数情况下，相比三引号形式还是略显复杂，一般使用率不高。
-
-**建议**： 注释是我们的好伙伴，应尽量多写，尤其是复杂的 Python 代码。
-
-作者 & 更新时间
-------------------------------------
-作者: `一去丶二三里 <http://blog.csdn.net/liang19890820>`_
-
-提交: 2017/12/6
diff --git a/course/source/python-base/cn/python-base-keyword.rst b/course/source/python-base/cn/python-base-keyword.rst
deleted file mode 100644
index 2d304d4..0000000
--- a/course/source/python-base/cn/python-base-keyword.rst
+++ /dev/null
@@ -1,124 +0,0 @@
-Python关键词
-================
-
-
-
-简述
------
-关键字是预先保留的标识符，每个关键字都有特殊的含义。编程语言众多，但每种语言都有相应的关键字，Python 也不例外，它自带了一个 *keyword* 模块，用于检测关键字。
-
-
-关键词列表
--------------
-进入 Python 交互模式，获取关键字列表：
-
-
-::
-
-    import keyword
-    keyword.kwlist
-
-<button>Run ...</button>
-
-共 33 个关键字，除 True、False 和 None 外，其他关键字均为小写形式。
-
-**注意**： Python 是一种动态语言，根据时间在不断变化，关键字列表将来有可能会更改。
-
-
-关键词判断
-----------------
-除此之外，keyword 模块还提供了关键字的判断功能：
-
-::
-
-    import keyword
-    keyword.iskeyword('and')
-    keyword.iskeyword('has')
-
-<button>Run ...</button>
-
-如果是关键字，返回 True；否则，返回 False。
-
-
-
-关键词含义
-------------------------
-
-
-
-+------------+------------------------------------------------------------------------------------------+
-| 关键字     | 含义                                                                                     |
-+============+==========================================================================================+
-|False       | 布尔类型的值，表示假，与 True 相反                                                       |    
-+------------+------------------------------------------------------------------------------------------+
-| None       |None 比较特殊，表示什么也没有，它有自己的数据类型 - NoneType                              |
-+------------+------------------------------------------------------------------------------------------+
-| True       |布尔类型的值，表示真，与 False 相反                                                       |
-+------------+------------------------------------------------------------------------------------------+
-| and        |        用于表达式运算，逻辑与操作                                                        |         
-+------------+------------------------------------------------------------------------------------------+
-| as         |用于类型转换                                                                              |
-+------------+------------------------------------------------------------------------------------------+
-|assert      |断言，用于判断变量或者条件表达式的值是否为真                                              |
-+------------+------------------------------------------------------------------------------------------+
-| break      |中断循环语句的执行                                                                        |
-+------------+------------------------------------------------------------------------------------------+
-| class      |用于定义类                                                                                |
-+------------+------------------------------------------------------------------------------------------+
-| continue   |      跳出本次循环，继续执行下一次循环                                                    |
-+------------+------------------------------------------------------------------------------------------+
-|def         |	用于定义函数或方法                                                                      |
-+------------+------------------------------------------------------------------------------------------+
-|del	     |删除变量或序列的值                                                                        |
-+------------+------------------------------------------------------------------------------------------+
-|elif	     |条件语句，与 if、else 结合使用                                                            |
-+------------+------------------------------------------------------------------------------------------+
-|else	     |条件语句，与 if、elif 结合使用。也可用于异常和循环语句                                    |
-+------------+------------------------------------------------------------------------------------------+
-|except	     |except 包含捕获异常后的操作代码块，与 try、finally 结合使用                               |
-+------------+------------------------------------------------------------------------------------------+
-|finally     |	用于异常语句，出现异常后，始终要执行 finally 包含的代码块。与 try、except 结合使用      |
-+------------+------------------------------------------------------------------------------------------+
-|for	     |for 循环语句                                                                              |
-+------------+------------------------------------------------------------------------------------------+
-|from	     |用于导入模块，与 import 结合使用                                                          |
-+------------+------------------------------------------------------------------------------------------+
-|global	     |定义全局变量                                                                              |
-+------------+------------------------------------------------------------------------------------------+
-|if	         |条件语句，与 else、elif 结合使用                                                          |
-+------------+------------------------------------------------------------------------------------------+
-|import	     |用于导入模块，与 from 结合使用                                                            |
-+------------+------------------------------------------------------------------------------------------+
-|in	         |判断变量是否在序列中                                                                      |
-+------------+------------------------------------------------------------------------------------------+
-|is	         |判断变量是否为某个类的实例                                                                |
-+------------+------------------------------------------------------------------------------------------+
-|lambda      |定义匿名函数                                                                              |
-+------------+------------------------------------------------------------------------------------------+
-|nonlocal    |	用于标识外部作用域的变量                                                                |
-+------------+------------------------------------------------------------------------------------------+
-|not	     |用于表达式运算，逻辑非操作                                                                |
-+------------+------------------------------------------------------------------------------------------+
-|or	         |用于表达式运算，逻辑或操作                                                                |
-+------------+------------------------------------------------------------------------------------------+
-|pass	     |空的类、方法或函数的占位符                                                                |
-+------------+------------------------------------------------------------------------------------------+
-|raise	     |异常抛出操作                                                                              |
-+------------+------------------------------------------------------------------------------------------+
-|return      |用于从函数返回计算结果                                                                    |
-+------------+------------------------------------------------------------------------------------------+
-|try	     |try 包含可能会出现异常的语句，与 except、finally 结合使用                                 |
-+------------+------------------------------------------------------------------------------------------+
-|while	     |while 循环语句                                                                            |
-+------------+------------------------------------------------------------------------------------------+
-|with	     |简化 Python 的语句                                                                        |
-+------------+------------------------------------------------------------------------------------------+
-|yield       |用于从函数依次返回值                                                                      |
-+------------+------------------------------------------------------------------------------------------+
-
-
-作者 & 更新时间
-------------------------------------
-作者: `一去丶二三里 <http://blog.csdn.net/liang19890820>`_
-
-提交: 2017/12/6
diff --git a/course/source/python-base/cn/python-base-operator.rst b/course/source/python-base/cn/python-base-operator.rst
deleted file mode 100644
index e3ee414..0000000
--- a/course/source/python-base/cn/python-base-operator.rst
+++ /dev/null
@@ -1,319 +0,0 @@
-简述
-================
-
-在 Python 中，运算符是执行算术或逻辑运算的特殊符号，操作的值被称为操作数。例如：
-
-::
-
-    5 + 6
-
-<button>Run ...</button>
-
-
-这里，+ 是执行加法的运算符，5 和 6 是操作数，11 是操作的输出。
-
-
-
-运算符种类
-
-----------------
-
-在 Python 中，支持以下类型的运算符：
-
-
-
-- 算术运算符
-- 比较（关系）运算符
-- 逻辑（布尔）运算符
-- 位运算符
-- 赋值运算符
-- 特殊运算符
-   - 成员运算符
-   - 身份运算符
-- 算术运算符
-
-----------------
-
-算术运算符用于执行加、减、乘、除等数学运算。
-
-
-+------------+-----------------------------------------------------------+-------------------------------+
-|  运算符    |                  含义                                     |          示例                 |
-+============+===========================================================+===============================+
-|   +        | 	        加 - 两个操作数相加，或者一元加                  |          x + y                |
-+------------+-----------------------------------------------------------+-------------------------------+
-|   -        | 	        减 - 两个操作数相减，或者一元减   	           |          x - y                |
-+------------+-----------------------------------------------------------+-------------------------------+
-|   *        | 	   乘 - 两个操作数相乘，或是返回一个被重复若干次的字符串 | 	    x * y                   |
-+------------+-----------------------------------------------------------+-------------------------------+
-|   /	     |         除 - 两个操作数相除（总是浮点数）                 |          x / y                |
-+------------+-----------------------------------------------------------+-------------------------------+	
-|   %        | 	        取模 - 返回除法（/）的余数	                      |          x % y（x/y 的余数）  |
-+------------+-----------------------------------------------------------+-------------------------------+
-|  //	     |         取整除 - 返回商的整数部分                         | 	     x // y               |
-+------------+-----------------------------------------------------------+-------------------------------+
-|   **       | 	        幂 - 返回 x 的 y 次幂	                           |           x ** y              |
-+------------+-----------------------------------------------------------+-------------------------------+
-
-
-
-示例：
-
-::
-
-    x = 8
-    y = 3
-    x + y
-    x - y
-    x * y
-    x / y  # 总是浮点数
-    x // y
-    x ** y
-
-<button>Run ...</button>
-
-
-**注意**： 除法 / 运算的结果总是浮点数。例如：4 / 2 返回的是 2.0，而不是 2。
-
-比较运算符
-
-----------------
-
-比较运算符又称关系运算符，用于比较值，根据条件返回 True 或 False。
-
-+------------+-----------------------------------------------------------+-------------------------------+
-|   运算符   | 	                    含义                              | 	          示例              |                  
-+============+===========================================================+===============================+
-|    >       | 	        大于 - 如果左操作数大于右操作数，则为 True       | 	          x > y             | 
-+------------+-----------------------------------------------------------+-------------------------------+
-|    < 	     |   小于 - 如果左操作数小于右操作数，则为 True	            |                 x < y         | 
-+------------+-----------------------------------------------------------+-------------------------------+
-|    ==	     |      等于 - 如果两个操作数相等，则为 True	               |              x == y           | 
-+------------+-----------------------------------------------------------+-------------------------------+
-|    !=	     |         不等于 - 如果两个操作数不相等，则为 True          |              x != y           | 
-+------------+-----------------------------------------------------------+-------------------------------+	
-|    >=	     |    大于等于 - 如果左操作数大于或等于右操作数，则为 True   |               x >= y          | 
-+------------+-----------------------------------------------------------+-------------------------------+
-|    <=	     |   小于等于 - 如果左操作数小于或等于右操作数，则为 True    |                x <= y         | 
-+------------+-----------------------------------------------------------+-------------------------------+
-
-示例：
-
-::
-
-    x = 6
-    y = 10
-    x > y
-    x < y
-    x == y
-    x != y
-    x >= y
-    x <= y
-
-<button>Run ...</button>
-
-
-逻辑运算符
-----------------
-
-逻辑运算符又称布尔运算符，通常用于测试真假值。
-
-
-+------------+-----------------------------------------------------------+-------------------------------+
-|    运算符  |     含义	                                                |        示例                   |
-+============+===========================================================+===============================+
-|    and     |    逻辑与 - 如果两个操作数都为 True，则为 True	          |     x and y                   |
-+------------+-----------------------------------------------------------+-------------------------------+
-|    or	     |       逻辑或 - 如果任一操作数为 True，则为 True	       |      x or y                   |
-+------------+-----------------------------------------------------------+-------------------------------+
-|   not	     |     逻辑非 - 如果操作数为 False，则为 True（补数）	     |               not x           |
-+------------+-----------------------------------------------------------+-------------------------------+
-
-示例：
-
-
-::
-
-    x = True
-    y = False
-    x and y
-    not x
-    not y
-
-
-<button>Run ...</button>
-
-
-位运算符
-----------------
-
-位运算符作用于操作数，就像它们是二进制数字的字符串一样，它一点点地运行，因此而得名。
-
-+------------+-----------------------------------------------------------+-------------------------------+
-|运算符      |	        含义                                              |	示例                        |
-+============+===========================================================+===============================+
-|  &         |	     按位与（AND）                                       |	x & y                       |  
-+------------+-----------------------------------------------------------+-------------------------------+
-|  |         |	     按位或（OR）                                        |	x | y                       |
-+------------+-----------------------------------------------------------+-------------------------------+
-|  ~         |	按位翻转/取反（NOT）                                     |	  ~x                         |
-+------------+-----------------------------------------------------------+-------------------------------+
-|   ^        |	按位异或（XOR）                                          |	x ^ y                      |
-+------------+-----------------------------------------------------------+-------------------------------+
-|   >>       |	   按位右移                                              |	x >> 2                       |     
-+------------+-----------------------------------------------------------+-------------------------------+
-|   <<       |	   按位左移                                              |	x << 2                       | 
-+------------+-----------------------------------------------------------+-------------------------------+
-
-例如：2 是二进制 10，7 是 111。
-
-令 x = 10（二进制 0000 1010），y = 4（二进制 0000 0100），进行位运算：
-
-::
-
-    x = 10  # 二进制 0000 1010
-    y = 4  # 二进制 0000 0100
-    x & y  # 0000 0000
-    x | y  # 0000 1110
-    ~x  # 1111 0101
-    x ^ y  # 0000 1110
-    x >> 2  # 0000 0010
-    x << 2  # 0010 1000
-
-<button>Run ...</button>
-
-
-赋值运算符
-----------------
-
-赋值运算符用于为变量赋值。
-
-例如：x = 5，是一个简单的赋值运算符，它将右侧的值 5 分配给左侧的变量 x。
-
-在 Python 中，有各种各样的复合运算符，例如：x += 5，先将变量 x 的值与 5 相加，再将最终结果分配给变量 x，等价于：x = x + 5。
-
-
-+------------+-----------------------------------------------------------+-------------------------------+
-| 运算符     | 	      示例                                            | 	等价于                      | 
-+============+===========================================================+===============================+
-| =          | 	x = 5                                                    | 	x = 5（相同）                | 
-+------------+-----------------------------------------------------------+-------------------------------+
-| +=         | 	x += 5                                                   | 	x = x + 5                    | 
-+------------+-----------------------------------------------------------+-------------------------------+
-| -=         | 	x -= 5                                                   | 	x = x - 5                    | 
-+------------+-----------------------------------------------------------+-------------------------------+
-| *=         | 	x *= 5                                                   | 	x = x * 5                    | 
-+------------+-----------------------------------------------------------+-------------------------------+
-| /=         | 	x /= 5                                                   | 	x = x / 5                    |  
-+------------+-----------------------------------------------------------+-------------------------------+
-| %=         | 	x %= 5                                                   | 	x = x % 5                    | 
-+------------+-----------------------------------------------------------+-------------------------------+
-| //=        | 	x //= 5	                                                 |      x = x // 5               | 
-+------------+-----------------------------------------------------------+-------------------------------+
-| **=        |  x **= 5                                                  | 	x = x ** 5                   |
-+------------+-----------------------------------------------------------+-------------------------------+
-| &=         | 	x &= 5                                                   |      x = x & 5                | 
-+------------+-----------------------------------------------------------+-------------------------------+
-| =          | 	x = 5                                                    | 	x = x | 5                    | 
-+------------+-----------------------------------------------------------+-------------------------------+
-| ^=         | 	x ^= 5	                                                 |      x = x ^ 5                | 
-+------------+-----------------------------------------------------------+-------------------------------+
-| >>=        | 	x >>= 5                                                  |       x = x >>                | 
-+------------+-----------------------------------------------------------+-------------------------------+
-| <<=        | 	x <<= 5                                                  | 	x = x << |                   |
-+------------+-----------------------------------------------------------+-------------------------------+
-
-特殊运算符
-----------------
-
-除以上运算符外，Python 还提供了一些特殊类型的运算符：
-
-- 身份运算符
-- 成员运算符
-
-
-身份运算符
-----------------
-
-身份运算符用于检查两个值（或变量）是否位于存储器的同一部分。
-
-**注意**： 两个变量相等，并不意味着它们也相同。
-
-
-+------------+-----------------------------------------------------------+-------------------------------+
-|   运算符   |	含义                                                      |	示例                        |
-+============+===========================================================+===============================+
-|    is      |	如果操作数相同，则为 True（引用同一个对象）              |	x is True                     |
-+------------+-----------------------------------------------------------+-------------------------------+
-|   is not   |	如果操作数不相同，则为 True（引用不同的对象）            |	x is not True                |
-+------------+-----------------------------------------------------------+-------------------------------+
-
-示例：
-
-::
-
-    x1 = 5
-    y1 = 5
-
-    x2 = 'Python'
-    y2 = 'Python'
-
-    x3 = [1, 2, 3]
-    y3 = [1, 2, 3]
-    x1 is not y1
-
-
-    x2 is y2
-
-    x3 is y3
-
-<button>Run ...</button>
-
-
-可以看到 x1 和 y1 是相同值的整数，所以它们相等且相同。x2 和 y2（字符串）同样是这样。
-
-但 x3 和 y3 是列表，它们相等，但不相同。由于列表是可变的（可以更改），即使它们相等，解释器也会将它们分别存储在内存中。
-
-成员运算符
-----------------
-
-成员运算符用于测试在序列（字符串、列表、元组、集合和字典）中是否可以找到一个值/变量。
-
-**注意**： 在字典中，只能检测 key 是否存在，而不能检测 value。
-
-+------------+-----------------------------------------------------------+-------------------------------+
-|   运算符   |	含义                                                      |	示例                        |
-+============+===========================================================+===============================+
-|    in      |	如果在序列中找到值/变量，则为 True                       |	5 in x                     |
-+------------+-----------------------------------------------------------+-------------------------------+
-|   not in   |	如果在序列中没有找到值/变量，则为 True                   |	5 not in x                   |
-+------------+-----------------------------------------------------------+-------------------------------+
-
-
-示例：
-
-
-::
-
-    x = 'Hello, World!'  # 字符串
-    y = {1:'Java', 2:'Python'}  # 字典
-
-    'H' in x
-
-    'hello' not in x
-
-    1 in y
-
-    'Python' in y
-
-<button>Run ...</button>
-
-1这里，'H' 在 x 中，但 'hello' 不存在于 x 中（切记，Python 是区分大小写的）。同样地，1 是 key，'Python' 是字典 y 中的值。因此，'Python' in y 返回 False。
-
-
-作者 & 更新时间
-------------------------------------
-作者: `一去丶二三里 <http://blog.csdn.net/liang19890820>`_
-
-提交: 2017/12/6
-
diff --git a/course/source/python-base/cn/python-base-python-datatype-number.rst b/course/source/python-base/cn/python-base-python-datatype-number.rst
deleted file mode 100644
index 536bb5a..0000000
--- a/course/source/python-base/cn/python-base-python-datatype-number.rst
+++ /dev/null
@@ -1,397 +0,0 @@
-简述
-================
-
-Python 中的每个值都有一个数据类型。
-
-在 Python 编程中，**一切（万物）皆对象**，数据类型实际上是类，变量是这些类的实例（对象）。
-
-
-数据类型
--------------
-
-Python 中有各种数据类型，下面列出了一些重要类型：
-
-  - Number（数字）
-  - String（字符串）
-  - List（列表）
-  - Tuple（元组）
-  - Set（集合）
-  - Dictionary（字典）
-
-Number（数字）
-----------------
-  
-
-Python 支持三种不同的数字类型：
-
-   - int（整型）
-   - float（浮点型）
-   - complex（复数）
-
-
-**注意**： Py3.x 去除了 long 类型，现在只有一种整型 - int，表示为长整型。
-
-可以使用 type() 函数获取变量或值的类型，使用 isinstance() 函数来检查一个对象是否属于一个特定的类。
-
-::
-
-    i = 5  # 整型
-    type(i)
-
-    f = 2.5  # 浮点型
-    type(f)
-    type(f)
-
-    c = 1+2j  # 复数
-    type(c)
-
-<button>Run ...</button>
-
-
-
-整数可以是任何长度，只受到可用内存的限制。
-
-浮点数精确到 15 位小数。
-
-复数以 x + yj 的形式写成，其中 x 是实部，y 是虚部。
-
-::
-
-    i = 2 ** 500  # 2 的 500 次幂
-    f = 0.12345678901234567890
-    c = 5+6j
-
-<button>Run ...</button>
-
-String（字符串）
------------------
-
-
-字符串是 Unicode 字符的序列。
-
-可以使用单引号（''）或双引号（""）来表示字符串，多行字符串可以使用三重引号 ''' 或 """ 来表示。
-
-::
-
-    s = 'Hello, Python!'
-    type(s)
-
-<button>Run ...</button>
-
-各种表示方式：
-
-::
-
-    s = 'Hello, Python!'  # 单引号
-    s = "Hello, World!"  # 双引号
-    s = '''Hello,  # 三重单引号
-    World! '''
-
-    s = """  # 三重双引号
-    Hello,
-    World!
-    """
-
-
-
-字符串可以被索引和截取，截取的语法格式如下：
-
-变量[头下标:尾下标]
-
-索引值以 0 开始，-1 为从末尾的开始位置。
-
-::
-
-    s = "Hello, World!"
-    s[4]  # 第五个字符
-    s[7:10]  # 第八个开始到第十个的字符
-    s[-4:-1]  # 倒数第四个开始到倒数第一个的字符
-    s[5] = 'p'  # 生成错误，字符串是不可变的
-
-<button>Run ...</button>
-
-加号（+）是字符串的连接符， 星号（*） 表示复制当前字符串，紧跟的数字为复制的次数。
-
-::
-
-    s1 = "Hello,"
-    s2 = " World!"
-    s1 + s2  # 连接字符串
-    (s1 + s2) * 3  # 复制 3 次字符串
-
-<button>Run ...</button>
-
-List（列表）
--------------
-
-列表是有序的元素序列，它是 Python 中使用最频繁的数据类型，非常灵活。
-
-列表中元素的类型可以不同，支持数字、字符串，甚至可以包含列表（所谓的嵌套）。
-
-列表用 [] 标识，内部元素用逗号分隔。
-
-::
-
-    l = [1, 5.5, 'Python']
-    type(l)
-
-<button>Run ...</button>
-
-和字符串一样，列表同样可以被索引和截取，列表被截取后返回一个包含所需元素的新列表。
-
-::
-
-    l = [3, 2, 5, 4, 1]
-    l
-    l[2]  # 第三个元素
-    l[0:3] # 从第一个元素到第三个元素
-    l[3:] # 从第三个元素开始的所有元素
-
-<button>Run ...</button>
-
-列表是可变的，意思是说，列表中元素的值可以被改变。
-
-::
-
-    l = [1, 2, 3]
-    l[2] = 5  # 将第三个元素 3 变为 5
-    l
-
-<button>Run ...</button>
-
-Tuple（元组）
--------------
-
-元组与列表相同，也是有序序列，唯一的区别是元组是不可变的。
-
-元组适用于保护性的数据，通常比列表快，因为它不能动态更改。
-
-元组用 () 标识，内部元素用逗号分隔。
-
-::
-
-    t = (5, 'Python', 1+2j)
-    type(t)
-
-<button>Run ...</button>
-
-元组也可以被索引和截取，但是不能被更改。
-
-::
-
-    t = (3, 2, 5, 4, 1)
-    t
-    t[1]  # 第二个元素
-    t[0:2] # 从第一个元素到第二个元素
-    t[0] = 10  # 生成错误，元组是不可变的
-
-<button>Run ...</button>
-
-虽然元组中的元素不可变，但它可以包含可变的对象，例如：List（列表）。
-
-构造一个空的或者包含一个元素的元组比较特殊，所以要采用一些额外的语法规则：
-
-::
-
-    tup1 = ()  # 空元组
-    tup2 = (5, )  # 一个元素，需要在元素后添加逗号
-
-<button>Run ...</button>
-
-Set（集合）
--------------
-
-
-集合是一个无序不重复元素集。
-
-集合用 {} 标识，内部元素用逗号分隔。
-
-可以使用大括号 {} 或者 set() 函数创建集合，注意： 要创建一个空集合，必须使用 set() 而不是 {}，因为 {} 用于创建一个空字典。
-
-::
-
-    s = {5, 'Python', 1+2j}
-    type(s)
-
-<button>Run ...</button>
-
-
-既然集合是无序的，那么索引就没有任何意义，也就是说，切片操作符 [] 不起作用。
-
-
-::
-
-    s = {"Java", "Python", "PHP"}
-    s[1]  # 生成错误，不支持索引
-
-<button>Run ...</button>
-
-
-不重复，是指集合中相同的元素会被自动过滤掉，只保留一份。
-
-
-::
-
-    s = {"PHP", "Python", "Java", "Python", "PHP"}
-    s
-
-<button>Run ...</button>
-
-
-除了去重之外，还常用于成员关系的测试。
-
-::
-
-    if ('Python' in s) :
-        ^4$print('Python 在集合中')
-    else :
-        ^4$print('Python 不在集合中')
-
-<button>Run ...</button>
-
-
-
-集合之间也可执行运算，例如：并集、差集、交集。
-
-::
-
-    a = set('abcdefg')
-    b = set('abghijk')
-    a
-    >>>{'b', 'f', 'e', 'd', 'a', 'c', 'g'}
-    b
-    >>>{'b', 'k', 'h', 'i', 'j', 'a', 'g'}
-
-    a - b  # 差集
-    >>>{'f', 'c', 'd', 'e'}
-
-    a | b  # 并集
-    >>>{'b', 'k', 'f', 'h', 'i', 'e', 'j', 'd', 'a', 'c', 'g'}
-
-    a & b  # 交集
-    >>>{'b', 'a', 'g'}
-
-    a ^ b  # 对称差 - 不同时存在的元素
-    >>>{'e', 'c', 'k', 'f', 'h', 'i', 'j', 'd'}
-
-
-
-对称差公式：A Δ B = (A ? B) ∪(B ? A)。也可表示为两个集合的并集减去它们的交集：A Δ B = (A ∪B) ? (A ∩B)。
-
-
-Dictionary（字典）
----------------------
-
-字典是键值对的无序集合。
-
-通常在有大量的数据时会使用，在检索数据时字典做了优化，必须知道要检索的 value 所对应的 key。
-
-字典用 {} 标识，其中的每个元素都以 key:value 对的形式出现，key 和 value 可以是任何类型。
-
-**注意**： 字典中的 key 必须是唯一的。
-
-::
-
-    d = {1:'value', 'key':2}
-    type(d)
-
-<button>Run ...</button>
-
-可以用 key 来检索相应的 value，但相反则不行。
-
-::
-
-    d = {}  # 创建空字典
-    d['name'] = 'Python'  # 增加新的键/值对
-    d['site'] = 'www.python.org'
-    d
-    d['site']  # 键为 'site' 的值
-    d['Python']  # 生成错误，不能用 value 检索 key
-
-<button>Run ...</button>
-
-
-字典有一些内置的函数，例如：keys()、values()、clear() 等。
-
-::
-
-    d.keys()  # 所有键
-    dict_keys(['name', 'site'])
-
-    d.values()  # 所有值
-    dict_values(['Python', 'www.python.org'])
-
-    d.clear()  # 清空字典
-    d
-
-<button>Run ...</button>
-
-
-数据类型之间的转换
----------------------
-
-可以使用不同的类型转换函数来转换不同的数据类型，例如：int()、float()、str() 等。
-
-从 int 转换为 float：
-
-::
-
-     float(5)
-
-<button>Run ...</button>
-
-
-从 float 到 int 的转换，值将会被截断（使其接近零）：
-
-::
-
-     int(10.8)
-     int(-10.8)
-
-<button>Run ...</button>
-
-字符串的转换必须包含兼容的值：
-
-::
-
-    float('2.5')
-    >>>2.5
-
-    str(25)
-    >>>'25'
-
-    int('str')
-    >>>Traceback (most recent call last):
-     File "<stdin>", line 1, in <module>
-     ValueError: invalid literal for int() with base 10: 'str'
-
-
-
-甚至可以将一个序列转换为另一个序列：
-
-::
-
-    set([1, 2, 3])
-
-    tuple({4, 5, 6})
-
-    list('hello')
-
-<button>Run ...</button>
-
-
-要转换为字典，每个元素必须是一对：
-
-::
-
-    dict([[1, 'value'], ['key', 2]])
-
-<button>Run ...</button>
-
-
-
-作者 & 更新时间
-------------------------------------
-作者: `一去丶二三里 <http://blog.csdn.net/liang19890820>`_
-
-提交: 2017/12/6
diff --git a/course/source/python-base/cn/python-base-variable.rst b/course/source/python-base/cn/python-base-variable.rst
deleted file mode 100644
index b0f9ba0..0000000
--- a/course/source/python-base/cn/python-base-variable.rst
+++ /dev/null
@@ -1,184 +0,0 @@
-简述
-================
-
-变量只不过是保留的内存位置，用于存储规定范围内的值。这意味着，在创建变量时会在内存中开辟一个空间。
-
-基于变量的数据类型，解释器会分配指定内存，并决定什么数据可以被存储在内存中。因此，变量可以指定不同的数据类型，例如：整数、小数或字符串等。
-
-
-何为变量？
------------
-
-简单地说，变量就是给数据起一个名字。
-
-变量更多情况下是一种引用，对应的只是内存当中的一个值，该值可以根据需要存储不同的数据类型。
-
-
-如下等式，一个名为 age 的变量被赋值为 18 。正如每个人都有姓名一样，变量的名字叫做标识符。
-
-.. image:: http://img.blog.csdn.net/20170331192942534
-
-
-
-
-
-**注意**： 在很多语言中，声明变量必须要声明变量的类型。例如，要在 C++ 中声明一个整形变量，必须先声明变量的类型为 int，然后使用变量名以及变量对应的值。而在 Python 中，
-
-变量命名
--------------
-
-变量可以任意取名，但必须遵循以下命名规则：
-
-   - 由字母、数字、下划线（_）组成
-   - 不能以数字开头
-   - 不能使用 Python 关键字
-   - 不能使用特殊符号，例如：!、@、#、$、% 等
-
-
-例如，定义以下变量：
-
-::
-
-   age = 18  // 正确
-   _age = 18  // 正确
-   age_18 = 18  // 正确
-   18_age = 18  // 错误，以数字开头
-   global = 18  // 错误，使用关键字 global
-   a@e = 18  // 错误，使用特殊符号 @
-
-除此之外，还需要注意：
-
-- 变量对大小写敏感（区分大小写）
-
-例如，定义以下变量：
-
-::
-
-    AGE = 18  // 正确
-    age = 18  // 正确
-
-AGE 和 age 是两个不同的变量，而非同一个。
-
-**建议**： 变量名应做到见明知义，避免晦涩难懂。虽然可以是任意长度，但一般应确保精简、无歧义。
-
-
-变量赋值
-================
-
-变量的基本赋值
-----------------
-
-变量是数据的引用，更像是标签。
-
-
-例如，有一个 teacher 叫 Jack Ma，相当于将 teacher 这个标签拴在了 Jack Ma 上。通过这个 teacher，就顺延到了 Jack Ma，于是当我们叫 teacher 时，实际是在叫 Jack Ma，给人的
-
-感觉似乎 teacher 就是 Jack Ma，而事实上是 teacher 这个标签贴在了 Jack Ma 上。
-
-::
-
-    teacher = "Jack Ma"
-    teacher
-
-<button>Run ...</button>
-
-在这里，teacher 标签就是所谓的变量，Jack Ma 就是该变量所对应的值，存储于内存中。
-
-在 Python 中，有非常重要的一句话：**对象有类型，变量无类型。** 可以这样理解，就是标签不仅可以贴在整形类型的对象上，还可以贴在其他类型的对象上（例如：字符串）。变量的这个特点（随意贴标签）非常重要，它没有类型。
-
-
-变量的重新赋值
-----------------
-
-变量，从字面意思理解就是变化的量，也就是说，数值是可变的。将变量从一个存储空间移至另一个存储空间中（也就是将标签移走，换到另一个位置），被称作变量的重新赋值。
-
-例如，原来 teacher 叫 Jack Ma，后来年龄大退休了，换了一个老师叫 Pony，当我们再叫 teacher 时，其实叫的是 Pony，而非 Jack Ma。
-
-::
-
-    teacher = "Jack Ma"
-    teacher
-    teacher = "Pony"
-    teacher
-
-<button>Run ...</button>
-
-一开始，相当于将 teacher 这个标签拴在了 Jack Ma 上，后来标签换了位置，拴在了 Pony 上。
-
-如果学过其他编程语言，例如：C++，很容易想到的是 teacher 开辟了内存空间，里面存了 Jack Ma，然后这个空间当中又换为 Pony。就好比 teacher 是一个容器，里面放着值。实际在 Python 当中，正好相反，Python 是以数据为主，Jack Ma、Pony 都存储在内存当中，teacher 无非是对数据的一个引用。所以，当重新赋值后，查看 teacher 所对应的地址时，会发生变化。
-
-每个对象在内存中都有对应的地址，这就是它的“身份”，使用 Python 的内建函数 id() 来查看：
-
-::
-
-    teacher = "JacK Ma"
-    id(teacher)
-    teacher = "Pony"
-    id(teacher)
-
-<button>Run ...</button>
-
-可以看到，重新赋值后，teacher 对应的内存空间已经变了。所以，JacK Ma 和 Pony 实际上存储在两个空间中。
-
-为多个变量指定同一个值
------------------------
-
-同一个内存地址，可以有多个标签。这个很容易理解，一个人可以有多重身份，JacK Ma 既是 teacher，又是 ceo。
-
-::
-
-    ceo = teacher = "Jack Ma"
-
-或者：
-
-::
-
-   teacher = "Jack Ma"
-   ceo = teacher
-
-<button>Run ...</button>
-
-看似不相关的变量，但他们指的都是同一个人 Jack Ma，不妨看一下：
-
-::
-
-    teacher = "Jack Ma"
-    ceo = teacher
-    id(teacher)
-    id(ceo)
-
-<button>Run ...</button>
-
-显然，对应的是同一个内存地址。也就是说，teacher、ceo 是不同的标签，但是所引用的数据是同一地址上的。
-
-将多个值分配给多个变量
-
-要将多个值分配给多个变量，可以单独分开来写：
-
-::
-
-    age = 18
-    teacher = "Jack Ma"
-
-<button>Run ...</button>
-
-也可以连在一起：
-
-::
-
-    age, teacher = 18, "Jack Ma"
-    age
-    teacher
-
-<button>Run ...</button>
-
-
-这里，值为 18 的整形分配给了变量 age，值为 “Jack Ma” 的字符串分配给了变量 teacher。
-
-
-
-作者 & 更新时间
-------------------------------------
-作者: `一去丶二三里 <http://blog.csdn.net/liang19890820>`_
-
-提交: 2017/12/6
diff --git a/course/source/python-base/cn/python-controller-break.rst b/course/source/python-base/cn/python-controller-break.rst
deleted file mode 100644
index 92eb4a6..0000000
--- a/course/source/python-base/cn/python-controller-break.rst
+++ /dev/null
@@ -1,98 +0,0 @@
-Python break 和 continue 语句
-======================================
-
-简述
---------------------------
-在 Python 中，break 和 continue 语句用于改变普通循环的流程。
-
-通常情况下，循环遍历一段代码，直到判断条件为 False。但有时，可能会希望不检测判断条件就可以终止当前迭代，甚至是整个循环。这种情况下，就需要使用 break 和 continue 语句。
-
-break 语句
-----------------------------------
-break 用于终止循环语句。即使循环条件不是 False 或者序列还没被完全递归完，也会终止。
-
-**注意：** 如果 break 语句在嵌套循环内，break 将终止最内层循环。
-
-语法格式：
-
-::
-
-    break
-
-
-流程图：
-
-.. image:: http://img.blog.csdn.net/20170416165752222
-
-当我们陶醉在单曲循环的世界中时，突然一声：老师来啦，^_^以迅雷不及掩耳之势关闭歌曲吧：
-
-::
-
-    i = 0
-    while i < 3:
-        ^4$if i == 1:
-            ^8$print('老师来啦')
-            ^8$print('关闭歌曲')
-            ^8$break
-
-        ^4$print('正在播放：双节棍')
-        ^4$i += 1
-
-<button>Run ...</button>
-
-运行程序，输出如下：
-
-::
-
-    正在播放：双节棍
-    老师来啦
-    关闭歌曲
-
-continue 语句
---------------------------------
-语法格式：
-
-::
-
-   continue
-
-
-流程图：
-
-.. image:: http://img.blog.csdn.net/20170416170554216
-
-列表播放时，遇到不喜欢的歌曲经常会选择下一曲，直接跳过当前歌曲：
-
-::
-
-    songs = ['安静', '蜗牛', '稻香']
-    # 通过索引遍历列表
-    for i in range(len(songs)):
-        ^4$if i == 1:
-            ^8$print('不想听', songs[i])
-            ^8$print('快进，下一曲')
-            ^8$continue
-        ^4$print("正在播放：", songs[i])
-
-<button>Run ...</button>
-
-运行程序，输出如下：
-
-::
-
-   正在播放： 安静
-   不想听 蜗牛
-   快进，下一曲
-   正在播放： 稻香
-
-遍历歌曲列表，当播放到“蜗牛”时，发现这首歌曲太煽情了，直接进入下一曲。
-
-**两者的根本区别：** break 用于终止整个循环；continue 用于跳出本次循环，还会继续下一次循环。
-
-
-
-作者 & 更新时间
-------------------------------------
-作者: `一去丶二三里 <http://blog.csdn.net/liang19890820>`_
-
-提交: 2017/12/6
diff --git a/course/source/python-base/cn/python-controller-for.rst b/course/source/python-base/cn/python-controller-for.rst
deleted file mode 100644
index f0c73ba..0000000
--- a/course/source/python-base/cn/python-controller-for.rst
+++ /dev/null
@@ -1,134 +0,0 @@
-Python 运算符
-================
-
-简述
------
-在 Python 中，for 循环用于迭代序列（例如：列表、元组）或其他可迭代对象，迭代序列称为遍历。
-
-for 循环
-----------
-语法格式：
-
-::
-
-    for <val> in <序列>:
-        ^4$<循环体>
-
-
-val 是一个变量，在每次迭代中，用于接收将序列中元素的值。
-
-循环会一直继续，直到到达序列的最后一项。循环体与其余的代码使用缩进分隔。
-
-流程图：
-
-.. image:: http://img.blog.csdn.net/20170416152544984
-
-
-::
-
-    for letter in 'Jay':
-        ^4$print('当前字母：', letter)
-
-    songs = ['安静', '蜗牛', '稻香']   
-    for song in songs:
-        ^4$print('正在播放：', song)
-
-<button>Run ...</button>
-
-运行程序，输出如下：
-
-::
-
-    当前字母： J
-    当前字母： a
-    当前字母： y
-    正在播放： 安静
-    正在播放： 蜗牛
-    正在播放： 稻香
-
-这里，可以将 for 循环视为歌曲中的顺序播放。
-
-通过索引遍历
----------------
-
-可以使用 range() 函数生成一个数字序列。
-
-还可以定义 range(start, stop[, step]) 中的 start、stop 和 step，如果没有提供 step，步长默认为 1。
-
-要强制该函数输出所有元素，可以使用函数 list()：
-
-::
-
-    # 输出：range(0, 10)
-    print(range(10))
-
-    # 输出：[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
-    print(list(range(10)))
-
-    # 输出：[2, 3, 4, 5, 6, 7]
-    print(list(range(2, 8)))
-
-    # 输出：[2, 5, 8, 11, 14, 17]
-    print(list(range(2, 20, 3)))    
-
-<button>Run ...</button>
-
-可以在 for 循环中使用 range() 来遍历一个数字序列，它可以与内置函数 len() 结合，使用索引进行序列迭代。
-
-len() 返回列表的长度，即：元素的个数。
-
-::
-
-    songs = ['安静', '蜗牛', '稻香']
-
-    # 通过索引遍历列表
-    for i in range(len(songs)):
-        ^4$print("正在播放：", songs[i])
-
-<button>Run ...</button>
-
-运行程序，输出如下：
-
-::
-
-    正在播放： 安静
-    正在播放： 蜗牛
-    正在播放： 稻香
-
-
-for … else
-------------------
-
-for 循环也可以有一个可选的 else 块，如果循环正常执行完（即：不是通过 break 跳出而中断的），则执行 else 部分。
-
-**注意：** break 语句可以用来跳出 for 循环，在这种情况下，else 部分会被忽略。
-
-::
-
-    songs = ['安静', '蜗牛', '稻香']
-    for song in songs:
-        ^4$print('正在播放：', song)
-    else:
-        ^4$print('播放结束')
-
-<button>Run ...</button>
-
-运行程序，输出如下：
-
-::
-
-    正在播放： 安静
-    正在播放： 蜗牛
-    正在播放： 稻香
-    播放结束
-
-
-可以看到，当 for 循环结束时，执行 else 中的代码块。
-
-
-
-作者 & 更新时间
-------------------------------------
-作者: `一去丶二三里 <http://blog.csdn.net/liang19890820>`_
-
-提交: 2017/12/6
diff --git a/course/source/python-base/cn/python-controller-if.rst b/course/source/python-base/cn/python-controller-if.rst
deleted file mode 100644
index c7fb4c6..0000000
--- a/course/source/python-base/cn/python-controller-if.rst
+++ /dev/null
@@ -1,191 +0,0 @@
-简述
-================
-
-当仅在满足某个条件才会执行相应的代码时，需要进行决策。在 Python 中，由 if … elif … else 语句来实现。
-
-
-if 语句
----------------
-
-
-语法格式：
-
-if <判断条件>：
-    <执行语句……>
-
-
-当“判断条件”成立（True）时，才执行语句；反之，则不执行。
-
-执行语句可以为多行，以缩进来区分表示同一范围。
-
-**注意**： 在 Python 中，非零值表示 True；None 和 0 表示 False。
-
-流程图：
-
- .. image:: http://img.blog.csdn.net/20170416144600526
-
-
-以购物为例（预算 500），进入商场，价格都是 1000+。
-
-
-
-::
-
-    price = 1000
-    print('纳尼，居然', price)
-    print('简直太贵了！')
-    print("货比三家，再转转。")
-
-<button>Run ...</button>
-
-执行程序，输出如下：
-
-::
-
-    纳尼，居然 1000
-    简直太贵了！
-    货比三家，再转转。
-
-
-if…else 语句
------------------------
-
-语法格式：
-
-::
-
-    if <判断条件>:
-        ^4$<执行语句1……>
-    else:
-        ^4$<执行语句2……>
-
-
-当“判断条件”为 True 时，执行语句1；否则，执行语句2。
-
-流程图：
-
-.. image:: http://img.blog.csdn.net/20170416145014731
-
-
-既然 1000 贵了，好吧！开始讨价还价，降价到 300：
-
-::
-    price = 300
-    if price > 500:
-        print('纳尼，居然', price)
-        print('简直太贵了！')
-    else:
-        print(price, '还算地道')
-        print('来一打！')
-        print("合适才买")
-
-<button>Run ...</button>
-
-执行程序，输出如下：
-
-::
-    300 还算地道
-    来一打！
-    合适才买
-
-
-if…elif…else 语句
--------------------------------------
-
-语法格式：
-
-::
-
-    if <判断条件1>:
-        ^4$<执行语句1……>
-    elif <判断条件2>:
-        ^4$<执行语句2……>
-    else:
-        ^4$<执行语句3……>
-
-elif 是 else if 的缩写，允许我们检查多个表达式。
-
-如果 if 的条件为 False，则检查下一个 elif 的状态，依次进行。倘若所有条件都为 False，则执行 else 中的语句（语句3）。
-
-**注意**： if 和 else 只能有一个，但 elif 可以有多个，if … elif … else 中只有一个语句块可以根据条件来执行。
-
-流程图：
-
-.. image:: http://img.blog.csdn.net/20170416145420452
-
-一般，大部分顾客买东西都要讨价还价。当然，其中不乏土豪，只管买买买。
-
-::
-
-    price = 2000
-    if price > 1000:
-        ^4$print('这么贵，肯定是好东西。')
-        ^4$print('买、买、买！')
-    elif price > 500:
-        ^4$print('价格还行，值得拥有。')
-        ^4$print('买买买！')
-    elif price > 200:
-        ^4$print('先入手，合不合适再说。')
-        ^4$print('买买买。')
-    else:
-        ^4$print('这么便宜，赶紧抢。')
-        ^4$print('买买买')
-
-    print('管它多钱，反正我要买买买。')
-
-<button>Run ...</button>
-
-
-执行程序，输出如下：
-
-::
-
-    这么贵，肯定是好东西。
-    买、买、买！
-    管它多钱，反正我要买买买。
-
-
-嵌套 if 语句
--------------------------------
-
-可以将一个 if … elif … else 语句加入至另一个 if … elif … else 语句中，这被称为嵌套。
-
-任何数量的这些语句都可以嵌套在一起，要找出嵌套级别，缩进是唯一的方法。
-
-买东西，追求的是性价比，所以既要价格适中，又要质量有保证：
-
-::
-
-    price = 300
-    quality = True
-    if price > 500:
-        ^4$print('纳尼，居然', price)
-        ^4$print('简直太贵了！')
-    else:
-        ^4$print(price, '价钱合适')
-
-        ^4$if quality:
-            ^8$print('质量也还不错')
-            ^8$print('来一打')
-         ^4$else:
-            ^8$print('质量不行')
-            ^8$print('算了，不要')
-         ^4$print('性价比较高再买')
-
-<button>Run ...</button>
-
-输出如下：
-
-::
-
-    300 价钱合适
-    质量也还不错
-    来一打
-    性价比较高再买
-
-
-作者 & 更新时间
-------------------------------------
-作者: `一去丶二三里 <http://blog.csdn.net/liang19890820>`_
-
-提交: 2017/12/6
diff --git a/course/source/python-base/cn/python-controller-pass.rst b/course/source/python-base/cn/python-controller-pass.rst
deleted file mode 100644
index 6ad0570..0000000
--- a/course/source/python-base/cn/python-controller-pass.rst
+++ /dev/null
@@ -1,73 +0,0 @@
-Python pass 语句
-================
-
-简述
------------------------------------
-在 Python 中，pass 是一个空语句，为了保持程序结构的完整性。一般情况下，pass 被用作占位符。
-
-pass 和注释之间的区别在于：解释器会完全忽略注释，但不会忽略 pass。
-
-然而，执行 pass 时什么都不会发生，导致无操作（NOP）。
-
-pass 语句
-------------------------------------
-
-语法格式：
-
-::
-
-   pass
-
-假设，欧阳锋写了一个循环或者函数，尚未实现（暂未想好如何实现或者出差交付给其他人），但是会在将来的某个时候实现。这时，如果循环体或者函数体为空，解释器就会报错。所以，可以使用 pass 语句构造一个不做任何事情的主体。
-
-上课了，开始听歌，随机播放、顺序播放、单曲循环、列表循环。。。反复思量着，到底选哪一个呢？没想好，那么就先纠结着，什么都不干！
-
-::
-
-    songs = ['安静', '蜗牛', '稻香']
-    for song in songs:
-
-<button>Run ...</button>
-
-执行语句，输出如下：
-
-::
-
-    SyntaxError: unexpected EOF while parsing
-
-
-报错了，所以呢？还是乖乖的加上 pass 吧！
-
-
-::
-
-    songs = ['安静', '蜗牛', '稻香']
-    for song in songs:
-        ^4$pass
-
-<button>Run ...</button>
-
-相应的，也可以在空函数或类中做同样的事情：
-
-::
-    def function(args):
-        ^4$pass
-
-<button>Run ...</button>
-
-::
-
-    class example:
-        ^4$pass
-
-<button>Run ...</button>
-
-总之，pass 什么也不做，就是为了占位，防止语法错误。
-
-
-
-作者 & 更新时间
-------------------------------------
-作者: `一去丶二三里 <http://blog.csdn.net/liang19890820>`_
-
-提交: 2017/12/6
diff --git a/course/source/python-base/cn/python-controller-while.rst b/course/source/python-base/cn/python-controller-while.rst
deleted file mode 100644
index 24f48d9..0000000
--- a/course/source/python-base/cn/python-controller-while.rst
+++ /dev/null
@@ -1,88 +0,0 @@
-Python while 循环
-============================
-
-简述
----------------------------------
-在 Python 中，while 循环用于遍历代码块，只要判断条件为 True，就会一直不停地循环执行。
-
-通常，在事先不知道迭代次数的情况下使用 while 循环。
-
-while 循环
----------------------------------
-语法格式：
-
-::
-
-    while <判断条件>:
-        ^4$<循环体>
-
-
-进入 while 循环，首先检测判断条件，只有当其为 True 时，才会进入循环体。一次迭代后，再次检测判断条件，此过程一直持续到判断条件为 False。
-
-和 for 循环一样，while 的循环体也通过缩进来确定。
-
-流程图：
-
-.. image:: http://img.blog.csdn.net/20170416154810480
-
-如果说 for 循环是**顺序播放**，那 while 循环可以视为**单曲循环**。
-
-
-::
-
-    i = 0
-    while i < 3:
-        ^4$print('正在播放：双节棍')
-        ^4$i += 1
-
-<button>Run ...</button>
-
-运行程序，输出如下：
-
-::
-
-    正在播放：双节棍
-    正在播放：双节棍
-    正在播放：双节棍
-
-这里，只要计数器 i 小于 3（单曲循环 3 次），判断条件将为 True。
-
-**注意**：要在循环体中增加计数器的值，这非常重要（很容易忽视），否则将导致无限循环。
-
-while … else
----------------------------------
-与 for 循环相同，在 while 循环中也可以有一个可选的 else 块。
-
-如果 while 循环中的判断条件为 False，则 else 部分被执行。
-
-**注意** ：while 循环可以用 break 语句终止，在这种情况下，else 部分被忽略。
-
-::
-
-    i = 0
-    while i < 3:
-        ^4$print('正在播放：双节棍')
-
-        ^4$i += 1
-    else:
-        print('播放结束')
-
-<button>Run ...</button>
-
-运行程序，输出如下：
-
-::
-    正在播放：双节棍
-    正在播放：双节棍
-    正在播放：双节棍
-    播放结束
-
-可以看到，在第四次迭代中，while 中的条件变为 False。因此，else 部分被执行。
-
-
-
-作者 & 更新时间
-------------------------------------
-作者: `一去丶二三里 <http://blog.csdn.net/liang19890820>`_
-
-提交: 2017/12/6
diff --git a/course/source/python-base/cn/python-datatype-character-string.rst b/course/source/python-base/cn/python-datatype-character-string.rst
deleted file mode 100644
index f909a97..0000000
--- a/course/source/python-base/cn/python-datatype-character-string.rst
+++ /dev/null
@@ -1,445 +0,0 @@
-简述
-================
-
-字符串（String），是由零个或多个字符组成的有限序列，在 Python 中表示文本的数据类型。
-
-字符只是一个符号，例如：字母（A-Z）、数字（0-9）、特殊符号（~!@#￥%…）等。
-
-
-字符串
-----------
-
-
-在 Python 中，字符串由内置的 str 类型定义。
-
-::
-
-    s = 'Hello, Python!'
-    type(s)
-    <class 'str'>
-
-
-
-可以使用单引号（''）或双引号（""）来表示字符串。
-
-**PS**： 单引号比双引号更常用。
-
-::
-
-    s = 'Hello'  # 单引号
-    s
-
-    s = "Hello"  # 双引号
-    s
-
-
-<button>Run ...</button>
-
-
-字符串可以跨多行，但是每行末尾必须添加反斜杠（\）以转义换行符。
-
-::
-
-    s = 'Hello \
-    World'
-
-    s
-
-<button>Run ...</button>
-
-三重引号（''' 或 """）内的字符串可以跨多个文本行。
-
-::
-
-
-    s = '''Hello
-    Python
-    '''
-
-    s
-
-    s = """
-    Hello
-    Python
-    """
-
-    s
-
-<button>Run ...</button>
-
-
-**注意**： 三重引号通常用于表示多行字符串和 docstring。
-
-访问字符串
-================
-
-
-索引
--------------
-
-
-字符串是字符的有序序列，可以通过其位置来获取具体的字符。在 Python 中，位置有一个优雅的称呼 - 索引（index）。
-
-索引分为两种形式：
-
-- 正向索引（从左到右）：索引从 0 开始，直到 length - 1，可以很方便地访问字符串开头附近的字符。
-- 反向索引（从右到左）：索引从 -1 开始，直到 -length，可以很方便地访问字符串末尾附近的字符。
-
-其中，length 为字符串的长度。
-
-为了更清晰以上概念，来看一个直观地索引图。假设，有一个字符串 s = "Hello"：
-
-.. image:: http://img.blog.csdn.net/20170510181247783
-
-
-使用 [index] 的语法形式来访问指定索引处的字符：
-
-::
-
-    s = 'Hello'
-    s[0]  # 第一个字符
-    s[4]  # 最后一个字符
-
-<button>Run ...</button>
-
-
-负索引（反向索引）从字符串的末尾开始计数，也可以起到同样的效果：
-
-::
-
-    s = 'Hello'
-    s[-5]  # 第一个字符
-    s[-1]  # 最后一个字符
-
-<button>Run ...</button>
-
-
-试图访问索引范围外的字符会引发 IndexError。索引必须是一个整数，不能使用 float 或其他类型，这将引发 TypeError。
-
-::
-
-    s = 'Hello'
-    s[10]
-    s[1.5]
-
-
-<button>Run ...</button>
-
-
-
-切片
-------------------------
-
-
-切片操作（slice）是一种很方便的方法，用于引用序列（通常是字符串和列表）的子部分。
-
-切片操作的语法格式：
-
-[start:stop:step]
-
-
-- start（开始索引）：第一个索引的值是 0，最后一个是 -1。
-- stop（结束索引）：切片操作符将取到该索引为止，但不包含该索引的值。
-- step（步长）：默认是 1，也就是说，一个接一个切取。如果为 2，则表示隔一取一。步长为正数时，表示从左向右取；如果为负数，表示从右向左取；步长不能为 0。
-
-
-通过上图，其实也可以很直观地看到切片。
-
-::
-
-    s = 'Hello'
-    s[1:4]  # 第 2 个到第 4 个字符
-    s[1:]  # 第 2 个到最后一个字符（将索引默认设置为字符串的开始或结束）
-    s[1:100]  # 索引太大，被截断至字符串长度处
-    s[:]  # 所有字符（从开始到结束）
-
-<button>Run ...</button>
-
-**PS**： s[:] 形式会忽略开始和结束索引，总是获得一个完整的副本。是 pythonic 使用的方式，用于复制序列（例如：字符串或列表）。
-
-和提取字符类似，切片操作也可以使用反向索引：
-
-::
-
-    s[-4:-1]  # 第 2 个到第 4 个字符
-    s[:-3]  # 开始到第 2 个字符
-    s[-3:]  # 第 3 个到最后一个字符
-
-<button>Run ...</button>
-
-
-对于任何索引 n，s[:n] + s[n:] == s 是一个整齐的切片，甚至对于负数或超出界限的值也是如此。换一种说法，s[:n] 和 s[n:] 总是将字符串分成两部分，来保存所有的字符。
-
-
-更改或删除字符串
-------------------
-
-
-字符串是不可变的，也就是说，一旦分配了字符串的元素就不能被更改。
-
-但是，可以将不同的字符串重新分配给同一个变量。
-
-::
-
-    s = 'Hello'
-    s[2] = 'a'
-
-    s = 'Python'
-    s
-
-<button>Run ...</button>
-
-无法从字符串中删除字符，但是可以使用关键字 del 完全删除字符串。
-
-::
-
-    s = 'Hello'
-    del s[2]
-    del s
-    s
-
-
-<button>Run ...</button>
-
-基本操作
--------------
-
-
-字符串能成为 Python 中最常用的数据类型之一，很大原因是因为它使用非常方便，提供了大量的字符串操作，例如：连接字符串、遍历字符串…
-
-所有的序列（例如：字符串、列表）都可以进行以下基本操作：
-
-- +：连接两个序列
-- *：重复序列元素
-- in：判断元素是否在序列中
-- min()：返回最小值
-- max()：返回最大值
-- len()：返回序列长度
-- enumerate()：返回一个枚举对象，包含字符串中所有元素的索引和值（作为一对）
-
-
-连接字符串
--------------
-
-连接是指将多个字符串合并为一个单独的字符串。
-
-::
-
-    s1 = "Hello,"
-    s2 = " World!"
-
-    s = s1 + s2  # 连接字符串
-    s
-
-
-<button>Run ...</button>
-
-如果想在不同的行中连接字符串，可以使用括号。
-
-::
-
-    # 两个字符串一起
-    'Hello,' ' World!'  
-
-
-    # 使用括号
-    s = ('Hello,'  
-    s
-
-<button>Run ...</button>
-
-
-操作符 + 不会将数字或其他类型自动转换为字符串形式，需要通过 str() 函数将值转换为字符串形式，以便它们可以与其他字符串组合。
-
-::
-
-    age = 18
-    s = 'My age is ' + age
-    s = 'My age is ' + str(age)
-    s
-
-
-<button>Run ...</button>
-
-重复字符串
--------------
-
-
-操作符 * 用于以指定的次数来重复字符串。有时很好用，比如打印一条华丽的分割线：
-
-::
-
-    s = 'Hello'
-    s * 3  # 重复字符串 3 次
-    print('-' * 20)  # 无需输入很多 -
-
-
-<button>Run ...</button>
-
-
-
-最大值/最小值
--------------
-
-
-在一个字符串中，每个字符在计算机中都有对应的 ASCII 码。min() 和 max() 就是根据对应的 ASCII 码进行比较的，从而获取最小值和最大值对应的字符。
-
-Python 提供了两个内置函数 ord() 和 chr()，用于字符和 ASCII 码之间的转换。
-
-::
-
-    ord('H')  # 字符转 ASCII 码
-    chr(72)  # ASCII 码转字符
-
-<button>Run ...</button>
-
-别犹豫啦，开始比较吧！
-
-::
-
-    s = 'Hello'
-
-    min(s)  # 最小字符
-
-    max(s)  # 最大字符
-
-<button>Run ...</button>
-
-
-字符串成员测试
-------------------------
-
-
-使用关键字 in，可以测试字符串中是否包含指定的子串。
-
-::
-
-    't' in 'python'
-
-    'th' not in 'python'
-
-
-<button>Run ...</button>
-
-
-
-字符串长度
-------------------------
-
-
-要获取一个字符串的长度，可以使用内置函数 len()：
-
-::
-
-    s = 'Hello'
-
-    len(s)  # 字符数
-
-<button>Run ...</button>
-
-
-迭代字符串
-------------------------
-
-
-
-使用 for 循环，可以遍历一个字符串。
-
-::
-
-     # 查找字符串中 l 的数量
-     count = 0
-     for letter in 'Hello':
-         ^4$if (letter == 'l'):
-            ^8$count += 1
-     print(count, 'letters found')
-
-<button>Run ...</button>
-
-
-还可使用 enumerate()，它会返回一个枚举对象，包含字符串中所有元素的索引和值（作为一对），这对于迭代很有用。
-
-::
-
-    s = 'Hello'
-    e = list(enumerate(s))
-    print(e)
-
-    for index, item in e:
-        ^4$print(index, item)
-
-
-<button>Run ...</button>
-
-
-运行程序，输出如下：
-
-::
-
-[(0, ‘H’), (1, ‘e’), (2, ‘l’), (3, ‘l’), (4, ‘o’)]
-0 H
-1 e
-2 l
-3 l
-4 o
-
-
-字符串的方法
-------------------------
-
-
-字符串有许多方法，可以通过 dir() 来查看方法列表：
-
-::
-
-     dir(str)
-
-
-这么多，当然不需要逐一介绍了，重在掌握如何使用（授人以鱼不如授人以渔），能做到随用随查就好。
-
-利用 help() 函数，可以查看函数或模块用途的详细说明：
-
-::
-
-    help(str.lower)
-    >>>Help on method_descriptor:
-
-    lower(...)
-        S.lower() -> str
-
-        Return a copy of the string S converted to lowercase.
-    (END)
-
-
-**注意**： 要终止查询，使用 q 键。
-
-按照说明，可以在交互模式下进行实验，这里仅列举一些比较常用的方法。
-
-::
-
-    s = 'Hello'
-
-    s.lower()  # 转换所有字符为小写
-    s.upper()  # 转换所有字符为大写
-
-    s.find('ll')  # 返回子串的开始索引
-
-    s.endswith('llo')  # 检查字符串是否以指定的子串结束
-
-    s.isdigit()  # 检查字符串是否只包含数字
-
-    s.replace('ll', 'r')  # 替换字符串中的子串
-
-    s = 'I like Python'
-    s.split()  # 分割字符串
-
-<button>Run ...</button>
-
-
-
-作者 & 更新时间
-------------------------------------
-作者: `一去丶二三里 <http://blog.csdn.net/liang19890820>`_
-
-提交: 2017/12/6
-
diff --git a/course/source/python-base/cn/python-datatype-dictionaries.rst b/course/source/python-base/cn/python-datatype-dictionaries.rst
deleted file mode 100644
index 616c234..0000000
--- a/course/source/python-base/cn/python-datatype-dictionaries.rst
+++ /dev/null
@@ -1,322 +0,0 @@
-Python pass 语句
-================
-
-
-
-简述
------
-记得上学时，《新华字典》绝对堪称神器，检索内容相当给力。但随着网络的发展，现在几乎很少有人在用了。
-
-如何使用《新华字典》呢？先查索引，然后通过索引查找相应的内容，简单、方便、快捷。
-
-出于这种需求，Python 也提供了这样一种数据类型 - dict，翻译为中文就是“字典”。
-
-字典的特点
------
-像列表和字典，可以很容易地被改变 - 在运行时可以随意地变小/变大（无需复制）。所以，没有列表和字典的 Python 程序或脚本，几乎是无法想象的。
-
-词典可以包含在列表中，反之亦然。但字典和列表（或其它序列）之间有什么区别呢？
-
-- 列表是对象的有序集合，而字典是无序集。
-- 对于列表（其他复合数据类型也一样）来说，元素只有值；而对于字典，元素由 key:value - 对的形式组成。
-- 字典中的元素通过 key 来访问，而非通过他们的位置来访问（主要区别）。
-
-字典是一个关联数组（也称为哈希），其中的任何 key 都与 value 相关联（或映射），value 可以是 Python 中的任何数据类型，所以字典是无序的 key:value 对。
-
-字典不支持序列数据类型（例如：字符串、元组和列表）的序列操作（例如：索引 
-切片、连接 +、重复 *），字典属于内置映射类型，同时也是该类型的唯一代表！
-
-虽然没有报错，但前面的值被后面的覆盖了，这样做毫无意义。
-在 Python 中，字典由内置的 dict 类型定义。
-
-要创建字典，需要将所有项（元素）放在花括号（{}）内，以逗号（,）分隔。其中，每个元素都以 key:value 对的形式出现，key 和 value 可以是任何数据类型。
-
-**注意：** 字典中的 key 必须是唯一的。
-
-:: 
-    d = {'name':'Python', 'site':'www.python.org'}
-    type(d)
-    <class 'dict'>
-
-<button>Run ...</button>
-
-字典的创建方式很多，还可以使用 dict()：
-
-由于列表是可变的，不可 hash，因此不能作为字典的 key。
-
-key 唯一，意思是说：不要有重复的 key 出现。
-
-虽然 value 可以是任何数据类型，并且可以重复，但 key 必须是不可变类型（例如：字符串、数字）或具有不可变元素的元组。
-
-::
-     d = {'name':'python', (1, 2):'C++'}  # 具有不可变元素的元组
-     d
-    {(1, 2): 'C++', 'name': 'python'}
-    
-     d = {'name':'python', [1, 2]:'C++'}  # 列表
-    ...
-    TypeError: unhashable type: 'list'
-
-<button>Run ...</button>
-
-由于列表是可变的，不可 hash，因此不能作为字典的 key。
-
-key 唯一，意思是说：不要有重复的 key 出现。
-
-::
-     d = {'name':'Python', 'name':'C++'}  # key 都是 'name'
-     d
-    {'name': 'C++'}
-
-<button>Run ...</button>
-
-虽然没有报错，但前面的值被后面的覆盖了，这样做毫无意义。
-
-
-从字典访问元素
------
-
-字典通过 key 来检索 value，有两种方式：
-
-- 在方括号（[]）内使用 key
-- key 与 get() 方法与一起使用
-
-区别在于：使用 get()，如果没有找到 key，则返回 None，而不是 KeyError。
-
-::
-    d = {'name':'Python', 'site':'www.python.org'}
-    
-    print(d['name'])
-   Python
-    
-    print(d.get('site'))
-   www.python.org
-    
-    print(d['version'])  # 没有找到 key，引发 KeyError
-   ...
-   KeyError: 'version'
-    
-    print(d.get('version'))  # 没有找到 key，返回 None
-   None
-    print(d.get('version', 3.5))  # 没有找到 key，返回默认值
-   3.5
-   1
-
-
-<button>Run ...</button>
-
-更改或添加元素
------
-
-字典是可变的，可以添加新元素，也可以使用赋值运算符（=）更改现有元素的值。
-
-如果 key 存在，value 将被更新；否则，新的 key:value 对会被添加到字典中。
-
-::
-     d = {}
-     
-     d['name'] = 'Python'  # 添加
-     d
-    {'name': 'Python'}
-     
-     d['site'] = 'www.python.org'  # 添加
-     d
-    {'site': 'www.python.org', 'name': 'Python'}
-     
-     d['name'] = 'Py'  #  更新
-     d
-    {'site': 'www.python.org', 'name': 'Py'}
-
-<button>Run ...</button>
-
-删除字典或元素
------
-
-通过使用 pop() 方法，可以删除指定 key 对应的元素，并返回其 value。
-
-popitem() 方法可以用来删除并返回一个任意元素 (key, value)，clear() 方法则会一次性删除所有元素。
-
-还可以使用 del 关键字来删除单个元素或整个字典本身。
-
-::
-     d = {'name':'Python', 'site':'www.python.org', 'version':3.5, 'from':1991}
-    >>> 
-     d.pop('site')  # 删除某一项
-    'www.python.org'
-     d
-    {'from': 1991, 'version': 3.5, 'name': 'Python'}
-     
-     d.popitem()  # 删除任意项
-    ('from', 1991)
-     d
-    {'version': 3.5, 'name': 'Python'}
-     
-     del d['version']  # 删除某一项
-     d
-    {'name': 'Python'}
-     
-     d.clear()  # 清空 - 删除所有项
-     d
-    {}
-     
-     del d  # 删除字典本身
-     d
-    ...
-    NameError: name 'd' is not defined
-
-<button>Run ...</button>
-
-基本操作
------
-
-字典不支持序列数据类型（例如：字符串、元组和列表）的序列操作，例如：索引 
-切片、连接（+）、重复（*）等。
-
-成员测试
->>>>>>>>>>
-
-可以测试一个 key 是否存在于字典中，使用关键字 in。
-
-注意： 成员测试仅适用于 key，不适用于 value。
-
-::
-     d = {'name':'Python', 'site':'www.python.org'}
-    
-     'name' in d
-    True
-     
-     'version' not in d
-    True
-     
-     'Python' in d  # 不能检测 value
-    False
-
-<button>Run ...</button>
-
-遍历字典
->>>>>>>>>>
-
-使用 for 循环，可以遍历字典中的每个 key。
-
-::
-     d = {'name':'Python', 'site':'www.python.org'}
-     
-     for key in d:
-    ...     print(key)
-    ... 
-    name
-    site
-
-<button>Run ...</button>
-
-字典的方法
------
-
-字典提供了许多方法，可以通过 dir() 来查看方法列表：
-
-::
-     dir(dict)
-    ['__class__', '__contains__', '__delattr__', '__delitem__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__getitem__', '__gt__', '__hash__', '__init__', '__iter__', '__le__', '__len__', '__lt__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__setitem__', '__sizeof__', '__str__', '__subclasshook__', 'clear', 'copy', 'fromkeys', 'get', 'items', 'keys', 'pop', 'popitem', 'setdefault', 'update', 'values']
-1
-2
-可以看到，有以下方法可用：
-+-----------------------+----------------------------------------------------------------------------------------------------------------+
-| 方法	                |         描述                                                                                                   | 
-+=======================+================================================================================================================+
-|clear()                | 	删除字典中的所有元素                                                                                     |
-+=======================+================================================================================================================+
-|copy()	                |           返回字典的浅拷贝                                                                                     |
-+=======================+================================================================================================================+
-|fromkeys(seq[, v])	|从 seq 返回一个新的字典，值等于 v（默认为 None）                                                                |
-+=======================+================================================================================================================+
-|get(key[,d])	        |返回 key 的值。如果 key 不存在，返回 d（默认为 None）。                                                         |
-+=======================+================================================================================================================+
-|items()	        |以列表形式，返回可遍历的 (key, value) 元组数组。                                                                |
-+=======================+================================================================================================================+
-|keys()	                |以列表的形式，返回字典中所有的 key。                                                                            |
-+=======================+================================================================================================================+
-|pop(key[,d])	        |用 key 删除元素并返回其 value。如果没有找到 key，则返回 d；如果没|有提供 d 并且没有找到 key，则会引发 KeyError。|
-+=======================+================================================================================================================+
-|popitem()	        |删除并返回一个任意元素，如果字典为空，则会引发 KeyError。                                                       |
-+=======================+================================================================================================================+
-|setdefault(key[,d])	|如果 key 在字典中，则返回对应的 value；否则，插入 key，将其值设为 d，并返回 d（默认为 None）。                  |
-+=======================+================================================================================================================+
-|update([other])	|将 other 字典中的键/值对更新到字典中，覆盖现有键。                                                              |
-+=======================+================================================================================================================+
-|values()	        |以列表的形式，返回字典中所有的 value                                                                            |
-+-----------------------+----------------------------------------------------------------------------------------------------------------+
-
-其中一些在上述示例中已经被使用过了。
-
-::
-    all_stars = {}.fromkeys(['Lebron', 'Kobe', 'Curry'], 'NBA')
-     
-    all_stars
-    {'Lebron': 'NBA', 'Curry': 'NBA', 'Kobe': 'NBA'}
-    
-    all_stars.keys()  # 返回所有 key
-    dict_keys(['Lebron', 'Curry', 'Kobe'])
-         
-    all_stars.values()  # 返回所有 value
-    dict_values(['NBA', 'NBA', 'NBA'])
-     
-    for item in all_stars.items():
-   ...     print(item)
-   ... 
-   ('Lebron', 'NBA')
-   ('Curry', 'NBA')
-   ('Kobe', 'NBA')
-
-<button>Run ...</button>
-
-字典推导式
------
-
-
-字典推导式（Dict Comprehensions）是一种优雅、简洁的方式，可以从一个 iterable 中创建新的字典。
-
-字典推导式由花括号标识，其中包含一个表达式对（key:value），紧随其后的是 for 语句。
-
-来看一个例子，NBA All-Star。
-
-::
-     all_stars = ['Lebron', 'Kobe', 'Curry']
-     
-     d = {key: value for key, value in enumerate(all_stars)}  
-     d
-    {0: 'Lebron', 1: 'Kobe', 2: 'Curry'}
-
-<button>Run ...</button>
-
-列表推导式可以可选地包含更多的 for 或 if 语句，if 语句可以过滤出新字典的元素。
-
-::
-    # value 的长度需要大于 5
-     d = {key: value for key, value in enumerate(all_stars) if len(value) > 5}  
-     d
-   {0: 'Lebron'}
-
-<button>Run ...</button>
-
-666，老詹独一档。。。
-
-字典与内置函数
------
-
-下述内置函数通常与字典一起使用，来执行不同的任务。
-+--------+--------------------------------------------------------------------------------------+
-函数     |描述                                                                                  |   
-+======+========================================================================================+
-all()	 |如果字典的所有 key 都是 True（或者字典为空），返回 True。                             |   
-+======+========================================================================================+
-any()	 |如果字典的所有 key 都是 True，返回 True；如果字典为空，返回 False。                   |   
-+======+========================================================================================+
-len()	 |返回字典的长度（元素个数）                                                            |  
-+======+========================================================================================+
-sorted() |	返回一个新的排序字典（不排序字典本身）                                          |
-+------+----------------------------------------------------------------------------------------+
-
-
-作者 & 更新时间
-------------------------------------
-作者: `CSDN <http://blog.csdn.net/liang19890820>`_
diff --git a/course/source/python-base/cn/python-datatype-escape-sequence.rst b/course/source/python-base/cn/python-datatype-escape-sequence.rst
deleted file mode 100644
index 79a7922..0000000
--- a/course/source/python-base/cn/python-datatype-escape-sequence.rst
+++ /dev/null
@@ -1,127 +0,0 @@
-简述
-================
-
-在字符串中，有时需要包含一些特殊的符号，但是有些符号不能直接输出，就需要使用转义序列。
-
-关于转义，维基百科这样定义：
-
-转义是当由于技术等原因、无法直接在代码中写出所要的字符时采用的，以多个字符的有序组合来表示原本需要的字符的手段，而转义序列（escape sequence）指在转义时使用的有序字符组合。
-
-
-
-转义序列
--------------
-
-
-作为一名伟大的程序员，在向别人介绍自己的职业时，往往会很自豪的说：Hi, "I'm a Pythoner"。如果要在 Python 中表示这句话，不能直接使用单引号或双引号。
-
-
-::
-
-    print('Hi, "I'm a Pythoner"')
-    print("Hi, "I'm a Pythoner"")
-
-<button>Run ...</button>
-
-
-要解决这个问题，有两种方式：
-
-- 使用三重引号
-- 使用转义序列
-
-转义序列以反斜杠（\）开头，并以不同的方式解释。如果使用单引号来表示字符串，那么字符串内的所有单引号都必须转义，双引号也不例外。
-
-::
-
-    print('''Hi, "I'm a Pythoner"''')  # 使用三重引号
-
-    print('Hi, "I\'m a Pythoner"')  # 转义单引号
-
-    print("Hi, \"I'm a Pythoner\"")  # 转义双引号
-
-
-<button>Run ...</button>
-
-
-
-以下是 Python 支持的所有转义序列的列表：
-
-+------------+------------------------------------------------------------------------------------------+
-| 转义序列   | 说明                                                                                     |
-+============+==========================================================================================+
-|\（行尾）   | 续行符                                                                                   |    
-+------------+------------------------------------------------------------------------------------------+
-| \\         |反斜杠                                                                                    |
-+------------+------------------------------------------------------------------------------------------+
-| \'         |单引号                                                                                    |
-+------------+------------------------------------------------------------------------------------------+
-| \"         |       双引号                                                                             |         
-+------------+------------------------------------------------------------------------------------------+
-| \a         |响铃                                                                                      |
-+------------+------------------------------------------------------------------------------------------+
-|\b          |退格（Backspace）                                                                         |
-+------------+------------------------------------------------------------------------------------------+
-| \f         |换页                                                                                      |
-+------------+------------------------------------------------------------------------------------------+
-| \n         |换行                                                                                      |
-+------------+------------------------------------------------------------------------------------------+
-| \r         |      回车                                                                                |
-+------------+------------------------------------------------------------------------------------------+
-|\t          |	水平制表符                                                                              |
-+------------+------------------------------------------------------------------------------------------+
-|\v	         |垂直制表符                                                                                |
-+------------+------------------------------------------------------------------------------------------+
-|\ooo	     |值为八进制 ooo 的字符                                                                     |
-+------------+------------------------------------------------------------------------------------------+
-|\xhh	     |值为十六进制 hh 的字符                                                                    |
-+------------+------------------------------------------------------------------------------------------+
-
-
-测试其中的转义序列，感受一下实际的意思：
-
-::
-
-    print('C:\\Windows\\System32')
-
-    print('I like \nPython')
-
-    print('This is \x48\x45\x58')
-
-
-<button>Run ...</button>
-
-
-原始字符串
-----------------
-
-
-原始字符串（Raw String），简单理解就是：字符串中的每个字符都表示原始含义。
-
-有时，可能希望忽略字符串中的转义序列：
-
-::
-
-    print('This is \x48\x45\x58')
-
-
-<button>Run ...</button>
-
-
-遗憾的是，这里的反斜杠并不是原始符号的含义，而是和后面的字符一起表示十六进制（被转义了）。
-
-如何避免呢？很 easy，为字符串加上前缀 r 或者 R（大小写均可）：
-
-::
-
-    print(r'This is \x48\x45\x58')
-
-
-<button>Run ...</button>
-
-R 是 Raw 的缩写，以其开头的字符串被称作原始字符串，并将反斜杠视为原义字符。因此，其中的任何转义序列都不会被特别处理，将会被忽略。
-
-作者 & 更新时间
-------------------------------------
-作者: `一去丶二三里 <http://blog.csdn.net/liang19890820>`_
-
-提交: 2017/12/6
diff --git a/course/source/python-base/cn/python-datatype-gather.rst b/course/source/python-base/cn/python-datatype-gather.rst
deleted file mode 100644
index aa8ad50..0000000
--- a/course/source/python-base/cn/python-datatype-gather.rst
+++ /dev/null
@@ -1,396 +0,0 @@
-Python 集合
-================
-
-
-
-简述
------
-数学上有一个基础概念 -集合，上高一的时候学过。集合的作用大吗？高考必考，你说呢？
-
-关于集合，维基百科这样描述：
-
-.. 集合是基本的数学概念，它是集合论的研究对象，指具有某种特定性质的事物的总体，（在最原始的集合论─朴素集合论─中的定义，集合就是“一堆东西”。）集合里的事物（“东西”），叫作元素。若然 x 是集合 A 的元素，记作 x ∈ A。
-
-在 Python 中，集合分为两类：
-
-- set：可变集合
-- frozenset：不可变集合
-set 可以原地修改，或者说是可变的，也可以说是 unhashable（不可哈希）的。
-
-frozenset，顾名思义，是一个被“冻结”的集合，不能原地修改，是 hashable（可哈希）的。
-
-集合
------
-在 Python 中，集合由内置的 set 类型定义。
-
-要创建集合，需要将所有项（元素）放在花括号（{}）内，以逗号（,）分隔。
-
-:: 
-    d = {'name':'Python', 'site':'www.python.org'}
-    type(d)
-    <class 'dict'>
-
-<button>Run ...</button>
-
-字典的创建方式很多，还可以使用 dict()：
-
-由于列表是可变的，不可 hash，因此不能作为字典的 key。
-
-key 唯一，意思是说：不要有重复的 key 出现。
-
-虽然 value 可以是任何数据类型，并且可以重复，但 key 必须是不可变类型（例如：字符串、数字）或具有不可变元素的元组。
-
-::
-     s = {'P', 'y', 't', 'h', 'o', 'n'}
-     type(s)
-    <class 'set'>
-
-<button>Run ...</button>
-
-集合可以有任意数量的元素，它们可以是不同的类型（例如：数字、元组、字符串等）。但是，集合不能有可变元素（例如：列表、集合或字典）。
-
-::
-     s = {1, 2, 3}  # 整形的集合
-     
-     s = {1.0, 'Python', (1, 2, 3)}  # 混合类型的集合
-      
-     s = set(['P', 'y'])  # 从列表创建
-     
-     s = {1, 2, [3, 4]}  # 不能有可变元素
-    ...
-    TypeError: unhashable type: 'list'
-
-<button>Run ...</button>
-
-创建空集合比较特殊。在 Python 中，空花括号（{}）用于创建空字典。要创建一个没有任何元素的集合，使用 set() 函数（不要包含任何参数）。
-
-::
-     d = {}  # 空字典
-     type(d)
-    <class 'dict'>
-         
-     s = set()  # 空集合
-    type(s)
-    <class 'set'>
-
-<button>Run ...</button>
-
-集合的特性
------
-
-.. 子曰：“温故而知新，可以为师矣。” 
-–《论语》
-
-回顾数学相关知识，集合有以下特性：
-
-- 无序性：一个集合中，每个元素的地位都是相同的，元素之间是无序的。 
-集合上可以定义序关系，定义了序关系后，元素之间就可以按照序关系排序。但就集合本身的特性而言，元素之间没有必然的序。
-- 互异性：一个集合中，任何两个元素都认为是不相同的，即每个元素只能出现一次。 
-有时需要对同一元素出现多次的情形进行刻画，可以使用多重集，其中的元素允许出现多次。
-- 确定性：给定一个集合，任给一个元素，该元素或者属于或者不属于该集合，二者必居其一，不允许有模棱两可的情况出现。
-
-当然，Python 中的集合也具备这些特性：
-
-::
-    # 无序性
-     s = set('Python')
-     s
-    {'y', 'n', 'h', 'o', 'P', 't'}
-    
-     s[0]  # 不支持索引
-    ...
-    TypeError: 'set' object does not support indexing
-
-    # 互异性
-     s = set('Hello')
-     s
-    {'e', 'H', 'l', 'o'}
-
-    # 确定性
-     'l' in s
-    True
-     
-     'P' not in s
-    True
-
-<button>Run ...</button>
-
-**注意：** 由于集合是无序的，所以索引没有任何意义。也就是说，无法使用索引或切片访问或更改集合元素。
-
-
-集合运算
------
-
-集合之间也可进行数学集合运算（例如：并集、交集等），可用相应的操作符或方法来实现。
-
-考虑 A、B 两个集合，进行以下操作。
-
-::
-     A = set('abcd')
-     B = set('cdef')
-
-<button>Run ...</button>
-
-子集
------
-
-子集，为某个集合中一部分的集合，故亦称部分集合。
-
-使用操作符 < 执行子集操作，同样地，也可使用方法 issubset() 完成。
-
-::
-     C = set('ab')
-     
-     C < A
-    True
-    
-     C < B
-     False
-     
-     C.issubset(A)
-     True
-
-<button>Run ...</button>
-
-并集
------
-
-一组集合的并集是这些集合的所有元素构成的集合，而不包含其他元素。
-
-使用操作符 | 执行并集操作，同样地，也可使用方法 union() 完成。
-
-::
-     A | B
-    {'e', 'f', 'd', 'c', 'b', 'a'}
-  
-     A.union(B)
-    {'e', 'f', 'd', 'c', 'b', 'a'}
-
-<button>Run ...</button>
-
-交集
------
-
-两个集合 A 和 B 的交集是含有所有既属于 A 又属于 B 的元素，而没有其他元素的集合。
-
-使用 & 操作符执行交集操作，同样地，也可使用方法 intersection() 完成。
-
-::
-     A & B
-    {'d', 'c'}
-    
-     A.intersection(B)
-    {'d', 'c'}
-
-<button>Run ...</button>
-
-差集
------
-A 与 B 的差集是所有属于 A 且不属于 B 的元素构成的集合
-
-使用操作符 - 执行差集操作，同样地，也可使用方法 difference() 完成。
-
-::
-     A - B
-    {'b', 'a'}
-     
-     A.difference(B)
-    {'b', 'a'}
-
-<button>Run ...</button>
-
-对称差
------
-
-两个集合的对称差是只属于其中一个集合，而不属于另一个集合的元素组成的集合。
-
-使用 ^ 操作符执行差集操作，同样地，也可使用方法 symmetric_difference() 完成。
-
-::
-     A ^ B
-    {'b', 'e', 'f', 'a'}
-     
-     A.symmetric_difference(B)
-    {'b', 'e', 'f', 'a'}
-
-<button>Run ...</button>
-
-更改集合
------
-
-虽然集合不能有可变元素，但是集合本身是可变的。也就是说，可以添加或删除其中的元素。
-
-可以使用 add() 方法添加单个元素，使用 update() 方法添加多个元素，update() 可以使用元组、列表、字符串或其他集合作为参数。
-
-::
-     s = {'P', 'y'}
-     
-     s.add('t')  # 添加一个元素
-     s
-    {'P', 'y', 't'}
-     
-     s.update(['h', 'o', 'n'])  # 添加多个元素
-    s
-    {'y', 'o', 'n', 't', 'P', 'h'}
-     
-     s.update(['H', 'e'], {'l', 'l', 'o'})  # 添加列表和集合
-     s
-    {'H', 'y', 'e', 'o', 'n', 't', 'l', 'P', 'h'}
-
-<button>Run ...</button>
-
-在所有情况下，元素都不会重复。
-
-从集合中删除元素
------
-
-可以使用 discard() 和 remove() 方法删除集合中特定的元素。
-
-两者之间唯一的区别在于：如果集合中不存在指定的元素，使用 discard() 保持不变；但在这种情况下，remove() 会引发 KeyError。
-
-::
-     s = {'P', 'y', 't', 'h', 'o', 'n'}
-     
-     s.discard('t')  # 去掉一个存在的元素
-     s
-    {'y', 'o', 'n', 'P', 'h'}
-     s.remove('h')  # 删除一个存在的元素
-     s
-    {'y', 'o', 'n', 'P'}
-     
-     s.discard('w')  # 去掉一个不存在的元素（正常）
-     s
-    {'y', 'o', 'n', 'P'}
-        
-     s.remove('w')  # 删除一个不存在的元素（引发错误）
-    ...
-    KeyError: 'w'
-
-<button>Run ...</button>
-
-类似地，可以使用 pop() 方法删除和返回一个项目。
-
-还可以使用 clear() 删除集合中的所有元素。
-
-::
-     s = set('Python')
-     
-     s.pop()  # 随机返回一个元素
-    'y'
-     
-     s.clear()  # 清空集合
-     s
-    set()
-
-<button>Run ...</button>
-
-**注意：** 集合是无序的，所以无法确定哪个元素将被 pop，完全随机。
-
-集合的方法
------
-
-老规矩，利用 dir() 来查看方法列表：
-
-::
-     dir(set)
-    ['__and__', '__class__', '__contains__', '__delattr__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__gt__', '__hash__', '__iand__', '__init__', '__ior__', '__isub__', '__iter__', '__ixor__', '__le__', '__len__', '__lt__', '__ne__', '__new__', '__or__', '__rand__', '__reduce__', '__reduce_ex__', '__repr__', '__ror__', '__rsub__', '__rxor__', '__setattr__', '__sizeof__', '__str__', '__sub__', '__subclasshook__', '__xor__', 'add', 'clear', 'copy', 'difference', 'difference_update', 'discard', 'intersection', 'intersection_update', 'isdisjoint', 'issubset', 'issuperset', 'pop', 'remove', 'symmetric_difference', 'symmetric_difference_update', 'union', 'update']
-
-<button>Run ...</button>
-
-可以看到，有以下方法可用：
-
-::
-
-可以看到，有以下方法可用：
-+-----------------------------+--------------------------------------------------------------------------------------+
-| 方法	                      |                                描述                                                  | 
-+=============================+======================================================================================+
-|add()	                      |            将元素添加到集合中                                                        |
-+=============================+======================================================================================+
-clear()                       |	    删除集合中的所有元素                                                             |
-+=============================+======================================================================================+
-copy()	                      |             返回集合的浅拷贝                                                         |
-+=============================+======================================================================================+
-difference                    |          将两个或多个集合的差集作为一个新集合返回                                    |
-+=============================+======================================================================================+
-difference_update()	      |            从这个集合中删除另一个集合的所有元素                                      |
-+=============================+======================================================================================+
-discard()	              |         删除集合中的一个元素（如果元素不存在，则不执行任何操作）                     |
-+=============================+======================================================================================+
-intersection()	              |            将两个集合的交集作为一个新集合返回                                        |
-+=============================+======================================================================================+
-intersection_update()	      |              用自己和另一个的交集来更新这个集合                                      |
-+=============================+======================================================================================+
-isdisjoint()	              |             如果两个集合有一个空交集，返回 True                                      |
-+=============================+======================================================================================+
-issubset()	              |          如果另一个集合包含这个集合，返回 True                                       |
-+=============================+======================================================================================+
-issuperset()	              |         如果这个集合包含另一个集合，返回 True                                        |
-+=============================+======================================================================================+
-pop()	                      |          删除并返回任意的集合元素（如果集合为空，会引发 KeyError）                   |
-+=============================+======================================================================================+
-remove()	              |        删除集合中的一个元素（如果元素不存在，会引发 KeyError）                       |
-+=============================+======================================================================================+
-symmetric_difference()	      |             将两个集合的对称差作为一个新集合返回                                     |
-+=============================+======================================================================================+
-symmetric_difference_update() |	          用自己和另一个的对称差来更新这个集合                                       |
-+=============================+======================================================================================+
-union()	                      |           将集合的并集作为一个新集合返回                                             |        
-+=============================+======================================================================================+
-update()	              |           用自己和另一个的并集来更新这个集合                                         |
-+-----------------------------+--------------------------------------------------------------------------------------+
-
-其中一些方法在上述示例中已经被使用过了，如果有方法不会用，可利用 help() 函数，查看用途及详细说明。
-
-集合与内置函数
------
-下述内置函数通常作用于集合，来执行不同的任务。
-
-+-----------+----------------------------------------------------------------------------------------+
-函数        |描述                                                                                    |   
-+===========+========================================================================================+
-all()	    |如果集合中的所有元素都是 True（或者集合为空），则返回 True。                            | 
-+===========+========================================================================================+
-any()	    |如果集合中的所有元素都是 True，则返回 True；如果集合为空，则返回 False。                | 
-+===========+========================================================================================+
-enumerate() |	返回一个枚举对象，其中包含了集合中所有元素的索引和值（配对）。                       | 
-+===========+========================================================================================+
-len()	    |返回集合的长度（元素个数）                                                              | 
-+===========+========================================================================================+
-max()	    |返回集合中的最大项                                                                      | 
-+===========+========================================================================================+
-min()	    |返回集合中的最小项                                                                      | 
-+===========+========================================================================================+
-sorted()    |	从集合中的元素返回新的排序列表（不排序集合本身）                                     | 
-+===========+========================================================================================+
-sum()	    |返回集合的所有元素之和                                                                  |
-+-----------+----------------------------------------------------------------------------------------+
-
-不可变集合
------
-frozenset 是一个具有集合特征的新类，但是一旦分配，它里面的元素就不能更改。这一点和元组非常类似：元组是不可变的列表，frozenset 是不可变的集合。
-
-集合是 unhashable 的，因此不能用作字典的 key；而 frozensets 是 hashable 的，可以用作字典的 key。
-
-可以使用函数 frozenset() 创建 frozenset。
-
-::
-     s = frozenset('Python')
-     type(s)
-    <class 'frozenset'>
-
-<button>Run ...</button>
-
-frozenset 也提供了一些列方法，和 set 中的类似。
-::
-     dir(frozenset)
-    ['__and__', '__class__', '__contains__', '__delattr__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__gt__', '__hash__', '__init__', '__iter__', '__le__', '__len__', '__lt__', '__ne__', '__new__', '__or__', '__rand__', '__reduce__', '__reduce_ex__', '__repr__', '__ror__', '__rsub__', '__rxor__', '__setattr__', '__sizeof__', '__str__', '__sub__', '__subclasshook__', '__xor__', 'copy', 'difference', 'intersection', 'isdisjoint', 'issubset', 'issuperset', 'symmetric_difference', 'union']
-
-<button>Run ...</button>
-
-由于 frozenset 是不可变的，所以没有添加或删除元素的方法。
-
-
-作者 & 更新时间
-------------------------------------
-作者: `CSDN <http://blog.csdn.net/liang19890820>`_
diff --git a/course/source/python-base/cn/python-datatype-list.rst b/course/source/python-base/cn/python-datatype-list.rst
deleted file mode 100644
index 4f46390..0000000
--- a/course/source/python-base/cn/python-datatype-list.rst
+++ /dev/null
@@ -1,530 +0,0 @@
-简述
-================
-
-
-Python 提供了一系列复合数据类型，通常被称为序列，列表（List）就是其中的一种。
-
-在 Python 中，列表是使用最频繁、用途最广泛的数据类型之一，非常灵活。
-
-
-列表
--------------
-
-
-列表是有序的元素序列，在 Python 中，列表由内置的 list 类型定义。
-
-要创建列表，需要将所有项（元素）放在方括号（[]）内，以逗号（,）分隔。
-
-::
-
-
-    l = ['P', 'y', 't', 'h', 'o', 'n']
-    type(l)
-
-<button>Run ...</button>
-
-
-列表可以有任意数量（n >= 0）的元素，当 n 为 0 时，表示一个空列表。
-
-列表中元素的类型可以不同，支持数字、字符串，甚至可以包含列表（所谓的嵌套）。
-
-::
-
-    l = []  # 空列表
- 
-    l = [1, 2, 3]  # 整形列表
-
-    l = [1, 5.5, 'Python']  # 混合类型列表
-
-    l = [6, ['Python'], ['P', 'y', 't', 'h', 'o', 'n']]  # 嵌套列表
-
-<button>Run ...</button>
-
-
-访问列表中的元素
-================
-
-
-
-列表索引
--------------
-
-
-和字符串类似，可以使用索引操作符（[]）访问列表中的一个元素。
-
-.. image:: http://img.blog.csdn.net/20170516213732766
-
-
-::
-
-    l = ['P', 'y', 't', 'h', 'o', 'n']
-
-    l[0]  # 第 1 个元素
- 
-    l[4]  # 第 5 个元素
-
-
-<button>Run ...</button>
-
-序列也可以反向索引，-1 表示最后一个元素，-2 表示倒数第二个元素，依此类推。
-
-::
-
-    l[-6]  # 第 1 个元素
-
-
-    l[-2]  # 第 5 个元素
-
-<button>Run ...</button>
-
-
-同样地，嵌套的列表可以使用嵌套索引来访问。
-
-::
-
-    l = [6, ['Python'], ['P', 'y', 't', 'h', 'o', 'n']]  # 嵌套列表
-
-    l[0]
-
-    l[2][3]  # 使用嵌套索引
-
-<button>Run ...</button>
-
-
-对列表进行切片
-------------------------
-
-
-可以使用切片操作符（:）来访问列表中的一系列元素。
-
-**PS**： 通过上图，可以很直观地看到切片。
-
-::
-
-    l = ['P', 'y', 't', 'h', 'o', 'n']
-
-    l[1:4]  #  第 2 个到第 4 个元素
-
-    l[1:]  # 第 2 个到最后一个元素（将索引默认设置为列表的开始或结束）
-
-    l[1:100]  # 索引太大，被截断至列表长度处
-
-    l[:]  # 所有元素（从开始到结束）
-
-
-<button>Run ...</button>
-
-
-
-和索引类似，切片操作也可以使用反向索引：
-
-::
-
-    l[-5:-2]  # 第 2 个到第 5 个元素
-
-    l[:-3]  # 开始到第 3 个元素
- 
-    l[-3:]  # 第 4 个到最后一个元素
-
-<button>Run ...</button>
-
-
-更改及添加元素
-----------------
-
-
-列表是可变的，也就是说，其中的元素可以被改变（不像字符串或元组）。
-
-可以使用赋值运算符（=）改变一个或一系列元素。
-
-::
-
-    l = ['P', 'y', 't', 'h', 'o', 'n']
-
-    l[0] = 'H'  # 更改第 1 个元素
-    l
-
-    l[1:4] = ['e', 'l', 'l']  # 更改第 2 个到第四个元素
-    l
-
-
-<button>Run ...</button>
-
-
-可以使用 append() 方法将一个元素添加到列表中，或者使用 extend() 方法添加多个元素。
-
-::
-
-    l = ['P', 'y']
- 
-    l.append('t')  # 添加一个元素
-    l
-
-    l.extend(['h', 'o', 'n'])  # 添加多个元素
-    l
-
-
-<button>Run ...</button>
-
-
-此外，可以使用 insert() 方法将一个元素插入到所需的位置。要插入多个元素，可以将其挤压进到一个列表的空切片中。
-
-::
-
-    l = ['P', 'n']
-
-    l.insert(1, 'y')  # 在指定索引处插入一个元素
-    l
-
-
-    l[2:2] = ['t', 'h', 'o']  # 插入多个元素
-    l
-
-<button>Run ...</button>
-
-
-从列表中删除元素
-----------------
-
-可以使用关键字 del，从列表中删除一个或多个元素，甚至可以完全删除列表。
-
-::
-
-    l = ['P', 'y', 't', 'h', 'o', 'n']
- 
-    del l[2]  # 删除第 3 个元素
-    l
-
- 
-    del l[1:3]  # 删除第 2 个到第 3 个元素
-    l
-
- 
-    del l  # 删除列表
-    l
-
-<button>Run ...</button>
-
-
-可以使用 remove() 方法删除指定的元素，或用 pop() 方法删除一个给定索引处的元素。
-
-如果没有提供索引，pop() 方法将删除并返回最后一个元素。这有助于我们将列表实现为栈（先进后出数据结构）。
-
-还可以使用 clear() 方法清空一个列表。
-
-::
-
-    l = ['P', 'y', 't', 'h', 'o', 'n']
- 
-    l.remove('h')  # 删除指定的元素
-    l
-
-
-    l.pop(2)  # 删除并返回第 3 个元素
-
-    l
-
- 
-    l.pop()  # 删除并返回最后一个元素
-
-    l
-
- 
-    l.clear()  # 清空列表
-    l
-
-<button>Run ...</button>
-
-
-最后，还可以通过将一个空列表分配给一个元素切片的方式来删除列表中的项目。
-
-::
-
-    l = ['P', 'y', 't', 'h', 'o', 'n']
- 
-    l[1:3] = []
-    l
- 
-    l[1:4] = []
-    l
-
-
-<button>Run ...</button>
-
-基本操作
-----------------
-
-
-在字符串一节中已经提过，所有的序列都有几种基本操作。列表是序列的一种，固然也适用。
-
-连接列表
-
-操作符 + 用于组合列表，也被称为连接。
-
-::
-
-
-    l1 = ['P', 'y']
-    l2 = ['t', 'h', 'o', 'n']
-
-    l1 + l2
-
-
-<button>Run ...</button>
-
-重复列表
-----------------
-
-
-操作符 * 会以给定次数重复一个列表。
-
-
-::
-
-    l = ['P', 'y', 't', 'h', 'o', 'n']
- 
-    l * 2  # 重复列表 2 次
-
-<button>Run ...</button>
-
-最小值/最大值
-----------------
-
-
-min() 和 max() 用于获取最小值和最大值。
-
-::
-
-
-    l = ['P', 'y', 't', 'h', 'o', 'n']
- 
-    min(l)  # 最小值
-
-    max(l)  # 最大值
-
-
-<button>Run ...</button>
-
-列表长度
-----------------
-
-
-要获取一个列表的长度，可以使用内置函数 len()。
-
-::
-
-    l = ['P', 'y', 't', 'h', 'o', 'n']
-
-    len(l)  # 元素个数
-
-<button>Run ...</button>
-
-成员测试
-----------------
-
-
-使用关键字 in，可以测试一个元素是否存在于列表中。
-
-::
-
-    l = ['P', 'y', 't', 'h', 'o', 'n']
-
-    't' in l
-
-    'o' not in l
-
-
-<button>Run ...</button>
-
-
-
-遍历列表
-----------------
-
-
-使用 for 循环，可以遍历列表中的每个元素。
-
-::
-
-
-    l = ['P', 'y', 't', 'h', 'o', 'n']
-    for letter in l:
-        ^4$print(letter)
-
-<button>Run ...</button>
-
-
-列表的方法
-------------
-
-列表中有许多方法，可以通过 dir() 来查看方法列表：
-
-
-::
-
-     dir(list)
-
-<button>Run ...</button>
-
-
-
-以双下划线开始和结尾的（例如：__add__）暂不考虑，后面讨论。剩下以下几个：
-
-+------------+------------------------------------------------------------------------------------------+
-| 方法       | 描述                                                                                     |
-+============+==========================================================================================+
-|append()    | 将元素添加到列表的末尾                                                                   |    
-+------------+------------------------------------------------------------------------------------------+
-| extend()   |将列表的所有元素添加到另一个列表                                                          |
-+------------+------------------------------------------------------------------------------------------+
-| insert()   |在定义的索引处插入一个元素                                                                |
-+------------+------------------------------------------------------------------------------------------+
-| remove()   |       从列表中删除一个元素                                                               |         
-+------------+------------------------------------------------------------------------------------------+
-| pop()      |从列表中删除所有元素                                                                      |
-+------------+------------------------------------------------------------------------------------------+
-|clear()     |断言，用于判断变量或者条件表达式的值是否为真                                              |
-+------------+------------------------------------------------------------------------------------------+
-|index()     |返回第一个匹配项的索引                                                                    |
-+------------+------------------------------------------------------------------------------------------+
-| count()    |统计某个元素在列表中出现的次数                                                            |
-+------------+------------------------------------------------------------------------------------------+
-| sort()     |      按升序排列列表中的元素                                                              |
-+------------+------------------------------------------------------------------------------------------+
-|reverse()   |	反转列表中元素的顺序                                                                    |
-+------------+------------------------------------------------------------------------------------------+
-|copy()	     |返回一个列表的浅拷贝                                                                      |
-+------------+------------------------------------------------------------------------------------------+
-
-
-按照惯例，在使用不熟悉的方法时，先看官方文档描述：
-
-
-::
-
-
-    help(list.append)
-
-    >>>Help on method_descriptor:
-
-    append(...)
-        L.append(object) -> None -- append object to end
-
-
-
-可以使用 list.method() 的方式来访问方法，上面已经列举了一些。
-
-::
-
-
-    l = ['P', 'y', 't', 'h', 'o', 'n']
- 
-    l.index('h')  # 返回第一个匹配 'h' 的索引
-
-
-    l.count('o')  # 统计 'o' 在列表中出现的次数
-
-
-    l.sort()  # 按升序排列列表中的元素
-    l
-
-
-    l.reverse()  # 反转列表中元素的顺序
-    l
-
-
-<button>Run ...</button>
-
-列表推导式
-----------------
-
-
-列表推导式（List Comprehension）是一种优雅、简洁的方式，通过现有列表来创建一个新列表。
-
-列表推导式由方括号标识，其中包含一个表达式，紧随其后的是 for 语句。
-
-假设，要创建一个正方形：
-
-::
-
-    squares = []
-    for x in range(10):
-        ^4$squares.append(x**2)
- 
-    square
-
-<button>Run ...</button>
-
-
-
-用列表推导式，可以获得相同的结果：
-
-::
-
-    squares = [x**2 for x in range(10)]
-
-<button>Run ...</button>
-
-这也相当于：squares = map(lambda x: x**2, range(10))，但它更简洁可读。
-
-列表推导式可以可选地包含更多的 for 或 if 语句，if 语句可以过滤出新列表的元素。
-
-::
-
-    squares = [x**2 for x in range(10) if x > 5]
-    squares
-
-<button>Run ...</button>
-
-::
-
-    odd = [x for x in range(10) if x % 2 == 1]
-    odd
-
-<button>Run ...</button>
-
-
-::
-
-    [x+y for x in ['I love ','I like '] for y in ['C++','Python']]
-    ['I love C++', 'I love Python', 'I like C++', 'I like Python']
-
-<button>Run ...</button>
-
-
-
-列表与内置函数
----------------
-
-
-下述内置函数通常与列表一起使用，来执行不同的任务。
-
-
-+------------+------------------------------------------------------------------------------------------+
-|函数        |描述                                                                                      |
-+============+==========================================================================================+
-|all()       |	如果列表中的所有元素都是 True（或者列表为空），则返回 True。                            |
-+------------+------------------------------------------------------------------------------------------+
-|any()	     |如果列表中的所有元素都是 True，则返回 True；如果列表为空，则返回 False。                  |
-+------------+------------------------------------------------------------------------------------------+
-|enumerate() |	返回一个枚举对象，其中包含了列表中所有元素的索引和值（配对）。                          |
-+------------+------------------------------------------------------------------------------------------+
-|len()	     |返回列表的长度（元素个数）。                                                              |
-+------------+------------------------------------------------------------------------------------------+
-|max()       |	返回列表的最大项                                                                        |
-+------------+------------------------------------------------------------------------------------------+
-|min()       |	返回列表的最小项                                                                        |
-+------------+------------------------------------------------------------------------------------------+
-|sorted()    |	返回一个排序的新列表（不排序列表本身）                                                  |
-+------------+------------------------------------------------------------------------------------------+
-|sum()       |	返回列表的所有元素之和                                                                  |
-+------------+------------------------------------------------------------------------------------------+
-|list()	     |将 iterable（元组、字符串、集合、字典）转换为列表                                         |
-+------------+------------------------------------------------------------------------------------------+
-
-
-作者 & 更新时间
-------------------------------------
-作者: `一去丶二三里 <http://blog.csdn.net/liang19890820>`_
-
-提交: 2017/12/6
diff --git a/course/source/python-base/cn/python-datatype-number.rst b/course/source/python-base/cn/python-datatype-number.rst
deleted file mode 100644
index fe84be4..0000000
--- a/course/source/python-base/cn/python-datatype-number.rst
+++ /dev/null
@@ -1,309 +0,0 @@
-简述
-================
-
-
-在 Python 中，对于数字的定义很简单 - 整数、浮点数和复数，分别被定义为 int、float 和 complex 类。只要数学不是体育老师教的，绝大部分人学起来都不会有任何问题。
-
-对于数字而言，除了进行基本的运算（例如：算术运算、比较运算）之外，Python 还提供了类型间的相互转换功能，以及其他的高级操作。
-
-
-
-数字类型
----------
-
-
-在 Python 中，支持三种不同的数字类型：
-
-- int（整型）：也被称为整数，是正或负整数，不带小数点。
-- float（浮点型）：由整数部分与小数部分组成，浮点型也可以使用科学计数法表示。
-- complex（复数）：以 x + yj 的形式构成，其中 x 是实部，y 是虚部。
-
-**注意**： Py3.x 去除了 long 类型，现在只有一种整型 - int，表示为长整型。
-
-
-::
-
-    type(5)  # 整型
-    type(2.5)  # 浮点型
-
-    c = 2.5 + 5.0j  # 复数
-    type(c)
-
-
-<button>Run ...</button>
-
-
-整数溢出？
--------------
-
-
-天文数字：形容非常大的数字，已经无法用一个确切的数来形容。
-对于很多编程语言来说，天文数字是需要被正视的，因为往往会引发整数溢出问题。但是，在 Python 中无需忧虑，因为它支持“无限精度”的整数。
-
-来感受一下：
-
-::
-
-    i = 2 ** 500  # 2 的 500 次幂
-    i
-
-<button>Run ...</button>
-
-
-哇，多么幸运，Python 做出了如此精妙的安排。
-
-虽然整数可以是任意长度，但是浮点数只能精确到 15 位小数（16 位不准确）。
-
-::
-
-    f = 0.12345678901234567890  # 值被截断
-    f
-
-<button>Run ...</button>
-
-**注意**： f 的值被截断。
-
-数字系统
-------------------------
-
-
-对于绝大部分人来说，每天处理的数字都是十进制（以 10 为基数）数字系统。但是程序员（尤其是搞嵌入式的），往往需要使用二进制（以 2 为基数）、八进制（以 8 为基数）和十六进制（以 16 为基数）数字系统。
-
-在 Python 中，可以在数字前面放置相应的前缀来表示这些数字。
-
-+--------------------------------+-----------------------------------------------------------------------+
-| 数字系统                       | 前缀                                                                  |
-+================================+=======================================================================+
-|十进制（Decimal）               |                                                                       |    
-+--------------------------------+-----------------------------------------------------------------------+
-| 二进制（Binary）               |‘0b’ 或者 ‘0B’                                                     |
-+--------------------------------+-----------------------------------------------------------------------+
-| 八进制（Octal）                |‘0o’ 或者 ‘0O’                                                     |
-+--------------------------------+-----------------------------------------------------------------------+
-| 十六进制（Hexadecimal）        |       ‘0x’ 或者 ‘0X’                                              |         
-+--------------------------------+-----------------------------------------------------------------------+
-
-来看一些示例：
-
-::
-
-
-   5  # 十进制
-   0b1010  # 二进制
-   0o15  # 八进制
-   0xFB  # 十六进制
-
-
-
-十进制还可以转换为二进制、八进制、十六进制：
-
-::
-
-   dec = 20  # 十进制
-   bin(dec)  # 十进制转二进制
-   oct(dec)  # 十进制转八进制
-   hex(dec)  # 十进制转十六进制
-
-
-<button>Run ...</button>
-
-类型转换
-------------------------
-
-
-一般情况下，数据的类型的转换通常是由编译系统自动进行的，不需要人工干预，所以被称为隐式类型转换。但如果程序要求一定要将某一类型的数据转换为另外一种类型，则可以利用强制类型转换运算符进行转换，这种强制转换过程称为**显式类型转换**。
-
-在进行数字运算（例如：加法、减法）时，如果其中的一个操作数是浮点数，则会将整数隐式（自动）转换为浮点数。
-
-::
-
-   2 + 3.0
-
-
-<button>Run ...</button>
-
-可以看到，2（整数）被转换为 2.0（浮点数），计算结果（5.0）也是一个浮点数。
-
-除此之外，还可以使用 int()、float() 和 complex() 等内置函数来**显式转换**类型，这些函数甚至可以将字符串转换为数字。
-
-
-::
-
-    int(2.3)
-    int(-2.8)
-    float(5)
-    complex('3+5j')
-
-<button>Run ...</button>
-
-从 float 转换为 int 时，数字将被截断（整数更接近零）。
-
-Decimal
-
-----------------
-
-通常，在使用内置类 float 时，会执行一些“匪夷所思”的计算。
-
-我们知道 1/3 等于 0.333…（无限循环），0.1 + 0.2 等于 0.3，但 Python 似乎不这么认为。
-
-::
-
-   1/3
-   0.1 + 0.2
-
-
-<button>Run ...</button>
-
-What？难道是眼花了？和数学知识不一样？
-
-事实证明，浮点数在计算机硬件中以二进制小数来表示，因为计算机只能理解二进制（0 和 1）。出于这个原因，大部分十进制小数都不能准确地存储在计算机中。
-
-例如，十进制小数 0.1，将转换为无限长的二进制小数 0.000110011001100110011…，而计算机存储的位数是有限的。也就是说，转换为二进制后，只会接近十进制的 0.1，但永远不会相等。
-
-因此，**这是计算机硬件的局限性，而不是 Python 中的错误**。
-
-为了克服这个问题，可以使用 Python 自带的 decimal 模块。虽然浮点数的精度最高可达 15 位，但 decimal 模块可自定义精度。
-
-
-::
-
-
-    import decimal
-    0.1
-    decimal.Decimal(0.1)
-    decimal.getcontext()  # 当前上下文
-    decimal.getcontext().prec  # 精度默认 28 位
-
-    d = decimal.Decimal(1) / decimal.Decimal(9)
-    d
-    decimal.getcontext().prec = 3  # 将精度修改为 3 位
-
-    d = decimal.Decimal(1) / decimal.Decimal(9)
-    d
-
-<button>Run ...</button>
-
-除此之外，它也保留了重要意义。我们知道 25.50 比 25.5 更精确，因为 25.50 有两个有效的小数位。
-
-
-::
-
-    from decimal import Decimal as D
-    D('0.1') + D('0.2')
-    D('1.2') * D('2.50')
-
-
-
-<button>Run ...</button>
-
-**注意**： 计算结果末尾的 0
-
-有人可能会问：既然 Decimal 这么棒，为什么每次不使用 Decimal 来代替 float 呢？主要原因是效率，float 运算要比 Decimal 运算更快。
-
-何时使用 Decimal，而不是 float 呢？在以下情况下，通常使用 Decimal。
-
-- 当在做金融应用时，需要精确表示。
-- 当想要控制所需的精度级别时
-- 当想要实现有效的小数位概念时
-- 当我们想要像在学校里学到的那样进行小数运算时
-
-
-fractions（有理数）
---------------------
-
-fractions 模块提供了涉及分数的操作，该模块支持有理数运算。
-
-一个 Fraction 实例可以由一对整数、另一个有理数、或者一个字符串来构造。
-
-
-::
-
-
-   import fractions
-   fractions.Fraction(1.5)
-   Fraction(3, 2)
-
-   fractions.Fraction(10)
-   Fraction(10, 1)
-   fractions.Fraction(3, 5)
-   Fraction(3, 5)
-
-   print(fractions.Fraction(1.5))
-
-<button>Run ...</button>
-
-
-当从 float 创建 Fraction 时，可能会得到一些不寻常的结果，这是由于之前讨论的不完美的二进制浮点数造成的。
-
-幸运的是，fraction 也允许用字符串进行实例化，这是使用十进制数时的首选方案。
-
-::
-
-    from fractions import Fraction
-    print(Fraction(1.1))  # 浮点数 1.1
-    print(Fraction('1.1'))  # 字符串 '1.1'
-
-<button>Run ...</button>
-
-
-此外，该数据类型也支持所有基本操作。
-
-::
-
-    from fractions import Fraction as F
-
-    F(1,3) + F(1,3)
-    Fraction(2, 3)
-
-    1 / F(2,3)
-    Fraction(3, 2)
-
-    F(-2,5) < 0
-
-<button>Run ...</button>
-
-
-math（数学函数）
-----------------
-
-
-math 模块用于处理数学相关的运算，例如：三角学、对数等。
-
-::
-
-    import math
-    math.pi  # 圆周率 π
-    math.e  # 自然常数
-
-    math.cos(math.pi)  # 余弦
-    math.log10(100)  # 以 10 为基数的 100 的对数
-
-    math.pow(2, 5)  # 2**5（2 的 5 次方） 运算后的值
-    math.sqrt(9)  # 9 的平方根
-    math.factorial(5)  # 5!（5 的阶乘）
-
-<button>Run ...</button>
-
-random（生成伪随机数）
-----------------------
-
-
-random 模块用于生成伪随机数。随机数可用于数学、游戏、安全等领域，还经常被嵌入到算法中，用以提高算法效率，并提高程序的安全性。
-
-::
-
-    import random
-    l = ['p', 'y', 't', 'h', 'o', 'n']
-    random.choice(l)  # 从序列 l 中随机挑选一个元素
-    random.shuffle(l)  # 将序列 l 的所有元素随机排序
-    random.randrange(0, 10)  # 从指定范围内，按指定基数（缺省值为 1）递增的集合中获取一个随机数
-    random.random()  # 随机生成一个实数，范围 [0,1)。
-
-<button>Run ...</button>
-
-
-作者 & 更新时间
-------------------------------------
-作者: `一去丶二三里 <http://blog.csdn.net/liang19890820>`_
-
-提交: 2017/12/6
diff --git a/course/source/python-base/cn/python-datatype-tuple.rst b/course/source/python-base/cn/python-datatype-tuple.rst
deleted file mode 100644
index 7fee742..0000000
--- a/course/source/python-base/cn/python-datatype-tuple.rst
+++ /dev/null
@@ -1,279 +0,0 @@
-简述
-================
-
-
-在 Python 中，元组（Tuple）与列表类似。两者之间的区别在于：元组不能更改，列表可以。也就是说，一旦被分配，就不能从元组中添加、更改或删除元素。
-
-
-元组的优点
--------------
-
-既然和列表类似，那元组的作用到底是什么呢？用列表代替不就可以了？
-
-与列表相比，元组有很多优点：
-
-- 通常将元组用于不同的数据类型，将列表用于相同（或相似）的数据类型。
-- 由于元组不可变，所以遍历元组比列表要快（较小的性能提升）。
-- 元组可以用作字典的 key，而列表不行。因为字典的 key 必须是不可变的，元组本身就是不可变的。
-- 如果数据不需要更改，将其作为元组来实现，可以确保“写保护”。
-- 元组可以被用在字符串格式化中。
-
-
-元组
--------
-
-
-在 Python 中，元组由内置的 tuple 类型定义。
-
-要创建元组，需要将所有项（元素）放在括号（()）内，以逗号（,）分隔。
-
-**注意**： 括号是可选的，但还是建议写上。
-
-::
-
-
-    tup = ('P', 'y', 't', 'h', 'o', 'n')
-    type(tup)
-    <class 'tuple'>
-
-<button>Run ...</button>
-
-
-元组中的元素可以是任意数量，还可以是不同类型（例如：数字、字符串、列表等）。
-
-::
-
-    tup = ()  # 空元组
- 
-    tup = ('P', 'y', 't', 'h', 'o', 'n')   # 字符串类型元组
-
-    tup = (1, 5.5, 'Python')   # 混合类型元组
-
-    tup = (6, 'Python', ('P', 'y', 't', 'h', 'o', 'n'))  # 嵌套元组
-
-    tup = 1, 5.5, 'Python'   # 创建元组时可以没有括号，也称为元组包装
-    tup
-    (1, 5.5, 'Python')
- 
-    a, b, c = tup  # 元组解包
-    a
-    1
-    b
-    5.5
-    c
-   'Python'
-
-<button>Run ...</button>
-
-
-构造包含一个元素的元组比较特殊，括号内只包含一个元素是不够的，还需要在元素后添加一个逗号。
-
-::
-
-    tup = ('Python')  # 只有括号
-    type(tup)
-    <class 'str'>
- 
-    tup = ('Python',)  # 在元素后面添加逗号
-    type(tup)
-    <class 'tuple'>
-
-<button>Run ...</button>
-
-可以看出，没有逗号就不是元组，加了逗号才是。
-
-索引和切片
------------
-
-有了前面关于字符串和列表的基础知识，元组很容易掌握。因为它们都属于序列，因此，基本操作是一样的。
-
-可以使用索引操作符（[]） 来访问元组中的一个元素，使用切片操作符（:）来访问元组中的一系列元素。
-
-.. image:: http://img.blog.csdn.net/20170517202258988?watermark/2/text/aHR0cDovL2Jsb2cuY3Nkbi5uZXQvbGlhbmcxOTg5MDgyMA==/font/5a6L5L2T/fontsize/400/fill/I0JBQkFCMA==/dissolve/70/gravity/SouthEast
-
-::
-
-
-    tup = ('P', 'y', 't', 'h', 'o', 'n')
-
-    tup[0]  # 第 1 个元素
-    'P'
-
-    tup[-2]  # 第 5 个元素（反向索引）
-    'o'
-
-   tup = (6, 'Python', ('P', 'y', 't', 'h', 'o', 'n'))  # 嵌套元组
- 
-   tup[2][3]  # 嵌套索引
-   'h'
-
-   tup = ('P', 'y', 't', 'h', 'o', 'n')
-
-   tup[1:4]  #  第 2 个到第 4 个元素
-   ('y', 't', 'h')
- 
-   tup[:-3]  # 开始到第 3 个元素
-   ('P', 'y', 't')
-
-<button>Run ...</button>
-
-
-
-其他的一些基本操作，例如：连接（+）、重复（*）、成员测试（in）、遍历（for）等，就不再一一展示了，请参考：Python 列表
-
-更改元组
-
-元组是不可变的，也就是说，元组中的元素在被赋值后不能改变。
-
-但是，如果元素本身是一个可变数据类型的列表，那么其嵌套项可以被改变。
-
-::
-
-    tup = (6, 'Python', ['P', 'y', 't', 'h', 'o', 'n'])
- 
-    tup[0] = 8  # 不能改变元素
-
-
-   tup[2][3] = 's'  # 可变的元素可以被改变
-   tup
-   (6, 'Python', ['P', 'y', 't', 's', 'o', 'n'])
-
-<button>Run ...</button>
-
-删除元组
----------
-
-如上所述，不能更改元组中的元素，这也意味着无法删除元组中的元素。
-
-但是，使用关键字 del 可以删除整个元组。
-
-
-::
-
-
-    tup = ('P', 'y', 't', 'h', 'o', 'n')
-
-    del tup[2]  # 无法删除元素
-
-
-    del tup  # 可以删除整个元组
-    tup
-
-<button>Run ...</button>
-
-
-列表和元组互转
-----------------
-
-列表和元组之间可以进行相互转换，分别使用 list() 和 tuple() 实现：
-
-::
-
-    tup = ('P', 'y', 't', 'h', 'o', 'n')
-    listx = list(tup)
-    listx
-    ['P', 'y', 't', 'h', 'o', 'n']
- 
-    l = ['H', 'e', 'l', 'l', 'o']
-    tupx = tuple(l)
-    tupx
-   ('H', 'e', 'l', 'l', 'o')
-
-<button>Run ...</button>
-
-
-既然可以互转，那么要改变元组，可以先将其转化为列表，对列表进行更改，然后再将列表转换为元组。
-
-例如，删除元组中的元素：
-
-::
-
-    tup = ('P', 'y', 't', 'h', 'o', 'n')
-    listx = list(tup)  # 将元组转换为列表
-    listx.remove('h')  # 删除列表中的元素
-    tup = tuple(listx)  # 再将列表转换为元组
-    tup
-   ('P', 'y', 't', 'o', 'n')
-
-<button>Run ...</button>
-
-**注意**： 元组本身是不可变的。这里说的并不是传统意义的改变，相当于元组的重新赋值。
-
-
-元组的方法
------------
-
-元组中的方法相对较少，可以通过 dir() 来查看方法列表：
-
-::
-
-     dir(tuple)
-     ['__add__', '__class__', '__contains__', '__delattr__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__getitem__',      '__getnewargs__', '__gt__', '__hash__', '__init__', '__iter__', '__le__', '__len__', '__lt__', '__mul__', '__ne__', '__new__', '__reduce__', '__reduce_ex__',      '__repr__', '__rmul__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', 'count', 'index']
-
-
-<button>Run ...</button>
-
-
-可以看到，有两个可用的方法：：
-
-+------------+------------------------------------------------------------------------------------------+
-| 方法       | 描述                                                                                     |
-+============+==========================================================================================+
-|index()     | 返回第一个匹配项的索引                                                                   |    
-+------------+------------------------------------------------------------------------------------------+
-| count()    |统计某个元素在列表中出现的次数                                                            |
-+------------+------------------------------------------------------------------------------------------+
-
-
-使用很简单，也非常好记。
-
-::
-
-    tup = ('P', 'y', 't', 'h', 'o', 'n')
- 
-    tup.index('h')  # 返回第一个匹配 'h' 的索引
-
- 
-    tup.count('o')  # 统计 'o' 在元组中出现的次数
-
-<button>Run ...</button>
-
-
-
-**注意**： 元组不可变，所以添加或删除元素的方法不适用于元组。
-
-元组与内置函数
---------------
-
-
-下述内置函数通常与元组一起使用，来执行不同的任务。
-
-+------------+------------------------------------------------------------------------------------------+
-|函数	       |描述                                                                                      |      
-+============+==========================================================================================+
-|all()       |	如果元组中的所有元素都是 True（或者元组为空），则返回 True。                            |
-+------------+------------------------------------------------------------------------------------------+
-|any()	     |如果元组中的所有元素都是 True，则返回 True；如果元组为空，则返回 False。                  |
-+------------+------------------------------------------------------------------------------------------+
-|enumerate() |	返回一个枚举对象，其中包含了元组中所有元素的索引和值（配对）。                          |
-+------------+------------------------------------------------------------------------------------------+
-|len()	     |返回元组的长度（元素个数）                                                                |
-+------------+------------------------------------------------------------------------------------------+
-|max()	     |返回元组中的最大项                                                                        |
-+------------+------------------------------------------------------------------------------------------+
-|min()	     |返回元组中的最小项                                                                        |
-+------------+------------------------------------------------------------------------------------------+
-|sorted()    |	返回一个新的排序元组（不排序元组本身）                                                  |
-+------------+------------------------------------------------------------------------------------------+
-|sum()	     |返回元组的所有元素之和                                                                    |
-+------------+------------------------------------------------------------------------------------------+
-|tuple()     |	将 iterable（字符串、列表、集合、字典）转换为元组                                       |
-+------------+------------------------------------------------------------------------------------------+
-
-
-作者 & 更新时间
-------------------------------------
-作者: `CSDN <http://blog.csdn.net/liang19890820>`_
-
-提交: 2017/12/6
-
diff --git a/course/source/python-base/index.rst b/course/source/python-base/index.rst
deleted file mode 100644
index 8f0d55c..0000000
--- a/course/source/python-base/index.rst
+++ /dev/null
@@ -1,19 +0,0 @@
-Python Basics
-=======================
-The courses are still in the planning. Welcome to reward us. 
-
-1. **Primary course**
-
-If the total number of people who reward us is more than 100, we promise to complete the primary course within 1 week, so that, after finishing your study, you can master the basic concepts and write simple scripts.
-
-2. **Intermediate course**
-
-If the total number of people who reward us is more than 500, we will launch the intermediate course within 2 weeks, so that, after finishing your study, you will be familiar with the knowledge and framework of the course and apply it to practical development.
-
-3. **Advanced course**
-
-If the total number of people who reward us is more than 2,000, we will launch the advanced course in a month, so that, after finishing your study, you will have a better understanding of the principles of the framework and be able to do advanced development.
-
-
-**The primary course is free to all, and the intermediate and advanced courses are only open to those who have rewarded us.**
-
diff --git a/course/source/qpy101/README.md b/course/source/qpy101/README.md
deleted file mode 100644
index 95fc9fd..0000000
--- a/course/source/qpy101/README.md
+++ /dev/null
@@ -1,82 +0,0 @@
-QPython 101
-=========================================
-
-    QPython 系列入门教程
-
-用短视频形式, 引导学习者快速进入编程状态, 可以利用 QPython 移动编程环境进行各种实用软件的开发和探索.
-
-
-
-
-
-
-
-内容规划
------------------------------------------
-
-:发布状态:
-    - <+ 完成并发布
-    - <- 完成未发布
-    - <| 筹备完成中
-    - <? 规划未决定
-
-
-
-=
-状态 编号 幻灯 视频
-= = = =
-<+ 起源综述 和River对谈 视频:|QR2QPyC100|
-<+ 101:语法/关键字 幻灯:`QPyC101`_  视频:|QR2QPyC101|
-<+ 102:语法/数据类型,运算符 幻灯:`QPyC102`_  视频:|QR2QPyC102|
-<+ 103:语法/变量,集合 幻灯:`QPyC103`_  视频:|QR2QPyC103|
-<- 104:语法/流程控制,if..else 幻灯:`QPyC104`_  ...
-<- 105:语法/流程控制,for..in 幻灯:`QPyC105`_  ...
-<- 106:语法/流程控制,while 幻灯:`QPyC106`_  ...
-<- 107:语法/流程控制,try:except 幻灯:`QPyC107`_  ...
-<- 108:语法/函式,创建 幻灯:`QPyC108`_  ...
-<| 109:语法/函式,可变参数 TBD  ...
-<? 110:语法/函式,作用域 TBD  ...
-<? 111:语法/函式,匿名函式 TBD  ...
-<? 112:语法/函式,高阶函式 TBD  ...
-<| 113:游戏/猜数字,伪代码 TBD  ...
-<| 114:游戏/猜数字,接受输入 TBD  ...
-<| 115:游戏/猜数字,随机数字 TBD  ...
-<| 116:游戏/猜数字,整体可用 TBD  ...
-=
-
-
-
-PS:
------------------------------------------
-
-官方视频号是 **QPYIO**
-
-|QPyIOQA|
-
-已经开通客服, 可以随时通过微信和我们交流.
-
-
-
-.. |QPyIOQA| image:: https://ipic.zoomquiet.top/2022-07-02-QPyIOQA.jpeg
-             :width: 360px
-
-.. |QR2QPyC100| image:: https://ipic.zoomquiet.top/2022-07-02-QPyC100.jpg
-             :width: 200px
-
-.. _QPyC101: https://slides.101.camp/QPyCamp101
-.. |QR2QPyC101| image:: https://ipic.zoomquiet.top/2022-07-02-QPyC101.jpg
-             :width: 200px
-
-.. _QPyC102: https://slides.101.camp/QPyCamp102
-.. |QR2QPyC102| image:: https://ipic.zoomquiet.top/2022-07-02-QPyC102.jpg
-             :width: 200px
-
-.. _QPyC103: https://slides.101.camp/QPyCamp103
-.. |QR2QPyC103| image:: https://ipic.zoomquiet.top/2022-07-02-QPyC103.jpg
-             :width: 200px
-
-.. _QPyC104: https://slides.101.camp/QPyCamp104
-.. _QPyC105: https://slides.101.camp/QPyCamp105
-.. _QPyC106: https://slides.101.camp/QPyCamp106
-.. _QPyC107: https://slides.101.camp/QPyCamp107
-.. _QPyC108: https://slides.101.camp/QPyCamp108
diff --git a/course/source/qpy101/index.rst b/course/source/qpy101/index.rst
deleted file mode 100644
index c33a08b..0000000
--- a/course/source/qpy101/index.rst
+++ /dev/null
@@ -1,82 +0,0 @@
-QPython 101
-=========================================
-
-    QPython 系列入门教程
-
-用短视频形式, 引导学习者快速进入编程状态, 可以利用 QPython 移动编程环境进行各种实用软件的开发和探索.
-
-
-
-
-
-
-
-内容规划
------------------------------------------
-
-:发布状态:
-    - <+ 完成并发布
-    - <- 完成未发布
-    - <| 筹备完成中
-    - <? 规划未决定
-
-
-
-====  ============================  ===============  =================
-状态              编号                   幻灯              视频
-====  ============================  ===============  =================
-<+    起源综述                      和River对谈      视频:|QR2QPyC100|
-<+    101:语法/关键字               幻灯:`QPyC101`_  视频:|QR2QPyC101|
-<+    102:语法/数据类型,运算符      幻灯:`QPyC102`_  视频:|QR2QPyC102|
-<+    103:语法/变量,集合            幻灯:`QPyC103`_  视频:|QR2QPyC103|
-<-    104:语法/流程控制,if..else    幻灯:`QPyC104`_  ...
-<-    105:语法/流程控制,for..in     幻灯:`QPyC105`_  ...
-<-    106:语法/流程控制,while       幻灯:`QPyC106`_  ...
-<-    107:语法/流程控制,try:except  幻灯:`QPyC107`_  ...
-<-    108:语法/函式,创建            幻灯:`QPyC108`_  ...
-<|    109:语法/函式,可变参数        TBD              ...
-<?    110:语法/函式,作用域          TBD              ...
-<?    111:语法/函式,匿名函式        TBD              ...
-<?    112:语法/函式,高阶函式        TBD              ...
-<|    113:游戏/猜数字,伪代码        TBD              ...
-<|    114:游戏/猜数字,接受输入      TBD              ...
-<|    115:游戏/猜数字,随机数字      TBD              ...
-<|    116:游戏/猜数字,整体可用      TBD              ...
-====  ============================  ===============  =================
-
-
-
-PS:
------------------------------------------
-
-官方视频号是 **QPYIO**
-
-|QPyIOQA|
-
-已经开通客服, 可以随时通过微信和我们交流.
-
-
-
-.. |QPyIOQA| image:: https://ipic.zoomquiet.top/2022-07-02-QPyIOQA.jpeg
-             :width: 360px
-
-.. |QR2QPyC100| image:: https://ipic.zoomquiet.top/2022-07-02-QPyC100.jpg
-             :width: 200px
-
-.. _QPyC101: https://slides.101.camp/QPyCamp101
-.. |QR2QPyC101| image:: https://ipic.zoomquiet.top/2022-07-02-QPyC101.jpg
-             :width: 200px
-
-.. _QPyC102: https://slides.101.camp/QPyCamp102
-.. |QR2QPyC102| image:: https://ipic.zoomquiet.top/2022-07-02-QPyC102.jpg
-             :width: 200px
-
-.. _QPyC103: https://slides.101.camp/QPyCamp103
-.. |QR2QPyC103| image:: https://ipic.zoomquiet.top/2022-07-02-QPyC103.jpg
-             :width: 200px
-
-.. _QPyC104: https://slides.101.camp/QPyCamp104
-.. _QPyC105: https://slides.101.camp/QPyCamp105
-.. _QPyC106: https://slides.101.camp/QPyCamp106
-.. _QPyC107: https://slides.101.camp/QPyCamp107
-.. _QPyC108: https://slides.101.camp/QPyCamp108
diff --git a/course/source/qpython-quick-start/cn/community-howto.rst b/course/source/qpython-quick-start/cn/community-howto.rst
deleted file mode 100644
index f2eabf8..0000000
--- a/course/source/qpython-quick-start/cn/community-howto.rst
+++ /dev/null
@@ -1,58 +0,0 @@
-社区指南
-================
-很多QPython用户喜欢在QPython各类社区里交流分享编程经验，你将有机会在这里遇见像你一样聪明的人，互相学习，获得成长。
-
-获得QPython的新消息和提示
---------------------------
-我们衷心希望你可以在微信和微博上关注我们，关注后可以及时获取QPython的新闻。
-QPython的微信会及时发布我们的最新消息。
-QPython的微博会分享一些QPython的小贴士，回答你的问题等。
-
-* 在微信搜索并关注我们: qpython
-
-* `在微博关注我们<http://weibo.com/qpython>`_
-
-
-和其他开发者一起聊聊QPython
-------------------------------------
-如果你想和其他人一起学习Python编程，可以考虑加入以下学习小组
-
-* `QPython 百度贴吧<https://tieba.baidu.com/f?kw=qpython>`_
-
-* 还可以在QQ中搜索QPython编程交流QQ群，群号：540717901
-
-* QPython还有几个微信交流群，请添加优趣小言微信quseitlab,留言qpython,它会把你加到QPython社区中
-
-第三社区
---------------
-这里有一些讨论QPython的帖子，像StackOverfolow或Reddit
-
-* `Stackoverflow上的QPython <http://stackoverflow.com/questions/tagged/qpython>`_
-* `Reddit上的QPython <https://www.reddit.com/search?q=qpython>`_
-
-
-贡献
--------------------
-如果你想快人一步获得新版本，并帮助测试，请加入QPython测试社区:
-
-* `加入QPython测试员社区 <https://plus.google.com/communities/111759148772865961493>`_
-
-
-在QPython里课程是非常重要的一个部分。如果您想为课程社区做出一些贡献，例如发布您自己的课程或帮助将当前课程翻译成其他语言，欢迎加入课程社区：
-
-* `加入QPython课程贡献社区 <https://plus.google.com/u/1/communities/111340957575273631204>`_
-
-
-如果您在使用QPython时遇到任何问题，请随时与我们联系，我们将尽快解决
-
-* `报告一个问题 <https://github.com/qpython-android/qpython/issues>`_
-* `报告QPython3的问题 <https://github.com/qpython-android/qpython3/issues>`_
-* `希望能支持一个库 <https://github.com/qpython-android/QPYPI/issues>`_
-* `直接给我们写信 <mailto:support@qpython.org>`_
-
-作者 & 更新时间
-------------------------------------
-作者: `River <https://github.com/riverfor>`_
-
-提交: 2017/8/6
-
diff --git a/course/source/qpython-quick-start/cn/editor-howto.rst b/course/source/qpython-quick-start/cn/editor-howto.rst
deleted file mode 100644
index 8cd7dfe..0000000
--- a/course/source/qpython-quick-start/cn/editor-howto.rst
+++ /dev/null
@@ -1,81 +0,0 @@
-编辑器指南
-==============
-这是Python开发者最经常用的工具了 - 编辑器。
-
-.. image:: http://edu.qpython.org/static/editor-1.png
-    :alt: QPython - 编辑器
-
-
-特性
----------
-在QPython编辑器里你可以编写或运行你的QPython脚本程序或项目。 QPython编辑器支持Python语法高亮显示，并显示行号。
-
-编辑器顶部右上角分别是打开、新建、编辑器设置，分别让你打开脚本或项目，新建脚本或项目，以及设置编辑器属性（主题，默认字体等）。
-
-在文件比较大时，语法高亮功能在某些设备上会让打开大文件的速度变慢，因此当总行数低于某个限制（默认值为300）时，编辑器才会开启语法高亮，当然，您可以根据需要在辑器设置中对高亮行数数值进行调整。
-
-在编辑器底部，工具栏上的按钮分别对应切换到热键工具栏，锁定键盘，跳转，保存，运行，查找，撤销，重做，最近打开，代码片段。
-
-.. image:: http://edu.qpython.org/static/editor-toolbar-1.png
-    :alt: QPython - 编辑器开发工具栏
-
-
-切换到热键助手之后，工具栏的按钮分别为切换为开发工具栏, 反TAB, TAB，DEF(def), IF(if), EF(elif), EL(else), FOR(for), IMT(import), IMF(from import), CLZ(class), RET(return), @, :, =, ", ', ;, ,, +, -, *, /, <, >, (, ), [, ], {, }, #.
-
-.. image:: http://edu.qpython.org/static/editor-toolbar-2.png
-    :alt: QPython - 编辑器热键工具栏
-
-
-熟练使用工具栏能够大大加快你的开发。
-
-保存新文件时，不要忘记添加.py的后缀因为编辑器不会自动添加后缀。
-
-当你打开一个QPython项目时，你还可以划出侧栏，让你更容易编辑相同项目中的其他文件。
-
-.. image:: http://edu.qpython.org/static/editor-left.png
-    :alt: QPython - 项目结构树
-
-
-其他
--------
-在手机上高效输入是一个比较有挑战的事情，为了加快输入效率，QPython团队提供了各种辅助工具。
-
-**扫描二维码**
-在QPython首页，还有一个二维码扫描功能，能够方便地扫描二维码，将里面的内容读取到编辑器中。
-
-**QEditor WebApp**
-在QPYPI的工具集中，有一个QEditor WebApp程序，在安装和运行之后，它会在手机上开启一项WebApp服务，允许用户从相同局域网的电脑用户通过访问指定地址来打开一个在线的编辑器，可以通过电脑浏览器来直接在手机上进行编程。
-
-.. image:: http://edu.qpython.org/static/qeditor.png
-    :alt: QPython - QEditor
-
-.. image:: http://edu.qpython.org/static/qeditor-run.png
-    :alt: QPython - 启动QEditor
-
-*如上图启动后，你可以在电脑上输入http://192.168.1.191:10000即可在浏览器上进行开发*
-
-
-在编辑器中试试运行以下代码？
----------------------------
-
-..  code-block :: none
-
-    class TestC():
-        ^4$def ask_age(self):
-            ^8$v = raw_input("> How old are you ?")
-            ^8$return v
-
-    tc = TestC()
-    tc.ask_age()
-
-<button>Run ...</button>
-
-.. image:: http://edu.qpython.org/static/course-index-principle.png
-    :target: data-video: "http://player.youku.com/embed/XMzE0OTI4NDgyMA=="
-    :alt: QPython - 编辑器帮助
-
-作者 & 更新时间
-------------------------------------
-作者: `River <https://github.com/riverfor>`_
-
-提交: 2017/8/6
diff --git a/course/source/qpython-quick-start/cn/index.rst b/course/source/qpython-quick-start/cn/index.rst
deleted file mode 100644
index 8066366..0000000
--- a/course/source/qpython-quick-start/cn/index.rst
+++ /dev/null
@@ -1,74 +0,0 @@
-QPython是什么
-================
-
-
-
-关于
------
-QPython (for Android)是安卓上的Python脚本引擎，它能让你在安卓设备上运行Python程序，它包含了Python解析器、控制台、编辑器。除了集成了基础的Python库之外，它还集成了支持WebApp开发的bottle库、支持调用安卓API的SL4A库等常见有用的库。
-
-针对iOS用户的QPython (for iOS)应用也已在appstore上线。
-
-QPython历史
--------------
-QPython诞生于2012年, 最初是其作者River为了实现能在安卓程序中实现热更新的一个Python插件SDK，后来作为安卓上的Python App独立发布，一经发布后便引起了广大Python开发者的喜爱，在很短的时间内，在没有任何推广的前提下，有数万的Python用户安装和评论，在15年，其安装使用用户量超过了2,000,000+，它也是目前安卓上最受欢迎的Python集成开发环境。
-
-
-支持的程序类型
-----------------
-目前QPython能够运行控制台程序、WebApp程序，后台应用程序，基于图形界面的应用程序等，各个类型的程序适合不同的应用场景，QPython通过特定的头部声明来确定如何运行应用程序，如"#qpy:webapp"表明该程序将会按照WebApp方式发布，"#qpy:kivy"表明该程序是一个kivy程序，"#qpy:qpyapp"声明该程序将会是QPYApp（适合于后台应用场景），而不带头部声明则默认会按照控制台的形式运行。
-
-
-*WebApp头部声明*
-::
-
-    #qpy:webapp
-
-
-*QPYApp头部声明*
-::
-
-    #qpy:qpyapp
-
-
-*Kivy程序头部声明*
-::
-
-    #qpy:kivy
-
-
-*控制台程序头部声明*
-::
-
-    #qpy:console
-
-
-如何安装第三方库
------------------
-QPython内置了PC版Python的绝大部分库。由于安卓里本身不支持编译工具链，因此你无法像PC上的Python一样能够安装所有的库，但是你可以通过QPYPI安装一些预编译的库，目前已经支持包括kivy, numpy, opencv, pycrypto, tornado 在内的库。此外你还可以用pip控制台从官方或者你指定的第三方QPYPI来安装大多数Python实现的库。
-
-
-QPython媒体、社区
---------------------
-QPython是一个更新迅速，注重倾听用户反馈的项目，包括facebook fan page, twitter, weibo, facebook群组，G+, Wechat/QQ group 等在内的媒体、社群是QPython的重要组成部分。我们鼓励你广泛参加到我们的社群讨论、活动以及QPython的建设当中，这样才能了解和掌握QPython最新的发展动态。
-
-
-QPython的社区版和商业版
-------------------------
-QPython分为社区开源版和商业版。社区版提供了基础的Python运行环境（会与商业版保持一致）,使用Apache开源协议，欢迎感兴趣的个人和团队在此基础上定制自己的业务，QPython开发团队同样会积极响应社区版用户的各种问题。
-QPython的商业版则致力于提供独特便利的功能和服务，包括在线教程，在线开发，代码分享等。
-
-无论是社区版或者商业版，我们希望都能让大家真正享受到在手机进行Python编程的快乐。
-
-
-
-.. image:: http://edu.qpython.org/static/codeanywhere.png
-    :target: data-video: "http://player.youku.com/embed/XMzAzMjE3NjI1Ng=="
-    :alt: QPython - 随时随地编程
-
-作者 & 更新时间
-------------------------------------
-作者: `River <https://github.com/riverfor>`_
-
-提交: 2017/8/6
-
diff --git a/course/source/qpython-quick-start/cn/principle.rst b/course/source/qpython-quick-start/cn/principle.rst
deleted file mode 100644
index 6246f18..0000000
--- a/course/source/qpython-quick-start/cn/principle.rst
+++ /dev/null
@@ -1,31 +0,0 @@
-随时随地学习编程
-====================================
-
-QPython专注为Python用户提供方便易用的移动Python IDE工具和编程学习服务。
-
-
-支持Python2和Python3
----------------------
-QPython支持最新的Python2和Python3，满足用户对于不同Python版本的要求
-
-
-移动IDE和的学习社区
---------------------
-QPython的控制台，编辑器，开发快捷工具集（QRCode代码读取器、本地文件管理器）、QPYPI、课程系统、代码分享社区，QPY.IO在线开发服务，为Python用户提供便捷的掌上编程学习环境和交流社区。
-
-
-强大、开源
-----------
-除了可以在安卓上学习Python编程外，QPython还允许你编程控制你的安卓设备。
-此外开源版QPython方便你在APP开发中使用Python引擎。
-
-.. image:: http://edu.qpython.org/static/course-index-principle.png
-    :target: data-video: "http://player.youku.com/embed/XMzA1NzI1NTE5Ng=="
-    :alt: QPython - 专注的掌上开发环境和编程学习服务
-
-作者 & 更新时间
-------------------------------------
-作者: `River <https://github.com/riverfor>`_
-
-提交: 2017/8/7
-
diff --git a/course/source/qpython-quick-start/cn/qpypi-howto.rst b/course/source/qpython-quick-start/cn/qpypi-howto.rst
deleted file mode 100644
index 6a6ef7a..0000000
--- a/course/source/qpython-quick-start/cn/qpypi-howto.rst
+++ /dev/null
@@ -1,52 +0,0 @@
-QPYPI指南
-=============
-第三方库可以快速扩展你的编程能力。 QPython主要通过QPYPI来管理第三方模块，你可以采用以下方法来更好地使用QPYPI。
-
-工具
-----
-工具栏目提供了常用的QPython工具，它能帮助你快速安装某些QPython上的应用（如SQLMap tool, Fabric tool等），为编程工作提供快捷助手（如Django manage script, QEditor WebApp, Scapy tool等)，并且我们还会陆续完善更多工具。
-
-.. image:: http://edu.qpython.org/static/qpypi-tools.png
-    :alt: QPYPI - 工具
-
-
-PIP控制台
-------------------------------------
-INSTALL WITH PYTHON'S PYPI
-
-.. image:: http://edu.qpython.org/static/qpypi-pip.png
-    :alt: QPYPI - PIP控制台
-
-
-通过该工具，你可以使用QPython内置的pip工具来安装很多Python库，它能帮你安装大部分由纯Python语言开发（包括依赖库）的Python库。
-
-
-注意：一些与c / c++文件混合的库不能通过pip控制台安装，这是由于安卓本身缺少开发编译环境支持，您可以向QPython团队寻求帮助。
-
-
-库
--------
-在库中，你可以安装一些由QPython团队整理和维护的Python的库。它们主要是由PIP控制台无法安装的Python库，或者由混合c/c++开发的但无法在QPython中编译的Python库，如kivy, Paramiko, PIL, Tornado等等。 你可以从这里方便地安装它们。
-
-.. image:: http://edu.qpython.org/static/qpypi-libraries.png
-    :alt: QPYPI - 库管理
-
-
-AIPY库
---------
-在AIPY库栏目中，QPython团队专门将与AI相关（包括神经网络、深度学习、数据统计和数学运算等等）的库整理进来，以便用户能够借助QPython在手机上进行AI相关编程学习。
-
-.. image:: http://edu.qpython.org/static/qpypi-aipy.png
-    :alt: QPYPI - AIPY
-
-
-手动管理
-------------
-如果通过QPYPI无法安装你希望安装的库，你可以将你所要的安装的库复制到设备的 /sdcard/qpython/lib/python2.7/site-packages 目录，这样你就可以在代码中使用 import <module> 的方式来让QPython加载对应的库。
-
-作者 & 更新时间
-------------------------------------
-作者: `River <https://github.com/riverfor>`_
-
-提交: 2017/11/6
-
diff --git a/course/source/qpython-quick-start/cn/terminal-howto.rst b/course/source/qpython-quick-start/cn/terminal-howto.rst
deleted file mode 100644
index e4201ac..0000000
--- a/course/source/qpython-quick-start/cn/terminal-howto.rst
+++ /dev/null
@@ -1,32 +0,0 @@
-终端使用帮助
-=======================
-是的, 这是一个可以直接输入Python指令的终端。
-
-.. image:: http://edu.qpython.org/static/terminal.png
-    :alt: QPython - 终端
-
-
------------------------
-它与电脑上的Python终端无异, 大多数人通常使用它来探索对象的属性, 测试语法或实现他们的想法. 你可以直接输入命令, Python解释器会帮你运行它们. QPython的终端有以下便捷功能:
-
-* 点击右上角的加号按钮新建终端
-* 点击左上交“终端 ?"，在下拉列表切换不同的终端，也可以点"X"关闭终端
-* 在终端窗口长按，可以通过弹出菜单来完成类似选择文本，拷贝所有内容，粘贴，发送control键，发送fn键等复杂的操作
-
-.. image:: http://edu.qpython.org/static/terminal-menu.png
-    :alt: QPython - Terminal 
-
-
-注意
--------
-除非您已完全关闭终端, 否则在通知栏中会有通知. 您可以在任何界面通过点击通知回到已打开的终端.
-
-.. image:: http://edu.qpython.org/static/course-index-principle.png
-    :target: data-video: "https://v.qq.com/iframe/player.html?vid=j0569yn4knh"
-    :alt: QPython - 终端帮助
-
-作者 & 更新时间
-------------------------------------
-作者: `River <https://github.com/riverfor>`_
-
-提交: 2017/10/7
diff --git a/course/source/qpython-quick-start/community-howto.rst b/course/source/qpython-quick-start/community-howto.rst
deleted file mode 100644
index d7eee98..0000000
--- a/course/source/qpython-quick-start/community-howto.rst
+++ /dev/null
@@ -1,56 +0,0 @@
-QPython Communities
-================
-Many QPython users like to exchange and share programming experience in QPython community, you have a great chance to meet smart and pythonic guys like you there, learn from each other and gain experience and growth.
-
-
-Get QPython news & tips
---------------------------
-We recommend you follow us on facebook and twitter to get QPython's news.
-QPython facebook page publishes official news and alerts.
-QPython twitter shares QPython tips and an active response to your question.
-
-* `Like us on facebook <https://www.facebook.com/QPython>`_
-
-* `Follow us on twitter <https://twitter.com/qpython>`_
-
-
-Talk about QPython with other guys
-------------------------------------
-If you want to learn Python programming with other guys, please, join our facebook group where many python users are there to help each other and the QPython google group maillist is a good way if you like communicate with email.
-
-* `Join Facebook community <https://www.facebook.com/groups/qpython>`_
-
-* `Join Google group <https://groups.google.com/forum/#!forum/qpython>`_
-
-Third community
---------------
-There are some communities which like to talk about QPython like, stackoverfolow and reddit
-
-* `QPython on Stackoverflow <http://stackoverflow.com/questions/tagged/qpython>`_
-* `QPython on Reddit <https://www.reddit.com/search?q=qpython>`_
-
-
-Contribute
--------------------
-
-If you want to checkout the latest version and want to help test beta, please join the Beta Tester community:
-
-* `Join QPython testers G+ community <https://plus.google.com/communities/111759148772865961493>`_
-
-The course is a very important part to QPython as it helps new users to learn more about QPython, If you want to contribute on course, like post your own course or help translate current courses to different languages, please join the course community:
-
-* `Join QPython course contributors G+ community <https://plus.google.com/u/1/communities/111340957575273631204>`_
-
-Feel free to contact us if you encounter any problem with QPython, we will try our best to solve it.
-
-* `Report QPython's issue <https://github.com/qpython-android/qpython/issues>`_
-* `Report QPython3's issue <https://github.com/qpython-android/qpython3/issues>`_
-* `I want QPython add a library <https://github.com/qpython-android/QPYPI/issues>`_
-* `Email us <mailto:support@qpython.org>`_
-
-Author & updated time
-------------------------------------------------------
-Author: `River <https://github.com/riverfor>`_
-
-Submitted: 2017/10/7
-Updated: 2018/04/20
diff --git a/course/source/qpython-quick-start/editor-howto.rst b/course/source/qpython-quick-start/editor-howto.rst
deleted file mode 100644
index dc14964..0000000
--- a/course/source/qpython-quick-start/editor-howto.rst
+++ /dev/null
@@ -1,82 +0,0 @@
-Editor
-==============
-This is the most used tool by Python developers - editor.
-
-.. image:: http://edu.qpython.org/static/editor-main.png
-    :alt: QPython - editor
-
-
-
-Features
----------
-The editor allows you to edit or run your QPython script program or project. The QPython editor supports Python syntax highlighting and shows line numbers (there is no ability to go to the line by number).
-
-There are the open(folder icon) and new(+ icon), setting on the editor's right-top area, which allow you to open script or project, create script or project, and set the editor's behaviors(like theme, font etc.).
-
-Syntax highlight feature makes the editor slow down for a bit when opening a large file, so the editor's highlight is enable when the total lines are lower than the limit(default is 300), and you can adjust it to the amount you want in editor's setting page.
-
-
-In the editor's bottom, the icons are switched to hotkeys, lock keyboard, goto, save, run, find, undo, redo, recent open, snippets.
-
-.. image:: http://edu.qpython.org/static/editor-toolbar-1.png
-    :alt: QPython - editor's toolbar
-
-When you switch to the hot keys toolbar, the toolbar shows the following icons: switch to develop toolbar, untab, tab, TAB，DEF(def), IF(if), EF(elif), EL(else), FOR(for), IMT(import), IMF(from import), CLZ(class), RET(return), @, :, =, ", ', ;, ,, +, -, *, /, <, >, (, ), [, ], {, }, #.
-
-.. image:: http://edu.qpython.org/static/editor-toolbar-2.png
-    :alt: QPython - editor's hotkey toolbar
-
-
-Skilled using the toolbar can greatly accelerate your development
-
-When saving the script, don’t forget to add .py extension to the file name since the editor don’t do it for you.
-
-When you are opening a QPython project, you could swipe on the editor's left to unfold the project's tree drawer, which allow you edit other files in the same project easily.
-
-.. image:: http://edu.qpython.org/static/editor-left.png
-    :alt: QPython - project tree
-
-Other
--------
-It's a big challenge for mobile inputting, the QPython team offers many kinds of tools for improving development efficiency.
-
-**Scan**
-In QPython's dashboard, there is a QRcode scanner, which can scan the QRcode and read the code into editor.
-
-
-**QEditor WebApp**
-In QPYPI's tools, there is a QEditor WebApp. After installing and running, it starts a WebApp service, allow user edit files from the computer's browser in the same lAN by visiting some online editors.
-
-
-.. image:: http://edu.qpython.org/static/qeditor.png
-    :alt: QPython - QEditor
-
-.. image:: http://edu.qpython.org/static/qeditor-run.png
-    :alt: QPython - Start QEditor
-
-
-*As above shows, you could start to develop by pressing enter http://192.168.1.191:10000 on your computer browser*
-
-
-Give a try to run the code in editor ?
-------------------------------------------------
-
-..  code-block :: none
-
-    class TestC():
-        ^4$def ask_age(self):
-            ^8$v = raw_input("> How old are you ?")
-            ^8$return v
-
-    tc = TestC()
-    tc.ask_age()
-
-<button>Run ...</button>
-
-
-Author & updated time
-------------------------------------------------------
-Author: `River <https://github.com/riverfor>`_
-
-Submitted: 2017/10/7
-Updated: 2018/04/20
diff --git a/course/source/qpython-quick-start/in/principle.rst b/course/source/qpython-quick-start/in/principle.rst
deleted file mode 100644
index b56cd71..0000000
--- a/course/source/qpython-quick-start/in/principle.rst
+++ /dev/null
@@ -1,29 +0,0 @@
-Dasar - dasar QPython
-======================
-
-Bab Penulis, Kontributor dan updated time
-------------------------------------------------------
-Bab Penulis: Muhammad Khoiruddin Harahap
-
-About
-----------
-QPython menyediakan Python IDE mobile yang mudah digunakan dan layanan pengembangan untuk pengguna Python.
-
-
-
-Supports Python2 dan Python3
--------------------------------------------------
-QPython mendukung program Python2 dan Python3 yang terbaru, memenuhi persyaratan pengguna versi Python yang berbeda.
-
-
-
-Mobile IDE dan Komunitas Pembelajaran
------------------------------------------------------------------
-QPython memiliki konsol, editor, toolkit pengembangan (QRCode Code reader, local file manager), QPYPI, kursus, komunitas untuk berbagi kode dan layanan pengembangan online QPYIO, menyediakan IDE Python versi mobile yang mudah digunakan untuk  komunitas pengguna Python.
-
-
-
-Powerful dan open source
------------------------------------------
-Selain belajar pemrograman Python di Android, QPython membantu mengendalikan perangkat android Anda dengan program.
-Versi open source QPython mudah bagi untuk menanamkan Python SDK ke dalam pengembangan aplikasi Anda.
diff --git a/course/source/qpython-quick-start/index.rst b/course/source/qpython-quick-start/index.rst
deleted file mode 100644
index 9f75be8..0000000
--- a/course/source/qpython-quick-start/index.rst
+++ /dev/null
@@ -1,77 +0,0 @@
-What is QPython
-========================
-
-About
---------
-QPython(for Android) is a Python script engine for Android system, which runs Python programs on any Android device. It contains a Python interpreter, a console, and an editor. Besides a basic Python library, QPython also integrates a bottle library that supports WebApp development, and an SL4A library that supports invoking Android APIs.
-
-A QPython (iOS) application designed for iOS users is also released in Appstore.
-
-
-History of QPython
-------------------------------------------------------
-Born in 2012, QPython at first was a Python script SDK created by River for the rapid release of hotfix on Android programs, and was later independently released as a Python App on Android system. Ever since its release, QPython has been widely favored by Python developers, and received hundreds of thousands of installations and reviews in a very short time even in the circumstances of none market promotion. In 2015, the total installations exceeded more than 2,000,000. Until now, it is the most popular Python integrated development environment on Android.
-
-Supported program types
-------------------------------------------------------
-Currently, QPython can run console applications, WebApp applications, backend applications, graphic interface-based applications, and so on. Each type of program fits with a different application scenario, Python determines how to  run an application according to a unique head statement. For example, #qpy:webapp indicates that the program will be released in the WebApp manner, #qpy:kivy indicates that the program is a ivy program, #qpy:qpyapp indicates that the program is a QPYApp (fits with the backend application scenario), and a program without head statement will by default run in the console manner (for easy debugging).
-
-How to install a third party library
-------------------------------------------------------
-QPython is imbedded with most of the Python libraries for PCs. As Android system itself does not support compilation tool links, you cannot install all libraries like Python on PCs. However, you can install some pre-compiled libraries via QPYPI, which supports libraries including kivy, numpy, opencv, pycrypto, tornado and so on . In addition, you can use the pip client to install most libraries implemented by Python from the official website or from a third party Pypi designated by you.
-
-
-*WebApp header declaration*
-::
-
-    #qpy:webapp
-
-
-*QPYApp header declaration*
-::
-
-    #qpy:qpyapp
-
-
-*Kivy header declaration*
-::
-
-    #qpy:kivy
-
-*Console header declaration*
-::
-
-    #qpy:console
-
-
-
-
-QPython media and community
-------------------------------------------------------
-QPython is a rapidly updated program which pays attention to user feedbacks, including feedbacks from Facebook fan page, twitter, weibo, Facebook group, G+, WeChat/QQ group, and other medias. Social groups are a significant part of QPython. We welcome you to join in our social group discussion, activities, and QPython construction, so as to learn and understand the latest development state of QPython.
-
-
-Open source branch and business branch of QPython
-------------------------------------------------------
-QPython has two branches: the open source branch and the business branch. The open source branch provides a basic Python operating environment (which is consistent with the business branch), and uses the Apache license. We welcome any person or company of interest to customize your own service on this basis. Our QPython development team will also actively respond to any kinds of questions from users of the open source branch.
-The business branch of QPython, on the other hand, is dedicated to providing unique and convenient features and services, including online tutoring, online development, code sharing, etc.
-
-No matter which branch you are using, we hope everyone can really enjoy Python programing on cell phones.
-
-
-
-*Video: QPython - Code Anywhere，Study Anytime*
-
-
-.. image:: http://edu.qpython.org/static/codeanywhere.png
-    :target: data-video: "https://youtu.be/HqfUUiftrRw"
-    :alt: QPython - Code Anywhere，Study Anytime
-
-Author & updated time
-------------------------------------------------------
-Author: `River <https://github.com/riverfor>`_
-
-Translator: `TGJ <http://quseit.com/>`_
-
-Submitted: 2017/8/6
-Updated: 2018/04/25
diff --git a/course/source/qpython-quick-start/notebook.rst b/course/source/qpython-quick-start/notebook.rst
deleted file mode 100644
index ef8028c..0000000
--- a/course/source/qpython-quick-start/notebook.rst
+++ /dev/null
@@ -1,74 +0,0 @@
-Share a live documents with QPyNotebook
-========================================
-
-Do you want an application which helps you create and share documents that contain live Python code, equations, visualizations and narrative text?
-
-Now, QPyNotebook for QPython is published, which is developed based on the great opensource project jupyter notebook, it will help you do the following jobs easily like data cleaning and transformation, numerical simulation, statistical modeling, data visualization, machine learning and much more.
-
-Before Use
------------
-**1. Make sure you have installed the newest QPython (>=2.2.0)**
-
-**2. Download QPyNotebook**
-
-- Click the QPyNotebook App button to download QPyNotebook, the button will take you to the Google Play App Store.
-
-- If you could not connect to Google you can download directly from our `Github page <https://github.com/qpython-android/notebook/releases/>`_ *(please check the "Trust unknown source" in system's setting if you want to install it with APK downloaded from github)*
-
-.. image:: http://edu.qpython.org/static/notebook-setting.png
-    :alt: Enable QPyNotebook in QPython Setting
-
-
-
-**3. Download related libraries**
-
-Click the QPyNotebook Libraries button and there'll be a terminal window to download those libraries. It may take a while, and after downloading is finished, the terminal window will be closed automatically.
-
-.. image:: http://edu.qpython.org/static/notebook-lib.png
-    :alt: Enable QPyNotebook in QPython Setting
-
-
-
-.. image:: http://edu.qpython.org/static/notebook-download.png
-    :alt: Download libraries
-
-
-**Now all set!**
-
-.. image:: http://edu.qpython.org/static/notebook-downloaded.png
-    :alt: all set
-
-
-How to use
------------------------
-Open the Explorer from QPython dashboard, find some *.ipynb files (For example notebooks/Welcome.ipynb) and open it, then you will start the QPyNotebook.
-
-- You could start the QPyNotebook from QPython's explorer*
-
-.. image:: http://edu.qpython.org/static/notebook-explorer.png
-    :alt: You can start QPyNotebook from QPython's explorer
-
-- Or from QPyNotebook App
-
-.. image:: http://edu.qpython.org/static/notebook-app.png
-    :alt: Start QPyNotebook
-
-*QPyNotebook's live documentation*
-
-.. image:: http://edu.qpython.org/static/notebook-page.png
-    :alt: Start to access Welcome.ipynb
-
-
-Feedback
------------------------
-Feel free to give us any feedback.
-
-* `@qpython <http://twitter.com/qpython>`_
-* `support@qpython.org <support@qpython.org>`_
-
-Join the community
-----------------------------
-
-* `QPython@G+ <https://plus.google.com/u/2/communities/111759148772865961493>`_
-* `QPython@Facebook <https://www.facebook.com/groups/qpython>`_
-
diff --git a/course/source/qpython-quick-start/principle.rst b/course/source/qpython-quick-start/principle.rst
deleted file mode 100644
index 9ef1015..0000000
--- a/course/source/qpython-quick-start/principle.rst
+++ /dev/null
@@ -1,31 +0,0 @@
-Principle
-====================================
-
-
-About
---------
-QPython is focused on providing the convenient mobile Python IDE and learning service for Python users.
-
-
-Supports Python2 and Python3
-----------------------------
-QPython supports the newest Python2 and Python3, which satisfy users requirements for different Python versions.
-
-
-Mobile IDE and learning community
----------------------------------------
-QPython has a console, an editor, a develop toolkit(QRCode Code reader, local file manager), QPYPI, courses, a code share community and QPYIO online development service, which provide a convenient mobile Python IDE and learning community for Python users.
-
-
-Powerful and open source
-------------------------
-Besides learning Python programing on Android, QPython helps you control your android device with programs.
-The open source version of QPython is convenient for you to embed Python SDK into your app development.
-
-
-Author & updated time
-------------------------------------------------------
-Author: `River <https://github.com/riverfor>`_
-
-Submitted: 2017/8/7
-Updated: 2018/04/25
diff --git a/course/source/qpython-quick-start/qpypi-howto.rst b/course/source/qpython-quick-start/qpypi-howto.rst
deleted file mode 100644
index 8b3afc3..0000000
--- a/course/source/qpython-quick-start/qpypi-howto.rst
+++ /dev/null
@@ -1,54 +0,0 @@
-QPYPI howto
-=============
-Third party libraries could extend your programming abilities upto a greater extent, QPython manages libraries through QPYPI, There are some ways to use QPYPI as well.
-
-Tools
--------
-Tools category contains the useful tools for QPython, which could help you install QPython applications (Like SQLMap tool, Fabric tool and so on) easily, offer help for programming (Like django manage script, QEditor WebApp, Scapy tool and so on), and we will keep to improve more tools.
-
-.. image:: http://edu.qpython.org/static/qpypi-tools.png
-    :alt: QPYPI - Tools
-
-PIP Console
-------------------------------------
-INSTALL WITH PYTHON'S PYPI
-
-.. image:: http://edu.qpython.org/static/qpypi-pip.png
-    :alt: QPYPI - PIP Console
-
-
-By using QPython's built in pip console, you could install many Python libraries, it can help you install many pure-python libraries (including the dependencies).
-
-Notice: Some libraries mixed with c/c++ files can not be install through pip console for the android lacks the compiler environment, you could ask for help from qpython developer team.
-
-Libraries
-----------
-In libraries, you can manage some Python libraries which are directly maintained by QPython team. There are some libraries which you can not install with pip console, or that are written in c/c++ code that can not be compiled on android. Like kivy, paramiko, PIL, tornado etc. You can install them easily from here.
-
-.. image:: http://edu.qpython.org/static/qpypi-libraries.png
-    :alt: QPYPI - Libraries manage
-
-
-
-
-AIPY Libraries
-------------------
-In AIPY libraries, QPython team has added the AI related programming libraries (like neural network, deeplearnning, data analysis and mathematical operation etc.), which help users to start the AI program learning with QPython's help easliy.
-
-
-.. image:: http://edu.qpython.org/static/qpypi-aipy.png
-    :alt: QPYPI - AIPY
-
-
-
-Manage Manually
------------------
-Besides these ways above, you could copy libraries into the /sdcard/qpython/lib/python2.7/site-packages in your device to install them.
-
-
-Author & updated time
-------------------------------------------------------
-Author: `River <https://github.com/riverfor>`_
-
-Submitted: 2017/10/7
-Updated: 2018/04/25
diff --git a/course/source/qpython-quick-start/terminal-howto.rst b/course/source/qpython-quick-start/terminal-howto.rst
deleted file mode 100644
index 1c35e18..0000000
--- a/course/source/qpython-quick-start/terminal-howto.rst
+++ /dev/null
@@ -1,31 +0,0 @@
-Terminal howto
-=================
-Yes, it’s regular Python terminal, feel free to comunicate with interpreter directly.
-
-.. image:: http://edu.qpython.org/static/terminal.png
-    :alt: QPython - Terminal
-
-Features
----------
-There is an ordinary Python terminal. Many people usually use it to explore objects’ properties, consult about syntax and test their ideas. You can type your commands directly and Python interpreter will execute them.
-
-QPython's terminal has the following features:
-
-* open additional terminals by tapping the plus button 
-* Switch terminal by click the drop-down list on the upper left corner, click the "X" to close the terminal
-* Long clicking on the terminal window, you could complete the complex operations like select text, copy all, paste, send control key, send fn key from the popup menu.
-
-.. image:: http://edu.qpython.org/static/terminal-menu.png
-    :alt: QPython - Terminal popup menu
-
-
-Note
-------
-There will be notification in the notification bar unless you explicitly close the terminal and you always can reach the open terminal by tapping the notification.
-
-Author & updated time
-------------------------------------------------------
-Author: `River <https://github.com/riverfor>`_
-
-Submitted: 2017/10/7
-Updated: 2018/04/25
diff --git a/course/source/qpython-webapp/cn/django-howto.rst b/course/source/qpython-webapp/cn/django-howto.rst
deleted file mode 100644
index 20f4742..0000000
--- a/course/source/qpython-webapp/cn/django-howto.rst
+++ /dev/null
@@ -1,15 +0,0 @@
-
-.. image:: http://edu.qpython.org/static/djangoman-installdjango.png
-    :alt: djangoman.py
-
-*2 运行Django manage script，根据提示安装Django库*
-
-.. image:: http://edu.qpython.org/static/djangoman-createproject.png
-    :alt: djangoman.py
-
-*3 运行Django manage script，创建新项目*
-
-.. image:: http://edu.qpython.org/static/djangoman-startserver.png
-    :alt: djangoman.py
-
-*3 运行完syncdb, createapp之后，运行startserver，启动服务*
diff --git a/course/source/qpython-webapp/cn/index.rst b/course/source/qpython-webapp/cn/index.rst
deleted file mode 100644
index 166c18c..0000000
--- a/course/source/qpython-webapp/cn/index.rst
+++ /dev/null
@@ -1,39 +0,0 @@
-QPython WebApp Principle
-====================================
-
-
-关于
---------
-QPython支持WebApp，能让所有的Web开发者马上开始快速移动开发。
-
-优势
-----------
-移动开发领域已经有了很多WebApp解决方案，比如cordova, phonegap等等，和它们对比，QPython的WebApp方案有下面的优势：
-
-*QPython是一个强大的Python引擎，能够处理许多逻辑事务，能有效地减少数据传输时间由此极大地提升应用的用户使用体验*
-
-*QPython支持大量的Python库，安装之后，你可以很方便地在项目中使用它们，由此大大节省开发时间*
-
-*使用QPython开发非常方便，你可以在掌上就完成开发任务，同时你也可以在PC上开发然后传输到移动设备上运行*
-
-QPython中的WebApp是如何工作的
-----------------------------
-QPython的WebApp包含以下几个模块： WebApp服务和Webview。WebApp服务在后台运行并且负责响应用户的输入， WebView是一个前端组件，它负责显示用户交互界面和负责响应用户的交互，比如界面展示，界面滚动，点击等等。
-
-**1** 当你从QPython运行一个WebApp程序时，QPython在后台启动WebApp服务，同时，QPython会在前端启动一个Webview去载入程序头部声明的WebApp入口链接。
-
-::
-
-#qpy:webapp:<webapp-title>
-#qpy://<webapp-address>:<webapp-port>/<webapp-path>
-
-**2** 然后用户能够开始用WEB的模式和应用进行交互，因此开发者能够用Web开发的模式去编写代码。
-
-**3** 当用户靠点击左上角的箭头退出应用时，程序将会请求 **http://<webapp-address><:webapp-port>/__exit** 这个URL来退出程序，开发能够重写其对应的逻辑函数 __exit 来定义退出行为。
-
-作者
-------------------------------------------------------
-作者: `River <https://github.com/riverfor>`_
-
-更新: 2017/10/16
-
diff --git a/course/source/qpython-webapp/cn/web-frameworks.rst b/course/source/qpython-webapp/cn/web-frameworks.rst
deleted file mode 100644
index 9cb2e6b..0000000
--- a/course/source/qpython-webapp/cn/web-frameworks.rst
+++ /dev/null
@@ -1,133 +0,0 @@
-QPython中的WEB框架
-==========================
-使用WEB开发框架，开发者能够高效迅速地开发出WEB应用。同样，在QPython中，你能用下列WEB框架来快速开发WebApp。
-
-Bottle
-----------
-Bottle是QPython内置的WebApp开发框架，您无需额外安装，只需要遵循WebApp的规范，参考bottle开发的方法就可以进行。
-
-来看一个最简单的bottle的Hello world。
-
-
-::
-
-    #qpy:webapp:Hello Qpython
-    #qpy://127.0.0.1:8080/
-    """
-    This is a sample for qpython webapp
-    """
-    from bottle import Bottle, ServerAdapter
-    from bottle import run, debug, route, error, static_file, template
-
-    ######### QPYTHON WEB SERVER ###############
-    class MyWSGIRefServer(ServerAdapter):
-        ^4$server = None
-        ^4$def run(self, handler):
-            ^8$from wsgiref.simple_server import make_server, WSGIRequestHandler
-            ^8$if self.quiet:
-                ^12$class QuietHandler(WSGIRequestHandler):
-                    ^12$def log_request(*args, **kw): pass
-                ^8$self.options['handler_class'] = QuietHandler
-            ^4$self.server = make_server(self.host, self.port, handler, **self.options)
-            ^4$self.server.serve_forever()
-
-        ^4$def stop(self):
-            ^8$import threading
-            ^8$threading.Thread(target=self.server.shutdown).start()
-            ^8$self.server.server_close()
-
-    ######### BUILT-IN ROUTERS ###############
-    @route('/__exit', method=['GET','HEAD'])
-    def __exit():
-        ^4$global server
-        ^4$server.stop()
-
-    @route('/assets/<filepath:path>')
-    def server_static(filepath):
-        ^4$return static_file(filepath, root='/sdcard')
-
-
-    ######### WEBAPP ROUTERS WRITE YOUR CODE BELOW###############
-    @route('/')
-    def home():
-        ^4$return template('<h1>Hello {{name}} !</h1><a href="/assets/qpython/projects/WebAppSample/main.py">View source</a><br /><br /> <a href="http://edu.qpython.org/qpython-webapp/index.html">>> About QPython Web App</a>',name='QPython')
-
-    ######### WEBAPP ROUTERS ###############
-    app = Bottle()
-    app.route('/', method='GET')(home)
-    app.route('/__exit', method=['GET','HEAD'])(__exit)
-    app.route('/assets/<filepath:path>', method='GET')(server_static)
-
-    try:
-        ^4$server = MyWSGIRefServer(host="127.0.0.1", port="8080")
-        ^4$app.run(server=server,reloader=False)
-    except Exception,ex:
-        ^4$print "Exception: %s" % repr(ex)
-
-
-<button>Run ...</button>
-
-
-点击运行，将代码复制到编辑器中点击运行即可看见效果。
-
-
-Tornado
-----------
-QPython同样支持异步通信Web开发框架Tornado，您可以通过QPYPI来安装它。
-
-
-.. image:: http://edu.qpython.org/static/qpypi-tornado.png
-    :alt: tornado library in QPYPI
-
-*通过QPYPI来安装tornado库*
-
-安装完毕之后，点击以下代码底部的运行即可将代码拷贝到编辑器中，保存运行即可看到基于Tornado的WebApp示例效果。
-
-
-::
-
-    #qpy:webapp:Tornado Sample
-    #qpy://127.0.0.1:8080/
-
-    import tornado
-    import tornado.web
-
-    class MainHandler(tornado.web.RequestHandler):
-        ^4$def get(self):
-            ^8$self.write("Hello, world")
-
-    class ExitHandler(tornado.web.RequestHandler): 
-        ^4$def get(self):
-            ^8$import os,signal
-            ^8$os.kill(os.getpid(), signal.SIGKILL)
-
-    def make_app():
-        ^4$return tornado.web.Application([
-            ^8$(r"/", MainHandler),
-            ^8$(r"/__exit", ExitHandler),
-        ^4$])
-
-    if __name__ == "__main__":
-        ^4$app = make_app()
-        ^4$app.listen(8080)
-        ^4$tornado.ioloop.IOLoop.current().start()
-
-<button>Run ...</button>
-
-此外值得一提的是，在QPython的AIPY扩展插件中，matlibplot也是默认支持了用Tornado作为绘图展示支持后端。
-
-Django
-----------
-作为Python WEB开发的最常用框架，Django同样获得了QPython的支持, QPython提供了一个djangoman.py脚本来帮助您快速安装django库和初始化django项目，通过运行该工具，你可以快速创建或者管理django项目。
-
-.. image:: http://edu.qpython.org/static/djangoman.png
-    :alt: djangoman.py
-
-*可以通过Django manage script来管理Django项目*
-
-通过djangoman.py来完成项目的初始化之后，您就可以按照WebApp的规范来开发您的WebApp并在QPython上运行。
-
-
-其他
-------
-除了上述框架，诸如其他框架如Flask,cherrypy等也有可能被QPython支持，你若感兴趣可以通过QPYPI中的INSTALL FROM PYTHON'S PYPI来探索。
diff --git a/course/source/qpython-webapp/cn/your-first-webapp.rst b/course/source/qpython-webapp/cn/your-first-webapp.rst
deleted file mode 100644
index f187e1a..0000000
--- a/course/source/qpython-webapp/cn/your-first-webapp.rst
+++ /dev/null
@@ -1,96 +0,0 @@
-QPython的WebApp
-==========================
-WebApp是一种Web开发者能够快速上手开发的应用，QPython的WebApp支持常见WebAApp的优势。此外，QPython让手机本身就能成为WebApp的运行容器，支持处理复杂的程序逻辑，因此能极大地提高WebApp使用体验，同时也增加了开发的灵活度。
-
-WebApp声明
------------
-在QPython中，通过头部声明来区分程序类型，当头部有以下定义时，我们知道它是一个WebApp程序。
-
-::
-
-    #qpy:webapp:<WebApp程序名称>
-    #qpy://<WebApp运行所在的IP，一般为127.0.0.1>:<WebApp运行所在的端口>/<初次载入时对应的WebApp Url路径>
-
-
-WebApp运行流程
---------------
-在程序被识别为WebApp运行后，QPython会让程序作为后台服务的方式启动，同时在前端会启动一个WebView组件，载入你在程序头部声明所对应的URL（由IP、端口、URL路径组合而成）
-
-如，如果IP为127.0.0.1，端口为8080，URL路径为hello/test，则WebView载入的URL为：http://127.0.0.1:8080/hello/test
-
-
-WebApp退出原理
---------------
-当你点了后退键退出WebView时，WebApp后台进程也需要退出，由于一开始QPython让其按照后台服务的方式启动，所以我们需要通知其退出，QPython是在WebView退出时，发出一个http://IP:端口/__exit 的GET请求到后台服务，由其处理结束后台进程，退出服务的工作，因此，作为一个完成的流程，您需要在自己的WebApp中定义/__exit 对应的退出服务操作，我们在结尾示例中附有示例。
-
-
-
-例子
---------
-作为QPython内嵌的Web开发库，来看一个最简单的bottle的Hello world
-
-
-::
-
-    #qpy:webapp:Hello Qpython
-    #qpy://127.0.0.1:8080/
-    """
-    This is a sample for qpython webapp
-    """
-    from bottle import Bottle, ServerAdapter
-    from bottle import run, debug, route, error, static_file, template
-
-    ######### QPYTHON WEB SERVER ###############
-    class MyWSGIRefServer(ServerAdapter):
-        ^4$server = None
-        ^4$def run(self, handler):
-            ^8$from wsgiref.simple_server import make_server, WSGIRequestHandler
-            ^8$if self.quiet:
-                ^12$class QuietHandler(WSGIRequestHandler):
-                    ^12$def log_request(*args, **kw): pass
-                ^8$self.options['handler_class'] = QuietHandler
-            ^4$self.server = make_server(self.host, self.port, handler, **self.options)
-            ^4$self.server.serve_forever()
-
-        ^4$def stop(self):
-            ^8$import threading
-            ^8$threading.Thread(target=self.server.shutdown).start()
-            ^8$self.server.server_close()
-
-    ######### BUILT-IN ROUTERS ###############
-    @route('/__exit', method=['GET','HEAD'])
-    def __exit():
-        ^4$global server
-        ^4$server.stop()
-
-    @route('/assets/<filepath:path>')
-    def server_static(filepath):
-        ^4$return static_file(filepath, root='/sdcard')
-
-
-    ######### WEBAPP ROUTERS WRITE YOUR CODE BELOW###############
-    @route('/')
-    def home():
-        ^4$return template('<h1>Hello {{name}} !</h1><a href="/assets/qpython/projects/WebAppSample/main.py">View source</a><br /><br /> <a href="http://edu.qpython.org/qpython-webapp/index.html">>> About QPython Web App</a>',name='QPython')
-
-    ######### WEBAPP ROUTERS ###############
-    app = Bottle()
-    app.route('/', method='GET')(home)
-    app.route('/__exit', method=['GET','HEAD'])(__exit)
-    app.route('/assets/<filepath:path>', method='GET')(server_static)
-
-    try:
-        ^4$server = MyWSGIRefServer(host="127.0.0.1", port="8080")
-        ^4$app.run(server=server,reloader=False)
-    except Exception,ex:
-        ^4$print "Exception: %s" % repr(ex)
-
-
-
-<button>Run ...</button>
-
-
-点击运行，将代码复制到编辑器中点击运行即可看见效果。
-
-当然，在QPython中，还提供了其他Web开发框架的支持来帮助你简化工作。
-
diff --git a/course/source/qpython-webapp/index.rst b/course/source/qpython-webapp/index.rst
deleted file mode 100644
index 267c733..0000000
--- a/course/source/qpython-webapp/index.rst
+++ /dev/null
@@ -1,40 +0,0 @@
-QPython WebApp Principle
-====================================
-
-
-About
---------
-QPython supports WebApp, the goal is to enable all WEB developers to the fastest development of mobile applications.
-
-Advantages
-----------
-There are lots of mobile WebApp solutions, like cordova, phonegap etc, and compared to them, QPython's WebApp has the following advantages.
-
-*QPython is a powerful Python engine that can handle many logical jobs, effectively reducing data transfer time and enhancing your app's user experience*
-
-*QPython supports massive Python libraries, you could import them in your project, so that you could save much develop time.*
-
-*It is very convenient for development with QPython, you can complete your development just in palm. And you could develop from PC then transfer to your mobile to run*
-
-How does WebApp work in QPython
-----------------------------
-QPython WebApp contains the following components: WebApp service and Webview. The WebApp service runs in the background and is responsible for responding to user's input. WebView is a front component, which shows the user interface and and is responsible for responding to render user interactions, such as scrolling, click the button and so on.
-
-**1** When you run a WebApp program from QPython, QPython launch the WebApp service in the background, at the same time, QPython will start a webview which loads the entry link declared in the program header in the front.
-
-::
-
-#qpy:webapp:<webapp-title>
-#qpy://<webapp-address>:<webapp-port>/<webapp-path>
-
-**2** Then the user can start to interact with the applicaton with the web mode. So the developer could write the logic code with the web development way.
-
-**3** When the user exit by clicking the left arrow, the application will request the **http://<webapp-address><:webapp-port>/__exit** to exit the application
-The developer could rewrite the __exit function to defines the exit behaviors.
-
-Chapter Author, contributor and updated time
-------------------------------------------------------
-Author: `River <https://github.com/riverfor>`_
-
-Submitted: 2017/10/16
-
diff --git a/course/source/qpython-webapp/web-frameworks.rst b/course/source/qpython-webapp/web-frameworks.rst
deleted file mode 100644
index 3d643d6..0000000
--- a/course/source/qpython-webapp/web-frameworks.rst
+++ /dev/null
@@ -1,132 +0,0 @@
-WEB framework in QPython
-==========================
-By using the WEB development framework, developers can efficiently and quickly develop Web applications. Similarly, in QPython, you can quickly develop WebApp with the following web frameworks.
-
-Bottle
-----------
-Bottle is a QPython built-in WebApp development framework, you do not need to install additional, just follow the WebApp's specifications, reference bottle development methods can be carried out.
-
-Look at one of the most simple Hello world with bottle
-
-::
-
-    #qpy:webapp:Hello Qpython
-    #qpy://127.0.0.1:8080/
-    """
-    This is a sample for qpython webapp
-    """
-    from bottle import Bottle, ServerAdapter
-    from bottle import run, debug, route, error, static_file, template
-
-    ######### QPYTHON WEB SERVER ###############
-    class MyWSGIRefServer(ServerAdapter):
-        ^4$server = None
-        ^4$def run(self, handler):
-            ^8$from wsgiref.simple_server import make_server, WSGIRequestHandler
-            ^8$if self.quiet:
-                ^12$class QuietHandler(WSGIRequestHandler):
-                    ^12$def log_request(*args, **kw): pass
-                ^8$self.options['handler_class'] = QuietHandler
-            ^4$self.server = make_server(self.host, self.port, handler, **self.options)
-            ^4$self.server.serve_forever()
-
-        ^4$def stop(self):
-            ^8$import threading
-            ^8$threading.Thread(target=self.server.shutdown).start()
-            ^8$self.server.server_close()
-
-    ######### BUILT-IN ROUTERS ###############
-    @route('/__exit', method=['GET','HEAD'])
-    def __exit():
-        ^4$global server
-        ^4$server.stop()
-
-    @route('/assets/<filepath:path>')
-    def server_static(filepath):
-        ^4$return static_file(filepath, root='/sdcard')
-
-
-    ######### WEBAPP ROUTERS WRITE YOUR CODE BELOW###############
-    @route('/')
-    def home():
-        ^4$return template('<h1>Hello {{name}} !</h1><a href="/assets/qpython/projects/WebAppSample/main.py">View source</a><br /><br /> <a href="http://edu.qpython.org/qpython-webapp/index.html">>> About QPython Web App</a>',name='QPython')
-
-    ######### WEBAPP ROUTERS ###############
-    app = Bottle()
-    app.route('/', method='GET')(home)
-    app.route('/__exit', method=['GET','HEAD'])(__exit)
-    app.route('/assets/<filepath:path>', method='GET')(server_static)
-
-    try:
-        ^4$server = MyWSGIRefServer(host="127.0.0.1", port="8080")
-        ^4$app.run(server=server,reloader=False)
-    except Exception,ex:
-        ^4$print "Exception: %s" % repr(ex)
-
-
-
-
-<button>Run ...</button>
-
-
-Click Run, copy the code to the editor and Run to check out the result.
-
-
-Tornado
-----------
-QPython also supports the asynchronous Communication Web Development Framework - Tornado, which you can install with QPYPI.
-
-.. image:: http://edu.qpython.org/static/qpypi-tornado.png
-    :alt: tornado library in QPYPI
-
-*To install tornado library by QPYPI*
-
-After the installation is complete, click on the bottom of the following code to run the code can be copied to the editor,save the run to check out Tornado-based WebApp sample effects.
-
-
-::
-
-    #qpy:webapp:Tornado Sample
-    #qpy://127.0.0.1:8080/
-
-    import tornado
-    import tornado.web
-
-    class MainHandler(tornado.web.RequestHandler):
-        ^4$def get(self):
-            ^8$self.write("Hello, world")
-
-    class ExitHandler(tornado.web.RequestHandler): 
-        ^4$def get(self):
-            ^8$import os,signal
-            ^8$os.kill(os.getpid(), signal.SIGKILL)
-
-    def make_app():
-        ^4$return tornado.web.Application([
-            ^8$(r"/", MainHandler),
-            ^8$(r"/__exit", ExitHandler),
-        ^4$])
-
-    if __name__ == "__main__":
-        ^4$app = make_app()
-        ^4$app.listen(8080)
-        ^4$tornado.ioloop.IOLoop.current().start()
-
-<button>Run ...</button>
-
-Also worth mentioning is that in QPython's AIPY extension,matlibplot also supports Tornado as the default drawing support backend by default.
-
-Django
-----------
-As the most commonly used framework for Python Web development, Django also received QPython support, QPython provides a djangoman.py scripts to help you quickly install and initialize django library project,By running this tool,You can quickly create or manage django projects.
-
-.. image:: http://edu.qpython.org/static/djangoman.png
-    :alt: djangoman.py
-
-*Django projects can be managed by Django manage script*
-
-After djangoman.py to complete the project's initialization,You can follow the WebApp's specifications to develop your WebApp and run it on QPython.
-
-Other
-------
-In addition to the above framework,Other frameworks such as Flask, Cherrypy, etc. are also likely to be supported by QPython,If you are interested, you can explore through INSTALL FROM PYTHON'S PYPI in QPYPI.
diff --git a/course/source/qpython-webapp/your-first-webapp.rst b/course/source/qpython-webapp/your-first-webapp.rst
deleted file mode 100644
index 0e7228b..0000000
--- a/course/source/qpython-webapp/your-first-webapp.rst
+++ /dev/null
@@ -1,86 +0,0 @@
-QPython’s WebApp
-==========================
-WebApp is an app allows developers to quickly get started application development, QPython's WebApp has the advantage of supporting common WebApps.Moreover, QPython lets the phone itself become a WebApp's run container, support for handling complex program logic, therefore, it can greatly improve the WebApp user experience, while also increasing the flexibility of the development.WebApp statement
-
------------
-In QPython, through the header statement to distinguish between program types, When the header has the following definition, we know that it is a WebApp program.
-
-::
-
-    #qpy:webapp:<WebApp Program name>
-    #qpy://<WebApp runs on which the IP，generally for this 127.0.0.1>:<The port on which WebApp is running>/<Corresponding WebApp URL path when Initializing>
-
-
-WebApp's Run process
----------------------
-After the program is recognized as a WebApp run，QPython will let the program starts as a background service，At the same time in the front end will start a WebView component，Load the URL you specified in the program header(By the IP, port, URL path combination)For example, If the IP is 127.0.0.1, the port is 8080 and the URL path is hello / test, then the URL that WebView loads is：http://127.0.0.1:8080/hello/test Web application exit principle
-
-Exit the WebApp
------------------
-When you click the Back button to exit WebView, the WebApp daemon also needs to quit. Since QPython started it as a background service in the first place, we need to tell it to quit,QPython is when WebView exits,Issue a http://IP:port/__exit GET request to the background service, By its end of background process, exit the service of work, Therefore, as a completed process, you need to define it in your own WebApp/__exit The corresponding exit service operation, we have an example in the end.
-
-E.g
---------
-As QPython embedded Web development library, Let's look at one of the easiest bottles - Hello world
-
-::
-
-    #qpy:webapp:Hello Qpython
-    #qpy://127.0.0.1:8080/
-    """
-    This is a sample for qpython webapp
-    """
-    from bottle import Bottle, ServerAdapter
-    from bottle import run, debug, route, error, static_file, template
-
-    ######### QPYTHON WEB SERVER ###############
-    class MyWSGIRefServer(ServerAdapter):
-        ^4$server = None
-        ^4$def run(self, handler):
-            ^8$from wsgiref.simple_server import make_server, WSGIRequestHandler
-            ^8$if self.quiet:
-                ^12$class QuietHandler(WSGIRequestHandler):
-                    ^12$def log_request(*args, **kw): pass
-                ^8$self.options['handler_class'] = QuietHandler
-            ^4$self.server = make_server(self.host, self.port, handler, **self.options)
-            ^4$self.server.serve_forever()
-
-        ^4$def stop(self):
-            ^8$import threading
-            ^8$threading.Thread(target=self.server.shutdown).start()
-            ^8$self.server.server_close()
-
-    ######### BUILT-IN ROUTERS ###############
-    @route('/__exit', method=['GET','HEAD'])
-    def __exit():
-        ^4$global server
-        ^4$server.stop()
-
-    @route('/assets/<filepath:path>')
-    def server_static(filepath):
-        ^4$return static_file(filepath, root='/sdcard')
-
-
-    ######### WEBAPP ROUTERS WRITE YOUR CODE BELOW###############
-    @route('/')
-    def home():
-        ^4$return template('<h1>Hello {{name}} !</h1><a href="/assets/qpython/projects/WebAppSample/main.py">View source</a><br /><br /> <a href="http://edu.qpython.org/qpython-webapp/index.html">>> About QPython Web App</a>',name='QPython')
-
-    ######### WEBAPP ROUTERS ###############
-    app = Bottle()
-    app.route('/', method='GET')(home)
-    app.route('/__exit', method=['GET','HEAD'])(__exit)
-    app.route('/assets/<filepath:path>', method='GET')(server_static)
-
-    try:
-        ^4$server = MyWSGIRefServer(host="127.0.0.1", port="8080")
-        ^4$app.run(server=server,reloader=False)
-    except Exception,ex:
-        ^4$print "Exception: %s" % repr(ex)
-
-<button>Run ...</button>
-
-
-Click Run, copy the code to the editor and click Run to see the result.
-
-Of course, in QPython, It also provides support for other web development frameworks to help you streamline your work.
diff --git a/course/source/qsl4a-develop/cn/index.rst b/course/source/qsl4a-develop/cn/index.rst
deleted file mode 100644
index 7ce0e3d..0000000
--- a/course/source/qsl4a-develop/cn/index.rst
+++ /dev/null
@@ -1,71 +0,0 @@
-QSL4A - QPython的安卓脚本层
-==============================================
-SL4A是Scripting Layer for Android 的缩写，中文直译为“安卓的脚本层”，QSL4A是QPython的安卓脚本层。
-
-
-什么是SL4A
-----------
-SL4A的全称为Scripting Layer for Android, 顾名思义就是Android的脚本架构层，它的目的就是可以用熟知的脚本开发语言来开发Android应用程序。其工作原理基于RPC远程调用，通过本地的脚本解析器和远端的原生态Android Server层的APK进行信息交互，即实现一个远程代理，把本地脚本的函数调用通过json格式的封装，传递给远程原生态Server APK进行实际的android系统函数呼叫，最后将操作的执行结果反馈给本地脚本解析器，然后再在终端显示出运行结果。
-
-QPython中集成了SL4A的支持，通过SL4A，用户可以很方便地通过Python代码来调用安卓特性。
-
-
-QSL4A支持的接口
--------------------
-
-您需要在安卓系统设置中开启QPython对应的权限设置才能使用相关的SL4A接口。
-
-**系统应用接口**
-
-* Android：包括剪贴板、Intent对象、广播、震动、网络状态、版本号、发送邮件、提示、输入框等在内的接口 `(接口文档) <http://www.qpython.org/en/guide_androidhelpers.html>`_
-* 应用：主要为应用管理接口 `(接口文档) <http://www.qpython.org/en/guide_androidhelpers.html#applicationmanagerfacade>`_
-* ActivityResult：安卓里控制ActivityResult行为的接口 `(接口文档) <http://www.qpython.org/en/guide_androidhelpers.html#activityresultfacade>`_
-* 通用Intent：访问二维码，用XX应用查看的接口 `(接口文档) <http://www.qpython.org/en/guide_androidhelpers.html#commonintentsfacade>`_
-
-**安卓相关设备接口**
-
-* 相机：访问&调用设备的相机接口 `(接口文档) <http://www.qpython.org/en/guide_androidhelpers.html#camerafacade>`_
-* 联系人：联系人访问接口 `(接口文档) <http://www.qpython.org/en/guide_androidhelpers.html#contactsfacade>`_
-* 地理位置：访问地理位置接口 `(接口文档) <http://www.qpython.org/en/guide_androidhelpers.html#locationfacade>`_
-* 电话：访问电话接口 `(接口文档) <http://www.qpython.org/en/guide_androidhelpers.html#phonefacade>`_
-* 媒体录制：能够控制录音或者视频 `(接口文档) <http://www.qpython.org/en/guide_androidhelpers.html#mediarecorderfacade>`_
-* 感应器管理：访问&控制设备的传感器 `(接口文档) <http://www.qpython.org/en/guide_androidhelpers.html#sensormanagerfacade>`_
-* 设置：能够设置设备的屏幕、飞行模式、铃声模式、震动模式、音量、亮度等 `(接口文档) <http://www.qpython.org/en/guide_androidhelpers.html#settingsfacade>`_
-* 短信：能够访问设备的短信 `(接口文档) <http://www.qpython.org/en/guide_androidhelpers.html#smsfacade>`_
-* 语音识别：能够识别用户的讲话并返回最可能的结果 `(接口文档) <http://www.qpython.org/en/guide_androidhelpers.html#speechrecognitionfacade>`_
-* 音调生成器：能够由给定的电话号码生成DTMF的语音 `(接口文档) <http://www.qpython.org/en/guide_androidhelpers.html#tonegeneratorfacade>`_
-* 唤醒锁：设备的唤醒锁接口 `(接口文档) <http://www.qpython.org/en/guide_androidhelpers.html#wakelockfacade>`_
-* WIFI：设备的WIFI管理 `(接口文档) <http://www.qpython.org/en/guide_androidhelpers.html#wififacade>`_
-* 电池管理：提供设备的电池管理接口 `(接口文档) <http://www.qpython.org/en/guide_androidhelpers.html#batterymanagerfacade>`_
-* 媒体播放器：媒体播放器设置 `(接口文档) <http://www.qpython.org/en/guide_androidhelpers.html#mediaplayerfacade>`_
-* 偏好设置：访问／控制偏好设置 `(接口文档) <http://www.qpython.org/en/guide_androidhelpers.html#preferencesfacade>`_
-* 文字语音：控制文字到语音输出 `(接口文档) <http://www.qpython.org/en/guide_androidhelpers.html#texttospeechfacade>`_
-* 蓝牙：控制手机上的蓝牙设备 `(接口文档) <http://www.qpython.org/en/guide_androidhelpers.html#bluetoothfacade>`_
-* 信号强度：访问手机信号强度信息 `(接口文档) <http://www.qpython.org/en/guide_androidhelpers.html#signalstrengthfacade>`_
-* 网络摄像头：能够控制手机的摄像机 `(接口文档) <http://www.qpython.org/en/guide_androidhelpers.html#webcamfacade>`_
-* 用户交互界面：用于控制UI界面相关的展现，如文本对话框，密码输入框、状态栏、进度条、日期选择器等 `(接口文档) <http://www.qpython.org/en/guide_androidhelpers.html#uifacade>`_
-* NFC：NFC相关的控制，主要提供了NFC中的master/slave交换信息的方式 `(接口文档) <http://www.qpython.org/en/guide_androidhelpers.html#nfc>`_
-* USB宿主序列：用于控制来自安卓的拥有USB主机控制器的类USB序列的设备 `(接口文档) <http://www.qpython.org/en/guide_androidhelpers.html#usb-host-serial-facade>`_
-
-
-**QPython相关的接口**
-
-* QPy接口：在手机上运行QPython脚本 `(接口文档) <http://www.qpython.org/en/guide_androidhelpers.html#qpyinterfacefacade>`_
-
-
-示例代码
------------
-在安卓中，可以引入androidhelper模块来载入QSL4A的支持。下面我们来运行一个简单的QSL4A脚本来体验下：
-
-
-::
-
-    ^0$import androidhelper
-    ^0$droid = androidhelper.Android()
-    ^0$line = droid.dialogGetInput()
-    ^0$s = "Hello, %s" % (line.result,)
-    ^0$droid.makeToast(line)
-
-
-<button>Run ...</button>
-
diff --git a/course/source/qsl4a-develop/index.rst b/course/source/qsl4a-develop/index.rst
deleted file mode 100644
index e17d40f..0000000
--- a/course/source/qsl4a-develop/index.rst
+++ /dev/null
@@ -1,71 +0,0 @@
-QSL4A - QPython's Android script layer
-==============================================
-SL4A is an acronym for Android's script layer, QSL4A is QPython's Android script layer.
-
-
-What is SL4A?
-----------
-The full name of SL4A Scripting Layer for Android, as the name suggests is Android's script architecture layer, Its purpose is to develop Android applications using well-known scripting languages.Its working principle is based on RPC remote call, through the local script parser and the remote native Android server layer APK information exchange,That is to achieve a remote agent,The local script function calls is encapsulated by JSON format,pass to the Remote ECS APK and proceed to the actual android system function call,and the results of the operation will be fed back to the local script parser,finally the terminal will show the results of the operation.
-
-QPython integrated support of SL4A, users can easily be invoked by Andrews characteristics Python code, by SL4A.
-
-
-QSL4A supported interfaces
--------------------
-
-You need to turn on the QPython permission settings in your Android system settings to use the associated SL4A interface.
-
-**System application interface**
-
-* Including clipboard, Intent object, broadcast, vibration, network status, version number, send mail, prompts, Input boxes, etc. `(APIs Document) <http://www.qpython.org/en/guide_androidhelpers.html>`_
-* Application: Mainly as an application management interface `(APIs Document) <http://www.qpython.org/en/guide_androidhelpers.html#applicationmanagerfacade>`_
-* ActivityResult��In Android control ActivityResult the interface of behavior `(APIs Document) <http://www.qpython.org/en/guide_androidhelpers.html#activityresultfacade>`_
-* Common Intent��Access two-dimensional code, Use XX application to view the interface `(APIs Document) <http://www.qpython.org/en/guide_androidhelpers.html#commonintentsfacade>`_
-
-**Android related device interface**
-
-* Camera��Access & recall device's camera interface `(APIs Document) <http://www.qpython.org/en/guide_androidhelpers.html#camerafacade>`_
-* Contact��Contact access interface `(APIs Document) <http://www.qpython.org/en/guide_androidhelpers.html#contactsfacade>`_
-* Location��Access location interface `(APIs Document) <http://www.qpython.org/en/guide_androidhelpers.html#locationfacade>`_
-* Phone: Access the phone interface `(APIs Document) <http://www.qpython.org/en/guide_androidhelpers.html#phonefacade>`_
-* 
-* Media recording: able to control recording or video `(APIs Document) <http://www.qpython.org/en/guide_androidhelpers.html#mediarecorderfacade>`_
-* Sensor Management: Access Control & Sensor device `(APIs Document) <http://www.qpython.org/en/guide_androidhelpers.html#sensormanagerfacade>`_
-* Settings: Ability to set device screen, flight mode, ringtone mode, vibration mode, volume, brightness, etc. `(APIs Document) <http://www.qpython.org/en/guide_androidhelpers.html#settingsfacade>`_
-* SMS: equipment can be access to SMS `(APIs Document) <http://www.qpython.org/en/guide_androidhelpers.html#smsfacade>`_
-* Speech recognition: Can recognize the user's speech and returns the most likely outcome `(APIs Document) <http://www.qpython.org/en/guide_androidhelpers.html#speechrecognitionfacade>`_
-* Tone Generator: The ability to generate DTMF speech from a given phone number `(APIs Document) <http://www.qpython.org/en/guide_androidhelpers.html#tonegeneratorfacade>`_
-* Wake lock: The device's wake lock interface `(APIs Document) <http://www.qpython.org/en/guide_androidhelpers.html#wakelockfacade>`_
-* WIFI: Device WIFI Management `(APIs Document) <http://www.qpython.org/en/guide_androidhelpers.html#wififacade>`_
-* Battery Management: Provides the device's battery management interface `(APIs Document) <http://www.qpython.org/en/guide_androidhelpers.html#batterymanagerfacade>`_
-* Media Player: Media Player Settings `(APIs Document) <http://www.qpython.org/en/guide_androidhelpers.html#mediaplayerfacade>`_
-* Preferences: Access / Control Preferences `(APIs Document) <http://www.qpython.org/en/guide_androidhelpers.html#preferencesfacade>`_
-* Text Voice: Control text to voice output `(APIs Document) <http://www.qpython.org/en/guide_androidhelpers.html#texttospeechfacade>`_
-* Bluetooth: Control Bluetooth devices on your phone `(APIs Document) <http://www.qpython.org/en/guide_androidhelpers.html#bluetoothfacade>`_
-* Signal strength: access phone signal strength information `(APIs Document) <http://www.qpython.org/en/guide_androidhelpers.html#signalstrengthfacade>`_
-* Webcam: A camera that controls the phone `(APIs Document) <http://www.qpython.org/en/guide_androidhelpers.html#webcamfacade>`_
-* User interface: UI interface is used to control related exhibits, such as a text box, the password input box, the status bar, progress bar, date picker, etc. `(APIs Document) <http://www.qpython.org/en/guide_androidhelpers.html#uifacade>`_
-* NFC: NFC related control, mainly provided in the NFC mode master/slave exchange information `(APIs Document) <http://www.qpython.org/en/guide_androidhelpers.html#nfc>`_
-* USB Host Sequence: A device used to control USB-like sequences from Android that has a USB host controller `(APIs Document) <http://www.qpython.org/en/guide_androidhelpers.html#usb-host-serial-facade>`_
-
-
-**QPython relevant interface**
-
-* QPy interface: Run QPython script on your phone `(APIs Document) <http://www.qpython.org/en/guide_androidhelpers.html#qpyinterfacefacade>`_
-
-
-Sample Code
------------
-In Android, you can import the androidhelper module to load QSL4A support.Let's run a simple QSL4A script to experience the following:
-
-
-::
-
-    ^0$import androidhelper
-    ^0$droid = androidhelper.Android()
-    ^0$line = droid.dialogGetInput()
-    ^0$s = "Hello, %s" % (line.result,)
-    ^0$droid.makeToast(line)
-
-
-<button>Run ...</button>
diff --git a/course/source/qtheme/__init__.py b/course/source/qtheme/__init__.py
deleted file mode 100644
index 1928849..0000000
--- a/course/source/qtheme/__init__.py
+++ /dev/null
@@ -1,7 +0,0 @@
-#/usr/bin/env python
-# -*- coding: utf-8 -*-
-
-import os.path
-
-pkg_dir = os.path.abspath(os.path.dirname(__file__))
-theme_path = os.path.split(pkg_dir)[0]
diff --git a/course/source/qtheme/__init__.pyc b/course/source/qtheme/__init__.pyc
deleted file mode 100644
index cacbe31..0000000
Binary files a/course/source/qtheme/__init__.pyc and /dev/null differ
diff --git a/course/source/qtheme/layout.html b/course/source/qtheme/layout.html
deleted file mode 100644
index 3e24154..0000000
--- a/course/source/qtheme/layout.html
+++ /dev/null
@@ -1,281 +0,0 @@
-{% set html_tag = '<html lang="en-US">' %}
-
-{% extends "basic/layout.html" %}
-
-{%- block doctype -%}
-<!DOCTYPE html>
-{%- endblock -%}
-
-{%- block htmltitle %}
-    <link href="https://cdn.bootcss.com/bootstrap/3.3.7/css/bootstrap.min.css" rel="stylesheet">
-    <title>{{ title|striptags|e }}</title>
-{%- endblock %}
-
-{%- block extrahead %}
-    <meta charset="UTF-8">
-    <meta name="viewport" content="width=device-width, initial-scale=1,maximum-scale=1, user-scalable=no">
-    <script src="https://cdn.bootcss.com/bootstrap/3.3.7/js/bootstrap.min.js"></script>
-{% endblock %}
-
-{% block content %}
-            <div class="header doc-header web-show">
-                <div class="col-sm-offset-2 col-sm-8  index-header">
-                    <div class="navbar-header">
-                        <button type="button" class="navbar-toggle" data-toggle="collapse" data-target="#example-navbar-collapse">
-                            <span class="sr-only">切换导航</span>
-                            <span class="icon-bar"></span>
-                            <span class="icon-bar"></span>
-                            <span class="icon-bar"></span>
-                        </button>
-                        <a href="/index.html" class="logo"><img src="http://www.qpython.org/static/img_logo.png"></a>
-                    </div>
-                    <ul class="header-title collapse navbar-collapse" id="example-navbar-collapse" >
-                        <li>
-                            <a href="http://www.qpython.org/index.html">Home</a>
-                        </li>
-                        <li>
-                            <a href="http://www.qpython.org/document.html">Guide</a>
-                        </li>
-                        <li class="header-phone-selected">
-                            <a href="/index.html" class="header-selected">Course</a>
-                            <span class="header-title-selected"></span>
-                        </li>
-                        <li>
-                            <a href="http://www.aipy.org">AIPY</a>
-                        </li>
-                        <li>
-                            <a target="_blank" href="https://github.com/qpython-android/qpython">Github</a>
-                        </li>
-                    </ul>
-                    <div class="search-box hidden-xs">
-                        <form action="/search.html">
-                            <input type="search" name="q" placeholder="keyword">
-                            <input type="hidden" name="check_keywords" value="yes" />
-                            <input type="hidden" name="area" value="default" />
-                            <button><img src="http://www.qpython.org/static/ic_search.png"></button>
-                        </form>
-                    </div>
-                </div>
-            </div>
-            <main class="contain web-content">
-                {{ super() }}
-            </main>
-{% endblock %}
-
-{# add only basic navigation links #}
-{%- block scripts %}{%- endblock %}
-{% block sidebar1 %}{% endblock %}
-{% block sidebar2 %}{% endblock %}
-{% block relbar1 %}{% endblock %}
-{% block relbar2 %}{% endblock %}
-{% block linktags %}{% endblock %}
-{% block footer %}
-    <div class="star-footer">
-        <img onclick="openPurchase()" src="{{ pathto('_static/star.jpg', 1) }}">
-        <div id="praise"><span>0</span> persons have rewarded</div>
-    </div>
-    <div class="foo"></div>
-    <footer class="clearfix fixfooter web-show">
-        <div class="col-sm-offset-2 col-sm-6 footer-div1">
-            <div class="footer-block-item">
-                <span class="footer-item1">Built with Sphinx using a theme provid by QPython.</span>
-                <span class="footer-item2 visible-xs">Copyight 2017, QPython.</span>
-            </div>
-        </div>
-        <div class="col-sm-2 footer-div2">
-            <a href="/index.html" class="pull-right"><img src="http://www.qpython.org/static/img_logo.png"></a>
-        </div>
-    </footer> 
-    <div id="purchase-box">
-        <div class="zh-img purchase-img"></div>
-        <div class="en-img purchase-img">
-            <form action="https://www.paypal.com/cgi-bin/webscr" method="post" target="_top">
-                <input type="hidden" name="cmd" value="_s-xclick">
-                <input type="hidden" name="hosted_button_id" value="ST6KWAK8R63JW">
-                <div class="en-thanks-tit">
-                    <input type="hidden" name="on0" value="Thanks">
-                    Thanks
-                </div>
-                <div class="en-thanks-sel">
-                    <select name="os0">
-                    <option value="Reward this course">Reward this course $ 0.99 USD</option>
-                    <option value="Reward this course">Reward this course $ 1.49 USD</option>
-                    <option value="Reward this course">Reward this course $ 1.99 USD</option>
-                    <option value="Reward this course">Reward this course $ 2.99 USD</option>
-                    <option value="Reward this course">Reward this course $ 3.49 USD</option>
-                    </select>
-                </div>
-                <input type="hidden" name="currency_code" value="USD">
-                <input type="image" class="pay_img" src="https://www.paypalobjects.com/en_GB/i/btn/btn_paynow_LG.gif" border="0" name="submit" alt="PayPal – The safer, easier way to pay online!">
-                <img alt="" border="0" src="https://www.paypalobjects.com/zh_XC/i/scr/pixel.gif" width="1" height="1">
-            </form>
-        </div>
-    </div>
-<script>
-    var milib;
-    if (milib==undefined) {
-        var milib = function(){
-        };
-        milib.qeditor = function(str){
-            alert('Please share this page to QPython then run it')
-        };
-        milib.getPurchaseNumber = function(str){
-            //alert('getPurchaseNumber:'+str)
-            return "10+";
-        };
-        milib.a8playVideoFromGW= function(url){
-            window.open(url)
-        };
-        milib.qpylibinstall = function(cat,url,target){
-            window.open(url)
-        };
-        milib.gotoIfPay = function(url, packageId, packageUrl){
-            window.open(url)
-        };
-    } else {
-    }
-
-    function openPurchase() {
-        //获取标识
-        var u = window.location.href.split('/')
-        var s = u[u.length-1].replace('.html', '')
-        s = u[u.length-2]+'-'+s
-        if (milib.openPurchaseActivity){
-            milib.openPurchaseActivity(s)
-        }else {
-            if (navigator.language== "zh-CN" || navigator.language== "zh"){
-                $('.zh-img ').show();
-                $('.en-img').hide();
-            }else {
-                $('.zh-img ').hide();
-                $('.en-img').show();
-            }
-            $('#purchase-box').show();
-        }
-    }
-    $(function() {
-        $('#purchase-box').click(function() {
-            $(this).hide();
-        })
-        $('.purchase-img').click(function(e) {
-            e.stopPropagation();
-        })
-    })
-
-    //按钮事件
-    function btnEvent(){
-        var seq = this.getAttribute('target')-2
-        var a = this.parentElement.getElementsByTagName('pre')
-        if ( a && a.length > 0){
-            /**********有代码，调用控制台*************/
-            var code = a[seq].lastChild.nodeValue
-            milib.qeditor(code)
-        }else{
-            //没有代码，调转
-            var url = this.getAttribute('data-url');
-            window.open(url);
-        }
-    }
-    //获取赞赏
-    function setPraise(){
-        var u = window.location.href.split('/')
-        var s = u[u.length-1].replace('.html', '')
-        s = u[u.length-2]+'-'+s
-
-        var i = document.getElementById('praise').getElementsByTagName('span')[0]
-        var num = milib.getPurchaseNumber(s);
-        i.innerHTML = num
-    }
-    //初始化代码显示
-    function initCode(){
-        var prearr = document.getElementsByTagName('pre');
-        for (var i = 0; i < prearr.length; i++) {
-            var temp_data = prearr[i].lastChild.nodeValue
-            //正则处理 lavel5
-            temp_data = temp_data.replace(/\^2\$/g, spaceString(2));
-            temp_data = temp_data.replace(/\^4\$/g, spaceString(4));
-            temp_data = temp_data.replace(/\^6\$/g, spaceString(6));
-            temp_data = temp_data.replace(/\^8\$/g, spaceString(8));
-            temp_data = temp_data.replace(/\^10\$/g, spaceString(10));
-            temp_data = temp_data.replace(/\^12\$/g, spaceString(12));
-            temp_data = temp_data.replace(/\^16\$/g, spaceString(16));
-            temp_data = temp_data.replace(/\^20\$/g, spaceString(20));
-            //set
-            prearr[i].lastChild.nodeValue = temp_data;
-        }
-    }
-    //产生空格
-    function spaceString(num){
-        var v = ''
-        for (var i = 0; i < num; i++) {
-            v += ' ';
-        }
-        return v;
-    }
-function setSearchHistory (t) {
-    if (!t)
-        return;
-  if (localStorage.course_search_list) {
-      var list = localStorage.course_search_list.split('%,%');
-      var i = list.indexOf(t);
-      if (i !== -1) {
-        list.splice(i, 1);
-        list.push(t);
-        localStorage.course_search_list = list.join('%,%');
-      }else {
-        localStorage.course_search_list += '%,%'+t;
-      }
-    }else {
-      localStorage.course_search_list = t;
-    }
-}
-
-    window.onload = function(){
-        setPraise();
-        initCode();
-        //将所有的button绑定事件
-        var b = document.getElementsByTagName('button');
-        for (var i = 0; i < b.length; i++) {
-            if (b[i].className=='button') {
-                 b[i].setAttribute('target', i)
-                b[i].onclick = btnEvent ;
-            }
-
-        }
-    }
-    $(function(){
-        var url = location.href.split('?');
-        var arg = url[1];
-        if (!arg){
-            return;
-        }
-        var args = arg.split('&');
-        for (var i = 0; i < args.length; i++) {
-            var c = args[i].split('=');
-            if (c[0]=='form' && c[1] == 'web') {
-                $('.web-show').css('display','block');
-                $('.web-content').addClass('margin-header');
-            }
-        }
-})
-$(function() {
-    $('form').submit(function() {
-    setSearchHistory($("input[name='q']").val());
-    })
-    $('.bodywrapper .body form input').val("");
-    $($('.bodywrapper .body form input')[0]).attr('placeholder', 'search');
-})
-
-</script>
-<script>
-  (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
-  (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
-  m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
-  })(window,document,'script','https://www.google-analytics.com/analytics.js','ga');
-
-  ga('create', 'UA-103260045-3', 'auto');
-  ga('send', 'pageview');
-
-</script>
-{% endblock %}
-
diff --git a/course/source/qtheme/static/course_ic_like@2x.png b/course/source/qtheme/static/course_ic_like@2x.png
deleted file mode 100755
index 073825e..0000000
Binary files a/course/source/qtheme/static/course_ic_like@2x.png and /dev/null differ
diff --git a/course/source/qtheme/static/course_ic_play@2x.png b/course/source/qtheme/static/course_ic_play@2x.png
deleted file mode 100755
index 692ea88..0000000
Binary files a/course/source/qtheme/static/course_ic_play@2x.png and /dev/null differ
diff --git a/course/source/qtheme/static/course_web.css b/course/source/qtheme/static/course_web.css
deleted file mode 100755
index f88af58..0000000
--- a/course/source/qtheme/static/course_web.css
+++ /dev/null
@@ -1,390 +0,0 @@
-.sc-course-item a {
-  display: block;
-  box-sizing: border-box;
-  padding: 10px 30px 10px 15px;
-  color: inherit;
-  font-size: 16px;
-  border-bottom: 1px solid #555;
-  position: relative;
-}
-.sc-course-item a::after {
-  content: "";
-  display: inline-block;
-  height: 6px;
-  width: 6px;
-  border-width: 2px 2px 0 0;
-  border-color: #C8C8CD;
-  border-style: solid;
-  -webkit-transform: matrix(0.71, 0.71, -0.71, 0.71, 0, 0);
-  transform: matrix(0.71, 0.71, -0.71, 0.71, 0, 0);
-  top: -2px;
-  position: absolute;
-  top: 50%;
-  margin-top: -4px;
-  right: 25px;
-}
-input{outline:none} 
-.list-enter-active, .list-leave-active {
-  transition: all .6s;
-}
-.list-enter .list-leave-to {
-  opacity: 0;
-  transform: translateY(-20px);
-}
-.list-enter, .list-leave-to
-  /* .list-leave-active for below version 2.1.8 */ {
-  opacity: 0;
-  transform: translateY(-20px);
-}
-.sc-doc-con {
-  padding: 0;
-}
-.sc-img,
-.sc-i-content {
-  cursor: pointer;
-}
-.sc-item a {
-  color: inherit;
-  text-decoration: none;
-}
-.sc-click-num {
-  position: relative;
-}
-.sc-click-num::after {
-  content: '';
-  position: absolute;
-  height: 15px;
-  width: 15px;
-  left: -15px;
-  background-size: 100%;
-  background-image: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAACgAAAAoCAMAAAC7IEhfAAAAS1BMVEUAAACbm5ucnJydnZ2enp6goKCcnJycnJycnJyioqKcnJybm5ubm5ubm5ucnJybm5ubm5ucnJycnJycnJycnJyfn5+hoaGqqqqbm5sNywpeAAAAGHRSTlMA98WGQhaXW0ghta3w5uLXzJ+LZ00tJgZFoc5uAAAAsklEQVQ4y+2Tyw6EIAxFQREEfD/5/y8dmbRCHB3ShQsT74qU096WB3v1DBVKdg3nTSdV8QcTlrtd3IoLTFt3kNVnnMJqrTEtVlW/XA5bUn+rS0jLL7hK7P1Wp+QIXqWffJr8xCWExphbwKhnbB38YlgZ66GZJQIzyJ59D+g4QzBDKrjUm68Dbe419hMKhlyBoIjCVDBtTR4mfTz0A09fYfpR0J9ZkDbuIKNJXyH9uV7doA/HHSMTrbNpqAAAAABJRU5ErkJggg==);
-}
-.sc-img {
-  height: 120px;
-  box-sizing: border-box;
-  border-bottom: 1px solid #eee;
-  position: relative;
-}
-.sc-img img {
-  height: 100%;
-  width: 100%;
-}
-.sc-lv {
-  position: absolute;
-  z-index: 2;
-  width: 30px;
-  height: 16px;
-  line-height: 16px;
-  color: #fff;
-  font-size: 10px;
-  background-color: #ECCD00;
-  text-align: center;
-  bottom: 0;
-  right: 0;
-  border-radius: 1px;
-}
-.sc-page {
-  display: flex;
-  justify-content: center;
-}
-.sc-page li {
-  display: inline-block;
-  padding: 5px 12px;
-  margin-right: 10px;
-  border-radius: 3px;
-  cursor: pointer;
-}
-.sc-page li.active {
-  background-color: #4BAC07;
-  color: #fff;
-}
-
-@media screen and (min-width: 768px){
-    .head-more {
-      display: block;
-      padding-top: 80px;
-    }
-  .head-more div {
-    display: block;
-  }
-    .head-title-tit {
-      font-size: 16px;
-      color: #FFFFFF;
-      line-height: 16px;
-      text-align: center;
-      margin: 40px 0 50px;
-    }
-    .index-search-box {
-      text-align: center;
-    }
-    .index-search-box input {
-      border: 0;
-      border-bottom: 1px solid #FFFFFF;
-      background-color: transparent;
-      font-size: 16px;
-      line-height: 26px;
-      color: #fff;
-      width: 300px;
-      text-align: center;
-      box-sizing: inherit;
-    }
-    .index-search-box input:active, .index-search-box input:focus {
-      border: none;
-      border-bottom: 2px solid #ECCD00;
-    }
-    .index-search-box input {
-      padding: 5px 0;
-    }
-    .index-search-box form {
-      position: relative;
-      box-sizing: border-box;
-      height: 38px;
-    }
-    .search-history {
-      position: absolute;
-      z-index: 999;
-      width: 300px;
-      background-color: #FFF;
-      border: 1px solid #DDD;
-    }
-    .s-headline {
-      display: flex !important;
-      justify-content: space-between;
-      font-size: 14px;
-      color: #9B9B9B;
-      padding: 5px 10px;
-    }
-    #clear_history {
-      color: #C64857;
-      text-decoration: underline;
-      cursor: pointer;
-    }
-    .history-list {
-      margin-bottom: 0;
-    }
-    .history-list li {
-      text-align: left;
-      padding: 2px 10px;
-      font-size: 18px;
-      color: #4A4A4A;
-      cursor: pointer;
-    }
-    .history-list li:hover {
-      background-color: #EEE;
-    }
-    .search-history li , .search-history ul,  .search-history div {
-      display: block;
-    } 
-    .index-search-box button {
-      position: absolute;
-      right: 0;
-      bottom: 5px;
-      background-color: transparent;
-      border: none;
-    }
-    .head-more .hot-search {
-      color: #FFF;
-      width:360px;
-      margin: 8px auto;
-    }
-    .head-more .hot-search > * {
-      display: inline-block;
-    }
-    .hot-search  a{
-      color: #ECCD00;
-      margin-right: 10px;
-    }
-    .hot-search {
-      line-height: 30px;
-    }
-    .hot-name {
-      font-size: 14px;
-      color: #aaa;
-    }
-    .head-more-other {
-      max-width: 800px;
-      padding: 0 20px;
-      margin: 30px auto;
-      text-align: center;
-      font-size: 13px;
-      color: #9B9B9B;
-    }
-
-
-      #example-navbar-collapse {
-        overflow-y: auto !important;
-        overflow-x: hidden !important;
-    }
-  header {
-    height: 320px;
-    background-color: #000;
-    background: url('http://www.qpython.org/static/img_background.png') no-repeat;
-    background-size: cover;
-  }
-  .sc-tag {
-    display: flex;
-    justify-content: center;
-    margin-bottom: 25px;
-  }
-  .sc-re,
-  .sc-la {
-    padding: 5px 20px;
-    cursor: pointer;
-    display: inline-block;
-    font-size: 22px;
-    color: #9B9B9B;
-  }
-  .sc-tag .active {
-    color: #4A4A4A;
-    position: relative;
-  }
-  .sc-tag .active::after {
-    content: '';
-    position: absolute;
-    height: 2px;
-    background-color: #ECCD00;
-    width: 40%;
-    bottom: 0;
-    left: 30%;
-  }
-  .sc-con {
-    width: 1000px;
-    margin: 0 auto;
-  }
-  .sc-h-logo {
-    margin: 25px 0;
-  }
-  .sc-content {
-    margin-right: -20px;
-  }
-  .sc-item {
-    width: 320px;
-    float: left;
-    min-height: 200px;
-    margin-right: 20px;
-    margin-bottom: 30px;
-    border: 1px solid #EBEBEB;
-    position: relative;
-  }
-  .sc-i-content {
-    box-sizing: border-box;
-    font-size: 14px;
-    padding: 5px 15px 0 15px;
-  }
-  .sc-info {
-    position: absolute;
-    right: 15px;
-    bottom: 0;
-    left: 15px;
-    height: 35px;
-    z-index: 2;
-    font-size: 12px;
-    color: #9B9B9B;
-  }
-  .sc-info-box {
-    background-color: #fff;
-    height: 100%;
-    width: 100%;
-    display: flex;
-    justify-content: space-between;
-    align-items: center;
-  }
-  .foo {
-    height: 80px;
-  }
-  .fixfooter {
-    position: fixed;
-    right: 0;
-    bottom: 0;
-    left: 0;
-    height: 80px;
-    z-index: 4;
-  }
-  .sc-course-item a {
-    font-size: 14px;
-    border-bottom: 1px solid #C8C8CD;
-  }
-  .sc-course-item a:first-child {
-    margin-top: 5px;
-    border-top: 1px dotted #C8C8CD;
-  }
-  .sc-course-item a:last-child {
-    margin-bottom: 35px;
-  }
-}
-@media screen and (max-width: 767px) {
-  #app {
-    background-color: #363636;
-  }
-  .desc-title {
-    position: fixed;
-    white-space: nowrap;
-    z-index: 5;
-    height: 50px;
-    color: #fff;
-    display: flex;
-    align-items: center;
-    font-size: 16px;
-  }
-  .desc-back {
-    padding: 10px 25px 10px 15px;
-  }
-  .carousel-inner img {
-    width: 100%;
-  }
-  .sc-tag {
-    height: 54px;
-    display: flex;
-    align-items: center;
-    color: #fff;
-    line-height: 54px;
-  }
-  .sc-re,
-  .sc-la {
-    width: 50%;
-    height: 100%;
-    text-align: center;
-    position: relative;
-  }
-  .sc-tag .active {
-    color: #49B300;
-  }
-  .sc-tag .active::after {
-    content: '';
-    position: absolute;
-    height: 2px;
-    background-color: #49B300;
-    right: 0;
-    bottom: 0;
-    left: 0;
-  }
-  .sc-i-content {
-    padding: 10px 15px;
-    font-size: 14px;
-    color: #FFFFFF;
-  }
-  .sc-info-box {
-    padding: 0 15px 10px;
-    display: flex;
-    justify-content: space-between;
-    align-items: center;
-    font-size: 12px;
-    color: #9B9B9B;
-  }
-  .sc-doc-con {
-    background-color: #363636;
-  }
-  .sc-info {
-    box-sizing: border-box;
-    border-bottom: 5px solid #4A4A4A;
-  }
-  .sc-page {
-    color: #fff;
-  }
-  .header-title {
-    margin-bottom: 0;
-  }
-  .sc-course-item {
-    color: #fff;
-  }
-}
diff --git a/course/source/qtheme/static/course_web.js b/course/source/qtheme/static/course_web.js
deleted file mode 100755
index 6a8dcf2..0000000
--- a/course/source/qtheme/static/course_web.js
+++ /dev/null
@@ -1,147 +0,0 @@
-var STATUS = false;
-
-var vm = new Vue({
-  el: '#app',
-  data: {
-    content: {
-      'new': {},
-      'featured': {}
-    },
-    tag: 'new',
-    c_page: {
-      new: 1,
-      featured: 1
-    },
-    page_size:12,
-    new_page: false,
-    new_page_data: {},
-    is_zh: navigator.language == 'zh-CN'  ? true : navigator.language == 'zh' ? true : false
-  },
-  created: function () {
-    var that = this;
-    var _url = this.is_zh === true ? '/index/zh.json' :  '/index/default.json';
-    $.get(_url, function(data){
-      var _new = [];
-        var _featured = [];
-        $.each(data, function(k, v){
-          v.is_open = false;
-          if (v.cat == 'new') {
-            _new.push(v);
-          }else if (v.cat == 'featured') {
-            _featured.push(v);
-          }
-        })
-        that.content = {
-          new: _new,
-          featured: _featured
-        };
-    })
-  },
-  methods: {
-    toggle_tag: function(tag) {
-      this.tag = tag;
-    },
-    change_page: function(p) {
-      if (this.tag == 'featured') {
-        this.c_page.featured = p;
-      }else if (this.tag == 'new') {
-        this.c_page.new = p;
-      }
-    },
-    is_has_next: function() {
-      return this.content[this.tag] ? this.content[this.tag].length/this.page_size > this.c_page[this.tag] : false;
-    },
-    if_has_many_page:  function() {
-      return this.content[this.tag].length/this.page_size >1 ? true : false;
-    },
-    course_desc: function(data) {
-      this.new_page_data = data;
-      this.new_page = true;
-      window.STATUS = true;
-    },
-    go_back: function () {
-      this.new_page = false;
-      window.STATUS = false;
-    }
-  }
-})
-
-window.onload = function (){
-  var h = $(window).height();
-  $('#app').css("minHeight", h+'px');
-}
-window.onresize = function (){
-  var h = $(window).height();
-  $('#app').css("minHeight", h+'px');
-}
-
-history.pushState(null, null, document.URL);
-window.addEventListener('popstate', function () {
-  if (window.STATUS){
-    history.pushState(null, null, document.URL);
-    vm.go_back();
-  }else {
-    history.go(-1);
-  }
-});
-
-function setSearchHistory (t) {
-  if (!t)
-        return;
-  if (localStorage.course_search_list) {
-      var list = localStorage.course_search_list.split('%,%');
-      var i = list.indexOf(t);
-      if (i !== -1) {
-        list.splice(i, 1);
-        list.push(t);
-        localStorage.course_search_list = list.join('%,%');
-      }else {
-        localStorage.course_search_list += '%,%'+t;
-      }
-    }else {
-      localStorage.course_search_list = t;
-    }
-}
-
-$(function(){
-  $('.hot-search a').bind('click', function(){
-    var t = $(this).text();
-    $('#index_search').val(t);
-    $('#index_search').parent().submit();
-  })
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-  $('#index_search').bind('focus', function() {
-    var lists = localStorage.course_search_list;
-    if (lists) {
-      var list = lists.split('%,%');
-      var content = '';
-      list.reverse();
-      for (var i=0; i<list.length && i<5; i++) {
-        if (list[i]) {
-          content += `<li>${list[i]}</li>`;
-        }
-        $('.history-list').html(content);
-        $('.history-list li').click(function() {
-          var val = $(this).text();
-          $('#index_search').val(val);
-          $('#index_search').parent().submit();
-        })
-        $('.search-history').show();
-      }
-    }
-  })
-  $('#index_search').bind('blur', function() {
-    setTimeout(function (){
-      $('.search-history').hide();
-    }, 200)
-  })
-  $('.index-search-box form').submit(function(){
-    setSearchHistory($('#index_search').val());
-  })
-
-  $('#clear_history').click(function() {
-    localStorage.clear();
-    $('.history-list').html('');
-    $('.search-history').hide();
-  })
-})
diff --git a/course/source/qtheme/static/purchase-zh.jpg b/course/source/qtheme/static/purchase-zh.jpg
deleted file mode 100755
index aa6d88e..0000000
Binary files a/course/source/qtheme/static/purchase-zh.jpg and /dev/null differ
diff --git a/course/source/qtheme/static/style.css b/course/source/qtheme/static/style.css
deleted file mode 100644
index b6cad98..0000000
--- a/course/source/qtheme/static/style.css
+++ /dev/null
@@ -1,569 +0,0 @@
-* {
-    margin: 0;
-    padding: 0;
-}
-a {
-    text-decoration: none;
-}
-body {
-    font-family: arial,"Hiragino Sans GB","Microsoft Yahei","微软雅黑",Tahoma,Helvetica;
-    color: #3d3d3d;
-    letter-spacing: .1px;
-}
-ol {padding-left:10px}
-ul {list-style:none;padding-left:10px;}
-h1 {
-    font-size: 20px;
-}
-h2 {
-    font-size: 28px;
-}
-h3 {
-    font-size: 18px;
-}
-input{outline:none} 
-#purchase-box {
-    display: none;
-    position: fixed;
-    top: 0;
-    right: 0;
-    bottom: 0;
-    left: 0;
-    background-color: #C5D3D6;
-    filter: alpha(opacity=60);
-    background-color: rgb(80, 149, 174);
-    background-color: rgba(80, 149, 174, 0.6);
-}
-.zh-img, 
-.en-img {
-    width: 240px;
-    height: 240px;
-    margin: 120px auto;
-    border-radius: 5px;
-    box-shadow: 0 0 9px #aaa;
-    background-size: 100%;
-    z-index: 1000;
-}
-.zh-img {
-    background-image: url(purchase-zh.jpg);
-}
-.en-img {
-    background-color: #FFF;
-    box-sizing: border-box;
-    padding: 30px 0;
-}
-.en-thanks-tit {
-    text-align: center;
-    font-size: 25px;
-    margin-bottom: 25px;
-}
-.en-thanks-sel select{
-    width: 90%;
-    margin-left: 5%;
-}
-.pay_img {
-    display: block;
-    margin: 0 auto;
-    margin-top: 30px;    
-}
-#purchase-box::after {
-    content: "x";
-    display: inline-block;
-    border-radius: 50%;
-    text-align: center;
-    width: 24px;
-    height: 24px;
-    left: 50%;
-    margin-left: -12px;
-    cursor: pointer;
-    background-color: #ccc;
-    color: #FFF;
-    line-height: 24px;
-    position: fixed;
-    top: 370px;
-    z-index: 9999;
-}
-/************* common  ***************/
-/* same css is useless */
-@media screen and (min-width: 768px) {
-    #example-navbar-collapse {
-        overflow-y: auto !important;
-        overflow-x: hidden !important;
-    }
-    .foo {
-        height: 80px;
-    }
-    header {
-        height: 250px;
-        background-color: #000;
-        background: url('img_background.png') no-repeat;
-        background-size: cover;
-        font-family: Roboto-Thin;
-    }
-    header * {
-        display: inline-block;
-    }
-    .header {
-        background-color:  #000;
-        height: 80px;
-    }
-    .index-header {
-        height: 100%;
-        display: flex;
-        justify-content: space-between;
-        -moz-justify-content: space-between;
-        -webkit-justify-content: space-between;
-        align-items: center;
-    }
-    .logo {
-        margin-left: -25px;
-    }
-    .header-title {
-        text-align: center;
-    }
-    .header-title li{
-        display: inline;
-        font-family: Roboto-Thin;
-        font-size: 16px;
-        margin-right: 25px;
-        position: relative;
-    }
-    .header-title li:last-child {
-        margin-right: 0;
-    }
-    .header-title li a {
-        color: #8a8787;
-    }
-    .header-title li .header-selected {
-        color:  #fff;
-    }
-    .header-title .header-title-selected {
-        width: 6px;
-        height: 6px;
-        display: block;
-        background: #49B300;
-        position: absolute;
-        left: 50%;
-        bottom: -15px;
-        margin-left: -6px;
-        border-radius: 50%;
-    }
-    .search-box {
-        position: relative;
-        margin-right: 4%;
-    }
-    .search-box input {
-        border: 0;
-        border-bottom: 1px solid #FFFFFF;
-        background-color: transparent;
-        font-size: 14px;
-        color: #fff;
-        width: 120px;
-    }
-    .search-box button{
-        background-color: transparent;
-        border: 0;
-    }
-    .search-box button {
-        position: absolute;
-        top: -3px;
-        right: 2px;
-    }
-    .index-main-pic {
-        margin-top: -150px;
-    }
-    .index-main-pic {
-        margin-top: -150px;
-    }
-    .clear-margin {
-        margin-left: 10px;
-        margin-right: 10px;
-    }
-    .index-content {
-        margin-top: 50px;
-    }
-    .index-content h2 {
-        font-size: 36px;
-        color: #4A4A4A;
-        letter-spacing: 0.07px;
-        margin-bottom: 30px;
-    }
-    .index-tag {
-        display: inline-block;
-        background: #FFDE00;
-        width: 30px;
-        height: 30px;
-        margin-right: 10px;
-        margin-top: 5px;
-    }
-    .index-content-text {
-        font-size: 20px;
-        color: #000000;
-        letter-spacing: 0.01px;
-        line-height: 30px;
-        margin-bottom: 40px;
-    }
-    .index-more-item {
-        background: #FFFEFE;
-        border-radius: 3px;
-        padding: 10px 20px;
-    }
-    .index-more .index-more-li {
-        padding-left: 10px;
-        padding-right: 10px;
-    }
-    .index-more-item {
-        min-height: 350px;
-    }
-    .index-faq {
-        margin-top: 60px;
-        padding-bottom: 40px;
-    }
-    .index-faq-li {
-        padding-left: 0;
-        padding-right: 0;
-    }
-    .index-faq-li > img {
-        width: 30px;
-    }
-    .index-link-logo {
-        margin-right: 35px;
-        margin-top: 20px;
-    }
-    .span-faq {
-        font-size: 25.65px;
-        color: #000000;
-        letter-spacing: 0.55px;
-        vertical-align: middle;
-    }
-    .index-shop-img {
-        height: 40px;
-        margin-bottom: 10px;
-        border-radius: 5px;
-        border:1px solid #ccc;
-    }
-    footer {
-        min-height: 80px;
-        background-color: #2F2F2F;
-        font-size: 18px;
-        color: #FFFFFF;
-        letter-spacing: 0.11px;
-    }
-    .footer-block-item {
-        color: #fff;
-        margin-top: 25px;
-        font-family: Roboto-Thin;
-    }
-    .fixfooter {
-        position: fixed;
-        right: 0;
-        bottom: 0;
-        left: 0;
-        height: 80px;
-        z-index: 4;
-    }
-    .doc-header {
-        position: fixed;
-        top: 0;
-        height: 80px;
-        width: 100%;
-        z-index: 10;
-    }
-    .margin-header {
-        margin-top: 100px !important;
-    }
-}
-
-@media screen and (min-width: 992px) {
-    header {
-        height: 350px;
-    }
-    .index-main-pic {
-        margin-top: -220px;
-    }
-    .index-more-item {
-        min-height: 320px;
-    }
-}
-
-@media screen and (min-width: 1200px) {
-    header {
-        height: 482px;
-    }
-    .index-main-pic {
-        margin-top: -300px;
-    }
-    .index-more-item {
-        min-height: 270px;
-    }
-}
-
-@media screen and (max-width: 767px) {
-    .doc-header {
-        background-color: #000;
-    }
-    .index-header {
-        padding: 0;
-    }
-    .logo img {
-        height: 50px;
-        margin: 10px 18px;
-    }
-    .header-title li {
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-        line-height: 35px;
-    }
-    .index-header .navbar-header {
-        border-bottom: 1px solid #555;
-    }
-    .header-title li a {
-        color: #fff;
-    }
-    .navbar-header button {
-        margin-top: 20px;
-    }
-    .navbar-toggle .icon-bar {
-        background-color: #fff;
-    }
-    .header-phone-selected {
-        background-color: #555;
-    }
-    .index-main-pic img {
-        width: 100%;
-    }
-    .index-content h2{
-        font-family: Roboto-Condensed;
-        font-size: 15.75px;
-        color: #000000;
-        letter-spacing: 0.03px;
-        font-weight: 600;
-    }
-    .index-content > div {
-        margin: 35px;
-        margin-bottom: 20px;
-        text-align: center;
-    }
-    .index-content-text {
-        font-family: Roboto-Light;
-        font-size: 10px;
-        color: #000000;
-        letter-spacing: 0;
-        line-height: 20px;
-        margin-top: 10px;
-    }
-    .index-shop-img {
-        height: 40px;
-        margin-bottom: 15px;
-    }
-    .index-shop-logo-ul {
-        margin-top: 30px;
-        margin-bottom:20px;
-    }
-    .index-more-li {
-        margin: 0 30px 30px 30px;
-    }
-    .index-faq-li {
-        margin: 0 30px 30px 30px;
-    }
-    .span-faq {
-        font-family: Roboto-Condensed;
-        font-size: 22px;
-        color: #000000;
-        letter-spacing: 0.47px;
-        vertical-align: middle;
-    }
-    .index-faq-li > img {
-        width: 22px;
-    }
-
-    footer {
-        padding: 25px;
-        background-color: #000;
-        color: #fff;
-        position: relative;
-    }
-    .footer-item1 {
-        font-family: Roboto-Light;
-        font-size: 11px;
-        letter-spacing: 0.07px;
-        line-height: 18px;
-        width: 70%;
-    }
-    .footer-item2 {
-        font-family: .PingFangSC-Regular;
-        font-size: 11px;
-        letter-spacing: 0.46px;
-        line-height: 30px;
-    }
-    .footer-div1 {
-        width: 70%;
-    }
-    .footer-div2 {
-        position: absolute !important;
-        bottom: 30px;
-        right: 30px;
-    }
-    .footer-div2 img {
-        width: 57px;
-    }
-}
-/*********end**********/
-
-.contain {
-    max-width: 738px;
-    margin: 0 auto;
-    padding-left: 15px;
-    padding-right: 15px;
-}
-div.highlight {
-    /*padding: 10px 20px;*/
-    background-color: #f4f4f4;
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-    margin-bottom: 25px;
-}
-.highlight span {
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-.headerlink {
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-img {
-    max-width: 100%;
-}
-pre {
-    overflow-x: auto;
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-    padding-bottom: 5px;
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-}
-.document {
-    margin-bottom: 40px;
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-h1 {
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-    font-size: 20px;
-    font-weight: 600;
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-h2 {
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-    font-size: 18px;
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-}
-h3,
-h4,
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-    font-weight: 600;
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-p {
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-.highlight-default .highlight .c1 {
-    font-style: normal;
-}
-.image-reference {
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-    max-width: 100%;
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-}
-.image-reference img {
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-.video {
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-.video::after {
-    content: '';
-    margin: auto;
-    width: 40px;
-    height: 40px;
-    background: url(course_ic_play@2x.png);
-    background-size: 100%;
-    position: absolute;
-    z-index: 999;
-    top: 0;
-    left: 0;
-    right: 0;
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-}
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-.button {
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-.star-footer {
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-}
-.star-footer img {
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-}
-#praise span {
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-}
-.web-show {
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-}
-.bodywrapper .body form {
-    position: relative;
-}
-.bodywrapper .body form input:first-child {
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-    border-bottom: 1px solid #333;
-    background-color: transparent;
-    font-size: 16px;
-    line-height: 26px;
-    width: 200px;
-    text-align: center;
-    padding-right: 25px;
-}
-.bodywrapper .body form input:first-child:hover {
-    border-bottom: 2px solid #333;
-}
-.bodywrapper .body form input:nth-child(2) {
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-    position: absolute;
-    width: 20px;
-    height: 20px;
-    left: 180px;
-    bottom: 3px;
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-    background-color: transparent;
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-.docutils th,
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-table.docutils {
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-    /*border-collapse: collapse;*/
-    width: 100%;
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\ No newline at end of file
diff --git a/course/source/qtheme/theme.conf b/course/source/qtheme/theme.conf
deleted file mode 100644
index 44f1a3c..0000000
--- a/course/source/qtheme/theme.conf
+++ /dev/null
@@ -1,4 +0,0 @@
-[theme]
-inherit = basic
-stylesheet = style.css
-pygments_style = none
diff --git a/course/source/sample.rst b/course/source/sample.rst
deleted file mode 100644
index 2f4ffb4..0000000
--- a/course/source/sample.rst
+++ /dev/null
@@ -1,78 +0,0 @@
-A hands-on introduction to Python for beginning programmers
-===========================================================
-
-If you are a new guy to QPython, you should watch the tutorial video and just follow the steps to learn how to run script with QPython
-
-<jumpbutton data-url="http\://www.baidu.com">Watch Tutorial</jumpbutton>
-
-Running with Console
------------------------------------
-
-Running QPython’s simple script with console model
-
-..  code-block :: none
-
-    qpy:2
-    #qpy:console
-    #qpy:http://qpython.com/s/qpy-sample.py
-    class Calculator(object):
-        ^4$#define class to simulate a simple calculator
-        ^4$def __init__ (self):
-            ^8$#start with zero
-            ^8$self.current = 0
-        ^4$def add(self, amount):
-            ^8$#add number to current
-            ^8$self.current += amount
-        ^4$def getCurrent(self):
-            ^8$return self.current
-    myBuddy = Calculator() # make myBuddy into a Calculator object
-    myBuddy.add(2) #use myBuddy's new add method derived from the Calculator class
-    print(myBuddy.getCurrent()) #print myBuddy's current instance variable
-
-<button>Run ...</button>
-
-Running with Kivy
----------------------------
-
-Running QPython’s script with GUI model ( Kivy library )
-
-..  code-block :: none
-
-    #qpy:2
-    #qpy:kivy
-    #qpy:http://qpython.com/s/qpy-guisample.py
-
-    from kivy.app import App
-    from kivy.uix.button import Button
-
-    class TestApp(App):
-        ^4$def build(self):
-            ^8$# display a button with the text : Hello QPython 
-            ^8$return Button(text='Hello QPython')
-
-    TestApp().run()
-
-<button>Run ...</button>
-
-Running as Web App
----------------------------------
-
-Running Web App with QPython
-
-..  code-block :: none
-
-    #qpy:2
-    #qpy:webapp:Hello Qpython
-    #qpy://localhost:8080/hello
-    """
-    This is a sample for qpython webapp
-    """
-    from bottle import route, run
-
-    @route('/hello')
-    def hello():
-        ^4$return "Hello World!"
-
-    run(host='localhost', port=8080)
-
-<button>Run ...</button>
diff --git a/course/source/unity3d/index.rst b/course/source/unity3d/index.rst
deleted file mode 100644
index 9bd34fb..0000000
--- a/course/source/unity3d/index.rst
+++ /dev/null
@@ -1,19 +0,0 @@
-Unity 3D with IronPython
-=======================
-The courses are still in the planning. Welcome to reward us. 
-
-1. **Primary course**
-
-If the total number of people who reward us is more than 100, we promise to complete the primary course within 1 week, so that, after finishing your study, you can master the basic concepts and write simple scripts.

-
-2. **Intermediate course**
-
-If the total number of people who reward us is more than 500, we will launch the intermediate course within 2 weeks, so that, after finishing your study, you will be familiar with the knowledge and framework of the course and apply it to practical development.
-
-3. **Advanced course**
-
-If the total number of people who reward us is more than 2,000, we will launch the advanced course in a month, so that, after finishing your study, you will have a better understanding of the principles of the framework and be able to do advanced development.
-
-
-**The primary course is free to all, and the intermediate and advanced courses are only open to those who have rewarded us.**
-
diff --git a/course/source/vr/index.rst b/course/source/vr/index.rst
deleted file mode 100644
index 56c1176..0000000
--- a/course/source/vr/index.rst
+++ /dev/null
@@ -1,19 +0,0 @@
-VR Programming with Python
-==============================
-The courses are still in the planning. Welcome to reward us. 
-
-1. **Primary course**
-
-If the total number of people who reward us is more than 100, we promise to complete the primary course within 1 week, so that, after finishing your study, you can master the basic concepts and write simple scripts.

-
-2. **Intermediate course**
-
-If the total number of people who reward us is more than 500, we will launch the intermediate course within 2 weeks, so that, after finishing your study, you will be familiar with the knowledge and framework of the course and apply it to practical development.
-
-3. **Advanced course**
-
-If the total number of people who reward us is more than 2,000, we will launch the advanced course in a month, so that, after finishing your study, you will have a better understanding of the principles of the framework and be able to do advanced development.
-
-
-**The primary course is free to all, and the intermediate and advanced courses are only open to those who have rewarded us.**
-
diff --git a/course/source/wego/index.rst b/course/source/wego/index.rst
deleted file mode 100644
index 80db98f..0000000
--- a/course/source/wego/index.rst
+++ /dev/null
@@ -1,19 +0,0 @@
-WegoX (Wechat openplatform SDK)
-==============================================
-The courses are still in the planning. Welcome to reward us. 
-
-1. **Primary course**
-
-If the total number of people who reward us is more than 100, we promise to complete the primary course within 1 week, so that, after finishing your study, you can master the basic concepts and write simple scripts.
-
-2. **Intermediate course**
-
-If the total number of people who reward us is more than 500, we will launch the intermediate course within 2 weeks, so that, after finishing your study, you will be familiar with the knowledge and framework of the course and apply it to practical development.
-
-3. **Advanced course**
-
-If the total number of people who reward us is more than 2,000, we will launch the advanced course in a month, so that, after finishing your study, you will have a better understanding of the principles of the framework and be able to do advanced development.
-
-
-**The primary course is free to all, and the intermediate and advanced courses are only open to those who have rewarded us.**
-
diff --git a/course/tasks.py b/course/tasks.py
deleted file mode 100644
index af75ca4..0000000
--- a/course/tasks.py
+++ /dev/null
@@ -1,97 +0,0 @@
-# -*- coding: utf-8 -*-
-"""invoke for replace Makfile to building edu.qpython.org
-"""
-__version__ = "EduBuilder v.220628.2142"
-__author__ = "Zoom.Quiet"
-__license__ = "MIT@2022"
-
-#from cProfile import run
-import os
-from pprint import pprint as pp
-
-from invoke import task
-
-SROOT = os.path.dirname(os.path.abspath(__file__))
-print(SROOT)
-
-
-
-@task
-def ver(c):
-    """echo self Version info."""
-    # print('\n\t powded by {}'.format(__version__))
-    print('''\n{}
-    CLI tools for build site
-    '''.format(">"*79))
-    print("\t powered by <- {} ".format(__version__))
-    print("\t build with ~> {}\n".format(__author__))
-    return None
-
-
-#   support stuff func.
-def cd(c, path2, echo=True):
-    os.chdir(path2)
-    if echo:
-        print('\n\t crt. PATH ===')
-        c.run('pwd')
-        print('='*42)
-        c.run('echo \n')
-
-@task
-def built(c):
-
-    c.run("rm -fr ../docs")
-    c.run("sphinx-build -b html -d build/doctrees source  build/html")
-
-    print("BUILT & export => build/html")
-    #ver(c)
-    return None
-
-@task
-def pub(c):
-    """$ inv pub ~ build all and publish through githuba-pages
-    """
-    built(c)
-    
-    with c.cd("build/html"):
-        c.run("find . -name '*.html' -exec python ../../analytics.py {} \;")
-        c.run("find . -name '*.html' -exec sed -i -e 's/_static/static/g;s/_images/images/g' {} \;")
-        c.run("python ../../replace.py")
-        c.run("find . -name '*-e' -exec rm {} \;")
-        #c.run("mv _static static")
-        #c.run("[ -d _images ] && mv _images  images")
-        #c.run('ls')
-        c.run('python ../../append_ann.py ./')
-        print("="*42, "pwd")
-        c.run('pwd')
-        print("="*42)
-
-    with c.cd(SROOT):
-        c.run('ls')
-        print("="*42, "pwd")
-        c.run('pwd')
-        print("="*42)
-        print("mv build/html  ../docs")
-        c.run("mv build/html  ../docs")
-        print("cp -r comments ../docs")
-        c.run("cp -r comments ../docs")
-        print("cp -r index ../docs")
-        c.run("cp -r index ../docs")
-        print("cp CNAME ../docs")
-        c.run("cp CNAME ../docs")
-        print("cp favicon.ico  ../docs")
-        c.run("cp favicon.ico  ../docs")
-        print("cp index.html ../docs")
-        c.run("cp index.html ../docs")
-
-        #c.run("ls ../docs")
-        print("mv ../docs/_sources ../docs/sources")
-        c.run("mv ../docs/_sources ../docs/sources")
-        print("mv ../docs/_static ../docs/static")
-        c.run("mv ../docs/_static ../docs/static")
-        print("mv ../docs/_images ../docs/images")
-        c.run("[ -d _images ] && mv ../docs/_images ../docs/images")
-
-    ver(c)
-    return None
-
diff --git a/docs/.buildinfo b/docs/.buildinfo
deleted file mode 100644
index 32a64b4..0000000
--- a/docs/.buildinfo
+++ /dev/null
@@ -1,4 +0,0 @@
-# Sphinx build info version 1
-# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: 752479c9512546629f9112836933c07d
-tags: 645f666f9bcd5a90fca523b33c5a78b7
diff --git a/docs/AIPY/index.html b/docs/AIPY/index.html
deleted file mode 100644
index 52fa47f..0000000
--- a/docs/AIPY/index.html
+++ /dev/null
@@ -1,344 +0,0 @@
-<!DOCTYPE html>
-<html lang="en-US">
-<head>
-<meta charset="utf-8" />
-<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
-<link href="https://cdn.bootcss.com/bootstrap/3.3.7/css/bootstrap.min.css" rel="stylesheet">
-<title>AIPY - Learn AI programming on Mobile</title>
-<link rel="stylesheet" type="text/css" href="../static/pygments.css" />
-<link rel="stylesheet" type="text/css" href="../static/style.css" />
-<meta charset="UTF-8">
-<meta name="viewport" content="width=device-width, initial-scale=1,maximum-scale=1, user-scalable=no">
-<script src="https://cdn.bootcss.com/bootstrap/3.3.7/js/bootstrap.min.js"></script>
-</head><body>
-<div class="header doc-header web-show">
-<div class="col-sm-offset-2 col-sm-8  index-header">
-<div class="navbar-header">
-<button type="button" class="navbar-toggle" data-toggle="collapse" data-target="#example-navbar-collapse">
-<span class="sr-only">切换导航</span>
-<span class="icon-bar"></span>
-<span class="icon-bar"></span>
-<span class="icon-bar"></span>
-</button>
-<a href="/index.html" class="logo"><img src="http://www.qpython.org/static/img_logo.png"></a>
-</div>
-<ul class="header-title collapse navbar-collapse" id="example-navbar-collapse" >
-<li>
-<a href="http://www.qpython.org/index.html">Home</a>
-</li>
-<li>
-<a href="http://www.qpython.org/document.html">Guide</a>
-</li>
-<li class="header-phone-selected">
-<a href="/index.html" class="header-selected">Course</a>
-<span class="header-title-selected"></span>
-</li>
-<li>
-<a href="http://www.aipy.org">AIPY</a>
-</li>
-<li>
-<a target="_blank" href="https://github.com/qpython-android/qpython">Github</a>
-</li>
-</ul>
-<div class="search-box hidden-xs">
-<form action="/search.html">
-<input type="search" name="q" placeholder="keyword">
-<input type="hidden" name="check_keywords" value="yes" />
-<input type="hidden" name="area" value="default" />
-<button><img src="http://www.qpython.org/static/ic_search.png"></button>
-</form>
-</div>
-</div>
-</div>
-<main class="contain web-content">
-<div class="document">
-<div class="documentwrapper">
-<div class="bodywrapper">
-<div class="body" role="main">
-<section id="aipy-learn-ai-programming-on-mobile">
-<h1>AIPY - Learn AI programming on Mobile<a class="headerlink" href="#aipy-learn-ai-programming-on-mobile" title="Permalink to this heading">¶</a></h1>
-<a class="reference external image-reference" href="http://www.aipy.org"><img alt="AIPY Logo" src="http://www.aipy.org/img/img_logo.png" /></a>
-<p>AIPY  is a high-level AI learning app, based on related libraries like Numpy, Scipy, theano, keras, etc…. It was developed with a focus on helping you learn and practise AI related programming well and fast.</p>
-<p>Now, AIPY is released as a QPython plugin, after being installed, you can see the AIPY category in QPYPI, where you could find Numpy, Scipy, Pandas, Matplotlib, Scikit-learn, Theano, Lasange, Keras.</p>
-<p>By installing these libraries, you could start mathematics, scientific, data  analytics, deeplearning etc. with QPython.</p>
-</section>
-<section id="how-to-use">
-<h1>How to use<a class="headerlink" href="#how-to-use" title="Permalink to this heading">¶</a></h1>
-<p>You should install AIPY application first, then please make sure you have the latest QPython(version &gt;=2.2.3-1) installed.</p>
-<p>If you have completed the previous step, then you could launch QPython and enter the QPYPI, you will see the AIPY category, from where you can install the related libraries. They are numpy, scipy, pandas, matplotlib, scikit-learn, theano, Lasagne, keras etc.</p>
-<img alt="AIPY libraires" src="http://edu.qpython.org/static/qpypi-aipy.png" />
-<p><em>You can install AIPY libraries from QPython’s QPYPI</em></p>
-</section>
-<section id="keras">
-<h1>Keras<a class="headerlink" href="#keras" title="Permalink to this heading">¶</a></h1>
-<p>Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.</p>
-<p>It runs on top of theano in QPython now, we will try to add tensorflow and other deeplearning frmaework in the future.</p>
-</section>
-<section id="lasange">
-<h1>Lasange<a class="headerlink" href="#lasange" title="Permalink to this heading">¶</a></h1>
-<p>Lasagne is a lightweight library to build and train neural networks in Theano.</p>
-</section>
-<section id="theano">
-<h1>Theano<a class="headerlink" href="#theano" title="Permalink to this heading">¶</a></h1>
-<p>Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently.</p>
-<p><strong>Besides these high-level AI related libraries, QPython supports the following libraries also.</strong></p>
-</section>
-<section id="numpy">
-<h1>Numpy<a class="headerlink" href="#numpy" title="Permalink to this heading">¶</a></h1>
-<p>NumPy is the fundamental package for scientific computing with Python. It contains the following things:</p>
-<ul class="simple">
-<li><p>1 A powerful N-dimensional array object</p></li>
-<li><p>2 Sophisticated (broadcasting) functions</p></li>
-<li><p>3 Tools for integrating C/C++ and Fortran code</p></li>
-<li><p>4 Useful linear algebra, Fourier transform, and random number capabilities</p></li>
-</ul>
-</section>
-<section id="scipy">
-<h1>Scipy<a class="headerlink" href="#scipy" title="Permalink to this heading">¶</a></h1>
-<p>SciPy is a Python-based ecosystem of open-source software for mathematics, science, and engineering</p>
-</section>
-<section id="pandas">
-<h1>Pandas<a class="headerlink" href="#pandas" title="Permalink to this heading">¶</a></h1>
-<p>Pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Additionally, it has the broader goal of becoming the most powerful and flexible open source data analysis / manipulation tool available in any language. It is already well on its way toward this goal.</p>
-</section>
-<section id="matplotlib">
-<h1>Matplotlib<a class="headerlink" href="#matplotlib" title="Permalink to this heading">¶</a></h1>
-<p>Matplotlib is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms.</p>
-<p>In QPython you could use the webagg as backends with tornado, please install tornado from QPYPI and make sure your script contains the below declarations.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1">#qpy:webapp:QPyMatplot</span>
-<span class="c1">#qpy://127.0.0.1:8988/</span>
-<span class="kn">import</span> <span class="nn">matplotlib</span>
-<span class="n">matplotlib</span><span class="o">.</span><span class="n">use</span><span class="p">(</span><span class="s1">&#39;webagg&#39;</span><span class="p">)</span>
-<span class="o">...</span>
-</pre></div>
-</div>
-</section>
-<section id="scikit-learn">
-<h1>Scikit-learn<a class="headerlink" href="#scikit-learn" title="Permalink to this heading">¶</a></h1>
-<p>Scikit-learn is a Machine Learning framework in Python.</p>
-<p>It has the following features:</p>
-<ul class="simple">
-<li><p>1 Simple and efficient tools for data mining and data analysis</p></li>
-<li><p>2 Accessible to everybody, and reusable in various contexts</p></li>
-</ul>
-<p><strong>It’s AIPY’s first release, we will keep developing, and bring more amazing features. Please give us any feedback to support&#64;qpython.org.</strong></p>
-<a class="reference external image-reference" href="http://www.aipy.org"><img alt="Get AIPY Application" src="http://edu.qpython.org/static/aipy.png" /></a>
-<p><em>Get AIPY Application http://www.aipy.org</em></p>
-</section>
-<div class="clearer"></div>
-</div>
-</div>
-</div>
-<div class="clearer"></div>
-</div>
-</main>
-<div class="star-footer">
-<img onclick="openPurchase()" src="../static/star.jpg">
-<div id="praise"><span>0</span> persons have rewarded</div>
-</div>
-<div class="foo"></div>
-<footer class="clearfix fixfooter web-show">
-<div class="col-sm-offset-2 col-sm-6 footer-div1">
-<div class="footer-block-item">
-<span class="footer-item1">Built with Sphinx using a theme provid by QPython.</span>
-<span class="footer-item2 visible-xs">Copyight 2017, QPython.</span>
-</div>
-</div>
-<div class="col-sm-2 footer-div2">
-<a href="/index.html" class="pull-right"><img src="http://www.qpython.org/static/img_logo.png"></a>
-</div>
-</footer>
-<div id="purchase-box">
-<div class="zh-img purchase-img"></div>
-<div class="en-img purchase-img">
-<form action="https://www.paypal.com/cgi-bin/webscr" method="post" target="_top">
-<input type="hidden" name="cmd" value="_s-xclick">
-<input type="hidden" name="hosted_button_id" value="ST6KWAK8R63JW">
-<div class="en-thanks-tit">
-<input type="hidden" name="on0" value="Thanks">
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-<div class="en-thanks-sel">
-<select name="os0">
-<option value="Reward this course">Reward this course $ 0.99 USD</option>
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-</select>
-</div>
-<input type="hidden" name="currency_code" value="USD">
-<input type="image" class="pay_img" src="https://www.paypalobjects.com/en_GB/i/btn/btn_paynow_LG.gif" border="0" name="submit" alt="PayPal – The safer, easier way to pay online!">
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diff --git a/docs/activity-edu.html b/docs/activity-edu.html
deleted file mode 100644
index d255504..0000000
--- a/docs/activity-edu.html
+++ /dev/null
@@ -1,273 +0,0 @@
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-<link rel="stylesheet" type="text/css" href="static/style.css" />
-<meta charset="UTF-8">
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-<a href="http://www.aipy.org">AIPY</a>
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-<h1>QPython Course<a class="headerlink" href="#qpython-course" title="Permalink to this heading">¶</a></h1>
-<p>QPython courses is a social Python learning project, based on QPython community, we hope more and more people could join us wo help write or translate and share QPython learning materials which could help other friends learn Python with more fun.</p>
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-<h2>How to contribute<a class="headerlink" href="#how-to-contribute" title="Permalink to this heading">¶</a></h2>
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-<div class="en-thanks-sel">
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diff --git a/docs/activity-qpypi.html b/docs/activity-qpypi.html
deleted file mode 100644
index 8f541de..0000000
--- a/docs/activity-qpypi.html
+++ /dev/null
@@ -1,269 +0,0 @@
-<!DOCTYPE html>
-<html lang="en-US">
-<head>
-<meta charset="utf-8" />
-<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
-<link href="https://cdn.bootcss.com/bootstrap/3.3.7/css/bootstrap.min.css" rel="stylesheet">
-<title>What package do you want</title>
-<link rel="stylesheet" type="text/css" href="static/pygments.css" />
-<link rel="stylesheet" type="text/css" href="static/style.css" />
-<meta charset="UTF-8">
-<meta name="viewport" content="width=device-width, initial-scale=1,maximum-scale=1, user-scalable=no">
-<script src="https://cdn.bootcss.com/bootstrap/3.3.7/js/bootstrap.min.js"></script>
-</head><body>
-<div class="header doc-header web-show">
-<div class="col-sm-offset-2 col-sm-8  index-header">
-<div class="navbar-header">
-<button type="button" class="navbar-toggle" data-toggle="collapse" data-target="#example-navbar-collapse">
-<span class="sr-only">切换导航</span>
-<span class="icon-bar"></span>
-<span class="icon-bar"></span>
-<span class="icon-bar"></span>
-</button>
-<a href="/index.html" class="logo"><img src="http://www.qpython.org/static/img_logo.png"></a>
-</div>
-<ul class="header-title collapse navbar-collapse" id="example-navbar-collapse" >
-<li>
-<a href="http://www.qpython.org/index.html">Home</a>
-</li>
-<li>
-<a href="http://www.qpython.org/document.html">Guide</a>
-</li>
-<li class="header-phone-selected">
-<a href="/index.html" class="header-selected">Course</a>
-<span class="header-title-selected"></span>
-</li>
-<li>
-<a href="http://www.aipy.org">AIPY</a>
-</li>
-<li>
-<a target="_blank" href="https://github.com/qpython-android/qpython">Github</a>
-</li>
-</ul>
-<div class="search-box hidden-xs">
-<form action="/search.html">
-<input type="search" name="q" placeholder="keyword">
-<input type="hidden" name="check_keywords" value="yes" />
-<input type="hidden" name="area" value="default" />
-<button><img src="http://www.qpython.org/static/ic_search.png"></button>
-</form>
-</div>
-</div>
-</div>
-<main class="contain web-content">
-<div class="document">
-<div class="documentwrapper">
-<div class="bodywrapper">
-<div class="body" role="main">
-<section id="what-package-do-you-want">
-<h1>What package do you want<a class="headerlink" href="#what-package-do-you-want" title="Permalink to this heading">¶</a></h1>
-<p>What pakcage do you need, pelase file a ticket in <a class="reference external" href="https://github.com/qpython-android/QPYPI/issues">QPYPI</a></p>
-</section>
-<div class="clearer"></div>
-</div>
-</div>
-</div>
-<div class="clearer"></div>
-</div>
-</main>
-<div class="star-footer">
-<img onclick="openPurchase()" src="static/star.jpg">
-<div id="praise"><span>0</span> persons have rewarded</div>
-</div>
-<div class="foo"></div>
-<footer class="clearfix fixfooter web-show">
-<div class="col-sm-offset-2 col-sm-6 footer-div1">
-<div class="footer-block-item">
-<span class="footer-item1">Built with Sphinx using a theme provid by QPython.</span>
-<span class="footer-item2 visible-xs">Copyight 2017, QPython.</span>
-</div>
-</div>
-<div class="col-sm-2 footer-div2">
-<a href="/index.html" class="pull-right"><img src="http://www.qpython.org/static/img_logo.png"></a>
-</div>
-</footer>
-<div id="purchase-box">
-<div class="zh-img purchase-img"></div>
-<div class="en-img purchase-img">
-<form action="https://www.paypal.com/cgi-bin/webscr" method="post" target="_top">
-<input type="hidden" name="cmd" value="_s-xclick">
-<input type="hidden" name="hosted_button_id" value="ST6KWAK8R63JW">
-<div class="en-thanks-tit">
-<input type="hidden" name="on0" value="Thanks">
-Thanks
-</div>
-<div class="en-thanks-sel">
-<select name="os0">
-<option value="Reward this course">Reward this course $ 0.99 USD</option>
-<option value="Reward this course">Reward this course $ 1.49 USD</option>
-<option value="Reward this course">Reward this course $ 1.99 USD</option>
-<option value="Reward this course">Reward this course $ 2.99 USD</option>
-<option value="Reward this course">Reward this course $ 3.49 USD</option>
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-<input type="hidden" name="currency_code" value="USD">
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diff --git a/docs/aipy-keras/index.html b/docs/aipy-keras/index.html
deleted file mode 100644
index ed5b4ae..0000000
--- a/docs/aipy-keras/index.html
+++ /dev/null
@@ -1,291 +0,0 @@
-<!DOCTYPE html>
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-<head>
-<meta charset="utf-8" />
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diff --git a/docs/aipy-numpy/cn/10sample.html b/docs/aipy-numpy/cn/10sample.html
deleted file mode 100644
index ff9fff7..0000000
--- a/docs/aipy-numpy/cn/10sample.html
+++ /dev/null
@@ -1,1113 +0,0 @@
-<!DOCTYPE html>
-<html lang="en-US">
-<head>
-<meta charset="utf-8" />
-<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
-<link href="https://cdn.bootcss.com/bootstrap/3.3.7/css/bootstrap.min.css" rel="stylesheet">
-<title>各种绘图实例</title>
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-<h1>各种绘图实例<a class="headerlink" href="#id1" title="Permalink to this heading">¶</a></h1>
-<section id="id2">
-<h2>简单绘图<a class="headerlink" href="#id2" title="Permalink to this heading">¶</a></h2>
-<p>plot 函数：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="o">%</span><span class="n">matplotlib</span> <span class="n">inline</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="n">t</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">,</span> <span class="mf">0.01</span><span class="p">)</span>
-<span class="n">s</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">*</span><span class="n">t</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">s</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;time (s)&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;voltage (mV)&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;About as simple as it gets, folks&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
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" 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" />
-</section>
-<section id="id3">
-<h2>子图<a class="headerlink" href="#id3" title="Permalink to this heading">¶</a></h2>
-<p><em>subplot</em> 函数：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">matplotlib.mlab</span> <span class="k">as</span> <span class="nn">mlab</span>
-<span class="n">x1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">5.0</span><span class="p">)</span>
-<span class="n">x2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">)</span>
-<span class="n">y1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">x1</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">x1</span><span class="p">)</span>
-<span class="n">y2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">x2</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x1</span><span class="p">,</span> <span class="n">y1</span><span class="p">,</span> <span class="s1">&#39;yo-&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;A tale of 2 subplots&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;Damped oscillation&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x2</span><span class="p">,</span> <span class="n">y2</span><span class="p">,</span> <span class="s1">&#39;r.-&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;time (s)&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;Undamped&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
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-<img 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" 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" 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" 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" />
-</section>
-<section id="id4">
-<h2>直方图<a class="headerlink" href="#id4" title="Permalink to this heading">¶</a></h2>
-<p>hist 函数：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">matplotlib.mlab</span> <span class="k">as</span> <span class="nn">mlab</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="c1"># example data</span>
-<span class="n">mu</span> <span class="o">=</span> <span class="mi">100</span> <span class="c1"># mean of distribution</span>
-<span class="n">sigma</span> <span class="o">=</span> <span class="mi">15</span> <span class="c1"># standard deviation of distribution</span>
-<span class="n">x</span> <span class="o">=</span> <span class="n">mu</span> <span class="o">+</span> <span class="n">sigma</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">10000</span><span class="p">)</span>
-<span class="n">num_bins</span> <span class="o">=</span> <span class="mi">50</span>
-<span class="c1"># the histogram of the data</span>
-<span class="n">n</span><span class="p">,</span> <span class="n">bins</span><span class="p">,</span> <span class="n">patches</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">num_bins</span><span class="p">,</span> <span class="n">normed</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">facecolor</span><span class="o">=</span><span class="s1">&#39;green&#39;</span><span class="p">,</span>     <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
-<span class="c1"># add a &#39;best fit&#39; line</span>
-<span class="n">y</span> <span class="o">=</span> <span class="n">mlab</span><span class="o">.</span><span class="n">normpdf</span><span class="p">(</span><span class="n">bins</span><span class="p">,</span> <span class="n">mu</span><span class="p">,</span> <span class="n">sigma</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">bins</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="s1">&#39;r--&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;Smarts&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;Probability&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;Histogram of IQ: $\mu=100$, $\sigma=15$&#39;</span><span class="p">)</span>
-<span class="c1"># Tweak spacing to prevent clipping of ylabel</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span><span class="n">left</span><span class="o">=</span><span class="mf">0.15</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<img 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" 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" />
-</section>
-<section id="id5">
-<h2>路径图<a class="headerlink" href="#id5" title="Permalink to this heading">¶</a></h2>
-<p>matplotlib.path 包：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>import matplotlib.path as mpath
-import matplotlib.patches as mpatches
-import matplotlib.pyplot as plt
-fig, ax = plt.subplots()
-Path = mpath.Path
-path_data = [
-    (Path.MOVETO, (1.58, -2.57)),
-    (Path.CURVE4, (0.35, -1.1)),
-    (Path.CURVE4, (-1.75, 2.0)),
-    (Path.CURVE4, (0.375, 2.0)),
-    (Path.LINETO, (0.85, 1.15)),
-    (Path.CURVE4, (2.2, 3.2)),
-    (Path.CURVE4, (3, 0.05)),
-    (Path.CURVE4, (2.0, -0.5)),
-    (Path.CLOSEPOLY, (1.58, -2.57)),
-     ]
-codes, verts = zip(*path_data)
-path = mpath.Path(verts, codes)
-patch = mpatches.PathPatch(path, facecolor=&#39;r&#39;, alpha=0.5)
-ax.add_patch(patch)
-# plot control points and connecting lines
-x, y = zip(*path.vertices)
-line, = ax.plot(x, y, &#39;go-&#39;)
-ax.grid()
-ax.axis(&#39;equal&#39;)
-plt.show()
-</pre></div>
-</div>
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-<img 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" 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" />
-</section>
-<section id="id6">
-<h2>三维绘图<a class="headerlink" href="#id6" title="Permalink to this heading">¶</a></h2>
-<p>导入 Axex3D：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>from mpl_toolkits.mplot3d import Axes3D
-from matplotlib import cm
-from matplotlib.ticker import LinearLocator, FormatStrFormatter
-import matplotlib.pyplot as plt
-import numpy as np
-fig = plt.figure()
-ax = fig.gca(projection=&#39;3d&#39;)
-X = np.arange(-5, 5, 0.25)
-Y = np.arange(-5, 5, 0.25)
-X, Y = np.meshgrid(X, Y)
-R = np.sqrt(X**2 + Y**2)
-Z = np.sin(R)
-surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1,        cmap=cm.coolwarm,
-    linewidth=0, antialiased=False)
-ax.set_zlim(-1.01, 1.01)
-ax.zaxis.set_major_locator(LinearLocator(10))
-ax.zaxis.set_major_formatter(FormatStrFormatter(&#39;%.02f&#39;))
-fig.colorbar(surf, shrink=0.5, aspect=5)
-plt.show()
-</pre></div>
-</div>
-<button class="button">Run …</button>
-</section>
-<section id="id7">
-<h2>流向图<a class="headerlink" href="#id7" title="Permalink to this heading">¶</a></h2>
-<p>主要函数：plt.streamplot</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="n">Y</span><span class="p">,</span> <span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mgrid</span><span class="p">[</span><span class="o">-</span><span class="mi">3</span><span class="p">:</span><span class="mi">3</span><span class="p">:</span><span class="mi">100</span><span class="n">j</span><span class="p">,</span> <span class="o">-</span><span class="mi">3</span><span class="p">:</span><span class="mi">3</span><span class="p">:</span><span class="mi">100</span><span class="n">j</span><span class="p">]</span>
-<span class="n">U</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span> <span class="o">-</span> <span class="n">X</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">Y</span>
-<span class="n">V</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">+</span> <span class="n">X</span> <span class="o">-</span> <span class="n">Y</span><span class="o">**</span><span class="mi">2</span>
-<span class="n">speed</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">U</span><span class="o">*</span><span class="n">U</span> <span class="o">+</span> <span class="n">V</span><span class="o">*</span><span class="n">V</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">streamplot</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">U</span><span class="p">,</span> <span class="n">V</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">U</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">autumn</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">()</span>
-<span class="n">f</span><span class="p">,</span> <span class="p">(</span><span class="n">ax1</span><span class="p">,</span> <span class="n">ax2</span><span class="p">)</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">ncols</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
-<span class="n">ax1</span><span class="o">.</span><span class="n">streamplot</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">U</span><span class="p">,</span> <span class="n">V</span><span class="p">,</span> <span class="n">density</span><span class="o">=</span><span class="p">[</span><span class="mf">0.5</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
-<span class="n">lw</span> <span class="o">=</span> <span class="mi">5</span><span class="o">*</span><span class="n">speed</span><span class="o">/</span><span class="n">speed</span><span class="o">.</span><span class="n">max</span><span class="p">()</span>
-<span class="n">ax2</span><span class="o">.</span><span class="n">streamplot</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">U</span><span class="p">,</span> <span class="n">V</span><span class="p">,</span> <span class="n">density</span><span class="o">=</span><span class="mf">0.6</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="n">lw</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
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-<img 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" 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oWf/ioVk036wKkfQtPj7M21bC+8fxb4SmD7d/DyONj4gwTmAPqdBx2Hw9ArIb2p/e8gERxFUrWDI2/x7l4Bs/+f/BOG3wEDEygJDmPhfdXbrlQ48S9w1UZIN6/iAWD7a7LWXHDKz9DtZuscwwLjkbh5AvXw0QjsB89A8FjkS6o4VU2aHYvXDY62gLKoIouC08jSCC2wOK6juqxYn2MxbgISiFsCyuL8jguNY02xN0fT8w0ArSOwD9R8k3O1Ncp+KyBUj6YoYTeOmf88dXL1trtf7fc1F3JDAtydwGnDYoyELyLlr9OL1qSrFPxyvZBuWk/ofm/sa7xwJcweKqTbpD+c9pOQLogrb+G9Qrp9b4KLF9sn3UAVvHcmlEbMeee3gIJ+58NNc+G6aTDh9sNHunDMW7xHlnh3LIZ/jYENP8KQO2DiPxM/xtZZckEAtJ8IV2+F4Y9CSpx/YtGy6u2h0yE7xg8kEsESKPoGcEDTM2KPKfsZ1oyHA2+ZH8e7DiqWGMIzJnC0BPdQ8eXGfN+GxQvgMKLLIZvBJOcQWesLrcc5JhrHtSLeFAhriehW2QYG8aqZmOb92oGmgcPIHghOA3197DJip5FWFpot2RR1cXE4DUIKbTaZi0MCoAAek5RDVzdZN/1/iZ9/3wuyzhwNLeMYKtufg7wP5AntxI/AFcONtn8efDkOqvZBy7Ew8TtIiQj8ZrSHsa/C6R/DmOftFSQBbJwNT7aHvUtrvu7W4NYf4Zpp0H3skSmoaCReA1t/gufGQVUxHHcWTHrIvg5DGBX7xNEP0HUynPsFpLWwt+9q44Lvfoc9AZ2Cz8QFkD0CkprHHlM8G0q+NkqKTeAzIt6enhZjVoF3vnnw7ZCrodw6hchpEK9uk3gdAwAP6OswLQsGcE4wjjtP3BimxzOeDHSL4JnW2ag+KwdVj1QvpUA7XlK0/M9DZS+oiiI9pQOdJIjifQFKsqFscOLncnYEHJLVYJZF4e5obJgEKjUjkuPpn9i5Q5XVubsdHrYeW7QA1hk57ce/DhlGwLl4GRwwdCu2vwvfT4CmPaDDBXDK55AUwyjocQl0seEKUgo2fwnP9Yb3ToOqWJWJCgosgq6HA8c48R4ZH++mefDSGVIGPOBCuOrdxNNQlA5zrpBgQNux4nOyi4IFsPczcKVBT5spK3veh7QR0MKiwCKsEJU90XyMz/AreyyyLnSD9MxEdDQXkAx4xUo0s4ydHWUd2m5+rhrHTQLHCaAvgNAicJl8Dq0VaH1B/SKiOc5xscc5ToeQC9T3oA5KuW3M410kLgl9CjjOszfXaPhvg8C/jD/CrpqoX1LlZRAIXyeGdW0mhWkFLQkc7UHfLt+tq3uMMYb/ONaNSSkIGoE5V4Ltu/LfhWChyJGmDzEf5zsAyy4Qt1Wn26C18WQRKIUfx8oTXIvJouAH0HQQ9HsCnHWkAH8lrHoXFj4L+RHB2Zwu0PcSaNpNshr8ZfJE0OcC82OF0YB6vMe6j/fIEO+Lp0LAC4OugMter9s/e+njsOtrSGkOE99JzFpefa+su90GySbWayTKN0D+Z6L90N/ksTlYBGWLhBQzR8ceo0LgM8RJPCZBKaUiZCMt0oMc3aQySq8Ah5lLIkFXA4ifV18gATYz4gVwToTgL+LnNSNeLceQk/wK9FngvNpknheAfieoWaAq6lZGGw5I1jhut6j5xHhEdtZRxdTZxSDeLSbEa+jSxnJl6EXyuiOj2m1kB7oOeYYWd6vbrB/RNz0I3j2QMwx6Plb9+s43q1XJ9k2TGpb+z0D3W+3PIxKle+Dn52HJy6LVAJDRGk66BQZeD6kRN1ulrOccDMC2JbDuG1j7DWTa1N22g6OYKmYHR4Z4A14Yeh1c9FJ8XYRY2PY1LDOsmwlvQXoCVsPOmZJu5sqCHjFyLmPu85ysW18OSU1ijyn5BtAhY7jkScaCf4dYQO42sfMwQX6Qyi8WkyM59hgQEfTQXqpTz2LA0VHSykjAh+YcAv6U+GTtmAi8Kxaf1cOKdj7oB0BfbE68WgfgVCHd0GxwTY49zgqeZ0DtgVCEKHq09m7KcxDaCKGfql9z1ZV4u4J/rqTVxZ6QrGJZvIGdYoG54gR/o1E4DfBDzhnQNM531OsxKSDqfEd1IZEKwZaIJgoaIkbetg5PGQfXwuLHYfMnUBYCbym0GQQn3w69J9d+gt3wGcz4DQz/Eww3So51HfJ+gV++FqLd8D14I6oY00xK6uuCRosXGPV7OP/ZujnV9y6FaedJHuGIx6GjmSh3DPgKYeGNos0w7A1zQZBIBEpgz5uy3eH35uMa3M0QT50/fGGb+BhBXA3BLSJBaBeOkyVY6Z8BKa/HTssC0IZDqAyYCvpuI4Mi1hzOhsDvxG/segg0k8+lTYLgraAy60a8WhIkTwXv+RAySpWjtS60VEibCeUng248eWg2o/O1ztcJgm4ImNygVCrQjph3Jd9mIAM8I+yfTw/ArnvAtwXa3Bm/KtOZAr2fqvna5uehMmq+wWIh4742UtqUgj0/wuLHYKvxHWsOGHgL9LwY2g6J/Zv+ZQpMu0wyl+Y/I9Kr67+SpWw/JLWDAiPzpVUP6DUWeo+FnqPgpQayehuJl7qTbsF6+HCS+Il6nw4D/5jY/j//QUg3dxh0vsLePnvehFA5NBkDGSa5rUpB8ZeynT3B/Fheo9LJKrAWz78bhpZknNvK4m2NBIH2GlZ0kvUxQfJ/tdaSAqavMc/ndSSD8xQIfQKhz8Fxvck8W4osoz5PxrpMfOTOiyHwR6kqU/nVWQGJQEuC5GlQYTwphAm4xrybQPpcKDVUs0I/AHWw+By5QMC8vY9eCKFdscu+fSslFTCRNLIDr4F3MyR3g+a/SXy+O9+GNbfVfC2pOTQdAR2sdXxRuli2ix+DvUbGiysZ+vxGfoPZXcz3/fklmHUTh5ivLA/ev6o65pjdBvpfAq36Qq8x0CTBpwC7aCRe6ka6JTvhgwlQWQCdJ8FZb8W2xgp+gYwOUnYciR0zYOt7YgkMe8OeT1iFYOe/ZbuDRRVN+SpwtoKUbEizeHQtWwKOLpAy3HxMsEgqn9zxhEbCj48WFq/mEvLVd0tqVTjYFg+uYRKECs43J14A1xlCpsFPwW1CvADOiwzinWJOvFquuC/0zyH0X8Mt4U68yEHzgPtfEPgDkBTbr+hoKxkc+nIOBdkSRbz2PqrSmE8M/7vP6P+abDOjIVQBu43eZe0fjm/tRmPrc7DKeFpzJEOvh6DVGZDWLf5vMVAB7w6EQiMbJ7kJ9L8FBtwCqSY3x9I8+OY+WD0NfDGyY1p3hZNuhp4ToGWvI5dOdgzj6KuTxUJFvpBu6S5oOwwmT5fywmgUb4GPxsG0EVCZX/26t0BcDAAn/hMyu9XeNxYOfAaVWyC5A+SeaT5u/5tQvBDSTjZ/NFcKyn+QR8UUi+KJYJ5YNnocwjlU1WZh8QI4w5ZdDEEXM7gMgfegSSHCoWMbifuhudY5uM7zQWkQ/AIqukNFm9jZBI5L5Qfi+39QkQNVFm4bKyT9DmgG7AbdpDFlyiPG3C3aHVkhsqGlUrUj8OHvI1ZAzxsmXptl8XufFaHztEHQ5Pz44w/NQcH6B6pJN70XnJYP3f4I6d3tEZ47DbK7SheJMf+C63fCsAdqkq5SsG8VzPsHvDwYHm8jetqxSBegaSsYcxu06n1kSBca08kShq9U3AsHN0Du8XDRp+COYUVUFcAnp0JVPuSeAJ4IX+LCm8B7AFqOgZ5xpCLDUDpseAQyx0CbyeaCIroP9hk18K1+a/E5Nku5sCsXki1cDQEjWHMoD9QEh1wNFhYvgKMdsEAee+3CaQi8RwahYh67tZF+tgxC88AVw9+ubwTfDaCHr+xNxrzzOdSUEyAwBfx/Ni5+g5RVDC0EO9Bc4DoXgq9KMYUzxo3ONRJIhtAyURBz2Mz/Bij6K5S9JHMNroKdDnD1hFbLJPMFzC3e4AG5uTrSwW3xiB5G4CDkGYVFHR61T1ShKlh2Dez5L+CA456ALnEyIcww8TWxdCP7qulB2P0T/DId1s+E4oggo9MDOZ2gzUAYcI0E8favhgNrxF3Yu44pg/XBMW7xHlvEW3EQZv4O9i6DJl3gkjmQHCM4E6yCWWdD8SZo3h9Om1odVd38DuyYCq50GGYRLIrGzjegaCEkt4U2Frm7+TOMvMoBkGGhWFZuqGalj7S++APbZR2PeJ1dwe2Jn+sY7kIcS8LQdJ/jgTTpXhGPlJynG8T7aWziDX4ipBwNLSoYF3wLVFSlmRZHG9kKrgsM4p0KSQ/U/s61FCHf4JcQ/AqSTPQLYsG/ElSUgl1oT81z6AbxOqIsXq+hseDpZ+9aPPCKqOFlTYQsGwpgAFV5sOhsKF4ilWoDP4SWCbQFikaqEeDSQ7D7B9gwBTZNh8oDQGsRvElvAT3OgB5nQZdTICnqhtPZ5twPF+qRTma0c38GaQnzH6VUHUpsrXHsEG/BZnjjNCjYBP0mw2mPQXqMH2K4kGLvfEhvB2d9Jq1HAPYvgm9vgFbjoPslkN7R3rkDxbDWaBfe53EptDDD3ldlbWXtApQZxJsRRwDILvH6t4B/cXwL5pCrIQGLV3OBazAEv4HgAkiyaBnuOgMCD0Lwe0iK4U91/04Uw0KRMpI5tS1Bz3PiWlCbIuZRD+J1jgHVBNCNIGGM7AXXJCHewA+JEW/WX8EbpYKWcVN17i5EuBqiPqfPIF67bobWd4K7pXXsIBLFi2Djg0K6qZ1gyCzI7GNv31jQQ7DnJyHbjdOgMqJzcnZX6HUttB8jqWR1SQ09UqijxatpmhN4DjgF2AMs1jRtplJqnfWeieHYIN5tP8Lb50DlQWjdH05/BrJMop0/3Ambp0uZ49mfV+f0lmyGz88QazitI3SzEQne+gaseQia9AR/vkR821xkPr5qCxR/I1ZN7qXWxy43en2lj7IeFybepI5xJht2McTJVHB0BMeJoCeoSeA8XZTDgoutidcxELTREPoe9FXgjNK80NIh+TPwXgihcH+1GJeZoxOk/AhVk0AZPtBoqzIRaC5wXQ6+f4F/CqTEIt7xoLcF70xIfcn+Y3jyKEieCN6wVoUHMqP0dNMulEKJ6GIQ73pIGQ0pNhX4NFfc7sGHsPs1WHMTEIL2V0Cfp8CToAAPyFNU/hLY8SUseb5a6xogqzP0vAi6XwC5/Y+cj7a+qLur4SRgs1JqO4CmaR8i7VN+ZcS74gOYcrUo4Pc8DS79EDwmxQaLn4ZlT4lb4YwZ0Mz4cVUVwGenSlCt3UQY+aK9C2Tr61C+RRYX0PtJ6/32GupmzS8At0XerW87+HeCMyc2AYShlH2LN+zbjZci5mgOgaWYNs4E6Zjg/Y/4bFONFD1nHwgtxbo6AnlcdnQTd0Lw49rEC2IJJk+Hyv6g1gL5tceApGilzoOKVkiHh6+szx0PrjOEeANTIDmGu8HZR3J91T4IrQOXjW4nYWQ/BPsM4k2ZBM4od0yzF2rvo1dByX/F/9vm7cQ+ixV0P6y7FXa9JH+3vxl6PiVSp4mgfDdsfle6ShSvg4wuQrpZnaDHhbLkDvi/Q7aRqDvxtgEiHxd3A3UQ+LDG0SNepeDbh2HO3+Tvk2+GM5+pWU7sr5QMh+z28O3dMP9R6DYBjr8c2hk+pGAVzD5LLN5m/WFChL/XCnoICiMUy4LA8jthxIzYhRahSihaJI+S8dwMRV9A8nBI72/t1wsVgl4Ojsz4BRS2idcgBKtW43o+VD0pVVxh4nWfDDiEfFWluR4ESCAr8CoEZoDnvthjNDckz4Eqw/WhrxM921rjMsHzDvjOl/OrYvOii3hwjRF9CH0D6Ktrp8ZpGiSNBd/7EJibGPF6Ivz5dtP0KubKd5l8IrgbqPuudy+smAzF86UdUO+XpIGlXQQqYPvHsPEt2PM1hxgquTl0OB1GXwotT/q/SbaRMCHeeQtlSXzPhsXRIV5/Jcy6E35+Qf7Bpz8Fw2+t+c9WCt46BzbPlUTr3XMl06DnpdDLKIbQQ/D1ZbB/AaS3h9Mi/L3xULZeyDQSB74T9abuMSrWdjwLBd9As0kpsT5gAAAgAElEQVSQZZGXC1DwjrSEz7XIdQXwrhYrLHVQ/As9UeINpz3FOq7LiPoHV0tBhuYWAnQeD6EVEFgIzm6S/xpz/7FABoRWgu95IfLk20GLKgJxtgXnNRB6AwLvgMdEXct9HgTHil84+IH4iesCzSXH8r9quBti5CS7x1UTb4pFZWIsuHpAcAOk2gxclRo6Hxn1EHyPRNF8IV3fXgkCD5gBWQPt77/8YVjxCASMMl1HEnQ4C7pfCe0mJZ4vfCzDhD5HD5YljAf+VWvIHqQEMYx2iNXboDjy3vEdS+HhE+HnGZDZDq74CEbESHvZ+CVs+kqCaevmSiHERbOgX0TGwbKHYdsMSMqC0z+HtAQ0HLa+XvNvzQWdroGOMSrc/Pmw1cgD7XinNUl6twjpOtKgSRwB9aol4FtD3Md7iNAAiEe8aYiMpB9TqUdHliGo44fQBnktsAxUslQYlZwGhe3B+3yMeSgIfg0qRyLHlbeA9wHwz6o9FiDJ8FcG3yFml94wXIbObOA1688XD+6wKteU2BkgbkPgJzCvdolxPLjCmRk2fjYqBGXGd5JZT+JVCva8CktOEdLNGQUnL02MdAHc6UK6uUNg+Itw+V4YPxU6nPnrIl2oT8+1JUA3TdM6apqWBFwEzIw5sh6ol8WraVo74G0gF7nHvKKUqn0PAbFOv3gUPr1fcgJb9oJrpkLrGBFYpeDziPYxCsjsB52jEux7Xw+7voSTHpT2JHYRrID14bp2DbrfAj3/BGkmzTa3/EN0CpqdCk3jpMnkGy2qm5wHTouuwABVi2SdaiH3dwg2LV6QZP9QmVi90W1ownD1B/82CK4QUi+O/BEbJB8rZzj4NZTHEIZ3mNTYO4aB1hHUdiP31+T7c50LvmzQl4olHct3bAeu0aA1k3xi/ZfaOb3ODlJNqG+B4HJwD0rg4DaLWACqFkLogFQkeiyqAeMhWAbrr4cDH0KTUZDaH3o8Xjei7HYFtJ0I2SZKeb8m1NFhoJQKapp2CzAHCZS81tAZDVB/izcA3K6U6oP07r5Z07Tajrz8rfDESJj5NyHdMX+Au5fGJl2A75+AvatqvrZjISyLClCktoBzvoc2o+3PWCn4+Ybqv8cvgBP/ZU66FZth1wsIQcdJ51OqmnibXxl/LpWGsyk1ju9e6RBKBq2nPQnFeOWtIMQLEFwJzs6GzkMU3DFcKs4BkjkRDYdJcYDmAJfxFBGcZj4fLQXcl4FKgsB083HxEHY3APhNJD2Twlbv3MSPDdZ6GWGUfiLrjLPr7i8t/wWWDBTSdaZD2xuh1zN1t049OUeOdA9uhU9ukSfXo4F6VK4ppWYrpXoopboqpR45HNOrF/EqpfYppVYY2+VIykXtX/A/+sHW+ZDVGv7wJVz0LCSZtBP5/C74LEazRIdThDqikehFvekF2PEeOFPhtNXQLA7pbb5HHknbXG0umhNG2XzwbQV3a8gaYz02kAeBXRJYs1IvA0OAZR+E9ktAJR6c3cRiDFl0lXAZFmVwhVjFmTOp2cnRA64YuaeOZpA+r1r7V14ERweLc10Jqjf43gJVajHvG0BPA++zIhlZV7guBr0bVH1g8v540EaAf0Nix1UO4wdrg3id2SJ6nmmRmmeFvDdgyWCo3AhpfWHgEmhxcd2OdSThr4Qv74Wne8OC52Hu34/OPP5XSoY1TesIDAAW1XrTVw4nXACXvQRpJvq2/kp4ZzJsMNrBuFNhyA3QegC06A3Ne4AnzqN7PBQsgOVG/uXg1yArjnvi4DzYOwWcydDVxgWUb/Rea365eclxGJXG15QyKH5F06FOxTYLDJRbqq3iWrxpiB6wAveJkPkOlBoSjY6mmHY9dnaAjO+gdACog4AeW5Xr0PiuIorDWvC/D54bTebUFxw9pfmm/33wXGfjw8aAe4TcrNRBCK6tnb3gPgn8P4C2Wp4m7FY36pWgUkAzuYYj0fxuaPbXxOceqoQNN8O+N+XvVr+B7v8WQ+FYQqAKKgtFDL14F2z7HnYvgV2LwR9xc927Gv5zufy+w4ueAQd3Q8AHQb8sAR+0r6NkZyz8L5QMa5qWDkwDbjUs35q45h046TJz63TvKnj/Eti/FtCgz9lw2VRwNWDSRdl2+OkS0Tntfit0iGM9BEph+XWQcgK0u0SiyFbw74cDn0L6BGh+dfz5HHIz2PDvhqUInTaJNywxqVsUJGjtQE8C349SJuxsCZ7zxY2gb4/dOLLGOdpB2qeidQvxS42TroeqeeB/GZJuML8WPDdD5QLwvwBJv63bY7rmAs854H0NfNPAFdVg0tEOHK1EPjO0SbIV7CC4RyrUXDaLFOwSehiVa2HjbVD4lRTp9HgBWl2d2DHqAz0kZcFle6B8j6zLdldvVwD71wnhBm0W6HhLYNF7NV9L7wZ7NtUeW15Y749wCL924tU0zQ1MB95VSsV0qt0/ezPMFpm70aNHM3r0aHlDKZj/HHx2JwR9kNsTLv1AqtcaEmU7YPY4sRranAcDHrcerxSsvEH8u5nHQwcbQjs7Hwb/XtBOgtQ4rgMA73bwDILUOKlpUG3x2iZeQyJTt3isdzjA1QcCP0pLn/CxM16HkrFAunk6WhjuIeAcJIpf/mmQbPE9uc8Fb1PQV0iusMskIu+eDNptktYWWlCtnJYoks43iHc6pEURr6aBezD4Pgb/ogSI18gqcsa5CScKpWDfi7DtDkhqI/nfvd+B9Aa0AMMIBaBsG5RshOINsi7ZCGVB2L1AsjHMkNJLJCBB1AJTm0JqEyFsb7Ec21cBoQhSdjrgN2+CJ030HJJSJUPJ5QFXEvMWLWPeoiXiSnS4gFWxzpw4fs3Eq2maBrwGrFVKPWM27v7776/9YtFumHEjrP9M/h58PZz5dG2xjfqidCt8PgYqdkKzE+GkV+IHJ3a8Cns+lBbZA6fIhWIF7w7IewnQoOOD8efk3wmFUySglGaDeEMJuhrCreSt/KkAruOqidczXl5zjxa3gDog7czDbcnN4PkDVF4Bgf9aE6+WDO4rwf80+F8xJ17NI5au71HwvVAP4h0necWhVRDcVPtzJA0R4g0sBGwEQvVSUGVSWBK3W0gCCOTDpmuhMJx6NgI6/1tEnhoKa16AnbMNgt0qAe5opJ8gpJvSDDLaQEZbSG8j2+F1Smsh2pQm4E6JfVMOBWDNDFjwAmz7TqpQB19qqoc9+ryejD6vuvz+gYcfbZjP/CvvuTYMuBxYpWmaUXDPXUqpL0z3CAZg3gsw635o3QZScmDyf6DvYZCOK9kIn4+Fyj3QfAhMnF1TPjLmPqvgF6MRYL+XIcOGNbTj75J6lXsppNtIHSqcIuvsM+KnnEEdLF4brgYAd1/RBQ9GaNhqmmQz+D8SMZl4xJt0FlR6IPgj6HvAYdFRIOk6Id7AB6Ce5FDb+mh4bgTfPyEwFfSnzFPVrKAlQdKZ4HsX/NPBFeVvdRsuHn/tkERMhK1dl0lRSV1Q9BVsvFK0d51Z0PUVaH5hwxw7EvlLYWe4O4cG6R0guztk9aheZ3QW0SlXgkL00XC64fgLZcnfKFZsIo1pGwq/ZotXKfUjiWRGrPsGPvwD5BktcVqdD2fdB9kNVE4ZiaK1MHssVO2HFiNggo2qtmA5LLlQBGbaXwvtLot/nsoNRiDECR0fsDk3I9qec4m98aEqSBoK7s72xms2XA0gwSyAQJR4uHukQbzfQ0ocsSEtE9ynSfmwfyok32Y+1tkLnCOk/Y7/A/CYVPY5OhgqaLPA/55UxdUFnslCvL7pkBpNvCcCDkmni1ciDRA0yvddDXCt6j7YcQ/sMfqeZQ6H7u+KAP/hQM/fQvtThWCzuoIrzhNcPOQthp+fgpYnwpA/mY9rHqMb85HCr5l4bePgTph6Byw18jibd4aLnoHjzzg8NeGFq2D2KeDNh1ZjYfxMUda3gtJh5e+ltXtGH+gbuw6kFrbdC+jQ6npI6Rp3ON6NULlM/LBZNht3VnwD/vWQa3NOjgRcDQDBNfKYGc7EcBtNGYO/xN4vGu6LpQOyf7418QIk3QqVVVD1vFjApkG22yGwEiqfkICbncKRWueaIHKRwSLRz3VGWOOOdNGqUEHwr6upxRALDeXfrVwNux6E/CmAE9rfD+3uip8FUx+0PLlhjrN3CfxwP2w23IM7v7cm3qOJRuIF/l9P8FeJ//a0e2DCH8Ft0cq8Ptg2W+rRvfnQdhKM+yj+HV7psPRm2DYVWp0Cxz8LLhu+5vzPoXg1pPaDDvfam1/hh7LOOde6nXsYermRb+oGj91gS4rk8cbTt3XkiB6DvhtCW8BlWCjO40HrI2XEoTxwxinFTjoDyq8FtVC0DKyCVe4zQL9FFMKC34LbrJJttEhM6mvFkvbYePqIhpYCZMh5fF9A6rVR73cF74cQtEG8/i2QNBI8dRSqUkHY+zjsuQ9wQdYI6PBPyGwgUoxEyAdV+8C7T5q9VpaBr0gEcoIVUjZ8aNtYBzOgPJ9DjBUut648AOX7JPgdXWJdVQj/vUby610e+U27cyXfOTkTUjIhNat6OyVTtj1ph1/Lt5F4EdIddBFMfhyaHAa3AhitpO+FJY9K8Gzg7TD4EWlLYoblD4pKU+sTYM9Uydftcidk2lCt8u2F1VdBoADaPAEeG5aQUlBiuL9zbCbDe1cASkjXTvFEGP6VxOz/FY2k08C/BAIbqonX4RLLLrRG/LxOC41ikMf0pAvA9wb43gHXPyzGeiD5d1B1H1Q9a068mgbJf4SK34qSWtKldXs68kwC30zwza5NvGHthZANDZTKL6RpZc7fEp9D1VrYejVUGP3ecq+Fdo+B06agUySUAt9+KF8HZeug7ACUbRaCDROtP7q/XSco2RbzcIfg6AiF2xObS8ALS96s+ZrHJFUsEk2Og/w9kJYD6U0gPUe2Wzega6KReIE/fQs9Rh++45fvgdmXwJ4fJHdyyAMw6C/WeZQVu2H5P0TbdOMWyPDAiJnQcrz5PkrBhvvAlQVFnwvpNjkFOtj0QRbNgJIFkHMmZI6zt4/XkK5MttmRAKgO6dqxKlLEsg2uAyIafLpHgH8O+H+E5DjEC+C5spp4U/9u/d0n3whVD4kPN7RZCixiHvMyqLwLQssh+J1kXCQKj+HO8X1VrcQWRtj1EI94gweEdLVk647R0VAh2PcU7L5X9DCS2kGn1yDL4hqLROVOKPtFCDZMtOXrpGNKGK7eEs+IhOaC5BaQ0gpSWorWs45kSrjTZHFFrZUn4n9m3OA0Taznws1QsQ92fA87vuHQ9ZWcBROfFgIOGktFAEoPgrcUqozFWwqVJdWvVZZBeZEs+7dWz7tzItd4HDQSL4eXdLfPgTmXixh6Wms49QNoa0Ptf8UjQrog/6SM4dDiFOt9ipdImxUQ+YyUZtD3bXuJ8ioEu4x80qxJ5lVh0fAulXVKnEfhGggTrw2/YdgtEIwqn00aLgnzgR/sndI9UoJi+g4JyiWNNh/ryAXPpeB7E7z/hrRnY4/TkiVFrep+qHqqbsTr6gjOnhBaL6ljSSOq3wv7a0NxmmxWfi3rlJG1e6qZoWqDYeUahTLNfwvtnpCbtl2s/gPs+6T2664syOglXYRT+4K7iZBsskG0nqaJF29YoYNR/j4cOLgR5j8Eq9+FZj1gkM1uGZEIBqCiGCqKqgm4okhcEE82kITmrzyd7OhBD8KC+2CxofHafgJMeqe6UZ8VynfB+pdqvrZnrqjxd4shCxlG3pTq7RCQOQqSbKZ4HZwCVWsgqb08atpFlUG8iVi8h5Lgbfz4wu6F4Maar7tPAtzSVVcvjp+7qjnAcwVU/QN8b1sTL0DyrUK8vjcg5cHqgGCtcb+DqkcM63gDOOsg8pJ8KlSsB+/s2MQbjGPxVhhCL6kT7J+z/CchXXcb6PQqZNsMpEYiZ6iok6X3qibajF7gaXn0hMqbdocz34JRD8WW3bQDlxuymstyuNBo8R4G7FsB390HeTPFSX/y32HQXfbu8pUH4KP+sbVhDy43J16ljNbZEcibDtlPQdc7rM+pgrD7ftlue699X20gD3wHIPVU8MRoWW4K47PF+z58i6HyAxnuWwR7e8vjd/NZhlrYIAjMl8VjQ/w72SBe/1RQz8XpYtEfXKPEheD9T3UnjGg4cg03xqtQ9SqkPxF/HtHwTIKKp8XPS4QYux1XQ6gUvD9BykRIPd3+OZtdIz3sml0DrjoWXHT7syxWWHQzbH5dsiJiLg7IHgz5m0W32pMl68htTzYktwR3FqS1lCaznszY5F6wAVa8BqveknLg362FHJtpjkcSjcTbgKgqgm/vhSUvCnH2nQyDboZ2o+3tv+8H+DTCDZHeDlqOgib9oGl/aGVxnLz/gjdG594qG23UC96TNDJPZ2hm0To+GiXTIJQvASm7j7iAmOMQ1+It+TP45sm2CoifN7hRikG0ZPHzBuaLoIwd4nV2B+fJIrzjmwnJcQKIKXdChQ6Vz0PKLeYpY8l/BN/PUPEcpNwBzlbx5xKJpJGSOpc0vGbanLMl4BCdiWj/bxglL0BgsyiNeXraP6emQcs65h8ngpC/ZoluLLjbQcFK6zHNRsDmCLeSK1kI2J0B3iL5zoJVRov3CHijA3nHCBqJtwGgdFjxFnz9F6jMl4tg8G0w+n5x8NvBxrfgx4iE/VM+ho42/Um+AlgaUeyQ1AxaTYY2F0LTOF2EgyWw713AA23vS0xLtcRwbWQlWM2kAhJ0cZpoDIeRdnU18YaRcmb1Y3/SKPDNr27IaQee30LZtVD5QnzidZ8K+l9A3wretyHFpJedq6doBgdXQuXTkPGY/fmA3ESax8hJ1tzgPAFwgl4hUo6R0CuhyBDMb3J3Yuc8UhjyApz0rNxQVEh0E8Lb6PJ30A+BMvAVg68E/Mbii1iHksCnJIhWvg/85VC8Pf75XzwVcjpBTgfIaQ/N+kBaLuR2gWYdIekwpY3GwzFOvJqqq5/G7gk0TdXrHHuXwec3w24jSNFhJJz6HLSwqeofrIT5v4eNRquftpNg5BuQatM36yuABeOg1BDvOO456HSjvYR3pWDdRVAwFZpdAL3et5ZPjERgN6xvJ6TR60B16pEKAC5rH1/pW3Dgaki/FFq+Zz5OKcgfA77vql9r9imkGI/U+kHY10zcDi1L7AUE9TIoaA2qHJquid9Q0vsBlF4Kjs7QdIP59xNYAoWDJLe32Q5w2JBmtIPtKWKhd6gAR5Rr5ODjcPDPkuPbfvHR86seDfjLoXw/lO6EzV9A3iLIW1rdry2MaEGxpgNh4xLZ1jTIaSMkXGPpBi26QFpto0nTNJRS9fqiNU1Tar7NsUOp9/nqgmPX4i3Jg/nPwuInxOJNbwnjn4C+CeRzFq+HuRdA0WrJ0R36HHT/jf39I0k3rTsM/VaEQuLh4FzY9S/IOkFI15kOnR62T7oAJVNlnXF6zXzPwufh4LOQ+3fINvFHR+oKWEHTIOdF2HccoqmbDskR7ZUcTUVUPbQJAqsgyUZmhSMDki+Dqpeh6hXIMNVOEnguBOd9cg7v+5BiIljjHiiVaP4vofLfkG7S3TgRKF1IF+QGFwnfeih8RFq5Z9/yv0W6AEnp0CQdmnSBjkZWQ6ASvn8QFj4hwe2UpnDfCijcAcU7ZV2QD+5sOLBFKlYLd8uyPuLmntMbtq+V4FqrbtCqK7TuBu0S6PocD8e4xXvsEW/RTpj7T1j0GmS2kk6og26C0feJw98O9CAsfw42vyK5j1ndYexUaJpAgMpXAPPHSh5leg8h3WSbvsXNd0HpYsifKW26er5sr5w4DKWgdI5cPNFuhtLp8uhvZX0moivg7iUqXqpIrNTom0PSyVC1yUjFspnSlnKDQbxvQfoj1oUcmhNS74aya6DyYSFts6eJtLsN4n1WgnGOOhQgREJVGXNIqRmIDJVC3rmgF4l4TbrNrsK/drhTYdwj0Pcy+PYeaH0SZLeVhWG1xwcDcHAHHNgqRBxe8vZLB5qSfFnWG+ZpWxtyqnbRmE5mEwe3wdePwOI3RVpO06DdIDjjIWgWRyErErvmwTe/h4OroWkv6HoVDP23/bbvAEWrYPltUL4W0nvC0G/sk27FJiHdMPQ0yIhxUVqhdA4Uz4H00ZAZ0VgykAeVP0mwLd0iwp6ooEvqNVDxlGjURiNpCFS9Df4FkGZDlxjAPQBcgyC4GLxTza3YMJIvg4oHJF3MN828YMM9EtzDIPCTEHtaPXUCtBRot6va6gWxgvddLdoYScdBy9fsWbvlS8Q9FNgHgf3Va2culK42bibhTANXddaBs7NRYtsGPK0j1q3jy5EeLeQeBxfFyC+OhssNLbrKEg1dh8I82LsZ9m6CvE2Qlg0v3NMwc2y0eOMgfxN8/TAseUcCAZoDTrgUxt8DLRN49CjbBd/9CTaGA1KdYPij0PlM+4+JSsHml2HZbaIg1eUaOP4h+6QLsPfdmn+HKmDZeDh5tVjvceegQ54RyMk6vabfsXQGoCB9onW5qV1XQxgp44V4Y1mQSWH5xIX2jnXomNdD2WIhyHjEq7kh7S4ouwEq/gmeC2KnwmmaWL3Fp0PVO5ByU22/bCLQHLW/I+9iKP9EpDXbfAQOG81FAbZeAd71tV9PHQylFtKTqr1UqMWCOwc8bSBlsAR0s46DzOMgs6e4zv4vw+GAZm1l6Ts64o1G4j18UAo2/ggL34OVrwrZOJww6Co45W7ITaBmO+iDpU/CoockkOZKgZPugoF3xm6OaQZ/Mfx8HewyFNS6XAcnPGNPLCdyLttjNCUNlBhBMRvEWzwdqpZLw8zmURZmmdF9N3Oy+f5KgUqSjAa7Fu+hrsT7a7/n6iv5uKEtktrmtJn0nnwxlP8RQoWiG5EUp1178lXg/UICfVVvQ+rVscclnSq6DZVToPI1SP+9vfnYRcpgaPuVUeKbwJNW5hhI7gbuFsbSUtbOptDOXZ1poEJASHK7VUgyCrz7wJcH3j2y9uWBNw8CRbLkbxMhmzA0h7TPCRNx1nGQNUA0df/XfNFmOAzEq2na48AZgB/YAlyjlIojem1yrCOa1VBVCvPfhW9ehN2r5bUex0GXITDuLmiWQCK2rsO66bDwLijZIq91mwyjnoDMBHVND/4MP10EFdvBlSFdKuL1ZKs1nyDM7y3+UJC82+ZnQe450PRUSU6PBxWEtceBbwO0ewmaG23olQLfFth1GVAKnRfUTn0Kw7cOtveWXNcue+z9EEN7Ia+1FCu0iUG+BadA6ABkPQXJccqqI1H6Nyh/CFKugZzX44+vfBdKrpB+aM03inRjLFR9DIXnSkv6lltqB8Z+DVA6+A9C1Q4o3Qala8RlUbIayjfVLABypkJxFSTlQPOToPlgyB0s28lNj95nqAMaLKvhW5tjx9jPatA0bTwwVymla5r2KIBSqg4dTY+UxbtzlZDtgnfBa6SjZLWAUdfBmBuhiUXXgmiEgrD6Q/jhEchfCx37Q5NeMPbf0N6m8EwYSof1T8PKvwrp5ZwAw/4LGQkEwkA0H365rJp0m50O/aYnpiYGcOBF8G6A5C7QzBAg922FdUOkDVFoOzS9zpx0ASq/kXXqKPvWj6M5oIGeL99DdIDNeZwoifmXJka8qVdD+cNQ9R5kPhy/g0bKpZKxEPhZXA4ZJm2Uks8Gdz/R6614DdJt+p7/L0FzgKe5LNkDgQuq3wt5oXR9NRFX5oH3SxH93/2FLGFkdhEibjECmgyC3H5Gb7NfOQ6DPamU+iriz0XA+XU91pH5D9wb8ZjZcxSMvQlOPAdcCYhbB31SRPHjP6HIUDTKag8Dboe+l0jLkUSQvwQW3A6hnUI23W+F/v+0lpGMhZAXVl0ABZ+CKxMGzIbsOvQJq1wDO++CtJHQ+vbqrIXimRDMl0XDPIXs0HGMW33qGPvn1lzgOB5Iku4b0e2I3IavPbC21q6WcHUVkvR+DBXPQ2acfnSaAzKfhoPDoPwJSL0udhGIpkHGvVA4GcofhbTfSsDxfwXOZMjpL0sYgxWU74T8RZD/MxxYBAVLoXSLLCVbYPPvwJ0ObYZCmxHQdgS0PEn6p0VC6VCwRqQkO5/2f5OoD7+P9zfAB3Xd+ch8o8kZMPwqsW7b9klsX38FLHkF5j8BZUaH06bdYMRdktaSCHkDVOTB4rulkg2g7Skw6F/Qtg6qSP4iId2iuaIQdcKXkJmAipgekBYwaf1h7wNSPZXUFrIi5lIREZhRwOZzoOvHkDGi1uFQurRQh8SIV+ngXyUniFWaXFfiBUi7Q4i38kVIvyt+MCxpqPiHvR9C6V8h5/3Y45LPlTLg4GqofAPSbkx8bvXB7r9B1WrIGClLav/E8rQbGpoGGR1k6WykIOoBKFwtRFy0FQoKoHgLbP9SFhBSdWeIey6jnchA7vtZqtwAJr4OfeugQHa0YZJONm+lLGbQNO0rINaj2d1KqVnGmHsAv1LK5OKMjyPj460qg+QEu6Ye3AY/vQLbPoHCdfJai+Nh5N3Qe3LiDfSCVbDqKZGDDFZI6e5xt8EJ94hQSKIoWgxLL4eUbAjugBO+ggyb1XRh7H8LNl4NaOBSkNYX+iwQt0IYK1tDYG/N/TLGQI9vah+vaiHsOV9+hJ132Xc16OWwI0OCaB0rar8fOgh5zUBLgzZliQVwlIKCwRBYDFkv2iPI4A7I7wl4oel8ySWOhaqpUHihWMUtNtWtPVBdsfp4qIooQ3akQ/owybvOPgWS45RrHy2U58HuH0W7evcPkB9Hw+HMadCjzk/UCaPBfLxf2hw7IbHKNU3TrgauA8YpFZmHmBiOrZLhUABWz4KfXoYNX8mPtucYcFfByHug++mJR22Vgq1TYNGf5VEMoOM5MPhxafyXKPQgbHoYNvxdotI5Q2Dg+5DaKfFjLR8k+Z9h9PkcmkTIB1auhbVRTwgZ46Dd45A6oPbx8q6F4teh+UPQPAFtgeA+2NVKgmsdYgTXAPa0AP0AtNoBrgRJpeq/UHSxiOjkrrOnIlJSzysAACAASURBVFdmBObSbofMp2KPUToc6AvBrdBsLnjiuHj0Kih+BryLAAXlX8s6ZbjkSHsGiPxm2pmQHOfa8G6F8h+g7HtZfJsBJ/hSIFQOmUMh9xJofgEktYj/eY8Wqgql2GjNW1ASIUoe/smWOaD1IOg0GjqPgXbDEjeiEkCDEe8cm2MnJhRcmwQ8CYxSShXUfYbHCvEe3A4L/gMLX4NSo5W5ywMDLoRhN0CnoXUgXB02z4QN70OeUX7btB+c/DS0TuAxPBLlm2DZFVBkPP53uR16PVy3nMqyn2FFVMGCpx0MWAbuZnLDWNUOAoZId86F0PIvkGaiyxvYC5s7Stpalw3SgsUuAptgd3dwdYF2m2OPOTBaUr2azZYy2kSggnCgi6wzn4fUc+Lvo5eD/ztRRbP63/uXSk84O4plKgRbso0KvQwIlcnrSf3Aa1h+rrbg2yOtlrIvg6xLpXNEPPjzoHQeHPgEDs4SkgfAAdljhISbnSsuqWMVWz6DTy+SPmwgbqFin+TXg9wwvVmSX999LPQYC52GNGj/xAYj3i/ijwPQJiVEvJuQnNCwQsUCpdRNdZrjUSNebwUsmwkr34H1X1SLKrfoCUNvgJOuhLQ6XKRBL6x9B5Y8CYUbAA26nAw9r4Yev0ncRQEytx2vwOo/QqgSktvCCW9C8wSzKCKxqD34Y8hM9v4Emp4F+x6H3YYWa/tXIddEuSuMA/dAwcOQcS60+yixufiWQ94JkNQf2iyPPabwJqh4UfSHM+ogd1jxDhT+RoTXW648ev7QomfB2QQ8g8DVRshED0jPOe9y8K6B4vcl/xggZQJozaDV3ZBiMz4RKhfyPfAhFM42criRgGnLGyBrAuSeZk9o6Ugj/xeYcSaU7oATb4ehD8COH2HbPMjfAAtm1UxlcydD52FCwj3GQvuB4Kz7/7bBiHe2zbGnHh2RnCNLvAEfrJoDCz6A5TPBVwl9ThI/U//JYt12Hl63JHBvEax4EZb9Cyr/P3vnHR5Vtb3/z6T3npDeISQhEHqRriiKghVFVMTeO/arol77tVwLdhR7xV4uioD0XpKQQnrvvUySmfP7Y02cSTIzmQkTFH7f93n2c+ac2adkMvOefdZ+17t0j8teUfLlSblSTD8Gg5YCSLsVKr+T9fClMPoVMQIZDBQtZF4INV/qt7nEgPds8J0PAedD0zrIOQPQQvT7EDBA5pemGXKixFsgegu4WamqaN8EFbPAeTqEmij10/gfaHkf3JeAz33WHR8kIaF8pEjifN8GDyuqcBxraDuh5RdoWAN166U8PIDPuRDygOmnDmPoqoeatVD9qUzCOo+B+n0SA468FsKvBOd/WCiirRoyP4GRS8CtT8JMeyPkbILs9ZC1HkoP9n4/ehpofWDUyTBqLkSOtqqisM2I9ycL+55xIhPvwV9h26ew+2speteD+Ckw9yqRlrkPUuhdnwf7X4aDb+kfkYJSYeLdkHDB4KUw7ZWQ/m/IeR0Cp0DnIRjzOoRZUPjRFLQdkHU51OgqWThHwuiN4BKt79N6ELJngaYBQh6GsEdMH09dCNUvi/62Kwcc/CHqe+uvq/kzaHxSiDfwFeN9ml6GulvA80bwN9FnILR+DHVLJTQQnHN06b49UDqh9TDYx0j5cFtDXShPHzVvy80DwOt0CH0QPKy8wakroPQDKH4T2nQhHZUjBJ8HkTeA7yAHHX8nmqshZ4OQcNZ6qfm2d5P+fQ8/SJ4DyXMh5WQIGWH2b7QZ8f5gYd8zT2TiXWqwISoVplwEkxdD0CAmpECqmh7+Bva8C6W7wL1dZDBR82DS3ZJIMdgvcGcjHH5OEiu6WwEVxC6DsU9J5dbBorMaDp8NTVvFZyHxC/A9rXef5h2QuRDcR0oWUtzX5iei8hZDgy5+bW8PI3aBq5FJt4FQfRs0vgS+94H/E8b7NL8BtdeBx9UQ8Kb15wAZ7VdOlKrG3k+A1yBGzn8dS4HWH6D9F2h+TbfxHoh9avDHNIfOcqj8D1SvEoN0EHVJ6BPgOcW6YylaqPkNil6Dqu/5S/vkMQqib4OQC6Ui8LFGdyf89ggkngkRU6waqf6FmhLI+APS1kPa71DbJ5zmGwqTL4bQJEidC8N6Z5najHgtHH+ozjqRifeuBJi6BKZcCKFWlE8xhKJA2V7Y+y4c+Bg6dDpDB2eYdQckL5aR7mDR3Q7Zr0D6U9Cpi++FL4Qx/wafUYM/LkDjDsi6WLLQnCMg+UeRjhmi5gs4cpmMin3OgISvzSdzdFXAoQigW9adoiHpMNgNYqKjMAG6siFsE7ga0QcDNK+G2ivA43IIWG39OXrQ8RtUzwOVF4Tkgn2A9cdQFGj5BJpeAzxA/SsEbQUPE7IzW6KrBqpehKqXJZZLjGh4Y/8DLlamqoOUjip+S1pnJXjPgqr9EHMVxN8E7tFWHKsGNt4ACZdC5Hzrk4py1sE7uoKeHsMgaSEknQ1xcwc3gaYoUr49fb0Qcfp6aKwSrXqVzsgpJBbGzBUSHjMHlX+IbYj3Owv7LjyRiVerHfwItKUKDn4ihFthEE8KmwDjlsPoJeDqO/gL7GqF3I8g41Fo1ykIgmZB6pMQeJQ/5O4WyF0JRS+A3zSwa4Wk78X2rweKAqVPQZFO/jXsGoh5ZeASQeWPQ/m/em/zvRBiPrXuGrvyoDBO3LhiakxPerV8BDWXgPvFEGimqoUlqD5NUlw9bgPfF6zfv6sEqq+Drn3g+xh4XGKZ85st0d0AVW9C/koZAdu5QPi9EH734OwctZ1QuRZy34fynpkhOwg7B0bcBv4nDfwbOvBf+PNWee0aBCOWQuIyCBjAoKgH1Vmw4w1IXwv1BfrtTh6QcDokLYKEM8BtkL83RYHidNi7Hg78AQc3QEuD/n0PX1Rf19uGeC1wrQRQLTqRidfaczRXwYFvYP9X0FoNzbqZdjd/SL1UCDfYmqq7RtCYC+mvQea7IjPyQDwaUp+EkFOPLtamKFD5FWTdJtIkVBB9N8T9q3dyhKYV8m6C6vekT9SzEHrHwOfWtMNBv94+sgDYQWqzdbHTuieh9n7wOB9CvjDdr/F1aFkjGtfBTK4ZonM/VE4Bh3Hg96roZw2h7YD2/0HLO+C/SgpNGqJjL9RcK+V/Qi0UbA4V1CWQv0ImzwCcoyH2efA/e/DfobrdkPMSFH+mV0T4jofht0HEYtM3mZYyyPoAMt+H+sP67QFjRNUz4mJwCxr4/IoCFYcg/RvI+AbKDJQuoTOg2wlSF8GYheA/iFF+DzQayNsP+9dL8w5Adc+HtiHebyzse/ZxSrwqlepdYAFQpShKv9Qti4m3sRwOrIV9X0LORr1kxcEJppwDoy+AhLOsTxE2hKKFknVw6GUo+om/VOLDpsLEf0HYaZaJ+82hNQcyb4ZaHSF4TYTE18B7Qu9+desg50Zw8gP1IRj+kfxYB4KmDdJjodsg0cElCTxPkRGvNRM+mibIT5CMr5A1YqxjCjU3QNMq8HsafAYoOW4J6ldC4yOiHQ7eJAY6HVtFOdF5AFCgczf4PgU+9/TeV9sE+YFAF0SWgqOVVYeHAg0bIfdmaNNls/mcCnEvgdsgQ2sA7WWQuwpyX4dOnV4/8GTwmQUJ14GrCYtORYGq3ZD5HmR/AmqdKsPOAaIuhbgFEH+mhOksQX0hZHwL6d9C9iFoqta/F5EKYxbB6LMgcmzvuLC6FSqzwTcCPC0LKdksxrvWwr7nHL/EOwNoAdZYTbxV+ZD+E+z7DPI267W89o4wch6MPR9SFoLHUVrbqRsgew2kvQqN2bpzOEP8Ehh1EwRa4a9gCt1tUPA05D8lM+0OPjD8SQi/urdeU10KR+6Aap1hu+ckSHgbPCxIN+6ugZzToH2vrPteCmFPgZMFdeCMoWoF1D8HLlMgcov5m07JWBmphmwE15mDO58htO1QuRC0FTLyt/cDpQ26DoPzSZJs4XEReCwBeyOTmjV3QNs68FgKfoNy5rM9lG4ofx0K/yWhCI8p4DZTnnSOZrJM0w5Fn0DOi6C2h+r98v2NuwSSbgU/M98djRryfxASbsiBwizZ7uILSRfC6GUQOtny0XlrHRz6CfZ/C+m/gNqw+KUdeAZKKaC2WqnBpigQPgYe2m/R4W1GvBZK2VXnHqfEC6BSqaKB7wck3u5OOPwn7P8J9v0EZZkweobkjTs4Q+JpOrI9C9wGqZPtgaYT8n6BjI+gNgM6df6/HhGQdD0kXmV6xGANuhoh/00oeg+0OUAXhF4Ow58GZ4PHOm03lL4MBQ/JpIydG0Q/BOG3WxafVOfBkfmgzhEz8piPwcsKi8a+6EiHwlRAA1E7wWWC6b5dpVA+B7rLILrKNjIwkCSFhofFUU1lJyZBnpeB22JwHqD+VutPUL5ADHwi9lpvwQlC+KW3QchKMZ63FTqrofBBqPhFqku4RMDIFyHonKMPYZWth4yXoPgH/npiCzkZkm+DiDPM3zxbKyH9Ezi4BioNwgd+wyHlMki5FLytCB10dUDmH7DnC9j+gXzHjSF2Gty7xaJD2ox4vxy4H4Dq/BOZeH97U8j20G96P14Q3eWsy2DUNEhecPQ6TEULJVuEbDO/gA6D2tNjl0L8uRC90DY2d+1lkPsS5L8O3U2yLWY5RF0hekxDNG6B7BtEowsQcDbEv2jZLLiilRhw9bug3gKuYyDup8GPckHSi4tOlkKOLikQPIA8rHI5NL8H3rdB4CAmwwaCtllimXa+lhOTooWKy6DpSwj6D/gOwpO3+AaoWQUuiRC/ERxtcCM2ROMuyLgemvbIesDpkPgyuMXZ4Ng5kPFfyFmtr07hNRyS74S4iweuMVh1CA6tgUMfQmuFfnvyFRAyDUafJwZQlqK7Gza/CWsfhPb63u+1qSBqGkw4S1p4osn/s82I18x0Ra++F5zAxPuwQabl7FExzF64GMaeAcOnSkG8o4GiQFUaZH4MGR9Dk0H9qoBRkLwUEpdYdyc3h+ZMyHkOij+QmWiAgNkw/G4YNr//F6qrFrZFysy3SwwMfxn8zRSqNERbOuRfB82bZT1shQj37Y/iBtVVDkVzoDMLXCZC1GbzI+72jVA6G3CCyDTryuEMNZq/Fjc2+yCIPWK+Dp0xdNdAzmwZ/bumQvx6cDgKhYwxKBoofgNy7ofuRhmZx9wHMffYpm6auh6y34GMl6G1CPxnQul+IdAxN4LPAGY/2m7I/01IOPs76HCHpip5Ak06C8ZdAiNPt3xupb0JXl4ARzbrtzU5QJfBaHj0QvCPhilnsqFeYcOWrX+9tXLlStsQ7+cW9l18AhOv8uwiSD0DUk+HAAvrgJmDphsKt0jA//B34BsCtbp/tGcEJF0shBtopU2jKShaKF8Hha9BRY9AUAWh58HwFeA3yfz+Rc9KWm/kfZZJjTRtUPoYlD8ncUPHIIh6AfyXHN2jancFFM6CzmxJXY38DRzMTHp07IfSBaIRdp0JAUbqyf2dUBQonAYd2yHgEQh42HS/mhclGzB4Ze/3uiogZ6aEcNwmQ/w66wncEqgrIftuKFsj665xkPCK3KxtAW03FH4DB96Bgh6HGBXELIDUmyFy3sDfnfZGOPQ17P0Qcv/Qz7m4+kJICqReBFOulXM1FMukW3M5xM4EH4PftboVXjsHDq+DsBS4ewvs/x/s/h72/QJ1HdCsy2B19YAJp8GUM2HS6aj8g21DvJ9Z2PfCE5l4bSFZUzdD9q9Ctlk/9n6cCUmFEZOEbMOnH70yoQdNRyDvfchbI6OJoGToOAJRyyH+Dik4aEsoCtR9A0V3gLoAUEHQtRD5hG1GYtoWKDpDVAEDkW7bBihbJH3dF0LoWvOfq6IG9UFQ7xI5WFc2OI0WKZj7mUNnitO2CYpmiR9u7BFwMDIR134Qssci3hff9DaaBzErypkBnYXgMRPifrZdHLsv6jbB4RtEN12xFYLPgpQXwMMG4YceVO2TNPqsj2VyDcRzOuo0mPm8hCE6G6XQZlcLBKSAY59qyg0lsO8T2PsBlBv4DqOiX3mH5EWwvI9+q6sD/ngVEmZDlMHkdXc3ZO0Ss53tP0C+wbHdvVB932Qb4rVQzq666DglXpVK9QkwC/AHqoCHFEVZbfD+4IhXUaAyCzL+BwU/Q956mTDrQcAIEXQnLoTIqYNzHTOGzkYo/AJy34NqgwkB9yiJn0UtPrrUYWPQdoqTVclzEuvszgT3MRDzuvXpqAOeq0VK1zsMoBSpexZq7gaPxRC8xvTkVdv/oP5Z6NgCTomg3isTXl0Z4DwJ1DvB6zrwvqa/XtdWKD5L0od9boLgl433qXoGyu8BOy8Ysbu/baY6T8i3qww850HMt4NLhLAE2i6ZG8h4ALqb5bMdfjeMuNe6qtYDoa0a0t6Gvf+BjlrT/RIuhtPNJMUU7YZvboai7cbfn3ojnDdI/47KQtj+o5Cwpx+qBz6yDfFaWJRHteQ4Jd4BT2AN8bbUQObvQraH10G9Ls87LkkE4ZHThGiTFkFggu0uUtMJpeuh8AMoXivyHQAHd4g8H+Iuh2EzbTeS7kF3I5S/CSUvQacua84pBOKfg4DFf591IujScteCxyLz9oUtX0GFrty801ixlnSKF3lYVzG0fiGqCJWdJDu4DqIe3UBQp0H+aMAeYjPBycjoUVGg8Hxo/BpcRkH89t7JLAAdmZAzCxyjgGEw4ovBpWBbio5ySLtH5gsAXCNl9Bt6lOqHvujuhC33wcFX9SNgQ4y7A2b+x4LrbYbXZkvqPsjAVwU0AFETYfISmHih+DEMBoqCys7ONsRrYVEe1cX/PxKvug3ydkCmjmiL9+rjSgAeAZA4D8adDcNng4cFWTeWorMFin6BvK+h8EeJW3loZDQ4bLaQbeR5UhzQ1ugohtKXhHR7zLjdkiDiLgi6eHDSqL8LmnrR07rOBgcj/x9FLaoI9V4hgIifwcmGN80elC+HxjUysg5ZZeJamyBnEqizwGcJRH7Un+Da0yHjFIn9ep8GCWuN16GzJWo2w8GboFFnxh40D1JeBK8k255H0wWfT4fKnbLe81PTBsK462D8NeAVbv4YnW3w1nzI19mHuvhBbadez+vsLlWNp5wLU88D3wEqS/eBzVQNH1rY95L/H4i3vRmytkLGRsjYBEd2QuIUKNf9Ex2cIX46JJ4qhBs+ZnAOSabQXgMF30PeWij+X++7v38KpFwNkWeC5yBd08xB2w2166D6e6h5SybNQKoThK8APyOKiBMFihbKFsvo2CkForbbPobaVQqFi6B9D0R+Bd7nGu/XkSHkq22F0Jcg8Jb+fdoOQcbJUtnZex6M+AbsB7jezmpwOgo5mqKB/Dcg40Hx8HUeBQFzYMzD4HyUCUSG6KiHz6ZCvS6RQuUJDbqbv8petLwjL4KEU0yH7zqa4I1ToHgXnPMqTLgcDv4IOz6BplrYqbOFVKkgaQactNhiErYZ8X5gYd9LT2Tiff9OIdr8vfoyIvImjF8AcQmQdKqQrpMNf5CKAlUHIe9XaEyHvA8N3PNVEDwVYs+R5m3DyQ3D8zftEQ/W8k+hs0rCB16BkvYZcRd42iBr7niAthkKJsikm9dyCHnX9ueoeQXKb5aR9/AM03Hshs+h8EJwGQ3Bz4O3kUoibelweC50VYHXXEj43jT51v8Je+fDiOcg/Lqju4Gqa8SnYf8T8l118oGUhyDhRrC3kRFQQy58OlnivuetB40d7F4Fh7+C4JMgfSP4RsHkK2DKFZKJ1u86WyFvEySc2pugWxth1/ew9QvY+4s85YCehKddIKNhf+PhCJsR7xoL+152IhOvLgSInT3EjofEmZA8C0ZOB/ejzFDri7YayF8nZJv3q14cHjEVWnZB2Fwh2phF4D5EOf5t+VD2EZR9CK1Z+u3uCRB6KYQvk/JB/1Ro1dBdDF0F0Fkgo3NtFTgEi6eCQ7BMpFkru1IfgsLJoLRD8Lvgvdy2161oIX8OtG6SemkRZp43K56BkgfAzh0St4Krkcf6tgwd+VaC12xI+KF/XBgg91HI00nZQi+Hka8d/cRc/UHYfQdU/C7rnvEw7lmIWGSbJ6P6HGjIFrlZD1oqYOcHsGWVVPkGic0nng5Tr4akBdaV9TFFwhETQXGCWefDzHNhmL54qs2I930L+y47kYn3o/uFaEdMBVcbayS7OqB4JxSvkxTh8j30krt4hEDsaWIMEnsKONuY6HvQnAPl30Hzfqgy+ME7DYPQJRB6CXiN++eEExQtqLOhIw069wnJ9rTucnp9ho7R0F3Qe3/XGSKJ8rkSPBaAysJEmMb3oGI5qFwgcoeMOi1BR5pch/0AMXf1EcgZLeQe9Q14LTLeT9HAkQugYS04RUHSdnA08ijcngkZcyXxxGsODP8GHI0ksJR/BBlXiweF53gY8xW4HmXSjqJA6Y+w5y5o0t3Ah82GCc+D3xApRAC0WshZD9vegkNrJTYM4BUCky6HCcsh2EopZVuTkPC2r+HPddDarH8vcRLMPA9mnocqPN42xPuehX0vP5GJ15bn6OqAoh2QtwFyN0DRNvmC+qgkZmvvBBEzhWxjT4PAUUNDdooGardD2Xc6ws2U7d4poOTCsHOEbP1PsU2K8tGiuwradujaTmnaRrDzBLtmmZ3+C3bgGCFE5xgtJjV2jpKA0V0B3SWgPgzoRjD2QRDwKHgvFT3tQKi4EhrfBcfhELV74Ey82teg/BYIuAOCnxn4+DUvQfltMjIfng4OJoqmatogaw607gS3CTByg/ERbXs2ZM4HxU+KRYz+CZyMTCQ2H4AD50B7Pjj6Q8qn8v8/Wmi7IPt1OPCImPR7J0ulkcmPgtcQzEcYoqUadq0REq7KgvAJcGg3jJgO0y+HSRdYn+rf1gzbfoRNX8GOn6BDV9HDzRPVz822IV4LvfpVy60nXpVKdSfwLBCgKErdQP2NHuMfT7yd7VC8A/I26om2u48kJjgFxp0LEZMhchY4DpH4vbMJqn6D8u+h/Ae9VR+Aoy+ELIDQRRA8/+8p3dIDpRvaDkDLZqkg3LpRRrJ94RAq2VpuKeAUoydax7CBR7DdVdD0ITS8A50ZQqJoIPRDcB3AQF7bDkVTxCTH4wIY9rz5/m27IG8yYA/x+0QOZg6KFvJmQtsW8LkMIsw8d3ZVwuGpoM4Hn0UQ/5Vx+Vx7PhyYBx254BoPo38F11gjx6uDQ0uh9hfADuKfhOgVtrn5q+vh0GNQuBEq98rNMPk6GP8AuA1xwUxFgfwtUhn811dF3QDg5Arjz4UZl0PiXOsnwzvaYOcvQsKunqjuesM2xGvhFILqCuuIV6VSRQBvAQnA+BOHeBvK4MhWyN0KR7ZA0V4YMR4qd+j7hIyG2NkQNxtiZoD7IMrHWAJtN9TsgrJ10qq2g18QdJbJ++5xQrShC6VCwN81stU0Qet2aNkirXW7zNoDeKSCer/EMl0ngtskHdlOFoI9WigKtP4K1feC+gBgBwGPg//d5vW/6kwomAmaagj/HLwuMH+eshugbhW4zYCYjQMTmTob8k8BAiBoBfgtMd23PVPIV9MAw26FyBeN9+ushIOnQ8s+cAqGlJ/B00i5KUUDuY9A/uPgOQNUXpC6GpxtZMLTlA87H4bsDwFF9OZjbofUu8DZ2zbnMIf2Ztj9FWx+DzI36rf7RcBJl8HUSyFscJJBm8V437aw71VWE+8XwGPAtxy3xKvphpI0yN0iRJu7FWoK+hzATmq1BQQOPdEqCjTl6Im2/A/oaup9LQnLwHeEEK7nyL8nZqsugabNQl5N30pKbE/BxB44DwePk8BzrtQEc0kyT4RHC60aah6SjDeXaTKaDvsQVGZm4uteg4obwd4f4tIkNGAKmnrIHgmaKgh7D3yXDXxN1W9B0TVy0xm5C1zNWE02bYTseRJ7DnkcQo3IzECc6NLOgYb1EiIZ9S34zjbet+ZX2LVEJw8LgXEfQuDcga/bUtQegh0PQoHOP0RlD5Gnw5zV4DZEv5G+qM6HzWtgy/vy2s0HylsgJhVmXQwzLwI/yyexbUa8b1nY92rLiVelUi0CZiuKcrtKpcrnuCBeRYHKfMjZqW95eyEuFaq26Xdw8YTYKRB/EsRNg9jJQ1O2G+SaGnOhdCOUboLueqjsU57UaziEzpMWPHvoJudMXqNGpE1NW4RsmzeDWufA5j0D1H8CDuA2Djym69o0cBziR09TaP4ZSi/SeTzMh4ivTGt2FQWKToPWdeBxJkR8Z/5GVv8BlF4mfsTDM03Hbg2PX3AJ1H0s1o8jd5qfnKv9DAofhrYsiHsVQm4w3k+rhsOXiZm9ygmSPobA84z3bSuCvUuhbjOgguH3QcIjA9fUswblW+H3S6Epz2CjSszS7Z3Fp2Huagi3Ien3hVYL2X/C/l/hq1dkVAwSehg9F2YvhWnngpv537LNiNfCQtiqa3oTr0qlWgcYGwE8ANwPnKooSpOOeCcoimImF9vMeY8J8T56uhBts5FrTDwJYmOEZOOnQdgo2/ku9IWihbrDQrKlG6FsE7SW698PGgfaQjGWDtORrYeN7CQthaYVGneKd2/TL9C8DTSNvfvYe4HnNPA5BbzGg/ukoTN1GQza90LRfAkjuJ4EET9IRQ5j6CqB3FEy0RfyDvheYfq4igL5s8UYx+86CDWRoWYITQtkToKOw+C3FKI/ME/u5asgV0e48W9D8JUmrkULR26F0lfAdSQEXQ3Rtxs/trYbsh+H7McALfhOhfEfg1v0wNdvKbRa2PM47H5Mn5xjiNlvQdJVtjufOajbYdcPsOFj2P0jdOtUEY7OMOksmLMMxp4Czv3TsW1GvG8Yf29DFmzI1q+v/MGyEa9KpRoF/A7ogtuEA6XAJEVRqqy+xmNCvD2lxLwCYfgkfYufCF42zMrpi652kZeVbIWaw1DxfX+zENdACJ0JYTNlGTja9p4M5tBRBA1boVHXWvbLKNdpGKgqRW3gHAme08HrJPCaDm7JQxs2sAXUWVA4DxwCQeUHUd+aJs+gBAAAIABJREFUvjk0fAhll4rCIvag2FCaQkc65I4TL+Ggf4OnmTpxPWg/DJkTJe4d+ToEXmu+f+mLkH87oIIRayDoEuP9FAWKn4fcZ8X2MWw5JK8ynfJds1FGvx2l4OANY96EsMUDX781aMiBz0aDxqAQqmIH45+A8TcP3cSzKbTUw+YvYePHkLZRPrPAZCgrhpnnwMkXwYST//Llthnxvm5h3+sGJyc7PkINmz8Tog2MGtqYaGMxlGwToi3ZBhX7RIojVwL+nuDsAWGzdG0m+B7DOK1GDU37oG6byLkaN0u81hAqe5kQ854GftPBayo4D8LDWNMiabTadl1rk6XSDio3kU05+IuZjYP/0JjBqPOg8BToyhetb+Ra42oJRYGS88XY3P00iPzJ/M2v6nkouxMcIyHhgOnRtCHqPob8pRIaSNgK7gNkDJY8DQX3AnaQ8AkEmiHIii/g4DL5fH2mwdivwdlEqKezFvZfCZU/gEM8eI2FCS+Dsw1jspnvw/rL9evtQBdSYXjyCki9Tn4HxxrVxbDlS/j2A8gxKD3k7Q+zz4eTL0I1fo5tiNeChyEA1fWDJt48JNTwDybeoThHRzOU7IGindLU+VC1t+/ZISgFIqZB+DSIOkl0j8eCaBUF2ouhYbsQbcN2aNyrr1rhN1LcsBx8hGR7mtdE20jR6r+EPBNKAXt3UFp7b/OYA3SA+3Rdm2ber9dSqDMhfzpoasFnGYSuNv75d1dD8QXQngMBN0GQmRLyShfknCQyM58LIeoTy/6nRTdC9SrwPAOi3xZlgtn+K6HoEcAeEr80XwW6aR/sXSQGSC7hMO47IVWj169A0Uew41rREjsHwcRVEGHCX8JaKAr87yLI/Rx8EmDC07D1cajYDQ6uoAqF0Uth6k3gYeNyR5aiKAt+/wx++wQKdRp4T19Uv9Tbhnhfs7DvDf+XQGEa3Z1ixtxDskU7oepwbyezhKnQmAHhU3VtGoRNAuchmpjri64WqN8DtbqbQPW30FHWv59HEvhOgcA54D0O3EcOTWijaT0UXSfOWnau8piv0r2mQ+LGmjrorpXmlgodu3sfw3kk+F4mpOx+FL7AbTugYK5UEfZfYToJoulnKDgDsIe4TUL+pqA+AlmpuvDBGvC7dODr0Koh/xqoXiMj3qSNxhMmeqAoUHg/lDwlk5Yht8Kw8033V1fCvnMldGTnCqPfh2AzMrmWPNhxBVTpJFmRF9lu9KtuhH1PQ/xiCEiVvyXvZ0j7Ara/J30cXGDCFTD9DvAfAq8SS6AokHsIfv8UHBxRXfWobYjXQntg1U3/R7yCLjWUpkPhHijYA4V7wcMdSjb07mfvCKFjIHKS5H5HTYaghGMTn9V2Q1MG1O4Qoq3dAU3pegOe4FnQvBEcfcBnCvhNlaXvJNn2T4OiSP2x9t3QuhlatwhZKh3gNg3at4ruN+A28D7P8vRgQ7T8CoVnAt0Q/BL4m5Brla2AmuckjDB8v/nKG7WrofgKiQ0n7AdnIwkNfdFVBWlTdAkTCyDhG8z6HisKlDwL2fcAKkhYBeFmYsRaNaRfD6W61Km4JyBuhWmNt6KFnFWw/+6hGf32O58CBZth0zOQ+YNsU9nBqPNh5grJTPsbYbMYrwk//H59b/7/kXjVbVCSDkUGJFtySJ8b3oOE6UC1jmQnQeREIV3HITSp7oGiQHMeVO+S1l0H5Z/Lj8QQKgfwGQ3+kyBwpuTSe4w4thN1toS2U9QJjV9B/TuiowVJuvC/Efxvt75YY8PHUP2YxDnDXpYwgbHz5k6H9l3gcTrE/Gg6jKAoULAYGr8Et6kwfJN5Eu1BexakT5P/ZdC1ELPKfKhCUaDgCch7UNZjH4PoB8xfV+GLUPGNKGf8Z8DEj8HNjDFS39Fv/E2QfD+4DZGRE0BlOvz5HOz/SP+bi50DU+6E5NNta8lqIWxGvP+1sO8tJzrxNlRB/n7I2y/L/P1QmgVx46But+EOMGwERI+HqHGyjEi1vYuZMSgKtJXqSHa3ZK3V7JZUzR746srauMeA/2QhWv/J4DNW4mcnIrRtoqGteUk8GlwniGlM2MvgfY51x6r6D1TcJZNcsb9LPLkv1HmQlQh0gkMIjMw3rRToroOs0TKRGPw4BD9g2XU0bxHPXUUNEU9C2L0D71PyBmRdDygQfguMeGGAScANsPtiqTTh5A/j35e0clPoGf1mPAmtagmxjX8Mkm4Y2qzIxhLY8hLsfAN8YiDrIATGw6wbYdpycD0G2XA62Ix4X7Kw760nMvEuC4G68v5v2tnDyKkQF9ObZG3tYGYMigItJVC5R99ay3Rpr33gOgwCJkJgT5sALn/TpMTfCUWBlnVQ/aIkSoCEHsJeMe7sZeoYZTdD7auiqIjbCi5G0kvzFkDLT/LaIQSGPQp+y4yHOZr/gIonoOE3iP8afC28GdR+BTkXAArEfwQBFw+8T+WXkL4UlE4IXgqJq80nQ3RUwZ5lUKmr/Dv8Tkh+AuzMZPQ1F8DWm6D4R1n3T4WTVkGQjevv9bvWRtj9Cfz8FNQVyjZnd5i8DGbfBCFmMv9sBJsRr4ms7359bzuRifcspIxz9BhJJYxNlWVkMjgfg1GiokBDIdTs1ZNs1V5or+7fNyQGfOIhYIKeaN3C/p7U4H8qFK3OMew+KZ5p7wOhz4Pv5ZZ9TooGCs6B5u/BKRbit/UvG6Rth7Q+mlOnWAh/Bzxm9z9m+TNQco/eX9fNQrvJ8heg8A5wTYWwxyHAzIi0B3W/w8GzRbIXsAiSPwAHM4MFRQs5z0H6/fK3+06CSZ/KU5PJfRQo+g623QItukzFhKtg4lPgMoTad5BiBQe/hz/+C9l/6LcnzoPZN0sYwhpfXitgM+J9wcK+t5/IxFuaA8GxxyZmpOmG2iwo3yetYh9U7Jdy8KFR0Fqo7+vsC8PGSwsaJ0vv2H8GyWo7QV0mtb86q6GrT+uskqVbPLT3VH9VGSwNXntOAU0pOEdLcoJzjP61U9jgkzE6i6DkOv3o1+MUiFgjBTsH/PtaIXe2TOi5TYbY9f0TLDKHQ+eR3tu8zobotf2PpyiQfxnUfqjz190FjhY+lZQ8Cfkr5RjJX4O/BeTbtBsOLAJVkEy2TvgW3AaY3KvdBjsvgvYicA6GpOcg+mLz37euVtj/bzj0nGjSnf2EfBOuPDbzB6VpsOFl2PGBJCS5+oASCDOXwazl4DfIwpYmYDPitaB2J4DqzhOZeIfqHOpWkZmVHoCWAij6AyoPyhekL9wCIOVsyZ4L0pGt1xAndJiDohG5WUeBVKxo79M6SsF/JrRuMH8cb53qwBw8p0L7NuPvuU+RkI/3aeA1H9zGW/eDVhRo+AhKb5Pqvuo8iP4cPOcMvG9XBRyZAl2F4HUuRH7WO5ZZcC40GZCsfaCMjp1NSJ+0HZA5S/x1PWfCiHXmH+kN/4bcW6H0ZYk9J38F/mcOvF9rHuxaAK2Z4OgH4z6HACNlhAzRWQd7rpSBQNlGCF8Ek1YNPInWkAlbb4Sy9RA4HVpaYcazEDnA+WyF1jrY+i7kbBcLR5DvzbgzYe7VkDrfJqn+NiPe5yzse9f/Ea9pKAo0lgrBlh6A0v1QdgCqc/Ra3pEzoEJXNNMnCoLHQoiuBY8Fr78pXNDZAC1Z0pozdcssiRu3rDezowoC5oGqXkZuhs3J4LWDr45cej5jRf+657NROmQyrLNAZFTqAmmdBeAULgqCHjgEgOc88J4PXqdaHrvtqoLiq6HpO8Aewp6HgJsH/sw7MiDvZLCPkEm7iFf1+1Q8BFWPyWutCrQKRL0H/macyTrLIGMidJVB4DUQ9bqF4Q8Fcm+X6s8qR0j6CgLOsuDvboL9S6HqB3lySHwOom8dWCVx5F3Ye4fs7+QLE18eePSrKJD3GWz9N9Skybbo02HGMxAwgEexraDVwqHf4Pc3Yc+38oQJ4B8Bs6+AOVdCwCAyLXWwGfE+a2HfFf9HvAJ1G5RmQPFBaUUHwc0F8n/u39fOAYKTIGwMxJ0Ew4ZDcCq4DeBaZWsoCrSXSQJHQ7oYpDf+CS2ZoDbhn+E9BlQV4BojzS0GXKJl6RoDLhGWjdaOFt0N0LIBGn8VU57OAv177lMksy7odiHjgQhM0UD5g1D1lKz7XQ7hqwZOR27eDEdOEYVByEoIeUi2q/Og4m4xM++qgKJrZUQ6YqP5hI6WXZA5Q44X+QoMu3GAD6Hn+hXIvUO8GlSOkPQlBCy0YD8NZD0EuU/IevgyGPX6wHK71mLYcQ2U6SbewhfCpNcHHv12t8PeF2HXk9DZLE8oycth6qPgYdtHf7NoqISN78H6t6AyV7ap7GDMYpi0EE462+o5HJsRrwWFSgBUd///RrxardhElmZCyV4h2OKDUGkwiu1B/CToOCIEGzoGwlNlGZwoJeGPFRQF2sqhMU2qFvcQbWMGdBk4iHkOB22OvLZ3BY8EaZ4J+tceI8DxGKg3rIGiiIF406/Q+Iuk+bbtlPdcksHvMvC/AhwDeu8DvUm5/jMoWi6+EE6xEP2VZMaZQ8M3kHceoIWIVRB4Xf8+RTdCzWvi2ztyl4zWTaH2Y6j4D7Qcgbg3IOAiiz4CId87ofQFId/EzyHQTKqwIco+gwPLdZ4Nk2Dcl+A6wOhPUSB3Ney5XT/6nfBfiFk68I2urRp2PAoHX5c4s4MbjL9LmvMx/G5ptZCxQUbBpZmwT6cMcvOCmRfAqcsg+SSL5nhsRrxPW9j3nhOZeGvLoCANCg5BYZqupcvoNiYFmg7pd7Czh5CREDkaIkZDRAqEp8ijzLEMFXTUQX061KWJ4XR9mry2cwL7yv79nfzAJxm8k6Qmlt9IIVjX8OM3iaK7FmregOpXJFTRAztvKXWubZOZfdfRQoSGk3Rt+yF3HmhqABWEPANBd5r/H9bojMtRQcwX4NvH41bpgpxToWUjeJ8PUW+Dg5mU8OInofh+SapI+Br8LAgdgO6RfgWUvCg+xyHXQLiFloqN+2HPIsAO2jUw9jUItSReXAI7rpbRr2u4lGOa+iL4jBh43/ps2HwvHFkr3zWPVEhaAhOuByczKdFDgcYa+ONT+G0NZBmEsIJj4JRLpYXFm9zdZsT7lIV97z2Rifd0E2/6h8KISRAfLwQbMRpCE8W381ihqx1qM6DmEFSnQWsa1B2CNiM+CyCzyhHjpNy2IdG6BP0z1BBDAW2nkGLJTcbfdwiElMr+f786H7LHSUkdEML2nAvuUyUV2X1a/33KH4fyf8nrsBdg2G293++ugfxroe5rsYQc+bPOf8IIFAWK7oPSp0HlDIk/go+Fk1GKAsX/hUzd+WPuh/jHLLuJqqvh4F1QsEbW42+C0c8MXPJdUSDvfXEXK9kg+uDRd8K4B8HRAgIt3QwH34Gd78m6exBMuxsmXHfsCRigMAN++0BaTal+++xlkDAZTrkAfHr7UtiMeJ+0sO99JzLxXuADUaMgOsVgmQyexzAWq9VA/RGZlKg5BNWHZNmQq/dYAAiKFLmPgxv4JoPfKF1LkaVbsO0JVtHqHjP/gT4OhtBoIPcUiQkbwu8yiH7f+D7aNkiPkZI9fRH+KgT0qfKgKJA9UzwjAEKfheC7evfpyIOM6TIK9zkLhn9lOolBUSD/Zqh4VeRqSevAy4z5Tl+UvAWHr5c4bvBFkLzaslRpRQvZz8Oh+2Wk7jUKpnwC3hZMgrVXw877IPMdWXcPh6n/gdgLLIizK5D7K2x4BEp1dQrdg2Dy7TDphmNnGmUIjQYObIB178O276DVHhrqRAs89TSYvxRmLgRXd9sR778t7PvAcUq8KpVqPvAiYA+8rfSJrqhUKkXRao/daFBRoLkMKtOg8hBUHIKqNKjKgNA4ic32ukB78EuAwBQISJHJOf9E8Iw+NiGCut2w+3JoyYHTMs2L6v8J0Koh52Qx0gG9mMLnfAh/VvTBfdGeBlkpfTbaQfwG8Jhh5BydkBYD3bqnDpfREHQz+F4I9rrYZVsaZMwUD4mASyH2PdP/L0ULR66A6vfB3huS14PHOMv/5ppf4cD5ElbxmQ6p30gKsCWo3wPbL4aWbEl7HvMcxN1o2e+hcgdsvhFq9si6WwjMeB2iLZnw6yHgh6F0p3575AyYeDO4GBBwQAL4RFv29xwt2pphw7fwy0ewc52QMoCrO8w+B9VjH9qGeB+3sO+DxyHxqlQqeyALOAUpg7ELWKIoymGDPkOn421rgLI0KE8Tj4XiTUK27fXG+486Cxw1QrA9ROuXcGwn6HqgUcPhxyDrKRlNAaS+AL7jQF0B6nIp4dKVL4Y8mlZdTLXPa9c46MoSe0N7D8ncsvcwWPeQChaOnlK5wjVJqh8cDbqqpKJDVxG4joWOLLke1xTwWQohd/VPyshbAE0/6dcdR8DI3Xoi7Qt1NaQPQ8/syN82bAWEPCzrLTvg8MmSjDHsZoh6yYxxTTdkL4HaL6Ue3cifwdOEX64xNB+AvQtAXQpuw2HcT5K8Ygm6W2D/bZCvG8GGnAnj3gA3CxQIWg1kvg1bbtF7ObsEQfg8cA8DlwAZJMScY9zPQVEg+0dYuxTUTf3fB3DyhHvqjn2V7NpK+O1zIeG0HTB8NKpPDtqGeB+zsO+/jk/inQo8rCjKfN36vQCKQWjbJsTb2Q6VmVB2SIi2Z9lgUL0hcRZU6JydXH1hWAoMG6Vb6l67HDuzj17QqKGtEFrzoexbyWBqzhTBvyEcAMMnZq8x0GnEO8IQ7qOgK818H49x0GFgEu8UDq7JQsQ9ZOyaZH6iqi86MqH6NQi+T24cJffIhFpHhki9Yt4HF4OJoeY/ILen2KKzfCauqRD/g2TPGcORM6Hpx97bnCJhlEH2YeNvkLVAvBPCHoHwh01fs7YTshZDR6VI/VK+lxGsxX9zKexbICTsOQ7in4FAKxIYSr6E3VeDx0iozoQJz0P85ZaNfhtz4eczoTHT+PunfQfRZiYPtVpYdzdsf55eNzOAgCS4Md3obscMxUegrhJV6nTbEO+jFvZ96Pgk3vOB0xRFuVq3fgkwWVGUmw36WE683Z1QkQPFadJK02UZGNbfjxfEFjIkGUJGSTXioBgITgHP0GM/0dXVDE3Z0JgtGWktB4Ro2/JF49v3y24Mzp7gnyLppM4houV1dhMFgZ27LO11SzvdazsnQCuPwZpWWWpb9evaFtC0gzpDqhW3HxZ9a194zZWqFL4LwfcscB1l/WfY+AsUXCVOYSoXCH8Sgm6REICiiMRMUwvBT4h0TJ0DbhMg8m1wH9P/eNWvQrHhhJ4Koj4E/z5mNnVfi9mNSwL4XQLh95m+dk0HHF4K1V+Lvjj5M8u0uj3oboaMa6BiM7SXQsLDkPCg5WnXbcWw8w4o/FLWg+fA1DekmrUl+H0ZHFnTe5u9C1yYA55m5HU9yPgKvuhj5t4GJCyCOSsg5iTLrmOIYLMY70oL+z58fBLvecD8gYj34Yf1o5DZs2cze8Z0KMsVSVltMRRug5I0KM/SZ8IYInY8OLcJwYaOgtAUWQbEDl1FYmPQdksJ7eYsaMwSou1p7QZyK7dQUBmqIuzALUKqymrVMguvUkFXPTTs5y9SDj4dphs8jg8FFI1MTrWnCxG3pctre1do3aHv5xwNPguFiD1nWJ7M0d0AxbdCrY4cPGZCzOr+JuXdtZC3VEr9dFVB/KdiTG4IdS6k6x7nVS7Q1SGZdSPXi9zKELWfQ/Y1UlkjbAVEP20m7KCB7Bug7E2kptqbEGqimrCp/bMehazHAEXShMd/BC4m6qz121+BvI9g1+2grpHYb+rDkHzXwGXfNV3w9XhR3vSgA/AYDic9DgnnDzw38fOtsFNnWOvsD3XNMugBiD8Zxl0JE84Dh6FP4NmwYQMbNmz4a33lypW2Id5HLOz7yPFJvFOARwxCDfcBWsMJNpVKpShb1kJBuhBtYTqUZEKX7h8dkQBtWT2dITAGIkZB+CjdMhlCEo6txKy7AxqyoP6wlIOv17WGbLB3AqcW6PuvsnOWUYvXCGm+seAeK5NlbhGmf1Dt5VDyOZT/CFGXQNRlQ/7nGYWmVR7bG76D+u+lBloP7L0khup7JrhPtmwk3PAdFFwD3ZUyWg9/DgKu6S2i13ZA/pWS7IAdRL4g5zE8ft5i6K6QVOGCm6DxZ3AMh+Hf9p8gq/4Usi+VeO6wqyF+lemRqKJAwSNQoHsmjX0CIu+1bpRftQ72LBUTI+dgmPAJBMy2fP+OGth9J+TqblK+KTD1LQicPMB5d8I3U2XS0D0COpyhQWcmFDYDxt4NCQtM/y3dalg9A8p2wZIfIGQCbH4FNr8KQaPhwEbwDoaZV8Osa8DPgpG0jWCzEa+ZiFOvviuPT+J1QCbXTgbKgJ0Ym1w71cjOQZEiKYsdA3EJQrKhieByDPWGXe1Qmwk16dKq06ErDxoO95aYGcIjEsLHg0e4EKx3gizdI47fRIm+UDTQshMavof676CzWMIXaMB9AgTdBP4XDpwK3FUDRTdJJpvbBLALgrh3ZXLrr3MpUPYolD4i60E36CbJjEz0aDvgyEXQUQLt2ZCwtr8ut+4nyDxP+gZcKOXZzY3WS1+D7Jv4y9w8fgBz875oL4M9S6B2E2AHIx+FEfdZd4yydbDtWmjJl2uNuR7GPwCuZtzVdj0MB5+DBf+DwEmQ9i5sXQmeIyB7o3iUzLwfEs81njHW2QK12RBicPNSt8LOT+F/L0iYD+SJMnUhzL0REucOeQjPZsT7Lwv7PnYcEi+ASqU6Hb2c7B2lj3RZpVIpyv3zhWSjkiEqCSKTwO0YpjR2q6EqU0at1Qf1JNuQR7/Ya+hwcZ3yjgffRGl+iVJ5wicBnP6Gsth/N9ozoeY9qHpLCmSCPPIHXg1B1w9cfr7uMyh+UApUOgRC7Or+YYWajyF/uUySec+HuE+Nqy+0ashZBrWfSUpv/GoIXNq7T+MmyDgTNM3gezqM/FLi4qZQ9SVkLAXncHAZA6NWg6MVE7Habsh6BLJ14tGw5ZC4Um7GlqK7DfY/AtX7IO83cPKC8Q/CmFvA3sKnva422PM2bHoSWipkW8BImHkfpCyROoWWQFEg+0/44zXY85U+/BecALOvh6mXDpkG32bE+6CFfR+3jnhVKtXNwA2ABvhRUZR7BnWN/ziTnKOBphtqc6SWVEWaTsubBrVHRJYTmwq1+w0uzh78hkNAsrRAXfMdLiGF/0NvaNuh9lOofBna9uk22kmCQ8Cl4GYmOaCzBPKWSfVjkFFz5DO9s86at0LO2RLmcE2G4d+BixGPW0ULBSug/HlZj3oaQlf0Ho217IG00ySW7DUDEr8R60ZTqPsDDt8GzQdFJjZ2LXha6fhV9StkPSl11uw9IPU5iLvaulFibTpsvQuKdMY5XjEw9RmIO8/y43R1wL7VsPlpKQAAotOdcS+Mvdw6+WRjBWx8Cza+AfWlEDYasrJg8jlw8nJIORnsbTfPYjPitbAClOrflhOvSqWaA9wPnKEoSpdKpQpUFMVINQULjnVcEq9WK6VJytKgLF2WdmrI+Q40nUYuwg784yHxNPDwFnINSAa/EX+Phvd4h6JAyzaofEVirppuUUT4XySSLlcT/gKKVoxrSh6QbC7XZIj7uHe1CHW+yMPsXKGtCEZ8BD7GYlVA2QtQcIe8Dr4ZYl7o4xdxGNLmiVFPRwWkfAvuZsrXtOVJefbmAzJCTn4bQpdY9dHQXg67b4DSb2Q9aC5Megs8LKiAbIjCX2DLnVCfIevuYTD5MUhcbvkxNF1w8CMZAddmy7aYeRA2G6ZdAx5WlJHXdMP+72HvL/DzW3pzpIAImLMM5lwOIUdfIt5mxHu/hX2fsIp4PwdeVxTFnJ+rZef9RxOvokBDBZQfFv/dsjRdwkS6xKMMkTgLyjeCbzQMSxbdbvAoWQYmgOMxKkSpaKGzCZz/4em/tkJHEVQ8B1VvSJgAOwi4DMIeAhcTWXiteyD3YujIFg+FiKd1k2q6WGR3A+RdCzWfAyqI/LcUozQ24qv5DHIuk3P7nQvxH0i691/XVwqHFoi8z94Tkj4y77OraYP066DsA1mPuhUSnh1YbWAIRYHiz2HPTTrVgiuMuAVGP25dkoK2GzLehq0roKtFtjl6SfKEZzg4ekgVlRFLwd2Mb7JWA+lfwp9PQmObSDYdXWDCJTDzVlEIWYPqIvjjfVj/HlTm6bcnzZRR8JTzBh1KtBnx3mdh3yetIt59wLfAfERLcpeiKLvN72XiWP8Y4m2uheJ0vYa3OE3WW+ogeQaU/9m7v3eITlqma+EpEDzy2NnhqRtEUtaYJdrdntdNOTBsBpz+v2NzHf8UqIug9HGofhfQyORY4FUQ+oDETvtC0wpFd0D1myI5wxniVoOzLplC0ULxo1CiE2T6nQ3x7xtP8mjcAJln6ybt/CDpU3CJ6n2uzCugSkfkMY9C1P1mUowVKH4dDt8qI3Pf6TDmc3CxstR6RzXsvQWKPpV1ew8Y918xPLc0bgvQVgU/LZQUYmMYcxtMt6DImFYL2b/Bxpcgw0C2OHwuzL4NkhZYV55Lq4WMP+H3d2Hbl+I26OIBzY4w4VQ4eQlMng9Olv+tNiNeC4pGA6ie6k28KpVqHWDsLvYA8G9gvaIot6pUqonAZ4qiWPkoozvPMSfe1iaRlvXIy9obIetnGdkag7svpJ4KgX5CsGGjJGnC4ygK/qlbIeM7SJgPbr6m+ykKtFVCXYbE3uoyQF0HdRukeqwpBEyERTtNv38ioyMXSh+Fmg8BrUjJxhaBg4n4av33cOQq6K4Ce1+Ifa23d27dD5BziehzXUbAyLXgltT/OK1pkH01NG2XcyV+An4GIQpFgaKnIO8BQIHA82Dke+BgZrK0YTvsO1/ShP1OhYgbIcSKZIseZL0I++6Uz6MHLsPALUpc7kY9Cp4WPKYfeh02Xd9/+8kfwMhLrLumyiz482XY8R506p4eA+Jg1p0w/iL53VmD9mbY8jmkbYGvV+u3e/jAzHNh3sU4/qo2AAAgAElEQVSQOnvAeLDNiNfElNeGImk9WLnFqhHvz8BTiqJs1K0fQfIWaq2+xmNCvK/fpSPbNKgq7t0hOBq6CqSMdESyyMr+Wo4C3xDbSljKD8EHi0XlMOM2WKQbKbRW6ZQOh/QkW5chvryGcHAH51ZwcAVvnZysVxshM9LHMzSt0pyCBu5rCu2HoeQRGeXFvmO+b2cF5F4FDT3lzC8SAnbQ/fjbj0DWudB2SIg8/j0IOL//cbrqIPMSqPsZUEH0Soh8oPfItuZHyLgYNE2Sbp3yLbiaGbSoqyDrXih4X0bhEZdBykvgaGUoqSUffp0g19gXqc/CyLv6bzeGP2+Fg//Vr3cDantIvgSmPQi+FvpH9KCtAba/A3++AnUFEHYSZO+BiefDrKshYYb1v7/yAlj/Gfz2MeQe1G/3C4Y5i+HkiyF5ktHj2ox4V1jY91mriPdaIFRRlIdVKtUI4DdFUSIHdY3HhHjnGmxwdIaoRJGWxYyC6GSIGwUBUUNbhVhRYOc7sPZmSZAA8AmFqJFQdUjc/FGBr7OklfbAyRv8k8AvGfySdK9HgsffpNvVqKG7XoomdtUDnTrPB8Oaa/R+be8pNwzHICFTY/pbRYHm3VD+JlR9AmhgcuHRkS+IJtiSdFpFEblawe1iuOMU9v/YO+/wqMrsj38mvffeE1IhhAQIvaMUsQB2XXt3XdeyVXcVt/3corvq2rsuYgULKlVCD70EAiGFJKQnpPdk5v7+OHeYSUiZmUwQ0e/zvM977533lpncfO+55z3ne8T14HWxfK5tgfy7Jews+kUIvr+f4+ig6C9QtAxQwOcSSHy/Z0RDaw5kXQGdlTBut4jeDHhtOih4HrJ/L7+1UyikvgGBC0z5BQxoKYKv4w1iNwDYwdTPIdyEysYgIWMfpUJDrrhzQhdDzirD75x8P6TeDiGDVPzoDZ0Wsr6E796WSTQ9guJh5p0w7RbwsOBeKMyGDStg4wooVcsDhY6B2jq4+GqYdzWMNpCw1YjXxOeY5l9mEa898BaQCnQCjyqKkmHRNZ4T4n3vT0KykaNEmtGK4Sf9QtsN1ScgfxMc+ABKD/RdfdgDyUJz9AD/ZIiZDB7h4KsSrauVLe6+oOuG9nLRAW4thrZT0tsir9BdKsl21crkjzF8J0FzZp+HPQOvqdC83bBu6yGEqnESEgchlO5eb0wTCqT+27lEWx7k3WT4TsG/hvAnRZdCUaB+nUQ5DPY3qV0jmgzdtVLLbuTKnmpk3Q1SHdhzkCwxYzTlwIFboU69tsg7YdQzMuFlKrKehKNGCi4dSERo9LWQ/g9J0BkMFZmwehGk/AImLJN49J1/g9JMOKkmPsTMhqmPQvxC8w2a6pOw+U3Y+jbUq6nvtvYw9gqYcSeMusj8/2FFgWN7IONT+PxDqDB68w2JhIuugvnXoEmZaB3ifdTEsc/8QBMoBj3BuYjjbaiQ2N2yg1B+WDR4K7MlcWIw3PA2jJgjZDucBNtRB02qrkPTCQm/atitEm0pPfx/evhPgqZepKqxA3tvseDsfaS+2xmRcU2vXl128IK2bOisEl0Epcu0aw65DzzSwTVV/Ko25yj0TumWqhElT4HjaHk4xL4K3hebd5z2Isi+Six59xlS+ifyYdMs8H6vTQt5z8DxP4rl6hwBY16HwH5C3nqjuxW+ToC2EghdAq6jIesf8pblkQiBi2Dcb8FlgKy1/lBbCDufg71vSGYagH8iTHkY0m4yP7JH2w2Hv4WM1+HQ12L5x0yBgpMw4waYdaPExpv7f6PTwaGdsO4TWP8pVKnVKdy90Oyotw7xPmLi2Gd/It7B0dUuoWWnDkOJUWuqgqQpUL6j53jvKPCJkmW/OCl/UnkU8jcb4n0f2AFRk61zfbou0XNozofGIyrR5krfUdNzbNB0qUSsh2MQuERIcw6X3jVSMqgcVJK19xafqanlyitXClEEX9Nze3e9EHB7MdSth6Y90LRbXvGNYcxPGjvwXSIxut4LwH1S32m91kTLYci5GVpUacyAWyDm2YETIXpD1w5Ff4P8vwpxeE6GUe+Aqwm1zAZCYzYcuAXq94JLmrgrUv8JriZYrDU7oPB9GP0XEalpLoY9vxXiLM2UMj8pD8DYX4GzGbG2erQ3wJ7XhYQbVOlUFz+YeD9MuAc8LKhEXFsK296Bg1tgj1HETvhIIeCZN0BglPnHNSZhRyc0D//dOsT7sIlj//0T8RqgKHC6BIoOS2s7LZEPFTnii+oNZw8YvxTcXUQWMiRFYnid+nkF7GiG3A3QUgPpt5mvcKbrhsY8qD0qBTH1rT5HrLXQmVC/uec+ti6i6eAeL1aq9yipKOASIT5Dc8KLBkPjQch+AOq2AzZwUe3gKbDaFij6M5Q8I9/BPhDCH4KWg0J8HUVCYhq939hT/K/eC8Brft8hY9aArgtKnxGfrdIhfuoRL4CfCWVwjFHzLWTfCR1l4uOO/RtEPDg061fXDYVvwL5HRHrT1hmSfguJv5HJV3NRtQ8yl0Hhalm3d4Mxv4C0R8HZgigebRcc/Qy2PQOle4XQW+0g4WJJoEiYa74bQlEgZxdk/A+2fChhoHqMmg4zb4TJV55VS81UWM3H+9Dg4wA0//mxEm9rE5QchcLDBqItzoKWesOYkVOhfLtMZgUlQFhKz+YzjG6ClkqoPgQ1h6XXaKHks14TJEZwj1arA7gLyerJ1nmYfMWKItq7zcehcb9YuTXr6THJNvpVcApRs40UVdPXCez9pdl5Ga6tJVtIzmu2uBr06G6UlN66NVC/Btpyel6HS7KQodd8cOt7xnpIaD0BuXdB4xZZ97kMYl8yj/C76iDnYSh/V9a9pkkNNVMrSfSHlmI49Bso/kjWXSIkXTj8Kst+h4rdsGsZFH0r6/bukPogpD0CThZoJCgKFG2DrJWw/gWD8eIXA1Pugkm3gYeJkpbG6O6CA+uEhDO/kIIFPqFQUAFj58DsK2HGEvAxfVLOasT7SxPHPnehE69OB2UFUHBYQkz0vacPVO05e0cPP4gcA5EpEDsOIpIgOAkchikDTdsFNcfh9AER0tGTbWuveF3/MdB2CNwixWr1HiURD96jwCvJtGqwpkDRivJVayG0FUlPt1ifnaeh67RMtnWeHtxnawcYGzbes6Apw7CusQN7PwMR2/tBwI0DC4S3n4S6tULC9RtFcN19OjRuFX0F/xukuQyQomsuFB1UvAEnfy3hYG7jJfQs7Bem6wUDVH8lYuadFZJVFvcPCL9vaNYvQNUWSZioV10j9t4w4TUhYEtQnikEXLxW1oPmgM8YGP+g5TXS6kth51uw4w2oUwNabexgzGKYeg/Ez7Esuqi1CTJXwdFd8PFrBmEdGxtInQGzroSZS8F/YDeH1Yj3F4OPA9C8cCET770T4WQWtLeePSAgHMK8hWAjUyBKJVuvwOGzYtsbofIwVByAioPSqo6AZzh05/cc6+AB/inglyKk65ciOg8OVs6Qq/4OTi0Xgm0tlAgHpZcovN8MaNpy9r4aR85Mzilazpqo8x6ninRr5Dd1CJQClF3V0rRNZx8z5l8QbuLUsK5TrOH6jVD5JnQaicK7pqokfN3gKmamoqMM8h+ExiyxhF3iRc7R7xLTj9FVC8cfhIrlUjdOp4NRz4HvzKFdm04Luf+FA49w5u+gsQXHQHDyl/jf0Msh0cTZH4DynbDv73B8o0yaaWwg4UqY+AiETrL8Oo+tg+2vwpHVBis4bDaMmApTfwYhCZYdu7EWtn4Jmz6F3evEMtZj9BSYdwtMuAjCz46fthrxPjD4OADNfy9k4tXfy36hMCIFYlLUfrQIodsPoxJYUyWUHBCth5o9UHkIavP6HusbD1EpQrR6kvWIHP5wMoCTr8Khe3tucwySqhUukdK7xUlcsYMv2PuqvY/4FvXQtkPFp1D8EtTvlG1jv4TAgepxtYt2bme1gYzd0sC1jwyxwaBooWEzVK+Amk9Ba+Qy8pghAuW+V4CdFR5cNd9A3sNCvgA+CyDu3+CaaMYx1sGhu+RBBxB8NST9U37zoaD5JGyaK+WfesM5DK44dfb2wVBxAHb/G7JXiH8ZIHQyTHgEEhZbXqxSbwUfWw/7jCZ8Y9KFgCdfB54WxnM3N8C2ryDjM8j8Fjo7RBGwIBcSUmDOYpi7GJIkOsJqxPtzE8e+eCET74EMIVmP4dHwBMSPVVcs8bol+4VsS/dDg1EJniB/aKsWyceAZCnlHpQKQWkQmNL/ZNy5QFMO1GSoRBslfkLbIbpVGg9BUxYEX2ueyIu1oOsQn3D1B1D7pWj4dpRIvTj/ayHwDvCYMrQHm64TSl6Ek8vE/aCxg7AHIOpJ07PLtG2Q/0/Ie1qkL22cYMRvpNkNwXWkKLDpYqja2HO752SYvkLcVZagqRT2vQj7X4F2NQ7bMwom/AZGXgOuFqbT67RwbAtsfx92fQrt6puQjS2kzIdpN8HYy6UOoCVoaYLMNfD1p7D1W1nXIzgC5i5G8/jz1iHefvJrzhr70oVMvMMhC1mVD0X7DU2phfL9Z491dIfQVAgbKwUxA0eJOPQ5qCf1E4zQ3Qi1X0P5y+IH1sM5EYLugICbh5Yl11kFBX+EstcBRfzUUX+B0FtNjz9uOwXHfgtlK2TdKQyS/qE+uCzMUmwthW9GyvcH8T60Iw/CuNsh5ffgbiEBd7ZA1rtiBdflgVMc1BZD8pWQfhfEzLT8odbZBvu+hO3/g8NrDD7bhLng6A9Tr4LxCyyvGNPZAbs2wcbP4bsvoKYCYkeh+eqodYi3D0mLPse+/BPx9g2tVopgFhuRbPEBaGvsOW7UVKg/DqFjhWTD0qT3HWG9VOT2erBzkvYTLEfrCah8Cyrfga5K2aaxA5/LIfhe8Jpj+URX0wHIfQi6GmSSyykCYv6gErCJVn/tNjjyIDQeEB0HxRtGLoPgAeqYDYT8N2HPnbKc/haUbIT8D5BCmTPAMQLSHoaAsQMepl/otHByPWz+D+SuM9LKjYP0O2HcreA2hIdaYzVkfgTb/genyqBSdZM4OsP4S2DqlTDhUsuryuh0kLUHmhvQTJtvHeK9d/BxAJpXfiJeccKfyob8fZC/X1rhIUgaD8W9JpW8QiByrFFLs25YWWcLVB2A8j1QsUf6+jxY+hXEXmqdc/zYoeuCum+g4k2xhlHANkhIOPgOCLpdStybC0WB6q8h93fQrKbQOsfAiCcg+EbTfKGKFk69A6c+gXI1qsB7HCQtM5+AFQWy/iBKaCNVodj645D1Tym33tEg20JmQNpDEH255dWzawth75uw9y1oNEr3HXmFWMGxc4dWmbviJOxYCds+heNGWZX2jjBuPky5EsYvAi/L3B1W8/HeY+LYV39sxNvRDgVZUnE4d5tKsocNZaaNMWEReDr2JFnPAYSfzYW2CyqzoDoLyjYLyZ7OPrvgpa0jzH0OUk38q/4E09FRBtWfQMkLIi0JgI1MmAXfBb6LzPdTK1qo+Bjylhkm4FziIXYZBF1jmlXd3QoFr0LO36FDtc69x4sFHHTJ0B/0DSfh0Atw9A3oUn2eHjEStzvydsujZ7TdknS053U4rqb7hkyDkjwYfx1MuAEixw/t+qtPwfaVsP1TyN4uD5iIZDh8DFKmwtRLYdqlEJVo8nmsRrx3mTj29QuZeFuaIO8QnNgvLWe/yERqtZCQBjUHDDsEx8KIcTBiLMSMhZg08DDj6dlUAzlbIe0ysO3DslEUqCuEkl1Qslv6sv2iWBYzHWpU/6PGVkRzgtKlBadLGNkPpRZbd7OQmT5SodOo6ddtXaEjx5BYoW/G647hoK0DB6MYX328r4N+PVAsU1srxDArOqjfJL7ampWGGGWHIAi6TfzBLmaWmNF1Q/kHkP8UtKkVE7xnCKGHmjjx2B8BJz0BwZcOnYA7GuHY23DwOWhUIyEcPGDMIxB3rcxLWIqGUtj7Nuz+AoqMCiYExEH6DULCgUNMoa4thx2rIOcQfPaWwScMEDbCQMJpMwaMYrIa8d5p4tg3LmTinaYx+J30sLGBiER5MiYkCdFGp4KrGdVde6NgLzy3BGpL4O53YPotojdatBuqsqF4g5BtSx/16fziIXYeBMcJ0QaMAXsLZ2/PFbRt0JIHLblqO2FYtveCzuMD7+8YBtqSgcc4RUFn4cBjnOOgPVd8lS5Jaks0LNv7WUZMndVQ+Z6QsD5Tzm0SaJwh5DYIuHLg6sG9oeuCsnch/8/gEAXVWyRdO/pBiLrbtCiI3gTsPQ2aayDxQYi5eegJNDotnPwSDvwbyraK+HvNCQibAmPugKRrLK90rShQuAf2fAB7P4TGSsNnkeMh/UZIuwp8h5j+3dwAmWth22rY8Q00GKUVu7jDgtskpHTGAggM7bGr1Yj3DhPHvnkhE+8MO4hJhvix0hLGShyvs5WyvAC2vQ9v3mVQJAuNAzdbqFTJxysMFCPBkPCJEDYRwiZAaDq4DGOo21Ch6ER4p+EA1O2XBIumHdA+QCyovY9oWDgEiGWqb/ZGy3Y+ko6qT6zAqOnXFR3oWgxWcle1xPx2GVvOLlJ+p78MOjsfCLgW4l6y8Psr0LANKt6RGGV9hICtBwRdByG3g4cZacq6Tij5QELImtRikrZuEHkHRP8SXE2QwuxuhYLXIOcNqFf9yA5eEHsnJDxgeaiYMaoOwt6XJG5Xrzbm4AYjrxMSDplouaWt7YacTbB7ORxcKaFjEelweA+MSIcJSyF9ieVJFGfOo4UjmULC21dD/hFwDIZKNckmIQVmLJSWNgWNg4N1iPd2E8e+dSETb0e7WXWXTEZVAXz3KuxYLqWnjWGDaO3aOUD4OIicAHGThGy9o85NUsRAKFoN2++F5IcgxUi1WdcNjUeh/oC0hgNQfxC6e2WXOTkCWnCJAdc4tcUblp3Dhp4Caw50XZJG3HoMWo+rvbqsbRQXQcIbQz9PdwNUfAhlb0OjUQ0y1yQh4KCbJEvMFCgKVK2B/GegRh9rawPBSyHmYfCdMvgxdF1QvBKOPwfVasKKxgbCF0PiLyHAggoOvdHZDMc+gUNvQomRrrLfSEi7FxKuAg8z68H1OH4bZH0Nx7bCutdlXY/QJAMJR48d+ncpK4RN30gcb+Z30GaUzeofhGZ7hXWI9zYTx759IROvNc7R2Q5FByAvE/IzpT9dPMAOGnh8g6Q/2p9nJdyPPA+ZD4s16R4D05+H6h1QsxNqdoGbjyQaGMMpBLzGgleaNM9kcIm2PFvpXEFRJIVY0VoWoTAQmo8KAVe8L3G8AJ4zQOMGITdCwBWmJ0A0HIT8Z6F0haRqe02B9jaIuQsibpCMwcFQs0cIuPAjQ7p32FLwnwOx11smcHPWOY4LAWe9JzoiQdNlcjp6Foy5DpKXgqtlymCAFKw8tBb2rIL9X/UUq/KLgIk3Q+JUSJkp4WRDQUc77NsGW76VFhqF5o1vrUO8t5o49p2fiNcARYHKQglXycmEllI4/KVEHxjD3gkc3cArWCbgasug4oTh878cFKGd8wXdnbDlFsj/sOd2J3qK2IQvAgcXlWRVsnUaYgmeCxm6LkkfLn8bmgokWw9ksi9wiZCw70WmPaTaSuHkf6FsI9Sq4k22LhB+jZCw7+TBrb7WMjjxMpx4FWyDRXTJxgGil0DibRB60dBCukD+F/JWw+HP4eCHBn1pGzuIuxhSroNRV4DTEOZMurvg2GbYvRL2fg7NdVCPEKajM6TOgfSFkH4JBFuhUkl7GxpnF+sQ780mjn3vx0y8rU2Qu0cl2l1CtvVGqmBpM6B4K4SOgthJMGKS9CGJZ9/A9RVwLENE06ff8v26FBQd1GVD0Vdw7GVo6ccn65cAUYvBfwr4TfqJZIeCjmoJIStbbtCqAPF1B18HwTeAZ/rgSTXadihdBQWvQ9Umw3aPkULAkTeJiPlA6G6Hk6sg520o2cAZqU7XMIi/GRJuBa9B6r2ZgvYGOPo5HPoQ8tYbBG/sHCFhEcQvgeRF5lcONoZOBwX7YOsXsOdbyOuVJRqeaCDh5GngaFmSkdUm124ycez7Pxbi1emg8Jg43I/ugo4m2PexbDeGhx8kToKESTByMsSNl8mi8xmKTgTRyzKgPAPKN0OHCZWfR/4cpv53mC/uR4iWfAkjK1sOLWpUhHM0dCgQejWEXgVe6YM/nJvy4OQbUPgOtKuRAO4jwSkRoq+HsEWDC583FcOJ9yDnHWg0UsCLvAKC50PcUnC1QBO3N5qr4ehKOLgCCrdA8Fg4tk+1hGdAyuUw5grwjRraeU6Xw941sPsb2L8eWtQkkPiJsP8QjJkK6XNhwlxIGmdyjTarEa+J1e41/7uQiXfLlwaizd4tFq4esaOh8RiMSBOS1ZNtUPT3PwFmCuoLJEyteAO0V0NdRs/PXUMhaCY4B0L4JRA4TUrIV+6Eqp1QewRSHxMf4PkCRZHIhfZTRq1YelsHaC8QARylQ5TNdB09m1uKTLTZeoCdh6G38wA7T1l3ihYBdudIcIqUaIvh+nsrCjTuEwJuzodSoyq6zhEQciWEXAU+kxiwcrSuC8q+Eiu4sx1KM2S7vTtELBESDp47cFywokD5VrGC8z8B33QoyJDzhs2EhGuEhF2s8NbTWAbZayBzOeRu7lm9JXQ0pFwhJBw+dmhp9d1dkL1TLOHiQljdy5Xm7gXjZgkJT5g7YEKF1Yj3RhPHLv+BEa9Go7kaWAYkAumKovShUKP+CL0LuQZFwKhJMGoiJE+U8LKhOurPFVqr4NR3ULxRyLax0PCZrSP4B0PQNAieBSGzZPLsfH2A6DqhOVs0CRoPiIRjY6YQrK69733cR0Hr0YGP6xwHbbmDjEmSqAc9bJyFgJ0iwTlK7UdKsoRrvHlC5wNB0cLpHVD2KZR9Bu1G0TBOIULCwVdJRMNAPuGWUplEO7kCThslJTj6QdTVEH0DBEwZmMi7miF/NRxbDoVrhdhBjYqYBfFXW4+EW+rg6Ldw+Avp242NnwXgGiQKZMkXydvmUFBbBXs3we6N0koLen4+dj64+sCk2TBxFkTFnvkfsRrx3mDi2A/MKu8+AfgvYA90A/critJHFQcTjjUE4k1EtJZeRerL90+8981SSVYlW78hhL6ca3S1waltULAOTmdC2baenzt6Q/gciLgIIuaCV+z5SbTdLdB0WERkGvcL0TYdAcUoRdtrghS9BLFMnSIkEsExXHqnCPGV2rmI4pfGUXrjplEjSLTNEvqlbZS4W32vX+6shbZ8qQTcXigFOHvDPgzaS0S7wSUO3EaBWzK4jpJll9ihyV0qOqjbBaWfChHrNXltQyVON2ghhFwKwQvAYQD/aGOuEPDJFdCgxo1rbMAhHoJnQtQVEDZn4Lp67fWQ/wWc+AQK1xmRsK2cP3I2xF8qmtFDvb+6OsQCPvQFHP0GKhtk4gzk2NHjhITHzJe5FLshSoqWFRpIeF8G1LRBvdHfOzBECHjiLDTX320d4jXxBVKzwizizQD+T1GUtRqNZiHwG0VRZlt0jUN1NWg0mk0MRrzDXd7dmlAUqUZRsE5a8RaZJAGImgh1hyB0upBsxEXgnzr0GerhQHcr1O2Amu+k2btC/Xdnj3OJBY+x4JEG7imqtRluHaFys663UUi4rVDtT0pWXvNRNc23j3tI4wCu48EtQb6D51hwH2OZhq6iSMXgym+h4APRRz5zHlvwmwYhl0HoZVJDr79j1B0SAq7NgrxvDZ/Zu0H4AoheDJGXyAO7P7TXQf6XkPMxNJ6CwizDZz6xQsDxl0LE9KHLm+p0UvPw8FoJI8vZKsSsh7M7pF4Cv1hhHYNCp4O8Y7Arw9Bq1QrcESPQZORbh3ivM3Hsh2YR7wpglaIoH2s0muuBRYrJ3uRex/qJeIHmKpmIyP8KCtZDc3nPz4PSIGYexC6E0InfnyxkdxvUZELgzLNfYXWdYr3pibYus2dBTu+JYNMG7mlCsh5jwWOM+F3Pd2hboeU4NB8RItY3pRNaynoN1oBbovr9xsrkmc9088/ZlCv+3LKvoHqrWlJJRcBFkgodMg+CZgmp9oaiiAbJyS+g8As4fcjoEm0hZCbEXAMhs8E7rn9Sa6uH/LVwYjXkfgNttYbPHNwhdj7EL4bo2eBpQdn23uholRCyQ2shax2UHhP3w+Prh37svmBMxLa2aG68zzrEe42JYz82i3gjgW2IFWADTFYUxYJSIoMQr0ajWQ/0JQP2mKIoX6ljBiXeJ5988sz6rFmzmDVrliXXaj1ou6BoJ+SshZw1UqkiahLUqjJ3bsFCtDHzIOYicB3Ex6booKkEPCKG75q7mmDjAkm0mPgyxN8LrSVQ9iU0HIKK/wlBnYFGLEC/2eA3B3ynnXsrFuSVuXEveIy3fhWMrkYh4Ib9qvtkv+o+MRJo8UyHqbuHdp7OeqhYI5NyFd/Km0HJZvnMxl7CAEPnCRH7ju3br9tYKAR88gso3yJE7jcViraDRxREz4fIeRA5Fxz7ib3VaaEkU0j4xGp5MwORkzy+BQJHQvxF0kbMAidrlFcqhtYGiBg99GP1gYyMDDIyMs6sP/XUU9Yh3qv7OV8VZBhJtTyV3ZN4B+C8x4EHgRcVRVmlznHdrSjKxRZd44/G4q0tFJLNWQt5GyWMTQ87R0hcCAnThWz9Rw3+WtXdDsXfySth/leS6ntf+cCTKZaiqxm+WwhVqn/ZNRzc/aFe/cltHMFBkZpsfnNUop05sF/yXKC9BA5dDQ2ZkPBviHxo+M+p7RDLWE/GztFSwsda0HVLhlrpGihbBzW7e8qHOvpC8EUQuggCJoPHiLPvpfZaKP5G3rJyV0KbUcihxhZCJkHUfIiaBwHj+lbZA1HZy/0aCvbAwU9FQ1oPGzuInAhxKhFHTPhBVF2x2uSaiYWdNZ+aZfE2KorioS5rgHpFUSzKULEW8f5KUZR9/Xz+/RBvZxuc2AxH18Dx70Q9q9topj4gERIWQPx8iJkhmWKDobUGCr6WSZDCddBldGTsuMsAACAASURBVKO7hcL126T2lTXRXg3r50D9kZ7bHZFJrqD5EHIFBF4MzlZ41bQWajfB4esMqbx+l0LCMyJVqW+6TvHtKlpAK5aqou3Z7HwBRSb77DwNIWn2noZt9n6y/n1ManbUQfl3QsKla6GlSLbbRUJjEbiGSHRL6CzpPXtNvuq0ULlfohqK1kHZTkMhS40tEAjB4yBqJkTNkhqBfc0pdHdC8S7IWQ+5G6B4d8/wMccY8IuB+BkQN13KYDmcf5FEViPeK00c+5lZxLsfeFhRlM0ajWYu8LSiKOkWXeMQohqWAM8DfkADcEBRlIV9jDs3xKsoUJEjRHt0jZBulxHRJs8ATz9ImC/N20T1qNoCOLYKavZB3kc9rZuANBhxOcReLsvW+sfXaaFsDez/DTRk9z0m9jaY8BLYnkdliNpOQenbUjJdLzxujN4/j0OIEPBAsAuCjoqBxzhESWKDc5jIPOqb8bpzpIjnDCc5K4okW5RtlFI85VugvabnGJdgAwmPvOvs6+lolDepwnUSI350bc/PHT0hcrqQcORMIeK+LOK2BsjfDCc2QHkW7M/o+bmtPURPgPjpQsYjpoDLENKLrQSrEe9SE8euNIt4xwMvImZPGxJOdmDgvfo51nmRMmwp2prgyHdQtAf2LYfThT0/jxwHoxZIi55oWliMokDFQTj2ORxfJZUpAKJnQO1OiJgtZDviMuv6dBWdqFsVfgTFn0CbMdloOGtWP+p6mP6B9c5vLjqqJCmhaR807JXl3sI+veEcKSFpjiHS7H0BG7HsNHZqb9SwFQ0CbZOEpnU1SN/d2HO9o1nkKQeCxh+0LeAaA64jwC1Wen1zibC+D1rRQW22VDUpy5C+Tb1On2S4LmugvQX1RVC4GQozoGgz1PWKiU1cAtetHPw4DZUipnNii7SSQz01soNHQqsdxE+C+MnSh8Rbr16hibAa8S4xceyqH1gChcknsCbxKgoUHYaDa+DQGji+TXRFk6ZC+XZw84NR84VoR84Dj16TYt1dsPdTSJgJ3iE9t5fsEMv2+Odys+vh6AHxiyBpqQilO1oxCkBRoPYwFL4PhR9Dq9EEqVsMeI2GmJsgfInM4DcXQnMBtJRA6AJwHcbJvN7oboKa9VLLrLMSTn999hiNowjK2Pup1S1KJANOj/Rt4D11eK6vq1GSIdpLpG9T+/ZSaC+DhgLoqut/f409OMSBWzS4J4CH2twTwMlKlrKiQN0xIWFbZ0gyUbvQGPXFQsCFGULI4+6GaRb4sFvrIW+HgYhtnGFPr3BDN2+ImygkHDcJ4ibItmGE1Yh3sYljP/+JePtG42k4skEl27VQZxTqpbGRm2L85TBmDkSM6/8J3dUBr1wLB76A1Mvgvo/lNezwSji5DTryOGNVugVB4mJIWgxRs60/KdFSCrnLIfc9qM8Bd3chBZdwiLpWms+47z8Ro7UAqlZLq80wCJ17T4GWQ+A+FjzGged46V3ie04uKgq0HIPT66GrFqJ///26RjrrRL+hJR9aCiR9WL+uaKGhH7eHvYcQsFcKOMWAVxJ4JoLHEBM4rAGd1jpx5B1tIoJzIlNtO0XtzxhxM6C0HOLHQcJ4iB8PcWngaj1jxGrEe7mJY7/8iXgFWi2c2At71sDetVCUDc7NhokC7xBIXSBt9EWmPYG72uHFK+HwN+pF2YCvc8/JsTELIWQ0JC2B0AnWf8XqaoHCz+HEu1BqpFTl5AfJ90PYPPCfPDxREaZCUSQaoOJjqF4t6cRnoAGvyRBwKfgvkJCqcym0PtzobhMibsqBRrXpl7vULCv3RKg2KqeksRPy9UwCr0S1HymWsqXlec4XKAqcLhESztkJuZnQrIWsXqF5Gg2ExQsRJ4yHuHSpNuNumb/YasR7mYljv/oxE+/pcti3TiXbddBkFCRuZw/zr5EaTWkLITzZPEuw+BC8uBSqe/nGnIHoNEhZAqOXQtBI61uYik5cICfegpOfSlgYiO5A5GUQd7NkM5lTQLO1VMLE7KxYD669DEreh+K3xKXQXS5uZTsP8Fugku1CcBggh1/bJpZtzTpIeu7CIWVFEanJxuPQXAQ1B6H+mKQGNxdylu/dZxyU7AO3cPBOMjQftXf2/z6+hXXQ3SVFanP2GlrBYdmuh1c85J2AsBhITIX4MdInpkJQ+KD/Y1Yj3ktNHLv6x0S8XZ1wcDvsWAvVuZDZa3IgKBomLIT0BZA6G5zNtB7qSmHXh/DN01KIsC+kLISHvjHvuKaiqQSy34Gjb0t+focqBhM4Wcg25hrzqhEoClRughMvwanPJZoh7u6hXaOuEypXC9lWfYvIbgCOQRBxC/jPl0KOA71Kd9aKz7dqFdSsNSRwTBwmX253M7SelHTiribRVuhukUkzbYvRcqssu8RDw1F50PXXnKOgs1UeZmeaT891e89+QrhaoeEENBxTyfgY6GzgxCqD1kJvOPlB+EJQnMEnEXwTwTdJJmq/z7cdS9HZASePCAnn7oP9h+H4QdneGx7ekDAGYsdD7EhITIH4keBkCGuzGvEuMnHs198P8Z67ujHFebBzLexYA3s2QZv6mj9+uiiTjZktRJu+AEItEJqpzIP9q2D/SikNZAw7R9Hybak1uCzqBwlRMhfaTkmmOPoWFK01hJ25h8PYpyDuevA0U/S6swEK3hPCbdSLr9iplpaFaDwKxW9A6f+gs8ZwzMDFEHG7EO5AqlxtpUK0laugbnPPVFrPdAhYLGRmKbQdUkGicb/4mFtPGsi20+gh6j0DarYMcjAbqBtEPMpnBlQMcpyEh2Dsv8/ebucCvqnSjDG7GxoLoPaYTKadacclvKyxCAp6ndPOGXwSDGTsNwY8o0UUx/78i7c9AwdHSBgnTY+uLijMEQLOOSgl348fgPrTcOIwbMgwjLWxgZh4IeHR4846vMU4D3K2BsK5sXgvjYGSXq/6sckweT5MXQhpU8HBzEkXRYHio7BrJWR/AcVGiXMOzpC8QKIX0haDnxqz290pLofKXAgbDX5RQ/puANQcEbI99j60qcRg6wAjFsOo20VIx9zJj7osIduT74vlBpIcEXc3xN4FLmYmSigKVK6F3H9JdETDVtnuPlrINvRGcBzgFVjRQtVaKHpR9Hbr1cKQGlvwmQUBSyDgcnC2oKZaV4OI+dRtk1a/W53EcwRda8+xegvVJUauXbGR6AlbFxHGsVWbnbpN4wQoYt332xSJAe6sM2q1PdcTH4XkP5r/3XpDUWRi9fQJuW9OH4fTx6D2OLT0MgQCp0H+NjFAPCPANwH8E6VaiX7ZPeT7n4A1FYoCVWWQewQO7ofjh+HYYSjIkXkdgMTRaNZnWcfiPSujoJ+x317IroZU5DVj0sVCtpPnQWCY+QdTFMjbI66JzJVQrmq+jhwHdbkw5jIYtxSS54OjFUvH90ZnC2R/BPtfBwcHqFatF7/RMOoOSLoRnM3UNFV0kkaa9Sx0lUKLmoAQOAvifw7hV5g/g67tgFMfQN4zYukC2HtD5PVCuJ6DVI3trIFTb0PRy2J1gsTe+k+DwCvBf5G8lpuDrkao3Sy+4NqtIlXZ2zxxGyVuDqcQSfl1UZtj8PfzOq4ow09w7fVCwKePS99YCye3QG2+IZOtN0JmQnMj+MdLC4g3LLt4De/1Wgvt7SKSc+wQ2NqhufIm6xDvAhPHrrmQiffQThiVbnL5jx7QdsOxbUK0u1bJTKseHn4wYTFMuwaSZgx/NeGKg7D/NTiyXDKMADwjYeRCSL4DAi0IAetuhdz3Ievf0KBKEYbMEr2I+PtlltxcdNZCwSuQ/4Ih68spBGJ/CVF3g8MA/5SKIlZn0UtQ9pFYuCCWZuS9EH77wNZxX9B1QvW3ULocqr6S6IhatY6Zxl7C0XymC9l6TzWfzC9kaLskaaImR23HDcuKl7jY+oKbvxBw8ERw8YXAOAiIleb8PYglmQir+XjnmTh23YVMvOaeo7MD9m+ELSuh9hScWGf4zDcMJi2VljjNMjI3Bx1NcPRDIdxyo0oDYZNh7N2QdDU4WGBdt1ZA9kvS9HXZXMMh+ZeQeKdp5cTPOmYJFL4LuX8zTHR5pkDsoxB+3cBVHBQFyj+HilVQ9r66UQMBCyHyfghYYF6kgqKDuu1CthWfSByvHoFLxdr2nibi67bnsQ/zfEbzaajJhaoTUK1vudJ36t00AdBQ1XM/zyAh4MA4CIwV10VANASNANfvN23YasRromaYZv2PnXhbm2H3t0K2mV8b6rI5u0NsGEy8AiYulRnRc+HXKjsIma9IleAy1Sfq5AUpN0PaXRCQbNlxT2dD9gsSYqbXy/VPh9GPQvSVppUg7422csj+P8h7TaxFpUo0Y+N/Bf5zB/69FAUqvoTjy6DhoIiKd1dBxJ0QeY+k05p1Laeg+GUh3PZiw3b30VJmPeR6qXM2GBTthROSdq6hKJIMUnUcSnKhKk/mNaryoCofuntFHLQ4SKQRgLuvEHDQCAiKNSz7R4Nv8LCnEFuNeC8yceyGHyPx1p+GzNWw5TPYs66n8n1sKsxYCtOXQtQwxNj2hc5WOPQR7HoVTu2SbWFjwd1FtW6vsnyGuWIX7H0a8j+XygE126Sk++hHIHCqZd+vrRKO/R3yXpZy5ADhV0PyHyTLaiAoClR8pRKuqvPhFAJxv4OI2/oW9x4IjYch72ko/wRcIqWsj1OYSrY3gocJeq6ddbJ/6XLZd+xy866hu0UUzuzNtNq6mtWJtHppXQ2G5c56SaCwcYfmElXfQKf2alPUdedwaKmWCAVbJ+l7L9t7y5uHo6favKS3dzk397hOC7UlBjI+XQy52VCRL62z7ex9HF2hvEUmwIOj1RYDIWoLjpFtLkNPGrEa8c41cezGCz2cTI+KU7Dxc/huFezbAuPGQ/4uuemSp8L0JdJCYs7dNVVmC9nuexfa1TLVTp4w7haYcDcEjbLsuIoCxeuEcEsyZJutE/hPhDlviUSgJWivhmP/hNz/SuICQNhSSH4SvE0g3MqvhXDrVSVPp2CI+z1E3WV+Sm9dJuT9DSrVyr0aewi4DIIWi992sMkwbbvEE5cuh6qvDWnJ9j7yRmBqkcuWQtg2U36Pi3N7kq+iQEcNNOepLV/6JnXdYwyU9VEWyRh+06F068Bj/KdD8SBjAmdCweazt2tshYCdvCTeu75SfLMufmpvvOwHzr7gFmCanKkxbGwlyscvEkb2YidFgboKlYTzDGTc1gytO6GhBoqOSesNn2Co6YLQaAiNkj4kymg5ske87rDjp3AyjaLkZ8PGVdKOGvlJ7exg8S2QOh6mXSGvMucKXR1w4DPx3x43KvcdPhEm3Qsp15h/U+uh00LeZ0K41ao16eABKT+H1F+Ca6Blx+1shJxX4fhThjCz0MsheRn4pA2+f80OOPk6lL4j645BEP87mXAzx8+qKFCzUQj3tDpJZuMMkXdDzKODh5UpCpzeAqXvQvlnojYGgAb85kp4W9BS0UgwBe3lsHW6aC4ApLwAzjES59t0HCo2STRFf/CdAQ35YO8lvnUHL3VZ3zwlmkOnRdTUNHKtZ3ob1FQ/UUrTton2c3ebNK3Rsp2XuAA6GoxafU+t6KDpkDcIgYfNlBI9Dq7gHiAkrO/1yx7hQuQegeLXdfUZmqugpRHKT0orK4DyArU/KdUudg4SMz16IjRrIDQSwqMMLSxStrm4WM/inW3i2E0XsqvB2B3q7AJTF8CcJTBjEXie4yoJ1QWw7VXY8ZZktSXOFK3dsT+DifdASOrgx+gP2k7Ieh/yP4ESVUfVJRDSHoHR9/Rf0mUw6Log5zU4+JRYuwEp4BYGyU+B7/jB928tgcO/heIPEHJLg4ifQfS9AxNud6uky7oaaRdXfwd5fzEQrp0HRD0A0Q8NHu2g6KB8FeT+WV65azNku+dYIduQ68TdYQ46amDrNGg2Kk6pegMM8BBedIsDN1US0i0W3NXeKej7j4fVdhqIuK0BWmqktZ029K1Gy06hkL3hbH+tMcKnwxEjAre1E0L2DAJPlYyn3QoJM4Z+/Tod1FRAaSGUnjT0ZYWyXF4ECWNh6wBlmJLT0Kw/YB3inWXi2IwL2dXg6QMzL4O5S2DKvHP7ygESknbka9jyChxba9AhDRsD6TfBuK/AcQghNt3tcOgt2Pl3aCyGoLHgGQPjfgNJt1heHFNRoPhz2Ps7aFTjegOmwPh/QuAUE66rDXL+BcefligHG0dI+BUk/m5wH66iwPZFULMVpq0Vmcqjjwpx+k4HB3+IeVgiHgbzqSpaKPsEcv8CTWo8sec4iPsDhNwA7kmDf5feaDgC2b+FqnU9a6zp4T0OAi6Wa/VKOz/IdSDYOoCLvzRTbRFFkaibpiop2Nq779JAlw00VkJDhUhB1pdJ0yNxNiRY4fptbCAgRFpaH/emVguVpXCqCE4VQonanyqE0iJZd7Fi7L1u8CHfJ86NxdvVJW6Fc426ctjxOmx/HerU+F87Rxh3Lcy4T8TRh/LP2NkCB1+DzH8aKhP7JsGUx2DktaLybymqMmHPr6Bqu6x7xMG4pyFyyeDXrChQ8gkc+jW0qpEFYVdByj9Eb9YUlHwGmWrhKnsP0HSA0iGZYfF/gBEPSnbYQNB1Q9mHcOIv0KJapE7h4k8Ov818f3JXE5SvhFPvQ/XGgceOfRcibjbv+Bc6ujpUElaJuLESkmZDgJmRK8MBnQ6aGtF4eVvH4jWxsLRm64Vs8Z5L0lUUyN4E61+GfZ9D7GghXf9YmHEvTLoV3HyHdo6ORtj3Eux+FlrVigKBqTD1D5CwZGjZVY0n4fDTkPuarDv6QeqTkHiPaZlr9Ucg63Eo/1LWvcZA6nMQMNP0a9B1QtZvDetdjfKqHnUjjPw7OIcOvL+ig7LPIOcxaFED/J2jIO4xCL/F9AkzEPKu3iBkW/65IT5ZYyuv26HXQfjPJE64tVjC2bStEHyF6ef4scDeEXwjpJ1vsLEBTytm253nk2vfgxk6TGiphy3vwoaXoVy1rmxsIWoyXPN3SJhr/sRCYwWsfhwm3Q4xUyXiYcfzULoTTn0rY0ImwNQ/QuyioVnPXS1w4O9w8B/glyrW4KiHYfRvTUum0LZD1l/g6N8lasLRD5L/CtF3mK8Vceghw0SVHgoQdtvgpNuYBQfvAbQSMeA6AuIeh7CfmZfyrOuC4veg7FOoWmPY7jMVwm+C0GusV0VZUUSEqGKT/I4jH7HOcX/C94efiHeYUbBXyHbHCkMMoncIzL4L5twFPoMQRX/QdsM710HeZsjNgCk/g53PS069vQskzodJj0LURUMjXEWB/I9hx6+kpA+IuPa8j8AtcuB99ajcDJl3Q5PqB/ZMgZlfgqOZqbfaDthxhQjq9IbGFnTtZ2/Xo7sFcv4E+c+Kz9UxGMa8oVq4Ztxmum4oWQ7H/yTqZCCl6v3niGXraoUwQ0URgfPKDJHbrMyAdjW7y9EPkh76YUo0/gQDfiLeYUB7K2z5CNa+LjqobWqFgOSL4KL7YOxlphW2HAhr/yykC3C6ANb9CeyB6JkwdxnEzBra8QFqDsG2B6UaLYDfWJj2PASbqGXbWS+ViPNel3XPJJj4OgRYoIXblAtbLoY2td6cgx9E3wWeyWK1usf1r6FQ+S0cvh9aCwENRP8ckv5qXiKDooWSFUK4Lar4kVsCJD4p1u1Qs9h0WqjcANU74MRr0N5LDcwpCIJmiyiRrts8cXpz0VgMW38DodMg9YHhO8+PGT8RrxVRkgPfvAIb3oFmlWwnLoCYJLjoXgiOt855Dq6ENX/quU3nBLd9BXEm5iIOhLbTsOcJyH5F/KFOfjDxb5B4u2luAUWB4s9gzy+EQGwcIPkxGPU7EV43B4oO8l+GrN8YIh+SnoCkx0z4HmVw5CGJWABJREh9DbwnmHf+0o8lNrlZ1Rx2jRXCDbt+6ITbkA0n34Wi/8n1OvhC+2kh2sBZhuYRf26iHvJWwbo7oKMOijfAmJ8bzqvTSshYR6PEA3c0QWezNP1yR5PIXTZUyVuZopX9dN1qry67BENNudxPtnbS65tG7V0DoFnNSLN3BHuns5cd3VU5TldwcgUnF0N/jisQm4VhiGrQaDRXA8uARCBdUZT9Rp/9Hrgd0AIPKoqyrs+DqDj/ibe7CzK/hG9ehoNGM9nx6bDofphxrQipWwP1pfDujZDfR2ZRVzu0DhCEbwoUHRx5B3Y8CY4AGhj9S0h/EhxN9Fe2VcPOB0VSsb0C/KfCpNfF2jUXradg7+1QtUHWI34Gqc+b5jstXg4F/5HS7rYukPhniHnQPLdC0zE49HOxlFtPivRjwhPiUrBEs0KPjtNQtEIEg2qNEnbcRkDULZJ04pVybsPL6nJhy6+h4AvDtvbTsGIetNVCUxm0VkHwdMjt4/4zRuh0OD5IgkXkDDg8iMB7zAzYP5gI/EzI7Od6HJ2FhEdOg5w8cHVXm4fRstq8A0GxBw9PcPeQmmxnlj2sPwE/PBZvFrAEeNV4o0ajGQlcC4wEQoENGo0mXlGUfun//CXeqhL49n1Y/wLUqqFajs4w8wZYdB/EWVGtvq0RtjwPa/4sgewg//hufqo2gwacPMDfzAoSxqjOgg33QZkaHhZ5E0z4LfiYkY5c9AVsvVv8kQGTIOUPIo5uiT+y5HPYd6voEjj4wdhXIOzKwffrboVDv4Cit8A1DgIvhZT/ij6DqdB1Q96/JG1Z1wE+0yD+MSk5NJSqvU0noOAtyHnWUHrH3gPCr4XoW8BvyvCTbWcz1GbD6aPSynZCxe6+Y40Bijb0tM7sHMA7GhzdwMG97945EOIXGaxXWzuDFXtm2Qkm3mawgrVGFrHeStY4Qfws6GyXULMute9sNyy7BUNSq1SMaW8x9B1thlZfKwLnA2HMDNg4AMm7uMDl15j7a/ePYSBeRVGOg+hJ9MIVwApFUbqAQo1GkwdMADJ7D9Tj/CJenQ72bICVL8O2ryToenQyhHvCJffB3JvBzYohJ+1NsOUF+O4ZaFVlC90CYN7jMOtB65yjswl2PAX7/yM3vEsgzHoGEm8wnQQ6G2DnQ5D7jqwHz4IZ74C7GWSnh7Yd9v0SCpeDqy/4z4Kxr4KTCWnMjdmw62poypZ/7PjfQNQd5pFZ4xHYfxvUq5ZoxO2Q/MzAGsGDoaUIsv8kFq6iBdd4SfiIugVCrxBxmuGAohM/fel3Esed8xk0Fg6wg4azGGHsnZB8F7gHSyr5cPqWrQmdTuZa2lugtQWam6ClSYTZW9XlFqNtNk7gEQKNDdKaG6VvapTW2mqoRGENnFsfbwg9SbYEsXz7xflBvA2nYfXbsOpVKFHjPm3tYO7VcPUDMGa6dS2V9hbY9iJs/Ae0qFq4I6bDwqcg3sQk78GgKJC7Ejb9EppLAQ2k/hym/kXy501F2SbYcis0F0uIWfr/wagHLbNym0/Ctqugbr/4chOfgJjbTUvIKHpH3ALaNilzPuFj8DRBcUwPXReceBpy/ixCOM7hkPo6BM43/3vo0VYOx/4GBa/K8TW2EH2nZOa5D0NSgKJA/Qkh2tKNULoJOtQHtu9YIV0be7Vu2ihDc48A39HCuyWboeBLyP8Cmk5BxCwINcMnfr7AxkbUyFzcYKi69TqdkHdHB7z0/uDjTUE/xJvRJK0/aDSa9UBQHx89pijKV31sN/MK1PN8b7KQigJZu2HVi7DxY0NV0sBwuOJuuPwO8LOyaE5nG2x6Bb5+Gvz9oDoboqfAJU9B/CC6teagvlCy2Y6+JOuB4+HiV6RChanoaoN9j8GR/8i63ziY+b6UCLcEpath500icegaDdM+BZ+xg+/X3QwH74di9R8i4mZIfRHszJAAbDoBe68VvV+AqHtg1D9MF8HpjY7TcPzvkKdXZ9NAxPUwaplEX1gTum7R3SjdDLkfSM00Y7hFQNhcCJ4J/hPAK9a0jEVFkRp9Lj/gcu/DAKuJ5JhwawNo9pufuabRaDYBj+on1zQaze8AFEV5Wl1fAzypKMqu/o5x7i3e5ib4ajmseAUKc8HHRkSYJy+EpffBlEusX1WiqwM2vwGr/wr1qr84aSZc9SwkzrMe4eq6YddzsPkJES8JS4PUuyDl7oGjFRQd5H8NYdPFGj59FNbdAPaqDy/tj5D6mGX+T103ZD0hQukgE0uT3jFtAq0pD448DuUfi5hO6ksQeat55z/1Eey/S179XaIh7Q2JybUE+giMvJfE7QEQugSS/yRhb9ZE3VFx7eT9D9oqIGC6kK6TP4TNgdA5EDoXPGIsu380mp9Idzgx/K4G4z/6l8AHGo3mWcTFEAcMoAY0BOLVaDT/BC4FOoF84DZFURr63SH7IHz4ipBuS7Ns8/GHmx+F+VdD6DDo73Z3wba34cu/SAkhgMg0WPInGDPETLPeKN8Hq++GCjXCZOS1MO/f4rsbDAdeho0PQFA6jLkFtv9KfLGBk+CynRCQbtk1tdfA9mugapO4JlL+Bkm/7t9NcfwFqN4GE14WYtu2WELMAuZKtIOHGfXfFC0c/SPkqITvNR7G/Mc8S9kYrcWw5zao+k5EbwLnwei/go8J6mymoqMW8j8Uwq0xkjj0TICIRTDlv+CT/FNyxQ8BwxNOtgR4HvADvtZoNAcURVmoKEq2RqP5GMgGuoH7Byu7Y7GrQaPRXAxsVBRFp9FongZQFOV3fYxTlKsnwUEj33P6DLj+Ppi3BByGoUClthsyV8DnT0K1Wh03LBkWPwXjTBCZMQedLWLh7vqPWGSeEbDwZYi7xLT9uzvg9RGqHxiwBRyApNtg+vPgYCFR1WXBxsvEv9h6AqZ8KPGq/X6PRvgsUAjfbQR0FosvNvgSmPwh2Juh3tZZD3tuhIpvxGIf/QzEPmjZ764oUPQeHHhQdHsd/dUIjKXmH6s/1B6C/BVw5N+GckwOnhBzHcTdKinY57Oy2QUEq7kaBqkHcGbs4R+YSI6iKOuNVncB/cciHcyUuL0lt8B190CsBZVzTYFOB9s/KrslzQAAIABJREFUgg+XgW+QkG5wIixeBulXWz/gO28tfHMPNBSJFTTxYZj1J/PI8sjbBtIFCb8e9SDMfs7y6yr5GjZfJ/5ZlxCYtw9cB0mdPrXSUD6oOV9epOLvgXH/NS+mtvEY7FwMzSckWWHixxBgoWuhvQr23Q1lauxryGIY9yo4BVh2vN5oyIGDT0LhR+A1Wkg3dJ6QbeTi4YuGsCZKD8gDP8zC8EpFkfCxlnpJSmquMyy31Mv9WFEGHe2G8LHOtp7hZP5RkHVEohJ0WuhWQ9f061otxKXCTrXiifFbuv6BptHATBPr9Zj0vax3qOGAtXy8twMr+v30b2/CJddaV2/TGIoCmatgxRNwStV7tbGDu96DyTeYLxIzGFpq4KuHoO6kkG5QGlz6OgSbefN3NEPGr87efuR9mPms+detKJD9H9j3K/lnjLoOpr5lGoEUvNfrWEgGlTmkW74adt8A3U2iFzH5c5nIswSlq2Dv3dBZI2LraS9I7LM1LM/mIjj0FOS/K7+TjYOkC8/5HNzPYcmpoUBRYMu/YfWvRe95WZXURtNDp4OmGqgrg7pStS/rue4ZAdu/he7O/s+TNAN2DJJkMUqB7AMDjwmOhsqKgcc09u+pNBs/ZOI1JbRCo9E8DnQqivJBvwe66vahXGP/UBTY940QboHqW/WPgGuegFk3D12voa/zHf4YvviFFDW0d4bLXoD0e83PtKo+AssniyqZMVz8If4q80lX1wW7HhAdAoAxy2DME6YRVeV2EYvpjY4a08+f/xqc+lg0c8OvgXFvgZ2JD1pthyHVWaeFrN+JUE9njfiX098CFytIGbZVwOG/wgmj8LO4u2DMH8F1kHJF5xO62uHTe2Cf+rDsaoW1fwXFBiqOQ+VxcAuBvesHPs4IZyFdOwdw9wZXL2lu3hIv7+oFPhEw6iJJXnJ0VtOInY2ak5TIeshWJsVtbCULzabXOhrAxlCEAAzL+t7ODv73Re+rtAw/ZOJVBqlOr9FobgUuAQZ8R1i2bNmZ5VmzZjFr1ixTr6+/C4PDm+GD38MJ1XfsHQxXPQ4X3ym55tZGYxmsug+yVZ3bmJlw5RvgZ0HBypKt8NFcQ3bViMtgzN0QkCb/MOZadR21kHE1VHwnsb5T34Hoa03bt+4wbDCKXXYOhoirIOxyKcxoCo7/Cw7/WpZT/iFVLkz9DvVZsD4dQi4VN8KeW8Vydg6DlGch/pdDn8zSdkDe27DnEUP4WcyN8nDysLDg6LmGokDxbsh8DQ5/Km8jxlj/f9BltB7mCG4+otTnFSK9T2jPda8gcPe3Xsq9BcjIyCAjI8P6Bz7PiXcok2sLgGeAmYqi9GsaDVje3RJkbYM3/wAlJ4AaeSpf+XuYf+/w3ECKAnvehK9/JXq8ju6w6F//3955h0dRdm38N/TeSxCQIr33DlKkSi+iWOBVUBSRplLUF6QogqgUKQICggiCAUIvIXRCr6FjQugBAiEkkLbn++Ns3CRski1pvt/e1zXXzuzMPPPs7uy9z57nnPuGuv3tjxmLCQ5/D3vH6Kx/1vzQaxsUtjHp0BqCr8GBT+DuJsicH1qu08kgWxDiDx4VlIyMDNBwEZR603bSFAGfsXBugm7X+hnKfGRf/0+MgEs/6HqGXOYJtHzQeA0UTAIvsIdnYP9b8OicCqfnqwk1J0DeeNLPRCD0VuIx8eRGxDO4cQz8DsC1g+C3D57ci//47EWgdn9wqwCFy0OhspDVwTzpVESSTa7ZmO5unP+XTa4BM9H59+3m2uWDImLnt84OXDgCv34FR8xasTnzQv8foHU/yOrgzH9iuO8Lm8fA2RW6XeFV6DYX8hSzv61nD2FTX7hqLn6pNxKaTnRODCbwDGxqp0RRqjs0/FET+m1B8BXY0UpJN2MuaOdtn9COCJwcDpd/0hFp3UVQ0k6rHRG46W7ZjnysgjvNvVTExhmYouD8D3DyS500y/kSNFgAbs3jPyfoEhwYrFkOvS6pM3RKQQRuHYWrnnByLdw8DlERsY9Jnwmy5FaL90w54P4ly8g3e27oPP75dv+/Io17rjmT1ZDEJULx4OppWPRf2G+O/WTLCT2HQa/hkMNB197EYDLB3jmwdiQUKAXZCkCXGVD9dccmd24fhfW9IMgPsuSF9kugTCfn+nh7D2ztrDoORV6Gl3+1zakC4JEP7HhF1c0KNISWm+zTSjBFwbEPwHehFnU0WOFYetejkxDiF/u5qFDVzXWGeIN94UBfCDAreJUbCLWmxm/wGRECJyfCmWka/smUBwJPg1sTx/tgC6Ii4dpeOL9Gl8fRvoBuWvjiVgVKNoISDfWxQNnY958pCm6f1lFx0ZrJ21dbYTLB44cQeA8eBFq0GGJqMzwxP/fsKdx5oOXCISFQJolkXSHNhxrShlaDNfhdhCX/hV1/6nbmrNBtMLz+GeQukHzXvXcVfn/PIs1XpDL08lRbbEdwfCEcnQNB/uBWBzqvgtwlneujrzvs7KOxy1I9oMUy252MHxyHnW207LZwC2jukbjjcEyYIuH4YCXd9Fk1JODmoN7C4f7PP5e/Ibi1c6w9EbjyKxwdqql0Wd2g4a9QtH38x/uuhkPDLe4f5d5TPYysyVRVFvEMrm5Xor3oofq70chVFCp0hTIdoERjyJrID2m69Eq4KUG6EeEQcB3uXIN7N+GaPwQGKMEGBljWH97T9LFMmeF2Atbz0XgaY930rxXJsRtpj3hv/A3zxsPGpVCtGmTMBJ0/hD6jIJ+1BIskgskEe36GdaMgPFSJ9vU5UCOBkZwIHJ4Pf++G15fFHo1EhsHmT+C4Ocvg5S+hyZfqcuwMzs2F/YM0XlzpQ2g00/YMiHuH4cA7SrpFX4Wmq+zLVRUTHOoPAbsgW2movxgK2mjnGhcHXodH5kyUTPl1Qq7E65C9pGPtRYbC6a/BZ4pul+gF9edo3NsaHl3QsMItsxZxgdrQ6GcoZGN83F4EXoajMyHQD87F0FrJXw4qdYeK3eCFOqkrLv4oAK6fA99LcNsP7vgp0d69BvdvWbIPDAOC0kFkPESZMzfkKwTFC6qITg6z5m6u3Jb1aE3eLNkhR05NNc2dB0onkbiRi3htxN0bMH8irF2oCdgZMkCttjDFAwolc6pPwBVY9i5cNf81rdMHes1I2I04LBjcP4CT5vTluu9CGXNyx+Mb8GdPuHlI06Q6zoMafZ3rowic+gEOm/N+64yHml/aHvp4cAK2tdV4bql3VDzdHglCETg+XN0c0meDFttUhN0RXPhBNRwAinSGZk6mED0LAK9O8OCwZmKUGQCl4pHdFIHzM8F/k5Ju5rxQ51so3z/p871FwG8nHPkJrmwEBHIU1bzvyj2UbAtWTPqquGvH4M9h0Ho41Oj6/P4nj8DfB66dBf+z+njtLDw2z5FLPggKjH1OunRQsBgUKQmFS0CeEpAzH+QvpCSbr6A+5i2QPNWo9sJFvIngwV1Y+C2snqsKZenSQcd3YOBYKOZAMrsIeC2Fnb/BuM0J5/KaTLBjFhxfAdcPQs7C8MZcqG7lZo2J26dhWS+d3MiUHbrPs5Cu325Y/RqEBGiC+mvu8IKTou0isO9TOL9EzR5rjYKKA2w//6EPbG2jpbxFWkLDhfZP6p2dABena0y32VrHfN0AfBfDqRG6/uJb0NBJGcCgi+DVXiUvs5eE+nMhdwXrx0Y8gf0DwHeF2TVjANT5Rq2XkhIRT8HndzgyXfO1QX+Aq7wJdYdAIScnDhPC394wvS08e6zl7FU7gt8JuLRPXSvu3IFzB62fmy0XvFgZiteBnAXArQS4ldTHgkWTPi8+OeEi3njwKBD++BmWTFZBZYA2r8HAceqh5gj8z8Gcj+CsOT67709o/qb1YwP+hgXvwoXdmtdYvx/0mAbZExAXFYHDC8DjE4h8ppMfb66CQhV036EZsG2EpoqVagU9V+jEnDMQE+waBGfmKum1mAtle9p+ftBl2PqKFkMU6wAv/2E/6V6cAWfGavZCoz+gSILp3fHDbxkcNhfTVJ8CFT5zrJ1oBOyFXV0hPFDFclpsUHcGa3h0Abx6aFpZhhzQ5Fco1cu568dFWBCcWQx7J6ivHkB2N6g9CGp+ANmTWY3syj6Y0Q7CzEU5/sdhQC4t641GsXpaBFG8EpSoosuL5scCxf53NClcxBsHj4NgwY+w8EeoVFVJt3ln+HA8lK/uWJvPQmDFBFg7TWeKcxeEd79Xm6C4EAGvefDHp3qD5ioEfWdD7S4JXyMsGNwHwklzgV7d96DzDK3aiXgGHoPhuqeSbqPPodUk51LFQCeyPPvrSDd9ZnjVHUraKL4DEOwHW1tpxVaRltBitf0OB75L1bECoN58eNEGeyBr8F8Jh/sCAlUnOk+6fis1Xm0Kh6KdoOkf8VfK+a2Gvf/RCbfcFaHlX5DHwR93azBFqebGvjFaAPP0ARSpA3WHQsVeye8qcXYzrB8Hfkd4jnFMT8GtLJRvAuWaQJmGagqb1NKraQ3/q+lkdiM0BBbNhLlTIOihPpclJ/zmDdWcmNDwXge/fAL3/PXXuv1AeHuSxp/i4r4/LHwPfMwTKvV7wzuz9G9VQrjlAys/goenIGM26D4Xar2t+4Juwm/d4MYRKFQReqyEKkngHRUVAdvegst/QoZs0MkDitshIhJyE7a0hJDrGhZotc5+0ZfbO8D7P7pe83t4ycHS79vb4PgQHb1XHguVvnCsnWhcmAlHzdZM5QZBnenW47OmCDg6CnzMBRqlekPjBfZlcSSGG/vUZSTAPFGYrzz08YQSLZJv9Ciio9kT7nDkD803jw+VGsPn+5KnH2kZrhEvsOAnmP0t3A/Q7frN4NOJUN/BGXHQWdc/J8MOs+Fn6Zrw0Rwob4XERWDPr/D7MPVZy5Ef+s2Bejb81TyyHJYP0EyH6l2gy7dQ2DxauuYNS7tD8G3IWxL6rIAiSRC/i3wGm3uDr4cm8XfeBC/YEVN9eh+8R2jcs0AdaL3RfrJ5cAJ2dtE838LNoOII+86PxsOTsLe7yjlW+lKJ1xn4TIGriyFDTqg2FioOt05wEcFwbDSc/1mr8up+D5UclKa0hsfXYe9IuGCeXM1ZHJpNhfKvJQ/hRkXB1f1KtifXQKC/ZV/6jJD3RShVH16oDA/84O5FCLgMJR3Ucv63w0W8wPhh+lizvhJuEzttdu7fhX5NoVw1+G45/PkjLP4acuXXUMHrX6oZZnorLyfwNsweoF/EZ8Gqx9tvDuROxNwxIgzch8Mes31P3bd04i1aAerYEvjrfXUlLt0c3loF2ZNgkibiKeweoaSbJR902arWQTafHwpbOkGAN7zUC5rOtb2wIhqht8GrsxYzZC8N1Sfad340nt6G3Z0gMkQLLKqMd46ULs2F4yN1vdFieClGpsi9I3BqIlQbpeGEHe3hnje88ArUGAeFHZwMjIvIMDgyBQ5P1hS2DFmgzudaiZgxW9JcIyZCAuHQQjiyWNW9QszZBnlegBrdoGZ3KNvM+r3//xku4gUq14ARE6CVg64P0z6Fa5d1uVQaHpv1a6s2hkHTdMbVGvaugF8+Uo3RvEVg4O/Q8I3E+/DgGizsBdeOqHJTz+nQ5AM9LyoSNn0O+37UYxsOgk4/Wny2Ht1wrKQYdKS7thv4boWX2sPLk6GAHSNoUyTs6K2km6MENPxJdQ/s6sNT8OoCoTc0XazhL459ZpGhsLuzuZ0mUH++c6T79+9wyFyRXm92bNIFODsVrnvAzS2qs/DEF7K/CA3nQq4kyg0NvABb39a5gchQKNcLmk2xvSAmMgyO/Aw139PS34Rw6wzsmwnHlumPMUDZ9lCoipJtyXqpm/PrLEwmuHUTrlyGq5fh6hXIa4Mdla1wES+w8ZjjN8mhnbBhmWXb/yaULwmfzYN6bayf8/iBEu5+c9VbzXbw8ULI90Li1/PZAkve1JFFvhLQfzWUMI84Qx/BhlFwbJ4SbZefoX6MtK4Lm2FZT2j/LTS20x4+8hmsMZNutoL6hS5gh4+YCOz9EPw3KNl22ALZbXi9sdowwf5+8OAI5CgFLdZY5Brtbefg2xB4VL3WmjrYTjSur9MSYARqTobyH8a5nsCdXbpuCteS4cz5oN1uyFnS8evGxIVlsHOglhcXaQKtvKB4c9vOFYFLHprx8vAqBN+CNt8/f1xUJJxdp4R7dbfl+fJtoelgqND+30e2YWHw92W46AOXzuliZAD3tfDsWexjKyThhKeLeHH8ZgkNgc/jyBsKULFV/KR7dCPM7g8P72hVzH9+gNYDEh9tmUzgMQF8PJR0K7WHvkstRRT3rsDcThBwCSq1gHbjoVSMWv4ji8B9gM5w3zyhXzZbR3iRYbC2B/hugawFoLcnFLTTvPHYeLiwQGUh262HvPHksiaEU1/DtT+1yKLlejV2dASnvlBHi4y54eWNzuXJ3t4Be17TbJEqY6DKyOePeeQDz+Iod4UFanm0s8QbEQq7PwGfhbpdvg+0nAuZbLRCCvCBrUPB1zyhW7ASlIlTEh0Zrtkym/4Lj8zegJlzQt1+0GQQFCrv3GtICYjA9b+VYI8dtpCs7xWNT8dErUZKuoUKQ5myULqMPpavCF2SyNLJRbwOYuc6+Ox1tSWJi0M7nn8u5DEsHQ1bzTHZSk1h8GJwi6cII/wZfNEYMmeD4Stg8QdwaqPqk3acBG1HWX4wLu2CBT0gNBBeqAq9F+loGPSG2zkJtn2l2y3GQNuJ9pHuup7w9yaVieztCQWr2nZuNM7Ph2PjNM/2lZXg1si+8wH8/oTT47WNZishT2X72wDwXwPnJqvIeNPV8Rcz2IIHxzXsYQqH8h9DjXhizVeWPP9ctqKQ20nRlcALsKkXPDirP2jNZ0Ll92z7bJ8Gwq6xqtMhUeoe3Xw81PnQkmYoAj7usGUkBF7VsFKmbND4Y6j7DmSJRx3NFJX0VXYxEREOf5+EcnXjf62PH8GZw3D6kGV5eB8q1YF9Ry3HGQaUKgPlKlmWshXhpfKQ0w4fP3vhSiezE09DYeYXsPQny3OlK0D1RlCgCOQvDPXjeHid2g3f9YNCRVUE/c1J0HFowrmKe5ZZXCuGlgdCVDj6g6VQNYboy4EFsOJDjZ9W6Qj9lmsaHOgXYN1g8J6jN1jnmdBokO2vNSocPF6Dqxt0Iu21HfZXNV3bAscm6XqTOVCys33ngypxHRwOeapD2fegqIMiNQ9Pw94+kK8hlHkH3F5xrB1Q0ZqdnSBvDZV0rDvdOgk8Og8+P1q2CzWBykPhxc5acOIoYoYW8pSDDqugoA2fjQicWwWbPlTyNdJBnY+g+dexi2muH4ZNI+CaOdWrYAVo/TVU6hz/P8THd8BzGpxeB2NOQ0YbhZHsQdhT+G8HOL0LRiyBV8xSn3evw6m9sGsbnPIG34vPn5u3ABQrCcPaWUi2dDnImgpC664Rrx3YsxG++Rhu+ul2uWowwwOKlrB+fPgzFUVf/YPe8DnzwffH4cVEzDSjomDdVMt2SAgULgpf71PjPlBSXfs57DTngLb6FLpMtow0Ip7CH33AZ60K37yxHKrY8TfJFAmbB6meQ5a80HsHFK5h+/kA90/riCx9FlXUqvS+feeDykp69oDQm1C0NVT42P42QLNGdvdSw8zcFaHsQMfaAW1jVzd4ekvbarDAugtF5DPY0EBHlOkyQZvtUMRJ8XQROPQV3PZW0k0otCAm2Pq+pvw1n6pOzRvfhRv79f4p2QLaTofCMf7BPLwGW0fDaXMaWrYC8MrXUHeAZYI2LgKvwfYpcHCh/kMCOL8VqiVS9GMvIsJhYg8lXYBVU+HcSTi0FfzOqWDVIyA8XNcr1dIc/OilWKm0U/mWDMRrGEYvYBxQAagnIsfMz7cGvkX1ycOBz0TEipeWBWmDeO/cgO+GwA6zKHaFGvDV3IQLKy6fgG/fBj8fJcO3xsDbX9lWT37IHW5div1cwG0Iuq/EG/oYlr4DZ9bp38LX50Kj9yzHhj6CPz+Ey1shax7o6wGl7MhJFhN4vAunl2rMr+sSKGyntF/oXVjfSfUHSnWCmlZin4n2Q2BPP3h8BfJVh0azHbdgP/gBPL4EeapAvZn2txGzLe+B8OAo5CipYY8MViq/xAR73oKIxxrW6HgQ8jvh5PFPm5/AmZ/N5dlzoOoH8b8n/l5wxhz7DfJTIfPg65A5N3ScA5Vi6DdHPIUDM8BzrJJnhszQeBi8PCr+DIe7l2D7ZDi8VH+oAap3gzZjLBO+SYWoSBjXGY6bjQYEFdDxOavrWXNAnVZQozVUrqdVppmSuSLPGSTPiPcM0A2YF+cK94COInLHMIzKwFYgwdSm1CXeyEhYPh/mjIYnQZA1O3w8AfoMNhvkWUFUJKyYAkvGQWQEFCsHo3+DijZWv/mdhulvWd/3JBAeXIcfX1XB9Wz5YIA7FK8Ns7pDuWZa7Ta7Hdw6DRVbQ/cfwc2OeKgIbBqkpJsxO3RcoOWl9iDyGWzoBsH+ULg+vLLQMcI8Ow2urdU831Z/OW5nfnk++JlLdl9epZV2juLCTHX/zZANmq+FLPEoxB37Aq79pdds6+k86UZFwM534eIyHT23+xNKJzKiPLPIsn5ljcYVi9SDLisgTwx35Tun4K839POODIPqfaDNN5A3xjxBzM/vgS+sGw0nVumPgZEO6r4JbUarPnRSIsAfZg+CI1ss5A4WB/a6zeCd8VCloY5ykxpPn8KZ03DqRNKK8CQD8YrIBVB7ojjPn4yxeQ7IahhGRhGJYyFiQeoR78kjMHognDkOTZqBW34YNR3cEpCAvH4F5n0GB9bqdteP4f3vIIuNX/QDq+CH1/VmBihVA6q+AuXqQ7mGEHwHJtSHR7fBrTx8eggKlYFdv8DxNbpsnaCiLIXKw+sL1IXVVojAjpFwbK6mV73uAcUb2n5+dBue78Gdg5CjOHRc6xhh3t4DR0bperMljue6Bp6Ew+bUuQbznJtMu+MFR4freqNFOgq3hovz4Yx5Aq+lu/MaupHPYKu5UjBjdnjVA4q1TPicsCC4tDr2c+mAWh9aSFdM4D0dPEdpPL9ABRiwG0qZwyGmKFjwMjx9CAP2aAHGjkmwayrkq6j/5Oq/B61HQsEkykUGCA2G/e6wcymc9Ez42Dx5oKaNpqeJQUTzdY8egiPecNQbIiLg+GndXzGREKFd10q6puxED+BYQqQLqSWSM+VLWPKzfhBFX4T3PoXWCVjhiMCa+fDDcChcXPVAh/8CdeNJKbOGHQtg3kD9MmTNBRP3QckYsbfj62BuHy0NrtAcBrtDdnNC98EY0oUPA1Umb9g+yGFnmtTeiXBwqoYveq2GUol8ua3hyCS4uFwJovMGVb+yF6G3wau3xkarjYQSDsYKwx/D7tfAFKY26aXjUYKzBU+uaVsSBZVHQsl49C5uboWD5jzehnOgqB33gDWEB8PGLnDTS7V5O20GNxuI3HOoOoDERehdfQy+Dev6wdVtul17ILSdFru6zecvuLZf1xe1VQ3cQLPuQrlm8P56yJtEWtSRkXBiu5LtwbUWxbL0GaFoWWjQGXqN1pBIgL9qnwTehto2uIt4rYPvh8HQKdA6hnJeaAh4H1CSPeKthBv4IPa5WbJA1apQtQbUrgMfD0ma1xtPVsOuCNgVaX0fgGEY2wFrX6oxIrLeyvMxz60MTAYSle9z2GXYVvzjMiwCG1fD2CFw97ZmHPQfBiPGqfp8fLh/Byb0h30bdbvtGzByFuS2sSIrIhx+HQLb5up2gx4w+DfLKFkEtv0EK0boepN+0G+eJa4YcBVGxbEANwwYsR0q2SFa4/0TbBumfxu7L4fKNtqvx8TfG2FDZ0Cg4zoo7YBvmykKdr0Ld7ZDnvLQbrvjKmoHBkLQOYh4BB0OOR6qMEWAVy94ckmrzVputJ4u9fgqeA+Bm5ug6kgVMHcGYUGwuTvc2AnZ3KDLdshvQ/70njFwKMa1C9WEsl2hdAcoXBsurYd176pKWdb80HkhVIjz4yYCs2pqGCIakUChqtBjrnqsJQWehcDOhXr/37oLweaS4yrNoOVb0LQX5LDDby8u9m6CYV017FepDoyZB7u3wp6tcMIbonLBvRg51oUKQ72GUKc+1G0ANetADouOSJK5DNtYJW8E2e8ybBiGFzBCRI7HeK4Y4An0E5F4BI8tSJkR701/GDUQvDbrdq0GMHkeVEokPcdrLUwcAI/uQ848MGo2tHvD9us+vA1Te8LFA5pm9v5caNnPsj8qEn4fAjvNub89JkHH0bHjbUutzM6LwJN4He2fx7El4GN2Weg43zHSvXcWPHrr3+8qbzlGugCnvodLv0GuMvDy746Trr8HXJxndtg46jjpApyapNVp2UtA22XWSTcqTEfpD46pL1rtSY5fL7q9zV0h7JHm+3beBLlt+Dt/bIaFdHO+CG8egJzmkvWoCNgzQfN3AUq3hq6LIaeVCsILHrFJF/Tb2G1m0pDu4/uweSZsmaVzFwA1OkLpBtDyTShc0vlreO+A4d2UdAHOHYXOtS2jzXTpoGVzKFBUSbZuAyj+YspkPiR/qOGfF2EYRh5gIzDSFtKFlCLeqCg4uEs9lUZPhj4DEq5me/IYpg0FD/PkRb1WMG4xFLZDA+G8N8x8G+5egfzF4HN3KBNDqelpMMx9H3x36Qxz/8XQ4PXYbSwbZJGQBCjXFGp0huqdoIiN1UTnN4D7ezrS7D4XajogrfjsIaztqulN+SpBzWH2twEQcBSOfKnrTWbYX1L8T3/uwwFz6lrtbyGfnVV2sfp0EE5PBAxosij+Krejo5R0c5RUpTFr6WW2Qkzg2Q9u7oJsRaD7Pshtg9vJkR9gl1mlrcQr0HOr5T4OCwb3nhBwVgXPG4+CBkOt99P/IPxhLZRi6GftDO76woYfdJQbHq3x0AC6joQ6CeQI24MT++GnzzSfNy7D5c8FrV6DZm2hcSvInYT6C/YgedLJugEzgALARsMwTohIe+Bj4CVgrGEY0fIzEgjrAAAQP0lEQVR7rUUk3tFZyoUatq+HGvWgYCKqYEf3waLJ4L0RMmeBwd9B74/tu2G2LIIZA6FMDcidBUb8CXliXPfhbfjmVfA9AS/Vgf7ToWycUca6r2HdOF0vUgFG7U1ctzcu/PbBwtY6edPiC2jjgMqXKQrcO4HvZihUA/rsd0wFK+IJrK6ljhRVPoEm0+1vA3S0v6s3+K1Sucj2Xo6TYEQweNSE4KtQ5TOoM8X6cf4esKOL1vh33A8F6zl2vWgcGAknpqhUZre9UDCR/GkR2PcleH+j2y2nQ+0YWhxP7sCKDnD3hOps9FoLxeIZtR5dAGve5x9mKFwNyreHonXUHSKPHZO1MXHnCqwcCwdWWtx6a72qhFuhifOjzMB7sH0VrPsVzh2L/7gKNWHF8fj3i4DHKnB7Aeo3eW53koUabFRBNZ7YH2pICqTc5FpCk2egSdkzxsG879SJtG5LGDUTStsx0xkZAfM+hbUzdLtcPfhgWuw0mOs+MKmDiqK7lYGhy6FI2djtbJ5qId3qHWFIgjF167hzBpZ0UtKtOwBaT7C/DYAD45R0s+SDrmsclx7cP0RJN19VaPCdY20A+K5U0s2QHZoucm7keXiYkm7e6lAznvfnib/mGgPUmew86Z6epaSbLgO0+ytx0jVFwY5BcGqeZlG0XwyVY6QjPrgIf7TTPN68ZeCNLZDXSsjCZILd38IO8z+ObAXggwNQoOzzx9qD0CAdJOyYqd5+hgEvvwOdP1NLH2cQEgy71sGm5eC9zaK5kC6dKgLWaQ7VGkDATbjlB7evQZMEHFJEYPJX8NMk/fd75o7295A3bN+m8d+kQhqvXENEknXRSySCy+dEOtUSKY1ImXQiU0eLhIUlfl5MPLon8mkLkVcQaZdRZNOC5485s1Pk7dwiPRAZ3UDkUUDs/SaTyF9fivwHXTx/tq8P0Qj0FZlURGQUIku7iURFOtbORXeRKYhMTSfiu92xNkRErqwSmYPIL5lFHpxxvJ2QWyLL8or8isj5uY63IyLi5y6yCJHfsogEnrV+TFS4yPpGIgsR2faqiCnKuWtedReZZYjMQuT84sSPN0WJ7B0j8n16kR+yiFxZH3v/9QMi0/KJTETk13oiTwKstxMaKLKko8gYdFk3SCQi3LnXEhUlsnuhyOBCIn0R6WeILP1EJOCac+2KiFw9I/Jdf5EOpUSqo0ut9CKDOohsWCYSEmx/myaTyLdfihTCsjSrK5Inh0gGdOn0qpj5wnnOyWbbkhTXc6iPyX6BhIjXZBJZPEOkYhYl3WYlRY7steFTjIOrp0TeKqmk+5qbiM+B54/ZvUykd0Yl3e+6iTwLjb0/Kkpk2WAl3PfSixxYan8/RESC74pMLaukO+9lkfCnjrVz/7zIjzmUeA9NdawNEZHg6yKLiyjxnpnleDsmk8je95V0t7bVbUcRckfEvaIS77kZ8R93epqS7h9FRZ7ec/x6IiJ3j4nMyaKke2SCbefsGi4yDZHlDUX8d8fed2GtyOQsSrorXhUJe2K9jZvHRaaWUsKdkFfkwibnXoeIyBVvkXF1lXD7IjKxsYjfcefajIoSObhJZFhrkabo8n5jkX5NRFbOFnkQz4+KLXj4UKRn69ikWwiR3GbCrVZJZMQwka1bko54s9i2/P8j3js3Rfq2UcItjchn/UQeB9n4ScbAnjUiHbMp6Q6qK3LvRuz9JpPI+plKuD0Q+XWoSGScEWhUpMiKMSL9EBmQSeTYGvv7ISLy7InIL6+KfPOiyPQaIk8fOdZO2BORuVVEfmsmsq634yRnMomsaSOyuLTIjnecI8urf4rMQ2RDK5En1x1vR0TEs7fIb3lE9vSLfxQbcERkQXoRj8Yit3Y5d73QAJHfSoqsbiTi2d+29+HoNCXdHzOK+MX5t3H+T5HfWynpbugvEhVhvY3zHiKTiyrpzqol8uBv517Hozsi8/tZCHfICyIHfnfuc30WKrJunsibFSyE2zqbyI8fi1y76ES7z0S2rhdpUf15wo1eimUWuXwp1mlJRryZbVtSi3hTp3JtvbuWCh/eBnnywaRfoJ2d7rUmEyz4GtznQM5c0LQnDJ2n1tXRiIqCnz+Gzb9ofXmLPvBqnATtyAj4uS/s/wOqt9LJiMoO2JdHRcKS3nBuIxSrCf02Ju4yYA0isGmgpo8VqAivbXB8YsRnAfhv0/hwg+8cbyc8CPabJ5NK9YLsDjpsAFzfrHHi9FmhxljrMeKocNjzrhZTFGoARZyonBITeL6jjsvZi0KzWYm/DxdWqP0SQNvFmsEQDd9t4PGm5h6/PB4af2m9vZO/wdr/6GdYpi10/tk5NbETa2D3bDi5Q3PM242AjmMgi4PGncGBsPJHWDcHgsxFDQWLQY/B0GkA5HQgGyE8HPZ6wrqVsHmtFktFI316yJNfv2/RZremKMgVj/Sls/hfjfECE4BTwEk0cbh4vL8+0Xj8WGTQf0TyIpLPEBnWT0e+9iIkWOTzbiL1EGmQTmTVrOd/9Z+GiIztItIGkVczi+xZ9Xw7EWEiU7uJ9ELknZwi5x0Ic4jotVcMEBmCyJj8InedGCkc+0VkAiKTs4kE+DjeTpCfyOwcItMRufiH4+2IiOz9SEe7axs6F2cNfyKyooTIAkROTYn/uGNfi8xHZOVLIhEhjl9PROTYtyI/I7Iwv4ZdEsM1Tx3lTkPkSJwQz42DIt9nE/kWke1D4x9pHpot8l902TnOuRFpWKjI8o9EPkCXhX1F7lx2vL1noSIrvxXpkVPkw7o6wh1QR2T7csfiziaTyElvkW9Hi5TLG3tE26K6yKQvRPZ5xT7ncZDI6eMil84/1xxJNeLNaNuSFNdzqI9OvLicMdYHAwvifRNERA4dEKlZWkm3SBaReTMcuyFv+or0qaak2zK3yMEtzx8TdF9kSEMl3e55Rc5YIdSwpyLfdFDS7ZdH5PKhf3Z5eXnZ16etE5V0P80i8reV+LKtuH1c5JvMSrynbY8xP9dfk0nEvZWS7obuzn3x73qLzDNEfskg8uC04+2IiBz6TEnXvbp4ecYzWfjgjMjCjEq8N72cu97NPSKz0yvx+tkQWw04KTIzp5LuziGx37eAs+I1KIeSrsfb8f8A7fveQrr7nIjNi4jcOicyvqoS7qBMIp7T7fosY90XUVEinr+J9C0u0gFdJr8hcmqvY/fH01CRvxaJdKstUg6R15qYJ8yqiEwbb5VUbUGSEW8G25Z/HfHGeaGjgcnxvgnf/Fckfzol3WY1RM7FM4udGI7vFmlTQEm3ZzkRvwvPH3PbV+Td8kq6bxYX8bMyanz6RGR8KyXddwuI+J6ItXvs2LG29+nQYiXdoYbIKQdjwyIaD571kpLuhvftOvW5/p6eo6Q7r4BIyF3H+xQVLrKqmo52D41yvB0RkfsnRBamF1lgiAQcsv4eR0WKrK2npLv3A+euF3pPZHFRJd0DIxM/PviGyF9tlXTXvxabWB/6isx8Qca2QmRVJ5FIKyNDk0lHt9Gke2i24303mUT2zhf5OKuS7ldlRa4ds7uZf97jkztFBteyEO6g6iLHtznWN/+/Rb77TKRePiXccuj61FEi5x38XsdAkhFvetuW1CJep2K8hmFMAt4GQoEG8R441Wzr/cnnMHo8ZHbA+HDtfJjykcZSG7SFiSu0jDgmrpyArzpA4B0oVQ0mbtJyxZgIfQzfdYTzeyGPG3y1A4o7KLV3YRus6K/r3WdCta6OtSMCG95VI8TCNVQ821EE+cK+T3W9xWzIVsjxts5MV3eKnKWg1leOt2OKgv0faMy20mBzLu6m54/zmQ73DmsMuV48xRS2IDquG3IT3BpDvURyqCPDYH13CDgOFd+G1vMtseeQu7CyNTy5BblehC4rnxcsF4HtI2H/VD2v669Qo69jfX8aBL9/AEdX6naDd+D1WRbXE3sQdB++7gSHN+h2/qLwziRo8VbC7izWcNATlkyHXRv09QJUrg1vfQwdekOWVHCZSAhpPMabIPEmptQjIl8AXxiGMQr4EfiP1YaKFoc5v0GT5o71UgROH1DS7TMcBn1nXa/3ji88vAvVW8DYNZDdyuRW6CMI8NMy4q884QUnfLke+uuXvOVn0NQOy5+4iAiF8BB1OeixCjI4MQkTcksdEUp0gLK9HG8HtLLMSA9NZjunsRv+SCfTshWF2glU74WbRc0bz9XX4CginqiLReZ80PqP+J0dohEZoq8vR1Fo/oOWkEcj5K6K6RSuCZXaQEYrBGOKhLtntCij53Ko7MT7HhYCFzwhc3Z4Yw40eNvxtoIfwKUNKmLecxR0HWa7hGpcbFoJXuu1GKlDb3hzEFSrl3YcJ+IijXuuJUnJsGEYLwKbROS5UhnDMNL4b48LLriQliBJUDKcktdzBA6HGgzDKCsil82bXYAT1o5LjRflggsu/P/Fv4FzHB7xGoaxGigPRAFXgQ9FJCAJ++aCCy648D+JZFcnc8EFF1xwITaSQJwzcRiGMcEwjFOGYZw0DMPTMIwk8jNJHhiGMdUwjPPmPrsbhuFACVrKwjCMXoZh+BiGEWUYhpPOj8kHwzDaGYZxwTCMy4ZhOGCNnLIwDONXwzDuGoZxJrX7YisMwyhuGIaX+X44axjGJ4mflXowDCOLYRiHzPxwzjAMJ61F0j5SZMRrGEZOEQk2rw8GqotI/2S/sIMwDKM14CkiJsMwJgOIyKhU7laCMAyjAjqXO484tiRpBYZhpAcuAq8AN4EjwBsicj5VO5YADMNoCjwBfhORqokdnxZgGIYb4CYiJw3DyAEcA7qm8fc5m4iEGoaRAdgHfCoi+1K7X8mFFBnxRpOuGTkAO3xzUh4isl0k2oqYQ4AT4gQpAxG5ICKXUrsfiaAecEVE/ERdWFegE7NpFiKyF3DSFiJlISJ3xGw5LiJPgPOAg3YjKQMRCTWvZgLSA4Gp2J1kR4oQL2ixhWEY/kBf1Inz34J3sZrt74IDKApcj7F9w/ycC8kEwzBKAjXRAUSahWEY6QzDOAncBbxE5Fxq9yk5kWTqZElWbJFCsMXG2TCML4BwEVmeop2LB85YT6cRuGZyUxDmMMNqYIh55JtmYf6HWcM8n7LVMIzmIrIrlbuVbEgy4hURW7UUl5MGRpCJ9dcwjH5AB8AOD/fkhR3vcVrFTSDmxGpxdNTrQhLDMIyMwF/AMhFZm9r9sRUiEmQYxkagDrArlbuTbEiprIaYxlLxFlukFRiG0Q74DOgiIs9Suz8OIK0mkB8FyhqGUdIwjExAb8Ajlfv0PwfDMAxgIXBORH5K7f4kBsMwCpgt0jEMIyvQmjTOEc4ipbIa/lXFFoZhXEaD/NEB/oMi8lEqdilRxLGeDgKirafTFAzDaA/8hE6gLBSRNJ06ZBjGH8DLQH4gAPiviCxK3V4lDMMwmgB7gNNYwjujRWRL6vUqfhiGURVYgg4E0wFLRWRq6vYqeeEqoHDBBRdcSGGkWFaDCy644IILChfxuuCCCy6kMFzE64ILLriQwnARrwsuuOBCCsNFvC644IILKQwX8brgggsupDBcxOuCCy64kMJwEa8LLrjgQgrj/wDVLiRTbrp89wAAAABJRU5ErkJggg==" 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" 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" 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" 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" />
-</section>
-<section id="id8">
-<h2>椭圆<a class="headerlink" href="#id8" title="Permalink to this heading">¶</a></h2>
-<p>Ellipse 对象：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>from pylab import figure, show, rand
-from matplotlib.patches import Ellipse
-NUM = 250
-ells = [Ellipse(xy=rand(2)*10, width=rand(), height=rand(),     angle=rand()*360)
-for i in range(NUM)]
-fig = figure()
-ax = fig.add_subplot(111, aspect=&#39;equal&#39;)
-for e in ells:
-    ax.add_artist(e)
-    e.set_clip_box(ax.bbox)
-    e.set_alpha(rand())
-    e.set_facecolor(rand(3))
-ax.set_xlim(0, 10)
-ax.set_ylim(0, 10)
-show()
-</pre></div>
-</div>
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-<img 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" 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" />
-</section>
-<section id="id9">
-<h2>条状图<a class="headerlink" href="#id9" title="Permalink to this heading">¶</a></h2>
-<p>bar 函数：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="n">n_groups</span> <span class="o">=</span> <span class="mi">5</span>
-<span class="n">means_men</span> <span class="o">=</span> <span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">35</span><span class="p">,</span> <span class="mi">30</span><span class="p">,</span> <span class="mi">35</span><span class="p">,</span> <span class="mi">27</span><span class="p">)</span>
-<span class="n">std_men</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
-<span class="n">means_women</span> <span class="o">=</span> <span class="p">(</span><span class="mi">25</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">34</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">25</span><span class="p">)</span>
-<span class="n">std_women</span> <span class="o">=</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
-<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
-<span class="n">index</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">n_groups</span><span class="p">)</span>
-<span class="n">bar_width</span> <span class="o">=</span> <span class="mf">0.35</span>
-<span class="n">opacity</span> <span class="o">=</span> <span class="mf">0.4</span>
-<span class="n">error_config</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;ecolor&#39;</span><span class="p">:</span> <span class="s1">&#39;0.3&#39;</span><span class="p">}</span>
-<span class="n">rects1</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">index</span><span class="p">,</span> <span class="n">means_men</span><span class="p">,</span> <span class="n">bar_width</span><span class="p">,</span>
-<span class="n">alpha</span><span class="o">=</span><span class="n">opacity</span><span class="p">,</span>
-<span class="n">color</span><span class="o">=</span><span class="s1">&#39;b&#39;</span><span class="p">,</span>
-<span class="n">yerr</span><span class="o">=</span><span class="n">std_men</span><span class="p">,</span>
-<span class="n">error_kw</span><span class="o">=</span><span class="n">error_config</span><span class="p">,</span>
-<span class="n">label</span><span class="o">=</span><span class="s1">&#39;Men&#39;</span><span class="p">)</span>
-<span class="n">rects2</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">index</span> <span class="o">+</span> <span class="n">bar_width</span><span class="p">,</span> <span class="n">means_women</span><span class="p">,</span> <span class="n">bar_width</span><span class="p">,</span>
-<span class="n">alpha</span><span class="o">=</span><span class="n">opacity</span><span class="p">,</span>
-<span class="n">color</span><span class="o">=</span><span class="s1">&#39;r&#39;</span><span class="p">,</span>
-<span class="n">yerr</span><span class="o">=</span><span class="n">std_women</span><span class="p">,</span>
-<span class="n">error_kw</span><span class="o">=</span><span class="n">error_config</span><span class="p">,</span>
-<span class="n">label</span><span class="o">=</span><span class="s1">&#39;Women&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;Group&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;Scores&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;Scores by group and gender&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(</span><span class="n">index</span> <span class="o">+</span> <span class="n">bar_width</span><span class="p">,</span> <span class="p">(</span><span class="s1">&#39;A&#39;</span><span class="p">,</span> <span class="s1">&#39;B&#39;</span><span class="p">,</span> <span class="s1">&#39;C&#39;</span><span class="p">,</span> <span class="s1">&#39;D&#39;</span><span class="p">,</span> <span class="s1">&#39;E&#39;</span><span class="p">))</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<img 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" 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" />
-</section>
-<section id="id10">
-<h2>饼状图<a class="headerlink" href="#id10" title="Permalink to this heading">¶</a></h2>
-<p>pie 函数：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>import matplotlib.pyplot as plt
-# The slices will be ordered and plotted counter-clockwise.
-labels = &#39;Frogs&#39;, &#39;Hogs&#39;, &#39;Dogs&#39;, &#39;Logs&#39;
-sizes = [15, 30, 45, 10]
-colors = [&#39;yellowgreen&#39;, &#39;gold&#39;, &#39;lightskyblue&#39;, &#39;lightcoral&#39;]
-explode = (0, 0.1, 0, 0) # only &quot;explode&quot; the 2nd slice (i.e. &#39;Hogs&#39;)
-plt.pie(sizes, explode=explode, labels=labels, colors=colors,
-    autopct=&#39;%1.1f%%&#39;, shadow=True, startangle=90)
-# Set aspect ratio to be equal so that pie is drawn as a circle.
-plt.axis(&#39;equal&#39;)
-plt.show()
-</pre></div>
-</div>
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-<img 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" 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" />
-</section>
-<section id="id11">
-<h2>图像中的表格<a class="headerlink" href="#id11" title="Permalink to this heading">¶</a></h2>
-<p>table 函数：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>import numpy as np
-import matplotlib.pyplot as plt
-data = [[  66386,  174296,   75131,  577908,   32015],
-    [  58230,  381139,   78045,   99308,  160454],
-    [  89135,   80552,  152558,  497981,  603535],
-    [  78415,   81858,  150656,  193263,   69638],
-    [ 139361,  331509,  343164,  781380,   52269]]
-columns = (&#39;Freeze&#39;, &#39;Wind&#39;, &#39;Flood&#39;, &#39;Quake&#39;, &#39;Hail&#39;)
-rows = [&#39;%d year&#39; % x for x in (100, 50, 20, 10, 5)]
-values = np.arange(0, 2500, 500)
-value_increment = 1000
-# Get some pastel shades for the colors
-colors = plt.cm.BuPu(np.linspace(0, 0.5, len(columns)))
-n_rows = len(data)
-index = np.arange(len(columns)) + 0.3
-bar_width = 0.4
-# Initialize the vertical-offset for the stacked bar chart.
-y_offset = np.array([0.0] * len(columns))
-# Plot bars and create text labels for the table
-cell_text = []
-for row in range(n_rows):
-    plt.bar(index, data[row], bar_width, bottom=y_offset,     color=colors[row])
-    y_offset = y_offset + data[row]
-    cell_text.append([&#39;%1.1f&#39; % (x/1000.0) for x in y_offset])
-# Reverse colors and text labels to display the last value at the     top.
-colors = colors[::-1]
-cell_text.reverse()
-# Add a table at the bottom of the axes
-the_table = plt.table(cellText=cell_text,
-    rowLabels=rows,
-    rowColours=colors,
-    colLabels=columns,
-    loc=&#39;bottom&#39;)
-# Adjust layout to make room for the table:
-plt.subplots_adjust(left=0.2, bottom=0.2)
-plt.ylabel(&quot;Loss in ${0}&#39;s&quot;.format(value_increment))
-plt.yticks(values * value_increment, [&#39;%d&#39; % val for val in values])
-plt.xticks([])
-plt.title(&#39;Loss by Disaster&#39;)
-plt.show()
-</pre></div>
-</div>
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-<img 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" 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" />
-</section>
-<section id="id12">
-<h2>散点图<a class="headerlink" href="#id12" title="Permalink to this heading">¶</a></h2>
-<p>scatter 函数：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">import</span> <span class="nn">matplotlib.cbook</span> <span class="k">as</span> <span class="nn">cbook</span>
-<span class="c1"># Load a numpy record array from yahoo csv data with fields date,</span>
-<span class="c1"># open, close, volume, adj_close from the mpl-data/example directory.</span>
-<span class="c1"># The record array stores python datetime.date as an object array in</span>
-<span class="c1"># the date column</span>
-<span class="n">datafile</span> <span class="o">=</span> <span class="n">cbook</span><span class="o">.</span><span class="n">get_sample_data</span><span class="p">(</span><span class="s1">&#39;goog.npy&#39;</span><span class="p">)</span>
-<span class="n">price_data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">datafile</span><span class="p">)</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">recarray</span><span class="p">)</span>
-<span class="n">price_data</span> <span class="o">=</span> <span class="n">price_data</span><span class="p">[</span><span class="o">-</span><span class="mi">250</span><span class="p">:]</span> <span class="c1"># get the most recent 250 trading days</span>
-<span class="n">delta1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">diff</span><span class="p">(</span><span class="n">price_data</span><span class="o">.</span><span class="n">adj_close</span><span class="p">)</span><span class="o">/</span><span class="n">price_data</span><span class="o">.</span><span class="n">adj_close</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
-<span class="c1"># Marker size in units of points^2</span>
-<span class="n">volume</span> <span class="o">=</span> <span class="p">(</span><span class="mi">15</span> <span class="o">*</span> <span class="n">price_data</span><span class="o">.</span><span class="n">volume</span><span class="p">[:</span><span class="o">-</span><span class="mi">2</span><span class="p">]</span> <span class="o">/</span> <span class="n">price_data</span><span class="o">.</span><span class="n">volume</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span><span class="o">**</span><span class="mi">2</span>
-<span class="n">close</span> <span class="o">=</span> <span class="mf">0.003</span> <span class="o">*</span> <span class="n">price_data</span><span class="o">.</span><span class="n">close</span><span class="p">[:</span><span class="o">-</span><span class="mi">2</span><span class="p">]</span> <span class="o">/</span> <span class="mf">0.003</span> <span class="o">*</span> <span class="n">price_data</span><span class="o">.</span><span class="n">open</span><span class="p">[:</span><span class="o">-</span><span class="mi">2</span><span class="p">]</span>
-<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">delta1</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">delta1</span><span class="p">[</span><span class="mi">1</span><span class="p">:],</span> <span class="n">c</span><span class="o">=</span><span class="n">close</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="n">volume</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;$\Delta_i$&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;$\Delta_{i+1}$&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Volume and percent change&#39;</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
-<span class="n">fig</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
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-<img 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8U773zIqlXbCARiCAa9FBSkcMMNlzJ58uShjqdpEe1c/WXe2cBhpVQZgIi8AlwHHK+klFKNQKOIfKmXbZzRQdPODS6Xi5///Pc0NqaSlXUVZnMMSimczhp+/evXuOeeZhYsuGioY2raWS0am/tygMpu01XheX2lgBUiskVE7u3XZIMsmtqmoyVr95xvvPEOjY2p5OVNx2yOAULfDJOSchg27FKef/59nM5eT8gHLWeki5asOmdkisZK6kzb4c5XSk0DrgS+JSLz+yGTdpZpb29n3brdZGdP7HG51WoHclm/XndratpAisbmvmogt9t0LqGzqT5RStWG/20UkbcINR+uObnc4sWLKSgoACAxMZGpU6eyYMEC4NNvMnr6800fEyl5eppesGABRUVF1NXVAfGYTBbKyrYCUFAwA+D4tMORTmlptT6enzF9bF6k5In26WPzIiVP9+mioiKWLFkCcPzz80xF48AJE6GBE5cANcAmTho40a3sfwBtxwZOiEgsYFRKtYmIHfgQ+E+l1IcnracHTpzjqqqqePjh58jLu6rXMvX1h5k+3c+99945iMk0LXqckxfzKqX8wAPAB8A+4FWlVLGI3Cci9wGISKaIVAL/CvxERCpEJA7IBNaIyA5gI/DOyRVUNDn5G3Uki5asx3JmZWWRmCh0dPTe59TVVc6sWZMGKdmJouV4QvRk1TkjUzQ296GUeg9476R5T3f7u44TmwSPaQemDmw67WxgNBq57rqLefbZleTnX4LJZDlheX39ITIzA0yYMGGIEmrauSHqmvsGg27u0wCUUrzxxt9ZunQLZvNwHI50fD4P7e1HSE/38N3vfoP09PShjqlpEas/mvt0JdUDXUlpxyilqKqqYs2aDZSV1REba+W886YyZcoUrFbrUMfTtIh2TvZJaZ+KprbpaMl6ck4RITc3l9tuu4kf//ifefDBbzB79uwhr6Ci5XhC9GTVOSOTrqQ0TdO0iKWb+3qgm/s0TdPOnG7u0zRN085qupKKYtHUNh0tWXXO/hctWXXOyKQrKU3TNC1i6T6pHug+KU3TtDOn+6Q0TdO0s5qupKJYNLVNR0tWnbP/RUtWnTMy6UpK0zRNi1i6T6oHuk9K0zTtzOk+KU3TNO2spiupKBZNbdPRklXn7H/RklXnjEy6ktI0TdMilu6T6oHuk9I0TTtzuk9K0zRNO6tFZSUlIotEZL+IHBKRH/SwfKyIrBcRt4g89HnWjSbR1DYdLVl1zv4XLVl1zsgUdZWUiBiB3wOLgPHArSIy7qRiTcA/A//7BdbVNE3TIkTU9UmJyDzgEaXUovD0DwGUUr/soewjQLtS6jefZ13dJ6VpmnbmztU+qRygstt0VXjeQK+raZqmDTLTUAf4As7kFKfP6y5evJiCggIAEhMTmTp1KgsWLAA+bRMe6ulj8yIlz+mmd+zYwYMPPhgxeXqbPvnYDnWe3qaj5XgCPPbYYxH5/yda/z9F8vEsKipiyZIlAMc/P8+YUiqqHsBc4P1u0z8CftBL2UeAhz7vuqHDEvlWrlw51BH6LFqy6pz9L1qy6pz9L/xZekaf+dHYJ2UCDgCXADXAJuBWpVRxD2X/A2hTn/ZJ9Wld3SelaZp25vqjTyrqmvuUUn4ReQD4ADACzymlikXkvvDyp0UkE9gMxANBEfk2MF4p1d7TukPzTDRN07TPEo0DJ1BKvaeUGqOUGqmU+kV43tNKqafDf9cppXKVUglKqSSlVJ5Sqr23daNV97b0SBctWXXO/hctWXXOyBSVlZSmaZp2boi6PqnBoPukNE3Tzty5ep2Upmmado4YtEpKRL41WPs6V0RT23S0ZNU5+1+0ZNU5I1O/je4TkVygt9M6AS4A/tBf+9M0TdPOfv3WJyUifwO+fJoiSill7JedDTDdJ6VpmnbmIq1P6m7gP5VShp4ewNP9uC9N0zTtHNBvlZRSqo3QDVt7s7a/9qWFRFPbdLRk1Tn7X7Rk1TkjU78OnFBKPXeaZS/15740TdO0s5++TqoHuk9qYLS2tlJcXIzb7SYpKYlx48ZhNpuHOpamaQOkP/qkdCXVA11J9a9AIMAbby3jg6J9BKxjwehAfHXEGapYfNtlzJgxbagjapo2ACJt4MQpROTfBnL757poaZt+5bW3eealjWRNepCCCddRMHYh+ZNuIybvbn73p1Xs2rV7qCMe98477/DGm+/w0I8f5aEfP8qbb72Ly+Ua6liniJbXHqInq84ZmQb6LuhxA7x9bZAopSguLubj1duoq3eRnhbPwgunMX78eAyG3r/r1NfXs3ztETILF2AyW09YZnekkVx4Iy+9/gYTJ0447XYGg9Pp5PmX3yWu8CbS8hajlGLp5u18suVZ/u27XyMpKWlI82nauWhAm/tE5BdKqR8N2A4GiG7uO1EwGOTFl95kxcYm7OnnEZeQQUdbI+1167loRhyL77wJo7HnS+CWvbecvxUZyRuzsNftl+94ike+s6j/fsnzC3rx5Tf5eF8qeWMuPGF+xf4iLp/s4pavXDdEyTQtOkV8c592dti6dRvLN7ZSMPUeMnInYY9PJz1nAgXT7qZom5eNGzf3um5TczsWW/Jpt28wJ9Pe3t7fsT8XpRRrNhSTVTDz+Dyfp5PmhlIS00exesO+IUynaeeufq2kROR+EXnk2AO4oPu0iHy/P/d3rhustun3P9pCcu4CDMYTW4cNBiOp+RezbHnvlVRKUhzeLidl+4t6XK6UIuhzEhc39C3DgWCQikPrAKg8vIn33/odq9eu4uP3/kJJySEi6ew6mvoloiWrzhmZ+rVPSin1ZPdpEYlRSv1nf+5DG3wV1UfJnJLb47L45GFUHHailELk1LP6mTOm8Po7LxCImdLj+q3NVWQmecjPz+/XzJ+XiDBzygje+aSUrnYn27YUYR/7TUwxibgaD9NW8yQlJSWMHDlySHNq2rkm6n4+HkBEFgGPEfoJ+GeVUr/qoczvgCuBTmCxUmp7eH4Z0AoEAJ9SavZg5T6Zz+dj3759VNfUk5GewsSJE7FarZ+9YtiCBQsGLlw3jjgbni4XsY5UFIpWVyuVNZU4W514Pa1YGg5TXFzMmDFjTumbMhqNpMc1s3bnSppdgs2eRHp2LimpKXS2NuIs/Rvfvf+yHiu4wXbNlRexfe9fObzrfYJxozBa4mlrqcXgqyNt+HxWrVmH2WwmNjaWlJSUIR3oMVivfX+Ilqw6Z2SKuoETImIEDgCXAtXAZuBWpVRxtzJXAQ8opa4SkTnA40qpueFlR4AZSinnafYx4AMn2tvb+c3jSzjSkIghpoCgp5pMRw0/+M7iiBtFtuy95bz2sZfccYvYW7yXhvZ6EnLicSQ7aDy4hvEjikm2x+FwJ3Lv7feSkJBAZ2cnf3/1Bar3bmByhuJQlYs1BxVdptE0uW14fc2MGW7jOw/cyvTpUwcsu1KKyspKqqqqMJvNjB49moSEhF7LV1ZW8uQzf+X1j6ux5l1PjDmAzdSBr34dCwsOMLIwg/bOIAFTKjPPv4rpM2Zht9sHLL+mRbOIv5hXRH6ulPpxP29zHvCIUmpRePqHAEqpX3Yr8xSwUin1anh6P3CRUqo+XEnNVEo1nWYfA15JvfLa27y/yUbB2MuPz6sqWcucUVXc+7Vb+rSNoqKiQflW1dHRwc9//QxrimOQrALyJ48k6PfQVLaDJONG7vj+JdjibOz9pJiO7W7uve1eXnrmd4w1VbBg0jBMRgNFO8qYMTqL4oqjeHx+XB0elu1uwTFiPjnDcpk+aTgzZkzDZrP1W26Xy8XTz7zGvsNuxDIKpTwYfPu5+vKJXHvtFTidTpqamvD7/VgsFtLT09mxYwczZ87knnu/xeHmFHILR2E3NJNn2srDXx9HrC10h4ya+jY2722iuMrE5NlXccVV1/U6wnEgDNZr3x+iJavO2f/6o5Ia6Oa+xwZgmzlAZbfpKmBOH8rkAPWAAlaISAB4Win1zABk/ExrN+wnq/D+E+Zl5c9mw5aV3LM4OOTXDHVnt9u54+Yr2P2rhwn40zi6Iw6D6mDq3HTOv3ohsY5YACaeP551tet5/De/YH5yE5dOzTthO45YK7PH5tDQ3MH/vF1Cs2EKOw4kMM4+gW3vHuGt957ge/98O5mZmWecORAI8NvfvUCVaxr5E84L/WdB4Wyax6PPPsWf336N/BljMKclIiYjQa8fb10TtXsOkfbSM3x5boCUhGYOVK+jINPEhTPGHK+gALIzHFyX4eByt4+/r/w7f/1LLbfccS8Wi+WMs2ua9qkBraSUUg0Dsdk+luut9r5AKVUjImnAchHZr5Ra00/Z+sxkMhIMBk6YFwwGMBj6/qVjML9N7SreyaL75jNy+gjcHW5iYmOwxJz6gZw7ZRivv/BHfvJPl50wf8HUAgCCQcX/LSulI+kqxmSNxFLnossXw+gpX6ahag//98dX+dlP/vmMK+ni4mLKah0UjD8fAI/Xw94D+6ntdGEaexXOzte58hs3Yo75tA/Q5/PR9vFyvFX7WF1WxRUOC9+4YTgWS+9nSLYYMzdfUcjfP97EG69aufn2rw3KF4xo+SYN0ZNV54xM0ThwohroPtQsl1N/IuTkMsPC81BK1YT/bRSRt4DZwCmV1OLFi49fXJqYmMjUqVOPvzmODQE9k+nUhAAHj6yicMJ1lB9chVIKA14uOW88q1evPuPt9/f08qLlXP3vi7BYLexbH+r+m7ogNGJvR9HO49Me1UlXZysfbTvCotmhkXBFO8pC25taQEmNk81lfjJHhK6Lyk628+Gmv2P01jBi0iWUbd7Aiy++SF5e3pnl/WgtlrgrCQQD7Nr6Dw5VlpM0/VIyJk6k6fAntNZ7aCqvInPMCA4UbQAgmGYnO91HwB2HL34kK9s72fX6TqZm2XHYrSyYUxDa/sbw8wlPr95cToJdUVG3iq1bJtPR2Tnkr5ee1tNDMV1UVMSSJUsA+u3i/AG/wayImACUUv5+3N4B4BKgBtjE6QdOzAUeU0rNFZFYwKiUahMRO/AhoR9q/PCkfQx4n5TH4+GJp//KzoNuJKYA5alidG6Qb3/rzj53xBcNYtv0f//uZ0y8fTwJKfGnLbdzywb2P/Mhv75mPnG2T8+0inaUsWBqAWt2l/PcnnEUjPh0UOXmIy7GzbmEOHscZfs+5u5LjFx00UVnlPfR3z7Nu2vj6PJaqW5rxzYmn/j0DByORGJjY3EeeIUrvjWKrLEjAPB6fWxa9z5JzZVMuGjC8e2UbSvBsGY7371+AgmOmNPus6yqhXe3J/BP//ofAz5acTBf+zMVLVl1zv4XUX1SIrIXuFIpVSEiM4F84B1Cw8TvFpF9SqmVZ7ofpZRfRB4APghv+zmlVLGI3Bde/rRSapmIXCUih4EOQr8aDJAJvBn+ADEBfz25ghosVquVB//5bsrKymhoaCAlZTTDhw+PqL6o7jJTsjhaffQzK6k2ZztmH8Rae/4JDqvZBP7Ok+Yq/D5/6GJZfwcWS+oXzqmU4uOP3qe24l2sgRw8tgsYMW0ksUlxdHS20+xspd0ViyVYSXLepxVhXW0NqY4g0nri8S+YPoLDXT5eWHGQb10/8bSVT35OAqwvp7y8fMhv8aRpZ4t+O5MSkWnAXqWUV0SeAcyEznbeAp4nNKLuqX7Z2QDT9+47VXFxMS9/8lcu/tqFp/2gfuOJ1xh7sIZvXTOrx+WtHR4e/MthjHm3UN+laHL7KW/xkJI5DEMwgKHsJf713iu5dP5Fpx1AoZSipKSE2tpaYmNjGT9+PDabjS2bN7Jp7e+5/cYc7v7ORg7J9dhzxtPU0I4IJKXG0lyzhsmXOrjivpuPb2/D2o8YnxsgPu7U69SCwSA7X1jJXflW5kzOOe1x2rSzmgr/Bfy/W+46bTlNOxdE1JnUsYtlw3YDLwMe4HZCo/x29de+tME3ZswYEouS2LN2H5PmT+ixTHVJDYFKE1h6vg5JKcWR+haa/XXs2fMeCTP/H8aEVJLM8SQn2GnZ/RbDb5nL+nQ7q17+C5OT07nt6mtJSUmhubmZoo/eZ8/WVQSBQ41CIGUc1qzx4K4OJl1KAAAgAElEQVQn9o0VfPOWq1hT9AY3X5OOwSAkjrRgLV9O+d49WPNmo7xdVOxaw8SZitjRCwgEghiNBoLBIB53O464nnMbDAYKL5/OP179iFkTs087uCU3K55tW8o+7+HtM7fbzc6du9i3r4LOTi8JCTZmzBjLmDFjIvYsXNPOxID0SYmIAbgOWKGUauv3HQywaDmTGuy26dbWVp5+4Sm86W5GzR1B2rA0RIRWZyuHtpTQvLOFr934dd56/g/cPFYxLO3TpsEVW0uo7PCxNmgl68r5NJU3sHt1ObUuO7FJGSTEeZmwaDojF8xDRAgGg1Rv3oZv1SZumnM+f/vrM1S3NmJPTqamrAaVPh3zqCuYNusiYmNjaXfWU/n+75iXX8qD3xzDx2vLeafLwY49DQTHjuDwW3vAaCFrdiaWpnocsycRl5DPuMJCUlNS2b5xOedPTuDA2mLGXDCux+e/6y8reGBCAmOGp6KUwtXmASDBYT1+dtns6uIvK4I8+MNfn7CuUor6+noMBgNpaWmfu88qGAzy3nsfsXTpVjyeETQ1tZKXdz5ebwde705SU1u47baFA3ph9BcVLX0oOmf/i6gzqWNEpAD4gVLq/s8oqkWZ+Ph4/uXr32brti2s+vsqNrVvxWA0YMHKBdPmM/feuSQmJnLVV+7htSX/w1fnmkhNiMXnD7B0ZwVd82Yx+vK5GE1GEjJS8GWmk94Vw7iJU4jPzsRo+vTtaDAYyJ0zk44RhTz8/YfxN7dx4b0PEDTHsO3jw8RV7mOcvY2y0kOMnziFuOQMOpIn0Nq+nbr6dn79YglNyZm4GhqxGFOJv/FBJCmVspcfxWH3ET9hBM2BBNbXV5NYWorVHzjNMw+xj81jX0UZ2ekO/r6mhC2NHQDMTLMzZ2wGbo+f9k4PweCJfWotLS388eUl1PmaUYEgBY4s7rnlq32+qW4wGORPf3qNVav8DBv2LaxWB0oVkZo6NlxiBm1ttTz22Ovcc08HF110ft9eUE2LAv3ZJ5UH/ARYTGgE3eBdft/PouVMaigppXC73QSDQWw22ylNTTu2bWP5q08yMyNAXWsnHydmMurq+YgYaGrtpLLZgyEhhwlTpmMynf670kv//ghtHSlcc/lcfP4Aa8uMcGQvM7MdlHRkMv/iqxER9mxcSUbZL4lLtrPMM4PsLy2g9O1ldOz/BGbfhMcUj2fl86RmBYi9aCZxC87DFh9Hx4EjGA6VcMslhTjsPd870R8IsnPjYWr++DYjLH72JqUw+64LMRgNbHxhFecbjjJhhI16p4dNhxxc95Vvc+GCK0hJSeHpF57jaIEw5oLQkP09yzcxui2Z2268ucd9nezDD1fywgtVFBbehsHQ+38rj6eN2tpn+clPrqGgoOAzj6umDbSI+D0pEckRkSeAg8DXic5rr7TPSUSw2WzY7fYe+0KmTp/O4u/+nPL4OTx2sAvn6Alsr2jnkxIXlb5Eciacz+RpM/v0QWpx2LGkJbC9vBmL2YjydKA8XZjMlhN+PsPka6PDFaCm3YI1bzgWh538yy7CEgjg+/jPsPZZRl9fwKj/vh3xdUBFNY60BFKmjSGYksDq3TWn/ByH2+OnpdXN1p0VGH0d5DgUk1Nh5JgkuuqrKSuvYNSUeNISA6QlKEYNU/zLncNJDi7nT0/+BxUVFew+sp9R80IjA0WEkedNYvuh3X06zj6fj6VLN5Od/aXTVlBKKVpb3Rw8nM2ddz/M1//p5/zr9/+X995fQUtLS5/2pWmR6AtXKCKSBfwIuBewAkeBXwHTgVv7JZ12WpHeNp2WlkZXjI053/k2rY1N5M+ag8Vsxhpz+uuNTlY4bzZ7X1tKnQ9qTT4o2YOv5CDtI4aTGJ9C1Z511JWsprNsIyMTk+l0HqXTVo3zaC5HN23FYPZz1aM34zRaseZl0uYJkHHlDOre2E76xbMxWkzY4h24OgNs/mAHsxdNw+nqYn91Ow0eodMH3mY/2a0dVDQFMBZmsWblEfL80FTRyf51LSQPm4VxtxFv/RHG5zQxKtdBfIyTX//8B5iy8+h0teNISQSgo7mVePvph/Ifs2/fPtrasklJOfGHI8vKiigoWABAIBhg+469VNb6MMVcQMB3hPQx3wAV5PXlW1n6/h/5zgNfHrKfGYn09+kxOmdk+tyVlIikE6qc7gNiACfwn8D/KaU6ROTZ/o2oRavm5ma21VQw8pYr8a3dgM1qpa22nvZAgNiUZGwJffugHj1lOhUbNlDx8VI+bvYwIieJRpOJd199nniTj7Ez7Iydnsy0W2ZRX9bMu8/tpmnrcxhrihg7OoajBRaCNsAdxN3uIWixEJOWRDB8Zwj30RaSk+NxxyicZZU0ODv4pNyNuWAkCckJNH6yh2BZDVs278KclsyeLiudtjjWv7SbgH0isZc9QO7EQjq6fLia2ti18S38wXTyM3IorduOb2MJVe5XmHfLQkSEypV7uWP+9X167mVlNZhMp69cdu/eT2W9icT0SYgIruZCOlrrSMsaT96oK3A5x/Prx1/mpz+5i4yMjD7tV9MiRZ8rKRFJBX4A3A/EAi3ALwjdzSHqRvCdDSL921RZWRmmkfmoQAB3p5t//PIpfI5UDCYzQWc9uQUZTLx8PvFZvV8PFfD52PvWB7T7s7Hd9j0CZbWY05IYn5JFbUM9+Mvx1e0hLzWNiTPGMmaSn7r6Elpf2cAltw4jf3wa617fTcuafRzZ7cVZG8A6LJGEkTasw7LwOF34SyvJmlhI3UEfDMtnQ0krsROnYLbH4DpSS/u61Uy6fgyZI/04Dx2lZFMjc35xFzseXUFX4mVgjsXV5qGlxYchYMU+/Dxq9i6jwx/HlPlXcKDUj7ejkhW/eocLZ03mvkW3MXbs2F6fc3cejx+D4dT/psfOojo6OiirdJGYMbfbiEETgYDveNmE5FzaXBewfMU67rj9y31+/fpLpL9Pj9E5I9NnVlIikgx8D3gAsANtwM+AR5VSroGNp0WzstpqSElk9TMvUx+bTfyN3yAuIdTkFfT5qCneTc0zf2PhV68luSDvlPWVUmx9dSllnXGkf/k2xGikybSDaaPHIyI0+5oZM2shvs65LF/2JjVlH+ByNdLhqyMuI5blv13LeV+fj9tjYtMTLcRO+hK2/ELaaqtpXFVE+txmMsYdYczYPJQCq9dAcs5oNlRXYWlox+NuxrVqA4WzswjYLQQMNhKGpdBS0U7jlkN4mgPYphbQWu7F2+UHv4EchwFsuXTs85MjPqoq6jGY0pj0pftpb7iaA7vfpq7+KN3rqEAggNPpxG63Exsbe8IxSEyMxedr7fUYV1bVIJYsQld9HDtwrZgtJ24nI2caq9Y/zpdv6DxlH5oWyfoycGIPoTMoBfwSKFBKPaIrqKF37MaOkepoWxs1+w7TEDcM4hIwhysoAIPZTPzk6ZguuZF1Ly0l4D/11o7NZRWUVrhIWngtYjTib2/D39FCc3U5zroaxBykoqqW0tom6rMn8eayfUy+IYukwngMowvwTh3DmnVH2fpuOyMXfoN4Wyb+qnayEkeSccFdBBsScIjQcbSV5r1VTB83hYrNewjGxIHbQ6bFT6rRiyngJUgsKhiDNWUMvqYuvIeasZmMNB+swhxjw2q0YDUKBhH8na3EmI2kxRppa25FEUQpRVL2cLLmf53n3tvJyqLQPY23bt3Co795iJf/+kMef+xfeOONF/B6vcePwaRJ41FqJ0oFTzg2ZWVFoWPU0okl5tOLkH1eJxbrUZJSCk8ob7bYCEoyTmevv/U5YCL9fXqMzhmZ+tLcdxvwX8AO4Be6aU/rK7/fT9n+clLuv53W3dt7LBObX4jTkUFD8UGyJo0/Ydnh9dsxjJ1JZ0Upzk/W07r7EJ5mL75YB+0dR+lMUIy47hISRo3H2eDHPvs81r27jWq/g2H3XURyl3DgozLc1QkcONyGfVg6icOTyMvLIxAIcMRZStmSj0gdk8/ocaOwW620tzgx+NPJjLOD30sgLp6qTWUUxDRgtiTQVOPB1ZCIt/oQ7tZOcJeSeOFkbGYTLe1+vP4AHYc3MjktkYbOIGabCb8yYjaF7mVojXUw7IKvsmTZc/i8Xezd/RJ33JJGenouHo+f9z5YwdKlcOONdwKQlZXFuHEOysuLSU8/9U4fIpzw4zXtbRuYOGMqBuOp/7VFGPAb32paf/vMMymlVJFS6gJgKfCqiPw4fAdxbYhFetu0wePFG5eKKc5B8rwLey1nGjGemgOlJ8wLBgIc2XmItt37OPLEa7Ttt2DNuBpz4oU4Mi/DY70Yb8NIyl/eQPmrf8Pf2YFj4mT27VLY5kyirVORkWzD39mF3+YgYLXjCSbQ2hWkraWF4lXrqdxfSnOsDcvCuexrdbHsyRfwdXXhem8Fjet3UFd5lPakQlrs49n8v+vY99J+Dq1ooT2QhcyfS8qcieS7D9G1+U1aK/ZgaT5E2arXMDYeoU4ycHp9uH0+MrJGnFA5WGMdJE29nl/87imcrfU8+WIljz59gN17Grh0YTYHDxTR0dFxvPwtt1yG17uMtraa4/OO9UmlJMfhdTcD0Na6DUf8AfJHzjvlGHvd7RiVk5SUlC/0Wp6JSH+fHqNzRqY+D5xQSn0AfCAiXwL+JiIfA79XSnUNWDotqmUkp8D+k3/q61RithDwn9ic5e3spGHPQXyWeGJyLkMMRlRQIf4AHo8HMTuwp0/D48nGdeAwxqZ1WC6fQbvLR9XhdmLdDlprW/EaYvDU7ULFj0SUEb/Xx/ZV6wkGfVhz2/HZjaz/+es4EtPprDtMbIqbrCsn03rUg2vpGpIuvo7OQ/XYp96PISUTu9NJpmczbaVVxOBh5IRxKHsCZXtXYGl3kRUXR3xaIeYYC+1KUd4IhVNsALQ0VFC2fxXurmYCbX72HLXjrZ/O7AvG0djRzs53dzNh/SGsFjsdHR3Hf7IlPz+f733vWh599EWam2eRkTEDqzU0MnJYTjY79izH2biThKQK5l16J5aYU+9kUVe1lUUXjifmcw7/17Sh9rkv5lVKvauUupLQxbtviciDItLzZfragIr0tukpU6YQLC8h4PXhXL+613L+hmoS05JOmFe7ax/t5WDNmYevuZnOyjJaq6vAH6DF1UogKFhsDmJiMvHIWFoPCmXvbORouRNfXQv20dlUtwTodGQiiV58217A6yzH1+ki0NmC6tiOJbMZX106xuF34S68HXfKVRzd2YAlN4PkSVmYsxKo/fNbJKfNJj4uibaScmx15YxLTyalTnFkp5PVRSvZs3EFXSlG5KaLcF0yhU1NZaxrCLDVOJ6W1LGs3r6L155/iqWv/YT6zKP4ZydSYT2IOWsUtd4sakrL2PDxRkqqvHxSkc/WXa0kJ396XVQwGKrAZ8zIwpGwkiNH/ouNGx+isvJ5Ghv/zIj8D8jI3M75V9xNbNypP3PSVH8Qe3ATlywcmtslRfr79BidMzJ94Yt5lVJvi8g/gBuAt0XkHeCP/ZZMi3qFhYVMSUmgeOM6rKaevw8FPR7k8G5yv3Tn8XlKKQ4t30Js0hi6Kspoa2zCZ3YQbKgmYfgUPH7o7GzDhxmH3Y7FlonfMRvv/r8Rn5RCoLyOQ08sxTBpIu7l2/HV+cHUQOf738OclYMh6MGU5qWrsRAVOwKLNY6AXyBtLEol014bJOjpwtDaQYbbTFp7GzEuJ472Tkoba1nd5KTDNgXb+bdhoJXyrUtIuSCBlLQcWojF8pXRtL69EXvOaAwqgMMqtK7fQe6CMYycNorKyjIYNoK2Letx1pby0Z5Wht/+U1qqD9O0fyUTkgsIBAKYTCb8fj/P/elVPtnajiVuGmIaj8mxlRTzFh56aCYJCQmkp9/La68vZfma5zEnzCY1YxwGo5mOtgZaGraQbD3Cd75z65A09WnamTqjWxiFb3D3Zvhn2G8C/gHkcEJXrjZQoqFt+nt3f5U7/+cx4h/8t1OWBT0emt99nYnTx2BL/HSEmquqGmdZGxmjJnFoewne5EIkNR+Dx4fVZMackILXW4UvKBx1NhNrUSQmJOMqVRjMXmxZ2ZgcSTQ8+wru4EjiF1xOQm4qzg/eIdjcgt9nxt3VgXLHgqsNHG4s8VbcxSXEJSbRsmIXbU4hcMhFWoeRgs4GDEYru+uO0NpShS/+emIcSaSPGkkAwVVRR7DhEF6Pl7icQrq8fuwxRqwmAxlxZqwmE/7UBDotoFAYUXTsr4ALbibWNo62139IY20HwVYozBlPclKAlpYWMjIyWLt2PWu3CsPH3YuEbz+lhk2n7EA6h0squPaaRQDcdusNXHB+BUWrN7Nt52p8vgDpaQl85eZpTJt2FTabbXBe8B5Ew/sUIjtnQ0MDn2zYSnnNUbrcXnYdqGLs8GzmzJ5BQkLPPzFztuiX++yFK6vXRORvwC3AqZ9I2jlp1qxZ/OCay/n1H36Oe/5lWEaOx2A201VyEN+uDYwYn0/B3AUnrFNffBBXtRuTrRXf0ToCbkG63FjdHSiLjY7ODsRixttWSxArNqOZttpKfC2Cu6WGileaMdhNOGJcZF8ziZjRqTTub8IxMo+4Vi9VtbnYc1LptBnwr1tPV3MnQaMDVbsXKKU5dR4daROgoAv/ri3sPlKBz5FJs9WPpA7HOuYSvG31uKoaiMlMx54+nI59a4hfaMUWF0v7JxsJmDKItyis4XsT2tPG0LxvBc2FmRiNRpS7g2B9PQwbRWxcMuk1RcQ5Epl24Q00bV5yvO9o+Uc7SMv58vEKCkIj9LLyL+bDFU9yzdVXHL8nYH5+Pnfdmc9dn56U0tzcTE1NDVarlezsbP2bU1GmrKyMN99dye4SJ5I+nbjkeRjizQR8HnZsKuGV955i3uR8rrtqIenp6UMdd0D0681gVehijpfCD22ARcs9vO664072HTzIgcYDeLpqaDhQSUeLwp4/nprqGKp/+RqZwxMZs2A6tXtL2PbmJzQ3xRGTmoFPnKjGGswYIHM4nd52bLFmYmwxWKwB3C0ttJWVY6gtxRH0MTz3PHb6YrEZq5lzVy7lJXugI5PA0Tps1WvJvmwWDUtbUKXNiMmP3d+Ocu7E5g5QYIujkQQCwy7HGDcaS1YsnvJSqlxVJCWmkORIxOcz4/O4cPuF9qZWbFnpGE1+/GXVtC5bizt+L50NAcxjryEuxkLrkR10Va0DMeBzmTj0zLvE2RX+A/XEWqDrwG7SsqZx3tVfIxBUHDm4jVFx6vjPeDhb2knOTz7lmNZWbkM8vuPNgierr6/n1dc/YMeeegzmDIKBDlITPdx43QXMnj1jUIeiR8v7NNJybt++k9+/uBxL4RXkXjQegzF0g+Gy3UUUTFpASs5oAr5L2Fi6jZ2P/oXvfvMmCgoKhjb0AIjKO5aLyCJCv/ZrBJ5VSv2qhzK/A64EOoHFx345uC/rav3LYDBw5cJLSN29i+fe347tvFvJvWoeRrMFAG9HB2X7trLjB89jjrGRNucOWupXEgiYwZxG2rBRtNRvBe8OjHmFiCQSUOCrrSBQW4nJ10WiLUiQdHzShTV1CqqriY5GFxfcnMP6v7yCeW8z4y9LwFW6BeXsxJGTjPHQIczWXDyJY5kWSCMrycaepnoOHT2KKXEiNm8TZoOBuFg7IxJjqa0/wOG4RDoPvY5iAh0+P6a2FtrLllM4eiadagKG+JEkjhyJx92Jp6Ecf9Nyxt8yAxUMsvflTxjumEduYgytVa9imhSD5I/CW1bLOy/+gPS0EcQ3FZE2xcFjv/o+V9/4DUaNyKSk4QhpmSdeQ9bRXs/Y/ESMxlPvjF5XV8d//eoFfJaFDBt32/FrptpcNfzhubdpbW3nsssWDPjrrn1xBw4c4HcvriBt+mJiE04dDHOM0Wxh2Ji5NCdk8OunXuffH7yTzMzebzMWjQbkl3kHkogYgQPApUA1sBm4VSlV3K3MVcADSqmrRGQO8LhSam5f1g2vr39Pqp8dOnSI/35qKdYxN3CotoEWFcCUmUanqxXn0UYMSQk4dx1BVe8mszCfth0bSE76ErW1LVgcaRjMfoINm4hJtGK22kAFaaquxZA1FiM+gtUlJNvLMJvTachYQLu7i0z1OImZBprUMBzzL8K3cQW0VmKfNRdrewMjFuTg95op/6CS2H35jIydSZy9lZcPb6UzLZd4fMTHTEE1LWPi+DGopjLWd3kxWu20NvoIdHkZkW3Fmj2czLGXsb+yisCISQRi7DidTVC2idETK8icNRG7xUj1slXkNw+nrbMG6wUZBJNs1DS1EvB2YTiwh6nNTh6+dzrDsuKprHHx0gcuMgovZMmrO1HxiwhKDCJglgDBlo/412/O5ZprQr+l1dTUxDvvrWT/4VpKSw5hTbyW4WMuPeV18Ho6qDv0B37zi3tJSkrq4ZXShlogEOB7j/wWNeJmEtJy+7xeXel2Rhm289ADXxvAdJ9PRP4y7yCYDRxWSpUBiMgrhH6qvntFcy3wFwCl1EYRSRSRTKCwD+tqZ8DtdtPQ0EBycjJxcXEUFxfz/pplfLxqJ7Hj7qFgWCGZwwpwuVwU79/P4ZL9SHIcgTInYrYTM+0W2jb/kXkXXEbVwWbSUpOpaWkjMTYOry0bkzjxth6lw92KijHh9rRhMhjA3UDB2Diau9Lx16zGlHkhrqYJeEr3EJg3haA/lZbOLHKGWbHYYhhx/ggS8+y4Ww3YkkqpZxeWTitZgTTSEmdT6zaCoYtG50Fi3PvYebiUQIcX66ybSB53Hp1NbaQd3c6EnHhqPCNw+4P4lNBSehDruEmQkoGxq4C2yn3ET1V0NNbTuL+OxIThlDZVkp0yGa9SmFMTiW/ew+jJiRz5sI6lm2uZWdCGAkqOHmazL8D4m4azb93biCEfxIDHXcKoKxIpqtzK3icOct3FV/Lnl97DaZyDLWEW22vfJ6lxK8MK5mGxnnjdvcVqR1mnsHHjNhYtumRo3iTaaRUXF+MMplLwOSoogIyCKexetZL6+vqz6m730VhJ5QCV3aargDl9KJMDZPdh3agRaW3oW7Zs49mXP8RnSgXPUS6eXcie6k0Uzs+hcmkLdncXmS3NJCUmkZCQQF3zUVKmjyR5TD6dzS5qN1cQExuHOzEPqyMRo2EfsVYHNkMngYANMZhxtzah7ELmvAkoI7iKa/A0OQkYd5OccgHNzkPY4z1YJx/AYD6PxhVeOoo2QrUfS20pSXddSev6LWDNptMnrP3f5fiSUjFcMorSTjeb33uToDuWLmsaLamzMHo2kzkjhcKFo+mormP7x8up8gi21AxcezezrXESpvg4uhICmIwtOKpXE6x5AatKwz/8Kpoacmj79RsoEWwZ57E+kITdasOUmYIPEx5XK1Vv7+HoqHzsC69g74hUXv5wM6rsIF95aAGHWixcuPBqLrzDT2NZDSoYJK3gAg5u2M2Ei2ZQd7iSnz31G+prxjH/qvNDN6rNmIenrZSGmt0MK5x7yutkixtGVc3eQXtfRNr7tDeRknP56i3Yh83udfmxPqmTicGAMWMGaz/ZzI03XD2ACQdXNFZSfW2HO6NTzMWLFx/vhExMTGTq1KnH38DHLqYb6uljIiFPe3s7f/9oL8mT76WhYjd+0zBeWraOqQuClOw6gtdvIivLTktLC66KnXg8HrrwYc9JpW33BnweDzFZhXhr6vF63NSUbGXuNV9lxctPYvW1oVqS8TUHMcV5EWM77qNBEvJH4Jhsp3rrSswZitV7VpN9yyIc5c101ZZgyGwjY2IAX7UT14FXsc2ej+fgEYJWGxse30BHjQG/PRdDBwSP1OCYlk3MjGSaisoxjJ6NMTuHYNpCnA0fkOKKJX7UNPJLlrN3y1KkcAqB4RdibKzm/7d33+FRHufCh3+zvWjVey8gUUQVvWNsjG3iFrfYcYnjxOntJCdOcr6045PEOXGSkziJ7dixTRz3XgAHbASYXoUAgSjqve5qtbvaNt8fEhiMAIGE2IW5r0sXevuj2WWffWfmnfGY2jA4OrBFVTD62/Oo2HUAb30zzYdeRzfyVoROT7fQ4o7JxDZ+DO2vrKd82duM+sYXaf14M92mKHwBE1eNy6XG5cWZlY7UQH1lO0QnsXfNdoRGULhgCgB7i7dTsfsghQumkDIyE1NGDBXbDxBTto/0lDS6GjdBjwOfr7c6r/JQ7+uVPbL39ao6vJ5YTeuwvX9279497O/H81k+5mLGEwwG+ffqtSRMziAhs7cdsrK0d/uxxNR4dPdJyyduj00dzZvv/Q9xMREXJf7i4mKeffbZ3niGqBNHOLZJzQB+LqVc0rf8IyB4YgcIIcTjQLGU8qW+5QPAfHqr+854bN961SZ1jnbs2MFjb1aTNfEmfD0uDpcUs+3Dt5Geg0xckEG7NgeS5jE6pZDk5GR6vD28v241wVFJWNOSQAgadh+iuwpERxtXjrAwasq1HN6/nZ1r/47w9NB2tBl9jBlNbBTWzAi0miYi08A4MYG67Q101vVgSkrEEmghIlJH06Zyxn9rDomzxtKwdh+HXqnG0eVEH5dA58EIRPRYrFOmYIizEfT7CbZX4tzyJt3tjVju+R3a2GTcuzaj++ARkgsnIIypuBuP0nzkIOKq+4msO0BifDbx827Avm8F2dOdRI7IxOnpQQT8VL61E695Kfr4TKqaWwhadZhG54PHBR8/R0Kanvo9WzHcsIjIyAhGWoJ4YiNIKUimp8NBxxPPM/3mhUyeMuuMZe/scLDsp+/R6Z3F+Kwi9uwuxdu0kyVLvoLVdnK3ZCklVfuf4sffnU9+fv6FfEso58Hj8fDVh/5A1pU/Oq/je1xduEue4I//8/0hjuz8XK5tUtuBkUKIbKAeuJ1Tp6t/h975r17qS2qdUsomIUTbAI5VzkNERASypx2f18PG956lvS0La/QDJEa4OLB9Dw1N65n62YkkTeytKzcajOSmZHCwuh67xws6Hd76FoKtfmLoxhKRCkB6XmFLeTIAACAASURBVCFHq6YS7/83S+f7WLO1BofGQGpRAaZYK22dXur3NOPYX0faFVMx6+1kL11AT2sbvpZaDD47fnsn1owYPG1bkREZODsLIT4TY4wVghK9zUjArUWXP46uagHOFQTXPoUsXIzc9ho97oloIq4HnRGveQrCdgg2LCd69DSMI6ag1ekJoqPL3o3F04NNCJxS4Hb78egkbqcHs9WGSInGEh0LMhbnjM/T1XQYT9OHmJKSCBp11LY2MnZaLgjQWMy0tHvJyDp7IomIiWTpg9NZ+fRatq56jbyEGPzxcRiMtpP2k1JSV7mekZn+izaVvHJmOp0OGfQjpTyvxwSCQT96fTh+rJ9e2P01Ukq/EOIbwAf0diN/WkpZJoR4sG/7E1LK5UKIa4UQh4Fu4AtnOvbi/CWDFyp16AAjRowgP2kN6z/4M4010RgjR2PTNDJ96kxgJi+/0kln+X5YeGPf/BIwbvRYrBUWjtRW4fN6yU8dSdyYSEre+AXeBjs1rioCnnZsLeu5ckkPX/rKfCa9e4Bn3j/M/tVlRF0zA5mcjMfZis9bhd/uIHlxBt6aRhylB9EJP5X/PoB+ax0uJwQ72vC2xxB342IcZbWYo8x0N7QSSIzA7/ajs+jw15ehzfkMOt87aP3lBEQMwagZNBw2EvR58Ae0GEbdiXd/HXa/JDE+gaAM0uEz41pZSoQugDkxnuZtJXQfbcWYWUK8dgJ2fTQ9HZ2YUhLxHNpFd+VOAl0NRFuCpJkD1LY60VqMON0+fH5Jy74askfkkpAQj6/HS1ebHaERRCbEoNVq2Vu8/Xj1H0BmYR5f/F02u979mGm6XHSYeOO9vyBMk7FEpOL1duN17mZkhpdvfv2uYX2oN5Tep2cSCnHqdDpiI824HW2n7Xp+ujYpAGdHIyMSL61em2GXpACklCuAFZ9a98Snlr8x0GOVwdNqtXz36/dS8/3/ocs6krTkADnZkzEYep+Fys0aQUX9DrraG4iMTwPA5/UhAgEKMrLJyMpCp9VRtfsDvnP/9SyaP5M9e/bw7MsbafWY+PVf6liz7k0mT46nqUqPddZkNNEpdDZ0kTRzGgZbFNVvrKdjVznYTEQvmIYnykLj8j1YCrLwVOzFFtQSk1BIsNOBp60TND60Gmj7qBSt1YTD1QMuJ7qgloA3Cm3AR9AdhdDF4zONQUTHIh1V+LvtSG0WPY5q9CYTNRvexVHvQK+7go1PvIfVWE/cqCym3D0eKZw07H0Lx+Fo3IZc5NG3MY5KJuHKbKhzEmMvoOqJl6FoMjGLJ+AORIHDSUTpUeZfM4vNb3zMnrXV+AMxSOnHGtHNlGsKkMFTq6O1Wi2jF0xm0xOr+c0PfsHUKRNYt34z9Y01REdamTF9Nvn5+WrUiRB39bxJvLRtO1kTlpzzsa7a7Sy6fdIFiOriCcskpfS60N/6uru72b59J6WlFXg8Pmw2E1OnjmbcuHHo9fpT9jebzYwaVYDWPIr4xJEnbZs5+3a6lm+hpmQFBfPuRqs3ULJ1E/H+TtwByf6ONmIMXWSKcm658Yt0d3fzj9c+ZEdrJL7sa9DmwKqSN1hbUkJKUR6Zi+ZjiU9g/75mOusb6CxtxkkqjoZu9FFZmDMK6RKZBLMh9+5bqX7NSOsHq4nSavHuLicuJxp7nR1sESRdVYj0BejpcNEVDBIV78Ne6ka77V00nVnEjVuIpIzudi+eYCreji70nlakp4WD7/0ZZ3sLhsxbMCWMxWgoY/zCNGIKsomIiUGnEWSOTqa8eD+b33uTqK88gG5kLoHqMnQGH/EzxyBFgJr3l9PdUoEuIY1Y6eeKBRPYsXwvdXV5xGQ/iL5v+g1PVysfvbya8bP7rxKyRtvo9HXz+JPL2L2vHn9Qjwz6SU+xMaogA6/XO+zTdVzsu5OBCpU4p08r4uWVTxDwXXH8gfcTne4uyuVoI1I2MmbMpdWCoZKUcopAIMDbb69gxYqd+Hx5RETko9Ua8PlcbNq0g4iIFdx++0LmzJl5yofkjGkF7CjZRVxCwUnbnI56Zk9PZ9LkWNas+yO+yFG0V1eSnWwj0NlI5a6XWHz3Ddx9x/1YLBaef+FlNu4pxVtwI6kpicTExtNoM1CzqhO3Q0dF8V46KhvwBSRa6SVIBMLgxzhvEb7KXXRUu4hOseHRenCWlmJ1V9Nt9uMOBkkbFYMpPRopBG5M9DQ70Oq1CEAfE0lXTSf+ri5ikzVY8wJ4vK14gjFozGC078Xf0krAv5ueqHz8vnaMMwuQ9v0495WQN9uKOSUBMz3oNb1/v1ajIWN0BEdLIYgG90vPoHdVEJFmxd8KllYPuYvy0Te3c+v1N5Ocm07phzuoq8kkYfTik8rRZIvHMOYWSjcsY+TUKtIKso9vk1Ky58OdbN5kpzU9jtHjb0NvtCClxNFew+MvbyVtxWa++827iI8//SgGysUVHR3NVTMKWLnrbbKn3jKgtqmAz0vD7jd4YOmsfofJCmfqvj+MXYh5ZQKBAE899QJvvdVIYuL9ZGcvIT5+JDExWSQmjiY7+2as1tt58snNrFix6pTjJ02ayKgRdioPv4vb1U7A76W5cS/7S37Fffcs4fOfu5k//Oyr3Ds3ivHWg7gPvkx0x1oe+urNfOWLd2G1WmlqamLl5reIzNCQOTEBl6sKT7cTg8mKVqdnTHYBnWu3IkaNQ1c0B09Qi3vbRqypcViq1qNrr0bfeASxfQ0jIivJdK1i/DgPsakaYpLsiIAk2qrDGm1C+AOIQACDzYDObKCnoQxnczfCVUNUqg2jvhZH1YsEzZ1Ikx1v1wawv4Bl0R2IcQsR8+4g0NCAad48NMHDRKXHoPV1ojVZTiqXYMBBbE407uXPM3phFEXfX0LRHRNY8I3ZjPn8eCQeOhq7Sc5JQwjBrlVHiMo49UsAgEarw+kwsXfNwZPWH/i4lLWvNxI58h7iUiejN/bGIIQgKi6T7LG30Crn8rv/+ydOp3MI3zVnFi7zH4VSnLfevJTJKU4qt79BMOA/aduxLufH+HrcVG35F9dNTWTe3DP3BA1Hl1bKvYwEg0FqampYseoDAsEgaYnJjB079ngb0PlavbqYjz92k5t7E0L0/x3GbI4hM/M2Xn75RXJyMhg9evTxbQaDge9++14+/HA9H675B+1OD2PGpDO7aDbjxhUCEBkZyaIrFrBg/lwOHjyI0WgkNzf3+DmqqqpIKjDT0xGkvrEMU+I4Wppb0dhr0LSW0XTQgOeohsCbDvzCh3Qkoakz4e9agybFTJJsJuVAFbFxOmbenYrRZGL7uzXkTEmi29NCq91O464Azu4AXrsXn8tH255m/O4g/oZO9Ppusid6Wfi9BbQfbMa9NgJHxSp6GrWI6BFoe7oJNlWgjZ2EMEQh/B58RzYScDrx1u/FWJiAzhJ58usV8OGqqiJ3wSQy5k8k0N6K1ZBAc4cHaY1m7N0z2fHjd+hsascabcPVJYjPiUPKID6fH5BotbrjY/WZIuNpqjpw/Py+Hi/rXi8lZtQDtNfYT9vulJI5mYr99WzYuIWrF6sRJ0KVTqfjaw98nhdeeYs16/6ENqmIxOzJGC2f9Nh0OdpoqdiOaC/h9isnce2SK4d14ODhopJUGKquruapN1+hzQwmkxmNVovnwGa0/36XO65Ywoypp39a/Ux8Ph/vvbeZ1NTbT5ugjtHrzUREzGbFig0nJSnobZtaunQxS5cuPuM5tFotY8aMOWW9zWaj8XATyRMMtOzeSO2Wjbh7EhHt5Ri7BfV7rRgMs8CcjzcoiY9JQrojsCT6cbRsQBPRQVf2tfg8jWx4dx/ubkFidAR6nY+pN0bz7yfWY4u/lqzUDJpa7OzdVk8gcxIiPQXZ3Y2of5IJN6WjCbQSkajF5j1CS4sNbe5t+BpbEMmpuCtKMLo/wL+9A02EG//mMoy2dCq27SdhTB7Q+9S5z9tDwN6M42gV3a3djLlmMgQCmLUakpJTQIDT6aSxrYnUyalU7z9C4bwi/D4PrS2NuLq70GmCIMAfAJPZhi0yhpjMCeg85cfLrLLkED7yMFij8Xe3YE21nlKuxyRlzGDF6me5ctGCfgeoHWqh0tZzNqEWp16v5967buXqRY2s37iNDzf9BZ+wotUaqF5bgk3v5bb5k5gx7dIeh1ElqTBTV1fH7156lrgb5zMu/5O7D2aBs62DZ194HyklM6ed+2hP+/fvp6srnri4gb3h4+JGUFpaTHNz85DNZVNXV8dbrz5J+dYqqIlDagzEaG3k5xpp1+YQOfpG3HYX2o4KDJ2HaHZKCgvG0yqSyJ6Vh5QzObD5eZzllbS0t2FeWoQtP5Gu+iY615dQd7Sd9LE+PAffobouj5Y2GxZLDEHaEC07kK7VWFLN+P16gl4XhggzuflujqwBr9mOMXcSXq8OTFb8R35P4sIv03FkM9EZcQRsLrxRI9myfDspcXqypyahDTjocfs4tLUbfUIiOp2BoL2dpMT442OiGA0GnFVNpI/JoKujg65uJxpbK56m3aTl5KPV9nZSkVLS7XLS1uzA23qAxbd8MrZbZWkDhpipBPx+gi4v1ojTJymLLZ7Wo73VqqmpqUPyuikXTnJyMrfe/BluWHo1DocDr9eL0WgkKirqkmt/6o9qkwozr37wPpFXTycpP5fy4k0nbYuIi2HkXUt58aOV9PT0AL0fbA0NDRw+fJgDBw5QWVmJy+Xq99w1NQ1otZkDjkWj0SJEGo2NjWfddyD1/X6/nxeX/Z6A7whjlt7JvJvu4sqbPkfmwhuxxSRijpyHOSKR2LRsZFweyZEm8uKjmTB2DHGRsXS1O9DpTeSMu56Ynh7mPDibws8vJH5UOpGzx9ETnUz58lraN9Rg9VbiqX4Ro/dtTL7XsTY8SYqphNTcdCw5N3FwdQcdNT2YRCzetiD42yApkx4s+KUFjcGPLmUkeaZOcqlFHywl/9YJZCyZhWneDCoqOjj67700lThoLg1iyJ1EwCfw7NlPosWCqW+mXL/Xh72yHkuHByEFwaCkrHQTV9+cgsGzgYC3+3j5CCGIsBqI0rbTefRFbBmfVP14PX60eiOdDW2kxaeg1Zz5DkmjM+H1es/6mgyFUGrrOZNQj9NgMBAfH095eTlxcXGXRYICdScVVpqbmznQ0cT4wtNXo1ljoyE3ma1bt6LRaflo+8e048YQbUVoNQQ9PrzNDqbnT2DO1Jmkp6cfr8f2en0IYTzHqLQEAoFB/FWfOHToEBG6RvbbLeQtmkVzczXxsQbi2u3sLKkhJX3e8X1tiansL61m4tiR6HQ6stIy2VOzj+iEGCxRCbi7o7B32Un3OdDh53Cti24i6dZNJinOwp7977Pkx5Ox++Nw2uM4unI/5uhs2h0mUqffSfN2Mx88/GeiE49gjNJgTDTRXfk8wfg5aJyN6OIiCMgsju74N9dPz6d+bAR6rR+/LQJD4XhsAT89W8opPyLx+g0YOsvx2+soEAvxltfRXNUEGgEuL5kJyYydMoN//e5FRl8xi6xEH+kp6cjPBfnw5ecJGiZijslEBoN4Osoxafex+NZ0WtqrGBEsQKPRYIk04qnrxNXkYULhlDOUcu8Xl4DXgcViOeN+ihIKVJIKI01NTZiyUo7P0Jm/YGa/+3VHGPjts3+l8NoZpN8wgZEZySc1qHrdHo7uLmfLm08xwpbKF277PFarlZgYG35/2zlG5cBqPX3V0jEDqe/v6emho8NOMGkaJouFiMh4mttb6HG0EwwmodH2dgqRSFyeAC59EtaIKADi4+OJrLXRcKie5JEpGIwjaF23FuaN4MBRLwFdCgF7OdbMKUhjDFZ/HRX73dTVdqIdNZOu3GxausyY/dW4mo+CKZ6MnGuxeJMpK9uMiE1ASxdCsx9NRh5S6KGxkU67m0rNWJoO7CcjV0u6COKpPEBH8SbsVVpi5t5GekoccQYXW/75Dzq3tbL461fjlwECwSDO5q7eu93ddcR22ag/eIDZMycAUDA1k9QRcZRvO0zt4b1otZA7L4K8CRMxWgzsOdhJc3MLyclJjCjKYu2rH1I45kEibZGnLWMAe1sVGUlaEhISzvqaDIVQa+s5HRVnaFJJKowIISAYPOM+RysrOdRQxbirJjHxswv73cdgNjFi5njyZozjYPEOfv/UY3z7C19l3LhChHiCYHAumrNUFwG43R3YbJ0n9cwbjJycHI7WBfCP6X1wNSY2jg4Eh6qP4vEbsDs9BIPQ3QMavZnY5MzjvdiEEEwaN4Hde/dQtf0ofl+QeL+XdT99je6YEfg7/KSnTEKvldgMWhoT89i/cQ+JS5didxvxGRKxTJiDcLVSt/avxGhdZIwbj685gQR7Pd4p92DyteBoKifodxIUelwZM7ClTCQq7wp0jQbaNzfSGunC2lBNnseOdekd5EzuHXuvw24hIn8eBZHxfPz4OuIKEyjddZRDrQY8Tkmqq53/uO9+/v7cL6jaHUvelCwAbDFWihaPpKifm+e4KA2OzjaSkhJpr2ggIdhEvO3Mr5uUkra6j7njrqmXZE8w5dKjklQYSU9Pp+f9evxeLzqDgfLiTSfdTdU3NHCwqRIrQbLGnD1xCCEYtXAKhwy7efxf/+Db93+VSZNS2bdvHykp4896fFPTdm67bcqA6saLi4spKiqipKSEmrIyGisq8Pf0YDCbScnLI2PUKCZMmMCEKVezbHcjphQHWq2goUVDclYRvpoaAvp4hNBgNgTxerqxtzbSGRFDRkYGQvR22y2aMJnOzg7WbzyM39eFDEaSnTed/EmZ6PCxa18tSfERVNVqiShYhKfBT5LFTYVHEmzeg7d6NwazltTUeFx+M7qgFkt0Fr66jUTN+gJx45ciZZCyTW9gaDpI0qQ7OFLTyFXz76Kj/hC1BzeTrZME4nrYVFLFwZ4EZECi19hIS0pl7qxp5OTk8Mbbb1Bd20b21XeTnJRCZ+lGKqtrWDIpm6bNh9lR20Hh4jEYLf0/UrD348PE5SfhbLOz5bkPyJZx/Ok3D/GHx99Er7+DyNj0U46RwSBVB96nKN/HlClFZ33NhkoojIk3ECrO0KSSVBiJjo6mKDOP8m17yJ19crtDIBBg75GDWBIt0N5F+gCS1DEjZk1gV/1HbN62hVtvXUJZ2TN0dEQRE5N12mPq63eSllbH/PmfOev5u7u7WbtqFR//85+MCATItFiYYrWi1+vp8Xho3rKFI2vXslqvJ3fWLNKNLTh8meCDtNwUYiZE0FHyW6yRFrqcPfQ4W4iyaND66+lubaG2JoqMzN4OH0L0llPhaCs5yYvYtPcARWPyCQL7KzoJSBMpURHo/RpkXBIRVkFstKDLOoLo+CQcwSZ0gSiS4gKUHzaQkpDEhJlz8Nkr2L/lCbrN8QS8Tji4maxFP8cSn4e9zo7T2U1i1lhssSkEDyxj3oLprHnszwgxBkNiKh17DlMU72fMmDEYjUamTp7KeruFrFFjAXBao6GnFV9Qz/dvn8TKjRV8+NdiosalkTI6hejkSPSG3v+uPW4v7fWdVB9owllh4Rt3fonZM2ej0Wj44bcMPPbkC1TWZxGZWITZGksw4Kej9RAB+3ZmTIznC/fcedk0uivhL+zmkxoOoTyfVEdHB4/843HkrDFkTh2Ptu/Dpq6ujq27t6Ipq2DxZxaSesJwOQPRXtdE46vb+dm3fkh1dTWPPvoCTudIEhImYLHEAb1VRQ5HHR0du8jIaOe7372P2NjYk87T09PDvn37aGttxWgyodPpWPfKKxQ4HMxMTcV4hg9Hl8/Hx/X1vNTqwLToyxTMuPr4tj0fvEbF9iB2dwwpsRpcrY1k6hoZmWajtBpmzr3qk7+lvYq4uBJuu20xD3zjhxzqsqHVm7DYTJgtJgpHT2RvXTWHG1uIGZGPSS8xJU8nJjEZGQzi+OhxEhPzSU2ZycgRBcfPG/B5cbTXIYSGDR++iGH8g+gtMdgb9zGzMIHEhESq92/AWPMetY3tdGi6CEo/lmg9Zhnkpw98j6lTp/b+rS4XP/+/x2lJGY/GaEVbtp7/euAOXn/xMe5YGCAl0Yajq4ete+spqbFT0+4CvRYpQRcMkhlr4fDBHr7347+Qk5NzUjl6vV5KSvawZl0JrR1O9DothaPSmTd3Kmlpaef0vlCUwRiK+aRUkupHKCcpgPb2dl5+/232NFZjHJGB0GjYvupDopMjufJz15Kcl3H2k/Rj65Pv8uCCW8nPz8dut7N58zZWrNhKV5cR0COlm9RUA9deO4PJkyedNFCplJLijz5i1WuvEenxEAHU2+2UlZby2ZQUFs2aNeCBTXfW1fHzgzWk3/xdRk5ZhEano7u9mTVP/Y2GhiwSzAZs7hrmjotHCNhc5mbuFdcB4PE4aGh4mxtvHM97aw5j146l5EgL3S11+JwWujuaCYi9pCwspOvgJrQzr4GINHyuVNIyknDuXYu+4QjTFjzAiLz8k9ptpJQEg0G0Wi2Hdq9mT0UjpuRJdFRtZsrEkViNJta/8mfGTVyKsGTR6O8gPt1KZ/U2XIfWsuxPv2XUqFHHz2e321n78SY8Ph8ziyaSkZHB+nXF1JQ8zeeW5p507UAgiKfHjxACs0nH9tJGShsKuP/B753Xa60ow0ElqQsk1JPUMW+//TaZmZl0dHTw/Jq3uOoX9w9qGobDW0sZ2WDiths+e3yd3++nra0Nn8+HyWQiLi6u3wb3le+/z8aXXmJ2ejpWoxGnx8P7H33EUiHQ+Xxs9vl4YOlSDMaBdXHfVV/Pn+pbsI2ehjZ9IsJgxV5XzuYXXyBGl8c1syZjMZs4UtOJz5jLqDFjaGk5jMezjTvvnMWr7+8kKv8LBIN+3n3ucTpd1yGsuXR5PAQd+9EFXmbKnRZsySmUbaugrsmKpq0JbWctOWOvZvSEKWQVTMNsiyUYDHK44iiH6hrwBYPEWq2Mycli84pf02ZsxjAyDV1NA22bm5mz5BcUjp9OIBhgz75Smjub0KAhJQLyovfyy//6GsYzlIHf7+efz/yFGLGTpQuy0OlOfj2llOze38STb7Ty8G+fIC4uboCv7sUTam0oUkoqKyspLt5IeXkVwaAkNTUem03PPffcE/JVoaFWnmdyuc7Mq/SJiopi0qRJ1NbWEnckZdDzBJkjI3Ac6TxpnU6nIykp6YzHtbW1seb111mcmYmxbwqPTXv3MsnvJyUyEsxm9LW1VBw5QsEJwyD5AgF6AgGsev0piW9Saip3+Xy48+PJHW2j2+UhYvxozDf/jr/++THW7X4ZGUzHbEsnO9dMbe1uJk5M57rr7uLQ4Up8lvFYIxMo3fgWERFXk5g6mc7OTvxuJ6as0Whc1xN0bOGah28nYflqXv2PZzBFLSAqfQLdYjxb9nRycN9TjCuagUuXSLnbS+SEKViMRpztbRRvLsaWHUnRzZ/B37EXQ7KN0q4ZJCRlA6DVaJk0biKBQACNRoMQgsrSDkpK9jBt2tTTlqVOp+Oue7/KO2++yB+eX8vEkZCbHoFWq6G5rZvtZT4w5XL1Z5aERYIKNU6nk8cff5a9exswGDKIju5tEywv7+DIkWIOHKjl29/+oqoWDSEqSYWxY9+mAoEAQjv4wUM0Wg2+T424PBBbNm0iDY4nqHanE3ttLZOioo7vsyg5mYqjRxmRn09QCJZXVlLscOHTGUgK+rg1NYlRn3puZ25aGk9t2cJNd9yBzfbJ6ApPPfMPamtrqaioQK/XYzAYyMzMPD79xGvvrCUyvrf7fVtjI0bTNHRaHVFRUbS7nVjjEnD3xOG263E0t7Lzna3YRnwWnXE26UmpBKWkpqsHc8Y17Nz2LD5bJEnXfP7482mW2DjsFj3+RBsBXxcTx+ZTvOojYkZdTVd3F0l8ktRPHBsvMqmIj9atOWOSgt6RBW65/V7a2payY9smPj5UTjAYICommWtun0N2dnZYdR8PlW/9brebRx/9K9XVRrKyTh6M1WKJIiHhPlpbq/nVr/7K//t/3yQ5OfkiRnt6oVKewyWskpQQIhZ4GcgCKoHbpJSd/ey3BPgjvVPEPyWlfKRv/c+BB4CWvl1/JKVceeEjv7BMJhMB9+CHuPG6e4gznfsoBJUHDpB8QhIpr62lUAi0J3wI6HU6tIEALrebFY2NfJSYTeq1s9EbTThamvjL6nf5ocFA+gmJzajTMTIQYNeOHcz71H/M9PR00tNP7WYNoBGid3RXwGyz0mVvRW9KRqfTYdLq6XG5kUEfgm5ajtbS7c0iKiadQEc9fl8MwUAPMqhFa4hAn7aE1oPPkfypu1RLci7te1eQOymK+NgJ+AM6Ag4vkWmnf5DWbI2lrXHgU2TExcWxeMnSAe+vnNmHHxZz9KgkJ2fCafeJj8+ksdHLsmWv8p//+c1hjE45nXAbu+8hYJWUMh/4sG/5JEIILfAYsAQYA3xOCHFsmG4J/F5KOanvJ6wT1LGxxuLi4tC5Ajjb7YM6X8ehekakZ5/zcRqNhhPb8Fqamsj8VLvLts7e7xIur5eP3X7SZ87DYDL3znWUmIx26lzWNTWfcu5sq5XKffvOKZ4JY7NobyylouIorR0uqg+/xJEDe6mrqSHCZMbb2IS7fQNpE2I4sLaUYCAdfZebdFHGkR1fp8P+GBrH4zhrP8QSk4vPE6Snrf6ka3gcHVhr6zDvOkzlhl20HzxEPAbiE04/maC3x0mEdWhmxQ31ceZOFAqx+nw+PvjgY5KTTx11/5jKyt0AJCXlsn9/LQ0NDcMV3jkJhfIcTmF1JwVcD8zv+/05oJhTE9U04LCUshJACPEScANQ1rc9fOpJBkin07Fg4gw27yhjzFUzzuscHqcLz6FmJl036ZyPzR07ln2lpaTFxhKUErvdTlxExEn7+AIBpNlMQKtFRkSi+9S02JboGBp9p44BmBQRQfGRI+cUT0F+Hgd+/j1MMUvJicnGOqKDmpoX8bWPo7nVQMC3lbEjKhitK6KkpIqizMU42uvodJUxVQ9WcwAAGJxJREFUamwG8ZPHotFGUL1+Na4GA9G2OOwH94IhAunppGPfR5grVvGP3/+C2NhY6urqsF3RTZ1Xi+YMU5x0NO7ihqVjz+lvUYZGRUUF3d0G4uJsZ91XCA1CJFNaupeUlJRhiE45k3C7k0qSUjb1/d4E9NeinwbUnLBc27fumG8KIUqEEE8LIaIvUJzD4sS66elF0+jYVYXf5zuvc1XuLGP2mKIBdxM/0bTp02nUaHD19BAMBiEYRP+p6rFMjYb0vDzibTYMjnZ6uk+u9uqqryXfdOroCiadjp7TjNreHyklb77wAouTINL+b7rt5aSmjWfCxEKSk3YTb3qBgthifvLQF/nVfzzEPTctxWYKotEcYu49V7PontsYU5BDSqKJzKkJOGqfR+M8RI7nIO73foFv+UPcmFLDm0/+iqKiInJycpgzZw733Hkj3ta1eFz9383WV+6gs3o5NtvQDOoaTu0SoRBr78j/Z+5Zmp098fjver2Fzs6uCxzV+QmF8hxOIZekhBCrhBCl/fxcf+J+fX3E++snfqa+438DcoCJQAPw6JAFfpHFxsYyPW88e9/bwLl2n2+vb8a++SjzZsw+r2tHR0dz3d13s7auDrvbjRSCYF8MwWCQBrsdGR1Ndm4uBq2WG2OjaVj9Ph0Ndbi77NTt30PE7k3MST31W6svGETX1yFjICorK2k5eJDZBfkUxProrHuHfdsfwX70n4yLquRb8xL47lVz2fDBBwQCAebMKiLQvg2PuwVrbBRCCAwGAwnxcYydMIooUxufv2Ykv/n2Fbz152+zfeWL/PaXPz9lvMKcnBwe+NxMGsuepr5yO35f71QpPW4HR0rfY+M7/4vGfxV/+tNGdu7cfV7lrJy/3hmrB94pyO/3Yjaf64wAyoUQctV9UsqrTrdNCNEkhEiWUjYKIVKAUxsxoA448WnWDHrvppBSHt9fCPEU8O7prnXfffeRnZ0N9H4IT5w48fg3mGN1whd7+di6Y8u3XX8zrc/9nfd+8ww50wspvKJ3ht59xdsAGLtg6inL7XVNrH54GTfMvOr4xIXnG8/1X/say19+mQN2O691dDAzOhqXENSZzTj0emYdSzYBP7M6Wmj/6F3aA0ESHR0UJcQT3TfH0seVlQDMyc6mxemkw+9n+fLlmC1GdpauY2/pPkwmK7fdfA9TiqaxY8eO4/Hs2bULR1MTf95fgcy+lvQZ+VQf3oCneRufGTeeKIuF3ZWVVDY1UV1dTU5ODrmJHnbs2E9jeRo+T98HWTCIxuQn3ghFk0bT3t7O+PHjz1oeGRkpPPbXp9m54m8kpY7AZIDoYAvJ8ZmMH/9ZGhp28vrr7+JwdA7q9d+9ezff+c53BvV6DdfyH//4x4v+/8ftdqPVOvD5eqir6635P3bndKwt6ti6ysrdNDbuYNSoJRct3jMth0J5nm65uLiYZ599FuD45+dghdXDvEKI3wJtUspHhBAPAdFSyoc+tY8OOAgsAuqBrcDnpJRlQogUKWVD337fBaZKKe/s5zph8TBvcT8P9Xk8Hp575V+UB5pJnzWW5JGZ/T4/5bJ3UbXjAM4dVXzp+rsoKCg4ZZ/zEQgE+OujjxK3YQMTk5KIiY3FZDLxcWUlc87jTbu+uprOuXOpbt1LxpggYyanEBllxtHpYt/ORmrLNNx927fIyuodZ/BfzzxD1apVrO+MIanw1uPnaarewTzdHmaO6B1CaENNDTd///uMHj0aKSWvvf4mf335SXRJBkbNG0dXcyOOkg5+9OUfHR/K6FzLwefzYTAYcDqd/PSnT9DZOQY4xPe+t4jx48ed8zlP1N9rH6pCJdZly15k3bo20tML+91eWbmb7OyJ2O1NGI2H+PWvfxKSXf1DpTwH4rIbcaKvC/orQCYndEEXQqQCf5dSXte33zV80gX9aSnlr/vWL6O3qk8CFcCDJ7RxnXidsEhSpxMIBCgtLWXN9o+pcrYQNSETc3QEWp0On6cH+5FGAlWdzBs/jdnTZg75Q6FHjx7lnV/+knuysgb1nzwQDPKnw4fxFURy3T0ZZOac2nOu+mgLxa+3880v/4yoqCiWv/cea59+mj2BPJJGfdJ9u6V+P1P9m1gwKg8pJaurq/nKL39JRsYnN93V1dW8tfwN6lvryE7N4ZpF1x1PfgPV3t5OS0sLZrO5b3R2cXz9wYMHSUxMJC8v7zxLRBmM1tZWfv7zPyDEaOLi+n98we120Ni4gf/8z3sYO1Z1chmsyy5JDZdwT1Inqq+vZ8++Ujq7Hfj8fqwmC5kpaYwfN76vnn7oSSl57L//m8mNjYzpq0I8Hzvq61lp0DDpeitzF+efdr+1K8tJ1i/mqkVX09jYyP/+4AccqetGO+IWbNGpeD1O2sre4J7REaTFxtJst3PEYuGhhx8esm/Kdrud5194h+2ljWiMqQT9DpJjvNx759UnjdenXFw1NTU8+ujfsdutxMWNJCKid4Dknp5umpoOodE08OCDtzJ16plnN1YGRiWpCyRcklQo3/bX1tay7Kc/5a6kJCKNxlOq+/zBIAeam1lXYafVFSDKqGFOto1xKckYtFraXC5ettvxpBu55WtZxMRFnPZabS1dfPDPNh763q8B+Mfjj3Nw1Sqqu6AzYMYgPSzMimZiVjpur5d1NTXc+p3vMLno1DmVzqdMPR4PD//mCRp6JpOWPRONtrept7OtCkf1q/z4uzcyYsSIczrn2YTya/9poRar0+lk69ZtLF++jrY2J0IITCYtyclWvvKVLx9vmw1VoVaeZ6LG7lNCVnp6OnPvuYfXn36aWz41MoTb5+PvW8spa04nwjATsyGaOkcXT2/fS2Z0GXdOTGdlRwfXfOtbvPre34mKOXO37agYCw7nJ89S3XnffTztcmHYtYtkg4H0uFSEEJTW1lIbCHDV3Xf3m6DO1/btO6npTCOncO5J66PjsvB7l/LqW2v40feHNkkp5y8iIoIrrljIwoULcLvdBINBzGYz69evD/kEdTlSd1L9CJc7qVAnpWR9cTEbli1jocVCQd/YfP/cWc7m6kLSo2aeXN0mJfuaN+G3FfPfjz3M1GnT+OUj3+Pmr2QSGWU+7XXsHd28/VQ9//WD3x1fFwgEKCsr4+PVq6mvrESr0zGmqIjZ8+eTmpo6pH/nbx59mgbvQmISTp1oUgaD1JQ8yh9+9WWiThjySVEuB+pOSglpQgjmLVxIdl4ebzz1FNurqsiWknUVfrKjp/YmKClx+/3YPR6aAgE0KXOIStCS0pdIJoydQ9nuTUyff/o7kf27G5gwds5J67RaLYWFhRQW9t+Tayj1eHzojP0/BC00GtAY8Z3nQ9aKcrkLuYd5lYE78XmpUJaZmcn4BQu46qGHKElLoyKYx/aubrbb7Wyx2zkgJa6MDArmz2fq3LmYzVM5ePAQADOnzaFsq4eWJke/525utHNwu4eZ0+b0u/1cnU+ZjilIo7P1cL/bXM42bKYeoqOHdnCTcHntIXxiVXGGJnUnpQwLjUZDQUEBV15/PYfrW0lNXXx8lttPTzInhB6/v3fEhsTERG665su8sewJCmdaGTspDYvViKu7h707a9m/2cVnr/vq8Wk6LoZ5c6exsngZ3V2jsdo+mW4kGPDTcGQld18/JeQn0lOUUKXapPqh2qQunOrqan7607fJyvrGabt/V1Y+yw9+MPWk51QaGxvZuGUtu/auR+JHoGdy4VxmzZh/1kkZh8OuXSX89R8r8RvGY47MxNvjwG/fwYLpidx912dPmldKUS4Xqgv6BaKS1IUjpeThhx+noWEOiYmnjrrQ2VmJTvc6jzzynX4/2KWU+Hw+9P3M5nux2e12tm7bSVV1C1GRZqZNHU9mZmbIxakow2UokpRqkwpj4VQ3fSxWIQQPPHAjWu1Kamo+xudzAxAIeKmv34bb/Spf+9pNp73zODYA7IX64B9MmUZFRXHVlQt54P7buPWWz5A1yBE3ziQcX/tQp+IMTaqiXBl2KSkp/OxnX2DlynWsW/dHAgETQniYOTOXa665a8i7iIcCKSUNDQ20trai1+vJzc3FaFSjbCvK2ajqvn6o6r7h4/V6cblcmM3mS/ZDu7a2lhfff4NaTzuW9HgC7h78dXauKprN1Vdc1e8AwIpyKVBtUheISlLKUKmvr+fR5x8n8drJpI8dcbz6z+Vwsv+ttUyPzOP2Gz97kaNUlAtDtUld5sKpbjpcYh3qON9c9T5xV40no3DkSe1TlsgIJn5uMRsq91BXV3fO5w2X8oTwiVXFGZpUklKUC6Sjo4MDzdVkjBvZ73adXk/slBFs2bV9mCNTlPChqvv6oar7lKFQUVHBX4tfY9IXrj3tPo2HqjBtrudrdz8wjJEpyvBQ1X2KEsLMZjO+Lhdn+sLj7uom0nz6aUgU5XKnklQYC6e66XCJdSjjTEpKIl5jpbWqvt/tUkradh5h6rhJ53zucClPCJ9YVZyhSSUp5bIlpcTv95/xTmcwhBDcsOBqDr+9ke4O+ynXPvDRVtICVkaO7L/NSlGUMGuTEkLEAi8DWUAlcJuUsrOf/f4BXAc0SynHncfxqk3qEnf06FGeX/keVa0tpMXEctfiaykoKLgg19q0dQsvf/QuplEp2DIS8Lo8dJZUkmdO4P7b7yYiQlX3KZemy+45KSHEb4FWKeVvhRA/BGKklA/1s99cwAks+1SSGujxKkldwrq6uvjx3/5ExI1XEj8yj/bKatpfXc7DX/wqcXFxF+Sa3d3d7CrZTUNrEyaDkfGjC9W4fsol73LsOHE98Fzf788BN/a3k5RyPdBxvseHi3Cqmw6lWMvKyvDlZ5GQ3/twbVxOFowvYO++vRcsTqvVypxZs7n1+pv5zJLrBj2uXyiV59mES6wqztAUbkkqSUrZ1Pd7E3CuczQM9njlEqDRaCAQPHllIKim01CUEBRyA8wKIVYByf1s+smJC1JKKYQ47zq5sx1/3333kZ2dDUB0dDQTJ05kwYIFwCffZNTyuS0fc7HjaW1tpW3VOmpzM0gsGMGul97Et6WEcb/9PVFRURc9vnArz7MtH1sXKvGE+/KxdaESz4nLxcXFPPvsswDHPz8HK9zapA4AC6SUjUKIFGCNlHLUafbNBt79VJvUgI5XbVKXvvr6el5btZLy+jpyk5K55cqryczMvNhhKcol5XJsk3oHuLfv93uBt4b5+JDy6W/UoSzUYk1NTeVb997PYz/6f3zv/i8dT1ChFufphEucED6xqjhDU7glqd8AVwkhyoEr+pYRQqQKId4/tpMQ4kVgI5AvhKgRQnzhTMcriqIooSmsqvuGi6ruUxRFGbyhqO4LuY4TinKu6uvr+bB4MzX17YzMSWLh/JnEx8df7LAURRkC4Vbdp5wgnOqmL1SsR44c4We//RfrjqTSYljMipJIfv7IMzQ2Np7X+cKlTMMlTgifWFWcoUklKSWsvfj6aozp15OWN4Oo+EwyR82nJ3I+7yxfc7FDUxRlCKg2qX6oNqnw4PP5+NJ3HiFr1k9OGr3B63Hi2P8XHvvdDy9idIqiXI5d0BXlOK1Wi9Wso8ftOGm929lObLQatFVRLgUqSYWxcKqbvhCxajQarr2iiPqy9/F7PUDvXVTrkQ+49sqp53XOcCnTcIkTwidWFWdoUr37lLC25OorcLlX8MG6PyK10ehkJ3ddN43p088vSSmKElpUm1Q/VJtU+HG5XNjtdmJiYjCZTBc7HEVRuAznkxouKkkpiqIMnuo4cZkLp7rpcIlVxTn0wiVWFWdoUklKURRFCVmquq8fqrpPURRl8FR1n6IoinJJU0kqjIVT3XS4xKriHHrhEquKMzSpJKUoiqKELNUm1Q/VJqUoijJ4qk1KURRFuaSFVZISQsQKIVYJIcqFEP8WQkSfZr9/CCGahBCln1r/cyFErRBiV9/PkuGJ/MIIp7rpcIlVxTn0wiVWFWdoCqskBTwErJJS5gMf9i335xmgvwQkgd9LKSf1/ay8QHEOi927d1/sEAYsXGJVcQ69cIlVxRmawi1JXQ881/f7c8CN/e0kpVwPdJzmHIOqHw0lnZ2dFzuEAQuXWFWcQy9cYlVxhqZwS1JJUsqmvt+bgKTzOMc3hRAlQoinT1ddqCiKooSGkEtSfW1Opf38XH/ifn3d7861C97fgBxgItAAPDo0UV8clZWVFzuEAQuXWFWcQy9cYlVxhqaw6oIuhDgALJBSNgohUoA1UspRp9k3G3hXSjnuXLcLIcKnUBRFUULYYLugh9ukh+8A9wKP9P371rkcLIRIkVI29C3eBJT2t99gC1VRFEUZGuF2JxULvAJkApXAbVLKTiFEKvB3KeV1ffu9CMwH4oBm4KdSymeEEMvoreqTQAXw4AltXIqiKEqICaskpSiKolxeQq7jxHAIp4eChyDWAR0/jHEuEUIcEEIcEkL88IT1F7RMT3fdT+3zp77tJUKISedybAjFWimE2NNXhlsvZpxCiFFCiE1CCI8Q4j/O5dgQizWUyvSuvtd8jxBigxBi/ECPDaE4z608pZSX3Q/wW+A/+37/IfCb0+w3F5gElH5q/c+A74VJrAM6fjjiBLTAYSAb0AO7gdEXukzPdN0T9rkWWN73+3Rg80CPDZVY+5YrgNhheF8OJM4EYArwMPAf53JsqMQagmU6E4jq+33JxXifDibO8ynPy/JOivB6KHiwsQ7o+CEwkOtMAw5LKSullD7gJeCGE7ZfqDI923XhhPillFuAaCFE8gCPDYVYT3xmcDjem2eNU0rZIqXcDvjO9dgQivWYUCnTTVJKe9/iFiB9oMeGSJzHDLg8L9ckFU4PBQ821qH4W4fqOmlAzQnLtX3rjrlQZXq2655pn9QBHDuUBhMr9HYKWi2E2C6E+NIFi3JgcV6IY8/HYK8XqmX6RWD5eR47GIOJE86xPMOtC/qACSFWAcn9bPrJiQtSSinO/bmovwG/7Pv9v+l9KPiL5xxknwsc65AdPwRxnunaQ1qm53DdE4XCoweDjXWOlLJeCJEArBJCHOi7yx5qg+lxNdy9tQZ7vdlSyoZQKlMhxELgfmD2uR47BAYTJ5xjeV6ySUpKedXptvV1MEiWnzwU3HyO5z6+vxDiKeDd84/0wsYKDPb4oYyzDsg4YTmD3m9hQ16mA73uGfZJ79tHP4Bjh9L5xloHIKWs7/u3RQjxJr1VMxfiA3UgcV6IY8/HoK4n+56tDJUy7euE8HdgiZSy41yODYE4z7k8L9fqvmMPBcN5PhR8wuJpHwoeIoOKdQiOH8rrbAdGCiGyhRAG4Pa+4y50mZ72up+K/56+WGYAnX3VlwM5diidd6xCCIsQwta33gos5sK9N8+lXD591xeKZXrMSbGGWpkKITKBN4DPSykPn8uxoRDneZXnhej9Eeo/QCywGigH/g1E961PBd4/Yb8XgXqgh9462C/0rV8G7AFK6P0wTgrhWPs9/iLGeQ1wkN7eQT86Yf0FLdP+rgs8SO8D3cf2eaxvewkw+WwxX8DX/LxiBXLp7Wm1G9h7oWM9W5z0Vg3XAHZ6O/VUAxGhWKanizUEy/QpoA3Y1fez9WK8T883zvMpT/Uwr6IoihKyLtfqPkVRFCUMqCSlKIqihCyVpBRFUZSQpZKUoiiKErJUklIURVFClkpSiqIoSshSSUpRFEUJWSpJKYqiKCFLJSlFCRNCiAVCiHohROzFjkVRhotKUooSPh6md/ieH1zsQBRluKgkpShhQAhxNb3jnTmBbwgh4i9ySIoyLFSSUpTw8G3gR/QOLGsFfnhxw1GU4aEGmFWUECeEuA6YIKX8lRAiBqgEtECuPGEeLkW5FKk7KUUJfd8A/g9A9k4e90fAQu+dlaJc0tSdlKKEMCHEDUC+lPJ/T1gXBVQAJiBP9s10qiiXInUnpSghSgghgK/S2w51nJTSDvyB3iSl7qaUS5q6k1KUECWEuBnIlFL+sZ9tNnrvpqzACCll3XDHpyjDQd1JKUoI6ruL+jLwt/62Sym7gN8BRuAnwxiaogwrlaQUJTTdCrwnpew5wz5/BlqB+4UQmSduEEJMEkLsu5ABKspwUElKUUKMEEID3A88eab9pJTdwCOAAfivT23eB1xzQQJUlGGk2qQUJcQIIe6kt0PElgHsbgY+B/jp7QVYeQFDU5Rhp5KUooSYvmq6UYAY4CHH/hM/CXyd3ueqxgFPSCm3D32EijJ8dBc7AEVRTialHHu+xwohbgJeBCYDWYBKUkpYU3dSinIJ6euaLuhtkxpxlo4XihLyVMcJRbmE9HVNvwt4E9AKIVRtiRLWVJJSlEvPXcAy4AsXOxBFGSyVpBTl0rMHmALsl1L6L3YwijIYqk1KURRFCVnqTkpRFEUJWSpJKYqiKCFLJSlFURQlZKkkpSiKooQslaQURVGUkKWSlKIoihKyVJJSFEVRQpZKUoqiKErI+v/t1cdmLh3N3wAAAABJRU5ErkJggg==" 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" />
-</section>
-<section id="id13">
-<h2>设置按钮<a class="headerlink" href="#id13" title="Permalink to this heading">¶</a></h2>
-<p>matplotlib.widgets 模块：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>import numpy as np
-import matplotlib.pyplot as plt
-from matplotlib.widgets import Slider, Button, RadioButtons
-fig, ax = plt.subplots()
-plt.subplots_adjust(left=0.25, bottom=0.25)
-t = np.arange(0.0, 1.0, 0.001)
-a0 = 5
-f0 = 3
-s = a0*np.sin(2*np.pi*f0*t)
-l, = plt.plot(t,s, lw=2, color=&#39;red&#39;)
-plt.axis([0, 1, -10, 10])
-axcolor = &#39;lightgoldenrodyellow&#39;
-axfreq = plt.axes([0.25, 0.1, 0.65, 0.03], axisbg=axcolor)
-axamp  = plt.axes([0.25, 0.15, 0.65, 0.03], axisbg=axcolor)
-sfreq = Slider(axfreq, &#39;Freq&#39;, 0.1, 30.0, valinit=f0)
-samp = Slider(axamp, &#39;Amp&#39;, 0.1, 10.0, valinit=a0)
-def update(val):
-    amp = samp.val
-    freq = sfreq.val
-    l.set_ydata(amp*np.sin(2*np.pi*freq*t))
-    fig.canvas.draw_idle()
-sfreq.on_changed(update)
-samp.on_changed(update)
-resetax = plt.axes([0.8, 0.025, 0.1, 0.04])
-button = Button(resetax, &#39;Reset&#39;, color=axcolor, hovercolor=&#39;0.975&#39;)
-def reset(event):
-    sfreq.reset()
-    samp.reset()
-button.on_clicked(reset)
-rax = plt.axes([0.025, 0.5, 0.15, 0.15], axisbg=axcolor)
-radio = RadioButtons(rax, (&#39;red&#39;, &#39;blue&#39;, &#39;green&#39;), active=0)
-def colorfunc(label):
-    l.set_color(label)
-    fig.canvas.draw_idle()
-radio.on_clicked(colorfunc)
-plt.show()
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<img 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" 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" />
-</section>
-<section id="id14">
-<h2>填充曲线<a class="headerlink" href="#id14" title="Permalink to this heading">¶</a></h2>
-<p>fill 函数：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-<span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="mi">4</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">x</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="mi">5</span> <span class="o">*</span> <span class="n">x</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
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-<img 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" 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" />
-</section>
-<section id="id15">
-<h2>时间刻度<a class="headerlink" href="#id15" title="Permalink to this heading">¶</a></h2>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">Show how to make date plots in matplotlib using date tick locators and</span>
-<span class="sd">formatters.  See major_minor_demo1.py for more information on</span>
-<span class="sd">controlling major and minor ticks</span>
-<span class="sd">All matplotlib date plotting is done by converting date instances into</span>
-<span class="sd">days since the 0001-01-01 UTC.  The conversion, tick locating and</span>
-<span class="sd">formatting is done behind the scenes so this is most transparent to</span>
-<span class="sd">you.  The dates module provides several converter functions date2num</span>
-<span class="sd">and num2date</span>
-<span class="sd">&quot;&quot;&quot;</span>
-<span class="kn">import</span> <span class="nn">datetime</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">import</span> <span class="nn">matplotlib.dates</span> <span class="k">as</span> <span class="nn">mdates</span>
-<span class="kn">import</span> <span class="nn">matplotlib.cbook</span> <span class="k">as</span> <span class="nn">cbook</span>
-<span class="n">years</span>    <span class="o">=</span> <span class="n">mdates</span><span class="o">.</span><span class="n">YearLocator</span><span class="p">()</span>   <span class="c1"># every year</span>
-<span class="n">months</span>   <span class="o">=</span> <span class="n">mdates</span><span class="o">.</span><span class="n">MonthLocator</span><span class="p">()</span>  <span class="c1"># every month</span>
-<span class="n">yearsFmt</span> <span class="o">=</span> <span class="n">mdates</span><span class="o">.</span><span class="n">DateFormatter</span><span class="p">(</span><span class="s1">&#39;%Y&#39;</span><span class="p">)</span>
-<span class="c1"># load a numpy record array from yahoo csv data with fields date,</span>
-<span class="c1"># open, close, volume, adj_close from the mpl-data/example directory.</span>
-<span class="c1"># The record array stores python datetime.date as an object array in</span>
-<span class="c1"># the date column</span>
-<span class="n">datafile</span> <span class="o">=</span> <span class="n">cbook</span><span class="o">.</span><span class="n">get_sample_data</span><span class="p">(</span><span class="s1">&#39;goog.npy&#39;</span><span class="p">)</span>
-<span class="n">r</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">datafile</span><span class="p">)</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">recarray</span><span class="p">)</span>
-<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">r</span><span class="o">.</span><span class="n">date</span><span class="p">,</span> <span class="n">r</span><span class="o">.</span><span class="n">adj_close</span><span class="p">)</span>
-<span class="c1"># format the ticks</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">xaxis</span><span class="o">.</span><span class="n">set_major_locator</span><span class="p">(</span><span class="n">years</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">xaxis</span><span class="o">.</span><span class="n">set_major_formatter</span><span class="p">(</span><span class="n">yearsFmt</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">xaxis</span><span class="o">.</span><span class="n">set_minor_locator</span><span class="p">(</span><span class="n">months</span><span class="p">)</span>
-<span class="n">datemin</span> <span class="o">=</span> <span class="n">datetime</span><span class="o">.</span><span class="n">date</span><span class="p">(</span><span class="n">r</span><span class="o">.</span><span class="n">date</span><span class="o">.</span><span class="n">min</span><span class="p">()</span><span class="o">.</span><span class="n">year</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-<span class="n">datemax</span> <span class="o">=</span> <span class="n">datetime</span><span class="o">.</span><span class="n">date</span><span class="p">(</span><span class="n">r</span><span class="o">.</span><span class="n">date</span><span class="o">.</span><span class="n">max</span><span class="p">()</span><span class="o">.</span><span class="n">year</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="n">datemin</span><span class="p">,</span> <span class="n">datemax</span><span class="p">)</span>
-<span class="c1"># format the coords message box</span>
-<span class="k">def</span> <span class="nf">price</span><span class="p">(</span><span class="n">x</span><span class="p">):</span> <span class="k">return</span> <span class="s1">&#39;$</span><span class="si">%1.2f</span><span class="s1">&#39;</span><span class="o">%</span><span class="n">x</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">format_xdata</span> <span class="o">=</span> <span class="n">mdates</span><span class="o">.</span><span class="n">DateFormatter</span><span class="p">(</span><span class="s1">&#39;%Y-%m-</span><span class="si">%d</span><span class="s1">&#39;</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">format_ydata</span> <span class="o">=</span> <span class="n">price</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
-<span class="c1"># rotates and right aligns the x labels, and moves the bottom of the</span>
-<span class="c1"># axes up to make room for them</span>
-<span class="n">fig</span><span class="o">.</span><span class="n">autofmt_xdate</span><span class="p">()</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
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-<img 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" 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" />
-</section>
-<section id="id16">
-<h2>金融数据<a class="headerlink" href="#id16" title="Permalink to this heading">¶</a></h2>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>import datetime
-import numpy as np
-import matplotlib.colors as colors
-import matplotlib.finance as finance
-import matplotlib.dates as mdates
-import matplotlib.ticker as mticker
-import matplotlib.mlab as mlab
-import matplotlib.pyplot as plt
-import matplotlib.font_manager as font_manager
-startdate = datetime.date(2006,1,1)
-today = enddate = datetime.date.today()
-ticker = &#39;SPY&#39;
-fh = finance.fetch_historical_yahoo(ticker, startdate, enddate)
-# a numpy record array with fields: date, open, high, low, close, volume, adj_close)
-r = mlab.csv2rec(fh); fh.close()
-r.sort()
-def moving_average(x, n, type=&#39;simple&#39;):
-    &quot;&quot;&quot;
-    compute an n period moving average.
-    type is &#39;simple&#39; | &#39;exponential&#39;
-    &quot;&quot;&quot;
-    x = np.asarray(x)
-    if type==&#39;simple&#39;:
-        weights = np.ones(n)
-    else:
-        weights = np.exp(np.linspace(-1., 0., n))
-    weights /= weights.sum()
-    a =  np.convolve(x, weights, mode=&#39;full&#39;)[:len(x)]
-    a[:n] = a[n]
-    return a
-def relative_strength(prices, n=14):
-    &quot;&quot;&quot;
-    compute the n period relative strength indicator
-    http://stockcharts.com/school/doku.php?    id=chart_school:glossary_r#relativestrengthindex
-    http://www.investopedia.com/terms/r/rsi.asp
-    &quot;&quot;&quot;
-    deltas = np.diff(prices)
-    seed = deltas[:n+1]
-    up = seed[seed&gt;=0].sum()/n
-    down = -seed[seed&lt;0].sum()/n
-    rs = up/down
-    rsi = np.zeros_like(prices)
-    rsi[:n] = 100. - 100./(1.+rs)
-    for i in range(n, len(prices)):
-        delta = deltas[i-1] # cause the diff is 1 shorter
-        if delta&gt;0:
-            upval = delta
-            downval = 0.
-        else:
-            upval = 0.
-            downval = -delta
-        up = (up*(n-1) + upval)/n
-        down = (down*(n-1) + downval)/n
-        rs = up/down
-        rsi[i] = 100. - 100./(1.+rs)
-    return rsi
-def moving_average_convergence(x, nslow=26, nfast=12):
-    &quot;&quot;&quot;
-    compute the MACD (Moving Average Convergence/Divergence) using a fast and slow exponential moving avg&#39;
-    return value is emaslow, emafast, macd which are len(x) arrays
-&quot;&quot;&quot;
-    emaslow = moving_average(x, nslow, type=&#39;exponential&#39;)
-    emafast = moving_average(x, nfast, type=&#39;exponential&#39;)
-    return emaslow, emafast, emafast - emaslow
-plt.rc(&#39;axes&#39;, grid=True)
-plt.rc(&#39;grid&#39;, color=&#39;0.75&#39;, linestyle=&#39;-&#39;, linewidth=0.5)
-textsize = 9
-left, width = 0.1, 0.8
-rect1 = [left, 0.7, width, 0.2]
-rect2 = [left, 0.3, width, 0.4]
-rect3 = [left, 0.1, width, 0.2]
-fig = plt.figure(facecolor=&#39;white&#39;)
-axescolor  = &#39;#f6f6f6&#39;  # the axes background color
-ax1 = fig.add_axes(rect1, axisbg=axescolor)  #left, bottom, width, height
-ax2 = fig.add_axes(rect2, axisbg=axescolor, sharex=ax1)
-ax2t = ax2.twinx()
-ax3  = fig.add_axes(rect3, axisbg=axescolor, sharex=ax1)
-### plot the relative strength indicator
-prices = r.adj_close
-rsi = relative_strength(prices)
-fillcolor = &#39;darkgoldenrod&#39;
-ax1.plot(r.date, rsi, color=fillcolor)
-ax1.axhline(70, color=fillcolor)
-ax1.axhline(30, color=fillcolor)
-ax1.fill_between(r.date, rsi, 70, where=(rsi&gt;=70),     facecolor=fillcolor, edgecolor=fillcolor)
-ax1.fill_between(r.date, rsi, 30, where=(rsi&lt;=30),     facecolor=fillcolor, edgecolor=fillcolor)
-ax1.text(0.6, 0.9, &#39;&gt;70 = overbought&#39;, va=&#39;top&#39;,     transform=ax1.transAxes, fontsize=textsize)
-ax1.text(0.6, 0.1, &#39;&lt;30 = oversold&#39;, transform=ax1.transAxes,     fontsize=textsize)
-ax1.set_ylim(0, 100)
-ax1.set_yticks([30,70])
-ax1.text(0.025, 0.95, &#39;RSI (14)&#39;, va=&#39;top&#39;, transform=ax1.transAxes,     fontsize=textsize)
-ax1.set_title(&#39;%s daily&#39;%ticker)
-### plot the price and volume data
-dx = r.adj_close - r.close
-low = r.low + dx
-high = r.high + dx
-deltas = np.zeros_like(prices)
-deltas[1:] = np.diff(prices)
-up = deltas&gt;0
-ax2.vlines(r.date[up], low[up], high[up], color=&#39;black&#39;,     label=&#39;_nolegend_&#39;)
-ax2.vlines(r.date[~up], low[~up], high[~up], color=&#39;black&#39;,     label=&#39;_nolegend_&#39;)
-ma20 = moving_average(prices, 20, type=&#39;simple&#39;)
-ma200 = moving_average(prices, 200, type=&#39;simple&#39;)
-linema20, = ax2.plot(r.date, ma20, color=&#39;blue&#39;, lw=2, label=&#39;MA (20)&#39;)
-linema200, = ax2.plot(r.date, ma200, color=&#39;red&#39;, lw=2, label=&#39;MA (200)&#39;)
-last = r[-1]
-s = &#39;%s O:%1.2f H:%1.2f L:%1.2f C:%1.2f, V:%1.1fM Chg:%+1.2f&#39; % (
-    today.strftime(&#39;%d-%b-%Y&#39;),
-    last.open, last.high,
-    last.low, last.close,
-    last.volume*1e-6,
-    last.close-last.open )
-t4 = ax2.text(0.3, 0.9, s, transform=ax2.transAxes, fontsize=textsize)
-props = font_manager.FontProperties(size=10)
-leg = ax2.legend(loc=&#39;center left&#39;, shadow=True, fancybox=True,     prop=props)
-leg.get_frame().set_alpha(0.5)
-volume = (r.close*r.volume)/1e6  # dollar volume in millions
-vmax = volume.max()
-poly = ax2t.fill_between(r.date, volume, 0, label=&#39;Volume&#39;,     facecolor=fillcolor, edgecolor=fillcolor)
-ax2t.set_ylim(0, 5*vmax)
-ax2t.set_yticks([])
-### compute the MACD indicator
-fillcolor = &#39;darkslategrey&#39;
-nslow = 26
-nfast = 12
-nema = 9
-emaslow, emafast, macd = moving_average_convergence(prices, nslow=nslow, nfast=nfast)
-ema9 = moving_average(macd, nema, type=&#39;exponential&#39;)
-ax3.plot(r.date, macd, color=&#39;black&#39;, lw=2)
-ax3.plot(r.date, ema9, color=&#39;blue&#39;, lw=1)
-ax3.fill_between(r.date, macd-ema9, 0, alpha=0.5,     facecolor=fillcolor, edgecolor=fillcolor)
-ax3.text(0.025, 0.95, &#39;MACD (%d, %d, %d)&#39;%(nfast, nslow, nema),     va=&#39;top&#39;,
-    transform=ax3.transAxes, fontsize=textsize)
-#ax3.set_yticks([])
-# turn off upper axis tick labels, rotate the lower ones, etc
-for ax in ax1, ax2, ax2t, ax3:
-    if ax!=ax3:
-        for label in ax.get_xticklabels():
-            label.set_visible(False)
-    else:
-        for label in ax.get_xticklabels():
-            label.set_rotation(30)
-            label.set_horizontalalignment(&#39;right&#39;)
-    ax.fmt_xdata = mdates.DateFormatter(&#39;%Y-%m-%d&#39;)
-class MyLocator(mticker.MaxNLocator):
-    def __init__(self, *args, **kwargs):
-        mticker.MaxNLocator.__init__(self, *args, **kwargs)
-    def __call__(self, *args, **kwargs):
-        return mticker.MaxNLocator.__call__(self, *args, **kwargs)
-# at most 5 ticks, pruning the upper and lower so they don&#39;t overlap
-# with other ticks
-#ax2.yaxis.set_major_locator(mticker.MaxNLocator(5, prune=&#39;both&#39;))
-#ax3.yaxis.set_major_locator(mticker.MaxNLocator(5, prune=&#39;both&#39;))
-ax2.yaxis.set_major_locator(MyLocator(5, prune=&#39;both&#39;))
-ax3.yaxis.set_major_locator(MyLocator(5, prune=&#39;both&#39;))
-plt.show()
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<img 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" 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" />
-</section>
-<section id="basemap">
-<h2>basemap 画地图<a class="headerlink" href="#basemap" title="Permalink to this heading">¶</a></h2>
-<p>需要安装 basemap 包：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>import matplotlib.pyplot as plt
-import numpy as np
-try:
-    from mpl_toolkits.basemap import Basemap
-    have_basemap = True
-except ImportError:
-    have_basemap = False
-def plotmap():
-    # create figure
-    fig = plt.figure(figsize=(8,8))
-    # set up orthographic map projection with
-    # perspective of satellite looking down at 50N, 100W.
-    # use low resolution coastlines.
-    map = Basemap(projection=&#39;ortho&#39;,lat_0=50,lon_0=-100,resolution=&#39;l&#39;)
-    # lat/lon coordinates of five cities.
-    lats=[40.02,32.73,38.55,48.25,17.29]
-    lons=[-105.16,-117.16,-77.00,-114.21,-88.10]
-    cities=[&#39;Boulder, CO&#39;,&#39;San Diego, CA&#39;,
-        &#39;Washington, DC&#39;,&#39;Whitefish, MT&#39;,&#39;Belize City, Belize&#39;]
-    # compute the native map projection coordinates for cities.
-    xc,yc = map(lons,lats)
-    # make up some data on a regular lat/lon grid.
-    nlats = 73; nlons = 145; delta = 2.*np.pi/(nlons-1)
-    lats = (0.5*np.pi-delta*np.indices((nlats,nlons))[0,:,:])
-    lons = (delta*np.indices((nlats,nlons))[1,:,:])
-    wave = 0.75*(np.sin(2.*lats)**8*np.cos(4.*lons))
-    mean = 0.5*np.cos(2.*lats)*((np.sin(2.*lats))**2 + 2.)
-    # compute native map projection coordinates of lat/lon grid.
-    # (convert lons and lats to degrees first)
-    x, y = map(lons*180./np.pi, lats*180./np.pi)
-    # draw map boundary
-    map.drawmapboundary(color=&quot;0.9&quot;)
-    # draw graticule (latitude and longitude grid lines)
-    map.drawmeridians(np.arange(0,360,30),color=&quot;0.9&quot;)
-    map.drawparallels(np.arange(-90,90,30),color=&quot;0.9&quot;)
-    # plot filled circles at the locations of the cities.
-    map.plot(xc,yc,&#39;wo&#39;)
-    # plot the names of five cities.
-    for name,xpt,ypt in zip(cities,xc,yc):
-        plt.text(xpt+100000,ypt+100000,name,fontsize=9,color=&#39;w&#39;)
-    # contour data over the map.
-    cs = map.contour(x,y,wave+mean,15,linewidths=1.5)
-    # draw blue marble image in background.
-    # (downsample the image by 50% for speed)
-    map.bluemarble(scale=0.5)
-def plotempty():
-    # create figure
-    fig = plt.figure(figsize=(8,8))
-    fig.text(0.5, 0.5, &quot;Sorry, could not import Basemap&quot;,
-        horizontalalignment=&#39;center&#39;)
-if have_basemap:
-    plotmap()
-else:
-    plotempty()
-plt.show()
-</pre></div>
-</div>
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-<img 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" 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" />
-</section>
-<section id="id17">
-<h2>对数图<a class="headerlink" href="#id17" title="Permalink to this heading">¶</a></h2>
-<p>loglog, semilogx, semilogy, errorbar 函数：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span><span class="n">hspace</span><span class="o">=</span><span class="mf">0.4</span><span class="p">)</span>
-<span class="n">t</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">0.01</span><span class="p">,</span> <span class="mf">20.0</span><span class="p">,</span> <span class="mf">0.01</span><span class="p">)</span>
-<span class="c1"># log y axis</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">221</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">semilogy</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">t</span><span class="o">/</span><span class="mf">5.0</span><span class="p">))</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;semilogy&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
-<span class="c1"># log x axis</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">222</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">semilogx</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">*</span><span class="n">t</span><span class="p">))</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;semilogx&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
-<span class="c1"># log x and y axis</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">223</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">loglog</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="mi">20</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">t</span><span class="o">/</span><span class="mf">10.0</span><span class="p">),</span> <span class="n">basex</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;loglog base 4 on x&#39;</span><span class="p">)</span>
-<span class="c1"># with errorbars: clip non-positive values</span>
-<span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">224</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_xscale</span><span class="p">(</span><span class="s2">&quot;log&quot;</span><span class="p">,</span> <span class="n">nonposx</span><span class="o">=</span><span class="s1">&#39;clip&#39;</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_yscale</span><span class="p">(</span><span class="s2">&quot;log&quot;</span><span class="p">,</span> <span class="n">nonposy</span><span class="o">=</span><span class="s1">&#39;clip&#39;</span><span class="p">)</span>
-<span class="n">x</span> <span class="o">=</span> <span class="mf">10.0</span><span class="o">**</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">,</span> <span class="mi">20</span><span class="p">)</span>
-<span class="n">y</span> <span class="o">=</span> <span class="n">x</span><span class="o">**</span><span class="mf">2.0</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">errorbar</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">xerr</span><span class="o">=</span><span class="mf">0.1</span><span class="o">*</span><span class="n">x</span><span class="p">,</span> <span class="n">yerr</span><span class="o">=</span><span class="mf">5.0</span><span class="o">+</span><span class="mf">0.75</span><span class="o">*</span><span class="n">y</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="n">ymin</span><span class="o">=</span><span class="mf">0.1</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Errorbars go negative&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<img 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" 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" />
-</section>
-<section id="id18">
-<h2>极坐标<a class="headerlink" href="#id18" title="Permalink to this heading">¶</a></h2>
-<p>设置 polar=True：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="n">r</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mf">3.0</span><span class="p">,</span> <span class="mf">0.01</span><span class="p">)</span>
-<span class="n">theta</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">r</span>
-<span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">111</span><span class="p">,</span> <span class="n">polar</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">theta</span><span class="p">,</span> <span class="n">r</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;r&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_rmax</span><span class="p">(</span><span class="mf">2.0</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;A line plot on a polar axis&quot;</span><span class="p">,</span> <span class="n">va</span><span class="o">=</span><span class="s1">&#39;bottom&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
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-<img 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" 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" />
-</section>
-<section id="id19">
-<h2>标注<a class="headerlink" href="#id19" title="Permalink to this heading">¶</a></h2>
-<p>legend 函数：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="c1"># Make some fake data.</span>
-<span class="n">a</span> <span class="o">=</span> <span class="n">b</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span> <span class="mf">.02</span><span class="p">)</span>
-<span class="n">c</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
-<span class="n">d</span> <span class="o">=</span> <span class="n">c</span><span class="p">[::</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
-<span class="c1"># Create plots with pre-defined labels.</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="s1">&#39;k--&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Model length&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">d</span><span class="p">,</span> <span class="s1">&#39;k:&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Data length&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">c</span><span class="o">+</span><span class="n">d</span><span class="p">,</span> <span class="s1">&#39;k&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Total message length&#39;</span><span class="p">)</span>
-<span class="n">legend</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s1">&#39;upper center&#39;</span><span class="p">,</span> <span class="n">shadow</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="s1">&#39;x-large&#39;</span><span class="p">)</span>
-<span class="c1"># Put a nicer background color on the legend.</span>
-<span class="n">legend</span><span class="o">.</span><span class="n">get_frame</span><span class="p">()</span><span class="o">.</span><span class="n">set_facecolor</span><span class="p">(</span><span class="s1">&#39;#00FFCC&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<img 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" />
-</section>
-<section id="id20">
-<h2>数学公式<a class="headerlink" href="#id20" title="Permalink to this heading">¶</a></h2>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>from __future__ import print_function
-import matplotlib.pyplot as plt
-import os
-import sys
-import re
-import gc
-# Selection of features following &quot;Writing mathematical expressions&quot; tutorial
-mathtext_titles = {
-    0: &quot;Header demo&quot;,
-    1: &quot;Subscripts and superscripts&quot;,
-    2: &quot;Fractions, binomials and stacked numbers&quot;,
-    3: &quot;Radicals&quot;,
-    4: &quot;Fonts&quot;,
-    5: &quot;Accents&quot;,
-    6: &quot;Greek, Hebrew&quot;,
-    7: &quot;Delimiters, functions and Symbols&quot;}
-n_lines = len(mathtext_titles)
-# Randomly picked examples
-mathext_demos = {
-    0: r&quot;$W^{3\beta}_{\delta_1 \rho_1 \sigma_2} = &quot;
-    r&quot;U^{3\beta}_{\delta_1 \rho_1} + \frac{1}{8 \pi 2} &quot;
-    r&quot;\int^{\alpha_2}_{\alpha_2} d \alpha^\prime_2 \left[\frac{ &quot;
-    r&quot;U^{2\beta}_{\delta_1 \rho_1} - \alpha^\prime_2U^{1\beta}_&quot;
-    r&quot;{\rho_1 \sigma_2} }{U^{0\beta}_{\rho_1 \sigma_2}}\right]$&quot;,
-    1: r&quot;$\alpha_i &gt; \beta_i,\ &quot;
-    r&quot;\alpha_{i+1}^j = {\rm sin}(2\pi f_j t_i) e^{-5 t_i/\tau},\ &quot;
-    r&quot;\ldots$&quot;,
-    2: r&quot;$\frac{3}{4},\ \binom{3}{4},\ \stackrel{3}{4},\ &quot;
-    r&quot;\left(\frac{5 - \frac{1}{x}}{4}\right),\ \ldots$&quot;,
-    3: r&quot;$\sqrt{2},\ \sqrt[3]{x},\ \ldots$&quot;,
-    4: r&quot;$\mathrm{Roman}\ , \ \mathit{Italic}\ , \ \mathtt{Typewriter} \ &quot;
-    r&quot;\mathrm{or}\ \mathcal{CALLIGRAPHY}$&quot;,
-    5: r&quot;$\acute a,\ \bar a,\ \breve a,\ \dot a,\ \ddot a, \ \grave a, \ &quot;
-    r&quot;\hat a,\ \tilde a,\ \vec a,\ \widehat{xyz},\ \widetilde{xyz},\ &quot;
-    r&quot;\ldots$&quot;,
-    6: r&quot;$\alpha,\ \beta,\ \chi,\ \delta,\ \lambda,\ \mu,\ &quot;
-    r&quot;\Delta,\ \Gamma,\ \Omega,\ \Phi,\ \Pi,\ \Upsilon,\ \nabla,\ &quot;
-    r&quot;\aleph,\ \beth,\ \daleth,\ \gimel,\ \ldots$&quot;,
-    7: r&quot;$\coprod,\ \int,\ \oint,\ \prod,\ \sum,\ &quot;
-    r&quot;\log,\ \sin,\ \approx,\ \oplus,\ \star,\ \varpropto,\ &quot;
-    r&quot;\infty,\ \partial,\ \Re,\ \leftrightsquigarrow, \ \ldots$&quot;}
-def doall():
-    # Colors used in mpl online documentation.
-    mpl_blue_rvb = (191./255., 209./256., 212./255.)
-    mpl_orange_rvb = (202/255., 121/256., 0./255.)
-    mpl_grey_rvb = (51./255., 51./255., 51./255.)
-    # Creating figure and axis.
-    plt.figure(figsize=(6, 7))
-    plt.axes([0.01, 0.01, 0.98, 0.90], axisbg=&quot;white&quot;, frameon=True)
-    plt.gca().set_xlim(0., 1.)
-    plt.gca().set_ylim(0., 1.)
-    plt.gca().set_title(&quot;Matplotlib&#39;s math rendering engine&quot;,
-                    color=mpl_grey_rvb, fontsize=14, weight=&#39;bold&#39;)
-    plt.gca().set_xticklabels(&quot;&quot;, visible=False)
-    plt.gca().set_yticklabels(&quot;&quot;, visible=False)
-    # Gap between lines in axes coords
-    line_axesfrac = (1. / (n_lines))
-    # Plotting header demonstration formula
-    full_demo = mathext_demos[0]
-    plt.annotate(full_demo,
-                 xy=(0.5, 1. - 0.59*line_axesfrac),
-                 xycoords=&#39;data&#39;, color=mpl_orange_rvb, ha=&#39;center&#39;,
-                 fontsize=20)
-    # Plotting features demonstration formulae
-    for i_line in range(1, n_lines):
-        baseline = 1. - (i_line)*line_axesfrac
-        baseline_next = baseline - line_axesfrac*1.
-        title = mathtext_titles[i_line] + &quot;:&quot;
-        fill_color = [&#39;white&#39;, mpl_blue_rvb][i_line % 2]
-        plt.fill_between([0., 1.], [baseline, baseline],
-                         [baseline_next, baseline_next],
-                         color=fill_color, alpha=0.5)
-        plt.annotate(title,
-                     xy=(0.07, baseline - 0.3*line_axesfrac),
-                     xycoords=&#39;data&#39;, color=mpl_grey_rvb, weight=&#39;bold&#39;)
-        demo = mathext_demos[i_line]
-        plt.annotate(demo,
-                     xy=(0.05, baseline - 0.75*line_axesfrac),
-                     xycoords=&#39;data&#39;, color=mpl_grey_rvb,
-                     fontsize=16)
-    for i in range(n_lines):
-        s = mathext_demos[i]
-        print(i, s)
-    plt.show()
-if &#39;--latex&#39; in sys.argv:
-    # Run: python mathtext_examples.py --latex
-    # Need amsmath and amssymb packages.
-    fd = open(&quot;mathtext_examples.ltx&quot;, &quot;w&quot;)
-    fd.write(&quot;\\documentclass{article}\n&quot;)
-    fd.write(&quot;\\usepackage{amsmath, amssymb}\n&quot;)
-    fd.write(&quot;\\begin{document}\n&quot;)
-    fd.write(&quot;\\begin{enumerate}\n&quot;)
-    for i in range(n_lines):
-        s = mathext_demos[i]
-        s = re.sub(r&quot;(?&lt;!\\)\$&quot;, &quot;$$&quot;, s)
-        fd.write(&quot;\\item %s\n&quot; % s)
-    fd.write(&quot;\\end{enumerate}\n&quot;)
-    fd.write(&quot;\\end{document}\n&quot;)
-    fd.close()
-    os.system(&quot;pdflatex mathtext_examples.ltx&quot;)
-else:
-    doall()
-0 $W^{3\beta}_{\delta_1 \rho_1 \sigma_2} = U^{3\beta}_{\delta_1 \rho_1} + \frac{1}{8 \pi 2} \int^{\alpha_2}_{\alpha_2} d \alpha^\prime_2 \left[\frac{ U^{2\beta}_    {\delta_1 \rho_1} - \alpha^\prime_2U^{1\beta}_{\rho_1 \sigma_2} }{U^{0\beta}_{\rho_1 \sigma_2}}\right]$
-1 $\alpha_i &gt; \beta_i,\ \alpha_{i+1}^j = {\rm sin}(2\pi f_j t_i) e^{-5 t_i/\tau},\ \ldots$
-2 $\frac{3}{4},\ \binom{3}{4},\ \stackrel{3}{4},\ \left(\frac{5 - \frac{1}{x}}{4}\right),\ \ldots$
-3 $\sqrt{2},\ \sqrt[3]{x},\ \ldots$
-4 $\mathrm{Roman}\ , \ \mathit{Italic}\ , \ \mathtt{Typewriter} \ \mathrm{or}\ \mathcal{CALLIGRAPHY}$
-5 $\acute a,\ \bar a,\ \breve a,\ \dot a,\ \ddot a, \ \grave a, \ \hat a,\ \tilde a,\ \vec a,\ \widehat{xyz},\ \widetilde{xyz},\ \ldots$
-6 $\alpha,\ \beta,\ \chi,\ \delta,\ \lambda,\ \mu,\ \Delta,\ \Gamma,\ \Omega,\ \Phi,\ \Pi,\ \Upsilon,\ \nabla,\ \aleph,\ \beth,\ \daleth,\ \gimel,\ \ldots$
-7 $\coprod,\ \int,\ \oint,\ \prod,\ \sum,\ \log,\ \sin,\ \approx,\ \oplus,\ \star,\ \varpropto,\ \infty,\ \partial,\ \Re,\ \leftrightsquigarrow, \ \ldots$
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<img 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" 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-</section>
-<section id="id21">
-<h2>作者 &amp; 更新时间<a class="headerlink" href="#id21" title="Permalink to this heading">¶</a></h2>
-<p>作者:<cite>李金  &lt;lijinwithyou&#64;gmail.com&gt;</cite></p>
-<p>提交: 2017/12/14</p>
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-<section id="pyplot">
-<h1>Pyplot 教程<a class="headerlink" href="#pyplot" title="Permalink to this heading">¶</a></h1>
-<section id="matplotlib">
-<h2>Matplotlib 简介<a class="headerlink" href="#matplotlib" title="Permalink to this heading">¶</a></h2>
-<p><strong>matplotlib</strong> 是一个 <strong>Python</strong> 的 2D 图形包。</p>
-<p>在线文档：<a class="reference external" href="http://matplotlib.org">http://matplotlib.org</a> ，提供了 Examples, FAQ, API, Gallery，其中 Gallery 是很有用的一个部分，因为它提供了各种画图方式的可视化，方便用户根据需求进行选择。</p>
-</section>
-<section id="id1">
-<h2>使用 Pyplot<a class="headerlink" href="#id1" title="Permalink to this heading">¶</a></h2>
-<p>导入相关的包：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-</pre></div>
-</div>
-<p>matplotlib.pyplot 包含一系列类似 <strong>MATLAB</strong> 中绘图函数的相关函数。每个 matplotlib.pyplot 中的函数对当前的图像进行一些修改，例如：产生新的图像，在图像中产生新的绘图区域，在绘图区域中画线，给绘图加上标记，等等…… matplotlib.pyplot 会自动记住当前的图像和绘图区域，因此这些函数会直接作用在当前的图像上。</p>
-<p>下文中，以 plt 作为 matplotlib.pyplot 的省略。</p>
-</section>
-<section id="plt-show">
-<h2>plt.show() 函数<a class="headerlink" href="#plt-show" title="Permalink to this heading">¶</a></h2>
-<p>默认情况下，matplotlib.pyplot 不会直接显示图像，只有调用 plt.show() 函数时，图像才会显示出来。plt.show() 默认是在新窗口打开一幅图像，并且提供了对图像进行操作的按钮。</p>
-<p>不过在 ipython 命令行中，我们可以使用 magic 命令将它插入 notebook 中，并且不需要调用 plt.show() 也可以显示：</p>
-<ul class="simple">
-<li><p>%matplotlib notebook</p></li>
-<li><p>%matplotlib inline</p></li>
-</ul>
-<p>不过在实际写程序中，我们还是需要调用 plt.show() 函数将图像显示出来。</p>
-<p>这里我们使图像输出在 notebook 中：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="o">%</span><span class="n">matplotlib</span> <span class="n">inline</span>
-</pre></div>
-</div>
-</section>
-<section id="plt-plot">
-<h2>plt.plot() 函数<a class="headerlink" href="#plt-plot" title="Permalink to this heading">¶</a></h2>
-<p><strong>例子</strong></p>
-<p>plt.plot() 函数可以用来绘图：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">])</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;some numbers&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-<img 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L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%0A" src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAYYAAAEACAYAAAC3adEgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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%0A" />
-<p><strong>基本用法</strong></p>
-<p>plot 函数基本的用法有以下四种：</p>
-<p>默认参数</p>
-<ul class="simple">
-<li><p>plt.plot(x,y)</p></li>
-</ul>
-<p>指定参数</p>
-<ul class="simple">
-<li><p>plt.plot(x,y, format_str)</p></li>
-</ul>
-<p>默认参数，x 为 0~N-1</p>
-<ul class="simple">
-<li><p>plt.plot(y)</p></li>
-</ul>
-<p>指定参数，x 为 0~N-1</p>
-<ul class="simple">
-<li><p>plt.plot(y, format_str)</p></li>
-</ul>
-<p>因此，在上面的例子中，我们没有给定 x 的值，所以其默认值为 [0,1,2,3]。</p>
-<p>传入 x 和 y：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">9</span><span class="p">,</span><span class="mi">16</span><span class="p">])</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">[</span><span class="o">&lt;</span><span class="n">matplotlib</span><span class="o">.</span><span class="n">lines</span><span class="o">.</span><span class="n">Line2D</span> <span class="n">at</span> <span class="mh">0xa48a550</span><span class="o">&gt;</span><span class="p">]</span>
-</pre></div>
-</div>
-</section>
-<section id="id2">
-<h2>字符参数<a class="headerlink" href="#id2" title="Permalink to this heading">¶</a></h2>
-<p>和 <strong>MATLAB</strong> 中类似，我们还可以用字符来指定绘图的格式：</p>
-<p>表示颜色的字符参数有：</p>
-<p>表示类型的字符参数有：</p>
-<p>例如我们要画出红色圆点：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">9</span><span class="p">,</span><span class="mi">16</span><span class="p">],</span> <span class="s1">&#39;ro&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-<img 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X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%0ADHNJaoBhLkkNMMwlqQGGuSQ14P8AGTGlG2xI8vsAAAAASUVORK5CYII=%0A" 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X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%0ADHNJaoBhLkkNMMwlqQGGuSQ14P8AGTGlG2xI8vsAAAAASUVORK5CYII=%0A" />
-<p>可以看出，有两个点在图像的边缘，因此，我们需要改变轴的显示范围。</p>
-</section>
-<section id="id3">
-<h2>显示范围<a class="headerlink" href="#id3" title="Permalink to this heading">¶</a></h2>
-<p>与 <strong>MATLAB</strong> 类似，这里可以使用 axis 函数指定坐标轴显示的范围：</p>
-<p>plt.axis([xmin, xmax, ymin, ymax])</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">9</span><span class="p">,</span><span class="mi">16</span><span class="p">],</span> <span class="s1">&#39;ro&#39;</span><span class="p">)</span>
-<span class="c1"># 指定 x 轴显示区域为 0-6，y 轴为 0-20</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span><span class="mi">6</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">20</span><span class="p">])</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-<img 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/>
-</section>
-<section id="numpy">
-<h2>传入 Numpy 数组<a class="headerlink" href="#numpy" title="Permalink to this heading">¶</a></h2>
-<p>之前我们传给 plot 的参数都是列表，事实上，向 plot 中传入 numpy 数组是更常用的做法。事实上，如果传入的是列表，matplotlib 会在内部将它转化成数组再进行处理：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>import numpy as np
-import matplotlib.pyplot as plt
-# evenly sampled time at 200ms intervals
-t = np.arange(0., 5., 0.2)
-# red dashes, blue squares and green triangles
-plt.plot(t, t, &#39;r--&#39;,
-        t, t**2, &#39;bs&#39;,
-        t, t**3, &#39;g^&#39;)
-plt.show()
-</pre></div>
-</div>
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-<img 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u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%0A" 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u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%0A" />
-</section>
-<section id="id4">
-<h2>传入多组数据<a class="headerlink" href="#id4" title="Permalink to this heading">¶</a></h2>
-<p>事实上，在上面的例子中，我们不仅仅向 plot 函数传入了数组，还传入了多组 (x,y,format_str) 参数，它们在同一张图上显示。
-这意味着我们不需要使用多个 plot 函数来画多组数组，只需要可以将这些组合放到一个 plot 函数中去即可。</p>
-</section>
-<section id="id5">
-<h2>线条属性<a class="headerlink" href="#id5" title="Permalink to this heading">¶</a></h2>
-<p>之前提到，我们可以用字符串来控制线条的属性，事实上还可以通过关键词来改变线条的性质，例如 linwidth 可以改变线条的宽度，color 可以改变线条的颜色：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span>
-<span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">2.0</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;r&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
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-<img 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8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%0AfxeZmr/ZF8WeAAAAAElFTkSuQmCC%0A" 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8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%0AfxeZmr/ZF8WeAAAAAElFTkSuQmCC%0A" />
-</section>
-<section id="id6">
-<h2>使用 plt.plot() 的返回值来设置线条属性<a class="headerlink" href="#id6" title="Permalink to this heading">¶</a></h2>
-<p>plot 函数返回一个 Line2D 对象组成的列表，每个对象代表输入的一对组合，例如：</p>
-<ul>
-<li><p>line1, line2 为两个 Line2D 对象</p>
-<p>line1, line2 = plt.plot(x1, y1, x2, y2)</p>
-</li>
-<li><p>返回 3 个 Line2D 对象组成的列表
-lines = plt.plot(x1, y1, x2, y2, x3, y3)</p></li>
-</ul>
-<p>我们可以使用这个返回值来对线条属性进行设置：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># 加逗号 line 中得到的是 line2D 对象，不加逗号得到的是只有一个     line2D 对象的列表</span>
-<span class="n">line</span><span class="p">,</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="s1">&#39;r-&#39;</span><span class="p">)</span>
-<span class="c1"># 将抗锯齿关闭</span>
-<span class="n">line</span><span class="o">.</span><span class="n">set_antialiased</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
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-<img 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V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%0AJyfj1DY0XlkAAAAASUVORK5CYII=%0A" 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V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%0AJyfj1DY0XlkAAAAASUVORK5CYII=%0A" />
-</section>
-<section id="plt-setp">
-<h2>plt.setp() 修改线条性质<a class="headerlink" href="#plt-setp" title="Permalink to this heading">¶</a></h2>
-<p>更方便的做法是使用 plt 的 setp 函数：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">lines</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-<span class="c1"># 使用键值对</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">setp</span><span class="p">(</span><span class="n">lines</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;r&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">2.0</span><span class="p">)</span>
-<span class="c1"># 或者使用 MATLAB 风格的字符串对</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">setp</span><span class="p">(</span><span class="n">lines</span><span class="p">,</span> <span class="s1">&#39;color&#39;</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">,</span> <span class="s1">&#39;linewidth&#39;</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
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-<img 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8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%0AfxeZmr/ZF8WeAAAAAElFTkSuQmCC%0A" 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8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%0AfxeZmr/ZF8WeAAAAAElFTkSuQmCC%0A" />
-<p>可以设置的属性有很多，可以使用 plt.setp(lines) 查看 lines 可以设置的属性，各属性的含义可参考 matplotlib 的文档。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">setp</span><span class="p">(</span><span class="n">lines</span><span class="p">)</span>
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>agg_filter: unknown
-alpha: float (0.0 transparent through 1.0 opaque)
-animated: [True | False]
-antialiased or aa: [True | False]
-axes: an :class:`~matplotlib.axes.Axes` instance
-clip_box: a :class:`matplotlib.transforms.Bbox` instance
-clip_on: [True | False]
-clip_path: [ (:class:`~matplotlib.path.Path`,               :class:`~matplotlib.transforms.Transform`) |                    :class:`~matplotlib.patches.Patch` |       None ]
-color or c: any matplotlib color
-contains: a callable function
-dash_capstyle: [&#39;butt&#39; | &#39;round&#39; | &#39;projecting&#39;]
-dash_joinstyle: [&#39;miter&#39; | &#39;round&#39; | &#39;bevel&#39;]
-dashes: sequence of on/off ink in points
-drawstyle: [&#39;default&#39; | &#39;steps&#39; | &#39;steps-pre&#39; | &#39;steps-mid&#39; |                   &#39;steps-post&#39;]
-figure: a :class:`matplotlib.figure.Figure` instance
-fillstyle: [&#39;full&#39; | &#39;left&#39; | &#39;right&#39; | &#39;bottom&#39; | &#39;top&#39; | &#39;none&#39;]
-gid: an id string
-label: string or anything printable with &#39;%s&#39; conversion.
-linestyle or ls: [``&#39;-&#39;`` | ``&#39;--&#39;`` | ``&#39;-.&#39;`` | ``&#39;:&#39;`` | ``&#39;None&#39;`` |                   ``&#39; &#39;`` | ``&#39;&#39;``]
-linewidth or lw: float value in points
-lod: [True | False]
-marker: :mod:`A valid marker style &lt;matplotlib.markers&gt;`
-markeredgecolor or mec: any matplotlib color
-markeredgewidth or mew: float value in points
-markerfacecolor or mfc: any matplotlib color
-markerfacecoloralt or mfcalt: any matplotlib color
-markersize or ms: float
-markevery: [None | int | length-2 tuple of int | slice |             list/array of int | float | length-2 tuple of float]
-path_effects: unknown
-picker: float distance in points or callable pick function         ``fn(artist, event)``
-pickradius: float distance in points
-rasterized: [True | False | None]
-sketch_params: unknown
-snap: unknown
-solid_capstyle: [&#39;butt&#39; | &#39;round&#39; |  &#39;projecting&#39;]
-solid_joinstyle: [&#39;miter&#39; | &#39;round&#39; | &#39;bevel&#39;]
-transform: a :class:`matplotlib.transforms.Transform` instance
-url: a url string
-visible: [True | False]
-xdata: 1D array
-ydata: 1D array
-zorder: any number
-</pre></div>
-</div>
-</section>
-<section id="id7">
-<h2>子图<a class="headerlink" href="#id7" title="Permalink to this heading">¶</a></h2>
-<p>figure() 函数会产生一个指定编号为 num 的图：</p>
-<blockquote>
-<div><p>plt.figure(num)</p>
-</div></blockquote>
-<p>这里，figure(1) 其实是可以省略的，因为默认情况下 plt 会自动产生一幅图像。</p>
-<p>使用 subplot 可以在一副图中生成多个子图，其参数为：</p>
-<blockquote>
-<div><p>plt.subplot(numrows, numcols, fignum)</p>
-</div></blockquote>
-<p>当 numrows * numcols &lt; 10 时，中间的逗号可以省略，因此 plt.subplot(211) 就相当于 plt.subplot(2,1,1)。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>def f(t):
-    return np.exp(-t) * np.cos(2*np.pi*t)
-t1 = np.arange(0.0, 5.0, 0.1)
-t2 = np.arange(0.0, 5.0, 0.02)
-plt.figure(1)
-plt.subplot(211)
-plt.plot(t1, f(t1), &#39;bo&#39;, t2, f(t2), &#39;k&#39;)
-plt.subplot(212)
-plt.plot(t2, np.cos(2*np.pi*t2), &#39;r--&#39;)
-plt.show()
-</pre></div>
-</div>
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-<img 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V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%0ASXD+P5ePy54P9ccKAAAAAElFTkSuQmCC%0A" 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V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%0ASXD+P5ePy54P9ccKAAAAAElFTkSuQmCC%0A" 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V%0ApswLI/hK/1L69r2Azp1vYc6cXUBTTAGXIb7KgXTiqj2HlStv5OKLP2bZskxgErAOc9M+FajG5s1b%0AKFUqCbOxnw3sxTiwbWfJkk0Yr+ZvgcZAG7KyBjB27PtA4U8BsZ6LcBVzJE83Cxcu5803N0d003P7%0ABuH2Tcot+aKe6wbzK58AoKoLRKSqiNRS1e0Oxz5G586duffee93qLmJycnJ4/vnn+fDDD2MyXkHF%0AV6pUdZYu3UC1av6wyB0+vAlzT/8rxtTifGO6sJtDUlINzJpjPmYT/FngaU/MWMF+nGbVPhKzMv8E%0AmM6mTdX49NMUqlWrw7ZtD2LqBVc+1s/ZZ9+Pqgaylx5P1apX89tvD2C8klYDM4Gn+eqrNSxY8Bp7%0A9/bDPOWdA7Q67qbntmIJV2kHU8zr1t3F5s0befHFz8nKSsdszh8EDpCVlcNNN92Jag4bN54P3IlR%0AXaXJyirHv/71EgcO3AZ8HJi/KmRl/Yknn/wvR49mM2LEV66axWJ1kypKvrCVfXH+l0W9gAHAS/mO%0ArwbGF2jzGdAx3/E04OwgfUXsR/r7779rhQoV9MCBAxH34QZffvmltm3b1lMZ3nvvPW3evLkeOnTI%0AUzlmzJihVapU1Vq1Li0Q+OR+8Jhq/vgFVchReFuhgdaq1UyzsrJi5n9/YnzA71qr1qVaufLpCpUV%0ALlR4ReFnBdUuXUYXEiBm5qmwz9q0+etx53Jf3bvfpWeddZ3CeIWhCi0VUhTO1bp12+nf/naP1qt3%0AvcKRE4LRigpUiyS4Le//5IDCWoXpChO0fPlWCn9R6KvQWqG6QjlNSammVao0VLhK4SaFOxRGKTyq%0Aqam9NDW1t8JTCv9UeEzhIYX7tGzZZgpDFC5RSFNoq5CqpUuXVxFRqKLQMHC+h8IVWr9+W23UqKPC%0AvwP/Hx8rzFT4Xlu1GqQNG45Q+F0hO+Q56tnzXoV9CtsU1it8rzBf27W7Wtu0uUJhosKrCs8G/oYx%0AWq5cU4VbFK5TGKTQT6GHVq1aTytVqqXQVCE1IH99hbpatmwFrVmzpnbr1k1VY+NHryG2K5grOeh1%0AoaRACEZKSgqtWrXim2++oUuXLiGK5D4vv/wy118flZCBkBkwYABvvfUWjz76KA899FDMxs2/ytm7%0AdxXr10/h448/4sCBpELt8G5yvBlLgME0arSI88//jbPOakOpUs3Zs+czTDI856aMou3t92NWl+8C%0Ak9m+/VwqVKiKedo46bh+Cq8udvw8FfwM4Lbb7jtutZeaei+3396XceOmYFxQc9kPLKFSpYeYOPEz%0Atm07AlTFPEE0ISurKffe+zTJyZUDsu8DKgJCVtaj3H//9ezdW+uElWV2djZPPfUJWVl/woTSbAe2%0AkpX1K9deez0HD+7DbJjvxeQ6rA/UJycHoBVm264+JqFtDc4550GSk7ODPsGcfvpBVJWsrNtP+KxS%0ApXXs2jXhhPPdu9/PwYOlmDXrdmD3ca9y5d7gyJGDGNPi8uM+W7FiPaplgf8Ah4FSZGWVpX//pxAp%0AxZEj5QOfHQWyyco6ysUXPx5YsD6D2SNKCfxbgXXrdpCUVBaj9lLyfZ77vkngfflj5xs2nEipUmVY%0AtOhvQBmMg2QSkERq6gj69GlAUlLScTqzSIq7ExT1wjyXT853PBK4u0Cb/wKD8h2vBmoF6cvRSuqO%0AO+7QRx55xFEfTti5c6dWqVJFf/31V89kyGXz5s1aq1YtnTt3bkzGO36VM1HhZD3llKExj1qdNGmm%0ApqeP0i5dRh+LolVV7dJluMIwhWoKtx1bTedGK4e72i8sqnfUqEf15JPPUKiq0FXhvwo7FFRbtryx%0A0FW7239vUU8IXbqMDpz7TWG+wusK92mNGi20cuV6Co0CTwApCicr1NdSpSoGngyaKZwaOF9JRUpp%0A2bIVFc5U6KVwjcKdCuO0ZcuB2r79tYHV7dHjZDnppCuOOy4Y+Rzu083o0c8Ves3xT3rHj1XYZ9Wq%0ADcl3nKPm6We/dux4l5533h2B/9NdCnvURG4f1M6dRwVW9OGNVdRcFCV7foh2CgRCy3XTB/hc824M%0A8wvpK+IvvKrqJ598or169XLUhxOeeuopveaaazwbvyAff/yx1qpVR7t1+3vUzRV5X8iXFOoqLA36%0AhfSKPOW2WWFEQOFfqC1aXK4ffTQlbHNF3t+7S+ENhf+nUFmrVz9VmzVLV9haqBILppijQWFjhab4%0AchT2KmxR+FErV74k8H+6Qo1JYqvCr3rBBQ8U2V8kirko2Yv6LJKbXrhmseKUr9s3qaL6y0/UFb0Z%0Ao+hcN4HjZwOfLwXaFtKPoy/2rl27tFKlSnr48GFH/URCTk6OtmjRQmfO9E/elUmTZmrlymcpXH2C%0AAnMbo0ifUrPaW3tsvC5dRrs+ViSc+OP8XWGCVqvWQJOSyirk2n6/Cii3HG3T5s8FfmT79JRTrtXb%0Abx+pp5xyrppVbEWFi9XYXXcWa2/3A24rvuL+3nAVc7T+5nBuHJHMUTRuUqHOU0wUvVsvp4peVbVd%0Au3aeKNu5c+dq48aNfZVczCi3/Wo2c1497sfpJjk5OXraaZ0VGiv8dIIi8ANF/Tg7drxL4X01m4Od%0AFU5SKKciyYEbVx2FZIVyCq21du2W2rhxV4W5Cn8E/XtjqcQiwU3FV1x/8YpT5RtLSpyiv/vuu/WB%0ABx5w3E+4XHfddfrEE0/EfNyiyDNXrFCoqcajwN1Vdk5Ojt5+++3asGGqnnrqcN+uYlXDNWXs1ypV%0ALldjptikxmsk7ynF76v2aOA35WbJIxRF7w+Ha5fo3r07Y8aM4cEHH4zJeBkZs3jqqc+YNettOnUa%0ARsuWs3wRnAP5o2ZbAG9i8u7MceTDnt/TpEyZw4gsY9++3SxatJC5c7+PiXdNpBTmf3+8t44hNfVR%0AKleuweLFJ2ZEDdVLJtEIN7jN4jOKuxMU9QKqA1OBtcAUoGqQNvWBGZh8tMuB4YX05fjOduDAAa1Q%0AoYLu2bPHcV/Fkbeqe1Ghf1Rt4JFw4qrzP1qmTHV9881PXOhvl0I3TUlJ1fff/8JlyWNPJOYKi8Uv%0AEMKKXky7yBCRfwK/qOo/ReRuoJqq3lOgTW2gtqouEZGKwHdAf82XJiHQTp3Ikku3bt0YMWIEF110%0AkeO+iiI9fVTA37c9cD9wUeD8/Uye/HBUxw6VjIxZjB8/9diqs3r1jSxdupBp06aFnQAs7+9dhQl2%0AvgR4gvT0Mb75e92m4PzdemtPu6q1+A4RQVULxiodh1PTTT+gS+D9BCATE+t+DFXdBmwLvN8vIquA%0AuhyfJsE1unfvzldffRV1RW/yuHyPSfGTV5jcL5kjIfjj9uOPP86ZZ7YhObkvmza9cux8cQFE5u96%0AGpM58p+YBGr++nvdxporLImCU0WfP2fNdqDITF4i0hBoAyxwOG6hlC9/Ei+/PI7Fi6tENYudsYG/%0AAlxL/mn0S+bIwhg5ciQTJy5k2bL/YRKA3Q6UOZYLBU7M8VGvXhWWLXsNk2xrPnD6sf78/vdaLJYQ%0AFL2ITAVqB/novvwHqhrIK1FoPxWBD4DbVHV/sDaRpkDIJSNjFs899xP79x9m5sy/ArWilrXvL3/p%0AwrRpl5CTs+LYudx86n6nWrXWwJOYZGMvYIqWDGDTph35kijtBaYzZ871JCfvYuDAa5g6tTzr1+cp%0A+Xj5ey2WRCIzM5PMzMywrnFqo18NpKnqNhGpA8xQ1WZB2pXB5GH9QlWfKaQvxzb6PDtyf4yXyZWB%0A8+7bzSdOnMgTT/ybWrUujDsbbt48gSnZ9yKQgcghVFMxuU52YfYfrqR797VMm/aEtVlbLD4kFjb6%0AT4GhmFpwQzH5VwsKIRgbx8rClLxb5FVcSsckWTKKPhp25Jdffpl77rmTgQMHut53tDnepfAC4AJO%0AO20kpUtvZ+3a2zGJluphslpAdvYYwNqsLZZ4xami/wfwnoj8GdhAoEiqiNTFpC/uC3TCpC9eJiK5%0AlaVGqupkh2OfQJ7veF+MJ8xRIMl1O/L69etZunQp/fv3d7XfWBHcD/xCxo2bwtq1Z5zQ3trhLZb4%0AxpGiV9XdmOpSBc9vwWhbVHUOMSpCfvxK9RRgPqmpGa7bkV999VWuvvpqkpOTXe03lhRfqs9g7fAW%0AS/zjyEbvJm750efakVetyiQpKYfx4x931dyQnZ1Nw4YNmTx5Mq1atXKtX79g7fAWS3wRio0+4RR9%0ALvPmzeP6669nxYoVxTcOg4yMDB5++GHmz5/var8Wi8USCaEo+ohNKiJSXUSmishaEZkiIlWLaJsk%0AIotF5LNIxwuX9u3bs2fPHlatcjcu66WXXvK8ipTFYrGEgxPb+T3AVFVtAnxFgYjYAtwGrISQSw86%0AplSpUlx22WV88MEHrvX5448/MmfOHAYNGuRanxaLxRJtnCj6fpi0BwT+DeqCIiL1MFWmXubE2rFR%0AZcCAAa4q+vHjx3PddddRsWJF1/q0WCyWaOPE6ybU9AdPA3cClR2MFRGdOnXil19+YdWqVTRv3txR%0AX3v37mXChAksXry4+MYWi8XiI4pc0Qds8N8HefXL3y43VWaQ6y8CdqjqYmK8mgdjvrn66quZMOHE%0ACvGhkpExi/T0UbRtewVlytTi++83uCegxWKxxIAiV/Sq2rOwz0Rku4jUzpf+YEeQZh2BfiLSBygH%0AVBaR11V1SLA+nea6CcbQoUPp2bMnjz76KElJ4UXIZmTMCuR+eQhoDLzNbbeZ/WTrcmixWLwgprlu%0AArnod6nqEyJyD6boSKEbsiLSBfi7ql5cyOeuulfmp3379owZM4YLL7wwrOvycsJ8jEnNOy9w3j85%0A5y0WS8kmqu6VmPQHPUVkLdAtcIyI1BWRjEKu8cRp/4YbbuC5554L+zqTO0eBh4G7jp1P5BzsFosl%0A8YhY0avqblXtoapNVLWXqv4WOL8lkOOmYPuZqtrvxJ6iz1VXXcU333zDmjVrwrrO5M7JzdOW51Rk%0Ac79YLJZ4IiY5aLymfPnyDBs2jLFjx4Z13c03d6NMmRuBB8ndSza5XwrdurBYLBbf4TR7Zdxwyy23%0A0LhxE5YvL0WpUjVCqj71888rad68AbVrL+Dw4e8CuV96241Yi8USV0Ss6EWkOvAupr7cBuCKXPNN%0AgXZVMcFSLTEG7+tUNeaJYr79dg2lS5/B7Nl7gGeBouukbt++nYceeogZM2bQsmXLWIpqsVgsrhKL%0AFAhjgc9VtTnQmigVBS+OceOm8OuvXwDTgK8BAnVSp57QVlW57rrruP76662St1gscY8T000/oEvg%0A/QQgkwLKXkSqAJ1VdSiAqmYDexyMGTHGg6YSpkbq1cASoAqHDiWRkTHruILY9ertZseOHTz44INe%0AiGqxWCyuEu0UCI2AnSLyGnAm8B2mOPgBB+NGRF71qX7Al8AA4BP27t2UryA2wKuULj2eF198iTJl%0AysRaTIvFYnGdqKZAwNxI2gL/UdW2wO8UneUyagwf3ovU1PsCR+OAuiQnN2b//t1kZT2M2Wb4C/AQ%0A2dkLePfd770Q02KxWFwn2ikQNgGbVHVh4PgDilD00UiBkEvBOqnJyfVp1uwaXnzxJUx2hirAQGAp%0AuSYdi8Vi8Ru+TIEgIrOA61V1rYiMAcqr6t1B2kUtBUJRmDQHDwBJgVfueZvmwGKx+B+/pEC4FXhL%0ARJZivG4eczCm6xiTzoPkV/I2KMpisSQSCVszNhxsQWyLxRKvlOji4BaLxVISiLbpxmKxWCxxgFX0%0AFovFkuBErOhFpHrAz36tiEwJ5LQJ1m6kiKwI+N+/LSLJkYtbMgjXdSqRsXORh52LPOxchEdUc92I%0ASEPgBqCtqp6BcW0Z5GDMEoH9Eudh5yIPOxd52LkIDyeKvh8mxw2Bf/sHabMXOAKkiEhpIAXY7GBM%0Ai8VisYSJE0VfbK4bVd0NPAn8DGwBflPVaQ7GtFgsFkuYFOleKSJTgdpBProPmKCq1fK13a2q1Qtc%0Anwp8BnTGZK18H/hAVd8KMpb1rbRYLJYIKM69Mtq5btoBc1V1V+Caj4COwAmKvjhBLRaLxRIZTkw3%0AnwJDA++HkldFOz+rgQ4iUl5EBOgBrHQwpsVisVjCxElSs+rAe0AD8pUSFJG6wEuq2jfQ7i7MjSAH%0AWIRJcHbEBdktFovFEgK+SYFgsVgslujgeWSsiPQWkdUisk5ETkhfXJIQkVcDex8luuqJiNQXkRmB%0AQLvlIjLca5m8QkTKicgCEVkiIitF5HGvZfIaEUkSkcUi8pnXsniJiGwQkWWBufimyLZeruhFJAlY%0Ag7HdbwaetlZSAAAgAElEQVQWAoNV1ZMC4l4jIp2B/cDrgQCzEomI1AZqq+oSEamIKUHZvwR/L1JU%0A9UAgFmUO8HdVneO1XF4hIiOAs4FKqtqvuPaJioj8CJwdcGMvEq9X9OcCP6jqhoDdfiJwiccyeYaq%0AzgZ+9VoOr1HVbaq6JPB+P7AKqOutVN6Rr8ZyWUx0ebE/7ERFROoBfYCXAeupF+IceK3oTwE25jve%0AFDhnsQDH0mi0ARZ4K4l3iEgpEVmCCUycoaol2XPtaeBOjHNHSUeBaSLyrYjcUFRDrxW93Qm2FErA%0AbPMBcFtgZV8iUdUcVT0LqAdcICJpHovkCSJyEbBDVRdjV/MAnVS1DXAhcHPA9BsUrxX9ZqB+vuP6%0AmFW9pYQjImWAD4E3VTVYjEaJQ1X3ABmYQMSSSEegX8A2/Q7QTURe91gmz1DVrYF/dwIfY0zhQfFa%0A0X8LNBaRhiJSFhiICcSylGACwXWvACtV9Rmv5fESEamRmwJcRMoDPYHF3krlDap6r6rWV9VGmCy4%0A01V1iNdyeYGIpIhIpcD7CkAvoFBvPU8VvapmA7cAX2IiZt8tqZ4VACLyDjAXaCIiG0XkWq9l8ohO%0AwNVA14Dr2GIR6e21UB5RB5gesNEvAD5T1a88lskvlGTTby1gdr7vxSRVnVJYYxswZbFYLAmO16Yb%0Ai8VisUQZq+gtFoslwXGs6EMJ2xeRcYEUB0tFpI3TMS0Wi8USOm6s6F8DCt0oE5E+wOmq2hi4EXje%0AhTEtFovFEiKOFX0IYfvHasuq6gKgqoicUHbQYrFYLNEhFjb6YGkO6sVgXIvFYrFQTClBFykYrnyC%0AT6etGWuxWCyRUVwp1lis6AumOagXOHcC2q8f2rMnevgwqlqyXr/+ir75JvrHH4y+4w40Pd3MR3a2%0A97LF8rVgAXryyehrr6FHjjD69tvR33/3Xi4vXrNmoTVrom+/jWZnM3r0aO9l8uo1dSr61lvHjo/N%0Axf79aE6O9/J5+AqFWCj6T4EhACLSAfhNVbcHbfnhh1C6NDzySAzE8hlVq8JVV0GZMlCxInz2GZxx%0ABuzb57VksWXUKPjPf+BPfzLfhcqVISXFa6liz2+/me/DhAkweDAkJXktkXfs3AlDhkCdOid+duml%0AMHFi7GXyihAVe0HccK/MDdtvGgjbv05EhonIMCOXfg6sF5EfgBeAmwrtrHRpeOUVeOEFWLTIqWjx%0ATZky5oZXtarXksSWzz6Dyy7zWgrvGTECLr4YLrww+Oc7d8K2bbGVyStuvdXc9Lp2PfGzxx6D226D%0A7cHXjgnH4MEwb17Ylzm20avq4BDa3BJyh3XqmJV93RJbZ4K0tDSvRfCO5OTjDkvkXBw+DBs3wkcf%0AHXf6uLkYPx5++cU8/SQyixfD7Nnwww/HnT42F+3awcCB8K9/wb//HXv5YsnXX8OCBXD22WFf6ptc%0ANyKifpHF4mN++sk8+Z1SwuvT7NwJTZvCsmVQL4Gd2C69FNLSzKq9MDZtgtatYfVqOPnkmIkWc3r1%0AgssvhxuOrzEiIqgPNmMtwfjlFzj/fDh61GtJ4otXX4WHHvJaCu+pWRP+/Gd48kmvJYkehw4ZE+YN%0ARRZPMje6QYNg7NjYyOUF335rbmRDh0Z0uV3Re8XTT5t9iDfeCK39smXQoEHJs9kXZOtWaNECfv4Z%0AKlXyWhpv2bDBmC42bYJy5byWxlt+/BE2bzaLp0TkxhuhYUO4994TPrIrer+iCi+9VPxKJT//+Ifx%0AwEg0cnLMo/mOHaG1r1MHunSBd9+NqlhxQcOG0LbtCbb8EkmjRomr5AGys40nWoT4W9Grwty5Rhkk%0AEvPmGZNN50JLPJ7IDTeYm0OiPfVMnw579hhTRKjkzoXFeJ2ceabXUliizauvOnJQ8beiB2OH/OYb%0Ar6Vwl7ffNrY2CaO+cVoa7N8Py5dHTSxPiGQu0tPNpmxWVvTk8oJbb4U5c8K7pl07aNkyOvJYEgZ/%0AK3oRs8v8wQdeS+Iua9bAgAHhXSNirkmkuThyBP73v/D95kuXNiv68uWjI5cXHDxo9muaNvVaEksC%0A4m9FD3nKLZFMFlOnQpMm4V+XaIp++nQzD/XrF9+2IBdfnFixFl98YfyjwzFhJSoffujMg2bPHvdk%0ASRD8r+jPOMME0Sxc6LUk3nPuucY+nZ3ttSTusHCheWKzmBu4nQvDG29AtWqRXXvokNmk3r3bVZHi%0Anfhwr7znnpKbAyfRUQ3PPp+IHDliAn1WrgyezyWcfsqUcU8uLzh40MzFTz9B9eqR9XHJJXDFFSZt%0AQjyzZw/ccgu8/nqRv5HEca8cPNis7C2JR0lX8mA22Js0cabk//c/kwog3pk5E846K3IlD9CnjzGF%0AxTvTppkIaBd+I/GxordYiiIRngqcrsZ37IDGjY1iKFvWPblizfDh5oY3cmTkffz8s9nv2LYtvrN+%0AXn+9Se0wfHiRzRJnRZ8ovPoq/FpU1UVL2PzpT5CR4bUUznFqcjn5ZGjWLHz3TL8xbZpZkTuhQQOo%0AXdukDYhXVOHzz53PRQCr6GPFvn0mMVM8r7b8SMuW5gdhSQyTxYIFZhXrlCFD4jt18bJlUKECnH66%0AK91ZRR8rvv7aPE5WqOC8r507TRqAeDV1rVhh0s+6QY8eMGOGO33FO927G5fVeKZSJXfMcHfeCf36%0AOe/HK6ZPN99tl4hVzVhLZqaJbnWDGjVg7VqT1KpRI3f6jCUvvGAyDrZp47yv1q2NLXbbNvO4XpI5%0A5xyTLuSPP+yTY7wzbJiJhHcJNypM9RaR1SKyTkTuDvJ5mojsEZHFgdeoiAd75RV46y1H8npGZqZZ%0AhbuBiLlpZGa601+scfOml5RkcgbNnOlOf7Fmxgz3UlUnJ5snJavk45+UFFdz6ztS9CKSBDwL9AZa%0AAINFpHmQpjNVtU3gFbkzfNmy8MknEV/uGfv2GRe6Dh3c6zNeFf0vv5gnkbZt3eszLc2Yg+KNjRuN%0Av3e8ewxZfI/TFf25wA+qukFVjwATgUuCtHPnm9yli1m5xZttWtU8jbiZmyV3LuKNWbOgUycTAOcW%0At98en8VIZs40/4+l7FYZGzfC3r1eS5GwOP2GnQJszHe8KXAuPwp0FJGlIvK5iLSIeLQGDaBiRRNB%0AGE9Urux+MEvTpqa26KZN7vYbbdw02+QSrytiN8158c7dd5scN24zbhwcOOB+v3GGU0UfytJ6EVBf%0AVc8ExgPObC/xarJwGxGTpjfe6oV26mRC1C3RuenFI6rRu+m98w7Mn+9+v9EiJycqsTZOn583A/lT%0AD9bHrOqPoar78r3/QkT+IyLVVfWErENjxow59j4tLe34qvd5H8Bnn8HNNzsUPQFISfFagvBJhDB9%0AN9i4EX77LTq55H/+GX74Abp1c7/vaLBunTHlRcODLHdhGC9zsWwZXHllkVaLzMxMMsNc7DpKgSAi%0ApYE1QHdgC/ANMFhVV+VrUwvYoaoqIucC76lqwyB9hZYCYf9+4z7mJBeGxeI169aZBcuIEe73PXu2%0A2beIl8jQF180Eb2vv+5+319+aapwxct+1jPPmCLg//1vyJdEPQWCqmYDtwBfAiuBd1V1lYgME5Fh%0AgWYDgO9FZAnwDDDIyZhUrGiVvCU4CxeaVXI80LhxdJQ8mHTWq1fHz1xE04TVqRN8953JihkPRGku%0AHG/3q+oXqtpUVU9X1ccD515Q1RcC759T1VaqepaqdlTVODKYucDAgfG3eRyvPPSQKepS0klONsr+%0A66+9liQ0Tj4ZunaNTt8VK0KrVia1gt9RNU9jF1zgetfWryua/PEHTJoUWQWlUDl0KPFqp0ZKx47x%0AtfEWTeJpLp55JroR3o88AqeeGr3+3WLdOnNjikLlNKvoo8myZZCaavJ3RIulS8OvueoFCxdCvs32%0AqNChA8ybF90x4oXzzrNzkUuPHvGRKmT7drj00qh0Hb+5bg4eNIEmycleS1I4CxZA+/bRHaNNG7MS%0A2LcvujcUp2RmRj9F8znnmBvf4cP+/l7EgvPOM543lvihc2fzigLxu6Lv39//9thYKPqyZU1FHr/X%0A1I3FXFSsaCo1LVkS3XGc8sQTsH59dMeoXt2kxbZYiGdF3769/x9Nv/km+soNjMnC7/bYWCh6gL/8%0Axd8pBVThn/90Nx2GxVIMPv5FFMN55/lfuc2bBy0iz/gQMn63x27ebDaNTzst+mMNG2ZMOH7lhx9M%0ATQIn9WEThR9/jN9stHFG/Cr69u2NucKtFK/RoFq12NSs7NgRataM/jiRsmCBcfeL15w0bhKrJ5t4%0AYOrU2Jlf16+HQc5CeOKZ+FX01asbN6Tly72WxHvq1jX1aP1Kjx7w7LNeS+EPrKLPI5ZzUbeuiUT2%0Aa4KzL74wBd6jRPwqeoC+fWHLFq+lsBRH5crx4d4WCxYscLcuQXE89ZR/C4bPnx87RV+unAmc8mta%0AiJtugt0npP9yjfhW9E8+CRde6LUUFkvoPP64qR0cK3btgilTYjdeqOzdCz/9BGecEbsx/bqXtWOH%0ASVfRpEnUhohvRe9XDh707yNiSWDbNqNQ/Uj37rH1uPGrR9bChcYtuEyZ2I3pVweO3D2sKHqLWUUf%0ADT76CIYO9VqKkkuFCibs/Y8/vJbEezp0MIokJ8drSY6nYUN44IHYjpkbOe23CnUx2Kuwij4aeLXh%0ANmmSyVroJ7zwiqpUybhyLlsW+7H9Rs2aUKOG/74XqanQq1dsx2zQwBRP95v3l1X0cYpXin7qVONZ%0A4CfatfOmcHf79vGRsTAW2LkwiPgzfuHCC6O+QR//in7PHpg82Wsp8jh82Lh8xnLDLZf27f1lg9y7%0AF9aujeomU6H4bS685OGH4aKLvJbCUhgjRsBJJ0V1iPhX9AcOwFVX+cfutmSJUWxelPnz28rt229j%0Av+GWi9/mYvJk+OtfvRk7NdXfAXWWqBP/ir5OHbP55pdMfTt3Qp8+3ox92mnmiWLzZm/GL4iXwUEt%0AW5rkYX5ZAHz9ddRXbRZLYThW9CLSW0RWi8g6Ebm7kDbjAp8vFZE2Tsc8gVzPAj9w0UXw6KPejC1i%0A3LT8MhexDg7KT1KSye3tl403GxGbx/XXG5OeVxw5Ej+lBV3CkaIXkSTgWaA30AIYLCLNC7TpA5yu%0Aqo2BG4HnnYwZFGuPzWPECFOP1A/89JNVbmBcG2OVydTvHDkC77wDtWt7J8Mtt8Brr3k3vgc4XdGf%0AC/ygqhtU9QgwEbikQJt+wAQAVV0AVBWRWg7HPR6/2WO9pHv32EYbFsWiRcalraSzZo3JzXTyyd7K%0A4Qcz1rJlJh1G5creydCunT8WhosXw7//HZOhnCr6U4CN+Y43Bc4V16aew3GPp21bSE93tUuLC4j4%0Ax3TiJYsWeb+a37wZmjXzVgbwhwnLLwvDr74yT70xwGkpwVCXCAV/7UGvG5OvpmhaWhppaWmh9Z6S%0AYiIhLRY/cuWVUasFGjJ165q8N1u2RKX4dMgsWADnn+/d+GA26rdsMaUtq1XzTo4FC+CSggaQ4snM%0AzCQzMzOsa0QdPM6JSAdgjKr2DhyPBHJU9Yl8bf4LZKrqxMDxaqCLqm4v0Jc6kcUXfP45dOoEVap4%0ALYkll/vvhzPPhAEDvJbEe/r0gRtu8Pam06wZvPcetG7tnQwAaWkwcqS3loAGDcyq3uGemoigqkU+%0AOjs13XwLNBaRhiJSFhgIfFqgzafAkIBAHYDfCir5hCAnx/jzHzrktSSW/FSpArNmeS2FP/CDyeLL%0AL82K2mt69IDtHqqhLVvg99/h9NNjMpwjRa+q2cAtwJfASuBdVV0lIsNEZFigzefAehH5AXgBuMmh%0AzP5k7VqoWhVqubvPHBFbtnibVG3HDrMB6Qf8oNz8gh/m4tRTY1N1rThGjYIhQ7wbP3evIkZ7WI5M%0AN24S96abCRNMlZiJE72WxLiwVa0KW7d6493w/PMmDa0fql4dOGCiQnfvhuRkr6Xxlt27jRnr55/t%0AJrnX7NxpXi7UlI6F6cZfvPIKzJ3rzdh+8CbIpUwZk3rAq2o6fpqLlBSTkmLxYm/G37jRP3WNq1c3%0AXh5WyXtPzZquKPlQSSxFv2GDWVV7gZdRoMHwMlrYb3ORW0jeCzp1MoWp/UIUi1tY/ItT90p/0b49%0AjBvnzdh9+0Ib97M7REz79vD227Ef97ffYNMmf2y45fKvf5l8SLFm69aYbrj5mj/+MLZ5P9jnSyCJ%0AdXvPXbl5UU3noYdMAWK/kLvxFut9j4ULTQBbaR+tISpV8mYlm1sizppK4IMP4OqrvZbieA4e9FeK%0A8yiSWIq+Zk0TAOFlwiS/0KABTJ8e+3GTkoybqcVfexVes2CBv554wSwIL7vMZHyNJR44nSSWogd/%0AuJD5ARFo3jz2q8lu3eDGG2M7pl+ZP99fexW5bNwYex/yefP8NxcVKphgpSVLYjtux46wbl1Mh0w8%0ART9qlFE2FovXVKjgzxX900/HNnvjwYOm6lq7drEbM1RivTDcs8fMRcOGsRuTRFT0LVtC/fpeS2Hx%0AG6qxX8VOmuRtLpXCiLVyW7TIuBJ6UXWtOGI9FwsWmD2sGFddSzxFH2tmzChxua3jkp07TZ4VLzbq%0A/UasN+o3bjTps/1IrBW9R+Y8q+id8tlnsG2b11IUjqqJlC3pnHyyiRa2G/UmDcHRo8YNNhYMGmTK%0AOvqR5s2hX7/YBbXNmwfnnRebsfJhFb1TPPqPC5nhw+Hll2Mz1r/+Zfzo/YrdqDeI2LnIpVQpeOqp%0A2Pj3q8KKFVbRxx2HD5uKOX7cZMrljDNi84Petw8efNCfdthcrHLL47LLbPBSrBGBH3/0JPFhYir6%0Abdtio3wXLzZ5VCpWjP5YkRIr5bZwocmvU7Zs9MeKlFilhVixwv+pkYcO9b4YSknEo5trYir6WrXM%0AnXPr1uiO43ezDRgvpE2bom9S8aOfdEHatDEuj9HekH37bZg2LbpjWCxhkJiKXsSEnkd79XbZZTBi%0ARHTHcErp0sadK9pJveLhpleunFlpRzsdgl8Dpbxg2jRbjMcHJKaih9iYLBo0iI+EVZ07m8ye0ULV%0AKrdcjh41N1U7F0bBX3KJf9I0F8X77yf0U1jEmadEpDrwLnAqsAG4QlVPsA+IyAZgL3AUOKKq50Y6%0AZlh06OBfl65YE+3C6UePwvjxcMop0R0nHli+3BTfrl7da0m8Z9EiE7vgRebQcNm0ycTE9OgRnf5z%0AXbBr145O/8XgZEV/DzBVVZsAXwWOg6FAmqq2iZmSB2NGWLYMsrNjNmSJpXRpGDzYayn8wcyZcMEF%0AXksRGocOGZfYaBFPc9G5c3Q30J97ziyGPMKJou8HTAi8nwD0L6Jt7PO0Vqliaqf6KV2uJfFp3Rqu%0AvdZrKUIjOdko+p9+ik7/mZnQpUt0+nabs84yq/qdO6PTf2YmpKVFp+8QcKLoa6lqbvKQ7UBhzqEK%0ATBORb0XkBgfjhU80a4TGc33bks6yZdHbv0lL8/+mdC4iRt6ZM93v+8gRs0EfLyv60qXh/POjs6o/%0AcMC4Ynfs6H7fIVLkcldEpgLBjEr35T9QVRWRwjRfJ1XdKiI1gakislpVZwdrOGbMmGPv09LSSPPw%0ADlgkW7aYDJmrVtmiEvHIokWm4IQfCrl7TVqaWW0OGeJuv/v2wR13xNdeRe5cXHaZu/3On2+e9Fza%0Aq8jMzCQzMzOsa0QjXJmKyGqM7X2biNQBZqhqs2KuGQ3sV9Ung3ymkcoSc955B957Dz7+2GtJwuOL%0AL6BXLxsRuWGD2azfutXeqFeuhIsu8lddW6/YssXEm7hdtPuBB8xe4WOPudtvABFBVYv8Ijsx3XwK%0ADA28Hwp8EkSAFBGpFHhfAegFfO9gTH/gsb0tYu64A5YudbfPW26BL790t89o07Ch8alfs8ZrSbyn%0AeXPYvz96dvp4om5d95U8mKeaiy5yv98wcKLo/wH0FJG1QLfAMSJSV0QyAm1qA7NFZAmwAJikqlOc%0ACBw2e/e6X80lXhV97qOpW6jCJ5/Aaae512es6NLF3bmIV0Tg//7P32k84p2//c1T+zw4UPSqultV%0Ae6hqE1XtletDr6pbVLVv4P16VT0r8Gqlqo+7JXjIzJoFf/2re/1t3Qq//GKShcUbbiu39euNso+H%0AoLGCuH3TW7HC3e9ZLOnTB046yWspLFEkcSNjczn/fONh4VYB4OXLTVBFtMPoo0GXLjB7tnuRirlP%0ANvFo505Pd/dxevp0G7Nh8S1xqK3CpGpVaNrUvVwvPXvGr7dG7drm5ZadPl5NWGDssVdf7V5/8TwX%0AbnP//TEvfu06CVaJLPEVPUDXrvDVV+71F48r2FzuvNO9epXz5lnlBuYJaeZMOxdgnpzHjYsvt8qC%0AXHstfPCB11K4SslQ9L17G9dCC1x3nXv7C99/D40bu9NXPPPNN+YJweb6gTlzjCdPPNv8zz7bHX3x%0A9de+efovGYq+c2ej3BLsccxzypf3WgJ/MHmy2dCMZ/bvN78Rp/sMn38e/3PRp49R9E71xZtvwubN%0A7sjkkIgDptwmrgKmLJb8/PEHHDxo8ivFM23bwtixZmEUKc2bwxtv+Lu8Zig0a2YKyLRtG9n1qiZe%0AY/JkMydRJNoBUyWLAwcgI6P4dpb44n//g3ypNyKibNn4V/JgVrJOvuM//gi7d0euHP2E07lYudJ4%0A5jUrMllAzLCKPlQmT4ann/ZaCovbnHYavPaaTVIHcPHFJgAu0rlo0MDY6OPR9bgg/frBDz9Efv0n%0Anxj3XZ84biTA/0iMeP99uPxyr6Vwj/vuMz/KSJg5M3HKw7VqZdIhRLvUYjxw7rnmyXXlysiuT0pK%0AnM35tDSYMKHYZoXiM31hFX0oHDxoNmcuvdRrSdyjQoXIPAL27jUrv0RR9CLmB5lg7nQRIQJXXGGy%0ALVqc8eqr0KmT11Ico2Qp+iNHYOBAs3kWDlOmGLvjySdHRy4vGDAAPvww/CjZjAyTY7xq1ejI5QUD%0ABpgVWLgmizVrYNeu6MjkFf/6F/z5z15LEf+0beurLLElS9GXKWNSkX7+eXjXvf46DBoUHZm8okkT%0A4/sdbiDZ668nXtnAM8+EypXDz2Z5ww3RLT/nBT6xKVvcpeS5V06YYFZvkyaFfs2778KFFxplkEg8%0A/7wpiPzee6G1/+knE0yycWPi+dBnZ4dXdnLNGpM7aONG9yKN45VcX3EbMOYJ1r0yGJdfbkL3N20K%0A/ZqBAxNPyYNZmc+dazbgQuG118w1iabkIfzawq+8AkOHWiUP8PjjZj4SkZwcGDkyfHOvzyh5K3qA%0Am2+GatXgkUdiM56fOXIkdGU1fTrUq2fMPiWZAwegUSOTCbSkz8WePZCaasozNmjgtTTRoVs3s29x%0A1VVFt9u1y7xi/J2wK/rCuOMO+Ogj99L1xjPhrEi7dbOKDeC//zXRo4k8F4cPm4IZR44U3W78eBNc%0AlKhKHuCee+DRR4tPifDkk/DUU7GRKUyc1Iy9HBgDNAPOUdVFhbTrDTwDJAEvq+oThbSLbQqEcG2y%0AFksu331nKjI1beq1JNGlWze45hqTzTEY+/aZgLM5cxJ7LlThvPNgxAjjfhqM3btNDMGiRXDqqTEV%0AL9or+u+BS4FC3Q5EJAl4FugNtAAGi0h0Ez+ESnFK3sOgoHArvCcyMZ+Lo0fNPsT27YW3OftsTxRb%0AzOfi4YdNbvndu4N/Pno09O2b+HMhYsy8d99t4kiCcffdxjMvxko+VJyUElytqmuLaXYu8IOqblDV%0AI8BE4JJIx4wZK1fCZZcZV0wP8EzRHz3quwyfMZ+LpCRjfx82zHdpEWI+F506md/BzTcHn4thwzwz%0AVcR8Lnr0MEWHJk8+8bOMDJg2Df7xj9jKFAbRttGfAmzMd7wpcM6/LFkCvXqZL3A8Fr12wsiRcOWV%0AeU8y33xjHldLGqNHw44dxmQR594Wjnn8cVi7Fm666cRFQNOm8V1gJFxeeOFE082hQ2Yv4/XXoVIl%0Ab+QKgSIVvYhMFZHvg7wuDrF/fy2JimLgQGjTxlSjeuYZGDLEa4liz0MPmRVtnTpw1lkmdqBrV6+l%0Aij3JyWaFtn+/mYtrrvFaIu9ISTGxFm3a2GCqYH9/uXKwbJmz1M4xwLF7pYjMAO4IthkrIh2AMara%0AO3A8EsgJtiErIvFzU7BYLBYfUdxmrFtuJ4UN8i3QWEQaAluAgUDQ+PniBLVYLBZLZERsoxeRS0Vk%0AI9AByBCRLwLn64pIBoCqZgO3AF8CK4F3VXWVc7EtFovFEiq+iYy1WCwWS3TwPDJWRHqLyGoRWSci%0Ad3stj5eIyKsisl1EvvdaFi8RkfoiMkNEVojIchEZ7rVMXiEi5URkgYgsEZGVIvK41zJ5jYgkichi%0AEfnMa1m8REQ2iMiywFx8U2RbL1f0gYCqNUAPYDOwEBhcUs07ItIZ2A+8rqpneC2PV4hIbaC2qi4R%0AkYrAd0D/Evy9SFHVAyJSGpgD/F1VIywPFv+IyAjgbKCSqvbzWh6vEJEfgbNVtZCItjy8XtHHZ0BV%0AlFDV2cCvXsvhNaq6TVWXBN7vB1YBdb2VyjtUNTe9aFlMKpFif9iJiojUA/oAL1O4E0hJIqQ58FrR%0Ax19AlSWmBDy22gALvJXEO0SklIgsAbYDM1Q1wqKuCcHTwJ2Av0K4vUGBaSLyrYjcUFRDrxW93Qm2%0AFErAbPMBcFtgZV8iUdUcVT0LqAdcICJpHovkCSJyEbBDVRdjV/MAnVS1DXAhcHPA9BsUrxX9ZqB+%0AvuP6mFW9pYQjImWAD4E3VfUTr+XxA6q6B8gA2nkti0d0BPoFbNPvAN1E5HWPZfIMVd0a+Hcn8DHG%0AFB4UrxX9sYAqESmLCaj61GOZLB4jIgK8AqxU1We8lsdLRKSGiFQNvC8P9AQWeyuVN6jqvapaX1Ub%0AAYOA6apaAnOVmA16EakUeF8B6IXJKBwUTxW9Dag6HhF5B5gLNBGRjSJSSCLwhKcTcDXQNeA6tjhQ%0A16AkUgeYHrDRLwA+U9UwK7onLCXZ9FsLmJ3vezFJVacU1tgGTFksFkuC47XpxmKxWCxRxip6i8Vi%0ASZjh7gwAAAA0SURBVHCsordYLJYExyp6i8ViSXCsordYLJYExyp6i8ViSXCsordYLJYExyp6i8Vi%0ASXD+P5ePy54P9ccKAAAAAElFTkSuQmCC%0A" 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V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%0ASXD+P5ePy54P9ccKAAAAAElFTkSuQmCC%0A" />
-</section>
-<section id="id8">
-<h2>图形上加上文字<a class="headerlink" href="#id8" title="Permalink to this heading">¶</a></h2>
-<p>plt.hist() 可以用来画直方图。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">mu</span><span class="p">,</span> <span class="n">sigma</span> <span class="o">=</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">15</span>
-<span class="n">x</span> <span class="o">=</span> <span class="n">mu</span> <span class="o">+</span> <span class="n">sigma</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">10000</span><span class="p">)</span>
-<span class="c1"># the histogram of the data</span>
-<span class="n">n</span><span class="p">,</span> <span class="n">bins</span><span class="p">,</span> <span class="n">patches</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">50</span><span class="p">,</span> <span class="n">normed</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">facecolor</span><span class="o">=</span><span class="s1">&#39;g&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.75</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;Smarts&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;Probability&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;Histogram of IQ&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="mi">60</span><span class="p">,</span> <span class="mf">.025</span><span class="p">,</span> <span class="sa">r</span><span class="s1">&#39;$\mu=100,\ \sigma=15$&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">([</span><span class="mi">40</span><span class="p">,</span> <span class="mi">160</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mf">0.03</span><span class="p">])</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
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-<img 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M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%0A60kAAAAASUVORK5CYII=%0A" 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M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%0A60kAAAAASUVORK5CYII=%0A" />
-<p>对于这幅图形，我们使用 xlabel ，ylabel，title，text 方法设置了文字，其中：</p>
-<ul class="simple">
-<li><p>xlabel ：x 轴标注</p></li>
-<li><p>ylabel ：y 轴标注</p></li>
-<li><p>title ：图形标题</p></li>
-<li><p>text ：在指定位置放入文字</p></li>
-</ul>
-<p>输入特殊符号支持使用 Tex 语法，用 $&lt;some Tex code&gt;$ 隔开。</p>
-<p>除了使用 text 在指定位置标上文字之外，还可以使用 annotate 函数进行注释，annotate 主要有两个参数：</p>
-<ul class="simple">
-<li><p>xy ：注释位置</p></li>
-<li><p>xytext ：注释文字位置</p></li>
-</ul>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>ax = plt.subplot(111)
-t = np.arange(0.0, 5.0, 0.01)
-s = np.cos(2*np.pi*t)
-line, = plt.plot(t, s, lw=2)
-plt.annotate(&#39;local max&#39;, xy=(2, 1), xytext=(3, 1.5),
-    arrowprops=dict(facecolor=&#39;black&#39;, shrink=0.05),
-    )
-plt.ylim(-2,2)
-plt.show()
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<img 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m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%0ARVGUgPP/Qde6gvF4TtQAAAAASUVORK5CYII=%0A" 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m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%0ARVGUgPP/Qde6gvF4TtQAAAAASUVORK5CYII=%0A" />
-</section>
-<section id="id9">
-<h2>作者 &amp; 更新时间<a class="headerlink" href="#id9" title="Permalink to this heading">¶</a></h2>
-<p>作者:<cite>李金  &lt;lijinwithyou&#64;gmail.com&gt;</cite></p>
-<p>提交: 2017/12/14</p>
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diff --git a/docs/aipy-numpy/cn/2pyplot-style.html b/docs/aipy-numpy/cn/2pyplot-style.html
deleted file mode 100644
index d103ab9..0000000
--- a/docs/aipy-numpy/cn/2pyplot-style.html
+++ /dev/null
@@ -1,350 +0,0 @@
-<!DOCTYPE html>
-<html lang="en-US">
-<head>
-<meta charset="utf-8" />
-<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
-<link href="https://cdn.bootcss.com/bootstrap/3.3.7/css/bootstrap.min.css" rel="stylesheet">
-<title>使用 style 来配置 pyplot 风格</title>
-<link rel="stylesheet" type="text/css" href="../../static/pygments.css" />
-<link rel="stylesheet" type="text/css" href="../../static/style.css" />
-<meta charset="UTF-8">
-<meta name="viewport" content="width=device-width, initial-scale=1,maximum-scale=1, user-scalable=no">
-<script src="https://cdn.bootcss.com/bootstrap/3.3.7/js/bootstrap.min.js"></script>
-</head><body>
-<div class="header doc-header web-show">
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-<div class="navbar-header">
-<button type="button" class="navbar-toggle" data-toggle="collapse" data-target="#example-navbar-collapse">
-<span class="sr-only">切换导航</span>
-<span class="icon-bar"></span>
-<span class="icon-bar"></span>
-<span class="icon-bar"></span>
-</button>
-<a href="/index.html" class="logo"><img src="http://www.qpython.org/static/img_logo.png"></a>
-</div>
-<ul class="header-title collapse navbar-collapse" id="example-navbar-collapse" >
-<li>
-<a href="http://www.qpython.org/index.html">Home</a>
-</li>
-<li>
-<a href="http://www.qpython.org/document.html">Guide</a>
-</li>
-<li class="header-phone-selected">
-<a href="/index.html" class="header-selected">Course</a>
-<span class="header-title-selected"></span>
-</li>
-<li>
-<a href="http://www.aipy.org">AIPY</a>
-</li>
-<li>
-<a target="_blank" href="https://github.com/qpython-android/qpython">Github</a>
-</li>
-</ul>
-<div class="search-box hidden-xs">
-<form action="/search.html">
-<input type="search" name="q" placeholder="keyword">
-<input type="hidden" name="check_keywords" value="yes" />
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-<button><img src="http://www.qpython.org/static/ic_search.png"></button>
-</form>
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-<main class="contain web-content">
-<div class="document">
-<div class="documentwrapper">
-<div class="bodywrapper">
-<div class="body" role="main">
-<section id="style-pyplot">
-<h1>使用 style 来配置 pyplot 风格<a class="headerlink" href="#style-pyplot" title="Permalink to this heading">¶</a></h1>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="o">%</span><span class="n">matplotlib</span> <span class="n">inline</span>
-</pre></div>
-</div>
-<p>style 是 pyplot 的一个子模块，方便进行风格转换， pyplot 有很多的预设风格，可以使用 plt.style.available 来查看：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">available</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">[</span><span class="sa">u</span><span class="s1">&#39;dark_background&#39;</span><span class="p">,</span> <span class="sa">u</span><span class="s1">&#39;bmh&#39;</span><span class="p">,</span> <span class="sa">u</span><span class="s1">&#39;grayscale&#39;</span><span class="p">,</span> <span class="sa">u</span><span class="s1">&#39;ggplot&#39;</span><span class="p">,</span>
-<span class="sa">u</span><span class="s1">&#39;fivethirtyeight&#39;</span><span class="p">]</span>
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span>
-<span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
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k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%0Agg==%0A" 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k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%0Agg==%0A" />
-<p>例如，我们可以模仿 R 语言中常用的 ggplot 风格：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">use</span><span class="p">(</span><span class="s1">&#39;ggplot&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
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-<img 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A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%0AwU9EFGT+Pxvu78Bmq8eQAAAAAElFTkSuQmCC%0A" 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A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%0AwU9EFGT+Pxvu78Bmq8eQAAAAAElFTkSuQmCC%0A" />
-<p>有时候，我们不希望改变全局的风格，只是想暂时改变一下分隔，则可以使用 context 将风格改变限制在某一个代码块内：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>with plt.style.context((&#39;dark_background&#39;)):
-    plt.plot(x, y, &#39;r-o&#39;)
-    plt.show()
-</pre></div>
-</div>
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-<img 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J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%0ASUVORK5CYII=%0A" 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J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%0ASUVORK5CYII=%0A" />
-<p>在代码块外绘图则仍然是全局的风格。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>with plt.style.context((&#39;dark_background&#39;)):
-    pass
-plt.plot(x, y, &#39;r-o&#39;)
-plt.show()
-</pre></div>
-</div>
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-<img 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4%0AvydPQmu3c3gbkRcCr/BLEqLWr0f1rFmik5APfX3yJA6qVMjJy3Mc4/A2Is8E3Bp/aEEB1FevwnL3%0A3aKjkA/lGY1YajY7HWOLLpFnAq7w69avR/Uzz3D0coBhiy6R7wRU4dd8+y1CDx1C7WOPiY5CPsYW%0AXSLfCajCH/Xee6iZPBlSRIToKORjHN5G5DsBs7mrLitDxN//jkv79omOQp3AaXjbd99BKi/Hna+9%0Axo1dIg8ovvA3TuEMO3sWtogIDP33v1kMApRjeJvViu533IGyXr1gEx2KSIEUXfhdTuF85RUAbPEL%0AaGFhME+dCt2GDah44w3RaYgUR9Fr/JzCGbxqpkxBxCefQH3liugoRIqj6MLPFr/gZTcYUHv//Yjc%0AulV0FCLFUXThZ4tfcDM/8wx0W7cCfKMn6hBFF/6ktDRkNWvdZItf8LDdfDPqBwxAxM6doqMQKYqi%0AN3eTfvELxGo0SB8+HNr6ek7hDELmZ55B9KJF127aU6lExyFSBEUX/iijEYnTp+PX8+eLjkKCWIcP%0Ax1fV1fh0/HhodDrYwsIwevZs3JqcLDoakWwptvCrTKZrN2zt2SM6CgmUv2cP8mtr8fr33zuOZf3n%0AP6h99VX+5EfkhmLX+CP//GdYRo2CvUcP0VFIoDyjEcvKypyOLT13ji29RK1QZuG32aAzGmGeMUN0%0AEhKMLb1EHafIwh/+6adoiI9H/cCBoqOQYGzpJeo4RRZ+3YYNvNonAK6ndmb27cuWXqJWKG5zN+Tf%0A/4amtBSWe+4RHYVkoOnUzpArV6A6fRopS5fi1uHDBScjki/FFP7GKZzhJ06gLiYGd3zxBbs2CECT%0AqZ0Auj74IBpsNlwVnIlIzhRR+FtM4Swr4xROcql6+nREr1sHjBwpOgqRbClijZ9TOKm9LOPHQ33+%0APLQnToiOQiRbiij8bNmjdgsJQf2MGYgyGkUnIZItRRR+tuxRR9Q9/TTCP/0U6mY3dhHRNYoo/EnT%0ApmFBSIjTMU7hJLfi4lB7332I/OMfRSchkiVFbO4Oj41FbGws0vv3h9Zq5RROapM5LQ1xkyejetYs%0AIDRUdBwiWVFE4ddt3IjbX3wRv54+XXQUUghbv37YGxuL3ffeC3VMDGxhYUhKS+PFAhEUUPjVpaUI%0A++ormHJyREchBcnPzUV+eTmWX7rkOJZx/jwAtgATyX6NX7d1K2omToSk14uOQgqSZzRiWZOiD7AF%0AmKiRvK/4a2sR+f77uMKP1qMOYgswkXuyvuKP+Ogj1A8ciIYbbxQdhRSGLcBE7sm68Edt3AgzN3TJ%0AA66mdrIFmOgar5d6jh49is2bN8Nut2PUqFGYMGFCi3OMRiOOHj2KsLAwzJo1C3379m3fN7dYYL3r%0ALm8jUhBymtp59SrUJ0/izoULubFLBC8Lv91ux8aNG7Fw4UIYDAZkZWUhMTERvXr1cpxz5MgRXLx4%0AEW+99RZOnz6NDRs2YPHixe36/n9QqXDH55/zHyt5pOnUzrjHH0dNTQ1qBWeiwNI4NVhrtSqqZdir%0Awn/mzBn06NED3bp1AwAkJyejoKDAqfAXFBRgxIgRAICbb74ZZrMZJpMJsbGxbX7/5WfOcAon+UR1%0AWhr0q1ah9uGHAZVKdBwKAC2mBkM5LcNerfGXl5cjLi7O8dhgMKC8vLzVc+Li4lqc0xq24JEvWEeP%0AhtpkQsiRI6KjUIBQ8tRgv7RzSpLU5jlFRUUoKipyPE5NTXX8Oqy+HnqF9fGHhoYqLnMjJWcH3Oe3%0APfccYv/4R1hSUvwfqgMC9fVXivbmD7PZXB+XQb3avn2749cJCQlISEhw+rpXhd9gMKCsyQTEsrIy%0AGAyGDp/jLlwja0gIqqqqvInqd3q9XnGZGyk5O+A+v2rCBHR//XWYT5+GvUcPAcnaJ1Bff6Vob36r%0A1nX5FF2v9Hq904WzK14t9dx000346aefcOnSJdhsNuTl5SExMdHpnMTERHz55ZcAgJKSEuh0unat%0A7zdiCx75ihQTg9oHHoDuT38SHYUCQFJaGjJ/3t9spJR65dUVv0ajQVpaGhYvXuxo5+zVqxd2794N%0AABg7dixuv/12FBYW4sUXX0R4eDhmzpzZ7u+fnpLCKZzkU+Zp0xCXmoqqF18E3NzkRdQeQ8eMgb5r%0AV2QaDFDFxipqarBKas8CvCClpaWiI3hMyT/uKjk70Hb+k+PGYXdDA9SxsbJswQv011/u2ptfe+oU%0A4h5/HBfz82U1+js+Pr7Nc+Q9q4fIx/Jzc5F/+TKndpLXdEYjzE89Jaui316yHtlA5Guc2km+oDKZ%0AEPHxx6iZPFl0FI+w8FNQ4dRO8oXIP/8ZltGjYW+2uasULPwUVDi1k7zW0ADdpk2KHiDJwk9BhVM7%0AyVvhu3fD3q0b6m+7TXQUj3Fzl4KKy6mdf/gDN3ap3XQBMC6ehZ+CTtOpnYannkJtVRWndlKrGqdw%0Ahly9CtU33yBx+nQMFR3KCyz8FNTMaWmIXrIEtampnNpJLrmcwvnaa4BWq9ifFLnGT0HNOmIEYLUi%0A9MAB0VFIppQ8hdMdFn4Kbmo1zGlp0BmNopOQTAViCzALPwW92kcfRVheHjTffSc6CslQILYAs/BT%0A0JN0OtSkpkK3ebPoKCRDSWlpyLzuOqdjSm8B5uYuEa5N7ex6772o+t3vIEVGio5DMjJ0zBhER0cj%0Ao1s3qPV6RU3hdIeFnwhAQ+/eqBsyBBF/+QtqpkwRHYdkJKSwECPq6tBv715AoxEdxydY+Il+lnvb%0Abfg6OxvS3/8uy3HNJIZu40aYp00LmKIPsPATAfi5V/vPf8bymhrg59ZOjmsm9Y8/InzvXlQsWSI6%0Aik9xc5cIgdmrTd7TbdmCmocfhhQdLTqKT/GKnwiB2atN3lHV1iLy/fdxZedO0VF8jlf8RAjMXm3y%0ATsTf/ob6QYPQcOONoqP4HAs/EdyMa77hBkX3apMXJAm6DRtQPWOG6CSdgks9RHAe16y1WKA6fx4j%0AhwzBQG7sBqWwr74C1GrUDRsmOkqnYOEn+lnTcc3a4mLEPfkkLlqtgJtlIAo8+bm5yN+yBaGHD8Pa%0AvTvu2LMnILu6WPiJXLD16wfbr36FiI8+Qu2jj4qOQ37QOH7Z0d1VUYGMV14BEHgtvVzjJ3Kj+pln%0AEPXee4AkiY5CfhBMLb0s/ERuWEeOvDarPy9PdBTyg2Bq6WXhJ3JHrYZ5xoxrV/0U8IKppZeFn6gV%0AtY88gpAjR6A5e1Z0FOpkSZMnY4HauSQqffyyO9zcJWqFFBGB3UlJ+OLxx6Hq3ZvD2wLYSJMJ+ltu%0AQdZ11wFmc0CMX3aHhZ+oFfm5uThQWIjlpaVAaSkADm8LSHY7dO++i9sWL8bQ8eNRVVUlOlGn4lIP%0AUSvyjEYs//57p2OB2ukRzMI+/xxSeDjqkpNFR/ELFn6iVgRTp0cwi1q/HubnnwdUKtFR/IKFn6gV%0AwdTpEaxCjh2D5sIF1N5/v+gofsPCT9QKl8PbArTTI1jp3n0X5unTgZAQ0VH8hpu7RK1oPrxNfeIE%0A7nroIdzOjd2AoPnhB4R/8QUqli0THcWvWPiJ2tB0eFv4Rx8hauNGXElPF5yKvJGfm4s8oxHhp06h%0APioKQ/Pzg6pLi4WfqAMs992H6GXLEHroEOoGDxYdhzzQOIwtp8lcnkAdxuYO1/iJOkKjQfVzzyFq%0A7VrRSchDwTSMzR0WfqIOqklNRcjRo9CWlIiOQh5giy4LP1HHRUTA/PTTiFq3TnQS8gBbdFn4iTxi%0AnjoV4Z99BvXPYxxIOZKmTsUCrfP2ZrC16HJzl8gDUpcuqHnkEURt3IjKhQtFx6EOSKmpgf6mm5B+%0A/fXQWiwBPYzNHRZ+Ig/t6d8fB+fPh72gADadjlM7lcBuR9Tbb2PQwoXoP3Kk6DTCsPATeSA/NxcH%0AVq/GcpsNKCgAwKmdShCWmwtJq4U1JUV0FKG4xk/kAbYEKpAkQf/WW6h+8cWgGcbmDgs/kQfYEqg8%0Aofv3Q1VZCcv48aKjCMfCT+QBtgQqj/7tt1E9ezag0YiOIhwLP5EHXE3tzDAYgqolUElCCguh+fZb%0A1D78sOgosuDx5m51dTXefPNNXLlyBddddx3mzZsHnU7X4rzZs2cjIiICarUaGo0GS5cu9SowkRw0%0An9rZUFeH8efP49d33SU4GTXlGMZ27Bjq4uJwx7593HyHF4V/586duPXWW/Hggw9i586d2LlzJ558%0A8kmX57766quIioryOCSRHDWd2gkAhiefhGXHDtS4+XdA/tViGNvVq0E3jM0dj5d6CgoKMGLECABA%0ASkoKDh065PZcSZI8fRoixaiaOxdRa9YA9fWioxDYedUajwt/RUUFYmNjAQAxMTGoqKhweZ5KpUJ2%0AdjYyMzORm5vr6dMRyV794MFo6NMHEX/7m+goBHZetabVpZ7s7GyYTKYWx5944gmnx6pWemKzs7PR%0ApUsXVFZWIjs7Gz179kS/fv1anFdUVISioiLH49TUVOj1+jb/AHIVGhqq2PxKzg6Izd/w8suImTUL%0A2qefBrSeraTy9fcNKTLS9Rd0ulbzySW/N7Zv3+74dUJCAhISEpy+3urfzIWtzCCJiYmByWRCbGws%0Arl69ipiYGJfndenSBQAQHR2NIUOG4MyZMy4Lv6twVVVVrcWTNb1er9j8Ss4OCM5/662I69EDtj/+%0AEbWPPOLRt+Dr7xtJo0djwRdfYInd7jg2v08f3DllSqv55JLfU3q9Hqmpqa2e4/HmbmJiIr744gtM%0AmDAB+/btw2AXn0ZktVpht9sREREBi8WCY8eO4REP/zEQKUXV3Lk4PmcOPvnrX6Gtq4MtLIxzfAQY%0Au38/tI8+ivSLF4N2GJs7Hhf+CRMm4M0338TevXsd7ZwAUF5ejnfffRdZWVkwmUx44403AAB2ux3D%0Ahg3DwIEDfZOcSKa+qq3FQZMJOV9+6TjGOT7+pT1xAqGHD2PQ/v24LSJCdBzZUUkybrkpVfCscyX/%0AuKjk7ID4/G9OmoScfftaHE9PScG8bdva/P2i83tLDvkNU6fCetddME+f3uHfK4f83oiPj2/zHN65%0AS+Rj7CYRK+TIEYQUFcHM+yncYuEn8jHO8RFL/8YbqJozB+Dr7RYLP5GPuZrjE2wf7SdK6MGD0H77%0ALWoef1x0FFnjB7EQ+VjTOT4h5eVQFxfjzqwsbux2osaZPBFHjsB6/fW448sv+Xq3goWfqBM0neMT%0AO2cOGk6dQtVvfiM4VWBqMZOnqoozedrApR6iTlaVng7dpk1QX74sOkpA4kyejmPhJ+pkDTfcgJqJ%0AExG1erXoKAGJXVQdx8JP5AfVL72EiJ07oWl2ZUres4WEuD7Orh63WPiJ/MAeFwfz9OnQ5+SIjhJw%0ARsbHI6vZ3bnsomodN3eJ/MT87LP4ZvBgfPLAA9CGhHCGjw+oy8tx92efwfbKK0jftYszedqJhZ/I%0ATw58/TXyNRrkHD7sOMYZPt6JWrkSlgcfROKUKUicMkV0HMXgUg+Rn+QZjVhWXu50jN0nntOeOYOI%0Av/8dVb/7negoisPCT+Qn7D7xrejsbFTPng27wSA6iuKw8BP5CWf4+E7ol19Ce/o0zNzA9QjX+In8%0AJCktDRnnzzvdbDS/Vy92n7RT41gGrcUC1YkTSJ42DYPcvJlS61j4ifyk6QwfrcUClJZiTHw8buHG%0AbptajGUAkPHxx6gbPJgb4x5g4Sfyo6YzfFQ1NbguJQWmvDzUJSUJTiZveUajU9EHrm2Mp2/axMLv%0AAa7xEwkiRUai8r//GzF/+ANQXy86jqxxY9y3WPiJBLLcey8auneHji2dreLGuG9xqYdIJJUKFdnZ%0AOHnfffjks8+glSRIkZEYOnUqlzCaSHrqKSzYvx9LbDbHMY5l8BwLP5FgeefP46BajZyvv3Ycyzh7%0AFgDv6G005uRJRA0YgPToaGitVo5l8BILP5FgeUYjciornY5x4/L/05aUIHLzZgzYtQsJ8fGi4wQE%0ArvETCcaNy1bY7YhNT0fV734HO4u+z/CKn0gwblw6c9yoZbXCfuUKxgHoxwFsPsXCTySYqzt6M7p1%0AC8qNS5c3asXHo/Lzz7ns5UMs/ESCNb+jV1Vfj3vOnkW/22+HXXA2f3N5o1ZpKfc7fIyFn0gGmt7R%0Aq9froUpPhyYzE1fffRdQqQSn8x/ud/gHN3eJZKhy/nxoT59GxIcfio7iV9zv8A9e8RPJUXg4TG+9%0AhROPPIJPtm2DFgiKj2pMmjwZC/LysKTJCAveqOV7LPxEMvWvixeRr9Ui58ABx7FA/6jGcZ9/Dl1i%0AItJDQ3kyS9fjAAAI6UlEQVSjVidi4SeSqTyjETkmk9OxQL6xK+Ivf0HowYMY8M9/4hadTnScgMbC%0ATyRTwbTRqT19GtGLFqHsgw8gseh3OhZ+IpkK9I1Ox41atbVQnTiBYY89htv69xcdKyiw8BPJlKsb%0Au7K0WiQ9+qjAVL7h8katzz+HNTc3IJex5IaFn0immt/YZQsPx+iQEKR88AHK778f0Cr3ny8/UUss%0A5f7NIQoCTW/sAgDYbMCUKTg2Ywb21NVd63xRYJtnMO1fyBELP5GSaLX452OP4dCcOchp8qEkSmvz%0AbDCbXR4PlP0LueOdu0QKk/fBB06fRAVcWybJU8jHN4YUFGD8uXPI7NHD6Thv1PIfXvETKYzSlkma%0Ajlm21dfj3pIS3LpuHUx2u9P+BW/U8h8WfiKFUVKbp6vuncyuXWGy21vuX5DfcKmHSGGS0tKQ0aeP%0A07EstRop//VfghK5l2c0OrWjAsCyK1cUsywVqHjFT6Qwrto8h48di3vWrMEuiwV7z52TTbeP0pal%0AggULP5ECuVom+YdWi/wFC5DT0OA4Jrrbx95s1lAjOS5LBRMWfqIA8a9PPnEq+oB/b4pq3MQNs9lg%0AVasxOjIS95SVISM+HstLSx3nsXtHPBZ+ogAhclnF1SZuVng4hqxYgTuioti9IzMs/EQBwl23D378%0AEfmffoq8rVs7be3f1QiGpRYL0nfswLxt21joZcbjwv/1119jx44d+OGHH7B06VLceOONLs87evQo%0ANm/eDLvdjlGjRmHChAkehyUi91wNdZvfqxd6qlQ49PzzyGnyqVberP079eX//CYS4mYtn5u48uRx%0A4e/duzd+//vf47333nN7jt1ux8aNG7Fw4UIYDAZkZWUhMTERvXr18vRpicgNV90+SdOmIW/jRuT8%0AXOgbNV37d1XI3b0huFzSKSiAqbbW5fncxJUnjwt/z5492zznzJkz6NGjB7p16wYASE5ORkFBAQs/%0AUSdx1e1zcN06l+eGfvcd8v/xDxxYssR5PHKTN4nmbwgul3TMZkzt1w8ZNTXOP21wE1e2OnWNv7y8%0AHHFxcY7HBoMBZ86c6cynJKJm3K39SyYTCmbNwnIXnUBTc3LQo6rK+Y7b48dhbbJc1FR8TAyGZGYi%0AfdMmhNXXwxoSwk1cGWu18GdnZ8PkYu3uiSeeQGJiYqeFIiLfcbn236cP7nztNRx66y3g8OEWv8d6%0A4gSWNzu2rLwcqZGRLp/DFh7u+GlDr9ejqqrKl38E8jGVJEmSN99g0aJFeOqpp1xu7paUlGDHjh14%0A+eWXAQAffvghVCqVyw3eoqIiFBUVOR6npqZ6E4uIKGht377d8euEhAQkJCQ4fb1TZ/XcdNNN+Omn%0An3Dp0iXYbDbk5eW5/UkhISEBqampjv+aBlciJedXcnaA+UVjfrG2b9/uVEubF33Ai8J/8OBBzJw5%0AEyUlJVi6dCmWLFkC4Nq6/tKlSwEAGo0GaWlpWLx4MebNm4ekpCRu7BIRCebx5u6QIUMwZMiQFscb%0A2zYbDRo0CIMGDfL0aYiIyMdkO5bZ1Y8nSqLk/ErODjC/aMwvVnvye725S0REyiLbK34iIuocLPxE%0AREFGdtM5lTzU7Z133kFhYSGio6OxYsUK0XE67MqVK1i7di0qKiqgUqkwevRo3HvvvaJjtVtdXR1e%0AffVV1NfXw2azYfDgwZg0aZLoWB1it9uRmZkJg8GAzMxM0XE6bPbs2YiIiIBarYZGo3F0+CmB2WzG%0A+vXr8f333wMAZs6ciV/96leCU7VPaWkpVq1a5Xh88eJFPPbYY+7//Uoy0tDQIL3wwgvSxYsXpfr6%0Aeun3v/+99N1334mO1W4nT56Uvv32W+m3v/2t6CgeuXr1qnTu3DlJkiSptrZWmjNnjqJef0mSJIvF%0AIkmSJNlsNmnBggVScXGx4EQd8/HHH0urV6+Wli1bJjqKR2bNmiVVVVWJjuGRNWvWSHv27JEk6drf%0AH7PZLDiRZxoaGqRnnnlGunz5sttzZLXU03Som1ardQx1U4p+/fpBp9OJjuGx2NhY/OIXvwAAhIeH%0Ao2fPnrh69arYUB0U9vNcGpvNBrvdjqioKMGJ2q+srAyFhYUYNWoUJAX3XCgxe01NDb755huMGjUK%0AwLV7kCLdjKeQu+PHj6N79+7o2rWr23NktdTDoW7ycenSJZw/fx4333yz6CgdYrfbkZGRgYsXL2Lc%0AuHGKumFwy5YtmDx5MmrdjDhWApVKhezsbKjVaowZMwZjFDKk7dKlS4iOjsY777yDCxcuoG/fvpg2%0AbZrjQkJJ9u/fj2HDhrV6jqyu+EkeLBYLVq5ciaeffhrhCpunrlarkZOTg/Xr16O4uNhp/pOcHT58%0AGNHR0ejbt68ir5gbZWdn4/XXX8eCBQuwa9cuFBcXi47ULg0NDTh37hzGjRuH5cuXIzw8HDt37hQd%0Aq8NsNhsOHz6MO++8s9XzZFX4DQYDysrKHI/LyspgMBgEJgo+NpsNK1aswPDhw13ema0UkZGRGDRo%0AEM6ePSs6SrucOnUKhw8fxuzZs7F69WoUFRXh7bffFh2rw7p06QIAiI6OxpAhQxTzE3tcXBwMBgN+%0A+ctfAgDuuOMOnDt3TnCqjissLMSNN96I6OjoVs+TVeHvyFA38j1JkrB+/Xr07NkT9913n+g4HVZZ%0AWQmz2QzgWofP8ePH0bdvX8Gp2mfSpElYt24d1q5di7lz5yIhIQEvvPCC6FgdYrVaHctUFosFx44d%0AQ+/evQWnap/Y2Fh07doVpaWlAIBjx44papmw0f79+5GcnNzmebJa42861K2xnVNJL/6qVatQXFyM%0AqqoqzJw5E6mpqRg5cqToWO126tQpfPXVV+jduzfmz58P4FpBuu222wQnax+TyYS1a9fCbrdDkiTc%0AddddGDBggOhYHlGpVKIjdFhFRQVycnIAXNtrGTZsGAYOHCg4VftNmzYNa9asgc1mQ/fu3TFr1izR%0AkTrEYrHg+PHjeO6559o8lyMbiIiCjKyWeoiIqPOx8BMRBRkWfiKiIMPCT0QUZFj4iYiCDAs/EVGQ%0AYeEnIgoyLPxEREHm/wEVP/FL2qENYwAAAABJRU5ErkJggg==%0A" 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4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%0AYeEnIgoyLPxEREHm/wEVP/FL2qENYwAAAABJRU5ErkJggg==%0A" />
-<p>还可以混搭使用多种风格，不过最右边的一种风格会将最左边的覆盖：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">use</span><span class="p">([</span><span class="s1">&#39;dark_background&#39;</span><span class="p">,</span> <span class="s1">&#39;ggplot&#39;</span><span class="p">])</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="s1">&#39;r-o&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
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-<img 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4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%0AYeEnIgoyLPxEREHm/wEVP/FL2qENYwAAAABJRU5ErkJggg==%0A" 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4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%0AYeEnIgoyLPxEREHm/wEVP/FL2qENYwAAAABJRU5ErkJggg==%0A" />
-<p>事实上，我们还可以自定义风格文件。</p>
-<p>自定义文件需要放在 matplotlib 的配置文件夹 mpl_configdir 的子文件夹 mpl_configdir/stylelib/ 下，以 .mplstyle 结尾。
-mpl_configdir 的位置可以这样查看：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib</span>
-<span class="n">matplotlib</span><span class="o">.</span><span class="n">get_configdir</span><span class="p">()</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="sa">u</span><span class="s1">&#39;c:/Users/Jin</span><span class="se">\\</span><span class="s1">.matplotlib&#39;</span>
-</pre></div>
-</div>
-<p>里面的内容以 属性：值 的形式保存：</p>
-<p>axes.titlesize : 24</p>
-<p>axes.labelsize : 20</p>
-<p>lines.linewidth : 3</p>
-<p>lines.markersize : 10</p>
-<p>xtick.labelsize : 16</p>
-<p>ytick.labelsize : 16</p>
-<p>假设我们将其保存为 mpl_configdir/stylelib/presentation.mplstyle，那么使用这个风格的时候只需要调用：</p>
-<blockquote>
-<div><p>plt.style.use(‘presentation’)</p>
-</div></blockquote>
-</section>
-<section id="id1">
-<h1>作者 &amp; 更新时间<a class="headerlink" href="#id1" title="Permalink to this heading">¶</a></h1>
-<p>作者:<cite>李金  &lt;lijinwithyou&#64;gmail.com&gt;</cite></p>
-<p>提交: 2017/12/14</p>
-</section>
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diff --git a/docs/aipy-numpy/cn/3text-base.html b/docs/aipy-numpy/cn/3text-base.html
deleted file mode 100644
index eef1cfc..0000000
--- a/docs/aipy-numpy/cn/3text-base.html
+++ /dev/null
@@ -1,478 +0,0 @@
-<!DOCTYPE html>
-<html lang="en-US">
-<head>
-<meta charset="utf-8" />
-<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
-<link href="https://cdn.bootcss.com/bootstrap/3.3.7/css/bootstrap.min.css" rel="stylesheet">
-<title>处理文本（基础）</title>
-<link rel="stylesheet" type="text/css" href="../../static/pygments.css" />
-<link rel="stylesheet" type="text/css" href="../../static/style.css" />
-<meta charset="UTF-8">
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-</head><body>
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-<a href="http://www.qpython.org/index.html">Home</a>
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-<span class="header-title-selected"></span>
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-<li>
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-<a target="_blank" href="https://github.com/qpython-android/qpython">Github</a>
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-<section id="id1">
-<h1>处理文本（基础）<a class="headerlink" href="#id1" title="Permalink to this heading">¶</a></h1>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="o">%</span><span class="n">matplotlib</span> <span class="n">inline</span>
-</pre></div>
-</div>
-<p>matplotlib 对文本的支持十分完善，包括数学公式，Unicode 文字，栅格和向量化输出，文字换行，文字旋转等一系列操作。</p>
-<section id="id2">
-<h2>基础文本函数<a class="headerlink" href="#id2" title="Permalink to this heading">¶</a></h2>
-<p>在 matplotlib.pyplot 中，基础的文本函数如下：</p>
-<ul class="simple">
-<li><p>text() 在 Axes 对象的任意位置添加文本</p></li>
-<li><p>xlabel() 添加 x 轴标题</p></li>
-<li><p>ylabel() 添加 y 轴标题</p></li>
-<li><p>title() 给 Axes 对象添加标题</p></li>
-<li><p>figtext() 在 Figure 对象的任意位置添加文本</p></li>
-<li><p>suptitle() 给 Figure 对象添加标题</p></li>
-<li><p>anotate() 给 Axes 对象添加注释（可选择是否添加箭头标记）</p></li>
-</ul>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span># -*- coding: utf-8 -*-
-import matplotlib.pyplot as plt
-%matplotlib inline
-# plt.figure() 返回一个 Figure() 对象
-fig = plt.figure(figsize=(12, 9))
-# 设置这个 Figure 对象的标题
-# 事实上，如果我们直接调用 plt.suptitle() 函数，它会自动找到当前    的 Figure 对象
-fig.suptitle(&#39;bold figure suptitle&#39;, fontsize=14,     fontweight=&#39;bold&#39;)
-# Axes 对象表示 Figure 对象中的子图
-# 这里只有一幅图像，所以使用 add_subplot(111)
-ax = fig.add_subplot(111)
-fig.subplots_adjust(top=0.85)
-# 可以直接使用 set_xxx 的方法来设置标题
-ax.set_title(&#39;axes title&#39;)
-# 也可以直接调用 title()，因为会自动定位到当前的 Axes 对象
-# plt.title(&#39;axes title&#39;)
-ax.set_xlabel(&#39;xlabel&#39;)
-ax.set_ylabel(&#39;ylabel&#39;)
-# 添加文本，斜体加文本框
-ax.text(3, 8, &#39;boxed italics text in data coords&#39;,     style=&#39;italic&#39;,
-    bbox={&#39;facecolor&#39;:&#39;red&#39;, &#39;alpha&#39;:0.5, &#39;pad&#39;:10})
-# 数学公式，用 $$ 输入 Tex 公式
-ax.text(2, 6, r&#39;an equation: $E=mc  &#39;, fontsize=15)
-# Unicode 支持
-ax.text(3, 2, unicode(&#39;unicode: Institut f\374r Festk    \366rperphysik&#39;, &#39;latin-1&#39;))
-# 颜色，对齐方式
-ax.text(0.95, 0.01, &#39;colored text in axes coords&#39;,
-    verticalalignment=&#39;bottom&#39;, horizontalalignment=&#39;right&#39;,
-    transform=ax.transAxes,
-    color=&#39;green&#39;, fontsize=15)
-# 注释文本和箭头
-ax.plot([2], [1], &#39;o&#39;)
-ax.annotate(&#39;annotate&#39;, xy=(2, 1), xytext=(3, 4),
-    arrowprops=dict(facecolor=&#39;black&#39;, shrink=0.05))
-# 设置显示范围
-ax.axis([0, 10, 0, 10])
-plt.show()
-</pre></div>
-</div>
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C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%0A" 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C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%0A" />
-</section>
-<section id="id3">
-<h2>文本属性和布局<a class="headerlink" href="#id3" title="Permalink to this heading">¶</a></h2>
-<p>我们可以通过下列关键词，在文本函数中设置文本的属性：</p>
-<p>其中 va, ha, multialignment 可以用来控制布局。</p>
-<ul class="simple">
-<li><p>horizontalalignment or ha ：x 位置参数表示的位置</p></li>
-<li><p>verticalalignment or va：y 位置参数表示的位置</p></li>
-<li><p>multialignment：多行位置控制</p></li>
-</ul>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>import matplotlib.pyplot as plt
-import matplotlib.patches as patches
-# build a rectangle in axes coords
-left, width = .25, .5
-bottom, height = .25, .5
-right = left + width
-top = bottom + height
-fig = plt.figure(figsize=(10,7))
-ax = fig.add_axes([0,0,1,1])
-# axes coordinates are 0,0 is bottom left and 1,1 is upper right
-p = patches.Rectangle(
-    (left, bottom), width, height,
-    fill=False, transform=ax.transAxes, clip_on=False
-    )
-ax.add_patch(p)
-ax.text(left, bottom, &#39;left top&#39;,
-    horizontalalignment=&#39;left&#39;,
-    verticalalignment=&#39;top&#39;,
-    transform=ax.transAxes,
-    size=&#39;xx-large&#39;)
-x.text(left, bottom, &#39;left bottom&#39;,
-    horizontalalignment=&#39;left&#39;,
-    verticalalignment=&#39;bottom&#39;,
-    transform=ax.transAxes,
-    size=&#39;xx-large&#39;)
-ax.text(right, top, &#39;right bottom&#39;,
-    horizontalalignment=&#39;right&#39;,
-    verticalalignment=&#39;bottom&#39;,
-    transform=ax.transAxes,
-    size=&#39;xx-large&#39;)
-ax.text(right, top, &#39;right top&#39;,
-    horizontalalignment=&#39;right&#39;,
-    verticalalignment=&#39;top&#39;,
-    transform=ax.transAxes,
-    size=&#39;xx-large&#39;)
-ax.text(right, bottom, &#39;center top&#39;,
-    horizontalalignment=&#39;center&#39;,
-    verticalalignment=&#39;top&#39;,
-    transform=ax.transAxes,
-    size=&#39;xx-large&#39;)
-ax.text(left, 0.5*(bottom+top), &#39;right center&#39;,
-    horizontalalignment=&#39;right&#39;,
-    verticalalignment=&#39;center&#39;,
-    rotation=&#39;vertical&#39;,
-    transform=ax.transAxes,
-    size=&#39;xx-large&#39;)
-ax.text(left, 0.5*(bottom+top), &#39;left center&#39;,
-    horizontalalignment=&#39;left&#39;,
-    verticalalignment=&#39;center&#39;,
-    rotation=&#39;vertical&#39;,
-    transform=ax.transAxes,
-    size=&#39;xx-large&#39;)
-ax.text(0.5*(left+right), 0.5*(bottom+top), &#39;middle&#39;,
-    horizontalalignment=&#39;center&#39;,
-    verticalalignment=&#39;center&#39;,
-    fontsize=20, color=&#39;red&#39;,
-    transform=ax.transAxes)
-ax.text(right, 0.5*(bottom+top), &#39;centered&#39;,
-    horizontalalignment=&#39;center&#39;,
-    verticalalignment=&#39;center&#39;,
-    rotation=&#39;vertical&#39;,
-    transform=ax.transAxes,
-    size=&#39;xx-large&#39;)
-ax.text(left, top, &#39;rotated\nwith newlines&#39;,
-    horizontalalignment=&#39;center&#39;,
-    verticalalignment=&#39;center&#39;,
-    rotation=45,
-    transform=ax.transAxes,
-    size=&#39;xx-large&#39;)
-ax.set_axis_off()
-plt.show()
-</pre></div>
-</div>
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-<img 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W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%0A/g8NfX/IA8ubvAAAAABJRU5ErkJggg==%0A" 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W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%0A/g8NfX/IA8ubvAAAAABJRU5ErkJggg==%0A" />
-</section>
-<section id="id4">
-<h2>注释文本<a class="headerlink" href="#id4" title="Permalink to this heading">¶</a></h2>
-<p>text() 函数在 Axes 对象的指定位置添加文本，而 annotate() 则是对某一点添加注释文本，需要考虑两个位置：一是注释点的坐标 xy ，二是注释文本的位置坐标 xytext：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>fig = plt.figure()
-ax = fig.add_subplot(111)
-t = np.arange(0.0, 5.0, 0.01)
-s = np.cos(2*np.pi*t)
-line, = ax.plot(t, s, lw=2)
-ax.annotate(&#39;local max&#39;, xy=(2, 1), xytext=(3, 1.5),
-    arrowprops=dict(facecolor=&#39;black&#39;, shrink=0.05),
-    )
-ax.set_ylim(-2,2)
-plt.show()
-</pre></div>
-</div>
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-<img 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m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%0ARVGUgPP/Qde6gvF4TtQAAAAASUVORK5CYII=%0A" 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m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%0ARVGUgPP/Qde6gvF4TtQAAAAASUVORK5CYII=%0A" />
-<p>在上面的例子中，两个左边使用的都是原始数据的坐标系，不过我们还可以通过 xycoords 和 textcoords 来设置坐标系（默认是 ‘data’）：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>fig = plt.figure()
-ax = fig.add_subplot(111)
-t = np.arange(0.0, 5.0, 0.01)
-s = np.cos(2*np.pi*t)
-line, = ax.plot(t, s, lw=2)
-ax.annotate(&#39;local max&#39;, xy=(3, 1),  xycoords=&#39;data&#39;,
-    xytext=(0.8, 0.95), textcoords=&#39;axes fraction&#39;,
-    arrowprops=dict(facecolor=&#39;black&#39;, shrink=0.05),
-    horizontalalignment=&#39;right&#39;,     verticalalignment=&#39;top&#39;,
-    )
-ax.set_ylim(-2,2)
-plt.show()
-</pre></div>
-</div>
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-<img 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F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%0A/wdcAmhbrixx1QAAAABJRU5ErkJggg==%0A" 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F%0AsmeesUoi71FRwdyxY8u1kdpa5osvljIvv+ysfU5y7Bhzerqc54YN0ctUV0tIB2B+6y1n7XOSgwfN%0AGmxpafQyp08z9+olWixd6qx9TrJrl1mDNcKaTYl8Kv7kE0fNc5StW+Uc09PlGokkFkfvuXR3ycnA%0AAw/I8tNPu2uLnSxYAFRWAtOnA1OmRC/ToQNw//2yHGQt/vY36To3e3bzvUnS04F//VdZDrIWL7wA%0A1NdLL6zhw6OX6dgR+NGPZDnIWjz7LMAMfOc7QE5O9DJduwI//KEsB1kL49zuuAPo0yeOHbT2T+DU%0AC+EaPbM0tBj/0k3jckHB6DXQWk29osKMQwaxB04oxDx8eGw19aNHmVNTpZa3d68z9jlJQ4OELAHm%0AJUtaLnvgAHNystT+Dx92xDxHqa2VXmix1NR37TJruydOOGOfk1RVMV9wQfP+EH6s0QNARgZw++2y%0A/Mwz7tpiB+vXy6tbN+Cb32y5bOfOUqMBgqnFp58CZWUyUO6661ou26MHMGeO1PKee84Z+5zkww+B%0AvXul9jpzZstl+/YVverrgf/9Xyesc5b33pMBlCNGAFOntlx20CDRq6YGeOklZ+xzkjffBE6eBMaP%0AB8bFmSnMk44eAP75n+X9//5PLuYgsWCBvN92m4QkWsPQ4tVXgYgstYHA0OJ735NQVWsYWrzyijj8%0AIGFoMXcuEMscIpFaBA1Di7vuAqjFvIxCe9Eiblqr8jv1QkTohlke6YcMkceVjz5K5MHHW4RCZhfS%0AlStj+05Dg9nV8rPP7LXPSRoamC+8UM6ruDi279TVMXfvLt/ZvNle+5ykttZ8PI/sOtcS1dXMnTrJ%0Ad3btstc+J6mqMsOVzTXCNuXUKbNBv2ljpZ+pqDDDlYcORS8Dv4ZuAPkXnzNHlt94w11brKSkBNi9%0AG+jdu/VHUoOkJDPEEyQt1qwBDh2SUEV+fmzfSUmRhkogWFoUFsrj+YgR8oqF9HRJFwEES4ulS4Ez%0AZyRU0VwjbFM6dQKuvlqW33rLNtMc5913Jd/RpZcmlgfMs44eMB39m28GJ2Rh3JA33yw9jGIl8k8v%0AKCELQ4tvfjO2x3ODIFYAjHMxzi1WVAsT1aJ5PD3DFLP8o+/bBxQXyz+838nLAzZulFrLrFmxf6+h%0AAbjwQuDoUWDbtthrfV5m6FBgxw7gk0+AadNi/15trWT2PHUK2LNHMlz6GWagf3/g4EFppG9Lg1tV%0AFdC9uzREHjkC9Opln51O0NAgT7vHj0sj/bBhsX+3vFyuC2b5fpcu9tnpBLW10mHjzBlppB8wIHo5%0AR2aYshMi6VsNAIsXu2uLFRw6JE4+M1P6z7eF5GTgqqtkOQhafPmlOPmuXYFJk9r23dRU4MorZXnJ%0AEuttc5qtW8XJZ2e3PStlZiYwY4YsL11qvW1O8/nn4qQvukgqAm2ha1cZk9LQACxfbo99TvLpp+Lk%0Ac3Obd/Kx4mlHD5hxtyDc0MaNWFAApKW1/fvGn14QtDDOYeZMibu3FeO6CMKfnnEOV13VthCWQRC1%0AuPrqxLQI0j0yu9n5+2LH847+yiulNrt6NVBR4bY1iWFcxPH+cEaNfsUKoLraGpvcIlEtjBt6+XKZ%0AqMTPJHpDG99butT/bVlWabF4sf/bshK9RyLxvKPv0sV8HPvoI7etiZ9QCFi2TJYNJ9VWevWS3ik1%0ANcDKldbZ5jR1deZvGa8WOTkSvz11qvEkJX6jqkp+S6K2tdlEMny4xPiPHgU2bLDWPicpL5ffMiUF%0AuPzy+PYxbpzE6fftA774wlr7nOTwYQnzZmRIj5tE8byjB8wboLDQVTMSYssWiT0OGAAMGRL/foKg%0AxeefS56f4cNl2sR4CYIWa9ZIo9u4cTLyNx4i/yT8rMWqVVIhmjxZRoTHQ1KS2X7jZy2M2eamT49t%0AUGVr+MLRG41Nfq7FGrbPmBFf7NEgaFokgmpholqYqBbn4wtHP3Gi9LTYuFEe7/yI8cO1tbdNU6ZO%0AlVrLunXy2O9HrNLCmE939Wr/xumt0sL4vlEr9iNWa7FypX/j9FZpYeALR5+RAVxyifxon37qtjVt%0Ah9n84RKd7LtLF2DsWHFsRUWJ2+Y0DQ3Sbx5IXIvevSVOX1UVOYG2fzh71mxfSDQOO3CgxOlPnpTu%0Amn6jslJCesnJzaftjpURI2RswcGDMgrdbxw/LqHetDRgwgRr9ukLRw80/pf2Gzt2mINZ2to3OBp+%0A1mLzZuk9lZMjjilR/KxFcbE0rOfmxh+fNyDytxZr1kglID9f0hkkApFZifCjFkZFaPLk+LphR0Md%0AvQNEPoYlEp83CIoWVqBamKgWJqpFYxJ29EQ0m4jKiGgHET0QZXsBEVUQ0Ybw6xfxHMeITRcXy2gx%0AP2H1D2fUVoweG34isjeBFUTGphsarNmnU9jp3PwWm7ZLC+N68xN2OPpEUwsnA9gJIAdABwAlAEY0%0AKVMAYGEM+2o1Zef48ZKG9MMPY03y6Q2MWYNKSqzb54gR3OJ8s14kFGLu0UPs3r7duv3aoa/d1NWZ%0AKYYPHLBmn6EQc8+e1utrN9XVzGlpYvfx49bsM1Lf/fut2acTnDol8wWnpDBXVsb2HTiQpngigJ3M%0AvIeZ6wC8AuAbUcpZELAwa7J+apA9eFASEnXpAowaZd1+/ajFjh3AsWPSiDp4sHX79aMWW7YAp0/L%0A7Eh9+1qzz8jYtJ+0WL9eGqZHjZIkXlaQkmKmAV+92pp9OkFRkfSays8HsrKs22+ijr4vgP0Rnw+E%0A10XCAKYS0UYiep+IRsZ7sMmT5d1PvU0MWydObFta4tbwsxaTJ1vTVmHgdy2sRLUwUS1M4kgn1YhY%0AIoHrAfRn5ioiugbA2wCi9j2ZN2/eueWCggIUFBQ02m5kOSwqkhiklc7CLowfrq0ZGlsjUgu/oFqY%0AqBYmqoVJLFoUFhaisK3DfluL7bT0AjAZwOKIzw8CeKCV7+wG0C3K+lZjUaEQc69eEnfbuTO2+JXb%0AzJgh9i5aZO1+GxqYO3eWfX/1lbX7tosJE8Te5cut3e/Zs2aM98QJa/dtFyNH2jM1ZGUlc3KyvM6c%0AsXbfdpGTI1ps2mTtfo8dk/2mp8tUjV4nXv8GB2L0xQCGEFEOEaUC+DaAhZEFiKg3kdS9iWgiZLKT%0AE/EcjMhf/9INDdJLCJDQjZUkJckgMsAfWtTUyMhmIusGgRikpppTEa5bZ+2+7eDUKaC0VCZDb2v+%0A+dbIypJYd0ODPwaRff21TB6TlQWMjDuoG53u3aUtqKZGxm94nb17RY/u3aXtxkoScvTMXA/gPgBL%0AAGwD8CozlxLRPUR0T7jYLQA2E1EJgD8C+E4ixzQcph+c29at0hU0J8eemX/8pMWGDTKad8SI+BNW%0AtYSftFi3TkKPeXnWDYiJxE9aGDZOmGBtG5aBH7WYONH6sHTC/eiZ+QNmHsbMg5n5t+F1TzHzU+Hl%0APzPzKGbOY+apzJxQUlk/1ejtij0aqBYmqoWJamGiWgi+GRlrYIQrNmyQLllexqmLeN067w8WWrtW%0A3u3WYu1a7w8WclILr+PUPdLetfCdo+/aVfKY19ZKzNfL2H0RZ2dLfvvKSon5ehm7tbjoIskXc/So%0AxHy9CrP9WowYAXTsKDHfI0fsOYYVhEL2/+nl5UkbTlmZt2eoq6sz21Ssbs8DfOjoAX88jlVWSow+%0AJUUmlbALP2hx7JhMBp6Zae2gsUj80lB/4IDMHtStm7WDxiJJTvZHQ/327dIw3bevdYPGmpKWJs6e%0A2dsN9Zs3S6PxkCHWDRqLxJeO3riIvdyroKRELq5RoyTNsl34QQvDtry8+CYCjxU/aPH55/I+fry9%0A40D8pIXVvbCaolr41NEbXem8/MMZc3cattqFamHiBy0M21QLvS4isVsLXzr6sWOlH/nWrUB1tdvW%0ARMepG9oIC23cCNTX23useHHDuXm1QdZp52bUFL2I09dFe9bCl44+M1ManBoavDsQwqmLuFs3aYis%0ArpYGJy/ilBZ9+kjCtJMnvdsg65QWF18sE3h89ZW0CXgNZue0yM2VBtmdO73ZIFtfb3Yssas9z5eO%0AHvD241hNjTxtJCUBY8bYfzwva1FRITdYaqr1Ix+bQuRtLQ4fFsfbubP1Ix+bkpRkamE8RXiJ3bvl%0A2ujdG7jwQnuP1aGDeR+WlNh7rHgoKxOfcdFFwAUX2HMMdfQ2sHmzPG0MH25tqtHm8LIWxo01Zozc%0AcHbj5cd0w+GOGyeO2G68rEVkbd6J5IRevkeceLLxraMfP17evX4RO4FqYWJo0V5v6Ejau3OLpL3f%0AI7519EYyqM2bvTedntMXsRHX27BBBqF4CTedm9caZNXRm6gWJuroW6BTJ2DoUBlRtnWr29Y0xumL%0AuFcvoF8/SaC2Y4czx4wVp7UYMEAaqI8elcFJXsLQws4BdJEMGyYdF/buBY4fd+aYsRDZEOuUFqNG%0AyRiOsjJvzTkdCjUO6dmFbx094M3Hsbo6YNMmWbY6BW1LeFGLqiq5sZKTgdGjnTkmkTfDNydOSE+g%0AjAxxwE6QnGxeg17S4uBB+SPu2lUyuzpBero4e2ZvNcju3Cmj6Pv2lYZpu/C1o/fi41hpqYSSBg+W%0AeWKdwotabNokNZbcXLnRnMKLWhi1trFj7R0d3BQva+FUQ6yB17WwE3X0FuN0qMJAtTDxYm8Tt7Tw%0A4pOe29dFe7xHAuHovTQq1O0b2kuNkF7QwiuoczPR68JEHX0MdO0qA09qaryTptfpRiaDCy+UtMUV%0AFZIp0gu4pcWgQRI2O3RIXl7ALec2YoRkcNy1Cygvd/bYzeGWFmPGeCt1ipON0r529IC3aiwNDWZD%0Aj9PODfCWFmfPAlu2SAx27Fhnj01k6u8FLU6dkpS8HTpIe4WTRI4K9cII2a+/lt5QHTtKSl4n8Vrq%0AlH37pJG+Rw/pNWcnvnf0Xnoc27FDum717w/07On88b2kxdat0gNp6FDpCus0XtLCyGMyerSkgnAa%0AL2lh/Nnk5TkzOrgpXtLCydHBCUtNRLOJqIyIdhDRA82UeSK8fSMRWVrX9VLDm1uPpAaqhYmXtHCq%0AZ0VzeEkLvS5MnNQiIUdPRMkA/hvAbAAjAdxKRCOalLkWwGBmHgLg+wD+ksgxm2I8opeUuD8q1CsX%0A8YYN7jfIekkLt3GrrcJAtTDxohaed/QAJgLYycx7mLkOwCsAvtGkzI0AXgQAZi4C0JWILBsa0LOn%0AhEq8MCrUbefWvz/QvbtM3ef2qFC3tRgyRBLK7dsneriJ21oYo0K/+EIG57iJ21p4KXWKnxx9XwD7%0AIz4fCK9rrYylTQ9eaIRkdv8R3StpeuvrzdHBbtXcIkeFull7q64Gtm1zLmV1NNLSzFGhRnuBG5SX%0AS4+wtDRpFHUDI3VKba38Lm5x6JCkre7Sxf6U1QCQ6Bi9WAMETZsaon5v3rx555YLCgpQUFAQ087z%0A84F33hHnduutMVpkMXv2yIXsRH7tlsjPB5YtEy2+0fTZyiG++EIcnJ35tWMhPx/49FPRYtYsd2ww%0AUlbn5kqvD7fIz5fw5vr1wLRp7tjgdMrq5sjPl15Q69c7m6Ykksj8Nm1tiC0sLERhYWGbvpOooz8I%0AoH/E5/6QGntLZfqF151HpKNvC16oxTqdX7s5vKaFm6gWJvn5wPPPqxbG8V95Rey56y53bEhEi6aV%0A4F/96letfifR0E0xgCFElENEqQC+DWBhkzILAXwPAIhoMoByZj6S4HEbEdln2q1GSK9cxF7oP65a%0AmHhFC/3TM2mP10VCjp6Z6wHcB2AJgG0AXmXmUiK6h4juCZd5H8CXRLQTwFMA7k3Q5vPo00dS9ZaX%0AuzdXqNu9CQy8MFeoV7QYOdL9uUK9okXkqNCaGnds8IoWkT31GhrcscFpLRLuR8/MHzDzMGYezMy/%0ADa97ipmfiihzX3j7WGa2/H/U7UZIZrNfrjEgwy2SkhpPROI0kfm13a65RY4KdaMRsrbWHIHptnPL%0AypKpLRsaZMSy05w5IymrU1KcS1ndHN27AwMHSjvSF184f/zjx2WOgMxM51JW+35krIGbjt7Ir33B%0ABXIBuY2bWuzcCZw+bX9+7VhxU4utW8XZDxkiE4K7jZtalJRIhcjplNXN4aYWxjHz8qR3mBOoo7cA%0ArzTEGnhFCy+gWpi4GZv2mhbt7boIpKN3ukHW+OHcDtsYeOEiVi1Ui0hUCxM3tAiMo8/JkbTFX3/t%0AfGpaIz7vldrKsGEyZd2ePcDJk84e22tajB4tj8elpTK1oZN4rRZr9BnftEkSzjmJ17SITIXgdOoU%0AN+6RwDiVYPfDAAAVx0lEQVR6NxtkvXYRp6SYqYGdbJCNzK/tFS3S0yUuHAqZo3WdoL7ebAB2uyHW%0AoGtX6ZV19qyz8zdUV0t7RVKS8ymrmyM7WwY2njoF7N7t3HHLy2VuAKdHBwfG0QPuOPrDh6UrY6dO%0AchN5BTe0MEYH9+olXV69ghtalJWZo4O7dXPuuK3hhhbG6OARI9wdHdwUN7Rwa3RwoBy9G41NkTVY%0AN/JrN4cbWkR2MfVCo7SB29eFl3DDuXlVC7fvESfxkGtKHL2ITVQLE9XCRLUwaU9aBMrRG6lp9++X%0Afu1O4NWLODdXHg23b5d+7U7gVS3GjpUnjC1bnEtN65UBdE1xY/4Gr2rhRk89dfQW4EZqWq9exE6n%0ApvXS6OCmGKlp6+qkUdBuIkcHe6Uh1sDp+RsiRwe7lSmyOQYMkPYTp+ZvOH1aRuJ26CD3ppMEytED%0Azj6OHTsmE1tkZooj8RpOanHggOjRrZvcQF7DSS2MuYP79ZOGaa/hpBZuzx3cEk731Nu4USpEo0ZJ%0ARcxJ1NEnQOREx04NZW4LTmrhtdHBTXFSC68+2RioFiZu3SNOo44+Abw2OKgpqoVJe7mhY0G1MGkv%0A90jgHP2IEfJYtGuX9Om2E68N626KkZp22zbp020nXtfCiJVv3CiDmezET87N7kZIP2lhN27eI4Fz%0A9JGpaY3BCXbh9Ys4M1P++BoazAYxu/C6FhdcIIOX7E5NGzk62Kt/ehde6Mz8DZGjg716XTg1f0NV%0AlVS4kpPdmTs4cI4eMGtvxqOSHbg1lLmtOKHFoUPy6tzZmYmO48UJLb78UiY5cXvu4JaIbIS0U4vS%0AUpnk5KKLJP2CF0lKMnsD2Vmr37RJemMNHy55qJwmkI7+kkvkfe1a+45h7HvcOHcnOm4NJ7QoKpL3%0ACRO8NTq4KU5qMXGifcewAtXCpD1o4eHbMn4mTZJ3Q1w7MPZtHMurqBYmqoWJamHSHrQIpKMfORLo%0A2FGm6zpi6TTkJm7/cLGSlyfzppaV2Tdvql+0mDBBwhYbN9o3b6pftDBqlsXF9jVO+0ULw761a+1r%0AnHZbi7gdPRF1I6JlRLSdiJYSUdQoHBHtIaJNRLSBiGx8ODJJTpabGrDnX5rZ/R8uVtLSxNkzA+vW%0AWb//hgZzv17XolMnqQTU1dkzcvrsWXO/xvXnVXr2lNh5VZU9o4UrK2W/KSneGx3clAEDpE3lxAmZ%0ACtNqjh6VtpvMTOdHxBokUqP/NwDLmHkogOXhz9FgAAXMPI6ZHYtQ2fk4tnu3jALt0UNuFq9jpxal%0ApXJTDxggOb69jp1abNwoQ/6HD/du42MkdmpRXCyNj2PGuNP42BaI7NXCiP2PHy9/fG6QiKO/EcCL%0A4eUXAdzUQlnHx0ra+cNF1ua9OAq0KU5p4QdUCxPVwiToWiTi6HszsxEBPwKgdzPlGMCHRFRMRHcn%0AcLw2YYi6bp31WfqMf2i/XcR2xCD9qoWdNTe/aWFHbxMvOLe2YKcWXrguWnyQIKJlAKI9kP888gMz%0AMxE150KmMfMhIuoJYBkRlTHzqmgF582bd265oKAABQUFLZnXIn36SFKpAwdkgIyVfd39dhFffDHQ%0Avbs0TO/bBwwcaN2+/aZFbq7ESnfvlthpz57W7dtvWhhdg7dulcyKViYd85sWl1wiT+clJdLWYlXS%0AMWbrHX1hYSEKCwvbagjH9QJQBiA7vHwhgLIYvvMwgJ82s42tZs4cZoD5hRes2+fZs8xpabLfkyet%0A26/dXHON2Pzqq9bts7KSOTlZXmfOWLdfu5k+XbRYtMi6fR4/LvtMT2eurbVuv3YzYYLY/dFH1u1z%0A/37ZZ5cuzA0N1u3XbkaOFLs/+8y6fX7xhezzwguZQyHr9htJ2He26HsTCd0sBHBHePkOAG83LUBE%0AmUTUKbycBeAqADYPxjeZMkXeP/3Uun2uXy//+H5pcDOwQ4uiIul1M3ast+YCbQ07tFi9Wt4nTPD2%0AALqm2KGFsa9Jk7w9gK4pdmoxebK77XmJ/AyPAZhFRNsBXBH+DCLqQ0TvhctkA1hFRCUAigC8y8xL%0AEzG4LUyfLu8rV1q3T2NfM2ZYt08nUC1MVAsT1cIkyFrE3dmHmU8AmBll/VcArgsvfwnAtXllxo2T%0AqQW3b5eERVZ0/zN+OOOi8AsTJ8rAqY0bJU+PFU8jftVi2jSpXa1bJ/3IrXga8asWl10m76tXy/gC%0AK55G/KqFYe+qVdKBw4qnEa9o4aMHq7aTkgJMnSrLq6I2/7aNhgbgk09k2bhB/EJGhjQ4MVvzaFpb%0AC6xZI8uXXpr4/pykSxcJN9XVWdP7prJSkoMlJZmP/36hd2+Z/enMGWsGkR0/LnPzpqWZOWT8wsCB%0A0oHjxAnJNJkoBw7IQKnOnd3JWBlJoB09YO3j2ObNkkYgJ0fm3fQbVmpRXCxpBEaOlIFjfsNKLT77%0ATNII5Od7b7q8WLBSC6MiNGmS89PlJQqRtVoYlctp09yfgU4dfRvwymNYvKgWJqqFiWphElQtAu/o%0Ajdj05s3ySJYIXvrh4mHqVAkvFBfLo3oi+F0LI/S2Zo2EoRLB71o0jU0nQlC0WLky8cGFXtIi8I4+%0APV0eIxONTTN764eLh86dpYG6vl7CDfHi57YKg169pItsdXVik2/U1Jha+q2twmDgQMlVVF6e2Exk%0Ap09L9+PkZP+1VRgMHy6hyEOHZGKheDl6VOL86eneSHAXeEcPmF2bPvoo/n1s2yY/XnY2MHiwNXa5%0AgRVarF8vN/WgQdJ45Ves0OKzz2RcxahRMvrYr1ihhfFEMH68pAn3I0TWaLFihbxPmSIRBbdpF45+%0A1ix5X7Ik/n0sXizvV13lj0RmzWG1Fn5GtTBRLUyCqEW7cPRTpkhviNJSyfUSD8aPfvXV1tnlBtOn%0AS2+I9evlCSUegqLFlVdKmGHNGuDUqfj2YWgxe7Z1drmB4ZBWrJBwVjwERQvjuv7wQ+mC21aYvXeP%0AtAtH36GD3NRAfP/SVVUSnycy/+39SmamPJoyA8uWtf37J0+KY0xJAa64wnr7nKRrVxmaXl8f32P6%0A4cOSBCsjw79tFQa9e0v7TU1NfGNOdu+WgYlduvgnkVlz5OQAw4bJn3884yxKS6UPfe/eMl7DC7QL%0ARw+Y/6zGI1VbWLFC4rDjx1ub7dAtEtFi+XKJw06dKo27fseofcajxdJwMo+CAml08zuJaGFUoGbO%0AdG9yDStJ5B6JDNt4JdePR8ywH+MiXrq07fOFLlzYeB9+55pr5P2999r+aBpULRYtanvXwqBqsXBh%0A27sWBlmLtuJJLVpLb+nUCzakKW5Kfr6kDH3nndi/U1/P3KuXfG/DBvtsc5JQiHn4cDmnZcti/97Z%0As5J6FpD0q0EgFGIeMEDO6dNPY/9eZSVzRoZ8b98+++xzkniv9ZMnmTt0YE5KYv76a/vsc5Kamviu%0A9cOHmYmYU1OZy8vtsy8S2Jym2HfMmSPvr78e+3c+/RT4+mvpSuiVeFuiEJlavPFG7N9bvlxSQIwa%0AJflRgkC8WixeLI2Wkyb5Mx1GNJKTgZtvluW2aLFokTwZzpgRjNAmIB0WbrhBltuixdtvy9PQrFnS%0AXuEV2qWjX7gw9tGQxo88Z46/u1U2xdDirbdkAFQsRGoRJCIdfawhi6Br0ZbKUNC1aIuj96wWrVX5%0AnXrBgdANM/Po0fI49uabrZc9e9Z8lC0qst82JwmFmC++WM5t8eLWy585w9y1q5TfvNl++5ykoYG5%0ATx85t5UrWy9fXs6cmSnld+2y3z4nqa1l7t5dzq24uPXyX38tYYqkJOaDB+23z0mqqpg7dhQttm1r%0AvfyBA6JDSgrzsWP222cADd2cz513yvvTT7deduFCCdvk5vov5WprELVNi9dflyHyl1wioZsgkZQE%0AzJ0ry7Fo8Y9/SJfbggIJ6QWJDh2A22+X5Vi0+Nvf5Ol49myZpzlIZGQAt94qy88803r555+XBv2b%0AbvLgKOnW/gmcesGhGv2xY1IDIWLes6flslddJf/mf/qTI6Y5zsGDMt9rSgrzoUMtl730UtHimWec%0Asc1pvvxSzi8tTeZ/bY5QiDkvT8r+4x/O2eckW7fK+XXsyHz6dPPlQiHmYcOk7NtvO2efk6xbJ+fX%0AvTtzdXXz5errmQcOlLJLlzpmHjPHVqN33cGfM8QhR8/MfNttcuY/+1nzZbZt43OTPbd04/udm26S%0A8/z3f2++zOefx3bj+x3jj/2xx5ovs2pVbDe+35k2Tc7ziSeaL7NkCZ+b+LquzjnbnCQUYh43Ts7z%0AueeaL/fWW1LmooucnxBdHX0zGP/SGRnMX30Vvcwtt0iZH/7QMbNcYeVKOc9OnZqPK157rZT56U+d%0Atc1pPvhAzrNbN+aKivO3h0LMM2ZImV/+0nHzHOWNN+Q8s7OlfaYpoRDzJZdImd/+1nn7nOTvf5fz%0AzMmRdrumNDSYbX8t/THaha2OHsA/AdgKoAFAfgvlZgMoA7ADwAMtlLNZjsbcfLOc/b33nr+tuNis%0AzQetgSkaV1/d/BOOUYPt2DE4faSbIxQyQ1TRnnCMGuwFF0jf8SATCpnjTh5//PztRg22d28ZUxBk%0A6uuZR46U833yyfO3v/yybOvfX/rfO43djn44gKEAPm7O0QNIBrATQA6ADgBKAIxopqztgkSyebO0%0AkBMxr1hhrq+qMmOwQa/BGhhPOCkpzJ99Zq4/fZp5xIj2UYM1WLFCzjc1lbmkxFxfXs48aFD7qMEa%0ALF4s55uZyVxaaq4/epS5b18OdPtVU958U863c+fGPa0OHTJ75j37rDu2ORK6acXRTwGwOOLzvwH4%0At2bK2ipGNB58kM89qi9Zwrx/P/Ps2bJu0KBgx6Ob8uMfy3n36sX88cfSUH355bJu+HD5A2wv3H23%0AnHefPsyffCI3thGzzsuL/vgeVIz2rIEDpYvx9u3MEybIusmTgxubb0ooZEYBBg9mXr9eGq3HjJF1%0Al1/ufGzewAuO/hYAz0R8/i6AJ5spa6sY0airY77xRlEh8tWtW/D6irfG2bNmY2Tkq1ev4KQ7iJXq%0Aaubp08/Xom9f5t273bbOWSormSdNOl+LnJz2EdaMpLzcfNqPfA0ZwnzkiHt2xeLoW8wzR0TLAGRH%0A2fQQMy9q6bthYhxnKMybN+/cckFBAQoKCtry9TaTkiIj2R5/HPjrXyUF76xZwPz5wesf3RqpqTKU%0A/de/lj7Dp09L3+j582WaufZEerpkY/zVr4AXXpA+89dfD/z+98HrK94aWVmSwvmXvwT+/nfJ4nrT%0ATcDvfifTMbYnunSRdOUPPQQsWCAjyufMAf7zP4Fu3Zyzo7CwEIWFhW36DskfQvwQ0ccAfsrM66Ns%0AmwxgHjPPDn9+EECImR+PUpYTtUVRFKW9QURg5hYTtFg1Mra5gxQDGEJEOUSUCuDbAOJI/KkoiqLE%0AS9yOnohuJqL9ACYDeI+IPgiv70NE7wEAM9cDuA/AEgDbALzKzKWJm60oiqLESsKhG6vQ0I2iKErb%0AcTJ0oyiKongUdfSKoigBRx29oihKwFFHryiKEnDU0SuKogQcdfSKoigBRx29oihKwFFHryiKEnDU%0A0SuKogQcdfSKoigBRx29oihKwFFHryiKEnDU0SuKogQcdfSKoigBRx29oihKwFFHryiKEnDU0SuK%0AogQcdfSKoigBJ5E5Y/+JiLYSUQMR5bdQbg8RbSKiDUS0Nt7jKYqiKPGRksB3NwO4GcBTrZRjAAXM%0AfCKBYymKoihxErejZ+YyQCamjYGYCimKoijW40SMngF8SETFRHS3A8dTFEVRImixRk9EywBkR9n0%0AEDMvivEY05j5EBH1BLCMiMqYeVVbDVUURVHio0VHz8yzEj0AMx8Kvx8lorcATAQQ1dHPmzfv3HJB%0AQQEKCgoSPbyiKEqgKCwsRGFhYZu+Q8yc0EGJ6GMAP2Pmz6NsywSQzMyniSgLwFIAv2LmpVHKcqK2%0AKIqitDeICMzcYjtoIt0rbyai/QAmA3iPiD4Ir+9DRO+Fi2UDWEVEJQCKALwbzckriqIo9pFwjd4q%0AtEavKIrSdmyt0SuKoij+QB29oihKwFFHryiKEnDU0SuKogQcdfSKoigBRx29oihKwFFHryiKEnDU%0A0SuKogQcdfSKoigBRx29oihKwFFHryiKEnDU0SuKogQcdfSKoigBRx29oihKwFFHryiKEnDU0SuK%0AogQcdfSKoigBRx29oihKwFFHryiKEnASmRz8d0RUSkQbiehNIurSTLnZRFRGRDuI6IH4TVUURVHi%0AIZEa/VIAucw8FsB2AA82LUBEyQD+G8BsACMB3EpEIxI4ZrugsLDQbRM8g2pholqYqBZtI25Hz8zL%0AmDkU/lgEoF+UYhMB7GTmPcxcB+AVAN+I95jtBb2ITVQLE9XCRLVoG1bF6O8C8H6U9X0B7I/4fCC8%0ATlEURXGIlJY2EtEyANlRNj3EzIvCZX4OoJaZ/xGlHCduoqIoipIIxBy/LyaiuQDuBnAlM9dE2T4Z%0AwDxmnh3+/CCAEDM/HqWs/ikoiqLEATNTS9tbrNG3BBHNBnA/gBnRnHyYYgBDiCgHwFcAvg3g1ngM%0AVRRFUeIjkRj9kwA6AlhGRBuI6H8AgIj6ENF7AMDM9QDuA7AEwDYArzJzaYI2K4qiKG0godCNoiiK%0A4n1cHxmrA6pMiOh5IjpCRJvdtsVNiKg/EX1MRFuJaAsR/YvbNrkFEaUTURERlRDRNiL6rds2uQ0R%0AJYejCIvctsVNiGgPEW0Ka7G2xbJu1ujDA6q+ADATwEEA6wDc2l7DO0R0GYBKAH9j5tFu2+MWRJQN%0AIJuZS4ioI4DPAdzUjq+LTGauIqIUAJ8A+Bkzf+K2XW5BRD8BMB5AJ2a+0W173IKIdgMYz8wnWivr%0Ado1eB1RFwMyrAJx02w63YebDzFwSXq4EUAqgj7tWuQczV4UXUwEkA2j1xg4qRNQPwLUAngWgHThi%0A1MBtR68DqpQWCffYGgcZfd0uIaIkIioBcATAx8y8zW2bXOQPkN5+odYKtgMYwIdEVExEd7dU0G1H%0Ary3BSrOEwzavA/hxuGbfLmHmEDPnQdKMTCeiApdNcgUiuh7A18y8AVqbB4BpzDwOwDUA/l849BsV%0Atx39QQD9Iz73h9TqlXYOEXUA8AaAl5j5bbft8QLMXAHgPQAT3LbFJaYCuDEcm14A4Aoi+pvLNrkG%0AMx8Kvx8F8BYkFB4Vtx39uQFVRJQKGVC10GWbFJchIgLwHIBtzPxHt+1xEyLqQURdw8sZAGYB2OCu%0AVe7AzA8xc39mvgjAdwB8xMzfc9suNyCiTCLqFF7OAnAVgGZ767nq6HVAVWOIaAGA1QCGEtF+IrrT%0AbZtcYhqA7wK4PNx1bEN4JHZ75EIAH4Vj9EUAFjHzcpdt8grtOfTbG8CqiOviXWZe2lxhHTClKIoS%0AcNwO3SiKoig2o45eURQl4KijVxRFCTjq6BVFUQKOOnpFUZSAo45eURQl4KijVxRFCTjq6BVFUQLO%0A/wdcAmhbrixx1QAAAABJRU5ErkJggg==%0A" />
-</section>
-<section id="id5">
-<h2>极坐标系注释文本<a class="headerlink" href="#id5" title="Permalink to this heading">¶</a></h2>
-<p>产生极坐标系需要在 subplot 的参数中设置 polar=True：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>fig = plt.figure()
-ax = fig.add_subplot(111, polar=True)
-r = np.arange(0,1,0.001)
-theta = 2*2*np.pi*r
-line, = ax.plot(theta, r, color=&#39;#ee8d18&#39;, lw=3)
-ind = 800
-thisr, thistheta = r[ind], theta[ind]
-ax.plot([thistheta], [thisr], &#39;o&#39;)
-ax.annotate(&#39;a polar annotation&#39;,
-    xy=(thistheta, thisr),  # theta, radius
-    xytext=(0.05, 0.05),    # fraction, fraction
-    textcoords=&#39;figure fraction&#39;,
-    arrowprops=dict(facecolor=&#39;black&#39;, shrink=0.05),
-    horizontalalignment=&#39;left&#39;,
-    verticalalignment=&#39;bottom&#39;,
-    )
-plt.show()
-</pre></div>
-</div>
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-<img 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Y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%0A" 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Y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%0A" />
-</section>
-<section id="id6">
-<h2>作者 &amp; 更新时间<a class="headerlink" href="#id6" title="Permalink to this heading">¶</a></h2>
-<p>作者:<cite>李金  &lt;lijinwithyou&#64;gmail.com&gt;</cite></p>
-<p>提交: 2017/12/14</p>
-</section>
-</section>
-<div class="clearer"></div>
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-<div id="praise"><span>0</span> persons have rewarded</div>
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-<option value="Reward this course">Reward this course $ 1.49 USD</option>
-<option value="Reward this course">Reward this course $ 1.99 USD</option>
-<option value="Reward this course">Reward this course $ 2.99 USD</option>
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diff --git a/docs/aipy-numpy/cn/4text-math.html b/docs/aipy-numpy/cn/4text-math.html
deleted file mode 100644
index d147562..0000000
--- a/docs/aipy-numpy/cn/4text-math.html
+++ /dev/null
@@ -1,359 +0,0 @@
-<!DOCTYPE html>
-<html lang="en-US">
-<head>
-<meta charset="utf-8" />
-<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
-<link href="https://cdn.bootcss.com/bootstrap/3.3.7/css/bootstrap.min.css" rel="stylesheet">
-<title>处理文本（数学表达式）</title>
-<link rel="stylesheet" type="text/css" href="../../static/pygments.css" />
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-<section id="id1">
-<h1>处理文本（数学表达式）<a class="headerlink" href="#id1" title="Permalink to this heading">¶</a></h1>
-<p>在字符串中使用一对 $$ 符号可以利用 Tex 语法打出数学表达式，而且并不需要预先安装 Tex。在使用时我们通常加上 r 标记表示它是一个原始字符串（raw string）</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="o">%</span><span class="n">matplotlib</span> <span class="n">inline</span>
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># plain text</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;alpha &gt; beta&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
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2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%0A" 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2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%0A" />
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># math text</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;$\alpha &gt; \beta$&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
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-<img 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a%0AtWD8JupNwFJVrfWfZc9l06zF6xndKwGj66vvSHK4qm5bzBQXYpp1OAg8XlVPAU8l+SZwAdAt7tOs%0AxR8CnwKoqn9L8u/AKxndf/N8M3M3531Z5uhNT0lOZXTT0+ofztuA98PRO2CPe9NTAxPXIslLgVuB%0A91bVgS2Y46JMXIuqenlVvayqXsbouvtHmoUdpvv5+Efgj5JsS3IaozfPfrDgeS7CNGuxD7gEYHx9%0A+ZXADxc6y5PHzN2c65l7edPTUdOsBfBJ4MXADeMz1sNVdeFWzXmzTLkW7U3587EvyR3Ag8DTwE1V%0A1S7uU35PfBq4JckDjE5EP1ZVP92ySW+iJF8C3grsSHIQuI7RJbp1d9ObmCSpIf83e5LUkHGXpIaM%0AuyQ1ZNwlqSHjLkkNGXdJasi4S1JDxl2SGvp/zy4/4DuVo0MAAAAASUVORK5CYII=%0A" 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a%0AtWD8JupNwFJVrfWfZc9l06zF6xndKwGj66vvSHK4qm5bzBQXYpp1OAg8XlVPAU8l+SZwAdAt7tOs%0AxR8CnwKoqn9L8u/AKxndf/N8M3M3531Z5uhNT0lOZXTT0+ofztuA98PRO2CPe9NTAxPXIslLgVuB%0A91bVgS2Y46JMXIuqenlVvayqXsbouvtHmoUdpvv5+Efgj5JsS3IaozfPfrDgeS7CNGuxD7gEYHx9%0A+ZXADxc6y5PHzN2c65l7edPTUdOsBfBJ4MXADeMz1sNVdeFWzXmzTLkW7U3587EvyR3Ag8DTwE1V%0A1S7uU35PfBq4JckDjE5EP1ZVP92ySW+iJF8C3grsSHIQuI7RJbp1d9ObmCSpIf83e5LUkHGXpIaM%0AuyQ1ZNwlqSHjLkkNGXdJasi4S1JDxl2SGvp/zy4/4DuVo0MAAAAASUVORK5CYII=%0A" />
-<section id="id2">
-<h2>上下标<a class="headerlink" href="#id2" title="Permalink to this heading">¶</a></h2>
-<p>使用 _ 和 ^ 表示上下标：</p>
-<p>$alpha_i &amp;gt; beta_i$：</p>
-<p>r’$alpha_i &gt; beta_i$’</p>
-<p>$sumlimits_{i=0}^infty x_i$：</p>
-<p>r’$sum_{i=0}^infty x_i$’</p>
-<p>注：</p>
-<ul class="simple">
-<li><p>希腊字母和特殊符号可以用 ‘+ 对应的名字’ 来显示</p></li>
-<li><p>{} 中的内容属于一个部分；要打出花括号是需要使用 {}</p></li>
-</ul>
-</section>
-<section id="stacked-numbers">
-<h2>分数，二项式系数，stacked numbers<a class="headerlink" href="#stacked-numbers" title="Permalink to this heading">¶</a></h2>
-<p>$frac{3}{4}, binom{3}{4}, stackrel{3}{4}$：</p>
-<p>r’$frac{3}{4}, binom{3}{4}, stackrel{3}{4}$’</p>
-<p>$frac{5 - frac{1}{x}}{4}$：</p>
-<p>r’$frac{5 - frac{1}{x}}{4}$’</p>
-<p>在 Tex 语言中，括号始终是默认的大小，如果要使括号大小与括号内部的大小对应，可以使用 left 和 right 选项：</p>
-<p>$(frac{5 - frac{1}{x}}{4})$</p>
-<p>r’$(frac{5 - frac{1}{x}}{4})$’</p>
-<p>$left(frac{5 - frac{1}{x}}{4}right)$：</p>
-<p>r’$left(frac{5 - frac{1}{x}}{4}right)$’</p>
-</section>
-<section id="id3">
-<h2>根号<a class="headerlink" href="#id3" title="Permalink to this heading">¶</a></h2>
-<p>$sqrt{2}$：</p>
-<p>r’$sqrt{2}$’</p>
-<p>$sqrt[3]{x}$：</p>
-<p>r’$sqrt[3]{x}$’</p>
-</section>
-<section id="id4">
-<h2>特殊字体<a class="headerlink" href="#id4" title="Permalink to this heading">¶</a></h2>
-<p>默认显示的字体是斜体，不过可以使用以下方法显示不同的字体：</p>
-<p>$s(t) = mathcal{A}sin(2 omega t)$：
-s(t) = mathcal{A}sin(2 omega t)</p>
-<ul class="simple">
-<li><p>Tex 语法默认忽略空格，要打出空格使用 ‘‘</p></li>
-<li><p>sin 默认显示为 Roman 字体</p></li>
-</ul>
-</section>
-<section id="id5">
-<h2>音调<a class="headerlink" href="#id5" title="Permalink to this heading">¶</a></h2>
-</section>
-<section id="id6">
-<h2>特殊字符表<a class="headerlink" href="#id6" title="Permalink to this heading">¶</a></h2>
-<p>参见：<a class="reference external" href="http://matplotlib.org/users/mathtext.html#symbols">http://matplotlib.org/users/mathtext.html#symbols</a></p>
-<p><strong>例子</strong></p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>import numpy as np
-import matplotlib.pyplot as plt
-t = np.arange(0.0, 2.0, 0.01)
-s = np.sin(2*np.pi*t)
-plt.plot(t,s)
-plt.title(r&#39;$\alpha_i &gt; \beta_i$&#39;, fontsize=20)
-plt.text(1, -0.6, r&#39;$\sum_{i=0}^\infty x_i$&#39;, fontsize=20)
-plt.text(0.6, 0.6, r&#39;$\mathcal{A}\ \mathrm{sin}(2 \omega t)$&#39;,
-    fontsize=20)
-plt.xlabel(&#39;time (s)&#39;)
-plt.ylabel(&#39;volts (mV)&#39;)
-plt.show()
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<img 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T%0Apk2ZnsOWc2PHjqVJLj3N06dPZ/Xq1Zx88smZj/3xxx9s3ryZRo0aZT526NAh+vTpk+fvcscdd1C3%0Abl0+++wz2rdvT+PGjdmyZQsTJkygffv2/O9//zvi+G++gQ4dkmjWrBk//PBDcG+Yj6680pJGXCpo%0AJ0g03vCoI7x3b9XHH/fkVBHzwQeqHTr4HUXBbNmyRRs0aJDZmTx48GAVEX3nnXfyfN2iRYv0oosu%0AOuKxoUOHardu3Y547Pzzz9fu3btn3t+2bZuWKVNGL730Uh0yZEjm4/fcc4/WqFHjqHLOP/98ffvt%0At4P+fR588EEtUqSITps2LfOxF198UefNm5fn67p3764iolu2bFFV1TfffFNLlCih+/fvP+rYxYtV%0Aq1ZVTU9X7dOnj3bu3Dno+Pxy4IBquXKqGzb4HYnJDdYRXnjp6bHVn5Hh0kth/HjI1loS1Xr06MFT%0ATz2V2Zlcvnx5gKOafLKbP38+mzdv5q8ssxo7dOhA6dKljzgu+/3y5ctTvnx5Vq5cSffu3TMfr1On%0ADqtXr2bbtm1HHL9q1SrKlSsX1O+S0ener18/GjduDLhmsrVr11KvXr0jjt2+ffsR9wcOHEhycnJm%0AWRMnTqRRo0aUKFHiqHK++cZduYtAcnIyK1euDCo+PxUv7ppQR470OxLjJUsaAbNmuRFJtWv7HUnB%0AlC8PDRrAuHF+RxKcN954g+OPP55//OMfmY+VLVsW4Kg/3tmlpKSwefNmqlWrxj//+U9ee+019u3b%0Ax8AgN6g+99xzj7if0Sy2Z8+eIx7fuXNnUEnjwIEDXH/99fTo0YP//ve/mY+PGTOGdu3aHXHs8OHD%0Aj+pYL1WqFC1btqRo0aKAm2PSokWLHMsaPtwlDYDjjz+enTt35htfNLAmqvhjSSNg+HDo0MHvKAqn%0AQ4fY+GLOmDGD++67j9GjR1OsWLHMW9u2bQFYv359nq+vUqUKM2bMoHPnzqSmpnLPPfdQvXp1Pvnk%0Ak3zLFpEcr+BzOzY9PT3PY1SV7t27c8kll/D8888f8dy4ceO4INtKlyNGjOD8888/4rFVq1ZRp04d%0AABYuXMjmzZtzTBobNrjl8Fu2dPfT09MzmmWjXvv2bhj77t1+R2K8Ykkj4Ntv4fLL/Y6icK680sUf%0ABUsk5WrTpk106tSJYcOGMXr0aObNm5d5mxzY7u3XX3/N8xyzZ89GVXnzzTdZs2YNa9asoVOnTvTo%0A0YNDhw55Fmu5cuWOakrK7tFHH6VOnTr0798/87GM5UZWrVp1VOf+woUL6dq16xGPPfbYY5m1j4kT%0AJ1K0aNHMDvidO3eybt06wDXvtGvnmnvANXMF23zmt7Jl4cILIQb67U2QLGkAa9a4q7ksg19iSo0a%0AcOKJEFiaKCosXbqUq6++mgMHDrBlyxYuv/xyevXqRceOHaldu/YRtwsvvJDk5GTmzp3Lrl27cj3n%0AwoULGZZlYkq1atUYMmQISUlJ7Nixw7PYTznllDybyt59912KFCnCww8/fMTjPwa2VDx8+PARo5sG%0ADhzIrFmzMms66enp/Oc//6Fo0aKcHtirdcqUKdSvX5+SgQ0yXnnllcxmKzdq6u9ytm/fTs2aNUP/%0ARSOkQwdXkzfxoajfAUSD775z1egiRfyOpPCuvNJ9MZs39zsS56effuLrr78mOTmZpKQkbr/99jyH%0AoDZo0IBx48bRs2dP0tLSGDp06FFX66rKyy+/TPfu3alSpQoAa9eupVatWlSsWDHzuIMHDx5V88jp%0AsYz7Bw8ePOLxZs2asWjRohzjnDBhAg888ADt27fnhizLIKelpWVOKGzQoAE33XQTV111FWvWrGHR%0AokU0a9aMtm3b0rJlS6ZMmULx4sUza1jgEknGMN5Zs2ZRsmRJKleuzO7dbnvfrC1wixYt4uKLL871%0AvYw2V1wBjzwChw5F58q8poAKOtwqGm+EOOT20ktVP/kkpFP4bs4c1VNPdUMyo8Hu3bu1Xbt2mpyc%0ArI8HMY556NChWrJkSb3ssst06dKluR4zYMAAve+++7Rfv376yCOP6N13363r1q1TVdVJkyZpw4YN%0AVUS0RIkS2rp1ax0+fLg2aNBAk5KStHjx4tq8eXPdsGGDXnvttVq+fHlNSkrSU045RZ944onMcsaO%0AHat16tTJMYbk5GRNSkpSEdGkpKQjfv7Pf/6jqqrbt2/X1q1ba6lSpbR169a6bNkynTdvnp522mla%0AoUIFvfnmm3Xbtm1HnHfevHnatGlT7dOnjz777LOZj3/+uWrbtn8fd+jQIS1Tpky+w3mjzfnnq44f%0A73cUJjsKMeQ24fcI37sXKld2TVQx0kycI1W3nPvo0bE3AizaHDhwgKpVqzJ//nxOPPFEX2O58UbX%0AJ3Dnne7+Tz/9xO23355v/0+0efJJt3JBYM6liRK2R3ghTJjghqzGcsIAN37/kktg1Ci/I4l9JUqU%0A4O677+aVV17xNY70dHcRcMklfz/20ksv5TvTPBpdeql9NuNFwieN775zH+h40K6d7R3ulb59+zJq%0A1Cj+/PNP32L4+We3rE2NGu7+0qVLWbNmzVH7e8SCc86BnTvdNrUmtiV00lB1Q1Uvu8zvSLzRujVM%0An27bwHqhZMmSDB48mNtuu823ORGjRrkBGgD79++nV69efPzxx0g0blyfj6Qku6iJFwmdNBYscGPf%0Aa9XyOxJvlCkD55/vtqs1oWvYsCE9evQIesa510aPdn9owe3vMWDAAE6N9o1e8mBJIz4kdEf4gAGw%0AaRP43HTtqWefhdWrIcQ9iIzPtm93zVKbN7vVjOPBtm1Qs6b7nYKcnG/CLOY6wkWknYgsEZHlIvJg%0ADs+3FJGdIjI3cHvEy/K///7ITsZ40K6da9aIg2uBhDZunJtzEy8JA+D4493IvsAcSBOjfEsaIlIE%0AeA1oB9QGuojIWTkcOklV6wduT3pV/s6dMG8e5LLfTcw6+2w4cACWL/c7EhOKrP0Z8STjosbELj9r%0AGo2AFaq6SlUPAZ8AV+ZwXFh6/SZOdOPfjz02HGf3j4i1Hcc61SP7M+JJ+/b22Yx1fiaNqsDaLPfX%0ABR7LSoEmIjJPRL4XEc+mrY0dC4HFVeNO+/Z2NRfL5s2D0qXhtNP8jsR7DRq4fsS1a/M/1kQnP9ee%0ACqbV/WeguqruFZH2wDdAjpuxZl1ttGXLlrTMWEc6F2PGwJdfBhtqbGnTBrp3h3374q8mlQjitZYB%0Abn23tm3d73jbbX5Hk3hSU1NJDXF4pW+jp0SkMdBfVdsF7j8EpKvqM3m8ZiXQQFW3Z3u8QKOnfv8d%0AmjRxK9vG4JD3oDRv7haJy7LXkYkRLVtC377xN0gjw4cfupV74/WiLZbE2uip2cDpIlJDRIoDnYAR%0AWQ8QkUoSmMkkIo1wSS7vjQ6CMHYsXHxx/CYMsH6NWLV7N8yZ8/eGS/GobVu3RXFamt+RmMLwLWmo%0AahpwN/ADsAj4VFUXi0gPEekROOwaYIGI/AK8DHT2ouwxY+K3PyNDmzbui2liy+TJ0LAhBLbViEuV%0AKrnFNWfN8jsSUxgJN7kvLQ1OOAEWLoTAlgxxKS0NKlaExYvdKr4mNtx3n/t/69fP70jCq08fSE52%0ATajGP7HWPOWL2bOhevX4ThgARYtCixZuFV8TO8aNc2uIxbs2bdzvamJPwiWNMWNcf0YisCaq2LJx%0AI6xb54alxrvmzd0F3J49fkdiCiohk0a892dkaN3aXc3FQQtkQpgwwXWAF02ATZhLl4bzzrMlRWJR%0AQiWNXbvcxKlo2Uc73M480/Vt/Pab35GYYIwfnxhNUxkyLmpMbEmopDFlihuZkigT3kTsixkrVN3/%0AU5s2fkcSOdZ8GpsSKmlMmACtWvkdRWTZFzM2/PabqxXGy94uwWjUyP3eW7f6HYkpiIRKGhMnJl7S%0AaN3aJcv0dL8jMXnJqGXE84TT7IoVc03FNsIvtiRM0ti+3S0X3qiR35FEVtWqbl7KL7/4HYnJS6L1%0AZ2SwmnDsSZikMXmyWwq9eHG/I4k8GxMf3dLT3dV2oiYN+2zGloRJGonYNJXBOsOj2y+/uNpg1ewb%0AAySAOnXcXI2VK/2OxATLkkYCaNkSpk2Dgwf9jsTkZOJEuOgiv6Pwh4j73SdO9DsSE6yESBpbtsDq%0A1Ykx0zYn5crBGWfYAnHRavJkt+RLomrZEkLc4sFEUEIkjUmToGlTN1ojUbVo4d4HE13S0938oUSZ%0AcJqTjM+mrVwQGxIiaSRy01QGu5qLTr/+ChUqxP8Cmnk54wzXdLpqld+RmGBY0kgQzZtbv0Y0SvSm%0AKXD9GnZREzviPmls3Oi2da1f3+9I/JWcDKed5lYWNdFj8mRISfE7Cv9Z82nsiPukkZrqrrKLFPE7%0AEv+1bGlfzGiiakkjg9U0YkfcJw1rmvqbfTGjy/LlUKKE2/o00dWqBfv3W79GLEiIpJGoY+Czy+jX%0AOHTI70gMuFqf1TIc69eIHXGdNNavd2tOnX2235FEh/LloWZN69eIFtYJfiTr14gNcZ00Jk50H8Sk%0AuP4tC8b6NaKH9WccyWoasSGu/5xaf8bR7IsZHVavhgMH4PTT/Y4kepx5Juzd694bE70saSSYlBT4%0A6Sfr1/BbRn9GIu2fkZ+Mfg2rCUe3uE0aa9bAX39B7dp+RxJdypeHU06BOXP8jiSxWX9Gzlq0sJpw%0AtIvbpJHRXmxXckezqzn/WX9Gzqz5NPrFfdIwR7Mvpr82bIBt29xeEuZIZ53lWgjWrPE7EpMbSxoJ%0AqHlzmDrV+jX8MmUKNGtmo/pyImJDb6Ndvh9bESknIu1F5A4R6Ski7USkbCSCK6zNm92aUzY/I2cV%0AKkCNGvDzz35HkphsUl/erCYc3XJNGiLSXERGAJOBzsBJQA2gCzBFREaISLOIRFlAU6a4/TNsvanc%0AWb+Gf6wTPG/22YxuRfN4riPQR1WX5/SkiJwB9AR+DEdgobCmqfy1bAmDBkHfvn5Hkli2bXPt9eee%0A63ck0at2bdi1C9auherV/Y7GZJdX89RzuSUMAFVdpqr/CkNMIbOkkb+UFNevkZbmdySJ5ccf4cIL%0AoWhel2sJTsR9Pq22EZ3yShpzRWSciNwiIuUiFlGIdu50q4cm6n7gwapQAapVg3nz/I4ksdgFTXBS%0AUlwzs4k+eSWNasDzQHNgqYgMF5HOInJsZEIrnKlToVEjKF7c70iin41SiTzrBA+OfTajV65JQ1XT%0AVHW0qnbDdYK/C1wJrBSRjyMUX4HZlVzwUlLc+2UiY9cuWLIEGjb0O5LoV7cubNrkbia6BDVSXFUP%0AAIuAxcBu4KxwBhUKSxrBa97cNQGkp/sdSWL46SeXMEqU8DuS6FekiJvLYk1U0SfPpCEiJ4lIXxH5%0AGfgWKAJcrqpRueP23r2ujb5xY78jiQ1Vq7q9wxct8juSxGAXNAVjNeHolNc8jZ9ww2lPAG5T1TNU%0A9TFVXRKx6ApoxgyoVw9KlvQ7kthhbceRY/0ZBWMjqKJTXjWNh4Aaqnq/qsbEmqh2JVdwdjUXGRm1%0A4Asv9DuS2HHeebBypdt900SPvDrCJ6lquojUFJGXRORrERkZuI2IZJDBsqRRcBlJQ9XvSOKb1YIL%0Arlgx19Q8darfkZisgpli9A0wCBgJZHSZRt2fmIMHYeZMt3yICV6NGm6i2YoVtotcONkFTeFkXNRc%0AfrnfkZgMwYye2q+qr6rqBFVNDdyirqXx55/h1FOhXMxMQ4wOGbNvrYkqvCxpFI59NqNPMEljoIj0%0AF5ELReS8jFvYIysg+1IWnnWGh5fVgguvUSNYuBB27/Y7EpMhmOapOsCNQCv+bp4icD9qTJ4M3br5%0AHUVsSkmBAQP8jiJ+zZ4NZ5wBZaN6Q4HodMwxbkmgadOgbVu/ozEQXE3jWuAUVW2hqq0ybuEOrKCm%0ATnWT1UzB1arlRvesXu13JPHJasGhsSaq6BJM0lgAJIc7kFCdcAJUquR3FLEpo1/DZt+GhyWN0FjS%0AiC7BJI1kYImIjInmIbf2pQyN9WuEx+HDbvkQqwUX3oUXuoEu+/b5HYmB4Po0HsvhsagbcmtJIzQp%0AKfDaa35HEX9++cUtQV+hgt+RxK7Spd0ChjNn2o6H0SCvZUQEIMsw29TsQ24zjimswH7jS0RkuYg8%0AmMsxrwaenyciua55ZUkjNHXr/r23uvGONU15w5qookdezVOpIvJAYFvXI4hIrcAf+UI3aIhIEeA1%0AoB1QG+giImdlO+YS4DRVPR24HXgjt/OdfHJhIzFgq4qGiyUNb1jSiB55JY22wDbgdRHZICLLAlf8%0AG3B/7Dch6oblAAAgAElEQVQBbUIouxGwQlVXqeoh4BPcfh1ZXQG8D6CqM4ByImLd3WHSooV9Mb2U%0Anu6SsPVnhK5pU5g+HQ4d8jsSk2ufRmAPjSHAkECtIKNVdquqHvag7KrA2iz31wEXBHFMNVzCMh5L%0ASYFbb/U7ivixaJFboaBqVb8jiX3JyW7FhzlzbOsDvwW1vX0gSXj9hzrYzvTs/SY5vq5///6ZP7ds%0A2ZKWLVsWKqhEVr8+rFrlVhUtX97vaGLf5MnWceuljJqwJY3CS01NJTU1NaRziPq0vKmINAb6q2q7%0AwP2HgHRVfSbLMW8Cqar6SeD+EqCFqm7Kdi716/eIN23bwt13wxVX+B1J7OvcGdq3h5tu8juS+PDl%0Al/Duu/Dtt35HEj9EBFUt0ICmoLZ7DZPZwOkiUkNEigOdgOzzP0YA/4TMJLMje8Iw3rIOR2+oWie4%0A15o3dys/HPaicdwUWr5JQ0RKB/o0MkZNXSEixUItWFXTgLuBH3D7j3+qqotFpIeI9Agc8z3wu4is%0AAN4C7gy1XJM36wz3xm+/uRFpNWr4HUn8OOEEqFwZFizwO5LElm/zVGB/8Ga4meFTgVnAQVXtGv7w%0AgmPNU97Zv99NRNuwAcqU8Tua2DV4MEyYAB995Hck8aVHD6hdG+691+9I4kO4mqdEVfcCVwH/U9Vr%0AgbqFCdBEv4xVRX/6ye9IYpt1goeH1YT9F1SfhohcCHQFvivI60xssn6N0Fl/Rng0b27bE4fq559D%0AW/khmD/+vYGHgK9VdaGInApMLHyRJtrZ1Vxo1qyBPXvckvPGW9Wru2bTJUv8jiR29ekDc+cW/vXB%0AzNOopKqZAzBV9TcR+bHwRZpod+GF7kO1bx8ce6zf0cSeKVNcLSO0ldlMblJS3IrMZ52V/7HmSAcP%0Auk3BmjQp/DmCqWk8FORjJk6UKuUWMJwxw+9IYtOkSdafEU5WEy682bPh9NND20Uy15qGiLQHLgGq%0Aisir/D0zuwxgK8DEuYx+DZtYX3CTJ8Ndd/kdRfxKSYFHH3X9GlabKxgv+tryqmn8AcwB9gf+zbiN%0AAP4RWrEm2llneOFs2uRudW18YdjUrOkSxsqVfkcSezKaTkMRzDyNYoFVaKOWzdPw3o4drtNx2zYo%0AXtzvaGLHF1/A++/DyJF+RxLfunSBf/wDunXzO5LYcfgwHH88LFvmJkqCx/M0RGSBiCwAfs74Octt%0AfkjRm6hXrhycdppbVdQEz4baRkZGZ7gJ3vz5UKXK3wmjsPIaPXV5aKc2sS6jierCC/2OJHZMmgTv%0AvON3FPGvRQt4/nm/o4gtXu3tkmtNI7A50ipVXQXsA87GzQTfG3jMxDnr1yiY7dtdO3v9XDclNl45%0A6yzYtQvWrfM7ktjhVS04mAULrwNmAtcC1wEzReTa0Is20S4lxVYVLYipU91eD8VCXs7T5EfEXTXb%0A9sTB8XLV5WDmaTwCNFTVf6rqP4GGwKOhF22iXcWKcOKJMG+e35HEBuvPiCzr1wjesmVuou5JJ4V+%0ArqAWLAS2ZLm/jaN30zNxypqogmeT+iLLPpvB8/KCJpikMRr4QUS6iUh34HtglDfFm2hnX8zg7N7t%0A9gRv2NDvSBLHOefAH3/A5s1+RxL9Ipo0VPUB3AZI5+A6w99S1b7eFG+iXUqKaze2aTB5mzbNLSl/%0AzDF+R5I4ihSBpk3hR1sJL19ejZyC4DrC+wDTVfU+Vf2Xqn7tTdEmFlSrBscdB4sX+x1JdJs0yfoz%0A/GA14fytWuUWH/Vq1eVgmqfKAGNE5EcRuVtEKnlTtIkV1uGYv9RUW6fLD/bZzF9GX5tX63QF0zzV%0AX1XrAHcBVYDJIjLem+JNLLCrubzt2eNGmNkkyMhr0ABWrHDL3picTZrk7QVNQXbg2wxsxI2equhd%0ACCbaZSQN69fI2bRpbkJfyZJ+R5J4iheHCy5wc2RMzryuBQfTp3GniKQC44EKwK2qWs+7EEy0q1nT%0AVW1//93vSKKTNU35y2rCuVu9Gv76y9sNq4KpaVQHeqtqbVV9TFUXeVe8iQUi9sXMS2qqzc/wk302%0Ac+d1fwYE16fxkKr+4l2RJhZZh2PO9u6FX36x/gw/XXABLFjg+pbMkbzuz4CC9WmYBGZXczmbNg3O%0APddtkWv8ceyxrk9p2jS/I4k+4Wg6taRhgnLWWW7W89q1fkcSXaxpKjrYRc3R1q51KwHXru3teS1p%0AmKBk9GvYqqJHsk7w6GBJ42jh6M8ASxqmAKxf40h798LcudCkid+RmCZNYPZs2L/f70iiR7guaCxp%0AmKDZ1dyRpk93i+ZZf4b/ypRxTaizZvkdSfQIV9LIa7tXY45Qrx5s2OBWFQ11n+F44FV/xpYtW+jX%0Arx+rV6+mUqVKPPjgg9StWxeAhQsX8s4771C+fHlKly5Nz549KWmzCHPUooW7qPFqYb5Ytm6dmyXv%0AdX8GWNIwBVCkCDRr5vo1rr7a72j8l5oKjzwS2jlUlSeffJIXXniB4447js8++4xmzZoxaNAgypYt%0Ay8SJE3nxxRdJSkri0KFDvPvuu9x+++2exB9vUlLg9dfh4Yf9jsR/Gf0ZSWFoS7KkYQoko4kq0ZPG%0Avn3w88+h92csX76crl27ctxxxwFw3XXXUaJECbp06ULHjh356KOPMo8tVqwYlStXZv/+/Rxja7Af%0ApVkzuOEGSEuDogn+ly2cAzSsT8MUiHWGO9Onu+a60qVDO8/evXuPam668soradSoEWPHjmXFihVH%0APHf48GH27dsXWqFxqnx5qFHDJfNEZ0nDRI0GDeC33+DPP/2OxF9e9WfUq1ePcePGZd5XVR599FH6%0A9etHp06daNu2bWbi2LVrFzNmzCA5OTn0guOUDdaA9evd97NOnfCcP8ErcaagihWDxo3dqqKXXeZ3%0ANP5JTYV+/UI/T1JSEm3atKF///4kJSWxdetWOnfuTJMmTWjbti316tXj2muvJTk5mQoVKvDyyy+H%0AXmgca9ECPvwQ7r/f70j8k7EhWDj6MwBE42C9axHRePg9YsUTT7jZ4c8+63ck/ti3DypWhI0bQ2+e%0AMt7auNGNGNq6NXx/NKPd7bdD3bpwzz35HysiqGqBpv8l6NtqQpHo/RrTp8PZZ1vCiEaVK7uEvmCB%0A35H4J9yrFFjSMAV2wQWwaJFb1yYRTZxoS4dEsxYt3B/ORLR+PWzf7moa4WJJwxTYMcdAo0aJ2+E4%0Afjy0bu13FCY3rVu7/6NENH48tGoV3qY5SxqmUNq0gSyDfhLGrl0wfz40bVqw15155pkkJSWF7da3%0Ab9/w/MIx6KKLXPPpoUN+RxJ548a572Y4WdIwhZKoV3OTJ7ta1rHHFux1jz/+eObPpUqVYvHixaSn%0Ap+d5O3z4MPv372f37t2sX7+eBQsWMH78eF555RW6detGpUqVkMASpoMHD7b5GwEVK7otihNtHSpV%0ASxomijVo4Na32bjR70giq7Bfyk6dOnHLLbcAsGfPHq677jr257Mkq4hQvHhxSpUqRZUqVahTpw6t%0AWrWiV69eDBkyhHXr1jF8+HBSUlLYsWMHH374YWF+pbiUiBc1S5ZA8eIuYYaTJQ1TKEWKuM7gCRP8%0AjiSyQunPePXVVznrrLMAWLBgAb179w4pliJFinDZZZeRmprKCy+8wOuvvx7S+eJJIjafZlzQeL1/%0ARnaWNEyhJdoXc+NGV7tq0KBwrz/22GP59NNPM9eNevvtt/nss888ia13795cccUVTEi0LJ6L5s1h%0AzpzE2jc8UgM0LGmYQmvd2iWNRJlXOX68q10VKVL4c9StW5cXX3wx8/7tt9/OypUrQw8OeOSRR9iw%0AYYMn54p1pUq55J4oO02mpblhxhddFP6yLGmYQqtVC9LTIduaenFr/HhvOhl79uzJ1YFlgnft2kWn%0ATp045MFQnxIlStC1a9eQzxMvEqkmPHs2nHwyVKoU/rIsaZhCE0mcL6bXI1MGDRrEySefDMDs2bNt%0AyGwYJFJneCTnDlnSMCFJlC/mihWuVnXGGd6cr2zZsgwbNoyigY0fXnnlFb799ltvTm4AaNgQfv8d%0AtmzxO5Lwi8RQ2wy+JA0RKS8iY0VkmYiMEZFyuRy3SkTmi8hcEZkZ6ThN/lq3dstqHD7sdyThFY6R%0AKY0bN+a///1v5v1u3bqxbt067wpIcMWKuSVF4n1swN69bk5KSkpkyvOrpvFvYKyqngGMD9zPiQIt%0AVbW+qjaKWHQmaCee6NpR5871O5LwClf1/8EHH6R14MTbt2+nS5cupKene19QgsoYrBHPfvwR6teP%0A3AKafiWNK4D3Az+/D3TI49gwjzo2oWrbFsaM8TuK8ElL864TPDsRYejQoZxwwgkATJ06lUcffdT7%0AghJUxmcznkf4/fADXHxx5MrzK2lUUtVNgZ83Abn1+SswTkRmi8htkQnNFFS7djBqlN9RhM/06W4b%0A0SpVwnP+SpUq8cEHH2QuCfL0008fsZufKbwzz3T/LlnibxzhNHo0tG8fufLCtnOfiIwFKufw1MNZ%0A76iqikhu1wFNVXWDiFQExorIElXNceR1//79M39u2bIlLW3t6ohp0QKuvRZ27IByOfZOxbZIfCnb%0Atm3L/fffz3PPPYeqcuONN7Jw4ULKly8f3oLjnIj7vxs1CgKT8ePKmjWuoz/YCaepqamkhrhuvC87%0A94nIElxfxUYRqQJMVNUz83nNY8BfqvpCDs/Zzn0+a98ebrkFrrnG70i816ABvPRS+Dsa09LSaNas%0AGTNnzqRJkyZMmDCB4sWLh7fQBPDNN/C//8VnE+rbb7tFNIcOLdzrC7Nzn197hI8AbgKeCfz7TfYD%0ARKQkUERVd4tIKaAt8Hj240x0aNfOXZHHW9LYtAl++w0uvDD8ZRUtWpQWLVrw559/MmLEiEInjDlz%0A5vDhhx9SpEgRVq1axaBBg3jrrbfYsWMH69ev5/HHH6dmuFe1iyIXXQQ33uiWFClVyu9ovDVqFATm%0AiUaOqkb8BpQHxgHLgDFAucDjJwLfBX6uCfwSuP0KPJTH+dT4a+lS1apVVdPT/Y7EW++/r3rVVZEp%0Aa+jQoVqxYkVdsWJFoc/x22+/6V133ZV5/6abbtIzzjhDp02bplOnTtWkpCR98cUXvQg3prRsqfrt%0At35H4a0DB1TLllXdvLnw5wj87SzQ329fahqquh04aiyKqv4BXBr4+Xfg3AiHZgrp9NPdssy//ur2%0Az44Xo0e7WlS4TZ48mbvuuotRo0Zx6qmnFvo8L7zwAs8++2zm/T179lC+fHkaN27MunXr6NOnD926%0AdfMg4tiSURO+9FK/I/HOtGnue1exYmTLtRnhxhNZOxzjxeHDrh083Elj2bJlXHPNNQwaNIgLQ2wH%0Ae+CBByiVpQ1m2rRptAmMFa5WrRrPPvssycnJIZURi+Ltswnu94nkqKkMljSMZzKu5uLF7NlQuTJU%0Arx6+MrZu3coll1xC3759ucaDDqEaNWpk/rx06VL++OMPWrVqFfJ5Y93ZZ8O+ffG1uGakasHZWdIw%0AnmnVyi1nsHu335F4I9xDbQ8cOECHDh34xz/+wf333+/5+TNGXzVp0iTzsd9///2o4+bOnUudOnU8%0ALz+aiMTXfKI//oC1a93Ww5FmScN4pnRpuOCC+FnrZ9So8F3JqSrdunUjOTmZ1157zZNz7ty5k1NO%0AOYWFCxcCMHbsWM4555zMTZ/S09N57rnnjnpdnTp1GBUvf03zEE814R9+cCsUFPWhV9qShvFUvLQd%0Ab90KixZBs2bhOf/DDz/M8uXL+fTTTzNngofqzTffZPXq1fz6668sWbKEFStWUKJEicznn3rqqRw7%0AwYsXL85JJ53kSQzR7OKL3aZM+/b5HUnownlBkx9LGsZTl10GI0e6ZcRj2XffuSu5LH9zPTNkyBA+%0A+ugjvvvuO0qWLOnJOYcOHcojjzxC9+7dmTNnDu+99x7Tp0+nZs2a9OzZk3vuuYcmTZpwwQUXZL4m%0APT2dgQMHcttttzF79mxP4ohm5crBeefF/lL+Bw7A2LFwySU+BVDQMbrReMPmaUSVWrVUZ870O4rQ%0AdOyo+t573p933LhxWrFiRV24cGHI50pPT9dJkyZphw4dVES0V69eBXr9V199pZs3b9abbrpJv/ji%0Ai5DjiQUvvqh6661+RxGa0aNVmzTx5lwUYp6GL8uIeM2WEYku//6320f7qaf8jqRw9u1zo6Z+/x2O%0AP9678y5atIiWLVvyySefcFEQmzmrKocOHeLQoUPs3buX7du3s23bNhYtWsTcuXMZPXp05v7iIsKs%0AWbM477zzgo5n9+7dqCp16tQ5qikrXv3+u5vd/8cfoe317qc77oCaNeGBB0I/VywtI2Li2JVXwm23%0AxW7SGDfO7U/gZcLYtGkTl156KVu3bs2cN+GlunXrFihhAJQpU4Y33niDjh07cvjwYdLS0jJ3EoxX%0ANWu6/V9mzIAsg8piRno6jBjhNj7zS3x/QowvLrjAdSSvWAGnneZ3NAU3fLhLfF564oknOPbYYznz%0AzDzX5Sy0u+66q1Cv++ijj3j55Zd599136dGjh8dRRacrr3T/x7GYNObMgeOO827b4cKw5ikTFrff%0ADrVqQZ8+fkdSMIcPu90Ip01zV6Xx7s4776RevXrUqlUrYSYBzpkD118PS5f6HUnBPfywq2383/95%0Ac77CNE9Z0jBh8d138MwzbtnmWDJ1qmsznj/f70hMuKjCSSe5EUhhqviFTd26MGgQNG7szfkKkzRs%0AyK0Ji9atYd48t0FMLPnmG++bpkx0EYErrnD/17FkxQrYts2fWeBZWdIwYXHMMW4y1bff+h1J8FRd%0AW3eHvHasN3GhQwf3fx1Lhg93yS7J57/aljRM2GR0OMaKJUvccNsCDkIKmldrPM2ePZt7772XDz/8%0AkJ49e/Lbb795EF1iadHC/X9v2OB3JMELxwCNwrA+DRM227dDjRpuTHzp0n5Hk78BA2D9enj99fCc%0A/+DBg2zcuDGkJTsOHDhArVq1mDFjBpUqVWL27NnceeedzJw508NIE8P117stfHv29DuS/G3a5AaW%0AbNzoavFesT4NE1XKl4emTd248lgwbBh06hS+83uxxtPkyZMpXbo0lSpVAqBBgwYsXryYVatWeRBh%0AYrnuOvjkE7+jCM7nn7slerxMGIVl8zRMWF1/vftjfP31fkeSt19/hR07wrNAYXp6Oq+//jrz58+n%0AR48enH/++ZnP/fnnnzz33HPkVVMuWrQojz32GEWLFmXVqlUcn2XWoYiQnJzMwoULj9hLw+SvfXu4%0A+WZYtw6qVfM7mrwNG+aG20YDSxomrDp0gLvvdqM+vJxh7bVhw6Bz5/B0Mg4fPpzOnTszZ84cVq9e%0AfUTSSE5OZsCAAUGfa+vWrUctcnjMMcewO142MYmgEiWgY0f49NPonk+0ahUsW+YGlkQDa54yYVWm%0ADLRtC19+6XckuVN1SaNLl/Ccv02bNpQoUYLx48dz2WWXhXSucuXKHVUr+euvv6hQoUJI501UXbq4%0A//to9skncPXVUKyY35E4VtMwYXf99fDqq26WeDSaMQOKF3frTYVDXms8bd++neeffz7P5qkiRYrQ%0Av39/ihYtyplnnslbb72V+VxaWhrbt2/n5JNPDk/wca5VK9c8tWyZv0tz5GXYMBg40O8o/majp0zY%0A7d/vluZYsACqVvU7mqPde6/rtH/ssfCV0axZM15++WVmzJhBjx49Cr0wYFpaGieffDLTp0+nevXq%0AjB8/nr59+zJnzhyPI04c99zjmk7D+f9fWAsXus2WVq8OT9OpjZ4yUemYY9z48s8+8zuSox0+7OIK%0AV9NUhnr16jF79mxq164d0kqyRYsW5cMPP+Spp57igw8+YOjQoXz66aceRpp4MgZrRON1Z8aIPr8n%0A9GVlNQ0TEWPHQr9+MGuW35Ecadw4t/9HAmxcZ3KhCqee6vrdwtVEWRiqbpXozz8P34RTq2mYqNWq%0AFaxdC8uX+x3JkcLZAW5ig4gbOffxx35HcqSZM6Fo0ehKZGBJw0RI0aLui/nBB35H8rc9e+Drr11c%0AJrF17QoffQRpaX5H8rcPPnBxSYHqAeFnScNEzM03w3vvuX6EaPDFF24jnmjsnDeRVacOnHwyjBrl%0AdyTOvn1uqG337n5HcjRLGiZi6tWDKlVgzBi/I3EGD4ZbbvE7ChMtbrnFfSaiwZdfuiXQq1f3O5Kj%0AWdIwEXXrrW4TGb8tXerG5oc4187EkU6dIDU1Ola+HTTIfVeikSUNE1GdO8OECf5/Md95B266KXpm%0A2Rr/lSkD11wDQ4b4G8fSpbB4MVx+ub9x5MaG3JqIu+MOqFQJ+vf3p/w9e1z79axZcMop/sRgotPc%0AuW6jo5Ur3eANP/TqBWXLwpNPhr8sG3JrYsJdd8Fbb8HBg/6U/9FHrgPcEobJrn59d0Hh1+Zhu3a5%0Az2c07/FhScNEXN26cNZZ/ixiqAqvveau5ozJSa9e7jPihw8+gNato3updksaxhf33AMvvRT5pRsm%0AToRDh6BNm8iWa2LHVVe5Sai//BLZcg8fdgt7RvsFjSUN44srrnBV8dTUyJb79NPQt2/0TZgy0aNY%0AMejdG555JrLlfv21WzixefPIlltQ1hFufDNkiNsA54cfIlPenDluU6jffnNLoRuTm127oGZNt2z+%0AqaeGvzxVaNgQHn3ULe4ZKdYRbmJK165u6edIrer9zDPwr39ZwjD5O+441xn97LORKW/8eNi7N3qH%0A2WZlNQ3jq4EDXU3j22/DW868eW5fguXLoXTp8JZl4sOWLXDmmW4F5HCOtFOFpk3dqMKuXcNXTk6s%0ApmFizu23u82Zpk4NbzkPPwwPPWQJwwSvYkW3v3245xN9+y3s3h07C2daTcP47t133W3SpPB0UE+d%0A6jbaWbYMSpTw/vwmfu3cCaef7kbd1anj/fnT0+Hcc91Eviuu8P78+bGaholJN94If/4JX33l/bnT%0A0+G+++CJJyxhmIIrW9bVUP/1r/AMDx882C1fEgt9GRksaRjfFS3qJlP9619uiQ8vDR7sOr5vuMHb%0A85rEcffdsH69GxLrpW3b4JFH4PXXY2sIuDVPmajRtatbCvrpp70539atrklhzBg45xxvzmkS06RJ%0A8M9/utF+XvWL3X47HHOMm9Dnl8I0T1nSMFFj40bXvvvVV25tqFCoupm9p50Gzz3nTXwmsWUsVe7F%0A0v7ff+8W7pw/3zWB+cX6NExMq1zZLWR4441uNEkohgyBVasis1KoSQwvv+xWMAi1mWrzZpeAPvjA%0A34RRWFbTMFGnZ0/YtMltx1qkSMFfP3s2tG/vvuDhGPFiEtf06W6U06RJbtHNgjp40M0XatwYBgzw%0APr6CipmahohcKyILReSwiJyXx3HtRGSJiCwXkQcjGaPxz6uvwo4dbo2oglqzxi3DMGiQJQzjvcaN%0A3SzxSy91NYaCUIUePdxoqSeeCE98keBX89QCoCMwObcDRKQI8BrQDqgNdBGRQuR2UxCpkV5BMAfF%0Ai7tl00ePhgcfDH6o4/Ll0LKle00k1+/JSzS8n/EkGt7Pbt1cp3irVrBuXXCvOXzYJYxFi+DjjwtX%0Ag44WviQNVV2iqsvyOawRsEJVV6nqIeATIEr+FMSvaPhSApQvD5Mnu2aAG25wNY+8jBsHLVq4MfX3%0A3BOZGIMRLe9nvIiW97N/f5c8mjaFn37K+9gtW6BjR7cb4LhxUKpUJCIMn2juCK8KrM1yf13gMZMg%0Ajj/eLeR23HFu46Z33oG//jrymHnz3Jf35pvhvffgttv8iNQkogcecJ3jV18Nd94JS5Yc+fyOHW5t%0AtbPPhjPOcMuFlCnjT6xeCtsuuCIyFqicw1P9VHVkEKewnm1DqVLwxhtuGZAXXoD773dLVZct60ZH%0ApaXBLbfAr7+65GJMJHXsCCkpblh3q1bu83rSSW7i3qpVrtN75Ei37Hm88HX0lIhMBPqo6s85PNcY%0A6K+q7QL3HwLSVfWorVFExBKMMcYUQkFHT4WtplEAuQU8GzhdRGoAfwCdgC45HVjQX9oYY0zh+DXk%0AtqOIrAUaA9+JyKjA4yeKyHcAqpoG3A38ACwCPlXVxX7Ea4wxxomLyX3GGGMiI5pHTx0hmIl+IvJq%0A4Pl5IlI/0jHGkvzeTxFpKSI7RWRu4PaIH3HGAhEZIiKbRGRBHsfYZzNI+b2f9tkMnohUF5GJgcnU%0Av4pIjgPSC/T5VNWovwFFgBVADaAY8AtwVrZjLgG+D/x8ATDd77ij9Rbk+9kSGOF3rLFwA5oD9YEF%0AuTxvn01v30/7bAb/XlYGzg38XBpYGurfzlipaQQz0e8K4H0AVZ0BlBORSpENM2YEO3HSBhgEQVWn%0AAH/mcYh9NgsgiPcT7LMZFFXdqKq/BH7+C1gMnJjtsAJ9PmMlaQQz0S+nY6qFOa5YFcz7qUCTQHX1%0AexGpHbHo4o99Nr1ln81CCIxErQ/MyPZUgT6f0TDkNhjB9tZnv/qwXv6cBfO+/AxUV9W9ItIe+AY4%0AI7xhxTX7bHrHPpsFJCKlgS+AewM1jqMOyXY/189nrNQ01gPVs9yvjsuGeR1TLfCYOVq+76eq7lbV%0AvYGfRwHFRKR85EKMK/bZ9JB9NgtGRIoBXwJDVfWbHA4p0OczVpJG5kQ/ESmOm+g3ItsxI4B/QuZs%0A8h2quimyYcaMfN9PEakk4nYuFpFGuOHZ2yMfalywz6aH7LMZvMD7NBhYpKov53JYgT6fMdE8papp%0AIpIx0a8IMFhVF4tIj8Dzb6nq9yJyiYisAPYA3X0MOaoF834C1wB3iEgasBfo7FvAUU5EhgEtgAqB%0ASauP4Ual2WezEPJ7P7HPZkE0BW4A5ovI3MBj/YCToHCfT5vcZ4wxJmix0jxljDEmCljSMMYYEzRL%0AGsYYY4JmScMYY0zQLGkYY4wJmiUNY4wxQbOkYUw2IlJWRO7Icv9EEfk8TGVdJiL983i+nogMDkfZ%0AxhSGzdMwJpvAwm4jVfXsCJQ1Eeic1wxcEUkFrlPVzeGOx5j8WE3DmKM9DZwa2ODnGRE5OWNDIBHp%0AJrtkU18AAAHJSURBVCLfiMgYEVkpIneLyP0i8rOITBOR5MBxp4rIKBGZLSKTRaRW9kJEpDpQPCNh%0AiMi1IrJARH4RkUlZDh0FXBv+X9uY/FnSMOZoDwK/qWp9VX2Qo1cArQN0BBoCTwG7VPU8YBqBNXyA%0At4Feqno+8ADwvxzKaYpbsTXDo0BbVT0XuDzL4zOBlNB+JWO8ERNrTxkTYflt8DNRVfcAe0RkBzAy%0A8PgCoJ6IlAKaAJ8H1tUDKJ7DeU4CNmS5PxV4X0Q+A77K8vgG3C6LxvjOkoYxBXcgy8/pWe6n475T%0AScCfqhrMXuCZWUVV7wis2nopMEdEGgRWbxVs/w0TJax5ypij7QbKFOJ1Am6/B2CliFwDbnlqEamX%0Aw/GrcXs4EzjuVFWdqaqPAVv4e/e0KoFjjfGdJQ1jslHVbcDUQKf0M7ir/Iwr/aw/k8PPGfe7AreI%0AyC/Ar7h9mLObCpyX5f6zIjI/0Ok+VVXnBx5vBEwO5Xcyxis25NYYH4nIBKCrqm7I45hUbMitiRJW%0A0zDGX88DPXN7MtCstcIShokWVtMwxhgTNKtpGGOMCZolDWOMMUGzpGGMMSZoljSMMcYEzZKGMcaY%0AoFnSMMYYE7T/Bxi7FV50SWh9AAAAAElFTkSuQmCC%0A" 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T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%0AoFnSMMYYE7T/Bxi7FV50SWh9AAAAAElFTkSuQmCC%0A" />
-</section>
-<section id="id7">
-<h2>作者 &amp; 更新时间<a class="headerlink" href="#id7" title="Permalink to this heading">¶</a></h2>
-<p>作者:<cite>李金  &lt;lijinwithyou&#64;gmail.com&gt;</cite></p>
-<p>提交: 2017/12/14</p>
-</section>
-</section>
-<div class="clearer"></div>
-</div>
-</div>
-</div>
-<div class="clearer"></div>
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-<div class="star-footer">
-<img onclick="openPurchase()" src="../../static/star.jpg">
-<div id="praise"><span>0</span> persons have rewarded</div>
-</div>
-<div class="foo"></div>
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-<div class="col-sm-offset-2 col-sm-6 footer-div1">
-<div class="footer-block-item">
-<span class="footer-item1">Built with Sphinx using a theme provid by QPython.</span>
-<span class="footer-item2 visible-xs">Copyight 2017, QPython.</span>
-</div>
-</div>
-<div class="col-sm-2 footer-div2">
-<a href="/index.html" class="pull-right"><img src="http://www.qpython.org/static/img_logo.png"></a>
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-<input type="hidden" name="on0" value="Thanks">
-Thanks
-</div>
-<div class="en-thanks-sel">
-<select name="os0">
-<option value="Reward this course">Reward this course $ 0.99 USD</option>
-<option value="Reward this course">Reward this course $ 1.49 USD</option>
-<option value="Reward this course">Reward this course $ 1.99 USD</option>
-<option value="Reward this course">Reward this course $ 2.99 USD</option>
-<option value="Reward this course">Reward this course $ 3.49 USD</option>
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-<img alt="" border="0" src="https://www.paypalobjects.com/zh_XC/i/scr/pixel.gif" width="1" height="1">
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-milib.getPurchaseNumber = function(str){
-//alert('getPurchaseNumber:'+str)
-return "10+";
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-milib.a8playVideoFromGW= function(url){
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-milib.qpylibinstall = function(cat,url,target){
-window.open(url)
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-s = u[u.length-2]+'-'+s
-if (milib.openPurchaseActivity){
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-//按钮事件
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-var seq = this.getAttribute('target')-2
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-/**********有代码，调用控制台*************/
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-}else{
-//没有代码，调转
-var url = this.getAttribute('data-url');
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-//获取赞赏
-function setPraise(){
-var u = window.location.href.split('/')
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-temp_data = temp_data.replace(/\^12\$/g, spaceString(12));
-temp_data = temp_data.replace(/\^16\$/g, spaceString(16));
-temp_data = temp_data.replace(/\^20\$/g, spaceString(20));
-//set
-prearr[i].lastChild.nodeValue = temp_data;
-}
-}
-//产生空格
-function spaceString(num){
-var v = ''
-for (var i = 0; i < num; i++) {
-v += ' ';
-}
-return v;
-}
-function setSearchHistory (t) {
-if (!t)
-return;
-if (localStorage.course_search_list) {
-var list = localStorage.course_search_list.split('%,%');
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-list.push(t);
-localStorage.course_search_list = list.join('%,%');
-}else {
-localStorage.course_search_list += '%,%'+t;
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-}else {
-localStorage.course_search_list = t;
-}
-}
-window.onload = function(){
-setPraise();
-initCode();
-//将所有的button绑定事件
-var b = document.getElementsByTagName('button');
-for (var i = 0; i < b.length; i++) {
-if (b[i].className=='button') {
-b[i].setAttribute('target', i)
-b[i].onclick = btnEvent ;
-}
-}
-}
-$(function(){
-var url = location.href.split('?');
-var arg = url[1];
-if (!arg){
-return;
-}
-var args = arg.split('&');
-for (var i = 0; i < args.length; i++) {
-var c = args[i].split('=');
-if (c[0]=='form' && c[1] == 'web') {
-$('.web-show').css('display','block');
-$('.web-content').addClass('margin-header');
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-$(function() {
-$('form').submit(function() {
-setSearchHistory($("input[name='q']").val());
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-$('.bodywrapper .body form input').val("");
-$($('.bodywrapper .body form input')[0]).attr('placeholder', 'search');
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-(i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
-m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
-})(window,document,'script','https://www.google-analytics.com/analytics.js','ga');
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-ga('send', 'pageview');
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\ No newline at end of file
diff --git a/docs/aipy-numpy/cn/5image-base.html b/docs/aipy-numpy/cn/5image-base.html
deleted file mode 100644
index 6b6192f..0000000
--- a/docs/aipy-numpy/cn/5image-base.html
+++ /dev/null
@@ -1,381 +0,0 @@
-<!DOCTYPE html>
-<html lang="en-US">
-<head>
-<meta charset="utf-8" />
-<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
-<link href="https://cdn.bootcss.com/bootstrap/3.3.7/css/bootstrap.min.css" rel="stylesheet">
-<title>图像基础</title>
-<link rel="stylesheet" type="text/css" href="../../static/pygments.css" />
-<link rel="stylesheet" type="text/css" href="../../static/style.css" />
-<meta charset="UTF-8">
-<meta name="viewport" content="width=device-width, initial-scale=1,maximum-scale=1, user-scalable=no">
-<script src="https://cdn.bootcss.com/bootstrap/3.3.7/js/bootstrap.min.js"></script>
-</head><body>
-<div class="header doc-header web-show">
-<div class="col-sm-offset-2 col-sm-8  index-header">
-<div class="navbar-header">
-<button type="button" class="navbar-toggle" data-toggle="collapse" data-target="#example-navbar-collapse">
-<span class="sr-only">切换导航</span>
-<span class="icon-bar"></span>
-<span class="icon-bar"></span>
-<span class="icon-bar"></span>
-</button>
-<a href="/index.html" class="logo"><img src="http://www.qpython.org/static/img_logo.png"></a>
-</div>
-<ul class="header-title collapse navbar-collapse" id="example-navbar-collapse" >
-<li>
-<a href="http://www.qpython.org/index.html">Home</a>
-</li>
-<li>
-<a href="http://www.qpython.org/document.html">Guide</a>
-</li>
-<li class="header-phone-selected">
-<a href="/index.html" class="header-selected">Course</a>
-<span class="header-title-selected"></span>
-</li>
-<li>
-<a href="http://www.aipy.org">AIPY</a>
-</li>
-<li>
-<a target="_blank" href="https://github.com/qpython-android/qpython">Github</a>
-</li>
-</ul>
-<div class="search-box hidden-xs">
-<form action="/search.html">
-<input type="search" name="q" placeholder="keyword">
-<input type="hidden" name="check_keywords" value="yes" />
-<input type="hidden" name="area" value="default" />
-<button><img src="http://www.qpython.org/static/ic_search.png"></button>
-</form>
-</div>
-</div>
-</div>
-<main class="contain web-content">
-<div class="document">
-<div class="documentwrapper">
-<div class="bodywrapper">
-<div class="body" role="main">
-<section id="id1">
-<h1>图像基础<a class="headerlink" href="#id1" title="Permalink to this heading">¶</a></h1>
-<p>导入相应的包：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">import</span> <span class="nn">matplotlib.image</span> <span class="k">as</span> <span class="nn">mpimg</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="o">%</span><span class="n">matplotlib</span> <span class="n">inline</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<img alt="https://github.com/riverfor/notes-python/raw/9e0c173527920683229678ba0e4482a74d74618a/06-matplotlib/stinkbug.png" src="https://github.com/riverfor/notes-python/raw/9e0c173527920683229678ba0e4482a74d74618a/06-matplotlib/stinkbug.png" />
-</section>
-<section id="id2">
-<h1>导入图像<a class="headerlink" href="#id2" title="Permalink to this heading">¶</a></h1>
-<p>我们首先导入上面的图像，注意 matplotlib 默认只支持 PNG 格式的图像，我们可以使用 mpimg.imread 方法读入这幅图像：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">img</span> <span class="o">=</span> <span class="n">mpimg</span><span class="o">.</span><span class="n">imread</span><span class="p">(</span><span class="s1">&#39;stinkbug.png&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">img</span><span class="o">.</span><span class="n">shape</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">(</span><span class="mi">375</span><span class="n">L</span><span class="p">,</span> <span class="mi">500</span><span class="n">L</span><span class="p">,</span> <span class="mi">3</span><span class="n">L</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>这是一个 375 x 500 x 3 的 RGB 图像，并且每个像素使用 uint8 分别表示 RGB 三个通道的值。不过在处理的时候，matplotlib 将它们的值归一化到 0.0~1.0 之间：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">img</span><span class="o">.</span><span class="n">dtype</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">dtype</span><span class="p">(</span><span class="s1">&#39;float32&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-</section>
-<section id="id3">
-<h1>显示图像<a class="headerlink" href="#id3" title="Permalink to this heading">¶</a></h1>
-<p>使用 plt.imshow() 可以显示图像：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">imgplot</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">img</span><span class="p">)</span>
-</pre></div>
-</div>
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9%0ABs58mYDr0pYCJBteClktY00A/HSSJOfJuT8FcF+SJP+3c+5PLhz/Kc8ogSo/L0kMwHLni2VCMYBA%0Ap/k8ndZR2mCIcX4rrcWaQ/p6SZtFH03P9+FqOkPAzfPGvhCE6/a6+Dm+hKOVKwVaqR6erUrlS5K1%0AfUMMl9uq+fNqysqiQ8ojAaWlP2a8SXqz2Jy1HyTJ9niEYgc7/gUAd1/4fTeAO2KUcEe2/lYUThqZ%0Abjmh1yS9F1VCAWtumyZaWVaakN6YOlv5pGPpt5Re05tVPFj5GYpkB7fT/9E8Up1Dg8Pqb84ELT08%0ArQS6Po01K7J8is+cpHIsP8jSN1KQCY0zy2b/WzrWiAYlQ9pMQjsn2SGxfcmnNf9Za+a6WmBNANzv%0AnDvgnPutC+e2JkkyeeH3JICtUkZr8AOyM0ri10GTJDHXRGPFmvJruqXBwh3Giq6SvlAd+HqWZF8I%0AYLnNPL22kyGLhJyblssHdZapoZROqhttMwtwpfRaOVb5dOM59c9QgKe/qe9oU2fua6EARvNQEOR/%0Amm1SIPDHfJsX1cX103aW2lDyC81/JAmRDx7E10pWuxRwY5Ikp5xzmwHc55x7ll5MkiRxzomeR6dJ%0AgN441hSB5rOuaxGxF4nRJQ2E0BKBdCzpozZojh+aHtG8VptrDEUDZU1nLBhyXXxwSnqt2QlPy8FS%0AS2+BpQWwoSAi+YVUnnUs6QsxSU2v1Q/cx7IyOpqP+ktoPEv5sxASaWyEMCDWpiyyKmBNkuTUhf9n%0AnXP/BuB6AJPOuYkkSU4757YBOCPlve+++9KK7N27F/v3748CrRCD0NLzDu6lESXWIB3731rZofK5%0AQ2vgpzFVbi8dgBo74OVL9dHSxaSJAVNuc0i04LKaaV3WINCrL2mAKx2H9HBf0YL4asAxq1iALonF%0AWq30MSDK00llPffcc3juueeCdsZKz8DqnKsAyCdJUnPOVQHcCuAvAHwewK8C+F8X/n9Oyn/rrbem%0Aldde6qCBqDX4pIFsAWIM2GqD12I29HeMk9Gy6VQq9PKWkC4N9Lm9IZap5Y2R2EAYAmqJuWs2SSzP%0AarOsAZvmkWyIJQmaSEDQC4vU+tZicRyoY0HZGicx/iXNerTAoQXAmDEtnd+3bx/27duX6r3//vuD%0A9bVkNYx1K4B/u2BgAcC/JknyNefcAQD3Oud+Axe2W2kKkuTiZ/UlhgfIjhYzleG/QywsRvjgpgNS%0Acxhel5jrFrvVzknRmdqpRW1/3Ol00u+RAfI2JWuQZAFQC/T9NbouGRNgYlmPNdi1OvDzWV7oIrEo%0AqQ70vAQiIVANAY5mR0wdNLstX9Sux9jO7dZs0uyitmh6tXJXKz0Da5IkLwC4Rjh/HsAtMTq0R+68%0AcAYREzlDDaZNH7JEZYlhW45usWgrSEhRO+SkIVCQQCyfz+PEiRMYHBzE0NAQ2u22agPvkxhmFgtU%0AVCdw8bPuIaaVRSwwiNUf8hfuhyHQtGxbC9FmTprvSyCuMWmaR2KZWrmSSMFGssWyS6u3VM5LAbDr%0A+uSVxvY0tiqBgyQWC6Dlclu4Dum8ds6yRbLDKt9qh5hy6DnJsXm7djodLCws4F/+5V+wfft23H77%0A7ZiYmEC73Uan00GhULjoCZuY8lcr0js0LcnSTlJabaa0WpFmEjF+pflBrGj9RHfShNL787EEJ4YN%0Ah0iMFnB4GVr9rIC1lv1qyVrsY12V8EaIYZvasZdutxuMjD6N1gnS+V4imlQGPZdFZ1Z7gYuXFfw5%0AGqScc9i3bx9+//d/H1dccQU+/vGP4zOf+QympqbQ19e3YsuQT8//QhJKb80gQoFX+m2BhDa4OHhk%0AZdqWSLMPbhe9TtNwf6bp/e8YoPNppX5YS7Ym6dNYpybcRqk/pC1SVr/z/xKZWivAXVdgtSpp5ZEG%0AlNRxMVFPOh8aUBwYNRDOUqcs563O1wac5IA8baFQwBvf+Eb83u/9HhYXF/EP//AP+MY3voFms5l+%0ARjvL4JDKCwUAmt4CYM0HrDpatkm6eH17rXusrCWQxwQWrzs0DrVZl6bb0pEFCEPXtMAqlRMbNNdK%0A1g1YJYbiz/cKiMDF63K8zNCg0ADAH2cBNe5M2nXNITVnsNiKNHBonhAD6HQ6KJVKeM973oP3ve99%0A+MY3voG///u/x8GDB1Euly9i+pRhZXHWmAFFp6yaflq/0MtOrLJi/U5r2yy6NP1Sm4bs8b95va1x%0Aor03gY4hPkuJqR/3Ke6z0iwkhsTQMjVg5rp5eVo6TddqZd2XArxIHWWxQfqbOkLMwOLgaUU/fo0C%0Ai6U7FOV5B1ugaTEBLlIZIaYlgXG73cbIyAj+/M//HK985SvxyU9+Ep/61KcwNzeHYrEoAjavd4yD%0AWvWKYTm8fGvwxYrkb5p/+rJD09aQLTzQ9sKqLJZGz4XAigO11X6Wj/pxSQOkRKCkcrRlI/6Zbs1u%0AjWBIEgL5XmRdgVWrkNaowMVTxJh1oiwDyxpAFsCvtlxLrGmXVS4foFrQ4W3t83W7XXQ6Hdx22234%0A3d/9XRw+fBh33303Hn/8cZH5xIK/Bn5WcKX1im2HrBIz+GiZa1WuBTCSXdb4CIFEFpuzsH9Nv+Rb%0A9BpPS9PRNvZ6rPcvSLhgtdFLKesGrFqjhqYb/ppnjtJnbenvUNSNZQa9gnasY1IQjCnLAvkYtiyJ%0ANGALhQJarRZGRkbwP/7H/8B1112Hj370o7j33nvRarXML9HyqTy1JwaYrPpyu3uZpVjlcf38uBdG%0AGeuD0rTWEqm/LTIQasssjFWzJTbAan4hlS35OE3r2XEM4eB61pq1rvvNK6mjLWYSC3BS5JR0WODL%0Ar4WcMquNGhPluiSd2uDOWkbIRh+8crkcarUafuqnfgof/OAH8dxzz+HDH/4wDh48iEKhkKblU/JY%0AcKRprPbTgLkXdiXpl+zk7GitmCovQ2Jf/rwmMV9hpb95HaT60P7TfEwaR1SswKORKgkstYAj2cff%0A8axJaEa8FrLuN6/8lNOLxVw1B4gdsNqxBGJZGpg6ohcaOaXrIZu8WOAkOSD9z/OGHFrKT23I5/Po%0AdDoYHx/HBz/4QYyNjeETn/gEHnjgAbGc0PfneR1oG4UCCT8Ota9WR20A87KpX/TCVGm+EIBwG6X6%0AhurIhV/jfWPVX/Mxrk/qi1Dwpn3gx0wM6FksXSuX55Gw5b89Y9XWRrQ0XjyDsjosliWtFkg1MJYc%0AS2M6fHDxP36e67eckNthBaZQcHHOodVqpfra7Tbe+9734l3vehfuuece/Ou//mt63jNc/wQXBSZJ%0ALAbhr4ck9AE/qR/oDhJpN0mvDJWDlOTrVp0lAJZEYvAx9mq+YNUBwEUPFnAGGTvuYsrtJX8ITzRA%0A53nWAlzXlbFmYVuxkdlf1zpai2RaNLOAPKZ8SzcvW7JZY25crGgeYg08r6a/WCyuaItGo4H9+/fj%0AQx/6EI4ePYq/+Zu/wfT0NAqFAjqdTtoO/D25Wj2lABMrMWCi/Y8Brixi6daYkqVHS2cFVBpU+bUs%0AdeDlSWzfsivWBl5XPkvQ2jFmFiallfKupVwSjJWf76XzYx00lD6kM2Yg8oBBdYWYisR2JN2helAd%0A1vkskTpJfvQ1gE6nk07/qtUq/viP/xjOOfzt3/4tfvjDHyKfz2cqP2v/ZU2jDUD/ey1BXCsza5rQ%0ArETL/1IBhh+b0qzKSyxg9RKwLLus85SoSTOxkJ5e5JLZFaABB88TioZ8Tyu/Tv+HypbKiu0I7btY%0AtLyQhBiBtDTAj/muCa5bY9V88NA03W4XfX19AIBOp4NisYhcLoc/+qM/wsTEBD7xiU/goYceQrlc%0ATtuCOjZ9BwAvi9eTsyQtTey3pVZ7XcsTC4JSW/ci1swmywwlpFtLz8dZTKDkvsYBTiIkGnjHpJPE%0A+0qWQNWLrPsDArxDLOAKgQTN40E2ZloW0knT0kFkTZcs59SYqTQd1nRbQYCWJzk9/y0xbT4No+V5%0Axup/d7td5PN5NBoNfOADH8C1116Lz372s/jUpz6FYrGYLgvQ8qyBIAVArc29baHP8sT2dezg1EAh%0AZkZDy5GeFqNp+A2dXC6HQqGwoo9C7ajZT+2R+pvbKvmw5FMxTz9KLNKaVUhlZgFcCqirnR3FyLq+%0A3YoLnWpo17U8VtrY6QmXWNCNjZbcHvqf6tLK4PmzCB+43PnWwqGcc2g2m7jzzjtRLpfxxS9+EQBw%0Axx13XATEvK856wuVw+vCwTg0SKXBHVO2ZYMmWa/Ttsnn82i325ibm8PZs2cxMzODvr4+vOIVr0i3%0AuVF7svbjWjP4LH3njyXf9EFFa+fYftL8no9Bnna1sq7AKk0ftIYLHXPhYGVFUan8GLu18mJt1OrE%0AB7kGfDTSa1E/NjjQ8yG7rDoUCgU0Gg3cfvvt6O/vx2c/+1m0223cddddqNfr6Y4B7ti9ApoXqjNL%0AoNCAOVRPnlYqW2N8GpBSgC8UCqjVajh+/DieffZZHD9+HCMjI7j22muxe/duFIvFNC2fyUnlcTul%0AskMi5eX6tTzSTIPqkPLxMnn5Wp9ZY4WXHUNqepFLirECcSBLxWoEyQmkcmI6JnQ95CAhNu7TSsCg%0AOYo2FZNEAm3JMaUobjmflBcAms0mbr75ZiwuLuLhhx/GP/3TP+Fd73oXOp2OGeSyCGU2VGIGhgbQ%0AtA6hPqVpODBSXSHA9/b7LWrnzp3Ds88+i4MHD6K/vx+vetWr8MY3vhFDQ0PpW8aofi2YxgRVDWSk%0APFqbabpjCAg95u/7kGYT2tjTxgjXJ9VJ8/HVyCUFrCEg7WX6GzM4skrMFEJim2sRKDQGQPNK9mVh%0A4hpYxwQxCiTNZhNve9vb0G638eijj+Lhhx/GTTfdhFarldmu2IGcBRxC+ni+XpiXJLxN/Zr18ePH%0A8f3vfx9HjhzByMgI3vjGN2Lv3r0rnmzz+TUQ4uVY9ckqazFmrPOxvqYxfyufNKujAT7LLCdG1n0p%0AwDsJX1iWpoeSM8Q6SKwzSc7Jr1lTkKyMO9YWzjC16CvZJwWAUD2lKM/PWYMFWGZgi4uL+Pmf/3k0%0Am018+ctfxubNm3HFFVek6SQmHWoDqT7cJmmax9tPq4vkd3xQSnolkfqE+nySJJiensbDDz+MH/7w%0AhxgZGcGdd96JzZs3m0zKr1Xzulr1ltohJL2ADWeBWYEwlhBpeCCx6yx9txYAu27AqrEpCVD9+V7Y%0AgdaZ9IUh1uCQdiJItligpaWz6hGK8BILjonyseX4a7Q87Yuxlnjm+o53vAOXXXYZvvCFL+BNb3oT%0Arr766hVP8tAtWCEJ2SCBLD3Pt39ZdfG2aYAQ6nd/npIIz5Tm5+fxgx/8AI888giq1Sre+c53YseO%0AHSgUCioY8aDA6yD5a6hNQ7MfrzcLidHqH5PPSucDiiQ8aGizi17GQla5pJYCvGgRK4bBavrof+m3%0AxQI1liTZp0XGGNHya+lC9njJCrhW23OwlQa9lLfT6eCNb3wjnnnmGTz44IPI5XK45ppr0jdkaQBn%0AgV9M3XnQ5nlj2lpKHzsIue/4p9KOHj2KBx54ADMzM7j++uvx2te+9qK9z5J9FpsO1YHqldL0wt5i%0AgSjEQq1+yFIGFYvJW/l7nWFSCe5jdc59zDk36Zz7Pjk36py7zzl30Dn3NefcCLn2Z865Q865Z51z%0At8YYYbFUPl2UphhUsjgY12eVESrXssFiMtJxKNpSBpClbTSxBm/sAKfnJDDrdruYnZ3Fa1/7Wjz/%0A/PO49957ceTIEZF9J8nK10FK/Zb1MWOqi9vJg0ZIYpm1cyv3TubzebRaLTz00EO45557kMvl8Fu/%0A9Vu44YYbVqSlf7zfpT9qu1YHa0+3NfbosVR2THtxH5VskFgt9/PVAB6fucTMQFYjMQ8IfBzA7ezc%0AnwK4L0mS/QC+fuEYzrmrAPwigKsu5Pmwc84sQ+tUCfS440miAaa/FisSSPhjq2wtj+TMHBhj7PG/%0AOfDQtqFlSvZJdscELO3rnhbD9A7t7fMfLWw2m7jnnnvQaDTEQU7vfkvXJT/Q+oyek2wLMfoQAPjz%0A/Okv+j+fz+P48eP45Cc/iW9/+9t4y1vegl/+5V9OP3fj+zRJlr+aS4HLAjBeLw30aX6+tBEiF7Te%0AVB9vOysI8GDBfVWqB/dzep3XSwPK2PFg+U4vEgTWJEkeAjDNTv8CgLsv/L4bwB0Xfr8dwCeTJGkl%0ASXIEwGEA1wf0i+clcI2NtL2UlyW9pUMqX4vGgP40mddl6fO/pTaSjkNtI9mnga40oLgOKX+328We%0APXvwtreXwmh0AAAgAElEQVS9DTMzM/jwhz+cPpnlAUVrF87OtOuhPpZs1wKc1WZSXblt/qm0733v%0Ae7j33ntRq9Xwa7/2a3jVq16lBtuY1y1q9sfmsdpKagsJRHnfWjZoZIfXI1Yk/5ds0cavFiDXSnp9%0ApHVrkiSTF35PAth64fd2AMdJuuMAdkgKJKbBjykLow0W6iQq3IFCDS79ltKEOiXkhDyNNT3hddbY%0AlxWRYyQmiEj9FgI03madTge33norrrrqKszMzOAjH/kI8vn8CpZK03NdHBikdFmmqP6/ttFeOxfT%0AXoVCAffffz++9KUvoVqt4v3vf396x5+/pFpjjlpZlh3aNcvHeVDjAcKfC/mA1m7SLI37OO8TrV8l%0Av7Rms5KfhGYrq5FV37xKkiRxzlnWide++tWvAkA6Pbzyyisl3elvDq70P8+jARQVa2oVYmCWSB1I%0AH+O07AgNVM2pNPCzWF3oOgdDi6nyQSENEGDl4KzX67jrrrvwkY98BMeOHcO9996Lu+66C/Pz8ysC%0ACdUXw6R4Xlq2NH2lwm+ixQQKrZ1yuRyWlpbw6U9/Gk8//TRuuOEG3HbbbSum/Zo9Wn39ef5fs6lX%0A4bpp/aS0WUWrr3QcW5dQAJJ8kwbSw4cP4/Dhw5nKtKRXYJ10zk0kSXLaObcNwJkL508A2EXS7bxw%0A7iK57bbbzOgkOZ0FcpZDZXG2mMir5ZE6TzvmttHBpJUtDawsogFyDPOOSRsqk9vcbDbx27/92/jQ%0Ahz6EJ554Al/4whfwsz/7s2i32ysGssVSYsUCoCztaIEtfQdCu93GV77yFTz55JO49dZbcfPNN6/Y%0AAWFtGwqVFUoj+aMkUhtYea2yNP+I9VPLN2PyW+liyNbevXuxd+/e9Pi+++4LlmlJr0sBnwfwqxd+%0A/yqAz5Hz73LO9TnnrgBwJYBHJAWc0UnAIwFSqPGk9SmpYa1B6fVYUxpujwaEllPzvbQaqGptY5Xj%0AB3oWBqb95qCvDQLOMq0B4fW12238yZ/8CZxz+Na3voUjR46k73Ll+mh5FnuPmWVowStWpCklbZ9v%0Af/vbOHDgAO6880781E/9FDqdDvL5vMhCqU1S8AwBC71OmbC3jdsq1UN6QIfnlwJdyO+4P3A2rrUn%0ArQ8/L/mFNQ6lGZCvs5R2LSRmu9UnAfwngJc55445534NwP8E8Gbn3EEAb7pwjCRJngZwL4CnAXwF%0AwO8kilf4xqWVjmFFIYDkDCnUidJ56gBUp3SsTZE0gKJ/9KuS1CG1P0mH1GYWe+DHfGYQivz0tzSw%0AQuzX//kXZQNItx3Nzs7in//5n3H+/PkVG+mpXgo4lCFagSamj7KK/zS4f77f21Gv1/HAAw/g2Wef%0AxR/+4R/i1a9+9UV3/XlbaYEqtFxA24Pr9Ndi+0jzcV6W5o+axBAOazxKRCYr+Gk2aDdIV+MXXmJ2%0ABbw7SZLtSZL0JUmyK0mSjydJcj5JkluSJNmfJMmtSZLMkPR/mSTJviRJXp4kyb/HGKFFVy0ds2/F%0A75jBH2o4y5ljG11yTEmXVSce5bmNVpk0jWa3xSpCur1+XkZo0FGG5F8l2Ol0sGvXLrznPe/B4uIi%0A7r333vTl2dT5k+TiJ+b49ZigwG3JKj6f3+zvb7q1Wi185StfweOPP45NmzZhy5YtK+zkDMkCrxjR%0A/DMmvwbq1hjk/kL10DTarE7LI9VBK4+ft3xXyy/ZoLVHr7LuL7oGVj4uZw3OmOgoMRhr8Gsszovl%0AvBpox3aulscqV7oeW6Z0rAGTVUeePhTMPFOjjNyn9e3dbDbxmte8Btdffz2OHTuGe+65Z8ULnb2d%0AnKXSoKz1uWS3di1GPEh6UO12uyiVSvjc5z6Hp59+GsViEXfccQeazWbKbC2/tETqIy2ddayll4CU%0AB3LKHEOgKJWtTc+5aJ+vzgKQWp61DKwxsq7A6juQPyce6sSQc3HGGQO00rHUyZKTaeATiqi8Llra%0AEBBwO63AIIkVaLR03E4LgDnIJUmSslWfz7PTd77znXj5y1+OH/7wh3jmmWeQz+fT72x54YHY/w8N%0AfG5PrGgBDUBaj7vvvhtPP/00tm3bhj/4gz9Ir3v7pbK1wc5919rXai3dxNSH+0wI3Lj/87HBH1SR%0A8mj2hL74IeULjbvYQBBTXhZZV2ClT6pInZGV5fnzXIfU0CHmoDEFDUg0e2i9JEbNywl92pv/piLl%0AldhIiIVq53kw0WzQZgi0TzU9i4uLePe7340tW7bgc5/7HGZnZ9PPaNM6cVYlBSErCPDzscLbs1Ao%0A4MEHH8ShQ4cwOjqKu+66a8Vnwq32jOkDaxxYa5NWmRq4+yULCxQtYiDpCy3t8XaixxLASzpoWkkP%0Ar3NI1gJgL4mlAElWWznaoBogSNMzq2ye1oq+Uj5pUGt2SDZxQNZAVGKSFtAAF7OFGBs0O1YT/YvF%0AIgDg13/919Hf348HHngAU1NT4uO0GjOR7NFmGdI5qc34sV8GePjhh/HQQw+h2+3iN3/zN1EqlS4K%0AIlq7WDMnLtbsSAqetN5ZxSpfWjrz/0NAGBprMX4qLQMlif6JdWlmI5W/FmBK5ZIA1lBUzjI99ed8%0AXnrMr3P9ksPSTqM6tbKlMqguLZ016Hh6DYAlx5TKkwBF+s11xgC19DtGqE7nlr/1tHXrVjz11FP4%0AzGc+g3K5fJFtIdDQ6qGVy/NqzC1JfvTs/4MPPohut4u/+Iu/QKVSQZKsfM2g1A6h/pHOSU9D0aDI%0Ar2UVzW8kn5TaRvqtnQsFMC/SDIzPIrV7BKEPB4aC02pl3ddYeeP48zzNavRznfSaBhL+vzRIYu3S%0AHCeGocRezwIY9Bzf6sWvhfRrdsW2g1RPvgvgzjvvRKfTweTkJE6fPi0uo4TK1dKF2kvrMz9VPnv2%0ALD7xiU9gbm4O733ve1dsD6NbsPh6aUwA0tg3Tc8ZvNZfEjjF+H7IFn/NAlnJ7pDQtCFGL6Xz/7kP%0AS/m08tdC1g1YaaPETKl7ARIvWgfRc7xcqWNibLPqy9OFpkwhvTHMUqqjVaZmKyC3idWe/nfMOq9z%0ALr0R5MtyzuEtb3kLFhYW8Hd/93fpgPE6LH28nay2D01j6Tm/G+ALX/gC5ubmcOedd2LPnj0ryqFf%0AB9AAk5crtaVmA68TPdYkC2BoPq/ZagFzrH4JpCXfycostWUS6fdagSqwjsBqLe5n6ZwYsJIcnE9r%0AQmCddfBazhaqO88bAjSJYcbUw7I11gFD4K21LW93Ooj87yRJcN1112HXrl3odDp47LHHUCqVVtTX%0AP6UVsl8buBSsrUDn27m/vx/3338/XnzxRdx000249tpr0Ww21TYLtaPmA54BS3ktG7Oe18aQFIyk%0AIGEBbAxYaf4j6fTHPIhoNlj1l/Lyuq1G1p2x+t8xgCDl4+n5Xkkv2g0KKQ09ztpRPo20yK7VSwJ+%0AKny7Dd8XSsuUbFkL0fqGXgsBN9UR+6LqVquFO+64I31B9MLCwop6UpZLhTNSbeBa9aVAkyTLb///%0AwQ9+gMceewxXXXUV3vKWt1y08X8tpsJeT8ySQKgsjbFp66Wx9mpAFhIp4GXJGzv+YkRbR18LuSRu%0AXgHyNgsuvDM587PW4CyHtYCB5+d5NIlJ04tTaYPGiuKXkkjBTGO1Pv2OHTuwY8cOnD9/Hl//+tdX%0AbNOjSwNWP4YGTQh0nXOYnJzEAw88gMsvvxzvfve70Wg00mtrATRel5bGAtOQDq5H+t2LraEAZtmd%0AtTx+PpYghWStb1wBlxCwxrJSKw8FZWlg+d+htwoB9hYWbQBrOmIHnaZDc5xQ+VIa689aUugFMKhO%0AqV40nVSOH7jdbhfve9/7kM/n8dhjj63oZ78UoAEnPR9qK1o+zddut9FsNnHvvffixRdfxO23346l%0ApSUUCoWePt7H6y0BUSyIelu5/1M9UkC21pS189ISVqgfQz4a0ye8zNXIWrJSS9Z1KSB05y7UEXQQ%0A0Lw8vXTNYje9gDy3OZTPqp/0X5sWelmLKB1zk0krX6t7zHKK1Q5UbrjhBiRJgo997GOpXvoZE0li%0AAg//TRlxkiTo7+/HN7/5TZw7dw67du3C8PAwAKy4SUUlVOeQrdqxVi8pj5XPWkLqdWoes4wm5bdu%0A3kkBQ5q6WzMeK3Bo9V4LEL9kGKsVabV1U5qe/teux5637iTGsB8tOEjT9VjJmtYKFpbukL3WcWiq%0AbYGvZA8dVO12GzfffDNKpRJOnz6NY8eOrcgbE5BjxLnl3Qn0CaRz587hySefTN/C5R+xzdInFAAk%0A0cBRCrQckGJEC5BaAIgJfl5iQNQqN8SeY+zSmLO/ppGGl+q+xLoD62oroUW4kCNZ5VqRTxu0MdFZ%0A0qnZaUVQCUS0OtOorumkA4MPEo0pUBtD4B17Q0eyidrT39+Pt7/97eh0Orj//vtRrVZXfN4klrlZ%0AfuGn9n5blXMOn/70p1Gv1/Ga17xGnXZzXdo1a8mCpwndd1gLAIiZUXD7VmOHBYSaH1EftIJOyM9j%0A7c3yzTFVx6o19ChWg2qNZwEm/S+VwwedVa5mb0xeDbyl8xzMtAEvOYpWHy6xW7Gs31nKo/WJEWoX%0ArSe12w+WTqeDvXv3YmxsDEePHsVjjz2GZrMZFdQ4IPIB7tPRZYBCoYBjx47h3LlzKJfLuPXWW9Nd%0ACNb0k9eLtpXUrpbfcICl7RMzQ7Aky/pqaFzEiOZPtFzL16XAZI15TULr8WuxBrvujFUTqZJWxWMc%0AM6ZMSacFiCEJOZN03hp8oXJ4GmuQSKyA/w6BKBetDaXyY+wCVq5l5nI5/NIv/RLy+TwefPBBjIyM%0ArCgnBlBCgcg5l77u76tf/So6nQ5uvvlmlMvlYB16HZTcp2IDPs1P01lriKGgwG3KAp6WSGNZKm+1%0AwLYadh/TLjGy7sDqB0uIIUkgxxuAPhLpr3PmE7KF5vOiAY3kxDHOqAUBK782pZfSSGXEgJs13dOm%0AjLRdaXtzPZoNWho+CClD27RpE3bt2oWpqal07VNbp7PqxM9TZpzL5fD444/j3LlzaLfbuO6669Bu%0At0WmKPknn776dLytJDt53hiWRvVYgKy1j6RXY/baEoW1zknTWEshoXMxujSJsem/PWOlnaJt8qYD%0AV3MA6rTS54u16aU20LUBIQ0Yms/rjukU3pFaGvrbYi4aoGrtkFU0JuH1xm72Dw2eUAADlvuw1Wrh%0A9ttvR7vdxr/927+h1WqteAF2qL2k+tGAmsvlUC6X8Z3vfAetVgu/8Ru/gUKhsIIEZNFJhbYVt9O3%0Ap7/OX+otlavlpW3q60V1+SWP0HfRLGYp2ULz0f+aWIEv1M6hAOP/W/77Usm6v4SF/rfS8bQc+GJF%0AcxSuS2JSIYfSnCSLLku/ZK903dtiOU7W6VIWe18qHTRwTUxM4Nprr0WSJDh69Kj4MbzYGQoVDzhf%0A+9rXcPbsWWzbtg27d+9W25sH01D/WgzRAs/Qb+k4FLT8ef8nfTrGYuIhUhADrjHtpEkW/44lMGsl%0A6w6sNHrSc5KDaixGczBJF/+zNnhLTkQfJ+Xsg5cdW/9exAoOMSKBPT8v5ZHyZWXp9JxVjjXoms0m%0AbrzxRszPz+OBBx5AqVRCs9lUl0JibPL9miQJHn30UbRaLbzjHe+4aK8sf4Q1S59LPsPrrP1ZdbBA%0AmbNZLQDxvqSgG8taY0Qrh17PSpa4XRp7Xi1gx0rMV1o/5pybdM59n5z7c+fccefc4xf+fo5c+zPn%0A3CHn3LPOuVtDxvuppHTdchrfOSHmxnVSMNCm5BREqV4e3aWAkEViOzBLtLUYuQamvSwVaO1uLUlo%0AtmvgIQEx7b/R0VG87W1vw8mTJ3Hw4EEUCoUVaUOMSSrTvw+g3W7jDW94Q/pBQOn1kRyULJGCkAWG%0A1L+tgCSxyZh60msxYG/pkcDXCjaS367WH7kNWvkhwF0r1hrDWD8O4HZ2LgHwV0mSvObC31cuGHUV%0AgF8EcNWFPB92zolltNvtFYxV+t4Nd8YQm+L/eSPyiG0xAqmxNfs059FYE2cQWRgPTycFGG4jr48E%0AiqG1Nmng8QHA66wNNlqeNVBpHglAut0ubr75ZoyNjeGRRx7B0tLSCl/S+luqn0+zsLCAhx9+GLVa%0ADddffz1arZaYntqkBX5ut9W+Gnhqtlugzn1XY51S2RbISqK1gcXkJbD2ZIVf12zXgNAf0/3Nmljt%0AtlqJ+fz1QwCmhUuSBW8H8MkkSVpJkhwBcBjA9WLBQiMaNlx07CMtH9hUL08v6YuZ5ljAq7Fqq9M4%0AI5F0WvZwHZqtMaxQq6/kYJYOqofXVWIG/E4+bxsNnDmLazab2LRpE86cOYPPfOYzaLVaqQ/4/36j%0Av/RWedp3+Xwe3/3udzEzM4Mbb7wR4+PjYj21OlP7pXaUAiDXw3XRNpD6k9aL5g1JDNBa4zKWBGg2%0ASeMl5Jva2AiRHG6LFHiorrUA19Wssf6ec+4J59w/OudGLpzbDuA4SXMcwA5NAWdugM7ANOHRW3u3%0AZhZHiMnngd2LBAAhXbQTeQS3QNrrl9pNYjJZB5vU/tZ0iQcZi6lqTFHSw0Vy/Ha7jZGREeRyOZw5%0Acwbf+973Vnw2G8BFd8A5Q/PH+Xwezz33HHK5HN785jdHvayH2y21T0xb0a+bcj2Sb0kkImbcSAGA%0AS8xYCfkC1yeBmdTXVltqoBgz9mLq45f31hNY/18AVwC4BsApAP+PkVbsJWnqIP3n10NsUuvUl1Ik%0A4JCiseawFghZYMzbJOYLr7H16DVdr6AeWx4H4k6ng9e85jU4evQo5ufn8a1vfQtzc3NigJFszefz%0AKVt95plncPLkSdxyyy3mVNIaxBoYhtJrdeVpeVAI2dOraME8Bsh4oIx9RDQUUKS0Uj4trwXoay2F%0AcJKLJUmSM/63c+4fAHzhwuEJALtI0p0Xzl0k999/fwoSe/fuxb59+7zudPBwwJGmVj6tBKB8EHKx%0AGtbrpA8d0IHKmZ3Gurm91NbVdKzG/GLagguvB68brTv/HSPU8Tkz14CYs03/VVQ+o0mSBNu3b8cr%0AX/lKHDt2DLVaDU888QRuuOGGVJ9/7p/b4vUUCgW0Wi3cf//9mJiYwE033YRGoxEMNKGgzX1AY8BZ%0ACIYWZOhDNlJfSf5Cz2v9nlViGLM1A9Pqr7Fy7ltS+lCABYBDhw7h8OHD4QpGSk/A6pzbliTJqQuH%0A7wDgdwx8HsAnnHN/heUlgCsBPCLpePOb3+x1cd3ieX4uprG0KagEuBYYUdCS9FligQfXZQ3UUNmh%0AQR7bTpqd2kzABx9t4Dr3o6empKUT3hc86HDgkAZeu93GjTfeiI9+9KMYGRnBU089hVtuuQX1en1F%0Av0m7OHK5HKampvDNb34T+XweExMT6RNWPqhye6T20eymkgX8tPa0+kPyIwvELH3SeV5vaexYvqm1%0Am2WDFnCtdCECIZGe/fv348orr0yP//3f/13UEStBYHXOfRLAzQDGnXPHAPxfAH7aOXcNlqf5LwD4%0AbQBIkuRp59y9AJ4G0AbwO4kxoiXHlJhqLEPS0mZhcNxJYiK4pJ8z2VAdpLJWw2glWQ0b0RgyP0fT%0AUlDV8sS2jyZe59jYWPo9rFqthpmZGRSLRdE2WlatVsN9992H8+fPo16vY3R0NA0A2vtWs9gl+ZIF%0AIFlnG5pYwBnDKrOct9LxcbBaCTHw1eiJIWuxEgTWJEneLZz+mJH+LwH8ZYReMWpbkZlPAUNRPCTS%0AVI3fELCirDRQuK7YQaHVRRuIawlSqwE1Wq5mV5ayrOu0PP5+iZGREdxwww149NFH0el0cPToUezf%0Av19cAvDAmc/n8cUvfhGTk5Oo1WrI5/N49atfjWaziWKxuIK1hmY8lq1SwNGCOv3P7Q61Va8zlpBo%0AMy7KvkNs1LoWw3ytuvbKvlcbQCxZ16+0xgABHTzSYn0vkVwCJOrUVhprukbPU6bTS0dxm6TrXLQp%0Aq5aHT8VXK0mSXPTeh1j9scxGY/adTgc33XRT+pDAf/3Xf4nfw/Jgmc/n8Z3vfAdnzpzB/Pw8isUi%0A3v/+96NUKq1Yk6XgIQVQehzTzxaBoH9SGt4+MW2l6bdARZt9WYyO2qz9aXZ54U+0abZK7RTT/tYu%0AnrWWdX2kVZoiWtHaYrixNF4aIJZ+KW/WjrY6Mcu0THNIy6YYvVklRm+sfl4/KSDQ6/Q3L6NarWJ0%0AdBStVgtHjhxJ10opGOfzeXS7XczOzuLAgQPodDpot9t485vfnL6C0Lnl3QIesPngtwayBsBaeq3O%0ALyWb8uVa9oVAmOYPBQAulu+GAk7Iv6z25jo0orEWsu7vCgCw4gaBBiRA3NqN1jChz7tY5dLzlg6e%0AluezwNUqkwYeySk5q4qx0WLmVLdUplVXSei0nS630B0XNC3XGwu0uVwOmzdvxuLiIpIkwfT0NHK5%0AXPpmKueWlwH6+vrwxBNPYGlpCZ1OB5dffnn6PS1fR85SaX7/lzV4SfXg+ybXavYglc/P9wIiUp01%0A3wzl066HAD2GAGUtN6Qzq1wyjBWQAUZygl6cjzKNkE0Wq9XqEHNeArFQFOU66Y0VDoIUYLN8XiKm%0AbK1OUhqJhfrpt7etVCqlf55Z+pedcFuoD/D9pT5dPp9Hp9PBtddei82bN6NUKuHEiRPpeZq+1Wrh%0A0KFDaLVamJ2dxa233oqFhYWL2kx6X2qpVEJfXx/6+vouWpqyAEFqPylf6M9qa02kNNITilZ+bTxY%0AwGaBVgxTjSk71E4x5cUEwKzS03artRCLHVhC0/vjLOVRPVYZPO9aRLFexVpP1ZYHenEOnj+2zjFt%0A2W63cebMGUxOTqbnR0ZGMDAwgJe97GWoVCrodruo1+tYWlpCq9VKGa4PKJS907pTgBseHsbi4iK6%0A3S5Onz6NfD6f6gKQrq2eP38ejUYDN910EyYmJlJ9PgDk83n09/ejUqmgWCyi1WqhXq/j1KlTaLfb%0AqNfrGBoaQrVajWJFtE1jp8m9trUXabYjzZ5i+90CJm18xazzx5Yh5dcki+9mwYZYWTdglRiVNKWg%0A16wKawvsFIAl/dbCPC0z5DghUNPsz9Kp1o0LuueyF+F1DQGAllcSP3U+deoUDh48mLK9fD6PZrOJ%0Axx57DHv37sU111yDvXv3olarpWufSZKgVquhr68PzWYTCwsLcM6hVCohn8+njyEWCgV0u11s27YN%0Aw8PDmJ6exuTkJLZu3Zpu9l9YWEChUMCBAweQJAna7TZ+7ud+Dv39/SgUCuh0OhgbG0OlUknbtFgs%0A4syZMzh06BBOnTqFkydPYnFxEe12G1dffXUKrFnWSGl6zedj/coqS7puARk9zsroYnxbGxc8vw9w%0AnDTEBHutLzSbJV//sWGsVGKiJwcArtMqT0vH2VBWscBIm95KYBYSjZXwtUqr7bI6jhUUYmz2QfTl%0AL385Zmdncfr06RX5PHv8/ve/j/379+MNb3gDBgcH4ZxDoVDA6OgoFhcXUa1WUalU0jv2Hgy73S6a%0AzSZGRkZQKBSwe/dunD9/HrVaDcDy111brRYqlQqmpqaQy+VQr9dx9dVXY2xsLLWjUqmgWq2iUCig%0A2Wyi0+ngqaeewoEDB3D27Fl0Oh10Oh00m038xE/8BMbHx1cwQOrTdA2Wzsik5S5JQjMS3/6xOiSC%0AYBEM/5vay5m51P/S3l+JvGj2aLNRaYYb2xZ0/IXsXwtQBdYRWDUJRXBLuDNqjhlijxIQSjpiREuX%0AlQGHmDNPqwGe5MhSObEAHOusfl31qquuwtTUVApcANItUs45HD58GNVqFXv27MGWLVtQLBbRbDbT%0Az690Oh0Ui0V0Oh2cP38efX19aLVaKBQKOH36NHK5HHbs2IFvfetbyOVyeP7557FlyxYsLS0hn8/j%0Am9/8Jmq1GpxzuO2221IgaDQaKfstlUooFov4j//4Dzz77LNYWFhAu91Gq9VCu93GFVdcgd27d6Nc%0ALqdtHQJB3jd8Si71i+bHoTJoHqlvsjBf7g/cLql+MexdY7NSkLLyxer1umLY6X9rxipFJS2NlxBD%0ACjUq/S05LQUkqSwtgmpMRIrUks1WORbjWGvRQDVUbwkouF4/9R8ZGcHVV1+N73znO+h0OikD9eua%0AuVwOhw8fxtGjRzE4OIhbbrkF1WoV7XZ7BZNsNBpYWlpCkiyvw/o7/NVqFf39/ekNsdOnT6+w8emn%0An0aSJJiYmMDOnTtRq9VQLBbR19e3QudDDz2Eubm5VG+SJGg2m9i/fz9e/vKXp6CapT9o+lBg5e0u%0AMUMLCKU8kh2xs7QYe/156Wm7UMCPtT3GthB7pd//CtnRq1wSX2mVOkc7H5OfXqO/Q1uuaETm6agj%0AWVM7yeFi66LZLUXw0EC0hEduDdylgcDP8d/8Pbv+ej6fB7B8V33Hjh346Z/+6XT90zmHpaWlNH+z%0A2US73caJEyfwkY98BE899RRGR0cxNjaGLVu2pFum+vv7kSQJFhcX0xteU1NTGBgYSJcHTp1afqVF%0Ao9HAiy++iHa7jVqthuuuuw7T09OYmprC1NQU5ubmMDQ0hOHhYRw/fhyTk5NYWFhIb35Vq1W87nWv%0Aw/79+1GpVNIdDlqf0f9We2l9I523jqlIQKOxSWs8WLq1sSL9p34gvVA9ZINms1SulJd+TslKH2NL%0ArKwrY81K97X0WpoYoImJWtI03bJFmw5J+b3emGgdKlOTELOypvIWC6WDlduvBYMkSVCpVFAqlTAy%0AMoIzZ87g4MGDWFxcRKvVgnMOxWIRSZKgVCqhUCjgxIkT2LJlC/bu3YtisYjdu3enn6VeWlpCu91O%0A8ydJgnK5jOHhYXQ6HczMzKDVaqG/vx+HDx9Ot15de+212LRpU7qWOz4+jhdffBGPPvooGo0GAKCv%0Arw+5XA7bt2/HZZddhsHBQRQKhRXvd9XeJaCxf+13ryIFfCqab4V8mEuIPGg2SQEhRAakgCUxbClY%0AhADesnstZd2AlT8UoDkpP+bPiAMycEiD3upwqbwkSS662645bgi8qD0ae+A2xDg8r5PGaCWGGgJM%0AqmAaXjYAACAASURBVIO+XYoP4l72zBaLReTzeVQqFYyPj+Pxxx/H4uJi+mpAP+0fGBjA3NwcvvSl%0AL2HXrl1461vfmi4Z9PX1pcyx1WqluwaGhoYwODiI6elpOLe8ravT6aQgvGfPHmzfvh3dbhfDw8Po%0A6+vD/Pw8vvrVrwL40S6G/v5+7NmzB6Ojo+mjrlIAoe0stbUXySd5n4RYrjU+JB/V/MyarXAd/Dz3%0Aee4TUmCWXr3Jy7Gm6NrYpTdteZ34tdgZ3VrIJbEUwL957kUa/BITjClDOhei/ZJumod2Nn8unTqZ%0ABeJZoijXZQ0+Dw5S/lCbSm+m5x9W1OrBy5PK9n3t94yOjIxg79696bolgBUPDfT19WHTpk04deoU%0Azpw5k67N+htXXodfKuh0OituLM3MzKDZbOLcuXNoNpvpU1Z+bbdaraLRaGBgYAB9fX1wbnk3wvj4%0AOMbHx9Hf33/R1N//ll4ubjE5rZ0oKEptJaWNEQtUrbJD+iTfpW1C20UCP3oskQ2un49XWiZn7dxO%0AaptVrxhMiJV13RWgTRXodX9Oovu9lplVQlMbSS9nJjSNxqRjbIi5pgFuKD91bmkfIRepflYQodfo%0AE1RJsrxOeu7cOWzdunUF6ObzebTbbeTzeYyPj+OZZ55BsVjE6Ogojh8/nu4sWFpaQqVSwc6dO1Eo%0AFPD5z38eSbK87FCpVDAwMICFhQU0m01s374dfX196bsBvv71r6PdbqcBxAPpwMBA+pmXUP20dtX8%0AgrdrzIxH0m9d1/RIxKAXkcBQSsOvaWNCK4OKhgWhOkg+beldrVwy262sBrZANdZJYpzWAih6LA20%0AUN7Qez1Dg1a6zsvPOo3kwqO/NoVzzl309q4szJ/3lWc3/iEAf3Oo3W6jVCrBOZdO5U+dOoWnnnoK%0A1WoVv/Irv4KZmRkkSYLNmzejXq+nb6bK5XJotVro6+vDl7/8ZfzMz/wMms0mut0uNm3alLKq2dlZ%0AHDx4EOVyOX2fq9/+VSwWxZeuxIClBoS0z2JnXxZAS8CmBQJrjMUAm8Y2tXxaeSEWze3izDWGfdL8%0AgLyE+FLKui0FaI4Ymn77NFnYHhdpuiXpk6ay/NVmod+8DrQeMWuT1mCT2GKI2WqDSrrGdWrgYpUd%0AE3BarRYWFxcxPDycvhe12+2ueFF1q9XCiRMn8OKLL6Z66vU6Nm3ahMXFRSwuLqb7W2dnZ9OHDrZv%0A344zZ85gaWkJi4uLAIDR0VH09fWhXC7j1KlT6VNhtVoN5XI53X7lb15J/sfbKaa+MVNNizhovir1%0Aj89LXxoj2aH90bL5Oa9XAnJ/TTofG3wlMJd8NBboLdDmx6ExFCvr+j5WL3RgapE+trIe/HptNG6L%0A1JlaZ1v6uRN6NqgBOgcwWpb23kqpLSSRGJg0iKy20epnlS8d+3VU/7XVG264AYODg+jr60vvwPsn%0ArPz+1VarBQA4efIklpaWsH37dpRKpXTL1eLiIkqlEoaGhnDgwAEAwJe//GXk8/lUb7VaxWOPPYaH%0AHnoIjUYDCwsL6XSxv78fIyMj6RYxCSBiwFEjBBYp4L6UBZhjgp227k7ZYAhceZtY25ikcRTDHLUx%0Ap9VVy+uPpXprs8C1kHW/eUUltlK847W3/mu6NdDl17iTecnyZiCqnzNUDZhDA1AbNDHRWnJCPt2V%0AQD6GDUtlhwaRZ6yFQgGDg4PYsmVLOlj9DSj/2Gq5XEZ/f3+6h/WRRx5JN/zncjmUy2V0u108+eST%0Aqe7Dhw9jcXERp06dQrPZRLlcxpNPPolnn30Wp0+fRrVaTW+A+RtXSZKk4J6lj32dLQYptYnVdjFC%0Ag68EipwoWOVIN2El20O2aemsoKDZodWX/raANnTeizRGe5VLZo1VE87c6HkaYWlaS0+oLIk1a6Ar%0A2RN7XSs79ryUTivXYq7WecpiYtJZ+rX+63a76VrowsICTpw4gfn5eTSbzTSNfxqqXC4jl8ul+19H%0AR0cxMzODyclJ5HI5PPXUU5idnU13D/i3Z01PT2NkZAT9/f04ceIEDhw4gOHhYeTz+RV7ZoHlBxnm%0A5+fR39+ParWa2s7rogU1ek0a9NQPLPCIlZBP8S1M2niRWGtMGau1XRtTWWyQ0vNyeJ2lPuU2rEYu%0AKWDVIrcU+QH9q5cxOrXr0nQq1m4tCMTYZKUJpbfYNrdHAsMQ86WDTZpahurPBy1N45xLH1ddXFzE%0A+fPn0W63048B+vR+P3GlUsE111yD48ePY3Z2Fs8//3y6rgoAR48exfnz5zEyMoKrrroKR48ehXPL%0A+1xf8YpXpK8UPHHiBCYmJrBlyxaUy2VMTk6iUCiseKyVfrOMt4E2MH2b8D7waaTPC2ltFhIJBKXr%0AWYK8FECsmaBlm3QcartYnRYT1oK8lSd27MbKJQWsXEJgKb1BiF4PgS7XpzGL1djN7fI28c8583yS%0ASHXhui3G7XVo1+i52CmcVFerHtRG/7hoqVTC1q1b0+1TIyMj6ZNVpVIJlUolvaPf39+PF154AbVa%0ALW3LTZs24T//8z9x+vRpVCoVOOfSBwS63W66lWpubg7btm3DCy+8gPn5eVx77bXpQwkDAwNwzqVv%0A0apWqyu+cCABjOZb/ry14Z3roG0Xsx4YAlWpLM1O/5vbpPmaVR+JHUuzOZqGjmGt3lYdQnZw+7Vz%0AvQQQTcwFBefcLufcN51zTznnfuCc+z8vnB91zt3nnDvonPuac26E5Pkz59wh59yzzrlbsxgTGsy+%0A86kTkHIvSqfUacUfL0cDasnJ+HlJt5YmCxumDF3Sy+vOb25J9ZPaKqtjWXq1dNRG/xDAZZddht27%0Ad6cgOjw8jJGRETSbzVQ3fWu/f8/q5Zdfjp07d2JkZAS7du1Cs9nEzMwMut0uJiYmsLCwgEqlgq1b%0At2LTpk3pQwXz8/O44oorMD4+DgCo1+vpum2xWMTAwACGhobSp8BoO3H/s84DKz8JRH9rDEsDLakf%0AtXbnsxEOnhYwch3cNs1WzQ6JtUvH2rKIvybdILPakPePZJu0nurc2qyzhjS0APxhkiSvBPB6AL/r%0AnHsFgD8FcF+SJPsBfP3CMZxzVwH4RQBXAbgdwIedc2oZ3Dm0CEmPpT9Lfwx4WbbF6tTOaQPAYqsS%0Ae/C/aVo+aCQQ9/9XG4Ulx8yaj/aZf4TZP+9fq9XSV/MVCgW8/vWvxy233JKCqX87VqPRSB8KqFar%0ASJIkfdfA8PBwCsKTk5PpY6jz8/MolUpIkuV12i1btuDVr341CoUCWq1WevMqn8/j8ssvT9/JGvKz%0AUBtIA19iSFobS7ok0LBeLmSRDOm6lTY0Pml+i91aQoFUqlcIVC0iw22PJQa9iAmsSZKcTpLkexd+%0AzwN4BsAOAL8A4O4Lye4GcMeF328H8MkkSVpJkhwBcBjA9Yb+VRmv6cgCftZ5qwwrf0ifNVApQGos%0AoFehLCSrI1kzAHrdsldidY1GA6dOnUKr1UK5XE5fplKpVDA0NIRNmzbBOZfeXPJ7TOfm5tDtdrF5%0A82ZUKhUsLCygr68P27dvR6FQwLZt27Br1y40Gg2cPXsWzz//fPrOgNHR0ZSdeubiQbdYLKJara5Y%0A36XgZbFDqY5WWt6OMX0rsVFrpmDZE2MXTxebN9ZPLVDXdGgBSZoxSBJDylYr0ZzXOXc5gNcA+A6A%0ArUmSTF64NAlg64Xf2wEcJ9mOYxmILxIpAl8oRys/ZF/PjaWBTUhnlmk+n5JoA4mel5yW5/d/0tdN%0AqfAtVZYT9spwJHv5eiFN6/ev1ut11Ot1OLf8+kC/VzWXy2FiYgKzs7PI5/Pp3tSRkREsLS2hXC6n%0A+009AOdyOYyOjmL79u1p+X6JwH9YsFKpYHBwMGWr/rMuw8PDaDQa6Q0uqX34dF4CL34u9Lo6nkdr%0Ab+qjElvV/CVmTEj28+tcr5S/V6bK9WrT8ZBv+v/abJen54x2rbZbRWlxzg0A+AyA30+SpMaMSwBY%0ArShesxiZNqXl6cTCDEZgAafkEJqTSnbz/CFHpGkoWGrlaEsJ/pz0nkurLElig1woH7dRssODArD8%0A6ZRut4uzZ88C+NHe1na7jfHxcWzduhUvvvhiepOp2+1ifn4eS0tLqFarmJycRLFYTB8UmJ6eThmn%0A31L1+te/HvPz8wCAiYkJnDlzBufPn0ehUEh3Azi3/DQXn4qGGGGoHaS6W0AKyC934eVwX+N6efC1%0AdITGGz+n7W6Q6mKJVlZoHNO82jiS2ojqlpYDrP7OIsFdAc65IpZB9V+SJPnchdOTzrmJJElOO+e2%0AAThz4fwJALtI9p0Xzl0kX/va19IK7N27F/v27VNt0CIQ/c/PS+ck57IaMaaBQ+VbQGVNo7V8UpT1%0AaeiAssCApve/LQaSpZ20unAmDvzokyxLS0vo6+vD8PBw+rHAxcXF9N2p27dvR6vVwqlTp/C6170O%0A5XI53RbVbDbTt//v3r0bSZLgiSeewIEDB9I3Xfky8vk8duxYnkCVy2W0221Uq1XUajXs3r07fU+A%0A31kgtbPVpqFzvM21PDFBih5rNvYSJGPTx/pDLIOV6qCd40FHK0MCWk6i/O/Dhw/j0KFDpo1ZxARW%0At1zqPwJ4OkmSvyaXPg/gVwH8rwv/P0fOf8I591dYXgK4EsAjku5bb701OCCpZJ1ahKJejGggFYru%0APD89pv81ts7t504kgZ0WfLTpmaRTS6eVZ7VBaCrpp1yNRgO1Wg0jIyOYmJjA0tIScrlcuiQAAAMD%0AA9ixYwd27tyJhYUF7NixA8451Go1VCqV9IXVhw4dQrvdxpYtW9L3A3gdfsN/o9FI38GaJMvLEbt2%0A7UKxWESj0UhfxuLbg+7TjfFBH5BCL92x2gZY2XdSH3L/jgEu7RHqWJadpZ95oJfqJqXTwJGfk/rE%0AAmefh/YLTXvllVfiyiuvTI/9u3l7ldBSwI0AfhnAzzjnHr/wdzuA/wngzc65gwDedOEYSZI8DeBe%0AAE8D+AqA30kyImJsh2sSAq9YUOWAarFAK490XXphckivF2kaKU0FgZVP9mhrcR5clpaW0hdB0ykw%0Afb0ft4mXy22hwsv2NgFAs9nE0tISCoVCutWqv78fzi1/TcDvNe3v70elUsGDDz6IhYWF9AYUALzw%0AwgvodDo4fvw4kiTB2NgY+vr6UK/XUSwW0wcQ/NcGPFMdHR1NH52l392SXuzN6yrVj7aN1G8xwsEv%0AFBi1/uH+SPuSXqPHmt9bn6uXJPZaluDA21D6LdVDIitandZKTMaaJMnD0MH3FiXPXwL4y1DBWiS6%0AoEOM0DE6QmVaTLZXvVp+KQrHTvMsoemltS6pPI3pdDod1Ov19IaN3y9aqVTSu+T+g3rOLT8lpTF5%0ACTw0xuu3TvmtVrVaLd3+VCqV0N/fn758Op/Po9lspl8U2LJlCx555BFcddVV6HQ6mJycxNTUFEZH%0ARzExMYHFxcUUuP1XXv00v9VqoV6v49y5c+nygN/m5cGavgvCCiihPuL5QhKrO0YPt4P3mWVfiD1q%0AvsRFCkzWLI2XFTNOLQIlXeN6Q/b1Kuv25BWtHI0cfn+jlD6mU7SpTpbpnKRPAxNuDy9TstnrjZn+%0ASI4g1UNjitQWYOVd+k6ng7m5OUxPT6efMXFu+aml4eHh9C1Q/iupuVwOCwsL6U0gX1boG1BJ8qNP%0A3PgXWC8sLKRPRnm27N9MVS6XMTQ0lO4v9ezSb8MaHx9HtVrFd7/7XfzkT/5k+unqer2e2nbw4EFc%0AdtllaLVa6Yb/drsNAJibm0OlUsG+ffuQJMvvge10Oim4W+0sBX/eTzSAx05v6TWe1zn5mX9un8Rw%0AtXQWONI8XAftc9qvXBcHdG6fNA6sgCDVk6aNCRg8j9ZmaxHc1v1jgjHApTWOFbFCOtdKtA7XmCpP%0Aw89RkZw+ttO5Q9O28M5Tr9cxMzOD06dPo9lspt+Z8gzWv6ZvfHwcg4ODaLfb6c0g/zJpP+hpOVLZ%0A9FytVsP8/Hz6eelCoYCFhQVs2bIFzWYTuVwu/XLq6OgoAODcuXMYHx/H+fPnUSwWceWVV6LdbuP8%0A+fPodDo4evQoNm3ahKNHj6bbq4aGhuDc8gMD999/f/pegLGxMVSrVSwsLKSA1el00iWJZrO54l2w%0AEkjG+GkoPT2vgTC9JoGu9L5YSQcNeNa3oCwAkxiv9VklTUK28jK5Pg0UtTL8MfVVn0/aDrgWeLGu%0A7wqQIk+WdY8QcPWiJ3YKZKWPuaZNkVcrFhOhg8GvNXoQ9VP8breb7in125rm5ubSTfMeWOkr9egA%0A92XzMv15D+YeyJrNJgYHB9O0/kGA6elpnDx5Ejt37kxBzjPbqamp9EGC559/Hpdffnm6q8SX/cIL%0AL6BQKCBJEgwMDGBmZib9IisAzM7OpgDa39+fsmMPrH5nALXdavMQC7XaRUsbCrgaE1wN45ICIbdZ%0Aqo/F0Gl9tGu8DHosMfAspENi0SGbVivruhSgRXiJ7WUFHz/tpNMoryuLXVnSW8CrTTU05mMBZKxt%0Ako3Actt4UK3ValhcXIRzLn2xs2ez/nHTRqORrnt6Xf5bUvl8Pn0htXMufQm11IeNRiP9sJ8vr9Pp%0AIEmWX8gyNzcHYHkbVqlUwunTp/GDH/wAe/bsSW9EnTt3DocOHYJzy1uyRkdHUSwWsXXr1vT1gUtL%0AS9iyZQuKxSKmp6fR6XSwf//+dAmhXq/j4MGDGB8fT1nt0NBQWg/PzAuFQvo1A6lvrD6U2p33WSxw%0Ahwa/xZS9aA9qWPbR6xrpCfmqNO40AKZjQwsSHBy1umjl8VmbZM9/+6UAQF7HkURjlVpnSgNb0i/p%0A0RzeipwWS+OdJgEx7XRJT0i0+tJj55anvAsLC+l0vNVqodvtYmFhAQBSIPHMtN1uo9FopBvuC4UC%0AisVi+oITvx4KIL3mmS9lsq1WC1NTU5ienk5Z6fz8fPrKQP9OVg/upVIJo6OjOHLkCEZGRjA1NYVC%0AoYC5uTksLi5iYWEBS0tLGBwcxPHjyw/7zc7O4sSJE9i1a3krtd+61Wg0cOLECYyPj2PLli1otVo4%0Af/48SqVSuv3KBwofTKxBJ/muBJhSUM1CECQbQkxNKpPaxP2B/87KzDU9GkukZcS0hTWWOUhqIE6v%0AaZ/h1l712KusG7CGXvlHRYs0/hpPqx1LTkB1SlHZ56d3ivlWHF4fXw8pAnM7+H+N0VgDWBvgUv2X%0AlpawsLCAmZmZ9OklD4R+W5Ovb5Ik6V11ugXJs1d/F71er2N4eBilUindBwosb6Pyuuv1OmZnZzE/%0AP4++vr5Ub6fTSdP7HQkzMzNotVrpjoVz586h0WikrxHctWsXzp8/j+PHj2NhYQGDg4M4efIk6vU6%0Atm3bhsHBQSRJgmPHjiGXy63QOzQ0hGq1ir1796YgSh+t9W3Y6XTSIKGBBe9Xyc+0GQkXiwVzduWv%0Aa2yTg40GSjyYSzZI/k+veZ/QXnVo1Znr8//5E2/WjFOqj9RO3D6N8WYNfppcEoyV/rdEYw1alAmx%0AQA7SkjNYnUuXG6Q6aeVoaXqdGmrTJm6Tvxk1Pz+P2dnZFKx8Xebn57F582aMjo4in89jamoK8/Pz%0AKYv1jNIzRs9QBwYG0Gg0UK1WMT4+Dud+9ChpkiSYn5/H1NQUZmdn0Wq10g/7eR3+lX2e2U5PT2Nu%0Abg612vLT097OdrudTvsrlQpKpRKazSZGR0dRKpXwzDPPoFQqodVqwbnl9w549j06OopCoYCJiQn0%0A9fWh1WphdnY2Be/BwcE0qGhTf84aNTYYEgnQLB+O0cdFY2+xtob8k573sxu/Q8AKHNbM1I8nD+L+%0ABilPL5EUesz7SAoy3EZOiFYr6/6iaymihqKb9Zvr1qZhUkdbaSRd0lMcEpOwJIaBxgShmIHpb1r5%0AF43QG1aFQgFjY2O44oorMDw8nDJZD6Zzc3NoNBro6+tLddVqtRQ8/dul5ubmMDY2hqGhIZTL5RWf%0Asl5cXESlUlnx8mkPfP6jgf5m2qlTp1Cv1zE2NgYAaVCYnZ3F0tISzp49i23btmHHjh2YnJxEs9lM%0A6+S3hTUaDeTzeZw+fToF+mKxmL7Mxa+ntlotNBqNNHhQBsaBIsRQJbECrjRrC+WT+pYfSyREukbT%0AhMBPO6asVfssekxdeMCK/WQ1B1XtvMbQtYC5Gll3YM0iMcyUM4kQAIca0QJjns5yJIllS8LtWW0k%0A5YzVM8ZWq7XCgYvFYgqGw8PDaLfb6O/vT5ni2bNnUa/X0yUAf1PIOZd+VtoPLr8XdfPmzahWq8jn%0A85iYmEiB05dZrVZT5trtdlMGSpcefPlJkmB0dDRlvZdffjn27NmDQ4cO4fz582g2m+nLsr3+TqeD%0Acrmcfpl1YWEh/QTL0tJS+m6Ber2OZrOJkZGRNGDyqa7Wtr0yV4mlxoIQT2+VHZoFWXXT0nCbqd10%0A+1UoIEm28HEsjYfVAh8nLRIAr1bWHVgl57WiLD+nTdel/BogWtMD7rxSxKO/pQFD80oMyOfjrFli%0ArRpjktqG18MDq59ae/CijzrmcjmUSqV0eu5vbg0MDKDdbqdPQR0/fhzPPfdcejPMr4u1221UKpX0%0ARdHz8/PpF1T9l0/9u1SB5ZeheMbrP+Lnlxj8Wunp06dRq9Wwc+dOdDod7Ny5E1u3bsXp06dTXYOD%0AgygWi8jn82lb+3cC+Be1+Acd6Bru7t27sXXr1vRrBbSt+RcEeH9JfWidC0lM4NXS036mvy3bvGjl%0AhWZL0jkOVJp+abxTQJXqIuWTwFFrO8qAeTpJ/2rkkthuZQGiJtKU25rWSyDp02r6eFmS42v/aT29%0AhLZ9xTIjSegTMFokpu8E8Hfhfd4kSdI1Sf8oaJIk6btK6VardrudPibqv5x69uxZnD17Nn3Df19f%0AH44ePZreqPLP5Pf19WFoaAjNZhOtVivdGeCBu16vo9VqoVarpR8IXFpagnMOZ8+exfXXX59+96pe%0Ar6f5/A0q386+rf1nrdvtNoaGhtDf34+xsTFMTU2h3W7j1KlTGBwcxK5du1Aul9MHFvg2K9pHIRLA%0A+1LyRYt5WQRAC9gWMEhgKOnWQNHSx22W1lhDQYaPW69HK4+3pfVBS6pTKl/bhrZaWXfGGhKJTXKA%0A45EnC1uImc7FshNNPy+L/w6Jli6mrvQ6XU/0T1r5RzjpNisPVJS5efHvSt28eXO6B9QD5+DgIBqN%0ARnqDrN1uo1wuo9vtYnp6GvV6PX2naqlUwqZNm9BsNtFoNJAkCebm5tJXAvq7/H6f66ZNm3DjjTei%0AXq/j6NGj6Yurz507lz68kCRJWge/V9YPOs+ol5aW0g8H+u1Y9XodCwsL2L59O7Zt25a+lEUCG7qO%0AyGcU1sxH6g+eJgbEpGm2FnRjSQrVw+2QACoEwBoQZgFW3rbcTs32mHT/f8glAaxa1I8FIokR0GuS%0AXi1vaBoW24G0HCkAAFAX+jXd2lRRWmKg1zwgeKbptzn5P8/Q/Bv1/dqpVLckSdLn9+lboYrFIkZG%0ARtKbQPPz8yvA0W/l8jejGo0G+vv7MT4+jkqlkq5zOre8WX94eBj1eh1JkmDHjh1461vfirm5OczN%0AzWFhYQGlUmnF9ij/xQFgeZuXfxuWZ+b+09qlUgmLi4vpOit9F8Fzzz2Xrh/v27cPIyMjF80C6PKA%0A5mcx/mzl5+esYC+xX2ucWP7tg4bFFkPgp7WNBWia72qiEQoL+KW6SHrXStYdWK0peJIkF7EDTUJs%0AIdbxY87FpNXsiGEzmsQ6ieTUlI369VXPBpMkSW/w+HS+7alOemPCv17PH/uvAPg108HBQQwMDODY%0AsWOYnJyEcy6d+jcajRRMy+UyAKTvIqjX66hWqxgcHESlUsHevXuxc+dOPPvss1hcXExZpt814L9x%0AxV/F6INFoVBAp9NJX/ziP0rY39+fvg1rYGAAtVoNp0+fTpn1wsICdu3ahfHxcZTL5fTxWAl0NGZF%0A00mgGjvr0Rik5gexgM3t832slSXp5iAr1VeaVcbWxwpQ0myQjjOp3aV6WwGsV1l3YA0BppR+LXTS%0Aho+ZlocAUAJOrj/Ly49jxQoY3E76yZFut5t+74nuFfRslgYz/ltzaH/Dym/f8mu1/r9/Ft+zYgCY%0AmppCuVxGuVxe8Xjs0NAQbrjhBpTLZZw4cSIFYwA4fvw46vU6tmzZgoGBgTRIeLt9m/uHA+hOhFqt%0AhiRZfvBhbm4O7XYbW7duxejoKMbGxnD06NE0vd/WNTY2hq1bt6ZBgNZdG9B8sFqzml6ZlJQ3dlbH%0A02tgG2KEki7LB6kdWdc3OVCHliRCMwSe/8cKWIGLbwbxAe3PU9Eajevi57gOf13LK+WRomrIESkb%0AkNJbkZPrDLEjzW5/I4c+OdbpdFasQ9LtTbRNrAHL35zkp5P+zz/dRL986pcHms0marUaxsfHMTY2%0Aln7Tau/evVhYWMDx48fT7VedTgcnT55Ep9PB9u3b0+1gAFYsYeRyufRF2XRbmV9T9jsdPHv2L4bZ%0AtGkTNm3ahGq1inPnziGXy2Fubg4nTpzA1q1bsXPnTmzbtg2Avhne9w997wIFEr+UwPuQ97006DWw%0AtsBRO6Y+oDFVugyi2ZEVyHmgpm3IH8yQ8lLheTWwldqUj/MQy88q6/7kFf+dZbqkMSitPAuQJOZq%0AMQLJFkknrxM9T69LUynJjhDwa5LL5dLPmExMTGBycjIFJX/dT3et4EHtkwaEr4sHF8pSk2R5ycDf%0AwJqYmEiBZmlpCTMzM9i8eTP6+vpw7NixdIfB0tIS+vv7sbS0hKGhoXSvrN/GlSTLU3S/rcozX/9+%0AV/8mLA/Q/f396UMClUoFtVoNk5OTOHPmTLp27Jc6/Bu9/Kdezp07hx07dmB4eDhl6byNpOUr2q4U%0AYLmEQJIHMEo+ePoYMKZptetZgrikS8vDd8nE+LRGpviXLmLbRGuP1colwVg10aI6/Z2lY6TIxcvj%0AOmNtixEtAISmg1K9pQBggbtfVxwcHMTmzZvT1+8BSKfqXjz783tCabto76/0aSgTyuVyGBkZ7oMJ%0ApAAAIABJREFUwe7du9MtWH7rU6fTwfz8fPoylXw+j8suuwwAcOTIkfRJK+eW96NOT09jfn4+Zd6V%0ASgXVajXdp+qB3DmXsu5Go5E+geXr4z/vMjIygvHx8fSpMP+Y67lz59J9rnNzc+nDEiMjI5ifn0ez%0A2cT8/Hy6NusB2z/R5R/R9S/09v/98ghtxxg/0/yB/9F+CM0yYoAy1rYQkGpl0+Bs5dNmstr40eoQ%0AIkI/NoyVsx4rHRAHhhbYaaxRmjaEmG0oUkvHUqSk9efMIzStk65JbJqeKxaL6WOmIyMj6eZ6yqT8%0A0oD00opQVPdpvI5arZY+6rq4uIjp6Wn09/djdnY2vZG0sLCAnTt34mUvexlqtRqmp6fTdwR0u11U%0AKhWcOXMmTevZpgduX6avB12/pb9LpVIKyJs3b8bAwED6pdbh4eH0Zd7A8o6FM2fO4MSJEzh58mS6%0Aturf0eofWBgcHMT27dvR19eHJ598MrW3UqmkNwP7+vrSvb4+sFSr1XRfsMYMQ/4qBTmt/7kfct+m%0AdlhB3PJpXi4XzSd5fbx9MTbT8zHgznXGvJilV1l3xqqBGI+6sdFXe9hA6pgsdsUySl+W5ggUBCS2%0AESpPGjzcXqutCoVCCk59fX3pk1H+ur95pT0r78vx71317HNpaQmzs7Oo1WoXvUTbb5vy27mcW/7C%0Aarlcxv/X3rsGSZqdZWLPybrktbKyMrMuXd1d3T3d0kgaTAwaYgIQZhGE1rJxcIlwmHWEDWFYxxLA%0AsmEvGMQPWNhAtjdYecN/iHCwjtBiI5vwhlkRClihNZIQBAJZEhrNjKzu6e7pa1XXJe+3qso8/lH1%0AnHry7fNl9XQ3U0y7TkRFZX75fef6nud93ve853zf+Z3fidnZWezs7ITYVEYtTE9P486dO9jb2wtM%0AUw/aZl6Mz+XOMOZBnyt9q8yDoLy7u4tMJoNisRiAdmpqKhyFuLa2hn6/j2azGSIFBoMB8vl82MTA%0ANxdcunQJw+EQ165dw61bt8JJX3yRIdkrlVq5XEapVEI6nU4cu+OUfEwOmEeSoo8BdIzhWtDXsmJz%0AM8YAtYwYqCXJu9Y/Nk+A8dc5xfLQ8pIii2I7sWLtfpx04sDK9Chm/HFM0f6edDShfe5RTPpJIP1W%0A0yStb+t3HGN/K2XSXKbZylhQgr1uBACO+o9bYWn2drvd4LvsdDrB1O50OsHXSVM+lUqF91DxFdPc%0AUPDyyy9jMBjg/v376Ha7wQ9LQNzY2AgvOfT+yI/Gw1V49CBfSsh7dLKQKdLvqr5J+nq5YYHtJxDP%0AzMwEIKR7YXt7G9/4xjfQaDQCO63VauFw7eeffx61Wg1vvvkm1tfXw84zHvAyPT2N7e1t1Ov1EM7F%0AzRi6uKnjlmTtqJzExjv23ya7QKWfYzKXBLZWziwQvpU8JxGVJCWgoX+2bo/iBrAA+6RpIrA6584D%0A+FcAlgB4AP+z9/5/cs79EwB/H8Dm4a2/7L3/w8NnPgLgJwAMAfyc9/7TCXknaku9h+mtMEbN357t%0AqPdPAq9HFeBYPrbuOnBJbUo6Q8DWl7/FJmGsT2KCzegAMjTg6NAS23YGzDcaDdTrdXS7XUxNTYU3%0AD9B3yR1MwNE5rPwdQDDfr1y5gunpabzwwgu4f/9+yJcscm9vD9lsFvfv3w+mPNklD1WhW4ALS8qw%0A+fpuHXfWD8DYK655fgGZpT2tjHUiONPUX1lZwauvvorbt2+HV7lMTU3hzp07yOfzmJ+fx/d8z/fg%0A9ddfx9e//vUQakafcbfbRafTwWAwQLvdxvLyclgMOy7FWKcd9yTwS7LajrOOJjHPmNXF/8oqjysr%0AVk9tp84dlf0YcUpSNLaeSez07WCsewD+a+/9V51zBQD/j3Puj3EAsh/z3n/MVP59AH4UwPsAnAXw%0AGefcu733xwasJQnVceDLFOu0JCGMsVyClQ7ecXGnOpAxINb62HrG8knS3EkTwj6vv8cUgQINWSvr%0Axx1YGpJ048YNbG5uBvDj+QDaLsbD8gV+9FtOT0/j0qVLWF1dRS6Xw8bGBnZ2duC9x3d913fhxo0b%0A4bBtMmLW68GDBwGwWQaBv9frBQZK0OOqfzqdfmilXNk4/Zv8zE0IzM+CKhPZEPtuamoKL774ItbW%0A1vDlL38ZGxsbWFhYAICww6vb7aJSqeAHfuAHcPXqVdy5cwf9fj9EXnBrcaPRwMzMTNiWa9Mk2bG/%0AHSdbSbKsMqr/Y+VNIhCxcifVI2ZJajmxvCYB6KPMuRgOKMg+DVAFjgFW7/06gPXDz23n3Os4AEwA%0AiPXoDwH4hPd+D8BN59w1AC8D+At7Y6xz3oq5cNxvj0vnk+pgNd4kzT2p/Fj+j6I1kyZ9Up4xgOc1%0ABVa+ZmU0GiGfz6NUKqFWq+H27duo1Wqo1+tjby7l1lHugBoMBtjf38fc3BwuXLiA5eVlpFIpXL58%0AGfPz8+HVKM65EKr06quvBjbLLabFYjG83YB1ZjgY20VWyUU24Ii9cKFIr2mEQjqdRj6fDyv3hUIh%0AfCeoTpIZ748OpGHeS0tL+OAHP4ivfOUruHv3bjgTgbu8qtUqlpeX8d73vhfVahWDwQCLi4sheuHu%0A3bsBcGOWFVNSvGxs/GOyk3TfpJREZGJyGZsnMVmM5aPP2tjapP6IzTNemxTqlqQUlPla3+/jpkf2%0AsTrnLgL4NhyA5AcA/EPn3I8B+BKAf+y9rwNYxTiI3sERECflG+1EbShTUqfEkh1cG9CdBJqxPGLX%0AJz03iWXb8o+bFLHnk4DUsm/N1+ZFnyN3JpXLZVy+fBn1eh3NZjO8ndU5Fw6Q7nQ6YXcVF6+mp6fx%0Afd/3fTh79mwAKXdoQtPNMBwOkc/nsba2FpgvD0/pdDqoVqvh2D4egM0/fdmg7sxi3nQXkHVqe8mC%0A6U/mIdbpdBpzc3Nj9dV+s3Kj/WflIZPJ4KWXXsL58+fx13/912ERrVgsYjgcYmtrCzMzM1haWsLC%0AwkKov/cHh86QpVrZVAC3cvKopMNaYapY9Vn1s06S56RTpGIkw9YvRkZi8zHpHjtGTFbp2PGL9U9s%0AIwKfsWclPG56JGB1B26A/xPAPzpkrr8F4NcPf/6nAP45gJ9MeHwi/B/HRG1H2mesZlKtd1j3h56Z%0ApP35mx0g5h0r3+b1KOwyqb22LlbpWPPF1pv/ta7W/CIw8UT9qakprK6u4urVq8GfyqB+msGM5eRi%0AzHd8x3eEuiwuLgY2aYHcex/8uRcuXMD29vbYYSdnz54NJ20x6fu1bBs4DgRcugk0EZAVjAmqMzMz%0AmJ+fD+/mSvIDxoBD5VHbOTs7i9XVVVSrVfzRH/0RvPdhu7D3Hjdu3IBzDu9///vDmbJ8zQwXyWLB%0A/mxfEugoM3sUluWcC/0BYIwla4hdEnBbmdJ6PCpbtvWxfZx0TwzsrGwnvcbF5mHr7v3RgumjsPlH%0ASccCq3NuBsC/BvC/eu9//7BiD+T33wbwB4df7wI4L4+fO7z2UPr0p4/WtC5fvhzeCx/r5JjwxzrA%0AUvukwbf3T/p+HBBqnWMs+FGERvOK5Z/0bBJw62/K/LV/CKwEPR6WMjs7i3PnzoVDURgEf+PGDXz4%0Awx8O/lPGfAIYM6Vtuzl2mUwGd+7cwbVr1wKTPH/+fHBH8GQrAGNsmAJP4dcIAfXNallWIZGhzczM%0AhHd65XK5AGixMZkkX3xGla9zDrOzs/jwhz+Mr371q9je3ka328W9e/eQyWSwsLCAV155Bb1eD2tr%0Aa+F5ZcwxxpikSO09kywbftfNCvY5O2cepdxYHrbvkubrcfnaeye1K5a/yv5xOOC9x/Xr1/HGG288%0Acp2OS8dFBTgA/xLAa977fyHXz3jv7x9+/REArxx+/iSA33XOfQwHLoB3AfjLWN4f+tCHEoU3SftZ%0A2q6fk/JKEtZJ5VEIlQHH6jGJLVjmMamOmtejakw7CWxemqf+xkTmwoWTu3fvwnuParWK8+fPo1Kp%0AIJ/PAzhYkHn++efDif72rQNkh4w/te1wzqFQKODGjRtoNpsol8thuyjvnZ2dDSFTXOnX1XyCGOvN%0A2FXbTgu8zh3EsabTaSwuLqJUKoXwKjsGsX6y/adlxfbbp9NpvPjii+F14p///OfR7Xaxvr6OpaUl%0AvP7669jb28Nzzz0XfMiWCU5SuHbMbT2TAFbraFMS67SMMCkdx+iPIyyxuk+aV0lzRJlrEkOOyYr3%0AHleuXMHly5dD3T/zmc9MbPNx6TjG+gEA/zmArznnvnJ47ZcB/GfOuRdxYObfAPAPDhv2mnPu9wC8%0ABmAfwE/7CaMSG8wkLRMDHiuIsXtig2A7194fmzixPB6lLUnP6W+2Dkl7wC1YJ7UxpmwsEDMAn281%0ApenKZ3TlnYxPWaG2mXGaNg5W6zYajbC0tITt7W2USiWsr6+HSAS2lXXiIhE3ADCpz5Z5KyPlghoB%0AlyCcyWSwuLiISqUSXg9j/Yy231W5xia+7X/1adOf65zDD//wD4fxffDgAf7kT/4E7XY7KBCWa/N6%0AVCstVp8Yi9R7HoVoeO/HXAW2n5Ket9E0zEvjTNW9ESMBtg2aLD7Y3/isjWeOtZ110fGLbTx4nHRc%0AVMAXAMRK+sMJz3wUwEcftQKxgdMBtQOk6TiBt89Nui8pv6TfrbBMYqX2eU1WCKzGj7FiVSCxMh+l%0AnbyeTqfDnv1KpTK2pZXt0xOZYvkQIGK/UWAVnMmSufefgM7fyEgZk8qdS6wL3xbAGFfuzdeTuciI%0Ac7kcqtUqFhcXQ8yqBTKVO9bDMj0LHhYEJhEAtp2v7d7Y2MBzzz03dsi4JusrjI3no7BaJQiT5gpl%0Azv6pctF2KYtPmpP2mgW5WD1svZNIlz6n1ybJvZYRIyUWa540PR14foykmksHTiej1W6abCdPKif2%0ArP6mE0v/YtrVam0brJxUzyRNGBtQ/T5Jg04C8uP6RgPfyfSAowD72ISxJwhNypf32XsJJNVqNQT6%0Ac0OBMlBdWLPhYcARa9WjCNUNwJchLi4uYmVlJYCqbi9V8FQXhlWgVk71Nx2L45RYKpXC6uoq6vV6%0AiNXV+sSetSCj6VHqwHZxXGzkhPablmt3MsV2NllSlARaSSCXlI5TVFrHWLttnaxyTGK89vknSSd6%0ACIv+t5rjuE7Q549jdJp/rMwkxklGZDWmFbgktpr0PcZEdGJpyEdMS8f6wvr7YuVb4SNbbDQaIQyI%0Ae+ztLix9LsYUjgNb1nF3dzesyBeLRWxsbDwE2FRseqYpzwHw3odXeDNv59wYayZbZRwpjwdUn67t%0Ac21bjJ3FUpIMJ8mC9x6XL1/G66+/jp2dnRB1YV0/SXMgKVxQy2Q/a334u2WoScxRnzuODSaxvBh4%0Ax/ou9ozWz/5ulZutqxKzGAbQ706LLDa/n0Y6cWDVz0mCznuShEp/jwFtUpm2I5NYga3PJKGI5ZMk%0AqFbQbRmTgNFO6uPaq3kTQJl/o9EIC1Vc9IlNvKQ68HOMcWsiAx0Oh5ifn8fi4mLYgKA7qYBx5kzh%0A53fGiup1LgR578O207W1tbAIp7uyYn2i3yeNZRKIJm0xtgy3UqkgnU5jZ2cnhK5ZhW1ByJ55Yetv%0A/yfJic3H1i1JtmNs8DjWZ5OWmfRcTJZjpCKWhwXjJKVi49kfpy2Pkk782EDgYcCKBUWz4yaBTdL3%0AmDA8ioCowNuBSBrwGGuJaVZbxyQgnSRkk7RtjLVaweWuqG63i1wuFyaf9bNNqrf+FmNZ+gxX4vn2%0A1mq1imaziVu3boVDqrmAZScKAVUPSgGOzFiCUSaTwfLyMs6ePRveSKAHrChrsfKgbE/zTupHfo4x%0AYGXfCrypVArVahXr6+s4d+4cnHPRcYz1pSargK0M2fracDvN77j41UlzVe9LmhfaD7HfmLQfLAYk%0ABe1Pkn9bz5gPXe+fVLe3mk70dCsrPHqdSTvsuJVcToykQGBNyg5sXlY47eBNMo+SQDKWkkB4UkoS%0AVjWdjzNp+Nvs7Czq9Xrw9fX7/YfaYFleTCj1/hig8l6eR6qHbi8tLYXDXVgWIwMYSO/90alcmshw%0A9SjBM2fOYG1tDYuLi+EgbAtcBGUFJTsWtj1Jk5DPJC2IafsJ6mfPnsXt27eRzWbDAS4xuTmORMSU%0Ag73fykjS2MWej5UTA+SkOk5iz7HfNRoiSaknEScbUzwprCzpO6+9bTuv/iZSbJCBh1nlcZokJiRJ%0AnWdBMTZIVsgsI4iFYCVNIv183BbTpDYlXbf1iwlxjGVb4d7a2grvjrILdjGA1j5OAhs7EansWM72%0A9jamp6eRzWZRLpexurqKmzdvBkbKM1FZJwUG1oGfNVa1Uqng3LlzqFQqY4eraP2SgMuOuypeTbaf%0AY2FRk8aLYWfe+7DV167ax5S7BY9YvfW52MJObOysErHJslb9r8/HFMOk/ojNP22jLXPSXLXjkVSP%0A2Nw+rp6Pm04sKgB4eDWS6VHA0nbcJJameeiKf+x3+zdJw8fqyvpN0tTaBrJCnVwx8E+qn01JyiMG%0AHNxWWigUAkBx8Wo0Go29nM+y9aT8k8Dfex9OlSIL5a6vhYUFzM/Ph5cOAgf+2EwmM8YuudOK57/q%0AOQI8zZ/mfy6XG3sVitZPwTJpEsXkKQbOVlZiMqjPpVIpzM3NoVgsYnt7G/l8/ljAVLBMYnExOZoE%0AqJr0uUmWDsu0cyNWb3Un8fsk+VXFoz5+6+9PitrR34+bK3aeKh4kYdJbTSfKWIH4Yo0Cjn63QBf7%0Ar/loh8b8X/ps7LfYZ62XzSPGMmJlaJ4xhmQ1rv7O/BTokph9TBNreTzebn5+fuy0KCts+vwkQbVt%0Atn3FcCcCq3MuuATK5XI4MJohQdzAsLu7GzYxsI70w/KeM2fO4MyZM+FwFWUuViYsM7L9p2zPAp51%0AMxGQkpit7UO6bM6ePYtvfvObY2ckMNltp0kAb8HcjntsJT7GCjVPW0YsX7Y7aYHT1tvOz1g7YsrO%0Azg+WmzTHJ7UnqW3MUz+/o4E1abDsRLD3aIqBLD/rpI4Nps0n9l2d3bxuNXsS0CQBtH6nQE3yzylw%0AHsdUY8/HJjbT9vZ2qCvZKYCxw6NjAp80kWx9FXS4KAUAnU5n7Ho+n0e5XEa328XNmzcDuNo+IUNl%0AefTVrq2t4fz58yGsyu6/1zGZBChJLDUJyOw9MQUbq8doNMKZM2fwta99Lbp1N7bQo6w2CaD4vAJo%0AUlLZTgoptPUGMHY6F8F1ksuG/y0ZsPVWJRaL6aWsTMICW/8YSbHzRJWYPv+k6W8FY40JuwpPjK1a%0AANHnbZrEJJM0swX4JPaZBGKT6mnrlQTKMWFhv1jWEWM2modt1/7+Pra2tsLWS+bBBSMex6er2bE+%0AOq4t+pn+U4ZLcVKn02mUy+VQr7t3747FqerrrMlWebDJhQsXsLKygoWFhbFdVTq5dbLa/tOJpYBh%0A6x4DB+03fcYqIwUc9jF3mG1vb+P8+fNjxyXG+tf2Z0yBxuQo9ozeG1MWMearbVTznv2WVD8Llknz%0AyJZvx03rqn1tgVHzirFc/e24ELknSSf+zqsYeOpvkwY8pv1irErZaxL4JGmvJCCNCbIdqJj2jw1o%0AbPIntU3rlHSfrZ/2LYWs3W5jZ2cnvFiQLxXktlBdkbeM1gp3LGldCI7sYw3M1p1HZLXFYhF37txB%0Aq9UKJj+3v9InWy6Xwwv59E0AFsQUHGIunJi5bMO8NKnFoiCurhRlnTEGNhqNwhm4tVoNs7OzY4Hr%0AFrR1/K0fNeayiYFXbF5pHhqZoM+xLbEyVQasste+svfY/tbPMVC3beJnu4POzk1lrlr348Lb3tHA%0AasEnxhKBhzvJpiSmpmVYpz9TzO8aA3KdIDHWnOTL0jz0/qRkXQ98RvuI12KsKgkktG18ptFooN/v%0AY2FhAZlMZmznkl0cYHm2LklKMGmseHp/r9d76OAWe9rW8vIyBoNBmBi7u7vBRZDL5cICFRe4yIa1%0Azy1IqHJgPbm7Tj8nTX7b7thCieZtFaEC8t7eHsrlMu7cuYNmsxk2aMTuj/WrZc9aro69Ze4WEK2c%0AKhjFgN26Grz3Y5aN1sPWV2XUuhOSnqcytuMR23Gndde+1HFQmXbu6JwKHc/Y2RdvNZ04Y2WaxFQn%0A3Zt0zQrOcc/HmIXu0tDOtyAcUwyxSRBjEBYcJ9VR89LvtmyrfVVLU/C2trYwNTUVTHD+zklCV4DN%0AM6aMLKjqDioFKoIfY0v1Psap0synO4L11T+eA8D8aGlYcIuNTex3ZWTWGtA8YoyGk1CvWZPZAh8t%0AgnQ6jdFohPX1dTz33HNj42CBztYjpsjs75pfEsvV/7YP7DhrP8XGJRbZop9jCleB2va/ZZ02P9tP%0AOm4qd0n9RgtN+5w+4ydNJ7p4lRRixP+PMti8NwZyFiiTAJX/FTRjAmk1rr1X87OftW4EMBv1kFTP%0AGCOyDNUyETvRtfzd3V3s7OygUCigUqkEk5uvR7FvPuWzNHntIqOCkx1THQf6U/XcVjsGzrlw2Eom%0AkwljwvowPz28mqxG+9YCmx0LVZgEu9iCmQU4BWlVWEmAp/dp6vV64Y23d+/exaVLl8aYW0x2Y+xL%0Ax98q/9gLEm0dVQ4tm1O2q7KgoKX9Y9vK/7rd2NaZY8fn+QzrFYsC0LHggmgqlRqTWeuOYV9woU3b%0AxfYksefHSScGrLGVP+BhDR0DHTtJgLhfRAdB74lpPFu2vS92bRLY6z0xgbY+X+u3sopDV9hjQMs8%0AbLIKAAB2dnawvb0dwpN2d3eRy+XCS+3oW9W/JPal9dO+0nbrJOb5r4PBIKww23z5jIKl9T9z0hCk%0A9Vnmd5xVoHG6liHxun639yrzSzJlWaYCFid3u90OxyfyPFw7dqrEYotElplagqDlWzBUmdDr6gpS%0AebRtVuDjczEyEusv3qPts8/z3tjah8qjc0fvYKMS1uf5WU18JTeqkG3fPW46MWDV4G1gfBXXCjPv%0Ae9QGJz0TY0iatOzjziDV+5MA27aJSTVpTAB1AtvV7CRQteXYvtLv3EK6srKCbDaLubk5LC8vY3d3%0AN7wllaa4Ti6te2y11rYpphDJMriV07JubVtS/9k2W3NcJ7AFGC1HJ7S9xzJylQk7lvqc1tcqNTJ+%0ARjfwMJr19fXwyhveG1Natu7aTj3/wAKPlVPtFwXtJPnSNtg+sT5c26eal8oAy9TDyy0rtWsjSios%0A0YjV0yoiupr4unZl64zK4MHrT5pOfPEKGN/nbhlHzNH+KPnGwC8JjOxz1pyNaXsLiNSUViC1XP0f%0AY+yx9lmwmNSWGBjYsofDIXZ2dpBOp7G0tIRsNovR6GCbJQDcu3dvzL+aVK4KNMvmeMX80KPRKLxj%0AazQaodlsIpvNPsRmtM5qVsaAwjKTWH8p60lSlPbZ2NjF5JCyYd8sq6BjlQ7rxle35HI57O/vY2dn%0AB2fPnn0IoDhmWkfbNtt+C1i2P2KM0jJC+/ZaC3qsn1od3MiRpCxZjj3JjImyo/VRhcRyeZaEgqKN%0AcbaYMjU1hWKxiJmZGfT7fbRaLQwGgzC3GU9s++Jx04mfFcAOVhYHHAmINvKt0HQd/Jiwsgy9X6/b%0AFWSbNM+k8q3ZHgM6qwgs25jU5iRwtd/1vsFggM3NTSwsLITDozWNRiPs7OyEcCvbj5aRKNPRiaF1%0A5CTd29sLwNpqtVCpVEL7YsxHwdMyGsv6dRFMFVdMAUxa6Erq9yS2Z+XW3q9jmUqlwmtn2J5cLofR%0AaIR79+5hZWVlorlvGZrep3W1xCC2ah+Tb6tArUKw4MprBEqVbSvnbLdzbuyNvrrwpfkSFK0vXN8y%0Ay3wtcHP8dR57f7C2sLu7G1wxvMfudLOH/TxOOvGoAKvR1OHO362AxwTCmg28HitjEiBqGToBYz5G%0AYNys13y17NjvMcDid+vzSxJybXPMlLGLJt4fvMu+2WyGoPRutwvnDhaMCoUC5ubm0Ov1xhYB7CRJ%0A0uqWdSo48jkCq56pqqxBx9Kap6qINV9lP7Gxjo2DLcu+aVb7zLI99j/ztotESdaXcy64WGiKzszM%0AoFgs4sGDB1HLzbZJ/ZqT5oa1WqyyYVJ5taAek1WrCNmmGLhxbKz1wsN22F5LOoDxaAu2mQtU6hsn%0A66Q8xGSV+RHUWT9l81y8ZKTAk6a/NVtaFSisiWBNSx0QnQz2HlsGf1NhtM9pmTHfW6w8ncB28jLF%0AJqRlYEkLerYdyvLZF2SeFhwUILnbivGgjUYjvPhuNBqNvSKF9deJpICqAqusQusAILBV5kk2QOZg%0AQc0Cnk5m7Wvmaeuj4G6v6/02IkP7VMfTvgOMz2q9OCm1zzVUTfuE/by/vx92l5VKJdy7dw+tVgvz%0A8/Nj9WNeyrLI+Pif/ah9YfvMKiNeT4rysLLJpOfg6nyxSo55UIGyr3nvcDjE7OzsWJkkBXxbL/t0%0Ad3c3uqhLINS1ALZxf38/hOSpP5VtYZ30kBd+T5qHbyWdGLBaZ7vVrDH2qR2rgqv32lAPJiss9r+y%0AHssceU0FN7aaCowDmSarDHjN+poUMGKMy7Iny6gUqGJm4/r6OkajUVhA6vV6IUh6MBiEFep0Oj22%0A0KKgwXrrhLBgYttFAea7rBji5f1RCEyMCdtJb88B4ESKKTk7Ntonqgw44SZtMFA2pkcR2n5mPZQJ%0AW9khsPBgmUqlgrt372Jrayv0Ja0JgoKeSzs/Px9eIc7yrUXFMeJ1lQk1j5U5qj+TbeDvBEKek8s6%0AMY7Y+kL5pgjg6I2/9MFSdnd3d8MY2Fhk9pmOj5r3KjssX9ns7OwsgCMAnZ6exmAwCCDN8tjfKqN8%0A9knSRGB1zmUAfA5AGsAsgH/jvf+Ic64M4P8AcAHATQD/qfe+fvjMRwD8BIAhgJ/z3n86lrcKBRup%0ArDWm6WNAasGZKWYq6WS3fswYEPM+5mOPobOT2JZtr9lYSW1fkiOfyU5eZa9aZ5bDdimA7O7uBpZa%0AKBRCv9P53+v1sL+/Hw6kptlEhmvrYPuc9bRRBFYROefQarXG2JL+piCtfcTrFiSUZesInIiOAAAg%0AAElEQVTY2edVIXFiWsuEdbbjbFmt5q9xmHyOGx4sqNINwM/0dzvnsLW1heFwGH5Pp9Po9/vY398P%0AY9HtdtHpdLC7u4vFxUXkcrkANLYNyhSVeWs92Q8KTFpf7S8yUKvUCZBUmtwlByCcNEZQUwVFELNg%0Ayu+cK1xMVf8q3Uhk7GwrAZ3t8d6HHX3eH/hZ0+l0sDTU4tvd3R1TnE+Sjnv9dd8590Hvfdc5Nw3g%0AC8657wbwgwD+2Hv/z5xzvwjglwD8knPufQB+FMD7AJwF8Bnn3Lu999FlNp387BQCgzq3ldHqIoUu%0AeilgWh8PtaqCnTUzORmk7UGQLOhbza558ln+ZxiNBWqdjDFw1joqiCkr4D06eVU47bVmsxnMTe6r%0A1z7ndlEAyOfzIW9dyGJ+FF419TlR1XelbeTWUzJlZR6qEPQ7+5Fjo+xF+1OZo4K43qds0rJ7Khct%0Aw7IlBRx9+aL+zp1l3NxAhkV2OhwOUSwWcf78edy9exeNRiP0Z6vVgvcHfvBSqYRU6uBts7lcbgwo%0AGF0BYIyxKTiq/FmLUFmfghCf49ZjlkdA2t3dxfT0dHAdqetkZmYmKAXKvParJVAa+kRGm0odvCpI%0AFTllirKm1hEZpzJplsP76FYgcNIyowshk8mEfiZAt9vt6Hx8K+lYV4D3vnv4cRbAFIAaDoD17xxe%0A/ziAz+IAXH8IwCe893sAbjrnrgF4GcBf2HwtYPKaskplLdYc5ERShzsngjXh9D4LPAqGFCytk+ah%0Ampr3qRArq7FmI+8Bxv2OCkLWr2hZK/NQDW/NVwUare9wOESz2US/38fZs2eD5qcAzs7OhokPIGw7%0A5ZtEbdutn5HX7Jgqs2CdCazNZjO8a0snoCrcWN+rMrFgqgsdynwsMFoGnQTKLMu6idSMHY1GY2fF%0A6jjwP015Kgue6OWcQz6fRyaTQa1Ww9mzZ1GtVpHP51EoFMI8IABWq9UxPyaAMEbsD/a5NfkJZARV%0AKjoFVbJsSzq0T7lDj9fIUtnPehiOmtwA0O/3A1sEDqJUyMjJbDk/yNKJAxrxQaAdDAZjb7vlnNAQ%0ArJmZmTFiwK3SBHIAweVCV8GTpmOB1TmXAvBlAJcB/Jb3/lXn3LL3fuPwlg0Ay4efVzEOondwwFwf%0ASpwgyuZUEDnoliXpZFHWaJmbBRpbtgqmsjt9Vk1Umm46wbQd/Gx386hprN+VJetvwPgxbOqKiDFb%0APYFKFQrrwTwZKzkcDlGtVlEqlcZ8m8PhMPhVrZmvjn/dWx0TQO07lj0zMxNMyOFwiFwuh93dXbRa%0ArVA+LQMdLwKW9duxbNtf2qdq5qq8AEcHvqgVRNDTe2imkhVy4W00Go0tHGn+XKW2ckMzmAAwPz+P%0AQqEQ8i6Xy7h37x76/T4uXLgQDqBhuWRdzFPHPZvNAkAAd/YHDxXXmFY9k0HnAsFHwXk4HAbmrWYy%0AQ8RU3rR/pqamAnjpWa0Ec9aLLFd9+exHq4xZDssYDAbBuiIAM2/KGttE85/lqBWjzFp9r0+aHoWx%0AjgC86JybB/BvnXMfNL9751zclj28JXbxs5/9LIADIbhy5QouXboU9anYyWDBxjIXBbrD+gEYX7VU%0A1qVJNTSfZf4agmF3Z8QYrk5wa9LrpCB4UNPrdWVS7ANtH32h+/v7gQlYgOUz/X4f29vbwYxUxaZs%0AgJPX+kl5jaaUcy6YcMra7aRIpVJhgYzmMNlIq9UKdSZjUNCwCyT9fn9MAalbJZPJBADi7jFOFO17%0ANfNp0ioTojnIich6sJ81vpLXuB2Vf8459Ho97O3tBSDgWPJkr0wmg16vh2w2i0wmg3K5jPX1ddRq%0ANezt7WFubi6MGxmYTnh1YfT7/bGjB8lW9/f3kc/nx/zjNPM7nc4YuycDZx+wLQTVqampcDyj9z4E%0A2OsZuHx/l/cevV4vyD/BeW9vD4VCAf1+H9lsNoSbsX2FQiGMXSaTgXNHrjiWy3Gksp6eng4hg5xb%0A6XQavV4vEDS6wQjmVIq8BwBu3bqFmzdvBsb+pOmRowK89w3n3KcAvARgwzm34r1fd86dAfDg8La7%0AAM7LY+cOrz2Uvv/7vx/Aw6uV1req7M66AYCHQ5Z4zbJG3qO+NF2x1fvtZwvg1mVAIJC+eigWTs0U%0AfldfKc0oPq9gbf2L+qcsRM9RtT6wfr+Per2OdDodjgm0TFpNenVj6D3e+4cUAE04ThQFduZLkCLY%0AOOdCfQCM+eY40RnvSPDSMdN2UhGr749lW4VKAJ2dnQ2Tn4nsSReedGysxcAx4A4y9VUWCoVwP0HL%0AWidcICQLJDB0Oh2k02mk0+kQV8z6qcIl82RbdI5w/DRWmWBGS2FmZibsAOMrwnmvMnnnXFjw2dnZ%0AwWg0wtzcHACg1WphZmYm+DJ5Lm61WkW73Q5jNxqNkM/nwwE7hUIB9Xodo9HB2bRUtABQLpdDLHW7%0A3cbS0hI6nQ7a7Tay2WxYbOJiK9uRSh0ssrXb7bE1jNFoFFwv2WwW6+vrwQVBJXPhwgWsra1hbm4O%0Aw+EQn/vc5yKo9ejpuKiAKoB9733dOZcF8CEAvwbgkwB+HMD/cPj/9w8f+SSA33XOfQwHLoB3AfjL%0AWN7WHKfAkJ1wIpKuq6+Q9wNHE007kt91cUFZjoKKLrYwDzIdCiZ9SsoYLDPUSafhOMCRr5juDbIB%0AanayTVUqzI/xjrqFT0HOBtqrSUuQ29/fR71eR6vVwtLSUphEFPiYAtI+Z79rDKr2NZ8hMGg91X3D%0A/NLpNPL5fOh7VRzMn/2oVgj7RpkZAYQgo8ozk8mEuNB2ux3kgPlwtTgWWWAtAObNxRZVLOpvZb3J%0A6tm+2dnZcMgNy0in02MgS9Z9/fp1LC4uBvltt9vB0shkMsEP2Gq1xs4YYF0INhwLyk+320Uul0M2%0Am0Wr1QpsmPVUJggc+W41ukEVHd+02+/3wz21Wm3stePqxqFLpFAoBIXS7XZDeTxfV4P+y+UyAODs%0A2bO4d+8eRqODt/3yZLDRaBTK7/f74chJtplRCbS0arUacrkcGo0GlpeXwwHvlCGO25Om4xjrGQAf%0Adwd+1hSA3/He/zvn3FcA/J5z7idxGG4FAN7715xzvwfgNQD7AH7aWzV/mDiZOPik59bs1PvVj6XZ%0AqnmnpjsFwS4gaRk6oTSUR1ekAYwJj/ocNdrA1pkTmYtAWg6d7gQKlk02pWBPYVOGy/yt35dgQTOH%0ALKdWq2E4HGJhYQG5XG6MKXLCMZHRqiKzJiId/ayHrRsnE81nsi62Q01s9ZvyDazT09MolUro9Xro%0A9Xqhb9nWTCYT5EcVF/tkeno6LEywfwkcVG40g7VMnvZFdqfKRhnd3NxccE3QtOW7vM6dOxeuExR5%0A9momk8Hs7Cw6nc6YIpybm8PU1BRu376N4XCISqWCK1euwLmDxa1+vx+Y/NTUwevBC4UCNjY2gqmd%0ATqdDlAGjL7z3KBaLoT9s1ECv18Pc3BwymUzoL/WzLiwsAEBQDPV6PZwvsbu7G57j2DCaJJVK4Vu+%0A5Vvw9a9/HVtbWyEsjO6Azc1NdDqdcNj69vZ2kFOO7czMDLLZLGZnZ1Gr1dDv9wM4U67I2Dn+XISl%0AXFDh1uv1MIdZT75Mc25uDu12G/Pz8/DeB5/1kySXgHt/o8k553/zN38zMAAKrTqzCVh0dANHDnLn%0AXBACAGNAak0u9aNatstJpixPQYr3EUjVxNQylLkq41V2xGepuS075TUyI55bqv5YHrfHOhAcFOwp%0AlGoCbW5u4ktf+hKazSaef/55PPfcc2Gi7+7ujjESNQOV3XNSZLPZwFjU16p9wPrpai1/39jYwLVr%0A11AoFLC6uorV1dVg8g8GgxCjOTs7OxZvSGAfDodj224JiHZcKTOpVArZbDb47qgYOPYaBsX+nJ2d%0ADbGlBI/t7W3s7+8jl8uF+tLvy7yKxWJQIPSzEuw5Fuxb9iMnNtneYDDAa6+9hu3tbVQqleBHV1dS%0AuVzG/fv3g1ul2+0Gl4RzDi+//HJga7lcDoPBAM1mM4wVQYQ+bsoL+63b7WJhYQHNZjOADU1ssmrK%0AKZUaQ5g4Ns45LCwshP5QmSgWi9ja2gpjxfnSarVQLBZD7G6pVMLs7CwGg0EIw6KiIVFhWWTflGuO%0AD8e20WiEV6Kr66XRaIR5kMvl0Gw2USgU8Gu/9mvw3j82dT2xnVek8tTCnLjKAAEEs4eTns5/Ai4H%0AS0FMTTICAxPBS32gBAAKvj90nvN1GVzBVn8vTQ+CNdmcMihloMpO2Faa1vv7+4EFsHwKJEH31q1b%0AqNfraDabY6vanGhTU1Not9tjJ//wb2dnB/V6PbRJV2YZD6kugf39/RDfx/p1Op1QR2WiwNFh06og%0A1M3A/+l0Oph+3/qt34rRaIR6vf7QAhxNTH1WmSpBg75ImnDsE9aHE3B6ehq5XC4wXwIbJytBSRfP%0AgAOW1mw2A8scDoeBfbEsMsnV1VV0u90xl8nCwgI6nU4AYJXFjY0NTE1NhcUcstb5+XlcvnwZ3/jG%0AN3D//v0xeaEi2djYQDabRS6Xw5tvvom1tTW0Wi3cvn07sO0zZ85gZWUlsOpOpxNCtQaDAba2tlAs%0AFkN/shz6dmdnZ1GpVDAcHsTd7u3toVqtYm9vD/l8Ho1GA/v7++HV6alUCp1OZ8xvTh823WnOucBU%0AOR67u7vh3V9c4Mxms2g0GlhfXw/RK+or5i4qWgBk/7oouLe3h4WFBTQajaCYyFyJA8DBEZqlUgnT%0A09Nh0wr9zk+STvTYQDXhyMjUR6oLR2oK6qIEwYgTkL6oVCqFbrcbmB81IwU8k8kE9pDP5wPrJeCx%0Ag2n25vP5MHHoZ9N4Pfp1uJpJBkTTRM1kjQ0l2+I9ysB43+bmJq5fv45ut4vFxUWsrKxgY2MD+Xwe%0ApVIpCCH7Qv3NVBJXrlxBLpcLr2IBDgCx3W4/FGBNhqesfHFxMZidzrkANqPRCIVCAYPBIAik+rgJ%0AeqlUClevXkWtVgvnwKqfnMDY7/dRLBbDuNPUpCVDwJyZmUGpVEKhUECr1Qosl33OEJv9/X1sbGyg%0AUqmgXC4H0Gu322FSdzodlEol9Pt99Ho9VCoV9Ho9tFotFAoFbG9vo91uY3FxMQAhrarFxUW0221s%0Abm4GBcl+7HQ6KBQKAUD29/fRaDQwNzc39nZculW2t7eRy+Xw4MGDwOi5aYBMfjAYYG5uDu95z3tC%0AVMH58+dRq9Vw5coVvPrqq8hms2FRhtZEsVhEu91GLpfD6uoqarVaGGOOG8GqWCyGtlP5UYlx5Z/j%0AQUXvnEOxWASAYLIPBgPMzs4in8+HOUhwp8IjINPkz2azYQWfLpB+vx+Y8+LiYojHppKcn59Hs9nE%0A9vY2ZmdnceHCBfzVX/1VKJ+gqotedJnQBVYqlYJrSln246YTPTZQ/SLqQyQjo3+K4KCDwaBkBWYC%0A5dzcXJi49NGxTAoBQVyfZ+fTR8N76EvU+DsAwdSjKUazjxqW4E9GTjZFxk3GR02tCykEOK6OLy0t%0AYX5+PpidFy9eDBqaPjcGV9PPRXOI+Tnn0Ol0MBgM0Gq1QrgUQ1SAI01+5syZMJkJkryHQMjT78nq%0AOC5kOezv7e1tbGxsYDAYYHV1FZcuXRqLACkWi1hfXw8LTQx1YuwkJ+zc3FxYHaZprS8cJDhPTU1h%0AYWEB+XwetVoNlUoF2Ww2hDFxslarVTQajeCvpDmpoTpUrtlsFjMzM8GPSgClMqzX6ygWi6jVaigW%0Ai6hUKtje3g67lXhMI8dQx2BlZQVvvvkmqtUq9vf30Wq1kM/nwyu9uWi1s7ODarUaFpjS6TSKxSKG%0AwyFWV1exu7uLH/mRHwkuFc4x+tQJaCrD3nvMz8+HxZ3hcBgUB6MHqtVq8IVS2c3NzeH+/fvodrso%0AlUoADhj+zs4OyuUy5ubmkM/nUa/Xcf369bDaznovLy/j+vXrWF5eDv3KCIP5+fkwd9n3VE537txB%0ApVLB3Nwctra2MD8/HxQGXQrb29t47rnncOvWLXS73SCLe3t7mJ+fD2CuUSwbGxvR9ZvHTScGrMqG%0ANJ6NkxHAmDlI2k93gTJABS0AQTAIbmQQ9MsSKJi37prhBCJI8JqawNvb22FlfTQaBZNDV5vp4qDZ%0ASV8Q60Otz/II9lwEAY7iZS9evAjnjl4BzVhE+lvPnz+Pvb29oMXJEKgQyN64AEHWQ2aRSh3E9JGB%0Ako2Rjd6+fTvEMQII9ePEVbeI9x4PHjxAJpNBo9FAJpPBnTt3wuRfWFgISotj1Ol0sLq6GuIx8/l8%0AYELscwAhbrLZbIbJogdrMApgb28vhN8sLCyM5TMzM4OVlZVg/dDHS58f3UetViswS8pit9vF/v4+%0AisVi6F+6TWZnZ1Eul7GwsICtrS1sbBzsn+HvDBXilmIqUsrs6uoqdnZ2kEqlsLi4iHw+Hxgn3Umr%0Aq6tjq+CLi4t48OABSqVSODi83W6jUCgglUqhVCqNHah9/fr1sSgJtoN1ou+12+1iaWkJ9Xodw+Ew%0A+Jc3NjaCsgEQ+ofnTtDdwJV5/l25ciW4CKj479y5E1g15yPrz/FaWFhAKpUKyo9zam9vD41GAxcu%0AXEC73R7bvUV5GAwGWFxcHFsspsLmYiOJBmWBjJmE6EnSiQIrNSTNX3UDcJKRzfF+mpy6e4SxbWpG%0AczJSQzNvmvAEa92tofGGzrng8CcTJtiQ6RHM9FCN6emj145kMpngSyIIlUqlwGC5jZHhJ2RJ73//%0A+3Hjxo3QHioUvvaZE1pXmnu9HhYWFgJrI8jNzc1hYWEhrIDS70UXwPPPP4+bN28Gs5lK6u7du4Gt%0Azc/PY3l5GTdv3gSA4A6gMHc6HSwuLuILX/gCOp0ONjc3x0zgYrGIF154ISxGtdttlMvl4BPOZrPB%0A10XAJbtiPCj7MJfLYX19HYuLi+j1emERhmNJs5mThkDOswnW19eDEqjVapiamkK1Wh1b6CR4Egjz%0A+Tx2d3dRKpUC42ff0jy+ffs2dnZ20Gw2w2JLuVxGOp1GvV7H3NwcWq0WVldXg0uFjPvevXvBOiMb%0A5aLZ1NRUADvGbXLVend3FysrK2g2mwAQlCV9k1NTU2g2m1heXsbdu3cDa6V1QIABEBR/o9FAsVgM%0AbpJ79+4hlUrh4sWL2N7ext7eHprNJkajg7hUAhHnVKPRCMSCSvrevXvBIqQVyPLb7Xaod6PRCKa+%0ArgPMzc0FBcRzFNbW1rC8vDzmnrt27Rqmp6extLQUXB/D4RCdTicsuvX7fbznPe/B7u4uNjY2Qtxt%0Ao9EIc+JpAOuJRQV89KMfDWYS2SMnEjUQd58QwAAEdkuho8lDnwzZH81ImoZcaFAfJKMM6NNJp9NB%0AoxMUmA+ZmUYQAAgAQpeGhnkACD7B4XAYmATBl74emnpkcMDR9kQyHobP8B30NLkZaL20tBTYhLJH%0Amq21Wi34e+neYHwlgACsahmQpW1ubmJpaSmAbrPZxNLSEmq1Gubn5/H5z38euVwOX/ziF5FOp/G9%0A3/u9uHr1Kl566SV86lOfwrd927eFbZq6dVVXY2kdFAqFALic7Oz7QqEQfNGso0ZbpFKp4H+j6U+F%0ASIbSbreRTqdRrVZRr9eDMu31emEFne4FMq9DmQ11npubC6vfDM9Jp9NoNpvhsA8qcwIIAZpAQP8l%0A/bn1eh25XC4s7tFSAoBisRjAgcycckGfsPpi5+bmAvlotVoolUpjK/Hs416vF0L5GBu6tLSE7e3t%0AYMrfvn07RJpUKpXg/6VriUSBpGN7ezucccD+IWGg5UbfLpUEXXGdTid85kIyQbNer2NxcRGdTiec%0AMZFKpYLsqMW3vr4eIjs4H/XIQiqtUqkUsIIKgVEyv/Ebv/FEUQEnGm51+BmdTicwBt2ZQ3puYxXp%0AgwUQVvPoqNaFLF0xZn50qpNRAhhzmFOYa7VaMI3oO+33+1hcXMTOzk4wxbmiSEbJicm2cdWV7JM+%0AH/p4eITfzs5OaE86ncb6+jpWVlbCJOOAcyXfex+OnKOApVIpbGxsjJk0zrmwo6VaraJWqwVQzufz%0AaLVa2NrawtLS0ljIFBkL/ZH0S9F3uLa2hnq9js3NTQAHCuTu3bvY2dnBCy+8MBYzOD8/HzZCLC8v%0Ao1KpBKbMbYS0XriApRZMs9nECy+8gAcPHgR/NoFienoa1Wo1+DlXV1eRTqfH+pOuoHa7jX6/jzNn%0AzgRWVCgUgp+XwO+9D6FGZKeVSiVYJtwxxDFmv9Hvn0qlAmPy3uPMmTO4desWUqlUACW6Xyi/XECl%0Ar5yyS1DgPAAOFlrW19eDpUPZ53jRlG42m8F8pjyocqJJX6vVxny3VFZ0p3D+0Foiq2PoG0GLMqIk%0AgTG/Dx48CFE2nOtcN+j1erhx40Z4/xdZOhfHaCVyg4X6SrngRt+thm/RX85QO4Z0cSMC13bogiCY%0At1qtdy6w/tIv/VIAHZp/NPWAoy2fZH8UYGWENJHYKbotUDcG0PFPwaB2orCVy+WwkECzkueWkrXR%0A/OXCQLVaDWZ1uVwOJjsFYWrq4IT+4fDgTZw0gxkN0Gg0QtjK9PQ0KpUK7t+/H1ZCmR+VTrPZxLlz%0A54KDn8DA4P7Z2VnU63Xs7e1heXl5zBfd6/Xwrne9C3/+53+OW7du4aWXXkIqlcK3f/u345VXXgnR%0ABqyLBnDTpGNoEIWXmr/X62FlZQWNRiMwDOcc3nzzzRD0DhzF3ZJtafQBw6IIrnt7e2FMVldXsbm5%0AiWKxOOYLJpDxHV5nz54NIXG9Xg+rq6th4ZJsP5PJoFAo4P79+8Gi4Cr1+vo6rly5EuSJAEuLpNFo%0AoFqtYmdnB9lsFktLS2P5q3+4Xq8HcOr3++H0KgBYX1/H0tJS8BPTYiDIsV/oc+RYczGHTLhQKASW%0ASf85Y191sXR/fz8sPnU6ncDq6JukTz+XywU5HQwGaDQaoT48cFvdEgRozkVdqOWCFttGIkNZ2Ns7%0AONyb1hbNfhII+sgZ5kWfP8GahEsXLjVumv1KfyoBmT5nJWckQVSOtNw+8pGPvDOB9Vd/9VfR7XbD%0AwhBjA7lAQxOLgkTNT/bEcCNqIWoqAGOrvFzZ5kQkQz537lxwOQAYM58BhDqdOXMG6+vrgU3RFGdd%0A6coYDAaoVqtBY6fT6eCT4wHG1JoEnOXlZbz++utBEZD1qnIgqyWgzMzMoFarje30yeVyWFpaQr/f%0AD69eoWuD4EVTeDQaoVwuo9FoBPfK7OzsQ0H3zWYTKysraLfbQRhHoxGuXLmCN998M6xGU5gZnqaL%0ARLpZgSE+5XI5sDcyDyqPbreLc+fOBXOZDLJUKqFUKoUdZfV6HTMzB++KajaboQ5sc6/XG1sAAxD6%0AEcBYRMRwOAxRI2T6dFGR2fAZKlYuggyHQywuLoYA/IsXL4aFEwIN5YoATfkiy0yn0yF8L5vNYmNj%0AIygByurc3NzYOgB3KpFt00rTDRkaAwwgyKnGbdKvu7OzM7YjieyQDK7X64U2EewY40vXCJk6d0uR%0AAJE1qwVFgqGLSIe4EFw6rVZrbAF1d3c3KAaOnboQ+Dz7lGspJFKUfa5t0LpQwqYLab/+67/+zgTW%0AQ40QKD8nEbU0G83AX8ZCUiD4XLFYxObmZgCaUqkUYjXJTBiaQyBQTVkul0PQMPOj/4mCUqlUwqBS%0AGBkSwolGVlkul4OQEUwzmcyYb4qv1ajVamGFPJVK4fnnn8fm5mZgAFtbW2HFVp3/Crw0aahElpeX%0Aw6JbJpMJ5wNwQrGtDA/K5XIBPCmM3vuwjZAKhqyfYM0J1Wq1cObMGTx48GBs9Z6TlBObC1M05dmP%0AZEO5XC5se+QWQ/q/yJTpu2TdCIylUikAFTeQEFBbrVYALwIr/YO8vrd3cOAxJzkV+crKClqtFjY2%0ANkIkhvc+gCUXqFhPRn1wiykZIfOjO4nuDo4pZWUwGARfMn3iXHBivKluymBkAvtB2VsulwsuBs4f%0A+oA5zpRxWouZTCaQl3q9HuRA1zjIVCl/3NjBMEfKPhmlxpCzbwl4GsdN2dY3Dui6CyM1OFYaeun9%0Awc4vKhquz8zOzoY3LhDQiSf8TAXS7XbDwu/e3t4TuwJONI6VZiFP8eGhEpy0XIygoHD1kx2Wz+dD%0AKMjFixdx9epVTE1NYXNzM5gt9DmRYTBsJJVKhZVpmoUUbvp6CFK6e4YThKeM03TQRSpOXLLbe/fu%0AhZ0y+/v7gQHTt9dut1GpVPDNb34TAMLCi/cely9fDr7BZrMZzCs+NxqNAnhcvHgRvV4PFy9exPXr%0A15FKpbC8vByEh+ZRJpPB2toabty4EcKi3v3udwc/KF0MXGwbjUbBN1soFNDpdEKsbCaTwbVr1wLj%0AGgwGKBaLIQaTCyZkE8psudpLc5qhNex7gli1Wg0r7br4QJ9erVYL4MqNIVwBnp+fD+YlFSz9lQSy%0A4XAY/OPs92w2izt37oTFIl1VzmazmJubw+bmJobDg/MXOA5U/tyJR/ari4V0Q9EHC4yfWcv+mJo6%0A2Cbb7XbDriMCMiM7CBBkfWTK6otXPyVJAF0X7Csqg9u3b4fxUnZKsKdZzcVajckmuBJQSZzURaZ+%0AVmB8swzrpRE2VEIamaOx7nTz0IrkZhGGctGdxQ0ZuquKCph+dLZHd2U+bjoxYGVncDJxoBgMTjOm%0AVCpha2sL6XQaS0tLABAEkgOcz+dx9epVAAeCxZNr9vb20Ov1xlZBGYtJVkSfEgVwYWEh5EFBHgwG%0AgaEyrnA0GoWthKwPTTCuUC4sLARwoj9Ot0hy9ZYmDRdEKMwLCwu4d+9ecDGsrKyMxRBOTU1hZ2cn%0AxHRubW3Be4+trS2Uy+Vg3jC4m66G6elprK+vB4aWy+Vw+/Zt5HK5MDHYDoIoQdB7H4COWwArlQrO%0Anj2LtbU1bGxsYHNzE81mExsbG2FLIpUWAYP+LU4OKrTz58+HdhFsWq1W6DeCFq2HarUa2OTCwkJY%0AgecCl0Y60N+mi4mcqAzNIUBwrAGEPPlKm/39/QAUVEwcc91MACAsdOl5otpulpBBwfEAAB4nSURB%0AVKUxyGRXGjpGc5iLuwwJY5iTRtiwrzkf6AYjYyeAU86oXDjnKI80/xkpw80KBE6NHyfYkYVyA4mC%0ApS42EejIQLlwTCLESBwdE2D87RtsN5UKXS1UGKwf+4GhXRpXDhyd5kZ3HMfuSdKJuQJ+5md+Jph4%0A7CgOTD6fx+bmZgBaxiESTKl16ffj9lSayRQw9eG1Wq2wrXFnZycIcrvdxsrKSmBvAMJCWKvVCiEg%0ANFlyuVxYJKDTn4H+BDaa/ARXBofTDNne3g5bUff29oIft1KpBHDmYRBc1SVb4kTk4Ot2WXXKs97F%0AYhH3798PkQOMfWU7uDBF5sK8qbDu3LkTgrp5LBvNZ06cCxcuhMW0bDaLzc3NMf8zGZWGvdBkI7Bz%0AwUXPVVhcXMTU1FQIVOfiAlkdlYTKBsfvUM4COOoJ/syHfln62Dgp6SYAjrYqAwjAStZGEKAPkS4B%0AAh9BnOGAHC/doELlD2AM/DjWwNFrcniNgMOFROBoww19kUw02XVO0JTX59kH6u8kQ+T4cI2ArJhg%0ArNYQy+CiFhknx5yMnfmoK4tsnGOtZzZwbLVcjqMe1EIwJQDzO/NkFA37kDLBcd/d3UW/38cv/MIv%0AvDN9rD/1Uz+FfD6Pd73rXbhx4wYuXboU2Bt3WtBvSr+Ngmmn0wkhF/Tj0EScn5/HrVu3QlhKOp0O%0Ag0btSDcAY+I4ScmG6LNR0CHL5hmOetQa68jDfAEEv6wyDC489Xo91Go1XLp0KexDLxaLuHv3Lkql%0AUjDvc7kc6vV6mCBkMroLjaujFCg11TgpGZJGIfLeh5Xh0WiE+fn5YNIykoALVI1GI0x6LjSRSdA3%0A22g0wutGGIPLfpqeng4bCRgwz7FkPbmwQHlk2BkXbHgf21mr1cbC8VhvsmK2m0HstD5Yji5ecYIy%0AXIll6klbZH00mQkuytgYTUIQIDARJMjcCDjWJ61mOgGDbaepTIXEvFkPbpIhaOoimr66hfLDOqgr%0AgcCqgKYROZxrZPyck2wjZY8+VZIdDRljnak02E5GPdB6ZFtZHzXT2f8KiFz81J1wCvC6aYdzhomW%0AGHB0POTP/uzPvjOB9Vd+5VeCmc5AfbIWTkQVai4AMSB5ZubgsBU9foz+tFKpFPySNMM52ehnInCQ%0AddLpT1CnZnfu4JiywWAQVpuVKfMoNWWRXORxzgVfmq6GkhnSXGEUwe7ubvBJEXxtyBmAoN119xkP%0AE6FJy/s58bn6zYnIuMlut4tGo4FSqRTYLF0kZB/e++A6oYnHWEUuFmlQPHAkoLqoQnZKRahgBxyd%0APAYgMBsuWHDSs81kqAT84XAYgJvl6QYIAMGnSpCgnCmb46Tj5CXI6JkJVCqsK/uZ9xMUeD/BmiyL%0Az6jMKxip6at+RZrflFXKPceU7hv+MXFsWF+NGtBFUQIay2AbFTxZB/q4mQfBTfuU/aGbdfgMy6G1%0AyX7moh9BmvJDVwrvYVtZvu6aVLmjsuL2Ws5d1oF9zv7i95//+Z9/Zy5e3b17N/hMh8OD/cgM9aA/%0AUv1DjUYDa2trWF9fH4vj5IoeGRj9gly95rZHri7ypCKeisTFDjrY9f08PCdTt8BS6AgONLsZUkPm%0ASs1K5zqFg5EJ9DMxCJvCQpZbq9WCs50+KzJh1cbAAbvjIScMBmf5NA/pd6NfmzGRNAn5meBC5UXA%0AUababrfH3pnEMwm4GEBGRcHmJNP3QJGBa0ws+5eTmL5FKhfdSQVgDKympw8OqSbgEHi56KMgy4mm%0AZznoMzoRqcApp5ycrAP9hmS9egI+LQmGhvX7/bHjMslSyfB5jXXhNQ0JYj3YP+xP3qcslCChY6Q+%0AVMqdsk8FWV0k4tizHO1/JRXMl3lzPDRigQBORgscvc1VF+J4nXWzOx+ZF5/hZwVNyi3rwLGhslLX%0Ahfb/k6YTA1auVnMBp1KphH3WXA2lj5MnA9HPQzNd4yV1xU9DtzqdTogBZUA7wZfPMCyJpi1wdIp+%0ALpdDrVbDwsJCcCkwLISRATRJKLwUIoaI8CVpNOvW19fDWZgMx9GwH25z1bAY+rAoODSNyOZYLoWP%0A5iWAMSZJFs9gfY1R5MQksBFUdnZ2Ql/zSD8CC4VcTTSyN4Kgumt0oUStJbJlThACrTJvmvSsIxeV%0ACPLA+Lmy7A81gRWoqDwYqkWFpKvEajqz7lQIVLzcZqngpgyXK+FkeVSIBBcySQUcTnqWz3zZVwQY%0AjpGyZf4R1NRtoWsQTPysgM2+V1+ksjpV3Kw7AV77n+2gXGi92L/AUfgYFTsVrypKto+AzvHW/lEr%0AQ8db89GFMN01pi7HJ00nBqzUaJubm+G0Ifo/aaKTHWgIBgFMF1kISFNTUwEwOUj8zPt1vzLNC3a2%0A7pqi/0+3HSqTUkHngBP8CTRc3FF/YbfbxdmzZ8cc8gRknv7D7alsdy6XC/4zXawjeKp/kqYaA90V%0AADnRVcDpi+RzZCcAgtKqVCrhMGbg6CxdMlPLgMiQ2T4CFf2KfIbjwjpxAnI8rNlLk5ll0z3A/NgO%0A+tMVCDl2BBkAganq73TVcMJTPpjoItHFQwBjOwaZnyo01kVBTGWF7dZVb9ZHzWfmz37TFXnmq+4A%0ANffVxcBxZj/bRR4FH46B+rLZP5xX6qfUs1LtfNd2kKCQeaty5ny3riWOuwIl81eFxbar7Cnz1gVV%0A/VNG/iTpxIC1UCiE1TyyVACBESnT0LAQ4EBIuXuGfhquwNLUVa1HEKE/kCBHRzfz50IIowM4MShc%0AOgGAIwbAulHbKchyMWtqaioEZPMemqPqJ6RpSTCmJiUgqQ+RgAwcLZ4BR+Yp/dMUIgVhTkiybQ3a%0ABo4mFMeCdQWOVqmZtwLl1NTRYcIUYoIEgVuZDyeD9ZtRwQEYawM/E0gViNgWMkOdtMo6yVYJirpy%0ArxNby1P/KoFBz+7kf7VatB/5nLqUCBI2YkHZH5OOhQ1z0rqStanVpHIBHCkHyjjbTjOZQMzn+ccx%0AYvvoT1Xg18gC9omyS/1M0sSy2HZVfqogVFZU2VnftZarrhUuWPEa8UXl7R0PrDxRZzQaheO6aJLS%0Al1gsFsNikpok3vsx8wpAWARhx3DxQzUTBxVAWECiMLJjCQxcFeZihh6Wa4FJzVuCB4FOD9Qls6C/%0AEzhiGGTc6hO05hcnHv2NBAmCP9k1nyfjZ9nU2LrLhG2gnxo4AAOuMrP+OqEAPDTJCFLsC1WCaqYq%0A41E/G+uheas1QcbFCcl60oWgITwK0N77sXMaWH/6Q5Xlq6JiPaypzjzoM9aJqWDKFWg+x3HjXwxE%0AKJ+UR5ZF1wOVrPqx1XeqLhb1marcaHiVyhU/a73sYiT/sz+UHav7wfYH62OvkVDprjWdo0xatv6u%0AAMq5QmWsRMIqZlpedoFXXR9Pmk4MWNvtdlg8YsgSzXoVcvWf8Bo7lwCosY/6ShFd7BkOh2G3CU1G%0Aale9jwtAdCHQ3ObLyHTFk3WhgBGs1GSiINPso5tBF0LUb0QBVb8nv2tMpvW1AgjHLBJkqQhU0XBS%0AcCGJAK8mn5qxuqhlTXJOfF2kAY7YDJmQgqYCkfp19TsVKc1MZc9UBtwswDx5P8eS93EMlXEpC6JC%0A4G92ctOc1HHj9RgTI4OmRcF6sy80XwKmJioRVaqcB6wHx0ZBlUDLexXEdEycO3rxHsdSXS5qpXHc%0AVOGqv1JJBZUT71MQ1PFT85sKmf2u46nzXMPKFMTZVuabBKqaJ5+lbKgbSRfxnjRNBFbnXAbA5wCk%0AAcwC+Dfe+4845/4JgL8PYPPw1l/23v/h4TMfAfATAIYAfs57/+lY3gzwpknOWDU1L9WkoabmRCBw%0AcsVcGZmyEC54pFKp4FNVgCZg8JAIBXAKIjta9ydbUxlAYOD6dgE1h60/i4NIs1KFT0GWpuxwOBzb%0AKXPY3+GzshgVQOt/YyC/+qo46Tl52MciC6Es+lCB8Vd+c3GOZVFQ1U2irIoTMKlcZbCsA9mpnuCk%0AfyyTCpr/LRPi/SxPGRfLZxvYPwoo6t5gslaVlgFgTL6ZP9uu9dK2M1/OCcqrXQDUcVbXiGXGKo/6%0AneBoGaf6Wvlf+4qkSMfAMkuWowxRx55KRvPQ51Q5qyxYRqyfFSC1PsrodQwn1f1x0kRg9d73nXMf%0A9N53nXPTAL7gnPtuAB7Ax7z3H9P7nXPvA/CjAN4H4CyAzzjn3u29j9aUwqCmmzU5gaMFAhUc3V2j%0A5owOPEGJjEh9atb8UdOc1wmaurhC/6z6+dTMYB6qdSlIKhxq9rE8dV+oKWaFWkN9rPlHM5Tla9QA%0AGa6yBjWjrAmti1U6CfQQDiZdJFNzl8+wbnaRQQVe71H/qTIUBRVOOK07n9P8LagwWcWpcmdZkbJ5%0AVWr8XeuhCkHBgnlo3hpyZuVI89e8tK6aFxfqtF1ad6vYmFTRqcVlZcMuQDEv7W/LtK2i4O/at9pX%0A2h59ThmolhFj5poPP/N5BVzFG7Us/saB9bDhPLVgFsAUgBrbELn9hwB8wnu/B+Cmc+4agJcB/IW9%0Akb4x749W7MjcdHLQ16cn5dgJb1kA/SX0WwJHK9N2cMgidFKqSUCB0sB0FbDYILIeCvAK+Lr4oIKk%0AgKWaWSeWCpJVDofjNcb4yKKVYegEZf46idlWgpK2SZmtKo3YooWCM/tAy7WsRWRuDFwIxhpuE2N2%0ABAY7SbR/rOJW14COIa0Xu0pu28c8VLFP6luVFSpVPke5UpeB1lX7IyYjLENlT6/pApqCF/PQPrMy%0AwvGPATLrYRWUymoMbO34aVtUWdl+5Biz3/RerZuWYd0TSrRs0nweNx0LrM65FIAvA7gM4Le89686%0A5/4TAP/QOfdjAL4E4B977+sAVjEOondwwFwfSjSfrRanxuT2Nw4WgZcCqZOFnUqA1kmrgmTZnLIk%0A5gMcCbkVLgVvCo0NOWK+ykgsW7OTHjgyYXRBieyP9Vb3AsvRe3mNSVmcTi4Z24dYF+tjQ1q0LAsc%0AMdBXpcT+VZNP2ai6SDhmasayXN3SaJmfZR4WWBRIVdlp/6tP27omrAmq9dRFJjsp7UKI9hnro/Wg%0AHFvmZJ/jNTVpk9i2WicAHnLXaF1VkShL1PItq9S+0f6yZMPKQqy+McaoJMfGO8dA1Y6XKjDiC3AU%0ARxtjz0+aHoWxjgC86JybB/BvnXPfC+C3APz64S3/FMA/B/CTSVnELv7pn/5p+Ly2toYLFy6EDiKT%0A00FQzcYVfTUrVZvpAPI3nQDMi/43BWvmryAJPByHRyCxrNSWYcGLeQEYE2IKg43d07ZZtmHbxEmk%0AgqfsQ+uqbVIgce5oQUHryKSAyLppG/S7siB+1omsZeqqv2VCTLrIYhkNyydz1rzVVNUxjfWJ5mXN%0AV/09xhZ1rFiOnbjKRpV92nrYZBm9/WxBjP2nfaWRLDqutu3aDgUlvWeS31qJg4KfBbxYXbU+2s+q%0A0G0fxeQ8xor5G61UJV58TZDt68dNjxwV4L1vOOc+BeDbvfef5XXn3G8D+IPDr3cBnJfHzh1eeyh9%0A4AMfCAJltbUuttA04k4h3hcbOAAPsVJdrWT+OgE5eOpb5WfV7CoEZIgqSPwtJkgxQebz/J15WEe6%0ACqAKZYztUriU+anPjSBjF01sHbR+Ms4PAbEyG2WeOhYEe+YRswBsGy3AWGBT9sn26X0cTxvjyvFQ%0ApatJx04Zui1fFYdes79pn6gStfXXsbPKU4HKMlEFItufvK5jx6QREla+1d2lssWk9VDFrmVQPjRc%0AS4mKVVhWJrQ9lqBYxRtTMnac9PkkZnvp0iVcuHAhjNWf/dmfRWXkUdNxUQFVAPve+7pzLgvgQwB+%0AzTm34r1fP7ztRwC8cvj5kwB+1zn3MRy4AN4F4C9jecd8W6PRaCymVDuaAJvEJtWc4nX177EsfTOB%0AndiWnelZj0kaPvZnTWi7WGOZltZZ68PftQ62zpqXnRAWmPmc3gOM+/d0bHQiKzirplfgVMDSdtt+%0AseWr2akgw3FVgLagRZbLNijo23paNqmJ8qCgqkmVClOMJTIvK6N6v36391r2llRXtj/Gcq2CjyUr%0AgyrTfM4uLOvYK2mwMqgKTPvM1lV90yQESQrCWodJedr22aQK1vZpzA3xuOk4xnoGwMfdgZ81BeB3%0AvPf/zjn3r5xzL+LAzL8B4B8AgPf+Nefc7wF4DcA+gJ/2sVbjKAaO7EgnjHXMxwRO/UYKTnZysyw+%0Aw//WB6MLH3bhKWmQVHis5tUBt8xaFYcKiAI8BZu/2cmr7bFlK8OI+bRiYMB+4GfNK0nYdELqNT7L%0AftTy7GSxiwrabqt07Bgo87PKjSBsn1dWqH1hx8gyJAu2+ru2ybKkpAUjVZiWgdn+tuARY6S2zhbo%0AksbMgiLz5dhbVqpjp4rfgrjKrs5hC/ixdlm3EvsoNge1fVZp2TGx46eYYmXgSdNx4VavAHh/5PqP%0ATXjmowA+elzBFHzuDGJSzWVBRFfsdeugDpY1Ya0QJA1CzDcJxOM7VdgAPASsNn9bR2UBnIQKhlq+%0AgrCyMgU/bYPNzzIsJmX41qxlvWJAkcQaLGhbn6ftH81Xy9P77ORlXdUisJOU8qO/a9tjE9r+FgPf%0AJMaaBMZ8zvoik3zUOsFt/jHAYF/Y+1XOLejwL2bV2DJpDbCcSYRFy1V3lI6vBf9Ye+wYWIWlc2IS%0A+FmZsHOV/23c7tsGrH+TSSeA7XALREm+OuuPSQLN2GCqGaNlatIJb4P8rZbn/THhVVZmn4kxCM1H%0ATWFtuz6nefMZyy4sq08SdNsX2r+2HhZ4dMwo0FZparv53zIey64njZFOOjU9rQmufRCTCa1fzC0T%0As2TscxYwrWtJ6xsDDV3YU3lWP7b2iZ0DMfIQY/uWwccsD1uWVZ6xcizLtH0bU2ravtgz2oeKGbE6%0A6nO2n6xMWEDl/e94YKWgU7PpAgjwcCNjbMmCkGVrNg9lDzrZYpqb92g5Nl87Aa2jnJ+1vayfLU+1%0AvLI0K/xJGl9ZIu+3q+xaH9s+ayrpM+p+0USGYieVlhUDBCsHSS6PmD8sBmiWwdp6U0asNRMr1/qX%0A2T5OcHVvqALRz7E6WEWn8qL58zdLGjQPC8ox0qD9r30dU/wxudff7JzTOtjNMHaOWrCNgSrvU8ar%0AfcfyYmBv6xyrr71m66n9FuuPx0knBqwUUg0uTwIPYJw9AkeT2gKCmi0KDsp2FeRYF2uOWjNV66Ex%0Aq6rN7UDqPbqgogpBU8w0t78reOuEVP+oKip+j5mavF/bpv/tBLOCruWpYPK6nQyaYhPPTj6raPU3%0AO1n0N5t/7B4qMis7FsRjeejvWi8LglYGJrVTlSHHIAZQ1sWjbiWrhKyvVctSWZ3Ul0ngZutvxz92%0A3fqONQ87Fvq84oHFAHtfrJ52F6BtmwXVdzSwErA0JEOd5Qouyhx0GynziW0GUGGzg23DbRSMFKwt%0A2PNeO6HshLOmbNKg62SxAm5ZrwVs7TN7v9W6au7Ydtt2UOHFgCzGFPSe2HVbFwsUwMMRF1rOJHeD%0ALSOmEGx7bdtjoVdaR+07HS8LTLYvbFm2XHvNsmdV7DGA1Xu1XAsQ1uzXsmOgmaSoNA97v62LHYMk%0ApaTX7TyL5Rdj7jF/bwzcbZmT2vY00okBq5pUZK1c6eeiljVXtHM1HlMFxHawdioZgU4OYNxkUoHm%0AcyyPifWwoMm8NF8LhEkDaoXeDrKdvNbkZJ2tP07bpxPTApn1NVl/MMu04G3bZSec1vlRPidNQPWf%0A2nGNAYidpLE+t66Z2ETWa0kuHP2vbgHNKzZpdcxt+/SeWFt0LgDJ8b8KXDFfKu+btOCnc8r6QmOK%0ANqndMcVvx1Gv2XbEyonNMVtWrD9j+cTuf9x04q4AncTA+GEIwLhW0metf1RXba25ZBOFP3bIR0wT%0A24WbGJjYCRZjoJp/khDF/IqaHwHBMjmNpGAf2nhOjXzQfG0dYmWr75f3aPuT8rL9Ebs/xkZs3pP8%0ArDF3jQU4O1b6W8z817HQFW5bdkwhOueiC5wx2bD9Zv/HFlf4u/VJal+pYrR1t/XR67HPNn7Y9kMM%0A0LW+VqmojNp1CW2D9kEMNJPGjTKRpEQmseKnAarAQWzqiaSYP8aCZZIJq88waeiEnuBjmYcOqg6k%0ALdMuymh+CjRJ4Akc+XXfeOONh+oSA3Jbd1sXXrftsxp7kvCzDB5qYk87sv1in7f7y/kMJ8gbb7zx%0A0PMEeev7tc9b4ImxPG0z87HPxUDQ+q0n9a/tPwVt5s9ojTfeeCMqC6q8J7FFWxeWYfvXKluVSSsz%0AWn/Ny/rZ7f2x+aB/V69ejc5brXNM9nRtQPtU2br+bn2its56TwxcNXpD+8VuiU4ai6cBricGrLEB%0AiYHNo054+5nfreZTYdQTk/R39d9aMDyuXrHBfuONN8bKVgVgw0lsuywg8uxaPqt/FiR4QLXd1msF%0AbhLAJCkcG2HBNly7dm1sfDhxtF0xAH0rYKp5JykPy8yT2hIDo1hKqt+1a9cSZURlKGnC2vYksXrb%0Az7G8rNxRCdgIE9snSXWyiur69evRvkmqjyUg9k8BlXlYuYyBeEwGYqQiJqva7phMPK10oi8TZIPY%0AydT0wMMakNcoMNoplv3oX6yzYpqa121na542D/5Pcj0kmVoKSLqqG2NbScIUaxP/J7HXJCGyjAF4%0AOAic17Rs65dme+wBODpGMR+d1s/WwZp99h47eXlN+1Q/T+rHWBlWvmyKgWkSONt2aj0ss4zJiuaR%0AlLQP7KaWpOdjgGznFP/b8UuqQ6zPrJ9fWf5x+U1SLjpGSfNGy7WfY33ypOnEgFVfc6GDbzW2Ntj6%0A0vS6vTc2aWKmWqxj7UThfepnjLElqxT0NwtmfFa386q5mMRIbP/wszUT7fM6SW0feT9+inpSH9pk%0AhdP+pv9jjMmOsVWYMdDS+yxbieUZAzJb70msR/NIspa0X2NMOakfY0pK81NXlNbTPpskE7YeSW21%0AYDrpf2xeaprk8kiq36QxjxEDlqN9naR4YqCs32Nz4mmArHvaSP1IhTr39hd6mk7TaTpNbyF57x87%0A/upEgPU0nabTdJqe5XRii1en6TSdptP0rKZTYD1Np+k0naannN52YHXOfdg59w3n3FXn3C++3eU/%0A7eSc+1+ccxvOuVfkWtk598fOuW865z7tnCvJbx85bPs3nHN/92Rq/XjJOXfeOfcnzrlXnXNfd879%0A3OH1Z7W9GefcF51zX3XOveac++8Orz+T7QUA59yUc+4rzrk/OPz+LLf1pnPua4ft/cvDa0+nvcet%0AyD3NPxy85fUagIsAZgB8FcB73846/A206d8H8G0AXpFr/wzAf3v4+RcB/PeHn9932OaZwz64BiB1%0A0m14C21dAfDi4ecCgP8XwHuf1fYetiF3+H8aBy/K/O5nvL3/DYD/DcAnD78/y229AaBsrj2V9r7d%0AjPVlANe89zf9wSuy/3ccvDL7HZu893+Ko1eCM/0ggI8ffv44gB8+/BxeD+69v4mDwXn57ajn00je%0A+3Xv/VcPP7cBvI6DV/A8k+0FAB9//fsz2V7n3DkA/xGA3wbC6+2fybZKsiv/T6W9bzewngVwW74n%0Avh77HZ6Wvfcbh583ACwffl7FQZuZ3rHtd85dxAFT/yKe4fY651LOua/ioF1/4r1/Fc9ue/9HAL8A%0AQIM7n9W2AoAH8Bnn3Jecc//V4bWn0t63e4PA/+9iu7z3/pi43XdcnzjnCgD+NYB/5L1vmUD0Z6q9%0A/uHXv3/Q/P5MtNc59x8DeOC9/4o7eMX9Q+lZaaukD3jv7zvnFgH8sXPuG/rjk7T37Was9vXY5zGu%0ABZ6VtOGcWwEA59wZAA8Orz/y68H/tibn3AwOQPV3vPe/f3j5mW0vk/e+AeBTAF7Cs9ne7wLwg865%0AGwA+AeD7nHO/g2ezrQAA7/39w/+bAP4vHJj2T6W9bzewfgnAu5xzF51zswB+FAevzH7W0icB/Pjh%0A5x8H8Pty/e8552adc5cw4fXgfxuTO6Cm/xLAa977fyE/PavtrXJV2B29/v0reAbb673/Ze/9ee/9%0AJQB/D8D/7b3/L/AMthUAnHM559zc4ec8gL8L4BU8rfaewErcf4iD1eRrAD5y0iuDT6E9nwBwD8Au%0ADvzH/yWAMoDPAPgmgE8DKMn9v3zY9m8A+A9Ouv5vsa3fjQP/21dxADBfAfDhZ7i9/x6ALx+292sA%0AfuHw+jPZXmnD38FRVMAz2VYAlw7H9asAvk4selrtPd3SeppO02k6TU85ne68Ok2n6TSdpqecToH1%0ANJ2m03SannI6BdbTdJpO02l6yukUWE/TaTpNp+kpp1NgPU2n6TSdpqecToH1NJ2m03SannI6BdbT%0AdJpO02l6yukUWE/TaTpNp+kpp/8PTYoQ8rA9sPAAAAAASUVORK5CYII=%0A" 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9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%0AdJpO02l6yukUWE/TaTpNp+kpp/8PTYoQ8rA9sPAAAAAASUVORK5CYII=%0A" />
-</section>
-<section id="id4">
-<h1>伪彩色图像<a class="headerlink" href="#id4" title="Permalink to this heading">¶</a></h1>
-<p>从单通道模拟彩色图像：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">lum_img</span> <span class="o">=</span> <span class="n">img</span><span class="p">[:,:,</span><span class="mi">0</span><span class="p">]</span>
-<span class="n">imgplot</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">lum_img</span><span class="p">)</span>
-</pre></div>
-</div>
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-<img 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D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%0A2+yGbQPWzTbbbLMbtv8GmbHHpC2s7ygAAAAASUVORK5CYII=%0A" 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D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%0A2+yGbQPWzTbbbLMbtv8GmbHHpC2s7ygAAAAASUVORK5CYII=%0A" />
-</section>
-<section id="colormap">
-<h1>改变 colormap<a class="headerlink" href="#colormap" title="Permalink to this heading">¶</a></h1>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">imgplot</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">lum_img</span><span class="p">)</span>
-<span class="n">imgplot</span><span class="o">.</span><span class="n">set_cmap</span><span class="p">(</span><span class="s1">&#39;hot&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-<img 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I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%0A5bbcltslbsvAutyW23Jbbpe4/X9nGwcxiuGaUwAAAABJRU5ErkJggg==%0A" 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I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%0A5bbcltslbsvAutyW23Jbbpe4/X9nGwcxiuGaUwAAAABJRU5ErkJggg==%0A" />
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">imgplot</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">lum_img</span><span class="p">)</span>
-<span class="n">imgplot</span><span class="o">.</span><span class="n">set_cmap</span><span class="p">(</span><span class="s1">&#39;spectral&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-<img 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a%0AJHUB4GvrGEqX2Nsha5Isvi5GPXjANFE+n35fBdbrUfrIAOO+qNWhgaXGwXEFiaubIhkwGqQFrD6Y%0AUySA2AXn0vZa1rRMT2P6qxffcpcmi/ZQPXkBf6cPDg4cEuqmlCObxlRXddSv9V+AcG3wBcJyVGXE%0AfJZj+UzVTQ3Szq6+lovoOBJIU2d2JtxrYDdKOyMnPOBuaC1cfGfZYxTiYo6RoEauddrb3A8laALp%0At/weShDhs6Ya2804Py2fRECk2KpgRpDWudFvsiTzfLEMmB9VCcy3eGdBO5RUsu2DezZo8plxwxPp%0Ao3lwTn/WJM622EikD7RbWEbOm/O8MuhpGo4hmZFZSPN2LbgERGMbjbNH4aqDW5HmFecy7Le7kLws%0AeODkKNxlDBDDLgFWPQtULcCB3n9fBdZDKA1UgJ/JY+MMVENIXOl+OEgCCVQbAPe35DwCSbcqWrCX%0AkQwhALCXwIdxMY8iUxfSrt3ZpFvqXU+3JuA/l8VAEQ4YF4tIyrXwZEobS1HtRDQfQSuKZVqO6v06%0AJAPJnuiLhNmshlIyUiCPMHewuzlVFonRtVia9Yay8bAOEcBKJdptJp5TzK7Lg3GNWjb1mponQUZ1%0ArpwDi4i6WyvGRXZJo9wr9byQd8bGU6ke25rmC+YbJaCmesa8tN0nKo+Mx1amAfPrgqsmiF3sc23z%0AXMaBn9dCOmgApM2bagEevFEDWYR7vFAtdxwJpHUeB8tjQx2YVdwkN7L3vgqst9sXFflp+gaANjny%0AryJxmGcj4e85cBeqHgk0D9irBN7TkESJfVwYSMr/KMWRG6w5F05+nVS6AJU7mjVJHFruXJycNSmP%0AWXDDAjlZqhNG+wTp92l0XV1AAnSKXUtmYOgNcZRz6ZoSrAhUS71P1BgcyPn7pPe6A7YQrK+zgSM6%0A2Ex6N6KwnGB1CZZn27vFmABNTivCQaeJbjlnH0x7ZF/SiWxsfK8P5XiMLX62NSLVhWVxUU8INNF1%0AnBOpX5CyIO+yvVNZ/EtmBFNgitaOWtoZI1URdDY2k5ikHC4FxQBukEvRJSBygSTWm/NoGksJh1w9%0A+54gyvkzb1J7VnrfBNgvE2mXog7bybXFuZA3nKqP5sFVCS3cQ0I3nc7SrYYEpDeiNAzTjtAjgSz9%0AZVeQVAFHLM2d9tsyEjgfgcRiiJKJxgtYua8CK3lzDbRiBqkpUifQGf1+SEfpIpL+Tk+c7IdzpPuR%0AuNYewO7oOiLqj5S4+Cc9BqIqRUk+76vPgIMTgacLacItmb5vo3ExbNa4zgySz7xxwFHdoIqhBLRZ%0A44aGCDciZBCOvuAJVG3vCxe6COF6w8JVKzhIsi71Z9ZnM+6J1MTEba43pYYH0QE3ROf0m4iC02Mf%0AE+RUxM9jZPVgPblJ6slHAiU3Vu3fmpNsJL3SxCSWWVv2y8SAlZuU9n1XT6xoKg1ybPaezgP2P+ut%0AgMVNYR68XG27ton9N7N32C+N/a59QKCtRX/2R10XNVjV6wLwTV4BOLtvSdu5IXRwtULNWXfWR5So%0A5kggSv3rOhJYRvhBHXrEEISBpNai4YzcL4nqhnVtRHtfBVYNxxQATBNA7kLiNvcgOYOTkeVpoRpY%0A6Vh/LpKICzjH2chkaqNbhrmogTINF3Hm9jrXB5Hz4cIZAx3SPJzYWgorb6NNeVPHy7LrhU0RtSbl%0AAiZVebTukotWqzVf4+LpJD093ZhODRErnatJZrJ5LKL1trTIE9jIOfNz3TYFN3LJEQ5elDSUU9d3%0Aag4TSCCzd542vQbpvY2RgBtNLCWTzVQ0taSwWboAoOmHz5VmzZADZdfMg3OfBLoAnwPKyef8gxvl%0AgMX9vNaY8TY64Nd6ZiXmOTb2Y2qJ2rClWTP9uo3FxOYWn1Pq24Bz8oeRAJLRxg7DwZ26WYIunx9G%0AevcOuBpBDzFE+CEchPsqsM6Q2Yo9ABCSSL8fyVhCsX4f3CcVSBzpPrhb1HKUOC4GThQnuaAo6pH0%0As6oCatGGXKsapIJMKHKO6u6z0Q6NG7VjuBI5tFov18Yyca2D44KiSFu/Xy8OcgwZPMkhSf26Kj05%0ASnJSGwaSGw2wqyu5vbpubKuK71qfTsqux0HziRCdJ1ydomVo+gD3UQXSBqBtUnXM2KqpgS+niUCg%0AxND4D8Hk+Nj650HlFizP0FteTB6Avk116AMKbwFa7Kl24nNKSFPb4GjVH6N58E2zHjOqnGbyfq26%0AGeOiCX46loAwFsFVHfVnBWYyBH1IG+C8cYAlEFNtAaR8jgcX7Y+gPBCj4RmPIHGwa0hzaBUJXKmN%0A5KnCHknlcBTpcMZ9E1ij60lPR9YX5whG5yBxqowrObXfz4dZ+KPrfLjwl4zzo1hMJ3GqA6grUiAc%0AE/cJvDx1Ei0P2MRQkZGqgKzUl8nIiUMxK2CxRVfrOpakh3O0rEfaVdPvMbhoX4uImcMJsmFI2gZ+%0AMKCe6KwX293K/0V1XfSc9aw3KmCxcSjCuUf2n+ZNHbICfBNdDcLvwTaqrgVaA6fAApj5CBCGLr2r%0AS6yRvIN17sBvdFF7LK0CON+NQUA7An3j+maW1dFNKg7fV+qDAy834Flw6Q0o5/xSJ6K+cM3B+m6t%0AdSMuQY+qERLX1dzEv2BlzExCIECq3pb115NplPiAoaF43pSqLaoH1oJHCzsMPzoLJPC9FQlQl5AA%0AVo9/ryNxv4fhaoTP31eBdb8t2AZpkX6Nfd5rfzzfvxfJGNUg6VwPGGhS/OoCsH/m3MpyX4pD1C/x%0A9AhdSeoem8Q0uYCUjouVHBonKMVg+vap7o/lcgLlCRyG6gKCW22EifDJTqMJncpZT4rtyiWowQtY%0AbCXOwBzkO0oDRtcM+xAoRV62l3UcE0NHy5a2s61tTHpYlsNsVO8JJDWELrTlzsoWfWbbez8MuEkY%0AWKgsvYCa3oBtC2J+6EqOtShPJQ3hev3lKg2TCthSTxXtf9du3YNkbeKnvGo9PzccrgudP6qiYFnk%0Aolvpy83cyjjGbAvXDFDOTwVV6qszlyrp1ltTh/Ru+Fpvfe6thrQ2voQEtgwtyWbfgTIgDLnaANfd%0AbiDpb//nfRVYHxATJ3o+PIgwA/ruQxL5DyJxrg2ShZ+ifYMEgsvWYxSDuMBrVx7qT4Hh4iVRd5ed%0AoKNziOQwou2Y9NOjY3reeSW/Wm+oYpWK7T2S9ZXinJ404q7PY44EaE5QbiJjJ35qX9exegDjeldd%0AYJDfVNSr03LjONFJJjUeqQtOhLtZsT+WOtPRsiEGBApEbed1CZKOmRIY+2bIKdfLhmNceImMdaJ2%0AEOfkHOinQGPOlb2E5mpGQpHx96wOYCcooFqHZZCtLUlSf+0TzhNVNWhy3WTGRAu+NzYP2DV1X2mf%0ADuAolBId10zE+PztJb1yrxFpPijjwgMW9MyISLEXbrT0jJrGY+f0JGBgnx5lfIJTBaz3SqDrrdBp%0ASP6mPLN+OpJlfy8SsB6w3w9EO3se3ZK/1KfPKtYQQOmzp0YMclXkXvVc9lQMK8s8uiGLJsDFSBjA%0ATsSAReunWokBcSGK7nZFymqK6BbhXVx8tmgoggEiPtr3XXPnwvtqIhP8eRWKGi4AM9g0ziUQnGnA%0A6oIZSKopRfUK4AtXj08qYI4R1RTKnLV9GVGMnGuIqR9pGMv5RhkLfqeaoCq7AMQINOI9n3XqPQbI%0AkfOT/7FJ4EiQCn1ZXt/aOK9b+hZoefwPUt+QuFs0QJhVv8HTxyblSbVSri/1q9H/F22xuoyNw8DV%0ATzappjMgDSg2p0Jd0ZbtppSWwVX/Fx1q80PUA8yDRtWqWqObOhkFlVqoEuAcYbC7/UhxXRln5UZ4%0AoCW6zPdI6VeRRP/T4MGHxo54313aNmC9EC76B7gKYA8SsD4wps5oY7nAlnrnYLhQM7j2ZfAMDhAd%0AzHXQpl01gDo5LP+mt4naJY6Hk7GBZ0QQGDO8KDgGCPcU5HcruxGAiI0tQMmryFu4raY2VnATkUed%0AiM902akd4kkEvqzPhXPXFL2z9TYOxcsxUs8Acih8TzAnbZax7MuAEjwV3OrFn/vMlMKRC7f3zAge%0ABAo1RLGMDDrBALmr8hcKnelsW6l3LIGYlL/3o4wiIvWnvY9DBljZqFmP2FbvB6Cd++d+MpxHA31x%0AJ3Ne+6Ea11rXXHOqCrh9rf7QQUYa575xg6Z6OhSnF1lnuL1jHpLhlC6Ioa/c1eCG7BA80DePh2vQ%0AogYJhBmDGEhg/ECcPG0bsPK0FP/ujwSkewFcGM3SH50DbG0Ra0AOuigpd7q7S64jBOF6oQK28/Y+%0AkLXYqOJMUJCVRV1bhnVRAcY9i56NEx7RnCGszLa3cpUDEo6Vej79X1BfGjgAU4XwWQPslkVCwwST%0AL1ldNhrXXW80aTPKvqHSLsDO7S8Ah5r6Btg9Ex1odJVHcaTVuE8andrg37Xs0I3rPQvQq7hOwEC2%0AH25cTNPW4rpwgjVA1gDL+URRvZlhQDUHXWwSBMqmqnNIAG1TJQHMxNNO1oGOUXCAFJRIgK+t6sHf%0A2gr0WW9uYmNxZ+s86jVT1FmeFWtGNgJyt0u2Wa/ab7VnAVVyLfMOrgrgZr2GSiqLwFEzmO2P6e4t%0AXmGkQY542SKjq/Fer9tx8rRtwMoz6/dD0qVOkAxWDfzcfBsTd0qL/wS+GCmKBpQGq4i0cKlrjXDu%0AptbPRZQWXlKIJaBy4tGaDCBb42u9UtY9CefA3T6XKyslWB0hk1knJj9TlC2+o+RkNH/l1gqdZLUQ%0AOgPsXcJBrlhbm5rzwImpmTunFFtfxK2BT2+cYquZWd4Z/PisEvMLwxMEhKTt7WwcGLpJeo36zhrE%0ASKEbAZ2qrFovmsGsAlzVrRYbdZT0wfNq5r4ZUmXA32vVROjLPKNx6QHePyoO5DyCqy60zWrMWwSq%0AwAJjHudy5+A5qmtl35mrTZ6Lwf2js5+4pY8Qo6gBLD1ZABdGJsZ158MaMeHFUXuXIShvQuJeDyId%0Al9WbbRkzdrM4GlulbQNW3m7Jo48XIAXaDXAD0iS6T2oL93Ukl8r+1iOKUwExLtJCL9TZ4m/LNK1d%0AXjZf8t1+bnEL8oSHgEu1+6ruTzm8IJNrzNVmjHTxLQKAZu4TNOuxDNQameC9cGex8Y0gN0MXeiwX%0A66jhxhqY6zUmDRhX2WNYXuYaZbFwweeNpRa5LU0w6SIK6CIM9Zm6MeZ2LuAS67pn0GqQ9Y+5jrFM%0Am8eoLfPTzbqfOOeYN9SRcpkP28C2AeVmkN8hh79RGsraWZrbsbHPEy8364u7kbLqem1hFw192rAa%0AqX8hGdQMSzUm2WvA2jaJxrAE/69G1jzPo9sO1DeZ8Y77kJiDPqQTmMetHgfhOlReGMpm8zobHn09%0AWTopYA0hXI/k1dABmMUYHx1COB3Au5Cw8noA3xZjvLN+9xz4Vc6nI1n9J9H1pJOY1AAUA8iBqpGE%0AYiUtyEtzH9jaT1B1XhE+2QmwnVhpgaSXJLg0nf1ObtUATUX9E4JQl4ChsN42rgbQRT8mqtZtQkTm%0AihFS/ep6BAm1FwNcRK4WUaHmGAFZAkUBhp1sKFV7FVgGdVJuTcofoyyWCreoINqZtWJh32sb52W/%0ALiyv9fR50zKJRdtYc8l9Na5sV+ikb8cuKlNJo+KOyQHqnCl0vjZ3ss5f62RRyWsjV66/vD9K1Vwd%0AI/5eu6UN1FUjxDnHtNNu5AgwKhfF6EZqVS3xsAp1/wzaDST11h4kh38GYGdg+T0or+8+lXSyHGsE%0A8IQYo6olLgVwZYzxdSGEH7fvl9Yv7oZ7Auw2INWz4uoUzrFvgGwNb+CnRXZRD9U4t5SV6mPuOOSO%0AYgk8TB8b4zRkEavbTrvh+XVTmeC9A7X2UF7M+hnj+sP82bg4AkvBxVVADOOwldvqkZ51Ij73IqYB%0AAp5xgd4ylp83ExEXvUuuplCthPL33CxpTx476YNajz1ZR+mqtAkV6dh/bflebFw/ml2RwhCMC3cl%0AlO3R593EVCFhpM9HSOfnidqT25S/IG8EnNvkTntrJ0906TzMeZyAQ63rnPsn+nzbEumGC/FHJxD2%0ABnwh6VNjKE868nAC4LaFWiPEqlFNw6pzr+QV8Q3KQO+MOVBfQX5PaAt7ywmpHpJnAXirfX4rgOeM%0AvcSrnNmofO4eSWdKrpUBVCa9Ay+51hCBJVsITedcCXVI9RHDpkt/1MMBJVfKxUtVABdLL9xrFvlj%0AOSnbrgSJZo6B/2LxfRFHoAu6T/UYBVVUHIe0t5k7R07RnGcJw9wtwaGTdtPVrAL/otxOytsC0fjW%0AzhLANNKGePfWAAAgAElEQVSHoSvLZP7NHFnEbWfellzPShKpxWbtC7Yrl2MbDEzMP6GXYvCNqj56%0AOsbN13pFBdUQy3Fc5DGgYF9Y/TlGtQqFfdn7b/l5j2KO8HMNpkVf9cN+Zpv7tmxj3qyCcdb0YLC5%0Anblt2VRYL45zH8YPYkyi+zXDiymCz3D4u5Ck1gBXHVDa5ft7Y5J8z4UDsV5oyOvETwUgAqeGY/1A%0ACKED8OsxxjcDODvGyBjJh5CO9Q+I0fTpV6ZRnSLcDYi+p1x/hWgLYG3JrMwWGQswroO9Xu3+Y5wS%0AUHJ7+gwoF0RTTW7AdbBZbxtd79XOElfbdKUubBFAEewWEkX6tgTXgZGFecGfR9tAeuHCFnGPIaZ+%0A7EzPvBVxe4waLvjeyxwz8mQ8ko1w9ESSPhvhJvm8NijlTVK40jHKFvKtqHmwWOyNjfkYc14BiFNr%0AazRXQYKygk8ltVAK65ZMn9o4KNVzVXXEMI6cKiSCIvPN73Rl+XXbmmpO5PIo/YxsEH0QzwPdbLrh%0Ae40t7DE1wDx4sB9ym/S/VhUBPYDWW28Gl72ebiTxmnRYmiX4LbJ3YXj56z2hkwXWx8UYbwwhnAng%0AyhDCtfpjjDGGMC5A/guAC+3zBpIngJ7a4Q7VAwjBPQAUK6kyoMV5jHuhkWfs9ItXVN7dLFlAcXSx%0AaBnFMAOvznYM/qc/ohoRClCIPhnHdKU6+WksqUVZXTAKUDl/23AoHi4CDPpkFhydcNiFKCyLdZEF%0AXTcUShKDsqW+dITPXJiARPFKg4G+O5dbjWmh6qlE96LOGHJ0+lw5wyJ/GrBkjNmWTL0DsYrPAzFb%0ADUuxnL8Nb3QEsjqo1gHruOhG0VerfaB6kHqwj/oaHLfC0pGDtffUuKUR4eqytB/00E19WCUfxxYp%0AF0ieBYxcpgF/on3fGxNonhmSuH8jEmPHuAE93AXrZOmkgDXGeKP9vyWEcDmARwM4FEI4J8Z4Uwjh%0A/iivNc/0l5clL4AGwCOfADzm8e4moR0Sq0nM46B1VPkCGAVguRi6qU/OfuKcA2kr+sMsroVysueT%0AN6Kfy5yqWOqZtp8u4LQAd8Ru/G/sXHhtic6LWhdH47/1UwfzfgG3wclNkZAncgiENUAWIh6fV3Vl%0APtkYlH+o0gGJoyEQ5EYs+I8h16abVs6U9eQurc9riihBqt48xwC12drcqakR172B5wQ9PrS8amxz%0A/aS9EQs2XEvX1f2iVIO7gvNY2SOkQ6QcKD/X3iB5mUYfnhiQT1kx5GVdRbpbzgVEyTct9QBs05J9%0AOcehXUJSMaxZZi3Su/9yFfDPV6Vnp8Ld6h7HCggh7AbQxhiPhBD2APhzAK8C8CQAt8UYXxtCuBTA%0AwRjjpdW78VdiOuEwAbCn99NVk1jGTV3q3ZBVu1hNTRdL3Z0aYRYCV+8g101L74BFIji5TiBNgmZe%0APtPndXqg5HLz6ZqqrAzKpiNuDYjGRLUggLcpqSi6yeLopsONRjcFBdba6dwbuTj/raoS1A+S3wuO%0Atfo996VycVWa/GyC4nTRIN0W6li4ElGyqHxA67Qnym8zwNK5Un8ea2cGWnlOJoKeI1o3zUe5+Myl%0AilQ1KKsuOgw5S4rsYwFgxqg1/aluMjxhBXgsAPV3BRyImca6IgdT0rCDTN8BOGLPecV2hN+q/FMB%0A2xYr4GwAl4fkqDkB8Hsxxj8PIXwcwB+EEL4b5m419jJPONzfOp/HHBmDkZxrF4DQuCK6h/uw8dCJ%0AWv5yVxhQRpSTXMFjIUgsIIqXHW+UtQVGoxhBEXBOIkQAxgH2BriFq5F85rsZ7CcAJqYTVB0IRhZ0%0AkDJ774dicdtuXixAe58gOl8GpqtWZpM+d0tu8CrEbGMFaj/VzThTfZ/AMsZ587dBsBLNemTDqMES%0AGL7HZ3Vdc31U1ROki1UFIhvpgPkTEV7bMzC81e3SjVe40SzhsF+k7+qjqKObSvB5mg8H1KqCql55%0AU69UMJ3N6cIdLzrXyTw0bq7G2h3j0vnbhulIVU0waxIDtd640Zo3KAAC5gauDNTdwnWl2Yglhq8+%0AAAdjSrM3JCxagvvCnizdY2CNMX4WwFeOPL8diWvdlI4BeHD0wAkIvrsF+IkrPZIaDIQLCUkXOlDo%0Aa+hmksWoiBykgrs0YFbnfshpDtpmINIK0FF86SbDtLA6ZA6ztgrLTptDsTVAZNvn3u6B/jJU/61D%0AchkVF5Xf40IMsujuAj503Vdg+cxb8Jh9N6E/6JtC9gueJ4ANEYU/ZuZ0xsCUQFxzK2Ogr83g5BdX%0ANsAXebHYA1Dc5jdSTq6LgsZIeubNjfpEstwifib7jnZlOj1kUp8C9B+qMYTUtZYKNpESVKWR18OI%0AYTbPg5F2jxmoFDBDHIaY1BCXXM/Lna/bNVt/DeSqIa7D6GXkaFiw4Nv2naeulAPVd4Lks2Qb77rV%0ASQO5027ThnTi8zQkzvVEY75V2ooq+l6hiBTeix1BFl+D4fJ7Hjzr1Pqyv5oyd4X0Tra8GlG8bWfO%0AHdbAqL8VlYbpSScjPqsjjSzAVFxSsouTUTf1OvYTB3nqcwEM1AeDcnSRBe+DwoXG8st6uQnQrgA/%0Adv71+NGnvgXn/ty78Sd7gN23Au06sLHbVR90W2I7tE+1DLo4jRqppB8LqmdiBBpzLCw2lRpY+D9I%0A/25CrFddf8A3v+xitVleYcHnsaTkgMcMZFX5Aw4a5dhtlUJvngCtzS1hIvI8a8r5VYjzUhZ1pD2c%0A2enEuKS01PvapGePChUhnlgt0Idk4V+X/tcpk09a1m2Gc871BY91SEv9zI898tmLk6ZtA1YgseHH%0AQnn1Mq3/88YHJAdztjQcUKZRoqtTbfRKP/rHbpr++lZEewx/11kx6neKMk39XI1QJH12Ij0bFrw3%0AKEc48JTYfqJuV4Ai+3YiifwNgL/ZdQS/9r5vwg899G14w1PfjWdf8YN43Z0JYHsBLAXOMd/SQRcY%0A6DXGhYc49FmU6uZ3Ci8DFelj2Sa+rG3fDGCLfhDuPasAyCVKH422SSu8BTYnq6L6Ba9SDcGNS0C2%0Am5btUdc+wDZ6NXZBuGNtU29Sh41ZcfR2C0jAwxqwLBdx7NSLUvcZkUByzeboWrs4EDtpuUsGatpX%0ANCJbhLlYdaWvKjlar7AzaaoC4KED3uoRkSTog0jcq4ayvKe0bcA6R3KzWofH9AS8oRwQfgbKDiTx%0AXXVG5k5cHNOsJj/9UXVxIogFWytjn7POra3+Kj0jqvqMAq9wBoUVW9L2rXRE8PxGrdDByyUpcBd6%0AUf2Nr0+Biy8CXvmc9+LNV3wrlg+djV95+h9j1zVPx/WHlzA5Xr5TLP7N6iMLl+43Y4u4PgQR26rL%0AuLmZekfdjCgFZFVO9Oej4nZV53rTK8a4+q5tylluRX5ctPnC8ww66aXNY0bVKP2pGzipkKZi9Y6K%0A0L24gC3YkPJljQZidKEikFHaXDPw1Gu/KZ7TIL3UezeQM1VApLqPNx3nekZ/b27ATX0vn2n3ahCm%0AQu1gn3PYTEu/B+nQ0jrGtUp3l7btBoGfjulY60XwOAE8JBCRoi1prAAGlWawZXoIrHTmx2p6oq4B%0AJiLCq342P9sCv69GBIildcyIUrTNFkc39ZM3WbSUiRAD8kkxfd6IqFaoIsjFVWqBwo8zApExOA2M%0Aa6vuwJosm4f6T86PAv9w6AK86/k/go9/w5djz396Gq4MwLGLkoqA5dCtDLF0IToR1e5i+blw8Krn%0AZd/m/tT2NPAjyHBjW27i3ZTvij4SEb7g+I377iuVQW3BH6OF3LS81y2ldt0dA2v2crFNpW/dKKlg%0A3cyBbtnnZd7oDHQJWFkf2QPzEWvMomvQj7dpzRJMybFqkGpeO69GL9h3XofOiwQB534ZDJ3EWKw8%0AfcWTmhv2/KjVm3ZbBm4hkM9CkpqBZMDiddkvCzgpr4BtVQXsQ7kLkWhz4jUMSjW7v9ame31mpkdq%0Ae1mcsgsWedTcpu72NddXfa+P9dVEUbM5AaeZAcOAgouwl2Avqq5YqPOrVB1qWCp0iVKP+v2aUwk9%0A0JwGPOqCz+HSq/8zLviqy7Dr2e/COdf8HP7yVmByK9ztyICZEZNGfYM34dZGuUqpR9Yfk5Mj9yiS%0ACiUNjmPN4Z1I7zpWJ6/ECCcXSkDKOuWu/D7W5s1UFArg9DRJDzarrL+vkhaP0Bb6WesneqeQgy1i%0AMMR0PLvpLXavSn4jxa+1CcRmjXOs3KuPT9LfqoHbqoFrQFrDVBfQnWqtdVDlFeRUBZCLnDV+A0d9%0AxxyPwM8MZBVTtO7anQ3SwYEp0omsFZwa2jZg5a2rAPKlcdR/hJh2NO54yprTssd1liPRh8oHTkQq%0AiqGZxsRXckkjsyeLSqH8G+RbUdZ7ap20DsVWXf0u7xTGONZp4qBSvEMA5aKxxYV+Mbdd6xNjmwC6%0AnwJ79gNvfdLf4NK/+kk85D1X4QcufQu+9pavRr8KhFlKw2Orm204yjHVHhJZlB1pT70BAsjW6xB9%0A88n9vIm4venvwGJdcSx/q0F9IW1RGCw2Pri+dPSEHxws6+Ovqu9WI5xSfaw6n0jTDdHamK9zGZu/%0A8pjukvzMqazGLhINUn1I61svoVzpPO28cdAGXMfKwEt6saHGDyERkHnbiA7T2NU1XI6nQg0AbCOw%0A7pbPAS7is5Op7NagtoBb+zaatDMdb0s3Cp7Prg06xaJXbrY2vixYKItAdFMwQfXbJp/JsQzccKSu%0AamQb46pqg5Vy39rGTU992R/7cOk40J8BPPLAZ3Dlj/wpvvF7v4Cl51yEsz76PrxvBrRrcD9JPTEU%0AnPtWbnugsxzrD0ha2CYmnHytM1dL9yIqDIYLdImjeYiaJVc1CrBtVZMWxssEKpC0jTRfBSQGJq1T%0AofsX1U/uM9obJmW7KP3w3ZylrAeYYYvgVR8hJS26WwvwdTzGO0QkMCSHCziA8hm/q3Q6lzxzOY2n%0AIec8drmmvq/toCpjxVSOo4FN7gFtqyrgCNKVCbWFsO6YDpXblRC/blQtqS3KFFtrINRFVk98FYM2%0AW5CafhHQDspdsD1StCZYxSa5gg1Oei2wyG/lNJEaoBb9lo17LdCtJPBaeyzwhof+FH7o967Ew/5T%0Ah+974evw2ruehvY2r2/WJwfhmGw15fJOxOVXY5k5o4mLsbSEs30MXs7+u1ukOmbd2HRzCt7fm51i%0Au6ekrl6AHD6giiDKnxADyqhxkv7JvJlWOdd2lr5rzNjCmMg5DOMG+/JIdR8S8M2a8rjpXNYsgVOv%0ATFKeZlrNSQa2Z3CaLrgxqnaRUnBkGQRZunoFOLBRh1vEIYn+Lh/zxPUgcPQ9pG0D1ruQGnMGgBus%0As5o4dBpW52BedUsi91pbBIsoPOSQKs61Fun5NwqOQfIQEV5PsBTO1yGBIZ/VQaTz54oLrnXDgC0q%0A0Y0y4HYRgAMo9Ht8L+tAlRuJ1Xf5n8VP+89jrjSyLR0Duv3Asx59GH999dNxv6/7A1z17S/C8qe/%0ACYe+6CDAPCaMZlHrdlkH5VwJzDR4KOcVPYQggYQ6SG4AjMegfVzENqg5dgWcpuyb4vABgUo43jEv%0Ak1HinGu8b7T8YvwUbGI5Dgrqdd1iPVesX/P8QFk253A+5GKUXeAMiBQZFITbPonseT1aNrs6F9Nj%0AcOt/J2ks+7Sm7fu0dyBdNTdJHlWvu1jbmk97xfJ3NqOXZ0xCTwDWK1+djRQ7oEOSpHfh5GnbgPUA%0A0mmHKRK4AkOH45WutCxSb7Pe+qkLdtosuFihASDUH7AQfRREVVStQFcpu1tZeopZ1Heq7q22JDPv%0AmrScwsFeF5GBnB5mUFBcZF1XF57C/UvLbqq/tvyf294Dx09LHgGhBTb2Ax/99o/jhW96M57/4gfj%0Ay/7o3eg3gPUDZpWfARtmncwne0ZcgHIb5hW4aJ+cwKofg3sEKGlEs3zUmFb7AD/ZZcBcc/d5c7qb%0Axi8FqUbGVCWIhVG+UAEwxudNHlM5/8+NSV3RRl+VzSw/qzY+rRfbs27ARxUdb0sNcA6W2cyq9Znz%0Asjplq3xTrnve3KocMElBk6+oKoB5tyPtJpDmcnqXfhskAxbvv7ofTp627c6r40iqgP0A9lgPUuRg%0AB/Ha5B4AQnnkbYwa+Z36oaZHEYVed+/MkRCx7VktCo6FOtuMNMIQFycwBOumL53l8ztMb0CowLJI%0An6juS6ojHYCCir18RMAVTmlg9GiB3XclN5205QPxQcBz1z+Ifb90FNf/xi147qd+Fwdf/UL87gow%0AO83Fc977NeoiZv2CiY+RirUFAAi3rRSigCSGaWKT6lGcOmrdk2CRWD+2sW7FAUf9QmvcHIxdgzKR%0AtLHgNHXean2q99hWILn7Kddeu/YV9Rrps5p4/xyQbu1QIKURSsP1AeXV6oADboglV6euWzwlFY2L%0ApKColB38qzHh2uf/OZxr1q7um1S3sdNjp4K2jWPdjXTSoSY980uiqM9n/K9nhWGdV+tgAZQWY4qg%0AdTo+F4NJN+Ja1Qd3Q8n587+4cQGlyMj0BNNFVzjXIfgYbo8nxPQqGKAExWHDy7TazkINoKDKBlX5%0AUbScrAPNBjBfSZ+XeuBJ3/oxvOD334FvuOtj+Ot/+/v4iU/+G0yPW3vl2u+iXhTLlCtTAK3VOfJ/%0AQGrgGemH0HssWo5NdmUimI9xxVoneBknMpSRiohhi+pXzwMpL3sFLOBs0WMUJDc7QLJlGsmDLk4r%0A9ONFqeMkTcbq6lXO6gJ9xiJVW7UZH5OlvKp8xQi9hHQS3fOok8z13c2Oyd9d2rYDApdLsXvt85Ls%0AiBN51iN5DeipDcAHsN4Vmwjs7sbjmBb1UH3eJpOuayRICtJu10TheEaI17/oBCAHrc8GOk8FlHZ8%0AwU/keslRHZ21LeoFg1UfjTnAb1ouJ2NIov7y4XSL7XwXsHwEWD0IHPhnYOk3/gln/u4n8fg/vRbv%0AaP4L1i4W8dQ41WbuRqixdhdtEMOiqjWKugd/3k1R3tqq7TI9eluFLxq4mp3oQEHVZ7H18S5C71WU%0ADUW6gdec6AKufFAFqWdut0ZFMzVEJ54ZpBNd/levB6rW6LTfRvMTtXXAdavO/2vtUCRn7AB+JjDX%0Av7MLlItVg/Zmx2HnTfk7LxmM8h4jYJExK9Ij6Vu/OeC+e0AASKDaoDwT3MDjrfbwzl5vyg0+xwwI%0Arnfh+xtNUobn460i0qs4XBxTlEHuRMHO0yDMi4rzfFhAXiX3U7tn6aJjuvQBhYsTJ3XTobxCxJ7l%0Ae5Qaf57zkzZkcbtDPjQwZsBRA1J9uqzwIRWOp91I4QVpoOqmyS3r+LnAsZc9BA+67IM4+rxDeMav%0A/zLuugGYraAIX1f418LrwfZkMFXjU3WCjJ9782Gdr3jdxnSiep5eDZl1nrkfaxL1hB7uIMfb29j1%0ALYqTTHUd9FAIYAY5PemlRrI+qV6o/+WcmN7lm072JtAN2dpIr4Ca9IbUMa+XvjEPHPuNTIUGMmGI%0AT2WGuIY3BFQHDHpwUZ0hACnWK4PEU5eQPOjLSlUhA7xwXevNrSTe5LzSAXvn6R2CdD7taGlnSLaa%0AU0HbCqyFLUNAkm4XZOEBB1sSRYC26hzAd9FspYQPZAaTEXXAQK/YlGI/RRiqAzhJ9JTLlkkXuHBz%0AmeOpwIH3HCGKT+fYJKifWTo1RtXeBwMObStcU6j+Gxe3fhbw/jN/FXd+59U4cPk/4Vv/7DvxD5/x%0AI5TthnOrBNhcJ8kn16OuFzeL3vskyDu132auro0RL4nMF07SQj/mV6t9XPV3oQfmb2MSSCjbNkZ9%0AIxsdfJMFgMkqcKQB3t4Ar8fj8dC152BlcjEOV3o0em50UxTW8hPxXGOGWyUF2NqnlOI2MHTyHxMC%0A+Z3ASHBUUl9VVQsADnoNhhuGhhnU//Rx70I6+TXth5jRRAfpU0XbZrwCgCUbgM0cjevvHDD1kauJ%0Avna5nB5YbcpOzUr2YLEGoh+n26yHA4b60YEqwfI4kUhZ69+4WJV7oUcAg14XRp+mNMKouxLzLbwi%0A9Lly78pZj4jChTGtaDgGYuzScaB7MnDlRX+H+z387/CtL/sBfP3NP4tjT/sJNI9yUZ1GFJY3EIcX%0AkIKuAiE5VW5wA1/YsTHjMVxRzyxS7RTvs/8rjw9yjqy+BunO6WPV5dH7oV9GPhjQ3AX8w7kNHn/L%0AMjbefxnwpmdj5XvmaJ74BPz9hbdi/43I0ta8OofJ47RjV/AMJKfo3O2JQHij8SuRJpyrKMP7Uchi%0AEeREySVmLjQ486TpYX0zszXVwSVWlSobOAgr4PfyXdujx2d56IjH35mehwSOnaAftkLbzrH2KAea%0AIoQapsZiJ5K4U9bPybUud/5Z+4vXaQPlrqyixRhFS7+hItOirRkOeIBM9BHOgGmpg9QG6amj4hlQ%0ABhupOWZVNwgYF2lDufBC72Cj7+h79cmuoo0TJHH2ocAtlwCfvvQ6PPz3H4KDV7wen7oBiJPSUu2N%0AH3l2AgrzZEjT8oETAwQgHgIj7dfPYW5A1cMBVbg8NXwx8Entt6rudIM2RKBfSu9O1hIwffcacPHn%0AfwCPedJvYuNtH8LzLr4Cf/T2J+KGb3k4bt5zKy64w99F9NjBrbnk9RNxAawkHz30onU7kcfLWls6%0A9vNk5HqVP41D9Rpi9npVSkSp31xv/Dda/CFpGgFo7cpFRic+JkfK+o+VTZXELLjN52Ro2zhWns7s%0Ao3sBUDSYmAiVWXW42MFOUZeK2rLHXWsjmA7IOJJ8vYtyiJZfvsAweJ519B6+R6NZ6IEQ/BADSUXT%0AOtiKljtGhdtQla6dwe8tgqRRbitKmSKq5jLHOFj4e2zXGNddiLQ1Ryh9GUMCpO5i4KrDf47n/sfz%0Agbd+G/7vb3k53nTLz2N2sAL4inPPk6HzOoz6kgbnAgEUeupaV0r/YlrSi8Aj8Hf6xl6TOpG7pp64%0A3jhh7S0s/6HkXtX4Vwg3bdKbHm6B356fj0uveCrwgR/G+f2f4Lve+E78Xw/6cxw4mt7rzVeXtzmw%0A3+mmVscBKPT2+tNI/wyMmyiZjDaaryq2RjWDr1dW63CHWJbBdT5rgLqKQJoSfMh1mv3L4euQGMC1%0AzcsH57Km19vSWM71/q/h+ut7TDMAu2Oy9m80KVgC77aZh+R8rAGuqWOtd1bdRbnTKbGzZ02K2qMK%0A+MxJyGcFWcZ0JIXgHGqA7/wap6CxiQ7Rmy0SrcdIT3PVp2r0MrgcTo5p9VACwZPtq/xHc5q63Ojp%0Axq6Tydys+svSz7Ti9BDSYYL5I4B37/1NXPA1X4U/fs752PvaZ+LVz3svmlnF7amaQyWUyrCUDz3o%0Ab7IBqBuZqmiazlQ9PcrrcmhEmlnbgIE6JRvzxg4RKOjruBqo1ka3bpq8OnjJ30YDfGhlF579kZ/F%0Aeb9yCfasX4dXfeQS/MAXYirzmAO7SifNRgJX+ueqgSsHeg/luALIKi+g2iSqdcOvvMVjqxThYF9f%0AX10zQQqE+mzWDNc5STnhOg2BOgaLPRKdQcoXGrLtSAyS5heRpOi1u9HeRbRtwLpLuMes94RwrEIt%0AfMfrQ/k7GQH9rP0yk8XDCUJdEd1F1I2kptWqhxiVh6oK3i5bD1AMQFNbm2Mpho35smr6gW8nkGOe%0AZlXAvEqfMyg/qyW89k4Y1SsayDHf2LqBRPNdFB5P8+gfBFw3eRme9c5l3PVLr8c3P3wPXn/2O3HB%0AaUBjbWnXx7krzSu3peJeBxtXJS1wgyMwtTOMRn8a8/mNoTJMRn9eqxEGPsX2OeuR7d1+mjjcdx4E%0Afu4Tz8DnfuS7setJE7zk516KF1/8WZx1M4qYCOrry5tyY+ObRY4jESRuqoyJegeMzRGNCheQxOEs%0AnRlHuN4O14fglPdBXbaVyQsF6/WpxPWpeFBNN6z0wFpVaOZ4e19f5FbnkqfqZPvq3Qg/5HAq9KPb%0ApmPVAzUUvXKHBwx2MsC8BASMiovNNK+KGKCFuy87nRyuumLle7ZCmkzUzfL8slKNi8UxOQWvBR4D%0Ag5M9FddV/ugfG1qQo8VrpU6tXtgj5QysqeRiK7BNL1bv2eLIlnR2vHLBmpdwi9PzOrz/muP4x6/9%0AC3zwv7wav/eR78DkprQQCKpavvaZnr7KEbjqPlrA4QTqq6Pnm+vee36pM6p8hIOu9eIDx/9BwV73%0AHBeiNbe0DnjNDQfwvT/7Ntz10ifiOT/zfnz6Zc/EK879LM60gxVFeWx7LA86xMY31s0CBA028GrD%0Apn814L6kDVxfGoMPc93N1E3yDyi7sZF0NZ/ANF1wsV+7k9NprIyifZRsZQ5RrGdyHpUde5dl81rs%0AXZtIlFulEwJrCOEtIYRDIYRPyrPTQwhXhhA+HUL48xDCQfntlSGE60II14YQnrJp3vZ/0ifWnd/Z%0A0XS3YgfxPnGN/Qj4IOkONUbaueutn29eaz1P3tdTBN81kCUga0zJegz0mhnSWMQmYDwdIJybcsFi%0AAOumyAFI6JhOowl9NQt3rXrG6k8ED30m+sLiWK2A6ELwDWV+2Q1qGei+H3jJ4yKe/vHfwade9o14%0A5Xt3oxPXosLbYWmYB/tI4zQUbaypHhzhwnPgFZT/N1PT5Gw246ylLqFPKoZgnP5kDbi9A87+7PPx%0Aa0++AueddSFe89EfxW899NdwYMl9Whk7Vw+g8NRdN7UAP2YoU8+DhRHPQqke6LX+wbxi7CvtECEm%0AqY5MBiVGnrlnJH5dt9r8AiCtT2m4ynWS9au+rSSC40RwYKuUbSb2vQ8em1XdNMmYMTbBbGRN3xPa%0ACsf62wCeWj27FMCVMcYvB/AX9h0hhEsAPA/AJfbOr4YQRsvQgQRQ+Kwtm7jLAWRDlzq/r4YgWe+i%0AynHWgRw20xXpZGijcaqxvFGys8FZ6t1qGYDCZWVSL05bzIVTP5CPtSpHtBmpFb+dufECSN8n6yiO%0AyoYeA1/Y4pJFqZ86hwNI+k4Ce/A0Ob6qihsEZrGw6+/U/bYbaeG/6BHvwksuuwYdNnD5m9+An7gd%0AmCn0bWAAACAASURBVFVAHCsudsxqn0FE/T8VKOsxUFFYOOyBCiWWf0V7gnOgY+5blCSytwEPJEyA%0AyXHgGWeegQdd+jY8+lUvxv6rXoi/e/Y34NuOpbEMXRL/Ywt0u9Jniv10qRujHN5R6jdQlVDK4Dtz%0A/05QU26OTEeOiQoHVyYjOCkQqUVf/6ju4/FXrh2ufQVNrllyzrMKPWjf0HoEuMskn9cGsbZfEJ7U%0A8stXPcWhJHpP6ITAGmP8CIA7qsfPAvBW+/xWAM+xz88G8I4Y4yzGeD2AzwB49Fi+nN8mJQ2Zqijg%0AGHwC5FMTSOJ5rN7LO26ZFXrITotxdxDqljjw9RW6rC/DnmUjuaXns0KpbtxjzUnkE2EKUnUfGXAM%0AnLaXkEe/n5gRw4w/jN1Kjk45neLsutRPOSw2NNr/fpKAoTihBgE7cj/CaeXOgm8oNMB0S8DTnncl%0AHv/qy/GVNzd4/zf9Nu687iBCn47HkjvOFm7ZObPXA1UB1UBnIBbQz22U9mYjnmx2g+PNdZ8oRa+H%0AdmP2HqDutk8HIyarwH/4+6/Gh573/+Ixt1+NV7zh5bj2rH/GdG/MZRH8Qge0awKYleRStydzrFtl%0AswKyWiICxclEMh7096YdoYGvB+UAuT75XE9b1X8KtMoM9XA9bK0O2Ewj1sjY8mJBdX1ULx9iB+AM%0AWRcSt023ywJDttiVm9E91bGeHWM8ZJ8PwQNvPwDAFyTdFwCcO1qwNKaeFG1MhweWe/cEyC5S8AHd%0ATNHMQemkYwNcfzTGvarvrIr7OtgEVb2ad4zU8toLJ1E7lXPgiwhb8jtgRiPmQb1agF9JEl00LN4f%0A4fQ2UwuMGTZqsXcQUxYOuqP+qfzdACdEoFsBfuhRf4YPf//34rxbj+GR7/gLrH12BZPVElAzgIYy%0Ar+IocjPctIr2bkJZp20g21fXoI9yvsIhj4WEzG018f+2mxo8/NqfwK0/9PPYf/xqvOFNv46nnPFJ%0A9JOkGqhPn2VpAQn0OKb12CptppYYxAHQz9JWDb/Hu+ZUnN5oxLgsa5fBTZiXvkcK8l4+lgoHZcDX%0AOOcUy6jFfwVMANmVq+3dX1WPuuZ9PpidRKUj2zxqN7Ct7lGb0Ul7BcQYYwib7pejv73lVd5xX/UN%0AwNf+O/clA1z8IOW7cYK7QNWdzp1xYGTSxQLfXcdo2pfih56Fjhg6RNdEsYe+s8E2h64td9ksvkb5%0AXi+QirsqjrRK/WuRufaX1dHpZMQHHGxwsOhb477t2CiPgA6s4AY2WRSGg12uC4GL3FUHbFwC/O0N%0AczzyR27COb/4P/ESfBP+4LF/hI2nmbVbLN0ZPNV1KSBzyAOXrAYDcbifOFAvCqVYez0M0im3buOR%0A3ZqsLv3E5vUceE8LvPCaP8QzfnSOA7/013j/E38S7a6kd50vIwfvLsqEqwAYF0KvuFE3uOIQgoz9%0AwNd2wUaqNNF5LyCnADq2Zraq99RkA5dIm/9RftfgKzXVa7yX9i6ysfAqbPquBqTNgmq8T1wFfOJD%0AKe3GFvrrRHRPgfVQCOGcGONNIYT7A7jZnn8RwAMl3Xn2bEAvvsyV1ZMIRGPLWSHuPDl2gHGnRVQe%0AEU2Uq1UKcN/SadzcuNXDJxcHtw5JtjKi6+IxOQblztGwDIgiKv891bXOnDMhR0fVQr1dZSf2tjRs%0ADHQf2v7egaBnIyzffpLK4OIe4/K6iZcVUen6WC7/dOOw/+qWRF9R/nbGM4HrPvgmfPP5r8AX3/5s%0APP7iW3DlsY+gPw1FkOqBd4DJkpmbNZAt6jDSD+zD3C+qUwaGsudIvy6yvMcmqTkYuetwC3zf+16H%0Ah7z8Jpz9q9fjl7/utVg7AMB8WCcb1rc1AAYfs1ovP2zU+CbBcV3I7nAjgXObnMPzxpoc/Rpp7vn1%0AefyApD6jBEiGRYvlGh5wygZubN48mFslko87dZ9cu9GrPcrF8gBA/bwL6ZQkY8lyqrCdzPsrnpj+%0AYOW/61UL+m6LdE9VAX8M4Lvs83cBeI88//YQwlII4SIAFwP42GjBAlq75n7dA+BeATmMmIgZ2nna%0Av72lXRkBh2jiiQZ4qAGSNz3CftszH6YJJnKQY+aRPgVVJQ3SUlMwi34vC51cblMZwAqreOMLL5dj%0Ax2trLraTO6KA8nNOZxwR8+wmvqB5eSG5UTrXF0YslByzhvlT969aFRG6BEJ7/t0deP+HX4lvOXgr%0ArvtvP4wPH3o0JnfBj4aOcPObHYYYi3WgxicFfb3ArwDReoOoiWMqkgTjFPQtcGgOPOevfhyPf/kK%0Adr/5Mvzi178W82Vgetx9Ytk/epcZvTqYT6Pc9yZ10iuFuqkYRTF0tSo2I/g8W+6A5bn7iNJAS6NT%0AL2m5VlS/qu6K8yb9UYIEfC3E4F5AgOtYc7xU+G8RfgIrogw5yLVMlVv2K6842WBtY/wQ1ilEt9GQ%0A5kCO4XqydEJgDSG8A8D/APCQEMLnQwj/EcB/B/DkEMKnAfwf9h0xxn8E8AcA/hHAFQC+Py4I+Eqd%0ASBMTOOl1K4vEi/ok1FhaFR0ovii32MRxEATcA2DDLKLc8TTqeQw+mMud/9VUGAVskmk4tn7iRiXq%0AMXlPVv3HUG7Ml0CqYd8aAVsCWqNgBgxuFW06d/GhmN6q4QieXwwGuuToWdcqbQFWgOuGg7cjNsBs%0Al3kKxMS9zV7/KjwC1+KXX/BKvOMTF6CfJHG5UJVYfow1209dJNbTTwWoQ9QItQQg3HPZCGxOfRq3%0A2Z7UhtkelyTe90/Aw/7lv+KjH/xmvOryV+EDjzuEbiX93i2VXHcrahb2M+B9Ol/yuhfqAuFkGVuV%0AHKqOud7jNhgjcv1wibBv3ACl6jAFR/W4AdxgFCpAYzfWMVkJbCo50h9d0wC+flUt0DWbxw4ZozE8%0AYbyPIh2SGuD4icZ/C7Rtga4/MnMuc/c8gSpdqYC0S7VwPU9A6W+3GU2rgeIAF2lMp0RRhYcP5o1b%0ADgni661zrz1K8aXOn1z2Wut+gLMmtXGRg/Lg3DZKrrQRUKC4mK9IZvronGVWJShnq1yjlMEQerkh%0AcK5YO3HshJZGksrXgczczzaf5hmbYlZuTn8z8MqPvhgf+LFH4tgj78B7XvlqXPgwYLoENBsGTMvJ%0A4JPjKcQRbi56X4z52hbt1+Ood4NCn+owOZ5AlVfQ/PEtwHe85034qr95CG54xlW48eteg41zkL0c%0AilixQfTdFcetdaP/sKoJsr47uIQDiPRjfTAWbNsbYeMmHDy7iZ446tsd5b9SfUyV60djR/SNr6UG%0Arrbj8Gj0KgoCVqUB4HZN7qpcJ6oLdHpTxcG1vSFtpUSsBwp6a8taSJzrfTrQNQfkmOmEIsR3lRNN%0APnciYowR9bKDi8OqvOfBffQK3acN0rQrxX3A399o/e7z+Uh95k35Dnf+MZ9bILW7azG87qVSedQn%0Af/KiaZyj1HYM9HNhCCJ01Sreq8T6OnpV8b4dVlCLdWe+mBrliWl4tj1zaAaK7RyYnQe89ht/Bw/4%0AztPxsL++AP/5w+/E8s0hH1aY73bOOgfNnjjwhGgctQATddw15TbeQ5mvn5gBandypZovAzddG/Ad%0AH/5dPO5nz8bFd1yF9z7qNZidldo+X/G6Z9E/Il9FPWDpgAJotX9rzjXXqeZoTwAJUeZ/zTGSK2QW%0AelCnNgwXNwAY6KnUSIaobp4WyJ81rCfXbAy2xkRSjHDgznE66vbZeuP6VBUiGbV8IzS8rdXlEveY%0AthVYO+NSd3UJgFQVMHaXjr439pP6qmUvApQnPvh7sPTcyWb2edYkDpVc6CQmt68A0+F26TuDxgCp%0A3rUjM+D6V37mX90WvSudBw1yKEOCQw2UudHOmaleTQ8tFBSGn1V0XnicNqAUubEAfEfyzxwGZ3Rj%0APquWB40582Xgff/nd+Hgd38IN71hHV9720HgSAIluiYhJICme5puBK04vm9GW3HF0jYMPCGkjbPd%0AwNF14Cs/+ad4yqVTXPLcK/Azb34NHv5lKe30OIqbILJRLmA0KHf2LqB3RoPB4QCVAPJ1LxXVx2GL%0A30Rtwr5oO5vjc2Bl7iqz2jqvAKsnmIAEoDzEo8CsIr3GVSZRF0oVHMsrvHmqBU8fc2XCSAy6AqQ2%0A1F3A4e8aZO+jLpwabwDStgIrlcrrTekXSlDLzr0j75J71VMjNHjppk9QpaLdsKrwbwV8J+ZEWWuB%0A4xPPi1ZTDv56BaYbjSvHC0+C6KexVrr0t97anUGW33rr9VZuOOsXgyxo5R6r/3otSPaBrTovO+y3%0A8p3PahCpOOhg7aGD+QBEA/xq7mBcq+hXKaL3jaSZCocVgCMPBF704rdh7+M+hH98xp/gmjsuxGw3%0Asg6G6o7eNj/WYaDOaJEjZgEOYtkHtuLMRynA2Zkm/QX4sxiA5TuBl171U1h6xZ249bHvwjf94G/h%0ArPsjO/j3UzdKonGAyJw+NxugGF/dJPXgg0py6kEC+P+ml76p1Az0PmF5ujGFmIK4kIMlB6rAyJNP%0AzF79vJVbjSg53BjKpgIW6N5+n0lZ5FLzoRtTHRTqBctjGofqOb5D6fH4xLlTvpdDClqdNpC41QmA%0Az+PkaVuBFXAQVSLwVbaX8ffjcDcDvOOoHpjaIHIQMnjDAZbl1mIDn5OjZXHkZJXGwqyRo11tHXzp%0A2kK/V8YsYHnzqh40LmRn5laAr+JY1Uo8MIRZ49WwQT1rdrquwJmUQVieF/cmsW6NL9Qx2szrefkw%0A8HUPBn7nx67A9GG34YlXPw3vmD8Qs12pncqJ0QoOlFxdNqgZeDMylx5FVbDPekwD3RwkukFxjYxu%0AAN1SUgM882+/B9d8z8Px3Is+j1969eV4snGqzcwwrTIm0VipkkYGfBv84iy/9JlKIfyu81/Hj5tk%0AU+vKR9Za/TwfpundYLzSpTWknGiAuzCF6I75PACgRHBUFR31mqRZMx5PQIPBTHrnZjW+8oasd24K%0AHVK9aTtRRkpDfUbYqUskgD0VtK3ASlBb6hc73qtVcIwoMhenSEIJ2DXQqRKe+avqgLTUOyBqfUiM%0AHTtWvVrR34zkD/j7eposwMUwPcEV4WoCUiGat/63iLgwc6yCKBwkfNHqIt3sj6TWZ/28VfV/VicY%0ACH7Zw2/EOd91Gvb+5PNx9UtfgPVrkV2aABeZN6NB8JYRwMqgxucECrPGFwFa7MhpPwGaNeAr9wP/%0A+PKnonnUB/FjP/0L+JrzkeOsZu584n2ifTVeYa9HAXSNc+SFH3crm0uoQHOTNTNGevJKx2xd6soQ%0AmbWulXdcAaXbIpA+azwQwIGRzMNYYCXApUTAwVxxgobhKN8ZRWu3XRy4bn1UGLaCryE+Z0Sr9bHO%0AuQe0bcCqZ42PTsp5oI7IOazfgnyKSwL1Hfjg9ShvXySQzTi4oRTrWV4XErjqPVl1/auiizI0ChDV%0AEHWEoDpf5qcTrhsZJS6ErnEuqyi/qcRc4ciya9dExHVJn2+fFdF5DKwLfeMCAFWueYzjzWJ5TFZz%0AGtMigDc85fG45OK/wec/+jD83OEDiOvIkZ0Ghjb2kXF9zFu52sJNSfqLBrYcYyFInqEsb26uU++9%0A62J89nEfwtNvvxM/8yufwbmPuxmdGakmq8DGHj9Z1fG6GgJ4K+MxIh0QPFVHTs+LbMitgm7n8WvK%0A/uhG9Lix8fmjGzXnHeBAtyI6SvqUkqgzHbvBg8R1VM/xmsnI6gfhWJkX19A8JC60CPEJL58S4Lzx%0AY7kz+6OXA31pe5T1pOr7OE4NbSuwUg8z5utWiJuQHU7+OEAEn3njFs0I5JsHNGyZvltwivA0PcyI%0A1SQRQ7nPXtLkZwRnyVfBtrN31iw/Db6rFODiz3rjd7evtSUIM3/qoAiwxV9lfS9OaUk52bFc9LMU%0ARemtwPdo8S8iYRG8I0rwtkUb2xIoahUFnzd9MgSxI/ol4MmnAf/2eZ/GV91xF9753LfjyG17ikML%0ACqz0Oqg5ZZ4sKzaSgBx/AaYKaEVVkAHLRPZmzokEtOvAjbcCL3jDz+PR138Wt7zndfj2/R9IYrHp%0Afufmo0uwzm5g1CsbYBexYFFuQDXVUdBo4OKmkf15AwZ627xBcrw4pDKXuPnPg4fKrJ9R3M9zLrie%0AlVNLdaz6p55vZBjqdVIbrPVoK50nqFNlWXkOQ3xrR9aYqg103bJuXQBux/8CwEoXDB0I6kHWBUhq%0A0t1qRlDVjuzzGk/pMVQXZAslSjXAmMpARXSNrK4U4aI+B5t5BfgkyhbRBSoKDjK5a4K4eivQi2Fd%0AQG91koBYPQ96W8yQ9hNwCaL5T8CI/dSpyNX45NWLFxGE21HwFgqdc8W6e6oagqJuNviYgaVbAn74%0AmW/Gn73kkehwGK/7u8dj103Je4CnvyarAkQRHo9WQiT2diy3nZuYDn/eLRmXHDGMnWATqZ8CMQKz%0AJWD5c8Dz3/lfcNHb1/H/vebl+LUHfxqrZ1l+0me9bADsKxXzC0Mayr6JwTlzfTZGRSCcMhsApbQT%0AJD2t5htNciHkHANct0pDLbnYeZPmZkA5h2t/V7W06/oeq6BKlwyqpACta4TPsxHNgHgjuLP/RlPm%0ArbFHxogHAli/3QDuvyDt3aFtA9ba77I+h8zO1l1Tdz5gCHL0MyX3qj/rFdcqimu5ykEG+ADqzloD%0APndPfcwgERzY+s5yBeCai67rEVH62/UwgA0GqGIM46aS/wJG1QhF/UcW7JguuNYLqk4ug7GKmGYA%0AUpeizE0B+egsUOp0c7stz4PnAi/8lhfjEctH8Ydv+VG8lddnBxNj66ufbUDamYNIazEK1LFe/XcH%0A5RrQE9gYb3b5KPCTeBrW3/41+IpXfB43P+NWLB9IsXBJ2Z2KYxgWbzpFuZKWfaK3FBTeGiMbFTNR%0ALxmgclmMPo4ab7g+zYgFDATggEEDl87buon5u+SlRXXNuKErS2TVs3wsVrLlXyEhhuH7VTWykW1Z%0AOOIpgCUAqzh52lZVQHaVgXN8vGZXzw6r0hsoXSr4HUAOvJvTyeKnk3FT+dj18EWthwc2mlIvW3DW%0AMsAm1aGav6V1U54LczWIcj42l9lN3AyilhtlUsfSi4CBiuvJCJSLr97AuGAIzLlNI5XTZ8rpqQtQ%0AUXAFEDyCybT1SbLQA2iB77/kM7jlks/hMdd8Ce+9/I3oj3leWSfbln8ah7Y3g1gt/itpuEIebtAB%0ACT3wh7t34Rc/8Is494XreMeLfhRHH5DSNDPR4+p72vYFVPSr1kt28+zHXIMpN4mmTKduSYiymRho%0Ad4279PEouYbxrP2+SbVKLEQ/1w+IgbZigubwZycyZlLkD0jMA5MHCB7YQ3VN1C6r46suIladhquD%0AAPYAWN7CuyeibQPWmghqAe52AZSsPdOR1H2Dk0EV3tlwhMT1UZRVx31gOP+b6BZQ/q6GpHnj4g7L%0AHZuIWdfLiVYBnHLCbIPu6NQP1+JQcXY6lKoQpcLvVtQE+mze+Pfj1W/rjW8S/LyIBk7axr2uT8a5%0AH4a3q/WKBWds4jlWgD9+43/HOlYw/YUz0N+1K9/9NDWlWNZXVtxvO3Pd5+B+sBFiZCiqDRCBtT3A%0A1RPgBc/+f/CyN34UL3js63D8fsDykWFeowclRvpK2z2Imdpg1FjI37wTrY098qEZ+rBmCaItpYto%0A6el1wvgY9fiNxb8gQ8K5RlUaUK7FHt4m2j3GjLCcWwrkjfymDMsiDhqSRni1hTTp5fp6tss+c9g2%0AcarZMm0bsNISr1xVA1EoB3e3yFxn8OdrBgI8K8zf68WfVQlNOTidPOdfXz0jKAKyU8ZKvEI5+OoF%0AACA7O9e7Nf1ldTIESaO6pwYSvBeuixL1XTYqbEbK3XKj4b3sAe5rqH1Xn7EGyqA0OW8bF7rAkJY6%0A13/pYQSV47LuUfoiO8Dbuwfu3+Pgz/wP/P0dU3z5K16MbiMt3o09I0BpmYQeRaAbBAzctFRNQd1u%0AM0sdzSOoe28Efug3fhIX/+0d+PCzvohHPORv07guI4cJ1LKLmKjR86+p9sHOgMq5I+qImrJHCEqV%0AjKpjAsr8uYmuT5zT22jSGDEcBSPqL7IlqJTG+R74o3R/2yM79atkppxslLVLA2+e93A971z6UT0A%0Apr1It2FEMkO5VrVN9c0BS7Z+VgF8abzL7xZtK8dqqqsEHiL60xmffqLK8qthiE7CBC5yt8oJquhQ%0Ac4j15OHgaJRx7vzsKNZXATn7mAYgGHgwjTo763UXdP/QiTZad/ik4y5eq0gg7ygXkF3J5DPzUF0Z%0A+085BMDVC1wgPOjATWndxoUbzpiHR62HjYCDqTW2cNoPyFZzWvGbHtjYBzzqRa/HIx97HIevfiL+%0A8JOPQx9RBNfO+swKsPPvcYQbDGUaXhEzX3F96V9NzsP1b30CHnv6MVz58lfhwgOutx3VAynZ71Rt%0A1D6+eqgC0ld0gyLnl/0uOR+jb8SZ28c4RbhniVrzCWQx+HzgeNdHtGvHfp23NRBnAJW1wK5QQKbY%0ATz0ruV8yDQqMWXUVHLjztfV9aUzLqkI4U8IjuKybzmsg6VeBxK2eingB2wqswOYs/laI5/rZgbV4%0ArPNc/enGRHeS6io5YO0mOzknOzAEb01TUxbhBPizdTT64Ghdg3zXNtZWWKatr8Tge3pRnBrp6oMS%0AnOxTeXelS+/zt40GWJu450JdBx5VLsZiRNwt9K/G9fGSPQD4njnwrB9/Gx407/DGv3gpdn0quT/l%0AU0nCwg+s7gvKAVAYl+inOlkH1vcAh48Dz/q2d+PJN9yCW348Yrp3ltPn8HthmNfgWhcrVy98zB0E%0A31wYJL1RsGhGDEzwYD/Mpg6QQlo01+netBmIknTubCX/sSJVXaD56RxWJoB7DdMyxChVVZSEeGig%0AvpqFbdFuCShVEjUALgO4aKTud5e2DVjXQ3Jz2ICrBeYL/lRnWivWo74fhqCmFnhNz4FU8V2d9nP+%0AoRTta2s+f+tG6kf9rr4HiJqhcXGcbS2uUQmuNyapWoSPVc9crz+KUlq3grsIpfqlPglGYgDkCJ/I%0A9Tjwb9KPpwEwWHF6OEHBKDu2G4CFmEL0ff1DP4ADL9qD+a8EPH/+HTh8YERHWZdheRNoc7jAIGoB%0AA+PJGnIw78kR4Jc/fyH+zbV34I6v2YeX/oeXYrYHmQMt6o1hmepXqlxqrq89jy1yfFvAx4NiNL09%0ACAghpo1s0pfzWo27VJf1odxUlWrpQp/XTDg5xZruDmNUeARU+SlzVDMUqlajK5jWq25a7RnBtKSJ%0AbFr6fCUmL4Hlkb66u7RtwGpqrMIKSOKEUkUz9THcZXUhUy+jJy8AETPiEHCyuGLfe4y4RTV+hjhK%0AvuZuma6kkPxqqtUQalXV/5oGwVUIZIYUyCsGKVPWdVVgtyhCGN9hH9TRiHpJA8k3SN1rYAXMjQfy%0AfjP0LZy1VRsUfIKLxSyrb82lqQHO3g+c8dpnYc/+/bjtpy/GWX8dXIXQYtzBvppgxc0HFTvTLSUx%0Af74EfLEBfvt3fh63zzbw4B9+LZ46F7VF4/mqGJ+t+nWZAYXYPkrROdNs4LL+okU/8nsFTPQ3ZYzh%0AaV/egKEc73I/bnBlV/CxAtuiabSZ+oH/+TnHFYAHdinWW5WO+RMjor1XxzvQORns/dr2Vni4SBu1%0A/B6J4VvkQ393aFtVAS2A20NSnq+HcmdSyhZ5mZj8rpNg19x1SdT/HZs411b7qdaknJ1awdVwlD0X%0Agg84RRDmof+1PA6oco26u/I3tpkTmgBZR1SvSY8XKsdZGNOkHUA5AYK0oZ5wIQ45YuqJGZqtC+5Z%0AEFD62NIjQeN/5oMbcCDJhqzexzr0CegYh/XdR4FnPPNy/MvVD8X9LgjYOOzcbtOVC6+41nqEihB6%0AdqPCxq4Erm/45KXo374XD8Bh/PS//yhmexLw1hxqjj0rqge90FG51XwKrQXmYnRjsOaZ+NoGE331%0AOiCOD9+pb8egtMD5TzsFaZcoEGfBvQJCLN0LAeSA7/zM+cuNdx5KTpjrhJ4AQLm/NHDmqJ7CytRk%0Ayad6ln+zhwyw0o6kq4dbJcmAco6Qo28B7InufnUytG3AegcSy30agM9NXFlNUuMPOVeCmxqEFDxW%0AJ+UATPoE2uzEdXK0YageyNZ2S6u6RjWYEdRUZKHvK8UukS4LYt00iMUivRit9fysKgsSJy3zC3AQ%0ADVVeizjXRXUt6h18A9EJ09qkXp2M69m0vRHj14XzVojQl3Us4gDI6aRuChw9CJz73/4KX90B57/g%0Az3D97Ql42xnyKascm2ALvjM8+RVb5NtT9/wzcPkvXYgH4yiedc33Y99sjm6aOOfBdTQjfUs1QMFB%0AW/1Zp7ZHcTKw7VOQ9d7m/XpbRmaqdaH0+15rfdPlZkYQXOnS+6umOlidAKtN2gx500U22mKoX9d5%0ACDig0jCkpP7l3Agm1ZjX00Rv3mD+TLOIc2R96mhYepOsrlMajmuGREMJajVnOHnaNmA9hsStzpAi%0AytQ7DokiuHoEEEyYlhFutHPUappPAxkXkM8NQ/Q0oRTXOagRyVGZx1kVuDt9H8KJCacJDPWh2Rq7%0ASf/QBQoowZTqhczpocyL7eK5bmAcNMdCNS4SU1lG/TN1yNrWMeLEr32SC0CSzMdcp2D1C6ZX/Nb2%0AOhz+2WvwyU/swbtnj8mnnzaM663j0I5RHaELAJoNYD4HzvvU43HxX56GL3v6x/CSpaN+aqs2uIWS%0Agy1CNI7srEVaZefgAAv4xp69XoIDaU4ffNNUhoO03CEfJJn2YnkPfqSbXO5Sn9LrkNCdkWUDpSFp%0A0WbNtUfgGjO8kRQ8A5CPtG7mPpiZid4NXMyL+THWM+Brc1FduUmwT0bu3LzbtG3AGgHcgsS5XiCN%0AViNQbUwiAKrLDxe9gg85u3zWWT5ThUDlPoNZa78reAeUOyEDVKy3KZwg9bt1iMLNvA7Y/kWO/XW6%0Aek70GE7WMS+IzbLW+mWPgk3qPVZfGr7YT4sWQhs9yLfSQi7aOL2x47gB6TbRwweB73vKG/CCi4yf%0A8QAAIABJREFU5tP4hd9+I5a/ANy+L0koNae4KEyfhk5kum4Z+MzfLOGm//p92IujuPn3fhlLZwJo%0APb/iau4o3PUJxhJIRiq2S7lBiu76R1fDGlC1n5YMFBfeYtEkneqkdxUNrevzkMaE6gDqZ9nPGlKT%0AYzy22W+h2ZnItOh1MFRz8Tvn2aJ8VYIEysMMlK5mTSl9ch3XEuLYHFwkRd4dOiGwhhDeEkI4FEL4%0ApDy7LITwhRDCJ+zvafLbK0MI14UQrg0hPGVRvufZ/9MAfL7qQe4emwVImTfuhAxgcEKp3iS5uGc2%0AAVvbxakfZEfQMhnhHJbqtJZMZF3u0pUyJD0tpu1YVB/mdyLiBB+jiUx21llFHp2o9eajIDnmf7pZ%0AfZSjz/nDDYB8RtXIGFhTp0iJYqMtOYxsyJG2M2hLbBNYPOOcOX7/6vfiEb/5Gby2//c4wDP7BljB%0AdqVF3OuYQam9A/ip+UMQ7zwL97vsY/jTQxtJTWHXvkQgG730sMGmhqngKo3C66Mpb48gF6b6y3nw%0AZyTdoP5/7s473rKqvPvftfdpt0zvDEMZhpGqIyIgzVFRg4gFJJZYEluiiRqNiaYYSyyY2N4klqgx%0ACkEiCXYEjSIoNnpvgzPADEy9d+6d207be71/rPXs9ex1zp0Zkbzzyft8Pveec3Zde+1nPev31CUC%0AGcqmFl25X8KTpG81H7QTBy5E66nmQQuJwY0eh9oUEC+7osMPu5GEEV6V74IShR/12J+NtONLzGSy%0AAoksaR2XPxSAovm2KMytIg3iCluPlfYHsf4b8DvRNgt8wlr7ZP93JYAx5hjgpcAx/pzPGGP63uNH%0AuDJd24AVFjYZ58CywB6jUJQJqo6U5StIMXPckTpmTgzmMvunnmGLTJSkHB0gqkjJTkM5FClT57eT%0AXjtbXLdAZlLdHtmmQ7n6jc14W6kGgj+na8qpeHEsr1bdinAeQh+1kl4UK6TVzLioh5hVElysa+Fk%0AhBAu5M+RfrKUbXsywEQ46XTiUulDf6zxk2GnAe2pb3L6+vu4/CMv4+pJ79FX6NImFDUCCuHn76X5%0AJ+m6jKT7W/CDL1/MizqP8OLn/JLWfEKca6LehQhLsaUqO7Dct2i38KbikwK14virlgdNSiYk4UMx%0ABci7bCfueBGCMkYKdT0PAqfuEaloDNqPILxQLH2C08Kkn7Rwk3sXkzVhAoh9Fsa/exmTxfZoHIES%0AqNZNlvI8WmvUsakysWsfRWojPiLUkY0d1on6rtthbIhVfxzk6r4Fq7X2pziNPaZ+MuCFwKXW2o61%0A9kHgAeCkftddjhMEe3BLzkJ/o7HBMwPB/qI992k0S9e93aVfQRIhcTQJAq1FiKYHHZuywBZBJbap%0AWh7UN30N/YL0LCwmDRHwuT4mLwuWfu1B9YPM2lBmSigPIGHgTN1PdpfUe7muOlf3ez/tIX7mRA2E%0AdhKQlKB9g/NOS/u040rQTqGJ5JRCmMTOafxk210Jty0d4fZfP53zv34n1wBVr6pLVlZngKIua2kZ%0AGgupj2nNKzA8CZ+/5S9YedVOfvjJb/LsZbf1HyF60knKdladllokEfhz0nx2s4RW4+NgfJmctINS%0AJvSBDIa6oSB7IwshhrMhL73+miY5XFDb3sxZe4tfjVN1C1ORCcIx1vBi9R567cZ6DImQbiduMpD+%0AEHOHCOCiTf5T+kWDporiPcve0fL+0m9jY32LMeY2Y8y/GmPm+20HAVvUMVuAlbNdYBgYAHbhynWB%0ASxpoWIdeIXSOzJbx+xQbn+S+t71TJ17adl/OotmoJ2zKz45io8VfuyduMLpZTnkVSJntY6+oRuqF%0AjbAPE+vJw/rvMvjkmkV9TRvO2RtVbBkB9NsXP5ugArl3QyEk2a6FRmw3FZMAhLAiHV5U5NwTJiST%0AB+dcewiqT1wLqzL48y4f/daZ1CYphTkVZQZ9qcKSOl4NiDZpwhcuP41jVtzAjadcRTZA3+D/mMRO%0AK+tL9T3HhmPFa55YFwWQWDfRSN9V8zA5QTBZSd+L2jvcKdtf477uF63QyHrNAzHNJlBj9R96Y7/7%0A2WMlBVer2xqJaptmzMNpn33icJIiMuJ4k3s2sv5jXfb3GwcVda0DKVg/i8v8WgdsBT6+l2P7NnMG%0AmPDfmzghCy4Eq4JTKy0h/KJfSp3MfAVSIqg1Qt0kCN3fhESQ7420EJTfYm7Qth1xpknkgb6utFmO%0ALXLqTTA36DhUEaCy+qsgC2NDX0jfxGUVI5Nl3+eRc/cCSAo1TY6Jv0N5gMs946ItxbZ+iFz252X7%0ApQjMIs5zENY/6y2ccdVXOdnewc8++w9c69V/iWmVhQ31eXgBnbYgS6EyBcc113HcjQMsvmiEpata%0AhdkhJi04C2Fq1T5LkS1WeibCpCDPoh9d3rkIpoYSpvJb0H9qXfYV6jiL2y/X3Wtgf7Qv/j6bXVyn%0AjRb9od+rOjf1k4SEPGokO5vTSMa6mEiqtsxbRalMJVwlOkDbRwXoaF9NPSubRqpRG4z6+22psu9D%0Aeslau6NojDFfBL7jfz4CrFKHHuy39dDN73NhVnXgyPWweL0zDbSM69BpA8M2QPrMwIwpB++K6ixh%0AKkYxnfWfXYLNKqZ+cZVCmZ/VG7kLeofwAjuJt+HkIWc+Xgp7wDvK9D1E9W+mMOiRSkawjcbqS0xa%0AQAlaTdSzio1O7hUHQXe1YFEk50o/CyPGhv5+32OK0bLY9aSt2uY8kyqhYMCY4GiSUKSKV+unKzDs%0A0031MiQ2gXcsg//603nc+6kaR975AJfc/FGedsa7nDbTdu9JzAFF8WtfEtCm0JiE2zPY+IJ3c9L6%0AH3PxvI8zDcXCirO9CFmUsd+KtOI4K8wE1qHjeNBK8XX5XlOCQfpPkzY3NTL3u4NbPE8mdm1ySTwy%0AiTUQQXha6xKB2S81HEJMre4GQYCFjyK6T3ydgtdM73bR4EQLQn2WHK3qugW6tGDzsubXNa4f5Zra%0A5xLTzde6v8eLjLX7xnLGmMOA71hrj/e/V1hrt/rvbweeaq19hXdefRVnV10J/BBYY6ObGGPshV7o%0ADeE6agnQwAnTWdtBQKZp3vtyYruSUdu1w0YEryEwoUQK6PhYeRm6ypOsoxXfV2eoZMYx/bRP3Rzo%0Ag3o0UtXtLT2DKQvTArXRy7DGKsO8ao9OD9z3mw7Xl2fR7ZLtOcFG3TUwlAXbVr+QqlbqGFwC3SEg%0Aq8T6uE/cwC0qN9ny+9WIUGdlOWgGl951Orue9XI+xpEsft7tPPCOdzJ5tL+fF8Bp233PfVvzBCpN%0A+I8OvP57X4ZfPY3fXf02Ln7FVUwvdsdndY98+5gmNMUTVrzarXZq5WoC0dfKoslZv8M4nE/U4o5x%0A/SpCVh9fMtlE/am1m5h0xp/mAbHpx6qyDo2KTW6WEBam9wkPa57YX6So48T96y/6SiYGPUaEd1Nc%0AzYuK4h85Jjdh0cHUwkl1sHZ/Auj60z4RqzHmUuDpwGJjzGbgvcB6Y8w6365NwB8CWGvvNsZcBtyN%0Ak5tvjoWq0DSuYrdMwC2cYJ0yMKiQatOjVDEJSEdiegWcDHYh+SqzcUyCSoUSG5wtgv6qebBFGes6%0ALBaiUjzFEJi1mYYwkmbaK3A0CfOJvTEOxobyoOgLoiJp268fJPRpXwI23i9IFoIjS66l5YagLB3+%0A09XvREYCwTauA+KhLJx60KKgQGkETujZBNYufpTvMkTODJt/eTo/fzIc1w4C0XhkaVPAm4aMhR/s%0Agddf8lm47Snw/VEGPreQ7oBvYw0qM5TrrUbPMRtJce0CgScBseuJEygV+mknvUWmxTkab4tD6LRz%0AtGMckjX+OcU2CeFa1t87nuDjNFm5ZjyGdDfoXVrwytgTtFhMzmoiKkxhs/Rp7AgjCaFZmreL46NP%0AGeKyomviG2wIabyJdWbIfk60x0L7hVgfbzLG2D+3LjJAeDZGrB21T0hCcmSyj190nD5X3I/ZDdKi%0AflSsizIYzCgKugBFzdh6HpCgCONGFlIIhRo+42M6dUhVwrxEJRHqh1jjZxWU0C/GVIR6P5qtH/oJ%0A1tnaEU9S+nhBMroNFm/8z1zapAhaCQ3S1G9A6+iOfuYKcCaAAn12KFWJSsdhwU1/z5EvW8O9LGHu%0AXevZOpAVQlJXzsor0LWw42HDMT+7Cv76cJbN3EGravnVrpcwJ4e549CuQWPGpbmWCq14Ia2vG2dk%0AySqqUh+gWJFWV9dSJCtTxM7SvU3IYgbbF0lo4WMlaYMIZPk9naoJsg8vxrymx6tof1kS+FX7D+L2%0Aap6OTQm5kgl7I2lngnMka4EvJPjkyQO/HWL9baICfisaxHVsgyBU9VMUSNaEcKxprzLq8KK92U2E%0A9C4d8CwvQyNXqRKkERo4odtMHYqVdkoEQkxdE2ysUF6bfW9t06Rnzn4osx8jS6CzpAH3CxuLGT12%0A9umGGfpoBVEb42fZUyvft5b3ep8Nve9LBISua9tzcShCmDTLJ13I5sFF6/+K1Wt2uuPe/306Fddg%0A7bTKai7fP5mBV919BHz8UJbM3M5JSx7gwjt+jxUTMNCEbsMPRL8oojidxAEZe/5N1rutH4kzLHaK%0ASZidOFfE4x1TWwnfvYU8ybGiXYjHW8e0zuZ3iD3jwseikcQZXvp96mSB2PMvQktXadMCs56F60h9%0AEG2ekybFpg4xKcVmL32O9IfQbIkAjwNYdW18nK7zG1MFJ0wtMNfCZtxDtYyr0WpVRw3Y8PJksOvM%0ACwgIR7zvMQlgkZfsNYpSFkcc9hHbqaSoS1G8wd9HwmHqeUBukgUiKlTNO77ipvWLFED9Tm14SbH6%0AVHhp82A3kv2VfN9OJrmv9E2JeVU7NJOIagkUtRJku6DcStRvenE7Qf3aTivnFoVmKNsEi0/xWhiK%0Atak0ijy72aV++J0sZJRTrniAGx5yqFOW1s4rbo2sbgO+PL6Cmz59FSab5El02PmtB3nFnFZhYqo2%0A3TlZg+Dht+78tEORiSXhVSZ3HakFbByClfRBn3HcrrxDCbmCEPUBjo9ij/xs5hMTuqdku5dKZZbA%0Ay9qLLseIABaBKuOtMH8ReFsXJbL+YM3v4uDS48zgPPP98vXlXBlHVRuAgIFi1WXU9UX7Snw/Va0D%0AaNKuIhZY+DEajLHD97ehAyZYwSUHtIAHjQslGLTOK1oDxJMpg9fi4lvluQsDezT4kj6dY41fFtuU%0As0OEYgSlM6E0+pJVLfvlZIvAEWYWFUkcPHF+tAiznF5bWdFuwqASAawFqvxJkRoxNwjjz8YjgoZF%0ApZI2W/Xscm25Z/GcagAJ8xd1XU2wM+tzql2H+FsGxmtQbbtzau2QTpnj2lITdK/QtUQz6I6xBFur%0ALFddz2B06Am8mB2MZHPpJCdR67plq/OKE3pZHRqj8J2bXwQ3NUm3dcnOu4xrap917c89Aq54gemF%0AYV5x27sNZxrIqhQLFfYsIOgZVhfRNtC7fpWFNCs7YuNCNRC0kJh0aF5xSfW9X9SLjA/hK0GSJTOV%0A7R9OJQk1Eicqt9IJNwJ8hHfkOO2Ag3JXJdH+OIhfnyPjSihOppF7aXlQ2FXVMcLvMaKOAdZjpccU%0AbvV40Vwcal2MQ6lTvjMGVUcMRC9eBjKUjdd7I0F1+4L5Fsc44rQSASoqWT+mr8+i5kuQvW5fbM/J%0ATPm5ZKLop6JYf8126j8TPyPnykblBXRFCdh+FF9f2lBSrym3PR58s1EtL1+/mzgmG8hwL7kOpg3z%0Ax8BOwcJ58NAytz9PYLhLUQOiZVy4W0XUZyWsUh+GlVWg2gLThe4ADHzyk3x74kuM/KjNR6enuXIK%0AWsNOgMkzTs6BH3/reaTpdt7U3cSiP76JPYfC4LQXwB5adWsUQjHpuO2D28BW3faZBRTLaovgFbJi%0Ae1cSSNtaIZgXsMH0JfxeHGPDJcTJJRRrTrORZHzJazG4hIQ8md2cpSnxbZQQOEHUxjttpbqcNKOe%0Ahdq7qO0JQaiXHGQE4a0RtghRARgQtCGN4rV9SgCIaHcijPVqC/r6uk1QvtdvQwdMsDbVzR8FDiMs%0AiSCDut8Lny3ebV8Q3nr0q68j/ZfjhJTMuoL8fKijq/qTl2dCuZ3OaUZdb3/fjY0++5F+0RaK6ASJ%0AgRRUqe1GBRrdR7+Icw7KNqrZ6hbEpGf4GN3i29gxMFmBL0wu4R/uPhySBAYqvHXJDGsenmT9393H%0Aim0wfTTUlkPnBMgOheEB6NT9oPCaQi13wjTJXE0AY13NAGshb8EZya+5/kdtYIobbz0au+ROuvMg%0A70LFQNqEZ/78OXDVQo7uPsxPL72Pa5/wMMmMF6ozwBiYW2DgEZjaCZ2DVzPw8CNUn9biK0+E1gCM%0AdGHFXHjZmBPqqcrHlhjbQigqp1ssXEvhW/L+oo43fl+/yU0fbiiruRLWJUJVBKxeQ8tQjkLo+uiF%0AnlJ/tnx9GT8WsApxyzUbmRunWuj2E1o6OUY/X2qDVic8JucbynwbX8+q7xDMTKXj5Bq2DHj+1yPW%0AZdHvDm4AGtysrYWrhC3tDTFJgYsioNj3erGGuH9D+uWJnQ+C0BVHS0fdU4SrJrFhSlRAqpiz1Se8%0ASicL6MHQ74XPRkWNWOO+y8sz1gmcmbSs/u2vvUgYVq+CoKMF9oaILGGik3emD5eoiMkKXPTw8+HO%0Ad8HcPdAd4B8rkzC5EJ57HXPWvY37b+qy5U1NVqcwbxB4CpiFwGuATZAfApNrITEwuB3SCUhngElg%0AF9gBePe34b9eP87YF6uw6/XUzv9PBl8O1CE7A7bcbNj0b88maeesO+h2/qrzOYbvhmQCGIbdH4Hd%0A1/jGV6tM2A4rhzbyzQfglWMD8PNToX4O2PnMWf5aXoK3rUdItJSSKx1lwz45PvedFjuzxJ5cWqq7%0ATwpqLEyL7RFSFkoi/ojNBTJeSuYf0RiidsghFYWCNMvFk3Nf34dCovo8qUQngriWO3QtKns/+y0E%0A4KXbHyc2FNsVSPv/BrFO+c8hta1CQK0ZLiSi7pFjjoskgDBbikoh/dBNILNOrdKqNTbMxFAOIBb+%0Ay0xZxVCRPPtlbpDUWb16qZCOOICgmlhT9rTWIoO8TCr6ciLopX2Zcc/bSjyDyYweMZe2k4m6pCnm%0Apb3FNhbPbAKSKNrsO8yqd1PLYX4G/3Dyv/HqHQfBpldDtQKDBkhh53ombv5PVkxVOeVf3sWVEzdh%0ALnUo014B2c1QPQ+Se2HuL3C1JqvABmCF+54/AezHoHMH5O/6KHXezdT3V9A8Agb/FZIzId0F+RHL%0A2H3QuTzljhu5/ZVP5cifTpAMumbs+Q6MbHDa1BAwMGcOD311lBOnFtO96p1QPQjqc2FmBUc85QK+%0AWYPU23eTrot3FUdWVvV8lIX9GIqyh/q9lASO7z+Te7SphWVOiSFLy3urSbcwL3SDCUHOLRIUBB3L%0AeEvKQlruV9iPUaYLASt9tJoZH2bYr9aFjCtdvUsXEpIMxiIywG9Lcq+dUA7h0/6SuPKdHrd66XDN%0Axxo4CQp+PIQqHEDB2sU5qYZRgpGAyoZt8Oh1jdunzQf9SPKGO17IVHL/gv3LynE3K/KxbWA8bLBR%0AygyYJU7Nie9hcC9XArDBIz56jetSBi62eYpXVUjqSPYzLWi0Lowo54pXuGQftWVVTVMcUF54owkz%0AvURL6LC0fv0umkSxNDaurxsqT97iGju3DacCf3Di3/NvW86G9gpIBtxLrrS8qpLwS/shXnncebz6%0AvGnOvx2aG2BoDbDOC6jFwCNgh2H0HJgzCu0FUJuE7HOQtuHMhw/lhezk725t8B//Aa/dBu0nQWUz%0ArK6uh+/vpEqFc15wHuk1wAzkT4SBh2BhDlMPpmQf/wP+8rAv8uWRVVB5NdQPgfYSaC2mctDv8641%0AD3PYKCRtCsRpBEZZZTPMAW+yyMQOmzuzQ7HelZ9khQ8Tb/rQAkn6ssg6M2WUa/w/XSvWps5MIcwi%0AAjYpue/D/jjCABRatV7IEu4h6bJyqTxx6F1WVgZlv1ff2yaEEMoc00kgF360UYiXCSBIbMJaERAH%0AtwhYARaWXn5P82CesP543R2iDf62dMAE69zoU5M8l+RNS691TK/9Q/dB1zOWCBQN/3XsqwhcY4LA%0AtR5xVr2Tq2t67UyltE4lAMXRVcq7JjBHsdheH1WuX1aWdmhp1UaQtXQJeESrBobOwNofpK37T2el%0A9CsILIwqg0RCb0RtkzRO7ZwQlDFZhTkd+MPlHf7jWecxc/HVMDAAB+/xifzT0NgOdLjikQ9xxYKf%0Aw/k380B7C8s3tBhaCMk251Aya2BkCWxbCIfUXDGSqQUwmcLq7XDQsoxJOgzR5KbaAM983QxZBrce%0AnsK5H8SYzcx98XW8fxN0z4FkBJItkLwU5p8DM9dlHPvES5nYdgakTwezAPIaTC5hYOlJXP6MNk/b%0A4xxassZWvwSB4rvvi9RHGxQCUO0ToZarUZ5k5RdUSplNe80HsUlBwsRiFbxIcDBBUPdE0yg0C2W+%0A0s+n1yMDjy4r4f1Xc8f3wjeN3AnQfqSdXtpEIYBDSBdPj8eEYfZYaJEJgorjeHY56P/JCgL/UySG%0A6Uf9X1vta1hXcGXGlB+63+qJUmyhX1/ECFGq1EvhYCH5LkH1XRMyYYTEhij7deaRjhYQhmipQs6t%0ANBwj7az3qX7Ur91i+5SwqDg8JvZ07s9kK/F8EuYkiDiJjinF8YrKZHtVNaFKDgMd1wa9KmtuYW7L%0AfV89Bf++dgfr/uJsWPkFGJnjJFTzYFxrOmCXQO3JsOtDrDn6u7zzE9B8K+x+E2RvgG4V5o/Dyt1O%0AwFw/F+4ahJ/V4e2HQJpeTUaHM5lk55K3YoEbh+Hu7efAgxkvNJs45QUw9nS47Fi46ySYOgXGPwVT%0AH4Br/wYmRv8KKi+Azl3AHMi+zbKXPJ/LzmnztBGozighZimFW4mwkfCqIlrAlp1cEgerKcmVyk04%0Ar19Cwmwk9WD1b0lyiFertQmlAtzhBnu5trpmscaXPHtSHhOtlGJliQSXraVrC5TWgFMTsqY4waSZ%0AlM+TWGARrDInCG/LSrWSXJHTH5UKz85WTvE3oQMmWBfhKlvVcNVavMYEOIGaobz4vhOmvbBVUS0l%0AbcYS1Azo9dS31HkZQcjGJAJEG/JlZhThJrGgcXgRhMwV/e5EXTY4oarTF/stX6HbIv2g42EL5Kzb%0AGqEK+RoLzZin5HqCDDRzFgUv1OQlZpacUPwjsWGyqOQw1CmXvRM1EgunTcF/1jbz/tdcyHOf/RQY%0A+Kkb9dkCoAJmDEwV6jvh8E18fxQ+9HQYP+14dt4C08+C2i9g3r0wsBOObsGvLfxnAncC3ZXjfI2D%0AmQDu+PkQmxtw1Ci8778WUelOM5bDid1/Zt7FcMFn4MiPQ/eMGpcdW+GoS87i92453cGs5nWQjMPq%0AS/nouRdxQ7qFM0eh1nKCLatRFHYpdTiUlnPRHZ9Vw7GzLcndl/S11XdZtwuUcI+QZhGP679rZGv7%0AaGbF/YwyQ6SEaAedZaYmXImCkISZig0aoAApEbA5wStf1AAmCFcZG1oLlLx+SUvN+/BmJwkCtSis%0AgrteUwnYZuqEvF5j7HEAqgUdMMH6EA6lLsHFrXbxCNDvFznRNK4QttBQ7ho9adzfjAgnLxBEOGdq%0Au4RmCOKNlzHRJGhWkGZuHNKVF6RVXIubDa3pLZwhAsn4v1ZSzrmu5t6GbMtalzChLlqhy6TFJGhW%0ACsHIZBKjyVJsqQnH6IwZQcISFVDPw/WlvQO+loJ2tg11yoH9cSZQ4j/lftUMFk3DH07C59aM84pX%0AvJmk8o+QjuHK81jo3Osu1vovNo0dwQdPfx//9IGVDCbQ3A6MQmUHDDwAqzbDk7uw28L122F8ITy6%0A6EkkWBY14ZT74fAJYHvK0Mwk9/31Sp7/PTAPQvevYez9MH1wm/e+8FM8uul4yF4GZhGYXZz+7Ku5%0A85gf88YZWDABlWZ4qVL1qnAqyTuTlFujEF7iZHXaoUCgxXlQrESQe2dRnFWmjxNzgZgO9O/StVHn%0AQ4nRZKmb2fiqqIGgZ2ETmT3y4BCyJginqk96SJWjSSfbaMGnL99Ky4BHF55JrQMn4jDN/HVaSXiG%0ALAmRPhKnatW1JR69SIJh/2KzHwsdMMEKrnO2Ag95Qdc27ncHN7wa1kUFzCfMcFr1rRKiCDTFm/pV%0AhipU/j4qAVB4KuW7UeeJCi0vJTPOGwqUlumW3O9Y6Ar6LVQgghCf7T0LE5QyRPKANjXqKBapkwHn%0At4uwjW1IcTk3Sc0tHAXWMbmxoTyg9KU1hHq40XVTNaAFNZnMC6TECeN5TfjHEXj+SV+DfAOFS7B6%0ANFCDdDkMPAjmY3ziFsPgZ2GgAjO/BC4HuwyqU3DKJrhqJ3x7CK4HOB8sCfceDZ88CMbmADOvYZBH%0A+Ienvx7mAUe659oObD8Kds1cCnaTb8MeOO4GPt2Ag8egNuNtnsITMkgr9GRUxao4vg+TTCFW6Wzf%0AN8UqBEpYFyUSta9AI9SYdyOkKp9FrKxsU4I4EfRqy/csN15dNwmfIpxlTDY8P85UnN1bzADNtKz6%0AQzDxyTgQ85qYz7QglmP0GnK6wpxoljqDUdBq27+beAwKCe9n6p7/q51X4BBrBYdWfRGiYvXWBjBu%0AXNiLoKaOP0eWONFeeU2p/9MTtaDWWJDKPi08dRhHkdZJOT5OtC7JPZagaxG6Bh/ilQd1p6lecjcJ%0AAq5qfZiY7TMp+M/YfmoJTCgFuAtPbB5mzJ6A/X79FalhuXFCT3t3BdnGtTdLFYuSoBLGNxUVstiU%0AhWOTFG6eXAzj50Ljw1BZBN3NUH8a5DthLIPBSTjsSs48An74Rnh446Ec/u6HMAPQbEFzISzaCM/6%0ADJw6DQcdDXPJeV4F5nwblg7C8NaEc+szrHlkDu2fws6LK+TnHcwVZz7IXa+Azg93QuUkmP4GZJs5%0AZ3Gbg1sU+f5Sc6D0YvYyCLUHXyIFJPxKrlE4rzIlkPsIth4HVNTPcWprD3kEnKcB4YoCElMzAAAg%0AAElEQVTDqqjBEN2z3yxfJD74CVXHiIowKwQkYSxpDU6bpnSNVw2arNofN0sXodGCVM7JcXJFo8Zu%0A4sadHv9FeKX6/ngB2AOKWMWOn+BMAw/73xmujkDNH9M24VgRqvrFtI37XSPMbDOm/KJ0tSjhGeH5%0A2WowisAyNtiDRJiKRxyCQNKz6aBfw0gEWz0LRS0EsSLt8IJ5bzF0/QaNNVEChLTbhpArvY6Vnjxi%0ASggTQGKDCicD1hKQhaj/xRpYCjHMVu5PC1URVDZ1A71dhS35GAzvgvoZzrbZuRbyPdD9b6/OJDC5%0AgBtuns+8Z3yKY99+Ms1zoPpTGL4B5r4aqvdA/eYQsL6OFvUE3jLwRX58WoWp6yz1OaOkd05gD4N6%0At8uyYx/krxcczldvmAONX8PUvwM/AbOJI2tQ6fh40NzZU/s6nDQaspScWhreS0pu2nF/Wt3v10/F%0A9pyeF6cjCMSMoBcy1GaIomkiRLya3lO8Jf6DUhxrgSST4BSaroRF/LQNVLKuYsey5qOuGgMCSGKK%0Aww7j+slyvpwrwrzleVGQqqxkoR1T8Xjb2/j4TemACdYp9X0Zbg2sAZwd1OKQqsE1cBtlO+ugdX/g%0AzAVC0jHauaVJz5j9/qA31El4TI4ZVLUBxDAPTn0GipViY4qjA1LrhG9cIEK/bG0Dii+pF9yT8+LC%0AFfoc/dzMsl23O6G8qoJkd2k7VYFGfPhKtZ9QiM0gNnya3AmtrQ1g92HA16F1kot9skPQvh6qQ1Cp%0AQPcomPgcjH4drMEYGH4NjL4D2sdD/XxgI/BkqDwTsn8e5342kiz5NNxxBumOtZzAJqydZMvlo0yN%0Aw86lDe49Hxg6EsY+BKNnwqKdLgZwCQwOQKXtQqT08tb9ivwUz6fVJP38XvDladmBZSKhXHJoGcrB%0A+ZGqnkSfxT7hGePaLn+xnVb/FYJZBKkJn2LqsYqHZ1ScrV62WpYegiD4dKRNP0ooC0k9BrTKL3Go%0AQsWSNvLblO22CcGk1U9ox2UZZyv+8ljogAlWnXH1iP89B4c0xT4hM9FyYCHO7gpOcLbxCNV/+roY%0ApBbm5kHwQkCZ2qGjZ6ZCPbEh9EhWERCHkAjtVlIWYvXMOXSkzFol7x14EISUju8Tg3pGSJEVxFix%0AYfbV7RO7rQxmfU5C+YXGi6UJaSQLAZUKY+qkAQjXzk0QrkVdUhsSK0q3i4UGwY4o9wRno9xUAfZk%0ADC34Pm9Ztx7WNGFwHLp3gR2HgRyqR0G+APYcDHYu3fHN/N0zIX8BtE8Hvg6cBGYF3L4E0jVzGGUd%0AX7zqddBNOPuST5E35pMnkD71KHbfPw/zjSrrbn4eTK2E9nGu9+rACqitg+V1105ZN6vwjAsylEeN%0ARpF2WBX7/EQi5gCbBKRblBkkhGYlGUXhFzm/JPj0/eR9iSSBYvkZY0MYobXO/qlBqbHuvXTSYMrJ%0A0uBEE4TXTIMfocjuw737KSVkdQyzAJI4q0uqsWk+k7FeCEWrVHuFbrVz2qrvch25j1HXkxUYpFqe%0AgIRmUo4htwRU/9vSATUFxMhyZ59jRFjkBGHZNsEkkBkY9M6WKePGRTzIZTYVioWfOIHEXimqf1Gh%0ASqNIjzStKWdwaNIvJiGEncTFhXUhXxGcqXWJEXopDbFJaQQQ1+UUwafbEs/+UJ7RZcIZ6Lp7iOmi%0AcIjZsCRzjBakXdaEtsYMKbGOhcMjms0MYLrwUwPYX1M77FFevxiwww5mLJlws2wbSBZB/T5YuNXZ%0AXau38b58Hdd+NaFSh+4odD4Ko7vgQ0eup2NyHibHfPksKldnDH5igBXNES7tnErrDTN0Lp7gHYfP%0AQPUH0FkMlTHIbyuM869aDuu9AOwppBurkDKJWIoVAiAIUjlH7Ju5N4EUpe2UM6pQu1M36cx2T3cC%0AJduq2EvFHCD2XakhnCnPeCHMPJJM/P27/p5SrKUU223K8aNRUwres4TJtrCtKqEqpO3/2gkmKNPg%0AwyfVtWUsF7Hnpszn8kxx23LjrplAkfJeiXi2lruxvb81NvZGB9R5Fd98sM8xDYWWpPBI4rdPGyds%0ApVNjs4Di6f2C92kejNhCWqDVlZpVU4UpxA6rX26MjCWMKSZBwjoNVd8b2b6P9vczZcjvuhpMYoow%0ABPtTfGmJzW2lDqHX814nGIRtBcqVAe4vWiAXEUy5Q01ASNtM4MoEWAy7t8AzFwP2OS4ra/ISv5Tv%0AcyCZD61roNGF4X+BPR8APshrd+Vc8WnojsMbX2gY3zDM6NdT3nTjjVw09CTsCQ2yKx9k5qUHccOW%0AY/m9X97EBXe/AFbW4Oo7Xd2BXVVIt7sGNoAN8K8r4G3LKALe9YKCuUKbQvKMeQqp1AcQVC6Ti3Wm%0ADzEFZL4sYS5QSzOsbNsbRZN+aZfnmWYlaEeW8qKOuhi6aGvtNMQmS3N0ckt8uzgTEMqO1H6l+opz%0Ak/JkrcdoPA5Lji31XbpqNueTxUfI+HMkVTvHlwg1QfCahN6IiMdIBxSxxrSoz7Zp3yGDFuZbp/bL%0As8/zzqVJEyIERKWQDo7XQIcQq2oIzh9hmix6gRo1ije0UDmKA8MM2C9ES0gLu37tKglVG/5kAAh4%0A6ldPVSYPmYW1itNK3J84pKQuQScJ6p2+pDgjrO8DKWTck0xhywstSnvlvOJZVDB6capXO62F7hTO%0AFtSew8iNQy4KoHM2TA25NKvOBGRbHWqt3sBdJ/ya+ae8E+ZMseenL+HDT055+YlPYMPuZ7Bj++eZ%0At/XdfLpxFBOf/SakVWhXsJsSdr75y3x6eCUsnA83/BnMexnDJ3RZsu6foPltaEzAyFxIzoLMaUJp%0AK7TXmmBrzSNUkNWcwLSJ+15kJolQNe53Z8CdK7UCBFUWSLYStpW6uo8AleW3NSoutSkJ76TRde+9%0AYkPMcel1Cp/nYQyJBqRNRN3oTyPV4r4Edb8ImfK/i6gapQFJmqk1ZURc1H/1z5+ZMCkUxxEEoyXE%0Av8qf8KcGLaJp6QxMSW5oJ/37+jelA4pY5cVmOHiuFxCsUQ5el8+KDY2eSZzaPGBdIoHYFKu+E6Xz%0A+wkwuSeU41H1DKiLYIgwj4v36rZZgsNJblmkpPp2dTzDVP01Y9PALGOkuL6e1TXSrVpnM9K2KXkW%0AvZChFIWRttXzXkaSYt8ygeQmmF6MUajYlh141iO7VF8v8qBZjwpkOermIKyqw4lHw1fHZ6BlYfhb%0AMPVGSA+G5D4Y+BlM3QF2IdQe5NidwEMHQWsazCGsOjbjynd+GP55OTxvPhPtCvx+AlPnATlVJmDZ%0AUtrjz8W+52h4z1Gs3fUwrTf8OyMPwc7hCRi80jHE1FoY/iHLD4HGNGSykKANn0W4UZVSbCvGmQFk%0A2Zis6p9fmUJioSmZUdIvsgihRk5SpKVUK8ALVV3bVacvyqGaj3UhIvx7LcxLXjsB964F8WqNyisd%0APbZRCI4kXVBa7i1oWMK7xO5qjXsGib7pmPL4kvBFCQeMnV8ygWsWE2Ev1a90UXzpP0G2uXH+EZE5%0A7aQ/6n0stFfEaoxZZYz5sTHmLmPMncaYt/rtC40x/22Mud8Y8wNjzHx1zl8aYzYYY+41xjxnfxoh%0AfLZHbesQsqqEZoxDsPKn0q5Lzqq9BflWvDe9przqJdXfhjx6ESD1vNfT35NLT3COac+idpiJIC3C%0AlOzeX6IIaWmfZELp+xt8fQLV7oFuQJD1vFxLtpoHlA4BnUKY3bW5IKZS+IsaxAZK1Y70Q/TzdCdd%0AaDecM+Xb34QvfRFY2oVDMhY8Eeh+CYZfAWnqOmJoD1QfcqaB255F48xHefvZY3DUJ3jPVuC0L7GY%0AbQxedS+2ugfO+AX8zMBBO7F/thZ76hQ0fgHDYzw1vwfzqq/x/kNdcRg24dSlgwA2wlr4Sg6D7YBQ%0AdTiTrkxVoEyPTPNqeEbw6DTxf+q4Usk/lVAwKzK1wXuP9LX/LGqsmnC8jlUVPm8nTtWXbCU9Rlqq%0APWLH1EH6UA7Ah7Kg01Eq0sbMBOEIwZ6ri6RkHq22kiBwNWWqzXFygA7ub6VByOYm1HKVv9g/kBu3%0AWoWe5wxO0xvcSx2G/aV9mQI6wNuttccCpwB/bIw5Gng38N/W2rXAj/xvjDHHAC8FjgF+B/iMMWbW%0Ae3gTE4fi1rzSpoAEGIqkTsX6NbH8X1UEWZ9ryyzbb+0e6GXgnqW01e+40lM/BKwzosSkqAWvCFs5%0AtevRopxTUYNDDPnCJLoAtbShakOEgMExYDUPCFScShKtIIUo9marjSJ/SqRDUyxlr+7eKE6tlDzz%0ArOLsjbvq0D0Ctp+DK1g9BouG4JvnXs4tx7+XwaXDzsvZSR1DdE6GZA2njcCrvK3z8D3A9HGMnHA8%0Axy/r0lm6EO44DYa3QqPLsXdsZbixAZJBGNrADc+tM/Yn3+KwHObvAmbmO9vqAKx/2QyHHgSH+SVi%0AZs1Ggr4ZVqX9GpmK+Snvv1+QsOuo3uvqIieFOcIL0EybFSLqJK7rrL9u/F7j1NEYkGhUGqdfJ4p/%0A5a+IYsnD/feXRAi2vQrfTMO2QtVXwlS3URQHSbMWc6CM157ltG0wB8pxUkDp8ci82utjW2u3WWtv%0A9d8ngXtwNVNeAHzFH/YV4EX++wuBS621HWvtg8ADwEn9ri2DuEG5stXeGlbFCWP9pztYqy1iRtC8%0AWlKj1ffZHEtC/VJNpX0FYlS/NcPp7UJ63R7dFrEfaftq7HTTQdUizItsLxHcVn1X98+NQ4idxAng%0ARuS80iaNmLQDQ1QsCdWRPgJ6Qqx0qcQiDdILpHYFLknh/cdBeyksPiiFLXVGx2H9blh3N7x8bgsG%0AF8HSzDfsDqjcyo+2we4U3vpEeNF8YOJJ2KcYfvXao6A2Aet+AKd8kIW7dnLWDzax+jOHQvJZ6A7D%0AWZOcUt3O0DiMWcCMgYUlKxwieMlcWDJNUbZPhFkuzg2B6OqZimc0wV5aSiWVyTJC74XDL1coVkuv%0AiJF0FIDYV/WihahLNNNQ7GdvS7WLB39ffptYmBmCLbXUPsqodLahFaPmIouKIDxzyu3SwlVIxnor%0ACcvTx3UQdDc2st7qcnoMzBam+JvQfs8nxpjDgCcDvwKWWWu3+13bCSutHARsUadtwQniHprCAZTd%0AlMOsBmd5qDg0IiYRQiKINF/q3/1IZkS9lC+477LOuyDR+IVqG5aoQ92kV/WQmVfsrbHXs3SsqJGm%0AV+DL81Vzx0gyqw90w4ATlVG3QcKmxBTQSoLqr+1KMRqIqZ0GFBILiiwSMjB7CbYscR7rH+fwiUn4%0A8hxoJhm0VjM6AmNDQBuefXgTmm+G4UFXsWftNBzyK9gB8++GP94M+SCw7L+gYWA6g5NvheE3g6nT%0AzYapMMUz7r0H5i2E7hCM/Tvz1sKeGs7+NAQ04ZODcM8k/Pe0ywaTZVO0Ci7osFDvU3oD7UXd959Z%0Aqt7pLMhSn6N1U202kNJ/Jg+mBTEvyD2kfJ7wZGFfn+WdynsX2z/0D6fqJ8xK+5OQwg3h/HjM7IuK%0AVSey3ibvizfFRyL2XC3cpGaHiY7Xpj/ov7LtY6H9EqzGmGHgcuBt1toJvc9aq+fXftR331zc6gEV%0AXJhV02+fMa6wSilGM7qCxaWs6YeQB8lNWf0WklhVbToQe6gurKuN4XEMqz6nlA7o7yX50zKT633a%0ADCCfuiaBtFdsYjq2legaqXUCVUKpEmCqGtTzrvE2swhhSD+IdzgjMKAhDK5CPcoDgs7VdYpr2WAW%0AKKEW/08SCnRBEOnTrnHpkJUUnrDQVyv71VLXkiqMDAAL4PzrgaMvgQ0fhrkVWItjltZ5XGbh8Afh%0A67cuhYEZOONqsDfB17q+gnqbaebTZJCPfeB2aD0DHtoG637MRTfCh7q4rJS5VdJdQ9Sr8JNdkLX9%0Acuney184cmx4DqAoQiKea23bLPpC8ZB4yYu00KQsaLVAzJQdt8QkBAQdo0Mphyfe7mICTUIOv1wm%0ALkKkx8psoXU61lQ0mHj1Y2mTfpbZSHhLxoiQtrXqSyQEM5icI/bUUqKM0uS6Ud9LbQN9vlxbogX+%0An8SxGmOqOKF6sbX2m37zdmPMcmvtNmPMCmCH3/4IzlwqdLDf1kNXv88F8wMcuR6euj5kUTWgVNYv%0AN72CTKpapaqTLRSBzvLbEJCbFmSGshDWs7WQLMMsgkbCqoo/9RJRxxTtQHk/KddkBeWR9ReMU1SL%0AY224nmSPNDKHBiXMTGIPU4Lg1PyhhZs8W5KGvqopVVTuM5M6W3Y36e9ok8w07QyTAZHkZYEhqrD8%0AzgzMb8NQFW6bhNcsgEVHjTPy0BgY+KdB+EID0jr8yyEbedNDf0p++4u54ohv8NHj4fqf3cY9u6Cz%0AEtLdO8gyYNEb4cUVsJfAd2+DY66h267RrRioPw+2fh9W/wlMj/GU1XC1BVYD9yesP3uKLRXI5sDT%0AFwCTFAH+Peo75d8Wikwneb64v6XPW6kXAnk4togHTsrvXne4TSinxxrfPuP7OqGIHy142JTNY7qA%0Ajg2H9KjrCWBt+Rjh95h3+5UerOaQeORcOIiTcJ70j7Z8JHEj/M/YH1qxYbKQeGtxChfrefljC+ez%0AR6rtpByL3k2CU/qma+GGn/C4kbE27ha10xiDs6GOWGvfrrb/vd/2UWPMu4H51tp3e+fVV3F21ZXA%0AD4E1NrqJMcb+nXWV2zQN45GsZdZ6qTGJYM0JMw74TBN5uQptGAIS3NeMaggFe4FQqEQJkjioX1Os%0Axguqi1dB1SQFRORZ5JBSrKp/NklSkBhTnbQA4RnTvIw2IASMQ4iQMID1jiWZzAQZt41aKoegRqW2%0AN+d6f0jsl4sA7oLPngrzEvg3C4cAP+zAjY+6AT61BC4YhFPbVW5tdPjvO+B3D4f2FvjkSjhhAA4f%0Agbt++lfAHHfSQy+G5RuYc9ES3njdXXz8I9th8bVQfwIc8k+sXAePJMAjcO4qeEkF/iKHxQa+1IHD%0ApqHRdH3RswzJXqjtEeNAtz9/tRLH28JD/cIBwXvTkzC5S3k/vVKB9qDrFU1jyo1rTzFRxny5l3Gg%0AEV+cqCKxpf2oNP5yhUA9z7Q9X4npTABQv8Lzs7VJJmhN2hEnCLaThGSYuopSmK3vAY4fBGsfe0Tr%0AvhDracArgduNMbf4bX8JXAhcZox5HfAg8LsA1tq7jTGXAXfjAOibY6G6LyqKUeNVyf04W2Y1UTG1%0AF7Ck4kAhTfYmVAuTQaSe9DulNNNH147tqMWKBJGQEwGYWHqmf5kY5LsI0cw4VGBsQLHxUjKy5LYs%0ASSNtk3Caeu5m8fEqTPh1ioa7QX0f6rhstnbq0vW1/JT+zgkrCEj8a1V94u8ltRJkMNTxA6QD7IbL%0ADbw/g8+0YLjjYvjrOVT3wA9WwxMNnGk7vHgChrefz5PmX87iRbBkBk5vwg9+dgHY+fC9F8Mz/g8c%0Afg3YBmuvG2SsUnOrDjaeDrYLu4b4eG2Kl90OX17rEk1mLByRODQwlEG14+yWsogeqv/0uxYSE4dM%0AvN3EFaXRanqhFaEGnn932klZJKV4dGU8rCxqEJheLU7WlpJ3LzwsVajkfev3AmH9Jy3PhM8sClRk%0Aveh0NqFq1PmaRHMRx6fcR3buC+iIDVX/1mNCzFxCwm8SW1tVQrWIFnjMonPvtFfBaq29jtntsGfN%0Acs6HgQ/v68YxWk2ApTjP/5Rxqr7UXq1QTlctzomQZJFFIW1Rx1gTcqabfZCHBDEbykHve+v3wq5L%0AsFNCEMLxmuXQG6eX+5toW1W/l23V8bm6rth0E8UkmWcia8JgE4Eq/WT9fcYq8M91uGgS2m04dQjW%0AD8IFTVi6HWq7oLkUOsMOuY0PBiecmD1kIM2o8BhwiQLSB/qZEgsD03DHAmB7HfacxbVbruAXh8KS%0AChzbhJe2YNtyOGwKFrfg1w34DvDeBBqHX86NF18Kz3sT5x85xnU/mwutN4D9HJw9BNlzfSZEnU3p%0AICckOQytgO4IVFbD2Am87KGfUt89RKM+xWYDVwPXj8BZwy5LqRCoEd/NqukogWvw2URp4D+N6vtN%0AtuDRqZ8sdd3TuLxgcbtIeBkokjaExP4oaq+Uz5OJDryZKg+/ZQl3SRKRa4saX7xfO7twLSpNWXcD%0AEYIiqOW0dhK26YScxJbHio3+8NeSe6S61i9hPCd4oEDQ+OLImsyE1Vr7aZGPhfbLefU/QXv83wxu%0AafiFOPCy0fQK0S5O2MYapy4oomfYHMekurK4VGGazUutq9poXtFZWTnlgSDNlE7UAckShK3TV3Uc%0AoLzAfqtRaobX62uVgrNN8NSKXS0zYcE0qYUpjiy5jQRjZwb2VB1K/OJt0P7ukXDFXH5+eZUP3wjn%0AdmHHQki2wtAlMO9amHsVLL+vPBByEzyumYGhbug3QdO53941bgDMmYJ7F8NzLNDqQvVEOjvhi8Cb%0At8P7E4eQR+tQGYcV2+CWDC6ahskKvHcNcPhWeOuFXHM/dLdbqEw5O+roOphZxvP/cA/cdjDd1g6+%0AkhwCfAW6twITUP8D2HAUF66fYjCFp3bg2TPQvRsebjkGkFAmjVaBothKPEEWxxvHe3UfB5vETIub%0A4CWCRP8V15Ei4OLFVqq09GfS59oSwSIOHL1NkwAHQ9inEbNExmhEKZOnmDpi+6VORBCNSCNfq++l%0A2iP8Lf0mWVj6efUn+BhuqxJyKMeXC9ARh5Rs0+NVO+5EbiTWjZ+pxyEf9YAufz2JQ6gJrt7qKlyJ%0AwJTyTDxsZ4l1VehBzzTGX1Nm3ljNgbLA1Ntlm1a/Y4ErTgJBfbKt1AYbmEpepKEsmIW5KzGjqcZq%0AlSq+H/RRz9Uz6CpVhZ3OOtWym8C7G/DdO4C7XgC1p4F5BCprYcP1bNn5PU48eZT3/g78ft1lfCZH%0AQT7krjlZcYMqte67PH8rVaErhPJsWeqW7gD48hK4sAtTdwE7LOSbYHQNp/EAl+6BX9XgdcAv74GR%0ALuxuwp7rYM0p8OymM+AvWvcORn7/M7Dl+bCwAe84FN54JKdceA+LRic5hPtYtWUV87L53L9yOWe/%0A4j38fP0ixg+aB2dvgEUP8I8t2NN2UVyPtoERuHscblkBz5wJtuki5dQGJ5HueOERA+VUVPX+0tgL%0AI+/MM1LPKqoRFE19YkVqKKUFV7o+QkBO6zMBV6yr2iRCWZCoaEui3Rhbtr1XLKUKdIl1k0KBWm0w%0AJYgDtmTWiviyCIdKyu3VqDgmXatDulZ8FEbtk+cx6pjMOFOiCHgRnLISSMWG6IZc3U+Px8dKB0yw%0AdvHLruA6qYGLaV3VB4rnuGiBnu2m/F2ESKHWK5QYkxaocUprJwnpstp5JYUbBI0V6pEJs2RxHSD3%0A+7pQiq8TZhJ1R9oZUwKlbKi4T6T9GrUXNqScHjVNkEErgY01uGEM2DzkkJ4dhMog0IDac2AsYde1%0AV/CWVaN85nTLH50Fqy1s84j/1HYYKLlxKqYUFNbOQSnVJn31n8PwtnFcyNQIsCuHxkXQHGLcwsBC%0A2FGBHRuWwD2v40unXMixixxyfngM2ruqfOeuNZA9HSqHwwMfZuEJX2H0IzdCt8Iv33AILDqW135k%0AmLYdZoattM9PsOMJ46u3waF12LkR5h7FpvENMNxiZAUwjmPIHfCNlbDeUFSnAiUw++h4IpRmK4Bd%0AEVuprv2pNSe5jzpG1sES4adnV11bAAtJtyz4dZigds4IciylhlpndpPD6lkoYh0np2gTgPCvXFNU%0Aag0+IIyTGkoAq8cR85T0V676DYIpSdqrQym1ecmofpTuKkXdoBxx/t2mXjOQyUHa0E/A/6Z0wASr%0AFDQSM1YTF5slpQCn1NNNG4da95dEwAiiaqYhJnNvAcZyLoSObqWB2TNCwQbd+yJouxEzJ4C1ZQ+6%0AzKQ6PrVnET7FgJoZY9qbB1XH44lhX6iZwqcT2P4wMHYKmMMgaUPaxPVSFWrPgu5quOti7nn017xt%0AKVSWu8mCBBqHwqeAo6yzgS5oOhPOyIBjqgqhcIvYhe+ZA28fAcZwaskQ0BkE24GBKS6X1JIUmDsG%0Am17A3xyykfWrLiNfDO1fAxvWwU0XQVqBKwy83bJn91WQfAKSz8PKxbC7xpd+5wRYDgM8AJ2Uq/5m%0ACcy9DOwfQfpHcN/X4ZArYPOX4Z4pOPQBF1Bdh5uByTrMb1KsICAUe+blHfUjicjQs58WqPEaYO4L%0APUigWDureLnlzyR3fCZoN+aZroHuLCYwsT8KtdKyYNHp1NVcCUy5vQ1qf2p7ebJU4MiDCuF/HUMq%0AwEOvgdUvUSEWkrpL9HuQNlkckJD7SaF2KbgC7nsjC5EV/6sXExzwN58GNvjfBmcSmGvcb79WJgtx%0ACQGC/PQKA3VbThaoE4RVV3R2fNB0Tsmjqmd2LcwqSqcQRJZYVwlHhLMObM4ph4fF5gFBwaKSV/KA%0Afo1158o9NML+TV6wRsJIGwnMK6E7kxX4WAo/eBC4PwVzJGSD8MgaWFmBygTYBuRzXUOH/wyaD8PG%0Au+hubELlETD3Mb05442r4Mhl8Nmae7Z5LVg8Bd2qs8W1E1fQopXAw3W4YAd0bsDZeyywcTFU1zjj%0A5sxiFs/b6opWJ3DsUIdrjtlI6yvn8v3By+CeJ4F5CGb+AL4Pc+7fzAn1HVw7/lS6g58Bk5LcsJzn%0AfuF6rnzryW4WvMOSUcP8qMuZN+zk1he9nPEX/R6MDsGF0/DR9dA4BrgYJh9wRSsmYXQEJgdcmcq4%0AilSxNLVCh0nMS16Yppk/X/hVV62aDSio7aV7KnW9VI/UtxGZSDN3Hx2hktJrominwdapJ97iMxIw%0ACSHAXqJQUoLZTCZP4eEitDBRoMMLTTF3FZrgLF0hArYfbwtKFuQqmZMxeJKShPXMHTNdCSi5Smjn%0AtJKE+1MDY190wATrdhxAmIcTrstxqHUQJ0AncOaBubjsg0U4Ido0ZXvrhAhDXMdOGHeswQlmPVO3%0A0qD2SK1KYx2yyo1DyXPy3o5NbAjVEGeNhHWJDU2YUycsaIdTNQ82Lq3SGHUPCEI/4LcAACAASURB%0AVN57KB9ThJGZMMAqamDoSAEhbRowvi++WYOvjQC3ptB6MdRPhk7VHbh7lQveNAmkHTdCK6OQLIXq%0AcZC0XE/nG+HXl8HWnWxYNMZZB3d53mq4oAFnTcG8Mffu9sxzTN1O4GMGxu6uwejRsPA2N1vW1znB%0A3vwl5A2WD8MbDbxlJ1wzWXejf/J4uON4GHqZ45b5u+A9DzDx/SNYfPko5scWe8FO+OA68oc3cuXr%0A18Hxj8AxKekH2sxZVMceV2Hs1nms/eY4N5y+BhZugX9+CFgKk6th+BCYGYZNk5BC+zAXflasYOon%0A2kKgqe8y8ULQEHSQej80W1pZwIbracGpHVOlYi5yL3Fw2XDN3DsnTAa2EpxceRIm/q5/H1WP2ipZ%0A4NW4YIrwfGYcohPhCYH3q7lHxEnZNKTjWA0ByKguLISgJZT5q1hXDjRR4EVAiSFUwSp8DCbYW8Vs%0AYAnLyde81qptx9b3Y7xN+yJ+WzqgzitwJgGDM3HFJC/B4sZh04RIAilOMI4L0xrDpUWC907jUr4W%0AAEvVzKhn4cyEc6q44kki2GS2FBPCTBoM3lXPIJLJoYP38fcoIdiEnmVoCoEnqlJS9pbuLfEgjYR/%0AEXrV5wTdpl1VuKSFK40z/SqonwZ2LpDDTA53DsLZj0D2cbBbIPkAjD8FquPQHYLBzc77nhwNQ+8D%0AJmDkEXjwQr63bYarjoAfHAzH5DDYgjmTDm19axF85zZg5wugsQpGboNHgcoLId8Bi6uwfAfNBC4F%0AuB7YfQbsOA7qAzB4AZi5OK55ENrnwzl/weUnvgzGDAxcDu/fBa89AQ6rw8xSyGdYtWuUwQkLD8Jt%0AL17tmGblxdD5FSRPhdZyaNVh+FSY/HgYDVKcJsEtV+3tVcZvEwYRz3VcF0JoX+mRJcfVLMfFKbL9%0A7qHTZt2FXbtNUj6/7cFEXAJTBIkuw9k1FNJLeL2ROUckBAdu5lGgwQmx6bQ39lzQvEapmleNfw4R%0A+AO+fRMVd089bsWkVmhiJght+S1rybUTpd4nZWFfLIdDOXqgX2jbY6EDWui6jhNAM2rbHoLQjXlt%0AG+Hlb/afg8AoAcUO4oRqhhOqc60Tak3jbICiwuwwDi2L7XbSuFi2QRvsvplnLkGiRY69n9VklrX4%0AF+uPjdNJZyNjQxyqRc2UJsycEnSeEJgrRtTGlpMihLRaJJPDxB5g5xyonQF2njd6DcGcBJ71Y1j6%0AeldqZydwyyeh9kIwD0PlAthzKAzt8hcehIlhGNwNcwdh6zbybfdw1tzv8ZR18JIl8HszsGwrLJ8D%0ANOtOmHW+5154s+pc1Qz4lwPPBL62E2ieDFMfhusH4bkbwU4Ch8D4ETBxFHznPF7w6A6uOO1usudu%0AhtG3wgMNnsQD3Da+yOXJ3pdyysQoG0h46Y9/wr1U2VVZxCPzzoUVz4SFv4Q8c6EpA0ugcT6M/Rjq%0AW2lmMOHVdp15BUEl14JsXyhHC1BshExVBS2jB7f1DizoTakVoFDYGNRvmUjToLGIHVXaKchUUKrE%0AcGrnlP4tNK3qE4iTyeI1uMQnkqikCG3rFSQ52/JEYlKwJpj2GlkIkdSp4yhbr6BPQanaBivfM+PX%0AzlLZXhotF2jaj6HHAbAeWMEqpBthcbWMhwgoT0KzhnDIVCjDmRFk6eylOLNBCydol+JMAyl+SRfj%0Ajm3j0mc7hLXQq5RnbGmL9ox2E+e8aeQU4TWyW9Ct2JQing+MQwiBEYbQvFaynxEGm9jxhAnjgRwn%0A38lusWd1E7jBwMgUMHMY2AVgvSsuacG8BtS/y9Ah8JFFcO0CuH7hz3mk/XPyCeCe22DoaTCwC6bW%0AwOi5rgahmQ/DTwDWQv4M2Nnmpqvv4aaDm1z5xBGefxisSeH8U1t8/eZ3YR+quwZVO9C8BBrnwdQ6%0AuH8rX1iyGTsCNJe7WWwh0GpAbSHYARj6KjTOhmes5dvTd8HJH4TN34d/BB4Z5bYPnQCLZsBAZV6N%0ABbS4mZXcxCo4pMGiRzZiProHMzyf/A9Ohyf9LdReDDvWw+FrYMlXIYGBirMNZxWodIINU5OuhC8I%0ASTLnjDqm9DIVFbbbqNhKEWEAvSm0pvypz4udX9YojcvzjZSJ7Ki2FJXKIn7SPFwE1JuyQMKjUJ2m%0A2/HoW2fawd6TCWTekvAobZpoJcG8YHBCXC8hr0kEvowR/zp6x4YSvjoaQNqwLwf3/tABFax+OSGO%0AoVyppQksxtlZobyygKZJHOoc8L83EdDqYeq43MJm465pcah2BIdoh62rqNWhN/KgYAYDNnfMM5Ap%0A1cm/7JSgOgmajGc9McJLbK1QRSEgsb9Kup8Uq9Zxtbo2glC/6AAd/yrX2GQh2wM0DwWWQGXSRQJU%0A90BnPiSWOQZObcGpBqYWwS8r8DDw6bXXwcR1bkbaBfzoezD6FYc8x46FpoW8C4s+APV7YNvD/KTz%0Afn6yDEwd7CrgaGBRC+7CzXB5DtNfBzMI0yeTb9jsCk9WRyHdCScOQb4Q7rgAPgn86zzIarDmUlj+%0At/Dgn8C3Uthm4eVLYD5QnYS0TZeD+BzHYJcuhh07Yc4AIyuOJNnS4qW7f8V/fPJQ7Hs+DWs+Bub/%0AgH2677iw+Fy1FYRbsSyLt1lqoSaB77nMtlAqPtJHLgcyZQGsi770mAdi4afQLlCEYUllK+ElLZg6%0AppxqDO5Zh7PejESdSy/eem2HjTUom5TTRCWpRvhYQIRV17P+mpKU0DXO3FDLg81WL24o6dMyVnpM%0AAcoRp6lCSDGWFWCLWHUTxvnj4biS+x0QkjKYKa56tqj/kuoq5oEGTlDO4BrbwQnS5ThAM6SuOe0/%0AJ3GmgoNxTD1lXPKBmA9m1H3ExhqXKtQk6r3YW4XhDIFh+pUq1OqSIQhVmfUTKMVF6nJmEBivqAcg%0Ahi3CNYUPGllYGFAQsTB4x7jvv7I4r2F6FkwudrAsnXGhVlkC7cMZtc4K8IS2sw68sOWA6WsMjAw7%0ARLFhGfzJE39GN3ktLJ6ARgIPnQePvgxGl0F7LgweDCOXwM47sWyFjV921VUWAsfh7Dd3N6H6SjDL%0AoH2ZY4opIF8Od6+CbwB0GR7bxGRnPly1Et76KqjeCnd8CMwz4XnAGQYWd2Foh+ug7hCMWywpzKlC%0Aezls7MCf1shX1bl042Ek/7IA+2gFFvwZzDsV5rzBFQrYCQsH3eW6VYqgf+s7PK+UbYYixETFllUb%0AdGaWLK1SaB95EILaeaX3x5Roga61mszFseaVMBl3UiVcTHmytybYSYUkUL6W9yLEruJfbcfEs6I0%0AR3i2GTmXZF8J6RLaYwgOMFHnc1z0ijzmVKXM7zKG5NpS3NrYkNSREQSmgAydmCD0m6xw8JvQAROs%0AVZyNVeB7TN76xk5cI2v4lZBxKbAQhPOQF3LDOPvMXJyQlbDI3F+nSlhiWzq4YZ39pWV8FpgtyTpA%0ACTTvRYUw40I5S0tI9hXhVLhUPBGo8j2mOHQLgiqnvc7C9FKVqh+DGAJymTJwT4bz9mVHwu4aNBp+%0AZHagvgcqy2k3YWsVjmu7ZzU4+Wtw/T7UgZUN+LPj4AdrfkI3hSfW4RubbmL6vrugdRCMr4Cx02Bo%0AATTuAHMo7PwdGNvmUPKKB5wjae3d8NCD0F4L6WLYdSbUfgLto5n7wUdZz6OcTJtxYMuJVb76vk87%0Ax9vOr8PWY9wL+/MZeM0ALDAOzeZ1aNfhFlxPzmRQA1OfwO5Y7F7WoR0WrZ2iuT1jYsl8+NmZcPov%0AYPqv4MRf8NKBSaqtMEHJiqzdxE0ytSx4wRPrhJ04VNrGCSgdwlQ4t5RwtVry4GytxfItCknFamwp%0AOcCfn6siOyLoi+QYE+y5vgl9Sdo+4CdoOS6uZwpBVTd6v2qnmMOEBHy0o2tIm+tZEPhxNS2dMSiq%0AfSvidYuKDpBtJghyHeGWmRC2WQAXtd/CrEk5vwkdMMHaIdhQJwmIVdN8wuoCB+GcV5rEBDBlghrf%0AxQnaKlAzDqUmlIWlXluraVxFLRFm+3JEyMwOZftpPyoKQRuKmgFa4MoAlNg/CQETEkaAMiqQcBZB%0AOol1zNbPaSbMuS3BGajbR0Jzjq9wMwi1QRe7WpkEVsNuBxor3vQhCEau001gfgdeb+GVqRO+w3vg%0AVYfCTw7/KhdmQAvsfWDvfwckT4Z8CJqvhO5uGLgI7v8MbM2dpF7yeXjo81B7EwycC5O7YfBqZl79%0AElZdtJWHgW8/YStbv/Uhp9rs+VuYOQ4WTMBd810ntIE9KdQWu4fejWOYJy+CW7twcoWBu3aRXTJD%0Ay66CE45g507glhE3sZyewP0LYfRzkFzBx29+D8l547yzSVFPt5k6rWBOu7yksrx/rUK20lCmTlPs%0A+CqFVPVR//sdJ8wgJgqTeZNAJWhHcc3Xol1JQNU9bbPBFCAprwXKVM/WUWrStHdWtdJgv5UqXnr5%0AdbGVatL9NVOZfcyJxqZJT1raDKbjaEVj0GOziFP3vwUvyeWbpjd657HSXuux/k+RMcb+uXXCdBBK%0AQf9Cc3DCV9bEkljXqjpmKwG9guuomg12z0nVqbsoRx+s8lOVwQllUedjGalrUKaWov5oHIcqgs/0%0AOa+I3/P7rQnfY4pDdGRWTSjneEs9BWEkqZuqZ+EiP9rAT+vwJ1uAX50LWz8Mk4POHjL/QWfTNDnY%0AcZj3Ri54FnxyytcUTYKdbqbitsm9uh7B595uNe29snsM3FCBP20CVx4P+ftg4gkOSS64Eyoj0L4G%0AzDTkX3fcXAc4Aiqvg8mPwqZrmfvBzVTe8jNGT7wY6u+A1pNgch40EmofaXLEhhHuecexsMI6j2K9%0A63pmR8WZGsSzeT9ww6M8rbuFu+ctoL3AMLPsIDghgTOvgc7nYeYc2PL7sHQChsdh/uUcufrTPPco%0AeHvbJT9kabDPSahRghvk7Uhg1VWsqkEJR9nmf5ciASKSvhe+gCBQC3uu2l8E6ytwIGteCWlNC0Js%0AqqwoIbZLaapGfkKFIFJeeHGUSfzoYDeg3FqmkCfByy8TUuwvKIrZ2P79olPCRbBKKJWg21y1eza7%0AqYxZQbGyLQWenfA/Wo/1f5S6OL4XtCpqehP3gE3CKgNtnClgBPfgTWYnvT45/h7GX3/Snz/or9mw%0AQfgIatXMrL3yQn3z+kWgq4EiA09eXpWyzVS8t6V7UmZ8YW6rmCwxbhAkBKZJ6BXYoj79X+rePMqy%0ArKj3/+xz7ph5c6jKrHkeeix6npupm1FkejxEBAEVR3ChqKCgD0VBHk8XigNLHiBPBJn0pwhoQzfQ%0AzWQ39ABd1d3V3VVd1VWVVZU1ZeV8h3Pu2b8/IuLufbMKUFpfrXfWynUzb957zh5ix/5GxDdiJzoG%0Awkkbg6yuKA8YWAZJUxVrBaZgX6HZKkl/u6yClWWxeGRBLjoN4nehksPyMqzvQKsBb0kfk+fVjkF5%0AANrrIB+FypBK/S9B6X7wt0MxDd17hCu75TvM/vOd0PkCtD8MJ7fAyMPQHGbs7TnXHj/MLS/cCpce%0AkwBcPiS1DQuLMiK78yDwkIfccZKEV80cZHCm4BuPH+PkPWPsPfVsih8pwcj/hsH3wEMfhOEbIflx%0A9jx8AXse/T986LK9/OXqOW52/ZtntaBXhtIsEpvTLkgdVYKyixWenV1sAShn4Wwd7D53gH42pleZ%0ApRL7PT0hyBPp3N7/40pPSfS+ox/ZxdZTTlB8cbp3jBTNh2rWF2g2FCEN1rrXSfrdVwVnKr6ltRes%0AfdYvc2/ZXFgtYlsrtueU1SLUIe4LGscJNYW216rq/Weg1nOqWEvIYBh3NdHXIwiYmiMkDmzT165+%0Afg1istpgN3THbbrgFrDXkgtuA3MDJAhn1ZCHpZXGmVNEE2tyZNzVvn74/omyL5kwW3mz+Jygnv8O%0AQvR/iW/KrjiDyugglkrba+BZfo/R6wgI/Pd3Q7oAviEf6gyIwkvaQBn8au4rJns+26UL2BB3LKiN%0ALCyqhYr4Ydupum6SNTC9FroNQYIuB18CX4fpi+FUAX49nLccOq+TqFG7gDWvhYMpZJ+HhWEoL0I+%0AgvstOOXnueWXroId3UAtmU0FoZakG6z1sgPNoqZLyl4arKXNM2hyHjnP6R7gsU9P8nffvoriFz8J%0Am+6BJ90NZHDoJljXhuwDtO5a5OfKb2P9jV/j82MyjNVCEHopOZPiUzgYylW2Eklt9U58l4088gkW%0AYTxj0/6M+U/65yAuSgLBhP9BJwPk0RzGMmi1V2MFWtDPd4VQJNsi8WUvKP5siDBPoqNTIpRcUVSb%0AqYLNI45q7C4oQUDjLHGRueCWKhWygRm9yyiPMVK1I5HikwxsDMsFpG5JUsMPjVOXtP9cXANn+b1J%0A/26xGjH37f8DXncWpOEj9B8Rm3D2gtggftSmE/9rlzMd1KYAY7MnFma7jO5hecoQAlNLrzga2vc+%0AIekAQkpgr/zZEsSdFsHXZXnZS10R36PbvaDXBg+Xr4Hvnv8g5O+F6m/AQj18MF2EZB6yISp+sock%0AjKyeeFng1g5rfzeRFERbqF0n77VS2OEAfwAerQusvXgYlp2EZAZa/wIb3gkbZuHkY8IndW+F6keh%0AsQ8m3gnZZeAH4fRyWLkIzZX411VgZkyqXjcS2J4J0m4DjwDnIbtzbRZmG3BHCnsnqXKCJzPDNAkf%0AocE+VpO+apihtQdo3Aezv3MYt2k5ft2r4RoPawtBzuQSEOv+LhP3foHLh+/kmqu/yV/UZc5HOxqJ%0ATvQcsiRk/cQ1KTqJbN6Zjs1QJkqlRP86js8diystxScRlJFsp6oWDompdXaZ0oyDRfZ+/Gq/WyT/%0ADApVJGs+stktP3/pFVO0LNMrvl83EljLtLL2n42XuvQRvUM6ff/nLQki9dLOmAHQe6QLLgMbk3gz%0AtM+3/l9WrCXoHR5YJlCpTOHNIWb7Bv27AE64UGrwqH5/AwJUMidZVks75PS+KQHVVnTGIlAqfDoX%0ATHf43r4Z81N9vyBXLBS97Ck1b2I6VUI4ysRuFz/XzDKbfDNrem3Vh5mfzGQ/gR7J23lYncEbqvCb%0A58OpUx+B9BpoPkukMB+AilKVioL2LBwdhZFcFbkq1F5KoOs3c22BgxS0tkDPSiC9LKebvA9YDXO3%0AQ/c+KF0IpSE4dRXwHDhQgjU5XPxXMJ/D/vdBZSO0N4iNvOkB2H2pnL5aFLA1kQODzpuBgUlY3ADZ%0AYDiCYhFYHIF7ga+cZkV2kG3k7KHKoWQDXLcKXjlDd+UXmfaLcMUD8LpDeL8FPprDXTfCA0MkT99A%0A8YxBgeFbH4DWUyG/iLtvO4/rz/s0bF1k6DQszg2xrDbHi6owXIPlFXAlGE1hs4eNOj+jevJj4jXA%0AFAVg4voR0K/4lpqw5tu02qIxkrWNLS36EWpkSPVAQOzbdIR7WVtiUBC7D1z0k7sQzDX/c3wZWjUf%0ArrnbzEp00Vq0yxCqI6TfOoLrydxoPc+Ij3zQ0WZUVmUbB4UTdA1FD3TRGHvOnh32H73OKY91AAEX%0ABeLyW9qfLoF7ugFRtlP0Ky37/zICIrXv2iS2CH5Jez8mB4PcsCA4zr9X7nZcciwOpJ1tYSw9wjtX%0APkgaOfNjkz5WqPZ7/Bq74UyoCsKbS8sW9nx9+t4VbfjlAfiDLQUcfCsM3Qh5HdK2jIofgNJ2yB9j%0AMoHtTsbNMmnMXLRAmpmQJvRdB3OJ/HRSeHEGHK7C1Oeg/vNQej2Uj8DcNTAxBosePu7hzffApa+A%0Ax8fB/TYs3gzzHWnP6D649XLx++w9CCvXwcV13Y0zehylHBGoo8ABRLLvAtotPHAXo+CG4OrV8AtH%0AYXAXLPwjVK6A5BbcJRNs3A7+pXDkOHS/DEX+eXjbJvipVHaX6l4gg6m3wCM/Ars+w9zl/wSjc5z8%0Axr/w4W4K7kHh5Ca7oFHAMg/rOtLe5Z7rB+FdBexYlDGKs6vi40Vi+lxGiLIbwrJkk/jqBZIijXm2%0AGhR98QKVG/OJxjLYXSJX5grw0X3aSfjO9+KBg3LBoVcPuIcerT8IWCj5kDoO8r84eaBESF21Y2ji%0A/rRSBRNF0BMGguyeqd5v6Wkiuev3KT+R65z6WFPEgluJBKUMwYEo0XWIny5HaFfWaU9Qmiv0taav%0AB5xkWNluaHGMwSWTfjaKlE3CUtPEdjFDiku/agqs1y/fT62yXdPM+Ljqejd6Vqw84/ztmAtrbVvq%0AejBKl6EIU3bOOqbfubqA5StgajyDU0dh7nwojcsKTzKoXgbzX+TQSg1MRYEZqzCfFnJUlXdSJOvr%0AHiamYXIf7Jm7mlZ3NbS3wcwW6FwFB5dDUhMEuf9JMOqp3vUoncuH8B/4VSjdC48/C3g1dBfgcBdW%0AVOUMl7eugMmTcNU4bN8kNK0MmeTFZdBow0JZjq98ECn9twX5+5Qc23eSQZGUy8bgRV6dnrNQuwkW%0APwWXTLBxK/xxARsK2DsGx34Cbmu/gLufV+f05Hb8oTIcXwcDz4PhndBeCZX/Bg9eDPwujL8ABq+A%0A2/8QvvERuDET98XCFBzch1QJ63BXY5Jn1T/IlZfD24fh8mbYpHLXzy6Ia6WebbN3BKsk/pfJ1VKk%0AawVMTF4SBO31MrRccF3Efn27qkVQwiUvvFMXyai1wxGsMGPSGD/c+xDwM7eatRmV8TyBrhd59YTE%0AF0dAx7XumfTC2OqLXWUDWUD4tkZKHtBgrLnizPUXZ4H+sNc5VawOQa5VZJ00ov+10GgfgZY1SVC8%0Adp1A+K45Ql+EsOMccYJkK17iF1ZgBQTdVnxAryakZrr0TgXwS0wFF875sSveyY0KBf0mjV8ivPH/%0A84TeOe6mEE3YbZHEKX6Of9+uakJm7e86WNGFHVX4+kgb5nZDcr74KLs1ia77DDw0HCykQZk+UIGd%0ATehOw/u7cOr0KpjaANOrYW6r1FVt7UCKs6wQztUD8vDk83Okw/Nk2+rwwCxsKzN27e0ced9t8PgM%0ATP8pcCE0V0CnJStgPoM/rMKpfZRpk91dqBR4+ExVXAFrSlCMwN4yfLkLcwWsKAtf9zRQaJqJ2wiN%0AQdiQiGmUtMDVoZiEDYepb4E3prBJK/lcmkkTbkhhblmTQ8t28fUd8HBxH4/d9TlO7f4H2LMJzlsN%0AA98A/3o4dS1M/xZc+0K48vXwsR+DTy6DbWOw/XxBEOPA6ALFshdyz/H/xU8+/U7eu7zghkLr1qZB%0ABiEyXwt6aa52Eq7JVa4Cs3Sz76VAF/3urVguCoKbpwcCHD06k8mMyaMVbY8RYEG/JWXtW6pUDTQY%0A8izi+1t/iYKA+jwrfmSbTOyXbVrAzdak73dRWUaXBZyz6H929Yodad/nXXA/PpHr+ypW59wG4G8R%0AUOmBD3jv/9w593bg5wj8/d/23t+i33kr8FpEL/6K9/7Ws917GAk+NRFkeoT+0oENBIVWEOVrxVeW%0AKlboL8wCopRXePHHTugkjaK7IeKcNtPDJtf8lUUkOPGgx075s6EH+wza8Rjtpj5QQmLSdkxZiXdT%0ACOgWBO0WkelS64bCvPHpmvGVR0LkCYig3oU1NWC8C8f2Q7UDlMQdkI1ApQSpuFych2/V4JWnSrCz%0ABsdukmpXfhZa18LJtaKoBpBE7F1OVtRdngoZnYcmSErTjNUKTrRWwfwQfHQeVzrB0Sf/Eex8PnRe%0AL6cYlEswfQSKTbD2EPzGKuAIDeZZoETCBFvosI/t+KMN+EcHNwKPDcFBD7O5jPaekjjjTwLdDlCG%0AZXWhg3R1AErTQAb5p+D8ghvG4epclFuu1of5AFdn4iu9sQOLCSxeD5+98cf4q7+/mcUT74PGleDH%0AwS1A5yNw6h7o/BG85/3wxy+B978KPrsZVtYl/Dw0ADfvgG0fYObLf8nPbPtrnnxDhw83hYprlLlY%0ArmI2ZbkIm/jiktVrymtp6mZ8YGB8e/u8pej27kNgy4AgS0OhpuA8gmBbafiuVYcy5kAtAiCmoHv+%0AS9fveoOg/E2eez5op4E+fb+p5n7qtViOIs5KV9Z3nJxgll2cUWm1B3qHcUbjlSEB8Sd6/SDEmgG/%0A5r3/rnOuAdzrnLsNacefeO//JP6wc+5i4OVIXZV1wJecc+d7H8cTz7ycfji+LO9/GdLR1chi/14c%0AswVkIdSQoNe8EzfBOMFXu1IXzRRSRyBHkCsIa2AhkZoBZo4MFlEUEvHbOIKT3rJrLEJuftaC4ETv%0AFWjRy+oB2GWKtxR9zv5vqNX4o3Yt9Q3FCtkhG0ZLs2Li59j9rgA+PQoU9wBN6I4IB7Q6BcV+GIG7%0APXz8QcfuE6+A2evg8FOhPSzlwhyiTOeQXawN3JOy5bvfYf/wVlzzOEPrhzn19scpNr+fExd5eOw1%0AMHUzTO3hz172Bn79rkHy/CUwfzm0B2SS882wIYP/sR7Zs4eZx1NjlhaDZGRcwT7uG10OW5woy2Hg%0AntM4JvFuC4w62WkXjOnbhekOrK1IjYJ1k5AsQvsbcEHGpWvhpwoYyQIaayfBKuk6SYqwKPRoBj+R%0AwDNfcjt/Nf18Pve5/wkfvwBe2ZRJr10G81+DW98Fzz4Mr38bfOQ1cMvNcF8LTiyDmTJsqMFT3gSz%0AT+Xbg6/kwEWwrROsnXiubfM0n6ohyqXE/5jcb1cSyaMR8g0s9OQnkvOeXzUJz7NbZonEOuMAV7WL%0A1H2NlKsh1Vj2ekWrde8195YFRbHZitpozzalaPn/pmCrS+S7nYZnG/o3F4ttKIULBa5tEyickFaO%0AoRnfPPHr+ypW7/0kSkf03s8753YTdOBS6wPgxcAnvPcZ8Lhzbi9yqOZdSz84Qr/pD6Hk30pCMgCI%0AspwhFMVeerBgi36FW0YoWAte0l3L+qxYuzcJgR0rUZggA5wgboNYmZV8CNRUu/3HVic++GmMEO2W%0ACK9dccHemIJiQSEzq5YuEAtmxL5UCArdaxsttXWg27/4YmHf6tGsjDuhdhqmVLEO3gmbdsEDa/jn%0AoxvAvxkOXwoTJbgd0cg7PYw7+Fobxspw7DiOEwzfDBPP/Qr84i58BZ504d9quAAAIABJREFU0e9z%0A9Sa4IIGPNuHrcw/Dvju4/Kb7+ZXvvBaaF0JRg8pRWBiBzY/Dwga4rwyZzeZpIKFFDcg5xBAHGYDT%0AXTiUyMQeAvIuPh2DS+syqVmhaUBVmdnBEtwEbJ+C6iFgERpfpbwNfqwM52WBueEJhZbtstjYaCZo%0AqVIIA+Vdjf1c+aqf4O9G3sbeIz8tQvtvF4tTf/F34Mf3wn2/x8BLf53FS28EdxWMfwgqTSlTeeAN%0AcO8byY7Cwg6ZeO+DDBgybOkz46ypmNhvcmbB0KXVzs4g4C/539ncBKZwYuXtOZO90k37A1AQ0r57%0AmWmm3AmurCxRyyDpb7vVFjZL0tphdQ8Kwji00nCUt7XTnlsggVd8iAtUCinoYpmElmiQFjDklKHk%0AxNh5ote/28fqnNuMLK27gCcDb3DOvQa4B/gN7/00kqEdK9EJzgSjgAzyvH5hwItCG0SUKsigr/Ww%0A38nONocIt2VrNRDzvooMRC2692lkYMecPKOD8DgPOCmuZC6BaeidEGuHF6b64xE0W49MepOx3GmQ%0AWP1JtQjdWCEXq/W49CTJOL/cTPTYB2bCY1d8fAQEH5XdyyqkW1Fg49caadwqchlaMDfGSA1mRoDO%0ADGQe1t4Gl74R9q6DxddD6zI4skMk5O9zONYkfXAKWIZPytRHHiRpF9R2NJh9x8sYe0HGb5bhOl0U%0AAzkMK3l//wB8fWMHjv8eDxx9lpw59fDNUP4aDNwA278FfjW4VCIH5QQR8xKOGWpkNBnGMwJuVFO7%0AtFMP5OCbMDIuQas6wtrvJLAng4EqPC+BLR4ax4EStL8MF0wxPg5XFAEBln3wX1qh5MEIvpivO56L%0Al+ew7b+9gzftfRdTt36UfMd1cD9CiSiGoPhVFid+G658iOds/jeeioDtdg4/e+iLsP6NsvijCJTx%0AXhe14AuEEyxSHwKbZkrH3M7YpI9Tba2+hEM23d59S+F0XfPn22WyW+0GUn/P76tCZW4vU6wx9Q5t%0Ao7ncegFXbUcrCcrNUG28J6Q+KhNIACyWVOCBdpleogVo0oYPwbvMFH+kwLMkjJOh5cKLp2adD1bs%0AE7n+XYpV3QD/APyqIte/Av5A//0O4D3IUfBnu84G3HpBntPIzpLRXwIQRNkuQ9gzHf18hwDV55Hl%0AV0fAQoFUpmsTygIu01dTnAWCZguCEj+J0jgQRZ8Bh00Jm8J14hpICGT+DsG0sKudQifqcRIJlIez%0A4nwTGquWbv7bShH8QeZXsyIaFp09g92gu7H5mXI1vTJta6WAVR6uS+DWEeD4A+DWwYo3w/1/CY9d%0AB59sw9EZVnUf5Liv4WerlJml9Oo1FFOTdFdNs/DOt7Gy1aFIy/zpxoyLM1GkPeRBiLjemAHrM5aN%0A/CutXXvJT74fvtmEyYvgl3fBZAHrT4HfJJ0cTeS4VzLZ4Mh11jsyWqMIUp3x4DtADbJSGMgETc9T%0ABbwVGJmFogzJYRi5hfJG+EVgpAhzY0EVXGBC9PLvCciplQbWhwOuacO/bu5y6GWv5CWf/Q6sd7A4%0AJLVhSyXY9yGYKPPQs36Uvx6TkngPlwAehVqTZFQ25G4SAixmvsdV8WM6k5nvcX2AOLgpwhc2aocE%0AaxsdqGrKYrciBIm8BElJlBIEczyWaeiv42p+3F5Q1YfgUqEAw1C3fSe+SoWsKUfw6Z7tMkWeRWvM%0AEVwbuZP/lZHNYimK7/FgfSg/2E2iQFoi6ezfceLd2sh/TkT/B97DOVcG/j/gY977zwB4749H//8Q%0AkgcDgjfioNp6vgd74V/fLq8V4JKb4LKb5O8jiHIbIZwEsBqF7ohLYA5BqjH3tYqkhlfRlEMfjnho%0AePG5jkedbhISCqqRIFhqW9nJ751YIFRSLN01FsA4mktkukC/orUrJfhmzZTx9PumOkk/AombYnza%0ApYErW/y5E4Ve8pB0hTNZUV9tvQsXl1Wxcj+svA4m/xxufTqsOgUv/wJcvsDxh1O4qUn9WMrl/7Ke%0A9p+/jPrJFsMD8OaGjDEehpuhOHGqY2Y83lohwvqqGuz89pXs/NZ7YHQDrD8Nr0+h3gY/D74OaQeK%0AChy0ShDLdbYrOmOL4Bcgr8lkT+QIcbUhKHWdCsUKZNdtIbt1TQc5OQSVt8N1TV45As/oSoopLkSk%0Ay/ZRH+bV/Hr2Xj1CkZaPXvKwcRm8+1VX8JYv/TQ8cBXkz4Q/XgdPnYObjzFx1yd52gW/yHu2zCg0%0Aq0JllqTUj5KXzqdtpFmkMNIkKC77nrmJzAIyShUqa/Vcind3y/TquhrbwKhUrTS4AKzGr7XJlJaN%0AS2IyqvJsplF8UoHFFOKAko3dUn+qKdcYtZoitCs+pcMyEVP6Nx2HvG+ULFO01SIchR0/Y87B1B3w%0AlTtk+/6+AaF/5/WDWAEO+GvgIe/9e6P313jvj+qfLwF26e+fBT7unPsTRMzPQ46GO+O66e3hCGwI%0A5rm5BmzHrhOUXt1D3YmiPY6gsHUI6o2rXDn9TtkL6jUWgOoBDqH8VhXa+NyrjirUQd2J7RXoocEe%0A9y4R5JAtifZD/44fVx7S28jkJZosgJaaK868T3ydLRvHxqmVBme+Kel4odl9HSLwlztwQ+BrM+Ae%0Ahc/eCKe68Pw7YPXvs/WaLu99IRz38OlFOPgs+CsHfoXw3uudgFhmyhL8OVmVzKul6b6ph7cuwou/%0A8SaY2ggbH4HnboDaY0jduw5wAqqnYXEVZLZ9jiBbZYEo1jLUhoWr+jiwYHwQL+6DRUTPnkYUqpk0%0Awx7qh6D+XrhslpePw095esVAkiIsNLdk0SVI0DI+WtkQeV3dL+VCNpksgec72PGcj3DbdR/j69Wc%0Aw92vMvs52PKBU+x+/cXs3/1pfqH2Xv73llvAXQA+Jymr++kscrJU0ZobaKlSMlPbIv3Wh7gAUOKh%0ASM/crE2GbXOJ2QAWcbexsaBsXDvCEhAsODpf6ld05SJYqI4gHzaWsYVj/TYf65Lu9zNrou95Ty/D%0AyhIP4jVhY2GvRiNcdCIy198EG28KKfS3/z5P6PpBiPXJCGNwp3PuO/rebwOvcM5dLl1jP2JV4b1/%0AyDn3aYSenQOv99+jLqFDAEUZkf3jyMBvRASk7eTvAS8oddaJEjWlu9JJMGuGoFRPE3irtpsPeEG+%0AyoCUc62cYKEmwsxZpb/XkEQCE2wzc5ZGOKFfcZqfyybczCTzadl3HEFI4zPdLeBki9r8TuZ7sgUW%0A+67iQV2KWs33GtfxjE8UKByMF7BsEKaqR2Duu7DnmbCtED9nqcLTqk3GMtjYhQurAgSrXvxzdltD%0AauMd6UMj7w+E2BhmiZhhq65+hL23XgfuU5C/GfIGVKYRCXBQasnxLfeOwGITYQaYN7EEaB3Zg2jx%0AlTIiJYMSmr4N0cWPdQR6rCnDNgfDk5C+Ey7cxSWb4KVOcvVNsfaSLtRmbuucVyKFEM95Sv+5UQmh%0A8n21gI2J52dGcn7Og3vd05n8ZWF4fWfhan797z/F6W3b5PvJJsg7JOVgDqde0FxMVYp96kvn2d6K%0AEWuWhMM1q5H/sUiQExCWyJGlxloUvVKIW6CThnvafDvE8ollOmYjtAmBtljuGoX6N1XhxSyEpZzx%0AHgpfEoSzdPDUnxmLgLCmLOZg6b/mJvEuWgMI4NqNxGv2oQFKQkW9J3L9IFbANzg7dfSW7/OddwHv%0A+kEPLgiKdQV6fDXiN/CJLKkaun6cMGuWFkdYo9+fQAVE77HBhaJHQzoJ84gv1SFW5CJCu6oj2CjR%0Ae3X0eUuZBzbppkQLF0jJ5gzvpGcqFhMUQ7rx0dVd5PdBrV1p0d8sDUESqwIUZ6bgtMCF/l4q1GWh%0Ai8OQQEyKNvPQEwUzPJA+BK3z4fET8KJ1UAzAomeyCwNq2g9nAbnYvSHwFc03Z+jYxssEGySiXqQK%0Ac/Nd0PgunL5Gag2WlUiX5pJYv64KewapMkWdLjMWuKIkyvLpKjC3DECnBlfWhOWwG5joyvlb9VRg%0A+bZ5KP0h7Libi7bC2xys7QafdYzCOols6sagMHdGL+ChfWsmAfXEjIwen1J3Q5urjQW4YVheuYdG%0AdTfzX38Sf3YJUL4A0haluixE81UayoqrV5U8UtYhCWNv7bExtjUQB+MKVTI299beXOXF5sprvK+V%0AyLHTmcqyzbGZ3TFvuqyBv3ZkmpuL2xMYAWkhHGCT5xgF2zog6n8va7EIKN7Wn8UrbO3YeFnFKxsT%0Ac5OVvMyXpWMvOrFYPaIjSrqmKjp2df4vJAj8V14ziHKrIcEph+wWU/qzRl/nnLgH4l0kQdxmAwiS%0AdchAVwguBYcgi6Yq2ZgfOIJA/g6iZOcRBLsHesJWQixO52Sh9OoCuP5d25zq36sgS8+f4/vfQ9tk%0AEVcIdU7jzJtaNwi2Nc9MvS5KA3PR3/S7K6zd9vjUSwS01kUOaR0HJp4F0yuh5KBYBcUAmQ8Vb50+%0Ap9Q9E8HHl40LhBRBCD4u5xMxDyo5ND4DJ66Bk1thtAH1CbG5y16KrBwdoz3foc08MAbDy2FVIs4l%0AgEMeOiU4L5HVkADHVKPVy3BtAld3Yflfwo4vMH4BvDmB89r9dB7rT8ULUu0VroFe0ZvuUnKp9slk%0AAYKf01CkjZnRnxIPQxXYcvFX2fW5n+POOxPwFSidoD4AI90wrrYZ44P/0BRcN5JPU1J2GT/UdKbz%0Aws4oeyljWMrEijEfqwVADSjEZfYSo+upgkpUyWWJ/M8RNh6gd4w7iFluMpg7Gb+qrRcCEj2bGFkb%0AKgW9gj9mWfTuT9j8jEFh/7dNzhEsPAMpp1N5/lokKXA0umcBvRrHSw8V/WGuc6ZY5wmoNSGUBhxE%0ATPxJRKgaBCXYd+mIzBC46ikyOHsQALMW4cJaanlbn2dVtIaQiZhF0Ow8ouhtHc06sUyNElUuwo5q%0Au2MlEvi42AWcqWxLhQh0H2pIIqI1wdyJj/g1BWu55Ko+elfXaUpkIsrPFqjt8naZSWnZZWmqgzB/%0AJVyQijR0lkPHcSqDrCJF+WMkYChtqW9saUAgvipdUwItuL/DwIU7GRzbyYnlL4OvXQ3XrQCnLHx0%0A0uY7yBaYyszNzsGqEdmNbwOOngAKSakd1Em3ayyBqz1s/Ras/zvqO+DdKVzZDqako9//5hD93E2C%0AgjQL42yXISQbVxtvm49SNE4Q0iu57o/hc78E0z8DyQDkJyDVwj76WcvDN6VZ9sE0N3+wZV3ZvJjy%0A7iZBliyVM0cRXKpKrxAShRUTMiRr/bHN3iP9j+sJ4MJ6yJ2sJftcLGtLubGLS3y75guOr7gmRSeN%0A3C1J8OUa2u25TqLXkrqd2qphbQzMTVL2sNsJALP0+Av0d4/omqXc+h/2OmeKdUpfUwLdyXL9y9Dj%0Al+bR5+3wwfj0AFOCC8j6mkMWyElkkE7o92YRxZkh/lQNhbCckHhQ1u8a6rWMrTLiXnBOkYJOuFf4%0AaGahXb1FRTCfev9ToW1HCrbrJaiQQK+2p+Vau0I6bYJvsmZFXczBvzQl1tpni9sQVNeJL3S2rO13%0AwB0r4Nm2quVY1qkMutVgVhoK66Fp+mkt9uA4PTIOxKUe0s5+qJUYmoMVT4YTk/8LtnwUDtRg+yiU%0AFmSwcxBbf05noS5vTim94egCeHXkzCMO+uOdMHM31uHyO2HDG1l95SLvSuH6pqJ01+83jNkcvRNX%0AXT/VyhAshCI8iW2o6pJZVH9zaYlCiX2J3QQ+m8KW2T1w+tkwPAPFLlwpKPKuk+BPvHEu6txakMmG%0A2zZ5T5CxJJIJy0RyiLzlZclYLuehjisEilQr6TfRHUH27DK/s+Xl981/jLjtfz5Yj10ilKwylNl9%0A6WfZ2H17vG+C5WD+UTvfLkWUfysVC7dMCF7H/tUyobDTmD6jTCiaD/3n4T2R63uBjP/yaxY9zw5Z%0ANk2kc00EgY4TfKYVfe80olTT6GceQa2WPDAf3ecA4awrQ7Y2YVV97gpCPVjzs3j9ewwJhm314m9M%0AoJehUy1kZ48DCEv9Rp4QnbT/G1IqFYG8XzgharcSCVwYKs2cFEJZKNHLQql25dlGJ0m8CHu10NOs%0AtTG2+w/mEr220mtGMHcJjFUAdzN8aQGWGQyoQROmC1mMHRfKvPVQKyHoUajCMIpOloSNx8bDlJkr%0A7oUhSKs3s3kEuOA+2PoZmPQw3YB8UCbjfDCPd5U2AlNPw6l5OJTLKmIUGIJmC47YdtmCq2rwozth%0A2U8xeN1p3jEINzRhWUcWWjkacwuC9HyBOpb1brBOzMdnexAEs9+sDuMq14qgXI16ZqjTAkHVHCob%0A/gX+eRw4CNU2pSq9fPVyNGe2YduJvjbupkjNNPdO5qKpG3bfhqd9bafyM5fCfEXm1e5lh/1Z5X0I%0AfYv7akkGdk9rT3yuVgwkCv1crn7bVtS2jhNueUd/5l1EcdM1YvINkm4+78KPAa4FJ35Tr88e8GEu%0AbVzsmQecgDXLzDT+u0d0wwCqQ2J08kNe57S6VQsJ8BpyNFpVA+mwQzpv/tWLkAYnCBLtItzv48jS%0Am0dI/xOI0p3Rz1bgjBoDQ4gCfky/F6NXMwfG9X2ja5lCtUyQIhI4Q2l5Ij7MpUValqYVVnzYrb2j%0Ad7hcbHYvXRxm0tjVTMPi7SSB7B6bVHaZGQf0UgxfXIKHDmyGgROSnjpZE7oFjnZXfcBFcCF4bbfz%0AIcvLng260dDve7PFK50+AIMnce2H+c0c9myFRw6+F67+UZgYhnpDPrwBxJkzTcIUvaTksYZoqLkK%0ASlCFkRLMKKN5yxD8/MOw5rVUngJ/VoFrWiHJIlF0s7wTgi6WSx5XL7MAoPlIbdMwAB4HUExJxBlZ%0AaRE2RwgLu+ylZvYrXzLN39y+GhY+CcNPp1wLrA/vgzKpFmH8umnYBOJz2Ky0nil48zuWtV8mIzGH%0Ac1HlINNN0doa9yG+DE3bZgoaCFLvkYlWntAr7ZnqOCw9RdUhcY9FH2oo2zjNJzCkLoVO2u/LH/By%0A73Yk/wWy9gcQK29AQUPZR5WvomsYQat2iwb9FfPM/3ro7MPwH7rOGWLdg/C0ZhGX2lGCiT9HOFF1%0AhGCKtfTHDEQQRdjWz3T0XnZO3mm9T4dw7MsoMsAL+hljD+TIwLYRRWvvaxIkC052TVsAcSAhRgj2%0Af0f/Z1ppOH64F8VVhazWfh+1qudLciEab9+PjyfupR0W/e3Kkv4foJe0YG1b8MC/XARjK2G+ptt3%0ACfIy7UIWhqHjHs1Fn9lWlGMLzXK2LaBWKoJSqmoH3ThQWwm1d7O+DT9fBa44AcveCMtaYv3niKnw%0AkjKkw2Q9foaHhQIW7OB0dejMzMkMr67BG6dhzesYvnKWNw3AlR1xexQEJGmsDOcFFda7qpxcGCNH%0AMLFtDkx52PsFwYdXK/rdPU7/33X0H2TnxfpYPfAJWFEHvwb87Qylcg+TH6u8ZO0peznNoVyIv9ra%0A4KIN3pJNDJXXu4GU710ofrJQEvR4uiy/Z9rGVOfMvlfvLnFruDA2ls6aRvPeduKyyPRZi06UY9PJ%0ATwaccgJSZvT3o06m/BjhmPpmEhRwHr0u6H0KnflJBBRNIpbpbuBeF04TqSh6rdkYEapWLUfMf3P3%0AtZA2ZYhOmOWJX+cMsS4iHajR86Axr//LUE4g0uEUQZ0JglOWIbvNEYIStGOyZwk+U/Q+04ggjkTP%0AsKNg0Pe6+vccPSOT4/qsg8hOZjt24qOaAEm/iQghVc5M/V45QUcvjc/5YFZZ2p0pKqN0mZKMg1lo%0AO2xLtAVryiOOlvZ4lz7cp6QmZN3DV6aAY8+AVzdEWitAawiqqyE/xMkUVvvgmzRFY5lIi8rd7PFm%0Au4KAetWKfEBEXaeDOb4I2Th5Ak/N4Npx+Pa6r0Lnu3DH9bBdJ+9i4P4V5Psz8DNAC1oLwAIlTpEz%0AoDNWg9oIvMDD2r+ByyZ4zRi8alHanRIQqLXTqHIlH+bTTG1DhJUC0iRQyXpjTUC5njAuheunQLno%0AtYcwETP/hu05XDMHs78F215IqxsqmFlGndf5tEzA3EndgmYaaIcVRWj2PXSuLC06S4P8ORexS3xo%0AoFM0GyvS2CpKCBu/KVVzEXgPs0ngnIPW/PCwV+8xjJxOvs8FemWBxExmCMWRViHrcD/Sp0HUH2oy%0ArPe39TurbVrQNppV20TW71pH71Tko4TDREH0wRCSFno/ss4H9TMGyJ7odc4Uq0XiUkSpKu4AZLCX%0AIVXeHKHQtaFTq0w1jwzqvN5rTn8a+r0FQuWr1XrvEqKEnb7G6DdBBtyCZyujNtW8fDf1gVIEQNHP%0A83MEhdrLPooE1S5TgJaSZ4gQtHCGcqpsHcT+ukUHI4q+zNe6lCwevwK9Ck1OFyMFzHwbuHsIrkd2%0AOo9onvooNGVDeZI2wAILHRd8V8WS56G0mi4RjUkj1FkKy9YBA0263WEKRWm/XoafPR8WTnwUNl4P%0AjyIrqgRcBUyvgqkScJJRpijwLCdnAsgpAwPwnBSe/mG44H08dz28OhNFUThxy2BK1QX+bjsNUfi4%0AWlnilU7kJYpe6Yr/2yyEXpUxfa0oWm1Fpj8oPSoNChtCQGlrAaz4Bbj3E/DUYDKbYhvJglWylN5W%0ALoIP244ySSP6UycNqLfnjnD0TihNfDCzC+3HUuaDbQZWUMU2dZNz52XumopSjxEUSRdY7mRdWfLb%0AnMreFGE9WWIQ+hlL4/SE4kgnCG65QWRtGg3T4iUGpGyIFoiokQRUOh+9F2dpVpF17vRzy/i/kCDw%0AX3kVqpxmoh20m8iAFUgHLWNKyxJTQTpdInI06+et+pWdgDxCGMBMfyxIpSWRe3UFOoRJTgg73eN6%0An2FChLSii9AyWmLz3JCJMQKMU2hBCDvfyA56M1RqwSUItCWLvJoZXlUF3k4F9RYEzqojKHVDq+j7%0All9ua8eSGPIuHE1/HyotSBsykCnirErPg+ZtHHYauNLvmrncTPuDF9bnrt7CAjeVQhGYKtFrHXwu%0AW6A4UBFBTiQD7Jo1cMfaL8D8e+H+N8CuBDZrw8XgpEKHDXSo4imAUxTM0YAfqcOLPgib383a8+FN%0AhSimkg9FcswfbDVAIZjpcWZSWoiv2xU6VzoXAyowmfY7Pn+qp7QiC8EGxebOEf6XJWICDz1/irk/%0AarL+V3ewJX2QRls+P1cKG6k2IQRAFXFbERHzzZuShBCEwsvZY20viLKUBNSWAFNOAYwqSavfW9Yd%0AxEBBR+fZ3FujHU1ocTDioFGSBbPfhQDwPmsLwTI1EDRCWIfjiFlubsCqvm4gBJEzfX9Kv2PFkmJQ%0AVDK50/uv1LYfdRK8XqGfWdDnVXV854EdiFvCFPAYwQ35RK5zF7xqQY/voOkPdoaO/XsBGUALZpWR%0AwZxBdq+4CEuXUJwlI0yCmQlG2XQE82NB/7dam7Ko73f0ezP6/DEC6di4pz2Hd6R4LDUV/VzvuGpd%0AgLFJCP0cWENChghK+n9HUFSecJ59Fj3LgjAx3craZJ+xhZiqojvZdeS7dsDGunCHWokMUstJRtC0%0AFHl4eRKUid3D7t/nc4NeFpb53cw/7BEF/yQPzEzRqq7mWEk2x6ECfr6Ag1fCPv8BeOl6+IcXwT1l%0A+eJyB7NDdPJRhlhkNTk7qTG/YhU8I4WX/QVsfC+bL4Vfr4TAVFMd82a2JhZY0/GyzKISEjQBUaCJ%0AmrVxQLJIQkTbEKpD7llErpGmmt7tJIyRIeQugvzaCexxMFc/AekpJg4+jf+540GmqmKlxG4XH8mL%0AZUo56B23kkfPMcVuG1zhtPaMswKMwmxZSASoHFHZPu1ge0n61E4ExbeTEADCyfjMOTl0wvy0iQ+U%0AwRVqDVhAuaNrZhFRZNOIcrNSn56Qfj5HQJmeEA+ZQhSf1RxN9LMZsEM3i7VIquy+yCSoIwrb1nEV%0AKRnaduL+sjWSFFBVV9YoAUl3EdfAE73OKSug5+VXszcroK7KdREZcCv/Z6gUZAc8hijKIX1vSD8z%0AD70qWAqEqSMT5AgTlRL8KSuQCVvhhZJhwTCnzzyBCIFDFlJXlVvvWGpEsPbrgqijO6OT/PqYokK0%0AAMs+CGgHQQxm0hniMxRq37eIf4cQze4hLh+i/4bUDP3YAq8W8vobSQP+Zg28NIHGMXh0tUD8wRIU%0Ao9CGAz6Ym/OlYC6b66GXUUXYdCxxII4wD2aSWtxKALef+dpq5nJBO8MZXN6FD5bhHde2uaPxW1D5%0AIjz9XfDIuAhAqQSf2MQ9B8q468Zpv7QJK78KtXfABadZth3+R0XK91n2WlvRpR1E11REpmAOUJeG%0ADp5tdp1UKpv1qGTRRuKAWi4KNSmE8ouHvCqZtQ5VnmmIxudOaHDxdSgBHpqB13fgzjqXPEOQauph%0AvhxcPAnBN47+XusKwrRAZKabmSMEJuOCMXbV9POzSLBnEKnLAeInXeZFUWeo+a+fP5xIYOk8FBkW%0A0rfchSpficrjpMq+xUO2AGNerFKPKHNTWk29n/2dI+tuDmUcEM65Q3+vI+t6vZP2j+SCni+yZAek%0AHTNO9EMDWYePu9CuhUQ2c6vPmnqp0dNxoUbEYjRuP+x17hSrOUJSqCbQVuXapkek6flbFhBz3Dho%0AhlyXIYIyhAya0arqyCAb2h1DBtiUsD1+nQ+nvNp31yKD0kIUqtfvWcDDFgvQI2zPJz1qes/XO0KI%0ADCf6eVM+hkpj4e+6UIbOglAWUY/J4w75TNmFaHWPErQkwNXSRZ4QAldt3biO7K6K9K49ArUDMLFa%0ABss7GYF2mXY3Y6YkO31VOYVVVd6m5O3qulB5vxeB9yEQl3RhwyxQnqC4BYb+O4yNSik7gOEO/HkZ%0A/nYHfGLjVzh89Hq4cQy605IC9oJr6czXIZ+G+rcY2Ob50TG4sA5PKWBtWwqreMKGZ8VFvA6+uWsq%0AajqXC8h1w6gUMFeW11YpBLJsDkHnRPsCkFXlQ1kpRPLjxJBWEuhq9v3Ew9oCWf1rboMHXs2723/C%0AL6sLwDK0bOO1gJSh315tXxcQ91Ae5Ms28SzRKlNpsBjmgczLseYIWlnIAAAgAElEQVSHtW8NrwEl%0A3WSMX91KpSbxCWSNGNpvlkK03pguuxPYq3KwTMdtObDcS3uHUZYAQVGOI8pvHRLdnyYwcUAU6SjS%0A5usRHWCUyRmEWljrSqILLgRRG14aYGv+tPZhM1JwKUPksa6yvLIdNq4skYy2q6OA5Q97nXvEqggR%0A3XVzRFmayQD0aiQmBMU5qF+3MSihp43oZxaQyakSHN/2yEEfFKEh05MORrswmIhSHQVGPazTSTAU%0AsJAqlxX1oRZaLCYJft81BLMDQnGP2JSuLFGsxjDQtdQX/EKfZfQsQ54WbW+l6ipRJGOVlkDMuRyh%0AntgJl48BfHMN1FdB4w/Ab4XajcJVOwX4CmSraC1OCEnAh3oGSaHBNoJJbOmUphjKBQyqbZV0oUhF%0AIVXaAKfhyVCZgmEtwYqaaSMOfmMAXjkABy6ET7hTrHHwiG8y4b7KTA7VNlxchzd3BbEMLcJIW/ih%0AnkBlM+TW2wQI429R9TwJZzYl0KsqlkCv6r0n8HV76NXmyUGzQq/YjdM5qyjBNbYU7Cp7eMADySpo%0AfRXGfpGd31pP91kTFB7KXSjMeR5tsrah5qoAW2nYdM2KsQCVcYs9wR86AKzUDWGhJGUXrA8mh7Vu%0AeIZDFBMI6lxwcDSBdUVwi3UcnE5Enk4gLrUUUZqroj6XvKzZERc45mVkXz+GKLsRZJ0a6rUEonFE%0ASZ4kZFeaX7VZCpuoWUpdlaVGoUXnkbXfRDaTjODquQKgIoAg0034pIOv/j+NWHOCzUCozWrRuwUk%0AQLwVGUyjVxiSaCADZBSpAWTAB5BBHEEoHAkBHFd96LCZr3aywHGgkorJWiJERIcKennQeSIBATMn%0AzBQaLHoU9r4MmdgUsx3VzMudJTjfh8UYZ2GZC8DuY6dUxjSYTiJmayS/EnRQNGzZUAmhznNJF83O%0AFlD+XXgeMPIJOH4hbPhxeKQKFwLdQWA1eXeit6FVjeqTCjdUKmP1I5yqBvUqhTTedUOEPXHg2jop%0Ae2TAy8tlYypKusATyUzaNA9rU7gi1eM7PMyWYDaFgRLU2qJU8TDSlGc1WhJwqnelPmyeCJoxZLqU%0AmbGQRgdHephPBfkZako9vRRM+9v6OaDj2yyJa6BwglrNV17Szcd7zTAqCTJP4gYcb8DgT8DxkzzS%0AejfHeBWnUlivczpUhE3fUm0tCyr291tGnlH5QObJeelP1wV2AigbIglj8mAip9aUVL7NoikVcDjV%0AxBtTiE42yQ0WrEMUYY2QTm5raz0hMHh3Kv8/hKxNtYmYQgJdFyMK9LR+L0EQ7EbgUi+mvNULmkdc%0Adk6fP9JRF00hczhfkrGfSyV4ZUr6PC8K+itOlHSCmP7jOuYHkboBj/CfkyBw7hSrchpqiiqqak6b%0AyZ8jimo1mkdMSEM1/2mX/qyJmJsKMtFDhE62FR03vHyv5UISAchCqxRQcgHBxOese/0M6GJKQkDK%0AEdCD+R7bTs15L89yTvoLkiZrStVQZsupOZ0o2tXXThLcEPMlehV9YsUNoliWsgKseHZFkVQCPHYM%0A+OaVcMGfwUUZNI/AkztwV1WgR2c51Ou0C/G/NQgK3JCpoVNLV41TOO2guDSFShK5ParAigIeKFG8%0AokZroEVZFaRTZYwLimQkg0aqyLKQhRXXHE2RvIZaN8xDMw1o1bih9tPbcPRRuxIYTmBTETEuvPRr%0AQJkaxmlNvJLOFQF2VQm1K/RKR8ZZc6kq1/io5R7PNAPyCrSvhssn6O5vcKwFKwcCnXDUB6K+cVut%0AoLa5fEyxZio3hdM2qWJtIq+dRCyXGUXzHScsAYcoHNvAjcZ13GkdcWAbooisONI+BKU2EI7zFi+0%0Aq5Z+bgahIpsbqJUEv+5xBKE6xP9qaBR9/yiiOMeRtT6PyMJFhYAZQ7EDiKKvONigQcg5XeQlLzJr%0A/ttxZC+fRI5faWpfjyLK9HxEB8wD9yFgzo5zeiLXOVOsF6aiQNcg/peD+v4s0rGVSNm+IQIP1dgB%0ABSGVzjpg/hejWmWIkh4juAsGVYjmXRBgO3iwSj/ZupEH5WU+L/Np9YJWLgRDEi+LwHK1MwdHnAhj%0A4aSfFW3LcSeuhoeB1bobT6DIOxFhHiokQmsL1XLCrZhH35ExeiWOvkwfU4LmYzUU/JnsmfCZDD53%0AB5V10Jk6Bflfwo/9GnyzJh1uVVmcgoXxUO/SE7K7LCBj5nXqBfmlXsbe6aZgwaNKgZwRWCTwlAGy%0Axgry9BA+oZfO69NQtb4AKAdk1qvYRQjWWZDKeJZGCVq0eyia80nkh9b7zToxdecRK8SZXDnZDNqp%0AKFFT4tWubEyFjmWhaNfmpVf31ot1UfZBKVYLDY6kgrwfWgCSZdAZhqFPw4Ov5YRfzjammETrt3p6%0ARXRSL4jMTPxuEopRd9XVY+4YAwTzqbJifAiGzkYAYdyHDSh3gc6XO1mDVqs4l2mghsQfcl1j6730%0A8VQSknRO62ePoEfNp+JKNrCjLmkOIet6A3CZjuM9TtZ9jijnUWQd2HwvIqb8KeBBJz7WBJhO1bJA%0A1rudCDLjBIE6/V4duCaHizqwswK3luBOZBMogH/V7z9P2/9Er3OmWLfo6wKiVC9HJmUKGaiVyC65%0AQj9jPNYBIhOJsMjssvKD5mOcIhy85fTDXv+fIRMGoXJW5sS0jylOcyUlXhMUTBx5tYU/XQ6uBY8s%0AXKs7ewSZ/MzJexdFfTAmwxj95RHjqPRiOVJg7uwnSaZqIlklJEtzhZBhdLoMPLAGLpjleRc8xIU1%0A+NPzgJkPw1ULsOadsDgGNXFa7gUuSvqLfiRe+YnlEJ1OCln8RszHyyZiYzhTVn7hONCpsmdlgwv1%0Au+1yUKjma7Y2GxOhrcrWCpKkPtDM4hNpzb9sh/DNOxnn0Qi9OqRt5qs3WTFuaxdxCcXZboZSZ8rB%0AjC4VEuw4WRZLJFHLwLK6FlKRnfgoksJBJwNqr4FWB6oPQWuYd07Uue18OVnYqo6ZbM2Xgpy31D3S%0A86US8uct0LvRw6pMxnExVcYKsnFPI4BljEDjWlCFbS6zdQiQqauiLvuAwM33bFlrazPYrlbHvjr8%0Ao5NnPKTrxGhTVmh+P8Id3UFIADILYpW+Gnr12pfZBHbqfA0jLJI1Ha2J7ESmp8oy1w1EqT4KXKNK%0AO3VwUQ5XHIDaNGxfC8tWCYLfB3wdeKwLl6SCbE+dubT+w9c5U6yPImtsHaIMjyK72Byy880iMN2g%0Av3FWl+urOb89gSlgtQYahMpVxxDnupHXzew3f26HkHCwiCy0TqILogjBJghCAErlcSHCbAEd+/ig%0AF+qW8fIsjW6BcBQN2v4K/am2jwLbEhHs2ZIu2iXPl1yk8DeIQK1SJNHL6uqG2poAf+SA226En51n%0AZCDnxR0YHIE/ua5g8Z5PQroGFn8K0vUwL5Z7RRGX5dgvOkEKFrnNnaDNHqI2hI8s+tFcfXYDsK0+%0Ay2OtlN/rVLhhOJxAaxQ0c3PY4iUNSsoKjnQSeMwJajoCbHTqwkkEZcZ+7jRS2OafnHQy19OEDbZM%0AcBvVELfMWCryWEK+O6uTWzH/pw/HUmcO0kQUUDsNAat5XWFGk1rRhjwHfANcBrWvQwb+zudx3wUf%0A4kKVSaN8daF3LLfd2+qROsJYV73I+yABGJQKUcJ7nJjiNZ2TErDTSX/NpzzmZN3Ze0eAcRcyn0qp%0Asm8UzTiV27qX+a3nYhm8sCaybTU6Onq/awuhIy5z8ApELpchJv6jDrZ7CRxVEAW7XNt70IXfx4CL%0AuzDeplcEe0VLZSaFmhMEfQxRyMcRBV9BeLjTy6FYDrM1OJIKIt6PgJx9qfT/OPAi4PM8seucKVYr%0ACXgaUaoFMiBXErIz7MhrC5J6AqcUAvJoIqjU3k8RBbZf/7bSgimyg7YJfkOjZW0iZH0ZUmgpR30+%0AEQd6hkw6BErXXicC7RTt1AksgyqyeGvIdzuIkLS1rcParklEQa1HdsvliNLMnbzXdhp40wVsqbWb%0APb2D2u5JYXMB43kQuoWSRIVN+bYTeHwfMPVMWPF57s/hzwbFhbClAQ9u83D04zD0FCgGoBDEeqMi%0AKEO/BTIegwjaMTO9nmgAycum0yikrZNlaf9JB09zTR47CYcX4LjWqG6XJBC0U9HOxUgkulvIfSyN%0AdrAQX+JgLnW596Yy9pWu+GNt8zCifEddJgmhdkPhxPT/ro690XKu8KGG76jeyHnp0wNOAyPAJYiP%0AcZvKbo4Ec6aRqPdcAstzDTomkR9dX09V4eFFoLMC2g2Jwl00CTO/zKm5D1E0QsQ9QWIPCwjqmkuF%0Ac1ntqnyqH6qmsjjvlD7lA0pe2YaNNbnfGJIKvd/JmlmFBIISVapbtZ0TLoCYispmqnO6P5HnLapM%0AX5XCVEW4vIva1sOEOMgKBCANJxKMOgX8E3BRKuvzWm27WW4WfD6g7x3XtbkBWNaFnSlUBiTZoZFD%0AWocTifTrHhfqNk84WV9H9P4PleAVDbi4I5v0NPBVYLSQNVzMwqkBsZ6+whO/zplinUWU50FE4WxC%0AuGYD+poSFsoEMskJgnIfR3wjhgYH9F6D+gUzF80sXyD4WQ3xWmrrCLITZwQfrJ3nPpcExThDKJA9%0AiCi8IS9oaR8hZW8roYzhowSkvdraqO1IkQVccdK/I9pfo5nFnL6jwICDhvZnuBDT1ojojRye1aJH%0AUC8p5xQXiOpzKXwthYcevxkeKUF3jt2PwO4xcGPg49JgA21BVG2Y98HPuODg3mg8TiALYNKFjJcE%0AQc4jyB9XeyGbr3CiaGpMyUCchH0XCipanou1sQpR5PchZwBuUwU56cQ1ZAFDy29f7wXtXErw5XYS%0AHQO1JBZVSZxwoiQt4rtJuzqMZgV1YVW3v6Zpx4lS3YeYohcgC/di3SR2If0+ov0seU2dLImb5LAT%0ArjQEpfeA03GeG4KpQbhkLcz9BSz+Ifceg5VDIpfrtV1TiZiqt6kcnO/gGYmUUVjXFl/3qYr6dNOw%0AicyUpGhLJ5F+no88/9su8K33A8ec+P+HETQ54JU25QUBHkZ8mmXd0K9StD6fBE6zR8xxhwS01hDc%0ABztL8G/6vGXIWtyp/XmTytBaxOe70ss6fG8iz32qytoHkfknlfHOEOvgKRXZLP4BSdG9RMfsC8DR%0AJryiLsejvQ+4owsbKlAqi0x/Sdt0F5Bp/mzTwd2ExIkncp0zxTqTyQ5XSeQY6jaCGFJkd1pOcHqX%0AEFRnxeJLwIPQOwxsi34uIXA5hxMpIFLW743oTp77cLCZuQ4yJ2Rmo09ZdLuBtM0BQ07MwlPIppAA%0Am11gLJjiNq7cQUS52v8PI0jMuHstRFFs8HANIa1vRBdi6iSH+RDBF1xDNpVSIveqqfK0qLP5Hoe1%0AQnyzFPiVJa8+qSM/AYNTkO+VAT8E3nJ9Z8ag/jpor4HSXliAfyrgNYmgxekkRHZXIItxBHHnHESO%0A5j2AKC2QBVOoQnlUvzvUfEQm7PE1nHf9/cwkgkIOOWnOQ8jiaSGm5VYvi+q0jvFYFlwCWQKjDg4k%0AchoLyBwdV0VgForTOcgI0emDyIa6CVEEh1PxsZnFMq1j3tS5niGkO15ewEgiimcaUXgnnWTaVRC5%0AWYFUdTI6UD2RsVibIpkFiwNys2IbXPUg/DM8cPIFTKz4PEkTDrVgbsI6sA38KkjGOeEPcOeG+7n2%0AUvh9db/MpWJBOO1LE1ESWxQVpkgCRrcEjzip6DSqfbpOZeoKZI4nEzkPyjnZSJ5UyNx8x8FzM1jX%0AEUW+J5FU0k0luMaJJTGdwHedAKMNRUDGP17IAY63l0VxdZCN42+8tH8T4vo6qutrUf+/E9EFK5H1%0Av0Vlb7ILO9R0P4go4PMQJfltL5v19rqsp39DFPZICn+fwSfKEjC+AbFarktgX1UDVouyKfYCNU/g%0A+r6K1TlXQxBzFZGZf/bev9U5txz4FOGE9x/33k/rd94KvBaRw1/x3t961geXxdeU5eDLglROIE5t%0A48VZilsZEeBdiMBuQBaJpayZSWOEdVCzRf9X1caMdPWIZkLkuKx+q6qG0w0ll70ISysRBLPFCbWk%0Ai5i4Vh/1OMHPC6Fm7CIhQ2yYoGi3R4PeRHbacS9mjupJUYSJLNSW/v0oQv9M0fhPIhtBVSPIZmq2%0AHBwp6+mziWSe1BHFensnga+Mw2AT0o+FEupWIqh8M1TXQPUItL8Fy6DZgfcNSvbLJCHYN6D9GiRU%0AA2sj81hTYdmMLNI8ESWbAB03rylNr2ai/AVWFKIcJ3QcN6hA7dSxK5ym7yIIMi3B6pxeuuVuBCHb%0AuWcQkkJMGdopFFbndxWyEC0foolwHs3E3aV9W6597iCWyDLt08lE3DMWUL0SVc6EbKY1Xdjc1EqM%0A6nPNEvjHFGhvhrwstJD918DwZ6GSM7n7Iib3fx3yzRJSdzshuRlKF0B6DPwguOdS7L6fu05+gF97%0AGvxOWfyI1/pwXpv5FL/tZFo3I6myDzhRXlZlaiXwoypXmxF/5t0qq4WOzYQTebvJi4V2oCaMjwsL%0AIXhMAr+Uyvw+H3hmBreU4T0JTDt4q4exQhTax7xkfpHBjIdOFy4ehMMeko7ULJ9OZYOdLOAKJ26K%0AR3VRPViFS2uCOHd34VQqiPVB4IMdQcgDVdiXy3x8t6SZWw5+EnFJfdjDG3UuG4rIV3rZVP+pAVNe%0A5nMXT+z6vorVe99yzt3svV90zpWAbzjnnoL4d2/z3v+Rc+63gLcAb3HOXQy8HAFU64AvOefO994X%0AS+/dReB83hYEuYiYMgeQnychwn2MUGHKhLiDLIzLvOyaqxAl6BBld8gF03rcy9HLRpaGwMHM0mAq%0AV4qQGmgHnaUeTlRD4KSmQQE7uG+qLDVC7Lwcq0cwgSiCk8C3gGchPrkyIthzCA/fduIZB9sN8tLP%0AdEgQ82yVKsjHEBJzAlymPrfUS+S06QWJ7HGyeA7pBI8D9zv48sEa3DcGN7ZDWHiQUCYsfwRcGfwi%0ApP+KVbLZjZijBmytnOI2ZEGNIYtwCHgBcA9iTj1G4D8OAJd7+FKjKyt5bBXfnIWfrIs1MOXgi8hm%0AaWcG3NWFv3FAAeuU+L0tlUDDZh2ffTrOeGhaGTNLPPFQLomindNoT8lJm2eRTfpSRFbuAu7Q8doG%0AfAMxiTcj/d+m7dqLKIwbkMX7VR2bzdrfq4CnFnDhnASuuk6YBN8pw616X9Y9Cut+El72cej8rPhb%0AH52DtU+GbdsguxYGvwCLTWh/GYZv01QjwNWAnTAu/s6vOGHULO+K6T9dlg1nDlk7X0TWxyonU7wP%0Acc3sLoQjfDuCBO8EHnYybD9XwKYOzFSk/aeBTzr4oofVKfwS4p7ZgGxebeBpyOb/sTI81BW0XCBZ%0ATHeXRO6f6uBuB0NVeBVwXSFj/twclhVQa8KtA/B/Erhcg1AFsKEmfOEtiWzwuxMomhoArsFPp4Ki%0Av40oysESPBvZIO6Yh29U4WfVMqo6+EMEjX8O+B2d26sy+HYdjrTgb6d5wtcPdAV47w2kGA3zNKJY%0An67vfwQZn7cALwY+4b3PgMedc3sR//RdZ9y3LZtyL081kaCAd4JaM8Sfd4igVFv6ej2Cbh9yUukG%0AxBSudUUJjZRkUD1KsyoJurNIeSuVnywJFde7TswAT6A0Jcj7U05Q1OpEzP/eiQEedunvRmXZAGzz%0AATFfT0gym9PXJ6E7phc/XIY40Dci5uO0E92zHRHWsrVFn2EBrweAq5xsBFOI0OzTPttJC6sRxfN4%0AF9hdwrU6+MseFY2XEhzMy4GZXdDaFRxiFWAW9o0Jb7OdQkVpPFOpKKTdyMZXR5TLKkJqr0cW9mVI%0AlPaUk+9zZwbnbeb2GfjuMJwoYGoRTna04ZbpYT8dOeqKOjyumSD7U0LIudC2ekJtSS1z1vbQrtGr%0ASZchxWWoiCDv84LAigwO60QdSKUDjw9IQCQvwR6llg2lct/PeFnsy4HDBdylPLmHq+BSuH5UnvWI%0AExm+x2u94EXExBg8BYO3QHcOmp+Cpz0F9l4CG98gEG/14VD5ZB5BHlWgeH8vqDCUikx8GVF4A6n4%0AQ7+JKI5diElt1aWen8EdZfhiBk9JZV5uQKyDr+jQvVrnM0vELTCqgGWVg5GKZNL9XSp924QAhP+f%0AuveOkqu8tn1/e+/K1TmouxVaOSIJASKKJETOOZhgg8FGRBtsgw2YAwbb4IBNsDEOZA4GTDYiByGE%0AAElIIKlRaKUO6hwrV+393T/mLhrf8944912/9+6gxtCQutVdtcO317fWXHPOdTLwNxTg9/Bgjj0y%0AVn5vdP4T0RpuykBnCD70YcCncvBmCE4NKMA9bmlzKkeb3hadKi0eHOU3x3bzIBbUOPFXcvCIIyhj%0Aor8ODzHwlgWtvn3kEUE9Kw0oux1IwJIojHLgwRx0B+DwKHzqQs8g/68Ysv63gdWyLBu/nwD80Riz%0A3rKsOmNMcdJBJyPN8tH8axBtZcRQ6l9fniCAsYERm68kylZXGzjCzwoO8g8yih7iIqF/rqdu4Go/%0AvSuzhHttY2Rzz6LyJmGBHRop2Qdt0UNCrsqZIocShEkOWwokEaOfzxt9b6UFMyx13YvPbgJliUWJ%0AXh9qAuyBgn8HWtxFfG4XyvhiKLbNMHqPdksZz3ZLATXiX7xd/t/1/vsFGDGqCaAsaMi/AUW6SBro%0AK4DtqLkTM5BNAd3fxmyPwoyPtOKLtbFBD24tI7ZDQb4Ejr0MWCFIFyCdUiZDULhVK3zJA3OAoSL3%0AzIK0p/ceY8M/Leg10FYKnB6ENtiwswasnhFW+Fdnk+Of6HAQslEwSZUKmSg4/nCdQgSNzS7WDPkR%0A7pyNdmQPvX/cPzeHEXeeABSKwnOPEWmPzZcWZQVXH5UK60YnfMwjWKEqaVvRdd1Xrwz3wl8qIBDQ%0A/UgBb3sw8FVT4SAweSekrhlJ58s2wvY6qLqQQ+fdyjJPQgNKoC4KAwbOCflQOPCxB+E8VATh267W%0A0Kf+0zzVP6SDUTZdlKxuDmpT7w3BwZ6C3w6jZOE2D1b4z89WGz6wYV0ergjqe08bzW2cGIIbXdGv%0AhvGrBYTVLnQlcJnkwae2TvN1H34IosptXETZ6lsG5hRgjxB84cFztrDfBSj4zUEJSqcRPk1BUMRx%0AASVK/8zA73IQDME3jBKTsQXYIw/bwzq2YwLwKfBOCqqiypa3p8GExTjpBPYLwIFZeCECM43Gvv2X%0AAXn/G6//lYzVA+ZZllUOvGZZ1sL/6f+NZf3Po/P+9S3+L7/7c/1PMgBTD4WSQ/3OL/CypXJyH7Qw%0AB4ED0ELpwm+S2NrVQujGNTMCfBcxPlDZDLDODxKT/J8jqN22OJYlgIJEKb4zlaWbE0E77lL0XNjo%0A5hcnHhSTpGIjzULxIWLUPCv3328X2nWyKL3vQpvBNiA1CE4MDgrq/z9HO3VRX51AgX6GNeL01Qfc%0ACOyWhq0h+LMz4i07hDCqGNq4+ixw24D0+VARAtM2ctLl+C4t/sUvgsVfncCYQ96gYf8Eu/T3YC0K%0AQMX5GAVGXMODfMk/e6M4g6MYPANouzVHwrYnRki5Xx0NQQkEp0Bwhg4+twEK06RY8qr0g8HpYIUh%0A3wSp/9SFcYG2uL7PgC8pYgQIDn7lRlb7/44wIucrZodV/t+uf1zFr30+XT4H+Sr/Yhft8v2bnQ/A%0APQlGbM4sRviCrWgnjqPSotxfGG/cAIFlsHM27+5tccgoGXp/MAidMR3XQ1/NpPyK4xUL7rbgdEew%0AUzliADQb2JiGe2IKrF0+HnpIRuvyW2FleJP9kn6NPSIhXwEsCMB8tJk/56kMv8OBBX1KRh6ogPsc%0AeNBv+noBOGMIAgUYduGGegXbccDFPmT3JPDHDLwWgWvzYi6cihqbbxl4w4Wr/Q3pFp9a1x3QJbs9%0AomMJeWKYzImo+XQocFVOdK8bAgrQJ+RgSUS38qwoJNPweLvWn1UOpkqeE9kW+NCDD13gY1j9AQTK%0ABH//u6//ZVaAMWbQsqx/oky907KsemNMh2VZDYzw3dv8a1l8FUVP//V1q/DBibaCXxyVMI3+QbX7%0AZfae/vcq0LM421NW2eSX0GPRzpNEpWkvWljVqFRJoBtTVHW9D6zyRMPZzdJC2omem+noOcn4v9vO%0AiLw27n+91T8WP5lgGAX14q4Mih9LrJHYZFAQj6NM1kJlyTr/ONeX6Vhmo7hUHDHT4r/nbAQtzEG7%0AegtyirooDFdEBTccZ+BDS5hfvz85MRWUMkhGtVOwlpRgH5zEdd4bmSNePMAizmIYGasQ8U/IZYRJ%0A7/kXpZiZ5lCw6UQBqIwR3aOf3YWAXNFFfDAKpRmwC7Bh7MiA96EQWDPA/QICOWAihBeIvRDcE0L7%0AqsbObwNvGIJT9ZQQhuB+UDoGMivAroJQu1Jst8v/4Dy4GeGTmQrwWqF0SCsz8pVzSX7lvGsYyWRj%0AjJBFix1Gz/9ZFyob/DlJAb2HA1xeAskSXZqnCrok4RDsyvrXp0hmLod4DJI1u7SgghOhtYwvqgY5%0APwCLyuHBtCq5L8swHyoJl8ExLkyx4YfGn5phwV7GVz7F4EVgaV7Q+U0WdDtwgaUkvzcOyxI6n2Mj%0AKo3vzCvbHpOBkE8Zm5mD80KiUn2vWsYt9yZ17kdFYK8gtPRCcyVck4bKBLywC16vgaUWPGHU7Pvm%0AENSm4fwYjPoCmubAQAAeD8FpwFV5dfyfTsI75ZrI81C3guTLDtw3DK/V6Vla7MFdBXjPaBNY3gVL%0AqnQtLo9IdbXnEGws89dtDfymAU5Kwb6D0FukfMQRzncgsC+Mnqyhv4O38W+9/jtWQA1QMMYMWJYV%0ARZjwLeh+fRO4w//7ef9XXgSesCzrtygOTkWY8n99GTUSig5Uzaizv7cPxn/i44ZN/ofWGyU1nbbW%0AVod/8FOMGkjF0jqNgtEsxHP8zFIA2+Z/TqrgP2thWB2EvW343FUy1uMoY50GLPJUkr9nw0OICdAO%0AdBnAGZlgkDZKZHpArAIDe1ojloUB1LDYhQDouS7U5KEipaAvCxoAACAASURBVPHTr5bCXX7WUI+e%0AtVYU4FPA51no9f3SdgZ9d3pXXNVCHp4KakOqs6DFCAccSgEZsCvAK6Ao3TYVXmnDXBuRf5yPn5Jl%0ABFMqDgzzYGZQ2ULCQMbTwURLlQAOFOeKf5VnVsxui/pcGy3oDOQKjGSELlA7DKENUOgb+XxjgdcG%0AdgCYDnYMnB5lp3YcTAbcPnCqwRmnf3sDYNeDGYLcOrB3gd0NXjUE54H7KMzs03FtAtwIeClw8rp5%0AJWgjyDJCOC6gINrmn5vjH3tR+2ozgv0E9bv9A1/5WQNWHN7Pqbk0kIdRQZgXhbe/OtgNBL24kCz6%0A440fhEAXrLyVQ3e7mmoLPixAohltQNVwSQge82D/HHzUDPfXgV0ilGTAwIokNMdU4vamoL8E+nsh%0AUAq3B+G6AFwH3JaHpkGwI4LkVhiJJL4JvJOBLgeSSZhpQXUIptoKdONsNX16orC7Cz8IwstJiJSK%0AB74gDPeEYY+sMuVHgzC54O+fFfBMJSzsgsDrEJkOz5bA5iFIl8DiECxMwZKYGtp1g/AzD1qH4Xtr%0AofIArfWzs3BnCA7wRK3b2AejY7BqUOqqwCA8nRL0VBEWbBEHnnPh2iGY2gC9TfDzyfCTokIoCgfa%0AsHwASm3+7dd/l7E2AA/7OKsNPGqMecuyrE+BpyzL+jY+3QrAGLPBsqynULVeAC4zpjhg4l9fE2x9%0A+GuoJDkDke3b0FqfjLI72z/ICa74mWlHwWQYX/3kY5Kb/d8rRRndMtQd3wv9KcJ2zeC7hGhBDSDZ%0AY9ITiD8FZchL7a/o+D1YP6AEKWvDKgNjLOjxxL8rSmvrEF6Ff/LFEb11wEcFGBcQzavagUAEnkV4%0A0g5XF2ySo6z5myizLgGqwnBqGB7NQbZYRrtQ8IA0bM+qyVLUkB5gww4/KfOK2GkeaD0Ey+rDq+hn%0ASj3Mjsh4Itfr38EJfImfHFguPNkxkCn6vIWFr6aLDt4ptPPBiHLCx1YxqHSIMDJfp8inKlkA6Qhk%0ACxCuUKmQBMJjIbsTIt+C4GTILIfyp6F7NJjRUNgueYzbCtFjIPVn8FwIzYfQLJ28tVEyruDxkFsF%0A0Quh6UHJc4MzwRmCzNtK94tTI8No18uFIJbTjlx0Ri4OUjOMyOp2+uc4E2XpRZLyWLC3yVbv1kO1%0Anp7NwMAW6CqHt+uALNhjYFYM1rUx4qE3oGTd/HklPLs/9mA9f590Ev2HvUxJ0GXyaOj1tbWv5mGP%0AAPwkCNFGWNANZ5XABsfmB3hc6EmcMNGGJX0KzOc1wLEGrrP1nKw3oiqRBa8bDp0A37TgFgu2BGFH%0AsYk4DKeP85V5KXgmBL9s01DIPhueseHKPPw6qoB7IHCUD0F4KWjYF77xMXwrCovjSjqezsE5Qej4%0AIfwtAvdk1bU/MAFbymBxGqpKtWSPi8MvclCdgFWHwZIUfNQN75RCoV9Mi+oYuLVabuNL9OxcFQHT%0ABW/ZYpFE4jC4BpZ1KEBsDkBgEtzciUqBWcAmFQseajy+xr/3sv5v4t7/py/LssyDRkHtdbRWD0Xl%0A7BpLWeoMFJgWeeoggrh6E3Nqqr4Y4csByFuADwz0GO10B6Bnog8FaAs9u5uN1kvWVdDZM6jP7USB%0AeJtRJ3KXpfK/Hf1cTxbCYempowi/3N1WrOj1fyZn5EZV7TfR5qH/jzDS4Qcd21MoQx0PfGqEgban%0AwAkqUwwXhDW9AHziiltnOTCcUEJjA7kM/tgSgfkmCURhYrXoYUNGaqrTQvDPT2Dw7ythXQRO+w84%0A6hkCdVAoqq2Ktu/FMtNTGZjLMxIYi5jhaEawig5GiLUF/rVUzjIyoLAIMRSAzmuJ3XUqsVMtehru%0AgjlPq46OopIhsT+ED4LMUrCW62ZnS0R0dCaDPQEaNkFvHaTTEBgn/JUCOGN0EPlV4MxUu99tF6m0%0Af5JvBfWZeEkVeQHh/cBwyJ9ml1OnpMojujeU2NDdB8fH4eV1/jmPRws3ysjskDKworDbFFjXByU5%0A2Rl+qUIo8CWBdkJajbD8eOjPAs1QNk0d/klrp/F+NsaUf17Elkd3hysAaxNU+dBJ0IJgEiIPQdcP%0AtSHUISqA3QAT2oFh5l5wLeEyONqDcxOwvhRuHJZJyuFhWNIB11XDM33QHAfjQTQPY8ugfxj62uCs%0AqfBkO1zQCE0BmOHCa30wvlId/Kqc71URFF3w4QKUBeA/hmBFXE5m17VDS62eje0JqClVlXhmJ3yv%0AFJqicKQnJ6xwDl7Kwo/i8JkHDwXggiVw2hj4pBHWlytTrvHgHBeaHMEe9yOfgMEOqAzD7DJVlVMd%0A2DsD/4jAJwZYCxXjYFpI0yYWVkAiD3cB/TkVQLPrYd1mfJkaGPNVp5D/Z6//Y4G1Oi+98AQLHvNH%0ANE709L1BhCMMoqRigg9+H4LW6lF+R/NDWxnfS6gbHbQ1N6vB1prfD2GOjfgwXk7Q2xQUSPdGiccT%0ArjLPWaBuNvAdF2Z3AR50lsNtJXqfKiNN+Jw8PBBUw2CMB6sL0BiCzoxcjkwEZtnKQOd50GRE6n/N%0Agt4cDBZgQkyl+9YsXBRRtTqI/s7ju1XlIJ0A8+ZB0HkUtBwC9S7EE3rYSjywfi1UP7EveJskSXVc%0ABQ7HhR0nwO9OUdfi3FOxD1nHlErYmhOPGBhpOvnZZqzBn/2TRAFlC9oNSlEQ7mFkAPsYRrrvxQmM%0AHYyoI0ABuxb46CHCfx5H9tdXwWALDDdCrAMKPTA4DuxaCIwH5zndfBMT3mKnwdsDnCZoyKguTR4A%0A1noI9UJ+OmTbIBQXhttvw7h+RbiyjI+tzILCJpkNTEO40tDukBiCmnZI5qVaKQEOhYNL4POP4NhJ%0A8Hg30BaE9tFQs0MbTBxYacOkIM8fkOXyHLS1QqxEWvZ9YtDeBmtKgL5qiPQKEg5Bfh0KzlP9azcM%0AJQ0yFXs0AN/58BhaNiwCr0H3OezbFSUt2FUOJZ9AbQvBv84n/+118HyQ4Map1HYY2u/5gJqJT3D+%0AHtt5IapT6miGH8+CcBJ+sl0P1sGTxe+c4sKyAvSEtIGvKsBCW3F8wzBcloeuUkjlpZg8OAf3VsBb%0ADjyegOYgbAzBQ2k1o4+NiBlzegaqC/BeHtpDEAjBT7aXcFUgT+foLKvyUJqE31XADxMq2/eNwvwU%0APJKBt7Pwiwr4IAYrC9DUqerymnpYOaxnY3QNPNYHpCFeDfcE4U5bj8cVH8KKw7Q8N3XBxEr4WYeW%0A49HjYfcU3LE+ztwpSTbkodCLL9MEZn1NA2upUQCrQUGwHPg0DwT19TmMCAOKjdU69Pz3oYZXDj3v%0AK4BCEoJhOCgAlxjFmcscuMn//+MQtHino7L7RiNV0gSg2pUrzmuWQOIzgaOAhqzgh4QjPtwDUWG1%0A7+fh2CBcYGCZpThzewFIwRFlqm4/MHCtBfvkYZWvhNoFPG5g4wAEgxAogdMRCN1ltBi3ejDDVoxb%0Al5Hiq/nlICz7BD6OwMVt4FwD3AWxdZC9BQoXQ8d3lCW6iF9lIbzAANWDUL0KCm/BrKdhDMytlCTz%0A410oYy12xF2gCpxtysKMB3YWxo+DLW+gbqHDCMUhC2MmQH8fpHbCl7NzehHeWAGmEyhzYNs90HwU%0AVLXCIYsku9vhL4Auyzd0PQJK3hjp4leg0iEFOCeCNQSN745IwKaiEqctrBktcX9RbaiAPQeg2Qb3%0Al1g192NKt+rmGLR79SG8aRM4eym5pRVCh0CuB2WmRULo5jAszI64BeF/Tpm/GBvRpjIOrDREk5Aa%0A8o+vxT8HB+g+FKfyXdyiQGMM2ry6gJZGqN7Jl3PBWysgfa46T9YekBlNoLsBu74DM/Vw8p0lkLoa%0Adv85rApD1QxY+hjWlAswM1d/qQ0PFKA0BvTDuWFYZMNZBSik4IJ6eNnVsfT0gpWB08bDS8PwHyXw%0AlCV++Io2OCkCe1bCpX1wdo0SgqqoTHqMB0d4MFyAxwOqREMB+I6B0wbgV5VwnCv11xRLXgGtrtZg%0A2tZ7nFiAvYfghkr4W7+yymYPdnbBnQ3wmxysTsP34vCk32T5Ra2mrf5gALw1cPlEuGIZzB6DNs9a%0AiUQOtuEw4K4B6On06ZWO+pn72XBmneLCmS58P6jj/FoGVlxoSMOkuALhnRZs6IdgGRxiwekuXAbM%0ACUK/UcfsVT9rrXE1OG5ZWNBBD1IHJgycY8GPt8OENsjHoL8RPqsQBcQOqltarOp6UbA7Az1vc4BW%0AIwpLhdHF7zXQMux7UZaBm4QxMS2OjzzdsL8W5/LGYWZYEjw3CfUlGsn8oIHNBSnLTrTgHx7Mc9So%0AK6Bjewe4wIG7czApKBzsGhveysGnz0+GzGKw71HHO+yDrEWqRFM15HaDwTpwBmH8kFLhUFJOGZ0b%0AITgW8r1QGPa7z/iSNSACC0phu6cuMGF4vgA3RaAjAeVBOc0vWc+Ik0wW4pMg6TO4w/WQbdd7kQZG%0Ag5WFSAzSr8+D/lsk1ax5Dva4Gwb6tHtsQyD4O+NgWgtsLoeJg3ra+jz9zEwU8JOodBk24NRBaaeC%0ARz9qzgU8GLVVzicu1DVC52uTYJRFtKyZ9DAs2g3eG4LCqvGw9w7YMgUqtnDKXvD8ajD9MLMcmkK6%0ANnZGQcN0+8e5DQXXsUACyspgaK1/bLOAnRCYBSfVwj+K8r8gxGKw0IK1GdgrDi8kUUCeBHcPwFUZ%0AKKuBoSTQXAFzByAcgDYXKg17xmHtxiC1B+T5dh6eCEBbhxqDVlr4YG4A7qiDJTa826l7RB1cUgpV%0AA3B6FJaktf7X2NCZh+9HYGZa5P91JbDRhncHYHQvLK+CYBb+EIALgT/lYbwLh3TDixPhzCEIbIey%0A3WBREvoaoHc5nDUGJsRhTg7+Ohru2lDB2Mmw/8AAT5fDHnF4aCUcWg5/r4JZIXglCosd+DgNdQlY%0AXA/D22DNeFWg2RS0DcHcDFwxBt5OwMPNcO1ceDUJB5XCe8NwSRCmBSSX/e56dfgnTZfmIhGG60rh%0A+xnobAEyECyHfDXa4LPgdEPQKiVzzPDXM7COM5AxMNVSp7zRFjl+I+KJNuRhe0BO4VlHKqUFHrxj%0ASX42Gng+LILzi5bMTPaxVPEtTsPYFCTjSljaQkoaHs6JtjQqqOexDAXWdF6+p8N5uCok6eoUYGZW%0AvqwFS+5IncPw4SA8bUGoGqJRfeY1LhzqQsRWd77UhgEPegdgYhwuCsADngJrLgBNQ1BVpub0jQEY%0Al5Vx/7PAd4Hrk+qp3BcRq+HztPo073fJXX58FeyqhC0DesZnl8KGLNht4EVgWgO0ZCE5BNOzsDEB%0A1EK4Cmp3QGs/lE+HwW6U+dUBBagr85MxF96w4Mcl8J0gfOc9FIBnwX7DsMI3SKicAIPbwRuNsrui%0AKH/LidBQUA08ahiabwInAjW3QM0LypCL7seZCIzJEZnmkekGtsHE3WHbJhRcy+MQ83e2or9i6yyc%0Aug24eSAbhqEytZxjm6mfBx0pYNcsGLUBXHW+A1HIbbP4Q2AMe8b62a9KF3liFYzrhqVFr7w0kuNt%0ARBnPKH/RliHQfBAmTZEenS4oTsaLl8NF5XDKVnh4Ljy8Hr3fEFqsPSgYbwzB7NyXJgXTxsGtZbAh%0ADC9/Aas9oAq+Vw5/GIL8gAjt88dBQwC6U5DoAasWLivA4g6Y3wjrmqG+TuSKyyvESrgvC39Jw/ha%0AWNAMe9iwxxSYlIfHtsE3xsLLYah14PcDcHoAfr4NfjoengzAuRaEMzB7SCKHUFBDD35qYLBEGedz%0AObENKrNSZFWG4XsGlufg5AxcGYObhuHxXdA4Ddo92KcAdxtJTI919KzVpsCuhk9yCrRVFszIQV8H%0APFIJ421Jbldk4FsxeHQnPDoa7rVhaSeUhaC0DCYWlFCtH4BLSmBzHFpysHYIRlfA9x24ZzNUlUNv%0ABPp2gFsOmSyCuYq0u0lf04z1bAPPZP2xECEprNJGzjTfMvA9S8TiQ114MwALDPzdggdd0RynRGUg%0A0WqUBY4uSGY5JufLVI2Stiej4ne2G2hx4TgHLvIx226UYO3hwkcO/NUSFWWG39w6LyD8KYeC5e6W%0AAs9TCVFc8oNgpSBWoWzZHQLKJLUnBnPC8Bvghw6szYuq9I4NL1hqxH/bwDFpLb7DLHjB0eipC86F%0Ax/8CFWUK6DMLsCmgTH3fHs2hGwjBWwFJJk9Kw8sRODotj9I5AXjfgbuG4VdxuHgH3FEP9xkYXo2C%0ApAFqpNCqqlFwbstIIRR0IJdVl5tSGTC91AMnpvXgNlRqsF+yyG1tgdkLoOmPz+GmZsM9XTiH1OA+%0A34rjGdwjxmOf/V28wEooS4jHtXdOOE4GZYJNNuG9PPJN4DWC3RbC7PIwczzJZHaCnQJvX7BX3oy3%0A2y3aSCYhoD0vmpOZjHaj4lybonVYL8psQ6j87/R/Lwr2weCtF/5uimq0KphUC1u3IlVJGg4fB292%0AqVw2LpSOg5ODcMA2uCYL6Xp1oDO9wGaomw+hWmj5HI4thyXtcM58eOIjsQMCcZi9C9YGwXwBXoU+%0AlygcUAdDMdiSgTvC8J+eZLPhISiMVTV1lgcLcwoW6Wa4aILW8ZZ1cOxEuL0bZo2C1S7sE1ScP9+B%0AR9aA2V3X5aJSaAtIAXi3J6XZbUHJwg/Lwu69It9vN3DGKIj0wWfDuvd71WtibglwdBLODkK+Be6e%0AJFXjvAz8OguPuUAAvl8Fj2bgEQM7DbwRg+c6YfEoQQ59n8HiuXBvn9bd1CCM6VajrTYBS0vhpxvg%0AP6dB4wBMfgyCBwmGbw3DNBesEnirDLDVmNpkQ/sOGBwLtwbhXgc2roHeath3gs5nVgze2IY2ymqU%0AbOz7NQ2sCw2sS4o71haVnGyZBzcX4I0AfODAU1np0j8JwgMFaA7IY2ByGK53JZ37nlEfwxmEsyvg%0AMA8sF94MwseuOLB7o6B2b1ANmdnInGW8C5NzcJsrr9LDovDdNhgXhqZK+EcAnvFgOCeNe02pYMD2%0AFMQzMnT+Ro3KusoInGXBEmCzB6f4/MeEA5ekYKcLe3TAWBvyPcryWqvh6Jjw5AUpyW7XONK3v24r%0AWx9MwYKYYs8STw9NcBLsVwIf5eE+Cy5PwU97IdQNHY3QUyE44osyZdAXunBqWg4+Gwzs3g/vGyAC%0AM2OwMwrHONq1PwrC9xxo2gmL6uExC976EE7dV3EoYsG6TXD/LLikHW4OWNzy0t8oj33Bfj/en9da%0AY8x+52F2vH0Mw6dcwEmXXsTy71cxtO+dZLuBoai6+Wmgag8wc4GXIJpkdDpA3+QkmWaIxSG/K0A+%0AGIR8GsogOg3Sg/JEODUmvX46Bvsn4MMa3zGtCRiYCBXblC2GEHQxGsEYG4E6+GkMbnWAbmnsU+1i%0AZbk9QE8ATIFRY0J0JXMjHFxTDslh7FkeN1TCPwfU8Z5bC0dH4YJBOMvneTb0arM9fQIsT8Cn20Rq%0AsJqhkIHQFKjOwq9D8EAlfLEe9qmDl/wNIJgHa5zEMEeOh9e6IFYvV6mTB2BbNZybhYe74GbgzSpY%0AWoBlfvPmwywEyuG+HritEmo+gtf2EX3uSuC6OKzvh4PK4N0MHLgdVjbAHz24PgAXxeHiJMxOQnq0%0Aqsand8H5ZXBTGLJJsMrg8hZoLlPFWHDgmw7cGBHl6oUoJDzYacMFaTglBE+7cFfG9zZIwNQaTePF%0Ahl8lYEJU/YxLjBRsTR68YEPvp3DhbHihH/o2AePlq1HVAPs5er+mPJztwpkt4FTBbXHoi8LHGd93%0AdQBmpGHXgCDCB8ohNgDr86J1BeLqoVIJ1H9NA+ujfn8lV4ArLfA8ue0MJaAxqsVeb8N5Bj7Iw8qQ%0AAuZMR6qK+pxYAJOzcG0JvJWCvWJQkYPVtlQkxxYkmXvJhgpH1MlllpQoUwNweUBZ7GEDcFcFrHPh%0Aclsu/Muba/lzzwBVs/Mc6sE7YfA64PcT4ZIuEaKHO+E7jTA+BSvK4McuPB6ElQlYWwLjXLjcgXE5%0A2OzAvgZucGCfJhieKVnuVQb274KfZCDZAHMDcGgWjuuBP5dAa0yc+WU5WaY1DSvwlzhw41Y4bypM%0A9uDUhHiCpxXgpAKUV4IbFKxxxzAcOgw31cCF3TC7RpntyQX4mwObl8Mh+8P4AfjANw8p64Gr6+Bm%0AB7KD6uh2bofDZsKaz+GN6bAoBAOfToTBbRAeDS2Xi7kw5yjoqQSnWTjhnDzsCkNvOXvv08XZgyVM%0AD15G+8d3cv2esoF1uiCXhtFRSPRVMhTrHwloA4t4bs5bXJSE/hYgCxNnRdj2SRSCA5w+1bApAp9t%0ACnPZmCwHVcAjSWhxoKMK0kmoTEHrDsA5krI9X2doPcwOwbgJMLhJHfysBZ3jZJW4pAv2HKOmaGkB%0ArvPgg7GCe27ZCeRhXhrWxMXeSho59e/04ZCLxsCj3VCRhtcbYUGTje147NYo8+cKB9ztMFADuS9Q%0APZyFcw0kkrBiKhyWg7SrMvxIS3OZgsCPyuCMFjipBuJl0JyAqaVwy2Z4oxb2NBLU3Fcp0sjOFBwV%0AVPa2oB3sUYLW/ur7DP5lK/xkHBySgPdjkpJODML1thg576UFY90dhod95dRmV1DAeRlojat6anDh%0AgSScHZfpTFMebBv+7sA52+HgBlWVmx0Yl4e3+qFsKWyJwf6L4MyY3jMG/KYfWkPwszKIZ8W7fjID%0AuzzIDML1DTL2edxTorkxBTPL4ISs+jLVBTWrHwvCH1JKqA7OwDu7INwIJ8RhfxvWp+HIMFziqALI%0ABuCxTjD/ZmDFGPP/+x/AsB1TPYzZK48hgaEXY7VjJhvMBA9zloupdzHxBGamh1lkMKMLmFG9mNqd%0AmJjBrElhLnH1u+UG87KLeTWNudHDPJjHfJTFvJvFNHdjTjCYNWnMX3MYqw9TkcH8wmAWeZiudsyv%0AcpgPk5i5BnNYGsNaTKwdMyqFqd6BcQqYaXkM3RhWYE7I6t9OAnPvAGZNP6Z3M+a9HOZNF7M8hwll%0AMaEhzD89zKcDmE1DmM3dmM39mHMN5qMkZm4Kc66L+WI7JpTDPG8w8/KYKwzm6QTm+DxmVgHDF5jV%0AScyMLOY3HmZCOyaexNCECaQxJ3gYNmL4CHN5ATM/i7k6hTnbwyxswvT/CcNLUcOjz5jZnZjaAcxn%0AR2EuzWOcPsxL3ZgGF3NxAbOkG3PuRgyrMGzARFdh2IwpdzE/8TAvpTFvuJiLXcyobZjmHsxZCcwR%0ABd2Hg3IYe8k+hpUY1vt/1mDsZsy0Xgy7MKGdmG/0YSavxMzvxESGMDRj+AzDUkwgj7E+w7ycx4S2%0AYy7KY67zMDVDmP1dzAVJzM1ZTHUew7uY6wsYejDlPZjoOsybKcyxecyebZjyYUywFXNAEvPaEMbe%0AoK/LW/T+P12KmbUG07sV804XZoGLqUxjpvdjnvQwdUbHdaHBOBsxR3Rgtg5hXvAwJ3VjFhoMg5ij%0AhzEPpzGlw5hYFjMjiYntwCwoYB5wMb93MRVrMDcbTFUH5sce5sMs5uUMZoHB7O9h9nAxz7iYt/KY%0AzR2Ytg2YN7KYF1zMLBfjpDGNXZg/GsyaBOaSAmZFFrOjF1PrYjb3aN0/42LecjG3GEz/BszHXZhL%0AezHHFzCv5jCzk5hAJ2ZPg4m3Yg7PYtYlMa0tmJXDmKkeZqzBzEph2DqyFmZ6GKsFs4+HCecx9iCm%0A2sVYaUw0hQm2YWLvYcYNYvbzMMd6GFKYijzm1CxmnsHEU5j1Cczw53pevkhg2vswTf2YZTnMbz2M%0AncM8mcP8xGD2N7r3BxoMHmaei7nRYGoNpsHDjMphyGld0Y/5tYdZlcG8nMMcZTChAYzTi3mwgKlw%0Adb+u9zCvePr/gw1mpsHUG8xfPMzJBqPQ+L8f4/7PsQLSCGgcA2Tg1CqYbcMz3bChBIIFOL0UPstB%0AhwORvPTA7f5s6ws8WFSlDO+aYZgZkXhm77Qs0ea1Q2QUXBeFpTnNRJoUhSoPXu2AZTYkamBLSJLN%0A5Sl4Ngp1WX1etgBXxuG3a+DCuWqCLSzAEx2w7xiNx/huChrDUlKtG4LXHXgkCt/yYPIwDPwDUrvB%0Ar/aHnWnhyZUR+OGg35z/Dlz/AMTD0F0COzNwbiXMGlK2enga1oXhzIJA+t1sONPAdZ0wdxT8ow9O%0ATsL0Gjh8OyRGwfVx+HwARg1CVwPMrYLPumH/SqnFxvc5HGu53FYLHw9pmNvoUvhZJ7xVo+b7CmDH%0AZnhgrJprzibYVQ8zBuDpbjhmJlxfAa0ZWLZdbvNeDWwfhJ5SiA1CslZGH6tDyh5ueBheOQ0uXgP7%0AHSRS+PROOKoO7jRwaB/8LS1O8/Y6eLka1ichOwQ/rYeTtsOna2DhYXBODE504IAv4JWpkO6FeA0k%0AhiFeItL477LwsxwU4lo7LQbmxyGYgi1d0F0LkwKQyoK7CVpLYWw5zAvBW2GYFYQzczDDH+2wNgQ7%0ASuHTFMyNweocNIdhTTtsqYY7XHhsGSTGQLoOWvJQWQ6fR0QkODcFD4Vk6h0OwC+N1slCFzYGYc8c%0ANHbDeTU67/FlMN6BF7vgsgaY7cImD/5kwatpuDID51UJmunzZL24woKSoK8eNGK0NHqQCcD0Ajy7%0AFt6bAn9wYLaB+0rkEnd5tySv4x0JU2ak4eMYLMpJcPOMA7+xIJxS1vqeA6dYOieThxsqxHQZ58gp%0A6ugMZGJiGFyXkgvaZRH4xBZ1shYNvfxtAW53BIfnLXCy0vrHXfhRTCKhHQZuS8MyAz+ISCQzLwK1%0AYTW5Iwau92XCow0cGZTvwKw0tG2DSCPcH4KdEQl1hnNwRhD2L8AnITWJ+5BtYlsLmCh4YdQr+TpC%0AAWzXv79VDZtD0gv3DYE1Fo4Lws5haAlBtwsnWFJlVARhVAQ27YCzJ0B4EE4BTjewICNd/5U1sPgz%0A2fCdOwuOCcrublQvZOtguVGH/vg8nJaFCzLweJkc+5eG4Pd5uM+D5zIqweeF4N4heLEGNmfgrV5w%0AKuHHADGB7udk1PC5PAWPxWBNWBvDgAN5F6Zl4O4OMwpDowAAIABJREFUmDkeaodUYn3YCVvL4N0K%0A+CwN8SAsfR6WHgL7RuBNA/dGYdtGmLYbdEbk9rU2I/pJUx4mB+CCgoL7P8Pw7i54Ni4dtRUQb3Bp%0AK1wRg+ficGEKbitXY+rJHlgwWcYXfy3AO90wrUwq09qUSuNb2oAcnBtS0Lm3DYL1sHepzJT/moH7%0AU8Lxzq+BjVVaqJ09wtz27YfqcSJib/9PBa/oIVCIwS9K4IAwrO+AoX6oaoRTDOyMwa9cuN0SZrop%0AJQz10riYE60h2DsL9/ZDWRieicAVnWI81GTgkzhMicCogqCy7wLHh2BzDk4Kq+m7GvGXj8uJzrY8%0ADucXYOEAOAVYmoJxJTDnPuiZCY+eIHrcvIJcnF4Ow6YhcY6vd+R0NVQJPa6CxNGubCZfj8HpSeGT%0AOwpQHoa7AuJI/76gAHtIARqDcnNbloRxUbgtKwhrQVhr5/gQ1Bbg3oiUgf059Q0+sXUuswPwmRFh%0AohnoLsCvA3BeFq4KiEWzOgS39Ug5dXQAGpNaEw22pgaEsnBsAG5/HjLT4U8zoCIAB+ZgeRCez8Cu%0AoLr5q4OSfO5v4F0LPuwXY+BvJfCfiNJ7cAEeC0BHDqYFRWWssaCpAGe48EpIDnITfYOavV01oCYl%0AIZ6D7aVQ6sGaIJwwoGbzHyrktPUjW3OujgSmGzWmnzbwC1d92dYAHNUOpQOwazQ8W6UpARE0LmZ6%0ATiybWBDuD8C3PfimLcvHURHoSgJ1X9fAuhlCo6EiApfZcOAQvJSEv0dhZhyuQUYj1+SFM17oKjjV%0AVIKXg9KchoX92YDdB00vwJjF4ve9EIDxFixKwytBzbjZZcEHFrQmoaUPyoLwZhxeLBMO2efC0w5c%0AYsOeebjAhaX98OdqGbVEUVayYReYMjg2DU1lcGyJ/F6Trswe9i7AX4GLbfhRK9SPg80bYFGZsnIr%0ADpf1ASnpyn9ZCte1wrXT4bc5uGAX1E6Qq020C54Jw50xNSO2dEOuWtTUH4Xh0RRcEIS/heC0MFy3%0AFTpGwazlcOqeeiAXpqCzSkYz32qHk0fBuaWw9wCcvhHu2geOWw/vBMCaAM87sL8Hi8NajHsiGtwH%0APkPgxmr40zBcVQoze4VHng+MqpRf6DNJuNaGM3aBWQ+dR2qMxim75KOyoQJ+E4d3KmS4/ISjIDPP%0AgeNCcP8OOCgK96Iu/co4HLEK2svglenwhdE0geVGGc2jPWDthG+NhocyMK1RFcqUKLT3wq8rxEt+%0AeRBGh9RkWoA/ZcGSi/2LFtzTL2rO5rC4/CcnNSMqWpAl7NhuKE/CQJ0mA4zfDEt2VzY+/wsgBBsm%0A+MEiIb3Czog+Y14OPgiJXpsFJhX0vvfHRzQIs4BzPBEQ5luwxtNGelcrfFEPfwqLDfSkgVkpuCEK%0AVw6qcVTtSGkbNXLsryzAxQH5TZwI/IdRBVYCfDsoksUuS6ZA38nLCPoEJE19DsHacQP3+ucRMTA6%0ADedV6DO6LG0ORxfUXH7JhsaCWDO1WVgbFcH+HQMPZKBxEB6v1Wyuc4YFne+MiBDyYVD3sTEPHwcg%0AbolV9wQSzxxkZDBTsOGftvwtrkSimjM8neuSABxm9Nk/isKHKdHFUo6w090Qu2gKcHFuxB1zdExW%0ApBvRBIW6HMwPwQu9QM3XNbB+BHvNgZogrO6CvkpRoS51YDALuSBUJqWaODsDV5aKvhGIwhWWLvbU%0AvGhRU0rh7T7NPK+Pw3ud8Oxo0Ui+l4U/eCLfz2mCsrmwMg9Ph+DJvEr6lzPQVgJtn8HCmTBzCG7a%0AAVNnQ1uneIPVu0MmB5PTsKlEO97S1WDmw80BZai9YXU/n7CgJgD7uHD6INxSDR8MwKxKeHcr5MfK%0AcOXnefhdCDa0w4/HiWaStOR8d42BiW1wUwZuHQffT8FvIvBiSLOsft8KrXVSwVxeL+ZSiw/8rxqE%0Au6OiaMU9uCsML/fLJf+DmB7qM41mBN0ah+uyGjZ3eC38YhCeDWmcNg48PQCnV6msPNyWsOL8lMZY%0APOLBoAtXu7DgBkhWnsKclufYcA2snyxXsN8PQyImQdErGahPw1+qIT9FTcjJHtydhzUpeLIMZgxB%0AxQAsnqSM+w0b7hlWYDkcDXfcEoLX0vDUALxZC/GNEB/WKK/sbHi+VoY6+xgZoX/s6lw+H4RIAK4P%0Aw9u2xCbHF+C+oEyVW21pESoMTMzAjBRgKXjtcuCIFmkQciXySH0mJg/SVFhTKJaF9XAesxk610Jt%0ADXQvUEAhrwZQyNP6mZSEv5do49pkwd2IDfK3ghpLbwfUbFqcU1MynhXN7nLgFFvZV2PehypCsD4C%0AlWklAPEQPBjQiKIw8EZBpPkfe5qJtsTni48xkmcf54pTuh25Lu2DjrPewEYHzmoHbFhbDY0Z+DwK%0Am/xgHgZMQR4ZFTnBXSkHXgrBGRl9beXhrbiubZmBLQbm58F14aU4nJyATFj+A8vjSpreCYklNxs4%0AKCd5d39A0JVjRPvb4MHHtoLxha7gvRetkdH2a4BZnu7hLAueHYabg7BfBj4tka9rABkr7bAkqV9h%0Awf0FSIa+roH1VQemuhwe1m63slwLttuDH7bD9ZYIv9cmoKMO7s8DBg7rgKaJcHifeOGxAiyKSDix%0A3oPaTumd96+FTwbhegOvVsLFGZXXu78ALadoBEQ4BX9x4IluPbQnl8B7JbAgApcauA1Y0QplMVFX%0AztkFDfVwYxvcNAXuSMGFPfBpDE5NwY0h0bGOHwVfBCDcA3fF4achOC4F7QG5DJ2dUtZ2aR4Ygkds%0AuDwHV9cKM64zcE8GFoY19ohykaqfCMpbYb0Fx3swUIDVARhlazjbHq4sCWe68Jgj7PP73boWE11l%0AlPOTMmPaowf+MFmE7Xk23J6G+WEF1CM9WGRgegc8sDPK87PSnOLAgzH4gSUe7uc5uDUA2zJw009h%0A7VngbIXjw/DBoXB/BexnhMElAjBUADcE76dlLgxAwr9XNfBZRlSgiWWwzZMpyEd5KEnDTfUKBAkD%0Ai1Fm9fOsWB3HAz9x9CBtcWGvDOwVEKm+zMi+rtKC1wOiA9V40iUcndXGsj3oU+hCMuGZ76tidxuE%0ASBAGIrAmpjEf322HaK+yVi8k/9HSvIyeGzrA8mCoAiL9MBSH1ioYU1Aw2xCCGhvWBXUMjQVVShuC%0A8EtknHOmBSfmlMU+FRJckQeOzimReCYqU6GU0dTUI3NSNxkj6bVlw7YoXBHSzw85mj81F1GaJiHZ%0AeFdCm01DBRwaUCbci+6LY+vWjPGgztb4ljMTsC4quKy6ILZFuQWrLEEPpxWEaSYCMhDaFZNl4m5Z%0Arcf1JQrmDnBGHmpzur+pEIzu05Sa/nrYXiFa5FYHogGxezIIshkIqFIrGNEuP0cQwpl5mJSGphj8%0AKiDvjjJgIQqwq/yvPzW6V6/kpPjO2xIFFSxhunVJcW/bS2FpFBbbX9fA+ixEx8Oq0XByFyxrgKXl%0AMDYPp0bgZguOSMK9MVhbEBWmIwAHuqIKdbXCOQ2wtwPvWzA3D0tCcHoa9t0Ah8xUMD6/D3rrIdMJ%0AUwpqLhSG4OlqWOzCxq1Q1wCnlMP8gsa9/NmB5n74WbkaatdnYO+IiNGXRuCODMxy4YgK+IGnBVPZ%0ApZLpolHCtCosuCaohlRNDHrSIicvTsGMEihph1c3wz0HqDH1bgJGfQrfOQRu3QWXW/DdCMzrgs+m%0Aw0suHBbSeIpmD47IaNBhRV4WAv2uMpBJnqwVy7IwJgXRbnAysGUqbC9RNlTrZ2I/icFrLsTS8McI%0AHL9VGCJG2fDEz8ErhaVToXEIxiZhW4NmVyUD8oL5VVgUoW8OwE9L4Jw+6IlpnM39ZWpU7OdBdQbu%0Aj4ritTilkjjvy5fPN9BsCS9sNxouuL+RE/zqIDxmw0keTMspy4gYlYHBEJwYgJcR398x4HaDKYF9%0AY/rsa9MwvgecnBpWyYACQEMGSrKQCPnKzz4oeRS8+WDGwHAVfDxKJfzRBT2AdVmo61JQLcT0cIby%0AEB+Q8U4uAq+OEaUoaaDBgwl+lvipDT+zRaw/1lYp/TrKmJ7JawDepZZEK39Pw0lRXdd/WBpR1Gk0%0AC60Ula0TEaTxHtoIvpFWwO4KazrpowY2DEEmA7NHSRvRkhJFMeBCQ1wUrhAShZUgn4waYHNWQX9h%0AFE4z/vwqS5OEmxxt3K2O/I4HLYlzXEsN4kQA6rOqirK2egPdjiwGzzIq3TOOAr9rweSEPMdzIdhY%0ArllUCRTo61AFthK40Iha2RnWuKW1KKM+KifLhbQF14VgkwuX2nBiHpaHFFDPNHrGFgDHZ0Sf2xj1%0APX6AsRk5ZDlGk4DLCzC59OsaWAegrAXMDBEDJuQ0IK6QV7m/CFg5CI1l2iXPzMNxg/DDOs0OP8kS%0ABri4G7pDMC2sDvbOMJy1Cz6ql1plrAt/KgAxZYXHhWFMEpbE4YwdsDkq8D8TkAPVDyrhxpwWwwOO%0AysLf5lQe/yMJF4XgEFszdmYMwdIymJqE6ig0+N3J4204JaoM5rwM3BDTnPV0AXYPwfycMpRLbM2T%0AOswSvPFHC65sk0zc+xQyc4E4FMog2uebVhttDq0VUJ2Ti0+uAPUvQP5AaJ8IA2EYlfDtUDNQNSjZ%0AXnO1zsuyYH6vgujfS+CeJNxTBtMS0GGES17jquz9yNE023O64Ldl8EpAI4QnD2n21YoymJWB5WHJ%0AYItOgR3IXWwNKhd3oGt5uwtbA8q0EgVYGhOmmUEY2CKjB6UhBVV5+Eu5JMSf5cEE1MhciHCyL4AT%0ADYwfht4A3BGVmi1udB5jPJiaA1xldW0lypZcS1lmRU4E8WACnDRkKyGQhkSd+IypsBRuHirz53j6%0AvVYHJhSg3H+oAz6skgxoLeYcZV6lBRmYvBGVN+g6HQopZGp+BsLjWwtwZEAahiTKEiehDL3G07jv%0AkNFU4M8dTbHYhT77RA9W2brm44x6CxEjf4OsgZwn+OgjA/taClTVRqXxNoSZznFhXUCVTBIF615k%0AfXkQ6povMAr671oKdDd7up9DDpQbNZqSljjg9WkoT2mIY3cYtkdkJDY+L8OjhJ8pNjmwMClGQH9Y%0AmXrSlm/IPCRDX+2vn8f8QOxZgjgqjT5/0JJtxouONoB9gfGexHdRAwt835yesOZ4HeBqTM0Ga2TS%0AcKceMyyj+5I2cJHzdQ2sj0QIHpqhulYLbegzmF8NmybCUI8UUc0FCI2Hb6c0lvZbbXD1RGiMw7Ie%0AoFQZ6gsD0siXjlaWclEKTnOV4n9cDvsMQmcUprVKbrfZgquH4P0AdObULNtnCK6ugm8WgAC8PgQX%0AIXOKQje8UitwfYyj7ubfLfj1IGRK4X5bHpK/y0k+u9aGMS6sCGnw2reBcRbcHoBPPG0kvbbI3w8U%0AfAlhQR3LeRbMTyvraLDgN2Fl2r0p6bBtB44pwEZ/EEASmFtQ1tlcKmehy7MKwqGAHoqxrhzhl4bh%0A7GGVPJvD8lDY6ij47VNQGfi2rY5zEAWlkC244aD/0d6ZB1t23PX98zvn3OXdt8ybN/uMRtJI1u5F%0A8o5tYRkKYxPbQIrFlRRLEidVIYVJoIDYVJGEVAxFEZykkpAihJQhYCqJwdjBcbwhHMCWsS3JtmRZ%0AGmkkzaLZ581b7nrO6fzx/fXtMyNjAxpr0OR21at737ln6e7T/e3vb+1aeqhfGyoh+bmOwuj3okGa%0Ao1wOrwya6HO1XGeWKhkVTrXg3W24McAba+kdlypNsLMGv+tD+PnAdRXcNIE961rwNtvaQvoDpvu+%0AsIa1Cs71VOdv7cPCREBfmtIzfhy9q81Mevhr3DsjM0klp3IZG7fVcONZWfOto61sSoOzbVnB948F%0A8sfmpW88k8HNJeydwNjFyseWpHt+8QB6E0kGgxwezDVR2YRvXYWHl+E989r1YneA3TVQwmMtifFX%0Au0g/Me0rVQMLpUB6nMGHOgLG30ML2CKKgP22UikqJ5Ukvpaf362TyPuhtmcpDErj0K6VPXGzUOTi%0AE5lAa3cpfeuG+bbZtd5FZdq99u5cDPdNQeD1CEqh8J1B9X5lJf309oGkgXMd7cq6joBrATn09zOB%0A96scMFcm6rO7C+0ScBUpp/gu4PtquGqsZ6y2ZVB9HI3vRxDzXsYXJD9+W9Cc2kCBfm11NwfRgjGH%0AcpJ8tqUEafuQ+iWgfcSem8B6FHgK/sGN8KsTePEy3PuwYrDpwZv3wwePAWP4h9fDEYOf6sN3TjQR%0A37oAX6jgNS25d6xV8B+DVvm/2YLtI+2P/uNB4PDtXeUP+LmxmMiPIEbzM8A7TastiOW2gizbV9fw%0A6nPwtm3wM8dgcQ4Ob4X/acqx+v5Mq+zLgkJO73XF+SND+KOuonYmwG/0tJPkyzOF2P7SZ+H2G5Sp%0A39MLkNXwviAVwH8y7d3+0onUHu9EK+jBACd8Nf+OSszgKx2Jd7snyhx/PtMe81/KpKtdNLFFyzSg%0AXhKUK3TPBJYm8LlF+Lu1rK67gLuC3FjOGHzzJly9AT+wU9ts31lLhGvXAoAaJanZLBSCu4EiXGJy%0A/tuAm0ewcyzd31dysfX1Aj6XpyRbqwgsVzMB1Uk8Sf8Edg3EANdaYoCtGlb6YphPbIN3Lar/rkOL%0AzGE0oc6aJtqLJ/DJlqzL+/sCm81cLjnva0vU3TFSO7ccgdESnN4GT82pjUUQsx3lYqXn2rB1LPF1%0A4V60IeYt8MXr4LoNpb8929X1oPredlQLT9WGE0sCBkOAErfhrE05Z9rBjyN23K30GwjsCeqHMlMm%0A/FvGivxba+n3TiUmO8wEyFmAI3PaP86CJKeX9NWuIui6WFZbWlTuLxS+/L2VxPm5oONlgLsz9euT%0AKPPikwiwXh/Enrs17BrK9lH7AnWiDedzSRIY3O/tefVY7c2Q18GJLnywK7XFJ1BKh4PISPZaV61s%0AGnw4E3uukUpgiMbSGxEL/zAaR/0gldyttVj2OFNq26VCAPpNJgB+PwLyJ0rYnkva4jmrYz2FLBI5%0A/Ps7tPr954E2OfuJXfBfzylvIkvK5LMwgV4HdrTgS33p0J5v8IpKjvk/VsI/C/CJ47BtrxjegUWt%0A4psjOf1+Sy5G9K8qTcy39+BfB3hXkJj+i30YjYF5+BsL8KOldIWPt5RtZ0+tvdbvBW6vxEjawCcC%0AfJfJifu/5QpBLDfhbYti428ZiW18fqxkLP+hC8/bVBrDicGxrvpl61hqjy0TKCbKoPdIobDCpYkM%0AII+YEre0Mg3ahRIOtbT1LwYfysVqvnUdnpqX5Xi1JR3fAWChkqi4XAkk+yZm9vlczPOtQcy0F+At%0AZ2DbEe3GemSbQKXj141MRoR9CHg2igRaf5ArC9l1KIz3MJoQLwwC+10VrGUSa7+CVAB3TgQW9+ea%0AKLtQutYDmbYB6QTtcb97pDZXwME5bdNzC9L7bSvhvjb8rMmH9YdR/bYEWC51XR58B96WQCsgYN26%0ADt1VCJmMqIN5scZJJkCqTSA1P4DWGDqrSpkXtsFoh7I+EaAYKmnJYA7OdgSSB45DZ0O7ddeF0q2O%0Au0oePcjhCU9nuWFyPdoeYKlW/RYc3Pu5xNm5Urre+P/z1iSZVZlY/Uah8wPpujVPwLJlont2aljZ%0AlEqoLNQXUTw/29EGgD3k1dLxd/qFTCB2HyIyPQRKn0WuWm9z6WOugidzOesvTdRvG4XG+WYmZnge%0AeR/kAV410jWbubxqPmDaxXiIVBODWqqA78gEtH8M/GmtpNtZ7VsUIde86XbtpX8foVW7RMi7RRtR%0AYMrh+qKWksGcHPh9xkyzlTH/HAXWfxTgi5tKUHHHAuws4MND+OEtMrB8cAyPHUOKwgJYhu/N4XQG%0AJyr48mENaoawuF0612Xg7iAF/MTg4ED7oH9upIQOjOGGDryuBZ+aSFS+0wSMb0a+c4eG8NY5TdxX%0ABr3glVIuTqMM7jGx0FvQuzoA9Ct42UR6poc7ilTpAfdU8LOVHOXXluB3W4r3/u423NTXHua/k8v3%0A9ZtHcLjQ7pin5x1kh3B8TtE7J0z3v2MicDxTwL9rpz3xjnldMrRtcRtFoU0yZU/ayGRsWs2UEev1%0AlVyD5ku5roD2zjpjyhy/s4QD67DnMRjPw+ndcPcW7aDw/DWoB/CpLXLtuXUowD3e1nP7Ofx8WxPv%0AJiSuHsQBtJaz+9j11B/LxcBfFATYx3NZmzvAa4LA7N5Mk/GbgrbV2DLR39C0uWsraJK1xwKufq6t%0APravKZnJYEnPW1iH1imgBf3tcH4JqOVqVht0RtpqZLOnPmlXAp1hJoDruRGrXcLcKmQbulfVhdGi%0Ap4ztegrWoLrNVfJ/7a7p3pM5GLZh0JJ/9udzuecdQYvAS5HO73tcVTBXCRRPZnCmhleNE3i2aul5%0AawSYp+ckTawVev4gl4vSot9jsdR7vuoMFCMtIsN5Rf0d6sB7Ci10e5Eb2A21Ah3uM1nXn0TMbivy%0Au70GRVJdHWQ8ehLpfleQ1NAOnhfcmfhR0/07SAy/Mwhobw16l2dMhsgPozkGmkcLSKXiTjQcnfg/%0AzoCnG0Fm/leTdrSomW7yyIS0WWQgbag3Fo4wD515SSe0nqPAmpWKQPrgOnzxOHpbZ8BWYP+yQOCW%0AHN5zGL1Nc5+/FmxUsFDIQn6HucK9jzqooxf32q7Y2APIMjoYKRP+zhyqbUo5eIKU9KiDXDVWkF/m%0A7+fwqwNFy5w9B7+wDI/n8L4S1kdwTU8W6j3I0Xp3rYH+cXdfeS8adKs1vB2JbPd2pPPZ7s/tBPgl%0AkwjzQ+uySH5sXuz4hgHsmig71qd7GhtXBU3wPopWawUxwU3gthr+KQpmeDPS9ZGJjY0zDdCVscbV%0AhxbFSv7JQCqBooRPLOv8RWS5vek8zI3kg7rREkB9fov0utf0NY7PtTR5t4/hTFuTfc9ADPn+Oehl%0AcNVIRpfPZQLaa4EbfIJ/pZAY9iTwj4HbSiXo+H1/1/t9Ybs+CByWXCe4d6B9pfIS2msSsTtrsvyX%0AHb3nrBRDjDvG5mNt5WHOSkJLSX3qFowXBY5nt6gNFrQgTTIB6bIbJTMkSWQVWAnnlqUe6BeydBe1%0AAHlpAkubYsAgsI8gVra1xdbEpC54Xyax13eh5mq0D9siikZanqjdK33VfXVJE/9MW0w1eq61azHT%0AuUp1WSgFwL1Sx8a5xtKeIWxdTSqIKoPVLXBoXsEhDyB96C40rs6S0sqeRXr17/Bjqwj0Xobvx4hY%0A7UPIF3aXt2uCgPEphGktf8bDSF1UIXXOnyH/47hBAwEWTOevIBXB2Qo1euSfhhSobhuZpsTM/cYT%0A/xyRtnuP123xlzoG2rCwqPqyqRfwnARWHgBKbdI3OoaW6zmSlvm0nxzQ25xHs6zQZwbUO6AzANsO%0Aw7N+/nHdZ24X3NWT6HAHSuV3/DTcsl1hfA8Ap8fQM+VdnZg2M3yXiWXtDPDuU1CPVJ8swO27BQyb%0AKKplD2LJrwDeEGS9fjlSvn8Qib1vMXiBA+ATiJEcmMgQcH+m5jyG9hUa5wrLrdFAe3kp8PxMC96F%0AotHGwHuAEzWMRvCqOYX1XhfklTBBjPYDPQHWCq4amMDNZxUG/NROuGeLJsWXvdteGmQQeNjg9aVc%0AUHoT6Ldk+Pt4Lr3WHQHu7Gt/+DNdie8nugKV3UMH/lzHhrnGcQsxZQsa2xsm/8cH0P/XoXq/Ei02%0A874ojDPpyuPuvLnrF/MAK4chW9f8sIneUWiDnfIxs+o3XYFyv4CToOTi+VgeG2UbRstq/6QHVe4M%0ANU9jdZTJx3W5lK44+O37hX6rzDezy3T+Sgk7R0oJmE9g0Bbo9Qsx2NqvH+YC8a+0ZK1e8+c9HyV+%0An2RSbfRcHbA8lE/t8kQuVWfa8FCuKbM3iEQsV+5yZvrs1PpcKBNx61VwvtCx4GL6WgEPtWQ7+KiL%0A1yuFDME3IUw6isb+ik/RCSIHb67ggRw+iUAxptftkvaTvJ2Ea9v8vPsRwXjCnxHB+FM+v0ApB+fQ%0Ajs6Puh2gxh8+8JNq0hbMHaYstNdSHggGJICtG585xN2HWfDPWgEkQ9/36hsGrGbWRd4XHQR3vx9C%0AeIeZ/XPgbWiuAbwzhPC//Zp3IIN6Bbw9hPCRr3LfwIfRm1pD1oo9/v95v3JI2h54yS/s+vlO/7s3%0AKLHIowMghy1dOB+3SclV4zuX5Zh9ptKLOWyw1pcbyxOVfFpZgb81L6fllQJ+HfnmPVr6y6n0zPk5%0AWJjTKjtC4tstwI9Uysu5C+1hVwS4N5f4m+n2bEOD5BBaIzro/KcQOB9GQBj3ADuPtksaAr+FRz4Z%0AvLlUrtrHXCWx5Ne/BgH5thIGhQbqR0xj6IVBngTXrcNCXyxttacJWpn+7iu0o8M+pK/dOZY+b7NQ%0AfP7BliYBKKHwrkrrXaeSbs+Q8WX3SGL1E204mIll3zLUhB7kmsjDDL7kRoKR98/zkAvNBEWLWRAo%0AzLuxLAsSdyeZ9gbsTCTO5iOBpAXIJkqGbUOghHoJxisSvyeFUthVft8MsU7cGj5ui72ttQXoIKb9%0ARKH6namlBllC9elWYogbedq0Naby3OGeEC0X1zOAAN1SzLWfq1/PtdWew6YFE+QKtb1Oi1DXvVvm%0AS7Hjs22934MtLYIjBGRngdsmemZlei9zldpSBIFnhhj4QbcNdNG+b18xhYh+2nWXTV1jx50995sW%0Avlc7STiCwHZNp03zFGSkde2cj899iIV+HxI+P48s+ud9ip9EaqwbkA43qjuPAedr/d8DljL5a08q%0ART7GreCp5NFhpvSjuRu0D5WIGo98IkV1QO2VLkgst6fcFL2x8OGZhrQWX+vHEMLQzF4XQuibWQH8%0AsZm9xqv4yyGEX26eb2a3At+PVDD7gI+Z2Y0hhPppN8+8U3roLZs3fg51hPlfjd5ShhAp1nqnVv1j%0AbT+nreij6Z7YQfe5Z6TkuQ9nfh/TRp4bLfT23M/xt0v19WRT9zvX0j1tTgCyHUWw/GCl7PyPoZX2%0AWqSU3+IDLkf6sJ5X9RCacC9HrHJiGmRr6B6g0d4xAAAgAElEQVRv9PPmUR7VXSNZzddb2pfoUcRi%0AuqbP+Vyi4pI/+88QgK8B15v2nN9fa8ubDeBjriPrtGCyDHs9/HKjENCt5nJ72Y7UGUVQhFJA7kvB%0AVLe4i8uTSA+613WjWZAK46pSr+VER4D6JdTOFxjsLTTJt7nhaa2AF7ThukzGuApt5PZiEzOMLG2+%0ATNbzylSXYQ7WhbKlzFWdWqqMrNL+TNmCDEgECLmL+l2Nn7HpvUfV3NaRqwPyBDqFj9RJpuctIwD5%0ASqZx8JIgfXtWSG848nZe6+9km+tFQaAWEEi2EahuFEnFkAUZZe/wBWPsusDKp3PhC0plztiz6e7k%0AdE1jbOD1A7HihVL6+Xat60oE5P1CksLnTCC85tNkgkDuDDIUXtOBj7eVW4CBpCI6MGqpjY+ZxvQG%0AytewXsOuPO0E3kcEMPd7x+l5M5pfpxGgbyNt7JAjEN2FwHgV9WsP2OtzaR8iH8eNJPZHBurvKpi8%0AX5b9vlQkpG/5dR3/DEwJEwEYazfbtYk//BmWrwmsACGEvn+Ndre46fFXQ/PvBN4bQpgAj5vZQYQp%0An37amdsRqNVo5i6iHj+BOsF1Jb19cFsBf7ZG2pq2z1RPMoCkc/EOYgnuaimyakstQ8b/aitN2nCs%0AmPm1TBOKbcDEDTaFchMcq6UfHAQR6VtRtqEDzjj2IjA7hG/BZLBmmnhHvAqPekcd8Y57KbK2r3tn%0AXIN0VefQIChQIpTlXIacQ66T7KKx8AXvlqMmBpUhb4Hc0oaphhymd5vu90LgpbWOHTa1+YhHJEU3%0Al1ZQRM+OoIFZGuwYCgTX2wL52tS1jwEfq+QLekOmPlgwZS46U4gl1KjecUurzyAGcWcmXWXbBaQs%0AiN1uNb2HUZYG1NiBJ5hYc1ZpI7jVlgBoM3dbRZDao2q7mqALC26Vt6D7lm0BbzBl/Oo02DBButi2%0Aj6duDp0FBUSsF7DVGeB8JsYWy9D0/JH3yw1BVvwsuEdHcF5Qqy2dWgEKg1ztqkz33eaGp6jiiHrR%0AVXeBak4wc91v5sC9tYIXZNoHajcCtUAC4XPOvPOgvn0gk07zDFLBbDC111CTNpy9F2U/o0Uy+rhq%0A5JP+bqP6cs2NRKec4Ve+KI0daEe1xt3AxFQzH8+RO3URS4WpKpyut9uzNbIL7U93zM+xAFt6CucG%0A9KBSodHzfu0E7V9HFMYjPY9UODLXNf/uWylNy9dFxa9fvu4tzCxD7P164FdCCA+Y2fcAP2pmP4g8%0ALn4ihLCK5ngTRKOx8+klKo5HXouAVpJFYA3y7VDNyxftaIF6LTLbHtMOBb9PDrt72p1xe1Du0j5S%0AFaxnArTrCiV3WUGD/gGDmw1e0dFtWkBhioU+gEBjiz9mV/AdmtHAfNzP/xM0sL8NGdMWLDkuL6Ac%0ABDtQVvelWrrGGGP9JHpOgcTs25HzdqeGWzLIC/iy6fhNwE21HKtPIbA9ZBp4O4MCE0Z+HzLVbU/Q%0AJF8JWsnvM2UdKtBCEv1N5xBI9Exbf2TIlWfkQDA2Tdw2sJJrErr3Cn3EQjJ0TheN2RGq2wHk5xvQ%0AhC9cbxwBqGWysvdzqRQi8Excf1lmss73xhJvT3f0rjfzZGACGYwy5D6Um7s0FS72B4EztYB6Ylos%0A8hJazm6rFlQdAVdhul/bwayTiSnX6NjWWvWcZAK5+UrPWJqIKU5LDWudNL8jU66QOJ8HsdIVzwVQ%0Aob7Z6s7yQ19s8ihJuN45ODBTw02Z3OcWzc8x9WXsl1EGD2YSsZ/09/QmBKz3oQW7RvNjI8Bmn8T0%0AOkylum5XY/8Erv+Les2ANnU0vwbNwXG0ymcKTjmJIitf7OPuiM+LwsdN1DEfC8qYBlrwi3bydb4d%0AeIWr2E4Wqsc6evaCj09i/SKw5t6OaLhqIdQOiBVlJMV3xJOol3kG5S/CWGvgdjPbAvwfM7sL+BXg%0A5/yUf4n2zPt7f94tvurRd5No+iuQkjBDL2gZqugWUcOxSOHRpNk6B6sTiYJT9toR+/uWXPq6m4AV%0A77RWEOLf6C91Nxqg6wHur2FrpkxHtyJm8YagCRJMg7tCUULtXHqtNYTzT5B0SefQ3ug3opV/v1dr%0AC1oDMsR0xiZjzRwa1E/5uWMcsFtpTKwyVRWz4m1/GA2cCj3rOgfPvSY28pjXa8nbuyOX7nIO1Tsy%0A5BFimgN0z29GfbMvkzgfncs3WmLkeJvv8vqM0RhdQIvILf7c7YgJ7fDvWxBTv94B83zL2aODa+71%0AX55I9B/lAobIvAJaBEE6zUHj9+jk3q30OTHY7HhU0UQJQbJKxqqsStb5vNC7ySb6v879t1wMVgNN%0AIFwhkOrWqku71t/IgbYVdE7U22ZVqu+onfxmI5tcbbQ/IJa6OJbhbJyncyEx1oDabD6mJpZwbegL%0AZd9k+Js4wAXgWC6p6qSP+Zf4uNhVi8EO/f/H/H1uoIGZ11pMxt5Xkbye9/e9gnI7lFGULrVw57mC%0AcbaiyLYJSsITAW2Sy93KSEabU7j3QYBhtNhHUb0Lp2vp7q/3OdP2MVsG1avyDjo/Zkqw9EJIxQnZ%0AlpYAv2zLP30qMZfIGv1JEpt9huUvTHpDCOfN7A+Al4YQ7o7HzezXkBEcpO7Z37jsKpIK6MLydlzx%0ARJIDcjS7e5rYoxFCgL6Of39XA2k3sqo/hOKQNUs08W8EXheEyTnwxUI+eAV6mS9F4PJHaBDuMN1n%0AD+rPW53Vkcm/tEb/X1skK+c1SGTqIlZ2ld/7tH+O0AAcIeDcg6JNzpoGQx/4KAKfHjKGbUUD7mG0%0AkEaysIQG0hmvb+2/n0CDLUcX3hqkrzyJxsg68D/QevUq032WvP2f9m6+0bt/NxLjl9EY28wUKdPz%0AGR7QZNqF9FwTJMKV3varghjvyOsf2/LKoLp/xuRsf4svcgTpefu5PlsIvGpnW9F1KQ8JkPLgQFYr%0Ay1KGgHjedYrg+mD3kd1oy6shegFkpf7qQv/j/TZFrwh+Lj0NCwHM0kTssqydPQbVp+sWeEOMeuR1%0ADpmDa6Xk5+1SWZzOt/y6Wm0wXJfsbmOYIpPKLC0c8XlR/xtZcumMfM0ZYt+vHWQaMz3vq9PevD0+%0AVgrU1w9mmpR7EQY9hQBuziThVQ5kpY/ha33MthHTfMJ/oyVxdL4lIrPfz4+E8KMthctmKLVf7s/a%0AhebqYR//e3D9ooN0nM+Mga6+Rqeho37dlJW68ar5HqeYEsmZaZGfQwtA4XXsz0lHzAAxi1f6DyPg%0A3/KMytcEVjPbDpQhhFUzm0MS778ws90hhON+2nejLF6gBDm/bWa/jPriBqRme3px4xAVyYclaruH%0Ayh4kRQ5TpfOHu/C3ESh8GbkcxXu93B/WQlEsNVqAoi5pBQHLb/n1p1HURWVu9UX9uY5ewBA4USj1%0AXAsZiTa8mjEbUMfvuROB07J36KNoTJz1+97r91sHTtaKmppHA+Z5aCzc702ZI72URQS+16OB9yAa%0A2NFKegqBWLQ+78yVVnBI0nEWXgd3zSNDjBnSeHwxcE2QYaCL2Hke3ICDDAi7cUMRWtwmJn3vaaRf%0ABuUB2GrTHbH5tCUfxZ0IfAYIUKLlvV9oO/NuKVCZq9xZv9bfQqksVHXuEUReN0yqgQX3glgYJlY6%0AV0pXHkwstOw6sPpYC7lE/8gcQcawcSFVQJW5+F2rTrlLMIPcGSECidon8SBPwlcwdboVqmPl7cyD%0A1BfdOmHAxMRSM5eORrn6ZeheAxZ0fpkl33fQOQPTRoFj06LWwpOumCdeR4vsfjSlCqb8hAp/z/5e%0Abvaxaz7ejvr4fjHS468EOJYptLVvGoMbmRb3fQgY3bA+1a8fQzr+pyzF/K8H35U805R+kV9rKC/G%0ApwqpyqoosvlEGCFW/SRizWcgOfCWJAyJHRR/byycMZDmRgQpB+NPEXtKNHHKdM0zKV+Pse4B3uN6%0A1gz4zRDCx83sN8zsdq/SIbQDBiGEB83svyMMKIEfCX+eP1cLveUavYnog1YDc0qCe4FVr5TV/0uu%0AT7kvaBdl3Gr52Z50ji8j6agfQhPgen/c/V6pz8BU3s5ypR58PorsOWoC7aO49dQ02KKlMzbGHQzY%0Ag8BlAw3O3D/PosFdIfHnnD8zb0nJf03mUVt+3jpiEGfRgL0KgW5ASSbcWYEn/PMq7+SRi9kLrqLo%0Aens7fu3NXuE1FxVbzl6jBiWuW3NBSactpJDITqWJvV5ILRBcnM+97dfVcmHaQRLvlmoB74ppQTnl%0A/TGPDEAdZ3m1A8m8i9tlpr8iJH1pDDkduzEn1qvtYGN+r/mJi99BDDUrIXQFWASJftZ2tywfU5UD%0AbwS1TWeUZMmdKor0G23V1RojuQgp4XJk1ufbic226uTvOsmkUho7AGe1WG7mDHXccrCMOm1XP2WW%0AmGr0MMCnRDckVjsf3HAWtNBOMr3bmxxozzlQLCAwfoEl08bVJnXNK0gW+0gSXojsBp1Kx9s+Pq7P%0AtTXPYTRvOi6xxJwCXQSymz4vbkIgUQVNiH6WSEjUrW76GLEMTkdXAp/74yC7RWG6r0UwdOPZBdb9%0AWKI6wPW/Gy0t+tF7soWM09Pggsh+4/2eYbl8AQKPoje6yIU6jYLkhxqtIH2miLV7XsDxJ+fjzfRn%0AC7CYJXcPSMCR4f6FCBCPxx99Yt6Yi+0uk7LvPIL696EKQg373OjVQoCIV71ArkW7UL2imtjQ6noa%0ARXQdRu0xv89ub9p1JFfdAgHrk2igXeN/1yFmcHWQjneAQMwQwEag+xRakY+R9Ljx7W4gcM+9jXuC%0AMkURZFmPEUdF0CRu18mQFEvuE2fgOsraJ3zfJxWIPUdXoi9kAtd9aMFbdLG27T7FLdfjQooiyoOr%0AC7xNhVvBW67XbNUJgAyx2nnXoVoQcFatNDaCs5jaHAQdLKdMsvYEKA2K0a3c+8/1qKUlNUUEM/z3%0ACHhF7XaSKtU/C2LZ/TzZURYnWqzyoGe3ncGOMzHaysEkGg5B7ySWWJfa2xL8e/ytW6X2jjK5ANZI%0A8ipCAkfQteNMoJih6TY0jZU9QdKAkXIsRI4zyFLylrjYGtBylUiJDLQVmksPI3JxHB3bjebj1alZ%0ADJCkV6N5Axq/8bd1aCiWSQanJrBOxQYSxS+ANuwokscBwJEAdQwLi+A6JoHsi/jG+bF+Q0tGomFt%0ALkSkaMGLq5ZHRTBSEpTjsRMjIOcQRsoNCfLNBJLvRSuJtQtBSTQmVXpuT6dwgvROSjQourkMC0cq%0ApYw74ExsHgHsSfReCv8e05fdqupyCok8qyg9XNfPj/7Ju0kDKbpWdRAIXuvXXhumBlb2BYmQOx0Q%0AKsRIHvU69YJi7KMf49BkXd2NAPUpk59r25lIu04TN3ZrFpJVeehgNszFJCtnBhlJBG4FgV9pmnSd%0AoLo9P8iolvt98zih0fkBd3x39hXnwsjZKJYs4jFAIKoQ4oivzZ3/DTpD+a3WufKpRjyKbDBGIk0s%0Axdrn1hCUnEVutpKBeK52gCrE3CeZWGDZmHJZkF9p14GFeJ8inRdPj//HcRY9IIaZWFVcyE66uA+w%0Av0rvMy4mmTPmvhu8ClfTRFXK2NUqmYN67MOatKhEFVLbmXMHsd5dXq/4W+HvMzgzj4Ebo0zHMm9D%0A5WqTDUuavU0EkMs+7qOvflS9gVQO1+N5Jrxf2qQdAKIbe535xUYCVkjMInZ0/J4xnWjn0SaL0zIm%0AgeqkcX4E62dYLh+wenzuNCIiyq6gjorWvbpxbEKqcQTdEdMZaW2dttdP76JsPcPgjtAVjGInVn7t%0AnF7cBlpFo5I+xjOPffIxVnjiai4Gdgr4U9Iidy3JSrkDRdCUJkbpeT9iNO40bnqb17HtzX/Ur62Q%0AGmCL/77NwaWyxFQje6yjrjLW38QcomFnmMNioWvnTCI6CLhGWdIh9spkIDI0YUufIIOWunjdWUqc%0A/F232B4xpWLbGtQf5zO1aSnAjjoxqqJOInTp4LZRKBdthgBv4CJ37pO+MvdbdYAovb1ZSJb1zVzO%0A85jE7cgiR37NOFObBr5IQDJ+5VXSnU6cqRNQ5idTMMNmIePTwJQa8hTJ8LiBB3Ag9lb4AlPmquPA%0AF/nCmdSoAWrRGDfMfZuQTK6BFkQeTiHpZsXvERemuNiMswSAtY/5cZYCDYwkFdTevpYbEOdKX5gd%0ArIsqeSTEz0EhtUphyUWt1bhvjZhr5u9h1QSqj/vciSqupjflmi+Y0UZxo8+JTTSvov2iIulBPeEc%0ANS6+x4ip6D3QBMOcpCKIv5tcwKZRV012GgG68v9j8MEzLJcPWGvUo11SJtocLW+RwjeL61KnMz9m%0ArfWoGly02pXJraSFdKwVOr8fHYNb/lxnqyGIaWYIzI6h1fNGf8zRTID16BzUQSvtbUi3GvNAHkeT%0ArO/V6SLH6BX/fnWQ2HPcxET3edVXSNZ/EAjHMbPgXdHCxT3EiHpVYpWjTBO+mytTUI3CQXulBv7E%0AdZa9UuAwdrbVIgFUFI3BGQjSC8ZJB4ml9n2CR2APiNFci0TOBy3Fee9B6o1pFFGDFdfmukV/RqdO%0AFm8QIGYkNcB8lSKimqoJfMLH+k0cQGOkVmVigvG+kBhvt1KfDBttaldJPwliYBFwgyUjXQ+ds4kW%0Av8Ilh+gihvdPDCeFFJZbmZ4ZKl1HllzH5sdidgHYWrirnzlDzrQIRG+EWO+e9816S9JJXMQwvZO5%0A4IzUj0fmOcqTaqAIWpyit0EgLXz9PLm9FUFuTvEdxL6sTdnXHvc+ieRkDc2NGPp6rqG73Migbfot%0AQzrYvf4e4mkxCmsRAet5QxMmiv7ugkkMw43vOWvcJJAMCs1zI9JfDKqRLj/DcvmANYZebJCYafRV%0AihQudlCz4fhv0XIYwTXIJeZkJnejqK8cR6th7PQK5noabGM0GLchP8wDft0uv+VONCCOIYZZmF5w%0ANFxFjYX574tepWPonnFBXDCJx9EN6YxX5RwC0wMo8ikLYkUjPB4iJBG6VXsEjwngo7jTrhUl1naW%0AZz5pFnxgWVAG/pFPxLiYZyQdX9zyIlqkV/PETGoHy22hIU76s6MIPbbkfRGFDlDKuXOk/AchSwAU%0AXYQMLQ4TcwHEJ3TXxdOYa6NrUl1EHaN53wwz/x8ZPCPjiiGwTdG76bwf+7RXJqNk1QDICKrD3Jk9%0Amp8xqfYQvc+F0NCBhgRMeQNE8oueO1+6eO3HOpV0r0aKOlsoJWH0c3mRTBej4OI+elZUFwydZT7q%0A76vr9TtlEtW3IzVRZPo1atcwSwt17Lcz6L7mwN/C89kEpa6EtIBtmFjpuvePr3Gc9//76H4bdeNH%0AZ5hnfYxgMixtoKl/PWKu60wJJ2soN6s6sfHpIa3TyKqIF4E0QCMQR9ba9ESCpIOKAUiXoFw+YI1Z%0AZ0AosolGa2STMVMJJCSI4jukEB/8mAPtJNOk7UNS9ER5ogMHCg2yq5wFnkMgeDXqjH3+mBi3G5Nm%0A3eW3GyIQjfpUd7Vju/++RNIJ7fTrC5J4dbbR7NiM7SH5NnZC0p81S2RsNRrwRa3PaKUHF2XdBSka%0AdzAdm0MiYm5Jz4hP3CLIEnvGF58tQVFmrUZ9F0yp+yITnvqWZuriZkKVDZLjN35s01Kqh2FkT2gC%0Ad/GIIb/P7sbgrqNBZ+Kv08Eu9k+ZCbjGDhJ5kIqimYV/5MCUhcS2o36xQKqT2uSGFedu6exz7Nfm%0AJH/dVuw/0mLjXU23TGt4XACqzD0U0Lnx91ZIYL80cQ8Cnm4wrBtjYbrYWCMXAfJmOef9Hl2rzvk7%0AaOO+x8428yazRW0654vQUyRAW/drt/k9xyaQz0geGvNB4/AkaTpGXekIzYccqWpqvC1xPhtMXBQf%0AWVLBHSOBckDRlxdY6yNoNgjG07wCssa5zQnXVDE2gT5Oovqi8/+K5fLqWKOeNbpG1CQgbIbJNb0G%0AIkuNPq/NNGAe5dFvX3RdQxQcIsaxg+R03EeGqwwNhHVS2q6DaIDegQZdFAFrBCAZ8hXc5udv8+bM%0Ak4D1FClWeh8Sf2LCrlMoHLHONOnmXHcV9YOx5FkyNsU9jaJ4FmrtDLrgC8tGofMWy8RGywxO5/J2%0AmHeQJ5foeZ4LF/DHTF28xf/fSfJtnXN1RDTMGHruTZZydkbSYN7eFlowIrNbCIlB5g7Sj/l5Mf55%0AbGki9hDIRTE7qjGiLnZcJGs4fm6vTCGekYlVWQo8KBx8B66SiEMkgjCona1ablStWsOtbUmjlPvz%0Ao3sVCCyzIENXDKeNIB/rWDSOjzIPhok6ZH92DAQY5QrYOI0Ml6Ula/9i7VuwhIQ1C6gvh+jdbqCF%0ArSa5bl2snokSxDlS/tTIQPHxERNNb/NrzZJhKor6sZxGwDLvn4vxvSCpbxOm9pIFV99sxTc6RFP5%0ANG5fiuSoiQERHJtAayR9aUbyb724NF04mwYwuGSgCpcTWKNLVZzRkaZHPWrsSHe0vsCNgsY1ccXa%0A4AJ96/QTkgrBM+4/jsT9NgLF6HQ7RmzzSVJ0SYylf8hvkaEBcw0CxTUk6szhOzwiIKr9vjuAwd1w%0A/C79FvNxB5K/4EkkCuUNBhpIzK4iuTHVrgeMEzKyx1YQ+4t7pnerNEljzP1hb1vcSbUkRaQseLv2%0A4+5ofk70mR674SMasJoW/iK4K1UGh+6GHXdp8sRoxoWQrP+lKaqpVaX5UATptMdIj7Y1TF/XdA4V%0AJECYhn2awlbN9ZeR/UUgjSw1b/xWZsnpfmIJCKPOGC7UR0bWO7ZU18KB2YDP/xG88s40DCemuva8%0A/9t10tsO8+Sb69XX+p9daDTKwoUqjE5Q1F5pAtcCMcV+pvd51pIL3onGO1tDoBbzXTzpC0jHlPui%0AbymhzALJoH7K390qSUM3B3zxbpjcpXGy1dtwxD+jb3RkudHTpUbS4GmcZMTGt5PP9wFSaPSZRhuq%0ACKoxwVI0WBkptWEN032so4gfSVlTfRh/u7hEESUC8yXIEwCXE1gjS23oPqf0PG/8XpK8BKzxF/Un%0AzZWpeS5cqKB2Y9dqphjq+9HkP+Sn9xEjPUTSFcZkJ8MglhdF/wyxghg40IyWWiP5oPbRYLnnbnjJ%0AXUlfeN7PX0WDdAmtCVtCalbfNBjPxSabdodd8InQN4FZhSbWYa/XTZl0YbRS8p61TLo29zyb5gzA%0A69BBIN9FAztGqkX34RrFjm+xCxf5jGQYqf1ed98Nb7pLz+l5m2L6v+hLWtRJr2e1jndw16eQHETi%0Ac6L+F3R9HPtxsTEuNBxFEXyhTOy2KTaPsyQV9Er1bQTYeH58fgS7pm9qBFwDPvtJeMlrG04qvuDF%0AMsgTcMb2NCXbuCgCUz/iuHdUmclYFB3qj6J3sBJkSDPSohPHa8SUyFiv8Xd5lT87iuiRHR8lZUtr%0AEr1ASkDX82ccvRvuuEvjPap1oqqsSR5B7z8GCjQTS01MetkuMlbd0HjuGTSGzsQOiheFix4SwbK+%0A6HjzWDRSNXWuEUPivSNhg+QlcInK5QPWmB8xMte4Z0N07o55BBqDdDrLm0atJvNt+KZeEAM4naFa%0ABc+TUqhF8XzD/++j39fQvfu+Uga7UF1bInDN0Iq7xIXiUCCpBzZIxo/zyK0q5sO8GQH0YWDJ9L1L%0AEsWiSvk8nsDCkhqhqWhfUzVlLDJthzwwDfyAQDrqrY6Rdg4AbQUcuyqKf496t+5udPdDdmEiiKWa%0AadglKIHLvgAvahg4iosG8HzpIGNJNwnusZB5ukyvzFrmgIesyCCGFGJ7TSn9bm6wSPDQ21o6y677%0AlMYcs1P3LS5UHwQS+DbF8Zh6L5a68VvcQTUanGKJ3hMXF+PC3QlAw7kXPQT8nH6R1ARrXqeYcGNM%0A8k5oaL+mJUY0bUcG2T2oT3veNwuN9kVwP9S4fp200MYSVUIxJLTR1GmJW8vkpMxnoPEbk7gs+7M2%0As+RqFTcGga+Ba3HeNwH3YhG+SdDi58V62Wj1j0DbBNlLCKpwOYF1iWTdiCtJfAOu2J42PE7M6Mfa%0A7NR4vKkaiEv5xeoA7+i1trIgnTcpzx8xqGq5oYAr1JtiREurbNZWJqwDSNyPu8dEPe023H+TNMii%0APe4plFAhst6BV+duNIhjir2z/tgY5x9fUB+B706/bwTXCRrI0VhUIvDrXzT6z3gdYj5NSAEJT3hd%0AYuai6EbWFA9jO5tlbHLniRb4ZokAVEU/ziiuO5uEZAAJDTE7uheNnU31TZM9+nJHnd06yVA4MK+b%0AG7JK03OLkMCxJvmORhVB6eJBDFCI7DcarCJbjXrJmHQlzsmoZx7k6fhmlqLQSmQE3B0uUPODXx/9%0AP8ssuWMNc/0f0Lg8T0oVuoXkmbjLj20lDfEuadgvkdyXxni/465aLg2t+V/cNiXmC+iSJKkFpIrI%0AgXuCVE25v5fIeWJ4LGgOXJwnOmLXHCIxx0mADxrXMUtcHxLwRWod53YTKGOEZtH4PZZ4LlwIyvG8%0AixO9xBcKTzeC/RXL5QtpnZVZmZVZ+WtcnnObCc7KrMzKrFzJ5WIJZVZmZVZmZVaeYZkB66zMyqzM%0AyiUuzzqwmtkbzOwhM3vEzH762X7+pS5m9utmdsLMvtg4tmJmHzWzh83sI2a23PjtHd72h8zs9Zen%0A1n+1Ymb7zewPzewBM/uSmb3dj1+p7e2a2T1mdp+ZPWhmP+/Hr8j2AphZbmb3mtkH/f8rua2Pm9kX%0AvL2f8WOXpr0hhGftD9noDpKSQd0H3PJs1uEb0KY7UWDWFxvHfhH4Kf/+08Av+Pdbvc0t74ODQHa5%0A2/CXaOtu4Hb/voA8wG65Utvrbej5Z4F2tXnNFd7eH0cbbXzA/7+S23oIWLno2CVp77PNWF8OHAwh%0APB60RfbvoC2zn7MlhPB/Sf72sbwFeI9/fw/wXf59uj14COFx9HJe/mzU81KUEMLxEMJ9/n0DucPu%0A4wptL0D46tu/X5HtNbOr0K7sv0ZyQLoi29ooF1v+L0l7n21g3Ycn0/dyhD9ve+zndtkVQjjh30+Q%0A3A73kqIA4TncfjO7FjH1e7iC22tmmazz9p0AAAIDSURBVJndh9r1hyGEB7hy2/tu4Ce5MCznSm0r%0AyGP1Y2b2WTP7+37skrT32Q4Q+P/OtyuEEL6O3+5zrk/MbAF4H/BjIYR1s7ToX2ntDU/f/v11F/1+%0ARbTXzN4EnAwh3Otb3D+tXCltbZRXhxCeMrMdwEfN7KHmj8+kvc82Y714e+z9XLgKXCnlhJntBjCz%0APSjPCvxltgf/a1rMrIVA9TdDCO/3w1dse2MJIZwH/gDlUb8S2/sq4C1mdgh4L/AtZvabXJltBSCE%0A8JR/ngJ+D4n2l6S9zzawfha4wcyuNbM28P1oy+wrrXwA+CH//kPA+xvH32pmbTM7wNfaHvyvYTFR%0A0/8CPBhC+DeNn67U9m6PVuHG9u/3cgW2N4TwzhDC/hDCAeCtwCdCCD/AFdhWADPrmdmif58HXo+i%0Azi9Ney+DJe6NyJp8EHjH5bYMXoL2vBflNRkj/fHfQSH3H0O5Xj4CLDfOf6e3/SHg2y93/f+SbX0N%0A0r/dhwDmXuANV3B7XwB83tv7BeAn/fgV2d5GG15L8gq4ItuKUnPc539filh0qdo7C2mdlVmZlVm5%0AxGUWeTUrszIrs3KJywxYZ2VWZmVWLnGZAeuszMqszMolLjNgnZVZmZVZucRlBqyzMiuzMiuXuMyA%0AdVZmZVZm5RKXGbDOyqzMyqxc4jID1lmZlVmZlUtc/h8Kz3lJYE3qlwAAAABJRU5ErkJggg==%0A" 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a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%0A" />
-<p>显示色度条：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">imgplot</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">lum_img</span><span class="p">)</span>
-<span class="n">imgplot</span><span class="o">.</span><span class="n">set_cmap</span><span class="p">(</span><span class="s1">&#39;spectral&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">()</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-<img 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I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%0ARK5CYII=%0A" 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I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%0ARK5CYII=%0A" />
-</section>
-<section id="id5">
-<h1>限制显示范围<a class="headerlink" href="#id5" title="Permalink to this heading">¶</a></h1>
-<p>先查看直方图：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">lum_img</span><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span> <span class="mi">256</span><span class="p">,</span> <span class="nb">range</span><span class="o">=</span><span class="p">(</span><span class="mf">0.0</span><span class="p">,</span><span class="mf">1.0</span><span class="p">),</span> <span class="n">fc</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">,</span> <span class="n">ec</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-<img alt="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAYYAAAEACAYAAAC3adEgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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%0A" src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAYYAAAEACAYAAAC3adEgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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%0A" />
-<p>将显示范围设为 0.0-0.7：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">imgplot</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">lum_img</span><span class="p">)</span>
-<span class="n">imgplot</span><span class="o">.</span><span class="n">set_clim</span><span class="p">(</span><span class="mf">0.0</span><span class="p">,</span><span class="mf">0.7</span><span class="p">)</span>
-</pre></div>
-</div>
-<img 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u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%0AbtWA5gcAAAAASUVORK5CYII=%0A" 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u%0ASXfOgoy5pEL9HlMvBgdPYu4k31egjNtKIe1FSeFHa2+Eex4bLkxCNG0ZF26X32X+4rxTUTIIZCUn%0AlePr1TGP636/hUFUCp47LnS2S6rH3++nMDfv2U1Vdd1GhIOZ86rdXfczClnzuANwHCPzvSEreoIO%0A9MyrGWSyl+n7VC4R4nAqJN/LwB1517AK58fXvYYY3IuBNFrFUr0tmePqsbVRwvlyP92+SVk8K6S1%0AFaxS86AMngMwqso4Z5TlaT1ZABg2IO7qhWtrl8T3xIHkPamJG3ZCGS9G7ubhbqC2vdwWlhY4VAJk%0AWDOyVC8Gan0uZDKRx8RjJHz2u5lW436y7knZEW1uLgWI58RMHceZffXnJVwj9k0B6Lo5dz08w3bQ%0ASnGWCZWL55W4quvJ4cBcoG6379NyzfVXagoVuuAW7FT6xIGF666fcFMkC1vGEty/afRnUeM8ZQVN%0AqIJ9Zn1cIyZjpHyPFRIDURZorj+uE7bdGQsdfPd426ihl8aUSreJayYqBSrVnUBrJ1jdcZMtUrvO%0AQ5UDbyt2Ec+RqQrUxQMZ6FKY6fmMBzEyiCePViEZ3draAsPWD3EoTzyj9JE5/S5aeAxWRQHl+iLR%0AnbIVT6yJwoDMH8ciZhGY3Cb3i8FEpu+Q6MpyZ5nr9+J2PXHDBdslNZULhYUtrnioDuuwklVV1ttq%0AIzwgNfFo9iunmEzcoRVd+DgOrpduqPviuunykwiT0AKnSxytbvfJfKbqnrFLt6GvckeSjRvneNIK%0AppVNDJbzGolrggfxRPzZ7TffWmC67Z5jZi6Yr7kby23g+rRHQQ+EBkMPzzA4vEpaW4s1BhyY7G8G%0A4meptj4t0BjMcXlbPbQ+KCw4qbSQcgvD9fm6A1q+Hw812aDa8nLf7NLmsCnjw2YC18VEcddDsttt%0ALM9Wqt9nMJ8YGl1xBww8tg4Q5MaAwQC66V5YERZgHbZKbKlRqBDDZoCK9bi99GYoqIehvFQHQH0/%0AliVGGi3vNjeQvEZX0tYdU6RygjWnFOl+S+UcWdCSX6xoY9DG7WIgdqQ8nzFP1RYe6ZbqP+9ZkHmN%0AMLVKaio8Gi48fYsBKlqTFoqG1uxFMV3KEIbxbQeu3G/2xwqXUBG9GVrG/nN/YvwjppTtAK2txerO%0A2DLkArPLRtytr9p1WcQ1CxXuwKFApitoy85EVya6kWQkYmmxXlt87ovUdFcihpQjWxG5CLbvE9sz%0AM0Ucjpid+xujz8QzO/jsBSrVVoD7Z01OqyoKfxMFV7TSPZa2hF2uLSBlC8WChRaen3M9fCfxbEI+%0AM2oKXguiiDObFlHGz2xDGVpGpGgNm+wNUYFSYZlHeOAOo+QM4tDCNrle4vLMWCBERJeYCiZmrxSq%0Ao+ZcD1Kz3xGHpmVtz8T9pyB28I4GAfNN6V1xxxRPljNf0K2ncmXWgb0H4vEWvm1e2K2ktROsxG5m%0AVB4t544vqV60FrL8TCtyStJm3LerZ/IEW9DZKo5MzzxJalZqP4L4zJuzoOBuEON5UhNWoNtIF41M%0ARq08UhnI8HPcc03X1y6Rmb9tZt0HBtT8nMfe1iut7qHq9BS332lk7styLhQtOTO+vYkNah6EQ6VJ%0AVzla8fYOKAxch4WiMT/3KdYbyfPeljLldvuzBR5dV2LZJreJRMw2Kk7j88TxPT8s2yYInJ5kfvC7%0AOYeME6RwjQKcwTjiv7kxjPybKxPPBvCcsF05y9s8b8E7pzpbKO5k4xGSTJXrhzoIlxC2WAWtnWD1%0AYMSTqPhH7MyfTZ7sRTUjplIzR8+Ta7eUQS0S3XEyE5nNApXWCrEnWl22jEzUksQUab3RTeb/+B6p%0AVhYWlDmMMhfUiLhj5AD3Izc+o/CdQoxuYI7M9IQ7LDRsGccdVzmrKEa4WYZpWcQDbdlwDmPgzv3j%0APJgP26xP4bohIr+Pgj4nWHyfOwjbYJci/A1D2SgM3IcllKMX4XEyL0fLN2fRui8JZfisPxMmcZ1W%0AulQ2NIw4HlSe5lNiyFLTGOFYGxO2d8Xt4kzBYt3myTntVFpbKIARQ58JYEFjrUhIwMKYAD43C4zw%0ARzwtuiVmNi9ihbLR+qHbbGEQy0frgcxKt01qLtboFlEg0AWlMPGCMaPEyD5d4yhgC9WpKCOVVijT%0AWXJCx88Z5mBAymPSJkBMubNO6Xn4HRYonlsKdEIvUnP+o9DPCQQGgPz+aGX3w/+cNRnr93jbm7JS%0AjXzkxdzD8xR43VCf/5OXXQ/vmY8tYIeox0Q+t6UZx4FuN+8bd/Sa9X1CSYWafeG7rPzJk+wnLdNc%0AkNfeUS71jt6D8BwNIM+f6+J73D7LGe4IWwWtnWAlFmlLzpNpLJV4qyfOFoHUDNhItZvnzzn8SLjG%0ACfREkaHsXniREP8kfkdmcZ3M4XRbI8PnoABaVG1WkrWwKQoRW060aITPFGi0rCnE+LzngViX22FF%0AyLZEF46BuYhnuk1UgOyPecOLg5Z/dDO5732AsrSq7ArSXWd7ielzQbLfbB8zDigAKVhNjIzneDKH%0AbZof6JrznbQqifEnlVZYX3n+dJ+97ijIzSPstwUP08kI51DZsd8jlKNiY2aG4TtDEBSIjE9YaUV8%0AngG/KOAJmdFAKXCdwSqO4SpobbMCIoN6kXoS7R5KtYak0KPwlcoBnVcN8NNSJXFHF0eA6SFmWFpE%0AdDnIABbSnlBreLouxngi0YU1RSE8wrW4WBlcMhGUj8Ge+Pw8yjKZfBLm5bmgEI1QBbFCuu8FvnMB%0AWNiY+YlH892cO1I8iIX/pVrY2l1l/rT7RGK74iqJ80X334Iito/1u98xR5jjygg414GfmVYzQ8X3%0AOM5eY54X32MQzM/Rs8rhzxEWoGdFis9yR1fuQCIqPfeV7WKwjWshrnFCIUPVuyOdV26eIrQQLW7L%0Al0nnGq+Q1k6w2mqxlWl3OFoj1rjGWO3+0bqxdWotRguL7iNNfy9iW0PWjsSj/Cz34ncyZewO0z2n%0AwKIF4mtSLcAijBCxZKmOfkcsMybGu35HlGOqVxRK3FJr8hxEyzFnQccy/l2mBdWngRlDtXWQC0JJ%0Adb5idC0ZKKLVNAjP+1oUAmyf+aIt+uuDtE18l9tiRcY8SbcD7x1ViroopE5XSt1QhhazF72j2hYI%0AjKK7D8QHDQPQ26Ll5rq9LberptCWmoqP/YjuuO+Z2O+u6h1jHrfoUpsfKRRdj9cBBazCNcYVhuEe%0Ac1sZVJ3Bs0nN7AanRno+mUWzSlrbrAAOXl/NA1W82IcoS4HpgfNkJZR3Oo2Zj27KUPViZ+TbbZlT%0ArSUdcfQCMNkiNRG/VWgbGTam2uTcQakJYxjbyllAHfznYuRuGjOZ63Iaj4kuH62VfrhHokUesSuP%0AL70ICn8rudxiYaAy9pV4MstwUbod9GxYhxeen4lwij2RgZo5kdytxfKDUmimao58UNxgSer2yu/d%0AWWkwL42GUqfK7kiRl8w/TAnyPWlcifr9hhyYV00PhmNGHJ/5p1LTrb41tKRSOJmcCkVDgn2N/ETM%0A2N/djxSe8by5L7REqaiketNGzkL1JpoYm6ASyincW0mrEqwppUtUJjsNJfWLojgqpbSnpFMlHSjp%0AEkmPLYriprGHbakyzYeZALZI7fq5DF173zOoHa0jU1d1kIbvl5rWo1QvbluTjCR7su1eewHE3UW0%0AmCzcLOTJ0Az60J2hq+r/OeyRC9CMwX7TqrGiaRNc1tYUiMSC3V6/O7rk0QL1WLnvpmhxUxBGS9OL%0AI8I/DFCwn7EdJAbPiB2ThqotGuJsXLxqtjNJGmyVephXfx4uSd1hKWST2xDxZy5iCn33a0Ya8zhM%0AXieErtyoYfgcLc2kpmKMZK9nEvGgFb4vl2nieyZnx0R4LRKvWR54HZGXp1F2SbXlPMB9t8O8nYPW%0AdhKt1mItJB1TFMUNuPYySV8riuL1KaWXVt9fNvakhWoUprT2otbzNZaVaoHRVf278ma03I8Q0pUX%0ArtlaNn7KnFi6/V50tsb64X4UMrTq4oJlO+LGBUIUo3CPlqDHxNrWmtoWi++ZuWgt0rKJFDV3m4VN%0AooCzh8Aj2WJqWcRB2T+6yjHqL9Vz1eb6k7oo5/oZcHKZbXin1BTsFQ2XSre+UymjTlcaoky36sfU%0AnNSfL+/72mhYfs+S64gKIApjE3cpca2QFz3HvmZ+Y6DP75xEjOzH77mzV0nRQrfwNd+yHYRafNId%0AD7JmsNVrhMEuoZ85heO28/S6Trh/a38LLUM7AU0YG85HS/pg9fmDkn43+5QZxy1glN3WKdOr7N4y%0AimlMj1CCTwsnTkhBQpeI7yNmS5dKamYEFGoyrtsehV+0EqTxRRwtIV+nsGA7pNrCs7am+2Sr2T9N%0AIzzv/jnv19afFRHPFo25vgXK3xqX0WO0TfUWZr7bLjYhHLed990Hzp3n1e8gF+ZgJPchqRnMaGu3%0AedNWDQRPpyel6tn+Yi04/VdUz/cXynLdXilQFxekwvPVKctt/6VlBqUI3bg9HhMaGRxLegXEpqNX%0AY4VC5eF5cT02eoRnLYyIRXojSVf1D/95XKer7+ZltmtJNd95bONaoYD1GFghUJlwPunFRQiPp9XR%0AmzIxgLcTaLWCtZD09ZTS2SmlP6mu7VsUxTXV52sk7Zt9kpipNC7w+NMYdGkj/rRR9eD6d2uYPsRn%0A/B7fI3kkONg8MV74TO3vvvBZL3rm3UW8tc0FsxtqQZgjwwbeMut3EDOWmu66lRKDggpt8lgTv/P7%0A2I+Vkhk94qIRa6MAJD6ac0VzEfrYJi/2mPbjOqmkI1kwMMgVlGSSlCr+IQQQ2zA1I/XmtN2qnd5Q%0ACtLBklQMy/9jOCPnj5AAt3i67RwLCmSm01Ex8x2Fapx8OvzZOKG3JjXHOf72lKmnUqDSi3S9HXye%0AVY3/t7nk9hYiBBiFfg7rz7XZ5PHx2NiwiEbMKmi1UMD9iqK4KqW0t6SvpZR+xJtFURQppbwOmFWt%0AgR1FzwlAJi7TvTHFRSmNBzJ4ApFwj5p0JSrGmptWayS+U/jP6C7dddIglHN9XkS2FOwO8feSuP2y%0AqzyDUDjGgAbJAptEzI3vodXDnNzocpPhqZRMnAPu6rFg47ZUkxcxhTHfw3dQuOeCNRzv6FqyjmBF%0AJVo/1fwlWooY3zSSpqZLqzV1S8FbkA9N3KVmKIX1EW81HMC1Y8Xidnkscql6pijc6ObTI1jJdk/z%0AKHF7XpOac2NrmnPrZ+nqmzg/9ER8jxCXy3VU4+xMsYpQhnlulbQqwVoUxVXV/1+klD4t6ShJ16SU%0Abl8UxdUppf0kXZt79sRvavsCPGZ/6Zg91YwomqIgdWAnCoToLlMIe9EzCMP3rDQqSizGbeXznEi+%0A05Ptz3Nqx3GsIMjQOcuKWJOtdLpp7qeqe5Xl1GDgyEBe5LY27GbStWTZnJUQLXErBlrgtNBYjruS%0AhuF/9D44xlGRdlBeKM/3x/n2OwjPxP5wQZralNgESh1p1C//pzgOUh21j1i438/rVOKG0KgYc30l%0ADst+8D5Tj3KpU9mO4T8lS3Td/Vn47Hv8s1Wt8IzXiNPGqEhi8JXplA5sJzV/BSRJp19S/jXgyVXQ%0ADv+YYEppg6RuURS3pJQ2SvqqpNdIOk7S9UVRvC6l9DJJuxdF8bLwbFG8QvUALah50Aqtj3nVAR8K%0AQ2YLDFGXF7Dhg9wCyll5k9IsYprLUOMqKdZL4pFytkaiYGU7qGAsbDjZvtaWDmXqolwuWmti3l+8%0AxgAboY1Ik6x+BiVNOcFqzI1WsOeFu6lMzlFcCM/Efg5ULiRbq7lyOaUQqVLIo1EVgGJgMPJODjck%0AFWoqhRy5f4uZzzzJ3+Rxtodga2+Iz1Kddsd6qCDMb/x5luWS5nPCl/j0cmMrjc8/6zWm636w7xSs%0AMY3M92wxszzPgTVVZdKrtWY/JrivpE+nUuX2JH20KIqvppTOlnRaSunpqtKtsk/TXPckLoTrXsze%0AxmZt7VQNax8C3Bz4Ab7HPeW0MFeqW6xZzXhe8MTj6OqYIYwR+aCH3LGFQnm62s6ldX0WULZQ3XZa%0Ao8btaN25XlrPFS5XTEnFotSZkbRJ5UljFQ1vkbob1Ny3T+szLoSYwhODAoQC/Gx0SWkl8QCO6BIa%0AMuA4xPQc84c/m3IBxwDXjKp7Hc9p1a+USnd+1C/vxdU3GkodK7I2eIeQEnmGJzJZ+M9iXMxv5kHO%0AqYWP0FdiuKPwjMcrejHE2HOZE7QwhfIcR7+XkFAKz/AzXfKcx8oUx41q7kQj3EaIjFas16GFakf1%0AzjXLH/OtTcH1AAAgAElEQVT1WkIBRVH8TNLdM9dvUGm1TiZ3jAMeI4AjNZk/59J4MDx4xEm8+yi6%0AUR50WjkWkpPIaVjGgykU4rOeQCfue9JZF3HW6CZHVzu6OoQlTByrGCWlG2UBXrVr2/XSBXf+TXU2%0AXK39z7tce+8mdaoF1+lLxUAazUvdTaq9gUh2+dkmK69orRAj5DUqiaRSqfB546xO3+K45CxpjhEF%0Aaw7jlZqLKkGgxuZX9XRb+KXjsSc0xP9uQ4QRzKcM/AhlIk7KMoSm/J3KjBCR8FxXtYJKoWy06Dxu%0AhETiGBBKsVFjnnNdEWcnz0vj65OKlhgqsyZ8zV6e63Eb/E5fo3yxDNmJ26V2gmxeBXFrmzEQD2gX%0A33uqzxv1wubOm0jcqeTJIOjuyXHQS8q7j7kEcj8/KV2H5SmEGB2N9+juUwt7XKT2dCcv4jZMkZCJ%0AF4zrn5HmNkl3OvEiHb3Hj/TQt56j+cfO6cafStoipd2l0aLUtZXAgAn7H2GZmGsc2xspCp2RyjQt%0A481Sc4FKTYXrhbbc6e9ul5UkyULAAmzSQotjPYmo/PmuSLYeo/XtLI5b45i6D7ZyaUR4LXkMmOif%0AC9yRFwmF5fif3kwOIlkp5BLnh+8yZm+Kli+9yti23OfoyewEWlvBatzDwkaqGYuuEq9JTS2dm7hZ%0AlIlBE5PTPSLjmSw8KEDbFlpM7YnXXRdBdF6fNAuRwZkUzjKxHi9O4qN+jnmP81JnJBUn3Kzzf3F7%0A/dGHPqDbX3qVXvKWd+qqZ0/r5p9L3TieleAccWdabKubUVmK/UVpOJCG/SrNaFI/qWzdB6mpEGlh%0A0MIxT7Vhgp3wHK0hvjOnWP3YpJPH2ohKLkfmNUM0PDzHOaG0fDnXtOaEMn4vBY8hMisY38vxb6To%0AyrfxrQ0jlucaWI7nFdoo1XnNUr3u59VUOByXHE7rdpgHaF27TudOr5LWTrBG8z5iL/zrhO9kAgLT%0AXjDUyFIT4zERn/N7Pbh0t6P27aheuP6L1qtxYU9eTvBGyzyOicsQz4rMYOIYxaRnX2d6ifEsv3ck%0A7TmUDrx+i0743tv0i+t2189/djsdeN6N+sJnH6sr9tpF/Zu0fXEUlUVguCAL1VRzlTrV62ZK17k7%0Alc/9LOIxdUi0H3NPaTUbl6TAHOF6TA+iG+t30W2mlefxTmoIhm6os1jJQqSQyd2jtcy2uD+R4pqI%0AK5kCJrrm0TW2wGtTSIxFEG5znX4fx0zhvuuIuH+0hhknibAHLXmeKexr5D/DKeZ9Kn+fmys1xzgH%0Amewg7XBWwKpemlJRvEZ1oi6P/6OGstvPSH88b5WZBRZy/oE3oT52cyVb1sxgttAchXTgrI1sxW1U%0A8+dYptRkBGPM8bpPu5fGgzp2i80cbudINfzh/GAzVYQ6YjScEduqz0UhXdWXLtvvznrITadr8Ljd%0Ade5xh2vxuJ/r1w+StFXbk8AXt0kzG8o2DJfacccxYkYGiUrWGDnb7vFklNxKwsfk+SAdocytIY4R%0AXVe0YzQqLdepuJc/5vPmqG2M3CfvdXegaaXkDBC6wrbmKVyHKseICkqq4xIu7zHsK3/Cfs5jlOqf%0AEqIQteKg8qNyE677/dHSNB7sg1+iovV7B6rPot0Y2hqFf0/1uGMe099qVVkBawsFcFIiI1ubResm%0AameX2aiakZwD6sGLuoPWZnSvbW0qfPcQ2y1uc528I8rWhzV5ZEJHOi0oeJgEmclM4H7GqY5YUfxZ%0AGlrNOcjD+YDucyGlJen2G6R7XHWxfjx1gP5i8QU64kU/1t++86P6+b331iXVzrCikLpVv4Z9aTSl%0A7Xvmi1H9eaIVEC0czpUXJ1Pr7DHQ7acAoZIycf5WQhR8tFZNndJan4rK1/3159w7c0LV7/Dz/BFK%0A199G9K6YJcPMAlqaFi70gBgc83gzR5hBolx/IuTleZpVvdXV/YpJ+/RA6ZExsCSUsfCkwnU7fd3G%0ASu78V7+b17m1eifZmWsnWDloTIuKTCE13RpGBaNLQvcoTgoHM4cLEreN5ImL7ldOULFO5t1JzQVi%0A5nN5t9lEiCI34dybTXeMVoctVi78nEIgflUJq86S1O1Kt5+SnvSB9+q7+9xDl375Wt33kNP149c+%0AUNcvdLRYBbUGAymlMopuizXFqHQuUqxwjdibx9sYPIUb3UZid5MUXoRZcpSzvtz+eG/S/PO5+Ewk%0ACjgqd7cnBr2k+khHWuIWDPTocpHuXniGMBHbGDHnHMUsDtdPKIPZEb7mZ+yJ0trns5xz86jPBvFc%0Aeiw8TgXqFv5zLnJeQNJOE6rSWgrWmNweXVZaWMTwfN3acIPyGJkXJbHMKIDcjqhFc9SWBbBcdkDE%0Ag3OfbdXajfMhMm4TmZbPCGV4eLEZLQZqTG3u5SL+ulJnStIN0p160j0vvEB//cUX6pF7fkcPf8kr%0A9OAfnaErf1W6ebPU60rFVBWUQpCu691eUWjkrMC4gJnKE4VmXAAxWyBHPdWQQRuWmKvDim2UuUZB%0AsBzF4BLropC0QWEPp595LqbVGfohXs8f6ePYWlH5nsllvB78XrY7N0eRaHVLTb6jEKVbzrbkXHW2%0A3c+6/1MoY2PDcFmk+C62L/dTP6ugtcNYX6F6YpxWYyuEh4dEVzCpeWyfGYFYGAfeGo45ii6zXNdz%0AGI8XQsSFaEULbbGLFy3sjupDp9nvHH7LBUwlQEEs1QndPOs0l75jrc6yJm+86EpFV+VhI968sat0%0A843St+97iB6/5Rwd/Ovn6K9u9yb95us/rzvNlUGcJIxTDAzR2s9Zh26Tx4KnMLlO42xxcTiabsG0%0Akig3D+vpVM/7Z0s8thQE0aprs3BzNMlatqJxhJtJ+TlcP3c4Oev3TjMqZf5yQ1KNsUrjCpuK3JZc%0AVHJxLLiJh+/NjZG9Na4Ve1dz6AsPfHfbjLvzkBh6D55HWusuxzMBzJfedWYFUnlNv7wYK7ExA+PM%0A7aSLTheXk+ZB9G4VEz8TA+LCpltPV6wbyrst8TuFdY7Z5jSOAUXtTWZN6B9nhZYD6zKzWzBbSDA5%0AOifsKSwspBlQ8+IeSGlQf1ZX0g3SbrPScef8VJeevpvuXnxMj//yx/Qf7/0DXbxpVksdaTSURlX5%0AG7aGNku1wHWfbD3aurYLG09zMqTB/jHYaWHEuadwtTDyWFkZes6puLnRwa6sF14M7EyiDv4TY7eA%0AM25uZS3VVhMDdV3VLq/nx4KBvGIh6/4QVqExUKg0ZtiHyAtUwh5HY97cGei+bVNznBxjyBkKnBfz%0AqNeyBWouZ5v9GeEvrlO2n/KC2Q2MWXjteUx3gq25tharJ8mRfAoIZwFIzQXENBGesUgsUWq6uzH/%0ALX72oLZFj21FLEeM1MfPZvToajjYYqHRzZSRmoJAqjMKcjubTLmINslCmVkPbVUNpHQHSZdKw2mp%0A05fOknTN/R+lR3/6ufqN0/fU1590lPbaJI02S6MtUm839Cu3ndfKzNkeVERcfLl98SR7MQwmRYvV%0AQpJH8Pm7sd1ovdtKjXUtE+ToL5WnWEklPLI9vYzKhUJgOWrLoCDfehunLXcLKypSU4RBzIMU0jZk%0AWMa8yayHjRpfN2zvTLifa0+OaAHHdkfDIgazpXFvh/dUPePskeiVSkqv/2W1WDkg0doyQ9OUj78x%0AlSMzOyPEHlgLEboDDCrxp1psRRmPm9PKF4H74YwDwxh+Z8xCsGY3Tsqp7OEaoYCcIOViocsXsUwr%0AJo4Dd7Uxxw+UpiVdLmmT1J2VUle6167S0Wd/Xn91/EnSqy7Ubx53vi57nnTDLVJv97KewuPpcXFb%0AmQBv68HtcBAm96u5OXKKlSnOFXHCiPXGhdnodLjXwnfRNrFQVU/jObv0jNie7ZWxopbPLMuIPjNQ%0A7GmQfA4qid4fqQ2SIiZJI4BwU86oYRvoxufeZ1jEvON1qFDGsEDMtnFbva5y2SIxgLqTaW2DVzlG%0ANYNEbIUmuv9zsFP4H9MrOvifS/+hACZWE8uSeeO7LZC9mGKQwdZhblePrfUYvfTBNIyScixiykys%0AM1pXFlIWnvypFLqjsb7KEhptVWkt7yLpFmnDNunVF5yhf5p7kg74zIU68JOX6RvPPEZbb5SGIyn1%0A8XyB+v2dh2nETAh/JiyQI7qDbdY+M09Yzl5Ebot00vjcu60s1mLXjJa0/SCX7YohjmvkA9Zl973t%0Ahx25hmJboxKKQazlqG0c6eabn+O7JsEknucUrvm/73tO45o3eS4ir+Z4jNCKBXKurf9PZAXEQzQ4%0AqCROAF19adytdJaAMwXiOxgxpba1QCWu5MVIi8D3TP3MNdNW1ZhdF5/9PisLLowIFTBfk+R+EWLg%0AwnMuYVflWOSsbeKatFJtOfPnUbyBY0nlbqsZaf7yCiHZUxpdKd35Uun91/+uOk/dV4/7/sf0Fx98%0Ai865uqrPz9uK90/ozKoOqvh9xKkt8Hw9phjZwh6q3hTCQ6EjtbnfLL8SyCfOd6/csluMyv/9Skh3%0AOqoPcilU/2LAqPnsxLpz8y/VvOE8ZO7ei8qBWRk5aCNShGJy2Rs8lyGmTrJ9sc0+IpQZDPZQvE64%0Axg0TRex8hOuGBaNhYuEfjZV5jdfJduwEWjvBauIOK6kWZgSmzYiOGJroTlnLeaF11dxuynQdDzgD%0AJ3wPo4xSzYzW+kM8z4CYVC/+mONIK5wWlDEyZwSwL0z274bnohU3RBmPiceDWLXJ4+5x8p8FGNOS%0AaLkXkhakuV2q7zdLUxsk3STd5SDpqhOm9eBHfFSnPP1ovf7pp+p7PUm7lu8uGMG2sGX2gPtjT4Rp%0AM44Qx80b3rCxEeVygoNR8+jdsM74KwWmAn8eN8/7sNwskLrS1CyggCDgeuZHqZ6LJWlYYchFkopq%0Ag8moWwpqbSrbNxqWgnlpXrrqZtW4qhVjziuJnp+JUBmNDkJgEa6SmgFaC6FhuCY1syriWLpewwp8%0AJzFuW8OEY/g7aLSWo+FEPrcys/fnvtMapleZi0XsAK2tYKXmNhPY1WXkz2XjD6YxqJXLM7SVmPCd%0AUcioUbt43t8pIAmGU2D7hwwdsc0xcxSyXOj8/Sq3mVkLdt0ZbHM7opCIEIXr4aLuqclcuaBNjjPY%0AHqa6+Jm+tPd+0t+87iV62ovfpk++79f1z/f9e521UdKG0mUutqnGQ506JTWVHiGJaIHYOrF7XIRy%0APAMhEnNPrYQslLzAc3PMvvtVFLTEgBGcGdn9nBRtLlSePbAkpaHKn3VZqizdkTS6SRrMSUuPntE3%0AX/lgnfrPT9R1Fx1W/rh87Js9EFr9OWuX143j83rE+RlhNxG24T3DXKPMM7RqPW8Rg2VMgUZGUo0P%0A58YyzpX/89BvnukqNWULlfxOoJ0kn3eQLOhykxA7yHQQW1W5cq4n7jDxoJIRGBDyRNJlb6NcUCAq%0AiUlZBiZmKLidbge1Oi2MGOmnpddTU1ubmSLmRYsuus253Fa3Kwqs6LaNpP510n3mpI3v+LDuf83N%0Aesbt36DrX7+3rnrV0/XwTaVFp62q3T6/LxeAyBH5gJbmNtUwAH/Js61OCxCf7cqMhLb3VsIgeQHG%0AsgNpOND23WcdZilIeUuaFnu1LbsopG3XSjPHdbX02hn9/qEf0Tff9TuafuRm/efN99ahz/+p9HPV%0AOZgxsGNBxGi3yYKKxgvyNycSAz6T4AyFe55nw0Juh40SfydOTkOLBpKJGTKxfzksnPAbYwo0Euzt%0AxADfDtDaW6xMe5LqRcyWEfdoszLjdYLWFEYmalVq5Zz7QrLQNlNHl59tscaMu2HI9CRnLiyoqQAi%0A5iU1LTz2icRsBJ5FIDVdNeKKTr3ijjQC/txC6GeBT6cZqehKR/Sko3/j83rGyW/S+7/4UH3krZ/Q%0AmXupGdzLtfXW0IJKIR0DEivJ4OCmjVy+qz8bE+ShInQZqVx6UteeATF8BloCjYYqcfAZabhZGkxJ%0A/RdKf3v2q3XPo8/Sob3z9KyP/6N+ctbBuuUxe+vQx/xU+i81vbKYbmj8MrfDzAqYeL+0vKUWlXbM%0AbuH16cx1k91+C0C2z9k30SDgjqu2XO8cuYzXOjfXeF0xCOy/W3toz4RX//eTBag1JvErMgpd+Nye%0AY+I+Uu3m0VUuwv+4mPhuasacwCRE4bYRWoipLsbsqJ2ji0uaU81UsUwOcI+WgwW+mZu4Jhky9pEa%0A3OMt1biW08bcfypEKK5eV+U5AQvSnTcWOuEv362HHftuffKzD9EXn/MaXXsN2mPhE12wyPRcEGop%0A52Ce5zAnqGNwNLjuksaFoN8RXWzW6QAco9CjSmj6PYQK+LopaTgv3dKRLn7RITrpU6/Upr0X9YMP%0AHamTdnulrnjSITr+Lz+vA75zpbS5emiomk/4o5NR4BvmIPRUqLmlNY5xJPI8M1OWI6a4Ucn4nseD%0A7zVuS+MlkpUCvdJcZkRUGOZZ5o0zN5pQwK1JrWyhtYMCuqotAf/QmwM4jnLTYjKjRK1Pt9XME91h%0AC5QY2W8TMgX+c+LNLNHFshBk9J+aj/mYLt9GnFhOMLdpOuOBfeDPlcTj9rhZgRqaxNxXHsXGyKoj%0A+8TKbKENQl0dSVule+wivfGUkzT87q/q745/pvTKc/TX//sz6lhRMrfViopQT8zUoNAk9kdFZhiF%0AisQKjkE+8gp/VI8Wi8faWGTujAu2EbzTqfi4kJTsjcyqDErOSaOFMsti4REb9Ow3vl2nvvyPdLdT%0Av6/No12ldy1qtqf6QHb337Sg0tJ1392nGFjzOOSIBkabMGnzrtrIY0oB3MM99iFCdi4Ty8X2UBiS%0AqCidS24PzDxB5ejNIax7pJVlhixDa2exUjPRZcolwPtaLlE4Bq0iExG7oTYjljrKPGdixJyJzTHK%0ATEjC3/kshfFyWt+CheMwxH8L3+jC0mKJVhWv02pqs1QcLOTWRFqyqvrEM3Jb+vFrPekdxz1Zn7zf%0A/fXhL7xF73nr43RpRxo55WqjVMQfh8uNkec33ouuW0z9ib90sKASkyXk4vIxhc+8sliVNy8ykmyy%0AReTnqnamDdouGIuuVMxIo0Vp/oHSP3zx6Tr0dhfqX//9gTpz6V76jy/cS7MfWtTsnDS0EqfF7n7H%0ADRa+N6c6FYrJ+PzzWBKLjFCYy1qgL7c11e2L3iAtV67PnMcWeTpKp5GaZy3HdpAPCTWY5xmPiArF%0ACnsnBbDWTrDS1WWEdRLl8hitIWnyR4rCkNHPGKHnNQt9uHcNisLYllrEdJ1aFCm6pe5bDqynciAD%0AeCEzbSwG4GJQkN+5QH3fVg6hE88X4ZkO7km1hRuDkUPpsMWhdv3wT/TIB39Wz7rw43rPw0/QVbeU%0AZfoOZqkuX/RDvbTAiI+bcsEvBxAZ8OQOu6HqPNnqvWN4Pw9jifPFPvIzx9jjW+HR/eulW3rSOc+6%0Amx7whO/pLSe/VM/f62Rd8ueH6u5f+750rbYrzi4hLdfruXGwiSlOhEXYrmHmusfURgVTqhjcYp+4%0ATkw0HpgCGL0zti2WYWCJMsBtsyLNGVbunzS+e4tB0TbjiYGyCNmtgpYVrCml96eUrkkp/QDX9kwp%0AfS2ldGFK6asppd1x7+UppYtSSj9KKT1kYuXE8YgfUjgRu3JaEpOhpabrk9uNZYoMTwHq61HQ9kIZ%0AA+u5+n0tx3w5jc9ybLsZifeJi1GA0eWlpREjom2uHlNN3J8lNZk+4RphAT/PujLByFEVwf/tfaQD%0AzvqhdNJ39dpPv1OnvvgozW+scFm6+F0pbVTND7mgSQyQxLHkc1TgVgoeQ2X+R4pKbtJ4si0jSQtS%0AUcEM8zdJw72n9LHTn6GjLj5HV27aVz++6i56yWv/XmmhhAa2ewkDaeifa7YC9Z8FkefbgpGnwMX2%0A84B2Wvy0iDkGnuMOysSgmOGuOG45ASm03W2KxgyVslQLOcZclqPIM3wHeTYaE17fFPiroGUPYUkp%0AHS1pi6QPFUVxRHXt9ZKuK4ri9Smll0raoyiKl6WUDpf0MUn3krS/pK9LuktRFKNQZ1H8qZrWR8Ty%0AaG3YkvBPndg9iUfkSeMpHLR8Cjyfgw14EAiFRC49yW4SsxjYB1rQQ9TB3SJJNfa3XKoXI/GLKl2+%0AhXCfh4uYSchAtEZMMeWLqUoeZ2t7wgFe1DyNygLI2FXV79GClGakVEgLS9I7dv09/cXiG7XfkTM6%0A/YD9tc/p0u43l6lKKipXeZs0HdNeiGW7j17wqX7fWBDS/aiEaTEo29PoS/V8MZISBbYPulmuHYU0%0AWFR5NoCxPbevJ23bIt34ir30mAM/r7O+cqTOOPiBOuKUs7WhN1THVpnnxT+vw6ARXdnYFithC4hI%0ANkqI349UpxXRYpOa809DwwrJ/XK9JmLXuXaauFZMVtoMuDrmEnmYz3iuI9zhehgXicJ8VuPCvfqf%0A3qLb9hCWoij+VdKN4fKjJX2w+vxBSb9bfT5e0seLougXRXGJpIslHZWvWE2gOgcFWDvSInO6RaGm%0AFWWiac9eerB5/mIkWr5ScyKEazHiSLeZ112nFxq3jfpdbo/wvMnQR2yTf57C9fPoRPfTfaSlQ8jE%0AROvcdVooDDWOEzMdRniXMWFmFlT1d6YrYVVIsxuk5978Kf39ASfrqjP30P3O/YXO27aXlgZSdzcp%0AdaXeqMp3lZoL0PNKL4bzEzM9IiRRzVFycI9zWQmKRM/E1yPRAEh1M7tTUlFIw0WVO6cG0miDtHmz%0A9I03PUwHLPxCm76+RWcM76O7ve172rRhqA6xTGO8W0OfI4+zzzxAp40iP9Ol7uJ6dN8tjK2A6NnZ%0ANe/hWc9JyvxR6HF8Cc9FOCDnFeQgv1m0Jcxnow1+znwcd3IJZVZJO4qx7lsUxTXV52sk7Vt9voPK%0AM5BMl6u0XMcpYqwk50puUzOFxs9xENsGwQNsxrNFwC2vkXKLNAo7l8nhfKToplr4cZcLhZrTneK7%0AkkrL1FCBhZj75EVFQD72wfUvR8Sf42fWU4TP3vKaw7+k5lbDkbRhF+m+571ZL3/B7+u6S+Z09Et/%0AoXNHs9JmqTNblilGwFk5B1QUtMpjtHlSQNIU06H8iwe8T9608PM1zHGnU1rkqSd1ktQZSEVXumCx%0Ao7874zV69Ke+pN869wx96Ju/rd/67rnaOCUNblbNk7bWuEOO/GGBFKnNSjVNusf6+K54+n9HtXVH%0AYSQ184Hdzhw0MAh/tLSlmoc9tm3551FZEis3rMUNP3ENxGA3xzuF/6ugVadbFUVRpJQm4QnZeyee%0Are0Dccx+0jH7qOlGRByLkUmemESyZoxWRq6XsYytgujKxMW13IgR3zM25V/dJKPmcL2Iw9KakWo4%0AILpHfo5Mybrb2h/7xjG3MjCz2nohs3oR2WUzLDOl5ulZHlNboYvS0beTBh/9hr598j/rjFf9L73p%0AeW/Vk974LD1qShoMpB5PwHeqDNPvvKDsnrJPXY3Pk1OlfJ9WILMFyHOxjog7M4A3lAb9st2jobRt%0ASZo/XHrK356p/3zcPfTEP/yI3vEPT9ZuG6XRvJR2kXreu84zEcjDDFTZSzBU4zH3OJsHeL5sbPck%0AIt97nqlE6XGxzpUGeiJGnbvH8R22lJXGzxUg37atT3oo7lelxNWRTr9COv2qCe+8lbSjgvWalNLt%0Ai6K4OqW0n8pYpiRdIemOKHdAdW2MTjxKNf7oAzaItTqS7gnOgdvRfWnDdijk2mx04k9Sjd9GtdAW%0A3fdCoyBg0KFNEPrnrn0gMPFaj4VxNtdDN9x9ZL/cRmKLdC09hsSJY9DJRNc4WoKMpPL5XFCRmFkq%0Aiz5osKSXPveFeuqvHKrTPvwMPfBF/0eXfeiDuqPzmv2+KFA9NjEzIbrMJOb9eq48fsaHc3mRUSjF%0AvEdTR+ruXra7OyNpd+n5f/oxnfvC3fSMJ7xeb3njy7TxDpJuKTHkYovKNCzmmXKMO3W9Y+2gtUYv%0AwX23EozzYYoGCXFU1mNvhOPFttriM39yDZgG4TmpKdwY2LLnQbiPeHmbMLeCjfmnxt8jDGYlBajg%0AmAPLP7fvNWdoVbSjUMDnJD2l+vwUSZ/B9cellKZTSgdLOlTSmdkaGJnjZgCpuV2UroazAkzRlRmo%0AxKdMDOJQqNo1aWvPQPVPTeTKMH+QDBWJaSSxzUsqharL2JWxJcbFZGY1PmYL0szmCHm0fjeoPiXd%0AGjoStTgxVOOzLmNhFq2DhHLGsIU2WXgzYJikzlDSgvQ7s9fr8uvvq90vv0nP/doHdPGhD9KNW/Bs%0At8QtCwo0W9LuP7HWnMfCw1UYrPA4e16pMKQmf5G6VaCtasNSv3xH6ktLPWk0I534tlfrCy85Rh94%0Ax5/p7e94mTZuknRD2bY0rCAPqemluH10i52PSsUYyfPQVTnnEesm/zHSz3mxZ+UDchxk9cYdWpH8%0ABVivTc8PMX+3ye12WeY++zrbFY+w9HOeV8oBj5+/c+7dJkJnbuNIpSET4b9ovO0gLStYU0ofl3SG%0ApMNSSpellJ4q6e8l/XZK6UJJx1bfVRTF+ZJOk3S+pC9Jek7RlnaAtJLti9WTHQ8BMfUz96JAo+A1%0AU3Cwc7hNrMvt8WKLwaouyufSW1yG1loE8GdVMrHPS51WvU0xRjcZ4HIwjGXcFn825hlTTnKHnQzD%0AszELws/TJZXqRRXLxvmxQrQCsMDeo4z8d5PUWZA+9JwjpUvO1bFbvqnPP+LXyrHYWLrVDTjElq8X%0Ary0pZkT08C63KZeHzHmJbZ5AxUBKc5L2lBa3SlP7aLsL/v1776WXn3GSPrb0TH1v6n76vSd/UbMO%0AXjp5n54Cx5vKQVX5+LM1bh/T76KC8HWvqbjK6YlRYHtsoweEX0TYzqtuTzzb2O+2wI6C3ePN/kQ8%0A3J8NP7BPMUtjJaZhDq7weiNZWeyEnVdr95tXz1AdiHG+nrWIVE8WMwbafnUgkjWUKRcYIrxA99Ja%0Ay1aaVA42tagpBnV8zRNkN88RyBx2w2R7kxnSzMcdQoxoRub0eFJBEXbIHQ/IfliA2uqLQjgy8QjX%0AGXpOtlMAACAASURBVNRj6libe+6thlWO5xWFdOIfPV/v/fTzdOz9LtVrzn6I7nadtGtSDQvNqfRI%0A7EI7a8H8QWvf4y+1K1JuR70VNBpKnTlp8UZpZo8yzao7I/30COnFz/uUPvOV4/XUB7xbr37Ks3Xg%0AXtLSkjTNX4CV8gJdql17n7dqq8pC0ml1hq5oyfm752VS3yzcaMETYuB353znAmhRqPrcBPKWMWGp%0AnWdS+JPyEAitd3pb0Rtx/fbk2HaPJQUoy3e16t+8WlvBKtWDEvHMAteIxShT1kQcrYPvTlniQFpI%0A0yLyO5gbaXyyzXp1HyiETbQEiaUVLWVy6VDSeH/JzH6fXSnmkObq4s4142euP0ZR2cZcfWQ7MnvM%0AL3Z7LEilMvI/K6Vbqu97SsUvpIc+8av62ocfqF971nn61sn30t4bR+XYblQJnVjBerFbAPnMCStR%0Aj8+Mmn2WmkGfHSEHkeak+Zulqb2l/7Ml6ZUnn67v/NUD9JvpLH1001G6y9WqT2zaoqbSMkWLkr/7%0AFZP4KTg8rgy4xvloEwv0IvydfM24BuuwEiN8wffM4HvbCVHE9JkRQEOAuayGvzznJgYhpXG+NW9b%0A8PK0LL6bv3Rgb1CrF6w7irHuHPJhH8ZwqMH4I3y553Jd5vPRNY4QgTW6NTcwve0TyJQPM7DxYEIA%0AOXdLqFuh/liGFmNHzTZR4OX6zWg5LTQzeM6tl5qWgcfHFm+uH0nj49jFtXiPFK3tKZW/KuBr09r+%0AO1r/eMpD9PS7fFgXfuYIXf3e2+vnHWm4IOkWlDdubKtXqhfjSgTmrRGqUSFXEMOwWqSzu5VNe/bJ%0AP9B3XvQA/d70R3TajUfpzjdUbb1ZzbzUIerl3LNt3Oxh95yUU8yRL6L1R2JAymNh5erzEKIbP41y%0AQ9VWKdeo4wZL4Vmul1w2jD2sWTXza6NHRuIaiX0kVNGWuy01LfH4vlXSTqxqB2ibau2Z+3UAMkcc%0A2JhrF7ffSU2t7jr6KEdAe6DmeyjYpHryoxY32WqKrq+ZiQEuWgdeuM7XZW4qx8DvNxPYvbE7O6ja%0AwOP9OD4mupVSc2FHa4SR/l74TLeTLv8G1ZkOdts8Hgx49FVuY8X7UpL2/1Xpz+aeoZ/fdxfd7bQr%0A9I3DDtGv/PxnZeDHFpEtEHoBFqwcC86no9fur4VDPICZRIsuZmB0pNFIuuEG6VVvfrt+eNJddeyx%0A79WJm5+jgxdU5x4jpWeMv+jG0orzvBraoAVJz4CQFWEtP+O1YI/M82GMuot7fsecmsHRAs/YeiQ+%0AG4Ozro/8zvaSFlULNxtZxpMtE+ydRSvac9MP93h2awd/DAAL4+Mx5Pdf6tOtSG35qG7dJLSiLXjE%0ASCIPZybYb2bhZEXrk7iTLbS2dkvNoImfN+PYzeG7kponU0Xrju+3sPd7CDNIzegntTD/psJ1WguM%0AmrLvbA+tbgvi+ByzFHJzl8PJrUiuko64XPqLL71Ae+x3nT74jy/T+Q84WMNdNJ7KQysj4nyuk9AE%0AczHt9tk78qKcUW05OUDm8ubJJHU3SYvz0smnvFjvfPdztHc6W2//yct1xJn9sk3ckirV880FTAze%0A0e+2PFzGAOLYxowBt7OL51gPKWWuExrzf+7X53sZAxng/whlXL/7T9ebHpXP1OBZIORjQz/uH9Mb%0A/Xt4TtPj+qdVT6+WhlIv3F8lra1gpVXVZju3CU4T3VgTo67K1B0jxnaDcj890lMzTYlupN3a3ChG%0AAWuLgeRFT5eaFlWMGHsRsq/RTZ3kkrseKxe/w0ER94mWQXfCH8eVgZBu+LwS8qEeldV99PxV2vjA%0AW/Sh5z9Tz/zh+3T2jaqtTqlewJOIP2nNY/RIU2ouZqYAMovCAriy9jZvk9I/SK879a+1xyGf1xu2%0APkqH/eS67b9EOqTQdBuU+Uwi3BMFHT00CytblRYqfGbSmskR8fWovEwW0h6HeN3vHYZ7TK3yfwtZ%0Ap04RMjPxuE33mZF8YKKS6nk0pGEPj+/2cx5nygGpfW5uJa2dYCW+GfMraTV2w/dIucCKFzkthJgv%0A2FVtmcTTp+jqMucuChLm8eUwLi9ma0OmBtkiykUz+dl9iHWzXzOqt2P6bwr3fI3Hu/GzTz7q4brf%0AMav6Z6pT+ONijAKU40EMO86lBepIGsxrOy90JX3lrYdon61n6d8231db/3Av3SzUQfec408ohv2I%0AkeOoiGnRe/zYXs/DRmnYly6++69p7muFFv51Wu/c9QN6yvy16m1QeSLVLVJ3L9XWkuueCe8gbxBi%0AssKgJRUxdu/k43xH78RzHnnf7yWe73vkP7bF40RBx5RJk70vPhOND/aLUF/kKcMjPTUFMYUwISrO%0Aleeev7JAoyDyYQ4aWwWtnWC14MoJxgjqRw1HxuGCsKAYoHyM2PNZM2xkRGtC55WSobso0w31chtm%0AFCxO2PfiksZTuOyadNXMeSRjst4IG0SXn8nT0eLJLUB/puVLV9vtZ5DBAsOLgAe2eEwYsOFcUiiP%0ApN6e9dj15qTDL5X+aO406YJ5PfiHV+vm4aayDVJzYbj/MdLtMRqqmR9svrOryIM4Yi4ot8F2JC1I%0AN83O6vHPOlX60kD/8tgH6UGf++z2st1plT9Z7d/i4vhZ6LidtPzIizkeonch1Tiox8N4JRWY+2Bl%0AToEmjQu7CFfEa8yOcZ3mJdcxFepjHb5uz4F9zKVTGjd3Wy3M3T/2hf0hbzAGk+s/25cLJu4grZ1g%0AjT97nLNSc+TrFnjRLeVWSAsqMwEH24zKBPlodXkR+HkKGpLfx7YQh+RilmrmyZ3RSvfG74+n90TB%0AxHGgRmZSPS3SXHmpZi7XaQa0oPZnCkRivrmxcQpNzGiwIGPbHEBy4GJW+uPz3qjuKZJuKvQvD/oD%0AXXmFpN1VL8xbNL64LKSoDD3X0QLzRo1cMjv62x+W5f7rOumVT36jLnzNIXrTt56ve337O9rnALyb%0AwTLPH8fKwnsSbGMeoJvbth78Pm6QiO3Plecckmdcpshcp/fEXVCsi0KKiowKL0INNJpsuRrO45r0%0A9ZifTWPFz7m9OSiAFD2BmOmwg7S2FqspDja1JN2o6P5FpjGIbVeXTMZzQ2nd0QXjgHqhRcaI7/Tz%0A0eLmjrJB5j4Zim2J7WDb/ax3jTjg5LFwNJYauafx8SXlIIwcLtoNZdlewhIWvAwE8YQxP8OtisyF%0ATM2yh/Wkf/rqA9S96Qb9+W6naHA/qdimeg6dtiXVQsvBFqb7WHEzEyNHLa7iVE9aukm6+KSH6d3f%0Ae7KO/t0v6mmHv0v7SCpuxvNUnoxUR+uK0X3/76BdXOARDvLYxjVkQ6KNpwd4xkqkLd+5jV/o8fg/%0AhRIpF6SkxHFmRyzXtqbiOimUb7t5Nd5jeWP0hF18v63vt4LWHgpwK8yE1lrUmLSipFrIDnDdFiEZ%0ALn52kIbMRCakEDcMwPa6PAWy2xoVA628HP7nZ3m/LfvBgS/3x89au0vjB8xEayG2Lwoy/sVFHz+b%0A+D4ybRQgnGcLWc95LucWOcO9nvSo//yBfuPul2rp4139wUM/q+so+DZVz3h8LNipsLqqPRP3Lwa+%0A+O4onFQ+e8Mf76Mn3O0zOvyYn+rzb/xf2v2Iskzij9LR7Z0ksIgHUjDFHUpSbRVGYUpc0nXm5s/l%0AYo4oeZnzF/tuItTiMWUwl8FWjwO9B1qQbWS+sCXK/N/onUZBa/5ty0aJ5L7wHOKVPrsM5Zy3tSG6%0AGHRJPIEmulteNLZAjNNEYJqbEOyeUVNGJjJMQAb1UX3W1G3nGZBiZJhuiuvlQoiYsIVBtK6qQz/G%0AdsDk9ukvx8gk7phiHzyek6LwORfP/+PPdjPVLSqVHt5TzWfaJH3yrAfqThu36PvveaiGV2+U9txa%0AtvfG6hkees52MK1mJWPhOaLluY90/V7SXTf9SItPmdGLj/0T7XagpKsl7aXxXwr2TqGcxeYyUQmS%0AmIwfiSuWHo55J25aiZYbISk/G70RKb/d12NJvN45xRHzNpzjdZkT1FGZEkem4lemLTlaiUC0EOXW%0A2JgathME69pZrLYM7c6aMSKgHF1bwgS2XBx9jSlOfsbuM7VpdFlt7RCHpDVgTRnzAv0O94l5ol7Q%0A/BUEE4NLnFBaBK5nqHoDgRUD01osqFbiwtgyITNxgXKRM+jBfuYsMsIO7CcPEKGbT5c7WtZccEnq%0ADKV9p+b1+Ae/WYvdGT3+BZ8tf6xzStIeqr2LmM/pDQmM8HP+IrRk/vImiwqDvfan0iuPf5tuOnVG%0AD3rJaXrkRWeXZb0ZIv7Oml3ujpqCjmXcPo6hrW6Pgfkwt9BzME+bu8xnvBb4ixCECBibyBG9JfNH%0AJBs4cb1wXbMuzwP5wgrZgVLCOu4LYZ+cl0EjTKqVJjMaYr3kiVXQ2p0V8GfVF+7nb6OcMDNx901H%0A9e9ARTyFyeXLddn7z61x4/vaNLkXd44xeaao65dqDUqGNqPEPqilDxFwp8COVrHPfKXbZgvLAo6W%0AKa17WoTOAvBnCyL3gfNF945CPZ6BEIMntobmJW2SNt9ROqh7tW68Zl+9f/+H6qkXfLV5rOSCaqUT%0AeYbnA7g99nTIe32V5xIU5XuX5qQr73aADp6+TLufe7V+cu2B2nO3pfJ577bje6g84o6hvsZPVGL7%0AqKCpZLm7jnMULc047qQ2YeHtxMtZ8+6r20rKeUYe2+XaQsyWPCU13X6SeZfPRVy8LRDp+eEmBI57%0AZVilk/RLfFbAzmgBIQQvREehowXLCOckRsrtrjI2m5voiKfm+jQpYk4NbiuCgoFtZaSUWFlMdHfZ%0ApfDn90RXnecjsA678bQ6uE3SdTAoGPufg1xy1k7ukI0llYIuSRu+L72/e4L0C+kNx71BZ1+hclu0%0AFQsj8OQBEheVBT03Duyq7UJVe0pbt0n3O/Lfpe9Iv/XYs7XrzFIdpGs7Ys9ucFzcVlIxM0Fq5koP%0A1fRIesrDMHGuJ0EPObK1F3kzJxD56xCRVgo3MQjHE7nMM/YgPW9WkEt4xuUpdGlte3wJLUXxyD7E%0AMSvU/JHOHaS1hQK4zZPJ+vHPE0J3gUQQnuRFFIWXB7sbnu1pfCKs0SiIWJb3Y/uoDSnU7e7Zzfbe%0Adu4s8jM5jFmhnQTzo8vp5H7uPIo4HTMLPN50S6V6sUX3OUdxxxgpPmMIxvnH7j8hospt6+0mHXvx%0AF3W7Ey7UBa+7m775ohNU3FHjgiEGM3pq4tL2GOwGo1yxWdvHffNN0vmPOEK/+NZeSsds0ye+9Sj1%0ANqkZXLUgjGS+I9yhzGdDRXO4Ti/DSoPjbmXA4GWEzDivbR6f64+WXTx202tluSj/chSNnIi7e5eW%0A++VgGccrZgLkAm0RwxbKFKqPoeR1qRbKy+3oWwGtnWClGxMHy5rHP1wm1W6OtVjE5IjV0aV08ndu%0AoFnWLnjEvAjM0yIyI0wSMpxgCnSF/xGDtPXnxRWFfg7K6GWue1HlyBieGZcBJgeWpOaOK4V3UKD5%0Avq00jisVoceR9cRI8lR4ZkalZdqTNm3t6yfXHSbtP6/PnftEnfXdVAsv7igiWUHZvWZ0POCtaVPV%0A/12kmSnpiff6lPo3zeh9OkGdc9R0XYmD+53k1xiEScoLAo7vQE1+5ty7rK/HMfQfsUcT+cDxiJxA%0AEsbJ/D6VKRufaSP2jXBQtCw9ptJ4YNl96oR7wrNsHxU728HPuUCV1+JOkIprCwV4AObVtCoj89nS%0AoDDmYvEzxkTNjNzt5N1BpjbhZAuKv9hpoePFW6j+1crIxNyRRPLk29LJCR7jtF78DOr4HZPOTojw%0AAdNw4vuYL2lyvTFg4n7mcift3nph+BcRuFAYnPDC4jUvHisyZndIdRBjSep0pY0flh53wyf0bxfd%0AX0ee39G1tHSMIZss6HIuIYWsLZlFSbtJ2iq98o//Tpd+4s7SldITf3yqNu2pGiogcftwxM4ZELRg%0AkGor1WNiq5RJ8lbccc4pAHNYu8fV3gnXEzMNjEe7zlz2idvn+fAYe/4ovM0HdudjG82f3IrLZxln%0AMCYd+0xe9nrwe0htc+R7tNQt5N231ZzVG5r730/UPrtnWmO8xa4bf2iPjErhEbVwxJ02qBaeU+FZ%0ApmFJTezJQlyqB91ReqmGMhyYIuOTaNXFM1QjUTNbW0dtaqZ1fWTKuODaLNfokuXI9ZGR3Q8LvZwF%0AxHa6fCSe/clFzj37tKxnpd6+0l8+6O+kG4aaPmmg/i6zpcDzbzZZIJiHNKFtUhOT3Shpm3T+z6TP%0AXP/70tXST55xoDrX98u6t2hcAOXGz4YCBXqnqt9tYkDTQopBRAvZXLaF6/OzFhRU6BaIHoeRakjI%0A565y1xJ5noHGCE/x+EaS2+n0Rmk8KB3Hijiy62f+bo6I7dPYisLS/w23RYHpNkbPcqWY8QRaW8HK%0APfcx6h3LuSw1PN3ViKtEbU7Lz9rXliMZ3wuC+XdzqhmQmpPYrCfPrjzrjXioF/2kxU4mYb8IS7hP%0ArMsL0ILQYxEpMlm0IEh032MZWtVtRC+DlMsImPQZY3zoP12k57z8FGko/eg+99Fgs2rBYOyUC7/N%0AQ4nwyry0NUkfu+DluvgLB+meR31VG95zpXoWTFFp+l1uH2MBcXVZ0HE8OG50+ZldwU0VEX6hBxDn%0AgK52P5SlS95XU6ibDEkRBmMgaVKmjj0APpOjqAhszdoqbiPCgm4HZQDlwyRIzP1gfTthS+vapVu9%0AovoyUgkFTKKcVWe33/+jNTXJEpSaTJ0r68gwGc3uFZOq/U4zEXMaJ6WQ5dpEcipSDttcCdkCXInq%0AdIDMymA5xqJ73VXzwGLeN7VZzG1zRLzYbfEGDz+zUbr0iDvqkNMu0cwHtugHT9tNd7q7pOvV7LeF%0AbdvWTam5ILvSF6f20hMOvEg3f1fa8qzba8PHF5WsaO0d5LI8llOWVtJWEHx37tkZ1b9tthw/9zLl%0AHEW3sCJWGZ/PBXzaaCZ8X0m/Gfzsqk6NIybsMXX9sa2sz7zQU3M+Omoe+pOjOHfsTzVe6R9u43Sr%0AlNL7U0rXpJR+gGsnppQuTymdU/09HPdenlK6KKX0o5TSQ1or9oSOlP+1RKac0BU3Wah6kJhuk+uZ%0AtbM1sN8xUFNLORmf+Bu30PqZJTUFCE98Yj9MOfeiTYvGdrcJOlsG7puFYizv8YtQC13AGLhrIy9S%0A9pUWGg9SsYua4zLXQzw8ehyxL7beKoFzwLcv0wfe9ceaf8WuOvPFDy93YRXhWWPTub5FV1LS9TdL%0AxfPuoZsHu+tPj3+/9K7F8vaCmmfWmg+4u65tGRo/jti158DWJlOEnEft2AD5J6ZreR7jGalSLbQZ%0AIKRgqeCVRgaKeYWZOVITp+TccKzZX2l8TURPys8RrlguMk9r2cYH4w9052OmBftuvuAvOUcDbQdp%0AJTrqHyU9LFwrJL25KIojq78vSVJK6XBJfyjp8OqZU1JK+XfsqSYuafzTWj1GOen+mog7cfKlJsNJ%0ATayKWK3xNU6EXRFuXrAwMfbVw3M5nDJuOyXuSQCfEfQ2vDNeIy7Go9vIEO5XLuXGlqmf66heXHxe%0AKO/vTPEhhmfgn+ltTPQXyrndjG57jo29Fyjn/7ZkKwHS3U068gkf1lH7fkfvu+mF0kGqfwmVEXtu%0AZ7ZFExdclYI1OmiTHrn1K9K/b9GjbvioNu6jcXy7QN0eX7fTZTzvOG+20X/CQ3SBPdbmOZ+Yb8Hj%0A+xRIhmn8bo97Up1/TSVAoeXvboODj37W5Hf7HfZWCG1xfIYaD+SadziWrmOkMvODuLH5yzGWEerI%0ArSW+w9BHDFhTIFMeGBdmXaugZQVrURT/qtIWiJR7/fGSPl4URb8oikskXSzpqGzFDkZZQEl5q85M%0AzEH1wDtQYfKPmRmkN0WL14xDy5gUgxMUkIQPPBGMxJqiBcZfDqXLHY8ztCUcLZtIHocoMNnXaDHH%0ABZDDPl1vDH75etyEQHKf4xZct4NWDYN/EU92O/2Mx8NzZmVSSIdtkg6auVrf2Oc4bfqbLdJ+qqEl%0A/99d9cJ1doMXlK3CWUnXS2+4z6ukT2/TS//9dbrXmf+ZH3vOq4UEdyUx95rHY0bLk8TrMRmfHpLn%0AgkpjXk3rnGlNObLVGK1C98t83TbPLJujCP0wq4YKk+uFyttkpcF6PM7EuOMxmLl4TS4zQWqOg9fG%0AGmcFPD+l9P2U0vtSSo7r30HS5ShzuaT9W2uwCc7BmNb4/nL/p6YyGWuxhvdh04t4zsJ5RzRRzpWx%0A1epfPrAwIaPGyWGE2s/0UM795Q6qTrhHokVga9ptYNqU0K7loAczWQ6bJQPGE43M8LYsHAzx4o44%0AtYmWGhc73xXdOs93JUg6e0m6v6RN0ta3b9R7n/sUbb1RTctja/XZ1hhzJP2z2h1pYYv0lXs/WAds%0AvkxP/p23apcNGhc+kQg7+C8eOiPVGC/hJD5jD4ljt4hnOYd+1j/GGQNe/pwTrIQu2oR821bU6P67%0AviJThu93DISnSNGKHoTnmfLUD/c4bvGz1IQaIlG4RrIMWUDfVkE7KljfKelgSXeXdJWkN00om9eb%0AxGEYtXM+InPdOFhkhkLNn7cw07GMg023VqguB+a7bYz+m8kteF0PMwpy2Quj8Dwj/8yckJr70735%0AwcKCeaZ0gwjsTxoHPjsJb2V6j9S0UGiVS816YpaGr+Ui2nTtmEI2xPeR1L2d9De9F0l/eYH0FelP%0AzvyARsfO1r955PFhQMMCaEnlz1PPSFvmpTM/fk/913vuoSc95ZM6fLhFHSpoUhQEFCS2GHPnINDt%0AFp5JavIT6+K4RWwxpm15DdBia5vveI/WJKENkgUYN+6wfX7WfLSkch7sReb4hOSxscBcVHMcvVbo%0AvluRRSt5EfURd49CncSyq6ScLbQsFUVxrT+nlN4r6fPV1ysk3RFFD6iujdGJX9b2iN4xB0rHHKAa%0AGnCkn1an3YKowbzPneUZyGEqVKRJFpzx00XVlildbk9aF/9NtkS5oKUadzWuPERdUjMLIEfE0Lyo%0ArDRs5but8XAQR047Gu83sxzcZo8dnzdNSp+hi+a6CXVQyDNv2At9GJ63gJyTtFnNSHll9f/KOy7X%0AQ9/+Q33l5LtK/yL92XNP0fu//7Syvm3V87uq6QJ6sc9Iuk7atJ/01FNP00OO/oKe+ed/pXSAxnFn%0AE6Gf6HFxHMwDnosNGidGw6kMPUeRb+Oc+Lvzp2MGhNdCNBJchp6SBSa3Msc+kcxLDFDG98R6+sqf%0AQucxja569Oi83lO4TwUsvMPwGhVRRnCefpV0+tXj13eUdkiwppT2K4riqurrYyQ5Y+Bzkj6WUnqz%0ASgjgUEln5uo48cHKg8xticVJzZ9dGaiZX+fe+LpUMzwtS1o9ZMJoEUn1JHrxWPBwMUSK2Cgxm2hh%0AWDuyvcQhaVVEpuE7Co2f6sQoLwV/TplEjNXfo3tF195jPa3S1Y7Cxc9yPL2JwJaHDzHxOFFIWSB4%0Afr3g+2q2ZyDN7Ck9/+vv11fOeIz08K7Ou+Cu+sEF0hGHqYaGpFKYkhemJN0ibX7CRh377G/qkjMO%0A1LM/907tNaUSItiq8iBt/qAkPa0YnadL6n64r1amxo950hZTplgfrXpaqlITR/WcUbj5GXohjA9E%0ATJPrjYE0jzOxbbfdGDbnhUE5qYapFtAnphJSyOaMn6lwL8JpXj8eOx/jOMqUm1ep3JzHjq3bx+wj%0AHbNfVb6QXvP9TFtuBS0rWFNKH5f0QEl7pZQuk/RqSceklO5eNfdnkk6QpKIozk8pnSbpfJVD8Zyi%0ALVG2q6aZ7wU3o+bA2/qLbou1m8L1RuPxrtykRWuEWpOCN1oU0Y3lIvI9RoIn5SG63W4j+zkIZWK/%0A+J3WnsJnLqiVYM25+plmYxfZO4PoaknNFLjceaJ0fZnyJY3zA4mC3e2oTgi703UXSft2pa3SWf37%0A6IgHS6OrpI77bIVtDL8rFYW0+ehNOu74b+g/vnaU9G1pafeBNvqgFVvJ06ENuTGKZMssuun0GBhE%0ApOKOvBK9B7cjl1Lo+milWjD6Pr0GCnYTv7PO3EHqEc5we00ee64hX/dzET6KFANh3vrMtbiYKR+D%0Ao34n4Rt7eFTmq4k8VbR2GwReq9odk+qOeoCiG+0y7HhkiLaEfC76SBaeFgIOglH72zWhIPX7I8N7%0AcuyyWovTPXNfJglbumrxWal51F+ktnHICdaITZlykIHbZU8jlncQizBFbh6lccEZlUju3XZ3p1T/%0ACmpV/oat0ivOfYv+9wNeIB0sbXtTT937DDW9R1WGpyZVedOXzic94tTzdP5ph0vfXtTUzFBLsxtL%0AYXqVSqv1RtV7+nPz7npjkNPj0EF5W2iLmfJMByMtl8+5Ep9zuXjBchQtalqPNnzajhOcZCg4y8T8%0A6qMCGZg1kadjxs1K+8d4xJzy5y1Xyi99RL+k57HaRbTQi6Ax8/2YExg1Pq2jNuLwMC2KLjhTvugm%0AMQeT+XfSuMVr6qjOP5QmL462qWMbclZmjpEdTXXALJaJbpqDIW1ckFNIbX12m3cJZXLWluvO1Ruz%0AQUyeBy9EvqMv7bmLdPLTXqK9d7tEmpLu+W/namqjamXieneRtFXavE361nueoPM/c7j0dWnfpRt1%0A9X4HaPBTlecB7CoVA9UJ8g56RGzPtJLDO2g4xJQ6pkv5c46nKXwnzYXv21JmBoIDN22Ks23OI/5t%0AYqDNyjUX+WfanttFgTmPephhYo+VFnFMvbKB4DHNBYoJCzH9MfZlJ9DaCdY4GWZ8WwbElZbw3f+H%0AocwwPE+yBWrrwVYlGVlqCsA2YR8xUF93tNSBMlsyXkxO44guepulwrop3BnUYKrOIu7bbZsUZLJA%0AJeZIS91tjxBDbA8VkJVe26L1mCd8JwZsRenrxNCk+vAPp67ZAinKvvbOGujeox9IP5cue8VBunjf%0A2RIjtfcwo9IC3ShddJ876KnnfaS0Tm8a6YUnvU57nHNjyYYDSZulNFD5vBd/UV7fniZo4Ugsyt4c%0AbQAAIABJREFU1ffIX/6fmw/3ma5xdN0ZsSf/uU1F5rNp1FKO4+xyzFjwd64Pkw0M8zbLEeahp8W5%0AIuzjnFkKf46p+djCeoQ6cpkpvraAul3GwjhCFyb+GvIqae0Eq1RHH2dVMx4niAve7ieDOTG4I+UZ%0Awe4fNRgZMFpmxL5y2G7bqBFrNcZqWMBWD61Q/4+bC9huMzBx0mh5uH5nGvQ0+bQpCzEG9CKzue6I%0AwdGSotsWg38c3ypPVD1tP5JPSaVlyOMRbelbmFLYxzQy475+XxUg6fz6SPp+oa0bN+iS3e9ZLrDr%0AVPPYBmn+59KXj3yqdKGka6THPOkjeuzj36pEvM7YoBfinFRUPxGjDVV9noPcLipCPeaXeA6t1NxO%0ASZiJRGVEimsg8utQ489RaFPYRmEdv/s/DQgqWBsU7Dv5y8ZGrJOBNh61mLOczRcxQyW3lpmJklNG%0AjBXw9LxoRe8gra1gdWes4X1qPSlGgaltV2q2D1Wm3bRZUiYzS8Qdc5NsaqszpjtJ431zIKWvOkLZ%0AhpnZAvAhHhvVjJg74mwMb1tLPVIeu/Y72sqY4SZBLn4uYmnOrLhJpZDdJt10sXTZldLSxSp/6XRW%0ApdDlSU62wJk874VkD8AK6xZJu0uvv9dL1Hn5FhWHdJTuOqelG1SmWnk+ulLaXzplvxOkyyR9Vnrl%0AjSfpoEPVPFHJsIbbsU1KhXTlJdKNl0qbL9f4r6lSIHjx0ruJ/GDB7f45NSi67fxPitfa1sNA48KH%0AGRnLkd/PIGWEKzqhvN9Lz2mIz1FBx5/9Zq42BbLrpIVL4WjLlu1WuO73xHa4H3Gd7gDtULrVTiEv%0AOMMA3JtvDZ3T0rbIyLx+ZhJFa4AugrUg8Vtij17gtBzZj/h+17MS6NtlJjF4TIeiK8RtpMSNbJUu%0ANy6xzx6TnHXVRjw1P4cndyTtKl389IN10eMOVmduoOLGKe3xjZt1+Q/nde2nztdF5xU6NEkH3K5M%0A+r9DV9pjqBInZf5vX/VWZqf5/F/q3jtMsqra+/+cququqs55untyzgMDzMCQBwkiWVQuov4EMaGC%0A+cK93ldEr5jgVa8RwxWViygKCIgSBIY8mUkMk0P3THdP5xyq6rx/nP5Sq/acHlD4PfPc9Tz1VNUJ%0A++y9z9orfNfae5fy2gCbtmU7mZeLg+yAS49h3q8ep97AQ6k++O6dn+LATyfCKyk+cdd3Oe7fdgb1%0AzgsmCjRHocuHgT2wbyjLmhUFcNalUFLl0TyzipI1h/CfA8+daeV6AjYjQS5xmOdlYRFLvrnP7V8r%0AsK0FKn7XfZCLN6fNPa5gcY/JkrPGjA2EuoaI+FNKyY6HsM059VzxMGQFnsViJTPc7ApL1kq3v90+%0AtWXEnONvgcV69ASru1SgxevcJfN0/kjWkvYAcnNGLT6mb6tlbfQfcgNbUcI1MmSZKWb+i8J6daz6%0Ah2UVjEUSdjYYo2cPEripg85x66KORW6aj7sGwetZNXY2mEsZAmu0GJ542xl8tu97VCcP0FtSSMll%0APRw4s54Lv/wQt27+Dw6d0cRf2wcZac3eHiNIlN4KLIrBpDkQS0PfLugahK6hgJX2Eazrs+llqPvl%0Adg7eOZOfZD7KUMtt1LYEXXdGEl4s9/j9966GCNT4f+fsm25l6w5oGgkM2ycJjF9RxIP8Uvjs5Xl0%0AfKGUO2ZfySvMpYUaHn7yXXgvEngHVmDYvrM8LCFr35/WZbCDWfCO5WcIfw9WmFoIxg2Mhf0e6325%0A5+warHD44uFjKW+rXCC8/naCgyiPw5fsTDvXWTjDGkHW8IJs34ZZoSlzrQvNvUk6eoJVbmzYvHPI%0Aze2UVreugwSMa2XZpHiLUdrAT8w5nyFXu1uMKcxKDSNbjstoLq5qgzjWwnRdtrDcVqvFdc0oBvia%0AoLftEVksLcwbsM91YRbhZy7JxXcxPhuEEWN3wEdv/xWb/3Med+z5JKkEUOURjcL9hy5j18JpDG4p%0A5rpdX+PYd/+epw5k4yf3AdOAR1PgbYLK0cc1EayvUg3MzIM/jfLOf+y5metSd7HvhelUjF5XDKwe%0AgKqSejadPx++2Uv8i+PY99F2do9k0SL31V17Aay79yzeG7+aPgropJyD1PHgnkvgh+TguznbsoSl%0AnA2Sixer310cX+PALjytGIO11GzWh9vvUrbifd2b51zjjgnL96NWfM5YtVali6VD7nKeapfN3BGU%0Ap8k+drsUtcN6rrrP1k87IFh+C9s9ROetsWRfsA32Cr99iyTi0cNYLWidMccUiLEaSRaV1gWQVnGF%0Ag5hXzDhk7ouYcuycehsdVxBJHWy3cNHHMovL2PbbttMyvesC2rqL0ex+QZDLfHajQR1zgz0Wo3Ld%0ANPtcC4eo/XLJrOUb5fABpGfHRq9VP9ogk72/F9gJN/3hNuoijWQGo/R2l+JHfUrLuoiQgZoRfnnS%0A9VT9dipL/iXG9Eg2vlkDnJAXrEVZCkyPwHUL4bwKeMcMmFkEN46Hm8og4cfhpQw8GeHEj0dYHINz%0AK2B6PmxZdykjj+VDNMr6V46ldCSo9snRYC62qp8HvPP2Ip568DJWxE+nkzIamcC+9CRu3v9VFn59%0AG2wiCMQpACWeHSsSb/vWYpbuvRIslkf1Eb7oBl60XqwNBGliip0W6kb+XRfehROEWwp/VmpihNw2%0AqL4SgtaaVfts2zR+hJNKMIoH7bixvG0DxJiydK1Nm7RjH7J4rAwqjTc3O+YtkIpHz2K1KVQuWRzH%0AUpg1OFYHKxovsgEaaVQxh5hFQLsEhpuXaK1ji0VJqLia0c1jDXthYbOywgB++2y1Q8ct47yRYIQl%0AW6YdaGE7IYixZT3JlXInHljLQBZIEdAFdU8388wFpzJv51Z6WsuoPWE33d3FJOKDjIs0k2SA25Z/%0AhgnLGzj5t8/jTVhDZ+sA9SloGYF4EZwwBIXzIG8m1EaCspOzCSTuixArGAa/GzrK6Lr4GC45ex2k%0AoDMWY+kffgDDcPb59/DUd+D8ODSkYHs6WHGwBmiZ5LFo81yeLTqRPUyhi1KGiNPYNZ67Y+/l3JtX%0AwMtkZ5+JX9ReN1ClPpbQEa9Y/reWo/7b3XLddya81pIbZLW5stYLsZtuWivYrXMY9uoqZRcWSJOL%0Az1uFq7Fn3X43m0K/3XFujRA348Ba9eob9x79l+GWNtdZKegK7X+Sjp7Fas30sMR1uSD2eFgC9ljW%0Aq86J8skurO0uAmHTYaLmGoubSYBa8F+CzD7HDhDbRvvyVd5YL9Bawkr7sUErFyd262ifE0aKliqw%0AIEvDMvOI+UDuQLHCPuXcY2ebqZ8yQPvo8Z0w4evNrG9azAdn/pTmbZOIJ4ZoYDz5DJOknwYmcIhq%0Avpr6P9z89CouvhUeBVYAv+6FwhIY3gf+VoJBshCYQWByfhiOz1sFfgfsgRUzLgrauAAeevu7Alxg%0AZQcLexs4bxHUXAvHvAOWT4PtwCrggnfF+O+iq9nMfEroppkalvirWPnCqZz7+RXwEkGGgxSgtXzg%0A8EWvrcCwPCzX2vVcXLzaRretcByLrEem+ihQ7Cpeu9ODJZuWaPnULjxu10G1StV6LuIDtTVJ7riS%0Ah+ZCApZc86/APF9jVmPaeoaqh2bOafzr+rDnjJX6+A/S0bNYLTZncUPhQGFk9x1yBYg6VEziam9Z%0ACXZnRjg84m7r5FqLbnQVDp8VYttjj0soq/42ncjFfuxvG1hysSq1Q+6eFbIWb1N97GCxZDMeImQF%0AOeTiYday0fU2ACBBK8vEri+gaK4HrIVpu/bw36s/xqcv/D7/Nv4WtiSCgFAeI9TQQpooU+K7qZzV%0ASsOli1m0eR1rfxUgCt9sC5ZgHTcMNZUwrhFYQCBY82F283ZYNBU6oH1zLUyAwY3wyiXz4R6AJFcd%0AuIUVG6BqH6Rj8MQhmH1NHjVfvpAHJwUTyOs5QJIBPrTz11x4z1/x/g40E1RCuPYguTxn+UICxVpQ%0AslR1rYtTWqvQxR3D0gyta22tYPdei7vbgM5Y+LlccFmHsvI8slix3bkgY36nzW+NG/G6hLuNm9jx%0AovvdiL0EqKA+GV+YsiCbU63y7PZKUXIVwRvJmvkn6egJ1iS5+YkJwoWDDUhBFje1G46JiV1cz0YI%0A5VpBuPstkoslZhIDCiKwlkOE7EIbVvDhtEP10TUacDaIJUqba9w8X5ENDFnGt6vy67hNB7OZD6qz%0AJRuwUJ6oBqRtv13lyCeLx0oJ2ICAG8hSXRqBLjhmzRYeXv4uPve5/+TVvJkU00OKGJW0MUCCZbzA%0A9hnTWfXfV3DGmS+w8YMPwGhTd/ZBTx/kpaCkFmJTgRMh+oAP7/DhtxkYgJ4/QKIUmoaroS1F7Z37%0AeWT5SBB8Ht0bo64a/vStOyip7KSPQgZJMJPt3PCXO+CXwE6CdAHxhtqhPrTGgA3eWOtVEIqdPBIl%0At99s2VZIu1krvvNf90jAqyy9MzfQa+MOLn/pmeJxkYJgyh1OkZ0oIYEli9sqZqXIqW+sRWuVuMpS%0A39hn28kwGkfie+txiufTHL4vm52Iof5yg4dvER09KEBPl4ku7WTdejvv2Go0kXWRLIUFkNxzcumP%0AhEmGBSF0n16ejlmrxA6UsQSYmMum3ei+seqi9ooh7HRA6xrmk3WX9DzITTa3pMGtdg6Y45BlUlkf%0ArkU9lvtkLf5h89FkhgGCXVXvgc8/9V3m8Cpxhsjg0UoVfRTRQzGT2McFPMzmK2dxwWhKwBaC/P7J%0AwHA79N8H/BT4BlAOnOCBF4WFELsSIlPh3vZ3wY4o9/3sKpIEGQWvUSmcWfAkE9lPkgGqaOW8XU8E%0AS7q/QjCV1bZbfZjgcA8rbL1RCT23nxRQkfuvSRD2Y91Wy08un7tBSshahyIJeyl1JdynnWdasvdb%0AflU51pPLEPCKDTSLdzD3WNhEH6uQbBBX5y3EofvVX25bZVG7cQ6X7HjV5381FACHWzeQm2rhujZu%0Aw48Urbba35r9ruDSOVd42nJUH7sMnOpkA1Mq37pFvinHuvJWMMuisBCDrZ99nj2u/pClqvbaOc9H%0ACvaJXGhC7r+1LKwF40aBRTbTA+e4goMiWbmjQntf+UTu7bqCD5XeQTUxCumlhG4K6aOZGkrp4l/y%0Af0fVHaWcfnUXW304Lx8KfGgvhtoq2LsDHl4D0bsIANkhiDUMs7IHCl6B7pcroTHDnoEMPQQGaAGw%0A8KvTOW/CLu5O9LKbKcxiG4X0Me7V5sD1t23Xe7DRb9c9F9lNDCFrIFhIJmWutQtMu+W5kJUbXLI4%0AZxhp/FhhI2GvLBwLXY0VxImYa/X+rbCzKXjuWLX1trEI3ylHZUH41khWuNtnW6NJmK4NKGujSZG1%0A6vX8t8h6PbqC1TKYm9caBqjruO5xo9QSfmIiGyiwHW/dU1uPsOh8xlzjBo6sYLEvSRijJUWQRZ65%0AVoPNhTMIud6ShCrOOSv8bc6hfdtjWTsqT4JQAUQLj2i7DYuhylpwA4yyrO1/YWpSVIWQKB5kXOkB%0ADlDPAjZSShftVDCNncQZpo8i9jOR3e+M8ft3vpuTD67ht/W/YhFAFzyyN1hVvQWY5AFxH5oypIqH%0A+M5P7+W+j/wL7IfC5CtsL2yliAAqrQDav3QMa0kwjV0MkKSKVobJo6BzIEhulZWverujxsWT5bJa%0AV1rCxEbfbSDU9r1LErq2DyVgLAQlQSNXXL8tn1thbsnNChC56UgWXnIDt+JRQWQam2nnGrf/juSp%0A2SUJZf3avcAsXqprrAelZ2sB7CPBdRaXfpN09ASrFUpKfdDcac2icnEkXW+3WrDBATHcWKC0G8wJ%0A60gxqsUv7bOtVekG4Fwcy5INLgjcHyQXo8Kpu7XU3Ta5i0+EPdsyjoUL3GwHqxxse1xBafNbrQWi%0AZ7sRbtXTksVZR0bLmwkdk0s5i7/zABdzKs8ySIJqWl8r8AD1fI/rOdA3nlMKn8Pvg1qCrKdrKmGw%0AJdgSOAFMqgZ+7kH8Za4f/i6LVu/h5YG5sB7y6Qf6X9tM4G0XwOMU8B0+z2LWcSrP4uORzzDxPens%0ADCCrUF3esqPIjd6LP93AiZ3Dbt+TxgHkBlzdfrTek/WAxFtWwPjmOggXYr5z3AbLrCWnvrAr+2v8%0AWlxZXpTaLAUdls3gBtssf6pvhcXa+20mgcfhmQXyvqznZcm1kt14x5ugo4exWi0mgDtDbg6crDnL%0AMJCbIuQyVIZgMLhWkrApm/rinpfroAFvB5SEto3kWoxKZYwlWF3BJ9zS5hGmzEdzo62rouPafx1z%0AvWsNwZEXvFZiuYUUXLzUdQvtu3CDYi5D+iHPd7M5fILFZKqgLVHO2/qe4O+/Oo8TutdSx0EqaSXO%0AEGV00E0J3ZSwt2MG7VSQnpFmyTUw1YNftgSpUosIZmhVTgdmAZOO40un3caIF+Oqz9wNtZBPEUn6%0ASQPvnlPNrx/6AjuZziYWMEIeCQaYltrNgle2Bviv+lGKXALE3SLEklxr66oKw7QwlSuobSDQ4q06%0AZ/FIS67Qg+w4sjnGPtk1SS2EoayNqPlt05LEF+7mn7IgC8h1+S0GLKFrFa9ySaPmOgvJQdZokaFk%0AZ7KpfbbdkNse2x8aa9ayVTut9X4kWOcfpKMbvHIH41ia1A5qyB38wk70wmzyMOYaW7aNnuu/7rN4%0ApQSILUuurXVfwgIVIgUnZO25aVUSVBKsdrWjjClD10IWAkg5Zblwh1wySy7EomCSgoT6qN+lcMJc%0ARRd3dJ+vAWQHgR3UECiJaqiilZM7X6L2Z2109ZQzQJLZbCOPEQZJMkSc+Wxh2oRXqecAM9nBhl9c%0Awfs/G3ltvGwshHQ1NN709qDsPPjj16+E1RFe/e582Olz6JRZLJowl7Nmxan6qs9SVtHIeErppJaD%0AxEiRTkcpeGwE9pC1/F4PvxTfyFUVv1q33w5umxLkQkL6jprjYWMj45Rl+1x9LQjLjRGoPRYiihAI%0AXlmkMiIk2LRrcNgi6vadShDbNqgfXIzT3m9jIqqHXVZSdbQ5wzbYZdtj35fqJt53x5XIGklvko4u%0Axuo2IKxBwoj0MjXg5WIpQABjR/5coH8sEuDtugf2RYhcGECaGYKXay0NOBwns+WL8axVrnOqVxi0%0AYUnR47DnjtX2saLL1p2Mcni93PpZ3E3PttBFzJzX+5MVkQ+Mhwk0sLN+CrO/sZftZTPZy3jyGGGE%0APHYxjWHymcNW8hniAh7iZ3yYd/MHIldmOP6vMOzBpXdGeGbSEtavnga3AldA5OwhMr+MwweAJZD5%0AZhN/3nolJ1ZMpTvazjia8fGYzF6ipCmhh1XxExh3/uPkPzMSnjvsBu3g8Gh31PltrSkN/KS534Wm%0A3gje5/a7JdvHFou0QSrL2xLSsjitZLAezFhBNVtPG0i1Y8kGk1SuCzXZ32HBLPe3jWmErUHsBtFk%0AQLnvwoX83iQdXYvVpbBG2TUvB8jF8bS1sbSa1fyisHQnuzCzna7p4kxW04a5+L75Vlkp55gllSUr%0Acci51pZnrUe7enqY5WjXrHWzFSAX37Na2lq0Xsj11lqyzGfbb3Et1c291qbD6FkawGmgBspSXXR6%0AZaw9bR4dhcX8jfNop4I+CvHIUEA//SRZxEbOP+tJfrbpE9RxkFuO/wILf1BP9P6382jlWXy5+Ct8%0Ae+Bf8d6T4YPf+wkZPx60cT/c8MVv4p1VR3NFFT/b9jF29s5kwepX+eqOW1jERippZyfT2cl0MnmR%0AwMPoM3VXu8KySzRTMEJ2po+1JmVpFY+e19baOh8jaxHa46IwAavg1VhK12bVSDlbTN2Fw/St+qoN%0ANj805nzc8abnupat0qXEl+JrRo8nnPItD0pRq38sZBUz93kcXkcLxVjs1eVRqzT/Vwev4HANbTWi%0Ai6u6GB9kX5QihzbApKhp2GQAvagwUN8+x1pZ1sW13/Z+3WetWbtBobSqkv/lponsQstjuSNp5xrr%0AqkfGuM+NRFu3KmyacFjqiU1BszhY1BxTUMZ1r+yz9cwYQb5TPfjboei2Pqr+9RC9FHN271P8d9HV%0AdFLGeBpYxEaaqKWfAmaxje7flrC6ehEjmRhDkTj1Kw7wjnMeZsuPjg2mZEUhekGah/suDATjISAO%0ALx06Be+2Ee7/yZWk/prHX757Pi3TK6mPNLKM54mRYgvzWZJZRd6qkSBtoIjse3UtVbmWlo/TZAOS%0A+aat4iVXYErBaMqpMgpcS85CLuJti59i+nks69E1POx4s0LIbuqn54mHrCVuy1I2jl3fwC4EY7dn%0A1zhQ1oIwbDuG5ZXaPnfhBwvxWZ7We7DC03qtukbPd/nzLTA3j1iE53kTPc970vO8zZ7nbfI87/rR%0A4xWe5z3med42z/Me9TyvzNxzk+d52z3P2+p53rn/dG2koSwpaVifMKsIck1/lxRh1Z44ul4krNPN%0AWnCt6bBkaouX2hQa+0LdFdWPpB1Vjupng3Iqx4UqPHL3YrIKQtdYwWdTZtzvMKvb/ndTgMI8Dhv0%0AsMcGCXKdkvC3b8LK78DkziYWdm5jxvo9XMhD7GMSeYyQzzB1HKSWgwyQ5Jf1V3Hucyt4+w+f4v+0%0AfYuuh+HaxT8NZkfdDfTB2895gIEXixk/ew/cAhMv2sns6s3UVjeSfiTKx2b/hKIfDRPPG+Ll0nnU%0AZZqYldlONS0c07WB6F8y0EUWH46Ztti0HQ1uKcyEaSNB+17DKzVPXh8rlKxgcEmBTytEw3jTCu/o%0AGOfd3E/7DPs7LFAWc+6x55TRY8uyKXtuuYy2RWt4JMnO6rOUP3ouGVJvi6taj8J6CuLvsNzqPg73%0AACXo3yS9nmweAT7j+/584CTgE57nzQVuBB7zfX8W8MTofzzPmwdcAcwjWOHtR57njf0MCQJpajsw%0Apf0suZFz16q1ZKP29nm61tbKBsZE9j57rXXh7bOsG65vN5XJtWQHzPXuClLWlUyZe1QHCUjBANZa%0AFlNZWMBmM4xFR/JfbP/YIAvkKgdXUYRtq6EpjoNALcyLw6xJ0FFWSHNZKbGWNF+64hvccNK/U9g8%0AyCAJBklQQQevMotuSmjurCT6Yob+TJyuX09i6/BceA/EIiMwDR5+7jImFO8jLzJC9LEhZmS2MZSJ%0AMye2lQkf38N17/8etMDeoUm0MI4SuikYHuTSnz1K+ZqBYGKAnammdtg2x53/bmDOCiu70wNkvSbd%0Aawd4WLDKCispNAl7CfqwkaYIv627JRu8gsPjALY9Gee/eM9+BD9o7CZ44yShnyTINCggKxgtZGKD%0AVTbQB1lvVddLVrh9Y70tBa1dK/dN0BGL8H2/yff99aO/ewkm940HLgbuHL3sTuDS0d+XAHf7vj/i%0A+/4egtTCpaGFuy/rsIeHHEuHfKLOPVag2rJd3NF1V8Oirm5dLdkUmbDULtfas781UK1lqIEqQar6%0AuwLNMr7r+lk4Jaz/xJAa4G7wKu38t+Riz3JNLYwia9l1EUUSArLQ4sBcmPRvMK4SClen2MwCUuOj%0ADG2A7S/BuCdayWOEaezCx6OWJibQwOAZCUhC+c97qG1oYlvfHLzpPkXf6qK0tI3LzrqbD57yc/wt%0AHunb4jz5lbdzQeQhemKFTHrHLooa+qABvDgkGSAdiZJclw5Wv3qO4FvvNY8sJirXMWLaIOFhMT5Z%0Auh65g98KBAkNq/xskAty+xJyrTHxiDujy/a3K3hcEs4eO8I1IpcHbBaCbacsUXtdGElQ2vqqTjpn%0AXXOVb9cHgKyRoTaExVow9bDwmUjluWPun6Q3LJs9z5sCLCZYNG2c7/vNo6eagXGjv+uBBnNbA4Eg%0APpzEZHaKHYy9HJqE1VjkAvOiN5I6IY1oBRpkhY91u+wL0znV387tdtuhQWSTusNW8bIBIdea0DMg%0A6xpKUNrFLNwUF8hmPKTNvSrbuqJ2UISRixeGuZBWsIeVJQFfSsA5S4FTIRZJs49JdE0qxBsXQJwz%0A/7iFv3EehfQyiX2cMfIMF/oP0Vhew86D4P8e/NYoZ1Y8gR/3yC8c5Nhlq7iRbwDQm1cE6QPwOyim%0Al97hYpb0rWbq1v0wETrjpZTTQW+mEO/PfuBrHUeArbo4pkc2MKVBbJeaFB/lOx/dg7nG7Y8wiMCW%0Apb4Uj0YJBHrCXGMDSlZQ2BiBS1ZBhE0VxRyzZbhxC7s0odqp5/8jpPrKm3PrOtbKd3qmAtCui+96%0AxJDFdC1cMchbQm9IsHqeVwT8EbjB9327JRC+749lH712SehRWW02kgxZ5nHzPXMqRK7AsZ0oASnX%0AWE+3UVE3j81CBi64b5nJuve2VbpfA0Ga3L3HCiT7bV32NIcLQcj2l9x5dxZXgrEFniVXuOoYZK0y%0AGyyRxeFaK5jzrlWhc7KMraDQR3mdKYINrRLwxHGnkCFCpi9CvAZqPVj9KLx7131cz38RP+hTt76d%0APr+QFzgJbzbs2AJbLp3NuPxmLj/3N8wdeoWu31RTPdBBhAyZ8gjEKrh2/Y9Z3beU6j0dXOf9lP3X%0A1NB/eYKavGYKOgfZ2j8Pbxh6T4hnBVUB2WR/G6RSW90IvrVU1RcWMtFvi9na+60LK2yWMe7NN/da%0ADNMqvgxZ40VTbO04s1F7K3TChKFNx4Ksle4upu1al/odRhYCcVOt3AWEIAslKR9cYzZi/lsozhoP%0AEXOdxpBVmhZGewss1tfNCvA8L49AqP7G9/37Rw83e55X6/t+k+d5dQRTtCFYDG6iuX3C6LHD6OYn%0Aea1jzpwMZ04k1411zXTXrbaWnwSMZWD9t666jRD65HZiWPBgrAwB+/KsJW2v1fPtXHs3o0BttLNk%0ARFHnWpUbI8sEsiJ853+YSx8mzG0WhRWmNiAzSFazW4HhO2WJbN6nfaYbnQboAD/qwUzwdvmceGAV%0AO8o6KC7oJXYsFD8EmQGYffcmbvzct/h+3ce49aavMPO0BqZeuZfoTKiuhHR3N4UlA3wn7wsMTSvk%0AkuH7WLj1ZS6dex/9g4VQMMLGogX0por58Mwfg5+heP0A8YlDzG/ewfNlJ3DJPX+DOBRSYExKAAAg%0AAElEQVS1DwWrXSt6nSZ3lLimiP7LSrIusc/h/OR6E/Z9WU/FwjqyaDURRt6PFG3aucc+0xoRYRh7%0AWDaJi6Xa2IHICjVXcCqoGiGLw0pQWhzTGkM2H12k/rN1Ez5vMwhsO225lr8h6zGqn/J4bSW3p5rg%0AqUPm3JskLzA4xzjpeR4Bhtrm+/5nzPFvjR77pud5NwJlvu/fOBq8+h8C52488Dgww3ce4nme73+F%0Aw4NA0l5vJCFeJIElS1VMNUTuy7RBA93zRjpQ0z81AGRRWoE3Vhe6assGHcLyUSE78yMMdrBWratZ%0ALcwgEnOrX+yAs3WzVniKbOK6XEStwWmtE3kWYfDLG6HRdLOh7+TRdEo5k7/YEjxvJkG0dgL4/wqt%0ASaieAFwBAx0JkolBWj8PZd+MMXBMlMR3hohe49FXkeCrZ9xIOxXk+cPc1fR+5lZuYeXXz4Dvpbhy%0A629ZNG49/RRw3eCPqP1rF+SBPx/8v3pE9vnBTgSDwFPALoJdAhKmX8MS0C1JgIwlKCDrroYJAktS%0AlDZDRQv56D3ad3ik/Mvo6L1aA9kKQ9V5rHstXOR6kkdyzTX+PHKXPtT4TpIdS5ryGpYVEEb2mWEZ%0AJyJruWr82N1f7YIuDnl/AN/3/2kR+3pQwCnA+4DlnuetG/28nWDVy3M8z9sGnDX6H9/3twC/J1gu%0A8xHgOleovi5ZTfhGs2ytdaB7BKhbwefmHI5FYRkFlqmOFJRysxAs2Y3fwrR/mICSdSMha7FpNzBi%0AXW0Llwh/U//IDWT0fynBJPspBCh5gqybrtVK9K3yreAPS+GxnBUx18gSHk2Sz0SiNEbGB89vAx4g%0A8I+2wvAwDLQRLALQAcmdg2TWetzfcxU9cwsp2jFE3j4YiUT44Rkfodsv4bdPX82Il8eFdQ9Sm98U%0ABKKSMQqTfWxlDl2Usj0xE56FA0uq8O+EyDo/GOCdo5++0Y+d86+AiaugbBvtzEBX0cktDQsyuf8t%0A/GPT9+zq+FY5u660hbdsqpVW/nfjERLa+ljDRjw9yOH8OZZQFVTlkg0+ady4GP2RyGa2WCtY/KW2%0A2Y/GsoJ9NntAkz/+f6Ajii7f959lbOF79hj3fB34+us+OUxT2KX/1AnS1q41BtmXL0ZRp1tB5Lob%0AGiiHVdy533XXRSo3zOpw8Vw7nVAUluSsCQOvF/CxFqwSsS2MoN/aAcAOYgk2m/IVIdg7egmkFkdI%0AJyPE96RgH7AaBjfDoVaomQT5peClgCpy11Z13dU8cmeUqU8V0NM7aQUWwdD4fFZwOvNP30bpob4g%0AmLUL2AzxBVDyHMF+WVOBKRB50ueKg3/gonEPc8cx1zKtbD9PLjud3/EvXO79kfPOeIgtzCeVjjIY%0ATUIdRDZn6EkUUUIXDYznJU7k2GtfYdvgLOo7W4N82vEEQatXCJSIMGiLM6tNYQLAWoDyFOyCQlYo%0AhbnqcPjuAhZLD4vIh8UA3DJl9UoZWu8lLNYA2TFnc8XtuFJd7JJ+LknQ6d3LotY4Vv3tbCwb8HKD%0Au5b/rVcaM/dgfsv69QlmaKrPLLTjke1zTeoIkzP/BB29mVfWNbDYkcWSRNJU1gWHrJurayAryFSG%0ABE2YO2zJHTwiG+VUXV2r12Klul8DxF0P07r2qqdNTRF2pnI1wC1jQJZpdF++ud8ju4iFjuk+BSxS%0ABEvoXwDt7yrgocQF5DFMYukwy9IvUPtoJ/mHYEcz/GUX1Eeg34fTSqB+sWkjZHccKCRgYrm7dvAP%0AEASDBghmQi0BroJIkc9upnKopJrSCX1wLMFagA8BtdDcB8mVED+PwHJdDcU/GWbCRxv4/x6/i4fe%0Aew7tlJFgkHUcy4bm40kVeTQunUrRn9phP0TOT3Nm/pO0Uw7ACk5n8fT1vO2W54I2NECmwmP/khom%0AtzbnBlXyTTsgd/ac6524wsMm9lsaK/NFHpbNLJGRIUhCPKxnuJCOvj1znWtB2qU2VW8rmKw3Yo2G%0ACNmg1cDo/36yvGaFsdpslYC2VbLQgIXv3M0ZMc+341ZwhnjMtj3f3GMFpR0D1rhS3Wze7ev58W+A%0Ajp5gVedaLSnXxXYYZF+IS7bDXY2VIXA3FWEPA+1djS+yQtZqdhu4ct1tq/FtcEdtdBOnRT651ru7%0APqcVxu7zcK61AkCDRQxvB4gExslw6Oxy7khczQHqKaeDfZFJrIkcx7nnPsbpZStZ/jsY90fYcDDo%0AzkINiHKCQTVCkJokJhZUIeVgI7yCE84HzoSDp5ezPzmeStrYWzeBGR17GJgQJdmXZvjqPJpOqqTj%0A6SZqIrD7vOnMeXQn6asjRBozfDfzad5/0Z3cz2V0DZfz0jfOpPjKDnquKYUdPjQ9S8+Di6AwTWpm%0AHh+f/2NKr+0nf/4wx56+mpm9u4LkweLgnQwX5dEQqad2ejvxqSNBHqtyje2UaZt3KnI9GJErbMN4%0ATRCTyrfv1/KglhCUJSwM1lXM1qK0ddO9NqAqci1kkbtwUIbsO1Q/yCuzyw66glpjQfVy0/ustQ+H%0A1w1yg6F5HO4hqk4am7KS1e8ydJT5oj6RorDB4n8MvAyloydYXQtUnS/rx3aujZy6ZdjfEiISZNJa%0AYQyte11GDHP9be6ogjkaSGJKNwjhCl8xrRhE1oSdl+1SmLUTVn+LV0nThykjMXeCAAI4BrbXTmYj%0AC+iilFoKGCDJWhYTyUvTe0oRixevZ855Hcx7yYcDQMnoM/eQO5C0o4AbeXUH2VKCuXlF0JysoZEJ%0AnMqzRPOGIR+SO9PgwYHl1Xyz6PN8/ewbKW8ZZn8mj4HJ+bSPK+PR889iC/No6h3H57b/gE8Xf4sL%0AP/pH8msGWffNk2ifWErX208P+raHALR62GPKrN20VddQ3NbN8+OX0n3ZJmqHmineM8RIdZRSuuip%0ALyQ+pRNWcni6HoS7vhHClbf1ThSoEdl0PPGJ5QMZBLLOLOwlQS/ISX0vvreeWVhcwPKO6md/a7aW%0Au46E9QqtVS9r1EIftr4yHlS2fbabHeDm+WrM6HyY1an2WQ/SZhPkO98SrHq26/39rxasYgzbIFk3%0A7gZqWsjkjZIgBW2rUUjuos5HIpchrbASdumSBk7M/FcZVrgIE7LpS26kFXJx2SMJ1yNFcy3D2uin%0AT2B6LoTWxcU8y2lsZS5xhiihhwgZRshnDcfTSTl7Ciax4ILNTDlnH5Oam7PlvgisJQj2HBr99BFg%0ApMqksAGtGEHE/z2QmgqxISihmy5KKaSPUrrgHaNtb4OCxmF+3/1+lt66jqsf+g3TkjtoPb6cu6JX%0AcvPfvsFIPI/MmiglH21mdnwrZ0Se4sfex+mYXkpPQwV8CZjqw50etMO0+3ZzftWDHMt6VnM87992%0ADxfP+gPLE0+RN2eE5eknKct0UUB/oHRKgA4CwWyX93Mj83pfYWRhHQlPm5/prjOh63xyLSd3SmbG%0A+Xbdfdd1PlIg2DU8ZASIxDd2F2DBa3qGtcrd+IDaL8/NtSxVX1ntNj9bub6WxFPucQvB6Hk2uCrB%0AK+Vlx7WMASmkt0AqHj3BKoGizrNYiPAkaS+5PW6qCuacSJpUbopIW1grIGaxS8gVZlZTiwEy5C5w%0ArWeEJRW78IANyClQZWEC9YNeesQpZyyyA8LFkK27CFmFVQwsgZHzI6wrOYYmahny42zduZD0jCjF%0AdDNIkhRlePi0U8E2bza18SbyJw1TSRvjaGZWzS7qjmshuXEIHhut9yGCrVMLyC44Isy1HlIfglWL%0Aj2V6eheZdJRnOZVOysgQ4QB1wSIqfUG/Vm9v4/j3Ps+PXvw4yy94nEcS51FOBysPLWNobSKwKLdD%0A9ztq+PKMr0AUdj81i9RHEkTuSkEkApu9oB5bYNeNE/juB26k9obddB2sIvXTOM9+9TR2F0zjbB5j%0AcWQdE/oO0FVUTMHM4eC+Vue96Z3oPUm4iEfU9zaaL8/JvhPITf9zA1ASbPaZdqUn18NStoGeayG2%0AMJjCrZcbyJIF6ioPQQ8SVuoX1wtTBgPkZlaobSlzreoYZiDoWhe2k9CUoJenpPFphbtmlI2Yc+pH%0AO9YsnPK/WrAqCqfpZOoUy1RiCDGkXCDLWDYhXRiKIuyuxrORaYvTyH1S4MyWL6EpQSy4Qgyl8gvI%0AbtftO9dD7q6n1pIJS2KOOOdURzGyBoK1JsQ8rhtlsaNCYDakl0Z4bvISXuQkdjKNYT+PkaEEu5pn%0AUlDcCwmffIYZieQxTJw9TKGEbhIMMkw+09jFjsQ26mccYNbUbUxa0kTJI33BDKqNBFF9CCLto96D%0Av8xj8/zZrGIJ4zlAfV8LTfm1dFPCRPbhE4G9wF5IXR6lOa+Kobw4mwYW8OfqC9nGLLoopbq6iWU3%0A/Z2XnziB/s8UwQsRiuv7WHvHMngaIrenmLFkM6nj8tj1iXmB5TkJqChm8A/QeNEU5k7eyIm3PUcr%0AVewankp3fimtXhUvFy0kRoq6vM7gPllmEmrWQrPRclmjYSlEVqBJ6afNcZdPJKTdVC37bMguGuIK%0AYosdSoC4MQMXC1UbLO9IUOu4uwaEtqpWVN0u9iLjQQJQdZRhBLlQUYRsKliKYCxZHNvCb8p/tUoh%0A7Vxny7XKxpIEqMV60xze9/8kHV2LFXKjf0ciCUOLR+k+CTiVoTw5CT/rvrtJ1XYvHQk/i9daLahB%0AYYH7AdMW6/aE4WmWXKzOXV/gSBMPXNdQCicM/1M/xQgCTvOgaWEFf+csNjOPDsrxIhDpTtOxdRzT%0A37mNd3p/ooxOfs97WD18AkX5vQyOJKjLO0AxvWxhHi9zDBW0Mz7ayNIJK1n2oReYuaMBfjhan3aC%0AAFAecBa0XlvIo5GzaaWShmg9W8vmsIFFVNBOHQeZzavBtRNg7XHzeIozWbtrGYN+AduYRTsVHKKK%0A+WzhOn7Is2edxj2PX0nnvnFcXHo/Uz69iz9tuor8eX0cGqom7g3BNoIFBzqAayFSP8zHp36f2kgz%0ALdTwVP9yBgcKWFu5mDN4mhK68IjmLv0n+EbvQ4LWuoxjDUQXd3dJk0HEwzZQKXojXouFXGTdSRC5%0AeaI2s0QkwWd5yuKOKsda7hpzUQIvyCcYZyUcDpfJM7RCy1U+EbIrnvWPHi8mazlbTFrCHHKxZf23%0AGQA2d9ruMqA6WmWn8t8COnqCFbLMGmZVhpF13W26hH1JbgqGBoEdEApCKTgA2XnfYnYxrLAluQ7W%0AbR80ZUfMtXZBlCORrHEX8IfcQWLhAp1zyxkifMk3O+gKgHGwKzmFlzmGdspJE6PPLyRT7fO2Y//C%0A7cOfZt6GHQxMjDNcm09trIkyOnkqvZzdA9OpKzlABo8BkqxsOpnKghaGSuKsixzLxFkNnH7r8yx6%0AYTP5f0vBWhhcC4l26Bqq4NXkbBaxgT6K6BhdwrefAnopJnMwAYcG4DTYxAL+q/t6eleVMP/ta4mQ%0AoZMydvTM5OWDS7ln/ftp/mUdkz61kyve8T/8ouXDDDQWwEYYbkmSSsboWFUY5KQWAF8AKiEzOZ//%0A+eYHKRnfw8m1TxH3h+hvLqe5oI4tybn0U0AtB4N3UkB2FppdMlAQlX2/diBbshipfdeWhxXtlyCT%0AhWkDWa4xYI+LP3TOwkj6b9On7HiRhevCRrLyLG/a58n4UJn9ZFf56iGr4G1utYyBsXLIrStv1yGw%0AuKuudaEOaylbDy1qzutjjSX1j2++3YyJf5KOrmCFw/EfNdhGKm2Uz80WgKybbEFpaW2bWyo3SMwo%0AbYa515LKEFkX3Wp5DQi5P9YqsC6cGNyC5W4Uciy8zfaBjZTqHpcZbJRX7S6DTC3sZiqtVJEaff0J%0Ab5DSKW2cEnuGGc17iP3Jp7h+kOuPv4NP1NxBX2ER99RdxprE8cxlC2s4nodaL2Gwq5g2z2djyQIi%0A+DzvR+krKGDr22Yw+8TtLHzyFRKrRqATZvx8H1+6/Jtsr51CJJKhkD5OYBVbmcsW5tFfV8AFZz/O%0A0AyPA9QRy0tBmU9ffxEdJeWkvBjvKf49jyfPYV3XSSz99+e56bSv8r5Dd9P7y4pggcq7oGzGISKR%0ADK0FhdCbhsXRYFZXLfAstH67lta6WoYvz+ejp/4X+YkMLx9cxIFp47mIh4iSyirHBIHFK36y70nC%0AQgPWelG+8+2+V6vwLLQlgeXyJua8tSythzJkrrG8Yhfuca1Fz5ThKgvrMVmFbQWS7tMi1Zr67BP0%0AndxyjaOxvFLbF5qcIYFqA0+qlxWktl4S+haesfW1CshCjFbAulDjP0lHV7CKefqcmliwGcZ+IcoW%0AEMPoZYqhRALTVZ4ViNKKUcK3axbjaUEOLcqt59sIptrkRmVVlhK9rVB0taeEppjCzhVXOWFum0vW%0ArZSSKoKe6iTbmMUhqiimjziDFNFLZ6aMGCmig6O7k+4HbxPE6qA02ctH5vwGv+q3eO0+6ckeP1q4%0Ais8f+D7DnQVsLlrAUHcR0eE0bZMrWcAmKova+PRF/5fpZ+8i0h8hsTrNlC0N1Kab2DBx7uheVjCH%0ArWSIsIGFHDN/A1MPHaSqpI2q5CH6ziugc7CY3669Gh6McejGagYyhVx//Lf4eORH3J75HL0PVQT8%0AcwEUTTpESV4XCYZojU2Ei6MwnQDznQGcRgAL/BIaDkzmq95/8qlTvs37+G8OUI+PR5pYFlPVDgLq%0AY5vqFJaYbwekTeC3OGPYe7Ielu6xedEi978yL8Lccytc5fmojjalS23r53BBZbNxJODswtVWwFl+%0AtWNBfG2NCI9sUMsaGjJMlIJoFYJ9lvUobeqU2hKWQSTDx670ZY2fNwpJvkE6eoLV4lZKh8J8WyEI%0AucySIXcygch2lKzYMFfHdqq0sTvP35LdjlpMoYikhJYVfCKrNV0tbrWmSC/cTvvTt4JrkNs3+vQT%0AJOrD4XiYpppWQkNiPDuYQVNfHbHCBpL0kWCIkeE89uRPIZ2MQUEqWJOsmwAnLQK2gFfgwxBEK33e%0A94XfM7AoyXx/MwlvkF9Gr+We/Veyt3EGXeWljC9o5PPcxvhkI+OSzbzr3HuZ1raX8uY+Tti7ifaS%0ABprLx7GSpRyimpN5jra8CrqryjlIHdvWL6D74fKg3VuCNqy/fxlPXriMaa17+GLNrazylsDyFCyK%0AUDS1g/GFjXj49GUKA0szOlr3acB+iL17iMT8Hkqv6KHxrqmMdObzwwOfZmnpi3yt4CZmdu/hQGlV%0AkN3QRjaIYrF1BYVs0EMBINdNF+m4C1FZIWG9M5cPrfJ2U73Em3YtA/GW6ucGpdxRr2wZGRIZ818G%0AiTwlC93pGltnQSjWehfPu3W3GQuKkUgQWxgiSq5ySjv/pfQUuNMzLEyn57ueXVj84y2goydYbQeF%0AYRrqOGk2MbYsP8jiR3YvcGlPq8nFTBlzncoXg0vQh1kKdvqdzQ11LQ3XjbftVGRVTG93ELAUpjEt%0AA+oaMZTaHsYgal8UKAW/Eg7lV7FnaAq9+6sYmnWISMQnxghlRZ0MkGSgIJ+CqhTsJqvkeslN9K6B%0A8p9288U5/xX0yUE4/twNnDLzOXbEp7FzYDrrDi6hva6CeWxhkAQPeJcwoaqBkqpulvEC1Qc7uLD3%0AEfYVTWIbsxgiwYrI6cxPbGZ7ZibdXyyHlwehJQ6xfiov6OYvx1xEY1E1V0V+w+qNJ5NX3Q+3x+BM%0AGKmPM0icoUyC/v7C7AQGeQTDkB6I0b+9gqkT9tIYnwLbYKi4gBVNZ3PFGTO5vuZ2PrjzblhPbtRc%0A7z9GIFjdGVASruIzvW+bwqT3lzFl2XcpPnSFoo2kQ+7kAMjyua6zmLzqZF3psfBDCWVl6VisVtar%0A+FWC1/Kd5T8b+IJsAMoKNte917PUL4rwW+FtXXsXZtG70HH7vIi5zs3osBCPx9jy6B+koydYbYPH%0AwjSs9ZcKuU4dYs1/ufTSvjZ/LqxcO4MFXj+Sa60S+6LDyE6nk5ViMVPN2ZaycIMF1rqwlqyi1apv%0AmizO5TKFmDMBfo3HgWgdLR31+J0ROkfKKIt3UkonRfTSQjXN0VoqS3ZlmdRl7AjBpIAXCVKrRoAO%0AKNvby3XVvwhW4C+GlmMq+C6fYB3H0k0pmwYXUBLp4sr83+EDNXWHmMl2ruaXLGYdK1nKAeqJkuL0%0AyAruvuIaeDoBhe3Mfc9B1txwArvn1vE4V7KRBZRObqV5w6TXcp6HGgtoK6kCoHdXRbAOwnEEebXT%0AgbXg/ySKPw821h4P1eC/DF4ZeKekOLhyMjeVfY9Npy3k8i/8iUvzH8H7PdnBKQ9ImKv6Vjxh+13X%0AimwQyypOa8XGnGtVrq6zZKPk4vUCcoVhmIFgg7Eu2aBRgixvu1amhQKk2G171Rcp556w+IVIVnIY%0ARTh8BpjGuwS+SOPMTlRwsyIwz5IHbGM1bwG+qqKOLin30yVpRbtAi+v625cjC9JG/uHwF65BMkgu%0ANiV33sXBrNC12k11tpZpxjlvtaeEu7uAxLBzj2uxWs1uNbCFENz6WTdMe0wVgl8eoZUqOkbKIAm9%0AvcWMxPNIkUcdB6mgg6FIAipH+0bbY1iXS3XtJuhDuXmbCdJjNgTX1Uxo5z/P+Rq/Oety7vA+Sn7+%0ACK3dNbyQv4ztzOAYNrCG45nDVtJEOIOnaaWKtRzHYtZR/bb9HDp1Ihfc/DTvOu0ePsQP2JxeQHPn%0AeAor+2j6whR4FvhumoL6HiIFGfITw2TSEchEgxlgZYCWYS8lmFQwRAAPHO9T/NEuLlp2L8t4kWfq%0Az+BP6/6FRxvOZ23Ncbz82YV8ZPmd1P+pOZhhpskCrtCRwpQl63pEkOtW611J4blejxsg0/3iAVdg%0AFJjnie8tH7v5qZA7rVTnpUi1gZ+91z7f3QLIWt5W+eqYtaDFRxL8Eeej9rnWrkuy7tPOdTbrwsIa%0ARwqayTNR/cMU0j9BR1ewSsgpvcoypb6tRredNJa5bjFNnGtt0EoM6C6Cgfm2bpkVXrIM7TPEOHY6%0Aq16s8DG5kPovl9E+Myy6bwWx2x825cbFpa2rEwE/Cf0kGRpMQB8MHyyhvbyCRGQQD58mxtEcrYHi%0ALblpZqqjBL+1RhSQGyEQPoMEgqsZvEKfmlPb6IsXUhNpprism31MpJUquigj4qe5a+Qq5uVtYaG3%0AkWJ6qKCdYno4rmYdZz5yOzPztnEL/4ddnTNZWLIeLzXCgY9Mgyc9Yv8+wuQTXyXBIL3pInr8YjKp%0AKMTTkBcNhERlGp6IBv1QSDBLbDgFL3j03FfG3V/8AO1nVXNZ0R+58rS7+PbWG1nZfRJ3z7mKnYun%0Ac87CJzhx7Rpm3bsTbwO5g05rlCpv2cXXMddHzbdv/tvIud61BKMVYC42KuFhI/zii6hzDnI9Lfe3%0AxU8xbVIZMedaO5PK8FcOZKX/8nZsuy0m7Zlr1Q4rjDH10zV2SyfVJW7u13gZJHfjQff9qH26x0KM%0Ab5KOrmC1EULNllJHWY0G2YReHZOVKbI4jhsIC1sX0wLdNjCm8zaYZfFNdwqtfbabCiIGlgBPmuut%0AYNW9Nj/PkoIWkE0ns27lWG/Rau9hiPRnmMh+krEBBtIlEIWuwVIqCtqJkCFFlLZYOelKj2ienxtk%0Ac4MOkBUKiigrEDnAa7t3DkQTNHRMoWOkmHE1zeQxQpoIfRSyqWcBwztLeYXF9C++j/fyPyxlJf1+%0AAT8ruIZNLOTLfIXutjIKinvoTpfQ9B9Tg2j9jyGyaJC2oUp836NrfzV0RUcX0PbJX9RFfnyYvv2l%0A+MPRoH41BMK/MRZ87/HxP5DHXz98CY9/+HyWT3uEE+c8z4TMHu7d/X6S0/tZETuDwiV9fGzpj/n4%0A5l+Q9yU/G2EuNPwgC0mDtIds/0kRJwmgBNcDsy6oXdNB/eyu6+umTVlcEnPM5QubeqgIfIpsapnl%0AJ+sVucEqqzRcoSiyY8kKeI0RKSPNpPLJXZPBKm/xsKUoWSjC5qXbFbnc/rJrkKTNsSi5XvNbIBWP%0AnmC1HeUuVCES00ooqtNsHqnVvoICXNJxO3U2LB1KLoaN0LrXqT7W7VeQxCVZke6MKCX028wEMbnF%0A80Q2oJAml2ldK98l1bUfIi/6nFvzBNeMv4OflX2UrpYAk0wRpXzUWkyRh5/xwPNzgwvqazuoZKnY%0AASuhP5oDWjzcS//eAtIHiji0MMKEiXsoSfdwfGQN15d8n6LFPaTIZy+TeITz+SC/oq63mZuKv8EO%0AZtDll3Lg4GQqyg+wr3tSsNF6L7AGUvEk6WMGyHgR6I8Eaw1Mh/xxvVRWtNLZWIO/Jj9Y33UPQdpV%0AnAAHngOxC1NMn/wK2zfMJfWlfB5LXsKKZedQPKeX2KQRCukjmkkzkEry8/xr2Tt/CkvuWMtlKx4k%0A/tBI0M5Wspa8lvCz78vygHgwPtoGGRD2XVlesVCBFW6Q3WZFQtnlUwl0F1fUf3eRIzeB33qGFvby%0AzPkwnnPxVpc/9XyfLHyi+odhya6hIchP3qEsXCkkCVe7QJEdp3bLJskX12oea/Huf4COblaAi7/Y%0AiJzFfkQJchnEuuV6Se6LsO6/LMuwxR8EhNvUkSNhM3A4I1iyTCGmsYtzqG4Zsi6Lbbt9hphEjK9I%0AtVUwapcVfPqkCfI3t0B1dTdXn30nu4um8uf2d+GlfQZIUsdBPHwyeGRqIlCRCe7RYFf5Fn/Tc5QY%0AniGwVhVc6IOlDav53NTv0Datkkklu5lAA23RSvoo5HlOZsPgIob2FTI4OcatA/+BVwyfzbuNTr+U%0AncPTiZJi6exneX7lcjJPRYPnVgPL0oxbso/xiUb2j0ykb6gqwFRjkG7Lp6l1Iv76fHiGAPftItgp%0AYBFwCoy7dD+XjbuXQnppW/4s429o5FCqhr33zGDlSycy/GiC5+vfRuHFHQy0FHHi4md43juZvdWT%0A2Hz5HK465vfMadgBdbB1eCYFXf0UtPRRPNRLfGcqeKfd5pMgSOGyKYA2Gu4KEct79rj41PKBdXWt%0AJWdhIgsPyQiwlqQUpz7yCMPetx1TNkPBDb6qvuJza3m6MIF7j/rJwoS6xxo+aq1V+rAAACAASURB%0AVLe9V6T4jBRH2D1uXEIe9Juko5/HalcNH+s6yLXmws4L07Tzry2AbyP4Ejbuc3WPtH0YWazVkqtx%0A3YGiXEPrTknwqX7uJAn7LUUSxqCec61VDPqMAAeB1TB5XgMXTHmIp8vPoJ8C4gyTJkovRXRRRiYZ%0AyUaZbW6wXRlIdbH9L5iiCjJxj8wZURon1lKXbKDDL+X+zKXM8rfz5MhZ7N8yA3rAe9bnjKv/xgPp%0A9/NC2RLu4Rqe7TmdRN8A3cMlzKvZzLP3nR0I6heA+aOfWJREYiiwsNNeAEfkAw2Qbo8H9dtHIFA9%0AggkC5cCpPjM+uJnj8tcwm1dpp4ITeYRLDjxE/mOj3bc8yl+OWc7Xh77Exh8vwVvu4aV9FsQ2kc8Q%0APxq8jmdnnMrpM1ZwfdNPGN/SyDnLHmbIi3Ns5mXmxzYxnkaqOcR4GpnSeJDYQAZvr0+0IY33nA+v%0Acvj6DtbAsILVGgPWErYRevEI5HpPec5/m64ogaI8Z4un2vJswEz84Ap7GHvMuNdLkdhgrfgnRSAQ%0ArfEj6MWulGXTI+1zbUaEFeq23cNkF3vBufctwFdV1NEj4Uu2g60wtPOybVRfgxoOd4ktZiOtJ2sV%0AwheBsPWRULYujYSeBevdtBgraPUSrRtk3S3t8yNBJHdLL9Y317iMJyvEXehCeX82sKABaHHqdijc%0APsT0KbuYUNTAhr2LacuvJB2Pks8wjYwnXRSDguFsf9r8XQ2uutH/1QRZBLNh63FTWV+0mMHiODuL%0Ap7COxbw6PIeDq6bQn07iN8R48cDyoIxHgCXw5DvPJJno5r6Ci/grb6eIXrz9Pj2FpZTXtbHxliXB%0AZIXFwEkEqV4+RCYP0tQ5jnjZID1t5dBAMJmhYvRziADnjBPs0zUuKCOyJE1+bIgyOmmilqns5pTu%0AF8l/GlgHdEPs5TQXP/g4F894nNZjK9hTM5ldsQm8zDHsYirHJF6mmRqe4xQO1VZzac39/Kjvkwx0%0AlnJ97//ld698gNjUEcqmtlBb1kTZ+A7K6CI+Y5DjWMvpFz/PCSs3BJsm7iA7BuR52ZXPxA9usBSy%0AfGgDl3pH1pLE8MJY66OqHPGZm3kCWStYZclCtfdbC9Ael7cma1SKQccsBGH3TlP5dhEk1cVNX7Pu%0AvJUL8lQljFUnbS+j52XMdW+Sjn66lQSYdZEh10W3UcQwoeameegaCSdZizYdRq6YTU+xwSe54/ac%0AyNXMlvndPFdr8aWdY2Iym4bjliOIQEyta95ovp1ltCiBsDkI4/1GJkX2sSF/McOZfAb9BAlvkChp%0AfM8L6lM4Wq8CgiUA66G3PknvnCIO1VVzYHwN6/MWsYMZdFLGxq5j6fOLaGkfR2pPhMyGRCCYnxgt%0AZxqB8KqHJQte5NvXfI7OiUX8nPexmXm09I0jrzdDqjtOIm+I5v+cAmsIpqxqCxiATZApSzAy1ad1%0ApJbBLYXBSlYDZFdE6iO75fNUAsFaBX5fhDx/hGHyKaSPZf4L1G5uDRZs6Rrt2yYCt/0gVOW3U5Vs%0A54Rx63hP1YOsWnosn0vezpYDx9F00kEWRTfwQOQS9hRP4SPFd/CrzFXcV/tufrHmWvY9NJOGaTOD%0AnW8HIH9yL38pv4Q5FVu47Lx7ua7y5xTeORTgv4JTrAcivrOBK+G3ynu2+LalEXO9m0JkvTeVId60%0A3p2NOdiPxpe9xwaHNXZsLEJWphXOaoN17W0GgKxaTSeH3PFuYZCw9EfVTxNzXAhPz1Leq7DwI1ne%0Ab5COKFg9z5sI/JognuoDd/i+/33P824GriWwCwD+zff9R0bvuQm4ZrT61/u+/2ho4ZobrIa5ZrjN%0Ae7OayGUgnPv0X1P95C5Zy80KKGloi4e62Qm6x1qwth5iuDCXyeLArnC3WI60tw3UWXzLRnnddlkX%0AUs+0SewWKxsE2mBi6yHmVW/h78VngwdJf4Aur4QMEby0/5pS8pd6DF4SZ9MJ03kk8w42spBSunja%0AP4M9jbNI9cTxiodJFvfSv6IiqMvu0fqvAjrBS2eIzMqQ8SIs/p/nqfTbeXDbu/jtxHfzCOfzaOt5%0AxEpHqO1qYWdqGpPrGtj+g7mBwGknEI76vZgg8PMCpBqTtHYlAyHYO9oHzQRCtZss3ltJIJQzEKtI%0AURbtJJ8h5rGFOTt3E1lHkOsqOEEeyMhoWb2jbSqEJSvXs2LCWXz7hk/ylfYvs7d8Mg3eBAqjfdzM%0AzSyJrOJt5U+wqWwhX77gy3z/ls+SnhSDShheU8RwsogXZtewfsFiGhdP5Ia5P2TqXfvhQbLbErnk%0AekPiLdeilEBzx5LKVW61G8dwIQAdt1kB1vK0wswNEtmAnfKg4fDtUfQc17JVGyF3nQI7/qz1ajN7%0AVDcrGG02j5vZIwF+pD3D/kl6PYt1BPiM7/vrPc8rAtZ4nvcYQTfc7vv+7fZiz/PmEexoNI/Axnnc%0A87xZvu+HJRHlkqslrBaznTYWFusuTyZIwEY7bWTSwgxqqSxcda7dqM0Kesi1YpXDKJdO5bpMpd+u%0A9SBtawW+6mnbhDluyS51KEvd5gdDblpZN0SaM0yr3kVBapBeL0FXXgkVdFBNC9GWNIyHHVdP4q/T%0AzuGJ6Fk8u+NttHVU4/d5EPUhFgkGziBEIh79OyrgTuBEAre8Ds6++WE+nvwJFcWd/GrcVfy55XIK%0ADw7zwJ3v4cnPnszDXMBL6RPp7SuDnRG6ispJju9j+/fmBgKybLS+HQSCsYNAWE8hEJYlBO95DcEi%0A2XPIpnvZQGV3UJY3I8X0OZspppuJNLBs6EXyXxqdvttJVpFab0Z8oIHfCfTAF774A9556sN88Kyf%0Asfpvp1LzgX0wDBtjC7i39QquO+G/OC31FO/87AN8p/4GHrjvPYFVXBnUb2BfKd+b/a+8MPcU/njR%0A5UxY2RJAHu44sJihfe9Weeq6GIfzhh03bmDK8o+bomSnsFp4zlqwErRWWFsBaesqoa60LgsX2ACV%0AraMtzxpY6iM3eCwFoPaojhb2sN6lgnyCGpTWOJaM+QfoiILV9/0mAscI3/d7Pc97hUBgMsbjLwHu%0A9n1/BNjjed4Ogu3jXjzsSruIiUgCyU1ulsluo59uK2ywyCdrqVmQ25KYw+KQPtmZH9ZihKyV6BNY%0ANm6aTNyUp+eF9ZAN9qg9epn2mIvbWmvbWrYZc/8wWUxUg0cDxs5w64FoU4bJ8/dSkXeIQ20LGe5L%0AsqRiNcc3v8yGxbN48cST+HP6Ep7dcQZD24sCLHAaQcpSsRcsjFIGbIV0cx7lp7Rx6gdXMGVZA6Th%0Apq6vUfenlqCvFkDp+1t5pOUiLh15gB+e8km2elPooIwp3m5ausYze94mdg1Noe+lqqxy6hitb5JA%0AWNYRuPqDBAIuOvo9QuDqzyCbNgeBMB4igDJmQ92xDUyINFBKNyf6L1G/vj1oTwe5/GMVksWWW0bL%0A9IFumH7fbp7ZfDafv+hWvt/8KVJ9+TS9MBXK4Yfep1l4/lo+dtpP+P6uT3PuDY+wOzaNyw88QGJ4%0AkFhkhJ9M+TA/XPkZds+ezIR4y+EBVj3XutA6Lj61BoPeu7vamfjYGiv2XFhUXcIp4pyzfKk+s4FV%0A/beTDOwC8hZTlUDDXKexJxlgJ+NYDNTyv+2HQbIBPmug6NnqNz0nYr5Vbthqcf8gvWGM1fO8KQSO%0A2IvAKcCnPM/7ALAa+Jzv+50EaJIVog1kBbFTIFmLwm6Frc7Q8nw28qmXI8vQCkw3MmixTFkdSXPM%0AWr8STpj/sgjsIscWnxX+Y7W6zTiQ9WvJWrnqebtWQJgr4gpYm9ubNueFEwlTVrK1VTaJbBlep09N%0AqpX6ZCOvDhzDZbV/5FfdH+WxiafxABeyZuh4ntv5NoimgymdzcBzBBZXEhLbBsHzocbjz/efyzkP%0APhPgnL8afWYfAWYJUAJzGvfwxQVfZ+PwQroHS7hv3RWcxtO86J/Bucf/mf2xiURT6eA5eWQDCu2j%0AZVURTEutJRBu2jqlYfQ5U3kt3eo1/jhIkAkwF6Jz+plQuJcR8pjBDo5r3RjUt42sNaSJDerLCAEM%0AIIqRTeHRPa/Ad/pv4ms3/Aen8wyrx5+If9Cj4LIeuinhLu+91E9v5D07HqDqsY4cyOGkf1/Jz6eM%0A7ietlbQkdDR1VkFKBVeUzC8BJqWud20Foa4Vnwpv7SE3h1pWpCw2kcoSz1medoOZErLWu9KYUGDV%0A5vfKYxQcaAWzKE1u8FTywVqgdrcOG1dRvWQBWyvXKgxrLftkA1pvkt6QYB2FAe4Fbhi1XH8M3DJ6%0A+qvAbcCHxrg9DBXNMqe0lotbCuPJkE1TcvFWuWzWrZclrBdkNbyea9OFIFejqyy9PLuy0ZA5JtfG%0AakaVlTS/5cqrTWFkNbzVxhKSslSsMoqFXOuTyyBiPNVJbR/FWctHOqgtOEhZtIN/TdzKFYlfsWLz%0AuQw9FCezIwr7PSiKBfe3Ae+DqZkdFCd6ufvyK/HyMtAdZe63Xwm2xtbaAep39eN+SGwc4rrdv+CZ%0ARSfySX5A5MUMK7adxfHXv8hgY5LKyW3sTk8L7k0SCM9BAsFYTFbRKk2mm0DgDhC4/26+5QBBdkAZ%0AUOdTU9/MIHFmsIOzeZyKV7sC3FbbgFgrCtMOKziE8xWSta5GMeXEv6d4ovocZt+ykREvj9b2akrr%0AgwkXn2r6KfmlQzx71ikkP5l+bSeHspEOCou6qB5sya5dETXP9cidu28VpvBNWz87eQay40p8OECg%0AKKRM4mR3SEiTVVYWN4WslLALHUkYKjBkrUM7tqzbbcnCCK5CsKSxY+MRtkwJfPG1taB1v54jZWWD%0AZ1LgpaP/7Rofb4JeV7B6npdHkBjyW9/37wfwfb/FnP85AfQOAUo00dw+YfTYYXTzitEfGThzIpw5%0AydRInaEOklax2kbMhDlncVAJH8jmwLkQgtKTbLK9tShdSMI+C7La0HXjR9v12ssN6+WM87FYrsi6%0AeZD7wjWLx3XjVO84AaOJ8dVGWUFdUDnQRl1BE3VTd/Nv3Mqjf7uYusoGznv7I0wv3U1qSx7F0zvo%0ALi/k+btO5wnvXIbq8igb6IG7TJ8pAV6rv49OZ30tOg94W3wevXg5X+r9GvsTE4lUpJn0/e2Uem3U%0A0kQDE8iPjwRy7sBoe8YTWKxxsgGpLgKrtWD0WAOB4LWDQ+v7jnpCXqmPH4XxHOADmV+zbP06vJWj%0A9yrAon4f/H/UvXeYZVd15v0758aqWzlXdc5qdatbAeWAEBaSACMJBAYBDmCPMTiMP3s+nMYYPmOP%0Asc1gG4wD2IAxNkkgDAiQEMoZWmp1UAepc1VXjjeHM3/s8/ZedSXMjMV8enyep56quveEHdZ611rv%0AWnufpv81r3aDcy1XFS0Ry1b75BKjH1rHn/z3X+Xu0et5qH4pz3zmfFrPm2fzxc/wuo1f4YN/+j4u%0A+vguiCBKBHQmFkhHFS8DzfJmDWnzdn7Ww5WHrfm3Bpl4PMrxGApMLOAphM6x3IDbHfUVcmtcNNca%0AH31uSxrV3ubNVLTznHQZc25zDsKCsn1LhxwF0T8WzDUG9qWG0plmzzgJ9xyEe06avr/I40dVBQTA%0AJ4F9URR9xHw+HEXRWPzvzbgN5AC+BnwuCIIP49RiE25Poecdf3ANyzN1tsat+TXTsJw3BT9p4kJt%0A2G3LVCREAiUJos6r8Py3napIOWl+23vbYm0lvWy2X+2zz7S0gg2bZGHlefx7ab4XAlnrwVvrbsMk%0AjYUdu3lom66ysvckF6ce5favvolkWOaKl32PP4p+l41fPQkPAfcCO3H85nehJVVyXo/e91XCAdoc%0ArqZVCmyNTw24D/6i77+xZ+ACzrv2YY7dsobBcJyQOklqjDDK8dQa5iqDDkxncR6n5bUzuPqUXvzL%0ACq1hquKrAbKceeNucqDI2sRRfi76FK8cu4/E/biElQ0PbbKkOUEpULUyKYNi32KRBQ7Ar//O3/Bz%0AWz7HQN8U684/Anth5rYhJv+6l1/Y/jd84F3v58Z//CYzQS/UIlopLKef5GEparOJIYXq+txGLEqg%0A2jJBRYUyFs28qfoYmvOtQyDQhuW5iGYdwjxH4bu9v/RK51s6y/Kker59lj1eaNvDpPlOnmszr6z2%0AY9rRwhna6epVcPUGf4/338OLOn6Ux3o58DZgdxAEu+LPfgd4SxAE58bNOwL8IkAURfuCIPgCLrVR%0AA94dRdEPd6xlXTXwAk9ZSvCToImx25ZZlx48YEmo5O1agl5hjIDG7mMa4cN9cAraXNf2QjyoFFsT%0ALgupAmerGAppbCa/2WNWm63Ft3+rHTrqTZ/ZVSo6FDoS3ycPTMCazccYZYT8dI7k2ipJaoRR3a1a%0Amo3Pm8F7BwU8mGv8J/Hvh2qebfGWbXDu9kf53vQ1XBd+i4/O/yr5bI4+piiToUFAJlGC4Qj6A/fc%0AU3iDlMYBeCNuj+X9uuL2HMDN51h8Xi8wAuuGDvMO/oFXnb6L5J34FVkWeMTDE9+jgldQW4oHPoTE%0A3KM1bk8e0qcqDIxOQQOOlNZBO0RVeOgvL+GK6x9kf+tWbkx9k6VEG9l8lTRVP7/amMTKoWgpORYa%0AYxu12QSupaOaF3fIebCH9Eu6o9rqLMujIjkoopnUfz07YJlBO/N8yVs9vkeL+V73t8ZA+vXD6IPm%0A+lmbjFI/hRNqW9r8b/VOr63XeNma2Rdx/KiqgAd4Ycf4jn/nmj8C/uhHPtlm8uwu/rDcM0iav19o%0AJYi1nhZA9Zk8K8tfWe/A1tFZ+qB5cK1lVdhVwCumQLrZ49QkymOw1QZSJrsyRO0qmXMsrdBMN+i3%0ArRawe1pKYKwnJpoFWMVxnii/jMoTWZIXl5ink6WwzWfUleyQQbIem4yeCvebue0QF1pGwAykwjpk%0AIoaCMXZ27uIHEy8jHGjQyxQpaqRTFRIr69SHkg48x3EAOIjzXuPlsqzHVwbkcBtZZ+PztSlKFlgB%0AvWeP8vbgn3jT+G103lN0sdU8yxdraOz0+mV5+VoOKsOpOWjHA5YUVV6QNbCa03l36SX3Pcb2l+3l%0AjnU38FuX/jlzQSftqQVaF4seJC31lTS/NZ62MiZsOk/AIeOvNsnAY75v9lrDeMxy8TgoQaZ9Ti0/%0AL93QGOp6G8rbZFQDv3xURsxypqH5LR2RfMlrl/41vznBVhO8UP5FcycaxUaw1mjamtwfwwKBHwOb%0A8B881MEUbtBtWJ2Lz0nihLi5lTaE10DapJT15uzqDB0SUgGqzm8xPzm81RagKFttrXZzoX7zISsr%0A7g68YEpQJOzNE1rDc4DWQ7DCLA+l0XSOSHhFBBbwlLUvw8roJCeLq4jaA2rJJLN0U6B1uclVO8Wh%0A/rDXk2tciuY6AUDoTkgMlOmoL/HT4WepLWR55vRWTrCaMmmS1CAbOfBcCazB/d0b/70Vx6+GOG86%0ACazGUx+iIVqB9ZA8t8pbRv6ZXy78NZ33F1x1wwTOUJTwiqu+yTDZ2kx5WOIErfGzr+exPKfmocoy%0AKilRafDqoa/w+NhF7L55M/kwR2/HJNnxhk+82KSOQENjHjT9SCb1v13cIEOqBN8ivlJDfKSAWPpn%0AE3cFli/gKeINljXyNlFmcw42KSX5fKFEcvOPxlG0h4ybPFVdV8cbfvHPukfDfK6kc8Wco340mu5p%0Ao9YXefxvl1v92A9NgAZeHbRbqwlA/z1eRaG6JbTtZNjSKwmjBrFi7iHvQt4Y8T0V+qotCpWkQCL9%0AVeJjQ+FmoLVAY7O1DTzw6n8JcMXcVwDfbDxs2YoFvmawl5dgMucd80Xurb0chiDKBszS5YC1C0+z%0AWLCwfJnGqllxmo+YDsgl8jQOJlm38ggjS2NcvvIe7rnjOsZfNUwiVyfUDZbin474+ir+LQBVXMh/%0AMG6LQGEBP/Y5CFY1ePXZt/Gu/N/TdWfRFQWO4hOj8sZ0yBjZTPu/Z0QkL5jfNmmjcZNcx4r9gcc/%0AyIe6fp+vJG5ikl7StYovh7I11JYLbeANvkCxmUMUv6hoRwAtz07gI844i6cFJIuW6tC9ZSRtna/m%0AW3ohOf1hsh/w/B2jrIerQ21T9GAjVBsh2d3e1A4BrV2AQNx+GaAKftGJ6BV9Dx4zfgzHSwes8vRs%0AokAD2RxOwvKsfrM3BV4ZbFZc9IHAzAKohEPAKFAT32p5RIFdZJ4F3vpZawpeqXSN/U5KIU9YYN3K%0Acgua4oUVKDLf29d72BBdhzVassQpHGjFFntmvJdjj2whdWWJehjSSpE5ulx4Hzb9aHzVx+aqBY2N%0A2mzfuFuD7sos2b4CM5Uert71CD9zw6d4YvMlLO7tYvaCPLlEnqCl4c6fxIX6NRzAlnFeVw0HkGO4%0ANmpl1QxnvPPk1grXXXc7H1h8P9seOOR2xTqEM5I2QaTxtKGo3fjYAqz1tiQnSo428PsqWLBW6Klx%0ASUHmsxU2/8Zevj59E329p+kPJpdvumKpFR12E2erN9aTFXVkZRc8ELfgOWkBt+ZJsmg3u9YzInMe%0A+DrP5kSX5X71uZVZ6aANy+3Yi5ILzf30OSznja0uS38C3BxoabgcIOvVg68SsHy1Tez+GPhVNfGl%0AOSzhLSWVUkpAZUllyWydp37k3drwSBa6FS80tiQElofIAiQBmcIJWX4tkbTWsYQPH63XoMN6lmqz%0AhEn9sVxcC75MKWXuIU9F46Jr9WMJ/QLew1JN3jw+i6/PFPYtwv7aVngcgr4qUSWgQCt5ckTZpjbZ%0AUMly1LYMSIqr9tvXZSRhXXSUCllGqyOwG649dA9v2PR5glMJZk4NstTIEbRUXHWBooUCDmBncIAq%0AAO3DAe4CjhZYdOcGWxtc/uq7+f3KB9l5z35X1aCFAEpI2bIkzbU8WYXNiiAsZWQjGRkVm/TRZjGi%0AfuwCEgFbAV6TvIPRu1ezhmN0hAt+a0Pi6zSPkksBhAUs9SNrfufMcyzQSY5acV5/G5620He6jwVK%0A6+navAcs53XTeC/SVljo/iqFy5nrBLJ6hnWcongM8uYzyaE8bSFXGi+fljrR3NrXlWsRRh1Pa+k+%0Amt+QH8t+rC8dsOrpOdygy8WX0tvVJBpc8UVF3ABWcKChMETZQFsmYrlRm2XUVoUCLQu8Cr8Fuilz%0AjkprJDT6sZtMNO+TCc/37ASGNuS0nFTzj9pgldpm+W3fBL4vtDRTzw6Ao/B01w6Yg9xQnmgiRURA%0AREDdblxtazhLeO9IACvlFKA2b7QR930kOsVw50k6G/OwF1bsHued6U8w/PIjVE+2MDffTaMRumRV%0AL74QXx7hCE45WnHce1/8W+O2AS55y/18KHgvF937lPNUj+LkRgoW4UB2Lr63OGy7jFK0izhShdi2%0AosNyc1lzH/ChtjWQkq8O2Dh5iPmFDraynxx5ZxxkkAQCsNzzslGc5kKGwHL2dumrvFDNjUBVL1rs%0AM2NoX4vSfCg8Vz5Ah66ziaGW+HPpgN2BS5GPXYJtIywlf+Vh2/zID8vWy5HS/Gr+BMJp82MjQN2z%0AWceUhH6Rx0sHrLKSsoiW+LferM1oBk2/ddiwwgKQtVYWGBUS2+cohLL3AG+lZRmlYM0cW2D+Vrho%0Aw2JrNKy1V/ttGySMdmWJkihZlr8pVIJklVmGKWt+YDl/FHsYD5y6lHBrncXRTshDjSSLtFPLJHwS%0AQ0ZGXkCK5Uk9WE7TyOgJqGIgXlc+wli0gn/lza4GdRdctGs3/7Xnf5LrXKByvJN6IQN9Dbd4up/l%0A3nIR780VcYA0Ht9/CIZvPcFvd/wh539/DzyKL6tSyGh5YSXkBJ62PEhAK+Ol+ZH3qDmL4msVEdjk%0AiY2e5CVFQAe8fvqr5NPtVAoZzgt+sPxttwIB2x7RIPK01DZbmqT+qT8Vc095YQmcUcq5dpDDg4nG%0Apmru08w32hxAcwZfMiGZFaDLwxT9Jb2TfEpHZAykW7png+U6YKNAPUPeeJe5n0ozqyzXFfDGyt4r%0Aw/K2vMjjpQPWBMtDAwt+zeGkJdlhuZI3Zx0FeBasrLDm8Esg7fUCTJ1reVibxVTbRAnYsMEmyprC%0A4GW8kARUE6yQ1wKYJlghlh2HlPnfclICWJuos1lsyxWXoH5xwPemr6FxbUhtOgNZmJ7pI08rUSbw%0ASxzzcfvkHTVnXW1SUMoAXnjjMevIF2gfmWeaXnfeMcg8WeF15a9z4foHXci+kIRGCGtxu1WtxBvY%0AeZwHuhvnceoV3F2QfUWeX1r9l1xz+EGS9zTcvq8qvbKJPVFEkiUZJs2LDb8tRwzLy4z0uS0fKvH8%0AJKvlvevAEgztmmD4miN8OX8L5zWe9LssaQ4lL+LFFUktmTEFX6YnIFH224K/9MEaarXdUl5lPOXV%0A3H7pif6XvkR4usiOg7z4drynKNnNmvMkvwLpFM6j1r4QLbgyuzY8ILfigVTOStrcrw0P5tJnjacO%0Am6RTVZL0vTlp9h88XlqP1YbRsj5teKtqPSJxgynzeTMI6jO7k5OU3/I3NsSC5R6wrVe1oYf1BJbw%0AAKZQVdfKUurzF8qUW9JcHrQoDusx1c3/ti0Ceuul6FrwIGcNioRa31fggS0XM/dkP/TVIRlCAyql%0ALBUyVDOp5aU1GtuMuWdovrNzZf+WstYgsRs6umdYiDp8AmkvbHzmOK9v+TLB1pIDVwHiStyOVcM4%0AUH0Stz3gbPxTwe0lsA3ecOW/8OsLf0XuvqLbns++DVULMCQL2gXMGkJLMS3hDcl809xYOUtzZnnw%0AsoUrsPwNwTbU7HDn/17LB3n8scvJzZaXh/+Y+2ls7WEpJMm65YNtaaCNcNRmhbrWk2320Cxvb+VO%0A9yUeH7vWvjmktpl7/W09TNvfNE7vxf1mm/5X1KByPqsvdilrcxsxz7FI15xMtjSDdPdFHi9dVYA4%0APFkshTU181uJKylAw5wnzscS67LeAT6MheWhnE0ANJp+aDpHu/LYiRPnJuCUB5s059myrch8bq2+%0A+q0srZTD1n0qNLPcmXbvsZ6v9X6bebDmJYxG6P6t7SehDGG6RmMmCSFEU+IJVgAAIABJREFUQcgC%0AHZQyWTpai8uvD8097Jypz/KwNEYKcdM4JZiGzHSdxUf7veezBOFDEa9e+x2e3vz3/OPud1F7OuW8%0AFj1L3qC2CAxwHk0XsBPeetMneX/5/bTdVXZlVbP4twdYflrjCd47sVSTrY2U561w3/a7gpMtyaeS%0Arc1JIwG2Hbsk0A7XPP0APAkHBs5i5fS4B6p2nGNhVxglWb6xj9ohGbYlfAq35U3aihHrebeaOSri%0AZVYess6XEVf5ozYVlxdcxM2VTQwJOG0UozbZ3IV0OMJXxYQsL4GyEaBdyag5gufrgJLO8j5tXbLG%0AVc8Sv21xwya5/4PHSwesGnwNhEBRh6yvBkIhmyW+7fkSAsv/WGCz/JglrW2JjKUHlLComWsVpoGf%0AVAGK+mRrYy1QwnLwhuUTLG9SgCWQBO8F6zN5js17FqiNGg8BnfpmQq5ab4LvTl0LmyMS6SqNahYi%0AaOTTzNDDYls7A92zrk8hfo4s9WDHWMlAKYDCbgsOszAyP8az6U7naQp4DsGGO0/w/772Q4zf2s93%0APn8TpWezXjl6cAmXURzXOAhsi+i5aZJfuv4veXf+bxj55rQL/6WUbXiZEGhIGVvxhr2Cpwa06EMg%0AqcMmQ2Us1GcrR9Yj1Bgl8MbOJJH6gkl6k9M82H05rzx6r0vWNXOCdh5t9truCWGfI29VICvHQ3Ok%0ADWMkP1av7PulVKKo/mi8tDeCysEks+KoLS2kMkaVNym5p7YrOagw3Tof1ustmD5oTvLm+hJ+VZfG%0AStGDPG61T1Gm5k4Y1Fym+GNAxZcOWHVI+O2aflvmIpCD5ckihXc2Q2rB1S4qUClS1HS+5aekUDZM%0AiVi+8EDALgG0WXnrVQhQtbzRLlFU+CSjYu9pqxssDdC8BloGQMCl8bHZY1s2onI2eZNJyF+Q49S3%0AV5O+osjqrmMcntkOLVAbTzK1oY9Ca9p5hVLANjxIKhqw3JWAVGGk5er0epMs5MK8AxHxgyoHy8PG%0A/Ek+c+PP8ek33s2Hx/8bxx7dFJdeRXBn4MbpIuh76wmu6/oOt2S+yLWLd5O7u+q2+jmG381LvJkF%0AO3nSFrTk1dk6VY2t9dikgFrxJ69V3lY3vnpBPGBzFlpHOyxcnqVt1RxPF85xeyK0x21TJYQMtmQB%0APDioDEntazbKzfoCHrCVRLJVI1p4oPNVC2qTXQW8bspAyNO151oqzZYpqj12aWsDvwLK8sHgnQW7%0ATaIMo0J3bbhTwCdTFdVp/PQM8J6v9gDRZwVzbcO08UUcLx2wWu9Rk2rrymwoIstnM7ryJgVW+i7E%0AA6XCDU2EDellyTQCAlpNoEId2xYppq7RElyBsVVYXWMpAt1XCoC5xrbDhi6qPxWA2sRK2HR9c5mT%0AMrUCOwM0k1d3M/s/e1iz8hCbwoMcnt0GQUCjHlCNUiyFOde3VpZn+VX/Z3lrPV9gqnBKnpK8i0PQ%0A1rYE34baMUi24TxQcOH7JHSOlfjlC/+eW1bfzsSlA2QSZerZBGNbhzjWNkIbeXZWnmL4mSm6di+5%0AVVhTuPrWOXzoqfCuuWJD4yePRsaviN8foZ3l0YbNJlug1KovKbVoE3nHabwh1bgkgSWYSPTz6nVf%0A45/vegdcj9tEHPzrgEQ5yLDa6KqZDrP9kV6Jz8yZPmgstEDE5hvk7WquZRwEcLCcWxZgZXBGRWCk%0A51taATwvb0vWRPHICFrKSlEaONmw+iWPuIyPTGwCT8ZSXHoW7/nKsIgWGme5UdW1L/J4aT1WCaC8%0AN/BgKuW0YKXfymTaAbD8mQba1onaMLxizpHyt+CUXEAgSyieVcKrPTgFcjZLKkVU221xs8JG9UPl%0AOVJUG0pqPNRmecuiKaSgzYmUtLmPhFSgLyWNhfFrra+m1pfidb1fpdJIE2YaNIoJonzIUqON6bDP%0AeZYCB1EwdfOje4onliEo4fcsVfiZhPISdJTnYVvEfAl6q3hgncOtppqA8GkY6Z9gZOWEe3YLbG97%0AxhfAP4fbPy1+/xTTrk9nEnSWc5PXZ5fm2tVQMmICAo29rfawC1h0nXhOKXbW3Edhr4C43HSvblii%0AjbUcZT7RxbeufAXXP/Y975FaMLc1pFrvLh61Zr7Tj8BD7VvEy6bGQ4AoedJv8bzSC+mk5E0UgJ13%0AJZu1q5XCbPtiRFsDa3Mb1pmSQ2FfqikKSp6rdFr6oHGQXOqeiqjkqcow2frUAAfYfXjaQjRRLy/6%0AeOmAVeBlS1gsj6LQQucIbAWm1quT16oBlsI0b2Jsl57qPlbJ5LHIMxM3ZGv1VF6i+kXwAAzLOchm%0AztgCYh8+lJTg6Zqy+Vz9k0ektr+QoIgjFLhaBZFBSQFr4LbGG8i+ap438QXuD68k01emeKwVooDF%0Aeifzyc7lK1psBrho7mU9HBkjCyhm0UV9EQY6xqkvJTiZTtI7WfNjr2RhBQecJ3E7UeXiZ/fieeU4%0AEQa4pa+iFDRfWnCiDXzk3dtD99VGLDKs8rwkc2qbElu2bMsujZSHLFrLgkkry/eoBfZxNk9zDkPB%0AKf6q61e4Pvs9b4A1l83JWim/qILmkkIZY8mFkkySOXmmamOI46+1TFURimRFZYmh+RwcFWGTxXJq%0ArEzazXiG8OBojZvKq5SUFKdK3M4C3ruum3Ps21u1U1kJ/8oeUSqiJtL4BSF9eCMo+k9LhYvmuhd5%0AvHTAqgEUSEigZLlsYkueoEJ7Wb7A3M9mqfVbAqRDQibgknDaujZxdM1gBx6w9Ty7DE/htuWGbfG1%0APEoJhdog662wS16T9dZVIlTF7/Bu97TUoWyyPhfPJa8nplNGNw3w8MNX8bZzPsV5o3s4PTJEbucc%0Axf2tkIdZuqiQ9t6R5kH9lHJorBSaqZ9qg+iUuI/ZNlgVnqQ8kaE13QE9Mz6DKyOie9fw9bNSMgGY%0AKJg6Z956uiyck6LbpKhoAJtR1xsHBEDam1M8qvqsl0faHbFk+DUmWhIqWVKUk8cbY43ZIBSjFh7m%0AUkbWnuLo7HoqvSnS81UP5JLFQvzcLJ6ykGelfimpqeoU8bsKebWKScBoaZyC+VzUV9bcP8/yaEw1%0ApeKb7cv7VDtq+ViVemkM5MiIf1VkI65YOmDnahFvuKQv6puMmDbsqXDmVedn2qn9ifVONMzz4l3e%0ASOApjf/UVQHT+IFRVlz8mLwggYIsuBTWgqeEwnKzEoLmZXA2hBEw21pGCaqsrc20WmFXyC6PUKAu%0AL856mDZDK9pACYoOvBctoVNBu/qrPtq6QHkuzYc8KYVjAisJYxwiP7VmJ3wp5K0XfZbMQ3Uu7XmU%0At6//R/72yv9K4ckc9VqCqUwfldWQTuFBTSBqeUv1T56QLWfR73jswjSUC2kqa9Mk2tugPuNDWnh+%0AXbK4TgsENoNr6ybBlwRJ+WWMRYWoplKepU266N7WmGjj7hTLi/9tssW+iA/TXtEc8rAFVu3QWBVQ%0AjjLM08Elqx7hm/fdzEK2g76ZaXetTU4p0pFDocSM/lf23JYxVfBLfSVz6oeOqvktZyNl7mnnReeJ%0AphDdoaSmDI3AW6AloLQVDTKODZxcLcTfy8hZrz+Pn2/xs9JdRaMCVM2x2iaAt/0exUU4Q7htKDvx%0Asj0Y92ecH8uS1pcOWOUNSDglxFJalReJH1F1gLyVH3ZIYDSY8jItR2ZJcI2ADbttiUaAEx6FMdaj%0AkiGQJVUW3YZvVvFD/DJC8bngwdDuL6v2SSjF5TUn1uwhIVToXjPXqx3t8NTADtpHltj5rf1wBAbb%0A5njHpZ8muCLgI8O/zvjcEPlcjmo6SbqlttyTbjHPsev05dUqUywKQp5LDjgFyWMRpVqWpUs74Jt4%0ARWrHA1JzJl+KZasiangDZXd80pjZ3ZwifKmQQEbn23I4a5zFK4MPW9UvtUEUSB/L5VVj32nGRqAR%0AQjmX4OnaDtLUOC+9i8+W3knhxlb4y2nfT4GqkkOST7sG3kYFar947Rk8vSUdE2BqK0zRC6rFlvGS%0AEbFlZQJ3GSTpod4628DtjbuCM3IGZtzsUtc8LjSP34d2xskRWMoIgQO6VrwXWsOB4wSe+urhzNuD%0Az3inrfEYyOjNQP3rMDEBg1sgfCWwJT6vJ56rWZZ71S/ieOmAVSUeKluxHoT1yCyhn8WH9rLE1iOz%0AiSC7Vt2u3Eiac2yG3mbYJRi2ikCH9dTkodlMsJRWGy5bjkrKJQ4Uc41tl32thkpj5Lk2lzfZQ+Nj%0AV301n7cZ7jx6LQO3nKLr67PwrPt4e+0Q/+XKv6OwoYUvlm9hnk4W0u3k6rPemxEoqRIiwldWyDOx%0A3J/K3sSDrYau8hz1WpLyJVm3kkreiy0qt6GmaoPFier5HXgeXKvXlLQQVycDKEDUmMpICnQ1f5a3%0A1znywoos3x9WYyseT/3X88TFyiAW47GagFqQYqneTpoK5/A0USnBJwbfxge6/9g5GNYD1FiqJK95%0AJZKlsYjPVRJKUUMHvgBfh/a21SGjLx5ZfLYMl00EY/pfxIHkkmmXXaoq+R+Px6KCS1IKvMVpKskm%0AmdL+upp3ycM0Dlg17xN4+kM5EHn9aZxRivUusR6G1+NK+NrjZ+bMbxmZMV708dIBq+W+7JK3efxA%0AymrbhJA4Nh3yUhQ+gwdZgZdd+SFLL7CSMiiTbJNhoidy5m8JsDwdKb5N9IgD0rNtKZi8HSmnvYd4%0AMoGV9eA0JknzDG1vF+KERa82FrerWl4pYRoKr2jhgbErubXn0wSDwLnxvY/A2tUnuXLj/ezJbqdM%0Axt1DxsFm0+XFK3SWpxPilNj2o4bzCGJAa50qEFUCaqsTTugr8X1b42sz+F3MiL9TMkrJMYXKegeW%0AuDoBuwBa3ofCQXFpSRzfZtf363opMGZ+2uO2peIxTuBBnfgzOQLtOM9HbbcVKTU3Twvt7ZyqrWAh%0A6qA/McW6K57hb0rv5vda/szxrKG5FpbvESHuV46GPErpk+REnr4t39LObPJi5X1r3PJmrMBTZ8pz%0AqApA3rxoij5ceC3P3u7speSQotCW+Nwl3JzP4Q22nKsU/i0iaTwPmsfJkjL5BdxrepR01pzW8Y6b%0ADKRopeN4fe6LP2vFLZueids1wIs+XjpglcALOBVG2tUROnJ4QVH4JdLbAo04MJXLKJywHouURiBq%0Ai9nt/qiq49O1AmQlCiJzvkIpeTY245kwn0sJMNdL6OWlKuSzlEDS3Ef9m8WHSAvAYbw3p9ATvLfR%0ABgzBV1a8hsp9rXRcv8Dkte0M7pgledw9vzDQSoEW0lQokWUp0QapWXe9SnQ68AAvY9jSNI4yTB34%0AzapxfR1oTALQWIUzBvW4L3a1j2RCSi5lsQkSJU70Ti2BnMJkW0AP3sOWwdOrR+TpLpr5sPtY5OKx%0A68Qn0xbx69kFOLaf7Tglb8EbCBm5Tljsbmeh1sbUqRHKgzluLN/OR068l6nzuxl5YGJ52ZqW7vbH%0A872E89KWcJ5VHhcGCzAVDXWyfJMWJYu68V5wxpwno6Okn+Q0Gd9fidaZ+G/7NoMQ/0JHGT/RB0P4%0A5cnyhtvi/jyLNxYVXD1yAtgct20mvv96vDGx0dtMfN0IfmWYgL4ft2fEBG6/iYG4bdIztakbnxCO%0A4v9FUbyI46UDVu3lKE/NJmXs1mjgLZCtc5OAiAQHD1ZSQnk28vyk+NaTBS94GlBbCC5AkwdpS44U%0AHtkkmoBBRePyvFXOY4vXpUCzeIrA7kGQMfcTZymOzWblbYa4gfP6ZfVlGBoQBQH/Un0bFCP6mOJU%0AuJIDw2fRMzxDSIOjrOVxLmKUYVZznEIqB6twAirKQ89X/1WzKI9TvKi8Tilrl+tLR36BYHPEPf2X%0Ac/nMo66dg/hkh8BB46M5FNgu4o2jvDp5ybA8K269L4GJQj5lf2XQszjF1HNtNKX7ah7tW2sVfqot%0AMp4Cq3kzn3FIX0y1kK+1wbMhsxs7uKnyNT5S/E3u3XYFb6nfBu2Q78twvG8lM+3dzIUdLCQ7KJOh%0Asz7PJePfZ/gbk36jGYG/pTMU6spIzOA3w+7CRxWzeJAU8HTinYG5+N5duPcxT+CAtjv+XcCF5rbc%0ASVGAeNZj8XPW4DPzxfh/5TOWWP5uMV0vCqUFX5vaFvcnE/dnJu5rT9yuKs4zDXAAK3psA06ei/E1%0AXbgIT1Ul3fFz/m8DaxAEWdwe7FLx26Mo+u0gCHqAz+OG5ijwpiiK5uJrfht4B27IfjWKou+84M2V%0AuWuNO5KLn7CET77Ia1DIKY7GFnLLc1EpjA3nlS0V+Mma1uLnWgK/aq4Fn4xRG/UMecYKNW3RM6bN%0AogAE6io7sa+IVhvFKQlALS0hgLfes8JnhYU6X8rSjQ9xVJZShupwkuee3EDf8DiX8jAdLJAnxxR9%0AzNDNQbYwRxerOMlajjBQn3BjfhZOENUG8CVNNkGlZIm4Pc1rO2c4r2SySpQOeLB6JXR+2M8HeMOY%0AxYf/AlSBbTsOrPQsVQBoHmH5aiJ5bJoLKe0ky3fqsuvNVQspflGyo3BUHo7apZ22bHndPHAivo9A%0APA2sgOneTkrHszABn0m+ndetuJ30szXuzP0EmesLnGQVFdKs4RhHWMcE/YwwxhJt7Ets5bGRC/mZ%0An/80Z33huHtBYjvek1Q/EzjgS3Jma8UzXKvaWY3HQaAlANXeCfL+Y26YCi5kTps5aAFeFo/NWNzn%0AtTgvMcS9EmcJlyhag9cbbQt4FO/NKy8hakgrrobi8xdwmf0IX9csAN4an7sY90Hefpy44lR83zW4%0A6E7etoB0X9zm1rhvL/L4d4E1iqJSEASviKKoEARBEnggCIIrgNcBd0ZR9KEgCN4L/BbwW0EQnA38%0AFHA2Lj94VxAEm6Moen4aSAKot6GCLwa29WWypuJ15vCeiwq6VdCvEEU/xOfM4rkxPdsqmpRI3JiU%0AuY4TJNW1Srm0IKAPH56ABwiBrQRoEu9xavme9hIVjbBs4Jv+Fw+m/in8FW8mj15tUM1gzvRtBCo3%0ApBn/9BArrjjBSk6SoE4nc9RIMUMP7Syyjb2ENDiPH5Cl5JcNKvNt26MNnuW51nBJCiWWFCpLmWah%0AJzUDB+HY6jVubKbwNMMA3iuPcN6GEjcCDnlVqioRFSHeVzW/qtrQUmglyepm/uQppfHeDnjPSEZb%0AhlUcYwrn6RTiNvfivap5nGe3Bx8xdOC2QBxwz7loehe/u+YP+eAbfpdvcR1pyrSeKPDMwDZKnWnu%0A4WpewzfJUmKYMc7haXLkmaWbGkkGGWe4ftrdewDvsRXifsjgZ+Pv5bEJCBN4uVTo243ff/dQPC89%0AeG53DXCVm0OejcekYPp4Cgdwonhm4rHsw4P1VPyMRdwCkOm4fSfj++aB7bjQfx5P30zg9EWr7CzV%0AMh2P9zje45zHy9Wm+PehuC01HMgv4XT7YHz+sfj/vrifL/L4kVRAFEUKtGXbZ3HA+vL4808D9+DA%0A9UbgX6IoqgJHgyA4DFwEPPK8GwsQxY9J+CN8iKPBszWgCnWT+HBHrdNmH7LI4tQ68ZSDwFz1fzYp%0A1Im35OIqZbkFgMo2yjDoGvGK2sczGT9H5LyK5+XJiYOSd6BSEbs7k7wIa5bU7jROSGfxNEoDT8wr%0AIaFVOt3w2OBO5ka7uXDrQ3Q25klSoyeYoRxkWMEpxhlkkTYiAkYYI12vOGGzO1vZcjSVSSkqyOOr%0AMXR+D04p4rXa1XIKTsDBybNZelWGth+UfSJBmxrLU2yBegbKbUnKQYZsqUQ1mSLVqJIs1im1Zqhl%0AUtSDkGyjRIOQTKlCcqlBYgmiVohCCOfjtik0FQUjmkZepWof5fnP48Fcq40EyuvxXpYoEPActDxD%0AvaakN75+EIKgwTqOcEvPlwB4GU/w9bNu4eFnruIXz/oYb+VznLuwh+G9E1CHek9IozugMZfgJ7iX%0AdKFGqlj30cpQ3J/2ePx68W+51YKCIu6tDStxIDkdy848/jU3DRxAKvnaZsZJY5CMryf+bAnnRa7G%0Ag/tRI3cz+HA9wvGhFfyy0U1uTDgZ338d/hXdC/jooDV+jmgERXnrcGCuDVkaOFDtxwFtAgeaMn7t%0AcRsUvZ4V338tTv7W4THlRRw/EliDIAhxAccG4ONRFO0NgmAwiqLx+JRx3NAQN9mC6Emc5/rCTy7j%0AgMcmPbSMThtrKMxV2UgGN5nzeNAE77VJwKXY4h9tqYi8FVuLqiV74njAe0X6fAQP+rqfpRFS+DIW%0AJTjkfYt3lfcjMFrA867aMEJ0ga5XEqS5PAjcyAtAxVErZDYhdtQDt5VvpjGd4KaW2xn+2pz3JsIC%0A9MAQMxS7UoSViHSxRmCTMQV8zZ+Mh+oX1Sa12Rb8E7dtyH3XsWqBYHWd2qEMe8/bxoVX/4AwA/N9%0ALZRaU5TCLPW4DKRAjnEGmKeLJDU6O+ZYoJMqKTKUKNJKQERLnCFKUiPVXqWzf56QBgPVCarJFNUg%0ARXspTzGbIUGd1lKJlpkqSWqQhESpTj2ZoNSdoJBqIdGok6uXaDlao5GGWneS1FKd8FTk5fB43M8V%0A8bxMx+OzAR+BKSQfwilsnHxqLZe4tn4nFyceYY5uWopl7trxXb469mb2Fs/hQ/f9dxprIYxrNsPR%0ARux11f19+/DVMAfNc8RLhzgdGsDXTPfi9GYUB7JK7uVxWtxvZHAYJ8v7cQC7El/xsAef+RcnPRvf%0AN8DJdH8sG4NGbifj56pdY/E9e+Jx7IzHUUlPGTMZuiC+JojH+ZF4Dk7hcEOlWMPx+afjPm+K+9GF%0A02HVqOuaVNxebWyuBOeLOP53PNYGcG4QBJ3At4MgeEXT91EQBM3B67JTXujDP/hM/Ecerl4JV5+F%0Am5BpnICoPEJJhGlcSKBQWEv8xJMo9JFXK+BRDwXAqnMEXzMoakA1c+Jwu/CA3YvP1quOTtSCstnq%0AqYRhDl8bKEBN4JRC3nIOJ7RzODOkVSgt+DXP4m2t90t8/nM4Id3Ccq7TFsZnYWKkly82boEVMBKN%0A+dUtpzkTaidS0PZM9cy8nKEoVPqilSwb4/F6DieIGluFvdoBX5zyznjMVkAx00I0n4B1EbvbdzC0%0A8gRJ6pxkJQt0UCfBND1UyLBIO3N00UKRLuZ4lg0UaaFKioDIvZuLJO0ssolDDHGaGgme5FzqJGhN%0A5clRICIgma0R0iBHnlI2SzjSYCUnKdJCBwvUSZClyDS9zNHNSk7SuXWeGgmWaGdFbpR0T5XMAr5K%0A4ihOiQdx8inAqgMXxvNyijPOQRRA5RzIjEH3gSLRWQH91Sly+xu84Zwv8dXcT/GN6g18oOMPyd5d%0AdrK2LZ53JfgkXzPx/Azj+MVj+MSNaJhB3AsV53D6lMDxi6dx4W4en2TsxNNdvfE5E7EcrYjl6C6c%0Ai5UCXoHz9p7AJ3sedtfXQ0i8Awe2pXicWnCe5T3xdU/GbZrEVzbcBVwct/NkfO1wLH9P4B2IYjyu%0AK+JxuBTvrfbgwv6d8flTcXtvgMogpMdxRrGAe69aAiphggcfqfOd/Qky36v//7ukNYqi+SAIvgFc%0AAIwHQTAURdHpIAiGcVNA3N1V5rKV8WfPO/7gZrzHZjP98oIUAs/jXyCn8ED1kuI3ZT3FLYmfVbmJ%0AuBp5hX04oZ3H87hKTKigWWvUdR8pUwsOcJVEkrerJBHxZwP4UhTwYbJ4XJHuoiIWTd/k6Si7LQ8k%0Az/LdhNrwXsQYTjh78KU5ZvXXWMcIE19cC2fD+spzbvxs4b927UrE7RCJL09bxkhCr5VHIv83QaMb%0AwtP4FVGYPsRh2XS2l1zLAplMkXtqr2AT+4kIGGOYaXrpYo5WCkzTxyT95MgzQw8TMeHawQIpqizQ%0AwRxdFGlhkn7ytDLMIGUytFCkToIKaRboICAiRY06IVXStFJgNcc5wBZaY+BNUGeOTgrkyJFnaG6a%0AdKJMENTpSi7QUqxRTYcQNjx11BGPyy68oVeIew+e+lnv5jmYgowqCNqgbaxCeqwB07Ctvh8yEXOZ%0ALibP62TV8QlnuL4HnBPLm6gSrS46jQPzZ3FAosTuZHyeNgW/CCfPT8fzfG08R1WcQW7gk0zb8XKt%0Arfbq8fdl4K3xvb8dy8Yrcdq/JR6DR6B2ChIfB66Mnx+DY/U+YD+kRuHYo7Dm+vheV+EMw7XxuH4S%0AV189AlwN/ClwGdSuDknuavjywnNxujwGnI8D1wkcSalNV8SLPwfpzwBPQvUPQxpdCaKeOkwleG7F%0Aaq4+71kuviBF6z116IL3f44XdfyoqoA+oBZF0VwQBC1x198PfA34GeBP4t9fjS/5GvC5IAg+jLMn%0Am3BbED//UOJFPJG8xTmcQnfhKwFUEqFQG3xxtEqzlEBRydFi/F0XnnsS99WLB9FpnMAKxLUSbBw3%0AgUP4F9pp2zRYvreAOE7wYZHoC4GK6vKmcVZ0PL72ApxQquxHAC5wHcHV9U3irbLAvBKPsjaOFq83%0AEPf5BE6JB+GBgYvgG9DzG6cZXDzt+jYcn7fguMigD78McJLlm1n34b2x1fjwLK6QiDIQ1qHRB6Hd%0A6q3dfRckIKq4kq++1aNcUP8B7ZkZFmmng3kahLRQJKTOKUZoENLGEhXS1EmQokaKKhXSzNFFghpp%0AKuTJ0cE8/UzRwwwNQuboYo4uBhnnmso9kGzwaHgxJbKUaJCmTJ4c3czSxyR52qiQJqRBhTRtLHK4%0Aa42jFqhSJ6QrO0eDBEEuYnBuikQdkqUI2qCx3s1LWIJ6GJCYidyciTaYxSn8dmjkgDCgVg/JHK+5%0AOe6Akeoo/WvGGWmc5t3Vj3P70i2ECxEMw8LlGVrGq6SONtz+s2uc/Favg1RvLFOqZtjo5puxWDbm%0A489O47MdTwKvwmmnFpkocXcC5xo18NzlaXx0lY/vPxLLyD6cB3o0lse3QeZBHFAuxd/FDkVqDc7I%0A3AtPrYA1c3GbVsTPexzYG1/TClEVgs84+br/7+CyRuRkbh+uEkFLULcBo1DalCJLlagf2ACzRzvp%0APj0PJQiqcX/fDqnHGlRuikj/WURwdYOzC8/CPLSOlWhsjuX3RR68ajlaAAAgAElEQVQ/ymMdBj4d%0A86wh8E9RFH03CIJdwBeCIHhnPKRvAoiiaF8QBF+Iu14D3h1F0QvTBEoMDOIm4BQ+86raVFuwr703%0A23D8ipIoCfzuPyr9acV7bfPxj8qoWvCJM/uKhxQ+Y1nGr76QB3sWzjuo43lGZSDllaokC3xCKocD%0AxCF8UqsLN8kCtnacYGtTiJPxM1Q8vwcHvsqyazZUC6w21HHCfgLnwaTiZ2XhuxOvgml4xeDd9B+c%0Ad15Ow43l+I5OBvfO+z1i98Vjoewx8Rz14hR2GqKOwAFsBOWOiPlcB8moBoGLedPrq7TN1pjvypLL%0AF9nfvZGIkKPFdRQrLaSKNRYyHayMTjIXdFEmQ5YiBziLeToYZIJX8032sJ19nE0rBdJUznijWzhA%0ARMAMPczRTYOQXqYpkeU0g/QyzX62Uk0nKZBjgQ4S1BlhlE4W6GWaDGXaWeQwm1iknT6muGzhMU51%0ADLLx+Elm29shW6eRCYmq0BYVeDq7nbCjwcrHpqluhQZJTq/oZtWxKfZtWkOy2mDz/zgOAUQ7AspX%0ARaTHITzo5ivcAxyPSF9aP5OFrwwk6X1wgS2dB3joo1fRmAv5zEffxNv/7ksE9Yi271cJNkVElQDW%0AQDAXwdOQvA+i9wZETwPtAcGhCG6EoBY5mVgfy/od8bxtj+dQEVcLDnS/CTyDAyjwUVE/zmMOcJn3%0ALcDNOGBui6/dE8uynJwHnf4s1eBj/wS/OQ+J9+AohFYcWH8PXnceDlS34wo3f97JKW+J5S+EaAyi%0A70JwGK78CahuDUgMRo6v7sfp6gTOs09CdrwKRyEYgtoQZDuKBIvADExd18mRllX0MMP6wijpByJ4%0AGxxZPcy6p8eYujRH2x1lEpkG4VPPL2L6Pz2CH4Z7/zePIAii6L344vBW/MYKWj64hA93tEpCIW8D%0Axyup/yoHyprztKRVy/QEEsryh3giPYsPsxbwXI7KeLrxgFvD1w2qiF/hu/XC9ZIy1ZOKh5rBecB6%0A5bUKpkfi/6fi/5+Jx6Yfv1/AMOQ3pGgQEOVCWmdKJGWAuiFagmAfnvNtAZ6FQzev4eqlexm9Zw2/%0A9s4/4UNjv0N6X8P1dy0OyLVyR/TAEs4o9Li/Gz1QGEzRtrcKA7DQneVYbgUJGqyunqAcZKgFCRpB%0ACGFEiSzT9JGkSq5RoBGGVEjzsalf418//VbS1y/x9k2f5Tfrf85kSw/tpQLHsivZxXmcYoQRxljP%0Ac4Q0mKWbDGXm6WSWLi7jYfaxlRItZzzPkAZLtJGkRj+TzNDjeFYKbGU/e9lGlRTdzNDDDCOM0cEC%0Ao4wwziAFWmlnkRx5VtZPcdb9RwkqEayH4u40p14zyEymi01jx5gfbiGcgQFmebZ1NX3VadrKRXJ3%0AleAcqCYgVYbps3J05IvkgyylVJaB07PM0UqmUCO3pwy9sHBeKx0PFKAETw6eQ3V9ivfufj/ff/xK%0Aem6doqdlhp51p0lVq2RSJTpY5HXlf+Mze/4LxVqa8vaAwve7WAzb6V8xRa6yxG37bqF1puiM5+M4%0Ap+C18dwexYXXX4H6KyGhlUwpnEEegfmdGTq/WKZ4Q4KWr9bdd5PANTiPUqVSaXy55KZYxu/EheUl%0AqH8eEufinZpMLOffBt4V68vJWO7ncbztt3G63QsHdkNmL/T2Qvu6+BkAD8X3qeJokioOoGdxFIGS%0AUkuxDvW4N2bkniySmqtRnU6S3lMiUYLgdRB1QW1VSOr7DaZf0UbvI0sE10IURXb94//R8dKtvDoP%0A54UWcJ3vxJPRWm2UwFmnJRyYVYAdOM9xHB/yDuPLo5Tt07JPbeSrIcrja/REHShkOoyvbT0IPAjl%0ABmTW4YRKZR4NHMCfjU8YDODLPFTkLC/7OXyJl7KtQ/gs6gDeC1A51blxe1uhMQAfvfgXuHfiGnZN%0AnE+yvUpnaYFUrkq4tsIbEl+mnUUe4RJ6d07TxzQpyrSxRCtF7ph6LaP/uobElhoXph8lfbrhFKWB%0AC9mI27fkKIHGSkhoNU0A+fYsx1cPsXHqGGQhmoV0d5W2WoEwUSNzqkaYhXQYMZHuI5PIM9XazmLC%0AeYHFsIVsVGR98Rj7MmexNNXB41zDYjrFN7mONRxjOlvnEJtIUqObObax98zS2haKpKkwTS9bOMgg%0A41RI8wPOZw3H6GeCk6yil2mS1BiMxqkFCc4t7GEq201/OMl29rCfrbRQIkOF1ZNjnOwd5Gi4ljw5%0Azi7uZymVo5JMMxd1ESQiOAaHz1/JUGOK4eIEC5k2vjh8Izt5ipXVMWZnuzh81joSxQaDfzILl0C1%0AMyQ51qBKSO9X8oxv7WGwdYaTPR0Uu5Lklkpkwjp7fmoDuUSelbsnqG5JEAxGnPvU07Ab7v7WT/KJ%0AX/9p7qpcx+H2dSzMd1JqyZCv5qhOZPnC5Ns5J72bjR2HuOMvXsOrb/oGx4+u4qnbzqd8JMN7vvRh%0A3nrqc1wT3k94tpOh+fNa6fxbB+DTw+20vbtA5ut1/yYGLSB4FjoPlOE4tPxtHXqg+g1IrQA+7uS3%0A8Ci0/iTQB/XbIKFa2MtxVezPOB1K7HD6HA3gPO06nL51ALY2GDo65cBwDfApaNwK4UysF7ugcHOa%0AtbdGZL5SdV72IA6wa8BpiDZDIcyQe64Mlzm6iTwEe2LdWQUHPgtbLgCOQGaiQrZQIng9LC620lop%0AQQjPXr6KDQ+eINXSYP7KLOVkluKF4uT+48dL57F+Dl8uorXtg3i+9DieN7V7AGg1Ukf8t8LoLhzw%0AjeLClhIu1DgYfz+JA8+zcSByCO+ZzuNAbQAHfKdwIDmJs3yqg70gPm9F/IwT+HXUO+I2PI2v0zsS%0A92MI79WuwoVceZyHfgq/ikTZ2Tkc6K0EluDbr385b9v1r8z/oIdNP/0078l+jI/xHi7mEV4V3cld%0A0U/wD4ffQzQXQAWCwQZBAEGxQVCHvk2neHnue5wX7eJXCh8luz8ieDoOGa/CeTGiONLAMWhcGgt6%0AEiptIekHGzz++u1smD9Oa3qBzD4IcvE83ANshIVz01SyKToWC5xsH6JeTzBYneJoHIK9q/E3fOMH%0AtzC09gi7ixcStUQc6ltDG0scYR0RIYfZxCU8TIYyIQ1GOMUBtrDQ6ORAuIUsJW7kdhZpJyJg+7Fn%0AeXL1FnYHOxhmjB5mWFU+wR2ZG3h9/naeyJ3HbdzMm+ufZ+PCMfrnZwhTEbtXbOIYq3nVwvf4fscO%0ALj+4i9FVPRxq2cjLH32MypaA2c52Bh9e4PCFK3k4eQlvOf0lwihgdKSHKApYMTVNMAaHd4yw4egY%0AM/05+vYvQjsUOpMsdnQw8OQM8ztaeK51DZmwwl3RtVzeuJ/VieMkGg3a9pdJdTcYG+jm/uSV7OQp%0AimTZUDnCE+kLuJeXUyPBE1zIKUY4tnsr67bu5TvR9eyrbuc7uVfy/tN/zDeGXkkmKvPmb9/Ob131%0APn42+ylWPDdNcBRIQXQSgg3A4xDtA26NOfU7cFFLFefZ5nDUwDnAfTjPMASugvndbXTuWoJvAL8U%0Ay/U4fuXZeKwL22I9KjsdiB6D4Bqcd9rJmeL8qAWiIoTp+B7HgN3AT+K86l2xTp2Dowt2AH+J44ff%0AHN//q7Gu3wDPXLCOsz5xhC9/NGTr7tWcfeioc2pKuOh00t23ei5EZUjWAihEhM8BD0LUDRyG4HwI%0AfuHFeawvHbB+GQcmKhk6CxcCjOIA5wguq3gap+x7cZM1hfMC53HerBJDHTjgOgFzn4PR45BKw8b1%0AEOzEly514Hcq0k48ozjB0k7yCsuT+GLwCCc4PfjNL3ritl4U32c6bq82njiF80TX43dyGsWBq+p0%0AZShGcKGbQpoO4AA0Vgd89qdu4TvBq7g2eSeD0ThtjTy1RIIBxtk8e5Q7O6/mcH4zR1lHNl3ggswT%0ApKjSGc2TK5SotYQUw6yr2wR6mSZLiRVz46RnYz7uGZzQa1nx7cBl+ARGAaZvbSVcDMkUyqTa6qQe%0AaLBwSSuJoEJub43K2UlS+RoLbVly+yokO1zG+5PX3MpHK7/MidIa3pj4Au89/GFa1uZJ1csc6t7A%0Aufv28oVtN/Oq6p3cl7iSS4OHWaq2M5oepoUC543tI0xXWehsY1fyXKbppUSWdTzH2eznZHUVD6cu%0AIUWVC3mcTUtHKDcyZB+tctu1r2Z99BwjE+OUB1IMfHme3CV5blv5Gm44dBcPD13E2Y19rP6XcU7/%0AXCenM0O0HK4ysHaM9GKN+UYnqZYqJ9MjnPPkAabPz1ErtNB/coZqewpORRy4cB3zQScXTO7mqcR2%0Arpx6DD4P1Z9JUg8CosUELUsllo600rahQP1kSGVDkufOXsmmk8eYen+K6sc7aX+2RDQAT/Tu5KJD%0AT9HIh5R2BLSfLhA+FfDsdavYecdBGqMQDkP+3BQLS10kOiN6Ds7QWB2R/ATU1ydIXlWjkkuSOBiR%0A/H6d4hFoWYDGCmi0Q7AZEiPAU5zhRTk7lrttMNnWRX84R7UtJPnxBsE7ofoRSFbhxF3Q+xrIvi/g%0AYM86Nu86SiGfITdeYu9lm1hx1zhth+fhIUi/EcZ+uZfsnXU6SnPumQfhyN9C3+cS8Id12k/hyqwu%0AAP4Z54z8DJS2JclO1lwk1Q+VlSkCqqT/0eHFkTesYN3fn4JLINoD0faA4PEInoUoghP/3whLYY5t%0ABw9RmMnQShkegOgaKF+WoJxJkTgR0DZZZGFHlgKtHE2vZm66hxtW3v2fFFj/EafEizhLUsWv/Z7G%0AKXsPDsC0vHACB67H4/8348ByEGeVpnFg+CQO1AZwgjIQ30PriWfxuxrZRQUtuNCoggOTMTxgpyFf%0AgVI/dP0sJA7hs/x7cGvOVO6iZZdrcAbiCVxdoxJb6+L2pXAWehSXkFuBs9qXxO38Fn638xosXp6l%0AvJSm7+SCG7PN8f2edeMyPtRHW3KR3ONlGHRKtFDJEW6p0nG4AkehsCPJRGcfa588TWlLkqWgnb7T%0As1RaEqSP1n1Fwh8DtwAH4fDvrKa1VGboB5NEqQbz2zpJZMt0PlZickcX/cfnqK0IWOxsoXCigzu6%0ArqW1VuRoYS0j/Sd539wHyNc6+JV1H+a1lTvonpunNJBghh5m6WYbe1j/g9PMrW7jYN86du7ex8GN%0AGym0ZmgrFulcXKI1k+d4boT++jT3Zq5g28R+kv1lloJ2TrCKPDk2cpjzdu0jGg54YugcLlzaRbHU%0AytLxNkZaxnmqZyvpDzVIPXGUjf9QolGA9P4GxeEki5e3EOXTzCXaaU/N82Dqci6rP0Tv+AKlUpbO%0ApSVmtuSYSvew8vuTJHrrzK5oJ92oUltK0TazRNunyxy4Eza/AWZ+qYNEVGO2s4PBY3OMdfQzMjrO%0AwQ3r2XngGRaHMyykOhjeO0n4aTf3Ezd3kRmp0jFWILg/onhRmsaKkNZaieBuzmy1l9+cITdahkdg%0A6TcytN1f5tS5AwyMTpOK6nA3sBqiL0J0PoQRzDwInV+GxO1OZGpvhOTtsXzncfU8vwj5f4PczcCn%0A3PzX2wOmyr30nDVFfTFNdrbieFslv7bgElwLOCpg3OlQbQAauyB9s+NaZ9+QpudQhXAj1B+C0k0t%0A5HqKTt9m8KVdqkwBOAnFIrTcjF/Lvxa4C+Z/toXO3y3CVVC7LiA5E7kIsgC1coLorIDUA24BCP04%0AfvYgcAAmbuyla26WZKXBqQ3DDEUThF11EuNQXMoSDNdpWVv9TwqsH4FoU0AQRU5gVMhbxoHLOThQ%0AUmXsTPx7PQ6QT+NfozARf98e30ebszRw2XFtRtGHAyHtyagds8ZxYfcqnPese1yJ36asjN8haCd+%0AnXM/LpRO40Bf9aQqqXoUx/kMxZ3/SvysTpyX+HZcYfQMTmiOwIc/AT//c9Chte+TOOORx22Jsy5+%0AhlbDPBU/Q6Uvk7gQ6lLgu8BngXdA47yQE9f30RYuQQQ9J4rUr4hIfh44CNFgQLAuOmPk9q1cx2A4%0ATu9jBfgfsPSFJKX5HH1j8xBBLRNCGsbWdbPy09NcecPdPFZ9GdU/bYdXQds3FyksZODVSf7sje/h%0A7NQ+RqqjPBts5Mbpb7NraAuFqI2de/fz3PYVrP/eKaZe3s6aY+McWzXIc4l1XDH+OKQCnuldx1yt%0Ai4v37eL3znoffzL+Po4MjLDxyAl2nbWFSiNDW22JbSee4/EN26nU05CAy/bvotEdMj7UwVg0TKZe%0AYetjR4jOrnNfx2XkwiUuvm83o1d1k6pWmWn0UUxnGGSc7JNVjm5ay/bxg4yV+lm5b5T6xSkq3dBI%0AB7R/v0LjUMDiQyGFj3TQe3KB5GQd1oc8ObCZ8x99Bg7C5Os6SD4eMX9ZG2v+aYxj7xpk5alxkuOw%0Av7iJNduP8Hjn+QxVxhlOjtE2WyV8KPL5h3/DeXQroD4IiSdjWTqBo7RugJkL2+j5+BL1TSFzb8jS%0A/eUSjT9rkPwraPwRhO/FgeEuOPbLgwzeNUdmqEL0/YggdLWnqZfFOnh3LPOHoPbHAcmFyIX+v4CL%0ArNY4+eBRXMRZBfZA/fUhtZ0hmX+oEeUhOAbcigO1SnzfLTgQz8ayejUuYTUCc6/P0PUXZYpXpsnO%0AVAhm4v5/CxqjIeH2Bou/lqT9AzUa++Cf74NXNKC3BVKD0LgH0rMQ/YnT3+AaiHJQuxJS+3A03Qo4%0A9R7oG4bMeyFaAY2jIbWb4ETnEAPT8+Q704ykZ/+TAuvHoLY6JDHdICjhPNdh4AGcZerDTUSIA7zN%0A+CWvE7hBWsKBRwoHXA/jvL8arhRI1QZzOG9xR3yu1q7Px99fh5vAg1D6CqQbEO7EgfgaPHVwCm8B%0A23CCej+OU9JmEEM4UDuGT8A9BnTC3P2QXXDYvbkdktuAm3DCucedwyBOqIdx9MUgbjleDLyjV/Qx%0Asm/Kl2TdC7wRCn8Mow1HfSiremY57R4cJ3XS3beWhtpwispkho6pJfec56DakyTVWoN9EK2GYBdw%0AARy4eS1bvnT0zFsVTl48wMpPTFB6S5rgSwE3vuZ2VmeO8Pf/z5uhvZOP/ey7+YfBt/KdLa/lJ2/9%0AKJf//nE+OPR7zK5p53RikGK9hXyYo1pq4bloLZsWDjGUGmd6qZcrao9wcMMaWhdKHMxsojXMU02k%0A6AsmWbV7nMpgiiiKSO5pkNtcJLNQY3R+gL76HKlqldOXdrO7vINz0nuYa22ntVyhnXla95dJVRpM%0AvKyd7IGIzs8vwI2wOJKjMJ1jcVuGVaOnmBnq5rnaelJhlb6xGRr9AWQiyAfkoxxRB+w4dADuiggu%0ArlOPUvBESOZ4GV4N0UFgbUBtMCCRbMDXQmobAwpXtdD2/SKFSxMUog76n54myACfhPC8iLnXtpB+%0AFMKrK0y09jJf7GT93DFyYcVxnpfjDOeSk1P6gb1QOw/4O0i+EmoZSF4G3AHl89NkpiqugP+rsHsX%0A7HhtrID3AK+A6rlJUgdqsBbGR2FwE/DnOBD9JvAoVP86QepgHeqQ/yTkfj2+Xku3/wW3/FYbIr0T%0AuA/qQUB4DpCNCJI4R2g31M5LEL4sIpxvUOpMk9pTJzFVdzp7Bb5W+iC+NHEYxq7vYfgzM0SXBBze%0AuZKNp05QK0HqUZyBUenmk9D4X+2dd5TeV3nnP/f3tnmnz2jUu63i3kC40GQgGLBDcAKxs+nZBDic%0AE8hmU4CEJdkkB3aTOEvOZskhpJCsAYfmQOgGFzBgjI0suciWZI3qSCPNaDT9bb+7fzz3O/eOcMKC%0AhbXSzj1nzsy876/c8tynfJ9y/wA44HC3e9zi0NdQU2CmUOBEfy/L94/iPuFp1TJqy8rwOqhM1aiv%0AK1A90MRdebZirG8nZi9dQzxOYyXGHBUwvw9jssL/RsPPMDFk6scwZjxIjEdVdsYQJjFVTHcpJjWP%0AYwyrgIHkN2KMaps9d/vlF1F87SwXfedpaicKlCo5I6u7WfwPJ+GX7LnNizKKdwQP+yqMsW8mppe2%0AsM2gyjvHIX8+7H4QNm0IY9oDrbuh8MvESIbdwLuA/4IJlSoGFcgRNggUYfptRdpvbxrz/LaNr/UO%0AR3N/RmVbK2ZXvRpmmwXKf92i9rNQHQrv/hrwctj5urVs+ugBZgsl2ss1yKC2vIT7aIPsYihuBqbg%0A5NXt9HxomqFbFrH8bSPwBnjopZfRNjDJzEQnf9r9m9y96wY+u/EV7GxdzIrCQSrUuOrpHRxdNcCe%0A8npectcDND/T5O5vwtrzYOm7eug+PsPsmhKtlTm1XR0MLx5gdXU/I6NL6O4c4/7qC3ndmz/P9Fvb%0AOLR+Ca43Z/FXxnn0lRvoZYwVdxynsnSWA1etZv3fDtK2p8Hkz5ZxtSIdU1YHIe+Ex9dsYB/rufH+%0ALzO0ZYCZfe2cN7ifPYUq2VdqLFoC3StzKMDUTWU6Pl5n5oISvuIp/E1OZX0ODqbfUCHrbnL08FJW%0ADRzmxEQfixafoHmoyFh3N4uro/BZmHhBB+W+WSq/12Lnu85n6dLD9N03g+8AdxJmtmQUD2XsPP98%0ANh3YS97r4RE4vmgRq0eO0DhUoNifk6/xFAaBFdDKofBxmPm9EtWvN0zYnw/shaFPw/I14Eew+M1X%0Ahz20Cvx3gUVwaDusUnLJVbZfpn66TMcH6rbXgsOUUvh+OuyfWeC+IHD3B3ruJzq/1mCKyoswpWfA%0A3kdu10z/dpnqt+o4FVuahNpfwNceg1WXwwV/AjwU7hsDPm31LdxriL6Yi4FOqK/IKH8yN2VMGV8r%0AMY3lUOi/CsxchflACsAETF1fpuPhuvls9ge+8RAW6rXHxt16JRQvO1sZ6x7In3S4YY9bTjx69kGM%0AYZ4kFtQdAmYtpKIVFqx4ADOrr8GkuWL2FPo0ik1uEWOgP4Npw6vB/6SlF/IQJvn/AZPsezHttQ1O%0A/ESVvj0ztHZA4XIs4yXguoOblrPuY0MRErgdOAbN7VB8DcYIJzFttBP4V0wCp0VXXhf6c7Pdy98B%0Afwx8DfzF4I5gTPmF9ryZG0q0vbWBewVGZDcBw1DfklF82JPt8TRuhdGebpb+5biZ9Sc9zELtSsdT%0Ab/KMPf583lF8D/fe+2MUpuHDvwq3/lZGdm0OtwE/Dv44cB+MvamT6edX6S2fYKrRQW9jgvK9uc3z%0AARuDXw+Nq8G/Cyo3Ecvv1eDOn7uBF/uv4Y8W6WifoV4q0ni6yqJjJ6j3lKhlZboHJzl6cTeLHx6H%0ApRluU069D/Y2z+eCe/Zw8sfK9Ly9TuNtBYqfy3ET3uZ8f3jXYWAD7LplFRv/20Ga1xcozrQ4sHoZ%0Aq//8CFwBs5cXaDvZso2/A/KvO1p/mNE6Am15C/4X7OyBgV/povcVE7hrHYWt3jZqmbmU0vGb2uj+%0A4Cz+52D8W530vH/SNvqWQGvK1/8m8FPEcoqXhHV+AnBw9Dd6WfrBMaOzL2LMIMMyiTDa41Gjn9p2%0AaO2D9hsxS+0KTDHIMK1xZ3j/k5iwfTPw18RKTU3bC1N/CR0XwczLodofnv8SjCk9BM1rCxQfbJm3%0Afxdmqb2OWKf2BTCyrIPuySlKd4Y9dw9mVfWFdVBx7HWwa90qNt5zcC6Uz28AN048IWC50fvYV6H3%0AFmJkQQVbpxX2Tr4Uru3FmK2OZZkINBgy3uaKr68L++4LYT56wxgvDWv5MQxSOUZU0M4nxryXwzot%0AB/fWs5Wxfh4azy+QPZZT6PSWajeCSTzl/2/DmGTIZ8/fCTOXW2pk529hRDwKvIKo5ebAI1C/D8qb%0AMJz0PLufIQuzyF8E02+DrjXEikAXYsRRIlbK/w6Mvhv6/wu2SMeB12CaXgjd4Cgm1TcDf4VBEzuB%0AJ+CTB2HzGrj4ZuIJrieAL8PYDHxmL7z+96EqwH4N8A1oNR2FAR8LnOwCroDWAcgOgP9ZyLbbu0ZG%0Au+nePs3hv2+ydC2U3uQobPIcrfayZGgMtw0aWwqUtluZudFaL52fGaP8U8BGqPsS5UMNI8YrIK86%0A6IOpG8p0vacG18OxTX10j01R68sYuXWW9bdhuFe3Y3Sjx9VgURFmthSpjjWZ6i/TfqQOj4Jz0CwW%0A+PjfVbjhhdPs/JcC1/5JC2ZgpLuHRV8+CddB3RUpX9dk5jegdGVGcVEO6yymNvskTH8MDmyEzePY%0Apj8Ch74JK64DljmY9Ez1tdO5b5r8yozs/hx/IzgliwjTdtC4vkDpYGuuZN7+NUtYvGyE6p4Wze9k%0AFM/Pba0fYC4LqPGyIqVWk9YmR2GXnwvzq90GlQuBV8KD74TlvbByAFp9UBRddWPM9RJicZ1NmJDa%0AS9S2tgPvCPQqJ+p1GAM+jjHVtcBHgLdg2LyyD7sxhqTCKjo9QAWFjsFQDyy7DtwdgX5favQ7fUsb%0A7SdmbT4eCP26FGPY8ilsAN8D7vOYVjuICfcyhuOr3N4R2zesCHvmlZBPQ/YI8aSHJrE4z8NEP4Yg%0Awcmwp3WqwN1hTq7F+MFhjFeMhHuvJVq/+zElZhCOHoCl6zBrVOnsPZi2OkRM3Q3wXv6iEGI4De76%0As5Wx7gn/fBrog/rGjOm+dnp3TdrkrCUWY9mHEWiQescu62fxvaM0uzP8F3NKvw6tR8FdB9nHYfcX%0AoVaAC98K2WGgAvkeyHqYS6njDVAfLjD14RZ9ryXmWn8BSwa4BFvowzD9QWi/DlgOra1QOIhpy93Y%0Agu/GCOBVwD9iknOKCF88CjNvL1C9q2Wax+2wvwUrXwuFJdjGmoRPvRFe/krofApa/ZC92XHwqmWs%0AvvcoWZ4bE19r/cqXQjaCbcADwGaYfWGRtvc0jeDr9t6pV1To+LMardc73Mc82U9bKErtDSXaPtcw%0Awn4J5NdAthvb6KGyu78VatUStXvb6Dk4wcgbuuk9OEFhmzci/SqwFSbeDYW3Q7u0ls3QmMxofCOn%0AfRpmG2XuP1Bn+SpY0gEDRbuPA3Dywk7YkNP9yDTuQaMF7grf12wum1+HwuPgNmAbay8WiD5mc3zs%0AX2HxmkAnl2Ma/Vigr1dijGIp8VwwHR+yK9DDMsh3QushmNzAGDgAACAASURBVPUw3QUDDbjrEehe%0ADdfcDG4P0cF6QejnZvD/CI06lNcxV5/XPwF5FQoXYlaRqq61BzoewbTdWeIBdj2YyX000OfPYPtg%0AT6ClE5hWq+r2B8OY1mFMchGxjm0Pxty2YhDR/tDnG4na7d7wPB1pUgUeg/s+DBu7YfkNGMPZiwn8%0AzdgeuRdjmrIKB8KcT2MCYE3o76Ewnj4MttMhocWwTuuJqeo+XKOQw4lw72T47H6oLYLKizGmvhPb%0AM4pNb8M0z69jjHYRTN0FY6OwciMmnFT8vANjqk2iA20vpvlvYM4p7Z53ljLW/LDDbfe2OLuBndB8%0AhaO4zJvU24ZNxg3Emos3hmtV2Sp43Jtr4b4b4GV/Dv4ucOuwRdmJTZzMBaVwikn/MebQUlWnC7CF%0APYhppu8J72xghH0N+KvBDUM+BJny6pXaWof8qL0jG8FMwgdg8qeLVB5qUvoIRpA3YcTfBD4DY7/U%0ATu8/TJNf5phoVul5bNqcTGuh+KfACyG/OKN5WU45lKFrfAGKW0MK6yrrd/0DUL4CHvwSbPkAxqAe%0AwzZxGzTvhOJ7Q5+3wZ6/Xsn57zvE2LurNLfOMHA5toGOYqZYLcyThMZxzFy8BrMwHgK/DtwbgafB%0APwJssOyXmdvhvlm4YavN7cS3oemgsAWyS6BzHSZULwd/GeYB3gPNW6D5ZIG2P2rNhfM8/b9h2Upo%0Av5mYSaff18J0h42NzdB5ErIN2ObZjMXjrsViJEcwxnKIGP98BGOun8C0yOXEilXtzBW3aWyH6SPQ%0As8nWbuh+WP4rNj8jn4R2B9VXBbrcHdZZtVoPE4Wtag2fxDa8Qg6nMUanzMClGAP6+3DdZYEO12Fa%0A6oOBjlTcXVXhBsL3u4mVny4mHr++k1in+LDNH/sx4Vwl1sioYxDWSvDN4OG/lXh44jBzkBCrMQY5%0AHmjnPGyPLsKExJOBfjqxpIMixoAdMb37aJh7H+ZkNTGoX+ntK0P/9oXnS4CuD9cdxiC988J6tofx%0AjWPCZBYTtIR3H8b2dUfoaxbm61pwW85SxjqU97Ds4ydtYrYARyE/5Mi2+ShZlPL5Fsz8HsSI5/rw%0A+SRzJcEaV2SU8pz8YZh4XSc9/3PSTJGXYHjQy2Fw0UrWPXrIcNWvYBJqNybt2mD4hh6WfPYk+Swc%0A+vklrLzrGBMbO2hsyBj43Dj0QCPLKMx6sorn0d+BS67A4IsS5jzaDqyFQlj41t1QuAWT6jug/uNQ%0AfgzbWF8EroaJLR10fW6K5oszij63jb8Ljm+Htj8s0fn+Bo3XFyl9uGlgfAbTH4K2y6HxSxmVz+Zm%0AmgrCuADTnLVJLsZCrmpYKvEx4D9BIy9QuqMFb7Dxcz/GjDdiwqdBrIjUZmtEhm3qjRgBB+3oK3fC%0A7mOQzcBNW2D588KYPwn0w9H90LwImILCJlimqkwnTFixHtyHgUdheBaW3Ipt+FUYc3wVJhAcc4Wj%0Am1dD8a/g+AjUTgKjsLTTBA499v/coXc5xmgVsrYG20SDmHUyTizCU7e1Yy+xJmo3luUjDH8ZthYP%0AEg/nO8/mdviT8J0u6JyGl1wevlfpx3pYH52tofeuxxjOfeFZG8Na7gh9Hwl0+nyiF3w4PKcz3K/8%0A+K7wDpVsXB2+E1NdQTyorxnmeVF4zkyYtwbGiBZB604ouNDfg2Gci4nMXNXMVCNWtQMeI5bevCDM%0AwWzo1z6MSV8R5lAhikqQ0XycJBYV7yGWGFWCz2KiIFlKrJSlqnLq44mwXndh4YmXEivrtRHPUgu1%0Ajd3NZyljfah1IeftOkDxYJPZ8TYGKmM2+DZo3g+8JaN4OLewj35M4yjASNbNosPj5FnAQ9qwDdfG%0A3LEPEyegK8S95ndC9kJMkn0QvvgduGELc2mlfisceS8sviOjtR0qPrcN9SCwFSZf0E51cprCERid%0A7qJ/xwStN0Phg9C8pkDhwZzaIs/UY7DojVi/7gE6oL6iSPn2pmFZj2CLdxh4I5aidzPMLC5RfFeD%0A5ruLVP++aRutBvmNkN1rfWQFJr0vC/+3YwSuTSONexibw2OYQDmKaZj/Ndy3DdgFrXY4uQ36fyHM%0A20ls0xwOzx6yZ01/G5741Uu4ePmTtH2jYUT/BEa8y4EHIC/BV2+HlcvgcBOy++Ha10PbddhmVg3Z%0AWYzBnI8RryeeQfQgxkD7sLnXcSa3wcwEVH+SGPK2C9u4V2Fm7jC2OV14vmqLquqZUqPXEtOGCxgO%0Ap/qpCkyvYBBIC5pPQinDNu4G62/zTjh+CJZtxJjDFcyd1dTaZmMprIGjx6GrH9o3YIpDmRjHrHjk%0Ak6F/YtCEvqpexQXEjb+PeB7UZOh/XxjDULhXRdDXYQ5PF376Qx8VhL+MaMrLi66jiAaIJyY3wvUn%0AMFhBjE6CAIzRdYf5HiRWclsdvlOxlgvCO3PMIhwmHvr3BIzthd5LsMyvGtEfoTO39hBrF2dh7J3M%0AxdoyGN6R1u4YCdf3YsJMz7qdeBpHH7HO8d5w33rgQnDvOUsZ65dnXsRlrUdY8ooJTtzRTtct0xSv%0Awib9dRiQ/wj468CtJYLxx4gq/qeI9SEPYBvgCRi8C9bdBnwSpt9VpP3pJlwEkzMVOp+qGXH9DXA9%0AzLyqSPXhpm3u4xhxLcM0trsxgnweRoAbMBznpZDPQrYDY3ZgjKGOaRza2NeGvg1jG/QLWIHF78Kx%0A9X0cuH4FV/3+Y5Y98j549EG47PeAO2DiZ6D6bZjeDj03WTREUdqUvJdLMMLfDD4HJ6dFG+QjkO2H%0AqUMwOw09W6F4PrZZ92FE+TKi2fYpOPEJ6KiAn4XKFfDE12BpCfpvgpOj0PY4VLaE+e/CNrM0qJ/A%0ANMuvhrnwGHOvhfEPYRDCbgwikSA4jjGUTqLzkPD57rAWQRNkkFhb4j9gm3xfuEaVxMTMnsY20Lcs%0A1MjPQnZx6LdwtlGg14TD1CB8/Em4rATnOeiTs2RteIf6uwPyNshU0LoG+UFo1Q3DL/w48w+MnAh/%0A9xO90wfDu3V8yXoi5qtspl1hXqRtqjhPk1i28gQmGBrYflH94OWh3+djzOMhYgEgMacgPOdgslA9%0AbY7Z+zDfh4jntU2GdysBZZqYyl0i7tEhIoS0IqzrGqLp3kE8sj5N2LkUY5BKJZ9NxjuIKReq9qba%0AIIPEDMrnhfnYScSGVZd4Q5jzneH3BuKZZ4eJmnAYj/v8WcpY/X1w8tIKPV+twUmoH4GyJF045sKH%0A+DznsAn5LeDjFkvHQXCHwL8ZyMEfgewzwBrIPwrZSzH8bgK4MmB4+7CF2G3fTf0rFLeUqTxej7VT%0Ab8Yk9HJMM2tiZco2YBrKNzHC6sA05ZdiJk8b8TTWX8C0CRH89USzaxIjvA4MAgk1AXgCWAvDX4LC%0Ag7DTw5IVsKgCvSUYHoVGE3IPa9dhmy8wphMV+MrDsLULBp4PLILmAcvrHitDawi6ylC+HGNghTCm%0ANozZ3QzcAa2noN4P1auxjT2LCYYq8EXItwb88hth7P02t3POxqUwd0R3G7bxe4g1cmsYdjlAjOK4%0AkHj8jOpreqK2cz3x5M8GsUBORmSQT2Ib7YVh3pvYxjwZngPkOWTnEUN+puz5rQMwOQrjNViyBCYn%0AoX99KDDTQ8zaW0s8SbQb25xyvDQwgafrdQyPElEuxhiMtCxp04cwDVPa3Wx41wSxzKS020YY4xJi%0ATWHNU3/4WyUkVdRIjEMMdjSsRY2YKt4IcyLhVA7vqRCPSioRywQOhPftCP2Vp1/PGw3jGgrvXIYJ%0AWpXyU03lYvh+e3juYmJluvOJZ8b1EfdghXjsUJ14QkCTWEhmNNwrH0aITWUgvOMIMTpA2qpqOWsO%0AM3B/dpYy1n/2N3HT41+i+lQdCnDkhl6q32zSMzQJF9hGaC7KKH87h8PQWgO7PgQb3+wo7Pc0t0Dx%0ACBbvd01GYRyK23MjmocxjWUKY4YPAytg6mvQ8WvYwt6NEXzAYJo7ofiTGEMpY2bXF4A6jB6B3ish%0AO594LtCK8P1KjOn2YVjueoyY8vCch4glB7/L/NNXW5iUvR8j4k4bD4NERnIVRpAbMAbiMEa2kqgN%0AKQ5wA0aUjxALceuoFVWX/wa2IeQI6A19Pxr6vALbyDlGhFkYj84bu4JY0HuGqMXkST90ImYzvPsY%0ARtiXhD4fSsYqTWxVII7x8IyjYQ07Q997iBqVGNqKMP5HkzFqzKrhK01kPMwbxCNtSmEMShFtJ5Z8%0A7A/jOBrW4zxizd/jzFV08rNW0GQOppjAnGCq8VsHhizioHAJtt7jxMMYZ7C1LmL0qnrARaIfoUbE%0ALfMwD0Xi+WwHwrzUMVpRdICcPj0YXXjiEdJNIpPrwxhahjGmpeG+WnjGMeJZbCrROYTRTgcRmiqE%0A+TpMxG1lARaIpy1XiOs+TDzDq4oJ0QFivQ2FUQ2GZ2ttOsP35fD+PIxpNsxbmXjEUAjXnNPWO8P6%0ACTJYScScQ4lR9/azlLG2HnUMLVrK8k8dJbvKwxMw8rJuqtU6bVOzZF+E/OUOX4XCR7wRyj0YPqkY%0A10XYJhVAfQU2iQrZeBzTxmoY0efEYih7w+cXh+ukaa3FFuZRjNC/hZ3z81FsQS7CmMAwJiXFuEaJ%0A2tkS4hEwDgPMX4BtyGHY9fNr2fjIPnuvFrwQnvsEtomFf/Vhm3EwPLOKMZYDRFOxFJ7dSyQaMTdp%0AjyXiiQozROZweXi+D5+PEA9LfCzM5S8Ti+VMhudOhsXsD89dRjThVKdhGttwh8KY+sPf3UR8VVig%0AvLM14vlhR7BNWiVihQ2iqXkRFu8ojakQrh0nMgaF8SjleIooTC4I/Ttq4zy+A/r7IbuAWNVL2XCl%0A0KfgdGscgq/vtmm7ahn0vhqjsXpYfwmKGnAPNHMotIfSfb3hO51OIc1S2leZKMClxcrjnxMF6uIw%0AP4PJnNbCNVViXPdK4qkBE5j1Jy1bNOqJxyEtwWj5aWIBJO0PlfYUHKS1OsZcERSGiFj2ovCjELe0%0A4PxgeGYr3Lscs2CkGZ8gnhB8jHj0/DJi4aQu4h46Fj5fSixu3yCW4xRTnyDugUp473QYfyh0415/%0Alha6zmY8jR7H8Ju7WPbecWhC/+A4M+1V3L1WD7QxAKVhjPmVsA2yj3jkxBA2mauJWst1RBN2DRGj%0AuQQjQnkEn0c8+bGETeiNGGMaJZ7ncyW28a4nYkh1jBhUhWcJUSPaQDywUM8fw+CHFcAW2Pi+fcaw%0AZBquDs/ZHa79FrbQl2AL/wBzNSxpxzZGAdOMpohFwOWs6MYEzzqMOB1zRy/PaZKV8Nn+MK4lxCy3%0AYBr5/XD8Kej7gKX4zR2U1yKavbPEamG1MPZ14buO8O6NRE1vUejHaOhrF1Erk1BSsLjyvDuIJxwc%0AIprES8PnU8SNt4R4IJ5SL08QI0jKRKrfRjz3bBzai9CcgrI0wCLxGBwxnyeBGZg+ah+tBXpaYa3V%0Al5PhZ4C5U4Nnh6Gjm6ild2F0XCWeSiycUiUsHZHBCsKph7mT5XOYqJHLsaO1yImMT4K0RjzTzCXz%0AqsiGLuafvKFaxT3E8+O0VoTnHwlzLc3wkmQeXHjfbLi2I8yrrCZZlquI+KlCn9aEvuTEU4qXEGkr%0AS76vEZ1qEgYdxAy64xhtC4JoJ1bU6yHW/6gR8d9n0c4YY+VuWPf0EH4zcw4H56D9qzOQG36aryhA%0AsxWPET6MTeZhrD7AXmxSnsQk3TDR1B3FGNlxbGIdptEOYQxsOTaZT2CEfQBb5AGMcUk6Z8zHexaH%0A/mcYcU2H+w4TvY0ZxoxXE/HIUUw7lCSX13N9uHYnMTZTgco7wzPWEpnYrvBOFZbQ5lhNPJ56hpjZ%0Ak4akTBNr0h4mFvmWE+g8oiPjhOGMrSY0hqE4jBF6ZtfWJqCiouGTYe4HwjN3E7UZeWUrRK1FG0Ha%0AY050aEwxd6Irx8Ncl4nMVYJtLMyFTN889GM/pmlPQX0vtGagGiCDqWE4PmGPXrwIKqvC/AUaaR9g%0A/im+2njtzEVriPG0T8LmzLrlCX4AVTgj6VMoTtLRAa4c5kCbeSD8KGKhRtT6DxE1Wh/eK4xZTPBg%0AmJMSURAcJWquVWJhadGFt3nLJ8EVwXkis9pMDLk6QTzRoi/8VMN3wiaDL4QJ4pH00mhh/mnDWrsO%0AIuZ+PLxzNMydhH13mH/NY8oAlWhRDt+LdhRaJitIUIGyuQrYvpITDozp1sL9UqgEgz3LduYY60Zg%0AJ/hOzAF00KrkFDqB3eblrn6hSfPGAtlgi7mzp7RQ38QWaAe2+JdgCytYYBBb9E5scUTQQYPgKBHr%0AkUmgDJfdGBO5jnjK5WeJZkYv0flSI+JNMv9PEItZXI1tiqFw7YnQF+FlKpRSw5jFVWFcY0TPqCdi%0AmqXwDpUoVAruASxCQYxmlHj8y6HwzCMYYSnL5Hi4djb0U+buGPAo+JOw7GpiPKDwLgeVduJGbWJC%0ArEk80XO/XccQEWssEUOvCOtVIdatnSYexa2MmgxjapNETFDvmAKehHo7zB6HqSnoaIfpBtTr0N1h%0AaaUze2BmFqZalllVBEbHoL4H2jPoG4BiDyZYWkQzsko8XZfw2bSNpdQFyy9JxiE6k0AYgcajVjSl%0AUgVfBNcd1ruXmJveS9yFYrwjxGPFm+F5gqZWh3lYTNT0MuKJxOpzIzxXDqNqeHYIz8qESwonl5Wg%0AfvVhDFp9K4RrikSBpxAoMXc5FJcStWNhxcKJG8QkgA1hbVcSYYmUqep3gZjP306sGlcIz5IlIoeh%0A9gvEo+KloWbhPdoPrTAPh4gnJ/fwrNsZw1hnR2G6XKU6U2df/0o2HNtPNhFyrMdhdE07zVaRrJjT%0AOzhJURhcE5v8HNMU2zEmGrS3+kYoTxLxwX6MUXRhkIIcO3KItDCNt4gR3SZscVqYViyMJ8OYuTQD%0Ahbw0wvOHMGY8hhGlHC0K66liCyyssgsjXE8ssC2J74hHXTSwzSMNNMM2V0f42Uc80UCEMYFpx/LE%0AS9A8FKIvLsMgDmkOJHPUgTHpw8ZYXQ/GNGVSjWCa4gGMcQuiqWOEqk28Ijxb+Jc0FW2qangf2MZW%0AemOLeOqskhLUt0Z4ftPicI/WIMthJmQKdzoY9JG/t0IXL6tAVxfkLSucPD1r31XboGcpZDKhxdg0%0AJ8I9l4X3DhFP5JXpLgbckczjUWAY/HSILlCeukxcJXJ4jI6ET0Okj7bwrDrx+OzFxCD2gdBnaaKV%0AMI9i7q0w7w2Mnl3ohywC1VDoDWOW916OJgXzi/YrRHoXRDKN0d9iIgQln0NOZIBjRM27QYx/rRML%0AFil8TzgvxPCyCeZOtJ07DLSLGGHRFq5X2myLCP0ow67CXMH6uSOZFAUwzPwEjgFwW3+EGKtzrg3L%0AU1Egxr9479/hnPsD7MDaY+HSd3rvPx/ueQfwK2F4b/Xef+mZnv1E3yYuPb6LkZ5uxrNuTrR3MzA4%0ATrFkedZtUw2mqxmuleO7HLUuR3k8Z3J5GwXXotXm6drdtB5sZM4jWq4Qy4ipDGADYzAPhu/WYwvf%0AgzGyDkwjUPaH6hQcwYhWOM5KYjkyaVQzmLQDgxUUVhJA8Dnpr0wWbVxtiFJ41vkYkTXCzFXDdw5j%0AUgL994dnqqh3CyNG4ZMT2AapEcNmhHV1QlHFZL5L9PCmsMdY6ANmKs45ToTNqT6CqiepHCMYtno0%0AvKsrXDtDNBfbidqaNq8gFcXoZmHcLaKHtzO8aybM7xHoWQtdE9CaMq3QNWG6BgNNmMhNxi0tWIZX%0AKWhyGdDloEuZOXqfGIU2qTZ2LzFOtI8YYSAtv52IGfpwXw9zXn2nkCDhkUoBFRzTx3xBAhEqmSDi%0AusUwn1MY49P9ClMTgxHtyLKYIjIV1XFQckEq5HqIGqpwUFkWYqwTxOPYlV1VJR6Z3SA6xOTxLxEZ%0AqSIJhLUKa54lwkl1osPOEbVv0bfMdmnXjfCMaWI1Mf2vscNcHWefQR403oJwXwlTObmkFT/L9u8y%0AVu/9rHPueu/9tHOuCHzdOfcibElu897fll7vnLsIK49xEcaG7nLObfLefw9qUSCnmRXpnp6mrWeG%0AQrkB3RZ3mrWgLW/ishyynLH+Dlzu6B+dpFBqUJpt0f4YcXOMEzXJ40RzoQNjkocxwlTgujI/pEmU%0AMWahUAxHXODU5B/HtGQRt3K828L3ulfmfRq+M0I0p6vJu4bDhDSJISVyEgikVxiOHBRiqFMYg+vA%0ACGqKqLFOhr8FXayx92b7iUy3g+iskPdbWTgymTKiqVckat79RDxKcY4+zI3wMGldGsPxMIb+MGZF%0AORTCPTkxFGk8eb4YMcR89mnIFhutlIJ5XpmBvlmYGbNwvWIBitUwVjHOLqKwEBSiDQixGlQaoiNB%0AJu1RZnWLKAhU5EPMSIxBzHGWiCEqTMgTw9HE1OWogYgXKqJD2piwS1W20i7uDHN2nHiMufBpmcti%0Acs8kTNSXJeEzKQEzxKpZelYhrKOsMCkDmjNPFJrCZNV3adJyHKdJCqlW2UzuU7QI4btWcq3mxSfP%0AbkueUbAxuCoUEojJlwPGnBHDsjqJ1sezaN8XY/Xey0cm8j4R/n8mNfkngI947xvAoHNOGdHfOvXC%0A1a0DzHY5Wq5AH2N0Hq/DODQuhMKUIzvpKeNx26Ht2CRuMzAA1YMtq8l6KmG1EzWlwxiw/yRxc24O%0AI2gjSqUGZsooQFjeam2iQvhsCtNeh4laqhZAWR/pYotpCq+S5im9vy88Q/GB2tgrMUY4SmSW0q66%0AMMJRSA5EJ90sUeNJcdkJ5tKE5zb1JiIh5uG3HDXysh4mCqh+ogbXQcRS5S0WQ1bcrZw40nYXEUO+%0ARpl/4u5wMl/S4iFuOPVLkQ9LiWvnw88k0TkXnA9t1VA4pA2c4hNlQs4k98pqOBHuLzPHtOdwXoUW%0A6RkQmaKamI4EgxhLM3mnoA0xNXmfJbgUwtZBjIzQO3x4tkxpCdAqMR46xUCV/isLSIJADkVp2WKC%0AEMv5KQpDuL8iEcA02hLzTXLBOlo3iHQlYVwketpzIq0J49UYNU6IvhApNbIa09A8WaLaE7IMtP8k%0AoGaIVpsKOFWguTKkLWtuIcJwz7J9X8bqnMuwEPvzgfd77x9zzr0e+HXn3C9ggSb/2Xs/hm27lIke%0AxNjF97TeQzVaHvJ2R1tXjUI5hzKUhmB2eUb16RaFkTwSzHHs3PAcw1uVgy1iS50zMq90rwqUyAkh%0AptmBbQSVmJPU02aQ2SfTQ2ZIDzEouR9bXMV4CixXVssIMahdG7Y3uW4mebbiYVOtRxtcTFJMHOIG%0AEK6bEbUkmdLaMJ6IbaWbVIQswSBBNJCMV5tYnnfNt8w+aRK6RprQrK3ZXEC/tC5hpdJQZFbLkRM0%0AjLlNqbz1g5hGLGdRGjo1EfvgCvZDGzFOUia5HCpy+MhDXAnPlHAQU13EfOdMTjy0spQ8Qw5HMSrC%0A333Md8ZII5V2qggHCUMBxDL1tU6yJLS+zXBv0NYpJ/MpAa41kcNMAkmhVsJXxdwFGWjepGVXiOsq%0AqE3C1hHxc41dyoQsA4WXKQ1XfUwdWWKC0ojVRKvFMM+ygGTGS/lIGav6J8x0Kpk/hfw5KO1Nni3n%0AGMy3YH7I9n+jsebAFc65HuCLzrmtwPux0h4Af4SdlPMf/61HPNOHf/A+LBXVea6/zvPS5wEtcC2o%0A7m/FzAllfEiyyYRT5k6aQSMP/yQRsBeTqRE3szS8jIgVKg5SBCX8rRx+S5uQJqm84lQrFRCvxYTI%0ASKRtKsNGYSWKRhCWqLQ9Xe+T+9NQE0fUlCS5pc0pvEXpemIqYlQyc1XQo4oxCzFjOaPkcRXmJK1Q%0A5p0Yg2CIKlHLEVOXg0KMoPuUe/X5DDFsR2MUtCJNRaZpG/MxReG8Yo6af2ldk+BbIdxJ40294GIY%0A8ixr7qXNStOUd1tZQdLmUs1P4UEpw1Esp5yeEBnGRPgR/YgupcGRjF1maypQxFgkgMSghIcLdpAA%0AlgNLWLDCoPRcOa1kWcwkfdU4x4n4vQSF1lRrLgtJkTdSUERLeqYYcBvRryCFSDQleEvMM40r1rWd%0ARGxZCpciFjQGzbOSSdS3CtyzDe7ZwXwL8lm0HygqwDn3LmDGe/9nyWfrgM947y91zr0dwHv/3vDd%0AF4B3e+8fOOU53od01LlKOg2iliCmqNRDeeyfJGozYnLCzxRIvZ+YsifGoFhJhc4UmXcyARAdTtNE%0AM/Qw0YQSoWlDLdNgiJ53aZ/qj2AKfS9zpg1jXgLcxQTSOE2ZW03mm3r6kcd8NrlW2GYfkYGOYsJG%0AuJOwMWlYhH6lGroybWQSyeud4sMSBkoDlICRiaaYSoWvKO5RTEfWgfqhtegimrCaT/VNGJj6MIvR%0AjCyRLLlOXvJgkTRnoCht69RwHm14adyCXqQJKcZSQk7msGhMQkuOJwk74bCdxLRVwRuCDSaIpfQU%0AQtRODC0UY0iZjq4R/KV+6TsxMTFVOR/VV4Uf6e+eZE6FMQum0N+pMiH8VbHRomHdI2feMqIWqNoF%0AxTCeevJMhR3KEpHQFNRTI1p+io0W1i9tM4V3RF8KjxPUIuEnLVUYtBSaDubSZd3SH21UwADQ9N6P%0AOeeqWADSHzrnlnljjWBJozvC358GPuycuw2DADZixd2+t8mTJ2ITsUibU+GJDmIa2jJibUY5pzwx%0APlQ4VycxC0UTr2yRlUSzQCC1gH5pv5KK64iLoVASSbTUs1wnak3p+7UhxfxlNreIhCOHlmLnUjNd%0A2pi0cWmPYrjSRKRpilhE3CJwacpiBNKmtSkVcyjzVI49PUtavN5VTn70bJL7FLStcYuRqO+Km4QY%0AEqa8dqU9atwqoKz1ERShcJ+J0A8JZQXai0YCsy8KItK4pKlKG5ZWKg1MTFVzKCelTGBpS/qdalJV%0AIqOUkFaGUyoIU2xQayZNry15j/ouQa2+zhI1Pn0vwS1YKMWUU8GcRkVoTiHWGWhhVpwKuyjcTX0U%0ALQuKSOdLfTyZjEfaqRQZ0YH6J4dqi2i1SbALgxcjWpdWywAAElJJREFU1drJsafrNQaItK79JDpW%0Ago6Eo4SRQrhEA8+yfT8oYDnwoYCzZsA/ee+/4pz7R+fcFaFre4E3AXjvH3fO/TMWldkE3uL/LZW4%0AhmFJJQzPk/dPcXAyd2VKiZi6wmf9xLqMCk1pxzaiiEfmgRwPwloVOiOTVdlHCsyWyZlifCl2ppkT%0AQQgSSHGylOEIDxSmWiZqKmkAtjRFRRUI/0tDr7ThYL5gEIFIexDDGA/XCNPSJpeQ0GZVDYE0e0UC%0AozP5TP1NIQ59J4xWjEBQR4rz6h5tfGXFyBSX5SLNT+OHyAgkLKT1qnJYToy1FINXP6StCWMV4xOT%0A7iBqV4KCtFFTfFItDaPSvGqdJpPvlL0kelK/Ndcu+U50or5Jw5NyIGEky0KMXRaRtEdFdZwkMh6I%0AziDRmTRZMUatm2AcaaGyGKT1SnBIERJDlaNMioP6rcQOKRclIj1ozqWdK8EnFVaKGpGpL9qV4JTp%0ALwcuRKVDhVwkIFLBotA3jacEeRFa6R77IduZKxt4kFgCTtIDImGIOcnZU8ecF0ewSbqUuHlyotMn%0AxbvECLT5pfmJcKWxyLGUYoLCguQVHyWa6Mrq0P9ihMJoIUpomU1awJlwrSADPSsF8iWBZQYLtxXO%0AK6KDSKDSeieT96SSVwQqKZ6a2GKgmifdL20oNTelrYiRiyHK/JSJJ/NOabpKCdW7JYjUR61dytC0%0AkcSoxAyqyTsUs6j+K8xNG0gCSYxJzxbNafNKkMP3pmRKQ+pmvioizUwmdIvImEjmQE5AOf/EjIT3%0Aa0zCPfXsVIil75QwTONw9Z2871obMVVp8dIQIUYqaI3TmF6tmSwcafsSQp3JM0RXckLKMaZ57SJC%0ALbI05BcgeY4EqPDsdK3SsDIJQe1vwTRiuqIjQQBKa03nsYtYQKiHOf9EvR3yAlQ7f4RQwI+01THN%0AURJeGocA59TjJ7BZuE66gQjXS+rrf5gfH9dKPktNKRGMGCHMN9W00cU805J/Ci1KtSiZWNK2FMtZ%0AJDozZA6JqaVhKmmoipwnGr+IN0t+J+bu3FiGiV51MWoF+CtVUsxUxCcS0hpoLgSNVIkhK+q7IiOE%0AtQnOkRYoE1OamdZAjglhtNJuxPTllU4jF8T0NXdpNIM2ojzGsiDUVwlKbWjFHaeQjDZjyrhJxtVF%0AjPlNvdgk96drLsUg1TRJ/k61pjTWVOvcyXyIZZb5NKD5kXYlzVPXaV2eCasvJvfoWRpv6oXX/AhW%0AUVOUhu5JBaygIgm29DmpUHDJM1OhKx9KinVLIxVerXemSRUSFtp/cqSlSQsq0gKRJhTXq3uBepsy%0AG374duYYq4LGNVHaaNpcDSIBizCEx0FcsC5i7GCaV6+NKA+qwqRkPmvx0wXX3ymjFXOXJ1kebHkx%0AxdxkPqUmtLyZaeiPNFF567W5PDGMBOYLG5l40lI0RyJoiLikni/8UdlBheQaMTlhqCJmbSBPTEcV%0ApCIi17sgOl2kxWptxHAVDqQNIEae4phptpkcIyT3SyCJQWgzS4iof2nUg9YqxTkJz5E2Khw/xYzT%0AGGAxl15imJrCkWRmSgAp1Ev0K00QIn2nFoly7LVW7UQYQJCDhEOqQKSavLRHOc1kfUmL1hxKgIhW%0AFCkhWhWDl6CSdg8RaxUzVNSHFBvtqTy5PsXd07lPLQiNRZCIaFOpuaJDabBySuo5ikxItXoJZjFR%0A/Wg/S8mQxaqEgEQ5aVahVXRUZp4dU1V3zkhrVKBQgGwc89BrsceJJlLaUvNUwfwCyLX40pKUqyyz%0ABCK+qpjRlJHIaZOG2aRZUmJmclzpHTLJ1acUOxJRitGLCWXJe2UGivFMJ9crLlAMQzjRbPJcEV6Z%0AWFVfUIPMdqWdpuEs0qYghrFB3MDpxkg3mpiEtAcJJs2b8ESIWJ7uFQ4rBimhqfkoJNeKMcupKFOb%0A5PkQQ6cKyXepx/fU70nmrMn8ugCad+GTMF87FT5IMnaFEakvxeRe0aSsp1MZnISsHGJapxQTTwWD%0AlChp8Nq5ErqdxIgH0Xw5uSdlQJpvabVKghDzccl1wh81Z2JE6iPEPSRhrGenAuvUVFEx9DT2NHVW%0Aqa+iYdFzmmIu2EMhWZrr1OIShCNISNEEKd5bFFOFQtNTPpX3/BDtjDHWerVAZarF7JIiecmTZxmV%0A2RaVjnx+0HW6QQREKxBfC6qwoRQ/lXYgKdpMrtH1qWmlmFA5Q4QFioHr3anZKiYB0fwTJgeRyLXB%0AUu0YooYkhikNWxI6zYoRZiRvqAgyrRqUMmIxInnopQnJBMuTe7SRpO3IQShhkmq0clTAfG1G16VN%0ATCrVNrSe0sYgEn6e/FZcruZXoTPqp8zOanKNNo/GmDKBdM4gRnNMEjVfkufLRBcNibmmEQeiET1X%0AVo5wRDUJH81ZKnT0v2hO9CrtSyFiEuop1ql+ksyV6pSmVp+wX9FQuq4ueV+KKYvGU2tBUEM61kry%0ALFk7JM+S8Cgzn/4gxrlqvaVdyqmrfaTxpQ5jiIy/RdRgJbhSpSeFKNIQLe3/JhTrUGiAk4L1LNsZ%0AY6wT5Q7qxTqOnJ6ROicHikx3FJipeiqzDarH86jBiTEJi4J4WJmcRy0Mj2wm9wh/lEalzA9pxCnT%0AkzdSGU8pwYJFDMD8YHIRuMxWaXCpd1mLp01+KuYmRi4TSyZSyoxk+qm/KY6k/9VX5XWnaZcNYjiT%0AMNPUFG8SHQy6Ps1bz5JnpKZWGjsphjmbfJ/itnJwwXyMLX1OuiHUTsXkZM6piXGLCUogpcwldcCJ%0AYYkGxHC05iljkCakuRJufSpOnifPlkMwFWCac4hrCRFWgljzV9ZCimWnwiw1raWhir7lLFVfUlNY%0AAiQNJ1QTBKP1EP2mkQOplpxGgkgASqDAfLpJY3055ZpTr9M1Ui5OdeSlCovGkNJy6jhuJPemuHKa%0ARJEKqCY4H6ICpKE/i3bGGGuNCrWsQoU6zYESBZrkLqNWqEB1mmq1FhlMWhRBTqU05lX4qBwXYsgp%0AIejeIrFoi7SwNCRF/6dpmhViGBHEhZEEFb4KcZMKsE8lo56fapRiSiISVXwSbEBynxiBTHsxcmFd%0A0l7lyJLTTO9QYHUKIaXxl2qCUNTHWWIpRTk8xHTScB85rdRvabit5G+S32lgvMxjfae1E6YmxglR%0AO9M7hderyUTXfIjJiQmlYVcSVtrUacqwcvKFTaZOHt0j77buF7wjrT8Vrml2lpiKnGrqS2rCppaa%0A8HExfAlRmcpaP8XLZkQhKDy0kDxX15J8Luala1I6lzDSGmheU+iF5LvUeZbCJbLqqkTNXTQkOhHM%0AJVpKHdMQrbI0njX1GSiy51RoBKJFlDJ1tcLpCbWCM8hYZ2inRIMSTbxztChSp0wbM+SZo94GhQwK%0AYibS9FKQPZ2EfqJUloe3kFwLEeRPi3Eok0ZMV+aCFlabJ8XihIdJS0tDf1IHUZe9856nYOum5B1a%0ATJmjeo6YnE+uhUiQpeQd2uwykURYioRQUH2KaRaJRKrPU4KW9paaQmJK8qRLyEnai/E17Zp7DsHW%0A9ck4FWUg3LtArHGgjSmLQZqFTGf9aD1TRqjfaQB8quXDfO2tnnynkD5p0Vrj1JMNUSjJAki1t8AA%0A7hmCrYuY78BR9IiYmjTxUvIemB8FkZrT0sAhanNirmLcKUwgHFjjSk18mf/CFyEy/DTWWp8r9lOW%0AmeYrh3v2w9ZVzI+yOVUTFC1KUXFEhUD9hQi7KRFEzDIN+BfdpSFtml8lW2hPSNsWfaVWTArRnNoS%0A5atVNpz1dLQzqLGWKdKgGMRmkyItMjLvKTabNEsZzudkHpzMNhGi1H4tvkYhrBHixtMiKUNHGpti%0ABoU9ptpBCs7rOzkNtMDSFmUKpuE/2vDBdL1nCLZeljxXDE+asdI3U81ajFBjkxNAfVIcaEp8cnDN%0AEMODpP0KJxQueao3VRifNqmIVgxYlkGq3aaYc2BI9wzB1g1EbaUZ5l7POXXehKf55HeqgZGsjeYm%0A3cwSJqmmLNMwDQlKKT01e/VeMbBUY0y1HTHmFFcH7hmGrcuZr72dqv3r3Snem5q6EmZi8GLA0r6k%0A/YpWxGglNCSwpMmnNKv00VSw6P1ihKkZnTIf0ZeEyNOBsfrkejV3yv8SPqdq4YJzFFM7ndwvGEK0%0AWkr+Tq0F7YGUblJoCeKe0DM1l4JDwjN9FuDjEBHgTlNc/xljrO3MUKdCjToFWnTkU5SzjLbpGs4b%0AmJwXoFmGkrzsmpjUaSJAP3WyiEBTJguRmAW4a2PVk+elzFomsfDYdGMUkvsFKaTFGxSfCfOD1FNv%0Ar5wcksTSJCRtU1MlXSmZcF3JZyKyEvGMptRRJgLVBkxj+kaSv3V9mukiIaYMrHSMijOGGN94Irkm%0ADQLX/ymT0mepENE8Kp/dJ5+l1wgKUMC93pWGb6XxmxAFTWqaw3zMMvV2p/iorhMtiHGkGB7Ju05t%0AYgLptal3WgxLa6Wg+tQ6S4VM6ktQkymdBsWLrtRSmCXVZPWdNHO1dF7TKJK0pTHgqeNVVos+T/0D%0Aimg4VYid2lLrUFptOma9Q/33yfWp5qw9VoRmCYoNaFbAO+M1p4upqhtnpPXno0xkFr9TokHWzCk3%0AG2QtA5ELTchaxlg94LQhUgYH8yWviFPmcKr5SEsT05wiEnQaAwnzpWWqzc4kP6qiA3HjpmY1zMdx%0AqkRmlZqzOiRQJlEa7pJqBvKuiimJaUsStyXPlKZBMn4xXD0X4qYTQxFzkjkuppZqqGkLoP8zmlli%0ALqlDTJBBCkOkTj3hb3K6KEohjToQQ9S7lcShpmwkwUapwEwxYGm5Mpe1CXVPisuKtgRRpFphkQiP%0AaPOmYUOKfjh17lKHZJoOnaYvK3xNa635IvksxcvFXDRHKYaqdRdklDqEBD+l3nbFG8vPIQ1Ultup%0AERupo/RUWhDTFNSgOU/hE621lB9hyVr3CvP3qCAL0Wv6zjSBQ98XzNTPM2Oi3kGrBDPtZXJXwIXO%0AOO/xLnUw/HDtjKW0PucvXWgLbaEttB+gPZuU1jPCWBfaQltoC+1cbs/kJ1toC22hLbSF9izaAmNd%0AaAttoS2009yec8bqnHuVc26nc26Xc+53n+v3n+7mnPs759xR59yO5LN+59yXnXNPOee+5JzrTb57%0ARxj7TufcK89Mr3+45pxb7Zy72zn3mHPuUefcW8Pn5+p425xzDzjntjnnHnfOvSd8fk6OF8A5V3DO%0Afdc595nw/7k81kHn3PYw3m+Hz07PeL33z9kP5qvbDazDfJPbgAufyz78CMb0YuBKYEfy2X8Hfif8%0A/bvAe8PfF4Uxl8Ic7AayMz2GH2Csy4Arwt+d2GE5F56r4w1jaA+/i9hBmS86x8f7m8DtwKfD/+fy%0AWPcC/ad8dlrG+1xrrC8AdnvvB70dkf1R7Mjss7Z577/G/MhNgNcCHwp/fwh4Xfh77nhw7/0gtjgv%0AeC76eTqa9/6I935b+HsSeAI77OacHC+Af+bj38/J8TrnVgGvAT5IDKY6J8eatFM9/6dlvM81Y10J%0AHEj+P8i/cTz2Wd6Weu+Phr+PAkvD3yuwMaudteMPh0heCTzAOTxe51zmnNuGjetu7/1jnLvj/Qvg%0At4kRpnDujhUs+vYu59x3nHO/Fj47LeN9rhME/r+L7fLe++8Tt3vWzYlzrhP4BPA27/2Ec1Hon2vj%0A9d97/Pv1p3x/TozXOXcTMOy9/2444v572rky1qS90Hs/5JxbDHzZObcz/fLZjPe51lgPAauT/1cz%0AXwqcK+2oc24ZgHNuOXZYCnzv+FeFz86a5pwrYUz1n7z3d4aPz9nxqnnvTwKfBZ7HuTne64DXOuf2%0AAh8BXuac+yfOzbEC4L0fCr+PAZ/CTPvTMt7nmrF+B9jonFvnnCsDt2BHZp9r7dPAL4a/fxG4M/n8%0AVudc2Tm3nn/vePD/B5sz1fRvgce99/8j+epcHe+AvMLJ8e/f5Rwcr/f+nd771d779cCtwFe99z/P%0AOThWAOdcu3OuK/zdAbwS2MHpGu8Z8MS9GvMm7wbecaY9g6dhPB8BDmPZzweAX8aKGN4FPAV8CehN%0Arn9nGPtO4IYz3f8fcKwvwvC3bRiD+S7wqnN4vJcCD4fxbgd+O3x+To43GcNLiVEB5+RYgfVhXbcB%0Aj4oXna7xLqS0LrSFttAW2mluC5lXC22hLbSFdprbAmNdaAttoS2009wWGOtCW2gLbaGd5rbAWBfa%0AQltoC+00twXGutAW2kJbaKe5LTDWhbbQFtpCO81tgbEutIW20BbaaW4LjHWhLbSFttBOc/s/4Dqc%0AbtWA5gcAAAAASUVORK5CYII=%0A" />
-</section>
-<section id="resize">
-<h1>resize 操作<a class="headerlink" href="#resize" title="Permalink to this heading">¶</a></h1>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">PIL</span> <span class="kn">import</span> <span class="n">Image</span>
-<span class="n">img</span> <span class="o">=</span> <span class="n">Image</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="s1">&#39;stinkbug.png&#39;</span><span class="p">)</span>
-<span class="n">rsize</span> <span class="o">=</span> <span class="n">img</span><span class="o">.</span><span class="n">resize</span><span class="p">((</span><span class="n">img</span><span class="o">.</span><span class="n">size</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">/</span><span class="mi">10</span><span class="p">,</span><span class="n">img</span><span class="o">.</span><span class="n">size</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">/</span><span class="mi">10</span><span class="p">))</span>
-<span class="n">rsizeArr</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">rsize</span><span class="p">)</span>
-<span class="n">imgplot</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">rsizeArr</span><span class="p">)</span>
-</pre></div>
-</div>
-<img 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/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%0AgPz/QCjzUOP0/xAAAAAASUVORK5CYII=%0A" 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/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%0AgPz/QCjzUOP0/xAAAAAASUVORK5CYII=%0A" />
-<p>上面我们将这个图像使用 PIL 的 Image 对象导入，并将其 resize 为原来的 1/100，可以看到很多细节都丢失了。</p>
-<p>在画图时，由于画面的大小与实际像素的大小可能不一致，所以不一致的地方会进行插值处理，尝试一下不同的插值方法：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">imgplot</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">rsizeArr</span><span class="p">)</span>
-<span class="n">imgplot</span><span class="o">.</span><span class="n">set_interpolation</span><span class="p">(</span><span class="s1">&#39;nearest&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-<img 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b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%0AhP8H0Tz8GZcurRgAAAAASUVORK5CYII=%0A" 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b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%0AhP8H0Tz8GZcurRgAAAAASUVORK5CYII=%0A" />
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">imgplot</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">rsizeArr</span><span class="p">)</span>
-<span class="n">imgplot</span><span class="o">.</span><span class="n">set_interpolation</span><span class="p">(</span><span class="s1">&#39;bicubic&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-<img 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h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%0AHVnusMMOOySwI8sddthhhwT+Pzl/C8KJDU7hAAAAAElFTkSuQmCC%0A" 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h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%0AHVnusMMOOySwI8sddthhhwT+Pzl/C8KJDU7hAAAAAElFTkSuQmCC%0A" />
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-<h1>作者 &amp; 更新时间<a class="headerlink" href="#id6" title="Permalink to this heading">¶</a></h1>
-<p>作者:<cite>李金  &lt;lijinwithyou&#64;gmail.com&gt;</cite></p>
-<p>提交: 2017/12/14</p>
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-m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
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\ No newline at end of file
diff --git a/docs/aipy-numpy/cn/6annotation.html b/docs/aipy-numpy/cn/6annotation.html
deleted file mode 100644
index c4e517c..0000000
--- a/docs/aipy-numpy/cn/6annotation.html
+++ /dev/null
@@ -1,604 +0,0 @@
-<!DOCTYPE html>
-<html lang="en-US">
-<head>
-<meta charset="utf-8" />
-<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
-<link href="https://cdn.bootcss.com/bootstrap/3.3.7/css/bootstrap.min.css" rel="stylesheet">
-<title>注释</title>
-<link rel="stylesheet" type="text/css" href="../../static/pygments.css" />
-<link rel="stylesheet" type="text/css" href="../../static/style.css" />
-<meta charset="UTF-8">
-<meta name="viewport" content="width=device-width, initial-scale=1,maximum-scale=1, user-scalable=no">
-<script src="https://cdn.bootcss.com/bootstrap/3.3.7/js/bootstrap.min.js"></script>
-</head><body>
-<div class="header doc-header web-show">
-<div class="col-sm-offset-2 col-sm-8  index-header">
-<div class="navbar-header">
-<button type="button" class="navbar-toggle" data-toggle="collapse" data-target="#example-navbar-collapse">
-<span class="sr-only">切换导航</span>
-<span class="icon-bar"></span>
-<span class="icon-bar"></span>
-<span class="icon-bar"></span>
-</button>
-<a href="/index.html" class="logo"><img src="http://www.qpython.org/static/img_logo.png"></a>
-</div>
-<ul class="header-title collapse navbar-collapse" id="example-navbar-collapse" >
-<li>
-<a href="http://www.qpython.org/index.html">Home</a>
-</li>
-<li>
-<a href="http://www.qpython.org/document.html">Guide</a>
-</li>
-<li class="header-phone-selected">
-<a href="/index.html" class="header-selected">Course</a>
-<span class="header-title-selected"></span>
-</li>
-<li>
-<a href="http://www.aipy.org">AIPY</a>
-</li>
-<li>
-<a target="_blank" href="https://github.com/qpython-android/qpython">Github</a>
-</li>
-</ul>
-<div class="search-box hidden-xs">
-<form action="/search.html">
-<input type="search" name="q" placeholder="keyword">
-<input type="hidden" name="check_keywords" value="yes" />
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-<div class="document">
-<div class="documentwrapper">
-<div class="bodywrapper">
-<div class="body" role="main">
-<section id="id1">
-<h1>注释<a class="headerlink" href="#id1" title="Permalink to this heading">¶</a></h1>
-<section id="id2">
-<h2>使用文本框进行注释<a class="headerlink" href="#id2" title="Permalink to this heading">¶</a></h2>
-<p>先看一个简单的例子：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>import numpy.random
-import matplotlib.pyplot as plt
-%matplotlib inline
-fig = plt.figure(1, figsize=(5,5))
-fig.clf()
-ax = fig.add_subplot(111)
-ax.set_aspect(1)
-x1 = -1 + numpy.random.randn(100)
-y1 = -1 + numpy.random.randn(100)
-x2 = 1. + numpy.random.randn(100)
-y2 = 1. + numpy.random.randn(100)
-ax.scatter(x1, y1, color=&quot;r&quot;)
-ax.scatter(x2, y2, color=&quot;g&quot;)
-# 加上两个文本框
-bbox_props = dict(boxstyle=&quot;round&quot;, fc=&quot;w&quot;, ec=&quot;0.5&quot;, alpha=0.9)
-ax.text(-2, -2, &quot;Sample A&quot;, ha=&quot;center&quot;, va=&quot;center&quot;, size=20,
-    bbox=bbox_props)
-ax.text(2, 2, &quot;Sample B&quot;, ha=&quot;center&quot;, va=&quot;center&quot;, size=20,
-    bbox=bbox_props)
-# 加上一个箭头文本框
-bbox_props = dict(boxstyle=&quot;rarrow&quot;, fc=(0.8,0.9,0.9), ec=&quot;b&quot;, lw=2)
-t = ax.text(0, 0, &quot;Direction&quot;, ha=&quot;center&quot;, va=&quot;center&quot;, rotation=45,
-    size=15,
-    bbox=bbox_props)
-bb = t.get_bbox_patch()
-bb.set_boxstyle(&quot;rarrow&quot;, pad=0.6)
-ax.set_xlim(-4, 4)
-ax.set_ylim(-4, 4)
-plt.show()
-</pre></div>
-</div>
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" 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" />
-<p><em>text()</em> 函数接受 <em>bbox</em> 参数来绘制文本框。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>bbox_props = dict(boxstyle=&quot;rarrow,pad=0.3&quot;, fc=&quot;cyan&quot;, ec=&quot;b&quot;, lw=2)
-t = ax.text(0, 0, &quot;Direction&quot;, ha=&quot;center&quot;, va=&quot;center&quot;, rotation=45,
-    size=15,
-    bbox=bbox_props)
-</pre></div>
-</div>
-<p>可以这样来获取这个文本框，并对其参数进行修改：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">bb</span> <span class="o">=</span> <span class="n">t</span><span class="o">.</span><span class="n">get_bbox_patch</span><span class="p">()</span>
-<span class="n">bb</span><span class="o">.</span><span class="n">set_boxstyle</span><span class="p">(</span><span class="s2">&quot;rarrow&quot;</span><span class="p">,</span> <span class="n">pad</span><span class="o">=</span><span class="mf">0.6</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>可用的文本框风格有：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span>       <span class="nc">name</span>        <span class="n">attrs</span>
-<span class="n">LArrow</span>      <span class="n">larrow</span>      <span class="n">pad</span><span class="o">=</span><span class="mf">0.3</span>
-<span class="n">RArrow</span>      <span class="n">rarrow</span>      <span class="n">pad</span><span class="o">=</span><span class="mf">0.3</span>
-<span class="n">Round</span>       <span class="nb">round</span>       <span class="n">pad</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span><span class="n">rounding_size</span><span class="o">=</span><span class="kc">None</span>
-<span class="n">Round4</span>      <span class="n">round4</span>      <span class="n">pad</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span><span class="n">rounding_size</span><span class="o">=</span><span class="kc">None</span>
-<span class="n">Roundtooth</span>  <span class="n">roundtooth</span>  <span class="n">pad</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span><span class="n">tooth_size</span><span class="o">=</span><span class="kc">None</span>
-<span class="n">Sawtooth</span>    <span class="n">sawtooth</span>    <span class="n">pad</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span><span class="n">tooth_size</span><span class="o">=</span><span class="kc">None</span>
-<span class="n">Square</span>      <span class="n">square</span>      <span class="n">pad</span><span class="o">=</span><span class="mf">0.3</span>
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>import matplotlib.patches as mpatch
-import matplotlib.pyplot as plt
-styles = mpatch.BoxStyle.get_styles()
-figheight = (len(styles)+.5)
-fig1 = plt.figure(figsize=(4/1.5, figheight/1.5))
-fontsize = 0.3 * 72
-ax = fig1.add_subplot(111)
-for i, (stylename, styleclass) in enumerate(styles.items()):
-    ax.text(0.5, (float(len(styles)) - 0.5 - i)/figheight, stylename,
-        ha=&quot;center&quot;,
-        size=fontsize,
-        transform=fig1.transFigure,
-        bbox=dict(boxstyle=stylename, fc=&quot;w&quot;, ec=&quot;k&quot;))
-# 去掉轴的显示
-ax.spines[&#39;right&#39;].set_color(&#39;none&#39;)
-ax.spines[&#39;top&#39;].set_color(&#39;none&#39;)
-ax.spines[&#39;left&#39;].set_color(&#39;none&#39;)
-ax.spines[&#39;bottom&#39;].set_color(&#39;none&#39;)
-plt.xticks([])
-plt.yticks([])
-plt.show()
-</pre></div>
-</div>
-<img 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" 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" />
-<p>各个风格的文本框如上图所示。</p>
-</section>
-<section id="id3">
-<h2>使用箭头进行注释<a class="headerlink" href="#id3" title="Permalink to this heading">¶</a></h2>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>plt.figure(1, figsize=(3,3))
-ax = plt.subplot(111)
-ax.annotate(&quot;&quot;,
-    xy=(0.2, 0.2), xycoords=&#39;data&#39;,
-    xytext=(0.8, 0.8), textcoords=&#39;data&#39;,
-    arrowprops=dict(arrowstyle=&quot;-&gt;&quot;,
-        connectionstyle=&quot;arc3&quot;),
-    )
-plt.show()
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<img alt="data:image/png;base64,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" 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/>
-<p>之前介绍了 <em>annotate</em> 中 <em>xy</em>, <em>xycoords</em>, <em>xytext</em>, <em>textcoords</em> 参数的含义，通常我们把 <em>xy</em> 设在 <em>data</em> 坐标系，把 <em>xytext</em> 设在 <em>offset</em> 即以注释点为原点的参考系。</p>
-<p>箭头显示是可选的，用 <em>arrowprops</em> 参数来指定，接受一个字典作为参数。</p>
-<p>不同类型的绘制箭头方式：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>import matplotlib.pyplot as plt
-import matplotlib.patches as mpatches
-x1, y1 = 0.3, 0.3
-x2, y2 = 0.7, 0.7
-fig = plt.figure(1, figsize=(8,3))
-fig.clf()
-from mpl_toolkits.axes_grid.axes_grid import AxesGrid
-from mpl_toolkits.axes_grid.anchored_artists import AnchoredText
-#from matplotlib.font_manager import FontProperties
-def add_at(ax, t, loc=2):
-    fp = dict(size=10)
-    _at = AnchoredText(t, loc=loc, prop=fp)
-    ax.add_artist(_at)
-    return _at
-grid = AxesGrid(fig, 111, (1, 4), label_mode=&quot;1&quot;, share_all=True)
-grid[0].set_autoscale_on(False)
-ax = grid[0]
-ax.plot([x1, x2], [y1, y2], &quot;.&quot;)
-el = mpatches.Ellipse((x1, y1), 0.3, 0.4, angle=30, alpha=0.2)
-ax.add_artist(el)
-ax.annotate(&quot;&quot;,
-    xy=(x1, y1), xycoords=&#39;data&#39;,
-    xytext=(x2, y2), textcoords=&#39;data&#39;,
-    arrowprops=dict(arrowstyle=&quot;-&quot;, #linestyle=&quot;dashed&quot;,
-        color=&quot;0.5&quot;,
-        patchB=None,
-        shrinkB=0,
-        connectionstyle=&quot;arc3,rad=0.3&quot;,
-        ),
-    )
-add_at(ax, &quot;connect&quot;, loc=2)
-ax = grid[1]
-ax.plot([x1, x2], [y1, y2], &quot;.&quot;)
-el = mpatches.Ellipse((x1, y1), 0.3, 0.4, angle=30, alpha=0.2)
-ax.add_artist(el)
-ax.annotate(&quot;&quot;,
-    xy=(x1, y1), xycoords=&#39;data&#39;,
-    xytext=(x2, y2), textcoords=&#39;data&#39;,
-    arrowprops=dict(arrowstyle=&quot;-&quot;, #linestyle=&quot;dashed&quot;,
-        color=&quot;0.5&quot;,
-        patchB=el,
-        shrinkB=0,
-        connectionstyle=&quot;arc3,rad=0.3&quot;,
-        ),
-    )
-add_at(ax, &quot;clip&quot;, loc=2)
-ax = grid[2]
-ax.plot([x1, x2], [y1, y2], &quot;.&quot;)
-el = mpatches.Ellipse((x1, y1), 0.3, 0.4, angle=30, alpha=0.2)
-ax.add_artist(el)
-ax.annotate(&quot;&quot;,
-    xy=(x1, y1), xycoords=&#39;data&#39;,
-    xytext=(x2, y2), textcoords=&#39;data&#39;,
-    arrowprops=dict(arrowstyle=&quot;-&quot;, #linestyle=&quot;dashed&quot;,
-        color=&quot;0.5&quot;,
-        patchB=el,
-        shrinkB=5,
-        connectionstyle=&quot;arc3,rad=0.3&quot;,
-        ),
-    )
-add_at(ax, &quot;shrink&quot;, loc=2)
-ax = grid[3]
-ax.plot([x1, x2], [y1, y2], &quot;.&quot;)
-el = mpatches.Ellipse((x1, y1), 0.3, 0.4, angle=30, alpha=0.2)
-ax.add_artist(el)
-ax.annotate(&quot;&quot;,
-    xy=(x1, y1), xycoords=&#39;data&#39;,
-    xytext=(x2, y2), textcoords=&#39;data&#39;,
-    arrowprops=dict(arrowstyle=&quot;fancy&quot;, #linestyle=&quot;dashed&quot;,
-        color=&quot;0.5&quot;,
-        patchB=el,
-        shrinkB=5,
-        connectionstyle=&quot;arc3,rad=0.3&quot;,
-        ),
-    )
-add_at(ax, &quot;mutate&quot;, loc=2)
-grid[0].set_xlim(0, 1)
-grid[0].set_ylim(0, 1)
-grid[0].axis[&quot;bottom&quot;].toggle(ticklabels=False)
-grid[0].axis[&quot;left&quot;].toggle(ticklabels=False)
-fig.subplots_adjust(left=0.05, right=0.95, bottom=0.05, top=0.95)
-plt.draw()
-plt.show()
-</pre></div>
-</div>
-<img 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" 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" />
-<p>字典中，connectionstyle 参数控制路径的风格：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Name</span>        <span class="n">Attr</span>
-<span class="n">angle</span>       <span class="n">angleA</span><span class="o">=</span><span class="mi">90</span><span class="p">,</span><span class="n">angleB</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span><span class="n">rad</span><span class="o">=</span><span class="mf">0.0</span>
-<span class="n">angle3</span>      <span class="n">angleA</span><span class="o">=</span><span class="mi">90</span><span class="p">,</span><span class="n">angleB</span><span class="o">=</span><span class="mi">0</span>
-<span class="n">arc</span>         <span class="n">angleA</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span><span class="n">angleB</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span><span class="n">armA</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span><span class="n">armB</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span><span class="n">rad</span><span class="o">=</span><span class="mf">0.0</span>
-<span class="n">arc3</span>        <span class="n">rad</span><span class="o">=</span><span class="mf">0.0</span>
-<span class="n">bar</span>         <span class="n">armA</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span><span class="n">armB</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span><span class="n">fraction</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span><span class="n">angle</span><span class="o">=</span><span class="kc">None</span>
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>import matplotlib.pyplot as plt
-import matplotlib.patches as mpatches
-fig = plt.figure(1, figsize=(8,5))
-fig.clf()
-from mpl_toolkits.axes_grid.axes_grid import AxesGrid
-from mpl_toolkits.axes_grid.anchored_artists import AnchoredText
-#from matplotlib.font_manager import FontProperties
-def add_at(ax, t, loc=2):
-    fp = dict(size=8)
-    _at = AnchoredText(t, loc=loc, prop=fp)
-    ax.add_artist(_at)
-    return _at
-grid = AxesGrid(fig, 111, (3, 5), label_mode=&quot;1&quot;, share_all=True)
-grid[0].set_autoscale_on(False)
-x1, y1 = 0.3, 0.3
-x2, y2 = 0.7, 0.7
-def demo_con_style(ax, connectionstyle, label=None):
-    if label is None:
-        label = connectionstyle
-    x1, y1 = 0.3, 0.2
-    x2, y2 = 0.8, 0.6
-    ax.plot([x1, x2], [y1, y2], &quot;.&quot;)
-    ax.annotate(&quot;&quot;,
-        xy=(x1, y1), xycoords=&#39;data&#39;,
-        xytext=(x2, y2), textcoords=&#39;data&#39;,
-        arrowprops=dict(arrowstyle=&quot;-&gt;&quot;, #linestyle=&quot;dashed&quot;,
-            color=&quot;0.5&quot;,
-            shrinkA=5, shrinkB=5,
-            patchA=None,
-            patchB=None,
-            connectionstyle=connectionstyle,
-            ),
-        )
-    add_at(ax, label, loc=2)
-column = grid.axes_column[0]
-demo_con_style(column[0], &quot;angle3,angleA=90,angleB=0&quot;,
-    label=&quot;angle3,\nangleA=90,\nangleB=0&quot;)
-demo_con_style(column[1], &quot;angle3,angleA=0,angleB=90&quot;,
-    label=&quot;angle3,\nangleA=0,\nangleB=90&quot;)
-column = grid.axes_column[1]
-demo_con_style(column[0], &quot;arc3,rad=0.&quot;)
-demo_con_style(column[1], &quot;arc3,rad=0.3&quot;)
-demo_con_style(column[2], &quot;arc3,rad=-0.3&quot;)
-column = grid.axes_column[2]
-demo_con_style(column[0], &quot;angle,angleA=-90,angleB=180,rad=0&quot;,
-    label=&quot;angle,\nangleA=-90,\nangleB=180,\nrad=0&quot;)
-demo_con_style(column[1], &quot;angle,angleA=-90,angleB=180,rad=5&quot;,
-    label=&quot;angle,\nangleA=-90,\nangleB=180,\nrad=5&quot;)
-demo_con_style(column[2], &quot;angle,angleA=-90,angleB=10,rad=5&quot;,
-    label=&quot;angle,\nangleA=-90,\nangleB=10,\nrad=0&quot;)
-column = grid.axes_column[3]
-demo_con_style(column[0], &quot;arc,angleA=-    90,angleB=0,armA=30,armB=30,rad=0&quot;,
-    label=&quot;arc,\nangleA=-90,\nangleB=0,\narmA=30,\narmB=30,\nrad=0&quot;)
-demo_con_style(column[1], &quot;arc,angleA=-90,angleB=0,armA=30,armB=30,rad=5&quot;,
-    label=&quot;arc,\nangleA=-90,\nangleB=0,\narmA=30,\narmB=30,\nrad=5&quot;)
-demo_con_style(column[2], &quot;arc,angleA=-90,angleB=0,armA=0,armB=40,rad=0&quot;,
-    label=&quot;arc,\nangleA=-90,\nangleB=0,\narmA=0,\narmB=40,\nrad=0&quot;)
-column = grid.axes_column[4]
-demo_con_style(column[0], &quot;bar,fraction=0.3&quot;,
-    label=&quot;bar,\nfraction=0.3&quot;)
-demo_con_style(column[1], &quot;bar,fraction=-0.3&quot;,
-    label=&quot;bar,\nfraction=-0.3&quot;)
-demo_con_style(column[2], &quot;bar,angle=180,fraction=-0.2&quot;,
-    label=&quot;bar,\nangle=180,\nfraction=-0.2&quot;)
-#demo_con_style(column[1], &quot;arc3,rad=0.3&quot;)
-#demo_con_style(column[2], &quot;arc3,rad=-0.3&quot;)
-grid[0].set_xlim(0, 1)
-grid[0].set_ylim(0, 1)
-grid.axes_llc.axis[&quot;bottom&quot;].toggle(ticklabels=False)
-grid.axes_llc.axis[&quot;left&quot;].toggle(ticklabels=False)
-fig.subplots_adjust(left=0.05, right=0.95, bottom=0.05, top=0.95)
-plt.draw()
-plt.show()
-</pre></div>
-</div>
-<img 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" 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" />
-<p><em>arrowstyle</em> 参数控制小箭头的风格：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Name</span>        <span class="n">Attrs</span>
-<span class="o">-</span>           <span class="kc">None</span>
-<span class="o">-&gt;</span>          <span class="n">head_length</span><span class="o">=</span><span class="mf">0.4</span><span class="p">,</span><span class="n">head_width</span><span class="o">=</span><span class="mf">0.2</span>
-<span class="o">-</span><span class="p">[</span>          <span class="n">widthB</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span><span class="n">lengthB</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span><span class="n">angleB</span><span class="o">=</span><span class="kc">None</span>
-<span class="o">|-|</span>         <span class="n">widthA</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span><span class="n">widthB</span><span class="o">=</span><span class="mf">1.0</span>
-<span class="o">-|&gt;</span>         <span class="n">head_length</span><span class="o">=</span><span class="mf">0.4</span><span class="p">,</span><span class="n">head_width</span><span class="o">=</span><span class="mf">0.2</span>
-<span class="o">&lt;-</span>          <span class="n">head_length</span><span class="o">=</span><span class="mf">0.4</span><span class="p">,</span><span class="n">head_width</span><span class="o">=</span><span class="mf">0.2</span>
-<span class="o">&lt;-&gt;</span>         <span class="n">head_length</span><span class="o">=</span><span class="mf">0.4</span><span class="p">,</span><span class="n">head_width</span><span class="o">=</span><span class="mf">0.2</span>
-<span class="o">&lt;|-</span>         <span class="n">head_length</span><span class="o">=</span><span class="mf">0.4</span><span class="p">,</span><span class="n">head_width</span><span class="o">=</span><span class="mf">0.2</span>
-<span class="o">&lt;|-|&gt;</span>       <span class="n">head_length</span><span class="o">=</span><span class="mf">0.4</span><span class="p">,</span><span class="n">head_width</span><span class="o">=</span><span class="mf">0.2</span>
-<span class="n">fancy</span>       <span class="n">head_length</span><span class="o">=</span><span class="mf">0.4</span><span class="p">,</span><span class="n">head_width</span><span class="o">=</span><span class="mf">0.4</span><span class="p">,</span><span class="n">tail_width</span><span class="o">=</span><span class="mf">0.4</span>
-<span class="n">simple</span>      <span class="n">head_length</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span><span class="n">head_width</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span><span class="n">tail_width</span><span class="o">=</span><span class="mf">0.2</span>
-<span class="n">wedge</span>       <span class="n">tail_width</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span><span class="n">shrink_factor</span><span class="o">=</span><span class="mf">0.5</span>
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>import matplotlib.patches as mpatches
-import matplotlib.pyplot as plt
-styles = mpatches.ArrowStyle.get_styles()
-ncol=2
-nrow = (len(styles)+1) // ncol
-figheight = (nrow+0.5)
-fig1 = plt.figure(1, (4.*ncol/1.5, figheight/1.5))
-fontsize = 0.2 * 70
-ax = fig1.add_axes([0, 0, 1, 1], frameon=False, aspect=1.)
-ax.set_xlim(0, 4*ncol)
-ax.set_ylim(0, figheight)
-def to_texstring(s):
-    s = s.replace(&quot;&lt;&quot;, r&quot;$&lt;$&quot;)
-    s = s.replace(&quot;&gt;&quot;, r&quot;$&gt;$&quot;)
-    s = s.replace(&quot;|&quot;, r&quot;$|$&quot;)
-    return s
-for i, (stylename, styleclass) in enumerate(sorted(styles.items())):
-    x = 3.2 + (i//nrow)*4
-    y = (figheight - 0.7 - i%nrow) # /figheight
-    p = mpatches.Circle((x, y), 0.2, fc=&quot;w&quot;)
-    ax.add_patch(p)
-    ax.annotate(to_texstring(stylename), (x, y),
-        (x-1.2, y),
-        #xycoords=&quot;figure fraction&quot;, textcoords=&quot;figure     fraction&quot;,
-        ha=&quot;right&quot;, va=&quot;center&quot;,
-        size=fontsize,
-        arrowprops=dict(arrowstyle=stylename,
-            patchB=p,
-            shrinkA=5,
-            shrinkB=5,
-            fc=&quot;w&quot;, ec=&quot;k&quot;,
-            connectionstyle=&quot;arc3,rad=-0.05&quot;,
-            ),
-        bbox=dict(boxstyle=&quot;square&quot;, fc=&quot;w&quot;))
-ax.xaxis.set_visible(False)
-ax.yaxis.set_visible(False)
-plt.draw()
-plt.show()
-</pre></div>
-</div>
-<img 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" 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-</section>
-<section id="id4">
-<h2>作者 &amp; 更新时间<a class="headerlink" href="#id4" title="Permalink to this heading">¶</a></h2>
-<p>作者:<cite>李金  &lt;lijinwithyou&#64;gmail.com&gt;</cite></p>
-<p>提交: 2017/12/14</p>
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diff --git a/docs/aipy-numpy/cn/7tag.html b/docs/aipy-numpy/cn/7tag.html
deleted file mode 100644
index 03fb256..0000000
--- a/docs/aipy-numpy/cn/7tag.html
+++ /dev/null
@@ -1,448 +0,0 @@
-<!DOCTYPE html>
-<html lang="en-US">
-<head>
-<meta charset="utf-8" />
-<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
-<link href="https://cdn.bootcss.com/bootstrap/3.3.7/css/bootstrap.min.css" rel="stylesheet">
-<title>标签</title>
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-<section id="id1">
-<h1>标签<a class="headerlink" href="#id1" title="Permalink to this heading">¶</a></h1>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">matplotlib</span> <span class="k">as</span> <span class="nn">mpl</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="o">%</span><span class="n">matplotlib</span> <span class="n">inline</span>
-</pre></div>
-</div>
-<p>legend() 函数被用来添加图像的标签，其主要相关的属性有：</p>
-<ul class="simple">
-<li><p>legend entry - 一个 legend 包含一个或多个 entry，一个 entry 对应一个 key 和一个 label</p></li>
-<li><p>legend key - marker 的标记</p></li>
-<li><p>Nlegend label - key 的说明</p></li>
-<li><p>legend handle - 一个 entry 在图上对应的对象</p></li>
-</ul>
-<section id="legend">
-<h2>使用 legend<a class="headerlink" href="#legend" title="Permalink to this heading">¶</a></h2>
-<p>调用 <em>legend()</em> 会自动获取当前的 <em>Axes</em> 对象，并且得到这些 <em>handles</em> 和 <em>labels</em>，相当于：</p>
-<blockquote>
-<div><p>handles, labels = ax.get_legend_handles_labels()</p>
-<p>ax.legend(handles, labels)</p>
-</div></blockquote>
-<p>我们可以在函数中指定 <a href="#id2"><span class="problematic" id="id3">*</span></a>handles * 的参数：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">line_up</span><span class="p">,</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Line 2&#39;</span><span class="p">)</span>
-<span class="n">line_down</span><span class="p">,</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="mi">3</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Line 1&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">handles</span><span class="o">=</span><span class="p">[</span><span class="n">line_up</span><span class="p">,</span> <span class="n">line_down</span><span class="p">])</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-<img 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" src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAXYAAAEACAYAAACnJV25AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAFUdJREFUeJzt3X2MVfWdx/HP10LbIZKCMctskQnNStNt61Otgop6+WMbhUj9wwTNUtpGKO2WWtKYJVQbgdQ2WWLStZtUSqdFYlKSgjUqVtNlvcXWIFmYUSi61qytxbaUFKo7UAT0u3/MnfFyuQ/nnnuez/uV3OTO3N+ce+bk+OPtmXPONXcXAKA4zkl7BQAA0WJiB4CCYWIHgIJhYgeAgmFiB4CCYWIHgIJpO7Gb2fvN7DkzGzazA2b27Rbj7jez35jZ82Z2WTyrCgAIYkK7F939hJnNc/fjZjZB0i/NbK67/3JsjJnNl3Shu88ys9mSvidpTryrDQBopeOhGHc/Xnv6XknvkXSkYchCSQ/Wxj4naYqZTYtyJQEAwXWc2M3sHDMblnRI0tPufqBhyHRJv6/7+qCkC6JbRQBAN4IU+zvufqlGJ+vrzKzSZJg1/lgE6wYACKHtMfZ67v6GmW2X9ElJ1bqXXpc0o+7rC2rfO4OZMdkDQAju3hjPbXU6K+Z8M5tSe94n6Z8kDTUMe1TSktqYOZL+6u6Hmi1v456NOv/fztc3f/FNnXr7lNydR4jHPffck/o6FOnB9mR7ZuWxdaurv991552u48dHvxdGp0Mxfy/pv2rH2J+T9Ji77zCz5Wa2XJLc/QlJ/2tmr0jaIOlfWi1s6SeWas8X9mjnazs15wdztP/P+0OtNAAUyeHD0qJF0l13SQ8/LK1fL/X1hV9e24nd3fe5+yfc/VJ3v9jd19e+v8HdN9SNW+HuF7r7Je6+t90yBz4woCf/+Ul98ZNf1LwH5+nenffq9Dunw/8GAJBj27ZJF18sDQxIQ0PSVVf1vsxUrjw1M+q9B5VKJe1VKBS2Z7TYnsFEXen1LOwxnK7fyMybvZe7a3BoUKt3rNbK2Su1au4qTTgn8N90ASB3tm2TVqyQFi+W1q1rP6GbmbzLP56mPrGPee2N17TssWX6y/G/aNPNm/Txv/t4IusFIDpmXc0/aNBsjsz1xC5R70De1SahtFcjl1ptu9xP7GOodyCfmNjDi3Jiz+RtezlzBgDCy2Sx16Pegfyg2MMrfLHXo94BxOmZZ57RRz7ykbRXI1KZn9glznsH0LuZM2dqx44dZ33/2muv1UsvvRT5+x0+fFi33Xabpk+frilTpmju3LnavXt35O/TTC4m9jHUO4CwzCzR0zFHRkY0e/Zs7d27V0ePHtVnP/tZLViwQMeOHYv9vXM1sUvUO4BoVatVzZjx7g1qZ86cqfvuu0+XXHKJpkyZoltvvVVvvfXW+OuPP/64Lr30Uk2dOlXXXHON9u3b13S5H/rQh7Ry5UpNmzZNZqZly5bp5MmTevnll2P/nXI3sY+h3gHEwcz0k5/8RE899ZReffVVvfDCC9q0aZMkaWhoSLfffrs2btyoI0eOaPny5Vq4cKFOnjzZcbnDw8M6efKkLrzwwph/gxxP7BL1DuSNWTSPuN1xxx3q7+/X1KlTddNNN2l4eFiS9P3vf1/Lly/XFVdcITPTkiVL9L73vU+7du1qu7w333xTn/nMZ7RmzRpNnjw59vXP9cQ+hnoH8sE9mkfc+vv7x5/39fVpZGREkvS73/1O9913n6ZOnTr+OHjwoP74xz+2XNbf/vY33XTTTbr66qu1atWq2NddKsjELlHvAOIz9kfXgYEB3XXXXTp69Oj4Y2RkRIsWLWr6c2+99ZZuvvlmDQwMaMOGDU3HxKEwE/sY6h1AKydPntSJEyfGH2+//Xagnxu7cGjZsmV64IEHtHv3brm7jh07pu3bt48Xfb1Tp07plltu0aRJk8aP0SelcBO7RL0DaG7+/PmaNGnS+GPt2rUdT4Osf/3yyy/Xxo0btWLFCp133nmaNWuWNm/e3PTnnn32WW3fvl0///nPNWXKFE2ePFmTJ0/Wr371q1h+tzPWOeu3FOgVd4wEksMtBcIr/N0d48A9Z4D4MbGHV6p7xUSFY+8AyqI0xV6PegfiQbGHR7H3iHoHUGSlLPZ61DsQHYo9PIo9QtQ7gKIpfbHXo96B3lDs4VHsMaHeARQBE3sDrloFyoWPxisR6h0olqQ/Gk+SvvGNb+iiiy7SxIkTtXbt2ljeoxkm9jaod6A4kv5oPEmaNWuW1q9frwULFiT63kzsAVDvQHHF9dF4krRkyRLdcMMNmjx5cqJ/VGZiD4h6B8ohro/GSxK3OezSWL0PDg1q3oPzuGMk0AVbG83hCL8n3vod+2g8SS0/Gk8aLfJvfetb2rVrl6677rpY16kbzEYhjNX7p/7hU1r22DL99Ac/5bx3IIC4J+SoNH403h/+8AdJox+Nt3nzZn33u98df/3UqVNtPxovDRyK6QHH3oFyCPvReM2WkQQm9h5x7B3IjyQ/Gk+STp8+Pf4+p06d0okTJ/TOO+9E9vu0wsQeEeodyL4kPxpPkpYuXapJkyZpy5YtuvfeezVp0iQ99NBDkf9eZ60z94qJHvecQVlxr5jwuFdMxlHvANJEsceMekeZUOzhUew5Qr0DSBrFniDqHUVHsYdHsecU9Q4gCRR7Sqh3FBHFHl6Uxc7EniJ31+DQoFbvWM09Z1AISd8Wt2iY2AuEekeZHD4srVghPf+89KMfSVddlfYaZRvH2HOKY+8oi23bpIsvlgYGpKEhJvW4UOwZQ72jiKj08CIvdjObYWZPm9mvzWy/md3RZEzFzN4ws6Ha4+5uVxzvot5RNFR68toWu5n1S+p392EzO1fSHkk3u/uLdWMqkr7m7gvbvhHF3jXqHXlGpUcj8mJ39z+5+3Dt+YikFyV9sNl7d/OmCIZ6R15R6ekKfIzdzGZK+oWkj9Um+bHvXy/pYUkHJb0u6U53P9Dk5yn2HlDvyAMqPXphij3QSdO1wzBbJX21flKv2StphrsfN7MbJT0i6cPNlrNmzZrx55VKRZVKpZt1LTU+axVZt23b6KS+eLG0aZPU15f2GuVTtVpVtVrtaRkdi93MJkp6XNLP3P07HRdo9qqky939SMP3KfaIUO/IEio9XnGcFWOSBiUdaDWpm9m02jiZ2ZUa/cfiSLOxiAbH3pEVHEvPpk5nxcyVtFPSC5LGBn5d0oAkufsGM/uypC9JOi3puEbPkNnVZFkUewyod6SBSk8OtxQoKe45gyTVH0tft45j6XFjYi856h1xotLTwb1iSo5j74gLx9LzhWIvKOodUaDS00exYxz1jl5R6flFsZcA9Y5uUOnZQrGjKeodQVHpxUCxlwz1jmao9Oyi2NER9Y5GVHrxUOwlRr2XG5WeDxQ7ukK9lxeVXmwUOyRR72VBpecPxY7QqPfio9LLg2LHWaj3YqHS841iRySo9+Kg0suJYkdb1Hs+UenFQbEjctR7/lDpoNgRGPWebVR6MVHsiBX1nl1UOupR7AiFes8GKr34KHYkhnpPH5WOVih29Ix6TxaVXi4UO1JBvSeHSkcQFDsiRb3Hg0ovL4odqaPeo0elo1sUO2JDvfeGSodEsSNjqPfwqHT0gmJHIqj3YKh0NKLYkVnUe2dUOqJCsSNx1PuZqHS0Q7EjF6j3d1HpiAPFjlSVtd6pdARFsSN3yljvVDriRrEjM4pe71Q6wqDYkWtFrncqHUmi2JFJRal3Kh29othRGEWodyodaaHYkXl5q3cqHVGi2FFIeap3Kh1ZQLEjV7Ja71Q64kKxo/CyWO9UOrKGYkdupV3vVDqSQLGjVNKsdyodWUaxoxCSqncqHUmj2FFaSdQ7lY68oNhROFHXO5WONFHsgKKtdyodeUSxo9DC1juVjqyIvNjNbIaZPW1mvzaz/WZ2R4tx95vZb8zseTO7rJsVAOIUpt6pdORd22I3s35J/e4+bGbnStoj6WZ3f7FuzHxJK9x9vpnNlvTv7j6nybIodqSqU71T6ciiyIvd3f/k7sO15yOSXpT0wYZhCyU9WBvznKQpZjatm5UAktCu3ql0FEngY+xmNlPSLyR9rDbJj33/MUnfdvdna1//p6RV7r6n4ecpdmTGWL0fevMv6t+1Sb/d/XEqHZkUptgnBFzwuZK2Svpq/aReP6Th66Yz+Jo1a8afVyoVVSqVQCsJRG3gAwNa9v4ndfvgoF6+bp7+9XMrdcXsVQr4nwQQm2q1qmq12tMyOha7mU2U9Likn7n7d5q8/oCkqrtvqX39kqTr3f1QwziKHZnQeCx9+kezecdIQIrnrBiTNCjpQLNJveZRSUtq4+dI+mvjpA5kRbNj6Vm8YyTQi05nxcyVtFPSC3r38MrXJQ1IkrtvqI37D0k3SDom6fPuvrfJsih2pCboGS9p3zESaBSm2LlACYW3bdvopL54sbRundTX1368u2twaFCrd6zWytkrtWruKk04h2PvSAcTO1Cn1/PSqXdkAfeKAWqiOC+dY+/IK4odhRLX1aPUO9JCsaPU4rx6lHpHnlDsyL2k7/FCvSNJFDtKJ417vFDvyDqKHbmUlTsxUu+IG8WOUsjSnRipd2QRxY7cyEqlt0K9Iw4UOworS5XeCvWOrKDYkWlZr/RWqHdEhWJHoeSh0luh3pEmih2Zk9dKb4V6Ry8oduReniu9FeodSaPYkQlFq/RWqHd0i2JHLhWx0luh3pEEih2pKUult0K9IwiKHblRpkpvhXpHXCh2JKrsld4K9Y5WKHZkGpXeGvWOKFHsiB2V3h3qHfUodmQOld496h29otgRCyo9GtQ7KHZkApUeHeodYVDsiAyVHi/qvZwodqSGSo8f9Y6gKHb0hEpPB/VeHhQ7EkWlp4d6RzsUO7pGpWcL9V5sFDtiR6VnD/WORhQ7AqHS84F6Lx6KHbHYupVKzwvqHRLFjjao9Hyj3ouBYkdkqPT8o97Li2LHGaj0YqLe84tiR0+o9OKi3suFYgeVXjLUe75Q7OgalV4+1HvxUewlRaVDot7zgGJHIFQ6xlDvxUSxlwiVjnao92yi2NESlY5OqPfioNgLjkpHGNR7dlDsOAOVjrCo93yj2AuISkeUqPd0Ueyg0hE56j1/KPaCoNKRBOo9ebEUu5n90MwOmdm+Fq9XzOwNMxuqPe7uZgXQOyodSaHe86FjsZvZtZJGJG1294uavF6R9DV3X9hhORR7xKh0pIl6T0Ysxe7uz0g62um9u3lT9I5KR9qo9+wKdIzdzGZKeqxFsV8v6WFJByW9LulOdz/QZBzFHgEqHVlEvccnTLFPiOB990qa4e7HzexGSY9I+nCzgWvWrBl/XqlUVKlUInj78ti6VfrKV6TFi6VNm6S+vrTXCBg1Vu+DQ4Oa9+A8rZy9UqvmrtKEc6KYYsqlWq2qWq32tIyei73J2FclXe7uRxq+T7GHRKUjT6j3aKVyHruZTTMzqz2/UqP/WBzp8GMIiGPpyBuOvacvyFkxP5Z0vaTzJR2SdI+kiZLk7hvM7MuSviTptKTjGj1DZleT5VDsXaDSUQTUe+/CFDsXKGVQ/bH0des4lo58c3cNDg1q9Y7VHHsPgYk956h0FBn1Hg73iskxjqWj6Dj2nhyKPWVUOsqIeg+OYs8ZKh1lRb3Hi2JPAZUOvIt6b49izwEqHTgT9R49ij0hVDrQGfV+Noo9o6h0IBjqPRoUe4yodCA86n0UxZ4hVDrQG+o9PIo9YlQ6EL0y1zvFnjIqHYgH9d4dij0CVDqQnLLVO8WeAiodSBb13hnFHhKVDqSvDPVOsSeESgeygXpvjmLvApUOZFdR651ijxGVDmQb9f4uir0DKh3InyLVO8UeMSodyKey1zvF3gSVDhRH3uudYo8AlQ4USxnrnWKvodKB4stjvVPsIVHpQDmUpd5LXexUOlBeeal3ir0LVDpQbkWu99IVO5UOoFGW651i74BKB9BM0eq9FMVOpQMIKmv1TrE3QaUD6EYR6r2wxU6lA+hVFuqdYq+h0gFEIa/1Xqhip9IBxCWtei91sVPpAOKUp3rPfbFT6QCSlmS9l67YqXQAach6veey2Kl0AFkRd72XotipdABZksV6z02xU+kAsi6Oei9ssVPpAPIgK/We6WKn0gHkVVT1Xqhip9IB5Fma9Z65YqfSARRNL/We+2Kn0gEUUdL1nolip9IBlEW39Z7LYqfSAZRJEvXesdjN7IeSFkj6s7tf1GLM/ZJulHRc0ufcfajJmDOKnUoHUHZB6j2uYv+RpBtavWhm8yVd6O6zJH1B0vc6LZBK7021Wk17FQqF7RkttmdwcdV7x4nd3Z+RdLTNkIWSHqyNfU7SFDOb1mzg4cPSokXS3XdLDz8srV8v9fWFWe1y4z+caLE9o8X27I6ZaeknlmrPF/Zo52s7NecHc7T/z/t7WmYUx9inS/p93dcHJV3QbCCVDgDNRVnvUf3xtPH4T9MD91Q6ALTWrN5DLSfI6Y5mNlPSY83+eGpmD0iquvuW2tcvSbre3Q81jEvuk6wBoEC6/ePphAje81FJKyRtMbM5kv7aOKmHWTEAQDgdJ3Yz+7Gk6yWdb2a/l3SPpImS5O4b3P0JM5tvZq9IOibp83GuMACgvcSuPAUAJCPSK0/N7AYze8nMfmNmq1qMub/2+vNmdlmU7180nbanmVXM7A0zG6o97k5jPfPAzH5oZofMbF+bMeybAXXanuybwZnZDDN72sx+bWb7zeyOFuOC75/uHslD0nskvSJppkYP1QxL+seGMfMlPVF7PlvSrqjev2iPgNuzIunRtNc1Dw9J10q6TNK+Fq+zb0a7Pdk3g2/LfkmX1p6fK+l/ep07oyz2KyW94u6/dfdTkrZI+nTDmMAXMyHQ9pTOPtUUTXiEF9oh0PaU2DcDcfc/uftw7fmIpBclfbBhWFf7Z5QTe7MLlaYHGNP0YiYE2p4u6era/5o9YWYfTWztiod9M1rsmyHUTi2/TNJzDS91tX9GcbrjmKB/hQ10MRMCbZe9kma4+3Ezu1HSI5I+HO9qFRr7ZnTYN7tkZudK2irpq7VyP2tIw9ct988oi/11STPqvp6h0X9V2o25oPY9nK3j9nT3/3P347XnP5M00czOS24VC4V9M0Lsm90xs4mStkl6yN0faTKkq/0zyon9vyXNMrOZZvZeSYs0evFSvUclLZGkdhczQVKA7Wlm08zMas+v1Ojpq0eSX9VCYN+MEPtmcLXtNCjpgLt/p8WwrvbPyA7FuPtpM1sh6SmNntEx6O4vmtny2utczNSFINtT0i2SvmRmpzV6L/xbU1vhjONCu2h12p5i3+zGNZIWS3rBzMY+y+LrkgakcPsnFygBQMGk/tF4AIBoMbEDQMEwsQNAwTCxA0DBMLEDQMEwsQNAwTCxA0DBMLEDQMH8P5XN8EjCdoj2AAAAAElFTkSuQmCC" />
-<p>可以将 <em>labels</em> 作为参数输入 <em>legend</em> 函数：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">line_up</span><span class="p">,</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">])</span>
-<span class="n">line_down</span><span class="p">,</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="mi">3</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">])</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">([</span><span class="n">line_up</span><span class="p">,</span> <span class="n">line_down</span><span class="p">],</span> <span class="p">[</span><span class="s1">&#39;Line Up&#39;</span><span class="p">,</span> <span class="s1">&#39;Line Down&#39;</span><span class="p">])</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-<img 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" 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" />
-</section>
-<section id="marker-key">
-<h2>产生特殊形状的 marker key<a class="headerlink" href="#marker-key" title="Permalink to this heading">¶</a></h2>
-<p>有时我们可以产生一些特殊形状的 marker：</p>
-<p>块状：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.patches</span> <span class="k">as</span> <span class="nn">mpatches</span>
-<span class="n">red_patch</span> <span class="o">=</span> <span class="n">mpatches</span><span class="o">.</span><span class="n">Patch</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="s1">&#39;red&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;The red data&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">handles</span><span class="o">=</span><span class="p">[</span><span class="n">red_patch</span><span class="p">])</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-<img 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" src="data:image/png;base64,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" />
-<p>点线组合：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.lines</span> <span class="k">as</span> <span class="nn">mlines</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="n">blue_line</span> <span class="o">=</span> <span class="n">mlines</span><span class="o">.</span><span class="n">Line2D</span><span class="p">([],</span> <span class="p">[],</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;blue&#39;</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;*&#39;</span><span class="p">,</span>
-<span class="n">markersize</span><span class="o">=</span><span class="mi">15</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Blue stars&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">handles</span><span class="o">=</span><span class="p">[</span><span class="n">blue_line</span><span class="p">])</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-<img 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" 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" />
-</section>
-<section id="id4">
-<h2>指定 legend 的位置<a class="headerlink" href="#id4" title="Permalink to this heading">¶</a></h2>
-<p><em>bbox_to_anchor</em> 关键词可以指定 <em>legend</em> 放置的位置，例如放到图像的右上角：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>plt.plot([1,2,3], label=&quot;test1&quot;)
-plt.plot([3,2,1], label=&quot;test2&quot;)
-plt.legend(bbox_to_anchor=(1, 1),
-    bbox_transform=plt.gcf().transFigure)
-plt.show()
-</pre></div>
-</div>
-<img 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yTxGVzUClAdhYJMSssnA1MriBuFAsQoTZ8MTK0A76FQkBlpqZJaqBXEgUIBIpamKqmFWkHeUShItbRXSS3UCqJCoQARyEKV1EKtII8oFKROVqukFmoF7aBQgBZluUpqoVaQFxQKUiG0KqmFWkGzKBSgCSFWSS3UCkJGoSAxeamSWqgVNIJCAerIU5XUQq0gNBQKOirvVVILtYJaKBSgCqqkNmoFIaBQEDuqpDnUCspRKEAJVdI8agVZRaEgFlRJNKgVUCjINaokOtQKsoRCQWSoknhRK/lEoSB3qJL4UStIOwoFbaFKkkGt5AeFglygSpJDrSCNKBQ0jSpJF2olbBQKgkWVpA+1grSgUNAQqiQbqJXwUCgIyooVVElWUCtIEoWCmqiSbKNWwkChIPOokuyjVtBpFAqOQJWEiVrJLgoFmUSVhItaQSdQKKBKcoZayRYKBZlBleQPtYK4UCg5RZVAolaygEJBqlElGEKtIEoUSo5QJRgJtZJOFApShypBPdQK2kWhBI4qQSuolfSgUJAKVAlaRa2gFRRKgKgSRIlaSRaFgsRQJYgatYJG1S0UM7tf0iWS9rv7WVV+XpC0StLvS99a6e63VxlHocSIKkEnUCudF1qhPCBpVp0xG9z9nNJ21GKCeFEl6BRqBSNp6ByKmfVKWj1CofyLu19WZx8USsSoEiSJWumM0AqlHpd0gZk9bWaPmtmZEewTdVAlSBq1gkpRFMo4Se+4+wEzu1jSd9z99CrjKJQIUCVII2olPlkqlFHt7sDd3yh7vMbM/s3MJrj7K5VjFy1aNPy4UCioUCi0++tzZcUK6ctflubOlQYGpDFjkp4RMGioVvq39KtvWZ8WTF2ghdMXalRX239icqdYLKpYLCY9jZZEUSjdGnwHmJvZ+ZL+w917q4yjUFpElSBLqJVoZalQ6p5DMbMfSPqlpDPMbLeZ/YOZXWdm15WGXClpm5ltlXSXpM/HN9384VwJsoZzK/nFlfIpRZUgBNRK+4IqFHQeVYJQUCv5QqGkCFWCkFErraFQ0DSqBKGjVsJHoSSMKkEeUSuNo1DQEKoEeUWthIlCSQBVAryHWhkZhYKaqBLgSNRKOCiUDqFKgPqolaNRKDgCVQI0hlrJNgolRlQJ0DpqZRCFAqoEaBO1kj0USsSoEiB6ea4VCiWnqBIgHtRKNlAoEaBKgM7JW61QKDlClQCdRa2kF4XSIqoESF4eaoVCCRxVAqQDtZIuFEoTqBIgvUKtFQolQFQJkG7USvIolDqoEiB7QqoVCiUQVAmQTdRKMiiUKqgSIBxZrxUKJcOoEiAs1ErnUCglVAkQvizWCoWSMVQJkA/USrxyXShUCZBfWakVCiUDqBIg36iV6OWuUKgSAJXSXCsUSkpRJQCqoVaikYtCoUoANCpttUKhpAhVAqAZ1Errgi0UqgRAu9JQKxRKwqgSAFGgVpoTVKFQJQDiklStUCgJoEoAxIlaqS/zhUKVAOi0TtYKhdIhVAmAJFAr1WWyUKgSAGkRd61QKDGiSgCkCbXynswUClUCIO3iqBUKJWJUCYAsyHutpLpQqBIAWRVVrVAoEaBKAGRZHmsldYVClQAITTu1QqG0iCoBEKK81EoqCoUqAZAXzdYKhdIEqgRAnoRcK3ULxczul3SJpP3uflaNMXdLuljSAUlfdPctVcYcUShUCYC8a6RWQiuUByTNqvVDM5st6cPu/hFJX5L07/V2SJW0p1gsJj2FoHA8o8XxbFxotVJ3QXH3JyS9OsKQyyUtK43dJGm8mXVXG/jyy9JVV0m33CL9+MfS4sXSmDGtTDvf+B82WhzPaHE8m2NmmveJedr8pc3auGujpn13mrbv3570tFoSxTmUkyXtLvt6j6RTqg2kSgCguhBqJaqT8pWv71U9MUOVAEBt1WolSxp627CZ9UpaXe2kvJndK6no7g+Vvn5O0gx331cxrjPvTwaAwGTlpPyoCPbxsKQbJT1kZtMkvVa5mEjZOSAAgNbUXVDM7AeSZkg6ycx2S7pV0mhJcvel7v6omc02s52S3pJ0TZwTBgCkU8eulAcAhC3SK+XNbJaZPWdmvzWzhTXG3F36+dNmdk6Uvz809Y6nmRXM7HUz21LabklinllgZveb2T4z2zbCGJ6bDap3PHluNs7MJpvZejN7xsy2m9lXaoxL//PT3SPZJB0jaaekXg2+JLZV0t9VjJkt6dHS46mSnorq94e2NXg8C5IeTnquWdgkXSTpHEnbavyc52a0x5PnZuPHcqKkKaXHYyU9n9W/nVEWyvmSdrr7H9z9kKSHJF1RMabhiyDR0PGUjn7LNqrwCC/QRUPHU+K52RB33+vuW0uP35S0Q9KkimGZeH5GuaBUu8Dx5AbGVL0IEg0dT5d0QSmBHzWzMzs2u/Dw3IwWz80WlC7ROEfSpoofZeL5GcXbhoc0ena/oYsg0dBx+bWkye5+wMwulvRTSafHO62g8dyMDs/NJpnZWEkrJH21VCpHDan4OnXPzygL5SVJk8u+nqzBVXSkMaeUvoej1T2e7v6Gux8oPV4jabSZTejcFIPCczNCPDebY2ajJa2UtNzdf1plSCaen1EuKP8j6SNm1mtmx0q6SoMXPZZ7WNIXJGmkiyAhqYHjaWbdZmalx+dr8G3gr3R+qkHguRkhnpuNKx2nfknPuvtdNYZl4vkZ2Ute7n7YzG6U9J8afIdSv7vvMLPrSj/nIsgmNHI8JV0p6QYzO6zBe9F8PrEJpxwX6Ear3vEUz81mXChprqTfmNnQvaS+IalHytbzkwsbAQCRSPwWwACAMLCgAAAiwYICAIgECwoAIBIsKACASLCgAAAiwYICAIgECwoAIBL/D9F4VsmYJAjFAAAAAElFTkSuQmCC" src="data:image/png;base64,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" />
-<p>更复杂的用法：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>plt.subplot(211)
-plt.plot([1,2,3], label=&quot;test1&quot;)
-plt.plot([3,2,1], label=&quot;test2&quot;)
-# Place a legend above this legend, expanding itself to
-# fully use the given bounding box.
-plt.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3,
-    ncol=2, mode=&quot;expand&quot;, borderaxespad=0.)
-plt.subplot(223)
-plt.plot([1,2,3], label=&quot;test1&quot;)
-plt.plot([3,2,1], label=&quot;test2&quot;)
-# Place a legend to the right of this smaller figure.
-plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
-plt.show()
-</pre></div>
-</div>
-<img 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" 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" />
-</section>
-<section id="axes-legend">
-<h2>同一个 Axes 中的多个 legend<a class="headerlink" href="#axes-legend" title="Permalink to this heading">¶</a></h2>
-<p>可以这样添加多个 legend：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">line1</span><span class="p">,</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Line 1&quot;</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;--&#39;</span><span class="p">)</span>
-<span class="n">line2</span><span class="p">,</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="mi">3</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Line 2&quot;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>
-<span class="c1"># Create a legend for the first line.</span>
-<span class="n">first_legend</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">handles</span><span class="o">=</span><span class="p">[</span><span class="n">line1</span><span class="p">],</span> <span class="n">loc</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-<span class="c1"># Add the legend manually to the current Axes.</span>
-<span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span><span class="o">.</span><span class="n">add_artist</span><span class="p">(</span><span class="n">first_legend</span><span class="p">)</span>
-<span class="c1"># Create another legend for the second line.</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">handles</span><span class="o">=</span><span class="p">[</span><span class="n">line2</span><span class="p">],</span> <span class="n">loc</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-<img 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" 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+gJUzFAwjE5g2IkdiAFqN7RFYkdsdDWlrtIetllUUeSbFTvkLh4ioh1XYnx+OOjjib5mHtHtUjsCESt7j1ab5icQTVoxaDffvADacUK5tLDRu+9PtFjRyT++ldp5Mjkr8SYFPTe6wuJHagTVO/1g8SO0HV0SEOGRB0FCqje04+pGISmMPHyzW9GHQm6YnIGpZDY0aeuEy+LF0cdDYoxOYNitGLQI1ZiTB567+lDjx2B+vd/l956Kx33Hq039N7Tg8QOoBPVezqQ2AF0Q/WebEzFoCrZrPT730cdBcLC5Ez9IbHXucLEy7p1UUeCMDE5U19oxdQpJl7qF733ZKEVg7KsWsVKjPWM6j39qNjr0G9/Kw0aREIH1XsSMBUDoCpMzsQXiR1A1aje44nEjoO0tUmvvCJddVXUkSBJqN7jhYunkHTwvUc/9amoo0HSMPeefCT2lOHeowgCkzPJRismRX70I2npUubSESx679EKpcduZndIOkfS39x9Yon3myQ9IOlP+Zfa3P37JbYjsYds27bcKoysxIgw0HuPRliJfYqkHZJW9JLYv+nuM/rYD4kdSDiq99oL5eKpu6+XtL2vz67kQ9F/O3dGHQHqEb33ZAji4qlLOtXMnjOzVWbW/d82AlOYePn616OOBPWMyZl4K+viqZmNk/RQD62YEZL2u/suM5sm6RZ3/0iJ7XzBggWdz5uamtTU1FR95HWorS23cNfMmdzVCPFB7z1YmUxGmUym83lLS0s4X1DqLbGX2PZVSSe6+7ai1+mxV4mVGBF39N7DE8kXlMzsCDOz/M8nKfeXxbY+fg0VaG1lLh3xRu89XsqZivmFpDMkHSbpTUkLJA2SJHdfamZXSLpc0j5Ju5SbkNlQYj9U7EAdoHoPFmvFAIgNeu/BYK2YhMtmpfXro44CCAaTM9EhscdEYY2XLhfDgcSj9x4NWjERY+IF9YLee3VoxSTMmjWsxIj6QfVeO1TsEXr2WWn3bhI66g/Ve/mYigGQKEzO9I3EDiBxqN57R2KPqba2XNtl0aKoIwHii+q9NC6exkzXe49+9rNRRwPEG3PvwSGxh4R7jwKVY3ImGLRiQrBkiXTzzcylA/1B7z2HHntMvPOONHAg66UDQaj33juJHUAq1XP1TmKPwN//Lo0cGXUUQH2ox+qdxF5DhTVe9u/P3QgDQG3UW/XOuGONtLa+N/Hys59FHQ1QX5ic6RsVewVYiRGIl3qo3mnFhOzuu3NJ/brrmHgB4iTNvXcSO4C6ldbqncQOoO6lrXrn4mlAsllp7dqoowBQDdacIbF3U5h4efzxqCMBUK16n5yhFZPHxAvQf2YVdQxQpFSOrKYVMzCwiBLs0UelCy+UZs6Uli9n4gXojzgXcHEW5F+KVOySNm+Wtm+nSgf6K19dRh1GIvV07JiKARApEnv1gkzsXDwFgJSpq8Te2ipdeWXUUQBAuOoisRfuPXrNNdIFF0QdDYA4Wb9+vY455piowwhU6hN715UYufcoUL/GjRundevWdXt9ypQp2rx5c+Cfl81m9aUvfUmjR49WQ0ODTj/9dD311FOBf04pqU7sK1bkqvT77pNuvJExRqCemVlN5+x37Nihk08+Wc8++6y2b9+uiy66SOecc4527twZ+meneirm3Xdz/yShA7XR42RHS/AJ1RdUlk+OOuoo3X777TrzzDMPej2TyejCCy/Ua6+9JilX2V955ZVasWKFtmzZoqlTp+quu+7S4MGDJUkPP/ywrrnmGm3ZskUTJkzQkiVLNHHixLJiGDlypDKZjCZNmtTtPaZiyjR0KEkdQGXMTCtXrtSaNWv06quvatOmTVq+fLkkaePGjZo9e7aWLVumbdu2ac6cOZoxY4b27NnT537b29u1Z88eHX300SH/CVKU2N96K+oIAKTFvHnz1NjYqFGjRmn69Olqb2+XJN12222aM2eOJk+eLDPTrFmzNHjwYG3YsKHX/f3jH//QhRdeqIULF2rEiBGhx5/4xF6YeJk1K+pIAKRFY2Nj589Dhw7Vjh07JElbtmzR4sWLNWrUqM7H1q1b9cYbb/S4r3fffVfTp0/Xqaeeqquuuir02KWErxVTmEufOTN3VyMA8VRpPzxuChddx44dq/nz5+vqq68u6/d2796tc889V2PHjtXSpUvDDPEgiUzsXVdivO8+RhgBlGfPnj3q6OjofD5o0KCyfq9wUfPSSy/V5z73OZ111lmaPHmydu3apUwmozPOOEPDhw8/6Hf27t2r8847T8OGDevs0ddKIlsxGzYwlw6gcs3NzRo2bFjno6Wlpc8xyK7vn3jiiVq2bJnmzp2rQw89VOPHj9eKFStK/t4TTzyhRx55RL/+9a/V0NCgESNGaMSIEXq8Bjd7SPW4I4DaYhGw6jHuCADoUawTezYr3X9/1FEAQLLENrEX1nh5+umoIwGAZIndVAwTLwDQP7Gq2H/zG1ZiBID+itVUzJYt0l/+QkIHkoqpmOpxz1MAsVTLZXHTaNUfV2na+GkHvRZKYjezOySdI+lv7l5ybUozu1XSNEm7JH3F3TeW2IbEDqDudezrUEumRTc8cYMO+IFu7198wsW66eyb1DCkQVJ4c+x3Spra05tm1izpaHcfL+lrkn7S1w5bW6WLLio7RhTJZDJRh5AqHM9gcTx7N2TgEF1/1vXaMHuDJhw+odv7d7bfqeN+fJxWv7y66s/oM7G7+3pJ23vZZIaku/LbPimpwcyOKLVh13uPXnZZNeFC4j+coHE8g8XxLM/k0ZP1zNee0XdP+64G2MGp+PV3Xlfzz5t1yQOXVLXvIKZiRkt6rcvzrZKOLLUhEy8A8J5yqvdqBDXuWNz/KdlM596jANBdb9V7NcqaijGzcZIeKnXx1MyWSMq4+z3555slneHubxZtx5VTAKhCpRdPg/jm6YOS5kq6x8xOkfR2cVKvJjAAQHX6TOxm9gtJZ0g6zMxek7RA0iBJcvel7r7KzJrN7BVJOyVdHGbAAIDe1ewLSgCA2gh0rRgzm2pmm83sZTMreddWM7s1//5zZjYpyM9Pm76Op5k1mdnfzWxj/nFNFHEmgZndYWZvmtnzvWzDuVmmvo4n52b5zGyMmT1mZi+Y2R/MbF4P25V/frp7IA9J75P0iqRxyrVq2iUdW7RNs6RV+Z9PlrQhqM9P26PM49kk6cGoY03CQ9IUSZMkPd/D+5ybwR5Pzs3yj2WjpBPyPw+X9D/9zZ1BVuwnSXrF3f/P3fdKukfSvxRtU/aXmVDW8ZS6j5qiBA/wi3Yo63hKnJtlcfe/unt7/ucdkl6S9MGizSo6P4NM7KW+qDS6jG1KfpkJZR1Pl3Rq/n/NVplZ9284oFycm8Hi3KxCfrR8kqQni96q6PwM8kYb5V6FLevLTCjruDwraYy77zKzaZLul/SRcMNKNc7N4HBuVsjMhktqlfSNfOXebZOi5z2en0FW7K9LGtPl+Rjl/lbpbZsj86+huz6Pp7u/4+678j+vljTIzA6tXYipwrkZIM7NypjZIEltku5291J3eq7o/Awysf9e0ngzG2dmh0g6X7kvL3X1oKRZktTbl5kgqYzjaWZHWH4BbDM7Sbnx1W21DzUVODcDxLlZvvxxul3Si+5+cw+bVXR+BtaKcfd9ZjZX0hrlJjpud/eXzGxO/n2+zFSBco6npPMkXW5m+5RbC/+LkQUcc3zRLlh9HU9xblbiNEkzJW0ys8K9LK6WNFaq7vzkC0oAkDKxupk1AKD/SOwAkDIkdgBIGRI7AKQMiR0AUobEDgApQ2IHgJQhsQNAyvw/a8UJ8l9tWroAAAAASUVORK5CYII=" />
-<p>其中 <em>loc</em> 参数可以取 0-10 或者 字符串，表示放置的位置：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">loc</span> <span class="n">string</span>      <span class="n">loc</span> <span class="n">code</span>
-<span class="s1">&#39;best&#39;</span>              <span class="mi">0</span>
-<span class="s1">&#39;upper right&#39;</span>       <span class="mi">1</span>
-<span class="s1">&#39;upper left&#39;</span>        <span class="mi">2</span>
-<span class="s1">&#39;lower left&#39;</span>        <span class="mi">3</span>
-<span class="s1">&#39;lower right&#39;</span>       <span class="mi">4</span>
-<span class="s1">&#39;right&#39;</span>             <span class="mi">5</span>
-<span class="s1">&#39;center left&#39;</span>       <span class="mi">6</span>
-<span class="s1">&#39;center right&#39;</span>      <span class="mi">7</span>
-<span class="s1">&#39;lower center&#39;</span>      <span class="mi">8</span>
-<span class="s1">&#39;upper center&#39;</span>      <span class="mi">9</span>
-<span class="s1">&#39;center&#39;</span>            <span class="mi">10</span>
-</pre></div>
-</div>
-</section>
-<section id="id5">
-<h2>更多用法<a class="headerlink" href="#id5" title="Permalink to this heading">¶</a></h2>
-<p>多个 <em>handle</em> 可以通过括号组合在一个 <em>entry</em> 中：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">numpy.random</span> <span class="kn">import</span> <span class="n">randn</span>
-<span class="n">z</span> <span class="o">=</span> <span class="n">randn</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
-<span class="n">red_dot</span><span class="p">,</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">z</span><span class="p">,</span> <span class="s2">&quot;ro&quot;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mi">15</span><span class="p">)</span>
-<span class="c1"># Put a white cross over some of the data.</span>
-<span class="n">white_cross</span><span class="p">,</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">z</span><span class="p">[:</span><span class="mi">5</span><span class="p">],</span> <span class="s2">&quot;w+&quot;</span><span class="p">,</span> <span class="n">markeredgewidth</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mi">15</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">([</span><span class="n">red_dot</span><span class="p">,</span> <span class="p">(</span><span class="n">red_dot</span><span class="p">,</span> <span class="n">white_cross</span><span class="p">)],</span> <span class="p">[</span><span class="s2">&quot;Attr A&quot;</span><span class="p">,</span> <span class="s2">&quot;Attr A+B&quot;</span><span class="p">])</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-<img 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" 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" />
-<p>自定义 <em>handle</em>：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>import matplotlib.pyplot as plt
-import matplotlib.patches as mpatches
-class AnyObject(object):
-    pass
-class AnyObjectHandler(object):
-    def legend_artist(self, legend, orig_handle, fontsize, handlebox):
-        x0, y0 = handlebox.xdescent, handlebox.ydescent
-        width, height = handlebox.width, handlebox.height
-        patch = mpatches.Rectangle([x0, y0], width, height, facecolor=&#39;red&#39;,
-            edgecolor=&#39;black&#39;, hatch=&#39;xx&#39;, lw=3,
-            transform=handlebox.get_transform())
-        handlebox.add_artist(patch)
-        return patch
-plt.legend([AnyObject()], [&#39;My first handler&#39;],
-    handler_map={AnyObject: AnyObjectHandler()})
-plt.show()
-</pre></div>
-</div>
-<img 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lQgw12SCmS4S1KBDHdJKpDhLkkFMtwlqUCGuyQVyHCXpAIZ7pJUIMNdkgpkuEtSgQx3SSqQ4S5JBTLcJalAhrskFaiucI+ItohYExEvRcRlfaw/JyJWR8SvI+KXETGn8aVKkuoVmTlwg4hRwG+ALwAbgaeBhZnZ2aPNXwEvZubWiGgD2jPzxF7byVr7kiR9UESQmTHY+9Uzcj8eWJuZr2bmDuBe4IyeDTLzV5m5tbr4JDBlsIVIkhqnnnCfDKzvsbyhelt/zgce3JuiJEl7Z/862tQ9lxIRc4HzgJP6Wt/e3t59vVKpUKlU6t20JH0kdHR00NHRsdfbqWfO/US65tDbqstXALsy85pe7eYAK4C2zFzbx3acc5ekQRrOOfdVwIyIaI2IA4GzgAd67XwaXcF+bl/BLklqrprTMpm5MyKWAg8Do4BbMrMzIi6srr8R+AdgAnB9RADsyMzjh69sSdJAak7LNGxHTstI0qAN57SMJOlDxnCXpAIZ7pJUIMNdkgpkuEtSgQx3SSqQ4S5JBTLcJalAhrskFchwl6QCGe6SVCDDXZIKZLhLUoEMd0kqkOEuSQUy3CWpQIa7JBXIcJekAhnuklQgw12SCmS4S1KBDHdJKpDhLkkFMtwlqUCGuyQVyHCXpAIZ7pJUIMNdkgpkuEtSgQx3SSqQ4S5JBTLcJalAhrskFchwl6QCGe6SVKCa4R4RbRGxJiJeiojL+mnzr9X1qyPi6MaXKUkajAHDPSJGAT8E2oDZwMKIOKJXm9OBv8jMGcAFwPXDVGsxOjo6RrqEfYZ9sZt9sZt9sfdqjdyPB9Zm5quZuQO4FzijV5u/Ae4AyMwngfERManhlRbEJ+5u9sVu9sVu9sXeqxXuk4H1PZY3VG+r1WbK3pcmSRqqWuGedW4nhng/SdIwiMz+czgiTgTaM7OtunwFsCszr+nR5gagIzPvrS6vAT6fma/32paBL0lDkJm9B9A17V9j/SpgRkS0ApuAs4CFvdo8ACwF7q2+GfxP72AfanGSpKEZMNwzc2dELAUeBkYBt2RmZ0RcWF1/Y2Y+GBGnR8Ra4G3g74a9aknSgAaclpEkfTg1/AxVT3rarVZfRMQ51T74dUT8MiLmjESdzVDP86La7riI2BkRC5pZX7PU+fqoRMR/R8TzEdHR5BKbpo7XR0tErIyIZ6t9sWQEymyKiLg1Il6PiOcGaDO43MzMhl3omrpZC7QCBwDPAkf0anM68GD1+gnAE42sYV+51NkXfwUcUr3e9lHuix7tHgN+AnxlpOseoefEeOAFYEp1uWWk6x7BvmgH/vn9fgD+COw/0rUPU3+cDBwNPNfP+kHnZqNH7p70tFvNvsjMX2Xm1urik5R7fkA9zwuAi4D7gTeaWVwT1dMPZwP/npkbADLzD02usVnq6YvNwLjq9XHAHzNzZxNrbJrM/C/gzQGaDDo3Gx3unvS0Wz190dP5wIPDWtHIqdkXETGZrhf3+19fUeLBoHqeEzOAiRHxeESsiohFTauuuerpi5uBz0bEJmA18I0m1bYvGnRu1voo5GB50tNudT+miJgLnAecNHzljKh6+uJfgMszMyMi2PM5UoJ6+uEA4BhgHjAG+FVEPJGZLw1rZc1XT19cCTybmZWIOBx4JCKOzMy3hrm2fdWgcrPR4b4RmNpjeSpd7zADtZlSva009fQF1YOoNwNtmTnQn2UfZvX0xbF0nSsBXfOrfx0ROzLzgeaU2BT19MN64A+ZuR3YHhH/CRwJlBbu9fTF/wH+ESAzX46I3wGz6Dr/5qNm0LnZ6GmZ7pOeIuJAuk566v3ifABYDN1nwPZ50lMBavZFREwDVgDnZubaEaixWWr2RWZOz8zPZOZn6Jp3/7+FBTvU9/r4f8DnImJURIyh6+DZi02usxnq6Ys1wBcAqvPLs4BXmlrlvmPQudnQkXt60lO3evoC+AdgAnB9dcS6IzOPH6mah0udfVG8Ol8fayJiJfBrYBdwc2YWF+51Pif+CbgtIlbTNRD9+8zcMmJFD6OIuAf4PNASEeuBZXRN0Q05Nz2JSZIK5M/sSVKBDHdJKpDhLkkFMtwlqUCGuyQVyHCXpAIZ7pJUIMNdkgr0v4W1q8xEcgEdAAAAAElFTkSuQmCC" src="data:image/png;base64,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" />
-<p>椭圆：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>from matplotlib.legend_handler import HandlerPatch
-import matplotlib.pyplot as plt
-import matplotlib.patches as mpatches
-class HandlerEllipse(HandlerPatch):
-    def create_artists(self, legend, orig_handle,
-            xdescent, ydescent, width, height, fontsize,     trans):
-        center = 0.5 * width - 0.5 * xdescent, 0.5 * height - 0.5 *     ydescent
-        p = mpatches.Ellipse(xy=center, width=width + xdescent,
-            height=height + ydescent)
-        self.update_prop(p, orig_handle, legend)
-        p.set_transform(trans)
-        return [p]
-c = mpatches.Circle((0.5, 0.5), 0.25, facecolor=&quot;green&quot;,
-    edgecolor=&quot;red&quot;, linewidth=3)
-plt.gca().add_patch(c)
-plt.legend([c], [&quot;An ellipse, not a rectangle&quot;],
-    handler_map={mpatches.Circle: HandlerEllipse()})
-plt.show()
-</pre></div>
-</div>
-<img 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" 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" />
-</section>
-<section id="id6">
-<h2>作者 &amp; 更新时间<a class="headerlink" href="#id6" title="Permalink to this heading">¶</a></h2>
-<p>作者:<cite>李金  &lt;lijinwithyou&#64;gmail.com&gt;</cite></p>
-<p>提交: 2017/12/14</p>
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diff --git a/docs/aipy-numpy/cn/8figures-subplots-axes-ticks.html b/docs/aipy-numpy/cn/8figures-subplots-axes-ticks.html
deleted file mode 100644
index 9b22cd7..0000000
--- a/docs/aipy-numpy/cn/8figures-subplots-axes-ticks.html
+++ /dev/null
@@ -1,371 +0,0 @@
-<!DOCTYPE html>
-<html lang="en-US">
-<head>
-<meta charset="utf-8" />
-<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
-<link href="https://cdn.bootcss.com/bootstrap/3.3.7/css/bootstrap.min.css" rel="stylesheet">
-<title>figures, subplots, axes 和 ticks 对象</title>
-<link rel="stylesheet" type="text/css" href="../../static/pygments.css" />
-<link rel="stylesheet" type="text/css" href="../../static/style.css" />
-<meta charset="UTF-8">
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-<section id="figures-subplots-axes-ticks">
-<h1>figures, subplots, axes 和 ticks 对象<a class="headerlink" href="#figures-subplots-axes-ticks" title="Permalink to this heading">¶</a></h1>
-<section id="figures-axes-ticks">
-<h2>figures, axes 和 ticks 的关系<a class="headerlink" href="#figures-axes-ticks" title="Permalink to this heading">¶</a></h2>
-<p>这些对象的关系可以用下面的图来表示：</p>
-<p>示例图像：</p>
-<p>&lt;img src=”./artists_figure.png” width = “600” height = “400” alt=”图1” align=left /&gt;</p>
-<p>具体结构：
-&lt;img src=”./artists_tree.png” width = “600” height = “400” alt=”图2” align=left /&gt;</p>
-</section>
-<section id="figure">
-<h2>figure 对象<a class="headerlink" href="#figure" title="Permalink to this heading">¶</a></h2>
-<p><em>figure</em> 对象是最外层的绘图单位，默认是以 <em>1</em> 开始编号（<strong>MATLAB</strong> 风格，<em>Figure 1</em>, <em>Figure 2</em>,*…*），可以用 <em>plt.figure()</em> 产生一幅图像，除了默认参数外，可以指定的参数有：</p>
-<ul class="simple">
-<li><p>num - 编号</p></li>
-<li><p>figsize - 图像大小</p></li>
-<li><p>dpi - 分辨率</p></li>
-<li><p>facecolor - 背景色</p></li>
-<li><p>edgecolor - 边界颜色</p></li>
-<li><p>frameon - 边框</p></li>
-</ul>
-<p>这些属性也可以通过 <em>Figure</em> 对象的 <em>set_xxx</em> 方法来改变。</p>
-</section>
-<section id="subplot-axes">
-<h2>subplot 和 axes 对象<a class="headerlink" href="#subplot-axes" title="Permalink to this heading">¶</a></h2>
-</section>
-<section id="subplot">
-<h2>subplot<a class="headerlink" href="#subplot" title="Permalink to this heading">¶</a></h2>
-<p><em>subplot</em> 主要是使用网格排列子图：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="o">%</span><span class="n">pylab</span> <span class="n">inline</span>
-<span class="n">subplot</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span>
-<span class="n">xticks</span><span class="p">([]),</span> <span class="n">yticks</span><span class="p">([])</span>
-<span class="n">text</span><span class="p">(</span><span class="mf">0.5</span><span class="p">,</span><span class="mf">0.5</span><span class="p">,</span> <span class="s1">&#39;subplot(2,1,1)&#39;</span><span class="p">,</span><span class="n">ha</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span><span class="n">va</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span><span class="n">size</span><span class="o">=</span><span class="mi">24</span><span class="p">,</span><span class="n">alpha</span><span class="o">=</span><span class="mf">.5</span><span class="p">)</span>
-<span class="n">subplot</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">)</span>
-<span class="n">xticks</span><span class="p">([]),</span> <span class="n">yticks</span><span class="p">([])</span>
-<span class="n">text</span><span class="p">(</span><span class="mf">0.5</span><span class="p">,</span><span class="mf">0.5</span><span class="p">,</span> <span class="s1">&#39;subplot(2,1,2)&#39;</span><span class="p">,</span><span class="n">ha</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span><span class="n">va</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span><span class="n">size</span><span class="o">=</span><span class="mi">24</span><span class="p">,</span><span class="n">alpha</span><span class="o">=</span><span class="mf">.5</span><span class="p">)</span>
-<span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-<p>Populating the interactive namespace from numpy and matplotlib</p>
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alt="data:image/png;base64,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" 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" 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" 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" />
-<p>更高级的可以用 <em>gridspec</em> 来绘图：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.gridspec</span> <span class="k">as</span> <span class="nn">gridspec</span>
-<span class="n">G</span> <span class="o">=</span> <span class="n">gridspec</span><span class="o">.</span><span class="n">GridSpec</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
-<span class="n">axes_1</span> <span class="o">=</span> <span class="n">subplot</span><span class="p">(</span><span class="n">G</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="p">:])</span>
-<span class="n">xticks</span><span class="p">([]),</span> <span class="n">yticks</span><span class="p">([])</span>
-<span class="n">text</span><span class="p">(</span><span class="mf">0.5</span><span class="p">,</span><span class="mf">0.5</span><span class="p">,</span> <span class="s1">&#39;Axes 1&#39;</span><span class="p">,</span><span class="n">ha</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span><span class="n">va</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span><span class="n">size</span><span class="o">=</span><span class="mi">24</span><span class="p">,</span><span class="n">alpha</span><span class="o">=</span><span class="mf">.5</span><span class="p">)</span>
-<span class="n">axes_2</span> <span class="o">=</span> <span class="n">subplot</span><span class="p">(</span><span class="n">G</span><span class="p">[</span><span class="mi">1</span><span class="p">,:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
-<span class="n">xticks</span><span class="p">([]),</span> <span class="n">yticks</span><span class="p">([])</span>
-<span class="n">text</span><span class="p">(</span><span class="mf">0.5</span><span class="p">,</span><span class="mf">0.5</span><span class="p">,</span> <span class="s1">&#39;Axes 2&#39;</span><span class="p">,</span><span class="n">ha</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span><span class="n">va</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span><span class="n">size</span><span class="o">=</span><span class="mi">24</span><span class="p">,</span><span class="n">alpha</span><span class="o">=</span><span class="mf">.5</span><span class="p">)</span>
-<span class="n">axes_3</span> <span class="o">=</span> <span class="n">subplot</span><span class="p">(</span><span class="n">G</span><span class="p">[</span><span class="mi">1</span><span class="p">:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">])</span>
-<span class="n">xticks</span><span class="p">([]),</span> <span class="n">yticks</span><span class="p">([])</span>
-<span class="n">text</span><span class="p">(</span><span class="mf">0.5</span><span class="p">,</span><span class="mf">0.5</span><span class="p">,</span> <span class="s1">&#39;Axes 3&#39;</span><span class="p">,</span><span class="n">ha</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span><span class="n">va</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span><span class="n">size</span><span class="o">=</span><span class="mi">24</span><span class="p">,</span><span class="n">alpha</span><span class="o">=</span><span class="mf">.5</span><span class="p">)</span>
-<span class="n">axes_4</span> <span class="o">=</span> <span class="n">subplot</span><span class="p">(</span><span class="n">G</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">])</span>
-<span class="n">xticks</span><span class="p">([]),</span> <span class="n">yticks</span><span class="p">([])</span>
-<span class="n">text</span><span class="p">(</span><span class="mf">0.5</span><span class="p">,</span><span class="mf">0.5</span><span class="p">,</span> <span class="s1">&#39;Axes 4&#39;</span><span class="p">,</span><span class="n">ha</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span><span class="n">va</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span><span class="n">size</span><span class="o">=</span><span class="mi">24</span><span class="p">,</span><span class="n">alpha</span><span class="o">=</span><span class="mf">.5</span><span class="p">)</span>
-<span class="n">axes_5</span> <span class="o">=</span> <span class="n">subplot</span><span class="p">(</span><span class="n">G</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="o">-</span><span class="mi">2</span><span class="p">])</span>
-<span class="n">xticks</span><span class="p">([]),</span> <span class="n">yticks</span><span class="p">([])</span>
-<span class="n">text</span><span class="p">(</span><span class="mf">0.5</span><span class="p">,</span><span class="mf">0.5</span><span class="p">,</span> <span class="s1">&#39;Axes 5&#39;</span><span class="p">,</span><span class="n">ha</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span><span class="n">va</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span><span class="n">size</span><span class="o">=</span><span class="mi">24</span><span class="p">,</span><span class="n">alpha</span><span class="o">=</span><span class="mf">.5</span><span class="p">)</span>
-<span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
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" 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" />
-</section>
-<section id="axes">
-<h2>axes 对象<a class="headerlink" href="#axes" title="Permalink to this heading">¶</a></h2>
-<p><em>subplot</em> 返回的是 <em>Axes</em> 对象，但是 <em>Axes</em> 对象相对于 <em>subplot</em> 返回的对象来说要更自由一点。<em>Axes</em> 对象可以放置在图像中的任意位置：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">axes</span><span class="p">([</span><span class="mf">0.1</span><span class="p">,</span><span class="mf">0.1</span><span class="p">,</span><span class="mf">.8</span><span class="p">,</span><span class="mf">.8</span><span class="p">])</span>
-<span class="n">xticks</span><span class="p">([]),</span> <span class="n">yticks</span><span class="p">([])</span>
-<span class="n">text</span><span class="p">(</span><span class="mf">0.6</span><span class="p">,</span><span class="mf">0.6</span><span class="p">,</span> <span class="s1">&#39;axes    ([0.1,0.1,.8,.8])&#39;</span><span class="p">,</span><span class="n">ha</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span><span class="n">va</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span><span class="n">size</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span><span class="n">alpha</span><span class="o">=</span><span class="mf">.5</span><span class="p">)</span>
-<span class="n">axes</span><span class="p">([</span><span class="mf">0.2</span><span class="p">,</span><span class="mf">0.2</span><span class="p">,</span><span class="mf">.3</span><span class="p">,</span><span class="mf">.3</span><span class="p">])</span>
-<span class="n">xticks</span><span class="p">([]),</span> <span class="n">yticks</span><span class="p">([])</span>
-<span class="n">text</span><span class="p">(</span><span class="mf">0.5</span><span class="p">,</span><span class="mf">0.5</span><span class="p">,</span> <span class="s1">&#39;axes    ([0.2,0.2,.3,.3])&#39;</span><span class="p">,</span><span class="n">ha</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span><span class="n">va</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span><span class="n">size</span><span class="o">=</span><span class="mi">16</span><span class="p">,</span><span class="n">alpha</span><span class="o">=</span><span class="mf">.5</span><span class="p">)</span>
-<span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-<img 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KamBiaTCUql0mGc+Hp1NDc3IysrC1FRUXjppZek7YsWLUJ5eTmam5vx3nvv4f7775cmBBsaGuDi4oINGzZMedxYr9fDbDZj3rx5UzpORAzne1BoaCgyMzNRUFCA6upqWK1WhIWFYdu2bfD09HQI55ycHJhMJqxdu9ZpbG/jxo24evUq8vPzERUVhfDwcLi4uGDbtm2oqKiARqPBlStXYLFY4OPjg8DAQKxatQrz58+XzvHII4+gtrYWLS0tqK6ulh6gs2bNGqSmpjoEWUBAAGJjY1FTUwOLxTLlcefAwEDs3LlTevBRdXU1/Pz8sHjxYmRkZEw46TZeALS2tjqNzRsMBqmXrVAoHMLZbDaju7sbkZGRThOp3t7eePXVV6UHH2m1WsjlcixcuBArV650Kn+9dnV1dTm1q6+vz2HbteFss9nQ3t4OhUIx4ZDO2HoiIiKwa9cunDlzBvX19Th37hxcXFwQGBiI5cuXY9myZdJEp11bWxuA0Tv0JlOHq6srXnrpJZw9exa//vorLl26hJGREfj4+CAxMRFLly4dd7L4Ri5dugQAWLhw4ZSPFY3sejPjMpnMNp2Zc6LpqqysxNdff+3UmwNGHxkaHR2NzMzMO9S6iVVVVeHAgQPYvn27dNefCNra2rBv3z5s2LABqampt6yef//733B1dcWOHTtuWR12RqMRe/bswYIFCxwekGWz2fDee+/Bw8MDr7322i1vx0yRyWSw2WxOn8TsOZNQ5s2bh/DwcKjVaqSkpDj1uBoaGqTnOov0PGetVouwsDChghkY/ffy9fW9pT3JgYEBtLW1YevWrbesDsD5ec5j/fbbbzAYDNi2bdstbcftwp4zCUer1eKzzz5zetj+yZMnHcJ6yZIlM/JcYbo76HQ6hztbw8LCpMcE2B+27+3t7TC2fTeYqOfMcCYiuoMmCmcupSMiEhDDmYhIQAxnIiIBMZyJiATEcCYiEhDDmYhIQAxnIiIBMZyJiATEcCYiEhDDmYhIQAxnIiIBMZyJiATEcCYiEtA98zznu/1/g04zh09SpHvBPRPOAH8piR/SdO/gsAYRkYAYzkREAmI4ExEJiOFMRCQghjMRkYAYzkREAmI4ExEJiOFMRCQghjMRkYAYzkREAmI4ExEJiOFMRCQghjMRkYAYzkREAmI4ExEJiOFMRCQghjMRkYAYzkREAmI4ExEJiOFMRCQghjMRkYAYzkREAmI4ExEJiOFMRCQghjMRkYAYzkREAmI4ExEJiOFMRCQghjMRkYAYzkREAmI4ExEJiOFMRCQghjMRkYAYzkREAmI4ExEJyO1ON+Buk5ubi56eHmzbtg0AoNFokJOTI+1/++23HcqXlZWhuLgYRqMRCoUCixcvRmpq6nXrsNlsUKvVuHLlCvR6PYaHh6FUKpGeno4FCxZAJpPdsJ0mkwlHjhxBXV0dbDYbYmNjsW7dOgQEBFz3uObmZpSWlqKxsRHd3d2Qy+WIiorCqlWroFAobljveI4fP47q6mqYTCaMjIwgICAA8+fPx9KlS+Hu7j7l89XU1ODMmTPo6OjAwMAA5HI5VCoVVqxY4VCuoqIC2dnZ0uu33noLMpkMvb292Lt3L1588UVERERM65qIbjWG8xR0dHTgl19+wa5du5z2bd26FX5+fg7bysrK8OOPP2L58uWIjY1FXV0dcnNzAeC6AT00NIRTp04hOTkZS5cuhYeHB6qrq3H48GHo9XqsWbPmuu0cGhpCVlYW3N3d8dRTTwEACgoKkJWVhddff/26gXj58mXo9Xo89NBDCA0NRXd3N4qKivCf//wHr732Gvz9/a9b93gGBwexcOFCBAcHw9XVFTqdDkVFRWhpaZE+5KZiYGAA4eHhSE9Ph1wuh8lkwunTp/Hxxx87lIuLi8OOHTtQVlaGCxcuSNt9fX2RlpaGvLw87NixY8r1E90ODOcpKC4uRkREBGbNmuW0b/bs2Q69UqvVioKCAqSkpGDVqlUAgOjoaPT09KCgoACLFi2Ci8v4o0ru7u5488034eXlJW2LiYmB2WxGSUkJVq5cCTe3iX90ZWVlMBqN+Otf/4rAwEAAQGhoKPbu3YvS0lIsWbJkwmOXLVsGHx8fh22RkZF49913UVZWhpUrV0547EQ2bNjg8DomJgZDQ0M4ffo0zGYzvL29p3S+pKQkJCUlOWybM2cO3n//fYdtcrkccrkc1dXVTudITU2FWq2GVqtFVFTUlOonuh2EDOeuri6cPHkSOp0Ovb298PX1RVxcHFavXi0FVm9vLz788ENERkZi69at0rH23upzzz2H+Ph4AEBDQwMKCwtx9epV2Gw2REZG4tFHH3UI2ZqaGhQWFqKjowNWqxX+/v6YP38+MjIyAAAWiwWXLl3C2rVrJ3UNOp0O/f39SE5OdtiekpICjUaDxsZGREdHj3usTCZzCGa78PBwaDQa9Pf3X7cHW1VVhYiICCmYAUChUCAyMhJVVVXXDeexwQwAAQEB8PHxQU9Pz4THTZU9kCf6gJru+SYrMDAQc+bMQVlZGcOZhCTkhGBPTw/8/f2xdu1aPP/888jIyEBdXR32798vlfH19cWTTz6JyspKlJaWAhgddsjLy8NDDz0kBfOVK1fw+eefw9PTE5s3b8aWLVswODiITz/9FN3d3QAAg8GA//3vfwgMDMQzzzyD5557DkuWLMHQ0JBUn1arxdDQECIjIyd1DR0dHQDg1MsOCQlx2D8VWq0WXl5eTsMn49U9Xu8+JCRkWvV2dHSgr69Pavt0Wa1WWCwW1NXVobi4GCkpKfD09Lyp842MjKCzsxM//PDDuB8s1xMZGYna2tpp1090KwnZc46KinLozahUKiiVSnz66adobW1FWFgYACA+Ph4PPfQQjhw5AgD49ttvERQU5DAmm5eXh+joaIexzejoaOzZswdqtRrr1q1DS0sLrFYrHn/8cXh4eEhlrnX16lXIZLJJB5TZbAbg3KOzv7bvn6yamhpcvnwZq1atuuGE4ERDBd7e3lOu12q14scff4SPjw8WLlw4pWOv1d7ejg8//FB6nZSUhI0bN077fADw8ccfo6WlBcBo7z4zMxP/+Mc/Jn18aGgo+vv7pclaIpEIGc4jIyNQq9UoLy+HyWTC8PCwtK+zs1MKZwBYs2YNGhoaAIz2gHfu3AlXV1eprMFgwPLly2G1WqVj3N3dERERAa1WCwAICwuDi4sLDh48iIULFyIqKsqpF9bb2zvuUMPt0NHRgUOHDiEmJgYPP/zwba37p59+QlNTE7Zv335T169UKrFz505YLBbodDqcOnUK33//PTZv3jztc27evBmDg4MwGAxQq9X44osvpnS8XC4HMPqzZTiTaIQM5+PHj+PcuXNYsWIFVCoVPDw80N3dja+//tohqAHA1dUViYmJAID77rsPwcHB0r6+vj4AwOHDh3H48GGneuwTeEqlEi+88AJOnz6N7OxsDA8PY86cOVizZs20xyPtQWY2m+Hr6yttn6hHPRGDwYDPP/8cgYGB2LZt26SW0U3UQ57q5Nvx48dRVlaGp556CrGxsZM+bjxubm6YPXs2gNFvRr6+vsjJyUF6evq0l7PZf9Zz5sxBXFwc3n333ZtqI5FIhAznS5cuYcGCBVi+fLm0bXBwcNyy7e3tKCoqAgBUVlaiqqoKc+fOBfB7z+iRRx4ZN1zsPWxgdBgjOjoaIyMjaGxsxIkTJ7B//3787W9/g7e3N3x9fTEwMACbzTapgLSP+ba3tzuEs33MdzLDI93d3cjKyoKXlxdeeOEFacjlRkJCQtDe3u60vaOjY9LDMkVFRThz5gzWr1/vNKk5E8LDwwGMfvjMxFpjLy8vKJXKKR3T398PAA4/HyJRCDkhODw87DSLr9Foxi136NAhKXAeeOAB5OTkSKsKgoKCoFAo0N7ejtmzZzv9N96kmaurK2JiYrBs2TIMDQ3BYDAAGA0Tm8026Qk1lUoFuVyOixcvOmyvqKiAt7f3DScW+/r68Pnnn0Mmk+HFF1+cUo937ty5aGpqktoOAEajETqdTvrgup6SkhKcOHECq1evRlpa2qTrnQr7UNS1K0puRm9vL/R6/ZSOaWtrg1wu55AGCUnInnNcXBw0Gg1mzZoFpVKJ3377DTqdzqnc0aNHYTAYsGvXLuzatQtPPPEE9u3bh+zsbLz44ouQyWRYv349Dhw4gJGRESQkJEAul6Ovrw86nQ4BAQFYsmQJSktLodVqER8fD39/f/T39+P06dPw8/OTAjwyMhJubm7QarXjhvpYLi4uWLlyJXJzc+Hn54fY2FjU19fjwoULWL9+vcOHT05ODsrLy/HWW28BGL2J5Msvv4TRaMSmTZtgMplgMpmk8iEhIdIqh4aGBmRlZWHTpk1YsGABAODBBx/EuXPncODAAWmNdUFBAQICAhxufjEajdi7dy8yMjKkJYOXLl1CXl4e4uLiEB0djaamJqm8p6enQ8/7s88+g9FoxJtvvilty8rKgslkwhtvvAFgNACPHj2KxMREKBQKjIyMQKvVoqSkBPHx8Q69Zvvdli+99JI0nFReXo6cnBxkZmZK2w4cOIDw8HDMmjULnp6e6OzsxNmzZx2+CU1GY2PjTQ/XEN0qQobzY489BpvNhoKCAgCjqzKefvppfPTRR1KZK1eu4Pz589i4cSOCgoIAjI61bt68GVlZWThz5gyWLVuG+Ph4vPzyyygqKsIPP/yAoaEh+Pr6QqVSSTcyhIWFoaamBvn5+ejr65N6tlu2bJFu9vD09ERSUhIuXrw46d5kamoqZDIZ1Go11Go1AgICsGHDBqe7A202G2w2m/S6r68Pra2tAIDvvvvO6bzXhpfFYgEAh+V17u7uyMzMxJEjR6Tj7bdvj7078Np6gdFVIfY/7X+3i46ORmZmpvTaYrE4Lesbey2+vr6Qy+U4deoUent74e7ujsDAQDz66KNYtGiRw7H2a7l2Mnbs+YDRbyWXL1+GWq2WbgePjo7G8uXL8c9//tPp32s8BoMBzc3NeOSRRyZVnuh2k4194zvslMls19svEplM5vRLPNM6Ojqwb98+7Ny5E6GhoQB+7+298cYbCAgImLGbKiYrPz8fV65cweuvv35b67VYLPjXv/6FLVu2ICEhYUbOeejQIQwODuK5556b9jnGvg+sVisKCwtRVFQkPVsDAI4dOwatVsvbt+mO+//3rNNElpBjzqIKCQnBokWLcOLECad9e/fuxTvvvHPb26TVah0mTm8XnU4HpVI5Y8EMjF7LH/7whxk7X0VFBd555x1pwtiut7cX58+fx7p162asLqKZxp7zTTKbzTAajdJr+3IxujOufR/wZ0N3g4l6zgxnuqfwfUB3Gw5rEBHdRRjOREQCYjgTEQmI4UxEJCCGMxGRgBjOREQCYjgTEQmI4UxEJCCGMxGRgBjOREQCYjgTEQmI4UxEJCCGMxGRgBjOREQCYjgTEQmI4UxEJCCGMxGRgBjOREQCYjgTEQmI4UxEJCCGMxGRgBjOREQCYjgTEQmI4UxEJCCGMxGRgBjOREQCYjgTEQmI4UxEJCCGMxGRgBjOREQCYjgTEQmI4UxEJCCGMxGRgNzudANmkkwmu9NNICKaEfdMONtstjvdBCKiGcNhDSIiATGciYgExHAmIhIQw5mISEAMZyIiATGciYgExHAmIhIQw5mISEAMZyIiATGciYgExHAmIhIQw5mISEAMZyIiATGciYgExHAmIhIQw5mISEAMZyIiATGciYgExHAmIhIQw5mISEAMZyIiATGciYgExHAmIhIQw5mISEAMZyIiATGciYgExHAmIhKQ240KyGSy29EOIiK6hsxms93pNhAR0Rgc1iAiEhDDmYhIQAxnIiIBMZyJiATEcCYiEtD/AVGhDgsTjgbFAAAAAElFTkSuQmCC" 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" />
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">axes</span><span class="p">([</span><span class="mf">0.1</span><span class="p">,</span><span class="mf">0.1</span><span class="p">,</span><span class="mf">.5</span><span class="p">,</span><span class="mf">.5</span><span class="p">])</span>
-<span class="n">xticks</span><span class="p">([]),</span> <span class="n">yticks</span><span class="p">([])</span>
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-<span class="n">xticks</span><span class="p">([]),</span> <span class="n">yticks</span><span class="p">([])</span>
-<span class="n">text</span><span class="p">(</span><span class="mf">0.1</span><span class="p">,</span><span class="mf">0.1</span><span class="p">,</span> <span class="s1">&#39;axes    ([0.2,0.2,.5,.5])&#39;</span><span class="p">,</span><span class="n">ha</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">,</span><span class="n">va</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span><span class="n">size</span><span class="o">=</span><span class="mi">16</span><span class="p">,</span><span class="n">alpha</span><span class="o">=</span><span class="mf">.5</span><span class="p">)</span>
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-<span class="n">xticks</span><span class="p">([]),</span> <span class="n">yticks</span><span class="p">([])</span>
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-<span class="n">xticks</span><span class="p">([]),</span> <span class="n">yticks</span><span class="p">([])</span>
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-</pre></div>
-</div>
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" 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/>
-<p>后面的 Axes 对象会覆盖前面的内容。</p>
-</section>
-<section id="ticks">
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-</section>
-<section id="id1">
-<h2>作者 &amp; 更新时间<a class="headerlink" href="#id1" title="Permalink to this heading">¶</a></h2>
-<p>作者:<cite>李金  &lt;lijinwithyou&#64;gmail.com&gt;</cite></p>
-<p>提交: 2017/12/14</p>
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-<title>不要迷信默认设置</title>
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-<section id="id1">
-<h1>不要迷信默认设置<a class="headerlink" href="#id1" title="Permalink to this heading">¶</a></h1>
-<p>导入相关的包：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-</pre></div>
-</div>
-<p>生成三角函数：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span>
-<span class="n">c</span><span class="p">,</span> <span class="n">s</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">x</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-</pre></div>
-</div>
-</section>
-<section id="id2">
-<h1>默认绘图<a class="headerlink" href="#id2" title="Permalink to this heading">¶</a></h1>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="o">%</span><span class="n">matplotlib</span> <span class="n">inline</span>
-<span class="c1"># 画图</span>
-<span class="n">p</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">c</span><span class="p">)</span>
-<span class="n">p</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">s</span><span class="p">)</span>
-<span class="c1"># 在脚本中需要加上这句才会显示图像</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>默认效果如图所示，我们可以修改默认的属性来得到更漂亮的结果。</p>
-</section>
-<section id="id3">
-<h1>图<a class="headerlink" href="#id3" title="Permalink to this heading">¶</a></h1>
-<p>图像以 <em>Figure #</em> 为窗口标题，并且数字从 <em>1</em> 开始，<em>figure()</em> 函数的主要参数如下：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>参数          默认值                      描述
-num            1                      图号
-figsize        figure.figsize         图大小（宽，高）（单位英寸）
-dpi            figure.dpi             分辨率（每英寸所打印的点数）
-facecolor      figure.facecolor       背景颜色
-edgecolor      figure.edgecolor       边界颜色
-frameon        True                   是否显示图框架
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># 设置图像大小</span>
-<span class="n">f</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="mi">6</span><span class="p">),</span> <span class="n">dpi</span><span class="o">=</span><span class="mi">80</span><span class="p">)</span>
-<span class="c1"># 画图</span>
-<span class="n">p</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">c</span><span class="p">)</span>
-<span class="n">p</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">s</span><span class="p">)</span>
-<span class="c1"># 在脚本中需要加上这句才会显示图像</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-</section>
-<section id="id4">
-<h1>设置线条颜色，粗细，类型<a class="headerlink" href="#id4" title="Permalink to this heading">¶</a></h1>
-<p>首先，我们使用 <em>figure()</em> 函数来创建一幅新图像，并且指定它的大小，使得长宽比更合适。
-然后，我们使用 <em>color</em>, <em>linewidth</em>, <em>linestyle</em> 参数，指定曲线的颜色，粗细，类型：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># 设置图像大小</span>
-<span class="n">f</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="mi">6</span><span class="p">),</span> <span class="n">dpi</span><span class="o">=</span><span class="mi">80</span><span class="p">)</span>
-<span class="c1"># 画图，指定颜色，线宽，类型</span>
-<span class="n">p</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;blue&quot;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">2.5</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s2">&quot;-&quot;</span><span class="p">)</span>
-<span class="n">p</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;red&quot;</span><span class="p">,</span>  <span class="n">linewidth</span><span class="o">=</span><span class="mf">2.5</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s2">&quot;-&quot;</span><span class="p">)</span>
-<span class="c1"># 在脚本中需要加上这句才会显示图像</span>
-<span class="c1"># plt.show()</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<img 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" 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" />
-<p>也可以像 <strong>Matlab</strong> 中一样使用格式字符来修改参数：</p>
-<p>表示颜色的字符参数有：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span> 字符  颜色
-‘b’  蓝色，blue
-‘g’  绿色，green
-‘r’  红色，red
-‘c’  青色，cyan
-‘m’  品红，magenta
-‘y’  黄色，yellow
-‘k’  黑色，black
-‘w’  白色，white
-</pre></div>
-</div>
-<p>表示类型的字符参数有：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span># 设置图像大小
-f = plt.figure(figsize=(10,6), dpi=80)
-# 画图，指定颜色，线宽，类型
-p = plt.plot(x, c, &#39;b-&#39;,
-    x, s, &#39;r-&#39;, linewidth=2.5)
-# 在脚本中需要加上这句才会显示图像
-# plt.show()
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<img 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" 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" />
-</section>
-<section id="id5">
-<h1>设置横轴纵轴的显示区域<a class="headerlink" href="#id5" title="Permalink to this heading">¶</a></h1>
-<p>我们希望将坐标轴的显示区域放大一些，这样可以看到所有的点，可以使用 <em>plt</em> 中的 <em>xlim</em> 和 <em>ylim</em> 来设置：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span> # 设置图像大小
-p = plt.figure(figsize=(10,6), dpi=80)
-# 画图，指定颜色，线宽，类型
-p = plt.plot(x, c, &#39;b-&#39;,
-    x, s, &#39;r-&#39;, linewidth=2.5)
-####################################################################
-# 设置显示范围
-p = plt.xlim(x.min() * 1.1, x.max() * 1.1)
-p = plt.ylim(c.min() * 1.1, c.max() * 1.1)
-####################################################################
-# 在脚本中需要加上这句才会显示图像
-# plt.show()
-</pre></div>
-</div>
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-<img 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" 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" />
-</section>
-<section id="id6">
-<h1>设置刻度<a class="headerlink" href="#id6" title="Permalink to this heading">¶</a></h1>
-<p>对于三教函数来说，我们希望将 x 轴的刻度设为与 $pi$ 有关的点，可以使用 plt 中的 xticks 和 yticks 函数，将需要的刻度传入：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span># 设置图像大小
-f = plt.figure(figsize=(10,6), dpi=80)
-# 画图，指定颜色，线宽，类型
-p = plt.plot(x, c, &#39;b-&#39;,
-    x, s, &#39;r-&#39;, linewidth=2.5)
-# 设置显示范围
-plt.xlim(x.min() * 1.1, x.max() * 1.1)
-plt.ylim(c.min() * 1.1, c.max() * 1.1)
-####################################################################
-# 设置刻度
-p = plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi])
-p = plt.yticks([-1, 0, 1])
-#####################################################################
-# 在脚本中需要加上这句才会显示图像
-# plt.show()
-</pre></div>
-</div>
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-<img 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" 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" />
-</section>
-<section id="x-y">
-<h1>设定 x 轴 y 轴标题<a class="headerlink" href="#x-y" title="Permalink to this heading">¶</a></h1>
-<p>我们想让刻度的位置显示的是含有 $pi$ 的标识而不是浮点数，可以在 xticks 中传入第二组参数，这组参数代表对应刻度的显示标识。这里，我们使用 latex 的语法来显示特殊符号（使用 $$ 包围的部分）：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span># 设置图像大小
-f = plt.figure(figsize=(10,6), dpi=80)
-# 画图，指定颜色，线宽，类型
-p = plt.plot(x, c, &#39;b-&#39;,
-    x, s, &#39;r-&#39;, linewidth=2.5)
-# 设置显示范围
-plt.xlim(x.min() * 1.1, x.max() * 1.1)
-plt.ylim(c.min() * 1.1, c.max() * 1.1)
-# 设置刻度及其标识
-p = plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi],
-    [&#39;$-\pi$&#39;, &#39;$-\pi/2$&#39;, &#39;$0$&#39;, &#39;$\pi/2$&#39;, &#39;$\pi$&#39;],     fontsize =&#39;xx-large&#39;)
-p = plt.yticks([-1, 0, 1],
-    [&#39;$-1$&#39;, &#39;$0$&#39;, &#39;$+1$&#39;], fontsize =&#39;xx-large&#39;)
-# 在脚本中需要加上这句才会显示图像
-# plt.show()
-</pre></div>
-</div>
-<button class="button">Run …</button>
-</section>
-<section id="id7">
-<h1>移动坐标轴的位置<a class="headerlink" href="#id7" title="Permalink to this heading">¶</a></h1>
-<p>现在坐标轴的位置是在边界上，而且有上下左右四条，我们现在想将下面和左边的两条移动到中间，并将右边和上面的两条去掉：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span># 设置图像大小
-f = plt.figure(figsize=(10,6), dpi=80)
-# 画图，指定颜色，线宽，类型
-plt.plot(x, c, &#39;b-&#39;,
-    x, s, &#39;r-&#39;, linewidth=2.5)
-# 设置显示范围
-plt.xlim(x.min() * 1.1, x.max() * 1.1)
-plt.ylim(c.min() * 1.1, c.max() * 1.1)
-# 得到轴的句柄
-ax = plt.gca()
-# ax.spines参数表示四个坐标轴线
-# 将右边和上边的颜色设为透明
-ax.spines[&#39;right&#39;].set_color(&#39;none&#39;)
-ax.spines[&#39;top&#39;].set_color(&#39;none&#39;)
-####################################################################
-# 将 x 轴的刻度设置在下面的坐标轴上
-ax.xaxis.set_ticks_position(&#39;bottom&#39;)
-# 设置位置
-ax.spines[&#39;bottom&#39;].set_position((&#39;data&#39;,0))
-# 将 y 轴的刻度设置在左边的坐标轴上
-ax.yaxis.set_ticks_position(&#39;left&#39;)
-# 设置位置
-ax.spines[&#39;left&#39;].set_position((&#39;data&#39;,0))
-#####################################################################
-# 设置刻度及其标识
-p = plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi],
-    [&#39;$-\pi$&#39;, &#39;$-\pi/2$&#39;, &#39;$0$&#39;, &#39;$\pi/2$&#39;, &#39;$\pi$&#39;],     fontsize =&#39;xx-large&#39;)
-p = plt.yticks([-1, 0, 1],
-    [&#39;$-1$&#39;, &#39;$0$&#39;, &#39;$+1$&#39;], fontsize =&#39;xx-large&#39;)
-# 在脚本中需要加上这句才会显示图像
-# plt.show()
-</pre></div>
-</div>
-<button class="button">Run …</button>
-</section>
-<section id="id8">
-<h1>加入图例<a class="headerlink" href="#id8" title="Permalink to this heading">¶</a></h1>
-<p>使用 legend() 加入图例：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span># 设置图像大小
-plt.figure(figsize=(10,6), dpi=80)
-# 画图，指定颜色，线宽，类型
-plt.plot(x, c, &#39;b-&#39;,
-    x, s, &#39;r-&#39;, linewidth=2.5)
-# 设置显示范围
-plt.xlim(x.min() * 1.1, x.max() * 1.1)
-plt.ylim(c.min() * 1.1, c.max() * 1.1)
-# 得到画图的句柄
-ax = plt.gca()
-# ax.spines参数表示四个坐标轴线
-# 将右边和上边的颜色设为透明
-ax.spines[&#39;right&#39;].set_color(&#39;none&#39;)
-ax.spines[&#39;top&#39;].set_color(&#39;none&#39;)
-# 将 x 轴的刻度设置在下面的坐标轴上
-ax.xaxis.set_ticks_position(&#39;bottom&#39;)
-# 设置位置
-ax.spines[&#39;bottom&#39;].set_position((&#39;data&#39;,0))
-# 将 y 轴的刻度设置在左边的坐标轴上
-ax.yaxis.set_ticks_position(&#39;left&#39;)
-# 设置位置
-ax.spines[&#39;left&#39;].set_position((&#39;data&#39;,0))
-# 设置刻度及其标识
-plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi],
-    [&#39;$-\pi$&#39;, &#39;$-\pi/2$&#39;, &#39;$0$&#39;, &#39;$\pi/2$&#39;, &#39;$\pi$&#39;],     fontsize =&#39;xx-large&#39;)
-plt.yticks([-1, 0, 1],
-    [&#39;$-1$&#39;, &#39;$0$&#39;, &#39;$+1$&#39;], fontsize =&#39;xx-large&#39;)
-#####################################################################
-# 加入图例，frameon表示去掉图例周围的边框
-l = plt.legend([&#39;cosine&#39;, &#39;sine&#39;], loc=&#39;upper left&#39;, frameon=False)
-####################################################################
-# 在脚本中需要加上这句才会显示图像
-# plt.show()
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<img 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" 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" />
-</section>
-<section id="id9">
-<h1>注释特殊点<a class="headerlink" href="#id9" title="Permalink to this heading">¶</a></h1>
-<p>我们可以使用 <em>anotate</em> 函数来注释特殊的点，假设我们要显示的点是 $2pi/3$：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span># 设置图像大小
-plt.figure(figsize=(10,6), dpi=80)
-# 画图，指定颜色，线宽，类型
-plt.plot(x, c, &#39;b-&#39;,
-    x, s, &#39;r-&#39;, linewidth=2.5)
-# 设置显示范围
-plt.xlim(x.min() * 1.1, x.max() * 1.1)
-plt.ylim(c.min() * 1.1, c.max() * 1.1)
-# 得到画图的句柄
-ax = plt.gca()
-# ax.spines参数表示四个坐标轴线
-# 将右边和上边的颜色设为透明
-ax.spines[&#39;right&#39;].set_color(&#39;none&#39;)
-ax.spines[&#39;top&#39;].set_color(&#39;none&#39;)
-# 将 x 轴的刻度设置在下面的坐标轴上
-ax.xaxis.set_ticks_position(&#39;bottom&#39;)
-# 设置位置
-ax.spines[&#39;bottom&#39;].set_position((&#39;data&#39;,0))
-# 将 y 轴的刻度设置在左边的坐标轴上
-ax.yaxis.set_ticks_position(&#39;left&#39;)
-# 设置位置
-ax.spines[&#39;left&#39;].set_position((&#39;data&#39;,0))
-# 设置刻度及其标识
-plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi],
-    [&#39;$-\pi$&#39;, &#39;$-\pi/2$&#39;, &#39;$0$&#39;, &#39;$\pi/2$&#39;, &#39;$\pi$&#39;],     fontsize =&#39;xx-large&#39;)
-plt.yticks([-1, 0, 1],
-    [&#39;$-1$&#39;, &#39;$0$&#39;, &#39;$+1$&#39;], fontsize =&#39;xx-large&#39;)
-# 加入图例，frameon表示图例周围是否需要边框
-l = plt.legend([&#39;cosine&#39;, &#39;sine&#39;], loc=&#39;upper left&#39;, frameon=False)
-###################################################################
-# 数据点
-t = 2 * np.pi / 3
-# 蓝色虚线
-plt.plot([t,t],[0,np.cos(t)], color =&#39;blue&#39;, linewidth=2.5,     linestyle=&quot;--&quot;)
-# 该点处的 cos 值
-plt.scatter([t,],[np.cos(t),], 50, color =&#39;blue&#39;)
-# 在对应的点显示文本
-plt.annotate(r&#39;$\sin(\frac{2\pi}{3})=\frac{\sqrt{3}}{2}$&#39;, # 文本
-    xy=(t, np.sin(t)), # 数据点坐标位置
-    xycoords=&#39;data&#39;,   # 坐标相对于数据
-    xytext=(+10, +30), # 文本位置坐标
-    textcoords=&#39;offset points&#39;, # 坐标相对于数据点的坐标
-    fontsize=16,       # 文本大小
-    arrowprops=dict(arrowstyle=&quot;-&gt;&quot;,     connectionstyle=&quot;arc3,rad=.2&quot;)) # 箭头
-# 红色虚线
-p = plt.plot([t,t],[0,np.sin(t)], color =&#39;red&#39;, linewidth=2.5,     linestyle=&quot;--&quot;)
-# 该点处的 sin 值
-p = plt.scatter([t,],[np.sin(t),], 50, color =&#39;red&#39;)
-# 显示文本
-p = plt.annotate(r&#39;$\cos(\frac{2\pi}{3})=-\frac{1}{2}$&#39;,
-    xy=(t, np.cos(t)), xycoords=&#39;data&#39;,
-    xytext=(-90, -50), textcoords=&#39;offset points&#39;,     fontsize=16,
-    arrowprops=dict(arrowstyle=&quot;-&gt;&quot;,     connectionstyle=&quot;arc3,rad=.2&quot;))
-##################################################################
-# 在脚本中需要加上这句才会显示图像
-# plt.show()
-</pre></div>
-</div>
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-<img 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" 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" />
-</section>
-<section id="id10">
-<h1>最后调整<a class="headerlink" href="#id10" title="Permalink to this heading">¶</a></h1>
-<p>调整刻度值的大小，并让其显示在曲线上方。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span># 设置图像大小
-plt.figure(figsize=(10,6), dpi=80)
-# 画图，指定颜色，线宽，类型
-plt.plot(x, c, &#39;b-&#39;,
-    x, s, &#39;r-&#39;, linewidth=2.5)
-# 设置显示范围
-plt.xlim(x.min() * 1.1, x.max() * 1.1)
-plt.ylim(c.min() * 1.1, c.max() * 1.1)
-# 得到画图的句柄
-ax = plt.gca()
-# ax.spines参数表示四个坐标轴线
-# 将右边和上边的颜色设为透明
-ax.spines[&#39;right&#39;].set_color(&#39;none&#39;)
-ax.spines[&#39;top&#39;].set_color(&#39;none&#39;)
-# 将 x 轴的刻度设置在下面的坐标轴上
-ax.xaxis.set_ticks_position(&#39;bottom&#39;)
-# 设置位置
-ax.spines[&#39;bottom&#39;].set_position((&#39;data&#39;,0))
-# 将 y 轴的刻度设置在左边的坐标轴上
-ax.yaxis.set_ticks_position(&#39;left&#39;)
-# 设置位置
-ax.spines[&#39;left&#39;].set_position((&#39;data&#39;,0))
-# 设置刻度及其标识
-plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi],
-    [&#39;$-\pi$&#39;, &#39;$-\pi/2$&#39;, &#39;$0$&#39;, &#39;$\pi/2$&#39;, &#39;$\pi$&#39;],     fontsize =&#39;xx-large&#39;)
-plt.yticks([-1, 0, 1],
-    [&#39;$-1$&#39;, &#39;$0$&#39;, &#39;$+1$&#39;], fontsize =&#39;xx-large&#39;)
-# 加入图例，frameon表示图例周围是否需要边框
-l = plt.legend([&#39;cosine&#39;, &#39;sine&#39;], loc=&#39;upper left&#39;, frameon=False)
-# 数据点
-t = 2 * np.pi / 3
-# 蓝色虚线
-plt.plot([t,t],[0,np.cos(t)], color =&#39;blue&#39;, linewidth=2.5,     linestyle=&quot;--&quot;)
-# 该点处的 cos 值
-plt.scatter([t,],[np.cos(t),], 50, color =&#39;blue&#39;)
-# 在对应的点显示文本
-plt.annotate(r&#39;$\sin(\frac{2\pi}{3})=\frac{\sqrt{3}}{2}$&#39;, # 文本
-    xy=(t, np.sin(t)), # 数据点坐标位置
-    xycoords=&#39;data&#39;,   # 坐标相对于数据
-    xytext=(+10, +30), # 文本位置坐标
-    textcoords=&#39;offset points&#39;, # 坐标相对于数据点的坐标
-    fontsize=16,       # 文本大小
-    arrowprops=dict(arrowstyle=&quot;-&gt;&quot;,     connectionstyle=&quot;arc3,rad=.2&quot;)) # 箭头
-# 红色虚线
-p = plt.plot([t,t],[0,np.sin(t)], color =&#39;red&#39;, linewidth=2.5,     linestyle=&quot;--&quot;)
-# 该点处的 sin 值
-p = plt.scatter([t,],[np.sin(t),], 50, color =&#39;red&#39;)
-# 显示文本
-p = plt.annotate(r&#39;$\cos(\frac{2\pi}{3})=-\frac{1}{2}$&#39;,
-    xy=(t, np.cos(t)), xycoords=&#39;data&#39;,
-    xytext=(-90, -50), textcoords=&#39;offset points&#39;,     fontsize=16,
-    arrowprops=dict(arrowstyle=&quot;-&gt;&quot;,     connectionstyle=&quot;arc3,rad=.2&quot;))
-####################################################################
-for label in ax.get_xticklabels() + ax.get_yticklabels():
-    label.set_fontsize(16)
-    label.set_bbox(dict(facecolor=&#39;white&#39;, edgecolor=&#39;None&#39;,     alpha=0.65 ))
-####################################################################
-# 在脚本中需要加上这句才会显示图像
-# plt.show()
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<img alt="data:image/png;base64,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" 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" />
-</section>
-<section id="id11">
-<h1>作者 &amp; 更新时间<a class="headerlink" href="#id11" title="Permalink to this heading">¶</a></h1>
-<p>作者:<cite>李金  &lt;lijinwithyou&#64;gmail.com&gt;</cite></p>
-<p>提交: 2017/12/14</p>
-</section>
-<div class="clearer"></div>
-</div>
-</div>
-</div>
-<div class="clearer"></div>
-</div>
-</main>
-<div class="star-footer">
-<img onclick="openPurchase()" src="../../static/star.jpg">
-<div id="praise"><span>0</span> persons have rewarded</div>
-</div>
-<div class="foo"></div>
-<footer class="clearfix fixfooter web-show">
-<div class="col-sm-offset-2 col-sm-6 footer-div1">
-<div class="footer-block-item">
-<span class="footer-item1">Built with Sphinx using a theme provid by QPython.</span>
-<span class="footer-item2 visible-xs">Copyight 2017, QPython.</span>
-</div>
-</div>
-<div class="col-sm-2 footer-div2">
-<a href="/index.html" class="pull-right"><img src="http://www.qpython.org/static/img_logo.png"></a>
-</div>
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-<div id="purchase-box">
-<div class="zh-img purchase-img"></div>
-<div class="en-img purchase-img">
-<form action="https://www.paypal.com/cgi-bin/webscr" method="post" target="_top">
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-<input type="hidden" name="hosted_button_id" value="ST6KWAK8R63JW">
-<div class="en-thanks-tit">
-<input type="hidden" name="on0" value="Thanks">
-Thanks
-</div>
-<div class="en-thanks-sel">
-<select name="os0">
-<option value="Reward this course">Reward this course $ 0.99 USD</option>
-<option value="Reward this course">Reward this course $ 1.49 USD</option>
-<option value="Reward this course">Reward this course $ 1.99 USD</option>
-<option value="Reward this course">Reward this course $ 2.99 USD</option>
-<option value="Reward this course">Reward this course $ 3.49 USD</option>
-</select>
-</div>
-<input type="hidden" name="currency_code" value="USD">
-<input type="image" class="pay_img" src="https://www.paypalobjects.com/en_GB/i/btn/btn_paynow_LG.gif" border="0" name="submit" alt="PayPal – The safer, easier way to pay online!">
-<img alt="" border="0" src="https://www.paypalobjects.com/zh_XC/i/scr/pixel.gif" width="1" height="1">
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-var milib = function(){
-};
-milib.qeditor = function(str){
-alert('Please share this page to QPython then run it')
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-milib.getPurchaseNumber = function(str){
-//alert('getPurchaseNumber:'+str)
-return "10+";
-};
-milib.a8playVideoFromGW= function(url){
-window.open(url)
-};
-milib.qpylibinstall = function(cat,url,target){
-window.open(url)
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-milib.gotoIfPay = function(url, packageId, packageUrl){
-window.open(url)
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-function openPurchase() {
-//获取标识
-var u = window.location.href.split('/')
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-s = u[u.length-2]+'-'+s
-if (milib.openPurchaseActivity){
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-$('.purchase-img').click(function(e) {
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-function btnEvent(){
-var seq = this.getAttribute('target')-2
-var a = this.parentElement.getElementsByTagName('pre')
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-/**********有代码，调用控制台*************/
-var code = a[seq].lastChild.nodeValue
-milib.qeditor(code)
-}else{
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diff --git a/docs/aipy-pandas/index.html b/docs/aipy-pandas/index.html
deleted file mode 100644
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diff --git a/docs/aipy-scipy/cn/10linear-algebra.html b/docs/aipy-scipy/cn/10linear-algebra.html
deleted file mode 100644
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-<h1>线性代数<a class="headerlink" href="#id1" title="Permalink to this heading">¶</a></h1>
-<p>numpy 和 scipy 中，负责进行线性代数部分计算的模块叫做 linalg。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">numpy.linalg</span>
-<span class="kn">import</span> <span class="nn">scipy</span> <span class="k">as</span> <span class="nn">sp</span>
-<span class="kn">import</span> <span class="nn">scipy.linalg</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">linalg</span>
-</pre></div>
-</div>
-<p>%matplotlib inline</p>
-</section>
-<section id="numpy-linalg-vs-scipy-linalg">
-<h1>numpy.linalg VS scipy.linalg<a class="headerlink" href="#numpy-linalg-vs-scipy-linalg" title="Permalink to this heading">¶</a></h1>
-<p>一方面scipy.linalg 包含 numpy.linalg 中的所有函数，同时还包含了很多 numpy.linalg 中没有的函数。</p>
-<p>另一方面，scipy.linalg 能够保证这些函数使用 BLAS/LAPACK 加速，而 numpy.linalg 中这些加速是可选的。</p>
-<p>因此，在使用时，我们一般使用 scipy.linalg 而不是 numpy.linalg。</p>
-<p>我们可以简单看看两个模块的差异：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span> <span class="s2">&quot;number of items in numpy.linalg:&quot;</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="nb">dir</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">linalg</span><span class="p">))</span>
-<span class="nb">print</span> <span class="s2">&quot;number of items in scipy.linalg:&quot;</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="nb">dir</span><span class="p">(</span><span class="n">scipy</span><span class="o">.</span><span class="n">linalg</span><span class="p">))</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">number</span> <span class="n">of</span> <span class="n">items</span> <span class="ow">in</span> <span class="n">numpy</span><span class="o">.</span><span class="n">linalg</span><span class="p">:</span> <span class="mi">36</span>
-<span class="n">number</span> <span class="n">of</span> <span class="n">items</span> <span class="ow">in</span> <span class="n">scipy</span><span class="o">.</span><span class="n">linalg</span><span class="p">:</span> <span class="mi">115</span>
-</pre></div>
-</div>
-</section>
-<section id="numpy-matrix-vs-2d-numpy-ndarray">
-<h1>numpy.matrix VS 2D numpy.ndarray<a class="headerlink" href="#numpy-matrix-vs-2d-numpy-ndarray" title="Permalink to this heading">¶</a></h1>
-<p>线性代数的基本操作对象是矩阵，而矩阵的表示方法主要有两种：numpy.matrix 和 2D numpy.ndarray。</p>
-</section>
-<section id="numpy-matrix">
-<h1>numpy.matrix<a class="headerlink" href="#numpy-matrix" title="Permalink to this heading">¶</a></h1>
-<p>numpy.matrix 是一个矩阵类，提供了一些方便的矩阵操作：</p>
-<ul class="simple">
-<li><p>支持类似 MATLAB 创建矩阵的语法</p></li>
-<li><p>矩阵乘法默认用 * 号</p></li>
-<li><p>.I 表示逆，.T 表示转置</p></li>
-</ul>
-<p>可以用 mat 或者 matrix 来产生矩阵：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">A</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mat</span><span class="p">(</span><span class="s2">&quot;[1, 2; 3, 4]&quot;</span><span class="p">)</span>
-<span class="nb">print</span> <span class="nb">repr</span><span class="p">(</span><span class="n">A</span><span class="p">)</span>
-<span class="n">A</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">matrix</span><span class="p">(</span><span class="s2">&quot;[1, 2; 3, 4]&quot;</span><span class="p">)</span>
-<span class="nb">print</span> <span class="nb">repr</span><span class="p">(</span><span class="n">A</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">matrix</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span>
-<span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]])</span>
-<span class="n">matrix</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span>
-<span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]])</span>
-</pre></div>
-</div>
-<p>转置和逆：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span> <span class="nb">repr</span><span class="p">(</span><span class="n">A</span><span class="o">.</span><span class="n">I</span><span class="p">)</span>
-<span class="nb">print</span> <span class="nb">repr</span><span class="p">(</span><span class="n">A</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">matrix</span><span class="p">([[</span><span class="o">-</span><span class="mf">2.</span> <span class="p">,</span>  <span class="mf">1.</span> <span class="p">],</span>
-<span class="p">[</span> <span class="mf">1.5</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.5</span><span class="p">]])</span>
-<span class="n">matrix</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span>
-<span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">]])</span>
-</pre></div>
-</div>
-<p>矩阵乘法：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">b</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mat</span><span class="p">(</span><span class="s1">&#39;[5; 6]&#39;</span><span class="p">)</span>
-<span class="nb">print</span> <span class="nb">repr</span><span class="p">(</span><span class="n">A</span> <span class="o">*</span> <span class="n">b</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">matrix</span><span class="p">([[</span><span class="mi">17</span><span class="p">],</span>
-<span class="p">[</span><span class="mi">39</span><span class="p">]])</span>
-</pre></div>
-</div>
-</section>
-<section id="numpy-ndarray">
-<h1>2 维 numpy.ndarray<a class="headerlink" href="#numpy-ndarray" title="Permalink to this heading">¶</a></h1>
-<p>虽然 numpy.matrix 有着上面的好处，但是一般不建议使用，而是用 2 维 numpy.ndarray 对象替代，这样可以避免一些不必要的困惑。</p>
-<p>我们可以使用 array 复现上面的操作：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">A</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">]])</span>
-<span class="nb">print</span> <span class="nb">repr</span><span class="p">(</span><span class="n">A</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span>
-<span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]])</span>
-</pre></div>
-</div>
-<p>逆和转置：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span> <span class="nb">repr</span><span class="p">(</span><span class="n">linalg</span><span class="o">.</span><span class="n">inv</span><span class="p">(</span><span class="n">A</span><span class="p">))</span>
-<span class="nb">print</span> <span class="nb">repr</span><span class="p">(</span><span class="n">A</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">array</span><span class="p">([[</span><span class="o">-</span><span class="mf">2.</span> <span class="p">,</span>  <span class="mf">1.</span> <span class="p">],</span>
-<span class="p">[</span> <span class="mf">1.5</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.5</span><span class="p">]])</span>
-<span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span>
-<span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">]])</span>
-</pre></div>
-</div>
-<p>矩阵乘法：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">b</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">])</span>
-<span class="nb">print</span> <span class="nb">repr</span><span class="p">(</span><span class="n">A</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">b</span><span class="p">))</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">array</span><span class="p">([</span><span class="mi">17</span><span class="p">,</span> <span class="mi">39</span><span class="p">])</span>
-</pre></div>
-</div>
-<p>普通乘法：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span> <span class="nb">repr</span><span class="p">(</span><span class="n">A</span> <span class="o">*</span> <span class="n">b</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">array</span><span class="p">([[</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">12</span><span class="p">],</span>
-<span class="p">[</span><span class="mi">15</span><span class="p">,</span> <span class="mi">24</span><span class="p">]])</span>
-</pre></div>
-</div>
-<p>scipy.linalg 的操作可以作用到两种类型的对象上，没有区别。</p>
-</section>
-<section id="id2">
-<h1>基本操作<a class="headerlink" href="#id2" title="Permalink to this heading">¶</a></h1>
-<p><strong>求逆</strong></p>
-<p>矩阵 $mathbf{A}$ 的逆 $mathbf{B}$ 满足：$mathbf{BA}=mathbf{AB}=I$，记作 $mathbf{B} = mathbf{A}^{-1}$。</p>
-<p>事实上，我们已经见过求逆的操作，linalg.inv 可以求一个可逆矩阵的逆：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">A</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">],[</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">]])</span>
-<span class="nb">print</span> <span class="n">linalg</span><span class="o">.</span><span class="n">inv</span><span class="p">(</span><span class="n">A</span><span class="p">)</span>
-<span class="nb">print</span> <span class="n">A</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">scipy</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">inv</span><span class="p">(</span><span class="n">A</span><span class="p">))</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">[[</span><span class="o">-</span><span class="mf">2.</span>   <span class="mf">1.</span> <span class="p">]</span>
-<span class="p">[</span> <span class="mf">1.5</span> <span class="o">-</span><span class="mf">0.5</span><span class="p">]]</span>
-<span class="p">[[</span>  <span class="mf">1.00000000e+00</span>   <span class="mf">0.00000000e+00</span><span class="p">]</span>
-<span class="p">[</span>  <span class="mf">8.88178420e-16</span>   <span class="mf">1.00000000e+00</span><span class="p">]]</span>
-</pre></div>
-</div>
-</section>
-<section id="id3">
-<h1>求解线性方程组<a class="headerlink" href="#id3" title="Permalink to this heading">¶</a></h1>
-<p>例如，下列方程组 $$</p>
-<p>begin{eqnarray*}
-x + 3y + 5z &amp;amp; = &amp;amp; 10 \
-2x + 5y + z &amp;amp; = &amp;amp; 8  \
-2x + 3y + 8z &amp;amp; = &amp;amp; 3
-end{eqnarray*}</p>
-<p>$$ 的解为： $$</p>
-<p>begin{split}left[begin{array}{c} x\ y\ zend{array}right]=left[begin{array}{ccc} 1 &amp;amp; 3 &amp;amp; 5\ 2 &amp;amp; 5 &amp;amp; 1\ 2 &amp;amp; 3 &amp;amp; 8end{array}right]^{-1}left[begin{array}{c} 10\ 8\ 3end{array}right]=frac{1}{25}left[begin{array}{c} -232\ 129\ 19end{array}right]=left[begin{array}{c} -9.28\ 5.16\ 0.76end{array}right].end{split}
-$$</p>
-<p>我们可以使用 linalg.solve 求解方程组，也可以先求逆再相乘，两者中 solve 比较快。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">time</span>
-<span class="n">A</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span>
-<span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
-<span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">8</span><span class="p">]])</span>
-<span class="n">b</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">10</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span>
-<span class="n">tic</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
-<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">xrange</span><span class="p">(</span><span class="mi">1000</span><span class="p">):</span>
-<span class="n">x</span> <span class="o">=</span> <span class="n">linalg</span><span class="o">.</span><span class="n">inv</span><span class="p">(</span><span class="n">A</span><span class="p">)</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">b</span><span class="p">)</span>
-<span class="nb">print</span> <span class="n">x</span>
-<span class="nb">print</span> <span class="n">A</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="o">-</span><span class="n">b</span>
-<span class="nb">print</span> <span class="s2">&quot;inv and dot: </span><span class="si">{}</span><span class="s2"> s&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">tic</span><span class="p">)</span>
-<span class="n">tic</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
-<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">xrange</span><span class="p">(</span><span class="mi">1000</span><span class="p">):</span>
-<span class="n">x</span> <span class="o">=</span> <span class="n">linalg</span><span class="o">.</span><span class="n">solve</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">b</span><span class="p">)</span>
-<span class="nb">print</span> <span class="n">x</span>
-<span class="nb">print</span> <span class="n">A</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="o">-</span><span class="n">b</span>
-<span class="nb">print</span> <span class="s2">&quot;solve: </span><span class="si">{}</span><span class="s2"> s&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">tic</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">[</span><span class="o">-</span><span class="mf">9.28</span>  <span class="mf">5.16</span>  <span class="mf">0.76</span><span class="p">]</span>
-<span class="p">[</span>  <span class="mf">0.00000000e+00</span>  <span class="o">-</span><span class="mf">1.77635684e-15</span>  <span class="o">-</span><span class="mf">8.88178420e-16</span><span class="p">]</span>
-<span class="n">inv</span> <span class="ow">and</span> <span class="n">dot</span><span class="p">:</span> <span class="mf">0.0353579521179</span> <span class="n">s</span>
-<span class="p">[</span><span class="o">-</span><span class="mf">9.28</span>  <span class="mf">5.16</span>  <span class="mf">0.76</span><span class="p">]</span>
-<span class="p">[</span>  <span class="mf">0.00000000e+00</span>  <span class="o">-</span><span class="mf">1.77635684e-15</span>  <span class="o">-</span><span class="mf">1.77635684e-15</span><span class="p">]</span>
-<span class="n">solve</span><span class="p">:</span> <span class="mf">0.0284671783447</span> <span class="n">s</span>
-</pre></div>
-</div>
-</section>
-<section id="id4">
-<h1>计算行列式<a class="headerlink" href="#id4" title="Permalink to this heading">¶</a></h1>
-<p>方阵的行列式为</p>
-<p>$$
-left|mathbf{A}right|=sum_{j}left(-1right)^{i+j}a_{ij}M_{ij}.
-$$</p>
-<p>其中 $a_{ij}$ 表示 $mathbf{A}$ 的第 $i$ 行 第 $j$ 列的元素，$M_{ij}$ 表示矩阵 $mathbf{A}$ 去掉第 $i$ 行 第 $j$ 列的新矩阵的行列式。</p>
-<p>例如，矩阵 $$</p>
-<p>begin{split}mathbf{A=}left[begin{array}{ccc} 1 &amp;amp; 3 &amp;amp; 5\ 2 &amp;amp; 5 &amp;amp; 1\ 2 &amp;amp; 3 &amp;amp; 8end{array}right]end{split}</p>
-<p>$$ 的行列式是： $$</p>
-<p>begin{eqnarray*} left|mathbf{A}right| &amp;amp; = &amp;amp; 1left|begin{array}{cc} 5 &amp;amp; 1\ 3 &amp;amp; 8end{array}right|-3left|begin{array}{cc} 2 &amp;amp; 1\ 2 &amp;amp; 8end{array}right|+5left|begin{array}{cc} 2 &amp;amp; 5\ 2 &amp;amp; 3end{array}right|\  &amp;amp; = &amp;amp; 1left(5cdot8-3cdot1right)-3left(2cdot8-2cdot1right)+5left(2cdot3-2cdot5right)=-25.end{eqnarray*}
-$$</p>
-<p>可以用 linalg.det 计算行列式：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">A</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span>
-<span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
-<span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">8</span><span class="p">]])</span>
-<span class="nb">print</span> <span class="n">linalg</span><span class="o">.</span><span class="n">det</span><span class="p">(</span><span class="n">A</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="o">-</span><span class="mf">25.0</span>
-</pre></div>
-</div>
-</section>
-<section id="id5">
-<h1>计算矩阵或向量的模<a class="headerlink" href="#id5" title="Permalink to this heading">¶</a></h1>
-<p>矩阵的模定义如下： $$</p>
-<p>begin{split}leftVert mathbf{A}rightVert =left{ begin{array}{cc} max_{i}sum_{j}left|a_{ij}right| &amp;amp; textrm{ord}=textrm{inf}\ min_{i}sum_{j}left|a_{ij}right| &amp;amp; textrm{ord}=-textrm{inf}\ max_{j}sum_{i}left|a_{ij}right| &amp;amp; textrm{ord}=1\ min_{j}sum_{i}left|a_{ij}right| &amp;amp; textrm{ord}=-1\ maxsigma_{i} &amp;amp; textrm{ord}=2\ minsigma_{i} &amp;amp; textrm{ord}=-2\ sqrt{textrm{trace}left(mathbf{A}^{H}mathbf{A}right)} &amp;amp; textrm{ord}=textrm{‘fro’}end{array}right.end{split}</p>
-<p>$$ 其中，$sigma_i$ 是矩阵的奇异值。</p>
-<p>向量的模定义如下：</p>
-<p>$$
-begin{split}leftVert mathbf{x}rightVert =left{ begin{array}{cc} maxleft|x_{i}right| &amp;amp; textrm{ord}=textrm{inf}\ minleft|x_{i}right| &amp;amp; textrm{ord}=-textrm{inf}\ left(sum_{i}left|x_{i}right|^{textrm{ord}}right)^{1/textrm{ord}} &amp;amp; left|textrm{ord}right|&amp;lt;infty.end{array}right.end{split}
-$$</p>
-<p>linalg.norm 可以计算向量或者矩阵的模：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">A</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span>
-<span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]])</span>
-<span class="nb">print</span> <span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">A</span><span class="p">)</span>
-<span class="nb">print</span> <span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">A</span><span class="p">,</span><span class="s1">&#39;fro&#39;</span><span class="p">)</span> <span class="c1"># frobenius norm 默认值</span>
-<span class="nb">print</span> <span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">A</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span> <span class="c1"># L1 norm 最大列和</span>
-<span class="nb">print</span> <span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">A</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="c1"># L -1 norm 最小列和</span>
-<span class="nb">print</span> <span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">A</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">)</span> <span class="c1"># L inf norm 最大行和</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="mf">5.47722557505</span>
-<span class="mf">5.47722557505</span>
-<span class="mi">6</span>
-<span class="mi">4</span>
-<span class="mi">7</span>
-</pre></div>
-</div>
-</section>
-<section id="id6">
-<h1>最小二乘解和伪逆<a class="headerlink" href="#id6" title="Permalink to this heading">¶</a></h1>
-<p><strong>问题描述</strong></p>
-<p>所谓最小二乘问题的定义如下：</p>
-<p>假设 $y_i$ 与 $mathbf{x_i}$ 的关系可以用一组系数 $c_j$ 和对应的模型函数 $f_j(mathbf{x_i})$ 的模型表示：</p>
-<p>$$
-y_{i}=sum_{j}c_{j}f_{j}left(mathbf{x}_{i}right)+epsilon_{i}
-$$</p>
-<p>其中 $epsilon_i$ 表示数据的不确定性。最小二乘就是要优化这样一个关于 $c_j$ 的问题：</p>
-<p>$$
-Jleft(mathbf{c}right)=sum_{i}left|y_{i}-sum_{j}c_{j}f_{j}left(x_{i}right)right|^{2}
-$$</p>
-<p>其理论解满足：</p>
-<p>$$
-frac{partial J}{partial c_{n}^{*}}=0=sum_{i}left(y_{i}-sum_{j}c_{j}f_{j}left(x_{i}right)right)left(-f_{n}^{*}left(x_{i}right)right)
-$$</p>
-<p>改写为：</p>
-<p>$$
-begin{eqnarray*} sum_{j}c_{j}sum_{i}f_{j}left(x_{i}right)f_{n}^{*}left(x_{i}right) &amp;amp; = &amp;amp; sum_{i}y_{i}f_{n}^{*}left(x_{i}right)\ mathbf{A}^{H}mathbf{Ac} &amp;amp; = &amp;amp; mathbf{A}^{H}mathbf{y}end{eqnarray*}
-$$</p>
-<p>其中：</p>
-<p>$$
-left{ mathbf{A}right} _{ij}=f_{j}left(x_{i}right).
-$$</p>
-<p>当 $mathbf{A^HA}$ 可逆时，我们有：</p>
-<p>$$
-mathbf{c}=left(mathbf{A}^{H}mathbf{A}right)^{-1}mathbf{A}^{H}mathbf{y}=mathbf{A}^{dagger}mathbf{y}
-$$</p>
-<p>矩阵 $mathbf{A}^{dagger}$ 叫做 $mathbf{A}$ 的伪逆。</p>
-<p><strong>问题求解</strong></p>
-<p>注意到，我们的模型可以写为：</p>
-<p>$$
-mathbf{y}=mathbf{Ac}+boldsymbol{epsilon}.
-$$</p>
-<p>在给定 $mathbf{y}$ 和 $mathbf{A}$ 的情况下，我们可以使用 linalg.lstsq 求解 $mathbf c$。</p>
-<p>在给定 $mathbf{A}$ 的情况下，我们可以使用 linalg.pinv 或者 linalg.pinv2 求解 $mathbf{A}^{dagger}$。</p>
-<p><strong>例子</strong></p>
-<p>假设我们的数据满足：</p>
-<p>$$
-begin{align}
-y_{i} &amp;amp; =c_{1}e^{-x_{i}}+c_{2}x_{i} \
-z_{i} &amp;amp; = y_i + epsilon_i
-end{align}
-$$</p>
-<p>其中 $x_i = frac{i}{10},i = 1,dots,10$，$c_1 = 5, c_2 = 2$，产生数据</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">c1</span><span class="p">,</span> <span class="n">c2</span> <span class="o">=</span> <span class="mf">5.0</span><span class="p">,</span> <span class="mf">2.0</span>
-<span class="n">i</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">r_</span><span class="p">[</span><span class="mi">1</span><span class="p">:</span><span class="mi">11</span><span class="p">]</span>
-<span class="n">xi</span> <span class="o">=</span> <span class="mf">0.1</span><span class="o">*</span><span class="n">i</span>
-<span class="n">yi</span> <span class="o">=</span> <span class="n">c1</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">xi</span><span class="p">)</span> <span class="o">+</span> <span class="n">c2</span><span class="o">*</span><span class="n">xi</span>
-<span class="n">zi</span> <span class="o">=</span> <span class="n">yi</span> <span class="o">+</span> <span class="mf">0.05</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">yi</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">yi</span><span class="p">))</span>
-</pre></div>
-</div>
-<p>构造矩阵 $mathbf A$：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">A</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">c_</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">xi</span><span class="p">)[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],</span> <span class="n">xi</span><span class="p">[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]]</span>
-<span class="nb">print</span> <span class="n">A</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">[[</span> <span class="mf">0.90483742</span>  <span class="mf">0.1</span>       <span class="p">]</span>
-<span class="p">[</span> <span class="mf">0.81873075</span>  <span class="mf">0.2</span>       <span class="p">]</span>
-<span class="p">[</span> <span class="mf">0.74081822</span>  <span class="mf">0.3</span>       <span class="p">]</span>
-<span class="p">[</span> <span class="mf">0.67032005</span>  <span class="mf">0.4</span>       <span class="p">]</span>
-<span class="p">[</span> <span class="mf">0.60653066</span>  <span class="mf">0.5</span>       <span class="p">]</span>
-<span class="p">[</span> <span class="mf">0.54881164</span>  <span class="mf">0.6</span>       <span class="p">]</span>
-<span class="p">[</span> <span class="mf">0.4965853</span>   <span class="mf">0.7</span>       <span class="p">]</span>
-<span class="p">[</span> <span class="mf">0.44932896</span>  <span class="mf">0.8</span>       <span class="p">]</span>
-<span class="p">[</span> <span class="mf">0.40656966</span>  <span class="mf">0.9</span>       <span class="p">]</span>
-<span class="p">[</span> <span class="mf">0.36787944</span>  <span class="mf">1.</span>        <span class="p">]]</span>
-</pre></div>
-</div>
-<p>求解最小二乘问题：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">c</span><span class="p">,</span> <span class="n">resid</span><span class="p">,</span> <span class="n">rank</span><span class="p">,</span> <span class="n">sigma</span> <span class="o">=</span> <span class="n">linalg</span><span class="o">.</span><span class="n">lstsq</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">zi</span><span class="p">)</span>
-<span class="nb">print</span> <span class="n">c</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">[</span> <span class="mf">4.87016856</span>  <span class="mf">2.19081311</span><span class="p">]</span>
-</pre></div>
-</div>
-<p>其中 c 的形状与 zi 一致，为最小二乘解，resid 为 zi - A c 每一列差值的二范数，rank 为矩阵 A 的秩，sigma 为矩阵 A 的奇异值。
-查看拟合效果：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">xi2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">r_</span><span class="p">[</span><span class="mf">0.1</span><span class="p">:</span><span class="mf">1.0</span><span class="p">:</span><span class="mi">100</span><span class="n">j</span><span class="p">]</span>
-<span class="n">yi2</span> <span class="o">=</span> <span class="n">c</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">xi2</span><span class="p">)</span> <span class="o">+</span> <span class="n">c</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">*</span><span class="n">xi2</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">xi</span><span class="p">,</span><span class="n">zi</span><span class="p">,</span><span class="s1">&#39;x&#39;</span><span class="p">,</span><span class="n">xi2</span><span class="p">,</span><span class="n">yi2</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span><span class="mf">1.1</span><span class="p">,</span><span class="mf">3.0</span><span class="p">,</span><span class="mf">5.5</span><span class="p">])</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;$x_i$&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;Data fitting with linalg.lstsq&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-<img 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g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%0AgIuIRNT/AXU6OlSJKzNiAAAAAElFTkSuQmCC%0A" 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g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%0AgIuIRNT/AXU6OlSJKzNiAAAAAElFTkSuQmCC%0A" />
-</section>
-<section id="id7">
-<h1>广义逆<a class="headerlink" href="#id7" title="Permalink to this heading">¶</a></h1>
-<p>linalg.pinv 或 linalg.pinv2 可以用来求广义逆，其区别在于前者使用求最小二乘解的算法，后者使用求奇异值的算法求解。</p>
-</section>
-<section id="id8">
-<h1>矩阵分解<a class="headerlink" href="#id8" title="Permalink to this heading">¶</a></h1>
-</section>
-<section id="id9">
-<h1>特征值和特征向量<a class="headerlink" href="#id9" title="Permalink to this heading">¶</a></h1>
-</section>
-<section id="id10">
-<h1>问题描述<a class="headerlink" href="#id10" title="Permalink to this heading">¶</a></h1>
-<p>对于给定的 $N times N$ 矩阵 $mathbf A$，特征值和特征向量问题相当与寻找标量 $lambda$ 和对应的向量 $mathbf v$ 使得： $$
-mathbf{Av} = lambda mathbf{v}
-$$</p>
-<p>矩阵的 $N$ 个特征值（可能相同）可以通过计算特征方程的根得到： $$
-left|mathbf{A} - lambda mathbf{I}right| = 0
-$$</p>
-<p>然后利用这些特征值求（归一化的）特征向量。</p>
-</section>
-<section id="id11">
-<h1>问题求解<a class="headerlink" href="#id11" title="Permalink to this heading">¶</a></h1>
-<ul class="simple">
-<li><dl class="simple">
-<dt>linalg.eig(A)</dt><dd><ul>
-<li><p>返回矩阵的特征值与特征向量</p></li>
-</ul>
-</dd>
-</dl>
-</li>
-<li><dl class="simple">
-<dt>linalg.eigvals(A)</dt><dd><ul>
-<li><p>返回矩阵的特征值</p></li>
-</ul>
-</dd>
-</dl>
-</li>
-<li><dl class="simple">
-<dt>linalg.eig(A, B)</dt><dd><ul>
-<li><p>求解 $mathbf{Av} = lambdamathbf{Bv}$ 的问题</p></li>
-</ul>
-</dd>
-</dl>
-</li>
-</ul>
-<p><strong>例子</strong></p>
-<p>矩阵为 $$
-begin{split}mathbf{A}=left[begin{array}{ccc} 1 &amp;amp; 5 &amp;amp; 2\ 2 &amp;amp; 4 &amp;amp; 1\ 3 &amp;amp; 6 &amp;amp; 2end{array}right].end{split}
-$$</p>
-<p>特征多项式为： $$
-begin{eqnarray*} left|mathbf{A}-lambdamathbf{I}right| &amp;amp; = &amp;amp; left(1-lambdaright)left[left(4-lambdaright)left(2-lambdaright)-6right]-\  &amp;amp;  &amp;amp; 5left[2left(2-lambdaright)-3right]+2left[12-3left(4-lambdaright)right]\  &amp;amp; = &amp;amp; -lambda^{3}+7lambda^{2}+8lambda-3.end{eqnarray*}
-$$</p>
-<p>特征根为： $$
-begin{eqnarray*} lambda_{1} &amp;amp; = &amp;amp; 7.9579\ lambda_{2} &amp;amp; = &amp;amp; -1.2577\ lambda_{3} &amp;amp; = &amp;amp; 0.2997.end{eqnarray*}
-$$</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">A</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span>
-<span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
-<span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">2</span><span class="p">]])</span>
-<span class="n">la</span><span class="p">,</span> <span class="n">v</span> <span class="o">=</span> <span class="n">linalg</span><span class="o">.</span><span class="n">eig</span><span class="p">(</span><span class="n">A</span><span class="p">)</span>
-<span class="nb">print</span> <span class="n">la</span>
-<span class="c1"># 验证是否归一化</span>
-<span class="nb">print</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="n">v</span><span class="o">**</span><span class="mi">2</span><span class="p">),</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-<span class="c1"># 第一个特征值</span>
-<span class="n">l1</span> <span class="o">=</span> <span class="n">la</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-<span class="c1"># 对应的特征向量</span>
-<span class="n">v1</span> <span class="o">=</span> <span class="n">v</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">T</span>
-<span class="c1"># 验证是否为特征值和特征向量对</span>
-<span class="nb">print</span> <span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">A</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">v1</span><span class="p">)</span><span class="o">-</span><span class="n">l1</span><span class="o">*</span><span class="n">v1</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">[</span> <span class="mf">7.95791620</span><span class="o">+</span><span class="mf">0.</span><span class="n">j</span> <span class="o">-</span><span class="mf">1.25766471</span><span class="o">+</span><span class="mf">0.</span><span class="n">j</span>  <span class="mf">0.29974850</span><span class="o">+</span><span class="mf">0.</span><span class="n">j</span><span class="p">]</span>
-<span class="p">[</span> <span class="mf">1.</span>  <span class="mf">1.</span>  <span class="mf">1.</span><span class="p">]</span>
-<span class="mf">3.23301824835e-15</span>
-</pre></div>
-</div>
-</section>
-<section id="id12">
-<h1>奇异值分解<a class="headerlink" href="#id12" title="Permalink to this heading">¶</a></h1>
-<p><strong>问题描述</strong></p>
-<p>$M times N$ 矩阵 $mathbf A$ 的奇异值分解为： $$
-mathbf{A=U}boldsymbol{Sigma}mathbf{V}^{H}
-$$</p>
-<p>其中 $boldsymbol{Sigma}, (M times N)$ 只有对角线上的元素不为 0，$mathbf U, (M times M)$ 和 $mathbf V, (N times N)$ 为正交矩阵。</p>
-<p>其具体原理可以查看维基百科： <a class="reference external" href="https://en.wikipedia.org/wiki/Singular_value_decomposition">https://en.wikipedia.org/wiki/Singular_value_decomposition</a></p>
-<p><strong>问题求解</strong></p>
-<dl class="simple">
-<dt>-mU,s,Vh = linalg.svd(A)</dt><dd><ul class="simple">
-<li><p>返回 $U$ 矩阵，奇异值 $s$，$V^H$ 矩阵</p></li>
-</ul>
-</dd>
-<dt>-mSig = linalg.diagsvd(s,M,N)</dt><dd><ul class="simple">
-<li><p>从奇异值恢复 $boldsymbol{Sigma}$ 矩阵</p></li>
-</ul>
-</dd>
-</dl>
-<p><strong>例子</strong></p>
-<p>奇异值分解：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">A</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">],[</span><span class="mi">4</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span><span class="mi">6</span><span class="p">]])</span>
-<span class="n">U</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">Vh</span> <span class="o">=</span> <span class="n">linalg</span><span class="o">.</span><span class="n">svd</span><span class="p">(</span><span class="n">A</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>$boldsymbol{Sigma}$ 矩阵：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">M</span><span class="p">,</span> <span class="n">N</span> <span class="o">=</span> <span class="n">A</span><span class="o">.</span><span class="n">shape</span>
-<span class="n">Sig</span> <span class="o">=</span> <span class="n">linalg</span><span class="o">.</span><span class="n">diagsvd</span><span class="p">(</span><span class="n">s</span><span class="p">,</span><span class="n">M</span><span class="p">,</span><span class="n">N</span><span class="p">)</span>
-<span class="nb">print</span> <span class="n">Sig</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">[[</span> <span class="mf">9.508032</span>    <span class="mf">0.</span>          <span class="mf">0.</span>        <span class="p">]</span>
-<span class="p">[</span> <span class="mf">0.</span>          <span class="mf">0.77286964</span>  <span class="mf">0.</span>        <span class="p">]]</span>
-</pre></div>
-</div>
-<p>检查正确性：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span> <span class="n">A</span>
-<span class="nb">print</span> <span class="n">U</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">Sig</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">Vh</span><span class="p">))</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">[[</span><span class="mi">1</span> <span class="mi">2</span> <span class="mi">3</span><span class="p">]</span>
-<span class="p">[</span><span class="mi">4</span> <span class="mi">5</span> <span class="mi">6</span><span class="p">]]</span>
-<span class="p">[[</span> <span class="mf">1.</span>  <span class="mf">2.</span>  <span class="mf">3.</span><span class="p">]</span>
-<span class="p">[</span> <span class="mf">4.</span>  <span class="mf">5.</span>  <span class="mf">6.</span><span class="p">]]</span>
-</pre></div>
-</div>
-<p><strong>LU 分解</strong></p>
-<p>$M times N$ 矩阵 $mathbf A$ 的 LU 分解为： $$
-mathbf{A}=mathbf{P},mathbf{L},mathbf{U}
-$$</p>
-<p>$mathbf P$ 是 $M times M$ 的单位矩阵的一个排列，$mathbf L$ 是下三角阵，$mathbf U$ 是上三角阵。</p>
-<p>可以使用 linalg.lu 进行 LU 分解的求解：</p>
-<p>具体原理可以查看维基百科： <a class="reference external" href="https://en.wikipedia.org/wiki/LU_decomposition">https://en.wikipedia.org/wiki/LU_decomposition</a></p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">A</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">],[</span><span class="mi">4</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span><span class="mi">6</span><span class="p">]])</span>
-<span class="n">P</span><span class="p">,</span> <span class="n">L</span><span class="p">,</span> <span class="n">U</span> <span class="o">=</span> <span class="n">linalg</span><span class="o">.</span><span class="n">lu</span><span class="p">(</span><span class="n">A</span><span class="p">)</span>
-<span class="nb">print</span> <span class="n">P</span>
-<span class="nb">print</span> <span class="n">L</span>
-<span class="nb">print</span> <span class="n">U</span>
-</pre></div>
-</div>
-<p>print P.dot(L).dot(U)</p>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">[[</span> <span class="mf">0.</span>  <span class="mf">1.</span><span class="p">]</span>
-<span class="p">[</span> <span class="mf">1.</span>  <span class="mf">0.</span><span class="p">]]</span>
-<span class="p">[[</span> <span class="mf">1.</span>    <span class="mf">0.</span>  <span class="p">]</span>
-<span class="p">[</span> <span class="mf">0.25</span>  <span class="mf">1.</span>  <span class="p">]]</span>
-<span class="p">[[</span> <span class="mf">4.</span>    <span class="mf">5.</span>    <span class="mf">6.</span>  <span class="p">]</span>
-<span class="p">[</span> <span class="mf">0.</span>    <span class="mf">0.75</span>  <span class="mf">1.5</span> <span class="p">]]</span>
-<span class="p">[[</span> <span class="mf">1.</span>  <span class="mf">2.</span>  <span class="mf">3.</span><span class="p">]</span>
-<span class="p">[</span> <span class="mf">4.</span>  <span class="mf">5.</span>  <span class="mf">6.</span><span class="p">]]</span>
-</pre></div>
-</div>
-</section>
-<section id="cholesky">
-<h1>Cholesky 分解<a class="headerlink" href="#cholesky" title="Permalink to this heading">¶</a></h1>
-<p>Cholesky 分解是一种特殊的 LU 分解，此时要求 $mathbf A$ 为 Hermitian 正定矩阵 （$mathbf A = mathbf{A^H}$）。
-此时有： $$
-begin{eqnarray*} mathbf{A} &amp;amp; = &amp;amp; mathbf{U}^{H}mathbf{U}\ mathbf{A} &amp;amp; = &amp;amp; mathbf{L}mathbf{L}^{H}end{eqnarray*}
-$$ 即 $$
-mathbf{L}=mathbf{U}^{H}.
-$$</p>
-<p>可以用 linalg.cholesky 求解。</p>
-<p><strong>QR 分解</strong></p>
-<p>$M×N$ 矩阵 $mathbf A$ 的 QR 分解为： $$
-mathbf{A=QR}
-$$
-$mathbf R$ 为上三角形矩阵，$mathbf Q$ 是正交矩阵。</p>
-<p>维基链接： <a class="reference external" href="https://en.wikipedia.org/wiki/QR_decomposition">https://en.wikipedia.org/wiki/QR_decomposition</a></p>
-<p>可以用 linalg.qr 求解。</p>
-<p><strong>Schur 分解</strong></p>
-<p>对于 $Ntimes N$ 方阵 $mathbf A$, Schur 分解要求找到满足下式的矩阵： $$
-mathbf{A=ZTZ^H}
-$$
-其中 $mathbf Z$ 是正交矩阵，$mathbf T$ 是一个上三角矩阵。</p>
-<p>维基链接： <a class="reference external" href="https://en.wikipedia.org/wiki/Schur_decomposition">https://en.wikipedia.org/wiki/Schur_decomposition</a></p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">A</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mat</span><span class="p">(</span><span class="s1">&#39;[1 3 2; 1 4 5; 2 3 6]&#39;</span><span class="p">)</span>
-<span class="nb">print</span> <span class="n">A</span>
-<span class="n">T</span><span class="p">,</span> <span class="n">Z</span> <span class="o">=</span> <span class="n">linalg</span><span class="o">.</span><span class="n">schur</span><span class="p">(</span><span class="n">A</span><span class="p">)</span>
-<span class="nb">print</span> <span class="n">T</span><span class="p">,</span> <span class="n">Z</span>
-<span class="nb">print</span> <span class="n">Z</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">T</span><span class="p">)</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">Z</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">[[</span><span class="mi">1</span> <span class="mi">3</span> <span class="mi">2</span><span class="p">]</span>
-<span class="p">[</span><span class="mi">1</span> <span class="mi">4</span> <span class="mi">5</span><span class="p">]</span>
-<span class="p">[</span><span class="mi">2</span> <span class="mi">3</span> <span class="mi">6</span><span class="p">]]</span>
-<span class="p">[[</span> <span class="mf">9.90012467</span>  <span class="mf">1.78947961</span> <span class="o">-</span><span class="mf">0.65498528</span><span class="p">]</span>
-<span class="p">[</span> <span class="mf">0.</span>          <span class="mf">0.54993766</span> <span class="o">-</span><span class="mf">1.57754789</span><span class="p">]</span>
-<span class="p">[</span> <span class="mf">0.</span>          <span class="mf">0.51260928</span>  <span class="mf">0.54993766</span><span class="p">]]</span> <span class="p">[[</span> <span class="mf">0.36702395</span> <span class="o">-</span><span class="mf">0.85002495</span> <span class="o">-</span><span class="mf">0.37782404</span><span class="p">]</span>
-<span class="p">[</span> <span class="mf">0.63681656</span> <span class="o">-</span><span class="mf">0.06646488</span>  <span class="mf">0.76814522</span><span class="p">]</span>
-<span class="p">[</span> <span class="mf">0.67805463</span>  <span class="mf">0.52253231</span> <span class="o">-</span><span class="mf">0.51691576</span><span class="p">]]</span>
-<span class="p">[[</span> <span class="mf">1.</span>  <span class="mf">3.</span>  <span class="mf">2.</span><span class="p">]</span>
-<span class="p">[</span> <span class="mf">1.</span>  <span class="mf">4.</span>  <span class="mf">5.</span><span class="p">]</span>
-<span class="p">[</span> <span class="mf">2.</span>  <span class="mf">3.</span>  <span class="mf">6.</span><span class="p">]]</span>
-</pre></div>
-</div>
-</section>
-<section id="id13">
-<h1>矩阵函数<a class="headerlink" href="#id13" title="Permalink to this heading">¶</a></h1>
-<p>考虑函数 $f(x)$ 的泰勒展开： $$
-fleft(xright)=sum_{k=0}^{infty}frac{f^{left(kright)}left(0right)}{k!}x^{k}
-$$</p>
-<p>对于方阵，矩阵函数可以定义如下： $$
-fleft(mathbf{A}right)=sum_{k=0}^{infty}frac{f^{left(kright)}left(0right)}{k!}mathbf{A}^{k}
-$$</p>
-<p>这也是计算矩阵函数的最好的方式。</p>
-</section>
-<section id="id14">
-<h1>指数和对数函数<a class="headerlink" href="#id14" title="Permalink to this heading">¶</a></h1>
-</section>
-<section id="id15">
-<h1>指数<a class="headerlink" href="#id15" title="Permalink to this heading">¶</a></h1>
-<p>指数可以定义如下： $$
-e^{mathbf{A}}=sum_{k=0}^{infty}frac{1}{k!}mathbf{A}^{k}
-$$</p>
-<p>linalg.expm3 使用的是泰勒展开的方法计算结果：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">A</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]])</span>
-<span class="nb">print</span> <span class="n">linalg</span><span class="o">.</span><span class="n">expm3</span><span class="p">(</span><span class="n">A</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">[[</span>  <span class="mf">51.96890355</span>   <span class="mf">74.73648784</span><span class="p">]</span>
-<span class="p">[</span> <span class="mf">112.10473176</span>  <span class="mf">164.07363531</span><span class="p">]]</span>
-</pre></div>
-</div>
-<p>另一种方法先计算 A 的特征值分解：
-$$
-mathbf{A}=mathbf{V}boldsymbol{Lambda}mathbf{V}^{-1}
-$$</p>
-<p>然后有（正交矩阵和对角阵的性质）：
-$$
-e^{mathbf{A}}=mathbf{V}e^{boldsymbol{Lambda}}mathbf{V}^{-1}
-$$</p>
-<p>linalg.expm2 使用的就是这种方法：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span> <span class="n">linalg</span><span class="o">.</span><span class="n">expm2</span><span class="p">(</span><span class="n">A</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">[[</span>  <span class="mf">51.9689562</span>    <span class="mf">74.73656457</span><span class="p">]</span>
-<span class="p">[</span> <span class="mf">112.10484685</span>  <span class="mf">164.07380305</span><span class="p">]]</span>
-</pre></div>
-</div>
-<p>最优的方法是用 Padé 近似 实现，Padé 近似往往比截断的泰勒级数准确，而且当泰勒级数不收敛时，Padé 近似往往仍可行，所以多用于在计算机数学中。</p>
-<p>linalg.expm 使用的就是这种方法：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span> <span class="n">linalg</span><span class="o">.</span><span class="n">expm</span><span class="p">(</span><span class="n">A</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">[[</span>  <span class="mf">51.9689562</span>    <span class="mf">74.73656457</span><span class="p">]</span>
-<span class="p">[</span> <span class="mf">112.10484685</span>  <span class="mf">164.07380305</span><span class="p">]]</span>
-</pre></div>
-</div>
-</section>
-<section id="id16">
-<h1>对数<a class="headerlink" href="#id16" title="Permalink to this heading">¶</a></h1>
-<p>指数的逆运算，可以用 linalg.logm 实现：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span> <span class="n">A</span>
-<span class="nb">print</span> <span class="n">linalg</span><span class="o">.</span><span class="n">logm</span><span class="p">(</span><span class="n">linalg</span><span class="o">.</span><span class="n">expm</span><span class="p">(</span><span class="n">A</span><span class="p">))</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">[[</span><span class="mi">1</span> <span class="mi">2</span><span class="p">]</span>
-<span class="p">[</span><span class="mi">3</span> <span class="mi">4</span><span class="p">]]</span>
-<span class="p">[[</span> <span class="mf">1.</span>  <span class="mf">2.</span><span class="p">]</span>
-<span class="p">[</span> <span class="mf">3.</span>  <span class="mf">4.</span><span class="p">]]</span>
-</pre></div>
-</div>
-</section>
-<section id="id17">
-<h1>三角函数<a class="headerlink" href="#id17" title="Permalink to this heading">¶</a></h1>
-<p>根据欧拉公式，其定义为： $$
-begin{eqnarray*} sinleft(mathbf{A}right) &amp;amp; = &amp;amp; frac{e^{jmathbf{A}}-e^{-jmathbf{A}}}{2j}\ cosleft(mathbf{A}right) &amp;amp; = &amp;amp; frac{e^{jmathbf{A}}+e^{-jmathbf{A}}}{2}.end{eqnarray*}
-$$</p>
-<p>正切函数定义为： $$
-tanleft(xright)=frac{sinleft(xright)}{cosleft(xright)}=left[cosleft(xright)right]^{-1}sinleft(xright)
-$$</p>
-<p>因此矩阵的正切函数定义为： $$
-left[cosleft(mathbf{A}right)right]^{-1}sinleft(mathbf{A}right).
-$$</p>
-<p>具体实现：</p>
-<ul class="simple">
-<li><p>linalg.sinm</p></li>
-<li><p>linalg.cosm</p></li>
-<li><p>linalg.tanm</p></li>
-</ul>
-</section>
-<section id="id18">
-<h1>双曲三角函数<a class="headerlink" href="#id18" title="Permalink to this heading">¶</a></h1>
-<p>$$begin{eqnarray*} sinhleft(mathbf{A}right) &amp;amp; = &amp;amp; frac{e^{mathbf{A}}-e^{-mathbf{A}}}{2}\ coshleft(mathbf{A}right) &amp;amp; = &amp;amp; frac{e^{mathbf{A}}+e^{-mathbf{A}}}{2}\ tanhleft(mathbf{A}right) &amp;amp; = &amp;amp; left[coshleft(mathbf{A}right)right]^{-1}sinhleft(mathbf{A}right).end{eqnarray*}$$</p>
-<p>具体实现：</p>
-<ul class="simple">
-<li><p>linalg.sinhm</p></li>
-<li><p>linalg.coshm</p></li>
-<li><p>linalg.tanhm</p></li>
-</ul>
-</section>
-<section id="id19">
-<h1>特殊矩阵<a class="headerlink" href="#id19" title="Permalink to this heading">¶</a></h1>
-<p>Scipy 提供了一些特殊矩阵的实现，具体可以参考：</p>
-<p><a class="reference external" href="http://docs.scipy.org/doc/scipy/reference/tutorial/linalg.html#special-matrices">http://docs.scipy.org/doc/scipy/reference/tutorial/linalg.html#special-matrices</a></p>
-</section>
-<section id="id20">
-<h1>作者 &amp; 更新时间<a class="headerlink" href="#id20" title="Permalink to this heading">¶</a></h1>
-<p>作者:<cite>李金  &lt;lijinwithyou&#64;gmail.com&gt;</cite></p>
-<p>提交: 2017/12/14</p>
-</section>
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diff --git a/docs/aipy-scipy/cn/11sparse-matrix.html b/docs/aipy-scipy/cn/11sparse-matrix.html
deleted file mode 100644
index f2389ce..0000000
--- a/docs/aipy-scipy/cn/11sparse-matrix.html
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-<h1>稀疏矩阵的线性代数<a class="headerlink" href="#id1" title="Permalink to this heading">¶</a></h1>
-<p>对于稀疏矩阵来说，其线性代数操作可以使用 scipy.sparse.linalg 实现：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">scipy.sparse.linalg</span>
-</pre></div>
-</div>
-</section>
-<section id="id2">
-<h1>矩阵操作<a class="headerlink" href="#id2" title="Permalink to this heading">¶</a></h1>
-<ul class="simple">
-<li><dl class="simple">
-<dt>scipy.sparse.linalg.inv</dt><dd><ul>
-<li><p>稀疏矩阵求逆</p></li>
-</ul>
-</dd>
-</dl>
-</li>
-<li><dl class="simple">
-<dt>scipy.sparse.linalg.expm</dt><dd><ul>
-<li><p>求稀疏矩阵的指数函数</p></li>
-</ul>
-</dd>
-</dl>
-</li>
-</ul>
-</section>
-<section id="id3">
-<h1>矩阵范数<a class="headerlink" href="#id3" title="Permalink to this heading">¶</a></h1>
-<ul class="simple">
-<li><dl class="simple">
-<dt>scipy.sparse.linalg.norm</dt><dd><ul>
-<li><p>稀疏矩阵求范数</p></li>
-</ul>
-</dd>
-</dl>
-</li>
-</ul>
-</section>
-<section id="id4">
-<h1>线性方程组求解<a class="headerlink" href="#id4" title="Permalink to this heading">¶</a></h1>
-<p>提供了一系列求解方法： <a class="reference external" href="http://docs.scipy.org/doc/scipy/reference/sparse.linalg.html#solving">http://docs.scipy.org/doc/scipy/reference/sparse.linalg.html#solving</a>
--linear-problems</p>
-<p>主要使用的是迭代方法求解。</p>
-</section>
-<section id="id5">
-<h1>特征值分解和奇异值分解<a class="headerlink" href="#id5" title="Permalink to this heading">¶</a></h1>
-<p>对于特别大的矩阵，原来的方法可能需要太大的内存，考虑使用这两个方法替代：</p>
-<ul class="simple">
-<li><dl class="simple">
-<dt>scipy.sparse.linalg.eigs</dt><dd><ul>
-<li><p>返回前 k 大的特征值和特征向量</p></li>
-</ul>
-</dd>
-</dl>
-</li>
-<li><dl class="simple">
-<dt>scipy.sparse.linalg.svds</dt><dd><ul>
-<li><p>返回前 k 大的奇异值和奇异向量</p></li>
-</ul>
-</dd>
-</dl>
-</li>
-</ul>
-</section>
-<section id="id6">
-<h1>作者 &amp; 更新时间<a class="headerlink" href="#id6" title="Permalink to this heading">¶</a></h1>
-<p>作者:<cite>李金  &lt;lijinwithyou&#64;gmail.com&gt;</cite></p>
-<p>提交: 2017/12/14</p>
-</section>
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diff --git a/docs/aipy-scipy/cn/1synopsis.html b/docs/aipy-scipy/cn/1synopsis.html
deleted file mode 100644
index dc6e881..0000000
--- a/docs/aipy-scipy/cn/1synopsis.html
+++ /dev/null
@@ -1,398 +0,0 @@
-<!DOCTYPE html>
-<html lang="en-US">
-<head>
-<meta charset="utf-8" />
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-</div>
-<ul class="header-title collapse navbar-collapse" id="example-navbar-collapse" >
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-<a href="http://www.qpython.org/index.html">Home</a>
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-<a href="/index.html" class="header-selected">Course</a>
-<span class="header-title-selected"></span>
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-<a href="http://www.aipy.org">AIPY</a>
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-<a target="_blank" href="https://github.com/qpython-android/qpython">Github</a>
-</li>
-</ul>
-<div class="search-box hidden-xs">
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-<section id="scientific-python">
-<h1>SCIentific PYthon 简介<a class="headerlink" href="#scientific-python" title="Permalink to this heading">¶</a></h1>
-<p><strong>Ipython</strong> 提供了一个很好的解释器界面。</p>
-<p><strong>Matplotlib</strong> 提供了一个类似 <strong>Matlab</strong> 的画图工具。</p>
-<p><strong>Numpy</strong> 提供了 ndarray 对象，可以进行快速的向量化计算。</p>
-<p><strong>Scipy</strong> 是 <strong>Python</strong> 中进行科学计算的一个第三方库，以 <strong>Numpy</strong> 为基础。</p>
-<p><strong>Pandas</strong> 是处理时间序列数据的第三方库，提供一个类似 <strong>R</strong> 语言的环境。</p>
-<p><strong>StatsModels</strong> 是一个统计库，着重于统计模型。</p>
-<p><strong>Scikits</strong> 以 <strong>Scipy</strong> 为基础，提供如 <strong>scikits-learn</strong> 机器学习和**scikits-image 图像处理** 等高级用法。</p>
-<section id="scipy">
-<h2>Scipy<a class="headerlink" href="#scipy" title="Permalink to this heading">¶</a></h2>
-<p><strong>Scipy</strong> 由不同科学计算领域的子模块组成：</p>
-<p>在使用 <strong>Scipy</strong> 之前，为了方便，假定这些基础的模块已经被导入：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">scipy</span> <span class="k">as</span> <span class="nn">sp</span>
-<span class="kn">import</span> <span class="nn">matplotlib</span> <span class="k">as</span> <span class="nn">mpl</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-</pre></div>
-</div>
-<p>使用 <strong>Scipy</strong> 中的子模块时，需要分别导入：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">linalg</span><span class="p">,</span> <span class="n">optimize</span>
-</pre></div>
-</div>
-<p>对于一些常用的函数，这些在子模块中的函数可以在 <strong>scipy</strong> 命名空间中调用。另一方面，由于 <strong>Scipy</strong> 以 <strong>Numpy</strong> 为基础，因此很多基础的 Numpy 函数可以在scipy 命名空间中直接调用。</p>
-<p>我们可以使用 numpy 中的 info 函数来查看函数的文档：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">np</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="n">optimize</span><span class="o">.</span><span class="n">fmin</span><span class="p">)</span>
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span> fmin(func, x0, args=(), xtol=0.0001, ftol=0.0001, maxiter=None,     maxfun=None,
-    full_output=0, disp=1, retall=0, callback=None)
-Minimize a function using the downhill simplex algorithm.
-This algorithm only uses function values, not derivatives or second
-derivatives.
-Parameters
-func : callable func(x,*args)
-The objective function to be minimized.
-x0 : ndarray
-Initial guess.
-args : tuple, optional
-Extra arguments passed to func, i.e. ``f(x,*args)``.
-callback : callable, optional
-Called after each iteration, as callback(xk), where xk is the
-current parameter vector.
-xtol : float, optional
-Relative error in xopt acceptable for convergence.
-ftol : number, optional
-Relative error in func(xopt) acceptable for convergence.
-maxiter : int, optional
-Maximum number of iterations to perform.
-maxfun : number, optional
-Maximum number of function evaluations to make.
-full_output : bool, optional
-Set to True if fopt and warnflag outputs are desired.
-disp : bool, optional
-Set to True to print convergence messages.
-retall : bool, optional
-Set to True to return list of solutions at each iteration.
-Returns
-xopt : ndarray
-Parameter that minimizes function.
-fopt : float
-Value of function at minimum: ``fopt = func(xopt)``.
-iter : int
-Number of iterations performed.
-funcalls : int
-Number of function calls made.
-warnflag : int
-1 : Maximum number of function evaluations made.
-2 : Maximum number of iterations reached.
-allvecs : list
-Solution at each iteration.
-See also
-minimize: Interface to minimization algorithms for multivariate
-functions. See the &#39;Nelder-Mead&#39; `method` in particular.
-Notes
-Uses a Nelder-Mead simplex algorithm to find the minimum of     function of
-one or more variables.
-This algorithm has a long history of successful use in     applications.
-But it will usually be slower than an algorithm that uses first or
-second derivative information. In practice it can have poor
-performance in high-dimensional problems and is not robust to
-minimizing complicated functions. Additionally, there currently is     no
-complete theory describing when the algorithm will successfully
-converge to the minimum, or how fast it will if it does.
-References
-.. [1] Nelder, J.A. and Mead, R. (1965), &quot;A simplex method for     function
-minimization&quot;, The Computer Journal, 7, pp. 308-313
-.. [2] Wright, M.H. (1996), &quot;Direct Search Methods: Once Scorned,     Now
-Respectable&quot;, in Numerical Analysis 1995, Proceedings of the
-1995 Dundee Biennial Conference in Numerical Analysis, D.F.
-Griffiths and G.A. Watson (Eds.), Addison Wesley Longman,
-Harlow, UK, pp. 191-208.
-</pre></div>
-</div>
-<p>可以用 lookfor 来查询特定关键词相关的函数：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">np</span><span class="o">.</span><span class="n">lookfor</span><span class="p">(</span><span class="s2">&quot;resize array&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>Search results for ‘resize array’</p>
-<dl class="simple">
-<dt>numpy.chararray.resize</dt><dd><p>Change shape and size of array in-place.</p>
-</dd>
-<dt>numpy.ma.resize</dt><dd><p>Return a new masked array with the specified size and shape.</p>
-</dd>
-<dt>numpy.oldnumeric.ma.resize</dt><dd><p>The original array’s total size can be any size.</p>
-</dd>
-<dt>numpy.resize</dt><dd><p>Return a new array with the specified shape.</p>
-</dd>
-<dt>numpy.chararray</dt><dd><p>chararray(shape, itemsize=1, unicode=False, buffer=None, offset=0,</p>
-</dd>
-<dt>numpy.memmap</dt><dd><p>Create a memory-map to an array stored in a <em>binary</em> file on disk.</p>
-</dd>
-<dt>numpy.ma.mvoid.resize</dt><dd><p>warning</p>
-</dd>
-</dl>
-<p>还可以指定查找的模块：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">np</span><span class="o">.</span><span class="n">lookfor</span><span class="p">(</span><span class="s2">&quot;remove path&quot;</span><span class="p">,</span> <span class="n">module</span><span class="o">=</span><span class="s2">&quot;os&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>Search results for ‘remove path’</p>
-<dl class="simple">
-<dt>os.removedirs</dt><dd><p>removedirs(path)</p>
-</dd>
-<dt>os.walk</dt><dd><p>Directory tree generator.</p>
-</dd>
-</dl>
-</section>
-<section id="id1">
-<h2>作者 &amp; 更新时间<a class="headerlink" href="#id1" title="Permalink to this heading">¶</a></h2>
-<p>作者:<cite>李金  &lt;lijinwithyou&#64;gmail.com&gt;</cite></p>
-<p>提交: 2017/12/14</p>
-</section>
-</section>
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diff --git a/docs/aipy-scipy/cn/2interpolation.html b/docs/aipy-scipy/cn/2interpolation.html
deleted file mode 100644
index 2fdd848..0000000
--- a/docs/aipy-scipy/cn/2interpolation.html
+++ /dev/null
@@ -1,570 +0,0 @@
-<!DOCTYPE html>
-<html lang="en-US">
-<head>
-<meta charset="utf-8" />
-<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
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-<div class="header doc-header web-show">
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-<div class="search-box hidden-xs">
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-<div class="body" role="main">
-<section id="id1">
-<h1>插值<a class="headerlink" href="#id1" title="Permalink to this heading">¶</a></h1>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="o">%</span><span class="n">matplotlib</span> <span class="n">inline</span>
-</pre></div>
-</div>
-<p>设置 <strong>Numpy</strong> 浮点数显示格式：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">np</span><span class="o">.</span><span class="n">set_printoptions</span><span class="p">(</span><span class="n">precision</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">suppress</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>从文本中读入数据，数据来自 <a class="reference external" href="http://kinetics.nist.gov/janaf/html/C-067.txt">http://kinetics.nist.gov/janaf/html/C-067.txt</a> ，保存为结构体数组：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>data = np.genfromtxt(&quot;JANAF_CH4.txt&quot;,
-    delimiter=&quot;\t&quot;, # TAB 分隔
-    skiprows=1,     # 忽略首行
-    names=True,     # 读入属性
-    missing_values=&quot;INFINITE&quot;,  # 缺失值
-    filling_values=np.inf)      # 填充缺失值
-</pre></div>
-</div>
-<p>显示部分数据：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>for row in data[:7]:
-    print &quot;{}\t{}&quot;.format(row[&#39;TK&#39;], row[&#39;Cp&#39;])
-print &quot;...\t...&quot;
-</pre></div>
-</div>
-<p>0.0     0.0</p>
-<p>100.0   33.258</p>
-<p>200.0   33.473</p>
-<p>250.0   34.216</p>
-<p>298.15  35.639</p>
-<p>300.0   35.708</p>
-<p>350.0   37.874</p>
-<p>…     …</p>
-<p>绘图：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">p</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="s1">&#39;TK&#39;</span><span class="p">],</span> <span class="n">data</span><span class="p">[</span><span class="s1">&#39;Cp&#39;</span><span class="p">],</span> <span class="s1">&#39;kx&#39;</span><span class="p">)</span>
-<span class="n">t</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">&quot;JANAF data for Methane $CH_4$&quot;</span><span class="p">)</span>
-<span class="n">a</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">6000</span><span class="p">,</span> <span class="mi">30</span><span class="p">,</span> <span class="mi">120</span><span class="p">])</span>
-<span class="n">x</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">&quot;Temperature (K)&quot;</span><span class="p">)</span>
-<span class="n">y</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s2">&quot;$C_p$ ($\frac</span><span class="si">{kJ}</span><span class="s2">{kg K}$)&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-<img 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d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%0AZmaWKwcWMzPLlQOLmZnl6n8BKYzjzfpoiFsAAAAASUVORK5CYII=%0A" 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d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%0AZmaWKwcWMzPLlQOLmZnl6n8BKYzjzfpoiFsAAAAASUVORK5CYII=%0A" />
-<section id="id2">
-<h2>插值<a class="headerlink" href="#id2" title="Permalink to this heading">¶</a></h2>
-<p>假设我们要对这组数据进行插值。</p>
-<p>先导入一维插值函数 interp1d：</p>
-<p>interp1d(x, y)</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">scipy.interpolate</span> <span class="kn">import</span> <span class="n">interp1d</span>
-</pre></div>
-</div>
-<dl class="simple">
-<dt>::</dt><dd><p>ch4_cp = interp1d(data[‘TK’], data[‘Cp’])</p>
-</dd>
-</dl>
-<p>interp1d 的返回值可以像函数一样接受输入，并返回插值的结果。
-单个输入值，注意返回的是数组：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">ch4_cp</span><span class="p">(</span><span class="mf">382.2</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">array</span><span class="p">(</span><span class="mf">39.565144000000004</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>输入数组，返回的是对应的数组：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">ch4_cp</span><span class="p">([</span><span class="mf">32.2</span><span class="p">,</span><span class="mf">323.2</span><span class="p">])</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">array</span><span class="p">([</span> <span class="mf">10.71</span><span class="p">,</span>  <span class="mf">36.71</span><span class="p">])</span>
-</pre></div>
-</div>
-<p>默认情况下，输入值要在插值允许的范围内，否则插值会报错：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">ch4_cp</span><span class="p">(</span><span class="mi">8752</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>报错:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="ne">ValueError</span>                                <span class="n">Traceback</span> <span class="p">(</span><span class="n">most</span> <span class="n">recent</span> <span class="n">call</span> <span class="n">last</span><span class="p">)</span>
-<span class="o">&lt;</span><span class="n">ipython</span><span class="o">-</span><span class="nb">input</span><span class="o">-</span><span class="mi">10</span><span class="o">-</span><span class="mi">5</span><span class="n">d727af9aa33</span><span class="o">&gt;</span> <span class="ow">in</span> <span class="o">&lt;</span><span class="n">module</span><span class="o">&gt;</span><span class="p">()</span>
-<span class="o">----&gt;</span> <span class="mi">1</span> <span class="n">ch4_cp</span><span class="p">(</span><span class="mi">8752</span><span class="p">)</span>
-<span class="n">d</span><span class="p">:</span>\<span class="n">Miniconda</span>\<span class="n">lib</span>\<span class="n">site</span><span class="o">-</span><span class="n">packages</span>\<span class="n">scipy</span>\<span class="n">interpolate</span>\<span class="n">polyint</span><span class="o">.</span><span class="n">pyc</span> <span class="ow">in</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span>
-<span class="mi">77</span>         <span class="s2">&quot;&quot;&quot;</span>
-<span class="s2">     78         x, x_shape = self._prepare_x(x)</span>
-<span class="s2">---&gt; 79         y = self._evaluate(x)</span>
-<span class="s2">     80         return self._finish_y(y, x_shape)</span>
-<span class="s2">     81</span>
-<span class="s2">d:\Miniconda\lib\site-packages\scipy\interpolate\interpolate.pyc in _evaluate(self, x_new)</span>
-<span class="s2">    496         #    The behavior is set by the bounds_error variable.</span>
-<span class="s2">    497         x_new = asarray(x_new)</span>
-<span class="s2">--&gt; 498         out_of_bounds = self._check_bounds(x_new)</span>
-<span class="s2">    499         y_new = self._call(self, x_new)</span>
-<span class="s2">    500         if len(y_new) &gt; 0:</span>
-<span class="s2">d:\Miniconda\lib\site-packages\scipy\interpolate\interpolate.pyc in _check_bounds(self, x_new)</span>
-<span class="s2">    526                 &quot;range.&quot;)</span>
-<span class="s2">    527         if self.bounds_error and above_bounds.any():</span>
-<span class="s2">--&gt; 528             raise ValueError(&quot;A value in x_new is above the interpolation &quot;</span>
-<span class="s2">    529                 &quot;range.&quot;)</span>
-<span class="s2">    530</span>
-<span class="s2">ValueError: A value in x_new is above the interpolation range.</span>
-</pre></div>
-</div>
-<p>但我们可以通过参数设置允许超出范围的值存在：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>ch4_cp = interp1d(data[&#39;TK&#39;], data[&#39;Cp&#39;],
-    bounds_error=False)
-</pre></div>
-</div>
-<p>不过由于超出范围，所以插值的输出是非法值：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">ch4_cp</span><span class="p">(</span><span class="mi">8752</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">array</span><span class="p">(</span><span class="n">nan</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>可以使用指定值替代这些非法值：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>ch4_cp = interp1d(data[&#39;TK&#39;], data[&#39;Cp&#39;],
-    bounds_error=False, fill_value=-999.25)
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">ch4_cp</span><span class="p">(</span><span class="mi">8752</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">array</span><span class="p">(</span><span class="o">-</span><span class="mf">999.25</span><span class="p">)</span>
-</pre></div>
-</div>
-</section>
-<section id="id3">
-<h2>线性插值<a class="headerlink" href="#id3" title="Permalink to this heading">¶</a></h2>
-<p>interp1d 默认的插值方法是线性，关于线性插值的定义，请参见：</p>
-<ul class="simple">
-<li><p>维基百科-线性插值： <a class="reference external" href="https://zh.wikipedia.org/wiki/%E7%BA%BF%E6%80%A7%E6%8F%92%E5%80%BC">https://zh.wikipedia.org/wiki/%E7%BA%BF%E6%80%A7%E6%8F%92%E5%80%BC</a></p></li>
-<li><p>百度百科-线性插值： <a class="reference external" href="http://baike.baidu.com/view/4685624.htm">http://baike.baidu.com/view/4685624.htm</a></p></li>
-</ul>
-<p>其基本思想是，已知相邻两点 $x_1,x_2$ 对应的值 $y_1,y_2$ ，那么对于 $(x_1,x_2)$ 之间的某一点 $x$ ，线性插值对应的值 $y$ 满足：点 $(x,y)$ 在 $(x_1,y_1),(x_2,y_2)$ 所形成的线段上。</p>
-<p>应用线性插值：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">T</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span><span class="mi">355</span><span class="p">,</span><span class="mi">5</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="n">ch4_cp</span><span class="p">(</span><span class="n">T</span><span class="p">),</span> <span class="s2">&quot;+k&quot;</span><span class="p">)</span>
-<span class="n">p</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="s1">&#39;TK&#39;</span><span class="p">][</span><span class="mi">1</span><span class="p">:</span><span class="mi">7</span><span class="p">],</span> <span class="n">data</span><span class="p">[</span><span class="s1">&#39;Cp&#39;</span><span class="p">][</span><span class="mi">1</span><span class="p">:</span><span class="mi">7</span><span class="p">],</span> <span class="s1">&#39;ro&#39;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mi">8</span><span class="p">)</span>
-</pre></div>
-</div>
-<img alt="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAXUAAAEACAYAAABMEua6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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%0ASSViqEtSiRjqklQihroklcj/A8xgms4BB0nlAAAAAElFTkSuQmCC%0A" src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAXUAAAEACAYAAABMEua6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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%0ASSViqEtSiRjqklQihroklcj/A8xgms4BB0nlAAAAAElFTkSuQmCC%0A" />
-<p>其中红色的圆点为原来的数据点，黑色的十字点为对应的插值点，可以明显看到，相邻的数据点的插值在一条直线上。</p>
-</section>
-<section id="id4">
-<h2>多项式插值<a class="headerlink" href="#id4" title="Permalink to this heading">¶</a></h2>
-<p>我们可以通过 kind 参数来调节使用的插值方法，来得到不同的结果：</p>
-<ul class="simple">
-<li><p>nearest 最近邻插值</p></li>
-<li><p>zero 0阶插值</p></li>
-<li><p>linear 线性插值</p></li>
-<li><p>quadratic 二次插值</p></li>
-<li><p>cubic 三次插值</p></li>
-<li><p>4,5,6,7 更高阶插值</p></li>
-</ul>
-<p>最近邻插值：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">cp_ch4</span> <span class="o">=</span> <span class="n">interp1d</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="s1">&#39;TK&#39;</span><span class="p">],</span> <span class="n">data</span><span class="p">[</span><span class="s1">&#39;Cp&#39;</span><span class="p">],</span> <span class="n">kind</span><span class="o">=</span><span class="s2">&quot;nearest&quot;</span><span class="p">)</span>
-<span class="n">p</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="n">cp_ch4</span><span class="p">(</span><span class="n">T</span><span class="p">),</span> <span class="s2">&quot;k+&quot;</span><span class="p">)</span>
-<span class="n">p</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="s1">&#39;TK&#39;</span><span class="p">][</span><span class="mi">1</span><span class="p">:</span><span class="mi">7</span><span class="p">],</span> <span class="n">data</span><span class="p">[</span><span class="s1">&#39;Cp&#39;</span><span class="p">][</span><span class="mi">1</span><span class="p">:</span><span class="mi">7</span><span class="p">],</span> <span class="s1">&#39;ro&#39;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mi">8</span><span class="p">)</span>
-</pre></div>
-</div>
-<img 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e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%0AiKEuSQUx1CWpIIa6JBXk/wGLsxItFlRd2gAAAABJRU5ErkJggg==%0A" src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAXUAAAEACAYAAABMEua6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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%0AiKEuSQUx1CWpIIa6JBXk/wGLsxItFlRd2gAAAABJRU5ErkJggg==%0A" />
-<p>0阶插值：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">cp_ch4</span> <span class="o">=</span> <span class="n">interp1d</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="s1">&#39;TK&#39;</span><span class="p">],</span> <span class="n">data</span><span class="p">[</span><span class="s1">&#39;Cp&#39;</span><span class="p">],</span> <span class="n">kind</span><span class="o">=</span><span class="s2">&quot;zero&quot;</span><span class="p">)</span>
-<span class="n">p</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="n">cp_ch4</span><span class="p">(</span><span class="n">T</span><span class="p">),</span> <span class="s2">&quot;k+&quot;</span><span class="p">)</span>
-<span class="n">p</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="s1">&#39;TK&#39;</span><span class="p">][</span><span class="mi">1</span><span class="p">:</span><span class="mi">7</span><span class="p">],</span> <span class="n">data</span><span class="p">[</span><span class="s1">&#39;Cp&#39;</span><span class="p">][</span><span class="mi">1</span><span class="p">:</span><span class="mi">7</span><span class="p">],</span> <span class="s1">&#39;ro&#39;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mi">8</span><span class="p">)</span>
-</pre></div>
-</div>
-<img 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d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%0AxFCXpIIY6pJUEENdkgpiqEtSQf4fzDOJu6Be93AAAAAASUVORK5CYII=%0A" src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAXUAAAEACAYAAABMEua6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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%0AxFCXpIIY6pJUEENdkgpiqEtSQf4fzDOJu6Be93AAAAAASUVORK5CYII=%0A" />
-<p>二次插值：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">cp_ch4</span> <span class="o">=</span> <span class="n">interp1d</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="s1">&#39;TK&#39;</span><span class="p">],</span> <span class="n">data</span><span class="p">[</span><span class="s1">&#39;Cp&#39;</span><span class="p">],</span> <span class="n">kind</span><span class="o">=</span><span class="s2">&quot;quadratic&quot;</span><span class="p">)</span>
-<span class="n">p</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="n">cp_ch4</span><span class="p">(</span><span class="n">T</span><span class="p">),</span> <span class="s2">&quot;k+&quot;</span><span class="p">)</span>
-<span class="n">p</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="s1">&#39;TK&#39;</span><span class="p">][</span><span class="mi">1</span><span class="p">:</span><span class="mi">7</span><span class="p">],</span> <span class="n">data</span><span class="p">[</span><span class="s1">&#39;Cp&#39;</span><span class="p">][</span><span class="mi">1</span><span class="p">:</span><span class="mi">7</span><span class="p">],</span> <span class="s1">&#39;ro&#39;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mi">8</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>三次插值：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">cp_ch4</span> <span class="o">=</span> <span class="n">interp1d</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="s1">&#39;TK&#39;</span><span class="p">],</span> <span class="n">data</span><span class="p">[</span><span class="s1">&#39;Cp&#39;</span><span class="p">],</span> <span class="n">kind</span><span class="o">=</span><span class="s2">&quot;cubic&quot;</span><span class="p">)</span>
-<span class="n">p</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="n">cp_ch4</span><span class="p">(</span><span class="n">T</span><span class="p">),</span> <span class="s2">&quot;k+&quot;</span><span class="p">)</span>
-<span class="n">p</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="s1">&#39;TK&#39;</span><span class="p">][</span><span class="mi">1</span><span class="p">:</span><span class="mi">7</span><span class="p">],</span> <span class="n">data</span><span class="p">[</span><span class="s1">&#39;Cp&#39;</span><span class="p">][</span><span class="mi">1</span><span class="p">:</span><span class="mi">7</span><span class="p">],</span> <span class="s1">&#39;ro&#39;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mi">8</span><span class="p">)</span>
-</pre></div>
-</div>
-<img 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Y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%0AAElFTkSuQmCC%0A" 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Y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%0AAElFTkSuQmCC%0A" />
-<p>事实上，我们可以使用更高阶的多项式插值，只要将 kind 设为对应的数字即可：</p>
-<p>四次多项式插值：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">cp_ch4</span> <span class="o">=</span> <span class="n">interp1d</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="s1">&#39;TK&#39;</span><span class="p">],</span> <span class="n">data</span><span class="p">[</span><span class="s1">&#39;Cp&#39;</span><span class="p">],</span> <span class="n">kind</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>
-<span class="n">p</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="n">cp_ch4</span><span class="p">(</span><span class="n">T</span><span class="p">),</span> <span class="s2">&quot;k+&quot;</span><span class="p">)</span>
-<span class="n">p</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="s1">&#39;TK&#39;</span><span class="p">][</span><span class="mi">1</span><span class="p">:</span><span class="mi">7</span><span class="p">],</span> <span class="n">data</span><span class="p">[</span><span class="s1">&#39;Cp&#39;</span><span class="p">][</span><span class="mi">1</span><span class="p">:</span><span class="mi">7</span><span class="p">],</span> <span class="s1">&#39;ro&#39;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mi">8</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>可以参见：</p>
-<ul class="simple">
-<li><p>维基百科-多项式插值：<a class="reference external" href="https://zh.wikipedia.org/wiki/%E5%A4%9A%E9%A1%B9%E5%BC%8F%E6%8F%92%E5%80%BC">https://zh.wikipedia.org/wiki/%E5%A4%9A%E9%A1%B9%E5%BC%8F%E6%8F%92%E5%80%BC</a></p></li>
-<li><p>百度百科-插值法：<a class="reference external" href="http://baike.baidu.com/view/754506.htm">http://baike.baidu.com/view/754506.htm</a></p></li>
-</ul>
-<p>对于二维乃至更高维度的多项式插值：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">scipy.interpolate</span> <span class="kn">import</span> <span class="n">interp2d</span><span class="p">,</span> <span class="n">interpnd</span>
-</pre></div>
-</div>
-<p>其使用方法与一维类似。</p>
-</section>
-<section id="id5">
-<h2>径向基函数<a class="headerlink" href="#id5" title="Permalink to this heading">¶</a></h2>
-<p>关于径向基函数，可以参阅：</p>
-<ul class="simple">
-<li><p>维基百科-Radial basis fucntion：<a class="reference external" href="https://en.wikipedia.org/wiki/Radial_basis_function">https://en.wikipedia.org/wiki/Radial_basis_function</a></p></li>
-</ul>
-<p>径向基函数，简单来说就是点 $x$ 处的函数值只依赖于 $x$ 与某点 $c$ 的距离：</p>
-<p>$$Phi(x,c) = Phi(|x-c|)$$</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">100</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>常用的径向基（RBF）函数有：</p>
-<p>高斯函数：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span> <span class="o">*</span> <span class="n">x</span> <span class="o">**</span><span class="mi">2</span><span class="p">))</span>
-<span class="n">t</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">&quot;Gaussian&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-<img 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Z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%0AwKmQi4gUOBVyEZEC9/8BloffFHVXiTcAAAAASUVORK5CYII=%0A" 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Z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%0AwKmQi4gUOBVyEZEC9/8BloffFHVXiTcAAAAASUVORK5CYII=%0A" />
-<p>Multiquadric 函数：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">x</span> <span class="o">**</span><span class="mi">2</span><span class="p">))</span>
-<span class="n">t</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">&quot;Multiquadric&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-<img 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R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%0AJJwSuYhIwv0/gG56R7Cwt98AAAAASUVORK5CYII=%0A" 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R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%0AJJwSuYhIwv0/gG56R7Cwt98AAAAASUVORK5CYII=%0A" />
-<p>Inverse Multiquadric 函数：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mf">1.</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">x</span> <span class="o">**</span><span class="mi">2</span><span class="p">))</span>
-<span class="n">t</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">&quot;Inverse Multiquadric&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-<img 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u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%0A9/9saTRo/aYs3AAAAABJRU5ErkJggg==%0A" 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u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%0A9/9saTRo/aYs3AAAAABJRU5ErkJggg==%0A" />
-</section>
-<section id="id6">
-<h2>径向基函数插值<a class="headerlink" href="#id6" title="Permalink to this heading">¶</a></h2>
-<p>对于径向基函数，其插值的公式为：</p>
-<p>$$
-f(x) = sum_j n_j Phi(|x-x_j|)
-$$</p>
-<p>我们通过数据点 $x_j$ 来计算出 $n_j$ 的值，来计算 $x$ 处的插值结果。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">scipy.interpolate.rbf</span> <span class="kn">import</span> <span class="n">Rbf</span>
-</pre></div>
-</div>
-<p>使用 multiquadric 核的：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">cp_rbf</span> <span class="o">=</span> <span class="n">Rbf</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="s1">&#39;TK&#39;</span><span class="p">],</span> <span class="n">data</span><span class="p">[</span><span class="s1">&#39;Cp&#39;</span><span class="p">],</span> <span class="n">function</span> <span class="o">=</span> <span class="s2">&quot;multiquadric&quot;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="s1">&#39;TK&#39;</span><span class="p">],</span> <span class="n">data</span><span class="p">[</span><span class="s1">&#39;Cp&#39;</span><span class="p">],</span> <span class="s1">&#39;k+&#39;</span><span class="p">)</span>
-<span class="n">p</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="s1">&#39;TK&#39;</span><span class="p">],</span> <span class="n">cp_rbf</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="s1">&#39;TK&#39;</span><span class="p">]),</span> <span class="s1">&#39;r-&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-<img alt="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAX4AAAEACAYAAAC08h1NAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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%0A" 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E%0AQyFCEycePFaze5Hkoit3E9QLDz/MI+vWwZQp8Le/wfXXE+zXT59+JhKndOWuHNnu3dC/P73eeANO%0APhkWLoQbblBZR0QOUvAnCudY0K8fm5s2JXvoUNrs3k3QjOCzzx4s+6ikIyKgUk/cC4VCBOrX97ZX%0AWL8eXnkFunUjGAyqrCOSQFTqEc+2bVR/7DHo3t27+GrOHOjWze9eiUiM03LOeLVhA1x8MakHDnh1%0A/Pr1D3lYZR0RORKVeuLQtyNHckKvXnx/8smc/803ZGZmAl7YK/BFElMkSz0K/jgSCoUIHH889OgB%0At98Ojz+uWr5IklCNP0nNGznSu/K2Vy94/HG/uyMicUo1/nixaBG/efddGDjQC/4wlXZE5Fip1BPj%0AQhMmsOmVV7ho3Dju37mTn6meL5KUIlnq0Yw/li1ZQuDJJ2HzZpgwgZ999pnq+SJSbqrxx6CJY8dC%0A//7wi1/AFVfA9OnQqZPf3RKRBKEZf6yZNYs2v/61F/qzZ0PLlgcfUmlHRCKhzDV+M+sD3AoUAPOB%0AO4CawDCgFZAD3OCc21LsearxH8nSpXDuuYw45xyuGzFCG6uJyEG+r+M3swzgK+AU59wPZjYMGAO0%0ABTY45541sz8B9ZxzvYs9V8Ffgm9Gj+bnd97JlC5duOqzz3RRlogcIhaCPw2YAnQBtgOjgFeAV4Gu%0Azrk8M2sMhJxzPy/2XAV/cXv3wkUXwZlnwnPP6aIsETmM7xdwOec2Ac8DK4G1wBbn3Dgg3TmXFz4s%0AD0iPRCcTVSgUAufgnnsgLQ2efdbvLolIEijTyV0zOx74PZABbAU+NLNbix7jnHNmVuLUvuhsNplL%0AGaFQiMDXX0N2NkycCJW838PJ+u8hIj8KhUIHP0sj0spa6rkRuNA5d3f4/m14ZZ8LgG7OuVwzawJM%0AUKnnyD669lqunTkTpk6FJk387o6IxLBYqPF3AP4FdAL2AO8A0/FW82x0zg00s95AXZ3cPVThb/HW%0Ay5dz6fvvM6JXL/LT05P6Lx8R+Wm+B3+4E48BPfGWc84G7gaOA4YDLdFyziP75hu45hoGX3EFdwwe%0A7HdvRCQOxMSWDc65Z4HiZyM3AT3K1aNEN2MG/OpXMGQIKyZP9rs3IpKEdOVulIRCIQL16nlbMLz9%0ANlx4IYGqVf3ulogkIe3VEyULPvwQLrkEXnsNrrwS0OodEfGHtmWOhmXL2NqxI3Veew1uu83v3ohI%0AHIqJk7tlfsMkCv5QKMT0zz7j7rffps/mzTTRNgwiUkYK/nixZw907w6BAMGqVbUNg4iUme9bNkgp%0AFBTAHXdAixbe3voiIjFCq3oqSmYmrFgB48dDpUoq7YhIzFCpJ8JCoRCBnBxvlj91KjRs6HeXRCQB%0AxMQFXFKynMGD4fPPIRRS6ItITNKMP5KWL2dH+/bUGj0aLrjA796ISALRjD/GhEIhQhMmcOu//sWg%0AnTupMWkSTJqkZZsiEpMU/BEQCAQI5OdD9erU+stfeELLNkUkhmk5ZyRs2wYPPwz/+AcFlSv73RsR%0AkaPSjD8S+vaFSy+Fc84hsG+f370RETkqndwtr9mzvdDPzob69f3ujYgkKF25GyNC48dDr14wYIBC%0AX0TihoK/HHa+8AKkpkLPnn53RUSk1FTjL6vcXAKhEEybBpX0+1NE4oeC/xgVflj6tR99xJhdu9g9%0AYgSMGKE1+yISNxT8xygQCHh78Rw4wOg+fbTVsojEHdUojlVWFjz6KAwfzr6UFL97IyJyzDTjPxY7%0Ad8INN8Czz0K7dgQ2bPC7RyIix0zr+I/FHXd4H7DyzjtgEVlOKyJSKtqkzQ/vvOOt4JkxQ6EvInFN%0ANf5SmD548MG6PjVr+t0dEZFyUannp+zcSX7r1jQaONAr9YiI+EBbNkRTZia5jRvDb37jd09ERCJC%0AM/4jCIVCzBs5knveeouf7d5Nr8xMAF2oJSK+iOSMv8zBb2Z1gbeAtoAD7gCWAMOAVkAOcINzbkux%0A58VF8ANw/fXQoQPB/ft1oZaI+CpWSj0vA2Occ6cA7YFFQG9gnHPuJGB8+H58+vZbmDoVHnnE756I%0AiERUmYLfzOoA5znn/gngnNvvnNsKXAW8Gz7sXeCXEelltDkHf/gDPPUU1Kih0o6IJJSyzvhbA+vN%0AbLCZzTazN82sJpDunMsLH5MHpEekl9E2fDjs3Qu33gqg4BeRhFLWC7iqAB2B/3HOzTCzlyhW1nHO%0AOTMrsZhftF4ecydL9+yB3r1h8GBttywivincCbgilOnkrpk1BqY451qH758L9AF+BnRzzuWaWRNg%0AgnPu58WeG9Mnd5f16sXx69bBf/7jd1dERA7yfcuGcLCvMrOTnHOLgR5AVvirJzAw/P3jSHQyajZs%0AoMl773mfoysikqDKs5yzA95yzhRgGd5yzsrAcKAl8bic83e/Y/q0aZw1fbrfPREROURMrOMv8xvG%0AYPCHQiHmjRrFPW++Savdu3lAF2uJSIxR8FeEu++GJk0IVq6si7VEJObEygVciWP5chg1Ch5+2O+e%0AiIhUOAU/wNNPw/33Q1qaSjsikvBU6lm+HDp1giVLIC3N796IiJRIpZ5IKjLbFxFJBsk949dsX0Ti%0AhGb8kaLZvogkoaT9sPWpQ4bQZdQob7YvIpJEknbGn/Lcc5rti0hSSs4a//Ll7GrXjhqrVyv4RSQu%0A+L5JW7wq3Ob06v/8h0937+bAK68A2ppBRJJL8s34s7MhEGDAnXfSe8AA//ohInIMtKqnPB5/HB57%0AjD3VqvndExERXyRVqYepU2HmTBgyhMC0aX73RkTEF8lT6nEOunWD226Du+6K/vuLiJSDSj1l8cUX%0AkJcHPXv63RMREV8lR/AXFHgfoP7UU1AluapbIiLFJUfwDxsGqalwzTV+90RExHcJH/wTx42Dv/wF%0ABgwAi0h5TEQkriV88O946SU44QTvxK6IiCT4cs4dOzh/0iSYONHvnoiIxIyEDP7CrRm6TZjA6h07%0AWDJ6NIwera0ZRERI5HX8K1ZAx468cPvtPPLiixX/fiIiFUibtJVC3p13kv6737HN746IiMSYxAz+%0AyZOpNnOmV96ZMcPv3oiIxJTEK/U4B50781Hz5lw7cmTFvY+ISBRFstSTUMEfCoVY949/EJg4kWa5%0AuTyRmQlov30RiX8K/iNxDjp2hMxMgnPnEgwGK+Z9RESiLGY2aTOzymY2x8w+Cd9PM7NxZrbYzMaa%0AWd1IdLK05j/1lHfj6quj+bYiInGlvFfuPgRkA4VT+N7AOOfcScD48P3ocI4Gr78OmZlgptKOiMgR%0AlDn4zaw5cBnwFlD458dVwLvh2+8CvyxX747F6NHe9/BsX8EvIlKy8iznfBF4FKhdpC3dOZcXvp0H%0ApJfj9UslFAox8auvuHfQIO7Ly+O0fv0AndAVETmSMgW/mV0B5Dvn5phZoKRjnHPOzEo8i1v0pGt5%0AAzoQCBBYvBhOOonT7r1XJ3RFJCEUbj1TEcq0qsfMngZuA/YD1fBm/SOBTkDAOZdrZk2ACc65nxd7%0AbkRX9Xz96aecd/fdMGYMwdGjFfwikpB8X9XjnPuzc66Fc641cBPwlXPuNmA0UPjZhj2BjyPRyaOp%0A9MwzcPnl0LGjSjsiIqVQ7nX8ZtYV+INz7iozSwOGAy2BHOAG59yWYsdHbsa/dCm72renxvLl0Lhx%0AZF5TRCQGJf0FXIW1r5uHDmXw4sVU0xW6IpLgtDsnEDzzTBg6lJqPP05f1fVFREotLj968V9vvw0P%0APQSvvsqBKnH7u0tExBdxmZq/+PprOPNMuOgiAikpfndHRCSuxE3wv/TSS2zZsoX2333HnStW8OI1%0A17A1GFRNX0TkGMVN8M+dO5ezq1blV6NG8QDQsE4dv7skIhKXYib4Q6FQibP3wvZT69fnt8OGwciR%0AZL38MiGd0BURKZOYDv5QKMSAvn1ZmZHB+R98wPhu3fh63jyqVavmTydFRBJAzAQ/zsHatZCVBdnZ%0AkJVFYP58zp41ixTnGNejBxeOHUt3swrbv0JEJBn4egFX4YVY7RYsoPtHH5FSowb5jRqxtXlz1tSu%0ATX6jRjzwzjs8lplJKBQiqJO5IpKkEu/K3UceYfy8eXQfP/6w44PBIMFg8IjnAEREkoHvm7RFXF4e%0AW2vXPuohCn0RkciImeA//pxzSnxIgS8iElmxUepp1w6GDIH27aPaFxGReJGQpR7SK/xTGkVEhFiY%0A8e/bBzVqwJ49ULlyVPsiIhIvEmvGv3491K+v0BcRiRL/g19lHhGRqPI/+HNz9bGJIiJR5H/wa8Yv%0AIhJVsRH8mvGLiESN/8Gfm6sZv4hIFPkf/Cr1iIhEVWwEv0o9IiJR43/wq9QjIhJV/ge/Sj0iIlHl%0A75YN2q5BRKRUEmfLhvx8aNBAoS8iEkX+Br/KPCIiUVem4DezFmY2wcyyzGyBmT0Ybk8zs3FmttjM%0AxppZ3aO+kFb0iIhEXVln/PuAh51zbYEuwANmdgrQGxjnnDsJGB++f2Ra0SMiEnVlCn7nXK5zbm74%0A9g5gIdAMuAp4N3zYu8Avj/pCKvWIiERduWv8ZpYBnA5MA9Kdc3nhh/KAo6e6duYUEYm6cgW/mdUC%0APgIecs5tL/pYeM3m0deKasYvIhJ1Vcr6RDOrihf67zvnPg4355lZY+dcrpk1AfJLem4wGPRuTJ1K%0A4MwzCZS1EyIiCSoUChEKhSrktct0AZeZGV4Nf6Nz7uEi7c+G2waaWW+grnOud7Hn/ngBV5s2MHw4%0AtGtXjiGIiCS+SF7AVdbgPxeYBHzHj+WcPsB0YDjQEsgBbnDObSn23B+Dv359WLQIGjYsY/dFRJKD%0A78FfrjcsDP69e6FWLW+7hkr+bxkkIhLLEmPLhsLtGhT6IiJR5V/qakWPiIgv/A1+reEXEYk6/4Jf%0A2zWIiPhCpR4RkSSjUo+ISJJRqUdEJMloxi8ikmQ04xcRSTI6uSsikmT8Cf4ffoAdOyAtzZe3FxFJ%0AZv4Ef36+tzGbtmsQEYk6f5JXZR4REd/4F/xa0SMi4gt/gl8rekREfKNSj4hIklGpR0QkyajUIyKS%0AZFTqERFJMir1iIgkGZV6RESSjDnnovuGZs5VrQp79ujKXRGRUjIznHMWidfyJ3kbNVLoi4j4xJ/0%0AVZlHRMQ3/gS/TuyKiPhGM34RkSSj4BcRSTIq9YiIJJmIB7+ZXWJmi8xsiZn9qcSDNOMXEfFNRIPf%0AzCoDrwGXAG2Am83slMMOTODgD4VCfnehQml88S2Rx5fIY4u0SM/4zwKWOudynHP7gH8DVx92VAKX%0AehL9h0/ji2+JPL5EHlukRTr4mwGritxfHW47VALP+EVEYl2kg790+z/UqxfhtxURkdKK6F49ZtYF%0ACDrnLgnf7wMUOOcGFjkmupsDiYgkiEjt1RPp4K8CfA90B9YC04GbnXMLI/YmIiJSLlUi+WLOuf1m%0A9j/AF0Bl4G2FvohIbIn6tswiIuKvqF65W6qLu2KMmf3TzPLMbH6RtjQzG2dmi81srJnVLfJYn/D4%0AFpnZRUXazzCz+eHHXo72OI7EzFqY2QQzyzKzBWb2YLg9IcZoZtXMbJqZzTWzbDN7JtyeEOMD7/oZ%0AM5tjZp+E7yfS2HLM7Lvw+KaH2xJpfHXNbISZLQz/fHaOyvicc1H5wiv9LAUygKrAXOCUaL1/Ofp9%0AHnA6ML9I27PAY+HbfwIGhG+3CY+ranicS/nxr6rpwFnh22OAS/weW7gvjYHTwrdr4Z2jOSXBxlgj%0A/L0KMBU4N8HG9wjwL2B0Av58/h+QVqwtkcb3LnBnkZ/POtEYXzQH+Avg8yL3ewO9/f6HL2XfMzg0%0A+BcB6eHbjYFF4dt9gD8VOe5zoAvQBFhYpP0m4B9+j+sIY/0Y6JGIYwRqADOAtokyPqA58CXQDfgk%0A0X4+8YK/frG2hBgfXsgvL6G9wscXzVJP6S7uig/pzrm88O08oPCKtKZ44ypUOMbi7WuIwbGbWQbe%0AXzfTSKAxmlklM5uLN44JzrksEmd8LwKPAgVF2hJlbOBdG/Slmc00s3vCbYkyvtbAejMbbGazzexN%0AM6tJFMYXzeBPyLPIzvsVG/djM7NawEfAQ8657UUfi/cxOucKnHOn4c2OzzezbsUej8vxmdkVQL5z%0Abg5Q4vrueB1bEec4504HLgUeMLPzij4Y5+OrAnQE/u6c6wjsxKuEHFRR44tm8K8BWhS534JDf0vF%0AkzwzawxgZk2A/HB78TE2xxvjmvDtou1rotDPUjGzqnih/75z7uNwc0KNEcA5txX4DDiDxBjf2cBV%0AZvZ/wFAEftooAAABaUlEQVTgAjN7n8QYGwDOuXXh7+uBUXj7gSXK+FYDq51zM8L3R+D9Isit6PFF%0AM/hnAieaWYaZpQA3AqOj+P6RNBroGb7dE68uXth+k5mlmFlr4ERgunMuF9gWPmNvwG1FnuOrcH/e%0ABrKdcy8VeSghxmhmDQpXRZhZdeBCYA4JMD7n3J+dcy2cc63x6rpfOeduIwHGBmBmNczsuPDtmsBF%0AwHwSZHzhfq0ys5PCTT2ALOATKnp8UT6ZcSneqpGlQB+/T66Uss9D8a5C3ot3juIOIA3vhNpiYCxQ%0At8jxfw6PbxFwcZH2M/B+aJcCr/g9riL9OhevPjwXLxDn4G2rnRBjBE4FZofH9x3waLg9IcZXpG9d%0A+XFVT0KMDa8GPjf8taAwMxJlfOF+dcBbcDAPGIl3wrfCx6cLuEREkow/H70oIiK+UfCLiCQZBb+I%0ASJJR8IuIJBkFv4hIklHwi4gkGQW/iEiSUfCLiCSZ/wfUE99TkRdDtgAAAABJRU5ErkJggg==%0A" />
-<p>使用 gaussian 核：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">cp_rbf</span> <span class="o">=</span> <span class="n">Rbf</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="s1">&#39;TK&#39;</span><span class="p">],</span> <span class="n">data</span><span class="p">[</span><span class="s1">&#39;Cp&#39;</span><span class="p">],</span> <span class="n">function</span> <span class="o">=</span> <span class="s2">&quot;gaussian&quot;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="s1">&#39;TK&#39;</span><span class="p">],</span> <span class="n">data</span><span class="p">[</span><span class="s1">&#39;Cp&#39;</span><span class="p">],</span> <span class="s1">&#39;k+&#39;</span><span class="p">)</span>
-<span class="n">p</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="s1">&#39;TK&#39;</span><span class="p">],</span> <span class="n">cp_rbf</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="s1">&#39;TK&#39;</span><span class="p">]),</span> <span class="s1">&#39;r-&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-<img 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q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%0AwgAAAABJRU5ErkJggg==%0A" 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q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%0AwgAAAABJRU5ErkJggg==%0A" />
-<p>使用 nverse_multiquadric 核：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">cp_rbf</span> <span class="o">=</span> <span class="n">Rbf</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="s1">&#39;TK&#39;</span><span class="p">],</span> <span class="n">data</span><span class="p">[</span><span class="s1">&#39;Cp&#39;</span><span class="p">],</span> <span class="n">function</span> <span class="o">=</span>     <span class="s2">&quot;inverse_multiquadric&quot;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="s1">&#39;TK&#39;</span><span class="p">],</span> <span class="n">data</span><span class="p">[</span><span class="s1">&#39;Cp&#39;</span><span class="p">],</span> <span class="s1">&#39;k+&#39;</span><span class="p">)</span>
-<span class="n">p</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="s1">&#39;TK&#39;</span><span class="p">],</span> <span class="n">cp_rbf</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="s1">&#39;TK&#39;</span><span class="p">]),</span> <span class="s1">&#39;r-&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>不同的 RBF 核的结果也不同。</p>
-</section>
-<section id="rbf">
-<h2>高维 RBF 插值<a class="headerlink" href="#rbf" title="Permalink to this heading">¶</a></h2>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">mpl_toolkits.mplot3d</span> <span class="kn">import</span> <span class="n">Axes3D</span>
-</pre></div>
-</div>
-<p>三维数据点：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mgrid</span><span class="p">[</span><span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">/</span><span class="mi">2</span><span class="p">:</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">/</span><span class="mi">2</span><span class="p">:</span><span class="mi">5</span><span class="n">j</span><span class="p">,</span> <span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">/</span><span class="mi">2</span><span class="p">:</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">/</span><span class="mi">2</span><span class="p">:</span><span class="mi">5</span><span class="n">j</span><span class="p">]</span>
-<span class="n">z</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">x</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">y</span><span class="o">**</span><span class="mi">2</span><span class="p">))</span>
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span><span class="mi">6</span><span class="p">))</span>
-<span class="n">ax</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">gca</span><span class="p">(</span><span class="n">projection</span><span class="o">=</span><span class="s2">&quot;3d&quot;</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">,</span><span class="n">z</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="o">&lt;</span><span class="n">mpl_toolkits</span><span class="o">.</span><span class="n">mplot3d</span><span class="o">.</span><span class="n">art3d</span><span class="o">.</span><span class="n">Path3DCollection</span> <span class="n">at</span> <span class="mh">0x176b4da0</span><span class="o">&gt;</span>
-</pre></div>
-</div>
-<img alt="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqsAAAFdCAYAAAAkOCRoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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%0AIbFKEARBEARBqJb/D6nRM2cdX17QAAAAAElFTkSuQmCC%0A" 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u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%0AIbFKEARBEARBqJb/D6nRM2cdX17QAAAAAElFTkSuQmCC%0A" />
-<p>3维 RBF 插值：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">zz</span> <span class="o">=</span> <span class="n">Rbf</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">z</span><span class="p">)</span>
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">xx</span><span class="p">,</span> <span class="n">yy</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mgrid</span><span class="p">[</span><span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">/</span><span class="mi">2</span><span class="p">:</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">/</span><span class="mi">2</span><span class="p">:</span><span class="mi">50</span><span class="n">j</span><span class="p">,</span> <span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">/</span><span class="mi">2</span><span class="p">:</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">/</span><span class="mi">2</span><span class="p">:</span><span class="mi">50</span><span class="n">j</span><span class="p">]</span>
-<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span><span class="mi">6</span><span class="p">))</span>
-<span class="n">ax</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">gca</span><span class="p">(</span><span class="n">projection</span><span class="o">=</span><span class="s2">&quot;3d&quot;</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">plot_surface</span><span class="p">(</span><span class="n">xx</span><span class="p">,</span><span class="n">yy</span><span class="p">,</span><span class="n">zz</span><span class="p">(</span><span class="n">xx</span><span class="p">,</span><span class="n">yy</span><span class="p">),</span><span class="n">rstride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">cstride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">jet</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="o">&lt;</span><span class="n">mpl_toolkits</span><span class="o">.</span><span class="n">mplot3d</span><span class="o">.</span><span class="n">art3d</span><span class="o">.</span><span class="n">Poly3DCollection</span> <span class="n">at</span> <span class="mh">0x176e5c50</span><span class="o">&gt;</span>
-</pre></div>
-</div>
-<img 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0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%0ABAKBafl/CyJH4aoVOLIAAAAASUVORK5CYII=%0A" 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0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%0ABAKBafl/CyJH4aoVOLIAAAAASUVORK5CYII=%0A" />
-</section>
-<section id="id7">
-<h2>作者 &amp; 更新时间<a class="headerlink" href="#id7" title="Permalink to this heading">¶</a></h2>
-<p>作者:<cite>李金  &lt;lijinwithyou&#64;gmail.com&gt;</cite></p>
-<p>提交: 2017/12/14</p>
-</section>
-</section>
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-<div id="praise"><span>0</span> persons have rewarded</div>
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-<option value="Reward this course">Reward this course $ 1.49 USD</option>
-<option value="Reward this course">Reward this course $ 1.99 USD</option>
-<option value="Reward this course">Reward this course $ 2.99 USD</option>
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diff --git a/docs/aipy-scipy/cn/3csv.html b/docs/aipy-scipy/cn/3csv.html
deleted file mode 100644
index ff84f4a..0000000
--- a/docs/aipy-scipy/cn/3csv.html
+++ /dev/null
@@ -1,423 +0,0 @@
-<!DOCTYPE html>
-<html lang="en-US">
-<head>
-<meta charset="utf-8" />
-<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
-<link href="https://cdn.bootcss.com/bootstrap/3.3.7/css/bootstrap.min.css" rel="stylesheet">
-<title>CSV 文件和 csv 模块</title>
-<link rel="stylesheet" type="text/css" href="../../static/pygments.css" />
-<link rel="stylesheet" type="text/css" href="../../static/style.css" />
-<meta charset="UTF-8">
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-<a href="http://www.aipy.org">AIPY</a>
-</li>
-<li>
-<a target="_blank" href="https://github.com/qpython-android/qpython">Github</a>
-</li>
-</ul>
-<div class="search-box hidden-xs">
-<form action="/search.html">
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-<main class="contain web-content">
-<div class="document">
-<div class="documentwrapper">
-<div class="bodywrapper">
-<div class="body" role="main">
-<section id="csv-csv">
-<h1>CSV 文件和 csv 模块<a class="headerlink" href="#csv-csv" title="Permalink to this heading">¶</a></h1>
-<p>标准库中有自带的 csv (逗号分隔值) 模块处理 csv 格式的文件：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">csv</span>
-</pre></div>
-</div>
-<section id="csv">
-<h2>读 csv 文件<a class="headerlink" href="#csv" title="Permalink to this heading">¶</a></h2>
-<p>假设我们有这样的一个文件：</p>
-<p>%% file data.csv</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="s2">&quot;alpha 1&quot;</span><span class="p">,</span>  <span class="mi">100</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.443</span>
-<span class="s2">&quot;beat  3&quot;</span><span class="p">,</span>   <span class="mi">12</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.0934</span>
-<span class="s2">&quot;gamma 3a&quot;</span><span class="p">,</span> <span class="mi">192</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.6621</span>
-<span class="s2">&quot;delta 2a&quot;</span><span class="p">,</span>  <span class="mi">15</span><span class="p">,</span> <span class="o">-</span><span class="mf">4.515</span>
-</pre></div>
-</div>
-<p>打开这个文件，并产生一个文件 reader：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">fp</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="s2">&quot;data.csv&quot;</span><span class="p">)</span>
-<span class="n">r</span> <span class="o">=</span> <span class="n">csv</span><span class="o">.</span><span class="n">reader</span><span class="p">(</span><span class="n">fp</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>可以按行迭代数据：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>for row in r:
-    print row
-fp.close()
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">[</span><span class="s1">&#39;alpha 1&#39;</span><span class="p">,</span> <span class="s1">&#39;  100&#39;</span><span class="p">,</span> <span class="s1">&#39; -1.443&#39;</span><span class="p">]</span>
-<span class="p">[</span><span class="s1">&#39;beat  3&#39;</span><span class="p">,</span> <span class="s1">&#39;   12&#39;</span><span class="p">,</span> <span class="s1">&#39; -0.0934&#39;</span><span class="p">]</span>
-<span class="p">[</span><span class="s1">&#39;gamma 3a&#39;</span><span class="p">,</span> <span class="s1">&#39; 192&#39;</span><span class="p">,</span> <span class="s1">&#39; -0.6621&#39;</span><span class="p">]</span>
-<span class="p">[</span><span class="s1">&#39;delta 2a&#39;</span><span class="p">,</span> <span class="s1">&#39;  15&#39;</span><span class="p">,</span> <span class="s1">&#39; -4.515&#39;</span><span class="p">]</span>
-</pre></div>
-</div>
-<p>默认数据内容都被当作字符串处理，不过可以自己进行处理：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>data = []
-with open(&#39;data.csv&#39;) as fp:
-    r = csv.reader(fp)
-    for row in r:
-data.append([row[0], int(row[1]), float(row[2])])
-data
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">[[</span><span class="s1">&#39;alpha 1&#39;</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.443</span><span class="p">],</span>
-<span class="p">[</span><span class="s1">&#39;beat  3&#39;</span><span class="p">,</span> <span class="mi">12</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.0934</span><span class="p">],</span>
-<span class="p">[</span><span class="s1">&#39;gamma 3a&#39;</span><span class="p">,</span> <span class="mi">192</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.6621</span><span class="p">],</span>
-<span class="p">[</span><span class="s1">&#39;delta 2a&#39;</span><span class="p">,</span> <span class="mi">15</span><span class="p">,</span> <span class="o">-</span><span class="mf">4.515</span><span class="p">]]</span>
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
-<span class="n">os</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="s1">&#39;data.csv&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-</section>
-<section id="id1">
-<h2>写 csv 文件<a class="headerlink" href="#id1" title="Permalink to this heading">¶</a></h2>
-<p>可以使用 <em>csv.writer</em> 写入文件，不过相应地，传入的应该是以写方式打开的文件，不过一般要用 <em>‘wb’</em> 即二进制写入方式，防止出现换行不正确的问题：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="p">[(</span><span class="s1">&#39;one&#39;</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mf">1.5</span><span class="p">),</span> <span class="p">(</span><span class="s1">&#39;two&#39;</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mf">8.0</span><span class="p">)]</span>
-<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s1">&#39;out.csv&#39;</span><span class="p">,</span> <span class="s1">&#39;wb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
-<span class="n">w</span> <span class="o">=</span> <span class="n">csv</span><span class="o">.</span><span class="n">writer</span><span class="p">(</span><span class="n">fp</span><span class="p">)</span>
-<span class="n">w</span><span class="o">.</span><span class="n">writerows</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>显示结果：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>!cat &#39;out.csv&#39;
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">one</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mf">1.5</span>
-<span class="n">two</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mf">8.0</span>
-</pre></div>
-</div>
-</section>
-<section id="id2">
-<h2>更换分隔符<a class="headerlink" href="#id2" title="Permalink to this heading">¶</a></h2>
-<p>默认情况下，<em>csv</em> 模块默认 <em>csv</em> 文件都是由 <em>excel</em> 产生的，实际中可能会遇到这样的问题：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="p">[(</span><span class="s1">&#39;one, </span><span class="se">\&quot;</span><span class="s1">real</span><span class="se">\&quot;</span><span class="s1"> string&#39;</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mf">1.5</span><span class="p">),</span> <span class="p">(</span><span class="s1">&#39;two&#39;</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mf">8.0</span><span class="p">)]</span>
-<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s1">&#39;out.csv&#39;</span><span class="p">,</span> <span class="s1">&#39;wb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
-<span class="n">w</span> <span class="o">=</span> <span class="n">csv</span><span class="o">.</span><span class="n">writer</span><span class="p">(</span><span class="n">fp</span><span class="p">)</span>
-<span class="n">w</span><span class="o">.</span><span class="n">writerows</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>!cat &#39;out.csv&#39;
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="s2">&quot;one, &quot;&quot;real&quot;&quot; string&quot;</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mf">1.5</span>
-<span class="n">two</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mf">8.0</span>
-</pre></div>
-</div>
-<p>可以修改分隔符来处理这组数据：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="p">[(</span><span class="s1">&#39;one, </span><span class="se">\&quot;</span><span class="s1">real</span><span class="se">\&quot;</span><span class="s1"> string&#39;</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mf">1.5</span><span class="p">),</span> <span class="p">(</span><span class="s1">&#39;two&#39;</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mf">8.0</span><span class="p">)]</span>
-<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s1">&#39;out.psv&#39;</span><span class="p">,</span> <span class="s1">&#39;wb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
-<span class="n">w</span> <span class="o">=</span> <span class="n">csv</span><span class="o">.</span><span class="n">writer</span><span class="p">(</span><span class="n">fp</span><span class="p">,</span> <span class="n">delimiter</span><span class="o">=</span><span class="s2">&quot;|&quot;</span><span class="p">)</span>
-<span class="n">w</span><span class="o">.</span><span class="n">writerows</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>!cat &#39;out.psv&#39;
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="s2">&quot;one, &quot;&quot;real&quot;&quot; string&quot;</span> <span class="o">|</span> <span class="mi">1</span> <span class="o">|</span> <span class="mf">1.5</span>
-<span class="n">two</span><span class="o">|</span><span class="mi">2</span><span class="o">|</span><span class="mf">8.0</span>
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
-<span class="n">os</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="s1">&#39;out.psv&#39;</span><span class="p">)</span>
-<span class="n">os</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="s1">&#39;out.csv&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-</section>
-<section id="id3">
-<h2>其他选项<a class="headerlink" href="#id3" title="Permalink to this heading">¶</a></h2>
-<p><em>numpy.loadtxt()</em> 和 <em>pandas.read_csv()</em> 可以用来读写包含很多数值数据的 <em>csv</em> 文件：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="o">%%</span><span class="n">file</span> <span class="n">trades</span><span class="o">.</span><span class="n">csv</span>
-<span class="n">Order</span><span class="p">,</span><span class="n">Date</span><span class="p">,</span><span class="n">Stock</span><span class="p">,</span><span class="n">Quantity</span><span class="p">,</span><span class="n">Price</span>
-<span class="n">A0001</span><span class="p">,</span><span class="mi">2013</span><span class="o">-</span><span class="mi">12</span><span class="o">-</span><span class="mi">01</span><span class="p">,</span><span class="n">AAPL</span><span class="p">,</span><span class="mi">1000</span><span class="p">,</span><span class="mf">203.4</span>
-<span class="n">A0002</span><span class="p">,</span><span class="mi">2013</span><span class="o">-</span><span class="mi">12</span><span class="o">-</span><span class="mi">01</span><span class="p">,</span><span class="n">MSFT</span><span class="p">,</span><span class="mi">1500</span><span class="p">,</span><span class="mf">167.5</span>
-<span class="n">A0003</span><span class="p">,</span><span class="mi">2013</span><span class="o">-</span><span class="mi">12</span><span class="o">-</span><span class="mi">02</span><span class="p">,</span><span class="n">GOOG</span><span class="p">,</span><span class="mi">1500</span><span class="p">,</span><span class="mf">167.5</span>
-</pre></div>
-</div>
-<p>Writing trades.csv</p>
-<p>使用 pandas 进行处理，生成一个 DataFrame 对象：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span>
-<span class="n">df</span> <span class="o">=</span> <span class="n">pandas</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">&#39;trades.csv&#39;</span><span class="p">,</span> <span class="n">index_col</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-<span class="nb">print</span> <span class="n">df</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Order</span>  <span class="n">Date</span> <span class="n">Stock</span>  <span class="n">Quantity</span>  <span class="n">Price</span>
-<span class="n">A0001</span>  <span class="mi">2013</span><span class="o">-</span><span class="mi">12</span><span class="o">-</span><span class="mi">01</span>  <span class="n">AAPL</span>      <span class="mi">1000</span>  <span class="mf">203.4</span>
-<span class="n">A0002</span>  <span class="mi">2013</span><span class="o">-</span><span class="mi">12</span><span class="o">-</span><span class="mi">01</span>  <span class="n">MSFT</span>      <span class="mi">1500</span>  <span class="mf">167.5</span>
-<span class="n">A0003</span>  <span class="mi">2013</span><span class="o">-</span><span class="mi">12</span><span class="o">-</span><span class="mi">02</span>  <span class="n">GOOG</span>      <span class="mi">1500</span>  <span class="mf">167.5</span>
-</pre></div>
-</div>
-<p>通过名字进行索引：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;Quantity&#39;</span><span class="p">]</span> <span class="o">*</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;Price&#39;</span><span class="p">]</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Order</span>
-<span class="n">A0001</span>    <span class="mi">203400</span>
-<span class="n">A0002</span>    <span class="mi">251250</span>
-<span class="n">A0003</span>    <span class="mi">251250</span>
-<span class="n">dtype</span><span class="p">:</span> <span class="n">float64</span>
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
-<span class="n">os</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="s1">&#39;trades.csv&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-</section>
-<section id="id4">
-<h2>作者 &amp; 更新时间<a class="headerlink" href="#id4" title="Permalink to this heading">¶</a></h2>
-<p>作者:李金  <a class="reference external" href="mailto:lijinwithyou&#37;&#52;&#48;gmail&#46;com">lijinwithyou<span>&#64;</span>gmail<span>&#46;</span>com</a></p>
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diff --git a/docs/aipy-scipy/cn/4odd.html b/docs/aipy-scipy/cn/4odd.html
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-<section id="id1">
-<h1>概率统计方法<a class="headerlink" href="#id1" title="Permalink to this heading">¶</a></h1>
-<section id="id2">
-<h2>简介<a class="headerlink" href="#id2" title="Permalink to this heading">¶</a></h2>
-<p><strong>Python</strong> 中常用的统计工具有 <strong>Numpy</strong>, <strong>Pandas</strong>, <strong>PyMC</strong>, <strong>StatsModels</strong> 等。</p>
-<p><strong>Scipy</strong> 中的子库 scipy.stats 中包含很多统计上的方法。</p>
-<p>导入 numpy 和 matplotlib：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="o">%</span><span class="n">pylab</span> <span class="n">inline</span>
-</pre></div>
-</div>
-<p>Populating the interactive namespace from numpy and matplotlib</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">heights</span> <span class="o">=</span> <span class="n">array</span><span class="p">([</span><span class="mf">1.46</span><span class="p">,</span> <span class="mf">1.79</span><span class="p">,</span> <span class="mf">2.01</span><span class="p">,</span> <span class="mf">1.75</span><span class="p">,</span> <span class="mf">1.56</span><span class="p">,</span> <span class="mf">1.69</span><span class="p">,</span> <span class="mf">1.88</span><span class="p">,</span> <span class="mf">1.76</span><span class="p">,</span> <span class="mf">1.88</span><span class="p">,</span> <span class="mf">1.78</span><span class="p">])</span>
-</pre></div>
-</div>
-<p>Numpy 自带简单的统计方法：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span> <span class="s1">&#39;mean, &#39;</span><span class="p">,</span> <span class="n">heights</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
-<span class="nb">print</span> <span class="s1">&#39;min, &#39;</span><span class="p">,</span> <span class="n">heights</span><span class="o">.</span><span class="n">min</span><span class="p">()</span>
-<span class="nb">print</span> <span class="s1">&#39;max, &#39;</span><span class="p">,</span> <span class="n">heights</span><span class="o">.</span><span class="n">max</span><span class="p">()</span>
-<span class="nb">print</span> <span class="s1">&#39;standard deviation, &#39;</span><span class="p">,</span> <span class="n">heights</span><span class="o">.</span><span class="n">std</span><span class="p">()</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">mean</span><span class="p">,</span>  <span class="mf">1.756</span>
-<span class="nb">min</span><span class="p">,</span>  <span class="mf">1.46</span>
-<span class="nb">max</span><span class="p">,</span>  <span class="mf">2.01</span>
-<span class="n">standard</span> <span class="n">deviation</span><span class="p">,</span>  <span class="mf">0.150811140172</span>
-</pre></div>
-</div>
-<p>导入 <strong>Scipy</strong> 的统计模块：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">scipy.stats.stats</span> <span class="k">as</span> <span class="nn">st</span>
-</pre></div>
-</div>
-<p>其他统计量：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span> <span class="s1">&#39;median, &#39;</span><span class="p">,</span> <span class="n">st</span><span class="o">.</span><span class="n">nanmedian</span><span class="p">(</span><span class="n">heights</span><span class="p">)</span>    <span class="c1"># 忽略nan值之后的中位数</span>
-<span class="nb">print</span> <span class="s1">&#39;mode, &#39;</span><span class="p">,</span> <span class="n">st</span><span class="o">.</span><span class="n">mode</span><span class="p">(</span><span class="n">heights</span><span class="p">)</span>           <span class="c1"># 众数及其出现次数</span>
-<span class="nb">print</span> <span class="s1">&#39;skewness, &#39;</span><span class="p">,</span> <span class="n">st</span><span class="o">.</span><span class="n">skew</span><span class="p">(</span><span class="n">heights</span><span class="p">)</span>       <span class="c1"># 偏度</span>
-<span class="nb">print</span> <span class="s1">&#39;kurtosis, &#39;</span><span class="p">,</span> <span class="n">st</span><span class="o">.</span><span class="n">kurtosis</span><span class="p">(</span><span class="n">heights</span><span class="p">)</span>   <span class="c1"># 峰度</span>
-<span class="nb">print</span> <span class="s1">&#39;and so many more...&#39;</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">median</span><span class="p">,</span>  <span class="mf">1.77</span>
-<span class="n">mode</span><span class="p">,</span>  <span class="p">(</span><span class="n">array</span><span class="p">([</span> <span class="mf">1.88</span><span class="p">]),</span> <span class="n">array</span><span class="p">([</span> <span class="mf">2.</span><span class="p">]))</span>
-<span class="n">skewness</span><span class="p">,</span>  <span class="o">-</span><span class="mf">0.393524456473</span>
-<span class="n">kurtosis</span><span class="p">,</span>  <span class="o">-</span><span class="mf">0.330672097724</span>
-<span class="ow">and</span> <span class="n">so</span> <span class="n">many</span> <span class="n">more</span><span class="o">...</span>
-</pre></div>
-</div>
-</section>
-<section id="id3">
-<h2>概率分布<a class="headerlink" href="#id3" title="Permalink to this heading">¶</a></h2>
-<p>常见的连续概率分布有：</p>
-<ul class="simple">
-<li><p>均匀分布</p></li>
-<li><p>正态分布</p></li>
-<li><p>学生t分布</p></li>
-<li><p>F分布</p></li>
-<li><p>Gamma分布</p></li>
-<li><p>…</p></li>
-</ul>
-<p>离散概率分布：</p>
-<ul class="simple">
-<li><p>伯努利分布</p></li>
-<li><p>几何分布</p></li>
-<li><p>…</p></li>
-</ul>
-<p>这些都可以在 scipy.stats 中找到。</p>
-</section>
-</section>
-<section id="id4">
-<h1>连续分布<a class="headerlink" href="#id4" title="Permalink to this heading">¶</a></h1>
-<section id="id5">
-<h2>正态分布<a class="headerlink" href="#id5" title="Permalink to this heading">¶</a></h2>
-<p>以正态分布为例，先导入正态分布：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">scipy.stats</span> <span class="kn">import</span> <span class="n">norm</span>
-</pre></div>
-</div>
-<p>它包含四类常用的函数：</p>
-<ul class="simple">
-<li><p>norm.cdf 返回对应的累计分布函数值</p></li>
-<li><p>norm.pdf 返回对应的概率密度函数值</p></li>
-<li><p>norm.rvs 产生指定参数的随机变量</p></li>
-<li><p>norm.fit 返回给定数据下，各参数的最大似然估计（MLE）值</p></li>
-</ul>
-<p>从正态分布产生500个随机点：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">x_norm</span> <span class="o">=</span> <span class="n">norm</span><span class="o">.</span><span class="n">rvs</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="mi">500</span><span class="p">)</span>
-<span class="nb">type</span><span class="p">(</span><span class="n">x_norm</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span>
-</pre></div>
-</div>
-<p>直方图：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">h</span> <span class="o">=</span> <span class="n">hist</span><span class="p">(</span><span class="n">x_norm</span><span class="p">)</span>
-<span class="nb">print</span> <span class="s1">&#39;counts, &#39;</span><span class="p">,</span> <span class="n">h</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-<span class="nb">print</span> <span class="s1">&#39;bin centers&#39;</span><span class="p">,</span> <span class="n">h</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">counts</span><span class="p">,</span>  <span class="p">[</span>   <span class="mf">7.</span>   <span class="mf">21.</span>   <span class="mf">42.</span>   <span class="mf">97.</span>  <span class="mf">120.</span>   <span class="mf">91.</span>   <span class="mf">64.</span>   <span class="mf">38.</span>   <span class="mf">17.</span>    <span class="mf">3.</span><span class="p">]</span>
-<span class="nb">bin</span> <span class="n">centers</span> <span class="p">[</span><span class="o">-</span><span class="mf">2.68067801</span> <span class="o">-</span><span class="mf">2.13266147</span> <span class="o">-</span><span class="mf">1.58464494</span> <span class="o">-</span><span class="mf">1.0366284</span>  <span class="o">-</span><span class="mf">0.48861186</span>  <span class="mf">0.05940467</span>
-<span class="mf">0.60742121</span>  <span class="mf">1.15543774</span>  <span class="mf">1.70345428</span>  <span class="mf">2.25147082</span>  <span class="mf">2.79948735</span><span class="p">]</span>
-</pre></div>
-</div>
-<img 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P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%0AkhphoEtSI/4P1qxllG6H6EYAAAAASUVORK5CYII=%0A" 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P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%0AkhphoEtSI/4P1qxllG6H6EYAAAAASUVORK5CYII=%0A" />
-<p>归一化直方图（用出现频率代替次数），将划分区间变为 20（默认 10）：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">h</span> <span class="o">=</span> <span class="n">hist</span><span class="p">(</span><span class="n">x_norm</span><span class="p">,</span> <span class="n">normed</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
-</pre></div>
-</div>
-<img 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B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%0AAAAASUVORK5CYII=%0A" 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B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%0AAAAASUVORK5CYII=%0A" />
-<p>在这组数据下，正态分布参数的最大似然估计值为：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">x_mean</span><span class="p">,</span> <span class="n">x_std</span> <span class="o">=</span> <span class="n">norm</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_norm</span><span class="p">)</span>
-<span class="nb">print</span> <span class="s1">&#39;mean, &#39;</span><span class="p">,</span> <span class="n">x_mean</span>
-<span class="nb">print</span> <span class="s1">&#39;x_std, &#39;</span><span class="p">,</span> <span class="n">x_std</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">mean</span><span class="p">,</span>  <span class="o">-</span><span class="mf">0.0426135499965</span>
-<span class="n">x_std</span><span class="p">,</span>  <span class="mf">0.950754110144</span>
-</pre></div>
-</div>
-<p>将真实的概率密度函数与直方图进行比较：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">h</span> <span class="o">=</span> <span class="n">hist</span><span class="p">(</span><span class="n">x_norm</span><span class="p">,</span> <span class="n">normed</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
-<span class="n">x</span> <span class="o">=</span> <span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">50</span><span class="p">)</span>
-<span class="n">p</span> <span class="o">=</span> <span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">norm</span><span class="o">.</span><span class="n">pdf</span><span class="p">(</span><span class="n">x</span><span class="p">),</span> <span class="s1">&#39;r-&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-<img 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Q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%0AKZGLiETc/wfoqVYxTpVDEwAAAABJRU5ErkJggg==%0A" 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Q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%0AKZGLiETc/wfoqVYxTpVDEwAAAABJRU5ErkJggg==%0A" />
-<p>导入积分函数：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">scipy.integrate</span> <span class="kn">import</span> <span class="n">trapz</span>
-</pre></div>
-</div>
-<p>通过积分，计算落在某个区间的概率大小：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">x1</span> <span class="o">=</span> <span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">108</span><span class="p">)</span>
-<span class="n">p</span> <span class="o">=</span> <span class="n">trapz</span><span class="p">(</span><span class="n">norm</span><span class="o">.</span><span class="n">pdf</span><span class="p">(</span><span class="n">x1</span><span class="p">),</span> <span class="n">x1</span><span class="p">)</span>
-<span class="nb">print</span> <span class="s1">&#39;</span><span class="si">{:.2%}</span><span class="s1"> of the values lie between -2 and 2&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
-<span class="n">fill_between</span><span class="p">(</span><span class="n">x1</span><span class="p">,</span> <span class="n">norm</span><span class="o">.</span><span class="n">pdf</span><span class="p">(</span><span class="n">x1</span><span class="p">),</span> <span class="n">color</span> <span class="o">=</span> <span class="s1">&#39;red&#39;</span><span class="p">)</span>
-<span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">norm</span><span class="o">.</span><span class="n">pdf</span><span class="p">(</span><span class="n">x</span><span class="p">),</span> <span class="s1">&#39;k-&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="mf">95.45</span><span class="o">%</span> <span class="n">of</span> <span class="n">the</span> <span class="n">values</span> <span class="n">lie</span> <span class="n">between</span> <span class="o">-</span><span class="mi">2</span> <span class="ow">and</span> <span class="mi">2</span>
-</pre></div>
-</div>
-<p>[&lt;matplotlib.lines.Line2D at 0x15cbb8d0&gt;]</p>
-<img 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1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%0AFXhjjIlSVuCNMSZK/T8+DStxCxquAQAAAABJRU5ErkJggg==%0A" 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1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%0AFXhjjIlSVuCNMSZK/T8+DStxCxquAQAAAABJRU5ErkJggg==%0A" />
-<p>默认情况，正态分布的参数为均值0，标准差1，即标准正态分布。</p>
-<p>可以通过 loc 和 scale 来调整这些参数，一种方法是调用相关函数时进行输入：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">p</span> <span class="o">=</span> <span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">norm</span><span class="o">.</span><span class="n">pdf</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span>
-<span class="n">p</span> <span class="o">=</span> <span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">norm</span><span class="o">.</span><span class="n">pdf</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mi">2</span><span class="p">))</span>
-<span class="n">p</span> <span class="o">=</span> <span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">norm</span><span class="o">.</span><span class="n">pdf</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">loc</span><span class="o">=-</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">.5</span><span class="p">))</span>
-</pre></div>
-</div>
-<img alt="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAXIAAAEACAYAAACuzv3DAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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%0AAAAAAElFTkSuQmCC%0A" 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8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%0AAAAAAElFTkSuQmCC%0A" />
-<p>另一种则是将 loc, scale 作为参数直接输给 norm 生成相应的分布：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">p</span> <span class="o">=</span> <span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">norm</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">pdf</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
-<span class="n">p</span> <span class="o">=</span> <span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">norm</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">pdf</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
-<span class="n">p</span> <span class="o">=</span> <span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">norm</span><span class="p">(</span><span class="n">loc</span><span class="o">=-</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">.5</span><span class="p">)</span><span class="o">.</span><span class="n">pdf</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
-</pre></div>
-</div>
-<img alt="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAXIAAAEACAYAAACuzv3DAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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%0AAAAAAElFTkSuQmCC%0A" 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8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%0AAAAAAElFTkSuQmCC%0A" />
-</section>
-<section id="id6">
-<h2>其他连续分布<a class="headerlink" href="#id6" title="Permalink to this heading">¶</a></h2>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">scipy.stats</span> <span class="kn">import</span> <span class="n">lognorm</span><span class="p">,</span> <span class="n">t</span><span class="p">,</span> <span class="n">dweibull</span>
-</pre></div>
-</div>
-<p>支持与 norm 类似的操作，如概率密度函数等。</p>
-<p>不同参数的对数正态分布：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">linspace</span><span class="p">(</span><span class="mf">0.01</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
-<span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">lognorm</span><span class="o">.</span><span class="n">pdf</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;s=1&#39;</span><span class="p">)</span>
-<span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">lognorm</span><span class="o">.</span><span class="n">pdf</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;s=2&#39;</span><span class="p">)</span>
-<span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">lognorm</span><span class="o">.</span><span class="n">pdf</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mf">.1</span><span class="p">),</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;s=0.1&#39;</span><span class="p">)</span>
-<span class="n">legend</span><span class="p">()</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="o">&lt;</span><span class="n">matplotlib</span><span class="o">.</span><span class="n">legend</span><span class="o">.</span><span class="n">Legend</span> <span class="n">at</span> <span class="mh">0x15781c88</span><span class="o">&gt;</span>
-</pre></div>
-</div>
-<p>不同的韦氏分布：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">linspace</span><span class="p">(</span><span class="mf">0.01</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
-<span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">dweibull</span><span class="o">.</span><span class="n">pdf</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;s=1, constant failure rate&#39;</span><span class="p">)</span>
-<span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">dweibull</span><span class="o">.</span><span class="n">pdf</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;s&gt;1, increasing failure rate&#39;</span><span class="p">)</span>
-<span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">dweibull</span><span class="o">.</span><span class="n">pdf</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mf">.1</span><span class="p">),</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;0&lt;s&lt;1, decreasing failure rate&#39;</span><span class="p">)</span>
-<span class="n">legend</span><span class="p">()</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="o">&lt;</span><span class="n">matplotlib</span><span class="o">.</span><span class="n">legend</span><span class="o">.</span><span class="n">Legend</span> <span class="n">at</span> <span class="mh">0xaa9bc50</span><span class="o">&gt;</span>
-</pre></div>
-</div>
-<img 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U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%0AAAAAAElFTkSuQmCC%0A" 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U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%0AAAAAAElFTkSuQmCC%0A" />
-<p>不同自由度的学生 t 分布：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
-<span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">t</span><span class="o">.</span><span class="n">pdf</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;df=1&#39;</span><span class="p">)</span>
-<span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">t</span><span class="o">.</span><span class="n">pdf</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;df=2&#39;</span><span class="p">)</span>
-<span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">t</span><span class="o">.</span><span class="n">pdf</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">100</span><span class="p">),</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;df=100&#39;</span><span class="p">)</span>
-<span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">[::</span><span class="mi">5</span><span class="p">],</span> <span class="n">norm</span><span class="o">.</span><span class="n">pdf</span><span class="p">(</span><span class="n">x</span><span class="p">[::</span><span class="mi">5</span><span class="p">]),</span> <span class="s1">&#39;kx&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;normal&#39;</span><span class="p">)</span>
-<span class="n">legend</span><span class="p">()</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="o">&lt;</span><span class="n">matplotlib</span><span class="o">.</span><span class="n">legend</span><span class="o">.</span><span class="n">Legend</span> <span class="n">at</span> <span class="mh">0x164582e8</span><span class="o">&gt;</span>
-</pre></div>
-</div>
-</section>
-<section id="id7">
-<h2>离散分布<a class="headerlink" href="#id7" title="Permalink to this heading">¶</a></h2>
-<p>导入离散分布：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">scipy.stats</span> <span class="kn">import</span> <span class="n">binom</span><span class="p">,</span> <span class="n">poisson</span><span class="p">,</span> <span class="n">randint</span>
-</pre></div>
-</div>
-<p>离散分布没有概率密度函数，但是有概率质量函数。</p>
-<p>离散均匀分布的概率质量函数（PMF）：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">high</span> <span class="o">=</span> <span class="mi">10</span>
-<span class="n">low</span> <span class="o">=</span> <span class="o">-</span><span class="mi">10</span>
-<span class="n">x</span> <span class="o">=</span> <span class="n">arange</span><span class="p">(</span><span class="n">low</span><span class="p">,</span> <span class="n">high</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">)</span>
-<span class="n">p</span> <span class="o">=</span> <span class="n">stem</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">randint</span><span class="p">(</span><span class="n">low</span><span class="p">,</span> <span class="n">high</span><span class="p">)</span><span class="o">.</span><span class="n">pmf</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>  <span class="c1"># 杆状图</span>
-</pre></div>
-</div>
-<p>二项分布：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">num_trials</span> <span class="o">=</span> <span class="mi">60</span>
-<span class="n">x</span> <span class="o">=</span> <span class="n">arange</span><span class="p">(</span><span class="n">num_trials</span><span class="p">)</span>
-<span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">binom</span><span class="p">(</span><span class="n">num_trials</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">)</span><span class="o">.</span><span class="n">pmf</span><span class="p">(</span><span class="n">x</span><span class="p">),</span> <span class="s1">&#39;o-&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;p=0.5&#39;</span><span class="p">)</span>
-<span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">binom</span><span class="p">(</span><span class="n">num_trials</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">)</span><span class="o">.</span><span class="n">pmf</span><span class="p">(</span><span class="n">x</span><span class="p">),</span> <span class="s1">&#39;o-&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;p=0.2&#39;</span><span class="p">)</span>
-<span class="n">legend</span><span class="p">()</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="o">&lt;</span><span class="n">matplotlib</span><span class="o">.</span><span class="n">legend</span><span class="o">.</span><span class="n">Legend</span> <span class="n">at</span> <span class="mh">0x1738a198</span><span class="o">&gt;</span>
-</pre></div>
-</div>
-<img 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b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%0AaLJXSqk48P8B4O96y1bc/nYAAAAASUVORK5CYII=%0A" 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b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%0AaLJXSqk48P8B4O96y1bc/nYAAAAASUVORK5CYII=%0A" />
-<p>泊松分布：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">21</span><span class="p">)</span>
-<span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">poisson</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">pmf</span><span class="p">(</span><span class="n">x</span><span class="p">),</span> <span class="s1">&#39;o-&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">r</span><span class="s1">&#39;$\lambda$=1&#39;</span><span class="p">)</span>
-<span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">poisson</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span><span class="o">.</span><span class="n">pmf</span><span class="p">(</span><span class="n">x</span><span class="p">),</span> <span class="s1">&#39;o-&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">r</span><span class="s1">&#39;$\lambda$=4&#39;</span><span class="p">)</span>
-<span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">poisson</span><span class="p">(</span><span class="mi">9</span><span class="p">)</span><span class="o">.</span><span class="n">pmf</span><span class="p">(</span><span class="n">x</span><span class="p">),</span> <span class="s1">&#39;o-&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">r</span><span class="s1">&#39;$\lambda$=9&#39;</span><span class="p">)</span>
-<span class="n">legend</span><span class="p">()</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="o">&lt;</span><span class="n">matplotlib</span><span class="o">.</span><span class="n">legend</span><span class="o">.</span><span class="n">Legend</span> <span class="n">at</span> <span class="mh">0x1763e320</span><span class="o">&gt;</span>
-</pre></div>
-</div>
-<img 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K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%0Agg==%0A" 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K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%0Agg==%0A" />
-</section>
-<section id="id8">
-<h2>自定义离散分布<a class="headerlink" href="#id8" title="Permalink to this heading">¶</a></h2>
-<p>导入要用的函数：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">scipy.stats</span> <span class="kn">import</span> <span class="n">rv_discrete</span>
-</pre></div>
-</div>
-<p>一个不均匀的骰子对应的离散值及其概率：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">xk</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">]</span>
-<span class="n">pk</span> <span class="o">=</span> <span class="p">[</span><span class="mf">.3</span><span class="p">,</span> <span class="mf">.35</span><span class="p">,</span> <span class="mf">.25</span><span class="p">,</span> <span class="mf">.05</span><span class="p">,</span> <span class="mf">.025</span><span class="p">,</span> <span class="mf">.025</span><span class="p">]</span>
-</pre></div>
-</div>
-<p>定义离散分布：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">loaded</span> <span class="o">=</span> <span class="n">rv_discrete</span><span class="p">(</span><span class="n">values</span><span class="o">=</span><span class="p">(</span><span class="n">xk</span><span class="p">,</span> <span class="n">pk</span><span class="p">))</span>
-</pre></div>
-</div>
-<p>此时， loaded 可以当作一个离散分布的模块来使用。</p>
-<p>产生两个服从该分布的随机变量：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">loaded</span><span class="o">.</span><span class="n">rvs</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">array</span><span class="p">([</span><span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
-</pre></div>
-</div>
-<p>产生100个随机变量，将直方图与概率质量函数进行比较：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">samples</span> <span class="o">=</span> <span class="n">loaded</span><span class="o">.</span><span class="n">rvs</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="mi">100</span><span class="p">)</span>
-<span class="n">bins</span> <span class="o">=</span> <span class="n">linspace</span><span class="p">(</span><span class="mf">.5</span><span class="p">,</span><span class="mf">6.5</span><span class="p">,</span><span class="mi">7</span><span class="p">)</span>
-<span class="n">hist</span><span class="p">(</span><span class="n">samples</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="n">bins</span><span class="p">,</span> <span class="n">normed</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="n">stem</span><span class="p">(</span><span class="n">xk</span><span class="p">,</span> <span class="n">loaded</span><span class="o">.</span><span class="n">pmf</span><span class="p">(</span><span class="n">xk</span><span class="p">),</span> <span class="n">markerfmt</span><span class="o">=</span><span class="s1">&#39;ro&#39;</span><span class="p">,</span> <span class="n">linefmt</span><span class="o">=</span><span class="s1">&#39;r-&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>&lt;Container object of 3 artists&gt;</p>
-</section>
-<section id="id9">
-<h2>假设检验<a class="headerlink" href="#id9" title="Permalink to this heading">¶</a></h2>
-<p>导入相关的函数：</p>
-<ul class="simple">
-<li><p>正态分布</p></li>
-<li><p>独立双样本 t 检验，配对样本 t 检验，单样本 t 检验</p></li>
-<li><p>学生 t 分布</p></li>
-</ul>
-<p>t 检验的相关内容请参考：</p>
-<ul class="simple">
-<li><p>百度百科-t 检验：<a class="reference external" href="http://baike.baidu.com/view/557340.htm">http://baike.baidu.com/view/557340.htm</a></p></li>
-<li><p>维基百科-学生 t 检验：<a class="reference external" href="https://en.wikipedia.org/wiki/Student%27s_t-test">https://en.wikipedia.org/wiki/Student%27s_t-test</a></p></li>
-</ul>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">scipy.stats</span> <span class="kn">import</span> <span class="n">norm</span>
-<span class="kn">from</span> <span class="nn">scipy.stats</span> <span class="kn">import</span> <span class="n">ttest_ind</span><span class="p">,</span> <span class="n">ttest_rel</span><span class="p">,</span> <span class="n">ttest_1samp</span>
-<span class="kn">from</span> <span class="nn">scipy.stats</span> <span class="kn">import</span> <span class="n">t</span>
-</pre></div>
-</div>
-</section>
-<section id="t">
-<h2>独立样本 t 检验<a class="headerlink" href="#t" title="Permalink to this heading">¶</a></h2>
-<p>两组参数不同的正态分布：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">n1</span> <span class="o">=</span> <span class="n">norm</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">1.0</span><span class="p">)</span>
-<span class="n">n2</span> <span class="o">=</span> <span class="n">norm</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">1.0</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>从分布中产生两组随机样本：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">n1_samples</span> <span class="o">=</span> <span class="n">n1</span><span class="o">.</span><span class="n">rvs</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="mi">100</span><span class="p">)</span>
-<span class="n">n2_samples</span> <span class="o">=</span> <span class="n">n2</span><span class="o">.</span><span class="n">rvs</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="mi">100</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>将两组样本混合在一起：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">samples</span> <span class="o">=</span> <span class="n">hstack</span><span class="p">((</span><span class="n">n1_samples</span><span class="p">,</span> <span class="n">n2_samples</span><span class="p">))</span>
-</pre></div>
-</div>
-<p>最大似然参数估计：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">loc</span><span class="p">,</span> <span class="n">scale</span> <span class="o">=</span> <span class="n">norm</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">samples</span><span class="p">)</span>
-<span class="n">n</span> <span class="o">=</span> <span class="n">norm</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="n">loc</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="n">scale</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>比较：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">100</span><span class="p">)</span>
-<span class="n">hist</span><span class="p">([</span><span class="n">samples</span><span class="p">,</span> <span class="n">n1_samples</span><span class="p">,</span> <span class="n">n2_samples</span><span class="p">],</span> <span class="n">normed</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">n</span><span class="o">.</span><span class="n">pdf</span><span class="p">(</span><span class="n">x</span><span class="p">),</span> <span class="s1">&#39;b-&#39;</span><span class="p">)</span>
-<span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">n1</span><span class="o">.</span><span class="n">pdf</span><span class="p">(</span><span class="n">x</span><span class="p">),</span> <span class="s1">&#39;g-&#39;</span><span class="p">)</span>
-<span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">n2</span><span class="o">.</span><span class="n">pdf</span><span class="p">(</span><span class="n">x</span><span class="p">),</span> <span class="s1">&#39;r-&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>[&lt;matplotlib.lines.Line2D at 0x17ca7278&gt;]</p>
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x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%0AIYRwcpLIhRDCyUkiF0IIJ/d/XdaGnd6OVMIAAAAASUVORK5CYII=%0A" 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x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%0AIYRwcpLIhRDCyUkiF0IIJ/d/XdaGnd6OVMIAAAAASUVORK5CYII=%0A" />
-<p>独立双样本 t 检验的目的在于判断两组样本之间是否有显著差异：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">t_val</span><span class="p">,</span> <span class="n">p</span> <span class="o">=</span> <span class="n">ttest_ind</span><span class="p">(</span><span class="n">n1_samples</span><span class="p">,</span> <span class="n">n2_samples</span><span class="p">)</span>
-<span class="nb">print</span> <span class="s1">&#39;t = </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">t_val</span><span class="p">)</span>
-<span class="nb">print</span> <span class="s1">&#39;p-value = </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">t</span> <span class="o">=</span> <span class="mf">0.868384594123</span>
-<span class="n">p</span><span class="o">-</span><span class="n">value</span> <span class="o">=</span> <span class="mf">0.386235148899</span>
-</pre></div>
-</div>
-<p>p 值小，说明这两个样本有显著性差异。</p>
-</section>
-<section id="id10">
-<h2>配对样本 t 检验<a class="headerlink" href="#id10" title="Permalink to this heading">¶</a></h2>
-<p>配对样本指的是两组样本之间的元素一一对应，例如，假设我们有一组病人的数据：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pop_size</span> <span class="o">=</span> <span class="mi">35</span>
-<span class="n">pre_treat</span> <span class="o">=</span> <span class="n">norm</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-<span class="n">n0</span> <span class="o">=</span> <span class="n">pre_treat</span><span class="o">.</span><span class="n">rvs</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="n">pop_size</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>经过某种治疗后，对这组病人得到一组新的数据：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">effect</span> <span class="o">=</span> <span class="n">norm</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="mf">0.05</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">0.2</span><span class="p">)</span>
-<span class="n">eff</span> <span class="o">=</span> <span class="n">effect</span><span class="o">.</span><span class="n">rvs</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="n">pop_size</span><span class="p">)</span>
-<span class="n">n1</span> <span class="o">=</span> <span class="n">n0</span> <span class="o">+</span> <span class="n">eff</span>
-</pre></div>
-</div>
-<p>新数据的最大似然估计：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">loc</span><span class="p">,</span> <span class="n">scale</span> <span class="o">=</span> <span class="n">norm</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">n1</span><span class="p">)</span>
-<span class="n">post_treat</span> <span class="o">=</span> <span class="n">norm</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="n">loc</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="n">scale</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>画图：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span> <span class="o">=</span> <span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="mi">4</span><span class="p">))</span>
-<span class="n">ax1</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span>
-<span class="n">h</span> <span class="o">=</span> <span class="n">ax1</span><span class="o">.</span><span class="n">hist</span><span class="p">([</span><span class="n">n0</span><span class="p">,</span> <span class="n">n1</span><span class="p">],</span> <span class="n">normed</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="n">p</span> <span class="o">=</span> <span class="n">ax1</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">pre_treat</span><span class="o">.</span><span class="n">pdf</span><span class="p">(</span><span class="n">x</span><span class="p">),</span> <span class="s1">&#39;b-&#39;</span><span class="p">)</span>
-<span class="n">p</span> <span class="o">=</span> <span class="n">ax1</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">post_treat</span><span class="o">.</span><span class="n">pdf</span><span class="p">(</span><span class="n">x</span><span class="p">),</span> <span class="s1">&#39;g-&#39;</span><span class="p">)</span>
-<span class="n">ax2</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">)</span>
-<span class="n">h</span> <span class="o">=</span> <span class="n">ax2</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">eff</span><span class="p">,</span> <span class="n">normed</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-</pre></div>
-</div>
-<img 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1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%0A" 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1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%0A" 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1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%0A" 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1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%0A" />
-<p>独立 t 检验：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">t_val</span><span class="p">,</span> <span class="n">p</span> <span class="o">=</span> <span class="n">ttest_ind</span><span class="p">(</span><span class="n">n0</span><span class="p">,</span> <span class="n">n1</span><span class="p">)</span>
-<span class="nb">print</span> <span class="s1">&#39;t = </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">t_val</span><span class="p">)</span>
-<span class="nb">print</span> <span class="s1">&#39;p-value = </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">t</span> <span class="o">=</span> <span class="o">-</span><span class="mf">0.347904839913</span>
-<span class="n">p</span><span class="o">-</span><span class="n">value</span> <span class="o">=</span> <span class="mf">0.728986322039</span>
-</pre></div>
-</div>
-<p>高 p 值说明两组样本之间没有显著性差异。</p>
-<p>配对 t 检验：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">t_val</span><span class="p">,</span> <span class="n">p</span> <span class="o">=</span> <span class="n">ttest_rel</span><span class="p">(</span><span class="n">n0</span><span class="p">,</span> <span class="n">n1</span><span class="p">)</span>
-<span class="nb">print</span> <span class="s1">&#39;t = </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">t_val</span><span class="p">)</span>
-<span class="nb">print</span> <span class="s1">&#39;p-value = </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">t</span> <span class="o">=</span> <span class="o">-</span><span class="mf">1.89564459709</span>
-<span class="n">p</span><span class="o">-</span><span class="n">value</span> <span class="o">=</span> <span class="mf">0.0665336223673</span>
-</pre></div>
-</div>
-<p>配对 t 检验的结果说明，配对样本之间存在显著性差异，说明治疗时有效的，符合我们的预期。</p>
-</section>
-<section id="p">
-<h2>p 值计算原理<a class="headerlink" href="#p" title="Permalink to this heading">¶</a></h2>
-<p>p 值对应的部分是下图中的红色区域，边界范围由 t 值决定。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">my_t</span> <span class="o">=</span> <span class="n">t</span><span class="p">(</span><span class="n">pop_size</span><span class="p">)</span> <span class="c1"># 传入参数为自由度，这里自由度为50</span>
-<span class="n">p</span> <span class="o">=</span> <span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">my_t</span><span class="o">.</span><span class="n">pdf</span><span class="p">(</span><span class="n">x</span><span class="p">),</span> <span class="s1">&#39;b-&#39;</span><span class="p">)</span>
-<span class="n">lower_x</span> <span class="o">=</span> <span class="n">x</span><span class="p">[</span><span class="n">x</span><span class="o">&lt;=</span> <span class="o">-</span><span class="nb">abs</span><span class="p">(</span><span class="n">t_val</span><span class="p">)]</span>
-<span class="n">upper_x</span> <span class="o">=</span> <span class="n">x</span><span class="p">[</span><span class="n">x</span><span class="o">&gt;=</span> <span class="nb">abs</span><span class="p">(</span><span class="n">t_val</span><span class="p">)]</span>
-<span class="n">p</span> <span class="o">=</span> <span class="n">fill_between</span><span class="p">(</span><span class="n">lower_x</span><span class="p">,</span> <span class="n">my_t</span><span class="o">.</span><span class="n">pdf</span><span class="p">(</span><span class="n">lower_x</span><span class="p">),</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;red&#39;</span><span class="p">)</span>
-<span class="n">p</span> <span class="o">=</span> <span class="n">fill_between</span><span class="p">(</span><span class="n">upper_x</span><span class="p">,</span> <span class="n">my_t</span><span class="o">.</span><span class="n">pdf</span><span class="p">(</span><span class="n">upper_x</span><span class="p">),</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;red&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-<img 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f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%0AMSZFWYE3xpgU9f8B8tTCMYTHdwAAAABJRU5ErkJggg==%0A" 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f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%0AMSZFWYE3xpgU9f8B8tTCMYTHdwAAAABJRU5ErkJggg==%0A" />
-</section>
-<section id="id11">
-<h2>作者 &amp; 更新时间<a class="headerlink" href="#id11" title="Permalink to this heading">¶</a></h2>
-<p>作者:<cite>李金  &lt;lijinwithyou&#64;gmail.com&gt;</cite></p>
-<p>提交: 2017/12/14</p>
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diff --git a/docs/aipy-scipy/cn/5curve-fitting.html b/docs/aipy-scipy/cn/5curve-fitting.html
deleted file mode 100644
index e1e31cc..0000000
--- a/docs/aipy-scipy/cn/5curve-fitting.html
+++ /dev/null
@@ -1,556 +0,0 @@
-<!DOCTYPE html>
-<html lang="en-US">
-<head>
-<meta charset="utf-8" />
-<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
-<link href="https://cdn.bootcss.com/bootstrap/3.3.7/css/bootstrap.min.css" rel="stylesheet">
-<title>曲线拟合</title>
-<link rel="stylesheet" type="text/css" href="../../static/pygments.css" />
-<link rel="stylesheet" type="text/css" href="../../static/style.css" />
-<meta charset="UTF-8">
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-<script src="https://cdn.bootcss.com/bootstrap/3.3.7/js/bootstrap.min.js"></script>
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-<div class="navbar-header">
-<button type="button" class="navbar-toggle" data-toggle="collapse" data-target="#example-navbar-collapse">
-<span class="sr-only">切换导航</span>
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-</button>
-<a href="/index.html" class="logo"><img src="http://www.qpython.org/static/img_logo.png"></a>
-</div>
-<ul class="header-title collapse navbar-collapse" id="example-navbar-collapse" >
-<li>
-<a href="http://www.qpython.org/index.html">Home</a>
-</li>
-<li>
-<a href="http://www.qpython.org/document.html">Guide</a>
-</li>
-<li class="header-phone-selected">
-<a href="/index.html" class="header-selected">Course</a>
-<span class="header-title-selected"></span>
-</li>
-<li>
-<a href="http://www.aipy.org">AIPY</a>
-</li>
-<li>
-<a target="_blank" href="https://github.com/qpython-android/qpython">Github</a>
-</li>
-</ul>
-<div class="search-box hidden-xs">
-<form action="/search.html">
-<input type="search" name="q" placeholder="keyword">
-<input type="hidden" name="check_keywords" value="yes" />
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-<main class="contain web-content">
-<div class="document">
-<div class="documentwrapper">
-<div class="bodywrapper">
-<div class="body" role="main">
-<section id="id1">
-<h1>曲线拟合<a class="headerlink" href="#id1" title="Permalink to this heading">¶</a></h1>
-<p>导入基础包：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">matplotlib</span> <span class="k">as</span> <span class="nn">mpl</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-</pre></div>
-</div>
-</section>
-<section id="id2">
-<h1>多项式拟合<a class="headerlink" href="#id2" title="Permalink to this heading">¶</a></h1>
-<p>导入线多项式拟合工具：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">numpy</span> <span class="kn">import</span> <span class="n">polyfit</span><span class="p">,</span> <span class="n">poly1d</span>
-</pre></div>
-</div>
-<p>产生数据：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
-<span class="n">y</span> <span class="o">=</span> <span class="mi">4</span> <span class="o">*</span> <span class="n">x</span> <span class="o">+</span> <span class="mf">1.5</span>
-<span class="n">noise_y</span> <span class="o">=</span> <span class="n">y</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">y</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span> <span class="o">*</span> <span class="mf">2.5</span>
-</pre></div>
-</div>
-<p>画出数据：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="o">%</span><span class="n">matplotlib</span> <span class="n">inline</span>
-<span class="n">p</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">noise_y</span><span class="p">,</span> <span class="s1">&#39;rx&#39;</span><span class="p">)</span>
-<span class="n">p</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="s1">&#39;b:&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
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l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%0A/wEcY+GYZJlNlgAAAABJRU5ErkJggg==%0A" 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l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%0A/wEcY+GYZJlNlgAAAABJRU5ErkJggg==%0A" />
-<p>进行线性拟合，polyfit 是多项式拟合函数，线性拟合即一阶多项式：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">coeff</span> <span class="o">=</span> <span class="n">polyfit</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">noise_y</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-<span class="nb">print</span> <span class="n">coeff</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">[</span> <span class="mf">3.93921315</span>  <span class="mf">1.59379469</span><span class="p">]</span>
-</pre></div>
-</div>
-<p>一阶多项式 <em>y = a_1 x + a_0</em> 拟合，返回两个系数 <em>[a_1, a_0]</em>。</p>
-<p>画出拟合曲线：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">p</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">noise_y</span><span class="p">,</span> <span class="s1">&#39;rx&#39;</span><span class="p">)</span>
-<span class="n">p</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">coeff</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">x</span> <span class="o">+</span> <span class="n">coeff</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="s1">&#39;k-&#39;</span><span class="p">)</span>
-<span class="n">p</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="s1">&#39;b--&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-<img 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E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%0ArkJggg==%0A" 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E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%0ArkJggg==%0A" />
-<p>还可以用 poly1d 生成一个以传入的 coeff 为参数的多项式函数：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">f</span> <span class="o">=</span> <span class="n">poly1d</span><span class="p">(</span><span class="n">coeff</span><span class="p">)</span>
-<span class="n">p</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">noise_y</span><span class="p">,</span> <span class="s1">&#39;rx&#39;</span><span class="p">)</span>
-<span class="n">p</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">f</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
-</pre></div>
-</div>
-<img 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l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%0AkFFgFxEJGQV2EZGQ+X/DyfQNy7jRVwAAAABJRU5ErkJggg==%0A" 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l%0ATJJ9OcS2X7JKgV2kpUnWC07Qw371VbPjjzfr0cOL2Vu3Ro7V1Jx9EImOPWeOeuw5psAu0pIkC+Bx%0AAv7SpWZnnmnWpYvZ735nVlub19Yrx95MggR2zccuUij8w/6jj6FhaoAlS6BfPz4o+5iS+7pQWgqj%0ARsGVV8KOOzZz+6L8UxdobpdmoaXxRFoSf814dAqA6OurVvGfc4dx1dFLOHLQbvToupGKCq+cfMdN%0AzTSNbqqpC1rR0nOFToFdJBNNXZcz1X6+4fufL3yHGw9/kT5rSml/cA/e++E4Jnx6LbtsrWnegUFp%0ALJ8neZYqV5OrG8qxSxg0Na8cYL/1681u/sU668hau/JHn9nq1b59L7vMbPbs/OSwVfWSV+jiqUgz%0AaGrtdoL9Nm40u/12s2/uudXO77HYKhZ8vO1x8xVcVaeedwrsIs2lqYHWt19trVfd0rWr2emDt1j5%0AuZMCV8g0C1W9FAQFdpHmkEmP/ZRTbOtb5TbnxPus5351Vlxs9vL89YlnT4zWiecjuOayLl4CU2AX%0AyUSQQJZBjr3+qqE27/5P7OA9PrLD+26y50//ldVXViXfX8G11VNgF8lEkKAdDbT+gOt/niDgLpy6%0AyI4eUGsHHmj2xB++tPqrhnrzuZx6qlIbkpQCu0imgqZZAvbc33zT7OSTzbp1M3vgAbO6usgbhVRp%0Aol8FBU2BXSSIVIEsaNBN8iWwYoU3/XmnTt5MuZs2BdsvL3SRtKApsIsEkeYcLUnFfAmsWuWVnO+x%0Ah9mUKWZffpnGufOp0L5s5GsK7CJBRQPZ7NleJPYH9dhpb1OlYyorbc3FN9qwqzZahw5mo0ebrVuX%0A4LyZpD1ynTIppPSQfE2BXVqHbAW4AItBJzxuJKjXVFXb+PFmHXbfatf0WWD/vffJ3AXfXPb21WMv%0AWArs0jpkI8D5A9lll3m3NILa//78rE276Svbc0+ziy6KdHKbo+48FwG4UNNDYmYK7NKaZBLg4gWy%0AyGLQqdIQW7aY/eY3ZnvvbXbOOWbvvJPltgWR7ZSJqmIKWpDArvnYJTyqqqB7d6ishG7dgu8XO494%0ATQ0MHw5HHQVvvLHtDIbz5rF1wEDm/K2ICRNgv/1g0sgNHLFxYeIpapvatlSiszuOGAHTp2u2xVYg%0AyHzs6rFLOGSrV5wiDVFfb/bkwxusd4fV9t0jam3Bgjj7xPZ4q6u91M511zVcmPW/19SesFImrRJK%0AxUiopFpnMxsBLkka4qWXzAYMMOvb1+zpRzd4o0XjfZHElktGc/ZVVQ2P/VU3TQ3ESpm0SgrsEi6J%0Aeqhz5iQPcIkCYKKJtmIC42uvmZ1wgtl3vmP2yCO+xaKT5bbjlU9GX8/nXOrS4imwS/gkCpjR95KU%0AIm7zhRCvlNH3fNkys7PPNttnH7N7r15iWz6Nk15JFqATBX7Vh0sGFNglnGLrzc28+1NO8YK1WUMv%0APRrsEwXiOLn5Dz80u/BCs29+02zGDLOvvrLGQd+fXkmUUkmU81d9uGRIgV3CJ1m9ub8HHi/4Jiph%0AjHxR/GfxR3b11WYdO3pZmi++SHDuVL8WmvgLQSQIBXYJlyD15okCf6JBR9XV9vmlw23klTXWod0G%0Au/7qjbZ2bZI2BEmjZJjTF0kmr4EdGAysACqAkXHez/HHl9BJVEYYm17xB99EaZuhQ23DO6tsUv+/%0AWscOW+3yy80+XlYTeC4Y9bQlX/IW2IHtgfeBbsAOwNvAgTHb5PwPQEIsVbrD30uPSZ1s2mR219T/%0AWaf2623IDzbbypUxx03nAqyCuzSzIIE9JyNPnXPfBSaa2eDI81GRSD7Vt43l4tzSSsSOFgVYtQqu%0Avhoeesh7Pny4dz9jBgB1o8fzh4N+yU3Td6JPH5g0CQ4+OIPz1dTAokWJR5uK5ECQkae5Cuw/BAaZ%0A2eWR5xcAR5rZtb5tFNglu/zBN/oYqP/HIuZuPpXxY7fyzc0fM+XejgwcvEvDfgrQ0oIECextcnTu%0AQBG7pKTk68fFxcUUFxfnqDnSKvgD86mnYgbPPw9jSrzX7/zV9pzUvwg3bhQMiMypEp1rZfLkPDVa%0AJLmysjLKysrS2idXPfYBQIkvFTMaqDezX/q2UY9dcubll2H0aFizBm65BX7wA9huu8ibmjhLWrB8%0ApmLaAO8B3wf+A7wGDDGz5b5tFNgltTRz2+XlXsxeuhRKSuCnP4U28X6X5mq2RZEcCxLYt0v2ZlOZ%0AWR1wDTAfeBd4zB/URQIbONCL1DU13vNobzuSP4+qqIAhQ2DQIDjpJFi5Ei65JEFQr6nxeuqVld59%0A9NgiIaH52KXwJUmdrF4NN98Mc+fCsGHerX37AMeaHCfHrnSMtAB567FLCMybt21PtqbGe70px4g+%0A9h8j6PGKiryg3r27d19UxNq1cMMN0LcvdOjg9dDHjUsR1MFL4fiDeFGR93zRouCfS6TAKbBLfAFT%0AIIGPMXCgV1c+fLj3OJ3j+VIn6yffTcmoTRxwAGzcCO+8A1OnesE9kFNP3bZnXlSkUkcJFQV2iS/a%0Akx071rvQGC9dkapX7z+Gf7t00h+RbTeOm8xtT3Sjx1+nU/nk27z+4np+/Wvo3Dkrn1YkVJRjl+SS%0AVY8EzVf7jwFpVaPU/vVZ7q8s5uYZO9G/v1e6eNA+GlAkrZdy7JKZVNUjQXr1/mNMmuTd4h0vpvdf%0AXw+P3vc/el11HH+atxNz53oXSA86CKVORFJQj13iS9QbP/ZYr6bQH7yXLIF+/bbthceO6oyZu6XR%0A8SLPbdJk5s1vw9iRdXxjczVTfrsnx5+R6oqoSOuRtwFKQSiwF7hEA4Pmz4eFCxsC/qpVcNpp8PDD%0AMGtW4x57grlbvk6jxBzv74u2Z8yla1i/pR2TD3+S0x//KW53lSCK+CmwS25Ee+JXXAHnnw/PPAP7%0A7tvkmvA3yjYw5sKPed/14Oau9/HjRdewfeUHGhEqEody7JKeoLXr0bryfv28nvq++za8nkZN+PLl%0A8MMfwhnn78JZP9uL5R/tzPndX/aCukaEijSZArs0CFq77r8gOmtW4wAc4MLmqlXecP/jjoP+/aHi%0A9RquqhpJ2wvOg3btYPFiuPHGbduSzuAokdYs1UocubqhFZQKU6rl3xKtJDRnTsr1PD/5xOzaa806%0AdDAbP96spibO/v5FqKuqGpbD02pFImaW5zVPU55Ygb1wJVuwOdFCzXPmJFw6rrrabMwYL6APG2a2%0AZk0ax9P6oiKNKLBL4sAZb13P6HtNDagx+365utqmTDHbYw+zSy81W7UqzTYl+4IRaaUU2CW9RZiz%0AsWBzZaVtZge7u+Qz69zZ7NxzzVasaMJ5MvmCEQkxBXbxBA2S6fbuY9R9Vm0PHP+gdeuyxU7+1jJ7%0Ao2x909qUjS8YkZAKEthVxx5G8QYXJRodmgVm8JeH/se4n6+nQ889uXV6G47pE6CmPdE8NGmumiTS%0AmqiOvbWKLVtctcobSFRentX6cDMoLfVKFm8pqWPGvbvwj3HzvaDur2mPV6qYbB4aTa0rkplUXfpc%0A3VAqJrei6YvycrPevb3SQf/rGaY1XnnFrLjYrEcPr4Bl69aY8wbJnyvVIpI2lIpp5aKpjvJyb6mh%0AqAzSGkuXeisVvfkmTJwIF18cZ13RJEvZAUq1iGRAc8W0ZqmCa5o++MAL5C+8AKNGwZVXwo47Jtkh%0A2TzuItJkyrG3Vv7JuLp1a5gz/bHH0l7H9N//9oL4kUfC/vtDRYW3YHTSoJ5qHncRySkF9jBKtGAz%0ANFxUnTfPu6jqnwvGF+Q//9zr7PfpA7vsAu+9B+PHe4+TSvSlouAu0nxSJeFzdSOsF08zrAXPuRQX%0AVdd/VG033WTWsaPZlVearV6d5vEL/fOLtHDo4mkeBF0HNJ/8F1VnzYIRI9h06x38Zt+pTL3rG5x4%0AIpSUwH775buhIhJLOfZ8CLIOaD7FTLlbd9kV3Nd9Ej2fuY0Fr36D0lJ46KGYoB50nnYRKQjqsedK%0AIVaF+H491O9axJ9+vZYJN3zJPv32ZErX3zBg9uXxv4Bawq8QkVZCPfZ8KdSqkEWLsEmTefblIg47%0AuI7bbvkfM3+/Ey+dMoMBt5+beGGLQv8VIiKNpUrC5+pGWC+ephpVmcuLiymOvXCh2dFHm/XqZTZ3%0AzOtWvy6mjakWttA0uiJ5Ry5ndwR+BLwDbAUOjXlvNFABrABOSrB/M/wR5EGqwJ3L4fQJjv3m39fb%0AySebdetm9uCDZnV1SfZNtXKSptEVyatcB/YDgJ7AAn9gB3oBbwM7AN2A94Ht4uzfLH8IBSmXQdJ3%0A7BVDSuzcszdb585m99xjtnlzin0T9cg1t4tIwQgS2JucYzezFWa2Ms5bZwKPmlmtmVVFAnv/pp4n%0AlIqKvNE/3bt79+nmqpNVqRQV8dFPRvF/3V/g6PnjOKR/Wyoq4OqroW3bJMdMdl0g0YCnRYvSa7eI%0ANItcXDzdG1jte74a2CcH52m5Mr24Gjstb6RK5dOeR/OLoZs45Pu7s9fQH1Jx9khGXVnDzjsHaE+y%0A0aKaRlekRYmdl68R51wp0CnOW2PM7Ok0zhO3rrGkpOTrx8XFxRQXF6dxyBYqtlQwGkTTqTLx7zdi%0ABDWT7uG23W/j10e25YIuf+fdJYeyV88iqBkX7NjJeuQK3iJ5VVZWRllZWVr7ZFzH7pxbANxgZm9G%0Ano8CMLOpked/Ayaa2eKY/SzTc7dI6UxZm2Lbr5av4p5eM5nR8VZOPX17So59iX3PPlTT4YqEWLNM%0A2xsJ7MPN7I3I817AI3h59X2AF4D9YqN4qw3s6UgwMGjLxMnc94e2TC7ZwneP2YFbim7jwN9cp7py%0AkVYgp4HdOXc28CtgD+AL4C0zOzny3hjgUqAO+LmZzY+zvwJ7ENHgfthhbP3nKzx65J1MuHVHeta/%0Ax6SZu3P4dm825Nw1aEgk9LTQRkhYZRVPffvnjN1tJrv27MSUs1+n+Cd7w7RpcOyxMGiQt2E05aL0%0Ai0hoaUqBEHjxr18yYABMOOBPTO0/l0V9rqR4SGcvqE+e7AX1sWO9jaNB3T/Huoi0OgrshSZSo754%0AMZxQXMuVF33FsLE789a0Uk57/ELcls2N6981j4uIxFBgLzDvdDiGsw+t4gfn1HNun+W8+6+NDHmv%0AhO2OifTA27WD2bMb179nOuBJREJFgT2ffCNIP/wQLrwQvndGe44eCBWnXc/PbtiVHe6Y1nhZuxkz%0A4NJLGw8iKtTZJEUkL3TxNJ9qavjvsF8yqU0Jc/7Sjmsv38T1n49l1+njveDsn889UU37/PmwcKHm%0AShdpJXTxtICtWwejphZx0F8ns+O//smK5z+iZMMNXlCHbXvgiYb1t2+veVxEpBH12JvZl1/CXXfB%0AHXfAOefAhAnQpa6qoXdeVKTVikQkIfXYC8jmzfCrX0GPHrBsGbzyCvz2t9ClfUx+fP589cBFJCMK%0A7DlWVwf33w89e0JpKTz3HDz6qBfg486quHDhtgfRTIoikgYF9niSzXceUH09/PnP0KcPPPigF8yf%0AfhoOPti3keY5F5EcUGCPJ8F850FGc5p52ZT+/WHqVC+fvmABHHVUnI39F0SjXyb+3nmaXyYiIqDA%0AHl8TR3O+/DIUF8OwYTBqFLz+Opx0EriklzkiMvgyERHxU1VMMlVVjWvJEygv92LwsmVQUgIXXABt%0Aki5hkkA0mI8Y4V1IVSWMiMRQVUwmAozmrKiAIUNg8GBvLq733oOLL25iUAdNDSAiWaHAHk+KNUBX%0Ar4af/czLm/fp4wX4a6/1pnGJK+jFWE0NICJZoMAeT4JqlbXPvs4NN0C/ftCxI6xcCWPGeIM/kwqS%0AP0+1oLSISEDKsacybx7r+wzkttlF3HPHFoacZ4y9YROdP/hneotapMqfp7MWqoi0WlpBKUMbN8LM%0AGRuZdmsdJ5/ZjpJRm+h+9/XemzNmePfpDPcPeDFWRCQRXTxNJEXOu7YWZs3yRoe+/NY3WPCi8WCH%0AX9B9t3UN28+dC8OHNw7qyerOlT8XkeZiZnm5eafOk+pqs6FDvXvf862fV9vDD5t95ztmJ5xg9tpr%0Avn0qK83Au48+vuCCbY7x9fMA54u7rYhIEpHYmTS+tt5UjC/nbdOm88wxv2Tsre3ZaSeYMgWOPz7+%0Atkya5L02blzD46OO8kYnzZjRuPcezY8rfy4iWaJUTDKRmvGy7hczcPFtjJnSnltu8WZdjBvUY/Po%0ARUVeIN+8GS67zLuP3Sda9ZJoLnUFdRHJgVbbY//Xgg2MuXA1H2y3Hzf3fJgfzzmL7TvGuQDq721H%0AH4PX2x6mlkMZAAAHk0lEQVQ40MuzR3vs4PXkNWpURHJEVTFxLF8O40Zu4dWXvmL8zW259JqdaPtV%0AExaziO3J19R4o5QeekhVLyKSM0rF+FRVecP9jzsOjuz4ARUr4crrd6JtW5o2XW7sICbwhp7Onq2q%0AFxHJq1bRY//oIzj0UBg6FG64AXbbLcsniNd713J2IpIDSsX4fPFFgoCejYqVeMd47DHv/rzzmn5c%0AEZEYSsX4JOylZ2Me9HhVL4MGecvcaX51EWlmTQ7szrnpzrnlzrly59xc59xuvvdGO+cqnHMrnHMn%0AZaepOdLERTXydlwRkRSanIpxzp0IvGhm9c65qQBmNso51wt4BDgC2Ad4AehpZvUx++d3gFKsXM3j%0AovlhRCSLcpqKMbNSX7BeDHSJPD4TeNTMas2sCngf6N/U8zSLXM3jovlhRCQPspVjvxR4NvJ4b2C1%0A773VeD33wpSredA1v7qI5EnSwO6cK3XOLY1zO923zVhgi5k9kuRQBZRziZFgUY20atqb87giIikk%0AXZ3TzE5M9r5z7mLgFOD7vpf/DXT1Pe8SeW0bJSUlXz8uLi6muLg42elyw1966C9bjL7e1BLFeNtr%0AfhgRSVNZWRllZWVp7ZPJxdPBwG3AcWb2me/16MXT/jRcPN0v9kppwV08BQ00EpGCl9MBSs65CqAt%0AEF194hUzGxp5bwxe3r0O+LmZzY+zf+EFdki9hJ2ISB5p5GlTR5WqRFFECpRGnjZlVKlKFEWkhQt3%0Ajx0agvlhhyVf5ci/rXLsIlKg1GOHr1dKSrnKEahEUURCIfyB3Z9aadfOW/Eo0dwtWsJOREIg3KkY%0ArXIkIiGjVIxWORKRVijcPXY/XRgVkRBQHbtfNlZKEhHJMwV2EZGQUY5dRKQVUmAXEQkZBXYRkZBR%0AYBcRCRkFdhGRkFFgFxEJGQV2EZGQUWAXEQkZBXYRkZBRYBcRCRkFdhGRkFFgFxEJGQV2EZGQUWAX%0AEQkZBXYRkZBRYBcRCRkFdhGRkFFgFxEJGQV2EZGQaXJgd87d4pwrd8697Zx70TnX1ffeaOdchXNu%0AhXPupOw0VUREgsikxz7NzPqZ2cHAk8BEAOdcL+A8oBcwGPi1c67V/TIoKyvLdxNySp+vZQvz5wvz%0AZwuqyQHXzDb4nrYHPos8PhN41MxqzawKeB/o3+QWtlBh/8elz9eyhfnzhfmzBdUmk52dc5OBnwIb%0AaQjeewOv+jZbDeyTyXlERCS4pD1251ypc25pnNvpAGY21sy+BdwP3JnkUJbFNouISBLOLPOY65z7%0AFvCsmfV2zo0CMLOpkff+Bkw0s8Ux+yjYi4g0gZm5ZO83ORXjnOthZhWRp2cCb0UePwU84py7HS8F%0A0wN4Ld2GiYhI02SSY7/VObc/sBX4ALgKwMzedc49DrwL1AFDLRs/C0REJJCspGJERKRw5L2+3Dl3%0ArXNuuXNumXPul/luTy44525wztU75zrkuy3Z5JybHvm7K3fOzXXO7ZbvNmXKOTc4MrCuwjk3Mt/t%0AySbnXFfn3ALn3DuR/2/X5btNueCc294595Zz7ul8tyXbnHNFzrk/R/7fveucGxBvu7wGdufc94Az%0AgL5m1huYkc/25EJkRO6JwKp8tyUHngcOMrN+wEpgdJ7bkxHn3PbAPXgD63oBQ5xzB+a3VVlVC/zC%0AzA4CBgBXh+zzRf0cLxUcxnTEXXiFKgcCfYHl8TbKd4/9KuBWM6sFMLO1eW5PLtwO3JjvRuSCmZWa%0AWX3k6WKgSz7bkwX9gffNrCryb3IOXmFAKJjZJ2b2duTxl3hBYe/8tiq7nHNdgFOA+4BQFWhEfhEf%0AY2a/BzCzOjP7It62+Q7sPYBjnXOvOufKnHOH57k9WeWcOxNYbWZL8t2WZnAp8Gy+G5GhfYCPfc9D%0AO7jOOdcNOATvCzlM7gBGAPWpNmyBugNrnXP3O+fedM79zjm3U7wNMxp5GoRzrhToFOetsZHz725m%0AA5xzRwCPA9/OdZuyKcXnGw34J0FrcT2IJJ9vjJk9HdlmLLDFzB5p1sZlXxh/um/DOdce+DPw80jP%0APRScc6cBn5rZW8654ny3JwfaAIcC15jZ6865O4FRwIR4G+aUmZ2Y6D3n3FXA3Mh2r0cuMHY0s89z%0A3a5sSfT5nHO98b5hy51z4KUp3nDO9TezT5uxiRlJ9vcH4Jy7GO+n7/ebpUG59W+gq+95V7xee2g4%0A53YAngAeMrMn892eLDsKOMM5dwqwI7Crc+4PZnZhntuVLavxMgCvR57/GS+wbyPfqZgngeMBnHM9%0AgbYtKagnY2bLzGwvM+tuZt3x/lIObUlBPRXn3GC8n71nmtmmfLcnC/4F9HDOdXPOtcWbpfSpPLcp%0Aa5zXw5gNvGtmyaYAaZHMbIyZdY38f/sx8FKIgjpm9gnwcSRWApwAvBNv25z32FP4PfB759xSYAsQ%0Amr+EOML4M/9uoC1QGvlV8oqZDc1vk5rOzOqcc9cA84HtgdlmFrfqoIUaCFwALHHORUeKjzazv+Wx%0ATbkUxv9z1wIPRzoeHwCXxNtIA5REREIm36kYERHJMgV2EZGQUWAXEQkZBXYRkZBRYBcRCRkFdhGR%0AkFFgFxEJGQV2EZGQ+X/DyfQNy7jRVwAAAABJRU5ErkJggg==%0A" />
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">f</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">poly1d</span><span class="p">([</span> <span class="mf">3.93921315</span><span class="p">,</span>  <span class="mf">1.59379469</span><span class="p">])</span>
-</pre></div>
-</div>
-<p>显示 f：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span> <span class="n">f</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="mf">3.939</span> <span class="n">x</span> <span class="o">+</span> <span class="mf">1.594</span>
-</pre></div>
-</div>
-<p>还可以对它进行数学操作生成新的多项式：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span> <span class="n">f</span> <span class="o">+</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">f</span> <span class="o">**</span> <span class="mi">2</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="mi">2</span>
-<span class="mf">31.03</span> <span class="n">x</span> <span class="o">+</span> <span class="mf">29.05</span> <span class="n">x</span> <span class="o">+</span> <span class="mf">6.674</span>
-</pre></div>
-</div>
-</section>
-<section id="id3">
-<h1>多项式拟合正弦函数<a class="headerlink" href="#id3" title="Permalink to this heading">¶</a></h1>
-<p>正弦函数：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span><span class="mi">100</span><span class="p">)</span>
-<span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>用一阶到九阶多项式拟合，类似泰勒展开：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">y1</span> <span class="o">=</span> <span class="n">poly1d</span><span class="p">(</span><span class="n">polyfit</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">,</span><span class="mi">1</span><span class="p">))</span>
-<span class="n">y3</span> <span class="o">=</span> <span class="n">poly1d</span><span class="p">(</span><span class="n">polyfit</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span>
-<span class="n">y5</span> <span class="o">=</span> <span class="n">poly1d</span><span class="p">(</span><span class="n">polyfit</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">,</span><span class="mi">5</span><span class="p">))</span>
-<span class="n">y7</span> <span class="o">=</span> <span class="n">poly1d</span><span class="p">(</span><span class="n">polyfit</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">,</span><span class="mi">7</span><span class="p">))</span>
-<span class="n">y9</span> <span class="o">=</span> <span class="n">poly1d</span><span class="p">(</span><span class="n">polyfit</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">,</span><span class="mi">9</span><span class="p">))</span>
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="mi">3</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span><span class="mi">3</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span><span class="mi">100</span><span class="p">)</span>
-<span class="n">p</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">x</span><span class="p">),</span> <span class="s1">&#39;k&#39;</span><span class="p">)</span>
-<span class="n">p</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y1</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
-<span class="n">p</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y3</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
-<span class="n">p</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y5</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
-<span class="n">p</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y7</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
-<span class="n">p</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y9</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
-<span class="n">a</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">([</span><span class="o">-</span><span class="mi">3</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span> <span class="mi">3</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.25</span><span class="p">,</span> <span class="mf">1.25</span><span class="p">])</span>
-</pre></div>
-</div>
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P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%0AiBJ3hUKh0CFK3BUKhUKH/AdKQZTK2jwwogAAAABJRU5ErkJggg==%0A" 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P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%0AiBJ3hUKh0CFK3BUKhUKH/AdKQZTK2jwwogAAAABJRU5ErkJggg==%0A" />
-<p>黑色为原始的图形，可以看到，随着多项式拟合的阶数的增加，曲线与拟合数据的吻合程度在逐渐增大。</p>
-</section>
-<section id="id4">
-<h1>最小二乘拟合<a class="headerlink" href="#id4" title="Permalink to this heading">¶</a></h1>
-<p>导入相关的模块：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">scipy.linalg</span> <span class="kn">import</span> <span class="n">lstsq</span>
-<span class="kn">from</span> <span class="nn">scipy.stats</span> <span class="kn">import</span> <span class="n">linregress</span>
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span><span class="mi">100</span><span class="p">)</span>
-<span class="n">y</span> <span class="o">=</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="n">x</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span> <span class="o">*</span> <span class="mf">0.35</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">,</span><span class="s1">&#39;x&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<img alt="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAXkAAAEACAYAAABWLgY0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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%0AAAAASUVORK5CYII=%0A" 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8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%0AAAAASUVORK5CYII=%0A" />
-<p>一般来书，当我们使用一个 N-1 阶的多项式拟合这 M 个点时，有这样的关系存在：</p>
-<p><em>XC = Y</em></p>
-</section>
-<section id="scipy-linalg-lstsq">
-<h1>Scipy.linalg.lstsq 最小二乘解<a class="headerlink" href="#scipy-linalg-lstsq" title="Permalink to this heading">¶</a></h1>
-<p>要得到 C ，可以使用 scipy.linalg.lstsq 求最小二乘解。</p>
-<p>这里，我们使用 1 阶多项式即 N = 2，先将 x 扩展成 X：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">((</span><span class="n">x</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span><span class="mi">1</span><span class="p">))))</span>
-<span class="n">X</span><span class="p">[</span><span class="mi">1</span><span class="p">:</span><span class="mi">5</span><span class="p">]</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">array</span><span class="p">([[</span> <span class="mf">0.05050505</span><span class="p">,</span>  <span class="mf">1.</span>        <span class="p">],</span>
-<span class="p">[</span> <span class="mf">0.1010101</span> <span class="p">,</span>  <span class="mf">1.</span>        <span class="p">],</span>
-<span class="p">[</span> <span class="mf">0.15151515</span><span class="p">,</span>  <span class="mf">1.</span>        <span class="p">],</span>
-<span class="p">[</span> <span class="mf">0.2020202</span> <span class="p">,</span>  <span class="mf">1.</span>        <span class="p">]])</span>
-</pre></div>
-</div>
-<p>求解：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">C</span><span class="p">,</span> <span class="n">resid</span><span class="p">,</span> <span class="n">rank</span><span class="p">,</span> <span class="n">s</span> <span class="o">=</span> <span class="n">lstsq</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-<span class="n">C</span><span class="p">,</span> <span class="n">resid</span><span class="p">,</span> <span class="n">rank</span><span class="p">,</span> <span class="n">s</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">(</span><span class="n">array</span><span class="p">([</span> <span class="mf">0.50432002</span><span class="p">,</span>  <span class="mf">0.0415695</span> <span class="p">]),</span>
-<span class="mf">12.182942535066523</span><span class="p">,</span>
-<span class="mi">2</span><span class="p">,</span>
-<span class="n">array</span><span class="p">([</span> <span class="mf">30.23732043</span><span class="p">,</span>   <span class="mf">4.82146667</span><span class="p">]))</span>
-</pre></div>
-</div>
-<p>画图：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">p</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="s1">&#39;rx&#39;</span><span class="p">)</span>
-<span class="n">p</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">C</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">x</span> <span class="o">+</span> <span class="n">C</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="s1">&#39;k--&#39;</span><span class="p">)</span>
-<span class="nb">print</span> <span class="s2">&quot;sum squared residual = </span><span class="si">{:.3f}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">resid</span><span class="p">)</span>
-<span class="nb">print</span> <span class="s2">&quot;rank of the X matrix = </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">rank</span><span class="p">)</span>
-<span class="nb">print</span> <span class="s2">&quot;singular values of X = </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">s</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">sum</span> <span class="n">squared</span> <span class="n">residual</span> <span class="o">=</span> <span class="mf">12.183</span>
-<span class="n">rank</span> <span class="n">of</span> <span class="n">the</span> <span class="n">X</span> <span class="n">matrix</span> <span class="o">=</span> <span class="mi">2</span>
-<span class="n">singular</span> <span class="n">values</span> <span class="n">of</span> <span class="n">X</span> <span class="o">=</span> <span class="p">[</span> <span class="mf">30.23732043</span>   <span class="mf">4.82146667</span><span class="p">]</span>
-</pre></div>
-</div>
-<img 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u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%0ATkSuQmCC%0A" 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u%0AjFbK13y9SQzyRE5xszWZTaolhzTO/vHjte+55+pXSkp0wdy5euTGG1t/zlwHJFnNywPHbMIgT+QU%0At/LC2aRacgl+q1bpiUOH9I033tBwONyyDyOfy8mgy5Y8gzyRLxlItRgNfsePH9ejR49aUy8ngy5z%0A8gzyRI4y2qq3u/VvMPh98MEHevfdd2u3bt30iSeecGy/lmHvGgZ5IkcZDXJ2B8M0wS8cDmtVVZVe%0AffXVGggE9MYbb2yZesBs0CzgoOs0Bnkis3INWEbTFS7lkt9++20955xz9De/+Y0ePnw4eZ0KMP2R%0AbxjkyX353qozE/CMXnh0qVfIiRMnUr9YoBcy842RIG/Z8n9ESbmx3JmV4peJC4UiP++9t+3C34ka%0AGoAHHgBqayM/E5fyy7ZcDsLhMNasWYNQKJT09Xbt0vz7BwLA9OlA796Rn5k+L3lXprOAUzewJe9f%0AfmgVZtPaTmztL1nSeo6XWJklS2xJixw+fFh//etf65e+9CUdOHCgbtiwoW0hC3vlWCbfv/W5AEzX%0AkGeY7Kvt6j9/tgEvsb719ZEgv2RJ6+0tWWLp59q3b59OnDhRA4GAXn311bphw4aWvu1G61Rf715O%0AntcCssYgT95gwahL10eTmt23Ay3jUCik99xzj3744Yep9x//ORLXNY295uZJ1Q/f+hzEIE/uy6Mg%0AmZTdU/k6KdkxdLtOyXixTh7FIE/u81OQNMOCk1Q4HNZXX31Vr7zyyuR5diPij6EXW81erJOHORLk%0AATwOoA7A2yleDwJoRMtC37NSlLP5cFBeSZVDrqjIv39+k99mPvnkE3300Uf1wgsv1L59++r8+fO1%0AsbEx93rU1iZd8MP1NWSZk8+aU0H+mwAGZAjyLxjYjp3HgvJN4oXA+KCUb//8OX6bCYfD+sorr2iX%0ALl308ssv17Vr17ZcSM2W0R4/XlxDlr1rUjIS5CVSzhwRKQGwUlUvTPJaEMDtqvp/M2xDragL+Uis%0AT/2gQcAbbwDz5rX0125oAKqqgPJyd+tos8bGRjQ0NKBXr17mNrR6dWRsQnx/dyePYex3OX16ZDyA%0AkbEGlJGIQFUlbRkHgvwwAM8DOADgHwDuUNUdScoxyFNboVBkQE5tLVBS4nZtbHPo0CEEAgEUFRWZ%0A25DbwTydAvldOslIkG/vQD3eAtBDVZtE5DIAywGcl6zgnDlzPr8fDAYRDAYdqB55VuJoUB+2/rZu%0A3YqHHnoIy5cvx8tz5mDgtdeaC9CxEcaxYxVrQd97rz0fwKgC+F06obKyEpWVldm9KVM+x8gNQAlS%0A5OSTlK0F0CXJ89Ymqyi/+fgi3LFjx3Tx4sX69a9/XXv27Kn333+/Hjx40Lrctdd6qPj4d+k2ONWF%0AMl2QB1CMlrTQEAChFOVsPRiUZ3x6ES4cDuvTTz+tl1xyiS5btkybm5tbFzASoI0ETS91N/Xp79IL%0AjAR50zl5EfkTgGEAzkSkK+VsAB2iUXuBiEwBcDOAZgBNAG5T1Y1JtqNm60KUD1QVImnSqEZy1+ku%0AZPIiZ8Fw7MKrFRjkyU8++eQTPP300xg/fjxOPfVU42/MJkAnOxnE5+ATc/IM9L5jJMhzqmEiC+3a%0AtQvTpk1Dr169sG7dOtTX1xt/c3xALilpmeI42fTDqaYorqpqHdBjUyVXVZn+bJSf2JInykVCV8UN%0AGzbg5z/7GbbX1GDilCmYPHkyunfvbmqbAJL3rmFrnaKYrqHCZmef8YTAunHtWvz9V7/C9596CicV%0AF5vbdiZe7gtPjmK6hvLH6tVt0xINDZHnc2XnqlQJK0aVLl+Oq//8Z3sCfOKxiQXy+GMTCDDAU3KZ%0Aut84dQO7UBY2u/pSp+qSmGW3vqamJn3iiSd06NChWldX1/KCE10V/dTPnN0pLQVONUx5xa5BPMkC%0AscHAWVtbqzNmzNAvfOELOmLECF21alVL33YnBx15bYBTrvx0wvIABnnKP1a3jNMFxwyBc/78+XrG%0AGWforbfeqrt3727dCo29NxRqed7uYOWlAU5m+OWE5QEM8pRfrP7nNzkytK6uTo8cOZJ8e6tWRQJ8%0A4vYzpR1yTVf4LTD65YTlMgZ5yh92fI03OMdL7WuvGd+X2WCby+f0W4rDbycsFzHIU/5w+ILcZx99%0ApH/+7nd1WFmZduvWTRv37TMecMy2QrMNcn66WOm3E5bLjAR59pOnglJXV4fHHnsMCx58EH3OOw9T%0AfvxjjBkzBh06dDDW19yqeWEKdW519vG3FAdDESX45S9/if3792Pq1Kn4yle+kt2brRppygnEyCIM%0A8pQ9trRSs+LYcEoCshBHvFL27Bwl6pC9e/di3rx5sLzRUF7eNhBnO9KUE4iRwxjkqbWE4fr50soM%0Ah8NYs2YNysvLUVpairq6Onz22WduV6stK04URFlguqYQGUk7xC4M1tQA8blrD6ZuFi9ejF/84hfo%0A3LkzpkyZgh/+8Ic45ZRT3K4Wke2YrqHkMqVkYnOV19QA11wD7NuXvJxHnHnmmXjqqaewZcsW3HDD%0ADQzwRHHYki9UqXp4JF4I3LcP+N73gKefBhYsyIvUTVK8oEw+xN41lF6yvtrJguHf/gb07+9an+5/%0A/vOfWLBgAV566SVUVVWhXbscvoCyVwv5kO3pGhF5XETqROTtNGXmi8h7IlIjIgPM7M8UO+Yrz2ep%0Alo9LvDDY0BBpwSeWs5mq4q9//SuuvPJK9OvXDx9//DEWLlyYW4AH8vaCMpFpmYbEprsB+CaAAQDe%0ATvH6SAAvRu9/HcDGNNuyYpRvahxO3cLosXDxmI0bN0779u2r8+fP14aGBus2zImxyEfgxNw1AErS%0ABPlHAfxn3ONdAIpTlLX1YKgqJ0aKMToXiotzptTV1Wk4HLZ2o/z9k88YCfKmc/IiUgJgpapemOS1%0AlQDuU9U3oo/XAZihqluTlFWzdTGkUOcM8aATJ05g9+7dOP/88+3fGXPy5ENGcvLtnahHwuOUkXzO%0AnDmf3w8GgwgGg9bWJDEPzX/w3JnorXLo0CFUVFTgkUcewZe//GWsWbMGImn/Ts3XCUg90pS9ayhP%0AVFZWorKyMrs3ZWrqZ7ohc7rmqrjH9qZr0qUXmJO3Vg7Hc8uWLXrddddpIBDQa6+9Vt98803X60SU%0Az+CBnHz8hddS2H3hNd0/uZ/m5HZL4jGsr1edMEG1osJQMJ08ebLef//9evDgweTbi23TzO+EeXcq%0AILYHeQB/AvBPAJ8B2A/gBgCTAEyKK/MQgD0AagAMTLMtaz41/8ntk+wkOnZs5M+ooiL7gG1Xy5s9%0AaKhAGAny/hwMxYur9okfKXvPPZHnZs0C7rkHqorK0aOxPRTCtHHjjF3YNDu3emIevqEBuOMOYOhQ%0AYOtWXnchXyvMuWtSDfIhawQCkYDcuzfw738D8+bhkzPOwMN9+6Lf889j2vjxOLWpyXjPlfjtTZ9u%0ALCDHD2yLzcOzbx/wzDORAA8AY8a0DH7i3wAVskxNfadusDsnT9aIHdOKCtUJE3T6tGl6+umn6w9+%0A8ANdv3KlhhcuzC5Vkkt6LfH3Ggqp9uun+rvfRa4RJF434HUX8ikU3ELevLhqryQn0WUjRuj+7dtb%0Av240YJs5KSfuq6aGeXgqOIUX5Mk24XDY+i6qZk/KsQusNTW82E4FiUHea5z+pmFyf+FwWDdu3Khj%0Ax47V73//+7buK2uxk0hNTSRVEwq1fp6BngoAg7zXOH3NIMf9NTU16RNPPKGDBg3SPn366AMPPKAf%0Af/yxPXXMReL4h1Co7edkio4KgJEg788ulF5mtsugzftTVfTr1w+9evXClClTMGLECBQVFdlXv1xw%0AARAiAFw0xLuc7sef5f4aGxvRuXNn26tFROYUZj95r3O6H3+K/TU2NuKdd95J+hYGeCL/YJB3Uvz0%0AtiUl9g/WSbK/7ZMn4+YbbkBJSQmeeeYZe/bLVbiIPINB3klVVamnu7Vxfyc6dcLSpUsRHD0al776%0AKoqPHcOOHTtw991327Pf2CjUWKCPnWzKyuzZHxGlxJx8AQiHwxg3bhwuv/xyXHHFFejQoYP9O011%0AwZcXTYkswwuv5K5kF3y5QhORZXjhtYA0NTVh4cKFWLRokdtViUh1gTmWopo5M3ISYIAnshWDfJ7b%0Au3cvbr/9dvTs2RMrVqxAr1693K5S5gvMucw8SUQ5YZC3gwO9S5qamlBeXo7S0lIUFRVh8+bNWLly%0AJYYNG2bZPnKW6QIzp4Mmck6mIbFO3RA/rUG+zybp0LD7ZcuWadNzz+XXseJ00ESWQd7OXeOHQGDh%0ABFrHjx/PvJ98OVb5fgIn8pD8DfKq/lir1cRUuP/+97/1j3/8o5aVlemsWbPSF/bDsSKirDkS5AGM%0AALALwHsAZiR5PQigEcC26G1Wiu20/QRmFmR2u8WY46IWBw4c0Lvuuku7du2qF198sS5dujR9Sz6G%0Ai1dbw+2/G6Is2B7kARQB2AOgBEAHANUAzk8oEwTwgoFtta692dapm2mMVMvTxVr0Kerw8ccf6xln%0AnKE333yzbo+ttpTN/qJL8nH5OxPyLf1FBc2JIP8NAP8b9/inAH6aUCYIYKWBbbXU3Kp/NDvTGOla%0AfPGvxeoQCiVfQSnBp59+ml094rdXXx8J8rFAzwCVG6a/KE84EeR/AOAPcY/HAvhdQplhAA4BqAHw%0AIoALUmwrUuv6etXZs637ymxXGsPoiSjJyWDXm2/qe489Zk1qIHEbsUBfUcEAZQbTX5QHnAjy3zcQ%0A5DsBODV6/zIAu1NsS2fPmKGzBw/W2TNm6Pr1680fAbtbZFlsv7m5WVesWKGXXnqpnnXWWfrss8/a%0AlxpggDKHLXnyqPXr1+vs2bM/vzkR5EsT0jU/S3bxNeE9tQC6JHne2n8op3KrGQJqY2Oj3n///dqr%0AVy8dMmSILlq0qHVKxuqAwgBlDnPylEecCPLtAeyNXnjtmOLCazFaJkIbAiCUYlvWtjyd6CVhIKAe%0AOnRIJ0yYoJs3b069Hata3gxQ5rF3DeURp7pQXgbg3Wgvm59Fn5sEYFL0/hQA26MngDcAlKbYTn4F%0AJC9eHGaAIiooRoK8t6Yarq/Pn1kJ4+ZF379/Px599FEMGzQIl550kvF50TntLhGZkH9TDdu9UpKF%0AdORIvPLWWxgzZgz69++PI0eO4JyvfjW7hS+cXCkq3aRpXK6PyLe81ZL3SF0y2b17N0aPHo127dph%0A6tSpGDt2LE477TS3q5Veum8NAL9REOUhrgxlk2PHjmHjxo0YNmwYRNIeX29JtSRfpteIyJMY5E1q%0Abm5GOBxGx44d3a6KdZItyWfkNSLynPzLyXvERx99hLlz56JPnz5Y7ae8dLrFOriQB5EvMchHqSo2%0AbdqEcePGoW/fvvj73/+OFStW4IorrnC7atZItyRfpuX6iChvMV0T9dprr+G6667DLbfcguuvvx5d%0AunRxrS62iOvy+bmGhpaePKley6a3EBE5ijn5LITDYagqioqKXKsDEVE2/JOTt6gfdzgcxksvvYSD%0ABw+2ea1du3YM8ETkO/kR5MvKWueIYznksjJDb29sbMSDDz6I888/H3feeScOHDjQ8iIHAhGRj+VH%0AkI+NBJ05M9LNz+BAndraWkyePBklJSXYuHEjHn/8cWzbtg0DBgxoKWTyBGIZnmyIyAb5lZPPsh93%0ATU0NVqxYgRtvvBFnn3126oJeGAiUOMr0mWeAtWuBefNaD1jixVAiivJPTh7IqR93//79cdddd6UP%0A8EAkiE6fHjmBDBqUfN92t6gTv62sXdu2Dm58wyCi/JZpmkqnbkhcyDteiml9w4cP6+uvv65XXXWV%0Avvvuuxkm5Uwjfrrf+DVSk+3bbvFzy3MBECJKAwamGs6PlnzCbI1NHTtiYd++GDh4MK6//nqUlpai%0Aa9euuW07cSDQvHmR5++4I6v8vyUSv60ALd8wpk/nXDJElLX8yskDWL16Na699loMHToUU6ZMwfDh%0Aw9GunYlzVapBQs8/D0yY4Nw8Lslmibzjjshrs2Zx0jAiasNfOfmowYMHY/PmzXjhhRfw3e9+11yA%0AByIXMZMFzq1bnZ3HJXFu+ZjhwznVABHlzLMt+YaGBnTu3Nn5qXy9slpTumkI2LuGiOBQS15ERojI%0ALhF5T0RmpCgzP/p6jYgMSFYmZtu2bZg4cSJ69+6NvXv3mq1e9pxcrSmdZN8w4rtSxmN/eiJKwVSQ%0AF5EiAA8BGAHgAgA/FJHzE8qMBHCOqp4L4CYAv0+1vbKyMowaNQq9e/fGu+++i3POOcdM9XITH1xj%0AA5QCgZbOGqasAAAGIUlEQVTWs9sB1SuDt4goL5htyQ8BsEdVQ6p6HMASAJcnlBkF4EkAUNVNAAIi%0AUpxsY7fffjtqa2sxc+ZMnHXWWSarZgEvBtQcR/8SUWEyG+S7Adgf9/hA9LlMZbon29iYb38b7du3%0Ab3kiU6vZ7qkAvBpQ4wdvsWslEaVhNsgbvWqbeGEg+fuybTXn2tLO5uTgxYDKVZyIyKD2mYuk9Q8A%0APeIe90CkpZ6uTPfoc23M6dQp0mWwrAzBUAjB//mf9EE1vqWdzbwzsZNDsh40iRIDqtst+cTePrHP%0A73a9iMh2lZWVqKyszO5NmYbEprshcpLYC6AEQEcA1QDOTygzEsCL0fulADam2FZknG78sH6jcnmP%0AkSkDUkyn4Or0AqtWtd1/fX3keSIqKDAwrYEVc85cBuBdAHsA/Cz63CQAk+LKPBR9vQbAwBTbyW2u%0AFjPzu2Q6OTCgEpGHORLkrboByL7VbKalncvJgUGfiDwk/4J8tgE016Cb68nBi+kbIipYRoK8Z6c1%0AsJWZKQOSLTBSVcUpCIjIcUamNci/IO+FOV0SV6jyynw3RFRQfDkLpeujUJP1UffqoCkiKnj515IH%0A3FuTNVOLPcs1aImIzPBnuibGjYCaLlUU+4bh5mLgRFRQ/JmuAdwb1p9q+t/4EbRc4IOIPCT/WvJe%0AvMjphYvBRFRw/JmuKcSAWoifmYgy8meQL0Re/PZCRK5jkPcTt3oUEZFnMcj7DbtoElEc//auKURc%0AKISIcsAgnw/ic/DsoklEWWC6Jh+wdw0RJcGcPBGRjzEnT0RU4BjkiYh8jEGeiMjH2uf6RhHpAuAZ%0AAL0AhABcqaptunuISAjAvwCcAHBcVYfkuk8iIsqOmZb8TwGsVdXzALwcfZyMAgiq6gAGeGMqKyvd%0AroJn8FhE8Di04LHIjpkgPwrAk9H7TwIYnaZs2qu/1Br/iFvwWETwOLTgsciOmSBfrKp10ft1AIpT%0AlFMA60Rki4jcaGJ/RESUpbQ5eRFZC6Brkpdmxj9QVRWRVJ3cy1T1AxH5AoC1IrJLVV/LrbpERJSN%0AnAdDicguRHLtH4rI2QDWq+qXM7xnNoAjqvrrJK9xJBQRUZYyDYbKuXcNgBcAXAvg/0V/Lk8sICKn%0AAihS1U9E5P8AuBTAL3KpKBERZc9MS74LgGcB9ERcF0oR+SKAP6hquYj0AfB89C3tATytqveZrzYR%0AERnhmblriIjIeq6PeBWRESKyS0TeE5EZbtfHLSLyuIjUicjbbtfFbSLSQ0TWi8g7IrJdRH7kdp3c%0AIiIni8gmEakWkR0iUvDfhEWkSES2ichKt+viJhEJicjfosfizZTl3GzJi0gRgHcBXALgHwA2A/ih%0Aqu50rVIuEZFvAjgCYJGqXuh2fdwkIl0BdFXVahE5DcBWAKML8e8CiFzbUtUmEWkP4HUAd6jq627X%0Ayy0ichuAQQA6qeoot+vjFhGpBTBIVQ+nK+d2S34IgD2qGlLV4wCWALjc5Tq5ItqttN7teniBqn6o%0AqtXR+0cA7ATwRXdr5R5VbYre7QigCEDaf2o/E5HuAEYCWAgOsgQMHAO3g3w3APvjHh+IPkcEABCR%0AEgADAGxytybuEZF2IlKNyKDD9aq6w+06uei3AKYDCLtdEQ8wNNDU7SDPq76UUjRVsxTAj6Mt+oKk%0AqmFV/SqA7gC+JSJBl6vkChH5HoCPVHUb2IoHIgNNBwC4DMCUaMq3DbeD/D8A9Ih73AOR1jwVOBHp%0AAOA5AE+papsxGIVIVRsBrAYw2O26uGQogFHRXPSfAHxbRBa5XCfXqOoH0Z8HASxDJP3dhttBfguA%0Ac0WkREQ6AvhPRAZZUQETEQFQAWCHqv632/Vxk4icKSKB6P1TAAwHsM3dWrlDVf9LVXuoam8AVwF4%0ARVXHu10vN4jIqSLSKXo/NtA0ac88V4O8qjYDmArgLwB2AHimgHtQ/AnAGwDOE5H9InK923VyURmA%0AsQAujnYP2yYiI9yulEvOBvBKNCe/CcBKVX3Z5Tp5RSGne4sBvBb3d7FKVV9KVpCDoYiIfMztdA0R%0AEdmIQZ6IyMcY5ImIfIxBnojIxxjkiYh8jEGeiMjHGOSJiHyMQZ6IyMf+P3gCky4HG7kGAAAAAElF%0ATkSuQmCC%0A" />
-</section>
-<section id="scipy-stats-linregress">
-<h1>Scipy.stats.linregress 线性回归<a class="headerlink" href="#scipy-stats-linregress" title="Permalink to this heading">¶</a></h1>
-<p>对于上面的问题，还可以使用线性回归进行求解：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">slope</span><span class="p">,</span> <span class="n">intercept</span><span class="p">,</span> <span class="n">r_value</span><span class="p">,</span> <span class="n">p_value</span><span class="p">,</span> <span class="n">stderr</span> <span class="o">=</span> <span class="n">linregress</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-<span class="n">slope</span><span class="p">,</span> <span class="n">intercept</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">(</span><span class="mf">0.50432001884393252</span><span class="p">,</span> <span class="mf">0.041569499438028901</span><span class="p">)</span>
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">p</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="s1">&#39;rx&#39;</span><span class="p">)</span>
-<span class="n">p</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">slope</span> <span class="o">*</span> <span class="n">x</span> <span class="o">+</span> <span class="n">intercept</span><span class="p">,</span> <span class="s1">&#39;k--&#39;</span><span class="p">)</span>
-<span class="nb">print</span> <span class="s2">&quot;R-value = </span><span class="si">{:.3f}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">r_value</span><span class="p">)</span>
-<span class="nb">print</span> <span class="s2">&quot;p-value (probability there is no correlation) = </span><span class="si">{:.3e}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">p_value</span><span class="p">)</span>
-<span class="nb">print</span> <span class="s2">&quot;Root mean squared error of the fit = </span><span class="si">{:.3f}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">stderr</span><span class="p">))</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">R</span><span class="o">-</span><span class="n">value</span> <span class="o">=</span> <span class="mf">0.903</span>
-<span class="n">p</span><span class="o">-</span><span class="n">value</span> <span class="p">(</span><span class="n">probability</span> <span class="n">there</span> <span class="ow">is</span> <span class="n">no</span> <span class="n">correlation</span><span class="p">)</span> <span class="o">=</span> <span class="mf">8.225e-38</span>
-<span class="n">Root</span> <span class="n">mean</span> <span class="n">squared</span> <span class="n">error</span> <span class="n">of</span> <span class="n">the</span> <span class="n">fit</span> <span class="o">=</span> <span class="mf">0.156</span>
-</pre></div>
-</div>
-<img 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b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%0A" 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b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%0A" />
-<p>可以看到，两者求解的结果是一致的，但是出发的角度是不同的。</p>
-</section>
-<section id="id5">
-<h1>更高级的拟合<a class="headerlink" href="#id5" title="Permalink to this heading">¶</a></h1>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">scipy.optimize</span> <span class="kn">import</span> <span class="n">leastsq</span>
-</pre></div>
-</div>
-<p>先定义这个非线性函数： <em>y = a e^{-b sin( f x + phi)}</em></p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">function</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">a</span> <span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">f</span><span class="p">,</span> <span class="n">phi</span><span class="p">):</span>
-<span class="sd">&quot;&quot;&quot;a function of x with four parameters&quot;&quot;&quot;</span>
-<span class="n">result</span> <span class="o">=</span> <span class="n">a</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">b</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">f</span> <span class="o">*</span> <span class="n">x</span> <span class="o">+</span> <span class="n">phi</span><span class="p">))</span>
-<span class="k">return</span> <span class="n">result</span>
-</pre></div>
-</div>
-<p>画出原始曲线：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span> <span class="mi">50</span><span class="p">)</span>
-<span class="n">actual_parameters</span> <span class="o">=</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mf">1.25</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">/</span> <span class="mi">4</span><span class="p">]</span>
-<span class="n">y</span> <span class="o">=</span> <span class="n">function</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="o">*</span><span class="n">actual_parameters</span><span class="p">)</span>
-<span class="n">p</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">)</span>
-</pre></div>
-</div>
-<img 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r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%0ALSKSMApuEZGEUXCLiCSMgltEJGH+Px9Ir5f410BLAAAAAElFTkSuQmCC%0A" 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r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%0ALSKSMApuEZGEUXCLiCSMgltEJGH+Px9Ir5f410BLAAAAAElFTkSuQmCC%0A" />
-<p>加入噪声：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">scipy.stats</span> <span class="kn">import</span> <span class="n">norm</span>
-<span class="n">y_noisy</span> <span class="o">=</span> <span class="n">y</span> <span class="o">+</span> <span class="mf">0.8</span> <span class="o">*</span> <span class="n">norm</span><span class="o">.</span><span class="n">rvs</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
-<span class="n">p</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="s1">&#39;k-&#39;</span><span class="p">)</span>
-<span class="n">p</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y_noisy</span><span class="p">,</span> <span class="s1">&#39;rx&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-<img alt="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAXAAAAEACAYAAACqOy3+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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%0AAABJRU5ErkJggg==%0A" 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q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%0AAABJRU5ErkJggg==%0A" />
-</section>
-<section id="scipy-optimize-leastsq">
-<h1>Scipy.optimize.leastsq<a class="headerlink" href="#scipy-optimize-leastsq" title="Permalink to this heading">¶</a></h1>
-<p>定义误差函数，将要优化的参数放在前面：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>def f_err(p, y, x):
-    return y - function(x, *p)
-</pre></div>
-</div>
-<p>将这个函数作为参数传入 leastsq 函数，第二个参数为初始值：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">c</span><span class="p">,</span> <span class="n">ret_val</span> <span class="o">=</span> <span class="n">leastsq</span><span class="p">(</span><span class="n">f_err</span><span class="p">,</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">args</span><span class="o">=</span><span class="p">(</span><span class="n">y_noisy</span><span class="p">,</span> <span class="n">x</span><span class="p">))</span>
-<span class="n">c</span><span class="p">,</span> <span class="n">ret_val</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">(</span><span class="n">array</span><span class="p">([</span> <span class="mf">3.03199715</span><span class="p">,</span>  <span class="mf">1.97689384</span><span class="p">,</span>  <span class="mf">1.30083191</span><span class="p">,</span>  <span class="mf">0.6393337</span> <span class="p">]),</span> <span class="mi">1</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>ret_val 是 1~4 时，表示成功找到最小二乘解：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">p</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y_noisy</span><span class="p">,</span> <span class="s1">&#39;rx&#39;</span><span class="p">)</span>
-<span class="n">p</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">function</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="o">*</span><span class="n">c</span><span class="p">),</span> <span class="s1">&#39;k--&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-</section>
-<section id="scipy-optimize-curve-fit">
-<h1>Scipy.optimize.curve_fit<a class="headerlink" href="#scipy-optimize-curve-fit" title="Permalink to this heading">¶</a></h1>
-<p>更高级的做法：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">scipy.optimize</span> <span class="kn">import</span> <span class="n">curve_fit</span>
-</pre></div>
-</div>
-<p>不需要定义误差函数，直接传入 function 作为参数：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">p_est</span><span class="p">,</span> <span class="n">err_est</span> <span class="o">=</span> <span class="n">curve_fit</span><span class="p">(</span><span class="n">function</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y_noisy</span><span class="p">)</span>
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span> <span class="n">p_est</span>
-<span class="n">p</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y_noisy</span><span class="p">,</span> <span class="s2">&quot;rx&quot;</span><span class="p">)</span>
-<span class="n">p</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">function</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="o">*</span><span class="n">p_est</span><span class="p">),</span> <span class="s2">&quot;k--&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">[</span> <span class="mf">3.03199711</span>  <span class="mf">1.97689385</span>  <span class="mf">1.3008319</span>   <span class="mf">0.63933373</span><span class="p">]</span>
-</pre></div>
-</div>
-<p>这里第一个返回的是函数的参数，第二个返回值为各个参数的协方差矩阵：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span> <span class="n">err_est</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">[[</span> <span class="mf">0.08483704</span> <span class="o">-</span><span class="mf">0.02782318</span>  <span class="mf">0.00967093</span> <span class="o">-</span><span class="mf">0.03029038</span><span class="p">]</span>
-<span class="p">[</span><span class="o">-</span><span class="mf">0.02782318</span>  <span class="mf">0.00933216</span> <span class="o">-</span><span class="mf">0.00305158</span>  <span class="mf">0.00955794</span><span class="p">]</span>
-<span class="p">[</span> <span class="mf">0.00967093</span> <span class="o">-</span><span class="mf">0.00305158</span>  <span class="mf">0.0014972</span>  <span class="o">-</span><span class="mf">0.00468919</span><span class="p">]</span>
-<span class="p">[</span><span class="o">-</span><span class="mf">0.03029038</span>  <span class="mf">0.00955794</span> <span class="o">-</span><span class="mf">0.00468919</span>  <span class="mf">0.01484297</span><span class="p">]]</span>
-</pre></div>
-</div>
-<p>协方差矩阵的对角线为各个参数的方差：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span> <span class="s2">&quot;normalized relative errors for each parameter&quot;</span>
-<span class="nb">print</span> <span class="s2">&quot;   a</span><span class="se">\t</span><span class="s2">  b</span><span class="se">\t</span><span class="s2"> f</span><span class="se">\t</span><span class="s2">phi&quot;</span>
-<span class="nb">print</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">err_est</span><span class="o">.</span><span class="n">diagonal</span><span class="p">())</span> <span class="o">/</span> <span class="n">p_est</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">normalized</span> <span class="n">relative</span> <span class="n">errors</span> <span class="k">for</span> <span class="n">each</span> <span class="n">parameter</span>
-<span class="n">a</span>             <span class="n">b</span>               <span class="n">f</span>               <span class="n">phi</span>
-<span class="p">[</span> <span class="mf">0.09606473</span>  <span class="mf">0.0488661</span>   <span class="mf">0.02974528</span>  <span class="mf">0.19056043</span><span class="p">]</span>
-</pre></div>
-</div>
-</section>
-<section id="id6">
-<h1>作者 &amp; 更新时间<a class="headerlink" href="#id6" title="Permalink to this heading">¶</a></h1>
-<p>作者:<cite>李金  &lt;lijinwithyou&#64;gmail.com&gt;</cite></p>
-<p>提交: 2017/12/14</p>
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-<section id="id1">
-<h1>最小化函数<a class="headerlink" href="#id1" title="Permalink to this heading">¶</a></h1>
-<section id="minimize">
-<h2>minimize 函数<a class="headerlink" href="#minimize" title="Permalink to this heading">¶</a></h2>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="o">%</span><span class="n">pylab</span> <span class="n">inline</span>
-<span class="n">set_printoptions</span><span class="p">(</span><span class="n">precision</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">suppress</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>Populating the interactive namespace from numpy and matplotlib</p>
-<p>已知斜抛运动的水平飞行距离公式：</p>
-<p><em>d = 2 frac{v_0^2}{g} sin(theta) cos (theta)</em></p>
-<ul class="simple">
-<li><p><em>d</em> 水平飞行距离</p></li>
-<li><p><em>v_0</em> 初速度大小</p></li>
-<li><p><em>g</em> 重力加速度</p></li>
-<li><p><em>theta</em> 抛出角度</p></li>
-</ul>
-<p>希望找到使 <em>d</em> 最大的角度 <em>theta</em>。</p>
-<p>定义距离函数：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>def dist(theta, v0):
-    &quot;&quot;&quot;calculate the distance travelled by a projectile launched
-    at theta degrees with v0 (m/s) initial velocity.
-    &quot;&quot;&quot;
-    g = 9.8
-    theta_rad = pi * theta / 180
-    return 2 * v0 ** 2 / g * sin(theta_rad) * cos(theta_rad)
-theta = linspace(0,90,90)
-p = plot(theta, dist(theta, 1.))
-xl = xlabel(r&#39;launch angle $\theta (^{\circ})$&#39;)
-yl = ylabel(&#39;horizontal distance traveled&#39;)
-</pre></div>
-</div>
-<img 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/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%0AAAAASUVORK5CYII=%0A" 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/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%0AAAAASUVORK5CYII=%0A" />
-<p>因为 Scipy 提供的是最小化方法，所以最大化距离就相当于最小化距离的负数：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>def neg_dist(theta, v0):
-    return -1 * dist(theta, v0)
-</pre></div>
-</div>
-<p>导入 scipy.optimize.minimize：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">scipy.optimize</span> <span class="kn">import</span> <span class="n">minimize</span>
-<span class="n">result</span> <span class="o">=</span> <span class="n">minimize</span><span class="p">(</span><span class="n">neg_dist</span><span class="p">,</span> <span class="mi">40</span><span class="p">,</span> <span class="n">args</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,))</span>
-<span class="nb">print</span> <span class="s2">&quot;optimal angle = </span><span class="si">{:.1f}</span><span class="s2"> degrees&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">result</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">optimal</span> <span class="n">angle</span> <span class="o">=</span> <span class="mf">45.0</span> <span class="n">degrees</span>
-</pre></div>
-</div>
-<p>minimize 接受三个参数：第一个是要优化的函数，第二个是初始猜测值，第三个则是优化函数的附加参数，默认 minimize 将优化函数的第一个参数作为优化变量，所以第三个参数输入的附加参数从优化函数的第二个参数开始。</p>
-<p>查看返回结果：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span> <span class="n">result</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">status</span><span class="p">:</span> <span class="mi">0</span>
-<span class="n">success</span><span class="p">:</span> <span class="kc">True</span>
-<span class="n">njev</span><span class="p">:</span> <span class="mi">18</span>
-<span class="n">nfev</span><span class="p">:</span> <span class="mi">54</span>
-<span class="n">hess_inv</span><span class="p">:</span> <span class="n">array</span><span class="p">([[</span> <span class="mf">8110.515</span><span class="p">]])</span>
-<span class="n">fun</span><span class="p">:</span> <span class="o">-</span><span class="mf">0.10204079220645729</span>
-<span class="n">x</span><span class="p">:</span> <span class="n">array</span><span class="p">([</span> <span class="mf">45.02</span><span class="p">])</span>
-<span class="n">message</span><span class="p">:</span> <span class="s1">&#39;Optimization terminated successfully.&#39;</span>
-<span class="n">jac</span><span class="p">:</span> <span class="n">array</span><span class="p">([</span> <span class="mf">0.</span><span class="p">])</span>
-</pre></div>
-</div>
-</section>
-<section id="rosenbrock">
-<h2>Rosenbrock 函数<a class="headerlink" href="#rosenbrock" title="Permalink to this heading">¶</a></h2>
-<p>Rosenbrock 函数是一个用来测试优化函数效果的一个非凸函数：</p>
-<p>$f(x)=sumlimits_{i=1}^{N-1}{100left(x_{i+1}^2 - x_iright) ^2 + left(1-x_{i}right)^2 }$</p>
-<p>导入该函数：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">scipy.optimize</span> <span class="kn">import</span> <span class="n">rosen</span>
-<span class="kn">from</span> <span class="nn">mpl_toolkits.mplot3d</span> <span class="kn">import</span> <span class="n">Axes3D</span>
-</pre></div>
-</div>
-<p>使用 N = 2 的 Rosenbrock 函数：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">meshgrid</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">25</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="mf">0.5</span><span class="p">,</span><span class="mf">3.5</span><span class="p">,</span><span class="mi">25</span><span class="p">))</span>
-<span class="n">z</span> <span class="o">=</span> <span class="n">rosen</span><span class="p">([</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">])</span>
-</pre></div>
-</div>
-<p>图像和最低点 (1,1)：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span> <span class="o">=</span> <span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span><span class="mf">5.5</span><span class="p">))</span>
-<span class="n">ax</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">gca</span><span class="p">(</span><span class="n">projection</span><span class="o">=</span><span class="s2">&quot;3d&quot;</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">azim</span> <span class="o">=</span> <span class="mi">70</span><span class="p">;</span> <span class="n">ax</span><span class="o">.</span><span class="n">elev</span> <span class="o">=</span> <span class="mi">48</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;X&quot;</span><span class="p">);</span> <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;Y&quot;</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_zlim</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span><span class="mi">1000</span><span class="p">))</span>
-<span class="n">p</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">plot_surface</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">,</span><span class="n">z</span><span class="p">,</span><span class="n">rstride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">cstride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cm</span><span class="o">.</span><span class="n">jet</span><span class="p">)</span>
-<span class="n">rosen_min</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="mi">1</span><span class="p">],[</span><span class="mi">1</span><span class="p">],[</span><span class="mi">0</span><span class="p">],</span><span class="s2">&quot;ro&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-<img alt="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqsAAAFBCAYAAABQLOaIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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%0Abxj8dmObAAAAAElFTkSuQmCC%0A" 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H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%0Abxj8dmObAAAAAElFTkSuQmCC%0A" />
-<p>传入初始值：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">x0</span> <span class="o">=</span> <span class="p">[</span><span class="mf">1.3</span><span class="p">,</span> <span class="mf">1.6</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.5</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.8</span><span class="p">,</span> <span class="mf">0.8</span><span class="p">]</span>
-<span class="n">result</span> <span class="o">=</span> <span class="n">minimize</span><span class="p">(</span><span class="n">rosen</span><span class="p">,</span> <span class="n">x0</span><span class="p">)</span>
-<span class="nb">print</span> <span class="n">result</span><span class="o">.</span><span class="n">x</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">[</span> <span class="mf">1.</span>  <span class="mf">1.</span>  <span class="mf">1.</span>  <span class="mf">1.</span>  <span class="mf">1.</span><span class="p">]</span>
-</pre></div>
-</div>
-<p>随机给定初始值：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">x0</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
-<span class="n">result</span> <span class="o">=</span> <span class="n">minimize</span><span class="p">(</span><span class="n">rosen</span><span class="p">,</span> <span class="n">x0</span><span class="p">)</span>
-<span class="nb">print</span> <span class="n">x0</span>
-<span class="nb">print</span> <span class="n">result</span><span class="o">.</span><span class="n">x</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">[</span> <span class="mf">0.815</span> <span class="o">-</span><span class="mf">2.086</span>  <span class="mf">0.297</span>  <span class="mf">1.079</span> <span class="o">-</span><span class="mf">0.528</span>  <span class="mf">0.461</span> <span class="o">-</span><span class="mf">0.13</span>  <span class="o">-</span><span class="mf">0.715</span>  <span class="mf">0.734</span>  <span class="mf">0.621</span><span class="p">]</span>
-<span class="p">[</span><span class="o">-</span><span class="mf">0.993</span>  <span class="mf">0.997</span>  <span class="mf">0.998</span>  <span class="mf">0.999</span>  <span class="mf">0.999</span>  <span class="mf">0.999</span>  <span class="mf">0.998</span>  <span class="mf">0.997</span>  <span class="mf">0.994</span>  <span class="mf">0.988</span><span class="p">]</span>
-</pre></div>
-</div>
-<p>对于 N &gt; 3，函数的最小值为 <em>(x_1,x_2, …, x_N) = (1,1,…,1)</em>，不过有一个局部极小值点 <em>(x_1,x_2, …, x_N) = (-1,1,…,1)</em>，所以随机初始值如果选的不好的话，有可能返回的结果是局部极小值点：</p>
-</section>
-<section id="id2">
-<h2>优化方法<a class="headerlink" href="#id2" title="Permalink to this heading">¶</a></h2>
-</section>
-<section id="bfgs">
-<h2>BFGS 算法<a class="headerlink" href="#bfgs" title="Permalink to this heading">¶</a></h2>
-<p>minimize 函数默认根据问题是否有界或者有约束，使用 ‘BFGS’, ‘L-BFGS-B’, ‘SLSQP’ 中的一种。</p>
-<p>可以查看帮助来得到更多的信息：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">info</span><span class="p">(</span><span class="n">minimize</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">minimize</span><span class="p">(</span><span class="n">fun</span><span class="p">,</span> <span class="n">x0</span><span class="p">,</span> <span class="n">args</span><span class="o">=</span><span class="p">(),</span> <span class="n">method</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">jac</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">hess</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-<span class="n">hessp</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span><span class="n">bounds</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">constraints</span><span class="o">=</span><span class="p">(),</span> <span class="n">tol</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">callback</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-<span class="n">options</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>Minimization of scalar function of one or more variables.</p>
-<p>Parameters</p>
-<p>fun : callable</p>
-<blockquote>
-<div><p>Objective function.</p>
-</div></blockquote>
-<p>x0 : ndarray</p>
-<blockquote>
-<div><p>Initial guess.</p>
-</div></blockquote>
-<p>args : tuple, optional</p>
-<blockquote>
-<div><p>Extra arguments passed to the objective function and its</p>
-<p>derivatives (Jacobian, Hessian).</p>
-</div></blockquote>
-<p>method : str or callable, optional</p>
-<blockquote>
-<div><p>Type of solver.  Should be one of</p>
-<blockquote>
-<div><ul class="simple">
-<li><p>‘Nelder-Mead’</p></li>
-<li><p>‘Powell’</p></li>
-<li><p>‘CG’</p></li>
-<li><p>‘BFGS’</p></li>
-<li><p>‘Newton-CG’</p></li>
-<li><p>‘Anneal (deprecated as of scipy version 0.14.0)’</p></li>
-<li><p>‘L-BFGS-B’</p></li>
-<li><p>‘TNC’</p></li>
-<li><p>‘COBYLA’</p></li>
-<li><p>‘SLSQP’</p></li>
-<li><p>‘dogleg’</p></li>
-<li><p>‘trust-ncg’</p></li>
-<li><p>custom - a callable object (added in version 0.14.0)</p></li>
-</ul>
-</div></blockquote>
-<p>If not given, chosen to be one of <code class="docutils literal notranslate"><span class="pre">BFGS</span></code>, <code class="docutils literal notranslate"><span class="pre">L-BFGS-B</span></code>, <code class="docutils literal notranslate"><span class="pre">SLSQP</span></code>,</p>
-<p>depending if the problem has constraints or bounds.</p>
-</div></blockquote>
-<p>jac : bool or callable, optional</p>
-<blockquote>
-<div><p>Jacobian (gradient) of objective function. Only for CG, BFGS,</p>
-<p>Newton-CG, L-BFGS-B, TNC, SLSQP, dogleg, trust-ncg.</p>
-<p>If <cite>jac</cite> is a Boolean and is True, <cite>fun</cite> is assumed to return the</p>
-<p>gradient along with the objective function. If False, the</p>
-<p>gradient will be estimated numerically.</p>
-<p><cite>jac</cite> can also be a callable returning the gradient of the</p>
-<p>objective. In this case, it must accept the same arguments as</p>
-</div></blockquote>
-<p><cite>fun</cite>.</p>
-<p>hess, hessp : callable, optional</p>
-<blockquote>
-<div><p>Hessian (matrix of second-order derivatives) of objective function</p>
-</div></blockquote>
-<p>or</p>
-<blockquote>
-<div><p>Hessian of objective function times an arbitrary vector p.  Only</p>
-</div></blockquote>
-<p>for</p>
-<blockquote>
-<div><p>Newton-CG, dogleg, trust-ncg.</p>
-<p>Only one of <cite>hessp</cite> or <cite>hess</cite> needs to be given.  If <cite>hess</cite> is</p>
-<p>provided, then <cite>hessp</cite> will be ignored.  If neither <cite>hess</cite> nor</p>
-<p><cite>hessp</cite> is provided, then the Hessian product will be approximated</p>
-<p>using finite differences on <cite>jac</cite>. <cite>hessp</cite> must compute the Hessian</p>
-<p>times an arbitrary vector.</p>
-</div></blockquote>
-<p>bounds : sequence, optional</p>
-<blockquote>
-<div><p>Bounds for variables (only for L-BFGS-B, TNC and SLSQP).</p>
-<p><code class="docutils literal notranslate"><span class="pre">(min,</span> <span class="pre">max)</span></code> pairs for each element in <code class="docutils literal notranslate"><span class="pre">x</span></code>, defining</p>
-<p>the bounds on that parameter. Use None for one of <code class="docutils literal notranslate"><span class="pre">min</span></code> or</p>
-<p><code class="docutils literal notranslate"><span class="pre">max</span></code> when there is no bound in that direction.</p>
-</div></blockquote>
-<p>constraints : dict or sequence of dict, optional</p>
-<blockquote>
-<div><p>Constraints definition (only for COBYLA and SLSQP).</p>
-<p>Each constraint is defined in a dictionary with fields:</p>
-<blockquote>
-<div><p>type : str</p>
-<blockquote>
-<div><p>Constraint type: ‘eq’ for equality, ‘ineq’ for inequality.</p>
-</div></blockquote>
-<p>fun : callable</p>
-<blockquote>
-<div><p>The function defining the constraint.</p>
-</div></blockquote>
-<p>jac : callable, optional</p>
-<blockquote>
-<div><p>The Jacobian of <cite>fun</cite> (only for SLSQP).</p>
-</div></blockquote>
-<p>args : sequence, optional</p>
-<blockquote>
-<div><p>Extra arguments to be passed to the function and Jacobian.</p>
-</div></blockquote>
-</div></blockquote>
-<p>Equality constraint means that the constraint function result is to</p>
-<p>be zero whereas inequality means that it is to be non-negative.</p>
-<p>Note that COBYLA only supports inequality constraints.</p>
-</div></blockquote>
-<p>tol : float, optional</p>
-<blockquote>
-<div><p>Tolerance for termination. For detailed control, use solver-</p>
-</div></blockquote>
-<p>specific</p>
-<blockquote>
-<div><p>options.</p>
-</div></blockquote>
-<p>options : dict, optional</p>
-<blockquote>
-<div><p>A dictionary of solver options. All methods accept the following</p>
-<p>generic options:</p>
-<blockquote>
-<div><p>maxiter : int</p>
-<blockquote>
-<div><p>Maximum number of iterations to perform.</p>
-</div></blockquote>
-<p>disp : bool</p>
-<blockquote>
-<div><p>Set to True to print convergence messages.</p>
-</div></blockquote>
-</div></blockquote>
-</div></blockquote>
-<p>callback : callable, optional</p>
-<blockquote>
-<div><blockquote>
-<div><p>Called after each iteration, as <code class="docutils literal notranslate"><span class="pre">callback(xk)</span></code>, where <code class="docutils literal notranslate"><span class="pre">xk</span></code> is</p>
-</div></blockquote>
-<p>the</p>
-<blockquote>
-<div><p>current parameter vector.</p>
-</div></blockquote>
-</div></blockquote>
-<p>Returns</p>
-<p>res : OptimizeResult</p>
-<blockquote>
-<div><p>The optimization result represented as a <code class="docutils literal notranslate"><span class="pre">OptimizeResult</span></code> object.</p>
-<p>Important attributes are: <code class="docutils literal notranslate"><span class="pre">x</span></code> the solution array, <code class="docutils literal notranslate"><span class="pre">success</span></code> a</p>
-<p>Boolean flag indicating if the optimizer exited successfully and</p>
-<p><code class="docutils literal notranslate"><span class="pre">message</span></code> which describes the cause of the termination. See</p>
-<p><cite>OptimizeResult</cite> for a description of other attributes.</p>
-</div></blockquote>
-<p>See also</p>
-<p>minimize_scalar : Interface to minimization algorithms for scalar</p>
-<blockquote>
-<div><p>univariate functions</p>
-</div></blockquote>
-<p>show_options : Additional options accepted by the solvers</p>
-<p>Notes</p>
-<p>This section describes the available solvers that can be selected by</p>
-<blockquote>
-<div><p>the</p>
-</div></blockquote>
-<p>‘method’ parameter. The default method is <em>BFGS</em>.</p>
-<p><strong>Unconstrained minimization</strong></p>
-<p>Method <em>Nelder-Mead</em> uses the Simplex algorithm <a class="footnote-reference brackets" href="#id20" id="id3" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a>, <a class="footnote-reference brackets" href="#id21" id="id4" role="doc-noteref"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></a>. This</p>
-<p>algorithm has been successful in many applications but other algorithms</p>
-<p>using the first and/or second derivatives information might be</p>
-<p>preferred</p>
-<p>for their better performances and robustness in general.</p>
-<p>Method <em>Powell</em> is a modification of Powell’s method <a class="footnote-reference brackets" href="#id22" id="id5" role="doc-noteref"><span class="fn-bracket">[</span>3<span class="fn-bracket">]</span></a>, <a class="footnote-reference brackets" href="#id23" id="id6" role="doc-noteref"><span class="fn-bracket">[</span>4<span class="fn-bracket">]</span></a> which</p>
-<p>is a conjugate direction method. It performs sequential one-dimensional</p>
-<p>minimizations along each vector of the directions set (<cite>direc</cite> field in</p>
-<p><cite>options</cite> and <cite>info</cite>), which is updated at each iteration of the main</p>
-<p>minimization loop. The function need not be differentiable, and no</p>
-<p>derivatives are taken.</p>
-<p>Method <em>CG</em> uses a nonlinear conjugate gradient algorithm by Polak and</p>
-<p>Ribiere, a variant of the Fletcher-Reeves method described in <a class="footnote-reference brackets" href="#id24" id="id7" role="doc-noteref"><span class="fn-bracket">[</span>5<span class="fn-bracket">]</span></a> pp.</p>
-<p>120-122. Only the first derivatives are used.</p>
-<p>Method <em>BFGS</em> uses the quasi-Newton method of Broyden, Fletcher,</p>
-<p>Goldfarb, and Shanno (BFGS) <a class="footnote-reference brackets" href="#id24" id="id8" role="doc-noteref"><span class="fn-bracket">[</span>5<span class="fn-bracket">]</span></a> pp. 136. It uses the first derivatives</p>
-<p>only. BFGS has proven good performance even for non-smooth</p>
-<p>optimizations. This method also returns an approximation of the Hessian</p>
-<p>inverse, stored as <cite>hess_inv</cite> in the OptimizeResult object.</p>
-<p>Method <em>Newton-CG</em> uses a Newton-CG algorithm <a class="footnote-reference brackets" href="#id24" id="id9" role="doc-noteref"><span class="fn-bracket">[</span>5<span class="fn-bracket">]</span></a> pp. 168 (also known</p>
-<p>as the truncated Newton method). It uses a CG method to the compute the</p>
-<p>search direction. See also <em>TNC</em> method for a box-constrained</p>
-<p>minimization with a similar algorithm.</p>
-<p>Method <em>Anneal</em> uses simulated annealing, which is a probabilistic</p>
-<p>metaheuristic algorithm for global optimization. It uses no derivative</p>
-<p>information from the function being optimized.</p>
-<p>Method <em>dogleg</em> uses the dog-leg trust-region algorithm <a class="footnote-reference brackets" href="#id24" id="id10" role="doc-noteref"><span class="fn-bracket">[</span>5<span class="fn-bracket">]</span></a></p>
-<p>for unconstrained minimization. This algorithm requires the gradient</p>
-<p>and Hessian; furthermore the Hessian is required to be positive</p>
-<p>definite.</p>
-<p>Method <em>trust-ncg</em> uses the Newton conjugate gradient trust-region</p>
-<p>algorithm <a class="footnote-reference brackets" href="#id24" id="id11" role="doc-noteref"><span class="fn-bracket">[</span>5<span class="fn-bracket">]</span></a> for unconstrained minimization. This algorithm requires</p>
-<p>the gradient and either the Hessian or a function that computes the</p>
-<p>product of the Hessian with a given vector.</p>
-<p><strong>Constrained minimization</strong></p>
-<p>Method <em>L-BFGS-B</em> uses the L-BFGS-B algorithm <a class="footnote-reference brackets" href="#id25" id="id12" role="doc-noteref"><span class="fn-bracket">[</span>6<span class="fn-bracket">]</span></a>, <a class="footnote-reference brackets" href="#id26" id="id13" role="doc-noteref"><span class="fn-bracket">[</span>7<span class="fn-bracket">]</span></a> for bound</p>
-<p>constrained minimization.</p>
-<p>Method <em>TNC</em> uses a truncated Newton algorithm <a class="footnote-reference brackets" href="#id24" id="id14" role="doc-noteref"><span class="fn-bracket">[</span>5<span class="fn-bracket">]</span></a>, <a class="footnote-reference brackets" href="#id27" id="id15" role="doc-noteref"><span class="fn-bracket">[</span>8<span class="fn-bracket">]</span></a> to minimize a</p>
-<p>function with variables subject to bounds. This algorithm uses</p>
-<p>gradient information; it is also called Newton Conjugate-Gradient. It</p>
-<p>differs from the <em>Newton-CG</em> method described above as it wraps a C</p>
-<p>implementation and allows each variable to be given upper and lower</p>
-<p>bounds.</p>
-<p>Method <em>COBYLA</em> uses the Constrained Optimization BY Linear</p>
-<p>Approximation (COBYLA) method <a class="footnote-reference brackets" href="#id28" id="id16" role="doc-noteref"><span class="fn-bracket">[</span>9<span class="fn-bracket">]</span></a>, <a class="footnote-reference brackets" href="#id29" id="id17" role="doc-noteref"><span class="fn-bracket">[</span>10<span class="fn-bracket">]</span></a>, <a class="footnote-reference brackets" href="#id30" id="id18" role="doc-noteref"><span class="fn-bracket">[</span>11<span class="fn-bracket">]</span></a>. The algorithm is</p>
-<p>based on linear approximations to the objective function and each</p>
-<p>constraint. The method wraps a FORTRAN implementation of the algorithm.</p>
-<p>Method <em>SLSQP</em> uses Sequential Least SQuares Programming to minimize a</p>
-<p>function of several variables with any combination of bounds, equality</p>
-<p>and inequality constraints. The method wraps the SLSQP Optimization</p>
-<p>subroutine originally implemented by Dieter Kraft <a class="footnote-reference brackets" href="#id31" id="id19" role="doc-noteref"><span class="fn-bracket">[</span>12<span class="fn-bracket">]</span></a>. Note that the</p>
-<p>wrapper handles infinite values in bounds by converting them into large</p>
-<p>floating values.</p>
-<p><strong>Custom minimizers</strong></p>
-<p>It may be useful to pass a custom minimization method, for example</p>
-<p>when using a frontend to this method such as</p>
-<p><cite>scipy.optimize.basinhopping</cite></p>
-<p>or a different library.  You can simply pass a callable as the</p>
-<blockquote>
-<div><p><code class="docutils literal notranslate"><span class="pre">method</span></code></p>
-</div></blockquote>
-<p>parameter.</p>
-<p>The callable is called as <code class="docutils literal notranslate"><span class="pre">method(fun,</span> <span class="pre">x0,</span> <span class="pre">args,</span> <span class="pre">**kwargs,</span> <span class="pre">**options)</span></code>
-where <code class="docutils literal notranslate"><span class="pre">kwargs</span></code> corresponds to any other parameters passed to <cite>minimize</cite>
-(such as <cite>callback</cite>, <cite>hess</cite>, etc.), except the <cite>options</cite> dict, which has
-its contents also passed as <cite>method</cite> parameters pair by pair.  Also, if</p>
-<p><cite>jac</cite> has been passed as a bool type, <cite>jac</cite> and <cite>fun</cite> are mangled so that
-<cite>fun</cite> returns just the function values and <cite>jac</cite> is converted to a function
-returning the Jacobian.  The method shall return an <code class="docutils literal notranslate"><span class="pre">OptimizeResult</span></code>
-object.</p>
-<p>The provided <cite>method</cite> callable must be able to accept (and possibly ignore)
-arbitrary parameters; the set of parameters accepted by <cite>minimize</cite> may</p>
-<p>expand in future versions and then these parameters will be passed to</p>
-<p>the method.  You can find an example in the scipy.optimize tutorial.</p>
-<p>References</p>
-<aside class="footnote brackets" id="id20" role="note">
-<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id3">1</a><span class="fn-bracket">]</span></span>
-<p>Nelder, J A, and R Mead. 1965. A Simplex Method for Function</p>
-<p>Minimization. The Computer Journal 7: 308-13.</p>
-</aside>
-<aside class="footnote brackets" id="id21" role="note">
-<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id4">2</a><span class="fn-bracket">]</span></span>
-<p>Wright M H. 1996. Direct search methods: Once scorned, now</p>
-<p>respectable, in Numerical Analysis 1995: Proceedings of the 1995</p>
-<p>Dundee Biennial Conference in Numerical Analysis (Eds. D F</p>
-<p>Griffiths and G A Watson). Addison Wesley Longman, Harlow, UK.</p>
-<p>191-208.</p>
-</aside>
-<aside class="footnote brackets" id="id22" role="note">
-<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id5">3</a><span class="fn-bracket">]</span></span>
-<p>Powell, M J D. 1964. An efficient method for finding the minimum of
-a function of several variables without calculating derivatives. The</p>
-<p>Computer Journal 7: 155-162.</p>
-</aside>
-<aside class="footnote brackets" id="id23" role="note">
-<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id6">4</a><span class="fn-bracket">]</span></span>
-<p>Press W, S A Teukolsky, W T Vetterling and B P Flannery.</p>
-<p>Numerical Recipes (any edition), Cambridge University Press.</p>
-</aside>
-<aside class="footnote brackets" id="id24" role="note">
-<span class="label"><span class="fn-bracket">[</span>5<span class="fn-bracket">]</span></span>
-<span class="backrefs">(<a role="doc-backlink" href="#id7">1</a>,<a role="doc-backlink" href="#id8">2</a>,<a role="doc-backlink" href="#id9">3</a>,<a role="doc-backlink" href="#id10">4</a>,<a role="doc-backlink" href="#id11">5</a>,<a role="doc-backlink" href="#id14">6</a>,<a role="doc-backlink" href="#id32">7</a>)</span>
-<p>Nocedal, J, and S J Wright. 2006. Numerical Optimization.</p>
-<p>Springer New York.</p>
-</aside>
-<aside class="footnote brackets" id="id25" role="note">
-<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id12">6</a><span class="fn-bracket">]</span></span>
-<p>Byrd, R H and P Lu and J. Nocedal. 1995. A Limited Memory</p>
-<p>Algorithm for Bound Constrained Optimization. SIAM Journal on</p>
-<p>Scientific and Statistical Computing 16 (5): 1190-1208.</p>
-</aside>
-<aside class="footnote brackets" id="id26" role="note">
-<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id13">7</a><span class="fn-bracket">]</span></span>
-<p>Zhu, C and R H Byrd and J Nocedal. 1997. L-BFGS-B: Algorithm</p>
-<p>778: L-BFGS-B, FORTRAN routines for large scale bound constrained</p>
-<p>optimization. ACM Transactions on Mathematical Software 23 (4):</p>
-<p>550-560.</p>
-</aside>
-<aside class="footnote brackets" id="id27" role="note">
-<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id15">8</a><span class="fn-bracket">]</span></span>
-<p>Nash, S G. Newton-Type Minimization Via the Lanczos Method.</p>
-<ol class="arabic simple" start="1984">
-<li><p>SIAM Journal of Numerical Analysis 21: 770-778.</p></li>
-</ol>
-</aside>
-<aside class="footnote brackets" id="id28" role="note">
-<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id16">9</a><span class="fn-bracket">]</span></span>
-<p>Powell, M J D. A direct search optimization method that models</p>
-<p>the objective and constraint functions by linear interpolation.</p>
-<ol class="arabic simple" start="1994">
-<li><p>Advances in Optimization and Numerical Analysis, eds. S. Gomez</p></li>
-</ol>
-<p>and J-P Hennart, Kluwer Academic (Dordrecht), 51-67.</p>
-</aside>
-<aside class="footnote brackets" id="id29" role="note">
-<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id17">10</a><span class="fn-bracket">]</span></span>
-<p>Powell M J D. Direct search algorithms for optimization</p>
-<p>calculations. 1998. Acta Numerica 7: 287-336.</p>
-</aside>
-<aside class="footnote brackets" id="id30" role="note">
-<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id18">11</a><span class="fn-bracket">]</span></span>
-<p>Powell M J D. A view of algorithms for optimization without</p>
-<p>derivatives. 2007.Cambridge University Technical Report DAMTP</p>
-<p>2007/NA03</p>
-</aside>
-<aside class="footnote brackets" id="id31" role="note">
-<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id19">12</a><span class="fn-bracket">]</span></span>
-<p>Kraft, D. A software package for sequential quadratic</p>
-<p>programming. 1988. Tech. Rep. DFVLR-FB 88-28, DLR German Aerospace</p>
-<p>Center – Institute for Flight Mechanics, Koln, Germany.</p>
-</aside>
-<p>Examples</p>
-<p>Let us consider the problem of minimizing the Rosenbrock function. This</p>
-<p>function (and its respective derivatives) is implemented in <cite>rosen</cite></p>
-<p>(resp. <cite>rosen_der</cite>, <cite>rosen_hess</cite>) in the <cite>scipy.optimize</cite>.</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">scipy.optimize</span> <span class="kn">import</span> <span class="n">minimize</span><span class="p">,</span> <span class="n">rosen</span><span class="p">,</span> <span class="n">rosen_der</span>
-</pre></div>
-</div>
-<p>A simple application of the <em>Nelder-Mead</em> method is:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">x0</span> <span class="o">=</span> <span class="p">[</span><span class="mf">1.3</span><span class="p">,</span> <span class="mf">0.7</span><span class="p">,</span> <span class="mf">0.8</span><span class="p">,</span> <span class="mf">1.9</span><span class="p">,</span> <span class="mf">1.2</span><span class="p">]</span>
-<span class="n">res</span> <span class="o">=</span> <span class="n">minimize</span><span class="p">(</span><span class="n">rosen</span><span class="p">,</span> <span class="n">x0</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;Nelder-Mead&#39;</span><span class="p">)</span>
-<span class="n">res</span><span class="o">.</span><span class="n">x</span>
-<span class="p">[</span> <span class="mf">1.</span>  <span class="mf">1.</span>  <span class="mf">1.</span>  <span class="mf">1.</span>  <span class="mf">1.</span><span class="p">]</span>
-</pre></div>
-</div>
-<p>Now using the <em>BFGS</em> algorithm, using the first derivative and a few
-options:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">res</span> <span class="o">=</span> <span class="n">minimize</span><span class="p">(</span><span class="n">rosen</span><span class="p">,</span> <span class="n">x0</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;BFGS&#39;</span><span class="p">,</span> <span class="n">jac</span><span class="o">=</span><span class="n">rosen_der</span><span class="p">,</span>
-<span class="n">options</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;gtol&#39;</span><span class="p">:</span> <span class="mf">1e-6</span><span class="p">,</span> <span class="s1">&#39;disp&#39;</span><span class="p">:</span> <span class="kc">True</span><span class="p">})</span>
-<span class="n">Optimization</span> <span class="n">terminated</span> <span class="n">successfully</span><span class="o">.</span>
-<span class="n">Current</span> <span class="n">function</span> <span class="n">value</span><span class="p">:</span> <span class="mf">0.000000</span>
-<span class="n">Iterations</span><span class="p">:</span> <span class="mi">52</span>
-<span class="n">Function</span> <span class="n">evaluations</span><span class="p">:</span> <span class="mi">64</span>
-<span class="n">Gradient</span> <span class="n">evaluations</span><span class="p">:</span> <span class="mi">64</span>
-<span class="n">res</span><span class="o">.</span><span class="n">x</span>
-<span class="p">[</span> <span class="mf">1.</span>  <span class="mf">1.</span>  <span class="mf">1.</span>  <span class="mf">1.</span>  <span class="mf">1.</span><span class="p">]</span>
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span> <span class="n">res</span><span class="o">.</span><span class="n">message</span>
-<span class="n">Optimization</span> <span class="n">terminated</span> <span class="n">successfully</span><span class="o">.</span>
-<span class="n">res</span><span class="o">.</span><span class="n">hess</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">[[</span> <span class="mf">0.00749589</span>  <span class="mf">0.01255155</span>  <span class="mf">0.02396251</span>  <span class="mf">0.04750988</span>  <span class="mf">0.09495377</span><span class="p">]</span>
-<span class="p">[</span> <span class="mf">0.01255155</span>  <span class="mf">0.02510441</span>  <span class="mf">0.04794055</span>  <span class="mf">0.09502834</span>  <span class="mf">0.18996269</span><span class="p">]</span>
-<span class="p">[</span> <span class="mf">0.02396251</span>  <span class="mf">0.04794055</span>  <span class="mf">0.09631614</span>  <span class="mf">0.19092151</span>  <span class="mf">0.38165151</span><span class="p">]</span>
-<span class="p">[</span> <span class="mf">0.04750988</span>  <span class="mf">0.09502834</span>  <span class="mf">0.19092151</span>  <span class="mf">0.38341252</span>  <span class="mf">0.7664427</span> <span class="p">]</span>
-<span class="p">[</span> <span class="mf">0.09495377</span>  <span class="mf">0.18996269</span>  <span class="mf">0.38165151</span>  <span class="mf">0.7664427</span>   <span class="mf">1.53713523</span><span class="p">]]</span>
-</pre></div>
-</div>
-<p>Next, consider a minimization problem with several constraints (namely
-Example 16.4 from <a class="footnote-reference brackets" href="#id24" id="id32" role="doc-noteref"><span class="fn-bracket">[</span>5<span class="fn-bracket">]</span></a>). The objective function is:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">fun</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="mf">2.5</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span>
-<span class="n">There</span> <span class="n">are</span> <span class="n">three</span> <span class="n">constraints</span> <span class="n">defined</span> <span class="k">as</span><span class="p">:</span>
-<span class="n">cons</span> <span class="o">=</span> <span class="p">({</span><span class="s1">&#39;type&#39;</span><span class="p">:</span> <span class="s1">&#39;ineq&#39;</span><span class="p">,</span> <span class="s1">&#39;fun&#39;</span><span class="p">:</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span>  <span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="mi">2</span><span class="p">},</span>
-<span class="p">{</span><span class="s1">&#39;type&#39;</span><span class="p">:</span> <span class="s1">&#39;ineq&#39;</span><span class="p">,</span> <span class="s1">&#39;fun&#39;</span><span class="p">:</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="o">-</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="mi">6</span><span class="p">},</span>
-<span class="p">{</span><span class="s1">&#39;type&#39;</span><span class="p">:</span> <span class="s1">&#39;ineq&#39;</span><span class="p">,</span> <span class="s1">&#39;fun&#39;</span><span class="p">:</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="o">-</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="mi">2</span><span class="p">})</span>
-<span class="n">And</span> <span class="n">variables</span> <span class="n">must</span> <span class="n">be</span> <span class="n">positive</span><span class="p">,</span> <span class="n">hence</span> <span class="n">the</span> <span class="n">following</span> <span class="n">bounds</span><span class="p">:</span>
-<span class="n">bnds</span> <span class="o">=</span> <span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="kc">None</span><span class="p">),</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="kc">None</span><span class="p">))</span>
-<span class="n">The</span> <span class="n">optimization</span> <span class="n">problem</span> <span class="ow">is</span> <span class="n">solved</span> <span class="n">using</span> <span class="n">the</span> <span class="n">SLSQP</span> <span class="n">method</span> <span class="k">as</span><span class="p">:</span>
-<span class="n">res</span> <span class="o">=</span> <span class="n">minimize</span><span class="p">(</span><span class="n">fun</span><span class="p">,</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;SLSQP&#39;</span><span class="p">,</span> <span class="n">bounds</span><span class="o">=</span><span class="n">bnds</span><span class="p">,</span>
-<span class="n">constraints</span><span class="o">=</span><span class="n">cons</span><span class="p">)</span>
-<span class="n">It</span> <span class="n">should</span> <span class="n">converge</span> <span class="n">to</span> <span class="n">the</span> <span class="n">theoretical</span> <span class="n">solution</span> <span class="p">(</span><span class="mf">1.4</span> <span class="p">,</span><span class="mf">1.7</span><span class="p">)</span><span class="o">.</span>
-</pre></div>
-</div>
-<p>默认没有约束时，使用的是 BFGS 方法。</p>
-<p>利用 callback 参数查看迭代的历史：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">x0</span> <span class="o">=</span> <span class="p">[</span><span class="o">-</span><span class="mf">1.5</span><span class="p">,</span> <span class="mf">4.5</span><span class="p">]</span>
-<span class="n">xi</span> <span class="o">=</span> <span class="p">[</span><span class="n">x0</span><span class="p">]</span>
-<span class="n">result</span> <span class="o">=</span> <span class="n">minimize</span><span class="p">(</span><span class="n">rosen</span><span class="p">,</span> <span class="n">x0</span><span class="p">,</span> <span class="n">callback</span><span class="o">=</span><span class="n">xi</span><span class="o">.</span><span class="n">append</span><span class="p">)</span>
-<span class="n">xi</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">xi</span><span class="p">)</span>
-<span class="nb">print</span> <span class="n">xi</span><span class="o">.</span><span class="n">shape</span>
-<span class="nb">print</span> <span class="n">result</span><span class="o">.</span><span class="n">x</span>
-<span class="nb">print</span> <span class="s2">&quot;in </span><span class="si">{}</span><span class="s2"> function evaluations.&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">result</span><span class="o">.</span><span class="n">nfev</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">(</span><span class="mi">37</span><span class="n">L</span><span class="p">,</span> <span class="mi">2</span><span class="n">L</span><span class="p">)</span>
-<span class="p">[</span> <span class="mf">1.</span>  <span class="mf">1.</span><span class="p">]</span>
-<span class="ow">in</span> <span class="mi">200</span> <span class="n">function</span> <span class="n">evaluations</span><span class="o">.</span>
-</pre></div>
-</div>
-<p>绘图显示轨迹：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">meshgrid</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="mf">2.3</span><span class="p">,</span><span class="mf">1.75</span><span class="p">,</span><span class="mi">25</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="mf">0.5</span><span class="p">,</span><span class="mf">4.5</span><span class="p">,</span><span class="mi">25</span><span class="p">))</span>
-<span class="n">z</span> <span class="o">=</span> <span class="n">rosen</span><span class="p">([</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">])</span>
-<span class="n">fig</span> <span class="o">=</span> <span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span><span class="mf">5.5</span><span class="p">))</span>
-<span class="n">ax</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">gca</span><span class="p">(</span><span class="n">projection</span><span class="o">=</span><span class="s2">&quot;3d&quot;</span><span class="p">);</span> <span class="n">ax</span><span class="o">.</span><span class="n">azim</span> <span class="o">=</span> <span class="mi">70</span><span class="p">;</span> <span class="n">ax</span><span class="o">.</span><span class="n">elev</span> <span class="o">=</span> <span class="mi">75</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;X&quot;</span><span class="p">);</span> <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;Y&quot;</span><span class="p">);</span> <span class="n">ax</span><span class="o">.</span><span class="n">set_zlim</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span><span class="mi">1000</span><span class="p">))</span>
-<span class="n">p</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">plot_surface</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">,</span><span class="n">z</span><span class="p">,</span><span class="n">rstride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">cstride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cm</span><span class="o">.</span><span class="n">jet</span><span class="p">)</span>
-<span class="n">intermed</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">xi</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span> <span class="n">xi</span><span class="p">[:,</span><span class="mi">1</span><span class="p">],</span> <span class="n">rosen</span><span class="p">(</span><span class="n">xi</span><span class="o">.</span><span class="n">T</span><span class="p">),</span> <span class="s2">&quot;g-o&quot;</span><span class="p">)</span>
-<span class="n">rosen_min</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="mi">1</span><span class="p">],[</span><span class="mi">1</span><span class="p">],[</span><span class="mi">0</span><span class="p">],</span><span class="s2">&quot;ro&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-<img 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B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%0AYII=%0A" 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B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%0AYII=%0A" />
-<p>BFGS 需要计算函数的 Jacobian 矩阵：</p>
-<p>给定 <em>left[y_1,y_2,y_3right] = f(x_0, x_1, x_2)</em></p>
-<p><strong>J=left[ begin{matrix} frac{partial y_1}{partial x_0} &amp;amp; frac{partial y_1}{partial x_1} &amp;amp; frac{partial y_1}{partial x_2} \frac{partial y_2}{partial x_0} &amp;amp; frac{partial y_2}{partial x_1} &amp;amp; frac{partial y_2}{partial x_2} \frac{partial y_3}{partial x_0} &amp;amp; frac{partial y_3}{partial x_1} &amp;amp; frac{partial y_3}{partial x_2} end{matrix} right]</strong></p>
-<p>在我们的例子中</p>
-<p><a href="#id33"><span class="problematic" id="id34">**</span></a>J= left[ begin{matrix}frac{partial rosen}{partial x_0} &amp;amp; frac{partial rosen}{partial x_1} end{matrix} right] **</p>
-<p>导入 rosen 函数的 Jacobian 函数 rosen_der：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">scipy.optimize</span> <span class="kn">import</span> <span class="n">rosen_der</span>
-</pre></div>
-</div>
-<p>此时，我们将 Jacobian 矩阵作为参数传入：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">xi</span> <span class="o">=</span> <span class="p">[</span><span class="n">x0</span><span class="p">]</span>
-<span class="n">result</span> <span class="o">=</span> <span class="n">minimize</span><span class="p">(</span><span class="n">rosen</span><span class="p">,</span> <span class="n">x0</span><span class="p">,</span> <span class="n">jac</span><span class="o">=</span><span class="n">rosen_der</span><span class="p">,</span> <span class="n">callback</span><span class="o">=</span><span class="n">xi</span><span class="o">.</span><span class="n">append</span><span class="p">)</span>
-<span class="n">xi</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">xi</span><span class="p">)</span>
-<span class="nb">print</span> <span class="n">xi</span><span class="o">.</span><span class="n">shape</span>
-<span class="nb">print</span> <span class="s2">&quot;in </span><span class="si">{}</span><span class="s2"> function evaluations and </span><span class="si">{}</span><span class="s2"> jacobian     evaluations.&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">result</span><span class="o">.</span><span class="n">nfev</span><span class="p">,</span> <span class="n">result</span><span class="o">.</span><span class="n">njev</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">(</span><span class="mi">38</span><span class="n">L</span><span class="p">,</span> <span class="mi">2</span><span class="n">L</span><span class="p">)</span>
-<span class="ow">in</span> <span class="mi">49</span> <span class="n">function</span> <span class="n">evaluations</span> <span class="ow">and</span> <span class="mi">49</span> <span class="n">jacobian</span> <span class="n">evaluations</span><span class="o">.</span>
-</pre></div>
-</div>
-<p>可以看到，函数计算的开销大约减少了一半，迭代路径与上面的基本吻合：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">meshgrid</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="mf">2.3</span><span class="p">,</span><span class="mf">1.75</span><span class="p">,</span><span class="mi">25</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="mf">0.5</span><span class="p">,</span><span class="mf">4.5</span><span class="p">,</span><span class="mi">25</span><span class="p">))</span>
-<span class="n">z</span> <span class="o">=</span> <span class="n">rosen</span><span class="p">([</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">])</span>
-<span class="n">fig</span> <span class="o">=</span> <span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span><span class="mf">5.5</span><span class="p">))</span>
-<span class="n">ax</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">gca</span><span class="p">(</span><span class="n">projection</span><span class="o">=</span><span class="s2">&quot;3d&quot;</span><span class="p">);</span> <span class="n">ax</span><span class="o">.</span><span class="n">azim</span> <span class="o">=</span> <span class="mi">70</span><span class="p">;</span> <span class="n">ax</span><span class="o">.</span><span class="n">elev</span> <span class="o">=</span> <span class="mi">75</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;X&quot;</span><span class="p">);</span> <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;Y&quot;</span><span class="p">);</span> <span class="n">ax</span><span class="o">.</span><span class="n">set_zlim</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span><span class="mi">1000</span><span class="p">))</span>
-<span class="n">p</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">plot_surface</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">,</span><span class="n">z</span><span class="p">,</span><span class="n">rstride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">cstride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cm</span><span class="o">.</span><span class="n">jet</span><span class="p">)</span>
-<span class="n">intermed</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">xi</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span> <span class="n">xi</span><span class="p">[:,</span><span class="mi">1</span><span class="p">],</span> <span class="n">rosen</span><span class="p">(</span><span class="n">xi</span><span class="o">.</span><span class="n">T</span><span class="p">),</span> <span class="s2">&quot;g-o&quot;</span><span class="p">)</span>
-<span class="n">rosen_min</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="mi">1</span><span class="p">],[</span><span class="mi">1</span><span class="p">],[</span><span class="mi">0</span><span class="p">],</span><span class="s2">&quot;ro&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-<img 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B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%0ACOGxJFgVQgghhBAe6/8D6bfN7+5DVO4AAAAASUVORK5CYII=%0A" 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B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%0ACOGxJFgVQgghhBAe6/8D6bfN7+5DVO4AAAAASUVORK5CYII=%0A" />
-</section>
-<section id="nelder-mead-simplex">
-<h2>Nelder-Mead Simplex 算法<a class="headerlink" href="#nelder-mead-simplex" title="Permalink to this heading">¶</a></h2>
-<p>改变 minimize 使用的算法，使用 Nelder–Mead 单纯形算法：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">xi</span> <span class="o">=</span> <span class="p">[</span><span class="n">x0</span><span class="p">]</span>
-<span class="n">result</span> <span class="o">=</span> <span class="n">minimize</span><span class="p">(</span><span class="n">rosen</span><span class="p">,</span> <span class="n">x0</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s2">&quot;nelder-mead&quot;</span><span class="p">,</span> <span class="n">callback</span> <span class="o">=</span>     <span class="n">xi</span><span class="o">.</span><span class="n">append</span><span class="p">)</span>
-<span class="n">xi</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">xi</span><span class="p">)</span>
-<span class="nb">print</span> <span class="n">xi</span><span class="o">.</span><span class="n">shape</span>
-<span class="nb">print</span> <span class="s2">&quot;Solved the Nelder-Mead Simplex method with </span><span class="si">{}</span><span class="s2"> function evaluations.&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">result</span><span class="o">.</span><span class="n">nfev</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">(</span><span class="mi">120</span><span class="n">L</span><span class="p">,</span> <span class="mi">2</span><span class="n">L</span><span class="p">)</span>
-<span class="n">Solved</span> <span class="n">the</span> <span class="n">Nelder</span><span class="o">-</span><span class="n">Mead</span> <span class="n">Simplex</span> <span class="n">method</span> <span class="k">with</span> <span class="mi">226</span> <span class="n">function</span> <span class="n">evaluations</span><span class="o">.</span>
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">meshgrid</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="mf">1.9</span><span class="p">,</span><span class="mf">1.75</span><span class="p">,</span><span class="mi">25</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="mf">0.5</span><span class="p">,</span><span class="mf">4.5</span><span class="p">,</span><span class="mi">25</span><span class="p">))</span>
-<span class="n">z</span> <span class="o">=</span> <span class="n">rosen</span><span class="p">([</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">])</span>
-<span class="n">fig</span> <span class="o">=</span> <span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span><span class="mf">5.5</span><span class="p">))</span>
-<span class="n">ax</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">gca</span><span class="p">(</span><span class="n">projection</span><span class="o">=</span><span class="s2">&quot;3d&quot;</span><span class="p">);</span> <span class="n">ax</span><span class="o">.</span><span class="n">azim</span> <span class="o">=</span> <span class="mi">70</span><span class="p">;</span> <span class="n">ax</span><span class="o">.</span><span class="n">elev</span> <span class="o">=</span> <span class="mi">75</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;X&quot;</span><span class="p">);</span> <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;Y&quot;</span><span class="p">);</span> <span class="n">ax</span><span class="o">.</span><span class="n">set_zlim</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span><span class="mi">1000</span><span class="p">))</span>
-<span class="n">p</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">plot_surface</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">,</span><span class="n">z</span><span class="p">,</span><span class="n">rstride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">cstride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cm</span><span class="o">.</span><span class="n">jet</span><span class="p">)</span>
-<span class="n">intermed</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">xi</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span> <span class="n">xi</span><span class="p">[:,</span><span class="mi">1</span><span class="p">],</span> <span class="n">rosen</span><span class="p">(</span><span class="n">xi</span><span class="o">.</span><span class="n">T</span><span class="p">),</span> <span class="s2">&quot;g-o&quot;</span><span class="p">)</span>
-<span class="n">rosen_min</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="mi">1</span><span class="p">],[</span><span class="mi">1</span><span class="p">],[</span><span class="mi">0</span><span class="p">],</span><span class="s2">&quot;ro&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-<img 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L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%0AEEIIIYTP+v/YLseLtZ7EnQAAAABJRU5ErkJggg==%0A" 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L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%0AEEIIIYTP+v/YLseLtZ7EnQAAAABJRU5ErkJggg==%0A" />
-</section>
-<section id="powell">
-<h2>Powell 算法<a class="headerlink" href="#powell" title="Permalink to this heading">¶</a></h2>
-<p>使用 Powell 算法</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">xi</span> <span class="o">=</span> <span class="p">[</span><span class="n">x0</span><span class="p">]</span>
-<span class="n">result</span> <span class="o">=</span> <span class="n">minimize</span><span class="p">(</span><span class="n">rosen</span><span class="p">,</span> <span class="n">x0</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s2">&quot;powell&quot;</span><span class="p">,</span> <span class="n">callback</span><span class="o">=</span><span class="n">xi</span><span class="o">.</span><span class="n">append</span><span class="p">)</span>
-<span class="n">xi</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">xi</span><span class="p">)</span>
-<span class="nb">print</span> <span class="n">xi</span><span class="o">.</span><span class="n">shape</span>
-<span class="nb">print</span> <span class="s2">&quot;Solved Powell&#39;s method with </span><span class="si">{}</span><span class="s2"> function evaluations.&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">result</span><span class="o">.</span><span class="n">nfev</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">(</span><span class="mi">31</span><span class="n">L</span><span class="p">,</span> <span class="mi">2</span><span class="n">L</span><span class="p">)</span>
-<span class="n">Solved</span> <span class="n">Powell</span><span class="s1">&#39;s method with 855 function evaluations.</span>
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">meshgrid</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="mf">2.3</span><span class="p">,</span><span class="mf">1.75</span><span class="p">,</span><span class="mi">25</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="mf">0.5</span><span class="p">,</span><span class="mf">4.5</span><span class="p">,</span><span class="mi">25</span><span class="p">))</span>
-<span class="n">z</span> <span class="o">=</span> <span class="n">rosen</span><span class="p">([</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">])</span>
-<span class="n">fig</span> <span class="o">=</span> <span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span><span class="mf">5.5</span><span class="p">))</span>
-<span class="n">ax</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">gca</span><span class="p">(</span><span class="n">projection</span><span class="o">=</span><span class="s2">&quot;3d&quot;</span><span class="p">);</span> <span class="n">ax</span><span class="o">.</span><span class="n">azim</span> <span class="o">=</span> <span class="mi">70</span><span class="p">;</span> <span class="n">ax</span><span class="o">.</span><span class="n">elev</span> <span class="o">=</span> <span class="mi">75</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;X&quot;</span><span class="p">);</span> <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;Y&quot;</span><span class="p">);</span> <span class="n">ax</span><span class="o">.</span><span class="n">set_zlim</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span><span class="mi">1000</span><span class="p">))</span>
-<span class="n">p</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">plot_surface</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">,</span><span class="n">z</span><span class="p">,</span><span class="n">rstride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">cstride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cm</span><span class="o">.</span><span class="n">jet</span><span class="p">)</span>
-<span class="n">intermed</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">xi</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span> <span class="n">xi</span><span class="p">[:,</span><span class="mi">1</span><span class="p">],</span> <span class="n">rosen</span><span class="p">(</span><span class="n">xi</span><span class="o">.</span><span class="n">T</span><span class="p">),</span> <span class="s2">&quot;g-o&quot;</span><span class="p">)</span>
-<span class="n">rosen_min</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="mi">1</span><span class="p">],[</span><span class="mi">1</span><span class="p">],[</span><span class="mi">0</span><span class="p">],</span><span class="s2">&quot;ro&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-<img 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B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%0AQgghhBBeS4JVIYQQQgjhtf4/QWwBBPzDxt8AAAAASUVORK5CYII=%0A" 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B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%0AQgghhBBeS4JVIYQQQgjhtf4/QWwBBPzDxt8AAAAASUVORK5CYII=%0A" />
-</section>
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-<h2>作者 &amp; 更新时间<a class="headerlink" href="#id35" title="Permalink to this heading">¶</a></h2>
-<p>作者:<cite>李金  &lt;lijinwithyou&#64;gmail.com&gt;</cite></p>
-<p>提交: 2017/12/14</p>
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diff --git a/docs/aipy-scipy/cn/7integral.html b/docs/aipy-scipy/cn/7integral.html
deleted file mode 100644
index 496531f..0000000
--- a/docs/aipy-scipy/cn/7integral.html
+++ /dev/null
@@ -1,734 +0,0 @@
-<!DOCTYPE html>
-<html lang="en-US">
-<head>
-<meta charset="utf-8" />
-<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
-<link href="https://cdn.bootcss.com/bootstrap/3.3.7/css/bootstrap.min.css" rel="stylesheet">
-<title>积分</title>
-<link rel="stylesheet" type="text/css" href="../../static/pygments.css" />
-<link rel="stylesheet" type="text/css" href="../../static/style.css" />
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-<span class="header-title-selected"></span>
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-<a href="http://www.aipy.org">AIPY</a>
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-<li>
-<a target="_blank" href="https://github.com/qpython-android/qpython">Github</a>
-</li>
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-<div class="document">
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-<div class="body" role="main">
-<section id="id1">
-<h1>积分<a class="headerlink" href="#id1" title="Permalink to this heading">¶</a></h1>
-<section id="id2">
-<h2>符号积分<a class="headerlink" href="#id2" title="Permalink to this heading">¶</a></h2>
-<p>积分与求导的关系：</p>
-<p><strong>frac{d}{dx} F(x) = f(x)
-Rightarrow F(x) = int f(x) dx</strong></p>
-<p>符号运算可以用 sympy 模块完成。</p>
-<p>先导入 init_printing 模块方便其显示：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sympy</span> <span class="kn">import</span> <span class="n">init_printing</span>
-<span class="n">init_printing</span><span class="p">()</span>
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sympy</span> <span class="kn">import</span> <span class="n">symbols</span><span class="p">,</span> <span class="n">integrate</span>
-<span class="kn">import</span> <span class="nn">sympy</span>
-</pre></div>
-</div>
-<p>产生 x 和 y 两个符号变量，并进行运算：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">symbols</span><span class="p">(</span><span class="s1">&#39;x y&#39;</span><span class="p">)</span>
-<span class="n">sympy</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">x</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">+</span> <span class="n">y</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">sqrt</span><span class="p">{</span><span class="n">x</span><span class="o">^</span><span class="p">{</span><span class="mi">2</span><span class="p">}</span> <span class="o">+</span> <span class="n">y</span><span class="o">^</span><span class="p">{</span><span class="mi">2</span><span class="p">}}</span>
-</pre></div>
-</div>
-<p>对于生成的符号变量 z，我们将其中的 x 利用 subs 方法替换为 3：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">z</span> <span class="o">=</span> <span class="n">sympy</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">x</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">+</span> <span class="n">y</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span>
-<span class="n">z</span><span class="o">.</span><span class="n">subs</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">sqrt</span><span class="p">{</span><span class="n">y</span><span class="o">^</span><span class="p">{</span><span class="mi">2</span><span class="p">}</span> <span class="o">+</span> <span class="mi">9</span><span class="p">}</span>
-</pre></div>
-</div>
-<p>再替换 y：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">z</span><span class="o">.</span><span class="n">subs</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span><span class="o">.</span><span class="n">subs</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="mi">5</span>
-</pre></div>
-</div>
-<p>还可以从 sympy.abc 中导入现成的符号变量：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sympy.abc</span> <span class="kn">import</span> <span class="n">theta</span>
-<span class="n">y</span> <span class="o">=</span> <span class="n">sympy</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">theta</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span>
-<span class="n">y</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">sin</span><span class="o">^</span><span class="p">{</span><span class="mi">2</span><span class="p">}{</span>\<span class="n">left</span> <span class="p">(</span>\<span class="n">theta</span> \<span class="n">right</span> <span class="p">)}</span>
-</pre></div>
-</div>
-<p>对 y 进行积分：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Y</span> <span class="o">=</span> <span class="n">integrate</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
-<span class="n">Y</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">frac</span><span class="p">{</span>\<span class="n">theta</span><span class="p">}{</span><span class="mi">2</span><span class="p">}</span> <span class="o">-</span> \<span class="n">frac</span><span class="p">{</span><span class="mi">1</span><span class="p">}{</span><span class="mi">2</span><span class="p">}</span> \<span class="n">sin</span><span class="p">{</span>\<span class="n">left</span> <span class="p">(</span>\<span class="n">theta</span> \<span class="n">right</span> <span class="p">)}</span> \<span class="n">cos</span><span class="p">{</span>\<span class="n">left</span> <span class="p">(</span>\<span class="n">theta</span> \<span class="n">right</span> <span class="p">)}</span>
-</pre></div>
-</div>
-<p>计算 <em>Y(pi) - Y(0)</em> ：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="n">np</span><span class="o">.</span><span class="n">set_printoptions</span><span class="p">(</span><span class="n">precision</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
-<span class="n">Y</span><span class="o">.</span><span class="n">subs</span><span class="p">(</span><span class="n">theta</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span> <span class="o">-</span> <span class="n">Y</span><span class="o">.</span><span class="n">subs</span><span class="p">(</span><span class="n">theta</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="mf">1.5707963267949</span>
-</pre></div>
-</div>
-<p>计算 int_0^pi y dtheta ：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">integrate</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="p">(</span><span class="n">theta</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">sympy</span><span class="o">.</span><span class="n">pi</span><span class="p">))</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">frac</span><span class="p">{</span>\<span class="n">pi</span><span class="p">}{</span><span class="mi">2</span><span class="p">}</span>
-</pre></div>
-</div>
-<p>显示的是字符表达式，查看具体数值可以使用 evalf() 方法，或者传入 numpy.pi，而不是 sympy.pi ：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">integrate</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="p">(</span><span class="n">theta</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">sympy</span><span class="o">.</span><span class="n">pi</span><span class="p">))</span><span class="o">.</span><span class="n">evalf</span><span class="p">()</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="mf">1.5707963267949</span>
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">integrate</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="p">(</span><span class="n">theta</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">))</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="mf">1.5707963267949</span>
-</pre></div>
-</div>
-<p>根据牛顿莱布尼兹公式，这两个数值应该相等。
-产生不定积分对象：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Y_indef</span> <span class="o">=</span> <span class="n">sympy</span><span class="o">.</span><span class="n">Integral</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
-<span class="n">Y_indef</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">int</span> \<span class="n">sin</span><span class="o">^</span><span class="p">{</span><span class="mi">2</span><span class="p">}{</span>\<span class="n">left</span> <span class="p">(</span>\<span class="n">theta</span> \<span class="n">right</span> <span class="p">)}</span>\<span class="p">,</span> <span class="n">d</span>\<span class="n">theta</span>
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span> <span class="nb">type</span><span class="p">(</span><span class="n">Y_indef</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>&lt;class ‘sympy.integrals.integrals.Integral’&gt;</p>
-<p>定积分：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Y_def</span> <span class="o">=</span> <span class="n">sympy</span><span class="o">.</span><span class="n">Integral</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="p">(</span><span class="n">theta</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">sympy</span><span class="o">.</span><span class="n">pi</span><span class="p">))</span>
-<span class="n">Y_def</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">int_</span><span class="p">{</span><span class="mi">0</span><span class="p">}</span><span class="o">^</span><span class="p">{</span>\<span class="n">pi</span><span class="p">}</span> \<span class="n">sin</span><span class="o">^</span><span class="p">{</span><span class="mi">2</span><span class="p">}{</span>\<span class="n">left</span> <span class="p">(</span>\<span class="n">theta</span> \<span class="n">right</span> <span class="p">)}</span>\<span class="p">,</span> <span class="n">d</span>\<span class="n">theta</span>
-</pre></div>
-</div>
-<p>产生函数 <em>Y(x) = int_0^x sin^2(theta) dtheta</em>，并将其向量化：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Y_raw</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">integrate</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="p">(</span><span class="n">theta</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">x</span><span class="p">))</span>
-<span class="n">Y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vectorize</span><span class="p">(</span><span class="n">Y_raw</span><span class="p">)</span>
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="o">%</span><span class="n">matplotlib</span> <span class="n">inline</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span>
-<span class="n">p</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">Y</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
-<span class="n">t</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;$Y(x) = \int_0^x sin^2(\theta) d\theta$&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-</section>
-<section id="id3">
-<h2>数值积分<a class="headerlink" href="#id3" title="Permalink to this heading">¶</a></h2>
-<p>数值积分：</p>
-<blockquote>
-<div><p>F(x) = lim_{n rightarrow infty} sum_{i=0}^{n-1} f(x_i)(x_{i+1}-x_i)</p>
-</div></blockquote>
-<p>Rightarrow F(x) = int_{x_0}^{x_n} f(x) dx</p>
-<p>导入贝塞尔函数：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">scipy.special</span> <span class="kn">import</span> <span class="n">jv</span>
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>def f(x):
-    return jv(2.5, x)
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
-<span class="n">p</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">f</span><span class="p">(</span><span class="n">x</span><span class="p">),</span> <span class="s1">&#39;k-&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-</section>
-<section id="quad">
-<h2>quad 函数<a class="headerlink" href="#quad" title="Permalink to this heading">¶</a></h2>
-<p>Quadrature 积分的原理参见：</p>
-<p><a class="reference external" href="http://en.wikipedia.org/wiki/Numerical_integration#Quadrature_rules_based_on_interpolating_functions">http://en.wikipedia.org/wiki/Numerical_integration#Quadrature_rules_based_on_interpolating_functions</a></p>
-<p>quad 返回一个 (积分值，误差) 组成的元组：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">scipy.integrate</span> <span class="kn">import</span> <span class="n">quad</span>
-<span class="n">interval</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mf">6.5</span><span class="p">]</span>
-<span class="n">value</span><span class="p">,</span> <span class="n">max_err</span> <span class="o">=</span> <span class="n">quad</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="o">*</span><span class="n">interval</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>积分值：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span> <span class="n">value</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="mf">1.28474297234</span>
-</pre></div>
-</div>
-<p>最大误差：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span> <span class="n">max_err</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="mf">2.34181853668e-09</span>
-</pre></div>
-</div>
-<p>积分区间图示，蓝色为正，红色为负：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span> <span class="s2">&quot;integral = </span><span class="si">{:.9f}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">value</span><span class="p">)</span>
-<span class="nb">print</span> <span class="s2">&quot;upper bound on error: </span><span class="si">{:.2e}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">max_err</span><span class="p">)</span>
-<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
-<span class="n">p</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">f</span><span class="p">(</span><span class="n">x</span><span class="p">),</span> <span class="s1">&#39;k-&#39;</span><span class="p">)</span>
-<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mf">6.5</span><span class="p">,</span> <span class="mi">45</span><span class="p">)</span>
-<span class="n">p</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">fill_between</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">f</span><span class="p">(</span><span class="n">x</span><span class="p">),</span> <span class="n">where</span><span class="o">=</span><span class="n">f</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="o">&gt;</span><span class="mi">0</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;blue&quot;</span><span class="p">)</span>
-<span class="n">p</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">fill_between</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">f</span><span class="p">(</span><span class="n">x</span><span class="p">),</span> <span class="n">where</span><span class="o">=</span><span class="n">f</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="o">&lt;</span><span class="mi">0</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;red&quot;</span><span class="p">,</span>     <span class="n">interpolate</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">integral</span> <span class="o">=</span> <span class="mf">1.284742972</span>
-<span class="n">upper</span> <span class="n">bound</span> <span class="n">on</span> <span class="n">error</span><span class="p">:</span> <span class="mf">2.34e-09</span>
-</pre></div>
-</div>
-<img 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r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%0AR6zoG2NMHLGib4wxceT/AZhppPNX9o1QAAAAAElFTkSuQmCC%0A" 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r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%0AR6zoG2NMHLGib4wxceT/AZhppPNX9o1QAAAAAElFTkSuQmCC%0A" />
-</section>
-<section id="id4">
-<h2>积分到无穷<a class="headerlink" href="#id4" title="Permalink to this heading">¶</a></h2>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>from numpy import inf
-interval = [0., inf]
-def g(x):
-    return np.exp(-x ** 1/2)
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>value, max_err = quad(g, *interval)
-x = np.linspace(0, 10, 50)
-fig = plt.figure(figsize=(10,3))
-p = plt.plot(x, g(x), &#39;k-&#39;)
-p = plt.fill_between(x, g(x))
-plt.annotate(r&quot;$\int_0^{\infty}e^{-x^1/2}dx = $&quot; + &quot;{}&quot;.format    (value), (4, 0.6),
-    fontsize=16)
-print &quot;upper bound on error: {:.1e}&quot;.format(max_err)
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">upper</span> <span class="n">bound</span> <span class="n">on</span> <span class="n">error</span><span class="p">:</span> <span class="mf">7.2e-11</span>
-</pre></div>
-</div>
-<img alt="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAlQAAADICAYAAAAuo384AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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%0ATkSuQmCC%0A" 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g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%0ATkSuQmCC%0A" />
-</section>
-<section id="id5">
-<h2>双重积分<a class="headerlink" href="#id5" title="Permalink to this heading">¶</a></h2>
-<p>假设我们要进行如下的积分：</p>
-<blockquote>
-<div><p>I_n = int limits_0^{infty} int limits_1^{infty} frac{e^{-xt}}{t^n}dt dx = frac{1}{n}</p>
-</div></blockquote>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>def h(x, t, n):
-    &quot;&quot;&quot;core function, takes x, t, n&quot;&quot;&quot;
-    return np.exp(-x * t) / (t ** n)
-</pre></div>
-</div>
-<p>一种方式是调用两次 quad 函数，不过这里 quad 的返回值不能向量化，所以使用了修饰符 vectorize 将其向量化：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>from numpy import vectorize
-@vectorize
-def int_h_dx(t, n):
-    &quot;&quot;&quot;Time integrand of h(x).&quot;&quot;&quot;
-    return quad(h, 0, np.inf, args=(t, n))[0]
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>@vectorize
-def I_n(n):
-    return quad(int_h_dx, 1, np.inf, args=(n))
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">I_n</span><span class="p">([</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">,</span> <span class="mi">5</span><span class="p">])</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">(</span><span class="n">array</span><span class="p">([</span> <span class="mf">1.97</span><span class="p">,</span>  <span class="mf">1.</span>  <span class="p">,</span>  <span class="mf">0.5</span> <span class="p">,</span>  <span class="mf">0.2</span> <span class="p">]),</span>
-<span class="n">array</span><span class="p">([</span>  <span class="mf">9.804e-13</span><span class="p">,</span>   <span class="mf">1.110e-14</span><span class="p">,</span>   <span class="mf">5.551e-15</span><span class="p">,</span>   <span class="mf">2.220e-15</span><span class="p">]))</span>
-</pre></div>
-</div>
-<p>或者直接调用 dblquad 函数，并将积分参数传入，传入方式有多种，后传入的先进行积分：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>from scipy.integrate import dblquad
-@vectorize
-def I(n):
-    &quot;&quot;&quot;Same as I_n, but using the built-in dblquad&quot;&quot;&quot;
-    x_lower = 0
-    x_upper = np.inf
-    return dblquad(h,
-        lambda t_lower: 1, lambda t_upper: np.inf,
-        x_lower, x_upper, args=(n,))
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">I_n</span><span class="p">([</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">,</span> <span class="mi">5</span><span class="p">])</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">(</span><span class="n">array</span><span class="p">([</span> <span class="mf">1.97</span><span class="p">,</span>  <span class="mf">1.</span>  <span class="p">,</span>  <span class="mf">0.5</span> <span class="p">,</span>  <span class="mf">0.2</span> <span class="p">]),</span>
-<span class="n">array</span><span class="p">([</span>  <span class="mf">9.804e-13</span><span class="p">,</span>   <span class="mf">1.110e-14</span><span class="p">,</span>   <span class="mf">5.551e-15</span><span class="p">,</span>   <span class="mf">2.220e-15</span><span class="p">]))</span>
-</pre></div>
-</div>
-</section>
-<section id="id6">
-<h2>采样点积分<a class="headerlink" href="#id6" title="Permalink to this heading">¶</a></h2>
-<p><strong>trapz</strong> 方法 和 <strong>simps</strong> 方法</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">scipy.integrate</span> <span class="kn">import</span> <span class="n">trapz</span><span class="p">,</span> <span class="n">simps</span>
-</pre></div>
-</div>
-<p>sin 函数， 100 个采样点和 5 个采样点：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">x_s</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
-<span class="n">y_s</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">x_s</span><span class="p">)</span>
-<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
-<span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">p</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="s1">&#39;k:&#39;</span><span class="p">)</span>
-<span class="n">p</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x_s</span><span class="p">,</span> <span class="n">y_s</span><span class="p">,</span> <span class="s1">&#39;k+-&#39;</span><span class="p">)</span>
-<span class="n">p</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">fill_between</span><span class="p">(</span><span class="n">x_s</span><span class="p">,</span> <span class="n">y_s</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;gray&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-<img 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R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%0AQmCC%0A" 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R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%0AQmCC%0A" />
-<p>采用 trapezoidal 方法 和 simpson 方法 对这些采样点进行积分（函数积分为 2）：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">result_s</span> <span class="o">=</span> <span class="n">trapz</span><span class="p">(</span><span class="n">y_s</span><span class="p">,</span> <span class="n">x_s</span><span class="p">)</span>
-<span class="n">result_s_s</span> <span class="o">=</span> <span class="n">simps</span><span class="p">(</span><span class="n">y_s</span><span class="p">,</span> <span class="n">x_s</span><span class="p">)</span>
-<span class="n">result</span> <span class="o">=</span> <span class="n">trapz</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span>
-<span class="nb">print</span> <span class="s2">&quot;Trapezoidal Integration over 5 points : </span><span class="si">{:.3f}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">result_s</span><span class="p">)</span>
-<span class="nb">print</span> <span class="s2">&quot;Simpson Integration over 5 points : </span><span class="si">{:.3f}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">result_s_s</span><span class="p">)</span>
-<span class="nb">print</span> <span class="s2">&quot;Trapezoidal Integration over 100 points : </span><span class="si">{:.3f}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">result</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>Trapezoidal Integration over 5 points : 1.896</p>
-<p>Simpson Integration over 5 points : 2.005</p>
-<p>Trapezoidal Integration over 100 points : 2.000</p>
-</section>
-<section id="ufunc">
-<h2>使用 ufunc 进行积分<a class="headerlink" href="#ufunc" title="Permalink to this heading">¶</a></h2>
-<p>Numpy 中有很多 ufunc 对象：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">type</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">add</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">numpy</span><span class="o">.</span><span class="n">ufunc</span>
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">np</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">add</span><span class="o">.</span><span class="n">accumulate</span><span class="p">)</span>
-<span class="n">accumulate</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>Accumulate the result of applying the operator to all elements.</p>
-<p>For a one-dimensional array, accumulate produces results equivalent</p>
-<p>to:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">r</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">A</span><span class="p">))</span>
-<span class="n">t</span> <span class="o">=</span> <span class="n">op</span><span class="o">.</span><span class="n">identity</span>        <span class="c1"># op = the ufunc being applied to A&#39;s</span>
-</pre></div>
-</div>
-<p>elements</p>
-<blockquote>
-<div><p>for i in range(len(A)):</p>
-<blockquote>
-<div><p>t = op(t, A[i])</p>
-<p>r[i] = t</p>
-</div></blockquote>
-<p>return r</p>
-</div></blockquote>
-<p>For example, add.accumulate() is equivalent to np.cumsum().</p>
-<p>For a multi-dimensional array, accumulate is applied along only one</p>
-<p>axis (axis zero by default; see Examples below) so repeated use is</p>
-<p>necessary if one wants to accumulate over multiple axes.</p>
-<p>Parameters</p>
-<p>array : array_like</p>
-<blockquote>
-<div><p>The array to act on.</p>
-</div></blockquote>
-<p>axis : int, optional</p>
-<blockquote>
-<div><p>The axis along which to apply the accumulation; default is zero.</p>
-</div></blockquote>
-<p>dtype : data-type code, optional</p>
-<blockquote>
-<div><p>The data-type used to represent the intermediate results. Defaults</p>
-<p>to the data-type of the output array if such is provided, or the</p>
-<p>the data-type of the input array if no output array is provided.</p>
-</div></blockquote>
-<p>out : ndarray, optional</p>
-<blockquote>
-<div><p>A location into which the result is stored. If not provided a</p>
-<p>freshly-allocated array is returned.</p>
-</div></blockquote>
-<p>Returns</p>
-<p>r : ndarray</p>
-<blockquote>
-<div><p>The accumulated values. If <cite>out</cite> was supplied, <cite>r</cite> is a reference</p>
-</div></blockquote>
-<p>to</p>
-<blockquote>
-<div><p><cite>out</cite>.</p>
-</div></blockquote>
-<p>Examples</p>
-<p>1-D array examples:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">np</span><span class="o">.</span><span class="n">add</span><span class="o">.</span><span class="n">accumulate</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">])</span>
-<span class="n">array</span><span class="p">([</span> <span class="mi">2</span><span class="p">,</span>  <span class="mi">5</span><span class="p">,</span> <span class="mi">10</span><span class="p">])</span>
-<span class="n">np</span><span class="o">.</span><span class="n">multiply</span><span class="o">.</span><span class="n">accumulate</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">])</span>
-<span class="n">array</span><span class="p">([</span> <span class="mi">2</span><span class="p">,</span>  <span class="mi">6</span><span class="p">,</span> <span class="mi">30</span><span class="p">])</span>
-</pre></div>
-</div>
-<p>2-D array examples:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>I = np.eye(2)
-I
-array([[ 1.,  0.],
-    [ 0.,  1.]])
-</pre></div>
-</div>
-<p>Accumulate along axis 0 (rows), down columns:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>np.add.accumulate(I, 0)
-array([[ 1.,  0.],
-    [ 1.,  1.]])
-np.add.accumulate(I) # no axis specified = axis zero
-array([[ 1.,  0.],
-    [ 1.,  1.]])
-</pre></div>
-</div>
-<p>Accumulate along axis 1 (columns), through rows:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>np.add.accumulate(I, 1)
-array([[ 1.,  1.],
-    [ 0.,  1.]])
-</pre></div>
-</div>
-<p>np.add.accumulate 相当于 cumsum ：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">result_np</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">add</span><span class="o">.</span><span class="n">accumulate</span><span class="p">(</span><span class="n">y</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">-</span> <span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">/</span> <span class="mi">2</span>
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">p</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="s1">&#39;rx&#39;</span><span class="p">)</span>
-<span class="n">p</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">result_np</span><span class="p">)</span>
-</pre></div>
-</div>
-</section>
-<section id="id7">
-<h2>速度比较<a class="headerlink" href="#id7" title="Permalink to this heading">¶</a></h2>
-<p>计算积分： int_0^x sin theta dtheta</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">sympy</span>
-<span class="kn">from</span> <span class="nn">sympy.abc</span> <span class="kn">import</span> <span class="n">x</span><span class="p">,</span> <span class="n">theta</span>
-<span class="n">sympy_x</span> <span class="o">=</span> <span class="n">x</span>
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">20</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span> <span class="mf">1e+4</span><span class="p">)</span>
-<span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-<span class="n">sympy_y</span> <span class="o">=</span> <span class="n">vectorize</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">sympy</span><span class="o">.</span><span class="n">integrate</span><span class="p">(</span><span class="n">sympy</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">theta</span><span class="p">),</span> <span class="p">(</span><span class="n">theta</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">x</span><span class="p">)))</span>
-</pre></div>
-</div>
-<p>numpy 方法：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="o">%</span><span class="n">timeit</span> <span class="n">np</span><span class="o">.</span><span class="n">add</span><span class="o">.</span><span class="n">accumulate</span><span class="p">(</span><span class="n">y</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-<span class="n">y0</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">add</span><span class="o">.</span><span class="n">accumulate</span><span class="p">(</span><span class="n">y</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-<span class="nb">print</span> <span class="n">y0</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
-</pre></div>
-</div>
-<p>The slowest run took 4.32 times longer than the fastest. This could</p>
-<p>mean that an intermediate result is being cached</p>
-<p>10000 loops, best of 3: 56.2 μs per loop</p>
-<p>-2.34138044756e-17</p>
-<p>quad 方法：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="o">%</span><span class="n">timeit</span> <span class="n">quad</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">20</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span>
-<span class="n">y2</span> <span class="o">=</span> <span class="n">quad</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">20</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span> <span class="n">full_output</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="nb">print</span> <span class="s2">&quot;result = &quot;</span><span class="p">,</span> <span class="n">y2</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-<span class="nb">print</span> <span class="s2">&quot;number of evaluations&quot;</span><span class="p">,</span> <span class="n">y2</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">][</span><span class="s1">&#39;neval&#39;</span><span class="p">]</span>
-</pre></div>
-</div>
-<p>10000 loops, best of 3: 40.5 μs per loop</p>
-<p>result =  3.43781337153e-15</p>
-<p>number of evaluations 21</p>
-<p>trapz 方法：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="o">%</span><span class="n">timeit</span> <span class="n">trapz</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span>
-<span class="n">y1</span> <span class="o">=</span> <span class="n">trapz</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span>
-<span class="nb">print</span> <span class="n">y1</span>
-</pre></div>
-</div>
-<p>10000 loops, best of 3: 105 μs per loop</p>
-<p>-4.4408920985e-16</p>
-<p>simps 方法：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="o">%</span><span class="n">timeit</span> <span class="n">simps</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span>
-<span class="n">y3</span> <span class="o">=</span> <span class="n">simps</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span>
-<span class="nb">print</span> <span class="n">y3</span>
-</pre></div>
-</div>
-<p>1000 loops, best of 3: 801 μs per loop</p>
-<p>3.28428554968e-16</p>
-<p>sympy 积分方法：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="o">%</span><span class="n">timeit</span> <span class="n">sympy_y</span><span class="p">(</span><span class="mi">20</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span>
-<span class="n">y4</span> <span class="o">=</span> <span class="n">sympy_y</span><span class="p">(</span><span class="mi">20</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span>
-<span class="nb">print</span> <span class="n">y4</span>
-</pre></div>
-</div>
-<p>100 loops, best of 3: 6.86 ms per loop</p>
-<p>0</p>
-</section>
-<section id="id8">
-<h2>作者 &amp; 更新时间<a class="headerlink" href="#id8" title="Permalink to this heading">¶</a></h2>
-<p>作者:<cite>李金  &lt;lijinwithyou&#64;gmail.com&gt;</cite></p>
-<p>提交: 2017/12/14</p>
-</section>
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diff --git a/docs/aipy-scipy/cn/8differential-equation.html b/docs/aipy-scipy/cn/8differential-equation.html
deleted file mode 100644
index 5e373a5..0000000
--- a/docs/aipy-scipy/cn/8differential-equation.html
+++ /dev/null
@@ -1,363 +0,0 @@
-<!DOCTYPE html>
-<html lang="en-US">
-<head>
-<meta charset="utf-8" />
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-<title>解微分方程</title>
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-<section id="id1">
-<h1>解微分方程<a class="headerlink" href="#id1" title="Permalink to this heading">¶</a></h1>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="o">%</span><span class="n">pylab</span> <span class="n">inline</span>
-</pre></div>
-</div>
-<p>Populating the interactive namespace from numpy and matplotlib</p>
-</section>
-<section id="id2">
-<h1>积分求解<a class="headerlink" href="#id2" title="Permalink to this heading">¶</a></h1>
-</section>
-<section id="id3">
-<h1>简单的例子<a class="headerlink" href="#id3" title="Permalink to this heading">¶</a></h1>
-<p>frac{dy}{dt} = sin(t)</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>def dy_dt(y, t):
-    return np.sin(t)
-</pre></div>
-</div>
-<p>积分求解：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">scipy.integrate</span> <span class="kn">import</span> <span class="n">odeint</span>
-<span class="n">t</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="o">*</span><span class="n">pi</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
-<span class="n">result</span> <span class="o">=</span> <span class="n">odeint</span><span class="p">(</span><span class="n">dy_dt</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">t</span><span class="p">)</span>
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span> <span class="o">=</span> <span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span><span class="mi">4</span><span class="p">))</span>
-<span class="n">p</span> <span class="o">=</span> <span class="n">plot</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">result</span><span class="p">,</span> <span class="s2">&quot;rx&quot;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">r</span><span class="s2">&quot;$\int_</span><span class="si">{0}</span><span class="s2">^</span><span class="si">{x}</span><span class="s2">sin(t) dt $&quot;</span><span class="p">)</span>
-<span class="n">p</span> <span class="o">=</span> <span class="n">plot</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="o">-</span><span class="n">cos</span><span class="p">(</span><span class="n">t</span><span class="p">)</span> <span class="o">+</span> <span class="n">cos</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="n">label</span><span class="o">=</span><span class="sa">r</span><span class="s2">&quot;$cos(0) - cos(t)$&quot;</span><span class="p">)</span>
-<span class="n">p</span> <span class="o">=</span> <span class="n">plot</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">dy_dt</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">t</span><span class="p">),</span> <span class="s2">&quot;g-&quot;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">r</span><span class="s2">&quot;$\frac</span><span class="si">{dy}{dt}</span><span class="s2">(t)$&quot;</span><span class="p">)</span>
-<span class="n">l</span> <span class="o">=</span> <span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s2">&quot;upper right&quot;</span><span class="p">)</span>
-<span class="n">xl</span> <span class="o">=</span> <span class="n">xlabel</span><span class="p">(</span><span class="s2">&quot;t&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-<img 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8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%0A" 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8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%0A" />
-</section>
-<section id="id4">
-<h1>高阶微分方程<a class="headerlink" href="#id4" title="Permalink to this heading">¶</a></h1>
-<p>抛物运动（竖直方向）：</p>
-<p>**
-frac{d^2x}{dt^2} = g - frac{D}{m}frac{dx}{dt}
-**</p>
-<p>改写成如下形式：</p>
-<p>**
-y = left[x, frac{dx}{dt}right] <a href="#id5"><span class="problematic" id="id6">**</span></a><a href="#id7"><span class="problematic" id="id8">**</span></a>begin{aligned}
-frac{dy_0}{dt} &amp;amp;= y_1 \frac{dy_1}{dt} &amp;amp;= -g - frac{D}{m} y_1 \end{aligned}
-**</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>def dy_dt(y, t):
-    &quot;&quot;&quot;Governing equations for projectile motion with drag.
-    y[0] = position
-    y[1] = velocity
-    g = gravity (m/s2)
-    D = drag (1/s) = force/velocity
-    m = mass (kg)
-    &quot;&quot;&quot;
-    g = -9.8
-    D = 0.1
-    m = 0.15
-    dy1 = g - (D/m) * y[1]
-    dy0 = y[1] if y[0] &gt;= 0 else 0.
-    return [dy0, dy1]
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">position_0</span> <span class="o">=</span> <span class="mf">0.</span>
-<span class="n">velocity_0</span> <span class="o">=</span> <span class="mi">100</span>
-<span class="n">t</span> <span class="o">=</span> <span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">12</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
-<span class="n">y</span> <span class="o">=</span> <span class="n">odeint</span><span class="p">(</span><span class="n">dy_dt</span><span class="p">,</span> <span class="p">[</span><span class="n">position_0</span><span class="p">,</span> <span class="n">velocity_0</span><span class="p">],</span> <span class="n">t</span><span class="p">)</span>
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">p</span> <span class="o">=</span> <span class="n">plot</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">y</span><span class="p">[:,</span><span class="mi">0</span><span class="p">])</span>
-<span class="n">yl</span> <span class="o">=</span> <span class="n">ylabel</span><span class="p">(</span><span class="s2">&quot;Height (m)&quot;</span><span class="p">)</span>
-<span class="n">xl</span> <span class="o">=</span> <span class="n">xlabel</span><span class="p">(</span><span class="s2">&quot;Time (s)&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-<img 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/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%0ASUoJQkREklKCEBGRpP4fPzntqp99LaMAAAAASUVORK5CYII=%0A" 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/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%0ASUoJQkREklKCEBGRpP4fPzntqp99LaMAAAAASUVORK5CYII=%0A" />
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">y</span><span class="p">,</span> <span class="n">infodict</span> <span class="o">=</span> <span class="n">odeint</span><span class="p">(</span><span class="n">dy_dt</span><span class="p">,</span> <span class="p">[</span><span class="n">position_0</span><span class="p">,</span> <span class="n">velocity_0</span><span class="p">],</span> <span class="n">t</span><span class="p">,</span>     <span class="n">full_output</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">printmessg</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="p">)</span>
-<span class="nb">print</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">infodict</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
-<span class="nb">print</span> <span class="s2">&quot;cumulative number of function evaluations at each calculated     point:&quot;</span><span class="p">,</span> <span class="n">infodict</span><span class="p">[</span><span class="s1">&#39;nfe&#39;</span><span class="p">]</span>
-<span class="nb">print</span> <span class="s2">&quot;cumulative number of time steps&quot;</span><span class="p">,</span> <span class="n">infodict</span><span class="p">[</span><span class="s1">&#39;nst&#39;</span><span class="p">]</span>
-<span class="n">Integration</span> <span class="n">successful</span><span class="o">.</span>
-<span class="p">[</span><span class="s1">&#39;hu&#39;</span><span class="p">,</span> <span class="s1">&#39;imxer&#39;</span><span class="p">,</span> <span class="s1">&#39;leniw&#39;</span><span class="p">,</span> <span class="s1">&#39;lenrw&#39;</span><span class="p">,</span> <span class="s1">&#39;message&#39;</span><span class="p">,</span> <span class="s1">&#39;mused&#39;</span><span class="p">,</span> <span class="s1">&#39;nfe&#39;</span><span class="p">,</span> <span class="s1">&#39;nje&#39;</span><span class="p">,</span>     <span class="s1">&#39;nqu&#39;</span><span class="p">,</span> <span class="s1">&#39;nst&#39;</span><span class="p">,</span> <span class="s1">&#39;tcur&#39;</span><span class="p">,</span> <span class="s1">&#39;tolsf&#39;</span><span class="p">,</span> <span class="s1">&#39;tsw&#39;</span><span class="p">]</span>
-<span class="n">cumulative</span> <span class="n">number</span> <span class="n">of</span> <span class="n">function</span> <span class="n">evaluations</span> <span class="n">at</span> <span class="n">each</span> <span class="n">calculated</span> <span class="n">point</span><span class="p">:</span>     <span class="p">[</span> <span class="mi">45</span>  <span class="mi">49</span>  <span class="mi">51</span>  <span class="mi">53</span>  <span class="mi">55</span>  <span class="mi">59</span>  <span class="mi">61</span>  <span class="mi">61</span>  <span class="mi">63</span>  <span class="mi">65</span>  <span class="mi">67</span>  <span class="mi">67</span>  <span class="mi">69</span>  <span class="mi">71</span>  <span class="mi">73</span>  <span class="mi">73</span>  <span class="mi">75</span>  <span class="mi">77</span>
-<span class="mi">77</span>  <span class="mi">79</span>  <span class="mi">79</span>  <span class="mi">81</span>  <span class="mi">81</span>  <span class="mi">83</span>  <span class="mi">85</span>  <span class="mi">85</span>  <span class="mi">87</span>  <span class="mi">87</span>  <span class="mi">89</span>  <span class="mi">89</span>  <span class="mi">91</span>  <span class="mi">91</span>  <span class="mi">93</span>  <span class="mi">95</span>  <span class="mi">95</span>  <span class="mi">97</span>
-<span class="mi">97</span>  <span class="mi">99</span>  <span class="mi">99</span> <span class="mi">101</span> <span class="mi">101</span> <span class="mi">103</span> <span class="mi">103</span> <span class="mi">105</span> <span class="mi">107</span> <span class="mi">107</span> <span class="mi">109</span> <span class="mi">109</span> <span class="mi">111</span> <span class="mi">111</span> <span class="mi">113</span> <span class="mi">113</span> <span class="mi">115</span> <span class="mi">115</span>
-<span class="mi">117</span> <span class="mi">117</span> <span class="mi">119</span> <span class="mi">119</span> <span class="mi">121</span> <span class="mi">121</span> <span class="mi">123</span> <span class="mi">123</span> <span class="mi">123</span> <span class="mi">125</span> <span class="mi">125</span> <span class="mi">127</span> <span class="mi">127</span> <span class="mi">129</span> <span class="mi">129</span> <span class="mi">131</span> <span class="mi">131</span> <span class="mi">131</span>
-<span class="mi">133</span> <span class="mi">133</span> <span class="mi">135</span> <span class="mi">135</span> <span class="mi">135</span> <span class="mi">137</span> <span class="mi">137</span> <span class="mi">139</span> <span class="mi">139</span> <span class="mi">139</span> <span class="mi">141</span> <span class="mi">141</span> <span class="mi">143</span> <span class="mi">143</span> <span class="mi">143</span> <span class="mi">145</span> <span class="mi">145</span> <span class="mi">147</span>
-<span class="mi">147</span> <span class="mi">149</span> <span class="mi">149</span> <span class="mi">149</span> <span class="mi">154</span> <span class="mi">158</span> <span class="mi">274</span> <span class="mi">280</span> <span class="mi">280</span><span class="p">]</span>
-<span class="n">cumulative</span> <span class="n">number</span> <span class="n">of</span> <span class="n">time</span> <span class="n">steps</span> <span class="p">[</span> <span class="mi">20</span>  <span class="mi">22</span>  <span class="mi">23</span>  <span class="mi">24</span>  <span class="mi">25</span>  <span class="mi">27</span>  <span class="mi">28</span>  <span class="mi">28</span>      <span class="mi">29</span>  <span class="mi">30</span>  <span class="mi">31</span>  <span class="mi">31</span>  <span class="mi">32</span>  <span class="mi">33</span>  <span class="mi">34</span>  <span class="mi">34</span>  <span class="mi">35</span>  <span class="mi">36</span>
-<span class="mi">36</span>  <span class="mi">37</span>  <span class="mi">37</span>  <span class="mi">38</span>  <span class="mi">38</span>  <span class="mi">39</span>  <span class="mi">40</span>  <span class="mi">40</span>  <span class="mi">41</span>  <span class="mi">41</span>  <span class="mi">42</span>  <span class="mi">42</span>  <span class="mi">43</span>  <span class="mi">43</span>  <span class="mi">44</span>  <span class="mi">45</span>  <span class="mi">45</span>  <span class="mi">46</span>
-<span class="mi">46</span>  <span class="mi">47</span>  <span class="mi">47</span>  <span class="mi">48</span>  <span class="mi">48</span>  <span class="mi">49</span>  <span class="mi">49</span>  <span class="mi">50</span>  <span class="mi">51</span>  <span class="mi">51</span>  <span class="mi">52</span>  <span class="mi">52</span>  <span class="mi">53</span>  <span class="mi">53</span>  <span class="mi">54</span>  <span class="mi">54</span>  <span class="mi">55</span>  <span class="mi">55</span>
-<span class="mi">56</span>  <span class="mi">56</span>  <span class="mi">57</span>  <span class="mi">57</span>  <span class="mi">58</span>  <span class="mi">58</span>  <span class="mi">59</span>  <span class="mi">59</span>  <span class="mi">59</span>  <span class="mi">60</span>  <span class="mi">60</span>  <span class="mi">61</span>  <span class="mi">61</span>  <span class="mi">62</span>  <span class="mi">62</span>  <span class="mi">63</span>  <span class="mi">63</span>  <span class="mi">63</span>
-<span class="mi">64</span>  <span class="mi">64</span>  <span class="mi">65</span>  <span class="mi">65</span>  <span class="mi">65</span>  <span class="mi">66</span>  <span class="mi">66</span>  <span class="mi">67</span>  <span class="mi">67</span>  <span class="mi">67</span>  <span class="mi">68</span>  <span class="mi">68</span>  <span class="mi">69</span>  <span class="mi">69</span>  <span class="mi">69</span>  <span class="mi">70</span>  <span class="mi">70</span>  <span class="mi">71</span>
-<span class="mi">71</span>  <span class="mi">72</span>  <span class="mi">72</span>  <span class="mi">72</span>  <span class="mi">73</span>  <span class="mi">75</span> <span class="mi">130</span> <span class="mi">133</span> <span class="mi">133</span><span class="p">]</span>
-</pre></div>
-</div>
-</section>
-<section id="id9">
-<h1>作者 &amp; 更新时间<a class="headerlink" href="#id9" title="Permalink to this heading">¶</a></h1>
-<p>作者:<cite>李金  &lt;lijinwithyou&#64;gmail.com&gt;</cite></p>
-<p>提交: 2017/12/14</p>
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diff --git a/docs/aipy-scipy/cn/9sparse.html b/docs/aipy-scipy/cn/9sparse.html
deleted file mode 100644
index d545637..0000000
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-<html lang="en-US">
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-<title>稀疏矩阵</title>
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-<a target="_blank" href="https://github.com/qpython-android/qpython">Github</a>
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-<section id="id1">
-<h1>稀疏矩阵<a class="headerlink" href="#id1" title="Permalink to this heading">¶</a></h1>
-<p>Scipy 提供了稀疏矩阵的支持（scipy.sparse）。</p>
-<p>稀疏矩阵主要使用 位置 + 值 的方法来存储矩阵的非零元素，根据存储和使用方式的不同，有如下几种类型的稀疏矩阵：</p>
-<table class="docutils align-default">
-<thead>
-<tr class="row-odd"><th class="head"><p>Type</p></th>
-<th class="head"><p>Description</p></th>
-</tr>
-</thead>
-<tbody>
-<tr class="row-even"><td><p>bsr_matrix(arg1[, shape, dtype, copy, blocksize])</p></td>
-<td><p>Block Sparse Row matrix</p></td>
-</tr>
-<tr class="row-odd"><td><p>coo_matrix(arg1[, shape, dtype, copy])</p></td>
-<td><p>A sparse matrix in COOrdinate format.</p></td>
-</tr>
-<tr class="row-even"><td><p>csc_matrix(arg1[, shape, dtype, copy])</p></td>
-<td><p>Compressed Sparse Column matrix</p></td>
-</tr>
-<tr class="row-odd"><td><p>csr_matrix(arg1[, shape, dtype, copy])</p></td>
-<td><p>Compressed Sparse Row matrix</p></td>
-</tr>
-<tr class="row-even"><td><p>dia_matrix(arg1[, shape, dtype, copy])</p></td>
-<td><p>Sparse matrix with DIAgonal storage</p></td>
-</tr>
-<tr class="row-odd"><td><p>dok_matrix(arg1[, shape, dtype, copy])</p></td>
-<td><p>Dictionary Of Keys based sparse matrix.</p></td>
-</tr>
-<tr class="row-even"><td><p>lil_matrix(arg1[, shape, dtype, copy])</p></td>
-<td><p>Row-based linked list sparse matrix</p></td>
-</tr>
-</tbody>
-</table>
-<p>在这些存储格式中：</p>
-<ul class="simple">
-<li><p>COO 格式在构建矩阵时比较高效</p></li>
-<li><p>CSC 和 CSR 格式在乘法计算时比较高效</p></li>
-</ul>
-</section>
-<section id="id2">
-<h1>构建稀疏矩阵<a class="headerlink" href="#id2" title="Permalink to this heading">¶</a></h1>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">scipy.sparse</span> <span class="kn">import</span> <span class="o">*</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-</pre></div>
-</div>
-<p>创建一个空的稀疏矩阵：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">coo_matrix</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="o">&lt;</span><span class="mi">2</span><span class="n">x3</span> <span class="n">sparse</span> <span class="n">matrix</span> <span class="n">of</span> <span class="nb">type</span> <span class="s1">&#39;&lt;type &#39;</span><span class="n">numpy</span><span class="o">.</span><span class="n">float64</span><span class="s1">&#39;&gt;&#39;</span>
-<span class="k">with</span> <span class="mi">0</span> <span class="n">stored</span> <span class="n">elements</span> <span class="ow">in</span> <span class="n">COOrdinate</span> <span class="nb">format</span><span class="o">&gt;</span>
-</pre></div>
-</div>
-<p>也可以使用一个已有的矩阵或数组或列表中创建新矩阵：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">A</span> <span class="o">=</span> <span class="n">coo_matrix</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">0</span><span class="p">],[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">3</span><span class="p">],[</span><span class="mi">4</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">5</span><span class="p">]])</span>
-<span class="nb">print</span> <span class="n">A</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>      <span class="mi">1</span>
-<span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>      <span class="mi">2</span>
-<span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>      <span class="mi">3</span>
-<span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>      <span class="mi">4</span>
-<span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>      <span class="mi">5</span>
-</pre></div>
-</div>
-<p>不同格式的稀疏矩阵可以相互转化：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">type</span><span class="p">(</span><span class="n">A</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">scipy</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">coo</span><span class="o">.</span><span class="n">coo_matrix</span>
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">B</span> <span class="o">=</span> <span class="n">A</span><span class="o">.</span><span class="n">tocsr</span><span class="p">()</span>
-<span class="nb">type</span><span class="p">(</span><span class="n">B</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">scipy</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">csr</span><span class="o">.</span><span class="n">csr_matrix</span>
-</pre></div>
-</div>
-<p>可以转化为普通矩阵：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">C</span> <span class="o">=</span> <span class="n">A</span><span class="o">.</span><span class="n">todense</span><span class="p">()</span>
-<span class="n">C</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">matrix</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
-<span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span>
-<span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">5</span><span class="p">]])</span>
-</pre></div>
-</div>
-<p>与向量的乘法：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">v</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
-<span class="n">A</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">v</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">array</span><span class="p">([</span> <span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">3</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">])</span>
-</pre></div>
-</div>
-<p>还可以传入一个 (data, (row, col)) 的元组来构建稀疏矩阵：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">I</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">])</span>
-<span class="n">J</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">])</span>
-<span class="n">V</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">4</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span><span class="mi">7</span><span class="p">,</span><span class="mi">9</span><span class="p">])</span>
-<span class="n">A</span> <span class="o">=</span> <span class="n">coo_matrix</span><span class="p">((</span><span class="n">V</span><span class="p">,(</span><span class="n">I</span><span class="p">,</span><span class="n">J</span><span class="p">)),</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">4</span><span class="p">))</span>
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span> <span class="n">A</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>      <span class="mi">4</span>
-<span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>      <span class="mi">5</span>
-<span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>      <span class="mi">7</span>
-<span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>      <span class="mi">9</span>
-</pre></div>
-</div>
-<p>COO 格式的稀疏矩阵在构建的时候只是简单的将坐标和值加到后面，对于重复的坐标不进行处理：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">I</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">])</span>
-<span class="n">J</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">])</span>
-<span class="n">V</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">])</span>
-<span class="n">B</span> <span class="o">=</span> <span class="n">coo_matrix</span><span class="p">((</span><span class="n">V</span><span class="p">,(</span><span class="n">I</span><span class="p">,</span><span class="n">J</span><span class="p">)),</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">4</span><span class="p">))</span>
-<span class="nb">print</span> <span class="n">B</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>        <span class="mi">1</span>
-<span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>        <span class="mi">1</span>
-<span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>        <span class="mi">1</span>
-<span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>        <span class="mi">1</span>
-<span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>        <span class="mi">1</span>
-<span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>        <span class="mi">1</span>
-<span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>        <span class="mi">1</span>
-</pre></div>
-</div>
-<p>转换成 CSR 格式会自动将相同坐标的值合并：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">C</span> <span class="o">=</span> <span class="n">B</span><span class="o">.</span><span class="n">tocsr</span><span class="p">()</span>
-<span class="nb">print</span> <span class="n">C</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>        <span class="mi">3</span>
-<span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>        <span class="mi">1</span>
-<span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>        <span class="mi">2</span>
-<span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>        <span class="mi">1</span>
-</pre></div>
-</div>
-</section>
-<section id="id3">
-<h1>求解微分方程<a class="headerlink" href="#id3" title="Permalink to this heading">¶</a></h1>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">scipy.sparse</span> <span class="kn">import</span> <span class="n">lil_matrix</span>
-<span class="kn">from</span> <span class="nn">scipy.sparse.linalg</span> <span class="kn">import</span> <span class="n">spsolve</span>
-<span class="kn">from</span> <span class="nn">numpy.linalg</span> <span class="kn">import</span> <span class="n">solve</span><span class="p">,</span> <span class="n">norm</span>
-<span class="kn">from</span> <span class="nn">numpy.random</span> <span class="kn">import</span> <span class="n">rand</span>
-</pre></div>
-</div>
-<p>构建 1000 x 1000 的稀疏矩阵：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">A</span> <span class="o">=</span> <span class="n">lil_matrix</span><span class="p">((</span><span class="mi">1000</span><span class="p">,</span> <span class="mi">1000</span><span class="p">))</span>
-<span class="n">A</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="p">:</span><span class="mi">100</span><span class="p">]</span> <span class="o">=</span> <span class="n">rand</span><span class="p">(</span><span class="mi">100</span><span class="p">)</span>
-<span class="n">A</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">100</span><span class="p">:</span><span class="mi">200</span><span class="p">]</span> <span class="o">=</span> <span class="n">A</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="p">:</span><span class="mi">100</span><span class="p">]</span>
-<span class="n">A</span><span class="o">.</span><span class="n">setdiag</span><span class="p">(</span><span class="n">rand</span><span class="p">(</span><span class="mi">1000</span><span class="p">))</span>
-</pre></div>
-</div>
-<p>转化为 CSR 之后，用 spsolve 求解 $Ax=b$：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">A</span> <span class="o">=</span> <span class="n">A</span><span class="o">.</span><span class="n">tocsr</span><span class="p">()</span>
-<span class="n">b</span> <span class="o">=</span> <span class="n">rand</span><span class="p">(</span><span class="mi">1000</span><span class="p">)</span>
-<span class="n">x</span> <span class="o">=</span> <span class="n">spsolve</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">b</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>转化成正常数组之后求解：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">x_</span> <span class="o">=</span> <span class="n">solve</span><span class="p">(</span><span class="n">A</span><span class="o">.</span><span class="n">toarray</span><span class="p">(),</span> <span class="n">b</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>查看误差：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">err</span> <span class="o">=</span> <span class="n">norm</span><span class="p">(</span><span class="n">x</span><span class="o">-</span><span class="n">x_</span><span class="p">)</span>
-<span class="n">err</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="mf">6.4310987107687431e-13</span>
-</pre></div>
-</div>
-</section>
-<section id="sparse-find">
-<h1>sparse.find 函数<a class="headerlink" href="#sparse-find" title="Permalink to this heading">¶</a></h1>
-<p>返回一个三元组，表示稀疏矩阵中非零元素的 (row, col, value)：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">sparse</span>
-<span class="n">row</span><span class="p">,</span> <span class="n">col</span><span class="p">,</span> <span class="n">val</span> <span class="o">=</span> <span class="n">sparse</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="n">C</span><span class="p">)</span>
-<span class="nb">print</span> <span class="n">row</span><span class="p">,</span> <span class="n">col</span><span class="p">,</span> <span class="n">val</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">[</span><span class="mi">0</span> <span class="mi">0</span> <span class="mi">1</span> <span class="mi">3</span><span class="p">]</span> <span class="p">[</span><span class="mi">0</span> <span class="mi">2</span> <span class="mi">1</span> <span class="mi">3</span><span class="p">]</span> <span class="p">[</span><span class="mi">3</span> <span class="mi">1</span> <span class="mi">2</span> <span class="mi">1</span><span class="p">]</span>
-</pre></div>
-</div>
-</section>
-<section id="sparse-issparse">
-<h1>sparse.issparse 函数<a class="headerlink" href="#sparse-issparse" title="Permalink to this heading">¶</a></h1>
-<p>查看一个对象是否为稀疏矩阵：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">sparse</span><span class="o">.</span><span class="n">issparse</span><span class="p">(</span><span class="n">B</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kc">True</span>
-</pre></div>
-</div>
-<p>或者</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">sparse</span><span class="o">.</span><span class="n">isspmatrix</span><span class="p">(</span><span class="n">B</span><span class="o">.</span><span class="n">todense</span><span class="p">())</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kc">False</span>
-</pre></div>
-</div>
-<p>还可以查询是否为指定格式的稀疏矩阵：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">sparse</span><span class="o">.</span><span class="n">isspmatrix_coo</span><span class="p">(</span><span class="n">B</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kc">True</span>
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">sparse</span><span class="o">.</span><span class="n">isspmatrix_csr</span><span class="p">(</span><span class="n">B</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kc">False</span>
-</pre></div>
-</div>
-</section>
-<section id="id4">
-<h1>作者 &amp; 更新时间<a class="headerlink" href="#id4" title="Permalink to this heading">¶</a></h1>
-<p>作者:<cite>李金  &lt;lijinwithyou&#64;gmail.com&gt;</cite></p>
-<p>提交: 2017/12/14</p>
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diff --git a/docs/ar/index.html b/docs/ar/index.html
deleted file mode 100644
index bee9c81..0000000
--- a/docs/ar/index.html
+++ /dev/null
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-<p>The courses are still in the planning. Welcome to reward us.</p>
-<ol class="arabic simple">
-<li><p><strong>Primary course</strong></p></li>
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-<div class="en-thanks-sel">
-<select name="os0">
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new file mode 100644
index 0000000..8a8ad11
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+ * @remix-run/router v1.23.2
+ *
+ * Copyright (c) Remix Software Inc.
+ *
+ * This source code is licensed under the MIT license found in the
+ * LICENSE.md file in the root directory of this source tree.
+ *
+ * @license MIT
+ */function vl(){return vl=Object.assign?Object.assign.bind():function(e){for(var t=1;t<arguments.length;t++){var n=arguments[t];for(var r in n)Object.prototype.hasOwnProperty.call(n,r)&&(e[r]=n[r])}return e},vl.apply(this,arguments)}var pt;(function(e){e.Pop="POP",e.Push="PUSH",e.Replace="REPLACE"})(pt||(pt={}));const hs="popstate";function Mp(e){e===void 0&&(e={});function t(r,l){let{pathname:o,search:i,hash:u}=r.location;return ii("",{pathname:o,search:i,hash:u},l.state&&l.state.usr||null,l.state&&l.state.key||"default")}function n(r,l){return typeof l=="string"?l:Bc(l)}return Ip(t,n,null,e)}function we(e,t){if(e===!1||e===null||typeof e>"u")throw new Error(t)}function Ac(e,t){if(!e){typeof console<"u"&&console.warn(t);try{throw new Error(t)}catch{}}}function Op(){return Math.random().toString(36).substr(2,8)}function ms(e,t){return{usr:e.state,key:e.key,idx:t}}function ii(e,t,n,r){return n===void 0&&(n=null),vl({pathname:typeof e=="string"?e:e.pathname,search:"",hash:""},typeof t=="string"?Il(t):t,{state:n,key:t&&t.key||r||Op()})}function Bc(e){let{pathname:t="/",search:n="",hash:r=""}=e;return n&&n!=="?"&&(t+=n.charAt(0)==="?"?n:"?"+n),r&&r!=="#"&&(t+=r.charAt(0)==="#"?r:"#"+r),t}function Il(e){let t={};if(e){let n=e.indexOf("#");n>=0&&(t.hash=e.substr(n),e=e.substr(0,n));let r=e.indexOf("?");r>=0&&(t.search=e.substr(r),e=e.substr(0,r)),e&&(t.pathname=e)}return t}function Ip(e,t,n,r){r===void 0&&(r={});let{window:l=document.defaultView,v5Compat:o=!1}=r,i=l.history,u=pt.Pop,s=null,a=h();a==null&&(a=0,i.replaceState(vl({},i.state,{idx:a}),""));function h(){return(i.state||{idx:null}).idx}function p(){u=pt.Pop;let k=h(),d=k==null?null:k-a;a=k,s&&s({action:u,location:v.location,delta:d})}function m(k,d){u=pt.Push;let f=ii(v.location,k,d);a=h()+1;let c=ms(f,a),g=v.createHref(f);try{i.pushState(c,"",g)}catch(S){if(S instanceof DOMException&&S.name==="DataCloneError")throw S;l.location.assign(g)}o&&s&&s({action:u,location:v.location,delta:1})}function y(k,d){u=pt.Replace;let f=ii(v.location,k,d);a=h();let c=ms(f,a),g=v.createHref(f);i.replaceState(c,"",g),o&&s&&s({action:u,location:v.location,delta:0})}function w(k){let d=l.location.origin!=="null"?l.location.origin:l.location.href,f=typeof k=="string"?k:Bc(k);return f=f.replace(/ $/,"%20"),we(d,"No window.location.(origin|href) available to create URL for href: "+f),new URL(f,d)}let v={get action(){return u},get location(){return e(l,i)},listen(k){if(s)throw new Error("A history only accepts one active listener");return l.addEventListener(hs,p),s=k,()=>{l.removeEventListener(hs,p),s=null}},createHref(k){return t(l,k)},createURL:w,encodeLocation(k){let d=w(k);return{pathname:d.pathname,search:d.search,hash:d.hash}},push:m,replace:y,go(k){return i.go(k)}};return v}var gs;(function(e){e.data="data",e.deferred="deferred",e.redirect="redirect",e.error="error"})(gs||(gs={}));function Fp(e,t,n){return n===void 0&&(n="/"),Dp(e,t,n)}function Dp(e,t,n,r){let l=typeof t=="string"?Il(t):t,o=Wc(l.pathname||"/",n);if(o==null)return null;let i=$c(e);Up(i);let u=null;for(let s=0;u==null&&s<i.length;++s){let a=Zp(o);u=Kp(i[s],a)}return u}function $c(e,t,n,r){t===void 0&&(t=[]),n===void 0&&(n=[]),r===void 0&&(r="");let l=(o,i,u)=>{let s={relativePath:u===void 0?o.path||"":u,caseSensitive:o.caseSensitive===!0,childrenIndex:i,route:o};s.relativePath.startsWith("/")&&(we(s.relativePath.startsWith(r),'Absolute route path "'+s.relativePath+'" nested under path '+('"'+r+'" is not valid. 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+ * React Router v6.30.3
+ *
+ * Copyright (c) Remix Software Inc.
+ *
+ * This source code is licensed under the MIT license found in the
+ * LICENSE.md file in the root directory of this source tree.
+ *
+ * @license MIT
+ */function yl(){return yl=Object.assign?Object.assign.bind():function(e){for(var t=1;t<arguments.length;t++){var n=arguments[t];for(var r in n)Object.prototype.hasOwnProperty.call(n,r)&&(e[r]=n[r])}return e},yl.apply(this,arguments)}const eh=z.createContext(null),th=z.createContext(null),Qc=z.createContext(null),Fl=z.createContext(null),Dl=z.createContext({outlet:null,matches:[],isDataRoute:!1}),Gc=z.createContext(null);function ru(){return z.useContext(Fl)!=null}function nh(){return ru()||we(!1),z.useContext(Fl).location}function rh(e,t){return lh(e,t)}function lh(e,t,n,r){ru()||we(!1);let{navigator:l}=z.useContext(Qc),{matches:o}=z.useContext(Dl),i=o[o.length-1],u=i?i.params:{};i&&i.pathname;let s=i?i.pathnameBase:"/";i&&i.route;let a=nh(),h;if(t){var p;let k=typeof t=="string"?Il(t):t;s==="/"||(p=k.pathname)!=null&&p.startsWith(s)||we(!1),h=k}else h=a;let m=h.pathname||"/",y=m;if(s!=="/"){let 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e.UseBlocker="useBlocker",e.UseLoaderData="useLoaderData",e.UseActionData="useActionData",e.UseRouteError="useRouteError",e.UseNavigation="useNavigation",e.UseRouteLoaderData="useRouteLoaderData",e.UseMatches="useMatches",e.UseRevalidator="useRevalidator",e.UseNavigateStable="useNavigate",e.UseRouteId="useRouteId",e}(Kc||{});function ch(e){let t=z.useContext(th);return t||we(!1),t}function fh(e){let t=z.useContext(Dl);return t||we(!1),t}function dh(e){let t=fh(),n=t.matches[t.matches.length-1];return n.route.id||we(!1),n.route.id}function ph(){var e;let t=z.useContext(Gc),n=ch(Kc.UseRouteError),r=dh();return t!==void 0?t:(e=n.errors)==null?void 0:e[r]}const ys={};function hh(e,t,n){ys[e]||(ys[e]=!0)}function mh(e,t){e==null||e.v7_startTransition,e==null||e.v7_relativeSplatPath}function Yc(e){we(!1)}function gh(e){let{basename:t="/",children:n=null,location:r,navigationType:l=pt.Pop,navigator:o,static:i=!1,future:u}=e;ru()&&we(!1);let s=t.replace(/^\/*/,"/"),a=z.useMemo(()=>({basename:s,navigator:o,static:i,future:yl({v7_relativeSplatPath:!1},u)}),[s,u,o,i]);typeof r=="string"&&(r=Il(r));let{pathname:h="/",search:p="",hash:m="",state:y=null,key:w="default"}=r,v=z.useMemo(()=>{let k=Wc(h,s);return k==null?null:{location:{pathname:k,search:p,hash:m,state:y,key:w},navigationType:l}},[s,h,p,m,y,w,l]);return v==null?null:z.createElement(Qc.Provider,{value:a},z.createElement(Fl.Provider,{children:n,value:v}))}function vh(e){let{children:t,location:n}=e;return rh(ui(t),n)}new Promise(()=>{});function ui(e,t){t===void 0&&(t=[]);let n=[];return z.Children.forEach(e,(r,l)=>{if(!z.isValidElement(r))return;let o=[...t,l];if(r.type===z.Fragment){n.push.apply(n,ui(r.props.children,o));return}r.type!==Yc&&we(!1),!r.props.index||!r.props.children||we(!1);let i={id:r.props.id||o.join("-"),caseSensitive:r.props.caseSensitive,element:r.props.element,Component:r.props.Component,index:r.props.index,path:r.props.path,loader:r.props.loader,action:r.props.action,errorElement:r.props.errorElement,ErrorBoundary:r.props.ErrorBoundary,hasErrorBoundary:r.props.ErrorBoundary!=null||r.props.errorElement!=null,shouldRevalidate:r.props.shouldRevalidate,handle:r.props.handle,lazy:r.props.lazy};r.props.children&&(i.children=ui(r.props.children,o)),n.push(i)}),n}/**
+ * React Router DOM v6.30.3
+ *
+ * Copyright (c) Remix Software Inc.
+ *
+ * This source code is licensed under the MIT license found in the
+ * LICENSE.md file in the root directory of this source tree.
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diff --git a/docs/bottle-web/index.html b/docs/bottle-web/index.html
deleted file mode 100644
index 30aab41..0000000
--- a/docs/bottle-web/index.html
+++ /dev/null
@@ -1,291 +0,0 @@
-<!DOCTYPE html>
-<html lang="en-US">
-<head>
-<meta charset="utf-8" />
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-<link rel="stylesheet" type="text/css" href="../static/style.css" />
-<meta charset="UTF-8">
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-</li>
-<li>
-<a href="http://www.qpython.org/document.html">Guide</a>
-</li>
-<li class="header-phone-selected">
-<a href="/index.html" class="header-selected">Course</a>
-<span class="header-title-selected"></span>
-</li>
-<li>
-<a href="http://www.aipy.org">AIPY</a>
-</li>
-<li>
-<a target="_blank" href="https://github.com/qpython-android/qpython">Github</a>
-</li>
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-<div class="body" role="main">
-<section id="bottle-web">
-<h1>Bottle Web<a class="headerlink" href="#bottle-web" title="Permalink to this heading">¶</a></h1>
-<p>The courses are still in the planning. Welcome to reward us.</p>
-<ol class="arabic simple">
-<li><p><strong>Primary course</strong></p></li>
-</ol>
-<p>If the total number of people who reward us is more than 100, we promise to complete the primary course within 1 week, so that, after finishing your study, you can master the basic concepts and write simple scripts.</p>
-<ol class="arabic simple" start="2">
-<li><p><strong>Intermediate course</strong></p></li>
-</ol>
-<p>If the total number of people who reward us is more than 500, we will launch the intermediate course within 2 weeks, so that, after finishing your study, you will be familiar with the knowledge and framework of the course and apply it to practical development.</p>
-<ol class="arabic simple" start="3">
-<li><p><strong>Advanced course</strong></p></li>
-</ol>
-<p>If the total number of people who reward us is more than 2,000, we will launch the advanced course in a month, so that, after finishing your study, you will have a better understanding of the principles of the framework and be able to do advanced development.</p>
-<p><strong>The primary course is free to all, and the intermediate and advanced courses are only open to those who have rewarded us.</strong></p>
-</section>
-<div class="clearer"></div>
-</div>
-</div>
-</div>
-<div class="clearer"></div>
-</div>
-</main>
-<div class="star-footer">
-<img onclick="openPurchase()" src="../static/star.jpg">
-<div id="praise"><span>0</span> persons have rewarded</div>
-</div>
-<div class="foo"></div>
-<footer class="clearfix fixfooter web-show">
-<div class="col-sm-offset-2 col-sm-6 footer-div1">
-<div class="footer-block-item">
-<span class="footer-item1">Built with Sphinx using a theme provid by QPython.</span>
-<span class="footer-item2 visible-xs">Copyight 2017, QPython.</span>
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-</div>
-<div class="col-sm-2 footer-div2">
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-<select name="os0">
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-<option value="Reward this course">Reward this course $ 1.49 USD</option>
-<option value="Reward this course">Reward this course $ 1.99 USD</option>
-<option value="Reward this course">Reward this course $ 2.99 USD</option>
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diff --git a/docs/bottle-web/tutorial.html b/docs/bottle-web/tutorial.html
deleted file mode 100644
index 2171970..0000000
--- a/docs/bottle-web/tutorial.html
+++ /dev/null
@@ -1,1106 +0,0 @@
-<!DOCTYPE html>
-<html lang="en-US">
-<head>
-<meta charset="utf-8" />
-<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
-<link href="https://cdn.bootcss.com/bootstrap/3.3.7/css/bootstrap.min.css" rel="stylesheet">
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-<link rel="stylesheet" type="text/css" href="../static/pygments.css" />
-<link rel="stylesheet" type="text/css" href="../static/style.css" />
-<meta charset="UTF-8">
-<meta name="viewport" content="width=device-width, initial-scale=1,maximum-scale=1, user-scalable=no">
-<script src="https://cdn.bootcss.com/bootstrap/3.3.7/js/bootstrap.min.js"></script>
-</head><body>
-<div class="header doc-header web-show">
-<div class="col-sm-offset-2 col-sm-8  index-header">
-<div class="navbar-header">
-<button type="button" class="navbar-toggle" data-toggle="collapse" data-target="#example-navbar-collapse">
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-</div>
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-<li>
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-<a href="/index.html" class="header-selected">Course</a>
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-</li>
-</ul>
-<div class="search-box hidden-xs">
-<form action="/search.html">
-<input type="search" name="q" placeholder="keyword">
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-<main class="contain web-content">
-<div class="document">
-<div class="documentwrapper">
-<div class="bodywrapper">
-<div class="body" role="main">
-<p>pache Server:
-.. _Apache: <a class="reference external" href="http://www.apache.org/">http://www.apache.org/</a>
-.. _cherrypy: <a class="reference external" href="http://www.cherrypy.org/">http://www.cherrypy.org/</a>
-.. _decorator: <a class="reference external" href="http://docs.python.org/glossary.html#term-decorator">http://docs.python.org/glossary.html#term-decorator</a>
-.. _flup: <a class="reference external" href="http://trac.saddi.com/flup">http://trac.saddi.com/flup</a>
-.. _http_code: <a class="reference external" href="http://www.w3.org/Protocols/rfc2616/rfc2616-sec10.html">http://www.w3.org/Protocols/rfc2616/rfc2616-sec10.html</a>
-.. _http_method: <a class="reference external" href="http://www.w3.org/Protocols/rfc2616/rfc2616-sec9.html">http://www.w3.org/Protocols/rfc2616/rfc2616-sec9.html</a>
-.. _json: <a class="reference external" href="http://de.wikipedia.org/wiki/JavaScript_Object_Notation">http://de.wikipedia.org/wiki/JavaScript_Object_Notation</a>
-.. _lighttpd: <a class="reference external" href="http://www.lighttpd.net/">http://www.lighttpd.net/</a>
-.. _mako: <a class="reference external" href="http://www.makotemplates.org/">http://www.makotemplates.org/</a>
-.. _mod_wsgi: <a class="reference external" href="http://code.google.com/p/modwsgi/">http://code.google.com/p/modwsgi/</a>
-.. _Paste: <a class="reference external" href="http://pythonpaste.org/">http://pythonpaste.org/</a>
-.. _Pound: <a class="reference external" href="http://www.apsis.ch/pound/">http://www.apsis.ch/pound/</a>
-.. <span class="target" id="wsgi-specification">WSGI Specification</span>: <a class="reference external" href="http://www.wsgi.org/">http://www.wsgi.org/</a>
-.. _issue: <a class="reference external" href="http://github.com/bottlepy/bottle/issues">http://github.com/bottlepy/bottle/issues</a>
-.. _Python: <a class="reference external" href="http://python.org/">http://python.org/</a>
-.. _SimpleCookie: <a class="reference external" href="http://docs.python.org/library/cookie.html#morsel-objects">http://docs.python.org/library/cookie.html#morsel-objects</a>
-.. _testing: <a class="reference external" href="http://github.com/bottlepy/bottle/raw/master/bottle.py">http://github.com/bottlepy/bottle/raw/master/bottle.py</a></p>
-<section id="tutorial">
-<h1>Tutorial<a class="headerlink" href="#tutorial" title="Permalink to this heading">¶</a></h1>
-<p>This tutorial introduces you to the concepts and features of the Bottle web framework and covers basic and advanced topics alike. You can read it from start to end, or use it as a reference later on. The automatically generated <span class="xref std std-doc">api</span> may be interesting for you, too. It covers more details, but explains less than this tutorial. Solutions for the most common questions can be found in our <span class="xref std std-doc">recipes</span> collection or on the <span class="xref std std-doc">faq</span> page. If you need any help, join our <a class="reference external" href="mailto:bottlepy&#37;&#52;&#48;googlegroups&#46;com">mailing list</a> or visit us in our <a class="reference external" href="http://webchat.freenode.net/?channels=bottlepy">IRC channel</a>.</p>
-<section id="installation">
-<span id="id1"></span><h2>Installation<a class="headerlink" href="#installation" title="Permalink to this heading">¶</a></h2>
-<p>Bottle does not depend on any external libraries. You can just download <a class="reference external" href="/bottle.py">bottle.py</a> into your project directory and start coding:</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$ wget https://bottlepy.org/bottle.py
-</pre></div>
-</div>
-<p>This will get you the latest development snapshot that includes all the new features. If you prefer a more stable environment, you should stick with the stable releases. These are available on <a class="reference external" href="http://pypi.python.org/pypi/bottle">PyPI</a> and can be installed via <strong class="command">pip</strong> (recommended), <strong class="command">easy_install</strong> or your package manager:</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$ sudo pip install bottle              <span class="c1"># recommended</span>
-$ sudo easy_install bottle             <span class="c1"># alternative without pip</span>
-$ sudo apt-get install python-bottle   <span class="c1"># works for debian, ubuntu, ...</span>
-</pre></div>
-</div>
-<p>Either way, you’ll need Python 2.7 or newer (including 3.2+) to run bottle applications. If you do not have permissions to install packages system-wide or simply don’t want to, create a <a class="reference external" href="http://pypi.python.org/pypi/virtualenv">virtualenv</a> first:</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$ virtualenv develop              <span class="c1"># Create virtual environment</span>
-$ <span class="nb">source</span> develop/bin/activate     <span class="c1"># Change default python to virtual one</span>
-<span class="o">(</span>develop<span class="o">)</span>$ pip install -U bottle  <span class="c1"># Install bottle to virtual environment</span>
-</pre></div>
-</div>
-<p>Or, if virtualenv is not installed on your system:</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$ wget https://raw.github.com/pypa/virtualenv/master/virtualenv.py
-$ python virtualenv.py develop    <span class="c1"># Create virtual environment</span>
-$ <span class="nb">source</span> develop/bin/activate     <span class="c1"># Change default python to virtual one</span>
-<span class="o">(</span>develop<span class="o">)</span>$ pip install -U bottle  <span class="c1"># Install bottle to virtual environment</span>
-</pre></div>
-</div>
-</section>
-<section id="quickstart-hello-world">
-<h2>Quickstart: “Hello World”<a class="headerlink" href="#quickstart-hello-world" title="Permalink to this heading">¶</a></h2>
-<p>This tutorial assumes you have Bottle either <a class="reference internal" href="#installation"><span class="std std-ref">installed</span></a> or copied into your project directory. Let’s start with a very basic “Hello World” example:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">bottle</span> <span class="kn">import</span> <span class="n">route</span><span class="p">,</span> <span class="n">run</span>
-<span class="nd">@route</span><span class="p">(</span><span class="s1">&#39;/hello&#39;</span><span class="p">)</span>
-<span class="k">def</span> <span class="nf">hello</span><span class="p">():</span>
-<span class="k">return</span> <span class="s2">&quot;Hello World!&quot;</span>
-<span class="n">run</span><span class="p">(</span><span class="n">host</span><span class="o">=</span><span class="s1">&#39;localhost&#39;</span><span class="p">,</span> <span class="n">port</span><span class="o">=</span><span class="mi">8080</span><span class="p">,</span> <span class="n">debug</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>This is it. Run this script, visit <a class="reference external" href="http://localhost:8080/hello">http://localhost:8080/hello</a> and you will see “Hello World!” in your browser. Here is how it works:</p>
-<p>The <code class="xref py py-func docutils literal notranslate"><span class="pre">route()</span></code> decorator binds a piece of code to an URL path. In this case, we link the <code class="docutils literal notranslate"><span class="pre">/hello</span></code> path to the <code class="docutils literal notranslate"><span class="pre">hello()</span></code> function. This is called a <cite>route</cite> (hence the decorator name) and is the most important concept of this framework. You can define as many routes as you want. Whenever a browser requests a URL, the associated function is called and the return value is sent back to the browser. It’s as simple as that.</p>
-<p>The <code class="xref py py-func docutils literal notranslate"><span class="pre">run()</span></code> call in the last line starts a built-in development server. It runs on <code class="docutils literal notranslate"><span class="pre">localhost</span></code> port <code class="docutils literal notranslate"><span class="pre">8080</span></code> and serves requests until you hit <kbd class="kbd compound docutils literal notranslate"><kbd class="kbd docutils literal notranslate">Control</kbd>-<kbd class="kbd docutils literal notranslate">c</kbd></kbd>. You can switch the server backend later, but for now a development server is all we need. It requires no setup at all and is an incredibly painless way to get your application up and running for local tests.</p>
-<p>The <a class="reference internal" href="#tutorial-debugging"><span class="std std-ref">Debug Mode</span></a> is very helpful during early development, but should be switched off for public applications. Keep that in mind.</p>
-<p>This is just a demonstration of the basic concept of how applications are built with Bottle. Continue reading and you’ll see what else is possible.</p>
-<section id="the-default-application">
-<span id="tutorial-default"></span><h3>The Default Application<a class="headerlink" href="#the-default-application" title="Permalink to this heading">¶</a></h3>
-<p>For the sake of simplicity, most examples in this tutorial use a module-level <code class="xref py py-func docutils literal notranslate"><span class="pre">route()</span></code> decorator to define routes. This adds routes to a global “default application”, an instance of <code class="xref py py-class docutils literal notranslate"><span class="pre">Bottle</span></code> that is automatically created the first time you call <code class="xref py py-func docutils literal notranslate"><span class="pre">route()</span></code>. Several other module-level decorators and functions relate to this default application, but if you prefer a more object oriented approach and don’t mind the extra typing, you can create a separate application object and use that instead of the global one:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">bottle</span> <span class="kn">import</span> <span class="n">Bottle</span><span class="p">,</span> <span class="n">run</span>
-<span class="n">app</span> <span class="o">=</span> <span class="n">Bottle</span><span class="p">()</span>
-<span class="nd">@app</span><span class="o">.</span><span class="n">route</span><span class="p">(</span><span class="s1">&#39;/hello&#39;</span><span class="p">)</span>
-<span class="k">def</span> <span class="nf">hello</span><span class="p">():</span>
-<span class="k">return</span> <span class="s2">&quot;Hello World!&quot;</span>
-<span class="n">run</span><span class="p">(</span><span class="n">app</span><span class="p">,</span> <span class="n">host</span><span class="o">=</span><span class="s1">&#39;localhost&#39;</span><span class="p">,</span> <span class="n">port</span><span class="o">=</span><span class="mi">8080</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>The object-oriented approach is further described in the <a class="reference internal" href="#default-app"><span class="std std-ref">Default Application</span></a> section. Just keep in mind that you have a choice.</p>
-</section>
-</section>
-<section id="request-routing">
-<span id="tutorial-routing"></span><h2>Request Routing<a class="headerlink" href="#request-routing" title="Permalink to this heading">¶</a></h2>
-<p>In the last chapter we built a very simple web application with only a single route. Here is the routing part of the “Hello World” example again:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nd">@route</span><span class="p">(</span><span class="s1">&#39;/hello&#39;</span><span class="p">)</span>
-<span class="k">def</span> <span class="nf">hello</span><span class="p">():</span>
-<span class="k">return</span> <span class="s2">&quot;Hello World!&quot;</span>
-</pre></div>
-</div>
-<p>The <code class="xref py py-func docutils literal notranslate"><span class="pre">route()</span></code> decorator links an URL path to a callback function, and adds a new route to the <a class="reference internal" href="#tutorial-default"><span class="std std-ref">default application</span></a>. An application with just one route is kind of boring, though. Let’s add some more (don’t forget <code class="docutils literal notranslate"><span class="pre">from</span> <span class="pre">bottle</span> <span class="pre">import</span> <span class="pre">template</span></code>):</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nd">@route</span><span class="p">(</span><span class="s1">&#39;/&#39;</span><span class="p">)</span>
-<span class="nd">@route</span><span class="p">(</span><span class="s1">&#39;/hello/&lt;name&gt;&#39;</span><span class="p">)</span>
-<span class="k">def</span> <span class="nf">greet</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;Stranger&#39;</span><span class="p">):</span>
-<span class="k">return</span> <span class="n">template</span><span class="p">(</span><span class="s1">&#39;Hello {{name}}, how are you?&#39;</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="n">name</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>This example demonstrates two things: You can bind more than one route to a single callback, and you can add wildcards to URLs and access them via keyword arguments.</p>
-<section id="dynamic-routes">
-<span id="tutorial-dynamic-routes"></span><h3>Dynamic Routes<a class="headerlink" href="#dynamic-routes" title="Permalink to this heading">¶</a></h3>
-<p>Routes that contain wildcards are called <cite>dynamic routes</cite> (as opposed to <cite>static routes</cite>) and match more than one URL at the same time. A simple wildcard consists of a name enclosed in angle brackets (e.g. <code class="docutils literal notranslate"><span class="pre">&lt;name&gt;</span></code>) and accepts one or more characters up to the next slash (<code class="docutils literal notranslate"><span class="pre">/</span></code>). For example, the route <code class="docutils literal notranslate"><span class="pre">/hello/&lt;name&gt;</span></code> accepts requests for <code class="docutils literal notranslate"><span class="pre">/hello/alice</span></code> as well as <code class="docutils literal notranslate"><span class="pre">/hello/bob</span></code>, but not for <code class="docutils literal notranslate"><span class="pre">/hello</span></code>, <code class="docutils literal notranslate"><span class="pre">/hello/</span></code> or <code class="docutils literal notranslate"><span class="pre">/hello/mr/smith</span></code>.</p>
-<p>Each wildcard passes the covered part of the URL as a keyword argument to the request callback. You can use them right away and implement RESTful, nice-looking and meaningful URLs with ease. Here are some other examples along with the URLs they’d match:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nd">@route</span><span class="p">(</span><span class="s1">&#39;/wiki/&lt;pagename&gt;&#39;</span><span class="p">)</span>            <span class="c1"># matches /wiki/Learning_Python</span>
-<span class="k">def</span> <span class="nf">show_wiki_page</span><span class="p">(</span><span class="n">pagename</span><span class="p">):</span>
-<span class="o">...</span>
-<span class="nd">@route</span><span class="p">(</span><span class="s1">&#39;/&lt;action&gt;/&lt;user&gt;&#39;</span><span class="p">)</span>            <span class="c1"># matches /follow/defnull</span>
-<span class="k">def</span> <span class="nf">user_api</span><span class="p">(</span><span class="n">action</span><span class="p">,</span> <span class="n">user</span><span class="p">):</span>
-<span class="o">...</span>
-</pre></div>
-</div>
-<p>Filters can be used to define more specific wildcards, and/or transform the covered part of the URL before it is passed to the callback. A filtered wildcard is declared as <code class="docutils literal notranslate"><span class="pre">&lt;name:filter&gt;</span></code> or <code class="docutils literal notranslate"><span class="pre">&lt;name:filter:config&gt;</span></code>. The syntax for the optional config part depends on the filter used.</p>
-<p>The following filters are implemented by default and more may be added:</p>
-<ul class="simple">
-<li><p><strong>:int</strong> matches (signed) digits only and converts the value to integer.</p></li>
-<li><p><strong>:float</strong> similar to :int but for decimal numbers.</p></li>
-<li><p><strong>:path</strong> matches all characters including the slash character in a non-greedy way and can be used to match more than one path segment.</p></li>
-<li><p><strong>:re</strong> allows you to specify a custom regular expression in the config field. The matched value is not modified.</p></li>
-</ul>
-<p>Let’s have a look at some practical examples:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nd">@route</span><span class="p">(</span><span class="s1">&#39;/object/&lt;id:int&gt;&#39;</span><span class="p">)</span>
-<span class="k">def</span> <span class="nf">callback</span><span class="p">(</span><span class="nb">id</span><span class="p">):</span>
-<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="nb">id</span><span class="p">,</span> <span class="nb">int</span><span class="p">)</span>
-<span class="nd">@route</span><span class="p">(</span><span class="s1">&#39;/show/&lt;name:re:[a-z]+&gt;&#39;</span><span class="p">)</span>
-<span class="k">def</span> <span class="nf">callback</span><span class="p">(</span><span class="n">name</span><span class="p">):</span>
-<span class="k">assert</span> <span class="n">name</span><span class="o">.</span><span class="n">isalpha</span><span class="p">()</span>
-<span class="nd">@route</span><span class="p">(</span><span class="s1">&#39;/static/&lt;path:path&gt;&#39;</span><span class="p">)</span>
-<span class="k">def</span> <span class="nf">callback</span><span class="p">(</span><span class="n">path</span><span class="p">):</span>
-<span class="k">return</span> <span class="n">static_file</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="o">...</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>You can add your own filters as well. See <span class="xref std std-doc">routing</span> for details.</p>
-</section>
-<section id="http-request-methods">
-<h3>HTTP Request Methods<a class="headerlink" href="#http-request-methods" title="Permalink to this heading">¶</a></h3>
-<p>The HTTP protocol defines several <a href="#id5"><span class="problematic" id="id6">`request methods`__</span></a> (sometimes referred to as “verbs”) for different tasks. GET is the default for all routes with no other method specified. These routes will match GET requests only. To handle other methods such as POST, PUT, DELETE or PATCH, add a <code class="docutils literal notranslate"><span class="pre">method</span></code> keyword argument to the <code class="xref py py-func docutils literal notranslate"><span class="pre">route()</span></code> decorator or use one of the five alternative decorators: <code class="xref py py-func docutils literal notranslate"><span class="pre">get()</span></code>, <code class="xref py py-func docutils literal notranslate"><span class="pre">post()</span></code>, <code class="xref py py-func docutils literal notranslate"><span class="pre">put()</span></code>, <code class="xref py py-func docutils literal notranslate"><span class="pre">delete()</span></code> or <code class="xref py py-func docutils literal notranslate"><span class="pre">patch()</span></code>.</p>
-<p>The POST method is commonly used for HTML form submission. This example shows how to handle a login form using POST:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">bottle</span> <span class="kn">import</span> <span class="n">get</span><span class="p">,</span> <span class="n">post</span><span class="p">,</span> <span class="n">request</span> <span class="c1"># or route</span>
-<span class="nd">@get</span><span class="p">(</span><span class="s1">&#39;/login&#39;</span><span class="p">)</span> <span class="c1"># or @route(&#39;/login&#39;)</span>
-<span class="k">def</span> <span class="nf">login</span><span class="p">():</span>
-<span class="k">return</span> <span class="s1">&#39;&#39;&#39;</span>
-<span class="s1">        &lt;form action=&quot;/login&quot; method=&quot;post&quot;&gt;</span>
-<span class="s1">            Username: &lt;input name=&quot;username&quot; type=&quot;text&quot; /&gt;</span>
-<span class="s1">            Password: &lt;input name=&quot;password&quot; type=&quot;password&quot; /&gt;</span>
-<span class="s1">            &lt;input value=&quot;Login&quot; type=&quot;submit&quot; /&gt;</span>
-<span class="s1">        &lt;/form&gt;</span>
-<span class="s1">    &#39;&#39;&#39;</span>
-<span class="nd">@post</span><span class="p">(</span><span class="s1">&#39;/login&#39;</span><span class="p">)</span> <span class="c1"># or @route(&#39;/login&#39;, method=&#39;POST&#39;)</span>
-<span class="k">def</span> <span class="nf">do_login</span><span class="p">():</span>
-<span class="n">username</span> <span class="o">=</span> <span class="n">request</span><span class="o">.</span><span class="n">forms</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;username&#39;</span><span class="p">)</span>
-<span class="n">password</span> <span class="o">=</span> <span class="n">request</span><span class="o">.</span><span class="n">forms</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;password&#39;</span><span class="p">)</span>
-<span class="k">if</span> <span class="n">check_login</span><span class="p">(</span><span class="n">username</span><span class="p">,</span> <span class="n">password</span><span class="p">):</span>
-<span class="k">return</span> <span class="s2">&quot;&lt;p&gt;Your login information was correct.&lt;/p&gt;&quot;</span>
-<span class="k">else</span><span class="p">:</span>
-<span class="k">return</span> <span class="s2">&quot;&lt;p&gt;Login failed.&lt;/p&gt;&quot;</span>
-</pre></div>
-</div>
-<p>In this example the <code class="docutils literal notranslate"><span class="pre">/login</span></code> URL is linked to two distinct callbacks, one for GET requests and another for POST requests. The first one displays a HTML form to the user. The second callback is invoked on a form submission and checks the login credentials the user entered into the form. The use of <code class="xref py py-attr docutils literal notranslate"><span class="pre">Request.forms</span></code> is further described in the <a class="reference internal" href="#tutorial-request"><span class="std std-ref">Request Data</span></a> section.</p>
-<p class="rubric">Special Methods: HEAD and ANY</p>
-<p>The HEAD method is used to ask for the response identical to the one that would correspond to a GET request, but without the response body. This is useful for retrieving meta-information about a resource without having to download the entire document. Bottle handles these requests automatically by falling back to the corresponding GET route and cutting off the request body, if present. You don’t have to specify any HEAD routes yourself.</p>
-<p>Additionally, the non-standard ANY method works as a low priority fallback: Routes that listen to ANY will match requests regardless of their HTTP method but only if no other more specific route is defined. This is helpful for <em>proxy-routes</em> that redirect requests to more specific sub-applications.</p>
-<p>To sum it up: HEAD requests fall back to GET routes and all requests fall back to ANY routes, but only if there is no matching route for the original request method. It’s as simple as that.</p>
-</section>
-<section id="routing-static-files">
-<h3>Routing Static Files<a class="headerlink" href="#routing-static-files" title="Permalink to this heading">¶</a></h3>
-<p>Static files such as images or CSS files are not served automatically. You have to add a route and a callback to control which files get served and where to find them:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">bottle</span> <span class="kn">import</span> <span class="n">static_file</span>
-<span class="nd">@route</span><span class="p">(</span><span class="s1">&#39;/static/&lt;filename&gt;&#39;</span><span class="p">)</span>
-<span class="k">def</span> <span class="nf">serverstatic</span><span class="p">(</span><span class="n">filename</span><span class="p">):</span>
-<span class="k">return</span> <span class="n">static_file</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="n">root</span><span class="o">=</span><span class="s1">&#39;/path/to/your/static/files&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>The <code class="xref py py-func docutils literal notranslate"><span class="pre">static_file()</span></code> function is a helper to serve files in a safe and convenient way (see <a class="reference internal" href="#tutorial-static-files"><span class="std std-ref">Static Files</span></a>). This example is limited to files directly within the <code class="docutils literal notranslate"><span class="pre">/path/to/your/static/files</span></code> directory because the <code class="docutils literal notranslate"><span class="pre">&lt;filename&gt;</span></code> wildcard won’t match a path with a slash in it. To serve files in subdirectories, change the wildcard to use the <cite>path</cite> filter:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nd">@route</span><span class="p">(</span><span class="s1">&#39;/static/&lt;filepath:path&gt;&#39;</span><span class="p">)</span>
-<span class="k">def</span> <span class="nf">serverstatic</span><span class="p">(</span><span class="n">filepath</span><span class="p">):</span>
-<span class="k">return</span> <span class="n">static_file</span><span class="p">(</span><span class="n">filepath</span><span class="p">,</span> <span class="n">root</span><span class="o">=</span><span class="s1">&#39;/path/to/your/static/files&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>Be careful when specifying a relative root-path such as <code class="docutils literal notranslate"><span class="pre">root='./static/files'</span></code>. The working directory (<code class="docutils literal notranslate"><span class="pre">./</span></code>) and the project directory are not always the same.</p>
-</section>
-<section id="error-pages">
-<span id="tutorial-errorhandling"></span><h3>Error Pages<a class="headerlink" href="#error-pages" title="Permalink to this heading">¶</a></h3>
-<p>If anything goes wrong, Bottle displays an informative but fairly plain error page. You can override the default for a specific HTTP status code with the <code class="xref py py-func docutils literal notranslate"><span class="pre">error()</span></code> decorator:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">bottle</span> <span class="kn">import</span> <span class="n">error</span>
-<span class="nd">@error</span><span class="p">(</span><span class="mi">404</span><span class="p">)</span>
-<span class="k">def</span> <span class="nf">error404</span><span class="p">(</span><span class="n">error</span><span class="p">):</span>
-<span class="k">return</span> <span class="s1">&#39;Nothing here, sorry&#39;</span>
-</pre></div>
-</div>
-<p>From now on, <cite>404 File not Found</cite> errors will display a custom error page to the user. The only parameter passed to the error-handler is an instance of <code class="xref py py-exc docutils literal notranslate"><span class="pre">HTTPError</span></code>. Apart from that, an error-handler is quite similar to a regular request callback. You can read from <code class="xref py py-data docutils literal notranslate"><span class="pre">request</span></code>, write to <code class="xref py py-data docutils literal notranslate"><span class="pre">response</span></code> and return any supported data-type except for <code class="xref py py-exc docutils literal notranslate"><span class="pre">HTTPError</span></code> instances.</p>
-<p>Error handlers are used only if your application returns or raises an <code class="xref py py-exc docutils literal notranslate"><span class="pre">HTTPError</span></code> exception (<code class="xref py py-func docutils literal notranslate"><span class="pre">abort()</span></code> does just that). Changing <code class="xref py py-attr docutils literal notranslate"><span class="pre">Request.status</span></code> or returning <code class="xref py py-exc docutils literal notranslate"><span class="pre">HTTPResponse</span></code> won’t trigger the error handler.</p>
-</section>
-</section>
-<section id="generating-content">
-<span id="tutorial-output"></span><h2>Generating content<a class="headerlink" href="#generating-content" title="Permalink to this heading">¶</a></h2>
-<p>In pure WSGI, the range of types you may return from your application is very limited. Applications must return an iterable yielding byte strings. You may return a string (because strings are iterable) but this causes most servers to transmit your content char by char. Unicode strings are not allowed at all. This is not very practical.</p>
-<p>Bottle is much more flexible and supports a wide range of types. It even adds a <code class="docutils literal notranslate"><span class="pre">Content-Length</span></code> header if possible and encodes unicode automatically, so you don’t have to. What follows is a list of data types you may return from your application callbacks and a short description of how these are handled by the framework:</p>
-<dl class="simple">
-<dt>Dictionaries</dt><dd><p>As mentioned above, Python dictionaries (or subclasses thereof) are automatically transformed into JSON strings and returned to the browser with the <code class="docutils literal notranslate"><span class="pre">Content-Type</span></code> header set to <code class="docutils literal notranslate"><span class="pre">application/json</span></code>. This makes it easy to implement json-based APIs. Data formats other than json are supported too. See the <span class="xref std std-ref">tutorial-output-filter</span> to learn more.</p>
-</dd>
-<dt>Empty Strings, <code class="docutils literal notranslate"><span class="pre">False</span></code>, <code class="docutils literal notranslate"><span class="pre">None</span></code> or other non-true values:</dt><dd><p>These produce an empty output with the <code class="docutils literal notranslate"><span class="pre">Content-Length</span></code> header set to 0.</p>
-</dd>
-<dt>Unicode strings</dt><dd><p>Unicode strings (or iterables yielding unicode strings) are automatically encoded with the codec specified in the <code class="docutils literal notranslate"><span class="pre">Content-Type</span></code> header (utf8 by default) and then treated as normal byte strings (see below).</p>
-</dd>
-<dt>Byte strings</dt><dd><p>Bottle returns strings as a whole (instead of iterating over each char) and adds a <code class="docutils literal notranslate"><span class="pre">Content-Length</span></code> header based on the string length. Lists of byte strings are joined first. Other iterables yielding byte strings are not joined because they may grow too big to fit into memory. The <code class="docutils literal notranslate"><span class="pre">Content-Length</span></code> header is not set in this case.</p>
-</dd>
-<dt>Instances of <code class="xref py py-exc docutils literal notranslate"><span class="pre">HTTPError</span></code> or <code class="xref py py-exc docutils literal notranslate"><span class="pre">HTTPResponse</span></code></dt><dd><p>Returning these has the same effect as when raising them as an exception. In case of an <code class="xref py py-exc docutils literal notranslate"><span class="pre">HTTPError</span></code>, the error handler is applied. See <a class="reference internal" href="#tutorial-errorhandling"><span class="std std-ref">Error Pages</span></a> for details.</p>
-</dd>
-<dt>File objects</dt><dd><p>Everything that has a <code class="docutils literal notranslate"><span class="pre">.read()</span></code> method is treated as a file or file-like object and passed to the <code class="docutils literal notranslate"><span class="pre">wsgi.file_wrapper</span></code> callable defined by the WSGI server framework. Some WSGI server implementations can make use of optimized system calls (sendfile) to transmit files more efficiently. In other cases this just iterates over chunks that fit into memory. Optional headers such as <code class="docutils literal notranslate"><span class="pre">Content-Length</span></code> or <code class="docutils literal notranslate"><span class="pre">Content-Type</span></code> are <em>not</em> set automatically. Use <code class="xref py py-func docutils literal notranslate"><span class="pre">send_file()</span></code> if possible. See <a class="reference internal" href="#tutorial-static-files"><span class="std std-ref">Static Files</span></a> for details.</p>
-</dd>
-<dt>Iterables and generators</dt><dd><p>You are allowed to use <code class="docutils literal notranslate"><span class="pre">yield</span></code> within your callbacks or return an iterable, as long as the iterable yields byte strings, unicode strings, <code class="xref py py-exc docutils literal notranslate"><span class="pre">HTTPError</span></code> or <code class="xref py py-exc docutils literal notranslate"><span class="pre">HTTPResponse</span></code> instances. Nested iterables are not supported, sorry. Please note that the HTTP status code and the headers are sent to the browser as soon as the iterable yields its first non-empty value. Changing these later has no effect.</p>
-</dd>
-</dl>
-<p>The ordering of this list is significant. You may for example return a subclass of <code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code> with a <code class="docutils literal notranslate"><span class="pre">read()</span></code> method. It is still treated as a string instead of a file, because strings are handled first.</p>
-<p class="rubric">Changing the Default Encoding</p>
-<p>Bottle uses the <cite>charset</cite> parameter of the <code class="docutils literal notranslate"><span class="pre">Content-Type</span></code> header to decide how to encode unicode strings. This header defaults to <code class="docutils literal notranslate"><span class="pre">text/html;</span> <span class="pre">charset=UTF8</span></code> and can be changed using the <code class="xref py py-attr docutils literal notranslate"><span class="pre">Response.content_type</span></code> attribute or by setting the <code class="xref py py-attr docutils literal notranslate"><span class="pre">Response.charset</span></code> attribute directly. (The <code class="xref py py-class docutils literal notranslate"><span class="pre">Response</span></code> object is described in the section <a class="reference internal" href="#tutorial-response"><span class="std std-ref">The Response Object</span></a>.)</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">bottle</span> <span class="kn">import</span> <span class="n">response</span>
-<span class="nd">@route</span><span class="p">(</span><span class="s1">&#39;/iso&#39;</span><span class="p">)</span>
-<span class="k">def</span> <span class="nf">get_iso</span><span class="p">():</span>
-<span class="n">response</span><span class="o">.</span><span class="n">charset</span> <span class="o">=</span> <span class="s1">&#39;ISO-8859-15&#39;</span>
-<span class="k">return</span> <span class="sa">u</span><span class="s1">&#39;This will be sent with ISO-8859-15 encoding.&#39;</span>
-<span class="nd">@route</span><span class="p">(</span><span class="s1">&#39;/latin9&#39;</span><span class="p">)</span>
-<span class="k">def</span> <span class="nf">get_latin</span><span class="p">():</span>
-<span class="n">response</span><span class="o">.</span><span class="n">content_type</span> <span class="o">=</span> <span class="s1">&#39;text/html; charset=latin9&#39;</span>
-<span class="k">return</span> <span class="sa">u</span><span class="s1">&#39;ISO-8859-15 is also known as latin9.&#39;</span>
-</pre></div>
-</div>
-<p>In some rare cases the Python encoding names differ from the names supported by the HTTP specification. Then, you have to do both: first set the <code class="xref py py-attr docutils literal notranslate"><span class="pre">Response.content_type</span></code> header (which is sent to the client unchanged) and then set the <code class="xref py py-attr docutils literal notranslate"><span class="pre">Response.charset</span></code> attribute (which is used to encode unicode).</p>
-<section id="static-files">
-<span id="tutorial-static-files"></span><h3>Static Files<a class="headerlink" href="#static-files" title="Permalink to this heading">¶</a></h3>
-<p>You can directly return file objects, but <code class="xref py py-func docutils literal notranslate"><span class="pre">static_file()</span></code> is the recommended way to serve static files. It automatically guesses a mime-type, adds a <code class="docutils literal notranslate"><span class="pre">Last-Modified</span></code> header, restricts paths to a <code class="docutils literal notranslate"><span class="pre">root</span></code> directory for security reasons and generates appropriate error responses (403 on permission errors, 404 on missing files). It even supports the <code class="docutils literal notranslate"><span class="pre">If-Modified-Since</span></code> header and eventually generates a <code class="docutils literal notranslate"><span class="pre">304</span> <span class="pre">Not</span> <span class="pre">Modified</span></code> response. You can pass a custom MIME type to disable guessing.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">bottle</span> <span class="kn">import</span> <span class="n">static_file</span>
-<span class="nd">@route</span><span class="p">(</span><span class="s1">&#39;/images/&lt;filename:re:.*\.png&gt;&#39;</span><span class="p">)</span>
-<span class="k">def</span> <span class="nf">send_image</span><span class="p">(</span><span class="n">filename</span><span class="p">):</span>
-<span class="k">return</span> <span class="n">static_file</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="n">root</span><span class="o">=</span><span class="s1">&#39;/path/to/image/files&#39;</span><span class="p">,</span> <span class="n">mimetype</span><span class="o">=</span><span class="s1">&#39;image/png&#39;</span><span class="p">)</span>
-<span class="nd">@route</span><span class="p">(</span><span class="s1">&#39;/static/&lt;filename:path&gt;&#39;</span><span class="p">)</span>
-<span class="k">def</span> <span class="nf">sendstatic</span><span class="p">(</span><span class="n">filename</span><span class="p">):</span>
-<span class="k">return</span> <span class="n">static_file</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="n">root</span><span class="o">=</span><span class="s1">&#39;/path/to/static/files&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>You can raise the return value of <code class="xref py py-func docutils literal notranslate"><span class="pre">static_file()</span></code> as an exception if you really need to.</p>
-<p class="rubric">Forced Download</p>
-<p>Most browsers try to open downloaded files if the MIME type is known and assigned to an application (e.g. PDF files). If this is not what you want, you can force a download dialog and even suggest a filename to the user:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nd">@route</span><span class="p">(</span><span class="s1">&#39;/download/&lt;filename:path&gt;&#39;</span><span class="p">)</span>
-<span class="k">def</span> <span class="nf">download</span><span class="p">(</span><span class="n">filename</span><span class="p">):</span>
-<span class="k">return</span> <span class="n">static_file</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="n">root</span><span class="o">=</span><span class="s1">&#39;/path/to/static/files&#39;</span><span class="p">,</span> <span class="n">download</span><span class="o">=</span><span class="n">filename</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>If the <code class="docutils literal notranslate"><span class="pre">download</span></code> parameter is just <code class="docutils literal notranslate"><span class="pre">True</span></code>, the original filename is used.</p>
-</section>
-<section id="http-errors-and-redirects">
-<span id="tutorial-error"></span><h3>HTTP Errors and Redirects<a class="headerlink" href="#http-errors-and-redirects" title="Permalink to this heading">¶</a></h3>
-<p>The <code class="xref py py-func docutils literal notranslate"><span class="pre">abort()</span></code> function is a shortcut for generating HTTP error pages.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">bottle</span> <span class="kn">import</span> <span class="n">route</span><span class="p">,</span> <span class="n">abort</span>
-<span class="nd">@route</span><span class="p">(</span><span class="s1">&#39;/restricted&#39;</span><span class="p">)</span>
-<span class="k">def</span> <span class="nf">restricted</span><span class="p">():</span>
-<span class="n">abort</span><span class="p">(</span><span class="mi">401</span><span class="p">,</span> <span class="s2">&quot;Sorry, access denied.&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>To redirect a client to a different URL, you can send a <code class="docutils literal notranslate"><span class="pre">303</span> <span class="pre">See</span> <span class="pre">Other</span></code> response with the <code class="docutils literal notranslate"><span class="pre">Location</span></code> header set to the new URL. <code class="xref py py-func docutils literal notranslate"><span class="pre">redirect()</span></code> does that for you:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">bottle</span> <span class="kn">import</span> <span class="n">redirect</span>
-<span class="nd">@route</span><span class="p">(</span><span class="s1">&#39;/wrong/url&#39;</span><span class="p">)</span>
-<span class="k">def</span> <span class="nf">wrong</span><span class="p">():</span>
-<span class="n">redirect</span><span class="p">(</span><span class="s2">&quot;/right/url&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>You may provide a different HTTP status code as a second parameter.</p>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>Both functions will interrupt your callback code by raising an <code class="xref py py-exc docutils literal notranslate"><span class="pre">HTTPError</span></code> exception.</p>
-</div>
-<p class="rubric">Other Exceptions</p>
-<p>All exceptions other than <code class="xref py py-exc docutils literal notranslate"><span class="pre">HTTPResponse</span></code> or <code class="xref py py-exc docutils literal notranslate"><span class="pre">HTTPError</span></code> will result in a <code class="docutils literal notranslate"><span class="pre">500</span> <span class="pre">Internal</span> <span class="pre">Server</span> <span class="pre">Error</span></code> response, so they won’t crash your WSGI server. You can turn off this behavior to handle exceptions in your middleware by setting <code class="docutils literal notranslate"><span class="pre">bottle.app().catchall</span></code> to <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p>
-</section>
-<section id="the-response-object">
-<span id="tutorial-response"></span><h3>The <code class="xref py py-class docutils literal notranslate"><span class="pre">Response</span></code> Object<a class="headerlink" href="#the-response-object" title="Permalink to this heading">¶</a></h3>
-<p>Response metadata such as the HTTP status code, response headers and cookies are stored in an object called <code class="xref py py-data docutils literal notranslate"><span class="pre">response</span></code> up to the point where they are transmitted to the browser. You can manipulate these metadata directly or use the predefined helper methods to do so. The full API and feature list is described in the API section (see <code class="xref py py-class docutils literal notranslate"><span class="pre">Response</span></code>), but the most common use cases and features are covered here, too.</p>
-<p class="rubric">Status Code</p>
-<p>The <a class="reference external" href="http_code">HTTP status code</a> controls the behavior of the browser and defaults to <code class="docutils literal notranslate"><span class="pre">200</span> <span class="pre">OK</span></code>. In most scenarios you won’t need to set the <code class="xref py py-attr docutils literal notranslate"><span class="pre">Response.status</span></code> attribute manually, but use the <code class="xref py py-func docutils literal notranslate"><span class="pre">abort()</span></code> helper or return an <code class="xref py py-exc docutils literal notranslate"><span class="pre">HTTPResponse</span></code> instance with the appropriate status code. Any integer is allowed, but codes other than the ones defined by the <a class="reference external" href="http_code">HTTP specification</a> will only confuse the browser and break standards.</p>
-<p class="rubric">Response Header</p>
-<p>Response headers such as <code class="docutils literal notranslate"><span class="pre">Cache-Control</span></code> or <code class="docutils literal notranslate"><span class="pre">Location</span></code> are defined via <code class="xref py py-meth docutils literal notranslate"><span class="pre">Response.set_header()</span></code>. This method takes two parameters, a header name and a value. The name part is case-insensitive:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nd">@route</span><span class="p">(</span><span class="s1">&#39;/wiki/&lt;page&gt;&#39;</span><span class="p">)</span>
-<span class="k">def</span> <span class="nf">wiki</span><span class="p">(</span><span class="n">page</span><span class="p">):</span>
-<span class="n">response</span><span class="o">.</span><span class="n">set_header</span><span class="p">(</span><span class="s1">&#39;Content-Language&#39;</span><span class="p">,</span> <span class="s1">&#39;en&#39;</span><span class="p">)</span>
-<span class="o">...</span>
-</pre></div>
-</div>
-<p>Most headers are unique, meaning that only one header per name is send to the client. Some special headers however are allowed to appear more than once in a response. To add an additional header, use <code class="xref py py-meth docutils literal notranslate"><span class="pre">Response.add_header()</span></code> instead of <code class="xref py py-meth docutils literal notranslate"><span class="pre">Response.set_header()</span></code>:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">response</span><span class="o">.</span><span class="n">set_header</span><span class="p">(</span><span class="s1">&#39;Set-Cookie&#39;</span><span class="p">,</span> <span class="s1">&#39;name=value&#39;</span><span class="p">)</span>
-<span class="n">response</span><span class="o">.</span><span class="n">add_header</span><span class="p">(</span><span class="s1">&#39;Set-Cookie&#39;</span><span class="p">,</span> <span class="s1">&#39;name2=value2&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>Please note that this is just an example. If you want to work with cookies, read <a class="reference internal" href="#tutorial-cookies"><span class="std std-ref">ahead</span></a>.</p>
-</section>
-<section id="cookies">
-<span id="tutorial-cookies"></span><h3>Cookies<a class="headerlink" href="#cookies" title="Permalink to this heading">¶</a></h3>
-<p>A cookie is a named piece of text stored in the user’s browser profile. You can access previously defined cookies via <code class="xref py py-meth docutils literal notranslate"><span class="pre">Request.get_cookie()</span></code> and set new cookies with <code class="xref py py-meth docutils literal notranslate"><span class="pre">Response.set_cookie()</span></code>:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nd">@route</span><span class="p">(</span><span class="s1">&#39;/hello&#39;</span><span class="p">)</span>
-<span class="k">def</span> <span class="nf">hello_again</span><span class="p">():</span>
-<span class="k">if</span> <span class="n">request</span><span class="o">.</span><span class="n">get_cookie</span><span class="p">(</span><span class="s2">&quot;visited&quot;</span><span class="p">):</span>
-<span class="k">return</span> <span class="s2">&quot;Welcome back! Nice to see you again&quot;</span>
-<span class="k">else</span><span class="p">:</span>
-<span class="n">response</span><span class="o">.</span><span class="n">set_cookie</span><span class="p">(</span><span class="s2">&quot;visited&quot;</span><span class="p">,</span> <span class="s2">&quot;yes&quot;</span><span class="p">)</span>
-<span class="k">return</span> <span class="s2">&quot;Hello there! Nice to meet you&quot;</span>
-</pre></div>
-</div>
-<p>The <code class="xref py py-meth docutils literal notranslate"><span class="pre">Response.set_cookie()</span></code> method accepts a number of additional keyword arguments that control the cookies lifetime and behavior. Some of the most common settings are described here:</p>
-<ul class="simple">
-<li><p><strong>max_age:</strong>    Maximum age in seconds. (default: <code class="docutils literal notranslate"><span class="pre">None</span></code>)</p></li>
-<li><p><strong>expires:</strong>    A datetime object or UNIX timestamp. (default: <code class="docutils literal notranslate"><span class="pre">None</span></code>)</p></li>
-<li><p><strong>domain:</strong>     The domain that is allowed to read the cookie. (default: current domain)</p></li>
-<li><p><strong>path:</strong>       Limit the cookie to a given path (default: <code class="docutils literal notranslate"><span class="pre">/</span></code>)</p></li>
-<li><p><strong>secure:</strong>     Limit the cookie to HTTPS connections (default: off).</p></li>
-<li><p><strong>httponly:</strong>   Prevent client-side javascript to read this cookie (default: off, requires Python 2.7 or newer).</p></li>
-<li><p><strong>same_site:</strong>  Disables third-party use for a cookie. Allowed attributes: <cite>lax</cite> and <cite>strict</cite>. In strict mode the cookie will never be sent. In lax mode the cookie is only sent with a top-level GET request.</p></li>
-</ul>
-<p>If neither <cite>expires</cite> nor <cite>max_age</cite> is set, the cookie expires at the end of the browser session or as soon as the browser window is closed. There are some other gotchas you should consider when using cookies:</p>
-<ul class="simple">
-<li><p>Cookies are limited to 4 KB of text in most browsers.</p></li>
-<li><p>Some users configure their browsers to not accept cookies at all. Most search engines ignore cookies too. Make sure that your application still works without cookies.</p></li>
-<li><p>Cookies are stored at client side and are not encrypted in any way. Whatever you store in a cookie, the user can read it. Worse than that, an attacker might be able to steal a user’s cookies through <a class="reference external" href="http://en.wikipedia.org/wiki/HTTP_cookie#Cookie_theft_and_session_hijacking">XSS</a> vulnerabilities on your side. Some viruses are known to read the browser cookies, too. Thus, never store confidential information in cookies.</p></li>
-<li><p>Cookies are easily forged by malicious clients. Do not trust cookies.</p></li>
-</ul>
-<p class="rubric" id="tutorial-signed-cookies">Signed Cookies</p>
-<p>As mentioned above, cookies are easily forged by malicious clients. Bottle can cryptographically sign your cookies to prevent this kind of manipulation. All you have to do is to provide a signature key via the <cite>secret</cite> keyword argument whenever you read or set a cookie and keep that key a secret. As a result, <code class="xref py py-meth docutils literal notranslate"><span class="pre">Request.get_cookie()</span></code> will return <code class="docutils literal notranslate"><span class="pre">None</span></code> if the cookie is not signed or the signature keys don’t match:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nd">@route</span><span class="p">(</span><span class="s1">&#39;/login&#39;</span><span class="p">)</span>
-<span class="k">def</span> <span class="nf">do_login</span><span class="p">():</span>
-<span class="n">username</span> <span class="o">=</span> <span class="n">request</span><span class="o">.</span><span class="n">forms</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;username&#39;</span><span class="p">)</span>
-<span class="n">password</span> <span class="o">=</span> <span class="n">request</span><span class="o">.</span><span class="n">forms</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;password&#39;</span><span class="p">)</span>
-<span class="k">if</span> <span class="n">check_login</span><span class="p">(</span><span class="n">username</span><span class="p">,</span> <span class="n">password</span><span class="p">):</span>
-<span class="n">response</span><span class="o">.</span><span class="n">set_cookie</span><span class="p">(</span><span class="s2">&quot;account&quot;</span><span class="p">,</span> <span class="n">username</span><span class="p">,</span> <span class="n">secret</span><span class="o">=</span><span class="s1">&#39;some-secret-key&#39;</span><span class="p">)</span>
-<span class="k">return</span> <span class="n">template</span><span class="p">(</span><span class="s2">&quot;&lt;p&gt;Welcome {{name}}! You are now logged in.&lt;/p&gt;&quot;</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="n">username</span><span class="p">)</span>
-<span class="k">else</span><span class="p">:</span>
-<span class="k">return</span> <span class="s2">&quot;&lt;p&gt;Login failed.&lt;/p&gt;&quot;</span>
-<span class="nd">@route</span><span class="p">(</span><span class="s1">&#39;/restricted&#39;</span><span class="p">)</span>
-<span class="k">def</span> <span class="nf">restricted_area</span><span class="p">():</span>
-<span class="n">username</span> <span class="o">=</span> <span class="n">request</span><span class="o">.</span><span class="n">get_cookie</span><span class="p">(</span><span class="s2">&quot;account&quot;</span><span class="p">,</span> <span class="n">secret</span><span class="o">=</span><span class="s1">&#39;some-secret-key&#39;</span><span class="p">)</span>
-<span class="k">if</span> <span class="n">username</span><span class="p">:</span>
-<span class="k">return</span> <span class="n">template</span><span class="p">(</span><span class="s2">&quot;Hello {{name}}. Welcome back.&quot;</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="n">username</span><span class="p">)</span>
-<span class="k">else</span><span class="p">:</span>
-<span class="k">return</span> <span class="s2">&quot;You are not logged in. Access denied.&quot;</span>
-</pre></div>
-</div>
-<p>In addition, Bottle automatically pickles and unpickles any data stored to signed cookies. This allows you to store any pickle-able object (not only strings) to cookies, as long as the pickled data does not exceed the 4 KB limit.</p>
-<div class="admonition warning">
-<p class="admonition-title">Warning</p>
-<p>Signed cookies are not encrypted (the client can still see the content) and not copy-protected (the client can restore an old cookie). The main intention is to make pickling and unpickling safe and prevent manipulation, not to store secret information at client side.</p>
-</div>
-</section>
-</section>
-<section id="request-data">
-<span id="tutorial-request"></span><h2>Request Data<a class="headerlink" href="#request-data" title="Permalink to this heading">¶</a></h2>
-<p>Cookies, HTTP header, HTML <code class="docutils literal notranslate"><span class="pre">&lt;form&gt;</span></code> fields and other request data is available through the global <code class="xref py py-data docutils literal notranslate"><span class="pre">request</span></code> object. This special object always refers to the <em>current</em> request, even in multi-threaded environments where multiple client connections are handled at the same time:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">bottle</span> <span class="kn">import</span> <span class="n">request</span><span class="p">,</span> <span class="n">route</span><span class="p">,</span> <span class="n">template</span>
-<span class="nd">@route</span><span class="p">(</span><span class="s1">&#39;/hello&#39;</span><span class="p">)</span>
-<span class="k">def</span> <span class="nf">hello</span><span class="p">():</span>
-<span class="n">name</span> <span class="o">=</span> <span class="n">request</span><span class="o">.</span><span class="n">cookies</span><span class="o">.</span><span class="n">username</span> <span class="ow">or</span> <span class="s1">&#39;Guest&#39;</span>
-<span class="k">return</span> <span class="n">template</span><span class="p">(</span><span class="s1">&#39;Hello {{name}}&#39;</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="n">name</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>The <code class="xref py py-data docutils literal notranslate"><span class="pre">request</span></code> object is a subclass of <code class="xref py py-class docutils literal notranslate"><span class="pre">BaseRequest</span></code> and has a very rich API to access data. We only cover the most commonly used features here, but it should be enough to get started.</p>
-<section id="introducing-formsdict">
-<h3>Introducing <code class="xref py py-class docutils literal notranslate"><span class="pre">FormsDict</span></code><a class="headerlink" href="#introducing-formsdict" title="Permalink to this heading">¶</a></h3>
-<p>Bottle uses a special type of dictionary to store form data and cookies. <code class="xref py py-class docutils literal notranslate"><span class="pre">FormsDict</span></code> behaves like a normal dictionary, but has some additional features to make your life easier.</p>
-<p><strong>Attribute access</strong>: All values in the dictionary are also accessible as attributes. These virtual attributes return unicode strings, even if the value is missing or unicode decoding fails. In that case, the string is empty, but still present:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">name</span> <span class="o">=</span> <span class="n">request</span><span class="o">.</span><span class="n">cookies</span><span class="o">.</span><span class="n">name</span>
-<span class="c1"># is a shortcut for:</span>
-<span class="n">name</span> <span class="o">=</span> <span class="n">request</span><span class="o">.</span><span class="n">cookies</span><span class="o">.</span><span class="n">getunicode</span><span class="p">(</span><span class="s1">&#39;name&#39;</span><span class="p">)</span> <span class="c1"># encoding=&#39;utf-8&#39; (default)</span>
-<span class="c1"># which basically does this:</span>
-<span class="k">try</span><span class="p">:</span>
-<span class="n">name</span> <span class="o">=</span> <span class="n">request</span><span class="o">.</span><span class="n">cookies</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;name&#39;</span><span class="p">,</span> <span class="s1">&#39;&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span><span class="s1">&#39;utf-8&#39;</span><span class="p">)</span>
-<span class="k">except</span> <span class="ne">UnicodeError</span><span class="p">:</span>
-<span class="n">name</span> <span class="o">=</span> <span class="sa">u</span><span class="s1">&#39;&#39;</span>
-</pre></div>
-</div>
-<p><strong>Multiple values per key:</strong> <code class="xref py py-class docutils literal notranslate"><span class="pre">FormsDict</span></code> is a subclass of <code class="xref py py-class docutils literal notranslate"><span class="pre">MultiDict</span></code> and can store more than one value per key. The standard dictionary access methods will only return a single value, but the <code class="xref py py-meth docutils literal notranslate"><span class="pre">getall()</span></code> method returns a (possibly empty) list of all values for a specific key:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">choice</span> <span class="ow">in</span> <span class="n">request</span><span class="o">.</span><span class="n">forms</span><span class="o">.</span><span class="n">getall</span><span class="p">(</span><span class="s1">&#39;multiple_choice&#39;</span><span class="p">):</span>
-<span class="n">do_something</span><span class="p">(</span><span class="n">choice</span><span class="p">)</span>
-</pre></div>
-</div>
-<p><strong>WTForms support:</strong> Some libraries (e.g. <a class="reference external" href="http://wtforms.simplecodes.com/">WTForms</a>) want all-unicode dictionaries as input. <code class="xref py py-meth docutils literal notranslate"><span class="pre">FormsDict.decode()</span></code> does that for you. It decodes all values and returns a copy of itself, while preserving multiple values per key and all the other features.</p>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>In <strong>Python 2</strong> all keys and values are byte-strings. If you need unicode, you can call <code class="xref py py-meth docutils literal notranslate"><span class="pre">FormsDict.getunicode()</span></code> or fetch values via attribute access. Both methods try to decode the string (default: utf8) and return an empty string if that fails. No need to catch <code class="xref py py-exc docutils literal notranslate"><span class="pre">UnicodeError</span></code>:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">request</span><span class="o">.</span><span class="n">query</span><span class="p">[</span><span class="s1">&#39;city&#39;</span><span class="p">]</span>
-<span class="go">&#39;G\xc3\xb6ttingen&#39;  # A utf8 byte string</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">request</span><span class="o">.</span><span class="n">query</span><span class="o">.</span><span class="n">city</span>
-<span class="go">u&#39;Göttingen&#39;        # The same string as unicode</span>
-</pre></div>
-</div>
-<p>In <strong>Python 3</strong> all strings are unicode, but HTTP is a byte-based wire protocol. The server has to decode the byte strings somehow before they are passed to the application. To be on the safe side, WSGI suggests ISO-8859-1 (aka latin1), a reversible single-byte codec that can be re-encoded with a different encoding later. Bottle does that for <code class="xref py py-meth docutils literal notranslate"><span class="pre">FormsDict.getunicode()</span></code> and attribute access, but not for the dict-access methods. These return the unchanged values as provided by the server implementation, which is probably not what you want.</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">request</span><span class="o">.</span><span class="n">query</span><span class="p">[</span><span class="s1">&#39;city&#39;</span><span class="p">]</span>
-<span class="go">&#39;Göttingen&#39; # An utf8 string provisionally decoded as ISO-8859-1 by the server</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">request</span><span class="o">.</span><span class="n">query</span><span class="o">.</span><span class="n">city</span>
-<span class="go">&#39;Göttingen&#39;  # The same string correctly re-encoded as utf8 by bottle</span>
-</pre></div>
-</div>
-<p>If you need the whole dictionary with correctly decoded values (e.g. for WTForms), you can call <code class="xref py py-meth docutils literal notranslate"><span class="pre">FormsDict.decode()</span></code> to get a re-encoded copy.</p>
-</div>
-</section>
-<section id="id3">
-<h3>Cookies<a class="headerlink" href="#id3" title="Permalink to this heading">¶</a></h3>
-<p>Cookies are small pieces of text stored in the clients browser and sent back to the server with each request. They are useful to keep some state around for more than one request (HTTP itself is stateless), but should not be used for security related stuff. They can be easily forged by the client.</p>
-<p>All cookies sent by the client are available through <code class="xref py py-attr docutils literal notranslate"><span class="pre">BaseRequest.cookies</span></code> (a <code class="xref py py-class docutils literal notranslate"><span class="pre">FormsDict</span></code>). This example shows a simple cookie-based view counter:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">bottle</span> <span class="kn">import</span> <span class="n">route</span><span class="p">,</span> <span class="n">request</span><span class="p">,</span> <span class="n">response</span>
-<span class="nd">@route</span><span class="p">(</span><span class="s1">&#39;/counter&#39;</span><span class="p">)</span>
-<span class="k">def</span> <span class="nf">counter</span><span class="p">():</span>
-<span class="n">count</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span> <span class="n">request</span><span class="o">.</span><span class="n">cookies</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;counter&#39;</span><span class="p">,</span> <span class="s1">&#39;0&#39;</span><span class="p">)</span> <span class="p">)</span>
-<span class="n">count</span> <span class="o">+=</span> <span class="mi">1</span>
-<span class="n">response</span><span class="o">.</span><span class="n">set_cookie</span><span class="p">(</span><span class="s1">&#39;counter&#39;</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="n">count</span><span class="p">))</span>
-<span class="k">return</span> <span class="s1">&#39;You visited this page </span><span class="si">%d</span><span class="s1"> times&#39;</span> <span class="o">%</span> <span class="n">count</span>
-</pre></div>
-</div>
-<p>The <code class="xref py py-meth docutils literal notranslate"><span class="pre">BaseRequest.get_cookie()</span></code> method is a different way do access cookies. It supports decoding <a class="reference internal" href="#tutorial-signed-cookies"><span class="std std-ref">signed cookies</span></a> as described in a separate section.</p>
-</section>
-<section id="http-headers">
-<h3>HTTP Headers<a class="headerlink" href="#http-headers" title="Permalink to this heading">¶</a></h3>
-<p>All HTTP headers sent by the client (e.g. <code class="docutils literal notranslate"><span class="pre">Referer</span></code>, <code class="docutils literal notranslate"><span class="pre">Agent</span></code> or <code class="docutils literal notranslate"><span class="pre">Accept-Language</span></code>) are stored in a <code class="xref py py-class docutils literal notranslate"><span class="pre">WSGIHeaderDict</span></code> and accessible through the <code class="xref py py-attr docutils literal notranslate"><span class="pre">BaseRequest.headers</span></code> attribute. A <code class="xref py py-class docutils literal notranslate"><span class="pre">WSGIHeaderDict</span></code> is basically a dictionary with case-insensitive keys:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">bottle</span> <span class="kn">import</span> <span class="n">route</span><span class="p">,</span> <span class="n">request</span>
-<span class="nd">@route</span><span class="p">(</span><span class="s1">&#39;/is_ajax&#39;</span><span class="p">)</span>
-<span class="k">def</span> <span class="nf">is_ajax</span><span class="p">():</span>
-<span class="k">if</span> <span class="n">request</span><span class="o">.</span><span class="n">headers</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;X-Requested-With&#39;</span><span class="p">)</span> <span class="o">==</span> <span class="s1">&#39;XMLHttpRequest&#39;</span><span class="p">:</span>
-<span class="k">return</span> <span class="s1">&#39;This is an AJAX request&#39;</span>
-<span class="k">else</span><span class="p">:</span>
-<span class="k">return</span> <span class="s1">&#39;This is a normal request&#39;</span>
-</pre></div>
-</div>
-</section>
-<section id="query-variables">
-<h3>Query Variables<a class="headerlink" href="#query-variables" title="Permalink to this heading">¶</a></h3>
-<p>The query string (as in <code class="docutils literal notranslate"><span class="pre">/forum?id=1&amp;page=5</span></code>) is commonly used to transmit a small number of key/value pairs to the server. You can use the <code class="xref py py-attr docutils literal notranslate"><span class="pre">BaseRequest.query</span></code> attribute (a <code class="xref py py-class docutils literal notranslate"><span class="pre">FormsDict</span></code>) to access these values and the <code class="xref py py-attr docutils literal notranslate"><span class="pre">BaseRequest.query_string</span></code> attribute to get the whole string.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">bottle</span> <span class="kn">import</span> <span class="n">route</span><span class="p">,</span> <span class="n">request</span><span class="p">,</span> <span class="n">response</span><span class="p">,</span> <span class="n">template</span>
-<span class="nd">@route</span><span class="p">(</span><span class="s1">&#39;/forum&#39;</span><span class="p">)</span>
-<span class="k">def</span> <span class="nf">display_forum</span><span class="p">():</span>
-<span class="n">forum_id</span> <span class="o">=</span> <span class="n">request</span><span class="o">.</span><span class="n">query</span><span class="o">.</span><span class="n">id</span>
-<span class="n">page</span> <span class="o">=</span> <span class="n">request</span><span class="o">.</span><span class="n">query</span><span class="o">.</span><span class="n">page</span> <span class="ow">or</span> <span class="s1">&#39;1&#39;</span>
-<span class="k">return</span> <span class="n">template</span><span class="p">(</span><span class="s1">&#39;Forum ID: {{id}} (page {{page}})&#39;</span><span class="p">,</span> <span class="nb">id</span><span class="o">=</span><span class="n">forum_id</span><span class="p">,</span> <span class="n">page</span><span class="o">=</span><span class="n">page</span><span class="p">)</span>
-</pre></div>
-</div>
-</section>
-<section id="html-form-handling">
-<h3>HTML <cite>&lt;form&gt;</cite> Handling<a class="headerlink" href="#html-form-handling" title="Permalink to this heading">¶</a></h3>
-<p>Let us start from the beginning. In HTML, a typical <code class="docutils literal notranslate"><span class="pre">&lt;form&gt;</span></code> looks something like this:</p>
-<div class="highlight-html notranslate"><div class="highlight"><pre><span></span><span class="p">&lt;</span><span class="nt">form</span> <span class="na">action</span><span class="o">=</span><span class="s">&quot;/login&quot;</span> <span class="na">method</span><span class="o">=</span><span class="s">&quot;post&quot;</span><span class="p">&gt;</span>
-Username: <span class="p">&lt;</span><span class="nt">input</span> <span class="na">name</span><span class="o">=</span><span class="s">&quot;username&quot;</span> <span class="na">type</span><span class="o">=</span><span class="s">&quot;text&quot;</span> <span class="p">/&gt;</span>
-Password: <span class="p">&lt;</span><span class="nt">input</span> <span class="na">name</span><span class="o">=</span><span class="s">&quot;password&quot;</span> <span class="na">type</span><span class="o">=</span><span class="s">&quot;password&quot;</span> <span class="p">/&gt;</span>
-<span class="p">&lt;</span><span class="nt">input</span> <span class="na">value</span><span class="o">=</span><span class="s">&quot;Login&quot;</span> <span class="na">type</span><span class="o">=</span><span class="s">&quot;submit&quot;</span> <span class="p">/&gt;</span>
-<span class="p">&lt;/</span><span class="nt">form</span><span class="p">&gt;</span>
-</pre></div>
-</div>
-<p>The <code class="docutils literal notranslate"><span class="pre">action</span></code> attribute specifies the URL that will receive the form data. <code class="docutils literal notranslate"><span class="pre">method</span></code> defines the HTTP method to use (<code class="docutils literal notranslate"><span class="pre">GET</span></code> or <code class="docutils literal notranslate"><span class="pre">POST</span></code>). With <code class="docutils literal notranslate"><span class="pre">method=&quot;get&quot;</span></code> the form values are appended to the URL and available through <code class="xref py py-attr docutils literal notranslate"><span class="pre">BaseRequest.query</span></code> as described above. This is considered insecure and has other limitations, so we use <code class="docutils literal notranslate"><span class="pre">method=&quot;post&quot;</span></code> here. If in doubt, use <code class="docutils literal notranslate"><span class="pre">POST</span></code> forms.</p>
-<p>Form fields transmitted via <code class="docutils literal notranslate"><span class="pre">POST</span></code> are stored in <code class="xref py py-attr docutils literal notranslate"><span class="pre">BaseRequest.forms</span></code> as a <code class="xref py py-class docutils literal notranslate"><span class="pre">FormsDict</span></code>. The server side code may look like this:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">bottle</span> <span class="kn">import</span> <span class="n">route</span><span class="p">,</span> <span class="n">request</span>
-<span class="nd">@route</span><span class="p">(</span><span class="s1">&#39;/login&#39;</span><span class="p">)</span>
-<span class="k">def</span> <span class="nf">login</span><span class="p">():</span>
-<span class="k">return</span> <span class="s1">&#39;&#39;&#39;</span>
-<span class="s1">        &lt;form action=&quot;/login&quot; method=&quot;post&quot;&gt;</span>
-<span class="s1">            Username: &lt;input name=&quot;username&quot; type=&quot;text&quot; /&gt;</span>
-<span class="s1">            Password: &lt;input name=&quot;password&quot; type=&quot;password&quot; /&gt;</span>
-<span class="s1">            &lt;input value=&quot;Login&quot; type=&quot;submit&quot; /&gt;</span>
-<span class="s1">        &lt;/form&gt;</span>
-<span class="s1">    &#39;&#39;&#39;</span>
-<span class="nd">@route</span><span class="p">(</span><span class="s1">&#39;/login&#39;</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;POST&#39;</span><span class="p">)</span>
-<span class="k">def</span> <span class="nf">do_login</span><span class="p">():</span>
-<span class="n">username</span> <span class="o">=</span> <span class="n">request</span><span class="o">.</span><span class="n">forms</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;username&#39;</span><span class="p">)</span>
-<span class="n">password</span> <span class="o">=</span> <span class="n">request</span><span class="o">.</span><span class="n">forms</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;password&#39;</span><span class="p">)</span>
-<span class="k">if</span> <span class="n">check_login</span><span class="p">(</span><span class="n">username</span><span class="p">,</span> <span class="n">password</span><span class="p">):</span>
-<span class="k">return</span> <span class="s2">&quot;&lt;p&gt;Your login information was correct.&lt;/p&gt;&quot;</span>
-<span class="k">else</span><span class="p">:</span>
-<span class="k">return</span> <span class="s2">&quot;&lt;p&gt;Login failed.&lt;/p&gt;&quot;</span>
-</pre></div>
-</div>
-<p>There are several other attributes used to access form data. Some of them combine values from different sources for easier access. The following table should give you a decent overview.</p>
-<table class="docutils align-default">
-<thead>
-<tr class="row-odd"><th class="head"><p>Attribute</p></th>
-<th class="head"><p>GET Form fields</p></th>
-<th class="head"><p>POST Form fields</p></th>
-<th class="head"><p>File Uploads</p></th>
-</tr>
-</thead>
-<tbody>
-<tr class="row-even"><td><p><code class="xref py py-attr docutils literal notranslate"><span class="pre">BaseRequest.query</span></code></p></td>
-<td><p>yes</p></td>
-<td><p>no</p></td>
-<td><p>no</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-attr docutils literal notranslate"><span class="pre">BaseRequest.forms</span></code></p></td>
-<td><p>no</p></td>
-<td><p>yes</p></td>
-<td><p>no</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-attr docutils literal notranslate"><span class="pre">BaseRequest.files</span></code></p></td>
-<td><p>no</p></td>
-<td><p>no</p></td>
-<td><p>yes</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-attr docutils literal notranslate"><span class="pre">BaseRequest.params</span></code></p></td>
-<td><p>yes</p></td>
-<td><p>yes</p></td>
-<td><p>no</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-attr docutils literal notranslate"><span class="pre">BaseRequest.GET</span></code></p></td>
-<td><p>yes</p></td>
-<td><p>no</p></td>
-<td><p>no</p></td>
-</tr>
-<tr class="row-odd"><td><p><code class="xref py py-attr docutils literal notranslate"><span class="pre">BaseRequest.POST</span></code></p></td>
-<td><p>no</p></td>
-<td><p>yes</p></td>
-<td><p>yes</p></td>
-</tr>
-</tbody>
-</table>
-</section>
-<section id="file-uploads">
-<h3>File uploads<a class="headerlink" href="#file-uploads" title="Permalink to this heading">¶</a></h3>
-<p>To support file uploads, we have to change the <code class="docutils literal notranslate"><span class="pre">&lt;form&gt;</span></code> tag a bit. First, we tell the browser to encode the form data in a different way by adding an <code class="docutils literal notranslate"><span class="pre">enctype=&quot;multipart/form-data&quot;</span></code> attribute to the <code class="docutils literal notranslate"><span class="pre">&lt;form&gt;</span></code> tag. Then, we add <code class="docutils literal notranslate"><span class="pre">&lt;input</span> <span class="pre">type=&quot;file&quot;</span> <span class="pre">/&gt;</span></code> tags to allow the user to select a file. Here is an example:</p>
-<div class="highlight-html notranslate"><div class="highlight"><pre><span></span><span class="p">&lt;</span><span class="nt">form</span> <span class="na">action</span><span class="o">=</span><span class="s">&quot;/upload&quot;</span> <span class="na">method</span><span class="o">=</span><span class="s">&quot;post&quot;</span> <span class="na">enctype</span><span class="o">=</span><span class="s">&quot;multipart/form-data&quot;</span><span class="p">&gt;</span>
-Category:      <span class="p">&lt;</span><span class="nt">input</span> <span class="na">type</span><span class="o">=</span><span class="s">&quot;text&quot;</span> <span class="na">name</span><span class="o">=</span><span class="s">&quot;category&quot;</span> <span class="p">/&gt;</span>
-Select a file: <span class="p">&lt;</span><span class="nt">input</span> <span class="na">type</span><span class="o">=</span><span class="s">&quot;file&quot;</span> <span class="na">name</span><span class="o">=</span><span class="s">&quot;upload&quot;</span> <span class="p">/&gt;</span>
-<span class="p">&lt;</span><span class="nt">input</span> <span class="na">type</span><span class="o">=</span><span class="s">&quot;submit&quot;</span> <span class="na">value</span><span class="o">=</span><span class="s">&quot;Start upload&quot;</span> <span class="p">/&gt;</span>
-<span class="p">&lt;/</span><span class="nt">form</span><span class="p">&gt;</span>
-</pre></div>
-</div>
-<p>Bottle stores file uploads in <code class="xref py py-attr docutils literal notranslate"><span class="pre">BaseRequest.files</span></code> as <code class="xref py py-class docutils literal notranslate"><span class="pre">FileUpload</span></code> instances, along with some metadata about the upload. Let us assume you just want to save the file to disk:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nd">@route</span><span class="p">(</span><span class="s1">&#39;/upload&#39;</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;POST&#39;</span><span class="p">)</span>
-<span class="k">def</span> <span class="nf">do_upload</span><span class="p">():</span>
-<span class="n">category</span>   <span class="o">=</span> <span class="n">request</span><span class="o">.</span><span class="n">forms</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;category&#39;</span><span class="p">)</span>
-<span class="n">upload</span>     <span class="o">=</span> <span class="n">request</span><span class="o">.</span><span class="n">files</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;upload&#39;</span><span class="p">)</span>
-<span class="n">name</span><span class="p">,</span> <span class="n">ext</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">splitext</span><span class="p">(</span><span class="n">upload</span><span class="o">.</span><span class="n">filename</span><span class="p">)</span>
-<span class="k">if</span> <span class="n">ext</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">(</span><span class="s1">&#39;.png&#39;</span><span class="p">,</span><span class="s1">&#39;.jpg&#39;</span><span class="p">,</span><span class="s1">&#39;.jpeg&#39;</span><span class="p">):</span>
-<span class="k">return</span> <span class="s1">&#39;File extension not allowed.&#39;</span>
-<span class="n">save_path</span> <span class="o">=</span> <span class="n">get_save_path_for_category</span><span class="p">(</span><span class="n">category</span><span class="p">)</span>
-<span class="n">upload</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">save_path</span><span class="p">)</span> <span class="c1"># appends upload.filename automatically</span>
-<span class="k">return</span> <span class="s1">&#39;OK&#39;</span>
-</pre></div>
-</div>
-<p><code class="xref py py-attr docutils literal notranslate"><span class="pre">FileUpload.filename</span></code> contains the name of the file on the clients file system, but is cleaned up and normalized to prevent bugs caused by unsupported characters or path segments in the filename. If you need the unmodified name as sent by the client, have a look at <code class="xref py py-attr docutils literal notranslate"><span class="pre">FileUpload.raw_filename</span></code>.</p>
-<p>The <code class="xref py py-attr docutils literal notranslate"><span class="pre">FileUpload.save</span></code> method is highly recommended if you want to store the file to disk. It prevents some common errors (e.g. it does not overwrite existing files unless you tell it to) and stores the file in a memory efficient way. You can access the file object directly via <code class="xref py py-attr docutils literal notranslate"><span class="pre">FileUpload.file</span></code>. Just be careful.</p>
-</section>
-<section id="json-content">
-<h3>JSON Content<a class="headerlink" href="#json-content" title="Permalink to this heading">¶</a></h3>
-<p>Some JavaScript or REST clients send <code class="docutils literal notranslate"><span class="pre">application/json</span></code> content to the server. The <code class="xref py py-attr docutils literal notranslate"><span class="pre">BaseRequest.json</span></code> attribute contains the parsed data structure, if available.</p>
-</section>
-<section id="the-raw-request-body">
-<h3>The raw request body<a class="headerlink" href="#the-raw-request-body" title="Permalink to this heading">¶</a></h3>
-<p>You can access the raw body data as a file-like object via <code class="xref py py-attr docutils literal notranslate"><span class="pre">BaseRequest.body</span></code>. This is a <code class="xref py py-class docutils literal notranslate"><span class="pre">BytesIO</span></code> buffer or a temporary file depending on the content length and <code class="xref py py-attr docutils literal notranslate"><span class="pre">BaseRequest.MEMFILE_MAX</span></code> setting. In both cases the body is completely buffered before you can access the attribute. If you expect huge amounts of data and want to get direct unbuffered access to the stream, have a look at <code class="docutils literal notranslate"><span class="pre">request['wsgi.input']</span></code>.</p>
-</section>
-<section id="wsgi-environment">
-<h3>WSGI Environment<a class="headerlink" href="#wsgi-environment" title="Permalink to this heading">¶</a></h3>
-<p>Each <code class="xref py py-class docutils literal notranslate"><span class="pre">BaseRequest</span></code> instance wraps a WSGI environment dictionary. The original is stored in <code class="xref py py-attr docutils literal notranslate"><span class="pre">BaseRequest.environ</span></code>, but the request object itself behaves like a dictionary, too. Most of the interesting data is exposed through special methods or attributes, but if you want to access <a class="reference external" href="WSGIspecification">WSGI environ variables</a> directly, you can do so:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nd">@route</span><span class="p">(</span><span class="s1">&#39;/my_ip&#39;</span><span class="p">)</span>
-<span class="k">def</span> <span class="nf">show_ip</span><span class="p">():</span>
-<span class="n">ip</span> <span class="o">=</span> <span class="n">request</span><span class="o">.</span><span class="n">environ</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;REMOTE_ADDR&#39;</span><span class="p">)</span>
-<span class="c1"># or ip = request.get(&#39;REMOTE_ADDR&#39;)</span>
-<span class="c1"># or ip = request[&#39;REMOTE_ADDR&#39;]</span>
-<span class="k">return</span> <span class="n">template</span><span class="p">(</span><span class="s2">&quot;Your IP is: {{ip}}&quot;</span><span class="p">,</span> <span class="n">ip</span><span class="o">=</span><span class="n">ip</span><span class="p">)</span>
-</pre></div>
-</div>
-</section>
-</section>
-<section id="templates">
-<span id="tutorial-templates"></span><h2>Templates<a class="headerlink" href="#templates" title="Permalink to this heading">¶</a></h2>
-<p>Bottle comes with a fast and powerful built-in template engine called <span class="xref std std-doc">stpl</span>. To render a template you can use the <code class="xref py py-func docutils literal notranslate"><span class="pre">template()</span></code> function or the <code class="xref py py-func docutils literal notranslate"><span class="pre">view()</span></code> decorator. All you have to do is to provide the name of the template and the variables you want to pass to the template as keyword arguments. Here’s a simple example of how to render a template:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nd">@route</span><span class="p">(</span><span class="s1">&#39;/hello&#39;</span><span class="p">)</span>
-<span class="nd">@route</span><span class="p">(</span><span class="s1">&#39;/hello/&lt;name&gt;&#39;</span><span class="p">)</span>
-<span class="k">def</span> <span class="nf">hello</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;World&#39;</span><span class="p">):</span>
-<span class="k">return</span> <span class="n">template</span><span class="p">(</span><span class="s1">&#39;hello_template&#39;</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="n">name</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>This will load the template file <code class="docutils literal notranslate"><span class="pre">hello_template.tpl</span></code> and render it with the <code class="docutils literal notranslate"><span class="pre">name</span></code> variable set. Bottle will look for templates in the <code class="docutils literal notranslate"><span class="pre">./views/</span></code> folder or any folder specified in the <code class="docutils literal notranslate"><span class="pre">bottle.TEMPLATE_PATH</span></code> list.</p>
-<p>The <code class="xref py py-func docutils literal notranslate"><span class="pre">view()</span></code> decorator allows you to return a dictionary with the template variables instead of calling <code class="xref py py-func docutils literal notranslate"><span class="pre">template()</span></code>:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nd">@route</span><span class="p">(</span><span class="s1">&#39;/hello&#39;</span><span class="p">)</span>
-<span class="nd">@route</span><span class="p">(</span><span class="s1">&#39;/hello/&lt;name&gt;&#39;</span><span class="p">)</span>
-<span class="nd">@view</span><span class="p">(</span><span class="s1">&#39;hello_template&#39;</span><span class="p">)</span>
-<span class="k">def</span> <span class="nf">hello</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;World&#39;</span><span class="p">):</span>
-<span class="k">return</span> <span class="nb">dict</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="n">name</span><span class="p">)</span>
-</pre></div>
-</div>
-<p class="rubric">Syntax</p>
-<p>The template syntax is a very thin layer around the Python language. Its main purpose is to ensure correct indentation of blocks, so you can format your template without worrying about indentation. Follow the link for a full syntax description: <span class="xref std std-doc">stpl</span></p>
-<p>Here is an example template:</p>
-<div class="highlight-html+django notranslate"><div class="highlight"><pre><span></span>%if name == &#39;World&#39;:
-<span class="p">&lt;</span><span class="nt">h1</span><span class="p">&gt;</span>Hello <span class="cp">{{</span><span class="nv">name</span><span class="cp">}}</span>!<span class="p">&lt;/</span><span class="nt">h1</span><span class="p">&gt;</span>
-<span class="p">&lt;</span><span class="nt">p</span><span class="p">&gt;</span>This is a test.<span class="p">&lt;/</span><span class="nt">p</span><span class="p">&gt;</span>
-%else:
-<span class="p">&lt;</span><span class="nt">h1</span><span class="p">&gt;</span>Hello <span class="cp">{{</span><span class="nv">name.title</span><span class="o">()</span><span class="cp">}}</span>!<span class="p">&lt;/</span><span class="nt">h1</span><span class="p">&gt;</span>
-<span class="p">&lt;</span><span class="nt">p</span><span class="p">&gt;</span>How are you?<span class="p">&lt;/</span><span class="nt">p</span><span class="p">&gt;</span>
-%end
-</pre></div>
-</div>
-<p class="rubric">Caching</p>
-<p>Templates are cached in memory after compilation. Modifications made to the template files will have no affect until you clear the template cache. Call <code class="docutils literal notranslate"><span class="pre">bottle.TEMPLATES.clear()</span></code> to do so. Caching is disabled in debug mode.</p>
-</section>
-<section id="plugins">
-<span id="id4"></span><h2>Plugins<a class="headerlink" href="#plugins" title="Permalink to this heading">¶</a></h2>
-<div class="versionadded">
-<p><span class="versionmodified added">New in version 0.9.</span></p>
-</div>
-<p>Bottle’s core features cover most common use-cases, but as a micro-framework it has its limits. This is where “Plugins” come into play. Plugins add missing functionality to the framework, integrate third party libraries, or just automate some repetitive work.</p>
-<p>We have a growing <span class="xref std std-doc">/plugins/index</span> and most plugins are designed to be portable and re-usable across applications. The chances are high that your problem has already been solved and a ready-to-use plugin exists. If not, the <span class="xref std std-doc">/plugindev</span> may help you.</p>
-<p>The effects and APIs of plugins are manifold and depend on the specific plugin. The <code class="docutils literal notranslate"><span class="pre">SQLitePlugin</span></code> plugin for example detects callbacks that require a <code class="docutils literal notranslate"><span class="pre">db</span></code> keyword argument and creates a fresh database connection object every time the callback is called. This makes it very convenient to use a database:</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">bottle</span> <span class="kn">import</span> <span class="n">route</span><span class="p">,</span> <span class="n">install</span><span class="p">,</span> <span class="n">template</span>
-<span class="kn">from</span> <span class="nn">bottle_sqlite</span> <span class="kn">import</span> <span class="n">SQLitePlugin</span>
-<span class="n">install</span><span class="p">(</span><span class="n">SQLitePlugin</span><span class="p">(</span><span class="n">dbfile</span><span class="o">=</span><span class="s1">&#39;/tmp/test.db&#39;</span><span class="p">))</span>
-<span class="nd">@route</span><span class="p">(</span><span class="s1">&#39;/show/&lt;post_id:int&gt;&#39;</span><span class="p">)</span>
-<span class="k">def</span> <span class="nf">show</span><span class="p">(</span><span class="n">db</span><span class="p">,</span> <span class="n">post_id</span><span class="p">):</span>
-<span class="n">c</span> <span class="o">=</span> <span class="n">db</span><span class="o">.</span><span class="n">execute</span><span class="p">(</span><span class="s1">&#39;SELECT title, content FROM posts WHERE id = ?&#39;</span><span class="p">,</span> <span class="p">(</span><span class="n">post_id</span><span class="p">,))</span>
-<span class="n">row</span> <span class="o">=</span> <span class="n">c</span><span class="o">.</span><span class="n">fetchone</span><span class="p">()</span>
-<span class="k">return</span> <span class="n">template</span><span class="p">(</span><span class="s1">&#39;show_post&#39;</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="n">row</span><span class="p">[</span><span class="s1">&#39;title&#39;</span><span class="p">],</span> <span class="n">text</span><span class="o">=</span><span class="n">row</span><span class="p">[</span><span class="s1">&#39;content&#39;</span><span class="p">])</span>
-<span class="nd">@route</span><span class="p">(</span><span class="s1">&#39;/contact&#39;</span><span class="p">)</span>
-<span class="k">def</span> <span class="nf">contact_page</span><span class="p">():</span>
-<span class="sd">&#39;&#39;&#39; This callback does not need a db connection. Because the &#39;db&#39;</span>
-<span class="sd">        keyword argument is missing, the sqlite plugin ignores this callback</span>
-<span class="sd">        completely. &#39;&#39;&#39;</span>
-<span class="k">return</span> <span class="n">template</span><span class="p">(</span><span class="s1">&#39;contact&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>Other plugin may populate the thread-safe <code class="xref py py-data docutils literal notranslate"><span class="pre">local</span></code> object, change details of the <code class="xref py py-data docutils literal notranslate"><span class="pre">request</span></code> object, filter the data returned by the callback or bypass the callback completely. An “auth” plugin for example could check for a valid session and return a login page instead of calling the original callback. What happens exactly depends on the plugin.</p>
-<section id="application-wide-installation">
-<h3>Application-wide Installation<a class="headerlink" href="#application-wide-installation" title="Permalink to this heading">¶</a></h3>
-<p>Plugins can be installed application-wide or just to some specific routes that need additional functionality. Most plugins can safely be installed to all routes and are smart enough to not add overhead to callbacks that do not need their functionality.</p>
-<p>Let us take the <code class="docutils literal notranslate"><span class="pre">SQLitePlugin</span></code> plugin for example. It only affects route callbacks that need a database connection. Other routes are left alone. Because of this, we can install the plugin application-wide with no additional overhead.</p>
-<p>To install a plugin, just call <code class="xref py py-func docutils literal notranslate"><span class="pre">install()</span></code> with the plugin as first argument:</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">bottle_sqlite</span> <span class="kn">import</span> <span class="n">SQLitePlugin</span>
-<span class="n">install</span><span class="p">(</span><span class="n">SQLitePlugin</span><span class="p">(</span><span class="n">dbfile</span><span class="o">=</span><span class="s1">&#39;/tmp/test.db&#39;</span><span class="p">))</span>
-</pre></div>
-</div>
-<p>The plugin is not applied to the route callbacks yet. This is delayed to make sure no routes are missed. You can install plugins first and add routes later, if you want to. The order of installed plugins is significant, though. If a plugin requires a database connection, you need to install the database plugin first.</p>
-<p class="rubric">Uninstall Plugins</p>
-<p>You can use a name, class or instance to <code class="xref py py-func docutils literal notranslate"><span class="pre">uninstall()</span></code> a previously installed plugin:</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">sqlite_plugin</span> <span class="o">=</span> <span class="n">SQLitePlugin</span><span class="p">(</span><span class="n">dbfile</span><span class="o">=</span><span class="s1">&#39;/tmp/test.db&#39;</span><span class="p">)</span>
-<span class="n">install</span><span class="p">(</span><span class="n">sqlite_plugin</span><span class="p">)</span>
-<span class="n">uninstall</span><span class="p">(</span><span class="n">sqlite_plugin</span><span class="p">)</span> <span class="c1"># uninstall a specific plugin</span>
-<span class="n">uninstall</span><span class="p">(</span><span class="n">SQLitePlugin</span><span class="p">)</span>  <span class="c1"># uninstall all plugins of that type</span>
-<span class="n">uninstall</span><span class="p">(</span><span class="s1">&#39;sqlite&#39;</span><span class="p">)</span>      <span class="c1"># uninstall all plugins with that name</span>
-<span class="n">uninstall</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>          <span class="c1"># uninstall all plugins at once</span>
-</pre></div>
-</div>
-<p>Plugins can be installed and removed at any time, even at runtime while serving requests. This enables some neat tricks (installing slow debugging or profiling plugins only when needed) but should not be overused. Each time the list of plugins changes, the route cache is flushed and all plugins are re-applied.</p>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>The module-level <code class="xref py py-func docutils literal notranslate"><span class="pre">install()</span></code> and <code class="xref py py-func docutils literal notranslate"><span class="pre">uninstall()</span></code> functions affect the <a class="reference internal" href="#default-app"><span class="std std-ref">Default Application</span></a>. To manage plugins for a specific application, use the corresponding methods on the <code class="xref py py-class docutils literal notranslate"><span class="pre">Bottle</span></code> application object.</p>
-</div>
-</section>
-<section id="route-specific-installation">
-<h3>Route-specific Installation<a class="headerlink" href="#route-specific-installation" title="Permalink to this heading">¶</a></h3>
-<p>The <code class="docutils literal notranslate"><span class="pre">apply</span></code> parameter of the <code class="xref py py-func docutils literal notranslate"><span class="pre">route()</span></code> decorator comes in handy if you want to install plugins to only a small number of routes:</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">sqlite_plugin</span> <span class="o">=</span> <span class="n">SQLitePlugin</span><span class="p">(</span><span class="n">dbfile</span><span class="o">=</span><span class="s1">&#39;/tmp/test.db&#39;</span><span class="p">)</span>
-<span class="nd">@route</span><span class="p">(</span><span class="s1">&#39;/create&#39;</span><span class="p">,</span> <span class="n">apply</span><span class="o">=</span><span class="p">[</span><span class="n">sqlite_plugin</span><span class="p">])</span>
-<span class="k">def</span> <span class="nf">create</span><span class="p">(</span><span class="n">db</span><span class="p">):</span>
-<span class="n">db</span><span class="o">.</span><span class="n">execute</span><span class="p">(</span><span class="s1">&#39;INSERT INTO ...&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-</section>
-<section id="blacklisting-plugins">
-<h3>Blacklisting Plugins<a class="headerlink" href="#blacklisting-plugins" title="Permalink to this heading">¶</a></h3>
-<p>You may want to explicitly disable a plugin for a number of routes. The <code class="xref py py-func docutils literal notranslate"><span class="pre">route()</span></code> decorator has a <code class="docutils literal notranslate"><span class="pre">skip</span></code> parameter for this purpose:</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">sqlite_plugin</span> <span class="o">=</span> <span class="n">SQLitePlugin</span><span class="p">(</span><span class="n">dbfile</span><span class="o">=</span><span class="s1">&#39;/tmp/test1.db&#39;</span><span class="p">)</span>
-<span class="n">install</span><span class="p">(</span><span class="n">sqlite_plugin</span><span class="p">)</span>
-<span class="n">dbfile1</span> <span class="o">=</span> <span class="s1">&#39;/tmp/test1.db&#39;</span>
-<span class="n">dbfile2</span> <span class="o">=</span> <span class="s1">&#39;/tmp/test2.db&#39;</span>
-<span class="nd">@route</span><span class="p">(</span><span class="s1">&#39;/open/&lt;db&gt;&#39;</span><span class="p">,</span> <span class="n">skip</span><span class="o">=</span><span class="p">[</span><span class="n">sqlite_plugin</span><span class="p">])</span>
-<span class="k">def</span> <span class="nf">open_db</span><span class="p">(</span><span class="n">db</span><span class="p">):</span>
-<span class="c1"># The &#39;db&#39; keyword argument is not touched by the plugin this time.</span>
-<span class="c1"># The plugin handle can be used for runtime configuration, too.</span>
-<span class="k">if</span> <span class="n">db</span> <span class="o">==</span> <span class="s1">&#39;test1&#39;</span><span class="p">:</span>
-<span class="n">sqlite_plugin</span><span class="o">.</span><span class="n">dbfile</span> <span class="o">=</span> <span class="n">dbfile1</span>
-<span class="k">elif</span> <span class="n">db</span> <span class="o">==</span> <span class="s1">&#39;test2&#39;</span><span class="p">:</span>
-<span class="n">sqlite_plugin</span><span class="o">.</span><span class="n">dbfile</span> <span class="o">=</span> <span class="n">dbfile2</span>
-<span class="k">else</span><span class="p">:</span>
-<span class="n">abort</span><span class="p">(</span><span class="mi">404</span><span class="p">,</span> <span class="s2">&quot;No such database.&quot;</span><span class="p">)</span>
-<span class="k">return</span> <span class="s2">&quot;Database File switched to: &quot;</span> <span class="o">+</span> <span class="n">sqlite_plugin</span><span class="o">.</span><span class="n">dbfile</span>
-</pre></div>
-</div>
-<p>The <code class="docutils literal notranslate"><span class="pre">skip</span></code> parameter accepts a single value or a list of values. You can use a name, class or instance to identify the plugin that is to be skipped. Set <code class="docutils literal notranslate"><span class="pre">skip=True</span></code> to skip all plugins at once.</p>
-</section>
-<section id="plugins-and-sub-applications">
-<h3>Plugins and Sub-Applications<a class="headerlink" href="#plugins-and-sub-applications" title="Permalink to this heading">¶</a></h3>
-<p>Most plugins are specific to the application they were installed to. Consequently, they should not affect sub-applications mounted with <code class="xref py py-meth docutils literal notranslate"><span class="pre">Bottle.mount()</span></code>. Here is an example:</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">root</span> <span class="o">=</span> <span class="n">Bottle</span><span class="p">()</span>
-<span class="n">root</span><span class="o">.</span><span class="n">mount</span><span class="p">(</span><span class="s1">&#39;/blog&#39;</span><span class="p">,</span> <span class="n">apps</span><span class="o">.</span><span class="n">blog</span><span class="p">)</span>
-<span class="nd">@root</span><span class="o">.</span><span class="n">route</span><span class="p">(</span><span class="s1">&#39;/contact&#39;</span><span class="p">,</span> <span class="n">template</span><span class="o">=</span><span class="s1">&#39;contact&#39;</span><span class="p">)</span>
-<span class="k">def</span> <span class="nf">contact</span><span class="p">():</span>
-<span class="k">return</span> <span class="p">{</span><span class="s1">&#39;email&#39;</span><span class="p">:</span> <span class="s1">&#39;contact@example.com&#39;</span><span class="p">}</span>
-<span class="n">root</span><span class="o">.</span><span class="n">install</span><span class="p">(</span><span class="n">plugins</span><span class="o">.</span><span class="n">WTForms</span><span class="p">())</span>
-</pre></div>
-</div>
-<p>Whenever you mount an application, Bottle creates a proxy-route on the main-application that forwards all requests to the sub-application. Plugins are disabled for this kind of proxy-route by default. As a result, our (fictional) <cite>WTForms</cite> plugin affects the <code class="docutils literal notranslate"><span class="pre">/contact</span></code> route, but does not affect the routes of the <code class="docutils literal notranslate"><span class="pre">/blog</span></code> sub-application.</p>
-<p>This behavior is intended as a sane default, but can be overridden. The following example re-activates all plugins for a specific proxy-route:</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">root</span><span class="o">.</span><span class="n">mount</span><span class="p">(</span><span class="s1">&#39;/blog&#39;</span><span class="p">,</span> <span class="n">apps</span><span class="o">.</span><span class="n">blog</span><span class="p">,</span> <span class="n">skip</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>But there is a snag: The plugin sees the whole sub-application as a single route, namely the proxy-route mentioned above. In order to affect each individual route of the sub-application, you have to install the plugin to the mounted application explicitly.</p>
-</section>
-</section>
-<section id="development">
-<h2>Development<a class="headerlink" href="#development" title="Permalink to this heading">¶</a></h2>
-<p>So you have learned the basics and want to write your own application? Here are
-some tips that might help you being more productive.</p>
-<section id="default-application">
-<span id="default-app"></span><h3>Default Application<a class="headerlink" href="#default-application" title="Permalink to this heading">¶</a></h3>
-<p>Bottle maintains a global stack of <code class="xref py py-class docutils literal notranslate"><span class="pre">Bottle</span></code> instances and uses the top of the stack as a default for some of the module-level functions and decorators. The <code class="xref py py-func docutils literal notranslate"><span class="pre">route()</span></code> decorator, for example, is a shortcut for calling <code class="xref py py-meth docutils literal notranslate"><span class="pre">Bottle.route()</span></code> on the default application:</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nd">@route</span><span class="p">(</span><span class="s1">&#39;/&#39;</span><span class="p">)</span>
-<span class="k">def</span> <span class="nf">hello</span><span class="p">():</span>
-<span class="k">return</span> <span class="s1">&#39;Hello World&#39;</span>
-<span class="n">run</span><span class="p">()</span>
-</pre></div>
-</div>
-<p>This is very convenient for small applications and saves you some typing, but also means that, as soon as your module is imported, routes are installed to the global default application. To avoid this kind of import side-effects, Bottle offers a second, more explicit way to build applications:</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">app</span> <span class="o">=</span> <span class="n">Bottle</span><span class="p">()</span>
-<span class="nd">@app</span><span class="o">.</span><span class="n">route</span><span class="p">(</span><span class="s1">&#39;/&#39;</span><span class="p">)</span>
-<span class="k">def</span> <span class="nf">hello</span><span class="p">():</span>
-<span class="k">return</span> <span class="s1">&#39;Hello World&#39;</span>
-<span class="n">app</span><span class="o">.</span><span class="n">run</span><span class="p">()</span>
-</pre></div>
-</div>
-<p>Separating the application object improves re-usability a lot, too. Other developers can safely import the <code class="docutils literal notranslate"><span class="pre">app</span></code> object from your module and use <code class="xref py py-meth docutils literal notranslate"><span class="pre">Bottle.mount()</span></code> to merge applications together.</p>
-<div class="versionadded">
-<p><span class="versionmodified added">New in version 0.13.</span></p>
-</div>
-<p>Starting with bottle-0.13 you can use <code class="xref py py-class docutils literal notranslate"><span class="pre">Bottle</span></code> instances as context managers:</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">app</span> <span class="o">=</span> <span class="n">Bottle</span><span class="p">()</span>
-<span class="k">with</span> <span class="n">app</span><span class="p">:</span>
-<span class="c1"># Our application object is now the default</span>
-<span class="c1"># for all shortcut functions and decorators</span>
-<span class="k">assert</span> <span class="n">my_app</span> <span class="ow">is</span> <span class="n">default_app</span><span class="p">()</span>
-<span class="nd">@route</span><span class="p">(</span><span class="s1">&#39;/&#39;</span><span class="p">)</span>
-<span class="k">def</span> <span class="nf">hello</span><span class="p">():</span>
-<span class="k">return</span> <span class="s1">&#39;Hello World&#39;</span>
-<span class="c1"># Also useful to capture routes defined in other modules</span>
-<span class="kn">import</span> <span class="nn">some_package.more_routes</span>
-</pre></div>
-</div>
-</section>
-<section id="debug-mode">
-<span id="tutorial-debugging"></span><h3>Debug Mode<a class="headerlink" href="#debug-mode" title="Permalink to this heading">¶</a></h3>
-<p>During early development, the debug mode can be very helpful.</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">bottle</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>In this mode, Bottle is much more verbose and provides helpful debugging information whenever an error occurs. It also disables some optimisations that might get in your way and adds some checks that warn you about possible misconfiguration.</p>
-<p>Here is an incomplete list of things that change in debug mode:</p>
-<ul class="simple">
-<li><p>The default error page shows a traceback.</p></li>
-<li><p>Templates are not cached.</p></li>
-<li><p>Plugins are applied immediately.</p></li>
-</ul>
-<p>Just make sure not to use the debug mode on a production server.</p>
-</section>
-<section id="auto-reloading">
-<h3>Auto Reloading<a class="headerlink" href="#auto-reloading" title="Permalink to this heading">¶</a></h3>
-<p>During development, you have to restart the server a lot to test your
-recent changes. The auto reloader can do this for you. Every time you
-edit a module file, the reloader restarts the server process and loads
-the newest version of your code.</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">bottle</span> <span class="kn">import</span> <span class="n">run</span>
-<span class="n">run</span><span class="p">(</span><span class="n">reloader</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>How it works: the main process will not start a server, but spawn a new
-child process using the same command line arguments used to start the
-main process. All module-level code is executed at least twice! Be
-careful.</p>
-<p>The child process will have <code class="docutils literal notranslate"><span class="pre">os.environ['BOTTLE_CHILD']</span></code> set to <code class="docutils literal notranslate"><span class="pre">True</span></code>
-and start as a normal non-reloading app server. As soon as any of the
-loaded modules changes, the child process is terminated and re-spawned by
-the main process. Changes in template files will not trigger a reload.
-Please use debug mode to deactivate template caching.</p>
-<p>The reloading depends on the ability to stop the child process. If you are
-running on Windows or any other operating system not supporting
-<code class="docutils literal notranslate"><span class="pre">signal.SIGINT</span></code> (which raises <code class="docutils literal notranslate"><span class="pre">KeyboardInterrupt</span></code> in Python),
-<code class="docutils literal notranslate"><span class="pre">signal.SIGTERM</span></code> is used to kill the child. Note that exit handlers and
-finally clauses, etc., are not executed after a <code class="docutils literal notranslate"><span class="pre">SIGTERM</span></code>.</p>
-</section>
-<section id="command-line-interface">
-<h3>Command Line Interface<a class="headerlink" href="#command-line-interface" title="Permalink to this heading">¶</a></h3>
-<p>Starting with version 0.10 you can use bottle as a command-line tool:</p>
-<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="gp">$ </span>python -m bottle
-<span class="go">Usage: bottle.py [options] package.module:app</span>
-<span class="go">Options:</span>
-<span class="go">  -h, --help            show this help message and exit</span>
-<span class="go">  --version             show version number.</span>
-<span class="go">  -b ADDRESS, --bind=ADDRESS</span>
-<span class="go">                        bind socket to ADDRESS.</span>
-<span class="go">  -s SERVER, --server=SERVER</span>
-<span class="go">                        use SERVER as backend.</span>
-<span class="go">  -p PLUGIN, --plugin=PLUGIN</span>
-<span class="go">                        install additional plugin/s.</span>
-<span class="go">  -c FILE, --conf=FILE  load config values from FILE.</span>
-<span class="go">  -C NAME=VALUE, --param=NAME=VALUE</span>
-<span class="go">                        override config values.</span>
-<span class="go">  --debug               start server in debug mode.</span>
-<span class="go">  --reload              auto-reload on file changes.</span>
-</pre></div>
-</div>
-<p>The <cite>ADDRESS</cite> field takes an IP address or an IP:PORT pair and defaults to <code class="docutils literal notranslate"><span class="pre">localhost:8080</span></code>. The other parameters should be self-explanatory.</p>
-<p>Both plugins and applications are specified via import expressions. These consist of an import path (e.g. <code class="docutils literal notranslate"><span class="pre">package.module</span></code>) and an expression to be evaluated in the namespace of that module, separated by a colon. See <code class="xref py py-func docutils literal notranslate"><span class="pre">load()</span></code> for details. Here are some examples:</p>
-<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="gp"># </span>Grab the <span class="s1">&#39;app&#39;</span> object from the <span class="s1">&#39;myapp.controller&#39;</span> module and
-<span class="gp"># </span>start a paste server on port <span class="m">80</span> on all interfaces.
-<span class="go">python -m bottle -server paste -bind 0.0.0.0:80 myapp.controller:app</span>
-<span class="gp"># </span>Start a self-reloading development server and serve the global
-<span class="gp"># </span>default application. The routes are defined <span class="k">in</span> <span class="s1">&#39;test.py&#39;</span>
-<span class="go">python -m bottle --debug --reload test</span>
-<span class="gp"># </span>Install a custom debug plugin with some parameters
-<span class="go">python -m bottle --debug --reload --plugin &#39;utils:DebugPlugin(exc=True)&#39;&#39; test</span>
-<span class="gp"># </span>Serve an application that is created with <span class="s1">&#39;myapp.controller.make_app()&#39;</span>
-<span class="gp"># </span>on demand.
-<span class="go">python -m bottle &#39;myapp.controller:make_app()&#39;&#39;</span>
-</pre></div>
-</div>
-</section>
-</section>
-<section id="deployment">
-<h2>Deployment<a class="headerlink" href="#deployment" title="Permalink to this heading">¶</a></h2>
-<p>Bottle runs on the built-in <a class="reference external" href="http://docs.python.org/library/wsgiref.html#module-wsgiref.simple_server">wsgiref WSGIServer</a>  by default. This non-threading HTTP server is perfectly fine for development, but may become a performance bottleneck when server load increases.</p>
-<p>The easiest way to increase performance is to install a multi-threaded server library like <a href="#id7"><span class="problematic" id="id8">paste_</span></a> or <a href="#id9"><span class="problematic" id="id10">cherrypy_</span></a> and tell Bottle to use that instead of the single-threaded server:</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">bottle</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">server</span><span class="o">=</span><span class="s1">&#39;paste&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>This, and many other deployment options are described in a separate article: <span class="xref std std-doc">deployment</span></p>
-</section>
-<section id="glossary">
-<span id="tutorial-glossary"></span><h2>Glossary<a class="headerlink" href="#glossary" title="Permalink to this heading">¶</a></h2>
-<dl class="simple glossary">
-<dt id="term-callback">callback<a class="headerlink" href="#term-callback" title="Permalink to this term">¶</a></dt><dd><p>Programmer code that is to be called when some external action happens.
-In the context of web frameworks, the mapping between URL paths and
-application code is often achieved by specifying a callback function
-for each URL.</p>
-</dd>
-<dt id="term-decorator">decorator<a class="headerlink" href="#term-decorator" title="Permalink to this term">¶</a></dt><dd><p>A function returning another function, usually applied as a function transformation using the <code class="docutils literal notranslate"><span class="pre">&#64;decorator</span></code> syntax. See <a class="reference external" href="http://docs.python.org/reference/compound_stmts.html#function">python documentation for function definition</a> for more about decorators.</p>
-</dd>
-<dt id="term-environ">environ<a class="headerlink" href="#term-environ" title="Permalink to this term">¶</a></dt><dd><p>A structure where information about all documents under the root is
-saved, and used for cross-referencing.  The environment is pickled
-after the parsing stage, so that successive runs only need to read
-and parse new and changed documents.</p>
-</dd>
-<dt id="term-handler-function">handler function<a class="headerlink" href="#term-handler-function" title="Permalink to this term">¶</a></dt><dd><p>A function to handle some specific event or situation. In a web
-framework, the application is developed by attaching a handler function
-as callback for each specific URL comprising the application.</p>
-</dd>
-<dt id="term-source-directory">source directory<a class="headerlink" href="#term-source-directory" title="Permalink to this term">¶</a></dt><dd><p>The directory which, including its subdirectories, contains all
-source files for one Sphinx project.</p>
-</dd>
-</dl>
-</section>
-</section>
-<div class="clearer"></div>
-</div>
-</div>
-</div>
-<div class="clearer"></div>
-</div>
-</main>
-<div class="star-footer">
-<img onclick="openPurchase()" src="../static/star.jpg">
-<div id="praise"><span>0</span> persons have rewarded</div>
-</div>
-<div class="foo"></div>
-<footer class="clearfix fixfooter web-show">
-<div class="col-sm-offset-2 col-sm-6 footer-div1">
-<div class="footer-block-item">
-<span class="footer-item1">Built with Sphinx using a theme provid by QPython.</span>
-<span class="footer-item2 visible-xs">Copyight 2017, QPython.</span>
-</div>
-</div>
-<div class="col-sm-2 footer-div2">
-<a href="/index.html" class="pull-right"><img src="http://www.qpython.org/static/img_logo.png"></a>
-</div>
-</footer>
-<div id="purchase-box">
-<div class="zh-img purchase-img"></div>
-<div class="en-img purchase-img">
-<form action="https://www.paypal.com/cgi-bin/webscr" method="post" target="_top">
-<input type="hidden" name="cmd" value="_s-xclick">
-<input type="hidden" name="hosted_button_id" value="ST6KWAK8R63JW">
-<div class="en-thanks-tit">
-<input type="hidden" name="on0" value="Thanks">
-Thanks
-</div>
-<div class="en-thanks-sel">
-<select name="os0">
-<option value="Reward this course">Reward this course $ 0.99 USD</option>
-<option value="Reward this course">Reward this course $ 1.49 USD</option>
-<option value="Reward this course">Reward this course $ 1.99 USD</option>
-<option value="Reward this course">Reward this course $ 2.99 USD</option>
-<option value="Reward this course">Reward this course $ 3.49 USD</option>
-</select>
-</div>
-<input type="hidden" name="currency_code" value="USD">
-<input type="image" class="pay_img" src="https://www.paypalobjects.com/en_GB/i/btn/btn_paynow_LG.gif" border="0" name="submit" alt="PayPal – The safer, easier way to pay online!">
-<img alt="" border="0" src="https://www.paypalobjects.com/zh_XC/i/scr/pixel.gif" width="1" height="1">
-</form>
-</div>
-</div>
-<script>
-var milib;
-if (milib==undefined) {
-var milib = function(){
-};
-milib.qeditor = function(str){
-alert('Please share this page to QPython then run it')
-};
-milib.getPurchaseNumber = function(str){
-//alert('getPurchaseNumber:'+str)
-return "10+";
-};
-milib.a8playVideoFromGW= function(url){
-window.open(url)
-};
-milib.qpylibinstall = function(cat,url,target){
-window.open(url)
-};
-milib.gotoIfPay = function(url, packageId, packageUrl){
-window.open(url)
-};
-} else {
-}
-function openPurchase() {
-//获取标识
-var u = window.location.href.split('/')
-var s = u[u.length-1].replace('.html', '')
-s = u[u.length-2]+'-'+s
-if (milib.openPurchaseActivity){
-milib.openPurchaseActivity(s)
-}else {
-if (navigator.language== "zh-CN" || navigator.language== "zh"){
-$('.zh-img ').show();
-$('.en-img').hide();
-}else {
-$('.zh-img ').hide();
-$('.en-img').show();
-}
-$('#purchase-box').show();
-}
-}
-$(function() {
-$('#purchase-box').click(function() {
-$(this).hide();
-})
-$('.purchase-img').click(function(e) {
-e.stopPropagation();
-})
-})
-//按钮事件
-function btnEvent(){
-var seq = this.getAttribute('target')-2
-var a = this.parentElement.getElementsByTagName('pre')
-if ( a && a.length > 0){
-/**********有代码，调用控制台*************/
-var code = a[seq].lastChild.nodeValue
-milib.qeditor(code)
-}else{
-//没有代码，调转
-var url = this.getAttribute('data-url');
-window.open(url);
-}
-}
-//获取赞赏
-function setPraise(){
-var u = window.location.href.split('/')
-var s = u[u.length-1].replace('.html', '')
-s = u[u.length-2]+'-'+s
-var i = document.getElementById('praise').getElementsByTagName('span')[0]
-var num = milib.getPurchaseNumber(s);
-i.innerHTML = num
-}
-//初始化代码显示
-function initCode(){
-var prearr = document.getElementsByTagName('pre');
-for (var i = 0; i < prearr.length; i++) {
-var temp_data = prearr[i].lastChild.nodeValue
-//正则处理 lavel5
-temp_data = temp_data.replace(/\^2\$/g, spaceString(2));
-temp_data = temp_data.replace(/\^4\$/g, spaceString(4));
-temp_data = temp_data.replace(/\^6\$/g, spaceString(6));
-temp_data = temp_data.replace(/\^8\$/g, spaceString(8));
-temp_data = temp_data.replace(/\^10\$/g, spaceString(10));
-temp_data = temp_data.replace(/\^12\$/g, spaceString(12));
-temp_data = temp_data.replace(/\^16\$/g, spaceString(16));
-temp_data = temp_data.replace(/\^20\$/g, spaceString(20));
-//set
-prearr[i].lastChild.nodeValue = temp_data;
-}
-}
-//产生空格
-function spaceString(num){
-var v = ''
-for (var i = 0; i < num; i++) {
-v += ' ';
-}
-return v;
-}
-function setSearchHistory (t) {
-if (!t)
-return;
-if (localStorage.course_search_list) {
-var list = localStorage.course_search_list.split('%,%');
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diff --git a/docs/data/courses.json b/docs/data/courses.json
new file mode 100644
index 0000000..3753938
--- /dev/null
+++ b/docs/data/courses.json
@@ -0,0 +1,424 @@
+{
+  "courses": [
+    {
+      "id": "6492",
+      "title": "Python语法实践",
+      "description": "关于 Python编程\nPython：简单又强大的编程语言\n想象一下，有这样一种编程语言，它不仅容易学习，而且功能强大，这就是Python。就像我们用简单的话语就能表达复杂的想法一样，Python用简单的代码就能完成很多工作。\nPython的设计非常人性化，它的代码看起来就像是在讲故事，容易理解和编写。而且Python还能自我检查错误，比一些更古老的编程语言做得更好。它内置了很多有用的工具，就像是你手边有一个万能工具箱，可以随时拿出来解决问题。",
+      "image": "/assets/udemy_06.png",
+      "url": "https://www.qpython.com.cn/course/python-basic-syntax/",
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+        "入门",
+        "初级"
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+      "status": "publish",
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+      "slug": "python-basic-syntax"
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+      "title": "QPython入门实践",
+      "description": "关于QPython\nQPython是一款专为Android设备设计的Python编程引擎，它整合了解释器、控制台、编辑器，并集成了QSL4A库和APIGPT，全面支持Web开发、科学计算以及AI扩展。无论您是Python新手还是资深开发者，QPython都能为您提供一个功能强大的移动编程平台。\n核心功能包括：\n完善的Python环境：内建Python解释器，让您能够随时随地编写和执行代码。\n高效的代码编辑器：QEditor为您提供了一个在手机上便捷开发Python项目的编辑器。\nJupyter Notebook兼容：通过QNotebook浏览器，您可以轻松学习和运行Notebook文件。\n扩展库与PIP支持：轻松安装和管理第三方库，极大地扩展您的编程可能性。\n核心库：QPython专有的QSL4A和APIGPT库，为您的Android或AI项目带来独特的功能和强大支持。\n学习资源与社区：内置精选课程和社区链接，助您迅速提升编程技能。",
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+      "url": "https://www.qpython.com.cn/course/qpython-basic/",
+      "tags": [
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+        "入门",
+        "初级"
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+      "price": "¥9",
+      "status": "publish",
+      "catalog": "移动应用开发",
+      "slug": "qpython-basic"
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+      "title": "生成式AI编程入门实践",
+      "description": "课程简介\n随着生成式AI（AIGC）的兴起，特别是ChatGPT的广泛应用，学习如何在您的应用程序中融入AIGC技术已成为一个趋势。《Q派生成式AI开发系列 – 入门篇》课程旨在为Python开发者提供一个快速入门的路径，让您能够在程序开发中掌握应用AIGC的能力。\n课程特色\n深入理解AIGC：我们将探索生成式AI的基本原理，以及它在艺术创作、图像生成、对话生成和文字内容生成等领域的应用。\n掌握PGPT服务：PGPT作为一个聚合了多个大型AI服务的云API平台，提供了一个轻量级的AIGC开发库，帮助开发者快速构建应用程序。\nQPython特别支持：课程特别使用了在QPython可以调用PGPT服务的方式，使得学习者能够在移动端便捷地体验生成式AI的魅力。",
+      "image": "/assets/udemy_02.png",
+      "url": "https://www.qpython.com.cn/course/getting-started-with-qpy-generative-ai-development/",
+      "tags": [
+        "AI应用",
+        "生成式AI",
+        "入门",
+        "初级"
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+      "difficulty": "beginner",
+      "price": "¥19",
+      "status": "publish",
+      "catalog": "人工智能",
+      "slug": "getting-started-with-qpy-generative-ai-development"
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+      "title": "Scrapy网络爬虫编程",
+      "description": "关于 Scrapy\nScrapy是一个强大且高效的Python网络爬虫框架，它能够帮助开发者快速抓取和提取网站上的数据。Scrapy的设计初衷是为了抓取大规模网站，并将抓取数据结构化存储，用于数据分析、机器学习模型训练、内容聚合等多种应用场景。\nScrapy提供了灵活的API和丰富的功能，包括支持处理多种页面结构、轻松定义数据提取逻辑、配置代理、处理重定向和页面缓存等功能。\n此外，它还具有出色的扩展性，允许用户编写自定义组件，满足各种复杂的需求。",
+      "image": "/assets/udemy_11.png",
+      "url": "https://www.qpython.com.cn/course/scrapy-spider/",
+      "tags": [
+        "数据采集",
+        "爬虫",
+        "中级"
+      ],
+      "difficulty": "intermediate",
+      "price": "¥9",
+      "status": "publish",
+      "catalog": "数据分析与处理",
+      "slug": "scrapy-spider"
+    },
+    {
+      "id": "4200",
+      "title": "Pico小车基本运动编程",
+      "description": "什么是Q派Pico智能小车\nQ派Pico智能小车是一款基于树莓派Pico微控制器的迷你小车编程平台，专为编程教育和创客爱好者设计。利用Micropython工具，它允许编程爱好者在一个扁平式、小巧的车身设计中，通过多种传感器进行互动和探索，学习编程的同时了解物理计算和机器人控制的基本原理。\n互动性强：Pico智能小车允许学习者通过编程直接与硬件交互，增强了学习的趣味性和实践性。\n多种传感器支持：包括红外传感器、超声波传感器等，可实现自动避障、循迹等功能。\nQPython环境支持：可在QPython移动环境中进行编程实验，随时随地学习和创作。",
+      "image": "/assets/udemy_03.png",
+      "url": "https://www.qpython.com.cn/course/picocar-basic/",
+      "tags": [
+        "智能硬件",
+        "嵌入式",
+        "中级"
+      ],
+      "difficulty": "intermediate",
+      "price": "¥29",
+      "status": "publish",
+      "catalog": "智能硬件",
+      "slug": "picocar-basic"
+    },
+    {
+      "id": "4218",
+      "title": "Flask编程入门实践",
+      "description": "什么是Flask\nFlask是Python中一个简单、灵活和易用的Web框架，适合初学者使用。它提供了丰富的功能和扩展性，可以帮助开发者快速构建功能完善的Web应用程序。\n简单易用：Flask的设计理念是保持简洁和易用性。它的API简单明了，容易学习和理解。\n路由系统：Flask提供了强大的路由系统，可以根据URL路径来匹配不同的处理函数。\n模板引擎：Flask内置了Jinja2模板引擎，可以方便地将动态内容与静态HTML页面分离。\n数据库集成：Flask支持多种数据库扩展，可以轻松地进行数据库操作。",
+      "image": "/assets/udemy_04.png",
+      "url": "https://www.qpython.com.cn/course/flask-web-development-course/",
+      "tags": [
+        "Web开发",
+        "Flask",
+        "入门",
+        "初级"
+      ],
+      "difficulty": "beginner",
+      "price": "¥9",
+      "status": "publish",
+      "catalog": "WEB开发",
+      "slug": "flask-web-development-course"
+    },
+    {
+      "id": "6457",
+      "title": "诗词问答项目实战",
+      "description": "本课程旨在通过实际项目的开发，让学员掌握Python编程的基本技能，并进一步深入学习如何将编程技术应用于文化艺术领域。通过开发一个互动式的诗词问答系统，学员将学习到从构思、设计到实现完整应用的全过程。\n课程内容：\n1. Python编程基础\n2. 问答逻辑的设计与实现\n3. 生成式AI在诗词中的应用\n4. 界面的设计与用户体验优化\n预期成果：\n完成本课程后，学员将掌握使用Python进行复杂项目开发的实战技能。",
+      "image": "/assets/udemy_05.png",
+      "url": "https://www.qpython.com.cn/course/poetry-you-ask-and-i-answer/",
+      "tags": [
+        "AI应用",
+        "NLP",
+        "项目实战",
+        "高级"
+      ],
+      "difficulty": "advanced",
+      "price": "¥19",
+      "status": "publish",
+      "catalog": "人工智能",
+      "slug": "poetry-you-ask-and-i-answer"
+    },
+    {
+      "id": "6705",
+      "title": "Django编程入门实践",
+      "description": "关于Django\n想象一下，你正在搭建一栋房子。Django就像是一套预先设计好的蓝图和工具，它能帮你快速地搭建起一个坚固、美观、功能强大的网站。\nDjango是一个使用Python语言编写的开源Web框架。它能帮你处理网站开发中许多繁琐的工作，比如用户认证、数据库管理、URL路由等等。\n快速开发：Django的设计理念就是快速开发。它提供了大量的内置功能，能让你在短时间内搭建出功能完整的网站。\n安全：Django内置了许多安全机制，能有效地保护你的网站免受常见的网络攻击。",
+      "image": "/assets/udemy_07.png",
+      "url": "https://www.qpython.com.cn/course/django-basics/",
+      "tags": [
+        "Web开发",
+        "Django",
+        "入门",
+        "初级"
+      ],
+      "difficulty": "beginner",
+      "price": "¥9",
+      "status": "publish",
+      "catalog": "WEB开发",
+      "slug": "django-basics"
+    },
+    {
+      "id": "6898",
+      "title": "NumPy科学计算编程",
+      "description": "关于Numpy\nNumPy（全称为Numerical Python）是一个专门为Python设计的工具库，帮助我们更轻松地处理大量数据。可以把它看作是Python中的“计算小帮手”，它能让我们用更少的代码来完成复杂的数学运算和数据处理。\n通过NumPy，你可以：\n创建数组：不仅能处理像列表一样的一维数据，还能轻松处理二维、三维甚至更多维度的数组。\n快速计算：NumPy提供了一整套内置的函数，让你能快速进行加减乘除、统计分析等运算。\n节省时间：由于它的高效运算方式，NumPy比传统的Python方法要快得多。",
+      "image": "/assets/udemy_08.png",
+      "url": "https://www.qpython.com.cn/course/qpy-numpy-basic/",
+      "tags": [
+        "数据分析",
+        "NumPy",
+        "入门",
+        "初级"
+      ],
+      "difficulty": "intermediate",
+      "price": "¥9",
+      "status": "publish",
+      "catalog": "数据分析与处理",
+      "slug": "qpy-numpy-basic"
+    },
+    {
+      "id": "6948",
+      "title": "Pandas数据分析和处理编程",
+      "description": "关于Pandas\nPandas是Python中一个强大且易用的库，专门用于处理和分析结构化数据。它能让您像操作Excel表格一样轻松处理数据表，适合整理、清洗、筛选和分析各类数据集。\n通过Pandas，您可以快速执行数据清洗、缺失值处理、数据分组、聚合等常见任务。Pandas是数据分析的必备工具，它能大大简化处理大量数据的工作流程。\n学习Pandas的好处：\n提升效率：掌握Pandas可以显著提高您处理大量数据的效率。\n增强洞察力：通过Pandas，您可以快速从数据中提取有价值的信息和洞察。",
+      "image": "/assets/udemy_09.png",
+      "url": "https://www.qpython.com.cn/course/qpy-pandas-basic/",
+      "tags": [
+        "数据分析",
+        "Pandas",
+        "中级"
+      ],
+      "difficulty": "intermediate",
+      "price": "¥9",
+      "status": "publish",
+      "catalog": "数据分析与处理",
+      "slug": "qpy-pandas-basic"
+    },
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+      "id": "7168",
+      "title": "使用Beautiful Soup 4解析网页",
+      "description": "关于Beautiful Soup\nBeautiful Soup是一个Python库，用于从HTML和XML文档中提取数据。Beautiful Soup的魅力在于它的简单和灵活性。它提供了一个直观的API，让你能够轻松地解析网页，就像浏览自己的书架一样轻松。\n无论是遇到格式不规范的HTML文档，还是需要在复杂的数据结构中找到特定的信息，Beautiful Soup都能帮你轻松应对。\n使用Beautiful Soup，你可以快速定位到网页中的特定元素，提取出你需要的文本或属性，甚至还能进行数据的清洗和整理。",
+      "image": "/assets/udemy_10.png",
+      "url": "https://www.qpython.com.cn/course/qpy-bs4-basics/",
+      "tags": [
+        "数据采集",
+        "爬虫",
+        "入门",
+        "初级"
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+      "price": "¥9",
+      "status": "publish",
+      "catalog": "数据分析与处理",
+      "slug": "qpy-bs4-basics"
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+      "id": "7552",
+      "title": "Pillow图像处理编程",
+      "description": "关于Pillow\n在当今的编程世界中，图像处理是一个不可或缺的技能，而Pillow库正是这一领域的佼佼者。Pillow，作为Python Imaging Library（PIL）的现代分支，已经成为Python中最流行的图像处理库。\n想象一下，你想要编辑一张图片，无论是进行简单的操作，比如打开和保存图像、剪切图像的一部分、或者旋转图像，还是进行更复杂的处理，比如应用各种滤镜、合成多个图像、甚至改变图像的颜色模式，Pillow都能帮你高效地完成这些任务。\n它的易用性使得即使是初学者也能快速上手，而其强大的功能也能满足专业开发者的需求。",
+      "image": "/assets/udemy_12.png",
+      "url": "https://www.qpython.com.cn/course/py-pillow-started/",
+      "tags": [
+        "图像处理",
+        "Python库",
+        "中级"
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+      "status": "publish",
+      "catalog": "深入Python开发",
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+      "title": "使用QPython的QSL4A编程",
+      "description": "关于QSL4A\nQSL4A（QPython Scripting Layer for Android）是QPython团队在SL4A项目基础上开发的扩展库，旨在为Android开发者提供更灵活的脚本编程环境。通过QSL4A，开发者不仅可以使用Python快速实现Android应用的原型，还可以通过调用Android API，实现设备控制和自动化任务。\nQSL4A的功能亮点：\n轻量级用户界面（UI）支持：提供基础的用户界面功能，开发者可以快速创建简单的交互式应用程序。\nHTML和JavaScript集成：支持通过HTML和JavaScript构建移动应用界面。\n传感器和硬件控制：能够访问Android设备的传感器、相机、电话等功能。",
+      "image": "/assets/udemy_13.png",
+      "url": "https://www.qpython.com.cn/course/qpy-qsl4a/",
+      "tags": [
+        "QPython",
+        "Android",
+        "中级"
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+      "difficulty": "intermediate",
+      "price": "¥9",
+      "status": "publish",
+      "catalog": "移动应用开发",
+      "slug": "qpy-qsl4a"
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+      "id": "9562",
+      "title": "OpenPyXL数据表格编程",
+      "description": "关于OpenPyXL\nOpenPyXL是一个功能强大的Python库，专门用于处理Excel文件，特别是.xlsx格式的文件。它允许开发者创建、修改、读取和保存Excel文件，支持复杂的操作，如添加图表、设置格式、处理公式、合并单元格、设置单元格样式等。\nOpenPyXL被广泛应用于数据处理、自动化办公以及生成报告等场景。\n课程介绍\n本课程将带您深入学习如何使用OpenPyXL来处理Excel文件。无论您是数据分析师、开发者，还是需要自动化生成报告的用户，都能通过本课程掌握使用OpenPyXL的核心技能。课程特别加入了在QPython中进行实践的内容。",
+      "image": "/assets/udemy_14.png",
+      "url": "https://www.qpython.com.cn/course/qpy-openpyxl/",
+      "tags": [
+        "数据分析",
+        "Excel",
+        "中级"
+      ],
+      "difficulty": "advanced",
+      "price": "¥9",
+      "status": "publish",
+      "catalog": "深入Python开发",
+      "slug": "qpy-openpyxl"
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+    {
+      "id": "9799",
+      "title": "Scikit-learn机器学习入门实践",
+      "description": "关于Scikit-learn\nScikit-learn是一个开源的Python库，广泛应用于机器学习和数据科学领域。它提供了简单而高效的工具，适用于数据挖掘、数据分析以及构建机器学习模型。Scikit-learn建立在NumPy、SciPy和matplotlib等科学计算库的基础之上，专注于机器学习模型的实现，既适合初学者，也适合有经验的开发者。\n主要特点：\n简单易用：Scikit-learn提供了一致的接口来调用不同的机器学习算法，使用起来十分直观。\n广泛的算法支持：包含了众多经典的机器学习算法，如分类、回归、聚类、降维等。\n丰富的文档和示例：拥有详尽的文档和大量示例，帮助开发者快速上手。",
+      "image": "/assets/udemy_15.png",
+      "url": "https://www.qpython.com.cn/course/scikit-learn-startup/",
+      "tags": [
+        "机器学习",
+        "AI",
+        "入门",
+        "初级"
+      ],
+      "difficulty": "beginner",
+      "price": "¥19",
+      "status": "publish",
+      "catalog": "人工智能",
+      "slug": "scikit-learn-startup"
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+      "id": "10416",
+      "title": "PyTorch 从入门到实践",
+      "description": "关于PyTorch\nPyTorch是一个广泛使用的深度学习框架，它提供了动态计算图和强大的GPU加速功能，使得研究人员和开发人员能够快速实现和测试复杂的神经网络模型。它的张量计算和自动微分机制为深度学习提供了高效且灵活的支持。\nPyTorch通过简洁的API使得深度学习的学习和实践变得更加容易，特别适合用于学术研究和工业应用。\n课程介绍\n本课程旨在帮助你快速入门PyTorch，全面涵盖PyTorch的基础概念，包括张量操作、自动微分，以及构建和训练简单神经网络。",
+      "image": "/assets/udemy_17.png",
+      "url": "https://www.qpython.com.cn/course/pytorch-startup/",
+      "tags": [
+        "深度学习",
+        "PyTorch",
+        "入门",
+        "初级"
+      ],
+      "difficulty": "beginner",
+      "price": "¥29",
+      "status": "publish",
+      "catalog": "人工智能",
+      "slug": "pytorch-startup"
+    },
+    {
+      "id": "11000",
+      "title": "AI素养基础",
+      "description": "关于AI\n人工智能（Artificial Intelligence, AI）是一门研究如何让机器具备人类智能的技术和学科。AI技术已经广泛应用于日常生活，从语音助手到推荐算法，再到自动驾驶汽车。\nAI的核心是让计算机能够模仿人类的思考、学习和解决问题的能力，这包括理解自然语言、分析数据、识别图像、规划决策等。\n在本课程中，我们将通过生动易懂的方式向您介绍AI的基本概念和实际应用。无论您是否拥有技术背景，这门课程都能帮助您迈出学习AI的第一步。",
+      "image": "/assets/udemy_16.png",
+      "url": "https://www.qpython.com.cn/course/qpai-artificial-intelligence-open-course-ai-literacy-foundation/",
+      "tags": [
+        "AI基础",
+        "通识",
+        "入门"
+      ],
+      "difficulty": "beginner",
+      "price": "¥5",
+      "status": "publish",
+      "catalog": "人工智能",
+      "slug": "qpai-artificial-intelligence-open-course-ai-literacy-foundation"
+    },
+    {
+      "id": "11564",
+      "title": "OpenCV入门课程",
+      "description": "关于OpenCV\nOpenCV（Open Source Computer Vision Library）是一个开源的计算机视觉库，广泛应用于图像处理、视频分析、机器学习等领域。它提供了丰富的工具，可以让开发者处理图像、视频，甚至进行实时计算机视觉应用开发。\n本课程特别为Python编程新手设计，帮助你通过简单易懂的步骤掌握OpenCV库的基础知识和实用技巧，轻松开启计算机视觉的世界。\n课程内容：\n在本教程中，我们将从最基础的内容开始，逐步引导你学习如何使用OpenCV进行图像和视频处理。",
+      "image": "/assets/udemy_18.png",
+      "url": "https://www.qpython.com.cn/course/opencv-started-tutorial/",
+      "tags": [
+        "计算机视觉",
+        "OpenCV",
+        "入门",
+        "初级"
+      ],
+      "difficulty": "beginner",
+      "price": "¥29",
+      "status": "publish",
+      "catalog": "人工智能",
+      "slug": "opencv-started-tutorial"
+    },
+    {
+      "id": "12497",
+      "title": "OpenAI 入门实践",
+      "description": "关于OpenAI库\nOpenAI是一个人工智能研究实验室，即鼎鼎大名的ChatGPT背后的实验室，它致力于开发对全人类有益的AI技术。在这门课程中，我们将学习如何使用OpenAI提供的Python库，借助该库与PGPT对接的AI模型进行交互。\nOpenAI Python库使得与强大的AI模型（如GPT系列、DALL·E等）进行沟通和使用变得简单而高效。通过这个库，你可以轻松地向AI发送请求并获取回答，生成文本、图像，甚至代码！\n课程内容：\n本课程将帮助你理解如何通过OpenAI调用PGPT服务与AI模型进行交互，并通过实际案例来掌握常见的使用场景。",
+      "image": "/assets/udemy_19.png",
+      "url": "https://www.qpython.com.cn/course/pgpt-openai-introduction/",
+      "tags": [
+        "AI应用",
+        "OpenAI",
+        "入门",
+        "初级"
+      ],
+      "difficulty": "beginner",
+      "price": "¥9",
+      "status": "publish",
+      "catalog": "人工智能",
+      "slug": "pgpt-openai-introduction"
+    },
+    {
+      "id": "12759",
+      "title": "DeepSeek入门实践",
+      "description": "关于DeepSeek\n在人工智能浪潮席卷全球的今天，Deepseek如同一颗冉冉升起的新星，以其对通用人工智能（AGI）的执着追求和创新精神，吸引着全球的目光。\nDeepseek成立于2023年，是一家专注实现AGI的中国公司。AGI，即能够像人类一样思考、学习、推理和解决问题的通用人工智能，是人工智能领域的终极目标。\nDeepSeek模型支持多种功能，包括智能对话、准确翻译、创意写作、高效编程、智能解题和文件解读等。其“深度思考”和“联网搜索”功能使得DeepSeek能够更全面地理解用户问题并提供准确答案。",
+      "image": "/assets/udemy_20.png",
+      "url": "https://www.qpython.com.cn/course/aipy-deepseek-intro/",
+      "tags": [
+        "AI应用",
+        "DeepSeek",
+        "入门",
+        "初级"
+      ],
+      "difficulty": "beginner",
+      "price": "¥9",
+      "status": "publish",
+      "catalog": "人工智能",
+      "slug": "aipy-deepseek-intro"
+    },
+    {
+      "id": "13083",
+      "title": "Twisted入门篇",
+      "description": "关于Twisted\nTwisted是一个强大的Python异步编程框架，专为事件驱动的网络应用程序设计。它支持多种协议的开发，如TCP、UDP、HTTP、FTP等，适用于构建高性能的网络服务器和客户端应用。\nTwisted的核心思想是基于事件循环的异步编程，这使得它能够高效地处理大量并发连接，而无需使用传统的线程或进程。\n它的Deferred和Callback机制使得异步操作变得更加简单易懂，避免了回调地狱的问题。",
+      "image": "/assets/udemy_21.png",
+      "url": "https://www.qpython.com.cn/course/python-twisted-intro/",
+      "tags": [
+        "深入Python开发",
+        "网络编程",
+        "中级"
+      ],
+      "difficulty": "beginner",
+      "price": "¥19",
+      "status": "publish",
+      "catalog": "深入Python开发",
+      "slug": "python-twisted-intro"
+    },
+    {
+      "id": "13724",
+      "title": "提示词工程入门",
+      "description": "关于提示词工程\n大语言模型的强大能力来源于海量的数据训练，但它们对提示词的解读极为敏感，甚至是微小的变化都可能导致完全不同的结果。因此，精准设计有效的提示词，不仅能显著提升模型表现，还能避免浪费计算资源和减少错误输出。\n随着人工智能技术，尤其是大语言模型（如GPT和Deepseek）在各行各业的广泛应用，提示词工程（Prompt Engineering）已经成为提高人机交互效率的关键技能。掌握如何设计、优化和评估提示词，能够帮助我们更好地与这些强大的语言模型进行高效对话。\n在这门课程中，我们将深入探索提示词工程的基本概念、设计原理和优化技巧。",
+      "image": "/assets/udemy_22.png",
+      "url": "https://www.qpython.com.cn/course/qpy-prompt-engineering/",
+      "tags": [
+        "AI应用",
+        "提示词工程",
+        "入门",
+        "初级"
+      ],
+      "difficulty": "beginner",
+      "price": "¥9",
+      "status": "publish",
+      "catalog": "人工智能",
+      "slug": "qpy-prompt-engineering"
+    },
+    {
+      "id": "13934",
+      "title": "Langchain开发入门",
+      "description": "关于Langchain\nLangchain是一个开源框架，旨在帮助开发者与大语言模型（如GPT、LLMs等）进行高效的交互与应用开发。它提供了一套强大的工具，使得开发者能够在处理自然语言任务时，轻松构建复杂的应用程序。\nLangchain通过简化数据流、管理多步任务、与外部API集成以及提供内存和状态管理等功能，帮助开发者充分利用语言模型的潜力，解决实际问题。\n课程简介\n本课程将深入讲解如何使用Langchain框架开发AI应用，从基础知识到高级应用，帮助你掌握Langchain的核心用法。",
+      "image": "/assets/udemy_23.png",
+      "url": "https://www.qpython.com.cn/course/ai-development-getting-started-with-langchain/",
+      "tags": [
+        "AI开发",
+        "LangChain",
+        "中级"
+      ],
+      "difficulty": "beginner",
+      "price": "¥19",
+      "status": "publish",
+      "catalog": "人工智能",
+      "slug": "ai-development-getting-started-with-langchain"
+    },
+    {
+      "id": "14285",
+      "title": "LangGraph开发入门",
+      "description": "关于LangGraph\nLangGraph是一个基于LangChain的库，专门用于构建可靠且可扩展的语言模型应用程序的状态管理和工作流编排工具。它不仅仅是一个简单的编程库，而是一个革命性的平台，使开发者能够创建复杂、动态且智能的AI应用程序。\n在传统的AI应用开发中，开发者常常受限于线性、静态的工作流程。LangGraph的出现彻底改变了这一局面。LangGraph允许开发者创建复杂的、有状态的AI工作流，可以精确控制语言模型代理的执行过程。\n它特别适合需要多步骤、有状态交互的AI应用程序。",
+      "image": "/assets/udemy_24.png",
+      "url": "https://www.qpython.com.cn/course/ai-course-langgraph/",
+      "tags": [
+        "AI开发",
+        "LangGraph",
+        "中级"
+      ],
+      "difficulty": "beginner",
+      "price": "¥19",
+      "status": "publish",
+      "catalog": "人工智能",
+      "slug": "ai-course-langgraph"
+    }
+  ]
+}
\ No newline at end of file
diff --git a/docs/data/courses_en.json b/docs/data/courses_en.json
new file mode 100644
index 0000000..0c26044
--- /dev/null
+++ b/docs/data/courses_en.json
@@ -0,0 +1,91 @@
+{
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+    {
+      "id": "6492",
+      "title": "Python Syntax Practice",
+      "description": "About Python Programming\nPython: A Simple Yet Powerful Programming Language\nImagine a programming language that is not only easy to learn but also powerful - that's Python. Just as we can express complex ideas with simple words, Python lets us accomplish a lot with simple code.\nPython is designed to be human-friendly, its code reads like a story, easy to understand and write. And Python checks for errors better than some older programming languages. It comes with many useful tools built-in, like having a versatile toolkit at your side that you can use anytime to solve problems.",
+      "image": "/assets/udemy_en_06.png",
+      "url": "https://www.qpython.com/course/python-basic-syntax/",
+      "tags": [
+        "Python Syntax",
+        "Getting Started",
+        "Beginner"
+      ],
+      "difficulty": "advanced",
+      "price": "¥5",
+      "status": "publish",
+      "catalog": "Advanced Python Development",
+      "slug": "python-basic-syntax"
+    },
+    {
+      "id": "3995",
+      "title": "QPython Getting Started",
+      "description": "About QPython\nQPython is a Python programming engine designed specifically for Android devices. It integrates an interpreter, console, and editor, along with the QSL4A library and APIGPT, fully supporting web development, scientific computing, and AI extensions. Whether you are a Python beginner or an experienced developer, QPython provides you with a powerful mobile programming platform.\nCore Features:\nComplete Python Environment: Built-in Python interpreter allows you to write and execute code anytime, anywhere.\nEfficient Code Editor: QEditor provides a convenient editor for developing Python projects on your phone.\nJupyter Notebook Compatibility: Through QNotebook browser, you can easily learn and run Notebook files.\nExtension Libraries and PIP Support: Easily install and manage third-party libraries, greatly expanding your programming possibilities.\nCore Libraries: QPython's exclusive QSL4A and APIGPT libraries bring unique features and powerful support for your Android or AI projects.\nLearning Resources and Community: Built-in curated courses and community links to help you quickly improve your programming skills.",
+      "image": "/assets/udemy_en_01.png",
+      "url": "https://www.qpython.com/course/qpython-basic/",
+      "tags": [
+        "QPython",
+        "Getting Started",
+        "Beginner"
+      ],
+      "difficulty": "beginner",
+      "price": "¥9",
+      "status": "publish",
+      "catalog": "Mobile App Development",
+      "slug": "qpython-basic"
+    },
+    {
+      "id": "4198",
+      "title": "Generative AI Programming Getting Started",
+      "description": "Course Overview\nWith the rise of Generative AI (AIGC), especially the widespread adoption of ChatGPT, learning how to integrate AIGC technology into your applications has become a trend. The QPai Generative AI Development Series - Getting Started course aims to provide Python developers with a quick start path, enabling you to master AIGC application skills in program development.\nCourse Features\nDeep Understanding of AIGC: We will explore the fundamental principles of generative AI and its applications in artistic creation, image generation, dialogue generation, and text content generation.\nMaster PGPT Services: PGPT, as a cloud API platform that aggregates multiple large AI services, provides a lightweight AIGC development library to help developers quickly build applications.\nSpecial QPython Support: The course specifically uses the method of calling PGPT services in QPython, allowing learners to conveniently experience the charm of generative AI on mobile devices.",
+      "image": "/assets/udemy_en_02.png",
+      "url": "https://www.qpython.com/course/getting-started-with-qpy-generative-ai-development/",
+      "tags": [
+        "AI Application",
+        "Generative AI",
+        "Getting Started",
+        "Beginner"
+      ],
+      "difficulty": "beginner",
+      "price": "¥19",
+      "status": "publish",
+      "catalog": "Artificial Intelligence",
+      "slug": "getting-started-with-qpy-generative-ai-development"
+    },
+    {
+      "id": "6948",
+      "title": "Pandas Data Analysis and Processing Programming",
+      "description": "About Pandas\nPandas is a powerful and easy-to-use library in Python specifically for processing and analyzing structured data. It allows you to handle data tables as easily as operating Excel spreadsheets, suitable for organizing, cleaning, filtering, and analyzing various datasets.\nWith Pandas, you can quickly perform common tasks such as data cleaning, missing value handling, data grouping, and aggregation. Pandas is an essential tool for data analysis that can greatly simplify the workflow of processing large amounts of data.\nBenefits of Learning Pandas:\nImprove Efficiency: Mastering Pandas can significantly improve your efficiency in processing large amounts of data.\nEnhanced Insight: With Pandas, you can quickly extract valuable information and insights from data.",
+      "image": "/assets/udemy_en_09.png",
+      "url": "https://www.qpython.com/course/qpy-pandas-basic/",
+      "tags": [
+        "Data Analysis",
+        "Pandas",
+        "Intermediate"
+      ],
+      "difficulty": "intermediate",
+      "price": "¥9",
+      "status": "publish",
+      "catalog": "Data Analysis and Processing",
+      "slug": "qpy-pandas-basic"
+    },
+    {
+      "id": "12759",
+      "title": "DeepSeek Getting Started Practice",
+      "description": "About DeepSeek\nIn today's wave of artificial intelligence sweeping the globe, DeepSeek shines like a rising star, attracting global attention with its pursuit and innovative spirit toward Artificial General Intelligence (AGI).\nDeepSeek was established in 2023 and is a company focused on achieving AGI. AGI, the ability to think, learn, reason, and solve problems like humans, is the ultimate goal of artificial intelligence.\nDeepSeek models support multiple functions including intelligent dialogue, accurate translation, creative writing, efficient programming, intelligent problem solving, and file interpretation. Its \"deep thinking\" and \"web search\" features enable DeepSeek to more comprehensively understand user questions and provide accurate answers.",
+      "image": "/assets/udemy_en_20.png",
+      "url": "https://www.qpython.com/course/aipy-deepseek-intro/",
+      "tags": [
+        "AI Application",
+        "DeepSeek",
+        "Getting Started",
+        "Beginner"
+      ],
+      "difficulty": "beginner",
+      "price": "¥9",
+      "status": "publish",
+      "catalog": "Artificial Intelligence",
+      "slug": "aipy-deepseek-intro"
+    }
+  ]
+}
diff --git a/docs/data/courses_en_backup.json b/docs/data/courses_en_backup.json
new file mode 100644
index 0000000..577434a
--- /dev/null
+++ b/docs/data/courses_en_backup.json
@@ -0,0 +1,424 @@
+{
+  "courses": [
+    {
+      "id": "6492",
+      "title": "Python Syntax Practice",
+      "description": "About Python Programming\nPython: A Simple Yet Powerful Programming Language\nImagine a programming language that is not only easy to learn but also powerful - that's Python. Just as we can express complex ideas with simple words, Python lets us accomplish a lot with simple code.\nPython is designed to be human-friendly, its code reads like a story, easy to understand and write. And Python checks for errors better than some older programming languages. It comes with many useful tools built-in, like having a versatile toolkit at your side that you can use anytime to solve problems.",
+      "image": "/assets/udemy_en_06.png",
+      "url": "https://www.qpython.com/course/python-basic-syntax/",
+      "tags": [
+        "Python Syntax",
+        "Getting Started",
+        "Beginner"
+      ],
+      "difficulty": "advanced",
+      "price": "¥5",
+      "status": "publish",
+      "catalog": "Advanced Python Development",
+      "slug": "python-basic-syntax"
+    },
+    {
+      "id": "3995",
+      "title": "QPython Getting Started",
+      "description": "About QPython\nQPython is a Python programming engine designed specifically for Android devices. It integrates an interpreter, console, and editor, along with the QSL4A library and APIGPT, fully supporting web development, scientific computing, and AI extensions. Whether you are a Python beginner or an experienced developer, QPython provides you with a powerful mobile programming platform.\nCore Features:\nComplete Python Environment: Built-in Python interpreter allows you to write and execute code anytime, anywhere.\nEfficient Code Editor: QEditor provides a convenient editor for developing Python projects on your phone.\nJupyter Notebook Compatibility: Through QNotebook browser, you can easily learn and run Notebook files.\nExtension Libraries and PIP Support: Easily install and manage third-party libraries, greatly expanding your programming possibilities.\nCore Libraries: QPython's exclusive QSL4A and APIGPT libraries bring unique features and powerful support for your Android or AI projects.\nLearning Resources and Community: Built-in curated courses and community links to help you quickly improve your programming skills.",
+      "image": "/assets/udemy_en_01.png",
+      "url": "https://www.qpython.com/course/qpython-basic/",
+      "tags": [
+        "QPython",
+        "Getting Started",
+        "Beginner"
+      ],
+      "difficulty": "beginner",
+      "price": "¥9",
+      "status": "publish",
+      "catalog": "Mobile App Development",
+      "slug": "qpython-basic"
+    },
+    {
+      "id": "4198",
+      "title": "Generative AI Programming Getting Started",
+      "description": "Course Overview\nWith the rise of Generative AI (AIGC), especially the widespread adoption of ChatGPT, learning how to integrate AIGC technology into your applications has become a trend. The QPai Generative AI Development Series - Getting Started course aims to provide Python developers with a quick start path, enabling you to master AIGC application skills in program development.\nCourse Features\nDeep Understanding of AIGC: We will explore the fundamental principles of generative AI and its applications in artistic creation, image generation, dialogue generation, and text content generation.\nMaster PGPT Services: PGPT, as a cloud API platform that aggregates multiple large AI services, provides a lightweight AIGC development library to help developers quickly build applications.\nSpecial QPython Support: The course specifically uses the method of calling PGPT services in QPython, allowing learners to conveniently experience the charm of generative AI on mobile devices.",
+      "image": "/assets/udemy_en_02.png",
+      "url": "https://www.qpython.com/course/getting-started-with-qpy-generative-ai-development/",
+      "tags": [
+        "AI Application",
+        "Generative AI",
+        "Getting Started",
+        "Beginner"
+      ],
+      "difficulty": "beginner",
+      "price": "¥19",
+      "status": "publish",
+      "catalog": "Artificial Intelligence",
+      "slug": "getting-started-with-qpy-generative-ai-development"
+    },
+    // {
+    //   "id": "7229",
+    //   "title": "Scrapy Web Crawler Programming",
+    //   "description": "About Scrapy\nScrapy is a powerful and efficient Python web crawling framework that helps developers quickly crawl and extract data from websites. Scrapy was designed from the ground up for crawling large-scale websites and storing crawled data in a structured way for data analysis, machine learning model training, content aggregation, and more.\nScrapy provides a flexible API and rich features, including support for handling various page structures, easily defining data extraction logic, configuring proxies, handling redirects, and page caching.\nAdditionally, it has excellent extensibility, allowing users to write custom components to meet various complex needs.",
+    //   "image": "/assets/udemy_en_11.png",
+    //   "url": "https://www.qpython.com/course/scrapy-spider/",
+    //   "tags": [
+    //     "Data Collection",
+    //     "Web Crawler",
+    //     "Intermediate"
+    //   ],
+    //   "difficulty": "intermediate",
+    //   "price": "¥9",
+    //   "status": "publish",
+    //   "catalog": "Data Analysis and Processing",
+    //   "slug": "scrapy-spider"
+    // },
+    // {
+    //   "id": "4200",
+    //   "title": "Pico Car Basic Motion Programming",
+    //   "description": "What is QPai Pico Smart Car\nThe QPai Pico Smart Car is a mini car programming platform based on the Raspberry Pi Pico microcontroller, designed specifically for programming education and maker enthusiasts. Using MicroPython tools, it allows programming enthusiasts to interact and explore through various sensors in a flat, compact car design, learning programming while understanding the basic principles of physical computing and robot control.\nStrong Interactivity: The Pico Smart Car allows learners to directly interact with hardware through programming, enhancing the fun and practicality of learning.\nMultiple Sensor Support: Including infrared sensors, ultrasonic sensors, etc., enabling functions like automatic obstacle avoidance and line tracking.\nQPython Environment Support: Programming experiments can be conducted in the QPython mobile environment, allowing learning and creating anytime, anywhere.",
+    //   "image": "/assets/udemy_en_03.png",
+    //   "url": "https://www.qpython.com/course/picocar-basic/",
+    //   "tags": [
+    //     "Smart Hardware",
+    //     "Embedded",
+    //     "Intermediate"
+    //   ],
+    //   "difficulty": "intermediate",
+    //   "price": "¥29",
+    //   "status": "publish",
+    //   "catalog": "Smart Hardware",
+    //   "slug": "picocar-basic"
+    // },
+    // {
+    //   "id": "4218",
+    //   "title": "Flask Web Development Getting Started",
+    //   "description": "What is Flask\nFlask is a simple, flexible, and easy-to-use web framework in Python, suitable for beginners. It provides rich features and extensibility, helping developers quickly build fully functional web applications.\nSimple and Easy to Use: Flask's design philosophy is to keep things simple and user-friendly. Its API is straightforward and easy to learn and understand.\nRouting System: Flask provides a powerful routing system that can match different handler functions based on URL paths.\nTemplate Engine: Flask comes with the Jinja2 template engine, making it easy to separate dynamic content from static HTML pages.\nDatabase Integration: Flask supports various database extensions for easy database operations.",
+    //   "image": "/assets/udemy_en_04.png",
+    //   "url": "https://www.qpython.com/course/flask-web-development-course/",
+    //   "tags": [
+    //     "Web Development",
+    //     "Flask",
+    //     "Getting Started",
+    //     "Beginner"
+    //   ],
+    //   "difficulty": "beginner",
+    //   "price": "¥9",
+    //   "status": "publish",
+    //   "catalog": "Web Development",
+    //   "slug": "flask-web-development-course"
+    // },
+    // {
+    //   "id": "6457",
+    //   "title": "Poetry Q&A Project Practice",
+    //   "description": "This course aims to help students master basic Python programming skills through actual project development, and further learn how to apply programming technology in the field of culture and art. Through developing an interactive poetry Q&A system, students will learn the entire process from conception, design to implementing a complete application.\nCourse Content:\n1. Python Programming Basics\n2. Design and Implementation of Q&A Logic\n3. Application of Generative AI in Poetry\n4. Interface Design and User Experience Optimization\nExpected Outcomes:\nAfter completing this course, students will master practical skills for developing complex projects using Python.",
+    //   "image": "/assets/udemy_en_05.png",
+    //   "url": "https://www.qpython.com/course/poetry-you-ask-and-i-answer/",
+    //   "tags": [
+    //     "AI Application",
+    //     "NLP",
+    //     "Project Practice",
+    //     "Advanced"
+    //   ],
+    //   "difficulty": "advanced",
+    //   "price": "¥19",
+    //   "status": "publish",
+    //   "catalog": "Artificial Intelligence",
+    //   "slug": "poetry-you-ask-and-i-answer"
+    // },
+    // {
+    //   "id": "6705",
+    //   "title": "Django Web Development Getting Started",
+    //   "description": "About Django\nImagine you are building a house. Django is like a set of pre-designed blueprints and tools that help you quickly build a sturdy, beautiful, and powerful website.\nDjango is an open-source web framework written in Python. It helps you handle many tedious tasks in website development, such as user authentication, database management, URL routing, and more.\nRapid Development: Django's design philosophy is rapid development. It provides a large number of built-in features that allow you to build fully functional websites in a short time.\nSecurity: Django has many built-in security mechanisms that can effectively protect your website from common network attacks.",
+    //   "image": "/assets/udemy_en_07.png",
+    //   "url": "https://www.qpython.com/course/django-basics/",
+    //   "tags": [
+    //     "Web Development",
+    //     "Django",
+    //     "Getting Started",
+    //     "Beginner"
+    //   ],
+    //   "difficulty": "beginner",
+    //   "price": "¥9",
+    //   "status": "publish",
+    //   "catalog": "Web Development",
+    //   "slug": "django-basics"
+    // },
+    // {
+    //   "id": "6898",
+    //   "title": "NumPy Scientific Computing Programming",
+    //   "description": "About NumPy\nNumPy (Numerical Python) is a library specially designed for Python to help us handle large amounts of data more easily. Think of it as a \"computing assistant\" in Python that allows us to complete complex mathematical operations and data processing with less code.\nWith NumPy, you can:\nCreate Arrays: Not only can it handle one-dimensional data like lists, but also easily handle two-dimensional, three-dimensional, and even higher-dimensional arrays.\nFast Computation: NumPy provides a complete set of built-in functions for quick arithmetic, statistical analysis, and other operations.\nTime-Saving: Due to its efficient computation method, NumPy is much faster than traditional Python methods.",
+    //   "image": "/assets/udemy_en_08.png",
+    //   "url": "https://www.qpython.com/course/qpy-numpy-basic/",
+    //   "tags": [
+    //     "Data Analysis",
+    //     "NumPy",
+    //     "Getting Started",
+    //     "Beginner"
+    //   ],
+    //   "difficulty": "intermediate",
+    //   "price": "¥9",
+    //   "status": "publish",
+    //   "catalog": "Data Analysis and Processing",
+    //   "slug": "qpy-numpy-basic"
+    // },
+    {
+      "id": "6948",
+      "title": "Pandas Data Analysis and Processing Programming",
+      "description": "About Pandas\nPandas is a powerful and easy-to-use library in Python specifically for processing and analyzing structured data. It allows you to handle data tables as easily as operating Excel spreadsheets, suitable for organizing, cleaning, filtering, and analyzing various datasets.\nWith Pandas, you can quickly perform common tasks such as data cleaning, missing value handling, data grouping, and aggregation. Pandas is an essential tool for data analysis that can greatly simplify the workflow of processing large amounts of data.\nBenefits of Learning Pandas:\nImprove Efficiency: Mastering Pandas can significantly improve your efficiency in processing large amounts of data.\nEnhanced Insight: With Pandas, you can quickly extract valuable information and insights from data.",
+      "image": "/assets/udemy_en_09.png",
+      "url": "https://www.qpython.com/course/qpy-pandas-basic/",
+      "tags": [
+        "Data Analysis",
+        "Pandas",
+        "Intermediate"
+      ],
+      "difficulty": "intermediate",
+      "price": "¥9",
+      "status": "publish",
+      "catalog": "Data Analysis and Processing",
+      "slug": "qpy-pandas-basic"
+    },
+    // {
+    //   "id": "7168",
+    //   "title": "Web Scraping with Beautiful Soup 4",
+    //   "description": "About Beautiful Soup\nBeautiful Soup is a Python library for extracting data from HTML and XML documents. The charm of Beautiful Soup lies in its simplicity and flexibility. It provides an intuitive API that allows you to easily parse web pages, just like easily browsing your own bookshelf.\nWhether encountering non-standardized HTML documents or needing to find specific information in complex data structures, Beautiful Soup can help you easily handle it.\nWith Beautiful Soup, you can quickly locate specific elements in web pages, extract the text or attributes you need, and even clean and organize data.",
+    //   "image": "/assets/udemy_en_10.png",
+    //   "url": "https://www.qpython.com/course/qpy-bs4-basics/",
+    //   "tags": [
+    //     "Data Collection",
+    //     "Web Scraper",
+    //     "Getting Started",
+    //     "Beginner"
+    //   ],
+    //   "difficulty": "beginner",
+    //   "price": "¥9",
+    //   "status": "publish",
+    //   "catalog": "Data Analysis and Processing",
+    //   "slug": "qpy-bs4-basics"
+    // },
+    // {
+    //   "id": "7552",
+    //   "title": "Pillow Image Processing Programming",
+    //   "description": "About Pillow\nIn today's programming world, image processing is an indispensable skill, and the Pillow library is a leader in this field. Pillow, as the modern branch of the Python Imaging Library (PIL), has become the most popular image processing library in Python.\nImagine you want to edit an image, whether for simple operations like opening and saving images, cropping part of an image, or rotating an image, or for more complex processing like applying various filters, compositing multiple images, or even changing the color mode of an image, Pillow can help you complete these tasks efficiently.\nIts ease of use allows even beginners to get started quickly, while its powerful features can meet the needs of professional developers.",
+    //   "image": "/assets/udemy_en_12.png",
+    //   "url": "https://www.qpython.com/course/py-pillow-started/",
+    //   "tags": [
+    //     "Image Processing",
+    //     "Python Library",
+    //     "Intermediate"
+    //   ],
+    //   "difficulty": "advanced",
+    //   "price": "¥9",
+    //   "status": "publish",
+    //   "catalog": "Advanced Python Development",
+    //   "slug": "py-pillow-started"
+    // },
+    // {
+    //   "id": "8878",
+    //   "title": "QSL4A Programming with QPython",
+    //   "description": "About QSL4A\nQSL4A (QPython Scripting Layer for Android) is an extension library developed by the QPython team based on the SL4A project, designed to provide a more flexible scripting programming environment for Android developers. Through QSL4A, developers can not only quickly implement prototypes of Android applications using Python, but also achieve device control and automation tasks by calling Android APIs.\nQSL4A Feature Highlights:\nLightweight User Interface (UI) Support: Provides basic user interface functionality, allowing developers to quickly create simple interactive applications.\nHTML and JavaScript Integration: Supports building mobile app interfaces through HTML and JavaScript.\nSensor and Hardware Control: Can access Android device sensors, cameras, phone, and other features.",
+    //   "image": "/assets/udemy_en_13.png",
+    //   "url": "https://www.qpython.com/course/qpy-qsl4a/",
+    //   "tags": [
+    //     "QPython",
+    //     "Android",
+    //     "Intermediate"
+    //   ],
+    //   "difficulty": "intermediate",
+    //   "price": "¥9",
+    //   "status": "publish",
+    //   "catalog": "Mobile App Development",
+    //   "slug": "qpy-qsl4a"
+    // },
+    // {
+    //   "id": "9562",
+    //   "title": "OpenPyXL Spreadsheet Programming",
+    //   "description": "About OpenPyXL\nOpenPyXL is a powerful Python library specifically for handling Excel files, especially .xlsx format files. It allows developers to create, modify, read, and save Excel files, supporting complex operations such as adding charts, setting formats, handling formulas, merging cells, and setting cell styles.\nOpenPyXL is widely used in data processing, automated office work, and report generation scenarios.\nCourse Introduction\nThis course will take you to deeply learn how to use OpenPyXL to process Excel files. Whether you are a data analyst, developer, or need to automate report generation, you can master the core skills of using OpenPyXL through this course. The course specifically includes practical content in QPython.",
+    //   "image": "/assets/udemy_en_14.png",
+    //   "url": "https://www.qpython.com/course/qpy-openpyxl/",
+    //   "tags": [
+    //     "Data Analysis",
+    //     "Excel",
+    //     "Intermediate"
+    //   ],
+    //   "difficulty": "advanced",
+    //   "price": "¥9",
+    //   "status": "publish",
+    //   "catalog": "Advanced Python Development",
+    //   "slug": "qpy-openpyxl"
+    // },
+    // {
+    //   "id": "9799",
+    //   "title": "Scikit-learn Machine Learning Getting Started",
+    //   "description": "About Scikit-learn\nScikit-learn is an open-source Python library widely used in machine learning and data science. It provides simple and efficient tools for data mining, data analysis, and building machine learning models. Scikit-learn is built on NumPy, SciPy, and matplotlib, focusing on machine learning model implementation, suitable for both beginners and experienced developers.\nMain Features:\nEasy to Use: Scikit-learn provides a consistent interface to call different machine learning algorithms, making it very intuitive to use.\nWide Algorithm Support: Includes many classic machine learning algorithms such as classification, regression, clustering, dimensionality reduction, etc.\nRich Documentation and Examples: With detailed documentation and a large number of examples to help developers get started quickly.",
+    //   "image": "/assets/udemy_en_15.png",
+    //   "url": "https://www.qpython.com/course/scikit-learn-startup/",
+    //   "tags": [
+    //     "Machine Learning",
+    //     "AI",
+    //     "Getting Started",
+    //     "Beginner"
+    //   ],
+    //   "difficulty": "beginner",
+    //   "price": "¥19",
+    //   "status": "publish",
+    //   "catalog": "Artificial Intelligence",
+    //   "slug": "scikit-learn-startup"
+    // },
+    // {
+    //   "id": "10416",
+    //   "title": "PyTorch From Beginner to Practice",
+    //   "description": "About PyTorch\nPyTorch is a widely used deep learning framework that provides dynamic computation graphs and powerful GPU acceleration, enabling researchers and developers to quickly implement and test complex neural network models. Its tensor computation and automatic differentiation mechanisms provide efficient and flexible support for deep learning.\nPyTorch's concise API makes learning and practicing deep learning easier, especially suitable for academic research and industrial applications.\nCourse Introduction\nThis course aims to help you quickly get started with PyTorch, comprehensively covering the basic concepts of PyTorch, including tensor operations, automatic differentiation, and building and training simple neural networks.",
+    //   "image": "/assets/udemy_en_17.png",
+    //   "url": "https://www.qpython.com/course/pytorch-startup/",
+    //   "tags": [
+    //     "Deep Learning",
+    //     "PyTorch",
+    //     "Getting Started",
+    //     "Beginner"
+    //   ],
+    //   "difficulty": "beginner",
+    //   "price": "¥29",
+    //   "status": "publish",
+    //   "catalog": "Artificial Intelligence",
+    //   "slug": "pytorch-startup"
+    // },
+    // {
+    //   "id": "11000",
+    //   "title": "AI Literacy Fundamentals",
+    //   "description": "About AI\nArtificial Intelligence (AI) is a technology and discipline that studies how to make machines possess human intelligence. AI technology has been widely used in daily life, from voice assistants to recommendation algorithms, and self-driving cars.\nThe core of AI is to enable computers to imitate human thinking, learning, and problem-solving abilities, including understanding natural language, analyzing data, recognizing images, planning decisions, and more.\nIn this course, we will introduce you to the basic concepts and practical applications of AI in an vivid and easy-to-understand way. Whether you have a technical background or not, this course can help you take your first step in learning AI.",
+    //   "image": "/assets/udemy_en_16.png",
+    //   "url": "https://www.qpython.com/course/qpai-artificial-intelligence-open-course-ai-literacy-foundation/",
+    //   "tags": [
+    //     "AI Fundamentals",
+    //     "General Knowledge",
+    //     "Getting Started"
+    //   ],
+    //   "difficulty": "beginner",
+    //   "price": "¥5",
+    //   "status": "publish",
+    //   "catalog": "Artificial Intelligence",
+    //   "slug": "qpai-artificial-intelligence-open-course-ai-literacy-foundation"
+    // },
+    // {
+    //   "id": "11564",
+    //   "title": "OpenCV Getting Started Course",
+    //   "description": "About OpenCV\nOpenCV (Open Source Computer Vision Library) is an open-source computer vision library widely used in image processing, video analysis, machine learning, and other fields. It provides rich tools that allow developers to process images and videos, and even develop real-time computer vision applications.\nThis course is specially designed for Python programming beginners, helping you master the basic knowledge and practical skills of the OpenCV library through simple and easy-to-understand steps, easily starting your journey in the world of computer vision.\nCourse Content:\nIn this tutorial, we will start from the most basic content and gradually guide you through learning how to use OpenCV for image and video processing.",
+    //   "image": "/assets/udemy_en_18.png",
+    //   "url": "https://www.qpython.com/course/opencv-started-tutorial/",
+    //   "tags": [
+    //     "Computer Vision",
+    //     "OpenCV",
+    //     "Getting Started",
+    //     "Beginner"
+    //   ],
+    //   "difficulty": "beginner",
+    //   "price": "¥29",
+    //   "status": "publish",
+    //   "catalog": "Artificial Intelligence",
+    //   "slug": "opencv-started-tutorial"
+    // },
+    // {
+    //   "id": "12497",
+    //   "title": "OpenAI Getting Started Practice",
+    //   "description": "About the OpenAI Library\nOpenAI is an artificial intelligence research laboratory, the lab behind the famous ChatGPT, dedicated to developing AI technology that benefits all of humanity. In this course, we will learn how to use the Python library provided by OpenAI to interact with AI models connected through PGPT.\nThe OpenAI Python library makes it simple and efficient to communicate with and use powerful AI models (such as the GPT series, DALL·E, etc.). Through this library, you can easily send requests to AI and get responses, generate text, images, and even code!\nCourse Content:\nThis course will help you understand how to interact with AI models by calling PGPT services through OpenAI, and master common usage scenarios through practical cases.",
+    //   "image": "/assets/udemy_en_19.png",
+    //   "url": "https://www.qpython.com/course/pgpt-openai-introduction/",
+    //   "tags": [
+    //     "AI Application",
+    //     "OpenAI",
+    //     "Getting Started",
+    //     "Beginner"
+    //   ],
+    //   "difficulty": "beginner",
+    //   "price": "¥9",
+    //   "status": "publish",
+    //   "catalog": "Artificial Intelligence",
+    //   "slug": "pgpt-openai-introduction"
+    // },
+    {
+      "id": "12759",
+      "title": "DeepSeek Getting Started Practice",
+      "description": "About DeepSeek\nIn today's wave of artificial intelligence sweeping the globe, DeepSeek shines like a rising star, attracting global attention with its pursuit and innovative spirit toward Artificial General Intelligence (AGI).\nDeepSeek was established in 2023 and is a company focused on achieving AGI. AGI, the ability to think, learn, reason, and solve problems like humans, is the ultimate goal of artificial intelligence.\nDeepSeek models support multiple functions including intelligent dialogue, accurate translation, creative writing, efficient programming, intelligent problem solving, and file interpretation. Its \"deep thinking\" and \"web search\" features enable DeepSeek to more comprehensively understand user questions and provide accurate answers.",
+      "image": "/assets/udemy_en_20.png",
+      "url": "https://www.qpython.com/course/aipy-deepseek-intro/",
+      "tags": [
+        "AI Application",
+        "DeepSeek",
+        "Getting Started",
+        "Beginner"
+      ],
+      "difficulty": "beginner",
+      "price": "¥9",
+      "status": "publish",
+      "catalog": "Artificial Intelligence",
+      "slug": "aipy-deepseek-intro"
+    },
+    // {
+    //   "id": "13083",
+    //   "title": "Twisted Getting Started",
+    //   "description": "About Twisted\nTwisted is a powerful Python asynchronous programming framework designed for event-driven network applications. It supports the development of multiple protocols such as TCP, UDP, HTTP, FTP, etc., suitable for building high-performance network server and client applications.\nTwisted's core idea is event loop-based asynchronous programming, which allows it to efficiently handle a large number of concurrent connections without using traditional threads or processes.\nIts Deferred and Callback mechanisms make asynchronous operations simpler and easier to understand, avoiding callback hell issues.",
+    //   "image": "/assets/udemy_en_21.png",
+    //   "url": "https://www.qpython.com/course/python-twisted-intro/",
+    //   "tags": [
+    //     "Advanced Python Development",
+    //     "Network Programming",
+    //     "Intermediate"
+    //   ],
+    //   "difficulty": "beginner",
+    //   "price": "¥19",
+    //   "status": "publish",
+    //   "catalog": "Advanced Python Development",
+    //   "slug": "python-twisted-intro"
+    // },
+    // {
+    //   "id": "13724",
+    //   "title": "Prompt Engineering Getting Started",
+    //   "description": "About Prompt Engineering\nThe powerful capabilities of large language models come from massive data training, but they are extremely sensitive to prompt interpretation - even tiny changes can lead to completely different results. Therefore, precisely designing effective prompts can not only significantly improve model performance but also avoid wasting computing resources and reduce errors.\nAs AI technology, especially large language models (like GPT and DeepSeek), are widely used in various industries, Prompt Engineering has become a key skill for improving human-computer interaction efficiency. Mastering how to design, optimize, and evaluate prompts can help us have more efficient conversations with these powerful language models.\nIn this course, we will deeply explore the basic concepts, design principles, and optimization techniques of prompt engineering.",
+    //   "image": "/assets/udemy_en_22.png",
+    //   "url": "https://www.qpython.com/course/qpy-prompt-engineering/",
+    //   "tags": [
+    //     "AI Application",
+    //     "Prompt Engineering",
+    //     "Getting Started",
+    //     "Beginner"
+    //   ],
+    //   "difficulty": "beginner",
+    //   "price": "¥9",
+    //   "status": "publish",
+    //   "catalog": "Artificial Intelligence",
+    //   "slug": "qpy-prompt-engineering"
+    // },
+    // {
+    //   "id": "13934",
+    //   "title": "LangChain Development Getting Started",
+    //   "description": "About LangChain\nLangChain is an open-source framework designed to help developers efficiently interact with and develop applications using large language models (such as GPT, LLMs, etc.). It provides a powerful set of tools that enables developers to easily build complex applications when processing natural language tasks.\nLangChain helps developers fully leverage the potential of language models to solve practical problems by simplifying data flow, managing multi-step tasks, integrating with external APIs, and providing memory and state management.\nCourse Introduction\nThis course will deeply explain how to develop AI applications using the LangChain framework, from basic knowledge to advanced applications, helping you master the core usage of LangChain.",
+    //   "image": "/assets/udemy_en_23.png",
+    //   "url": "https://www.qpython.com/course/ai-development-getting-started-with-langchain/",
+    //   "tags": [
+    //     "AI Development",
+    //     "LangChain",
+    //     "Intermediate"
+    //   ],
+    //   "difficulty": "beginner",
+    //   "price": "¥19",
+    //   "status": "publish",
+    //   "catalog": "Artificial Intelligence",
+    //   "slug": "ai-development-getting-started-with-langchain"
+    // },
+    // {
+    //   "id": "14285",
+    //   "title": "LangGraph Development Getting Started",
+    //   "description": "About LangGraph\nLangGraph is a library based on LangChain, specifically designed for building reliable and scalable state management and workflow orchestration tools for language model applications. It is not just a simple programming library, but a revolutionary platform that enables developers to create complex, dynamic, and intelligent AI applications.\nIn traditional AI application development, developers are often limited by linear, static workflows. LangGraph has completely changed this situation. LangGraph allows developers to create complex, stateful AI workflows with precise control over the execution process of language model agents.\nIt is especially suitable for AI applications that require multi-step, stateful interactions.",
+    //   "image": "/assets/udemy_en_24.png",
+    //   "url": "https://www.qpython.com/course/ai-course-langgraph/",
+    //   "tags": [
+    //     "AI Development",
+    //     "LangGraph",
+    //     "Intermediate"
+    //   ],
+    //   "difficulty": "beginner",
+    //   "price": "¥19",
+    //   "status": "publish",
+    //   "catalog": "Artificial Intelligence",
+    //   "slug": "ai-course-langgraph"
+    // }
+  ]
+}
diff --git a/docs/dive-into-python/cn/actionscope.html b/docs/dive-into-python/cn/actionscope.html
deleted file mode 100644
index 1ce6a46..0000000
--- a/docs/dive-into-python/cn/actionscope.html
+++ /dev/null
@@ -1,495 +0,0 @@
-<!DOCTYPE html>
-<html lang="en-US">
-<head>
-<meta charset="utf-8" />
-<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
-<link href="https://cdn.bootcss.com/bootstrap/3.3.7/css/bootstrap.min.css" rel="stylesheet">
-<title>作用域</title>
-<link rel="stylesheet" type="text/css" href="../../static/pygments.css" />
-<link rel="stylesheet" type="text/css" href="../../static/style.css" />
-<meta charset="UTF-8">
-<meta name="viewport" content="width=device-width, initial-scale=1,maximum-scale=1, user-scalable=no">
-<script src="https://cdn.bootcss.com/bootstrap/3.3.7/js/bootstrap.min.js"></script>
-</head><body>
-<div class="header doc-header web-show">
-<div class="col-sm-offset-2 col-sm-8  index-header">
-<div class="navbar-header">
-<button type="button" class="navbar-toggle" data-toggle="collapse" data-target="#example-navbar-collapse">
-<span class="sr-only">切换导航</span>
-<span class="icon-bar"></span>
-<span class="icon-bar"></span>
-<span class="icon-bar"></span>
-</button>
-<a href="/index.html" class="logo"><img src="http://www.qpython.org/static/img_logo.png"></a>
-</div>
-<ul class="header-title collapse navbar-collapse" id="example-navbar-collapse" >
-<li>
-<a href="http://www.qpython.org/index.html">Home</a>
-</li>
-<li>
-<a href="http://www.qpython.org/document.html">Guide</a>
-</li>
-<li class="header-phone-selected">
-<a href="/index.html" class="header-selected">Course</a>
-<span class="header-title-selected"></span>
-</li>
-<li>
-<a href="http://www.aipy.org">AIPY</a>
-</li>
-<li>
-<a target="_blank" href="https://github.com/qpython-android/qpython">Github</a>
-</li>
-</ul>
-<div class="search-box hidden-xs">
-<form action="/search.html">
-<input type="search" name="q" placeholder="keyword">
-<input type="hidden" name="check_keywords" value="yes" />
-<input type="hidden" name="area" value="default" />
-<button><img src="http://www.qpython.org/static/ic_search.png"></button>
-</form>
-</div>
-</div>
-</div>
-<main class="contain web-content">
-<div class="document">
-<div class="documentwrapper">
-<div class="bodywrapper">
-<div class="body" role="main">
-<section id="id1">
-<h1>作用域<a class="headerlink" href="#id1" title="Permalink to this heading">¶</a></h1>
-<p>在函数中，<em>Python</em> 从命名空间中寻找变量的顺序如下：</p>
-<ul class="simple">
-<li><p>local function scope</p></li>
-<li><p>enclosing scope</p></li>
-<li><p>global scope</p></li>
-<li><p>builtin scope</p></li>
-</ul>
-<p>例子：</p>
-</section>
-<section id="local">
-<h1>local 作用域<a class="headerlink" href="#local" title="Permalink to this heading">¶</a></h1>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>def foo(a,b):
-    c = 1
-    d = a + b + c
-</pre></div>
-</div>
-<p>这里所有的变量都在 <em>local</em> 作用域。</p>
-</section>
-<section id="global">
-<h1>global 作用域<a class="headerlink" href="#global" title="Permalink to this heading">¶</a></h1>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>c = 1
-def foo(a,b):
-    d = a + b + c
-</pre></div>
-</div>
-<p>这里的 <em>c</em> 就在 <em>global</em> 作用域。</p>
-</section>
-<section id="id2">
-<h1>global 关键词<a class="headerlink" href="#id2" title="Permalink to this heading">¶</a></h1>
-<p>使用 <em>global</em> 关键词可以在 <em>local</em> 作用域中修改 <em>global</em> 作用域的值。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>c = 1
-def foo():
-    global c
-    c = 2
-print c
-foo()
-print c
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="mi">1</span>
-<span class="mi">2</span>
-</pre></div>
-</div>
-<p>其作用是将 <em>c</em> 指向 <em>global</em> 中的 <em>c</em></p>
-<p>如果不加关键词，那么 <em>local</em> 作用域的 <em>c</em> 不会影响 <em>globa</em> 作用域中的值：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>c = 1
-def foo():
-    c = 2
-print c
-foo()
-print c
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="mi">1</span>
-<span class="mi">1</span>
-</pre></div>
-</div>
-</section>
-<section id="built-in">
-<h1>built-in 作用域<a class="headerlink" href="#built-in" title="Permalink to this heading">¶</a></h1>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>def list_length(a):
-    return len(a)
-a = [1,2,3]
-print list_length(a)
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="mi">3</span>
-</pre></div>
-</div>
-<p>这里函数 <em>len *就是在</em> built-in* 作用域中：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">__builtin__</span>
-<span class="n">__builtin__</span><span class="o">.</span><span class="n">len</span>
-</pre></div>
-</div>
-</section>
-<section id="class">
-<h1>class 中的作用域<a class="headerlink" href="#class" title="Permalink to this heading">¶</a></h1>
-<table class="docutils align-default">
-<thead>
-<tr class="row-odd"><th class="head"><p>Global</p></th>
-<th class="head"><p>MyClass</p></th>
-</tr>
-</thead>
-<tbody>
-<tr class="row-even"><td><p>var = 0</p></td>
-<td></td>
-</tr>
-<tr class="row-odd"><td><p>MyClass</p></td>
-<td><p>var = 1</p></td>
-</tr>
-<tr class="row-even"><td><p>access_class</p></td>
-<td><p>access_class</p></td>
-</tr>
-</tbody>
-</table>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span># global
-var = 0
-class MyClass(object):
-    # class variable
-    var = 1
-    def access_class_c(self):
-            print &#39;class var:&#39;, self.var
-    def write_class_c(self):
-             MyClass.var = 2
-             print &#39;class var:&#39;, self.var
-    def access_global_c(self):
-             print &#39;global var:&#39;, var
-    def write_instance_c(self):
-             self.var = 3
-             print &#39;instance var:&#39;, self.var
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Global</span>         <span class="n">MyClass</span>        <span class="n">obj</span>
-<span class="n">var</span> <span class="o">=</span> <span class="mi">0</span>
-<span class="n">MyClass</span>
-<span class="p">[</span><span class="n">access_class</span><span class="p">]</span>
-<span class="n">obj</span>            <span class="n">var</span> <span class="o">=</span> <span class="mi">1</span>
-<span class="n">access_class</span>
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">obj</span> <span class="o">=</span> <span class="n">MyClass</span><span class="p">()</span>
-</pre></div>
-</div>
-<p>查询 <em>self.var</em> 时，由于 <em>obj</em> 不存在 <em>var</em>，所以跳到 <strong>MyClass</strong> 中：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Global</span>        <span class="n">MyClass</span>        <span class="n">obj</span>
-<span class="n">var</span> <span class="o">=</span> <span class="mi">0</span>
-<span class="n">MyClass</span>
-<span class="p">[</span><span class="n">access_class</span>
-<span class="bp">self</span><span class="p">]</span>
-<span class="n">obj</span>           <span class="n">var</span> <span class="o">=</span> <span class="mi">1</span>
-<span class="n">access_class</span>
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">obj</span><span class="o">.</span><span class="n">access_class_c</span><span class="p">()</span>
-</pre></div>
-</div>
-<p>class var: 1</p>
-<p>查询 <em>var</em> 直接跳到 <em>global</em> 作用域：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Global</span>       <span class="n">MyClass</span> <span class="n">obj</span>
-<span class="n">var</span> <span class="o">=</span> <span class="mi">0</span>
-<span class="n">MyClass</span>
-<span class="p">[</span><span class="n">access_class</span>
-<span class="bp">self</span><span class="p">]</span>
-<span class="n">obj</span>         <span class="n">var</span> <span class="o">=</span> <span class="mi">1</span>
-<span class="n">access_class</span>
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">obj</span><span class="o">.</span><span class="n">access_global_c</span><span class="p">()</span>
-</pre></div>
-</div>
-<p>global var: 0</p>
-<p>修改类中的 <em>MyClass.var</em>：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Global</span>       <span class="n">MyClass</span> <span class="n">obj</span>
-<span class="n">var</span> <span class="o">=</span> <span class="mi">0</span>
-<span class="n">MyClass</span>
-<span class="p">[</span><span class="n">access_class</span>
-<span class="bp">self</span><span class="p">]</span>
-<span class="n">obj</span>        <span class="n">var</span> <span class="o">=</span> <span class="mi">2</span>
-<span class="n">access_class</span>
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">obj</span><span class="o">.</span><span class="n">write_class_c</span><span class="p">()</span>
-<span class="k">class</span> <span class="nc">var</span><span class="p">:</span> <span class="mi">2</span>
-</pre></div>
-</div>
-<p>修改实例中的 <em>var</em> 时，会直接在 <em>obj</em> 域中创建一个：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Global</span>        <span class="n">MyClass</span> <span class="n">obj</span>
-<span class="n">var</span> <span class="o">=</span> <span class="mi">0</span>
-<span class="n">MyClass</span>
-<span class="p">[</span><span class="n">access_class</span>
-<span class="bp">self</span><span class="p">]</span>
-<span class="n">obj</span>   <span class="n">var</span> <span class="o">=</span> <span class="mi">2</span>
-<span class="n">access_class</span>  <span class="n">var</span> <span class="o">=</span> <span class="mi">3</span>
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">obj</span><span class="o">.</span><span class="n">write_instance_c</span><span class="p">()</span>
-</pre></div>
-</div>
-<p>instance var: 3</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">MyClass</span><span class="o">.</span><span class="n">var</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="mi">2</span>
-</pre></div>
-</div>
-<p><em>MyClass</em> 中的 <em>var</em> 并没有改变。</p>
-</section>
-<section id="id3">
-<h1>词法作用域<a class="headerlink" href="#id3" title="Permalink to this heading">¶</a></h1>
-<p>对于嵌套函数：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>def outer():
-    a = 1
-    def inner():
-             print &quot;a =&quot;, a
-         inner()
-outer()
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">a</span> <span class="o">=</span> <span class="mi">1</span>
-</pre></div>
-</div>
-<p>如果里面的函数没有找到变量，那么会向外一层寻找变量，如果再找不到，则到 global 作用域。</p>
-<p>返回的是函数的情况：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>def outer():
-    a = 1
-    def inner():
-            return a
-    return inner
-func = outer()
-print &#39;a (1):&#39;, func()
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">a</span> <span class="p">(</span><span class="mi">1</span><span class="p">):</span> <span class="mi">1</span>
-</pre></div>
-</div>
-<p><em>func()</em> 函数中调用的* a <em>要从它定义的地方开始寻找，而不是在 *func</em> 所在的作用域寻找。</p>
-</section>
-<section id="id4">
-<h1>作者 &amp; 更新时间<a class="headerlink" href="#id4" title="Permalink to this heading">¶</a></h1>
-<p>作者:李金  <a class="reference external" href="mailto:lijinwithyou&#37;&#52;&#48;gmail&#46;com">lijinwithyou<span>&#64;</span>gmail<span>&#46;</span>com</a></p>
-<p>提交: 2017/12/6</p>
-</section>
-<div class="clearer"></div>
-</div>
-</div>
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-<div class="clearer"></div>
-</div>
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-<span class="footer-item2 visible-xs">Copyight 2017, QPython.</span>
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-};
-milib.qeditor = function(str){
-alert('Please share this page to QPython then run it')
-};
-milib.getPurchaseNumber = function(str){
-//alert('getPurchaseNumber:'+str)
-return "10+";
-};
-milib.a8playVideoFromGW= function(url){
-window.open(url)
-};
-milib.qpylibinstall = function(cat,url,target){
-window.open(url)
-};
-milib.gotoIfPay = function(url, packageId, packageUrl){
-window.open(url)
-};
-} else {
-}
-function openPurchase() {
-//获取标识
-var u = window.location.href.split('/')
-var s = u[u.length-1].replace('.html', '')
-s = u[u.length-2]+'-'+s
-if (milib.openPurchaseActivity){
-milib.openPurchaseActivity(s)
-}else {
-if (navigator.language== "zh-CN" || navigator.language== "zh"){
-$('.zh-img ').show();
-$('.en-img').hide();
-}else {
-$('.zh-img ').hide();
-$('.en-img').show();
-}
-$('#purchase-box').show();
-}
-}
-$(function() {
-$('#purchase-box').click(function() {
-$(this).hide();
-})
-$('.purchase-img').click(function(e) {
-e.stopPropagation();
-})
-})
-//按钮事件
-function btnEvent(){
-var seq = this.getAttribute('target')-2
-var a = this.parentElement.getElementsByTagName('pre')
-if ( a && a.length > 0){
-/**********有代码，调用控制台*************/
-var code = a[seq].lastChild.nodeValue
-milib.qeditor(code)
-}else{
-//没有代码，调转
-var url = this.getAttribute('data-url');
-window.open(url);
-}
-}
-//获取赞赏
-function setPraise(){
-var u = window.location.href.split('/')
-var s = u[u.length-1].replace('.html', '')
-s = u[u.length-2]+'-'+s
-var i = document.getElementById('praise').getElementsByTagName('span')[0]
-var num = milib.getPurchaseNumber(s);
-i.innerHTML = num
-}
-//初始化代码显示
-function initCode(){
-var prearr = document.getElementsByTagName('pre');
-for (var i = 0; i < prearr.length; i++) {
-var temp_data = prearr[i].lastChild.nodeValue
-//正则处理 lavel5
-temp_data = temp_data.replace(/\^2\$/g, spaceString(2));
-temp_data = temp_data.replace(/\^4\$/g, spaceString(4));
-temp_data = temp_data.replace(/\^6\$/g, spaceString(6));
-temp_data = temp_data.replace(/\^8\$/g, spaceString(8));
-temp_data = temp_data.replace(/\^10\$/g, spaceString(10));
-temp_data = temp_data.replace(/\^12\$/g, spaceString(12));
-temp_data = temp_data.replace(/\^16\$/g, spaceString(16));
-temp_data = temp_data.replace(/\^20\$/g, spaceString(20));
-//set
-prearr[i].lastChild.nodeValue = temp_data;
-}
-}
-//产生空格
-function spaceString(num){
-var v = ''
-for (var i = 0; i < num; i++) {
-v += ' ';
-}
-return v;
-}
-function setSearchHistory (t) {
-if (!t)
-return;
-if (localStorage.course_search_list) {
-var list = localStorage.course_search_list.split('%,%');
-var i = list.indexOf(t);
-if (i !== -1) {
-list.splice(i, 1);
-list.push(t);
-localStorage.course_search_list = list.join('%,%');
-}else {
-localStorage.course_search_list += '%,%'+t;
-}
-}else {
-localStorage.course_search_list = t;
-}
-}
-window.onload = function(){
-setPraise();
-initCode();
-//将所有的button绑定事件
-var b = document.getElementsByTagName('button');
-for (var i = 0; i < b.length; i++) {
-if (b[i].className=='button') {
-b[i].setAttribute('target', i)
-b[i].onclick = btnEvent ;
-}
-}
-}
-$(function(){
-var url = location.href.split('?');
-var arg = url[1];
-if (!arg){
-return;
-}
-var args = arg.split('&');
-for (var i = 0; i < args.length; i++) {
-var c = args[i].split('=');
-if (c[0]=='form' && c[1] == 'web') {
-$('.web-show').css('display','block');
-$('.web-content').addClass('margin-header');
-}
-}
-})
-$(function() {
-$('form').submit(function() {
-setSearchHistory($("input[name='q']").val());
-})
-$('.bodywrapper .body form input').val("");
-$($('.bodywrapper .body form input')[0]).attr('placeholder', 'search');
-})
-</script>
-<script>
-(function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
-(i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
-m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
-})(window,document,'script','https://www.google-analytics.com/analytics.js','ga');
-ga('create', 'UA-103260045-3', 'auto');
-ga('send', 'pageview');
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diff --git a/docs/dive-into-python/cn/builder.html b/docs/dive-into-python/cn/builder.html
deleted file mode 100644
index d711312..0000000
--- a/docs/dive-into-python/cn/builder.html
+++ /dev/null
@@ -1,467 +0,0 @@
-<!DOCTYPE html>
-<html lang="en-US">
-<head>
-<meta charset="utf-8" />
-<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
-<link href="https://cdn.bootcss.com/bootstrap/3.3.7/css/bootstrap.min.css" rel="stylesheet">
-<title>生成器 &amp; 迭代器</title>
-<link rel="stylesheet" type="text/css" href="../../static/pygments.css" />
-<link rel="stylesheet" type="text/css" href="../../static/style.css" />
-<meta charset="UTF-8">
-<meta name="viewport" content="width=device-width, initial-scale=1,maximum-scale=1, user-scalable=no">
-<script src="https://cdn.bootcss.com/bootstrap/3.3.7/js/bootstrap.min.js"></script>
-</head><body>
-<div class="header doc-header web-show">
-<div class="col-sm-offset-2 col-sm-8  index-header">
-<div class="navbar-header">
-<button type="button" class="navbar-toggle" data-toggle="collapse" data-target="#example-navbar-collapse">
-<span class="sr-only">切换导航</span>
-<span class="icon-bar"></span>
-<span class="icon-bar"></span>
-<span class="icon-bar"></span>
-</button>
-<a href="/index.html" class="logo"><img src="http://www.qpython.org/static/img_logo.png"></a>
-</div>
-<ul class="header-title collapse navbar-collapse" id="example-navbar-collapse" >
-<li>
-<a href="http://www.qpython.org/index.html">Home</a>
-</li>
-<li>
-<a href="http://www.qpython.org/document.html">Guide</a>
-</li>
-<li class="header-phone-selected">
-<a href="/index.html" class="header-selected">Course</a>
-<span class="header-title-selected"></span>
-</li>
-<li>
-<a href="http://www.aipy.org">AIPY</a>
-</li>
-<li>
-<a target="_blank" href="https://github.com/qpython-android/qpython">Github</a>
-</li>
-</ul>
-<div class="search-box hidden-xs">
-<form action="/search.html">
-<input type="search" name="q" placeholder="keyword">
-<input type="hidden" name="check_keywords" value="yes" />
-<input type="hidden" name="area" value="default" />
-<button><img src="http://www.qpython.org/static/ic_search.png"></button>
-</form>
-</div>
-</div>
-</div>
-<main class="contain web-content">
-<div class="document">
-<div class="documentwrapper">
-<div class="bodywrapper">
-<div class="body" role="main">
-<section id="id1">
-<h1>生成器 &amp;  迭代器<a class="headerlink" href="#id1" title="Permalink to this heading">¶</a></h1>
-<p><em>while</em> 循环通常有这样的形式：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>&lt;do setup&gt;
-result = []
-while True:
-    &lt;generate value&gt;
-    result.append(value)
-    if &lt;done&gt;:
-        break
-</pre></div>
-</div>
-<p>使用迭代器实现这样的循环：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>class GenericIterator(object):
-    def __init__(self, ...):
-    &lt;do setup&gt;
-    # 需要额外储存状态
-    &lt;store state&gt;
-    def next(self):
-    &lt;load state&gt;
-    &lt;generate value&gt;
-    if &lt;done&gt;:
-        raise StopIteration()
-    &lt;store state&gt;
-    return value
-</pre></div>
-</div>
-<p>更简单的，可以使用生成器：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>def generator(...):
-    &lt;do setup&gt;
-    while True:
-        &lt;generate value&gt;
-        # yield 说明这个函数可以返回多个值！
-        yield value
-        if &lt;done&gt;:
-            break
-</pre></div>
-</div>
-<p>生成器使用 <em>yield</em> 关键字将值输出，而迭代器则通过 <em>next</em> 的 <em>return</em> 将值返回；与迭代器不同的是，生成器会自动记录当前的状态，而迭代器则需要进行额外的操作来记录当前的状态。</p>
-<p>对于之前的 <em>collatz</em> 猜想，简单循环的实现如下：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>def collatz(n):
-    sequence = []
-    while n != 1:
-        if n % 2 == 0:
-            n /= 2
-        else:
-            n = 3*n + 1
-        sequence.append(n)
-    return sequence
-for x in collatz(7):
-    print x,
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>运行的结果会是：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="mi">22</span> <span class="mi">11</span> <span class="mi">34</span> <span class="mi">17</span> <span class="mi">52</span> <span class="mi">26</span> <span class="mi">13</span> <span class="mi">40</span> <span class="mi">20</span> <span class="mi">10</span> <span class="mi">5</span> <span class="mi">16</span> <span class="mi">8</span> <span class="mi">4</span> <span class="mi">2</span> <span class="mi">1</span>
-</pre></div>
-</div>
-<p>迭代器的版本如下：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>class Collatz(object):
-    def __init__(self, start):
-        self.value = start
-    def __iter__(self):
-        return self
-    def next(self):
-        if self.value == 1:
-            raise StopIteration()
-        elif self.value % 2 == 0:
-            self.value = self.value/2
-        else:
-            self.value = 3*self.value + 1
-        return self.value
-for x in Collatz(7):
-    print x,
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>生成器的版本如下：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>def collatz(n):
-    while n != 1:
-        if n % 2 == 0:
-            n /= 2
-        else:
-            n = 3*n + 1
-        yield n
-for x in collatz(7):
-    print x,
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>事实上，生成器也是一种迭代器：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">collatz</span><span class="p">(</span><span class="mi">7</span><span class="p">)</span>
-<span class="nb">print</span> <span class="n">x</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>输出如下</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="o">&lt;</span><span class="n">generator</span> <span class="nb">object</span> <span class="n">collatz</span> <span class="n">at</span> <span class="mh">0x0000000003B63750</span><span class="o">&gt;</span>
-</pre></div>
-</div>
-<p>它支持 <em>next</em> 方法，返回下一个 <em>yield</em> 的值：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">collatz</span><span class="p">(</span><span class="mi">7</span><span class="p">)</span>
-<span class="nb">print</span> <span class="n">x</span><span class="o">.</span><span class="n">next</span><span class="p">()</span>
-<span class="nb">print</span> <span class="n">x</span><span class="o">.</span><span class="n">next</span><span class="p">()</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p><em>__iter__</em> 方法返回的是它本身：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">collatz</span><span class="p">(</span><span class="mi">7</span><span class="p">)</span>
-<span class="nb">print</span> <span class="n">x</span><span class="o">.</span><span class="fm">__iter__</span><span class="p">()</span>
-</pre></div>
-</div>
-<p>输出结果：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="o">&lt;</span><span class="n">generator</span> <span class="nb">object</span> <span class="n">collatz</span> <span class="n">at</span> <span class="mh">0x0000000003B63750</span><span class="o">&gt;</span>
-</pre></div>
-</div>
-<p>之前的二叉树迭代器可以改写为更简单的生成器模式来进行中序遍历：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>class BinaryTree(object):
-    def __init__(self, value, left=None, right=None):
-        self.value = value
-        self.left = left
-        self.right = right
-    def __iter__(self):
-        # 将迭代器设为生成器方法
-        return self.inorder()
-    def inorder(self):
-    # traverse the left branch
-    if self.left is not None:
-        for value in self.left:
-            yield value
-    # yield node&#39;s value
-    yield self.value
-    # traverse the right branch
-    if self.right is not None:
-        for value in self.right:
-            yield value
-</pre></div>
-</div>
-<p>非递归的实现：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>def inorder(self):
-    node = self
-    stack = []
-    while len(stack) &gt; 0 or node is not None:
-        while node is not None:
-            stack.append(node)
-            node = node.left
-        node = stack.pop()
-        yield node.value
-        node = node.right
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>class BinaryTree(object):
-    def __init__(self, value, left=None, right=None):
-        self.value = value
-        self.left = left
-        self.right = right
-    def __iter__(self):
-        # 将迭代器设为生成器方法
-        return self.inorder()
-    def inorder(self):
-    # traverse the left branch
-    if self.left is not None:
-        for value in self.left:
-            yield value
-    # yield node&#39;s value
-    yield self.value
-    # traverse the right branch
-    if self.right is not None:
-        for value in self.right:
-            yield value
-tree = BinaryTree(
-    left=BinaryTree(
-        left=BinaryTree(1),
-        value=2,
-        right=BinaryTree(
-            left=BinaryTree(3),
-            value=4,
-            right=BinaryTree(5)
-        ),
-    ),
-    value=6,
-    right=BinaryTree(
-        value=7,
-        right=BinaryTree(8)
-    )
-)
-for value in tree:
-    print value,
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>正确应输出：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="mi">1</span> <span class="mi">2</span> <span class="mi">3</span> <span class="mi">4</span> <span class="mi">5</span> <span class="mi">6</span> <span class="mi">7</span> <span class="mi">8</span>
-</pre></div>
-</div>
-<section id="id2">
-<h2>作者 &amp; 更新时间<a class="headerlink" href="#id2" title="Permalink to this heading">¶</a></h2>
-<p>作者:李金  <a class="reference external" href="mailto:lijinwithyou&#37;&#52;&#48;gmail&#46;com">lijinwithyou<span>&#64;</span>gmail<span>&#46;</span>com</a></p>
-<p>提交: 2017/12/6</p>
-</section>
-</section>
-<div class="clearer"></div>
-</div>
-</div>
-</div>
-<div class="clearer"></div>
-</div>
-</main>
-<div class="star-footer">
-<img onclick="openPurchase()" src="../../static/star.jpg">
-<div id="praise"><span>0</span> persons have rewarded</div>
-</div>
-<div class="foo"></div>
-<footer class="clearfix fixfooter web-show">
-<div class="col-sm-offset-2 col-sm-6 footer-div1">
-<div class="footer-block-item">
-<span class="footer-item1">Built with Sphinx using a theme provid by QPython.</span>
-<span class="footer-item2 visible-xs">Copyight 2017, QPython.</span>
-</div>
-</div>
-<div class="col-sm-2 footer-div2">
-<a href="/index.html" class="pull-right"><img src="http://www.qpython.org/static/img_logo.png"></a>
-</div>
-</footer>
-<div id="purchase-box">
-<div class="zh-img purchase-img"></div>
-<div class="en-img purchase-img">
-<form action="https://www.paypal.com/cgi-bin/webscr" method="post" target="_top">
-<input type="hidden" name="cmd" value="_s-xclick">
-<input type="hidden" name="hosted_button_id" value="ST6KWAK8R63JW">
-<div class="en-thanks-tit">
-<input type="hidden" name="on0" value="Thanks">
-Thanks
-</div>
-<div class="en-thanks-sel">
-<select name="os0">
-<option value="Reward this course">Reward this course $ 0.99 USD</option>
-<option value="Reward this course">Reward this course $ 1.49 USD</option>
-<option value="Reward this course">Reward this course $ 1.99 USD</option>
-<option value="Reward this course">Reward this course $ 2.99 USD</option>
-<option value="Reward this course">Reward this course $ 3.49 USD</option>
-</select>
-</div>
-<input type="hidden" name="currency_code" value="USD">
-<input type="image" class="pay_img" src="https://www.paypalobjects.com/en_GB/i/btn/btn_paynow_LG.gif" border="0" name="submit" alt="PayPal – The safer, easier way to pay online!">
-<img alt="" border="0" src="https://www.paypalobjects.com/zh_XC/i/scr/pixel.gif" width="1" height="1">
-</form>
-</div>
-</div>
-<script>
-var milib;
-if (milib==undefined) {
-var milib = function(){
-};
-milib.qeditor = function(str){
-alert('Please share this page to QPython then run it')
-};
-milib.getPurchaseNumber = function(str){
-//alert('getPurchaseNumber:'+str)
-return "10+";
-};
-milib.a8playVideoFromGW= function(url){
-window.open(url)
-};
-milib.qpylibinstall = function(cat,url,target){
-window.open(url)
-};
-milib.gotoIfPay = function(url, packageId, packageUrl){
-window.open(url)
-};
-} else {
-}
-function openPurchase() {
-//获取标识
-var u = window.location.href.split('/')
-var s = u[u.length-1].replace('.html', '')
-s = u[u.length-2]+'-'+s
-if (milib.openPurchaseActivity){
-milib.openPurchaseActivity(s)
-}else {
-if (navigator.language== "zh-CN" || navigator.language== "zh"){
-$('.zh-img ').show();
-$('.en-img').hide();
-}else {
-$('.zh-img ').hide();
-$('.en-img').show();
-}
-$('#purchase-box').show();
-}
-}
-$(function() {
-$('#purchase-box').click(function() {
-$(this).hide();
-})
-$('.purchase-img').click(function(e) {
-e.stopPropagation();
-})
-})
-//按钮事件
-function btnEvent(){
-var seq = this.getAttribute('target')-2
-var a = this.parentElement.getElementsByTagName('pre')
-if ( a && a.length > 0){
-/**********有代码，调用控制台*************/
-var code = a[seq].lastChild.nodeValue
-milib.qeditor(code)
-}else{
-//没有代码，调转
-var url = this.getAttribute('data-url');
-window.open(url);
-}
-}
-//获取赞赏
-function setPraise(){
-var u = window.location.href.split('/')
-var s = u[u.length-1].replace('.html', '')
-s = u[u.length-2]+'-'+s
-var i = document.getElementById('praise').getElementsByTagName('span')[0]
-var num = milib.getPurchaseNumber(s);
-i.innerHTML = num
-}
-//初始化代码显示
-function initCode(){
-var prearr = document.getElementsByTagName('pre');
-for (var i = 0; i < prearr.length; i++) {
-var temp_data = prearr[i].lastChild.nodeValue
-//正则处理 lavel5
-temp_data = temp_data.replace(/\^2\$/g, spaceString(2));
-temp_data = temp_data.replace(/\^4\$/g, spaceString(4));
-temp_data = temp_data.replace(/\^6\$/g, spaceString(6));
-temp_data = temp_data.replace(/\^8\$/g, spaceString(8));
-temp_data = temp_data.replace(/\^10\$/g, spaceString(10));
-temp_data = temp_data.replace(/\^12\$/g, spaceString(12));
-temp_data = temp_data.replace(/\^16\$/g, spaceString(16));
-temp_data = temp_data.replace(/\^20\$/g, spaceString(20));
-//set
-prearr[i].lastChild.nodeValue = temp_data;
-}
-}
-//产生空格
-function spaceString(num){
-var v = ''
-for (var i = 0; i < num; i++) {
-v += ' ';
-}
-return v;
-}
-function setSearchHistory (t) {
-if (!t)
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-<p>标准库中有自带的 csv (逗号分隔值) 模块处理 csv 格式的文件：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">csv</span>
-<span class="nb">dir</span><span class="p">(</span><span class="n">csv</span><span class="p">)</span>
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-<h2>读 csv 文件<a class="headerlink" href="#csv" title="Permalink to this heading">¶</a></h2>
-<p>假设我们有这样的一个文件：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1">#file data.csv, 内容如下</span>
-<span class="s2">&quot;alpha 1&quot;</span><span class="p">,</span>  <span class="mi">100</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.443</span>
-<span class="s2">&quot;beat  3&quot;</span><span class="p">,</span>   <span class="mi">12</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.0934</span>
-<span class="s2">&quot;gamma 3a&quot;</span><span class="p">,</span> <span class="mi">192</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.6621</span>
-<span class="s2">&quot;delta 2a&quot;</span><span class="p">,</span>  <span class="mi">15</span><span class="p">,</span> <span class="o">-</span><span class="mf">4.515</span>
-</pre></div>
-</div>
-<p>打开这个文件，并产生一个文件 reader：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">fp</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="s2">&quot;data.csv&quot;</span><span class="p">)</span>
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-</pre></div>
-</div>
-<p>可以按行迭代数据：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>fp = open(&quot;data.csv&quot;)
-r = csv.reader(fp)
-for row in r:
-    print row
-fp.close()
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1">#如运行正确，内容将会如下</span>
-<span class="p">[</span><span class="s1">&#39;alpha 1&#39;</span><span class="p">,</span> <span class="s1">&#39;  100&#39;</span><span class="p">,</span> <span class="s1">&#39; -1.443&#39;</span><span class="p">]</span>
-<span class="p">[</span><span class="s1">&#39;beat  3&#39;</span><span class="p">,</span> <span class="s1">&#39;   12&#39;</span><span class="p">,</span> <span class="s1">&#39; -0.0934&#39;</span><span class="p">]</span>
-<span class="p">[</span><span class="s1">&#39;gamma 3a&#39;</span><span class="p">,</span> <span class="s1">&#39; 192&#39;</span><span class="p">,</span> <span class="s1">&#39; -0.6621&#39;</span><span class="p">]</span>
-<span class="p">[</span><span class="s1">&#39;delta 2a&#39;</span><span class="p">,</span> <span class="s1">&#39;  15&#39;</span><span class="p">,</span> <span class="s1">&#39; -4.515&#39;</span><span class="p">]</span>
-</pre></div>
-</div>
-<p>默认数据内容都被当作字符串处理，不过可以自己进行处理：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>data = []
-with open(&#39;data.csv&#39;) as fp:
-    r = csv.reader(fp)
-    for row in r:
-        data.append([row[0], int(row[1]), float(row[2])])
-print(data)
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-<button class="button">Run …</button>
-</section>
-<section id="id1">
-<h2>写 csv 文件<a class="headerlink" href="#id1" title="Permalink to this heading">¶</a></h2>
-<p>可以使用 <em>csv.writer</em> 写入文件，不过相应地，传入的应该是以写方式打开的文件，不过一般要用 <em>‘wb’</em> 即二进制写入方式，防止出现换行不正确的问题：
-In [7]:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>data = [(&#39;one&#39;, 1, 1.5), (&#39;two&#39;, 2, 8.0)]
-with open(&#39;out.csv&#39;, &#39;wb&#39;) as fp:
-    w = csv.writer(fp)
-    w.writerows(data)
-</pre></div>
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-<button class="button">Run …</button>
-<p>如果运行正确，结果将会如下：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">one</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mf">1.5</span>
-<span class="n">two</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mf">8.0</span>
-</pre></div>
-</div>
-</section>
-<section id="id2">
-<h2>更换分隔符<a class="headerlink" href="#id2" title="Permalink to this heading">¶</a></h2>
-<p>默认情况下，<em>csv</em> 模块默认 <em>csv</em> 文件都是由 <em>excel</em> 产生的，实际中可能会遇到这样的问题：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>data = [(&#39;one, \&quot;real\&quot; string&#39;, 1, 1.5), (&#39;two&#39;, 2, 8.0)]
-with open(&#39;out.csv&#39;, &#39;wb&#39;) as fp:
-    w = csv.writer(fp)
-    w.writerows(data)
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>可以修改分隔符来处理这组数据：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>data = [(&#39;one, \&quot;real\&quot; string&#39;, 1, 1.5), (&#39;two&#39;, 2, 8.0)]
-with open(&#39;out.psv&#39;, &#39;wb&#39;) as fp:
-    w = csv.writer(fp, delimiter=&quot;|&quot;)
-    w.writerows(data)
-</pre></div>
-</div>
-<button class="button">Run …</button>
-</section>
-<section id="id3">
-<h2>其他选项<a class="headerlink" href="#id3" title="Permalink to this heading">¶</a></h2>
-<p><em>numpy.loadtxt()</em> 和 <em>pandas.read_csv()</em> 可以用来读写包含很多数值数据的*csv*文件：</p>
-<dl>
-<dt>::</dt><dd><p>#有文件 trades.csv,内容如下</p>
-<p>Order,Date,Stock,Quantity,Price
-A0001,2013-12-01,AAPL,1000,203.4
-A0002,2013-12-01,MSFT,1500,167.5
-A0003,2013-12-02,GOOG,1500,167.5</p>
-</dd>
-</dl>
-<p>使用 pandas 进行处理，生成一个 DataFrame 对象：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span>
-<span class="n">df</span> <span class="o">=</span> <span class="n">pandas</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">&#39;trades.csv&#39;</span><span class="p">,</span> <span class="n">index_col</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-<span class="nb">print</span> <span class="n">df</span>
-</pre></div>
-</div>
-<dl>
-<dt>::</dt><dd><blockquote>
-<div><p>Date Stock  Quantity  Price</p>
-</div></blockquote>
-<p>Order
-A0001  2013-12-01  AAPL      1000  203.4
-A0002  2013-12-01  MSFT      1500  167.5
-A0003  2013-12-02  GOOG      1500  167.5</p>
-</dd>
-</dl>
-<p>通过名字进行索引：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;Quantity&#39;</span><span class="p">]</span> <span class="o">*</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;Price&#39;</span><span class="p">]</span>
-</pre></div>
-</div>
-<p>将会输出如下内容：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Order</span>
-<span class="n">A0001</span>    <span class="mi">203400</span>
-<span class="n">A0002</span>    <span class="mi">251250</span>
-<span class="n">A0003</span>    <span class="mi">251250</span>
-<span class="n">dtype</span><span class="p">:</span> <span class="n">float64</span>
-</pre></div>
-</div>
-</section>
-<section id="id4">
-<h2>作者 &amp; 更新时间<a class="headerlink" href="#id4" title="Permalink to this heading">¶</a></h2>
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-<section id="datetime">
-<h1>datetime 模块<a class="headerlink" href="#datetime" title="Permalink to this heading">¶</a></h1>
-<p>datetime 提供了基础时间和日期的处理。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">datetime</span> <span class="k">as</span> <span class="nn">dt</span>
-<span class="nb">dir</span><span class="p">(</span><span class="n">dt</span><span class="p">)</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<section id="date">
-<h2>date 对象<a class="headerlink" href="#date" title="Permalink to this heading">¶</a></h2>
-<p>可以使用 date(year, month, day) 产生一个 date 对象：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">datetime</span> <span class="k">as</span> <span class="nn">dt</span>
-<span class="n">d1</span> <span class="o">=</span> <span class="n">dt</span><span class="o">.</span><span class="n">date</span><span class="p">(</span><span class="mi">2007</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">25</span><span class="p">)</span>
-<span class="n">d2</span> <span class="o">=</span> <span class="n">dt</span><span class="o">.</span><span class="n">date</span><span class="p">(</span><span class="mi">2008</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">25</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>可以格式化 date 对象的输出：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">datetime</span> <span class="k">as</span> <span class="nn">dt</span>
-<span class="n">d1</span> <span class="o">=</span> <span class="n">dt</span><span class="o">.</span><span class="n">date</span><span class="p">(</span><span class="mi">2007</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">25</span><span class="p">)</span>
-<span class="n">d2</span> <span class="o">=</span> <span class="n">dt</span><span class="o">.</span><span class="n">date</span><span class="p">(</span><span class="mi">2008</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">25</span><span class="p">)</span>
-<span class="nb">print</span> <span class="n">d1</span>
-<span class="nb">print</span> <span class="n">d1</span><span class="o">.</span><span class="n">strftime</span><span class="p">(</span><span class="s1">&#39;%A, %m/</span><span class="si">%d</span><span class="s1">/%y&#39;</span><span class="p">)</span>
-<span class="nb">print</span> <span class="n">d1</span><span class="o">.</span><span class="n">strftime</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">%a</span><span class="s1">, %m-</span><span class="si">%d</span><span class="s1">-%Y&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>可以看两个日期相差多久：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">datetime</span> <span class="k">as</span> <span class="nn">dt</span>
-<span class="n">d1</span> <span class="o">=</span> <span class="n">dt</span><span class="o">.</span><span class="n">date</span><span class="p">(</span><span class="mi">2007</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">25</span><span class="p">)</span>
-<span class="n">d2</span> <span class="o">=</span> <span class="n">dt</span><span class="o">.</span><span class="n">date</span><span class="p">(</span><span class="mi">2008</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">25</span><span class="p">)</span>
-<span class="nb">print</span> <span class="n">d2</span> <span class="o">-</span> <span class="n">d1</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>返回的是一个 timedelta 对象：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">datetime</span> <span class="k">as</span> <span class="nn">dt</span>
-<span class="n">d1</span> <span class="o">=</span> <span class="n">dt</span><span class="o">.</span><span class="n">date</span><span class="p">(</span><span class="mi">2007</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">25</span><span class="p">)</span>
-<span class="n">d2</span> <span class="o">=</span> <span class="n">dt</span><span class="o">.</span><span class="n">date</span><span class="p">(</span><span class="mi">2008</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">25</span><span class="p">)</span>
-<span class="n">d</span> <span class="o">=</span> <span class="n">d2</span> <span class="o">-</span> <span class="n">d1</span>
-<span class="nb">print</span> <span class="n">d</span><span class="o">.</span><span class="n">days</span>
-<span class="nb">print</span> <span class="n">d</span><span class="o">.</span><span class="n">seconds</span>
-</pre></div>
-</div>
-<p>查看今天的日期：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">datetime</span> <span class="k">as</span> <span class="nn">dt</span>
-<span class="nb">print</span> <span class="n">dt</span><span class="o">.</span><span class="n">date</span><span class="o">.</span><span class="n">today</span><span class="p">()</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-</section>
-<section id="time">
-<h2>time 对象<a class="headerlink" href="#time" title="Permalink to this heading">¶</a></h2>
-<p>可以使用 time(hour, min, sec, us) 产生一个 time 对象：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">datetime</span> <span class="k">as</span> <span class="nn">dt</span>
-<span class="n">t1</span> <span class="o">=</span> <span class="n">dt</span><span class="o">.</span><span class="n">time</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">38</span><span class="p">)</span>
-<span class="n">t2</span> <span class="o">=</span> <span class="n">dt</span><span class="o">.</span><span class="n">time</span><span class="p">(</span><span class="mi">18</span><span class="p">)</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>改变显示格式：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">datetime</span> <span class="k">as</span> <span class="nn">dt</span>
-<span class="n">t1</span> <span class="o">=</span> <span class="n">dt</span><span class="o">.</span><span class="n">time</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">38</span><span class="p">)</span>
-<span class="nb">print</span> <span class="n">t1</span>
-<span class="nb">print</span> <span class="n">t1</span><span class="o">.</span><span class="n">strftime</span><span class="p">(</span><span class="s1">&#39;%I:%M, %p&#39;</span><span class="p">)</span>
-<span class="nb">print</span> <span class="n">t1</span><span class="o">.</span><span class="n">strftime</span><span class="p">(</span><span class="s1">&#39;%H:%M:%S, %p&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>因为没有具体的日期信息，所以 time 对象不支持减法操作。</p>
-</section>
-<section id="id1">
-<h2>datetime 对象<a class="headerlink" href="#id1" title="Permalink to this heading">¶</a></h2>
-<p>可以使用 datetime(year, month, day, hr, min, sec, us) 来创建一个 datetime 对象。</p>
-<p>获得当前时间：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">datetime</span> <span class="k">as</span> <span class="nn">dt</span>
-<span class="n">d1</span> <span class="o">=</span> <span class="n">dt</span><span class="o">.</span><span class="n">datetime</span><span class="o">.</span><span class="n">now</span><span class="p">()</span>
-<span class="nb">print</span> <span class="n">d1</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>给当前的时间加上 30 天，timedelta 的参数是 timedelta(day, hr, min, sec, us)：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">datetime</span> <span class="k">as</span> <span class="nn">dt</span>
-<span class="n">d1</span> <span class="o">=</span> <span class="n">dt</span><span class="o">.</span><span class="n">datetime</span><span class="o">.</span><span class="n">now</span><span class="p">()</span>
-<span class="n">d2</span> <span class="o">=</span> <span class="n">d1</span> <span class="o">+</span> <span class="n">dt</span><span class="o">.</span><span class="n">timedelta</span><span class="p">(</span><span class="mi">30</span><span class="p">)</span>
-<span class="nb">print</span> <span class="n">d2</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>除此之外，我们还可以通过一些指定格式的字符串来创建 datetime 对象：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">datetime</span> <span class="k">as</span> <span class="nn">dt</span>
-<span class="nb">print</span> <span class="n">dt</span><span class="o">.</span><span class="n">datetime</span><span class="o">.</span><span class="n">strptime</span><span class="p">(</span><span class="s1">&#39;2/10/01&#39;</span><span class="p">,</span> <span class="s1">&#39;%m/</span><span class="si">%d</span><span class="s1">/%y&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<table class="docutils align-default">
-<thead>
-<tr class="row-odd"><th class="head"><p>子表达式</p></th>
-<th class="head"><p>匹配内容</p></th>
-</tr>
-</thead>
-<tbody>
-<tr class="row-even"><td><p>%a</p></td>
-<td><p>星期英文缩写</p></td>
-</tr>
-<tr class="row-odd"><td><p>%A</p></td>
-<td><p>星期英文</p></td>
-</tr>
-<tr class="row-even"><td><p>%w</p></td>
-<td><p>一星期的第几天，[0(sun),6]</p></td>
-</tr>
-<tr class="row-odd"><td><p>%b</p></td>
-<td><p>月份英文缩写</p></td>
-</tr>
-<tr class="row-even"><td><p>%B</p></td>
-<td><p>月份英文</p></td>
-</tr>
-<tr class="row-odd"><td><p>%d</p></td>
-<td><p>日期，[01,31]</p></td>
-</tr>
-<tr class="row-even"><td><p>%H</p></td>
-<td><p>小时，[00,23]</p></td>
-</tr>
-<tr class="row-odd"><td><p>%I</p></td>
-<td><p>小时，[01,12]</p></td>
-</tr>
-<tr class="row-even"><td><p>%j</p></td>
-<td><p>一年的第几天，[001,366]</p></td>
-</tr>
-<tr class="row-odd"><td><p>%m</p></td>
-<td><p>月份，[01,12]</p></td>
-</tr>
-<tr class="row-even"><td><p>%M</p></td>
-<td><p>分钟，[00,59]</p></td>
-</tr>
-<tr class="row-odd"><td><p>%p</p></td>
-<td><p>AM 和 PM</p></td>
-</tr>
-<tr class="row-even"><td><p>%S</p></td>
-<td><p>秒钟，[00,61] （大概是有闰秒的存在）</p></td>
-</tr>
-<tr class="row-odd"><td><p>%U</p></td>
-<td><p>一年中的第几个星期，星期日为第一天，[00,53]</p></td>
-</tr>
-<tr class="row-even"><td><p>%W</p></td>
-<td><p>一年中的第几个星期，星期一为第一天，[00,53]</p></td>
-</tr>
-<tr class="row-odd"><td><p>%y</p></td>
-<td><p>没有世纪的年份</p></td>
-</tr>
-<tr class="row-even"><td><p>%Y</p></td>
-<td><p>完整的年份</p></td>
-</tr>
-</tbody>
-</table>
-</section>
-<section id="id2">
-<h2>作者 &amp; 更新时间<a class="headerlink" href="#id2" title="Permalink to this heading">¶</a></h2>
-<p>作者:<cite>李金  &lt;lijinwithyou&#64;gmail.com&gt;</cite></p>
-<p>提交: 2017/12/6</p>
-</section>
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-<meta charset="UTF-8">
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-<script src="https://cdn.bootcss.com/bootstrap/3.3.7/js/bootstrap.min.js"></script>
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-<a href="/index.html" class="logo"><img src="http://www.qpython.org/static/img_logo.png"></a>
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-<ul class="header-title collapse navbar-collapse" id="example-navbar-collapse" >
-<li>
-<a href="http://www.qpython.org/index.html">Home</a>
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-<span class="header-title-selected"></span>
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-<a href="http://www.aipy.org">AIPY</a>
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-<a target="_blank" href="https://github.com/qpython-android/qpython">Github</a>
-</li>
-</ul>
-<div class="search-box hidden-xs">
-<form action="/search.html">
-<input type="search" name="q" placeholder="keyword">
-<input type="hidden" name="check_keywords" value="yes" />
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-<button><img src="http://www.qpython.org/static/ic_search.png"></button>
-</form>
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-</div>
-<main class="contain web-content">
-<div class="document">
-<div class="documentwrapper">
-<div class="bodywrapper">
-<div class="body" role="main">
-<section id="id1">
-<h1>动态编译<a class="headerlink" href="#id1" title="Permalink to this heading">¶</a></h1>
-<section id="id2">
-<h2>标准编程语言<a class="headerlink" href="#id2" title="Permalink to this heading">¶</a></h2>
-<p>对于 <strong>C</strong> 语言，代码一般要先编译，再执行。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="o">.</span><span class="n">c</span> <span class="o">-&gt;</span> <span class="o">.</span><span class="n">exe</span>
-</pre></div>
-</div>
-</section>
-<section id="id3">
-<h2>解释器语言<a class="headerlink" href="#id3" title="Permalink to this heading">¶</a></h2>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">shell</span> <span class="n">脚本</span>
-<span class="o">.</span><span class="n">sh</span> <span class="o">-&gt;</span> <span class="n">interpreter</span>
-</pre></div>
-</div>
-</section>
-<section id="byte-code">
-<h2>Byte Code 编译<a class="headerlink" href="#byte-code" title="Permalink to this heading">¶</a></h2>
-<p><strong>Python</strong>, <strong>Java</strong> 等语言先将代码编译为 byte code（不是机器码），然后再处理：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="o">.</span><span class="n">py</span> <span class="o">-&gt;</span> <span class="o">.</span><span class="n">pyc</span> <span class="o">-&gt;</span> <span class="n">interpreter</span>
-</pre></div>
-</div>
-</section>
-<section id="eval">
-<h2>eval 函数<a class="headerlink" href="#eval" title="Permalink to this heading">¶</a></h2>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">eval</span><span class="p">(</span><span class="n">statement</span><span class="p">,</span> <span class="n">glob</span><span class="p">,</span> <span class="n">local</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>使用 <em>eval</em> 函数动态执行代码，返回执行的值：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">a</span> <span class="o">=</span> <span class="mi">1</span>
-<span class="nb">eval</span><span class="p">(</span><span class="s2">&quot;a+1&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="mi">2</span>
-</pre></div>
-</div>
-<p>可以接收明明空间参数：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">local</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">a</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
-<span class="n">glob</span> <span class="o">=</span> <span class="p">{}</span>
-<span class="nb">eval</span><span class="p">(</span><span class="s2">&quot;a+1&quot;</span><span class="p">,</span> <span class="n">glob</span><span class="p">,</span> <span class="n">local</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="mi">3</span>
-</pre></div>
-</div>
-<p>这里 <em>local</em> 中的 <em>a</em> 先被找到。</p>
-</section>
-<section id="exec">
-<h2>exec 函数<a class="headerlink" href="#exec" title="Permalink to this heading">¶</a></h2>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">exec</span><span class="p">(</span><span class="n">statement</span><span class="p">,</span> <span class="n">glob</span><span class="p">,</span> <span class="n">local</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>使用 <em>exec</em> 可以添加修改原有的变量。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">a</span> <span class="o">=</span> <span class="mi">1</span>
-<span class="n">exec</span><span class="p">(</span><span class="s2">&quot;b = a+1&quot;</span><span class="p">)</span>
-<span class="nb">print</span> <span class="n">b</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="mi">2</span>
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">local</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">a</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
-<span class="n">glob</span> <span class="o">=</span> <span class="p">{}</span>
-<span class="n">exec</span><span class="p">(</span><span class="s2">&quot;b = a+1&quot;</span><span class="p">,</span> <span class="n">glob</span><span class="p">,</span> <span class="n">local</span><span class="p">)</span>
-<span class="nb">print</span> <span class="n">local</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">{</span><span class="s1">&#39;a&#39;</span><span class="p">:</span> <span class="mi">2</span><span class="p">,</span> <span class="s1">&#39;b&#39;</span><span class="p">:</span> <span class="mi">3</span><span class="p">}</span>
-</pre></div>
-</div>
-<p>执行之后，<em>b</em> 在 <em>local</em> 命名空间中。</p>
-</section>
-<section id="id4">
-<h2>警告<a class="headerlink" href="#id4" title="Permalink to this heading">¶</a></h2>
-<p>动态执行的时候要注意，不要执行不信任的用户输入，因为它们拥有 <em>Python</em> 的全部权限。</p>
-</section>
-<section id="compile-byte-code">
-<h2>compile 函数生成 byte code<a class="headerlink" href="#compile-byte-code" title="Permalink to this heading">¶</a></h2>
-<blockquote>
-<div><p>compile(str, filename, mode)</p>
-</div></blockquote>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">a</span> <span class="o">=</span> <span class="mi">1</span>
-<span class="n">c</span> <span class="o">=</span> <span class="nb">compile</span><span class="p">(</span><span class="s2">&quot;a+2&quot;</span><span class="p">,</span> <span class="s2">&quot;&quot;</span><span class="p">,</span> <span class="s1">&#39;eval&#39;</span><span class="p">)</span>
-<span class="nb">eval</span><span class="p">(</span><span class="n">c</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="mi">3</span>
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">a</span> <span class="o">=</span> <span class="mi">1</span>
-<span class="n">c</span> <span class="o">=</span> <span class="nb">compile</span><span class="p">(</span><span class="s2">&quot;b=a+2&quot;</span><span class="p">,</span> <span class="s2">&quot;&quot;</span><span class="p">,</span> <span class="s1">&#39;exec&#39;</span><span class="p">)</span>
-<span class="n">exec</span><span class="p">(</span><span class="n">c</span><span class="p">)</span>
-<span class="n">b</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="mi">3</span>
-</pre></div>
-</div>
-</section>
-<section id="abstract-syntax-trees">
-<h2>abstract syntax trees<a class="headerlink" href="#abstract-syntax-trees" title="Permalink to this heading">¶</a></h2>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">ast</span>
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">tree</span> <span class="o">=</span> <span class="n">ast</span><span class="o">.</span><span class="n">parse</span><span class="p">(</span><span class="s2">&quot;a+2&quot;</span><span class="p">,</span> <span class="s2">&quot;&quot;</span><span class="p">,</span> <span class="s2">&quot;eval&quot;</span><span class="p">)</span>
-<span class="n">ast</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">tree</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="s2">&quot;Expression(body=BinOp(left=Name(id=&#39;a&#39;, ctx=Load()), op=Add(), right=Num(n=2)))&quot;</span>
-</pre></div>
-</div>
-<p>改变常数的值：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">tree</span><span class="o">.</span><span class="n">body</span><span class="o">.</span><span class="n">right</span><span class="o">.</span><span class="n">n</span> <span class="o">=</span> <span class="mi">3</span>
-<span class="n">ast</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">tree</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="s2">&quot;Expression(body=BinOp(left=Name(id=&#39;a&#39;, ctx=Load()), op=Add(), right=Num(n=3)))&quot;</span>
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">a</span> <span class="o">=</span> <span class="mi">1</span>
-<span class="n">c</span> <span class="o">=</span> <span class="nb">compile</span><span class="p">(</span><span class="n">tree</span><span class="p">,</span> <span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="s1">&#39;eval&#39;</span><span class="p">)</span>
-<span class="nb">eval</span><span class="p">(</span><span class="n">c</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="mi">4</span>
-</pre></div>
-</div>
-<p>安全的使用方法 <em>literal_eval</em> ，只支持基本值的操作：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">ast</span><span class="o">.</span><span class="n">literal_eval</span><span class="p">(</span><span class="s2">&quot;[10.0, 2, True, &#39;foo&#39;]&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">[</span><span class="mf">10.0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="kc">True</span><span class="p">,</span> <span class="s1">&#39;foo&#39;</span><span class="p">]</span>
-</pre></div>
-</div>
-</section>
-<section id="id5">
-<h2>作者 &amp; 更新时间<a class="headerlink" href="#id5" title="Permalink to this heading">¶</a></h2>
-<p>作者:李金  <a class="reference external" href="mailto:lijinwithyou&#37;&#52;&#48;gmail&#46;com">lijinwithyou<span>&#64;</span>gmail<span>&#46;</span>com</a></p>
-<p>提交: 2017/12/6</p>
-</section>
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diff --git a/docs/dive-into-python/cn/modifier.html b/docs/dive-into-python/cn/modifier.html
deleted file mode 100644
index bc99ac4..0000000
--- a/docs/dive-into-python/cn/modifier.html
+++ /dev/null
@@ -1,524 +0,0 @@
-<!DOCTYPE html>
-<html lang="en-US">
-<head>
-<meta charset="utf-8" />
-<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
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-<li>
-<a href="http://www.qpython.org/index.html">Home</a>
-</li>
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-<section id="id1">
-<h1>修饰符<a class="headerlink" href="#id1" title="Permalink to this heading">¶</a></h1>
-<section id="id2">
-<h2>函数是一种对象<a class="headerlink" href="#id2" title="Permalink to this heading">¶</a></h2>
-<p>在 <em>Python</em> 中，函数是也是一种对象。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">foo</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
-<span class="nb">print</span> <span class="n">x</span>
-<span class="nb">print</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">foo</span><span class="p">))</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">type</span> <span class="s1">&#39;function&#39;</span>
-</pre></div>
-</div>
-<p>查看函数拥有的方法：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">dir</span><span class="p">(</span><span class="n">foo</span><span class="p">)</span>
-<span class="n">Out</span><span class="p">[</span><span class="mi">2</span><span class="p">]:</span>
-<span class="p">[</span><span class="s1">&#39;__call__&#39;</span><span class="p">,</span>
-<span class="s1">&#39;__class__&#39;</span><span class="p">,</span>
-<span class="s1">&#39;__closure__&#39;</span><span class="p">,</span>
-<span class="s1">&#39;__code__&#39;</span><span class="p">,</span>
-<span class="s1">&#39;__defaults__&#39;</span><span class="p">,</span>
-<span class="s1">&#39;__delattr__&#39;</span><span class="p">,</span>
-<span class="s1">&#39;__dict__&#39;</span><span class="p">,</span>
-<span class="s1">&#39;__doc__&#39;</span><span class="p">,</span>
-<span class="s1">&#39;__format__&#39;</span><span class="p">,</span>
-<span class="s1">&#39;__get__&#39;</span><span class="p">,</span>
-<span class="s1">&#39;__getattribute__&#39;</span><span class="p">,</span>
-<span class="s1">&#39;__globals__&#39;</span><span class="p">,</span>
-<span class="s1">&#39;__hash__&#39;</span><span class="p">,</span>
-<span class="s1">&#39;__init__&#39;</span><span class="p">,</span>
-<span class="s1">&#39;__module__&#39;</span><span class="p">,</span>
-<span class="s1">&#39;__name__&#39;</span><span class="p">,</span>
-<span class="s1">&#39;__new__&#39;</span><span class="p">,</span>
-<span class="s1">&#39;__reduce__&#39;</span><span class="p">,</span>
-<span class="s1">&#39;__reduce_ex__&#39;</span><span class="p">,</span>
-<span class="s1">&#39;__repr__&#39;</span><span class="p">,</span>
-<span class="s1">&#39;__setattr__&#39;</span><span class="p">,</span>
-<span class="s1">&#39;__sizeof__&#39;</span><span class="p">,</span>
-<span class="s1">&#39;__str__&#39;</span><span class="p">,</span>
-<span class="s1">&#39;__subclasshook__&#39;</span><span class="p">,</span>
-<span class="s1">&#39;func_closure&#39;</span><span class="p">,</span>
-<span class="s1">&#39;func_code&#39;</span><span class="p">,</span>
-<span class="s1">&#39;func_defaults&#39;</span><span class="p">,</span>
-<span class="s1">&#39;func_dict&#39;</span><span class="p">,</span>
-<span class="s1">&#39;func_doc&#39;</span><span class="p">,</span>
-<span class="s1">&#39;func_globals&#39;</span><span class="p">,</span>
-<span class="s1">&#39;func_name&#39;</span><span class="p">]</span>
-</pre></div>
-</div>
-<p>在这些方法中，<em>__call__</em> 是最重要的一种方法：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">foo</span><span class="o">.</span><span class="fm">__call__</span><span class="p">(</span><span class="mi">42</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="mi">42</span>
-</pre></div>
-</div>
-<p>相当于：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">foo</span><span class="p">(</span><span class="mi">42</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="mi">42</span>
-</pre></div>
-</div>
-<p>因为函数是对象，所以函数可以作为参数传入另一个函数：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">bar</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
-<span class="n">x</span> <span class="o">+=</span> <span class="mi">1</span>
-<span class="n">f</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">bar</span><span class="p">(</span><span class="n">foo</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="mi">5</span>
-</pre></div>
-</div>
-</section>
-<section id="id3">
-<h2>修饰符<a class="headerlink" href="#id3" title="Permalink to this heading">¶</a></h2>
-<p>修饰符是这样的一种函数，它接受一个函数作为输入，通常输出也是一个函数：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">dec</span><span class="p">(</span><span class="n">f</span><span class="p">):</span>
-<span class="nb">print</span> <span class="s1">&#39;I am decorating function&#39;</span><span class="p">,</span> <span class="nb">id</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
-<span class="k">return</span> <span class="n">f</span>
-</pre></div>
-</div>
-<p>将 <em>len</em> 函数作为参数传入这个修饰符函数：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">declen</span> <span class="o">=</span> <span class="n">dec</span><span class="p">(</span><span class="nb">len</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">I</span> <span class="n">am</span> <span class="n">decorating</span> <span class="n">function</span> <span class="mi">33716168</span>
-</pre></div>
-</div>
-<p>使用这个新生成的函数：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">declen</span><span class="p">([</span><span class="mi">10</span><span class="p">,</span><span class="mi">20</span><span class="p">,</span><span class="mi">30</span><span class="p">])</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="mi">3</span>
-</pre></div>
-</div>
-<p>上面的例子中，我们仅仅返回了函数的本身，也可以利用这个函数生成一个新的函数，看一个新的例子：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">loud</span><span class="p">(</span><span class="n">f</span><span class="p">):</span>
-<span class="k">def</span> <span class="nf">new_func</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">):</span>
-<span class="nb">print</span> <span class="s1">&#39;calling with&#39;</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kw</span>
-<span class="n">rtn</span> <span class="o">=</span> <span class="n">f</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">)</span>
-<span class="nb">print</span> <span class="s1">&#39;return value is&#39;</span><span class="p">,</span> <span class="n">rtn</span>
-<span class="k">return</span> <span class="n">rtn</span>
-<span class="k">return</span> <span class="n">new_func</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">loudlen</span> <span class="o">=</span> <span class="n">loud</span><span class="p">(</span><span class="nb">len</span><span class="p">)</span>
-<span class="n">loudlen</span><span class="p">([</span><span class="mi">10</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">30</span><span class="p">])</span>
-<span class="n">calling</span> <span class="k">with</span> <span class="p">([</span><span class="mi">10</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">30</span><span class="p">],)</span> <span class="p">{}</span>
-<span class="k">return</span> <span class="n">value</span> <span class="ow">is</span> <span class="mi">3</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="mi">3</span>
-</pre></div>
-</div>
-</section>
-<section id="id4">
-<h2>用 &#64; 来使用修饰符<a class="headerlink" href="#id4" title="Permalink to this heading">¶</a></h2>
-<p><em>Python</em> 使用 &#64; 符号来将某个函数替换为修饰符之后的函数：</p>
-<p>例如这个函数：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">foo</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
-<span class="nb">print</span> <span class="n">x</span>
-<span class="n">foo</span> <span class="o">=</span> <span class="n">dec</span><span class="p">(</span><span class="n">foo</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">I</span> <span class="n">am</span> <span class="n">decorating</span> <span class="n">function</span> <span class="mi">64021672</span>
-</pre></div>
-</div>
-<p>可以替换为：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nd">@dec</span>
-<span class="k">def</span> <span class="nf">foo</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
-<span class="nb">print</span> <span class="n">x</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">I</span> <span class="n">am</span> <span class="n">decorating</span> <span class="n">function</span> <span class="mi">64021112</span>
-</pre></div>
-</div>
-<p>事实上，如果修饰符返回的是一个函数，那么可以链式的使用修饰符：</p>
-<blockquote>
-<div><p>&#64;dec1</p>
-<p>&#64;dec2</p>
-<p>def foo(x):</p>
-<blockquote>
-<div><p>print x</p>
-</div></blockquote>
-</div></blockquote>
-<p>使用修饰符 <em>loud</em> 来定义这个函数：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nd">@loud</span>
-<span class="k">def</span> <span class="nf">foo</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
-<span class="nb">print</span> <span class="n">x</span>
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">foo</span><span class="p">(</span><span class="mi">42</span><span class="p">)</span>
-<span class="n">calling</span> <span class="k">with</span> <span class="p">(</span><span class="mi">42</span><span class="p">,)</span> <span class="p">{}</span>
-<span class="mi">42</span>
-<span class="k">return</span> <span class="n">value</span> <span class="ow">is</span> <span class="kc">None</span>
-</pre></div>
-</div>
-</section>
-<section id="id5">
-<h2>例子<a class="headerlink" href="#id5" title="Permalink to this heading">¶</a></h2>
-<p>定义两个修饰器函数，一个将原来的函数值加一，另一个乘二：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">plus_one</span><span class="p">(</span><span class="n">f</span><span class="p">):</span>
-<span class="k">def</span> <span class="nf">new_func</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
-<span class="k">return</span> <span class="n">f</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span>
-<span class="k">return</span> <span class="n">new_func</span>
-<span class="k">def</span> <span class="nf">times_two</span><span class="p">(</span><span class="n">f</span><span class="p">):</span>
-<span class="k">def</span> <span class="nf">new_func</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
-<span class="k">return</span> <span class="n">f</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">*</span> <span class="mi">2</span>
-<span class="k">return</span> <span class="n">new_func</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>定义函数，先乘二再加一：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nd">@plus_one</span>
-<span class="nd">@times_two</span>
-<span class="k">def</span> <span class="nf">foo</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
-<span class="k">return</span> <span class="nb">int</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">foo</span><span class="p">(</span><span class="mi">13</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="mi">27</span>
-</pre></div>
-</div>
-</section>
-<section id="id6">
-<h2>修饰器工厂<a class="headerlink" href="#id6" title="Permalink to this heading">¶</a></h2>
-<p><em>decorators factories</em> 是返回修饰器的函数，例如：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>def super_dec(x, y, z):
-def dec(f):
-def new_func(*args, **kw):
-            print x + y + z
-            return f(*args, **kw)
-return new_func
-return dec
-</pre></div>
-</div>
-<p>它的作用在于产生一个可以接受参数的修饰器，例如我们想将 <em>loud</em> 输出的内容写入一个文件去，可以这样做：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>def super_loud(filename):
-fp = open(filename, &#39;w&#39;)
-def loud(f):
-def new_func(*args, **kw):
-            fp.write(&#39;calling with&#39; + str(args) + str(kw))
-            # 确保内容被写入
-            fp.flush()
-            fp.close()
-            rtn = f(*args, **kw)
-            return rtn
-return new_func
-return loud
-</pre></div>
-</div>
-<p>可以这样使用这个修饰器工厂：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nd">@super_loud</span><span class="p">(</span><span class="s1">&#39;test.txt&#39;</span><span class="p">)</span>
-<span class="k">def</span> <span class="nf">foo</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
-<span class="nb">print</span> <span class="n">x</span>
-</pre></div>
-</div>
-<p>调用 <em>foo</em> 就会在文件中写入内容：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">foo</span><span class="p">(</span><span class="mi">12</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="mi">12</span>
-</pre></div>
-</div>
-<p>查看文件内容：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s1">&#39;test.txt&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span>
-<span class="nb">print</span> <span class="n">fp</span><span class="o">.</span><span class="n">read</span><span class="p">()</span>
-<span class="n">calling</span> <span class="k">with</span><span class="p">(</span><span class="mi">12</span><span class="p">,){}</span>
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
-<span class="n">os</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="s1">&#39;test.txt&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-</section>
-<section id="id7">
-<h2>作者 &amp; 更新时间<a class="headerlink" href="#id7" title="Permalink to this heading">¶</a></h2>
-<p>作者:李金  <a class="reference external" href="mailto:lijinwithyou&#37;&#52;&#48;gmail&#46;com">lijinwithyou<span>&#64;</span>gmail<span>&#46;</span>com</a></p>
-<p>提交: 2017/12/6</p>
-</section>
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diff --git a/docs/dive-into-python/cn/operator.html b/docs/dive-into-python/cn/operator.html
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-<h1>operator, functools, itertools, toolz, fn, funcy 模块<a class="headerlink" href="#operator-functools-itertools-toolz-fn-funcy" title="Permalink to this heading">¶</a></h1>
-<section id="operator">
-<h2>operator 模块<a class="headerlink" href="#operator" title="Permalink to this heading">¶</a></h2>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">operator</span> <span class="k">as</span> <span class="nn">op</span>
-</pre></div>
-</div>
-<p><em>operator</em> 模块提供了各种操作符（<em>+,</em>,[]*）的函数版本方便使用：</p>
-<p>加法：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span> <span class="n">reduce</span><span class="p">(</span><span class="n">op</span><span class="o">.</span><span class="n">add</span><span class="p">,</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">))</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="mi">45</span>
-</pre></div>
-</div>
-<p>乘法：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span> <span class="n">reduce</span><span class="p">(</span><span class="n">op</span><span class="o">.</span><span class="n">mul</span><span class="p">,</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">10</span><span class="p">))</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="mi">362880</span>
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span> <span class="n">my_list</span> <span class="o">=</span> <span class="p">[(</span><span class="s1">&#39;a&#39;</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="p">(</span><span class="s1">&#39;bb&#39;</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span> <span class="p">(</span><span class="s1">&#39;ccc&#39;</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="p">(</span><span class="s1">&#39;dddd&#39;</span><span class="p">,</span> <span class="mi">3</span><span class="p">)]</span>
-<span class="c1"># 标准排序</span>
-<span class="nb">print</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">my_list</span><span class="p">)</span>
-<span class="c1"># 使用元素的第二个元素排序</span>
-<span class="nb">print</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">my_list</span><span class="p">,</span> <span class="n">key</span><span class="o">=</span><span class="n">op</span><span class="o">.</span><span class="n">itemgetter</span><span class="p">(</span><span class="mi">1</span><span class="p">))</span>
-<span class="c1"># 使用第一个元素的长度进行排序：</span>
-<span class="nb">print</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">my_list</span><span class="p">,</span> <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">[(</span><span class="s1">&#39;a&#39;</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="p">(</span><span class="s1">&#39;bb&#39;</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span> <span class="p">(</span><span class="s1">&#39;ccc&#39;</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="p">(</span><span class="s1">&#39;dddd&#39;</span><span class="p">,</span> <span class="mi">3</span><span class="p">)]</span>
-<span class="p">[(</span><span class="s1">&#39;a&#39;</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="p">(</span><span class="s1">&#39;ccc&#39;</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="p">(</span><span class="s1">&#39;dddd&#39;</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="p">(</span><span class="s1">&#39;bb&#39;</span><span class="p">,</span> <span class="mi">4</span><span class="p">)]</span>
-<span class="p">[(</span><span class="s1">&#39;a&#39;</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="p">(</span><span class="s1">&#39;bb&#39;</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span> <span class="p">(</span><span class="s1">&#39;ccc&#39;</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="p">(</span><span class="s1">&#39;dddd&#39;</span><span class="p">,</span> <span class="mi">3</span><span class="p">)]</span>
-</pre></div>
-</div>
-</section>
-<section id="functools">
-<h2>functools 模块<a class="headerlink" href="#functools" title="Permalink to this heading">¶</a></h2>
-<p><em>functools</em> 包含很多跟函数相关的工具，比如之前看到的 <em>wraps</em> 函数，不过最常用的是 <em>partial</em> 函数，这个函数允许我们使用一个函数中生成一个新函数，这个函数使用原来的函数，不过某些参数被指定了：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">functools</span> <span class="kn">import</span> <span class="n">partial</span>
-<span class="c1"># 将 reduce 的第一个参数指定为加法，得到的是类似求和的函数</span>
-<span class="n">sum_</span> <span class="o">=</span> <span class="n">partial</span><span class="p">(</span><span class="n">reduce</span><span class="p">,</span> <span class="n">op</span><span class="o">.</span><span class="n">add</span><span class="p">)</span>
-<span class="c1"># 将 reduce 的第一个参数指定为乘法，得到的是类似求连乘的函数</span>
-<span class="n">prod_</span> <span class="o">=</span> <span class="n">partial</span><span class="p">(</span><span class="n">reduce</span><span class="p">,</span> <span class="n">op</span><span class="o">.</span><span class="n">mul</span><span class="p">)</span>
-<span class="nb">print</span> <span class="n">sum_</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">])</span>
-<span class="nb">print</span> <span class="n">prod_</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">])</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="mi">10</span>
-<span class="mi">24</span>
-</pre></div>
-</div>
-<p><em>partial</em> 函数还可以按照键值对传入固定参数。</p>
-</section>
-<section id="itertools">
-<h2>itertools 模块<a class="headerlink" href="#itertools" title="Permalink to this heading">¶</a></h2>
-<p><em>itertools</em> 包含很多与迭代器对象相关的工具，其中比较常用的是排列组合生成器* permutations <em>和</em> combinations*，还有在数据分析中常用的 <em>groupby</em> 生成器：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">itertools</span> <span class="kn">import</span> <span class="n">cycle</span><span class="p">,</span> <span class="n">groupby</span><span class="p">,</span> <span class="n">islice</span><span class="p">,</span> <span class="n">permutations</span><span class="p">,</span> <span class="n">combinations</span>
-</pre></div>
-</div>
-<p><em>cycle</em> 返回一个无限的迭代器，按照顺序重复输出输入迭代器中的内容，<em>islice</em> 则返回一个迭代器中的一段内容：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span> <span class="nb">list</span><span class="p">(</span><span class="n">islice</span><span class="p">(</span><span class="n">cycle</span><span class="p">(</span><span class="s1">&#39;abcd&#39;</span><span class="p">),</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">[</span><span class="s1">&#39;a&#39;</span><span class="p">,</span> <span class="s1">&#39;b&#39;</span><span class="p">,</span> <span class="s1">&#39;c&#39;</span><span class="p">,</span> <span class="s1">&#39;d&#39;</span><span class="p">,</span> <span class="s1">&#39;a&#39;</span><span class="p">,</span> <span class="s1">&#39;b&#39;</span><span class="p">,</span> <span class="s1">&#39;c&#39;</span><span class="p">,</span> <span class="s1">&#39;d&#39;</span><span class="p">,</span> <span class="s1">&#39;a&#39;</span><span class="p">,</span> <span class="s1">&#39;b&#39;</span><span class="p">]</span>
-</pre></div>
-</div>
-<p><em>groupby</em> 返回一个字典，按照指定的 <em>key</em> 对一组数据进行分组，字典的键是 <em>key</em>，值是一个迭代器：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>animals = sorted([&#39;pig&#39;, &#39;cow&#39;, &#39;giraffe&#39;, &#39;elephant&#39;,
-&#39;dog&#39;, &#39;cat&#39;, &#39;hippo&#39;, &#39;lion&#39;, &#39;tiger&#39;], key=len)
-# 按照长度进行分组
-for k, g in groupby(animals, key=len):
-    print k, list(g)
-print
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="mi">3</span> <span class="p">[</span><span class="s1">&#39;pig&#39;</span><span class="p">,</span> <span class="s1">&#39;cow&#39;</span><span class="p">,</span> <span class="s1">&#39;dog&#39;</span><span class="p">,</span> <span class="s1">&#39;cat&#39;</span><span class="p">]</span>
-<span class="mi">4</span> <span class="p">[</span><span class="s1">&#39;lion&#39;</span><span class="p">]</span>
-<span class="mi">5</span> <span class="p">[</span><span class="s1">&#39;hippo&#39;</span><span class="p">,</span> <span class="s1">&#39;tiger&#39;</span><span class="p">]</span>
-<span class="mi">7</span> <span class="p">[</span><span class="s1">&#39;giraffe&#39;</span><span class="p">]</span>
-<span class="mi">8</span> <span class="p">[</span><span class="s1">&#39;elephant&#39;</span><span class="p">]</span>
-</pre></div>
-</div>
-<p>排列：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span> <span class="p">[</span><span class="s1">&#39;&#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">p</span><span class="p">)</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">permutations</span><span class="p">(</span><span class="s1">&#39;abc&#39;</span><span class="p">)]</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">[</span><span class="s1">&#39;abc&#39;</span><span class="p">,</span> <span class="s1">&#39;acb&#39;</span><span class="p">,</span> <span class="s1">&#39;bac&#39;</span><span class="p">,</span> <span class="s1">&#39;bca&#39;</span><span class="p">,</span> <span class="s1">&#39;cab&#39;</span><span class="p">,</span> <span class="s1">&#39;cba&#39;</span><span class="p">]</span>
-</pre></div>
-</div>
-<p>组合：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span> <span class="p">[</span><span class="nb">list</span><span class="p">(</span><span class="n">c</span><span class="p">)</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">combinations</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">],</span> <span class="n">r</span><span class="o">=</span><span class="mi">2</span><span class="p">)]</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">[[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]]</span>
-</pre></div>
-</div>
-</section>
-<section id="toolz-fn-funcy">
-<h2>toolz, fn 和 funcy 模块<a class="headerlink" href="#toolz-fn-funcy" title="Permalink to this heading">¶</a></h2>
-<p>这三个模块的作用是方便我们在编程的时候使用函数式编程的风格。</p>
-</section>
-<section id="id1">
-<h2>作者 &amp; 更新时间<a class="headerlink" href="#id1" title="Permalink to this heading">¶</a></h2>
-<p>作者:李金  <a class="reference external" href="mailto:lijinwithyou&#37;&#52;&#48;gmail&#46;com">lijinwithyou<span>&#64;</span>gmail<span>&#46;</span>com</a></p>
-<p>提交: 2017/12/6</p>
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diff --git a/docs/dive-into-python/cn/orm.html b/docs/dive-into-python/cn/orm.html
deleted file mode 100644
index de32ec0..0000000
--- a/docs/dive-into-python/cn/orm.html
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-<link href="https://cdn.bootcss.com/bootstrap/3.3.7/css/bootstrap.min.css" rel="stylesheet">
-<title>对象关系映射</title>
-<link rel="stylesheet" type="text/css" href="../../static/pygments.css" />
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-<main class="contain web-content">
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-<section id="id1">
-<h1>对象关系映射<a class="headerlink" href="#id1" title="Permalink to this heading">¶</a></h1>
-<p>数据库中的记录可以与一个 Python 对象对应。</p>
-<p>例如对于上一节中的数据库：</p>
-<table class="docutils align-default">
-<thead>
-<tr class="row-odd"><th class="head"><p>Order</p></th>
-<th class="head"><p>Date</p></th>
-<th class="head" colspan="3"><p>Stock    |             Quantity  |        Price</p></th>
-</tr>
-</thead>
-<tbody>
-<tr class="row-even"><td><p>A0001</p></td>
-<td><p>2013-12-01</p></td>
-<td colspan="3"><p>AAPL      |              1000     |        203.4</p></td>
-</tr>
-<tr class="row-odd"><td><p>A0002</p></td>
-<td><p>2013-12-01</p></td>
-<td><p>MSFT</p></td>
-<td><p>1500</p></td>
-<td><p>167.5</p></td>
-</tr>
-<tr class="row-even"><td><p>A0003</p></td>
-<td><p>2013-12-02</p></td>
-<td><p>GOOG</p></td>
-<td><p>150</p></td>
-<td><p>167.5</p></td>
-</tr>
-</tbody>
-</table>
-<p>可以用一个类来描述：</p>
-<table class="docutils align-default">
-<thead>
-<tr class="row-odd"><th class="head"><p>Attr.</p></th>
-<th class="head"><p>Method</p></th>
-</tr>
-</thead>
-<tbody>
-<tr class="row-even"><td><p>Order id</p></td>
-<td><p>Cost</p></td>
-</tr>
-<tr class="row-odd"><td><p>Stock</p></td>
-<td></td>
-</tr>
-<tr class="row-even"><td><p>Quant.</p></td>
-<td></td>
-</tr>
-<tr class="row-odd"><td><p>Price</p></td>
-<td></td>
-</tr>
-</tbody>
-</table>
-<p>可以使用 sqlalchemy 来实现这种对应：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>from sqlalchemy.ext.declarative import declarative_base
-from sqlalchemy import Column, Date, Float, Integer, String
-Base = declarative_base()
-class Order(Base):
-    __tablename__ = &#39;orders&#39;
-    order_id = Column(String, primary_key=True)
-    date = Column(Date)
-    symbol = Column(String)
-    quantity = Column(Integer)
-    price = Column(Float)
-    def get_cost(self):
-        return self.quantity*self.price
-</pre></div>
-</div>
-<p>生成一个 Order 对象：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">datetime</span>
-<span class="n">order</span> <span class="o">=</span> <span class="n">Order</span><span class="p">(</span><span class="n">order_id</span><span class="o">=</span><span class="s1">&#39;A0004&#39;</span><span class="p">,</span> <span class="n">date</span><span class="o">=</span><span class="n">datetime</span><span class="o">.</span><span class="n">date</span><span class="o">.</span><span class="n">today</span><span class="p">(),</span> <span class="n">symbol</span><span class="o">=</span><span class="s1">&#39;MSFT&#39;</span><span class="p">,</span> <span class="n">quantity</span><span class="o">=-</span><span class="mi">1000</span><span class="p">,</span> <span class="n">price</span><span class="o">=</span><span class="mf">187.54</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>调用方法：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">order</span><span class="o">.</span><span class="n">get_cost</span><span class="p">()</span>
-</pre></div>
-</div>
-<p>使用上一节生成的数据库产生一个 session：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sqlalchemy</span> <span class="kn">import</span> <span class="n">create_engine</span>
-<span class="kn">from</span> <span class="nn">sqlalchemy.orm</span> <span class="kn">import</span> <span class="n">sessionmaker</span>
-<span class="n">engine</span> <span class="o">=</span> <span class="n">create_engine</span><span class="p">(</span><span class="s2">&quot;sqlite:///my_database.sqlite&quot;</span><span class="p">)</span>   <span class="c1"># 相当于 connection</span>
-<span class="n">Session</span> <span class="o">=</span> <span class="n">sessionmaker</span><span class="p">(</span><span class="n">bind</span><span class="o">=</span><span class="n">engine</span><span class="p">)</span> <span class="c1"># 相当于 cursor</span>
-<span class="n">session</span> <span class="o">=</span> <span class="n">Session</span><span class="p">()</span>
-</pre></div>
-</div>
-<p>使用这个 session 向数据库中添加刚才生成的对象：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">session</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">order</span><span class="p">)</span>
-<span class="n">session</span><span class="o">.</span><span class="n">commit</span><span class="p">()</span>
-</pre></div>
-</div>
-<p>显示是否添加成功：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>for row in engine.execute(&quot;SELECT * FROM orders&quot;):
-    print row
-</pre></div>
-</div>
-<p>成功则会输出以下内容</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">(</span><span class="sa">u</span><span class="s1">&#39;A0001&#39;</span><span class="p">,</span> <span class="sa">u</span><span class="s1">&#39;2013-12-01&#39;</span><span class="p">,</span> <span class="sa">u</span><span class="s1">&#39;AAPL&#39;</span><span class="p">,</span> <span class="mi">1000</span><span class="p">,</span> <span class="mf">203.4</span><span class="p">)</span>
-<span class="p">(</span><span class="sa">u</span><span class="s1">&#39;A0002&#39;</span><span class="p">,</span> <span class="sa">u</span><span class="s1">&#39;2013-12-01&#39;</span><span class="p">,</span> <span class="sa">u</span><span class="s1">&#39;MSFT&#39;</span><span class="p">,</span> <span class="mi">1500</span><span class="p">,</span> <span class="mf">167.5</span><span class="p">)</span>
-<span class="p">(</span><span class="sa">u</span><span class="s1">&#39;A0003&#39;</span><span class="p">,</span> <span class="sa">u</span><span class="s1">&#39;2013-12-02&#39;</span><span class="p">,</span> <span class="sa">u</span><span class="s1">&#39;GOOG&#39;</span><span class="p">,</span> <span class="mi">1500</span><span class="p">,</span> <span class="mf">167.5</span><span class="p">)</span>
-<span class="p">(</span><span class="sa">u</span><span class="s1">&#39;A0004&#39;</span><span class="p">,</span> <span class="sa">u</span><span class="s1">&#39;2015-09-10&#39;</span><span class="p">,</span> <span class="sa">u</span><span class="s1">&#39;MSFT&#39;</span><span class="p">,</span> <span class="o">-</span><span class="mi">1000</span><span class="p">,</span> <span class="mf">187.54</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>使用 filter 进行查询，返回的是 Order 对象的列表：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>for order in session.query(Order).filter(Order.symbol==&quot;AAPL&quot;):
-    print order.order_id, order.date, order.get_cost()
-</pre></div>
-</div>
-<p>成功则会输出以下内容</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">A0001</span> <span class="mi">2013</span><span class="o">-</span><span class="mi">12</span><span class="o">-</span><span class="mi">01</span> <span class="mf">203400.0</span>
-</pre></div>
-</div>
-<p>返回列表的第一个：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">order_2</span> <span class="o">=</span> <span class="n">session</span><span class="o">.</span><span class="n">query</span><span class="p">(</span><span class="n">Order</span><span class="p">)</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="n">Order</span><span class="o">.</span><span class="n">order_id</span><span class="o">==</span><span class="s1">&#39;A0002&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">first</span><span class="p">()</span>
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">order_2</span><span class="o">.</span><span class="n">symbol</span>
-</pre></div>
-</div>
-<p>输出： u’MSFT’</p>
-<section id="id2">
-<h2>作者 &amp; 更新时间<a class="headerlink" href="#id2" title="Permalink to this heading">¶</a></h2>
-<p>作者:<cite>李金  &lt;lijinwithyou&#64;gmail.com&gt;</cite></p>
-<p>提交: 2017/12/6</p>
-</section>
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diff --git a/docs/dive-into-python/cn/os.html b/docs/dive-into-python/cn/os.html
deleted file mode 100644
index 5078629..0000000
--- a/docs/dive-into-python/cn/os.html
+++ /dev/null
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-<html lang="en-US">
-<head>
-<meta charset="utf-8" />
-<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
-<link href="https://cdn.bootcss.com/bootstrap/3.3.7/css/bootstrap.min.css" rel="stylesheet">
-<title>与操作系统进行交互：os 模块</title>
-<link rel="stylesheet" type="text/css" href="../../static/pygments.css" />
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-</head><body>
-<div class="header doc-header web-show">
-<div class="col-sm-offset-2 col-sm-8  index-header">
-<div class="navbar-header">
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-<span class="header-title-selected"></span>
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-<li>
-<a href="http://www.aipy.org">AIPY</a>
-</li>
-<li>
-<a target="_blank" href="https://github.com/qpython-android/qpython">Github</a>
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-<form action="/search.html">
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-<section id="os">
-<h1>与操作系统进行交互：os 模块<a class="headerlink" href="#os" title="Permalink to this heading">¶</a></h1>
-<p>os 模块提供了对系统文件进行操作的方法：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
-<span class="nb">dir</span><span class="p">(</span><span class="n">os</span><span class="p">)</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<section id="id1">
-<h2>文件路径操作<a class="headerlink" href="#id1" title="Permalink to this heading">¶</a></h2>
-<ul class="simple">
-<li><p><em>os.remove(path)</em> 或 <em>os.unlink(path)</em> ：删除指定路径的文件。路径可以是全名，也可以是当前工作目录下的路径。</p></li>
-<li><p><em>os.removedirs</em>：删除文件，并删除中间路径中的空文件夹</p></li>
-<li><p><em>os.chdir(path)</em>：将当前工作目录改变为指定的路径</p></li>
-<li><p><em>os.getcwd()</em>：返回当前的工作目录</p></li>
-<li><p><em>os.curdir</em>：表示当前目录的符号</p></li>
-<li><p><em>os.rename(old, new)</em>：重命名文件</p></li>
-<li><p><em>os.renames(old, new)</em>：重命名文件，如果中间路径的文件夹不存在，则创建文件夹</p></li>
-<li><p><em>os.listdir(path)</em>：返回给定目录下的所有文件夹和文件名，不包括 <em>‘.’</em> 和 <em>‘..’</em> 以及子文件夹下的目录。（<em>’.’</em> 和* ‘..’* 分别指当前目录和父目录）</p></li>
-<li><p><em>os.mkdir(name)</em>：产生新文件夹</p></li>
-<li><p><em>os.makedirs(name)</em>：产生新文件夹，如果中间路径的文件夹不存在，则创建文件夹</p></li>
-</ul>
-<p>获得当前目录：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
-<span class="n">os</span><span class="o">.</span><span class="n">getcwd</span><span class="p">()</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>获得当前目录下的文件：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
-<span class="n">os</span><span class="o">.</span><span class="n">listdir</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">curdir</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>写文件：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
-<span class="n">f</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="s2">&quot;test.file&quot;</span><span class="p">,</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span>
-<span class="n">f</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
-<span class="nb">print</span> <span class="s2">&quot;test.file&quot;</span> <span class="ow">in</span> <span class="n">os</span><span class="o">.</span><span class="n">listdir</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">curdir</span><span class="p">)</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>重命名文件：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
-<span class="n">os</span><span class="o">.</span><span class="n">rename</span><span class="p">(</span><span class="s2">&quot;test.file&quot;</span><span class="p">,</span> <span class="s2">&quot;test.new.file&quot;</span><span class="p">)</span>
-<span class="nb">print</span> <span class="s2">&quot;test.file&quot;</span> <span class="ow">in</span> <span class="n">os</span><span class="o">.</span><span class="n">listdir</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">curdir</span><span class="p">)</span>
-<span class="nb">print</span> <span class="s2">&quot;test.new.file&quot;</span> <span class="ow">in</span> <span class="n">os</span><span class="o">.</span><span class="n">listdir</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">curdir</span><span class="p">)</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>删除文件：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
-<span class="n">os</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="s2">&quot;test.new.file&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-</section>
-<section id="id2">
-<h2>系统常量<a class="headerlink" href="#id2" title="Permalink to this heading">¶</a></h2>
-<p>当前操作系统的换行符：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
-<span class="n">os</span><span class="o">.</span><span class="n">linesep</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>当前操作系统的路径分隔符：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
-<span class="n">os</span><span class="o">.</span><span class="n">sep</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>当前操作系统的环境变量中的分隔符（<em>’;’</em> 或 <em>‘:’</em>）：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
-<span class="n">os</span><span class="o">.</span><span class="n">pathsep</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-</section>
-<section id="id3">
-<h2>其他<a class="headerlink" href="#id3" title="Permalink to this heading">¶</a></h2>
-<p><em>os.environ</em> 是一个存储所有环境变量的值的字典，可以修改。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
-<span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s2">&quot;USER&quot;</span><span class="p">]</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p><em>os.urandom(len)</em> 返回指定长度的随机字节。</p>
-</section>
-<section id="os-path">
-<h2>os.path 模块<a class="headerlink" href="#os-path" title="Permalink to this heading">¶</a></h2>
-<p>不同的操作系统使用不同的路径规范，这样当我们在不同的操作系统下进行操作时，可能会带来一定的麻烦，而 <em>os.path</em> 模块则帮我们解决了这个问题。</p>
-<ul class="simple">
-<li><p>os.path.isfile(path) ：检测一个路径是否为普通文件</p></li>
-<li><p>os.path.isdir(path)：检测一个路径是否为文件夹</p></li>
-<li><p>os.path.exists(path)：检测路径是否存在</p></li>
-<li><p>os.path.isabs(path)：检测路径是否为绝对路径</p></li>
-</ul>
-</section>
-<section id="split-join">
-<h2>split 和 join<a class="headerlink" href="#split-join" title="Permalink to this heading">¶</a></h2>
-<ul class="simple">
-<li><p>os.path.split(path)：拆分一个路径为 (head, tail) 两部分</p></li>
-<li><p>os.path.join(a, * p)：使用系统的路径分隔符，将各个部分合成一个路径</p></li>
-</ul>
-</section>
-<section id="id4">
-<h2>其他<a class="headerlink" href="#id4" title="Permalink to this heading">¶</a></h2>
-<ul class="simple">
-<li><p>os.path.abspath()：返回路径的绝对路径</p></li>
-<li><p>os.path.dirname(path)：返回路径中的文件夹部分</p></li>
-<li><p>os.path.basename(path)：返回路径中的文件部分</p></li>
-<li><p>os.path.splitext(path)：将路径与扩展名分开</p></li>
-<li><p>os.path.expanduser(path)：展开 ‘~’ 和 ‘~user’</p></li>
-</ul>
-</section>
-<section id="id5">
-<h2>作者 &amp; 更新时间<a class="headerlink" href="#id5" title="Permalink to this heading">¶</a></h2>
-<p>作者:<cite>李金  &lt;lijinwithyou&#64;gmail.com&gt;</cite></p>
-<p>提交: 2017/12/6</p>
-</section>
-</section>
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diff --git a/docs/dive-into-python/cn/parameterpassing.html b/docs/dive-into-python/cn/parameterpassing.html
deleted file mode 100644
index ea5cb8c..0000000
--- a/docs/dive-into-python/cn/parameterpassing.html
+++ /dev/null
@@ -1,629 +0,0 @@
-<!DOCTYPE html>
-<html lang="en-US">
-<head>
-<meta charset="utf-8" />
-<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
-<link href="https://cdn.bootcss.com/bootstrap/3.3.7/css/bootstrap.min.css" rel="stylesheet">
-<title>函数进阶</title>
-<link rel="stylesheet" type="text/css" href="../../static/pygments.css" />
-<link rel="stylesheet" type="text/css" href="../../static/style.css" />
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-<a href="http://www.qpython.org/index.html">Home</a>
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-<a target="_blank" href="https://github.com/qpython-android/qpython">Github</a>
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-<div class="document">
-<div class="documentwrapper">
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-<div class="body" role="main">
-<section id="id1">
-<h1>函数进阶<a class="headerlink" href="#id1" title="Permalink to this heading">¶</a></h1>
-<p>本章内容包括参数传递，高阶函数，lambda 匿名函数，global 变量，递归</p>
-<section id="id2">
-<h2>函数是基本类型<a class="headerlink" href="#id2" title="Permalink to this heading">¶</a></h2>
-<p>在 Python 中，函数是一种基本类型的对象，这意味着</p>
-<ul class="simple">
-<li><p>可以将函数作为参数传给另一个函数</p></li>
-<li><p>将函数作为字典的值储存</p></li>
-<li><p>将函数作为另一个函数的返回值</p></li>
-</ul>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>def square(x):
-    &quot;&quot;&quot;Square of x.&quot;&quot;&quot;
-    return x*x
-def cube(x):
-    &quot;&quot;&quot;Cube of x.&quot;&quot;&quot;
-    return x*x*x
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>作为字典的值：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">funcs</span> <span class="o">=</span> <span class="p">{</span>
-<span class="s1">&#39;square&#39;</span><span class="p">:</span> <span class="n">square</span><span class="p">,</span>
-<span class="s1">&#39;cube&#39;</span><span class="p">:</span> <span class="n">cube</span><span class="p">,</span>
-<span class="p">}</span>
-</pre></div>
-</div>
-<p>例子：</p>
-<dl>
-<dt>::</dt><dd><dl class="simple">
-<dt>def square(x):</dt><dd><p>    ”””Square of x.”””
-    return x*x</p>
-</dd>
-<dt>def cube(x):</dt><dd><p>    ”””Cube of x.”””
-    return x*x*x</p>
-</dd>
-<dt>funcs = {</dt><dd><p>‘square’: square,
-‘cube’: cube,</p>
-</dd>
-</dl>
-<p>}</p>
-<p>x = 2</p>
-<p>print square(x)
-print cube(x)</p>
-<dl class="simple">
-<dt>func in sorted(funcs):</dt><dd><p>    print func, funcs[func](x)</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<button class="button">Run …</button>
-</section>
-</section>
-<section id="id3">
-<h1>函数参数<a class="headerlink" href="#id3" title="Permalink to this heading">¶</a></h1>
-<section id="id4">
-<h2>引用传递<a class="headerlink" href="#id4" title="Permalink to this heading">¶</a></h2>
-<p>Python 中的函数传递方式是 call by reference 即引用传递，例如，对于这样的用法：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">11</span><span class="p">,</span> <span class="mi">12</span><span class="p">]</span>
-<span class="n">f</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>传递给函数 f 的是一个指向 x 所包含内容的引用，如果我们修改了这个引用所指向内容的值（例如 x[0]=999），那么外面的 x 的值也会被改变。不过如果我们在函数中赋给 x 一个新的值（例如另一个列表），那么在函数外面的 x 的值不会改变：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>def mod_f(x):
-    x[0] = 999
-    return x
-x = [1, 2, 3]
-print x
-print mod_f(x)
-print x
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>def no_mod_f(x):
-    x = [4, 5, 6]
-    return x
-x = [1,2,3]
-print x
-print no_mod_f(x)
-print x
-</pre></div>
-</div>
-<button class="button">Run …</button>
-</section>
-<section id="id5">
-<h2>默认参数是可变的<a class="headerlink" href="#id5" title="Permalink to this heading">¶</a></h2>
-<p>函数可以传递默认参数，默认参数的绑定发生在函数定义的时候，以后每次调用默认参数时都会使用同一个引用。</p>
-<p>这样的机制会导致这种情况的发生：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>def f(x = []):
-    x.append(1)
-    return x
-</pre></div>
-</div>
-<p>理论上说，我们希望调用 f() 时返回的是 [1]， 但事实上：</p>
-<dl>
-<dt>::</dt><dd><dl class="simple">
-<dt>def f(x = []):</dt><dd><p>    x.append(1)
-    return x</p>
-</dd>
-</dl>
-<p>print f()
-print f()
-print f()
-print f(x = [9,9,9])
-print f()
-print f()</p>
-</dd>
-</dl>
-<button class="button">Run …</button>
-<p>将会得到以下结果</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-<span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
-<span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
-<span class="p">[</span><span class="mi">9</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
-<span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
-<span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
-</pre></div>
-</div>
-<p>而我们希望看到的应该是这样：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">f</span><span class="p">(</span><span class="n">x</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
-<span class="k">if</span> <span class="n">x</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-<span class="n">x</span> <span class="o">=</span> <span class="p">[]</span>
-<span class="n">x</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
-<span class="k">return</span> <span class="n">x</span>
-<span class="nb">print</span> <span class="n">f</span><span class="p">()</span>
-<span class="nb">print</span> <span class="n">f</span><span class="p">()</span>
-<span class="nb">print</span> <span class="n">f</span><span class="p">()</span>
-<span class="nb">print</span> <span class="n">f</span><span class="p">(</span><span class="n">x</span> <span class="o">=</span> <span class="p">[</span><span class="mi">9</span><span class="p">,</span><span class="mi">9</span><span class="p">,</span><span class="mi">9</span><span class="p">])</span>
-<span class="nb">print</span> <span class="n">f</span><span class="p">()</span>
-<span class="nb">print</span> <span class="n">f</span><span class="p">()</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>输出应该是</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-<span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-<span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-<span class="p">[</span><span class="mi">9</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
-<span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-<span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-</pre></div>
-</div>
-</section>
-<section id="id6">
-<h2>高阶函数<a class="headerlink" href="#id6" title="Permalink to this heading">¶</a></h2>
-<p>以函数作为参数，或者返回一个函数的函数是高阶函数，常用的例子有 <em>map</em> 和 <em>filter</em> 函数：</p>
-<p><em>map(f, sq)</em> 函数将 <em>f</em> 作用到*sq* 的每个元素上去，并返回结果组成的列表，相当于：</p>
-<p><em>[f(s) for s in sq]</em></p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">map</span><span class="p">(</span><span class="n">square</span><span class="p">,</span> <span class="nb">range</span><span class="p">(</span><span class="mi">5</span><span class="p">))</span>
-</pre></div>
-</div>
-<p>输入：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">16</span><span class="p">]</span>
-</pre></div>
-</div>
-<p><em>filter(f, sq)</em> 函数的作用相当于，对于 <em>sq</em> 的每个元素 <em>s</em>，返回所有 <em>f(s)</em> 为 <em>True</em> 的 <em>s</em> 组成的列表，相当于：</p>
-<p><em>[s for s in sq if f(s)]</em></p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">is_even</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
-<span class="k">return</span> <span class="n">x</span> <span class="o">%</span> <span class="mi">2</span> <span class="o">==</span> <span class="mi">0</span>
-<span class="nb">filter</span><span class="p">(</span><span class="n">is_even</span><span class="p">,</span> <span class="nb">range</span><span class="p">(</span><span class="mi">5</span><span class="p">))</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>输出：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">]</span>
-</pre></div>
-</div>
-<p>一起使用：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">map</span><span class="p">(</span><span class="n">square</span><span class="p">,</span> <span class="nb">filter</span><span class="p">(</span><span class="n">is_even</span><span class="p">,</span> <span class="nb">range</span><span class="p">(</span><span class="mi">5</span><span class="p">)))</span>
-</pre></div>
-</div>
-<p>输出：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">16</span><span class="p">]</span>
-</pre></div>
-</div>
-<p><em>reduce(f, sq)</em> 函数接受一个二元操作函数 <em>f(x,y)</em>，并对于序列 <em>sq</em> 每次合并两个元素：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>def my_add(x, y):
-    return x + y
-reduce(my_add, [1,2,3,4,5])
-</pre></div>
-</div>
-<p>Out[12]:
-15</p>
-<p>传入加法函数，相当于对序列求和。</p>
-<p>返回一个函数：</p>
-<p>In [13]:</p>
-<dl>
-<dt>::</dt><dd><dl>
-<dt>def make_logger(target):</dt><dd><dl class="simple">
-<dt>def logger(data):</dt><dd><dl class="simple">
-<dt>with open(target, ‘a’) as f:</dt><dd><p>f.write(data + ‘n’)</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p>return logger</p>
-</dd>
-</dl>
-<p>foo_logger = make_logger(‘foo.txt’)
-foo_logger(‘Hello’)
-foo_logger(‘World’)</p>
-</dd>
-</dl>
-<button class="button">Run …</button>
-<p>In [14]:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>!cat foo.txt
-Hello
-World
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>In [15]:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
-<span class="n">os</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="s1">&#39;foo.txt&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-</section>
-<section id="id7">
-<h2>匿名函数<a class="headerlink" href="#id7" title="Permalink to this heading">¶</a></h2>
-<p>在使用 <em>map</em>， <em>filter</em>，<em>reduce</em> 等函数的时候，为了方便，对一些简单的函数，我们通常使用匿名函数的方式进行处理，其基本形式是：</p>
-<p>lambda &lt;variables&gt;: &lt;expression&gt;</p>
-<p>例如，我们可以将这个：</p>
-<p>In [16]:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span> <span class="nb">map</span><span class="p">(</span><span class="n">square</span><span class="p">,</span> <span class="nb">range</span><span class="p">(</span><span class="mi">5</span><span class="p">))</span>
-<span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">16</span><span class="p">]</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>用匿名函数替换为：</p>
-<p>In [17]:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span> <span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span> <span class="o">*</span> <span class="n">x</span><span class="p">,</span> <span class="nb">range</span><span class="p">(</span><span class="mi">5</span><span class="p">))</span>
-<span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">16</span><span class="p">]</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>匿名函数虽然写起来比较方便（省去了定义函数的烦恼），但是有时候会比较难于阅读：</p>
-<p>In [18]:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">s1</span> <span class="o">=</span> <span class="n">reduce</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">x</span><span class="o">+</span><span class="n">y</span><span class="p">,</span> <span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="o">**</span><span class="mi">2</span><span class="p">,</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">10</span><span class="p">)))</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">s1</span><span class="p">)</span>
-<span class="mi">285</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>当然，更简单地，我们可以写成这样：</p>
-<p>In [19]:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">s2</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">x</span><span class="o">**</span><span class="mi">2</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
-<span class="nb">print</span> <span class="n">s2</span>
-<span class="mi">285</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-</section>
-<section id="global">
-<h2>global 变量<a class="headerlink" href="#global" title="Permalink to this heading">¶</a></h2>
-<p>一般来说，函数中是可以直接使用全局变量的值的：</p>
-<p>In [20]:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="mi">15</span>
-<span class="k">def</span> <span class="nf">print_x</span><span class="p">():</span>
-<span class="nb">print</span> <span class="n">x</span>
-<span class="n">print_x</span><span class="p">()</span>
-<span class="mi">15</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>但是要在函数中修改全局变量的值，需要加上 <em>global</em> 关键字：</p>
-<p>In [21]:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="mi">15</span>
-<span class="k">def</span> <span class="nf">print_newx</span><span class="p">():</span>
-<span class="k">global</span> <span class="n">x</span>
-<span class="n">x</span> <span class="o">=</span> <span class="mi">18</span>
-<span class="nb">print</span> <span class="n">x</span>
-<span class="n">print_newx</span><span class="p">()</span>
-<span class="nb">print</span> <span class="n">x</span>
-<span class="mi">18</span>
-<span class="mi">18</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>如果不加上这句 global 那么全局变量的值不会改变：</p>
-<p>In [22]:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="mi">15</span>
-<span class="k">def</span> <span class="nf">print_newx</span><span class="p">():</span>
-<span class="n">x</span> <span class="o">=</span> <span class="mi">18</span>
-<span class="nb">print</span> <span class="n">x</span>
-<span class="n">print_newx</span><span class="p">()</span>
-<span class="nb">print</span> <span class="n">x</span>
-<span class="mi">18</span>
-<span class="mi">15</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-</section>
-<section id="id8">
-<h2>递归<a class="headerlink" href="#id8" title="Permalink to this heading">¶</a></h2>
-<p>递归是指函数在执行的过程中调用了本身，一般用于分治法，不过在 <em>Python</em> 中这样的用法十分地小，所以一般不怎么使用：</p>
-<p>Fibocacci 数列：</p>
-<p>In [23]:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">fib1</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
-<span class="sd">&quot;&quot;&quot;Fib with recursion.&quot;&quot;&quot;</span>
-<span class="c1"># base case</span>
-<span class="k">if</span> <span class="n">n</span><span class="o">==</span><span class="mi">0</span> <span class="ow">or</span> <span class="n">n</span><span class="o">==</span><span class="mi">1</span><span class="p">:</span>
-<span class="k">return</span> <span class="mi">1</span>
-<span class="c1"># recurssive caae</span>
-<span class="k">else</span><span class="p">:</span>
-<span class="k">return</span> <span class="n">fib1</span><span class="p">(</span><span class="n">n</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">+</span> <span class="n">fib1</span><span class="p">(</span><span class="n">n</span><span class="o">-</span><span class="mi">2</span><span class="p">)</span>
-<span class="nb">print</span> <span class="p">[</span><span class="n">fib1</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">)]</span>
-<span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">13</span><span class="p">,</span> <span class="mi">21</span><span class="p">,</span> <span class="mi">34</span><span class="p">,</span> <span class="mi">55</span><span class="p">]</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>一个更高效的非递归版本：</p>
-<p>In [24]:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">fib2</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
-<span class="sd">&quot;&quot;&quot;Fib without recursion.&quot;&quot;&quot;</span>
-<span class="n">a</span><span class="p">,</span> <span class="n">b</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span>
-<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">n</span><span class="o">+</span><span class="mi">1</span><span class="p">):</span>
-<span class="n">a</span><span class="p">,</span> <span class="n">b</span> <span class="o">=</span> <span class="n">b</span><span class="p">,</span> <span class="n">a</span><span class="o">+</span><span class="n">b</span>
-<span class="k">return</span> <span class="n">b</span>
-<span class="nb">print</span> <span class="p">[</span><span class="n">fib2</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">)]</span>
-<span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">13</span><span class="p">,</span> <span class="mi">21</span><span class="p">,</span> <span class="mi">34</span><span class="p">,</span> <span class="mi">55</span><span class="p">]</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>速度比较：</p>
-<p>In [25]:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="o">%</span><span class="n">timeit</span> <span class="n">fib1</span><span class="p">(</span><span class="mi">20</span><span class="p">)</span>
-<span class="o">%</span><span class="n">timeit</span> <span class="n">fib2</span><span class="p">(</span><span class="mi">20</span><span class="p">)</span>
-<span class="mi">100</span> <span class="n">loops</span><span class="p">,</span> <span class="n">best</span> <span class="n">of</span> <span class="mi">3</span><span class="p">:</span> <span class="mf">5.35</span> <span class="n">ms</span> <span class="n">per</span> <span class="n">loop</span>
-<span class="mi">100000</span> <span class="n">loops</span><span class="p">,</span> <span class="n">best</span> <span class="n">of</span> <span class="mi">3</span><span class="p">:</span> <span class="mf">2.2</span> <span class="n">μs</span> <span class="n">per</span> <span class="n">loop</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>对于第一个递归函数来说，调用 <em>fib(n+2)</em> 的时候计算 <em>fib(n+1)</em>, <em>fib(n)</em>，调用 <em>fib(n+1)</em> 的时候也计算了一次 <em>fib(n)</em>，这样造成了重复计算。</p>
-<p>使用缓存机制的递归版本，这里利用了默认参数可变的性质，构造了一个缓存：</p>
-<p>In [26]:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">fib3</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">cache</span><span class="o">=</span><span class="p">{</span><span class="mi">0</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">:</span> <span class="mi">1</span><span class="p">}):</span>
-<span class="sd">&quot;&quot;&quot;Fib with recursion and caching.&quot;&quot;&quot;</span>
-<span class="k">try</span><span class="p">:</span>
-<span class="k">return</span> <span class="n">cache</span><span class="p">[</span><span class="n">n</span><span class="p">]</span>
-<span class="k">except</span> <span class="ne">KeyError</span><span class="p">:</span>
-<span class="n">cache</span><span class="p">[</span><span class="n">n</span><span class="p">]</span> <span class="o">=</span> <span class="n">fib3</span><span class="p">(</span><span class="n">n</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">+</span> <span class="n">fib3</span><span class="p">(</span><span class="n">n</span><span class="o">-</span><span class="mi">2</span><span class="p">)</span>
-<span class="k">return</span> <span class="n">cache</span><span class="p">[</span><span class="n">n</span><span class="p">]</span>
-<span class="nb">print</span> <span class="p">[</span><span class="n">fib3</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">)]</span>
-<span class="o">%</span><span class="n">timeit</span> <span class="n">fib1</span><span class="p">(</span><span class="mi">20</span><span class="p">)</span>
-<span class="o">%</span><span class="n">timeit</span> <span class="n">fib2</span><span class="p">(</span><span class="mi">20</span><span class="p">)</span>
-<span class="o">%</span><span class="n">timeit</span> <span class="n">fib3</span><span class="p">(</span><span class="mi">20</span><span class="p">)</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<blockquote>
-<div><p>[1, 1, 2, 3, 5, 8, 13, 21, 34, 55]
-100 loops, best of 3: 5.37 ms per loop
-100000 loops, best of 3: 2.19 μs per loop
-The slowest run took 150.16 times longer than the fastest. This could mean that an intermediate result is being cached
-1000000 loops, best of 3: 230 ns per loop</p>
-</div></blockquote>
-</section>
-<section id="id9">
-<h2>作者 &amp; 更新时间<a class="headerlink" href="#id9" title="Permalink to this heading">¶</a></h2>
-<p>作者:李金  <a class="reference external" href="mailto:lijinwithyou&#37;&#52;&#48;gmail&#46;com">lijinwithyou<span>&#64;</span>gmail<span>&#46;</span>com</a></p>
-<p>提交: 2017/12/6</p>
-</section>
-</section>
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-</form>
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-<script>
-var milib;
-if (milib==undefined) {
-var milib = function(){
-};
-milib.qeditor = function(str){
-alert('Please share this page to QPython then run it')
-};
-milib.getPurchaseNumber = function(str){
-//alert('getPurchaseNumber:'+str)
-return "10+";
-};
-milib.a8playVideoFromGW= function(url){
-window.open(url)
-};
-milib.qpylibinstall = function(cat,url,target){
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-$('.purchase-img').click(function(e) {
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-//按钮事件
-function btnEvent(){
-var seq = this.getAttribute('target')-2
-var a = this.parentElement.getElementsByTagName('pre')
-if ( a && a.length > 0){
-/**********有代码，调用控制台*************/
-var code = a[seq].lastChild.nodeValue
-milib.qeditor(code)
-}else{
-//没有代码，调转
-var url = this.getAttribute('data-url');
-window.open(url);
-}
-}
-//获取赞赏
-function setPraise(){
-var u = window.location.href.split('/')
-var s = u[u.length-1].replace('.html', '')
-s = u[u.length-2]+'-'+s
-var i = document.getElementById('praise').getElementsByTagName('span')[0]
-var num = milib.getPurchaseNumber(s);
-i.innerHTML = num
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-var prearr = document.getElementsByTagName('pre');
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-//正则处理 lavel5
-temp_data = temp_data.replace(/\^2\$/g, spaceString(2));
-temp_data = temp_data.replace(/\^4\$/g, spaceString(4));
-temp_data = temp_data.replace(/\^6\$/g, spaceString(6));
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-temp_data = temp_data.replace(/\^10\$/g, spaceString(10));
-temp_data = temp_data.replace(/\^12\$/g, spaceString(12));
-temp_data = temp_data.replace(/\^16\$/g, spaceString(16));
-temp_data = temp_data.replace(/\^20\$/g, spaceString(20));
-//set
-prearr[i].lastChild.nodeValue = temp_data;
-}
-}
-//产生空格
-function spaceString(num){
-var v = ''
-for (var i = 0; i < num; i++) {
-v += ' ';
-}
-return v;
-}
-function setSearchHistory (t) {
-if (!t)
-return;
-if (localStorage.course_search_list) {
-var list = localStorage.course_search_list.split('%,%');
-var i = list.indexOf(t);
-if (i !== -1) {
-list.splice(i, 1);
-list.push(t);
-localStorage.course_search_list = list.join('%,%');
-}else {
-localStorage.course_search_list += '%,%'+t;
-}
-}else {
-localStorage.course_search_list = t;
-}
-}
-window.onload = function(){
-setPraise();
-initCode();
-//将所有的button绑定事件
-var b = document.getElementsByTagName('button');
-for (var i = 0; i < b.length; i++) {
-if (b[i].className=='button') {
-b[i].setAttribute('target', i)
-b[i].onclick = btnEvent ;
-}
-}
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-$(function(){
-var url = location.href.split('?');
-var arg = url[1];
-if (!arg){
-return;
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-$('.web-show').css('display','block');
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-setSearchHistory($("input[name='q']").val());
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-$('.bodywrapper .body form input').val("");
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-(i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
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diff --git a/docs/dive-into-python/cn/re.html b/docs/dive-into-python/cn/re.html
deleted file mode 100644
index f741766..0000000
--- a/docs/dive-into-python/cn/re.html
+++ /dev/null
@@ -1,464 +0,0 @@
-<!DOCTYPE html>
-<html lang="en-US">
-<head>
-<meta charset="utf-8" />
-<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
-<link href="https://cdn.bootcss.com/bootstrap/3.3.7/css/bootstrap.min.css" rel="stylesheet">
-<title>正则表达式和 re 模块</title>
-<link rel="stylesheet" type="text/css" href="../../static/pygments.css" />
-<link rel="stylesheet" type="text/css" href="../../static/style.css" />
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-</head><body>
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-<a target="_blank" href="https://github.com/qpython-android/qpython">Github</a>
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-<main class="contain web-content">
-<div class="document">
-<div class="documentwrapper">
-<div class="bodywrapper">
-<div class="body" role="main">
-<section id="re">
-<h1>正则表达式和 re 模块<a class="headerlink" href="#re" title="Permalink to this heading">¶</a></h1>
-<section id="id1">
-<h2>正则表达式<a class="headerlink" href="#id1" title="Permalink to this heading">¶</a></h2>
-<p>正则表达式是用来匹配字符串或者子串的一种模式，匹配的字符串可以很具体，也可以很一般化。</p>
-<p>Python 标准库提供了 re 模块。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">re</span>
-<span class="nb">dir</span><span class="p">(</span><span class="n">re</span><span class="p">)</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-</section>
-<section id="re-match-re-search">
-<h2>re.match &amp; re.search<a class="headerlink" href="#re-match-re-search" title="Permalink to this heading">¶</a></h2>
-<p>在 re 模块中， re.match 和 re.search 是常用的两个方法：
-re.match(pattern, string[, flags])
-re.search(pattern, string[, flags])</p>
-<p>两者都寻找第一个匹配成功的部分，成功则返回一个 match 对象，不成功则返回 None，不同之处在于 re.match 只匹配字符串的开头部分，而 re.search 匹配的则是整个字符串中的子串。</p>
-</section>
-<section id="re-findall-re-finditer">
-<h2>re.findall &amp; re.finditer<a class="headerlink" href="#re-findall-re-finditer" title="Permalink to this heading">¶</a></h2>
-<p>re.findall(pattern, string) 返回所有匹配的对象,
-re.finditer 则返回一个迭代器。</p>
-</section>
-<section id="re-split">
-<h2>re.split<a class="headerlink" href="#re-split" title="Permalink to this heading">¶</a></h2>
-<p>re.split(pattern, string[, maxsplit]) 按照 pattern 指定的内容对字符串进行分割。</p>
-</section>
-<section id="re-sub">
-<h2>re.sub<a class="headerlink" href="#re-sub" title="Permalink to this heading">¶</a></h2>
-<p>re.sub(pattern, repl, string[, count]) 将 pattern 匹配的内容进行替换。</p>
-</section>
-<section id="re-compile">
-<h2>re.compile<a class="headerlink" href="#re-compile" title="Permalink to this heading">¶</a></h2>
-<p>re.compile(pattern) 生成一个 pattern 对象，这个对象有匹配，替换，分割字符串的方法。</p>
-</section>
-<section id="id2">
-<h2>正则表达式规则<a class="headerlink" href="#id2" title="Permalink to this heading">¶</a></h2>
-<p>正则表达式由一些普通字符和一些元字符（metacharacters）组成。普通字符包括大小写的字母和数字，而元字符则具有特殊的含义：</p>
-<table class="docutils align-default">
-<thead>
-<tr class="row-odd"><th class="head"><p>子表达式</p></th>
-<th class="head"><p>匹配内容</p></th>
-</tr>
-</thead>
-<tbody>
-<tr class="row-even"><td><p>.</p></td>
-<td><p>匹配除了换行符之外的内容</p></td>
-</tr>
-<tr class="row-odd"><td><p>w</p></td>
-<td><p>匹配所有字母和数字字符</p></td>
-</tr>
-<tr class="row-even"><td><p>d</p></td>
-<td><p>匹配所有数字，相当于 [0-9]</p></td>
-</tr>
-<tr class="row-odd"><td><p>s</p></td>
-<td><p>匹配空白，相当于 [tntfv]</p></td>
-</tr>
-<tr class="row-even"><td><p>W,D,S</p></td>
-<td><p>匹配对应小写字母形式的补</p></td>
-</tr>
-<tr class="row-odd"><td><p>[…]</p></td>
-<td><p>表示可以匹配的集合支持范围表示如 a-z, 0-9 等</p></td>
-</tr>
-<tr class="row-even"><td><p>(…)</p></td>
-<td><p>表示作为一个整体进行匹配</p></td>
-</tr>
-<tr class="row-odd"><td><p>^</p></td>
-<td><p>表示匹配后面的子表达式的补</p></td>
-</tr>
-<tr class="row-even"><td><ul class="simple">
-<li></li>
-</ul>
-</td>
-<td><p>表示匹配前面的子表达式 0 次或更多次</p></td>
-</tr>
-<tr class="row-odd"><td><p>?</p></td>
-<td><p>表示匹配前面的子表达式 1 次或更多次</p></td>
-</tr>
-<tr class="row-even"><td><p>{m}</p></td>
-<td><p>表示匹配前面的子表达式 m 次</p></td>
-</tr>
-<tr class="row-odd"><td><p>{m,}</p></td>
-<td><p>表示匹配前面的子表达式至少 m 次</p></td>
-</tr>
-<tr class="row-even"><td><p>{m,n}</p></td>
-<td><p>表示匹配前面的子表达式至少 m 次，至多 n 次</p></td>
-</tr>
-</tbody>
-</table>
-<p>例如：
-- ca*t 匹配： ct, cat, caaaat, …
-- abd|acd 匹配： ab1, ac9, …
-- ([^a-q]bd) 匹配： rbd, 5bd, …</p>
-</section>
-<section id="id3">
-<h2>例子<a class="headerlink" href="#id3" title="Permalink to this heading">¶</a></h2>
-<p>假设我们要匹配这样的字符串：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">string</span> <span class="o">=</span> <span class="s1">&#39;hello world&#39;</span>
-<span class="n">pattern</span> <span class="o">=</span> <span class="s1">&#39;hello (\w+)&#39;</span>
-<span class="n">match</span> <span class="o">=</span> <span class="n">re</span><span class="o">.</span><span class="n">match</span><span class="p">(</span><span class="n">pattern</span><span class="p">,</span> <span class="n">string</span><span class="p">)</span>
-<span class="nb">print</span> <span class="n">match</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>一旦找到了符合条件的部分，我们便可以使用 group 方法查看匹配的部分：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>string = &#39;hello world&#39;
-pattern = &#39;hello (\w+)&#39;
-match = re.match(pattern, string)
-if match is not None:
-    print match.group(0)
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>获得第二个匹配的部分</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>string = &#39;hello world&#39;
-pattern = &#39;hello (\w+)&#39;
-match = re.match(pattern, string)
-if match is not None:
-    print match.group(1)
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>我们可以改变 string 的内容：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>string = &#39;hello there&#39;
-pattern = &#39;hello (\w+)&#39;
-= re.match(pattern, string)
-if match is not None:
-    print match.group(0)
-print match.group(1)
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>通常，match.group(0) 匹配整个返回的内容，之后的 1,2,3,… 返回规则中每个括号（按照括号的位置排序）匹配的部分。</p>
-<p>如果某个 pattern 需要反复使用，那么我们可以将它预先编译：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>pattern1 = re.compile(&#39;hello (\w+)&#39;)
-match = pattern1.match(string)
-if match is not None:
-    print match.group(1)
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>由于元字符的存在，所以对于一些特殊字符，我们需要使用 ‘' 进行逃逸字符的处理，使用表达式 ‘\’ 来匹配 ‘' 。
-但事实上，Python 本身对逃逸字符也是这样处理的：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pattern</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="se">\\</span><span class="s1">&#39;</span>
-<span class="nb">print</span> <span class="n">pattern</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>因为逃逸字符的问题，我们需要使用四个 ‘\\’ 来匹配一个单独的 ‘'：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pattern</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="se">\\\\</span><span class="s1">&#39;</span>
-<span class="n">path</span> <span class="o">=</span> <span class="s2">&quot;C:</span><span class="se">\\</span><span class="s2">foo</span><span class="se">\\</span><span class="s2">bar</span><span class="se">\\</span><span class="s2">baz.txt&quot;</span>
-<span class="nb">print</span> <span class="n">re</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">pattern</span><span class="p">,</span> <span class="n">path</span><span class="p">)</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>这样看起来十分麻烦，好在 Python 提供了 raw string 来忽略对逃逸字符串的处理，从而可以这样进行匹配：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pattern</span> <span class="o">=</span> <span class="sa">r</span><span class="s1">&#39;</span><span class="se">\\</span><span class="s1">&#39;</span>
-<span class="n">path</span> <span class="o">=</span> <span class="sa">r</span><span class="s2">&quot;C:\foo\bar\baz.txt&quot;</span>
-<span class="nb">print</span> <span class="n">re</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">pattern</span><span class="p">,</span> <span class="n">path</span><span class="p">)</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>如果规则太多复杂，正则表达式不一定是个好选择。</p>
-</section>
-<section id="numpy-fromregex">
-<h2>Numpy 的 fromregex()<a class="headerlink" href="#numpy-fromregex" title="Permalink to this heading">¶</a></h2>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># 有 test.dat 文件，内容如下</span>
-<span class="mi">1312</span> <span class="n">foo</span>
-<span class="mi">1534</span>    <span class="n">bar</span>
-<span class="mi">444</span>  <span class="n">qux</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>numpy.fromregex(file, pattern, dtype) 中，dtype 中的内容与 pattern 的括号一一对应：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pattern</span> <span class="o">=</span> <span class="s2">&quot;(\d+)\s+(...)&quot;</span>
-<span class="n">dt</span> <span class="o">=</span> <span class="p">[(</span><span class="s1">&#39;num&#39;</span><span class="p">,</span> <span class="s1">&#39;int64&#39;</span><span class="p">),</span> <span class="p">(</span><span class="s1">&#39;key&#39;</span><span class="p">,</span> <span class="s1">&#39;S3&#39;</span><span class="p">)]</span>
-<span class="kn">from</span> <span class="nn">numpy</span> <span class="kn">import</span> <span class="n">fromregex</span>
-<span class="n">output</span> <span class="o">=</span> <span class="n">fromregex</span><span class="p">(</span><span class="s1">&#39;test.dat&#39;</span><span class="p">,</span> <span class="n">pattern</span><span class="p">,</span> <span class="n">dt</span><span class="p">)</span>
-<span class="nb">print</span> <span class="n">output</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>显示 num 项：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pattern</span> <span class="o">=</span> <span class="s2">&quot;(\d+)\s+(...)&quot;</span>
-<span class="n">dt</span> <span class="o">=</span> <span class="p">[(</span><span class="s1">&#39;num&#39;</span><span class="p">,</span> <span class="s1">&#39;int64&#39;</span><span class="p">),</span> <span class="p">(</span><span class="s1">&#39;key&#39;</span><span class="p">,</span> <span class="s1">&#39;S3&#39;</span><span class="p">)]</span>
-<span class="kn">from</span> <span class="nn">numpy</span> <span class="kn">import</span> <span class="n">fromregex</span>
-<span class="n">output</span> <span class="o">=</span> <span class="n">fromregex</span><span class="p">(</span><span class="s1">&#39;test.dat&#39;</span><span class="p">,</span> <span class="n">pattern</span><span class="p">,</span> <span class="n">dt</span><span class="p">)</span>
-<span class="nb">print</span> <span class="n">output</span><span class="p">[</span><span class="s1">&#39;num&#39;</span><span class="p">]</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-</section>
-<section id="id4">
-<h2>作者 &amp; 更新时间<a class="headerlink" href="#id4" title="Permalink to this heading">¶</a></h2>
-<p>作者:<cite>李金  &lt;lijinwithyou&#64;gmail.com&gt;</cite></p>
-<p>提交: 2017/12/6</p>
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diff --git a/docs/dive-into-python/cn/sql.html b/docs/dive-into-python/cn/sql.html
deleted file mode 100644
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-<title>SQL 数据库</title>
-<link rel="stylesheet" type="text/css" href="../../static/pygments.css" />
-<link rel="stylesheet" type="text/css" href="../../static/style.css" />
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-<div class="body" role="main">
-<section id="sql">
-<h1>SQL 数据库<a class="headerlink" href="#sql" title="Permalink to this heading">¶</a></h1>
-<p>Python 提供了一系列标准的数据库的 API，这里我们介绍 sqlite 数据库的用法，其他的数据库的用法大同小异：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">sqlite3</span> <span class="k">as</span> <span class="nn">db</span>
-</pre></div>
-</div>
-<p>首先我们要建立或者连接到一个数据库上：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">sqlite3</span> <span class="k">as</span> <span class="nn">db</span>
-<span class="n">connection</span> <span class="o">=</span> <span class="n">db</span><span class="o">.</span><span class="n">connect</span><span class="p">(</span><span class="s2">&quot;my_database.sqlite&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>不同的数据库有着不同的连接方法，例如 cx-oracle 数据库的链接方式为：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">connection</span> <span class="o">=</span> <span class="n">db</span><span class="o">.</span><span class="n">connect</span><span class="p">(</span><span class="n">username</span><span class="p">,</span> <span class="n">password</span><span class="p">,</span> <span class="n">host</span><span class="p">,</span> <span class="n">port</span><span class="p">,</span>  <span class="s1">&#39;XE&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>一旦建立连接，我们可以利用它的 cursor() 来执行 SQL 语句：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">sqlite3</span> <span class="k">as</span> <span class="nn">db</span>
-<span class="n">connection</span> <span class="o">=</span> <span class="n">db</span><span class="o">.</span><span class="n">connect</span><span class="p">(</span><span class="s2">&quot;my_database.sqlite&quot;</span><span class="p">)</span>
-<span class="n">cursor</span> <span class="o">=</span> <span class="n">connection</span><span class="o">.</span><span class="n">cursor</span><span class="p">()</span>
-<span class="n">cursor</span><span class="o">.</span><span class="n">execute</span><span class="p">(</span><span class="s2">&quot;&quot;&quot;CREATE TABLE IF NOT EXISTS orders(</span>
-<span class="s2">      order_id TEXT PRIMARY KEY,</span>
-<span class="s2">      date TEXT,</span>
-<span class="s2">      symbol TEXT,</span>
-<span class="s2">      quantity INTEGER,</span>
-<span class="s2">      price NUMBER)&quot;&quot;&quot;</span><span class="p">)</span>
-<span class="n">cursor</span><span class="o">.</span><span class="n">execute</span><span class="p">(</span><span class="s2">&quot;&quot;&quot;INSERT INTO orders VALUES (&#39;A0001&#39;, &#39;2013-12-01&#39;, &#39;AAPL&#39;, 1000, 203.4)&quot;&quot;&quot;</span><span class="p">)</span>
-<span class="n">connection</span><span class="o">.</span><span class="n">commit</span><span class="p">()</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>不过为了安全起见，一般不将数据内容写入字符串再传入，而是使用这样的方式：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">sqlite3</span> <span class="k">as</span> <span class="nn">db</span>
-<span class="n">connection</span> <span class="o">=</span> <span class="n">db</span><span class="o">.</span><span class="n">connect</span><span class="p">(</span><span class="s2">&quot;my_database.sqlite&quot;</span><span class="p">)</span>
-<span class="n">cursor</span> <span class="o">=</span> <span class="n">connection</span><span class="o">.</span><span class="n">cursor</span><span class="p">()</span>
-<span class="n">orders</span> <span class="o">=</span> <span class="p">[</span>
-<span class="p">(</span><span class="s2">&quot;A0002&quot;</span><span class="p">,</span><span class="s2">&quot;2013-12-01&quot;</span><span class="p">,</span><span class="s2">&quot;MSFT&quot;</span><span class="p">,</span><span class="mi">1500</span><span class="p">,</span><span class="mf">167.5</span><span class="p">),</span>
-<span class="p">(</span><span class="s2">&quot;A0003&quot;</span><span class="p">,</span><span class="s2">&quot;2013-12-02&quot;</span><span class="p">,</span><span class="s2">&quot;GOOG&quot;</span><span class="p">,</span><span class="mi">1500</span><span class="p">,</span><span class="mf">167.5</span><span class="p">)</span>
-<span class="p">]</span>
-<span class="n">cursor</span><span class="o">.</span><span class="n">executemany</span><span class="p">(</span><span class="s2">&quot;&quot;&quot;INSERT INTO orders VALUES</span>
-<span class="s2">    (?, ?, ?, ?, ?)&quot;&quot;&quot;</span><span class="p">,</span> <span class="n">orders</span><span class="p">)</span>
-<span class="n">connection</span><span class="o">.</span><span class="n">commit</span><span class="p">()</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>cx-oracle 数据库使用不同的方式：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">cursor</span><span class="o">.</span><span class="n">executemany</span><span class="p">(</span><span class="s2">&quot;&quot;&quot;INSERT INTO orders VALUES</span>
-<span class="s2">    (:order_id, :date, :symbol, :quantity, :price)&quot;&quot;&quot;</span><span class="p">,</span>
-<span class="n">orders</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>查看支持的数据库格式：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">sqlite3</span> <span class="k">as</span> <span class="nn">db</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">db</span><span class="o">.</span><span class="n">paramstyle</span><span class="p">)</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>在 query 语句执行之后，我们需要进行 commit，否则数据库将不会接受这些变化，如果想撤销某个 commit，可以使用 rollback() 方法撤销到上一次 commit() 的结果：</p>
-<dl class="simple">
-<dt>::</dt><dd><dl class="simple">
-<dt>try:</dt><dd><p>    … # perform some operations</p>
-</dd>
-<dt>except:</dt><dd><p>    connection.rollback()
-    raise</p>
-</dd>
-<dt>else:</dt><dd><p>    connection.commit()</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<p>使用 SELECT 语句对数据库进行查询：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">sqlite3</span> <span class="k">as</span> <span class="nn">db</span>
-<span class="n">connection</span> <span class="o">=</span> <span class="n">db</span><span class="o">.</span><span class="n">connect</span><span class="p">(</span><span class="s2">&quot;my_database.sqlite&quot;</span><span class="p">)</span>
-<span class="n">cursor</span> <span class="o">=</span> <span class="n">connection</span><span class="o">.</span><span class="n">cursor</span><span class="p">()</span>
-<span class="n">stock</span> <span class="o">=</span> <span class="s1">&#39;MSFT&#39;</span>
-<span class="n">cursor</span><span class="o">.</span><span class="n">execute</span><span class="p">(</span><span class="s2">&quot;&quot;&quot;SELECT *</span>
-<span class="s2">    FROM orders</span>
-<span class="s2">    WHERE symbol=?</span>
-<span class="s2">    ORDER BY quantity&quot;&quot;&quot;</span><span class="p">,</span> <span class="p">(</span><span class="n">stock</span><span class="p">,))</span>
-<span class="k">for</span> <span class="n">row</span> <span class="ow">in</span> <span class="n">cursor</span><span class="p">:</span>
-<span class="nb">print</span> <span class="n">row</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>cursor.fetchone() 返回下一条内容， cursor.fetchall() 返回所有查询到的内容组成的列表（可能非常大）：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">sqlite3</span> <span class="k">as</span> <span class="nn">db</span>
-<span class="n">connection</span> <span class="o">=</span> <span class="n">db</span><span class="o">.</span><span class="n">connect</span><span class="p">(</span><span class="s2">&quot;my_database.sqlite&quot;</span><span class="p">)</span>
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-<span class="n">stock</span> <span class="o">=</span> <span class="s1">&#39;AAPL&#39;</span>
-<span class="n">cursor</span><span class="o">.</span><span class="n">execute</span><span class="p">(</span><span class="s2">&quot;&quot;&quot;SELECT *</span>
-<span class="s2">FROM orders</span>
-<span class="s2">WHERE symbol=?</span>
-<span class="s2">ORDER BY quantity&quot;&quot;&quot;</span><span class="p">,</span> <span class="p">(</span><span class="n">stock</span><span class="p">,))</span>
-<span class="n">cursor</span><span class="o">.</span><span class="n">fetchall</span><span class="p">()</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>读写完毕数据库之后需要关闭数据库：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">cursor</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
-<span class="n">connection</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
-</pre></div>
-</div>
-<section id="id1">
-<h2>作者 &amp; 更新时间<a class="headerlink" href="#id1" title="Permalink to this heading">¶</a></h2>
-<p>作者:<cite>李金  &lt;lijinwithyou&#64;gmail.com&gt;</cite></p>
-<p>提交: 2017/12/6</p>
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diff --git a/docs/dive-into-python/cn/sys.html b/docs/dive-into-python/cn/sys.html
deleted file mode 100644
index 3ae8085..0000000
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+++ /dev/null
@@ -1,374 +0,0 @@
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-<title>sys 模块简介</title>
-<link rel="stylesheet" type="text/css" href="../../static/pygments.css" />
-<link rel="stylesheet" type="text/css" href="../../static/style.css" />
-<meta charset="UTF-8">
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-<input type="hidden" name="check_keywords" value="yes" />
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-<main class="contain web-content">
-<div class="document">
-<div class="documentwrapper">
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-<div class="body" role="main">
-<section id="sys">
-<h1>sys 模块简介<a class="headerlink" href="#sys" title="Permalink to this heading">¶</a></h1>
-<p>通过以下方式引入sys模块</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">sys</span>
-</pre></div>
-</div>
-<section id="id1">
-<h2>命令行参数<a class="headerlink" href="#id1" title="Permalink to this heading">¶</a></h2>
-<p><em>sys.argv</em> 显示传入的参数：</p>
-<p>假设print_args.py的内容如下：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">sys</span>
-<span class="nb">print</span> <span class="n">sys</span><span class="o">.</span><span class="n">argv</span>
-</pre></div>
-</div>
-<p>用以下方法运行这个程序</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">python</span> <span class="n">print_args</span><span class="o">.</span><span class="n">py</span> <span class="mi">1</span> <span class="n">foo</span>
-</pre></div>
-</div>
-<p>将会得到以下的输出</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">[</span><span class="s1">&#39;print_args.py&#39;</span><span class="p">,</span> <span class="s1">&#39;1&#39;</span><span class="p">,</span> <span class="s1">&#39;foo&#39;</span><span class="p">]</span>
-</pre></div>
-</div>
-<p>第一个参数 （<em>sys.args[0]</em>） 表示的始终是执行的文件名，然后依次显示传入的参数。</p>
-<p>可以用os.remove删除文件：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
-<span class="n">os</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="s1">&#39;print_args.py&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-</section>
-<section id="id2">
-<h2>异常消息<a class="headerlink" href="#id2" title="Permalink to this heading">¶</a></h2>
-<p><em>sys.exc_info() *可以显示 *Exception</em> 的信息，返回一个 <em>(type, value, traceback)</em> 组成的三元组，可以与 <em>try/catch</em> 块一起使用：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>try:
-    x = 1/0
-except Exception:
-    print sys.exc_info()
-</pre></div>
-</div>
-<p>运行上述代码，将会得到：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">(</span><span class="o">&lt;</span><span class="nb">type</span> <span class="s1">&#39;exceptions.ZeroDivisionError&#39;</span><span class="o">&gt;</span><span class="p">,</span> <span class="ne">ZeroDivisionError</span><span class="p">(</span><span class="s1">&#39;integer division or modulo by zero&#39;</span><span class="p">,),</span> <span class="o">&lt;</span><span class="n">traceback</span> <span class="nb">object</span> <span class="n">at</span> <span class="mh">0x0000000003C6FA08</span><span class="o">&gt;</span><span class="p">)</span>
-</pre></div>
-</div>
-<p><em>sys.exc_clear()</em> 用于清除所有的异常消息。</p>
-</section>
-<section id="id3">
-<h2>标准输入输出流<a class="headerlink" href="#id3" title="Permalink to this heading">¶</a></h2>
-<p>以下属性代表python的标准输入输出流操作句柄</p>
-<ul class="simple">
-<li><p>sys.stdin</p></li>
-<li><p>ys.stdout</p></li>
-<li><p>sys.stderr</p></li>
-</ul>
-</section>
-<section id="python">
-<h2>退出Python<a class="headerlink" href="#python" title="Permalink to this heading">¶</a></h2>
-<p><em>sys.exit(arg=0)</em> 用于退出 Python。<em>0</em> 或者 <em>None</em> 表示正常退出，其他值表示异常。</p>
-</section>
-<section id="python-path">
-<h2>Python Path<a class="headerlink" href="#python-path" title="Permalink to this heading">¶</a></h2>
-<p><em>sys.path</em> 表示 Python 搜索模块的路径和查找顺序：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">sys</span>
-<span class="nb">print</span> <span class="n">sys</span><span class="o">.</span><span class="n">path</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>在程序中可以通过sys.append, sys.insert 等方式添加，修改新的路径。</p>
-</section>
-<section id="id4">
-<h2>操作系统信息<a class="headerlink" href="#id4" title="Permalink to this heading">¶</a></h2>
-<p><em>sys.platform</em> 显示当前操作系统信息：</p>
-<ul class="simple">
-<li><p>Windows: win32</p></li>
-<li><p>Mac OSX: darwin</p></li>
-<li><p>Linux: linux2</p></li>
-</ul>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">sys</span>
-<span class="n">sys</span><span class="o">.</span><span class="n">platform</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>‘win32’
-返回*Windows* 操作系统的版本：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">sys</span>
-<span class="n">sys</span><span class="o">.</span><span class="n">getwindowsversion</span><span class="p">()</span>
-</pre></div>
-</div>
-<p>将会输出</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">sys</span><span class="o">.</span><span class="n">getwindowsversion</span><span class="p">(</span><span class="n">major</span><span class="o">=</span><span class="mi">6</span><span class="p">,</span> <span class="n">minor</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">build</span><span class="o">=</span><span class="mi">9200</span><span class="p">,</span> <span class="n">platform</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">service_pack</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>标准库中有 <em>planform</em> 模块提供更详细的信息。</p>
-</section>
-<section id="id5">
-<h2>Python 版本信息<a class="headerlink" href="#id5" title="Permalink to this heading">¶</a></h2>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">sys</span>
-<span class="n">sys</span><span class="o">.</span><span class="n">version</span>
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">sys</span>
-<span class="n">sys</span><span class="o">.</span><span class="n">version_info</span>
-</pre></div>
-</div>
-</section>
-<section id="id6">
-<h2>作者 &amp; 更新时间<a class="headerlink" href="#id6" title="Permalink to this heading">¶</a></h2>
-<p>作者:<cite>李金  &lt;lijinwithyou&#64;gmail.com&gt;</cite></p>
-<p>提交: 2017/12/6</p>
-</section>
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diff --git a/docs/dive-into-python/cn/the-use-of-modifier.html b/docs/dive-into-python/cn/the-use-of-modifier.html
deleted file mode 100644
index e8919ba..0000000
--- a/docs/dive-into-python/cn/the-use-of-modifier.html
+++ /dev/null
@@ -1,455 +0,0 @@
-<!DOCTYPE html>
-<html lang="en-US">
-<head>
-<meta charset="utf-8" />
-<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
-<link href="https://cdn.bootcss.com/bootstrap/3.3.7/css/bootstrap.min.css" rel="stylesheet">
-<title>修饰符的使用</title>
-<link rel="stylesheet" type="text/css" href="../../static/pygments.css" />
-<link rel="stylesheet" type="text/css" href="../../static/style.css" />
-<meta charset="UTF-8">
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-</head><body>
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-<a href="http://www.qpython.org/index.html">Home</a>
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-<span class="header-title-selected"></span>
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-<a href="http://www.aipy.org">AIPY</a>
-</li>
-<li>
-<a target="_blank" href="https://github.com/qpython-android/qpython">Github</a>
-</li>
-</ul>
-<div class="search-box hidden-xs">
-<form action="/search.html">
-<input type="search" name="q" placeholder="keyword">
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-<div class="document">
-<div class="documentwrapper">
-<div class="bodywrapper">
-<div class="body" role="main">
-<section id="id1">
-<h1>修饰符的使用<a class="headerlink" href="#id1" title="Permalink to this heading">¶</a></h1>
-<section id="classmethod">
-<h2>&#64;classmethod 修饰符<a class="headerlink" href="#classmethod" title="Permalink to this heading">¶</a></h2>
-<p>在 <em>Python</em> 标准库中，有很多自带的修饰符，例如 <em>classmethod</em> 将一个对象方法转换了类方法：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>class Foo(object):
-@classmethod
-    def bar(cls, x):
-        print &#39;the input is&#39;, x
-    def __init__(self):
-        pass
-</pre></div>
-</div>
-<p>类方法可以通过 <strong>类名.方法</strong> 来调用：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Foo</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="mi">12</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="mi">12</span>
-</pre></div>
-</div>
-</section>
-<section id="property">
-<h2>&#64;property 修饰符<a class="headerlink" href="#property" title="Permalink to this heading">¶</a></h2>
-<p>有时候，我们希望像 <strong>Java</strong> 一样支持 <em>getters</em> 和 <em>setters</em> 的方法，这时候就可以使用 <em>property</em> 修饰符：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>class Foo(object):
-    def __init__(self, data):
-        self.data = data
-    @property
-    def x(self):
-        return self.data
-</pre></div>
-</div>
-<p>此时可以使用* .x* 这个属性查看数据（不需要加上括号）：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">foo</span> <span class="o">=</span> <span class="n">Foo</span><span class="p">(</span><span class="mi">23</span><span class="p">)</span>
-<span class="n">foo</span><span class="o">.</span><span class="n">x</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="mi">23</span>
-</pre></div>
-</div>
-<p>这样做的好处在于，这个属性是只读的：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">foo</span><span class="o">.</span><span class="n">x</span> <span class="o">=</span> <span class="mi">1</span>
-</pre></div>
-</div>
-<p>如果想让它变成可读写，可以加上一个修饰符 <em>&#64;x.setter</em>：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>class Foo(object):
-def __init__(self, data):
-    self.data = data
-@property
-def x(self):
-    return self.data
-@x.setter
-def x(self, value):
-    self.data = value
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">foo</span> <span class="o">=</span> <span class="n">Foo</span><span class="p">(</span><span class="mi">23</span><span class="p">)</span>
-<span class="nb">print</span> <span class="n">foo</span><span class="o">.</span><span class="n">x</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="mi">23</span>
-</pre></div>
-</div>
-<p>可以通过属性改变它的值：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">foo</span><span class="o">.</span><span class="n">x</span> <span class="o">=</span> <span class="mi">1</span>
-<span class="nb">print</span> <span class="n">foo</span><span class="o">.</span><span class="n">x</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="mi">1</span>
-</pre></div>
-</div>
-</section>
-<section id="numpy-vectorize">
-<h2>Numpy 的 &#64;vectorize 修饰符<a class="headerlink" href="#numpy-vectorize" title="Permalink to this heading">¶</a></h2>
-<p><em>numpy</em> 的 <em>vectorize</em> 函数讲一个函数转换为 <em>ufunc</em>，事实上它也是一个修饰符：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>from numpy import vectorize, arange
-@vectorize
-def f(x):
-    if x &lt;= 0:
-        return x
-    else:
-        return 0
-f(arange(-10.0,10.0))
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">array</span><span class="p">([</span><span class="o">-</span><span class="mf">10.</span><span class="p">,</span>  <span class="o">-</span><span class="mf">9.</span><span class="p">,</span>  <span class="o">-</span><span class="mf">8.</span><span class="p">,</span>  <span class="o">-</span><span class="mf">7.</span><span class="p">,</span>  <span class="o">-</span><span class="mf">6.</span><span class="p">,</span>  <span class="o">-</span><span class="mf">5.</span><span class="p">,</span>  <span class="o">-</span><span class="mf">4.</span><span class="p">,</span>  <span class="o">-</span><span class="mf">3.</span><span class="p">,</span>  <span class="o">-</span><span class="mf">2.</span><span class="p">,</span>  <span class="o">-</span><span class="mf">1.</span><span class="p">,</span>   <span class="mf">0.</span><span class="p">,</span><span class="mf">0.</span><span class="p">,</span>   <span class="mf">0.</span><span class="p">,</span>   <span class="mf">0.</span><span class="p">,</span>   <span class="mf">0.</span><span class="p">,</span>   <span class="mf">0.</span><span class="p">,</span>   <span class="mf">0.</span><span class="p">,</span>   <span class="mf">0.</span><span class="p">,</span>   <span class="mf">0.</span><span class="p">,</span>   <span class="mf">0.</span><span class="p">])</span>
-</pre></div>
-</div>
-</section>
-<section id="id2">
-<h2>注册一个函数<a class="headerlink" href="#id2" title="Permalink to this heading">¶</a></h2>
-<p>来看这样的一个例子，定义一个类：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>class Registry(object):
-def __init__(self):
-    self._data = {}
-def register(self, f, name=None):
-    if name == None:
-        name = f.__name__
-    self._data[name] = f
-    setattr(self, name, f)
-</pre></div>
-</div>
-<p><em>register</em> 方法接受一个函数，将这个函数名作为属性注册到对象中。</p>
-<p>产生该类的一个对象：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">registry</span> <span class="o">=</span> <span class="n">Registry</span><span class="p">()</span>
-</pre></div>
-</div>
-<p>使用该对象的 <em>register</em> 方法作为修饰符：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>@registry.register
-def greeting():
-    print &quot;hello world&quot;
-</pre></div>
-</div>
-<p>这样这个函数就被注册到 <em>registry</em> 这个对象中去了：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">registry</span><span class="o">.</span><span class="n">_data</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">{</span><span class="s1">&#39;greeting&#39;</span><span class="p">:</span> <span class="o">&lt;</span><span class="n">function</span> <span class="n">__main__</span><span class="o">.</span><span class="n">greeting</span><span class="o">&gt;</span><span class="p">}</span>
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">registry</span><span class="o">.</span><span class="n">greeting</span>
-</pre></div>
-</div>
-<p>结果:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="o">&lt;</span><span class="n">function</span> <span class="n">__main__</span><span class="o">.</span><span class="n">greeting</span><span class="o">&gt;</span>
-</pre></div>
-</div>
-<p><em>flask</em> ，一个常用的网络应用，处理 url 的机制跟这个类似。</p>
-</section>
-<section id="wraps">
-<h2>使用 &#64;wraps<a class="headerlink" href="#wraps" title="Permalink to this heading">¶</a></h2>
-<p>一个通常的问题在于：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span> def logging_call(f):
-    def wrapper(*a, **kw):
-        print &#39;calling {}&#39;.format(f.__name__)
-        return f(*a, **kw)
-    return wrapper
-@logging_call
-def square(x):
-&#39;&#39;&#39;
-square function.
-&#39;&#39;&#39;
-return x ** 2
-print square.__doc__, square.__name__
-</pre></div>
-</div>
-<p>None wrapper</p>
-<p>我们使用修饰符之后，<em>square</em> 的 <em>metadata</em> 完全丢失了，返回的函数名与函数的 <em>docstring</em> 都不对。</p>
-<p>一个解决的方法是从 <em>functools</em> 模块导入 <em>wraps</em> 修饰符来修饰我们的修饰符：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span> import functools
-def logging_call(f):
-@functools.wraps(f)
-    def wrapper(*a, **kw):
-        print &#39;calling {}&#39;.format(f.__name__)
-        return f(*a, **kw)
-    return wrapper
-@logging_call
-def square(x):
-&#39;&#39;&#39;
-square function.
-&#39;&#39;&#39;
-return x ** 2
-print square.__doc__, square.__name__
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<blockquote>
-<div><dl class="simple">
-<dt>square function.</dt><dd><p>square</p>
-</dd>
-</dl>
-</div></blockquote>
-<p>现在这个问题解决了，所以在自定义修饰符方法的时候为了避免出现不必要的麻烦，尽量使用* wraps* 来修饰修饰符！</p>
-</section>
-<section id="class">
-<h2>Class 修饰符<a class="headerlink" href="#class" title="Permalink to this heading">¶</a></h2>
-<p>与函数修饰符类似，类修饰符是这样一类函数，接受一个类作为参数，通常返回一个新的类。</p>
-</section>
-<section id="id3">
-<h2>作者 &amp; 更新时间<a class="headerlink" href="#id3" title="Permalink to this heading">¶</a></h2>
-<p>作者:李金  <a class="reference external" href="mailto:lijinwithyou&#37;&#52;&#48;gmail&#46;com">lijinwithyou<span>&#64;</span>gmail<span>&#46;</span>com</a></p>
-<p>提交: 2017/12/6</p>
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diff --git a/docs/dive-into-python/cn/with.html b/docs/dive-into-python/cn/with.html
deleted file mode 100644
index d103212..0000000
--- a/docs/dive-into-python/cn/with.html
+++ /dev/null
@@ -1,597 +0,0 @@
-<!DOCTYPE html>
-<html lang="en-US">
-<head>
-<meta charset="utf-8" />
-<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
-<link href="https://cdn.bootcss.com/bootstrap/3.3.7/css/bootstrap.min.css" rel="stylesheet">
-<title>with 语句和上下文管理器</title>
-<link rel="stylesheet" type="text/css" href="../../static/pygments.css" />
-<link rel="stylesheet" type="text/css" href="../../static/style.css" />
-<meta charset="UTF-8">
-<meta name="viewport" content="width=device-width, initial-scale=1,maximum-scale=1, user-scalable=no">
-<script src="https://cdn.bootcss.com/bootstrap/3.3.7/js/bootstrap.min.js"></script>
-</head><body>
-<div class="header doc-header web-show">
-<div class="col-sm-offset-2 col-sm-8  index-header">
-<div class="navbar-header">
-<button type="button" class="navbar-toggle" data-toggle="collapse" data-target="#example-navbar-collapse">
-<span class="sr-only">切换导航</span>
-<span class="icon-bar"></span>
-<span class="icon-bar"></span>
-<span class="icon-bar"></span>
-</button>
-<a href="/index.html" class="logo"><img src="http://www.qpython.org/static/img_logo.png"></a>
-</div>
-<ul class="header-title collapse navbar-collapse" id="example-navbar-collapse" >
-<li>
-<a href="http://www.qpython.org/index.html">Home</a>
-</li>
-<li>
-<a href="http://www.qpython.org/document.html">Guide</a>
-</li>
-<li class="header-phone-selected">
-<a href="/index.html" class="header-selected">Course</a>
-<span class="header-title-selected"></span>
-</li>
-<li>
-<a href="http://www.aipy.org">AIPY</a>
-</li>
-<li>
-<a target="_blank" href="https://github.com/qpython-android/qpython">Github</a>
-</li>
-</ul>
-<div class="search-box hidden-xs">
-<form action="/search.html">
-<input type="search" name="q" placeholder="keyword">
-<input type="hidden" name="check_keywords" value="yes" />
-<input type="hidden" name="area" value="default" />
-<button><img src="http://www.qpython.org/static/ic_search.png"></button>
-</form>
-</div>
-</div>
-</div>
-<main class="contain web-content">
-<div class="document">
-<div class="documentwrapper">
-<div class="bodywrapper">
-<div class="body" role="main">
-<section id="with">
-<h1>with 语句和上下文管理器<a class="headerlink" href="#with" title="Permalink to this heading">¶</a></h1>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># create/aquire some resource</span>
-<span class="o">...</span>
-<span class="k">try</span><span class="p">:</span>
-<span class="c1"># do something with the resource</span>
-<span class="o">...</span>
-<span class="k">finally</span><span class="p">:</span>
-<span class="c1"># destroy/release the resource</span>
-<span class="o">...</span>
-</pre></div>
-</div>
-<p>处理文件，线程，数据库，网络编程等等资源的时候，我们经常需要使用上面这样的代码形式，以确保资源的正常使用和释放。</p>
-<p>好在Python 提供了 with 语句帮我们自动进行这样的处理，例如之前在打开文件时我们使用：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>with open(&#39;my_file&#39;, &#39;w&#39;) as fp:
-    # do stuff with fp
-    data = fp.write(&quot;Hello world&quot;)
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>这等效于下面的代码，但是要更简便：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>fp = open(&#39;my_file&#39;, &#39;w&#39;)
-try:
-    # do stuff with f
-    data = fp.write(&quot;Hello world&quot;)
-finally:
-    fp.close()
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<section id="id1">
-<h2>上下文管理器<a class="headerlink" href="#id1" title="Permalink to this heading">¶</a></h2>
-<p>其基本用法如下：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>with &lt;expression&gt;:
-    &lt;block&gt;
-</pre></div>
-</div>
-<p><em>&lt;expression&gt;</em> 执行的结果应当返回一个实现了上下文管理器的对象，即实现这样两个方法，<em>__enter__</em> 和 <em>__exit__</em>：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>with open(&#39;my_file&#39;, &#39;w&#39;) as fp:
-    # do stuff with fp
-    data = fp.write(&quot;Hello world&quot;)
-    print fp.__enter__
-    print fp.__exit__
-</pre></div>
-</div>
-<p>输出如下：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="o">&lt;</span><span class="n">built</span><span class="o">-</span><span class="ow">in</span> <span class="n">method</span> <span class="fm">__enter__</span> <span class="n">of</span> <span class="n">file</span> <span class="nb">object</span> <span class="n">at</span> <span class="mh">0x0000000003C1D540</span><span class="o">&gt;</span>
-<span class="o">&lt;</span><span class="n">built</span><span class="o">-</span><span class="ow">in</span> <span class="n">method</span> <span class="fm">__exit__</span> <span class="n">of</span> <span class="n">file</span> <span class="nb">object</span> <span class="n">at</span> <span class="mh">0x0000000003C1D540</span><span class="o">&gt;</span>
-</pre></div>
-</div>
-<p><em>__enter__ *方法在 *&lt;block&gt;</em> 执行前执行，而 <em>__exit__ *在 *&lt;block&gt;</em> 执行结束后执行：</p>
-<p>比如可以这样定义一个简单的上下文管理器：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>class ContextManager(object):
-    def __enter__(self):
-        print &quot;Entering&quot;
-    def __exit__(self, exc_type, exc_value, traceback):
-    print &quot;Exiting&quot;
-with ContextManager():
-    print &quot;  Inside the with statement&quot;
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>即使 <em>&lt;block&gt;</em> 中执行的内容出错，<em>__exit__</em> 也会被执行：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>class ContextManager(object):
-    def __enter__(self):
-        print &quot;Entering&quot;
-    def __exit__(self, exc_type, exc_value, traceback):
-    print &quot;Exiting&quot;
-with ContextManager():
-    print 1/0
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>输出会是：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Entering</span>
-<span class="n">Exiting</span>
-<span class="ne">ZeroDivisionError</span> <span class="n">Traceback</span> <span class="p">(</span><span class="n">most</span> <span class="n">recent</span> <span class="n">call</span> <span class="n">last</span><span class="p">)</span>
-<span class="ne">ZeroDivisionError</span><span class="p">:</span> <span class="n">integer</span> <span class="n">division</span> <span class="ow">or</span> <span class="n">modulo</span> <span class="n">by</span> <span class="n">zero</span>
-</pre></div>
-</div>
-</section>
-<section id="enter">
-<h2><em>__enter__</em> 的返回值<a class="headerlink" href="#enter" title="Permalink to this heading">¶</a></h2>
-<p>如果在 <em>__enter__ *方法下添加了返回值，那么我们可以使用 *as</em> 把这个返回值传给某个参数：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>class ContextManager(object):
-    def __enter__(self):
-        print &quot;Entering&quot;
-        return &quot;my value&quot;
-    def __exit__(self, exc_type, exc_value, traceback):
-        print &quot;Exiting&quot;
-#将 *__enter__*返回的值传给 *value* 变量：
-with ContextManager() as value:
-    print value
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>输出应该是：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Entering</span>
-<span class="n">my</span> <span class="n">value</span>
-<span class="n">Exiting</span>
-</pre></div>
-</div>
-<p>一个通常的做法是将 <em>__enter__</em> 的返回值设为这个上下文管理器对象本身，文件对象就是这样做的：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">fp</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="s1">&#39;my_file&#39;</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span>
-<span class="nb">print</span> <span class="n">fp</span><span class="o">.</span><span class="fm">__enter__</span><span class="p">()</span>
-<span class="n">fp</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
-<span class="kn">import</span> <span class="nn">os</span>
-<span class="n">os</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="s1">&#39;my_file&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>实现方法非常简单：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>class ContextManager(object):
-    def __enter__(self):
-        print &quot;Entering&quot;
-        return self
-    def __exit__(self, exc_type, exc_value, traceback):
-        print &quot;Exiting&quot;
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>with ContextManager() as value:
-    print value
-</pre></div>
-</div>
-<p>输出是：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Entering</span>
-<span class="o">&lt;</span><span class="n">__main__</span><span class="o">.</span><span class="n">ContextManager</span> <span class="nb">object</span> <span class="n">at</span> <span class="mh">0x0000000003D48828</span><span class="o">&gt;</span>
-<span class="n">Exiting</span>
-</pre></div>
-</div>
-</section>
-<section id="id2">
-<h2>错误处理<a class="headerlink" href="#id2" title="Permalink to this heading">¶</a></h2>
-<p>上下文管理器对象将错误处理交给 <em>__exit__</em> 进行，可以将错误类型，错误值和 <em>traceback</em> 等内容作为参数传递给 <em>__exit__</em> 函数：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>class ContextManager(object):
-    def __enter__(self):
-        print &quot;Entering&quot;
-    def __exit__(self, exc_type, exc_value, traceback):
-        print &quot;Exiting&quot;
-    if exc_type is not None:
-        print &quot;  Exception:&quot;, exc_value
-#如果没有错误，这些值都将是 *None*, 当有错误发生的时候：
-with ContextManager():
-    print 1/0
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>输出是：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Entering</span>
-<span class="n">Exiting</span>
-<span class="ne">Exception</span><span class="p">:</span> <span class="n">integer</span> <span class="n">division</span> <span class="ow">or</span> <span class="n">modulo</span> <span class="n">by</span> <span class="n">zero</span>
-<span class="ne">ZeroDivisionError</span> <span class="n">Traceback</span> <span class="p">(</span><span class="n">most</span> <span class="n">recent</span> <span class="n">call</span> <span class="n">last</span><span class="p">)</span>
-<span class="ne">ZeroDivisionError</span><span class="p">:</span> <span class="n">integer</span> <span class="n">division</span> <span class="ow">or</span> <span class="n">modulo</span> <span class="n">by</span> <span class="n">zero</span>
-</pre></div>
-</div>
-<p>在这个例子中，我们只是简单的显示了错误的值，并没有对错误进行处理，所以错误被向上抛出了，如果不想让错误抛出，只需要将 <em>__exit__</em> 的返回值设为* True*：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>class ContextManager(object):
-    def __enter__(self):
-        print &quot;Entering&quot;
-    def __exit__(self, exc_type, exc_value, traceback):
-        print &quot;Exiting&quot;
-    if exc_type is not None:
-        print &quot; Exception suppresed:&quot;, exc_value
-        return True
-with ContextManager():
-    print 1/0
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>输出是：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Entering</span>
-<span class="n">Exiting</span>
-<span class="ne">Exception</span> <span class="n">suppresed</span><span class="p">:</span> <span class="n">integer</span> <span class="n">division</span> <span class="ow">or</span> <span class="n">modulo</span> <span class="n">by</span> <span class="n">zero</span>
-</pre></div>
-</div>
-<p>在这种情况下，错误就不会被向上抛出。</p>
-</section>
-<section id="id3">
-<h2>数据库的例子<a class="headerlink" href="#id3" title="Permalink to this heading">¶</a></h2>
-<p>对于数据库的 <em>transaction</em> 来说，如果没有错误，我们就将其 <em>commit</em> 进行保存，如果有错误，那么我们将其回滚到上一次成功的状态。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>class Transaction(object):
-    def __init__(self, connection):
-        self.connection = connection
-    def __enter__(self):
-        return self.connection.cursor()
-    def __exit__(self, exc_type, exc_value, traceback):
-        if exc_value is None:
-            # transaction was OK, so commit
-            self.connection.commit()
-        else:
-            # transaction had a problem, so rollback
-self.connection.rollback()
-#建立一个数据库，保存一个地址表：
-import sqlite3 as db
-connection = db.connect(&quot;:memory:&quot;)
-with Transaction(connection) as cursor:
-    cursor.execute(&quot;&quot;&quot;CREATE TABLE IF NOT EXISTS addresses (
-        address_id INTEGER PRIMARY KEY,
-        street_address TEXT,
-        city TEXT,
-        state TEXT,
-        country TEXT,
-        postal_code TEXT
-    )&quot;&quot;&quot;)
-#插入数据：
-with Transaction(connection) as cursor:
-    cursor.executemany(&quot;&quot;&quot;INSERT OR REPLACE INTO addresses VALUES (?, ?, ?, ?, ?, ?)&quot;&quot;&quot;, [
-        (0, &#39;515 Congress Ave&#39;, &#39;Austin&#39;, &#39;Texas&#39;, &#39;USA&#39;, &#39;78701&#39;),
-        (1, &#39;245 Park Avenue&#39;, &#39;New York&#39;, &#39;New York&#39;, &#39;USA&#39;, &#39;10167&#39;),
-        (2, &#39;21 J.J. Thompson Ave.&#39;, &#39;Cambridge&#39;, None, &#39;UK&#39;, &#39;CB3 0FA&#39;),
-        (3, &#39;Supreme Business Park&#39;, &#39;Hiranandani Gardens, Powai, Mumbai&#39;, &#39;Maharashtra&#39;, &#39;India&#39;, &#39;400076&#39;),
-    ])
-#假设插入数据之后出现了问题：
-with Transaction(connection) as cursor:
-    cursor.execute(&quot;&quot;&quot;INSERT OR REPLACE INTO addresses VALUES (?, ?, ?, ?, ?, ?)&quot;&quot;&quot;,
-        (4, &#39;2100 Pennsylvania Ave&#39;, &#39;Washington&#39;, &#39;DC&#39;, &#39;USA&#39;, &#39;78701&#39;),
-    )
-    raise Exception(&quot;out of addresses&quot;)
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>输出为：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="ne">Exception</span> <span class="n">Traceback</span> <span class="p">(</span><span class="n">most</span> <span class="n">recent</span> <span class="n">call</span> <span class="n">last</span><span class="p">)</span>
-<span class="o">...</span>
-<span class="ne">Exception</span><span class="p">:</span> <span class="n">out</span> <span class="n">of</span> <span class="n">addresses</span>
-</pre></div>
-</div>
-<p>最新的一次插入将不会被保存，而是返回上一次 commit 成功的状态：</p>
-<p>如果继续执行：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>cursor.execute(&quot;SELECT * FROM addresses&quot;)
-for row in cursor:
-    print row
-</pre></div>
-</div>
-<p>输出为：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="sa">u</span><span class="s1">&#39;515 Congress Ave&#39;</span><span class="p">,</span> <span class="sa">u</span><span class="s1">&#39;Austin&#39;</span><span class="p">,</span> <span class="sa">u</span><span class="s1">&#39;Texas&#39;</span><span class="p">,</span> <span class="sa">u</span><span class="s1">&#39;USA&#39;</span><span class="p">,</span> <span class="sa">u</span><span class="s1">&#39;78701&#39;</span><span class="p">)</span>
-<span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="sa">u</span><span class="s1">&#39;245 Park Avenue&#39;</span><span class="p">,</span> <span class="sa">u</span><span class="s1">&#39;New York&#39;</span><span class="p">,</span> <span class="sa">u</span><span class="s1">&#39;New York&#39;</span><span class="p">,</span> <span class="sa">u</span><span class="s1">&#39;USA&#39;</span><span class="p">,</span> <span class="sa">u</span><span class="s1">&#39;10167&#39;</span><span class="p">)</span>
-<span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="sa">u</span><span class="s1">&#39;21 J.J. Thompson Ave.&#39;</span><span class="p">,</span> <span class="sa">u</span><span class="s1">&#39;Cambridge&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="sa">u</span><span class="s1">&#39;UK&#39;</span><span class="p">,</span> <span class="sa">u</span><span class="s1">&#39;CB3 0FA&#39;</span><span class="p">)</span>
-<span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="sa">u</span><span class="s1">&#39;Supreme Business Park&#39;</span><span class="p">,</span> <span class="sa">u</span><span class="s1">&#39;Hiranandani Gardens, Powai, Mumbai&#39;</span><span class="p">,</span> <span class="sa">u</span><span class="s1">&#39;Maharashtra&#39;</span><span class="p">,</span> <span class="sa">u</span><span class="s1">&#39;India&#39;</span><span class="p">,</span> <span class="sa">u</span><span class="s1">&#39;400076&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-</section>
-<section id="contextlib">
-<h2>contextlib 模块<a class="headerlink" href="#contextlib" title="Permalink to this heading">¶</a></h2>
-<p>很多的上下文管理器有很多相似的地方，为了防止写入很多重复的模式，可以使用 <em>contextlib</em> 模块来进行处理。</p>
-<p>最简单的处理方式是使用 <em>closing</em> 函数确保对象的 <em>close()</em> 方法始终被调用：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>from contextlib import closing
-import urllib
-with closing(urllib.urlopen(&#39;http://www.qpython.com&#39;)) as url:
-    html = url.read()
-print html[:100]
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>另一个有用的方法是使用修饰符 <em>&#64;contextlib</em>：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>from contextlib import contextmanager
-@contextmanager
-def my_contextmanager():
-    print &quot;Enter&quot;
-    yield
-    print &quot;Exit&quot;
-with my_contextmanager():
-    print &quot;  Inside the with statement&quot;
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>输出应是：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Enter</span>
-<span class="n">Inside</span> <span class="n">the</span> <span class="k">with</span> <span class="n">statement</span>
-<span class="n">Exit</span>
-</pre></div>
-</div>
-<p><em>yield *之前的部分可以看成是</em> __enter__* 的部分，<em>yield</em> 的值可以看成是 <em>__enter__</em> 返回的值，<em>yield *之后的部分可以看成是</em> __exit__* 的部分。</p>
-<p>使用 <em>yield</em> 的值：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nd">@contextmanager</span>
-<span class="k">def</span> <span class="nf">my_contextmanager</span><span class="p">():</span>
-<span class="nb">print</span> <span class="s2">&quot;Enter&quot;</span>
-<span class="k">yield</span> <span class="s2">&quot;my value&quot;</span>
-<span class="nb">print</span> <span class="s2">&quot;Exit&quot;</span>
-<span class="k">with</span> <span class="n">my_contextmanager</span><span class="p">()</span> <span class="k">as</span> <span class="n">value</span><span class="p">:</span>
-<span class="nb">print</span> <span class="n">value</span>
-</pre></div>
-</div>
-<p>输出是：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Enter</span>
-<span class="n">my</span> <span class="n">value</span>
-<span class="n">Exit</span>
-</pre></div>
-</div>
-<p>错误处理可以用 <em>try</em> 块来完成：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>@contextmanager
-def my_contextmanager():
-    print &quot;Enter&quot;
-    try:
-        yield
-except Exception as exc:
-        print &quot;   Error:&quot;, exc
-finally:
-    print &quot;Exit&quot;
-with my_contextmanager():
-    print 1/0
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>输出为：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Enter</span>
-<span class="n">Error</span><span class="p">:</span> <span class="n">integer</span> <span class="n">division</span> <span class="ow">or</span> <span class="n">modulo</span> <span class="n">by</span> <span class="n">zero</span>
-<span class="n">Exit</span>
-</pre></div>
-</div>
-<p>对于之前的数据库 <em>transaction</em> 我们可以这样定义：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>@contextmanager
-def transaction(connection):
-    cursor = connection.cursor()
-    try:
-        yield cursor
-    except:
-        connection.rollback()
-        raise
-    else:
-        connection.commit()
-</pre></div>
-</div>
-<button class="button">Run …</button>
-</section>
-<section id="id4">
-<h2>作者 &amp; 更新时间<a class="headerlink" href="#id4" title="Permalink to this heading">¶</a></h2>
-<p>作者:<cite>李金  &lt;lijinwithyou&#64;gmail.com&gt;</cite></p>
-<p>提交: 2017/12/6</p>
-</section>
-</section>
-<div class="clearer"></div>
-</div>
-</div>
-</div>
-<div class="clearer"></div>
-</div>
-</main>
-<div class="star-footer">
-<img onclick="openPurchase()" src="../../static/star.jpg">
-<div id="praise"><span>0</span> persons have rewarded</div>
-</div>
-<div class="foo"></div>
-<footer class="clearfix fixfooter web-show">
-<div class="col-sm-offset-2 col-sm-6 footer-div1">
-<div class="footer-block-item">
-<span class="footer-item1">Built with Sphinx using a theme provid by QPython.</span>
-<span class="footer-item2 visible-xs">Copyight 2017, QPython.</span>
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-<div class="col-sm-2 footer-div2">
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-//获取赞赏
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-<title>Dive into Python</title>
-<link rel="stylesheet" type="text/css" href="../static/pygments.css" />
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-</button>
-<a href="/index.html" class="logo"><img src="http://www.qpython.org/static/img_logo.png"></a>
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-<li>
-<a href="http://www.qpython.org/index.html">Home</a>
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-<section id="dive-into-python">
-<h1>Dive into Python<a class="headerlink" href="#dive-into-python" title="Permalink to this heading">¶</a></h1>
-<p>The courses are still in the planning. Welcome to reward us.</p>
-<ol class="arabic simple">
-<li><p><strong>Primary course</strong></p></li>
-</ol>
-<p>If the total number of people who reward us is more than 100, we promise to complete the primary course within 1 week, so that, after finishing your study, you can master the basic concepts and write simple scripts.</p>
-<ol class="arabic simple" start="2">
-<li><p><strong>Intermediate course</strong></p></li>
-</ol>
-<p>If the total number of people who reward us is more than 500, we will launch the intermediate course within 2 weeks, so that, after finishing your study, you will be familiar with the knowledge and framework of the course and apply it to practical development.</p>
-<ol class="arabic simple" start="3">
-<li><p><strong>Advanced course</strong></p></li>
-</ol>
-<p>If the total number of people who reward us is more than 2,000, we will launch the advanced course in a month, so that, after finishing your study, you will have a better understanding of the principles of the framework and be able to do advanced development.</p>
-<p><strong>The primary course is free to all, and the intermediate and advanced courses are only open to those who have rewarded us.</strong></p>
-</section>
-<div class="clearer"></div>
-</div>
-</div>
-</div>
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-</div>
-</main>
-<div class="star-footer">
-<img onclick="openPurchase()" src="../static/star.jpg">
-<div id="praise"><span>0</span> persons have rewarded</div>
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-<footer class="clearfix fixfooter web-show">
-<div class="col-sm-offset-2 col-sm-6 footer-div1">
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diff --git a/docs/django/index.html b/docs/django/index.html
deleted file mode 100644
index 5df2a92..0000000
--- a/docs/django/index.html
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@@ -1,291 +0,0 @@
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-<a href="http://www.qpython.org/document.html">Guide</a>
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-<a href="/index.html" class="header-selected">Course</a>
-<span class="header-title-selected"></span>
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-<h1>Django Programming<a class="headerlink" href="#django-programming" title="Permalink to this heading">¶</a></h1>
-<p>The courses are still in the planning. Welcome to reward us.</p>
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-<div id="praise"><span>0</span> persons have rewarded</div>
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-<div class="foo"></div>
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-<div class="col-sm-offset-2 col-sm-6 footer-div1">
-<div class="footer-block-item">
-<span class="footer-item1">Built with Sphinx using a theme provid by QPython.</span>
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-<div class="en-thanks-sel">
-<select name="os0">
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-        <hr />
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-        <script src="https://utteranc.es/client.js" repo="qpython-android/comments" issue-term="url" label="💬"
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diff --git a/docs/favicon.ico b/docs/favicon.ico
deleted file mode 100644
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Binary files a/docs/favicon.ico and /dev/null differ
diff --git a/docs/featured/featured_2017_0821.html b/docs/featured/featured_2017_0821.html
deleted file mode 100644
index c3995cc..0000000
--- a/docs/featured/featured_2017_0821.html
+++ /dev/null
@@ -1,277 +0,0 @@
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-<link href="https://cdn.bootcss.com/bootstrap/3.3.7/css/bootstrap.min.css" rel="stylesheet">
-<title>QPython Daily Featured</title>
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-<link rel="stylesheet" type="text/css" href="../static/style.css" />
-<meta charset="UTF-8">
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-<script src="https://cdn.bootcss.com/bootstrap/3.3.7/js/bootstrap.min.js"></script>
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-<div class="header doc-header web-show">
-<div class="col-sm-offset-2 col-sm-8  index-header">
-<div class="navbar-header">
-<button type="button" class="navbar-toggle" data-toggle="collapse" data-target="#example-navbar-collapse">
-<span class="sr-only">切换导航</span>
-<span class="icon-bar"></span>
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-<span class="icon-bar"></span>
-</button>
-<a href="/index.html" class="logo"><img src="http://www.qpython.org/static/img_logo.png"></a>
-</div>
-<ul class="header-title collapse navbar-collapse" id="example-navbar-collapse" >
-<li>
-<a href="http://www.qpython.org/index.html">Home</a>
-</li>
-<li>
-<a href="http://www.qpython.org/document.html">Guide</a>
-</li>
-<li class="header-phone-selected">
-<a href="/index.html" class="header-selected">Course</a>
-<span class="header-title-selected"></span>
-</li>
-<li>
-<a href="http://www.aipy.org">AIPY</a>
-</li>
-<li>
-<a target="_blank" href="https://github.com/qpython-android/qpython">Github</a>
-</li>
-</ul>
-<div class="search-box hidden-xs">
-<form action="/search.html">
-<input type="search" name="q" placeholder="keyword">
-<input type="hidden" name="check_keywords" value="yes" />
-<input type="hidden" name="area" value="default" />
-<button><img src="http://www.qpython.org/static/ic_search.png"></button>
-</form>
-</div>
-</div>
-</div>
-<main class="contain web-content">
-<div class="document">
-<div class="documentwrapper">
-<div class="bodywrapper">
-<div class="body" role="main">
-<section id="qpython-daily-featured">
-<h1>QPython Daily Featured<a class="headerlink" href="#qpython-daily-featured" title="Permalink to this heading">¶</a></h1>
-<section id="how-to-help-with-translate-qpython-to-other-languages">
-<h2>How to help with translate QPython to other languages?<a class="headerlink" href="#how-to-help-with-translate-qpython-to-other-languages" title="Permalink to this heading">¶</a></h2>
-<p>Thank you very much for being willing to contribute, pelase see <a class="reference external" href="https://github.com/qpython-android/localization">QPython localization project</a> for more detail.  by <em>Railson Oliveira ( from facebook )</em></p>
-</section>
-<section id="run-kivy-app-don-t-show-log-information">
-<h2>Run Kivy app don’t show log information<a class="headerlink" href="#run-kivy-app-don-t-show-log-information" title="Permalink to this heading">¶</a></h2>
-<p>How to avoid the qpython miss important log in kivy project ? Just see  <a class="reference external" href="https://github.com/qpython-android/qpython/issues/24">an example of changing qpython log behavior</a> by  <em>yingshaoxo ( from github )</em></p>
-<p><em>If you have any question, please leave a message to us, we will answer in next featured.</em></p>
-</section>
-</section>
-<div class="clearer"></div>
-</div>
-</div>
-</div>
-<div class="clearer"></div>
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-<img onclick="openPurchase()" src="../static/star.jpg">
-<div id="praise"><span>0</span> persons have rewarded</div>
-</div>
-<div class="foo"></div>
-<footer class="clearfix fixfooter web-show">
-<div class="col-sm-offset-2 col-sm-6 footer-div1">
-<div class="footer-block-item">
-<span class="footer-item1">Built with Sphinx using a theme provid by QPython.</span>
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-<div class="en-thanks-sel">
-<select name="os0">
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-<option value="Reward this course">Reward this course $ 3.49 USD</option>
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-<input type="hidden" name="currency_code" value="USD">
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-var u = window.location.href.split('/')
-var s = u[u.length-1].replace('.html', '')
-s = u[u.length-2]+'-'+s
-var i = document.getElementById('praise').getElementsByTagName('span')[0]
-var num = milib.getPurchaseNumber(s);
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-temp_data = temp_data.replace(/\^10\$/g, spaceString(10));
-temp_data = temp_data.replace(/\^12\$/g, spaceString(12));
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diff --git a/docs/featured/featured_2017_0822.html b/docs/featured/featured_2017_0822.html
deleted file mode 100644
index 77d69d0..0000000
--- a/docs/featured/featured_2017_0822.html
+++ /dev/null
@@ -1,274 +0,0 @@
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-<link rel="stylesheet" type="text/css" href="../static/pygments.css" />
-<link rel="stylesheet" type="text/css" href="../static/style.css" />
-<meta charset="UTF-8">
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-<script src="https://cdn.bootcss.com/bootstrap/3.3.7/js/bootstrap.min.js"></script>
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-<div class="header doc-header web-show">
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-<a href="/index.html" class="logo"><img src="http://www.qpython.org/static/img_logo.png"></a>
-</div>
-<ul class="header-title collapse navbar-collapse" id="example-navbar-collapse" >
-<li>
-<a href="http://www.qpython.org/index.html">Home</a>
-</li>
-<li>
-<a href="http://www.qpython.org/document.html">Guide</a>
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-<input type="hidden" name="check_keywords" value="yes" />
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-</form>
-</div>
-</div>
-</div>
-<main class="contain web-content">
-<div class="document">
-<div class="documentwrapper">
-<div class="bodywrapper">
-<div class="body" role="main">
-<section id="qpython-daily-featured">
-<h1>QPython Daily Featured<a class="headerlink" href="#qpython-daily-featured" title="Permalink to this heading">¶</a></h1>
-<section id="how-to-copy-and-paste-text-in-terminal">
-<h2>How to copy and paste text in terminal?<a class="headerlink" href="#how-to-copy-and-paste-text-in-terminal" title="Permalink to this heading">¶</a></h2>
-<p><em>By Neelu from Facebook</em></p>
-<p>By long touching on the terminal, the menu shows, you can copy text through “Select text”  and paste text through “Paste”.</p>
-<img alt="http://edu.qpython.org/static/terminal-howto-select-text.png" src="http://edu.qpython.org/static/terminal-howto-select-text.png" />
-</section>
-</section>
-<div class="clearer"></div>
-</div>
-</div>
-</div>
-<div class="clearer"></div>
-</div>
-</main>
-<div class="star-footer">
-<img onclick="openPurchase()" src="../static/star.jpg">
-<div id="praise"><span>0</span> persons have rewarded</div>
-</div>
-<div class="foo"></div>
-<footer class="clearfix fixfooter web-show">
-<div class="col-sm-offset-2 col-sm-6 footer-div1">
-<div class="footer-block-item">
-<span class="footer-item1">Built with Sphinx using a theme provid by QPython.</span>
-<span class="footer-item2 visible-xs">Copyight 2017, QPython.</span>
-</div>
-</div>
-<div class="col-sm-2 footer-div2">
-<a href="/index.html" class="pull-right"><img src="http://www.qpython.org/static/img_logo.png"></a>
-</div>
-</footer>
-<div id="purchase-box">
-<div class="zh-img purchase-img"></div>
-<div class="en-img purchase-img">
-<form action="https://www.paypal.com/cgi-bin/webscr" method="post" target="_top">
-<input type="hidden" name="cmd" value="_s-xclick">
-<input type="hidden" name="hosted_button_id" value="ST6KWAK8R63JW">
-<div class="en-thanks-tit">
-<input type="hidden" name="on0" value="Thanks">
-Thanks
-</div>
-<div class="en-thanks-sel">
-<select name="os0">
-<option value="Reward this course">Reward this course $ 0.99 USD</option>
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-<option value="Reward this course">Reward this course $ 1.99 USD</option>
-<option value="Reward this course">Reward this course $ 2.99 USD</option>
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-temp_data = temp_data.replace(/\^10\$/g, spaceString(10));
-temp_data = temp_data.replace(/\^12\$/g, spaceString(12));
-temp_data = temp_data.replace(/\^16\$/g, spaceString(16));
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diff --git a/docs/featured/featured_2017_0923.html b/docs/featured/featured_2017_0923.html
deleted file mode 100644
index 820f56f..0000000
--- a/docs/featured/featured_2017_0923.html
+++ /dev/null
@@ -1,283 +0,0 @@
-<!DOCTYPE html>
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-<ul class="header-title collapse navbar-collapse" id="example-navbar-collapse" >
-<li>
-<a href="http://www.qpython.org/index.html">Home</a>
-</li>
-<li>
-<a href="http://www.qpython.org/document.html">Guide</a>
-</li>
-<li class="header-phone-selected">
-<a href="/index.html" class="header-selected">Course</a>
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-<div class="search-box hidden-xs">
-<form action="/search.html">
-<input type="search" name="q" placeholder="keyword">
-<input type="hidden" name="check_keywords" value="yes" />
-<input type="hidden" name="area" value="default" />
-<button><img src="http://www.qpython.org/static/ic_search.png"></button>
-</form>
-</div>
-</div>
-</div>
-<main class="contain web-content">
-<div class="document">
-<div class="documentwrapper">
-<div class="bodywrapper">
-<div class="body" role="main">
-<section id="qpython-featured">
-<h1>QPython Featured<a class="headerlink" href="#qpython-featured" title="Permalink to this heading">¶</a></h1>
-<section id="how-to-close-the-push-notification">
-<h2>How to close the push notification<a class="headerlink" href="#how-to-close-the-push-notification" title="Permalink to this heading">¶</a></h2>
-<p>Last time, someone asked how to close the push notification. The fact is QPython didn’t support that function, so we stopped sending push notifications. But now QPython supports closing push notifications. Besides, QPython supports closing log notifications too. You can find these options in your QPython’s setting page.</p>
-<p>We don’t recommend to close notifications because you may lost some amazing featured contents.</p>
-</section>
-<section id="what-s-new-recently">
-<h2>What’s new recently<a class="headerlink" href="#what-s-new-recently" title="Permalink to this heading">¶</a></h2>
-<p>We published a stable version (v2.0.5) a few days ago, which supports Russian language and fixes some bugs like FTP issue.</p>
-<p>We published a BETA version (v2.0.6) yesterday which fixes sqlite3 error (get it from <a class="reference external" href="https://github.com/qpython-android/qpython/releases">https://github.com/qpython-android/qpython/releases</a>) and allows users to call QPython API with service.</p>
-</section>
-<section id="the-qpython-community">
-<h2>The QPython community<a class="headerlink" href="#the-qpython-community" title="Permalink to this heading">¶</a></h2>
-<p>More and more talent people join the QPython community (<a class="reference external" href="https://www.facebook.com/groups/qpython">https://www.facebook.com/groups/qpython</a>)  recently.  And we hope that we could learn from each other.</p>
-<p><em>If you want to talk to us, please leave a message to us through twitter ( &#64;qpython ), and we will reply you as soon as possible.</em></p>
-</section>
-</section>
-<div class="clearer"></div>
-</div>
-</div>
-</div>
-<div class="clearer"></div>
-</div>
-</main>
-<div class="star-footer">
-<img onclick="openPurchase()" src="../static/star.jpg">
-<div id="praise"><span>0</span> persons have rewarded</div>
-</div>
-<div class="foo"></div>
-<footer class="clearfix fixfooter web-show">
-<div class="col-sm-offset-2 col-sm-6 footer-div1">
-<div class="footer-block-item">
-<span class="footer-item1">Built with Sphinx using a theme provid by QPython.</span>
-<span class="footer-item2 visible-xs">Copyight 2017, QPython.</span>
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-</div>
-<div class="col-sm-2 footer-div2">
-<a href="/index.html" class="pull-right"><img src="http://www.qpython.org/static/img_logo.png"></a>
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-<form action="https://www.paypal.com/cgi-bin/webscr" method="post" target="_top">
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-<input type="hidden" name="hosted_button_id" value="ST6KWAK8R63JW">
-<div class="en-thanks-tit">
-<input type="hidden" name="on0" value="Thanks">
-Thanks
-</div>
-<div class="en-thanks-sel">
-<select name="os0">
-<option value="Reward this course">Reward this course $ 0.99 USD</option>
-<option value="Reward this course">Reward this course $ 1.49 USD</option>
-<option value="Reward this course">Reward this course $ 1.99 USD</option>
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-<option value="Reward this course">Reward this course $ 3.49 USD</option>
-</select>
-</div>
-<input type="hidden" name="currency_code" value="USD">
-<input type="image" class="pay_img" src="https://www.paypalobjects.com/en_GB/i/btn/btn_paynow_LG.gif" border="0" name="submit" alt="PayPal – The safer, easier way to pay online!">
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-var milib = function(){
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-milib.getPurchaseNumber = function(str){
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-return "10+";
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-milib.a8playVideoFromGW= function(url){
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-milib.qeditor(code)
-}else{
-//没有代码，调转
-var url = this.getAttribute('data-url');
-window.open(url);
-}
-}
-//获取赞赏
-function setPraise(){
-var u = window.location.href.split('/')
-var s = u[u.length-1].replace('.html', '')
-s = u[u.length-2]+'-'+s
-var i = document.getElementById('praise').getElementsByTagName('span')[0]
-var num = milib.getPurchaseNumber(s);
-i.innerHTML = num
-}
-//初始化代码显示
-function initCode(){
-var prearr = document.getElementsByTagName('pre');
-for (var i = 0; i < prearr.length; i++) {
-var temp_data = prearr[i].lastChild.nodeValue
-//正则处理 lavel5
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-temp_data = temp_data.replace(/\^4\$/g, spaceString(4));
-temp_data = temp_data.replace(/\^6\$/g, spaceString(6));
-temp_data = temp_data.replace(/\^8\$/g, spaceString(8));
-temp_data = temp_data.replace(/\^10\$/g, spaceString(10));
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-var list = localStorage.course_search_list.split('%,%');
-var i = list.indexOf(t);
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-list.splice(i, 1);
-list.push(t);
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-localStorage.course_search_list = t;
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diff --git a/docs/featured/featured_2017_1013.html b/docs/featured/featured_2017_1013.html
deleted file mode 100644
index 7523c00..0000000
--- a/docs/featured/featured_2017_1013.html
+++ /dev/null
@@ -1,278 +0,0 @@
-<!DOCTYPE html>
-<html lang="en-US">
-<head>
-<meta charset="utf-8" />
-<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
-<link href="https://cdn.bootcss.com/bootstrap/3.3.7/css/bootstrap.min.css" rel="stylesheet">
-<title>Hello</title>
-<link rel="stylesheet" type="text/css" href="../static/pygments.css" />
-<link rel="stylesheet" type="text/css" href="../static/style.css" />
-<meta charset="UTF-8">
-<meta name="viewport" content="width=device-width, initial-scale=1,maximum-scale=1, user-scalable=no">
-<script src="https://cdn.bootcss.com/bootstrap/3.3.7/js/bootstrap.min.js"></script>
-</head><body>
-<div class="header doc-header web-show">
-<div class="col-sm-offset-2 col-sm-8  index-header">
-<div class="navbar-header">
-<button type="button" class="navbar-toggle" data-toggle="collapse" data-target="#example-navbar-collapse">
-<span class="sr-only">切换导航</span>
-<span class="icon-bar"></span>
-<span class="icon-bar"></span>
-<span class="icon-bar"></span>
-</button>
-<a href="/index.html" class="logo"><img src="http://www.qpython.org/static/img_logo.png"></a>
-</div>
-<ul class="header-title collapse navbar-collapse" id="example-navbar-collapse" >
-<li>
-<a href="http://www.qpython.org/index.html">Home</a>
-</li>
-<li>
-<a href="http://www.qpython.org/document.html">Guide</a>
-</li>
-<li class="header-phone-selected">
-<a href="/index.html" class="header-selected">Course</a>
-<span class="header-title-selected"></span>
-</li>
-<li>
-<a href="http://www.aipy.org">AIPY</a>
-</li>
-<li>
-<a target="_blank" href="https://github.com/qpython-android/qpython">Github</a>
-</li>
-</ul>
-<div class="search-box hidden-xs">
-<form action="/search.html">
-<input type="search" name="q" placeholder="keyword">
-<input type="hidden" name="check_keywords" value="yes" />
-<input type="hidden" name="area" value="default" />
-<button><img src="http://www.qpython.org/static/ic_search.png"></button>
-</form>
-</div>
-</div>
-</div>
-<main class="contain web-content">
-<div class="document">
-<div class="documentwrapper">
-<div class="bodywrapper">
-<div class="body" role="main">
-<section id="hello">
-<h1>Hello<a class="headerlink" href="#hello" title="Permalink to this heading">¶</a></h1>
-<section id="what-s-new">
-<h2>What’s new<a class="headerlink" href="#what-s-new" title="Permalink to this heading">¶</a></h2>
-<p>We are working on the AIPY branch which offers data analysis and deeplearning features. It mainly includes numpy, scipy, matplitlib, pandas, theano, scikit-learn and keras etc. Please follow us on facebook (<a class="reference external" href="http://www.facebook.com/qpython">http://www.facebook.com/qpython</a>) to get it’s progress.</p>
-<p>A Chinese qpython users group is going to launching a QPython Programming contest, if you want to join this contest, please follow wechat GONGZHONGHAO qpython to get the detail information.</p>
-</section>
-<section id="saving-scripts-to-cloud">
-<h2>Saving scripts to cloud<a class="headerlink" href="#saving-scripts-to-cloud" title="Permalink to this heading">¶</a></h2>
-<p>Newest QPython (2.0.6) has support saving script to cloud (require google login), Would you like give a try ?</p>
-<p><em>If you want to talk to us, please leave a message to us through twitter ( &#64;qpython ), and we will reply you as soon as possible.</em></p>
-</section>
-</section>
-<div class="clearer"></div>
-</div>
-</div>
-</div>
-<div class="clearer"></div>
-</div>
-</main>
-<div class="star-footer">
-<img onclick="openPurchase()" src="../static/star.jpg">
-<div id="praise"><span>0</span> persons have rewarded</div>
-</div>
-<div class="foo"></div>
-<footer class="clearfix fixfooter web-show">
-<div class="col-sm-offset-2 col-sm-6 footer-div1">
-<div class="footer-block-item">
-<span class="footer-item1">Built with Sphinx using a theme provid by QPython.</span>
-<span class="footer-item2 visible-xs">Copyight 2017, QPython.</span>
-</div>
-</div>
-<div class="col-sm-2 footer-div2">
-<a href="/index.html" class="pull-right"><img src="http://www.qpython.org/static/img_logo.png"></a>
-</div>
-</footer>
-<div id="purchase-box">
-<div class="zh-img purchase-img"></div>
-<div class="en-img purchase-img">
-<form action="https://www.paypal.com/cgi-bin/webscr" method="post" target="_top">
-<input type="hidden" name="cmd" value="_s-xclick">
-<input type="hidden" name="hosted_button_id" value="ST6KWAK8R63JW">
-<div class="en-thanks-tit">
-<input type="hidden" name="on0" value="Thanks">
-Thanks
-</div>
-<div class="en-thanks-sel">
-<select name="os0">
-<option value="Reward this course">Reward this course $ 0.99 USD</option>
-<option value="Reward this course">Reward this course $ 1.49 USD</option>
-<option value="Reward this course">Reward this course $ 1.99 USD</option>
-<option value="Reward this course">Reward this course $ 2.99 USD</option>
-<option value="Reward this course">Reward this course $ 3.49 USD</option>
-</select>
-</div>
-<input type="hidden" name="currency_code" value="USD">
-<input type="image" class="pay_img" src="https://www.paypalobjects.com/en_GB/i/btn/btn_paynow_LG.gif" border="0" name="submit" alt="PayPal – The safer, easier way to pay online!">
-<img alt="" border="0" src="https://www.paypalobjects.com/zh_XC/i/scr/pixel.gif" width="1" height="1">
-</form>
-</div>
-</div>
-<script>
-var milib;
-if (milib==undefined) {
-var milib = function(){
-};
-milib.qeditor = function(str){
-alert('Please share this page to QPython then run it')
-};
-milib.getPurchaseNumber = function(str){
-//alert('getPurchaseNumber:'+str)
-return "10+";
-};
-milib.a8playVideoFromGW= function(url){
-window.open(url)
-};
-milib.qpylibinstall = function(cat,url,target){
-window.open(url)
-};
-milib.gotoIfPay = function(url, packageId, packageUrl){
-window.open(url)
-};
-} else {
-}
-function openPurchase() {
-//获取标识
-var u = window.location.href.split('/')
-var s = u[u.length-1].replace('.html', '')
-s = u[u.length-2]+'-'+s
-if (milib.openPurchaseActivity){
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-$('#purchase-box').click(function() {
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-$('.purchase-img').click(function(e) {
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-function btnEvent(){
-var seq = this.getAttribute('target')-2
-var a = this.parentElement.getElementsByTagName('pre')
-if ( a && a.length > 0){
-/**********有代码，调用控制台*************/
-var code = a[seq].lastChild.nodeValue
-milib.qeditor(code)
-}else{
-//没有代码，调转
-var url = this.getAttribute('data-url');
-window.open(url);
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-function setPraise(){
-var u = window.location.href.split('/')
-var s = u[u.length-1].replace('.html', '')
-s = u[u.length-2]+'-'+s
-var i = document.getElementById('praise').getElementsByTagName('span')[0]
-var num = milib.getPurchaseNumber(s);
-i.innerHTML = num
-}
-//初始化代码显示
-function initCode(){
-var prearr = document.getElementsByTagName('pre');
-for (var i = 0; i < prearr.length; i++) {
-var temp_data = prearr[i].lastChild.nodeValue
-//正则处理 lavel5
-temp_data = temp_data.replace(/\^2\$/g, spaceString(2));
-temp_data = temp_data.replace(/\^4\$/g, spaceString(4));
-temp_data = temp_data.replace(/\^6\$/g, spaceString(6));
-temp_data = temp_data.replace(/\^8\$/g, spaceString(8));
-temp_data = temp_data.replace(/\^10\$/g, spaceString(10));
-temp_data = temp_data.replace(/\^12\$/g, spaceString(12));
-temp_data = temp_data.replace(/\^16\$/g, spaceString(16));
-temp_data = temp_data.replace(/\^20\$/g, spaceString(20));
-//set
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-function spaceString(num){
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-v += ' ';
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-return v;
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-function setSearchHistory (t) {
-if (!t)
-return;
-if (localStorage.course_search_list) {
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-var i = list.indexOf(t);
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diff --git a/docs/featured/featured_2018_0116.html b/docs/featured/featured_2018_0116.html
deleted file mode 100644
index c9daafc..0000000
--- a/docs/featured/featured_2018_0116.html
+++ /dev/null
@@ -1,301 +0,0 @@
-<!DOCTYPE html>
-<html lang="en-US">
-<head>
-<meta charset="utf-8" />
-<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
-<link href="https://cdn.bootcss.com/bootstrap/3.3.7/css/bootstrap.min.css" rel="stylesheet">
-<title>QPY Programming Challenge 0x00</title>
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-<link rel="stylesheet" type="text/css" href="../static/style.css" />
-<meta charset="UTF-8">
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-<script src="https://cdn.bootcss.com/bootstrap/3.3.7/js/bootstrap.min.js"></script>
-</head><body>
-<div class="header doc-header web-show">
-<div class="col-sm-offset-2 col-sm-8  index-header">
-<div class="navbar-header">
-<button type="button" class="navbar-toggle" data-toggle="collapse" data-target="#example-navbar-collapse">
-<span class="sr-only">切换导航</span>
-<span class="icon-bar"></span>
-<span class="icon-bar"></span>
-<span class="icon-bar"></span>
-</button>
-<a href="/index.html" class="logo"><img src="http://www.qpython.org/static/img_logo.png"></a>
-</div>
-<ul class="header-title collapse navbar-collapse" id="example-navbar-collapse" >
-<li>
-<a href="http://www.qpython.org/index.html">Home</a>
-</li>
-<li>
-<a href="http://www.qpython.org/document.html">Guide</a>
-</li>
-<li class="header-phone-selected">
-<a href="/index.html" class="header-selected">Course</a>
-<span class="header-title-selected"></span>
-</li>
-<li>
-<a href="http://www.aipy.org">AIPY</a>
-</li>
-<li>
-<a target="_blank" href="https://github.com/qpython-android/qpython">Github</a>
-</li>
-</ul>
-<div class="search-box hidden-xs">
-<form action="/search.html">
-<input type="search" name="q" placeholder="keyword">
-<input type="hidden" name="check_keywords" value="yes" />
-<input type="hidden" name="area" value="default" />
-<button><img src="http://www.qpython.org/static/ic_search.png"></button>
-</form>
-</div>
-</div>
-</div>
-<main class="contain web-content">
-<div class="document">
-<div class="documentwrapper">
-<div class="bodywrapper">
-<div class="body" role="main">
-<section id="qpy-programming-challenge-0x00">
-<h1>QPY Programming Challenge 0x00<a class="headerlink" href="#qpy-programming-challenge-0x00" title="Permalink to this heading">¶</a></h1>
-<img alt="QPython - QPY Programming Challenge 0x00" src="http://edu.qpython.org/static/qpyprogrammingchanlleage-0x00.png" />
-<p>Did you learn the word “Pythonic” when you first write Python?</p>
-<p>Have you read the P2P transmission of 15 lines, neural network of 11 lines or block chain python code of 50 lines?</p>
-<p>Even if you have written countless Bugs, do you still remember the heart that you wanted to write Simple and elegant code at first?</p>
-<p>Why not try to put down the impetuous and try to revisit Pythonic?</p>
-<section id="qpy-programming-challenge">
-<h2>QPY Programming Challenge<a class="headerlink" href="#qpy-programming-challenge" title="Permalink to this heading">¶</a></h2>
-<p>Since 2018, we plan to start QPython Challenge once a month.</p>
-</section>
-<section id="how-to-submit">
-<h2>How to submit<a class="headerlink" href="#how-to-submit" title="Permalink to this heading">¶</a></h2>
-<p>You can send your entries to us via email (<a class="reference external" href="mailto:support&#37;&#52;&#48;qpython&#46;org">support<span>&#64;</span>qpython<span>&#46;</span>org</a>)，note QPyC20181 participate in this event and your nickname.</p>
-<p>You can submit multiple times, we will take the final manuscript as your final work. The event at the end of the month, the highest percentage of votes is the champion.</p>
-</section>
-<section id="evaluation-methods">
-<h2>Evaluation methods<a class="headerlink" href="#evaluation-methods" title="Permalink to this heading">¶</a></h2>
-<p>At the end of the month, we will issue a document on QPython community to list the entries, Each contest will voted the user selected the most Pythonic code winner, Domestic winners receive QPython T-Shirt or themed souvenirs.</p>
-</section>
-<section id="the-0x00-issue-of-the-challenge">
-<h2>The 0x00 issue of the challenge<a class="headerlink" href="#the-0x00-issue-of-the-challenge" title="Permalink to this heading">¶</a></h2>
-<p>#The first topic
-The title of Challenge 2 is already in the article, please find it.</p>
-<p># The second topic
-100 * 100 a chessboard, the first piece at the position [0, 0],Every second piece random walk horizontal or vertical step, At the same time there are n traps on the board, traps arranged randomly. Known traps abscissa i(nt[]) coordx and ordinate (int[]) coordy,how many steps can a player move without falling into the trap?</p>
-<p>Take out QPython in your pocket and write out the Pythonic code comments on the subject two subject names and send them to us, Compared to other QPythoners, see who is more Pythonic?</p>
-</section>
-<section id="join-the-qpython-community">
-<h2>Join the QPython community<a class="headerlink" href="#join-the-qpython-community" title="Permalink to this heading">¶</a></h2>
-<ul class="simple">
-<li><p><a class="reference external" href="https://www.facebook.com/groups/qpython">QPython&#64;Facebook</a></p></li>
-<li><p><a class="reference external" href="https://plus.google.com/u/2/communities/111759148772865961493">QPython&#64;G+</a></p></li>
-</ul>
-</section>
-</section>
-<div class="clearer"></div>
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-</div>
-<div class="clearer"></div>
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diff --git a/docs/featured/featured_2018_0301_notebook.html b/docs/featured/featured_2018_0301_notebook.html
deleted file mode 100644
index 6669aba..0000000
--- a/docs/featured/featured_2018_0301_notebook.html
+++ /dev/null
@@ -1,315 +0,0 @@
-<!DOCTYPE html>
-<html lang="en-US">
-<head>
-<meta charset="utf-8" />
-<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
-<link href="https://cdn.bootcss.com/bootstrap/3.3.7/css/bootstrap.min.css" rel="stylesheet">
-<title>Share a live documents with QPyNotebook</title>
-<link rel="stylesheet" type="text/css" href="../static/pygments.css" />
-<link rel="stylesheet" type="text/css" href="../static/style.css" />
-<meta charset="UTF-8">
-<meta name="viewport" content="width=device-width, initial-scale=1,maximum-scale=1, user-scalable=no">
-<script src="https://cdn.bootcss.com/bootstrap/3.3.7/js/bootstrap.min.js"></script>
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-<a href="/index.html" class="logo"><img src="http://www.qpython.org/static/img_logo.png"></a>
-</div>
-<ul class="header-title collapse navbar-collapse" id="example-navbar-collapse" >
-<li>
-<a href="http://www.qpython.org/index.html">Home</a>
-</li>
-<li>
-<a href="http://www.qpython.org/document.html">Guide</a>
-</li>
-<li class="header-phone-selected">
-<a href="/index.html" class="header-selected">Course</a>
-<span class="header-title-selected"></span>
-</li>
-<li>
-<a href="http://www.aipy.org">AIPY</a>
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-<li>
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-</li>
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-<form action="/search.html">
-<input type="search" name="q" placeholder="keyword">
-<input type="hidden" name="check_keywords" value="yes" />
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-</div>
-</div>
-</div>
-<main class="contain web-content">
-<div class="document">
-<div class="documentwrapper">
-<div class="bodywrapper">
-<div class="body" role="main">
-<section id="share-a-live-documents-with-qpynotebook">
-<h1>Share a live documents with QPyNotebook<a class="headerlink" href="#share-a-live-documents-with-qpynotebook" title="Permalink to this heading">¶</a></h1>
-<p>Do you want some application which helps you create and share documents that contain live Python code, equations, visualizations and narrative text?</p>
-<p>Now, QPyNotebook for QPython is published, which is developed based on the great opensource project jupyter notebook, it will help you do the following jobs easily like data cleaning and transformation, numerical simulation, statistical modeling, data visualization, machine learning and much more.</p>
-<section id="before-use">
-<h2>Before Use<a class="headerlink" href="#before-use" title="Permalink to this heading">¶</a></h2>
-<p><strong>1. Make sure you have installed the newest QPython (&gt;=2.2.0)</strong></p>
-<p><strong>2. Download QPyNotebook</strong></p>
-<ul class="simple">
-<li><p>Click the QPyNotebook App button to download QPyNotebook, the button will take you to the Google Play App Store.</p></li>
-<li><p>If you could not connect to Google you can download directly from our <a class="reference external" href="https://github.com/qpython-android/notebook/releases/">Github page</a> <em>(please check the “Trust unknow source” in system’s setting if you install it with APK downloaded from github)</em></p></li>
-</ul>
-<img alt="Enable QPyNotebook in QPython Setting" src="http://edu.qpython.org/static/notebook-setting.png" />
-<p><strong>3. Download related libraries</strong></p>
-<p>Click the QPyNotebook Libraries button and there’ll be a terminal window to download those libraries. It may take a while, and after downloading finish, the terminal window will be closed automatically.</p>
-<img alt="Enable QPyNotebook in QPython Setting" src="http://edu.qpython.org/static/notebook-lib.png" />
-<img alt="Download libraries" src="http://edu.qpython.org/static/notebook-download.png" />
-<p><strong>Now all set!</strong></p>
-<img alt="all set" src="http://edu.qpython.org/static/notebook-downloaded.png" />
-</section>
-<section id="how-to-use">
-<h2>How to use<a class="headerlink" href="#how-to-use" title="Permalink to this heading">¶</a></h2>
-<p>Open the Explorer from QPython dashboard, find some <a href="#id1"><span class="problematic" id="id2">*</span></a>.ipynb files (For example notebooks/Welcome.ipynb) and open it, then you will start the QPyNotebook.</p>
-<ul class="simple">
-<li><p>You could start the QPyNotebook from QPython’s explorer*</p></li>
-</ul>
-<img alt="You could start QPyNotebook from QPython's explorer" src="http://edu.qpython.org/static/notebook-explorer.png" />
-<ul class="simple">
-<li><p>Or from QPyNotebook App</p></li>
-</ul>
-<img alt="Start QPyNotebook" src="http://edu.qpython.org/static/notebook-app.png" />
-<p><em>QPyNotebook’s live documentation</em></p>
-<img alt="Start to access Welcome.ipynb" src="http://edu.qpython.org/static/notebook-page.png" />
-</section>
-<section id="feedback">
-<h2>Feedback<a class="headerlink" href="#feedback" title="Permalink to this heading">¶</a></h2>
-<p>Feel free to give us any feedback please.</p>
-<ul class="simple">
-<li><p><a class="reference external" href="http://twitter.com/qpython">&#64;qpython</a></p></li>
-<li><p><a class="reference external" href="mailto:support&#37;&#52;&#48;qpython&#46;org">support<span>&#64;</span>qpython<span>&#46;</span>org</a></p></li>
-</ul>
-</section>
-<section id="join-the-community">
-<h2>Join the community<a class="headerlink" href="#join-the-community" title="Permalink to this heading">¶</a></h2>
-<ul class="simple">
-<li><p><a class="reference external" href="https://plus.google.com/u/2/communities/111759148772865961493">QPython&#64;G+</a></p></li>
-<li><p><a class="reference external" href="https://www.facebook.com/groups/qpython">QPython&#64;Facebook</a></p></li>
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-</section>
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diff --git a/docs/full-text-search/index.html b/docs/full-text-search/index.html
deleted file mode 100644
index ecc88dd..0000000
--- a/docs/full-text-search/index.html
+++ /dev/null
@@ -1,291 +0,0 @@
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-<ol class="arabic simple">
-<li><p><strong>Primary course</strong></p></li>
-</ol>
-<p>If the total number of people who reward us is more than 100, we promise to complete the primary course within 1 week, so that, after finishing your study, you can master the basic concepts and write simple scripts.</p>
-<ol class="arabic simple" start="2">
-<li><p><strong>Intermediate course</strong></p></li>
-</ol>
-<p>If the total number of people who reward us is more than 500, we will launch the intermediate course within 2 weeks, so that, after finishing your study, you will be familiar with the knowledge and framework of the course and apply it to practical development.</p>
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-<div id="praise"><span>0</span> persons have rewarded</div>
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diff --git a/docs/genindex.html b/docs/genindex.html
deleted file mode 100644
index 21ebada..0000000
--- a/docs/genindex.html
+++ /dev/null
@@ -1,308 +0,0 @@
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-<link rel="stylesheet" type="text/css" href="static/pygments.css" />
-<link rel="stylesheet" type="text/css" href="static/style.css" />
-<meta charset="UTF-8">
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-<script src="https://cdn.bootcss.com/bootstrap/3.3.7/js/bootstrap.min.js"></script>
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-<div class="header doc-header web-show">
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-</div>
-<ul class="header-title collapse navbar-collapse" id="example-navbar-collapse" >
-<li>
-<a href="http://www.qpython.org/index.html">Home</a>
-</li>
-<li>
-<a href="http://www.qpython.org/document.html">Guide</a>
-</li>
-<li class="header-phone-selected">
-<a href="/index.html" class="header-selected">Course</a>
-<span class="header-title-selected"></span>
-</li>
-<li>
-<a href="http://www.aipy.org">AIPY</a>
-</li>
-<li>
-<a target="_blank" href="https://github.com/qpython-android/qpython">Github</a>
-</li>
-</ul>
-<div class="search-box hidden-xs">
-<form action="/search.html">
-<input type="search" name="q" placeholder="keyword">
-<input type="hidden" name="check_keywords" value="yes" />
-<input type="hidden" name="area" value="default" />
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-<div class="document">
-<div class="documentwrapper">
-<div class="bodywrapper">
-<div class="body" role="main">
-<h1 id="index">Index</h1>
-<div class="genindex-jumpbox">
-<a href="#C"><strong>C</strong></a>
-| <a href="#D"><strong>D</strong></a>
-| <a href="#E"><strong>E</strong></a>
-| <a href="#H"><strong>H</strong></a>
-| <a href="#S"><strong>S</strong></a>
-</div>
-<h2 id="C">C</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-<td style="width: 33%; vertical-align: top;"><ul>
-<li><a href="bottle-web/tutorial.html#term-callback"><strong>callback</strong></a>
-</li>
-</ul></td>
-</tr></table>
-<h2 id="D">D</h2>
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-<td style="width: 33%; vertical-align: top;"><ul>
-<li><a href="bottle-web/tutorial.html#term-decorator"><strong>decorator</strong></a>
-</li>
-</ul></td>
-</tr></table>
-<h2 id="E">E</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-<td style="width: 33%; vertical-align: top;"><ul>
-<li><a href="bottle-web/tutorial.html#term-environ"><strong>environ</strong></a>
-</li>
-</ul></td>
-</tr></table>
-<h2 id="H">H</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-<td style="width: 33%; vertical-align: top;"><ul>
-<li><a href="bottle-web/tutorial.html#term-handler-function"><strong>handler function</strong></a>
-</li>
-</ul></td>
-</tr></table>
-<h2 id="S">S</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-<td style="width: 33%; vertical-align: top;"><ul>
-<li><a href="bottle-web/tutorial.html#term-source-directory"><strong>source directory</strong></a>
-</li>
-</ul></td>
-</tr></table>
-<div class="clearer"></div>
-</div>
-</div>
-</div>
-<div class="clearer"></div>
-</div>
-</main>
-<div class="star-footer">
-<img onclick="openPurchase()" src="static/star.jpg">
-<div id="praise"><span>0</span> persons have rewarded</div>
-</div>
-<div class="foo"></div>
-<footer class="clearfix fixfooter web-show">
-<div class="col-sm-offset-2 col-sm-6 footer-div1">
-<div class="footer-block-item">
-<span class="footer-item1">Built with Sphinx using a theme provid by QPython.</span>
-<span class="footer-item2 visible-xs">Copyight 2017, QPython.</span>
-</div>
-</div>
-<div class="col-sm-2 footer-div2">
-<a href="/index.html" class="pull-right"><img src="http://www.qpython.org/static/img_logo.png"></a>
-</div>
-</footer>
-<div id="purchase-box">
-<div class="zh-img purchase-img"></div>
-<div class="en-img purchase-img">
-<form action="https://www.paypal.com/cgi-bin/webscr" method="post" target="_top">
-<input type="hidden" name="cmd" value="_s-xclick">
-<input type="hidden" name="hosted_button_id" value="ST6KWAK8R63JW">
-<div class="en-thanks-tit">
-<input type="hidden" name="on0" value="Thanks">
-Thanks
-</div>
-<div class="en-thanks-sel">
-<select name="os0">
-<option value="Reward this course">Reward this course $ 0.99 USD</option>
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old mode 100755
new mode 100644
index 7fcba70..106f521
--- a/docs/index.html
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@@ -1,237 +1,22 @@
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-    <title>QPython - Course</title>
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-    <link href="//cdn.bootcss.com/bootstrap/3.3.7/css/bootstrap.min.css" rel="stylesheet">
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-<body>
-    <div id="app">
-        <section v-if="!new_page">
-            <header>
-                <div class="col-sm-offset-2 col-sm-8 index-header">
-
-                    <div class="navbar-header">
-                        <button type="button" class="navbar-toggle" data-toggle="collapse"
-                            data-target="#example-navbar-collapse">
-                            <span class="sr-only">切换导航</span>
-                            <span class="icon-bar"></span>
-                            <span class="icon-bar"></span>
-                            <span class="icon-bar"></span>
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-                        <a href="/index.html" class="logo"><img src="http://www.qpython.org/static/img_logo.png"></a>
-                    </div>
-
-                    <ul class="header-title collapse navbar-collapse" id="example-navbar-collapse">
-                        <li>
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-                </div>
-                <div class="head-more hidden-xs">
-                    <div class="head-title-tit">Learning Python Efficiently on mobile. </div>
-                    <div class="index-search-box">
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-                            <input type="text" id="index_search" name="q" placeholder="search" autocomplete="off">
-                            <input type="hidden" name="check_keywords" value="yes" />
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-                            <input type="hidden" name="form" value="web" />
-                            <button><img src="http://www.qpython.org/static/ic_search.png"></button>
-                            <div class="search-history" style="display: none;">
-                                <div class="s-headline">
-                                    <span>最近搜索</span>
-                                    <span id="clear_history">清除全部</span>
-                                </div>
-                                <ul class="history-list">
-
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-                        </form>
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-                    <div class="hot-search">
-                        <div class="hot-name">Hot： </div>
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-                        <div><a href="javascript:void(0)">Scikit-learn</a></div>
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-                    <div class="head-more-other">
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-            </header>
-            <main class="doc-content clearfix sc-doc-con">
-                <div class="sc-con">
-                    <div class="sc-body">
-                        <div class="sc-tag">
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-                                <div v-if="v.open == 1">
-                                    <div class="hidden-xs">
-                                        <a :href="v.link+'?form=web'" :title="v.title">
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-                                                <img :src="v.logo" alt="picture">
-                                                <span class="sc-lv">lv {{v.level}}</span>
-                                            </div>
-                                        </a>
-                                        <div class="sc-i-content" @click="v.is_open = !v.is_open"
-                                            title="click open course list">
-                                            {{v.title}}
-                                        </div>
-                                    </div>
-
-                                    <div class="visible-xs" @click="course_desc(v)">
-                                        <a href="javascript:void(0);">
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-                                                <span class="sc-lv">lv {{v.level}}</span>
-                                            </div>
-
-                                            <div class="sc-i-content">
-                                                {{v.title}}
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-                                        </a>
-                                    </div>
-
-                                    <div class="sc-info">
-                                        <div class="sc-info-box">
-                                            <span>{{v.rdate}}</span>
-                                            <span class="sc-click-num">{{v.downloads}}</span>
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-                                    <transition-group name="list" tag="div">
-                                        <div class="sc-course-item hidden-xs" v-if="v.is_open === true"
-                                            :key="v.is_open">
-                                            <a v-for="v1 in v.courses" :href="v1.link+'?form=web'"
-                                                title="click to enter">{{ v1.title }}</a>
-                                        </div>
-                                    </transition-group>
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-                                <div v-else="">
-                                    <a :href="v.link+'?form=web'" :title="v.title">
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-                                        <div class="sc-info-box">
-                                            <span>{{v.rdate}}</span>
-                                            <span class="sc-click-num">{{v.downloads}}</span>
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-                    </ul>
-                </div>
-            </main>
-
-            <div class="foo"></div>
-
-            <footer class="clearfix fixfooter">
-                <div class="col-sm-offset-2 col-sm-6 footer-div1">
-                    <div class="footer-block-item">
-                        <span class="footer-item1">Built with Sphinx using a theme provid by QPython.</span>
-                        <span class="footer-item2 visible-xs">Copyight QPython.</span>
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-        <section class="visible-xs" v-if="new_page">
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-            </div>
-            <div style="height: 50px;"></div>
-            <a :href="new_page_data.link+'?form=web'" :title="new_page_data.title">
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-                    <span class="sc-click-num">{{new_page_data.downloads}}</span>
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-    ga('send', 'pageview');
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+<!DOCTYPE html>
+<html lang="en">
+  <head>
+    <meta charset="UTF-8" />
+    <link rel="icon" type="image/svg+xml" href="/vite.svg" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>QPython+ - Personalized Programming Learning Community</title>
+    <!-- Google tag (gtag.js) -->
+    <script async src="https://www.googletagmanager.com/gtag/js?id=G-JH6WGHX4M2"></script>
+    <script>
+      window.dataLayer = window.dataLayer || [];
+      function gtag(){dataLayer.push(arguments);}
+      gtag('js', new Date());
+      gtag('config', 'G-JH6WGHX4M2');
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+    <link rel="stylesheet" crossorigin href="/assets/index-BXqFqG7T.css">
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+  <body>
+    <div id="root"></div>
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+</html>
diff --git a/docs/index/default.json b/docs/index/default.json
deleted file mode 100644
index 66d8cae..0000000
--- a/docs/index/default.json
+++ /dev/null
@@ -1,247 +0,0 @@
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-                "link":"http://edu.qpython.org/qsl4a-develop/index.html",
-                "smodule":"qsl4a-develop-index",
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-                "title":"QSL4A Introduction",
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-                "downloads": "10,000+"
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-
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-        "description":"As the most widely used web development language in today's application, Python has a web development library that includes bottle, django, tornado, and flask to meet your different development needs. This course will not only teach you how to use these development frameworks to develop web applications but also lets you master how to develop super WebApp with QPython.",
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-        "logo": "http://edu.qpython.org/static/course-qpython-webapp.png",
-        "courses":[
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-                "rdate":"2017-06-15",
-                "link":"http://edu.qpython.org/qpython-webapp/index.html",
-                "smodule":"qpython-webapp-principle",
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-                "title":"QPython WebApp Principle",
-                "ver":"1.0.0",
-                "downloads": "10,000+"
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-                "smodule":"qpython-webapp-web-frameworks",
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-                "title":"Web Frameworks on QPython",
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-                "rdate":"2017-06-15",
-                "link":"http://edu.qpython.org/qpython-quick-start/principle.html",
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-                "title":"QPython principle",
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-            },
-            {
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-                "rdate":"2017-06-15",
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-                "title":"Community howto",
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-            {
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-                "title":"AIPY howto",
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-            },
-            {
-                "rdate":"2018-03-05",
-                "link":"http://edu.qpython.org/featured/featured_2018_0301_notebook.html",
-                "smodule":"qpython-quick-start-notebook-howto",
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-                "link":"http://edu.qpython.org/qpython-quick-start/principle.html",
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-            {
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diff --git a/docs/kivy-qpython/cn/index.html b/docs/kivy-qpython/cn/index.html
deleted file mode 100644
index ae6ed41..0000000
--- a/docs/kivy-qpython/cn/index.html
+++ /dev/null
@@ -1,307 +0,0 @@
-<!DOCTYPE html>
-<html lang="en-US">
-<head>
-<meta charset="utf-8" />
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-<link rel="stylesheet" type="text/css" href="../../static/style.css" />
-<meta charset="UTF-8">
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-<section id="qpythonkivyapp">
-<h1>QPython的KivyApp<a class="headerlink" href="#qpythonkivyapp" title="Permalink to this heading">¶</a></h1>
-<p>Kivy是Python上一个支持多点触摸的跨平台图形界面开发框架，使用Kivy可以方便地开发包括游戏在内的图形界面程序。QPython能较好地支持Kivy。</p>
-<section id="kivy">
-<h2>Kivy的安装<a class="headerlink" href="#kivy" title="Permalink to this heading">¶</a></h2>
-<p>在使用Kivy之前，您需要先安装Kivy。您可以在QPYPI中安装及升级kivy相关的库，QPYPI能自动安装依赖库。如果Kivy有更新，也可以通过QPYPI来进行升级操作。</p>
-<img alt="在QPYPI中管理kivy库" src="http://edu.qpython.org/static/qpypi-kivy.png" />
-</section>
-<section id="kivyapp">
-<h2>KivyApp声明<a class="headerlink" href="#kivyapp" title="Permalink to this heading">¶</a></h2>
-<p>在QPython中，通过头部声明来区分程序类型，当头部有以下定义时，我们知道它是一个KivyApp程序。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1">#qpy:kivy</span>
-</pre></div>
-</div>
-<p>此外，如果想要让你的Kivy程序按照全屏模式运行，您需要增加下面声明。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1">#qpy:fullscreen</span>
-</pre></div>
-</div>
-</section>
-<section id="id1">
-<h2>KivyApp运行流程<a class="headerlink" href="#id1" title="Permalink to this heading">¶</a></h2>
-<p>在程序被识别为KivyApp运行后，QPython会启动OpenGL引擎，新建一个SDL视图，并在其中完成渲染，输出画面。</p>
-</section>
-<section id="id2">
-<h2>KivyApp退出流程<a class="headerlink" href="#id2" title="Permalink to this heading">¶</a></h2>
-<p>按退出退出时，QPython会结束Kivy程序，进行清理，结束OpenGL引擎，同时退出SDL视图。</p>
-</section>
-<section id="id3">
-<h2>例子<a class="headerlink" href="#id3" title="Permalink to this heading">¶</a></h2>
-<p>让我们来看一个最简单的KivyApp示例，这个是安装完QPython后默认带的示例程序。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>#qpy:kivy
-from kivy.app import App
-from kivy.uix.button import Button
-class TestApp(App):
-    def build(self):
-        return Button(text=&#39;Hello World&#39;)
-TestApp().run()
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-<button class="button">Run …</button>
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-</section>
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-<img onclick="openPurchase()" src="../../static/star.jpg">
-<div id="praise"><span>0</span> persons have rewarded</div>
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-<div class="foo"></div>
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-<div class="col-sm-offset-2 col-sm-6 footer-div1">
-<div class="footer-block-item">
-<span class="footer-item1">Built with Sphinx using a theme provid by QPython.</span>
-<span class="footer-item2 visible-xs">Copyight 2017, QPython.</span>
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diff --git a/docs/kivy-qpython/cn/your-first-kivyapp.html b/docs/kivy-qpython/cn/your-first-kivyapp.html
deleted file mode 100644
index fe10996..0000000
--- a/docs/kivy-qpython/cn/your-first-kivyapp.html
+++ /dev/null
@@ -1,475 +0,0 @@
-<!DOCTYPE html>
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-<title>Kivy App 教程&gt;&gt;一个简单的绘画应用程序</title>
-<link rel="stylesheet" type="text/css" href="../../static/pygments.css" />
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-<section id="kivy-app">
-<h1>Kivy App 教程&gt;&gt;一个简单的绘画应用程序<a class="headerlink" href="#kivy-app" title="Permalink to this heading">¶</a></h1>
-<section id="id1">
-<h2>贡献者<a class="headerlink" href="#id1" title="Permalink to this heading">¶</a></h2>
-<p>作者: <a class="reference external" href="https://kivy.org/">Kivy</a></p>
-<p>在接下来的教程中, 您将需要创建一个新的小部件作为引导。 这将在编写Kivy应用程序时提供了丰富并且重要的知识，它可以根据您想要达到的目的让您创建全新的用户界面与自定义元素。</p>
-</section>
-<section id="id2">
-<h2>基本事项<a class="headerlink" href="#id2" title="Permalink to this heading">¶</a></h2>
-<p>在创建应用程序时，你必须问自己三个重要的问题：</p>
-<ul class="simple">
-<li><p>我的应用程序要处理什么数据？</p></li>
-<li><p>我该如何直观地表示这些数据？</p></li>
-<li><p>用户如何与这些数据交互？</p></li>
-</ul>
-<p>举个例子，如果您想写一个非常简单的线画应用程序，让用户用手指在屏幕上画画， 这就是用户和您的应用程序的交互方式。 当用户这么做时, 应用程序将会记住用户手指的位置， 以便稍后您可以在这些位置进行画线。所以手指所在的点将是您的数据，你在它们之间画的线将是您的视觉表现。</p>
-<p>在Kivy上, 应用程序的用户界面由小部件组成。 您在屏幕上所看的的东西都是由这个小部件绘制而成。 通常情况下，你会想要重写之前在不同背景下的写的代码，这就是为什么小部件典型地代表了一个回答上述三个问题的具体实例。 小部件封装数据，定义用户与数据的交互并绘制其可视化表示。 您可以通过嵌套小部件来构建从简单到复杂的用户界面。 这里有很多内置的小部件，如按钮，滑块和其他常见的东西。在很多情况下， 你可能你需要一个超出Kivy附带范围的自定义小部件 (例如：一个医学可视化部件)。</p>
-<p>所以，在设计小部件的时候请记住这三个问题。 尝试以最小及可重用的方式写它们 (即一个小部件完全是它应该做的，只不过是其他事情而已。 如果您需要更多，可以编写更多的小部件或编写更小的小部件的其他小部件。 我们试图坚持单一责任原则).</p>
-</section>
-<section id="id3">
-<h2>绘制小部件<a class="headerlink" href="#id3" title="Permalink to this heading">¶</a></h2>
-<p>我们确信您从童年开始就梦想创造属于自己的多点触控画图程序。而我们，可以助您圆梦 。在下面的章节中，您将会学习如何使用Kivy来编写这样的程序。 在此之前，请确保您已经阅读和理解创建一个应用程序。准备好了吗？让我们开始吧！</p>
-<p><strong>初始结构</strong></p>
-<p>先来写一个我们需要的基础代码框架。顺便提一句，本节中使用的所有不同的代码片段也可以在Kivy附带的examples / guide / firstwidget目录中找到，所以您可以不用每次都要复制和粘贴 。下面是我们所需要的一个基础代码框架 ：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>#qpy:kivy
-from kivy.app import App
-from kivy.uix.widget import Widget
-class MyPaintWidget(Widget):
-    pass
-class MyPaintApp(App):
-    def build(self):
-        return MyPaintWidget()
-if __name__ == &#39;__main__&#39;:
-    MyPaintApp().run()
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>说实话，这很简单。保存为paint.py. 如果你运行它，你应该只会看见黑屏。这时你所看到，并不是使用像Button这样的内置小部件（请参阅创建应用程序），我们将继续编写小部件来完成绘图。我们通过一个从小部件(line 5-6)继承的类来实现这一点，虽然这个类暂时还没什么用，但我们还能像正常的Kivy部件一样对待它（line 11）。if __name__ …结构（第14行）是一种Python机制，可防止在从文件导入时执行if语句中的代码， 即：如果您编写导入绘图，它不会执行一些计划之外的事情，但可以很好地提供文件中定义的类。</p>
-<p><em>注意: 您可能想知道为什么您必须单独导入App和Widget， 而不是从kivy导入 *. 尽管时间较短，但这会使你的命名造成污染，并导致应用程序的启动速度变慢。它也可能引入歧义类和变量命名，所以通常在Python社区中被人们所诟病。 我们这样做的方式更快，更全面。</em></p>
-<p><strong>添加行为</strong></p>
-<p>现在就让我们开始添加一些实际行为，即：即使其对用户输入做出反应。</p>
-<p>像这样改编代码：</p>
-<dl>
-<dt>::</dt><dd><p>from kivy.app import App
-from kivy.uix.widget import Widget</p>
-<dl class="simple">
-<dt>class MyPaintWidget(Widget):</dt><dd><dl class="simple">
-<dt>    def on_touch_down(self, touch):</dt><dd><p>        print(touch)</p>
-</dd>
-</dl>
-</dd>
-<dt>class MyPaintApp(App):</dt><dd><dl class="simple">
-<dt>    def build(self):</dt><dd><p>        return MyPaintWidget()</p>
-</dd>
-</dl>
-</dd>
-<dt>if __name__ == ‘__main__’:</dt><dd><p>    MyPaintApp().run()</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<button class="button">Run …</button>
-<p>这只是为了显示对用户输入做出反应的容易程度。当发生MotionEvent（即触摸，点击等）时，我们将会把关于触摸对象的信息打印6给控制台。 在屏幕上您不会看到任何东西，但是如果您观察从中运行程序的命令行，你会看到每一个触摸的消息。这也表明一个小部件不必具有可视化表示。</p>
-<p>这些用户体验并不会让人无所适从。接下来让我们在绘制窗口中添加一些实际的代码：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>#qpy:kivy
-from kivy.app import App
-from kivy.uix.widget import Widget
-from kivy.graphics import Color, Ellipse
-class MyPaintWidget(Widget):
-    def on_touch_down(self, touch):
-        with self.canvas:
-            Color(1, 1, 0)
-            d = 30.
-            Ellipse(pos=(touch.x - d / 2, touch.y - d / 2), size=(d, d))
-class MyPaintApp(App):
-    def build(self):
-        return MyPaintWidget()
-if __name__ == &#39;__main__&#39;:
-    MyPaintApp().run()
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<img alt="screenshot of pong game" src="http://edu.qpython.org/static/kivy-guide-3.jpg" />
-<p>如果您通过这些修改运行代码，您会看到每次触摸时，你会触摸到一个小的黄色圆圈。</p>
-<p>它是如何工作的？</p>
-<ul class="simple">
-<li><p>Line 9: 我们使用Python的语句和小部件的Canvas对象 这就像是小部件可以在屏幕上绘制事物来表示自己的一个区域。通过使用with语句，所有正确缩进的连续绘图命令都将修改此画布。 with声明还确保在我们绘制之后，内部状态可以被妥善地清理。</p></li>
-<li><p>Line 10: 您可能已经猜到了：这将连续绘制操作的颜色设置为黄色（默认颜色格式为RGB，所以（1,1,0）为黄色）。直到另一个颜色设置。 然后将该颜色当作是您的画笔， 您可以使用它在画布上绘画，直到将画笔切换成另一种颜色。</p></li>
-<li><p>Line 11: 我们需要先确定即将绘制的圆的直径。 使用一个可取的变量，因为我们需要参考该值多次，我们并不希望在哪些地方会改变它，假如我们想圆更大或更小</p></li>
-<li><p>Line 12: 想要绘制一个圆，我们只需绘制一个宽度和高度相等的椭圆。 由于我们希望在用户触摸的位置绘制圆，我们会将触摸的位置传递给椭圆。 请注意，我们需要在x和y方向上（即向左和向下）将椭圆移动到-d / 2，因为位置指定了椭圆边界框的左下角，我们希望它以我们的触摸为中心。</p></li>
-</ul>
-<p>那很简单，不是吗？ 它变得更好了！ 更新代码如下所示：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>#qpy:kivy
-from kivy.app import App
-from kivy.uix.widget import Widget
-from kivy.graphics import Color, Ellipse, Line
-class MyPaintWidget(Widget):
-    def on_touch_down(self, touch):
-        with self.canvas:
-            Color(1, 1, 0)
-            d = 30.
-            Ellipse(pos=(touch.x - d / 2, touch.y - d / 2), size=(d, d))
-            touch.ud[&#39;line&#39;] = Line(points=(touch.x, touch.y))
-    def on_touch_move(self, touch):
-        touch.ud[&#39;line&#39;].points += [touch.x, touch.y]
-class MyPaintApp(App):
-    def build(self):
-        return MyPaintWidget()
-if __name__ == &#39;__main__&#39;:
-    MyPaintApp().run()
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<img alt="screenshot of pong game" src="http://edu.qpython.org/static/kivy-guide-4.jpg" />
-<p>发生了以下的变化：</p>
-<ul class="simple">
-<li><p>Line 3: 我们现在不仅导入了椭圆绘图指令，还导入了线绘图指令如果您查看Line的文档，您将看到它接受的点参数必须是二维点坐标列表，比如 (x1, y1, x2, y2, …, xN, yN).</p></li>
-<li><p>Line 13: 这是它变得有趣的地方。 touch.ud是一个Python字典（键入&lt;dict&gt;），它允许我们存储触摸的自定义属性。</p></li>
-<li><p>Line 13:我们利用我们导入的Line指令，并设置一个Line来绘制。由于这是在on_touch_down中完成的，所以每次新的触摸都会有一个新的线。 通过在with块内创建行， 画布会感应到该线，并将绘制它。 我们只是想稍后修改这行，所以我们在touch.ud字典下面存储了一个对它的引用，这个字典在任意选择的，但是恰当的命名键’line’下面。 通过我们创建初始触摸位置的线，这是我们的线开始的地方。</p></li>
-<li><p>Lines 15: 我们添加一个新的方法到我们的小部件。这与on_touch_down方法类似，但不是在发生新的触摸时被调用，当现有的触摸（已经调用on_touch_down）移动，即其位置改变时，他的方法被调用。 请注意，这是具有更新属性的相同MotionEvent对象。 这会让人觉得十分方便，你很快就会明白为什么会有这样的感觉了。</p></li>
-<li><p>Line 16: 请记住：这是我们在on_touch_down中获得的触摸对象，因此我们可以简单地访问我们存储在touch.ud字典中的数据！早些时候我们为这条线设置了触摸点，我们现在在当前位置添加新的触摸点。我们需要扩展这条线，因为这发生在on_touch_move上，这只是在触摸移动时才被调用，这正是我们要更新这一行的原因。为了使我们不必将这些触摸点记录到在线簿记中，在touch.ud中存储该行使我们在使用时可以更方便一些。</p></li>
-</ul>
-<p>到目前为止一切还不错，虽然还不算漂亮。这让它看上去有点像意大利面。如何让触摸点有自己的颜色？让我们接着往下看：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>#qpy:kivy
-from random import random
-from kivy.app import App
-from kivy.uix.widget import Widget
-from kivy.graphics import Color, Ellipse, Line
-class MyPaintWidget(Widget):
-    def on_touch_down(self, touch):
-        color = (random(), random(), random())
-        with self.canvas:
-            Color(*color)
-            d = 30.
-            Ellipse(pos=(touch.x - d / 2, touch.y - d / 2), size=(d, d))
-            touch.ud[&#39;line&#39;] = Line(points=(touch.x, touch.y))
-    def on_touch_move(self, touch):
-        touch.ud[&#39;line&#39;].points += [touch.x, touch.y]
-class MyPaintApp(App):
-    def build(self):
-        return MyPaintWidget()
-if __name__ == &#39;__main__&#39;:
-    MyPaintApp().run()
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<img alt="Screenshot of pong game" src="http://edu.qpython.org/static/kivy-guide-5.jpg" />
-<p>Here are the changes:</p>
-<ul class="simple">
-<li><p>Line 1: 我们导入Python的random（）函数，它会给我们在[0.，1）范围内的随机值。</p></li>
-<li><p>Line 10: 在这种情况下，我们只需创建一个包含3个随机浮点值的新元组，它们将代表随机的RGB颜色。这样，每一个新的触摸点都将有属于自己的颜色。 不要被使用元组困惑。我们只是将这个元组绑定在颜色上作为这个方法的一个快捷方式。</p></li>
-<li><p>Line 12: 和上面一样，我们设置画布的颜色。这一次，我们使用我们生成的随机值，并使用Python的元组解压缩语法将它们提供给颜色类（因为Color类需要三个单独的颜色分量，而不是一个。如果我们要直接传递元组，1值被传递，而不管元组本身包含3个值的事实）。</p></li>
-</ul>
-<p>这看起来好多了！假如你有更多 的技能和耐心，您甚至可以创建一个漂亮的小绘图！</p>
-<p><em>注意：由于默认情况下，颜色指令采用RGB模式，我们正在向它提供一个带有三个随机浮点值的元组，如果运气不好，很可能会得到黑色。 这并不是好的情况，因为默认情况下，背景颜色也很暗， 这样的话，你会很难看到你自己画的线。有一个很好的技巧来防止这种情况：而不是创建一个具有三个随机值的元组，像这样创建一个元组：（random（），1.，1）。然后，将它传递给颜色指令时，将模式设置为HSV颜色空间：颜色（</em> color，mode =’hsv’）。这样可能出现的颜色将会变少，但是你得到的颜色将永远是同样明亮的：只有色调会改变。</p>
-<p><strong>奖励积分</strong></p>
-<p>在这一点上，我们可以说我们已经完成了。小部件做了它应该做的事：跟踪触摸并画线。在一条线开始的位置绘制圆圈。</p>
-<p>但是，如果用户想要开始一个新的绘图呢？使用当前的代码，清除窗口的唯一方法是重新启动整个应用程序。不过，我们可以做得更好。现在让我们添加一个清除按钮，删除迄今为止绘制的所有线条和圆圈。 有两种选择：</p>
-<ul class="simple">
-<li><p>我们可以创建按钮作为我们的小部件的附件。这意味着如果您创建多个小部件，则每个小部件都有自己的按钮。如果你不小心，这也将允许用户在按钮上绘制，这可能不是你想要的得到的结果。</p></li>
-<li><p>如果你不小心，这也将允许用户在按钮上绘制，这可能不是你想要的。或者我们只在我们的应用程序类中设置按钮一次，当它被按下时，我们清除这个小部件。</p></li>
-</ul>
-<p>就我们这个简单的例子来说，这并不重要。对于较大的应用程序，您应该考虑一下谁在你的应用程序中做什么。 我们将在这里介绍第二个选项，以便您了解如何在应用程序类的build（）方法中构建应用程序的构件树。我们也将更改为HSV色彩空间（参见前面的注释）：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>#qpy:kivy
-from random import random
-from kivy.app import App
-from kivy.uix.widget import Widget
-from kivy.uix.button import Button
-from kivy.graphics import Color, Ellipse, Line
-class MyPaintWidget(Widget):
-    def on_touch_down(self, touch):
-        color = (random(), 1, 1)
-        with self.canvas:
-            Color(*color, mode=&#39;hsv&#39;)
-            d = 30.
-            Ellipse(pos=(touch.x - d / 2, touch.y - d / 2), size=(d, d))
-            touch.ud[&#39;line&#39;] = Line(points=(touch.x, touch.y))
-    def on_touch_move(self, touch):
-        touch.ud[&#39;line&#39;].points += [touch.x, touch.y]
-class MyPaintApp(App):
-    def build(self):
-        parent = Widget()
-        self.painter = MyPaintWidget()
-        clearbtn = Button(text=&#39;Clear&#39;)
-        clearbtn.bind(on_release=self.clear_canvas)
-        parent.add_widget(self.painter)
-        parent.add_widget(clearbtn)
-        return parent
-    def clear_canvas(self, obj):
-        self.painter.canvas.clear()
-if __name__ == &#39;__main__&#39;:
-    MyPaintApp().run()
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<img alt="screenshot of pong game" src="http://edu.qpython.org/static/kivy-guide-6.jpg" />
-<p>将会发生以下的事情：</p>
-<ul class="simple">
-<li><p>Line 4: 我们添加了一个导入语句，以便能够使用Button类。</p></li>
-<li><p>Line 25: 我们创建一个虚拟的Widget（）对象作为我们的绘画控件和我们即将添加的按钮的父对象。 这只是一个比较简陋的方法来设置一个小部件树层次结构。 我们也可以使用布局或做其他一些丰富的东西。再一次：这个小部件除了保存我们现在添加的两个小部件以外，完全没有任何功能。</p></li>
-<li><p>Line 26:我们像往常一样创建我们的MyPaintWidget（），只是这次我们不直接返回它，而是将它绑定到一个变量名。</p></li>
-<li><p>Line 27: 我们创建一个按钮小部件。 它上面会有一个标签，显示文本“清除”。</p></li>
-<li><p>Line 28: 然后，我们将按钮的on_release事件（当按钮被按下然后释放时触发）绑定到第33行和第34行上定义的回调函数clear_canvas。</p></li>
-<li><p>Line 29 &amp; 30: 我们通过制作虚拟父窗口小部件的画家和clearbtn子元素来设置窗口小部件层次结构。 这意味着画家和clearbtn在通常的计算机科学树术语中是兄弟姐妹。</p></li>
-<li><p>Line 33 &amp; 34: 到目前为止，按钮什么也没做。 它在那里，你可以看见它，也可以按它，但什么都不会发生。我们在这里改变：我们创建了一个小的抛弃函数，当按钮被按下时，它将成为我们的回调函数。该功能只是清除画家的画布内容，使其再次变黑。</p></li>
-</ul>
-<p><em>注意：Kivy Widget类在设计上保持简单。没有一般属性，如背景颜色和边框颜色。相反，这些示例和文档说明了如何轻松地处理这些简单的事情，就像我们在这里完成的那样，为画布设置颜色并绘制形状。从一个简单的开始，你可以逐渐使它成为更精细的定制。从Widget派生的更高级别的内置小部件（如Button）确实具有诸如background_color之类的便利属性，但这些属性因widget而异。如果您需要添加更多功能，请使用API文档查看小部件提供的内容以及子类。</em></p>
-<p>恭喜！ 你已经写了你的第一个Kivy部件。很明显，这只是一个快速介绍。还会有更多的东西等着你去发现。 不过我们建议您短暂休息一下，让刚刚学到的东西沉淀一下。 不如先画一些不错的图片让自己放松一下？如果您觉得您已经理解了所有内容，并准备好学习更多，请继续往下阅读。</p>
-</section>
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-<div class="star-footer">
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-<div id="praise"><span>0</span> persons have rewarded</div>
-</div>
-<div class="foo"></div>
-<footer class="clearfix fixfooter web-show">
-<div class="col-sm-offset-2 col-sm-6 footer-div1">
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-var u = window.location.href.split('/')
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diff --git a/docs/kivy-qpython/index.html b/docs/kivy-qpython/index.html
deleted file mode 100644
index ebecb60..0000000
--- a/docs/kivy-qpython/index.html
+++ /dev/null
@@ -1,316 +0,0 @@
-<!DOCTYPE html>
-<html lang="en-US">
-<head>
-<meta charset="utf-8" />
-<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
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-<section id="qpython-s-kivyapp">
-<h1>QPython’s KivyApp<a class="headerlink" href="#qpython-s-kivyapp" title="Permalink to this heading">¶</a></h1>
-<p>Kivy is a support multi-touch cross-platform graphical interface development framework in python.Use Kivy can easily develop GUI applications including games.QPython can support Kivy better.</p>
-<section id="kivy-s-installation">
-<h2>Kivy’s installation<a class="headerlink" href="#kivy-s-installation" title="Permalink to this heading">¶</a></h2>
-<p>Before using Kivy, you need to have Kivy installed.You can install and upgrade kivy related libraries in QPYPI,QPYPI can automatically install dependent libraries.If Kivy needs to be updated, You can also upgrade operated by QPYPI.</p>
-<img alt="Manage kivy libraries in QPYPI" src="http://edu.qpython.org/static/qpypi-kivy.png" />
-</section>
-<section id="kivyapp-sstatement">
-<h2>KivyApp’sstatement<a class="headerlink" href="#kivyapp-sstatement" title="Permalink to this heading">¶</a></h2>
-<p>In QPython, the program type distinguished by head declarations, When the header has the following definition, we know it is a KivyApp program.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1">#qpy:kivy</span>
-</pre></div>
-</div>
-<p>In addition, If you want to make your Kivy program run in full-screen mode, You need to add the following statement.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1">#qpy:fullscreen</span>
-</pre></div>
-</div>
-</section>
-<section id="kivyapp-s-run-process">
-<h2>KivyApp’s Run Process<a class="headerlink" href="#kivyapp-s-run-process" title="Permalink to this heading">¶</a></h2>
-<p>After the program is identified as KivyApp operation, QPython will start the OpenGL engine at the same time, Create a new SDL view, And in which to complete the rendering, output screen.</p>
-</section>
-<section id="exit">
-<h2>Exit<a class="headerlink" href="#exit" title="Permalink to this heading">¶</a></h2>
-<p>Press quit exiting, QPython will finish the Kivy program, clean up, end OpenGL engine, and exit SDL view.</p>
-</section>
-<section id="e-g">
-<h2>E.g<a class="headerlink" href="#e-g" title="Permalink to this heading">¶</a></h2>
-<p>Let’s look at a simple example about KivyApp,This is the default sample program installed after installing QPython.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>#qpy:kivy
-from kivy.app import App
-from kivy.uix.button import Button
-class TestApp(App):
-    def build(self):
-        return Button(text=&#39;Hello World&#39;)
-TestApp().run()
-</pre></div>
-</div>
-<button class="button">Run …</button>
-</section>
-</section>
-<div class="clearer"></div>
-</div>
-</div>
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-<section id="kivy-app-tutorials-a-simple-paint-app">
-<h1>Kivy App Tutorials &gt;&gt; A Simple Paint App<a class="headerlink" href="#kivy-app-tutorials-a-simple-paint-app" title="Permalink to this heading">¶</a></h1>
-<p>In the following tutorial, you will be guided through the creation of your first widget. This provides powerful and important knowledge when programming Kivy applications, as it lets you create completely new user interfaces with custom elements for your specific purpose.</p>
-<section id="basic-considerations">
-<h2>Basic Considerations<a class="headerlink" href="#basic-considerations" title="Permalink to this heading">¶</a></h2>
-<p>When creating an application, you have to ask yourself three important questions:</p>
-<ul class="simple">
-<li><p>What data does my application process?</p></li>
-<li><p>How do I visually represent that data?</p></li>
-<li><p>How does the user interact with that data?</p></li>
-</ul>
-<p>If you want to write a very simple line drawing application for example, you most likely want the user to just draw on the screen with his/her fingers. That’s how the user interacts with your application. While doing so, your application would memorize the positions where the user’s finger were, so that you can later draw lines between those positions. So the points where the fingers were would be your data and the lines that you draw between them would be your visual representation.</p>
-<p>In Kivy, an application’s user interface is composed of Widgets. Everything that you see on the screen is somehow drawn by a widget. Often you would like to be able to reuse code that you already wrote in a different context, which is why widgets typically represent one specific instance that answers the three questions above. A widget encapsulates data, defines the user’s interaction with that data and draws its visual representation. You can build anything from simple to complex user interfaces by nesting widgets. There are many widgets built in, such as buttons, sliders and other common stuff. In many cases, however, you need a custom widget that is beyond the scope of what is shipped with Kivy (e.g. a medical visualization widget).</p>
-<p>So keep these three questions in mind when you design your widgets. Try to write them in a minimal and reusable manner (i.e. a widget does exactly what its supposed to do and nothing more. If you need more, write more widgets or compose other widgets of smaller widgets. We try to adhere to the Single Responsibility Principle).</p>
-</section>
-<section id="paint-widget">
-<h2>Paint Widget<a class="headerlink" href="#paint-widget" title="Permalink to this heading">¶</a></h2>
-<p>We’re sure one of your childhood dreams has always been creating your own multitouch paint program. Allow us to help you achieve that. In the following sections you will successively learn how to write a program like that using Kivy. Make sure that you have read and understood Create an application. You have? Great! Let’s get started!</p>
-<p><strong>Initial Structure</strong></p>
-<p>Let’s start by writing the very basic code structure that we need. By the way, all the different pieces of code that are used in this section are also available in the examples/guide/firstwidget directory that comes with Kivy, so you don’t need to copy &amp; paste it all the time. Here is the basic code skeleton that we will need:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>#qpy:kivy
-from kivy.app import App
-from kivy.uix.widget import Widget
-class MyPaintWidget(Widget):
-    pass
-class MyPaintApp(App):
-    def build(self):
-        return MyPaintWidget()
-if __name__ == &#39;__main__&#39;:
-    MyPaintApp().run()
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>This is actually really simple. Save it as paint.py. If you run it, you should only see a black screen. As you can see, instead of using a built-in widget such as a Button (see Create an application), we are going to write our own widget to do the drawing. We do that by creating a class that inherits from Widget (line 5-6) and although that class does nothing yet, we can still treat it like a normal Kivy widget (line 11). The if __name__ … construct (line 14) is a Python mechanism that prevents you from executing the code in the if-statement when importing from the file, i.e. if you write import paint, it won’t do something unexpected but just nicely provide the classes defined in the file.</p>
-<p><em>Note: You may be wondering why you have to import App and Widget separately, instead of doing something like from kivy import *. While shorter, this would have the disadvantage of polluting your namespace and make the start of the application potentially much slower. It can also introduce ambiguity into class and variable naming, so is generally frowned upon in the Python community. The way we do it is faster and cleaner.</em></p>
-<p><strong>Adding Behaviour</strong></p>
-<p>Let’s now add some actual behaviour to the widget, i.e. make it react to user input. Change the code like so:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>#qpy:kivy
-from kivy.app import App
-from kivy.uix.widget import Widget
-class MyPaintWidget(Widget):
-    def on_touch_down(self, touch):
-        print(touch)
-class MyPaintApp(App):
-    def build(self):
-        return MyPaintWidget()
-if __name__ == &#39;__main__&#39;:
-    MyPaintApp().run()
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>This is just to show how easy it is to react to user input. When a MotionEvent (i.e. a touch, click, etc.) occurs, we simply print the information about the touch object to the console. You won’t see anything on the screen, but if you observe the command-line from which you are running the program, you will see a message for every touch. This also demonstrates that a widget does not have to have a visual representation.</p>
-<p>Now that’s not really an overwhelming user experience. Let’s add some code that actually draws something into our window:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>#qpy:kivy
-from kivy.app import App
-from kivy.uix.widget import Widget
-from kivy.graphics import Color, Ellipse
-class MyPaintWidget(Widget):
-    def on_touch_down(self, touch):
-        with self.canvas:
-            Color(1, 1, 0)
-            d = 30.
-            Ellipse(pos=(touch.x - d / 2, touch.y - d / 2), size=(d, d))
-class MyPaintApp(App):
-    def build(self):
-        return MyPaintWidget()
-if __name__ == &#39;__main__&#39;:
-    MyPaintApp().run()
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<img alt="screenshot of pong game" class="align-center" src="http://edu.qpython.org/static/kivy-guide-3.jpg" />
-<p>If you run your code with these modifications, you will see that every time you touch, there will be a small yellow circle drawn where you touched. How does it work?</p>
-<ul class="simple">
-<li><p>Line 9: We use Python’s with statement with the widget’s Canvas object. This is like an area in which the widget can draw things to represent itself on the screen. By using the with statement with it, all successive drawing commands that are properly indented will modify this canvas. The with statement also makes sure that after our drawing, internal state can be cleaned up properly.</p></li>
-<li><p>Line 10: You might have guessed it already: This sets the Color for successive drawing operations to yellow (default color format is RGB, so (1, 1, 0) is yellow). This is true until another Color is set. Think of this as dipping your brushes in that color, which you can then use to draw on a canvas until you dip the brushes into another color.</p></li>
-<li><p>Line 11: We specify the diameter for the circle that we are about to draw. Using a variable for that is preferable since we need to refer to that value multiple times and we don’t want to have to change it in several places if we want the circle bigger or smaller.</p></li>
-<li><p>Line 12: To draw a circle, we simply draw an Ellipse with equal width and height. Since we want the circle to be drawn where the user touches, we pass the touch’s position to the ellipse. Note that we need to shift the ellipse by -d/2 in the x and y directions (i.e. left and downwards) because the position specifies the bottom left corner of the ellipse’s bounding box, and we want it to be centered around our touch.</p></li>
-</ul>
-<p>That was easy, wasn’t it? It gets better! Update the code to look like this:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>#qpy:kivy
-from kivy.app import App
-from kivy.uix.widget import Widget
-from kivy.graphics import Color, Ellipse, Line
-class MyPaintWidget(Widget):
-    def on_touch_down(self, touch):
-        with self.canvas:
-            Color(1, 1, 0)
-            d = 30.
-            Ellipse(pos=(touch.x - d / 2, touch.y - d / 2), size=(d, d))
-            touch.ud[&#39;line&#39;] = Line(points=(touch.x, touch.y))
-    def on_touch_move(self, touch):
-        touch.ud[&#39;line&#39;].points += [touch.x, touch.y]
-class MyPaintApp(App):
-    def build(self):
-        return MyPaintWidget()
-if __name__ == &#39;__main__&#39;:
-    MyPaintApp().run()
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<img alt="screenshot of pong game" class="align-center" src="http://edu.qpython.org/static/kivy-guide-4.jpg" />
-<p>This is what has changed:</p>
-<ul class="simple">
-<li><p>Line 3: We now not only import the Ellipse drawing instruction, but also the Line drawing instruction. If you look at the documentation for Line, you will see that it accepts a points argument that has to be a list of 2D point coordinates, like (x1, y1, x2, y2, …, xN, yN).</p></li>
-<li><p>Line 13: This is where it gets interesting. touch.ud is a Python dictionary (type &lt;dict&gt;) that allows us to store custom attributes for a touch.</p></li>
-<li><p>Line 13: We make use of the Line instruction that we imported and set a Line up for drawing. Since this is done in on_touch_down, there will be a new line for every new touch. By creating the line inside the with block, the canvas automatically knows about the line and will draw it. We just want to modify the line later, so we store a reference to it in the touch.ud dictionary under the arbitrarily chosen but aptly named key ‘line’. We pass the line that we’re creating the initial touch position because that’s where our line will begin.</p></li>
-<li><p>Lines 15: We add a new method to our widget. This is similar to the on_touch_down method, but instead of being called when a new touch occurs, this method is being called when an existing touch (for which on_touch_down was already called) moves, i.e. its position changes. Note that this is the same MotionEvent object with updated attributes. This is something we found incredibly handy and you will shortly see why.</p></li>
-<li><p>Line 16: Remember: This is the same touch object that we got in on_touch_down, so we can simply access the data we stored away in the touch.ud dictionary! To the line we set up for this touch earlier, we now add the current position of the touch as a new point. We know that we need to extend the line because this happens in on_touch_move, which is only called when the touch has moved, which is exactly why we want to update the line. Storing the line in the touch.ud makes it a whole lot easier for us as we don’t have to maintain our own touch-to-line bookkeeping.</p></li>
-</ul>
-<p>So far so good. This isn’t exactly beautiful yet, though. It looks a bit like spaghetti bolognese. How about giving each touch its own color? Great, let’s do it:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>#qpy:kivy
-from random import random
-from kivy.app import App
-from kivy.uix.widget import Widget
-from kivy.graphics import Color, Ellipse, Line
-class MyPaintWidget(Widget):
-    def on_touch_down(self, touch):
-        color = (random(), random(), random())
-        with self.canvas:
-            Color(*color)
-            d = 30.
-            Ellipse(pos=(touch.x - d / 2, touch.y - d / 2), size=(d, d))
-            touch.ud[&#39;line&#39;] = Line(points=(touch.x, touch.y))
-    def on_touch_move(self, touch):
-        touch.ud[&#39;line&#39;].points += [touch.x, touch.y]
-class MyPaintApp(App):
-    def build(self):
-        return MyPaintWidget()
-if __name__ == &#39;__main__&#39;:
-    MyPaintApp().run()
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<img alt="Screenshot of pong game" class="align-center" src="http://edu.qpython.org/static/kivy-guide-5.jpg" />
-<p>Here are the changes:</p>
-<ul class="simple">
-<li><p>Line 1: We import Python’s random() function that will give us random values in the range of [0., 1.).</p></li>
-<li><p>Line 10: In this case we simply create a new tuple of 3 random float values that will represent a random RGB color. Since we do this in on_touch_down, every new touch will get its own color. Don’t get confused by the use of tuples. We’re just binding the tuple to color for use as a shortcut within this method because we’re lazy.</p></li>
-<li><p>Line 12: As before, we set the color for the canvas. Only this time we use the random values we generated and feed them to the color class using Python’s tuple unpacking syntax (since the Color class expects three individual color components instead of just 1. If we were to pass the tuple directly, that would be just 1 value being passed, regardless of the fact that the tuple itself contains 3 values).</p></li>
-</ul>
-<p>This looks a lot nicer already! With a lot of skill and patience, you might even be able to create a nice little drawing!</p>
-<p><em>Note: Since by default the Color instructions assume RGB mode and we’re feeding a tuple with three random float values to it, it might very well happen that we end up with a lot of dark or even black colors if we are unlucky. That would be bad because by default the background color is dark as well, so you wouldn’t be able to (easily) see the lines you draw. There is a nice trick to prevent this: Instead of creating a tuple with three random values, create a tuple like this: (random(), 1., 1.). Then, when passing it to the color instruction, set the mode to HSV color space: Color(*color, mode=’hsv’). This way you will have a smaller number of possible colors, but the colors that you get will always be equally bright: only the hue changes.</em></p>
-<p><strong>Bonus Points</strong></p>
-<p>At this point, we could say we are done. The widget does what it’s supposed to do: it traces the touches and draws lines. It even draws circles at the positions where a line begins.</p>
-<p>But what if the user wants to start a new drawing? With the current code, the only way to clear the window would be to restart the entire application. Luckily, we can do better. Let us add a Clear button that erases all the lines and circles that have been drawn so far. There are two options now:</p>
-<ul class="simple">
-<li><p>We could either create the button as a child of our widget. That would imply that if you create more than one widget, every widget gets its own button. If you’re not careful, this will also allow users to draw on top of the button, which might not be what you want.</p></li>
-<li><p>Or we set up the button only once, initially, in our app class and when it’s pressed we clear the widget.</p></li>
-</ul>
-<p>For our simple example, it doesn’t really matter that much. For larger applications you should give some thought to who does what in your app. We’ll go with the second option here so that you see how you can build up your application’s widget tree in your app class’s build() method. We’ll also change to the HSV color space (see preceding note):</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>#qpy:kivy
-from random import random
-from kivy.app import App
-from kivy.uix.widget import Widget
-from kivy.uix.button import Button
-from kivy.graphics import Color, Ellipse, Line
-class MyPaintWidget(Widget):
-    def on_touch_down(self, touch):
-        color = (random(), 1, 1)
-        with self.canvas:
-            Color(*color, mode=&#39;hsv&#39;)
-            d = 30.
-            Ellipse(pos=(touch.x - d / 2, touch.y - d / 2), size=(d, d))
-            touch.ud[&#39;line&#39;] = Line(points=(touch.x, touch.y))
-    def on_touch_move(self, touch):
-        touch.ud[&#39;line&#39;].points += [touch.x, touch.y]
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-        clearbtn = Button(text=&#39;Clear&#39;)
-        clearbtn.bind(on_release=self.clear_canvas)
-        parent.add_widget(self.painter)
-        parent.add_widget(clearbtn)
-        return parent
-    def clear_canvas(self, obj):
-        self.painter.canvas.clear()
-if __name__ == &#39;__main__&#39;:
-    MyPaintApp().run()
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<img alt="screenshot of pong game" class="align-center" src="http://edu.qpython.org/static/kivy-guide-6.jpg" />
-<p>Here’s what happens:</p>
-<ul class="simple">
-<li><p>Line 4: We added an import statement to be able to use the Button class.</p></li>
-<li><p>Line 25: We create a dummy Widget() object as a parent for both our painting widget and the button we’re about to add. This is just a poor-man’s approach to setting up a widget tree hierarchy. We could just as well use a layout or do some other fancy stuff. Again: this widget does absolutely nothing except holding the two widgets we will now add to it as children.</p></li>
-<li><p>Line 26: We create our MyPaintWidget() as usual, only this time we don’t return it directly but bind it to a variable name.</p></li>
-<li><p>Line 27: We create a button widget. It will have a label on it that displays the text ‘Clear’.</p></li>
-<li><p>Line 28: We then bind the button’s on_release event (which is fired when the button is pressed and then released) to the callback function clear_canvas defined on below on Lines 33 &amp; 34.</p></li>
-<li><p>Line 29 &amp; 30: We set up the widget hierarchy by making both the painter and the clearbtn children of the dummy parent widget. That means painter and clearbtn are now siblings in the usual computer science tree terminology.</p></li>
-<li><p>Line 33 &amp; 34: Up to now, the button did nothing. It was there, visible, and you could press it, but nothing would happen. We change that here: we create a small, throw-away function that is going to be our callback function when the button is pressed. The function just clears the painter’s canvas’ contents, making it black again.</p></li>
-</ul>
-<p><em>Note: The Kivy Widget class, by design, is kept simple. There are no general properties such as background color and border color. Instead, the examples and documentation illustrate how to easily handle such simple things yourself, as we have done here, setting the color for the canvas, and drawing the shape. From a simple start, you can move to more elaborate customization. Higher-level built-in widgets, deriving from Widget, such as Button, do have convenience properties such as background_color, but these vary by widget. Use the API docs to see what is offered by a widget, and subclass if you need to add more functionality.</em></p>
-<p>Congratulations! You’ve written your first Kivy widget. Obviously this was just a quick introduction. There is much more to discover. We suggest taking a short break to let what you just learned sink in. Maybe draw some nice pictures to relax? If you feel like you’ve understood everything and are ready for more, we encourage you to read on.</p>
-</section>
-<section id="chapter-author-contributor">
-<h2>Chapter Author, contributor<a class="headerlink" href="#chapter-author-contributor" title="Permalink to this heading">¶</a></h2>
-<p>Author: <a class="reference external" href="https://kivy.org/">Kivy</a></p>
-</section>
-</section>
-<div class="clearer"></div>
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-<div id="praise"><span>0</span> persons have rewarded</div>
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-<div class="footer-block-item">
-<span class="footer-item1">Built with Sphinx using a theme provid by QPython.</span>
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diff --git a/docs/objects.inv b/docs/objects.inv
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index cee460d..0000000
Binary files a/docs/objects.inv and /dev/null differ
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deleted file mode 100644
index 24f0557..0000000
--- a/docs/opencv/index.html
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-<title>OpenCV Programming</title>
-<link rel="stylesheet" type="text/css" href="../static/pygments.css" />
-<link rel="stylesheet" type="text/css" href="../static/style.css" />
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-<span class="sr-only">切换导航</span>
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-<li>
-<a href="http://www.qpython.org/index.html">Home</a>
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-<a href="http://www.qpython.org/document.html">Guide</a>
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-<a href="/index.html" class="header-selected">Course</a>
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-<a href="http://www.aipy.org">AIPY</a>
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-<section id="opencv-programming">
-<h1>OpenCV Programming<a class="headerlink" href="#opencv-programming" title="Permalink to this heading">¶</a></h1>
-<p>The courses are still in the planning. Welcome to reward us.</p>
-<ol class="arabic simple">
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diff --git a/docs/python-base/cn/index.html b/docs/python-base/cn/index.html
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-<h1>学习 Python 的由来<a class="headerlink" href="#python" title="Permalink to this heading">¶</a></h1>
-<p>第一次接触 Python 时，是在刚毕业不久，那时公司在做一个网盘客户端，需要调研一些 GUI 框架。由于当时 Python 很火（当然，现在也一样），便尝试了一下 PyQt（Python 语言和 Qt 库的融合）。</p>
-<p>很长时间里，我对 Python 的认知停留在“Life is short, You need Python ”上，就像“PHP 是世界上最好的语言”一样。直到去年的一次“机缘巧合”，要做一个邮件收发组件，我发现 Python 简直太不可思议了，它提供了很多方便的模块（例如：email、smtplib、poplib），使用起来十分简单，能够让我在很短的时间里出色地完成任务。</p>
-<p>在此以后，我几乎每天都要面对 Python，很高兴，我又迈出了新的一步！开始认真研究，包括它自带的一些文档、教程，里面的很多内容确实很好，但大部分都是概念相关的，不够全面，我觉得不太适合新手。所以，是时候该做笔记了，学习 -&gt; 编写 -&gt; 改进 -&gt; 学习… 这是一个漫长的过程，希望在多次的改进和重写后，它能成为了有用的 Python 学习指南。</p>
-<section id="id1">
-<h2>作者 &amp; 更新时间<a class="headerlink" href="#id1" title="Permalink to this heading">¶</a></h2>
-<p>作者: <a class="reference external" href="http://blog.csdn.net/liang19890820">一去丶二三里</a></p>
-<p>提交: 2017/12/6</p>
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-<section id="id1">
-<h1>简述<a class="headerlink" href="#id1" title="Permalink to this heading">¶</a></h1>
-<p>对于任何编程语言来说，都可以在代码中包含注释。这不仅有助于解释代码以提高可读性，也便于日后自己参考或者他人阅读，有时对跟踪问题也非常有用。</p>
-<p>添加注释很有必要，好的开发人员基本都会大量地使用注释。</p>
-<section id="id2">
-<h2>注释风格<a class="headerlink" href="#id2" title="Permalink to this heading">¶</a></h2>
-<p>关键字是预先保留的标识符，每个关键字都有特殊的含义。编程语言众多，但每种语言都有相应的关键字，Python 也不例外，它自带了一个 <em>keyword</em> 模块，用于检测关键字。</p>
-<dl class="simple">
-<dt>在 Python 中，注释的风格与其他语言相比有很大的不同，但使用起来也很简单，主要包含两种：</dt><dd><ul class="simple">
-<li><p>单行注释</p></li>
-<li><p>多行注释</p></li>
-</ul>
-</dd>
-</dl>
-<p>单行注释适用于简短、快速的注释（或者用于调试），而多行注释常用于描述一段内容，或屏蔽整个代码块。</p>
-<p><strong>PS</strong>： 注释是写给其他开发人员（包括自己）看的，Python 解释器会忽略注释。</p>
-</section>
-<section id="id3">
-<h2>单行注释<a class="headerlink" href="#id3" title="Permalink to this heading">¶</a></h2>
-<p># 被用作单行注释符号，<strong>使用 # 时，其右侧的任何数据都会被忽略，被当做是注释。</strong></p>
-<p>例如，在代码的上面或下面添加注释：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1">#print(&quot;这是单行注释&quot;)</span>
-<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Hello, World!&quot;</span><span class="p">)</span>
-<span class="c1">#print(&quot;这也是单行注释&quot;)</span>
-</pre></div>
-</div>
-<p>这将产生以下结果：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>Hello, World!
-</pre></div>
-</div>
-<p>也可以在代码的后面添加注释：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Hello, World!&quot;</span><span class="p">)</span>  <span class="c1">#这是单行注释</span>
-</pre></div>
-</div>
-<p>总之，# 号右侧的内容是不会被执行的。</p>
-</section>
-<section id="id4">
-<h2>多行注释<a class="headerlink" href="#id4" title="Permalink to this heading">¶</a></h2>
-<p>相比单行注释，多行注释略有不同，需要在想要注释的部分之前和之后添加 三个单引号（’’’）或三个双引号（”””）。</p>
-<p>例如，描述一段内容，或屏蔽整个代码块：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="sd">&#39;&#39;&#39;</span>
-<span class="sd">这在多行注释中</span>
-<span class="sd">这也在多行注释中</span>
-<span class="sd">这仍然在多行注释中</span>
-<span class="sd">&#39;&#39;&#39;</span>
-<span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">if True:</span>
-<span class="sd">    print(&quot;Hello&quot;)</span>
-<span class="sd">else:</span>
-<span class="sd">    print(&quot;Python&quot;)</span>
-<span class="sd">&quot;&quot;&quot;</span>
-<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Hello, World!&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>除此之外，还有一种方式，使用多个单行注释来表示多行注释：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1">#print(&quot;这是注释&quot;)</span>
-<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Hello, World!&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>这种方式适用于简短的代码注释。大多数情况下，相比三引号形式还是略显复杂，一般使用率不高。</p>
-<p><strong>建议</strong>： 注释是我们的好伙伴，应尽量多写，尤其是复杂的 Python 代码。</p>
-</section>
-<section id="id5">
-<h2>作者 &amp; 更新时间<a class="headerlink" href="#id5" title="Permalink to this heading">¶</a></h2>
-<p>作者: <a class="reference external" href="http://blog.csdn.net/liang19890820">一去丶二三里</a></p>
-<p>提交: 2017/12/6</p>
-</section>
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diff --git a/docs/python-base/cn/python-base-keyword.html b/docs/python-base/cn/python-base-keyword.html
deleted file mode 100644
index c204980..0000000
--- a/docs/python-base/cn/python-base-keyword.html
+++ /dev/null
@@ -1,302 +0,0 @@
-<!DOCTYPE html>
-<html lang="en-US">
-<head>
-<meta charset="utf-8" />
-<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
-<link href="https://cdn.bootcss.com/bootstrap/3.3.7/css/bootstrap.min.css" rel="stylesheet">
-<title>Python关键词</title>
-<link rel="stylesheet" type="text/css" href="../../static/pygments.css" />
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-<div class="header doc-header web-show">
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-<span class="sr-only">切换导航</span>
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-</button>
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-</div>
-<ul class="header-title collapse navbar-collapse" id="example-navbar-collapse" >
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-<a href="http://www.qpython.org/index.html">Home</a>
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-<section id="python">
-<h1>Python关键词<a class="headerlink" href="#python" title="Permalink to this heading">¶</a></h1>
-<section id="id1">
-<h2>简述<a class="headerlink" href="#id1" title="Permalink to this heading">¶</a></h2>
-<p>关键字是预先保留的标识符，每个关键字都有特殊的含义。编程语言众多，但每种语言都有相应的关键字，Python 也不例外，它自带了一个 <em>keyword</em> 模块，用于检测关键字。</p>
-</section>
-<section id="id2">
-<h2>关键词列表<a class="headerlink" href="#id2" title="Permalink to this heading">¶</a></h2>
-<p>进入 Python 交互模式，获取关键字列表：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">keyword</span>
-<span class="n">keyword</span><span class="o">.</span><span class="n">kwlist</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>共 33 个关键字，除 True、False 和 None 外，其他关键字均为小写形式。</p>
-<p><strong>注意</strong>： Python 是一种动态语言，根据时间在不断变化，关键字列表将来有可能会更改。</p>
-</section>
-<section id="id3">
-<h2>关键词判断<a class="headerlink" href="#id3" title="Permalink to this heading">¶</a></h2>
-<p>除此之外，keyword 模块还提供了关键字的判断功能：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">keyword</span>
-<span class="n">keyword</span><span class="o">.</span><span class="n">iskeyword</span><span class="p">(</span><span class="s1">&#39;and&#39;</span><span class="p">)</span>
-<span class="n">keyword</span><span class="o">.</span><span class="n">iskeyword</span><span class="p">(</span><span class="s1">&#39;has&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>如果是关键字，返回 True；否则，返回 False。</p>
-</section>
-<section id="id4">
-<h2>关键词含义<a class="headerlink" href="#id4" title="Permalink to this heading">¶</a></h2>
-</section>
-<section id="id5">
-<h2>作者 &amp; 更新时间<a class="headerlink" href="#id5" title="Permalink to this heading">¶</a></h2>
-<p>作者: <a class="reference external" href="http://blog.csdn.net/liang19890820">一去丶二三里</a></p>
-<p>提交: 2017/12/6</p>
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diff --git a/docs/python-base/cn/python-base-operator.html b/docs/python-base/cn/python-base-operator.html
deleted file mode 100644
index 417dd29..0000000
--- a/docs/python-base/cn/python-base-operator.html
+++ /dev/null
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-<meta charset="utf-8" />
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-<link rel="stylesheet" type="text/css" href="../../static/pygments.css" />
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-<meta charset="UTF-8">
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-<script src="https://cdn.bootcss.com/bootstrap/3.3.7/js/bootstrap.min.js"></script>
-</head><body>
-<div class="header doc-header web-show">
-<div class="col-sm-offset-2 col-sm-8  index-header">
-<div class="navbar-header">
-<button type="button" class="navbar-toggle" data-toggle="collapse" data-target="#example-navbar-collapse">
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-<a href="http://www.qpython.org/index.html">Home</a>
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-<div class="body" role="main">
-<section id="id1">
-<h1>简述<a class="headerlink" href="#id1" title="Permalink to this heading">¶</a></h1>
-<p>在 Python 中，运算符是执行算术或逻辑运算的特殊符号，操作的值被称为操作数。例如：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="mi">5</span> <span class="o">+</span> <span class="mi">6</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>这里，+ 是执行加法的运算符，5 和 6 是操作数，11 是操作的输出。</p>
-<p>运算符种类</p>
-<hr class="docutils" />
-<p>在 Python 中，支持以下类型的运算符：</p>
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-<li><p>算术运算符</p></li>
-<li><p>比较（关系）运算符</p></li>
-<li><p>逻辑（布尔）运算符</p></li>
-<li><p>位运算符</p></li>
-<li><p>赋值运算符</p></li>
-<li><dl class="simple">
-<dt>特殊运算符</dt><dd><ul>
-<li><p>成员运算符</p></li>
-<li><p>身份运算符</p></li>
-</ul>
-</dd>
-</dl>
-</li>
-<li><p>算术运算符</p></li>
-</ul>
-<hr class="docutils" />
-<p>算术运算符用于执行加、减、乘、除等数学运算。</p>
-<p>示例：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="mi">8</span>
-<span class="n">y</span> <span class="o">=</span> <span class="mi">3</span>
-<span class="n">x</span> <span class="o">+</span> <span class="n">y</span>
-<span class="n">x</span> <span class="o">-</span> <span class="n">y</span>
-<span class="n">x</span> <span class="o">*</span> <span class="n">y</span>
-<span class="n">x</span> <span class="o">/</span> <span class="n">y</span>  <span class="c1"># 总是浮点数</span>
-<span class="n">x</span> <span class="o">//</span> <span class="n">y</span>
-<span class="n">x</span> <span class="o">**</span> <span class="n">y</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p><strong>注意</strong>： 除法 / 运算的结果总是浮点数。例如：4 / 2 返回的是 2.0，而不是 2。</p>
-<p>比较运算符</p>
-<hr class="docutils" />
-<p>比较运算符又称关系运算符，用于比较值，根据条件返回 True 或 False。</p>
-<p>示例：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="mi">6</span>
-<span class="n">y</span> <span class="o">=</span> <span class="mi">10</span>
-<span class="n">x</span> <span class="o">&gt;</span> <span class="n">y</span>
-<span class="n">x</span> <span class="o">&lt;</span> <span class="n">y</span>
-<span class="n">x</span> <span class="o">==</span> <span class="n">y</span>
-<span class="n">x</span> <span class="o">!=</span> <span class="n">y</span>
-<span class="n">x</span> <span class="o">&gt;=</span> <span class="n">y</span>
-<span class="n">x</span> <span class="o">&lt;=</span> <span class="n">y</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<section id="id2">
-<h2>逻辑运算符<a class="headerlink" href="#id2" title="Permalink to this heading">¶</a></h2>
-<p>逻辑运算符又称布尔运算符，通常用于测试真假值。</p>
-<p>示例：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="kc">True</span>
-<span class="n">y</span> <span class="o">=</span> <span class="kc">False</span>
-<span class="n">x</span> <span class="ow">and</span> <span class="n">y</span>
-<span class="ow">not</span> <span class="n">x</span>
-<span class="ow">not</span> <span class="n">y</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-</section>
-<section id="id3">
-<h2>位运算符<a class="headerlink" href="#id3" title="Permalink to this heading">¶</a></h2>
-<p>位运算符作用于操作数，就像它们是二进制数字的字符串一样，它一点点地运行，因此而得名。</p>
-<p>例如：2 是二进制 10，7 是 111。</p>
-<p>令 x = 10（二进制 0000 1010），y = 4（二进制 0000 0100），进行位运算：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="mi">10</span>  <span class="c1"># 二进制 0000 1010</span>
-<span class="n">y</span> <span class="o">=</span> <span class="mi">4</span>  <span class="c1"># 二进制 0000 0100</span>
-<span class="n">x</span> <span class="o">&amp;</span> <span class="n">y</span>  <span class="c1"># 0000 0000</span>
-<span class="n">x</span> <span class="o">|</span> <span class="n">y</span>  <span class="c1"># 0000 1110</span>
-<span class="o">~</span><span class="n">x</span>  <span class="c1"># 1111 0101</span>
-<span class="n">x</span> <span class="o">^</span> <span class="n">y</span>  <span class="c1"># 0000 1110</span>
-<span class="n">x</span> <span class="o">&gt;&gt;</span> <span class="mi">2</span>  <span class="c1"># 0000 0010</span>
-<span class="n">x</span> <span class="o">&lt;&lt;</span> <span class="mi">2</span>  <span class="c1"># 0010 1000</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-</section>
-<section id="id4">
-<h2>赋值运算符<a class="headerlink" href="#id4" title="Permalink to this heading">¶</a></h2>
-<p>赋值运算符用于为变量赋值。</p>
-<p>例如：x = 5，是一个简单的赋值运算符，它将右侧的值 5 分配给左侧的变量 x。</p>
-<p>在 Python 中，有各种各样的复合运算符，例如：x += 5，先将变量 x 的值与 5 相加，再将最终结果分配给变量 x，等价于：x = x + 5。</p>
-</section>
-<section id="id5">
-<h2>特殊运算符<a class="headerlink" href="#id5" title="Permalink to this heading">¶</a></h2>
-<p>除以上运算符外，Python 还提供了一些特殊类型的运算符：</p>
-<ul class="simple">
-<li><p>身份运算符</p></li>
-<li><p>成员运算符</p></li>
-</ul>
-</section>
-<section id="id6">
-<h2>身份运算符<a class="headerlink" href="#id6" title="Permalink to this heading">¶</a></h2>
-<p>身份运算符用于检查两个值（或变量）是否位于存储器的同一部分。</p>
-<p><strong>注意</strong>： 两个变量相等，并不意味着它们也相同。</p>
-<p>示例：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">x1</span> <span class="o">=</span> <span class="mi">5</span>
-<span class="n">y1</span> <span class="o">=</span> <span class="mi">5</span>
-<span class="n">x2</span> <span class="o">=</span> <span class="s1">&#39;Python&#39;</span>
-<span class="n">y2</span> <span class="o">=</span> <span class="s1">&#39;Python&#39;</span>
-<span class="n">x3</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]</span>
-<span class="n">y3</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]</span>
-<span class="n">x1</span> <span class="ow">is</span> <span class="ow">not</span> <span class="n">y1</span>
-<span class="n">x2</span> <span class="ow">is</span> <span class="n">y2</span>
-<span class="n">x3</span> <span class="ow">is</span> <span class="n">y3</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>可以看到 x1 和 y1 是相同值的整数，所以它们相等且相同。x2 和 y2（字符串）同样是这样。</p>
-<p>但 x3 和 y3 是列表，它们相等，但不相同。由于列表是可变的（可以更改），即使它们相等，解释器也会将它们分别存储在内存中。</p>
-</section>
-<section id="id7">
-<h2>成员运算符<a class="headerlink" href="#id7" title="Permalink to this heading">¶</a></h2>
-<p>成员运算符用于测试在序列（字符串、列表、元组、集合和字典）中是否可以找到一个值/变量。</p>
-<p><strong>注意</strong>： 在字典中，只能检测 key 是否存在，而不能检测 value。</p>
-<p>示例：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="s1">&#39;Hello, World!&#39;</span>  <span class="c1"># 字符串</span>
-<span class="n">y</span> <span class="o">=</span> <span class="p">{</span><span class="mi">1</span><span class="p">:</span><span class="s1">&#39;Java&#39;</span><span class="p">,</span> <span class="mi">2</span><span class="p">:</span><span class="s1">&#39;Python&#39;</span><span class="p">}</span>  <span class="c1"># 字典</span>
-<span class="s1">&#39;H&#39;</span> <span class="ow">in</span> <span class="n">x</span>
-<span class="s1">&#39;hello&#39;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">x</span>
-<span class="mi">1</span> <span class="ow">in</span> <span class="n">y</span>
-<span class="s1">&#39;Python&#39;</span> <span class="ow">in</span> <span class="n">y</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>1这里，’H’ 在 x 中，但 ‘hello’ 不存在于 x 中（切记，Python 是区分大小写的）。同样地，1 是 key，’Python’ 是字典 y 中的值。因此，’Python’ in y 返回 False。</p>
-</section>
-<section id="id8">
-<h2>作者 &amp; 更新时间<a class="headerlink" href="#id8" title="Permalink to this heading">¶</a></h2>
-<p>作者: <a class="reference external" href="http://blog.csdn.net/liang19890820">一去丶二三里</a></p>
-<p>提交: 2017/12/6</p>
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-<section id="id1">
-<h1>简述<a class="headerlink" href="#id1" title="Permalink to this heading">¶</a></h1>
-<p>Python 中的每个值都有一个数据类型。</p>
-<p>在 Python 编程中，<strong>一切（万物）皆对象</strong>，数据类型实际上是类，变量是这些类的实例（对象）。</p>
-<section id="id2">
-<h2>数据类型<a class="headerlink" href="#id2" title="Permalink to this heading">¶</a></h2>
-<p>Python 中有各种数据类型，下面列出了一些重要类型：</p>
-<blockquote>
-<div><ul class="simple">
-<li><p>Number（数字）</p></li>
-<li><p>String（字符串）</p></li>
-<li><p>List（列表）</p></li>
-<li><p>Tuple（元组）</p></li>
-<li><p>Set（集合）</p></li>
-<li><p>Dictionary（字典）</p></li>
-</ul>
-</div></blockquote>
-</section>
-<section id="number">
-<h2>Number（数字）<a class="headerlink" href="#number" title="Permalink to this heading">¶</a></h2>
-<p>Python 支持三种不同的数字类型：</p>
-<blockquote>
-<div><ul class="simple">
-<li><p>int（整型）</p></li>
-<li><p>float（浮点型）</p></li>
-<li><p>complex（复数）</p></li>
-</ul>
-</div></blockquote>
-<p><strong>注意</strong>： Py3.x 去除了 long 类型，现在只有一种整型 - int，表示为长整型。</p>
-<p>可以使用 type() 函数获取变量或值的类型，使用 isinstance() 函数来检查一个对象是否属于一个特定的类。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">i</span> <span class="o">=</span> <span class="mi">5</span>  <span class="c1"># 整型</span>
-<span class="nb">type</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
-<span class="n">f</span> <span class="o">=</span> <span class="mf">2.5</span>  <span class="c1"># 浮点型</span>
-<span class="nb">type</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
-<span class="nb">type</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
-<span class="n">c</span> <span class="o">=</span> <span class="mi">1</span><span class="o">+</span><span class="mi">2</span><span class="n">j</span>  <span class="c1"># 复数</span>
-<span class="nb">type</span><span class="p">(</span><span class="n">c</span><span class="p">)</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>整数可以是任何长度，只受到可用内存的限制。</p>
-<p>浮点数精确到 15 位小数。</p>
-<p>复数以 x + yj 的形式写成，其中 x 是实部，y 是虚部。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">i</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">**</span> <span class="mi">500</span>  <span class="c1"># 2 的 500 次幂</span>
-<span class="n">f</span> <span class="o">=</span> <span class="mf">0.12345678901234567890</span>
-<span class="n">c</span> <span class="o">=</span> <span class="mi">5</span><span class="o">+</span><span class="mi">6</span><span class="n">j</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-</section>
-<section id="string">
-<h2>String（字符串）<a class="headerlink" href="#string" title="Permalink to this heading">¶</a></h2>
-<p>字符串是 Unicode 字符的序列。</p>
-<p>可以使用单引号（’’）或双引号（””）来表示字符串，多行字符串可以使用三重引号 ‘’’ 或 “”” 来表示。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">s</span> <span class="o">=</span> <span class="s1">&#39;Hello, Python!&#39;</span>
-<span class="nb">type</span><span class="p">(</span><span class="n">s</span><span class="p">)</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>各种表示方式：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">s</span> <span class="o">=</span> <span class="s1">&#39;Hello, Python!&#39;</span>  <span class="c1"># 单引号</span>
-<span class="n">s</span> <span class="o">=</span> <span class="s2">&quot;Hello, World!&quot;</span>  <span class="c1"># 双引号</span>
-<span class="n">s</span> <span class="o">=</span> <span class="s1">&#39;&#39;&#39;Hello,  # 三重单引号</span>
-<span class="s1">World! &#39;&#39;&#39;</span>
-<span class="n">s</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;  # 三重双引号</span>
-<span class="s2">Hello,</span>
-<span class="s2">World!</span>
-<span class="s2">&quot;&quot;&quot;</span>
-</pre></div>
-</div>
-<p>字符串可以被索引和截取，截取的语法格式如下：</p>
-<p>变量[头下标:尾下标]</p>
-<p>索引值以 0 开始，-1 为从末尾的开始位置。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">s</span> <span class="o">=</span> <span class="s2">&quot;Hello, World!&quot;</span>
-<span class="n">s</span><span class="p">[</span><span class="mi">4</span><span class="p">]</span>  <span class="c1"># 第五个字符</span>
-<span class="n">s</span><span class="p">[</span><span class="mi">7</span><span class="p">:</span><span class="mi">10</span><span class="p">]</span>  <span class="c1"># 第八个开始到第十个的字符</span>
-<span class="n">s</span><span class="p">[</span><span class="o">-</span><span class="mi">4</span><span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>  <span class="c1"># 倒数第四个开始到倒数第一个的字符</span>
-<span class="n">s</span><span class="p">[</span><span class="mi">5</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;p&#39;</span>  <span class="c1"># 生成错误，字符串是不可变的</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>加号（+）是字符串的连接符， 星号（*） 表示复制当前字符串，紧跟的数字为复制的次数。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">s1</span> <span class="o">=</span> <span class="s2">&quot;Hello,&quot;</span>
-<span class="n">s2</span> <span class="o">=</span> <span class="s2">&quot; World!&quot;</span>
-<span class="n">s1</span> <span class="o">+</span> <span class="n">s2</span>  <span class="c1"># 连接字符串</span>
-<span class="p">(</span><span class="n">s1</span> <span class="o">+</span> <span class="n">s2</span><span class="p">)</span> <span class="o">*</span> <span class="mi">3</span>  <span class="c1"># 复制 3 次字符串</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-</section>
-<section id="list">
-<h2>List（列表）<a class="headerlink" href="#list" title="Permalink to this heading">¶</a></h2>
-<p>列表是有序的元素序列，它是 Python 中使用最频繁的数据类型，非常灵活。</p>
-<p>列表中元素的类型可以不同，支持数字、字符串，甚至可以包含列表（所谓的嵌套）。</p>
-<p>列表用 [] 标识，内部元素用逗号分隔。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">l</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mf">5.5</span><span class="p">,</span> <span class="s1">&#39;Python&#39;</span><span class="p">]</span>
-<span class="nb">type</span><span class="p">(</span><span class="n">l</span><span class="p">)</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>和字符串一样，列表同样可以被索引和截取，列表被截取后返回一个包含所需元素的新列表。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">l</span> <span class="o">=</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
-<span class="n">l</span>
-<span class="n">l</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>  <span class="c1"># 第三个元素</span>
-<span class="n">l</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">3</span><span class="p">]</span> <span class="c1"># 从第一个元素到第三个元素</span>
-<span class="n">l</span><span class="p">[</span><span class="mi">3</span><span class="p">:]</span> <span class="c1"># 从第三个元素开始的所有元素</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>列表是可变的，意思是说，列表中元素的值可以被改变。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">l</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]</span>
-<span class="n">l</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="mi">5</span>  <span class="c1"># 将第三个元素 3 变为 5</span>
-<span class="n">l</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-</section>
-<section id="tuple">
-<h2>Tuple（元组）<a class="headerlink" href="#tuple" title="Permalink to this heading">¶</a></h2>
-<p>元组与列表相同，也是有序序列，唯一的区别是元组是不可变的。</p>
-<p>元组适用于保护性的数据，通常比列表快，因为它不能动态更改。</p>
-<p>元组用 () 标识，内部元素用逗号分隔。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">t</span> <span class="o">=</span> <span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="s1">&#39;Python&#39;</span><span class="p">,</span> <span class="mi">1</span><span class="o">+</span><span class="mi">2</span><span class="n">j</span><span class="p">)</span>
-<span class="nb">type</span><span class="p">(</span><span class="n">t</span><span class="p">)</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>元组也可以被索引和截取，但是不能被更改。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">t</span> <span class="o">=</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-<span class="n">t</span>
-<span class="n">t</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>  <span class="c1"># 第二个元素</span>
-<span class="n">t</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">2</span><span class="p">]</span> <span class="c1"># 从第一个元素到第二个元素</span>
-<span class="n">t</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mi">10</span>  <span class="c1"># 生成错误，元组是不可变的</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>虽然元组中的元素不可变，但它可以包含可变的对象，例如：List（列表）。</p>
-<p>构造一个空的或者包含一个元素的元组比较特殊，所以要采用一些额外的语法规则：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">tup1</span> <span class="o">=</span> <span class="p">()</span>  <span class="c1"># 空元组</span>
-<span class="n">tup2</span> <span class="o">=</span> <span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="p">)</span>  <span class="c1"># 一个元素，需要在元素后添加逗号</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-</section>
-<section id="set">
-<h2>Set（集合）<a class="headerlink" href="#set" title="Permalink to this heading">¶</a></h2>
-<p>集合是一个无序不重复元素集。</p>
-<p>集合用 {} 标识，内部元素用逗号分隔。</p>
-<p>可以使用大括号 {} 或者 set() 函数创建集合，注意： 要创建一个空集合，必须使用 set() 而不是 {}，因为 {} 用于创建一个空字典。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">s</span> <span class="o">=</span> <span class="p">{</span><span class="mi">5</span><span class="p">,</span> <span class="s1">&#39;Python&#39;</span><span class="p">,</span> <span class="mi">1</span><span class="o">+</span><span class="mi">2</span><span class="n">j</span><span class="p">}</span>
-<span class="nb">type</span><span class="p">(</span><span class="n">s</span><span class="p">)</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>既然集合是无序的，那么索引就没有任何意义，也就是说，切片操作符 [] 不起作用。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">s</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;Java&quot;</span><span class="p">,</span> <span class="s2">&quot;Python&quot;</span><span class="p">,</span> <span class="s2">&quot;PHP&quot;</span><span class="p">}</span>
-<span class="n">s</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>  <span class="c1"># 生成错误，不支持索引</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>不重复，是指集合中相同的元素会被自动过滤掉，只保留一份。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">s</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;PHP&quot;</span><span class="p">,</span> <span class="s2">&quot;Python&quot;</span><span class="p">,</span> <span class="s2">&quot;Java&quot;</span><span class="p">,</span> <span class="s2">&quot;Python&quot;</span><span class="p">,</span> <span class="s2">&quot;PHP&quot;</span><span class="p">}</span>
-<span class="n">s</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>除了去重之外，还常用于成员关系的测试。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>if (&#39;Python&#39; in s) :
-    print(&#39;Python 在集合中&#39;)
-else :
-    print(&#39;Python 不在集合中&#39;)
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>集合之间也可执行运算，例如：并集、差集、交集。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">a</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="s1">&#39;abcdefg&#39;</span><span class="p">)</span>
-<span class="n">b</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="s1">&#39;abghijk&#39;</span><span class="p">)</span>
-<span class="n">a</span>
-<span class="o">&gt;&gt;&gt;</span><span class="p">{</span><span class="s1">&#39;b&#39;</span><span class="p">,</span> <span class="s1">&#39;f&#39;</span><span class="p">,</span> <span class="s1">&#39;e&#39;</span><span class="p">,</span> <span class="s1">&#39;d&#39;</span><span class="p">,</span> <span class="s1">&#39;a&#39;</span><span class="p">,</span> <span class="s1">&#39;c&#39;</span><span class="p">,</span> <span class="s1">&#39;g&#39;</span><span class="p">}</span>
-<span class="n">b</span>
-<span class="o">&gt;&gt;&gt;</span><span class="p">{</span><span class="s1">&#39;b&#39;</span><span class="p">,</span> <span class="s1">&#39;k&#39;</span><span class="p">,</span> <span class="s1">&#39;h&#39;</span><span class="p">,</span> <span class="s1">&#39;i&#39;</span><span class="p">,</span> <span class="s1">&#39;j&#39;</span><span class="p">,</span> <span class="s1">&#39;a&#39;</span><span class="p">,</span> <span class="s1">&#39;g&#39;</span><span class="p">}</span>
-<span class="n">a</span> <span class="o">-</span> <span class="n">b</span>  <span class="c1"># 差集</span>
-<span class="o">&gt;&gt;&gt;</span><span class="p">{</span><span class="s1">&#39;f&#39;</span><span class="p">,</span> <span class="s1">&#39;c&#39;</span><span class="p">,</span> <span class="s1">&#39;d&#39;</span><span class="p">,</span> <span class="s1">&#39;e&#39;</span><span class="p">}</span>
-<span class="n">a</span> <span class="o">|</span> <span class="n">b</span>  <span class="c1"># 并集</span>
-<span class="o">&gt;&gt;&gt;</span><span class="p">{</span><span class="s1">&#39;b&#39;</span><span class="p">,</span> <span class="s1">&#39;k&#39;</span><span class="p">,</span> <span class="s1">&#39;f&#39;</span><span class="p">,</span> <span class="s1">&#39;h&#39;</span><span class="p">,</span> <span class="s1">&#39;i&#39;</span><span class="p">,</span> <span class="s1">&#39;e&#39;</span><span class="p">,</span> <span class="s1">&#39;j&#39;</span><span class="p">,</span> <span class="s1">&#39;d&#39;</span><span class="p">,</span> <span class="s1">&#39;a&#39;</span><span class="p">,</span> <span class="s1">&#39;c&#39;</span><span class="p">,</span> <span class="s1">&#39;g&#39;</span><span class="p">}</span>
-<span class="n">a</span> <span class="o">&amp;</span> <span class="n">b</span>  <span class="c1"># 交集</span>
-<span class="o">&gt;&gt;&gt;</span><span class="p">{</span><span class="s1">&#39;b&#39;</span><span class="p">,</span> <span class="s1">&#39;a&#39;</span><span class="p">,</span> <span class="s1">&#39;g&#39;</span><span class="p">}</span>
-<span class="n">a</span> <span class="o">^</span> <span class="n">b</span>  <span class="c1"># 对称差 - 不同时存在的元素</span>
-<span class="o">&gt;&gt;&gt;</span><span class="p">{</span><span class="s1">&#39;e&#39;</span><span class="p">,</span> <span class="s1">&#39;c&#39;</span><span class="p">,</span> <span class="s1">&#39;k&#39;</span><span class="p">,</span> <span class="s1">&#39;f&#39;</span><span class="p">,</span> <span class="s1">&#39;h&#39;</span><span class="p">,</span> <span class="s1">&#39;i&#39;</span><span class="p">,</span> <span class="s1">&#39;j&#39;</span><span class="p">,</span> <span class="s1">&#39;d&#39;</span><span class="p">}</span>
-</pre></div>
-</div>
-<p>对称差公式：A Δ B = (A ? B) ∪(B ? A)。也可表示为两个集合的并集减去它们的交集：A Δ B = (A ∪B) ? (A ∩B)。</p>
-</section>
-<section id="dictionary">
-<h2>Dictionary（字典）<a class="headerlink" href="#dictionary" title="Permalink to this heading">¶</a></h2>
-<p>字典是键值对的无序集合。</p>
-<p>通常在有大量的数据时会使用，在检索数据时字典做了优化，必须知道要检索的 value 所对应的 key。</p>
-<p>字典用 {} 标识，其中的每个元素都以 key:value 对的形式出现，key 和 value 可以是任何类型。</p>
-<p><strong>注意</strong>： 字典中的 key 必须是唯一的。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">d</span> <span class="o">=</span> <span class="p">{</span><span class="mi">1</span><span class="p">:</span><span class="s1">&#39;value&#39;</span><span class="p">,</span> <span class="s1">&#39;key&#39;</span><span class="p">:</span><span class="mi">2</span><span class="p">}</span>
-<span class="nb">type</span><span class="p">(</span><span class="n">d</span><span class="p">)</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>可以用 key 来检索相应的 value，但相反则不行。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">d</span> <span class="o">=</span> <span class="p">{}</span>  <span class="c1"># 创建空字典</span>
-<span class="n">d</span><span class="p">[</span><span class="s1">&#39;name&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;Python&#39;</span>  <span class="c1"># 增加新的键/值对</span>
-<span class="n">d</span><span class="p">[</span><span class="s1">&#39;site&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;www.python.org&#39;</span>
-<span class="n">d</span>
-<span class="n">d</span><span class="p">[</span><span class="s1">&#39;site&#39;</span><span class="p">]</span>  <span class="c1"># 键为 &#39;site&#39; 的值</span>
-<span class="n">d</span><span class="p">[</span><span class="s1">&#39;Python&#39;</span><span class="p">]</span>  <span class="c1"># 生成错误，不能用 value 检索 key</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>字典有一些内置的函数，例如：keys()、values()、clear() 等。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">d</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span>  <span class="c1"># 所有键</span>
-<span class="n">dict_keys</span><span class="p">([</span><span class="s1">&#39;name&#39;</span><span class="p">,</span> <span class="s1">&#39;site&#39;</span><span class="p">])</span>
-<span class="n">d</span><span class="o">.</span><span class="n">values</span><span class="p">()</span>  <span class="c1"># 所有值</span>
-<span class="n">dict_values</span><span class="p">([</span><span class="s1">&#39;Python&#39;</span><span class="p">,</span> <span class="s1">&#39;www.python.org&#39;</span><span class="p">])</span>
-<span class="n">d</span><span class="o">.</span><span class="n">clear</span><span class="p">()</span>  <span class="c1"># 清空字典</span>
-<span class="n">d</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-</section>
-<section id="id3">
-<h2>数据类型之间的转换<a class="headerlink" href="#id3" title="Permalink to this heading">¶</a></h2>
-<p>可以使用不同的类型转换函数来转换不同的数据类型，例如：int()、float()、str() 等。</p>
-<p>从 int 转换为 float：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">float</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>从 float 到 int 的转换，值将会被截断（使其接近零）：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">int</span><span class="p">(</span><span class="mf">10.8</span><span class="p">)</span>
-<span class="nb">int</span><span class="p">(</span><span class="o">-</span><span class="mf">10.8</span><span class="p">)</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>字符串的转换必须包含兼容的值：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">float</span><span class="p">(</span><span class="s1">&#39;2.5&#39;</span><span class="p">)</span>
-<span class="o">&gt;&gt;&gt;</span><span class="mf">2.5</span>
-<span class="nb">str</span><span class="p">(</span><span class="mi">25</span><span class="p">)</span>
-<span class="o">&gt;&gt;&gt;</span><span class="s1">&#39;25&#39;</span>
-<span class="nb">int</span><span class="p">(</span><span class="s1">&#39;str&#39;</span><span class="p">)</span>
-<span class="o">&gt;&gt;&gt;</span><span class="n">Traceback</span> <span class="p">(</span><span class="n">most</span> <span class="n">recent</span> <span class="n">call</span> <span class="n">last</span><span class="p">):</span>
-<span class="n">File</span> <span class="s2">&quot;&lt;stdin&gt;&quot;</span><span class="p">,</span> <span class="n">line</span> <span class="mi">1</span><span class="p">,</span> <span class="ow">in</span> <span class="o">&lt;</span><span class="n">module</span><span class="o">&gt;</span>
-<span class="ne">ValueError</span><span class="p">:</span> <span class="n">invalid</span> <span class="n">literal</span> <span class="k">for</span> <span class="nb">int</span><span class="p">()</span> <span class="k">with</span> <span class="n">base</span> <span class="mi">10</span><span class="p">:</span> <span class="s1">&#39;str&#39;</span>
-</pre></div>
-</div>
-<p>甚至可以将一个序列转换为另一个序列：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">set</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span>
-<span class="nb">tuple</span><span class="p">({</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">})</span>
-<span class="nb">list</span><span class="p">(</span><span class="s1">&#39;hello&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>要转换为字典，每个元素必须是一对：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">dict</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="s1">&#39;value&#39;</span><span class="p">],</span> <span class="p">[</span><span class="s1">&#39;key&#39;</span><span class="p">,</span> <span class="mi">2</span><span class="p">]])</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-</section>
-<section id="id4">
-<h2>作者 &amp; 更新时间<a class="headerlink" href="#id4" title="Permalink to this heading">¶</a></h2>
-<p>作者: <a class="reference external" href="http://blog.csdn.net/liang19890820">一去丶二三里</a></p>
-<p>提交: 2017/12/6</p>
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-<section id="id1">
-<h1>简述<a class="headerlink" href="#id1" title="Permalink to this heading">¶</a></h1>
-<p>变量只不过是保留的内存位置，用于存储规定范围内的值。这意味着，在创建变量时会在内存中开辟一个空间。</p>
-<p>基于变量的数据类型，解释器会分配指定内存，并决定什么数据可以被存储在内存中。因此，变量可以指定不同的数据类型，例如：整数、小数或字符串等。</p>
-<section id="id2">
-<h2>何为变量？<a class="headerlink" href="#id2" title="Permalink to this heading">¶</a></h2>
-<p>简单地说，变量就是给数据起一个名字。</p>
-<p>变量更多情况下是一种引用，对应的只是内存当中的一个值，该值可以根据需要存储不同的数据类型。</p>
-<p>如下等式，一个名为 age 的变量被赋值为 18 。正如每个人都有姓名一样，变量的名字叫做标识符。</p>
-<img alt="http://img.blog.csdn.net/20170331192942534" src="http://img.blog.csdn.net/20170331192942534" />
-<p><strong>注意</strong>： 在很多语言中，声明变量必须要声明变量的类型。例如，要在 C++ 中声明一个整形变量，必须先声明变量的类型为 int，然后使用变量名以及变量对应的值。而在 Python 中，</p>
-</section>
-<section id="id3">
-<h2>变量命名<a class="headerlink" href="#id3" title="Permalink to this heading">¶</a></h2>
-<p>变量可以任意取名，但必须遵循以下命名规则：</p>
-<blockquote>
-<div><ul class="simple">
-<li><p>由字母、数字、下划线（_）组成</p></li>
-<li><p>不能以数字开头</p></li>
-<li><p>不能使用 Python 关键字</p></li>
-<li><p>不能使用特殊符号，例如：!、&#64;、#、$、% 等</p></li>
-</ul>
-</div></blockquote>
-<p>例如，定义以下变量：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>age = 18  // 正确
-_age = 18  // 正确
-age_18 = 18  // 正确
-18_age = 18  // 错误，以数字开头
-global = 18  // 错误，使用关键字 global
-a@e = 18  // 错误，使用特殊符号 @
-</pre></div>
-</div>
-<p>除此之外，还需要注意：</p>
-<ul class="simple">
-<li><p>变量对大小写敏感（区分大小写）</p></li>
-</ul>
-<p>例如，定义以下变量：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">AGE</span> <span class="o">=</span> <span class="mi">18</span>  <span class="o">//</span> <span class="n">正确</span>
-<span class="n">age</span> <span class="o">=</span> <span class="mi">18</span>  <span class="o">//</span> <span class="n">正确</span>
-</pre></div>
-</div>
-<p>AGE 和 age 是两个不同的变量，而非同一个。</p>
-<p><strong>建议</strong>： 变量名应做到见明知义，避免晦涩难懂。虽然可以是任意长度，但一般应确保精简、无歧义。</p>
-</section>
-</section>
-<section id="id4">
-<h1>变量赋值<a class="headerlink" href="#id4" title="Permalink to this heading">¶</a></h1>
-<section id="id5">
-<h2>变量的基本赋值<a class="headerlink" href="#id5" title="Permalink to this heading">¶</a></h2>
-<p>变量是数据的引用，更像是标签。</p>
-<p>例如，有一个 teacher 叫 Jack Ma，相当于将 teacher 这个标签拴在了 Jack Ma 上。通过这个 teacher，就顺延到了 Jack Ma，于是当我们叫 teacher 时，实际是在叫 Jack Ma，给人的</p>
-<p>感觉似乎 teacher 就是 Jack Ma，而事实上是 teacher 这个标签贴在了 Jack Ma 上。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">teacher</span> <span class="o">=</span> <span class="s2">&quot;Jack Ma&quot;</span>
-<span class="n">teacher</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>在这里，teacher 标签就是所谓的变量，Jack Ma 就是该变量所对应的值，存储于内存中。</p>
-<p>在 Python 中，有非常重要的一句话：<strong>对象有类型，变量无类型。</strong> 可以这样理解，就是标签不仅可以贴在整形类型的对象上，还可以贴在其他类型的对象上（例如：字符串）。变量的这个特点（随意贴标签）非常重要，它没有类型。</p>
-</section>
-<section id="id6">
-<h2>变量的重新赋值<a class="headerlink" href="#id6" title="Permalink to this heading">¶</a></h2>
-<p>变量，从字面意思理解就是变化的量，也就是说，数值是可变的。将变量从一个存储空间移至另一个存储空间中（也就是将标签移走，换到另一个位置），被称作变量的重新赋值。</p>
-<p>例如，原来 teacher 叫 Jack Ma，后来年龄大退休了，换了一个老师叫 Pony，当我们再叫 teacher 时，其实叫的是 Pony，而非 Jack Ma。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">teacher</span> <span class="o">=</span> <span class="s2">&quot;Jack Ma&quot;</span>
-<span class="n">teacher</span>
-<span class="n">teacher</span> <span class="o">=</span> <span class="s2">&quot;Pony&quot;</span>
-<span class="n">teacher</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>一开始，相当于将 teacher 这个标签拴在了 Jack Ma 上，后来标签换了位置，拴在了 Pony 上。</p>
-<p>如果学过其他编程语言，例如：C++，很容易想到的是 teacher 开辟了内存空间，里面存了 Jack Ma，然后这个空间当中又换为 Pony。就好比 teacher 是一个容器，里面放着值。实际在 Python 当中，正好相反，Python 是以数据为主，Jack Ma、Pony 都存储在内存当中，teacher 无非是对数据的一个引用。所以，当重新赋值后，查看 teacher 所对应的地址时，会发生变化。</p>
-<p>每个对象在内存中都有对应的地址，这就是它的“身份”，使用 Python 的内建函数 id() 来查看：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">teacher</span> <span class="o">=</span> <span class="s2">&quot;JacK Ma&quot;</span>
-<span class="nb">id</span><span class="p">(</span><span class="n">teacher</span><span class="p">)</span>
-<span class="n">teacher</span> <span class="o">=</span> <span class="s2">&quot;Pony&quot;</span>
-<span class="nb">id</span><span class="p">(</span><span class="n">teacher</span><span class="p">)</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>可以看到，重新赋值后，teacher 对应的内存空间已经变了。所以，JacK Ma 和 Pony 实际上存储在两个空间中。</p>
-</section>
-<section id="id7">
-<h2>为多个变量指定同一个值<a class="headerlink" href="#id7" title="Permalink to this heading">¶</a></h2>
-<p>同一个内存地址，可以有多个标签。这个很容易理解，一个人可以有多重身份，JacK Ma 既是 teacher，又是 ceo。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">ceo</span> <span class="o">=</span> <span class="n">teacher</span> <span class="o">=</span> <span class="s2">&quot;Jack Ma&quot;</span>
-</pre></div>
-</div>
-<p>或者：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">teacher</span> <span class="o">=</span> <span class="s2">&quot;Jack Ma&quot;</span>
-<span class="n">ceo</span> <span class="o">=</span> <span class="n">teacher</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>看似不相关的变量，但他们指的都是同一个人 Jack Ma，不妨看一下：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">teacher</span> <span class="o">=</span> <span class="s2">&quot;Jack Ma&quot;</span>
-<span class="n">ceo</span> <span class="o">=</span> <span class="n">teacher</span>
-<span class="nb">id</span><span class="p">(</span><span class="n">teacher</span><span class="p">)</span>
-<span class="nb">id</span><span class="p">(</span><span class="n">ceo</span><span class="p">)</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>显然，对应的是同一个内存地址。也就是说，teacher、ceo 是不同的标签，但是所引用的数据是同一地址上的。</p>
-<p>将多个值分配给多个变量</p>
-<p>要将多个值分配给多个变量，可以单独分开来写：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">age</span> <span class="o">=</span> <span class="mi">18</span>
-<span class="n">teacher</span> <span class="o">=</span> <span class="s2">&quot;Jack Ma&quot;</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>也可以连在一起：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">age</span><span class="p">,</span> <span class="n">teacher</span> <span class="o">=</span> <span class="mi">18</span><span class="p">,</span> <span class="s2">&quot;Jack Ma&quot;</span>
-<span class="n">age</span>
-<span class="n">teacher</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>这里，值为 18 的整形分配给了变量 age，值为 “Jack Ma” 的字符串分配给了变量 teacher。</p>
-</section>
-<section id="id8">
-<h2>作者 &amp; 更新时间<a class="headerlink" href="#id8" title="Permalink to this heading">¶</a></h2>
-<p>作者: <a class="reference external" href="http://blog.csdn.net/liang19890820">一去丶二三里</a></p>
-<p>提交: 2017/12/6</p>
-</section>
-</section>
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diff --git a/docs/python-base/cn/python-controller-break.html b/docs/python-base/cn/python-controller-break.html
deleted file mode 100644
index bf59b53..0000000
--- a/docs/python-base/cn/python-controller-break.html
+++ /dev/null
@@ -1,337 +0,0 @@
-<!DOCTYPE html>
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-<section id="python-break-continue">
-<h1>Python break 和 continue 语句<a class="headerlink" href="#python-break-continue" title="Permalink to this heading">¶</a></h1>
-<section id="id1">
-<h2>简述<a class="headerlink" href="#id1" title="Permalink to this heading">¶</a></h2>
-<p>在 Python 中，break 和 continue 语句用于改变普通循环的流程。</p>
-<p>通常情况下，循环遍历一段代码，直到判断条件为 False。但有时，可能会希望不检测判断条件就可以终止当前迭代，甚至是整个循环。这种情况下，就需要使用 break 和 continue 语句。</p>
-</section>
-<section id="break">
-<h2>break 语句<a class="headerlink" href="#break" title="Permalink to this heading">¶</a></h2>
-<p>break 用于终止循环语句。即使循环条件不是 False 或者序列还没被完全递归完，也会终止。</p>
-<p><strong>注意：</strong> 如果 break 语句在嵌套循环内，break 将终止最内层循环。</p>
-<p>语法格式：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">break</span>
-</pre></div>
-</div>
-<p>流程图：</p>
-<img alt="http://img.blog.csdn.net/20170416165752222" src="http://img.blog.csdn.net/20170416165752222" />
-<p>当我们陶醉在单曲循环的世界中时，突然一声：老师来啦，^_^以迅雷不及掩耳之势关闭歌曲吧：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>i = 0
-while i &lt; 3:
-    if i == 1:
-        print(&#39;老师来啦&#39;)
-        print(&#39;关闭歌曲&#39;)
-        break
-    print(&#39;正在播放：双节棍&#39;)
-    i += 1
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>运行程序，输出如下：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>正在播放：双节棍
-老师来啦
-关闭歌曲
-</pre></div>
-</div>
-</section>
-<section id="continue">
-<h2>continue 语句<a class="headerlink" href="#continue" title="Permalink to this heading">¶</a></h2>
-<p>语法格式：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">continue</span>
-</pre></div>
-</div>
-<p>流程图：</p>
-<img alt="http://img.blog.csdn.net/20170416170554216" src="http://img.blog.csdn.net/20170416170554216" />
-<p>列表播放时，遇到不喜欢的歌曲经常会选择下一曲，直接跳过当前歌曲：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>songs = [&#39;安静&#39;, &#39;蜗牛&#39;, &#39;稻香&#39;]
-# 通过索引遍历列表
-for i in range(len(songs)):
-    if i == 1:
-        print(&#39;不想听&#39;, songs[i])
-        print(&#39;快进，下一曲&#39;)
-        continue
-    print(&quot;正在播放：&quot;, songs[i])
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>运行程序，输出如下：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>正在播放： 安静
-不想听 蜗牛
-快进，下一曲
-正在播放： 稻香
-</pre></div>
-</div>
-<p>遍历歌曲列表，当播放到“蜗牛”时，发现这首歌曲太煽情了，直接进入下一曲。</p>
-<p><strong>两者的根本区别：</strong> break 用于终止整个循环；continue 用于跳出本次循环，还会继续下一次循环。</p>
-</section>
-<section id="id2">
-<h2>作者 &amp; 更新时间<a class="headerlink" href="#id2" title="Permalink to this heading">¶</a></h2>
-<p>作者: <a class="reference external" href="http://blog.csdn.net/liang19890820">一去丶二三里</a></p>
-<p>提交: 2017/12/6</p>
-</section>
-</section>
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-</div>
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diff --git a/docs/python-base/cn/python-controller-for.html b/docs/python-base/cn/python-controller-for.html
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index 7c5c42e..0000000
--- a/docs/python-base/cn/python-controller-for.html
+++ /dev/null
@@ -1,360 +0,0 @@
-<!DOCTYPE html>
-<html lang="en-US">
-<head>
-<meta charset="utf-8" />
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-<link href="https://cdn.bootcss.com/bootstrap/3.3.7/css/bootstrap.min.css" rel="stylesheet">
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-<div class="document">
-<div class="documentwrapper">
-<div class="bodywrapper">
-<div class="body" role="main">
-<section id="python">
-<h1>Python 运算符<a class="headerlink" href="#python" title="Permalink to this heading">¶</a></h1>
-<section id="id1">
-<h2>简述<a class="headerlink" href="#id1" title="Permalink to this heading">¶</a></h2>
-<p>在 Python 中，for 循环用于迭代序列（例如：列表、元组）或其他可迭代对象，迭代序列称为遍历。</p>
-</section>
-<section id="for">
-<h2>for 循环<a class="headerlink" href="#for" title="Permalink to this heading">¶</a></h2>
-<p>语法格式：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>for &lt;val&gt; in &lt;序列&gt;:
-    &lt;循环体&gt;
-</pre></div>
-</div>
-<p>val 是一个变量，在每次迭代中，用于接收将序列中元素的值。</p>
-<p>循环会一直继续，直到到达序列的最后一项。循环体与其余的代码使用缩进分隔。</p>
-<p>流程图：</p>
-<img alt="http://img.blog.csdn.net/20170416152544984" src="http://img.blog.csdn.net/20170416152544984" />
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>for letter in &#39;Jay&#39;:
-    print(&#39;当前字母：&#39;, letter)
-songs = [&#39;安静&#39;, &#39;蜗牛&#39;, &#39;稻香&#39;]
-for song in songs:
-    print(&#39;正在播放：&#39;, song)
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>运行程序，输出如下：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>当前字母： J
-当前字母： a
-当前字母： y
-正在播放： 安静
-正在播放： 蜗牛
-正在播放： 稻香
-</pre></div>
-</div>
-<p>这里，可以将 for 循环视为歌曲中的顺序播放。</p>
-</section>
-<section id="id2">
-<h2>通过索引遍历<a class="headerlink" href="#id2" title="Permalink to this heading">¶</a></h2>
-<p>可以使用 range() 函数生成一个数字序列。</p>
-<p>还可以定义 range(start, stop[, step]) 中的 start、stop 和 step，如果没有提供 step，步长默认为 1。</p>
-<p>要强制该函数输出所有元素，可以使用函数 list()：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># 输出：range(0, 10)</span>
-<span class="nb">print</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">))</span>
-<span class="c1"># 输出：[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]</span>
-<span class="nb">print</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">)))</span>
-<span class="c1"># 输出：[2, 3, 4, 5, 6, 7]</span>
-<span class="nb">print</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">8</span><span class="p">)))</span>
-<span class="c1"># 输出：[2, 5, 8, 11, 14, 17]</span>
-<span class="nb">print</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">3</span><span class="p">)))</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>可以在 for 循环中使用 range() 来遍历一个数字序列，它可以与内置函数 len() 结合，使用索引进行序列迭代。</p>
-<p>len() 返回列表的长度，即：元素的个数。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>songs = [&#39;安静&#39;, &#39;蜗牛&#39;, &#39;稻香&#39;]
-# 通过索引遍历列表
-for i in range(len(songs)):
-    print(&quot;正在播放：&quot;, songs[i])
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>运行程序，输出如下：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>正在播放： 安静
-正在播放： 蜗牛
-正在播放： 稻香
-</pre></div>
-</div>
-</section>
-<section id="for-else">
-<h2>for … else<a class="headerlink" href="#for-else" title="Permalink to this heading">¶</a></h2>
-<p>for 循环也可以有一个可选的 else 块，如果循环正常执行完（即：不是通过 break 跳出而中断的），则执行 else 部分。</p>
-<p><strong>注意：</strong> break 语句可以用来跳出 for 循环，在这种情况下，else 部分会被忽略。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>songs = [&#39;安静&#39;, &#39;蜗牛&#39;, &#39;稻香&#39;]
-for song in songs:
-    print(&#39;正在播放：&#39;, song)
-else:
-    print(&#39;播放结束&#39;)
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>运行程序，输出如下：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>正在播放： 安静
-正在播放： 蜗牛
-正在播放： 稻香
-播放结束
-</pre></div>
-</div>
-<p>可以看到，当 for 循环结束时，执行 else 中的代码块。</p>
-</section>
-<section id="id3">
-<h2>作者 &amp; 更新时间<a class="headerlink" href="#id3" title="Permalink to this heading">¶</a></h2>
-<p>作者: <a class="reference external" href="http://blog.csdn.net/liang19890820">一去丶二三里</a></p>
-<p>提交: 2017/12/6</p>
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diff --git a/docs/python-base/cn/python-controller-if.html b/docs/python-base/cn/python-controller-if.html
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-<section id="id1">
-<h1>简述<a class="headerlink" href="#id1" title="Permalink to this heading">¶</a></h1>
-<p>当仅在满足某个条件才会执行相应的代码时，需要进行决策。在 Python 中，由 if … elif … else 语句来实现。</p>
-<section id="if">
-<h2>if 语句<a class="headerlink" href="#if" title="Permalink to this heading">¶</a></h2>
-<p>语法格式：</p>
-<dl class="simple">
-<dt>if &lt;判断条件&gt;：</dt><dd><p>&lt;执行语句……&gt;</p>
-</dd>
-</dl>
-<p>当“判断条件”成立（True）时，才执行语句；反之，则不执行。</p>
-<p>执行语句可以为多行，以缩进来区分表示同一范围。</p>
-<p><strong>注意</strong>： 在 Python 中，非零值表示 True；None 和 0 表示 False。</p>
-<p>流程图：</p>
-<blockquote>
-<div><img alt="http://img.blog.csdn.net/20170416144600526" src="http://img.blog.csdn.net/20170416144600526" />
-</div></blockquote>
-<p>以购物为例（预算 500），进入商场，价格都是 1000+。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">price</span> <span class="o">=</span> <span class="mi">1000</span>
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;纳尼，居然&#39;</span><span class="p">,</span> <span class="n">price</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;简直太贵了！&#39;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;货比三家，再转转。&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>执行程序，输出如下：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>纳尼，居然 1000
-简直太贵了！
-货比三家，再转转。
-</pre></div>
-</div>
-</section>
-<section id="if-else">
-<h2>if…else 语句<a class="headerlink" href="#if-else" title="Permalink to this heading">¶</a></h2>
-<p>语法格式：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>if &lt;判断条件&gt;:
-    &lt;执行语句1……&gt;
-else:
-    &lt;执行语句2……&gt;
-</pre></div>
-</div>
-<p>当“判断条件”为 True 时，执行语句1；否则，执行语句2。</p>
-<p>流程图：</p>
-<img alt="http://img.blog.csdn.net/20170416145014731" src="http://img.blog.csdn.net/20170416145014731" />
-<p>既然 1000 贵了，好吧！开始讨价还价，降价到 300：</p>
-<dl>
-<dt>::</dt><dd><p>price = 300
-if price &gt; 500:</p>
-<blockquote>
-<div><p>print(‘纳尼，居然’, price)
-print(‘简直太贵了！’)</p>
-</div></blockquote>
-<dl class="simple">
-<dt>else:</dt><dd><p>print(price, ‘还算地道’)
-print(‘来一打！’)
-print(“合适才买”)</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<button class="button">Run …</button>
-<p>执行程序，输出如下：</p>
-<dl class="simple">
-<dt>::</dt><dd><p>300 还算地道
-来一打！
-合适才买</p>
-</dd>
-</dl>
-</section>
-<section id="if-elif-else">
-<h2>if…elif…else 语句<a class="headerlink" href="#if-elif-else" title="Permalink to this heading">¶</a></h2>
-<p>语法格式：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>if &lt;判断条件1&gt;:
-    &lt;执行语句1……&gt;
-elif &lt;判断条件2&gt;:
-    &lt;执行语句2……&gt;
-else:
-    &lt;执行语句3……&gt;
-</pre></div>
-</div>
-<p>elif 是 else if 的缩写，允许我们检查多个表达式。</p>
-<p>如果 if 的条件为 False，则检查下一个 elif 的状态，依次进行。倘若所有条件都为 False，则执行 else 中的语句（语句3）。</p>
-<p><strong>注意</strong>： if 和 else 只能有一个，但 elif 可以有多个，if … elif … else 中只有一个语句块可以根据条件来执行。</p>
-<p>流程图：</p>
-<img alt="http://img.blog.csdn.net/20170416145420452" src="http://img.blog.csdn.net/20170416145420452" />
-<p>一般，大部分顾客买东西都要讨价还价。当然，其中不乏土豪，只管买买买。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>price = 2000
-if price &gt; 1000:
-    print(&#39;这么贵，肯定是好东西。&#39;)
-    print(&#39;买、买、买！&#39;)
-elif price &gt; 500:
-    print(&#39;价格还行，值得拥有。&#39;)
-    print(&#39;买买买！&#39;)
-elif price &gt; 200:
-    print(&#39;先入手，合不合适再说。&#39;)
-    print(&#39;买买买。&#39;)
-else:
-    print(&#39;这么便宜，赶紧抢。&#39;)
-    print(&#39;买买买&#39;)
-print(&#39;管它多钱，反正我要买买买。&#39;)
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>执行程序，输出如下：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>这么贵，肯定是好东西。
-买、买、买！
-管它多钱，反正我要买买买。
-</pre></div>
-</div>
-</section>
-<section id="id2">
-<h2>嵌套 if 语句<a class="headerlink" href="#id2" title="Permalink to this heading">¶</a></h2>
-<p>可以将一个 if … elif … else 语句加入至另一个 if … elif … else 语句中，这被称为嵌套。</p>
-<p>任何数量的这些语句都可以嵌套在一起，要找出嵌套级别，缩进是唯一的方法。</p>
-<p>买东西，追求的是性价比，所以既要价格适中，又要质量有保证：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>price = 300
-quality = True
-if price &gt; 500:
-    print(&#39;纳尼，居然&#39;, price)
-    print(&#39;简直太贵了！&#39;)
-else:
-    print(price, &#39;价钱合适&#39;)
-    if quality:
-        print(&#39;质量也还不错&#39;)
-        print(&#39;来一打&#39;)
-    else:
-        print(&#39;质量不行&#39;)
-        print(&#39;算了，不要&#39;)
-    print(&#39;性价比较高再买&#39;)
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>输出如下：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="mi">300</span> <span class="n">价钱合适</span>
-<span class="n">质量也还不错</span>
-<span class="n">来一打</span>
-<span class="n">性价比较高再买</span>
-</pre></div>
-</div>
-</section>
-<section id="id3">
-<h2>作者 &amp; 更新时间<a class="headerlink" href="#id3" title="Permalink to this heading">¶</a></h2>
-<p>作者: <a class="reference external" href="http://blog.csdn.net/liang19890820">一去丶二三里</a></p>
-<p>提交: 2017/12/6</p>
-</section>
-</section>
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-<section id="python-pass">
-<h1>Python pass 语句<a class="headerlink" href="#python-pass" title="Permalink to this heading">¶</a></h1>
-<section id="id1">
-<h2>简述<a class="headerlink" href="#id1" title="Permalink to this heading">¶</a></h2>
-<p>在 Python 中，pass 是一个空语句，为了保持程序结构的完整性。一般情况下，pass 被用作占位符。</p>
-<p>pass 和注释之间的区别在于：解释器会完全忽略注释，但不会忽略 pass。</p>
-<p>然而，执行 pass 时什么都不会发生，导致无操作（NOP）。</p>
-</section>
-<section id="pass">
-<h2>pass 语句<a class="headerlink" href="#pass" title="Permalink to this heading">¶</a></h2>
-<p>语法格式：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">pass</span>
-</pre></div>
-</div>
-<p>假设，欧阳锋写了一个循环或者函数，尚未实现（暂未想好如何实现或者出差交付给其他人），但是会在将来的某个时候实现。这时，如果循环体或者函数体为空，解释器就会报错。所以，可以使用 pass 语句构造一个不做任何事情的主体。</p>
-<p>上课了，开始听歌，随机播放、顺序播放、单曲循环、列表循环。。。反复思量着，到底选哪一个呢？没想好，那么就先纠结着，什么都不干！</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">songs</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;安静&#39;</span><span class="p">,</span> <span class="s1">&#39;蜗牛&#39;</span><span class="p">,</span> <span class="s1">&#39;稻香&#39;</span><span class="p">]</span>
-<span class="k">for</span> <span class="n">song</span> <span class="ow">in</span> <span class="n">songs</span><span class="p">:</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>执行语句，输出如下：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="ne">SyntaxError</span><span class="p">:</span> <span class="n">unexpected</span> <span class="n">EOF</span> <span class="k">while</span> <span class="n">parsing</span>
-</pre></div>
-</div>
-<p>报错了，所以呢？还是乖乖的加上 pass 吧！</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>songs = [&#39;安静&#39;, &#39;蜗牛&#39;, &#39;稻香&#39;]
-for song in songs:
-    pass
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>相应的，也可以在空函数或类中做同样的事情：</p>
-<dl class="simple">
-<dt>::</dt><dd><dl class="simple">
-<dt>def function(args):</dt><dd><p>    pass</p>
-</dd>
-</dl>
-</dd>
-</dl>
-<button class="button">Run …</button>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>class example:
-    pass
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>总之，pass 什么也不做，就是为了占位，防止语法错误。</p>
-</section>
-<section id="id2">
-<h2>作者 &amp; 更新时间<a class="headerlink" href="#id2" title="Permalink to this heading">¶</a></h2>
-<p>作者: <a class="reference external" href="http://blog.csdn.net/liang19890820">一去丶二三里</a></p>
-<p>提交: 2017/12/6</p>
-</section>
-</section>
-<div class="clearer"></div>
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-</div>
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-<div class="clearer"></div>
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-</main>
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-<section id="python-while">
-<h1>Python while 循环<a class="headerlink" href="#python-while" title="Permalink to this heading">¶</a></h1>
-<section id="id1">
-<h2>简述<a class="headerlink" href="#id1" title="Permalink to this heading">¶</a></h2>
-<p>在 Python 中，while 循环用于遍历代码块，只要判断条件为 True，就会一直不停地循环执行。</p>
-<p>通常，在事先不知道迭代次数的情况下使用 while 循环。</p>
-</section>
-<section id="while">
-<h2>while 循环<a class="headerlink" href="#while" title="Permalink to this heading">¶</a></h2>
-<p>语法格式：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>while &lt;判断条件&gt;:
-    &lt;循环体&gt;
-</pre></div>
-</div>
-<p>进入 while 循环，首先检测判断条件，只有当其为 True 时，才会进入循环体。一次迭代后，再次检测判断条件，此过程一直持续到判断条件为 False。</p>
-<p>和 for 循环一样，while 的循环体也通过缩进来确定。</p>
-<p>流程图：</p>
-<img alt="http://img.blog.csdn.net/20170416154810480" src="http://img.blog.csdn.net/20170416154810480" />
-<p>如果说 for 循环是**顺序播放**，那 while 循环可以视为**单曲循环**。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>i = 0
-while i &lt; 3:
-    print(&#39;正在播放：双节棍&#39;)
-    i += 1
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>运行程序，输出如下：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>正在播放：双节棍
-正在播放：双节棍
-正在播放：双节棍
-</pre></div>
-</div>
-<p>这里，只要计数器 i 小于 3（单曲循环 3 次），判断条件将为 True。</p>
-<p><strong>注意</strong>：要在循环体中增加计数器的值，这非常重要（很容易忽视），否则将导致无限循环。</p>
-</section>
-<section id="while-else">
-<h2>while … else<a class="headerlink" href="#while-else" title="Permalink to this heading">¶</a></h2>
-<p>与 for 循环相同，在 while 循环中也可以有一个可选的 else 块。</p>
-<p>如果 while 循环中的判断条件为 False，则 else 部分被执行。</p>
-<p><strong>注意</strong> ：while 循环可以用 break 语句终止，在这种情况下，else 部分被忽略。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>i = 0
-while i &lt; 3:
-    print(&#39;正在播放：双节棍&#39;)
-    i += 1
-else:
-print(&#39;播放结束&#39;)
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>运行程序，输出如下：</p>
-<dl class="simple">
-<dt>::</dt><dd><p>正在播放：双节棍
-正在播放：双节棍
-正在播放：双节棍
-播放结束</p>
-</dd>
-</dl>
-<p>可以看到，在第四次迭代中，while 中的条件变为 False。因此，else 部分被执行。</p>
-</section>
-<section id="id2">
-<h2>作者 &amp; 更新时间<a class="headerlink" href="#id2" title="Permalink to this heading">¶</a></h2>
-<p>作者: <a class="reference external" href="http://blog.csdn.net/liang19890820">一去丶二三里</a></p>
-<p>提交: 2017/12/6</p>
-</section>
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-<section id="id1">
-<h1>简述<a class="headerlink" href="#id1" title="Permalink to this heading">¶</a></h1>
-<p>字符串（String），是由零个或多个字符组成的有限序列，在 Python 中表示文本的数据类型。</p>
-<p>字符只是一个符号，例如：字母（A-Z）、数字（0-9）、特殊符号（~!&#64;#￥%…）等。</p>
-<section id="id2">
-<h2>字符串<a class="headerlink" href="#id2" title="Permalink to this heading">¶</a></h2>
-<p>在 Python 中，字符串由内置的 str 类型定义。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>s = &#39;Hello, Python!&#39;
-type(s)
-&lt;class &#39;str&#39;&gt;
-</pre></div>
-</div>
-<p>可以使用单引号（’’）或双引号（””）来表示字符串。</p>
-<p><strong>PS</strong>： 单引号比双引号更常用。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">s</span> <span class="o">=</span> <span class="s1">&#39;Hello&#39;</span>  <span class="c1"># 单引号</span>
-<span class="n">s</span>
-<span class="n">s</span> <span class="o">=</span> <span class="s2">&quot;Hello&quot;</span>  <span class="c1"># 双引号</span>
-<span class="n">s</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>字符串可以跨多行，但是每行末尾必须添加反斜杠（）以转义换行符。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">s</span> <span class="o">=</span> <span class="s1">&#39;Hello </span><span class="se">\</span>
-<span class="s1">World&#39;</span>
-<span class="n">s</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>三重引号（’’’ 或 “””）内的字符串可以跨多个文本行。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">s</span> <span class="o">=</span> <span class="s1">&#39;&#39;&#39;Hello</span>
-<span class="s1">Python</span>
-<span class="s1">&#39;&#39;&#39;</span>
-<span class="n">s</span>
-<span class="n">s</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;</span>
-<span class="s2">Hello</span>
-<span class="s2">Python</span>
-<span class="s2">&quot;&quot;&quot;</span>
-<span class="n">s</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p><strong>注意</strong>： 三重引号通常用于表示多行字符串和 docstring。</p>
-</section>
-</section>
-<section id="id3">
-<h1>访问字符串<a class="headerlink" href="#id3" title="Permalink to this heading">¶</a></h1>
-<section id="id4">
-<h2>索引<a class="headerlink" href="#id4" title="Permalink to this heading">¶</a></h2>
-<p>字符串是字符的有序序列，可以通过其位置来获取具体的字符。在 Python 中，位置有一个优雅的称呼 - 索引（index）。</p>
-<p>索引分为两种形式：</p>
-<ul class="simple">
-<li><p>正向索引（从左到右）：索引从 0 开始，直到 length - 1，可以很方便地访问字符串开头附近的字符。</p></li>
-<li><p>反向索引（从右到左）：索引从 -1 开始，直到 -length，可以很方便地访问字符串末尾附近的字符。</p></li>
-</ul>
-<p>其中，length 为字符串的长度。</p>
-<p>为了更清晰以上概念，来看一个直观地索引图。假设，有一个字符串 s = “Hello”：</p>
-<img alt="http://img.blog.csdn.net/20170510181247783" src="http://img.blog.csdn.net/20170510181247783" />
-<p>使用 [index] 的语法形式来访问指定索引处的字符：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">s</span> <span class="o">=</span> <span class="s1">&#39;Hello&#39;</span>
-<span class="n">s</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>  <span class="c1"># 第一个字符</span>
-<span class="n">s</span><span class="p">[</span><span class="mi">4</span><span class="p">]</span>  <span class="c1"># 最后一个字符</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>负索引（反向索引）从字符串的末尾开始计数，也可以起到同样的效果：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">s</span> <span class="o">=</span> <span class="s1">&#39;Hello&#39;</span>
-<span class="n">s</span><span class="p">[</span><span class="o">-</span><span class="mi">5</span><span class="p">]</span>  <span class="c1"># 第一个字符</span>
-<span class="n">s</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>  <span class="c1"># 最后一个字符</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>试图访问索引范围外的字符会引发 IndexError。索引必须是一个整数，不能使用 float 或其他类型，这将引发 TypeError。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">s</span> <span class="o">=</span> <span class="s1">&#39;Hello&#39;</span>
-<span class="n">s</span><span class="p">[</span><span class="mi">10</span><span class="p">]</span>
-<span class="n">s</span><span class="p">[</span><span class="mf">1.5</span><span class="p">]</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-</section>
-<section id="id5">
-<h2>切片<a class="headerlink" href="#id5" title="Permalink to this heading">¶</a></h2>
-<p>切片操作（slice）是一种很方便的方法，用于引用序列（通常是字符串和列表）的子部分。</p>
-<p>切片操作的语法格式：</p>
-<p>[start:stop:step]</p>
-<ul class="simple">
-<li><p>start（开始索引）：第一个索引的值是 0，最后一个是 -1。</p></li>
-<li><p>stop（结束索引）：切片操作符将取到该索引为止，但不包含该索引的值。</p></li>
-<li><p>step（步长）：默认是 1，也就是说，一个接一个切取。如果为 2，则表示隔一取一。步长为正数时，表示从左向右取；如果为负数，表示从右向左取；步长不能为 0。</p></li>
-</ul>
-<p>通过上图，其实也可以很直观地看到切片。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">s</span> <span class="o">=</span> <span class="s1">&#39;Hello&#39;</span>
-<span class="n">s</span><span class="p">[</span><span class="mi">1</span><span class="p">:</span><span class="mi">4</span><span class="p">]</span>  <span class="c1"># 第 2 个到第 4 个字符</span>
-<span class="n">s</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span>  <span class="c1"># 第 2 个到最后一个字符（将索引默认设置为字符串的开始或结束）</span>
-<span class="n">s</span><span class="p">[</span><span class="mi">1</span><span class="p">:</span><span class="mi">100</span><span class="p">]</span>  <span class="c1"># 索引太大，被截断至字符串长度处</span>
-<span class="n">s</span><span class="p">[:]</span>  <span class="c1"># 所有字符（从开始到结束）</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p><strong>PS</strong>： s[:] 形式会忽略开始和结束索引，总是获得一个完整的副本。是 pythonic 使用的方式，用于复制序列（例如：字符串或列表）。</p>
-<p>和提取字符类似，切片操作也可以使用反向索引：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">s</span><span class="p">[</span><span class="o">-</span><span class="mi">4</span><span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>  <span class="c1"># 第 2 个到第 4 个字符</span>
-<span class="n">s</span><span class="p">[:</span><span class="o">-</span><span class="mi">3</span><span class="p">]</span>  <span class="c1"># 开始到第 2 个字符</span>
-<span class="n">s</span><span class="p">[</span><span class="o">-</span><span class="mi">3</span><span class="p">:]</span>  <span class="c1"># 第 3 个到最后一个字符</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>对于任何索引 n，s[:n] + s[n:] == s 是一个整齐的切片，甚至对于负数或超出界限的值也是如此。换一种说法，s[:n] 和 s[n:] 总是将字符串分成两部分，来保存所有的字符。</p>
-</section>
-<section id="id6">
-<h2>更改或删除字符串<a class="headerlink" href="#id6" title="Permalink to this heading">¶</a></h2>
-<p>字符串是不可变的，也就是说，一旦分配了字符串的元素就不能被更改。</p>
-<p>但是，可以将不同的字符串重新分配给同一个变量。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">s</span> <span class="o">=</span> <span class="s1">&#39;Hello&#39;</span>
-<span class="n">s</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;a&#39;</span>
-<span class="n">s</span> <span class="o">=</span> <span class="s1">&#39;Python&#39;</span>
-<span class="n">s</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>无法从字符串中删除字符，但是可以使用关键字 del 完全删除字符串。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">s</span> <span class="o">=</span> <span class="s1">&#39;Hello&#39;</span>
-<span class="k">del</span> <span class="n">s</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
-<span class="k">del</span> <span class="n">s</span>
-<span class="n">s</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-</section>
-<section id="id7">
-<h2>基本操作<a class="headerlink" href="#id7" title="Permalink to this heading">¶</a></h2>
-<p>字符串能成为 Python 中最常用的数据类型之一，很大原因是因为它使用非常方便，提供了大量的字符串操作，例如：连接字符串、遍历字符串…</p>
-<p>所有的序列（例如：字符串、列表）都可以进行以下基本操作：</p>
-<ul class="simple">
-<li><p>+：连接两个序列</p></li>
-<li><p><a href="#id8"><span class="problematic" id="id9">*</span></a>：重复序列元素</p></li>
-<li><p>in：判断元素是否在序列中</p></li>
-<li><p>min()：返回最小值</p></li>
-<li><p>max()：返回最大值</p></li>
-<li><p>len()：返回序列长度</p></li>
-<li><p>enumerate()：返回一个枚举对象，包含字符串中所有元素的索引和值（作为一对）</p></li>
-</ul>
-</section>
-<section id="id10">
-<h2>连接字符串<a class="headerlink" href="#id10" title="Permalink to this heading">¶</a></h2>
-<p>连接是指将多个字符串合并为一个单独的字符串。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">s1</span> <span class="o">=</span> <span class="s2">&quot;Hello,&quot;</span>
-<span class="n">s2</span> <span class="o">=</span> <span class="s2">&quot; World!&quot;</span>
-<span class="n">s</span> <span class="o">=</span> <span class="n">s1</span> <span class="o">+</span> <span class="n">s2</span>  <span class="c1"># 连接字符串</span>
-<span class="n">s</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>如果想在不同的行中连接字符串，可以使用括号。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># 两个字符串一起</span>
-<span class="s1">&#39;Hello,&#39;</span> <span class="s1">&#39; World!&#39;</span>
-<span class="c1"># 使用括号</span>
-<span class="n">s</span> <span class="o">=</span> <span class="p">(</span><span class="s1">&#39;Hello,&#39;</span>
-<span class="n">s</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>操作符 + 不会将数字或其他类型自动转换为字符串形式，需要通过 str() 函数将值转换为字符串形式，以便它们可以与其他字符串组合。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">age</span> <span class="o">=</span> <span class="mi">18</span>
-<span class="n">s</span> <span class="o">=</span> <span class="s1">&#39;My age is &#39;</span> <span class="o">+</span> <span class="n">age</span>
-<span class="n">s</span> <span class="o">=</span> <span class="s1">&#39;My age is &#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">age</span><span class="p">)</span>
-<span class="n">s</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-</section>
-<section id="id11">
-<h2>重复字符串<a class="headerlink" href="#id11" title="Permalink to this heading">¶</a></h2>
-<p>操作符 * 用于以指定的次数来重复字符串。有时很好用，比如打印一条华丽的分割线：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">s</span> <span class="o">=</span> <span class="s1">&#39;Hello&#39;</span>
-<span class="n">s</span> <span class="o">*</span> <span class="mi">3</span>  <span class="c1"># 重复字符串 3 次</span>
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;-&#39;</span> <span class="o">*</span> <span class="mi">20</span><span class="p">)</span>  <span class="c1"># 无需输入很多 -</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-</section>
-<section id="id12">
-<h2>最大值/最小值<a class="headerlink" href="#id12" title="Permalink to this heading">¶</a></h2>
-<p>在一个字符串中，每个字符在计算机中都有对应的 ASCII 码。min() 和 max() 就是根据对应的 ASCII 码进行比较的，从而获取最小值和最大值对应的字符。</p>
-<p>Python 提供了两个内置函数 ord() 和 chr()，用于字符和 ASCII 码之间的转换。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">ord</span><span class="p">(</span><span class="s1">&#39;H&#39;</span><span class="p">)</span>  <span class="c1"># 字符转 ASCII 码</span>
-<span class="nb">chr</span><span class="p">(</span><span class="mi">72</span><span class="p">)</span>  <span class="c1"># ASCII 码转字符</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>别犹豫啦，开始比较吧！</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">s</span> <span class="o">=</span> <span class="s1">&#39;Hello&#39;</span>
-<span class="nb">min</span><span class="p">(</span><span class="n">s</span><span class="p">)</span>  <span class="c1"># 最小字符</span>
-<span class="nb">max</span><span class="p">(</span><span class="n">s</span><span class="p">)</span>  <span class="c1"># 最大字符</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-</section>
-<section id="id13">
-<h2>字符串成员测试<a class="headerlink" href="#id13" title="Permalink to this heading">¶</a></h2>
-<p>使用关键字 in，可以测试字符串中是否包含指定的子串。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="s1">&#39;t&#39;</span> <span class="ow">in</span> <span class="s1">&#39;python&#39;</span>
-<span class="s1">&#39;th&#39;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="s1">&#39;python&#39;</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-</section>
-<section id="id14">
-<h2>字符串长度<a class="headerlink" href="#id14" title="Permalink to this heading">¶</a></h2>
-<p>要获取一个字符串的长度，可以使用内置函数 len()：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">s</span> <span class="o">=</span> <span class="s1">&#39;Hello&#39;</span>
-<span class="nb">len</span><span class="p">(</span><span class="n">s</span><span class="p">)</span>  <span class="c1"># 字符数</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-</section>
-<section id="id15">
-<h2>迭代字符串<a class="headerlink" href="#id15" title="Permalink to this heading">¶</a></h2>
-<p>使用 for 循环，可以遍历一个字符串。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span># 查找字符串中 l 的数量
-count = 0
-for letter in &#39;Hello&#39;:
-    if (letter == &#39;l&#39;):
-        count += 1
-print(count, &#39;letters found&#39;)
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>还可使用 enumerate()，它会返回一个枚举对象，包含字符串中所有元素的索引和值（作为一对），这对于迭代很有用。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>s = &#39;Hello&#39;
-e = list(enumerate(s))
-print(e)
-for index, item in e:
-    print(index, item)
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>运行程序，输出如下：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>[(0, ‘H’), (1, ‘e’), (2, ‘l’), (3, ‘l’), (4, ‘o’)]
-</pre></div>
-</div>
-<p>0 H
-1 e
-2 l
-3 l
-4 o</p>
-</section>
-<section id="id16">
-<h2>字符串的方法<a class="headerlink" href="#id16" title="Permalink to this heading">¶</a></h2>
-<p>字符串有许多方法，可以通过 dir() 来查看方法列表：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">dir</span><span class="p">(</span><span class="nb">str</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>这么多，当然不需要逐一介绍了，重在掌握如何使用（授人以鱼不如授人以渔），能做到随用随查就好。</p>
-<p>利用 help() 函数，可以查看函数或模块用途的详细说明：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">help</span><span class="p">(</span><span class="nb">str</span><span class="o">.</span><span class="n">lower</span><span class="p">)</span>
-<span class="o">&gt;&gt;&gt;</span><span class="n">Help</span> <span class="n">on</span> <span class="n">method_descriptor</span><span class="p">:</span>
-<span class="n">lower</span><span class="p">(</span><span class="o">...</span><span class="p">)</span>
-<span class="n">S</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="o">-&gt;</span> <span class="nb">str</span>
-<span class="n">Return</span> <span class="n">a</span> <span class="n">copy</span> <span class="n">of</span> <span class="n">the</span> <span class="n">string</span> <span class="n">S</span> <span class="n">converted</span> <span class="n">to</span> <span class="n">lowercase</span><span class="o">.</span>
-<span class="p">(</span><span class="n">END</span><span class="p">)</span>
-</pre></div>
-</div>
-<p><strong>注意</strong>： 要终止查询，使用 q 键。</p>
-<p>按照说明，可以在交互模式下进行实验，这里仅列举一些比较常用的方法。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">s</span> <span class="o">=</span> <span class="s1">&#39;Hello&#39;</span>
-<span class="n">s</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span>  <span class="c1"># 转换所有字符为小写</span>
-<span class="n">s</span><span class="o">.</span><span class="n">upper</span><span class="p">()</span>  <span class="c1"># 转换所有字符为大写</span>
-<span class="n">s</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s1">&#39;ll&#39;</span><span class="p">)</span>  <span class="c1"># 返回子串的开始索引</span>
-<span class="n">s</span><span class="o">.</span><span class="n">endswith</span><span class="p">(</span><span class="s1">&#39;llo&#39;</span><span class="p">)</span>  <span class="c1"># 检查字符串是否以指定的子串结束</span>
-<span class="n">s</span><span class="o">.</span><span class="n">isdigit</span><span class="p">()</span>  <span class="c1"># 检查字符串是否只包含数字</span>
-<span class="n">s</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">&#39;ll&#39;</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span>  <span class="c1"># 替换字符串中的子串</span>
-<span class="n">s</span> <span class="o">=</span> <span class="s1">&#39;I like Python&#39;</span>
-<span class="n">s</span><span class="o">.</span><span class="n">split</span><span class="p">()</span>  <span class="c1"># 分割字符串</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-</section>
-<section id="id17">
-<h2>作者 &amp; 更新时间<a class="headerlink" href="#id17" title="Permalink to this heading">¶</a></h2>
-<p>作者: <a class="reference external" href="http://blog.csdn.net/liang19890820">一去丶二三里</a></p>
-<p>提交: 2017/12/6</p>
-</section>
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diff --git a/docs/python-base/cn/python-datatype-dictionaries.html b/docs/python-base/cn/python-datatype-dictionaries.html
deleted file mode 100644
index 524a7d0..0000000
--- a/docs/python-base/cn/python-datatype-dictionaries.html
+++ /dev/null
@@ -1,584 +0,0 @@
-<!DOCTYPE html>
-<html lang="en-US">
-<head>
-<meta charset="utf-8" />
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-<a target="_blank" href="https://github.com/qpython-android/qpython">Github</a>
-</li>
-</ul>
-<div class="search-box hidden-xs">
-<form action="/search.html">
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-<main class="contain web-content">
-<div class="document">
-<div class="documentwrapper">
-<div class="bodywrapper">
-<div class="body" role="main">
-<section id="python-pass">
-<h1>Python pass 语句<a class="headerlink" href="#python-pass" title="Permalink to this heading">¶</a></h1>
-<section id="id1">
-<h2>简述<a class="headerlink" href="#id1" title="Permalink to this heading">¶</a></h2>
-<p>记得上学时，《新华字典》绝对堪称神器，检索内容相当给力。但随着网络的发展，现在几乎很少有人在用了。</p>
-<p>如何使用《新华字典》呢？先查索引，然后通过索引查找相应的内容，简单、方便、快捷。</p>
-<p>出于这种需求，Python 也提供了这样一种数据类型 - dict，翻译为中文就是“字典”。</p>
-</section>
-<section id="id2">
-<h2>字典的特点<a class="headerlink" href="#id2" title="Permalink to this heading">¶</a></h2>
-<p>像列表和字典，可以很容易地被改变 - 在运行时可以随意地变小/变大（无需复制）。所以，没有列表和字典的 Python 程序或脚本，几乎是无法想象的。</p>
-<p>词典可以包含在列表中，反之亦然。但字典和列表（或其它序列）之间有什么区别呢？</p>
-<ul class="simple">
-<li><p>列表是对象的有序集合，而字典是无序集。</p></li>
-<li><p>对于列表（其他复合数据类型也一样）来说，元素只有值；而对于字典，元素由 key:value - 对的形式组成。</p></li>
-<li><p>字典中的元素通过 key 来访问，而非通过他们的位置来访问（主要区别）。</p></li>
-</ul>
-<p>字典是一个关联数组（也称为哈希），其中的任何 key 都与 value 相关联（或映射），value 可以是 Python 中的任何数据类型，所以字典是无序的 key:value 对。</p>
-<p>字典不支持序列数据类型（例如：字符串、元组和列表）的序列操作（例如：索引
-切片、连接 +、重复 <a href="#id3"><span class="problematic" id="id4">*</span></a>），字典属于内置映射类型，同时也是该类型的唯一代表！</p>
-<p>虽然没有报错，但前面的值被后面的覆盖了，这样做毫无意义。
-在 Python 中，字典由内置的 dict 类型定义。</p>
-<p>要创建字典，需要将所有项（元素）放在花括号（{}）内，以逗号（,）分隔。其中，每个元素都以 key:value 对的形式出现，key 和 value 可以是任何数据类型。</p>
-<p><strong>注意：</strong> 字典中的 key 必须是唯一的。</p>
-<dl class="simple">
-<dt>::</dt><dd><p>d = {‘name’:’Python’, ‘site’:’www.python.org’}
-type(d)
-&lt;class ‘dict’&gt;</p>
-</dd>
-</dl>
-<button class="button">Run …</button>
-<p>字典的创建方式很多，还可以使用 dict()：</p>
-<p>由于列表是可变的，不可 hash，因此不能作为字典的 key。</p>
-<p>key 唯一，意思是说：不要有重复的 key 出现。</p>
-<p>虽然 value 可以是任何数据类型，并且可以重复，但 key 必须是不可变类型（例如：字符串、数字）或具有不可变元素的元组。</p>
-<dl>
-<dt>::</dt><dd><blockquote>
-<div><p>d = {‘name’:’python’, (1, 2):’C++’}  # 具有不可变元素的元组
-d</p>
-</div></blockquote>
-<p>{(1, 2): ‘C++’, ‘name’: ‘python’}</p>
-<blockquote>
-<div><p>d = {‘name’:’python’, [1, 2]:’C++’}  # 列表</p>
-</div></blockquote>
-<p>…
-TypeError: unhashable type: ‘list’</p>
-</dd>
-</dl>
-<button class="button">Run …</button>
-<p>由于列表是可变的，不可 hash，因此不能作为字典的 key。</p>
-<p>key 唯一，意思是说：不要有重复的 key 出现。</p>
-<dl>
-<dt>::</dt><dd><blockquote>
-<div><p>d = {‘name’:’Python’, ‘name’:’C++’}  # key 都是 ‘name’
-d</p>
-</div></blockquote>
-<p>{‘name’: ‘C++’}</p>
-</dd>
-</dl>
-<button class="button">Run …</button>
-<p>虽然没有报错，但前面的值被后面的覆盖了，这样做毫无意义。</p>
-</section>
-<section id="id5">
-<h2>从字典访问元素<a class="headerlink" href="#id5" title="Permalink to this heading">¶</a></h2>
-<p>字典通过 key 来检索 value，有两种方式：</p>
-<ul class="simple">
-<li><p>在方括号（[]）内使用 key</p></li>
-<li><p>key 与 get() 方法与一起使用</p></li>
-</ul>
-<p>区别在于：使用 get()，如果没有找到 key，则返回 None，而不是 KeyError。</p>
-<dl>
-<dt>::</dt><dd><blockquote>
-<div><p>d = {‘name’:’Python’, ‘site’:’www.python.org’}</p>
-<p>print(d[‘name’])</p>
-</div></blockquote>
-<p>Python</p>
-<blockquote>
-<div><p>print(d.get(‘site’))</p>
-</div></blockquote>
-<p>www.python.org</p>
-<blockquote>
-<div><p>print(d[‘version’])  # 没有找到 key，引发 KeyError</p>
-</div></blockquote>
-<p>…
-KeyError: ‘version’</p>
-<blockquote>
-<div><p>print(d.get(‘version’))  # 没有找到 key，返回 None</p>
-</div></blockquote>
-<dl class="simple">
-<dt>None</dt><dd><p>print(d.get(‘version’, 3.5))  # 没有找到 key，返回默认值</p>
-</dd>
-</dl>
-<p>3.5
-1</p>
-</dd>
-</dl>
-<button class="button">Run …</button>
-</section>
-<section id="id6">
-<h2>更改或添加元素<a class="headerlink" href="#id6" title="Permalink to this heading">¶</a></h2>
-<p>字典是可变的，可以添加新元素，也可以使用赋值运算符（=）更改现有元素的值。</p>
-<p>如果 key 存在，value 将被更新；否则，新的 key:value 对会被添加到字典中。</p>
-<dl>
-<dt>::</dt><dd><blockquote>
-<div><p>d = {}</p>
-<p>d[‘name’] = ‘Python’  # 添加
-d</p>
-</div></blockquote>
-<p>{‘name’: ‘Python’}</p>
-<blockquote>
-<div><p>d[‘site’] = ‘www.python.org’  # 添加
-d</p>
-</div></blockquote>
-<p>{‘site’: ‘www.python.org’, ‘name’: ‘Python’}</p>
-<blockquote>
-<div><p>d[‘name’] = ‘Py’  #  更新
-d</p>
-</div></blockquote>
-<p>{‘site’: ‘www.python.org’, ‘name’: ‘Py’}</p>
-</dd>
-</dl>
-<button class="button">Run …</button>
-</section>
-<section id="id7">
-<h2>删除字典或元素<a class="headerlink" href="#id7" title="Permalink to this heading">¶</a></h2>
-<p>通过使用 pop() 方法，可以删除指定 key 对应的元素，并返回其 value。</p>
-<p>popitem() 方法可以用来删除并返回一个任意元素 (key, value)，clear() 方法则会一次性删除所有元素。</p>
-<p>还可以使用 del 关键字来删除单个元素或整个字典本身。</p>
-<dl>
-<dt>::</dt><dd><blockquote>
-<div><p>d = {‘name’:’Python’, ‘site’:’www.python.org’, ‘version’:3.5, ‘from’:1991}</p>
-</div></blockquote>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="go">&gt;&gt;&gt;</span>
-<span class="go"> d.pop(&#39;site&#39;)  # 删除某一项</span>
-<span class="go">&#39;www.python.org&#39;</span>
-<span class="go"> d</span>
-<span class="go">{&#39;from&#39;: 1991, &#39;version&#39;: 3.5, &#39;name&#39;: &#39;Python&#39;}</span>
-</pre></div>
-</div>
-<blockquote>
-<div><p>d.popitem()  # 删除任意项</p>
-</div></blockquote>
-<dl class="simple">
-<dt>(‘from’, 1991)</dt><dd><p>d</p>
-</dd>
-</dl>
-<p>{‘version’: 3.5, ‘name’: ‘Python’}</p>
-<blockquote>
-<div><p>del d[‘version’]  # 删除某一项
-d</p>
-</div></blockquote>
-<p>{‘name’: ‘Python’}</p>
-<blockquote>
-<div><p>d.clear()  # 清空 - 删除所有项
-d</p>
-</div></blockquote>
-<p>{}</p>
-<blockquote>
-<div><p>del d  # 删除字典本身
-d</p>
-</div></blockquote>
-<p>…
-NameError: name ‘d’ is not defined</p>
-</dd>
-</dl>
-<button class="button">Run …</button>
-</section>
-<section id="id8">
-<h2>基本操作<a class="headerlink" href="#id8" title="Permalink to this heading">¶</a></h2>
-<p>字典不支持序列数据类型（例如：字符串、元组和列表）的序列操作，例如：索引
-切片、连接（+）、重复（*）等。</p>
-<section id="id9">
-<h3>成员测试<a class="headerlink" href="#id9" title="Permalink to this heading">¶</a></h3>
-<p>可以测试一个 key 是否存在于字典中，使用关键字 in。</p>
-<p>注意： 成员测试仅适用于 key，不适用于 value。</p>
-<dl>
-<dt>::</dt><dd><blockquote>
-<div><p>d = {‘name’:’Python’, ‘site’:’www.python.org’}</p>
-<p>‘name’ in d</p>
-</div></blockquote>
-<p>True</p>
-<blockquote>
-<div><p>‘version’ not in d</p>
-</div></blockquote>
-<p>True</p>
-<blockquote>
-<div><p>‘Python’ in d  # 不能检测 value</p>
-</div></blockquote>
-<p>False</p>
-</dd>
-</dl>
-<button class="button">Run …</button>
-</section>
-<section id="id10">
-<h3>遍历字典<a class="headerlink" href="#id10" title="Permalink to this heading">¶</a></h3>
-<p>使用 for 循环，可以遍历字典中的每个 key。</p>
-<dl>
-<dt>::</dt><dd><blockquote>
-<div><p>d = {‘name’:’Python’, ‘site’:’www.python.org’}</p>
-<p>for key in d:</p>
-</div></blockquote>
-<p>…     print(key)
-…
-name
-site</p>
-</dd>
-</dl>
-<button class="button">Run …</button>
-</section>
-</section>
-<section id="id11">
-<h2>字典的方法<a class="headerlink" href="#id11" title="Permalink to this heading">¶</a></h2>
-<p>字典提供了许多方法，可以通过 dir() 来查看方法列表：</p>
-<dl>
-<dt>::</dt><dd><blockquote>
-<div><p>dir(dict)</p>
-</div></blockquote>
-<p>[‘__class__’, ‘__contains__’, ‘__delattr__’, ‘__delitem__’, ‘__dir__’, ‘__doc__’, ‘__eq__’, ‘__format__’, ‘__ge__’, ‘__getattribute__’, ‘__getitem__’, ‘__gt__’, ‘__hash__’, ‘__init__’, ‘__iter__’, ‘__le__’, ‘__len__’, ‘__lt__’, ‘__ne__’, ‘__new__’, ‘__reduce__’, ‘__reduce_ex__’, ‘__repr__’, ‘__setattr__’, ‘__setitem__’, ‘__sizeof__’, ‘__str__’, ‘__subclasshook__’, ‘clear’, ‘copy’, ‘fromkeys’, ‘get’, ‘items’, ‘keys’, ‘pop’, ‘popitem’, ‘setdefault’, ‘update’, ‘values’]</p>
-</dd>
-</dl>
-<p>1
-2
-可以看到，有以下方法可用：
-+———————–+—————————————————————————————————————-+
-| 方法                    |         描述                                                                                                   |
-+=======================+================================================================================================================+
-<a href="#id39"><span class="problematic" id="id40">|clear()                |       删除字典中的所有元素                                                                                     |
-+=======================+================================================================================================================+
-|copy()                 |           返回字典的浅拷贝                                                                                     |
-+=======================+================================================================================================================+
-|fromkeys(seq[, v])     |从 seq 返回一个新的字典，值等于 v（默认为 None）                                                                |
-+=======================+================================================================================================================+
-|get(key[,d])           |返回 key 的值。如果 key 不存在，返回 d（默认为 None）。                                                         |
-+=======================+================================================================================================================+
-|items()                |以列表形式，返回可遍历的 (key, value) 元组数组。                                                                |
-+=======================+================================================================================================================+
-|keys()                 |以列表的形式，返回字典中所有的 key。                                                                            |
-+=======================+================================================================================================================+
-|pop(key[,d])           |用 key 删除元素并返回其 value。如果没有找到 key，则返回 d；如果没|有提供 d 并且没有找到 key，则会引发 KeyError。|</span></a>
-+=======================+================================================================================================================+
-<a href="#id12"><span class="problematic" id="id13">|</span></a>popitem()              <a href="#id14"><span class="problematic" id="id15">|</span></a>删除并返回一个任意元素，如果字典为空，则会引发 KeyError。                                                       |
-+=======================+================================================================================================================+
-<a href="#id16"><span class="problematic" id="id17">|</span></a>setdefault(key[,d])    <a href="#id18"><span class="problematic" id="id19">|</span></a>如果 key 在字典中，则返回对应的 value；否则，插入 key，将其值设为 d，并返回 d（默认为 None）。                  |
-+=======================+================================================================================================================+
-<a href="#id20"><span class="problematic" id="id21">|</span></a>update([other])        <a href="#id22"><span class="problematic" id="id23">|</span></a>将 other 字典中的键/值对更新到字典中，覆盖现有键。                                                              |
-+=======================+================================================================================================================+
-<a href="#id24"><span class="problematic" id="id25">|</span></a>values()               <a href="#id26"><span class="problematic" id="id27">|</span></a>以列表的形式，返回字典中所有的 value                                                                            |
-+———————–+—————————————————————————————————————-+</p>
-<p>其中一些在上述示例中已经被使用过了。</p>
-<dl>
-<dt>::</dt><dd><blockquote>
-<div><p>all_stars = {}.fromkeys([‘Lebron’, ‘Kobe’, ‘Curry’], ‘NBA’)</p>
-<p>all_stars
-{‘Lebron’: ‘NBA’, ‘Curry’: ‘NBA’, ‘Kobe’: ‘NBA’}</p>
-<p>all_stars.keys()  # 返回所有 key
-dict_keys([‘Lebron’, ‘Curry’, ‘Kobe’])</p>
-<p>all_stars.values()  # 返回所有 value
-dict_values([‘NBA’, ‘NBA’, ‘NBA’])</p>
-<p>for item in all_stars.items():</p>
-</div></blockquote>
-<p>…     print(item)
-…
-(‘Lebron’, ‘NBA’)
-(‘Curry’, ‘NBA’)
-(‘Kobe’, ‘NBA’)</p>
-</dd>
-</dl>
-<button class="button">Run …</button>
-</section>
-<section id="id28">
-<h2>字典推导式<a class="headerlink" href="#id28" title="Permalink to this heading">¶</a></h2>
-<p>字典推导式（Dict Comprehensions）是一种优雅、简洁的方式，可以从一个 iterable 中创建新的字典。</p>
-<p>字典推导式由花括号标识，其中包含一个表达式对（key:value），紧随其后的是 for 语句。</p>
-<p>来看一个例子，NBA All-Star。</p>
-<dl>
-<dt>::</dt><dd><blockquote>
-<div><p>all_stars = [‘Lebron’, ‘Kobe’, ‘Curry’]</p>
-<p>d = {key: value for key, value in enumerate(all_stars)}
-d</p>
-</div></blockquote>
-<p>{0: ‘Lebron’, 1: ‘Kobe’, 2: ‘Curry’}</p>
-</dd>
-</dl>
-<button class="button">Run …</button>
-<p>列表推导式可以可选地包含更多的 for 或 if 语句，if 语句可以过滤出新字典的元素。</p>
-<dl>
-<dt>::</dt><dd><blockquote>
-<div><dl class="simple">
-<dt># value 的长度需要大于 5</dt><dd><p>d = {key: value for key, value in enumerate(all_stars) if len(value) &gt; 5}
-d</p>
-</dd>
-</dl>
-</div></blockquote>
-<p>{0: ‘Lebron’}</p>
-</dd>
-</dl>
-<button class="button">Run …</button>
-<p>666，老詹独一档。。。</p>
-</section>
-<section id="id29">
-<h2>字典与内置函数<a class="headerlink" href="#id29" title="Permalink to this heading">¶</a></h2>
-<p>下述内置函数通常与字典一起使用，来执行不同的任务。
-+——–+————————————————————————————–+
-函数     <a href="#id30"><span class="problematic" id="id31">|</span></a>描述                                                                                  |
-+======+========================================================================================+
-all()    <a href="#id32"><span class="problematic" id="id33">|</span></a>如果字典的所有 key 都是 True（或者字典为空），返回 True。                             |
-+======+========================================================================================+
-any()    <a href="#id34"><span class="problematic" id="id35">|</span></a>如果字典的所有 key 都是 True，返回 True；如果字典为空，返回 False。                   |
-+======+========================================================================================+
-len()    <a href="#id36"><span class="problematic" id="id37">|</span></a>返回字典的长度（元素个数）                                                            |
-+======+========================================================================================+
-sorted() |      返回一个新的排序字典（不排序字典本身）                                          |
-+——+—————————————————————————————-+</p>
-</section>
-<section id="id38">
-<h2>作者 &amp; 更新时间<a class="headerlink" href="#id38" title="Permalink to this heading">¶</a></h2>
-<p>作者: <a class="reference external" href="http://blog.csdn.net/liang19890820">CSDN</a></p>
-</section>
-</section>
-<div class="clearer"></div>
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-<section id="id1">
-<h1>简述<a class="headerlink" href="#id1" title="Permalink to this heading">¶</a></h1>
-<p>在字符串中，有时需要包含一些特殊的符号，但是有些符号不能直接输出，就需要使用转义序列。</p>
-<p>关于转义，维基百科这样定义：</p>
-<p>转义是当由于技术等原因、无法直接在代码中写出所要的字符时采用的，以多个字符的有序组合来表示原本需要的字符的手段，而转义序列（escape sequence）指在转义时使用的有序字符组合。</p>
-<section id="id2">
-<h2>转义序列<a class="headerlink" href="#id2" title="Permalink to this heading">¶</a></h2>
-<p>作为一名伟大的程序员，在向别人介绍自己的职业时，往往会很自豪的说：Hi, “I’m a Pythoner”。如果要在 Python 中表示这句话，不能直接使用单引号或双引号。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Hi, &quot;I&#39;</span><span class="n">m</span> <span class="n">a</span> <span class="n">Pythoner</span><span class="s2">&quot;&#39;)</span>
-<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Hi, &quot;</span><span class="n">I</span><span class="s1">&#39;m a Pythoner&quot;&quot;)</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>要解决这个问题，有两种方式：</p>
-<ul class="simple">
-<li><p>使用三重引号</p></li>
-<li><p>使用转义序列</p></li>
-</ul>
-<p>转义序列以反斜杠（）开头，并以不同的方式解释。如果使用单引号来表示字符串，那么字符串内的所有单引号都必须转义，双引号也不例外。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s1">&#39;&#39;&#39;Hi, &quot;I&#39;m a Pythoner&quot;&#39;&#39;&#39;</span><span class="p">)</span>  <span class="c1"># 使用三重引号</span>
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Hi, &quot;I</span><span class="se">\&#39;</span><span class="s1">m a Pythoner&quot;&#39;</span><span class="p">)</span>  <span class="c1"># 转义单引号</span>
-<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Hi, </span><span class="se">\&quot;</span><span class="s2">I&#39;m a Pythoner</span><span class="se">\&quot;</span><span class="s2">&quot;</span><span class="p">)</span>  <span class="c1"># 转义双引号</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>以下是 Python 支持的所有转义序列的列表：</p>
-<p>测试其中的转义序列，感受一下实际的意思：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s1">&#39;C:</span><span class="se">\\</span><span class="s1">Windows</span><span class="se">\\</span><span class="s1">System32&#39;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;I like </span><span class="se">\n</span><span class="s1">Python&#39;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;This is </span><span class="se">\x48\x45\x58</span><span class="s1">&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-</section>
-<section id="id3">
-<h2>原始字符串<a class="headerlink" href="#id3" title="Permalink to this heading">¶</a></h2>
-<p>原始字符串（Raw String），简单理解就是：字符串中的每个字符都表示原始含义。</p>
-<p>有时，可能希望忽略字符串中的转义序列：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s1">&#39;This is </span><span class="se">\x48\x45\x58</span><span class="s1">&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>遗憾的是，这里的反斜杠并不是原始符号的含义，而是和后面的字符一起表示十六进制（被转义了）。</p>
-<p>如何避免呢？很 easy，为字符串加上前缀 r 或者 R（大小写均可）：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;This is \x48\x45\x58&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>R 是 Raw 的缩写，以其开头的字符串被称作原始字符串，并将反斜杠视为原义字符。因此，其中的任何转义序列都不会被特别处理，将会被忽略。</p>
-</section>
-<section id="id4">
-<h2>作者 &amp; 更新时间<a class="headerlink" href="#id4" title="Permalink to this heading">¶</a></h2>
-<p>作者: <a class="reference external" href="http://blog.csdn.net/liang19890820">一去丶二三里</a></p>
-<p>提交: 2017/12/6</p>
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diff --git a/docs/python-base/cn/python-datatype-gather.html b/docs/python-base/cn/python-datatype-gather.html
deleted file mode 100644
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-<section id="python">
-<h1>Python 集合<a class="headerlink" href="#python" title="Permalink to this heading">¶</a></h1>
-<section id="id1">
-<h2>简述<a class="headerlink" href="#id1" title="Permalink to this heading">¶</a></h2>
-<p>数学上有一个基础概念 -集合，上高一的时候学过。集合的作用大吗？高考必考，你说呢？</p>
-<p>关于集合，维基百科这样描述：</p>
-<p>在 Python 中，集合分为两类：</p>
-<ul class="simple">
-<li><p>set：可变集合</p></li>
-<li><p>frozenset：不可变集合</p></li>
-</ul>
-<p>set 可以原地修改，或者说是可变的，也可以说是 unhashable（不可哈希）的。</p>
-<p>frozenset，顾名思义，是一个被“冻结”的集合，不能原地修改，是 hashable（可哈希）的。</p>
-</section>
-<section id="id2">
-<h2>集合<a class="headerlink" href="#id2" title="Permalink to this heading">¶</a></h2>
-<p>在 Python 中，集合由内置的 set 类型定义。</p>
-<p>要创建集合，需要将所有项（元素）放在花括号（{}）内，以逗号（,）分隔。</p>
-<dl class="simple">
-<dt>::</dt><dd><p>d = {‘name’:’Python’, ‘site’:’www.python.org’}
-type(d)
-&lt;class ‘dict’&gt;</p>
-</dd>
-</dl>
-<button class="button">Run …</button>
-<p>字典的创建方式很多，还可以使用 dict()：</p>
-<p>由于列表是可变的，不可 hash，因此不能作为字典的 key。</p>
-<p>key 唯一，意思是说：不要有重复的 key 出现。</p>
-<p>虽然 value 可以是任何数据类型，并且可以重复，但 key 必须是不可变类型（例如：字符串、数字）或具有不可变元素的元组。</p>
-<dl>
-<dt>::</dt><dd><blockquote>
-<div><p>s = {‘P’, ‘y’, ‘t’, ‘h’, ‘o’, ‘n’}
-type(s)</p>
-</div></blockquote>
-<p>&lt;class ‘set’&gt;</p>
-</dd>
-</dl>
-<button class="button">Run …</button>
-<p>集合可以有任意数量的元素，它们可以是不同的类型（例如：数字、元组、字符串等）。但是，集合不能有可变元素（例如：列表、集合或字典）。</p>
-<dl>
-<dt>::</dt><dd><blockquote>
-<div><p>s = {1, 2, 3}  # 整形的集合</p>
-<p>s = {1.0, ‘Python’, (1, 2, 3)}  # 混合类型的集合</p>
-<p>s = set([‘P’, ‘y’])  # 从列表创建</p>
-<p>s = {1, 2, [3, 4]}  # 不能有可变元素</p>
-</div></blockquote>
-<p>…
-TypeError: unhashable type: ‘list’</p>
-</dd>
-</dl>
-<button class="button">Run …</button>
-<p>创建空集合比较特殊。在 Python 中，空花括号（{}）用于创建空字典。要创建一个没有任何元素的集合，使用 set() 函数（不要包含任何参数）。</p>
-<dl>
-<dt>::</dt><dd><blockquote>
-<div><p>d = {}  # 空字典
-type(d)</p>
-</div></blockquote>
-<p>&lt;class ‘dict’&gt;</p>
-<blockquote>
-<div><p>s = set()  # 空集合</p>
-</div></blockquote>
-<p>type(s)
-&lt;class ‘set’&gt;</p>
-</dd>
-</dl>
-<button class="button">Run …</button>
-</section>
-<section id="id3">
-<h2>集合的特性<a class="headerlink" href="#id3" title="Permalink to this heading">¶</a></h2>
-<p>–《论语》</p>
-<p>回顾数学相关知识，集合有以下特性：</p>
-<ul class="simple">
-<li><p>无序性：一个集合中，每个元素的地位都是相同的，元素之间是无序的。</p></li>
-</ul>
-<p>集合上可以定义序关系，定义了序关系后，元素之间就可以按照序关系排序。但就集合本身的特性而言，元素之间没有必然的序。
-- 互异性：一个集合中，任何两个元素都认为是不相同的，即每个元素只能出现一次。
-有时需要对同一元素出现多次的情形进行刻画，可以使用多重集，其中的元素允许出现多次。
-- 确定性：给定一个集合，任给一个元素，该元素或者属于或者不属于该集合，二者必居其一，不允许有模棱两可的情况出现。</p>
-<p>当然，Python 中的集合也具备这些特性：</p>
-<dl>
-<dt>::</dt><dd><dl class="simple">
-<dt># 无序性</dt><dd><p>s = set(‘Python’)
-s</p>
-</dd>
-</dl>
-<p>{‘y’, ‘n’, ‘h’, ‘o’, ‘P’, ‘t’}</p>
-<blockquote>
-<div><p>s[0]  # 不支持索引</p>
-</div></blockquote>
-<p>…
-TypeError: ‘set’ object does not support indexing</p>
-<dl class="simple">
-<dt># 互异性</dt><dd><p>s = set(‘Hello’)
-s</p>
-</dd>
-</dl>
-<p>{‘e’, ‘H’, ‘l’, ‘o’}</p>
-<dl class="simple">
-<dt># 确定性</dt><dd><p>‘l’ in s</p>
-</dd>
-</dl>
-<p>True</p>
-<blockquote>
-<div><p>‘P’ not in s</p>
-</div></blockquote>
-<p>True</p>
-</dd>
-</dl>
-<button class="button">Run …</button>
-<p><strong>注意：</strong> 由于集合是无序的，所以索引没有任何意义。也就是说，无法使用索引或切片访问或更改集合元素。</p>
-</section>
-<section id="id4">
-<h2>集合运算<a class="headerlink" href="#id4" title="Permalink to this heading">¶</a></h2>
-<p>集合之间也可进行数学集合运算（例如：并集、交集等），可用相应的操作符或方法来实现。</p>
-<p>考虑 A、B 两个集合，进行以下操作。</p>
-<dl class="simple">
-<dt>::</dt><dd><p>A = set(‘abcd’)
-B = set(‘cdef’)</p>
-</dd>
-</dl>
-<button class="button">Run …</button>
-</section>
-<section id="id5">
-<h2>子集<a class="headerlink" href="#id5" title="Permalink to this heading">¶</a></h2>
-<p>子集，为某个集合中一部分的集合，故亦称部分集合。</p>
-<p>使用操作符 &lt; 执行子集操作，同样地，也可使用方法 issubset() 完成。</p>
-<dl>
-<dt>::</dt><dd><blockquote>
-<div><p>C = set(‘ab’)</p>
-<p>C &lt; A</p>
-</div></blockquote>
-<p>True</p>
-<blockquote>
-<div><p>C &lt; B
-False</p>
-<p>C.issubset(A)
-True</p>
-</div></blockquote>
-</dd>
-</dl>
-<button class="button">Run …</button>
-</section>
-<section id="id6">
-<h2>并集<a class="headerlink" href="#id6" title="Permalink to this heading">¶</a></h2>
-<p>一组集合的并集是这些集合的所有元素构成的集合，而不包含其他元素。</p>
-<p>使用操作符 | 执行并集操作，同样地，也可使用方法 union() 完成。</p>
-<dl>
-<dt>::</dt><dd><blockquote>
-<div><p>A | B</p>
-</div></blockquote>
-<p>{‘e’, ‘f’, ‘d’, ‘c’, ‘b’, ‘a’}</p>
-<blockquote>
-<div><p>A.union(B)</p>
-</div></blockquote>
-<p>{‘e’, ‘f’, ‘d’, ‘c’, ‘b’, ‘a’}</p>
-</dd>
-</dl>
-<button class="button">Run …</button>
-</section>
-<section id="id7">
-<h2>交集<a class="headerlink" href="#id7" title="Permalink to this heading">¶</a></h2>
-<p>两个集合 A 和 B 的交集是含有所有既属于 A 又属于 B 的元素，而没有其他元素的集合。</p>
-<p>使用 &amp; 操作符执行交集操作，同样地，也可使用方法 intersection() 完成。</p>
-<dl>
-<dt>::</dt><dd><blockquote>
-<div><p>A &amp; B</p>
-</div></blockquote>
-<p>{‘d’, ‘c’}</p>
-<blockquote>
-<div><p>A.intersection(B)</p>
-</div></blockquote>
-<p>{‘d’, ‘c’}</p>
-</dd>
-</dl>
-<button class="button">Run …</button>
-</section>
-<section id="id8">
-<h2>差集<a class="headerlink" href="#id8" title="Permalink to this heading">¶</a></h2>
-<p>A 与 B 的差集是所有属于 A 且不属于 B 的元素构成的集合</p>
-<p>使用操作符 - 执行差集操作，同样地，也可使用方法 difference() 完成。</p>
-<dl>
-<dt>::</dt><dd><blockquote>
-<div><p>A - B</p>
-</div></blockquote>
-<p>{‘b’, ‘a’}</p>
-<blockquote>
-<div><p>A.difference(B)</p>
-</div></blockquote>
-<p>{‘b’, ‘a’}</p>
-</dd>
-</dl>
-<button class="button">Run …</button>
-</section>
-<section id="id9">
-<h2>对称差<a class="headerlink" href="#id9" title="Permalink to this heading">¶</a></h2>
-<p>两个集合的对称差是只属于其中一个集合，而不属于另一个集合的元素组成的集合。</p>
-<p>使用 ^ 操作符执行差集操作，同样地，也可使用方法 symmetric_difference() 完成。</p>
-<dl>
-<dt>::</dt><dd><blockquote>
-<div><p>A ^ B</p>
-</div></blockquote>
-<p>{‘b’, ‘e’, ‘f’, ‘a’}</p>
-<blockquote>
-<div><p>A.symmetric_difference(B)</p>
-</div></blockquote>
-<p>{‘b’, ‘e’, ‘f’, ‘a’}</p>
-</dd>
-</dl>
-<button class="button">Run …</button>
-</section>
-<section id="id10">
-<h2>更改集合<a class="headerlink" href="#id10" title="Permalink to this heading">¶</a></h2>
-<p>虽然集合不能有可变元素，但是集合本身是可变的。也就是说，可以添加或删除其中的元素。</p>
-<p>可以使用 add() 方法添加单个元素，使用 update() 方法添加多个元素，update() 可以使用元组、列表、字符串或其他集合作为参数。</p>
-<dl>
-<dt>::</dt><dd><blockquote>
-<div><p>s = {‘P’, ‘y’}</p>
-<p>s.add(‘t’)  # 添加一个元素
-s</p>
-</div></blockquote>
-<p>{‘P’, ‘y’, ‘t’}</p>
-<blockquote>
-<div><p>s.update([‘h’, ‘o’, ‘n’])  # 添加多个元素</p>
-</div></blockquote>
-<p>s
-{‘y’, ‘o’, ‘n’, ‘t’, ‘P’, ‘h’}</p>
-<blockquote>
-<div><p>s.update([‘H’, ‘e’], {‘l’, ‘l’, ‘o’})  # 添加列表和集合
-s</p>
-</div></blockquote>
-<p>{‘H’, ‘y’, ‘e’, ‘o’, ‘n’, ‘t’, ‘l’, ‘P’, ‘h’}</p>
-</dd>
-</dl>
-<button class="button">Run …</button>
-<p>在所有情况下，元素都不会重复。</p>
-</section>
-<section id="id11">
-<h2>从集合中删除元素<a class="headerlink" href="#id11" title="Permalink to this heading">¶</a></h2>
-<p>可以使用 discard() 和 remove() 方法删除集合中特定的元素。</p>
-<p>两者之间唯一的区别在于：如果集合中不存在指定的元素，使用 discard() 保持不变；但在这种情况下，remove() 会引发 KeyError。</p>
-<dl>
-<dt>::</dt><dd><blockquote>
-<div><p>s = {‘P’, ‘y’, ‘t’, ‘h’, ‘o’, ‘n’}</p>
-<p>s.discard(‘t’)  # 去掉一个存在的元素
-s</p>
-</div></blockquote>
-<dl class="simple">
-<dt>{‘y’, ‘o’, ‘n’, ‘P’, ‘h’}</dt><dd><p>s.remove(‘h’)  # 删除一个存在的元素
-s</p>
-</dd>
-</dl>
-<p>{‘y’, ‘o’, ‘n’, ‘P’}</p>
-<blockquote>
-<div><p>s.discard(‘w’)  # 去掉一个不存在的元素（正常）
-s</p>
-</div></blockquote>
-<p>{‘y’, ‘o’, ‘n’, ‘P’}</p>
-<blockquote>
-<div><p>s.remove(‘w’)  # 删除一个不存在的元素（引发错误）</p>
-</div></blockquote>
-<p>…
-KeyError: ‘w’</p>
-</dd>
-</dl>
-<button class="button">Run …</button>
-<p>类似地，可以使用 pop() 方法删除和返回一个项目。</p>
-<p>还可以使用 clear() 删除集合中的所有元素。</p>
-<dl>
-<dt>::</dt><dd><blockquote>
-<div><p>s = set(‘Python’)</p>
-<p>s.pop()  # 随机返回一个元素</p>
-</div></blockquote>
-<p>‘y’</p>
-<blockquote>
-<div><p>s.clear()  # 清空集合
-s</p>
-</div></blockquote>
-<p>set()</p>
-</dd>
-</dl>
-<button class="button">Run …</button>
-<p><strong>注意：</strong> 集合是无序的，所以无法确定哪个元素将被 pop，完全随机。</p>
-</section>
-<section id="id12">
-<h2>集合的方法<a class="headerlink" href="#id12" title="Permalink to this heading">¶</a></h2>
-<p>老规矩，利用 dir() 来查看方法列表：</p>
-<dl>
-<dt>::</dt><dd><blockquote>
-<div><p>dir(set)</p>
-</div></blockquote>
-<p>[‘__and__’, ‘__class__’, ‘__contains__’, ‘__delattr__’, ‘__dir__’, ‘__doc__’, ‘__eq__’, ‘__format__’, ‘__ge__’, ‘__getattribute__’, ‘__gt__’, ‘__hash__’, ‘__iand__’, ‘__init__’, ‘__ior__’, ‘__isub__’, ‘__iter__’, ‘__ixor__’, ‘__le__’, ‘__len__’, ‘__lt__’, ‘__ne__’, ‘__new__’, ‘__or__’, ‘__rand__’, ‘__reduce__’, ‘__reduce_ex__’, ‘__repr__’, ‘__ror__’, ‘__rsub__’, ‘__rxor__’, ‘__setattr__’, ‘__sizeof__’, ‘__str__’, ‘__sub__’, ‘__subclasshook__’, ‘__xor__’, ‘add’, ‘clear’, ‘copy’, ‘difference’, ‘difference_update’, ‘discard’, ‘intersection’, ‘intersection_update’, ‘isdisjoint’, ‘issubset’, ‘issuperset’, ‘pop’, ‘remove’, ‘symmetric_difference’, ‘symmetric_difference_update’, ‘union’, ‘update’]</p>
-</dd>
-</dl>
-<button class="button">Run …</button>
-<p>可以看到，有以下方法可用：</p>
-<p>可以看到，有以下方法可用：
-+—————————–+————————————————————————————–+
-| 方法                          |                                描述                                                  |
-+=============================+======================================================================================+
-<a href="#id13"><span class="problematic" id="id14">|</span></a>add()                        |            将元素添加到集合中                                                        |
-+=============================+======================================================================================+
-clear()                       |     删除集合中的所有元素                                                             |
-+=============================+======================================================================================+
-copy()                        |             返回集合的浅拷贝                                                         |
-+=============================+======================================================================================+
-difference                    |          将两个或多个集合的差集作为一个新集合返回                                    |
-+=============================+======================================================================================+
-difference_update()           |            从这个集合中删除另一个集合的所有元素                                      |
-+=============================+======================================================================================+
-discard()                     |         删除集合中的一个元素（如果元素不存在，则不执行任何操作）                     |
-+=============================+======================================================================================+
-intersection()                |            将两个集合的交集作为一个新集合返回                                        |
-+=============================+======================================================================================+
-intersection_update()         |              用自己和另一个的交集来更新这个集合                                      |
-+=============================+======================================================================================+
-isdisjoint()                  |             如果两个集合有一个空交集，返回 True                                      |
-+=============================+======================================================================================+
-issubset()                    |          如果另一个集合包含这个集合，返回 True                                       |
-+=============================+======================================================================================+
-issuperset()                  |         如果这个集合包含另一个集合，返回 True                                        |
-+=============================+======================================================================================+
-pop()                         |          删除并返回任意的集合元素（如果集合为空，会引发 KeyError）                   |
-+=============================+======================================================================================+
-remove()                      |        删除集合中的一个元素（如果元素不存在，会引发 KeyError）                       |
-+=============================+======================================================================================+
-symmetric_difference()        |             将两个集合的对称差作为一个新集合返回                                     |
-+=============================+======================================================================================+
-symmetric_difference_update() |           用自己和另一个的对称差来更新这个集合                                       |
-+=============================+======================================================================================+
-union()                       |           将集合的并集作为一个新集合返回                                             |
-+=============================+======================================================================================+
-update()                      |           用自己和另一个的并集来更新这个集合                                         |
-+—————————–+————————————————————————————–+</p>
-<p>其中一些方法在上述示例中已经被使用过了，如果有方法不会用，可利用 help() 函数，查看用途及详细说明。</p>
-</section>
-<section id="id15">
-<h2>集合与内置函数<a class="headerlink" href="#id15" title="Permalink to this heading">¶</a></h2>
-<p>下述内置函数通常作用于集合，来执行不同的任务。</p>
-<table class="docutils align-default">
-<tbody>
-</tbody>
-</table>
-<p>函数        <a href="#id16"><span class="problematic" id="id17">|</span></a>描述                                                                                    |
-+===========+========================================================================================+
-all()       <a href="#id18"><span class="problematic" id="id19">|</span></a>如果集合中的所有元素都是 True（或者集合为空），则返回 True。                            |
-+===========+========================================================================================+
-any()       <a href="#id20"><span class="problematic" id="id21">|</span></a>如果集合中的所有元素都是 True，则返回 True；如果集合为空，则返回 False。                |
-+===========+========================================================================================+
-enumerate() |   返回一个枚举对象，其中包含了集合中所有元素的索引和值（配对）。                       |
-+===========+========================================================================================+
-len()       <a href="#id22"><span class="problematic" id="id23">|</span></a>返回集合的长度（元素个数）                                                              |
-+===========+========================================================================================+
-max()       <a href="#id24"><span class="problematic" id="id25">|</span></a>返回集合中的最大项                                                                      |
-+===========+========================================================================================+
-min()       <a href="#id26"><span class="problematic" id="id27">|</span></a>返回集合中的最小项                                                                      |
-+===========+========================================================================================+
-sorted()    |   从集合中的元素返回新的排序列表（不排序集合本身）                                     |
-+===========+========================================================================================+
-sum()       <a href="#id28"><span class="problematic" id="id29">|</span></a>返回集合的所有元素之和                                                                  |
-+———–+—————————————————————————————-+</p>
-</section>
-<section id="id30">
-<h2>不可变集合<a class="headerlink" href="#id30" title="Permalink to this heading">¶</a></h2>
-<p>frozenset 是一个具有集合特征的新类，但是一旦分配，它里面的元素就不能更改。这一点和元组非常类似：元组是不可变的列表，frozenset 是不可变的集合。</p>
-<p>集合是 unhashable 的，因此不能用作字典的 key；而 frozensets 是 hashable 的，可以用作字典的 key。</p>
-<p>可以使用函数 frozenset() 创建 frozenset。</p>
-<dl>
-<dt>::</dt><dd><blockquote>
-<div><p>s = frozenset(‘Python’)
-type(s)</p>
-</div></blockquote>
-<p>&lt;class ‘frozenset’&gt;</p>
-</dd>
-</dl>
-<button class="button">Run …</button>
-<p>frozenset 也提供了一些列方法，和 set 中的类似。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span> <span class="nb">dir</span><span class="p">(</span><span class="nb">frozenset</span><span class="p">)</span>
-<span class="p">[</span><span class="s1">&#39;__and__&#39;</span><span class="p">,</span> <span class="s1">&#39;__class__&#39;</span><span class="p">,</span> <span class="s1">&#39;__contains__&#39;</span><span class="p">,</span> <span class="s1">&#39;__delattr__&#39;</span><span class="p">,</span> <span class="s1">&#39;__dir__&#39;</span><span class="p">,</span> <span class="s1">&#39;__doc__&#39;</span><span class="p">,</span> <span class="s1">&#39;__eq__&#39;</span><span class="p">,</span> <span class="s1">&#39;__format__&#39;</span><span class="p">,</span> <span class="s1">&#39;__ge__&#39;</span><span class="p">,</span> <span class="s1">&#39;__getattribute__&#39;</span><span class="p">,</span> <span class="s1">&#39;__gt__&#39;</span><span class="p">,</span> <span class="s1">&#39;__hash__&#39;</span><span class="p">,</span> <span class="s1">&#39;__init__&#39;</span><span class="p">,</span> <span class="s1">&#39;__iter__&#39;</span><span class="p">,</span> <span class="s1">&#39;__le__&#39;</span><span class="p">,</span> <span class="s1">&#39;__len__&#39;</span><span class="p">,</span> <span class="s1">&#39;__lt__&#39;</span><span class="p">,</span> <span class="s1">&#39;__ne__&#39;</span><span class="p">,</span> <span class="s1">&#39;__new__&#39;</span><span class="p">,</span> <span class="s1">&#39;__or__&#39;</span><span class="p">,</span> <span class="s1">&#39;__rand__&#39;</span><span class="p">,</span> <span class="s1">&#39;__reduce__&#39;</span><span class="p">,</span> <span class="s1">&#39;__reduce_ex__&#39;</span><span class="p">,</span> <span class="s1">&#39;__repr__&#39;</span><span class="p">,</span> <span class="s1">&#39;__ror__&#39;</span><span class="p">,</span> <span class="s1">&#39;__rsub__&#39;</span><span class="p">,</span> <span class="s1">&#39;__rxor__&#39;</span><span class="p">,</span> <span class="s1">&#39;__setattr__&#39;</span><span class="p">,</span> <span class="s1">&#39;__sizeof__&#39;</span><span class="p">,</span> <span class="s1">&#39;__str__&#39;</span><span class="p">,</span> <span class="s1">&#39;__sub__&#39;</span><span class="p">,</span> <span class="s1">&#39;__subclasshook__&#39;</span><span class="p">,</span> <span class="s1">&#39;__xor__&#39;</span><span class="p">,</span> <span class="s1">&#39;copy&#39;</span><span class="p">,</span> <span class="s1">&#39;difference&#39;</span><span class="p">,</span> <span class="s1">&#39;intersection&#39;</span><span class="p">,</span> <span class="s1">&#39;isdisjoint&#39;</span><span class="p">,</span> <span class="s1">&#39;issubset&#39;</span><span class="p">,</span> <span class="s1">&#39;issuperset&#39;</span><span class="p">,</span> <span class="s1">&#39;symmetric_difference&#39;</span><span class="p">,</span> <span class="s1">&#39;union&#39;</span><span class="p">]</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>由于 frozenset 是不可变的，所以没有添加或删除元素的方法。</p>
-</section>
-<section id="id31">
-<h2>作者 &amp; 更新时间<a class="headerlink" href="#id31" title="Permalink to this heading">¶</a></h2>
-<p>作者: <a class="reference external" href="http://blog.csdn.net/liang19890820">CSDN</a></p>
-</section>
-</section>
-<div class="clearer"></div>
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-<div class="document">
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-<div class="body" role="main">
-<section id="id1">
-<h1>简述<a class="headerlink" href="#id1" title="Permalink to this heading">¶</a></h1>
-<p>Python 提供了一系列复合数据类型，通常被称为序列，列表（List）就是其中的一种。</p>
-<p>在 Python 中，列表是使用最频繁、用途最广泛的数据类型之一，非常灵活。</p>
-<section id="id2">
-<h2>列表<a class="headerlink" href="#id2" title="Permalink to this heading">¶</a></h2>
-<p>列表是有序的元素序列，在 Python 中，列表由内置的 list 类型定义。</p>
-<p>要创建列表，需要将所有项（元素）放在方括号（[]）内，以逗号（,）分隔。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">l</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;P&#39;</span><span class="p">,</span> <span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="s1">&#39;t&#39;</span><span class="p">,</span> <span class="s1">&#39;h&#39;</span><span class="p">,</span> <span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="s1">&#39;n&#39;</span><span class="p">]</span>
-<span class="nb">type</span><span class="p">(</span><span class="n">l</span><span class="p">)</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>列表可以有任意数量（n &gt;= 0）的元素，当 n 为 0 时，表示一个空列表。</p>
-<p>列表中元素的类型可以不同，支持数字、字符串，甚至可以包含列表（所谓的嵌套）。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">l</span> <span class="o">=</span> <span class="p">[]</span>  <span class="c1"># 空列表</span>
-<span class="n">l</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]</span>  <span class="c1"># 整形列表</span>
-<span class="n">l</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mf">5.5</span><span class="p">,</span> <span class="s1">&#39;Python&#39;</span><span class="p">]</span>  <span class="c1"># 混合类型列表</span>
-<span class="n">l</span> <span class="o">=</span> <span class="p">[</span><span class="mi">6</span><span class="p">,</span> <span class="p">[</span><span class="s1">&#39;Python&#39;</span><span class="p">],</span> <span class="p">[</span><span class="s1">&#39;P&#39;</span><span class="p">,</span> <span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="s1">&#39;t&#39;</span><span class="p">,</span> <span class="s1">&#39;h&#39;</span><span class="p">,</span> <span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="s1">&#39;n&#39;</span><span class="p">]]</span>  <span class="c1"># 嵌套列表</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-</section>
-</section>
-<section id="id3">
-<h1>访问列表中的元素<a class="headerlink" href="#id3" title="Permalink to this heading">¶</a></h1>
-<section id="id4">
-<h2>列表索引<a class="headerlink" href="#id4" title="Permalink to this heading">¶</a></h2>
-<p>和字符串类似，可以使用索引操作符（[]）访问列表中的一个元素。</p>
-<img alt="http://img.blog.csdn.net/20170516213732766" src="http://img.blog.csdn.net/20170516213732766" />
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">l</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;P&#39;</span><span class="p">,</span> <span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="s1">&#39;t&#39;</span><span class="p">,</span> <span class="s1">&#39;h&#39;</span><span class="p">,</span> <span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="s1">&#39;n&#39;</span><span class="p">]</span>
-<span class="n">l</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>  <span class="c1"># 第 1 个元素</span>
-<span class="n">l</span><span class="p">[</span><span class="mi">4</span><span class="p">]</span>  <span class="c1"># 第 5 个元素</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>序列也可以反向索引，-1 表示最后一个元素，-2 表示倒数第二个元素，依此类推。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">l</span><span class="p">[</span><span class="o">-</span><span class="mi">6</span><span class="p">]</span>  <span class="c1"># 第 1 个元素</span>
-<span class="n">l</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">]</span>  <span class="c1"># 第 5 个元素</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>同样地，嵌套的列表可以使用嵌套索引来访问。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">l</span> <span class="o">=</span> <span class="p">[</span><span class="mi">6</span><span class="p">,</span> <span class="p">[</span><span class="s1">&#39;Python&#39;</span><span class="p">],</span> <span class="p">[</span><span class="s1">&#39;P&#39;</span><span class="p">,</span> <span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="s1">&#39;t&#39;</span><span class="p">,</span> <span class="s1">&#39;h&#39;</span><span class="p">,</span> <span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="s1">&#39;n&#39;</span><span class="p">]]</span>  <span class="c1"># 嵌套列表</span>
-<span class="n">l</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-<span class="n">l</span><span class="p">[</span><span class="mi">2</span><span class="p">][</span><span class="mi">3</span><span class="p">]</span>  <span class="c1"># 使用嵌套索引</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-</section>
-<section id="id5">
-<h2>对列表进行切片<a class="headerlink" href="#id5" title="Permalink to this heading">¶</a></h2>
-<p>可以使用切片操作符（:）来访问列表中的一系列元素。</p>
-<p><strong>PS</strong>： 通过上图，可以很直观地看到切片。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">l</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;P&#39;</span><span class="p">,</span> <span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="s1">&#39;t&#39;</span><span class="p">,</span> <span class="s1">&#39;h&#39;</span><span class="p">,</span> <span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="s1">&#39;n&#39;</span><span class="p">]</span>
-<span class="n">l</span><span class="p">[</span><span class="mi">1</span><span class="p">:</span><span class="mi">4</span><span class="p">]</span>  <span class="c1">#  第 2 个到第 4 个元素</span>
-<span class="n">l</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span>  <span class="c1"># 第 2 个到最后一个元素（将索引默认设置为列表的开始或结束）</span>
-<span class="n">l</span><span class="p">[</span><span class="mi">1</span><span class="p">:</span><span class="mi">100</span><span class="p">]</span>  <span class="c1"># 索引太大，被截断至列表长度处</span>
-<span class="n">l</span><span class="p">[:]</span>  <span class="c1"># 所有元素（从开始到结束）</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>和索引类似，切片操作也可以使用反向索引：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">l</span><span class="p">[</span><span class="o">-</span><span class="mi">5</span><span class="p">:</span><span class="o">-</span><span class="mi">2</span><span class="p">]</span>  <span class="c1"># 第 2 个到第 5 个元素</span>
-<span class="n">l</span><span class="p">[:</span><span class="o">-</span><span class="mi">3</span><span class="p">]</span>  <span class="c1"># 开始到第 3 个元素</span>
-<span class="n">l</span><span class="p">[</span><span class="o">-</span><span class="mi">3</span><span class="p">:]</span>  <span class="c1"># 第 4 个到最后一个元素</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-</section>
-<section id="id6">
-<h2>更改及添加元素<a class="headerlink" href="#id6" title="Permalink to this heading">¶</a></h2>
-<p>列表是可变的，也就是说，其中的元素可以被改变（不像字符串或元组）。</p>
-<p>可以使用赋值运算符（=）改变一个或一系列元素。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">l</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;P&#39;</span><span class="p">,</span> <span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="s1">&#39;t&#39;</span><span class="p">,</span> <span class="s1">&#39;h&#39;</span><span class="p">,</span> <span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="s1">&#39;n&#39;</span><span class="p">]</span>
-<span class="n">l</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;H&#39;</span>  <span class="c1"># 更改第 1 个元素</span>
-<span class="n">l</span>
-<span class="n">l</span><span class="p">[</span><span class="mi">1</span><span class="p">:</span><span class="mi">4</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;e&#39;</span><span class="p">,</span> <span class="s1">&#39;l&#39;</span><span class="p">,</span> <span class="s1">&#39;l&#39;</span><span class="p">]</span>  <span class="c1"># 更改第 2 个到第四个元素</span>
-<span class="n">l</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>可以使用 append() 方法将一个元素添加到列表中，或者使用 extend() 方法添加多个元素。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">l</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;P&#39;</span><span class="p">,</span> <span class="s1">&#39;y&#39;</span><span class="p">]</span>
-<span class="n">l</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;t&#39;</span><span class="p">)</span>  <span class="c1"># 添加一个元素</span>
-<span class="n">l</span>
-<span class="n">l</span><span class="o">.</span><span class="n">extend</span><span class="p">([</span><span class="s1">&#39;h&#39;</span><span class="p">,</span> <span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="s1">&#39;n&#39;</span><span class="p">])</span>  <span class="c1"># 添加多个元素</span>
-<span class="n">l</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>此外，可以使用 insert() 方法将一个元素插入到所需的位置。要插入多个元素，可以将其挤压进到一个列表的空切片中。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">l</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;P&#39;</span><span class="p">,</span> <span class="s1">&#39;n&#39;</span><span class="p">]</span>
-<span class="n">l</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="s1">&#39;y&#39;</span><span class="p">)</span>  <span class="c1"># 在指定索引处插入一个元素</span>
-<span class="n">l</span>
-<span class="n">l</span><span class="p">[</span><span class="mi">2</span><span class="p">:</span><span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;t&#39;</span><span class="p">,</span> <span class="s1">&#39;h&#39;</span><span class="p">,</span> <span class="s1">&#39;o&#39;</span><span class="p">]</span>  <span class="c1"># 插入多个元素</span>
-<span class="n">l</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-</section>
-<section id="id7">
-<h2>从列表中删除元素<a class="headerlink" href="#id7" title="Permalink to this heading">¶</a></h2>
-<p>可以使用关键字 del，从列表中删除一个或多个元素，甚至可以完全删除列表。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">l</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;P&#39;</span><span class="p">,</span> <span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="s1">&#39;t&#39;</span><span class="p">,</span> <span class="s1">&#39;h&#39;</span><span class="p">,</span> <span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="s1">&#39;n&#39;</span><span class="p">]</span>
-<span class="k">del</span> <span class="n">l</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>  <span class="c1"># 删除第 3 个元素</span>
-<span class="n">l</span>
-<span class="k">del</span> <span class="n">l</span><span class="p">[</span><span class="mi">1</span><span class="p">:</span><span class="mi">3</span><span class="p">]</span>  <span class="c1"># 删除第 2 个到第 3 个元素</span>
-<span class="n">l</span>
-<span class="k">del</span> <span class="n">l</span>  <span class="c1"># 删除列表</span>
-<span class="n">l</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>可以使用 remove() 方法删除指定的元素，或用 pop() 方法删除一个给定索引处的元素。</p>
-<p>如果没有提供索引，pop() 方法将删除并返回最后一个元素。这有助于我们将列表实现为栈（先进后出数据结构）。</p>
-<p>还可以使用 clear() 方法清空一个列表。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">l</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;P&#39;</span><span class="p">,</span> <span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="s1">&#39;t&#39;</span><span class="p">,</span> <span class="s1">&#39;h&#39;</span><span class="p">,</span> <span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="s1">&#39;n&#39;</span><span class="p">]</span>
-<span class="n">l</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="s1">&#39;h&#39;</span><span class="p">)</span>  <span class="c1"># 删除指定的元素</span>
-<span class="n">l</span>
-<span class="n">l</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>  <span class="c1"># 删除并返回第 3 个元素</span>
-<span class="n">l</span>
-<span class="n">l</span><span class="o">.</span><span class="n">pop</span><span class="p">()</span>  <span class="c1"># 删除并返回最后一个元素</span>
-<span class="n">l</span>
-<span class="n">l</span><span class="o">.</span><span class="n">clear</span><span class="p">()</span>  <span class="c1"># 清空列表</span>
-<span class="n">l</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>最后，还可以通过将一个空列表分配给一个元素切片的方式来删除列表中的项目。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">l</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;P&#39;</span><span class="p">,</span> <span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="s1">&#39;t&#39;</span><span class="p">,</span> <span class="s1">&#39;h&#39;</span><span class="p">,</span> <span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="s1">&#39;n&#39;</span><span class="p">]</span>
-<span class="n">l</span><span class="p">[</span><span class="mi">1</span><span class="p">:</span><span class="mi">3</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
-<span class="n">l</span>
-<span class="n">l</span><span class="p">[</span><span class="mi">1</span><span class="p">:</span><span class="mi">4</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
-<span class="n">l</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-</section>
-<section id="id8">
-<h2>基本操作<a class="headerlink" href="#id8" title="Permalink to this heading">¶</a></h2>
-<p>在字符串一节中已经提过，所有的序列都有几种基本操作。列表是序列的一种，固然也适用。</p>
-<p>连接列表</p>
-<p>操作符 + 用于组合列表，也被称为连接。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">l1</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;P&#39;</span><span class="p">,</span> <span class="s1">&#39;y&#39;</span><span class="p">]</span>
-<span class="n">l2</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;t&#39;</span><span class="p">,</span> <span class="s1">&#39;h&#39;</span><span class="p">,</span> <span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="s1">&#39;n&#39;</span><span class="p">]</span>
-<span class="n">l1</span> <span class="o">+</span> <span class="n">l2</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-</section>
-<section id="id9">
-<h2>重复列表<a class="headerlink" href="#id9" title="Permalink to this heading">¶</a></h2>
-<p>操作符 * 会以给定次数重复一个列表。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">l</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;P&#39;</span><span class="p">,</span> <span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="s1">&#39;t&#39;</span><span class="p">,</span> <span class="s1">&#39;h&#39;</span><span class="p">,</span> <span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="s1">&#39;n&#39;</span><span class="p">]</span>
-<span class="n">l</span> <span class="o">*</span> <span class="mi">2</span>  <span class="c1"># 重复列表 2 次</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-</section>
-<section id="id10">
-<h2>最小值/最大值<a class="headerlink" href="#id10" title="Permalink to this heading">¶</a></h2>
-<p>min() 和 max() 用于获取最小值和最大值。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">l</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;P&#39;</span><span class="p">,</span> <span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="s1">&#39;t&#39;</span><span class="p">,</span> <span class="s1">&#39;h&#39;</span><span class="p">,</span> <span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="s1">&#39;n&#39;</span><span class="p">]</span>
-<span class="nb">min</span><span class="p">(</span><span class="n">l</span><span class="p">)</span>  <span class="c1"># 最小值</span>
-<span class="nb">max</span><span class="p">(</span><span class="n">l</span><span class="p">)</span>  <span class="c1"># 最大值</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-</section>
-<section id="id11">
-<h2>列表长度<a class="headerlink" href="#id11" title="Permalink to this heading">¶</a></h2>
-<p>要获取一个列表的长度，可以使用内置函数 len()。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">l</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;P&#39;</span><span class="p">,</span> <span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="s1">&#39;t&#39;</span><span class="p">,</span> <span class="s1">&#39;h&#39;</span><span class="p">,</span> <span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="s1">&#39;n&#39;</span><span class="p">]</span>
-<span class="nb">len</span><span class="p">(</span><span class="n">l</span><span class="p">)</span>  <span class="c1"># 元素个数</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-</section>
-<section id="id12">
-<h2>成员测试<a class="headerlink" href="#id12" title="Permalink to this heading">¶</a></h2>
-<p>使用关键字 in，可以测试一个元素是否存在于列表中。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">l</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;P&#39;</span><span class="p">,</span> <span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="s1">&#39;t&#39;</span><span class="p">,</span> <span class="s1">&#39;h&#39;</span><span class="p">,</span> <span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="s1">&#39;n&#39;</span><span class="p">]</span>
-<span class="s1">&#39;t&#39;</span> <span class="ow">in</span> <span class="n">l</span>
-<span class="s1">&#39;o&#39;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">l</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-</section>
-<section id="id13">
-<h2>遍历列表<a class="headerlink" href="#id13" title="Permalink to this heading">¶</a></h2>
-<p>使用 for 循环，可以遍历列表中的每个元素。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>l = [&#39;P&#39;, &#39;y&#39;, &#39;t&#39;, &#39;h&#39;, &#39;o&#39;, &#39;n&#39;]
-for letter in l:
-    print(letter)
-</pre></div>
-</div>
-<button class="button">Run …</button>
-</section>
-<section id="id14">
-<h2>列表的方法<a class="headerlink" href="#id14" title="Permalink to this heading">¶</a></h2>
-<p>列表中有许多方法，可以通过 dir() 来查看方法列表：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">dir</span><span class="p">(</span><span class="nb">list</span><span class="p">)</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>以双下划线开始和结尾的（例如：__add__）暂不考虑，后面讨论。剩下以下几个：</p>
-<table class="docutils align-default">
-<thead>
-<tr class="row-odd"><th class="head"><p>方法</p></th>
-<th class="head"><p>描述</p></th>
-</tr>
-</thead>
-<tbody>
-<tr class="row-even"><td><p>append()</p></td>
-<td><p>将元素添加到列表的末尾</p></td>
-</tr>
-<tr class="row-odd"><td><p>extend()</p></td>
-<td><p>将列表的所有元素添加到另一个列表</p></td>
-</tr>
-<tr class="row-even"><td><p>insert()</p></td>
-<td><p>在定义的索引处插入一个元素</p></td>
-</tr>
-<tr class="row-odd"><td><p>remove()</p></td>
-<td><p>从列表中删除一个元素</p></td>
-</tr>
-<tr class="row-even"><td><p>pop()</p></td>
-<td><p>从列表中删除所有元素</p></td>
-</tr>
-<tr class="row-odd"><td><p>clear()</p></td>
-<td><p>断言，用于判断变量或者条件表达式的值是否为真</p></td>
-</tr>
-<tr class="row-even"><td><p>index()</p></td>
-<td><p>返回第一个匹配项的索引</p></td>
-</tr>
-<tr class="row-odd"><td><p>count()</p></td>
-<td><p>统计某个元素在列表中出现的次数</p></td>
-</tr>
-<tr class="row-even"><td><p>sort()</p></td>
-<td><p>按升序排列列表中的元素</p></td>
-</tr>
-<tr class="row-odd"><td><p>reverse()</p></td>
-<td><p>反转列表中元素的顺序</p></td>
-</tr>
-<tr class="row-even"><td><p>copy()</p></td>
-<td><p>返回一个列表的浅拷贝</p></td>
-</tr>
-</tbody>
-</table>
-<p>按照惯例，在使用不熟悉的方法时，先看官方文档描述：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">help</span><span class="p">(</span><span class="nb">list</span><span class="o">.</span><span class="n">append</span><span class="p">)</span>
-<span class="o">&gt;&gt;&gt;</span><span class="n">Help</span> <span class="n">on</span> <span class="n">method_descriptor</span><span class="p">:</span>
-<span class="n">append</span><span class="p">(</span><span class="o">...</span><span class="p">)</span>
-<span class="n">L</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">object</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span> <span class="o">--</span> <span class="n">append</span> <span class="nb">object</span> <span class="n">to</span> <span class="n">end</span>
-</pre></div>
-</div>
-<p>可以使用 list.method() 的方式来访问方法，上面已经列举了一些。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">l</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;P&#39;</span><span class="p">,</span> <span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="s1">&#39;t&#39;</span><span class="p">,</span> <span class="s1">&#39;h&#39;</span><span class="p">,</span> <span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="s1">&#39;n&#39;</span><span class="p">]</span>
-<span class="n">l</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="s1">&#39;h&#39;</span><span class="p">)</span>  <span class="c1"># 返回第一个匹配 &#39;h&#39; 的索引</span>
-<span class="n">l</span><span class="o">.</span><span class="n">count</span><span class="p">(</span><span class="s1">&#39;o&#39;</span><span class="p">)</span>  <span class="c1"># 统计 &#39;o&#39; 在列表中出现的次数</span>
-<span class="n">l</span><span class="o">.</span><span class="n">sort</span><span class="p">()</span>  <span class="c1"># 按升序排列列表中的元素</span>
-<span class="n">l</span>
-<span class="n">l</span><span class="o">.</span><span class="n">reverse</span><span class="p">()</span>  <span class="c1"># 反转列表中元素的顺序</span>
-<span class="n">l</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-</section>
-<section id="id15">
-<h2>列表推导式<a class="headerlink" href="#id15" title="Permalink to this heading">¶</a></h2>
-<p>列表推导式（List Comprehension）是一种优雅、简洁的方式，通过现有列表来创建一个新列表。</p>
-<p>列表推导式由方括号标识，其中包含一个表达式，紧随其后的是 for 语句。</p>
-<p>假设，要创建一个正方形：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>squares = []
-for x in range(10):
-    squares.append(x**2)
-square
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>用列表推导式，可以获得相同的结果：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">squares</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span><span class="o">**</span><span class="mi">2</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">)]</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>这也相当于：squares = map(lambda x: x**2, range(10))，但它更简洁可读。</p>
-<p>列表推导式可以可选地包含更多的 for 或 if 语句，if 语句可以过滤出新列表的元素。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">squares</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span><span class="o">**</span><span class="mi">2</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span> <span class="k">if</span> <span class="n">x</span> <span class="o">&gt;</span> <span class="mi">5</span><span class="p">]</span>
-<span class="n">squares</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">odd</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span> <span class="k">if</span> <span class="n">x</span> <span class="o">%</span> <span class="mi">2</span> <span class="o">==</span> <span class="mi">1</span><span class="p">]</span>
-<span class="n">odd</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">[</span><span class="n">x</span><span class="o">+</span><span class="n">y</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;I love &#39;</span><span class="p">,</span><span class="s1">&#39;I like &#39;</span><span class="p">]</span> <span class="k">for</span> <span class="n">y</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;C++&#39;</span><span class="p">,</span><span class="s1">&#39;Python&#39;</span><span class="p">]]</span>
-<span class="p">[</span><span class="s1">&#39;I love C++&#39;</span><span class="p">,</span> <span class="s1">&#39;I love Python&#39;</span><span class="p">,</span> <span class="s1">&#39;I like C++&#39;</span><span class="p">,</span> <span class="s1">&#39;I like Python&#39;</span><span class="p">]</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-</section>
-<section id="id16">
-<h2>列表与内置函数<a class="headerlink" href="#id16" title="Permalink to this heading">¶</a></h2>
-<p>下述内置函数通常与列表一起使用，来执行不同的任务。</p>
-<table class="docutils align-default">
-<thead>
-<tr class="row-odd"><th class="head"><p>函数</p></th>
-<th class="head"><p>描述</p></th>
-</tr>
-</thead>
-<tbody>
-<tr class="row-even"><td><p>all()</p></td>
-<td><p>如果列表中的所有元素都是 True（或者列表为空），则返回 True。</p></td>
-</tr>
-<tr class="row-odd"><td><p>any()</p></td>
-<td><p>如果列表中的所有元素都是 True，则返回 True；如果列表为空，则返回 False。</p></td>
-</tr>
-<tr class="row-even"><td><p>enumerate()</p></td>
-<td><p>返回一个枚举对象，其中包含了列表中所有元素的索引和值（配对）。</p></td>
-</tr>
-<tr class="row-odd"><td><p>len()</p></td>
-<td><p>返回列表的长度（元素个数）。</p></td>
-</tr>
-<tr class="row-even"><td><p>max()</p></td>
-<td><p>返回列表的最大项</p></td>
-</tr>
-<tr class="row-odd"><td><p>min()</p></td>
-<td><p>返回列表的最小项</p></td>
-</tr>
-<tr class="row-even"><td><p>sorted()</p></td>
-<td><p>返回一个排序的新列表（不排序列表本身）</p></td>
-</tr>
-<tr class="row-odd"><td><p>sum()</p></td>
-<td><p>返回列表的所有元素之和</p></td>
-</tr>
-<tr class="row-even"><td><p>list()</p></td>
-<td><p>将 iterable（元组、字符串、集合、字典）转换为列表</p></td>
-</tr>
-</tbody>
-</table>
-</section>
-<section id="id17">
-<h2>作者 &amp; 更新时间<a class="headerlink" href="#id17" title="Permalink to this heading">¶</a></h2>
-<p>作者: <a class="reference external" href="http://blog.csdn.net/liang19890820">一去丶二三里</a></p>
-<p>提交: 2017/12/6</p>
-</section>
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diff --git a/docs/python-base/cn/python-datatype-number.html b/docs/python-base/cn/python-datatype-number.html
deleted file mode 100644
index 5001118..0000000
--- a/docs/python-base/cn/python-datatype-number.html
+++ /dev/null
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-<!DOCTYPE html>
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-<head>
-<meta charset="utf-8" />
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-<title>简述</title>
-<link rel="stylesheet" type="text/css" href="../../static/pygments.css" />
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-<a href="http://www.qpython.org/index.html">Home</a>
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-<span class="header-title-selected"></span>
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-<a href="http://www.aipy.org">AIPY</a>
-</li>
-<li>
-<a target="_blank" href="https://github.com/qpython-android/qpython">Github</a>
-</li>
-</ul>
-<div class="search-box hidden-xs">
-<form action="/search.html">
-<input type="search" name="q" placeholder="keyword">
-<input type="hidden" name="check_keywords" value="yes" />
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-<button><img src="http://www.qpython.org/static/ic_search.png"></button>
-</form>
-</div>
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-</div>
-<main class="contain web-content">
-<div class="document">
-<div class="documentwrapper">
-<div class="bodywrapper">
-<div class="body" role="main">
-<section id="id1">
-<h1>简述<a class="headerlink" href="#id1" title="Permalink to this heading">¶</a></h1>
-<p>在 Python 中，对于数字的定义很简单 - 整数、浮点数和复数，分别被定义为 int、float 和 complex 类。只要数学不是体育老师教的，绝大部分人学起来都不会有任何问题。</p>
-<p>对于数字而言，除了进行基本的运算（例如：算术运算、比较运算）之外，Python 还提供了类型间的相互转换功能，以及其他的高级操作。</p>
-<section id="id2">
-<h2>数字类型<a class="headerlink" href="#id2" title="Permalink to this heading">¶</a></h2>
-<p>在 Python 中，支持三种不同的数字类型：</p>
-<ul class="simple">
-<li><p>int（整型）：也被称为整数，是正或负整数，不带小数点。</p></li>
-<li><p>float（浮点型）：由整数部分与小数部分组成，浮点型也可以使用科学计数法表示。</p></li>
-<li><p>complex（复数）：以 x + yj 的形式构成，其中 x 是实部，y 是虚部。</p></li>
-</ul>
-<p><strong>注意</strong>： Py3.x 去除了 long 类型，现在只有一种整型 - int，表示为长整型。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">type</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>  <span class="c1"># 整型</span>
-<span class="nb">type</span><span class="p">(</span><span class="mf">2.5</span><span class="p">)</span>  <span class="c1"># 浮点型</span>
-<span class="n">c</span> <span class="o">=</span> <span class="mf">2.5</span> <span class="o">+</span> <span class="mf">5.0</span><span class="n">j</span>  <span class="c1"># 复数</span>
-<span class="nb">type</span><span class="p">(</span><span class="n">c</span><span class="p">)</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-</section>
-<section id="id3">
-<h2>整数溢出？<a class="headerlink" href="#id3" title="Permalink to this heading">¶</a></h2>
-<p>天文数字：形容非常大的数字，已经无法用一个确切的数来形容。
-对于很多编程语言来说，天文数字是需要被正视的，因为往往会引发整数溢出问题。但是，在 Python 中无需忧虑，因为它支持“无限精度”的整数。</p>
-<p>来感受一下：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">i</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">**</span> <span class="mi">500</span>  <span class="c1"># 2 的 500 次幂</span>
-<span class="n">i</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>哇，多么幸运，Python 做出了如此精妙的安排。</p>
-<p>虽然整数可以是任意长度，但是浮点数只能精确到 15 位小数（16 位不准确）。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">f</span> <span class="o">=</span> <span class="mf">0.12345678901234567890</span>  <span class="c1"># 值被截断</span>
-<span class="n">f</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p><strong>注意</strong>： f 的值被截断。</p>
-</section>
-<section id="id4">
-<h2>数字系统<a class="headerlink" href="#id4" title="Permalink to this heading">¶</a></h2>
-<p>对于绝大部分人来说，每天处理的数字都是十进制（以 10 为基数）数字系统。但是程序员（尤其是搞嵌入式的），往往需要使用二进制（以 2 为基数）、八进制（以 8 为基数）和十六进制（以 16 为基数）数字系统。</p>
-<p>在 Python 中，可以在数字前面放置相应的前缀来表示这些数字。</p>
-<p>来看一些示例：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="mi">5</span>  <span class="c1"># 十进制</span>
-<span class="mb">0b1010</span>  <span class="c1"># 二进制</span>
-<span class="mo">0o15</span>  <span class="c1"># 八进制</span>
-<span class="mh">0xFB</span>  <span class="c1"># 十六进制</span>
-</pre></div>
-</div>
-<p>十进制还可以转换为二进制、八进制、十六进制：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">dec</span> <span class="o">=</span> <span class="mi">20</span>  <span class="c1"># 十进制</span>
-<span class="nb">bin</span><span class="p">(</span><span class="n">dec</span><span class="p">)</span>  <span class="c1"># 十进制转二进制</span>
-<span class="nb">oct</span><span class="p">(</span><span class="n">dec</span><span class="p">)</span>  <span class="c1"># 十进制转八进制</span>
-<span class="nb">hex</span><span class="p">(</span><span class="n">dec</span><span class="p">)</span>  <span class="c1"># 十进制转十六进制</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-</section>
-<section id="id5">
-<h2>类型转换<a class="headerlink" href="#id5" title="Permalink to this heading">¶</a></h2>
-<p>一般情况下，数据的类型的转换通常是由编译系统自动进行的，不需要人工干预，所以被称为隐式类型转换。但如果程序要求一定要将某一类型的数据转换为另外一种类型，则可以利用强制类型转换运算符进行转换，这种强制转换过程称为**显式类型转换**。</p>
-<p>在进行数字运算（例如：加法、减法）时，如果其中的一个操作数是浮点数，则会将整数隐式（自动）转换为浮点数。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="mi">2</span> <span class="o">+</span> <span class="mf">3.0</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>可以看到，2（整数）被转换为 2.0（浮点数），计算结果（5.0）也是一个浮点数。</p>
-<p>除此之外，还可以使用 int()、float() 和 complex() 等内置函数来**显式转换**类型，这些函数甚至可以将字符串转换为数字。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">int</span><span class="p">(</span><span class="mf">2.3</span><span class="p">)</span>
-<span class="nb">int</span><span class="p">(</span><span class="o">-</span><span class="mf">2.8</span><span class="p">)</span>
-<span class="nb">float</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
-<span class="nb">complex</span><span class="p">(</span><span class="s1">&#39;3+5j&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>从 float 转换为 int 时，数字将被截断（整数更接近零）。</p>
-<p>Decimal</p>
-<hr class="docutils" />
-<p>通常，在使用内置类 float 时，会执行一些“匪夷所思”的计算。</p>
-<p>我们知道 1/3 等于 0.333…（无限循环），0.1 + 0.2 等于 0.3，但 Python 似乎不这么认为。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="mi">1</span><span class="o">/</span><span class="mi">3</span>
-<span class="mf">0.1</span> <span class="o">+</span> <span class="mf">0.2</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>What？难道是眼花了？和数学知识不一样？</p>
-<p>事实证明，浮点数在计算机硬件中以二进制小数来表示，因为计算机只能理解二进制（0 和 1）。出于这个原因，大部分十进制小数都不能准确地存储在计算机中。</p>
-<p>例如，十进制小数 0.1，将转换为无限长的二进制小数 0.000110011001100110011…，而计算机存储的位数是有限的。也就是说，转换为二进制后，只会接近十进制的 0.1，但永远不会相等。</p>
-<p>因此，<strong>这是计算机硬件的局限性，而不是 Python 中的错误</strong>。</p>
-<p>为了克服这个问题，可以使用 Python 自带的 decimal 模块。虽然浮点数的精度最高可达 15 位，但 decimal 模块可自定义精度。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">decimal</span>
-<span class="mf">0.1</span>
-<span class="n">decimal</span><span class="o">.</span><span class="n">Decimal</span><span class="p">(</span><span class="mf">0.1</span><span class="p">)</span>
-<span class="n">decimal</span><span class="o">.</span><span class="n">getcontext</span><span class="p">()</span>  <span class="c1"># 当前上下文</span>
-<span class="n">decimal</span><span class="o">.</span><span class="n">getcontext</span><span class="p">()</span><span class="o">.</span><span class="n">prec</span>  <span class="c1"># 精度默认 28 位</span>
-<span class="n">d</span> <span class="o">=</span> <span class="n">decimal</span><span class="o">.</span><span class="n">Decimal</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="o">/</span> <span class="n">decimal</span><span class="o">.</span><span class="n">Decimal</span><span class="p">(</span><span class="mi">9</span><span class="p">)</span>
-<span class="n">d</span>
-<span class="n">decimal</span><span class="o">.</span><span class="n">getcontext</span><span class="p">()</span><span class="o">.</span><span class="n">prec</span> <span class="o">=</span> <span class="mi">3</span>  <span class="c1"># 将精度修改为 3 位</span>
-<span class="n">d</span> <span class="o">=</span> <span class="n">decimal</span><span class="o">.</span><span class="n">Decimal</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="o">/</span> <span class="n">decimal</span><span class="o">.</span><span class="n">Decimal</span><span class="p">(</span><span class="mi">9</span><span class="p">)</span>
-<span class="n">d</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>除此之外，它也保留了重要意义。我们知道 25.50 比 25.5 更精确，因为 25.50 有两个有效的小数位。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">decimal</span> <span class="kn">import</span> <span class="n">Decimal</span> <span class="k">as</span> <span class="n">D</span>
-<span class="n">D</span><span class="p">(</span><span class="s1">&#39;0.1&#39;</span><span class="p">)</span> <span class="o">+</span> <span class="n">D</span><span class="p">(</span><span class="s1">&#39;0.2&#39;</span><span class="p">)</span>
-<span class="n">D</span><span class="p">(</span><span class="s1">&#39;1.2&#39;</span><span class="p">)</span> <span class="o">*</span> <span class="n">D</span><span class="p">(</span><span class="s1">&#39;2.50&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p><strong>注意</strong>： 计算结果末尾的 0</p>
-<p>有人可能会问：既然 Decimal 这么棒，为什么每次不使用 Decimal 来代替 float 呢？主要原因是效率，float 运算要比 Decimal 运算更快。</p>
-<p>何时使用 Decimal，而不是 float 呢？在以下情况下，通常使用 Decimal。</p>
-<ul class="simple">
-<li><p>当在做金融应用时，需要精确表示。</p></li>
-<li><p>当想要控制所需的精度级别时</p></li>
-<li><p>当想要实现有效的小数位概念时</p></li>
-<li><p>当我们想要像在学校里学到的那样进行小数运算时</p></li>
-</ul>
-</section>
-<section id="fractions">
-<h2>fractions（有理数）<a class="headerlink" href="#fractions" title="Permalink to this heading">¶</a></h2>
-<p>fractions 模块提供了涉及分数的操作，该模块支持有理数运算。</p>
-<p>一个 Fraction 实例可以由一对整数、另一个有理数、或者一个字符串来构造。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">fractions</span>
-<span class="n">fractions</span><span class="o">.</span><span class="n">Fraction</span><span class="p">(</span><span class="mf">1.5</span><span class="p">)</span>
-<span class="n">Fraction</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
-<span class="n">fractions</span><span class="o">.</span><span class="n">Fraction</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
-<span class="n">Fraction</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-<span class="n">fractions</span><span class="o">.</span><span class="n">Fraction</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
-<span class="n">Fraction</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">fractions</span><span class="o">.</span><span class="n">Fraction</span><span class="p">(</span><span class="mf">1.5</span><span class="p">))</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>当从 float 创建 Fraction 时，可能会得到一些不寻常的结果，这是由于之前讨论的不完美的二进制浮点数造成的。</p>
-<p>幸运的是，fraction 也允许用字符串进行实例化，这是使用十进制数时的首选方案。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">fractions</span> <span class="kn">import</span> <span class="n">Fraction</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">Fraction</span><span class="p">(</span><span class="mf">1.1</span><span class="p">))</span>  <span class="c1"># 浮点数 1.1</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">Fraction</span><span class="p">(</span><span class="s1">&#39;1.1&#39;</span><span class="p">))</span>  <span class="c1"># 字符串 &#39;1.1&#39;</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>此外，该数据类型也支持所有基本操作。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">fractions</span> <span class="kn">import</span> <span class="n">Fraction</span> <span class="k">as</span> <span class="n">F</span>
-<span class="n">F</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">)</span> <span class="o">+</span> <span class="n">F</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">)</span>
-<span class="n">Fraction</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
-<span class="mi">1</span> <span class="o">/</span> <span class="n">F</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">)</span>
-<span class="n">Fraction</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
-<span class="n">F</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span><span class="mi">5</span><span class="p">)</span> <span class="o">&lt;</span> <span class="mi">0</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-</section>
-<section id="math">
-<h2>math（数学函数）<a class="headerlink" href="#math" title="Permalink to this heading">¶</a></h2>
-<p>math 模块用于处理数学相关的运算，例如：三角学、对数等。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">math</span>
-<span class="n">math</span><span class="o">.</span><span class="n">pi</span>  <span class="c1"># 圆周率 π</span>
-<span class="n">math</span><span class="o">.</span><span class="n">e</span>  <span class="c1"># 自然常数</span>
-<span class="n">math</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span>  <span class="c1"># 余弦</span>
-<span class="n">math</span><span class="o">.</span><span class="n">log10</span><span class="p">(</span><span class="mi">100</span><span class="p">)</span>  <span class="c1"># 以 10 为基数的 100 的对数</span>
-<span class="n">math</span><span class="o">.</span><span class="n">pow</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>  <span class="c1"># 2**5（2 的 5 次方） 运算后的值</span>
-<span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">9</span><span class="p">)</span>  <span class="c1"># 9 的平方根</span>
-<span class="n">math</span><span class="o">.</span><span class="n">factorial</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>  <span class="c1"># 5!（5 的阶乘）</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-</section>
-<section id="random">
-<h2>random（生成伪随机数）<a class="headerlink" href="#random" title="Permalink to this heading">¶</a></h2>
-<p>random 模块用于生成伪随机数。随机数可用于数学、游戏、安全等领域，还经常被嵌入到算法中，用以提高算法效率，并提高程序的安全性。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">random</span>
-<span class="n">l</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;p&#39;</span><span class="p">,</span> <span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="s1">&#39;t&#39;</span><span class="p">,</span> <span class="s1">&#39;h&#39;</span><span class="p">,</span> <span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="s1">&#39;n&#39;</span><span class="p">]</span>
-<span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="n">l</span><span class="p">)</span>  <span class="c1"># 从序列 l 中随机挑选一个元素</span>
-<span class="n">random</span><span class="o">.</span><span class="n">shuffle</span><span class="p">(</span><span class="n">l</span><span class="p">)</span>  <span class="c1"># 将序列 l 的所有元素随机排序</span>
-<span class="n">random</span><span class="o">.</span><span class="n">randrange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>  <span class="c1"># 从指定范围内，按指定基数（缺省值为 1）递增的集合中获取一个随机数</span>
-<span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">()</span>  <span class="c1"># 随机生成一个实数，范围 [0,1)。</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-</section>
-<section id="id6">
-<h2>作者 &amp; 更新时间<a class="headerlink" href="#id6" title="Permalink to this heading">¶</a></h2>
-<p>作者: <a class="reference external" href="http://blog.csdn.net/liang19890820">一去丶二三里</a></p>
-<p>提交: 2017/12/6</p>
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diff --git a/docs/python-base/cn/python-datatype-tuple.html b/docs/python-base/cn/python-datatype-tuple.html
deleted file mode 100644
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-<section id="id1">
-<h1>简述<a class="headerlink" href="#id1" title="Permalink to this heading">¶</a></h1>
-<p>在 Python 中，元组（Tuple）与列表类似。两者之间的区别在于：元组不能更改，列表可以。也就是说，一旦被分配，就不能从元组中添加、更改或删除元素。</p>
-<section id="id2">
-<h2>元组的优点<a class="headerlink" href="#id2" title="Permalink to this heading">¶</a></h2>
-<p>既然和列表类似，那元组的作用到底是什么呢？用列表代替不就可以了？</p>
-<p>与列表相比，元组有很多优点：</p>
-<ul class="simple">
-<li><p>通常将元组用于不同的数据类型，将列表用于相同（或相似）的数据类型。</p></li>
-<li><p>由于元组不可变，所以遍历元组比列表要快（较小的性能提升）。</p></li>
-<li><p>元组可以用作字典的 key，而列表不行。因为字典的 key 必须是不可变的，元组本身就是不可变的。</p></li>
-<li><p>如果数据不需要更改，将其作为元组来实现，可以确保“写保护”。</p></li>
-<li><p>元组可以被用在字符串格式化中。</p></li>
-</ul>
-</section>
-<section id="id3">
-<h2>元组<a class="headerlink" href="#id3" title="Permalink to this heading">¶</a></h2>
-<p>在 Python 中，元组由内置的 tuple 类型定义。</p>
-<p>要创建元组，需要将所有项（元素）放在括号（()）内，以逗号（,）分隔。</p>
-<p><strong>注意</strong>： 括号是可选的，但还是建议写上。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>tup = (&#39;P&#39;, &#39;y&#39;, &#39;t&#39;, &#39;h&#39;, &#39;o&#39;, &#39;n&#39;)
-type(tup)
-&lt;class &#39;tuple&#39;&gt;
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>元组中的元素可以是任意数量，还可以是不同类型（例如：数字、字符串、列表等）。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span> <span class="n">tup</span> <span class="o">=</span> <span class="p">()</span>  <span class="c1"># 空元组</span>
-<span class="n">tup</span> <span class="o">=</span> <span class="p">(</span><span class="s1">&#39;P&#39;</span><span class="p">,</span> <span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="s1">&#39;t&#39;</span><span class="p">,</span> <span class="s1">&#39;h&#39;</span><span class="p">,</span> <span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="s1">&#39;n&#39;</span><span class="p">)</span>   <span class="c1"># 字符串类型元组</span>
-<span class="n">tup</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mf">5.5</span><span class="p">,</span> <span class="s1">&#39;Python&#39;</span><span class="p">)</span>   <span class="c1"># 混合类型元组</span>
-<span class="n">tup</span> <span class="o">=</span> <span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="s1">&#39;Python&#39;</span><span class="p">,</span> <span class="p">(</span><span class="s1">&#39;P&#39;</span><span class="p">,</span> <span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="s1">&#39;t&#39;</span><span class="p">,</span> <span class="s1">&#39;h&#39;</span><span class="p">,</span> <span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="s1">&#39;n&#39;</span><span class="p">))</span>  <span class="c1"># 嵌套元组</span>
-<span class="n">tup</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span> <span class="mf">5.5</span><span class="p">,</span> <span class="s1">&#39;Python&#39;</span>   <span class="c1"># 创建元组时可以没有括号，也称为元组包装</span>
-<span class="n">tup</span>
-<span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mf">5.5</span><span class="p">,</span> <span class="s1">&#39;Python&#39;</span><span class="p">)</span>
-<span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span> <span class="o">=</span> <span class="n">tup</span>  <span class="c1"># 元组解包</span>
-<span class="n">a</span>
-<span class="mi">1</span>
-<span class="n">b</span>
-<span class="mf">5.5</span>
-<span class="n">c</span>
-<span class="s1">&#39;Python&#39;</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>构造包含一个元素的元组比较特殊，括号内只包含一个元素是不够的，还需要在元素后添加一个逗号。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>tup = (&#39;Python&#39;)  # 只有括号
-type(tup)
-&lt;class &#39;str&#39;&gt;
-tup = (&#39;Python&#39;,)  # 在元素后面添加逗号
-type(tup)
-&lt;class &#39;tuple&#39;&gt;
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>可以看出，没有逗号就不是元组，加了逗号才是。</p>
-</section>
-<section id="id4">
-<h2>索引和切片<a class="headerlink" href="#id4" title="Permalink to this heading">¶</a></h2>
-<p>有了前面关于字符串和列表的基础知识，元组很容易掌握。因为它们都属于序列，因此，基本操作是一样的。</p>
-<p>可以使用索引操作符（[]） 来访问元组中的一个元素，使用切片操作符（:）来访问元组中的一系列元素。</p>
-<img alt="http://img.blog.csdn.net/20170517202258988?watermark/2/text/aHR0cDovL2Jsb2cuY3Nkbi5uZXQvbGlhbmcxOTg5MDgyMA==/font/5a6L5L2T/fontsize/400/fill/I0JBQkFCMA==/dissolve/70/gravity/SouthEast" src="http://img.blog.csdn.net/20170517202258988?watermark/2/text/aHR0cDovL2Jsb2cuY3Nkbi5uZXQvbGlhbmcxOTg5MDgyMA==/font/5a6L5L2T/fontsize/400/fill/I0JBQkFCMA==/dissolve/70/gravity/SouthEast" />
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span> <span class="n">tup</span> <span class="o">=</span> <span class="p">(</span><span class="s1">&#39;P&#39;</span><span class="p">,</span> <span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="s1">&#39;t&#39;</span><span class="p">,</span> <span class="s1">&#39;h&#39;</span><span class="p">,</span> <span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="s1">&#39;n&#39;</span><span class="p">)</span>
-<span class="n">tup</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>  <span class="c1"># 第 1 个元素</span>
-<span class="s1">&#39;P&#39;</span>
-<span class="n">tup</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">]</span>  <span class="c1"># 第 5 个元素（反向索引）</span>
-<span class="s1">&#39;o&#39;</span>
-<span class="n">tup</span> <span class="o">=</span> <span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="s1">&#39;Python&#39;</span><span class="p">,</span> <span class="p">(</span><span class="s1">&#39;P&#39;</span><span class="p">,</span> <span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="s1">&#39;t&#39;</span><span class="p">,</span> <span class="s1">&#39;h&#39;</span><span class="p">,</span> <span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="s1">&#39;n&#39;</span><span class="p">))</span>  <span class="c1"># 嵌套元组</span>
-<span class="n">tup</span><span class="p">[</span><span class="mi">2</span><span class="p">][</span><span class="mi">3</span><span class="p">]</span>  <span class="c1"># 嵌套索引</span>
-<span class="s1">&#39;h&#39;</span>
-<span class="n">tup</span> <span class="o">=</span> <span class="p">(</span><span class="s1">&#39;P&#39;</span><span class="p">,</span> <span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="s1">&#39;t&#39;</span><span class="p">,</span> <span class="s1">&#39;h&#39;</span><span class="p">,</span> <span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="s1">&#39;n&#39;</span><span class="p">)</span>
-<span class="n">tup</span><span class="p">[</span><span class="mi">1</span><span class="p">:</span><span class="mi">4</span><span class="p">]</span>  <span class="c1">#  第 2 个到第 4 个元素</span>
-<span class="p">(</span><span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="s1">&#39;t&#39;</span><span class="p">,</span> <span class="s1">&#39;h&#39;</span><span class="p">)</span>
-<span class="n">tup</span><span class="p">[:</span><span class="o">-</span><span class="mi">3</span><span class="p">]</span>  <span class="c1"># 开始到第 3 个元素</span>
-<span class="p">(</span><span class="s1">&#39;P&#39;</span><span class="p">,</span> <span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="s1">&#39;t&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>其他的一些基本操作，例如：连接（+）、重复（*）、成员测试（in）、遍历（for）等，就不再一一展示了，请参考：Python 列表</p>
-<p>更改元组</p>
-<p>元组是不可变的，也就是说，元组中的元素在被赋值后不能改变。</p>
-<p>但是，如果元素本身是一个可变数据类型的列表，那么其嵌套项可以被改变。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span> <span class="n">tup</span> <span class="o">=</span> <span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="s1">&#39;Python&#39;</span><span class="p">,</span> <span class="p">[</span><span class="s1">&#39;P&#39;</span><span class="p">,</span> <span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="s1">&#39;t&#39;</span><span class="p">,</span> <span class="s1">&#39;h&#39;</span><span class="p">,</span> <span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="s1">&#39;n&#39;</span><span class="p">])</span>
-<span class="n">tup</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mi">8</span>  <span class="c1"># 不能改变元素</span>
-<span class="n">tup</span><span class="p">[</span><span class="mi">2</span><span class="p">][</span><span class="mi">3</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;s&#39;</span>  <span class="c1"># 可变的元素可以被改变</span>
-<span class="n">tup</span>
-<span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="s1">&#39;Python&#39;</span><span class="p">,</span> <span class="p">[</span><span class="s1">&#39;P&#39;</span><span class="p">,</span> <span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="s1">&#39;t&#39;</span><span class="p">,</span> <span class="s1">&#39;s&#39;</span><span class="p">,</span> <span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="s1">&#39;n&#39;</span><span class="p">])</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-</section>
-<section id="id5">
-<h2>删除元组<a class="headerlink" href="#id5" title="Permalink to this heading">¶</a></h2>
-<p>如上所述，不能更改元组中的元素，这也意味着无法删除元组中的元素。</p>
-<p>但是，使用关键字 del 可以删除整个元组。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">tup</span> <span class="o">=</span> <span class="p">(</span><span class="s1">&#39;P&#39;</span><span class="p">,</span> <span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="s1">&#39;t&#39;</span><span class="p">,</span> <span class="s1">&#39;h&#39;</span><span class="p">,</span> <span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="s1">&#39;n&#39;</span><span class="p">)</span>
-<span class="k">del</span> <span class="n">tup</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>  <span class="c1"># 无法删除元素</span>
-<span class="k">del</span> <span class="n">tup</span>  <span class="c1"># 可以删除整个元组</span>
-<span class="n">tup</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-</section>
-<section id="id6">
-<h2>列表和元组互转<a class="headerlink" href="#id6" title="Permalink to this heading">¶</a></h2>
-<p>列表和元组之间可以进行相互转换，分别使用 list() 和 tuple() 实现：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span> <span class="n">tup</span> <span class="o">=</span> <span class="p">(</span><span class="s1">&#39;P&#39;</span><span class="p">,</span> <span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="s1">&#39;t&#39;</span><span class="p">,</span> <span class="s1">&#39;h&#39;</span><span class="p">,</span> <span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="s1">&#39;n&#39;</span><span class="p">)</span>
-<span class="n">listx</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">tup</span><span class="p">)</span>
-<span class="n">listx</span>
-<span class="p">[</span><span class="s1">&#39;P&#39;</span><span class="p">,</span> <span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="s1">&#39;t&#39;</span><span class="p">,</span> <span class="s1">&#39;h&#39;</span><span class="p">,</span> <span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="s1">&#39;n&#39;</span><span class="p">]</span>
-<span class="n">l</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;H&#39;</span><span class="p">,</span> <span class="s1">&#39;e&#39;</span><span class="p">,</span> <span class="s1">&#39;l&#39;</span><span class="p">,</span> <span class="s1">&#39;l&#39;</span><span class="p">,</span> <span class="s1">&#39;o&#39;</span><span class="p">]</span>
-<span class="n">tupx</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">l</span><span class="p">)</span>
-<span class="n">tupx</span>
-<span class="p">(</span><span class="s1">&#39;H&#39;</span><span class="p">,</span> <span class="s1">&#39;e&#39;</span><span class="p">,</span> <span class="s1">&#39;l&#39;</span><span class="p">,</span> <span class="s1">&#39;l&#39;</span><span class="p">,</span> <span class="s1">&#39;o&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>既然可以互转，那么要改变元组，可以先将其转化为列表，对列表进行更改，然后再将列表转换为元组。</p>
-<p>例如，删除元组中的元素：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span> <span class="n">tup</span> <span class="o">=</span> <span class="p">(</span><span class="s1">&#39;P&#39;</span><span class="p">,</span> <span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="s1">&#39;t&#39;</span><span class="p">,</span> <span class="s1">&#39;h&#39;</span><span class="p">,</span> <span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="s1">&#39;n&#39;</span><span class="p">)</span>
-<span class="n">listx</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">tup</span><span class="p">)</span>  <span class="c1"># 将元组转换为列表</span>
-<span class="n">listx</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="s1">&#39;h&#39;</span><span class="p">)</span>  <span class="c1"># 删除列表中的元素</span>
-<span class="n">tup</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">listx</span><span class="p">)</span>  <span class="c1"># 再将列表转换为元组</span>
-<span class="n">tup</span>
-<span class="p">(</span><span class="s1">&#39;P&#39;</span><span class="p">,</span> <span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="s1">&#39;t&#39;</span><span class="p">,</span> <span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="s1">&#39;n&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p><strong>注意</strong>： 元组本身是不可变的。这里说的并不是传统意义的改变，相当于元组的重新赋值。</p>
-</section>
-<section id="id7">
-<h2>元组的方法<a class="headerlink" href="#id7" title="Permalink to this heading">¶</a></h2>
-<p>元组中的方法相对较少，可以通过 dir() 来查看方法列表：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">dir</span><span class="p">(</span><span class="nb">tuple</span><span class="p">)</span>
-<span class="p">[</span><span class="s1">&#39;__add__&#39;</span><span class="p">,</span> <span class="s1">&#39;__class__&#39;</span><span class="p">,</span> <span class="s1">&#39;__contains__&#39;</span><span class="p">,</span> <span class="s1">&#39;__delattr__&#39;</span><span class="p">,</span> <span class="s1">&#39;__dir__&#39;</span><span class="p">,</span> <span class="s1">&#39;__doc__&#39;</span><span class="p">,</span> <span class="s1">&#39;__eq__&#39;</span><span class="p">,</span> <span class="s1">&#39;__format__&#39;</span><span class="p">,</span> <span class="s1">&#39;__ge__&#39;</span><span class="p">,</span> <span class="s1">&#39;__getattribute__&#39;</span><span class="p">,</span> <span class="s1">&#39;__getitem__&#39;</span><span class="p">,</span>      <span class="s1">&#39;__getnewargs__&#39;</span><span class="p">,</span> <span class="s1">&#39;__gt__&#39;</span><span class="p">,</span> <span class="s1">&#39;__hash__&#39;</span><span class="p">,</span> <span class="s1">&#39;__init__&#39;</span><span class="p">,</span> <span class="s1">&#39;__iter__&#39;</span><span class="p">,</span> <span class="s1">&#39;__le__&#39;</span><span class="p">,</span> <span class="s1">&#39;__len__&#39;</span><span class="p">,</span> <span class="s1">&#39;__lt__&#39;</span><span class="p">,</span> <span class="s1">&#39;__mul__&#39;</span><span class="p">,</span> <span class="s1">&#39;__ne__&#39;</span><span class="p">,</span> <span class="s1">&#39;__new__&#39;</span><span class="p">,</span> <span class="s1">&#39;__reduce__&#39;</span><span class="p">,</span> <span class="s1">&#39;__reduce_ex__&#39;</span><span class="p">,</span>      <span class="s1">&#39;__repr__&#39;</span><span class="p">,</span> <span class="s1">&#39;__rmul__&#39;</span><span class="p">,</span> <span class="s1">&#39;__setattr__&#39;</span><span class="p">,</span> <span class="s1">&#39;__sizeof__&#39;</span><span class="p">,</span> <span class="s1">&#39;__str__&#39;</span><span class="p">,</span> <span class="s1">&#39;__subclasshook__&#39;</span><span class="p">,</span> <span class="s1">&#39;count&#39;</span><span class="p">,</span> <span class="s1">&#39;index&#39;</span><span class="p">]</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>可以看到，有两个可用的方法：：</p>
-<table class="docutils align-default">
-<thead>
-<tr class="row-odd"><th class="head"><p>方法</p></th>
-<th class="head"><p>描述</p></th>
-</tr>
-</thead>
-<tbody>
-<tr class="row-even"><td><p>index()</p></td>
-<td><p>返回第一个匹配项的索引</p></td>
-</tr>
-<tr class="row-odd"><td><p>count()</p></td>
-<td><p>统计某个元素在列表中出现的次数</p></td>
-</tr>
-</tbody>
-</table>
-<p>使用很简单，也非常好记。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">tup</span> <span class="o">=</span> <span class="p">(</span><span class="s1">&#39;P&#39;</span><span class="p">,</span> <span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="s1">&#39;t&#39;</span><span class="p">,</span> <span class="s1">&#39;h&#39;</span><span class="p">,</span> <span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="s1">&#39;n&#39;</span><span class="p">)</span>
-<span class="n">tup</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="s1">&#39;h&#39;</span><span class="p">)</span>  <span class="c1"># 返回第一个匹配 &#39;h&#39; 的索引</span>
-<span class="n">tup</span><span class="o">.</span><span class="n">count</span><span class="p">(</span><span class="s1">&#39;o&#39;</span><span class="p">)</span>  <span class="c1"># 统计 &#39;o&#39; 在元组中出现的次数</span>
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p><strong>注意</strong>： 元组不可变，所以添加或删除元素的方法不适用于元组。</p>
-</section>
-<section id="id8">
-<h2>元组与内置函数<a class="headerlink" href="#id8" title="Permalink to this heading">¶</a></h2>
-<p>下述内置函数通常与元组一起使用，来执行不同的任务。</p>
-</section>
-<section id="id9">
-<h2>作者 &amp; 更新时间<a class="headerlink" href="#id9" title="Permalink to this heading">¶</a></h2>
-<p>作者: <a class="reference external" href="http://blog.csdn.net/liang19890820">CSDN</a></p>
-<p>提交: 2017/12/6</p>
-</section>
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diff --git a/docs/qpy101/index.html b/docs/qpy101/index.html
deleted file mode 100644
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-<section id="qpython-101">
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-<blockquote>
-<div><p>QPython 系列入门教程</p>
-</div></blockquote>
-<p>用短视频形式, 引导学习者快速进入编程状态, 可以利用 QPython 移动编程环境进行各种实用软件的开发和探索.</p>
-<section id="id1">
-<h2>内容规划<a class="headerlink" href="#id1" title="Permalink to this heading">¶</a></h2>
-<dl class="field-list simple">
-<dt class="field-odd">发布状态<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p>&lt;+ 完成并发布</p></li>
-<li><p>&lt;- 完成未发布</p></li>
-<li><p>&lt;| 筹备完成中</p></li>
-<li><p>&lt;? 规划未决定</p></li>
-</ul>
-</dd>
-</dl>
-<table class="docutils align-default">
-<thead>
-<tr class="row-odd"><th class="head"><p>状态</p></th>
-<th class="head"><p>编号</p></th>
-<th class="head"><p>幻灯</p></th>
-<th class="head"><p>视频</p></th>
-</tr>
-</thead>
-<tbody>
-<tr class="row-even"><td><p>&lt;+</p></td>
-<td><p>起源综述</p></td>
-<td><p>和River对谈</p></td>
-<td><p>视频:<a class="reference internal" href="https://ipic.zoomquiet.top/2022-07-02-QPyC100.jpg"><img alt="QR2QPyC100" src="https://ipic.zoomquiet.top/2022-07-02-QPyC100.jpg" style="width: 200px;" /></a></p></td>
-</tr>
-<tr class="row-odd"><td><p>&lt;+</p></td>
-<td><p>101:语法/关键字</p></td>
-<td><p>幻灯:<a class="reference external" href="https://slides.101.camp/QPyCamp101">QPyC101</a></p></td>
-<td><p>视频:<a class="reference internal" href="https://ipic.zoomquiet.top/2022-07-02-QPyC101.jpg"><img alt="QR2QPyC101" src="https://ipic.zoomquiet.top/2022-07-02-QPyC101.jpg" style="width: 200px;" /></a></p></td>
-</tr>
-<tr class="row-even"><td><p>&lt;+</p></td>
-<td><p>102:语法/数据类型,运算符</p></td>
-<td><p>幻灯:<a class="reference external" href="https://slides.101.camp/QPyCamp102">QPyC102</a></p></td>
-<td><p>视频:<a class="reference internal" href="https://ipic.zoomquiet.top/2022-07-02-QPyC102.jpg"><img alt="QR2QPyC102" src="https://ipic.zoomquiet.top/2022-07-02-QPyC102.jpg" style="width: 200px;" /></a></p></td>
-</tr>
-<tr class="row-odd"><td><p>&lt;+</p></td>
-<td><p>103:语法/变量,集合</p></td>
-<td><p>幻灯:<a class="reference external" href="https://slides.101.camp/QPyCamp103">QPyC103</a></p></td>
-<td><p>视频:<a class="reference internal" href="https://ipic.zoomquiet.top/2022-07-02-QPyC103.jpg"><img alt="QR2QPyC103" src="https://ipic.zoomquiet.top/2022-07-02-QPyC103.jpg" style="width: 200px;" /></a></p></td>
-</tr>
-<tr class="row-even"><td><p>&lt;-</p></td>
-<td><p>104:语法/流程控制,if..else</p></td>
-<td><p>幻灯:<a class="reference external" href="https://slides.101.camp/QPyCamp104">QPyC104</a></p></td>
-<td><p>…</p></td>
-</tr>
-<tr class="row-odd"><td><p>&lt;-</p></td>
-<td><p>105:语法/流程控制,for..in</p></td>
-<td><p>幻灯:<a class="reference external" href="https://slides.101.camp/QPyCamp105">QPyC105</a></p></td>
-<td><p>…</p></td>
-</tr>
-<tr class="row-even"><td><p>&lt;-</p></td>
-<td><p>106:语法/流程控制,while</p></td>
-<td><p>幻灯:<a class="reference external" href="https://slides.101.camp/QPyCamp106">QPyC106</a></p></td>
-<td><p>…</p></td>
-</tr>
-<tr class="row-odd"><td><p>&lt;-</p></td>
-<td><p>107:语法/流程控制,try:except</p></td>
-<td><p>幻灯:<a class="reference external" href="https://slides.101.camp/QPyCamp107">QPyC107</a></p></td>
-<td><p>…</p></td>
-</tr>
-<tr class="row-even"><td><p>&lt;-</p></td>
-<td><p>108:语法/函式,创建</p></td>
-<td><p>幻灯:<a class="reference external" href="https://slides.101.camp/QPyCamp108">QPyC108</a></p></td>
-<td><p>…</p></td>
-</tr>
-<tr class="row-odd"><td><p>&lt;|</p></td>
-<td><p>109:语法/函式,可变参数</p></td>
-<td><p>TBD</p></td>
-<td><p>…</p></td>
-</tr>
-<tr class="row-even"><td><p>&lt;?</p></td>
-<td><p>110:语法/函式,作用域</p></td>
-<td><p>TBD</p></td>
-<td><p>…</p></td>
-</tr>
-<tr class="row-odd"><td><p>&lt;?</p></td>
-<td><p>111:语法/函式,匿名函式</p></td>
-<td><p>TBD</p></td>
-<td><p>…</p></td>
-</tr>
-<tr class="row-even"><td><p>&lt;?</p></td>
-<td><p>112:语法/函式,高阶函式</p></td>
-<td><p>TBD</p></td>
-<td><p>…</p></td>
-</tr>
-<tr class="row-odd"><td><p>&lt;|</p></td>
-<td><p>113:游戏/猜数字,伪代码</p></td>
-<td><p>TBD</p></td>
-<td><p>…</p></td>
-</tr>
-<tr class="row-even"><td><p>&lt;|</p></td>
-<td><p>114:游戏/猜数字,接受输入</p></td>
-<td><p>TBD</p></td>
-<td><p>…</p></td>
-</tr>
-<tr class="row-odd"><td><p>&lt;|</p></td>
-<td><p>115:游戏/猜数字,随机数字</p></td>
-<td><p>TBD</p></td>
-<td><p>…</p></td>
-</tr>
-<tr class="row-even"><td><p>&lt;|</p></td>
-<td><p>116:游戏/猜数字,整体可用</p></td>
-<td><p>TBD</p></td>
-<td><p>…</p></td>
-</tr>
-</tbody>
-</table>
-</section>
-<section id="ps">
-<h2>PS:<a class="headerlink" href="#ps" title="Permalink to this heading">¶</a></h2>
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-<p><a class="reference internal" href="https://ipic.zoomquiet.top/2022-07-02-QPyIOQA.jpeg"><img alt="QPyIOQA" src="https://ipic.zoomquiet.top/2022-07-02-QPyIOQA.jpeg" style="width: 360px;" /></a></p>
-<p>已经开通客服, 可以随时通过微信和我们交流.</p>
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diff --git a/docs/qpython-quick-start/cn/community-howto.html b/docs/qpython-quick-start/cn/community-howto.html
deleted file mode 100644
index b8a4b5c..0000000
--- a/docs/qpython-quick-start/cn/community-howto.html
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@@ -1,319 +0,0 @@
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-<link href="https://cdn.bootcss.com/bootstrap/3.3.7/css/bootstrap.min.css" rel="stylesheet">
-<title>社区指南</title>
-<link rel="stylesheet" type="text/css" href="../../static/pygments.css" />
-<link rel="stylesheet" type="text/css" href="../../static/style.css" />
-<meta charset="UTF-8">
-<meta name="viewport" content="width=device-width, initial-scale=1,maximum-scale=1, user-scalable=no">
-<script src="https://cdn.bootcss.com/bootstrap/3.3.7/js/bootstrap.min.js"></script>
-</head><body>
-<div class="header doc-header web-show">
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-<div class="navbar-header">
-<button type="button" class="navbar-toggle" data-toggle="collapse" data-target="#example-navbar-collapse">
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-<span class="icon-bar"></span>
-<span class="icon-bar"></span>
-</button>
-<a href="/index.html" class="logo"><img src="http://www.qpython.org/static/img_logo.png"></a>
-</div>
-<ul class="header-title collapse navbar-collapse" id="example-navbar-collapse" >
-<li>
-<a href="http://www.qpython.org/index.html">Home</a>
-</li>
-<li>
-<a href="http://www.qpython.org/document.html">Guide</a>
-</li>
-<li class="header-phone-selected">
-<a href="/index.html" class="header-selected">Course</a>
-<span class="header-title-selected"></span>
-</li>
-<li>
-<a href="http://www.aipy.org">AIPY</a>
-</li>
-<li>
-<a target="_blank" href="https://github.com/qpython-android/qpython">Github</a>
-</li>
-</ul>
-<div class="search-box hidden-xs">
-<form action="/search.html">
-<input type="search" name="q" placeholder="keyword">
-<input type="hidden" name="check_keywords" value="yes" />
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-</div>
-<main class="contain web-content">
-<div class="document">
-<div class="documentwrapper">
-<div class="bodywrapper">
-<div class="body" role="main">
-<section id="id1">
-<h1>社区指南<a class="headerlink" href="#id1" title="Permalink to this heading">¶</a></h1>
-<p>很多QPython用户喜欢在QPython各类社区里交流分享编程经验，你将有机会在这里遇见像你一样聪明的人，互相学习，获得成长。</p>
-<section id="qpython">
-<h2>获得QPython的新消息和提示<a class="headerlink" href="#qpython" title="Permalink to this heading">¶</a></h2>
-<p>我们衷心希望你可以在微信和微博上关注我们，关注后可以及时获取QPython的新闻。
-QPython的微信会及时发布我们的最新消息。
-QPython的微博会分享一些QPython的小贴士，回答你的问题等。</p>
-<ul class="simple">
-<li><p>在微信搜索并关注我们: qpython</p></li>
-<li><p><a href="#id11"><span class="problematic" id="id12">`在微博关注我们&lt;http://weibo.com/qpython&gt;`_</span></a></p></li>
-</ul>
-</section>
-<section id="id2">
-<h2>和其他开发者一起聊聊QPython<a class="headerlink" href="#id2" title="Permalink to this heading">¶</a></h2>
-<p>如果你想和其他人一起学习Python编程，可以考虑加入以下学习小组</p>
-<ul class="simple">
-<li><p><a href="#id13"><span class="problematic" id="id14">`QPython 百度贴吧&lt;https://tieba.baidu.com/f?kw=qpython&gt;`_</span></a></p></li>
-<li><p>还可以在QQ中搜索QPython编程交流QQ群，群号：540717901</p></li>
-<li><p>QPython还有几个微信交流群，请添加优趣小言微信quseitlab,留言qpython,它会把你加到QPython社区中</p></li>
-</ul>
-</section>
-<section id="id3">
-<h2>第三社区<a class="headerlink" href="#id3" title="Permalink to this heading">¶</a></h2>
-<p>这里有一些讨论QPython的帖子，像StackOverfolow或Reddit</p>
-<ul class="simple">
-<li><p><a class="reference external" href="http://stackoverflow.com/questions/tagged/qpython">Stackoverflow上的QPython</a></p></li>
-<li><p><a class="reference external" href="https://www.reddit.com/search?q=qpython">Reddit上的QPython</a></p></li>
-</ul>
-</section>
-<section id="id4">
-<h2>贡献<a class="headerlink" href="#id4" title="Permalink to this heading">¶</a></h2>
-<p>如果你想快人一步获得新版本，并帮助测试，请加入QPython测试社区:</p>
-<ul class="simple">
-<li><p><a class="reference external" href="https://plus.google.com/communities/111759148772865961493">加入QPython测试员社区</a></p></li>
-</ul>
-<p>在QPython里课程是非常重要的一个部分。如果您想为课程社区做出一些贡献，例如发布您自己的课程或帮助将当前课程翻译成其他语言，欢迎加入课程社区：</p>
-<ul class="simple">
-<li><p><a class="reference external" href="https://plus.google.com/u/1/communities/111340957575273631204">加入QPython课程贡献社区</a></p></li>
-</ul>
-<p>如果您在使用QPython时遇到任何问题，请随时与我们联系，我们将尽快解决</p>
-<ul class="simple">
-<li><p><a class="reference external" href="https://github.com/qpython-android/qpython/issues">报告一个问题</a></p></li>
-<li><p><a class="reference external" href="https://github.com/qpython-android/qpython3/issues">报告QPython3的问题</a></p></li>
-<li><p><a class="reference external" href="https://github.com/qpython-android/QPYPI/issues">希望能支持一个库</a></p></li>
-<li><p><a class="reference external" href="mailto:support&#37;&#52;&#48;qpython&#46;org">直接给我们写信</a></p></li>
-</ul>
-</section>
-<section id="id10">
-<h2>作者 &amp; 更新时间<a class="headerlink" href="#id10" title="Permalink to this heading">¶</a></h2>
-<p>作者: <a class="reference external" href="https://github.com/riverfor">River</a></p>
-<p>提交: 2017/8/6</p>
-</section>
-</section>
-<div class="clearer"></div>
-</div>
-</div>
-</div>
-<div class="clearer"></div>
-</div>
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-<p>在手机上高效输入是一个比较有挑战的事情，为了加快输入效率，QPython团队提供了各种辅助工具。</p>
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-<p>提交: 2017/8/6</p>
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diff --git a/docs/qpython-quick-start/cn/index.html b/docs/qpython-quick-start/cn/index.html
deleted file mode 100644
index 6716509..0000000
--- a/docs/qpython-quick-start/cn/index.html
+++ /dev/null
@@ -1,319 +0,0 @@
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-<section id="qpython">
-<h1>QPython是什么<a class="headerlink" href="#qpython" title="Permalink to this heading">¶</a></h1>
-<section id="id1">
-<h2>关于<a class="headerlink" href="#id1" title="Permalink to this heading">¶</a></h2>
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-<p>针对iOS用户的QPython (for iOS)应用也已在appstore上线。</p>
-</section>
-<section id="id2">
-<h2>QPython历史<a class="headerlink" href="#id2" title="Permalink to this heading">¶</a></h2>
-<p>QPython诞生于2012年, 最初是其作者River为了实现能在安卓程序中实现热更新的一个Python插件SDK，后来作为安卓上的Python App独立发布，一经发布后便引起了广大Python开发者的喜爱，在很短的时间内，在没有任何推广的前提下，有数万的Python用户安装和评论，在15年，其安装使用用户量超过了2,000,000+，它也是目前安卓上最受欢迎的Python集成开发环境。</p>
-</section>
-<section id="id3">
-<h2>支持的程序类型<a class="headerlink" href="#id3" title="Permalink to this heading">¶</a></h2>
-<p>目前QPython能够运行控制台程序、WebApp程序，后台应用程序，基于图形界面的应用程序等，各个类型的程序适合不同的应用场景，QPython通过特定的头部声明来确定如何运行应用程序，如”#qpy:webapp”表明该程序将会按照WebApp方式发布，”#qpy:kivy”表明该程序是一个kivy程序，”#qpy:qpyapp”声明该程序将会是QPYApp（适合于后台应用场景），而不带头部声明则默认会按照控制台的形式运行。</p>
-<p><em>WebApp头部声明</em></p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1">#qpy:webapp</span>
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-<p><em>QPYApp头部声明</em></p>
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-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1">#qpy:kivy</span>
-</pre></div>
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-<p><em>控制台程序头部声明</em></p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1">#qpy:console</span>
-</pre></div>
-</div>
-</section>
-<section id="id4">
-<h2>如何安装第三方库<a class="headerlink" href="#id4" title="Permalink to this heading">¶</a></h2>
-<p>QPython内置了PC版Python的绝大部分库。由于安卓里本身不支持编译工具链，因此你无法像PC上的Python一样能够安装所有的库，但是你可以通过QPYPI安装一些预编译的库，目前已经支持包括kivy, numpy, opencv, pycrypto, tornado 在内的库。此外你还可以用pip控制台从官方或者你指定的第三方QPYPI来安装大多数Python实现的库。</p>
-</section>
-<section id="id5">
-<h2>QPython媒体、社区<a class="headerlink" href="#id5" title="Permalink to this heading">¶</a></h2>
-<p>QPython是一个更新迅速，注重倾听用户反馈的项目，包括facebook fan page, twitter, weibo, facebook群组，G+, Wechat/QQ group 等在内的媒体、社群是QPython的重要组成部分。我们鼓励你广泛参加到我们的社群讨论、活动以及QPython的建设当中，这样才能了解和掌握QPython最新的发展动态。</p>
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-<section id="id1">
-<h1>随时随地学习编程<a class="headerlink" href="#id1" title="Permalink to this heading">¶</a></h1>
-<p>QPython专注为Python用户提供方便易用的移动Python IDE工具和编程学习服务。</p>
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-<section id="ide">
-<h2>移动IDE和的学习社区<a class="headerlink" href="#ide" title="Permalink to this heading">¶</a></h2>
-<p>QPython的控制台，编辑器，开发快捷工具集（QRCode代码读取器、本地文件管理器）、QPYPI、课程系统、代码分享社区，QPY.IO在线开发服务，为Python用户提供便捷的掌上编程学习环境和交流社区。</p>
-</section>
-<section id="id2">
-<h2>强大、开源<a class="headerlink" href="#id2" title="Permalink to this heading">¶</a></h2>
-<p>除了可以在安卓上学习Python编程外，QPython还允许你编程控制你的安卓设备。
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-<p>作者: <a class="reference external" href="https://github.com/riverfor">River</a></p>
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-</section>
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-<p>注意：一些与c / c++文件混合的库不能通过pip控制台安装，这是由于安卓本身缺少开发编译环境支持，您可以向QPython团队寻求帮助。</p>
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diff --git a/docs/qpython-quick-start/community-howto.html b/docs/qpython-quick-start/community-howto.html
deleted file mode 100644
index fd6f3c1..0000000
--- a/docs/qpython-quick-start/community-howto.html
+++ /dev/null
@@ -1,319 +0,0 @@
-<!DOCTYPE html>
-<html lang="en-US">
-<head>
-<meta charset="utf-8" />
-<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
-<link href="https://cdn.bootcss.com/bootstrap/3.3.7/css/bootstrap.min.css" rel="stylesheet">
-<title>QPython Communities</title>
-<link rel="stylesheet" type="text/css" href="../static/pygments.css" />
-<link rel="stylesheet" type="text/css" href="../static/style.css" />
-<meta charset="UTF-8">
-<meta name="viewport" content="width=device-width, initial-scale=1,maximum-scale=1, user-scalable=no">
-<script src="https://cdn.bootcss.com/bootstrap/3.3.7/js/bootstrap.min.js"></script>
-</head><body>
-<div class="header doc-header web-show">
-<div class="col-sm-offset-2 col-sm-8  index-header">
-<div class="navbar-header">
-<button type="button" class="navbar-toggle" data-toggle="collapse" data-target="#example-navbar-collapse">
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-</button>
-<a href="/index.html" class="logo"><img src="http://www.qpython.org/static/img_logo.png"></a>
-</div>
-<ul class="header-title collapse navbar-collapse" id="example-navbar-collapse" >
-<li>
-<a href="http://www.qpython.org/index.html">Home</a>
-</li>
-<li>
-<a href="http://www.qpython.org/document.html">Guide</a>
-</li>
-<li class="header-phone-selected">
-<a href="/index.html" class="header-selected">Course</a>
-<span class="header-title-selected"></span>
-</li>
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-<a href="http://www.aipy.org">AIPY</a>
-</li>
-<li>
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-<button><img src="http://www.qpython.org/static/ic_search.png"></button>
-</form>
-</div>
-</div>
-</div>
-<main class="contain web-content">
-<div class="document">
-<div class="documentwrapper">
-<div class="bodywrapper">
-<div class="body" role="main">
-<section id="qpython-communities">
-<h1>QPython Communities<a class="headerlink" href="#qpython-communities" title="Permalink to this heading">¶</a></h1>
-<p>Many QPython users like to exchange and share programming experience in QPython community, you have a great chance to meet smart and pythonic guys like you there, learn from each other and gain experience and growth.</p>
-<section id="get-qpython-news-tips">
-<h2>Get QPython news &amp; tips<a class="headerlink" href="#get-qpython-news-tips" title="Permalink to this heading">¶</a></h2>
-<p>We recommend you follow us on facebook and twitter to get QPython’s news.
-QPython facebook page publishes official news and alerts.
-QPython twitter shares QPython tips and an active response to your question.</p>
-<ul class="simple">
-<li><p><a class="reference external" href="https://www.facebook.com/QPython">Like us on facebook</a></p></li>
-<li><p><a class="reference external" href="https://twitter.com/qpython">Follow us on twitter</a></p></li>
-</ul>
-</section>
-<section id="talk-about-qpython-with-other-guys">
-<h2>Talk about QPython with other guys<a class="headerlink" href="#talk-about-qpython-with-other-guys" title="Permalink to this heading">¶</a></h2>
-<p>If you want to learn Python programming with other guys, please, join our facebook group where many python users are there to help each other and the QPython google group maillist is a good way if you like communicate with email.</p>
-<ul class="simple">
-<li><p><a class="reference external" href="https://www.facebook.com/groups/qpython">Join Facebook community</a></p></li>
-<li><p><a class="reference external" href="https://groups.google.com/forum/#!forum/qpython">Join Google group</a></p></li>
-</ul>
-</section>
-<section id="third-community">
-<h2>Third community<a class="headerlink" href="#third-community" title="Permalink to this heading">¶</a></h2>
-<p>There are some communities which like to talk about QPython like, stackoverfolow and reddit</p>
-<ul class="simple">
-<li><p><a class="reference external" href="http://stackoverflow.com/questions/tagged/qpython">QPython on Stackoverflow</a></p></li>
-<li><p><a class="reference external" href="https://www.reddit.com/search?q=qpython">QPython on Reddit</a></p></li>
-</ul>
-</section>
-<section id="contribute">
-<h2>Contribute<a class="headerlink" href="#contribute" title="Permalink to this heading">¶</a></h2>
-<p>If you want to checkout the latest version and want to help test beta, please join the Beta Tester community:</p>
-<ul class="simple">
-<li><p><a class="reference external" href="https://plus.google.com/communities/111759148772865961493">Join QPython testers G+ community</a></p></li>
-</ul>
-<p>The course is a very important part to QPython as it helps new users to learn more about QPython, If you want to contribute on course, like post your own course or help translate current courses to different languages, please join the course community:</p>
-<ul class="simple">
-<li><p><a class="reference external" href="https://plus.google.com/u/1/communities/111340957575273631204">Join QPython course contributors G+ community</a></p></li>
-</ul>
-<p>Feel free to contact us if you encounter any problem with QPython, we will try our best to solve it.</p>
-<ul class="simple">
-<li><p><a class="reference external" href="https://github.com/qpython-android/qpython/issues">Report QPython’s issue</a></p></li>
-<li><p><a class="reference external" href="https://github.com/qpython-android/qpython3/issues">Report QPython3’s issue</a></p></li>
-<li><p><a class="reference external" href="https://github.com/qpython-android/QPYPI/issues">I want QPython add a library</a></p></li>
-<li><p><a class="reference external" href="mailto:support&#37;&#52;&#48;qpython&#46;org">Email us</a></p></li>
-</ul>
-</section>
-<section id="author-updated-time">
-<h2>Author &amp; updated time<a class="headerlink" href="#author-updated-time" title="Permalink to this heading">¶</a></h2>
-<p>Author: <a class="reference external" href="https://github.com/riverfor">River</a></p>
-<p>Submitted: 2017/10/7
-Updated: 2018/04/20</p>
-</section>
-</section>
-<div class="clearer"></div>
-</div>
-</div>
-</div>
-<div class="clearer"></div>
-</div>
-</main>
-<div class="star-footer">
-<img onclick="openPurchase()" src="../static/star.jpg">
-<div id="praise"><span>0</span> persons have rewarded</div>
-</div>
-<div class="foo"></div>
-<footer class="clearfix fixfooter web-show">
-<div class="col-sm-offset-2 col-sm-6 footer-div1">
-<div class="footer-block-item">
-<span class="footer-item1">Built with Sphinx using a theme provid by QPython.</span>
-<span class="footer-item2 visible-xs">Copyight 2017, QPython.</span>
-</div>
-</div>
-<div class="col-sm-2 footer-div2">
-<a href="/index.html" class="pull-right"><img src="http://www.qpython.org/static/img_logo.png"></a>
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diff --git a/docs/qpython-quick-start/editor-howto.html b/docs/qpython-quick-start/editor-howto.html
deleted file mode 100644
index e9f1d23..0000000
--- a/docs/qpython-quick-start/editor-howto.html
+++ /dev/null
@@ -1,313 +0,0 @@
-<!DOCTYPE html>
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-<link href="https://cdn.bootcss.com/bootstrap/3.3.7/css/bootstrap.min.css" rel="stylesheet">
-<title>Editor</title>
-<link rel="stylesheet" type="text/css" href="../static/pygments.css" />
-<link rel="stylesheet" type="text/css" href="../static/style.css" />
-<meta charset="UTF-8">
-<meta name="viewport" content="width=device-width, initial-scale=1,maximum-scale=1, user-scalable=no">
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-<li>
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-<li>
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-<main class="contain web-content">
-<div class="document">
-<div class="documentwrapper">
-<div class="bodywrapper">
-<div class="body" role="main">
-<section id="editor">
-<h1>Editor<a class="headerlink" href="#editor" title="Permalink to this heading">¶</a></h1>
-<p>This is the most used tool by Python developers - editor.</p>
-<img alt="QPython - editor" src="http://edu.qpython.org/static/editor-main.png" />
-<section id="features">
-<h2>Features<a class="headerlink" href="#features" title="Permalink to this heading">¶</a></h2>
-<p>The editor allows you to edit or run your QPython script program or project. The QPython editor supports Python syntax highlighting and shows line numbers (there is no ability to go to the line by number).</p>
-<p>There are the open(folder icon) and new(+ icon), setting on the editor’s right-top area, which allow you to open script or project, create script or project, and set the editor’s behaviors(like theme, font etc.).</p>
-<p>Syntax highlight feature makes the editor slow down for a bit when opening a large file, so the editor’s highlight is enable when the total lines are lower than the limit(default is 300), and you can adjust it to the amount you want in editor’s setting page.</p>
-<p>In the editor’s bottom, the icons are switched to hotkeys, lock keyboard, goto, save, run, find, undo, redo, recent open, snippets.</p>
-<img alt="QPython - editor's toolbar" src="http://edu.qpython.org/static/editor-toolbar-1.png" />
-<p>When you switch to the hot keys toolbar, the toolbar shows the following icons: switch to develop toolbar, untab, tab, TAB，DEF(def), IF(if), EF(elif), EL(else), FOR(for), IMT(import), IMF(from import), CLZ(class), RET(return), &#64;, :, =, “, ‘, ;, ,, +, -, <a href="#id1"><span class="problematic" id="id2">*</span></a>, /, &lt;, &gt;, (, ), [, ], {, }, #.</p>
-<img alt="QPython - editor's hotkey toolbar" src="http://edu.qpython.org/static/editor-toolbar-2.png" />
-<p>Skilled using the toolbar can greatly accelerate your development</p>
-<p>When saving the script, don’t forget to add .py extension to the file name since the editor don’t do it for you.</p>
-<p>When you are opening a QPython project, you could swipe on the editor’s left to unfold the project’s tree drawer, which allow you edit other files in the same project easily.</p>
-<img alt="QPython - project tree" src="http://edu.qpython.org/static/editor-left.png" />
-</section>
-<section id="other">
-<h2>Other<a class="headerlink" href="#other" title="Permalink to this heading">¶</a></h2>
-<p>It’s a big challenge for mobile inputting, the QPython team offers many kinds of tools for improving development efficiency.</p>
-<p><strong>Scan</strong>
-In QPython’s dashboard, there is a QRcode scanner, which can scan the QRcode and read the code into editor.</p>
-<p><strong>QEditor WebApp</strong>
-In QPYPI’s tools, there is a QEditor WebApp. After installing and running, it starts a WebApp service, allow user edit files from the computer’s browser in the same lAN by visiting some online editors.</p>
-<img alt="QPython - QEditor" src="http://edu.qpython.org/static/qeditor.png" />
-<img alt="QPython - Start QEditor" src="http://edu.qpython.org/static/qeditor-run.png" />
-<p><em>As above shows, you could start to develop by pressing enter http://192.168.1.191:10000 on your computer browser</em></p>
-</section>
-<section id="give-a-try-to-run-the-code-in-editor">
-<h2>Give a try to run the code in editor ?<a class="headerlink" href="#give-a-try-to-run-the-code-in-editor" title="Permalink to this heading">¶</a></h2>
-<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>class TestC():
-    def ask_age(self):
-        v = raw_input(&quot;&gt; How old are you ?&quot;)
-        return v
-tc = TestC()
-tc.ask_age()
-</pre></div>
-</div>
-<button class="button">Run …</button>
-</section>
-<section id="author-updated-time">
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diff --git a/docs/qpython-quick-start/in/principle.html b/docs/qpython-quick-start/in/principle.html
deleted file mode 100644
index c1d3216..0000000
--- a/docs/qpython-quick-start/in/principle.html
+++ /dev/null
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-<link href="https://cdn.bootcss.com/bootstrap/3.3.7/css/bootstrap.min.css" rel="stylesheet">
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-<section id="about">
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-</section>
-<section id="mobile-ide-dan-komunitas-pembelajaran">
-<h2>Mobile IDE dan Komunitas Pembelajaran<a class="headerlink" href="#mobile-ide-dan-komunitas-pembelajaran" title="Permalink to this heading">¶</a></h2>
-<p>QPython memiliki konsol, editor, toolkit pengembangan (QRCode Code reader, local file manager), QPYPI, kursus, komunitas untuk berbagi kode dan layanan pengembangan online QPYIO, menyediakan IDE Python versi mobile yang mudah digunakan untuk  komunitas pengguna Python.</p>
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-</section>
-</section>
-<div class="clearer"></div>
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-</div>
-</div>
-<div class="clearer"></div>
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-<img onclick="openPurchase()" src="../../static/star.jpg">
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-<section id="what-is-qpython">
-<h1>What is QPython<a class="headerlink" href="#what-is-qpython" title="Permalink to this heading">¶</a></h1>
-<section id="about">
-<h2>About<a class="headerlink" href="#about" title="Permalink to this heading">¶</a></h2>
-<p>QPython(for Android) is a Python script engine for Android system, which runs Python programs on any Android device. It contains a Python interpreter, a console, and an editor. Besides a basic Python library, QPython also integrates a bottle library that supports WebApp development, and an SL4A library that supports invoking Android APIs.</p>
-<p>A QPython (iOS) application designed for iOS users is also released in Appstore.</p>
-</section>
-<section id="history-of-qpython">
-<h2>History of QPython<a class="headerlink" href="#history-of-qpython" title="Permalink to this heading">¶</a></h2>
-<p>Born in 2012, QPython at first was a Python script SDK created by River for the rapid release of hotfix on Android programs, and was later independently released as a Python App on Android system. Ever since its release, QPython has been widely favored by Python developers, and received hundreds of thousands of installations and reviews in a very short time even in the circumstances of none market promotion. In 2015, the total installations exceeded more than 2,000,000. Until now, it is the most popular Python integrated development environment on Android.</p>
-</section>
-<section id="supported-program-types">
-<h2>Supported program types<a class="headerlink" href="#supported-program-types" title="Permalink to this heading">¶</a></h2>
-<p>Currently, QPython can run console applications, WebApp applications, backend applications, graphic interface-based applications, and so on. Each type of program fits with a different application scenario, Python determines how to  run an application according to a unique head statement. For example, #qpy:webapp indicates that the program will be released in the WebApp manner, #qpy:kivy indicates that the program is a ivy program, #qpy:qpyapp indicates that the program is a QPYApp (fits with the backend application scenario), and a program without head statement will by default run in the console manner (for easy debugging).</p>
-</section>
-<section id="how-to-install-a-third-party-library">
-<h2>How to install a third party library<a class="headerlink" href="#how-to-install-a-third-party-library" title="Permalink to this heading">¶</a></h2>
-<p>QPython is imbedded with most of the Python libraries for PCs. As Android system itself does not support compilation tool links, you cannot install all libraries like Python on PCs. However, you can install some pre-compiled libraries via QPYPI, which supports libraries including kivy, numpy, opencv, pycrypto, tornado and so on . In addition, you can use the pip client to install most libraries implemented by Python from the official website or from a third party Pypi designated by you.</p>
-<p><em>WebApp header declaration</em></p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1">#qpy:webapp</span>
-</pre></div>
-</div>
-<p><em>QPYApp header declaration</em></p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1">#qpy:qpyapp</span>
-</pre></div>
-</div>
-<p><em>Kivy header declaration</em></p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1">#qpy:kivy</span>
-</pre></div>
-</div>
-<p><em>Console header declaration</em></p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1">#qpy:console</span>
-</pre></div>
-</div>
-</section>
-<section id="qpython-media-and-community">
-<h2>QPython media and community<a class="headerlink" href="#qpython-media-and-community" title="Permalink to this heading">¶</a></h2>
-<p>QPython is a rapidly updated program which pays attention to user feedbacks, including feedbacks from Facebook fan page, twitter, weibo, Facebook group, G+, WeChat/QQ group, and other medias. Social groups are a significant part of QPython. We welcome you to join in our social group discussion, activities, and QPython construction, so as to learn and understand the latest development state of QPython.</p>
-</section>
-<section id="open-source-branch-and-business-branch-of-qpython">
-<h2>Open source branch and business branch of QPython<a class="headerlink" href="#open-source-branch-and-business-branch-of-qpython" title="Permalink to this heading">¶</a></h2>
-<p>QPython has two branches: the open source branch and the business branch. The open source branch provides a basic Python operating environment (which is consistent with the business branch), and uses the Apache license. We welcome any person or company of interest to customize your own service on this basis. Our QPython development team will also actively respond to any kinds of questions from users of the open source branch.
-The business branch of QPython, on the other hand, is dedicated to providing unique and convenient features and services, including online tutoring, online development, code sharing, etc.</p>
-<p>No matter which branch you are using, we hope everyone can really enjoy Python programing on cell phones.</p>
-<p><em>Video: QPython - Code Anywhere，Study Anytime</em></p>
-
-        <a class="reference external image-reference video" target="_blank" href="https://youtu.be/HqfUUiftrRw"
-    ><img alt="QPython - Code Anywhere，Study Anytime" src="http://edu.qpython.org/static/codeanywhere.png" /></a>
-</section>
-<section id="author-updated-time">
-<h2>Author &amp; updated time<a class="headerlink" href="#author-updated-time" title="Permalink to this heading">¶</a></h2>
-<p>Author: <a class="reference external" href="https://github.com/riverfor">River</a></p>
-<p>Translator: <a class="reference external" href="http://quseit.com/">TGJ</a></p>
-<p>Submitted: 2017/8/6
-Updated: 2018/04/25</p>
-</section>
-</section>
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diff --git a/docs/qpython-quick-start/notebook.html b/docs/qpython-quick-start/notebook.html
deleted file mode 100644
index dfb0713..0000000
--- a/docs/qpython-quick-start/notebook.html
+++ /dev/null
@@ -1,315 +0,0 @@
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-<html lang="en-US">
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-<meta charset="utf-8" />
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-<link href="https://cdn.bootcss.com/bootstrap/3.3.7/css/bootstrap.min.css" rel="stylesheet">
-<title>Share a live documents with QPyNotebook</title>
-<link rel="stylesheet" type="text/css" href="../static/pygments.css" />
-<link rel="stylesheet" type="text/css" href="../static/style.css" />
-<meta charset="UTF-8">
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-<div class="documentwrapper">
-<div class="bodywrapper">
-<div class="body" role="main">
-<section id="share-a-live-documents-with-qpynotebook">
-<h1>Share a live documents with QPyNotebook<a class="headerlink" href="#share-a-live-documents-with-qpynotebook" title="Permalink to this heading">¶</a></h1>
-<p>Do you want an application which helps you create and share documents that contain live Python code, equations, visualizations and narrative text?</p>
-<p>Now, QPyNotebook for QPython is published, which is developed based on the great opensource project jupyter notebook, it will help you do the following jobs easily like data cleaning and transformation, numerical simulation, statistical modeling, data visualization, machine learning and much more.</p>
-<section id="before-use">
-<h2>Before Use<a class="headerlink" href="#before-use" title="Permalink to this heading">¶</a></h2>
-<p><strong>1. Make sure you have installed the newest QPython (&gt;=2.2.0)</strong></p>
-<p><strong>2. Download QPyNotebook</strong></p>
-<ul class="simple">
-<li><p>Click the QPyNotebook App button to download QPyNotebook, the button will take you to the Google Play App Store.</p></li>
-<li><p>If you could not connect to Google you can download directly from our <a class="reference external" href="https://github.com/qpython-android/notebook/releases/">Github page</a> <em>(please check the “Trust unknown source” in system’s setting if you want to install it with APK downloaded from github)</em></p></li>
-</ul>
-<img alt="Enable QPyNotebook in QPython Setting" src="http://edu.qpython.org/static/notebook-setting.png" />
-<p><strong>3. Download related libraries</strong></p>
-<p>Click the QPyNotebook Libraries button and there’ll be a terminal window to download those libraries. It may take a while, and after downloading is finished, the terminal window will be closed automatically.</p>
-<img alt="Enable QPyNotebook in QPython Setting" src="http://edu.qpython.org/static/notebook-lib.png" />
-<img alt="Download libraries" src="http://edu.qpython.org/static/notebook-download.png" />
-<p><strong>Now all set!</strong></p>
-<img alt="all set" src="http://edu.qpython.org/static/notebook-downloaded.png" />
-</section>
-<section id="how-to-use">
-<h2>How to use<a class="headerlink" href="#how-to-use" title="Permalink to this heading">¶</a></h2>
-<p>Open the Explorer from QPython dashboard, find some <a href="#id1"><span class="problematic" id="id2">*</span></a>.ipynb files (For example notebooks/Welcome.ipynb) and open it, then you will start the QPyNotebook.</p>
-<ul class="simple">
-<li><p>You could start the QPyNotebook from QPython’s explorer*</p></li>
-</ul>
-<img alt="You can start QPyNotebook from QPython's explorer" src="http://edu.qpython.org/static/notebook-explorer.png" />
-<ul class="simple">
-<li><p>Or from QPyNotebook App</p></li>
-</ul>
-<img alt="Start QPyNotebook" src="http://edu.qpython.org/static/notebook-app.png" />
-<p><em>QPyNotebook’s live documentation</em></p>
-<img alt="Start to access Welcome.ipynb" src="http://edu.qpython.org/static/notebook-page.png" />
-</section>
-<section id="feedback">
-<h2>Feedback<a class="headerlink" href="#feedback" title="Permalink to this heading">¶</a></h2>
-<p>Feel free to give us any feedback.</p>
-<ul class="simple">
-<li><p><a class="reference external" href="http://twitter.com/qpython">&#64;qpython</a></p></li>
-<li><p><a class="reference external" href="mailto:support&#37;&#52;&#48;qpython&#46;org">support<span>&#64;</span>qpython<span>&#46;</span>org</a></p></li>
-</ul>
-</section>
-<section id="join-the-community">
-<h2>Join the community<a class="headerlink" href="#join-the-community" title="Permalink to this heading">¶</a></h2>
-<ul class="simple">
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-<li><p><a class="reference external" href="https://www.facebook.com/groups/qpython">QPython&#64;Facebook</a></p></li>
-</ul>
-</section>
-</section>
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-<section id="qpypi-howto">
-<h1>QPYPI howto<a class="headerlink" href="#qpypi-howto" title="Permalink to this heading">¶</a></h1>
-<p>Third party libraries could extend your programming abilities upto a greater extent, QPython manages libraries through QPYPI, There are some ways to use QPYPI as well.</p>
-<section id="tools">
-<h2>Tools<a class="headerlink" href="#tools" title="Permalink to this heading">¶</a></h2>
-<p>Tools category contains the useful tools for QPython, which could help you install QPython applications (Like SQLMap tool, Fabric tool and so on) easily, offer help for programming (Like django manage script, QEditor WebApp, Scapy tool and so on), and we will keep to improve more tools.</p>
-<img alt="QPYPI - Tools" src="http://edu.qpython.org/static/qpypi-tools.png" />
-</section>
-<section id="pip-console">
-<h2>PIP Console<a class="headerlink" href="#pip-console" title="Permalink to this heading">¶</a></h2>
-<p>INSTALL WITH PYTHON’S PYPI</p>
-<img alt="QPYPI - PIP Console" src="http://edu.qpython.org/static/qpypi-pip.png" />
-<p>By using QPython’s built in pip console, you could install many Python libraries, it can help you install many pure-python libraries (including the dependencies).</p>
-<p>Notice: Some libraries mixed with c/c++ files can not be install through pip console for the android lacks the compiler environment, you could ask for help from qpython developer team.</p>
-</section>
-<section id="libraries">
-<h2>Libraries<a class="headerlink" href="#libraries" title="Permalink to this heading">¶</a></h2>
-<p>In libraries, you can manage some Python libraries which are directly maintained by QPython team. There are some libraries which you can not install with pip console, or that are written in c/c++ code that can not be compiled on android. Like kivy, paramiko, PIL, tornado etc. You can install them easily from here.</p>
-<img alt="QPYPI - Libraries manage" src="http://edu.qpython.org/static/qpypi-libraries.png" />
-</section>
-<section id="aipy-libraries">
-<h2>AIPY Libraries<a class="headerlink" href="#aipy-libraries" title="Permalink to this heading">¶</a></h2>
-<p>In AIPY libraries, QPython team has added the AI related programming libraries (like neural network, deeplearnning, data analysis and mathematical operation etc.), which help users to start the AI program learning with QPython’s help easliy.</p>
-<img alt="QPYPI - AIPY" src="http://edu.qpython.org/static/qpypi-aipy.png" />
-</section>
-<section id="manage-manually">
-<h2>Manage Manually<a class="headerlink" href="#manage-manually" title="Permalink to this heading">¶</a></h2>
-<p>Besides these ways above, you could copy libraries into the /sdcard/qpython/lib/python2.7/site-packages in your device to install them.</p>
-</section>
-<section id="author-updated-time">
-<h2>Author &amp; updated time<a class="headerlink" href="#author-updated-time" title="Permalink to this heading">¶</a></h2>
-<p>Author: <a class="reference external" href="https://github.com/riverfor">River</a></p>
-<p>Submitted: 2017/10/7
-Updated: 2018/04/25</p>
-</section>
-</section>
-<div class="clearer"></div>
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-<section id="terminal-howto">
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-<img alt="QPython - Terminal" src="http://edu.qpython.org/static/terminal.png" />
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-<h2>Features<a class="headerlink" href="#features" title="Permalink to this heading">¶</a></h2>
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-</section>
-<section id="note">
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-<p>There will be notification in the notification bar unless you explicitly close the terminal and you always can reach the open terminal by tapping the notification.</p>
-</section>
-<section id="author-updated-time">
-<h2>Author &amp; updated time<a class="headerlink" href="#author-updated-time" title="Permalink to this heading">¶</a></h2>
-<p>Author: <a class="reference external" href="https://github.com/riverfor">River</a></p>
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-Updated: 2018/04/25</p>
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-<img onclick="openPurchase()" src="../static/star.jpg">
-<div id="praise"><span>0</span> persons have rewarded</div>
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diff --git a/docs/qpython-webapp/cn/django-howto.html b/docs/qpython-webapp/cn/django-howto.html
deleted file mode 100644
index ad6c1b1..0000000
--- a/docs/qpython-webapp/cn/django-howto.html
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-<div class="col-sm-offset-2 col-sm-6 footer-div1">
-<div class="footer-block-item">
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-<input type="hidden" name="on0" value="Thanks">
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-<div class="en-thanks-sel">
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-<section id="id1">
-<h2>关于<a class="headerlink" href="#id1" title="Permalink to this heading">¶</a></h2>
-<p>QPython支持WebApp，能让所有的Web开发者马上开始快速移动开发。</p>
-</section>
-<section id="id2">
-<h2>优势<a class="headerlink" href="#id2" title="Permalink to this heading">¶</a></h2>
-<p>移动开发领域已经有了很多WebApp解决方案，比如cordova, phonegap等等，和它们对比，QPython的WebApp方案有下面的优势：</p>
-<p><em>QPython是一个强大的Python引擎，能够处理许多逻辑事务，能有效地减少数据传输时间由此极大地提升应用的用户使用体验</em></p>
-<p><em>QPython支持大量的Python库，安装之后，你可以很方便地在项目中使用它们，由此大大节省开发时间</em></p>
-<p><em>使用QPython开发非常方便，你可以在掌上就完成开发任务，同时你也可以在PC上开发然后传输到移动设备上运行</em></p>
-</section>
-<section id="qpythonwebapp">
-<h2>QPython中的WebApp是如何工作的<a class="headerlink" href="#qpythonwebapp" title="Permalink to this heading">¶</a></h2>
-<p>QPython的WebApp包含以下几个模块： WebApp服务和Webview。WebApp服务在后台运行并且负责响应用户的输入， WebView是一个前端组件，它负责显示用户交互界面和负责响应用户的交互，比如界面展示，界面滚动，点击等等。</p>
-<p><strong>1</strong> 当你从QPython运行一个WebApp程序时，QPython在后台启动WebApp服务，同时，QPython会在前端启动一个Webview去载入程序头部声明的WebApp入口链接。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1">#qpy:webapp:&lt;webapp-title&gt;</span>
-<span class="c1">#qpy://&lt;webapp-address&gt;:&lt;webapp-port&gt;/&lt;webapp-path&gt;</span>
-</pre></div>
-</div>
-<p><strong>2</strong> 然后用户能够开始用WEB的模式和应用进行交互，因此开发者能够用Web开发的模式去编写代码。</p>
-<p><strong>3</strong> 当用户靠点击左上角的箭头退出应用时，程序将会请求 <strong>http://&lt;webapp-address&gt;&lt;:webapp-port&gt;/__exit</strong> 这个URL来退出程序，开发能够重写其对应的逻辑函数 __exit 来定义退出行为。</p>
-</section>
-<section id="id3">
-<h2>作者<a class="headerlink" href="#id3" title="Permalink to this heading">¶</a></h2>
-<p>作者: <a class="reference external" href="https://github.com/riverfor">River</a></p>
-<p>更新: 2017/10/16</p>
-</section>
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diff --git a/docs/qpython-webapp/cn/web-frameworks.html b/docs/qpython-webapp/cn/web-frameworks.html
deleted file mode 100644
index 297ee29..0000000
--- a/docs/qpython-webapp/cn/web-frameworks.html
+++ /dev/null
@@ -1,364 +0,0 @@
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-<li>
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-<li>
-<a href="http://www.qpython.org/document.html">Guide</a>
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-<a href="/index.html" class="header-selected">Course</a>
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-<main class="contain web-content">
-<div class="document">
-<div class="documentwrapper">
-<div class="bodywrapper">
-<div class="body" role="main">
-<section id="qpythonweb">
-<h1>QPython中的WEB框架<a class="headerlink" href="#qpythonweb" title="Permalink to this heading">¶</a></h1>
-<p>使用WEB开发框架，开发者能够高效迅速地开发出WEB应用。同样，在QPython中，你能用下列WEB框架来快速开发WebApp。</p>
-<section id="bottle">
-<h2>Bottle<a class="headerlink" href="#bottle" title="Permalink to this heading">¶</a></h2>
-<p>Bottle是QPython内置的WebApp开发框架，您无需额外安装，只需要遵循WebApp的规范，参考bottle开发的方法就可以进行。</p>
-<p>来看一个最简单的bottle的Hello world。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>#qpy:webapp:Hello Qpython
-#qpy://127.0.0.1:8080/
-&quot;&quot;&quot;
-This is a sample for qpython webapp
-&quot;&quot;&quot;
-from bottle import Bottle, ServerAdapter
-from bottle import run, debug, route, error, static_file, template
-######### QPYTHON WEB SERVER ###############
-class MyWSGIRefServer(ServerAdapter):
-    server = None
-    def run(self, handler):
-        from wsgiref.simple_server import make_server, WSGIRequestHandler
-        if self.quiet:
-            class QuietHandler(WSGIRequestHandler):
-            def log_request(*args, **kw): pass
-        self.options[&#39;handler_class&#39;] = QuietHandler
-    self.server = make_server(self.host, self.port, handler, **self.options)
-    self.server.serve_forever()
-    def stop(self):
-        import threading
-        threading.Thread(target=self.server.shutdown).start()
-        self.server.server_close()
-######### BUILT-IN ROUTERS ###############
-@route(&#39;/__exit&#39;, method=[&#39;GET&#39;,&#39;HEAD&#39;])
-def __exit():
-    global server
-    server.stop()
-@route(&#39;/assets/&lt;filepath:path&gt;&#39;)
-def serverstatic(filepath):
-    return static_file(filepath, root=&#39;/sdcard&#39;)
-######### WEBAPP ROUTERS WRITE YOUR CODE BELOW###############
-@route(&#39;/&#39;)
-def home():
-    return template(&#39;&lt;h1&gt;Hello {{name}} !&lt;/h1&gt;&lt;a href=&quot;/assets/qpython/projects/WebAppSample/main.py&quot;&gt;View source&lt;/a&gt;&lt;br /&gt;&lt;br /&gt; &lt;a href=&quot;http://edu.qpython.org/qpython-webapp/index.html&quot;&gt;&gt;&gt; About QPython Web App&lt;/a&gt;&#39;,name=&#39;QPython&#39;)
-######### WEBAPP ROUTERS ###############
-app = Bottle()
-app.route(&#39;/&#39;, method=&#39;GET&#39;)(home)
-app.route(&#39;/__exit&#39;, method=[&#39;GET&#39;,&#39;HEAD&#39;])(__exit)
-app.route(&#39;/assets/&lt;filepath:path&gt;&#39;, method=&#39;GET&#39;)(serverstatic)
-try:
-    server = MyWSGIRefServer(host=&quot;127.0.0.1&quot;, port=&quot;8080&quot;)
-    app.run(server=server,reloader=False)
-except Exception,ex:
-    print &quot;Exception: %s&quot; % repr(ex)
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>点击运行，将代码复制到编辑器中点击运行即可看见效果。</p>
-</section>
-<section id="tornado">
-<h2>Tornado<a class="headerlink" href="#tornado" title="Permalink to this heading">¶</a></h2>
-<p>QPython同样支持异步通信Web开发框架Tornado，您可以通过QPYPI来安装它。</p>
-<img alt="tornado library in QPYPI" src="http://edu.qpython.org/static/qpypi-tornado.png" />
-<p><em>通过QPYPI来安装tornado库</em></p>
-<p>安装完毕之后，点击以下代码底部的运行即可将代码拷贝到编辑器中，保存运行即可看到基于Tornado的WebApp示例效果。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>#qpy:webapp:Tornado Sample
-#qpy://127.0.0.1:8080/
-import tornado
-import tornado.web
-class MainHandler(tornado.web.RequestHandler):
-    def get(self):
-        self.write(&quot;Hello, world&quot;)
-class ExitHandler(tornado.web.RequestHandler):
-    def get(self):
-        import os,signal
-        os.kill(os.getpid(), signal.SIGKILL)
-def make_app():
-    return tornado.web.Application([
-        (r&quot;/&quot;, MainHandler),
-        (r&quot;/__exit&quot;, ExitHandler),
-    ])
-if __name__ == &quot;__main__&quot;:
-    app = make_app()
-    app.listen(8080)
-    tornado.ioloop.IOLoop.current().start()
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>此外值得一提的是，在QPython的AIPY扩展插件中，matlibplot也是默认支持了用Tornado作为绘图展示支持后端。</p>
-</section>
-<section id="django">
-<h2>Django<a class="headerlink" href="#django" title="Permalink to this heading">¶</a></h2>
-<p>作为Python WEB开发的最常用框架，Django同样获得了QPython的支持, QPython提供了一个djangoman.py脚本来帮助您快速安装django库和初始化django项目，通过运行该工具，你可以快速创建或者管理django项目。</p>
-<img alt="djangoman.py" src="http://edu.qpython.org/static/djangoman.png" />
-<p><em>可以通过Django manage script来管理Django项目</em></p>
-<p>通过djangoman.py来完成项目的初始化之后，您就可以按照WebApp的规范来开发您的WebApp并在QPython上运行。</p>
-</section>
-<section id="id1">
-<h2>其他<a class="headerlink" href="#id1" title="Permalink to this heading">¶</a></h2>
-<p>除了上述框架，诸如其他框架如Flask,cherrypy等也有可能被QPython支持，你若感兴趣可以通过QPYPI中的INSTALL FROM PYTHON’S PYPI来探索。</p>
-</section>
-</section>
-<div class="clearer"></div>
-</div>
-</div>
-</div>
-<div class="clearer"></div>
-</div>
-</main>
-<div class="star-footer">
-<img onclick="openPurchase()" src="../../static/star.jpg">
-<div id="praise"><span>0</span> persons have rewarded</div>
-</div>
-<div class="foo"></div>
-<footer class="clearfix fixfooter web-show">
-<div class="col-sm-offset-2 col-sm-6 footer-div1">
-<div class="footer-block-item">
-<span class="footer-item1">Built with Sphinx using a theme provid by QPython.</span>
-<span class="footer-item2 visible-xs">Copyight 2017, QPython.</span>
-</div>
-</div>
-<div class="col-sm-2 footer-div2">
-<a href="/index.html" class="pull-right"><img src="http://www.qpython.org/static/img_logo.png"></a>
-</div>
-</footer>
-<div id="purchase-box">
-<div class="zh-img purchase-img"></div>
-<div class="en-img purchase-img">
-<form action="https://www.paypal.com/cgi-bin/webscr" method="post" target="_top">
-<input type="hidden" name="cmd" value="_s-xclick">
-<input type="hidden" name="hosted_button_id" value="ST6KWAK8R63JW">
-<div class="en-thanks-tit">
-<input type="hidden" name="on0" value="Thanks">
-Thanks
-</div>
-<div class="en-thanks-sel">
-<select name="os0">
-<option value="Reward this course">Reward this course $ 0.99 USD</option>
-<option value="Reward this course">Reward this course $ 1.49 USD</option>
-<option value="Reward this course">Reward this course $ 1.99 USD</option>
-<option value="Reward this course">Reward this course $ 2.99 USD</option>
-<option value="Reward this course">Reward this course $ 3.49 USD</option>
-</select>
-</div>
-<input type="hidden" name="currency_code" value="USD">
-<input type="image" class="pay_img" src="https://www.paypalobjects.com/en_GB/i/btn/btn_paynow_LG.gif" border="0" name="submit" alt="PayPal – The safer, easier way to pay online!">
-<img alt="" border="0" src="https://www.paypalobjects.com/zh_XC/i/scr/pixel.gif" width="1" height="1">
-</form>
-</div>
-</div>
-<script>
-var milib;
-if (milib==undefined) {
-var milib = function(){
-};
-milib.qeditor = function(str){
-alert('Please share this page to QPython then run it')
-};
-milib.getPurchaseNumber = function(str){
-//alert('getPurchaseNumber:'+str)
-return "10+";
-};
-milib.a8playVideoFromGW= function(url){
-window.open(url)
-};
-milib.qpylibinstall = function(cat,url,target){
-window.open(url)
-};
-milib.gotoIfPay = function(url, packageId, packageUrl){
-window.open(url)
-};
-} else {
-}
-function openPurchase() {
-//获取标识
-var u = window.location.href.split('/')
-var s = u[u.length-1].replace('.html', '')
-s = u[u.length-2]+'-'+s
-if (milib.openPurchaseActivity){
-milib.openPurchaseActivity(s)
-}else {
-if (navigator.language== "zh-CN" || navigator.language== "zh"){
-$('.zh-img ').show();
-$('.en-img').hide();
-}else {
-$('.zh-img ').hide();
-$('.en-img').show();
-}
-$('#purchase-box').show();
-}
-}
-$(function() {
-$('#purchase-box').click(function() {
-$(this).hide();
-})
-$('.purchase-img').click(function(e) {
-e.stopPropagation();
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-//按钮事件
-function btnEvent(){
-var seq = this.getAttribute('target')-2
-var a = this.parentElement.getElementsByTagName('pre')
-if ( a && a.length > 0){
-/**********有代码，调用控制台*************/
-var code = a[seq].lastChild.nodeValue
-milib.qeditor(code)
-}else{
-//没有代码，调转
-var url = this.getAttribute('data-url');
-window.open(url);
-}
-}
-//获取赞赏
-function setPraise(){
-var u = window.location.href.split('/')
-var s = u[u.length-1].replace('.html', '')
-s = u[u.length-2]+'-'+s
-var i = document.getElementById('praise').getElementsByTagName('span')[0]
-var num = milib.getPurchaseNumber(s);
-i.innerHTML = num
-}
-//初始化代码显示
-function initCode(){
-var prearr = document.getElementsByTagName('pre');
-for (var i = 0; i < prearr.length; i++) {
-var temp_data = prearr[i].lastChild.nodeValue
-//正则处理 lavel5
-temp_data = temp_data.replace(/\^2\$/g, spaceString(2));
-temp_data = temp_data.replace(/\^4\$/g, spaceString(4));
-temp_data = temp_data.replace(/\^6\$/g, spaceString(6));
-temp_data = temp_data.replace(/\^8\$/g, spaceString(8));
-temp_data = temp_data.replace(/\^10\$/g, spaceString(10));
-temp_data = temp_data.replace(/\^12\$/g, spaceString(12));
-temp_data = temp_data.replace(/\^16\$/g, spaceString(16));
-temp_data = temp_data.replace(/\^20\$/g, spaceString(20));
-//set
-prearr[i].lastChild.nodeValue = temp_data;
-}
-}
-//产生空格
-function spaceString(num){
-var v = ''
-for (var i = 0; i < num; i++) {
-v += ' ';
-}
-return v;
-}
-function setSearchHistory (t) {
-if (!t)
-return;
-if (localStorage.course_search_list) {
-var list = localStorage.course_search_list.split('%,%');
-var i = list.indexOf(t);
-if (i !== -1) {
-list.splice(i, 1);
-list.push(t);
-localStorage.course_search_list = list.join('%,%');
-}else {
-localStorage.course_search_list += '%,%'+t;
-}
-}else {
-localStorage.course_search_list = t;
-}
-}
-window.onload = function(){
-setPraise();
-initCode();
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-var b = document.getElementsByTagName('button');
-for (var i = 0; i < b.length; i++) {
-if (b[i].className=='button') {
-b[i].setAttribute('target', i)
-b[i].onclick = btnEvent ;
-}
-}
-}
-$(function(){
-var url = location.href.split('?');
-var arg = url[1];
-if (!arg){
-return;
-}
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-var c = args[i].split('=');
-if (c[0]=='form' && c[1] == 'web') {
-$('.web-show').css('display','block');
-$('.web-content').addClass('margin-header');
-}
-}
-})
-$(function() {
-$('form').submit(function() {
-setSearchHistory($("input[name='q']").val());
-})
-$('.bodywrapper .body form input').val("");
-$($('.bodywrapper .body form input')[0]).attr('placeholder', 'search');
-})
-</script>
-<script>
-(function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
-(i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
-m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
-})(window,document,'script','https://www.google-analytics.com/analytics.js','ga');
-ga('create', 'UA-103260045-3', 'auto');
-ga('send', 'pageview');
-</script>
-</body>
-</html>
\ No newline at end of file
diff --git a/docs/qpython-webapp/cn/your-first-webapp.html b/docs/qpython-webapp/cn/your-first-webapp.html
deleted file mode 100644
index 262eeca..0000000
--- a/docs/qpython-webapp/cn/your-first-webapp.html
+++ /dev/null
@@ -1,339 +0,0 @@
-<!DOCTYPE html>
-<html lang="en-US">
-<head>
-<meta charset="utf-8" />
-<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
-<link href="https://cdn.bootcss.com/bootstrap/3.3.7/css/bootstrap.min.css" rel="stylesheet">
-<title>QPython的WebApp</title>
-<link rel="stylesheet" type="text/css" href="../../static/pygments.css" />
-<link rel="stylesheet" type="text/css" href="../../static/style.css" />
-<meta charset="UTF-8">
-<meta name="viewport" content="width=device-width, initial-scale=1,maximum-scale=1, user-scalable=no">
-<script src="https://cdn.bootcss.com/bootstrap/3.3.7/js/bootstrap.min.js"></script>
-</head><body>
-<div class="header doc-header web-show">
-<div class="col-sm-offset-2 col-sm-8  index-header">
-<div class="navbar-header">
-<button type="button" class="navbar-toggle" data-toggle="collapse" data-target="#example-navbar-collapse">
-<span class="sr-only">切换导航</span>
-<span class="icon-bar"></span>
-<span class="icon-bar"></span>
-<span class="icon-bar"></span>
-</button>
-<a href="/index.html" class="logo"><img src="http://www.qpython.org/static/img_logo.png"></a>
-</div>
-<ul class="header-title collapse navbar-collapse" id="example-navbar-collapse" >
-<li>
-<a href="http://www.qpython.org/index.html">Home</a>
-</li>
-<li>
-<a href="http://www.qpython.org/document.html">Guide</a>
-</li>
-<li class="header-phone-selected">
-<a href="/index.html" class="header-selected">Course</a>
-<span class="header-title-selected"></span>
-</li>
-<li>
-<a href="http://www.aipy.org">AIPY</a>
-</li>
-<li>
-<a target="_blank" href="https://github.com/qpython-android/qpython">Github</a>
-</li>
-</ul>
-<div class="search-box hidden-xs">
-<form action="/search.html">
-<input type="search" name="q" placeholder="keyword">
-<input type="hidden" name="check_keywords" value="yes" />
-<input type="hidden" name="area" value="default" />
-<button><img src="http://www.qpython.org/static/ic_search.png"></button>
-</form>
-</div>
-</div>
-</div>
-<main class="contain web-content">
-<div class="document">
-<div class="documentwrapper">
-<div class="bodywrapper">
-<div class="body" role="main">
-<section id="qpythonwebapp">
-<h1>QPython的WebApp<a class="headerlink" href="#qpythonwebapp" title="Permalink to this heading">¶</a></h1>
-<p>WebApp是一种Web开发者能够快速上手开发的应用，QPython的WebApp支持常见WebAApp的优势。此外，QPython让手机本身就能成为WebApp的运行容器，支持处理复杂的程序逻辑，因此能极大地提高WebApp使用体验，同时也增加了开发的灵活度。</p>
-<section id="webapp">
-<h2>WebApp声明<a class="headerlink" href="#webapp" title="Permalink to this heading">¶</a></h2>
-<p>在QPython中，通过头部声明来区分程序类型，当头部有以下定义时，我们知道它是一个WebApp程序。</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1">#qpy:webapp:&lt;WebApp程序名称&gt;</span>
-<span class="c1">#qpy://&lt;WebApp运行所在的IP，一般为127.0.0.1&gt;:&lt;WebApp运行所在的端口&gt;/&lt;初次载入时对应的WebApp Url路径&gt;</span>
-</pre></div>
-</div>
-</section>
-<section id="id1">
-<h2>WebApp运行流程<a class="headerlink" href="#id1" title="Permalink to this heading">¶</a></h2>
-<p>在程序被识别为WebApp运行后，QPython会让程序作为后台服务的方式启动，同时在前端会启动一个WebView组件，载入你在程序头部声明所对应的URL（由IP、端口、URL路径组合而成）</p>
-<p>如，如果IP为127.0.0.1，端口为8080，URL路径为hello/test，则WebView载入的URL为：<a class="reference external" href="http://127.0.0.1:8080/hello/test">http://127.0.0.1:8080/hello/test</a></p>
-</section>
-<section id="id2">
-<h2>WebApp退出原理<a class="headerlink" href="#id2" title="Permalink to this heading">¶</a></h2>
-<p>当你点了后退键退出WebView时，WebApp后台进程也需要退出，由于一开始QPython让其按照后台服务的方式启动，所以我们需要通知其退出，QPython是在WebView退出时，发出一个http://IP:端口/__exit 的GET请求到后台服务，由其处理结束后台进程，退出服务的工作，因此，作为一个完成的流程，您需要在自己的WebApp中定义/__exit 对应的退出服务操作，我们在结尾示例中附有示例。</p>
-</section>
-<section id="id3">
-<h2>例子<a class="headerlink" href="#id3" title="Permalink to this heading">¶</a></h2>
-<p>作为QPython内嵌的Web开发库，来看一个最简单的bottle的Hello world</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>#qpy:webapp:Hello Qpython
-#qpy://127.0.0.1:8080/
-&quot;&quot;&quot;
-This is a sample for qpython webapp
-&quot;&quot;&quot;
-from bottle import Bottle, ServerAdapter
-from bottle import run, debug, route, error, static_file, template
-######### QPYTHON WEB SERVER ###############
-class MyWSGIRefServer(ServerAdapter):
-    server = None
-    def run(self, handler):
-        from wsgiref.simple_server import make_server, WSGIRequestHandler
-        if self.quiet:
-            class QuietHandler(WSGIRequestHandler):
-            def log_request(*args, **kw): pass
-        self.options[&#39;handler_class&#39;] = QuietHandler
-    self.server = make_server(self.host, self.port, handler, **self.options)
-    self.server.serve_forever()
-    def stop(self):
-        import threading
-        threading.Thread(target=self.server.shutdown).start()
-        self.server.server_close()
-######### BUILT-IN ROUTERS ###############
-@route(&#39;/__exit&#39;, method=[&#39;GET&#39;,&#39;HEAD&#39;])
-def __exit():
-    global server
-    server.stop()
-@route(&#39;/assets/&lt;filepath:path&gt;&#39;)
-def serverstatic(filepath):
-    return static_file(filepath, root=&#39;/sdcard&#39;)
-######### WEBAPP ROUTERS WRITE YOUR CODE BELOW###############
-@route(&#39;/&#39;)
-def home():
-    return template(&#39;&lt;h1&gt;Hello {{name}} !&lt;/h1&gt;&lt;a href=&quot;/assets/qpython/projects/WebAppSample/main.py&quot;&gt;View source&lt;/a&gt;&lt;br /&gt;&lt;br /&gt; &lt;a href=&quot;http://edu.qpython.org/qpython-webapp/index.html&quot;&gt;&gt;&gt; About QPython Web App&lt;/a&gt;&#39;,name=&#39;QPython&#39;)
-######### WEBAPP ROUTERS ###############
-app = Bottle()
-app.route(&#39;/&#39;, method=&#39;GET&#39;)(home)
-app.route(&#39;/__exit&#39;, method=[&#39;GET&#39;,&#39;HEAD&#39;])(__exit)
-app.route(&#39;/assets/&lt;filepath:path&gt;&#39;, method=&#39;GET&#39;)(serverstatic)
-try:
-    server = MyWSGIRefServer(host=&quot;127.0.0.1&quot;, port=&quot;8080&quot;)
-    app.run(server=server,reloader=False)
-except Exception,ex:
-    print &quot;Exception: %s&quot; % repr(ex)
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>点击运行，将代码复制到编辑器中点击运行即可看见效果。</p>
-<p>当然，在QPython中，还提供了其他Web开发框架的支持来帮助你简化工作。</p>
-</section>
-</section>
-<div class="clearer"></div>
-</div>
-</div>
-</div>
-<div class="clearer"></div>
-</div>
-</main>
-<div class="star-footer">
-<img onclick="openPurchase()" src="../../static/star.jpg">
-<div id="praise"><span>0</span> persons have rewarded</div>
-</div>
-<div class="foo"></div>
-<footer class="clearfix fixfooter web-show">
-<div class="col-sm-offset-2 col-sm-6 footer-div1">
-<div class="footer-block-item">
-<span class="footer-item1">Built with Sphinx using a theme provid by QPython.</span>
-<span class="footer-item2 visible-xs">Copyight 2017, QPython.</span>
-</div>
-</div>
-<div class="col-sm-2 footer-div2">
-<a href="/index.html" class="pull-right"><img src="http://www.qpython.org/static/img_logo.png"></a>
-</div>
-</footer>
-<div id="purchase-box">
-<div class="zh-img purchase-img"></div>
-<div class="en-img purchase-img">
-<form action="https://www.paypal.com/cgi-bin/webscr" method="post" target="_top">
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index 1015051..0000000
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-<div class="document">
-<div class="documentwrapper">
-<div class="bodywrapper">
-<div class="body" role="main">
-<section id="qpython-webapp-principle">
-<h1>QPython WebApp Principle<a class="headerlink" href="#qpython-webapp-principle" title="Permalink to this heading">¶</a></h1>
-<section id="about">
-<h2>About<a class="headerlink" href="#about" title="Permalink to this heading">¶</a></h2>
-<p>QPython supports WebApp, the goal is to enable all WEB developers to the fastest development of mobile applications.</p>
-</section>
-<section id="advantages">
-<h2>Advantages<a class="headerlink" href="#advantages" title="Permalink to this heading">¶</a></h2>
-<p>There are lots of mobile WebApp solutions, like cordova, phonegap etc, and compared to them, QPython’s WebApp has the following advantages.</p>
-<p><em>QPython is a powerful Python engine that can handle many logical jobs, effectively reducing data transfer time and enhancing your app’s user experience</em></p>
-<p><em>QPython supports massive Python libraries, you could import them in your project, so that you could save much develop time.</em></p>
-<p><em>It is very convenient for development with QPython, you can complete your development just in palm. And you could develop from PC then transfer to your mobile to run</em></p>
-</section>
-<section id="how-does-webapp-work-in-qpython">
-<h2>How does WebApp work in QPython<a class="headerlink" href="#how-does-webapp-work-in-qpython" title="Permalink to this heading">¶</a></h2>
-<p>QPython WebApp contains the following components: WebApp service and Webview. The WebApp service runs in the background and is responsible for responding to user’s input. WebView is a front component, which shows the user interface and and is responsible for responding to render user interactions, such as scrolling, click the button and so on.</p>
-<p><strong>1</strong> When you run a WebApp program from QPython, QPython launch the WebApp service in the background, at the same time, QPython will start a webview which loads the entry link declared in the program header in the front.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1">#qpy:webapp:&lt;webapp-title&gt;</span>
-<span class="c1">#qpy://&lt;webapp-address&gt;:&lt;webapp-port&gt;/&lt;webapp-path&gt;</span>
-</pre></div>
-</div>
-<p><strong>2</strong> Then the user can start to interact with the applicaton with the web mode. So the developer could write the logic code with the web development way.</p>
-<p><strong>3</strong> When the user exit by clicking the left arrow, the application will request the <strong>http://&lt;webapp-address&gt;&lt;:webapp-port&gt;/__exit</strong> to exit the application
-The developer could rewrite the __exit function to defines the exit behaviors.</p>
-</section>
-<section id="chapter-author-contributor-and-updated-time">
-<h2>Chapter Author, contributor and updated time<a class="headerlink" href="#chapter-author-contributor-and-updated-time" title="Permalink to this heading">¶</a></h2>
-<p>Author: <a class="reference external" href="https://github.com/riverfor">River</a></p>
-<p>Submitted: 2017/10/16</p>
-</section>
-</section>
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-<div id="praise"><span>0</span> persons have rewarded</div>
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-<div class="foo"></div>
-<footer class="clearfix fixfooter web-show">
-<div class="col-sm-offset-2 col-sm-6 footer-div1">
-<div class="footer-block-item">
-<span class="footer-item1">Built with Sphinx using a theme provid by QPython.</span>
-<span class="footer-item2 visible-xs">Copyight 2017, QPython.</span>
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-</div>
-<div class="col-sm-2 footer-div2">
-<a href="/index.html" class="pull-right"><img src="http://www.qpython.org/static/img_logo.png"></a>
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diff --git a/docs/qpython-webapp/web-frameworks.html b/docs/qpython-webapp/web-frameworks.html
deleted file mode 100644
index 6183c5d..0000000
--- a/docs/qpython-webapp/web-frameworks.html
+++ /dev/null
@@ -1,364 +0,0 @@
-<!DOCTYPE html>
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-<head>
-<meta charset="utf-8" />
-<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
-<link href="https://cdn.bootcss.com/bootstrap/3.3.7/css/bootstrap.min.css" rel="stylesheet">
-<title>WEB framework in QPython</title>
-<link rel="stylesheet" type="text/css" href="../static/pygments.css" />
-<link rel="stylesheet" type="text/css" href="../static/style.css" />
-<meta charset="UTF-8">
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-<script src="https://cdn.bootcss.com/bootstrap/3.3.7/js/bootstrap.min.js"></script>
-</head><body>
-<div class="header doc-header web-show">
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-<li>
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-<li>
-<a href="http://www.qpython.org/document.html">Guide</a>
-</li>
-<li class="header-phone-selected">
-<a href="/index.html" class="header-selected">Course</a>
-<span class="header-title-selected"></span>
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-<li>
-<a href="http://www.aipy.org">AIPY</a>
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-<li>
-<a target="_blank" href="https://github.com/qpython-android/qpython">Github</a>
-</li>
-</ul>
-<div class="search-box hidden-xs">
-<form action="/search.html">
-<input type="search" name="q" placeholder="keyword">
-<input type="hidden" name="check_keywords" value="yes" />
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-<main class="contain web-content">
-<div class="document">
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-<section id="web-framework-in-qpython">
-<h1>WEB framework in QPython<a class="headerlink" href="#web-framework-in-qpython" title="Permalink to this heading">¶</a></h1>
-<p>By using the WEB development framework, developers can efficiently and quickly develop Web applications. Similarly, in QPython, you can quickly develop WebApp with the following web frameworks.</p>
-<section id="bottle">
-<h2>Bottle<a class="headerlink" href="#bottle" title="Permalink to this heading">¶</a></h2>
-<p>Bottle is a QPython built-in WebApp development framework, you do not need to install additional, just follow the WebApp’s specifications, reference bottle development methods can be carried out.</p>
-<p>Look at one of the most simple Hello world with bottle</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>#qpy:webapp:Hello Qpython
-#qpy://127.0.0.1:8080/
-&quot;&quot;&quot;
-This is a sample for qpython webapp
-&quot;&quot;&quot;
-from bottle import Bottle, ServerAdapter
-from bottle import run, debug, route, error, static_file, template
-######### QPYTHON WEB SERVER ###############
-class MyWSGIRefServer(ServerAdapter):
-    server = None
-    def run(self, handler):
-        from wsgiref.simple_server import make_server, WSGIRequestHandler
-        if self.quiet:
-            class QuietHandler(WSGIRequestHandler):
-            def log_request(*args, **kw): pass
-        self.options[&#39;handler_class&#39;] = QuietHandler
-    self.server = make_server(self.host, self.port, handler, **self.options)
-    self.server.serve_forever()
-    def stop(self):
-        import threading
-        threading.Thread(target=self.server.shutdown).start()
-        self.server.server_close()
-######### BUILT-IN ROUTERS ###############
-@route(&#39;/__exit&#39;, method=[&#39;GET&#39;,&#39;HEAD&#39;])
-def __exit():
-    global server
-    server.stop()
-@route(&#39;/assets/&lt;filepath:path&gt;&#39;)
-def serverstatic(filepath):
-    return static_file(filepath, root=&#39;/sdcard&#39;)
-######### WEBAPP ROUTERS WRITE YOUR CODE BELOW###############
-@route(&#39;/&#39;)
-def home():
-    return template(&#39;&lt;h1&gt;Hello {{name}} !&lt;/h1&gt;&lt;a href=&quot;/assets/qpython/projects/WebAppSample/main.py&quot;&gt;View source&lt;/a&gt;&lt;br /&gt;&lt;br /&gt; &lt;a href=&quot;http://edu.qpython.org/qpython-webapp/index.html&quot;&gt;&gt;&gt; About QPython Web App&lt;/a&gt;&#39;,name=&#39;QPython&#39;)
-######### WEBAPP ROUTERS ###############
-app = Bottle()
-app.route(&#39;/&#39;, method=&#39;GET&#39;)(home)
-app.route(&#39;/__exit&#39;, method=[&#39;GET&#39;,&#39;HEAD&#39;])(__exit)
-app.route(&#39;/assets/&lt;filepath:path&gt;&#39;, method=&#39;GET&#39;)(serverstatic)
-try:
-    server = MyWSGIRefServer(host=&quot;127.0.0.1&quot;, port=&quot;8080&quot;)
-    app.run(server=server,reloader=False)
-except Exception,ex:
-    print &quot;Exception: %s&quot; % repr(ex)
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>Click Run, copy the code to the editor and Run to check out the result.</p>
-</section>
-<section id="tornado">
-<h2>Tornado<a class="headerlink" href="#tornado" title="Permalink to this heading">¶</a></h2>
-<p>QPython also supports the asynchronous Communication Web Development Framework - Tornado, which you can install with QPYPI.</p>
-<img alt="tornado library in QPYPI" src="http://edu.qpython.org/static/qpypi-tornado.png" />
-<p><em>To install tornado library by QPYPI</em></p>
-<p>After the installation is complete, click on the bottom of the following code to run the code can be copied to the editor,save the run to check out Tornado-based WebApp sample effects.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>#qpy:webapp:Tornado Sample
-#qpy://127.0.0.1:8080/
-import tornado
-import tornado.web
-class MainHandler(tornado.web.RequestHandler):
-    def get(self):
-        self.write(&quot;Hello, world&quot;)
-class ExitHandler(tornado.web.RequestHandler):
-    def get(self):
-        import os,signal
-        os.kill(os.getpid(), signal.SIGKILL)
-def make_app():
-    return tornado.web.Application([
-        (r&quot;/&quot;, MainHandler),
-        (r&quot;/__exit&quot;, ExitHandler),
-    ])
-if __name__ == &quot;__main__&quot;:
-    app = make_app()
-    app.listen(8080)
-    tornado.ioloop.IOLoop.current().start()
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>Also worth mentioning is that in QPython’s AIPY extension,matlibplot also supports Tornado as the default drawing support backend by default.</p>
-</section>
-<section id="django">
-<h2>Django<a class="headerlink" href="#django" title="Permalink to this heading">¶</a></h2>
-<p>As the most commonly used framework for Python Web development, Django also received QPython support, QPython provides a djangoman.py scripts to help you quickly install and initialize django library project,By running this tool,You can quickly create or manage django projects.</p>
-<img alt="djangoman.py" src="http://edu.qpython.org/static/djangoman.png" />
-<p><em>Django projects can be managed by Django manage script</em></p>
-<p>After djangoman.py to complete the project’s initialization,You can follow the WebApp’s specifications to develop your WebApp and run it on QPython.</p>
-</section>
-<section id="other">
-<h2>Other<a class="headerlink" href="#other" title="Permalink to this heading">¶</a></h2>
-<p>In addition to the above framework,Other frameworks such as Flask, Cherrypy, etc. are also likely to be supported by QPython,If you are interested, you can explore through INSTALL FROM PYTHON’S PYPI in QPYPI.</p>
-</section>
-</section>
-<div class="clearer"></div>
-</div>
-</div>
-</div>
-<div class="clearer"></div>
-</div>
-</main>
-<div class="star-footer">
-<img onclick="openPurchase()" src="../static/star.jpg">
-<div id="praise"><span>0</span> persons have rewarded</div>
-</div>
-<div class="foo"></div>
-<footer class="clearfix fixfooter web-show">
-<div class="col-sm-offset-2 col-sm-6 footer-div1">
-<div class="footer-block-item">
-<span class="footer-item1">Built with Sphinx using a theme provid by QPython.</span>
-<span class="footer-item2 visible-xs">Copyight 2017, QPython.</span>
-</div>
-</div>
-<div class="col-sm-2 footer-div2">
-<a href="/index.html" class="pull-right"><img src="http://www.qpython.org/static/img_logo.png"></a>
-</div>
-</footer>
-<div id="purchase-box">
-<div class="zh-img purchase-img"></div>
-<div class="en-img purchase-img">
-<form action="https://www.paypal.com/cgi-bin/webscr" method="post" target="_top">
-<input type="hidden" name="cmd" value="_s-xclick">
-<input type="hidden" name="hosted_button_id" value="ST6KWAK8R63JW">
-<div class="en-thanks-tit">
-<input type="hidden" name="on0" value="Thanks">
-Thanks
-</div>
-<div class="en-thanks-sel">
-<select name="os0">
-<option value="Reward this course">Reward this course $ 0.99 USD</option>
-<option value="Reward this course">Reward this course $ 1.49 USD</option>
-<option value="Reward this course">Reward this course $ 1.99 USD</option>
-<option value="Reward this course">Reward this course $ 2.99 USD</option>
-<option value="Reward this course">Reward this course $ 3.49 USD</option>
-</select>
-</div>
-<input type="hidden" name="currency_code" value="USD">
-<input type="image" class="pay_img" src="https://www.paypalobjects.com/en_GB/i/btn/btn_paynow_LG.gif" border="0" name="submit" alt="PayPal – The safer, easier way to pay online!">
-<img alt="" border="0" src="https://www.paypalobjects.com/zh_XC/i/scr/pixel.gif" width="1" height="1">
-</form>
-</div>
-</div>
-<script>
-var milib;
-if (milib==undefined) {
-var milib = function(){
-};
-milib.qeditor = function(str){
-alert('Please share this page to QPython then run it')
-};
-milib.getPurchaseNumber = function(str){
-//alert('getPurchaseNumber:'+str)
-return "10+";
-};
-milib.a8playVideoFromGW= function(url){
-window.open(url)
-};
-milib.qpylibinstall = function(cat,url,target){
-window.open(url)
-};
-milib.gotoIfPay = function(url, packageId, packageUrl){
-window.open(url)
-};
-} else {
-}
-function openPurchase() {
-//获取标识
-var u = window.location.href.split('/')
-var s = u[u.length-1].replace('.html', '')
-s = u[u.length-2]+'-'+s
-if (milib.openPurchaseActivity){
-milib.openPurchaseActivity(s)
-}else {
-if (navigator.language== "zh-CN" || navigator.language== "zh"){
-$('.zh-img ').show();
-$('.en-img').hide();
-}else {
-$('.zh-img ').hide();
-$('.en-img').show();
-}
-$('#purchase-box').show();
-}
-}
-$(function() {
-$('#purchase-box').click(function() {
-$(this).hide();
-})
-$('.purchase-img').click(function(e) {
-e.stopPropagation();
-})
-})
-//按钮事件
-function btnEvent(){
-var seq = this.getAttribute('target')-2
-var a = this.parentElement.getElementsByTagName('pre')
-if ( a && a.length > 0){
-/**********有代码，调用控制台*************/
-var code = a[seq].lastChild.nodeValue
-milib.qeditor(code)
-}else{
-//没有代码，调转
-var url = this.getAttribute('data-url');
-window.open(url);
-}
-}
-//获取赞赏
-function setPraise(){
-var u = window.location.href.split('/')
-var s = u[u.length-1].replace('.html', '')
-s = u[u.length-2]+'-'+s
-var i = document.getElementById('praise').getElementsByTagName('span')[0]
-var num = milib.getPurchaseNumber(s);
-i.innerHTML = num
-}
-//初始化代码显示
-function initCode(){
-var prearr = document.getElementsByTagName('pre');
-for (var i = 0; i < prearr.length; i++) {
-var temp_data = prearr[i].lastChild.nodeValue
-//正则处理 lavel5
-temp_data = temp_data.replace(/\^2\$/g, spaceString(2));
-temp_data = temp_data.replace(/\^4\$/g, spaceString(4));
-temp_data = temp_data.replace(/\^6\$/g, spaceString(6));
-temp_data = temp_data.replace(/\^8\$/g, spaceString(8));
-temp_data = temp_data.replace(/\^10\$/g, spaceString(10));
-temp_data = temp_data.replace(/\^12\$/g, spaceString(12));
-temp_data = temp_data.replace(/\^16\$/g, spaceString(16));
-temp_data = temp_data.replace(/\^20\$/g, spaceString(20));
-//set
-prearr[i].lastChild.nodeValue = temp_data;
-}
-}
-//产生空格
-function spaceString(num){
-var v = ''
-for (var i = 0; i < num; i++) {
-v += ' ';
-}
-return v;
-}
-function setSearchHistory (t) {
-if (!t)
-return;
-if (localStorage.course_search_list) {
-var list = localStorage.course_search_list.split('%,%');
-var i = list.indexOf(t);
-if (i !== -1) {
-list.splice(i, 1);
-list.push(t);
-localStorage.course_search_list = list.join('%,%');
-}else {
-localStorage.course_search_list += '%,%'+t;
-}
-}else {
-localStorage.course_search_list = t;
-}
-}
-window.onload = function(){
-setPraise();
-initCode();
-//将所有的button绑定事件
-var b = document.getElementsByTagName('button');
-for (var i = 0; i < b.length; i++) {
-if (b[i].className=='button') {
-b[i].setAttribute('target', i)
-b[i].onclick = btnEvent ;
-}
-}
-}
-$(function(){
-var url = location.href.split('?');
-var arg = url[1];
-if (!arg){
-return;
-}
-var args = arg.split('&');
-for (var i = 0; i < args.length; i++) {
-var c = args[i].split('=');
-if (c[0]=='form' && c[1] == 'web') {
-$('.web-show').css('display','block');
-$('.web-content').addClass('margin-header');
-}
-}
-})
-$(function() {
-$('form').submit(function() {
-setSearchHistory($("input[name='q']").val());
-})
-$('.bodywrapper .body form input').val("");
-$($('.bodywrapper .body form input')[0]).attr('placeholder', 'search');
-})
-</script>
-<script>
-(function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
-(i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
-m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
-})(window,document,'script','https://www.google-analytics.com/analytics.js','ga');
-ga('create', 'UA-103260045-3', 'auto');
-ga('send', 'pageview');
-</script>
-</body>
-</html>
\ No newline at end of file
diff --git a/docs/qpython-webapp/your-first-webapp.html b/docs/qpython-webapp/your-first-webapp.html
deleted file mode 100644
index c28557b..0000000
--- a/docs/qpython-webapp/your-first-webapp.html
+++ /dev/null
@@ -1,334 +0,0 @@
-<!DOCTYPE html>
-<html lang="en-US">
-<head>
-<meta charset="utf-8" />
-<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
-<link href="https://cdn.bootcss.com/bootstrap/3.3.7/css/bootstrap.min.css" rel="stylesheet">
-<title>QPython’s WebApp</title>
-<link rel="stylesheet" type="text/css" href="../static/pygments.css" />
-<link rel="stylesheet" type="text/css" href="../static/style.css" />
-<meta charset="UTF-8">
-<meta name="viewport" content="width=device-width, initial-scale=1,maximum-scale=1, user-scalable=no">
-<script src="https://cdn.bootcss.com/bootstrap/3.3.7/js/bootstrap.min.js"></script>
-</head><body>
-<div class="header doc-header web-show">
-<div class="col-sm-offset-2 col-sm-8  index-header">
-<div class="navbar-header">
-<button type="button" class="navbar-toggle" data-toggle="collapse" data-target="#example-navbar-collapse">
-<span class="sr-only">切换导航</span>
-<span class="icon-bar"></span>
-<span class="icon-bar"></span>
-<span class="icon-bar"></span>
-</button>
-<a href="/index.html" class="logo"><img src="http://www.qpython.org/static/img_logo.png"></a>
-</div>
-<ul class="header-title collapse navbar-collapse" id="example-navbar-collapse" >
-<li>
-<a href="http://www.qpython.org/index.html">Home</a>
-</li>
-<li>
-<a href="http://www.qpython.org/document.html">Guide</a>
-</li>
-<li class="header-phone-selected">
-<a href="/index.html" class="header-selected">Course</a>
-<span class="header-title-selected"></span>
-</li>
-<li>
-<a href="http://www.aipy.org">AIPY</a>
-</li>
-<li>
-<a target="_blank" href="https://github.com/qpython-android/qpython">Github</a>
-</li>
-</ul>
-<div class="search-box hidden-xs">
-<form action="/search.html">
-<input type="search" name="q" placeholder="keyword">
-<input type="hidden" name="check_keywords" value="yes" />
-<input type="hidden" name="area" value="default" />
-<button><img src="http://www.qpython.org/static/ic_search.png"></button>
-</form>
-</div>
-</div>
-</div>
-<main class="contain web-content">
-<div class="document">
-<div class="documentwrapper">
-<div class="bodywrapper">
-<div class="body" role="main">
-<section id="qpythons-webapp">
-<h1>QPython’s WebApp<a class="headerlink" href="#qpythons-webapp" title="Permalink to this heading">¶</a></h1>
-<p>WebApp is an app allows developers to quickly get started application development, QPython’s WebApp has the advantage of supporting common WebApps.Moreover, QPython lets the phone itself become a WebApp’s run container, support for handling complex program logic, therefore, it can greatly improve the WebApp user experience, while also increasing the flexibility of the development.WebApp statement</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1">#qpy:webapp:&lt;WebApp Program name&gt;</span>
-<span class="c1">#qpy://&lt;WebApp runs on which the IP，generally for this 127.0.0.1&gt;:&lt;The port on which WebApp is running&gt;/&lt;Corresponding WebApp URL path when Initializing&gt;</span>
-</pre></div>
-</div>
-<section id="webapp-s-run-process">
-<h2>WebApp’s Run process<a class="headerlink" href="#webapp-s-run-process" title="Permalink to this heading">¶</a></h2>
-<p>After the program is recognized as a WebApp run，QPython will let the program starts as a background service，At the same time in the front end will start a WebView component，Load the URL you specified in the program header(By the IP, port, URL path combination)For example, If the IP is 127.0.0.1, the port is 8080 and the URL path is hello / test, then the URL that WebView loads is：<a class="reference external" href="http://127.0.0.1:8080/hello/test">http://127.0.0.1:8080/hello/test</a> Web application exit principle</p>
-</section>
-<section id="exit-the-webapp">
-<h2>Exit the WebApp<a class="headerlink" href="#exit-the-webapp" title="Permalink to this heading">¶</a></h2>
-<p>When you click the Back button to exit WebView, the WebApp daemon also needs to quit. Since QPython started it as a background service in the first place, we need to tell it to quit,QPython is when WebView exits,Issue a <a class="reference external" href="http://IP:port/__exit">http://IP:port/__exit</a> GET request to the background service, By its end of background process, exit the service of work, Therefore, as a completed process, you need to define it in your own WebApp/__exit The corresponding exit service operation, we have an example in the end.</p>
-</section>
-<section id="e-g">
-<h2>E.g<a class="headerlink" href="#e-g" title="Permalink to this heading">¶</a></h2>
-<p>As QPython embedded Web development library, Let’s look at one of the easiest bottles - Hello world</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>#qpy:webapp:Hello Qpython
-#qpy://127.0.0.1:8080/
-&quot;&quot;&quot;
-This is a sample for qpython webapp
-&quot;&quot;&quot;
-from bottle import Bottle, ServerAdapter
-from bottle import run, debug, route, error, static_file, template
-######### QPYTHON WEB SERVER ###############
-class MyWSGIRefServer(ServerAdapter):
-    server = None
-    def run(self, handler):
-        from wsgiref.simple_server import make_server, WSGIRequestHandler
-        if self.quiet:
-            class QuietHandler(WSGIRequestHandler):
-            def log_request(*args, **kw): pass
-        self.options[&#39;handler_class&#39;] = QuietHandler
-    self.server = make_server(self.host, self.port, handler, **self.options)
-    self.server.serve_forever()
-    def stop(self):
-        import threading
-        threading.Thread(target=self.server.shutdown).start()
-        self.server.server_close()
-######### BUILT-IN ROUTERS ###############
-@route(&#39;/__exit&#39;, method=[&#39;GET&#39;,&#39;HEAD&#39;])
-def __exit():
-    global server
-    server.stop()
-@route(&#39;/assets/&lt;filepath:path&gt;&#39;)
-def serverstatic(filepath):
-    return static_file(filepath, root=&#39;/sdcard&#39;)
-######### WEBAPP ROUTERS WRITE YOUR CODE BELOW###############
-@route(&#39;/&#39;)
-def home():
-    return template(&#39;&lt;h1&gt;Hello {{name}} !&lt;/h1&gt;&lt;a href=&quot;/assets/qpython/projects/WebAppSample/main.py&quot;&gt;View source&lt;/a&gt;&lt;br /&gt;&lt;br /&gt; &lt;a href=&quot;http://edu.qpython.org/qpython-webapp/index.html&quot;&gt;&gt;&gt; About QPython Web App&lt;/a&gt;&#39;,name=&#39;QPython&#39;)
-######### WEBAPP ROUTERS ###############
-app = Bottle()
-app.route(&#39;/&#39;, method=&#39;GET&#39;)(home)
-app.route(&#39;/__exit&#39;, method=[&#39;GET&#39;,&#39;HEAD&#39;])(__exit)
-app.route(&#39;/assets/&lt;filepath:path&gt;&#39;, method=&#39;GET&#39;)(serverstatic)
-try:
-    server = MyWSGIRefServer(host=&quot;127.0.0.1&quot;, port=&quot;8080&quot;)
-    app.run(server=server,reloader=False)
-except Exception,ex:
-    print &quot;Exception: %s&quot; % repr(ex)
-</pre></div>
-</div>
-<button class="button">Run …</button>
-<p>Click Run, copy the code to the editor and click Run to see the result.</p>
-<p>Of course, in QPython, It also provides support for other web development frameworks to help you streamline your work.</p>
-</section>
-</section>
-<div class="clearer"></div>
-</div>
-</div>
-</div>
-<div class="clearer"></div>
-</div>
-</main>
-<div class="star-footer">
-<img onclick="openPurchase()" src="../static/star.jpg">
-<div id="praise"><span>0</span> persons have rewarded</div>
-</div>
-<div class="foo"></div>
-<footer class="clearfix fixfooter web-show">
-<div class="col-sm-offset-2 col-sm-6 footer-div1">
-<div class="footer-block-item">
-<span class="footer-item1">Built with Sphinx using a theme provid by QPython.</span>
-<span class="footer-item2 visible-xs">Copyight 2017, QPython.</span>
-</div>
-</div>
-<div class="col-sm-2 footer-div2">
-<a href="/index.html" class="pull-right"><img src="http://www.qpython.org/static/img_logo.png"></a>
-</div>
-</footer>
-<div id="purchase-box">
-<div class="zh-img purchase-img"></div>
-<div class="en-img purchase-img">
-<form action="https://www.paypal.com/cgi-bin/webscr" method="post" target="_top">
-<input type="hidden" name="cmd" value="_s-xclick">
-<input type="hidden" name="hosted_button_id" value="ST6KWAK8R63JW">
-<div class="en-thanks-tit">
-<input type="hidden" name="on0" value="Thanks">
-Thanks
-</div>
-<div class="en-thanks-sel">
-<select name="os0">
-<option value="Reward this course">Reward this course $ 0.99 USD</option>
-<option value="Reward this course">Reward this course $ 1.49 USD</option>
-<option value="Reward this course">Reward this course $ 1.99 USD</option>
-<option value="Reward this course">Reward this course $ 2.99 USD</option>
-<option value="Reward this course">Reward this course $ 3.49 USD</option>
-</select>
-</div>
-<input type="hidden" name="currency_code" value="USD">
-<input type="image" class="pay_img" src="https://www.paypalobjects.com/en_GB/i/btn/btn_paynow_LG.gif" border="0" name="submit" alt="PayPal – The safer, easier way to pay online!">
-<img alt="" border="0" src="https://www.paypalobjects.com/zh_XC/i/scr/pixel.gif" width="1" height="1">
-</form>
-</div>
-</div>
-<script>
-var milib;
-if (milib==undefined) {
-var milib = function(){
-};
-milib.qeditor = function(str){
-alert('Please share this page to QPython then run it')
-};
-milib.getPurchaseNumber = function(str){
-//alert('getPurchaseNumber:'+str)
-return "10+";
-};
-milib.a8playVideoFromGW= function(url){
-window.open(url)
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-milib.qpylibinstall = function(cat,url,target){
-window.open(url)
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-/**********有代码，调用控制台*************/
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-//没有代码，调转
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-window.open(url);
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-//获取赞赏
-function setPraise(){
-var u = window.location.href.split('/')
-var s = u[u.length-1].replace('.html', '')
-s = u[u.length-2]+'-'+s
-var i = document.getElementById('praise').getElementsByTagName('span')[0]
-var num = milib.getPurchaseNumber(s);
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diff --git a/docs/qsl4a-develop/cn/index.html b/docs/qsl4a-develop/cn/index.html
deleted file mode 100644
index cbbbd33..0000000
--- a/docs/qsl4a-develop/cn/index.html
+++ /dev/null
@@ -1,326 +0,0 @@
-<!DOCTYPE html>
-<html lang="en-US">
-<head>
-<meta charset="utf-8" />
-<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
-<link href="https://cdn.bootcss.com/bootstrap/3.3.7/css/bootstrap.min.css" rel="stylesheet">
-<title>QSL4A - QPython的安卓脚本层</title>
-<link rel="stylesheet" type="text/css" href="../../static/pygments.css" />
-<link rel="stylesheet" type="text/css" href="../../static/style.css" />
-<meta charset="UTF-8">
-<meta name="viewport" content="width=device-width, initial-scale=1,maximum-scale=1, user-scalable=no">
-<script src="https://cdn.bootcss.com/bootstrap/3.3.7/js/bootstrap.min.js"></script>
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-</button>
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-<a href="http://www.qpython.org/index.html">Home</a>
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-<a href="http://www.qpython.org/document.html">Guide</a>
-</li>
-<li class="header-phone-selected">
-<a href="/index.html" class="header-selected">Course</a>
-<span class="header-title-selected"></span>
-</li>
-<li>
-<a href="http://www.aipy.org">AIPY</a>
-</li>
-<li>
-<a target="_blank" href="https://github.com/qpython-android/qpython">Github</a>
-</li>
-</ul>
-<div class="search-box hidden-xs">
-<form action="/search.html">
-<input type="search" name="q" placeholder="keyword">
-<input type="hidden" name="check_keywords" value="yes" />
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-</form>
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-</div>
-<main class="contain web-content">
-<div class="document">
-<div class="documentwrapper">
-<div class="bodywrapper">
-<div class="body" role="main">
-<section id="qsl4a-qpython">
-<h1>QSL4A - QPython的安卓脚本层<a class="headerlink" href="#qsl4a-qpython" title="Permalink to this heading">¶</a></h1>
-<p>SL4A是Scripting Layer for Android 的缩写，中文直译为“安卓的脚本层”，QSL4A是QPython的安卓脚本层。</p>
-<section id="sl4a">
-<h2>什么是SL4A<a class="headerlink" href="#sl4a" title="Permalink to this heading">¶</a></h2>
-<p>SL4A的全称为Scripting Layer for Android, 顾名思义就是Android的脚本架构层，它的目的就是可以用熟知的脚本开发语言来开发Android应用程序。其工作原理基于RPC远程调用，通过本地的脚本解析器和远端的原生态Android Server层的APK进行信息交互，即实现一个远程代理，把本地脚本的函数调用通过json格式的封装，传递给远程原生态Server APK进行实际的android系统函数呼叫，最后将操作的执行结果反馈给本地脚本解析器，然后再在终端显示出运行结果。</p>
-<p>QPython中集成了SL4A的支持，通过SL4A，用户可以很方便地通过Python代码来调用安卓特性。</p>
-</section>
-<section id="qsl4a">
-<h2>QSL4A支持的接口<a class="headerlink" href="#qsl4a" title="Permalink to this heading">¶</a></h2>
-<p>您需要在安卓系统设置中开启QPython对应的权限设置才能使用相关的SL4A接口。</p>
-<p><strong>系统应用接口</strong></p>
-<ul class="simple">
-<li><p>Android：包括剪贴板、Intent对象、广播、震动、网络状态、版本号、发送邮件、提示、输入框等在内的接口 <a class="reference external" href="http://www.qpython.org/en/guide_androidhelpers.html">(接口文档)</a></p></li>
-<li><p>应用：主要为应用管理接口 <a class="reference external" href="http://www.qpython.org/en/guide_androidhelpers.html#applicationmanagerfacade">(接口文档)</a></p></li>
-<li><p>ActivityResult：安卓里控制ActivityResult行为的接口 <a class="reference external" href="http://www.qpython.org/en/guide_androidhelpers.html#activityresultfacade">(接口文档)</a></p></li>
-<li><p>通用Intent：访问二维码，用XX应用查看的接口 <a class="reference external" href="http://www.qpython.org/en/guide_androidhelpers.html#commonintentsfacade">(接口文档)</a></p></li>
-</ul>
-<p><strong>安卓相关设备接口</strong></p>
-<ul class="simple">
-<li><p>相机：访问&amp;调用设备的相机接口 <a class="reference external" href="http://www.qpython.org/en/guide_androidhelpers.html#camerafacade">(接口文档)</a></p></li>
-<li><p>联系人：联系人访问接口 <a class="reference external" href="http://www.qpython.org/en/guide_androidhelpers.html#contactsfacade">(接口文档)</a></p></li>
-<li><p>地理位置：访问地理位置接口 <a class="reference external" href="http://www.qpython.org/en/guide_androidhelpers.html#locationfacade">(接口文档)</a></p></li>
-<li><p>电话：访问电话接口 <a class="reference external" href="http://www.qpython.org/en/guide_androidhelpers.html#phonefacade">(接口文档)</a></p></li>
-<li><p>媒体录制：能够控制录音或者视频 <a class="reference external" href="http://www.qpython.org/en/guide_androidhelpers.html#mediarecorderfacade">(接口文档)</a></p></li>
-<li><p>感应器管理：访问&amp;控制设备的传感器 <a class="reference external" href="http://www.qpython.org/en/guide_androidhelpers.html#sensormanagerfacade">(接口文档)</a></p></li>
-<li><p>设置：能够设置设备的屏幕、飞行模式、铃声模式、震动模式、音量、亮度等 <a class="reference external" href="http://www.qpython.org/en/guide_androidhelpers.html#settingsfacade">(接口文档)</a></p></li>
-<li><p>短信：能够访问设备的短信 <a class="reference external" href="http://www.qpython.org/en/guide_androidhelpers.html#smsfacade">(接口文档)</a></p></li>
-<li><p>语音识别：能够识别用户的讲话并返回最可能的结果 <a class="reference external" href="http://www.qpython.org/en/guide_androidhelpers.html#speechrecognitionfacade">(接口文档)</a></p></li>
-<li><p>音调生成器：能够由给定的电话号码生成DTMF的语音 <a class="reference external" href="http://www.qpython.org/en/guide_androidhelpers.html#tonegeneratorfacade">(接口文档)</a></p></li>
-<li><p>唤醒锁：设备的唤醒锁接口 <a class="reference external" href="http://www.qpython.org/en/guide_androidhelpers.html#wakelockfacade">(接口文档)</a></p></li>
-<li><p>WIFI：设备的WIFI管理 <a class="reference external" href="http://www.qpython.org/en/guide_androidhelpers.html#wififacade">(接口文档)</a></p></li>
-<li><p>电池管理：提供设备的电池管理接口 <a class="reference external" href="http://www.qpython.org/en/guide_androidhelpers.html#batterymanagerfacade">(接口文档)</a></p></li>
-<li><p>媒体播放器：媒体播放器设置 <a class="reference external" href="http://www.qpython.org/en/guide_androidhelpers.html#mediaplayerfacade">(接口文档)</a></p></li>
-<li><p>偏好设置：访问／控制偏好设置 <a class="reference external" href="http://www.qpython.org/en/guide_androidhelpers.html#preferencesfacade">(接口文档)</a></p></li>
-<li><p>文字语音：控制文字到语音输出 <a class="reference external" href="http://www.qpython.org/en/guide_androidhelpers.html#texttospeechfacade">(接口文档)</a></p></li>
-<li><p>蓝牙：控制手机上的蓝牙设备 <a class="reference external" href="http://www.qpython.org/en/guide_androidhelpers.html#bluetoothfacade">(接口文档)</a></p></li>
-<li><p>信号强度：访问手机信号强度信息 <a class="reference external" href="http://www.qpython.org/en/guide_androidhelpers.html#signalstrengthfacade">(接口文档)</a></p></li>
-<li><p>网络摄像头：能够控制手机的摄像机 <a class="reference external" href="http://www.qpython.org/en/guide_androidhelpers.html#webcamfacade">(接口文档)</a></p></li>
-<li><p>用户交互界面：用于控制UI界面相关的展现，如文本对话框，密码输入框、状态栏、进度条、日期选择器等 <a class="reference external" href="http://www.qpython.org/en/guide_androidhelpers.html#uifacade">(接口文档)</a></p></li>
-<li><p>NFC：NFC相关的控制，主要提供了NFC中的master/slave交换信息的方式 <a class="reference external" href="http://www.qpython.org/en/guide_androidhelpers.html#nfc">(接口文档)</a></p></li>
-<li><p>USB宿主序列：用于控制来自安卓的拥有USB主机控制器的类USB序列的设备 <a class="reference external" href="http://www.qpython.org/en/guide_androidhelpers.html#usb-host-serial-facade">(接口文档)</a></p></li>
-</ul>
-<p><strong>QPython相关的接口</strong></p>
-<ul class="simple">
-<li><p>QPy接口：在手机上运行QPython脚本 <a class="reference external" href="http://www.qpython.org/en/guide_androidhelpers.html#qpyinterfacefacade">(接口文档)</a></p></li>
-</ul>
-</section>
-<section id="id28">
-<h2>示例代码<a class="headerlink" href="#id28" title="Permalink to this heading">¶</a></h2>
-<p>在安卓中，可以引入androidhelper模块来载入QSL4A的支持。下面我们来运行一个简单的QSL4A脚本来体验下：</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>import androidhelper
-droid = androidhelper.Android()
-line = droid.dialogGetInput()
-s = &quot;Hello, %s&quot; % (line.result,)
-droid.makeToast(line)
-</pre></div>
-</div>
-<button class="button">Run …</button>
-</section>
-</section>
-<div class="clearer"></div>
-</div>
-</div>
-</div>
-<div class="clearer"></div>
-</div>
-</main>
-<div class="star-footer">
-<img onclick="openPurchase()" src="../../static/star.jpg">
-<div id="praise"><span>0</span> persons have rewarded</div>
-</div>
-<div class="foo"></div>
-<footer class="clearfix fixfooter web-show">
-<div class="col-sm-offset-2 col-sm-6 footer-div1">
-<div class="footer-block-item">
-<span class="footer-item1">Built with Sphinx using a theme provid by QPython.</span>
-<span class="footer-item2 visible-xs">Copyight 2017, QPython.</span>
-</div>
-</div>
-<div class="col-sm-2 footer-div2">
-<a href="/index.html" class="pull-right"><img src="http://www.qpython.org/static/img_logo.png"></a>
-</div>
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-<div id="purchase-box">
-<div class="zh-img purchase-img"></div>
-<div class="en-img purchase-img">
-<form action="https://www.paypal.com/cgi-bin/webscr" method="post" target="_top">
-<input type="hidden" name="cmd" value="_s-xclick">
-<input type="hidden" name="hosted_button_id" value="ST6KWAK8R63JW">
-<div class="en-thanks-tit">
-<input type="hidden" name="on0" value="Thanks">
-Thanks
-</div>
-<div class="en-thanks-sel">
-<select name="os0">
-<option value="Reward this course">Reward this course $ 0.99 USD</option>
-<option value="Reward this course">Reward this course $ 1.49 USD</option>
-<option value="Reward this course">Reward this course $ 1.99 USD</option>
-<option value="Reward this course">Reward this course $ 2.99 USD</option>
-<option value="Reward this course">Reward this course $ 3.49 USD</option>
-</select>
-</div>
-<input type="hidden" name="currency_code" value="USD">
-<input type="image" class="pay_img" src="https://www.paypalobjects.com/en_GB/i/btn/btn_paynow_LG.gif" border="0" name="submit" alt="PayPal – The safer, easier way to pay online!">
-<img alt="" border="0" src="https://www.paypalobjects.com/zh_XC/i/scr/pixel.gif" width="1" height="1">
-</form>
-</div>
-</div>
-<script>
-var milib;
-if (milib==undefined) {
-var milib = function(){
-};
-milib.qeditor = function(str){
-alert('Please share this page to QPython then run it')
-};
-milib.getPurchaseNumber = function(str){
-//alert('getPurchaseNumber:'+str)
-return "10+";
-};
-milib.a8playVideoFromGW= function(url){
-window.open(url)
-};
-milib.qpylibinstall = function(cat,url,target){
-window.open(url)
-};
-milib.gotoIfPay = function(url, packageId, packageUrl){
-window.open(url)
-};
-} else {
-}
-function openPurchase() {
-//获取标识
-var u = window.location.href.split('/')
-var s = u[u.length-1].replace('.html', '')
-s = u[u.length-2]+'-'+s
-if (milib.openPurchaseActivity){
-milib.openPurchaseActivity(s)
-}else {
-if (navigator.language== "zh-CN" || navigator.language== "zh"){
-$('.zh-img ').show();
-$('.en-img').hide();
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-$('.zh-img ').hide();
-$('.en-img').show();
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-$('#purchase-box').show();
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-$('#purchase-box').click(function() {
-$(this).hide();
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-$('.purchase-img').click(function(e) {
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-//按钮事件
-function btnEvent(){
-var seq = this.getAttribute('target')-2
-var a = this.parentElement.getElementsByTagName('pre')
-if ( a && a.length > 0){
-/**********有代码，调用控制台*************/
-var code = a[seq].lastChild.nodeValue
-milib.qeditor(code)
-}else{
-//没有代码，调转
-var url = this.getAttribute('data-url');
-window.open(url);
-}
-}
-//获取赞赏
-function setPraise(){
-var u = window.location.href.split('/')
-var s = u[u.length-1].replace('.html', '')
-s = u[u.length-2]+'-'+s
-var i = document.getElementById('praise').getElementsByTagName('span')[0]
-var num = milib.getPurchaseNumber(s);
-i.innerHTML = num
-}
-//初始化代码显示
-function initCode(){
-var prearr = document.getElementsByTagName('pre');
-for (var i = 0; i < prearr.length; i++) {
-var temp_data = prearr[i].lastChild.nodeValue
-//正则处理 lavel5
-temp_data = temp_data.replace(/\^2\$/g, spaceString(2));
-temp_data = temp_data.replace(/\^4\$/g, spaceString(4));
-temp_data = temp_data.replace(/\^6\$/g, spaceString(6));
-temp_data = temp_data.replace(/\^8\$/g, spaceString(8));
-temp_data = temp_data.replace(/\^10\$/g, spaceString(10));
-temp_data = temp_data.replace(/\^12\$/g, spaceString(12));
-temp_data = temp_data.replace(/\^16\$/g, spaceString(16));
-temp_data = temp_data.replace(/\^20\$/g, spaceString(20));
-//set
-prearr[i].lastChild.nodeValue = temp_data;
-}
-}
-//产生空格
-function spaceString(num){
-var v = ''
-for (var i = 0; i < num; i++) {
-v += ' ';
-}
-return v;
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-function setSearchHistory (t) {
-if (!t)
-return;
-if (localStorage.course_search_list) {
-var list = localStorage.course_search_list.split('%,%');
-var i = list.indexOf(t);
-if (i !== -1) {
-list.splice(i, 1);
-list.push(t);
-localStorage.course_search_list = list.join('%,%');
-}else {
-localStorage.course_search_list += '%,%'+t;
-}
-}else {
-localStorage.course_search_list = t;
-}
-}
-window.onload = function(){
-setPraise();
-initCode();
-//将所有的button绑定事件
-var b = document.getElementsByTagName('button');
-for (var i = 0; i < b.length; i++) {
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-b[i].setAttribute('target', i)
-b[i].onclick = btnEvent ;
-}
-}
-}
-$(function(){
-var url = location.href.split('?');
-var arg = url[1];
-if (!arg){
-return;
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-var args = arg.split('&');
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diff --git a/docs/qsl4a-develop/index.html b/docs/qsl4a-develop/index.html
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--- a/docs/qsl4a-develop/index.html
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-<title>QSL4A - QPython’s Android script layer</title>
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-<section id="qsl4a-qpython-s-android-script-layer">
-<h1>QSL4A - QPython’s Android script layer<a class="headerlink" href="#qsl4a-qpython-s-android-script-layer" title="Permalink to this heading">¶</a></h1>
-<p>SL4A is an acronym for Android’s script layer, QSL4A is QPython’s Android script layer.</p>
-<section id="what-is-sl4a">
-<h2>What is SL4A?<a class="headerlink" href="#what-is-sl4a" title="Permalink to this heading">¶</a></h2>
-<p>The full name of SL4A Scripting Layer for Android, as the name suggests is Android’s script architecture layer, Its purpose is to develop Android applications using well-known scripting languages.Its working principle is based on RPC remote call, through the local script parser and the remote native Android server layer APK information exchange,That is to achieve a remote agent,The local script function calls is encapsulated by JSON format,pass to the Remote ECS APK and proceed to the actual android system function call,and the results of the operation will be fed back to the local script parser,finally the terminal will show the results of the operation.</p>
-<p>QPython integrated support of SL4A, users can easily be invoked by Andrews characteristics Python code, by SL4A.</p>
-</section>
-<section id="qsl4a-supported-interfaces">
-<h2>QSL4A supported interfaces<a class="headerlink" href="#qsl4a-supported-interfaces" title="Permalink to this heading">¶</a></h2>
-<p>You need to turn on the QPython permission settings in your Android system settings to use the associated SL4A interface.</p>
-<p><strong>System application interface</strong></p>
-<ul class="simple">
-<li><p>Including clipboard, Intent object, broadcast, vibration, network status, version number, send mail, prompts, Input boxes, etc. <a class="reference external" href="http://www.qpython.org/en/guide_androidhelpers.html">(APIs Document)</a></p></li>
-<li><p>Application: Mainly as an application management interface <a class="reference external" href="http://www.qpython.org/en/guide_androidhelpers.html#applicationmanagerfacade">(APIs Document)</a></p></li>
-<li><p>ActivityResult??In Android control ActivityResult the interface of behavior <a class="reference external" href="http://www.qpython.org/en/guide_androidhelpers.html#activityresultfacade">(APIs Document)</a></p></li>
-<li><p>Common Intent??Access two-dimensional code, Use XX application to view the interface <a class="reference external" href="http://www.qpython.org/en/guide_androidhelpers.html#commonintentsfacade">(APIs Document)</a></p></li>
-</ul>
-<p><strong>Android related device interface</strong></p>
-<ul class="simple">
-<li><p>Camera??Access &amp; recall device’s camera interface <a class="reference external" href="http://www.qpython.org/en/guide_androidhelpers.html#camerafacade">(APIs Document)</a></p></li>
-<li><p>Contact??Contact access interface <a class="reference external" href="http://www.qpython.org/en/guide_androidhelpers.html#contactsfacade">(APIs Document)</a></p></li>
-<li><p>Location??Access location interface <a class="reference external" href="http://www.qpython.org/en/guide_androidhelpers.html#locationfacade">(APIs Document)</a></p></li>
-<li><p>Phone: Access the phone interface <a class="reference external" href="http://www.qpython.org/en/guide_androidhelpers.html#phonefacade">(APIs Document)</a></p></li>
-<li></li>
-<li><p>Media recording: able to control recording or video <a class="reference external" href="http://www.qpython.org/en/guide_androidhelpers.html#mediarecorderfacade">(APIs Document)</a></p></li>
-<li><p>Sensor Management: Access Control &amp; Sensor device <a class="reference external" href="http://www.qpython.org/en/guide_androidhelpers.html#sensormanagerfacade">(APIs Document)</a></p></li>
-<li><p>Settings: Ability to set device screen, flight mode, ringtone mode, vibration mode, volume, brightness, etc. <a class="reference external" href="http://www.qpython.org/en/guide_androidhelpers.html#settingsfacade">(APIs Document)</a></p></li>
-<li><p>SMS: equipment can be access to SMS <a class="reference external" href="http://www.qpython.org/en/guide_androidhelpers.html#smsfacade">(APIs Document)</a></p></li>
-<li><p>Speech recognition: Can recognize the user’s speech and returns the most likely outcome <a class="reference external" href="http://www.qpython.org/en/guide_androidhelpers.html#speechrecognitionfacade">(APIs Document)</a></p></li>
-<li><p>Tone Generator: The ability to generate DTMF speech from a given phone number <a class="reference external" href="http://www.qpython.org/en/guide_androidhelpers.html#tonegeneratorfacade">(APIs Document)</a></p></li>
-<li><p>Wake lock: The device’s wake lock interface <a class="reference external" href="http://www.qpython.org/en/guide_androidhelpers.html#wakelockfacade">(APIs Document)</a></p></li>
-<li><p>WIFI: Device WIFI Management <a class="reference external" href="http://www.qpython.org/en/guide_androidhelpers.html#wififacade">(APIs Document)</a></p></li>
-<li><p>Battery Management: Provides the device’s battery management interface <a class="reference external" href="http://www.qpython.org/en/guide_androidhelpers.html#batterymanagerfacade">(APIs Document)</a></p></li>
-<li><p>Media Player: Media Player Settings <a class="reference external" href="http://www.qpython.org/en/guide_androidhelpers.html#mediaplayerfacade">(APIs Document)</a></p></li>
-<li><p>Preferences: Access / Control Preferences <a class="reference external" href="http://www.qpython.org/en/guide_androidhelpers.html#preferencesfacade">(APIs Document)</a></p></li>
-<li><p>Text Voice: Control text to voice output <a class="reference external" href="http://www.qpython.org/en/guide_androidhelpers.html#texttospeechfacade">(APIs Document)</a></p></li>
-<li><p>Bluetooth: Control Bluetooth devices on your phone <a class="reference external" href="http://www.qpython.org/en/guide_androidhelpers.html#bluetoothfacade">(APIs Document)</a></p></li>
-<li><p>Signal strength: access phone signal strength information <a class="reference external" href="http://www.qpython.org/en/guide_androidhelpers.html#signalstrengthfacade">(APIs Document)</a></p></li>
-<li><p>Webcam: A camera that controls the phone <a class="reference external" href="http://www.qpython.org/en/guide_androidhelpers.html#webcamfacade">(APIs Document)</a></p></li>
-<li><p>User interface: UI interface is used to control related exhibits, such as a text box, the password input box, the status bar, progress bar, date picker, etc. <a class="reference external" href="http://www.qpython.org/en/guide_androidhelpers.html#uifacade">(APIs Document)</a></p></li>
-<li><p>NFC: NFC related control, mainly provided in the NFC mode master/slave exchange information <a class="reference external" href="http://www.qpython.org/en/guide_androidhelpers.html#nfc">(APIs Document)</a></p></li>
-<li><p>USB Host Sequence: A device used to control USB-like sequences from Android that has a USB host controller <a class="reference external" href="http://www.qpython.org/en/guide_androidhelpers.html#usb-host-serial-facade">(APIs Document)</a></p></li>
-</ul>
-<p><strong>QPython relevant interface</strong></p>
-<ul class="simple">
-<li><p>QPy interface: Run QPython script on your phone <a class="reference external" href="http://www.qpython.org/en/guide_androidhelpers.html#qpyinterfacefacade">(APIs Document)</a></p></li>
-</ul>
-</section>
-<section id="sample-code">
-<h2>Sample Code<a class="headerlink" href="#sample-code" title="Permalink to this heading">¶</a></h2>
-<p>In Android, you can import the androidhelper module to load QSL4A support.Let’s run a simple QSL4A script to experience the following:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>import androidhelper
-droid = androidhelper.Android()
-line = droid.dialogGetInput()
-s = &quot;Hello, %s&quot; % (line.result,)
-droid.makeToast(line)
-</pre></div>
-</div>
-<button class="button">Run …</button>
-</section>
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-<span class="footer-item2 visible-xs">Copyight 2017, QPython.</span>
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-//获取赞赏
-function setPraise(){
-var u = window.location.href.split('/')
-var s = u[u.length-1].replace('.html', '')
-s = u[u.length-2]+'-'+s
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-//初始化代码显示
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-var prearr = document.getElementsByTagName('pre');
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-var temp_data = prearr[i].lastChild.nodeValue
-//正则处理 lavel5
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-//产生空格
-function spaceString(num){
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-}
-function setSearchHistory (t) {
-if (!t)
-return;
-if (localStorage.course_search_list) {
-var list = localStorage.course_search_list.split('%,%');
-var i = list.indexOf(t);
-if (i !== -1) {
-list.splice(i, 1);
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-localStorage.course_search_list = list.join('%,%');
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-localStorage.course_search_list += '%,%'+t;
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-localStorage.course_search_list = t;
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-window.onload = function(){
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diff --git a/docs/sample.html b/docs/sample.html
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@@ -1,330 +0,0 @@
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-<link href="https://cdn.bootcss.com/bootstrap/3.3.7/css/bootstrap.min.css" rel="stylesheet">
-<title>A hands-on introduction to Python for beginning programmers</title>
-<link rel="stylesheet" type="text/css" href="static/pygments.css" />
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-<section id="a-hands-on-introduction-to-python-for-beginning-programmers">
-<h1>A hands-on introduction to Python for beginning programmers<a class="headerlink" href="#a-hands-on-introduction-to-python-for-beginning-programmers" title="Permalink to this heading">¶</a></h1>
-<p>If you are a new guy to QPython, you should watch the tutorial video and just follow the steps to learn how to run script with QPython</p>
-
-        <button data-url="http://www.baidu.com" class="button">Watch Tutorial</button>
-    
-<section id="running-with-console">
-<h2>Running with Console<a class="headerlink" href="#running-with-console" title="Permalink to this heading">¶</a></h2>
-<p>Running QPython’s simple script with console model</p>
-<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>qpy:2
-#qpy:console
-#qpy:http://qpython.com/s/qpy-sample.py
-class Calculator(object):
-    #define class to simulate a simple calculator
-    def __init__ (self):
-        #start with zero
-        self.current = 0
-    def add(self, amount):
-        #add number to current
-        self.current += amount
-    def getCurrent(self):
-        return self.current
-myBuddy = Calculator() # make myBuddy into a Calculator object
-myBuddy.add(2) #use myBuddy&#39;s new add method derived from the Calculator class
-print(myBuddy.getCurrent()) #print myBuddy&#39;s current instance variable
-</pre></div>
-</div>
-<button class="button">Run …</button>
-</section>
-<section id="running-with-kivy">
-<h2>Running with Kivy<a class="headerlink" href="#running-with-kivy" title="Permalink to this heading">¶</a></h2>
-<p>Running QPython’s script with GUI model ( Kivy library )</p>
-<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>#qpy:2
-#qpy:kivy
-#qpy:http://qpython.com/s/qpy-guisample.py
-from kivy.app import App
-from kivy.uix.button import Button
-class TestApp(App):
-    def build(self):
-        # display a button with the text : Hello QPython
-        return Button(text=&#39;Hello QPython&#39;)
-TestApp().run()
-</pre></div>
-</div>
-<button class="button">Run …</button>
-</section>
-<section id="running-as-web-app">
-<h2>Running as Web App<a class="headerlink" href="#running-as-web-app" title="Permalink to this heading">¶</a></h2>
-<p>Running Web App with QPython</p>
-<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>#qpy:2
-#qpy:webapp:Hello Qpython
-#qpy://localhost:8080/hello
-&quot;&quot;&quot;
-This is a sample for qpython webapp
-&quot;&quot;&quot;
-from bottle import route, run
-@route(&#39;/hello&#39;)
-def hello():
-    return &quot;Hello World!&quot;
-run(host=&#39;localhost&#39;, port=8080)
-</pre></div>
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-<button class="button">Run …</button>
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-<div class="foo"></div>
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-<img alt="" border="0" src="https://www.paypalobjects.com/zh_XC/i/scr/pixel.gif" width="1" height="1">
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-<script>
-var milib;
-if (milib==undefined) {
-var milib = function(){
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-milib.qeditor = function(str){
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-};
-milib.getPurchaseNumber = function(str){
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-return "10+";
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-$('#purchase-box').show();
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-var a = this.parentElement.getElementsByTagName('pre')
-if ( a && a.length > 0){
-/**********有代码，调用控制台*************/
-var code = a[seq].lastChild.nodeValue
-milib.qeditor(code)
-}else{
-//没有代码，调转
-var url = this.getAttribute('data-url');
-window.open(url);
-}
-}
-//获取赞赏
-function setPraise(){
-var u = window.location.href.split('/')
-var s = u[u.length-1].replace('.html', '')
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-var num = milib.getPurchaseNumber(s);
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-var prearr = document.getElementsByTagName('pre');
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-var temp_data = prearr[i].lastChild.nodeValue
-//正则处理 lavel5
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-temp_data = temp_data.replace(/\^4\$/g, spaceString(4));
-temp_data = temp_data.replace(/\^6\$/g, spaceString(6));
-temp_data = temp_data.replace(/\^8\$/g, spaceString(8));
-temp_data = temp_data.replace(/\^10\$/g, spaceString(10));
-temp_data = temp_data.replace(/\^12\$/g, spaceString(12));
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-return;
-if (localStorage.course_search_list) {
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-$('.bodywrapper .body form input').val("");
-$($('.bodywrapper .body form input')[0]).attr('placeholder', 'search');
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diff --git a/docs/search.html b/docs/search.html
deleted file mode 100644
index 1990f9f..0000000
--- a/docs/search.html
+++ /dev/null
@@ -1,288 +0,0 @@
-<!DOCTYPE html>
-<html lang="en-US">
-<head>
-<meta charset="utf-8" />
-<meta name="viewport" content="width=device-width, initial-scale=1.0" />
-<link href="https://cdn.bootcss.com/bootstrap/3.3.7/css/bootstrap.min.css" rel="stylesheet">
-<title>Search</title>
-<link rel="stylesheet" type="text/css" href="static/pygments.css" />
-<link rel="stylesheet" type="text/css" href="static/style.css" />
-<script src="static/searchtools.js"></script>
-<script src="static/language_data.js"></script>
-<script src="searchindex.js" defer></script>
-<meta charset="UTF-8">
-<meta name="viewport" content="width=device-width, initial-scale=1,maximum-scale=1, user-scalable=no">
-<script src="https://cdn.bootcss.com/bootstrap/3.3.7/js/bootstrap.min.js"></script>
-</head><body>
-<div class="header doc-header web-show">
-<div class="col-sm-offset-2 col-sm-8  index-header">
-<div class="navbar-header">
-<button type="button" class="navbar-toggle" data-toggle="collapse" data-target="#example-navbar-collapse">
-<span class="sr-only">切换导航</span>
-<span class="icon-bar"></span>
-<span class="icon-bar"></span>
-<span class="icon-bar"></span>
-</button>
-<a href="/index.html" class="logo"><img src="http://www.qpython.org/static/img_logo.png"></a>
-</div>
-<ul class="header-title collapse navbar-collapse" id="example-navbar-collapse" >
-<li>
-<a href="http://www.qpython.org/index.html">Home</a>
-</li>
-<li>
-<a href="http://www.qpython.org/document.html">Guide</a>
-</li>
-<li class="header-phone-selected">
-<a href="/index.html" class="header-selected">Course</a>
-<span class="header-title-selected"></span>
-</li>
-<li>
-<a href="http://www.aipy.org">AIPY</a>
-</li>
-<li>
-<a target="_blank" href="https://github.com/qpython-android/qpython">Github</a>
-</li>
-</ul>
-<div class="search-box hidden-xs">
-<form action="/search.html">
-<input type="search" name="q" placeholder="keyword">
-<input type="hidden" name="check_keywords" value="yes" />
-<input type="hidden" name="area" value="default" />
-<button><img src="http://www.qpython.org/static/ic_search.png"></button>
-</form>
-</div>
-</div>
-</div>
-<main class="contain web-content">
-<div class="document">
-<div class="documentwrapper">
-<div class="bodywrapper">
-<div class="body" role="main">
-<h1 id="search-documentation">Search</h1>
-<noscript>
-<div class="admonition warning">
-<p>
-Please activate JavaScript to enable the search
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-</noscript>
-<p>
-Searching for multiple words only shows matches that contain
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-<input type="text" name="q" aria-labelledby="search-documentation" value="" autocomplete="off" autocorrect="off" autocapitalize="off" spellcheck="false"/>
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-<div id="search-results">
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-</div>
-</div>
-</div>
-<div class="clearer"></div>
-</div>
-</main>
-<div class="star-footer">
-<img onclick="openPurchase()" src="static/star.jpg">
-<div id="praise"><span>0</span> persons have rewarded</div>
-</div>
-<div class="foo"></div>
-<footer class="clearfix fixfooter web-show">
-<div class="col-sm-offset-2 col-sm-6 footer-div1">
-<div class="footer-block-item">
-<span class="footer-item1">Built with Sphinx using a theme provid by QPython.</span>
-<span class="footer-item2 visible-xs">Copyight 2017, QPython.</span>
-</div>
-</div>
-<div class="col-sm-2 footer-div2">
-<a href="/index.html" class="pull-right"><img src="http://www.qpython.org/static/img_logo.png"></a>
-</div>
-</footer>
-<div id="purchase-box">
-<div class="zh-img purchase-img"></div>
-<div class="en-img purchase-img">
-<form action="https://www.paypal.com/cgi-bin/webscr" method="post" target="_top">
-<input type="hidden" name="cmd" value="_s-xclick">
-<input type="hidden" name="hosted_button_id" value="ST6KWAK8R63JW">
-<div class="en-thanks-tit">
-<input type="hidden" name="on0" value="Thanks">
-Thanks
-</div>
-<div class="en-thanks-sel">
-<select name="os0">
-<option value="Reward this course">Reward this course $ 0.99 USD</option>
-<option value="Reward this course">Reward this course $ 1.49 USD</option>
-<option value="Reward this course">Reward this course $ 1.99 USD</option>
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-</form>
-</div>
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-<script>
-var milib;
-if (milib==undefined) {
-var milib = function(){
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-milib.qeditor = function(str){
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-};
-milib.getPurchaseNumber = function(str){
-//alert('getPurchaseNumber:'+str)
-return "10+";
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-milib.a8playVideoFromGW= function(url){
-window.open(url)
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diff --git a/docs/searchindex.js b/docs/searchindex.js
deleted file mode 100644
index 3cf1f08..0000000
--- a/docs/searchindex.js
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\ No newline at end of file
diff --git a/docs/sources/AIPY/index.rst.txt b/docs/sources/AIPY/index.rst.txt
deleted file mode 100644
index a313434..0000000
--- a/docs/sources/AIPY/index.rst.txt
+++ /dev/null
@@ -1,98 +0,0 @@
-AIPY - Learn AI programming on Mobile
---------------------------------------------------------
-.. image:: http://www.aipy.org/img/img_logo.png
-    :alt: AIPY Logo
-    :target: http://www.aipy.org
-
-AIPY  is a high-level AI learning app, based on related libraries like Numpy, Scipy, theano, keras, etc.... It was developed with a focus on helping you learn and practise AI related programming well and fast.
-
-Now, AIPY is released as a QPython plugin, after being installed, you can see the AIPY category in QPYPI, where you could find Numpy, Scipy, Pandas, Matplotlib, Scikit-learn, Theano, Lasange, Keras.
-
-By installing these libraries, you could start mathematics, scientific, data  analytics, deeplearning etc. with QPython.
-
-
-How to use
---------------
-You should install AIPY application first, then please make sure you have the latest QPython(version >=2.2.3-1) installed.
-
-If you have completed the previous step, then you could launch QPython and enter the QPYPI, you will see the AIPY category, from where you can install the related libraries. They are numpy, scipy, pandas, matplotlib, scikit-learn, theano, Lasagne, keras etc.
-
-.. image:: http://edu.qpython.org/static/qpypi-aipy.png
-    :alt: AIPY libraires
-
-*You can install AIPY libraries from QPython's QPYPI*
-
-
-Keras
------------
-Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.
-
-It runs on top of theano in QPython now, we will try to add tensorflow and other deeplearning frmaework in the future.
-
-Lasange
------------
-Lasagne is a lightweight library to build and train neural networks in Theano.
-
-
-Theano
--------
-Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently.
-
-
-**Besides these high-level AI related libraries, QPython supports the following libraries also.**
-
-
-Numpy
------------
-NumPy is the fundamental package for scientific computing with Python. It contains the following things:
-
-- 1 A powerful N-dimensional array object
-- 2 Sophisticated (broadcasting) functions
-- 3 Tools for integrating C/C++ and Fortran code
-- 4 Useful linear algebra, Fourier transform, and random number capabilities
-
-
-Scipy
--------
-SciPy is a Python-based ecosystem of open-source software for mathematics, science, and engineering
-
-
-Pandas
---------
-Pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Additionally, it has the broader goal of becoming the most powerful and flexible open source data analysis / manipulation tool available in any language. It is already well on its way toward this goal.
-
-
-Matplotlib
-------------
-Matplotlib is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms.
-
-In QPython you could use the webagg as backends with tornado, please install tornado from QPYPI and make sure your script contains the below declarations.
-
-::
-
-    #qpy:webapp:QPyMatplot
-    #qpy://127.0.0.1:8988/
-
-    import matplotlib
-    matplotlib.use('webagg')
-    ...
-
-
-Scikit-learn
-------------
-Scikit-learn is a Machine Learning framework in Python.
-
-It has the following features:
-
-- 1 Simple and efficient tools for data mining and data analysis
-- 2 Accessible to everybody, and reusable in various contexts
-
-
-
-**It's AIPY's first release, we will keep developing, and bring more amazing features. Please give us any feedback to support@qpython.org.**
-
-.. image:: http://edu.qpython.org/static/aipy.png
-    :alt: Get AIPY Application
-    :target: http://www.aipy.org
-
-*Get AIPY Application http://www.aipy.org*
diff --git a/docs/sources/activity-edu.rst.txt b/docs/sources/activity-edu.rst.txt
deleted file mode 100644
index 5fc4e69..0000000
--- a/docs/sources/activity-edu.rst.txt
+++ /dev/null
@@ -1,8 +0,0 @@
-QPython Course
-===============================
-QPython courses is a social Python learning project, based on QPython community, we hope more and more people could join us wo help write or translate and share QPython learning materials which could help other friends learn Python with more fun.
-
-
-How to contribute
------------------
-Please visit `QPython course <https://github.com/qpython-android/course>`_
diff --git a/docs/sources/activity-qpypi.rst.txt b/docs/sources/activity-qpypi.rst.txt
deleted file mode 100644
index e2b189b..0000000
--- a/docs/sources/activity-qpypi.rst.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-What package do you want
-=========================
-What pakcage do you need, pelase file a ticket in `QPYPI <https://github.com/qpython-android/QPYPI/issues>`_
-
diff --git a/docs/sources/aipy-keras/index.rst.txt b/docs/sources/aipy-keras/index.rst.txt
deleted file mode 100644
index 431de56..0000000
--- a/docs/sources/aipy-keras/index.rst.txt
+++ /dev/null
@@ -1,19 +0,0 @@
-Keras Programming
-=======================
-The courses are still in the planning. Welcome to reward us. 
-
-1. **Primary course**
-
-If the total number of people who reward us is more than 100, we promise to complete the primary course within 1 week, so that, after finishing your study, you can master the basic concepts and write simple scripts.

-
-2. **Intermediate course**
-
-If the total number of people who reward us is more than 500, we will launch the intermediate course within 2 weeks, so that, after finishing your study, you will be familiar with the knowledge and framework of the course and apply it to practical development.
-
-3. **Advanced course**
-
-If the total number of people who reward us is more than 2,000, we will launch the advanced course in a month, so that, after finishing your study, you will have a better understanding of the principles of the framework and be able to do advanced development.
-
-
-**The primary course is free to all, and the intermediate and advanced courses are only open to those who have rewarded us.**
-
diff --git a/docs/sources/aipy-numpy/cn/10sample.rst.txt b/docs/sources/aipy-numpy/cn/10sample.rst.txt
deleted file mode 100644
index f62e824..0000000
--- a/docs/sources/aipy-numpy/cn/10sample.rst.txt
+++ /dev/null
@@ -1,1269 +0,0 @@
-
-各种绘图实例
-=============
-
-简单绘图
-----------
-
-plot 函数：
-
-
-
-::
-
-    %matplotlib inline
-
-    import numpy as np
-    import matplotlib.pyplot as plt
-
-    t = np.arange(0.0, 2.0, 0.01)
-    s = np.sin(2*np.pi*t)
-    plt.plot(t, s)
-
-    plt.xlabel('time (s)')
-    plt.ylabel('voltage (mV)')
-    plt.title('About as simple as it gets, folks')
-    plt.grid(True)
-    plt.show()
-
-
-<button>Run ...</button>
-
-
-.. image:: 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-子图
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-
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-*subplot* 函数：
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-::
-
-    import numpy as np
-    import matplotlib.mlab as mlab
-
-    x1 = np.linspace(0.0, 5.0)
-    x2 = np.linspace(0.0, 2.0)
-
-    y1 = np.cos(2 * np.pi * x1) * np.exp(-x1)
-    y2 = np.cos(2 * np.pi * x2)
-
-    plt.subplot(2, 1, 1)
-    plt.plot(x1, y1, 'yo-')
-    plt.title('A tale of 2 subplots')
-    plt.ylabel('Damped oscillation')
-
-    plt.subplot(2, 1, 2)
-    plt.plot(x2, y2, 'r.-')
-    plt.xlabel('time (s)')
-    plt.ylabel('Undamped')
-
-    plt.show()
-
-
-<button>Run ...</button>
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-.. image:: 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hG9AjHp9FEVW8KWB4iIstjJVCsqV79Ur744hZycw+TlpZcT+Ht2tVl5MhK/OpXx01O48Y1oXv3gcf8FpMnD8eZpCrRpcv5nHzy39i//wqqVv2pP0IbhpFShJOnsQC4T1XnecuXAk+rats4yBcW4eZpgHMmjxnTnSpVziAtrXbShKXu3buIlSuvZ9++J5k583XyFUOnTgOLlH/r1lf5+uvfs3v3I7zzzlsWjmsYxjFiladxJzBGRKp7y7uA3iUVLhHIjz7q02cv4CZLyRCWmpOzj9Wru9O06b857bSudOjQN+zP1q7djY8+Wsxbbw2kX7/cY+uT4XsbhpF4FDvTODbQUxqqmnDhtuHONAYNupabbnqv0PrJk6/l2WffiYVoUWHNmr5AOc4++5WIPp+s39swjNgSq5kGkJjKoqSEij5KtLDUwAS+I0d2ct55O/jNb76KeH/J8r0Nw0h8wlYaqYBqxRBbEschHiyBb+zY+px11ocRm5KS4XsbhpEchBNymzIEC0sdN64JnToN9EmiwgRL4OvV6wemTBke8T6Dfe+xYxsm1Pc2DCM5CDnTEJEuuO56QpAue6o6KYZyxYTAsNTs7B84fHgLPXs+m1DO4FiYkgqG4x46tJF27WpwxRXXRbzPRKKoelxWwNEwoktR5qlf4pTFaUA7YLa3/grgYyDplAa4G+iVV15PdvYuFiz4CZdcklgVb2NlSsr/3gB5eUdZsuRCtm17jdq1u5Vqv34TqlDjihWL+OKLcVbA0TCiTEjzlKr2UdW+QAVcefQuqtoFONdbl9Skp5/MSSedyb59i/0W5QQ6dx7E2LENTlgXbRNauXIVOOusV1i37l6OHt0etf36weTJzwQt1Dht2pNB15fGzGcYRniO8AbAloDlrcBPYiNOfMlvAVu9eqi6TfHnyiuvZ+PGnzFhQgVOOqk+UOmEjO9oUa3aRdSu3ZN16/6P5s3HR3XfsaCgqaljx5tp2nQ1+/YFLwlTsWLwEGzVfUH3Z6YrwwiPcJTGB8C7IjIB59+4FXg/plLFiRo1Mtm48d80bPiQ36IcIydnL2ec8Qm33LKSihXrxfRYZ5zxGIsWtWDHjrepVesXMT1WaQhmgnrllQ/o2PEmqlZtC8wr9JmcnKpAYf/Qnj2fMnZsB7KyVtGr1/fH1qeS6coUohFLwikjIsCNwGXeqrmqOjnWgpWEkpQRCSQ7eycLFjTkkkt2Uq5cegwkKzkbN/6b3buzOPfcN+NyvF275vDqq11Zvfo8ypXLTcibTFHJiZ06DQzSY6QJP/1pz0I+jXHjmnDLLY8yceJgunf/Ouj+kiXZsWTNuJrQrduzFhxgFCImyX2qqiKyFNinqu+LSGURydD8eX4pEJEOwFAgDXhZVZ8MMmYY0BE4CPRR1c9Ke9x80tNrHvNrVK/ufyktVWXjxudo1uyFuB3zs88OsnDhUfr2nX1sXaI9defmbg6x5XDQQo355rzZsy8Kun769JeAwkojWZIdi+rSGCxkO78ZF1Bkd8dkVihFyR7pNiM4xSoNEbkD+A1QE2gC1AeeB64qzYFFJA14Drga2AgsEpGpqro6YMx1wJmq2lRELvaO26Y0xy1I9ertPb+G/0pj9+7ZiJSnevXL43bMt94aRt++J+r/ROn4p6r88MOz7Nv3ZYgRLqIsMDIskFDrQ0WoHTy4nuzsncyb90lC3EhC3dBCKYYxY/rgnq0Ks3fvHEaP/oi+fQ8U+tykSc8AoRUKkBDnA4KfEyha9ki2FadE46lsYqEQ87dFQjg+jbuB1sACAFX9SkROi+hoJ9IaWKeqGwBE5DWgE7A6YMwNwGjvuAu9hlC1VXVrFI4POL/Gpk3P07DhH6O1y4jZuPE56tUbgLMIxodEKjFy4kWezoUXwjnnbKN79+cZP/5vhUxN3btHFlHmkh2/LpB134jMzHMYMaIxS5ak07v3jmPb/Jh5hWrtu2fPfA4fXhH0M5Uq1UakGvBJoW3O93MAKBwtuHfvbEaPnk/fvif+z3v0+JoXXniEjIy9cQ1dLpnp7WsOHarGr39dWIlOnPhX8vJygyrYN9/8C1AuollZUduKUjaR3OCLmlVGKmPgtuERBBOGozSOqOqR/BuZiJQnSLJfBNQDvg9Y/gG4OIwx9XERXFGhRo3LWbOmF3l52b76NQ4f/o7du+dy9tlj43rcRCkxEuzHMXJkFerW/S8dOtxMhQq1g5qaIiGYSatHD7e/u+9uR+/eJ950/Zh5hWrtO2bMi5QvX4sTAxod6en16dRpIOPHF/bxdO9+X8gny+rVryEvbxewqNC27ds/5847s09YF3g+ov0UHOommZ29kylTngt6k3/iieAPWQcPrkAkeFbB4cOf47pXF2bfvg8ZM+YTrxr2icd6/fWHgfIlVjZF5Q2F+gyENje++eaj5OXlBN02YcI9qObSo8eGQtvGjbsDVaVXr1Am3+IJR2l8KCIPA5VF5Brgt8C0iI94nHAVT8ErIujnhgwZcux9ZmYmmZmZYe08Pb0mlSo18d2vsWnTC9Su3Yvy5avG9bjBnrpHj65Fr17xLTES7Mfxq18dYPLkl7n66ptDmpoiJdT+ypcPlYJ0CIi+WSLY/tq2PZejR9cGHZ+R8VM6dbovhGII3owrcH3B/7X73KCQCqVixXQgu9D6Q4c+Z+LEAcycOZlevTYdWx/pU/DYsWvYv/9Opk+fEPRGOGpUP8qXD/6Ak5eXgWsueiIZGZfiAmQKB1FUrXpJyG1VqlyI6n5gWaFtOTkbycsrfD4gf8Y2j759TzQR9ujxNY8//jceeaSw8h0zpg+qefTuvbPQtlGjOiOSE/RYhw9/ibPwF0Ykh3LlgivLbdvS2LTpILm5QTeHRThK40GgH7AS6A/MAF6O/JDH2IjLAcmnAW4mUdSY+t66QgQqjZLi8jU+9E1p5OYeZvPml2nZ8qO4H7vgTSY3N4dWrVZw8cVNiv5glEkUM1momdeePUv43//u5Z13pkVkqgnXFj9y5IesWlWBnJzqIfZUqVjFUJSPp6jPBVMoNWtWAwrHnohUYebMt05QGOBudqNH90Ikjdtv31Fo25gxfVHNoXfvXSds69XrO0aPfpry5YPfkmrUaOf9bwrf5GvWbML48XuDKtFQ36vobQ+GVKKVK7cqQhG1I5QJsGLF8gRTvpUq1fHM0TsLbatevTVQGZf1UPBYbUPKUanS2d629YW2nXFGcxo10mPRiKNHFxpSLOFET+WKyGhgIe4pf01E8a2FWQw0FZFGwCZc/kfBmhZTgQHAayLSBtgdTX9GPjVqtGfTphE0bPhgtHcdFtu3v0HVqhdQuXIzX45f8CazceMLrF7dnQsu+IRy5UKZr6LL0aOhgvHiayYLNvMaN64JN93Ul8mT/1noZheOqSaUyWX//jT69y84uzrC5Mnt6d59UMjZBIRWDMVRUoXiZA0mx7+YMuVpgj3DVa78E1RzgB2FtlWqdJr3hLyr0LZq1VqEVAxQic6dBwb939xxx+NBZQ/8npFsK7myCW0CzMmpTP5sNZD09HreDX5loW0iGZ65cX0UFWLwbSUhnOip64EXgG+8VY1FpL+qzojoiB6qmiMiA4B3cSG3r6jqahHp720foaozROQ6EVmHU+Hht6wrAc6v0Tvufo38m8yBAx9ToUJTunad7nvEEkDduv3ZufMd1q//E02aPB3z423b9ibnnvs1Y8fWo1ev4zeh0ji7I6Wop/FZs94HCmegHz26npkzRzBp0tMlsks/8USoKj5Hip0VxIKiFFEwOULdIMuXr+PdCL8otC09vb63LZgzP7RiCMf0Fkr2or5XpLOyorYFk/+663oyfnzhvKHibvClkSO8be8GPS9FEU5y35fA9aq6zltuAsxQ1bNKfLQYEWlyXyCLFp1Hs2YjqF49qhG9ISkuCctvjh7dweLF57N9+53MmjUvZqGFmzePYv36h2nRYiaffppfAj68/ufxJlSS4dixdcjJ2V0o+ghg1KgKiGTTp0/h6/NvfzuZhx4q/MSdLEmGwSO8mtC9+7NAYdNbONvyZ2aJfB0URyj5i/pefn3nSJL7wlEai1T1ooBlAT4NXOc30VAaa9feQ4UKp8fNRJUMLVinTHmciRMfLdBbvHSKLdCEc+jQFs4/fyd9+syjcuWEeQYJSVE3ySlTnubGGwvPQiZNaoNqFbp0mVVo2wsvtCwUzhp480wGIr0RJrtiSBVi1e51iYjMAN7wlrsCi0XkJkjOvhrBcPka8fNrJIrjtyhmzfroBIUBpQs/DX7TbcjZZ6/jyisTX2kUZSoIZaoRqe6ZXDZEZItPdCIx/RS3zUhswlEalYBtQHtvebu37pfecooojfj6NRIlP6Iooq3YgucefJsQ2efhEupmF8qBXhpbvGEkIuFET/WJgxy+k55+CpUqNWL//qVUq1YwxzD6dOo0kFdemUO/fsfD8Pxw/BZFtBVbUTWkkp1Iw2ANI9kIJ3qqMTAQaBQwXlX1hhjK5Qv5/TXioTRatarG2rW1mTz5XBLVNBHs6fnll8txww0/Ydast5kyZXhY2b433NCPBg1msX//VyGOlDizq9JgisEoC4TjCF+BS+b7HMjPu1dVDd79xgei4QgH2L59Eps3v0SLFjOjIFXRrFrVk4yMC2nQ4P9ifqzSUNBh2bHjzXz77d/5+OOtJxS/y3eQQ+HImFdeKc8111xB3bq/5vXXH0pqx69hpBKxip76VFVbl0qyGBMtpXH06A4WLmzs9dcIx90TGdnZP7JgQRPatPmG9PSaMTtOrBg48Bq6dCmcpTpxYlvy8vK45ZaFhbblR4VZ1IxhJA6xip4aLiJDcFkgxzyjqrq0ZOIlPh99tJAxY5TXX29NuXKnxqzc8ZYtY6lV65dJqTAAypULXnvn4MHlIQvE5fstzIRjGMlNOErjXKAXcAXHzVN4yylDfjhonz77ya+1E4sS0KrK5s0v0qzZiKjtM96EcpBnZFwWsh5OqvgtDKOsE+qxMJCuwBmq2l5Vr8h/xVqweBOq1IMzpUSPPXs+ApTq1S+N6n7jiXOQn1jQcNy4JnTqNLDIbYZhJD/hzDRWAicTxR4WiUi8ku02b36R00+/I66NlqJNaevhGIaRvISjNE4G1ojIIo77NFIu5DYeyXbZ2TvZsWMaZ545NGr79AvL9jWMskk4SmNwzKVIAIJn9DaOarLd1q1jOeWU60lPPyVq+zQMw4gn4WSEZ8VBDt8paHLZu3cFN9xwa9SemFWVTZtepFmz/0Rlf4ZhGH4QTkZ4W2AYcA5QEdf7Yr+qVouxbHEn0Kyydet4tmyJoK1VCPbu/RjVXKpXvzxq+zQMw4g34URPPQd0B9biDPz9gJR/XK5Vqwv793/GoUPfFD84DDZtepG6dZPbAW4YhhFW2rOqrhWRNFXNBUaJyDJc7/CUJS2tErVr92Lz5pdp3PhvEe9n9uzpTJ78T/btm0tGxnfceONZ5iQ2DCNpCUdpHBCRisByEXkK2AKUicfl00+/g+XLr6BRo0cjKpdeuH9EFuPHfw9YOWzDMJKTcMxTt3vjBgAHgfpAl1gKlShUqXI2J53UjB9/nBrR5+OVMGgYhhEvwome2iAip3rvh0TjoCJSE3gdaAhsAG5R1d1Bxm0A9gK5QLYfhRPr1u3Ppk0jOPXUkuvJZOjOZxiGURJCzjTEMUREdgBfAV+JyA4RGSyl9+Y+CLyvqs2AWYT2jyiQqaot/aq0W6vWTRE7xLOzQykNq8NkGEZyUpR56l7gEuAiVT1ZVU8GWnvr7i3lcW8A8uNZRwOdixjrq//kuEP8pRJ9Li/vKOedt5nRo087Yb3VYTIMI5kJ2U/Di5C6RlW3F1h/Km6WcH7EBxXZ5SkhvFnLzvzlAuO+AfbgzFMjVDXonTta/TRCceDAGpYty6Rt2+8oV65CWJ/ZsOFx9u5dwPbtdzF16nNY/wjDMBKNaPfTKF9QYQCo6nYRCScp8H2gTpBNDxfYn4pIqDv+Jaq6OV9RicgaVZ0XbOCQIUOOvc/MzCQzM7M4EcOmSpWzqVz5LH78cVpYvo0DB1axceMwWrWik1cnAAAgAElEQVRaSosWDbjqql9ETRbDMIxIycrKIisrq1T7KGqm8ZmqtizptrAOKrIG56vYIiKnA3NU9exiPjMYl4n+zyDbYjrTAJg06T6mTRtJtWo/K9QTOxDVXJYuvYQ6dfpQr96dMZXJMAyjNEQy0yjKp9FCRPYFewE/K52oTAV6e+97A28VHCAilUUkw3tfBfg5rkx73Jk9ezozZkyid++d3Hjjh9x003u8+uo9zJ49vdDYH34YRrlylahb9w4fJKXUTxGphJ2L49i5OI6di9IRUmmoapqqZoR4lbaB9hPANSLyFXClt4yI1BWR/DtxHWCe51tZCLytqsFawsWct94aRs+eJ0ZPBeZbzJ49nUGDrmXgwIt5+OH72bKlexFtT2OL/SCOY+fiOHYujmPnonSU9uYfEaq6E7g6yPpNwPXe+2+AiJ3t0SRUvsWBA4uYNOk+ZsyYdIJSGT/+KSpWrGcOb8MwUg5/HoeTjFANmtLT6/L226OKnIUYhmGkEiEd4clEEdFXhmEYRhGU1BGeEkrDMAzDiA9mnjIMwzDCxpSGYRiGETamNAzDMIywSWqlISIdRGSNiKwVkQf8lsdPRGSkiGwVEV8SIBMFEWkgInNE5AsR+VxEBvktk1+ISCURWSgiy0RklYj83W+Z/EZE0kTkMxGZ5rcsfiIiG0RkhXcuPi3RZ5PVES4iacCXuHyPjcAioJuqrvZVMJ8QkcuA/cAYVS1txn7SIiJ1gDqqukxEqgJLgM5l+LqorKoHvXpxHwF/UNWP/JbLL0Tkd0ArIENVb/BbHr8QkfVAKy9nrkQk80yjNbBOVTeoajbwGtDJZ5l8wyvkuMtvOfxGVbeo6jLv/X5gNVDXX6n8Q1UPem8rAGlAiW8SqYKI1AeuA16mjLSsLoaIzkEyK416wPcByz946wwDABFpBLTElaEpk4hIOa8Uz1ZcYdBVfsvkI88A9wF5fguSACjwgYgsFpHflOSDyaw0ktOuZsQFzzQ1EbjHm3GUSVQ1z+t9Ux+4XEQyfRbJF0TkF8A2Vf0Mm2WAazvREugI3O2Zt8MimZXGRqBBwHID3GzDKOOISDrwP2CcqhaqoFwWUdU9wHTgQr9l8Yl2wA2eLf9V4EoRGeOzTL6hqpu9v9uByThzf1gks9JYDDQVkUYiUgG4FVdy3SjDeJ0gXwFWqepQv+XxExGpJSI1vPcnAdcAn/krlT+o6kOq2kBVzwBuA2ar6u1+y+UHpW07kbRKQ1VzgAHAu8Aq4PWyGiEDICKvAh8DzUTkexHp67dMPnEJ0BO4wgsn/ExEOvgtlE+cDswOaC8wTVVn+SxTolCWzdu1KUXbiaQNuTUMwzDiT9LONAzDMIz4Y0rDMAzDCBtflUY4pS9EZJhXJmS5iLSMp3yGYRjGifg90xgFhHRSish1wJmq2hS4A3g+XoIZhmEYhfFVaYRR+uIGYLQ3diFQQ0Rqx0M2wzAMozB+zzSKI1ipkPo+yWIYhlHmKe+3AGFQMOW/UIyw9Qg3DMOIjJL2CE/0mUbBUiH1vXWF0KZN0UGD0DZt0CpV0EsuQVu2dH87dkR37UJV7VXMa/Dgwb7LkFSvvDx01iz0yivRhg3RM89EcU82evnlDO7VC/3gA/c655zj2y66CD161H/5k+xl12cJXjk5aLt26Mkno+XLo2edhd55JzphAvr992jHjhHdlBNdaUwFbgcQkTbAblXdGnTkp5/Cs8/CJ5/Ali0wZAhs2gTz58PMmXDHHfGT2kh9VGH6dGjXDu66C3r1grVroWlTt/3CC2HKFGjcGK66yr0aNXLbmjWDChXgrLNgxAg4csS3r2GkKN9+C1dcAV98Abt2QU4OtGgBzz8P3bpB/fowYUJEu/Y75Da/9MVZXumLX4lIfxHpD6CqM4BvRGQdMAL4bcid1ahx/H3VqnD11XDBBW65fHn3g83OjtE3McoU110HGRlw223uYWTVKujTB9LT3Q+xa1d4//0Tr0k4vm3hQvjoIxg71imWmjWdsunYEXbv9uUrGSnEa6/BRRfBL34Bbdu6dRdeCC++eOK4gtdnuPg+hYrCy32NIOzapdq1q+rq1ao//7nqxRerrlsXfKyhqqpz5szxW4TEZvRo1fR0VTfXcNdXEYR1Pi+44Pj+br45OnKmKHZ9FsGePaq9eqk2a6a6eLFbl38P3LUr6Ee8e2eJ7reJbp4qHTVqwBtvwNlnOxNVt27Qpg2MHu1+okYhMjMz/RYhcRk2DP70J3cNQfCntwKEdT5re1HkVatCuXI2Iy4Cuz5D8PHHcP75ULkyLF0KrVq59fn3wEhnFUFIiYKFIqJhf48VK6B9e2dTbtnSTeWieEKNFEQVHnsMxo93Zqfq1Z1Z6sUXo3Pt7N7t9jd0KPz6186c+sYbUKlS6fdtpD5t28KSJfCzn8GsWSW6JkUELWH0VNlTGgCXXeZsyuBszG+8ERvBjOQnLw/uvRfmzoV33jk+K4gVR49C794umGPKFKhWLbbHM5Kb//4X7rzzeDBFCe9nkSiN1DZPhSIjw/096SQXyWIYwcjJgb593XR/zpzYKwxwM+Bx45xJ9aqrYMeO2B/TSE7eew8efBBae033wjCXRoOyqTTyo1iWLnU/0DFltuujEYp+/aBOHZgxA15/Pb4mzLQ0+M9/4Jpr4Mwznfnhuusssso4zrJl0LMnTJwIU6eGjtiLAWXTPBXIqlUunnn8eBemaxiqblaxfbtb9tOE2bgxrF/vvxxG4vDddy4/aOhQuPnmUu3KzFOR0Lw5vPkmdO8Oy5f7LY2RCDz/PBw+7N7HacofkrPPdn9r1nSJgEbZZtcul8/z+9+XWmFEiikNgMsvh+HDXTLM998XP95IXZYscdUEsrLiOuUPyYQJcNNNzlT25pv+yWH4z5EjcOON8POfu+AMnzDzVCD/+IeLRvjoIwvDLYvs3u3i25980renuJB8+SVceqlzfra0XmRljrw86NHD5fC88YbL54kCZp4qLb//vbNnN25sJR3KGqouUur66xNPYYCrUzV8uJv97NnjtzRGvGnd2tU627cP9u71VRRTGoGIQK1azm74zjtW5LAsMXQobNwITz/ttyShue02Z5ro188qGpQlli51/tZ9+9xM0+f7kimNglSp4v6mpcHDD/srixEfPvkEnnjCTfsrVvRbmqL5179cNNVzz/ktiREPjhxxyZ7Nm7tlvwMzMJ9GYfJLOpx7rnOGzpoVNfuhkYDs2OH8GMOHww03+C1NeHzzjat/9fbbxxO7jNTk4YddefNRo6B//+iVrvGwMiLRJDfXOR579IABA6K7byMx+M1v4H//cz/CpUuTK/hh8mS4/XbXI6F6dRdllUzyG8Xz6afuQWb58phVIzClEW2+/BIuuQQWLHCZuUZq0bQprFvn3idj4lz9+s4PA8kpvxGaw4ddlNyQIXDrrTE7jEVPRZuzznKlsPv0cTMPI3XYvNl1N4OEsBNHRL6du1mz5JTfCM2f/+yq1sZQYUSKrzMNEekADAXSgJdV9ckC2zOBKcA33qr/qepfguwnNjMNcPHRmZkuqcbHhBojynTrBnXrumTOKNuJ48bu3fDLX7rZxqpVVko9Vfj4Y+jSxbVxOPXUmB4qqcxTIpIGfAlcDWwEFgHdVHV1wJhM4HeqWqSHMqZKA+Drr+Hii12/8bPOit1xjPjw/vvOqfj5565pTbLTpYvzbQwe7LckRmk5eNA1U3riCVcJIMYkm3mqNbBOVTeoajbwGtApyLgSfaGY0KQJPPqoC30zM1Vyc/gw/Pa3LmQ1FRQGuByT4cNh7Vq/JTFKy8MPu/7ecVAYkeKn0qgHBBZ6+sFbF4gC7URkuYjMEJHmcZOuIHfd5apLNmtmZaqTmSeegPPOc//DVKFBA3joIbj7bkv6S2ZuuAH+/W/YujWh7y9+Ko1wru6lQANVPQ8YDrwVW5GKoFw59+P85hvXb9yyxZOPr75yP8qhQ/2WJPoMGgTbtrneH0bykZsLs2e72lKzZiX0/aV8qA0iMi1gUTnRTKTF+RnCYCPQIGC5AW62EXiQfQHvZ4rIf0SkpqruLLizIUOGHHufmZkZmwb0p5zi/taqZdEqyYaqexJ/6CEXqppqlC/vSrrffLOrm1a9ut8SGSXhv/+F9HT3PobRfFlZWWRlZZVqHyEd4Z4TGuBGoA4wDqc4ugFbVfX/SnVgkfI4R/hVwCbgUwo7wmsD21RVRaQ18IaqNgqyr9g6wvPZvdsVtfv4Y1c87MILY39MIzq8+qqrXrt4sbvBpir9+7uWscOH+y2JES5797oAm1dfdR0b4xjNF5PoKRFZoqqtilsXCSLSkeMht6+o6t9FpD+Aqo4QkbuBu4Ac4CAukmpBkP3ER2nk8/LL7slg3jxX5NBIbHbvdjkNkya58hupzM6d7ru+/bY91CQL99/vytmMHBn3Q8dKaawGfqGqX3vLjYHpqnpOxJJGmbgrjdxcF+Fw//2u8qiR2AwYADk58MILfksSH8aMgWHDYOFCV3jTSFzWrnU94D//3DXaijOxUhodgBcBr1ExjYA7VPXdSISMBXFXGuBmGT17wurVqRO6mYp06QLTpkH79q7zXTIm8ZUUVZe4mJHhyt9YXarEpVMn1+/7gQd8OXzMkvtEpBKQn9W2RlWPRCBfzPBFaYBL8W/e3JKqEhVV11s7P3yxLNVnatXKFWGEsvW9k4n333eh/F984VtJ/pgk94lIFeA+YICqLgd+IiK/iFDG1OKpp5wZwPqKJybvvutCGCF560tFSn5V1NNPL1vfO1nIyXFlif7xj8Tv4VKAcPI0RgFHgXbe8ibgrzGTKJlo2NCFcfo0tTSKIDfX+ZxGjHBP2u+/X7ZMNBMmuATGQ4dcaQojsRgxwvkwOgUrgpHYhB09JSKfqWpLb91yL+EuIfDNPAVw4ACcfbZLqmrXrvjxRnwYNcpFo8ydW7Yj3B54wEVUvfSS35IY+ezc6e4Zs2a5SrY+EqvaU0dE5KSAgzQBEsqn4StVqsDf/w733OMq4hr+c/AgPPKIm/qXZYUB8Mc/wtSpzm5uJAZDhrgkTJ8VRqSEozSGAO8A9UVkAjAbMHtMIN27u94MzZtbXapEYOhQN+u7+GK/JfGfGjWc4rj/fr8lMQBuucVl7n/1VdLeJ8KNnqoFXIzLCF+gqjtiLVhJ8NU8lU/LlrBsmXtv0Sr+sW2bU94LF7rqxAYcPQrnnONMVFde6bc0ZZtateDHH937BLhPxCp6SoD2uL4XVwKXRSZeinP66e5vgwYWreInjz3m+rqbwjhOhQrwt7/BffeZCdVPFi6E/fvd+ySO5gvHEf480AR4FTfTuAX4RlV/G3vxwiMhZhq7d7u8jaVLXdOmatX8lacs8tVXrqf76tXuic44jqoroTJokFOqRnxRdbO8G2+Ejz5KmG6RscoIXwM0V9U8b7kcsEpVz45Y0iiTEEojn9tvhzPOcE2bjPjSpQu0bm0h0KGYO9ddn2vWWGvYePPuuy5Y5vPPE6pgZqyip9YBPwlY/om3zgjGY4+5rnBbt/otSdli/nxYtMg9SRvBufxy14DKKuDGl7w8ePBB+OtfE0phREo4M425wEW40uWKa9O6CNhLdPpqlJqEmmmAe6JQddniRuxRdYlSJ58MjRtbraWiWLPG9aBu1cr13LBzFXteew3+9S/n00iwEPBYmacyi9isqvphSQ4YCxJOaWzb5qJVFi1yNzEjtkyZ4sKe8zOfEyAqJaE5/XTYssW9t3MVW44eddF8L76YkJFrMStY6O28GgGd/oJ1z/OLhFMa4Hwa69bB2LF+S5La5OY6k0ulSrBkiYtKKWslQ0rKFVdAVpY7b1lZdq5iyfPPw1tvOZ9GAhKrkNv+IrIFWAks8V6LIxOxDPG737mb14oVfkuS2kyY4CLV3n+/bNaYioTJk6FZM7jgAjtXseTAAXj8cVcxIoUIxzy1DmiTaAl9gSTkTAOcT+Pdd11rWCP6HD3qaviMGuX6ZRjhs2OHO3eWBBk7/vpXWLnS+TQSlFhFT30DHIpMpKIRkQ4iskZE1opI0DhJERnmbV8uIi1jIUfM6N8fVq1yoY5G9HnpJffEbAqj5NSq5SLN/vxnvyVJTX78EZ55xs00UoxwZhoXAP8FPsGVSAfnAC9VbKOIpAFf4jLNN+Iisrqp6uqAMdfh+nhcJyIXA8+qaqEmzwk70wDn0/jDH9xTXZUqFq0SLQ4cgKZNXS/sCy7wW5rkZN8+dw7few9atPBbmtSiRQs3mzv//IT+zccqemoxMBfn08jDZYWrqo6OVFBvv22BwarawVt+ELfjJwLGvADMUdXXveU1QHtV3VpgX4mrNHJznc3dInuiyxNPuOx7O5elY+hQV6J72jS/JUkdvv/eJfjm5rrlBP7NR6I0wsk0SVPV30UoU1HUAwJb3v2AK4pY3Jj6QPJkzqWlufDb/MieJK03k1Ds2gX//Kcrx2CUjjvvdGaU+fNdCRaj9Dz2mGvQ9s03KfmbD0dpzBSR/sBUAvpoRCHkNtypQUEtGPRzQ4YMOfY+MzOTzMzMiISKCe+/78wA/fol7DQ1qXj6adfx7Kyzih9rFE2lSq7H/UMPufDbBEs+Szq+/NKF2C5a5MrRJ0iNqXyysrLIysoq1T7CMU9tIMiNWlXPKNWBRdoAQwLMU38E8lT1yYAxLwBZqvqat5x85ql85s6F3r3dRVWhgt/SJC9btsC557oy9A0a+C1NapCT4xoCDR0K117rtzTJzS23OB/bgw/6LUlYxCR6SlUbqeoZBV+Ri3mMxUBTEWkkIhWAW3GzmUCmArfDMSWzu6DCSBouv9yZqVJsqhp3/vIXp3xNYUSP8uVdlM9DD1np9NKwZIkz86V4/bNwmzD9FGgOHCuNqapjSn1wkY7AUCANeEVV/+6ZwlDVEd6Y54AOwAGgr6ouDbKfxJ9pAHz2mevst3YtVK3qtzTJx/r1zka8Zg2ceqrf0qQWeXlw0UXuCblrV7+lSU6uvRY6d4a77vJbkrCJVfTUEFwTpnOB6UBH4CNVvTlCOaNO0igNgNtuc6aAhx/2W5Lko2lTl9B37rkJHcaYtLz7rjOvnH++hYeXlDlz4Ne/dr1cksj8HCul8TlwHrBUVc8TkdrAeFW9OnJRo0tSKY21a6FtW+fbOOUUv6VJHlascJVZc3LccgKHMSYtqq5S8J49btnOcXiout/0oEGucGYSEauM8EOqmgvkiEh1YBtgBuVIadrUNQt68snixxrH+eMf3bmDlAxjTAhEXEVWcM5cO8fhMWUKHDrkrAhlgHCUxiIRORl4Cee8/gz4OKZSpTp//jO88gps3Oi3JMlBVpab9s+ZY0UJY82MGVC/vvO92TkuntxcZ2r+29+gXDi30+Qn7NLoACJyBlBNVZfHTqSSk1TmqXzuv9+ZAUaM8FuSxEYVLr4Y7r0XunXzW5qyQb4Jdc0a67VeHKNHuxpo8+YlZY5LVH0aItKKIhLwgkUx+UVSKo2dO12xvU8+OW52MQozcaJ7ilu8uMw8ySUEd9/tHLrPPOO3JInLkSMuwXTcOLj0Ur+liYhoK40snNI4CWgF5DeGaAEsVtW2kYsaXZJSaYArnTxypMs5qFzZolUKkp3tIqX+/W+45hq/pSlb5CdRLl7s6igZhWnXzs3G2rRJ2t9urKKnJuEKC670ln8KPKqqXSKWNMokrdLYvx9q1nQ3R7BolYI8/zxMmuR8GEb8GTzY1U+y7pOF2bPH5Qol+W83Vkpjlao2L26dnySt0gCXs/H559amtCD79zuz3fTpVvrcL/JLp7/zjsvdMI7z4IPOLLVxY1L/dmMVcrtCRF4WkUwRuUJEXgISyhGe1GRludLp99yTlBddzHjmGdfL2hSGf2RkwJ/+lDR1lOLGhg3O+f3ee2Uymi+cmcZJwF3AZd6qucDzqno4xrKFTVLPNMBddHfd5br8JVE2aczYvt3V6fr0U2jc2G9pyjZHj7rcjREj4Kqr/JYmMejWzTVVGzzYb0lKTUzMU8lA0isNgOuvh6uvdqGlZZ1Bg1z44rPP+i2JAfD6664c/aefWgTbggVw882uokOVKn5LU2pi5dO4FBgMNOJ4/w1V1YR5BEwJpbFqFWRmumiMmjX9lsY/vv7a5WWsXm1FCROFvDxo3Rruuw9uvdVvafxD1TWquuMO6NPHb2miQqx8Gq8A/wIuBS7yXq1LLp5RJM2buyeYxx7zWxJ/ueYaVwG4d2/YvdtvaQxws4vatd3/5Npry+7/5c034fBhuP12vyXxlXBmGgtVtWAb1oQiJWYaANu2OeXx8ccu8a+sMWcOdOzokqYgacMYU5LMTPjwQ/e+LP5fDh92fraRI12ARooQq5nGHBF5WkTaikgr72UhLbHgtNOcCeCBB/yWJP5kZ8OAAccL5llRwsSicmX3Ny0NHnnEX1n8YPhwaNEipRRGpIQz05gTbL2qJszZS5mZBhx/ovnvf6F9e7+liR/PPOPyAV57Dfr3T7jeymWe3budLb9xY+d3evNNvyWKH/nRfPPnp1xf+miXEfl9gVUK7MA1YPomMhGP7bsm8DrQENgA3KKqhQylXn/yvUAukK2qQX0pKaU0wEWrPPWUa05fFqJVtmyBn/40JX+UKcehQ242+NJLLtqvLDBggJthpWA0X7TNUxlA1YBXBq4G1UwRKW250QeB91W1GTDLWw6GApmq2jKUwkhJbrnF5WuMG+e3JPHhgQegXz9TGMnASSfB0KEwcKDL4Uh1Vq92/ps//9lvSRKGEudpeLOEWaraMuKDiqwB2qvqVhGpA2Sp6tlBxq0HLlTVH4vZX2rNNAA6dXLmmssvd6aAVDXVzJ/vwjhXr3YZyEbio+r6bVx1FfzhD35LEztUXTHRihXdA02SFiUsilg5wk9AVXeW9DNBqK2qW733W4HaoQ4HfCAii0XkN1E4bvKwZ497kvvgA2dLTkVyc93U/+mnTWEkE/mJl088AZs2+S1N7Bg3zvlyvvkGZs5M3d9hCSlf/JATEZErgF1hjHsfqBNk08OBC6qqIhJqmnCJqm4WkVOB90VkjarOCzZwyJAhx95nZmaSmZlZnIiJTX60Snp6yiQSFeLFF13drTLSJjOlaNYMfvMb10wsFc2o27a5WdR557kQ+BSJ5svKyiIrK6tU+yjKEb4yyOqTgc3A7aq6OuKDOvNUpqpuEZHTgTnBzFMFPjMY2K+q/wyyLfXMU/nRKjfcAH/5CyxbBpUq+S1V9NixwzlUZ81ylX6N5GP/fhdVNH68M6OmEj16QN26rpXrHXekbDRftKOnGhVYpcCPqro/IulO3PdT3r6eFJEHgRqq+mCBMZWBNFXdJyJVgPdwfTzeC7K/1FMagdx8syuQ9pe/+C1J9LjjDjebGjrUb0mM0vDGG66Z2JIlUL7EhovEZPp0V/9s5crjM/4UJWkKFnrO9DeAnxAQcisidYGXVPV6EWkMTPI+Uh4Yr6p/D7G/1FYamze7afIHH7gEo2TnxhvdD7N9+9R28pcFVKFePXdzbdYs+Z3F+/a58O+RI8tEVd+kURrRJuWVBsDLL7sp8iefuJjxZOXgQahVy8X7Q9ksSZFqXHSRawsLyf//HDTImd1GjvRbkrgQl+gpwyf69XOF/IYN81uS0nH//cefRFPEuVjmya9GXLmy6+eerHzyCUycCP/4h9+SJDSmNJIFEXeD/etfYf16v6WJjLffdq9PPimTHc9SlgkT3P/zkkuSN2v6yBH3YPbss2W7NUEYmHkq2XjqKRdx9M47TpEkC1u3uj7Tb7wBl11W/Hgj+cj/H7/+evJFUz36KHz2GUyenFy/q1JiPo2yQE4O1Knj/AKNGyeH41HVdSa84ILUigAzCjN9Otx9twsRT/TrMp+bb4YpU9zDzKRJySN3FDClUVa48EIX4gjJ4Xh87jkYM8aVDElP91saI9YMGAA//ugeaBL9qX3vXvcQVkYDM8wRXlY47TT3t2JFePJJf2Upji++cFP/8eNNYZQVnn4ali93//NEJi8PevZ0s3awwIwwsZlGMpKfLX7qqbB2LcyYkZiJVYcPu37f99wDv/qV39IY8WTZMte699NP4Ywz/JYmOI884roRTpzoZkcpmvVdFGaeKmvk5Lhqoz/7GfyzUHUV/7n3Xvj+e5fAl+hmCiP6/Otf7oY8d27iPdS8+aarLbVo0fGZexnEzFNljfLlXae7KVOczyCRuPpq+M9/3Kxozx6/pTH84P/+D777zs00OnZ010IisHw5/Pa3LlKqDCuMSDGlkezUrOmUxu9/70wBicCMGW7af/SoCw+2ktJlk3LloGFD+OEHFyKeCNfBjh3QubPr+X3BBX5Lk5SY0kgFzj3XlRnp0sXVqfKTDz5wpdwvusgtm3OxbFO9uvtbsaKr6eQn2dkuOuq226wcfykwpZEqdOrknuRuusllt/rBvHnQrZuzY8+YYVnfxvFs8aVLYfRo94TvF7/7nSt1YrlCpcIc4alEXh40aeJiz1u1cvHm8bphL1jgen9MmOD8GYZRkG+/dZnif/qTa+AUL/Ly3Ix3zRpX6sQqKx/DHOFlnXLlXE/jnTvdE36PHvE57tKlTmGMGmUKwwhNw4bOx/XoozB2bHyOeeAA3HILrFvnEvhSuX1ynDClkWpUrer+NmjgylV/9FFsj/f55y7s94UXXKkQwyiKM890DzQPPBD7zOvvvoNLL3W/ibZt3TrzsZUaUxqpRr4NecUKF4bbpYtzkseCDh1cBEqdOnDllbE5hpF6nHOOi6bq08cpkQ4doh+OO38+tGnjMr5HjXJFFM3HFh1UNe4voCvwBZALXFDEuA7AGmAt8EAR49QIwZdfqjZrpjpokGp2dnT2uX69aqdOqpUqqbpyhKpdu0Zn30bZ4aKLjl8/V1wRvf2OHKl66qmqM2ZEb58pinfvLNH926+ZxkrgRmBuqAEikgY8h1MczYFuInJOfMRLIQkdCi0AAAalSURBVJo1g4UL4csvnU25bVtnTgrxZJeVlRV6X4cPw2OPuSn+RRcdL39tU/6QFHk+yzr5NZ8aN3ZO6t69XXn1IijyfPbpA/Xru5Ig06a5hEIj6viiNFR1jap+Vcyw1sA6Vd2gqtnAa0Cn2EuXgtSo4ZofpaW5KKeZM+HnP3dtLQsQ8kf59tsuH2T5cldh9+GHbcofBqY0iiDflLpkiXuoqV3b5XIMG+ZK5ASh0PnMyXHXc/fuzrm+caNrKZyIZXVShET2adQDvg9Y/sFbZ0RC+fLHk6saN4aTT3ZPZT17wrvvHv+RZme7H/B778FLL7nwyHr13I+7Zk145RU3YwGnKOIZ1mukFoHXT0aGazA2dy5MneqKcTZoAM2bw6uvOh9dfjkaVRexd++97hoeMgTatYMrrnDbbeYbU2JWRUxE3gfqBNn0kKpOC2MXlngRbSZMcOGG+dU8t21zs4VHHnHJgbm5TnlMmOAUS6NGTkFUqwabNrlorDvuKFP9Bow4c845buZ63nmwcqVb97vfuQeWb7911+c//uH+1q/vzFD51Qd69jzx+jZigq/JfSIyB/i9qi4Nsq0NMERVO3jLfwTyVLVQAwkRMQVjGIYRAVrC5L5EqFccSuDFQFMRaQRsAm4FugUbWNIvbRiGYUSGLz4NEblRRL4H2gDTRWSmt76uiEwHUNUcYADwLrAKeF1VV/shr2EYhuFIidpThmEYRnxI5OipExCRDiKyRkTWisgDIcYM87YvF5GW8ZYxmSjufIpIpojsEZHPvNef/JAzGRCRkSKyVURWFjHGrs0wKe582rUZPiLSQETmiMgXIvK5iAwKMS7867Ok2YB+vIA0YB3QCEgHlgHnFBhzHTDDe38xsMBvuRP1Feb5zASm+i1rMryAy4CWwMoQ2+3ajO75tGsz/HNZBzjfe18V+LK0985kmWmEk+h3AzAaQFUXAjVEpHZ8xUwawk2ctACDMFDVecCuIobYtVkCwjifYNdmWKjqFlVd5r3fD6wG6hYYVqLrM1mURjiJfsHG1I+xXMlKOOdTgXbedHWGiDSPm3Sph12b0cWuzQjwIlFbAgsLbCrR9ZkIIbfhEK63vuDTh3n5gxPOeVkKNFDVgyLSEXgLaBZbsVIauzajh12bJUREqgITgXu8GUehIQWWQ16fyTLT2Ag0CFhugNOGRY2p760zClPs+VTVfap60Hs/E0gXkZrxEzGlsGsziti1WTJEJB34HzBOVd8KMqRE12eyKI1jiX4iUgGX6De1wJipwO1wLJt8t6oWXTKz7FLs+RSR2iIi3vvWuPDsnfEXNSWwazOK2LUZPt55egVYpapDQwwr0fWZFOYpVc0RkfxEvzTgFVVdLSL9ve0jVHWGiFwnIuuAA0BfH0VOaMI5n8DNwF0ikgMcBG7zTeAER0ReBdoDtbyk1cG4qDS7NiOguPOJXZsl4RKgJ7BCRD7z1j0E/AQiuz4tuc8wDMMIm2QxTxmGYRgJgCkNwzAMI2xMaRiGYRhhY0rDMAzDCBtTGoZhGEbYmNIwDMMwwsaUhmEUQESqi8hdAct1ReTNGB3rFyIypIjtLUTklVgc2zAiwfI0DKMAXmG3aar6szgcaw5wW1EZuCKSBdyiqttiLY9hFIfNNAyjME8ATbwGP0+KSMP8hkAi0kdE3hKR90RkvYgMEJE/iMhSEflERE72xjURkZkislhE5orIWQUPIiINgAr5CkNEuorIShFZJiIfBgydCXSN/dc2jOIxpWEYhXkA+FpVW6rqAxSuAHoucCNwEfBXYK+qXgB8glfDB3gRGKiqFwL3Af8JcpxLcBVb83kE+Lmqng/8MmD9p8DlpftKhhEdkqL2lGHEmeIa/MxR1QPAARHZDUzz1q8EWohIFaAd8KZXVw+gQpD9/ATYHLA8HxgtIm8AkwLWb8Z1WTQM3zGlYRgl50jA+7yA5Tzcb6ocsEtVw+kFfkyrqOpdXtXW64ElItLKq94qWP8NI0Ew85RhFGYfkBHB5wRcvwdgvYjcDK48tYi0CDL+W1wPZ7xxTVT1U1UdDGznePe0072xhuE7pjQMowCq+iMw33NKP4l7ys9/0g98T5D3+cs9gH4isgz4HNeHuSDzgQsClp8SkRWe032+qq7w1rcG5pbmOxlGtLCQW8PwERGZDfRQ1c1FjMnCQm6NBMFmGobhL/8A7gy10TNrrTOFYSQKNtMwDMMwwsZmGoZhGEbYmNIwDMMwwsaUhmEYhhE2pjQMwzCMsDGlYRiGYYSNKQ3DMAwjbP4fLgxre1MOTEUAAAAASUVORK5CYII=
-
-
-直方图
---------------
-
-hist 函数：
-
-
-
-
-::
-
-    import numpy as np
-    import matplotlib.mlab as mlab
-    import matplotlib.pyplot as plt
-
-    # example data
-    mu = 100 # mean of distribution
-    sigma = 15 # standard deviation of distribution
-    x = mu + sigma * np.random.randn(10000)
-
-    num_bins = 50
-    # the histogram of the data
-    n, bins, patches = plt.hist(x, num_bins, normed=1, facecolor='green',     alpha=0.5)
-    # add a 'best fit' line
-    y = mlab.normpdf(bins, mu, sigma)
-    plt.plot(bins, y, 'r--')
-    plt.xlabel('Smarts')
-    plt.ylabel('Probability')
-     plt.title(r'Histogram of IQ: $\mu=100$, $\sigma=15$')
-
-    # Tweak spacing to prevent clipping of ylabel
-    plt.subplots_adjust(left=0.15)
-    plt.show()
-
-
-<button>Run ...</button>
-
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-.. image:: 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
-
-
-路径图
-----------
-
-
-matplotlib.path 包：
-
-
-
-
-
-::
-
-    import matplotlib.path as mpath
-    import matplotlib.patches as mpatches
-    import matplotlib.pyplot as plt
-
-    fig, ax = plt.subplots()
-
-    Path = mpath.Path
-    path_data = [
-        ^4$(Path.MOVETO, (1.58, -2.57)),
-        ^4$(Path.CURVE4, (0.35, -1.1)),
-        ^4$(Path.CURVE4, (-1.75, 2.0)),
-        ^4$(Path.CURVE4, (0.375, 2.0)),
-        ^4$(Path.LINETO, (0.85, 1.15)),
-        ^4$(Path.CURVE4, (2.2, 3.2)),
-        ^4$(Path.CURVE4, (3, 0.05)),
-        ^4$(Path.CURVE4, (2.0, -0.5)),
-        ^4$(Path.CLOSEPOLY, (1.58, -2.57)),
-        ^4$ ]
-    codes, verts = zip(*path_data)
-    path = mpath.Path(verts, codes)
-    patch = mpatches.PathPatch(path, facecolor='r', alpha=0.5)
-    ax.add_patch(patch)
-
-    # plot control points and connecting lines
-    x, y = zip(*path.vertices)
-    line, = ax.plot(x, y, 'go-')
-
-    ax.grid()
-    ax.axis('equal')
-    plt.show()
-
-
-<button>Run ...</button>
-
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-.. image:: 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VXcDGtNalffEHCwYNuLdgArYNrc9PJRjz82QNOG/NM8Rnum38f7w14j/CQcDauX8+Hzz9Px/37uT0+3mkHVasFBXF3gwZc9csvfDxlCgUFBXaNo5Sie3x3n7hVq1kYvmhbLwM1K3vybxbVjGevf5bkecmUWhz7ee2ozMxMDixZQlK9epVeNy0z0+HtT66axNrctSzdutThsQCeX/Y8nWI7MaDRAOZ/9hlrJ08muXp12kVHO/2snRV799K/fn3qbtnCx5MnU1RUZNc4Rn0EmVm/+4Yv2sI+YzuNRaOZtHaSR+NYvXAh14eEEOjv75Ht1w4I5cH8toyZ9wgWi8WhsX7c/yOzt8zmjd5v8NnMmZxMTeW+uDiiqrnu/GylFP3i4ojaupX5s2fbdazC2teWNqYxSE/bxDJyM+j4QUd+GPEDTSObun37R44c4ZNnnmGMAxfOOEOBpZi40nd4qc/rjOw+0r4xSgpInJbIhK4TKFhxlND16xkcH4+/m/IqLi1lZmYmrUeN4rquXSu1rtaa2LdiWTF8BQ1rNnRRhKKypKctLpFQM4EXe7zIsLnDKLGUuH37a5Yvp4Ofn0cLNkCIXyATzl7P39Oeo6jEvhbDhLQJNK/VnBMLM4natIlbGzRwW8EGCPT3566YGH5ISeHgwYOVWlcp5TOPIDMDwxdts/a1rBzN/8FrHyQsKIyJqyc6JyAbaa3ZuWYNLSMj7R7DGT1tqwfCriGiwJ8X5v690uv+fOBnUtJT6JrVmga//Ub/+vXxc8NVp3/OPzwkhN6BgaR+8kmlWx1G7Gub9btv+KItHOOn/Jg5aCZvrXmLzUc2u227x44dI/DECSKqVHHbNivip/x4u7QP7/w6hWP5x2xer7CkkOR5ydwRNJC4LXvoU7++R28TkFi7Nn5btrBx/fpKrSd9beMwfNH25SuibOGM/OvXqM+rvV/l3jn3UlRqX3ugsnZnZHC1g2PYezXk5dxYLYH2p6MZ/ekjNq/z0sqXqFZQhWt/PsuguDi3Fuzy8ldKMeCqq1j28cecOXPG5rEahDcgyD+I33J+c2KErmXW777hi7ZwjuTEZGKqx/DSypfcsr19W7cSF+KZZyRW5N3Avsw/MJ/N+678qyP9cDrvrHmHwb/UY0ic+w46Xkmd0FBanjzJ0tRUm9eRvrZxeMenzAFm7WtZOSt/pRTTB05n2vpprD9YuZ/W9jhx5AgRDhZtZ/a0rZoGR3LbqWY88sWDFS5XXFrM0M+G0vdAcx6p24TgAKc8ua9SKsq/R0wM2xYt4sSJEzaPZ7S+tlm/+4Yv2sJ56obV5e2+b3PvnHspKLHvCjtb5WdnU8ML97QB3q52I5vz0vlm0zeXXeYfS/+BPnKGicEdnH7rWGcICQiglcXCxnXrbF6nR4MepGWmSV/byxm+aJu1r2Xl7PyHthxK86jm/H155c+isFVpaSlnTpwgNCjIoXGc3dO2qulfhdGn2vNE6uhyL7j55dAvTPrpbSYe70K96hU/OceVrpT/NbVqsXHRIpsvGqpfoz5hwWFsPbbVCdG5nlm/+4Yv2sK5lFK8N+A9Pt78Mav3rXbJNk6fPk0VcMtpcfb6e/VunD5znHeWTLlofomlhDs+vp2hx1ox8KpGHorONrVDQ4nIzeX333+3eR3pa3s/wxdts/a1rFyRf1S1KKb2n8rwecM5XXTa6eMDOKNcu6KnbRWo/Hm58Ab++eM/OFt0tmz+uK/HoXJOMSm8p8efAGRL/teGhLB+yRKbxzRSX9us333DF23hGrc2u5WOMR15ZskzTh9bKYURuqbDwxKpe6YKd714F0nJSbS7qx2TJr3NA3tbU8XFjylzluZRURzcsIHjx49feWHO9bVX7F2BRTt2HxbhOoYv2mbta1m5Mv8p/aYwZ8cclu1Z5tRx/fz8sDjhYJeretoXujurGd+sWMD38d+zqfkmLL017x74mdS9trccXMWW/AP8/EgENvz0k01j1g2rS2TVSLdeaGUvs373DV+0hetEVIngPwP/w4h5I8gvzHfauH5+fmgv7mdfaFnWHnTvi+dl9D7OlAzbz8rwtLaRkWxeutTms0Kkr+3dDF+0zdrXsnJ1/v0b9af31b158rsnnTZmUFAQReDw3rYre9pWhX7l32+8wM/9N9j6M1vzj6xaFXJzyc3NtWl5o/S1zfrdd7hoK6VmKqWOKKV+dUZAwvu8mfQmi3cvZuHOhU4Zz9/fn6o1anCysNAp47lSsKX8+3yHWNx/MY29lFLEA5l79ti0fPf47qzat8rjD8gQ5XPGnvaHQF8njGMXs/a1rNyRf/Xg6swcNJORC0Zy/KxtB7SupEZUFPkOFm139LRHJ3QkYcnFT2tPWBLBYwkdXL7tK6lM/g2CgtizZYtNy9YOrU3dsLpsOrzJzsjcw6zffYeLttZ6FeCcb7LwWj0b9GRw08GMXjTaKeNVj4rihAH2tAfENWZSw74kLU3ghuVxJC1NYFLDvgyIc96DgN0hPjyczPR06Wv7AOlpG5w783+l9yusyVrDnO1zHB6rRnQ0J+x8GK2VO3racK5wL+p5D2ndh7Oo5z1eU7Ark394SAj+J06Qk5Nj0/JG6Gub9bvvlsbc8OHDiT//Uy48PJzExMSynzbWP7y90+np6Q6tb/Rpd+ZfLagao68azX2T7+P6168nqlqU3ePVjo1lF38UHutPfZl23XQD4Ksvv6RJ06ZXfH9u6HADI+aPYMnSJQT4B3jN592Xp9PS0khJSQEoq5flccozIpVS8cACrXWrcl6TZ0T6mKe+f4o9eXv44o4v7L4q8NixY/xv3DjGxMU5OTpxOemHD7OzQwfuGDHCpuXbTGvD+ze9T6fYTi6OTJRHnhEpnOafPf/JtmPbmL1ltt1jREZGciYkhDPFxU6MTFSkXvXqHNi+3eblpa/tnZxxyt+nwI9AY6XUfqVUsuNh2c6sfS0rT+QfEhDCrFtmMfa7sRw6eciuMZRS1G3UiAP59l+0466etreqbP7hISGczMmx+a5/3t7XNut33xlnjwzVWtfVWgdrretprT90RmDCu7WPac+odqMYuWCk3fdfjmnenIOnXXNDKnEpfz8/qmlNvo3/o+wW142fsn5y2yPohG0M3x4x67maVp7Mf/wN48nKzyIlPcWu9WPi4jjgwPbdcZ62N7Mn/3A/P/Ly8mxaNqJKBI1rNWbdAe+8ZN+s333DF23hOUH+QXw0+CPGLRnHvhP7Kr1+TEwMB7SWJ6W4UbjWNhdtkL62NzJ80TZrX8vK0/m3rt2asR3Hct/8+ypdfMPCwgiMiuKInS0S6WlnVnqdcIuFPBvvQQLe3df29GffUwxftIXnPX3905woOMG09dMqtZ5SiubdurGtEkVEOCY8OJi8Q7YfPO4a15V1B9a5/JmhwnaGL9pm7WtZeUP+AX4BzLplFuOXjycjN6NS6zZPTGSbnS0S6WnHV3qd8JAQThw+bPPy1YOr0+KqFqzJWlPpbbmaN3z2PcHwRVt4h2ZRzXiu63Mkz0uu1N3hYmJiKI6M5NiZMy6MTljVCA4m7+jRSq0jfW3vYviibda+lpU35T+m4xg0mklrJ9m8TlmLxMZ7YlxIetqZlV4n0N+f0kpe0OStfW1v+uy7k+GLtvAe/n7+pAxK4V+r/sX2Y7Zfedc8MZGtNl7wIRxX2VZUl/pd2HhoI2eK5deQNzB80TZrX8vK2/JPqJnAiz1eZNjcYZRYbHu6S2xsLIWRkRyr5Fkk0tOOd8t2QoNCaVOnDT/u/9Et27OVt3323cXwRVt4nwevfZDqwdWZuHqiTcsrpWjWtatdLRLhHtLX9h6GL9pm7WtZeWP+fsqPmYNm8taat2x+qneLtm3ZVsmf7dLTzqz0OgrQdrSivLGv7Y2ffXcwfNEW3ql+jfq82vtV7p1zr033rqhXrx5nIiLIlrNIvFLnep3ZfGQzJwtPejoU0zN80TZrX8vKm/NPTkwmtnosL6186YrLlrVIsrNtHl962vFu21aVwCpcU/caftj3g9u2eSXe/Nl3JcMXbeG9lFJMHzidaeun8fOBn6+4fJsOHdhosWCRe5G4jL0PrQDvbJGYkeGLtln7Wlbenn90WDRv932bYXOHXfFS6JiYGEKbNeN3Gw9ISk87s9LrFJaUEBgcbNf2vK1oe/tn31UMX7SF9xvacijNo5rz9+V/v+KyHfv1Y81J6Zu6Sl5BAeF16ti1bqfYTmw/tp0TBSecHJWoDMMXbbP2tayMkL9SivcGvMfHmz9m9b7VFS7bvEULcmrV4sipU1ccV3ra8ZVe53hBARExMXZtLzggmI6xHVm5d6Vd6zubET77rmD4oi2MIapaFFP7T2X4vOGcLrr8RTT+/v60HziQtZU4IClsd7yoiIjoaLvX97YWiRkZvmibta9lZaT8b212Kx1jOvLMkmcqXO6aDh3YFhh4xYf+Sk87s9Lr5Pn7Ex4RYfc2valoG+mz70yGL9rCWKb0m8KcHXNYtmfZZZepVq0aTXv2ZGMlbiEqbHMciHCgaLePac+u3F3knpV7oHuK4Yu2WftaVkbLP6JKBNMHTmfEvBHkF17+AbMdu3dnXWlphaf/SU87vtLrHLdYHCraQf5BdK7XmRWZK+wew1mM9tl3FsMXbWE8/Rr1o/fVvXnyuycvu0x0dDThLVuy/dgxN0bm2wpKSigNCqJq1aoOjeNNLRIzMnzRNmtfy8qo+b+Z9CaLdy9m4c6Fl12mU//+rK3gzn/S086s1PJHTp0isl49hy6wAe8p2kb97DvK4aKtlOqrlNqhlNqplHraGUEJ31c9uDozB81k5IKRHD97vNxlmjZtyonatTkk5207xc4TJ2jYoYPD4xzZeoTtn22n8187k5ScROriVCdEJ2yl7Hk2X9nKSvkDvwG9gQPAz8BQrfX2C5bRjmxD+LbHvn2MvMI8Ph78cbmv/7BiBYdnzuT2Bg3cHJnveXffPm5+8UViY2PtHiN1cSpjpo4ho+0fzwJN2JTApEcmMaDPAGeEKc5TSqG1vuRnkaN72h2AXVrrTK11MTAbGOTgmMJEXun9Cmuy1jBn+5xyX2/fqRN7wsM5WskHJIiL5RUUcDosjLp16zo0zuT/Tb6oYANktM1gyqdTHBpX2M7Roh0D7L9gOuv8PLcxa1/Lyuj5VwuqRsqgFB7+9mGOnb70oGNwcDCdb7uNtHIeRis97Uybl92Zk0PD9u3x87P/K3/w5EE2Hyv//ugFlorvK+MKRv/s2yvAwfVt6nsMHz6c+POnJ4WHh5OYmFh2uo71D2/vdHp6ukPrG33aV/K/p9U9PJT6EI9EPYJS6qLXi4uL2RcRweFTp9hx/kpJ6+lu1sIl0xVPH/DzI7FtW7venzNFZ1gTuIapP08lMDcQ9gDWbtWec/8K8QuxeTyZLn86LS2NlJQUgLJ6WR5He9qdgAla677np58FLFrrVy9YRnra4ooKSgpo9347xncbz9BWQy95fc3q1WROn84Qk5+bbY+i0lLeOHiQx6dOJSQkxOb1SiwlfLDxA/6x4h/0vro3L/V4iS0/b7m0p70xgUmPSk/b2S7X03Z0T3s90EgpFQ8cBO4CLv3GCXEFIQEhzLplFjd9ehPd47sTHXbx/TGu6dCBH+fM4eDJk9QNC/NQlMaUkZtLdIsWNhdsrTXzf5vP00ueJqZ6DKl3p9Iuuh0AcX3iAJjy6RQKLAWE+IXw2KOPScF2I4f2tAGUUv2AtwF/YIbW+t9/et2le9ppaWmmvTIKfC//8cvGs+nwJhYMXXDJ+cTr1qxh53vv8ZfzZ5KkZWaa+qpIW/LXWvPBnj10HjeOFi1bXnHMdQfW8dTip8g5k8PEPhPp17Cfw+d1u4qvffb/zFVnj6C1Xqi1bqK1bvjngi1EZY2/YTxZ+VmkpKdc8lq7a6/laO3a7D8h93O21a7cXIoTEmjeokWFy+0+vpshXw5h8GeDubf1vaQ/mE7/Rv29tmCbmcN72lfcgPS0RSVtPrKZXh/1YsOoDdSvUf+i1zasX8+2KVP4q5y3fUVaa6bv2cP1Tz992aKdcyaHl1a+xEebP2JdpOLFAAAT4ElEQVRsx7E8cd0TVAuq5uZIRXlctqcthLO1rt2axzs9zn3z78OiLRe9lti2Lbl167I3L89D0RnH7zk5lDZqRLPmzS95raCkgNdWv0bTqU0pLC1k28PbGH/DeCnYBmD4om3WczWtfDX/cV3GkV+Yz7T10y6a7+/vzw133sny48dZvmePh6LzDhWdp11qsbAsP5/ut912UYvDoi38d/N/afpOU1bvX82q5FW8O+BdaofWdkPEzuWrn/0rcfTsESFcIsAvgJRBKXT9sCtJCUkk1Ewoe611mzasqlePw9u3VzCCua3IyqJ65840bdasbN6yPct4avFTBPgF8NHgj+gW182DEQp7SU9beLU3f3qTuTvmsnzYcvz9/Mvm/7p5Mz+9/jojGzSQg2V/kpWfz+zSUh546SXCwsLYenQr45aMY0f2Dv7d69/c0fwO+ZsZgPS0hSGN6TgGjWbS2kkXzW/ZqhV+rVrxy5EjHorMO50qKuKr7Gz6jxrFSU5y//z76TGrB32u7sO2h7dxZ4s7pWAbnOGLtln7Wla+nr+/nz8pg1L416p/sf3YH+0QpRQ14uNZWlBAUWmpByP0nD/3tAtKSvhk/34a/+UOZh/9jFbvtaJmlZr89uhvjO00luCAYM8E6iK+/tm/HMMXbeH7Emom8GKPFxk2dxgllpKy+VFRUVzdpw+rDhzwYHTeoai0lI/37mHLDdUYsWUUe/L2sHHURib2mUhEFfsfLya8j/S0hSFYtIWkT5LoEd+D57o+VzY/Pz+faePGMbJGDSKqVPFghO6Tuvd3JmespdCvlGCLP8Ni2rCiRi6pcTtpUrcpr934Wtll58K4LtfTlqItDGPfiX1c859rWPLXJbSp06Zs/splyzg8axZ3muCCm9S9vzNm1yIyev/xtJ+Axf7Uah3JjLEz5CpGH+KzByLN2teyMlP+9WvU59XerzJs7jCKSovKcr+ua1cORkeTaYILbiZnrP2jYJ8/Tb2kTyltCtowoPEAUxVsM332L2T4oi3MJTkxmdjqsby08qWyeYGBgfS55x4W5eZi8fFfdYV+5R90LbQUujkS4SmGL9q+fJcvW5gtf6UU0wdOZ9r6aVRr9Mcl181btCC4bVs2HT7swehcL9jyx7nqXNANsj6EwEzM9tm3MnzRFuYTHRbNpL6TGDZ3GAUl5x5zpZSi75AhLC8qoqCk5AojGNfohI4kLLn4bJCEjQk8NvQxD0Uk3M3wRdusfS0rs+Y/pOUQoo5GMX7Z+LJ50dHRNO7bl5UHD3owMtcaENeYf1/dC5ZCyx9akrQ3ybRPjTHrZ9/wRVuYk1KKxzs9zie/fsLqfavL5vccMID0kBByzpzxYHSuFXJVAPV61WHK36awaOYiUxZsM5NT/oShzdk+h3FLxpH+QHrZbUV/XLmS3R98wF989L4k4/IW80t8CN+NXerpUIQL+ewpf8LcBjcbTMeYjjyz5JmyeR27dOFko0ZsPXbMg5G5zobAo1xTv6OnwxAeYviibda+lpWZ87fmPqXfFObsmMOyPcuAc/fcHpiczHdnz3K2uNiDEbrGjqAcbmh4g6nfezDvZ9/wRVuIiCoRTB84nRHzRpBfmA9AbGwsTQcOZLGP3Zckr/QsxwLy6dqkq6dDER4iPW3hM0bOHwnA9JunA1BQUMC7zz/PbSUlxIWHezI0p/n61HaeCFtN5gtZng5FuJj0tIXPeyPpDRbvXsy3O78FICQkhH4jRrAgJ4cSi+UKaxvDDyV7aR5x6TMfhXkYvmibta9lZeb8/5x79eDqzBw0k1ELRnH87Ln7czRt1oxa3bqx2kfaJBsDj3Ft/U6Aud97MG/+dhdtpdQdSqmtSqlSpZTcB1J4hZ4NejK46WBGLxoNnPuJ2f/OO1kbFES2D5y7vSM4hxsa3eDpMIQH2d3TVko1BSzA+8CTWuuNl1lOetrCrU4XnSbx/UQm9p7I4GaDAVizejU7pk1j2NVXG/bc7WMlp4nxf5v8v50kJMh89xoxG6f3tLXWO7TWvzsWlhDOVy2oGimDUnj424c5dvrcudodrruOohYtSDfwMyWXFeyhnn+0FGyTk562wZk5/4py71K/C/e0uoeHUh9Ca42fnx8Dhw1jSVERp4uK3BekE60u2U/zmi3Lps383oN58w+o6EWl1GKgTjkvPae1XmDrRoYPH058fDwA4eHhJCYmlt1W0fqHt3c6PT3dofWNPm32/Cua/mfPf9LkySYMWTeEvJw8CnUhh347QPrWMP7b7+Zzy59/OG73859Pb57eFJRNwxPNSUtL84q/r0w7dzotLY2UlBSAsnpZHofP01ZKLUd62sJLvf3Z2zz57pNYev5xyl+txeH8q3YXRrW61oORVd5VJW/x5T0L6Na0m6dDEW7g6vO0jXlkR/i8hYsWXlSwAXL65PHmvk2GusT9cMkpTvif5bqG13k6FOFhjpzyN1gptR/oBKQqpRY6LyzbWX9emJWZ87cl90Jd/mO4Aq8KZ1GWca4qXHp2N3H+MQQGBJbNM/N7D+bN35GzR+Zoretpratoretorfs5MzAhnCFYBZc7Pzoqhv2xsezIznZzRPZZXbqfFpGtPB2G8AKGP3vE2tA3KzPnb0vuo+8eTcKmhIvmxa6LZcxfxnDLAw+Qevo0ZwzQJtkUlE2HuItbI2Z+78G8+Vd49ogQRmd9qsuUT6dQYCkg70weB+IP0K5TO6LDoml1222kfvEFdzRocIWRPGtnUDY9GvfwdBjCCxh+T9usfS0rM+dva+4D+gxg0cxFpKWkkf55OqPvHM2g2YM4W3yWHklJHImPZ8vRo64N1gFZxSc45V9E+4T2F80383sP5s3f8EVbiMp6vtvzJNRMIHleMgEBAQweNYqFBQWc8tKLbpYU7KFBQD38/fw9HYrwAnI/bWFKZ4vP0n1WdwY0GsDfb/g7y777jiP//S9DvPC5kqPyFpDbNI4vH/za06EIN5L7aQtxgSqBVZh711xmbJrB51s/54bevclr3JhfvPDeJOnB2XSMk/OzxTmGL9pm7WtZmTl/R3OPDotm3pB5PPLtI2w8vJHB99/P4pIS8gvLP7fbU3YG5dCjyaUHIc383oN58zd80RbCEYl1Epk+cDqDPxtMSdUSOt59N/OysvCWlt7uouMU+JXQLl5uWS/OkZ62EMArP7zCF9u+IO3eNGZPfo82u3bRoW5dT4fF9PwNvF3zN7Y+L3dBNhvpaQtRgae7PE2LqBYkz0/mlhHJpPn5cfT0aU+HxU/6AK2uSvR0GMKLGL5om7WvZWXm/J2Zu1KK6QOnc+jUISZvnkyfkSP58vBhiktLnbYNe/wSlE3H+PIPQpr5vQfz5m/4oi2EswQHBDPnrjl88usnbA3cTlTv3izx8E2ldgXl0KtpL4/GILyL9LSF+JNfj/xKz4968sXgL0j/YBH9T5+mca1abo/j98Js2gbN4OQLp/Hzk/0rs5GethA2alW7FR8O+pC7591N+78OZP6pU5z0wGmASwv3kBAULwVbXMTwnwaz9rWszJy/K3O/qfFNPHndkzy86mGa33kLcz1wGuAafYDWddpe9nUzv/dg3vwNX7SFcJUnrnuC9nXbMy1vBgVtWrPm4EG3bv+XoBw6xXV26zaF95OethAVKCotos/HfUiMTCR2YQn3hIQQHRbm8u1atIUa+jXWPbCRZjHNXL494X2kpy2EHYL8g/jqzq/4Zvc3FPatw5dHj1LkhtMAtxdlg/KjSXQTl29LGIvhi7ZZ+1pWZs7fXblHVo1kwdAFTN4xhZPdr2bR/v0u3+aygj00DG5Q4UFIM7/3YN78DV+0hXCH5lHN+WjwR0w5/h82xIay1cUPTVjDAVrXkfuNiEtJT1uISpiydgpT107lL1s68nB4bWpVreqS7bQqmM4jN47nwZ4PumR84f2kpy2EEzza4VF6XN2D79rsYvahAy65zN2iLewOyqFXM7kSUlzK8EXbrH0tKzPn74nclVJM7jeZ4NAQFl2Tw7cu6G9vLjxCgAqgUXSjCpcz83sP5s3f7qKtlHpNKbVdKfWLUuprpVQNZwYmhLcK9A/kizu/YIdfBp81yGXT4cNOHX95USaNqiQ4dUzhO+zuaSul+gBLtdYWpdQrAFrrZ8pZTnrawif9nvM7XWZ04das6/hHUGPqhIY6Zdy78r8itHV7Zgyf6ZTxhDE5vaettV6stbacn1wLxNo7lhBG1LhWY2bfPpsv665mau4uCkpKnDLuluAcrmsgV0KK8jmrpz0C+NZJY1WKWftaVmbO3xty73V1L17u/TIpjdfzv6xdDt+fpFRb2B2UbdNBSG/I35PMmn9ARS8qpRYDdcp56Tmt9YLzy/wNKNJa/+9y4wwfPpz4+HgAwsPDSUxMpHv37sAff3h7p9PT0x1a3+jTZs/fG6ab0pRBrW7hjeLvObUmn9ZRV9H9/Oc9LTPz3PI2Tk/fuYGAwAAaXNXAa/KTafdMp6WlkZKSAlBWL8vj0HnaSqnhwEigl9a64DLLSE9b+LwSSwk3zrqRwt+ymV3Yi3o17DsuPzF/NV/VzmHt0xudHKEwGqf3tJVSfYGngEGXK9hCmEWAXwBfD/2aw1edZmzJj5wuKrJrnHUcIjH6WidHJ3yJIz3tKUAosFgptUkp9a6TYqoU688LszJz/t6We3hION8P/57ldX7nxex1WOz4hbklOIfOV3exaVlvy9/dzJp/hT3timitKz7zXwgTSqiZwFd3f82gj2+mxYFw7o1uafO6pdpCZlAOvZv3dmGEwujk3iNCuMDUn6YyYeHzLDxxB9dG1LVpnZ/O7mdgyNdk/+O4i6MTRiD3HhHCjR657hEGtbqNIVUXkF1w2qZ1lhdl0rhaQxdHJozO8EXbrH0tKzPn7+25v3/b+1xVN57biuZSYrnyjaXW+R2ibUx7m8f39vxdzaz5G75oC+Gt/P38WThyEbtrneHRvO+vuPzWoBy6XH29GyITRiY9bSFcbMfhHXSc1p6Xj3fl0YgO5S5TpEsJ4xX2PZFF7Rq13Ryh8EbS0xbCQ5rWacont/6PZ8LTWHJyd7nL/FSwn3CqS8EWV2T4om3WvpaVmfM3Uu4DWw9k/HUvcFeVOewuyL3k9RVFe2kc2rhSYxopf1cwa/6GL9pCGMXTfZ+mf8LNJDGbU6UXXzH5s98h2sbafhBSmJf0tIVwo1JLKZ3f7ETVQ8dZGno3furcftPVRe/yyqB3uLPjnR6OUHgL6WkL4QX8/fz57uHF7Ao/w6P53wFQYCkmK/A4PZv19HB0wggMX7TN2teyMnP+Rs09vGo4341Ywn+rb2PAyv9S+6vXKZ5XQtNeTZkwcYLN4xg1f2cxa/5233tECGG/5jHN6Zc/gM/yP4PzHZEccnj5q5cBmDBugueCE15NetpCeEhk+0hybsq5ZH6t1Fpkr8v2QETCm0hPWwgvU+Jf/jMlS/yc86xJ4ZsMX7TN2teyMnP+Rs89oLT87mSAxbaupdHzd5RZ8zd80RbCqB6941ECvr24QAekBvDo7Y96KCJhBNLTFsKDJkycwDtfvkOJXwkBlnMFWw5CCrh8T1uKthBCeCGfPRBp1r6WlZnzN3PuIPmbNX/DF20hhDATaY8IIYQX8tn2iBBCmIndRVsp9U+l1C9KqXSl1FKlVD1nBmYrs/a1rMycv5lzB8nfrPk7sqc9UWvdRmudCMwFXnBSTJWSnp7uic16DTPnb+bcQfI3a/52F22t9ckLJkMBj9wsIS8vzxOb9Rpmzt/MuYPkb9b8HbrLn1LqZeCvwBmgk1MiEkIIcVkV7mkrpRYrpX4t55+BAFrrv2mt6wMpwFtuiPcSmZmZntis1zBz/mbOHSR/s+bvlFP+lFL1gW+11i3LeU3O9xNCCDuUd8qf3e0RpVQjrfXO85ODgE22blQIIYR97N7TVkp9CTQBSoEM4CGt9VEnxiaEEOJPXH5FpBBCCOfxmSsilVJPKqUsSqmano7FnZRSrymltp+/0OlrpVQNT8fkDkqpvkqpHUqpnUqppz0djzsppeoppZYrpbYqpbYopUZ7OiZ3U0r5K6U2KaUWeDoWd/OJon3+asw+wF5Px+IB3wMttNZtgN+BZz0cj8sppfyBd4C+QHNgqFKqmWejcqti4HGtdQvOnWr7iMnyBxgDbANM1yrwiaINvAmM83QQnqC1Xqy1tpyfXAvEejIeN+kA7NJaZ2qti4HZnDsYbgpa68Na6/Tz/30K2A7U9WxU7qOUigX6Ax8ApjvRwfBFWyk1CMjSWm/2dCxeYATwraeDcIMYYP8F01nn55mOUioeaMu5/2GbxVvAU4DlSgv6IoeuiHQXpdRioE45L/2Nc+2AGy9c3C1BuVEF+T+ntV5wfpm/AUVa6/+5NTjPMN1P4vIopUKBL4Ex5/e4fZ5S6ibgqNZ6k1Kqu6fj8QRDFG2tdZ/y5iulWgINgF+UUnCuNbBBKdXBl04/vFz+Vkqp4Zz7udjLLQF53gHgwrtK1uPc3rZpKKUCga+AT7TWcz0djxt1Bm5WSvUHQoDqSqmPtNb3ejgut/GpU/6UUnuAa7TWuZ6OxV2UUn2BN4AbtNYeuWmXuymlAoDfOPc/qYPAOmCo1nq7RwNzE3VuD2UWkKO1ftzT8XiKUuoG4P+01gM9HYs7Gb6n/Se+838g203h3F0WF58/BepdTwfkalrrEuBR4DvOnUHwmVkK9nldgHuAHuff803n/+dtRqb7zvvUnrYQQvg6X9vTFkIInyZFWwghDESKthBCGIgUbSGEMBAp2kIIYSBStIUQwkCkaAshhIFI0RZCCAP5f8NyCaSKvRwCAAAAAElFTkSuQmCC
-
-
-三维绘图
----------
-
-
-导入 Axex3D：
-
-
-
-
-
-::
-
-
-    from mpl_toolkits.mplot3d import Axes3D
-    from matplotlib import cm
-    from matplotlib.ticker import LinearLocator, FormatStrFormatter
-    import matplotlib.pyplot as plt
-    import numpy as np
-
-    fig = plt.figure()
-    ax = fig.gca(projection='3d')
-    X = np.arange(-5, 5, 0.25)
-    Y = np.arange(-5, 5, 0.25)
-    X, Y = np.meshgrid(X, Y)
-    R = np.sqrt(X**2 + Y**2)
-    Z = np.sin(R)
-    surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1,        cmap=cm.coolwarm,
-             ^4$linewidth=0, antialiased=False)
-    ax.set_zlim(-1.01, 1.01)
-
-    ax.zaxis.set_major_locator(LinearLocator(10))
-    ax.zaxis.set_major_formatter(FormatStrFormatter('%.02f'))
-
-    fig.colorbar(surf, shrink=0.5, aspect=5)
-
-    plt.show()
-
-
-
-<button>Run ...</button>
-
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-.. image::data:image/png;base64,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-流向图
--------------
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-主要函数：plt.streamplot
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-::
-
-    import numpy as np
-    import matplotlib.pyplot as plt
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-    Y, X = np.mgrid[-3:3:100j, -3:3:100j]
-    U = -1 - X**2 + Y
-    V = 1 + X - Y**2
-    speed = np.sqrt(U*U + V*V)
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-    plt.streamplot(X, Y, U, V, color=U, linewidth=2, cmap=plt.cm.autumn)
-    plt.colorbar()
-
-    f, (ax1, ax2) = plt.subplots(ncols=2)
-    ax1.streamplot(X, Y, U, V, density=[0.5, 1])
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-    lw = 5*speed/speed.max()
-    ax2.streamplot(X, Y, U, V, density=0.6, color='k', linewidth=lw)
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-    plt.show()
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-<button>Run ...</button>
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-.. image:: 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
-
-
-
-
-椭圆
-------------
-
-
-Ellipse 对象：
-
-
-
-
-::
-
-
-    from pylab import figure, show, rand
-    from matplotlib.patches import Ellipse
-
-    NUM = 250
-
-    ells = [Ellipse(xy=rand(2)*10, width=rand(), height=rand(),     angle=rand()*360)
-            for i in range(NUM)]
-
-    fig = figure()
-    ax = fig.add_subplot(111, aspect='equal')
-    for e in ells:
-        ^4$ax.add_artist(e)
-        ^4$e.set_clip_box(ax.bbox)
-        ^4$e.set_alpha(rand())
-        ^4$e.set_facecolor(rand(3))
-
-    ax.set_xlim(0, 10)
-    ax.set_ylim(0, 10)
-
-    show()
-
-
-<button>Run ...</button>
-
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-.. image:: 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
-
-
-
-条状图
----------
-
-
-bar 函数：
-
-
-
-
-::
-
-    import numpy as np
-    import matplotlib.pyplot as plt
-
-
-    n_groups = 5
-
-    means_men = (20, 35, 30, 35, 27)
-    std_men = (2, 3, 4, 1, 2)
-
-    means_women = (25, 32, 34, 20, 25)
-    std_women = (3, 5, 2, 3, 3)
-
-    fig, ax = plt.subplots()
-
-    index = np.arange(n_groups)
-    bar_width = 0.35
-
-    opacity = 0.4
-    error_config = {'ecolor': '0.3'}
-
-    rects1 = plt.bar(index, means_men, bar_width,
-                     alpha=opacity,
-                     color='b',
-                     yerr=std_men,
-                     error_kw=error_config,
-                     label='Men')
-
-    rects2 = plt.bar(index + bar_width, means_women, bar_width,
-                     alpha=opacity,
-                     color='r',
-                     yerr=std_women,
-                     error_kw=error_config,
-                     label='Women')
-
-    plt.xlabel('Group')
-    plt.ylabel('Scores')
-    plt.title('Scores by group and gender')
-    plt.xticks(index + bar_width, ('A', 'B', 'C', 'D', 'E'))
-    plt.legend()
-
-    plt.tight_layout()
-    plt.show()
-
-
-<button>Run ...</button>
-
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-.. image:: 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
-
-
-饼状图
-----------
-
-
-pie 函数：
-
-
-
-
-::
-
-    import matplotlib.pyplot as plt
-
-
-    # The slices will be ordered and plotted counter-clockwise.
-    labels = 'Frogs', 'Hogs', 'Dogs', 'Logs'
-    sizes = [15, 30, 45, 10]
-    colors = ['yellowgreen', 'gold', 'lightskyblue', 'lightcoral']
-    explode = (0, 0.1, 0, 0) # only "explode" the 2nd slice (i.e. 'Hogs')
-
-    plt.pie(sizes, explode=explode, labels=labels, colors=colors,
-            ^4$autopct='%1.1f%%', shadow=True, startangle=90)
-    # Set aspect ratio to be equal so that pie is drawn as a circle.
-    plt.axis('equal')
-
-    plt.show()
-
-
-<button>Run ...</button>
-
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-.. image:: 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
-
-
-
-图像中的表格
---------------
-
-
-table 函数：
-
-
-
-
-
-::
-
-    import numpy as np
-    import matplotlib.pyplot as plt
-
-
-    data = [[  66386,  174296,   75131,  577908,   32015],
-            ^4$[  58230,  381139,   78045,   99308,  160454],
-            ^4$[  89135,   80552,  152558,  497981,  603535],
-            ^4$[  78415,   81858,  150656,  193263,   69638],
-            ^4$[ 139361,  331509,  343164,  781380,   52269]]
-
-    columns = ('Freeze', 'Wind', 'Flood', 'Quake', 'Hail')
-    rows = ['%d year' % x for x in (100, 50, 20, 10, 5)]
-
-    values = np.arange(0, 2500, 500)
-    value_increment = 1000
-
-    # Get some pastel shades for the colors
-    colors = plt.cm.BuPu(np.linspace(0, 0.5, len(columns)))
-    n_rows = len(data)
-
-    index = np.arange(len(columns)) + 0.3
-    bar_width = 0.4
-
-    # Initialize the vertical-offset for the stacked bar chart.
-    y_offset = np.array([0.0] * len(columns))
-
-    # Plot bars and create text labels for the table
-    cell_text = []
-    for row in range(n_rows):
-        ^4$plt.bar(index, data[row], bar_width, bottom=y_offset,     color=colors[row])
-        ^4$y_offset = y_offset + data[row]
-        ^4$cell_text.append(['%1.1f' % (x/1000.0) for x in y_offset])
-    # Reverse colors and text labels to display the last value at the     top.
-    colors = colors[::-1]
-    cell_text.reverse()
-
-    # Add a table at the bottom of the axes
-    the_table = plt.table(cellText=cell_text,
-                          ^4$rowLabels=rows,
-                          ^4$rowColours=colors,
-                          ^4$colLabels=columns,
-                          ^4$loc='bottom')
-
-    # Adjust layout to make room for the table:
-    plt.subplots_adjust(left=0.2, bottom=0.2)
-
-    plt.ylabel("Loss in ${0}'s".format(value_increment))
-    plt.yticks(values * value_increment, ['%d' % val for val in values])
-    plt.xticks([])
-    plt.title('Loss by Disaster')
-
-    plt.show()
-
-
-<button>Run ...</button>
-
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-.. image:: 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
-
-
-
-散点图
-------------
-
-scatter 函数：
-
-
-
-
-::
-
-    import numpy as np
-    import matplotlib.pyplot as plt
-    import matplotlib.cbook as cbook
-
-    # Load a numpy record array from yahoo csv data with fields date,
-    # open, close, volume, adj_close from the mpl-data/example directory.
-    # The record array stores python datetime.date as an object array in
-    # the date column
-    datafile = cbook.get_sample_data('goog.npy')
-    price_data = np.load(datafile).view(np.recarray)
-    price_data = price_data[-250:] # get the most recent 250 trading days
-
-    delta1 = np.diff(price_data.adj_close)/price_data.adj_close[:-1]
-
-    # Marker size in units of points^2
-    volume = (15 * price_data.volume[:-2] / price_data.volume[0])**2
-    close = 0.003 * price_data.close[:-2] / 0.003 * price_data.open[:-2]
-
-    fig, ax = plt.subplots()
-    ax.scatter(delta1[:-1], delta1[1:], c=close, s=volume, alpha=0.5)
-
-    ax.set_xlabel(r'$\Delta_i$', fontsize=20)
-    ax.set_ylabel(r'$\Delta_{i+1}$', fontsize=20)
-    ax.set_title('Volume and percent change')
-
-    ax.grid(True)
-    fig.tight_layout()
-
-    plt.show()
-
-
-<button>Run ...</button>
-
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-.. image:: 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-
-
-设置按钮
-----------
-
-
-matplotlib.widgets 模块：
-
-
-
-
-
-::
-
-    import numpy as np
-    import matplotlib.pyplot as plt
-    from matplotlib.widgets import Slider, Button, RadioButtons
-
-    fig, ax = plt.subplots()
-    plt.subplots_adjust(left=0.25, bottom=0.25)
-    t = np.arange(0.0, 1.0, 0.001)
-    a0 = 5
-    f0 = 3
-    s = a0*np.sin(2*np.pi*f0*t)
-    l, = plt.plot(t,s, lw=2, color='red')
-    plt.axis([0, 1, -10, 10])
-
-    axcolor = 'lightgoldenrodyellow'
-    axfreq = plt.axes([0.25, 0.1, 0.65, 0.03], axisbg=axcolor)
-    axamp  = plt.axes([0.25, 0.15, 0.65, 0.03], axisbg=axcolor)
-
-    sfreq = Slider(axfreq, 'Freq', 0.1, 30.0, valinit=f0)
-    samp = Slider(axamp, 'Amp', 0.1, 10.0, valinit=a0)
-
-    def update(val):
-        ^4$amp = samp.val
-        ^4$freq = sfreq.val
-        ^4$l.set_ydata(amp*np.sin(2*np.pi*freq*t))
-        ^4$fig.canvas.draw_idle()
-    sfreq.on_changed(update)
-    samp.on_changed(update)
-
-    resetax = plt.axes([0.8, 0.025, 0.1, 0.04])
-    button = Button(resetax, 'Reset', color=axcolor, hovercolor='0.975')
-    def reset(event):
-        ^4$sfreq.reset()
-        ^4$samp.reset()
-    button.on_clicked(reset)
-
-    rax = plt.axes([0.025, 0.5, 0.15, 0.15], axisbg=axcolor)
-    radio = RadioButtons(rax, ('red', 'blue', 'green'), active=0)
-    def colorfunc(label):
-        ^4$l.set_color(label)
-        ^4$fig.canvas.draw_idle()
-    radio.on_clicked(colorfunc)
-
-    plt.show()
-
-<button>Run ...</button>
-
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-.. image:: 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
-
-
-
-填充曲线
-----------------
-
-fill 函数：
-
-
-
-::
-
-    import numpy as np
-    import matplotlib.pyplot as plt
-
-
-    x = np.linspace(0, 1)
-    y = np.sin(4 * np.pi * x) * np.exp(-5 * x)
-
-    plt.fill(x, y, 'r')
-    plt.grid(True)
-    plt.show()
-
-
-<button>Run ...</button>
-
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-.. image:: 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/B5fe3qU+kqu8A74jID4H/A45tbrtx48aRlZUFQJcuXRg0aND+qVvvvvsuh/otcJbr+5odgePOQFZiIs8//zx33HFHw/NNXsyN48Z/fyyOCwoKwioeL8cFBQVhFY+NvRk33i8sLMQNtz3+ocBEVR3lG98D1KvqE618z2ZgiKruavJ4qz3+hx98kIqHHuLxCJ/V0+i+uDjq//AHHnvqKa9DMcZEKK96/MuA/iKSJSJJwNXA9xajEZFjRER89wcDNC36bbFp1Sr6R0nRB8iur2dBTo7XYRhjYpCrwq+qtcB4YDawBnhdVdeKyM0icrNvsyuAz0RkBfAMcE1HjrVx3bqIus7uwZwJFGzYQHmTE9L8/6SLdZYLh+XCYblwz22PH1XNoeFDW//HJvvdfxJ40u1xNm7dGvFz+P11AgalppKXl8fIkSO9DscYE0MiYj3+PXv20OuIIyitqYmKWT2N7o2PR+64g0f+9CevQzHGRKCoXo8/mqZy+htWV8eCmTO9DsMYE2MiovBHw6qczTmLA+fzW//SYblwWC4clgv3IqLwR8OqnM3pDJyUmkp+fr7XoRhjYkhEFP5oWJWzJcPKy1nw4Yf7x40nbBjLhT/LhcNy4V5EFP5N69dH1Ywef9l1deS+/77XYRhjYkhEFP5om8rp72zg07Vr96/Pb/1Lh+XCYblwWC7cC/vCv2fPHvZVVZHhdSBBkk7D+vzW5zfGhErYF/5oncrpb1hFBQvmzQOsf+nPcuGwXDgsF+6FfeGP1qmc/rJra1lgfX5jTIiEf+Ffv55+UTiV0985wJLPP6eqqsr6l34sFw7LhcNy4V7YF/5oW5WzOYcAx6WksGTJEq9DMcbEgLBfq+fM44/nqbVrOceDmELpjoQEDr33Xv574kSvQzHGRIioXatn49atUbUcc0uGWZ/fGBMiYV34v/32W6qqq6N2Kqe/HwKLV63igw8+8DqUsGG9XIflwmG5cC+sC39hYSFHRflUzkZdgP4pKaxbt87rUIwxUS6sC39JSQm9vA4ihLIrKyn97juvwwgbNl/bYblwWC7cC/vCn1lT43UYITOspobcGTO8DsMYE+XCuvAXf/klvSorvQ4jZH4ILFqxgpoY+mXXGuvlOiwXDsuFe64Lv4iMEpF1IrJRRCY08/xPRWSliKwSkY9F5OS27rtk82Yy3QYYQboBRyYk8Omnn3odijEmirkq/CISD/wVGAUcD4wVkeOabPYFcK6qngw8DPytrfsvKSyMqcIPcEldHbm+dXtinfVyHZYLh+XCPbfv+IcAm1S1UFVrgKnAaP8NVDVPVRs/sVwMbf+8tnjbtpj6cBdgWHU1C957z+swjDFRzG3hzwSK/MbFvsda8l9Am68uXrJzZ8y9448DPl6+nNraWq9D8Zz1ch2WC4flwr0El9/f5vUeRGQ48HMarj3SrHHjxpGVlQVAamoqFdXVdPU9l+v7mh3l40OBrKQkXnzxRY477rj9f9Y2vthjaVxQUBBW8Xg5LigoCKt4bOzNuPF+YWEhbrhaq0dEhgITVXWUb3wPUK+qTzTZ7mTgP8AoVd3Uwr6+t1bP+vXrueS009hYVtbh+CLVb5OSyJw4kQn33ON1KMaYMObVWj3LgP4ikiUiScDVwPQmgfWhoej/rKWi35ySkhIy4+NdhheZRlRX8+E773gdhjEmSrkq/KpaC4wHZgNrgNdVda2I3CwiN/s2ux/oCrwgIitEpE1rDxcXF9Orrs5NeBEpFxgG5BUUUFVV5XE03vL/8zbWWS4clgv33Pb4UdUcIKfJY5P97t8E3NTe/ZYUF5MZQydv+etCw/r8+fn5DBs2zOtwjDFRJmzP3C3ZvJnMGHzHn+37OqK8nA9nz/YyFM81frBlLBf+LBfuhW3hL/7ii5ibw+/vvLo65tm6PcaYIAjbwl9SUhJzc/jBmdZ5NrBywwbKYnBWUyPr5TosFw7LhXvhW/h37IjJwt8oDTgtJYWFCxd6HYoxJsqE5TV3a2pq6JSSQkV9vftPnyPYw3FxfHfLLfx50iSvQzHGhKGouubu9u3b6Z6SEtNFH2BEfT0f2nV4jTEBFpaFv7i4mMyE2Cz7uX73Twe+KC5m165dHkXjLevlOiwXDsuFe2FZ+GPtkostSQTOSU5m/vz5XodijIkiYVv4M/ft8zoMT2Q3GY8oLeXDGF2m2eZrOywXDsuFe2FZ+Iu3bCGzutrrMMLCecC8OXO8DsMYE0XCsvCXbN4cs62e3Cbjk4Hd335LcXGxB9F4y3q5DsuFw3LhXngW/q1bY3oOv784YHhCAvPscozGmAAJy3n8x2RkMGvHDvp7HFO4+F8g74oreHXaNK9DMcaEkY7O4w+7wq+qpCUlsau2ljSvgwoTG4HhXbtStGsXIu3+PzbGRKmoOYFr9+7dpMTHx2zRz23msX5AXFUVGzduDHE03rJersNy4bBcuBd2hb+4uJheyclehxFWBDhPlQ/nzvU6FGNMFAi7wh+rq3I2ym7h8RGVlcybPr2FZ6OTzdd2WC4clgv3wq7wFxcXk1lb63UYYec8YP6iRdTX13sdijEmwoVd4S8pKqJXRYXXYXgmt4XHM4HDRVi5cmUIo/GW9XIdlguH5cI914VfREaJyDoR2SgiE5p5/gcikici+0Tk9oPtr2TTpphu9bRmRE0N8z780OswjDERztV0ThGJB9YDI4ESYCkwVlXX+m3THegLjAG+VdW/tLAvVVVGnXkmt+bnc3GHo4pe/wGmnH02Mxct8joUY0wY8Go65xBgk6oWqmoNMBUY7b+Bqn6jqsuAmrbssGTbtphdruFgsoFFy5ZRbesYGWNccFv4M4Eiv3Gx77EOK9m5M6ZbPbmtPNcNGJCcTH5+foii8Zb1ch2WC4flwj23VzsJ6Gm/P/vZz9hbWcmzQFdgEM70xlzf12gfc5DnLywvZ+a77+6f3dM4ta3xhyGaxgUFBWEVj5fjgoKCsIrHxt6MG+8XFhbihtse/1BgoqqO8o3vAepV9Ylmtn0AKGutx79hwwZGDR7M5rKyDscU7fKBX/Tty2cu/+ONMZHPqx7/MqC/iGSJSBJwNdDSWUYHDa6kpITM+HiXIUW304Gvv/6arVu3eh2KMSZCuSr8qloLjAdmA2uA11V1rYjcLCI3A4hIDxEpAm4D7hORrSLSubn9FRcX06uuzk1IES/3IM/HA6Pi4ng/Bq7KZb1ch+XCYblwz/U8flXNUdVjVbWfqj7ue2yyqk723d+uqr1V9VBV7aqqfVS12V5OSXExmZWVbkOKehdXVPD+1Kleh2GMiVBhtSzz+Jtu4pgpU/i918GEuT1An+Rkvv72W1JTU70OxxjjkahYlrlkyxabw98GXYBTkpOZP3++16EYYyJQeBX+oqKYnsMPB+/xN7qotJT333ormKF4znq5DsuFw3LhXlgV/uIdO2K+8LfVxarMnDGDcGnVGWMiR1j1+BPj4iivryfR62AigAJZaWnkLF3K8ccf73U4xhgPREWP/7DkZCv6bSTAxXV1vD9jhtehGGMiTFgV/sxEK/u57dj24qqqqJ7Wab1ch+XCYblwL6wKv83oaZ/hwPI1a9izZ4/XoRhjIkhY9fh/k5zMc1VVXocSUS5OT+eGKVO46qqrvA7FGBNiHe3xu12dM6B6WdFvt4tKS3n/jTes8Eeouro6duzYQUlJCdu2bWPbtm2UbN3Kti++YHtxMQkJCXQ+5BDSu3Shc5cupHfrRuf0dA499FCOPfZYTjjhBLp16+b1P8NEmLB6x/8qcL3XgXgsF2cp5rYoBIakp7N9zx7i4sKqc+dabm7u/mVpo0lRURGzZ81i1rRpfLhwIclAZlISR6pyZE0NmZWVHAn0AOqAUmA50BMoA0oTE/k2MZF1CQmsrqykU2oqJwwYwAmnnsoJgwczcOBABg8eTGKUfmYWra+LjoiKd/w2h7/9soDuwNKlSznjjDM8jsY0p6qqigULFjB7xgxmvfsuO775hh/FxTG6ooK/0lDgOcgaVb3we0NQU9Nwo2Fab1FNDauXLWP1smXkpaXxXHw8W6qrOee00xh+6aWcN3IkgwYNIt5WvjU+YfWOfx1wrNeBRKC7EhJIvesuHnz0Ua9DMX5KS0uZ/Nxz/M8TT5BVX89FZWVcUF/PYBpWWQ2mncACYH5yMvOSkviqtpZzzziD8y+/nMtGj6ZPnz5BjsCEQkff8YdV4S8Fml2v2bRqAXB7//4s27DB61AMsGvXLib95S88P2kSI1W5u6KCgR7H9BUNbcRZqam8r0rvI49k9NixjL7iCgYNGoRIu2uHCQNRUfjDIxJv5dK+Hj80XMU+IzmZ1Vu20LNnz4DH5JVI6+UWFxfzl8ce49W//50r6+u5q6qKfgHady7tf120pBb4GHg3MZF3k5KoTUnhsssv58fXXMO5555LQkJYdYAPEGmvi2CKijN3TcckAucnJJAzc6bXocSkqqoq/njHHZzcvz9xU6bwWWUlfwtg0Q+0BGAY8D81NWwqL2fmrl30nDKFO8eMIbNbN351ww3MnTuX2tpar0M1QWLv+KPEq8D088/nrTlzvA4lpnz66aeM+8lP6Ld9Oy9UVjZ8UBvBvgCmifBm5858qcqYMWO48rrrGD58eNTOEopk1uqJcTuAASkpfL1nD8nJyV6HE/Wqq6t55P77mTxpEv+vspKxtOGi0hGmEOeXwOb6ei675BKuvP56RowYYa+xMGGtniiR28HvOwI4JTGRnJycAEbjrXBdk2XlypUMOeEEVjz7LAWVlVxL8It+bpD335ws4A5VFpeWsry8nJNff53Hx46lR9euXHf55bzzzjtUenCp1HB9XUQS14VfREaJyDoR2SgiE1rYZpLv+ZUicorbY5rmXVtayr/+9jevw4hatbW1PHz//Zx/5pnctmkT0ysqiJ6P0lvXB/g9sHDvXtZUVnLm228z6frr6dmtG5effz6vvPwyO3bs8DpM00auWj0iEg+sB0YCJcBSYKyqrvXb5iJgvKpeJCJnAM+o6tBm9mWtHpd2A0clJ1O0YweHHHKI1+FEldLSUq659FL2LV3KqxUVtqCgz05gJjCjUyc+qKnhuGOO4ZJrruHSMWM46aSTbJpokHnV6hkCbFLVQlWtAaYCo5tscxkNnz2iqouBLiKS4fK4phndgOzERN7+z3+8DiWqFBcX88PBg8nMz2eWFf3vOZyGZVbeLC9nR3U1D61dy45HH2XMWWeR1b07/zV2LP/85z/Ztm2b16EaP24n7GYCRX7jYqDpugHNbdML+Lrpzia6DCYaFNLQW+2oDWVljLvxRrYUFgYkHi8VFhaSlZXlaQwrVqxg+vTppNHwjsarc6MLcfe6CKWu1dVcV13NuvJypk+dystNrhlx4oknMmzYMA4//PAO7T8cXhehpKqUlZWxc+fOA24d5bbwt7U70/RPkWa/752BA+nSpQsAKSkp9OjRY/9/cKGvkEX72Pdgh7//0iOOYN1TT7H8wQfphlMsGvceSePtHh//E2AjDYXq1FNP5Uu8e31sz8+HCPp5+LKwkFTgFt948+bNbN68mVWrVvH555/z+eef06gT0A8YADReRLTQ9zUrSsdbgL00FOCvfOPdwD5Cw23hLwF6+4170/COvrVtevkeO0BBQYHLcAzA9i++4PS33uJWrwOJYJPi4phzyCHkz5pli98FQV1dHRs2bGDp0qUsXbSIpQsX8v7mzaxOSeGU+nqOrahggCrHAv1p+OUQ7hQop6GVsd3/JkJiSgrbExNZLcL2mhq+3rePzikp9OjalR5HHMGwzEwy+vQho3dvjjjiCDIyMvZ/7d69O6mpqc0es6Ofobj9cDeBhg93RwDbgCW0/uHuUODpFj/ctY93A2LWrFk8eNVV5JWWeh1KxKkHbktOZm6PHryfmxtTLQWvVVdXs3r1alauXMmGNWvYUFDA+vXr2fzVVxyWmMiAxESyamrosW8fPerr6QHfu3UmcNNq62lYAvvbJrfdvq+74uPZkZLCjoQEdgA7amvZUVUFImQceig9unenR48e9OjThx5ZWfTo2ZOMjAx69uytJ/IpAAANwElEQVRJT9/9QJwL4dkJXCJyIfA0DQsOvqSqj4vIzQCqOtm3zV+BUTT8QrxRVZc3sx8r/ARmHZKamhoyDzuM/NJSjg5MWJ7IJXDr07RFPfCr5GTWHn88M+bN2992DAexvD5NfX09RUVFrF+/nq1bt5L3ySekirC9qIjt27ax/Ztv+Orbb6mqrSUtMZG0+Hg6xceTFhdHJxHSaPiFoM3c6oFKoLy+nor6espra6moq2vYV1ISXTt1oushh9CtSxe6dutGtyOOoGtGBt0yMva/K/e/deoU2r9NPFuPX1VzgJwmj01uMh7v9jim7RITE7nqqqv49yuvcG99vdfhRAQFbklOZs1xx5GzYAHp6eleh2R84uLi6Nu3L3379gWgX79+zf4SrK2tpaKigvLy8v1fG++rKiJywC0uLo60tLT9t06dOpGWlkZKSkrUXdjIX3gt2RAmsUSDTz75hJsuuIDVZWVRt5RAoClwa3Iynw4YwOxFi+wcCBMxbMkG8z1nnnkmlamprPQ6kDCnwG1JSSw55hhmLVxoRd/EBCv8YSZQ65CICGNvuIF/RfCKirlB3r8CdyQlsejoo5nz8ccceuihQT5ix9n6NA7LhXtW+KPYT8eN49+JiViX/0AKTEhMZF7fvsz5+OOw+iDXmGCzHn+UG3j00Ty7ZQvneh1ImLkvMZEZffowb/FiDjvsMK/DMaZDrMdvmnXtL37Bv1JSvA4jrEyKi2Najx7Mzcuzom9ikhX+MBPo/uU1117LNKA6oHsNjdwg7PNN4IlDD2XWRx/RvXv3IBwhOKyv7bBcuGeFP8r17duX4wYMYLbXgYSBBcBvOnXi/Xnz7IxcE9Osxx8DXnj+eRbceSdTKyq8DsUznwMjUlP55/TpjBw50utwjAmI6LjmbpjEEm127dpF/969WRMFFwPviCLg7NRUHp88mZ9ed53X4RgTMPbhbpQIRv/ysMMOY+zYsTwbYXP6cwOwjz3AhWlp3Prf/x3RRd/62g7LhXtW+GPEH/74RybHxxNL63XuA8akpTHiuuu44+67vQ7HmLBhrZ4YcvUllzB05kxui4E81wNjU1OpHz6cqdOnEx8f73VIxgSc9fjNQS1btozLhw1jc0UFkdX0ab97EhNZeMIJzM3LI8XOYzBRynr8USKY/cvTTjuNfscfz+tBO0Jg5Xbw+14UYVr37rzzwQdRU/Str+2wXLhnhT/G3PXwwzzZuXObL5YcaeYA/52ezszc3A5fzNuYaGetnhijqgw85hie3LKFUV4HE2Cf0TBX/z9z5nDOOed4HY4xQWetHtMmIsKdDz7IU507ex1KQG0DLklL45kpU6zoG3MQVvjDTCj6l9dccw0bk5NZFvQjuZPbxu3KgEvT0rj5rrsYe+21QYzIO9bXdlgu3Otw4ReRbiLygYhsEJE5ItLsguYi8rKIfC0in3U8TBNIiYmJ/H7CBJ5KS/M6FNfqgGvT0jhlzBjuuf9+r8MxJiJ0uMcvIk8CO1X1SRGZAHRV1QPOkhGRH9Lwpuw1VT2plf1Zjz+ESktLOapnT5aUl3O018F0UOO1ctcPHszMBQtIjLAzk41xy4se/2XAq777rwJjmttIVRcC37o4jgmC9PR0fvnrX/M/ycleh9JhjyUk8HGfPrw1a5YVfWPawU3hz1DVr333vwYyAhBPzAtl//LWP/yBf4qwM2RHbJ/cVp6bEhfHy4cfTs5HH8XEBdKtr+2wXLiX0NqTIvIBNLug473+A1VVEXHdpxk3btz+ddK7dOnCoEGDyM7OBpz/7GgfNwrV8a684gqenTqV4XV1Dc83Ht/31ctxQQvPvwtMSE3lmaeeokePhpdnuPz/BWtcUFAQVvHY2Jtx4/3CwkLccNPjXwdkq+p2EekJzFfVH7SwbRYww3r84WfLli2cfuKJfFxRwbFeB9MGi4DLO3ViZm4up512mtfhGOMpL3r804EbfPdvAN5xsS/jkaOOOooHH3+cGzp1otbrYA7ic+CK1FT++fbbVvSNccFN4f8TcL6IbADO840RkSNF5P3GjUTk38AnwAARKRKRG90EHO2atnxC4dfjx9PpxBP5S5itYJnrd38rcFFaGk9PmcL555/vUUTe8eJ1Ea4sF+612uNvjaruBg64hp2qbgMu9huP7egxTGjExcXx0tSpnH7iiVxcXs6JXgfUxE7ggrQ0/vDgg1F7gpYxoWRr9Zj9Xpw8mf+9/Xbyy8vDZtnmr4Dz09IY/Zvf8OhTT3kdjjFhxdbqMa7d9MtfcsTgwTye0OE/BAPqS+DctDTG3nknjzz5pNfhGBM1rPCHGS/7lyLCi//6F39NSaHAsygabACGJCcz/qGHuHfiRETa/aYmqlhf22G5cM8Kv/meXr168ee//pUbOnWi2qMYVgHDU1O54dZb+d3tt3sUhTHRy3r85gCqypgf/YiTFizgkZqakB57CXBZairPvPwyV19zTUiPbUyksWvumoDavn07AwcM4N3SUoaG6JgLgJ+kpfHy669zySWXhOioxkQu+3A3SoRL/7JHjx5M+cc/uCwtjXeDfCwFJovwk06d+Pf06fuLfrjkIhxYLhyWC/es8JsWXXrZZbw3fz7ju3Xj0YSEoFynt4iGOfovHXccuUuWMGLEiCAcxRjjz1o95qC2bdvG5RdcQJ/Nm3mlspJOAdinAn8X4a6UFH4/YQIT7r2XhDCZRmpMpLAevwmqffv28asbbmDl++/zTnk5fV3s6yvgl2lpFB15JK9Om8bAgQMDFaYxMcV6/FEiXPuXKSkpvDJ1Ktc/8ABnpqWxsAP7qAJeAwalpnLKrbeyZPXqVot+uObCC5YLh+XCPfvb2rSZiHDbnXdywsCBXHnllQxXZWhZGWcApwApzXzPFiAHyElPZ0FVFQOPO473p0yx1TWN8ZC1ekyHbNu2jblz55Kfm8vijz5i3datnJiayhn79nFKdTWfJSaSk5zMbhFGXXABF15xBT/60Y/o1q2b16EbEzWsx288VVFRwfLly8nPy2P5woUcf+qpXHjJJZxyyinExVlH0ZhgsMIfJXJzc/dfbi3WWS4clguH5cJhH+4aY4xpE3vHb4wxEcre8RtjjGmTDhd+EekmIh+IyAYRmSMiXZrZpreIzBeR1SLyuYj81l240c/mKDssFw7LhcNy4Z6bd/x3Ax+o6gDgQ9+4qRrgNlU9ARgK3CIix7k4ZtQrKPD6Eijhw3LhsFw4LBfuuSn8lwGv+u6/CoxpuoGqblfVAt/9MmAtcKSLY0a9PXv2eB1C2LBcOCwXDsuFe24Kf4aqfu27/zWQ0drGIpJFwwmei10c0xhjjEutLtkgIh8APZp56l7/gaqqiLQ4JUdEOgPTgN/53vmbFhQWFnodQtiwXDgsFw7LhXsdns4pIuuAbFXdLiI9gfmq+oNmtksE3gNyVPXpVvZnczmNMaadOjKd080ibdOBG4AnfF/fabqBiAjwErCmtaIPHQveGGNM+7l5x98NeAPoAxQCV6nqHhE5EnhRVS8WkXOAj4BVsP8CTveo6izXkRtjjOmQsDlz1xhjTGiE9MxdERklIutEZKOITGhhm0m+51eKyCmhjC+UDpYLEfmpLwerRORjETnZizhDoS2vC992p4tIrYhcHsr4QqmNPyPZIrLCd1JkbohDDJk2/IwcLiKzRKTAl4txHoQZdCLysoh8LSKftbJN++qmqobkBsQDm4AsIBEoAI5rss1FwEzf/TOA/FDFF8pbG3NxJnCo7/6oWM6F33bzaJgocIXXcXv4uugCrAZ6+caHex23h7mYCDzemAdgF5DgdexByMUPaZgK/1kLz7e7bobyHf8QYJOqFqpqDTAVGN1km/0nhanqYqCLiLR6fkCEOmguVDVPVb/zDRcDvUIcY6i05XUBcCsNU4K/CWVwIdaWXFwLvKWqxQCqujPEMYZKW3LxFXCI7/4hwC5VrQ1hjCGhqguBb1vZpN11M5SFPxMo8hsX+x472DbRWPDakgt//wXMDGpE3jloLkQkk4Yf+hd8D0XrB1NteV30B7r51sBaJiLXhSy60GpLLl4EThCRbcBK4Hchii3ctLtuhvKau239YW06rTMaf8jb/G8SkeHAz4GzgxeOp9qSi6eBu1VVfVOEo3Xqb1tykQgMBkYAaUCeiOSr6sagRhZ6bcnFH4ECVc0WkWOAD0RkoKqWBjm2cNSuuhnKwl8C9PYb96bhN1Nr2/TyPRZt2pILfB/ovgiMUtXW/tSLZG3JxanA1Iaaz+HAhSJSo6rTQxNiyLQlF0XATlWtBCpF5CNgIBBthb8tuTgLeBRAVTeLyBbgWGBZSCIMH+2um6Fs9SwD+otIlogkAVfTcBKYv+nA9QAiMhTYo856QNHkoLkQkT7Af4CfqeomD2IMlYPmQlWPVtWjVPUoGvr8v47Cog9t+xl5FzhHROJFJI2GD/PWhDjOUGhLLtYBIwF8Pe1jgS9CGmV4aHfdDNk7flWtFZHxwGwaPrF/SVXXisjNvucnq+pMEblIRDYB5cCNoYovlNqSC+B+oCvwgu+dbo2qDvEq5mBpYy5iQht/RtaJyCwaToqsp+Fkyagr/G18XTwGvCIiK2l4E3uXqu72LOggEZF/A8OAw0WkCHiAhpZfh+umncBljDExxi69aIwxMcYKvzHGxBgr/MYYE2Os8BtjTIyxwm+MMTHGCr8xxsQYK/zGGBNjrPAbY0yM+f+eFChb1lWmfwAAAABJRU5ErkJggg==
-
-
-
-时间刻度
------------
-
-
-
-
-::
-
-    """
-    Show how to make date plots in matplotlib using date tick locators and
-    formatters.  See major_minor_demo1.py for more information on
-    controlling major and minor ticks
-
-    All matplotlib date plotting is done by converting date instances into
-    days since the 0001-01-01 UTC.  The conversion, tick locating and
-    formatting is done behind the scenes so this is most transparent to
-    you.  The dates module provides several converter functions date2num
-    and num2date
- 
-    """
-    import datetime
-    import numpy as np
-    import matplotlib.pyplot as plt
-    import matplotlib.dates as mdates
-    import matplotlib.cbook as cbook
-
-    years    = mdates.YearLocator()   # every year
-    months   = mdates.MonthLocator()  # every month
-    yearsFmt = mdates.DateFormatter('%Y')
-
-    # load a numpy record array from yahoo csv data with fields date,
-    # open, close, volume, adj_close from the mpl-data/example directory.
-    # The record array stores python datetime.date as an object array in
-    # the date column
-    datafile = cbook.get_sample_data('goog.npy')
-    r = np.load(datafile).view(np.recarray)
-
-    fig, ax = plt.subplots()
-    ax.plot(r.date, r.adj_close)
-
-
-    # format the ticks
-    ax.xaxis.set_major_locator(years)
-    ax.xaxis.set_major_formatter(yearsFmt)
-    ax.xaxis.set_minor_locator(months)
-
-    datemin = datetime.date(r.date.min().year, 1, 1)
-    datemax = datetime.date(r.date.max().year+1, 1, 1)
-    ax.set_xlim(datemin, datemax)
-
-    # format the coords message box
-    def price(x): return '$%1.2f'%x
-    ax.format_xdata = mdates.DateFormatter('%Y-%m-%d')
-    ax.format_ydata = price
-    ax.grid(True)
-
-    # rotates and right aligns the x labels, and moves the bottom of the
-    # axes up to make room for them
-    fig.autofmt_xdate()
-
-    plt.show()
-
-
-
-<button>Run ...</button>
-
-
-.. image:: 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
-
-
-金融数据
---------------
-
-
-
-::
-
-    import datetime
-    import numpy as np
-    import matplotlib.colors as colors
-    import matplotlib.finance as finance
-    import matplotlib.dates as mdates
-    import matplotlib.ticker as mticker
-    import matplotlib.mlab as mlab
-    import matplotlib.pyplot as plt
-    import matplotlib.font_manager as font_manager
-
-
-    startdate = datetime.date(2006,1,1)
-    today = enddate = datetime.date.today()
-    ticker = 'SPY'
-
-
-    fh = finance.fetch_historical_yahoo(ticker, startdate, enddate)
-    # a numpy record array with fields: date, open, high, low, close, volume, adj_close)
-
-    r = mlab.csv2rec(fh); fh.close()
-    r.sort()
-
-
-    def moving_average(x, n, type='simple'):
-        ^4$"""
-        ^4$compute an n period moving average.
-
-        ^4$type is 'simple' | 'exponential'
-
-        ^4$"""
-        ^4$x = np.asarray(x)
-        ^4$if type=='simple':
-            ^4$^4$weights = np.ones(n)
-        ^4$else:
-            ^4$^4$weights = np.exp(np.linspace(-1., 0., n))
-
-        ^4$weights /= weights.sum()
-
-
-        ^4$a =  np.convolve(x, weights, mode='full')[:len(x)]
-        ^4$a[:n] = a[n]
-        ^4$return a
-
-    def relative_strength(prices, n=14):
-        ^4$"""
-        ^4$compute the n period relative strength indicator
-        ^4$http://stockcharts.com/school/doku.php?    id=chart_school:glossary_r#relativestrengthindex
-        ^4$http://www.investopedia.com/terms/r/rsi.asp
-        ^4$"""
-
-        ^4$deltas = np.diff(prices)
-        ^4$seed = deltas[:n+1]
-        ^4$up = seed[seed>=0].sum()/n
-        ^4$down = -seed[seed<0].sum()/n
-        ^4$rs = up/down
-        ^4$rsi = np.zeros_like(prices)
-        ^4$rsi[:n] = 100. - 100./(1.+rs)
-
-        ^4$for i in range(n, len(prices)):
-            ^4$^4$delta = deltas[i-1] # cause the diff is 1 shorter
-
-            ^4$^4$if delta>0:
-                ^4$^4$^4$upval = delta
-                ^4$^4$^4$downval = 0.
-            ^4$^4$else:
-                ^4$^4$^4$upval = 0.
-                ^4$^4$^4$downval = -delta
-
-            ^4$^4$up = (up*(n-1) + upval)/n
-            ^4$^4$down = (down*(n-1) + downval)/n
-
-            ^4$^4$rs = up/down
-            ^4$^4$rsi[i] = 100. - 100./(1.+rs)
-
-        ^4$return rsi
-
-    def moving_average_convergence(x, nslow=26, nfast=12):
-        ^4$"""
-        ^4$compute the MACD (Moving Average Convergence/Divergence) using a fast and slow exponential moving avg'
-        ^4$return value is emaslow, emafast, macd which are len(x) arrays
-       """
-        ^4$emaslow = moving_average(x, nslow, type='exponential')
-        ^4$emafast = moving_average(x, nfast, type='exponential')
-        ^4$return emaslow, emafast, emafast - emaslow
-
-
-    plt.rc('axes', grid=True)
-    plt.rc('grid', color='0.75', linestyle='-', linewidth=0.5)
-
-    textsize = 9
-    left, width = 0.1, 0.8
-    rect1 = [left, 0.7, width, 0.2]
-    rect2 = [left, 0.3, width, 0.4]
-    rect3 = [left, 0.1, width, 0.2]
-
-
-    fig = plt.figure(facecolor='white')
-    axescolor  = '#f6f6f6'  # the axes background color
-
-    ax1 = fig.add_axes(rect1, axisbg=axescolor)  #left, bottom, width, height
-    ax2 = fig.add_axes(rect2, axisbg=axescolor, sharex=ax1)
-    ax2t = ax2.twinx()
-    ax3  = fig.add_axes(rect3, axisbg=axescolor, sharex=ax1)
-
-
-
-    ### plot the relative strength indicator
-    prices = r.adj_close
-    rsi = relative_strength(prices)
-    fillcolor = 'darkgoldenrod'
-
-    ax1.plot(r.date, rsi, color=fillcolor)
-    ax1.axhline(70, color=fillcolor)
-    ax1.axhline(30, color=fillcolor)
-    ax1.fill_between(r.date, rsi, 70, where=(rsi>=70),     facecolor=fillcolor, edgecolor=fillcolor)
-    ax1.fill_between(r.date, rsi, 30, where=(rsi<=30),     facecolor=fillcolor, edgecolor=fillcolor)
-    ax1.text(0.6, 0.9, '>70 = overbought', va='top',     transform=ax1.transAxes, fontsize=textsize)
-    ax1.text(0.6, 0.1, '<30 = oversold', transform=ax1.transAxes,     fontsize=textsize)
-    ax1.set_ylim(0, 100)
-    ax1.set_yticks([30,70])
-    ax1.text(0.025, 0.95, 'RSI (14)', va='top', transform=ax1.transAxes,     fontsize=textsize)
-    ax1.set_title('%s daily'%ticker)
-
-    ### plot the price and volume data
-    dx = r.adj_close - r.close
-    low = r.low + dx
-    high = r.high + dx
-
-    deltas = np.zeros_like(prices)
-    deltas[1:] = np.diff(prices)
-    up = deltas>0
-    ax2.vlines(r.date[up], low[up], high[up], color='black',     label='_nolegend_')
-    ax2.vlines(r.date[~up], low[~up], high[~up], color='black',     label='_nolegend_')
-    ma20 = moving_average(prices, 20, type='simple')
-    ma200 = moving_average(prices, 200, type='simple')
-
-    linema20, = ax2.plot(r.date, ma20, color='blue', lw=2, label='MA (20)')
-    linema200, = ax2.plot(r.date, ma200, color='red', lw=2, label='MA (200)')
-
-
-    last = r[-1]
-    s = '%s O:%1.2f H:%1.2f L:%1.2f C:%1.2f, V:%1.1fM Chg:%+1.2f' % (
-        ^4$today.strftime('%d-%b-%Y'),
-        ^4$last.open, last.high,
-        ^4$last.low, last.close,
-        ^4$last.volume*1e-6,
-        ^4$last.close-last.open )
-    t4 = ax2.text(0.3, 0.9, s, transform=ax2.transAxes, fontsize=textsize)
-
-    props = font_manager.FontProperties(size=10)
-    leg = ax2.legend(loc='center left', shadow=True, fancybox=True,     prop=props)
-    leg.get_frame().set_alpha(0.5)
-
-
-    volume = (r.close*r.volume)/1e6  # dollar volume in millions
-    vmax = volume.max()
-    poly = ax2t.fill_between(r.date, volume, 0, label='Volume',     facecolor=fillcolor, edgecolor=fillcolor)
-    ax2t.set_ylim(0, 5*vmax)
-    ax2t.set_yticks([])
-
-
-    ### compute the MACD indicator
-    fillcolor = 'darkslategrey'
-    nslow = 26
-    nfast = 12
-    nema = 9
-    emaslow, emafast, macd = moving_average_convergence(prices, nslow=nslow, nfast=nfast)
-    ema9 = moving_average(macd, nema, type='exponential')
-    ax3.plot(r.date, macd, color='black', lw=2)
-    ax3.plot(r.date, ema9, color='blue', lw=1)
-    ax3.fill_between(r.date, macd-ema9, 0, alpha=0.5,     facecolor=fillcolor, edgecolor=fillcolor)
-
-
-    ax3.text(0.025, 0.95, 'MACD (%d, %d, %d)'%(nfast, nslow, nema),     va='top',
-             ^4$transform=ax3.transAxes, fontsize=textsize)
-
-    #ax3.set_yticks([])
-    # turn off upper axis tick labels, rotate the lower ones, etc
-    for ax in ax1, ax2, ax2t, ax3:
-        ^4$if ax!=ax3:
-            ^4$^4$for label in ax.get_xticklabels():
-                ^4$^4$^4$label.set_visible(False)
-        ^4$else:
-            ^4$^4$for label in ax.get_xticklabels():
-                ^4$^4$^4$label.set_rotation(30)
-                ^4$^4$^4$label.set_horizontalalignment('right')
-
-        ^4$ax.fmt_xdata = mdates.DateFormatter('%Y-%m-%d')
-
-
-
-    class MyLocator(mticker.MaxNLocator):
-        ^4$def __init__(self, *args, **kwargs):
-            ^4$^4$mticker.MaxNLocator.__init__(self, *args, **kwargs)
-
-        ^4$def __call__(self, *args, **kwargs):
-            ^4$^4$return mticker.MaxNLocator.__call__(self, *args, **kwargs)
-
-    # at most 5 ticks, pruning the upper and lower so they don't overlap
-    # with other ticks
-    #ax2.yaxis.set_major_locator(mticker.MaxNLocator(5, prune='both'))
-    #ax3.yaxis.set_major_locator(mticker.MaxNLocator(5, prune='both'))
-
-    ax2.yaxis.set_major_locator(MyLocator(5, prune='both'))
-    ax3.yaxis.set_major_locator(MyLocator(5, prune='both'))
-
-    plt.show()
-
-
-
-<button>Run ...</button>
-
-
-.. image:: 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
-
-
-
-basemap 画地图
---------------------
-
-需要安装 basemap 包：
-
-
-
-
-
-::
-
-    import matplotlib.pyplot as plt
-    import numpy as np
-
-    try:
-        ^4$from mpl_toolkits.basemap import Basemap
-        ^4$have_basemap = True
-    except ImportError:
-        ^4$have_basemap = False
-
-
-    def plotmap():
-        ^4$# create figure
-        ^4$fig = plt.figure(figsize=(8,8))
-        ^4$# set up orthographic map projection with
-        ^4$# perspective of satellite looking down at 50N, 100W.
-        ^4$# use low resolution coastlines.
-        ^4$map = Basemap(projection='ortho',lat_0=50,lon_0=-100,resolution='l')
-        ^4$# lat/lon coordinates of five cities.
-        ^4$lats=[40.02,32.73,38.55,48.25,17.29]
-        ^4$lons=[-105.16,-117.16,-77.00,-114.21,-88.10]
-        ^4$cities=['Boulder, CO','San Diego, CA',
-        ^4$^4$'Washington, DC','Whitefish, MT','Belize City, Belize']
-        ^4$# compute the native map projection coordinates for cities.
-        ^4$xc,yc = map(lons,lats)
-        ^4$# make up some data on a regular lat/lon grid.
-        ^4$nlats = 73; nlons = 145; delta = 2.*np.pi/(nlons-1)
-        ^4$lats = (0.5*np.pi-delta*np.indices((nlats,nlons))[0,:,:])
-        ^4$lons = (delta*np.indices((nlats,nlons))[1,:,:])
-        ^4$wave = 0.75*(np.sin(2.*lats)**8*np.cos(4.*lons))
-        ^4$mean = 0.5*np.cos(2.*lats)*((np.sin(2.*lats))**2 + 2.)
-        ^4$# compute native map projection coordinates of lat/lon grid.
-        ^4$# (convert lons and lats to degrees first)
-        ^4$x, y = map(lons*180./np.pi, lats*180./np.pi)
-        ^4$# draw map boundary
-        ^4$map.drawmapboundary(color="0.9")
-        ^4$# draw graticule (latitude and longitude grid lines)
-        ^4$map.drawmeridians(np.arange(0,360,30),color="0.9")
-        ^4$map.drawparallels(np.arange(-90,90,30),color="0.9")
-        ^4$# plot filled circles at the locations of the cities.
-        ^4$map.plot(xc,yc,'wo')
-        ^4$# plot the names of five cities.
-        ^4$for name,xpt,ypt in zip(cities,xc,yc):
-        ^4$^4$plt.text(xpt+100000,ypt+100000,name,fontsize=9,color='w')
-        ^4$# contour data over the map.
-        ^4$cs = map.contour(x,y,wave+mean,15,linewidths=1.5)
-        ^4$# draw blue marble image in background.
-        ^4$# (downsample the image by 50% for speed)
-        ^4$map.bluemarble(scale=0.5)
-
-    def plotempty():
-        ^4$# create figure
-        ^4$fig = plt.figure(figsize=(8,8))
-        ^4$fig.text(0.5, 0.5, "Sorry, could not import Basemap",
-        ^4$^4$horizontalalignment='center')
-
-    if have_basemap:
-        ^4$plotmap()
-    else:
-        ^4$plotempty()
-    plt.show()
-
-
-<button>Run ...</button>
-
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-.. image:: 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
-
-
-对数图
------------
-
-loglog, semilogx, semilogy, errorbar 函数：
-
-
-
-
-::
-
-    import numpy as np
-    import matplotlib.pyplot as plt
-
-    plt.subplots_adjust(hspace=0.4)
-    t = np.arange(0.01, 20.0, 0.01)
-
-    # log y axis
-    plt.subplot(221)
-    plt.semilogy(t, np.exp(-t/5.0))
-    plt.title('semilogy')
-    plt.grid(True)
-
-    # log x axis
-    plt.subplot(222)
-    plt.semilogx(t, np.sin(2*np.pi*t))
-    plt.title('semilogx')
-    plt.grid(True)
-
-    # log x and y axis
-    plt.subplot(223)
-    plt.loglog(t, 20*np.exp(-t/10.0), basex=2)
-    plt.grid(True)
-    plt.title('loglog base 4 on x')
-
-    # with errorbars: clip non-positive values
-    ax = plt.subplot(224)
-    ax.set_xscale("log", nonposx='clip')
-    ax.set_yscale("log", nonposy='clip')
-
-    x = 10.0**np.linspace(0.0, 2.0, 20)
-    y = x**2.0
-    plt.errorbar(x, y, xerr=0.1*x, yerr=5.0+0.75*y)
-    ax.set_ylim(ymin=0.1)
-    ax.set_title('Errorbars go negative')
-
-
-    plt.show()
-
-
-<button>Run ...</button>
-
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-.. image:: 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
-
-
-极坐标
----------
-
-设置 polar=True：
-
-
-
-
-::
-
-    import numpy as np
-    import matplotlib.pyplot as plt
-
-
-    r = np.arange(0, 3.0, 0.01)
-    theta = 2 * np.pi * r
-
-    ax = plt.subplot(111, polar=True)
-    ax.plot(theta, r, color='r', linewidth=3)
-    ax.set_rmax(2.0)
-    ax.grid(True)
-
-    ax.set_title("A line plot on a polar axis", va='bottom')
-    plt.show()
-
-
-<button>Run ...</button>
-
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-.. image:: 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
-
-
-
-标注
----------
-
-legend 函数：
-
-
-
-
-::
-
-    import numpy as np
-    import matplotlib.pyplot as plt
-
-    # Make some fake data.
-    a = b = np.arange(0,3, .02)
-    c = np.exp(a)
-    d = c[::-1]
-
-    # Create plots with pre-defined labels.
-    plt.plot(a, c, 'k--', label='Model length')
-    plt.plot(a, d, 'k:', label='Data length')
-    plt.plot(a, c+d, 'k', label='Total message length')
-
-    legend = plt.legend(loc='upper center', shadow=True, fontsize='x-large')
-
-    # Put a nicer background color on the legend.
-    legend.get_frame().set_facecolor('#00FFCC')
-
-    plt.show()
-
-
-<button>Run ...</button>
-
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-.. image:: 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
-
-
-
-数学公式
---------------
-
-
-
-
-::
-
-    from __future__ import print_function
-    import matplotlib.pyplot as plt
-    import os
-    import sys
-    import re
-    import gc
-
-    # Selection of features following "Writing mathematical expressions" tutorial
-    mathtext_titles = {
-        ^4$0: "Header demo",
-        ^4$1: "Subscripts and superscripts",
-        ^4$2: "Fractions, binomials and stacked numbers",
-        ^4$3: "Radicals",
-        ^4$4: "Fonts",
-        ^4$5: "Accents",
-        ^4$6: "Greek, Hebrew",
-        ^4$7: "Delimiters, functions and Symbols"}
-    n_lines = len(mathtext_titles)
-
-    # Randomly picked examples
-    mathext_demos = {
-        ^4$0: r"$W^{3\beta}_{\delta_1 \rho_1 \sigma_2} = "
-        ^4$r"U^{3\beta}_{\delta_1 \rho_1} + \frac{1}{8 \pi 2} "
-        ^4$r"\int^{\alpha_2}_{\alpha_2} d \alpha^\prime_2 \left[\frac{ "
-        ^4$r"U^{2\beta}_{\delta_1 \rho_1} - \alpha^\prime_2U^{1\beta}_"
-        ^4$r"{\rho_1 \sigma_2} }{U^{0\beta}_{\rho_1 \sigma_2}}\right]$",
-
-        ^4$1: r"$\alpha_i > \beta_i,\ "
-        ^4$r"\alpha_{i+1}^j = {\rm sin}(2\pi f_j t_i) e^{-5 t_i/\tau},\ "
-        ^4$r"\ldots$",
-
-        ^4$2: r"$\frac{3}{4},\ \binom{3}{4},\ \stackrel{3}{4},\ "
-        ^4$r"\left(\frac{5 - \frac{1}{x}}{4}\right),\ \ldots$",
-
-        ^4$3: r"$\sqrt{2},\ \sqrt[3]{x},\ \ldots$",
-
-        ^4$4: r"$\mathrm{Roman}\ , \ \mathit{Italic}\ , \ \mathtt{Typewriter} \ "
-        ^4$r"\mathrm{or}\ \mathcal{CALLIGRAPHY}$",
-
-        ^4$5: r"$\acute a,\ \bar a,\ \breve a,\ \dot a,\ \ddot a, \ \grave a, \ "
-        ^4$r"\hat a,\ \tilde a,\ \vec a,\ \widehat{xyz},\ \widetilde{xyz},\ "
-        ^4$r"\ldots$",
-
-        ^4$6: r"$\alpha,\ \beta,\ \chi,\ \delta,\ \lambda,\ \mu,\ "
-        ^4$r"\Delta,\ \Gamma,\ \Omega,\ \Phi,\ \Pi,\ \Upsilon,\ \nabla,\ "
-        ^4$r"\aleph,\ \beth,\ \daleth,\ \gimel,\ \ldots$",
-
-        ^4$7: r"$\coprod,\ \int,\ \oint,\ \prod,\ \sum,\ "
-        ^4$r"\log,\ \sin,\ \approx,\ \oplus,\ \star,\ \varpropto,\ "
-        ^4$r"\infty,\ \partial,\ \Re,\ \leftrightsquigarrow, \ \ldots$"}
-
-
-    def doall():
-        ^4$# Colors used in mpl online documentation.
-        ^4$mpl_blue_rvb = (191./255., 209./256., 212./255.)
-        ^4$mpl_orange_rvb = (202/255., 121/256., 0./255.)
-        ^4$mpl_grey_rvb = (51./255., 51./255., 51./255.)
-
-        ^4$# Creating figure and axis.
-        ^4$plt.figure(figsize=(6, 7))
-        ^4$plt.axes([0.01, 0.01, 0.98, 0.90], axisbg="white", frameon=True)
-        ^4$plt.gca().set_xlim(0., 1.)
-        ^4$plt.gca().set_ylim(0., 1.)
-        ^4$plt.gca().set_title("Matplotlib's math rendering engine",
-            ^4$^4$            color=mpl_grey_rvb, fontsize=14, weight='bold')
-        ^4$plt.gca().set_xticklabels("", visible=False)
-        ^4$plt.gca().set_yticklabels("", visible=False)
-
-        ^4$# Gap between lines in axes coords
-        ^4$line_axesfrac = (1. / (n_lines))
-
-        ^4$# Plotting header demonstration formula
-        ^4$full_demo = mathext_demos[0]
-        ^4$plt.annotate(full_demo,
-            ^4$^4$         xy=(0.5, 1. - 0.59*line_axesfrac),
-            ^4$^4$         xycoords='data', color=mpl_orange_rvb, ha='center',
-            ^4$^4$         fontsize=20)
-
-        ^4$# Plotting features demonstration formulae
-        ^4$for i_line in range(1, n_lines):
-            ^4$^4$baseline = 1. - (i_line)*line_axesfrac
-            ^4$^4$baseline_next = baseline - line_axesfrac*1.
-            ^4$^4$title = mathtext_titles[i_line] + ":"
-            ^4$^4$fill_color = ['white', mpl_blue_rvb][i_line % 2]
-            ^4$^4$plt.fill_between([0., 1.], [baseline, baseline],
-            ^4$^4$                 [baseline_next, baseline_next],
-            ^4$^4$                 color=fill_color, alpha=0.5)
-            ^4$^4$plt.annotate(title,
-            ^4$^4$             xy=(0.07, baseline - 0.3*line_axesfrac),
-            ^4$^4$             xycoords='data', color=mpl_grey_rvb, weight='bold')
-            ^4$^4$demo = mathext_demos[i_line]
-            ^4$^4$plt.annotate(demo,
-            ^4$^4$             xy=(0.05, baseline - 0.75*line_axesfrac),
-            ^4$^4$             xycoords='data', color=mpl_grey_rvb,
-            ^4$^4$             fontsize=16)
-
-        ^4$for i in range(n_lines):
-            ^4$^4$s = mathext_demos[i]
-            ^4$^4$print(i, s)
-        ^4$plt.show()
-
-    if '--latex' in sys.argv:
-        ^4$# Run: python mathtext_examples.py --latex
-        ^4$# Need amsmath and amssymb packages.
-        ^4$fd = open("mathtext_examples.ltx", "w")
-        ^4$fd.write("\\documentclass{article}\n")
-        ^4$fd.write("\\usepackage{amsmath, amssymb}\n")
-        ^4$fd.write("\\begin{document}\n")
-        ^4$fd.write("\\begin{enumerate}\n")
-
-        ^4$for i in range(n_lines):
-            ^4$^4$s = mathext_demos[i]
-            ^4$^4$s = re.sub(r"(?<!\\)\$", "$$", s)
-            ^4$^4$fd.write("\\item %s\n" % s)
-
-        ^4$fd.write("\\end{enumerate}\n")
-        ^4$fd.write("\\end{document}\n")
-        ^4$fd.close()
-
-        ^4$os.system("pdflatex mathtext_examples.ltx")
-    else:
-        ^4$doall()
-    0 $W^{3\beta}_{\delta_1 \rho_1 \sigma_2} = U^{3\beta}_{\delta_1 \rho_1} + \frac{1}{8 \pi 2} \int^{\alpha_2}_{\alpha_2} d \alpha^\prime_2 \left[\frac{ U^{2\beta}_    {\delta_1 \rho_1} - \alpha^\prime_2U^{1\beta}_{\rho_1 \sigma_2} }{U^{0\beta}_{\rho_1 \sigma_2}}\right]$
-    1 $\alpha_i > \beta_i,\ \alpha_{i+1}^j = {\rm sin}(2\pi f_j t_i) e^{-5 t_i/\tau},\ \ldots$
-    2 $\frac{3}{4},\ \binom{3}{4},\ \stackrel{3}{4},\ \left(\frac{5 - \frac{1}{x}}{4}\right),\ \ldots$
-    3 $\sqrt{2},\ \sqrt[3]{x},\ \ldots$
-    4 $\mathrm{Roman}\ , \ \mathit{Italic}\ , \ \mathtt{Typewriter} \ \mathrm{or}\ \mathcal{CALLIGRAPHY}$
-    5 $\acute a,\ \bar a,\ \breve a,\ \dot a,\ \ddot a, \ \grave a, \ \hat a,\ \tilde a,\ \vec a,\ \widehat{xyz},\ \widetilde{xyz},\ \ldots$
-    6 $\alpha,\ \beta,\ \chi,\ \delta,\ \lambda,\ \mu,\ \Delta,\ \Gamma,\ \Omega,\ \Phi,\ \Pi,\ \Upsilon,\ \nabla,\ \aleph,\ \beth,\ \daleth,\ \gimel,\ \ldots$
-    7 $\coprod,\ \int,\ \oint,\ \prod,\ \sum,\ \log,\ \sin,\ \approx,\ \oplus,\ \star,\ \varpropto,\ \infty,\ \partial,\ \Re,\ \leftrightsquigarrow, \ \ldots$
-
-
-
-<button>Run ...</button>
-
-
-.. image:: 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
-
-
-
-作者 & 更新时间
-------------------------------------
-作者:`李金  <lijinwithyou@gmail.com>`
-
-提交: 2017/12/14
-
-
-
-
-
-
diff --git a/docs/sources/aipy-numpy/cn/1pyplot.rst.txt b/docs/sources/aipy-numpy/cn/1pyplot.rst.txt
deleted file mode 100644
index 70585a5..0000000
--- a/docs/sources/aipy-numpy/cn/1pyplot.rst.txt
+++ /dev/null
@@ -1,547 +0,0 @@
-
-Pyplot 教程
-================
-
-Matplotlib 简介
-------------------
-
-**matplotlib** 是一个 **Python** 的 2D 图形包。
-
-在线文档：http://matplotlib.org ，提供了 Examples, FAQ, API, Gallery，其中 Gallery 是很有用的一个部分，因为它提供了各种画图方式的可视化，方便用户根据需求进行选择。
-
-
-使用 Pyplot
------------------------
-
-导入相关的包：
-
-
-
-
-::
-
-    import numpy as np
-    import matplotlib.pyplot as plt
-
-
-matplotlib.pyplot 包含一系列类似 **MATLAB** 中绘图函数的相关函数。每个 matplotlib.pyplot 中的函数对当前的图像进行一些修改，例如：产生新的图像，在图像中产生新的绘图区域，在绘图区域中画线，给绘图加上标记，等等…… matplotlib.pyplot 会自动记住当前的图像和绘图区域，因此这些函数会直接作用在当前的图像上。
-
-
-下文中，以 plt 作为 matplotlib.pyplot 的省略。
-
-
-plt.show() 函数
-----------------------
-
-默认情况下，matplotlib.pyplot 不会直接显示图像，只有调用 plt.show() 函数时，图像才会显示出来。plt.show() 默认是在新窗口打开一幅图像，并且提供了对图像进行操作的按钮。
-
-
-不过在 ipython 命令行中，我们可以使用 magic 命令将它插入 notebook 中，并且不需要调用 plt.show() 也可以显示：
-
-
-- %matplotlib notebook
-- %matplotlib inline
-
-
-不过在实际写程序中，我们还是需要调用 plt.show() 函数将图像显示出来。
-
-
-这里我们使图像输出在 notebook 中：
-
-
-
-
-::
-
-    %matplotlib inline
-
-
-plt.plot() 函数
-------------------
-
-**例子**
-
-plt.plot() 函数可以用来绘图：
-
-
-
-
-::
-
-    plt.plot([1,2,3,4])
-    plt.ylabel('some numbers')
-
-    plt.show()
-
-
-.. image:: data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAYYAAAEACAYAAAC3adEgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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%0A
-
-
-**基本用法**
-
-plot 函数基本的用法有以下四种：
-
-默认参数
-
-- plt.plot(x,y)
-
-指定参数
-
-- plt.plot(x,y, format_str)
-
-默认参数，x 为 0~N-1
-
-- plt.plot(y)
-
-指定参数，x 为 0~N-1
-
-- plt.plot(y, format_str)
-
-
-因此，在上面的例子中，我们没有给定 x 的值，所以其默认值为 [0,1,2,3]。
-
-传入 x 和 y：
-
-
-
-
-::
-
-    plt.plot([1,2,3,4], [1,4,9,16])
-
-
-结果:
-::
-
-    [<matplotlib.lines.Line2D at 0xa48a550>]
-
-
-字符参数
--------------
-
-和 **MATLAB** 中类似，我们还可以用字符来指定绘图的格式：
-
-
-表示颜色的字符参数有：
-
-
-+------------+------------------------------+
-| 字符        | 颜色                         |                                           
-+============+==============================+
-|‘b’         |  蓝色，blue                   |                           
-+------------+------------------------------+
-|‘g’         |绿色，green                    |                               
-+------------+------------------------------+
-|‘r’         |红色，red                      |                             
-+------------+------------------------------+
-|‘c’         |青色，cyan                     |                                   
-+------------+------------------------------+
-|‘m’	     |品红，magenta                  |                              
-+------------+------------------------------+
-|‘y’	     |黄色，yellow                   |                                                     
-+------------+------------------------------+
-|‘k’	     |黑色，black                    |                                                       
-+------------+------------------------------+
-|‘w’	     |白色，white                    |                                               
-+------------+------------------------------+
-
-
-表示类型的字符参数有：
-
-
-
-+------------+------------------------------+-------------------------------+-----------------------------+
-| 字符        | 类型                         |字符                            |类型                         |
-+============+==============================+===============================+=============================+
-|'-'         | 实线                          | '--'                          |虚线                         |    
-+------------+------------------------------+-------------------------------+-----------------------------+
-|'-.'        |虚点线                         |':'                            |点线                         |
-+------------+------------------------------+-------------------------------+-----------------------------+
-|'.'         |点                            |','                            |像素点                       |
-+------------+------------------------------+-------------------------------+-----------------------------+
-|'o'         |圆点                           |'v'                            |下三角点                     |         
-+------------+------------------------------+-------------------------------+-----------------------------+
-|'^'	     |上三角点                       |'<'                            |左三角点                     |
-+------------+------------------------------+-------------------------------+-----------------------------+
-|'>'	     |右三角点                       |'1'                            |下三叉点                     |
-+------------+------------------------------+-------------------------------+-----------------------------+
-|'2'	     |上三叉点                       |'3'                            |左三叉点                     |
-+------------+------------------------------+-------------------------------+-----------------------------+
-|'4'	     |右三叉点                       |'s'                            |正方点                       |
-+------------+------------------------------+-------------------------------+-----------------------------+
-|'p'	     |五角点                         |'*'                            |星形点                       |
-+------------+------------------------------+-------------------------------+-----------------------------+
-|'h'	     |六边形点                       |'H'                            |六边形点2                    |
-+------------+------------------------------+-------------------------------+-----------------------------+
-|'+'	     |加号点                         |'*'                            |乘号点                       |
-+------------+------------------------------+-------------------------------+-----------------------------+
-|'D'	     |实心菱形点                     |'d'                            |瘦菱形点                     |
-+------------+------------------------------+-------------------------------+-----------------------------+
-|'_'	     |横线点                         |                               |                             |
-+------------+------------------------------+-------------------------------+-----------------------------+
-
-	
-例如我们要画出红色圆点：
-
-
-
-
-::
-
-    plt.plot([1,2,3,4], [1,4,9,16], 'ro')
-    plt.show()
-
-.. image:: data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAXMAAAEACAYAAABBDJb9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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%0ADHNJaoBhLkkNMMwlqQGGuSQ14P8AGTGlG2xI8vsAAAAASUVORK5CYII=%0A
-
-
-可以看出，有两个点在图像的边缘，因此，我们需要改变轴的显示范围。
-
-
-显示范围
------------
-
-与 **MATLAB** 类似，这里可以使用 axis 函数指定坐标轴显示的范围：
-
-plt.axis([xmin, xmax, ymin, ymax])
-
-
-
-
-::
-
-    plt.plot([1,2,3,4], [1,4,9,16], 'ro')
-    # 指定 x 轴显示区域为 0-6，y 轴为 0-20
-    plt.axis([0,6,0,20])
-    plt.show()
-
-
-.. image:: data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAW4AAAEACAYAAACTXJylAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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%0A
-
-
-传入 Numpy 数组
----------------------
-
-之前我们传给 plot 的参数都是列表，事实上，向 plot 中传入 numpy 数组是更常用的做法。事实上，如果传入的是列表，matplotlib 会在内部将它转化成数组再进行处理：
-
-
-
-
-
-::
-
-    import numpy as np
-    import matplotlib.pyplot as plt
-
-    # evenly sampled time at 200ms intervals
-    t = np.arange(0., 5., 0.2)
-
-    # red dashes, blue squares and green triangles
-    plt.plot(t, t, 'r--', 
-            ^4$^4$t, t**2, 'bs', 
-            ^4$^4$t, t**3, 'g^')
-
-    plt.show()
-
-
-
-<button>Run ...</button>
-
-
-.. image:: data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAXQAAAEACAYAAACj0I2EAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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%0A
-
-
-
-传入多组数据
---------------
-
-事实上，在上面的例子中，我们不仅仅向 plot 函数传入了数组，还传入了多组 (x,y,format_str) 参数，它们在同一张图上显示。
-这意味着我们不需要使用多个 plot 函数来画多组数组，只需要可以将这些组合放到一个 plot 函数中去即可。
-
-
-线条属性
--------------
-
-之前提到，我们可以用字符串来控制线条的属性，事实上还可以通过关键词来改变线条的性质，例如 linwidth 可以改变线条的宽度，color 可以改变线条的颜色：
-
-
-
-
-::
-
-    x = np.linspace(-np.pi,np.pi)
-    y = np.sin(x)
-
-    plt.plot(x, y, linewidth=2.0, color='r')
-
-    plt.show()
-
-
-<button>Run ...</button>
-
-
-.. image:: 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pnB+ef79bBhcWPJESV6kUyMGePJ/vDDYf/9Y0cjtXXOOX6A+2OPwbJlsaPJOiV6kUxsuHZekqtJ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%0AfxeZmr/ZF8WeAAAAAElFTkSuQmCC%0A
-
-
-
-使用 plt.plot() 的返回值来设置线条属性
----------------------------------------------
-
-plot 函数返回一个 Line2D 对象组成的列表，每个对象代表输入的一对组合，例如：
-
-
-- line1, line2 为两个 Line2D 对象
-
-  line1, line2 = plt.plot(x1, y1, x2, y2)
-
-
-- 返回 3 个 Line2D 对象组成的列表
-  lines = plt.plot(x1, y1, x2, y2, x3, y3)
-
-
-我们可以使用这个返回值来对线条属性进行设置：
-
-
-
-
-
-
-::
-
-    # 加逗号 line 中得到的是 line2D 对象，不加逗号得到的是只有一个     line2D 对象的列表
-    line, = plt.plot(x, y, 'r-')
-
-    # 将抗锯齿关闭
-    line.set_antialiased(False)
-
-    plt.show()
-
-<button>Run ...</button>
-
-
-.. image:: 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E/cgcFw1HzA2n5Q0k0R8YWK7x2QtBMRB6eXb5F0OiJur9iWcgDAFiJi6WS2dKLfwKIbeUTS5bYv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%0AJyfj1DY0XlkAAAAASUVORK5CYII=%0A
-
-
-plt.setp() 修改线条性质
---------------------------
-
-
-更方便的做法是使用 plt 的 setp 函数：
-
-
-
-
-
-::
-
-    lines = plt.plot(x, y)
-
-    # 使用键值对
-    plt.setp(lines, color='r', linewidth=2.0)
-
-    # 或者使用 MATLAB 风格的字符串对
-    plt.setp(lines, 'color', 'r', 'linewidth', 2.0)
-
-    plt.show()
-
-
-<button>Run ...</button>
-
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-.. image:: 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pnB+ef79bBhcWPJESV6kUyMGePJ/vDDYf/9Y0cjtXXOOX6A+2OPwbJlsaPJOiV6kUxsuHZekqtJ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%0AfxeZmr/ZF8WeAAAAAElFTkSuQmCC%0A
-
-
-可以设置的属性有很多，可以使用 plt.setp(lines) 查看 lines 可以设置的属性，各属性的含义可参考 matplotlib 的文档。
-
-
-
-
-::
-
-    plt.setp(lines)
-
-
-::
-
-      agg_filter: unknown
-      alpha: float (0.0 transparent through 1.0 opaque)         
-      animated: [True | False]         
-      antialiased or aa: [True | False]         
-      axes: an :class:`~matplotlib.axes.Axes` instance         
-      clip_box: a :class:`matplotlib.transforms.Bbox` instance         
-      clip_on: [True | False]         
-      clip_path: [ (:class:`~matplotlib.path.Path`,               :class:`~matplotlib.transforms.Transform`) |                    :class:`~matplotlib.patches.Patch` |       None ]         
-      color or c: any matplotlib color         
-      contains: a callable function         
-      dash_capstyle: ['butt' | 'round' | 'projecting']         
-      dash_joinstyle: ['miter' | 'round' | 'bevel']         
-      dashes: sequence of on/off ink in points         
-      drawstyle: ['default' | 'steps' | 'steps-pre' | 'steps-mid' |                   'steps-post']         
-      figure: a :class:`matplotlib.figure.Figure` instance         
-      fillstyle: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none']         
-      gid: an id string         
-      label: string or anything printable with '%s' conversion.         
-      linestyle or ls: [``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` |                   ``' '`` | ``''``]
-      linewidth or lw: float value in points         
-      lod: [True | False]         
-      marker: :mod:`A valid marker style <matplotlib.markers>`
-      markeredgecolor or mec: any matplotlib color         
-      markeredgewidth or mew: float value in points         
-      markerfacecolor or mfc: any matplotlib color         
-      markerfacecoloralt or mfcalt: any matplotlib color         
-      markersize or ms: float         
-      markevery: [None | int | length-2 tuple of int | slice |             list/array of int | float | length-2 tuple of float]
-      path_effects: unknown
-      picker: float distance in points or callable pick function         ``fn(artist, event)``         
-      pickradius: float distance in points         
-      rasterized: [True | False | None]         
-      sketch_params: unknown
-      snap: unknown
-      solid_capstyle: ['butt' | 'round' |  'projecting']         
-      solid_joinstyle: ['miter' | 'round' | 'bevel']         
-      transform: a :class:`matplotlib.transforms.Transform` instance         
-      url: a url string         
-      visible: [True | False]         
-      xdata: 1D array         
-      ydata: 1D array         
-      zorder: any number 
-
-        
-子图
---------
-
-
-figure() 函数会产生一个指定编号为 num 的图：
-
-     plt.figure(num)
-
-
-
-这里，figure(1) 其实是可以省略的，因为默认情况下 plt 会自动产生一幅图像。
-
-
-使用 subplot 可以在一副图中生成多个子图，其参数为：
-
-
-     plt.subplot(numrows, numcols, fignum)
-
-当 numrows * numcols < 10 时，中间的逗号可以省略，因此 plt.subplot(211) 就相当于 plt.subplot(2,1,1)。
-
-
-
-
-
-
-::
-
-    def f(t):
-        ^4$return np.exp(-t) * np.cos(2*np.pi*t)
-
-    t1 = np.arange(0.0, 5.0, 0.1)
-    t2 = np.arange(0.0, 5.0, 0.02)
-
-    plt.figure(1)
-    plt.subplot(211)
-    plt.plot(t1, f(t1), 'bo', t2, f(t2), 'k')
-
-    plt.subplot(212)
-    plt.plot(t2, np.cos(2*np.pi*t2), 'r--')
-    plt.show()
-
-<button>Run ...</button>
-
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-.. image:: 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swLI/hK/1L69r2Azp1vYc6cXUBTTAGXIb7KgXTiqj2HlStv5OKLP2bZskxgErAOc9M+FajG5s1b%0AKFUqCbOxnw3sxTiwbWfJkk0Yr+ZvgcZAG7KyBjB27PtA4U8BsZ6LcBVzJE83Cxcu5803N0d003P7%0ABuH2Tcot+aKe6wbzK58AoKoLRKSqiNRS1e0Oxz5G586duffee93qLmJycnJ4/vnn+fDDD2MyXkHF%0AV6pUdZYu3UC1av6wyB0+vAlzT/8rxtTifGO6sJtDUlINzJpjPmYT/FngaU/MWMF+nGbVPhKzMv8E%0AmM6mTdX49NMUqlWrw7ZtD2LqBVc+1s/ZZ9+Pqgaylx5P1apX89tvD2C8klYDM4Gn+eqrNSxY8Bp7%0A9/bDPOWdA7Q67qbntmIJV2kHU8zr1t3F5s0befHFz8nKSsdszh8EDpCVlcNNN92Jag4bN54P3IlR%0AXaXJyirHv/71EgcO3AZ8HJi/KmRl/Yknn/wvR49mM2LEV66axWJ1kypKvrCVfXH+l0W9gAHAS/mO%0ArwbGF2jzGdAx3/E04OwgfUXsR/r7779rhQoV9MCBAxH34QZffvmltm3b1lMZ3nvvPW3evLkeOnTI%0AUzlmzJihVapU1Vq1Li0Q+OR+8Jhq/vgFVchReFuhgdaq1UyzsrJi5n9/YnzA71qr1qVaufLpCpUV%0ALlR4ReFnBdUuXUYXEiBm5qmwz9q0+etx53Jf3bvfpWeddZ3CeIWhCi0VUhTO1bp12+nf/naP1qt3%0AvcKRE4LRigpUiyS4Le//5IDCWoXpChO0fPlWCn9R6KvQWqG6QjlNSammVao0VLhK4SaFOxRGKTyq%0Aqam9NDW1t8JTCv9UeEzhIYX7tGzZZgpDFC5RSFNoq5CqpUuXVxFRqKLQMHC+h8IVWr9+W23UqKPC%0AvwP/Hx8rzFT4Xlu1GqQNG45Q+F0hO+Q56tnzXoV9CtsU1it8rzBf27W7Wtu0uUJhosKrCs8G/oYx%0AWq5cU4VbFK5TGKTQT6GHVq1aTytVqqXQVCE1IH99hbpatmwFrVmzpnbr1k1VY+NHryG2K5grOeh1%0AoaRACEZKSgqtWrXim2++oUuXLiGK5D4vv/wy118flZCBkBkwYABvvfUWjz76KA899FDMxs2/ytm7%0AdxXr10/h448/4sCBpELt8G5yvBlLgME0arSI88//jbPOakOpUs3Zs+czTDI856aMou3t92NWl+8C%0Ak9m+/VwqVKiKedo46bh+Cq8udvw8FfwM4Lbb7jtutZeaei+3396XceOmYFxQc9kPLKFSpYeYOPEz%0Atm07AlTFPEE0ISurKffe+zTJyZUDsu8DKgJCVtaj3H//9ezdW+uElWV2djZPPfUJWVl/woTSbAe2%0AkpX1K9deez0HD+7DbJjvxeQ6rA/UJycHoBVm264+JqFtDc4550GSk7ODPsGcfvpBVJWsrNtP+KxS%0ApXXs2jXhhPPdu9/PwYOlmDXrdmD3ca9y5d7gyJGDGNPi8uM+W7FiPaplgf8Ah4FSZGWVpX//pxAp%0AxZEj5QOfHQWyyco6ysUXPx5YsD6D2SNKCfxbgXXrdpCUVBaj9lLyfZ77vkngfflj5xs2nEipUmVY%0AtOhvQBmMg2QSkERq6gj69GlAUlLScTqzSIq7ExT1wjyXT853PBK4u0Cb/wKD8h2vBmoF6cvRSuqO%0AO+7QRx55xFEfTti5c6dWqVJFf/31V89kyGXz5s1aq1YtnTt3bkzGO36VM1HhZD3llKExj1qdNGmm%0ApqeP0i5dRh+LolVV7dJluMIwhWoKtx1bTedGK4e72i8sqnfUqEf15JPPUKiq0FXhvwo7FFRbtryx%0A0FW7239vUU8IXbqMDpz7TWG+wusK92mNGi20cuV6Co0CTwApCicr1NdSpSoGngyaKZwaOF9JRUpp%0A2bIVFc5U6KVwjcKdCuO0ZcuB2r79tYHV7dHjZDnppCuOOy4Y+Rzu083o0c8Ves3xT3rHj1XYZ9Wq%0ADcl3nKPm6We/dux4l5533h2B/9NdCnvURG4f1M6dRwVW9OGNVdRcFCV7foh2CgRCy3XTB/hc824M%0A8wvpK+IvvKrqJ598or169XLUhxOeeuopveaaazwbvyAff/yx1qpVR7t1+3vUzRV5X8iXFOoqLA36%0AhfSKPOW2WWFEQOFfqC1aXK4ffTQlbHNF3t+7S+ENhf+nUFmrVz9VmzVLV9haqBILppijQWFjhab4%0AchT2KmxR+FErV74k8H+6Qo1JYqvCr3rBBQ8U2V8kirko2Yv6LJKbXrhmseKUr9s3qaL6y0/UFb0Z%0Ao+hcN4HjZwOfLwXaFtKPoy/2rl27tFKlSnr48GFH/URCTk6OtmjRQmfO9E/elUmTZmrlymcpXH2C%0AAnMbo0ifUrPaW3tsvC5dRrs+ViSc+OP8XWGCVqvWQJOSyirk2n6/Cii3HG3T5s8FfmT79JRTrtXb%0Abx+pp5xyrppVbEWFi9XYXXcWa2/3A24rvuL+3nAVc7T+5nBuHJHMUTRuUqHOU0wUvVsvp4peVbVd%0Au3aeKNu5c+dq48aNfZVczCi3/Wo2c1497sfpJjk5OXraaZ0VGiv8dIIi8ANF/Tg7drxL4X01m4Od%0AFU5SKKciyYEbVx2FZIVyCq21du2W2rhxV4W5Cn8E/XtjqcQiwU3FV1x/8YpT5RtLSpyiv/vuu/WB%0ABx5w3E+4XHfddfrEE0/EfNyiyDNXrFCoqcajwN1Vdk5Ojt5+++3asGGqnnrqcN+uYlXDNWXs1ypV%0ALldjptikxmsk7ynF76v2aOA35WbJIxRF7w+Ha5fo3r07Y8aM4cEHH4zJeBkZs3jqqc+YNettOnUa%0ARsuWs3wRnAP5o2ZbAG9i8u7MceTDnt/TpEyZw4gsY9++3SxatJC5c7+PiXdNpBTmf3+8t44hNfVR%0AKleuweLFJ2ZEDdVLJtEIN7jN4jOKuxMU9QKqA1OBtcAUoGqQNvWBGZh8tMuB4YX05fjOduDAAa1Q%0AoYLu2bPHcV/Fkbeqe1Ghf1Rt4JFw4qrzP1qmTHV9881PXOhvl0I3TUlJ1fff/8JlyWNPJOYKi8Uv%0AEMKKXky7yBCRfwK/qOo/ReRuoJqq3lOgTW2gtqouEZGKwHdAf82XJiHQTp3Ikku3bt0YMWIEF110%0AkeO+iiI9fVTA37c9cD9wUeD8/Uye/HBUxw6VjIxZjB8/9diqs3r1jSxdupBp06aFnQAs7+9dhQl2%0AvgR4gvT0Mb75e92m4PzdemtPu6q1+A4RQVULxiodh1PTTT+gS+D9BCATE+t+DFXdBmwLvN8vIquA%0AuhyfJsE1unfvzldffRV1RW/yuHyPSfGTV5jcL5kjIfjj9uOPP86ZZ7YhObkvmza9cux8cQFE5u96%0AGpM58p+YBGr++nvdxporLImCU0WfP2fNdqDITF4i0hBoAyxwOG6hlC9/Ei+/PI7Fi6tENYudsYG/%0AAlxL/mn0S+bIwhg5ciQTJy5k2bL/YRKA3Q6UOZYLBU7M8VGvXhWWLXsNk2xrPnD6sf78/vdaLJYQ%0AFL2ITAVqB/novvwHqhrIK1FoPxWBD4DbVHV/sDaRpkDIJSNjFs899xP79x9m5sy/ArWilrXvL3/p%0AwrRpl5CTs+LYudx86n6nWrXWwJOYZGMvYIqWDGDTph35kijtBaYzZ871JCfvYuDAa5g6tTzr1+cp%0A+Xj5ey2WRCIzM5PMzMywrnFqo18NpKnqNhGpA8xQ1WZB2pXB5GH9QlWfKaQvxzb6PDtyf4yXyZWB%0A8+7bzSdOnMgTT/ybWrUujDsbbt48gSnZ9yKQgcghVFMxuU52YfYfrqR797VMm/aEtVlbLD4kFjb6%0AT4GhmFpwQzH5VwsKIRgbx8rClLxb5FVcSsckWTKKPhp25Jdffpl77rmTgQMHut53tDnepfAC4AJO%0AO20kpUtvZ+3a2zGJluphslpAdvYYwNqsLZZ4xami/wfwnoj8GdhAoEiqiNTFpC/uC3TCpC9eJiK5%0AlaVGqupkh2OfQJ7veF+MJ8xRIMl1O/L69etZunQp/fv3d7XfWBHcD/xCxo2bwtq1Z5zQ3trhLZb4%0AxpGiV9XdmOpSBc9vwWhbVHUOMSpCfvxK9RRgPqmpGa7bkV999VWuvvpqkpOTXe03lhRfqs9g7fAW%0AS/zjyEbvJm750efakVetyiQpKYfx4x931dyQnZ1Nw4YNmTx5Mq1atXKtX79g7fAWS3wRio0+4RR9%0ALvPmzeP6669nxYoVxTcOg4yMDB5++GHmz5/var8Wi8USCaEo+ohNKiJSXUSmishaEZkiIlWLaJsk%0AIotF5LNIxwuX9u3bs2fPHlatcjcu66WXXvK8ipTFYrGEgxPb+T3AVFVtAnxFgYjYAtwGrISQSw86%0AplSpUlx22WV88MEHrvX5448/MmfOHAYNGuRanxaLxRJtnCj6fpi0BwT+DeqCIiL1MFWmXubE2rFR%0AZcCAAa4q+vHjx3PddddRsWJF1/q0WCyWaOPE6ybU9AdPA3cClR2MFRGdOnXil19+YdWqVTRv3txR%0AX3v37mXChAksXry4+MYWi8XiI4pc0Qds8N8HefXL3y43VWaQ6y8CdqjqYmK8mgdjvrn66quZMOHE%0ACvGhkpExi/T0UbRtewVlytTi++83uCegxWKxxIAiV/Sq2rOwz0Rku4jUzpf+YEeQZh2BfiLSBygH%0AVBaR11V1SLA+nea6CcbQoUPp2bMnjz76KElJ4UXIZmTMCuR+eQhoDLzNbbeZ/WTrcmixWLwgprlu%0AArnod6nqEyJyD6boSKEbsiLSBfi7ql5cyOeuulfmp3379owZM4YLL7wwrOvycsJ8jEnNOy9w3j85%0A5y0WS8kmqu6VmPQHPUVkLdAtcIyI1BWRjEKu8cRp/4YbbuC5554L+zqTO0eBh4G7jp1P5BzsFosl%0A8YhY0avqblXtoapNVLWXqv4WOL8lkOOmYPuZqtrvxJ6iz1VXXcU333zDmjVrwrrO5M7JzdOW51Rk%0Ac79YLJZ4IiY5aLymfPnyDBs2jLFjx4Z13c03d6NMmRuBB8ndSza5XwrdurBYLBbf4TR7Zdxwyy23%0A0LhxE5YvL0WpUjVCqj71888rad68AbVrL+Dw4e8CuV96241Yi8USV0Ss6EWkOvAupr7cBuCKXPNN%0AgXZVMcFSLTEG7+tUNeaJYr79dg2lS5/B7Nl7gGeBouukbt++nYceeogZM2bQsmXLWIpqsVgsrhKL%0AFAhjgc9VtTnQmigVBS+OceOm8OuvXwDTgK8BAnVSp57QVlW57rrruP76662St1gscY8T000/oEvg%0A/QQgkwLKXkSqAJ1VdSiAqmYDexyMGTHGg6YSpkbq1cASoAqHDiWRkTHruILY9ertZseOHTz44INe%0AiGqxWCyuEu0UCI2AnSLyGnAm8B2mOPgBB+NGRF71qX7Al8AA4BP27t2UryA2wKuULj2eF198iTJl%0AysRaTIvFYnGdqKZAwNxI2gL/UdW2wO8UneUyagwf3ovU1PsCR+OAuiQnN2b//t1kZT2M2Wb4C/AQ%0A2dkLePfd770Q02KxWFwn2ikQNgGbVHVh4PgDilD00UiBkEvBOqnJyfVp1uwaXnzxJUx2hirAQGAp%0AuSYdi8Vi8Ru+TIEgIrOA61V1rYiMAcqr6t1B2kUtBUJRmDQHDwBJgVfueZvmwGKx+B+/pEC4FXhL%0ARJZivG4eczCm6xiTzoPkV/I2KMpisSQSCVszNhxsQWyLxRKvlOji4BaLxVISiLbpxmKxWCxxgFX0%0AFovFkuBErOhFpHrAz36tiEwJ5LQJ1m6kiKwI+N+/LSLJkYtbMgjXdSqRsXORh52LPOxchEdUc92I%0ASEPgBqCtqp6BcW0Z5GDMEoH9Eudh5yIPOxd52LkIDyeKvh8mxw2Bf/sHabMXOAKkiEhpIAXY7GBM%0Ai8VisYSJE0VfbK4bVd0NPAn8DGwBflPVaQ7GtFgsFkuYFOleKSJTgdpBProPmKCq1fK13a2q1Qtc%0Anwp8BnTGZK18H/hAVd8KMpb1rbRYLJYIKM69Mtq5btoBc1V1V+Caj4COwAmKvjhBLRaLxRIZTkw3%0AnwJDA++HkldFOz+rgQ4iUl5EBOgBrHQwpsVisVjCxElSs+rAe0AD8pUSFJG6wEuq2jfQ7i7MjSAH%0AWIRJcHbEBdktFovFEgK+SYFgsVgslujgeWSsiPQWkdUisk5ETkhfXJIQkVcDex8luuqJiNQXkRmB%0AQLvlIjLca5m8QkTKicgCEVkiIitF5HGvZfIaEUkSkcUi8pnXsniJiGwQkWWBufimyLZeruhFJAlY%0Ag7HdbwaetlZSAAAgAElEQVQWAoNV1ZMC4l4jIp2B/cDrgQCzEomI1AZqq+oSEamIKUHZvwR/L1JU%0A9UAgFmUO8HdVneO1XF4hIiOAs4FKqtqvuPaJioj8CJwdcGMvEq9X9OcCP6jqhoDdfiJwiccyeYaq%0AzgZ+9VoOr1HVbaq6JPB+P7AKqOutVN6Rr8ZyWUx0ebE/7ERFROoBfYCXAeupF+IceK3oTwE25jve%0AFDhnsQDH0mi0ARZ4K4l3iEgpEVmCCUycoaol2XPtaeBOjHNHSUeBaSLyrYjcUFRDrxW93Qm2FErA%0AbPMBcFtgZV8iUdUcVT0LqAdcICJpHovkCSJyEbBDVRdjV/MAnVS1DXAhcHPA9BsUrxX9ZqB+vuP6%0AmFW9pYQjImWAD4E3VTVYjEaJQ1X3ABmYQMSSSEegX8A2/Q7QTURe91gmz1DVrYF/dwIfY0zhQfFa%0A0X8LNBaRhiJSFhiICcSylGACwXWvACtV9Rmv5fESEamRmwJcRMoDPYHF3krlDap6r6rWV9VGmCy4%0A01V1iNdyeYGIpIhIpcD7CkAvoFBvPU8VvapmA7cAX2IiZt8tqZ4VACLyDjAXaCIiG0XkWq9l8ohO%0AwNVA14Dr2GIR6e21UB5RB5gesNEvAD5T1a88lskvlGTTby1gdr7vxSRVnVJYYxswZbFYLAmO16Yb%0Ai8VisUQZq+gtFoslwXGs6EMJ2xeRcYEUB0tFpI3TMS0Wi8USOm6s6F8DCt0oE5E+wOmq2hi4EXje%0AhTEtFovFEiKOFX0IYfvHasuq6gKgqoicUHbQYrFYLNEhFjb6YGkO6sVgXIvFYrFQTClBFykYrnyC%0AT6etGWuxWCyRUVwp1lis6AumOagXOHcC2q8f2rMnevgwqlqyXr/+ir75JvrHH4y+4w40Pd3MR3a2%0A97LF8rVgAXryyehrr6FHjjD69tvR33/3Xi4vXrNmoTVrom+/jWZnM3r0aO9l8uo1dSr61lvHjo/N%0Axf79aE6O9/J5+AqFWCj6T4EhACLSAfhNVbcHbfnhh1C6NDzySAzE8hlVq8JVV0GZMlCxInz2GZxx%0ABuzb57VksWXUKPjPf+BPfzLfhcqVISXFa6liz2+/me/DhAkweDAkJXktkXfs3AlDhkCdOid+duml%0AMHFi7GXyihAVe0HccK/MDdtvGgjbv05EhonIMCOXfg6sF5EfgBeAmwrtrHRpeOUVeOEFWLTIqWjx%0ATZky5oZXtarXksSWzz6Dyy7zWgrvGTECLr4YLrww+Oc7d8K2bbGVyStuvdXc9Lp2PfGzxx6D226D%0A7cHXjgnH4MEwb17Ylzm20avq4BDa3BJyh3XqmJV93RJbZ4K0tDSvRfCO5OTjDkvkXBw+DBs3wkcf%0AHXf6uLkYPx5++cU8/SQyixfD7Nnwww/HnT42F+3awcCB8K9/wb//HXv5YsnXX8OCBXD22WFf6ptc%0ANyKifpHF4mN++sk8+Z1SwuvT7NwJTZvCsmVQL4Gd2C69FNLSzKq9MDZtgtatYfVqOPnkmIkWc3r1%0AgssvhxuOrzEiIqgPNmMtwfjlFzj/fDh61GtJ4otXX4WHHvJaCu+pWRP+/Gd48kmvJYkehw4ZE+YN%0ARRZPMje6QYNg7NjYyOUF335rbmRDh0Z0uV3Re8XTT5t9iDfeCK39smXQoEHJs9kXZOtWaNECfv4Z%0AKlXyWhpv2bDBmC42bYJy5byWxlt+/BE2bzaLp0TkxhuhYUO4994TPrIrer+iCi+9VPxKJT//+Ifx%0AwEg0cnLMo/mOHaG1r1MHunSBd9+NqlhxQcOG0LbtCbb8EkmjRomr5AGys40nWoT4W9Grwty5Rhkk%0AEvPmGZNN50JLPJ7IDTeYm0OiPfVMnw579hhTRKjkzoXFeJ2ceabXUliizauvOnJQ8beiB2OH/OYb%0Ar6Vwl7ffNrY2CaO+cVoa7N8Py5dHTSxPiGQu0tPNpmxWVvTk8oJbb4U5c8K7pl07aNkyOvJYEgZ/%0AK3oRs8v8wQdeS+Iua9bAgAHhXSNirkmkuThyBP73v/D95kuXNiv68uWjI5cXHDxo9muaNvVaEksC%0A4m9FD3nKLZFMFlOnQpMm4V+XaIp++nQzD/XrF9+2IBdfnFixFl98YfyjwzFhJSoffujMg2bPHvdk%0ASRD8r+jPOMME0Sxc6LUk3nPuucY+nZ3ttSTusHCheWKzmBu4nQvDG29AtWqRXXvokNmk3r3bVZHi%0Anfhwr7znnpKbAyfRUQ3PPp+IHDliAn1WrgyezyWcfsqUcU8uLzh40MzFTz9B9eqR9XHJJXDFFSZt%0AQjyzZw/ccgu8/nqRv5HEca8cPNis7C2JR0lX8mA22Js0cabk//c/kwog3pk5E846K3IlD9CnjzGF%0AxTvTppkIaBd+I/GxordYiiIRngqcrsZ37IDGjY1iKFvWPblizfDh5oY3cmTkffz8s9nv2LYtvrN+%0AXn+9Se0wfHiRzRJnRZ8ovPoq/FpU1UVL2PzpT5CR4bUUznFqcjn5ZGjWLHz3TL8xbZpZkTuhQQOo%0AXdukDYhXVOHzz53PRQCr6GPFvn0mMVM8r7b8SMuW5gdhSQyTxYIFZhXrlCFD4jt18bJlUKECnH66%0AK91ZRR8rvv7aPE5WqOC8r507TRqAeDV1rVhh0s+6QY8eMGOGO33FO927G5fVeKZSJXfMcHfeCf36%0AOe/HK6ZPN99tl4hVzVhLZqaJbnWDGjVg7VqT1KpRI3f6jCUvvGAyDrZp47yv1q2NLXbbNvO4XpI5%0A5xyTLuSPP+yTY7wzbJiJhHcJNypM9RaR1SKyTkTuDvJ5mojsEZHFgdeoiAd75RV46y1H8npGZqZZ%0AhbuBiLlpZGa601+scfOml5RkcgbNnOlOf7Fmxgz3UlUnJ5snJavk45+UFFdz6ztS9CKSBDwL9AZa%0AAINFpHmQpjNVtU3gFbkzfNmy8MknEV/uGfv2GRe6Dh3c6zNeFf0vv5gnkbZt3eszLc2Yg+KNjRuN%0Av3e8ewxZfI/TFf25wA+qukFVjwATgUuCtHPnm9yli1m5xZttWtU8jbiZmyV3LuKNWbOgUycTAOcW%0At98en8VIZs40/4+l7FYZGzfC3r1eS5GwOP2GnQJszHe8KXAuPwp0FJGlIvK5iLSIeLQGDaBiRRNB%0AGE9Urux+MEvTpqa26KZN7vYbbdw02+QSrytiN8158c7dd5scN24zbhwcOOB+v3GGU0UfytJ6EVBf%0AVc8ExgPObC/xarJwGxGTpjfe6oV26mRC1C3RuenFI6rRu+m98w7Mn+9+v9EiJycqsTZOn583A/lT%0AD9bHrOqPoar78r3/QkT+IyLVVfWErENjxow59j4tLe34qvd5H8Bnn8HNNzsUPQFISfFagvBJhDB9%0AN9i4EX77LTq55H/+GX74Abp1c7/vaLBunTHlRcODLHdhGC9zsWwZXHllkVaLzMxMMsNc7DpKgSAi%0ApYE1QHdgC/ANMFhVV+VrUwvYoaoqIucC76lqwyB9hZYCYf9+4z7mJBeGxeI169aZBcuIEe73PXu2%0A2beIl8jQF180Eb2vv+5+319+aapwxct+1jPPmCLg//1vyJdEPQWCqmYDtwBfAiuBd1V1lYgME5Fh%0AgWYDgO9FZAnwDDDIyZhUrGiVvCU4CxeaVXI80LhxdJQ8mHTWq1fHz1xE04TVqRN8953JihkPRGku%0AHG/3q+oXqtpUVU9X1ccD515Q1RcC759T1VaqepaqdlTVODKYucDAgfG3eRyvPPSQKepS0klONsr+%0A66+9liQ0Tj4ZunaNTt8VK0KrVia1gt9RNU9jF1zgetfWryua/PEHTJoUWQWlUDl0KPFqp0ZKx47x%0AtfEWTeJpLp55JroR3o88AqeeGr3+3WLdOnNjikLlNKvoo8myZZCaavJ3RIulS8OvueoFCxdCvs32%0AqNChA8ybF90x4oXzzrNzkUuPHvGRKmT7drj00qh0Hb+5bg4eNIEmycleS1I4CxZA+/bRHaNNG7MS%0A2LcvujcUp2RmRj9F8znnmBvf4cP+/l7EgvPOM543lvihc2fzigLxu6Lv39//9thYKPqyZU1FHr/X%0A1I3FXFSsaCo1LVkS3XGc8sQTsH59dMeoXt2kxbZYiGdF3769/x9Nv/km+soNjMnC7/bYWCh6gL/8%0Axd8pBVThn/90Nx2GxVIMPv5FFMN55/lfuc2bBy0iz/gQMn63x27ebDaNTzst+mMNG2ZMOH7lhx9M%0ATQIn9WEThR9/jN9stHFG/Cr69u2NucKtFK/RoFq12NSs7NgRataM/jiRsmCBcfeL15w0bhKrJ5t4%0AYOrU2Jlf16+HQc5CeOKZ+FX01asbN6Tly72WxHvq1jX1aP1Kjx7w7LNeS+EPrKLPI5ZzUbeuiUT2%0Aa4KzL74wBd6jRPwqeoC+fWHLFq+lsBRH5crx4d4WCxYscLcuQXE89ZR/C4bPnx87RV+unAmc8mta%0AiJtugt0npP9yjfhW9E8+CRde6LUUFkvoPP64qR0cK3btgilTYjdeqOzdCz/9BGecEbsx/bqXtWOH%0ASVfRpEnUhohvRe9XDh707yNiSWDbNqNQ/Uj37rH1uPGrR9bChcYtuEyZ2I3pVweO3D2sKHqLWUUf%0ADT76CIYO9VqKkkuFCibs/Y8/vJbEezp0MIokJ8drSY6nYUN44IHYjpkbOe23CnUx2Kuwij4aeLXh%0ANmmSyVroJ7zwiqpUybhyLlsW+7H9Rs2aUKOG/74XqanQq1dsx2zQwBRP95v3l1X0cYpXin7qVONZ%0A4CfatfOmcHf79vGRsTAW2LkwiPgzfuHCC6O+QR//in7PHpg82Wsp8jh82Lh8xnLDLZf27f1lg9y7%0AF9aujeomU6H4bS685OGH4aKLvJbCUhgjRsBJJ0V1iPhX9AcOwFVX+cfutmSJUWxelPnz28rt229j%0Av+GWi9/mYvJk+OtfvRk7NdXfAXWWqBP/ir5OHbP55pdMfTt3Qp8+3ox92mnmiWLzZm/GL4iXwUEt%0AW5rkYX5ZAHz9ddRXbRZLYThW9CLSW0RWi8g6Ebm7kDbjAp8vFZE2Tsc8gVzPAj9w0UXw6KPejC1i%0A3LT8MhexDg7KT1KSye3tl403GxGbx/XXG5OeVxw5Ej+lBV3CkaIXkSTgWaA30AIYLCLNC7TpA5yu%0Aqo2BG4HnnYwZFGuPzWPECFOP1A/89JNVbmBcG2OVydTvHDkC77wDtWt7J8Mtt8Brr3k3vgc4XdGf%0AC/ygqhtU9QgwEbikQJt+wAQAVV0AVBWRWg7HPR6/2WO9pHv32EYbFsWiRcalraSzZo3JzXTyyd7K%0A4Qcz1rJlJh1G5creydCunT8WhosXw7//HZOhnCr6U4CN+Y43Bc4V16aew3GPp21bSE93tUuLC4j4%0Ax3TiJYsWeb+a37wZmjXzVgbwhwnLLwvDr74yT70xwGkpwVCXCAV/7UGvG5OvpmhaWhppaWmh9Z6S%0AYiIhLRY/cuWVUasFGjJ165q8N1u2RKX4dMgsWADnn+/d+GA26rdsMaUtq1XzTo4FC+CSggaQ4snM%0AzCQzMzOsa0QdPM6JSAdgjKr2DhyPBHJU9Yl8bf4LZKrqxMDxaqCLqm4v0Jc6kcUXfP45dOoEVap4%0ALYkll/vvhzPPhAEDvJbEe/r0gRtu8Pam06wZvPcetG7tnQwAaWkwcqS3loAGDcyq3uGemoigqkU+%0AOjs13XwLNBaRhiJSFhgIfFqgzafAkIBAHYDfCir5hCAnx/jzHzrktSSW/FSpArNmeS2FP/CDyeLL%0AL82K2mt69IDtHqqhLVvg99/h9NNjMpwjRa+q2cAtwJfASuBdVV0lIsNEZFigzefAehH5AXgBuMmh%0AzP5k7VqoWhVqubvPHBFbtnibVG3HDrMB6Qf8oNz8gh/m4tRTY1N1rThGjYIhQ7wbP3evIkZ7WI5M%0AN24S96abCRNMlZiJE72WxLiwVa0KW7d6493w/PMmDa0fql4dOGCiQnfvhuRkr6Xxlt27jRnr55/t%0AJrnX7NxpXi7UlI6F6cZfvPIKzJ3rzdh+8CbIpUwZk3rAq2o6fpqLlBSTkmLxYm/G37jRP3WNq1c3%0AXh5WyXtPzZquKPlQSSxFv2GDWVV7gZdRoMHwMlrYb3ORW0jeCzp1MoWp/UIUi1tY/ItT90p/0b49%0AjBvnzdh9+0Ib97M7REz79vD227Ef97ffYNMmf2y45fKvf5l8SLFm69aYbrj5mj/+MLZ5P9jnSyCJ%0AdXvPXbl5UU3noYdMAWK/kLvxFut9j4ULTQBbaR+tISpV8mYlm1sizppK4IMP4OqrvZbieA4e9FeK%0A8yiSWIq+Zk0TAOFlwiS/0KABTJ8e+3GTkoybqcVfexVes2CBv554wSwIL7vMZHyNJR44nSSWogd/%0AuJD5ARFo3jz2q8lu3eDGG2M7pl+ZP99fexW5bNwYex/yefP8NxcVKphgpSVLYjtux46wbl1Mh0w8%0ART9qlFE2FovXVKjgzxX900/HNnvjwYOm6lq7drEbM1RivTDcs8fMRcOGsRuTRFT0LVtC/fpeS2Hx%0AG6qxX8VOmuRtLpXCiLVyW7TIuBJ6UXWtOGI9FwsWmD2sGFddSzxFH2tmzChxua3jkp07TZ4VLzbq%0A/UasN+o3bjTps/1IrBW9R+Y8q+id8tlnsG2b11IUjqqJlC3pnHyyiRa2G/UmDcHRo8YNNhYMGmTK%0AOvqR5s2hX7/YBbXNmwfnnRebsfJhFb1TPPqPC5nhw+Hll2Mz1r/+Zfzo/YrdqDeI2LnIpVQpeOqp%0A2Pj3q8KKFVbRxx2HD5uKOX7cZMrljDNi84Petw8efNCfdthcrHLL47LLbPBSrBGBH3/0JPFhYir6%0Abdtio3wXLzZ5VCpWjP5YkRIr5bZwocmvU7Zs9MeKlFilhVixwv+pkYcO9b4YSknEo5trYir6WrXM%0AnXPr1uiO43ezDRgvpE2bom9S8aOfdEHatDEuj9HekH37bZg2LbpjWCxhkJiKXsSEnkd79XbZZTBi%0ARHTHcErp0sadK9pJveLhpleunFlpRzsdgl8Dpbxg2jRbjMcHJKaih9iYLBo0iI+EVZ07m8ye0ULV%0AKrdcjh41N1U7F0bBX3KJf9I0F8X77yf0U1jEmadEpDrwLnAqsAG4QlVPsA+IyAZgL3AUOKKq50Y6%0AZlh06OBfl65YE+3C6UePwvjxcMop0R0nHli+3BTfrl7da0m8Z9EiE7vgRebQcNm0ycTE9OgRnf5z%0AXbBr145O/8XgZEV/DzBVVZsAXwWOg6FAmqq2iZmSB2NGWLYMsrNjNmSJpXRpGDzYayn8wcyZcMEF%0AXksRGocOGZfYaBFPc9G5c3Q30J97ziyGPMKJou8HTAi8nwD0L6Jt7PO0Vqliaqf6KV2uJfFp3Rqu%0AvdZrKUIjOdko+p9+ik7/mZnQpUt0+nabs84yq/qdO6PTf2YmpKVFp+8QcKLoa6lqbvKQ7UBhzqEK%0ATBORb0XkBgfjhU80a4TGc33bks6yZdHbv0lL8/+mdC4iRt6ZM93v+8gRs0EfLyv60qXh/POjs6o/%0AcMC4Ynfs6H7fIVLkcldEpgLBjEr35T9QVRWRwjRfJ1XdKiI1gakislpVZwdrOGbMmGPv09LSSPPw%0ADlgkW7aYDJmrVtmiEvHIokWm4IQfCrl7TVqaWW0OGeJuv/v2wR13xNdeRe5cXHaZu/3On2+e9Fza%0Aq8jMzCQzMzOsa0QjXJmKyGqM7X2biNQBZqhqs2KuGQ3sV9Ung3ymkcoSc955B957Dz7+2GtJwuOL%0AL6BXLxsRuWGD2azfutXeqFeuhIsu8lddW6/YssXEm7hdtPuBB8xe4WOPudtvABFBVYv8Ijsx3XwK%0ADA28Hwp8EkSAFBGpFHhfAegFfO9gTH/gsb0tYu64A5YudbfPW26BL790t89o07Ch8alfs8ZrSbyn%0AeXPYvz96dvp4om5d95U8mKeaiy5yv98wcKLo/wH0FJG1QLfAMSJSV0QyAm1qA7NFZAmwAJikqlOc%0ACBw2e/e6X80lXhV97qOpW6jCJ5/Aaae512es6NLF3bmIV0Tg//7P32k84p2//c1T+zw4UPSqultV%0Ae6hqE1XtletDr6pbVLVv4P16VT0r8Gqlqo+7JXjIzJoFf/2re/1t3Qq//GKShcUbbiu39euNso+H%0AoLGCuH3TW7HC3e9ZLOnTB046yWspLFEkcSNjczn/fONh4VYB4OXLTVBFtMPoo0GXLjB7tnuRirlP%0ANvFo505Pd/dxevp0G7Nh8S1xqK3CpGpVaNrUvVwvPXvGr7dG7drm5ZadPl5NWGDssVdf7V5/8TwX%0AbnP//TEvfu06CVaJLPEVPUDXrvDVV+71F48r2FzuvNO9epXz5lnlBuYJaeZMOxdgnpzHjYsvt8qC%0AXHstfPCB11K4SslQ9L17G9dCC1x3nXv7C99/D40bu9NXPPPNN+YJweb6gTlzjCdPPNv8zz7bHX3x%0A9de+efovGYq+c2ej3BLsccxzypf3WgJ/MHmy2dCMZ/bvN78Rp/sMn38e/3PRp49R9E71xZtvwubN%0A7sjkkIgDptwmrgKmLJb8/PEHHDxo8ivFM23bwtixZmEUKc2bwxtv+Lu8Zig0a2YKyLRtG9n1qiZe%0AY/JkMydRJNoBUyWLAwcgI6P4dpb44n//g3ypNyKibNn4V/JgVrJOvuM//gi7d0euHP2E07lYudJ4%0A5jUrMllAzLCKPlQmT4ann/ZaCovbnHYavPaaTVIHcPHFJgAu0rlo0MDY6OPR9bgg/frBDz9Efv0n%0Anxj3XZ84biTA/0iMeP99uPxyr6Vwj/vuMz/KSJg5M3HKw7VqZdIhRLvUYjxw7rnmyXXlysiuT0pK%0AnM35tDSYMKHYZoXiM31hFX0oHDxoNmcuvdRrSdyjQoXIPAL27jUrv0RR9CLmB5lg7nQRIQJXXGGy%0ALVqc8eqr0KmT11Ico2Qp+iNHYOBAs3kWDlOmGLvjySdHRy4vGDAAPvww/CjZjAyTY7xq1ejI5QUD%0ABpgVWLgmizVrYNeu6MjkFf/6F/z5z15LEf+0beurLLElS9GXKWNSkX7+eXjXvf46DBoUHZm8okkT%0A4/sdbiDZ668nXtnAM8+EypXDz2Z5ww3RLT/nBT6xKVvcpeS5V06YYFZvkyaFfs2778KFFxplkEg8%0A/7wpiPzee6G1/+knE0yycWPi+dBnZ4dXdnLNGpM7aONG9yKN45VcX3EbMOYJ1r0yGJdfbkL3N20K%0A/ZqBAxNPyYNZmc+dazbgQuG118w1iabkIfzawq+8AkOHWiUP8PjjZj4SkZwcGDkyfHOvzyh5K3qA%0Am2+GatXgkUdiM56fOXIkdGU1fTrUq2fMPiWZAwegUSOTCbSkz8WePZCaasozNmjgtTTRoVs3s29x%0A1VVFt9u1y7xi/J2wK/rCuOMO+Ogj99L1xjPhrEi7dbOKDeC//zXRo4k8F4cPm4IZR44U3W78eBNc%0AlKhKHuCee+DRR4tPifDkk/DUU7GRKUyc1Iy9HBgDNAPOUdVFhbTrDTwDJAEvq+oThbSLbQqEcG2y%0AFksu331nKjI1beq1JNGlWze45hqTzTEY+/aZgLM5cxJ7LlThvPNgxAjjfhqM3btNDMGiRXDqqTEV%0AL9or+u+BS4FC3Q5EJAl4FugNtAAGi0h0Ez+ESnFK3sOgoHArvCcyMZ+Lo0fNPsT27YW3OftsTxRb%0AzOfi4YdNbvndu4N/Pno09O2b+HMhYsy8d99t4kiCcffdxjMvxko+VJyUElytqmuLaXYu8IOqblDV%0AI8BE4JJIx4wZK1fCZZcZV0wP8EzRHz3quwyfMZ+LpCRjfx82zHdpEWI+F506md/BzTcHn4thwzwz%0AVcR8Lnr0MEWHJk8+8bOMDJg2Df7xj9jKFAbRttGfAmzMd7wpcM6/LFkCvXqZL3A8Fr12wsiRcOWV%0AeU8y33xjHldLGqNHw44dxmQR594Wjnn8cVi7Fm666cRFQNOm8V1gJFxeeOFE082hQ2Yv4/XXoVIl%0Ab+QKgSIVvYhMFZHvg7wuDrF/fy2JimLgQGjTxlSjeuYZGDLEa4liz0MPmRVtnTpw1lkmdqBrV6+l%0Aij3JyWaFtn+/mYtrrvFaIu9ISTGxFm3a2GCqYH9/uXKwbJmz1M4xwLF7pYjMAO4IthkrIh2AMara%0AO3A8EsgJtiErIvFzU7BYLBYfUdxmrFtuJ4UN8i3QWEQaAluAgUDQ+PniBLVYLBZLZERsoxeRS0Vk%0AI9AByBCRLwLn64pIBoCqZgO3AF8CK4F3VXWVc7EtFovFEiq+iYy1WCwWS3TwPDJWRHqLyGoRWSci%0Ad3stj5eIyKsisl1EvvdaFi8RkfoiMkNEVojIchEZ7rVMXiEi5URkgYgsEZGVIvK41zJ5jYgkichi%0AEfnMa1m8REQ2iMiywFx8U2RbL1f0gYCqNUAPYDOwEBhcUs07ItIZ2A+8rqpneC2PV4hIbaC2qi4R%0AkYrAd0D/Evy9SFHVAyJSGpgD/F1VIywPFv+IyAjgbKCSqvbzWh6vEJEfgbNVtZCItjy8XtHHZ0BV%0AlFDV2cCvXsvhNaq6TVWXBN7vB1YBdb2VyjtUNTe9aFlMKpFif9iJiojUA/oAL1O4E0hJIqQ58FrR%0Ax19AlSWmBDy22gALvJXEO0SklIgsAbYDM1Q1wqKuCcHTwJ2Av0K4vUGBaSLyrYjcUFRDrxW93Qm2%0AFErAbPMBcFtgZV8iUdUcVT0LqAdcICJpHovkCSJyEbBDVRdjV/MAnVS1DXAhcHPA9BsUrxX9ZqB+%0AvuP6mFW9pYQjImWAD4E3VfUTr+XxA6q6B8gA2nkti0d0BPoFbNPvAN1E5HWPZfIMVd0a+Hcn8DHG%0AFB4UrxX9sYAqESmLCaj61GOZLB4jIgK8AqxU1We8lsdLRKSGiFQNvC8P9AQWeyuVN6jqvapaX1Ub%0AAYOA6apaAnOVmA16EakUeF8B6IXJKBwUTxW9Dag6HhF5B5gLNBGRjSJSSCLwhKcTcDXQNeA6tjhQ%0A16AkUgeYHrDRLwA+U9UwK7onLCXZ9FsLmJ3vezFJVacU1tgGTFksFkuC47XpxmKxWCxRxip6i8Vi%0ASZjh7gwAAAA0SURBVHCsordYLJYExyp6i8ViSXCsordYLJYExyp6i8ViSXCsordYLJYExyp6i8Vi%0ASXD+P5ePy54P9ccKAAAAAElFTkSuQmCC%0A
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swLI/hK/1L69r2Azp1vYc6cXUBTTAGXIb7KgXTiqj2HlStv5OKLP2bZskxgErAOc9M+FajG5s1b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%0ASXD+P5ePy54P9ccKAAAAAElFTkSuQmCC%0A
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-
-图形上加上文字
-------------------
-
-plt.hist() 可以用来画直方图。
-
-
-
-
-
-::
-
-    mu, sigma = 100, 15
-    x = mu + sigma * np.random.randn(10000)
-
-    # the histogram of the data
-    n, bins, patches = plt.hist(x, 50, normed=1, facecolor='g', alpha=0.75)
-
-
-    plt.xlabel('Smarts')
-    plt.ylabel('Probability')
-    plt.title('Histogram of IQ')
-    plt.text(60, .025, r'$\mu=100,\ \sigma=15$')
-    plt.axis([40, 160, 0, 0.03])
-    plt.grid(True)
-    plt.show()
-
-
-<button>Run ...</button>
-
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-.. image:: 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DrQ8MFbZmWQ9uUmiaodoE5N6vGNh/tQUpZ6fO1wg2JmZh3hLq8SSr0PN/X4OnkfSqP7YEZz3CX1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%0A60kAAAAASUVORK5CYII=%0A
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-对于这幅图形，我们使用 xlabel ，ylabel，title，text 方法设置了文字，其中：
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-- xlabel ：x 轴标注
-- ylabel ：y 轴标注
-- title ：图形标题
-- text ：在指定位置放入文字
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-输入特殊符号支持使用 Tex 语法，用 $<some Tex code>$ 隔开。
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-除了使用 text 在指定位置标上文字之外，还可以使用 annotate 函数进行注释，annotate 主要有两个参数：
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-- xy ：注释位置
-- xytext ：注释文字位置
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-::
-
-    ax = plt.subplot(111)
-
-    t = np.arange(0.0, 5.0, 0.01)
-    s = np.cos(2*np.pi*t)
-    line, = plt.plot(t, s, lw=2)
-
-    plt.annotate('local max', xy=(2, 1), xytext=(3, 1.5),
-                ^4$arrowprops=dict(facecolor='black', shrink=0.05),
-                ^4$)
-
-    plt.ylim(-2,2)
-    plt.show()
-
-
-
-<button>Run ...</button>
-
-
-
-.. image:: 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7jnHjnnc881HdmpU8yjR8v748a5a5+T3HWXnPMFFzAfOiTvVVQwDx8u70+Y4K59TnLrrXLO/fuL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%0ARVGUgPP/Qde6gvF4TtQAAAAASUVORK5CYII=%0A
-
-
-
-作者 & 更新时间
-------------------------------------
-作者:`李金  <lijinwithyou@gmail.com>`
-
-提交: 2017/12/14
-
-
-
diff --git a/docs/sources/aipy-numpy/cn/2pyplot-style.rst.txt b/docs/sources/aipy-numpy/cn/2pyplot-style.rst.txt
deleted file mode 100644
index 90aee83..0000000
--- a/docs/sources/aipy-numpy/cn/2pyplot-style.rst.txt
+++ /dev/null
@@ -1,175 +0,0 @@
-
-使用 style 来配置 pyplot 风格
----------------------------------
-
-
-
-
-::
-
-    import matplotlib.pyplot as plt
-    import numpy as np
-
-    %matplotlib inline
-
-
-style 是 pyplot 的一个子模块，方便进行风格转换， pyplot 有很多的预设风格，可以使用 plt.style.available 来查看：
-
-
-
-
-::
-
-    plt.style.available
-
-
-结果:
-::
-
-    [u'dark_background', u'bmh', u'grayscale', u'ggplot', 
-    u'fivethirtyeight']
-
-
-
-
-::
-
-    x = np.linspace(0, 2 * np.pi)
-    y = np.sin(x)
-
-    plt.plot(x, y)
-
-    plt.show()
-
-
-<button>Run ...</button>
-
-
-.. image:: 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tkX6/3ohwyB4cPhvfcChpJYWLUKataERYvg8MNDpxHJrtgO3Tjnb8Lee2/oJBIHhx7qZ98MGBA6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%0Agg==%0A
-
-
-例如，我们可以模仿 R 语言中常用的 ggplot 风格：
-
-
-
-
-::
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-    plt.style.use('ggplot')
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-    plt.plot(x, y)
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-    plt.show()
-
-
-<button>Run ...</button>
-
-
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-.. image:: 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H7NOwX9Yh0kkZuxBTIRgSTdBnv5fNMpRK4VOIN/zWJI+67cniEISIeewOFvoIe/NZ1C5EoBMfi1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%0AwU9EFGT+Pxvu78Bmq8eQAAAAAElFTkSuQmCC%0A
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-有时候，我们不希望改变全局的风格，只是想暂时改变一下分隔，则可以使用 context 将风格改变限制在某一个代码块内：
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-::
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-    with plt.style.context(('dark_background')):
-        ^4$plt.plot(x, y, 'r-o')
-        ^4$plt.show()
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-<button>Run ...</button>
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-.. image:: 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/7Ro0dTUVFBVVUVM2fOjHrO7NmzqaqqYsOGDQwcOLDZj90BeCvRACWQwrt2jgXuxfxd0sauSIKJ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%0ASUVORK5CYII=%0A
-
-
-在代码块外绘图则仍然是全局的风格。
-
-
-
-
-
-::
-
-    with plt.style.context(('dark_background')):
-        ^4$pass
-    plt.plot(x, y, 'r-o')
-    plt.show()
-
-<button>Run ...</button>
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-.. image:: 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ydPQmu3c3gbkRcCr/BLEqLWr0f1rFmik5APfX3yJA6qVMjJy3Mc4/A2Is8E3Bp/aEEB1FevwnL3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%0AYeEnIgoyLPxEREHm/wEVP/FL2qENYwAAAABJRU5ErkJggg==%0A
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-还可以混搭使用多种风格，不过最右边的一种风格会将最左边的覆盖：
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-::
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-    plt.style.use(['dark_background', 'ggplot'])
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-    plt.plot(x, y, 'r-o')
-    plt.show()
-
-<button>Run ...</button>
-
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-.. image:: 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ydPQmu3c3gbkRcCr/BLEqLWr0f1rFmik5APfX3yJA6qVMjJy3Mc4/A2Is8E3Bp/aEEB1FevwnL3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%0AYeEnIgoyLPxEREHm/wEVP/FL2qENYwAAAABJRU5ErkJggg==%0A
-
-事实上，我们还可以自定义风格文件。
-
-
-自定义文件需要放在 matplotlib 的配置文件夹 mpl_configdir 的子文件夹 mpl_configdir/stylelib/ 下，以 .mplstyle 结尾。
-mpl_configdir 的位置可以这样查看：
-
-
-
-
-
-::
-
-    import matplotlib
-    matplotlib.get_configdir()
-
-
-结果:
-::
-
-    u'c:/Users/Jin\\.matplotlib'
-
-
-里面的内容以 属性：值 的形式保存：
-
-
-axes.titlesize : 24
-
-axes.labelsize : 20
-
-lines.linewidth : 3
-
-lines.markersize : 10
-
-xtick.labelsize : 16
-
-ytick.labelsize : 16
-
-
-假设我们将其保存为 mpl_configdir/stylelib/presentation.mplstyle，那么使用这个风格的时候只需要调用：
-
-
-     plt.style.use('presentation')
-
-
-作者 & 更新时间
-------------------------------------
-作者:`李金  <lijinwithyou@gmail.com>`
-
-提交: 2017/12/14
\ No newline at end of file
diff --git a/docs/sources/aipy-numpy/cn/3text-base.rst.txt b/docs/sources/aipy-numpy/cn/3text-base.rst.txt
deleted file mode 100644
index d6b7c27..0000000
--- a/docs/sources/aipy-numpy/cn/3text-base.rst.txt
+++ /dev/null
@@ -1,396 +0,0 @@
-
-处理文本（基础）
-=====================
-
-
-
-::
-
-    import matplotlib.pyplot as plt
-    import numpy as np
-    %matplotlib inline
-
-
-matplotlib 对文本的支持十分完善，包括数学公式，Unicode 文字，栅格和向量化输出，文字换行，文字旋转等一系列操作。
-
-
-基础文本函数
-----------------
-
-在 matplotlib.pyplot 中，基础的文本函数如下：
-
-
-- text() 在 Axes 对象的任意位置添加文本
-- xlabel() 添加 x 轴标题
-- ylabel() 添加 y 轴标题
-- title() 给 Axes 对象添加标题
-- figtext() 在 Figure 对象的任意位置添加文本
-- suptitle() 给 Figure 对象添加标题
-- anotate() 给 Axes 对象添加注释（可选择是否添加箭头标记）
-
-
-
-
-
-::
-
-    # -*- coding: utf-8 -*-
-    import matplotlib.pyplot as plt
-    %matplotlib inline
-
-    # plt.figure() 返回一个 Figure() 对象
-    fig = plt.figure(figsize=(12, 9))
-
-    # 设置这个 Figure 对象的标题
-    # 事实上，如果我们直接调用 plt.suptitle() 函数，它会自动找到当前    的 Figure 对象
-    fig.suptitle('bold figure suptitle', fontsize=14,     fontweight='bold')
-
-    # Axes 对象表示 Figure 对象中的子图
-    # 这里只有一幅图像，所以使用 add_subplot(111)
-    ax = fig.add_subplot(111)
-    fig.subplots_adjust(top=0.85)
-
-    # 可以直接使用 set_xxx 的方法来设置标题
-    ax.set_title('axes title')
-    # 也可以直接调用 title()，因为会自动定位到当前的 Axes 对象
-    # plt.title('axes title')
-
-    ax.set_xlabel('xlabel')
-    ax.set_ylabel('ylabel')
-
-    # 添加文本，斜体加文本框
-    ax.text(3, 8, 'boxed italics text in data coords',     style='italic',
-             ^4$bbox={'facecolor':'red', 'alpha':0.5, 'pad':10})
-
-    # 数学公式，用 $$ 输入 Tex 公式
-    ax.text(2, 6, r'an equation: $E=mc^2$', fontsize=15)
-
-    # Unicode 支持
-    ax.text(3, 2, unicode('unicode: Institut f\374r Festk    \366rperphysik', 'latin-1'))
-
-    # 颜色，对齐方式
-    ax.text(0.95, 0.01, 'colored text in axes coords',
-            ^4$verticalalignment='bottom', horizontalalignment='right',
-            ^4$transform=ax.transAxes,
-            ^4$color='green', fontsize=15)
-
-    # 注释文本和箭头
-    ax.plot([2], [1], 'o')
-    ax.annotate('annotate', xy=(2, 1), xytext=(3, 4),
-                ^4$arrowprops=dict(facecolor='black', shrink=0.05))
-
-    # 设置显示范围
-    ax.axis([0, 10, 0, 10])
-
-    plt.show()
-
-
-<button>Run ...</button>
-
-
-.. image:: 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wAAAOAR4RkAAADwiPAMAAAAeER4BgAAADwiPAMAAAAeEZ4BAAAAjwjPAAAAgEeEZwAAAMAjwjMA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%0A
-
-
-文本属性和布局
-----------------------
-
-我们可以通过下列关键词，在文本函数中设置文本的属性：
-
-+--------------------+--------------------------------------------------------------------------------+
-| 关键词             | 值                                                                             |
-+====================+================================================================================+
-|alpha	             | float                                                                          |    
-+--------------------+--------------------------------------------------------------------------------+
-|backgroundcolor     |any matplotlib color                                                            |
-+--------------------+--------------------------------------------------------------------------------+
-|bbox	             |rectangle prop dict plus key 'pad' which is a pad in points                     |
-+--------------------+--------------------------------------------------------------------------------+
-|clip_box	         |a matplotlib.transform.Bbox instance                                            |         
-+--------------------+--------------------------------------------------------------------------------+
-|clip_on	         |[True ， False]                                                                 |
-+--------------------+--------------------------------------------------------------------------------+
-|clip_path	         |a Path instance and a Transform instance, a Patch                               |
-+--------------------+--------------------------------------------------------------------------------+
-|color	             |any matplotlib color                                                            |
-+--------------------+--------------------------------------------------------------------------------+
-| family	         |[ 'serif' , 'sans-serif' , 'cursive' , 'fantasy' , 'monospace' ]                |
-+--------------------+--------------------------------------------------------------------------------+
-| fontproperties     |a matplotlib.font_manager.FontProperties instance                               |
-+--------------------+--------------------------------------------------------------------------------+
-|horizontalalignment |[ 'center' , 'right' , 'left' ]                                                 |
-+--------------------+--------------------------------------------------------------------------------+
-|label	             |any string                                                                      |
-+--------------------+--------------------------------------------------------------------------------+
-|linespacing	     |float                                                                           |
-+--------------------+--------------------------------------------------------------------------------+
-|multialignment      |['left' , 'right' , 'center' ]                                                  |
-+--------------------+--------------------------------------------------------------------------------+
-|name or fontname    |string e.g., ['Sans' , 'Courier' , 'Helvetica' ...]                             |
-+--------------------+--------------------------------------------------------------------------------+
-|picker	             |[None,float,boolean,callable]                                                   |
-+--------------------+--------------------------------------------------------------------------------+
-|position	         |(x,y)                                                                           |
-+--------------------+--------------------------------------------------------------------------------+
-|rotation	         |[ angle in degrees 'vertical' , 'horizontal'                                    |
-+--------------------+--------------------------------------------------------------------------------+
-|size or fontsize    |[ size in points , relative size, e.g., 'smaller', 'x-large' ]                  |
-+--------------------+--------------------------------------------------------------------------------+
-|style or fontstyle  |[ 'normal' , 'italic' , 'oblique']                                              |
-+--------------------+--------------------------------------------------------------------------------+
-|text	 	         |string or anything printable with '%s' conversion                               |
-+--------------------+--------------------------------------------------------------------------------+
-|transform	         |a matplotlib.transform transformation instance                                  |
-+--------------------+--------------------------------------------------------------------------------+
-|variant	         |[ 'normal' , 'small-caps' ]                                                     |
-+--------------------+--------------------------------------------------------------------------------+
-|verticalalignment   |[ 'center' , 'top' , 'bottom' , 'baseline' ]                                    |
-+--------------------+--------------------------------------------------------------------------------+
-|visible	 n       |[True , False]                                                                  |
-+--------------------+--------------------------------------------------------------------------------+
-|weight or fontweight|[ 'normal' , 'bold' , 'heavy' , 'light' , 'ultrabold' , 'ultralight']           |
-+--------------------+--------------------------------------------------------------------------------+
-|x		             |float                                                                           |
-+--------------------+--------------------------------------------------------------------------------+
-|y	                 |float                                                                           |
-+--------------------+--------------------------------------------------------------------------------+
-|zorder		         |any number                                                                      |
-+--------------------+--------------------------------------------------------------------------------+
-
-
-
-其中 va, ha, multialignment 可以用来控制布局。
-
-
-
-- horizontalalignment or ha ：x 位置参数表示的位置
-- verticalalignment or va：y 位置参数表示的位置
-- multialignment：多行位置控制
-
-
-
-
-
-::
-
-    import matplotlib.pyplot as plt
-    import matplotlib.patches as patches
-
-    # build a rectangle in axes coords
-    left, width = .25, .5
-    bottom, height = .25, .5
-    right = left + width
-    top = bottom + height
-
-    fig = plt.figure(figsize=(10,7))
-    ax = fig.add_axes([0,0,1,1])
-
-    # axes coordinates are 0,0 is bottom left and 1,1 is upper right
-    p = patches.Rectangle(
-        ^4$(left, bottom), width, height,
-        ^4$fill=False, transform=ax.transAxes, clip_on=False
-        ^4$)
-
-    ax.add_patch(p)
-
-    ax.text(left, bottom, 'left top',
-            ^4$horizontalalignment='left',
-            ^4$verticalalignment='top',
-            ^4$transform=ax.transAxes,
-            ^4$size='xx-large')
-
-    x.text(left, bottom, 'left bottom',
-            ^4$horizontalalignment='left',
-            ^4$verticalalignment='bottom',
-            ^4$transform=ax.transAxes,
-            ^4$size='xx-large')
-
-    ax.text(right, top, 'right bottom',
-            ^4$horizontalalignment='right',
-            ^4$verticalalignment='bottom',
-            ^4$transform=ax.transAxes,
-            ^4$size='xx-large')
-
-    ax.text(right, top, 'right top',
-            ^4$horizontalalignment='right',
-            ^4$verticalalignment='top',
-            ^4$transform=ax.transAxes,
-            ^4$size='xx-large')
-
-    ax.text(right, bottom, 'center top',
-            ^4$horizontalalignment='center',
-            ^4$verticalalignment='top',
-            ^4$transform=ax.transAxes,
-            ^4$size='xx-large')
-
-    ax.text(left, 0.5*(bottom+top), 'right center',
-            ^4$horizontalalignment='right',
-            ^4$verticalalignment='center',
-            ^4$rotation='vertical',
-            ^4$transform=ax.transAxes,
-            ^4$size='xx-large')
-
-    ax.text(left, 0.5*(bottom+top), 'left center',
-            ^4$horizontalalignment='left',
-            ^4$verticalalignment='center',
-            ^4$rotation='vertical',
-            ^4$transform=ax.transAxes,
-            ^4$size='xx-large')
-
-    ax.text(0.5*(left+right), 0.5*(bottom+top), 'middle',
-            ^4$horizontalalignment='center',
-            ^4$verticalalignment='center',
-            ^4$fontsize=20, color='red',
-            ^4$transform=ax.transAxes)
-
-    ax.text(right, 0.5*(bottom+top), 'centered',
-            ^4$horizontalalignment='center',
-            ^4$verticalalignment='center',
-            ^4$rotation='vertical',
-            ^4$transform=ax.transAxes,
-            ^4$size='xx-large')
-
-    ax.text(left, top, 'rotated\nwith newlines',
-            ^4$horizontalalignment='center',
-            ^4$verticalalignment='center',
-            ^4$rotation=45,
-            ^4$transform=ax.transAxes,
-            ^4$size='xx-large')
-
-    ax.set_axis_off()
-    plt.show()
-
-<button>Run ...</button>
-
-
-.. image:: 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PZVdeUs7rlGtUWsNq6qf1TVUd3Y+n1pIf/+3R8VVNVbu30vAN6V5JVVdWa3z3Avjc4VMzxuLlVk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%0A/g8NfX/IA8ubvAAAAABJRU5ErkJggg==%0A
-
-
-注释文本
---------------
-
-
-text() 函数在 Axes 对象的指定位置添加文本，而 annotate() 则是对某一点添加注释文本，需要考虑两个位置：一是注释点的坐标 xy ，二是注释文本的位置坐标 xytext：
-
-
-
-
-
-::
-
-    fig = plt.figure()
-    ax = fig.add_subplot(111)
-
-    t = np.arange(0.0, 5.0, 0.01)
-    s = np.cos(2*np.pi*t)
-    line, = ax.plot(t, s, lw=2)
-
-    ax.annotate('local max', xy=(2, 1), xytext=(3, 1.5),
-                ^4$arrowprops=dict(facecolor='black', shrink=0.05),
-                ^4$)
-
-    ax.set_ylim(-2,2)
-    plt.show()
-
-
-<button>Run ...</button>
-
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-.. image:: 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7jnHjnnc881HdmpU8yjR8v748a5a5+T3HWXnPMFFzAfOiTvVVQwDx8u70+Y4K59TnLrrXLO/fuL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%0ARVGUgPP/Qde6gvF4TtQAAAAASUVORK5CYII=%0A
-
-
-
-在上面的例子中，两个左边使用的都是原始数据的坐标系，不过我们还可以通过 xycoords 和 textcoords 来设置坐标系（默认是 'data'）：
-
-
-
-
-+--------------------+--------------------------------------------------------------------------------+
-| 参数               | 坐标系                                                                         |
-+====================+================================================================================+
-|‘figure points’   | points from the lower left corner of the figure                                |    
-+--------------------+--------------------------------------------------------------------------------+
-|‘figure pixels’   |pixels from the lower left corner of the figure                                 |
-+--------------------+--------------------------------------------------------------------------------+
-|‘figure fraction   |0,0 is lower left of figure and 1,1 is upper right                              |
-+--------------------+--------------------------------------------------------------------------------+
-|‘axes points       |points from lower left corner of axes                                           |         
-+--------------------+--------------------------------------------------------------------------------+
-|‘axes pixels’     |pixels from lower left corner of axes                                           |
-+--------------------+--------------------------------------------------------------------------------+
-|clip_path	         |a Path instance and a Transform instance, a Patch                               |
-+--------------------+--------------------------------------------------------------------------------+
-|‘axes fraction’   |0,0 is lower left of axes and 1,1 is upper right                                |
-+--------------------+--------------------------------------------------------------------------------+
-
-
-
-
-
-::
-
-    fig = plt.figure()
-    ax = fig.add_subplot(111)
-
-    t = np.arange(0.0, 5.0, 0.01)
-    s = np.cos(2*np.pi*t)
-    line, = ax.plot(t, s, lw=2)
-
-    ax.annotate('local max', xy=(3, 1),  xycoords='data',
-                ^4$xytext=(0.8, 0.95), textcoords='axes fraction',
-                ^4$arrowprops=dict(facecolor='black', shrink=0.05),
-                ^4$horizontalalignment='right',     verticalalignment='top',
-                ^4$)
-
-    ax.set_ylim(-2,2)
-    plt.show()
-
-
-<button>Run ...</button>
-
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-.. image:: 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meesUoi71FRwdyxY8u1kdpa5osvljIvv+ysfU5y7Bhzerqc54YN0ctUV0tIB2B+6y1n7XOSgwfN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%0A/wdcAmhbrixx1QAAAABJRU5ErkJggg==%0A
-
-
-极坐标系注释文本
----------------------
-
-
-产生极坐标系需要在 subplot 的参数中设置 polar=True：
-
-
-
-
-::
-
-    fig = plt.figure()
-    ax = fig.add_subplot(111, polar=True)
-    r = np.arange(0,1,0.001)
-    theta = 2*2*np.pi*r
-    line, = ax.plot(theta, r, color='#ee8d18', lw=3)
-
-    ind = 800
-    thisr, thistheta = r[ind], theta[ind]
-    ax.plot([thistheta], [thisr], 'o')
-    ax.annotate('a polar annotation',
-                ^4$xy=(thistheta, thisr),  # theta, radius
-                ^4$xytext=(0.05, 0.05),    # fraction, fraction
-                ^4$textcoords='figure fraction',
-                ^4$arrowprops=dict(facecolor='black', shrink=0.05),
-                ^4$horizontalalignment='left',
-                ^4$verticalalignment='bottom',
-                ^4$)
-    plt.show()
-
-<button>Run ...</button>
-
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-.. image:: 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/0TQQiZpFKpVv/+++/ybt26RU2O8vJy6HQ6iMXiqMnABY1hTbUhtm/fjt69e7sUXTTIycnBkCFD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%0A
-
-
-
-作者 & 更新时间
-------------------------------------
-作者:`李金  <lijinwithyou@gmail.com>`
-
-提交: 2017/12/14
-
diff --git a/docs/sources/aipy-numpy/cn/4text-math.rst.txt b/docs/sources/aipy-numpy/cn/4text-math.rst.txt
deleted file mode 100644
index 8f2b94d..0000000
--- a/docs/sources/aipy-numpy/cn/4text-math.rst.txt
+++ /dev/null
@@ -1,221 +0,0 @@
-
-处理文本（数学表达式）
-===========================
-
-
-在字符串中使用一对 $$ 符号可以利用 Tex 语法打出数学表达式，而且并不需要预先安装 Tex。在使用时我们通常加上 r 标记表示它是一个原始字符串（raw string）
-
-
-
-
-::
-
-    import matplotlib.pyplot as plt
-    import numpy as np
-    %matplotlib inline
-
-
-
-
-::
-
-    # plain text
-    plt.title('alpha > beta')
-
-    plt.show()
-
-<button>Run ...</button>
-
-
-.. image:: data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAXcAAAEKCAYAAADpfBXhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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%0A
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-::
-
-    # math text
-    plt.title(r'$\alpha > \beta$')
-
-    plt.show()
-
-
-<button>Run ...</button>
-
-
-.. image:: 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WD8JupNwFJVrfWfZc9l06zF6xndKwGj66vvSHK4qm5bzBQXYpp1OAg8XlVPAU8l+SZwAdAt7tOs%0AxR8CnwKoqn9L8u/AKxndf/N8M3M3531Z5uhNT0lOZXTT0+ofztuA98PRO2CPe9NTAxPXIslLgVuB%0A91bVgS2Y46JMXIuqenlVvayqXsbouvtHmoUdpvv5+Efgj5JsS3IaozfPfrDgeS7CNGuxD7gEYHx9%0A+ZXADxc6y5PHzN2c65l7edPTUdOsBfBJ4MXADeMz1sNVdeFWzXmzTLkW7U3587EvyR3Ag8DTwE1V%0A1S7uU35PfBq4JckDjE5EP1ZVP92ySW+iJF8C3grsSHIQuI7RJbp1d9ObmCSpIf83e5LUkHGXpIaM%0AuyQ1ZNwlqSHjLkkNGXdJasi4S1JDxl2SGvp/zy4/4DuVo0MAAAAASUVORK5CYII=%0A
-
-
-上下标
--------------
-
-
-使用 _ 和 ^ 表示上下标：
-
-
-$\alpha_i &gt; \beta_i$：
-
-r'$\alpha_i > \beta_i$'
-
-$\sum\limits_{i=0}^\infty x_i$：
-
-r'$\sum_{i=0}^\infty x_i$'
-
-注：
-
-- 希腊字母和特殊符号可以用 '\ + 对应的名字' 来显示
-- {} 中的内容属于一个部分；要打出花括号是需要使用 \{\}
-
-
-分数，二项式系数，stacked numbers
------------------------------------------
-
-
-$\frac{3}{4}, \binom{3}{4}, \stackrel{3}{4}$：
-
-r'$\frac{3}{4}, \binom{3}{4}, \stackrel{3}{4}$'
-
-
-$\frac{5 - \frac{1}{x}}{4}$：
-
-r'$\frac{5 - \frac{1}{x}}{4}$'
-
-
-在 Tex 语言中，括号始终是默认的大小，如果要使括号大小与括号内部的大小对应，可以使用 \left 和 \right 选项：
-
-$(\frac{5 - \frac{1}{x}}{4})$
-
-r'$(\frac{5 - \frac{1}{x}}{4})$'
-
-$\left(\frac{5 - \frac{1}{x}}{4}\right)$：
-
-r'$\left(\frac{5 - \frac{1}{x}}{4}\right)$'
-
-
-
-根号
------------
-
-
-$\sqrt{2}$：
-
-r'$\sqrt{2}$'
-
-$\sqrt[3]{x}$：
-
-r'$\sqrt[3]{x}$'
-
-
-特殊字体
----------------
-
-
-默认显示的字体是斜体，不过可以使用以下方法显示不同的字体：
-
-
-
-+--------------------------+--------------------------------------------------------------------------+
-| 命令	                 |显示                                                                      |
-+==========================+==========================================================================+
-|\mathrm{Roman}	           | $\mathrm{Roman}$                                                         |    
-+--------------------------+--------------------------------------------------------------------------+
-| \mathit{Italic}	       |$\mathit{Italic}$                                                         |
-+--------------------------+--------------------------------------------------------------------------+
-| \mathtt{Typewriter}	   |$\mathtt{Typewriter}$                                                     |
-+--------------------------+--------------------------------------------------------------------------+
-| \mathcal{CALLIGRAPHY}	   |$\mathcal{CALLIGRAPHY}$                                                   |         
-+--------------------------+--------------------------------------------------------------------------+
-| \mathbb{blackboard}	   |$\mathbb{blackboard}$                                                     |
-+--------------------------+--------------------------------------------------------------------------+
-|\mathfrak{Fraktur}	       |$\mathfrak{Fraktur}$                                                      |
-+--------------------------+--------------------------------------------------------------------------+
-| \mathsf{sansserif}	   |$\mathsf{sansserif}$                                                      |
-+--------------------------+--------------------------------------------------------------------------+
-
-
-
-$s(t) = \mathcal{A}\ \sin(2 \omega t)$：
-s(t) = \mathcal{A}\ \sin(2 \omega t)
-
-
-
-
-- Tex 语法默认忽略空格，要打出空格使用 '\ '
-- \sin 默认显示为 Roman 字体
-
-
-
-音调
-------------
-
-+--------------------------+--------------------------------------------------------------------------+
-|命令	                     |结果                                                                      |
-+==========================+==========================================================================+
-|\acute a	               | $\acute a$                                                               |    
-+--------------------------+--------------------------------------------------------------------------+
-|\breve a		           |$\breve a$                                                                |
-+--------------------------+--------------------------------------------------------------------------+
-|\ddot a		           |$\ddot a$                                                                 |
-+--------------------------+--------------------------------------------------------------------------+
-|\dot a		               |$\dot a$                                                                  |         
-+--------------------------+--------------------------------------------------------------------------+
-|\tilde a		           |$\tilde a$                                                                |
-+--------------------------+--------------------------------------------------------------------------+
-|\4vec a		           |$\vec a$                                                                  |
-+--------------------------+--------------------------------------------------------------------------+
-|\overline{abc}		       |$\overline{abc}$                                                          |
-+--------------------------+--------------------------------------------------------------------------+
-|\widehat{xyz}		       |$\widehat{xyz}$                                                           |
-+--------------------------+--------------------------------------------------------------------------+
-|\widetilde{xyz}	       |$\widetilde{xyz}$                                                         |
-+--------------------------+--------------------------------------------------------------------------+
-
-
-
-
-特殊字符表
---------------
-
-
-参见：http://matplotlib.org/users/mathtext.html#symbols
-
-
-
-**例子**
-
-
-
-
-::
-
-    import numpy as np
-    import matplotlib.pyplot as plt
-    t = np.arange(0.0, 2.0, 0.01)
-    s = np.sin(2*np.pi*t)
-
-    plt.plot(t,s)
-    plt.title(r'$\alpha_i > \beta_i$', fontsize=20)
-    plt.text(1, -0.6, r'$\sum_{i=0}^\infty x_i$', fontsize=20)
-    plt.text(0.6, 0.6, r'$\mathcal{A}\ \mathrm{sin}(2 \omega t)$',
-             ^4$fontsize=20)
-    plt.xlabel('time (s)')
-    plt.ylabel('volts (mV)')
-    plt.show()
-
-
-
-<button>Run ...</button>
-
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-.. image:: 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k2ZnsOWc2PHjqVJLj3N06dPZ/Xq1Zx88smZj/3xxx9s3ryZRo0aZT526NAh+vTpk+fvcscdd1C3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%0AoFnSMMYYE7T/Bxi7FV50SWh9AAAAAElFTkSuQmCC%0A
-
-
-作者 & 更新时间
-------------------------------------
-作者:`李金  <lijinwithyou@gmail.com>`
-
-提交: 2017/12/14
-
-
diff --git a/docs/sources/aipy-numpy/cn/5image-base.rst.txt b/docs/sources/aipy-numpy/cn/5image-base.rst.txt
deleted file mode 100644
index 101a260..0000000
--- a/docs/sources/aipy-numpy/cn/5image-base.rst.txt
+++ /dev/null
@@ -1,228 +0,0 @@
-
-图像基础
------------------
-
-
-导入相应的包：
-
-
-
-
-::
-
-    import matplotlib.pyplot as plt
-    import matplotlib.image as mpimg
-    import numpy as np
-    %matplotlib inline
-
-<button>Run ...</button>
-
-.. image:: https://github.com/riverfor/notes-python/raw/9e0c173527920683229678ba0e4482a74d74618a/06-matplotlib/stinkbug.png
-
-
-
-导入图像
-------------
-
-
-我们首先导入上面的图像，注意 matplotlib 默认只支持 PNG 格式的图像，我们可以使用 mpimg.imread 方法读入这幅图像：
-
-
-
-
-::
-
-    img = mpimg.imread('stinkbug.png')
-
-
-
-
-::
-
-    img.shape
-
-
-结果:
-::
-
-    (375L, 500L, 3L)
-
-这是一个 375 x 500 x 3 的 RGB 图像，并且每个像素使用 uint8 分别表示 RGB 三个通道的值。不过在处理的时候，matplotlib 将它们的值归一化到 0.0~1.0 之间：
-
-
-
-
-::
-
-    img.dtype
-
-
-结果:
-::
-
-    dtype('float32')
-
-
-显示图像
--------------------
-
-使用 plt.imshow() 可以显示图像：
-
-
-
-
-::
-
-    imgplot = plt.imshow(img)
-
-
-.. image:: 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s58mYDr0pYCJBteClktY00A/HSSJOfJuT8FcF+SJP+3c+5PLhz/Kc8ogSo/L0kMwHLni2VCMYBA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%0AdJpO02l6yukUWE/TaTpNp+kpp/8PTYoQ8rA9sPAAAAAASUVORK5CYII=%0A
-
-
-
-伪彩色图像
------------------
-
-
-从单通道模拟彩色图像：
-
-
-
-
-::
-
-
-    lum_img = img[:,:,0]
-    imgplot = plt.imshow(lum_img)
-
-
-<button>Run ...</button>
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-.. image:: 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qjn8jet3KgxHzwsbhuw0plEMJxgAdevF+3A60lORn/T5CePso79cIigq+E5ljvU4GXqlXXTwrfR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%0A2+yGbQPWzTbbbLMbtv8GmbHHpC2s7ygAAAAASUVORK5CYII=%0A
-
-
-改变 colormap
-------------------
-
-
-
-::
-
-    imgplot = plt.imshow(lum_img)
-    imgplot.set_cmap('hot')
-
-
-
-.. image:: 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HXomfFqrFQ20WfW4Qs857Ial3zbVWPX0JeLoWNFyYe36/FoAQJ3I9N4paH0tjw9Y/w+7r5/4OkJ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%0A5bbcltslbsvAutyW23Jbbpe4/X9nGwcxiuGaUwAAAABJRU5ErkJggg==%0A
-
-
-
-
-::
-
-    imgplot = plt.imshow(lum_img)
-    imgplot.set_cmap('spectral')
-
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-.. image:: 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HUB4GvrGEqX2Nsha5Isvi5GPXjANFE+n35fBdbrUfrIAOO+qNWhgaXGwXEFiaubIhkwGqQFrD6Y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%0A
-
-显示色度条：
-
-
-
-
-::
-
-    imgplot = plt.imshow(lum_img)
-    imgplot.set_cmap('spectral')
-    plt.colorbar()
-    plt.show()
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-.. image:: 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pNdyCo1YsSOEONVbx4yV5ZnyNAFlk3YatDrKHW1CFklwHuUQcYrKokU1wCA188BecpOkFIVRbCS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%0ARK5CYII=%0A
-
-
-限制显示范围
--------------------
-
-
-先查看直方图：
-
-
-
-
-::
-
-    plt.hist(lum_img.flatten(), 256, range=(0.0,1.0), fc='k', ec='k')
-    plt.show()
-
-
-.. image:: data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAYYAAAEACAYAAAC3adEgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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%0A
-
-
-将显示范围设为 0.0-0.7：
-
-
-
-
-::
-
-    imgplot = plt.imshow(lum_img)
-    imgplot.set_clim(0.0,0.7)
-
-
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-.. image:: 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XfOgoy5pEL9HlMvBgdPYu4k31egjNtKIe1FSeFHa2+Eex4bLkxCNG0ZF26X32X+4rxTUTIIZCUn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%0AbtWA5gcAAAAASUVORK5CYII=%0A
-
-
-
-resize 操作
------------------
-
-
-
-
-::
-
-    from PIL import Image
-    img = Image.open('stinkbug.png')
-    rsize = img.resize((img.size[0]/10,img.size[1]/10))
-    rsizeArr = np.asarray(rsize) 
-    imgplot = plt.imshow(rsizeArr)
-
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-.. image:: 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3fv3sUrr7yCo6MjrK2tYXNzEzs7OxPwjOizCEA5RGZpso8hEV9ljzq3xs6i1F1rGWcClj1AQd73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%0AgPz/QCjzUOP0/xAAAAAASUVORK5CYII=%0A
-
-上面我们将这个图像使用 PIL 的 Image 对象导入，并将其 resize 为原来的 1/100，可以看到很多细节都丢失了。
-
-
-在画图时，由于画面的大小与实际像素的大小可能不一致，所以不一致的地方会进行插值处理，尝试一下不同的插值方法：
-
-
-
-
-::
-
-    imgplot = plt.imshow(rsizeArr)
-    imgplot.set_interpolation('nearest')
-
-.. image:: data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAUsAAAD9CAYAAAA1U1VCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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%0AhP8H0Tz8GZcurRgAAAAASUVORK5CYII=%0A
-
-
-
-
-
-::
-
-    imgplot = plt.imshow(rsizeArr)
-    imgplot.set_interpolation('bicubic')
-
-.. image:: 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WhYBGKR44iT2tqmNdRIKzT3m1TltWvXTn6TG84Vp6YwvfJ77doCpp5LLy4K3O97qscKW41wQ6Vd%0AS2DkteC1u1ddRljMDY9mTQvZDm5Vo72w1MB5hKVQ+GOM9Fs+wXP58uVT7rgVu5wamN8aYbZCkqO2%0AQAacjRHTPgvZvmlVlXPF/3g/UJ6RWCJ2uegCzyh4inJpFzxTfou9vTb0TEISVAa/WKWqpP382XBL%0AVUYX5XmeaFrAiZJuyeJ3FgBn+5TOwejwxtqr3lq9S42DqZPvphd4RiKz4q4heyJ7SXkt0ogC3Vwp%0AWiqI0miqMhM/WyOWvGRdXE0RSfJ7VykGTHZReINCHfIcaf1rfctt7bdlb287Rxz31iXWxqIxS+mK%0AW99eGRGihaBWtLrdPSpyBGH2EE+kwjlZesc191srt8f2ka741D7uyc9VJT1vT5+Dg4MzcWCpNgGc%0AhET4Amim3hFplihjzvJGYpPKsmcFPdo3ClPch7kJk8rJwJuYuC2RArXK0myJyrH6wFsFzmJk30b2%0AyAmGkyV/3p6/nITiv/LNTvS8vSyX16+pSsv2uY6PzrdFLL7As1RZS7h4c8ci10KGwGQ6ieyFbBHz%0ASNKce5HPOi4Jk5MlfQ4ODgDoz9vTK+8yJNjqfi+hPOckSq+Nme0eLK4se13w3gE/N3G1EuYS6nIk%0Ael1P+ra2edncjc/Ul4mNaml7MSUUoC3u8NffEVnu7e2hlLPP2x8dHYWLZ1OJsjW2mcFFUpSEza2G%0Az0EWWyOhXsLcWjs0SHK0PgS5UET7sm311OZob2aE0pLP2fNHSLXn7ymfp8y17TUxtx1rtXPRJ3gy%0AatLLNwWtbnnrws5SGBnb7Im5Au0xZY0cqBwiSevvKbJYw3uQaaJ9ckGMxyStuwpa6uuxaaSq7Mnr%0A5bE8sxH19mATT/D0umCElgs/ShvN4EuQZsbGpQlTKhhvIEsVyd1PIkwAZwhDEial2QKmLJ4QKB7J%0AF3P4ivfe3p77NNQIGzwsSZRzxUznJM6QLEspPwngHwH4dK31i4/3PR/AzwF4KYCnAHxtrfUzQTnq%0Ad6L+VLoWMtuCuzKVEEcp3ymqKZOPx+r4f/lQfiIP2Y4tEmYGllKTTz1R+/lfddCz9toTUVF4pmff%0AqOugpZwtXHu9yLzP8qcAvF7s+04AN2utjwJ43/HvLmjkOeXi0OJic2OuulrakY0RZvLKfV6+TLny%0Ahmx+ryH/jx9+s7b1WODWEC2uSLeb/rvo2rVreOCBB/C85z3v5HPt2jVcvXpVfZuTVffa/dIztpas%0AfyRCZVlr/fVSysNi9xsAvPp4++0AnoRDmGspg1Hu6lzI2jeiHb0ud2+5nHQtdUkEYhG1RhhbPZ+e%0AapNxWfrN76uUtwxJV7zFJR9h/4g8axO5xNRFut6Y5YO11mePt58F8KCV0FvgaUXvbSxrkjXQ99x4%0AT1lLlOGVG6WRShPACVnyxR/+JAuRxnlxy62+kKv93vP2RKyWqtwCSZ0HG0Zj8gJPrbWWUlSr3/ve%0A955s37hxAy9/+ctPHe9ZjV3zQpkSF/TySxUypawR9swBeW8kJ07uph8eHp7E7Y6Ojk4WPYD7z6uv%0APQ5aweOwlpqWK+W9RDnFxhGLLksRWE9MVstz+/Zt3Lp1K1VnL1k+W0p5Ya31mVLKiwB8Wkv0+tff%0AD3WuObhHXFwZEhtBdNmFmxaCzdZload87kXIv6Hg9xTWeu/JFkpHcUt6ubCM+22ZKL3+5PZTWutc%0AS6JscR8jG+S4Gbk4s6aa7K37xo0buH79+snvJ554wkzbS5bvAvAmAD98/P3OznLOBXpifRlCbF25%0An5M4sza01CEXN8j15B9+OxFfBKFYHnD6DUhbJk2PfKStfJXbShOVKTGVrKbE9OYmytby5+izzK1D%0AP4N7izkvKKU8DeB7APwQgJ8vpbwZx7cOBWWYhlquhnXLRO9F0pNvhKvrldNKcq3KdC4y8eqQxyg+%0AJ1dF+f2W5H7TOeLbRJ6yDI1sJLZCpt6dHllvJEIvWU1Vm1PqHlF2xh2X+3rtzayGv9E49NoorzVT%0AGvVsZnCPRitxcnhxq1EXmoVW5auBFjX4a8b4Ig9dnHzRp9Z64pLL24ha6p5jTEmSnrrC6mEOotTs%0AHUGYc2AUUWbzR3k3+Yo2YBvqUiM5rYwWN3Wkmtyq+y3T85VgTpYHBwenVrqpPusZ6d42jOobTSFK%0A0tSIpxUj44hzY42JojVumVGVmXZsliwlRhHmlPq17SgtIbOyOYVkR12gLYtPWfDbfsid5ivdBwcH%0Ap9xs6w3sUxeyepW4l8+KPc4d+5u7/DXV5VxE2VMfx6bIMiLEEYSZvXBGo9cV73HDpyqxjA1ZcMXF%0AnwHn/xDJY5R0nP5il99nKRd5Wtvqxcgz+ehbjkFqp/a7xbY50k7JswbmJMqpcctNkaWGaDBmGr2G%0AC+thlKKc6iZnMYJAKT0nTHqChUiI//8M3TpE6pMT5ui2aMe0ftdCMT2k3WJTS5op6beApRVlSzpg%0AA2SZVYtTVGWU1uswTUW0oGeRJFKMcyhKC6PrIoUp3wJO916SG07PUF+9evVEYUpVORLcFhkn5fV6%0AhN2rdnuOjUjfmm8u17wnrNW6f6qqBFYgywzJrUGYXr4p6ImDtcQno7KmYmRdXI0RWdJvfm8lkSm5%0A6SOVpQYiR/rIhSUiSf73tLw9tD2K3LZGknNgqvIb1XctWF1ZWrBU1JQY0VrxSg2j4pNWWVMxQlF6%0ACow//8xVJXD/z7pIUWovlfAultY7HjhJ8hvlJVmSHfy1clMXnbL7W8uZmmcNBZlNvwZRAjOTpUd4%0AWYVoqSytDAtRrM9Dqxs+hYg9YlxSTY6qL1LVnCzleZaPR2rnwXKtspOiJEr+GjkiTG4Pf06d7G/F%0ACLd7LoKcUv7o8kbaMKqsRZSlNngtsssoqFZV2euC8zrmSN8bm/TqWXKRZ0q9nDA1YpYxwp6+8MIZ%0Aklz5/3rTOzf5Kj3/IzHNzgij43IZLOXOjy6zZ0JZIqSwqBueUZRZophKtFtApN56bB4xaEaoyEwZ%0AckWZ79du0dEWUKaoMc395n9Ve3BwcIosr1y5csY+7o73XsjRgkQLtkCQvWVvPfa6uBuedcEzRNJz%0AK80WSZOQ7QuJ0W3xFFlrGVp+r8wWBTniIpFuOL29nd7gTi/4oNuaZPySfmdtnqNNa7u5veVvidgz%0AWMUNH6UMo3wtMcC5QIpjzlX7OSeAqbHLlvZYE+Jc7qEXsyTSJLKstZ6QJKUjwuT936J6s0S5xfjd%0AVmxYsj2zK0vPlfKUYQsBRvnWVJPaYoRERjW31LXkrUQcU+vtHRNTYREnf9nHpUuXTr1OjqfXbG2N%0Au82hmucs76KgZTLO/GHZJEwZUDKvPJ6pL3tsTXjxtN7yMqom6uNR9bYQgbTLGi88btiy0BLV75Xt%0ATeA7osxhjbikFhP3fltYbIHHUpW9Ljjf7x2LXPCtxC574pWZuwB4usxFvFR/TAlNWAtDltuemTz5%0A6jvdJkTlUczS+j/vqJ7IhmwZPdgSUW4ZGYW5GTd8Cmlq5WXLnRqTWxNZwmm5YHpDAD1ltBK0R5ic%0AKKOBbx3TiJIv8NDTRBFpZuqaojDP0xjdCuSY0MZI1K+zK8vWC6I3bhnVlbVjzpl4rgWYuVbDR5br%0A2an1uZWWu8OSPFsIiGKT8tFGesyStslu/vhlliznIMroGGFtQrXOR8+CXeRd9V6zrXlXfTY8utCz%0AqrCVNNcaSJn2rD3IOdbuK8vd5vsyioGXyT/8/kp+bujJItrHFSf/b28rjrmFmCUPIYwsU8Lr77nu%0AZOitx1KTWRvPdcxSKzuTb+n43NyYk9S2RuASrfFJSZLyT9NqrademkFxTPlcuHyyqNf9nztmSViK%0AuDL1ttqSSb8EYW4qZtm6YOHVlTm2FhHM5TpfVMLUJlcrnfXNiVI+A85fnMEXejhByhd/aKpyqus8%0AEpYa77Vjiv0jCLO3Hisd0L7Qu0jMsnfVs6cuYOwCUFTfSBJZamFlrbJby7Am1CgWyI9rMUr6b/L9%0A/f2TZ8C118NJspRvbP9cwBIx/F6CG1FmS/vC+yxLKT9ZSnm2lPIhtu97SymfKKV88Pjz+nSNDBm3%0AJJuvtQwtDw/0W8dkOmv/WpjThmy53uLMlLo1V9r6czPrHPHnv+/cuYPnnnsOn/3sZ/HZz34Wzz33%0AHO7cuXOGQAGYRJklzvNGri3jKNMH3nHKP7IfeZny01t3Rln+FIB/D+Cn2b4K4C211rekLOcZk+72%0AHComW+5UslnbbSUbIixtY0+/SFeJE5iE5RZLF5xU5Z07d8xnwPktRLz80aqyZYFqKfTWL70A67hX%0AfjbN1LCAVkd0XjP/G/7rpZSHtTqzxs0RT9wCIXmY4t4vhTlsnLO9kdqxJkgZq6QXZvCXZhBZHh0d%0AnXq0sUVdjVqEmBNL2eDVk+0rgkW8U9sR1SEx5XHHbyml/F4p5W2llL85oRwTIxTe6DJ3sLEUUWqu%0AuBUqken4wg4pTIphcjLt+d/yXjdScw2nYCsTtKfCWxS6lXb0JD9ZWRr4MQDff7z9AwB+BMCbZaIn%0AnnjiZPvGjRu4fv26WtjWVeIULLnAdR4w1RX34sqa2pBEKW8XkuXxi0Z+y20NvQpzlDueJew53dyW%0A8rxF10w5VuglG267desWbt++HaYFOsmy1vpp2i6l/ASAd2vpHn/8cbB0p4xck0C2QEpe+AHYjjqY%0AA5mYVSaf7Cvep5b7LW8T4v80yVfBtf8rzyLj3s3htrfaODV22IKWtlgEmLGrtQ8effRRPProoyf7%0A3vOe95jpu8iylPKiWuunjn9+DYAPeemnojeuGeUlzE1MPfa3Bp97sRYpW+1rUTzaBchddc31lv+t%0AQ/8cCeDM/5XTfZVT4LUr056R5BDVnyl/joWVnnyjyTyDkCxLKT8D4NUAXlBKeRrAvwbwmlLKK3Bv%0AVfxjAL5xVitnRu/Ja62jl/ApXW/dFlrL6m2391vbr7m/0g6vP6Si5PdVcrLkZEjESf9Xbj0D3uuV%0AWMSUdb/nnCxHqdeRsd3sanrrIs0UZFbD36js/skZbIns6B4wo93u3vKmEuaUupeAdIP5frk4o4ET%0Al0WYdHHLi9xSlPwPyOS/NmqPMRJhSmWpxTY123tc21Hxyl6MUo8j7fZIMDOhWJPpFPs2+7/hGjSi%0AaFFmWyCZEYQ5AnOpSk1JEoHxbZmWCJI+/P9ttJghJ0z+0YiS/nzs4ODgzPPfnCD5/5RrREm3Fck+%0A4bb1xivnIMxe1ZUJbXl5RxN9jxvueSWEVjvPFVluCXMo3a0S/5R2ak/a8NtyePn8kULtZRUaYWok%0AKf9Hh8iSVKVUlFeuXDnldnNC5CQvX64R9U1LvFLb16KKonM0MsYXEc5c8cSemK6FHtsuBFmuFfc7%0Ar4S5BNFKIrNeXEHtIAIiVVdrxd7e3im1ye3XyufxSXoyhx5fpKd0qFwAJ+VzhckvPr5qLkk2iqty%0AZN1vL222rowtI0msdUKw8ktEijHrmreUHeHckaVHMkB/8NhC1iVZI4a5ldACoA9iSZTyBnDt2Wty%0AiXm/StdX1iGfyiFFSY8zEnFK4pN1EClLBczt4yROpM7bH/XRlPjbKGTL9q4zLW0rYfaqcqvcDGFe%0AiJjlqAt/LfXYW+/Uds9NmC3KiUNTlZzMePyQqzt5a49824+sg79JiLvdd+7cOfncvXv3lAtOhMdf%0A+ivtlgqY7ANOE2dP7LfVlVyKOFvT9xB/K0Yp4RFlbIYsW5AZpF7sZ476ZL0tdWrlz0WCo8v0yuOu%0Asqb+iOg4WUqiJLec0slyiTD57UFEks8999yJqiRi5OVRfm4vgDPxT37s0qVL5mOQWSWWJZUpxDOa%0AVDXMsZjTUk+2/lE2rkaWUwlirrSjytiSizwHWvqeyEVbhNFIiNxh+VZySkflcrLk6pIUplSxlF+S%0ALZVBF5984ocg87b0U4/LOEVRLkVkW8dIMl3lbyXWKHMN8ppq32ibp7hc2TK0FV2tXVzF0T5Kv7+/%0Aj0uXLp188zKoTzSylIs88s1B3KXnBMxjk7w8TXX2ojfG1pN2Sp45MdLbi8rXfkfpI6zqhlsEAfS5%0AG3MuxqyFKYQ5NVY0tTwiOR571NxgTpyHh4dniJJwdHR0ZsVaU4hypZ3K0f6hkcrhZKmNKU7a/NPS%0ALz3xyt60Xp2jMEW19Vy3LfX3HvOweszSIoMeksiegN7AfCuWIOURpDZHmZxQ+NMxXMXRca7iyA2W%0AipQTIRE9AUeKAAAgAElEQVQdJzZJmlS+dk/ltWvXTgiTyETaANx3uyXRa4tNc/S5dVH3kOZoaHW3%0AKjsrXQ/BZ72AKX22KFn2xP0Io1eZW1TmEsTaU9aaRGnVw/udK0rtdiD6UEyRSJQUJi9TKlFamdbU%0AJYE/BSSf+5ZkqRE0V5Pyf3mW+C+eUapwtLocSZTZ8nvSTEmvYdX/Dff2afk4MoN06TjlUnVZ9cxR%0Af6uCoguTu990Ezgdl+55KQUHBwcA7j8xQ6vXmmvNXWyNSDVlSW8TunLlyglxyvzcHi10IB+FXDuc%0As3RMMusOz22TV98odathFTd8BIktFX9siUeNrmeqyhthQ089nDApLsiflpGkZMUdtYUWqfwovfV2%0Acx4GsJ77JlVJtnok2fsvj1tzmUeUkSGmKJQw1Q6LKFvanE27esyS0Eugcy6ALEWU2TJbiDKrvHvK%0AzkAufmiLIkRqtIrNCcxacOFP8xBZ8r+JkLf8aOqLyuft4+mk+91LlL0kNVINTSXKLAG2KrqpaxLy%0Ady9RtmAzZLm2SyMxRyxzShlZd7jFpiVUOS2QyMFMcUd5Gw9XlvQbwKlVckpL5cr7KuneSgBnFpNo%0An3wRBhGjVMUyvuq9BakHcyrOnrJbF0da1KSWrleZjyLKlvSbIctetC6ATD024nhL3qVd8dHQ3Gbt%0ANhwCDV4+iPl9lESYRHh0Mzp/BpzIkgiZu/iHh4cnb0eXsUmpQrNE2YqlY3pT6x9NkjJPq0JvIcpo%0AraPlXK5Clmtf2Gu77dn8c6jJKRgRa5buOZWrfcu6iTA5qR0dHZ16Jpy/ko2ImbvmtMCjvfhX3o40%0AAnMRY3axZYotW1nQserqUZdT1jo2oSznIK9eYulVbSPVpLavxy7u2m4NkU0y1kmQz2zT8+acKLmy%0ALKWceXyRbgGiVXGqR7OpVUku0detRDkqttmqJq3+bEGmTUvEK4GNkGUP5iDKnjyZ45ErEB1rJUqL%0AaHuUhRab6xnw3kfaKl+fJgmLv9yCPw9Ojznyt6JzsuQr7Px2Jr5oRHXy70z7RqSZghFENqLsORZ2%0AsnbM3ceLk2Wra9lCNJnyWvKNinkutYizlNJsDcxz99pyc3l8kKfhCy3cfu6Wc4KUxMjbK2OU/H2a%0A/Dhte23KtHspzEGUPfmnLHJp/b2GevSwSWXJO27E6u4oopwrZrnlBRtvILcE5vmbhyRpchLjK9L8%0AmKzL+psKT7HKj1zk0bZb+2pptMQPRyz8jFrY0cponcx7CbT32nLJspTyEICfBvD5ACqAH6+1/mgp%0A5fkAfg7ASwE8BeBra62f6bLgbJ1D841w1+dc2BmpaDPoXbHU6vYIU1OTHlHSqnUpp58V50qP5/WU%0AiFSI3k3mfHEn+78/fN8IV3eUW7oGUY5Ea3xyJDLn4FJwfB/At9VavwjAVwD45lLKFwL4TgA3a62P%0AAnjf8e9ZDJxSzpxEaSmeLKy8LWqtBZY73BJPzOyL6tGezOFkRosu9Py2/OdF7xYeiyD539tqZctn%0AvqN7Kb1wgncsOjctimoUUWbrHe16Z7GEWs9ew66yrLU+A+CZ4+2/LKX8IYAXA3gDgFcfJ3s7gCfR%0ASZhTsTRRjiD43nABocU11mZl+d1qn6Y0LRKVBGk9wshvEucLLpKguA0aucm/fOBvPCKSzBJxy7ke%0AdVHLfsnU0Utka8X+smh1reduTzpmWUp5GMCXAPhtAA/WWp89PvQsgAe1PJm41hyYw43tdcW12NkI%0AuyLC9MgrUjKaG6rFkS333KpXI03Kry3iyG/pyls3lnPC5epS/kd4hiinKP3Wyail7JY0U7E0qc6p%0AYltDbBwpsiylfB6AXwDwrbXWvxCKopZS1FbcvHnzZPv69eu4fv162rCkXal9PWVp5JC1odWeKTEr%0AL3bHt+UCi/Y/MpwgvI9Mz0nTcsHl28y1F14Ap/82Qn7z/+uRytJ6KogTMU8rCTKrKK3JyDsHa2Dt%0A+lsxkghbyrp16xZu3bqVShuSZSnlCu4R5Ttqre883v1sKeWFtdZnSikvAvBpLe/jjz+eNPk+5lSj%0AI8tdQzFr8NSdJEoiKfmGHgInC0kwfL88R9Yg1YhSbluErUHWr9kXTR5RP3rbLeWNwBTXcgm3tBc9%0AdrW65Fo+bWzcuHEDL3/5y09+v+c97zHLjVbDC4C3AfhwrfWt7NC7ALwJwA8ff79TyR5iCjGOJqst%0AhgxaYA0mTpT8P2rkvYhki1Rr8sNt9vosUpb8L28jd14qV0nokiQ5AWuuf8stR73QyCp7rjMTUa8N%0ALcdH1DEnMnVn+jwTTgJiZfkqAF8P4PdLKR883vddAH4IwM+XUt6M41uHQosakI2F9cT5onIzWEtV%0AagMjIhhJVPxJF05YAFQ3lWJ6nEw4UfHzoBGWRpTyzeiyLV7c0kor65X/w0P1Xr58GQcHB6cI1yJN%0AqTBbz3vPOOmpY25CnGrDFCxJxFFd0Wr4b8C+vei1nTbJOhYnH4s011KXEXoWETSiIpKkF00QaXEC%0AlAsifDFF3gvJlaal7qSK48pSxiw9AuRleh9ej1a+VKN8gtBeHkz1yolhDkQhCOtcTyHM8+6uL2n/%0AJp/gIWTcsymEF8X7WuqdC73xHU4enCjlOx/5S3I5IUqio39k1GKYVrzQcr/lIg/VrxGmFj+ksIJG%0AxJabLRd/JFlq/zg50i2PMMrb6SHFLRLm1uwBNkKWEflExAWcdQFbBh9PP5WAR6JlwEhSsdxfIkpS%0Al/z2HSJKrTxJlJpCk4SnKUtO3jxm6S3QyHYSWfL4q3xLOhGnjHNKm+Wz5EsSJKEllpmJ0Y2Oby5J%0Apr1jfo70Epv4wzJvPz9OyOTPDCorf8bt2orLbikvjSi5uqRvfjsOgJP4Hn2TC64RjebK8rIAmKpS%0AroZLRamdS672qAz+lnTtZRpaXFU+/piJWU7FkmPFIre5SG9qmdn8vfWMavMmlCUhS0AWcUZue1RG%0AKzm21DcHskSpESZf4OFEQgqSE41cMadveXuR9jcRHlnKuKIs32vb0dHRmbCCJEypkku59y+SZKv2%0A6japLjlx9sYt1xgfPcTYk2cKEWXyzl1+CzZFlkCfC03Q4jZZ4mxRlZ4NGnpDDK3Q3F5Okpq7SspR%0Aa4NGGrxNXFnKF1LQsYgs5b2evHyql7eNq0pSljIGy9vPCYBsohXxvb09VeVaCpOPDWnn5yLmVHpz%0Aq8ie8mcly6kud+tA9NSfhkycM4pnZu2S9WXgzfRajJJ/LGXp3edIdcq4IycqqTKJfA4PD0+5tXSM%0AyE2LKVo3pst2chui0AInTK4IKaxw6dKlk1uIrAUh3lautHl4QZL55xpp9pBNlGeOMnvL1TC7spyi%0AnDKNnOIay7RTXK0MpqjmbPoMUVpP71hv3ZFkxfPxRw95Ov7YIrnMMqZokaXmAmuhBU0tWws1ROAW%0AYWuKXHv7EP+dmWyjY3PB8hTmKntK+rVItBWbc8NbYZHjCFdJI885Bn6rIrbKoG/NDbfIRFu4IVea%0AyrPs02KawH1i4vZo8VIZs/RIkt8aZK18ay49/SaborAA9Zc2YfCYLEdvzHsu9BJlxovpOT66vjls%0AyWARslxjZpX1A9twlTx3v6UMvq2RDLm/miLTVKW2Sszt5a4sr1O67Zw4OdlpKlBzvz2S1Agy68qT%0AbRQusEiSvwCY8u7t7Z2UT7dXcQUuJ1PqF++8zeW5ZPZl01gTT0vZ2fpGE+4cOPfKEsgPvFGkvSb5%0Ae4PDc1tlLE6SnSRK/ode5JLyd0V6F5jlNlur4JZbS4Tvuc2aHZoK1iYRjShpcUrLxwmT18UXyLRF%0ARs22HuJsUWJTSFI7poVJejCVcJckR4lzR5ZbUIdrIOsKaRe3jMdJaKvanDT4Aod8TpzX6cUVJdHx%0AuqkOCYtwLSXJiZyHB2SIwlOuBwcHp/qU59EInt9mlYGcaOVqfStaiLKHQCOSzExW2bxrEmGm/nNF%0AllOJUssvB64FOcinqMuWvC2zP9+vEZqWTypLeRsQpSey1MhDW0XmK9REQlqsVLqyFFeMFDJvh0Y+%0AfBU7Oq+yHhmr5OBhB6nO+b5IWWoLR1kX3to31b3NEmV24s6mWYskW+tdhCxHqMHPNUXZS5IWtAtT%0AW9zRVsUlkREJcfWmESV9pCK0iJLaoCk/L84p2wecVnzSTbYmSF43kSZPI/OSOy7jtvxceGQrF5Gs%0AuGdEgkuRZGu5mfrWVJN8gsrgXCnLNSAv5FHq0quv5ZgW+5IkGH2svPK+Qq6e6FsSG7/vkR5DlItK%0AvGxOwlwVRu63Zjf1h0Zu9C1fPWe9bZ3K0lQn73uaPLR8VqiAhzy0R0SjuGfPRJpN36omp4QQIlu2%0AJpBmJ8utNDhywXvRS5ga8Xppo7IIkuyke83VmUU4slxJGJrq4+R49+7dk20iS24jJ0pSptzl9xZ1%0AuE3cbkttclVJ/eD906P2nzy8Dw4PD0/9tkjWWkzj56LWeircoSntCFNUWlY1ZkMAGZu3oip7MCtZ%0AjiTKOVTc2naMiP0AZ+NdXLnQyjaRDV+gofSyPq6oOElot+1wstzf38edO3dOEaV8Z6ZUpATuzsun%0Ac3idvM3cbuucyInj8uXLuHr1qvohwtTerET18HgpV5Xy5SHaohqlI5Kmftjb2ztzDq029cYRvXJa%0Aj3uk2nJtRG7wVq55wipueG8HLNl5Le730u64lkYqSiJFIkq+yu3dl8hVJCk/vrKt3RAuXW/+FiCp%0AKjU3lruskiy9VXQZGpDl8+N84rh69SquXbt26kNkyV1j7ZySvfwJH6lApTrmkwURpZxAKD9X3VkC%0AHEGUWRKeqgZb44RTMbK+cxezHK3ssqS3JGH2xKqA0yqKXLyjo6MTwuQ3ZPOLlcqWpAXgTBxSI0zt%0AGW0txmipWKqHu/bWY5HUTo1o5H7ax/8Cl1TktWvX8MADD7hkyftFrtATIUobNMKn9KQqaQLhipfH%0AbOU5bR0La8b+1lKDXp1RDDiLc0eWwNigclRPC0GOGCiZ+JAH6RJqio0rJ40k+e02mnq01KX3dBAn%0Ack4unHgsd18qYUv1S5LkREmxyatXr56QJCdLilvyGCLZJ+/FlCEEqo+rSt5fnCzJ/Sb1T+RpPYcu%0AJwhvXGTd4161NUqheTFmywvxyppabxaLk+XoOOYcZXrlzUWYvWqSg9cr1aVc8eW2aupJ+2j3P2rk%0AqLmSUoEB9j9PyoUkL2TA1SSREO8DHiPk7rdUlZws+UTCV8EPDg7OqG5uC33kX3jQohCdE3LH5YSg%0AvSrPUkVT3WOLODwXfA5oBJmtd2kFey6V5Ui0kqO2T/stEQXrrbqz0BYF+BuBrDicvG+Sf2T58j5M%0AfkzGS7WnXni7uErUXG+PKCVBavs1ZSnjlESURJba445cZZMt/I/bNFWuLVBRPosoeRna00wWphCa%0AR0xTyXgK5iRB3ubWNkb/G/4QgJ8G8PkAKoAfr7X+aCnlewH8cwB/cpz0u2qt9r+TM0O3iB61KFVt%0ApHJbB1vP4JSEKV1g2SZ5QzknJkorH3GkfUSw3C3lF7781giF7JD5JYFofS1Vq+wHGauUypKvgksX%0AXHPxNVhkp91mxftZi3/y8Ic1EZBNGixFOgKRAo3EhIYoxJC1a1Q7M/VHynIfwLfVWn+3lPJ5AP5n%0AKeUm7hHnW2qtb5lu5jJo7dgMYVrpPGRsGKEWtPgXL1+qO03ByRvHPZKUF79GgNzV5v2mpddsknm0%0AvtReCEJEKT+cJK17K63zRX3CSV+2R8vDz5HW/9a5kMgcz8b8NHK26rHS9BDmCPRc15l9GqL/DX8G%0AwDPH239ZSvlDAC8+PrxNmdiJTKd7hEnIlDE3tFghr19+5AsoODHybXnPJidHXrZ2fyaP49V6fyXZ%0AsikiDE09Z4iSxyilouThBUlovB81tc77Tn74Tec8HCLVomx7RGQ8r+wXOuaRfVRmhii9ukYQ5pTr%0AMsrDv+W2hnTMspTyMIAvAfBbAF4F4FtKKd8A4HcAfHut9TMZA+eYbbLlRukyajIixhHK0kMLMVv5%0A6LdHTOTG0kUtVaOVX6pEIsq7d++eehGHjMvJwcu/I3WnETqRIV8F5yTJY5TWvZU8FMGh3V9JtvKY%0ArVwI0urzzk8POLnz/uP92EssmXQjCVOOhZZrt7cuDymyPHbB/yuAbz1WmD8G4PuPD/8AgB8B8GaZ%0A74knnjjZvn79Oq5fv76YPLcwgjD5MaCftKYO2h4XRBKbZpMkH00BWvklWZKapGNEnpKYLGjkJffJ%0AxxglQcqFHPl4o/VCC9qW6pJc70ixA/df6AHgVAzVImg5cbXG8zIhhEzZU8jaEhhSSUflePtaPTxN%0AQdZacfv2bdy+fTu0B0iQZSnlCoBfAPCfa63vPK7k0+z4TwB4t5b3da97nWn4aMJsIa5Mp/cQ4BTy%0A24L7TpCEqa3Oeu4LkSXdPwicfozRW51vtZHstBZyLKKUMUrpGkvSkbf1RLZTHv4Ek3wuPXpaiNef%0ARUv6XtLqtWEkMfd6eHL/jRs3cP369ZPfN2/eNOuMVsMLgLcB+HCt9a1s/4tqrZ86/vk1AD7klaM1%0ArJdgIrSWa3U6P+Etbn6EkSq1BRYhSPdMfuSihwc+Y8sb27VFFFmvtDe6IMg+7fagBx54wL2XUhKV%0Apyw5InKjY/JJKTpmqUvZj/zcZMnEcr01b0lCC8VoyE7wmWtmFHlmbLLCHC11RcryVQC+HsDvl1I+%0AeLzvuwG8sZTyCgAVwMcAfKNnoHaiNEk+kih6XeRRM3yvXZ4bkwG/OCLlQ98tnwx4G+i5dPkGJO1l%0AFZk6JInLhRxOlJwwM6qS94vVXwSuNKWbTopS3rRPZEkK01OWPeMtmuAzY8sba1baLEFFGOE9RWVM%0AqSNaDf8NANodsr/SUklWWWZjDtrxlrqj9K2KcjR665WK0UrjKagRZMnLkSvqGmFqH81+mUauektV%0ASUTJ3yoUud+yPwjaOaEQA701iMq1bqqXRE9k76nVjCrk9mrpI5U6laSmXCejCLVHxbbWPesTPBrB%0AecHrzKDwjvfK/uysPpI8lyJijxylCpVptW2eX4MkBo8ovZVoqy4qT7s9SCNK+ZIMz/322sb7iiDv%0AQ6VVfrqJX3sCSfYL/7b6OuseS8KM3HFethaDbiGTSMxMIeTRsdVeWxYlSw5OlKOUXG/cb454YUT8%0AI+rSlGEmrdwvyTRTrtdn2iSokRy/leby5csniz/aG3gkyWhEaT3OaC2oSGLKnhNJmDRurcdGOflo%0AE9YUWziyhKnl498j0FPWyPrnKH8xsvRcK37hjVBcU0gzqzJb0JpfUzpzD6ReeGEOiyTlirC18CNB%0AZWVJ0rpFSFtUyZwjbVKnc0MfeYO/JEurTEvltsIizEw+y8YRGF1u6zU+ov7FyDIinchdiMqnMuTx%0AKbGUJdxkibnqzA6WKepGOx9yIebw8BBXrlw587IJIk35R2H8osgoSnnjuRanlO2bOplJWyVBatte%0Amdp2q5sp82VIcwShjIh/9qT3zuEool7FDc8Qo0dWVrlax3n7uD2ZelswmmxHqEvLBdPiVVq6lvbw%0Ac6s9Kqm9nYffg8lv5o6I0rpNyFKtLSGLKJRktZn61dqOypblt57/JUkrm3ZO72gJcbNazJIwhxve%0Aaku2zhFu/cjyW+xo2Z89HoFIrtZ6atGDE+XVq1dPnvIhxcnf4k7fmcWc6OUYU+OTVv9YcUIrvVVW%0AhsR7z8mS7vWUulomkCxGhbEWj1lqwWYtFqT9bql3FEmNjCGNTDsCLfEsLW2232jVuNZ68qZwTV1K%0AouSPFXq3B1lk6d1Mn/FcsgpUm+yjeKHnknt1z+VOR23NkOISCnOt8BiwAFlas+4oYtQwhSh741ge%0AWoPRLeVaEw2H16bILR0x8WgvjSDCtP5jR/4dA/0ro0eU1p+ORYRpnZ/R5423m7Z5HaTEOaL4qFZH%0AZp8sXwqb1jJHK0wPaxHm7G9K9whTS6P9luWNwhJEyaHZrg3MFhfO6l+rHk4gcp9mTwu0/pRxOm63%0A9tcU5HLzf0DkZEn/ofO85z3vhCzlX9lmHiOkuqxtma+nb2QsWGszL5u/yi2qr8UryB7XiDMqryfE%0Ak72G11KQFjb/txJTlE1PPit/j5ti2WXl09y6TNmSMLVvKo8rGH6PYKQwo/ZEfSUnQyJD/iozIkqK%0AY8r92sKO9Z/fVnusiUQjTc1uD5a64h/5FnVJlhTbpW+rPzOIiMxTkFG7W0lyLpWZwSibZidL7SJq%0AdfsyjfIu3lb7WvIDOfuyrlDm4tQGu3axa4QJ4AyhWIsgMn/k5mtp+G9STJcvXz5jFxHFlStXTsUv%0AqYxLly6defUav02I/39O9K5IrZ8y7m40br36tNADfyMRTQiyLktl9pKPZWdL+a2xyqhvLBuiMjNe%0A2cjwwKxkOUVGR4oyc+FGiNRkxp7WgeOV4bmCUV2ccKWKke4ecP+t3bTPU2K951HrXx6Tk6qK/teb%0A/9c21S9vHZI3nGdfd9bbrilur6YqtQlB/lMmgXsArXVnj/cQSEt/eITpiYXMBJVV/1PV7WJueK/y%0Ay5SVKd+aRVtidZ7yyxCZlo4fl+50jwtI39ofgPGVZgCnYmSSMKX7SGSRsdWCrJfvpxdQcMUlyZI/%0ACSTfOu69vcf7PQf4udDccH4HgCRLjmhMT7mGLJsz5fYqS2uykmKhtV2ZcMUIhbmoG679tvYBuYsw%0A6zp79VplZDs444J5tngklIGsn5OjvC2HEyZvv6fIuF2WCtbaqJ0/XiepSdr2Fj9kyMB6c5HVJ9r2%0AktCUJZ0XwFau9BcV8nx53y02ZfZnlHpUR6Qw+XjxxpWGKddLZj9hUTe8Zyb0CNMbJF4H9ipLyz5Z%0AT1Sn5T5oM61Wl2UHfYgY+ZMx+/v72N/fVxcWLEXGP1EfWOdAtkW63gT5EgqZ1vtYfeSdm8yFmDkn%0AUX5LWXJXnJOEtIneZCTbG/WDZov1OxqvXjnWPnlcOxe8rl6itJC5djanLHuguaARYcptb588pqVp%0AcTW8gSDTZNRbpk6ZjxOl/DdF+nA1YxGRViZdsJ7rzQd75J5LRcHL1ggjq6QsFZMhBp7Wm9iyqovn%0A5/2oPe4piYLn5Qqa6qM+k9+WPRmVbak82SfyHGVUO09reSDRPg8eP3h5tG0Pq8Qse9Sc7JCpqrW1%0A7mhfNHP21pXNy1ULJ8m7d++eIkp5qw59R0TJX5sWEYemgKw+1NSrNj4yExrfF13MEfHLerV9fL+n%0AxCyFyT8WWcoVfk6WNMnI+zMtu7MEp018VtusMrTj8ltTkr2KMrLV6w+vDRybVJZzo+Ui9AZEtrN7%0AZr6oXH7y5QVIZHn37l3cuXPnhCytW1UyrnjGJq0sz0WUCsVra3RBZrdl3sjNjia/jIqOiJIv7PC3%0ALgG6G84Xu7gdGglZfRERndYXGrzzFtWn9VnLWMvYaE3sLcqYsEmy7CWW0ch0aNTZPW3JDGBJMpqy%0AvHv37illSRel5tbJ+mW5EelpLnLkQXhkGU1ovA+8fVMvNs+eTNhBI0vtOICTfqbzCODMIhZ/X6Zm%0An7zTgOrQ6pPHMm23yupRgxZa7Iv6n6C1odX2xcjSakgUh7LKkOW1XBSZcqIZUdrViogAsmVKtcJj%0AljJe6ZGa54p77dQu2imqUlN4LWTZksarw1KaXqjBqt/rQy1MIScqrQ7KE01sParSUsjSm7HaaF3n%0AEQdodVq2RnVE/NBDmKspyynqUZ44rbzoIuH5rLRZwhytKDXl4OXRFg74h9/krT3i6BGmpyq9i82a%0A/DRF3EOWLRe6TG8RoIXMBNOqKrk7zfdp5yEqM5rYPMKU23Q9aPfCRnV7/dqq+mSbrWPSPu0cRGMw%0AKpsQ/W/4AwD+O4BrAK4C+KVa63eVUp4P4OcAvBTAUwC+ttb6GZnfIjKlnlQ6reyW/drJ1GzMzsZe%0AB3uDx7r4M2SpzZDWDej8nkX+P94aQVpvBvLq19JEbeT5vItdQlNKUb3eBJohTFlW9NHaK9vGSZKe%0A/5b/42PdQ+rV51343pi1+pNipRp4m3i8lY95TQlr6SxodnqipLVMrb8iwoz+Cve5Uspjtda/KqXs%0AAfiNUspXAngDgJu11n9TSvkOAN95/GmC1pmRiuCdkiUumVd2rKYuLcLM1NmqXLSTaJ1AjdD4wNVe%0A/8XzSYKM3iJuDajsbC/TZpRR5lxq9ViK1vMevHMly7MmGNom23m7LcIkoiylnHG1tfMij8tzJRVe%0Ay6Quf3sTkxxv1gRknYsWO6L0Vn2yzkj8ZMdb6IbXWv/qePMqgMsA/gz3yPLVx/vfDuBJJMlSG5xR%0Ap7acfCtNNANlSFq7mLV6vFkucgki4tB+WyQpL2wAp1RN9CSMvPA0WzWbJDIkmSnPUxaW8vLUZVSW%0AVa7861q6tUo+966RCqWnlWztAQHrHygtotbOkzVGo3HOoalL65xp/WdNwBqssnomTnnuvYm+ZYIO%0AybKUcgnA/wJwHcCP1Vr/oJTyYK312eMkzwJ40CsjmgUy8NRBZlbiA1KWK220SNL6LW3UyvHqsWY3%0Ab9bjeT01SSqG3HD5PHX2lWa9pK7l0+zNTkbWJGi5rBZaiNd6gobq1e5F1fqK8lk34Wv2y/o0Qpf9%0A57XRgyRdXobcx0mej2mr37L1cpUt7bGuXVmvllZrTwtRAjlleQTgFaWUvwHgvaWUx8TxWkpxa8uo%0ALG07o1iibStf5o3UMo/WwRml0mJ3RETaBWFdkFzBcOKMCNO6EL0PT2dtR+3z2utNUKWUUwtRmvsa%0Aledd7DRerDItZQng1MTAbebvq5RtkduyPv7bmuA9RJMiJyhtdV07d9LubHiHlwng1KQTEZ81XjPn%0Auwfp1fBa65+XUn4ZwJcBeLaU8sJa6zOllBcB+LSW5/3vf//J9iOPPIKXvexlZ9K0Kkxmz5ntVsK0%0AbPBULOXVZvJeRHXJNBq5SHVCRMnvreT7PaK02soVRe/HaovVHxklIMmN79POpVYm5eF9Rfv4BSyV%0AJHPSR3cAABPbSURBVIGnsfqO2yVJhm97Ckq2w2qbhHZuOQFKRSxJmKfJEKWctCyy1OyVeeS9pZ7K%0A9vhEG1cf/ehH8bGPfczMwxGthr8AwEGt9TOllOcBeB2A7wPwLgBvAvDDx9/v1PI/9thjpxrjXZRW%0Ao3rgnQiyhw8IK41mj3YBtNjVMvC1NHJbU1lytZW3V5KldW4kyXCi5ISZ2fZIztuWF7Fmk7zIAT3W%0A5k0Csjz+LHzU77y9lnutkUukiDx45K+1WdbB90djUutfi+DkgpfcZ9lM/cfr1PrGI0vLfmmvtF2K%0AuCeffFItC4iV5YsAvL3ci1teAvCOWuv7SikfBPDzpZQ34/jWIauADCFlBslUApXlZAjTymuRZWbg%0ATbHZ27aIko7RdvbWFFm/JEHv21uV57Z627x9HrFJO7U+jvrdGhNeHZo6tWKWsg6trzUbM6rR62P5%0A26rXqye67mSZ3iv0eJmWkpUTO+9T7dl3bQL1vq02ZPglunXoQwC+VNn/pwBeGxVuXQRZZBtoDTSv%0AE6KLwqrLG1hWfVZdveSp2UHlcfWo2cUHtEeQUd2SQL17PKWN8g05HlnLujgx836wSF+WyceFfNku%0At0lu8zq0/rD6ybNRuvteeRZJa2Qp2yAVntaurFqVdWT6XdapEaVlv3yIQtbN+8Ky3bpWrDotLPoE%0Aj2WQJKtoRm0lm1GqVJbJTxKfEeesj7Y55OCS0O6Hi1bBPRu8C9YjS2vC0Ahcq5e/uFjrf0kMVhut%0ANmh903JjOJUnoRFLS/9b40ojSz4OZZ2yPo0wLFWppZPvF9DK58e9srTrxytHjiVtYtaglZ/FomQZ%0AKThKY21bFxtBdkSm86wTNAIZZawRQ6ZMmZf3B4+3AfcXH3haqWxaEM3KrYpHW3DSyiMCpvZo5beQ%0ApXaRSUKLyFJTTdL2iLikbVMm3ojEJOFk6tJIWrbTmji0azaCVbZ1zfOFtSxhaojyrvoiDa8zPSlt%0AzTSRq8JhzXTSPi2vrE+zK1OHRZieLbwsrX7rNhZJoJai8VS9vLhkfbRfuzlbkhAnI40wtbZzxcoJ%0AU9pnESaHfFa+RaF6Sse70OVHhiO0c6FdH9EkbO3TyDmqPwvP2/DK0a5vabO3LzOpZK+vKN2sZCln%0ASb4f0Gdh+VuShDZY+b7Wk2apVwv8xEQzYATrRHt2ywvbIkzappnXmjAyk4ZMb60Uy3RkH7dXWwCQ%0A935qdnGC5IRpEZ1Gdhw8b6YM7UPpebutftPK1topz6lGkrJubQx4dmr2ZtRipDC1cStfNC3TauV6%0AdmYEiERWba5KloAfwPVI0yM5fpK0GdoiSo14+AVtEaE361kDONsv2v5W+7ldXN3xF8dGs3y0n+rh%0AJMPr4x9tRZzK4IRp/VOjRrbyZmVNWfI6LLK0xohVhkWavCwLGoHJbXneeL975fPj0mvQ6o6gpZHh%0AG8sOiyB5uRoZWYQZTTqWeMi0aQpmV5bWfm3AaOmiztBmUU1dajdT8zTSDu3i0uzMDsrM7JZRmNag%0A0i5gTT1Fs3nU11rfccVHfS1vKZLlcLKU25zcKG9WWco6JNFp/cvr8VSgdtO712/R+dHSRiRJ0OLQ%0A2sTVQpQ96pL2SZKk/XSOrPh4ZkxaJMnzW9dFtj8zWIQsNRXSkpcrGX4MOHth8PwaQfCbX2U6Ko+X%0Aa3W2Nai0C6hlEGZVpQZNrVgfroq5DbLNlt0aYXJytCYnKsNywek/Z3j/8/ZEypLSamrQIkyrL6OP%0ARJYwrfRa//P9EnIy9NSrhWiyb1GXtC1db+kV8Hwyv2WfJFSNZOUYG0mUwMxkKVeptNlPu9HUIg5K%0AIztWG8CSLLg9HjFpykLC29dycryZNkOM0UUckW6mHl6XBq8vrad4PHWprYjLsSMXlTRVoSnClnMk%0A01tueHTBym2tv7UJgbc7Y2vUB9r1Icvwyrdslr+5B8An0EzfaeDHLIETEW1URxazK8voJMrfGslZ%0As4mnKvgFpp0sbqN3kzNf5ZX1yDrl/lbIgWERp+wv7xYZrSztfkUJ6xzxNkYD2SJpKkOqS6ksZd9w%0Am6Wy5OdNfmtkaZ0n7bx6k5J1AWvl0W/Zbxo5yjFnjUGPpK1zF/WD3MdJrwXe2NLsludQlsXLzE70%0AWp29mF1ZSmjEyGNL3kxN0EiTKxaqR9aplaF1Pk8rX47QMutbJ12zxfsty5Mk6d3grLVRTkgeWfJt%0Aa0KwSNOqX7ZBKssMWdK4sRSGp640MtH6Wea3Foq0dmplUTo+mfMyItLk21IQaGNU6we57dkooSl5%0A7zqw9lvXtHec9kciwsJUogRWIkv6pgEo9wP2rBSpL06arfnlMe1pGN6OaKB4M6WXXrOVlyOJ0roP%0AULuQvYUurZ6WC0wiqsdqg3b/oUe+3sTqkWbULmty4sd6FY42PizSjCbmSB1a51Me5/VnxICsT+tf%0AiYxY0PLQtzfBWzZniDiDxckSON2xXCXQMUI0w0j3Wt4qY8G64CRheApPDnhpu/bbG4DWhS7VhFQ6%0AnGisPuQf6idv0HnKy7Jfu5FcfmsTmiR+62LTyGkqYcp9Whs1EojOoTZ2rPFkTaqSKDXizBKodz6z%0ANkbw+jXTZ94+bQzxYxFnnAuytAYwNU67nUB2bEaSc9LUFGL0kfVEJMm3W2dfrR3ZvBZRWsqSly/v%0AedTa7l1gmbbI/dp50NJLVakpIVlmK2Hy8jzylG3IXvQ8nzaReufZI1eLKK26rf3R+cwIAw/WpNIy%0A5q3+0s6tljYaK17dGayiLLnC0e4XiwalJEqej5fD01q3tFCaUdAGVlaJWBcNfUtCsfZp7dfie5GK%0AyE4EngqUbdWUuEVY2gUuy86SpkYaGnm2tj0DabN2vrVJxiJMzcbIdosso3OklWEds67dTF9aZOiR%0ApFVGVKaVPsIqN6Xzkx5dvFY+ScRysJFbqBGltoqaxegLyiNIuS8ze1sXPp805P15Wj6N8Lw2RNtW%0Af1vkZalLi4g1ovQmA4soo0k6skGri/9u6c+MsszanxEfVjuscjKK00vnkVsPWVrljxJGi751SAMf%0ACHLwaReuJFpNvcpV9VorDg8P3ZfSysHokdIoRSKVhaY2NJvkdlRH9rdHtARNCWWIJrLRU0NaHfKi%0A1RQmt1crN0s02kWtTfhWfbJuTy1GaaO2eO2IYF0Xlm2Ux0sXtdFSthZ5t5JlixiLsJiy1AamvEha%0AB5q8rYfK4AOLK6rMwkZElhZx8e8sNCXiqQfrt2yvZ0d0LFIgWQKLVFSmnVqZ2ndElB4Zye3sBdVT%0AB6XLKkxZjqfKtboi8P6yXrailSsndFkm3/aIXuYZSZYtkydvl4dFlKVFlBwts6sccFxdSrKk4xph%0ASvs0GyPClPk1ZN0VWRbls/pNkqRXVlaFehe3NvAjwpRltNgT2aiRpnXBtSJSk1q/W2RG257nEI1/%0AWZ6so6U/ZR9FIsKrm5fljUXvGpB9uBZZRmUv5oZbitIiTW9QaYOLL/TIE+PNntqs2aowNfs1G1r7%0ASv7WBltGjbfWqZFcC1FqhK6d1ynQiF1evFn1pxGjlz46Rrbwb26fRioRYWp1jiBK7SPLp/QZUaDt%0Ak+PJyzeSLFsnztWVpRwskmxkGkAPpGdkvPVYozcgpG2andZ+q73yxGRs19prEVQvCVu2WhddC7H1%0AXLhZRBOWTMsJWiN6efFl2+kpR22/pnw9dRoRydS+zRBlVllGfab1sdy20raSnHa8RVl6iphjVrKM%0A1FkGmRlNyyO3PZKUNka/NfunqBDLJk6a8lhrHdbgtlRslNdT1t7gG6EqNfu0+umYRYiRt6LZrE1U%0AGbUtbdHyT7mQI1hk0aPaOLT+08ZK5FloXkDWM4jKyeaJeMZ+nu9e5gdKKb9dSvndUsqHSyk/eLz/%0Ae0spnyilfPD483qnjNDYwIYzpMX3yzoys5KnVHoVpayf//ZmbJknQ9CWuvSUQu8MzeEpH0sdZOrM%0ATAZZm7yx4vVnNn/GdmsisepqracF1hhoPUcaWgRP9uPZnCmvty2ZfNFf4T5XSnms1vpXpZQ9AL9R%0ASvlKABXAW2qtb8kYkRmkmQZ6A1zb1vJniImXkyFIDVNVVSuBROqoxw5v8GmulaeUetuesTF7zqUK%0AkvZ727wNGkG32i5tsNokx2vvuIqIJKtgZTprDGhticq3CDIzjrL9MkW8hW54rfWvjjevArgM4M+o%0A3ihvr2GWWrAIzBu81oUsy7GI0oI2oDOuTqZPWpRFy+DJooVMLZLJlpfpa8/G7ASh1auRKJUt7dH2%0A9UzYUX3aeM0Qpocpk7QmciK75LFo/I8eqxFJ9tbnuuHHlVwqpfwugGcBvL/W+gfHh76llPJ7pZS3%0AlVL+ppM/ZUjW9dFOoHVCrfI88o0I03I3M+4o32ed0Giwemk8Gz23JQtrYEr3yapD5vf62rqotDI9%0Au7JKKRon2bHTMt6zE3WP6Mj0v2WP9tvK02prNAYjBayllWVHaDlPHCFZ1lqPaq2vAPASAH+/lPIa%0AAD8G4GUAXgHgUwB+xDKKf7MyT76zqssbqNkB7eXz7FX6xP2dhecSeeSotckrv0UpZm3U0rUQc+tg%0A9SadzCTQS5z8WOa8WLZ7/ZCdqCNE7dfgXRdyn/VbltPThp7JW+aPIG1p7d/0anit9c9LKb8M4O/W%0AWp9kFf4EgHdreX7zN3/zxKiHHnoIL33pS62yT9Lxb9rWGpU9GaXoN0NHJ9uyk9JMIRZpS2SXl09z%0ADzW0pNsaon7vLTMDOX7mqIPXY9XH92tprLoiNSn7tlVdeuW22rMGnnrqKTz11FOptC5ZllJeAOCg%0A1vqZUsrzALwOwPeVUl5Ya33mONnXAPiQlv9Vr3qVqirlSTLqbjpRkkAyaTOEK4k8q4Q9m6wZTpux%0AM/X0oqXcnsnBKr+X3D0ymapcPTus3y1jTpYx1znldXiQ5JwRJNq4ledkBFFmCD6qK1NurRUPP/zw%0AKRH3a7/2a2b+SFm+CMDbSymXcM9lf0et9X2llJ8upbwCQAXwMQDfqGWOiFIS0RS0KAXtd0ZVynTy%0AwvHUT8tg9AanZ49mr4W5L1ayQaunZb83Zrw29owtGbaIwhjRxJkdBx6sNvPt3rBQD2GOQovomFK+%0Ad164DZk+i24d+hCAL1X2f0NYsl2metKtE9biRmh1jUbmIvTsicg6q3qnDLCpitLb3wPZLxpBTlEU%0AEhliyxJmqz1Zz6oFHnFm8/Z4Ti3w7IvqlP2r9bfVB1F9Lf22yivavDiJlc473nIRUZ6pA6FnsFuu%0AdeSGa3laYBG850a2XnStSi5Sl9Z3q22t56nWcS/jkLBUMy8/6z7PZcPIcUdljkhvtTvbH9Fk6NVN%0ACFfDR+DjH//4mX29FycN4J5ZpBe8zqg+IryPfvSjJ9vah6fV9svyPIXplS23eT7vdwtqrfjIRz5i%0AHsuWkUnT+pH5+G/PhqjcW7duhfVa5Wv1tGCka2xN4Nq+27dvT6prpDfSMil751VLY2ERsnz66afV%0A/VlpTr8zswNP2zJbW+glYIs8ZJ2e2x25+h7pTlGoPYOa2qu5R9l65PmKzl8vUWr1W+mtY7du3Tpj%0Ah2db1HavnaOQCel44y8a03Nj6oSujasWwlyELIF48PSUZ5WfTT8nLEUYERlPlznmkWErAW8ZWfWm%0A5eG/+beWxspvkVurSszavTQ0b6YHWdtH8EB0HXkTUquqBBYkS4ms6sumWRot9mdcaf4dleXVEdmw%0ANYw8t9mJclS51vE1VOMayF4DSwmVFtLsKn9Gwy/e6Nhhhx0uPGqtKuvORpY77LDDDhcJq7nhO+yw%0Aww7nCTuy3GGHHXZIYHayLKW8vpTyR6WUW6WU75i7vqVRSvnJUsqzpZQPsX3PL6XcLKX831LKE8V5%0Ahd15RCnloVLK+0spf1BK+d+llH95vP9CtrvY/xhwIdtLKKVcLvf+CeHdx78vdHsjzEqWpZTLAP4D%0AgNcD+NsA3lhK+cI561wBP4V77eP4TgA3a62PAnjf8e+LhH0A31Zr/SIAXwHgm4/P64Vsd631OQCP%0A1XuvKvw7AB4r9/4x4EK2l+FbAXwYAC1sXPT2uphbWb4SwO1a61O11n0APwvgq2euc1HUWn8d998e%0AT3gDgLcfb78dwD9Z1KiZUWt9ptb6u8fbfwngDwG8GBe43VX/x4AL295SyksA/EMAPwGc/CvChW1v%0ABnOT5YsB8Md3PnG876LjwVrrs8fbzwJ4cE1j5kQp5WEAXwLgt3GB2130fwy4sO0F8O8A/CsAR2zf%0ARW5viLnJ8nP+vqR6796sC9kPpZTPA/ALAL611voX/NhFa3c9+48Bj4njF6a9pZR/DODTtdYPAvp/%0AbV2k9mYxN1n+MYCH2O+HcE9dXnQ8W0p5IQCUUl4E4NMr2zMcpZQruEeU76i1vvN494Vvd631zwH8%0AMoAvw8Vt798D8IZSyscA/AyAryqlvAMXt70pzE2WvwPg5aWUh0spVwF8HYB3zVznFvAuAG863n4T%0AgHc6ac8dyr3nyt4G4MO11reyQxey3aWUF9DKb7n/jwEfxAVtb631u2utD9VaXwbgnwL41VrrP8MF%0AbW8Wsz/BU0r5BwDeintB8bfVWn9w1goXRinlZwC8GsALcC+O8z0AfgnAzwP4AgBPAfjaWutn1rJx%0ANI5Xgn8NwO/jviv2XQD+By5gu0spX4x7Cxr8HwP+bSnl+biA7eUopbwawLfXWt/wudBeD7vHHXfY%0AYYcdEtg9wbPDDjvskMCOLHfYYYcdEtiR5Q477LBDAjuy3GGHHXZIYEeWO+ywww4J7Mhyhx122CGB%0AHVnusMMOOySwI8sddthhhwT+Pzl/C8KJDU7hAAAAAElFTkSuQmCC%0A
-
-
-
-作者 & 更新时间
-------------------------------------
-作者:`李金  <lijinwithyou@gmail.com>`
-
-提交: 2017/12/14
-
diff --git a/docs/sources/aipy-numpy/cn/6annotation.rst.txt b/docs/sources/aipy-numpy/cn/6annotation.rst.txt
deleted file mode 100644
index 7ac6f1c..0000000
--- a/docs/sources/aipy-numpy/cn/6annotation.rst.txt
+++ /dev/null
@@ -1,517 +0,0 @@
-
-注释
-=========
-
-
-使用文本框进行注释
------------------------
-
-先看一个简单的例子：
-
-
-
-
-::
-
-    import numpy.random
-    import matplotlib.pyplot as plt
-    %matplotlib inline
-
-    fig = plt.figure(1, figsize=(5,5))
-    fig.clf()
-
-    ax = fig.add_subplot(111)
-    ax.set_aspect(1)
-
-    x1 = -1 + numpy.random.randn(100)
-    y1 = -1 + numpy.random.randn(100)
-    x2 = 1. + numpy.random.randn(100)
-    y2 = 1. + numpy.random.randn(100)
-
-    ax.scatter(x1, y1, color="r")
-    ax.scatter(x2, y2, color="g")
-
-    # 加上两个文本框
-    bbox_props = dict(boxstyle="round", fc="w", ec="0.5", alpha=0.9)
-    ax.text(-2, -2, "Sample A", ha="center", va="center", size=20,
-            ^4$bbox=bbox_props)
-    ax.text(2, 2, "Sample B", ha="center", va="center", size=20,
-            ^4$bbox=bbox_props)
-
-    # 加上一个箭头文本框
-    bbox_props = dict(boxstyle="rarrow", fc=(0.8,0.9,0.9), ec="b", lw=2)
-    t = ax.text(0, 0, "Direction", ha="center", va="center", rotation=45,
-                ^4$size=15,
-                ^4$bbox=bbox_props)
-
-    bb = t.get_bbox_patch()
-    bb.set_boxstyle("rarrow", pad=0.6)
-
-    ax.set_xlim(-4, 4)
-    ax.set_ylim(-4, 4)
-
-    plt.show()
-
-
-
-.. image:: data:image/png;base64,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
-
-
-
-*text()* 函数接受 *bbox* 参数来绘制文本框。
-
-
-::
-
-    bbox_props = dict(boxstyle="rarrow,pad=0.3", fc="cyan", ec="b", lw=2)
-
-    t = ax.text(0, 0, "Direction", ha="center", va="center", rotation=45,
-
-                ^4$size=15,
-
-                ^4$bbox=bbox_props)
-
-
-
-可以这样来获取这个文本框，并对其参数进行修改：
-
-::
-
-    bb = t.get_bbox_patch()
-
-    bb.set_boxstyle("rarrow", pad=0.6)
-
-
-可用的文本框风格有：
-
-::
-
-    class	name	    attrs
-
-    LArrow	larrow	    pad=0.3
-
-    RArrow	rarrow	    pad=0.3
-
-    Round	round	    pad=0.3,rounding_size=None
-
-    Round4	round4	    pad=0.3,rounding_size=None
-
-    Roundtooth	roundtooth  pad=0.3,tooth_size=None
-
-    Sawtooth	sawtooth    pad=0.3,tooth_size=None
-
-    Square	square	    pad=0.3
-
-
-
-
-
-::
-
-    import matplotlib.patches as mpatch
-    import matplotlib.pyplot as plt
-
-    styles = mpatch.BoxStyle.get_styles()
-
-    figheight = (len(styles)+.5)
-    fig1 = plt.figure(figsize=(4/1.5, figheight/1.5))
-    fontsize = 0.3 * 72
-    ax = fig1.add_subplot(111)
-
-    for i, (stylename, styleclass) in enumerate(styles.items()):
-        ^4$ax.text(0.5, (float(len(styles)) - 0.5 - i)/figheight, stylename,
-                  ^4$^4$ha="center",
-                  ^4$^4$size=fontsize,
-                  ^4$^4$transform=fig1.transFigure,
-                  ^4$^4$bbox=dict(boxstyle=stylename, fc="w", ec="k"))
-
-    # 去掉轴的显示
-    ax.spines['right'].set_color('none')
-    ax.spines['top'].set_color('none')
-    ax.spines['left'].set_color('none')
-    ax.spines['bottom'].set_color('none')
-    plt.xticks([])
-    plt.yticks([])
-
-    plt.show()
-
-
-.. image:: 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
-
-
-各个风格的文本框如上图所示。
-
-
-
-使用箭头进行注释
-----------------------
-
-
-
-
-::
-
-    plt.figure(1, figsize=(3,3))
-    ax = plt.subplot(111)
-
-    ax.annotate("",
-                ^4$xy=(0.2, 0.2), xycoords='data',
-                ^4$xytext=(0.8, 0.8), textcoords='data',
-                ^4$arrowprops=dict(arrowstyle="->",
-                                ^4$^4$connectionstyle="arc3"), 
-                ^4$)
-
-    plt.show()
-
-
-<button>Run ...</button>
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-.. image:: data:image/png;base64,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-之前介绍了 *annotate* 中 *xy*, *xycoords*, *xytext*, *textcoords* 参数的含义，通常我们把 *xy* 设在 *data* 坐标系，把 *xytext* 设在 *offset* 即以注释点为原点的参考系。
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-箭头显示是可选的，用 *arrowprops* 参数来指定，接受一个字典作为参数。
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-不同类型的绘制箭头方式：
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-::
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-    import matplotlib.pyplot as plt
-    import matplotlib.patches as mpatches
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-    x1, y1 = 0.3, 0.3
-    x2, y2 = 0.7, 0.7
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-    fig = plt.figure(1, figsize=(8,3))
-    fig.clf()
-    from mpl_toolkits.axes_grid.axes_grid import AxesGrid
-    from mpl_toolkits.axes_grid.anchored_artists import AnchoredText
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-    #from matplotlib.font_manager import FontProperties
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-    def add_at(ax, t, loc=2):
-        ^4$fp = dict(size=10)
-        ^4$_at = AnchoredText(t, loc=loc, prop=fp)
-        ^4$ax.add_artist(_at)
-        ^4$return _at
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-
-    grid = AxesGrid(fig, 111, (1, 4), label_mode="1", share_all=True)
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-    grid[0].set_autoscale_on(False)
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-    ax = grid[0]
-    ax.plot([x1, x2], [y1, y2], ".")
-    el = mpatches.Ellipse((x1, y1), 0.3, 0.4, angle=30, alpha=0.2)
-    ax.add_artist(el)
-    ax.annotate("",
-                ^4$xy=(x1, y1), xycoords='data',
-                ^4$xytext=(x2, y2), textcoords='data',
-                ^4$arrowprops=dict(arrowstyle="-", #linestyle="dashed",
-                                ^4$^4$color="0.5",
-                                ^4$^4$patchB=None,
-                                ^4$^4$shrinkB=0,
-                                ^4$^4$connectionstyle="arc3,rad=0.3",
-                                ^4$^4$),
-                ^4$)
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-    add_at(ax, "connect", loc=2)
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-    ax = grid[1]
-    ax.plot([x1, x2], [y1, y2], ".")
-    el = mpatches.Ellipse((x1, y1), 0.3, 0.4, angle=30, alpha=0.2)
-    ax.add_artist(el)
-    ax.annotate("",
-                ^4$xy=(x1, y1), xycoords='data',
-                ^4$xytext=(x2, y2), textcoords='data',
-                ^4$arrowprops=dict(arrowstyle="-", #linestyle="dashed",
-                                ^4$^4$color="0.5",
-                                ^4$^4$patchB=el,
-                                ^4$^4$shrinkB=0,
-                                ^4$^4$connectionstyle="arc3,rad=0.3",
-                                ^4$^4$),
-                ^4$)
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-    add_at(ax, "clip", loc=2)
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-
-    ax = grid[2]
-    ax.plot([x1, x2], [y1, y2], ".")
-    el = mpatches.Ellipse((x1, y1), 0.3, 0.4, angle=30, alpha=0.2)
-    ax.add_artist(el)
-    ax.annotate("",
-                ^4$xy=(x1, y1), xycoords='data',
-                ^4$xytext=(x2, y2), textcoords='data',
-                ^4$arrowprops=dict(arrowstyle="-", #linestyle="dashed",
-                                ^4$^4$color="0.5",
-                                ^4$^4$patchB=el,
-                                ^4$^4$shrinkB=5,
-                                ^4$^4$connectionstyle="arc3,rad=0.3",
-                                ^4$^4$),
-                ^4$)
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-    add_at(ax, "shrink", loc=2)
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-
-    ax = grid[3]
-    ax.plot([x1, x2], [y1, y2], ".")
-    el = mpatches.Ellipse((x1, y1), 0.3, 0.4, angle=30, alpha=0.2)
-    ax.add_artist(el)
-    ax.annotate("",
-                ^4$xy=(x1, y1), xycoords='data',
-                ^4$xytext=(x2, y2), textcoords='data',
-                ^4$arrowprops=dict(arrowstyle="fancy", #linestyle="dashed",
-                                ^4$^4$color="0.5",
-                                ^4$^4$patchB=el,
-                                ^4$^4$shrinkB=5,
-                                ^4$^4$connectionstyle="arc3,rad=0.3",
-                                ^4$^4$),
-                ^4$)
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-    add_at(ax, "mutate", loc=2)
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-    grid[0].set_xlim(0, 1)
-    grid[0].set_ylim(0, 1)
-    grid[0].axis["bottom"].toggle(ticklabels=False)
-    grid[0].axis["left"].toggle(ticklabels=False)
-    fig.subplots_adjust(left=0.05, right=0.95, bottom=0.05, top=0.95)
-
-    plt.draw()
-    plt.show()
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-.. image:: 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-
-
-
-字典中，connectionstyle 参数控制路径的风格：
-
-
-
-::
-
-    Name	Attr
-    angle	angleA=90,angleB=0,rad=0.0
-    angle3	angleA=90,angleB=0
-    arc	        angleA=0,angleB=0,armA=None,armB=None,rad=0.0
-    arc3	rad=0.0
-    bar	        armA=0.0,armB=0.0,fraction=0.3,angle=None
-
-
-
-
-
-::
-
-    import matplotlib.pyplot as plt
-    import matplotlib.patches as mpatches
-
-    fig = plt.figure(1, figsize=(8,5))
-    fig.clf()
-    from mpl_toolkits.axes_grid.axes_grid import AxesGrid
-    from mpl_toolkits.axes_grid.anchored_artists import AnchoredText
-
-    #from matplotlib.font_manager import FontProperties
-
-    def add_at(ax, t, loc=2):
-        ^4$fp = dict(size=8)
-        ^4$_at = AnchoredText(t, loc=loc, prop=fp)
-        ^4$ax.add_artist(_at)
-        ^4$return _at
-
-
-    grid = AxesGrid(fig, 111, (3, 5), label_mode="1", share_all=True)
-
-    grid[0].set_autoscale_on(False)
-
-
-    x1, y1 = 0.3, 0.3
-    x2, y2 = 0.7, 0.7
-
-
-    def demo_con_style(ax, connectionstyle, label=None):
-
-        ^4$if label is None:
-            ^4$^4$label = connectionstyle
-
-        ^4$x1, y1 = 0.3, 0.2
-        ^4$x2, y2 = 0.8, 0.6
-
-        ^4$ax.plot([x1, x2], [y1, y2], ".")
-        ^4$ax.annotate("",
-                    ^4$^4$xy=(x1, y1), xycoords='data',
-                    ^4$^4$xytext=(x2, y2), textcoords='data',
-                    ^4$^4$arrowprops=dict(arrowstyle="->", #linestyle="dashed",
-                                    ^4$^4$^4$color="0.5",
-                                    ^4$^4$^4$shrinkA=5, shrinkB=5,
-                                    ^4$^4$^4$patchA=None,
-                                    ^4$^4$^4$patchB=None,
-                                    ^4$^4$^4$connectionstyle=connectionstyle,
-                                   ^4$^4$^4$),
-                    ^4$^4$)
-
-        ^4$add_at(ax, label, loc=2)
-
-    column = grid.axes_column[0]
-
-    demo_con_style(column[0], "angle3,angleA=90,angleB=0",
-                   ^4$label="angle3,\nangleA=90,\nangleB=0")
-    demo_con_style(column[1], "angle3,angleA=0,angleB=90",
-                   ^4$label="angle3,\nangleA=0,\nangleB=90")
-
-
-
-    column = grid.axes_column[1]
-
-    demo_con_style(column[0], "arc3,rad=0.")
-    demo_con_style(column[1], "arc3,rad=0.3")
-    demo_con_style(column[2], "arc3,rad=-0.3")
-
-
-
-    column = grid.axes_column[2]
-
-    demo_con_style(column[0], "angle,angleA=-90,angleB=180,rad=0",
-                   ^4$label="angle,\nangleA=-90,\nangleB=180,\nrad=0")
-    demo_con_style(column[1], "angle,angleA=-90,angleB=180,rad=5",
-                   ^4$label="angle,\nangleA=-90,\nangleB=180,\nrad=5")
-    demo_con_style(column[2], "angle,angleA=-90,angleB=10,rad=5",
-                   ^4$label="angle,\nangleA=-90,\nangleB=10,\nrad=0")
-
-
-    column = grid.axes_column[3]
-
-    demo_con_style(column[0], "arc,angleA=-    90,angleB=0,armA=30,armB=30,rad=0",
-                       ^4$label="arc,\nangleA=-90,\nangleB=0,\narmA=30,\narmB=30,\nrad=0")
-    demo_con_style(column[1], "arc,angleA=-90,angleB=0,armA=30,armB=30,rad=5",
-                   ^4$label="arc,\nangleA=-90,\nangleB=0,\narmA=30,\narmB=30,\nrad=5")
-    demo_con_style(column[2], "arc,angleA=-90,angleB=0,armA=0,armB=40,rad=0",
-                   ^4$label="arc,\nangleA=-90,\nangleB=0,\narmA=0,\narmB=40,\nrad=0")
-
-
-    column = grid.axes_column[4]
-
-    demo_con_style(column[0], "bar,fraction=0.3",
-                   ^4$label="bar,\nfraction=0.3")
-    demo_con_style(column[1], "bar,fraction=-0.3",
-                   ^4$label="bar,\nfraction=-0.3")
-    demo_con_style(column[2], "bar,angle=180,fraction=-0.2",
-                   ^4$label="bar,\nangle=180,\nfraction=-0.2")
-
-
-    #demo_con_style(column[1], "arc3,rad=0.3")
-    #demo_con_style(column[2], "arc3,rad=-0.3")
-
-
-    grid[0].set_xlim(0, 1)
-    grid[0].set_ylim(0, 1)
-    grid.axes_llc.axis["bottom"].toggle(ticklabels=False)
-    grid.axes_llc.axis["left"].toggle(ticklabels=False)
-    fig.subplots_adjust(left=0.05, right=0.95, bottom=0.05, top=0.95)
-
-    plt.draw()
-    plt.show()
-
-
-.. image:: data:image/png;base64,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
-
-
-*arrowstyle* 参数控制小箭头的风格：
-
-
-::
-
-    Name	Attrs
-    -	        None
-    ->	        head_length=0.4,head_width=0.2
-    -[	        widthB=1.0,lengthB=0.2,angleB=None
-    |-|	        widthA=1.0,widthB=1.0
-    -|>	        head_length=0.4,head_width=0.2
-    <-	        head_length=0.4,head_width=0.2
-    <->	        head_length=0.4,head_width=0.2
-    <|-	        head_length=0.4,head_width=0.2
-    <|-|>	head_length=0.4,head_width=0.2
-    fancy	head_length=0.4,head_width=0.4,tail_width=0.4
-    simple	head_length=0.5,head_width=0.5,tail_width=0.2
-    wedge	tail_width=0.3,shrink_factor=0.5
-
-
-
-
-
-::
-
-    import matplotlib.patches as mpatches
-    import matplotlib.pyplot as plt
-
-    styles = mpatches.ArrowStyle.get_styles()
-
-    ncol=2
-    nrow = (len(styles)+1) // ncol
-    figheight = (nrow+0.5)
-    fig1 = plt.figure(1, (4.*ncol/1.5, figheight/1.5))
-    fontsize = 0.2 * 70
-
-
-    ax = fig1.add_axes([0, 0, 1, 1], frameon=False, aspect=1.)
-
-    ax.set_xlim(0, 4*ncol)
-    ax.set_ylim(0, figheight)
-
-    def to_texstring(s):
-        ^4$s = s.replace("<", r"$<$")
-        ^4$s = s.replace(">", r"$>$")
-        ^4$s = s.replace("|", r"$|$")
-        ^4$return s
-
-    for i, (stylename, styleclass) in enumerate(sorted(styles.items())):
-        ^4$x = 3.2 + (i//nrow)*4
-        ^4$y = (figheight - 0.7 - i%nrow) # /figheight
-        ^4$p = mpatches.Circle((x, y), 0.2, fc="w")
-        ^4$ax.add_patch(p)
-
-        ^4$ax.annotate(to_texstring(stylename), (x, y),
-                    ^4$^4$(x-1.2, y),
-                    ^4$^4$#xycoords="figure fraction", textcoords="figure     fraction",
-                    ^4$^4$ha="right", va="center",
-                    ^4$^4$size=fontsize,
-                    ^4$^4$arrowprops=dict(arrowstyle=stylename,
-                                    ^4$^4$^4$patchB=p,
-                                    ^4$^4$^4$shrinkA=5,
-                                    ^4$^4$^4$shrinkB=5,
-                                    ^4$^4$^4$fc="w", ec="k",
-                                    ^4$^4$^4$connectionstyle="arc3,rad=-0.05",
-                                    ^4$^4$^4$),
-                    ^4$^4$bbox=dict(boxstyle="square", fc="w"))
-
-    ax.xaxis.set_visible(False)
-    ax.yaxis.set_visible(False)
-
-
-
-    plt.draw()
-    plt.show()
-
-
-
-
-.. image:: 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
-
-
-
-作者 & 更新时间
-------------------------------------
-作者:`李金  <lijinwithyou@gmail.com>`
-
-提交: 2017/12/14
\ No newline at end of file
diff --git a/docs/sources/aipy-numpy/cn/7tag.rst.txt b/docs/sources/aipy-numpy/cn/7tag.rst.txt
deleted file mode 100644
index 305e2f9..0000000
--- a/docs/sources/aipy-numpy/cn/7tag.rst.txt
+++ /dev/null
@@ -1,317 +0,0 @@
-
-标签
-============
-
-
-
-::
-
-    import numpy as np
-    import matplotlib as mpl
-    import matplotlib.pyplot as plt
-
-    %matplotlib inline
-
-
-legend() 函数被用来添加图像的标签，其主要相关的属性有：
-
-
-- legend entry - 一个 legend 包含一个或多个 entry，一个 entry 对应一个 key 和一个 label
-- legend key - marker 的标记
-- Nlegend label - key 的说明
-- legend handle - 一个 entry 在图上对应的对象
-
-
-
-使用 legend
---------------
-
-调用 *legend()* 会自动获取当前的 *Axes* 对象，并且得到这些 *handles* 和 *labels*，相当于：
-
-    handles, labels = ax.get_legend_handles_labels()
-
-    ax.legend(handles, labels)
-
-
-我们可以在函数中指定 *handles * 的参数：
-
-
-
-
-::
-
-    line_up, = plt.plot([1,2,3], label='Line 2')
-    line_down, = plt.plot([3,2,1], label='Line 1')
-    plt.legend(handles=[line_up, line_down])
-    plt.show()
-
-
-.. image:: data:image/png;base64,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-可以将 *labels* 作为参数输入 *legend* 函数：
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-::
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-    line_up, = plt.plot([1,2,3])
-    line_down, = plt.plot([3,2,1])
-    plt.legend([line_up, line_down], ['Line Up', 'Line Down'])
-    plt.show()
-
-.. image:: data:image/png;base64,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
-
-
-产生特殊形状的 marker key
----------------------------------
-
-
-有时我们可以产生一些特殊形状的 marker：
-
-
-块状：
-
-
-
-
-::
-
-    import matplotlib.patches as mpatches
-
-    red_patch = mpatches.Patch(color='red', label='The red data')
-    plt.legend(handles=[red_patch])
-
-    plt.show()
-
-.. image:: data:image/png;base64,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
-
-
-点线组合：
-
-
-
-
-::
-
-    import matplotlib.lines as mlines
-    import matplotlib.pyplot as plt
-
-    blue_line = mlines.Line2D([], [], color='blue', marker='*',
-                          markersize=15, label='Blue stars')
-    plt.legend(handles=[blue_line])
-
-    plt.show()
-
-.. image:: data:image/png;base64,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-指定 legend 的位置
--------------------------
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-*bbox_to_anchor* 关键词可以指定 *legend* 放置的位置，例如放到图像的右上角：
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-
-
-::
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-    plt.plot([1,2,3], label="test1")
-    plt.plot([3,2,1], label="test2")
-    plt.legend(bbox_to_anchor=(1, 1),
-               ^4$bbox_transform=plt.gcf().transFigure)
-
-    plt.show()
-
-
-.. image:: data:image/png;base64,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
-
-
-更复杂的用法：
-
-
-
-
-::
-
-    plt.subplot(211)
-    plt.plot([1,2,3], label="test1")
-    plt.plot([3,2,1], label="test2")
-    # Place a legend above this legend, expanding itself to
-    # fully use the given bounding box.
-    plt.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3,
-               ^4$ncol=2, mode="expand", borderaxespad=0.)
-
-    plt.subplot(223)
-    plt.plot([1,2,3], label="test1")
-    plt.plot([3,2,1], label="test2")
-    # Place a legend to the right of this smaller figure.
-    plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
-
-    plt.show()
-
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-.. image:: data:image/png;base64,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
-
-
-同一个 Axes 中的多个 legend
------------------------------------
-
-
-可以这样添加多个 legend：
-
-
-
-
-
-::
-
-    line1, = plt.plot([1,2,3], label="Line 1", linestyle='--')
-    line2, = plt.plot([3,2,1], label="Line 2", linewidth=4)
-
-    # Create a legend for the first line.
-    first_legend = plt.legend(handles=[line1], loc=1)
-
-    # Add the legend manually to the current Axes.
-    ax = plt.gca().add_artist(first_legend)
-
-    # Create another legend for the second line.
-    plt.legend(handles=[line2], loc=4)
-
-    plt.show()
-
-
-
-.. image:: data:image/png;base64,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
-
-
-
-其中 *loc* 参数可以取 0-10 或者 字符串，表示放置的位置：
-
-
-::
-
-    loc string	    loc code
-    'best'	        0
-    'upper right'	1
-    'upper left'	2
-    'lower left'	3
-    'lower right'	4
-    'right'	        5
-    'center left'	6
-    'center right'	7
-    'lower center'	8
-    'upper center'	9
-    'center'	        10
-
-
-
-更多用法
--------------
-
-
-多个 *handle* 可以通过括号组合在一个 *entry* 中：
-
-
-
-
-::
-
-    from numpy.random import randn
-
-    z = randn(10)
-
-    red_dot, = plt.plot(z, "ro", markersize=15)
-    # Put a white cross over some of the data.
-    white_cross, = plt.plot(z[:5], "w+", markeredgewidth=3, markersize=15)
-
-    plt.legend([red_dot, (red_dot, white_cross)], ["Attr A", "Attr A+B"])
-
-    plt.show()
-
-
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-.. image:: 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
-
-
-自定义 *handle*：
-
-
-
-
-::
-
-    import matplotlib.pyplot as plt
-    import matplotlib.patches as mpatches
-
-    class AnyObject(object):
-        ^4$pass
-
-    class AnyObjectHandler(object):
-        ^4$def legend_artist(self, legend, orig_handle, fontsize, handlebox):
-            ^4$^4$x0, y0 = handlebox.xdescent, handlebox.ydescent
-            ^4$^4$width, height = handlebox.width, handlebox.height
-            ^4$^4$patch = mpatches.Rectangle([x0, y0], width, height, facecolor='red',
-                                       ^4$^4$^4$edgecolor='black', hatch='xx', lw=3,
-                                       ^4$^4$^4$transform=handlebox.get_transform())
-            ^4$^4$handlebox.add_artist(patch)
-            ^4$^4$return patch
-
-    plt.legend([AnyObject()], ['My first handler'],
-               ^4$handler_map={AnyObject: AnyObjectHandler()})
-
-    plt.show()
-
-
-.. image:: data:image/png;base64,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-椭圆：
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-::
-
-    from matplotlib.legend_handler import HandlerPatch
-    import matplotlib.pyplot as plt
-    import matplotlib.patches as mpatches
-
-
-    class HandlerEllipse(HandlerPatch):
-        ^4$def create_artists(self, legend, orig_handle,
-                           ^4$^4$^4$xdescent, ydescent, width, height, fontsize,     trans):
-            ^4$^4$center = 0.5 * width - 0.5 * xdescent, 0.5 * height - 0.5 *     ydescent
-            ^4$^4$p = mpatches.Ellipse(xy=center, width=width + xdescent,
-                                  ^4$^4$^4$height=height + ydescent)
-            ^4$^4$self.update_prop(p, orig_handle, legend)
-            ^4$^4$p.set_transform(trans)
-            ^4$^4$return [p]
-
-
-    c = mpatches.Circle((0.5, 0.5), 0.25, facecolor="green",
-                    ^4$edgecolor="red", linewidth=3)
-    plt.gca().add_patch(c)
-
-    plt.legend([c], ["An ellipse, not a rectangle"],
-                ^4$handler_map={mpatches.Circle: HandlerEllipse()})
-
-    plt.show()
-
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-.. image:: data:image/png;base64,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
-
-
-作者 & 更新时间
-------------------------------------
-作者:`李金  <lijinwithyou@gmail.com>`
-
-提交: 2017/12/14
-
-
diff --git a/docs/sources/aipy-numpy/cn/8figures-subplots-axes-ticks.rst.txt b/docs/sources/aipy-numpy/cn/8figures-subplots-axes-ticks.rst.txt
deleted file mode 100644
index 2214b67..0000000
--- a/docs/sources/aipy-numpy/cn/8figures-subplots-axes-ticks.rst.txt
+++ /dev/null
@@ -1,180 +0,0 @@
-
-figures, subplots, axes 和 ticks 对象
-==============================================
-
-figures, axes 和 ticks 的关系
-----------------------------------------
-
-
-这些对象的关系可以用下面的图来表示：
-
-
-示例图像：
-
-
-<img src="./artists_figure.png" width = "600" height = "400" alt="图1" align=left />
-
-
-具体结构：
-<img src="./artists_tree.png" width = "600" height = "400" alt="图2" align=left />
-
-
-figure 对象
----------------
-
-
-*figure* 对象是最外层的绘图单位，默认是以 *1* 开始编号（**MATLAB** 风格，*Figure 1*, *Figure 2*,*...*），可以用 *plt.figure()* 产生一幅图像，除了默认参数外，可以指定的参数有：
-
-
-- num - 编号
-- figsize - 图像大小
-- dpi - 分辨率
-- facecolor - 背景色
-- edgecolor - 边界颜色
-- frameon - 边框
-
-
-这些属性也可以通过 *Figure* 对象的 *set_xxx* 方法来改变。
-
-
-subplot 和 axes 对象
---------------------------
-
-subplot
--------------
-
-
-*subplot* 主要是使用网格排列子图：
-
-
-
-
-::
-
-    %pylab inline
-
-    subplot(2,1,1)
-    xticks([]), yticks([])
-    text(0.5,0.5, 'subplot(2,1,1)',ha='center',va='center',size=24,alpha=.5)
-
-    subplot(2,1,2)
-    xticks([]), yticks([])
-    text(0.5,0.5, 'subplot(2,1,2)',ha='center',va='center',size=24,alpha=.5)
-
-    show()
-
-
-Populating the interactive namespace from numpy and matplotlib
-
-
-.. image:: 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-.. image:: data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAV0AAADtCAYAAAAcNaZ2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAHYJJREFUeJzt3VtMFOffB/Dv7LKyLCynVagIohb4q5WDiqLBYJuoTS1abU1sPfWitfmnJ7nrNddvmtiLpvHCxAaN1moEkQSrpoJtQVGBoggqArKsCILrCsvCMjvvBe/My2F3OYgPit9PQiI7h+fZxf3uM8/8ZlZSFAVERCSGbro7QET0JmHoEhEJxNAlIhKIoUtEJBBDl4hIIIYuEZFAAf4WSpLEejIioklQFEXy9rjf0P2/Dae+N0REM5gkec1bAJxeICISiqFLRCQQQ5eISCCGLhGRQAxdIiKBGLpERAIxdImIBGLoEhEJxNAlIhKIoUtEJBBDl4hIIIYuEZFADF0iIoEYukREAjF0iYgEYugSEQnE0CUiEoihS0QkEEOXiEgghi4RkUAMXSIigRi6REQCMXSJiARi6BIRCcTQJSISiKFLRCQQQ5eISCCGLhGRQAxdIiKBGLpERAIxdImIBGLoEhEJxNAlIhKIoUtEJBBDl4hIIIYuEZFADF0iIoECprsD9PI0NTXh119/RVhYGHJycoS2nZ+fj+rqaqxfvx7vvvuu0LaHOnz4MFpbW/Hdd98hIiJi2vrxKigsLMTNmzfx2WefISkpabq788Zi6L4BJEmaEW3b7XZUVVXBaDRizZo1Y65fW1sLq9WKtLS0UYHb19eHuro6NDQ0wGaz4dmzZ1AUBWazGQsWLEBGRgbeeuutSfVTlmU0NjbCZrOhtbUVNpsN3d3dAIDdu3cjISFhUvsFgP7+fjQ2Nmr7tdls6O3tBQB8++23sFgsPrfNyspCVVUVLl68iMTExGn9f/EmY+jSa8Nut6OkpATh4eFjhq7H48GlS5cgSRKysrJGLT906BCePn2q/W4wGCBJkhbs1dXV2LhxI9auXTvhfnZ0dODYsWNel71o0D148AC//fbbpLYNCwtDamoqKisrUVVVheXLl79QX2hyGLo0I9XX16OrqwuLFi3yOq3g8Xgwd+5crFixAomJiQgLCwMAPH78GMXFxWhqasIff/yB2bNnIzExccLtG41GxMTEaD8nT5584eekCg4ORkxMDObNmwez2YzCwsJxb7tixQpUVlairKyMoTtNGLo0I924cQMAkJKS4nX5xx9/jPnz5496PDo6Grt378ahQ4fw5MkT/PPPPxMO3ejoaPzwww8T7/Q4/Oc//8HixYu13+12+4S2j42NRUREBDo6OtDS0oK4uLip7iKNgaE7DWRZRkVFBW7fvo2Ojg709/cjKCgIISEhiI+PR0pKCmJjYwEAly9fRklJCVJTU7Ft2zav+xvvSav6+nqUlZWhra0NHo8H0dHRWL16NZKTk72un5ubCwA4cOAA+vv7UVpaiqamJrhcLoSHhyMlJQWZmZnQ6/UTfg0GBgZQUVGBW7du4cmTJ5BlGWFhYUhKSkJmZiZCQkKGrX/w4EE8e/YMwGDQqH1TffTRR0hLSwMAOBwONDQ0QKfTYcmSJV7b9xa4qoCAACxbtgyXL1/Go0ePJvzcXuZc6VTs+5133sFff/2FyspKhu40YOgK5vF4kJeXh+bmZgCDb6LAwEC4XC44nU60t7fD6XRix44dw7Ybz5vN1zqKoqC8vBznz5/X2hsYGIDVaoXVakVLSws2b97sc78tLS0oLCyE2+1GYGAgAKCzsxN//vkn7t27h71792LWrFnjfQnQ09ODo0ePoq2tDcBgyAUEBKCrqwvl5eWoqqrC7t27tQ8eYPCQur+/H729vZAkCcHBwcP2aTAYtH83NTUBAObMmTOhfg0VFBQEYPDvNdOor2tjY+M09+TNxNAVrKamBs3NzTAYDNiyZQuWLl0KvV4PRVHgcDhw9+5d9PX1TWmbTqcTFy5cQGpqKjZu3Ijg4GC4XC6UlpairKwMFRUViIuL8zniLSoqQlRUFLZu3YqoqCjIsoyamhoUFRXBarXi/Pnz2LJly7j7c+bMGbS1tSEoKAjZ2dlYsmQJJEmCzWZDQUEB2tvbceLECXz99dcwmUwAgP379w8rgTtw4IDP/asfaDExMRN4lYZTgzsqKmrS+3hVzZs3D8DgEYPD4UBoaOg09+jNwosjBLNarQCA1NRUJCcna4fmkiQhLCwMq1atwrp166a0TbfbjYULF2Lbtm3aCNFoNGLTpk1ITU0FMDiN4UtAQAD27NmjBZBer0daWho+/PBDAEBlZaV26D+W5uZmNDQ0AAA++eQTLF26VBuhx8TEYN++fTAajejp6cHVq1cn9XzVKYE5c+ZMevu6ujoAmJEnm0JCQmA0GgEAra2t09ybNw9DVzD18Pz58+dC2/UV5Go5VVdXl3a4P1J6err2Jh0qNTUVoaGhUBQFd+7cGVc/amtrAQwG7Ntvvz1qeXBwMNLT0wEAt2/fHtc+R+rp6QHw/1MEE9HX14fTp09DURStumEmUl8bp9M5zT158zB0BVPPhNfX1+P48eO4c+eOVtz+suj1ep8njiIjI7WTVr5OGi1YsMDr45Ikafv1FdgjqW0sXLjQ5zrqsq6uLrjd7nHtdyg1SCYauh6PB6dPn0ZnZyeMRiN27NgxYy8gUF8b9QOKxOGcrmDx8fF47733UFJSgrt37+Lu3bsAAIvFgqSkJKSnpyMyMnJK2zSZTNDpfH++ms1mdHd3+xz1+JvzM5vNAMb/5lXbULfz156iKHA6nVoN7cukKAry8/Nx7949GAwG7Nq1a8r/DkQAQ3daZGVlISUlBbdu3UJTUxOsVis6OztRVlaGq1evYuvWrdpc60w1MDDw0vZtMpngcDjGfQShKArOnTuHmpoa6PV6fPrppzO+lEp9bUZWgdDLx9CdJuHh4Vi3bh3WrVsHRVHQ3NyMy5cvo7m5GUVFRUhISEBwcLA2QvUXUi6Xy29bTqcTHo/H52hXnV/29QZ0OBw+R31jbTuSyWRCZ2en3xNvDocDwOD0hVq9MBHBwcFwOBzjnq8sLi7GzZs3odPpsGPHDixatGjCbb5uGLrTh3O6rwBJkrBgwQLs2rULOp0Obrdbm/tUT2CpQTSSoihjFvDLsoyWlhavy7q6urSbscydO9frOmoJlre21WW+th1JLeNSS7K8UetHLRbLsPpbdX5VUZRxtdHe3j5mfy5evIhr165BkiRs37592NVeM1V3d7f2Qf0iZXU0OQxdwWRZ9rlMp9NpwaKObKOjowEMlvao4ThUTU2Nz0Ae6sqVK34ft1gsWlsjVVRUeB1N//vvv3j+/DkkSfJ55ddIS5cuBTB4Uxi1LGuo7u5uXL9+HcDglVNDqZUfY9Uxqyf3bDab3/VKSkrw999/Q5IkbNmyBcuWLRvXc3jdqWWL4eHhrNGdBgxdwc6cOYOCggI0NDQMCw+73Y78/HzIsgyDwYD4+HgAQFxcHMxmM2RZxqlTp7Rr7d1uN65fv47CwkKv5VxDGQwGNDY2oqCgQDvh5XK5cOHCBVRVVQGA38uHBwYGcPToUW3kKMsyqqqqcO7cOQCDN1EZ75t3/vz52q0NCwoKUFtbq41cbTYb8vLy4HK5EBISgoyMjGHbWiwW6HQ6uFwuvyVqavXDkydPfM7rlpeXa7XJH3zwwYTrcY8cOYLc3FwcOXLE6/Le3l44nU7tR6Veeaj+eLvi7eDBg8jNzUV+fr7XfQ/dfujzG9mmryMC9ajnTZhGeRVxTlewgYEB3L59Wwu7wMBAyLKsjWx1Oh2ys7O1kh6dTofNmzfj5MmTaG5uxk8//YRZs2bB7XZDURQsX74cHo8H1dXVPts0mUxYu3YtiouLtfvRDh25rl692u8oLzs7G2fPnsUvv/yCwMBAuN1uLSxiY2Px/vvvT+g12L59O/Ly8tDW1obff/8der0eer0e/f39AAbLmXbu3Dmq5MtgMCA5ORnV1dU4efIkAgMDtQ+cTZs2aaNos9mMhIQE3L9/H7W1tVi5cuWoPpw/fx7A4JRFSUkJSkpKfPb3q6++8vmh4quk7NChQ17nrU+dOjXs988//9xvSZ43//M//+P18cOHDw/7PScnx2vlh1orPRMv/HgdMHQF27BhA+bPn4/GxkZ0dXVpJ6IiIyMRHx+PNWvWjLr0dPHixdi7dy9KS0vx6NEjrXB/1apVSEtL8zkiUkmShIyMDERERGg3vDEYDGPe8EYVFxeHL7/8UrvhjSzLiIyMRHJy8qRueGMymfDFF19oN7zp7OyELMuwWCxITEz0esMbVXZ2NsxmM+rq6mC327VgG1nPu3LlSty/fx81NTVeQ1elKMqY5W7eRozq3+1F50RHBqvH49FGxi9jvrWlpQV2ux1RUVHD7m1B4kj+TkpIkqSMddKCZi71Tl6+RkyvMo/Hg59//hlPnz7FN9984/cbFSaqu7sbP/74IwwGA3JyciZVYeGL1WrF4cOHERoaiu+//35Sd3Dz5+zZs6isrMTWrVs50n2JJEmCoiheD1U4p0szkk6nw4YNG6Aois+TiJOlVl6kp6dPaeAO3fdkb5npj91uR3V1NebMmaPdBpPEY+jSjLVkyRLExsaipqZm2FfzvKiHDx/CYDAgMzNzyvY5dN9ms9nvlMhkXblyBR6PBxs2bJixlze/Dji9QD69ztMLRNOJ0wtERK8IjnSJiKYYR7pERK8Ihi4RkUAMXSIigRi6REQCMXSJiARi6BIRCcTQJSISiKFLRCQQQ5eISCCGLhGRQAxdIiKBGLpERAIxdImIBGLoEhEJxNAlIhKIoUtEJBBDl4hIIIYuEZFADF0iIoEYukREAjF0iYgEYugSEQnE0CUiEoihS0QkEEOXiEgghi4RkUAMXSIigRi6REQCMXSJiARi6BIRCcTQJSISiKFLRCQQQ5eISCCGLhGRQAxdIiKBGLpERAIxdImIBAoYawVJkkT0g4jojSApijLdfSAiemNweoGISCCGLhGRQAxdIiKBGLpERAIxdImIBGLoEhEJxNAlIhKIoUtEJBBDl4hIIIYuEZFADF0iIoEYukREAjF0iYgEYugSEQnE0CUiEoihS0QkEEOXiEgghi4RkUB+vyNNkiR+lw8R0SQoiuL1CybH/GJKfocaEdHE+PtCX04vEBEJxNAlIhKIoUtEJBBDl4hIIIYuEZFADF0iIoEYukREAjF0iYgEYugSEQnE0CUiEoihS0QkEEOXiEgghi4RkUAMXSIigRi6REQCMXSJiARi6BIRCcTQJSISiKFLRCQQQ5eISCCGLhGRQAxdIiKBGLpERAIxdImIBGLoEhEJxNAlIhKIoUtEJBBDl4hIIIYuEZFADF0iIoEYukREAjF0iYgEYugSEQnE0CUiEoihS0QkEEOXiEgghi4RkUAMXSIigQKmuwP08jQ1NeHXX39FWFgYcnJyhLadn5+P6upqrF+/Hu+++67Qtoc6fPgwWltb8d133yEiImLa+vEqKCwsxM2bN/HZZ58hKSlpurvzxmLovgEkSZoRbdvtdlRVVcFoNGLNmjVjrl9bWwur1Yq0tLRRgdvX14e6ujo0NDTAZrPh2bNnUBQFZrMZCxYsQEZGBt56661J9VOWZTQ2NsJms6G1tRU2mw3d3d0AgN27dyMhIWFS+wWAZ8+e4c6dO3jw4AEeP36M7u5u6PV6REREIDExEWvWrEFISIjXbbOyslBVVYWLFy8iMTFxWv9fvMkYuvTasNvtKCkpQXh4+Jih6/F4cOnSJUiShKysrFHLDx06hKdPn2q/GwwGSJKkBXt1dTU2btyItWvXTrifHR0dOHbsmNdlLxJ0z549w8GDB4c9FhgYCLfbjfb2drS3t+PGjRvYuXMnFixYMGr7sLAwpKamorKyElVVVVi+fPmk+0KTx9ClGam+vh5dXV1YtGiR12kFj8eDuXPnYsWKFUhMTERYWBgA4PHjxyguLkZTUxP++OMPzJ49G4mJiRNu32g0IiYmRvs5efLkCz8nRVEAAElJSUhLS8PChQthNBrh8Xjw4MEDFBUVwW6348SJE/j222+9jnhXrFiByspKlJWVMXSnCUOXZqQbN24AAFJSUrwu//jjjzF//vxRj0dHR2P37t04dOgQnjx5gn/++WfCoRsdHY0ffvhh4p0eQ1BQEP773/8iOjp62OM6nQ4JCQlav/v6+nDjxg2sX79+1D5iY2MRERGBjo4OtLS0IC4ubsr7Sf4xdKeBLMuoqKjA7du30dHRgf7+fgQFBSEkJATx8fFISUlBbGwsAODy5csoKSlBamoqtm3b5nV/4z1pVV9fj7KyMrS1tcHj8SA6OhqrV69GcnKy1/Vzc3MBAAcOHEB/fz9KS0vR1NQEl8uF8PBwpKSkIDMzE3q9fsKvwcDAACoqKnDr1i08efIEsiwjLCwMSUlJyMzMHDVKO3jwIJ49ewZgcJpB7Zvqo48+QlpaGgDA4XCgoaEBOp0OS5Ys8dq+t8BVBQQEYNmyZbh8+TIePXo04ef2suZKAwMDRwXuULNnz0ZsbCyampr89vudd97BX3/9hcrKSobuNGDoCubxeJCXl4fm5mYAg2/QwMBAuFwuOJ1OtLe3w+l0YseOHcO2G88b2dc6iqKgvLwc58+f19obGBiA1WqF1WpFS0sLNm/e7HO/LS0tKCwshNvtRmBgIACgs7MTf/75J+7du4e9e/di1qxZ430J0NPTg6NHj6KtrQ3AYMgFBASgq6sL5eXlqKqqwu7du7UPHgAIDg5Gf38/ent7IUkSgoODh+3TYDBo/25qagIAzJkzZ0L9GiooKAjA4N/rdTKefquva2Njo5A+0XAMXcFqamrQ3NwMg8GALVu2YOnSpdDr9VAUBQ6HA3fv3kVfX9+Utul0OnHhwgWkpqZi48aNCA4OhsvlQmlpKcrKylBRUYG4uDifI96ioiJERUVh69atiIqKgizLqKmpQVFREaxWK86fP48tW7aMuz9nzpxBW1sbgoKCkJ2djSVLlkCSJNhsNhQUFKC9vR0nTpzA119/DZPJBADYv3//sBK4AwcO+Ny/+oEWExMzgVdpODW4o6KiJr0P0TweDx4+fAjAf7/nzZsHYPCIweFwIDQ0VEj/aBAvjhDMarUCAFJTU5GcnKwdmkuShLCwMKxatQrr1q2b0jbdbjcWLlyIbdu2aSNEo9GITZs2ITU1FcDgNIYvAQEB2LNnj/ZG1uv1SEtLw4cffggAqKys1A79x9Lc3IyGhgYAwCeffIKlS5dqI/SYmBjs27cPRqMRPT09uHr16qSer3poPWfOnElvX1dXBwCv1cmma9euoaenB5IkaVMt3oSEhMBoNAIAWltbRXWP/g9DVzD18Pz58+dC2/UV5Go5VVdXl3a4P1J6err2Jh0qNTUVoaGhUBQFd+7cGVc/amtrAQwG7Ntvvz1qeXBwMNLT0wEAt2/fHtc+R+rp6QHw/4faE9HX14fTp09DURStuuF18PjxY1y6dAkAsHr1asyePdvv+upr43Q6X3rfaDiGrmDqmfD6+nocP34cd+7cQW9v70ttU6/X+zxxFBkZqZ208nXyxVvNJzA4Olf36yuwR1LbWLhwoc911GVdXV1wu93j2u9QapBMNHQ9Hg9Onz6Nzs5OGI1G7Nix47W4gOD58+c4ceIEBgYGEBMTg40bN465jfraqB9QJA7ndAWLj4/He++9h5KSEty9exd3794FAFgsFiQlJSE9PR2RkZFT2qbJZIJO5/vz1Ww2o7u72+eox9+cn9lsBjD+N6/ahrqdv/YURYHT6dRqaF8mRVGQn5+Pe/fuwWAwYNeuXVP+d3gZent7kZeXB7vdDovFgl27dk2qmoTEYehOg6ysLKSkpODWrVtoamqC1WpFZ2cnysrKcPXqVWzdulWba52pBgYGXtq+TSYTHA7HuI8gFEXBuXPnUFNTA71ej08//fS1KKVyuVzIy8tDR0cHwsLCsG/fvlFVHb6or81416epw9CdJuHh4Vi3bh3WrVsHRVHQ3NyMy5cvo7m5GUVFRUhISEBwcLA2QvUXUi6Xy29bTqcTHo/H52hXnV/29QZ0OBw+R31jbTuSyWRCZ2en3xNvDocDwOD0hVq9MBHBwcFwOBzjnq8sLi7GzZs3odPpsGPHDixatGjCbYrW39+PY8eO4dGjRwgJCcG+ffsmVIXA0J0+nNN9BUiShAULFmDXrl3Q6XRwu93a3Kd6AksNopEURRmzgF+WZbS0tHhd1tXVpd2MZe7cuV7XUUuwvLWtLvO17UhqGZdakuWNWj9qsViG1d+q86vq5bBjtdHe3j5mfy5evIhr165BkiRs374dixcvHnOb6eZ2u3H8+HFYrVaYTCbs27dvQlMh3d3d2gf1i5TV0eQwdAWTZdnnMp1OpwWLOrJVr0BqbW3VwnGompoan4E81JUrV/w+brFYfF7tVFFR4XU0/e+//+L58+eQJMnnlV8jLV26FMDgTWHUsqyhuru7cf36dQCDV04NpVZ+jFXHrJ7cs9lsftcrKSnB33//DUmSsGXLFixbtmxcz2E6ybKM3377DU1NTTAajdi7d++ES+PUssXw8HDW6E4Dhq5gZ86cQUFBARoaGoaFh91uR35+PmRZhsFgQHx8PAAgLi4OZrMZsizj1KlTsNvtAAZHO9evX0dhYaHXcq6hDAYDGhsbUVBQoJ3wcrlcuHDhAqqqqgDA7+XDAwMDOHr0qDZylGUZVVVVOHfuHIDBm6iM9807f/587daGBQUFqK2t1UauNpsNeXl5cLlcCAkJQUZGxrBtLRYLdDodXC6X3xI1tfrhyZMnPud1y8vLtdrkDz74YML1uEeOHEFubi6OHDnidXlvby+cTqf2o1KvPFR/vF05dvDgQeTm5iI/P3/Y42p1RUNDAwIDA7Fnz55J3X5SPep5HaZRZiLO6Qo2MDCA27dva2EXGBgIWZa1ka1Op0N2drZW0qPT6bB582acPHkSzc3N+OmnnzBr1iy43W4oioLly5fD4/GgurraZ5smkwlr165FcXGxdj/aoSPX1atX+x3lZWdn4+zZs/jll1+0WwmqYREbG4v3339/Qq/B9u3bkZeXh7a2Nvz+++/Q6/XQ6/Xo7+8HMFjOtHPnzlElXwaDAcnJyaiursbJkycRGBiofeBs2rRJG0WbzWYkJCTg/v37qK2txcqVK0f14fz58wAGpyxKSkpQUlLis79fffWVzw8VXyVlhw4d8jpvferUqWG/f/75535L8oZqaWnRPmxkWcbx48d99jksLAz79+/3ukytlX6dLvyYSRi6gm3YsAHz589HY2Mjurq6tBNRkZGRiI+Px5o1a0Zdwrl48WLs3bsXpaWlePTokVa4v2rVKqSlpY0aEY0kSRIyMjIQERGh3fDGYDCMecMbVVxcHL788kvthjeyLCMyMhLJycmTuuGNyWTCF198od3wprOzE7Isw2KxIDEx0esNb1TZ2dkwm82oq6uD3W7Xgm1kPe/KlStx//591NTUeA1dlaIoY5a7eZtDVv9uLzonOjJYPR6PNjIeue+h/RgYGPB7cnXoXPhQLS0tsNvtiIqKGnZvCxJH8ndSQpIkZayTFjRzqXfyysnJEVIrO5U8Hg9+/vlnPH36FN988w0sFsuU7bu7uxs//vgjDAYDcnJyJlVh4YvVasXhw4cRGhqK77//fsprbs+ePYvKykps3bqVI92XSJIkKIri9TCIc7o0I+l0OmzYsAGKovg8iThZauVFenr6lAbu0H1P9paZ/tjtdlRXV2POnDl+781ALxdDl2asJUuWIDY2FjU1NcO+mudFPXz4EAaDAZmZmVO2z6H7NpvNfqdEJuvKlSvweDzYsGHDa3F580zF6QXy6XWeXiCaTpxeICJ6RXCkS0Q0xTjSJSJ6RTB0iYgEYugSEQnE0CUiEoihS0QkEEOXiEgghi4RkUAMXSIigRi6REQCMXSJiARi6BIRCcTQJSISiKFLRCQQQ5eISCCGLhGRQAxdIiKBGLpERAIxdImIBGLoEhEJxNAlIhKIoUtEJBBDl4hIIIYuEZFADF0iIoEYukREAjF0iYgEYugSEQnE0CUiEoihS0QkEEOXiEgghi4RkUAMXSIigRi6REQCMXSJiARi6BIRCcTQJSISiKFLRCRQwFgrSJIkoh9ERG8ESVGU6e4DEdEbg9MLREQCMXSJiARi6BIRCcTQJSISiKFLRCTQ/wL05tG4souUcwAAAABJRU5ErkJggg==
-
-
-
-更高级的可以用 *gridspec* 来绘图：
-
-
-
-
-::
-
-    import matplotlib.gridspec as gridspec
-
-    G = gridspec.GridSpec(3, 3)
-
-    axes_1 = subplot(G[0, :])
-    xticks([]), yticks([])
-    text(0.5,0.5, 'Axes 1',ha='center',va='center',size=24,alpha=.5)
-
-    axes_2 = subplot(G[1,:-1])
-    xticks([]), yticks([])
-    text(0.5,0.5, 'Axes 2',ha='center',va='center',size=24,alpha=.5)
-
-    axes_3 = subplot(G[1:, -1])
-    xticks([]), yticks([])
-    text(0.5,0.5, 'Axes 3',ha='center',va='center',size=24,alpha=.5)
-
-    axes_4 = subplot(G[-1,0])
-    xticks([]), yticks([])
-    text(0.5,0.5, 'Axes 4',ha='center',va='center',size=24,alpha=.5)
-
-    axes_5 = subplot(G[-1,-2])
-    xticks([]), yticks([])
-    text(0.5,0.5, 'Axes 5',ha='center',va='center',size=24,alpha=.5)
-
-    show()
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-.. image:: 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
-
-
-
-axes 对象
--------------
-
-
-*subplot* 返回的是 *Axes* 对象，但是 *Axes* 对象相对于 *subplot* 返回的对象来说要更自由一点。*Axes* 对象可以放置在图像中的任意位置：
-
-
-
-
-::
-
-    axes([0.1,0.1,.8,.8])
-    xticks([]), yticks([])
-    text(0.6,0.6, 'axes    ([0.1,0.1,.8,.8])',ha='center',va='center',size=20,alpha=.5)
-
-    axes([0.2,0.2,.3,.3])
-    xticks([]), yticks([])
-    text(0.5,0.5, 'axes    ([0.2,0.2,.3,.3])',ha='center',va='center',size=16,alpha=.5)
-
-    show()
-
-.. image:: 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-    axes([0.4,0.4,.5,.5])
-    xticks([]), yticks([])
-    text(0.1,0.1, 'axes    ([0.4,0.4,.5,.5])',ha='left',va='center',size=16,alpha=.5)
-
-    show()
-
-
-.. image:: 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
-
-
-后面的 Axes 对象会覆盖前面的内容。
-
-
-ticks 对象
----------------
-
-
-*ticks* 用来注释轴的内容，我们可以通过控制它的属性来决定在哪里显示轴、轴的内容是什么等等。
-
-
-作者 & 更新时间
-------------------------------------
-作者:`李金  <lijinwithyou@gmail.com>`
-
-提交: 2017/12/14
diff --git a/docs/sources/aipy-numpy/cn/9dont-trust-default.rst.txt b/docs/sources/aipy-numpy/cn/9dont-trust-default.rst.txt
deleted file mode 100644
index 4db7c74..0000000
--- a/docs/sources/aipy-numpy/cn/9dont-trust-default.rst.txt
+++ /dev/null
@@ -1,641 +0,0 @@
-
-不要迷信默认设置
----------------------
-
-
-导入相关的包：
-
-
-
-
-::
-
-    import numpy as np
-    import matplotlib.pyplot as plt
-
-
-生成三角函数：
-
-
-
-
-::
-
-    x = np.linspace(-np.pi, np.pi)
-    c, s = np.cos(x), np.sin(x)
-
-
-默认绘图
-------------
-
-
-
-
-::
-
-    %matplotlib inline
-
-    # 画图
-    p = plt.plot(x,c)
-    p = plt.plot(x,s)
-
-    # 在脚本中需要加上这句才会显示图像
-    plt.show()
-
-
-<button>Run ...</button>
-
-
-.. image::data:image/png;base64,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-默认效果如图所示，我们可以修改默认的属性来得到更漂亮的结果。
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-图
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-图像以 *Figure #* 为窗口标题，并且数字从 *1* 开始，*figure()* 函数的主要参数如下：
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-
-::
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-    参数	        默认值	                 描述
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-    num	           1	                  图号
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-    figsize        figure.figsize         图大小（宽，高）（单位英寸）
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-    dpi	           figure.dpi	          分辨率（每英寸所打印的点数）
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-    facecolor	   figure.facecolor       背景颜色
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-    edgecolor	   figure.edgecolor       边界颜色
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-    frameon        True                   是否显示图框架
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-
-::
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-    # 设置图像大小
-    f = plt.figure(figsize=(10,6), dpi=80)
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-    # 画图
-    p = plt.plot(x,c)
-    p = plt.plot(x,s)
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-    # 在脚本中需要加上这句才会显示图像
-    plt.show()
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-设置线条颜色，粗细，类型
---------------------------------
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-首先，我们使用 *figure()* 函数来创建一幅新图像，并且指定它的大小，使得长宽比更合适。
-然后，我们使用 *color*, *linewidth*, *linestyle* 参数，指定曲线的颜色，粗细，类型：
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-::
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-    # 设置图像大小
-    f = plt.figure(figsize=(10,6), dpi=80)
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-    # 画图，指定颜色，线宽，类型
-    p = plt.plot(x, c, color="blue", linewidth=2.5, linestyle="-")
-    p = plt.plot(x, s, color="red",  linewidth=2.5, linestyle="-")
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-    # 在脚本中需要加上这句才会显示图像
-    # plt.show()
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-<button>Run ...</button>
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-.. image:: 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
-
-
-
-也可以像 **Matlab** 中一样使用格式字符来修改参数：
-
-
-
-表示颜色的字符参数有：
-
-
-::
-
-    字符	颜色
-   ‘b’	蓝色，blue
-   ‘g’	绿色，green
-   ‘r’	红色，red
-   ‘c’	青色，cyan
-   ‘m’	品红，magenta
-   ‘y’	黄色，yellow
-   ‘k’	黑色，black
-   ‘w’	白色，white
-
-
-
-表示类型的字符参数有：
-
-+------------+----------+---------------+----------------+
-|   字符     | 	  类型| 	        字符| 	       类型| 
-+------------+----------+---------------+----------------+
-|    '-'     |	    实线|	          '--'|	           虚线|
-+------------+----------+---------------+----------------+
-|   '-.'     |	  虚点线|	          ','|            点线|
-+------------+----------+---------------+----------------+
-|    '.'     |	     点 |	        ','|	      像素点|
-+------------+----------+---------------+----------------+
-|    'o'     |	    圆点|	           'v'|	       下三角点|
-+------------+----------+---------------+----------------+
-|    '^'     |  上三角点|	         '<'|        左三角点|
-+------------+----------+---------------+----------------+
-|    '>'     |  右三角点|	         '1'|	     下三叉点|
-+------------+----------+---------------+----------------+
-|    '2'     |	上三叉点|	         '3'|        左三叉点|
-+------------+----------+---------------+----------------+
-|    '4'     |  右三叉点|	         's'|	       正方点|
-+------------+----------+---------------+----------------+
-|    'p'     |	  五角点|	          '*'|	        星形点|
-+------------+----------+---------------+----------------+
-|    'h'     | 六边形点1|	         'H'|       六边形点2|
-+------------+----------+---------------+----------------+
-|    '+'     |    加号点|	          'x'|	        乘号点|
-+------------+----------+---------------+----------------+
-|    'D'     | 实心菱形点|	        'd'|	    瘦菱形点|
-+------------+----------+---------------+----------------+
-|   '_'	     |  横线点  |               |                |
-+------------+----------+---------------+----------------+
-
-
-		
-
-
-::
-
-    # 设置图像大小
-    f = plt.figure(figsize=(10,6), dpi=80)
-
-    # 画图，指定颜色，线宽，类型
-    p = plt.plot(x, c, 'b-', 
-             ^4$x, s, 'r-', linewidth=2.5)
-
-    # 在脚本中需要加上这句才会显示图像
-    # plt.show()
-
-<button>Run ...</button>
-
-
-.. image:: data:image/png;base64,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
-
-
-
-
-设置横轴纵轴的显示区域
-------------------------------
-
-
-我们希望将坐标轴的显示区域放大一些，这样可以看到所有的点，可以使用 *plt* 中的 *xlim* 和 *ylim* 来设置：
-
-
-
-
-::
-
-    # 设置图像大小
-    p = plt.figure(figsize=(10,6), dpi=80)
-
-    # 画图，指定颜色，线宽，类型
-    p = plt.plot(x, c, 'b-', 
-             ^4$x, s, 'r-', linewidth=2.5)
-
-    ####################################################################
-
-    # 设置显示范围
-    p = plt.xlim(x.min() * 1.1, x.max() * 1.1)
-    p = plt.ylim(c.min() * 1.1, c.max() * 1.1)
-
-    ####################################################################
-
-   # 在脚本中需要加上这句才会显示图像
-   # plt.show()
-
-
-
-<button>Run ...</button>
-
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-.. image:: 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-
-
-设置刻度
-------------
-
-
-对于三教函数来说，我们希望将 x 轴的刻度设为与 $\pi$ 有关的点，可以使用 plt 中的 xticks 和 yticks 函数，将需要的刻度传入：
-
-
-
-
-
-::
-
-    # 设置图像大小
-    f = plt.figure(figsize=(10,6), dpi=80)
-
-    # 画图，指定颜色，线宽，类型
-    p = plt.plot(x, c, 'b-', 
-             ^4$x, s, 'r-', linewidth=2.5)
-
-    # 设置显示范围
-    plt.xlim(x.min() * 1.1, x.max() * 1.1)
-    plt.ylim(c.min() * 1.1, c.max() * 1.1)
-
-    ####################################################################
-
-    # 设置刻度
-    p = plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi])
-    p = plt.yticks([-1, 0, 1])
-
-    #####################################################################
-
-    # 在脚本中需要加上这句才会显示图像
-    # plt.show()
-
-
-<button>Run ...</button>
-
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-.. image:: 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w2fQY4S27VKp7QSOA6dYqNu8+c4aWqDRuAp5/+x29yu9lOfPJkPteqBUydqtYCgW7hQu4cut1A3rzAxo1Ahgymo3IMFYKLBLF9+6zLUpkzA7NmKWEKBjlyANOnszYtMpJHr1eu/OM3ff21lTCVKsWbc0qYAl/lytwxBID9+1nspuG+fkFJk0gAi4hgD8Nr1/g8dSrw8MNmYxLvKV8e6NGD6wMHgI8+ijffddcuawhvzpw81kmVykic4gOdOllFiXPnAn36mI1HAChpEglYbjfQpAlfOwFetnn1VbMxife1b281if7+e2DECDBbrlGDW1AhIZwpp86lwSUkBJgyxeoX0rkz8MsvZmMS1TSJBKr4PQzLl2cX6dBQszGJb1y4ADz7LNsQJE8OHK/UAA/M+4b/smtX6yhHgs+uXSxUjIhg19NNm9RKwsfUEVwkyGzbxnllkZFA9uwcw5E9u+moxJd+/52jVapFT8d0/O92XNmy7OmjbDm4xb/p8fTTbPGfJo3ZmIKYCsFFgsjly6xj8pzMzJihhMkJnnsOGN/uIMahIQDgcvIsiJsyTQmTE9SoAbRqxfX27RzArM0II5Q0iQQQtxuoX99qydO7t9XPR4LcjRv4cGENpAOr/mtGT0KfyQ8aDkps07cvUK4c19OmAaNHGw3HqXQ8JxJAhg4FWrTg+vXXeakmRG99nOHzz4FhwwAA32RshQZhAxESAixbxpo2cYCzZ4EiRYDjx3lTcsuWRE51ljtRTZNIEFi/HnjhBY4W+89/+PMyc2bTUYktfv4ZqFqV6+LFsXP0ahR/PgUiI4Fs2VjTliOH2RDFJqtXc3vZ7ebtgPXr1ZjNy1TTJBLgzp8H3n3XmsU6e7YSJsc4dozjNAAgfXpg+nQUKpLi/6czZ86wRjgmxlyIYqMyZYC2bbnesgX48kuz8TiMkiYRP+d2s7XA8eN8HjwYKFbMbExik5gYoGZN4OJFPn/99f+vm9erZ+VSq1YBvXoZilHs1727NVOnTx/ephNb6HhOxM9NnswpCgBHacycqUkZjtGpk5UNNWoEjBlz07+OiOCtul27gGTJ2Jbg2WcNxCn227ULKFqUA5sffZR9SNKlMx1VUFBNk0iAOn4cKFiQbQayZwd279axnGMsWwa8/DK3GgsVYkaUOvW/ftvWrUDx4tyUKlAA2LwZSJnSQLxivyFDgJYtuW7QABg/3mw8QUI1TSIByO3mrLHLl/k8frwSJsc4cwaoXZt/CdKk4fbiLRImAChcmBM2ACbVag7uIM2bW1cnv/6aFwbEp7TTJOKnxo4FGjfmul49YMIEs/GITdxudi/98Uc+T5hgFS/dRnQ0O8Rv3swWFGvW8Fkc4OhR4Kmn+O4qa1Ye22kOYZLoeE4kwBw+zJ+D4eFArlzAzp1AhgymoxJbzJrFDtAAk6dZsxJUxLZ7N+uZoqKAxx9niYsmbTjEtGncmQSAKlXYwE2Fj4mm4zmRABIXx42F8HA+T5ighMkxzp0DPv2U6yxZgFGjEvziV6AA0LMn1wcOAF984aMYxf/UqmUl2vPmcZq3+IR2mkT8TPyu302a8HVTHKJGDe4sARwq6HkhTKDYWDZA9dxAX75c3cId4+JFXhg4eRK47z7OqMuTx3RUAUnHcyIBYt8+4JlnOIw3Tx4esaRNazoqscUPP/A4DgDeeovPiThiOXCALXyuXwdy5wZ27NBNdMdYupQ3LgEWta1axV4Uck90PCcSAGJi2I8pMpKvld9+q4TJMc6f57YiwCuSo0cnuibl8ceB/v25PnIEaN3aOyFKAHjpJeCzz7hevx7o189sPEFISZOInxgwgK14ALZeef55s/GIjZo35zBWABg+nE25kqBJE+tYbtw44JdfkhifBI5+/YB8+bju1o1XKsVrdDwn4gd27GBz3+ho/rzbsuW2bXkk2MydC7z5JtdvvAHMmeOVm09HjvAG5tWrQM6cvImeKVOS/1gJBJs3AyVKcPs6Xz6e86vjaYLpeE7Ej0VFAR9+yIQpNBSYNEkJk2NcvGg148qYkWNSvHRVPHduNowGWBvcrJlX/lgJBEWKWF1O9+7lfDrxCiVNIob17MmLLgDQvj1HYohDtGgBnD7N9dChQI4cXv3j69cHKlfmeupU4KefvPrHiz9r29Ya6tu7N/Dnn2bjCRI6nhMx6I8/eMklNpZHKX/8AaRIYToqscWCBcDrr3NduTIwf75PGhKePMn5hZcusWH07t38VRzgjz840dntZpHkypVsGS93pOM5ET8UFQXUrcuEKXlyYPJkJUyOERYGNGzIdfr0nJnjow7OOXMCI0dyfe4c0LSpTz6N+KNixaxz2TVrOJ9OkkRJk4ghAwYAe/Zw3aWLtZMuDtCyJbeAABYePfSQTz9dzZrA229zPWsWsGiRTz+d+JMePTiLCeCR3alTZuMJcDqeEzHg0CEemURGcvzF1q3cbRIH+OUX4NVXuX7lFWYwNswJO3kSyJ8fuHKFReK7d2s2nWPMm8ebmQBQvbrVdV5uScdzIn7E7WYfnchIPo8dq4TJMS5fBj7+mOt06dhEyabBqjlzAr16cX3kiDWnThygShUmSwAwezaTKEkU7TSJ2GzmTOC997hu0AAYP95sPGKjxo2ZJQP81VPXZJPYWLbv2bSJ0zW2beNOpzjAqVPcarx8mcd1u3drvs5taPaciJ8IC+PPrdOneYNp715OzRAH2LABKFWKW40VKnBOmE27TPFt2cL64Lg4XahynHHjgEaNuG7enG0u5F90PCfiJzp2tNryDBqkhMkxYmN5Jut2szOzF5tY3qtnn735QtXEiUbCEBMaNLDmMw0fDmzcaDaeAKSdJhGbbNzIoxG3m3PBfv3V2Oum2G3kSGuQapcuQPfuRsO5epU7nidOMHHfu1e9mxzjzz95VTc6mr/+8YeKKv9BO00ihsXEcFfc7WYvpq++UsLkGKdPc4sRAB59lG3fDUuXjhsNACe5tGljNh6xUf78wBdfcL19uzVrRxJESZOIDUaMYNEtwNfMJ54wG4/YqHVr3vMHuOPkJ4MF33rLakg+aRKwYoXRcMROHTpYP4S6dWMPFEkQHc+J+NixY3xzFx4OPPYYsHMnkCqV6ajEFr/9Brz4Itdvvw388IPZeP7hyBHgySeB69f5Grp9O0uuxAFWrQLKluX6pZeAxYu1/f0/Op4TMahZMyZMAI/llDA5RFQUi78BdpH0w5tKuXNzowEA9u0D+vc3GY3Y6oUXrJ5hS5cC06aZjSdAaKdJxId+/hmoWpXrWrX0c8lR+vblMQgA9OvHERZ+KDoaKFKEO6ApUwK7dnFHVBzg0iVug585A2TJwiLxLFlMR2Wc+jSJGBAezqOPo0eBDBn4Tj5bNtNRiS3+/ptf/IgI/rp1q19PY163Dihdmmud1DjMrFlAjRpcN2rEdhgOp+M5EQO6dWPCBHDTQQmTg3z+ORMmABg92q8TJoA9N+Of1MyYYTYesVH16pyBCLD55ZYtZuPxc9ppEvGB7dt55OEZW7F2rbouO8b8+Zz1BQAffABMnmw2ngS6eBHIlw84d44J/t69QMaMpqMSW+zdCxQqxN4opUqx66mDtxq10yRio7g4jhiLjQVCQzliTAmTQ0REWE0sM2QABgwwG889yJwZGDyY6zNnrFY+4gD58nF3FOBZrYovb0s/ykW8bOJEjhkDgBYtgKeeMhuP2KhPH97jB4BevQLuTPb9960OCWPGcLCvOETnzkD27Fy3bcu28fIvOp4T8aLLl4G8eYGzZ4EHH+Sud9q0pqMSW+zbxww5KooD3jZu5FZjgNm3jyc10dE6qXGcyZOBOnW4btPGsT0odDwnYpMePZgwATyZUcLkEG430LQpEyaXiw25AjBhAtjksnlzrtetA6ZPNxuP2Kh2baBkSa6HDmUGLTfRTpOIl+zbBxQsyFrK0qWB1av1Dt0xguza9pUrwOOPWzum+/YB991nOiqxxZYtQNGifCPwyivAokWO+0GmnSYRG7RsyYTJ5eIwVIf9nHGua9dYvAawMWDv3mbj8YL06VmeBQAnTrBlhjjEs89a/ScWLwbmzTMbj59R0iTiBQsX8h8A+Ogj/twRh+jTBzh5kuv+/XkNLQjUrcu2GQCPmj317eIAPXta/SZatAAiI83G40eUNIkkUVSUtdGQPj0vTYlDHDkCDBrEdbFiVhFtEAgJ4Y4pANy4AbRubTYesVHWrCzQBIDDh62/46KkSSSpRowA9u/numtX4IEHzMYjNmrblhkFwMLZIGvIVaoUZyYCwA8/AL/9ZjYesVHjxrxGCfDI+dgxs/H4CRWCiyTBmTNsMXDlCm8d7djh9xMzxFtWr+akeACoWRP47juz8fjI8eP8ux0RwdfQLVuAZMlMRyW2WLkSKFeO63ffBWbONBqOXVQILuIjHTsyYQKAIUOUMDlGXJzVQTl16qCulH7oIaBDB6537gTGjzcbj9iobFnrVuisWcCKFUbD8QfaaRJJpM2bWcbidgOVKwMLFpiOSGwzcSJQvz7XXboA3bubjcfHrl8HnnySJVz338/j6CCpd5e7OXaMY1YctNWonSYRL3O72QDQ7ebPjyFDTEcktrl61RrM9uCDrGsKcqlTAwMHcn3hAtCtm9FwxE65cll/33fuZONWB1PSJJIIM2YAa9dy3bw565rEIfr0AU6f5rpvX8d0fXz7baB8ea5HjwZ27zYbj9ioVSvg0Ue57tIFOHfObDwG6XhO5B6Fh7Mw9sQJ3pTbv58D7cUB/voLyJ+fN+aKFwfWrw+6G3N3smMHULgwS7oqVgSWLFETV8eYNw944w2uP/4YGDfObDw+pOM5ES/q148JE8BNByVMDhK/xcCwYY5KmADOI27cmOtly4CffzYbj9jo9deBSpW4/uYbYNcus/EYop0mkXtw5Ag3GiIj2S1540bHvW4616pVvE0EsHnRtGlm4zHkwgXOpbt0iSc2u3cDqVKZjkpssWcPM+fYWM6l++UX0xH5hHaaRLykTRtrosDw4UqYHCM21jEtBu7m/vuBL7/k+vBh9vQUh3jyyZvn0i1ebDYeA7TTJJJAK1ZYhbAO3mhwpgkTOFQQYNt3h18fi4kBnnmGu0z33ce6vpw5TUcltjh7FnjsMd4iLVgQ2LYNCA01HZVXaadJJIni4qz5cmnSsK5JHCJ+i4GHHnJEi4G7SZaMJV0AL0Z06mQ2HrHRAw9Y3U537eIbCgdR0iSSAFOn8g0VALRrx9dOcYjevTkvB+CxXJo0ZuPxExUqWJepvv2WN+vEIT7/HHj4Ya47d+YbC4fQ8ZzIXVy/zj5Mx48DOXIABw44pjWP/PUXuyFHRQElSgDr1umOfTx79/KEJjYWePllR5a4ONe0aUDt2lx36gT06GE2Hi/S8ZxIEgwdyoQJAHr2VMLkKG3aMGEC+BdBCdNN8uUDGjXieskSJU2OUrMmULQo14MGWT8kg5x2mkTuIH7NY6FCwNatQVfzKLcTv8VA7drAlClm4/FT+h5xsNWrgRde4PrDD4FJk8zG4yXaaRJJpC+/tI7rBwzQi4FjxMVxdATAFgN9+piNx4898ADQvj3XO3cGzeumJESZMsBbb3E9eTKH+QY57TSJ3Ma+fUCBAqzXeOklHj+IQ8yYweMHgIWunsZEcksRERwtdPw4Ww/s369jbMc4cID9m2JigHLlgOXLA/4YWztNIonQrh0TJpeLu0ziEDduWC0GsmZlXZPcUZo0rPcDgJMngcGDzcYjNnr8ceDTT7lesYIz6oKYdppEbiF+OUvdusDEiUbDETsNHWo15Ro1CmjSxGw8ASI2lnXB27Zxl+ngQSB7dtNRiS0uXgTy5AHCwnjVeNcuIHly01ElmnaaRO5BXBzQujXXqVMH1U1auZuwMOsLnjevNTJC7io01NqRDQ93fNN0Z8mcmcfYAM9mx4wxG48PKWkS+YdZs4A//uC6ZUs1snSUPn34rhlgI8sAfrdsQsWKwKuvcj1+POe7ikN8+iknOANA9+58AxKEdDwnEk9kJHvP/P03bwUdPAikS2c6KrHF0aPcXbpxAyhdmtepA7w/1kQzAAAgAElEQVSg1YRdu4Cnn+aO7WuvAfPnm45IbPPDD8A773DdunXAFoPqeE4kgUaOZMIE8M2SEiYH6dSJCRPAH/ZKmBKlYEGgfn2uFyzgZSpxiLffBp5/nuvhw9lRP8hop0nkfy5cYJO+sDDuNu3cycGk4gBbtwJFigBuN1CtGvD996YjCminTvF7KSICKFwY2LQJCNFbdGfYuBF47jmu330XmDnTbDyJoJ0mkQTo2dM6hu/fXwmTY7jdQNu2/DVZMjWy9IIcOaxODVu3At99ZzYesVHx4laPs1mzgN9/NxuPl2mnSQTAoUNA/vxAdDRbDfz2m05nHGPxYqBSJa6bNgVGjDAbT5C4do0tfE6fBnLlYrPY1KlNRyW2OHKE3U6jogLyB6p2mkTuokMHJkwAMHBgQH1/S1LExnKXCWABm+fatCRZ2rRW94Zjx4Bhw8zGIzbKndtqeLlyJbBokdFwvEk7TeJ469cDpUpxXasWMG2a2XjERt9+C9Srx3XPnkDHjkbDCTaxsbxJt3s3kD49b6NmzWo6KrHF+fNseHnlSsBNctZOk8htuN1WI8uUKYFevczGIza6fp035gAOTPN0ARevid/w8soV3kgVh8iS5eZJzkHyblRJkzja3LnAunVcN2vGXWVxiKFDgRMnuO7RgwPUxOsqVWLTSwAYO5a7TeIQzZvzVgDAo+/ISLPxeIGO58SxYmKAp54C/vwTyJSJxeCZMpmOSmxx7hyPDq5eZWOhbdsC5uggEG3Zwo4OAPDee8D06WbjERuNHw80bMj1wIFAq1Zm40kAHc+J3MKkSUyYABaCK2FykJ49mTAB7C+hhMmnnn2WyRIAzJgBbN5sNh6xUb16bHwHsP4hwMeraKdJHOn6dV6HPnGCs+X279d1aMc4eJD9JWJigBdfBJYt03VJGxw6xNfOmBjgpZeAJUtMRyS2mTMHeOstrtu141xHP6adJpF/GDHCKmfp3l0Jk6N88QVfuQHuMilhskWePECjRlwvXcpcVRyialXrivKwYcDx42bjSQLtNInjXLrEYdxhYcCTTwLbt6v7t2Ns2gQUK8a1+kvY7swZJk/h4axx2rhR41UcY80aoEwZrj/6CPj6a7Px3IF2mkTi6dvXOlbv3VsJk6N4rkAnT866JrFVtmxWHfDmzcDs2WbjERs9/zzwxhtcT5wI7NljNp5E0k6TOMrx46xliozkbvGaNTqdcYylS4GXX+b6s884hV1sd+UKh/meO8df9+xhDisOsGcPG13GxTGBmjvXdES3pJ0mkf/p1s1qFdKvnxImx4iLs3aZ0qa1mlqK7dKnt6bVHDzo16c04m1PPml14P/5Z75rDTDaaRLH+PNPtuSJiwOqVOH3rDjEzJnWnfeuXZk9izFRUbxJ99dfPLI7eJC5rDhA/O3+kiWBtWv97t2rdppEwEtTcXEsPO3d23Q0YpvoaGtnKWvWgGiuF+xSpLBKys6cYXN2cYiHHmKncICDP/30iO52tNMkjrBuHVC6NNd167IOURziq6+AJk24HjaM83LEuLg43qDbtg1Il459nDTM1yHCwniF+dIlbjnu3OlXN3K00ySO5nZb5SwpU2poqKOEhwNffsl17txWoyAxLiTE6nF49ap2fx0lY0agY0eu9+4NqHexSpok6C1YAKxezXXTpsDDD5uNR2w0bBhw+jTXX37JrFn8xssvA+XLcz16NHDkiNFwxE6ffmr9MO7aFYiIMBtPAilpkqAWG8u5cgCQIYO1Fge4cIFXJAFec65Vy2w88i8ul/UliooCunQxG4/YKFUqoEcPrk+d4hucAKCkSYLa1KnArl1ct2sH3H+/2XjERn36sCmQZ62hvH6pWDHgnXe4njoV2LHDbDxio/ff5xsagCONLl0yG08CqBBcglZkJPDEE8DRo0COHLzWnCaN6ajEFkePAnnzAjducHTDypV+d61ZLPv3s4VPbCxQuTKP1MUh5s9nDxjAb4b5qhBcHGn0aL52Aiz+VsLkIN26MWEC1MU0AOTNC3z8MdcLFzLHFYd47TXravOwYdYkdT+lnSYJSpcv80brxYvcbdq1y69utIovxR/VULUqMGeO6YgkAU6d4liViAjguefYwke5rkPEH+bbqBEwZozRcLTTJI4zYAATJkBDeR1HXUwDUo4cQIsWXP/+O/DTT2bjERs9/zx3nADO1TlwwGw8d6CdJgk6p08DefLwHWvx4sCGDXrH6hjr13MSM8AZVxMmmI1H7kn8HeL8+VkUrjc8DrFjB/DMM2ys9+67HH1kiHaaxFF69rRafvTtq4TJMf7ZxVTz5QJOhgzcKAQ4K3LKFLPxiI2eespqCzJrFrB5s9l4bkM7TRJUDh9mV/7oaOCll4AlS0xHJLZZuNDa4m/ZEhg0yGw8kijXr3Oe64kTQK5cvFmXKpXpqMQWhw+zCDUmhp1PFy82EoZ2msQxunZlwgSonMVR4uKszqXp01vbFRJwUqe2NgmPHTNeEyx2evRRa9TRkiXA8uVm47kF7TRJ0Ih/JF69Ond4xSGmTQNq1+a6Z09rrpUEpJgYoEAB7jJlycJhvunTm45KbOEHRanaaRJH6NiRCVNoqNWdXxwgKgro3JnrbNmAzz83G48kWbJkQK9eXJ8/DwwebDYesVH27NY1yo0b/a5liJImCQpr17KxLADUr89jcXGIr78G/vqL6y5dgPvuMxuPeEW1akCRIlwPGgScPWs2HrFRmzZA5sxcd+zIrUc/oaRJAl78S1OpUmnop6OEhwNffsn1I48ADRqYjUe8xuWyJmpcu6YaRUeJP13dz65RKmmSgLdoERvKAkDTpsBDD5mNR2w0bBhw5gzXPXoAKVKYjUe8qmJF4MUXuf7qK+DIEaPhiJ0+/RR48EGuu3blMFE/kOSkyeVyVXK5XHtdLtcBl8vVzhtBiSTUPy9NeXacxAEuXuRkdIA9XmrWNBuP+ESfPvw1Kkqttxzln9cov/rKaDgeSUqaXC5XKICRACoBeBJATZfLld8bgYkkxIwZvDUHAG3bAvffbzYesVG/fmwhDfDsJkQb58GoeHHg7be5njIF2L3bbDxio7p1Oc0Z4M2AK1eMhgMkfaepOICDbrf7iNvtjgYwA0DVpIclcnf/vDTVvLnZeMRGJ04Aw4dzXbo0ULmy2XjEp3r2ZE4cFwd06mQ6GrFN/GuUFy74RcPapCZNDwI4Fu/5+P8+Zh+3mw2w1AvKcb7+mg1kASZPadOajUds9OWXVo2DZuUEvfz5uekA8Ab6hg1GwxE7VasGFC3KtR9co0xq0mQ2U9m/HyhZEqhQgSMUxDHCw61eTI88Anz8sdl4xEb79wPffMP1a69xQroEva5drTr/9u31Ptkx4l+jDA+3dp4MSer86BMAcsV7zgXuNt2kW7zqvXLlyqFcuXJJ/LT/kyULsHcv1x06AK++qroGhxg+nI1jAW466NKUg3TpAsTG8oep7qE7xsMP80LVkCHAypWcsvHKK6ajEltUqMCrlMuWAePHs0A8Uyav/fErVqzAihUrEvR7kzRGxeVyJQOwD0AFACcBbARQ0+12/xnv9/h2jEqfPtacqalTgfff993nEr9w8SJHFF2+DBQqBGzdyi7g4gBbtlgdD2vV4vgUcYxz5zhh4+pVoHBhYNMmvU92jE2beFu2Rw+fdy/22RgVt9sdA6ApgMUA9gCYGT9hskWzZmy7DrCwJSrK1k8v9vvnpSklTA7ieYOULJnV1FIcI2tWoHVrrrduBWbPNhuP2KhoUQ4UNTzuITgG9n71FdCkCdcjRrDDoQSlEyeAxx5jDXDp0sDq1aoBdowVK4Dy5blu0gQYNcpoOGLG1avcbTp3jj8L9uwBkic3HZUEk+Af2NugAb+LAG7dXbtmNh7xGV2acii32+pimiaN7p07WLp01pf/4EFgwgSz8YizBEfSlDy5dZXq7Flg6FCz8YhPxL80VbmyLk05ys8/W/fMmzcHcuQwG48Y1agR8J//cN29OxARYTYecY7gOJ4D2PXs2WeB7ds5T+PQId6uk6BRowaPtF0uYNs2Ts4QB4iN5Rd7zx7emDl8GMiY0XRUYtjkyUCdOlz37Qu00xAv8ZLgP54DeIXCM6ToyhWrr4MEhc2bmTABvDSlhMlBpk1jwgSwQY8SJgEvShcowHXfvsClS2bjEWcInp0mgHUP5coBq1YBKVMCBw4AuXLd9X8m/u/ll4GlS3kSu3cvWw6IA9y4wdsyf/8N5MzJ7+k0aUxHJX7i55+Bqv8b3NWund4ri3c4Y6cJ4LmNZ7fpxg0edkvA+/VXJkwAaxmUMDnI2LFMmAC2hFbCJPFUqQKUKsX1sGG8XSviS8G10+RRtSrfgoSEcCR2vnz2fn7xGrebU843bQLuu4+latmymY5KbKG75ZIAq1cDL7zAdcOGzLNFksI5O00evXpx10kjsQPejz8yYQKAli2VMDnKoEFMmAB+TythklsoU4YjCAHert2/32w8EtyCc6cJ4LWKyZO53rgRKFbM/hgkSWJigIIFgX37gPvv5y5ThgymoxJbnD3LXaZr1zg2ZeNGzcuQ29qxA3jmGe5Mv/suMHOm6YgkkDlvpwlgPZPnnamnKZ4ElG+/ZcIEcHqGEiYH6dnTalLbt68SJrmjp57irVqAt2w3bzYbjwSv4N1pAtgEb/hwrpcu5ZRkCQjXrwOPP87Czly5uOWeKpXpqMQWhw+zDjE6mt+znlsAIncQ/6/NSy8BS5aYjkgClTN3mgCgY0dWDwPs72IqeZN7NnKkdROme3clTI7SpQtf+QDrNqzIXTz6KG/XAsyzf/3VbDwSnIJ7pwngNWXPNPTZs4F33jEXiyRIWBh/AF66BDz5JOsVQkNNRyW22L4dKFyYb3CqV7c6mookwJkzLIULDweKFmUpnOZTyr1y7k4TALRqxSpigDtPMTFm45G76t/f6u7bq5cSJkfp0IEJU2go65pE7kG2bLxlC/DW7Y8/mo1Hgk/wJ03p0zNZAlgYM3Gi2Xjkjk6dsuYtlyhhdfsVB1i5Eli0iOsGDYC8ec3GIwGpdWu9TxbfCf6kCQA++cQap9Ktm0Zi+7Evv2QROMBLU9padwi325q4mjo165pEEiH+++R9+3gLV8RbnJE0pUpljVQ5edK6USd+5cABYPx4ritVAsqWNRuP2GjOHOD337n+/HPOmRNJpH++T/a8ERNJquAvBPeIjQWefppjVTJkYKdEzx6u+IX33rOa0m3dymZ14gAxMUChQpzEnCkT745nzGg6Kglw334L1KvHdf/+QJs2RsORAOLsQnCP0FBrBPbly7rK7Ge2bLESppo1lTA5yqRJTJgAdjFVwiRe8MEHvH0L8Md9WJjZeCQ4OGenCWDdRNmynPCYIgULw//zH9NRCXgct3gxkCwZXz/z5DEdkdgifhfThx7iGa2acomXzJ0LvPkm1x06AL17m41HAoN2mjxcLqBfP66jolRs6id+/ZUJE8Ap5UqYHGTUKHUxFZ954w3ewgV4K9fzV00ksZy10+RRrRobeLhcwLZtHFwkRsTFcZbyli1s3n7wIJA9u+moxBbxu5jmywfs3MmtRhEvWrXKulTSoIF12UTkdrTT9E+9e7PGye3meBUxZuZMJkwA+5AqYXKQ+F1Me/dWwiQ+8cILwOuvcz1hArBnj9l4JLA5c6cJ4JCiceO4Xr4cKF/ebDwOdOMGkD8/8NdfwAMPcJcpXTrTUYktTp4EHnuMNU0lSgDr1qkpl/jM7t08UIiL45Hd3LmmIxJ/pp2mW+nWDUiThut27TTM14CvvmLCBHBEoBImB1EXU7FRgQJW+4Gff+ZdIJHEcO5OEwB06sThZoCG+drs8mUWfF+4wMtTu3cDyZObjkpssXcvULAge6e9+iqwcKHpiMQBTpzgzxptbsrdaKfpdtq0sRpcfvEFEB1tNh4H6dePCRPAchYlTA7Svj0TJpfL6p0m4mMPPshm8wCwYYOG+UriODtpypCBu00A+8N8/bXZeBzi+HFgyBCun3uOlxnFIVavtgpK6tbVzVWxVbt21vvkDh30PlnunbOTJoBDinLn5rp7d+DaNaPhOEHXrkBkJNcDBmiL3DHcbmuWRapUrGsSsVGGDEDnzlzHn3UpklBKmlKmBHr25PrMGWDwYLPxBLndu62p41WqAGXKGA1H7PT999ZQ3hYt2AFcxGaNGwOPPMJ19+7A1atm45HA4uxCcI+4OKBIETa6TJuWw3wfeMB0VEGpShVg/nwgJIS9DD2zoSTIRUXxi33oEJAlC/tLZMhgOipxqOnTgVq1uO7ShcmTiIcKwe8mJMQar3LtmrXzJF61ciUTJgCoX18Jk6OMHcuECeCrlBImMahGDb5PBoBBg4DTp83GI4FDO00ebjfw0kschJY8OfDnnxqC5kVuN6/5btwIpE7NjYacOU1HJbaI318iTx62ZE6RwnRU4nDLlwMVKnDduDH7xokA2mlKmPjDfKOjrVt14hXff8+ECWA5ixImB4nfX6JPHyVM4hdefBGoVInr8eOBffvMxiOBQTtN/1SzJjBjBtcbN3KarCRJdDSP4g4e5HXfQ4d0OuMYx44BefPyuuRzzwHr1+u6pPiNHTuAZ57hTvhbb6l3k5B2mu5Fz55Wp8VWrTRexQvGjWPCBKicxXG6dFF/CfFbTz0FfPgh1z/9xC7hIneinaZbadnS6r744498CyKJcuUK57KeOwc8+ihLxXQ64xDx38ZXrQrMmWM6IpF/OXqUm6E3bgClS7P/qnJ7Z9NO073q1AnIlInrtm15XVoSZeBAJkwAx/wpYXKQtm2ZMIWGalyK+K2HHwaaNeN67VqrYb3IrShpupXMmXmsAPBcSdcqEuXUKV7nBXi99913zcYjNlq6FFi8mOuPPwby5TMbj8gddOhgvU9u3x6IiTEbj/gvJU2306QJz5UAjnu4dMlsPAGoSxcgIoLr/v3ZDkscIC6Ou0wAcN99nJsj4scyZeLMdoC36DReRW5HL2O3kyKF1YLg4kU1vLxH27cD33zDdeXKvN4rDjFtGrvrA0yesmc3G49IAjRtao0h7dIFCAszGo74KRWC34nbDZQty8pANbxMMLcbqFiRzeNCQzkuJX9+01GJLSIjWVV77BiTpQMHOJpIJADMmsVu4QDQujUvfIrzqBA8sVwuqygnOpqH3XJX8+czYQKATz5RwuQoI0YwYQI40EsJkwSQ6tWBUqW4Hj7cmvwj4qGdpoSoXZtHDgCwZg3vpcotRUUBhQoB+/cDGTNaDS3FATxjUi5fZqa8YweQLJnpqETuycaN7MMKANWqcZqBOIt2mpKqd28gVSqu1fDyjr76igkTAHTurITJUbp1Y8IEsB5QCZMEoOLFgfff5/qHH4BVq8zGI/5FO00J9cUXnJsFANOnA++9ZzYeP3TxIi8cXrrEX3fvVl8mx9i9G3j6aSA2llX/y5apQ6AErPjTf4oU4e6Tbv86h3aavKF9e+CBB6y1ZzSE/F/8zgwDBihhcgy3m130Y2P5yjJkiBImCWi5crEQHAA2bwamTjUbj/gPJU0JlT49C1sB4O+/WSUo/7dvHzBqFNdly3JqhjjEwoXAkiVcf/wxB3qJBLh27YAcObju0AEIDzcbj/gHHc/di5gYHkHs2cMk6uBBIGtW01H5hapVgZ9/5gbD5s1A4cKmIxJbREcDBQuykE3fExJkJk4E6tfnuls39Wl1Ch3PeUuyZFbjjitX+F0kWL6cCRMA1K2rhMlRRo2yKv+7dFHCJEHlww85cxrgVIMTJ8zGI+Zpp+leud3Ayy+z0FWdGxEbCzz7LG+X33cfXz9z5jQdldji/Hng8cfZOlmV/xKkVqwAypfn+sMPgUmTjIYjNtBOkze5XMDAgfw1NtaaseVQ337LhAlgDYASJgfp1s2aNTFokBImCUrlygFvvsn15MnApk1GwxHDtNOUWB99BEyYwPWyZUCFCmbjMeDqVW40nDkDPPQQi8HTpDEdldgifouBihVZCK4bcxKkDhwAChRgCV+ZMsDKlfrrHsy00+QLPXpYGcLnn7NI3GH69mXC5FkrYXIItxto0cJqMTB4sF5BJKg9/jgH+gIcRfrjj2bjEXOUNCVWzpxseAkAu3axFbaD/P23NZavWDGgZk2z8YiNFiwAli7lulEjzs0RCXKdOwOZM3Pdti1w44bZeMQMHc8lRWQk92wPH+agtf37HXN7qFYtNkYHNI7PUeIPF8yQgecWDvk7LzJiBNCsGdcDBlgNMCW46HjOV1KlAoYO5TosDOjY0Ww8NtmwwUqY3n1XCZOjxG8x0LWrEiZxlMaNgXz5uO7RAzh3zmw8Yj/tNCWV2w1Urgz88gvrOjZuBIoWNR2Vz8TFAaVKAb//DqRMCezdC+TObToqscX582wtcPkyB3Pt3Kkbc+I4CxYAr7/OdaNGwJgxZuMR79NOky+5XMCwYUDy5EygmjVjZhGkJk5kwgSwFlgJk4N07cqECVCLAXGsypWBl17ietw4tSBwGu00eUu7dmwZC7D72Ycfmo3HBy5eBJ54ghsODz0E/PknkDat6ajEFrt2scVAXBxfMRYv1o05cay9ezliMToaeO45YN06XiSV4KCdJjt06mRNd2zblmNWgkznzkyYAG40KGFyCE+Lgbg4vjIMGaKESRwtXz5+SwDceZ840Ww8Yh8lTd6SLp2103TmDKsEg8jWrdbZ/YsvAtWrm41HbDR/Phu4AqyELVDAbDwifqBzZ+DBB7lu35478RL8dDznTW438Pzz3KtNloyFsp6rFgEsLo7/WevX8z9rxw5Hj9tzluvXmST99Rfbahw4AGTJYjoqEb8waxZQowbXTZrwcqkEPh3P2cXlYiMPl4sdwps3ZyIV4CZPZsIEcEtaCZOD9OnDhAkAevZUwiQST/Xq3HkHuBO/davZeMT3tNPkC40bA2PHcj1nDlC1qtl4kiAsjLfLz51jE/S9e3kSKQ6wfz8bWUZFAc8+y3YaoaGmoxLxK3/+yaLwmBigZEk2+1VReGDTTpPdevUCMmXiukULHnEEqM6drQZugwYpYXIMtxv49FMmTC4XxwQpYRL5l/z5OX4U4I785Mlm4xHfUtLkC/ffz6MMgEcbAweajSeRtm0DRo/munx56+xeHGD2bKv4u1EjoHhxs/GI+LEuXbgTD/DydFiY2XjEd3Q85ysxMUCRIqyaTp2a51oPP2w6qgRzu4EyZYC1a1n8vW2bLk05xpUrvMBw6hTHpOzbZ+2cisgtTZ/OmZwA8NlnwPDhZuORxNPxnAnJkrEoHODxXIBNdpwyhQkTwCbnSpgcpFs3JkwAp5IqYRK5q/feA8qW5XrUKGD7drPxiG9op8nXatWyptsuX85zLj93+TI7f585A2TPzo2G9OlNRyW22LGDRd+xsewzsWqVGlmKJNCuXcAzz/Dbp3RpYPVqffsEIu00mdS/P5AmDddNm7Kw1s917cqECWA5lhImh4iLAz75hD/xQ0NZ0Kaf+CIJVrAgd+YB7tRPmWI2HvE+JU2+9tBDvIIGAHv28LjDj+3YAYwcyfULL1hn9OIAkyaxMSvA60CFCpmNRyQAdevGHXqAReGeGdcSHHQ8Z4eoKBaF79oFpEzJzCRvXtNR/YvbzURpzRpuNGzbxndO4gAXLvBM9sIFzob480/1lxBJpGnTgNq1uW7eHBg61Gw8cm90PGdaihTAuHE86rhxg1e4/TCRnDaNCRPA2x9KmBzkiy+YMAH8Ca+ESSTRatXiG1CAO/c7d5qNR7xHO012atrUGk40YQJQr57ZeOIJC2OTttOngWzZWPydIYPpqMQWv//OVsZuN/DKK8CiRaplEkminTuBwoWtOxUrV6pTeKDQTpO/6N3bGovdqhVw9qzZeOJp25YJE8CyKyVMDhEby+Jvt5tHxyNHKmES8YJChfg+GeAO/vjxZuMR71DSZKf06a0q60uXOGLFD/z2m/UNXbGidRYvDvDVV9aU0XbtgMceMxuPSBDp0QPIlYvrtm2B48fNxiNJp+M5E95+G/jpJ64XLQIqVTIWSkQEh00eOsTOCDt3Ao8+aiwcsdPp0yz+vnKFX/Rdu9i9XkS8ZuFC4LXXuK5SBZg7V5u5/k7Hc/5mxAir0PaTT4DwcGOhdOvGhAnguDwlTA7SqhUTJoA7oEqYRLyucmXg/fe5njcPmDXLbDySNNppMmX0aE6RBzhixUD/pk2bgOeeY0/D4sXZokeD7B1i/ny+7QW48/nDD2bjEQli58/zos358xznuGcPkCWL6ajkdu6006SkyZS4OF6pWL+eVyr++IPjK2wSHQ0ULcqWUcmTA1u2qMWAY4SFcZjgyZOs+N+927qgICI+MWMGULMm17Vrq1u4P9PxnD8KCWHvpmTJmEA1bAjExNj26fv3Z8IEsEWPEiYHadmSCRMADBmihEnEBjVqAK+/zvXUqSxnlcCjnSbTOnUCevXievBgW27U/fknh0pGRQFPPsldppQpff5pxR8sWsQiC0A9mURsdvw4f+Zevcpbdbt3q4+sP9LxnD+LjOT1tQMHeH1t924gd26ffbq4OKBMGdYvuVz8tUQJn3068SeXL3NL8fhx/qTevdu6Dy0ithgzhvd/APZxGjHCbDzybzqe82epUgFjx3IdEQE0aeLTESujR1szWZs1U8LkKG3aWI1iBg1SwiRiQMOG1oiVUaOAtWvNxiP3RjtN/qJ+fWDiRK5nzOABuJf9/Tc3Gq5d42bWzp1A2rRe/zTij5YuBV5+meuKFYElS3QsJ2LI/v08YLhxA8iXj/1lU6UyHZV4aKcpEAwYwLuoALeAPMNTvcTtBho3ZsIEcHNLCZNDXL0KNGjAddq0bP+uhEnEmLx5ge7dud67lz3yJDAoafIX99/P6fIAZ9J55oF5ybRpwC+/cF23rrXpIA7Qrh1w9CjX/fv7tGZORBKmVSsO9AWAfv2A7dvNxiMJo+M5f+J2A2+9xT77ALuYO/oAABlqSURBVO+lelrJJsHZs2ysdvEikC0bG6tlzpzkP1YCwfLlQIUKXJcrB/z6q0ati/iJrVuBYsU4N7tIEWDDBnahEbN0PBcoXC72bnrgAT5/+qm1Q5AEzZoxYQI4LUMJk0Ncu2Ydy6VJA3zzjRImET9SuDAH+QLA5s1smyb+TT9B/c0DDwBff8315ctAnTrsE5BIc+cCM2dy/eabQLVqXohRAkOHDsBff3Hdt68GC4r4oS5dWOPkWe/fbzYeuTMdz/mrhg1ZsAvwenjLlvf8R5w8yRsaFy5wWsaePUDOnF6OU/zTqlVA2bJclykDrFihXSYRP7V6tdWGoEgRtoVJkcJsTE6m47lANHgwkCcP1x06sD/APYiL4yaV5xLeV18pYXKMiAi2sACA1KmBCROUMIn4sTJlOLcd4DFd585m45Hb009Sf5U2LSc6hoRw3knt2mzqkUBDhgDLlnH9wQfWoEhxgI4dgUOHuO7VC3jsMbPxiMhd9epl3aYbMIB3NsT/6HjO33XubDXxaNuWd1PvYutW4LnngOholrFs3QqkT+/jOMU/rFgBvPgib2KWKsVjutBQ01GJSALs3Qs8+yxw/TpPBnbsYDcasZdmzwWy6GigZEnu2bpcwG+/WbUqtxAezjPxffv4Wrl2LRMocYBz54CnnwZOnWJ74W3bgCeeMB2ViNyD8eNZ0grw8s6PP6oXrd1U0xTIkidnv6ZUqbh78OGHvFV3Gy1bMmECgG7dlDA5hqeI7dQpPg8froRJJAA1aMB2fQAwZw670Ij/0E5ToBg5EvjsM67r1AG+/fZfv+Wnn4C33+a6TBluSulkxiEGDuRAXoBzC6dP19tTkQB14QI3jU+c4F2OzZvZoFjsoeO5YBAXB7z6KgetAsD339/UdOnECbYXuHgRyJiRLfkffthQrGKv338Hnn8eiIlREZtIkFi+nLO13W7gmWfYLTxlStNROYOO54JBSAgwcSKQKROfGzb8/1FMXBxP7Txdv8eOVcLkGGFhwHvvMWFKnpydTJUwiQS8F1+0uoVv2wZ88YXZeISUNAWSnDmZEQHMkOrXB9xuDBzIdyUAUK8e8O675kIUG7ndwMcfA0eO8LlfP6BoUaMhiYj3fPklL/YAbN3nOWgQc3Q8F4g++IDF4QCOth6OPEM/Q0wM2/Fs3coWT+IAY8YAn3zC9euvAz//rDomkSCzfz/bEISHA9mzsw1B1qymowpuqmkKNpcvs4Dp6FFEIxnKYiX+SFYK69ZxYrY4wI4dQPHibHj64IPcv8+SxXRUIuIDEyYAH33Etd4f+Z5qmoJNhgzAzJmICUmO5IjB93gHg9ucUsLkFOHhvCF34wZr3aZPV8IkEsTq1QPeeYfr+fM5FkvMUNIUoL4/XgKfxo0AAOTEKTRdWZ3jViT4NW3K1sEAm3GVKWM0HBHxLZeL/Zpy5eJzq1bArl1mY3IqHc8FoD17gBIlgKtX3ZiSogFqR03gv/jsMzY1lOA1dSpr2gBer1myRM24RBxi5UqgfHneAcmbl91GMmY0HVXw0fFcELl4EahaFbh6FQBcyDB1lHVjasQIDvmV4LR/P9C4MddZszKBUsIk4hhlywJdunC9fz+7jcTGmo3JaZQ0BZCYGJayHDzI5+7dgSrVUwE//GDVtDRsyCt0ElwiI/nFDw/n8+TJQI4cZmMSEdt16cKZdACweDHQrp3ZeJxGSVMAadMGWLaM63feATp1+t+/ePhhYMYMFgVHRnKWiqfTpQQ+t5t1TNu28bltW6BSJbMxiYgRISF8z1SwIJ8HDeKz2EM1TQFi4kT2sgQ4k2jtWuC++/7xmwYMsFrIvvIKsGCBjm+CQfyva4kSwKpV7P4tIo51+DBbzFy8yPEqK1dqQLu3qE9TgFu3jsV/UVEsZfnjD+A//7nFb3S72Q78++/53LEj0LOnrbGKl/30E2cMut28OvP77zqWExEAHMr+0kusa8qRA9i0iYMjJGmUNAWwY8f4buLMGSBZMo5LueMN86tXuRuxZw+ff/rJOgCXwLJ5M7/Y16+zzfvatWxqKiLyP6NG8fQe4GvFypVA6tRmYwp0uj0XoCIigLfeYsIE8Jvjri150qUDfvyRvwKc5Ltvn0/jFB84dgyoUoUJU0gIB/EqYRKRf2jShPd/AJ5CNGzIjWnxDSVNfsrtZtv8zZv5HP8b466eeMKqDLx6lYXh7FEggeDqVSZMp07xeehQoHJlszGJiF9yudhtxvOGeupUFoeLbyhp8lP9+vFCHACUK8fXzXvy5pusaQJ4VFenjhp6BILYWKBmTWD7dj43bcqmpSIit5EiBUtZH36Yz23bAosWmY0pWKmmyQ/Nm8cGlm43kDs3t1wTNVosNhZ47TU28wCARo04tEiTHv3X558Dw4Zx/eqrnMyZLJnZmEQkIGzbBpQuzdKODBl4b+SJJ0xHFXhUCB5ArBEpbCmwfj1QqFAS/sBLl9hGdudOPnfoAPTu7ZVYxctGjwY+/ZTrQoWANWuA9OnNxiQiAWX2bF6iBjRqJbFUCB4gzpwB3njDKj+aMiWJCRMAZMrEnaZHH+Vznz468PZHv/wCNGvGdbZsHGWuhElE7lH16kDnzlzv388E6sYNszEFE+00+Ynz59mLyTO5unt3a8aQVxw+zH3b06f5PGECUK+eFz+BJNquXUCpUsyWU6XineHixU1HJSIBKi6O7d3mzOFz1arcgVJP3ITR8Zyfu3SJA+s9UzLq1wfGj+dNc6/auRN44QUgLIx/+Pffs6eBmHP6NNv4Hj3K59mzOSNHRCQJrl3jYIh16/hcvTrw3XcqkUwIHc/5scuX+RfbkzDVrg2MG+eDhAngWd/ChUCaNHwr8t57wK+/+uATSYJcusTWAp6EqU8fJUwi4hVp0/LHfbFifJ49G6hbV5eok0o7TQZdvcq5q7a/E1i8mC/W0dH8zlq+3PrOEntcuAC8/DKwZQuf69UDvvlGNxtFxKtsO8kIItpp8kMREcxbPAnTm28C06bZtHX6yivsgOZycQ/31VeBP/+04RMLAODcOaBCBSthqlYNGDtWCZOIeF2mTMDSpUDBgnyeMIHt37SXkThKmgyIjGRh3sqVfK5cmY0sbS3Se/ddYMwYri9c4NTHv/+2MQCHOnOGFf+e5pU1axr44ouIk2TJAixbBuTLx+evvgJatFDilBhKmmx24wY3FpYt43PFisAPPwApUxoIpmFDq2fTiRNMnDyD7sT7Tp5ke/fdu/n8wQfsK6HKTBHxsWzZWML62GN8HjYMaN9eidO9UtJko+hooEYNFucB7Dk5dy5vmRvTvj3QujXXBw6wyOr8eYMBBaljx/gF37uXzx99BEycCISGmo1LRBwjZ06WsObOzef+/YGuXY2GFHCUNNkkJgZ4/30mSQDb8syfz4tsRrlc/M6pX5/P27YxuEOHzMYVTI4cYcJ08CCfGzfmFUklTCJis1y5mDjlysXnHj2AXr3MxhRIlDTZIDaWVz1nz+ZzsWLcbUqb1mhYFpeLhcjvv8/nAwc4y2XDBrNxBYPDh5kw/fUXn5s147gUXV0REUMeeYSJU44cfO7UCRg40GxMgUI/uX3s6lXWME2bxufChXnjP0MGs3H9S7JkwOTJwBdf8NnTovzHH83GFcgOHGAzUU8fplatgKFDdUtORIx77DEmTg88wOc2bYB27dTH6W7Up8mHDh/mLDlP3W/BgsBvv/Emg18bPx745BN+97hcwODBwOefm44qsOzdy+Yop07xuUMH7oErYRIRP7JzJ39UeUpZK1dmv0C/e2NvI/VpMsDTL9KTML36KofW+33CBAAff8yCq7RpebWiRQugeXO9BUmoNWt4JOdJmLp2VcIkIn6pUCFWYjz5JJ8XLmR1xv79ZuPyV0qavMztBkaOZLPnixf5sTZtgHnzAixzr1QJWL2a1y0AYPhwnjNGRJiNy5+53cCgQWwrcPYsP9ajB9CtmxImEfFbefIA69ez4TLAjfLnngOWLDEblz/S8ZwXRUUBn34KfP01n1Om5Lp2bbNxJcmxY9yv3bWLz8WLMwP0HIQLhYVxFIpnrHiKFEw0GzUyG5eISALFxQFduli36UJCWCD++efOet93p+M5JU1ecvYsN2LWrOFzzpzATz8xxwh4ly9zkKynI+cjjwCLFgFPPGE2Ln+xdSv//zl8mM+5c/OqZNGiRsMSEUmMmTP5HvD6dT7XrcsBEkaaMBugmiYf27aNr4+ehKl4ceCPP4IkYQJ4rrhgAVCnDp//+gsoWRL45RezcZnmdrPfUsmSVsJUpQpnyilhEpEAVaMGX888vZy+/ZZVB54yTSdT0pREs2ezF+SxY3z+8EPOlPOUAgWNFCnYwbpbNz5fusTq9rp1reItJwkPZxLZqBFn44SGAv368XguUybT0YmIJMmzz/LNf+nSfN6wgZebNm0yG5dpSpoS6do1tt15911uYYaEsAb4228Nj0XxJZeLN8GmTAHSpePHJk3itQsn9XPyVElOmcLnHDl4XbJtWzWtFJGg4ZlX99FHfD5xAihThpednHqZWjVN98jt5nlvq1acvwrw9GrmTOCVV8zGZqtjx7jLsmiR9bFq1fjdlD27ubh8bfp0tmQID+dz+fL8WLZsZuMSEfERz63wFi2sZOmZZ/gxz05UMFFNk5fs3MnXyJo1rYSpRAlg40aHJUwAD7sXLOBuS+bM/NgPP3DXafLk4BudvX8/UL06UKuWlTB17AgsXaqESUSCmssFfPYZf9w98gg/tm0b8PzzLElxUq2TkqYECAvjlcvChVmvBPDG/cSJwNq1QN68ZuMzxuViP4U9e5hQAKx1qlOHbQo840MC2alTHLD75JPA99/zY5kzM2Hs2VNDd0XEMcqXZ8Pm7t2tMpQpU3iRevBgIDrabHx20PHcHcTFcdOkXTurV2FoKNC0KeuhM2Y0Gp7/+eknoEkT4PRpPqdNy+Loxo0Dr9bn8mWgf3/OivM09HS5ONS4d2/rWomIiAMdOQK0bMkf+x758wMjRgAVKhgLyyvUpykRtmxhcrR+vfWxsmX5F6JQIXNx+b1Ll1jwNXGi9bFnnwWaNeM9Vn+vko+MBEaPZne3+LcCX30V6NMHePppc7GJiPiZxYv54z3+2JXq1XkxKlDfWyppSiC3m/VJ48bxNd8Tds6c/AtQo4azuqImyZIlQMOGwN9/Wx+7/36gQQPuPOXObSy0W4qNBaZOZTvc+MeKxYtzt6xcOWOhiYj4s6goYMgQTo3ylHymScORpXXrBl4Ji5Kmuzh4EJg2ja+ZBw9aH0+enLcFOnWybtjLPbh2jdnmmDHWkR3AzLNKFc6cqVjR7NHdpUusT+rXzxoVA/CQvndv4K23lCmLiCTA8eOctTpjxs0fL16c5a81agTGBC6fJE0ul6s6gG4A8gEo5na7t9zm9/ll0nT+PDBrFovYNmy4+d+FhACvvcaSlnz5zMQXVKKiePA9cqTVNt3j8ceZPNWpY1+R2P79nJ83bx7jid9wJGdOFqzVqwckS2ZPPCIiQWTFCtYCb9x488dDQzkLvnZt4I03uBvlj3yVNOUDEAdgLIBWgZA0Xb/O18mpU9leKCbm5n9fuDDwwQfAe++xX6H4wPbtwKhR3NrzFFgD/O6pXp1vSZ56ioVjGTJ453PGxADr1vGL/9/27jdGrqoO4/j3KZRAW9h2BYvYQiui2EJr0TQEBbdIDZCWSogKAf+RAC+I+AIQ+ZNIokajRhMh8MZI1QZIVCCFloSCXSVKasBut1oKLAFbQEpVKNsApTg/X5wz7ex0ZvZuO7Mz230+yc3M3HvO3TM9vef+5p5z7l25cmjne9m0aenmlNdc07lHspnZGLJ5czrfrlgxdKQGpHlCF12UAqhFizprInJLu+ckraVDgqYI2L49VU6tZWBgb39r2fHHpwlRl14Kc+eOSjENUrfY8uVp0HVln2ilWbNSADV/fnqdNw9OPHHfo+u99+DNN9OMtzfeSK87dqTLiWvXwurV6e9Vmz07dRMuXQpnnZUeFWNmZk1VKqXfrStWpB6e6ua4uzs17SecUHsZ7ZnqB23QNDiYJmqVg6ItW/Y+lbmRrq50UeOyy9It4cfabPiDSqmUBo3feWcKcAYHG6efNCmNKty9e2+AtHNnsb8lpYfrlgOlOXM8XsnMbBTt2pV+x65YAQ89lEZvDOeoo/YGUKeemoabttJ+B02S1gC1nolxU0Q8mNO0LWjavTvNYC+V6qeZMmVoxHr22bBkSefPfB+XSqUU/fb3p268/v60DAzs/x3Gp0xJt2tfujTdcPOYY5pbZjMz2y+vv57uGbxu3dCLH7t21c+zYEG6JVArNQqaGo50jYjFzSjArbfeuud9T08PPU2avj1xYuq5mTCh/mW9adN8MWHMmDAhdZnNng3Llu1dv3Nnug1tOZgaGIDJk9Mlw66udO221mtXV7pRiLvdzMw6zrRp6VGeV1yxd12plG4mXW+YzSmnNL8cvb299Pb2FkrbrO656yLiqTrbO2YguJmZmVkjLXlgr6QLJW0FTgdWSXp4uDxmZmZmY5VvbmlmZmaWteRKk5mZmdl44qDJzMzMrAAHTWZmZmYFOGgyMzMzK8BBk5mZmVkBDprMzMzMCnDQZGZmZlbAQRE0Fb39uR18XPfjl+t+/HLdj1/trnsHTTamue7HL9f9+OW6H7/aXfcHRdBkZmZm1moOmszMzMwKGJVnz7X0D5iZmZk1Ub1nz7U8aDIzMzM7GLh7zszMzKwAB01mZmZmBXR00CTpu5I2SOqT9JikmXXS/VLSNkkb62y/VlJJUnf+vFjSk5L68+uiVn4PGzlJX5D0D0n/k3Rag3Qv5npcL+mvFevvzevWS3pB0vq8/n2S1koalHTbaHwXG56kcyVtlvScpBvqpPl53r5B0oLh8krqlrRG0rOSHpE0dTS+ixVXoO3ukbSj4li+Ja//aMW69TnNNXlbobbD2kvS4ZLW5fP7Jkk/qJHmZElPSHpH0rU1th+S6//BinU/lvR0bifuk9TVzHJ3dNAE/Cgi5kfEx4EHgO/USXcXcG6tDTnQWgz8s2L1dmBJRMwDvgr8pnlFtibZCFwI/GmYdAH0RMSCiFi4Z2XExXndAuD3eQF4G7gFuK4FZbb9IOkQ4HbSMTwHuETSx6rSnA98OCJOAq4E7iyQ99vAmoj4CPBY/mydpW7bXeGP5WM5Ir4HEBHPVBzfnwDeAu7P6Yu2HdZGEfEOsCif3+cBiyR9uirZf4BvAD+ps5tvAptI54GyR4C5ETEfeBa4sZnl7uigKSIGKz5OAf5dJ93jwOt1dvNT4FtV6fsi4tX8cRNwhKSJB1hca6KI2BwRzxZMXnOWA4AkAV8E7sn7fSsi/gzsOvBSWpMsBAYi4sWI2A3cCyyrSnMB8CuAiFgHTJV07DB59+TJr59v7dewkRqm7S6re3xn5wDPR8TWvM+RtB3WRhHxVn57GHAI8N+q7dsj4klgd3VeSTOA84FfUPF/JCLWREQpf1wHzGhmmTs6aAKQ9H1JW0hXhH44wrzLgJcior9BsouAp3KDa2NPAI/mbtYramw/E9gWEc/XyGed4YPA1orPL+V1RdIc1yDv9IjYlt9vA6Y3q8A2agI4I3e1rJY0p0aai4G7R7lc1gSSJkjqIx2fayNi0wiy/wy4Hig1SHM5sPoAiriPtgdNeczBxhrLUoCIuDkijgeWk/6Riu53EnATQ7v0VJVmLikQu+pAv4eN3HB1X9Cn8iX684CrJZ1Ztf0S3KB2uqIB7HBXHMpp9tlfpHurOFAee/4GzMxdLbeRhmnsIekwYCnw2zaUzQ5QRJRy99wM4CxJPUXySVoCvBYR66nTLki6GXg3Ipra/h/azJ3tj4hYXDDp3YwsYjwRmAVsSD00zACekrQwIl7Ll/buA74cES+MYL/WJCOo+0b7+Fd+3S7pflJ3zeMAkg4ljW3wYNDO9jJQOcljJumKUaM0M3KaiTXWv5zfb5N0bES8KukDwGtNLbW1XOUQjYh4WNIdkrojotyNcx6pp2B7e0pozRAROyStAj4J9BbIcgZwQR7reDhwlKRfR8RXACR9jdR199lml7XtV5oakXRSxcdlwPqieSNiY0RMj4jZETGb1MCelgOmqcAq4IaIeKK5pbYWqPdLYpKkI/P7ycDnSINAy84Bno6IV4ru09riSeAkSbPylYMvASur0qwEyg3i6cAbueutUd6VpG598usD2JgiaXoel4ikhaQbMleOe7mEPF6x3i5aWT7bf5KOLs9olXQEacJWvXP8kHqMiJsiYmY+t18M/KEiYDqX1G23LA82b66I6NgF+B3pJNhHmv30/rz+OGBVRbp7gFdIg3u3Al+vsa8XgO78/hZgJ6mCysvR7f6+XobU14W5Lt8GXgUerq574EP5/0Yf8Hfgxqp93AVcWWPfL5JmZQwCW4CT2/19x/tCumLwDDBQrkdSt/lVFWluz9s3kH4A1c2b13cDj5Jm0DwCTG339/SyT72X2+538/F+eWW9A1fnY7sP+AtwekXeyaTJQUdW7bNm2+GlsxbgVFL3ax/QD1yf11fW/7G5LneQJgxsAaZU7eczwMqKz8+RZsuXz+13NLPcfoyKmZmZWQEd3T1nZmZm1ikcNJmZmZkV4KDJzMzMrAAHTWZmZmYFOGgyMzMzK8BBk5mZmVkBDprMzMzMCnDQZGZmZlbA/wEMqEO0RIci1gAAAABJRU5ErkJggg==
-
-
-
-设定 x 轴 y 轴标题
---------------------------
-
-
-我们想让刻度的位置显示的是含有 $\pi$ 的标识而不是浮点数，可以在 xticks 中传入第二组参数，这组参数代表对应刻度的显示标识。这里，我们使用 latex 的语法来显示特殊符号（使用 $$ 包围的部分）：
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-
-
-
-::
-
-    # 设置图像大小
-    f = plt.figure(figsize=(10,6), dpi=80)
-
-    # 画图，指定颜色，线宽，类型
-    p = plt.plot(x, c, 'b-', 
-             ^4$x, s, 'r-', linewidth=2.5)
-
-    # 设置显示范围
-    plt.xlim(x.min() * 1.1, x.max() * 1.1)
-    plt.ylim(c.min() * 1.1, c.max() * 1.1)
-
-    # 设置刻度及其标识
-    p = plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi], 
-                   ^4$['$-\pi$', '$-\pi/2$', '$0$', '$\pi/2$', '$\pi$'],     fontsize ='xx-large')
-    p = plt.yticks([-1, 0, 1], 
-                   ^4$['$-1$', '$0$', '$+1$'], fontsize ='xx-large')
-
-    # 在脚本中需要加上这句才会显示图像
-    # plt.show()
-
-<button>Run ...</button>
-
-
-.. image::data:image/png;base64,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
-
-
-
-移动坐标轴的位置
-----------------------
-
-
-现在坐标轴的位置是在边界上，而且有上下左右四条，我们现在想将下面和左边的两条移动到中间，并将右边和上面的两条去掉：
-
-
-
-
-::
-
-    # 设置图像大小
-    f = plt.figure(figsize=(10,6), dpi=80)
-
-    # 画图，指定颜色，线宽，类型
-    plt.plot(x, c, 'b-', 
-             ^4$x, s, 'r-', linewidth=2.5)
-
-    # 设置显示范围
-    plt.xlim(x.min() * 1.1, x.max() * 1.1)
-    plt.ylim(c.min() * 1.1, c.max() * 1.1)
-
-    # 得到轴的句柄
-    ax = plt.gca()
-    # ax.spines参数表示四个坐标轴线
-    # 将右边和上边的颜色设为透明
-    ax.spines['right'].set_color('none')
-    ax.spines['top'].set_color('none')
-
-    ####################################################################
-
-    # 将 x 轴的刻度设置在下面的坐标轴上
-    ax.xaxis.set_ticks_position('bottom')
-    # 设置位置
-    ax.spines['bottom'].set_position(('data',0))
-
-    # 将 y 轴的刻度设置在左边的坐标轴上
-    ax.yaxis.set_ticks_position('left')
-    # 设置位置
-    ax.spines['left'].set_position(('data',0))
-
-    #####################################################################
-
-    # 设置刻度及其标识
-    p = plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi], 
-               ^4$['$-\pi$', '$-\pi/2$', '$0$', '$\pi/2$', '$\pi$'],     fontsize ='xx-large')
-    p = plt.yticks([-1, 0, 1], 
-               ^4$['$-1$', '$0$', '$+1$'], fontsize ='xx-large')
-
-    # 在脚本中需要加上这句才会显示图像
-    # plt.show()
-
-
-<button>Run ...</button>
-
-
-.. image::data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAjwAAAFdCAYAAAAHT7f4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XmcjeUbBvDrjH0XiZQhooikklCRNq2WNqWF0E+lJGTLvu9SlCRrIm1EpERk35kxY23Ivm9jxizn/P64Ot4ZWWY55zznvO/1/Xzm43lnxpybmTPnfp/nfu7H5fF4ICIiImJnYaYDEBEREfE3JTwiIiJie0p4RERExPaU8IiIiIjtKeERERER21PCIyIiIraX9Sof1551EfE5l8sFtcQQET9wXe4DmuERERER21PCIyIiIranhEdERERsTwmPiIiI2J4SHhEREbE9JTwiIiJie0p4RERExPaU8IiIz6xfvx7VqlXD3r17TYciIpLK1RoPiojDde/eHe+++y6uvfbay37OTz/9hJkzZ8LlcmH16tVwu90BjFBE5Oo0wyMiV7Rnzx7ExsZe8XPq16+P8ePH4/XXXw9QVCIi6aOER0R8RsdFiEiwUsIjIiIitqcaHhG5KpfrsufxXVFyMhAXd+mPnT2b+jpbNiBHjgw9jAQ7j4c/CCdPAqdO8c/YWKBkSaBMGSBM997if0p4ROSq0rpUlZDAPz/9FNi0CVi69L+JjVe+fKmvXS7g9tuBBx4A7r+fb8WKZSJoCRyPB9i4EZg9G9iwgQlNyuTm5EkgKenSfzdPHqBiRaByZf4AeN8KFAjsv0Fsz3WVX2RakBdxkBdffBGnT59O9b7Nmzfj5ptvRq5cuVK9v1WrVqhV60ksXw4sXsy35csXITGxDoAYAOFXeCQXgO4prmv/+5ZauXJMfB54gG8lSzIxkiAQHw8sXAj8/DMTnX/+8e3XL1mSiU/lyvwhePhhzQRJWlz2N4QSHhG5oqZNm6Jnz54ID2cCs2MH8MUXwKJFwLp1XLayLALAhOfmm8PxwAPArbf+N0lp396FwYNT/3o5dowzQitXWjNFF7vxRiY+zz0H1Kun17+AO3QImDOHSc5vv3FZ6mJlygBFiwIFC3KW5nJ/5swJbN/OqcBNmzhDdFGy/Z+v+9ZbQNOmQKFC/vs3SqhTwiMiGdO0aVP06NEDp0+XRL9+wLffApdqs1OpElCmzCLMnFkHK1fGoGrVy8/wuFyuyy6TxccDq1YBS5Zw1mjp0ku/rpYvD3TqBDRqxPof8ZMzZ4DPPwe+/57fmIu/b9myAbVrA08/zbdSpTL2OB4PsGcPE5+USdD27akfM1cu4OWXgXfeAapUyei/SuxLCY+IZMxTTzVFfHwPLFhQMtX7q1YFatXijEvNmrzp/uOPP/Dwww9j586duOmmmy77Na+U8FwsKYllId5ls4ULU08ElCoFdOgANGnCSQPxkYQEYMwYoHdv4MiR1B8rXBh48kkmOI8+CuTP7784Tp0CJk9mYdjWrak/VqMGE5/nngOyZ/dfDBJKlPCISNp5PEws+vYF/vijKYAeAEoiLAx46SWgY0fWmXotWrQIo0ePxooVK7Bv3z6ULVsWVapUQZcuXVAx5Sf+Kz0Jz8XOnOHr8NChwMGD1vuvvx5o2xb43/+AvHkz9KUF4PTdtGlA167Arl3W+8uVA+rXZ5JTvTqQJUtg4/J4gD/+YOIza1bqacaiRYEWLfjNv/HGwMYlwUYJj4hcncfD+tO+fVlLQ62QLdtHaNq0GD78kKUUmZWZhMcrPh4YPx4YNAiIibHeX6gQ0Lo10KqVSj3SxeMBfv2V64QbNljvL1MG6NMHeOGF4Cma2rOHy2xjxwJHj1rvz5KFWW+PHlz6EidSwiMiVzZnDl/rNm+23pc7N2+a27YFbrjBd4/li4THKzER+OYboH9/IDraen/evEx6unblv0OuYNUqrgsuWmS9r2hRoFs3oHnz4F0uio8HZswARo1KmaEDZcsC48Zxd5c4jRIeEbm02Fjggw+488qrQAHg3Xc5U3KFM0MzzJcJj5fbDfz4I9CvH3ePed1yCzB1KnDnnT59OHvYuhXo0oUFyV758gHt2wNt2oTW2uDKlcxw16yx3vf228CAAf9t+iR2poRHRP5rzRqgcWNg2zZeFyjA+py33/ZvHao/Eh4vjweYPx/o3t266c+WjbW37doFvvQkKCUnAz17Mjv09hXInp3f+M6dgSJFzMaXUUlJwIgRnNaLj+f7wsNZ9FW3rtnYJFCU8IiIJTkZGDiQSYG3AW6tWsCkSXx98Dd/Jjxepv+NQevQIW7r/uMPXrtcwCuvAL16ZXxLebDZvp1LcYsXW+977TVg+HAVdtmfEh4Rod27gVdfZZ8bAMialTWpgZz9CETC47V6NWextm/ndYECvOF/8cWAPHxwWbyYjYsOHOD1bbcBU6YAd9xhNi5/cLu5Tvvhh9zaBwDXXcd6n+eeMxub+NNlE54gKbkXkUCYOpWd+r3Jzi23ACtWsF7Vrks9VauypqdFC16fOsXX/Ndeu3JjX1vxeLidrU4dK9l59VWu+dkx2QG4o6xlSyAyEnj8cb7v8GHg+eeBZ59N3dNAHEEzPCIOcOoUyzOmTrXe17Ile9mY2MEUyBmelH76iSsdx47xulQpTnDUrBnwUALnxAl2ZZw1i9c5cgCffML/CKccTObxAF9/zSr848f5vuLFuTXRrgmfc2lJS8Splizhzfzu3bwuUoQ7dp9+2lxMphIegBMcTZqwsBngRECXLqxztd0RFWvXcvnG26joppuA775z7pa1w4e5/fDbb3mdNy//Px57zGxc4kta0hJxovHjecyRN9l5/HEeUWQy2THt+uuBuXO5mSdHDpZ69O7N/5NLndkVkjwe4LPPePSCN9mpX59re05NdgDW8EybBgwbxtmts2d5RMa4caYjkwBQwiNiU0OHAm+8wRf0nDm5ijFnDlCsmOnIzAsL4+rG6tU89BRgk+FHHrFWPELW2bPcdfX22zwPK0sWYMgQ4IcfeFK507lc7DE0YwafGMnJXN776KP/HowqtqIlLRGb8Xi4RNO/P68LFmSiU6OG2bhSMrmkdbHYWKBhQ2uJq2JFJj/Fi5uNK0MOHWLW5m2XXbw4MH06cN99ZuMKVsuXc2rPW9TVuDFne3LkMBuXZIZqeEScIDmZN/bersnFivHF+/bbzcZ1sWBKeABOhLz6qlXacdNNwG+/+ebcsIDZvx946CHrfI2HHmKV+nXXmY0r2G3fDjzxBLBjB69r1+Zs2DXXGA1LMkw1PCJ2d/48TzL3JjulSwNLlwZfshOMsmdnbvC///H67785KbJpk9m40uyff9hV0ZvsNGvGTFfJztWVLcuZnurVeb1oEbftpTyRVmxBCY+IDZw9y5n5GTN4XakS8NdfTHokbbJkYZ1vly68PngQeOABJo1BLSaGyY53hqJlS2a9dm2s5A/XXgssWMD+PAAQFQXcey93uYltKOERCXHHj7Ns47ffeF2jBvDnn9yNJOnjcrHr9NChvD51iv+3v/xiNq7L2rWLyc7ff/P6vfeA0aNZlS3pkysX1zTbtuX1oUPMeGfPNhuX+IxqeERC2L59bCESGcnrunXZViRPHrNxXU2w1fBcyoQJ3LyTnMzjNyZN4pJh0Ni+HXjwQf4QAHyhHjzYOc0E/enTT7mNz+1m8vjdd0CDBqajkrRR0bKI3ezYwdkHb6lBo0bAxImsRwl2oZDwAMDMmTxz6/x55hGffsqicOOio1MfE9GpE9C3r5IdX5o1i0+quDg+qebO5f+5BDsVLYvYSUQEi2q9yc5bb/GIhFBIdkJJvXp8ncuXj9v933mHeYVRERFcxvImO927K9nxh2ee4W6trFm5ja9ePTZukpClGR6RELNnDzeU7N/P665dgZ49Q+v1LlRmeLzWruVy4dGjvB49mklmwG3cCDz8sBVInz5WlbX4x7RpwMsvM+MtXJhntZQvbzoquTwtaYnYwYkTnNnZsoXXAwbwpPNQE2oJD8BVpAceAI4cYVnHDz/wpj9g1q7lGuaJE7weNAho3z6AATjYZ59Za5k33MCteyVLmo1JLkdLWiKhLj6exyF5k5333w/NZCdU3XorO1bnzs1a1pdeAlasCNCDb9zIRoLeZGfECCU7gfTWW5xNA1gk/sgjPIhUQopmeERCgPcF1tsJ+PnnOdMeqruPQ3GGx2vOHM7sJCdzhWPZMqBcOT8+4P79QLVqwN69vB41Kkgqpx3G4+FOuOHDeV2lCrBwIVCggNm45GKa4REJZe3aWcnOAw9wi3SoJjuh7skngc8/5/jYMdb2HDrkpweLjWXxrDfZGTxYyY4pLhcPYW3ShNfr1/N7ExdnNCxJO/3KFAlyw4dbN5UVKgA//cRDnsWc5s2Bbt04/vtvJkFnz/r4Qdxunnru7fb75ptWUzwxIywMGDuWa8sAsHgx8MILQGKi2bgkTbSkJRLEvv2WfWAAHny9fDkQHm42Jl8I5SUtL4+Hic9XX/H68cfZtydbNh89QIcOLEwGuDPrl198+MUlU+LjeeDowoW8fuUVNsHStGsw0C4tkVDz55/Ao4+yBUi+fNwNW7my6ah8ww4JD8Ab+2eeAebN4/UbbwBffumDFgFffgm0aMFx+fIsFCpYMJNfVHzqzBk2Ilyzhtfvvcdi8lDqD2FPSnhEQklkJLefnzzJm/q5c7lJxy7skvAAXMqqXdtaeereHejRIxNfcMECFgYlJfFQy5UrdQpssDp6FLj/fuuU+oEDgQ8/NBuTKOERCRX79vGgZm+d6pQpQOPGZmPyNTslPACLlqtXt87wHDuWy13pFh3NL3TyJJAjB/DHHzwNVoLXP//w7mTPHi5pzZvHbetiinZpiYSCU6dYC+JNdgYMsF+yY0dFi/J1rnBhXrdsmYET1o8eZfXzyZO8Hj9eyU4oKFGC527lymX1j/Ce+SJBRQmPSJBITgaeew7YvJnX77yj2fFQUq4c8PPP3EGXnMxeSRs2pPEvnz/P07h37eJ1jx5BdjS7XFHlypzWA9ir4NlntV09CCnhEQkS3bsDv//OcYMGwMcfq/4x1FSvbjWEPHeOCeypU1f5S97tXn/9xeuXX7b2vEvoaNwYaN2a43Xr2J3ZRsu2dqAaHpEgMHcud7kCQMWKrFPNndtsTP5ktxqeiw0YAHTqxHHDhsB3310hee3d20pwatZk1qtGS6EpMZG7C5Ys4bWxU2YdTUXLIsFqzx52qT9+HMibl7tcb7nFdFT+ZfeEx+3m8ROzZ/N62DCgTZtLfGLKRkulS/NwriJFAhan+MHBg8Bdd/FIkGzZgEWLVIsVWEp4RIJRQgKPili5ktfTplmvf3Zm94QHYAJ7553A7t1A1qzsq5TqdW/bNn5CbCzPY1qxgieUSuhbvhyoVYszPtdfzyWuYsVMR+UU2qUlEozat7eSnVatnJHsOEWhQsCMGbzJT0ri9/bo0X8/GB/Pd8TG8vqbb5Ts2En16sDIkRwfOMAKdh0/YZwSHhFDZsywfidWrcpzCcVeqla1zkHbu5cnELjdYKbr3cL14YfsRSD28r//AU2bcvzXXzwBWIzSkpaIAdu2AXffze7011zDg5dLljQdVeA4YUnLy+Phxqtp03g9vdGPeGFaQ17cey8PoNQZWfYUF8dOzN423JMnM+sVf1INj0iwOHeOM96bNvF69mz2m3MSJyU8ABPbqlWBuK27sQF34Bqc5NlY69cDpUqZDk/8afduFjEfO8bmhMuWAXfcYToqO1MNj0iwaNXKSnY6dnResuNE+fIB332TiOlhLzHZAXBs0DglO05QsqTVnCkujn0Kjh83HZUjKeERCaDx4/kGcBNH795m45HAqfhtN9zrXg4AGIW3UX9SQ9WxOsXDDwP9+3P8999c1nLQDGew0JKWSIBs2gRUq8YNOkWLcjXj+utNR2WG05a0MH8+8NhjAIB/ClVG2eMrcB450b49MGiQ4dgkMDwe4IUX2IUSAD79lOfHiK+phkfEpNOnWaS8fTtnthcsAGrXNh2VOY5KeA4e5FlLhw8DefIgfulaVG9yy4VNWjNnAs88YzZECZBTp/izsHs3u2mvX692BL6nGh4RU7xHJW3fzuvevZ2d7DhKcjKXLw4f5vXo0chZ+RbMmAHkz893vf46VznEAQoUACZO5Dkj8fH82dC6ZsAo4RHxs3Hj2HMH4HlZHTuajUcCaMAATucBwGuv8Q3AzTdbtVwnT/Jg9KQkQzFKYNWqZfXkWbtWhXwBpCUtET+KiQEqVQLOngVuuAHYuBEoXNh0VOY5Yknrr7/44uZ283C0NWt4WFoKrVtbzSf791cy7BjnzwP33MPCvrAw/qxUr246KrtQDY9IoLndPDh50SJe//or8OijRkMKGrZPeI4dY6+VvXuBHDl4fkjlyv/5tLg4HqcVHc3eg2vXMkEWB9i8mYV9CQlAmTLsvH1RQiwZohoekUAbNcpKdlq2VLLjGB4PjxTYu5fXw4ZdMtkB2Idu4kTe5Ccmsp4nISGAsYo5lSoB/fpxvHMn0Lat2XgcQDM8In6wbRtv8OPigJtu4sy1bt4stp7hGT8eeOMNjp99lgVcrsvedAIAunSxXvu6dQN69vRzjBIcLp4G/vln4KmnjIZkA1rSEgmU5GQen7N8OV/nFi5kKYdYbJvw7NsH3HYbtx9ffz0QGcnD0q4iZUlHlizAihVc7RAH2L0buP129q647joudV13nemoQpmWtEQCZehQJjsAi1KV7DiEx8MTsk+d4vWYMWlKdgCW+UyaxDqe5GQubcXH+zFWCR4lS3L9G2D7gjffVBdmP1HCI+JDERFA164c33KLtUwhDjBlCjBnDsevvAI8/XS6/nrlylzOAoAtW6yxOEDjxsDzz3M8c6bVs0B8SktaIj6SmMijI9avZxHqsmW8lv+y3ZLWgQNcyjpxgueGbNkCFCqU7i+TlATUqAGsXs3l0CVLgJo1/RCvBJ9jx1jIfOAAC/42bgRKlzYdVSjSkpaIv/Xrx2QHADp0ULLjGB4P8NZbTHYA4PPPM5TsAEDWrNy1lSMHv2yTJkBsrO9ClSBWuLA1s3P2LPDqq1zfFJ9RwiPiA2vXAn36cFypEtC9u9l4JICmTeMyBAA0agTUr5+pL1e+PNC3L8c7dqgZoaM89hjQqhXHy5bpZFkf05KWSCbFx3NHTWQk79BXr+aWdLk82yxpHToEVKgAHD8OFCnCpaxrr830l01O5nlrf/3F699/5+5lcYBz59iNcutW/kJZs+ayfZzkkrSkJeIv3bsz2QFYaKpkx0HeeYfJDgCMHu2TZAfg1vQJE4DcuXn9xhvctSwOkDs3C+CzZGFRV4sWWtryESU8IpmwbBkwZAjHd92l5QdHmTED+P57jp97jm8+VKYMMHgwx3v2AB984NMvL8Hs7rutA0ZXr7a2rUumaElLJINiYzmbs2MHi0zXruVGHbm6kF/SOnKES1lHj7LYdMsWvzSLc7tZ1vH777yePRt48kmfP4wEo3PnWBC4axeQJw9/xsLDTUcVCrSkJeJrH33EZAdgwbKSHQd5910mOwDw6ad+64wbFgaMGwfkz8/rFi2Akyf98lASbHLn5o4/gHdX77yjhoSZpIRHJAPWrQNGjuS4Rg2gTRuz8UgA/fADMH06xw0aAC++6NeHCw8HRozg+MABnrslDvHII9yeDnB6z7uEKhmiJS2RdEpOBu69l5snsmZlf7AKFUxHFVpCdknr2DF+sw8fZq+dyEigWDG/P6zHw11aCxeyIeGKFTx7SxzgyBH2Kjh2jD9rUVFAwYKmowpmWtIS8ZXPPmOyAwDt2yvZcZTWrZnsAJziC0CyAzDJ+ewzIHt268iupKSAPLSYVqQIMGwYxwcPamdEJmiGRyQd9u8Hbr0VOHMGuOkmnp3l3TosaReSMzyzZ1vnYz39NJsNui57M+kX3bsDvXpxPGyYllIdw+Ph8taCBbxesgS47z6zMQWvyz4plfCIpMMLL3A3MgDMnQvUrWs2nlAVcgnPuXOcytu9GyhQgDtmihcPeBjx8cDttwPbt3PjTlQUUKJEwMMQE3bs4K6t+Hguca1fz+2hcjEtaYlk1ty5VrLzwgtKdhylb18mOwAwYICRZAcAcuZkf0OAG3fee89IGGLCzTdbZ9ZERQEDB5qNJwRphkckDc6d47bzmBhuEY6KMvaaZwshNcOzdSvvrBMT2RBuxQp2wTWocWNg6lSOZ84EnnnGaDgSKImJ7HC6eTMLujZu5Bq7pKQZHpHM6NOHyQ7Am30lOw7h8bD/SWKiVTlsONkBWL/j3ajTqhUP1xYHyJYNGDuWP4sJCcCbb7I7paSJEh6Rq4iMtFr833038NZbZuORAJo+3SoUfest/gAEgaJFubIGAP/8A/TsaTYeCaBq1awT1ZcsAb76ymw8IURLWiJX4HYDtWrx1OqwMB5rc+edpqMKfSGxpHXqFJcLDh5kJ+XoaOCaa0xHdYHbDdSsaa2wrV2rQ7Ud48wZFtHv3cupvqiogLVICAFa0hLJiPHjmewAPE1AyY6DdO/OZAfgFF8QJTsAE/AxY5jsJCcDLVtqdcMx8uWzDhQ9eRJ4/32z8YQIzfCIXMaRI7zBP34cuOEG3kTly2c6KnsI+hmeDRtYHOp2A/ffD/z5Z8B77qRV+/bAkCEcf/45mxKKQzz3nHXchE6W9VIfHpH0atIEmDiR4++/Bxo2NBqOrQR1wuN2s6nb8uU8O2T9eqBiRdNRXVZsLFc39uzh6kZ0NGt8xAH272dPntOngdKlWXCYM6fpqEzTkpZIeixaZCU7Tz7JMyLFIcaPZ7IDsJVxECc7ABsQfvIJxydPAh98YDYeCaDixbltFAB27QKGDjUbT5DTDI/IRc6fZ/Hn1q1ArlxsqluqlOmo7CVoZ3iOHgVuuYXrmDfeyHXMvHlNR5UmDRoAP/3E8fz5PIlAHCApicWFmzfznJvoaKe339YMj0haDR7MZAcAevRQsuMonTox2QGAESNCJtkBeJapN9y33+YJBOIAWbNaU3znzrGoSy5JMzwiKcTEcEk8Pp4rGevWsdeX+FZQzvAsXw7UqMFx3brAL78EbaHy5Qwfbi1p9eoFdO1qNh4JoEaN2DcKABYuBGrXNhqOQSpaFkmLlJsedCCx/wRdwpOUxKaCGzfyQMaICJ5dFGKSkri5bNMmLsdu3er01Q0H+ecfbis9d45Hoaxbx9kf59GSlsjVLFxoJTsvv6xkx1FGjWKyA3BZKwSTHYCvbyNHchwXB3ToYDYeCaASJYDOnTnevJnHoEgqmuERwX/r/rZuZc2q+EdQzfAcOMBC5TNngDJlOLsT4lt7X3gBmDGDY81UOkh8PE853rWLPQq2bQOKFDEdVaBphkfkSsaOZbID8AZfyY6DtGvHZAdg8WeIJzsAC++9/4zWrdmJWRwgZ04W2wPsUdCli9l4goxmeMTxjh8Hypbln6VKcRt6rlymo7K3oJnhWbaMB1IB7CzpXdO0ge7dWbgMAF9+CTRrZjYeCRCPB3jiCWDePBbdr1oVNIfeBoiKlkUu5733rF2d330HPPus2XicICgSHrcbuPdengibIwf7l9ioB0FsLGtY9+7l2afbtgEFCpiOSgJi2zZuM01MBKpXt04/dgYtaYlcSkQEMHo0xw8+qOMjHGXKFCY7ANC2ra2SHYAdmAcN4vjwYaB3b7PxSACVK8cu4QDbLUyZYjaeIKEZHnEsj4fdaBcs4M3P+vXA7bebjsoZjM/wnD3LQuX9+4FixYDt20OqyWBaeTw8+3TpUu7giojgP1sc4MwZfrMPHODhatu2Afnzm44qEDTDI3KxmTOZ7ABAy5ZKdhxl0CAmOwDQv78tkx2AJRwjR/LPpCSds+Uo+fJZU3yHDmmKD5rhEYdKuXvzmmt4g1+4sOmonMPoDM/u3SxuiY9nl75Vq2xf39CiBQuXAWDOHNa0igN4POxJsGwZp/g2b+bPvr1phkckpeHDmewA3MmiZMdBOna0DpoaMcL2yQ7AA7W9qxlt2gAJCWbjkQBxubgjwzvF17o1kyCHsv8zXeQi+/fzBQDgLE/LlmbjkQBauhSYNo3jF190TEe+667jNnWApRyffmo2HgmgO+8E3nyT4/nzuZbvUFrSEsd57TVg8mSOf/8deOghs/E4kZElLbcbqFYNWLOGDdqio4GSJQMbg0EJCaxT27qVsz3btrGWVRzg6FHu3DpxArjpJjYbs0GDzcvQkpYIAKxYYSU7DRoo2XGUyZOZ7ADsruygZAcAsmfnUi4AnD4NfPSR2XgkgK691ipa/vtvq/GYw2iGRxzj4j5zW7YApUubjsqZAj7Dc/Ys73APHACuv57TGzbdmXU1Tz4J/PILyzpWr2bdtjhAUhKn+KKi2IFyxw4mQvajGR6RyZNT95lTsuMgAwYw2fGOHZrsAJzlyZqVtasOr2F1lqxZecgaAJw6BfTsaTYeAzTDI45w5gxv8A8eBIoXZx2Dg1/zjAvoDM/u3WzAdv48ULUq1zUdsDPrStq1A4YO5XjqVOCll8zGIwGSstuqfTtRaoZHnG3gQCY73rGSHQfp0IHJDuCYbehX07Urd24B/O+JizMbjwSIy8VM17tN/cMPTUcUUHrmi+3t3Wvdzd5zD/Dyy2bjkQD66y9g+nSOGzUCatQwG0+QKFAA6NOH43/+YTdmcYjKlYEmTTieNQtYtMhkNAGlJS2xvSZNgIkTOV68mGcLiVkBWdJyu5nhrl3LLbhbtwLh4f59zBCSlATccQcQGclt6jt2AEWKmI5KAmL/fqBsWeDcOfbpWb3aTjOfWtISZ9qwAZg0ieMGDZTsOMqkSUx2AKB9eyU7F0lZw3r6tI5acpTixfmcAIB16xxzmrpmeMS2PB7g0UfZXDBrVt7JlitnOioBAjDDc/Ys72C9VerbtgF58vjv8ULUxTWseo44SGwsnyMHDgA33MDnSO7cpqPyBc3wiPP8+iuTHYDHR+gXuYMMHWpVqffvr2TnMlwuYMgQq4a1Y0fTEUnA5MljFXLt2wcMG2Y2ngDQDI/YkuoTgptfZ3gOHgRuvpl3sParT/CLlHVuS5Y45ogxSU7mc2TTJib4KEykAAAgAElEQVRAO3YAxYqZjiqzNMMjzjJhApMdAOjUScmOo/TowWQHYJGKkp2r6tPHOlqpbVs1I3SMLFmsLayxsUC3bmbj8TPN8IjtpDxFoEQJbs7Jlct0VJKS32Z4oqKASpV45/r44zxDQdKkSxegXz+Op03jYfLiEN7zRsLCuNOjUiXTEWWGZnjEOYYOtU4R6NtXyY6jdOzIZCcsDBg0yHQ0IaVDB2smtFMnq1ejOMDgwZztcbvZhtumlPCIrRw4YG21rVIFaNzYbDwSQIsXs5EawKKUihWNhhNq8ufnaiDAA7VHjTIajgRShQpAixYcz58PzJtnNh4/0ZKW2MqbbwJjx3K8YAFQp47ZeOTSfL6k5fEA994LrFrFKb3t27nVVtIlMZGrGVu3AgULAjt3AoUKmY5KAuLwYRb7nzkD3HYbl7ayZjUdVUZoSUvsLzISGDeO4yefVLLjKDNmMNkBgA8+ULKTQdmy8aw5ADh5kkvC4hDXXQd07sxxZCTw1Vdm4/EDzfCIbaSsu9u8mbO0Epx8OsOTkACULw/s2gVcey2nJfLn983XdiCPB6hdmyuE2bIB0dFA6dKmo5KAiI/n6el79jAB2rEDyJfPdFTppRkesbcFC6wNOc2bK9lxlM8+Y7IDAN27K9nJJG8zQoBLXN6bfnGAnDnZqBPgEpf3B8EmNMMjIc/tBu66i0vO9umdZW8+m+E5eZJ1B8eOsU1+ZCSnJSTTXn4Z+OYbjpcvZ4mUOIDbDVSrBqxZE6q/UDXDI/Y1ZQqTHQD48MNQe25KpgwcyGQH4J2pkh2f6dcPyJ6d43bt1IzQMcLCrEKu2FigVy+z8fiQZngkpMXFscng3r3A9ddzc46OTQp+Ppnh+ecffvPj44Hq1YGlS7keIz7Tvr21qvHDD0CDBmbjkQCqW5cHEmbJAmzZEkqHEWqGR+xpxAgmOwDQu7eSHUfp2pXJDsDmS0p2fK5zZ2tb+ocfsqZHHGLgQD6nkpOBjz4yHY1PKOGRkHXsGDBgAMcVK7LXnDjExo3ApEkcN2gA1KxpNh6buuYa5pUASzm8Pa7EASpXtjq3pmz7EMK0pCUhq21bYNgwjufMAZ54wmw8knaZXtJ67DF2hM2ShYXKt9ziu+AklfPnuev/77+BokWZ+OTNazoqCYiYGD63EhKAWrWAhQtDYSZVS1piL7t3A59+yvEDD/CcSHGI+fP5BgD/+5+SHT/LkYPLxQBw6BAwfLjZeCSASpUC3nmH4z//DPkjJzTDIyGpSRNg4kSOtWU29GR4hic5mT0INm7kNMOOHZx2EL9yu4E77+R/e7587O3oPWhUbO7oUaBMGeD0aZ47sn49Z1aDl2Z4xD42b05dvqFkx0G+/pqvugCraJXsBERYmFUvd+aMjpxwlGuvBTp04HjzZj4HQ5RmeCTkPP00MHs2fwlHRgK33mo6IkmvDM3wnD/P5avdu9WDwACPB3joIZZxZMvGA0Zvusl0VBIQ586xweeBA0B4OL/5OXOajupyNMMj9rBkCZMdAHjjDSU7jvLZZ0x2AB4hoWQnoFwua5YnMRHo1s1sPBJAuXMDPXtyvGcPMGqU2XgySDM8EjI8Hu4+Xr6cNxc7duhQ7FCV7hme06dZR3D0qI6QMOz554HvvmMCtGEDcPvtpiOSgEhKYg1PdDT7FezaBRQsaDqqS9EMj4S+mTOZ7ADA++8r2XGUIUOY7AAsIFGyY0zfvqxZ9XiATp1MRyMBkzWrdbDoiRPW8RMhRDM8EhKSkngnGRUV7DcXkhbpmuE5dIizO7GxwN13swFa8PcCsbWWLYExYzhetIgtWsQBLp5m374duPFG01FdTDM8EtomTmSyA7DdvZIdB+ndm8kOwCISJTvGde/Osg6AG3h0sKhDuFzAoEEcx8cDPXoYDSe9NMMjQS8ujmUb+/bxZmL79mDeICBpkeYZnp07WZmelAQ88ojVcFCM69KFJ6oDwPffAw0bmo1HAqhePWDWLG6V3bwZqFDBdEQpaYZHQtcnnzDZAYBevZTsOEq3bkx2AKt+QILChx9aB4t27mx9m8QB+vVjsuN2h1QhlxIeCWonTlivcxUqAK+9ZjYeCaANG4CpUzl+4QV2WJagUaAAZ3kAtmUZP95sPBJAt91mndY8axbw119Gw0krLWlJUOvQwVoynjkTeOYZs/GIb6RpSevxx3l2T9aswJYtXNeUoBIfz16Qe/YAxYtzudlb2yM2t3cvn5Px8UCNGkx6gqO+TktaEnr27gVGjuS4Zk12WBaHWLTIOqiweXMlO0EqZ04uMwPA/v3W81Uc4MYbgffe43jZMmDOHLPxpIFmeCRoNW8OjBvH8ZIlwH33mY1HfOeKMzweDw9IW7WK0wU7dvAoCQlKycnAHXcAERFc5tq1y6rtEZs7cQIoXRo4eRKoWJHL0OYPFtUMj4SWLVusmoBnnlGy4yg//shkB2CHSSU7QS1LFuvIiVOnVFvuKNdcYx0sGhEBfPON2XiuQjM8EpQaNAB++okbATZtYo2c2MdlZ3iSkninuHUrpwl27eK0gQQ1j4fNB5csAXLkYC1PiRKmo5KASHmwaKlSfO5mz24yIs3wSOhYvpzJDgC8/rqSHUeZMIG/MAHudVayExJcLuukgfPn2ZhQHCJ3busk2ZgY4IsvjIZzJZrhkaDi8QC1awOLF/NOcds2IDzcdFTia5ec4VGHyZCXcmY2IgIoX950RBIQiYn8Zu/cCVx3Hf/Mm9dUNJrhkdDw669MdgDgnXeU7DjKp59aHSZ79lSyE4L69LH60X30keloJGCyZeM3HwAOHwaGDzcbz2VohkeChtvNsyHXrwfy5WP5xrXXmo5K/OE/MzwnT3K3x4kTvFPctIn9dyTkNGnCs+8AYOVK4J57jIYjgeJ2sznohg2mf4FrhkeC34wZTHYAoF07JTuOMnAgkx2AbeuV7ISsHj2smtXOnY2GIoEUFmZt0TtzJii362mGR4JCYiKPjtixAyhShEvA+fKZjkr8JdUMz/793OURFwdUrw4sXRosHVslg95/H/j4Y45/+w14+GGz8UiAeDzAgw8Cf/5psghTMzwS3L76iskOwLV/JTsO0rs3kx2Ad4VKdkJe585WzWrHjnwdFAdwuayZnfPnWYsXRDTDI8albONQsiR3JefIYToq8acLMzw7drBmJymJZ2f98ovp0MRHevSwXu9mzACee85oOBJI9evz8EMz2/Uue8ekhEeMGzTIatY5caJORA9VI0eOxLZt21CsWDFERESgV69eKFeu3CU/90LC8/LLVnfWdeuAKlUCGLH40+nTQJkywNGjQLlyQGSkSrMcIzISqFSJU3sNGwLffx/IR1fCI8Ep5VEst90GbNwYDEexSHqNGzcOX3zxBVauXAkAmDdvHlq2bIno6GjkvMT2cpfLBc/69VaC06hR0Lell/QbPhz44AOOx47l+XjiEOa266mGR4LT4MFMdgCgb18lO6GqT58+eP311y9cP/bYY0hISMDXX399+b/k3cKTNSvreMR23nrLOmKiRw+rVEscIOV2vSAp5AqKhGfRokWmQxADvv9+EUaM4Pjee3lIqISebdu2Yffu3ahYseKF97lcLtx222347bffLv8X587ln82bs4hLbCdnTquOZ98+YPRo/b53jFKlmPECwMKFwO+/G//eK+ERYwYMWHThjm/AAG3OCVU7d+4EAOTPnz/V+/PmzYuYmJj//oWUd3q5cgFdu/oxOjHt1VetmtV+/YB58xYZjUcCKOV2vU6dsGjhQqPhmE94IiOBs2dNRyEBtnMnsHYtx3Xr8qRlCU0n/m0YmCdPnlTvz5s374WPpTJ7tjV+7z2geHF/hieGZc3K5WoAOH4cWLbMbDwSQNddB7Rty/HatcCWLUbDMZfwJCayqKlSJUAzPI7TrZt1o9+vn9lYJHOy/rv1JstFBViJiYlITk5O/cnJyVbtTsGC1vY8sbX69a2a1eXLgUOHzMYjAdS2rdU2/48/+NpvyBV3ablcLvNVRiIiIiJp5PF4LlkgccWuCFfZsp55GzcCd9zBsbalOsaTT7K/XJYsQFQUULas6YgkM/bs2YNSpUphyZIlqFmz5oX316xZEyVKlMC0adP4joQE4JZbgJgYuAB4YmOB3LnNBC1GPPooj5rIlg2IjmZLCnGA8+fZefKtt9hg1L8Fm0G6Lb1yZTYeA4Bp06yTI8W2Fi+2muk2b65kxw7Cw8NRvnx5REVFXXhfYmIiIiMj8XDKQ5S++AJIWcSsZMdxvMvXiYlA9+5mY5EAypED+Pln4IknjO5OMV+03KuX1X6zUyezsYhfeTzWtzhnTtbxiD00a9YMEyZMuHD97bffolChQnjZe0Nz9izQpw/HZcoEPkAJCnffDTz/PMdffw1s3mw2HnEW8wlPmTLAm29y/Ouv3K8vtjR7trVDo3Vrbc6xkzZt2qBOnTpo0aIFevXqhR9++AHz589Hbu8szscfW5WqajLoaL17cznb47Hq10UCITiOljhwgI3Hzp0DqlVjGb+asthKcjLLtSIigAIFgF27gEKFTEclAXHsGIs1Tp/mMva6dXBlyeL/GkEJWi1aAF9+yfGSJcB995mNR2wlSGt4vK6/Hnj/fY5XruQpq2IrU6cy2QHYZTw9yU5MTAy6qjld6BowgMkOgJH33otW770HAGjUqBG2bdtmMjIxpHt3IHv2GABdg+XUAXGA4JjhAXigUunSPE2yfHku7upgJVs4f56bc3bvZm67Y0f66lUHDBiA6tWro1atWoiLi8OwYcNw9OhRREdHIywsDAMGDEClSpX89w+QjNu7l5Xp8fEYV7YsvrjmGqxcuRIulwtz58694gGjYm+1ag3A4sXVAdTCzz8DDz2k57b4RJDP8ABsQuataI2KAiZPNhuP+MyYMUx2ABYqp3dzzp9//ola/7Zi7t27N1577TUMHz4cc+fOxZ133on77rsP27dv93HU4hO9egHx8QCAPmfPpv+AUbGtbNn+RIECfF536gT07KnntvhX8CQ8ANCqlVXJ2r37hV+UErrOnLE259x8M9CsWfr+flRUFG699VYAQHx8PEaOHJlqN1CHDh0QFxeHkSNH+ihi8Zlt24CvvuLwwQex++DB9B8wKrYUFRWFSpVuvdBoOyIiHiNG6Lkt/hVUCc/aLVtQweNBGICwPXsQljs3wsLCLrw1b97cdIiSTsOGAUeOcNynDxuOpcc333xzYWtzcnIyrr32WsTGxl74eN68eVGoUKELB1hKEOnShdXqLhd2PvccgHQcMCq25n1ev/ceUKwYACQjKelanD6t57adrF27FhUqVEj1Om7yNT1oEp4jR46gV69eGD9jBtaFh6MjgJiCBfF28+aIiYlBTEwMRo8ebTpMSYcjR4AhQzi+9da16Nkz/T/4q1atQtWqVQHwcMqYmBgMGDDgwsdjY2Nx+PBhlFFvl+CyejXw3Xccv/IKThQsCCAdB4xKyMjIi5r3eZ0nj7cBYR4kJ8cgPFzPbbu48Jo+fjzWrVuHjh07IiYmBm+//bax1/SgSXhWrlyJ8ePHo1rNmoisVw91AYSfOAHXhg0IDw9HeHg4smfPbjpMSYe+fdlvDjiCggXT/4O/Zs0a3H333Vd8jKlTpyJ37txo06aNf/4Rkn4eD7fiAUD27ECvXuk7YFRCRkZe1C5+XjdrZvWi7N2by+CAntuh7sJrerVqiIyMRN26dREeHg6Xy2XsNf2KZ2llxsyZMzFlypSrfl7hwoXx+eef46mnnrrwvtmHD2NClSo4s349Tq5fDxw+zGPmJWTExACffcZxpUorMXv2eBQuXAhff/31f37wL2f69OmpilwvdvLkSfTp0wejR49GaR3KEzx++42nIgM8O6dUKRT5+28AgNvtTvWpsbGxKPjv7I+EHu+LWqFCaX9uX/y8zpaNy90vvcRZ4eHDgffe03M71KV6TZ89GxMmTMCZM2dw8uRJYzH5LeGpV68e6tWrl+6/d/jwYRw+cgQ5Bg7E348+irikJB7AMmKEH6KUjEhLMrt6NZCQUBjA5/jyy6dQuDDfn9YffI/Hg82bN6cqck3J7XajWbNm6NGjB1577bWM/lPE19xua3Ynb17W8QAXliUOHTqEm2+++cKnHz9+PNW1hJb0vqhd7nn9wgvAoEE8TnHwYDdWrdJz2y4OHz6Mw4cPI0eOHPj7778RFxdnLBa/JTwZNX78eNx3333Aww/j+F13Yd/atZwqeP99oFQp0+EJrp7MRkQAt9/OccOGwD33cJyeH/wlS5bg/vvvv+zHO3fujFdeeQUNGjQAAOzcuVNr/cHg22+tQ4DbtQOKFAGQ+oBR74nq3gNGmzZtaipa8ZG0Prcv97wOCwP69wfq1gXOnu0Ml+sVNG2q57YdXHhNB29w9u3bZyyWoKnhAYCkpCSMHj2aJyy7XCjQrh3WAjiTkAD06GE6PEmjzp1ZxhEWxjoer/T84E+fPh0vvfTSJT82ZswYVK9e/UKyAwCTJk3yTfCScQkJwEcfcVykCPDBB6k+fNUDRiVkpfW5faXn9aOPAuXKjQFQHfPnN7jQu0vP7dCV6jUdQIECBbB27Vqc8RZqBVhQzfBs3boV+fPnv3AHWKZ+fYTnyYMjsbHIN2kS7xgvs8QhwWHpUuDnnzlu2hT4t4XOhR9871JYyh/8fPnypfoaSUlJiImJueTa/axZs/Dtt9/i0UcfRVRUFADWgeTKlct//yhJmy+/BLxbiLt2BS76vrZp0wYnT55EixYtAOC/B4xKSErrc/tKz2sA+PnnWcif/1sAjyIhIQoNGwJPPKHndij7z2t6mTIIDw/HkSNH/vN7PyA8Hs+V3szbssXjCQvzeACP55lnTEcjV+B2ezz33cdvVY4cHs+ePdbHIiIiPBUrVvQkJyd7PB6PJy4uzlOmTBnPzp07//N15s2b5xk2bNh/3n/06FFPnjx5PGFhYR6Xy3XhLSwszDNjxgy//bskDc6c8XiKFuU3/6abPJ7z56/46fzVI3aQ1uf25Z7XHk/q5zbguvCm57ZkwGVzmuA5S+tKmjW70LEVf/0F/JstSnCZMwfw1jC2awcMHpyxr9OsWTP07t0bxb1dtyX49enDWR0AmDIFaNz4ip/ucrl0WrrDpPV5HRXFiXy3G6hXD/jppwAFKHYRAmdpXUmPHkCOHBx36KCjdYOQ220dhZY/v7VRJ73Onz+PI0eOKNkJJUePcosNAFSuzP3FIimk53ldvjzQpAnHM2cCy5f7NzZxjtBIeEqUAN59l+OURSISNKZO5QH3AHNS7zb09Prll1/w5JNP+i4w8b9+/axucf37s1pdJIX0Pq9T3uN27Kh7XPGN0FjSAoDjx9mO8+RJoEIFYONGIGtQ1Vw7VkICcMstbDZYrBiwYwdw0QkCada4cWOMHDkShTOaMUlg7d4NlCvHH4JatYCFCwHXZWeUL9CSlrNk5Hndrh0wdCjHv/wCPP64n4ITuwnxJS0AKFTIWjPZsgWYONFsPHLBF18w2QGAbt0ynux4PB5kzZpVyU4o6d6dyQ4ADBiQpmRHnCWjz+tOnbg87h1f1KRbJN1CZ4YHAOLieDe5dy9www3Atm2AtrQadfo0cPPNbAlfpgwLDtN7IrqEKG+HSY8HaNAA+OGHNP9VzfBIWvTta7V2mjwZeOUVs/FISLDBDA8A5MoF9OrF8b59wCefmI1HMGQIkx2AG3WU7DhIly6X7jAp4iPvv89lcoCJT3y82XgktIXWDA8AJCdzJ0hkJFCgABudaQnEiAMHOLtz7hxw113AqlWqV3WMpUuBfzvr4o03gHHj0vXXNcMjaTVmDNCyJcdDh/6ngbfIxWwywwMAWbJwJwgAnDpljSXgevZksgOw546SHYfweKy+Azly6NgX8atmzbgpAuAs8okTZuOR0BWaL1FPPQV4D6D75BNcOHRFAiY6micJANw98eCDZuORAJozhw1AAbaLKFHCbDxia1mzsh4eYLLjHYukV+gtaXktXw7UqMHx668DKQ4lFP9r0IAdUF0uYMMG63R0sbmkJC4pb9nCJeVdu7iDMp20pCXp4fFwBXXZMk4qbt+uPFsuy0ZLWl7Vq/NVFwAmTQI2bTIbj4MsXWq1e3/tNSU7jjJhApMdgHuFM5DsiKSXy2UdVXP+PNtfiKRX6M7wAFxXqViRhcxPPMGpdvEr3Wk5WGwsULYsq9VLlAC2buXOyQzQDI9kRMOGwI8/amZZrsiGMzwAcOutrGgD2Ipz0SKj4TjBTz8x2QGA1q2V7DjK8OFMdgBWj2Yw2RHJqP79uW8lZd28SFqF9gwPAOzfz73RcXHAPfcAK1ao26ufJCVxQm3rVuCaa9gR4JprTEclAXHoEJ9nZ8+yhmftWr7yZJBmeCSjWrbkVnUAWLAAqFPHbDwSdGw6wwMAxYsDbdpwvGoV8P33ZuOxsXHjmOwA7DmnZMdBevVisgPwZPRMJDsimdG9u9Vg/8MPdeSEpF3oz/AA7MdTpgxw7BhrDCIj1fLXx86e5X/twYNAyZIsn8qZ03RUEhBbtwK33cZauUcfBX79NdNfUjM8khndu1tN97/5BmjUyGw8ElRsPMMDcHus98CV7dvT3fVVrm7YMCY7AMs3lOw4SKdOTHZcLmDgQNPRiKBdO+C66zju3Jk7t0Suxh4zPAB/4m+9lcd2Fy0K7NgB5M1rOipbOHyYE2hnzwJ33MHyDXVVdoiUR0i89howcaJPvqxmeCSzRo0CWrXieMQIbqIQge1neADuke7Th+NDh3joivhEyvKNgQOV7DiGxwO0b89xyueXSBB4803W0QNA796sbBC5Enu9dL30EqcgABZW7t9vNh4b2L7d2hHxyCMs4RCH+OEHdjQHeGy1ehBIEMmWzTpK8dgx/soXuRL7LGl5/fEH8NBDHGfgFGdJ7fnnge++43jdOqBKFbPxSIAkJgIVKnBpuHBh/lmwoM++vJa0xBc8HjbdX7mSbaG2bwduuMF0VGKYA5a0vOrU4eGiADB+PLBxo9l4QtjKlVay07ixkh1HGTOGSQ4AdO3q02RHxFdcLmtmJy6Ou7dELsd+MzxA6iMnHn4YmD9fzQjTyeMBatcGFi8GsmfnzuRSpUxHJQFx+jSr1I8eBUqXBqKi+EPgQ5rhEV96+mlg9mzWF27axC4K4lgOmuEBuFvrf//j+PffgblzzcYTgmbNYrIDAO+8o2THUQYNYrIDAP36+TzZEfG1AQOY7LjdbEYocin2nOEBgCNHWMJ/+jRQvjzT/qxZTUcVEhISeIe0Ywe7KW/fzjIOcYB9+9hhMi4OqFqV65p+mB3VDI/42ptvAmPHcvzrr9pg4WAOm+EBgCJF2JEK4JT8l1+ajSeEjBpllW9066Zkx1G6dWOyAwCDB2spWEJGr15W67UPPuDZfyIp2XeGBwDi47m8tXs3E6AdO4D8+U1HFdSOHePE2MmTvNGPiNCKhmNs3sy2Dm43iyJmzfLbQ2mGR/yhf3/rPvfzz63KBnEUB87wADz/YMAAjo8cscZyWT17MtkBgCFDlOw4SocOTHbCwnSEhISk998HwsM57tpVzQglNXsnPADw4otAtWocDx8O7NljNp4gFh0NjB7N8YMP8iZfHGLePKu4v3lz1r2JhJhcuaxc/cgR1tyLeNl7Sctr2TKgZk2OGzcGpkwxG0+Q8m7tdLnYZNDbtFpsLjERqFyZtW758rFKvWhRvz6klrTEXzweoEYNYMUKzlBHRwM33WQ6Kgkghy5pedWoATz3HMdffw2sXm02niD0++9MdgA2qFay4yBjxjDZAYCPPvJ7siPiTy4XJ/MB7jjt0MFsPBI8nDHDAwA7d3KaPjERuP9+4M8/tQPlX8nJ7KK8eTOQJw9v8K+/3nRUEhDHj7M6/fhxNhncsoUHhfqZZnjE315+GfjmG46XLAHuu89sPBIwDp/hAdg59t13OV6yBPjxR7PxBJGvvmKyAwCdOinZcZSePZnsAKxSD0CyIxIIAwZw3wrAbeput9l4xDznzPAAwIkT3HN9/Dj/jIx0/Dak06d5g3/4MHc3REez8E8cICoKqFSJU3y1a/Pg3QDNemqGRwKhSxercHnyZOCVV8zGIwGhGR4AbBvcrRvHO3ZYW5IcrH9/JjsA74iU7DhI27ZMdlwuYMQILfGK7XTsCBQrxnGnTsC5c2bjEbOclfAAwFtvcXYHYGtO73S+A8XEWMV91aoBjRoZDUcCae7c1NvQK1c2G4+IH+TLB/Tpw/HevcDQoWbjEbOctaTl9eOPQMOGHLdpAwwbZjYeQxo1AqZP53jZMqB6dbPxSIAkJgK33871y3z5ONt53XUBDUFLWhIoycnAXXcBGzcCuXNzU0bx4qajEj/SklYq9etzpxYAfPIJf/E7zLJlVrLTqJGSHUf5/HPrZ75r14AnOyKBlCWLdU977hzresSZnDnDAwDr1zPt93h4rO68eY6pYXC7meCsWsVNOVu3AiVLmo5KAuLYMVapnzjBnYuRkUZ2ZmmGRwKtXj0eD+dyAWvWAHfeaToi8RPN8PxHlSrAm29yPH8+MHOm2XgCaNo0JjsAt2sq2XGQnj2Z7ADahi6OMngwkDUr73E/+IB/irM4d4YHAI4eBcqV4wtAqVJsumbzbUrnzvEA+X/+YUPd7dtZxiEOsGULa3eSk4E6ddhe29CspmZ4xIT33wc+/pjjH34AGjQwG4/4hWZ4Lunaa4HevTmOieEdr80NGcJkB+DuBSU7DuLdhh4Wxu15DlnCFfHq1o3dSQCgXTsgPt5sPBJYzk54AOB//+NdL8CmNDY+Tf3vv/lPBLgLuWlTs/FIAM2dyzo1AGjRwvqZF3GQQoWAHj043rWLy1ziHM5e0vJavBioVYvj558Hvv3WbDx+Ur++Vaqks2UcJOU29Pz5uY5peGeWlrTElKQkFixv3syjJ6KiWNEgtqElrSt64AGr696MGWyxbzNz51rJzqJq62IAABs1SURBVKuvKtlxlM8+s7ahd+tmPNkRMSlrVuDTTzmOj2crNnEGzfB47d0L3HILq3pvuw3YsIHPDBs4fx6oWJH95fLn5zZ0b7t1sbmU29CD6Pw4zfCIaa+8Anz9Ncdz5wJ165qNR3xGMzxXdeONwEcfcRwZaatztoYMYbID8DQNJTsO0rFj6m3oQZDsiASDwYOtTRvvvssbQ7E3zfCkdP48Z3d27gQKFAC2bQv56f/du4Hy5YG4OB6MvW6dbSau5GpWrLBaaNetC/zyS9DszNIMjwSD4cPZkwcA+vYFOnc2G4/4xGV/ySnhudjs2cDTT3PcvDkwdqzZeDLp2WfZbwIA/vyT5UriAElJQNWqXJrNkQOIiLAOzQ0CSngkGCQmsgdtZCRbsEVHA+HhpqOSTNKSVpo99RTwxBMcjxvHHuQh6tdfrWSncWMlO44yejSTHQDo1Cmokh2RYJEtGzBqFMdxcdZsj9iTZnguZft2Lm0lJgLVqvGkzbDQyg3Pn+cSlreT8tatwPXXm45KAuLAARbgnznD87IiIrj/NohohkeCycsvA998w/H8+cAjj5iNRzJFMzzpUrasleqvXAlMnmw2ngwYNozJDsBGW0p2HKRtWyY7APffBlmyIxJshgwB8ubl+N13gYQEs/GIf2iG53LOnuVd8v79PHRq61YWMoeAPXtYqOzdYb9+PaduxQEWLAAefpjj555jX6kgpBkeCTZDhgDt23M8YADQoYPZeCTDVLScIVOnsvgF4F1ziJy19fzzwHffcbxwIVC7ttFwJFASEthReetWIE8eVmDeeKPpqC5JCY8Em8REHrkTFQXkzs2nT4kSpqOSDNCSVoa89JLVkvjjj1kLEeR+/91Kdho1UrLjKEOHMtkBuI4ZpMmOSDDKls3qwHzuHO9xxV40w3M1GzYAd90FuN3safLXX0FbwJzyBj9vXt6h3HCD6agkIGJigAoVuNUkBNYxNcMjwapRI2D6dI5/+81aIZaQoRmeDLvjDuuwleXLgTFjzMZzBcOHWzf43bsr2XGU1q2Z7AA8OyuIkx2RYDZkCFeEARUw241meNIiNpZ3zbt38zCqLVuCLpvYuxe49VaGWr48sHGjXvMcY9YsoF49jl9/HZgwwWg4aaEZHglmgwZZRcuDBlnFzBISVLScaXPnWg0JGzYEvv/ebDwXeeEFa0POggVAnTpm45EAOXeOS1m7dwMFC3KKLwSOQ1HCI8EsIYEFzNHRnO2JilIBcwjRklamPf44i5gBti/+6Sez8aQwa5aV7LzwgpIdR+nXj8kOAPTvHxLJjkiwy54d+OQTjmNjgbffBpSfhz7N8KTHoUNcLzpxgktaW7ZwicugU6d4g79/P2/wt2xRk0HH2LqV7bQTE4G77+ZhoVmymI4qTTTDI6Hg1VeBKVM4/uYbFjRL0NMMj08ULWr14tm3D+jSxWw8AD78kMkOwF3JSnYcwuMB3nmHyY7LxULlEEl2RELF8OFAkSIcv/sucPSo2Xgkc5TwpFfTplZzm1GjeFdtyKJFwBdfcPzQQwxNHGLaNBZrAcBbb3GGR0R86tprgZEjOT561NqwK6FJS1oZsW0bG96cPw9UrAisWxfwLVHnzrGobscOIFcu9kQsXTqgIYgpR45wHfPoUdbsREcD11xjOqp00ZKWhAqPh5sgf/6Z13PmWPtXJChpScunypUDPvqI44gII0dO9OjBZAcA+vRRsuMorVpZc+sjR4ZcsiMSSlwuYPRoq1yzZUvrbF4JLZrhyaiEBKBKFVYJ58gBbN7MU9YDYM0aoFo1Nn++5x5g2TKVbzjG99/zUFCA7RG++46/kUOMZngk1IwZw2QHYPmc9xgKCTrqw+MXy5YBNWtyXKcOD7Ly84uPd0POpk1A1qxcTatUya8PKcHi6FE2wDx8GChUiMl20aKmo8oQJTwSatxu/pr/809eL1liHbUoQUVLWn5RowYLRgHgjz+ASZP8/pCDBzPZAYDOnZXsOErr1kx2AC5lhWiyIxKKwsKAsWOBnDl53bw5EB9vNiZJH83wZNapU+zNc+AA77qjo619jD4WHc1C5YQEPuT69VxNEweYOROoX5/jZ55h48sQXMry0gyPhKqUx0507gz07Ws2HvkPLWn51Q8/AM8+y/ErrwCTJ/v8Idxu4IEHgKVL+Tq3dCkPbxcHOH6cS1kHD9qmu6QSHglVSUmsoVy3jmUFq1fzjGkJGlrS8qsGDazDG6dMAX791ecP8dlnTHIANsBSsuMgbdow2QGAjz8O+WRHJJRlzQqMG8eNIklJQLNm/FOCn2Z4fGXvXq4znT0LFC/OXVuFCvnkS+/Zwxv8s2eBkiW5Ez5vXp98aQl2c+YATz3F8RNPALNnh/RSlpdmeCTUde7M4+sAnageZLSkFRBjxwJvvslxo0Y8fCWTPB7gySd5WDvAyaNHH830l5VQcPIkM939+9kEJDISuPFG01H5hBIeCXXx8ayp3LaNhcybNgWsM4lcmZa0AqJ5c+DppzmeNg2YOjXTX/Lrr61k5/XXlew4Stu21kFpw4fbJtkRsYOcObm0BTD5adGCtZYSvDTD42uHDnGv+JEjQIECXNoqUSJDX+rgQZ5ccewYTxCIivLZKpkEu3nzgMcf5/ixx5j12mApy0szPGIX77zDTswA//R2KhFjtKQVUCm3ENepA/z2G5s4pIPbzaWsefN4PX068MILPo5TgtPp01zK2rsXyJePRVvh4aaj8iklPGIXp0/zxvSff3iu4bp1wK23mo7K0bSkFVD16rF0H2BDwo8/TveXGDnSSnZefhl4/nkfxifBrX17JjsAz2mzWbIjYif58wMTJnACNi4OeOklnistwUczPP5y5gybM+zaxe6Aa9bwNiANNm7kGVkJCUCpUsCGDVwdEwf4/XfgkUc4fughzg7aaCnLSzM8YjcdOwIDB3Lctq2RM6WFtKRlxNKl7BbodrOcf+XKq7ZGPncOqFqVveXCwnheS40aAYpXzDpxgknynj1AnjxcyipVynRUfqGER+wmIYG/q9eu5bV21BqjJS0jatZk2g9w2qZ796v+lXbtmOwAQLduSnYcw+PhMuiePbwePNi2yY6IHWXPzo25efLw+vXXuXdFgodmePwtIYFtkdet49LEn38C999/yU+dNctq2FyzJrBoEbt6igOMGgW0asVxvXrAjz/acinLSzM8YldffWWVcD71FH+v2/ipHIy0pGXUli3AXXexWUOpUpztyZ8/1afs3w/cfju3oOfPz0/RDb5DbNjAw3kSEtjCYMMG2/cfUMIjduXxcEftd9/xetQo4O23zcbkMFrSMqpCBauaLSYGaN061Yfdbk5/HjvG688/V7LjGGfPAi++yGQnSxZ257Z5siNiZy4X8MUXVvu1tm3ZJF3MU8ITKK1aWbtvJkzgCev/Gj6cm3MA4LXXuK1RHOLtt9mbHgB69eJapoiEtGuuASZPZvITH8/f6fHxpqMSLWkF0r597MJ84gRQuDAQEYH1B4qhWjUgMREoXZqrGfnymQ5UAmLiRKBJE44ffpjbOtLZoDJUaUlLnKBLF6BfP45btwZGjDAbj0OohidoTJ/Og0UBJD38GCrt+QXR28KQJQt3sVerZjg+CYzoaNZ1nTvHc0M2bgSKFTMdVcAo4REnSEwE7rsPWLWK17/8Yp0YI36jGp6g8eKLQOPGAICsv/+KF7f1AgD07KlkxzHi4vhzcO4c57ynTHFUsiPiFNmycat63ry8btKExy2KGUp4TBg9GmduuAUA0AM90eG22Rfa9YgDtG0LbNrEcceOVm2XiNhOmTLAp59yfPgw0LQpd3JJ4GlJy4B9+4DnbovC/FP3IB/Owp2/AMLWrAbKljUdmvjb998Dzz3HcY0a7MvkwGZLWtISJ/F4eCbitGm8/vhj4L33zMZkY6rhCRbx8UDt2jxlogF+wA94lh+oWBFYvtya+xT7+ftvoEoV4NQpbuPYsMGxB4Mq4RGnOXmSJ8fs3s2lrj/+YH2P+JxqeIKBxwO0bMlkBwCKvNnQOnoiIgJo3lxznXaVmMi9qadO8fqrrxyb7Ig4UcGCrOfJlo2/Dp591jpJRgJDCU8ADR/OncgA26188gmAPn24JRngDq7hw43FJ37UpYuV6b77LlC/vtl4RCTgatRg52WA9Tz163PvggSGlrQC5NdfgSeeYFflEiWA1auBokX//eDRo8Ddd3OuM0sW4LffgAcfNBqv+NCPPwING3JcpQqXLnPkMBuTYVrSEid7912rkPnFF9lgXedt+YxqeEzatg245x6uZuTKxX47Vapc9Enr1nHaJz4eKFIEWLvW6k0uoWvNGuCBB7gVPW9efp9VnK6ERxwtMRF47DFg4UJe9+0LdO5sNiYbUQ2PKadOAc88Y5VuTJhwiWQHAO68k4doAcCRI1zgVS/y0PbPP8DTTzPZCQvjkqWSHRHHy5YNmDEDuOkmXnfpwlPVxb+U8PhRcjLrVLdu5XWXLjxF97Jef906Vnf1au1bDGVnzjDZOXiQ1x9/zDVNERHwdKFZs6yNuY0b65BRf1PC40edOwNz53Jcrx7Phryq4cOB6tU5HjsW+PJLv8UnfuLNdDdu5HWrVnwTEUmhYkU2WgeAs2e5GnDsmNmY7Ew1PH4yZQrw6qsc33Yb61TTfCjovn08Z+nQISB7dmDJEhYBSWh4/33O6AA8OGfWLEc2F7wS1fCIWPr0Abp25bhOHWDePC57SYaoaDmQVq8G7r8fOH8eKFSIB8eVKZPOL7JkCX/yk5KAG2/klubixf0Sr/jQqFHWbE6lSsBffwH585uNKQgp4RGxeDzcrTVjBq/ffRcYOdJsTCFMCU+gHDjAHeb793OH+a+/Ag89lMEvNnIk0Lo1xxUr8hiCQoV8Fqv42Lx5wJNPsvdA0aLMdNVc8JKU8IikFhvLzssbNvD6yy+BZs3MxhSitEsrEOLjgQYNmOwALMfJcLIDMM1v2ZLjiAi+mMbGZjpO8YOICFaku93sPfDzz0p2RCTN8uQBZs5kVxIAeOsttjAR31HC4yNuN7NxbzPd5s19UKfqcrE71Ysv8nrFCjawO38+k19YfOrgQSajZ87wevJkoGpVszGJSMgJDwd++ME6fqJhQ2DnTtNR2YcSHh9wuzkRM3Uqr2vWZCmHTzpnZskCTJoE1K3L6/nzWQ2dnOyDLy6ZFhfHLXjeQ3H692cPJRGRDLjvvtTHT9Spwyb8knlKeDLJ42GZzdixvK5QgScJZM/uwwfJnh347jsexAKwsu3tt3XQqGluN3snrVrF66ZNgQ4dzMYkIiGvRQur8/KePTxpaO9eszHZgRKeTPB4gPbtrTNRypYFfv/dWoP1qTx5gNmzufMHAL74Qr3ITfJ4gLZtrW0VtWuzU7YOxBERH+jTB/jgA47//pszPQcOmI0p1GmXVgZ5PMBHHwH9+vG6dGluorrxRj8/8IEDnPPctYvXgwcD7dr5+UElFe+03ief8LpcOTZa0g66NNMuLZGr83jYcN97U12+PLBoEXDddUbDCnbalu5rvXoB3btzHB4OLF4MlCwZoAfftYtJjzfdHzcOeOONAD24w7ndwDvvWOee3XQTTwAM2DffHpTwiKSNt0bUWzZx++3AH3/waAq5JCU8vjRwINCxI8c33MCZnXQ3FsysiAiewn3iBA+mnDGDJf3iP8nJwJtvAl99xeuyZfmbx+/TevajhEck7dxu3tNOnMjrO+8EFiwAChY0G1eQUsLjKyNGAG3acFy0KJOd/7d37zFV1n8cwN8HBFJMSlkQKS4t02RiWZGmBZguSSZr5bKw2LqsYW1lq2VlEdrNqK0apauWDVwXyzyRmrYmRuWIvNDMWxuilc0LAqZ45Hb64/17fo8kFxU43+c85/3azuA5B+LbOe55Ps/3+/l+PldcYWgwGzcCN98MNDQwsXnVKh5Lz2tpYVJyURGPR47kGUfVr8+JAh6Rs9PSwg26H3/M45QUbtpVIffTKODpCe++azczj43lWuro0UaHxH/x06ezaEN0NI+t3VzSM5qbeab55BMejx7NYCcuzuy4gpgCHpGz19wM3Hkn8MUXPJ44kQ2qrY7rAkCVlrvvgw/sYOfCC7kby3iwAwBTpwLLlnF30PHjLO28YoXpUblHUxPPMFawk5zMnB0FOyISYH36sN5bZiaPf/iBHdYbGsyOK1go4DkDRUWsiwBw+nDdOl73HOOOO4APP2SRQp8PuP129rXQHXT3nDzJ99a6nRo3jjk7vVJ3QESka5GRTNm0atGuX8+WRj6f2XEFAwU8nfD7gddfB3Jy+H3//mwGes01pkfWjnvvZZ2e/v052LlzuXVaFZnPjc/HJHCvl8cpKZzW09ZzETEsKooT+enpPF63jpP9hw6ZHZfTKYenAz4fN+RYOar9+rEZ9qRJZsfVpa1b2dfJ6mA6YwbnQPv1MzuuYHLiBJCVxbMIwJyoNWuUHdiDlMMj0n3HjwMZGSyLArA6htfrsBWIwFMOz9nYvx+46SY72ElMZNdaxwc7ADB2LJuMWhWZvV5WAT5wwOiwgsaBA5wrtoKdG2/ktJ6CHRFxmOho3ohnZ/N4717en33+udlxOZUCnv/4+WcuWVntkSZOBCoqGEcEjSFDgLIye4t6RQUwfjywa5fZcTnd998DV11l3y6lpwOrV2sLhIg4Vt++7C+9aBH3rjQ0MPXw+edZv0dsCnhOUVTEG3qrgPEDD3D3cVCW8Y6J4cU6J4fHe/Yw6CkrMzosR/L7ebY4tVlNdjZzoqKjzY5NRKQLHg/7On79tT0ZnZ/P/SvHjpkdm5Mo4AHzep94ArjnHm7MCQ9n75IlS3q463mgRUSwKvALL/C4tpazPtYWa+F7kpXFLuctLcwGXLKEt0x9+5oenYjIGcvIAMrL2d4PAL78kktce/aYHZdThHzScl0dMGsW10EBbsJZvtzOfneNjz4C7r+flasAIC8PeOYZFnYIVZs28RaouprHl17Kxe+rrzY6rFCgpGWR3lNXx/Jha9fyeNAgntpSU40OK1CUtNye3buB66+3g52kJKa7uC7YAbht/Ztv7PnOvDxutd661eiwjPD7WTZ7wgQ72JkxA9i8WcGOiAS9Cy5gp6HHH+dxTQ0wZQpPe6EsJAMevx8oLgauu87O483KAn76CRg2zOzYetXkydxuNnIkjzdvBq69Fnj2Wa7lhYJjx5ifk5sLNDZy/fK11zj3q058IuIS4eFAQQEn96OiOLmfm8uWgDU1pkdnRsgtaVVWAg8/zJLclvnzOeERFirhn88HLFwIvPKKXZhw1Cj2zxg/3uzYetP27VzC2rGDxwkJwKefciueBJSWtEQCp7yc1ZitPRkDBwIvvcQsh/Bws2PrBVrSqq0FHnmEKxZWsBMXx64B+fkhFOwAwHnnMeD55RduwwYYBNxwA/Doo6xm5SZHjwLPPcfZLCvYmTwZ2LJFwY6IuF5KCtM1pk7l8ZEjwEMPcZVj40azYwsk11/mW1s5cTFiBHdetbYyop07lzk8t91meoQGjR3L0P/llznn6fcDb77JooXffWd6dN138iT/f4YPBxYsYIEKj4dTemvXBmm9ARGRs3fJJUzjXLGCFZkBZjVMmMBlrlCoTevqJa2KCmDOHH61pKcDb73lkE7nTrJzJ+c3f/zRfu6++7gIHGy5La2tbKcxf76dlAywomRBActoi1Fa0hIxp6EBePVVPqz0zQEDuNoxZ07Qb97tcEnLlQHP4cPA008D779vNwwfPBh44w2mcHg6fDtCXGsrUFgIzJtnL2tddBEz3R58ELj4YrPj64rfz1uYp54Cfv3Vfv6yy7hgrQ/fMRTwiJhXVQU89hjw1Vf2c0lJwNtvB/UW9tDI4dm3j9e1ESOA997j9S8igtfvnTtZblvXu06EhTHRads27mEEgIMHmdGdmMjCDmVldhTpJOXlQFoaK29ZwU58PPdhbt+uDz+AtmzZgpSUFPz555+mhyIinRg2jO0WV63iyj/A039aGk+Z69e7qz1F0M/w1NWxoFJxMbBhQ9vXbrmFKRxW1Uk5C34/KzIXFHCh91RjxnDe8+67zbZeaGxkAPbOO1yYtgwYADz5JBOw1RoiYFauXAmv1wuPx4OlS5eiuroaiYmJ7f6sZnhEnMXn4yrIwoXAiRP284MH81Sfnc3ZnyDgriWtxkZgzRoGOSUlp5eQSUoCXnwRyMzUTX23+f2cPSksBD77jG++JSaG2W65ucDllwdmPDU1/PBLSrh8dfSo/VpkJGsOzJsHxMYGZjxymg0bNiAtLU0Bj0gQ2rePp9Dly4GmpravJScDs2ezO0FCgpnxnYHgD3j8fhYGLC7mdffIkbavx8cDd93FDyM5WYFOrzh4kIlRixcDf/zR9rUpU5gRPmYMP4CEhJ77EHbt4iJzSQmTqv87xxoezluQ/Hx7+4EYU1paivT0dAU8IkGspoZBT1ERr72n8nhY2SM7mzudzz/fzBg7EBwBzz//AHv3tv+oquL19lTR0XyzZ8/mtdaFBZScqbmZwUdhYcfb1wcOtIMf6+uVV57ekNPvZ/Xjujqgvp4P6/tNm/h3fv/99P9+TAzXLDMzgWnT+PfEERTwiLhLVRWwbBknHHbvbvtaVBQn+IcObf8RFxfwOnfODHgWLWLkaAU1tbVd/054OIsnZWez/ZFSNAzbscPOodm/v/OfDQtjQlVUlB3c1NefeVbc8OEMcDIzgUmTmJEujqOAR8Sd/H7Wqy0qYornoUNd/05kJPe8DB3KrwsWsCZQL3JmwHPrrcDq1R2/HhEBDBliv1HjxgEzZzJiFAc6fJg7pKxHZSXw22/n3qcrLIzVnzMzgenT2QNMa5WOp4BHxP2amoBvv2UqZXW1PXFRX9/57/31V6/n/3R4kTBaXmjUKL5RHU2FxcdrmSqoxMZybfHUdvPNzVySqqxkELRtG5+PiWFBw86+xsfzqwSc1+tFcXFxlz83aNAgLF68+Jz+Rl5e3v+/T01NRWoQF/4QCTUREawCkpHR9vn6+o5TU/7+m6d1UxyVwyMiwU8zPCJiUGgUHhQRERFpjwIeEelRrf9LQm9paTE8EhERmwIeEekRpaWlmDlzJnJycuDxeDBt2jTMmjUL26y8LRERg5TDIyIBpxweEeklyuERERGR0KWAR0RERFxPAY+IiIi4ngIeERERcT0FPCIiIuJ6CnhERETE9RTwiIiIiOsp4BERERHXU8AjIiIirteni9c7rFgoItINfuj8IiIB1FVrCREREZGgpyUtERERcT0FPCIiIuJ6CnhERETE9RTwiIiIiOsp4BERERHX+xc1HT2p8IKu8QAAAABJRU5ErkJggg==
-
-
-加入图例
---------------
-
-
-使用 legend() 加入图例：
-
-
-
-
-
-::
-
-    # 设置图像大小
-    plt.figure(figsize=(10,6), dpi=80)
-
-    # 画图，指定颜色，线宽，类型
-    plt.plot(x, c, 'b-', 
-             ^4$x, s, 'r-', linewidth=2.5)
-
-    # 设置显示范围
-    plt.xlim(x.min() * 1.1, x.max() * 1.1)
-    plt.ylim(c.min() * 1.1, c.max() * 1.1)
-
-    # 得到画图的句柄
-    ax = plt.gca()
-
-    # ax.spines参数表示四个坐标轴线
-    # 将右边和上边的颜色设为透明
-    ax.spines['right'].set_color('none')
-    ax.spines['top'].set_color('none')
-
-    # 将 x 轴的刻度设置在下面的坐标轴上
-    ax.xaxis.set_ticks_position('bottom')
-    # 设置位置
-    ax.spines['bottom'].set_position(('data',0))
-
-    # 将 y 轴的刻度设置在左边的坐标轴上
-    ax.yaxis.set_ticks_position('left')
-    # 设置位置
-    ax.spines['left'].set_position(('data',0))
-
-    # 设置刻度及其标识
-    plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi], 
-               ^4$['$-\pi$', '$-\pi/2$', '$0$', '$\pi/2$', '$\pi$'],     fontsize ='xx-large')
-    plt.yticks([-1, 0, 1], 
-               ^4$['$-1$', '$0$', '$+1$'], fontsize ='xx-large')
-
-    #####################################################################
-
-    # 加入图例，frameon表示去掉图例周围的边框
-    l = plt.legend(['cosine', 'sine'], loc='upper left', frameon=False)
-    ####################################################################
-
-    # 在脚本中需要加上这句才会显示图像
-    # plt.show()
-
-
-<button>Run ...</button>
-
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-.. image:: 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
-
-
-注释特殊点
-----------------
-
-
-我们可以使用 *anotate* 函数来注释特殊的点，假设我们要显示的点是 $2\pi/3$：
-
-
-
-
-::
-
-    # 设置图像大小
-    plt.figure(figsize=(10,6), dpi=80)
-
-    # 画图，指定颜色，线宽，类型
-    plt.plot(x, c, 'b-', 
-             ^4$x, s, 'r-', linewidth=2.5)
-
-    # 设置显示范围
-    plt.xlim(x.min() * 1.1, x.max() * 1.1)
-    plt.ylim(c.min() * 1.1, c.max() * 1.1)
-
-    # 得到画图的句柄
-     ax = plt.gca()
-
-    # ax.spines参数表示四个坐标轴线
-    # 将右边和上边的颜色设为透明
-    ax.spines['right'].set_color('none')
-    ax.spines['top'].set_color('none')
-
-    # 将 x 轴的刻度设置在下面的坐标轴上
-    ax.xaxis.set_ticks_position('bottom')
-    # 设置位置
-    ax.spines['bottom'].set_position(('data',0))
-
-    # 将 y 轴的刻度设置在左边的坐标轴上
-    ax.yaxis.set_ticks_position('left')
-    # 设置位置
-    ax.spines['left'].set_position(('data',0))
-
-    # 设置刻度及其标识
-    plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi], 
-               ^4$['$-\pi$', '$-\pi/2$', '$0$', '$\pi/2$', '$\pi$'],     fontsize ='xx-large')
-    plt.yticks([-1, 0, 1], 
-               ^4$['$-1$', '$0$', '$+1$'], fontsize ='xx-large')
-
-    # 加入图例，frameon表示图例周围是否需要边框
-    l = plt.legend(['cosine', 'sine'], loc='upper left', frameon=False)
-
-    ###################################################################
-
-    # 数据点
-    t = 2 * np.pi / 3
-
-    # 蓝色虚线
-    plt.plot([t,t],[0,np.cos(t)], color ='blue', linewidth=2.5,     linestyle="--")
-
-    # 该点处的 cos 值
-    plt.scatter([t,],[np.cos(t),], 50, color ='blue')
-
-    # 在对应的点显示文本
-    plt.annotate(r'$\sin(\frac{2\pi}{3})=\frac{\sqrt{3}}{2}$', # 文本
-                 ^4$xy=(t, np.sin(t)), # 数据点坐标位置
-                 ^4$xycoords='data',   # 坐标相对于数据
-                 ^4$xytext=(+10, +30), # 文本位置坐标
-                 ^4$textcoords='offset points', # 坐标相对于数据点的坐标
-                 ^4$fontsize=16,       # 文本大小
-                 ^4$arrowprops=dict(arrowstyle="->",     connectionstyle="arc3,rad=.2")) # 箭头
-
-    # 红色虚线
-    p = plt.plot([t,t],[0,np.sin(t)], color ='red', linewidth=2.5,     linestyle="--")
-
-    # 该点处的 sin 值
-    p = plt.scatter([t,],[np.sin(t),], 50, color ='red')
-
-    # 显示文本
-    p = plt.annotate(r'$\cos(\frac{2\pi}{3})=-\frac{1}{2}$',
-                 ^4$xy=(t, np.cos(t)), xycoords='data',
-                 ^4$xytext=(-90, -50), textcoords='offset points',     fontsize=16,
-                 ^4$arrowprops=dict(arrowstyle="->",     connectionstyle="arc3,rad=.2"))
-
-
-    ##################################################################
-
-    # 在脚本中需要加上这句才会显示图像
-    # plt.show()
-
-
-<button>Run ...</button>
-
-
-.. image:: 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
-
-
-最后调整
--------------
-
-
-调整刻度值的大小，并让其显示在曲线上方。
-
-
-
-
-::
-
-    # 设置图像大小
-    plt.figure(figsize=(10,6), dpi=80)
-
-    # 画图，指定颜色，线宽，类型
-    plt.plot(x, c, 'b-', 
-             ^4$x, s, 'r-', linewidth=2.5)
-
-    # 设置显示范围
-    plt.xlim(x.min() * 1.1, x.max() * 1.1)
-    plt.ylim(c.min() * 1.1, c.max() * 1.1)
-
-    # 得到画图的句柄
-    ax = plt.gca()
-
-    # ax.spines参数表示四个坐标轴线
-    # 将右边和上边的颜色设为透明
-    ax.spines['right'].set_color('none')
-    ax.spines['top'].set_color('none')
-
-    # 将 x 轴的刻度设置在下面的坐标轴上
-    ax.xaxis.set_ticks_position('bottom')
-    # 设置位置
-    ax.spines['bottom'].set_position(('data',0))
-
-    # 将 y 轴的刻度设置在左边的坐标轴上
-    ax.yaxis.set_ticks_position('left')
-    # 设置位置
-    ax.spines['left'].set_position(('data',0))
-
-    # 设置刻度及其标识
-    plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi], 
-               ^4$['$-\pi$', '$-\pi/2$', '$0$', '$\pi/2$', '$\pi$'],     fontsize ='xx-large')
-    plt.yticks([-1, 0, 1], 
-               ^4$['$-1$', '$0$', '$+1$'], fontsize ='xx-large')
-
-    # 加入图例，frameon表示图例周围是否需要边框
-    l = plt.legend(['cosine', 'sine'], loc='upper left', frameon=False)
-
-    # 数据点
-    t = 2 * np.pi / 3
-
-    # 蓝色虚线
-    plt.plot([t,t],[0,np.cos(t)], color ='blue', linewidth=2.5,     linestyle="--")
-
-    # 该点处的 cos 值
-    plt.scatter([t,],[np.cos(t),], 50, color ='blue')
-
-    # 在对应的点显示文本
-    plt.annotate(r'$\sin(\frac{2\pi}{3})=\frac{\sqrt{3}}{2}$', # 文本
-                 ^4$xy=(t, np.sin(t)), # 数据点坐标位置
-                 ^4$xycoords='data',   # 坐标相对于数据
-                 ^4$xytext=(+10, +30), # 文本位置坐标
-                 ^4$textcoords='offset points', # 坐标相对于数据点的坐标
-                 ^4$fontsize=16,       # 文本大小
-                 ^4$arrowprops=dict(arrowstyle="->",     connectionstyle="arc3,rad=.2")) # 箭头
-
-    # 红色虚线
-    p = plt.plot([t,t],[0,np.sin(t)], color ='red', linewidth=2.5,     linestyle="--")
-
-    # 该点处的 sin 值
-    p = plt.scatter([t,],[np.sin(t),], 50, color ='red')
-
-    # 显示文本
-    p = plt.annotate(r'$\cos(\frac{2\pi}{3})=-\frac{1}{2}$',
-                 ^4$xy=(t, np.cos(t)), xycoords='data',
-                 ^4$xytext=(-90, -50), textcoords='offset points',     fontsize=16,
-                 ^4$arrowprops=dict(arrowstyle="->",     connectionstyle="arc3,rad=.2"))
-
-
-    ####################################################################
-
-    for label in ax.get_xticklabels() + ax.get_yticklabels():
-        ^4$label.set_fontsize(16)
-        ^4$label.set_bbox(dict(facecolor='white', edgecolor='None',     alpha=0.65 ))
-
-    ####################################################################
-
-    # 在脚本中需要加上这句才会显示图像
-    # plt.show()
-
-<button>Run ...</button>
-
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-.. image:: 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
-
-
-
-
-
-作者 & 更新时间
-------------------------------------
-作者:`李金  <lijinwithyou@gmail.com>`
-
-提交: 2017/12/14
-
diff --git a/docs/sources/aipy-numpy/index.rst.txt b/docs/sources/aipy-numpy/index.rst.txt
deleted file mode 100644
index 5bd2d46..0000000
--- a/docs/sources/aipy-numpy/index.rst.txt
+++ /dev/null
@@ -1,19 +0,0 @@
-Numpy Programming
-=======================
-The courses are still in the planning. Welcome to reward us. 
-
-1. **Primary course**
-
-If the total number of people who reward us is more than 100, we promise to complete the primary course within 1 week, so that, after finishing your study, you can master the basic concepts and write simple scripts.

-
-2. **Intermediate course**
-
-If the total number of people who reward us is more than 500, we will launch the intermediate course within 2 weeks, so that, after finishing your study, you will be familiar with the knowledge and framework of the course and apply it to practical development.
-
-3. **Advanced course**
-
-If the total number of people who reward us is more than 2,000, we will launch the advanced course in a month, so that, after finishing your study, you will have a better understanding of the principles of the framework and be able to do advanced development.
-
-
-**The primary course is free to all, and the intermediate and advanced courses are only open to those who have rewarded us.**
-
diff --git a/docs/sources/aipy-pandas/index.rst.txt b/docs/sources/aipy-pandas/index.rst.txt
deleted file mode 100644
index 96bdd68..0000000
--- a/docs/sources/aipy-pandas/index.rst.txt
+++ /dev/null
@@ -1,19 +0,0 @@
-Pandas Programming
-=======================
-The courses are still in the planning. Welcome to reward us. 
-
-1. **Primary course**
-
-If the total number of people who reward us is more than 100, we promise to complete the primary course within 1 week, so that, after finishing your study, you can master the basic concepts and write simple scripts.

-
-2. **Intermediate course**
-
-If the total number of people who reward us is more than 500, we will launch the intermediate course within 2 weeks, so that, after finishing your study, you will be familiar with the knowledge and framework of the course and apply it to practical development.
-
-3. **Advanced course**
-
-If the total number of people who reward us is more than 2,000, we will launch the advanced course in a month, so that, after finishing your study, you will have a better understanding of the principles of the framework and be able to do advanced development.
-
-
-**The primary course is free to all, and the intermediate and advanced courses are only open to those who have rewarded us.**
-
diff --git a/docs/sources/aipy-scikit-learn/index.rst.txt b/docs/sources/aipy-scikit-learn/index.rst.txt
deleted file mode 100644
index c0f29bc..0000000
--- a/docs/sources/aipy-scikit-learn/index.rst.txt
+++ /dev/null
@@ -1,19 +0,0 @@
-Scikit Learn Programming
-============================
-The courses are still in the planning. Welcome to reward us. 
-
-1. **Primary course**
-
-If the total number of people who reward us is more than 100, we promise to complete the primary course within 1 week, so that, after finishing your study, you can master the basic concepts and write simple scripts.

-
-2. **Intermediate course**
-
-If the total number of people who reward us is more than 500, we will launch the intermediate course within 2 weeks, so that, after finishing your study, you will be familiar with the knowledge and framework of the course and apply it to practical development.
-
-3. **Advanced course**
-
-If the total number of people who reward us is more than 2,000, we will launch the advanced course in a month, so that, after finishing your study, you will have a better understanding of the principles of the framework and be able to do advanced development.
-
-
-**The primary course is free to all, and the intermediate and advanced courses are only open to those who have rewarded us.**
-
diff --git a/docs/sources/aipy-scipy/cn/10linear-algebra.rst.txt b/docs/sources/aipy-scipy/cn/10linear-algebra.rst.txt
deleted file mode 100644
index 086a061..0000000
--- a/docs/sources/aipy-scipy/cn/10linear-algebra.rst.txt
+++ /dev/null
@@ -1,1163 +0,0 @@
-线性代数
---------------
-
-numpy 和 scipy 中，负责进行线性代数部分计算的模块叫做 linalg。
-
-
-
-
-::
-
-    import numpy as np
-    import numpy.linalg
-    import scipy as sp
-    import scipy.linalg
-    import matplotlib.pyplot as plt
-    from scipy import linalg
-
-
-%matplotlib inline
-
-numpy.linalg VS scipy.linalg
-------------------------------
-
-一方面scipy.linalg 包含 numpy.linalg 中的所有函数，同时还包含了很多 numpy.linalg 中没有的函数。
-
-另一方面，scipy.linalg 能够保证这些函数使用 BLAS/LAPACK 加速，而 numpy.linalg 中这些加速是可选的。
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-因此，在使用时，我们一般使用 scipy.linalg 而不是 numpy.linalg。
-
-我们可以简单看看两个模块的差异：
-
-
-
-::
-
-    print "number of items in numpy.linalg:", len(dir(numpy.linalg))
-    print "number of items in scipy.linalg:", len(dir(scipy.linalg))
-
-
-结果:
-::
-
-    number of items in numpy.linalg: 36
-
-    number of items in scipy.linalg: 115
-
-
-
-numpy.matrix VS 2D numpy.ndarray
------------------------------------
-
-
-线性代数的基本操作对象是矩阵，而矩阵的表示方法主要有两种：numpy.matrix 和 2D numpy.ndarray。
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-
-numpy.matrix
----------------------
-
-
-numpy.matrix 是一个矩阵类，提供了一些方便的矩阵操作：
-
-
-- 支持类似 MATLAB 创建矩阵的语法
-- 矩阵乘法默认用 * 号
-- .I 表示逆，.T 表示转置
-
-
-可以用 mat 或者 matrix 来产生矩阵：
-
-
-
-
-::
-
-    A = np.mat("[1, 2; 3, 4]")
-    print repr(A)
-
-    A = np.matrix("[1, 2; 3, 4]")
-    print repr(A)
-
-
-结果:
-::
-
-    matrix([[1, 2],
-
-        [3, 4]])
-
-
-    matrix([[1, 2],
-
-            [3, 4]])
-
-
-
-转置和逆：
-
-
-
-
-::
-
-    print repr(A.I)
-    print repr(A.T)
-
-
-结果:
-::
-
-    matrix([[-2. ,  1. ],
-
-        [ 1.5, -0.5]])
-
-
-    matrix([[1, 3],
-
-            [2, 4]])
-
-
-矩阵乘法：
-
-
-
-
-::
-
-    b = np.mat('[5; 6]')
-    print repr(A * b)
-
-
-结果:
-::
-
-    matrix([[17],
-
-        [39]])
-
-
-2 维 numpy.ndarray
--------------------------
-
-
-虽然 numpy.matrix 有着上面的好处，但是一般不建议使用，而是用 2 维 numpy.ndarray 对象替代，这样可以避免一些不必要的困惑。
-
-
-我们可以使用 array 复现上面的操作：
-
-
-
-
-
-
-::
-
-    A = np.array([[1,2], [3,4]])
-    print repr(A)
-
-
-结果:
-::
-
-    array([[1, 2],
-
-       [3, 4]])
-
-
-逆和转置：
-
-
-
-
-::
-
-    print repr(linalg.inv(A))
-    print repr(A.T)
-
-
-
-结果:
-::
-
-    array([[-2. ,  1. ],
-
-       [ 1.5, -0.5]])
-
-    array([[1, 3],
-
-           [2, 4]])
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-矩阵乘法：
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-
-
-
-
-::
-
-    b = np.array([5, 6])
-
-    print repr(A.dot(b))
-
-
-结果:
-::
-
-    array([17, 39])
-
-
-
-普通乘法：
-
-
-
-
-
-::
-
-    print repr(A * b)
-
-
-结果:
-::
-
-    array([[ 5, 12],
-
-       [15, 24]])
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-
-scipy.linalg 的操作可以作用到两种类型的对象上，没有区别。
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-
-
-基本操作
------------
-
-**求逆**
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-
-矩阵 $\mathbf{A}$ 的逆 $\mathbf{B}$ 满足：$\mathbf{BA}=\mathbf{AB}=I$，记作 $\mathbf{B} = \mathbf{A}^{-1}$。
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-
-
-事实上，我们已经见过求逆的操作，linalg.inv 可以求一个可逆矩阵的逆：
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-
-
-
-
-::
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-    A = np.array([[1,2],[3,4]])
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-    print linalg.inv(A)
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-    print A.dot(scipy.linalg.inv(A))
-
-
-结果:
-::
-
-    [[-2.   1. ]
-
-    [ 1.5 -0.5]]
-
-    [[  1.00000000e+00   0.00000000e+00]
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-    [  8.88178420e-16   1.00000000e+00]]
-
-
-求解线性方程组
----------------------
-
-例如，下列方程组 $$
-
-
-\begin{eqnarray*} 
-x + 3y + 5z &amp; = &amp; 10 \\
-2x + 5y + z &amp; = &amp; 8  \\
-2x + 3y + 8z &amp; = &amp; 3
-\end{eqnarray*}
-
-
-
-$$ 的解为： $$
-
-
-\begin{split}\left[\begin{array}{c} x\\ y\\ z\end{array}\right]=\left[\begin{array}{ccc} 1 &amp; 3 &amp; 5\\ 2 &amp; 5 &amp; 1\\ 2 &amp; 3 &amp; 8\end{array}\right]^{-1}\left[\begin{array}{c} 10\\ 8\\ 3\end{array}\right]=\frac{1}{25}\left[\begin{array}{c} -232\\ 129\\ 19\end{array}\right]=\left[\begin{array}{c} -9.28\\ 5.16\\ 0.76\end{array}\right].\end{split}
-$$
-
-
-
-我们可以使用 linalg.solve 求解方程组，也可以先求逆再相乘，两者中 solve 比较快。
-
-
-
-
-::
-
-
-    import time
-
-    A = np.array([[1, 3, 5],
-                  [2, 5, 1],
-                 [2, 3, 8]])
-    b = np.array([10, 8, 3])
-
-    tic = time.time()
-
-    for i in xrange(1000):
-       x = linalg.inv(A).dot(b)
-
-    print x
-    print A.dot(x)-b
-    print "inv and dot: {} s".format(time.time() - tic)
-
-    tic = time.time()
-
-    for i in xrange(1000):
-        x = linalg.solve(A, b)
-
-    print x
-    print A.dot(x)-b
-    print "solve: {} s".format(time.time() - tic)
-
-
-
-结果:
-::
-
-    [-9.28  5.16  0.76]
-
-    [  0.00000000e+00  -1.77635684e-15  -8.88178420e-16]
-
-    inv and dot: 0.0353579521179 s
-
-
-    [-9.28  5.16  0.76]
-
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-    [  0.00000000e+00  -1.77635684e-15  -1.77635684e-15]
-
-
-    solve: 0.0284671783447 s
-
-
-计算行列式
------------------
-
-
-方阵的行列式为
-
-$$
-\left|\mathbf{A}\right|=\sum_{j}\left(-1\right)^{i+j}a_{ij}M_{ij}.
-$$
-
-
-其中 $a_{ij}$ 表示 $\mathbf{A}$ 的第 $i$ 行 第 $j$ 列的元素，$M_{ij}$ 表示矩阵 $\mathbf{A}$ 去掉第 $i$ 行 第 $j$ 列的新矩阵的行列式。
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-
-
-例如，矩阵 $$
-
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-\begin{split}\mathbf{A=}\left[\begin{array}{ccc} 1 &amp; 3 &amp; 5\\ 2 &amp; 5 &amp; 1\\ 2 &amp; 3 &amp; 8\end{array}\right]\end{split}
-
-
-
-$$ 的行列式是： $$
-
-
-\begin{eqnarray*} \left|\mathbf{A}\right| &amp; = &amp; 1\left|\begin{array}{cc} 5 &amp; 1\\ 3 &amp; 8\end{array}\right|-3\left|\begin{array}{cc} 2 &amp; 1\\ 2 &amp; 8\end{array}\right|+5\left|\begin{array}{cc} 2 &amp; 5\\ 2 &amp; 3\end{array}\right|\\  &amp; = &amp; 1\left(5\cdot8-3\cdot1\right)-3\left(2\cdot8-2\cdot1\right)+5\left(2\cdot3-2\cdot5\right)=-25.\end{eqnarray*}
-$$
-
-
-
-可以用 linalg.det 计算行列式：
-
-
-
-
-::
-
-    A = np.array([[1, 3, 5],
-                  [2, 5, 1],
-                  [2, 3, 8]])
-
-    print linalg.det(A)
-
-
-结果:
-::
-
-    -25.0
-
-
-
-计算矩阵或向量的模
-----------------------
-
-矩阵的模定义如下： $$
-
-
-\begin{split}\left\Vert \mathbf{A}\right\Vert =\left\{ \begin{array}{cc} \max_{i}\sum_{j}\left|a_{ij}\right| &amp; \textrm{ord}=\textrm{inf}\\ \min_{i}\sum_{j}\left|a_{ij}\right| &amp; \textrm{ord}=-\textrm{inf}\\ \max_{j}\sum_{i}\left|a_{ij}\right| &amp; \textrm{ord}=1\\ \min_{j}\sum_{i}\left|a_{ij}\right| &amp; \textrm{ord}=-1\\ \max\sigma_{i} &amp; \textrm{ord}=2\\ \min\sigma_{i} &amp; \textrm{ord}=-2\\ \sqrt{\textrm{trace}\left(\mathbf{A}^{H}\mathbf{A}\right)} &amp; \textrm{ord}=\textrm{'fro'}\end{array}\right.\end{split}
-
-
-$$ 其中，$\sigma_i$ 是矩阵的奇异值。
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-
-
-向量的模定义如下：
-
-$$
-\begin{split}\left\Vert \mathbf{x}\right\Vert =\left\{ \begin{array}{cc} \max\left|x_{i}\right| &amp; \textrm{ord}=\textrm{inf}\\ \min\left|x_{i}\right| &amp; \textrm{ord}=-\textrm{inf}\\ \left(\sum_{i}\left|x_{i}\right|^{\textrm{ord}}\right)^{1/\textrm{ord}} &amp; \left|\textrm{ord}\right|&lt;\infty.\end{array}\right.\end{split}
-$$
-
-
-
-linalg.norm 可以计算向量或者矩阵的模：
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-
-
-
-
-::
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-    A = np.array([[1, 2],
-                  [3, 4]])
-
-    print linalg.norm(A)
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-    print linalg.norm(A,'fro') # frobenius norm 默认值
-
-    print linalg.norm(A,1) # L1 norm 最大列和
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-    print linalg.norm(A,-1) # L -1 norm 最小列和
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-    print linalg.norm(A,np.inf) # L inf norm 最大行和
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-
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-结果:
-::
-
-    5.47722557505
-
-    5.47722557505
-
-    6
-
-    4
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-    7
-
-
-最小二乘解和伪逆
--------------------------
-
-**问题描述**
-
-
-
-所谓最小二乘问题的定义如下：
-
-
-假设 $y_i$ 与 $\mathbf{x_i}$ 的关系可以用一组系数 $c_j$ 和对应的模型函数 $f_j(\mathbf{x_i})$ 的模型表示：
-
-$$
-y_{i}=\sum_{j}c_{j}f_{j}\left(\mathbf{x}_{i}\right)+\epsilon_{i}
-$$
-
-
-其中 $\epsilon_i$ 表示数据的不确定性。最小二乘就是要优化这样一个关于 $c_j$ 的问题：
-
-$$
-J\left(\mathbf{c}\right)=\sum_{i}\left|y_{i}-\sum_{j}c_{j}f_{j}\left(x_{i}\right)\right|^{2}
-$$
-
-
-其理论解满足：
-
-$$
-\frac{\partial J}{\partial c_{n}^{*}}=0=\sum_{i}\left(y_{i}-\sum_{j}c_{j}f_{j}\left(x_{i}\right)\right)\left(-f_{n}^{*}\left(x_{i}\right)\right)
-$$
-
-
-
-改写为：
-
-$$
-\begin{eqnarray*} \sum_{j}c_{j}\sum_{i}f_{j}\left(x_{i}\right)f_{n}^{*}\left(x_{i}\right) &amp; = &amp; \sum_{i}y_{i}f_{n}^{*}\left(x_{i}\right)\\ \mathbf{A}^{H}\mathbf{Ac} &amp; = &amp; \mathbf{A}^{H}\mathbf{y}\end{eqnarray*}
-$$
-
-
-其中：
-
-$$
-\left\{ \mathbf{A}\right\} _{ij}=f_{j}\left(x_{i}\right).
-$$
-
-
-当 $\mathbf{A^HA}$ 可逆时，我们有：
-
-$$
-\mathbf{c}=\left(\mathbf{A}^{H}\mathbf{A}\right)^{-1}\mathbf{A}^{H}\mathbf{y}=\mathbf{A}^{\dagger}\mathbf{y}
-$$
-
-
-矩阵 $\mathbf{A}^{\dagger}$ 叫做 $\mathbf{A}$ 的伪逆。
-
-
-
-**问题求解**
-
-注意到，我们的模型可以写为：
-
-$$
-\mathbf{y}=\mathbf{Ac}+\boldsymbol{\epsilon}.
-$$
-
-
-在给定 $\mathbf{y}$ 和 $\mathbf{A}$ 的情况下，我们可以使用 linalg.lstsq 求解 $\mathbf c$。
-
-
-在给定 $\mathbf{A}$ 的情况下，我们可以使用 linalg.pinv 或者 linalg.pinv2 求解 $\mathbf{A}^{\dagger}$。
-
-
-
-**例子**
-
-
-
-假设我们的数据满足：
-
-$$
-\begin{align}
-y_{i} &amp; =c_{1}e^{-x_{i}}+c_{2}x_{i} \\
-z_{i} &amp; = y_i + \epsilon_i
-\end{align}
-$$
-
-
-其中 $x_i = \frac{i}{10},\ i = 1,\dots,10$，$c_1 = 5, c_2 = 2$，产生数据
-
-
-
-
-
-::
-
-    c1, c2 = 5.0, 2.0
-    i = np.r_[1:11]
-    xi = 0.1*i
-    yi = c1*np.exp(-xi) + c2*xi
-    zi = yi + 0.05 * np.max(yi) * np.random.randn(len(yi))
-
-
-
-构造矩阵 $\mathbf A$：
-
-
-
-
-
-
-::
-
-    A = np.c_[np.exp(-xi)[:, np.newaxis], xi[:, np.newaxis]]
-    print A
-
-
-结果:
-::
-
-    [[ 0.90483742  0.1       ]
-
-     [ 0.81873075  0.2       ]
-
-     [ 0.74081822  0.3       ]
-
-     [ 0.67032005  0.4       ]
-
-     [ 0.60653066  0.5       ]
-
-     [ 0.54881164  0.6       ]
-
-     [ 0.4965853   0.7       ]
-
-     [ 0.44932896  0.8       ]
-
-     [ 0.40656966  0.9       ]
-
-     [ 0.36787944  1.        ]]
-
-
-求解最小二乘问题：
-
-
-
-
-::
-
-    c, resid, rank, sigma = linalg.lstsq(A, zi)
-
-    print c
-
-
-结果:
-::
-
-    [ 4.87016856  2.19081311]
-
-
-其中 c 的形状与 zi 一致，为最小二乘解，resid 为 zi - A c 每一列差值的二范数，rank 为矩阵 A 的秩，sigma 为矩阵 A 的奇异值。
-查看拟合效果：
-
-
-
-
-
-::
-
-    xi2 = np.r_[0.1:1.0:100j]
-    yi2 = c[0]*np.exp(-xi2) + c[1]*xi2
-
-    plt.plot(xi,zi,'x',xi2,yi2)
-    plt.axis([0,1.1,3.0,5.5])
-    plt.xlabel('$x_i$')
-    plt.title('Data fitting with linalg.lstsq')
-    plt.show()
-
-
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-.. image:: 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2VsWBcXB73R885r2jLdnTkfzeHGV2/k6B5Hc8859zCm75jkFCwiUof6hlAaG+A/cPcLzexqAHd/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%0AgIuIRNT/AXU6OlSJKzNiAAAAAElFTkSuQmCC%0A
-
-
-
-广义逆
---------------
-
-linalg.pinv 或 linalg.pinv2 可以用来求广义逆，其区别在于前者使用求最小二乘解的算法，后者使用求奇异值的算法求解。
-
-
-矩阵分解
------------------------
-特征值和特征向量
------------------------
-
-问题描述
-------------
-
-对于给定的 $N \times N$ 矩阵 $\mathbf A$，特征值和特征向量问题相当与寻找标量 $\lambda$ 和对应的向量 $\mathbf v$ 使得： $$
-\mathbf{Av} = \lambda \mathbf{v}
-$$
-
-
-
-矩阵的 $N$ 个特征值（可能相同）可以通过计算特征方程的根得到： $$
-\left|\mathbf{A} - \lambda \mathbf{I}\right| = 0
-$$
-
-
-
-然后利用这些特征值求（归一化的）特征向量。
-
-
-
-问题求解
-----------------
-
-
-- linalg.eig(A)
-    - 返回矩阵的特征值与特征向量
-- linalg.eigvals(A)
-    - 返回矩阵的特征值
-- linalg.eig(A, B)
-    - 求解 $\mathbf{Av} = \lambda\mathbf{Bv}$ 的问题
-
-
-**例子**
-
-
-矩阵为 $$
-\begin{split}\mathbf{A}=\left[\begin{array}{ccc} 1 &amp; 5 &amp; 2\\ 2 &amp; 4 &amp; 1\\ 3 &amp; 6 &amp; 2\end{array}\right].\end{split}
-$$
-
-
-
-特征多项式为： $$
-\begin{eqnarray*} \left|\mathbf{A}-\lambda\mathbf{I}\right| &amp; = &amp; \left(1-\lambda\right)\left[\left(4-\lambda\right)\left(2-\lambda\right)-6\right]-\\  &amp;  &amp; 5\left[2\left(2-\lambda\right)-3\right]+2\left[12-3\left(4-\lambda\right)\right]\\  &amp; = &amp; -\lambda^{3}+7\lambda^{2}+8\lambda-3.\end{eqnarray*}
-$$
-
-
-特征根为： $$
-\begin{eqnarray*} \lambda_{1} &amp; = &amp; 7.9579\\ \lambda_{2} &amp; = &amp; -1.2577\\ \lambda_{3} &amp; = &amp; 0.2997.\end{eqnarray*}
-$$
-
-
-
-
-
-::
-
-    A = np.array([[1, 5, 2], 
-                  [2, 4, 1],
-                  [3, 6, 2]])
-
-
- 
-    la, v = linalg.eig(A)
-
-    print la
-
-
-    # 验证是否归一化
-    print np.sum(abs(v**2),axis=0)
-
-    # 第一个特征值
-    l1 = la[0]
-    # 对应的特征向量
-    v1 = v[:, 0].T
-
-    # 验证是否为特征值和特征向量对
-    print linalg.norm(A.dot(v1)-l1*v1)
-
-
-
-结果:
-::
-
-    [ 7.95791620+0.j -1.25766471+0.j  0.29974850+0.j]
-
-    [ 1.  1.  1.]
-
-    3.23301824835e-15
-
-
-奇异值分解
-----------------
-
-
-**问题描述**
-
-
-$M \times N$ 矩阵 $\mathbf A$ 的奇异值分解为： $$
-\mathbf{A=U}\boldsymbol{\Sigma}\mathbf{V}^{H}
-$$
-
-
-其中 $\boldsymbol{\Sigma}, (M \times N)$ 只有对角线上的元素不为 0，$\mathbf U, (M \times M)$ 和 $\mathbf V, (N \times N)$ 为正交矩阵。
-
-
-其具体原理可以查看维基百科： https://en.wikipedia.org/wiki/Singular_value_decomposition
-
-
-
-**问题求解**
-
-
--mU,s,Vh = linalg.svd(A)
-    - 返回 $U$ 矩阵，奇异值 $s$，$V^H$ 矩阵
--mSig = linalg.diagsvd(s,M,N)
-    - 从奇异值恢复 $\boldsymbol{\Sigma}$ 矩阵
-
-
-
-**例子**
-
-
-奇异值分解：
-
-
-
-
-::
-
-    A = np.array([[1,2,3],[4,5,6]])
-
-    U, s, Vh = linalg.svd(A)
-
-
-$\boldsymbol{\Sigma}$ 矩阵：
-
-
-
-
-::
-
-    M, N = A.shape
-    Sig = linalg.diagsvd(s,M,N)
-
-    print Sig
-
-
-结果:
-::
-
-    [[ 9.508032    0.          0.        ]
-
-
-    [ 0.          0.77286964  0.        ]]
-
-
-检查正确性：
-
-
-
-
-::
-
-    print A
-    print U.dot(Sig.dot(Vh))
-
-
-结果:
-::
-
-    [[1 2 3]
-
-     [4 5 6]]
-
-    [[ 1.  2.  3.]
-
-     [ 4.  5.  6.]]
-
-
-**LU 分解**
-
-
-$M \times N$ 矩阵 $\mathbf A$ 的 LU 分解为： $$
-\mathbf{A}=\mathbf{P}\,\mathbf{L}\,\mathbf{U}
-$$
-
-
-
-$\mathbf P$ 是 $M \times M$ 的单位矩阵的一个排列，$\mathbf L$ 是下三角阵，$\mathbf U$ 是上三角阵。
-
-
-可以使用 linalg.lu 进行 LU 分解的求解：
-
-
-具体原理可以查看维基百科： https://en.wikipedia.org/wiki/LU_decomposition
-
-
-
-
-
-::
-
-    A = np.array([[1,2,3],[4,5,6]])
-
-    P, L, U = linalg.lu(A)
-
-    print P
-    print L
-    print U
-
-
-
-
-print P.dot(L).dot(U)
-
-结果:
-::
-
-    [[ 0.  1.]
-
-     [ 1.  0.]]
-
-    [[ 1.    0.  ]
-
-     [ 0.25  1.  ]]
-
-    [[ 4.    5.    6.  ]
-
-     [ 0.    0.75  1.5 ]]
-
-    [[ 1.  2.  3.]
-
-     [ 4.  5.  6.]]
-
-
-Cholesky 分解
-------------------
-
-
-Cholesky 分解是一种特殊的 LU 分解，此时要求 $\mathbf A$ 为 Hermitian 正定矩阵 （$\mathbf A = \mathbf{A^H}$）。
-此时有： $$
-\begin{eqnarray*} \mathbf{A} &amp; = &amp; \mathbf{U}^{H}\mathbf{U}\\ \mathbf{A} &amp; = &amp; \mathbf{L}\mathbf{L}^{H}\end{eqnarray*}
-$$ 即 $$
-\mathbf{L}=\mathbf{U}^{H}.
-$$
-
-
-可以用 linalg.cholesky 求解。
-
-
-
-**QR 分解**
-
-$M×N$ 矩阵 $\mathbf A$ 的 QR 分解为： $$
-\mathbf{A=QR}
-$$
-$\mathbf R$ 为上三角形矩阵，$\mathbf Q$ 是正交矩阵。
-
-
-维基链接： https://en.wikipedia.org/wiki/QR_decomposition
-
-
-
-可以用 linalg.qr 求解。
-
-
-**Schur 分解**
-
-
-对于 $N\times N$ 方阵 $\mathbf A$, Schur 分解要求找到满足下式的矩阵： $$
-\mathbf{A=ZTZ^H}
-$$
-其中 $\mathbf Z$ 是正交矩阵，$\mathbf T$ 是一个上三角矩阵。
-
-
-维基链接： https://en.wikipedia.org/wiki/Schur_decomposition
-
-
-
-
-::
-
-    A = np.mat('[1 3 2; 1 4 5; 2 3 6]')
-
-    print A
-
-    T, Z = linalg.schur(A)
-
-    print T, Z
-
-    print Z.dot(T).dot(Z.T)
-
-
-结果:
-::
-
-    [[1 3 2]
-
-     [1 4 5]
-
-     [2 3 6]]
-
-    [[ 9.90012467  1.78947961 -0.65498528]
-
-     [ 0.          0.54993766 -1.57754789]
-
-     [ 0.          0.51260928  0.54993766]] [[ 0.36702395 -0.85002495 -0.37782404]
-
-     [ 0.63681656 -0.06646488  0.76814522]
-
-     [ 0.67805463  0.52253231 -0.51691576]]
-
-    [[ 1.  3.  2.]
-
-     [ 1.  4.  5.]
-
-     [ 2.  3.  6.]]
-
-
-
-
-矩阵函数
---------------
-
-
-考虑函数 $f(x)$ 的泰勒展开： $$
-f\left(x\right)=\sum_{k=0}^{\infty}\frac{f^{\left(k\right)}\left(0\right)}{k!}x^{k}
-$$
-
-
-对于方阵，矩阵函数可以定义如下： $$
-f\left(\mathbf{A}\right)=\sum_{k=0}^{\infty}\frac{f^{\left(k\right)}\left(0\right)}{k!}\mathbf{A}^{k}
-$$
-
-
-这也是计算矩阵函数的最好的方式。
-
-
-
-指数和对数函数
-------------------
-
-指数
-----------
-
-指数可以定义如下： $$
-e^{\mathbf{A}}=\sum_{k=0}^{\infty}\frac{1}{k!}\mathbf{A}^{k}
-$$
-
-
-
-linalg.expm3 使用的是泰勒展开的方法计算结果：
-
-
-
-
-
-::
-
-    A = np.array([[1, 2], [3, 4]])
-
-    print linalg.expm3(A)
-
-
-结果:
-::
-
-    [[  51.96890355   74.73648784]
-
-    [ 112.10473176  164.07363531]]
-
-另一种方法先计算 A 的特征值分解：
-$$
-\mathbf{A}=\mathbf{V}\boldsymbol{\Lambda}\mathbf{V}^{-1}
-$$
-
-
-然后有（正交矩阵和对角阵的性质）：
-$$
-e^{\mathbf{A}}=\mathbf{V}e^{\boldsymbol{\Lambda}}\mathbf{V}^{-1}
-$$
-
-
-linalg.expm2 使用的就是这种方法：
-
-
-
-
-
-::
-
-    print linalg.expm2(A)
-
-
-结果:
-::
-
-    [[  51.9689562    74.73656457]
-
-    [ 112.10484685  164.07380305]]
-
-
-最优的方法是用 Padé 近似 实现，Padé 近似往往比截断的泰勒级数准确，而且当泰勒级数不收敛时，Padé 近似往往仍可行，所以多用于在计算机数学中。
-
-
-linalg.expm 使用的就是这种方法：
-
-
-
-
-::
-
-    print linalg.expm(A)
-
-
-结果:
-::
-
-    [[  51.9689562    74.73656457]
-
-
-    [ 112.10484685  164.07380305]]
-
-
-
-对数
------------
-
-
-指数的逆运算，可以用 linalg.logm 实现：
-
-
-
-
-::
-
-    print A
-    print linalg.logm(linalg.expm(A))
-
-
-结果:
-::
-
-    [[1 2]
-
-     [3 4]]
-
-    [[ 1.  2.]
-
-     [ 3.  4.]]
-
-
-三角函数
--------------------
-
-
-根据欧拉公式，其定义为： $$
-\begin{eqnarray*} \sin\left(\mathbf{A}\right) &amp; = &amp; \frac{e^{j\mathbf{A}}-e^{-j\mathbf{A}}}{2j}\\ \cos\left(\mathbf{A}\right) &amp; = &amp; \frac{e^{j\mathbf{A}}+e^{-j\mathbf{A}}}{2}.\end{eqnarray*}
-$$
-
-
-正切函数定义为： $$
-\tan\left(x\right)=\frac{\sin\left(x\right)}{\cos\left(x\right)}=\left[\cos\left(x\right)\right]^{-1}\sin\left(x\right)
-$$
-
-
-
-因此矩阵的正切函数定义为： $$
-\left[\cos\left(\mathbf{A}\right)\right]^{-1}\sin\left(\mathbf{A}\right).
-$$
-
-
-
-具体实现：
-
-
-- linalg.sinm
-- linalg.cosm
-- linalg.tanm
-
-
-
-双曲三角函数
----------------------
-
-
-$$\begin{eqnarray*} \sinh\left(\mathbf{A}\right) &amp; = &amp; \frac{e^{\mathbf{A}}-e^{-\mathbf{A}}}{2}\\ \cosh\left(\mathbf{A}\right) &amp; = &amp; \frac{e^{\mathbf{A}}+e^{-\mathbf{A}}}{2}\\ \tanh\left(\mathbf{A}\right) &amp; = &amp; \left[\cosh\left(\mathbf{A}\right)\right]^{-1}\sinh\left(\mathbf{A}\right).\end{eqnarray*}$$
-
-
-
-具体实现：
-
-
-- linalg.sinhm
-- linalg.coshm
-- linalg.tanhm
-
-
-
-特殊矩阵
-------------------
-
-
-Scipy 提供了一些特殊矩阵的实现，具体可以参考：
-
-
-http://docs.scipy.org/doc/scipy/reference/tutorial/linalg.html#special-matrices
-
-
-
-
-作者 & 更新时间
-------------------------------------
-作者:`李金  <lijinwithyou@gmail.com>`
-
-提交: 2017/12/14
\ No newline at end of file
diff --git a/docs/sources/aipy-scipy/cn/11sparse-matrix.rst.txt b/docs/sources/aipy-scipy/cn/11sparse-matrix.rst.txt
deleted file mode 100644
index 32ccde8..0000000
--- a/docs/sources/aipy-scipy/cn/11sparse-matrix.rst.txt
+++ /dev/null
@@ -1,71 +0,0 @@
-
-稀疏矩阵的线性代数
-------------------------
-
-
-对于稀疏矩阵来说，其线性代数操作可以使用 scipy.sparse.linalg 实现：
-
-
-
-
-
-::
-
-
-    import scipy.sparse.linalg
-
-
-
-矩阵操作
--------------
-
-
-- scipy.sparse.linalg.inv
-     - 稀疏矩阵求逆
-- scipy.sparse.linalg.expm
-     - 求稀疏矩阵的指数函数
-
-
-
-矩阵范数
---------------
-
-
-- scipy.sparse.linalg.norm
-    - 稀疏矩阵求范数
-
-
-
-线性方程组求解
-------------------------
-
-
-提供了一系列求解方法： http://docs.scipy.org/doc/scipy/reference/sparse.linalg.html#solving
--linear-problems
-
-
-主要使用的是迭代方法求解。
-
-
-
-特征值分解和奇异值分解
---------------------------------
-
-
-对于特别大的矩阵，原来的方法可能需要太大的内存，考虑使用这两个方法替代：
-
-
-
-- scipy.sparse.linalg.eigs
-      - 返回前 k 大的特征值和特征向量
-- scipy.sparse.linalg.svds
-      - 返回前 k 大的奇异值和奇异向量
-
-
-
-
-作者 & 更新时间
-------------------------------------
-作者:`李金  <lijinwithyou@gmail.com>`
-
-提交: 2017/12/14
diff --git a/docs/sources/aipy-scipy/cn/1synopsis.rst.txt b/docs/sources/aipy-scipy/cn/1synopsis.rst.txt
deleted file mode 100644
index b2a89c0..0000000
--- a/docs/sources/aipy-scipy/cn/1synopsis.rst.txt
+++ /dev/null
@@ -1,252 +0,0 @@
-
-SCIentific PYthon 简介
-===========================
-
-
-**Ipython** 提供了一个很好的解释器界面。
-
-
-**Matplotlib** 提供了一个类似 **Matlab** 的画图工具。
-
-
-**Numpy** 提供了 ndarray 对象，可以进行快速的向量化计算。
-
-
-**Scipy** 是 **Python** 中进行科学计算的一个第三方库，以 **Numpy** 为基础。
-
-
-**Pandas** 是处理时间序列数据的第三方库，提供一个类似 **R** 语言的环境。
-
-
-**StatsModels** 是一个统计库，着重于统计模型。
-
-
-**Scikits** 以 **Scipy** 为基础，提供如 **scikits-learn** 机器学习和**scikits-image 图像处理** 等高级用法。
-
-
-
-Scipy
---------------
-
-
-**Scipy** 由不同科学计算领域的子模块组成：
-
-
-+------------+------------------------------------------------------------------------------------------+
-| 子模块      | 描述                                                                                     |
-+============+==========================================================================================+
-|cluster     | 聚类算法                                                                                  |    
-+------------+------------------------------------------------------------------------------------------+
-| constants  |物理数学常数                                                                              |
-+------------+------------------------------------------------------------------------------------------+
-| fftpack    |快速傅里叶变换                                                                            |
-+------------+------------------------------------------------------------------------------------------+
-|integrate   |积分和常微分方程求解                                                                      |         
-+------------+------------------------------------------------------------------------------------------+
-|interpolate |插值                                                                                      |
-+------------+------------------------------------------------------------------------------------------+
-|io	         |输入输出                                                                                  |
-+------------+------------------------------------------------------------------------------------------+
-|linalg	     |线性代数                                                                                  |
-+------------+------------------------------------------------------------------------------------------+
-|odr	     |正交距离回归                                                                              |
-+------------+------------------------------------------------------------------------------------------+
-|optimize    |优化和求根                                                                                |
-+------------+------------------------------------------------------------------------------------------+
-|signal	     |信号处理                                                                                  |
-+------------+------------------------------------------------------------------------------------------+
-|sparse	     |稀疏矩阵                                                                                  |
-+------------+------------------------------------------------------------------------------------------+
-|spatial     |空间数据结构和算法                                                                        |
-+------------+------------------------------------------------------------------------------------------+
-|special     |特殊方程                                                                                  |
-+------------+------------------------------------------------------------------------------------------+
-|stats	     |统计分布和函数                                                                            |
-+------------+------------------------------------------------------------------------------------------+
-|weave	     |C/C++ 积分                                                                                |
-+------------+------------------------------------------------------------------------------------------+
-
-
-
-在使用 **Scipy** 之前，为了方便，假定这些基础的模块已经被导入：
-
-
-
-
-::
-
-    import numpy as np
-    import scipy as sp
-    import matplotlib as mpl
-    import matplotlib.pyplot as plt
-
-
-使用 **Scipy** 中的子模块时，需要分别导入：
-
-
-
-
-::
-
-    from scipy import linalg, optimize
-
-
-对于一些常用的函数，这些在子模块中的函数可以在 **scipy** 命名空间中调用。另一方面，由于 **Scipy** 以 **Numpy** 为基础，因此很多基础的 Numpy 函数可以在scipy 命名空间中直接调用。
-
-
-我们可以使用 numpy 中的 info 函数来查看函数的文档：
-
-
-
-
-::
-
-    np.info(optimize.fmin)
-
-
-::
-
-     fmin(func, x0, args=(), xtol=0.0001, ftol=0.0001, maxiter=None,     maxfun=None,
-          ^4$full_output=0, disp=1, retall=0, callback=None)
-
-
-
-    Minimize a function using the downhill simplex algorithm.
-
-
-
-    This algorithm only uses function values, not derivatives or second
-    derivatives.
-
-
-
-    Parameters
-
-    func : callable func(x,*args)
-        The objective function to be minimized.
-    x0 : ndarray
-        Initial guess.
-    args : tuple, optional
-        Extra arguments passed to func, i.e. ``f(x,*args)``.
-    callback : callable, optional
-        Called after each iteration, as callback(xk), where xk is the
-        current parameter vector.
-    xtol : float, optional
-        Relative error in xopt acceptable for convergence.
-    ftol : number, optional
-        Relative error in func(xopt) acceptable for convergence.
-    maxiter : int, optional
-        Maximum number of iterations to perform.
-    maxfun : number, optional
-        Maximum number of function evaluations to make.
-    full_output : bool, optional
-        Set to True if fopt and warnflag outputs are desired.
-    disp : bool, optional
-        Set to True to print convergence messages.
-    retall : bool, optional
-        Set to True to return list of solutions at each iteration.
-
-    Returns
-
-    xopt : ndarray
-        Parameter that minimizes function.
-    fopt : float
-        Value of function at minimum: ``fopt = func(xopt)``.
-    iter : int
-        Number of iterations performed.
-    funcalls : int
-        Number of function calls made.
-    warnflag : int
-        1 : Maximum number of function evaluations made.
-        2 : Maximum number of iterations reached.
-    allvecs : list
-        Solution at each iteration.
-
-    See also
-
-    minimize: Interface to minimization algorithms for multivariate
-        functions. See the 'Nelder-Mead' `method` in particular.
-
-    Notes
-
-    Uses a Nelder-Mead simplex algorithm to find the minimum of     function of
-    one or more variables.
-
-    This algorithm has a long history of successful use in     applications.
-    But it will usually be slower than an algorithm that uses first or
-    second derivative information. In practice it can have poor
-    performance in high-dimensional problems and is not robust to
-    minimizing complicated functions. Additionally, there currently is     no
-    complete theory describing when the algorithm will successfully
-    converge to the minimum, or how fast it will if it does.
-
-    References
-
-    .. [1] Nelder, J.A. and Mead, R. (1965), "A simplex method for     function
-           minimization", The Computer Journal, 7, pp. 308-313
-
-    .. [2] Wright, M.H. (1996), "Direct Search Methods: Once Scorned,     Now
-           Respectable", in Numerical Analysis 1995, Proceedings of the
-           1995 Dundee Biennial Conference in Numerical Analysis, D.F.
-           Griffiths and G.A. Watson (Eds.), Addison Wesley Longman,
-           Harlow, UK, pp. 191-208.
-
-
-
-可以用 lookfor 来查询特定关键词相关的函数：
-
-
-
-
-
-::
-
-    np.lookfor("resize array")
-
-
-
-Search results for 'resize array'
-
-numpy.chararray.resize
-    Change shape and size of array in-place.
-numpy.ma.resize
-    Return a new masked array with the specified size and shape.
-numpy.oldnumeric.ma.resize
-    The original array's total size can be any size.
-numpy.resize
-    Return a new array with the specified shape.
-numpy.chararray
-    chararray(shape, itemsize=1, unicode=False, buffer=None, offset=0,
-numpy.memmap
-    Create a memory-map to an array stored in a *binary* file on disk.
-numpy.ma.mvoid.resize
-     warning
-
-
-
-
-还可以指定查找的模块：
-
-
-
-
-
-::
-
-    np.lookfor("remove path", module="os")
-
-
-Search results for 'remove path'
-
-os.removedirs
-    removedirs(path)
-os.walk
-    Directory tree generator.
-
-
-
-作者 & 更新时间
-------------------------------------
-作者:`李金  <lijinwithyou@gmail.com>`
-
-提交: 2017/12/14
\ No newline at end of file
diff --git a/docs/sources/aipy-scipy/cn/2interpolation.rst.txt b/docs/sources/aipy-scipy/cn/2interpolation.rst.txt
deleted file mode 100644
index 46b1d2c..0000000
--- a/docs/sources/aipy-scipy/cn/2interpolation.rst.txt
+++ /dev/null
@@ -1,581 +0,0 @@
-
-插值
-=========
-
-
-
-::
-
-    import numpy as np
-    import matplotlib.pyplot as plt
-    %matplotlib inline
-
-
-设置 **Numpy** 浮点数显示格式：
-
-
-
-
-::
-
-    np.set_printoptions(precision=2, suppress=True)
-
-
-从文本中读入数据，数据来自 http://kinetics.nist.gov/janaf/html/C-067.txt ，保存为结构体数组：
-
-
-
-
-::
-
-    data = np.genfromtxt("JANAF_CH4.txt", 
-                      ^4$delimiter="\t", # TAB 分隔
-                      ^4$skiprows=1,     # 忽略首行
-                      ^4$names=True,     # 读入属性
-                      ^4$missing_values="INFINITE",  # 缺失值
-                      ^4$filling_values=np.inf)      # 填充缺失值
-
-显示部分数据：
-
-
-
-
-::
-
-    for row in data[:7]:
-        ^4$print "{}\t{}".format(row['TK'], row['Cp'])
-    print "...\t..."
-
-0.0	0.0
-
-100.0	33.258
-
-200.0	33.473
-
-250.0	34.216
-
-298.15	35.639
-
-300.0	35.708
-
-350.0	37.874
-
-...	...
-
-
-
-绘图：
-
-
-
-
-::
-
-    p = plt.plot(data['TK'], data['Cp'], 'kx')
-    t = plt.title("JANAF data for Methane $CH_4$")
-    a = plt.axis([0, 6000, 30, 120])
-    x = plt.xlabel("Temperature (K)")
-    y = plt.ylabel(r"$C_p$ ($\frac{kJ}{kg K}$)")
-
-.. image:: 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dddw/L1LC2zMcorQrXLC7dYCq2RcZPRrjWz9XCLxbpBtdYJwOzZszdomQyuPcm2TpYvX75By2TR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%0AZmaWKwcWMzPLlQOLmZnl6n8BKYzjzfpoiFsAAAAASUVORK5CYII=%0A
-
-
-插值
---------
-
-
-假设我们要对这组数据进行插值。
-
-
-先导入一维插值函数 interp1d：
-
-
-interp1d(x, y)
-
-
-
-
-::
-
-    from scipy.interpolate import interp1d
-
-
-
-
-::
-    ch4_cp = interp1d(data['TK'], data['Cp'])
-
-
-interp1d 的返回值可以像函数一样接受输入，并返回插值的结果。
-单个输入值，注意返回的是数组：
-
-
-
-
-::
-
-    ch4_cp(382.2)
-
-
-
-结果:
-::
-
-    array(39.565144000000004)
-
-
-输入数组，返回的是对应的数组：
-
-
-
-
-::
-
-    ch4_cp([32.2,323.2])
-
-
-结果:
-::
-
-    array([ 10.71,  36.71])
-
-
-默认情况下，输入值要在插值允许的范围内，否则插值会报错：
-
-
-
-
-::
-
-    ch4_cp(8752)
-
-报错: 
-::
-
-    ValueError                                Traceback (most recent call last)
-    <ipython-input-10-5d727af9aa33> in <module>()
-    ----> 1 ch4_cp(8752)
-
-    d:\Miniconda\lib\site-packages\scipy\interpolate\polyint.pyc in __call__(self, x)
-         77         """
-         78         x, x_shape = self._prepare_x(x)
-    ---> 79         y = self._evaluate(x)
-         80         return self._finish_y(y, x_shape)
-         81 
-
-    d:\Miniconda\lib\site-packages\scipy\interpolate\interpolate.pyc in _evaluate(self, x_new)
-        496         #    The behavior is set by the bounds_error variable.
-        497         x_new = asarray(x_new)
-    --> 498         out_of_bounds = self._check_bounds(x_new)
-        499         y_new = self._call(self, x_new)
-        500         if len(y_new) > 0:
-
-    d:\Miniconda\lib\site-packages\scipy\interpolate\interpolate.pyc in _check_bounds(self, x_new)
-        526                 "range.")
-        527         if self.bounds_error and above_bounds.any():
-    --> 528             raise ValueError("A value in x_new is above the interpolation "
-        529                 "range.")
-        530 
-
-
-    ValueError: A value in x_new is above the interpolation range.
-
-
-但我们可以通过参数设置允许超出范围的值存在：
-
-
-
-
-::
-
-    ch4_cp = interp1d(data['TK'], data['Cp'], 
-                      ^4$bounds_error=False)
-
-
-不过由于超出范围，所以插值的输出是非法值：
-
-
-
-
-::
-
-    ch4_cp(8752)
-
-
-
-结果:
-::
-
-    array(nan)
-
-
-可以使用指定值替代这些非法值：
-
-
-
-
-::
-
-    ch4_cp = interp1d(data['TK'], data['Cp'], 
-                      ^4$bounds_error=False, fill_value=-999.25)
-
-
-
-
-::
-
-    ch4_cp(8752)
-
-
-
-结果:
-::
-
-    array(-999.25)
-
-
-线性插值
---------------
-
-
-interp1d 默认的插值方法是线性，关于线性插值的定义，请参见：
-
-
-
-- 维基百科-线性插值： https://zh.wikipedia.org/wiki/%E7%BA%BF%E6%80%A7%E6%8F%92%E5%80%BC
-- 百度百科-线性插值： http://baike.baidu.com/view/4685624.htm
-
-
-
-其基本思想是，已知相邻两点 $x_1,x_2$ 对应的值 $y_1,y_2$ ，那么对于 $(x_1,x_2)$ 之间的某一点 $x$ ，线性插值对应的值 $y$ 满足：点 $(x,y)$ 在 $(x_1,y_1),(x_2,y_2)$ 所形成的线段上。
-
-
-应用线性插值：
-
-
-
-
-::
-
-    T = np.arange(100,355,5)
-    plt.plot(T, ch4_cp(T), "+k")
-    p = plt.plot(data['TK'][1:7], data['Cp'][1:7], 'ro', markersize=8)
-
-
-.. image:: data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAXUAAAEACAYAAABMEua6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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%0ASSViqEtSiRjqklQihroklcj/A8xgms4BB0nlAAAAAElFTkSuQmCC%0A
-
-
-
-其中红色的圆点为原来的数据点，黑色的十字点为对应的插值点，可以明显看到，相邻的数据点的插值在一条直线上。
-
-
-多项式插值
---------------
-
-
-我们可以通过 kind 参数来调节使用的插值方法，来得到不同的结果：
-
-
-- nearest 最近邻插值
-- zero 0阶插值
-- linear 线性插值
-- quadratic 二次插值
-- cubic 三次插值
-- 4,5,6,7 更高阶插值
-
-
-最近邻插值：
-
-
-
-
-::
-
-    cp_ch4 = interp1d(data['TK'], data['Cp'], kind="nearest")
-    p = plt.plot(T, cp_ch4(T), "k+")
-    p = plt.plot(data['TK'][1:7], data['Cp'][1:7], 'ro', markersize=8)
-
-
-.. image:: data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAXUAAAEACAYAAABMEua6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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%0AiKEuSQUx1CWpIIa6JBXk/wGLsxItFlRd2gAAAABJRU5ErkJggg==%0A
-
-0阶插值：
-
-
-
-
-::
-
-    cp_ch4 = interp1d(data['TK'], data['Cp'], kind="zero")
-    p = plt.plot(T, cp_ch4(T), "k+")
-    p = plt.plot(data['TK'][1:7], data['Cp'][1:7], 'ro', markersize=8)
-
-
-.. image:: data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAXUAAAEACAYAAABMEua6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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%0AxFCXpIIY6pJUEENdkgpiqEtSQf4fzDOJu6Be93AAAAAASUVORK5CYII=%0A
-
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-二次插值：
-
-
-
-
-::
-
-    cp_ch4 = interp1d(data['TK'], data['Cp'], kind="quadratic")
-    p = plt.plot(T, cp_ch4(T), "k+")
-    p = plt.plot(data['TK'][1:7], data['Cp'][1:7], 'ro', markersize=8)
-
-
-
-三次插值：
-
-
-
-
-::
-
-    cp_ch4 = interp1d(data['TK'], data['Cp'], kind="cubic")
-    p = plt.plot(T, cp_ch4(T), "k+")
-    p = plt.plot(data['TK'][1:7], data['Cp'][1:7], 'ro', markersize=8)
-
-
-.. image:: data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAXUAAAEACAYAAABMEua6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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%0AAElFTkSuQmCC%0A
-
-
-
-事实上，我们可以使用更高阶的多项式插值，只要将 kind 设为对应的数字即可：
-
-
-
-四次多项式插值：
-
-
-
-
-::
-
-    cp_ch4 = interp1d(data['TK'], data['Cp'], kind=4)
-    p = plt.plot(T, cp_ch4(T), "k+")
-    p = plt.plot(data['TK'][1:7], data['Cp'][1:7], 'ro', markersize=8)
-
-
-
-可以参见：
-
-
-- 维基百科-多项式插值：https://zh.wikipedia.org/wiki/%E5%A4%9A%E9%A1%B9%E5%BC%8F%E6%8F%92%E5%80%BC
-- 百度百科-插值法：http://baike.baidu.com/view/754506.htm
-
-
-对于二维乃至更高维度的多项式插值：
-
-
-
-
-::
-
-    from scipy.interpolate import interp2d, interpnd
-
-
-其使用方法与一维类似。
-
-
-径向基函数
----------------
-
-关于径向基函数，可以参阅：
-
-
-- 维基百科-Radial basis fucntion：https://en.wikipedia.org/wiki/Radial_basis_function
-
-
-径向基函数，简单来说就是点 $x$ 处的函数值只依赖于 $x$ 与某点 $c$ 的距离：
-
-$$\Phi(x,c) = \Phi(\|x-c\|)$$
-
-
-
-
-::
-
-    x = np.linspace(-3,3,100)
-
-
-常用的径向基（RBF）函数有：
-
-高斯函数：
-
-
-
-
-::
-
-    plt.plot(x, np.exp(-1 * x **2))
-    t = plt.title("Gaussian")
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-.. image:: 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zNbaWarzOzavRxTamZLzexlMyvLeEopeCrk9WOm7hVJX62F3MxKgLuA/sCxwDAzO6baMQcC/w8Y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%0AwKmQi4gUOBVyEZEC9/8BloffFHVXiTcAAAAASUVORK5CYII=%0A
-
-Multiquadric 函数：
-
-
-
-
-::
-
-    plt.plot(x, np.sqrt(1 + x **2))
-    t = plt.title("Multiquadric")
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-.. image:: 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Ei+3Xdf+HQ+YQKssUbsaKqXSWll9Qwu0hgYCTxYNYkDuPv3lR4/a2ZDzKy5u8+t2rZ///4/Pi4r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%0AJJwSuYhIwv0/gG56R7Cwt98AAAAASUVORK5CYII=%0A
-
-
-Inverse Multiquadric 函数：
-
-
-
-
-::
-
-    plt.plot(x, 1. / np.sqrt(1 + x **2))
-    t = plt.title("Inverse Multiquadric")
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-.. image:: 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bFkCey6K0yaBDvuGDtN5nvoIXjhBXj11dhJJDa1yCVjPPhg2I9TRbxizjwz7GE6ZUrsJJIN1CKX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%0A9/9saTRo/aYs3AAAAABJRU5ErkJggg==%0A
-
-
-径向基函数插值
-----------------
-
-对于径向基函数，其插值的公式为：
-
-$$
-f(x) = \sum_j n_j \Phi(\|x-x_j\|)
-$$
-
-
-我们通过数据点 $x_j$ 来计算出 $n_j$ 的值，来计算 $x$ 处的插值结果。
-
-
-
-
-::
-
-    from scipy.interpolate.rbf import Rbf
-
-
-使用 multiquadric 核的：
-
-
-
-
-::
-
-    cp_rbf = Rbf(data['TK'], data['Cp'], function = "multiquadric")
-    plt.plot(data['TK'], data['Cp'], 'k+')
-    p = plt.plot(data['TK'], cp_rbf(data['TK']), 'r-')
-
-
-.. image:: 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yFCEycePFaze5Hkoit3E9QLDz/MI+vWwZQp8Le/wfXXE+zXT59+JhKndOWuHNnu3dC/P73eeANO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%0A
-
-
-使用 gaussian 核：
-
-
-
-
-::
-
-    cp_rbf = Rbf(data['TK'], data['Cp'], function = "gaussian")
-    plt.plot(data['TK'], data['Cp'], 'k+')
-    p = plt.plot(data['TK'], cp_rbf(data['TK']), 'r-')
-
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-.. image:: 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ptxPPPM7rQePeDQQ+G55/zywRFEs1RCpGadhtu2cc2LL9Lk2mt5b/NmTo5wZ12wciX7DhoE//kP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%0AwgAAAABJRU5ErkJggg==%0A
-
-
-使用 nverse_multiquadric 核：
-
-
-
-
-::
-
-    cp_rbf = Rbf(data['TK'], data['Cp'], function =     "inverse_multiquadric")
-     plt.plot(data['TK'], data['Cp'], 'k+')
-     p = plt.plot(data['TK'], cp_rbf(data['TK']), 'r-')
-
-不同的 RBF 核的结果也不同。
-
-
-高维 RBF 插值
---------------------
-
-
-
-
-::
-
-    from mpl_toolkits.mplot3d import Axes3D
-
-三维数据点：
-
-
-
-
-::
-
-    x, y = np.mgrid[-np.pi/2:np.pi/2:5j, -np.pi/2:np.pi/2:5j]
-    z = np.cos(np.sqrt(x**2 + y**2))
-
-
-
-
-::
-
-    fig = plt.figure(figsize=(12,6))
-    ax = fig.gca(projection="3d")
-    ax.scatter(x,y,z)
-
-
-
-结果:
-::
-
-    <mpl_toolkits.mplot3d.art3d.Path3DCollection at 0x176b4da0>
-
-
-.. image:: 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A4O4plnnpnx/w8++CDOPvtsHHTQQQCA973vfbj66qtD70cYsFhlYkP0oVYqFWSzWWSzWUcfar1e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%0AIbFKEARBEARBqJb/D6nRM2cdX17QAAAAAElFTkSuQmCC%0A
-
-3维 RBF 插值：
-
-
-
-
-::
-
-    zz = Rbf(x, y, z)
-
-
-
-
-::
-
-    xx, yy = np.mgrid[-np.pi/2:np.pi/2:50j, -np.pi/2:np.pi/2:50j]
-    fig = plt.figure(figsize=(12,6))
-    ax = fig.gca(projection="3d")
-    ax.plot_surface(xx,yy,zz(xx,yy),rstride=1, cstride=1, cmap=plt.cm.jet)
-
-
-结果:
-::
-
-    
-<mpl_toolkits.mplot3d.art3d.Poly3DCollection at 0x176e5c50>
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-.. image:: 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NV0C9SFx5oZmjoFpKJ1ra2tSR9qaWlpUtypGqOFHmdn36+sCqnT/IVsOuAE6Ty09fX1ycQHu5ue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%0ABAKBafl/CyJH4aoVOLIAAAAASUVORK5CYII=%0A
-
-
-作者 & 更新时间
-------------------------------------
-作者:`李金  <lijinwithyou@gmail.com>`
-
-提交: 2017/12/14
-
diff --git a/docs/sources/aipy-scipy/cn/3csv.rst.txt b/docs/sources/aipy-scipy/cn/3csv.rst.txt
deleted file mode 100644
index a2c153a..0000000
--- a/docs/sources/aipy-scipy/cn/3csv.rst.txt
+++ /dev/null
@@ -1,248 +0,0 @@
-CSV 文件和 csv 模块
-=======================
-标准库中有自带的 csv (逗号分隔值) 模块处理 csv 格式的文件：
-
-
-::
-
-  import csv
-
-
-
-读 csv 文件
-----------------------------------------------------------
-假设我们有这样的一个文件：
-
-
-%% file data.csv
-
-::
-
-	"alpha 1",  100, -1.443
-	"beat  3",   12, -0.0934
-	"gamma 3a", 192, -0.6621
-	"delta 2a",  15, -4.515
-
-
-打开这个文件，并产生一个文件 reader：
-
-
-
-::
-
-	fp = open("data.csv")
-	r = csv.reader(fp)
-
-可以按行迭代数据：
-
-
-::
-
-	for row in r:
-	^4$print row
-	    
-	fp.close()
-
-
-结果:
-::
-    
-    ['alpha 1', '  100', ' -1.443']
-	['beat  3', '   12', ' -0.0934']
-	['gamma 3a', ' 192', ' -0.6621']
-	['delta 2a', '  15', ' -4.515']
-
-
-默认数据内容都被当作字符串处理，不过可以自己进行处理：
-
-
-
-::
-
-	data = []
-
-	with open('data.csv') as fp:
-		^4$r = csv.reader(fp)
-		^4$for row in r:
-
-	data.append([row[0], int(row[1]), float(row[2])])
-	    
-	data
-
-
-结果:
-::
-    
-	[['alpha 1', 100, -1.443],
-	['beat  3', 12, -0.0934],
-	['gamma 3a', 192, -0.6621],
-	['delta 2a', 15, -4.515]]
-
-
-
-::
-
-	import os
-	os.remove('data.csv')
-
-
-
-写 csv 文件
--------------------------------------------------------------
-可以使用 *csv.writer* 写入文件，不过相应地，传入的应该是以写方式打开的文件，不过一般要用 *'wb'* 即二进制写入方式，防止出现换行不正确的问题：
-
-
-::
-
-	data = [('one', 1, 1.5), ('two', 2, 8.0)]
-	with open('out.csv', 'wb') as fp:
-	w = csv.writer(fp)
-	w.writerows(data)
-
-
-显示结果：
-
-::
-
-	!cat 'out.csv'
-
-
-结果:
-::
-
-	one,1,1.5
-	two,2,8.0
-
-
-
-
-
-更换分隔符
-----------------------------------------------------
-默认情况下，*csv* 模块默认 *csv* 文件都是由 *excel* 产生的，实际中可能会遇到这样的问题：
-
-
-::
-
-	data = [('one, \"real\" string', 1, 1.5), ('two', 2, 8.0)]
-	with open('out.csv', 'wb') as fp:
-	w = csv.writer(fp)
-	w.writerows(data)
-
-
-
-
-::
-
-	!cat 'out.csv'
-
-
-结果:
-::
-
-	"one, ""real"" string",1,1.5
-	two,2,8.0
-
-
-可以修改分隔符来处理这组数据：
-
-
-
-::
-
-	data = [('one, \"real\" string', 1, 1.5), ('two', 2, 8.0)]
-	with open('out.psv', 'wb') as fp:
-	w = csv.writer(fp, delimiter="|")
-	w.writerows(data)
-
-
-
-
-::
-
-	!cat 'out.psv'
-
-
-结果:
-::
-
-	"one, ""real"" string" | 1 | 1.5
-	two|2|8.0
-
-
-
-
-::
-
-	import os
-	os.remove('out.psv')
-	os.remove('out.csv')
-
-其他选项
--------------------------------------------------------------------------
-
-*numpy.loadtxt()* 和 *pandas.read_csv()* 可以用来读写包含很多数值数据的 *csv* 文件：
-
-
-
-::
-
-	%%file trades.csv
-	Order,Date,Stock,Quantity,Price
-	A0001,2013-12-01,AAPL,1000,203.4
-	A0002,2013-12-01,MSFT,1500,167.5
-	A0003,2013-12-02,GOOG,1500,167.5
-
-
-Writing trades.csv
-
-
-使用 pandas 进行处理，生成一个 DataFrame 对象：
-
-
-
-::
-
-	import pandas
-	df = pandas.read_csv('trades.csv', index_col=0)
-	print df
-
-
-             
-
-结果:
-::
-
-    Order  Date Stock  Quantity  Price                                 
-	A0001  2013-12-01  AAPL      1000  203.4
-	A0002  2013-12-01  MSFT      1500  167.5
-	A0003  2013-12-02  GOOG      1500  167.5
-
-
-通过名字进行索引：
-
-
-
-::
-
-	df['Quantity'] * df['Price']
-
-结果:
-::
-    
-	Order
-	A0001    203400
-	A0002    251250
-	A0003    251250
-	dtype: float64
-
-
-::
-
-	import os
-	os.remove('trades.csv')
-
-
-作者 & 更新时间
-------------------------------------
-作者:李金  lijinwithyou@gmail.com
\ No newline at end of file
diff --git a/docs/sources/aipy-scipy/cn/4odd.rst.txt b/docs/sources/aipy-scipy/cn/4odd.rst.txt
deleted file mode 100644
index 0a3bffa..0000000
--- a/docs/sources/aipy-scipy/cn/4odd.rst.txt
+++ /dev/null
@@ -1,860 +0,0 @@
-
-概率统计方法
-=============
-
-简介
----------
-
-**Python** 中常用的统计工具有 **Numpy**, **Pandas**, **PyMC**, **StatsModels** 等。
-
-
-**Scipy** 中的子库 scipy.stats 中包含很多统计上的方法。
-
-
-导入 numpy 和 matplotlib：
-
-
-
-
-::
-
-    %pylab inline
-
-Populating the interactive namespace from numpy and matplotlib
-
-
-
-::
-
-    heights = array([1.46, 1.79, 2.01, 1.75, 1.56, 1.69, 1.88, 1.76, 1.88, 1.78])
-
-
-Numpy 自带简单的统计方法：
-
-
-
-
-::
-
-    print 'mean, ', heights.mean()
-    print 'min, ', heights.min()
-    print 'max, ', heights.max()
-    print 'standard deviation, ', heights.std()
-
-
-结果:
-::
-
-    mean,  1.756
-
-    min,  1.46
-
-    max,  2.01
-
-    standard deviation,  0.150811140172
-
-
-导入 **Scipy** 的统计模块：
-
-
-
-
-::
-
-    import scipy.stats.stats as st
-
-
-其他统计量：
-
-
-
-
-::
-
-    print 'median, ', st.nanmedian(heights)    # 忽略nan值之后的中位数
-    print 'mode, ', st.mode(heights)           # 众数及其出现次数
-    print 'skewness, ', st.skew(heights)       # 偏度
-    print 'kurtosis, ', st.kurtosis(heights)   # 峰度
-    print 'and so many more...'
-
-
-
-结果:
-::
-
-    median,  1.77
-
-    mode,  (array([ 1.88]), array([ 2.]))
-
-    skewness,  -0.393524456473
-
-    kurtosis,  -0.330672097724
-
-    and so many more...
-
-
-
-概率分布
------------------
-
-
-常见的连续概率分布有：
-
-
-- 均匀分布
-- 正态分布
-- 学生t分布
-- F分布
-- Gamma分布
-- ...
-
-
-
-离散概率分布：
-
-
-- 伯努利分布
-- 几何分布
-- ...
-
-
-这些都可以在 scipy.stats 中找到。
-
-
-连续分布
-=========
-
-
-正态分布
-------------
-
-以正态分布为例，先导入正态分布：
-
-
-
-
-::
-
-    from scipy.stats import norm
-
-
-它包含四类常用的函数：
-
-
-- norm.cdf 返回对应的累计分布函数值
-- norm.pdf 返回对应的概率密度函数值
-- norm.rvs 产生指定参数的随机变量
-- norm.fit 返回给定数据下，各参数的最大似然估计（MLE）值
-
-
-从正态分布产生500个随机点：
-
-
-
-
-::
-
-    x_norm = norm.rvs(size=500)
-    type(x_norm)
-
-
-
-
-结果:
-::
-
-    numpy.ndarray
-
-
-直方图：
-
-
-
-::
-
-    h = hist(x_norm)
-    print 'counts, ', h[0]
-    print 'bin centers', h[1]
-
-
-
-
-结果:
-::
-
-    counts,  [   7.   21.   42.   97.  120.   91.   64.   38.   17.    3.]
-    bin centers [-2.68067801 -2.13266147 -1.58464494 -1.0366284  -0.48861186  0.05940467
-    0.60742121  1.15543774  1.70345428  2.25147082  2.79948735]
-
-
-.. image:: 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W/Xywj98TDvPA34Th91TEtVzVfV8W71LuCcPuuZtKo6VFWH+65jgpp+Q1xVfRb4ft91TEtVHa2q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%0AkhphoEtSI/4P1qxllG6H6EYAAAAASUVORK5CYII=%0A
-
-归一化直方图（用出现频率代替次数），将划分区间变为 20（默认 10）：
-
-
-
-
-::
-
-    h = hist(x_norm, normed=True, bins=20)
-
-
-.. image:: data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAXIAAAEACAYAAACuzv3DAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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%0AAAAASUVORK5CYII=%0A
-
-
-在这组数据下，正态分布参数的最大似然估计值为：
-
-
-
-
-::
-
-    x_mean, x_std = norm.fit(x_norm)
-
-    print 'mean, ', x_mean
-    print 'x_std, ', x_std
-
-
-
-
-结果:
-::
-
-    mean,  -0.0426135499965
-
-    x_std,  0.950754110144
-
-
-将真实的概率密度函数与直方图进行比较：
-
-
-
-
-::
-
-    h = hist(x_norm, normed=True, bins=20)
-
-    x = linspace(-3,3,50)
-    p = plot(x, norm.pdf(x), 'r-')
-
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-.. image:: 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ZFLxhIl8jEMZBXNuJjh9bWut20KPYfUNsy+01tYrAnwFnA5MEHnWNHRw5clZ7rxd7rzIocwJ+xQ%0AikzDE/E3wHlM5zFi8OWXsP32WY9KCpsudkqDbcMaRjOYC7mbVWwXdjgCvERXpoJu3y9RSuTSYFdx%0AA2/Rlsn0DDsUqeH3AM88A6+/HnYokmcaI5eEao+RH8hcXqILbXkrhUe3aYw8f22D9v7443DTTTBz%0AJmxdexaRRJHmkUuWOfdwPn/gWj1/s1CdcQbstluwuJaUDFXkklDNinwAD3IBI+nM62ygLJXWRLWy%0AjWrc7g4LFwbL3c6aBXvtlcH+pBBo+qFkbGMi/z6fMYdDOJEpVNIh1dZENSFGNe5N59j118OMGcHD%0AKEzPS40yDa1I1tzCZTzOzxuQxCVUl10GCxbAhAlhRyJ5oIpcEjIzuhDnUc7iEOY0cLphdCvbqMa9%0A2Tk2fTqceSa89x5sp2miUaWKXDLWGLiH87mYuzRnPGq6doXu3YNb+KWoqSKXhIaZ0ZGe9GIiDb+F%0APLqVbVTj3uIc++yzYL3yKVOgg4bFokgXOyUzCxfyWatWHMYi/ks6sx+imxCjGned59iDD8LIkcGN%0AQmWpzDaSQqKhFUmfO1x4ITdBmklcCsaAAdC0KdxzT9iRSI4okUvdxo6FZcvQbSVFwCxI4hUVsHRp%0A2NFIDmhoRbb0xRfBw33Hj8eOPJLoDTNoaKVOV18N8+bB009n0Ifkm8bIJT3nnRes03H33Sk/fLlu%0AUWwbZt85TuRr1sChh8Idd8BPf5pBP5JPSuTScK+8EqzXMWcO7LCDEnlk2gbtk55jf/sbDBwY/P82%0Aa5ZBX5IvutgpDbNuXfBA3zvvhB12CDsayYXjjw/ml1dUhB2JZJEqcvnODTcEU9QmTdq0Pocq8qi0%0ADdqndI4tXw6tW8PUqdC+fQb9ST5oaEVSV8+KeUrkUWkbtE/5HNPc8sjQ0Iqkxh3OPz94TJiWPS0N%0AAwbA974Hd98ddiSSBSklcjMrN7N5ZrbAzC6v4/NfmNlbZva2mb1qZodmP1TJmT//GVasgF//OuxI%0AJF/MYPRouO46WLw47GgkQ0mHVsysDHgf6A4sAd4E+rn73BrbHAm85+4rzawcqHD3zrX2o6GVQvT5%0A58FaHJMnw+GHb/Gxhlai0jZo3+Bz7A9/gNmzYfz4DPqVXMrW0EpHYKG7L3L39cBYoFfNDdz9NXdf%0AWf32DaBFOgFLCH7/ezj99DqTuJSAK66AuXO1bnnENUphm+bARzXeLwY6Jdj+HOD5TIKS7LEET4eJ%0AAQ8DhwCrhg/PU0RSUJo0CYZYzjwTunXTtNOISiWRp/y3mpl1AwYCR9f1eUWNuauxWIxYLJbqriUj%0AW/4XNuVr7udQhjCcVZyUoK0eE1b0unaFHj2Cv85Gjw47mpIXj8eJx+MNapPKGHlngjHv8ur3Q4EN%0A7n5zre0OBZ4Byt19YR370Rh5COob476VS9mNTziLx5Ltoc72KfYewbZh9p2NuNOzPbCyRQt45JGg%0AMpeCkcoYeSoV+UyglZntDSwFTgf61epoT4IkfmZdSVwKS0fe4Oc8ThveCTsUybr0fhF8icGoUTBo%0AELz9drDsrURG0oud7l4FXARMA94DnnT3uWY22MwGV292DbATMMrMKs1sRs4ilow05hseYCCXcCef%0As0vY4Ugh+elPoVOnYJVEiRTd2Vnkag+tVHAt7ZhNbyaQ2p/iURxmKOWhlUz6hl2AdwimpTWkGtO5%0AnTu6s1M204a3uYCRDGEUuogpW3I+w7mEJxjDITRmLcEvhmQvCZsSeYkoo4oxnMNQbuRj9gg7HClg%0AT3I6H/IjhnJj2KFIijS0UuQ2Dq38jlsoZyrdeZGGVeNRHGYIe4gi+nHvwRJm047j+Dvv0iZpW53b%0AuaPVDwUzoxXv80+OoiMz+Dc/augeiF5SK5yEmL+22e/7XO7jPO7lSF7j24QT3JTIc0lj5IIB9zGI%0A6xmWRhKXUnY/5/IV2/Fbbg87FElCFXmRu9SMPhxNV6azgXTWnY5idVpYlW1+2uam7735NzPomGSI%0ARRV5LqkiL3XvvssVwC95JM0kLqVuEftwBTfxKGfRmG/CDkfqoURerNatgzPPZChoSEUy8gAD+S97%0AUkFF2KFIPZTIi1VFBey5J2PCjkOKgDGI+xjAQxzFq2EHI3XQGHkxevVVOOUUmD0b2313Cm3ctbDb%0Ahtl3YcfdiwncxqW0Yzar2G6ztjq3c0fTD0vRV19Bu3Zw223Qu3eGT/iBaCa1Ujzm/PQ9hoF8Sxnn%0Acd9mbXVu544SeSk67zxYvz54SjqZPqoNopnUSvGY89P3dnzJW7Tl1wxnMj03tdW5nTvZWsZWomLy%0AZPjrX4NlSEVy4Cu2pz8PM5YzaEtnPmPXsEMSVJEXj+XLoW1beOKJ4Ikv1VSRR6nv6MT9/7iMffmA%0AvowDtlJFnkOaR14q3IMhlV/8YrMkLpIrw7ie/VjI2TwYdiiChlaKw/Dh8NFHMHZs2JFIiVhHE/rx%0ABHFiDVq3XHJDFXnUzZgBN9wATz0VPBFdJE/e4xAu4xaeBli1KuxwSpoSeZStWAGnnx48+fxHuntT%0A8u9hBvA6wJAhwRCfhEKJPKrc4eyzoVcv6NMn7GikhF0IUFkJDzwQdiglS2PkUXXHHbBsGTz9dNiR%0ASIlbA8HPYZcucMQRcOihYYdUclSRR9Hrr8PNN8OTT0LjxmFHIwIHHRQUF6eeGtxdLHmleeRR8/nn%0A0KED/OlPcPLJSTfXPPIo9R3duDed24MGwddfw5//DKYHfGeD5pEXmw0boH//oOpJIYmL5N3w4TBn%0ADtx7b9iRlBRV5FFy000waRJMnw5bb51SE1XkUeo7unFvdm7Pnw9HHw1Tp8Jhh6W5T9lIFXkxmTgR%0ARowI5ounmMRFQrH//kFF3rs3LF0adjQlQbNWomD27GDs8bnnoEWLsKMRSa5PH5g3LxgCfOklaNo0%0A7IiKmoZWCt2yZdCpE9xyC5x2WoOba2glSn1HN+46z2334JrOmjXBDKutNACQDg2tRN2aNcENP+ec%0Ak1YSFwmVGdx3XzC8cu21YUdT1FJK5GZWbmbzzGyBmV1ex+cHmtlrZrbWzC7NfpglyB0GDoR994Wr%0Arw47GpH0NGkC48fDY4/B44+HHU3RSjq0YmZlwPtAd2AJ8CbQz93n1thmV2AvoDfwhbvfVsd+Snpo%0AxRo4p/ZqoAfQDVgLaa/3rKGVKPUd3biT/ny++y4cd1xw0f7II9PspzRla2ilI7DQ3Re5+3pgLNCr%0A5gbuvtzdZwLr0462JHhKr1N5knPYk958zNqMTmyRAtG6NTz0EPTtC//5T9jRFJ1UEnlz4KMa7xdX%0Af09yoCNvcDcXcjKT+ITdww5HJHt69IDLLoOePWHlyrCjKSqpJHKVhHlyKG8xiZPpz8O8TdvNPjOz%0AtF4iBeWSSyAWg5NOCm7ll6xIZR75EqBljfctCaryBquoqNj0dSwWIxaLpbObonQA85jCiVzECKbQ%0Ao44tMhn7FCkQZnDnnXDuucENQ88+C9tsE3ZUBSUejxOPxxvUJpWLnY0ILnYeDywFZlDrYmeNbSuA%0Ar3Sxc0uJLjruzb+ZTleu5joeoX9drettm0LPGbQNs28dc3T6TuFiZ23ffhs8Y3b1ahg3TncrJ5DK%0Axc6UbggysxOBO4EyYIy732hmgwHcfbSZ7U4wm2V7YAPwFXCwu6+qsQ8l8jpOlD1Ywkt04TYuZRQX%0A1Ne6zrYp9pxB2zD71jFHp+80EjnA+vXBxc+mTYPVEsvK0uy/uGUtkWcpGCXyWifKrnzKdLryAAO5%0AlcsStd6ibQN6zqBtmH3rmKPTd5qJHGDtWvjpT2HPPeH++3X3Zx10Z2cB24kV/JUf8xSnJUniIoUv%0A3Yvxtu22wdzy+fPh4ov13M80qSLPk5oV+XZ8yQucwCscw++4leQXJEuzytMxR6XvLFTzK1cGNwx1%0A7x4s16wZV5uoIi9Au7GM6XRlBh1TTOIiJWCHHWDaNHjhBbjwwuBiqKRMFXmemBn7soBp/ISHGMD1%0ADCP1JK4qL39tw+y7FOPe/BzYDhgPfAGcCXyTwh6KPa+oIi8gHYCX6MJNXMH1XI0qcZGNvlue4iuc%0AHqzlW05jKl3Znv9t9vmWLwEl8vx44QWmABcwkvsZFHY0IgVtHU3oxxO8Qxteogs/RE8ZSkaJPNfG%0AjoUzz+QUYCK9w45GJBKcrfg1w3mS03mVo2nF/LBDKmhK5Ll0113BIkEvvsjLYcciEjnGjVzJ9Qxj%0AOl05ghlhB1Sw9MzOXFi9Gi66CF5/HV55BfbaK+yIRCLrAc7hU37Ac5zEFdzEA5wTdkgFRxV5ti1Y%0AECycv3YtzJihJC6SBZPpSRde4lJuYwwD2ZbVYYdUUJTIs2ncODjqKDj//GDtiGbNwo5IpGjM4yA6%0AMoMmfMNrHMl+LAg7pIKhoZVsWL8eLr88eDbhlClw+OFhRyRSlL6mGWfyGIMZzT85iiFhB1QgVJFn%0AavHiYKH8+fNh1iwlcZGcM0ZzPicyhVsAfvMbWLcu7KBCpUSeLnd45BE47LBg9bZJk2DnncOOSqRk%0AzOJwDgNYuDC4LlVZGXZIodHQSjo++CAYB//8c3j++SCZi0jefQFBEfXQQ1BeDv37Q0VFsMZ5CVFF%0A3hDr18PNN0OnTvCTnwSzUpTERcJlBmefDe+8Ewx1tm4Nf/1r2FHllRbNStWbb8KgQbDbbnDPPbDP%0APg1qnuhRbym0DqltmH3rmKPTd7jHvEVemTIFLrgAjjkGbr8ddt01g/2HT08IyoZPPoHrrmPZ3Xdz%0AKfB4RjuL5okSvbhL8ZjD7DvsY95SU+CPBCsoVgBjgPV1bBeFnKTVDzOxYgVccQUcdBCUldEaeDzh%0AKmzJXiKSG1ueb6txfodzIrPozY+Zxz78kofYiiqK8ZxUIq/tyy/hj3+E/feHL76At96Cu+7i87Dj%0AEpEGq6QD5UxjAA9xDmN4l9acylMYG8IOLas0tLLR6tVw991w663Bhcxrr4V99930cWZj3BDtP12j%0AFncpHnOYfUflmJ0TeIHrGUZj1nE1b/Hshg0F/1i5ohsjHzFiJHPmpL+c5Y9/3JU+ffps/s0FC4KL%0Alw8/DN26wR/+AAcfvEVbJfJSaRtm36UYdxjH7JzMJP5Ib9oedBAMGQK//GXwuLkCVHSJ/IgjTmDm%0AzP2AA9JoHWfIkH0YOfIOqKqC556DkSODmwgGDoTBgxPORFEiL5W2YfZdinGHe8w+fXqQB6ZNg9NO%0AC2a7tG2bQTzZl0oij+ANQT8DTkijnbPjqjlwww0wejS0aBH8p02cCNtsk+0gRSQKunQJXsuWwf33%0AB3dp77lnUKX37h2Zhe8iWJH/noYk8n34kL6Moy8jaN3kU5r98qzgP6l9+wb1rYq8VNqG2Xcpxh1y%0ARV47J1VVweTJcO+98OqrcNxx0Lcv9OwZ2tBLyU4/PIB5XMkNzKJD9XKXC7maE9npm7XYffdhHTpg%0AZg16iUgJaNQoqMSffx4WLQq+fuopaNkSTjoJHnggWJqjwERwaGVLe7GIGPFNr0ZUMZ4+/IY7eIVj%0A2EAZcEf11plUDiJSMnbaKVi7pX//YFryc88Fzxy45BJo1SpY9bRbNzj22NAvlEZuaKVy5qUcSAsO%0AZyZdmU6MONuypkYaj/E+B7Bl4r0D+C1R/RNQceerbZh9l2LcBTa0kop16zi6SRNiQDegE/A+EAde%0ABv4FLE5hN6n2nZVZK2ZWDtwJlAH3u/vNdWwzHDgRWA0McPct1pNMK5GvXh0shFNZCZWVzHlsLHuv%0A/obF7EUl7ZlOV+LEmMeBJK+Ylcij1beOOTp9R/eY0y0ua14za8w3HMGbxIhzNK/SnkoaUUUl7Te9%0AZtOO+exfPTrQsL4znrViZmXACKA7sAR408wmufvcGtv0APZz91Zm1gkYBXROKUIIkvWHHwZrCi9Y%0AsPm/y5cHt8i3bw/t23N7y3/x1PvDWEWvlHefP3EgFnIMuRSneI8vTvEeG+j4cmsdTXiVY3iVYzZ9%0Ab3c+3pTG+zKO67ia5ixhEXuzcOND6kaOhP32C4ZpWrSArbdOO4ZkY+QdgYXuvgjAzMYCvYC5NbY5%0AGXgYwN3fMLMdzWw3d/9ki70NGwZLlgSvpUuDf9esCeZvt2oVHFS7dnDKKcH7li2hrGxT87cfnsgq%0ACnWd4Tg6WaIqTvEeG+j46periQzL+CFT+CFT6LHpe9uwhh/xIa1YwH5MDpb/GDcuKFyXLYPvfx+a%0AN4c99ggZ7HGoAAAD/UlEQVT+3fhKQbJE3hz4qMb7xQRDQsm2aQFsmcgbNw6WlqwZ7M47F/wtsiJS%0ArPI3+WEt2/Ieh/AehwBw6+jR331YVRWstFqzyF2yBOLxlPadLJGnepS1j6rudtdck+Lu6lZWBk2b%0AXk2jRsMb3Hbdug9Zuzaj7kVEcqNRo/or8EceSdo84cVOM+sMVLh7efX7ocCGmhc8zeweIO7uY6vf%0AzwO61h5aMbMCXjFLRKRwZXqL/kyglZntDSwFTgf61dpmEnARMLY68f+vrvHxZIGIiEh6EiZyd68y%0As4uAaQTTD8e4+1wzG1z9+Wh3f97MepjZQuBr4OycRy0iIpvk7YYgERHJjbyutWJm15nZW2Y228z+%0AZmYt89l/LpnZLWY2t/r4njGzwlzcOE1mdqqZzTGzb82sQ9jxZIuZlZvZPDNbYGaXhx1PNpnZA2b2%0AiZm9E3YsuWBmLc3sH9U/l++a2a/DjilbzGwbM3ujOle+Z2Y3Jtw+nxW5mW3n7l9Vf/0roK27n5u3%0AAHLIzE4A/ubuG8zsJgB3vyLksLLGzA4ENgCjgUvd/V8hh5Sx6hve3qfGDW9Av5o3vEWZmR0LrAIe%0Acfc2YceTbWa2O7C7u882s2bALKB3Ef3/NXX31WbWCHgF+J27v1LXtnmtyDcm8WrNgM/y2X8uufsL%0A7r7xQYBvEMylLxruPs/d0388U2HadMObu68HNt7wVhTc/WXgi7DjyBV3X+bus6u/XkVwo+Ie4UaV%0APe6+uvrLxgTXKFfUt23el7E1sxvM7L9Af+CmfPefJwOB58MOQpKq62a21G6lk4JSPbOuPUERVRTM%0AbCszm01wc+U/3P29+rbN+jK2ZvYCsHsdH13p7s+6+1XAVWZ2BcFKVpGZ5ZLs2Kq3uQpY5+6P5zW4%0ALEjl+IqMrvQXgephlb8AF1dX5kWh+i/8dtXX26aZWczd43Vtm/VE7u6pPr7ncSJWtSY7NjMbAPQA%0Ajs9LQFnWgP+7YrEEqHnBvSWprUAqBcLMtgbGAY+5+4Sw48kFd19pZs8BhxMsLLOFfM9aaVXjbS9g%0Ai+Vuo6p6ud/LgF7uXuyLARTLzV2bbngzs8YEN7xNCjkmSZEFK16NAd5z9zvDjiebzGwXM9ux+utt%0ACZ5vWW++zPeslb8ABwDfAh8AQ9z907wFkENmtoDgosTGCxKvufsFIYaUVWbWBxgO7AKsBCrd/cRw%0Ao8qcmZ3Id+vtj3H3hNO8osTMngC6At8HPgWucfcHw40qe8zsGOAl4G2+GyYb6u5Tw4sqO8ysDcGq%0AsltVvx5191vq3V43BImIRFtRPnxZRKSUKJGLiEScErmISMQpkYuIRJwSuYhIxCmRi4hEnBK5iEjE%0AKZGLiETc/wfoqVYxTpVDEwAAAABJRU5ErkJggg==%0A
-
-
-导入积分函数：
-
-
-
-
-::
-
-    from scipy.integrate import trapz
-
-
-通过积分，计算落在某个区间的概率大小：
-
-
-
-
-::
-
-    x1 = linspace(-2,2,108)
-    p = trapz(norm.pdf(x1), x1) 
-    print '{:.2%} of the values lie between -2 and 2'.format(p)
-
-    fill_between(x1, norm.pdf(x1), color = 'red')
-    plot(x, norm.pdf(x), 'k-')
-
-
-
-结果:
-::
-
-    95.45% of the values lie between -2 and 2
-
-
-
-
-
-[<matplotlib.lines.Line2D at 0x15cbb8d0>]
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-.. image:: 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PM7bYAyPh+z//Uv11FMKbMO3oTMU5ddxqa5cxmTm+s6iilgDPBl9epM2b7ddRQTInaapCk1OVlZ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%0AFXhjjIlSVuCNMSZK/T8+DStxCxquAQAAAABJRU5ErkJggg==%0A
-
-
-
-默认情况，正态分布的参数为均值0，标准差1，即标准正态分布。
-
-
-可以通过 loc 和 scale 来调整这些参数，一种方法是调用相关函数时进行输入：
-
-
-
-
-::
-
-    p = plot(x, norm.pdf(x, loc=0, scale=1))
-    p = plot(x, norm.pdf(x, loc=0.5, scale=2))
-    p = plot(x, norm.pdf(x, loc=-0.5, scale=.5))
-
-.. image:: data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAXIAAAEACAYAAACuzv3DAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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%0AAAAAAElFTkSuQmCC%0A
-
-
-另一种则是将 loc, scale 作为参数直接输给 norm 生成相应的分布：
-
-
-
-
-::
-
-    p = plot(x, norm(loc=0, scale=1).pdf(x))
-    p = plot(x, norm(loc=0.5, scale=2).pdf(x))
-    p = plot(x, norm(loc=-0.5, scale=.5).pdf(x))
-
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-.. image:: 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sSYCrFEbi4UjfVx0FZ4//5aVjImjFgiNxeKxvp4oYED9YPMmDBiidyczzlN5NHYIgdL5CYslZvI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%0AAAAAAElFTkSuQmCC%0A
-
-
-其他连续分布
-----------------
-
-
-
-
-::
-
-    from scipy.stats import lognorm, t, dweibull
-
-
-支持与 norm 类似的操作，如概率密度函数等。
-
-
-不同参数的对数正态分布：
-
-
-
-
-::
-
-    x = linspace(0.01, 3, 100)
-
-    plot(x, lognorm.pdf(x, 1), label='s=1')
-    plot(x, lognorm.pdf(x, 2), label='s=2')
-    plot(x, lognorm.pdf(x, .1), label='s=0.1')
-
-    legend()
-
-
-
-
-结果:
-::
-
-    <matplotlib.legend.Legend at 0x15781c88>
-
-
-.. image::data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAXcAAAEACAYAAABI5zaHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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%0A9P8BfIyZcbomsyAAAAAASUVORK5CYII=%0A
-
-
-
-不同的韦氏分布：
-
-
-
-
-::
-
-    x = linspace(0.01, 3, 100)
-
-    plot(x, dweibull.pdf(x, 1), label='s=1, constant failure rate')
-    plot(x, dweibull.pdf(x, 2), label='s>1, increasing failure rate')
-    plot(x, dweibull.pdf(x, .1), label='0<s<1, decreasing failure rate')
-
-    legend()
-
-
-
-
-结果:
-::
-
-    <matplotlib.legend.Legend at 0xaa9bc50>
-
-
-.. image:: 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O3atZMZM2aISMEp+QYMGCBNmzaVHTt2iN1ul1OnTknjxo1l3rx5YrfbZdOmTRIaGio7duwQEZGY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%0AAAAAAElFTkSuQmCC%0A
-
-
-不同自由度的学生 t 分布：
-
-
-
-
-::
-
-    x = linspace(-3, 3, 100)
-
-    plot(x, t.pdf(x, 1), label='df=1')
-    plot(x, t.pdf(x, 2), label='df=2')
-    plot(x, t.pdf(x, 100), label='df=100')
-    plot(x[::5], norm.pdf(x[::5]), 'kx', label='normal')
-
-    legend()
-
-
-
-
-结果:
-::
-
-    <matplotlib.legend.Legend at 0x164582e8>
-
-.. image::data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAXgAAAEACAYAAAC57G0KAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz%0AAAALEgAACxIB0t1+/AAAIABJREFUeJzsnWd4VEUXgN9L7wQIvVfpICggoURQWlAQREAREBSkiiAi%0AhBLFCCQoHQSkWQGlKUGqRCH0Kl+QntCRthtq+vl+TIgEki3Jbuq8z7NP9t57ZuZkkz137pkz5xgi%0Agkaj0WjSH5lSWgGNRqPROAdt4DUajSadog28RqPRpFO0gddoNJp0ijbwGo1Gk07RBl6j0WjSKVYN%0AvGEYbQzDOGEYxmnDMEZZkHveMIxIwzA629tWo9FoNI7HooE3DCMzMBtoA1QHuhuGUS0BuSnARnvb%0AajQajcY5WJvBNwDOiEiwiEQAy4EO8cgNAX4BbiSirUaj0WicgDUDXxK4+NjxpZhzsRiGURJluOfF%0AnHq0NdZqW41Go9E4D2sG3pY8BtOBT0TlPDBiXra21Wg0Go2TyGLl+mWg9GPHpVEz8cepDyw3DAPA%0AFWhrGEaEjW0xDEPfCDQajSYRiIhhTSDBF+oGcBYoB2QDjgDVLMgvATrZ01apkH6ZMGFCSqvgVNL8%0A7xcWJrJwoUjNmiL584t4eIhMnizy449iWrhQnqtQQYK8vGRgpUpiKlJEpFQpkc8+EzGZUlrzJJPm%0A/3ZWSO+/X4zttGjDLbpoRCQSGAxsAo4DK0TkH8Mw+huG0T8xbS3ebTSa5CI0FGbPhkqV4OefYdYs%0AuHUL1q+HUaMwt22L5+HDtOzShXITJuC9fz+enTtjXr4czp6FihXB01O10WhSKVbj4EXkdxF5RkQq%0AicikmHPzRWR+PLLviMhqS201mhQnMBAaNIANG2DVKti0CdzdIXPmWJGAgAC8vb3JkSMHAC4uLnh/%0A8QUBZjMsXQoHDsCNG1CrFmzcGP84Gk0Ko3eyOhl3d/eUVsGppKnfTwTmzlXGfNgw8POD55+PV9TD%0AwwMXF5c4v5+LiwseHh7qoHx5WLAAfvgB3nsPhg+HsDDn/w4OJE397RJBev/9bMGQFC74YRiGpLQO%0AmvSFn58fbm5uuLi4xJ4zX7tGQOfOeISFwY8/QpUqjhvw1i1l5IOD1U2jeHHH9a3RJIBhGFYXWfUM%0AXpPucHNzw9PTE7PZDID50iU8n3sOtyJFYNcuxxp3gEKFlKunUydo1gzOn3ds/2kEwzD0y0mvRP9N%0AUnr2rGfwGmdgNpvx9PRkZP/++LZqhffLL+OydGkcP7tTmDkTvvwStmxx/I0klRMzo0xpNdIdCX2u%0AtszgtYHXpFuCjxyh/LPPEvTOO5RbtAiSMBOyiyVLVITN1q1QvXryjJkK0AbeOSTFwGsXjSZdYv73%0AX3w9PAjq0wffHDkwh4Qk3+DvvAM+Pvi5u2M+eTKuXmYzfn5+yaeLJkOjDbwm3WE2mfBs1gzvevUo%0At3Ah3l98Eccnnyz06IHbu+/i2bQp5itXlF4xbiM3N7fk00OTodEGXpPuCBg8GO/cuXFZsQIyZVIx%0A7N7eBAQEJKseLt7eeLdsiWeTJgSfPYunpyfe3t5xons0KUPv3r0ZN24cAPPmzaNo0aLky5cPk8mU%0Awpo5Fm3gNemLn37CY9cuXDZsgFy5Yk/HiWFPLgwDl2XLGFmkCOUrVWLkyJHauKcSHkWnREZGMmLE%0ACLZt28adO3coUKCAzX2MGzeOWrVqkTVrVj799FMnapt4tIHXpB9OnYKhQ2HtWihWLKW1AcD84AG+%0ANWoQVKIEvoMGJa+bSGMREeHatWuEhoZSrZr9tYgqV66Mr68vHh4eSQpldCbawGvSB2Fh0K0bfPop%0A1KmT0toA//ncvb/8knIrVqh8NsOGaSOfAhw+fJh69eqRL18+unXrRmhoKOfOnaNq1aqAesJ76aWX%0A7OqzZ8+etGnThrx586ba6CFt4DXpg08+gXLlYMCAlNYklkf5bFxcXKBJE1yGDsX7zBkC/vorpVXL%0AUISHh9OxY0d69eqFyWSiS5curFq1iooVKxIYGAhASEgIW7duBaB27doUKFAg3tfgwYNT8lexG2v5%0A4DWa1M/69bB6NRw+nHyx7jbwlM9/9Ghctm3D4+hRePXVlFEqBXHUn8beyfKePXuIjIzkgw8+AKBz%0A5848H5ODKL6Z999//51kHVML2sBr0jY3b8K778Ivv0DBgimtjWUyZ1bJyerVgzZtEkx0ll5JKS/G%0AlStXKFkybrXQsmXLpowyyYx20WjSNsOGwVtvQZMmKa2JbZQooVIZvPsuRESktDYZguLFi3P58uU4%0A585byBdUo0YN8ubNG+9r4MCB8bbRi6wajaP5/XeVPOyzz1JaE/t4800oWRJ8fFJakwxB48aNyZIl%0ACzNnziQiIoLVq1ezf//+BOUDAwO5e/duvK+5c+fGykVGRhIaGkpUVBQRERGEhoYSHR2dHL+Szehc%0ANJq0yd27ULMmLFoEdkY/pArOn4f69WHnToiJ5EjrpOZcNAcPHuS9997jzJkztGvXDsMwqFy5Mn37%0A9qVChQpERESQKZN9893evXvz7bffxjm3dOlSevbs6UjVdbIxTQZkyBC4d08l9kqrzJoFK1fCn3+C%0AncYlNZKaDXxaRicb02Qs9u5Vi6pffpnSmiSNgQMhKkpVhtJonIBVA28YRhvDME4YhnHaMIxR8Vzv%0AYBjGUcMwDhuGcdAwjBaPXQs2DOPvmGv7HK28JgMSHa1m71OmpP6oGWtkzgzz5sGECXD7dkpro0mH%0AWHTRGIaRGTgJvARcBvYD3UXkn8dkcovI/Zj3tYA1IlIp5jgIqC8iCf73aheNxi6WLVNGcdeudOHW%0AANTmrGzZYMaMlNYkSWgXjXNIiovGWhx8A+CMiATHdLgc6ADEGvhHxj2GPMDNJ/WwMoZGYxt378Lo%0A0bBmjVOM+6U7l1jzzxrWnlzLpTuXYs+75HChfeX2dKrWieqFqzs+JG7iRKhWDfr1gxo1HNu3JkNj%0AzcCXBC4+dnwJaPikkGEYHYFJQHGg1WOXBNhqGEYUMF9EFiZNXU2GxtsbXn4ZGj71L5gk9lzaw4jN%0AIzhx8wSvVHmFoQ2GUtX1v8iWK3evsO7kOtr+0JZcWXPx2Yuf0aV6F8cZeldXGDcOPvwQNm1KVbtx%0ANWkcEUnwBXQGFj523AOYZUG+KXDysePiMT8LA0eApvG0EY3GKqdPixQqJHLlisO6ND80y8D1A6X4%0A1OLyw98/SHhkuEX56Oho2R60XWrMqSHtfmgnwaZgh+ki4eEi1auLrFvnuD6TGf1ddg4Jfa4x5y3a%0AcGsz+MtA6ceOS6Nm8QndLHYYhpHFMIxCInJLRK7GnL9hGMYalMtnx5PtvLy8Yt+7u7vj7u5uRS1N%0AhuPjj2HECChe3CHd+Qf702N1D9pVbkfgwEAK5LSeB9wwDNzLuXOo/yGm7ppK/QX1mfLSFPrW65t0%0AhbJmhenTVWRN27bqWKN5DH9/f/z9/e1rZMn6o1w4Z4FyQDbULLzaEzIV+W+xth5wNuZ9LiBvzPvc%0AQADQKp4xHH7H06Qz9uwRKVVK5MEDh3S35p81UtinsGw+szlJ/Zy4cUIqzKggn//5uURHRztEN2nV%0ASmTePMf0lczo77JzSOhzxYYZvMWLqg/aoiJpzgCjY871B/rHvP8Y+B9wGDU7fz7mfIWYG8KRmOuj%0AE+jfOZ+KJn0QHS3i7i6ycKFDult8aLEUm1pMDlw+4JD+rty5IrXm1pIPN34oUdFRSe/wwAGREiVE%0A7t9Pel/JTFr6Lvfq1UvGjh0rIiJz586VIkWKSN68eeX27dsprNnTONXAO/uVlv4pNM5n/fr1YjKZ%0A/juxcaOYKlaU9Q7wTU/fPV3KTisrJ26cSHJfj3P7wW1pvKix9F7b2zFG/o03RL74Iun9JDNp6bvc%0Au3dvGTdunEREREjOnDnl2LFjdrW/fv26dOvWTUqUKCH58+cXNzc32bt3r1N0TYqBTyeBxJr0gpub%0AG56enqrqUXQ05pEj8axUCbdmzZLU76rjq/hy95fseGcHz7g+4yBtFQVyFmDL21s4fes0E7ZPSHqH%0AEyfCV1/pzU9ORiTxJfvu3btHw4YNOXToECaTiV69euHh4cH9+/etN05GdC4aTarjUam7kVWr4jtx%0AIt4nT+JiRzHkJzl09RCtv2/N5h6bebb4sw7UNC437t+gwTcN8G7hzZu13kxaZ/37g4uL2rGbRkjN%0AG50OHz5M37594yQby5QpE+vWrePBgwfkzp2bhg0bxlZ1Sgz58+fH39+fZ5917P9YUjY6aReNJlUS%0AdPq0ABL0/fdJ6ufynctS6qtSsur4KgdpZplj/x6Twj6FZdeFXUnr6NIlkYIF1c80Qmr9LoeFhUmZ%0AMmVk+vTpEhkZKb/88otkzZpVxo0bJ8HBwWIYhkRF/edaq1Wrlri4uMT7GjRoULxjHD58WHLkyCF3%0A7txxuP4Jfa44IExSo0l2zGYzvv37E9SwIb67duHt4aHqmtpJaGQoHZZ3YMBzA+hUrZMTNH2amkVq%0AsqTDEjqv7Mzed/dSOn9p643io2RJ6N1bzeBnznSojimF8aljNnDJBPueEpxdsu/OnTu8/fbbeHl5%0AkTdvXrvaOhtt4DWpCrPZjOfo0XifO4fLkiV4162Lp6fnf8Wr7WDMtjGUzV+W0U1GO0nb+PGo4sGQ%0ABkPoubYn23puI5ORyKWukSOhenVVULxECccqmQLYa5gdhTNL9j18+JBXXnmFxo0bM2rUU7kYUxy9%0AyKpJVQQEBOBduzYuZcqAuzsuLi54e3sTEBBgVz/bzm1jZeBK5refnyLl1D52+5jI6Eim7Z6W+E6K%0AFYNevXTlpyTirJJ9YWFhdOzYkTJlyjB//nyn6Z8krPlwnP0ilfrtNClERIRI5coi27YluovbD25L%0A6a9Ky8bTGx2omP2cu31OXH1c5e9rfye6j/Xffium/PnjpGgwmUyyfv16R6joUFLrdzk8PFzKlCkj%0AM2bMkPDwcFm1alWsDz4oKOgpH7ytfbZv3146duwokZGRTtJckdDnig6T1KQ5li+HokXhxRcT3cXg%0A3wfT4ZkOtK7U2oGK2U/5AuXxecmHHmt6EBYZlqg+3F55Bc+yZTFPnAj8F2Hk5ubmSFXTNVmzZmX1%0A6tUsXbqUQoUKsXLlSjp37gyoSJTEPOHt2rULPz8/tmzZgouLS+wM394nTWejwyQ1qYeoKJUud/bs%0ARNdZXRm4kgn+EzjY7yC5suZysIL2IyJ0XtmZKoWqMPmlyYnqw/zPP3g++ywjd+zAd+nSRK1HJAep%0AOUwyLaNrsmrSBz/9pIz7zp2JSpkbEhpCtTnVWN11NY1KNXKCgonj+v3r1Jxbk209t1GraK1E9RHc%0Auzflly0jKCiIcuXKOVZBB6ENvHPQNVk1aR8RmDQJxo5NdD70cdvH0b5K+1Rl3AGK5C7CZy9+xgC/%0AAURLtN3tzWYzviIE5c+P78SJapevRmMD2sBrUge//64Me5s2iWp+6OohVgSuYFLLSQ5WzDG8V+89%0AwqPC+fbot3a1e+Rz954xg3KdOuFdrNh/qRw0GitoF40mddCsmapN2r273U2jJZrGixrTr34/+jzb%0AJ8mqhITAtWv/Hbu4qHXfpHLwykE8fvTg+KDjFMxpW8FwPz8/3NzclM/9n3/A3R3z4cMEHD6Mh4dH%0A0pVyINpF4xy0D16TtgkIgLffhlOnIIv9e+8WHlzI0qNL2fHOjkRvKoqKgj/+gMWL1cPE4wb9+nV4%0A/nl45x147TXIkSNRQwAweMNgIqMj+br914nroFMnaNECBg9OvBJOQht456Bz0WjSNu3bi8ydm6im%0ApocmKeJbRI5cPZLo4f/6S6R8eZF69URmzxa5dSvu9YcPRX76SeTll1XVwGXLEj2UmB6apNjUYnLw%0AysHEdbBnj0jZsqrEXypDf5edQ0KfKzbEwesZvCZlOXYMWrWCc+cgZ067m3+85WPMoWYWvLLA7rbR%0A0SrVy4wZsGgR2OLx+Ptv6NoVGjVSAT+5c9s9LF8f+JqVgSvZ1nNb4nbZtmihHifeftv+tk5Ez+Cd%0Ag46i0aRdfHzggw8SZdyDzcEsOryIT90/tbvtrVvQrh34+cGBA7YZd4DatWH/fuXSadAATpywe2je%0ArfcuV+9dZcPpDfY3BpWbZsoUdYfSaCygDbwm5bh4UVnY999PVHPPPzwZ2mAoxfPaV4j77l1o3Rqe%0AeQb8/aFUKfvGzZMHli2DYcOgZUs4e9a+9lkyZcHnJR8+3qry1djNyy+rtYqNG+1vqwGgd+/ejBs3%0ADoB58+ZRtGhR8uXLh8lkSmHNHIs28JqUY9YslUwrEbsyD1w5gH+wPyMaj7CrXWgodOgAzz0H06cn%0Aak0XUBGd770H48crD9OVK/a1b1+lPYVzFWbJ4SWJG3zECPjyS/vbaoD/UhRERkYyYsQItm3bxp07%0AdyhgR2GZcePGUatWLbJmzcqnnz79FPnjjz9StmxZ8uTJw2uvvRbn5hEWFkafPn3Inz8/xYsXZ9q0%0AJCSls4BVA28YRhvDME4YhnHaMIyn8mEahtHBMIyjhmEcNgzjoGEYLWxtq8nA3L2rHN9Dh9rdVEQY%0AuWUkXs29yJMtj83tIiNVFKarK8yZk+j9VHHo3x/69lVPBPZM/gzDYGqrqUzwn8C98Hv2D9y1q/IP%0AHTlif1sNkLSSfQCVK1fG19cXDw+Pp9ZSAgMDef/99/nhhx/4999/yZUrV5xMlF5eXpw9e5YLFy6w%0Afft2fHx82LRpU5J/p6ewtAILZAbOAOWArMARoNoTMrkfe18LOGNrW9FRNBmXadNEunRJVFO/U35S%0AfU51iYiKsKtd//4qEiY0NFHDJkh0tMjw4SKNG4uEhdnXtvsv3eVT/08TN/CkSSI9eiSurRNIzd/l%0AQ4cOybPPPit58+aVrl27Srdu3eTNN9+U3Llzi2EYkidPHmnZsmWi+u7Ro4d4eXnFOTd69Gh56623%0AYo/Pnj0r2bJlk3v37omISIkSJWTLli2x18ePHy/dunWLt/+EPlcckE2yQYzBDhaRCGA50OGJG8Tj%0AVWbzADdtbavJoERGKv/ICPvcK6AmJGP/GMvnL35Olky2+1d++knFua9aBdmz2z2sRQwDfH2hUCGV%0AacEePnvxM2buncnth4kosN2/P6xfD5cu2d82AxEeHk7Hjh3p1asXJpOJLl26sGrVKipWrEhgYCAA%0AISEhsfVYa9euTYECBeJ9DbZx/8Hx48epU6dO7HGFChXInj07p06dwmQycfXq1TjXa9euHauLI7H2%0ADSkJXHzs+BLQ8EkhwzA6ApOA4kAre9pqMiCrV6uVzYb2/zusObEGwzDoWLWjzW3OnVOeoE2bwFkV%0A1TJlUpuknn1WLby2tjFTcaWClXit6mt8uetLvFt62zdogQIqVHLWrLRRnNtRhVfsDMV0dsm++Lh3%0A7x758+ePcy5fvnzcvXuXe/eUS+7x64+uORprBt6mT1JE1gJrDcNoCnxnGEZVe5Tw8vKKfe/u7o67%0Au7s9zTVpCRG1OPjJJ3Y3jYqOYvz28fi87GNz/HhEhPK7e3pCvXp2D2kXrq7w7bfw1ltw+LDt6Q3G%0ANhtLvQX1GNZoGIVzF7Zv0GHD1DbbsWOdd/dyFCkUI+/Mkn0JkSdPHkJCQuKcCwkJIW/evOTJo9aN%0A7ty5g6ura5xrlvD398ff398uPawZ+MvA41WDS6Nm4vEiIjsMw8gCFIyRs6nt4wZek87ZvVsFob/6%0Aqt1NVwSuIF/2fLSt1NbmNuPHK8MbM3lzOi++CH36qOCgDRvUzN4aZV3K0r1md6YETGFqq6n2DVih%0Aghp0yZJELVhnBBIq2VepUqV45WvUqMGFCxfivfb2228zd+7cp84/OeGoUaMGR48ejT0+e/Ys4eHh%0AVKlShdy5c1O8eHGOHDnCSzF1D44ePUrNmjUt/h5PTn7ji9x5CksOetQN4CxqoTQb8S+yVuS/nDb1%0AgLO2thW9yJrx6NJFZMYMu5tFREVI5ZmVZevZrTa32blTpHhxkX//tXu4JBEeLtKokUp7YCuX71yW%0AApMLyOU7l+0fcMcOkUqVROwsO+doUut32Rkl+0REIiIi5OHDh9K9e3cZO3asPHz4MLafwMBAyZcv%0An+zYsUPu3bsn3bt3l+7du8e2/eSTT6R58+ZiMpnk+PHjUqxYMdm0aVO84yT0uWLDIqstuWLaAidR%0AETGjY871B/rHvP8Y+B9wGNgBPG+pbTz92/3BatIo58+LFCggEhJid9PFhxaL+1J3iY6Otkk+PFyk%0AZk2RFSvsHsohBAaKuLqKXLbDXg/fOFwG+Q2yf7DoaJH69UV++83+tg4kNX+XDxw48FQUzbhx4yQ4%0AOFgyZcqUKAPfq1cvMQwjzmvZY4mKfvzxRylTpozkzp1bOnbsKCaTKfZaWFiY9OnTR/LlyydFixaV%0AadOmJThOUgy8zkWjST5GjYLwcLBzU0dkdCTPzH6GpR2W0rRsU5va+PioqJlHaeZTgjFj1ALv8uW2%0AyV+/f52qs6tybMAxSuYrab3B43z3nVoA2LLFfkUdhM5F4xx0umBN6uf+fShXDvbuVX5jO/j26Lcs%0AObKE7b222yQfHKx2qu7dCxUr2q+qo3jwAGrWhHnzbI+qGbFpBFESxfQ20+0bLCxMfb5bt6q6timA%0ANvDOQScb06R+vv8eGje227hHRUfhvcObcc3G2SQvAkOGwIcfpqxxB8iVS+2YHTgQHj60rc1HjT/i%0A26Pfcu3eNevCj5M9uyqYMmOG/Ypq0i3awGucj4gyPIkIZVkZuBLXXK68WO5Fm+TXrVPJv0aOtHso%0Ap9C2LdSvD198YZt88bzFeavWW3y5KxF5Zvr3h59/VlFKGg3awGuSgy1bIHNmFc5nB9ESHTt7tyXu%0APTwcPvoIZs6EbNkSq6zj+eormDsXEoi8e4qP3T5m0eFF3Hxw07rw4xQtqjKpLVxov5KadIk28Brn%0AM3Ommr3budq55p815Mqai9YVbXNgz5unUgDHhBanGkqVUm4aW9MYlM5fmjdqvMG03YnIMPjBB8ov%0AFJmINMSadIdeZNU4l7NnVfmj8+eVU9pGRIR6C+rxmftnvPLMK1blTSZl3LdvT7E1RovcvQtVqqj0%0A97bsqA02B1N/QX3ODDlDgZy2p7AFoEkTtQjRuXPilE0kepHVOehFVk3qZe5cVV7ODuMOsOH0BqIl%0AmvZV2tsk7+2tCmKnRuMOKouAl5fKr2aLDSznUo5Xn3mV2ftm2z/YkCGqnqAmw6Nn8Brncf8+lCkD%0ABw+qED4bERGaLGnC0AZD6Vqzq1X5c+dUOpbAQChWLAn6OpnISFXyz8cH2ttw3zpx8wTNljQj6IMg%0Acmezo/hrRIT6vDduhFq1Eq2vvegZvHPQM3hNqsHPzw+z2awOvv8emjTB7OKCn5+fzX3suLCD6/ev%0A83r1122SHzNG5dxKzcYdVPUoX18V4WOLi7yqa1WalW3GwkN2LppmzaoiavQsPlWTKVMmzp0759xB%0ArG11dfaLVLy9WWM/JpNJBg4cKKbbt0Vq1hTTmjXq+LFt2tZo830bWXhwoU2yhw6JFCsmElNHIdUT%0AHS3SvLnI4sW2yR+4fEBKfVVKwiLtrCRy9aqIi4vI7dt265hY9HfZPgzDkLNnz1qVS+hzxQEFPzQa%0Au3BxccHb2xvPd94h+MEDPDdvxtvbGxcb664eunqIY/8e4+3ab9skP348jB4Nue3wYKQkhgETJ8Kn%0An6qwTmvUL1Gf6oWr893R7+wbqFgxaNdOZZlMQeI80cVgNpvteqJzRB9JJTKtRiVZuwM4+4W+66dL%0Aglq3FkCCgoLsavf6ytflq11f2SS7e7dI6dIiDx8mQsEUpnVrkblzbZP1D/KXyjMrS2RUpH2D7N4t%0AUqGCSKSd7RJJfN/l2Ce6mCe4J49tIal9lC1bVqZOnSq1a9eW/PnzS9euXSU0pm7jggULpFKlSlKw%0AYEF59dVX5cqVK7HtDMOQOXPmSKVKlaRChQri7+8vJUuWFB8fHylcuLAUL15c1qxZI35+flK5cmUp%0AWLCgTJo0Kbb93r17pVGjRuLi4iLFixeXwYMHS3h4eJz+nT2D1wZe43BMx47JwOzZJejYMbu+iCdu%0AnJDCPoXlbthdm+RfeklkwYKkaJpy7N8vUrKkyIMH1mWjo6PlhW9ekOXHlts3yKMsk+vXJ05JO0no%0Au/zIIAcFBdlt3B3RR7ly5aRhw4Zy9epVuX37tlSrVk2+/vpr2bZtm7i6usrhw4clLCxMhgwZIs2a%0ANYttZxiGtGrVSkwmk4SGhsr27dslS5YsMnHiRImMjJSFCxdKoUKF5M0335R79+5JYGCg5MyZU4KD%0Ag0VE5ODBg7J3716JioqS4OBgqVatmkyfPj1O/9rAa9IUJpNJBtavL6b33vvv2MYvZJ+1fcRru5dN%0A42zfrianj02I0hwdO4p8ZdvDivx64lep+3Vdm9Mlx7J4sUjbtvYrlwgsfZeDgoIS9UTniD7KlSsn%0AP/zwQ+zxxx9/LO+//7707dtXRo0aFXv+3r17kjVrVjl//ryIKAO8ffv22Ovbt2+XnDlzxv4N7ty5%0AI4ZhyL59+2Jl6tevL2vXro1Xj2nTpslrr70We6x98Jo0R4C/P94XLuDy4YfAfz75gIAAi+0u37nM%0AmhNrGNzAelFjERg3TsWVZ83qCK1Ths8+U6VUY0p0WsSjigcRURFsOWdnOuBu3WD/frXhLIUwm834%0A+voSFBSEr6/vU/705Oij2GMhVrly5eLevXtcuXKFMmXKxJ7PnTs3hQoVilP9qXTp0nH6KVSoUGza%0AjJw5cwJQ9LHajDlz5uT+/fsAnDp1ivbt21O8eHHy58+Pp6cnt5I5T5A28BqH4vHgAS61akG1arHn%0AXFxc8PDwsNhu2p5p9KrTi0K5ClkdY/NmuHkT3nwzyeqmKLVqqfQ8s2ZZl81kZGKU2ygm75xs3yA5%0Ac0Lv3iqPQwpgNpvx9PTE29ubcuXKqQV4T0+7DLQj+oiPEiVKcP78+djj+/fvc+vWrTj1W22t/Rsf%0AAwYMoHr16pw5c4aQkBC8vb2Jjo5Oks72og28xrHMmQODBtnVxPTQxOLDixn+wnCrsiIqAmX8eJW/%0ALK0zbpyC3AwzAAAgAElEQVSqf2LLLL5bzW6cNZ1l3+V99g0yYAAsXaoS1CczAQEBcaKobH2ic3Qf%0Aj6O8G9C9e3eWLFnC0aNHCQsLY8yYMTRq1CjOrD4p3Lt3j7x585IrVy5OnDjBvBS4yWoDr3EcR46o%0AlIl2FtSeu38urz7zKqXzl7Yqu20b3L4Nb7yRWCVTF9Wrq1l8PHWcnyJr5qyMeGEEUwKm2DWG3z//%0AYK5fP05pqeQKM/Tw8HgqRNaWJzpH9/E4hmFgGAYtW7Zk4sSJdO7cmRIlShAUFMTyxz6j+GbvT56z%0ANMOfOnUqP/74I/ny5aNfv35069YtjnxSng5sxpqT3tkv9CJr+uHdd0UmTrSryYPwB1LUt6gEXg+0%0AKhsdLdKkich33yVWwdTJsWMiRYvatlnrXtg9KexTWP658Y/N/ZtMJhno4SGm2rVFoqMTFapoC/q7%0A7BwS+lxxxCKrYRhtDMM4YRjGacMwRsVz/S3DMI4ahvG3YRgBhmHUfuxacMz5w4Zh2PlcqUlTmEzw%0Ayy/w3nt2NVtyZAkNSzWkeuHqVmX9/eHff9W6YXqiZk1o2hS+/tq6bO5suRncYDC+Ab429+/i4oL3%0At9/iGRxM8Nq1sf5sWzefadIwlqw/kBk4A5QDsgJHgGpPyLwA5I953wbY89i1IKCglTEcf8vTJD9f%0AfSXSvbtdTSKiIqT89PIScCHAJvnmzUWWLk2EbmmAo0dVyoX7963L3rx/UwpMLiCXQi7ZNUbQmDFJ%0ADlW0hP4uO4eEPlccMINvAJwRkWARiQCWAx2euEHsFpGQmMO9QKkn+kihmvaaZCM6WkVp2Lm4+nPg%0Az5TKV4rGpRtblf3zT7h0Cd56K7FKpm5q14YXXoAFC6zLFspViF51ejFtj+0FQcxmM77XrhGUNy++%0An32W5AgUTdrAmoEvCVx87PhSzLmE6AtseOxYgK2GYRwwDMO+Z3dN2mHbNhWO19i6oX6EiDAlYAqj%0A3J7y+sXLxIkqa2SWLIlVMvUzfrxKJRwaal12+AvDWXJkCaaHJquysWGGX35Juddfx7tMGYeEGWpS%0AP9a+LjYndzYM40WgD+D22Gk3EblqGEZhYIthGCdEZMeTbb28vGLfu7u74+7ubuuwmtTA3Llq9m5H%0AVMCms5uIkijaVW5nVXb3bjhzBt62Lf9YmqVuXXjuOVi8WJX4s0Tp/KVpX6U98w7MY0zTMRZl44QZ%0ADhyIy+uv433wIAEBAYmORNEkP/7+/vj7+9vXyJL/BmgEbHzseDQwKh652ihffSULfU0ARsRz3lGu%0AKk1KcP68SMGCIndtyx/zCPel7vLdUdvCYdq1E5k3LzHKpT327BEpU0YkzIbswP/7939S1LeoPAi3%0AIaHN4zRoIPLbb4lT0AL6u+wcEvpccYAP/gBQ2TCMcoZhZAO6Ar8+LmAYRhlgNdBDRM48dj6XYRh5%0AY97nBloBx+y7/WhSPQsWQI8ekCePzU32XtpLkCmIrjWsV2s6dAiOHlWbMTMCDRuq2rLf2ZAduEaR%0AGjQo2YClR5baN8jAgWpDmhN4FGOuX457JenvIWLZC2MYRltgOiqiZpGITDIMoz+AiMw3DOMb4DXg%0AQkyTCBFpYBhGBZThB+UK+kFEJsXTv1jTQZNKCQ9XJfn8/aFqVZubdVrRiRfLvciQhkOsynbuDM2a%0AwQcfJEHPNMaOHaqM7YkT1tccdl3cxdtr3ubk4JNkyWTjAsXDh+rvtmcPVKyYdIU1KYJhQ8k+XZNV%0Ak3h++gm++UYtstqIPXVGAwOhZUtVc9XOmt1pnubNoV8/26KGmi5pyqDnB9Gtph0bBD7+WEU/TZ2a%0AeCU1KYotBl6nKtAknrlzra8GPoFPgA+DGwy2qYi0tzd8+GHGM+4AY8eq39+W3FSfuH3C5J2TsWui%0A9P77Kj/Nw4eJ1lGT+tEGXpM4/v4bgoKgQwfrsjFcDLnI2hNrbUoJfPo0bNmi8mRlRF56CfLmhVWr%0ArMu2q9yOaIlm45mNtg9QoQI0aBAnP40m/aENvCZxzJ0L/fvbFZj+1e6veKfuOxTMWdCq7KRJMGQI%0A5MuXFCXTLoahMk16e6sMmpZlDT5p8gmTA+xMJTxokG1ZzjRpFm3gNfYTEgIrVsC779rc5OaDmyw7%0AusymlMDBwbBunTLwGZlHIeq2JH18o8YbXAy5yK6Lu2wfoE0blVh///7EKahJ9WgDr7GfZcugdWso%0AXtzmJrP3zaZTtU6UzGdpI7TCx0c9HBQokBQl0z6GoXzxn39ufRafJVMWRjYeaV9BkMyZlQ/MSSGT%0AmpRHR9Fo7ENEVWtauFClQLSBe+H3KD+jPAF9AqhSqIpF2StXVHbFkyehcGFHKJy2iY5Wn8fMmcov%0Ab4nQyFDKzyjPlre3ULNITdsGuHkTKldWW4ULWa+mpUk96CgajeP54w9VCLVJE5ubLDi4APdy7laN%0AO6iovd69tXF/RKZMKgfP559bl82RJQcfNPzAvlm8q6sq0LJ4ceKV1KRa9AxeYx+dO8PLL6swOxsI%0AiwyjwswK/NrtV+qXqG9R9vp1tV/qf/+DEiUcoWz6IDJS7W5dutT6Q1NIaAgVZ1Zk33v7qFCggm0D%0A7NunkuyfOaPuKJo0gZ7BaxzLpUuwfbtdOXuXHV1G7aK1rRp3ULVJu3XTxv1JsmSB0aNVRk1r5M+R%0An/efex+fAB/bB3j+eShYEDbaEWapSRPoGbzGdsaNA7MZZs2ySTwyOpJnZj/Dso7LaFLGskvn1i2o%0AUgUOH1a76DVxCQ9XrvIVK6BRI8uyNx/cpMqsKhwbcMymRW0AlixRFbmSoU6rxjHoGbzGcYSFqYVV%0AO4p6LP/fckrlK2XVuANMn668P9q4x0+2bLbP4l1zudKrTi++3P2l7QN066ZcNWfPJl5JTapDz+A1%0AtvHjj2ohbutWm8SjJZpa82oxrfU0WlVsZVHWZFKz0/37oXx5RyibPgkLg0qVYM0alTfeEpfvXKbW%0AvFqcGnIK11yutg2g89OkKfQMXuM4Zs+2a/a+7sQ6cmXNxcsVXrYqO3OmCuTQxt0y2bPDqFG2zeJL%0A5itJl+pdmLFnhu0DDBigVnIfPEi0jprUhZ7Ba6xz6BB07KjSOtqQmkBEaPBNA8Y0GcNr1V6zKBsS%0Aomalu3ernxrLhIaqDL/r18Ozz1qWPWc6R4OFDTg79Cz5c+S3bYBXXlF/6759k66sxqnoGbzGMcyZ%0Ao8Iibcw7s/HMRkIjQ+lQ1XoistmzoW1bbdxtJUcOGDnStrj4CgUq0K5yO2bvm237AIMGqT+KnnSl%0AC/QMXmOZ27fVlPHkSShSxKq4iPDCohcY/sJw3qjxhkXZO3eUYf/rL7vqhWR4HjxQf5KNG6FOHcuy%0AJ2+epOmSppwdepa82fNa7zw6WgXdL1tmVxF1TfKjZ/CapLN4MbRvb5NxB9h6bit3wu7QuVpnq7Iz%0AZ0KrVtq420uuXGo99NNPrcs+4/oML1V4ibn7bcwamSmTyvE/245ZvybVomfwmoSJilJT7OXLVbFQ%0AK4gITZc0ZeDzA3mz1psWZR/53gMCVPy7xj4ePlSz+A0boG5dy7KB1wNp8W0Lzg09Z1OhFcxmteJ9%0A/LhdCeU0yYuewWuShp+fSgpjg3EH8A/258aDGzYV054xA9q108Y9seTMqSJqvLysy9YoUoPmZZvz%0A9YGvbevcxQW6doX585OkoyYVICIWX0Ab4ARwGhgVz/W3gKPA30AAUNvWtjEyokmlvPSSyHff2Sze%0AfElzWXZkmVU5k0mkUCGR06eTopzmwQORkiVFDhywLnv02lEpNrWY3A+/b1vn//ufSLFiImFhSVNS%0A4zRibKdF+21xBm8YRmZgdoyhrg50Nwyj2hNi54BmIlIbmAgssKOtJrXyzz9w7Bh06WKT+J/Bf3Lp%0AziWrrhlQOWdeeUVHziSVnDnhk09sm8XXLlqbF0q9wPwDNs7Ka9SA6tVV+gJNmsWai6YBcEZEgkUk%0AAlgOxIl9E5HdIhISc7gXKGVrW03qws/PD7PZrA5mz4Z+/TA/fIiflfwkIsJ4//GMbTaWLJksh1Le%0Avq2iLseOdZTWGZt334UjR2wryjSh+QR8dvlwP/y+bZ0PGWJz3iFN6sSagS8JXHzs+FLMuYToC2xI%0AZFtNCuPm5oanpyfm8+fhp58wd++Op6cnbm5uFtv9EfQHV+9epUftHlbH8PWF115TC4SapJMjB3h6%0A2nbDrFOsDk3KNLE9ouaVV+DqVThwIGlKalIMaztXbA5vMQzjRaAP8Mga2NzW67FnTHd3d9zd3W1t%0AqnEgLi4ueHt74/nKK4x0c8N39my8vb1xcXFJsM2j2fuE5hOszt6vXYMFC9SMU+M4+vRRN05/f7D2%0A1fFq7kWLb1vw/nPvW4+Lz5xZhUzOmqXi4jUpir+/P/7+/na1sRgmaRhGI8BLRNrEHI8GokVkyhNy%0AtYHVQBsROWNnW7GkgyaZiY4muGJFygcHExQURLly5SyKbzyzkeGbhnNswDEyZ8psUXbIEFUM6quv%0AHKivBoDvv4d582DnTlXL1RJvrnqTmkVqMqbpGOsd37qlFkts3OimST4cESZ5AKhsGEY5wzCyAV2B%0AX58YpAzKuPd4ZNxtbatJfZh//hnf+/cJOncOX1/f/3zy8SAijN8+Hi93L6vGPShIJaQcPdrRGmsA%0AundXO4NtSec+ofkEpu2ZRkhoiHXhQoXUQrsOmUybWAuzAdoCJ4EzwOiYc/2B/jHvvwFuAYdjXvss%0AtY2nf2dEEGkSgclkkoGlSolp3rz/jgcOFJPJFK/8ryd+lVpza0lUdJTVvnv1Ehk3zpHaap5k7VqR%0A2rVFoqz/OaTnmp7itd3Lto6PHdMhk6kQbAiTtGrgnf3SBj71sH7OHDEVKSISGhp7zmQyyfr165+S%0AjYqOkrpf15XVx1db7TcwUKRwYRGz2aHqap4gOlqkQQORn36yLnvm1hkpNKWQ3Lx/07bOW7a0a0+E%0AxvnYYuD1TlZNLB5Hj+IyaJBKPB6Di4sLHh4eT8muDFxJtszZ6Fi1o9V+x46Fjz6C/DZmrNUkDsOA%0AL75Qn3d4uGXZigUr0qV6F6YETLEs+Ihhw9T2Y71elqbQBl6juHULVq6E/v2tikZERTD2j7FMajkJ%0Aw8qKXkCAirIbMsRRimos0bKlWhNdsMC67Ljm41h0eBGX7lyyKusngvnWLdi1K/ac2Wy2ukdCk7Jo%0AA69RLFwIHTpA0aJWRRcdXkSFAhVoUb6FRTmR/3KX58zpKEU11pgyRVV9CrGyhloibwn61evHZ39+%0AZrVPt6ZN8SxZErOvL6CMuy17JDQpjDUfjrNfaB98yhMeLlKqlMihQ1ZF74fflxJflpD9l/dblV21%0ASqROHZHISEcoqbGHXr1ExoyxLnf7wW1x9XGVEzdOWJU1XbggA7Nnl6CdOy0uvmuSB2zwwet0wRqV%0ADnjePPjzT6uiU3ZO4eDVg6zsstKiXESESmcyZw68bL0sq8bBXLyo0ggfPQqlSlmWnbxzMoeuHrL6%0ANwUI7tuX8osX27RHQuNcdLpgjXVE4MsvYfhwq6K3H95m6u6pTHzRetXnhQtVSnFt3FOG0qWhXz+Y%0AMMG67NCGQwm4GMD+y5YT2pjNZnzDwwnKnx9fb2+LeyQ0qQNt4DM6O3eqAg+vvGJV1PsvbzpV7cQz%0Ars9YlAsJgc8+U75gTcrxySeqOPfRo5blcmXNhVdzL0ZuGUlCT9OPfO7es2ZRrlUrvCtUUHmLtJFP%0A3Vjz4Tj7hfbBpywdO4rMmWNV7FHc9LW716zKjhgh0revI5TTJJU5c0RefFHFyFsiIipCasypIetO%0ArIv3+vr16//zue/ZI1KunJhu3Ih3j4QmeUD74DUWOX1aFVYODobclku5vfHzG9QpWgfPZp4W5U6d%0AUl0GBtoUkKNxMpGR8Oyzqn5rp06WZTee2ciwjcM4NuAYWTNntSzs5gYffgivv+44ZTV2oX3wGsvM%0AmKEctVaM++6Lu9l9aTcfvvCh1S5HjFCl5LRxTx1kyQLTp6uNZqGhlmVbV2xNmfxlWHDQhiD6ESPU%0A2o0mVaNn8BmV27dVUnYrhZVFhMaLG/N+/ffpVbeXxS43bYJBg9Ts/bHNsJpUwGuvQYMG1pO9Hb12%0AlFbft+LU4FPkz2Fh63FUlCqo+/338MILjlVWYxN6Bq9JmPnz1cYmC8Yd4OfjPxMaGWq1mEdEhHpi%0A/+orbdxTI1Onqgn3lSuW5eoUq4NHZQ8m7ZxkWTBzZpW+QM/iUzV6Bp8RCQ1VMYybN0OtWgmKPYh4%0AQLU51VjaYSkvln/RYpfTpsHvv6tZvLV85JqUYfRoFR///feW5a7cvULtebXZ8+4eKhW0UDj33j31%0Af7RrF1Su7FhlNVbRM3hN/Hz3HdSrZ9G4A/gG+NKwZEOrxv3yZfD2VoV/tHFPvXh6wo4dqvKTJUrk%0ALcFHjT9ixOYRlgXz5IEBA/QsPhWjZ/AZjagoqF5dZaNq3jxBsfPm89RbUI9D/Q5R1qWsxS67dlUT%0AuM8/d7SyGkezZo0y9EeOQLZsCcuFRYZRY24N5rSbQ+tKrRMWvH4dnnkGTpzQK+vJjJ7Ba55m3Too%0AUACaNbMoNnLLSIY2GGrVuG/eDPv3wxgbqr9pUp6OHZVXZdo0y3LZs2RnWutpDNs0jIioiIQFixRR%0A5aRmznSsohqHoGfwGQkRaNRIxTFaCIr2D/an99re/DPoH3JmTTgNZGio8vJMmwbt2ztDYY0zOHdO%0ARdQcPAhlLdy/RYR2P7ajVYVWlkNkz56Fhg1VXca8Vgp5axyGnsFr4vLXXyotQYcOCYpEREUw9Peh%0ATG011aJxB/DxgZo1tXFPa1SoAB98oF6WMAyD6a2n88XOL7h271rCghUrqkT0Cxc6VlFNktEz+IyE%0Ah4d6Rn/vvQRFvtz1JZvPbWbjWxstFvP45x9o2hQOHYIyZZyhrMaZhIWpbJOffw6dO1uWHb11NBfu%0AXOCHTj8kLHTwoPrfOnvWsnNf4zAcMoM3DKONYRgnDMM4bRjGqHiuVzUMY7dhGKGGYYx44lqwYRh/%0AG4Zx2DCMffb/ChqHcfQoHD4Mb7+doMiFkAtM2jmJOe3mWDTu0dHw7rsqoZg27mmT7Nnhm29Upa3b%0Aty3Ljms+joALAWw9tzVhofr1oWpV+MHCTUCT7Fg08IZhZAZmA22A6kB3wzCqPSF2CxgCTI2nCwHc%0AReRZEWngAH01ieWLL9T28hw5EhT5YOMHDG041HLsMzB3LmTKBO+/72glNcmJm5tKJTPCSjRkrqy5%0AmN1uNgP9BhIaaSHfwZgxMGmSitTSpAqszeAbAGdEJFhEIoDlQBwHrojcEJEDQEJL7ToyOqU5eRK2%0Ab7dYb/W3k79x/MZxRrk99ZAWh/PnVeKqb75RRl6TtvniC/WvsXmzZbn2VdpTs0hNfAJ8EhZydwdX%0AV/jlF4fqqEk81r6iJYGLjx1fijlnKwJsNQzjgGEYCTt+Nc5l0iT1LJ4nT7yX74ffZ8jvQ5jbbi7Z%0AsyScZ0BE3SOGD1ehz5q0T548KmtFv35qY6olZrSZwcy9Mzl963T8Aoahguy/+EL9s2hSnCxWrif1%0Ar+QmIlcNwygMbDEM44SI7HhSyMvLK/a9u7s77u7uSRxWE0twMPz2G5w5k6DIuO3jaFKmCS0rtLTY%0A1aJFal/LRx85WEdNitK6Nbz4oiqQPm9ewnKl85fGs6kn/db3Y1vPbWQy4pkftmsHY8eqSiM2FJHR%0A2I6/vz/+1rYhP4mlZPFAI2DjY8ejgVEJyE4ARljoK97r6IIfzmXAAJHRoxO8vPvibik2tZjcuH/D%0AYjdnz4q4uooEBjpaQU1qwGwWKVNGZMMGy3KRUZHScGFD+Xr/1wkL/fyzSMOG1quMaJIENhT8sOai%0AOQBUNgyjnGEY2YCuwK8JyMbxtRuGkcswjLwx73MDrYBj9t1+NEni6lVVUPvD+DephEWG0WddH2a0%0AmYFrLtcEu4mKgp491Rpa9erOUlaTkuTPD0uXquioW7cSlsucKTOLXl3E2O1juRhyMX6hTp1U3cY/%0A/nCKrhrbsRoHbxhGW2A6kBlYJCKTDMPoDyAi8w3DKAbsB/IB0cBdVMRNEWB1TDdZgB9E5KkcpDoO%0A3ol8+KHyhU6fHu/lsX+MJfBGIKvfWG0xLHLKFNi4EbZt0wur6Z3hw1XyuOXLLSeOm/jnRHZf2o3f%0Am37x/+98951aiff31xnonIQtcfB6o1N65epVqFFDVd+IJ+f7kWtHaPVdK46+f5TieRPOCX/0KLz0%0AEhw4YHlbuyZ98PChCmkfOxbefDNhuYioCJ5f+DwjXhjB23Xi2VsRGake9+bPVw5+jcPRqQoyMlOm%0AQK9e8Rr3sMgweq/tjc/LPhaN+/370K2bKuKhjXvGIGdOlS9+2DCVsyYhsmbOyuIOixmxeQSX71x+%0AWiBLFhg/HiZM0BE1KYiewadHrlxRWcACA6FYsacuf7L1E07eOmnVNdOnj/K/L1vmTGU1qZEZM5Sh%0ADwiwnHlg4p8T2XFhBxt7bHw6qiYyUj1Fzp2rctVoHIqewWcg/Pz8MJvN6mDyZOjdG3OOHPj5+cWR%0A23F+B98e/ZYF7RdYNO4//KAK9cyZ40ytNamVoUOhRAnrNVxHNx3NnbA7zN0/9+mLehaf8lgLs3H2%0ACx0m6RBMJpMMHDhQTIGBIgUKiOnkSXVsMsXKhISGSPnp5eXXE79a7OvUKRUSefiws7XWpGZu3hQp%0AXVpk/XrLcidvnhRXH1f558Y/T1+MjBSpWlVkyxbnKJmBwYYwSe2iSUeYzWY8mzVjZMOG+GbLhre3%0ANy4uLrHX+67rSyYjEwtfTTit68OHKkdJ374waFByaK1JzezcqfLV7NtnObHcvP3zWHxkMbv67CJr%0A5qxxL/70E8yerTrTETUOQ0fRZDSCgwmuW5fyISEEBQVRrly52EsrA1cyZtsYDvc/TN7s8RdlEIF3%0A3lGpZH/8UX8XNQpfX1i5UtVzTShXnYjQ/qf21CpSi8kvTY57MSoK6tRRC/8eHs5XOIOgffAZDPOY%0AMfhWrkxQUBC+vr6xPvlzpnMM3jCYFa+vSNC4g9qmfuiQCl/Wxl3ziI8+UmX+Bg1K2JVuGAZLOyzl%0A+7+/Z/PZJzKXZc6sqrKPGaNyTWuSD2s+HGe/0D54h2DatUsG5sghpuBgdRzjk//35r/y/ILnZfru%0A6RbbBwSIFCkicuZMcmirSWvcvStSvbrI/PmW5bYHbZfiU4vL1btX416IjhZp1Ejkhx+cp2QGA+2D%0Azzj4vfACbu3a4TJuXOw5s9lMn1l9iKocxdquaxOMmrl6FZ5/HhYsULmiNJr4OHUKmjRRddtfeCFh%0AOS9/L3Ze2MmmHpvInClz7Hm/SZNwW7AAl5MnY2MvzWYzAQEBeGjXjd1oF01GYe9ePC5dwuWJNI87%0Ar+/kYN6DLH51cYLG/cEDePVVGDBAG3eNZapUgSVLVIm/8+cTlhvXbByR0ZF8seOLOOfdBgzAMyoK%0A86xZQExQgKcnbm5uzlQ7Y2Ntiu/sF9pFk3RatBBZsCDOqdO3TksR3yKy68KuBJtFRYl06iTSs6dO%0A/KexnWnTRGrWFAkJSVjm8p3LUuLLEvL76d/jnDdt3y4Dc+WSoOPHnwrj1dgH2kWTAdi0Se1KCQxU%0AG0tQBTxeWPQC7z/3PgOfH5hg09Gj1U7FLVtUjU6NxhZEYOBANYv/9dfYf7un2HF+B6///Dp7+u6h%0AfIHyseeDPTwov2HDU5FeGvvQLpr0TlSUCnGYMiX2WyYivPfbe9QrXo8Bzw1IsOmSJfDzz7B6tTbu%0AGvswDJg5U2UieJSwND6alm2KZ1NPOq3sxIOIB4Byy/gWKECQiwu+n3763+5rjVPQBj4ts3gxFCwI%0AHf4rkztj7wxO3DzBPI95Cfrd/fzU7N3PT5XQ1GjsJWtWNUH48081v0iIIQ2GUL1wdfqv74/JZMLT%0A0xPv2bMp9847eAOenp7ayDsR7aJJq9y9q1a91q9X+V2BTWc20Xtdb3b12RXnkfhxdu1S94P166Fh%0Aw+RUWJMeuXJFRdaMHauS08XHg4gHuC12o+6dukx7b5raXX37NlStinndOgJu39ZRNIlA72RNz4wd%0ACxcuwLffAnD8xnHcl7qzuutqmpRpEm+TwEBo0UJlh2zTJjmV1aRnTp2C5s1V6vdXX41f5tKdSzT6%0AphGz282mY9WO6uT06bB5M2zYkHzKpiNsMfA6iiYtcuGCSMGC6qeIXL93XSrMqCDLjixLsMm5cypx%0A1HffJZeSmozEvn0ihQuLbN9uQebSPnH1cZVDVw6pE2FhIpUqiWzenCw6pjdwQE1WTWpk9GgVuF66%0ANGGRYXRa2YmuNbrSs07PeMUvXFDpuEeNgh49kllXTYbg+edVvpo33lCRWfHKlHyeue3m0mF5B67c%0AvaI2O02ZouoERkYmr8IZBO2iSWv89Zey0v/8Q3SunHT7pRuCsOL1FU8XXEDV12zeXOURSaD2tkbj%0AMDZvVv+ev/2W8BrPpB2TWBG4gj97/0n+7Png5ZeVb2fo0ORVNo3jkDBJwzDaGIZxwjCM04ZhjIrn%0AelXDMHYbhhFqGMYIe9pq7CQyEoYMgalTkVy5GLZxGNfvX+e7176L17hfu6Zm7v36aeOuSR5atYKl%0AS5W9PnAgfplPmnxCkzJNeG3Fa4RFhauYy4kT4fr1ZNU1I2DRwBuGkRmYDbQBqgPdDcOo9oTYLWAI%0AMDURbTX2MH8+FCoEXbowJWAKf57/k3Xd1pEjy9M5XC9cgGbN4O234eOPU0BXTYalXTtYuFBlBt61%0A6+nrhmEwo80MCuYsSM+1PYmuVhV69rRePkpjN9Zm8A2AMyISLCIRwHKgw+MCInJDRA4AEfa21djB%0AjRvg5QUzZ7LkyFLmH5zP72/9Tv4c+Z8SPXNGGfcBA8DTM/lV1WhefVUFeHXsCH/88fT1zJky832n%0A7/n33r988PsHyPjx8PvvsHdv8iubjrFm4EsCFx87vhRzzhaS0lbzJGPGQI8eLOd/jN0+lo1vbaRE%0A3qG01RYAABmsSURBVBJPiQUGgru7EtduGU1K0rq12gzVrZvaVPckObLkYG23tey6tIvR+ychkyer%0AxaKoqORXNp2SQBaJWJKy+mlzWy8vr9j37u7uuLu7J2HYdMjOneDnx/o1PgzbOIytPbfyjOszT4nt%0A2gWdOsHUqTpaRpM6aN5cLbi++qqqDNXziUAvlxwubO6xGfdl7uSqlpPxOXPC11/repHx4O/vj7+/%0Av32NLMVQAo2AjY8djwZGJSA7ARhhb1t0HLxlQkNFqlWTgzM+kSK+ReTglYPxiq1Zowplb9iQzPpp%0ANDZw/LhI2bIi3t7xZy69dveaPDPrGVn03QiRQoVELl5Mdh3TGjggDv4AUNkwjHKGYWQDugK/JiD7%0AZLiOPW01CeHjw7/F8tAm9BvWdVtHveL1nhKZO1dl9/v9d2jbNgV01GisUK2aesJcuTJ+L0zRPEXZ%0A1nMbX9xey65Xn1XRYpqkY+0OALQFTgJngNEx5/oD/WPeF0P52kMAE3AByJNQ23j6T7Y7XprjxAkJ%0AdckrdT0LyZ6Le566HBEhMmyYSJUqImfPpoB+Go2dhISIvPSSSNu2Imbz09cvhlyUGl9VkhulXSV6%0A1arkVzANgc4Hn4YR4XqDGswoeYnO8/yfmrmbzWrxKipKzYoKFEghPTUaO4mIUAEAf/yh8slXqhT3%0A+rV71xg17gVmfn+LfKcvYLi4pIyiqRydDz4Ns2NcLy7/e4Zuc/96yrifOgWNGsEzzyi3jDbumrRE%0A1qwwe7bywri5wbZtca8Xy1OML733s7VqNna92YRoiU4ZRdMB2sCnMkSE2StGUH36DxRcvo5aJerG%0Aub56tUrPOnw4zJiRcDUdjSa1M2AA/PQTvPUWTJ4M0Y/ZcddcrrT85SAV951h8rgXCYsMSzlF0zDa%0AwKcioqKjGLJ+EI3GzSfr6LGUbfzfimlEhCreNHy4iinu1y8FFdVoHESLFrB/P6xbB6+9plyPj3Ap%0AWpaCP6ym39f7eWNhK+6E3Uk5RdMo2sCnAvz8/Lh8/TJdfu5CtRVbqetai+j3h+IXszvk4kWVUyYw%0AEA4eVJn7NJr0QunSqjJU2bKqds2+ff9dy9a6HQXf6M3oX67RfGlzLt25lHKKpkG0gU8FlK9Vnrpv%0A1KX0hTAGbrzNvdlz8Bw/Hjc3N375Rf3Tt2unZu6FCqW0thqN48mWTeUcmzIFXnlFuWwehVJm8vGh%0A4fkoxt2qRaNvGnHgSgJZzDRPoaNoUphDVw/RYXkH3q3Uk3/f/YaPBw7E9/p1Ro/2xsvLhT//hB9/%0A1LN2Tcbh4kW1EztzZpXPplQp1G7uLl3YsMKbXntHMc9jHq9Xfz2lVU1RdBRNKufHYz/S+vvWzGgz%0Agwk7Ivi4Zk3Ke3nRtOlImjVzIToaDh3Sxl2TsShdWoVQtmwJ9erBkiUgbk3gvfdoN/EnNr+5keGb%0AhjN++3iionXeGkvoGXwKEBEVwUebP8LvtB+r3lhFnaPXMPfpw8ctWxNmjGfVKl8WLfKma1cd/6vJ%0A2Bw9Cr17Q4kSsGBuJEfa1cGtSxdCP3qfrr90JVfWXMxtMZfjh45nuMLdegafCrly9wovLnuRc+Zz%0A7H9vP3UoirlXL94t8zyb/vwKkXL873/e/PWXJ+bHQwo0mgxInToqg3CDBlD3uSxc6rScMT4+5Njz%0AP7a+vZUKOSpQ54065K2YN6VVTZVoA5+MbDi9gfoL6tO6YmvWdVtHgez5CevaE5+s7hy4upQFC1z4%0A9lsoV84Fb29vAhIqbqnRZCCyZYMJE2D7dvj2j1pkK7GATzp24nLgP/AHTJ8ync6/dWbW3llkNG+A%0ANbSLJhkIiwxj9LbR/HL8F77v9D3NyjYjIgIOtp+A/LEdvxF/MGZ8FnLlSmlNNZrUTXQ0fPMN3B7S%0Ai9Hh33L0yFlq16nAmdtn6L6qOyXylmDxq4splCv9h5tpF00qIPB6II0XN+ac6RyH+x+mWdlmbN4M%0AQ8v/RsU/F1No20o+n6yNu0ZjC5kywRtvmDnVLSd/uNan/3PdmTXLTAWXSgT0CaBKwSrUnV+XzWc3%0Ap7SqqQNr2cic/SKdZpOMjIoU3wBfcfVxlfkH5kt0dLQcOybi4SHSovQpCc1fWKJ37U5pNTWaNIXJ%0AZJKBAweKyWQSuXJF/nUtLnUKeEitWibZskXJbDm7RcpMKyMD1g+Qu2F3U1ZhJ4ID8sFrEsGpW6dw%0AX+bO+lPr2ffuPtoV7ce77xq0aAFtmtxjS97XyD5lIsYLjVJaVY0mTREQEIC3tzcuLi5QvDhF1v7M%0A9sx76dT4FwYMUGUCXe+8xN/v/83DyIfU+boOfwb/mdJqpxzW7gDOfpGOZvBhkWEy8c+JUmhKIZm+%0Ae7pcvhIlw4aJFCggMmqUiOlmpEjHjiJ9+sRf1kaj0djPnDki1atL2L8mmTVLpGhRkTfeUFWkfj3x%0Aq5T6qpT0XddXbj24ldKaOhT0DD75CLgQQL359dhzaQ+bOx/i8uoPqFkjE9HRKofM5Mng8sXHKpvS%0AvHlgWFwb0Wg0tjJwILRoQba3ujC4fwRnzqgNUs2bw4qJr7D25UByZMlBjbk1+PHYjxkq0kYb+CRy%0A9e5Veq7pSddfujKwxnjK7/mNl54vw/378PffKqVv8eIoo+7nB6tWqbgvjUbjOKZNg+zZYcAA8uQW%0ARo2CM2egalVo2yIf15fOZlLd1f9v787jo6zOBY7/HiStgUgGQYOBQKIIuKAIymIS9rC70CooV4Wr%0AXhXEigpXcKyimBYNRahtra1cREThI0tZAlEQAmEgXFlCsIRNEpIii1wyLRaBLM/94wwQYmAGMpnJ%0AhPP9fObDvDPnnXneAZ45c97zPod3XO/QbUY3th3aFuyIA8Im+Et0svgkk9ZNovX7ran9YzRdtu3g%0A1YGDCL9S2L4d/vhHTw0NgLQ0ePNNk+CvvjqocVtWjVS7Nsyebcqtvv02APXqwauvwt69ZoEc52Od%0AiFq4kduuGESPj3vwq2W/4uiPR4MceNXyOg9eRPoAU4ArgA9V9e0K2vwes/7qcWCYqm7xPJ4H/Aso%0AAYpUtX0F+2oo/WRSVeb8fQ6vfPUK13ALV6z4Hfs2t2DkSHj66Qry94YNpjze3/4Gd98dlJgt67Kx%0Afz906gQTJsDQoec8dfIkfPIJTJ4M1DlC1MO/5puSebwc/zLPtn+WK2tfGZyYL5Ev8+C9nQC9ArNg%0AdiwQBmQBN5Vr0w9Y6rnfAcgs81wucLWX96jK8xB+tXLvSm37fntt8mY7jY5fpW3aqM6YoXry5Hl2%0AyM42Z3xSUwMap2Vd1nJyVBs1Uj3Pot2lpappaaq9eqle3XK7Nv/1PdpkUqzO3DpTi0uKAxzspcOH%0Ak6zeEnwnIK3M9lhgbLk2fwYGl9neAUTp2QTfwMt7BOCjqJyMvLXadmo3vcrZXOt0mKUPDylRl8vL%0ARJjdu1Wjo1Vnzw5YnJZleWzapHrNNapffnnBZjt3qj7/vOpVrdO1/ui7NWbizTo7+3MtKS0JUKCX%0AzpcE720MvjFQUGb7H57HfG2jwAoR2Sgi/+XlvaqdhVkZtHqrN93/+B/sX/YIr9TLYe/CIXw6qxZ3%0A332BiTD5+ZCUBG+8AYMHBzRmy7Iw02jmzzcLvq5de95mLVrAlClwYH0XJt20lrquSTz6wUSuG9+W%0AqSs+D/lyxN4SvK+D4+dLdQmqegdmfP5ZEUn0ObIgOX5ccU7/ggajO/OLGcOof/ABUvvu5MDSxxn7%0A37WJivLyAnv3mvlZL7wATz4ZkJgty6pAQgLMmgW/+AWkp1+wad268PjjQs6ivmx6+ms6/jiBMfN/%0AR8TLtzD03Y84eLgoMDH7WW0vz+8HYspsx2B66Bdq08TzGKr6nefP70VkAdAeyCj/JuPHjz9zv2vX%0ArnTt2tWn4P2lqAiWfnGSt5fOZkOtyYTXKeWR68cy8ZHBOOpd+CNKTU0lPj7eXFm3ezf06IF71Chc%0AN9zA5VWd2rKqoaQkmDMHBg0yS6P17Ol1l9athYXv3ENR0QBS5q5i6pZkZr7za1q6n+OFzk/x8EAH%0AVwWhOnF6ejrpXr6ofuJC4zeYL4BvMSdZf4b3k6wd8ZxkBeoAV3nu1wVcQK8K3iMg41XlnTihunix%0A6qAnDmh47zc1bOx12uqtXvrp/6Zp6UVcZXqmNsb69aqNG2vhe++drZVhWVb1kJFhxuSXLLm03fds%0A1vhJj2jYq/U17N7ntPugHJ05U9Xt9nOcF4HKnmQ1r0FfYCdmNs04z2NPA0+XafMHz/Nbgbaex673%0AfCFkAd+c3reC1w/U56FHj6p+8onqg4NKte7Na/SaZx7S8DccOuSzpzT7YPYlv27hsmU6IjxccydP%0Atsndsqqr9etVr71Wdfr0S36Jgn8W6EupTo2cEKUNX+yhV94xX3v2PqV/+INqfr7/QvWFLwm+RteD%0AV4Xt2831RUuXwsacwzS792OOxn5IRASM7DCcoW2G4riyEkvjzZsHw4eTl5JC3LBh5ObmEhsb67dj%0AsCzLj3bsILVLF+KffBLHW2+dmSnhdrtxuVw+L/t3svgk83Lm8V7mn9hx+Fsafz+Mfyx6nLjIG+nX%0AD/r3hw4dzMLhVaXS8+ADccPPPfgjR1TnzFF94gnVmBjVpnEntPcLc7XjlPs08reROnTBUM3Yl3FR%0AwzAVKi1VnTpVNTpaC9PTdcSIEZqbm2t78JZVzRXm5OiIhg218LHHVE+dOrcE8SXYfni7vvTFS3rN%0AO9fo7VMSta/zL3rrnYV69dWqDz6o+te/qu7b5+eD0MukB3/smJkF9dVXZiX2PXugc5cS4rqt4fC1%0As/nqu3m0jmrNo7c9ygM3P0C9n9erfNAnT8LIkbBuHe7PPsP5wQdnSpi63W6cTufZkqaWZVU77oIC%0AnAkJjLnuOlJuvpnkyZMr/f/1VMkplu1exszsmSzfu5zE6F7E/fAwh9f1ZdWX4URGQvfu0KMHdO0K%0A115buWPwpQcfcgn+yBFYtw7WrIHVqyEnB+66C7p0K6Zh27XkMI/5O+cSfVU0g28ZzEO3PkTTyKb+%0AC3j/fvjlL6FxY/joI1LXrDk7i8bjYn/uWZYVeHnffktc8+bkRkcTu2gRtGvnt9cu/LGQudvnMufv%0Ac9h0YBP9mvenfcQDnMrpRcbKOmRkQKNG0LmzmVUdHw9Nm15ckdmQT/AlJSaBZ2aapO5ywcGDpnBQ%0AYiJ0SDjOvxquYOm3C1m8azExkTEMbDWQQbcMokWDFv4PdvVqGDIEnn0Wxo2zJX8tK0Sd/qU9ZswY%0AUp55huSvv8YxaRIMG+b3/9cHfzjIvO3zmL9jPhu/20jP63ty74330/RUP7IzG7B6tcltYWEm0Xfq%0AZG5t2pgCmecTUgle1VwA+vXXZ2+bNkFUlEnoHTuag49oksfy3DSW7FrCmn1ruDP6Tu5pcQ8DbxpI%0ArCO2aoIsKjLVID/8EKZPhz59quZ9LMuqcuWHUd1uN87hw0nOysLRpo0p7V1Fw6tHjh9h8c7FLNq1%0AiJW5K7kt6jYG3DiAPs37UudYa9atEzIzTad21y649VYzQnH61rLl2RO3IZPgk5KUzZvNN1jZg7nr%0ALgiL+Bdr9q1hxd4VpO1Jo/BEIb1u6MWAGwfQu3nvys2A8UVurum1R0bCRx+Z31WWZYWscy5O9HC7%0A3bhWrqT/ypVm2t2sWVVe/fVE8QnS89JZsmsJy/Ys40TxCXrf0Juk65PoHtedCIli06ZzO72HD8Pt%0At5tKDO+9FyIJPjVVueMOszDGv0/9m3UF60jPS2dV3iqyD2XTvnF7el7fkz7N+9CmURtqSQDK2JeW%0AmqLub7wBTic8/7xZ0t2yrJpt0SJ46ilTx+bNN00dgwDYc3QPaXvSWLF3Bav3raZJvSZ0j+1Ol9gu%0AdG7WmYZ1GlJYCFlZsHkzjB4dIgl+Qc4CXPku1hasZduhbbRp1IYuzbrQLa4b8THxhIeFBzaonBxT%0AR6ZWLTMs07JlYN/fsqzgOnIERo2C9evhL38xU18CqLi0mE3fbWJV3ipW71vNuoJ1xNSLIbFpIvFN%0A40lomkBc/bjQSPC9Z/YmoWkC8THxdGjSgTphdQLy3j/5qXbsGO7XXsM1bRr9J06EZ56xvXbLupwt%0AXWryQGKiWSnqzDJtgVVcWsyWA1twFbhYm78WV4GLg6MPhkaCD1YMZ062TJiAY9Ei3OPG4YyMJHnB%0AAhw33RSUmCzLqmZ++AEmTjQnX0eNgtGjITzAowrlqCq1atXymuAv6+6pIzKS5MREnC1bkjd1Ks74%0AeJIzM21ytyzrrIgIeOst2LgRtm6FFi1Ife453N9/f04zt9tNampqQEISH6dyXp4JXhW+/BI6dcKR%0AnMyY5GTisrIYM2mSvfrUsqyKxcXB3Lnw+efEb9uGs3lz3H/6ExQVnRkNiI+PD3aU57i8EnxxsakJ%0A3a6d+ak1ahTu1atJ2bqV3NxcUlJScLvdwY7SsqzqrGNHHOnpJM+ahTM5mbzYWJwDBpA8dmz16yB6%0AK1ZT1TcCUS740CHViRNVmzVT7dzZFIIvKflJkaHKFh2yLOvykpubq4Dm9u2r2qCB6ksvqe7aFZD3%0Axg9rsoaukhJTgeyhh8zCizt3wuefm3IDAwZArVq4XK5zioI5HA6Sk5NxuVxBDt6yrOrO7XaTkpJi%0Afv3HxeFevtzMuouPN9Mq58yBH38MaowhO4umwqvRCgtxffwx/fPzYfZsc9XpsGHw6KNVdumxZVmX%0AnwrLHZzeDg+HBQtg2jRTb+W++8xFU127Qm1vq6T6LmRKFVxKDGc+0PHjcXzzDe45c3DOmkVyw4Y4%0AHnnEfKCtWlVBxJZlXe7OW+6gfBXZAwdMZ/PTT03Zk/794f77SS0tJb5Hj0pVoa2ZCV7VLG69YgXu%0A1FScK1YwpkULUurWJfndd3F07GirPFqWVf0UFMDChbBgAe7MTJz165P8xBM47r0Xd2wsztdeu6h1%0AJEIqwZ/32+vECVN8YcMGyMgwq3uEhZnV0ZOSyGvRgri77rJL5VmWFTqOHTMd1N/8hjHHj5OSn09y%0AYiKO7t1NreB27UyBwwvwS4IXkT7AFOAK4ENVfbuCNr/HLM59HBimqlsuYl8tLCw0wy0vvogjPx+y%0As81t82ZzcrRVK2jfHhISzCXDzZoB5Wo6p6TYVZQsywopeXl5xMXFkbtxI7EFBaYTu2GD6dQ2bmwS%0A/W23mVvr1qZUgmeEotJrsmIS8x4gFggDsoCbyrXpByz13O8AZPq6r6edjmjUSAvr11eNjFRNSFAd%0APlz1/ffNKujHj1c4RShUpjiuWrUq2CFUKXt8oasmH5tq9T++0zmrwrWci4pUs7NVp09XffFF1aQk%0A1ago1YgI1bZtVYcM8WmapLcE3wlIK7M9Fhhbrs2fgcFltncAjXzZ1/O45n72merBg2Yhax8tWbLk%0AJ8m8sLBQlyxZ4vsnHACvv/56sEOoUvb4QldNPjbV6n18l9xBLSxUzcxUnTHDL/PgGwMFZbb/4XnM%0AlzbRPuwLQEpGBu6f//yiTo7279//J8MxDofDroNqWVa1d8nX4Dgc0KEDPPaYT+/jLcH7ega2UtNW%0AkpOTcTqdtkyAZVmXhUB1UC94klVEOgLjVbWPZ3scUKplTpaKyJ+BdFWd7dneAXQB4rzt63k8uNN4%0ALMuyQpR6Ocnq7bKqjcCNIhILfAcMBh4u12YRMBKY7flCcKvqIRH5Px/29X4W2LIsy7okF0zwqlos%0AIiOBLzCzYqapao6IPO15/gNVXSoi/URkD/Bv4D8vtG9VHoxlWZZ1VtAvdLIsy7KqRrWoJikiE0Rk%0Aq4hkichXIhIT7Jj8SURSRCTHc4zzReTCl6iFEBF5UET+LiIlItI22PH4i4j0EZEdIrJbRF4Odjz+%0AJCL/IyKHRGRbsGOpCiISIyKrPP8uvxGRXwU7Jn8SkStFZIMnX24Xkd+et2116MGLyFWqesxz/zng%0AdlV9Mshh+Y2IJAFfqWqpiEwEUNWxQQ7LL0SkFVAKfAC8pKqbgxxSpYnIFcBOoCewH/gaeLimDDGK%0ASCLwA/CxqrYOdjz+JiKNgEaqmiUiEcAm4P6a8vcHICJ1VPW4iNQG1gKjVXVt+XbVogd/Orl7RABH%0AghVLVVDV5apa6tncAARnafYqoKo7VHVXsOPws/bAHlXNU9UiYDZwX5Bj8htVzQAKgx1HVVHVg6qa%0A5bn/A5CDuS6nxlDV4567P8Oc4zxaUbtqkeABRCRZRPKBocDEYMdThR4HlgY7COuCfLnAzwoBnll8%0Ad2A6VjWGiNQSkSzgELBKVbdX1M5/1ee9B7QcU8KgvFdUdbGqOgGniIwF3sUzGydUeDs+TxsncEpV%0APw1ocJXky7HVMMEft7QqzTM8Mxd43tOTrzE8IwJtPOfzvhCRrqqaXr5dwBK8qib52PRTQrCH6+34%0ARGQYpjBbj4AE5EcX8XdXU+wHyp7oj8H04q0QISJhwDzgE1X9W7DjqSqq+k8RSQXuBNLLP18thmhE%0A5MYym/cBW4IVS1XwlE0eA9ynqieCHU8VqikXrZ25wE9Efoa5SG9RkGOyfCQiAkwDtqvqlGDH428i%0A0lBEHJ774UAS58mZ1WUWzVygJVACfAsMV9XDwY3Kf0RkN+ZkyOkTIetVdUQQQ/IbERkI/B5oCPwT%0A2KKqfYMbVeWJSF/OrmUwTVXPOxUt1IjIZ5hyIg2Aw8Brqjo9uFH5j4gkAGuAbM4Ot41T1bTgReU/%0AItIamIHpoNcCZqpqSoVtq0OCtyzLsvyvWgzRWJZlWf5nE7xlWVYNZRO8ZVlWDWUTvGVZVg1lE7xl%0AWVYNZRO8ZVlWDWUTvGVZVg1lE7xlWVYN9f98mteu43HACgAAAABJRU5ErkJggg==%0A
-
-
-离散分布
-------------------
-
-导入离散分布：
-
-
-
-
-::
-
-    from scipy.stats import binom, poisson, randint
-
-
-离散分布没有概率密度函数，但是有概率质量函数。
-
-
-离散均匀分布的概率质量函数（PMF）：
-
-
-
-
-::
-
-    high = 10
-    low = -10
-
-    x = arange(low, high+1, 0.5)
-    p = stem(x, randint(low, high).pmf(x))  # 杆状图
-
-
-.. image::data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAXsAAAEACAYAAABS29YJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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%0A
-
-
-二项分布：
-
-
-
-
-::
-
-    num_trials = 60
-    x = arange(num_trials)
-
-    plot(x, binom(num_trials, 0.5).pmf(x), 'o-', label='p=0.5')
-    plot(x, binom(num_trials, 0.2).pmf(x), 'o-', label='p=0.2')
-
-    legend()
-
-
-
-
-结果:
-::
-
-    <matplotlib.legend.Legend at 0x1738a198>
-
-.. image:: 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eQhH+8PFZGPRaRaRH7Rmn1jlbuEE4ppDUb0GsHnBz+n3tQH/djKqabG9x+81dXBG0arVLg1m+xF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%0AaLJXSqk48P8B4O96y1bc/nYAAAAASUVORK5CYII=%0A
-
-泊松分布：
-
-
-
-
-::
-
-    x = arange(0,21)
-
-    plot(x, poisson(1).pmf(x), 'o-', label=r'$\lambda$=1')
-    plot(x, poisson(4).pmf(x), 'o-', label=r'$\lambda$=4')
-    plot(x, poisson(9).pmf(x), 'o-', label=r'$\lambda$=9')
-
-    legend()
-
-
-
-
-结果:
-::
-
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A65P3TXdOwIX37p6yiUUso3yjuy7w7sEJGdIpIDLAQGFd5BRFaIyBH7w5XAecX68It6pTrXXikV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%0Agg==%0A
-
-
-自定义离散分布
-------------------
-
-
-导入要用的函数：
-
-
-
-
-::
-
-    from scipy.stats import rv_discrete
-
-
-一个不均匀的骰子对应的离散值及其概率：
-
-
-
-
-::
-
-    xk = [1, 2, 3, 4, 5, 6]
-    pk = [.3, .35, .25, .05, .025, .025]
-
-
-定义离散分布：
-
-
-
-
-::
-
-    loaded = rv_discrete(values=(xk, pk))
-
-
-此时， loaded 可以当作一个离散分布的模块来使用。
-
-
-产生两个服从该分布的随机变量：
-
-
-
-
-::
-
-    loaded.rvs(size=2)
-
-
-
-
-结果:
-::
-
-    array([3, 1])
-
-
-产生100个随机变量，将直方图与概率质量函数进行比较：
-
-
-
-
-::
-
-    samples = loaded.rvs(size=100)
-    bins = linspace(.5,6.5,7)
-
-    hist(samples, bins=bins, normed=True)
-    stem(xk, loaded.pmf(xk), markerfmt='ro', linefmt='r-')
-
-
-
-
-<Container object of 3 artists>
-
-.. image::data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAXgAAAEACAYAAAC57G0KAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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%0AiXLAm5klygFvZpYoB7yZWaIc8GZmifp/J4pqDI9mKMcAAAAASUVORK5CYII=%0A
-
-
-假设检验
--------------
-
-
-导入相关的函数：
-
-
-- 正态分布
-- 独立双样本 t 检验，配对样本 t 检验，单样本 t 检验
-- 学生 t 分布
-
-
-t 检验的相关内容请参考：
-
-
-- 百度百科-t 检验：http://baike.baidu.com/view/557340.htm
-- 维基百科-学生 t 检验：https://en.wikipedia.org/wiki/Student%27s_t-test
-
-
-
-
-::
-
-    from scipy.stats import norm
-    from scipy.stats import ttest_ind, ttest_rel, ttest_1samp
-    from scipy.stats import t
-
-
-独立样本 t 检验
---------------------
-
-
-两组参数不同的正态分布：
-
-
-
-
-::
-
-    n1 = norm(loc=0.3, scale=1.0)
-    n2 = norm(loc=0, scale=1.0)
-
-
-从分布中产生两组随机样本：
-
-
-
-
-::
-
-    n1_samples = n1.rvs(size=100)
-    n2_samples = n2.rvs(size=100)
-
-
-将两组样本混合在一起：
-
-
-
-
-::
-
-    samples = hstack((n1_samples, n2_samples))
-
-
-最大似然参数估计：
-
-
-
-
-::
-
-    loc, scale = norm.fit(samples)
-    n = norm(loc=loc, scale=scale)
-
-
-比较：
-
-
-
-
-
-::
-
-    x = linspace(-3,3,100)
-
-    hist([samples, n1_samples, n2_samples], normed=True)
-    plot(x, n.pdf(x), 'b-')
-    plot(x, n1.pdf(x), 'g-')
-    plot(x, n2.pdf(x), 'r-')
-
-
-
-
-[<matplotlib.lines.Line2D at 0x17ca7278>]
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-.. image:: 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FsNuew8JnduGDzYTH4KCoKaeWrSrXQ3GZYoAEnkIjbYv9/c0Vu9GlKGHl3SuzfUrAmVK9s+xcT3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%0AIYRwcpLIhRDCyUkiF0IIJ/d/XdaGnd6OVMIAAAAASUVORK5CYII=%0A
-
-
-
-独立双样本 t 检验的目的在于判断两组样本之间是否有显著差异：
-
-
-
-
-::
-
-    t_val, p = ttest_ind(n1_samples, n2_samples)
-
-    print 't = {}'.format(t_val)
-    print 'p-value = {}'.format(p)
-
-
-
-结果:
-::
-
-    t = 0.868384594123
-
-    p-value = 0.386235148899
-
-
-p 值小，说明这两个样本有显著性差异。
-
-
-
-配对样本 t 检验
----------------------
-
-
-配对样本指的是两组样本之间的元素一一对应，例如，假设我们有一组病人的数据：
-
-
-
-
-::
-
-    pop_size = 35
-
-    pre_treat = norm(loc=0, scale=1)
-    n0 = pre_treat.rvs(size=pop_size)
-
-
-经过某种治疗后，对这组病人得到一组新的数据：
-
-
-
-
-
-::
-
-    effect = norm(loc=0.05, scale=0.2)
-    eff = effect.rvs(size=pop_size)
-
-    n1 = n0 + eff
-
-
-新数据的最大似然估计：
-
-
-
-
-::
-
-    loc, scale = norm.fit(n1)
-    post_treat = norm(loc=loc, scale=scale)
-
-
-画图：
-
-
-
-
-
-::
-
-    fig = figure(figsize=(10,4))
-
-    ax1 = fig.add_subplot(1,2,1)
-    h = ax1.hist([n0, n1], normed=True)
-    p = ax1.plot(x, pre_treat.pdf(x), 'b-')
-    p = ax1.plot(x, post_treat.pdf(x), 'g-')
-
-    ax2 = fig.add_subplot(1,2,2)
-    h = ax2.hist(eff, normed=True)
-
-
-.. image:: 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NqqK49+8WjS91WZFf2DqXfv3uGFEREJQbzCaiLQOPq48hLgcmCHm0Hd/aBfX5vZc0Qea96pqJJs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%0A
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NqqK49+8WjS91WZFf2DqXfv3uGFEREJQbzCaiLQOPq48hLgcmCHm0Hd/aBfX5vZc0Qea96pqJJs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%0A
-
-
-
-独立 t 检验：
-
-
-
-
-::
-
-    t_val, p = ttest_ind(n0, n1)
-
-    print 't = {}'.format(t_val)
-    print 'p-value = {}'.format(p)
-
-
-
-
-
-
-结果:
-::
-
-    t = -0.347904839913
-
-
-    p-value = 0.728986322039
-
-
-高 p 值说明两组样本之间没有显著性差异。
-
-
-
-配对 t 检验：
-
-
-
-
-::
-
-    t_val, p = ttest_rel(n0, n1)
-
-    print 't = {}'.format(t_val)
-    print 'p-value = {}'.format(p)
-
-
-
-结果:
-::
-
-    t = -1.89564459709
-
-
-    p-value = 0.0665336223673
-
-
-配对 t 检验的结果说明，配对样本之间存在显著性差异，说明治疗时有效的，符合我们的预期。
-
-
-p 值计算原理
--------------
-
-
-p 值对应的部分是下图中的红色区域，边界范围由 t 值决定。
-
-
-
-
-::
-
-    my_t = t(pop_size) # 传入参数为自由度，这里自由度为50
-
-    p = plot(x, my_t.pdf(x), 'b-')
-    lower_x = x[x<= -abs(t_val)]
-    upper_x = x[x>= abs(t_val)]
-
-
-
-    p = fill_between(lower_x, my_t.pdf(lower_x), color='red')
-
-    p = fill_between(upper_x, my_t.pdf(upper_x), color='red')
-
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-.. image:: 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wBV/UJVt4YfzgIKfvvYQvU08P77UK2aFfdEVakSXH89DB7sO4kpTZEKfDVgdb7Ha8LPHcitQP7r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%0AMSZFWYE3xpgU9f8B8tTCMYTHdwAAAABJRU5ErkJggg==%0A
-
-
-作者 & 更新时间
-------------------------------------
-作者:`李金  <lijinwithyou@gmail.com>`
-
-提交: 2017/12/14
-
-
-
-
-
diff --git a/docs/sources/aipy-scipy/cn/5curve-fitting.rst.txt b/docs/sources/aipy-scipy/cn/5curve-fitting.rst.txt
deleted file mode 100644
index ceaf084..0000000
--- a/docs/sources/aipy-scipy/cn/5curve-fitting.rst.txt
+++ /dev/null
@@ -1,558 +0,0 @@
-
-曲线拟合
--------------
-
-导入基础包：
-
-::
-
-    import numpy as np
-    import matplotlib as mpl
-    import matplotlib.pyplot as plt
-
-
-多项式拟合
------------------------
-
-导入线多项式拟合工具：
-
-
-
-::
-
-     from numpy import polyfit, poly1d
-
-产生数据：
-
-::
-
-    x = np.linspace(-5, 5, 100)
-    y = 4 * x + 1.5
-    noise_y = y + np.random.randn(y.shape[-1]) * 2.5
-
-
-画出数据：
-
-::
-
-    %matplotlib inline
-
-    p = plt.plot(x, noise_y, 'rx')
-    p = plt.plot(x, y, 'b:')
-
-.. image:: 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1/BtekpsIuUI07QzgTacMANb+cKuOnXJ0xwX7V8U3Ddzk73ceOU2pC8FNhFyhU3zRKz5/722+7P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%0A/wEcY+GYZJlNlgAAAABJRU5ErkJggg==%0A
-
-
-
-进行线性拟合，polyfit 是多项式拟合函数，线性拟合即一阶多项式：
-
-
-
-
-::
-
-    coeff = polyfit(x, noise_y, 1)
-    print coeff
-
-
-
-结果:
-::
-
-    [ 3.93921315  1.59379469]
-
-
-一阶多项式 *y = a_1 x + a_0* 拟合，返回两个系数 *[a_1, a_0]*。
-
-
-画出拟合曲线：
-
-
-
-
-::
-
-    p = plt.plot(x, noise_y, 'rx')
-    p = plt.plot(x, coeff[0] * x + coeff[1], 'k-')
-    p = plt.plot(x, y, 'b--')
-
-.. image:: 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nnqHQcy1e3QoaqA5v7rS+3f6K8qclKbVXhTN6/YXLTP/fcX76n7W07oOgXusmXsxZcAcNpeolIk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%0ArkJggg==%0A
-
-
-
-还可以用 poly1d 生成一个以传入的 coeff 为参数的多项式函数：
-
-
-
-
-::
-
-    f = poly1d(coeff)
-    p = plt.plot(x, noise_y, 'rx')
-    p = plt.plot(x, f(x))
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-.. image:: 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JJ9OcS2X7JKgV2kpUnWC07Qw371VbPjjzfr0cOL2Vu3Ro7V1Jx9EImOPWeOeuw5psAu0pIkC+Bx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%0AkFFgFxEJGQV2EZGQ+X/DyfQNy7jRVwAAAABJRU5ErkJggg==%0A
-
-
-
-
-::
-
-    f
-
-
-
-结果:
-::
-
-    poly1d([ 3.93921315,  1.59379469])
-
-
-显示 f：
-
-
-
-
-::
-
-    print f
-
-
- 
-
-结果:
-::
-
-    3.939 x + 1.594
-
-
-
-还可以对它进行数学操作生成新的多项式：
-
-
-
-
-::
-
-    print f + 2 * f ** 2
-
-
-
-
-结果:
-::
-
-    2
-
-    31.03 x + 29.05 x + 6.674
-
-
-
-多项式拟合正弦函数
----------------------
-
-正弦函数：
-
-
-
-
-::
-
-    x = np.linspace(-np.pi,np.pi,100)
-    y = np.sin(x)
-
-
-用一阶到九阶多项式拟合，类似泰勒展开：
-
-
-
-
-::
-
-    y1 = poly1d(polyfit(x,y,1))
-    y3 = poly1d(polyfit(x,y,3))
-    y5 = poly1d(polyfit(x,y,5))
-    y7 = poly1d(polyfit(x,y,7))
-    y9 = poly1d(polyfit(x,y,9))
-
-
-
-
-::
-
-    x = np.linspace(-3 * np.pi,3 * np.pi,100)
-
-    p = plt.plot(x, np.sin(x), 'k')
-    p = plt.plot(x, y1(x))
-    p = plt.plot(x, y3(x))
-    p = plt.plot(x, y5(x))
-    p = plt.plot(x, y7(x))
-    p = plt.plot(x, y9(x))
-
-    a = plt.axis([-3 * np.pi, 3 * np.pi, -1.25, 1.25])
-
-
-.. image:: 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tBkGUf58P6tVE1uJeWSDF7If4E7Ft6BXueYgbr4ynRqErp59zdbuXbGtWw7tY3OvtH/OD//PHR3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%0AiBJ3hUKh0CFK3BUKhUKH/AdKQZTK2jwwogAAAABJRU5ErkJggg==%0A
-
-黑色为原始的图形，可以看到，随着多项式拟合的阶数的增加，曲线与拟合数据的吻合程度在逐渐增大。
-
-
-最小二乘拟合
---------------------
-
-
-导入相关的模块：
-
-
-
-
-::
-
-    from scipy.linalg import lstsq
-    from scipy.stats import linregress
-
-
-
-
-::
-
-    x = np.linspace(0,5,100)
-    y = 0.5 * x + np.random.randn(x.shape[-1]) * 0.35
-
-    plt.plot(x,y,'x')
-
-
-结果:
-
-    
-.. image:: data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAXkAAAEACAYAAABWLgY0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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%0AAAAASUVORK5CYII=%0A
-
-
-一般来书，当我们使用一个 N-1 阶的多项式拟合这 M 个点时，有这样的关系存在：
-
-
-*XC = Y*
-
-
-Scipy.linalg.lstsq 最小二乘解
-----------------------------------
-
-
-要得到 C ，可以使用 scipy.linalg.lstsq 求最小二乘解。
-
-
-这里，我们使用 1 阶多项式即 N = 2，先将 x 扩展成 X：
-
-
-
-
-::
-
-    X = np.hstack((x[:,np.newaxis], np.ones((x.shape[-1],1))))
-    X[1:5]
-
-
-
-结果:
-::
-
-    array([[ 0.05050505,  1.        ],
-               [ 0.1010101 ,  1.        ],
-               [ 0.15151515,  1.        ],
-               [ 0.2020202 ,  1.        ]])
-
-
-求解：
-
-
-
-
-
-::
-
-    C, resid, rank, s = lstsq(X, y)
-    C, resid, rank, s
-
-
-
-结果:
-::
-
-    (array([ 0.50432002,  0.0415695 ]),
-
-        12.182942535066523,
-
-        2,
-
-        array([ 30.23732043,   4.82146667]))
-
-画图：
-
-
-
-
-::
-
-    p = plt.plot(x, y, 'rx')
-    p = plt.plot(x, C[0] * x + C[1], 'k--')
-    print "sum squared residual = {:.3f}".format(resid)
-    print "rank of the X matrix = {}".format(rank)
-    print "singular values of X = {}".format(s)
-
-
-
-
-结果:
-::
-
-    sum squared residual = 12.183
-
-    rank of the X matrix = 2
-
-    singular values of X = [ 30.23732043   4.82146667]
-
-
-.. image:: data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAXkAAAEACAYAAABWLgY0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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%0ATkSuQmCC%0A
-
-
-Scipy.stats.linregress 线性回归
---------------------------------------
-
-对于上面的问题，还可以使用线性回归进行求解：
-
-
-
-
-::
-
-    slope, intercept, r_value, p_value, stderr = linregress(x, y)
-    slope, intercept
-
-
-
-结果:
-::
-
-    (0.50432001884393252, 0.041569499438028901)
-
-
-
-
-
-::
-
-    p = plt.plot(x, y, 'rx')
-    p = plt.plot(x, slope * x + intercept, 'k--')
-    print "R-value = {:.3f}".format(r_value)
-    print "p-value (probability there is no correlation) = {:.3e}".format(p_value)
-    print "Root mean squared error of the fit = {:.3f}".format(np.sqrt(stderr))
-
-
-
-结果:
-::
-
-    R-value = 0.903
-
-    p-value (probability there is no correlation) = 8.225e-38
-
-    Root mean squared error of the fit = 0.156
-
-.. image:: 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5XyNV9vEoM8kVPcbE1mk2rJIY2zf9IkHXDuuXphcbEuXrBAj9xwQ9vPmeuAJKt5eeCYTRjkiZzi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%0A
-
-
-
-可以看到，两者求解的结果是一致的，但是出发的角度是不同的。
-
-
-
-更高级的拟合
-----------------
-
-
-
-
-::
-
-    from scipy.optimize import leastsq
-
-
-先定义这个非线性函数： *y = a e^{-b sin( f x + \phi)}*
-
-
-
-
-::
-
-    def function(x, a , b, f, phi):
-        """a function of x with four parameters"""
-        result = a * np.exp(-b * np.sin(f * x + phi))
-        return result
-
-画出原始曲线：
-
-
-
-
-::
-
-    x = np.linspace(0, 2 * np.pi, 50)
-    actual_parameters = [3, 2, 1.25, np.pi / 4]
-    y = function(x, *actual_parameters)
-    p = plt.plot(x,y)
-
-
-.. image:: data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAW4AAAEACAYAAACTXJylAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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%0ALSKSMApuEZGEUXCLiCSMgltEJGH+Px9Ir5f410BLAAAAAElFTkSuQmCC%0A
-
-
-加入噪声：
-
-
-
-
-::
-
-    from scipy.stats import norm
-    y_noisy = y + 0.8 * norm.rvs(size=len(x))
-    p = plt.plot(x, y, 'k-')
-    p = plt.plot(x, y_noisy, 'rx')
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-.. image:: 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AkjRoxAn+bNgYgIIDkZCA83OiyPU1WEh4dj9erV1lkqjiyNk1mRV128eBHbt2/Hky1a5C4IjBkz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%0AAABJRU5ErkJggg==%0A
-
-
-Scipy.optimize.leastsq
-----------------------------
-
-
-定义误差函数，将要优化的参数放在前面：
-
-
-
-
-::
-
-    def f_err(p, y, x):
-        ^4$return y - function(x, *p)
-
-
-将这个函数作为参数传入 leastsq 函数，第二个参数为初始值：
-
-
-
-
-::
-
-    c, ret_val = leastsq(f_err, [1, 1, 1, 1], args=(y_noisy, x))
-    c, ret_val
-
-
-
-结果:
-::
-
-    (array([ 3.03199715,  1.97689384,  1.30083191,  0.6393337 ]), 1)
-
-
-ret_val 是 1~4 时，表示成功找到最小二乘解：
-
-
-
-
-::
-
-    p = plt.plot(x, y_noisy, 'rx')
-    p = plt.plot(x, function(x, *c), 'k--')
-
-
-.. image::data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAXAAAAEACAYAAACqOy3+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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%0AA5MAAAAASUVORK5CYII=%0A
-
-
-Scipy.optimize.curve_fit
---------------------------
-
-
-更高级的做法：
-
-
-
-
-::
-
-    from scipy.optimize import curve_fit
-
-
-不需要定义误差函数，直接传入 function 作为参数：
-
-
-
-
-::
-
-    p_est, err_est = curve_fit(function, x, y_noisy)
-
-
-
-
-::
-
-    print p_est
-    p = plt.plot(x, y_noisy, "rx")
-    p = plt.plot(x, function(x, *p_est), "k--")
-
-
-结果:
-::
-
-    [ 3.03199711  1.97689385  1.3008319   0.63933373]
-
-.. image::data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAXAAAAEACAYAAACqOy3+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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%0AA5MAAAAASUVORK5CYII=%0A
-
-
-这里第一个返回的是函数的参数，第二个返回值为各个参数的协方差矩阵：
-
-
-
-
-::
-
-    print err_est
-
-
-结果:
-::
-
-    [[ 0.08483704 -0.02782318  0.00967093 -0.03029038]
-
-     [-0.02782318  0.00933216 -0.00305158  0.00955794]
-
-     [ 0.00967093 -0.00305158  0.0014972  -0.00468919]
-
-     [-0.03029038  0.00955794 -0.00468919  0.01484297]]
-
-协方差矩阵的对角线为各个参数的方差：
-
-
-
-
-::
-
-    print "normalized relative errors for each parameter"
-    print "   a\t  b\t f\tphi"
-    print np.sqrt(err_est.diagonal()) / p_est
-
-
-
-结果:
-::
-
-    normalized relative errors for each parameter
-    a	          b	          f	          phi
-    [ 0.09606473  0.0488661   0.02974528  0.19056043]
-
-
-
-作者 & 更新时间
-------------------------------------
-作者:`李金  <lijinwithyou@gmail.com>`
-
-提交: 2017/12/14
\ No newline at end of file
diff --git a/docs/sources/aipy-scipy/cn/6minfunc.rst.txt b/docs/sources/aipy-scipy/cn/6minfunc.rst.txt
deleted file mode 100644
index 21f2a08..0000000
--- a/docs/sources/aipy-scipy/cn/6minfunc.rst.txt
+++ /dev/null
@@ -1,963 +0,0 @@
-最小化函数
-===============
-
-minimize 函数
-------------------
-
-
-
-::
-
-    %pylab inline
-    set_printoptions(precision=3, suppress=True)
-
-
-Populating the interactive namespace from numpy and matplotlib
-
-
-已知斜抛运动的水平飞行距离公式：
-
-
-*d = 2 \frac{v_0^2}{g} \sin(\theta) \cos (\theta)*
-
-
-- *d* 水平飞行距离
-- *v_0* 初速度大小
-- *g* 重力加速度
-- *\theta* 抛出角度
-
-
-希望找到使 *d* 最大的角度 *\theta*。
-
-
-定义距离函数：
-
-
-
-
-::
-
-    def dist(theta, v0):
-        ^4$"""calculate the distance travelled by a projectile launched
-        ^4$at theta degrees with v0 (m/s) initial velocity.
-        ^4$"""
-        ^4$g = 9.8
-        ^4$theta_rad = pi * theta / 180
-        ^4$return 2 * v0 ** 2 / g * sin(theta_rad) * cos(theta_rad)
-    theta = linspace(0,90,90)
-    p = plot(theta, dist(theta, 1.))
-    xl = xlabel(r'launch angle $\theta (^{\circ})$')
-    yl = ylabel('horizontal distance traveled')
-
-
-.. image:: 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7t3qlF0MVCyyJ3rrw+jnl57LbQ7i2TL/ffDLbfAW2/BxhsnHU39EFeJ8jPc/dNKF/ptjSKTgvbo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%0AAAAASUVORK5CYII=%0A
-
-因为 Scipy 提供的是最小化方法，所以最大化距离就相当于最小化距离的负数：
-
-
-
-
-::
-
-    def neg_dist(theta, v0):
-        ^4$return -1 * dist(theta, v0)
-
-
-导入 scipy.optimize.minimize：
-
-
-
-
-::
-
-    from scipy.optimize import minimize
-    result = minimize(neg_dist, 40, args=(1,))
-    print "optimal angle = {:.1f} degrees".format(result.x[0])
-
-
-结果:
-::
-
-    optimal angle = 45.0 degrees
-
-
-minimize 接受三个参数：第一个是要优化的函数，第二个是初始猜测值，第三个则是优化函数的附加参数，默认 minimize 将优化函数的第一个参数作为优化变量，所以第三个参数输入的附加参数从优化函数的第二个参数开始。
-
-
-
-查看返回结果：
-
-
-
-
-::
-
-    print result
-
-
-结果:
-::
-
-    status: 0
-
-    success: True
-
-    njev: 18
-
-    nfev: 54
-
-    hess_inv: array([[ 8110.515]])
-
-    fun: -0.10204079220645729
-
-    x: array([ 45.02])
-
-    message: 'Optimization terminated successfully.'
-
-    jac: array([ 0.])
-
-
-Rosenbrock 函数
----------------------
-
-Rosenbrock 函数是一个用来测试优化函数效果的一个非凸函数：
-
-
-$f(x)=\sum\limits_{i=1}^{N-1}{100\left(x_{i+1}^2 - x_i\right) ^2 + \left(1-x_{i}\right)^2 }$
-
-
-导入该函数：
-
-
-
-
-::
-
-    from scipy.optimize import rosen
-    from mpl_toolkits.mplot3d import Axes3D
-
-
-使用 N = 2 的 Rosenbrock 函数：
-
-
-
-
-::
-
-    x, y = meshgrid(np.linspace(-2,2,25), np.linspace(-0.5,3.5,25))
-    z = rosen([x,y])
-
-
-图像和最低点 (1,1)：
-
-
-
-
-::
-
-    fig = figure(figsize=(12,5.5))
-    ax = fig.gca(projection="3d")
-    ax.azim = 70; ax.elev = 48
-    ax.set_xlabel("X"); ax.set_ylabel("Y")
-    ax.set_zlim((0,1000))
-    p = ax.plot_surface(x,y,z,rstride=1, cstride=1, cmap=cm.jet)
-    rosen_min = ax.plot([1],[1],[0],"ro")
-
-
-.. image:: 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jCsm96iI2mN6LcquDEQxuoNzLPPPo8E1Ii8WGhls8OJM6DY0lixYkVlqicQVHmuRvW8q6GoFZ4v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%0Abxj8dmObAAAAAElFTkSuQmCC%0A
-
-
-传入初始值：
-
-
-
-
-::
-
-    x0 = [1.3, 1.6, -0.5, -1.8, 0.8]
-    result = minimize(rosen, x0)
-    print result.x
-
-
-结果:
-::
-
-    [ 1.  1.  1.  1.  1.]
-
-
-随机给定初始值：
-
-
-
-
-::
-
-    x0 = np.random.randn(10)
-    result = minimize(rosen, x0)
-    print x0
-    print result.x
-
-
-结果:
-::
-
-    [ 0.815 -2.086  0.297  1.079 -0.528  0.461 -0.13  -0.715  0.734  0.621]
-
-
-    [-0.993  0.997  0.998  0.999  0.999  0.999  0.998  0.997  0.994  0.988]
-
-
-对于 N > 3，函数的最小值为 *(x_1,x_2, ..., x_N) = (1,1,...,1)*，不过有一个局部极小值点 *(x_1,x_2, ..., x_N) = (-1,1,...,1)*，所以随机初始值如果选的不好的话，有可能返回的结果是局部极小值点：
-
-
-
-优化方法
----------------
-
-BFGS 算法
------------------
-
-
-minimize 函数默认根据问题是否有界或者有约束，使用 'BFGS', 'L-BFGS-B', 'SLSQP' 中的一种。
-
-
-可以查看帮助来得到更多的信息：
-
-
-
-
-::
-
-    info(minimize)
-
-
-结果:
-::
-
-    minimize(fun, x0, args=(), method=None, jac=None, hess=None, 
-
-    hessp=None,bounds=None, constraints=(), tol=None, callback=None, 
-
-    options=None)
-
-
-
-Minimization of scalar function of one or more variables.
-
-
-
-Parameters
-
-fun : callable
-
-    Objective function.
-
-x0 : ndarray
-
-    Initial guess.
-
-args : tuple, optional
-
-    Extra arguments passed to the objective function and its
-
-    derivatives (Jacobian, Hessian).
-
-method : str or callable, optional
-
-    Type of solver.  Should be one of
-
-        - 'Nelder-Mead'
-
-        - 'Powell'
-
-        - 'CG'
-
-        - 'BFGS'
-
-        - 'Newton-CG'
-
-        - 'Anneal (deprecated as of scipy version 0.14.0)'
-
-        - 'L-BFGS-B'
-
-        - 'TNC'
-
-        - 'COBYLA'
-
-        - 'SLSQP'
-
-        - 'dogleg'
-
-        - 'trust-ncg'
-
-        - custom - a callable object (added in version 0.14.0)
-
-
-    If not given, chosen to be one of ``BFGS``, ``L-BFGS-B``, ``SLSQP``,
-
-    depending if the problem has constraints or bounds.
-
-jac : bool or callable, optional
-
-    Jacobian (gradient) of objective function. Only for CG, BFGS,
-
-    Newton-CG, L-BFGS-B, TNC, SLSQP, dogleg, trust-ncg.
-
-    If `jac` is a Boolean and is True, `fun` is assumed to return the
-
-    gradient along with the objective function. If False, the
-
-    gradient will be estimated numerically.
-
-    `jac` can also be a callable returning the gradient of the
-
-    objective. In this case, it must accept the same arguments as 
-
-`fun`.
-
-hess, hessp : callable, optional
-
-    Hessian (matrix of second-order derivatives) of objective function
- 
-or
-
-    Hessian of objective function times an arbitrary vector p.  Only 
-
-for
-
-    Newton-CG, dogleg, trust-ncg.
-
-    Only one of `hessp` or `hess` needs to be given.  If `hess` is
-
-    provided, then `hessp` will be ignored.  If neither `hess` nor
-
-    `hessp` is provided, then the Hessian product will be approximated
-
-    using finite differences on `jac`. `hessp` must compute the Hessian
-
-    times an arbitrary vector.
-
-bounds : sequence, optional
-
-    Bounds for variables (only for L-BFGS-B, TNC and SLSQP).
-
-    ``(min, max)`` pairs for each element in ``x``, defining
-
-    the bounds on that parameter. Use None for one of ``min`` or
-
-    ``max`` when there is no bound in that direction.
-
-constraints : dict or sequence of dict, optional
-
-    Constraints definition (only for COBYLA and SLSQP).
-
-    Each constraint is defined in a dictionary with fields:
-
-        type : str
-
-            Constraint type: 'eq' for equality, 'ineq' for inequality.
-
-        fun : callable
-
-            The function defining the constraint.
-
-        jac : callable, optional
-
-            The Jacobian of `fun` (only for SLSQP).
-
-        args : sequence, optional
-
-            Extra arguments to be passed to the function and Jacobian.
-
-    Equality constraint means that the constraint function result is to
-
-    be zero whereas inequality means that it is to be non-negative.
-
-    Note that COBYLA only supports inequality constraints.
-
-tol : float, optional
-
-    Tolerance for termination. For detailed control, use solver-
-
-specific
-
-    options.
-
-options : dict, optional
-
-    A dictionary of solver options. All methods accept the following
-
-    generic options:
-
-        maxiter : int
-
-            Maximum number of iterations to perform.
-
-        disp : bool
-
-            Set to True to print convergence messages.
-
- 
-
-callback : callable, optional
-
-    Called after each iteration, as ``callback(xk)``, where ``xk`` is
-
- the
-
-    current parameter vector.
-
-
-Returns
-
-res : OptimizeResult
-
-    The optimization result represented as a ``OptimizeResult`` object.
-
-    Important attributes are: ``x`` the solution array, ``success`` a
-
-    Boolean flag indicating if the optimizer exited successfully and
-
-    ``message`` which describes the cause of the termination. See
-
-    `OptimizeResult` for a description of other attributes.
-
-
-
-See also
-
-minimize_scalar : Interface to minimization algorithms for scalar
-
-    univariate functions
-
-show_options : Additional options accepted by the solvers
-
-
-Notes
-
-This section describes the available solvers that can be selected by
-
- the
-
-'method' parameter. The default method is *BFGS*.
-
-
-**Unconstrained minimization**
-
-
-Method *Nelder-Mead* uses the Simplex algorithm [1]_, [2]_. This
-
-algorithm has been successful in many applications but other algorithms
-
-using the first and/or second derivatives information might be 
-
-preferred
-
-for their better performances and robustness in general.
-
-
-Method *Powell* is a modification of Powell's method [3]_, [4]_ which
-
-is a conjugate direction method. It performs sequential one-dimensional
-
-minimizations along each vector of the directions set (`direc` field in
-
-`options` and `info`), which is updated at each iteration of the main
-
-minimization loop. The function need not be differentiable, and no
-
-derivatives are taken.
-
-Method *CG* uses a nonlinear conjugate gradient algorithm by Polak and
-
-Ribiere, a variant of the Fletcher-Reeves method described in [5]_ pp.
-
-120-122. Only the first derivatives are used.
-
-
-Method *BFGS* uses the quasi-Newton method of Broyden, Fletcher,
-
-Goldfarb, and Shanno (BFGS) [5]_ pp. 136. It uses the first derivatives
-
-only. BFGS has proven good performance even for non-smooth
-
-optimizations. This method also returns an approximation of the Hessian
-
-inverse, stored as `hess_inv` in the OptimizeResult object.
-
-
-Method *Newton-CG* uses a Newton-CG algorithm [5]_ pp. 168 (also known
-
-as the truncated Newton method). It uses a CG method to the compute the
-
-
-search direction. See also *TNC* method for a box-constrained
-
-minimization with a similar algorithm.
-
-Method *Anneal* uses simulated annealing, which is a probabilistic
-
-metaheuristic algorithm for global optimization. It uses no derivative
-
-information from the function being optimized.
-
-Method *dogleg* uses the dog-leg trust-region algorithm [5]_
-
-for unconstrained minimization. This algorithm requires the gradient
-
-and Hessian; furthermore the Hessian is required to be positive 
-
-definite.
-
-Method *trust-ncg* uses the Newton conjugate gradient trust-region
-
-algorithm [5]_ for unconstrained minimization. This algorithm requires
-
-the gradient and either the Hessian or a function that computes the
-
-product of the Hessian with a given vector.
-
-**Constrained minimization**
-
-Method *L-BFGS-B* uses the L-BFGS-B algorithm [6]_, [7]_ for bound
-
-constrained minimization.
-
-Method *TNC* uses a truncated Newton algorithm [5]_, [8]_ to minimize a
-
-function with variables subject to bounds. This algorithm uses
-
-gradient information; it is also called Newton Conjugate-Gradient. It
-
-differs from the *Newton-CG* method described above as it wraps a C
-
-implementation and allows each variable to be given upper and lower
-
-bounds.
-
-Method *COBYLA* uses the Constrained Optimization BY Linear
-
-Approximation (COBYLA) method [9]_, [10]_, [11]_. The algorithm is
-
-based on linear approximations to the objective function and each
-
-constraint. The method wraps a FORTRAN implementation of the algorithm.
-
-Method *SLSQP* uses Sequential Least SQuares Programming to minimize a
-
-function of several variables with any combination of bounds, equality
-
-and inequality constraints. The method wraps the SLSQP Optimization
-
-subroutine originally implemented by Dieter Kraft [12]_. Note that the
-
-wrapper handles infinite values in bounds by converting them into large
-
-floating values.
-
-**Custom minimizers**
-
-It may be useful to pass a custom minimization method, for example
-
-when using a frontend to this method such as 
-
-`scipy.optimize.basinhopping`
-
-or a different library.  You can simply pass a callable as the
-
- ``method``
-
-parameter.
-
-The callable is called as ``method(fun, x0, args, **kwargs, **options)``
-where ``kwargs`` corresponds to any other parameters passed to `minimize`
-(such as `callback`, `hess`, etc.), except the `options` dict, which has
-its contents also passed as `method` parameters pair by pair.  Also, if
-
-`jac` has been passed as a bool type, `jac` and `fun` are mangled so that
-`fun` returns just the function values and `jac` is converted to a function
-returning the Jacobian.  The method shall return an ``OptimizeResult``
-object.
-
-The provided `method` callable must be able to accept (and possibly ignore)
-arbitrary parameters; the set of parameters accepted by `minimize` may
-
-expand in future versions and then these parameters will be passed to
-
-the method.  You can find an example in the scipy.optimize tutorial.
-
-
-
-References
-
-.. [1] Nelder, J A, and R Mead. 1965. A Simplex Method for Function
-
-    Minimization. The Computer Journal 7: 308-13.
-
-.. [2] Wright M H. 1996. Direct search methods: Once scorned, now
-
-    respectable, in Numerical Analysis 1995: Proceedings of the 1995
-
-    Dundee Biennial Conference in Numerical Analysis (Eds. D F
-
-    Griffiths and G A Watson). Addison Wesley Longman, Harlow, UK.
-
-    191-208.
-.. [3] Powell, M J D. 1964. An efficient method for finding the minimum of
-   a function of several variables without calculating derivatives. The
-
-   Computer Journal 7: 155-162.
-
-.. [4] Press W, S A Teukolsky, W T Vetterling and B P Flannery.
-
-   Numerical Recipes (any edition), Cambridge University Press.
-
-.. [5] Nocedal, J, and S J Wright. 2006. Numerical Optimization.
-
-   Springer New York.
-
-.. [6] Byrd, R H and P Lu and J. Nocedal. 1995. A Limited Memory
-
-   Algorithm for Bound Constrained Optimization. SIAM Journal on
-
-   Scientific and Statistical Computing 16 (5): 1190-1208.
-
-.. [7] Zhu, C and R H Byrd and J Nocedal. 1997. L-BFGS-B: Algorithm
-
-   778: L-BFGS-B, FORTRAN routines for large scale bound constrained
-
-   optimization. ACM Transactions on Mathematical Software 23 (4):
-
-   550-560.
-
-.. [8] Nash, S G. Newton-Type Minimization Via the Lanczos Method.
-
-   1984. SIAM Journal of Numerical Analysis 21: 770-778.
-
-.. [9] Powell, M J D. A direct search optimization method that models
-
-   the objective and constraint functions by linear interpolation.
-
-   1994. Advances in Optimization and Numerical Analysis, eds. S. Gomez
-
-   and J-P Hennart, Kluwer Academic (Dordrecht), 51-67.
-
-.. [10] Powell M J D. Direct search algorithms for optimization
-
-   calculations. 1998. Acta Numerica 7: 287-336.
-
-.. [11] Powell M J D. A view of algorithms for optimization without
-
-   derivatives. 2007.Cambridge University Technical Report DAMTP
-
-   2007/NA03
-
-.. [12] Kraft, D. A software package for sequential quadratic
-
-   programming. 1988. Tech. Rep. DFVLR-FB 88-28, DLR German Aerospace
-
-   Center -- Institute for Flight Mechanics, Koln, Germany.
-
-Examples
-
-Let us consider the problem of minimizing the Rosenbrock function. This
-
-function (and its respective derivatives) is implemented in `rosen`
-
-(resp. `rosen_der`, `rosen_hess`) in the `scipy.optimize`.
-
->>> from scipy.optimize import minimize, rosen, rosen_der
-
-A simple application of the *Nelder-Mead* method is:
-
-
-::
-
-
-    x0 = [1.3, 0.7, 0.8, 1.9, 1.2]
-    res = minimize(rosen, x0, method='Nelder-Mead')
-    res.x
-    [ 1.  1.  1.  1.  1.]
-
-
-
-Now using the *BFGS* algorithm, using the first derivative and a few
-options:
-
-::
-
-    res = minimize(rosen, x0, method='BFGS', jac=rosen_der,
-                   options={'gtol': 1e-6, 'disp': True})
-    Optimization terminated successfully.
-             Current function value: 0.000000
-             Iterations: 52
-             Function evaluations: 64
-             Gradient evaluations: 64
-     res.x
-
-
-     [ 1.  1.  1.  1.  1.]
-
-::
-
-    print res.message
-    Optimization terminated successfully.
-    res.hess
-
-结果:
-::
-
-    [[ 0.00749589  0.01255155  0.02396251  0.04750988  0.09495377]
-
-     [ 0.01255155  0.02510441  0.04794055  0.09502834  0.18996269]
-
-     [ 0.02396251  0.04794055  0.09631614  0.19092151  0.38165151]
-
-     [ 0.04750988  0.09502834  0.19092151  0.38341252  0.7664427 ]
-
-     [ 0.09495377  0.18996269  0.38165151  0.7664427   1.53713523]]
-
-
-
-Next, consider a minimization problem with several constraints (namely
-Example 16.4 from [5]_). The objective function is:
-
-
-
-::
-
-    fun = lambda x: (x[0] - 1)**2 + (x[1] - 2.5)**2
-
-    There are three constraints defined as:
-
-    cons = ({'type': 'ineq', 'fun': lambda x:  x[0] - 2 * x[1] + 2},
-            {'type': 'ineq', 'fun': lambda x: -x[0] - 2 * x[1] + 6},
-            {'type': 'ineq', 'fun': lambda x: -x[0] + 2 * x[1] + 2})
-
-    And variables must be positive, hence the following bounds:
-
-    bnds = ((0, None), (0, None))
-
-    The optimization problem is solved using the SLSQP method as:
-
-    res = minimize(fun, (2, 0), method='SLSQP', bounds=bnds,
-                   constraints=cons)
-
-    It should converge to the theoretical solution (1.4 ,1.7).
-
-
-默认没有约束时，使用的是 BFGS 方法。
-
-
-利用 callback 参数查看迭代的历史：
-
-
-
-
-::
-
-    x0 = [-1.5, 4.5]
-    xi = [x0]
-    result = minimize(rosen, x0, callback=xi.append)
-    xi = np.asarray(xi)
-    print xi.shape
-    print result.x
-    print "in {} function evaluations.".format(result.nfev)
-
-
-结果:
-::
-
-    (37L, 2L)
-
-
-    [ 1.  1.]
-
-
-    in 200 function evaluations.
-
-
-绘图显示轨迹：
-
-
-
-
-
-
-::
-
-    x, y = meshgrid(np.linspace(-2.3,1.75,25), np.linspace(-0.5,4.5,25))
-    z = rosen([x,y])
-    fig = figure(figsize=(12,5.5))
-    ax = fig.gca(projection="3d"); ax.azim = 70; ax.elev = 75
-    ax.set_xlabel("X"); ax.set_ylabel("Y"); ax.set_zlim((0,1000))
-    p = ax.plot_surface(x,y,z,rstride=1, cstride=1, cmap=cm.jet)
-    intermed = ax.plot(xi[:,0], xi[:,1], rosen(xi.T), "g-o")
-    rosen_min = ax.plot([1],[1],[0],"ro")
-
-.. image:: 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IWlO2wc269qekaPk/ArrqPlR+o6Z+1YlcqRnemMinuOyA2Xidl7Rt2FBAIPR4hVgUAg+A+TX5Sq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%0AYII=%0A
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-
-
-BFGS 需要计算函数的 Jacobian 矩阵：
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-
-给定 *\left[y_1,y_2,y_3\right] = f(x_0, x_1, x_2)*
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-**J=\left[ \begin{matrix} \frac{\partial y_1}{\partial x_0} &amp; \frac{\partial y_1}{\partial x_1} &amp; \frac{\partial y_1}{\partial x_2} \\\ \frac{\partial y_2}{\partial x_0} &amp; \frac{\partial y_2}{\partial x_1} &amp; \frac{\partial y_2}{\partial x_2} \\\ \frac{\partial y_3}{\partial x_0} &amp; \frac{\partial y_3}{\partial x_1} &amp; \frac{\partial y_3}{\partial x_2} \end{matrix} \right]**
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-
-在我们的例子中
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-**J= \left[ \begin{matrix}\frac{\partial rosen}{\partial x_0} &amp; \frac{\partial rosen}{\partial x_1} \end{matrix} \right] **
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-
-导入 rosen 函数的 Jacobian 函数 rosen_der：
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-
-
-
-::
-
-    from scipy.optimize import rosen_der
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-此时，我们将 Jacobian 矩阵作为参数传入：
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-
-
-::
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-    xi = [x0]
-    result = minimize(rosen, x0, jac=rosen_der, callback=xi.append)
-    xi = np.asarray(xi)
-    print xi.shape
-    print "in {} function evaluations and {} jacobian     evaluations.".format(result.nfev, result.njev)
-
-
-
-结果:
-::
-
-    (38L, 2L)
-
-    in 49 function evaluations and 49 jacobian evaluations.
-
-
-可以看到，函数计算的开销大约减少了一半，迭代路径与上面的基本吻合：
-
-
-
-
-
-::
-
-    x, y = meshgrid(np.linspace(-2.3,1.75,25), np.linspace(-0.5,4.5,25))
-    z = rosen([x,y])
-    fig = figure(figsize=(12,5.5))
-    ax = fig.gca(projection="3d"); ax.azim = 70; ax.elev = 75
-    ax.set_xlabel("X"); ax.set_ylabel("Y"); ax.set_zlim((0,1000))
-    p = ax.plot_surface(x,y,z,rstride=1, cstride=1, cmap=cm.jet)
-    intermed = ax.plot(xi[:,0], xi[:,1], rosen(xi.T), "g-o")
-    rosen_min = ax.plot([1],[1],[0],"ro")
-
-
-.. image:: data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqsAAAFBCAYAAABQLOaIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz%0AAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXd4FFUXh9+d3U02lZAEElroHUTxU1QUKYK9KwjSFSmK%0AdAFBERBFKQIK0pUiIEVp0osU6b0kBAgkoYaEQOputsx8f8QNKVsGCHHR+z6Pj2Tm3Dt3Npud355z%0AzzkaRVEQCAQCgUAgEAg8EemfXoBAIBAIBAKBQOAMIVYFAoFAIBAIBB6LEKsCgUAgEAgEAo9FiFWB%0AQCAQCAQCgccixKpAIBAIBAKBwGMRYlUgEAgEAoFA4LHo3JwXda0EAoFA4FGYzWaq1qxEh1kVqNW4%0AhEvb9Jtmuoes4b2GEgevw/5fZbfzZ5mh3DPQ++sA2nb1UbWmds+mYtIHMHhlPVp5beLyhxDiZKjR%0AAmV/hO+6Q/tn3M+dboSIDvDdOHj3Hed2u/dB/0+rs3ffKVVrFgg8EI2jg+7EqkAgEAgEHsX0GdMo%0AUU3rVqgC+Ad54e8rMXuXzJ4F6uaftliDt59WtVDd/aeZg7vNTI+vjSRJFPPTcjrZxuNlHNtPOaIh%0A0F9D+2fcC2eAMb9JhJaAd99xbV86HK5cTVQ1p0BwPyG2AQgEAoHgviE9PZ0RX37Bm19VUj1G8tJQ%0AvQLUr+XeNiMThv2oMHCsOqEqywpDe2TQuGMZ/IO8APAL8eZ0smP7dDOM/EthTBd1QjXhBoxbJjP1%0AB/f24WFw9WoKsqxuboHgfkGIVYFAIBDcN4yfMI6aTYpT8aEgVfYJ5zK4mWLjifoOo4sFmLRAQ1CI%0AjldaqROryxdkcT1J4f0J1XOOBVbwJirZ8fUmHdJQorjEW0+qmp6h8yRq1ZJopMLe2xsCA/Vcv35d%0A3eQCwX2CEKsCgUAguC9ISkriuwnjeH1ERdVj5n58Av/wAPYdd/+4u5kKX81QGPaDn6q5TUaFkX3S%0AaflFFSTp1vwRdQM4llTweilZ8PVuhYnd1Xk+oy/Cgq0y82aps1cUMHjbOHnypCp7geB+QYhVgUAg%0AENwXfPn1CB5rVYrwKv6q7CP/TCRyWyJvrWpF1Dkb7qLjY36WKBWhp+kL3qrmnzHehCHAmxd6ROQ5%0AXuOJYkQlFbzYuP0SZUtIvPCIqunpM0Pi6UYaKqvc8bBqDdy8aSElJUXdAIHgPkEkWAkEAoHA44mP%0Aj+fnn39i9El18XPZpjDzg2PUfb8+4Q+VwstLw5k4hepOnLKJyTBhnsycjYGq5k+6JjPl63QGrahf%0A4FzdJsFMSFWwyaD92yWUbITx+2RWjlA1PX+dhB0nZGIi1dmbTPBhHwgI8ebq1avqBgkE9wnCsyoQ%0ACAQCj2foF4Np1j2CoHCDKvutM+PISLXRbFxzAPxL+HEoyrn9qBkSFavrefRJL1XzjxlipGzNAOo1%0ACylwLqikNwadhgtpt46N3idRsZRE03ru51YU6DlVolUrCA5WtRy++15Ca9DzyvtBxF+IVTdIILhP%0AEGJVIBAIBB5NZGQkq1ev4sUB6vaqZqZYWDDoJE+PbZGzl9RQpQT7Tzh+5F1KgOlLZL6drW57wZko%0AK8t/yaT3grpObQICdUT/ned0LQMmH5SZ3kvd3tPluyEuUWHSGFXmXL4Co8fLDJldmrByOuIvxqgb%0AKBDcJwixKhAIBAKPZuCQfrw4sCK+xfSq7H8bfpqA0sWo2/aWmCzXKIKdRxxn6A+bIlGznhd16qub%0Af3ivTB5oHkqZqs4TsfzDDZy+kf3vUXskqpeTeLym+7ktVug1TUPf3gpe6py89P9UovpDvvyvaQAl%0Ay+q5IDyrgn8ZYs+qQCAQCDyWPXv2sO/AbsYsaKTK/urZdDZOPUe7XZ3zHK/+ek3mf7kNRQFNLs16%0A7gIs+EPmj6PqvKq7tpo5tMfMzIvOvaoAIVV9OBGbwaU0mRlHZHZ9p2p6pq/TYNNoGNhPXQPJPfth%0AzTqZxWfKAlCyrI6LFy+ru5hAcJ8gPKsCgUAg8EgURaH/oN689kUlvHy0qsbM6XmCck9XIOzB8DzH%0AS9QugVar4fzFvPZDvpd46HEvKld377uxNwBo0rksvoGu7SvXD+BYoobhuyQeqCTxYGX3a0/LhKFz%0AFL75St12AVmGbj01tGhXnBKls92wJcvquXQhEUUR3dIF/x6EWBUIBAKBR7J+/XouJMTQqEM5VfYn%0AtiRy6q/rvLrwDYfnA0r45kmyioyBlVtlxs9T51X9fX4WydcV3htfza1traeKczzBxvwTMj/1Uyc+%0Av1kqUTJc4p23VZkzbwFcTZQY8EOpnGN+AVr0XhI3btxQN4lAcB8gxKpAIBAIPA5Zluk/qDdvjqqE%0AVuf+USXbFGZ1PcoDXepjCHJcMcC7Yij7T97aA/DJdxKPNfWiVFn3XlVjpsKX/dJ5Z0TeBgDOqPZ4%0AECYrPFxNQ00VWvtKMnz3u8yMKeqEbWoq9B8CPb4NQ5fv9SlVzo8LFy6omkcguB8QYlUgEAgEHsev%0Av/6K1TuVR14v5d4Y2DI9lsx0maZjnnFqU+apcuw6kv3YOxQJf+6TGTdHXV3VGeNNGAK9ea5bhHtj%0A4FJUBno9fNFWXTh+6FyJunUknnhMlTkjv5EIKeXNy50K1rYqWdaLixcvOhglENyfCLEqEAgEAo/C%0AbDYzaOgA3h5dCY3GcQZ/bjJTLCwcHEmT8S1cej2rvVqDo9E2FAX6jZNo/KI3waHuH4OJCTI/jk6n%0A2/RaqtavKAo/vBeJl5eGBBXR+Kh4WLRNZt5sdV7VMzEwbZbM8IVlHZ4vURbhWRX8qxDVAAQCgUDg%0AUcyYOZ3QqlpqNymhyn7psGgCygZRu7XrDP2wh8Kw2mDRWjhwUmbPxgBV87tqAOCIbb9c5WqsidAH%0AS3My/pJb+z4zJZo0lqlYQdX09Owr8XATP6rV83F4vkQ5mQsX49RNJhDcBwixKhAIBAKPIT09nRFf%0AfkHvP1wLTztXzqSzefp52u19z62tJEkUC/Xhg+FGXmhlICDQvVf1dKSVFQsymXC8oar1ZKZamfHx%0AKZp905hrJ5M4tOsqYHNqv+ME/HVS5vwpVdOzfhPsO6iw4mIZpzYly+o4v+2sugkFgvsAsQ1AIBAI%0ABB7DdxPHU/3pYlR8KEiV/ZyPThDRuCJhdcNU2StBfmgkGDlFXQWALz7OoF7zUEpV9lVlv3DYOfzD%0AA/hflwco/1RZTsQ6D+0rCnz0o4Y2rSFIxe1aLNC9l4a3e4fg76J0Vlg5PReFZ1XwL0J4VgUCgUDg%0AEcTFxTF23LcM3/e4Kvvjm65xes91ul9op8remmUl5VI6VavrMRjc+2p2bjZzdL+FGRfUeXnjI9NZ%0AP/0CXfa+C0Dl5hEsTVYwW8DLQXOsZX/BxWSYqLKt6g9TNVgULR98UdKlXcmyOi5ccL/9QCC4XxCe%0AVYFAIBB4BD369ELWWAir7LyNqR2bVc4uVdX1fxgCHZeqys+e0btAr+dctNVt0XxZVvisRzpNVTQA%0AgOykqildTlHluUqE1cnea2sINODnK3H2SkF7ixV6T9PQv6+CToXb6FoijPha4ZNpZdyWzipZVs/l%0Ai0miMYDgX4MQqwKBQCD4x9m4cSPbD+7HbJJJjM10a795WhzGTIUmo5uqmv9GTDK7v91F/d8/QVY0%0AXHARngdYNjeLmzeg0zj3DQAAdi1LID4qnTd+eSHPcb/iPkTGF7SftlYDOg0DequansGfa6lQ05cn%0AX3SfFGZvDJCcnKxucoHAwxFiVSAQCAT/CIqiIMsyKSkptO/ahWJTh2AoU4KobUkux6XfMLPo05M0%0AnfCcqgL9iqKw5v0/CG5ch5AnquNXMoBj+y1O7Y2ZCqP6p9Pqy6qq5jdlWJna/RRPD2+IlyGvm9Sr%0ATGABsZqaCUPnKnw7Wl2pqkNHYOlyG6OWOi5VlR+rVUHS2jh+/Lgqe4HA0xFiVSAQCAT3FEVRsNls%0AWCwWTCYTGRkZpKamkpKSQkpKCv0GD8L6eB38nn8S5X/1OLbetVhd9nk0xSKKU6tVbVXXP7U0iqtH%0AEqi/pA8AmsoRHNrjPEN/2hgjPsW8ee4DdW1el4yMxVDcl8d71i9wrkS9EhyK0eY5NnqJRHgpiZaO%0Au8LmQVGg28cSjd8sRqnyXqrWs2hCMllZVlJSUlTZCwSejkiwEggEAkGhIMsysizniFP7f/Zjdk+q%0A/d+KonDw4EEWLltKqZNLAQho8wIn3tvq9BqXo9PYMiuWDvu7qFpTVmoW67qtoeqoNuh8s/e2hjSt%0Aw95lpx3aX7tqY+q3GXy6qqDwdLieMxms+j6OzjtaOzwf8WQZDq84ccv+OkxcLrN+tarpWbwMYi/A%0A9/ucl6rKTcIFM9O/SKBsvZLExzvYfyAQ3IcIsSoQCAQC1eQWnWoFqf1nSZLQaDRIkoRWq8VqtdLz%0Ak/4UG98PbWhxAHxfeIqkdCuJcZmUKF+wXNTPH50gomlFStRW1zBg2+CteIcHU7HHsznHSrd6gp3D%0AF2KzKWi1eTtkjRliJKJ2IA80dd8AQFEUpnQ9RcUm5Sld33HprCrNK7DymozNBlotDJ6jpV5dhcce%0Adb8FICMDen8C7w0Pw8tLXSB0dLcEKj5aklrPl+ZcbIyqMQKBpyPEqkAgEAgKYBeZdlGaW5w6EqL2%0Af0N28f38/2k0mgKtU8dO+I6UcqEEt3k+55gkSRhKZ+9bLdE+Io/9sQ3XOLsvme4X2qu6h6uHr3D0%0A5yM0PPRtnuN+lcLwMmiJOWWjWu1bj8HTJ62sXGRkwjF1XtX9qxOJOZRK34ttnNr4l/TFYNBwPkHB%0AbIWlO2wc269qekaPk/ArrqPlR+o6Z+1YlcqRnemMinuOyA2Xidl7Rt2FBAIPR4hVgUAg+A+TX5Sq%0A9ZTaPaR2L6n9Z0ei1BGnT59m4pTJhB1aWMBerv8Ax9efolEusWqzyszsepR6PR5RVapKtsmsbr+S%0A8JYNCaheMIRuCA/iyD5zHrE67ONM1Q0AzCYbU7pG0XBQA7z9Xe8l9Q8yEBlv5PvVEs2aypQv73Z6%0AYuNg4hSZyVvVhf+NGTJfvn+ZZ4c8gG+QFyEV/Nkde17VWIHA0xFiVSAQCP7l5BaZVqs1j7fUXeg+%0AvyjN7Sm9U2RZ5v2eHxLw+QfoI0oVOB/Q5nmOd9ue59imqbGYs6DxqCaqrnF46iHSrxl5ZMYHDs/r%0Aalbi4F+RtOyU/fPOTWaOHTAz46K6BgDLRsehNXjTaFADt7ZepQL5cY2RvadkYleomp7eAyXqPO5L%0AnQbua84CzBiWiKGYD899UgeAkAp+XIgVjQEE/w6EWBUIBIJ/CWpC93axqtVqc2xBfei+MJg5exZx%0AShahH7ZyeN7v5cYktbZw/UImIeV8SU82s+jTSJ6f/aqqUlLpCelsHbSZB+b2RHJScb9EiwfYP+kI%0A4IfNpjC0RzrN3i+Lb4D7x2JCrJHfx5yn/Ya33doCFK8TwrqfE+jWBQID3dv/uR227VD4PU5dqaqY%0AEyaW/HidT3bfqvEaUMKAMdNEeno6/v7qWssKBJ6KEKsCgUBwn3E3oXsgx1uq0+nuqSh1xKVLl/ji%0Aq1GE/DkDjVbr0EaSJHxKhxK57TpPtfVlyWfRBFUIpuZbtVRdY+NHGwioW4FSrzv3epZ6+3G29JpF%0AVpbCigUmUlI0dByjrgHA9B7RlHuiLBEN1YXo9T56fA3w3Wj3tlYrdOul4ZUPgikW7P4RLcsKIzpe%0Apt4r5Sj3QPGc4xqNhpLlixMXF0ft2upKfAkEnooQqwKBQOCB5M+6zy1M7d7QOwndZ2VlAeDlpa5m%0AZ2HfU/c+vfH9sBXetau4tJUfqsuJjWeo9L8g/vw5lg4H1JWqit1ynph1Z3g6ZrJLO+/QQPyK6Tm0%0A28Ko/hm0GV1Dldf20PokTu5Mpk98V1XryUjM5Oj8KBQbqPk+MOMnDakZEh+PcVxdID+rZt/kcpyV%0Ar3c3LHAutLw/sbGxQqwK7nuEWBUIBIJ/kNxe0vye0txCNLcgvdvQfVF5UfPz22+/cfjCOUquGOXW%0ANuCd5zjW8y+SLxop36wSJWq6L1VlzbKyuuNKIj5+EUPJYm7t9eGhDOpyDb/i3jzbxX3I3WKWmdwl%0Akga9/4dPkPskL4A/PtxKsZplMEbFEnNeoZoLjZ6cDEOGK3w6u7Qq4Xwj0cqEfldoPe0JdPqCXuri%0AFQzExcWpWqdA4MkIsSoQCARFQGFk3RdG6N5e77SouX79Or0HDSTw97FovPRu7f1ebUJS2yxiDlj5%0A8FInVdfYM3oXsqSn5ijHBfrzo69RnvjfrzByszrP48rx8cjoaDaioBfTEWc3xBK97hzPnR3LngZD%0AOHEyzaVYHTpSokwlb5q95V5oA3zX+xph1Yvz6DsVHZ4vVsFL1FoV/CsQYlUgEAgKiXsVuv830H/I%0Ap3i3bI7P4/XUDTCZ0ei01GjzAF5uSkMB3IhJZve3u3hkw2eqplcUBdPF6/gW01Hn6WC39kkXTfz6%0AZQxtVqrokQqYMyws67COqp+8iKFkIJQuybETabzxqmP7E5Hwy0KZOYfV7YM9tC2DbStvMjz6dac2%0AIeX9OHdY1FoV3P8IsSoQCAS3ibvQvcViAbLD9Pk9pkWZde9s7UUtgDdt2sT6v7YTfmKp6jHXe32D%0A2abFkmpxa6soCmve/4PgxnUIaVhD1fxXluwmLeoSGGXSki0EBLv29s78+DSlHy5FpaYRLu3sbB76%0AF9oAP2oPzVangQ9X5MDhWMDmYP3Qo7fE4y/4U766++0FFrPM8I6XeKp7DYJKO68JG1LBnwOi1qrg%0AX4AQqwKBQOCEOw3dAzkC9J/Kund1T/dqDfn31cqyTFpaGl17f0yxqZ8i+bsvtg+Q/sd2UhZvhFGT%0AOPdVb7drPrU0iqtHEmh6aYSq+bMSUzn6wXSCxw3ANOw7ovfc5H8vON8Te/zPZA5vTKL3eXVJXpcP%0AJbB/+jGa7vsi51hYs1ocW7rZof2K1RAZrbDqT3WlquZ9m4zFpuON0Q+5tAsp78eF2Iuq5hQIPJmi%0A37gkEAgEHkTuYvlZWVkYjUbS09NJSUnh5s2bpKamkpaWRnp6OpmZmZhMppz/zGZzzr5SnU6Ht7c3%0Afn5+6PV6dDodBoMBLy+vPGL134CiKNhsNiwWC1lZWZhMJjIzM8nIyMBoNGKxWFAUBa1Wy9fjxsKT%0A9fB7/klVc1sTk7nabgjyJyPg7bZYjFZuxNxwap+VmsW6bmuoOqoNOl91SU/Hu0zDu2Zlgt57A1uF%0Aipz6K8X5eiwyP7wXycPd6uEX6l5s26wyS9uupWzrxylW+1ZIP6x5LRITFTIz89qbTPBRP2g3uCQG%0Ag/tH8qVzZn7++hod5zd0u/c4MNyHtJTs34lAcD8jPKsCgeA/gbvQPeDUY+oobO9KfGo0mpw5PY3b%0A8azmf83s/9lfE/vroNVq0ev1BV6TAwcOsHDZEtXhf0VRuNb+MzSVasB7HwKgLV2W2M3nCa7ieF/p%0AtsFb8Q4PpmKPZ1Vd48pve0ncepKI82sB8G7agGMbna/vjx8uYjJpaPFNI1Xz7/nuEJk3rTw9vWOe%0A4zpfAwHFdESesvK/+reOj5+kQW/Q0f4T99UOFEVh1PtXqPp0ONUahbu1lyQNoWWLERsbS82aNVWt%0AXyDwRIRYFQgE/yruJus+d5KTJ4XuC5P8YtVR6N5ZeazcgtTda2I2m3m/54cEjuuHNrS4S1s7aT+t%0AIHPPceTdt5KCTP9rxLnVu6nf9eEC9lcPX+Hoz0doeOhbVfObr6dx9L2pBH/bF11wEACB777AuTEz%0AsdkUtNq893TjahYLPj/Dm4teVldK6nwKm7/YxeMrezu09woN5ERkco5YvXQZvhmvMG6NuqSqrb+l%0Acuqwka8vvqjKPi3RRPK1FPbu3SvEquC+RohVgUBw35FfYDmrTepIoP7bs+6dkXs/rdlszvP65X9N%0ACkOoj53wHSnlQglu87wqe0vsJa59PBp5/Ky8PUnbdCa29QIUWUEj3VqLbJNZ3WEV4S0bElBdndg7%0A0XUGXlXKE9T1VptUr+oV0Rt0XIhMp0LdgDz2s/ueoWSdktR4sbLbuRVF4beO6ynxdA3CmzkuhaWp%0AWJojx24C2b+H/p9KVKtv4OHG7tuhZqTZ+LrrZV4aUR9vP/elvwB+6boPm00hIyNDlb1A4KkIsSoQ%0ACDwWV6F7s9mM9u92nYURuv+34Cp0n/venYXuC4PTp08z6ccphB1aqGpuxWbj6tsDUJ5oDC/lKw1V%0A/1E0Oi3XjiUQ9uCt0PeRaYdJT8jkkRkfqFrT1RX7Sdh4jIiYNQXO6UqVJHp3Sh6xGrXrJvtWJtDz%0A9Puq5j86P4qrx5N44eIQpzYhj1XhwJooAHbvg7UbZJacLadq/imDkwgI86dpT3XVDg4vjydy8xUe%0A7NuI6JjTqsYIBJ6KEKsCgeAf505D9zabrVAL5t8vOAvdOxLquQWpoigYjUa8vb3v2dpkWeb9nh8S%0A8PkH6CNKqRpzc8wczPHXUJbsdnheKVOR2E3nc8RqekI6WwZt4oE5PZF07h9j5uR0jnT6keJf9kTn%0AYEuCXK8OJ/48zrMfZGfj22wKP3SO5IEOdQgsrcLrmZTJ6p5beGBiW5dJXuHP1WH/+OXIMnTrqeHZ%0AtkGEhrv3kp46ZGTVT9cZfOglt7YAGclZzO28mwZfPY9vqUCi50SrGicQeCpCrAoEgiKhsEP3ZrMZ%0AnU6HXq8uJFqUaDSanJD73ZD7NcgvTO+ks1VRJH3NmDWTOCWL0A9bqbLPOhpN0shpKAvWgJfj4v/m%0AJ1twdtVyGvR/HIBNPTcQUKcCpV5voOoaJz+chb5iWYr3bOPwvP9rTYgcsC3n5/VTL5J600aX75uq%0Amn/NR38SULUUFTs85dKu+COVMJlg7ERISJKYNdm9mLfZFIZ3uMzDrSpSqrq6zlYLPzpAQMUSPPBh%0AQxKPXmZ/zB5V4wQCT0WIVYFAUKg4C93nTtpRk3WfW4w5El/2gvv/BtyF7nML9bsJ3d/rhgDHjx9n%0A6IjhhP/1MxptwV71+ZFNWVx5qx/Ka23gkSecG7Z7n4vNvsdmtnFhZzxn157h6ZjJqtaU8Mchrv5x%0AiIizfzi18X+1MbHts0hLtqDICnMHn+aV2S+oSqo6uzGWU3/E8OwZ90lekiQREOzF5yPNDJkVhlbr%0Afv7fpt4gOVGm3/TH3NoCHF97iaOrL/Du6YEAFKsUzMVz8f9IMwiBoLAQYlUgENwRd1MwvzBC955c%0AHsoR+T3L+f99p1n3t7uGeyVYjEYj7d7rijkrC30ldcXtkwdPwmbWwDc/uDasWAV9gC/xO+L5o9NK%0AIj5+EUNJ915Gy80MjrT/gaAveqArGeLUTjIY8C3hz+m9N/lrcSIhVUOo81Y1t/ObMy381mEdVQe8%0AgE94kFt7ALOio1xlhZc6qGjxetXCD4Ou0ml+I3Q698LWmGrmp/a7+N9nzfELz05S8wowYAjw4cqV%0AK5QuXVrVGgUCT0OIVYFA4BRHeyOtVqtHZN0XVqi9sMn9etmbBuQO3ed+Xe6VKC1qFEWhW8/eXC5e%0AB21yIpmb9+L/0tMux2T+uZ8bM5ahrN0LKjyY1ohqrO64EkWrp+ao1qrWdbLnT+jKlSa4b3u3trYK%0AFfnjhwuc2HaDD092UjX/ls92Ifn7Ufvz11TZX9t2irTrJho8HejeGBjzYQJlHwjhwVfVJWEt7n0I%0A3/Bi1B/QOM/x4MoliYmJEWJVcN8ixKpAICiQtGSz2bBarVgslpxQqLvQvUajKdJSUP+0Z9VV6D73%0A+u5l1v2drLmw16AoCpN/nMr6fScxfbIbZrQhc/EGl2LVlpLGlVafoHTrB5XdezABrA83JH32IR7f%0ANlyV/bV1R7i8fB/lo1epsvdq/AgHRx2lfqfaFC/v3mt7+XAC+6Yepcnez1XNb0kzsuedH/Fv/DBR%0AB465td+7MZ29G9MYee4Nt7YAUZuvcGBJLK1P9i9wLqBycWJiYnjqKdd7agUCT0WIVYHgP8Tthu5t%0ANlvOWE/Lui8KsZr/NXHUxSm3l9TuNbV7n+9l1v2dcDdi1Vn1gb/++osR34zDNHAPePtC8z6kTn2J%0AEjab032rSd2+hNDS0HeouosbjbBuJYokEVg3wq25JSWTw+1+IGhoV3SlS6q8QdBI8NKPz7g1tVll%0AlrVdS5lWjxFUR53X82jvhWiDi1Ph15FEhT2HOUvGy9uxRznLJDOi0yWa9q1NQKj7FrKmdAuz2/5F%0Avf5PExBRsNqBT+VAzsSccTBSILg/EGJVIPiX4Sx0n7+8kZrQvSzLeHt7o1NRHqioKUyxWthdnP5p%0Ar+/d4K7Fqv3edTodly9fpnO3DzF1mg8lKmZPUP1pNFodpj3H8Gn4UIH505ZtIu2Pncg7I1WvSRrc%0AE9ChCwnl+taThL/6iEv7yN4/ow0vQcjAzqrmNx05xc0J8/EKMJB48jql64e5tN8z4RAZyRYazVS3%0AXeDq+uPEL95HtZOL0AUXwzfQi3Mns6hR38eh/U+jriN5efPKF/VUzf/bJ0fQB/nTYFgLh+cDKwdz%0Aaq0oXyW4f/G8J5BAIFCFo9C9q6z73KJUbejearV6rOiyC8Lb8RY68w466uLkKaH7wsT+WhWGODeZ%0ATLzdtiPpTfpA7eZ5riOH1SVj2eYCYtV6JZGEzsOQh42FUJUez6ULUNauRJkehXniBySuOuRSrCZu%0AOsalpXsod3K5qunlTCNXXu+D8mY7OLiduJ2XXIrVG7EpbB62i8dXOG6pmh/zjQz2vDuNEp91xisi%0Ae15dyRBOHzE5FKtxp7NYMD6RPn86Fp75ObPzGrvmnqXV0b5ObYpVDiE65i9V8wkEnogQqwKBh6Mm%0AdO/IUwp5RUduQapWgEmS5JFJTIDLWqKuiubn9w7+m9ut5q84YLVagex2q3fTYlVRFLp/3Ic4n0rY%0AWgwoeP6prqT+2peQcf1y5lIUhYR3P4U6D0Hrjupu4Gw0DO6J0udnCCkNzTtxZcb71HVibk0zcvjd%0A7wka+B5eKhsSXO89BlkywNeTyBrUk5gNf/L4x/Ud2iqKwvJOGwhtVJ3wZxy3VM3P4W5z8SpXirBP%0A2uUcs1X1gOvXAAAgAElEQVSpROT+U7ySz/GrKAojO12mZosyVHykhNu5zUYrM1vvpM5HTxJUOdSp%0AXWClEOJj4lStVyDwRIRYFQg8gMIM3ecWHHcrwCRJyrNv1ZOwe3zt3t+7Dd3fz6it02r3pHt5ed3V%0A6zB9xkzW7j6KceAecDTPo62RF36A+dR5vGtWAiD1x8WYjp5B3huj7iLGTDTtX0d5/A1o9Fb2sYav%0AY/2uLRlnr+JXJbzAkKg+c5FCgwkZqq4Fa/qqP0lZuA558+Hs+3jjHeI6zXPqrT+24BSXjybywsVP%0AVc1/8feDXF53jGpnluY57tf4IY7PO1zAft0vKZw/ZebrzU+qmn/FkKNovL1pOPoFl3a+Yf5kmUyk%0ApKRQrJi6xgICgSchxKpAUITkD91bLJYcMagmdG8XHkWVdS9JEhaL5Z7NrwZHiU25RanFYkGr1eaE%0A7gtLqHsarrzFauu0Go1GtFrtXb02u3fv5vNR32D8ZBd4+zk2kiQ0JaqR8dtmvIdUwnw6lmsDxqNM%0AXQS+vqquIw3qCbIWpf/PeeaVSkZwbf0RKlZ5Lo990tYTXFj0F+VO/qZqfuvVJK62G4o8+Eso+3fS%0A1iNPINsUkmNuElIlb6JS5nUjqz/cTJ3x77psqWrHdC2V/Z1mEfZtT/Ql89ZULfba05wbOhlZVpCk%0A7N9F6g0bY3pe4Y2xj+BlcP9oPr8viW3TTvP2gV5ubTUaDSGVwoiJiaF+fcdeY4HAkxFiVSC4B6gN%0A3du9grlrht5t6L4wsW8DKIruN+4Se3ILdbsYM5lMHtty9U5xtZ/0Tlqs5p/7bn6Ply9fpmW7jhg7%0AzIGSlV3a2h5uTfrCGRQf2Imrb/WHpi9As+dcjslhyXzkdatgRnSBGqyWui249vt2Kn54ay5ruolD%0AbSZRrH8HvMqXcTu9IsskvDMQatWDjt1vnZAktGHhxO24VECsrum5Fb8qpajUuZH7+RWFg51nY6hV%0AidCurxc4b6hcFp1e4mKMmYiq2RUjJg24Rkj5QJ58r6rb+S1ZNma8s5OanR8luKbrZDA7XiV92L59%0AuxCrgvsSIVYFgjvkTkL3+ZN57CLVYDB4pEfQvp7CEquFnXXvqS1X1VQDUBu695Rkr6ysLN5o3Y6M%0Apz6CuipEZ9OPyFr9GYk9vsKSlIayeq66C505BZ/2gr5zILhgqJ9XPiLpo2nIZiuSV/Yj7NSA+UjF%0AihH6RQ9Vl0iZtADTiXPIe84WOGes9SjnN0dRv1OdnGMxm+OIWhVDi9PuW6oCxP+ym2t/naH6eede%0AXu/QYkQfNhFR1ZvjezLZsOgGn598VdX8f3xxHKus5cmJr6iyv7o3ngu7Yrj88GVV9gKBpyHEqkDg%0ABldZ964EqZrQvaIoZGRk5Nh4Inbv6u2sz1ltUkdZ93dTs9XTS0TdaZ3WeyVK7/RLh6IofNSnP+f1%0A5bA+P1jdIG9fpOLhpMxbhfLbVlBT/iwzI3ufasO34Kk3HduUq47O35/k3acJfboW17dHEj9vG+WO%0ALlO1rKzjZ0gc8j3KT7873pLw0hvEDO+a86PFaGFZ+3VU6fc8vqXct1TNvHSDgz3mUvrHQeiCnHeq%0AspYvT/TBizR5I5Dh7S/xWIcqhJb3dzt//OFkNk2K4o2/PlT1N2nJMLP2rXkYakYQe/WSW3uBwBMR%0AYlUg+Jt/IuvenvByu2KwKHFVEeCf9g56SsvV/B5j+/vG/kXEU1qs3qlYnTX7J1ZtP+A8ocoR1+Ox%0AJSVBxcpQ72FVQ6SBHwF6lL6zXdpZS9Uiac1hgh6pzKE2kwj8uC1eld0X55eNpuwyVa++A082cWz0%0A3CtkftyO9IQM/MP82PLZbiQfH+p8UTCcnx9FUdjfdjq+j9Qh+N1nXdr6PF6X4zvP8uvEZNIzNLzz%0AvevasQBWi8zM1jup2ro+JR50v90BYMfHK9H4+VNt4gdE9V+saoxA4GkIsSr4T+EoDK3WU5pffOX2%0Agt2N6NBqtdhsNo8svA/ZgtDefvVOE3vu5dqK0rOqtk6rXeD7+vp6zLaOO32d9u7dy5DhozB+8hcY%0A3Hv+AMhMQTO2KUqZJyB2ByRfh+AQ12MWz0XZuAZl+qkC+1TzIz/1DleWf40t04zGx48SX32salnJ%0A/cYh27Tw7RTnRjod3iVDidt5ieDKQez98TBN9gxTNf+56du4cfwiNeJXurUt9vKTRE2cz6lDGXT9%0ArYmqL6vrvjqJMV2h8XR1LVjPr47k9OKjPHpyKpKPF6eizxTJ/nOBoLDxzKejQHCX5BaZdpGlNnTv%0AqBTUvQzNarVasrKy7snct4O7/aRwy4PsCe1W4d6JVXfJXu7qtNq/AHmiKFBTQ9V+/5cuXeLtdztg%0AbP8ThLlP/AHAakGa9BJIAShvrEeaXxV542po1cH5mNNRMKQPSv95jvep5ufZzmTO6kPc7C2UO/Sr%0AqmVlrN3JzXmrkTccdCuGjZXrcm7zBbYM202Ztx8jqK57r236+USO9ltI2V9GIKmoFuDToDZWq0KV%0AhiWp1aK0W/vLJ2+y7tsTvLqlmyphm3ktnY3tfqXCVx0wRJTI/p2ikJSURIkS7mu4CgSehBCrgvsa%0AR6FXu1CwnzObzTmeUGfJPP9k1n1RZtzD7YfuIbvkkT0JzJO4G7Fa2Mlejub3JPK/v9xVHbBarbRq%0A35n0J7tBvRfVXgTp506QGIfc8QxIEnKZF5GW/ILsTKza96k++TY0dB9qByArE/R6fN94Bq/qFd2a%0AW69d50qbwcj9h0N59/bKs69w4LPe+IT602i2+5atiiyzt9WP+DV5hKBX3VcLALgxYyVaLy1NPq7h%0A1tZmlZnxzk4qvV6X8AYR7tejKGxq9yu+tcoT0TM7CUuj0RBUPYLo6GghVgX3HUKsCu4Lcguq20ly%0Ayp3NfrfJPPcKu0AszK0A+YVI/n/fTmKPXXR5YvjwdrPu84uzwkz2yr8uTyF3cqCiKBiNRlX7irt/%0A3Ifz2tJYnx+i+lrSqhEox9ahtD8JuuySTDQYhDy7EqTchGIFE5SkAT1A8kbpM0vdRawWpGGvINu8%0A0KSkq7r/hDaDoVpt6NJT3TXq1gcJ6v3YQZUX88yEjaTHJVN950+qpjdFnudS34loSpbiwqEbPPSa%0AawG6afwp0q5beG1uK1Xzn5y+j6sHLvJYXN71GKqXJjo6miefVNd0QCDwFIRYFXgMd5N17yp0b7Va%0AsVgseHl5/dO36BR7ktXt4s47VhiJPfY5PM1TmBv72u6nUlCFiZovJ/bfu5qqA58OGcqyjTswDd7v%0ANmSew665yOvGQssd4Jer9qd/abRBpbBt/APeejfvmEVzUDavQ5l5WvV1pB8/hoR46LaetGmNCbNa%0A0bj4kpfywyJMh08j7z6j7j4y0tF0fxetrw9aL/ePyNRTlzn+2TIqrByLpOIzRjaaOP/qJ0ivvQah%0AJTiz03UVg4TTqawafpQX/3hPlXC+eTaJHX1XUXPBAHT+easdSFXDiYw+5XYOgcDTEGJVUOS4C93n%0A/+9uQ/darRaTyeSRnkE7Wq3WZaeofzrr3r5VQavVFtqcd4IjcQ6QkZEB/PtbrN7N1gX7lzZ33vs5%0Ac+bxww+T0TZ4B3ycl17KQ/Q2mNcdnl8AYQ8WOG0LfxbtsoXYcovV6Ej4rA/KgF8gqKSqy2jWzkDe%0AsgD6H4OQ8kgGX4y7juLbyHG1gayTZ0kcNBFlxmLwV5EcpihIfbqA1hdrxUe4timSUi/Uc2ouW23s%0AafkjgS83IqCZ+2x+gCu9JiDbtGgn/4By8BBxP091+tkkywozW+8k4rkalG3sugmDfT1r35xPyLMP%0AU/LVxwuc96lRhqNzj6pap0DgSQixKrhnqCkF5S7rvjCSeezloaxWq8d2OrIL6tyC3VliT+7XpKi8%0Ag67KV90L3HmM839Z8fb2vus2op6E2qoDhf3lZOasnxgybCzUXoLt4LvQPsN5S1U7l6Ng0svw2BdQ%0A1UlR+waDsc2tDulp4B+Q7b1s/zrKU+/AE6+pW9zJXSg/9oaOSyGkPABySG2MK/90KFblLHN2maoX%0A34DGzVVdQjNnGsqOrSizYmDLL1xZ8zX1xrd2ah/99R+YrmdSbcFwVfPfXL6N6ws3ot+9O/tv6uH6%0AyFaFGxczCS5X8HXe+n001y8a6bC3jar5D4zcQmaikQYHBzo871e9LGfOLMVqtXps9RGBwBHi3Sq4%0AK/KH7nNn39vFjSdk3et0Omw2m0eIVWdCDLITmTzROyhJkkvP751SGB5jm80GeNY+UbXcbdWB272W%0Aq7HTp8/ksxETMNbcAr5VkOJLIB/6HR5v63zSlAQY2xSqtoRHBzi3K1YeKTAMefNaeOVtpP7dQOuD%0A0lflPtXEizDsJWjyCdR6/tY9PdyB1GWfEzK2X4Ehyf3HY8sCxs9Qd40jB1C+/BQ+Xw6BwdC8A+mz%0A+mK+mYFXUEEhefNoPJGjV1Npy2RV4XnzhQTiO4xE+9UopIjs6gKSJKEtUZzY/dcLiNWk82n8/ulh%0AnvutA5IKYXl1XzwHx/7Jg9u+dWrvU6UUl8/Hk5GRQUBAgMfWdhYI8iPEqkAVrkL39v729n2N+T2m%0AnpB1by8PVZRbAW7XO2avWuCJe2vvxrNamMlejvDELla513Svqw6oxdV7f8qP0xj+1Q8Ya20Fn0oA%0AyIFvIP05BdmZWM3KRDO+ORSrgfLsTLfXl8OaIS1bgJyRgbJ1Y/Y+VTVkGdEMeRYiHkN5Pl+900fa%0AY13xEZa4y+jL3yr/lLFhFzd/WoG8bq+6vbDJ16HDG/Dih1D/by+sXyD6kGCSdpym9MsP5TG3ZVnY%0A/fZkglo/i1+DOg4mzItisxH3xmCkRxug65C3KoIpoiqxe69T/41bSVaKojDr3V2UaVKF8s9Wdzu/%0AJcPM2jfnUarL8xT7n/MSY1ofb/zDQjh16hQPPPAA3t7eQrAK7guEWBXk4U5D95D3oetpWff2dRV2%0A8X1He2vv1Dtm9/56Inbx5UrwFEWyl6u1/dPk/tuw/x4zMzPvadWBwmDS95MZ9e10jDX/BJ8Kt05U%0AGIa8txRcj4eQfNnqsg1p6ltgMiO336zuQg2GIM+rDbu2owxcqG6fqqIgjW2ffZ3eqwue1+nRhkSQ%0A8ccOgnpkZ8rbkm5w9Z2ByH2GQiUVtWFlGalbGyhZAbnLt3lOmUvV5drGyAJiNfLz5VjNUHH6IPfz%0AA9eGzybrYhLadX8WPNnwSc5sm5Pn0PZpZ7h6Oo32F9U1O9jRayUaXz+qTfjArW1A9bKcP3+eypWz%0A98B6Ykk6gSA/Qqz+B3EUus8tSuH2QvcajQaTyYSXl5dH74PS6XR3vFerKLxjWq0Ws9nskYlg9t+7%0ALMs5/y/q/ZSu1laUYlXt1gUgx3PlCb9PR++r7yZ8z+hxs7I9qobyeQfog5D8qqHsmoPy8md5TkmL%0AeqGcP4zS6Yz6agF6X9B5Qf0W8PgrqoZolnyLcmgLyuBop9exVn6BjMUbCOrRKrtMVdshUKk6dO+r%0A6hrSxNEoUVEoP50vcE5p+CZX1o0gd8rY9b0xnP5hE5X3zFTllUzffpiEcQvQr12L5GAbkvbVV7gw%0AaVzO7yf5QgZLBxyk2bw26Azuoyzn/4ji9K/ZXarUoKtWiri4uJwyZpIk4eXl5RHvUYHAGf85/3/n%0Azp0JCwujbt26OceSk5Np3rw51apVo0WLFty8eTPn3Ndff03VqlWpUaMGGzZsyDl+8OBB6tatS9Wq%0AVenVq1eR3oNa7B4ei8VCVlYWmZmZpKWlkZKSQkpKCmlpaaSnp5ORkYHRaMRkMpGVlYXJZMoT2tfr%0A9RgMBvz8/PDz88PHxwdvb2/0en2OYLULQU/G7rl0JWzyv2Ymk4nMzMyc18hisWCv2ert7Y2vry9+%0Afn74+vpiMBhyBPudCBS7vad4CfO/FvaHW0ZGBllZWTneQ51Oh7e3t9P3x71+CN4LsWoXpFarFbPZ%0ATFZWVs69575/eykoHx+fPPdv38rhSUlf+cXqmLETGD3+J4y1/iwoVP9GDvsYtk2DXK+vZuME5F3z%0AUVrtAi+17VeT0Cx6EmwGJHOmujH716H8MhKlyxrwD3Vu93RvMvccQ840kjp1CcZ9J7HN/0PdNXZs%0AQZ4yDmXkWjD4FjzftB0ZcUlkJWfXc7VmZrH77ckEd3sd37pV3E5vTU4h9s1PkT7uhfSg46oCUq1a%0AaLRaEmPSUBSFnzvsJvyxilR+zf32AmNiOhvbLaLCl+0xRKgr9G+8doOlK5bj7++PxWLBaDTm/H0L%0ABJ7Kf06sdurUiXXr1uU5Nnr0aJo3b87p06dp1qwZo0ePBiAyMpJff/2VyMhI1q1bR48ePXL+oLt3%0A786sWbM4c+YMZ86cKTBnUZJbVJhMJjIyMkhNTSUlJYXU1FTS0tJIS0vLEVx2EWZ/4NpFqZeXV85D%0AN7/4cvfQ1ev1OQLXU7ELyNyhWrsQyy9E7MLbLkpdCfXCEiP2qgVFuRUgvyjLLc7zizL7l5Lc74+i%0AFKXOuNsuVs7u32g0YjabczzGuUVpYXw5+adQFIWvvh7D2Elz/xaqLlqJhncCUwbE7M7++fAKlN+H%0AwqurIch9JygAslLRLH4ajS4cnt6PfHgLZKS4HnMhGka1hJe+hQoNXNuGlEcbWJybUxZzbcB4bBN+%0AgkAVJbeuXIIPWkObYVClvmMbX3/0IaEkbY8G4MQnS8DgS5lx7h0UiqJwoe1wNBEV0A92nJ1vR1ci%0AhNgD19k95xzxR2/w3AoXrWlzzb+p3WJ8a0QQ0ctJFYZ8XP1lK4lr9pP5d4QkICAAk8lESkqKxzsb%0ABP9tPDdme4946qmniI2NzXNs5cqVbNu2DYAOHTrQuHFjRo8ezYoVK2jdujV6vZ4KFSpQpUoV9u7d%0AS/ny5UlLS+PRRx8FoH379ixfvpznnnvunq1bTeg+/37Sosy6t+/J9KTi+85KQBmNRsAza3La99UW%0AdtUCZwlOzkL39tch92thsViwWq0eJ8put4uVJ2xdKAryv//tnxcjR41m2uwVGGttB+9SrieRJBS/%0ABkg7ZiBr9TDjXWgyGcqq7IBkyUSztBka2Qv5qR0gSWj9S2HbtQKat3c8JiMFzactUOq8Bk/1UHUZ%0Aa4mHSBowHl5vBc88736AxYKm01tQ9VGUt11UMQCySj/AtQ2R6IN8ifl5B1WPzle1putTfiN9byS6%0Ao+7rmpoq1ODI7+c5sfYST097Cy9f95+hkTP3c2XfBR6LVdc1K/P0JU51nUzAmEHEDpmQEyGyl/XL%0AysrK+RwXCDwN8a4EEhISCAvL7rgSFhZGQkICAJcvX+axxx7LsStbtiyXLl1Cr9dTtmzZnONlypTh%0A0qVLhbKW/A9Ve7a9PVMccChIHWXd325WtZ0nn36az4cMoUWLFrc1TqfTYTabc8ROUZH7dXBXk1OS%0AJMxmM35+fh4pRuwPjjulMEpBOcNe7cHTyJ95X1SloNytyb6ee/0+c/SFzNH7X1EUvh37HdN+Wo2x%0A1jbwDld3gYiRyPsaw+EV8GAvqOPe6weANQvp9xcgMw258YmcPae24i8jrZ+F7Eis2mxII98E72CU%0AtnPVvgBgyQIvL5g4W9UQaeQguJaIPHufe+On3ubS0oFc+O0Aof3fxVC5rNshxuNnufzJD2jnz0NS%0A4+Vt9BQHvthK2acqUq31Q27NU2Kus6PPSmr8MgBdoIPtC/mwmcwce3kE3i83w69bG7I+m5jz3LPZ%0AbPj6+pKRkVFgz7VA4CkIsZqPf8qzNnXqVJ555hlCQ0Mxm80kJycTGhpaYO9g/qzqe5FV/OjjT/DW%0A228z9PNhDOjbR/W8Wq0250F5LzodFYYQUxQlZ9+pJ4pVe/LSnWTdO/rSUpgeYzVrKwqceczT09Nz%0A1ulpHvPCwJ0Qt/8NOHr/K4rCFyO+YvYvG7JD/17qOkYB4BUMGi2EPgRPjVI3RrYirXoTbsQhN44C%0AKdejpvpQ5E0RkJIExfLuRZVmD0I5dwJl6DnVy5NWD0S+dAy8feDEEajnuJtVDmuWIy+aA98fATVe%0AxCZtME7uhm/VcpT6ootbcznTROyrA9G8+Ra6Zs1U3YNGAY1Ww/PL3X8RkK021r41n+LN6zvsUuWI%0Asx9NxWrVEPLLeDQaDb61qxEZGUlwcDBarRaDwYCiKDmC1WAwCMEq8CiEWCXbm3r16lXCw8O5cuUK%0AJUtmf5CXKVOGCxcu5NhdvHiRsmXLUqZMGS5evJjneJkyZW77umlpaURHRxMVFcWmTZv46aefuHHj%0ABvHx8TRu3Ji5c+fmPHzs3iMfH5+7v2E3fD5oIAsX/cpXY8dy4NgxfpoyGT8/N11sIGdfn8ViuWOx%0A6kyIOKvJebtCJHc3K0/ZrpCb3PtWc4t/dx7joiiFZJ+7qMSqWo+5/ffp4+Nz34fv78UXEUVRGDJ0%0AOLN/2Yix5lbwUpeIA0DaQTjyDBCKZMlAVaVdRUZa2xbl6mGUxlGgM+Q9byiJFBCBvGMpvNTt1vEt%0AC5D/mAp99oGXe28hgGbHZJSd0+C1vWi2d4K1K1BcidVzZ6H3+9BjMpRx374UQLPpZ9B7ETzARXOE%0AXFz+aBw2rTe6SRNU2csHDmAdMxYvfwNpcTcwBLu+9wNfbiEjIYMG+9WVzUr4dTtXFu+kxIm1OQJU%0AqVWFqKgoGjRokLPlyGAwYLPZyMzMzBGs9/PfkuDfhfjqBLzyyivMmZNd527OnDm89tprOccXLVqE%0A2Wzm/PnznDlzhkcffZTw8HACAwPZu3cviqIwb968nDHuuHjxIs2bN6dcuXKEhYXx/vvvs2bNGurU%0AqYO3tzcTJ04kPj6eJUuW5Enm8fb2zrM/9V4SFBTEiKFD8K5aiz/x54lmz3D+fMGyLo6wVwVQu4fQ%0AWWKLxWLJKZNU2Iktnla5IH+ylyzLOYlyjioQ5E/2Ksokn7tpDuAMV8luJpPJ7f0722Pryah5/6tJ%0AfHT3O5dlmfc/+JDZC7fcvlBN3giHG4PfB1DmCPK1Y3DjrLsbQ9rUDSV2K0qjo+DlOAQuh76NtC5X%0AI4Ezh2BiF2g1C8Jqqlvf8ZUoKweitFgOxWugVOkIyxc7tzdmomn/GjzyEjzjZL9sfo5sRZk+ACW4%0AFsath9ya31y6heSlW9GuXKnKM6kkJWFu2Rpe74UmtBxXdse5tE/Yf4GDY/6k1orPVXW1yjx7maj3%0AJxE4ZQS6iFtNE+TalTkcdRKz2ZzzpV2j0eDn55ezr99eRk8g8AT+c57V1q1bs23bNpKSkihXrhwj%0ARoxg0KBBtGzZklmzZlGhQgUWL87+wKtVqxYtW7akVq1a6HQ6pkyZkvNgmDJlCh07dsRoNPLCCy+o%0ATq4KCQmhb9++1KxZk4iIiDwfaDVq1GDr1q08+WTB5AW719JsNmMwGAqcL2w6d+rI97NmEdv4ZeJq%0AP8yTz7Rg/szpNGnSxOW43J4uvV5/T/dQ3ilarTZnHUUV6srvMcv/7/xeUqvViq+vr8eJrzsVq0Xh%0AMffEB+s/+f5PTk6mfftubN+5HeqtBy8X5Z/ykzAPTnWD4mMgMDvJSeNVF83xaciNxjgdJu34BCX6%0AN5Snj4DBxfWqDkTeMD67japOD0Oeg8e6wkMt1a0vdg/MbQMNJ0OZvz+TqndC2d8XzsdAxXxeU0VB%0AGtADrBqUgb+ou8blGBj+GrQYAaFVubm6I2VcRBXMcVeI7zwK7ehvkFRE2hSrFWubtmjK1kD54Cuy%0AEi9y5c9jPNDjCYf2OV2q3n+OYo9Uczu/nGXh2Csj8X6uMX5t81YL0NWuypGFG3Lee3Y0Gg3+/v6k%0ApqZiNBpznjue9jkk+O+hcfMB73mf/v9ibDYbDRs2ZMmSJQQFBRU4b99T5OvrWyQia/v27bTs9hGZ%0Aa6LgyB58+rVmUK+e9O75UZ4PL2dZx3YBUViJX4WJyWTKEQiFibMwrj107SjhK78os/+ePTEJzGKx%0AYLPZnH5hKoz7vxOMRiN6vf4fyWS237Oj5EjA4Xv/Xr//Dxw4QMuWHUlNewOL5SiasHLINea4HwhI%0AF8Ygnx8BIfPBL5fIyVgNKe2gxzXQFvy7kfaMRNk/HuWpfRDgvnOUtL028itt0fy1DBQ/lI+2qbu5%0Aa2dg/CNQqxc8OjzvnL/XRX7/XejeJ89xzYLZMGIwyvRTEKwisSwjFU33eiilH4O2C7O7XH3hT/XD%0Ac/CuWrDUl2K1cubR9zGHRaD/9VdVt2H77HOsi5Yi/xKXnRy2/Xf8pnSk8+WhDu23dllG3PYLPBo9%0AXdX8p7tP4dr6o4Sc3VLgeWG7dp2UGs9x4WyMwy1eNpuN1NRUfH198fHxKfTPSYHABQ4/GMU2AA9C%0Aq9XStWtXpk93/GGUe09oUdCoUSMef7Au2p/GQYPGGH/dwzcLl9Cm83vcvHnTaU1K+zd1e8j2dmu2%0AFgWFkXWfP3TtqnmAPXStJoxrFzRFseXjdsmdZOUsdH+3938nFIVn1VXoPjMzs0CjAAAfH58893yv%0Aa9IqisLkyVN58cVWJCV9h9n8HYr8A3LCYjAnuRksI8V8jBI7CkpuyitUAfxeQpIMcK5gwX3NoYnI%0A+8agPLFVlVAFkMPbwc9D0SQnonRT2bI17Rr80BgiXi4gVAHksm8hrci3FeDEUZTP+6MMmK9OqNps%0ASCNfR6Mvli1UASQJqXgEaRsdVw9I+Gwm5qs30f6izmtrW7Uay6yfkcdtzRaqAE+8jCk5E2NiegH7%0A2DVRRC86Qt0NI1XNf+23XVz+ZSvFt/7i0LEhlQhGljR5GuDkRqvV4u/vn/O+9qRtU4L/JkKsehht%0A27Zl/fr1OZnN+bGL1Xv5YM4tREYP+xz9z+Mh4TKUKU/mL3+x2eJFkxde4vLly06FiF6vz3lweyJq%0Au1m5ax5wr7o4FXVzAEfkrutrv3/7F5L8DQN0Op3LLmf3Uxer3L9zV/tJ3XUxK+o9tKmpqbRq1ZGR%0AIxdiNO4GzRvZJzS1kLSV0Fxx0Y5TNiNFvY1y5VeU8MNgcFyIX9a+jHR4Up5jmuOzUHYMhQZrIOhB%0Ah+IHaS8AACAASURBVOMKoChoTPEg6ZDbzFeXlZ+VgWZKMzT+VaDpPMc2dXshR0dC0rXsn1NuQvvX%0AoEVnaPCSqqVJM/vD+SjkD3flOW6NaEbaip0F7NO2HuTapF/RLl2iah+pfOYM5q7dUXr+ABE1bp3Q%0A6fAKCebqnvg89sbEdDa0XUSFke3wKR/mdn7j+atEdvyOgAmfoSvveDuCRqPBt1ZVoqOjnc6j1+vx%0A9fUlPT09Zy+9QPBPIcSqA77++mtq165N3bp1adOmDVlZWXfUkvVO0Ov1vPfee8ye7bheoH1P4916%0AV3N7iVwJsYoVK9K5fTsM3/2deerji+nb+cS+1J5GLZ5j165dDh/Int7RKnfWvSuPmavWmveyi1NR%0AitX7wWPojjsRq+4Su+zv36LsYnannDhxgkcfbcLWP0PJzNwFmrx7NmXLMJQL34Hs4HPDmop0tBnc%0AOIgSHgV6F52pin+FfHk3pP5dJSV6McqWXvC/xRCqslGAIiMd7QIXlqAxVEUTtdb9GJsVafZraLKs%0AKC9tdW5nCEIKKgub1mTvU+3RHikwHHp8r25t62ejrJuN3H1HwYoET3QndcdhlFxeRmvSTeLeHoLU%0Atx9SHfftUZX0dCxvtoSGr8PzBctUmUpW48rOW0lWiqKwqcNifKtHENHbfRKvbLZw/JUv8W7yBP6d%0A33ZtXDu7IoArvL29MRgMOX8TQrAK/imEWM1HbGwsM2bM4NChQxw/fhybzcaiRYtuqyXr3f5Bd+jQ%0AgeXLl5OZ6biHtpeXl2rvqjshYjabXQoxLy8vhnzyCd67NsKxv0NgGg22Tv1I/WYeb3boxOSpUwus%0Axf4A/6e9g7nJ7zFTFMVl1r0zj1lRZd278/zeLoXlMfTE5gDOxKqj97+jL2X53///hHf4Tpk7dz7N%0Amr3ClSufkZU1HTQO9hNrWqLBGxKX5T2edRXNoQZgSkEudRp0wa4vpgtF8q6B5sRMOLcG1nWCB2dD%0AuIquUQCKDelQO5RLq1CqHkYJ/Qxl9yxw9ZmpKEiLP4BLkcivHchpLuAMuUQLtMsXI00eh3LkIPK3%0AKvfCnvwLJvdEaf0LhDooa1WqDlqDL5n7Iv9elsKFd79AU6kK+gH93E6vKAq2rt1B6weDHe8fVh5q%0AzqWtMTk/R806wJU9F3hgvbrwf0y/2WSlmgj6fYpbW7l2FQ5HnXRrZzAY0Gq1OX8vnva3L/hvIMRq%0APgIDA9Hr9WRmZmK1WsnMzKR06dKsXLmSDh2yvwnbxSTgsCXrvn0quqK4wGAw0LZtW+bOddzBxZ6g%0Akdu7mt9L5E6I+Pn54e/vr0qIBQQE8PWwYfh93Su7W4ydJ57BtGg3I2fNpXP3HphMpjzjinJ/bW7c%0Ahe7tHjN7Mo5dlHmSx8x+7Tt5MNxrj6Gn7qd1VQrKvn3hXpRCK2rsAjw1NZX27bsyYMAkjMZtKLRz%0APc7SAU38V7cOZJ6Bg/XRUAY57AhI6uoOy74DUQ5MgFUtoc5EKKsyg1+2IB1oBQlbUaodB6+yEPQ2%0AGpsVzu1wOkzaMBLl6HLk1/aBl/t6zzw4ANuencgTvkIZtgp8A9yPSYiDz1+CxgOhlvPtAnJQFdLW%0A7wXg+qTFpB+MRlr+u/v5AXnyFKw7dyNP3OFccD/bjsRjl5GtNlLOXWd77xVUm9VLVZeqxJV7ufTz%0AJoK3zFeVgKurXZVd+/e7tbOXtAJyHBxCsAqKGiFW8xEcHEy/fv2IiIigdOnSBAUF0bx5c5ctWXO3%0AXrW3ZL1bunTpwuLFi8nKyspz3C5E7G1DHQkxwK0Qud0Hcps2rSkjm2H1wrwnylUic9Ee1tzIotGz%0Az+e5d/u+0HshbO4mdJ+7PqmnCi9wvRXgTu+/MN4L/6Rn1Vlim31PXVEndqlZ792MdZbEd/z4cRo2%0AbMG6dRqMxv2gqaVixuEoxjhI2Qup++HgI6Bvjlxyk1tvZb6VgWyDch2hwvv/Z++846Oo1jf+PWfT%0AGyV0EFR6EaSDUkUREASRIigWLjYExYbYsQECYrl2EURFsaGUCwIKAtJrEEjovUMIySa72XLO74/J%0AbDbJbnYiRX735vl88mGZOWdmzuzszDvved7nsdZFuZBre8HpNahaWyE8x0FLSnREK2xrPg/cb+1U%0A1O8T0Lf8DnEWjVc8WRAWAR0HQT0LDk8OO+K5zoir2kPnlwofRt3bSJ/1J1mbd3L0uY+xfT4VGRcX%0AchfelatwvT4G/eosSCgke13xKmzRkZxOOsavfb6m1I2NKXdbYCkrfzgPnmTbXW8RP/FZwqpXC9le%0Aa41zyo8c3Le/QJIhEExJK4/Hkyf5UYxiXCr8z+mshsKePXt455132L9/PyVKlKBv3758/fXXedqE%0AKpw434egUooTJ07QoEEDRo4cidvtZteuXdSsWZPx48cXkPyJjo6+6MUcUkreHz+OnvcMxtGpJ8T4%0AZThiYnG8/T27P3uTVh068v2X02jdurWv8OZ83KJCSSGdr4uTGVD/E5JHoWAqFvjrwgYav/nvpXCx%0AAgpk9S8GQlmLmuM0xwyGHNmlcHizCqvfQSgtVnM75pjnzJnLY4+NwuF8Ha0eAKvftYgA1QGx60F0%0A1m6IewxKW7RPBWMKP20UKv1jUNcg7cnWHK28TuSa7nBuN6rmNgjLZxRQ4TW8m9pC308gwu/7S1kE%0APwyDTjOgbGNrx5i2C2a1A10aW9pxQpKQlEKO6Q8qDHX3zNDbb/0gjtdeZn+PpxEDBmDr2D5kF338%0AOO4Bd8LA56BRaF6vSKzMonu+w5maTcu1z4Zsr9we/ur5BpFtmxP34IDQYwCyPpxO1pwlhIdHsnPn%0ATurWrRtSnkpKSXx8POnp6b5rsVjSqhiXCpffE/ofxvr167nuuutITEwEoHfv3qxatYoKFSpYtmQt%0AqvVqZmYmEydOJCUlheTkZHbu3EliYiI1a9YkPT2dfv360bdvX+rVq5dHe9PMql2qquNWrVrRoGZ1%0ANrx4P2rMFIj048YJgeeBUZyr3YieA+/ijeef4/4h/yI8PByn0xlSWPqfEk+32Ww4nU4iIiL+0enf%0AwsbvcDj+EfOEYLhQ2ejCXkTM/Vg1CvDXOr0cp/FDjdW8xs3P/moC5rjT0tIYMOA+Nm0+gNOxAEST%0AIIqEwQ7CBaIK2r4QSr4IJZ+z3ledQ57qg87+C1gLsizqTFXI3AexhRRkeTKRq7pA5nFU7e0gA0xn%0AxzRBRpZGbZ0NTfobyw5vhim9ocV4uLKHtWNM3w+/XA+lb4WqT+Fd3wyyHRAZ/AVGTnsevWM9euQu%0Aa9nl6FKIqCi8kbFETHorZHPtduPuPwBRsyn67sD6qfmRXeZq3JsW0eTPCZbUBfaNmobzlJ0y6wpR%0Ae/Df/ooNpI0cj35/NpHTJrJ3716uuOIKEhISQtpk22w24uPjycjI8F2fl+OLfjH++1BMA8iHOnXq%0AsHr1ahwOh1GJ+dtv1KtXjx49ehTJkrUoiIiIIDs7m27dujF58mROnDjBoUOHWLx4MT169KBEiRJ0%0A7NiR8uXL53kQmzeWS1nE9MHECejFsxG9GsOBANaL7bvi/GYFL370GQ8MfxS32+0rtMo/dR1MgcB8%0AY78UUkhmgHApqABWFRj8paDAqLy/nIp9TC6t1WlAczo7lLWozWYjIiLibxW2XS4Bqv93rLXG5XIV%0A4M76U3UiIiKIiIjw0VLM6mtTZcIsbpk69QsaNGjG6tXbcGV3MgLVIh3YNoRohBSzEaIaUp+w3te9%0AC3GkEWSfQuu9IOuCLIOUDZD7PyikXwZiRQfIOo2qtS1woJoDFdkduTIn2Eo9AB92gjr3wzWPWDtG%0A+xH45Too0QHqTYW4+sjoRFhXiNLA4m9Qs95H378YogLbwuaHnPcMyp6NbNnKUns16jn08TOocb9a%0Aas/JQ7BlBba4GEsuVafnrePQJ/Mp9ds0S4Gt9+gJzvR4ED14JFx/I5m1GrFt+3aio6PJyMiwdB8M%0ACwsjNjbWd8+6XGlUxfjvQrGDVQCMHz+eadOmIaWkSZMmTJ48mYyMDPr168fBgwd9lqymy9SYMWOY%0AMmUKYWFhvPvuu9x8880X7FjS0tLo3LkzCxcuDPjW63a7cbvdPirApcBrY8Yy4a23IDwCxnwOXQMU%0AWNgziB41iKvPHuWbzydTtqzhSX4xXYz+LrKzsxFC/G2qQn4UJWMYavz/pDNTYcjKyiIyMjLPNWk1%0AO55/3BcKmZmZREdHXxJ3NytT96ZBhv/36//nT+EIhqVLl/LII09z4kQcWVnPYkyG9QV2gbAwg6O9%0ACPkWWr0C9AQ+BNaD6AlXHAFbQae8PMhaACf7grgVZF46FGoRyNuh20mw5VMgcKUhVnRAeDSq+gaQ%0AIa5f90lIqQYjNyE+7golr0XfbK1wiawTiJktIOoadKO5ucv/GoithhPvSwGm93esg5Edod8X0KiP%0Apd2I5e/Bry+im49DbHuByL27Cr1+vT/8iHvEk+jJW6DilRbGYUc80BRdsiZy9yJa7/yUqCrBLWud%0Ah0+zpv5QYl9/kvjhd4fcvHa5ONWyL57YiqgpvxkL537Ljct+YO533/qKiuPj4y39Ls2Xr7i4OKKi%0Aoi6ZdXUx/usR8OIrDlb/H+C5556jdu3a9O7du8A6rTVZWVm+DMylgMPhoH6T5pxu1AuWTkV2H4B6%0A4T2IiMzbUCnCPnqduO8/4avPPqFt27aX5Q3NzPjFxISuuPVHMK/7QHzavxucmZW3kZGRoRtfApiB%0AuNPp9I0nEJ/0n3gRCRRAnw+C2ajmD0qDBaJutxullC+ALuza9zegUEqxb98+Ro58mZUrN+FwPAPc%0AjHkPl7IviOtQKoQ8kd6LkP1BH0TrqUAuX1LamqFL3IcuEYQTqTUiYyI6dTSIN0EOC9hMyitQ14yB%0Aqn5KBNlnEH+2RahYVPU1lou35O66KNcRZKk6qNssKqo4zyBmtkKEV0Vdm88FK2MrbGoBP57JSwU4%0AfQQeagTNH4BbxmAJST/AjPug6zyo0AYxvSQRC+cj6wcubFPbt5PdqTOMnAodLaglKIV8phscOYR6%0A4S8iXr+KmhP6U2FAYE6s8njZ0Pop3KXKk7hwqqUhnBvyHFkLVuNdsDfXhGH3dsoP78mB7VvRWmO3%0A233V/6F+s+azx+v1+gLWy2WGoxj/r1Fst/r/FY8//jiffPJJwOkWMyPocrkuyr4DuTgBjB39IjEp%0AS2D8Zli6EHFbEzi0N29nKfE88hJpL33EbXfexfBHR/wjUlah4F/AFAhWpLDM7VxIBQZzm/+EVm1h%0AagMmRcYMyi8XKajzkfoKZYrg8XjyjNecvvefxvefujfpDDabzccrN/dlXkvmfjIyMkhPT8dut3P2%0A7FlGj36d66+/kaVLa+BwzAe64H//Vuo1lPcL0MeCDQjBJ0BD0GXRejv+gSqA8j6HTpsAOrtgf+VE%0AnhkAZ8eCWBg0UAVQ7kGI3RNzFzhPIJa1BF26SIEqnjRUthOURvVcGbo9QHYaYlZbhK0cquGiguvj%0AGyCjS8GGBX59HIjnbkZUaWY9UN2zzAhU234GldqBlIj46qgFgQ1g9Ll0XLf3gxsHWQtUAfnx0+id%0Am1EjjXPmKnMt535PCtp+//Nf4TyaRql5n1nafuan35H5w694p6/M6xZ2ZS3OHDvqC1Lj4uLwer2W%0AFQLMF3zzd1KsEFCMi4XiYNUi0tLS6NOnD3Xr1qVevXqsWbPmkrlalS1bltatWzNv3ryA68PCwny2%0AmH8XRZVC6tOnD7XKlUAk/Yr69x502bpwayNYGGDKrdOteEZ/zFc//ECz1u3ZsGHD3z7OiwHTzcrt%0Adp+XFNbF4JOaxUwX6yFQmDxSMH3e2NhYIiMjfS9Kl4s+aajCr1DcWdMgA/LySQMFpP6f/c+BmT01%0A92UqOtjtdl9QampVgvHbjY6OJj4+nvnz59OkyfV89tlunM5ZeDxDgQAC/9RByhpIW4BgSx9DyhuB%0A54DP0HoGEKhiuzdSRIP9m7yLPUcQx5uDYx2aFJDXF37SxcvozL2QthEcRxBLWyBsV6KvXmY9UHUd%0AROxsgtCJgIQzwYM0H9x2xJyOCBWNuja4bqmKbIvt9xxrVq2Rb96JcLrQgwPfSwvg2F/weXdo8hLU%0Ayq20V5V7oWfNLtBcK4Vn8L8Q8eXgSWsFT2LeFNScz9BPLIeoHBmsxn04EyRYPbNwIwc/mEvJBV9Y%0A4qlmr9lM2uNvoCbMgApV8q4MCyOmRl22bt1qHIsQxMfHk52dXUA2MeCxF0taFeMSoZgGYBH33HMP%0A7du3Z/DgwXg8HjIzM3njjTcoU6YMI0eO5M033+Ts2bOMGzeO7du3M3DgQNatW8eRI0e48cYb2blz%0A53lNgR87dox+/foxd+7cgNsxRc/Nopxg8J/SzD+9WRiXNFAgsnXrVm64pRfOt5MhvjQs+QKmDEf2%0AGoR69u28tACtEbc3Rx+1E63SGHjH7bz28gvEWdAovJAIxSf15xheDnxaMLIWZkD0dxFKCirYdx8M%0AZvbFFAu/HGBSJiIiIookBfV3+aT+15I5fW/+a55X8+XF5XL5Mq75z+vGjRsZOvRJ9u51kJn5HNDU%0Awmi3AQOBfSBy/OL198D9CNEQrb8HQn037yDCp6Ir7wEhwbkaTtyCEM3Qer7lYFPom6GsRKdtQUQ1%0ARl81N3QnE1mbYU8nhGyHjv0ZkXkjosaVqPaTg/dxZyHndoIsO6rZZpCF0D4ytsCmVvBTKvKH8eif%0A/40euQNiQjh1AZw9BG83hqvugLbv513nOA3fVCZq1w5EyRK+xd4Jb+H+8BP09H0QY+HelrQMRnaD%0AId/DNd1yl7uciCcTaHt0GuGJucVf2cdSWV3vYWJefJSEJwaH3Lz3xGlONOiG6v0QPBk4kxzz/GDe%0AbN+E+++/37fM4/GQkZFBXFycJXkqr9dLenq67+XdVLMoRjH+BoppAH8X586dY/ny5QwebNwcwsLC%0AKFGixCV1tapYsSINGzZk8eLFAdeHh4f7pirBuovT+fieN2jQgD69ehDxQ46Qdsd7YeIW+H0e4vZm%0AcGhfbmMh0K9/Bo5DOLrMY/rKDBo2acWCBQsCbvt8Udj4g1mLAgGzZf/0TdcqFeBCGCVYzQ6bxgD/%0ApDlAfmUJs9jQdOoyOaOmukKwDGn+LKn/tW/uy/9aysrKwm63+6buTc90KSWRkZHExcWRkJBAfHy8%0A79zGxcUVqJzesWMHXbr0pHPn2/nrr15kZv6AtUAVoD5SXo2U40CnIuXtCHE/MBat/0PoQBXgUVDp%0A4JgH9qlwvBPooWixoEgmAVrdiT6+CKKaFy1QTV8Iu9pC2L3oWKOYSkeOQe38FtyBrabxZiPnd4PM%0AVFSzDYUHqgDxDZFRJeGDYajvx6PvX2QtUM1KRXzYHlG+TcFAFSC6DLYS5VFLluQe2uIluCa9jR4z%0Az1qgemQPPHsrdHkhb6AKEBFFeOkypP253bdIe71svX0M4U2usRSoareb1O4PQPWGQQNVgKza17I2%0AaUueZWFhYcTFxWG32y3de0xJq6ysLJxO52Vls12M/w5cXiXGlyn27dtH2bJlue+++0hKSqJp06a8%0A8847hbpatWqVK21yoVytRo4cyb333kunTp18hS1ut9vHuZRSkpWV5ePuFUWn8u/itZde4OemLXDd%0A+BBUbQDlr0K9vxcx8Xbo2QjGfwk3GjJf1GuM7NoXlj+I8/Z1OA8u4p6HH6LDdd/x3qRxPu1aq/DP%0AbAUqdso/fv+MWSCYU7aXm9C1SVEwcbGNEqwgf8X7xYIVZQVzXOZ1nz/g9v8cKlPqnx31/2xSRfzP%0AaaiiKX/YbDaio6Ox2+0kJSUxYcK/WbFiBdnZDuAboGGRz41SrwCDgC9BV0PrTUDw6vGCkGjvbXDq%0ALkCD+Bbkrda7a4VgHFq/AaIUIqaV5ak4kfo5+tBjEDkeoobmrghrgQxLRO39CWrns4/1upG/9oS0%0Ag6jm263bw9rqwqJp0G8qVL42dAe3A/FpZ0REWdTNvwRt5k1ohW3uf+C2XqhDh3Hdcx/c9wbUbR56%0AH/ZziCduRNfrAl1HBWziKl2PtMVbKNvTeJbsH/0tWftPU2b/T6G3D6Q/+jqeo6moRRsLb1inEesX%0AfltgsflCm5GRQUJCQshr3V/SyvxdXqqi32L896M4s2oBHo8nZ6puKBs3biQ2NpZx48blaRMqGDif%0AQMHlcrF9+3Y2bNhARkYGAwcOpE2bNlSuXJnp06fnyRICvkzppSh2SUxM5MVnniZm2mNgZtmkRI/8%0AGe5+G54ehHxtOORw9NTICai0FNg3F6reRFafv/jt2FVc2+w6vvzy64CZulAFMCbXMFSxT6iMoelm%0AdbnAvzrc6/WGzA7nz45f7OzwhbRdzZ+9DMQnza9Pao7R/BwdHe37jv3XmUGmf6bUvJbMc+rPJzVf%0ADMLCwoiJiSEhIYGEhATfufU/r1bhcrn48ccfadu2M7ff/iBLllQnO/srhGiMlO/9jTO2BilfAhRQ%0AH6X+oGiBKsAfwDxQDtCTixionkDSAdS7wHzQ49En3wIdooBSa+TxF9GHHofoH/IGqjlQ+g7k1nzn%0ARHmRv/WDM9tRzbdAWOF0Jx/OLoO0VYCEhgXVVAru3IucdjvCfg7Vc0XhbesPxbVgIdrhwNO3H6JB%0AO+g3IvQ+PB7kc7ciIkvBkBnB213Tk9RFmwFIXZzEgbd/oeT8KUgLEntZX8wkc/psvF+vyFtQFQi1%0AG7Jn+9aAfG/z/mm32y391iMiInwvZcUarMW4kCgOVi2gSpUqVKlShebNjTfmPn36sHHjRp+rFXDB%0AXa0OHDhAr169qF27NgkJCfTq1Ytp06bRtGlThBBMnDiR5ORkBg8e7AtOzGlcM3C7VHjg/iGUyToG%0Aa/NlIW78l6EWsHA2ok9zOHIASpdFDB+NXPEQKAXhMbhajcfe5VdGjp1Mx87dSUlJsVzsExcXd8GC%0AcjOzeimntq0YBfhn9i6FUYJV/B0nq8LoGebUfbAiJ3/h/EBT92Zmx+Px+M6b+dnhcPim7tPT03E6%0AnXg8Hl+RmBmUxsfHExsb67uezve8nj59mjFj3uSqq+owYsTH7NlzB1lZn6H1rUA0Wo9CqfXAOotb%0ATELKAcBDKFUDeBdYDxwtwlGdQMq7gTuBXsDNSPmR9e7qN/DWRSuN1ilAc+AOJGGQVohlqXYjDw1C%0An/oIYldCRNfA7aJeQqVuN6xTAbRCLhkEx9agmm6BMIs899O/wuZuUPpVZFQ52Dan8PZaI396EA5t%0ARvXeGFobtnIHhLDh7nU7OiMb/cYsS4cl3xuGPrQH9VQI1YNWg8jcfZSsPcfY2mcssS8OI6JR3ZDb%0Ad23YytlHRqPGfQ2Vq4U+oISS6MhotmzZEnC1qeGdmZlp6d5o3pPMZEJxwFqMC4HiYNUCKlSowBVX%0AXMHOnTsB+O2336hfv/5FdbUqVaoUd911FzNnzuTcuXPs3LmTWbNm8fHHH1OiRAmEED5TAn+Eh4df%0A8orMsLAw3p/4JjFfPQmufJInFaujPtgHCdWgxzWweA560KPoaBusfTm3XdnGZPVaw+aIXrTr1IWJ%0Ab73t0xe9VMGZmX27GNnV880Om1nzf9q9yh/BgtVgY7Xb7UXik+b/NxSf1Ol04nA4AHA6nWRkZATl%0Ak/q/5FyM6yk5OZkhQ4ZSu/Y1vPXWetLSXsVuHwu0JO9tNw64ASFepfB61mSkvA+4B6USgVnASKAB%0AQlRHSisyTF6E+BRoDBwCFgGPA6+gvGtBh5gu1m4kI0H1Ap5C64X4qxUoz0DEySDH4c1A7r0J0peh%0AY7dDWIPg+5FxCFsDZMqnRgC57AH0wd9RzTdDRAgTAxMnZ8JffaDUJCj1FEp2wba2kKItQP72Kjpp%0ApqHxGhFvaTdalsC7dRvq3T8t8XzFzPdRv81AP70SIkJkh2NKEl6qJBvbjSKsQR0Snnkw5Pa9p85w%0AptsQdP+H4caelsYgvpiE25HJ9u3bA68/D0mrjIyMYkmrYlwQFKsBWERSUhJDhgzB5XJRvXp1pk6d%0Aitfr/UdcrbZv387TTz/Nt99+G/ABawq2XyhHJqvofns//oy/FjVwDAR68C/6FL58Etl3CKp1J3jy%0ATrjrEETkszpM30/MiocoJw7z+cfv+TLalwJWVRWC4WIZBZiyWtHRwX3OLzXMrGVkZGRQPikU9L33%0A59D6j78ofFJ/Xqk/n9T8VymF0+kkLi7ukhlRKKVYuHAh48f/m6Skv/B4uuPx3AKUCtHTgxAD0Ho0%0AkK/Qhj1I+RZKrcDIYD4L5A+kDgBDgFXAVUH2sRkhHgJO5eync771w5C2MJT4T+Du+gBC34bgBErN%0ABgKJ4btAXgE1FkKsnx2p+yhi9w0IFYGKXgvSwm/LNRfcg5C1B6J3fY9utgmiqoTuB3D8S0geComf%0AQsJAY5nnMByuAS8dgdjEAl3EmsnoWU9Aj2VQ1gKvFRApU9F/PASVqsI3u0J3WLcIXugFD8+BOjdY%0A28ezVbBJO+UO/YkMpfTi8XCm/Z24sqNQ34SgMJhYPAeeGABNe/Bo88qMH/tG0KZKKdLT031Jg1Aw%0AtVfNAtZAShjFKEYAFDtY/bdAa03//v0ZPnw4115b8MZqvgHHxMRc9JuDf4HTunXruLlnb2SDjqhh%0AX0B8wYcCx3YhX7kBXaYMuF3gvRLdPcADUmvY9S3Rq5+kf5+evPHqS8THW8t2nA+UUjgcjpDn7kJL%0AQV2o47oYKGyskBtknq8UFJBn+/6Bqb8UlHk+Q6lVZGdn++wgL9Y501qzYcMGvv32B6ZPn05GxjmM%0AbOUNQFFeFn8AfgaW5PQ7hM32Dl7vIoS4Fq2fA4JXsQvxGEJUQ6n8bkbpSDkapb7BCIRfI3Bdbapx%0AzLa1IOrnXaV+BnUPQrRB6++C9DfRH1spiffKHEqQYxvsvgEhm6Cj/2NdZUB7ITPReOltthFiggXh%0AeSGOfITe+TSU+wZi83Jwbcdr4O0yElo/kLfT9v/AV/3hpp+gqsWkwq7p8MeDUO9rSBkI03dAZnuV%0A/AAAIABJREFUuSuCtz+QAg+1gFvHQMfgJgt5xrLkffSPTxFe90rKbwmtspA+4nUyv1uId9F+sJKo%0ASN4MA9vCve9C6Uo0XvImq37/tdAuViWttNacO3eO6OhosrKyiI6OJiYmpljSqhhWUCxddTHg9Xpp%0A3LgxPXr0ALgkRgFCCJ599lkmTZoUcHrFfIibBSkXAla4hk2aNGHo/UNQSYtgWC3YFECWqmJN1Pv7%0AIKYSem8K+vBvcGpzoEFCrYE4+m1jxrpsGjZpxfz58y/YeILBDKb8s6KXg1GAvwPSxYKV79g/k2lm%0ASkwOcWF80vxT96aSRX4+qTl17/F4fLMDsbGxeaSgrPJJzTam49aFPE+bNm1i1KjnueqqOnTrNpDP%0APjtIRsZjCBEL2ChaoArQN+f8vI+UzwPdUOoI8DVaT6SwQNU4pudRaj6QYi4BZgLXAMsxaANjCR5o%0AlkaI5kjhR83RTiQPgrobGIfWPxXS38QkvGkLwXUQMv6Ana3B1hsda123FZWKzLoB3NmImPqWA1V5%0AYAJ650goP7tAoArgDeuLXPNp3oUH1hiB6nXvWg9U9/yYE6h+BeV6IeOqwYqCBgE+nDsDT3SCxn0t%0AB6ps+AE9cxR0+Q53yl5Uhr3Q5lnfzCHj8x/xfrncWqB64ijcdxN0uh86DYarm5K8ZVNIfqlVSStz%0AXUREBPHx8TgcjmJJq2KcF4ozq+eJSZMm+ar0Z8+ezciRIy+JUYDWmttuu41nn32WevUKTsn9nexq%0Afpmg/J+t+L5nZmZS/9rmpFbsCCk/I9vdibp3EkTGFNzhrx/CV0+BLRbu2gcRhRROHFpMzIoHaXlt%0Abca8+gL169cP3raIyD9usxrcFJEPlCn9J4wCnE4nNpvtvKS1iioF5T99758l9Q88MzMzfVmT/PsK%0AlCX1p0Tkn76/0OfVPL6wsLC/Te0wt7N161a+//5Hpk//AbvdRXZ2Uzye5sAV5CYDlgPfAV9j8FGt%0AQAFJwGfAPoSogdYvAhaKY/LgGaSMQ6mxSDkMrbeh9eMY5gFWcBy4GWxJgELongiyc4Jg68cibW1Q%0A0dGQtQkiXoXoJ60PwZMEmV2RVEWpn0FWh1ZbIebq4H20Ru57GXXwXSi/EKJbBm6n0uFgeRi1E0pd%0AAad2wjstoMEIaD7a2vHtnwOL7oA6U6BCf2PZrpHI+BWoDwJMvbtdiOFtwB1m8FStYMcSeL87dJoC%0AdfoT9nVFSnzxGtHdOgRs7kpK5tT1/dFvfAFdLdi7ZmUaBa8lqqKfz82kxjxSjT/n/RLweZIfTqcT%0Ap9MZVNLKlK8yaUsul4vMzEzi4uJ8qh3FKEYQFGdWLzQOHz7MvHnzGDJkiC9zc6mMAoQQjBo1qtDs%0AqhAiYHY1lO+7ydsMJQUVaAo2NjaWSW++TuyZJHh4E2z6HfFYPdgboHijy1B4cyN4nTC1MqR8lSt/%0AlR9X3EBWny0sPVqe1u1u5L7BQ30Fb1Zh1SggLCwMrXWewq5/yu/eH0Up/rpU1qKmQoGZaXY6nQWk%0AoExnKdNa1JSCMh9ckZGRF+28msUeLpcrj1atVSQnJzN69KvUqtWQG264lX//ezsnTtxDZuYbeDy3%0AA1XJe29ti5SlkXKKha2fRojpwACEeA2D21oeIWpT9EAVYChKLQOuR+tItF6K9UAVoAJCXgvqDvA2%0ARetGKLW1iMeSjvKWgcz1EPVJ0QJV17dgvx5Ub5RaDVREiIbIox8E76M1cvfj6IPvQ8UVwQNVAJmA%0AjKwBm76B9OPwQXu48jbrgerBX2HRAKj1UW6gClDtKdSODZCRlre91sjx/0KcPol+fJm1fRzaDB/c%0ACq3egDrGPrwx9XAt+DNgc5WaxpmuQ9C9BlsLVJVCPtYX4dLoZ/NazsrqTVm9erWlGbnCJK201rhc%0Arjw1E6aklTlLU6wQUIyiojhYPQ88/vjjTJgwIc+bZWFGAVWq5BYIXAijgJYtW5Kens7evXsDrg8P%0AD/c9pIvi+36+UlC33XYbtSolIPYsRI3Yhb76FnihLfKn1yF/sFWlDtz/AUgNy0Ygv2sMJ9YH3nBY%0ANKrdBxCRwMw5W2jTriu9et/J+vW57f0LcYJJQZlT92a2LZA+qSkwfzkhkJNVqKn7QPqkgQJTU6c0%0A0NS9qaeaXwoqMzOT9PR0HA4HQghfpXB4eHgBKSh/fdJ/QmIrNjYWh8MRMtg/e/Yss2bN4qGHhlGp%0A0pU0b96Sd99N4ujRgWRljcPt7otRxBR8DEoNRamFwO4Aaz3ASqR8BrgHIZYA/dH6E+AJ4CmU+hXY%0AU4QRHjacrLgv57iqofXHQIDZjEKRhFapoLcBr4D+gqI9In4DGiDlfqQsh+CYtW7ag8x+HLIeBD0F%0AyHWM0t43UIc+M15oC/RTyB33o49+ja60DiILURjIgYq6D7HqY8RHHRAlG0DH/BzfIDj8OyzoAzUm%0AQaW7866LKIeMqwBr8tKU5IwJ6JX/QT29OrTWKcDpfTDpBqj/ADTN1WvVNfrhnLe0QHPt9ZJ621Ao%0AfyW8FMBlKwDkm0+ht25EjV1XgJaReWUzkrYlk5GRYemlOJiklcvlwmazFciemsobZuHV5XZ/Lcbl%0AjeJg9W9i7ty5lCtXjsaNGwf90V1MowCz/6hRo5g4cSLLly9n8uTJvPHGG75Awrwh+IucXwopKCEE%0AH7w9nsilr0DWGbj1A7j3N/jPB4hnW8KJfXk7tB+EqFgDEruiIhrDzPbIRXdB5vGCGw+Lgo6fgD6A%0AM3ErSzbfyC233kP7jrcwf/587Ha772YYyigg2LhNHuaF5PyeD/yL2LTWPommYHzSQFJQf5dPakpO%0AZWRk+PikXq/Xxyf1txYNDw9HKfWP6b4WBv8MsH9WJzs7m2XLlvHCCy/RuHFrrrqqJg8+OI6vvz7L%0AuXN3oHU4Llc9oDqFBah5UQFoipRvYUzxAxxGiM+APkj5DkolAO+h1JtAe3JvxRWBxkg5gdAsrG1I%0AORK4B633AxOBLxHiCIbgv1UcRMqhwN3A1UBdpCzKrE8aUv4LGADcjVJzUd7H0Y5xoF2Fd1WnkVkd%0AwPk96A1A/uxgJ6StBJz8IV8/D3LbQPTJuejKSRBR3dqhxt+DTj8OKhzdzaLV89HlML8nXD0WqjwQ%0AsImKbI9tsZ/A/4rZqC9eRT8yH0pUCL2P9JOIie3gis7Q/q286+oMwn3oKN7TqXkW2599C3fKQbzT%0ACgaygSBmfIL+8XP0q39CdEGKiq7enFUbk3yi/qGyn8EkrVwuV1C1AJOWVhywFqOoKLZb/ZtYuXIl%0As2fPZt68eTidTtLT0xk0aBDly5fn+PHjVKhQ4YIbBZw8eZKNGzeSnJyc589ut7N9+3bq1KlDnTp1%0A8rg1mWLzVqRGLiQaNGhA/z69mLHkBbJv+QiqtkY9cQgxozc8cQ0M+Td0uNcopBICPXQyvNAWWm2H%0Amq/Aln7wZQ1Ey5fQjUaAza9o4MruiPJN4Oy/0GXn4Ih7mE0HZjD4gdFUKBfJi8+PoGfPnufFiwoL%0ACyswlXWxYYVPCvgsTs3xBeOTWpGCCsYn9eeSRkREWOKTmtN82dnZ58UPvViIiIjA4/Gwbt06Vq1a%0AxezZC9m8eR0REZXIyqqO19seuAe3258TfBMGl/RNoCiyYfeh9ZPAB0i5E6X2IURVtH4EpZqE6DsU%0ArYcCS4EO+dYpYBVSTkWpgyh1LfApWufKY2ndDSFeReu2FH6LT0XKf6PUT2jdEINrWxo4hlJ3AzuB%0AWiGOdT5wP1A+57MZmHVHygko99cQEcTH3rMRMrsCNVBqH8GK0pRrEPLgRFTFHPtVlY3863Z0+mZ0%0Apa0QZtG5y30QcbQj2muDyp2sFXwdXwXzukG1l6Dq8ODtqj2Nd10LcGXDwRR47U7o92+4uhBaggmn%0AHfH2DRBfHboFcLSKiCGsZHmy/1hDTB/DSMHxw3zSP/oG/f06sPJbW/U7etwT8NQvULFG4DZXN2XH%0Alk0+Yxm73U58fHzIhEt8fDzp6emYttYej4e4uMB8bTPA9Z+RKZa0KoYVFBdYXQAsXbqUiRMnMmfO%0AHEaOHEliYiLPPPMM48aNIy0tLU+B1dq1a30FVrt37y7Sj/Tzzz9nxowZ1K1bl7p161KnTh3q1q1L%0AUlISP/74I5MmTSrQR2tNVlZWHmH5S4XU1FQaNG6BfeACqOQnsbX1R8Ts+xF12+SRuJIfDIakLagW%0AOdP6pxYgk+9HC4Xu8AlcdUvuNs7uhBnXQrmVEJmzba3A8R9iXWOIizrFc6MeY+DAAX8rUPcvHLrQ%0AOp2htFj9dUfzS0CZWU//4woVlAbyujeF8gPJQZ2vxJbdbic6Ovq8CsEuFJxOJ5s3b2bt2rV8991M%0AUlJSsNni8Hhqkp1dE6hJqOlyKccCdXICuFA4DWxGynUotRsIB64D7sFfQD805gLzMCStIgEXsBAh%0AvgCy0LoNMJjAAZ5Cyn+h1GPAHQHWOxHiC7T+CCmvQKkXya/PKsQTCFERpQp6xhs4i5QjcoqvHgH+%0AFaDNZIRtBjp+H4h816jrS8h8GCPQfSfIPnKPF1tZaLYcYmohk26BzP2oykkgE0L0NTexAY51Roh2%0AaDEExF1w36nCXapOrofZHeGKp+Hql0LuQq6tiHpkHHz0NDQeAHe8G/q4PC7kuzdBWipqQFLwAHpW%0AD+I7RlNi8hjc23ZxsmUf9OhP4dY7Q+9jTzL0bQn9XoPujxXaNHb41ayc/wu1atXCbrcjpbRUpGtK%0AWpm/+WDBqgmllE/aqljSqhj5UKyzerGwdOlS3nrrLWbPnk1qauolNwpQStGxY0c+/fRTKlWqVGD9%0APyko//nnU3jhgx/JvHdZXqOArDTkVzejUnfDiG+g8c2QfhoevhrqToMKt+W23fUq4uAkRPkmqPYf%0AQanaAMgVT0HKHFT5HXl3qjVk/0msayxh3k08PuIRhgy5j4QEiw+2HJxv9X1h+qShgtLC9ElNaafY%0A2FgftzaYaH5+fVL/wPRiPRw8Hg9ZWVnExsZe0hckrTUHDhxg7dq1LF++iuXLV7Fv306ioyvhclUm%0AOzsGY3r8SYzpdqs4jZFZfYKCmUYF7EGIzcA6tD6HzZaI11sX6ICUnwOVcgLHokHKx9C6ExCP1t8g%0AZRRKdQNuIzSDawnwOUZ2NjZnmRf4BRiPlDEo9QSGo1YgnMaY1l8G5FfemAM8hJSVUOpToGyQbSiE%0AvB4dPRkiDHc/tBuZPQLl/Ar0VOD2EOMwIOSNiHKl0Y4DiOxzqEqbrZkLAGTOheN3gG0ohI83tqfK%0AoztNgWq3BO5zejP80g4qD4cawYXy82BTV0hdiKzTATXi99DtlUJO7g971qIG7TAoTsGQ8h22pMco%0Av20eJ665Be91PeC1T4O3N5F6Cm5tBA27wdDCHbwA4t7py3v3dWPgwIForUlPT/cVR4WCy+XCbrcT%0AGxtrKUFgBrgmJS2/mkgx/mdRHKz+N2P27NksWrSIMWMK2h1e6uyqf/Dkdrtp3f4m9jYcBY0GFGy8%0A/C34Y7RP4kr8PgXx/Ruo647kzTJ47LBlIJz5DdngflSLV0HYYFo1iH0dEh4OfDDZSUS73kQ4FjJk%0AyH08OvwhHzUjFKwE+YGm7s1sJhA0CM3v5OS/rLB9mYFodna2b/smNzd/ptTMkv4TGQuXy0V2dvZF%0AFeQ/c+YMW7duZe3adfz++59s3rwej0dgs1XDbq+IUalfmbzZx28R4jhaP4Ohh2oVsxBiM1qPxSiS%0A2oqUG1AqCSHCgPJo3Qwj+PN/6KYDr2MYBTSyuC83sAUhZqH1XqQsg1KDgLZFOF6Q8hG07o7WI4A/%0AMSxdM9D6X4AVK85nkDIepWbm/P80Ug5HqcXAYxgc11AYiwjbgI7bDPoUIutWhPcwSi0nfza3cMwD%0AeiCj66Mqbiw8I+oHkf4++tQzEPYehPllf7PvRFY9h+oaQGz/zFb4pQ1U+BfUeqvg+kDwpMP668Cx%0AE97LhLAQL7haI79/FL32e/SgZIgqXEcX5UF8EkdEw9p4smx4fwphjQuQ7UQMuB6IQb+63No4fnqD%0A7o6N/PjtN4AhgZienk5sbGxISpQZfAohgkpa5Ye/pFVUVFRxwFoMKA5WLz4OHTrE3XffzcmTJxFC%0A8MADD/Doo4+SmppK//79OXDgQIFs69ixY5kyZQo2m4333nuPzp3z2yBag1KKtm3b8uWXXwYMxs7X%0ARjQQAmUOzWX+wdO6deu4fdADOB5JhsgA00Op+5BfdUZrN/rJ7xHv3ImO6gF1C9IayNiK/Ks/ynkE%0Arp8AtmjEssfQFY6BLORm6t5LpHMCOuNb6tevx1NPDqNTp04+D+tg48vMzCQ2NjboeAPpkwayFgXy%0ATLEXxVo0GJ/U7XYTHh5OZGTkBacqXAg4HA6UUuftuqW15tixY2zZsoWkpCRWrFjPX3/9xalTh9Fa%0AEh5+PW53FYzgtCSFF0IppHwTaJWTpbQCN3AI+BSDt3oOKRNQqipGcVSo4p4FwJ/AuwSnAniBrdhs%0Af+L1rkXKaJS6GoNXWgqlXrF4rP5IBl5GiHrATrTuCTyI9braNIyCp0UYlq4PI2W1nGxqiODKBxdC%0AXIeOegWyxyCog1a/UTTThG+BB4zjTnwBSj0duotWyLOPo9K+gLBZYOuQd706AJ7acPcRiPJz2ju7%0AA35uDeUGQO1CJLP84TqD2NQB4QEtjqEf/glqtS+0i1jwJswfhx6wEUpYCNq1RnxRFmED9dtBKOS+%0AZbaXj/WDLRtR7+ywqEZwCJ5uSuXSpdiTssW32AxC4+PjCw0mTW1VrTUejyck39WEqdlqStldjvey%0AYlxSFAerFxvHjx/n+PHjXHvttdjtdpo2bcovv/zC1KlTL4lRwPfff8+aNWsYPXp0gXXnw8EMZS0a%0ATDTfHzd1u5V1p2NQvadBbJCCiNmPwOYvoG4bSF4J1++ByCBZ0CNfIXY9CdFl0PajENkNynwTejCu%0AZDjalMiIymh9gtat23HHHd3p0qULiYmJBcbqdrt9U+3nM3Uf7LwG8roPxCcNZC3q9XqDCvJfDjAz%0A+v7i4KHg8XjYtWsXSUlJbNy4mdWrN5KSsg2vVxMRUZmsrDJ4POUxpvFjEOKdnCAsVNGSP44AHwOP%0AYgS4+eHEEOffgxApKHUEIaLROhZjenwwhjOUdUg5BmiMUvf4LVVAMlKuQKlVCBGB1tWALsCVOW0c%0AwIvACKCVxb1lAUsRYg5an8TQb51O0TizJp4CtmM8Cp7CoAYUBS6gF0awOwIYX4S+GUj5AFrPy8lo%0Au8E2Bq48AqKQ6105kKf6obPWocNWGMYCASBVLVTzYXDNo8aCc7thZiso3RPqfW7tELOPITa0RVAG%0AVWElnOiCbHoV6s5PgvdZ9QV8Oxx6L4YKzS3tRq5+GbVmLKJDV/RHs0K3f+9l9PQP0e/sgHgLLxYZ%0AZxAjm0GpOoQdWMbpE8fyTOWbGdCEhISAs3Naa9LS0nwZVbvdjhDCR1UKhaysLF9hVlRUVDF/9X8b%0AxcHqpUavXr0YNmwYw4YNY+nSpT6lgA4dOpCSksLYsWORUvLMM88A0KVLF0aPHk2rVlYfSnnh9Xpp%0A06YNM2bMoHTpgjcoc+o4EJ/ISiV6KPeqwrB3716atLweRRj0/BAaDsjLYTVxcBVyRm/UueNQuiW0%0AXB18o0pB8nA4Og2UG8rOhZibQh6LTHsG0meivH8AvxIbOwu3+w/q1GlE374306VLF664Itfn25Rj%0Ayh+gWglKQ/FJA3ndF4VP6na7cTgcxMXFXZYZCaUUmZmZPqksE16vl3379pGSkkJycjIbNvzFqlUr%0AOXv2DNHRiUAFMjPLoHUFjMA0nsD3sC0YPMzHsZ7tA2NaPwWtnwOygT1IuRutd6D1aQwnqNIYBVhN%0AMTK2AP9BiC1o/TxG4ZNVHAfeAl4CvEj5J0qtQAiJ1lWBzjn7CoTfcv4+o3BFgj1I+R+UWo6UJVCq%0ADdAaI9h9BwitQ5qL/TmKA6aY/ftAxyL0B1iCEC/l+HxkYPCFrQVnsBa4LSeD/QMmL1baGqLKfghx%0AQbiunpOI450RniyUbX3hBVjucYj4L9B3pED6fpjZAkp2hvpfWztExwHEhuvBVhddfoFBW8pcCGn9%0AYdJpkAFoJlvnwyd9oMt3UL27pd3Ita+j101E13sPdj4C69OgMDrX7Onw8kPw+iqoZuE7d2Yinr8e%0AQQzq/pXEf9qYuV++S8uWefnMpplIQkJCgfuTaTxi1gUUle+qtcZuNyxl4+LiiIyMLA5Y/3dRHKxe%0ASuzfv5/27duzdetWqlatytmzZwHjR1m6dGnOnj3L8OHDadWqFXfeaVR0DhkyhK5du3L77daKDgLh%0Aq6++Ijk5mWeffbbAOqWUj7tq/t//L1B29EJaYE56+z1eHfs2Gi+iSlPUbVOh5BUFG3o8MKM3pMyD%0AKx6E2uMgLD74hp3HYX1nsO9BRjVCxT8FMbcGz76oTDh8NXhHYGR7wMhG/U509GyUmk+VKlXp1687%0APXt2p1q1akRERBRKoQgkBWV+zm8teqH5pPkLri4nuN1udu3axebNm9m3bx8bN25j+/Zkjh7dT2Rk%0ASWy2cjgcpXG7EzGm2xcAD2FIIVmD4QKVjtaPEJqH6gKOYmRXF2DcFz05QVEZoC5Gljb4d23op9ZE%0Aqf5B2+RFBrALmAWkIUQMcEVO8ZQ122ApXwOao9SQfGscwDKEmI3WpxGiOlr3Ia/r1DSEOIDW0whN%0AAdiFlJNRaj1C1EXrh4CZOf1nY01rdj9SvozWf6F1fwyzgpeQUqDUbyH6epFyLEqNxVBQeDnf+tHI%0AqLWoKhsKdnXtgKMdEdRE25aElqZSLvAmQufvYOkQiL8eGvxQeB8TmTtgQ1tEZBt0+Zl5VomjZQJT%0AAfatgbc7Qdu34Zr7Le1GbpiAXv0auuUfULIJ4o/S6Cm/QsMWgTtsXAmDb4JhX0Gr3qF34HEjX78Z%0ATh5DDdsGUhL5n4d5rdfVPProo3mamjMlSqkCXPSMjAyflrMJpRTp6ek+Pe9QMANc01SkWNLqfxbF%0Aweqlgt1up3379rz44ov06tWLUqVK+YJVgNKlS5OamhowWO3WrRu9e1u4yQSBx+PhuuuuY9q0aRw5%0AcoQdO3bQunVrqlSp4svmAXkCp4tdHW7C5XLRqNn1HKnyMmLv5+jTqxFd3kQ3fzjwg2Xxq7BsAmiJ%0AqPkS+opHwBYkiHCnwR9Xgqc+0rYXpV2IEsPQcQ9BWIDK78xfEKfvRatdFPRw9wAriIiYTVjYHBIS%0AIrn11i7ccktn2rRp49MhzB+Y5tcn9T+3FxNaaxwOB5DrKnMp4fV6OXToEHv27GHv3r2kpOxi69Yd%0AbNmyBbv9DDExZRGiHFlZpfB6ywDlMDJlgXiLPyHEQbQehiH7ZAUepJwEtEAp/8x6bmBqsx1EqQNo%0AfQ4hYhAiHqVKYgSRA7EaNBpIxchUDsYIbvPDiZHlTEHrbWidlhMMlwOOGBxObaXAyR/HMKbQx2MU%0AJu3NyaIuy8miXg90I7CuqgchnkLrh4Fg2bytSPkZSm0DGmJwW0v69X8ArV/DoCgEQyZSvo9S0xGi%0ACVqPJlcWLAPog6FO0DRI/0MI0RfYh9ZTg7RzgrwGKi2GqGa5ix3L4FgPkL0h3KIrFYC7NXjWIsp2%0AQzeaY61PRhJs6AgxPaFcgH0duwnZ9Oq8VIDjO2BcC2j4OFw32tJuxKZ30StfhBaLoJSR5RSrWsAd%0A3dDDA2zj0F64rQl0fwr6vBB6B0oh3xkA21ehHt0JETn31k1f0Tl7FrN/mF6gi5kB9Ze0MmWoSpYs%0AWeDeY/Jd4+LiLKmqmAFuVFRUsaTV/y6Kg9VLAbfbTffu3enatSsjRhhZuzp16vDHH3/4jAI6duxI%0ASkoK48aNA2DUqFGAQQN45ZVXCky/BINZeJKcnOybTk1OTmbjxo24XC5q1KhBzZo1GT58OA0bNvSR%0A3x0Oxz+WhVu8eDEDBz9O1s3b4NgixNohULoaus/XULZ23saebMSkGmhPY6R3C0plQq2xUPnewNXA%0Ah6cikp9Ge44DPyNtr6PUTmyxnfHGPQFR7XKpB1ojT3ZCOyLQ+udCjlgDm7DZZqP1Z2h9jkqVatCg%0AQX1atryGa665hoYNG1KpUqV/dBrefIjkz25cKDgcDnbv3s3x48fZs2cPyck72b59J/v27ePkySNE%0ARpYgLKwMLldJnM4SQCLGufsRI7MWiBsaCAop3wWqo9StRTjCvcBXQAdsttM5gWmaX2BaDmOavQ55%0As6YrgcUYclZFkTZbAizHmGIPB/YjxE6E2IpSx3Oq6MticFsbkxuYm3zZEeTyUq3iU+AEQoSj9SmE%0AuAqt+1rczgpgBoZuqzlLYVzbUn6KUnsxMsr3U/DlDYzs6m9ovZiCLxEaQxf2tZzA+WUCmwm8gJTh%0AGFa0+fETcB9CNMvJABdSgCXuRsYnoMrlZEEzpsPJB8D2IoSPCt4vP7xLwNUDhBs6nAv+IuyPtFWw%0A6WaI/xeUeTtwm8wFkHZHLhXg7BF4owlU6wE3hZaPAhBJH6KXPwPN50Nim9wVu99Eqm9Qs5PydkhP%0AQ/RqjL6qJTwewFggP7RGTh2BXj4DPWxb3jqCM7spNb0jxw4EsgvOzYCabnjmzE4wbVW3243dbg/K%0Ad80Pf0kr06K1GP9TKA5WLza01txzzz0kJiby9tu5N7KLZRSgtaZ27dpUrFjRZxBQt25dqlevzh13%0A3MHcuXN9lez+cDqdmHaZ/wRu7zeIJSeuxdNgNHhcsHIgHJ2PbD8K1W4U2PxuTrsWwDd9odpByPge%0AefZltAhD13kLKvTJKzauFWJVM/S5+iC+yll2CHgCIX8HWwl0wpMQd7fBZXPvgSMNQc/HGpduJXAr%0A8CqQRnj4HqKj9+Jy7cRmE9SqVZ/mza+hSZOGNGjQgLp1615SJydTkL+oBVdut5ujR49y+PBh39+e%0APQfYu/cghw8f5uTJYzidmSilkTKS8PCGfgFp6Zy/wA8UKZei9aocNyerD52zGBzJPhTsr+P8AAAg%0AAElEQVTMXHoxipyOI8QJpDyM13scg3cajpEVrwXUo2BgGhhCTEEIjVI5FechkYmhDvA9Bu0gCyGi%0AgUS0rotxLRX83eViFkLsQOuXKPycaIxs41/AxpxiKYkRdD9OUQ0IpXwJaJmj+boaKT9F66No3RJD%0A1L/wcyXlQ2j9EFoP8lu6HSlfQOtDaD0EQwM2GMzs6nKMAB6MbOwwlPoJeAUjyx0Kh0C0hWq7kfYp%0AqNTxYPsCwizSp7RGqPFo16vAi8jw91G1x0OFEPtO/R2SekHC05BYuEmAjwpQpRFiTDOIq4XuNc/S%0A4Ymtk9F/jICms6HsDXlXutJgcXn48xiUzOFou93Ie26Acw7Um+st7UP+PBY9czz64Y1QOp8agdZE%0AjS/H1o2rqVKlSsD+/pJWDoeDmJiYQoNKs+LfqqSVGeAWS1r9T6I4WL3Y+PPPP2nXrh0NGzb0BZxj%0Ax46lRYsWl9wo4P333yczM5OhQ4cWWGd6OZ+vpNDfxaFDh2jWsi2OG9dDfM6N8uQK5Ir+6KgYdL9v%0AoXLuFKD8shv6qAtd5TejqCp1PCLtLYhMRNd5G8p0yc2Ypm+G1deDdzMIv4IVrYD3kGHvo7zHkAl3%0AoOJGILK+Q6RPR3m3Wzp2KYcAG1HqC7+lGjiDMaW8m9jYvUi5G4fjIOXKVSUqKpIWLZpSuXJZEhNL%0AU6pUKRITEylVqhSlSxv/L1Wq1AV5eXC5XJw4cQKXy0VaWhqpqamcPXuW1NRUUlNTOX78NCdOnObU%0AqdOkpKSglIfMzHNERZUkLKw0WifgcMTh8cRjTAOXyPk3FqNI6EPgLkLLNZlQSPk5WtvQOojtZkCs%0AxeCU9gbOYLMdQ6kjOdnSSKSMxeuNw9BRrY7B0bQh5WdADIbblNVr24UQk4C2aN2hwDojG3oIm20/%0AXu8BwJGTOY3HOCc3UtAWtTAopJwIXItS+QOsbCAFKZNQakvOZV0GrRthqAHsB6ZhaLcGE+MPhn3A%0AmwhRFjiH1u2AQVh/iVhOrtGAGyknotQcDP3XZ7EmR/UcUsai1DxgI0LchhCRKPU9RTFqEPImtDyH%0A0HZ02CKQFtUgdAbSexfasyJnRqUVMBJZ4k9U83XB+52aA1sHQKk3oKQFg4djNyEbVkIf247I1qh+%0Aa63Zu26fBosfgSY/QbnAzwK58krUC+OhWz8jQ/r8EFi+CPXebggP/R2I3z9HTxkB9y2BKs0Cton/%0ArgcfPzOg0PoJMwMKBKQA5IdZ8V8saVWMECgOVv+X4HA4aNeuHfPmzQuY3XM4HISFhV30KRb/wiP/%0AzxMmvs2HP6fguO6X3MZKwdqHYf/XyJYPojq9DhExcPYAvFsPKi6A2Da5bU+NQmR8hoitjqr9DpQ2%0A1sntD8HRlSjPlgBHBOiNCPk0Wq9BRFRDZ28HXgCeszCiMxj8xseAIO43PriAAwjxHFpL4DrCwzOJ%0AiMjEZssE7GidjtudgcuVQXh4JHFxJUlIKIndnkH58hXQ2uDFGtxYL16v8n3O5cx6cTqzyc524PE4%0ACQ+PJDw8HpstFiNwi8HjiSI7OxKtYzB4hDFIuRKt09B6OFYF8oVYAfyekym1GlxnYGiMdsKoTveH%0AwsikngJOYbMdR+tjKJWKcc+yIUQptC6PwdUMZY+ajRDvIUQblCqKiP4B4AsMXVEHNtsBlNqP1meR%0AMhaIR6lKGJne6uRmNbcCMzFksIoSPJ7AyB4Px3gh2IqUm1BqX04gXBnDprV2gZ5GJtiFUs8SOiB3%0AA39hs63A6/0rp3008AnWg9RcGBarVTEsZaug1KsYLwxWkY6RXR0CTAb6A2OLeBTrMfjCZyEyOag0%0AVQGoHQh3V6PqXS0hl5ObBbZK0Hwd/8feeYfHVV1d/3fOlUbNsmxZ7rhjm95rKDGhmhogBkIgdJzQ%0A85IADiFAQg0ldIzpHWOKDQZsgykGA6bYdPfeZFuyrS6N5p79/bHvSCPNjOaKl+QFPq3nmUfS6La5%0AM3fuOvusvRadtkler/RZmHsOdLsHOp8ebl/Vr0HpkdhuW+JOmRsuxGD+s/DmObDTc9CrDaeAz0bh%0A7RbBv/VpzMO3wNgbkdu+geLk9MIkfPoq/Pu3cOIEGD4y7WLmvev5w7D1/PvWtq3GKisricViFBUV%0AZZzij0uVvq+l1Y/VT7oDPzg6yOqPDVOmTOGSSy7B933OPvvsJgurHwq33nornudx9tmtO4h/+Opq%0Ae6NFGxoa2GXP/Vg//D7o26ppY9O32A9+jbg6ZNSTMPgA7NvXwseP4fovbbmsi8K686BqPLbr7rjh%0At0PewKDZ6k4wp5EWUgdcg/GeQvyNWPsLnDsS+BVKitKdl6cw5jJEJhLOu/I74Dw0sjP1tJpearVA%0ANeov+YhqdDkhOA6b8NO2+tugpOR24FDCJx3VBMe0H+FtiQRrH9IABwnX0az4DtWvHg5U4XmlOFeK%0AyCYgG89TUi3SDegHbAkUYe2TQAznUmXPp8MKVL96Oi274hNRjVZFS/G8NUHVthzICWyretMsJ8j0%0AHj+PMWvRpKhMpCRehV+OamU3All4XjG+PxzYn2YSlQ5RjLkeOA6RVO+bD8zF8z7E9z9Ho1WHoM1R%0A3TDm6mAq/8AM+0lEOcZMRWRy8BrOJ2xUajMEmEWzzvdxkgcvbaEaa/8ZWFkdg/VmIt5FiHdp5lX9%0AiRA9FU3uSm6KMnYEZoudcMNaBgGY1Q8gCy6FkiegMGTjq78RU3oY0vgtHPE8DM40qAUWvgBTToMd%0An4A+Gc7rhunw3Si48RH4yylw1XQYFqLPYe4H8M/D4Mh7YZc2vhcBlrzD8E+v4ItZ76W9P8S9VXNy%0AcmhsbExpaZVqnQ5Lqw5kQAdZ/THB932GDx/OW2+9Rd++fdl999159tln2XrrVN3F3w/V1dWMGDGC%0AqVOnJk0xxxutIpFIaD1Qay/WRP9QaH+06LRp0zh19BXUHvI1eCmagub8DRbcid3+eNwhN8M9u0DO%0AedD9yuRlY5Ww7iyoeR3b41Bc/laYFeOQxrVgMleQrDkc52YFue5rgAjWHohzh6JTvInVI8HaA3Au%0AH7gp1LnTbvWZOHdXqOVhPdqEcwbanR0GX6IE4DK0WhcGC9Cbd3sqg9XArSixSiTGDq2clQHlWLsB%0AY9bh+xtQIh7/nPUIHv3RKmUbtmTUAvcCOwGZPXSb8TbwOUqqaoG1GFOKMatwbh3qIJAPdAoaobRq%0Aa+0zQG5g3h+2iuOw9g5gOM79utX/osAqYDmetxjfXwGA53UOnBHWYszAVjrQMPgKbZi6HtUOO9SB%0A4EOcmxU0YQ1A/VtbE/bP0Eare2n7cyKoJvUVnPsKa3vj3DEYMw1jeuHcde043i+w9m5EVgfyg3fR%0AwUvYMIfpwEVY2wXnbkMHNK+DuQNy1oBJU+UXH+vG4BrvB/k3aoeVCu+DdzTsvwE8JVF2xS24xf+A%0AHi9DwUHhDrNxMWbNrzDSG+eX4G1dhH9ocld9Cyx+BV7/LezwCPQNaYf2dpHq/c99AEaEiL1d/jVc%0AuQ/sOwZGJNsaJqGhmqybe7Ji6aKUnt3Q7K1aWFiY1tIqFb6vpVVWVlZT7GsHYf1Zo4Os/pjw0Ucf%0Ace211zJlyhSAJGeAHwrXXXcdxcXFnHpq8s0wFosRjUaT7I7CBAS0rpRCc7wohI8WHXnUb5hZsR/+%0ANmm+QKtXYN8/Cle7CrY5HvP1eKT/SshK07kdW48p/T1SMwNcHXAsmJdSL5sIWYlO8d6DVntmAi/h%0Aed/i+6UY0w1jDg2skfZHp633Rm/4KaYOk1CHVqIOAUaFWB6MmQI8h8h1hJ2ytfYxYBXOhag2Na0z%0AEfgO5/5MZoIWJ6SfotrFXfC8Cpxbj0gFqhvNA/JwrjPQCyUWAwCLMQ8DBYicEvr4lOw9DpxEWq1s%0A5DsomQHZNdDYiNmYjdQ3AI0YkxtoXIvQyvZQoE+a1xoNZAR74lx7TPA3op+dY4AsrF2GyGJEygNS%0AXBRMn+9Ay+p6NZrCdRzpLZ1Sw5gHAMGYIWjAgENkC1RDmywfSIS1/wb64tyfUvy3Dk3AegWVqmyD%0Anvs4aakErkAHLDtmOMrvsPZenFsI7IvGpkaA27G2Cucm07aUoQxrr8C5d9Gp/5Zk09ojcN4NkHVG%0A8qpSjo0dC24hzk0h03Vqswfjhl4PvU/FLP07rLgL6TkN8sK5s1D3EawdCYwE+yy42WD3hT+WQ1aa%0AKuLS12HyKNjufugXgnQCbP4MZu0POx0Cl0/MvPz6ZXDZbrDtSXDUPeH2sewDeOQgnn3qMUaOHJlS%0ASlZVVUV2dja5ubmICFVVVXiel7KptzU6LK060AY6yOqPCS+88AJTp07lwQcfBOCpp55i1qxZ3H33%0A3T/ofioqKjj44IOZOnVqUgXVOdekXY3/HSep/8lo0UR/0iVLlrDvLw9Gdr4JGfqH9NquuXdivv47%0A0lAJnQ6Afm+3/cKjSzGlpyC1n2PYNfDtPAZMer2jMbdiuC24MSa+nijwBjAZaxfhXNw2qAZjoog8%0ABJSQmejNQhtR7kIjMDPBBZ3WkcAjMwxU2qB6x0NCrhPDmFsQGYjqCWtRHelGYCOeVwaU4Vw5ItUo%0AGctFxy51KGHpj1ooZbJ/qkSbtPYD9gl5fGDMJ8A7wXlYhzYarcHzqvC9ShgabTkGmFAAC/fFNH4X%0AeEEGzV2RBVAyC7Jj0JgFZXtCtLXN0hpUv/pb0qdK1QHz0WrmeqAa56qALDSatRcqI9ietjW20JzC%0A9We0SpoODvVbXYTnzcf3l6CfuQI00nRHwjeVVaJNWlfQHB27Gmtfx7l3AguqEaisJNXn+imMWYjI%0Ak6TWOy/C2vtw7mtgD1QGk0jYohhzRnDt/DLF+nHbs79i7SCcu53U18wTGPsSElnW0hnEzYbo4Vgz%0AEOfeJJxc52/YwilQvD+y5mmk1wzICZn6Vf08rDsDuAy85iADm9UP96vbYPgJyessfxNePRa2vgMG%0AJEu1UqJ8BnxyBGRvh+1Rg7s9jS4/jooNmMt2RXruASe/EG4fKz6GRw/GFgzg7xefwOjR5zZVNOOI%0Ae6sWFRU1ffc756iqqmqytMqE9lpaJToQdFha/azRQVZ/THjxxReZMmXKf5ysAlx66aV06dKFzp07%0AM3/+fHbddVeOOOIIEt/71hGq34eUpooWjf8uIi3M8hNN88+/4GIef/I5TEEfZK+HoWcazWX9RsyM%0Ao5D1s6DL+dD9avDajti06y/AbXwKazrjXBnWOwrnnwX8KjnhSmIYs01g5dOWNU016gs5DdViNqAV%0Arm5Y2xeRAUEVrS9awetLnLCofdASnLu5zeNuxjrUpugswsdlzgfuB/6HllP7UZSkVKJNT5UYU4m1%0Am3FuZaDZBK2A5mBtHs7lItIZnbbvjVZJ4+TLDyqlee2cxl4CPItqStM159RDZCaUfAXZ9RBzsEEg%0AaiBiobuFrGxozAXnwx/KkzcxbgisOS44F9tAZEsY+gaMag7oYEJXWDgyBWH9GCLToaQPZEehsQHK%0AIni+j+9VQEkUsj2I5cKGoRDdDq0iT8aYckTOo30NTM9gzCZE/kQz+XOornYRnjcP31+KMR7GdMG5%0AAWgldjM6pX8Z+h61B5NRqcRZWPsqzi0OJAmjUM1wW3AYc2mgW04MOFiBtWNx7hNUunERqX1bAR7E%0AmAWIvEvL+9MKrL0Yke8Q+TOqc27jOOxhSNaD4AW2Wf7DEL0I9Yy9JcPrSEQ52EEYrxPS+xOIDMq8%0Aigim4mZk43XAg2B/2/L//jl4/ZfhH/dmy+dXvgMTj4StboFByY4tKbF+Cnx2vH7vdT0PFnWHscug%0AS5q0t7oqzF/3hqxi5KwZqZdpjVWfwcO/gq0uha47sJcby5uvv0h1dTWFhYVNhY103qqJhDKMu8n3%0AtbTqIKw/a3SQ1R8TPv74Y6655pomGcCNN96ItfZ/3WQ1e/ZsZsyY0SIooL6+nq5du7L33nszbNgw%0ARowY0cJeq7GxkaysLCKRSNMXRiZSmigNSExzSoxsTSSmbUWL1tXVsdU2u1BWPwRqP8X2PQS3212Q%0An4bIzBkDc+8B52OLz8J1vRyy0zQu+ZWweBD4V6PT9tdjzIeINGLtqTh3OrBzQljAx+g06mTCdTl/%0Agk5t3ojqTBehN9sydAq1BpEaIBdrewFdcO5ztKq4HUpoIqieM5Lm77eD5pYr0OaZepQgR4OfLR/G%0ANCDyCToF3h2oCiqiDtXiRjAmgkgE53LRamgxUIcxXyByEW1n0CciXindH63mhkTOS9DtG8juBY0x%0AKCuEaBTPq8G5WiS7HoYaGJXwFTShEJbGYFAMRjU2Pz/JwI6S7I0/3cAeBXrKYtXwqQcj/eRjeaYH%0A1B0BFVGoWgeyBnLWwpabWlVrc2DpQBi0FkZVJjyfSHgd1t4L9AtsqcJWOh3W3o7I1oj0aKqcKjkt%0ACsjpLqT+TD6HMRsR+QvhXB3qgHl43teBQ4CHVj9HkbkKnIiPgKfRCmg1GtP6LsZsi8jFZJ49iGHM%0AmYjciiZr+RjzICI3owlYNxPuc3gHxpuDZH+GdecjjRPQBKyj2vFaPseYUYhsxHQ+FunxVOZVJIYt%0AH41UvowwFWwKr2a3CsyWcM4qyAuM91e9DxNHwtDrYMglyeukwpoJ8MUZ0PN26HYuAN7yofi/uwIO%0AStF82NiAvfYg2LwZd96X4Wyz1syBh0bA0Atg5+uhvozc14ZQtn4Nvu9TV1fXRCorKirIy8tLSUjj%0AU/yJ5LYttNfSqqGhgZqaGnJycujSpUuHQ8DPDx1k9ceEWCzG8OHDmT59On369GGPPfb4QRqsHnro%0AIb744oumgICtttqKXr16cdxxxzFs2DCuvPLKJM2p7/vU1NQkmcmnq5K2jhZtXSn9Pnj99dc57Zwr%0AqR08HbP4t0jNHMz2f0W2vjS5+cqvh4lDoP5QrP0G53+N1+V4/K5/g5ytkjde+TymdDTiL6L5ZjwN%0A1dx9hTFdgHNUR2kGYu1ZIB/jXAg9GGDt5cBXOJcm0QaHdn/PQ6uKS9HEo+7BPcQBDhE/4XcH+MFP%0Ah5rdA0QCAuOhtk76U31MPUSygJzgsRidIj4Q6Il2mbf1/gjWPg1Uogb5YbEYbfY5k9RemQ7YAJFZ%0AULIAcuog21duOzBYZEI2LBwODIWiCBS9B6eUJm9qPOp21BpvoyYOiXhoEGw+DrJi4M2D4qnwuxTr%0ATkaLgEXox6MqAh84ODKWvOwzWXByiufHDYE18epyFcbcCxyCSJoMdyqBucASPK8c56oQqUUHKLlo%0AoEE6ctoasUCDuhfOpeo8F3Qg9U3g37oSjX/dItjPJFQOEKKS2ArGjEEkD1iDMUMDkpqm0pcS4zHm%0AXUQex5gLgQ1oTGt7XAIawRwCFGONBDKesHZaPtbejHP/Qp03TgRzHAxc1fasjavElh4NDYtw8jHY%0AdC4fYL1huH0ugR3PgzUfwUuHwJArYWjI/oSVj8DXF0KfR6BLwod/9Xl4/Rbi/71V1dY57K3Hw8I5%0AuAvnNceotoXSr+HB/WDw2bDrrU1PF765HVNeHMeuu+5KXV0d0WiU/Px8qqur2/RWjUaj1NbWhqqY%0AttfSyjnH5s2b8TyPwsLCDkurnx86yOqPDW+88UaTddVZZ53FmDEhujS/J3zf59e//jXnnHMOBx3U%0AsrNVRGhoaKChoYHs7OwWldNUVdJEsvtD4vCjRvHBkn3xe4+BzdOxy89EcMhe46BvK0/A1W/AjJPA%0AWw6UY/xzEf8jbOEBuOJrIC+hyiGCXflLXG1nkAmt9uqAR4Oq0AKs3QrnTkB1n1ejGs5MqECrsb+l%0A7SnLpgPC2usCj9PLQiwP2mF/DdroEtYdYC2qj/0d4U3864J1tie85hWsfQfnPkOnhFcCa/E8JWDO%0A1SoHGwqMSqhsTg8Oa2Dw95sGRnhQUQSfVMPhDck7ei4bTmpMfv55D05I2PbzXWDRDhCNofZU1fg9%0AN8C5Kb7SxvWBNb8DCsCLQedK6DEefrsuedkJXsvX0PRaDAwaCKW9gkcjlE0BdxZaJZ8HkW+he7mS%0A50aB8iK82GB8vw8qIegJzEGZ9wWE0zXHsRo17D8fJZ2NqG70G5z7EmjA2m44NxxtdEqcvn0ZYxYj%0Acj3hpAsVwMcY8x4iZeg19BfaVVlvQikqV2lEB1VX075krjqMeTSopHZDbcvCeQarB/JJGLMS58YS%0AT9WyWYcgXc5EuqQhk7FV2vHvclXuYDOQQf9qbPdJuAMfghcOgEF/geFtJ2DFYZbeicz9G/R7Hgpb%0AfQc2LIal28LjGyEnGISLYMf9Efl4InLhPMjPZIMGrP8OHtgHBpwKe7R0K8mZ/UeuPXUQl1xyMSJC%0ATU0NsViM7OzsjI1UcXL7Q1ta1dXVEYvFMMYgIh2WVj8/dJDV/58hInz33Xccf/zxnHjiiaxatYr5%0A8+czZswYdtllFzzPa9Kc5ubm/kdJaTosW7aM3fbYn7rhsyEnyJJfeQ1m3b8xPfbE7X4/FDaTLvvO%0Akci6WiQraLZy6yH2R5Bp2LztcMX/UMsZYyC6EJbsBDIFSJ3aolrU2/C8F4KkoggwGr2JbUd67R3A%0AFIy5EhFNUMqMTcAf0DJh2Eaj9zHmRUT+SriGETBmBvBWO038V6HE53ckz637aAPSCtR2aRPW1uJc%0APSL1gMXz+iPSM7CEKtFHn5fg3MXJu0qsiD6xBSw5CzDQ58nUyz+eB6fVJT//YJ6mlEViEBUoc5jG%0AQqztiXM9EOkOkVoY+g6MSqiMPt8VFqXQrLZ3/w8PgJzh0GsR9NoAvWqhyIcNRvnYkjzwGuHYBKKd%0ARi9rzHigPGgmC6vJ89Hp+OVY2xfnFgb+qr3Qz/v2pK+qxxO19sS5dE4V9cBsrJ2Bc0uwtgTndkdt%0A3Z4JdLr/bmMfrbEQayfg3GxUhlKPNjGGlSE44DXg30GIwsVoDPIklIxnwrPA+RizByJjaXmeJ4J3%0AAwxck6xrb5gDaw7GyF4Ir4SbXne1YErAejDgItj6+szriGAXXYdbdAv0f705DKUV7LK+uPPHwu4q%0AebATrkVevRM57wvo0j/zfjbMhwd+Af1GwZ5jk/+/bDy/zHmKqZO1OSte1YxEIkl61eSXID+4pZWI%0AUFFRQadOnfA8r4UDQYel1c8GHWT1/zeMHTuWWbNmMXfuXObOnUtOTg79+/cnNzeXQw89lG233ZY9%0A99yzaTon/uVirQ1l2PyfwD/+eSN3P/EttQMS7KZilZhFJyKV72G2vhjZ7m+QXQA1K+GVrcC+Cl7C%0AHLCrhdglGF6ArJ5IyT+g8DhM+dWYTc/gYt+GOJKVaNTnsuCmvwljemDtTvj+bmh1cyuaCaBg7VmI%0AVCHyz5Cv9l2MeQCRGwh3kxasvQuRWkTOD7kPwdoHUBP/kB3H+BjzJiKfo+XQTXheLc7VBYQ0grXd%0AMKY7vgeUrIVsC40RKFsH0e1pSvfKq4VBSyH3DTi6OnlX79CcRzCuL6wJggYiC5KboZ7Pg2UFMGhT%0Ay+rm8wazpB9SPwRtKCtBNbgpKnSRb6FkEkQMRLdI4waQbv/Asi0DzWpNi/2zSCCah7XdgF5K1CNF%0A0HMu9PoOojlwbIrX/+AgWN3a+9Nh7T3AQJxLZUTv0Er7aqxdBSzDuTKMyUEkhg5kzqV9iVprUe1x%0AohzAB77F897H978MpAPbouECiUQlhjHXACcj0nYzFHyKtc/j3Cr0+jkFKMbaKxE5LOTneg7G3ABs%0AROR0mpPkbg2aBd9vY90KrB2NyFuIXIu6KCTDeHsi3e+DTgkG/TWvQ+kJYM4Bm07ykwIyHtzpULIf%0A7DUtxPKCnfdnZPkjyIB3IG+n9Msu+zV21yLchY9jpo1FHr8MzpoBfdpYJ47yRTB2L+h9NPzikdTL%0A1K4lf+q2lK1fjbW2aXofIDc3N2PXf3streINWuksreIzgJ07q+tInODm5ORQUFDQYWn180AHWf3/%0ADffddx/Z2dlN+tVu3dQW5+mnn2bKlCncf//9SVqfuH4oJyfnB8mqby/q6+vZZrvdWFd0H3RtlWxV%0A9Sl26cm42GbY414YMArz7U2Y7+7HsSy5yuFi4F+N4UEwEaT4Mii7DvzRhItWXYw2ntyK+lbOBD7E%0A8xY12ThZOwDYDed2Rqdxz0OnNcN4MwrWXotIddAcEwaVaDTs4YSfdq1Ck6pGoFXcGDqVuzl4bMLz%0ANiJSjnObUeuqbPQ7w6GVqm7BoxjVw6KErv8k6F2jBTUHrM2D+noYNhwGV0H3DbCiP8zeBCem6NiP%0AV1afz4NFjRD9DUqc1mBy1yPF1apvbTRQ3hUvNiAgyKUBQd4I5dnQcAnhq3rVwFiULKWKtWwAVkDk%0ACyhZCpHGoForELVKdEuyIDtL3QjKdobozqSudgvWvoDrPw9OTyEfeBsY0h+WDoIlg2F1X/Czgn1P%0AguxiaOwEZQMwjQ5jluFcKZp61QnfLwYGox6inYFajLkbOAzJLoKSmQk2XftAtC1N/ESMWYTIOVg7%0AC+c+RMMFBqOft15trPsF8Awa49pavtAATMeYF4AoIrujzVyJ1bNFwG2oy0a6/azG2ttw7lNUdvNH%0AWg5I6oGTURuwVI4iHwAnBVXhJ2n2jU2Ff2Jzv8Rt8RkApvIeZMPlYP4NNqSeW2JYcyku9ghwIkRe%0AgUNKW1psJa3jsN+MRta8jAz4CHLTWacFqHob1h8H5z0Ed58OJ0+CLUMkk21cCmP3hJ6HwD5tN5N1%0AmjKMd15/hu23357q6uqmmO62SGUiEj1S/7eWVqkau+IENz8/n7y8vA6HgJ8+OshqBxQiwiWXXMLg%0AwYM555zkyMx4w1VBQUEo/7sfGlOmTOHUsy6ndvg3YFNMB629C7PmGkzRcNwe92HeOw6pPxGy06RJ%0AOQf+3VhuxcVWgSkAmYn6YLYNY27CmHE49zzJZGgjWhr8BM9bhe+Xo0QoF2u3A7rhXDHaudMFJRNd%0Agr8Lgu1tRG+6JwN7ZTwexWw0deryYJuCEoKa4FHd9LsxNVhbiXNLA9P+rGDZCJ6Ycx8AACAASURB%0AVNbmoGlNeSjB6I42SPVFiUQdWm3bmpRa3F5jYWhpy9TO6cBGq3xj8dGwcruAfKWoVL5kYGNWUCi0%0AEG1EbbN6YUwfnOuBVkm7B+crFaIJ9lmnhzx/oIT4IbSKmI21FRhTh+/XBeenAM8rQaQHzsVJejeM%0A+Q74AJFzaZvsJKIR+twC50aT//XgYMj9BQxeBIMWQLcK+NyD8kY4KtEJwcLCksAia1ua/FhT+sYa%0AiDwHQzvBqIqEbXSDhUe1IqyCBlwsxdqFODcfMBgzAJGDyRQukAhN8eqFc3Gt52aMmYzIZKwtwLmD%0AUJKZmqzptdYf51pfx9VY+zDOjQ+cBsaQPpI2VXW1EWuvxrn7UAu4/wnxaurA7gF93sXWPImreBR4%0AGWzrLr40kHUYOQbDysDndSgmuzey67PQPU0Sm4thvzwF2fAuMvAziKRv2mqBxV2hsR5+/RDslKqD%0AsBU2r4D794CSX8J+4zMunvf5WdxwzvaMHj26hbdqe3xSfwhLq1gsRnV1NUVFRUnV0/ixdOrUidzc%0A3NCpjB34UaKDrHagGY2NjYwcOZIxY8aw997JnbfxKL1OnTr9n3RaHnXMicxYtCex3n9LvYCrh0Wn%0AwubXoGgrqFwI3mKwGbwmY+Mh9idNt7FDETkekaPQylSqaySKMTsjshtwcYgjX45mn29GvSorsbYu%0AsJOKIqI/tbqZjzGFgbVVI9YOCxy0fPTSS3QGaPlwbg3aSBInnwbIxphsrM0GsnEuG5FclOh1xphy%0AYEVgTRW2al6K6lePJSkBaPBN8Pv65FWezMUu3Q3n5qD2QdpwZXLLgkqpg0aL2VgCDUMQ6YES0mKs%0AfRbwce5s2lcpfQCtMB6b8LxDu+CXod3qG7G2Tq2xpAGthEaDfe9AnJAqEUp3sxOsnYLIt4GXakiN%0AZeRLGDqxpRXX8x5mSR40+MHx5GELuuL6bITf1SZv45kesHEUlHcDsakHAHEdbMlkOLcyeRvjhsKa%0Aw4GleN7CIFiAIASgD0reX0M9SsMTVUUVGjRwBtbOx7n3A83w8WROugK9Zv6KVr23Q6+DScBdWFuM%0Ac5eReYDZgDY6vozaqS3EmBMwZhPOPYxW08PiVLBfYkw+IjPBhmxSlI/BPxJjtkZkCs3X2m/x+sTw%0Ad03hMuI3YGcfh2z6Ehn8BWSVhNvX5udg9e9h+Eg4ZVLm5StWw9g9MF32QH75crh9LH6MXd3jTJ8y%0AMclbtaGhoYWlVVuIE8rva2kVr+qmq87Gj6VTp07k5eV1OAT8dNFBVjvQEqWlpRx11FGMHz+eXr2S%0Ap97q6upwzpGfn/9f1wEtX76cHXfem8YhL0FRG9WM2rnYxSfgqr8Fbwhkf9lmShUAshrqhwMjsHYZ%0Azi0H8rH21zh3DDpVnkjmZqG6uEcJZ4mzArVxuhStSqZCHVrdW4dWtj5Gm652Rkla/OGh127898S/%0Ap6IE7SjC+VH6GDMW6IzIbzMu3YTIlKB61yeY9g50nsNvgN+mqBY+A2ZhPiJ1gMXa/oj00SYn4o90%0AZLkOY8YBPRE5KeQB1qJd9G8CXfC87EBfW4fGv3bDmB74fnzf8UppBPgWnXoeRfq0qtZwWPsCsBbn%0Azid5KlrJOazHmM1YW68NaNm1UOIg20BjPpQNguhWaPW4G01NPgMehTOWJ+92ci78Ig86VcO6nvBp%0AJRyXipAO0Uprqm08CmZFXuDd2h/17GrdiDMDlbxcSdtNhXEIsApjvkbkPXQgNgj4PeEtpOJ4CGvL%0Ace5ijLkR9So+B63IhsVtWFuOyBmBvOYA4E7CD35Are3+AtSA/RZsCOIuguF+xP8LcCHQWru+FOyO%0AcPAqiCQklcVqsJ+OhJpVuIFfQVaIcy6CKb8JWXcD2FMh90W4orTZLzoVKtcqUe28IzJicuZ9ANSu%0AxUzbl+zYRpYvmZuyMtoen9T2kNtES6vc3FyqqqratMuKH0tjY2OHpdVPGx1ktQPJmDlzJldddRUv%0AvfRS0pdQ3KqkrdHsfxJnnHE2z78wCVu4G27AHVCwc/qF1z8BS84B8bCRs3HmArDpqzDGvwti1yHu%0APfQm9iZqrr4AkWo870B8/zjUvqkYa88H3se5x0IduzFPY8x4nLuDcDfJzejN8UjCywGWoVWoswlP%0ACjYDd6M3/xQm5q2Rqnr3craGJZU3wtEp1nmwCFafBBRizCNAj3YQz/gxjkOrnYm65U2ojnglxpRj%0AbU0wbR/FmM4Y0wPnlqIEfgRKADNXPo35HJHXUHKVaepVrbCUkL6NyhaKMEYJqVowFWBtMcaU4Ptx%0AYtw1eKwDnkRP3PYp95DWiWBcFqwZAzmN0KsUOk+C4zclL/d4RDW256Sw+Ho9ApW/gflbaXU2Dawd%0AB3TCudGkm3GABVj7Nc59iTEClCCyI9bOAnbAuVPSbj81GoHPgCfQa+YI9LPdXsLxDVqhjaB68/YQ%0A3VKsHYPI54ichbXTwB6G47a2V5M6rDkb8V9H5Gkg9VS/zd4WN/wCGHSRPtG4GfPxgZhoHW7Q7Mw2%0AWKBa2LWjkc0vI5GpYHeFWFc4623ou2vqdarXYe7fE/KHIwdOzbwPgOrlMPUXmLztyHfLeHn8Pey3%0A335JZDF+nwBC+aR+H0srgOzsbPLz276e48ciIk0pVx0NVz85dJDVnwMmTJjANddcw7x58/j000/Z%0AZZdd/tfbvO+++/jmm2+45ZZbki5s5xzV1dX/J8L1hoYGtt1uD9auzwG3FFt8KK7fLZCbejrOlN6N%0ArPg7uOEgX2GzdsbZS8EenSJa1cc07oj4w4HWGrl5qPfqZzi3Dmu3w7lD0AaQC4DjyQw/SOfpjVpU%0AhcEnQYLP5UBhqDWsnQZ8iHOXEt6fch7a2j6a5Cz6BlTKsBJYB32WpdZaPpcPy3eEIV/CbxKmrCd0%0AgoVHJ3TYb0an6Hch3Q08GeVoNfsToAvWinq14jCmGGt74/s9UMas8oFmb81FaKrSUcE+w8HaDxB5%0AF5EziVcKlViW43k1iNQHEo4GIAdruwRkdA1K3I5Hz2VnMvt8fovaTJ1ESv/blE4EXWGRgWg+OphZ%0ADn2+hnNTyDDeBIoKYV0Ujkrwqp0Q6KW7rADxYGNXWHYw1G+TvA3qMeZ24AhE4s1KFag7wBf4/qJA%0Ah9obbfRLrDyWo5XMPxFOSrASa9/DuZlYmxvovNej72N7ErXmB9rWuej1U4zaYYUhKz7GPIXIvzBm%0AK0RuQt/LL4D/AW8NmKLUq8oyjByOIRoEErQVinAbpuBh5IAFEC3DfLQ/RgpwAz4GG+L69auwK4+G%0AugW4nFlNYQSmYR/Mnr/AHZYiXramDPPAXhDphxz0TuZ9AFQugKn7Yjrtg2z9MpEVF3DFad35619T%0Ae8/GSWV7CWUYSyvf95saq8K41LR2IOiwtPrJoYOs/hwwb948rLWMHj2a22677QchqyLCmWeeyT77%0A7MPJJ5+c9P9YLEZtbe3/ScPV22+/zQknXUBd3juY6nOR6Exsj1Nwff8BkVbSBfExX+2A1O6JZoJf%0AibUTcdKIyb4AsaPB9Gle3s2Bhn1R4pAuC30T8ATWTse5FegU8HaIDEdkIDqF2p/U5HIJqv0b08b2%0AW8Lau4E1OBcyhhGHMXehDUa/D7lOA8a8DCxFpB/WVmJMLb5fj1YpC7A2aC7qtwDOSFG9e3QALD8j%0ATYNP64r2auAxtFKWaKlTBSxEzdnLMKYG52pQXWh3RLojMhf1Cd0HbUwLc9OZi5LxUSTpbAEll6tR%0AQl7apGX1/WriWuF4ZdS5EkSKaVkdTRy01QZpVYWIpIi9TANjPkFkKnA6LVO/NuuxRb6DkmWQ3RgE%0ACGRBQwyt7Fo8b0t8LwuGLmtlo9UVlh4E/bKg+ycgS/U1VQmUbQ3FpS1J8JsGqneCeUdAQ+uq3hzg%0AVWB/jPkGkTI8rxjfH4zqQVsPdBIxJVj/Jlp2/cdRiwYLTEekHGP6I3IEcU2qtdehiVx/bOs0BpiL%0AtY/g3Dz083U2kIsx56GRrYe1vTrzMOZ/gPVopHFLJwFrT0LMaMSkIGpuGrhRGDMCkVRNmK0Rg6xe%0AsPOTmG8vAm8A0v+dcH6tjasxyw7E+Nm4yCywCaSw8WnI/jNcvqalFKB2I+aBvSGrO3LgjHD72fQV%0ATBsBXY+GYY/pc+WT2LXwTma+90ba1cL4pMYRJ5RZWVkZyW19fT3RaBTf90O5DyQeS05ODvn5+WRn%0AZ3cQ1p8OOsjqzwkHHHDAD0ZWQadmDjnkEP71r3+x447JzRDRaJSGhoZQI+EfGieceDrTZm5JY+4N%0AEJuPrToVF/0W0+cipPcVkJVQ8aj+HL7dH9ynNGsQX8R6N+L8Bdjsg3DmT2APAGOw/sUQew3n0n8J%0ANyOGMeci8g3QH8/bhHPViFShDgD9gCE4NxgYAAzA2teAyTh3G+GmM2tQrev+tGyzbwubUHJ+BNqY%0AUoFaXKk9leepPZVzm4NjdYEnp0O/FwLiEamEkrmBVVQWVOwMg9+E4yuSdzmuB6w5L+TxRYG30Cne%0AHnheFN+vRYlxMdb2wfd7oxWpHrQkpYlVyPY0/MxBLYy2B6JBt388vECbq5q1rD1QMlqCtbODKeDz%0ACO9TWh0Q1uIMjgQVqIRgPVp9nIsOHDoBjYGHLRhTiLXFiBTjXFe04Sv+swGtVO8GHJR5sGAc9F4L%0AwyZC7YbUAWtvAftG4LPe8LHBq6sOPteN6FR6PF1qX8I35oG1twHb4Vw8htYB87D2XZz7Amu74tye%0AaMW9dVVxLfAvVLKSLgb2u6CSuhAlqefQshL7EsZ8EOhoU1Ut67H2zkDaMwK1hEu13FvAbeCtBROQ%0AMHFYrsP5/0LDCC5Mex6SYPYHmYUpGon0fy3cOnVfwbIDMXZ3JHtyCps+p1KAs9+FPoFcqm4zZtw+%0AGApxB38YjqiWfQJvHQzdT4chdzY/H6sgMnsL1pWubLO6mcknteUhZ7a0iocAxD1aw7oPJB5Lh6XV%0ATw4dZPXnhB+arIImSJ1wwgm89NJLFBcn2/L8XzVcrV27lh123Iva/PchK+jmjX6ErT4LF1uF2eIq%0ApNeFTXovu+wPsOEDXGxOqy2tBv6CMdPBFCLen8D7DTTsDHIWamuTCeuBQ9Gp/bjPqUOrqF8Di7F2%0AHVCFc9UoUfOBrnhef7QDPQeRHERyEYmglafs4GcEWAC8h4YSeChBaUCnZuuxtg5t0KoPpqfrA1sq%0AQStvuVibgzE5+H4uOp1ZjBLB3ijxsSihvR/YEyJ9kqee3zRQ0wNiDfCbzc3PP18Ai+ohei4tpzwd%0ASsYWASvxvM04p1PoxnRCJAsl1kejBKSYcAR+NjAZ1ZQObPW/2mB/y4F1eF5N0O1fhzadRVE9r1qJ%0ANTdXpbtxxbv9Z6EJUmFy7mPAUuBx9Fz3AqqwVt+3ZgcIMKYg0Lh2xbkuwfs2D431HRAcc6ZrKx6t%0AeiDh/HwBHAy8EU5PFVULDLawswMx8FV/mPkLKN8SMFg7FuiJc78LcWyJiMsBzsCYUkTewRgfkS3R%0AWN5M5/YRjKlC5O5W+/0mIKmLgV3RZsZU1TmHtRcGEpnWDYUzMeZSjMnGuRvI5DJg7bE4c60GAkgF%0AlpMQ9xkirwbHEAaCMfcF1VuBbcrACyH3qZoGK44H70zIvTPtYqZhb9hrf+TQm6G+EvPgvhg/gjvk%0Ak3BEdd178PYR0PtPMDA52KRwwS948oHLOeywtivVP6SlVTQabWrIMsaktLQKcywdllY/KXSQ1Z8K%0ADj74YEpLS5Oev+GGGzjqKI3V+0+QVYBp06Zxxx13MH78+KQvmv/Lhqt77rmPa298ndrc6S2nueom%0AYWsvxkkN9L8Jup8Gfg3MGQSx69BqS2s4YCzWuwfnrw6kAetB3kHJRiY8jzG3oDGNmSpN5cBHwFOo%0AP2Y+SjyjwSOGMTGMcahuzkfE4Zw2DnleEeAhkoVzHkpoc1FSkxdsrwAowJg5aM75BYRvSlmux9an%0ABM5dm/zvcUO0WteqemcalyAyBxiKteVAbTCFb7G2B9AX53qihKS5+9+YR1GSfS6pp4fTYTrwPrAl%0AxlQHkoF4c1WXQMeaWJ3tDuRgzCxEJqJkJZUkIBUEa6ch8hGa+mWId/dDOcZUYq1WaJ2LDyQiGFOE%0ASDX6+dqPZm/douBnLsnfw4K1ryDyJSJ/INlQPx3i2tzWlmJ+cJxrmo7X2hqMqcfvWQHnuuRNTcmC%0A3TtreEPXTdB/BRiBeVvBzH1gVTc0aOAQRMIEUVShvq1LcO5zlDT2wLmD0aa+sJ/NGBph/Ee0+vp1%0AQFKXBNs5k8yxw+8CzwEfotfLRqy9FufeQqOOR4c8lmcx5kXEvo5xh2NMN5x7h3BuCQDrsPb3wft8%0AOzbralyPy6Fb2zIHs+lBZM2fIOtfkJNhJiP6BCYyBvnTPMxDv8Q0ONxhs8MR1dVvwIzfwBbXQL/U%0AASV25d85+9CN3H7bzRlJX3tIZVuWVvGp/ERZQXvcB6DD0uoniA6y+nPCf4qsigg333wzmzdv5qqr%0ArvrRNFzFYjF22XV/Fpf9BfJSGF/XjMPUXQ1eDjLgDnD1mCXnIf5S2m7S+AbMFSDvAwbPOxjf3wdN%0AruqTZh1RHZt4gUF5Zlg7HpE3EbmacDfrKMbcEFShUrXcp0IjxtyDSB/CNYEFiLwKW3wO/VCeNYTm%0AAuaj/WD5XqjzQCmeV4Pvqy+sVn0FtQaKE9NOtF19c1j7AJCHc6eTPO0ar1IuAlbjeVUJ++uMeqru%0AhFZKe9KyuSodPkelBL8h2e/TodZhK9GK5QY8rxqR+qCpK65h7YYx8an5YpSIxqfni2iu1FZizJ0Y%0AkxfoLcO814K1kxD5KiBmqQzvHUoCy4LHZrQCvx5jioNBTrwBLBtrizCmGOe6IRJICCKbYOgHLYMC%0Anu8Kiw6D7gWw45ewbRBFXJDQNPdJd1gQg8ZN0NgPyg5sFSywCfVtXYxzCxGpQaNZS4BtsPZjYAuc%0AOzPEuWiND4BJWNs/sJjbHTiDzCS1Gdb+CZGTEekLXI21/XDuX6hlWFjEUJlNPRoRe3871n0dOC1o%0A3HoE/T56AJPzPDJ0UWq7KRHshjG4DfdBzgTIOjTzbpyDxi6Yzr0xLhs38otwTVvLX4CZp8Ggf0Pv%0ANtK5Kj5gcN2FzJzxRqiq6f/W0ipdCECipVUY9wFodiCIV1g7COuPGh1k9eeEAw44gFtvvZVddw07%0ABRUezjlOPPFERo0axZFHJsdRxhuu/tuBAZ988gmHH3EydYVzwabozHUOaq7D1N8JOVsgDaswbm/E%0AhTG/Xo1WPrfA8+rw/Q2oBdFeOLcvepPsR/N1tALtOL8MtVjKhBjG/BmRXsCpGZdWrAL+jU5/p9Pt%0AtcYG9EZ6FGmtkRKRqvN8Os2EdRyYtUVY2yuoXMa78ItRWcK9GLNj4JYQFjGsvTdIhtoSWBHEvcYb%0ArArwvD74fj90wNAHnb63WDsdkbcD782BofennrTvAr0xxgbhAPVNXqzGdA2qfz0RKSHufaqNRa8C%0Av0M9cMOgFmPuwZgYzl1Iah2koPrkDWiK2SbUa7cO6Inn+UAU5xoDCUEj+tnLx9pOGFMIdMb3Ab4E%0Afol+fotos9ofmQ8lMyB7BTR2gbKRLTWuXgy2XAQ7fAXbfqdjlMW0lE9P6AwLt8Pza/D9RcRnAFT7%0Auz3qLZz4mqsx5k5ETkQHgZkQRbWtn+Pcl8FzXVANa3tndBzqo/syGsBxEUo624NPgpmU9ehnfxHh%0A5BC1WHspzo0H/gyc1uK4jLcLMuBVKGgVDesasGtOQareQyLvgddWRG7iequgflvI6wLHLA5HVBc/%0ACp9cAEMegh4ZvJddI5HPS5j77Rw6depEYWFhm9//7SWVrS2tqqur8TwvpUa2Pe4D8eU7LK1+Mugg%0Aqz8HvPzyy1x00UWUlZVRVFTEzjvvzBtvhGkOah8qKys59NBDuf/++xk2LFnP1dDQ0DRS/W9e9Gef%0AfQEvTSmgIffu9Au5GFRdDA1Pgl+HNh+NJpO1kzH3A/9A5EW0IvYh8BaetyAgrzl43l5B5XVPjHkL%0AYx7GubGEq6CtAK5A9a7h0nCMmQ5MR+R/CN/c8iXGvBI0CSWS+hhaQVTTemsrcL3Xwzmx5E28DZR1%0AhkUjM+TJl2HMg8CBiLRFRNai2swVeF5l0HmvOk5r98C5LVA9bW8yERJr3wkiLM9GPVXjqEXdBZYC%0Aa/C86kAzWwvkYUx3RFaj5PcQms34M93sPkNlHEeijTjpEEU1u6XB6417+HbH82KIKPHUxqW4djQv%0AcF8oBArx/QaUHe6NkvFCVObRiXTvvzEzA2eB36O61zBYBTyC6q9bD3idHn/uEhj4AZzUkLQ2zxio%0ALQAvHxoLoewXEG2rAe5L4BU0aCBVRbMO+AbP+xzfnxvYYm2BkvDO6KDtUkINwAD9LLyHMZNR3bDB%0AmAMQ+WvI9QGWYO1tgcvAocDpGHMaIg+hg8G28AXGjMIYD+ceJ7V/7x/xuuTi90sYTMc2YpYfhmks%0Aw0U+Axsy0jc2A+p/DbIF5JbCb0rBtP2dZObfg8y+AoY9B92SixKpULD4CG64/FBOOeUUfN/PWDWN%0Ak8pIJJLRdiqRUObn51NZWdkU7ZoK7XEfiG+/w9LqJ4EOstqB9mHu3LmcccYZTJo0icLClo0AIkJd%0AXR0AeXl5/7WLfuPGjQzZcjsaI39A8i4Fr42ObVcNm34N0Y/RJKPfBUblu5H6enAYsxci3YBrk/4H%0AnwLTgijJ9SgRqUYJ0zHolHC34GfqL09jXsKY13Du74TzRXVYexciNkOnOSgBqg4er6JTxV2wtq6p%0AEQvyICcfukUh20KkCvbxk4uUz+TCsuNS2FClwlLgGVR6MAwlw/OBVVhbFVRLwdo+iPRHZAu06Skn%0AmDIfjnMnEl7LWA88i3bTd8faxmDKvqFJv+pcX9Tjtifa8BQnwIuBe1EP1rCm9VGUeE5EiW4XjKnB%0A2ubmqebqZ17Qza82V75fjuqCD0QtzjrRknym0rBOCzSVvyes5Zkx7yPyJlq9a51I1Rq1qJRgDiqR%0AGIg6Jmjql35OsrG2GNe/Ak5PEf36Etr/F8eEYlh4RJuE1ZingSrUR9hDZQ1fBX7GiwPpwCCUoLZu%0AvnoTHTTcQduDmdVY+wbOzQjcBn6FDjDK0K79h8l8TsuxdmwwINoZJcnxAc2jGPMlIl+T/jvkNkSu%0AR09QcqNSM1aBORiGL4XsXhBdgll6AEb64CLvh6uMimBidyL1V6IDgTGYSAkyYiL03C/tavbbG3Bf%0A3wRbvQJdRmTeD0DFDPj2SPbfd0+mTnmV6upqrLUZG26/j6WViJCVldXkApAO7XEfSDyWDkurHzU6%0AyGoH2o8XX3yRZ599lsceeyxphBuf5olEIqFGtj8Ubr/9dq666jowFltwOi5/DHj9Ui8sDZgNwxF/%0AS3R69luULJyOyMkk2yF9h1a17qTthhyHVoteB97CmE4YQ9Bwozd7TVXqhjHd8f341HIR8BBKekYF%0A2xHi2sjmvxN/bkJTj3ZHK4/VWFuFMZWIVCBSjUgN2lwTwZhsrI0E6U65aBUxINGRpW1P+8cxries%0AyeRx6aMuCAtRcloXHG82ntcX3x+AklIleKm/gyox5i6M2RrnRpFMWDegiURLsHZj8FprMaYrmoy1%0AKDgvBwfnN4wPcClwB8b0QeQC9P1aRtx3VYMA1FFAm7gagPxAs1qOvi8jabaTKgoenVIcv0azajPO%0AaehUfWYo+ZxEap2tblePuwIdnFShQQqrUDLmB84RSqJVThCv6Ao6UChAnSnWA1sFj7ifbEAI06Vp%0AvQ20TkF+ZACsSNXQGEc1cBfalFeBc6vwvK74/lCUoKbS6jbD2tuBnXDujBTnYg7WTsa5xRgzEJHj%0AaVl1B3gAjb59kNSfxXqMeQaRJ7B2AM5dRksPXN2XMaci8jBaaU/EKqw9GZHFiNxLmIQ4mzUSKTkJ%0AKTgMlh0G9lDIfS7jegBILTZ6BtL4JiIvo+cQMCOxQ3rh9n40xTqC+WIMMn8sbPsWFO4Wbl/rn4WF%0AZ0P+aDp7z1C6dinGmCbil6nhNhaLUVVVFYpUtjcEoD3uA/Htd1ha/ajRQVY70H6ICFdeeSWFhYVc%0AfPHFSf+PN1zl5+f/12xBRIT99juMOXN2wthPEfkKL/94/PyrICtFJbBhBmw6HGQqevN5GWufwLlF%0AGNMPTS06kXhDlTFXY8zjOPccYap91j4KTAyaNizNTTsraO4i34i1tWgsZ7x6Jahe0tB8fbb8Pf4/%0AEUGkJug4L0ArPUXoDb4bzf6kice7EfXkPJAmrWAY8vF8PiyKQnQ0zV6jjSgpXZRgzVUD5AXEtB9q%0AhfUF8EfSN6elQiVaMRuIEs6VAZmpBnys7Q0Mwrl+qG64N80NTV+ggQOH0bb5ewwl1gvQuNb1qOes%0AakOhM9aWYExPfD/uYBAfYBTTXAXfhDG3op6tfyUTwYrDmBloDOdI1G4q7oVbiRLNGrTiWQvUYUwU%0AkY1oo10WxniI+IjEgmOOoZ+TCMaoQ4QxuUHT30q0ujqM5ipuQcIjh8T7QcuqbKv3LWXcbhbsHEuu%0Axk830HNbmLkfrLWo7GM5nlcR+LY2oLKGmuDYRtG+hKoNqO/qX9FBZg3GvIPIZIxxiOyIkvt024xi%0AzOWI/I2Wcg6HaprvxNq8wE2jLX3yIxjzNSJf0XweXwBGY8zOiDxIeMnOZLB/BXzIuhRy/hFuNbcM%0AU38YRhzOfUhLacUsyDoQTigHL6GIIA772QXIkueQ7WZCQQgtrAh29c24FddD0WNQcDydKrdmymsP%0Asdtuu2W0nkpENBqlpqYmI6n8PiEAqRq02kKHpdWPGh1ktQPfD7FYjKOPPpoLLriAESNGJP2/sbGx%0AyRrkv9VwNXfuXPbd9zDq678E6jD2XEQ+xuYdiMu/FrJb3mxs5e+hbjbOTU54Ngo8gue9jO8vx9qd%0AcO4s4HCM2QeRvQhn9h0LtGz9COfVGteiTgymRMPd2Kx9CVjYTmuqhRB5PsVXpAAAIABJREFUDkp6%0AQ7YHWaWwX0Pqaf+GnoE11Q4Q/QIldSXEk6WM6YS1WyQ0PvUmmRi8gfqink/bpvprUV/aJXheBb5f%0ASXOF+SBUe7kF8caqtrEIuA+tZB2JkqQlxN0E1He1FsjH8/oiMqBJI2vtG4gsQOTPhNURQ2NgoTQb%0ATaDqjBKpclR6odVOaxsC0hlt0qsqyXRoZTMPJZn5QZUzH5ECnIsPRvKC8zEeHYycHDynXr3pvGJV%0AwzoBtbUK5xZi7duIvIemcLV631oHD7ga+EOytR5voxxwLnr6a7KgMh/KtoHoTiihykIbyd4FLiG8%0AVVccr6LNV9sH8azFgSXWvoS7JqYC76A6hhxgNsbcAmwKZlrCaDfj1/vDwC+x9nxEXg9I8Kh2vJYy%0ArP07zr0H2X+A3NvCrRZ7E+p/g+FARF4g1eu2OX1xe90D/Y/VJ5yP/fh0ZNVUZIdPITeEtlli2CV/%0ARNa/iBRPhRytFGfX/JlLz83lmmuu0sMJqqaprKdaI5OlVaoQgDDbhQ5Lq58ROshqB74/ysrKGDly%0AJE899RT9+iVPudfX1xOLxUJbiXwfOOeaHr7v8/e/X8cjj6yhvn58sEQpmNHAdGzObriCf0Ik0G25%0AjbB+MMgYtIraGpuBe/C8t/D9UozpFUyPjiNcJ/4itInrL4QjPYK1twREpg27mBZoDLqqexHamiqy%0AAPpPgN6NzUXfanRmeWDCcuNy8dYVBA1JUYzpGlR/LXoD7kP4TuyXUcJ4IUpGKlHJxEI8byO+rxVN%0Aa/shsiUiA9BKoMHaW4D+OHc26Y3741gVbHcxxqxDPU4bMaY71vZLkCH0JX3jlmDtRJybiKZkHZzw%0Av80o61qFkusNTfKAZm2nIV79NaYz0AWRLjjXBa1odkariZ2DRy3G/AtjGoPKbFuRpXGsw5ibMCY3%0AMLgPM7iJV5wPoXV8aDpYOxX1lj0draSXodX5CjTkoB5jovhZNbBlXUteNsGD3vmwRVWye8CkAvju%0ASGhoruQZ8yxQgUg6t4Q4BJ2ZWILnzQ/cByxK2i8ksz431eu8EpH9MGY1zn0VHOy5hJOQxPEwqvdt%0ACLS2TxI+8UzQSuw/sHYQzg3DZM1Hcr9KbWPVtJpgYjch9dcB16NkPx1Ox26xHnfA6+BHsR+cgKyf%0Ahew4JzmmOhX8Guy8Y5Gqb5CSWZCV8J1f/y5bdr2Ub776sOmpaDRKbW1tqMpmTU1N2uas1iEA7amY%0Axhu0gA5Lq582OshqB/53mD17NhdffDETJ05M0hKJCLW1tVhrQ+mM0kGnuwXf91sQU+ccIoLneVhr%0A8TyP+vp6dtppH9avvx/t1o2jEjgfzCvY7C1xBddBzmFQ/zSm8kLEzaTt6ceV6LT0u6jtTmesHY7v%0Ab4NOXw5FSUbLa8raR4BJCXKATNiMTmnGp4bDYAOq+zuGUPrHXmNhaGlL8jAdJazHBH9PyIKFwyC6%0ADVrB64beuGsDa6qdcG5kyOOLoaW111Di6CHSgLW9/h975x0nV1XG/e85d8rOtmQ3vZBeCUkgkAKh%0AFxMBUXoviogoKEVKBAVUiqKC9Cq9ComhJKGDkB4S0nvv2+v0e877x3Nn6+zuRIi87/vZ5/OZz+xO%0AudPv/Z3n+RUkilZiaOXAnm6fFPEAa0eM+QUCMFMJYcuALR5FoAoArftg7RCsHYiImx5DqcGeZVRb%0A4NogXNUViJ/nbgQIaoQPalCqo9dd7o7rdvfen87e8++CfFf+iNYdMeZ2BJi2VXG0ftoDhleQntuY%0ApJ4aEKGetxxBfG2zEEDZ8JRsctqLcHDzkdQsFwmfECqBtQZrXaD+XCgG4p+rVAfPpaADxuRjbR4C%0AwHMhUASdV4LfQKIaSqohfhX0egeu2NT85bzvg+T34OuDIRFAuLwPAYMxpuHCyyIAeaPnwrEesDhO%0AR1y3DyKODACPA1cjPNtMK4qIymYiXfAhwO/J3Ng/VWtR6gWsXYcsBPbFc3UzWv8Gazdh7bUIdSUO%0A6lTIeg98R6a/m61Gxy/EJudizQza5sNuBT0UTt+MnnsJVKzDjF4KvgxoK/G9qBUnopJJTOeFoJu8%0APzZBsKQra1YvoUePek5vU+upliqldUgnzmopBCCRSLS53dS22y2t/p+vdrDaXt+8nnvuOT7//HMe%0AfvjhZj/q1E4oGAy2yV+y1jYDo6m/lVJ1gLThuVKq2WN+8MEHXHjhDYTDK5BuS8OKAjei9MvgdMHm%0A/AEdeRAbz/JGeG1VNfX+lbkotQmlyjGmEhERDcKYA7F2KHLg645Sl3mdwkwN0Beh1DNYewOZpWeB%0AjC6nY+0vqQdHVdRzZIvRuhqIYPpViKi8ab2sIN5bgEPTPPlGVUq9NdXhaa7fS2MQWe2p4QfgumXe%0A9TeRWQcxVVuRsb5F62yMqQYCOE4/XHcIIpzph4DGpvu1MFr/HmuTSGBDNwTwrUREYFtxnHKPQ1kD%0ABNC6N8KJ7YtSH3hc0duQzz2TA1ctWt/veYJegLgPlHunFC+1xntucZQS0ZOEHaTAo6JeYOciu14f%0AAhz9gA+l/FirkUWOg9a9UMpXd72c5G+JtvV7qWefe89zEgJyU9G+Dc8DdSfpsM7xuNxNBUbpyqL1%0ADKxdju3TEX68o/lNXukGYzpC7x2wYBwsHAuRBAL0TkK4z+tx3XVAwgOnPRFbrXSTjU8RQdmfEB5u%0AS5UEVuE4s3Hd5V4XdDhK7UJCEzLkiAIS8/oCEvM6CuiCUkuw9jPadvZIoNTjWPsYAjT/QOPF1K3o%0AgB8TTGNDaNajIpNQhDBmNpnypHVgIEZVo335mFHLwJcBPzi8FpYfh3KGYgs/bjH9Kid8Ln+763gu%0Au+yyussaAr+2LA3TWVql6AQdO3akaQhAptuFdkur/w+qHay21zcvay1XX301w4YN4/LLm/MzXdel%0AtraWnJwcHMepA6UpQNoQmCqlmgHS1Glf6swzL+bjj4eRSNzVwi0McAfKeQJrqsAmgWcRnltb9Sky%0AbnyQ+oNEqtO3EFiL45TiupUIBzYHASZHI124rAanYIO/Q97/QbR+CtiKMb9GfnJxBGi3dpqLKL4D%0AdWIt6QAXYm1njC8JnXdChz2C6Zqq/V/JgnW3ZPD6QcDjy4i3pEU4g8UeiHRxnANw3dQD9KFh11qp%0Af2LtDuA6RKjUtOLIKH85jrPHex8NjjMA1w0jYQ1TyFRFL4B9HtI9s0iXNIaY/vfFmIEet7gPwolt%0AukBIovVzGDMdoVqk0tLKkM98K6koU62rUCqMpF1FkM9Fe4/bDa0LPGpAHtZKd7KuM1knegKlHgBK%0AvZH4QdSDz5YOmPOA+1FqqNedawsolXvUgxjGXEfr4E6q3j7rIloOXzDUTxA814PuK+FnbvObPtkV%0Adg2GzjtgYhEMi8JSDXNdqAx44rC+SDrZYDKZTGj9GNANY35O4/fKAhvRei7GLEDrAMYMQCgRKUus%0AKErdhfgXtxYhaxGngRcwZgfCAb6MVMdfqes8vvN5rWxjCUpd54kr7/ReY9MqA86EnKWgB9dfnHwX%0AoucDp4F9kcy56ouBE8BnYXwR6AyoI5WzYeXJkHUGFKZxEmhYtS9wwpipvPfu640uTgE/n8/XZmcz%0ABSqzs7MJBALU1ta2OJnbl+1Cu6XV/+PVDlbb69upeDzO5MmT+f3vf8+4caIyT+10jDEkEgmSySRK%0AiYo9BUDTdUq/jdq9ezejRk0gHP4Pkp7TUhngIVD3gC1H66O88faxtNb50/pqYB3G3NvGMylCAOxs%0AYDNKdUPrhmPXejV3vao7NXZ1qQc6CumkOV7nzFd3boyDtQHkYLkd4YSeTaOY07ZSqQCe6gI7f9nK%0Aa0nZUq1F610YU4EAywBaH+T5YabG+W2Yj6vnsHYrAliTiF/merQux5gqD0gOxXWHIrZL3Rps8zmk%0Ag3Y9zZPCtgELgDU4Tkkd0NV6INYehOzb3kHrE5Ho09YO2AYBoksQx4Bl1Ftxxb3X0RGluqBUd4zp%0A6XGHU3SAFD2gBK3v9ERbVwMnt/reSCXR+iWMeQmYiIjTUtZlqe+IafJ3EfAQSkU9UVR35HukvfOG%0AJx/Cq30Ca1chka4tWL01KKU+R9K7TkAWVnvJzt6Dz1dCLFZDPG5RChyfwtEK7SiMzyXWB2yDyb56%0AE7K2OzgmC9fNwnUt8UA+TPDBITtgfT7MroSia8i0aygVRqn7PWHUBGA3Ss3D2tkolQB6Y+0JtOyr%0A+jlC9UlFoDYsiyRXPQ8UY+14BLg3/Q59jvBP59CcdlKD1vdizFQkNet6WvutKPVzVGAsJvAkWINK%0A3o6N3Q/27winNpMyKPVXrL0TOBP0VDhkIWS3ofwv/hes+zHkToEOt7b9MG4xwbJBFO3d3qx7mQJ+%0AWVlZGVta5eTkUFtb+62GAPy3llaO45CXl0cwGGwHrN9NtYPV9vpmZa1l9+7drF69mnnz5vH000/T%0As2dPNmzYQCwWY9WqVQQCAbTWuK5LKonkf0Faf+ihR7jtthdJJh9DOiWt7WQSKHUg1lZ5XZcitO6P%0AtSdj7YkI4G14/3IE0J6PdGfaqlS0ajfSz+Ab31boBluQlKSLae4P2VKlolVPplG3pi17qn85sD4E%0A8WuoDy/YjYzKt6F1lWcbFcRx+nhcwd4IOP4MSeDKNClpMwJOvyY15pb3ehjWDkYQdFudko+AfyFg%0ANdoEmA7A2pFYO4QUFaPxZ7cTrW/G2oB3AO+EdHJXIt23EsSGqxqJc+2DUoM9ukFfJC3rA8Tg/Vpa%0AB+YG4Yhu8J7vHMROrB8CSJMeiEoCiQaLl6R3nkDAaBAByqnHUq2cUo8bQ4CUbeFEg9v7qY9vbXi9%0A6Huys8FxIBYDa6FnTxg4CEIh+OB9CUY6/GjNQ89mUdhZEYtCNGqJRSEWhU8/S/Lm+0mirsVn4MSx%0APg7opXnm4QTLlxg6d3M47JhsNq6GzVtrUYf5SYyJi93VlyfC1rFkJuYrBj5ABHaFWFuG1t2ReORD%0A2vispLT+KzDW686m3ss5HkitwtojEUFmy91r4aBe4i0CUvUhcAsSTHAvmSwOZJF0JeSsQMevxLrL%0AsOZDMotzBkmlOxdrV2PtE8A4lHMGqscRmP4PpL+Ltahdf8VuvRM6PA05rXWIG5QpR+8dyJOP38dF%0AFzUP19iXzmY8Hq/z687NbZ0/vK8d0321tIpGo3VR4qFQqN3S6rupdrDaXv9d7d69m9NPP53Vq1cT%0ADAYZPnw4w4cPJxgMsm3bNv7whz/Qv3//RjuDFM/I5/O1ubr+NiqRSDBkyEiKispQqoc3mruIlkee%0ACxChyrMIgJnqAZMdCB/wBIyZhHS6soH3UGoKYvbd9hhVFOQ3IZGgrcVQ1pfW7wNfYMz1ZJZuBSIO%0AmoZ0XjpJV7X3VDggKsfdht3UV4NQ3RtK+kN8NqBwnJCnzk8lTPVDEqZ6k1548ikiRrqa5l6qBjng%0ALkbrHR63V+E4g3Hd4YhMfCWSCNSWY8J24Eu0Xoe1ZUjoQapTeDUSu9kUmDatauALpNv9NQLQ4kAh%0AjjMAY4Z6wqx+3qmghe0tRoRwcWQh5CK84CqUEmcAoQFEkC54oSfK6oHrJhHQGgKu8B4jZUuV451n%0AN7gsiFKvYe0DnlL8HqRr21otQ6nfI6EUU0jP8WwYNDEH+AehkMV1DVprevX2M2iwZeTIGIMHw4CB%0AcuraFTZtgjt+Z5k5E8Yd4fDQc0F69s5sAbp3j+Hxvyf556NxCrv4uOqOAn54aT31wnUtm9ckWLIg%0AwjtLa1jmxEhWW4KLsmFdf2K1/ZDvmYssAhrG9Rq07ub5/SaQBKd9DSfZiwSA/BnYgVIvIPGsxyEL%0AlExe59cIx3o2Ip77LdbO9zre5+/Ts1HqLKwtQusRGPMfMhd/vQtchFIjsPZ56sH+f8C5CiYUN6cC%0AWBe9+Rrs3lexhTMgmI6TnqYSK6F4EhjDhReczDPPPJL+Zl5nsy3rKWst5eXlaK0zApX72jHN1NKq%0AIXc1Ho+Tl5dHVlZWRo/RXt9qtYPV9vrvKpFIMH/+fIYPH06nTo3H5Q8++CAbN27k7rvvbrYjSAUG%0A/K9SQpYuXcpxx51KLHYJWk/DmBK0vgRjfkU61bDW1wDvYcyrDS61yNj5TbRejzHlaD0aY05GqTcA%0AjbWZiTKUehuYhrV3kHm06gNY68faSzN6DHkdM4CVGN9kGPxBy+P/Jx2hW+JH664YU4KAp3MQPmmm%0AI68ZyLj8auRg/zWOs9vrdgZwnKG47jBk/Nq1yXbfQVq8v6C+Y2QQ8dNctN6MteVYm8RxhuG6ByN8%0A1YHIWHUK1iqs/RON89bjCI93Hlpv8gBuDUr19NwMRiEBDA8BXbD2bpqPhw0CqBcAK1BqK1pXeB6w%0AYWTcX+Hd7gdIN7sr9TSArjQX+QGsRvw0VyBiohMRYBtBFP/RBv9HkS5pmfd6LLKY6ot0RFNj/Xrh%0AVf13a7Z3vwHIIivPO+UgQQNryMmdSzS6A58D518It0xR9OwFySRs3QobN8DGjbBmlWLVKti8yVBR%0AAa4rWpusEPh8CscHfr/C5wOfX+H3g88vlwWC4PfL73/JAkMwW/Hruztx+k/y8ftb/465xvLhyloe%0A/6SM8mpDz90+yj9MsmezSzDkIxrJx00eikw/UouLlLPA8CbOAm2Vi1A/3gBKUKoj1n4PGdnv2zRI%0A61sxpjuwHKWGYO2fka56prUbrR/EmC+9xy4ls0VxBK2vw5iXgVuQYIcmz80/DjPoIejc4L1xI+i1%0AZ0HVYkzneeDLcFISngZlF4O9EPgN+flHs3v3xhYBXSadzVgsRjQaxefzYYzJSES1PyytEolEHRUh%0AFUzQbmn1nVQ7WG2vb7+MMVx66aWceOKJnH12c0PspoKr/V2/+c1vefbZ3USjTyGcs99j7VIPcP4G%0AOI36g3sNAlh+BDSNcEzVHuB1HGc+rrsLEesMxdoDEW5l6pSye2pYBqV+i0RaZso5KwfuQZTbbcc1%0ACkirgMBT0CsiuqGmHdVPgJIQbDgc4odSfxAsRylJuEqv9E/3WCtI0QXk/ywcZ6Q3Nk9ZerVV/0F4%0AfgfgOFFctxzw4TgjGoDTPqQHDAa4HwFyo9G6EijFmEqU6oTWoxpsYzDNx8lJ4HakQzwKGf2XACk7%0ALB9a90Op4V43OEVVSIHFogY8xPEINaAUAT3bEfBejOPUAk27rrbB86lBqSEolUMqgapedBfC2hAQ%0AwpgshHqxGonwPQWlQKkUbaDxubUJjNmKgNYEfn+AQDCCNZZYTABlXi6ccx7U1sCqVbBlC5SVQnYO%0AhHI0kbChuhICIcWBh+fzs7/1I6/AT7TWEI+4RCOGeNgQi7jEo4ZY2BKPGqpKE8x/t5w1C6oJBDWF%0AB2ST3zVA5a5aassSRMOGHn38HHhokIMPDzJkVICho4N07NR8v2CtZcHmCH+bXsr64gRmjqVHRTYj%0AxndiztRSkrFsojWHYu1IpPtYjthZnU3r6VPViChyBa671hModkK4xt/HmB+1ct90VYZSn2Dt+wj4%0AvRpZ/GVatWj9LMb8y9uv3OTRVq7zHEJaqxUo9SOUcjHmFVqmGvwWXbAVc5DnChEvRq08CZUIYzov%0AAp2BC4k16JrfYSofBPsIKKE35eaMZPr0fzBx4sQW79qa9VTKFSDV0GjJ0qql7WYaAtCWpVXq+qys%0ALILBICkrxlSKVrul1f+02sFqe+2fCofDnHTSSdx///0cdNBBza6Px+PEYrGMVszftGpraxkxYizF%0AxQ9AXX5oFWLA/TbGJFHqaqz9OTJGnoUc4N4gvVq9YSURMcYrQB/PID6MMWHqIzu7oVRPz3anG/K7%0AexDhoqZTATfkDKbOl3mPkXJbSNkfVeI4lUAFxlR4o3EXAg4MTsLZDX6uDTuqr2TBljNasKfahnBl%0AzwIObHJdBOmgrkXrUs+WqiNKDcGYQQhwXQf8hnqVdUuVGutvxJgyZMEQR0a8tyAWSa19NzYBH6D1%0AKqwtod4X1I+Az7G07HFagnAb56H1dowp9S4PeddNRMIcWgLbpQidYBGwGscpwnVLEeCTeh19cJwR%0AWNsLY3oi362u3vuS6rqmRrozUeo2rF2PCIPORvimTQFow7+/RjraPu95HoeIkTogjgYdvNezlqzQ%0Ap2A/pKBTnG7d5Fu1crnFTUJuniInzyG/i58eA4L0GZZF554+cgt8bFsVZeqjxSQTlu79s/jeT7oR%0ACGq0o9AOaO2dO6rR39bA568XM/+9Mjr2CHH8VQOYfN1gfL7Gi43KoihL3tnNqk+L2bGskuq9EWoq%0AEoRyNINGBBk9IcjwQwMcMNDPJ/+u5Y3Hq0BrhvyoA75jHVbsLOfEQ3px6rgD2PVVmJmP7WXhu0U4%0Avj5Ea8Z4n8MMRMiXok4YJIltNbAMa0s9W6y+3ufey7vdFsTo/w7aDhqwwCq0noUxKzye7PdR6kuU%0AKsCYTJKoksB04DEcpxOuex3CuQZZSD2NjEHSB1ko9QiSfncqcB+td4LLQY+HQ9eAjaOWHwu6P7bw%0AM9AZNBBMFbr8HGxsCdb9GFT9Pl7r27n8J6U89FDLr7m1zmbDbmZKkJtS5bdFH9vXEIDWBFpNwwhS%0A26+urkZrTU5OTrvg6n9X7WC1vfZfbdy4kfPPP59p06ZRUNA8QjESiWCMyWjF/E3r/fff56KLricc%0Anktz8c5UtP4rxmzEcSbhujeg9YPAGox5OoOtW7T+FdbWYO1NDS4PIyPkLQj3rQStBchK1CfU+2m2%0A9rNq+N5oROQkFleum4UAk0LqgVABdH9SjP9TCVUpkJoSVD05EHZd3MpjLkM4b2cjncGUUr8WrTsD%0AQzFmIDJebvp+vo6A1hto7MlZAXyJUisRW6YEWh+IMYdQzzfdhVJ/RKn+GHNzk21vB95H6xVYW4y1%0ACRxnNK57BOK9OQCo8EagexC/zSMQwPIF8B+0XusB2xq0HgAcjjHjEPuhvt57/RwSt1ngCWTKgaVo%0ALd1J4d1GUKqX9/xHY+1whFYyBNiF1vdgzJtIl/4MBGDspM7z1qlGqVrqkq9MauwfAJULttp7zQal%0ABnrOD964X8m58vi61joYsxf5rsW990yTm1tLPJ6kZy8BpPGYZetWCAahQ7cAx55VyICDQhTviLNh%0AWZStqyLs2hKjttIlK1ujNBg0eT1y8Yd8WM/y1VqLNXLC2Lr/I+URTMKAAqUViYQlEXXJLQjSfWg+%0A/Q7pQN+D8+k5PJ9ew/PIKUjvxOC6hnWzS5n94jbmv7EDbcX2Kh63DDy0A1c+Mpz+o6VzVlQRYfrc%0ArXy2bBcThnXj9In9KAwGmfvWXt59cC/bV9eAzSERSwKnoPUqjFntvZ+dsXYUsqhpidf6L5TaibX3%0AkJ62Uwt8gVIzgSjWDkPszVIOBmEkZOAvSHhBurLAXJT6q2cldjki3mxcWl+OMbcizhANqwStL8Da%0Ar7D2QcQHuu3SvpOxHYdgKz6C4KlQ+FJG9yOxDlUyCWU7YNwv5fva6OUsp7DwFLZuXdUq1asl66nq%0A6mr8fn8jYJoSUaU8T1urb8PSqmF3t+njtVtafSfVDlbba//WzJkzefTRR3nllVeajfz/14Krs8++%0AhA8/7EMicUcLt9gG3OrZ8/iR7tmNCNhoq4oRhfDFSGesrbIo9XckXvJn1HdBNC13RFLRqt2QrmcL%0AFVgHw9+A05P1l6W6qpuB4gLY8P00XdUk0hVdi9Z7Mabce8wC4BCsTRnvZyJYeRPpwB4PbEDrIoyp%0AQet+WJsa1Q5o4bVGPT5nBDgUrTdgbRHWRpH89xQ4HUz6OMwo4sO6COksRjw6wFhcdwICTEfQ3HKo%0AAngb+BCtN3gAMO69L/2pT0ca6r0PqceOImD4C2AJjrMVSynGrfDumwKaEZT/NKweCXQB1dk7dUEW%0AG1Gwu8BuA3cpJN+QvwkiHeJeCBBtqvzX3t/bcZxN+P3QtZtwSWtrFcV7LcEsqK4GrNhKWWsJ5fkI%0AZGms1uA4RCviRGsSBLId8rrnMPr8oWTlNQEbaQ7KO74qYs27W3ACDj0m9GbcjRPpe5wIupLxJDu+%0A2Mq2T7ew56vdVG0qJ1YWJlIZI5Dt0G1QrgdiO9LrwDzyu2axdOYe/vPsNoo21dCxXwcOuuBAxl97%0AGOve3sCCh76iZFUR2fk+jru4J8dc2J2+B+VRFY4zY+F2ZizczrDeHThjYn+GHdCRvZvDfPTPXbz/%0A5A5iYUOstqvn7pGpR69B678Axzbhvm5GfGfne+r+Y5HkqnTf52kotQprX6c54N2A1n/F2vVYeyqy%0A/2jp9z8LeBURaqa+ux8B56JUf6x9iczFVy7SbX4H8qdAhwyDECIzoPRchCb1YvrbWEtOzmDeeusR%0AjjrqqFapXk07myng2DQEAOotrdoSZ6XbblvVVKCV4sy2lJCVep7Z2dl1DgHtgHW/VjtYbS+pWbNm%0Ace211+K6Lj/96U+5+eabv5XtWmu56667iEajTJky5TsVXO3Zs4dRo8ZRW/sOrR+sxHtVqaexdi/Q%0AA63HeR3AUbTMwXwPpR7A2r+Qmc1OFaIoP45Ms9plRP0PRPAxOv1Nuj4AXSrkuJhEGobjkK7q9hDs%0APN0DqruAlSi1DaXEmkqpHLTu61lTHYCo5tcgPMy2KBEA64F5OM4Oj3dqEFP7UxGg19qBwyAA8xO0%0A3ubxRV2Eb/hLBCSmO0gZxBT/XbReizHFKNUTa4/1nv9mb8R+KfUA03jXvY3Wi4DdGFOOUgNQ6hiM%0AORpZdPQCbkKpl7E2H4nBrUWpdWhnr1AvTBXoTjj+IVhnJEYdBM5QOeleYHZB5E8QfQ5IgM5BqYAA%0AP5uUrqqNgQqAk4fyFaB8hShfZ6yvK0Z1gWQxVL4FxgPPTmfQPcHJAzeMSi4gGIRoVDYbyoZkAuJx%0ACITACfgI5gXwhxxQimTMpXpPBKUUvqDGTVp0Thahnh3RPkf8qaz8flOsFOMmie6tIl4eAUA5Ch30%0Ao3wOygE3HMfEXXJ65FEwqJAuI7vReXhnCgYXUjCokLze+ShPlGLlupaiAAAgAElEQVSMYc/CXWx4%0AZy2rX19BeGclTkBjXIt1Ib9fB374zMn0ntAz7T7j6+dXsOiRryhdU0peJz/HX9qTYy7oQZeBWXy0%0AZCf/nruFzvlZnDGxP4cO7gwWVn9ZzvM3b2Dd/Ap8WV1IRM6jsSCvpdoBPIEsgPag1AysLUapAVh7%0AOvW0gZbKIAlql2FtirtaitaPYszHSMf1ejLZZ2j9Y4z5E3AJWt+CMU8Cv0LEiZnWWpS6BijG4kLh%0AM5B9eut3sRZdczem8h6w94G6qtWb+3w38/MrE/zud7e0GYnasLMZj8dRSrXYEY3H44TD4YxEVP+t%0ApVVeXl6dz2tr92voB9tuabXfqx2stpf8qIcOHcpHH31Er169GDt2LK+++irDh7dhGp1hGWM466yz%0AuOiii5g8eXKz65PJZJ2P3f5WWD799NP89rcvUVv7AW2rey1a/wBj1gM9ESP8MiQffQyueygCGPuQ%0AGuVr/WusrfK4Y5nUcuAR4NdkHj+6FHgLuIqmhum5OXczzMTJQQaUa0JQ0x1pBG4BNvhx3Pw6ayrH%0A6Y0xfZEEp96k9zd9DRnBX0vzdKcyYLbnklAGKCQgYBSizv4ceB85oB6aZtsVwCy0XoIxRUh610SM%0AORx5bz8FHkfrSRhzLfVgdzPwFo6zGNfdi0TdHoPrHo+M/js3eIyZyCjWB/TEccob3Ge8d5/DEdDQ%0A8PWvAF5E6c9RaivGLfUevwYIQehaCJ4PziCwDiTnQfILSC5Gq01gSzBuJZgIKtAbnT0MkzUK6+uB%0AqpqFrfwYdBBUNvi6gg6gVQKlYlgTxZo4mJicu1ExM/XlgC9P9sJRiTD1+yGRgLx8EUgZA1m5imiN%0A7Kp9WQ6+oMYkLfFwsm4PrgIONu4lS2lVB1ClaaukiSrKrbq/naCPRHUMJzdI1x8cRt6ofmT360qo%0AXxdCfbvIN+KL1VTMX0/Nym3EthSRrKghWR3FjSXJ6ZFHx4GFFAwsYNe8HZStLyWrUy75ow+gz4VH%0A0PNHh7DlmS/Y9uJsatbvwfFrhp8+hBHnDKXvMX3wBRp36dykYfEzS1n8+NeUrSulY/cgx1/agyPP%0A686meBVTZ28hXJOkV2kOW1+ppqY4SceBBRxwVB9WvLION96fRHgS4t7QtBIIUN2KCABrvS7qeMTH%0AeF/AydcID/w1lHoHa19E634YcxNCf8m0pgPTPSu0Kox5AZkwZFJxtH4IY55AaAZ3AH9DZ23DdJnb%0A8t1MLbriQmzkS6yZCSoDkaddQPfuF7F8+RystW0KnlKdTWstHTt2bPU4EIlEiMfjbYLghtvdF0ur%0AFGDOZPvxeJza2tp2S6v9X+1gtb1g7ty53HnnncyaNQuAe++VVKZbbsk0erPtqqioYPLkyTz55JMM%0AGtQ8PSYWi9XZguzPcYoxhiFDDmHv3iDG/AQZ8bfWMdyBtCWvQ0BQErGx+hKtN3tWT+A4o3DdsUiX%0A5Q7E+D8zj0KtXwBWeF6qmYF1racC6zHm6rr75Gbfx8nJWl6P19/u3ADMyIWaQiDiF9okQ5Agg8yt%0AqbR+1gPhVyHK/6/RusTjsA7AmNFIt7pnmm1+AbyOUpdh7TEIH/ZDtN6KMRVoPcQzbR+PdHOb3n8v%0AEqiQBApRqhhrIzjOYbjuiUhE7oA099sAPIfW8zBmFyI4SnFBnwAuaHCfOGKh9RbaWYIxu8EmcAKH%0AYZwTsM5R4Bsr/Lz4xxC5AswOUEERpLg14OuADg2G0ChMcARkDQWnAKLroXY+RFfgmF2YZAU2UQE6%0AiMrtD7lDsdWroXIFKL+AUpsEm4oolfQyAU8tfD4+cLQiEW+8e9ZBByfoJxmOY5OmxfujFDgatEY5%0AckJrTE0EjMHp04PQcRNwOuVhyqpwyyoxFVWYymqoqsZW1+LWRrEJl0DXfLIO6ELO4B7kDu9FsiZK%0A2X9WUbV4E9YYnJxsjNZo18WtCdNhxAH0OnMMPSaPpGBM30bd193vLmXDox9TtXgLiZoo/Y/vx8jz%0AhzH45IGEChp3It2Ey8LHv2bRw19Rtb2Swp5ZVJfFSfS2qGMdVFfNUcdN4LDDDiYYDBKvjTP3/q/4%0A8t5FWHcEyehhQDlabwE2eTZ32UAHjOmFUutQagTGXNj03cugSoC/IVZr3TDmGlqcjLRYu9H6VYyZ%0Ai0wZppG5ndYSlLoGpeIY82fqJ0thUJOg2wLwp5k2JbcIP9VojDsXVIZpYnYzMIQPPpjBwQcfjOM4%0A5OS0brtVXV1NIpFoE6ymVPn7w9LKGENFRQU+ny8jRwGoB8/tllb7tdrBanvBm2++yfvvv89TTz0F%0AwEsvvcT8+fN56KGHvtXHWbFiBT/72c+YPn16sx2XtZZIRMaLoVBovwLWZcuWMXHiUUAXjClD6wkY%0AcwnSLWneWVTqaeBPWPss6eM51wCfovVqT/hTiXQIB6GU5L9b2zD7veF5LsJfvc3jhKb4sRYBxtEG%0Ap0iDv2sRNXsASdyKcFgowcJI82c3NgSLDgC2XABxi/BJM03FSiIWSUuR1mwCpToCY7D2IKSr09aI%0ALYZ0lRYhoMuP1od73NNDaDmtqgzxtp3v8Uc7eJcdj7gpNKUUxJGO83SU2oS11TjOUbjujxBw3hcR%0AvFwDTPX+D6KdEoxbhNKd0cGjcdVx4JsIepgAR1MEsRfBfQ9HbcBNFKH83dD5x+E6fVA1s7DRdQIu%0AA71QxFDEMMlqcGOonN7o/OG4OUPBjUG8FOKl6GQRJEox0Qpw45Dd2QOqLkQrJDJKaYhWk660g4ie%0AvLvg05A00iU1qfapku1o7XVODbhG/m5aYpQKPp+0Z8O1zW/jc1BZQQG2rsFGYyjHQXfqgNO1E4kd%0Ae7El5eD3oQIBrOOgrMGGI6jcEB3OPpG8SRPIOfoQfF0LSZZUUPbom1RP/5zEph2QdOl63DB6nX4o%0A3b53ENm96oWZFcu3s/Zvsyj5ZBXRvVV0HdmVURcNZ8CJ/ShdV8aGWVvY+P5mavbWEOySh68wl/ju%0ACpTrcvRth9Pzh91ZsHgxmzdv5dBDD2b8uEPJzc0hXBbhiz/NYeHjX4MJkoz1QKYCI5t8NyuAh5CF%0AaGtWWKkqBxaj1FysLUYWh2XAvbQeAd20dqD1yxgzF6UGIslsXyKL5rb4mGG0/rPnG30a4tLRBEyp%0AK9G5ozEdm4hJo59A6elgTwD7pnwXMyn7FvBjlOrAr351Ln/84+2Ew+FWo1attVRWVtb5qu6LWX9b%0AIBgyt7RKealaa1u0tEr3XNotrfZ7tYPV9oK33nqLWbNm7XewCvDGG2/w1ltv8cwzzzRbgVpr6yL2%0AMiHFf5O68867ePjhuYTDtwOPovWXntn/ZIy5CBmTpcZ8BqVOAvxY+7sMtl6EWMdsRzog1SgVRWtJ%0AS7I2ibVxr1MYp15U5SK8NZdU9rykH4lARykfylOCW+vDGAfhnY4EjuWY0P18lgasHhuCz/sAa+/w%0ALpmNjDWvovG4XF6rgO+lOM5eXLcSpbJRaijGDEbrT7HWh6SBpTO7T5VYQznOaly3FKW6Y+1olPoc%0ApUZ7Sv90B64twBtovcLrbI3AmJORz6MbsNALASjwlM8GeA7HWYTr7kIM/0/DmFOQznbDxcVm4B9o%0A5yOMu80TNlWAjULWbZB1vXROkysg/gLKfAZsxSbL0aFh2LyTsDnHQM4RoLOh7DWonIqTXIkb3QOB%0AAsgfhopsx0aKIFkNwc6gkgIS49VynlXggVLjAVIFNSU0j0BtvqtN3S1V/fr14+STT+aKK65gyJB0%0ANmSZVTKZZPv27Sxbtoxp06axaNEidu7cSTweT3+HoPfZxWLUcQdyO0IgCLgQroasLNQhh6KPPRZG%0AHASbNmE+mIlavQJbUorTtZDcE8aRO2k8OceMwd+jM7XzV1D++FtEPv+KxO5Ssrrl0+PUg+l56mi6%0AHD2UeGkN5Uu2UfTpajY+8SkaK9xZrfB1zufA237IAeePx5dV/7lveXE2K297k2RFLUfcOJ4hlwzk%0Aq+VLWbFiNQeNGMYRR4yjsLCAql3VfHLrXFa8tgaTOALjHkFzMLgIETrdRvqJTBWS1jYPY3bjOJ1x%0A3UMQTnoAmOotpJ6gbRrBZrR+CWMWo9RgJP1K6ApaX4+1v/Aua6m+BH6N1iGM+Tv1JstNaw2on0LP%0AXaA7Stxq7QPYitvA/gFUW96uXtkoWv8KY15DAPlIOne+mM2bV2CMIRwOt6jmj8VixGIx8vLyqKmp%0AQSnVpvVUSkTVGgiue2oZWFpZa6moqCAvLw+t9T4JtFLHrtTzbre0+tarHay2F8ybN4877rijjgZw%0Azz33oLX+1kRWDctay0033UTXrl355S+bWrDUC66ys7P3K2E9Fotx8MGHs23bzxDRDIgS/mGU+hpr%0A42h9NsZcgHAtNyEejLeRWVelAonSPAUZU7dUBjnAlSHq+QXAhQh/tTUwmKp1SOb8Tzgs9ETLndVO%0ABbDj1w0unYZSm7H2FwjgTSVOVSCm/kM9U/9BNE7dSaL1g1jrpAGsa4GPvfF+NVoP9binY5BkIZAx%0A6O+RRK57EbC8GLEP24gx1TjO4bjuJO99S2dOPh/pEMUR54CjvQXGSTQWuxik+/wYWi/FmFK0fyKG%0Ac8A5BVRvMC4k/wTu3xDxkyMcvbyJmLzJkHM0ZI8FE4HSF6D6bXRyPSZWhMrujep2IqbzCZB/IOye%0ACXvfRUc2YMIlqE6Dsb2PhF2LoGQFBLIgGYdEgw8plAORMGAFtAYDEI0Bnv6qQYO0U6fOTJs2jTFj%0AxgD/22kECD9v3rx5vPDCC7z//vuUlZWDP+S9HguOH7KyIR4FXwCy8yARlbavBsJh6H0A+vAjYNx4%0AKC7CLlqEWrkcW1yEU9iBnOPHkjd5PLpDLrH126h47j3iKzfi5Gbh1sbQAQcdCuIb0o/sCQeRe/JE%0AQmOHs3fKo9S88QFO0Mewm0+m/0+Owp/X+Pezc/pilt3wGtE95Yy7+lBG/2IEyzesYtGir+nfvw9H%0ATpxAz57dKd1Qzge/+ZKNH2zDjR6DtWNpCCyVehEJd7gREezVILSYeRizDa0LPVrMsTRfkBm0vhtr%0AT8Pac1t4pzeg9QsYsxzpwF5O/e8nVXOQacVCmk8mKtH6doyZicRLX9nmZ6t9Z2Jzf4XNvQpd8RNs%0AeBbWTAeVofDTrkWp01AqgTHvIHQeS27ueKZPf5Tx48eTTCbrBExN9+2VlZV1NlEp26hAIEAo1Po+%0AcF8trVoLAYhEInXd14bbzlSg1XT7gUCgHbB+e9UOVttLuipDhw7l448/pmfPnowbN+5bFVile7xT%0ATjmF6667jqOPPrrZ9YlEgkgkst8FV/Pnz+eUU84jEvk3TYVKMAelnkJG4NnAxZ4C+F2s/SeZiSvm%0AIDy1W0kPupqWQetHgRjGtJSe1byk2/kVOSE4OVnTiLN6TgBmhqAmcoHnAFDsvaatCB/XpT4ONQVO%0A2w5CkBhIBRyDUguA3VjrejzS8YgDQEvdDhe4GelABxFh2gkY8z2EH5zuoLMQeBal1mBtDK1Pw5jD%0APcFIEdLJuRShSjyB1m9i7QYsDtr/QwxngD5exEwgfNPkX9F6Bia5DR0cggn9EGJfoRJfYZO1kDMG%0AkiUoyrCxMnT+EGz3SdhOx0HuYNg5FYreQ4c3YSKl6C7DsP0nYf35ULISXbIEU7UT/EFUjyHYcDUq%0AUYOtLoFIjTyP7BCE06wwvMrPz2PduvV1B9Cm9b+2f2v62K7rYoxh3bp13HLLLcyePVu6sdoPJiGt%0A4FCuLAqiDWgFublCNUgkJLcVhH6gNDpLfls2Gsd26Ig++VTUYYdhu3bDvvAc6sv/oLP8FP7yLAou%0APw1/D5kOGGMof2IaZX95nmRRGf0uPYqhv5lE7oCujZ733k9W8/U1L1K7uYgxl49i3A1jWL97E3Pn%0ALaRTp0ImThzPwAH92LO0iFm//oJdi0pJhI9CFq0+hILzIDAMrasxZhOOU4DrjgBOoGVaS6o2ImED%0Aj9LYh3gNWj+PMWuR389PaG2/ofVNWHsB1jZchM4EbvRCCe6n7WCOVL0Fzj9RTmeUG8G4c0B1bftu%0AADwL9hrE8eNxGtIMtL6Piy7ay2OPPYAxhmQySSwWa8QfbRoCAPtmPZUSUX0TS6tUV7WpEGtfBVop%0AgBsKheoaLu2A9VupdrDaXlIzZ86ss666/PLLmTJlyn59vKKiIk455RReeeUVevVqbv0SjUZJJpMZ%0ApZB8k/rFL67l9dfLiEb/2MItDPCOxxnbhHAe+yKcz76IkrflnZjWf0bEGjdm+IyqgbsQMVdrHdnG%0Az1Hrl4EI2Vk1DLNV9W4AfqiJdsdxE7huDeCidTes7YO1PbyxfFcPHGeiZC0HvqiziAIXpY7D2uMQ%0AoNvS4sIiI9SZKLUNax3EyuprlPop1v40zX0XIQB1tQdQf+B5XU6g8WLheUTU5geiKGcQ6HOw+oeg%0ARtd7g5qvIXEf2vkSk9yLzp6Ayb4Acn4Avh5gwlBxPzryGia2EQJdUCSxsWIIdoEOIyC8BeVWYKMV%0A6K4HYQdMxgYLoHgFumghpmoHaAfVZyQ2GoZoJSpchq0qk+cQDECswWqiIb+0QY0dO5ZPPvkko8Xa%0A/rZ/M8bUnVLg1HVdrLVorXEcp9G51hqlFBs2bOCee+7h39OnE00oSHpBGP5sAa/+IPQYAdFKCJdC%0AuAJGHAknXQjDJ8A7j8PcqVBejDryaJxLLkWdeBI2EMC++grmkQdh62ayjziETteeQ973D0d5YKV2%0A7nL2Xvd3okvX0fnIoRz421PocuywRvuS0gUbWfzz56les4uDzh3Okb+bwI7qXcz+ch4mbuhtehH5%0AIsrGWZuxrgF/EBNxkcVWNsLF7otMQjJZjNaXUs+glPbETiu8TuomRHT1Y+rjj1urZcDDiG1bAolk%0Ane+JIFvq2qarJDKdeUySqOyczPiptgatr8DamVj7MMKJbVqbyc09kR07NuD3+zHGEI/HSSaTdWr7%0AmpqatIutffFV3RcRVbqOaSQSqeOcfpNtN3ze7ZZW32q1g9X2+u5qwYIF3HjjjUybNq3ZjipFWtda%0AtzkK+iZVVVXFiBGHUVb2B9pW78eQaNUnkPG3cFCV6o7WA3DdwcjBqx8yxlfIiPAKBHhOyvBZrQWe%0AQsZ/bXVGXIQfuhXpquTjOH7PmspF664eMO2JKPULaQwKY2j9CDAMY86h+T4hxWGd61l31aB1f89z%0AdgRav4C11Vh7B+k7souAGR5A1V4H9TgEqCpgFUrdgVIDMeYvCK+0KUA9A/lsGu70k8Cznjp6k8dt%0AHYlSb4MqxPruBn0GuO+DeRCllmDdapy87+OGzoWcyZJ/bqqg/K/o6JuY2GZ07mBsj4ux3c+ErH6w%0A4xnUzkexNesgWIjyBbDxKhFA+QLSPXQ9lb4/JF1Em4CaSpnhN5zlA4SCEImhs3yYqIQ2aC1NRoCj%0Ajj6S996dsc8WOK7rUltbS05Ozn9ln2OtJFE1BaTGGKy1aQFpCpRmWrFYjKeffpq77rqLyuqIeMYq%0ADcFcoQsU9oFEDEwUIlXQ90A48SIYdDDMeg6+/hBqK1Enn4Jz4cWoI4/CFhfj/vFO1EczwU1SeMXp%0AFF55OoEBsgBOFpWx+7r7qX3vC4Kdchh+6w/oe8EEnCwZN8eKqtj19hKW3fw6JhKnQ98OlG+uQI9w%0AsIcrKND06H8UQ8+7iJ1Pfsa6297Axo7EuhORbv8HiEdqU+53W1UK3I9MdCqRru2lZObPXF9a34ox%0AByCLvsFY+3f2DTjPRam7PZeAPmitMXZh23ezS72xfzbGvEt66y+pvLyTeO65m5k8eXLddywWE6pL%0AKBSiuro6bQgA1FtDZdLZ/G8trZRSVFZWtvoYmQq0mj7vlOCqHbB+42oHq+313dYzzzzD3Llz+cc/%0A/tFsJ5AirQeDwTb5SN+kZs6cySWX3EA4PI1MDhZKPYlSL3pjtgqkw7EGrXcBVRgjUUFa9wYGYUwY%0AObBdTj1YbHhSSFdT1V2m9XtIfOJFwB5kfF8KVOE4MayNYkwcAdB+lMoDsrF2JzJKPxQBzJnQKKpQ%0A6nGUOgJjvo90j2ej9Uqve+qg9WjPO3UoTUUnSj2KtduB2xFAvBilZgBbsZYmADXd81mPcFBBOkRn%0Aeh3UpgAVJHjhMaxdg1JdgZ9i7YXUZ7cbpDP1uvd+RtF5Z2LyroTQMWK8nyyBir+gY9MxsW3o/AMx%0APS6BbmdAsBfsfgW14xFs9UpUVgEMvwQ7+AII74ZFd6NKvsL6gzDhMkGZC18W8ZQ/AD0HwdZV8nFa%0AA/kdUVVl2Fpv1O94ndQme9ERI4cxfdq79OjRg/+24vE40Wi0VfpMCpQ2BaSu66KUagZIHcdBKfWt%0ATDdSHFtrbV3E8tSpU7n22mspLfUCJHxB4b4ajxqgACwU9oDjz4deg+Gz12D9QrAu+sxz0Oefjzpk%0ADOa9dzB/uw/WrSXr4CF0vvY8sg4bTnJ3CYmdRZT+/RUSqzaCUmT37UTtpmKssTgdctCdCqFvT5Kr%0AN2GLShjypwvpe833qdy9gc1z/k359rX0GTuZLl3GsPLS56hdHcat/RHwKRLHegOtK/PjwCYk7ncV%0A1go3XH5rf0WikvelqpCkvXeRhdvViBVbprXNcwlYjnREL/O2cx7wAagWUvisRalHPB/pCxCaU1v1%0AFD/4wUJee+2f3iYEsKb41m2p7qPRaJ34qi1Lq9raWqy1GVlaRaNRotFoXXe1NVeBTARaLW0/NzeX%0AUCjUbmn1zaodrLbXd1vWWq688krGjBnDJZdc0uz6VMdofwuuJk36IXPmVGDMWYhyt6mgoWElUeos%0ArO1My8kxOxEQux6JLi2lXvkP9T8j28oJQKFUR7TuABTguh0RwVO+d55HY47nh4hQ6yrvukzKIKKl%0AD5HxZhSte2DtIZ49VS/a9mP9O6Lkz0KCAY73AOpw0gPUGuAlHGcOrlvmiaR6Am+h9YkYcy/1POKv%0AgL9Ld9RqtL7Msxob2WB7FcDv0c40sSMLnYdhADr5IiaxFZU7Gev60GYxJr4T3fEQTI9LoduPwN8V%0A9k5FbXsAW7Mc/DmoFEBVDiy4HbX3C2wigj7sfMyo02HVTPTKf2OqilFHnIYN5aFXz8Hs2ojqPwgb%0ArkFXlmEqq1FBHzYmXVTliN7I51ckE/IZv/POOxx//PEZflatV4o+k52d3WKnVCnVYqd0f1dbHNt3%0A3nmHG264gZ07d8oFTlC601p7azktAi7XixJ2HMjK8n4uVjiwiQQohc7LxroG5XMgryM2rxDbqass%0AKpYthEgNHf98PflXnotqQJ+offsTKq74HYG8ICOf/QWFRx1ITckONs95m72r59Nz1FH41+Sx6bb3%0AsLEjwSzxqDSXUf87McAulFqLUisxZgda52BMF2TUfwgQ8Ba+QYyZQtu/MQtsROtZGPMVWnfBmBNQ%0AaiFK9cSY+zL4BKrR+gmMmYpSB2PtFBpTDv6IdnIwZkaahy9H64uxdo5n43dcBo8HUEIgcDBbtqyl%0AQwcRa6b4q5FIhFAo1Or0rKE1VKaWVj6fLyPbqdraWmKxGB06dGizc9uWQKul7bdbWn0r1Q5W2+u7%0Ar1gsxve+9z3uuuuuOqVzw/pfCK42b97MmDFjSSRCWJvKsJ+EmNgPJ73h/DnAjUi3sa2Ko9TNTbxU%0A26oy4AHgRCCD1BivRK1c69napOMwugjVYAWOU4TrVgJZKNUXa9eh1KlIfnpbtRzpLO1A9hl9kS7p%0AFNLHxxokEvU9jNnpuQWcjYhSUgfMErS+GmN2AiPrHAK0Psvj1TbMXzfAC2j9AMasRQfGYAK/hODp%0AoLyDX2w6KjoFm9gkHTu3Bt3lBEyvn8v12x9B1XyN1X70sIswQy6E7B6w4A/oHTMw4WKcUT/AHXsp%0AlGxEz3sKU7QBPeRQzKhjYc1c1IavsNnZ0LkLTlkx7p690nH1vEjrPxjwBxSJmOxCb77lN9x045Rv%0AJIxKB0iTyRS9QDcDpKlO6XdZKY5tVlZWqxOTsrIyHnjgAR74x6O4yQj488SPNtBRqALJMHQbASPP%0Akc720peheg8cexGceQv0HAz/eRVe+i3UlMLPboRLr4E8z91i2kuov9yEdgwF/5hC9lmTGgl8Kq67%0Ah9pn3qTL98cw4qGfEOxeQLS6jC3z3mPHko8o6D6c8PMlROYncWtLUOoIrO3k2bWtQymNUp0wZggy%0AJUg3no+j1L3AuVjb0oIliozrZ3gd2cHA6dRThKqAO5EkvJaCBlwk/eofHsi9hfQ+y+UIHWEJqAb7%0ANTsX+BFa98KYt1t4LemqGq1vxpi3uOee27n88svr+M+p72MymWxTcZ+asmmt67ryLdW+WFrV1NSQ%0ATCbx+XwZdUz3RfjV8Hm3W1p942oHq+31f0dt376dM844gzfffJMuXZrzn/a34Mpay8svv8x1191H%0AOPw3BFT9xwNNGq2Pw5gTkIOOAKt6OsDfyUyctBU5qPyYzAz5QZT7ryC815Z5YY3LoPXDQC+P72kQ%0AcLocrYswpgqxpxqC6w7ynkuqi7kGeBmxvDkszbaXodRnwA5PYHO4Z081GAGRXwLPodSFnjWPQtJz%0AXsHaDR5d4SysPZXmMZMG+LcnZtuGUABykLjWhnZhy4EpKD0HSxAV+jk28GNwPCqAKYPa36LNvzEm%0Ajuryc2zhzyDYD6o/gy3ngQ1L6hQWuoyGg66GokXo3R9hqnagB03EHP5TyOoAH98HOxaj8gqw37sE%0Aineil36IKS9BHXIYtmQves9OTHVjE31fToBkbRx/tkMy4tZRV0cdfCAvPv9q2iS3lqql0X1TkVPq%0AFA6HCQaD+92v+L+tfZ2YGGN4++23ueqqX1BVVSnANVELGAh68bOHXAh9JsCCp2D3Yhg8Ds6aAgef%0ABAvfhedvgLLdcOnV8NPfQIEXb/z4vain/4LTvROdHvkdWceNr3vc5K4iSk6/muTKdQy68zz6/foU%0AtM8hEa1l26IP2Dr/PXyxHMIv7MVusOAGkMXtBOqpKW3VCiccPDgAACAASURBVOAN4B4a0wF2o/WH%0AGPM5Wud5v7OTSO9E8gpK7cHaV2k+yfgKpe4CqrH2CmTx21rdhHZGYcxzYA1K/wVr/gj8HKH6ZFqf%0AAlcgEbXnM3Lk+3zxxaxGfOfU9zglYGqtu5nqbAaDwTZBaCa2U6nb5Ofn1/l7Z6KPaLe0+k6qHay2%0A1/899emnn3LPPffw5ptvNjuAfVuCq9aUzUopzjzzQhYs6EcyeXGDey0A3kbrDTJi1gdhzCTgKJS6%0ADms7Ac09Y9OVUu8CMzzOV2a0Bq1nAEsw5te0DYqTCChei4ibQkAMpbLRenAacJquliMHz8uAgxGA%0A+ikCUEHrI7wDZ0vq/43AX4B8tI5jTBStT8WYH5G+S70TuB+lFmJtEKWuwNoLEBrDVcDHwPlAHtr5%0AN8Ytwgmdjhu4EnxH1iuXY2+jY3/AJFah88ZiOl0HHU6RCNPwYth1PYQXogtGYQbfBJ2Pgc1PwZq7%0AvPfNimBK+yCnEKJVEPMA6NCxUL4bKovFCD81fvZ4bConC5IGG4vjywlgjcXnV8Sq4viD9d3U559/%0AnjPPPLNFU/JMRE7plPdN63/lV/xN6ptMTFasWMEFF1zAxo0bPeBa7WXOBiC/Fxz2Y9i1BDZ+BKE8%0AOPMmOP5SWL8AnroGijbDuVfAVbdAl+6QTMJd16OmPkvgkOEUPnQrgdHD6h4v/O5nlP/0Vvw5AUY9%0A+wsKj5ZoUjeZYNeyz9n4+VvEt1VhP3cxXx8LGU0m6kup54EYEjqyBK1nYswWlOqDtachv7XWKolS%0Av8Pa6xELKYBdaH0fxixCxJ0/JzMO+zZkfzYPrX/tcWxfQbjwmVQ1/4e98w6Pomrb+O+cTS+E3gQB%0AkSpVkY4NBJQmNkSUKiIoxS6KiKhYwQ6CIgqiL9IsFMUG0rEBioD0DqGmbbbNOd8fZyd1k2w00ei3%0A93XttbAzO1uyM/PM89xFyrEotQjD078H8BAVdQUbNnxNnTp1sq2tlMLr9WaIo/L7LRTGV7Ug26m0%0AtDSEEMTExBS6Y1pYSyulVIaXbMjS6k8hVKyGULIwZcoUjhw5wsSJE3PtzMFa9OQUkRRG2Xz48GGa%0AN2+N05lX4sspDK9yE0odIzMWtTvQECOgKktgr1AwXc+n0FqQf/pMVlgIMQ2IQOvbMx4zRek+4CgO%0ARzJKOdE6HdM1rYRllcXwZq/BBBoECx8mjnQzmRzUNgUUqGA4uUuRcq2fo2vzuuZiTMKzQmGiVD9C%0AqSM4HB2xLNs1Iev2twGjgd/N9h31oNRycNT0b8buon6KUm5EhWHossNMF1UpOD0VeeYVlPsosuZt%0AqNpjjIF/4irktvtRSduQdbqhWj0CpWvDl8Nh3zKoUBPqXgZHt8O+Dea16l8Cqefg2B5EWBjhva/B%0AWrUOfTyR+NYN8ew+hOdUEmGRDtznXERECTwuc7gcOmwwTz7xNAkJCXmKnJTfEiBnQfpnRU4+ny8j%0ANejPOAT8HSiKiYnH42Ho0KEsWLAAZCQoN4THgvbBBVeBOwUSt4LyQcdB0Os+c9Hx1l1wZCf0ug1G%0APgZVz4fUVBg7BLFyCVFdL6Ps5AcJq2mcBZRSnHvgBdJmzKNCl+Y0fGMIUVUMt93nTOfw+u/YvWIe%0A3vRU2FAZfhkBvoIKHw9mmrEd81sHKaMwoQK9CC4YxMYaYCkw3z+d+B9CXITWwfo827AwF6onEaIV%0AWgcnPDX4HhiClAkoNYOsIR3h4c8xbFgpnn/+mWzPsPcHt9tNMFGrRWFpZRe9CQkJGY/b2w22Yxqy%0AtPpbESpWQyhZUEpx2223ce2113L99bm5nVkteqSUuYrRvJTN9n0wJ/0ZM97mscfewel8hfw7mT5g%0AJbAAOIAQUWhtUpWMQr80UpZD6wp+YYVdyIIRJPXCpDt5stzcedyfwoQMxCGEzlKUVkSpKmhdEUMT%0AqEB2VfKvwGcYGkGNfD5LMrAGh2MnlnUGIeLQuhKGmzuC/DmzPyDEUrQ+jBCV0NqOR41FiKfQehum%0A09oOU2C/jBA/Y0b8w9D6FnJb/3yAMfw/gpR9UOo+4CzScQ9K7YHI7gi1E+39w99FHQMJ3U0X1ZsI%0AR+9HpC6DsBh03QehxgBwxMO+t5B7XkKlJyIvuQt1yRhQFuLLO9GH1yDrtEFdNwGcSciP70edOYy4%0AdQz6gotwzHgcdfYEUSMGYv2wGd+6HyjVvhGeQ4m4DxwnPD4KX3IalltljPzLV0pgwbxPady4cZH9%0APgsDj8eD2+0OSh39T6A4UrhefvllnnjiKSzL2CMRFmMCByzL2IpJCRVqQEIF8Hlh3y/msc69odXl%0ARtB17jTMnIJwphDbvxcRTeqiklKxTifh23sI1/JVSIcksmpZ3CfOoZxuZGy0CXmoHYZV/yyU8cGG%0Ay+HHy8AVjTkubAf+QMrjQApKpSFEPFJWx7JiMWLCe4Faf+KTn8bsZ+lIWQWlHsI4cAQLBaxGiHfQ%0AOhVjzbcd4ypSEFKR8lGUmo/ppo4MsM5u4uMHcPDgH7m6ovbFm9vtRkpZ4MWLx+PB6XQGVSgGsp1K%0ATU3F4XDkmtIVxiorr23nh5Cl1Z9GqFgNoeQhNTWVzp0789prr9GwYUPS09M5evQo1atXz1CRWv7U%0Am0DK5r8qIlFKcfnlXdi8ubnfHaAgWAgxAq1jgDsxnYlETELUMf+/k3A40tHalXEzHUQfpiB2IIR9%0AL7P9X2sHStnWVkeArhglfLDdju8wav9RZD/x7AHWIeURlEpGyhoo1Qy4iEx+7I8YG6ghmKACG8eA%0AjxFip59C0cUvygrE0/sEmIXp7qQiZReUGorh/2b9OxmhiEkJkwhxPyYswC7wFfAmQk5Cq3MgLER0%0AI3SVSRDfGVJXIo8/gkr/DVmhParOg1CxI1gu+PVhxNF5EBaObv0INBkEp3cgvr4bfeJX5CU9UT3G%0AwdEdyEWPGJV//wfRFzTEMXUs6uQRokbfgfX7H/hWfEepVg3xpTpx/baX6MrxeE+n4kpyZ3ySsHBB%0At+49eO2V1zNsa4rq91lYpKeno5QqUJjyT6G4UriUUrz++us8Nm4iWrnAEWO6q/GNDV/ZdcD8Nso2%0AgMgESPoDtBvQUKqyoRRYPkhLBJ8bImIMHaRUGShVFpLPwYYliPMqE7X4Ixw1My8Gtc+He/wofEcW%0AwwUWbA6D9T5EaoK/MK2OsXmrQvaLyy8w05DxFJyEBcYF4yeE2IDWiRh6zzlMklSwnFkFrEGIdzDC%0AzM7A9Uj5GNADpfIKTLGxBhiMlHH+bmrOKUom4uL68uabowJSYWwKjM23LojuFayvak7bKcuy8vV2%0AtS2ngimEQ5ZWfxtCxWoIJQfJycls376d7du3s27dOlasWIGUkqNHj3LppZeyaNGijBO+1+tFa11s%0Agqs9e/bQqtVlpKcboVLBOALcgYlIbBTE+gohZiPEfpTKy/4qN4RYA6xD65EUbkS4ENPVbIcQvwMn%0A0VrhcDTGsmz/1LwKhc2YUX5fjFr/B5Q6i5QtUaorRoEcqAuRCLyDEFv8hbyFEGXRejZGkGVjC2Bz%0A9Zr6lcrdsmzTAzyKkLNBhKOjxkHUAFDnwPkQeD81nqY6HVGhA/qSWRB3AaQdgC0j4NT3yPINUG0e%0Agwu7w57lyO8fRp3bh7x8MOqah2D7SuRn41HOJMTgR00n9bUHUCcOEj1mKNbR4/gWfE5so5qI+Bic%0Aa7cSXaUUqftPo7wKR4RE+RTSIYiOjeHdGbO49tprS0RxaJ9QA3WSSgqKO4XL5XIxZswY5syZB8Iy%0AdIGo6iDCwHMI4qpA+2egTD1YPgDO7YBO90GXhyAqHjbOhfmjoFJ1eOw9qNvM3jCM7QHb1hPx0iTC%0Ab++b7W/uW/YlrlF3QdN4aHoWdjSEte3hVN6+qlJOA8qg1HACn6NTgJ+RcoOfQlMey2oGXI6hH72P%0AlF6UejWP59tQwFqEmIkRX10N3EAmDecPjOhrG4G7q2lI+ThKfYQRjY4OsE5OLKB9+1UsXvxBwE66%0A3WG16Sv58VIL46uaVeRkWRZhYWH57guF6ZiGLK3+FoSK1RD+eaxZs4ZbbrmFs2fPUq9ePRo0aJDR%0AUd21axfTpk0LmHBV3JnoDz88ljfemIHD0QjLuhjTzaxLXnxUIUwHUeuJea6THW7/mPwCMkURBUEj%0A5f+Ac/6TWX44gRFHHUSIJOywAiGuQuvmmA5IQVf1PmA1hu6Qivlcd2AspPI6MK9Eyvn+E2lrLOtW%0AjLOABh7DdGLGAZFIORWljiLlrf5Rf8Ms2zkF3ANiOSKsJjrqCYjo5Tcr1eB6Del5AYUPyg1BOFeD%0AaxtaWSbS030aUeVi9NVvQpVLYPPbyI2TUOlnEF3vRXcaCRs/Ri6fhPKkI+58wnRSp4xGHd1H1MjB%0AaMvCN3MukVXLEdWkFinLN+I5m0ZYXAS+VBObGhEbxnmNyiCUg3JhVfngvY+oVq1aAd/r3wulFGlp%0AacUesPFX8FdTuILFoUOH6Nq1K/v37wdHnLnQCS8FOh0i4qHdRIivDt+MAFci9HwKLhsGSJg9EDYv%0Ahq79YcRzEOe3wfrmY3hpGI6LmxI5801k5czkOXXgIOm9+6MTNTQuDy1/gsPVYe1lcChQ99OFEC8D%0A12JijMGEJ2/2F6gHkLIcSjUCriL3RaYXIZ5G67sInJqngXX+TmoyWncEbiLQsUDKsUB3lHo6x5K1%0AmG5qDEpNJ3+KUVakERFxBWvWfEXt2rUDHru11hkerAXxUgvjq2qLnLTWlClTpsDiNqvlVHFaWsXE%0AxIQK1oIRKlZD+OeRkpLC6dOnOf/887ONRLTWTJgwASklDzzwwJ8WXP1ZWJbFJZe0Zdcu4++n9Rm/%0AB2sNtL4ErRtjuqi2sl4j5b1o7fF3PoPBIUwKzC0Eb2flxqQ4nU+mZ6sHI0LaicNxCstKBhQORzWU%0AquVftypSvg2URam7yJuPqzBK4HUodQIhygDt0boiQryHEF1RajDZT27JmJjUTX5aQB+07k1uuy0X%0AJq3qF/+/7wBeJLs7wXYQI4CNyMj2qMjxENbOcAmVAtfzCO+rIBzoqk9B2dtBhoNzG/LQIJTzNyjb%0AAuk5gnKeMIWtFOBJg2qNofcE2LsJse49tM8Nd02Eus2QLwxH7dtJ5HVdoGplrLkL8J1OIqxcAsrl%0AQaWlExYXAQjK1K9AWISD01uO0Pz6Guz57jR9et/K+HFPBMwXLwn4uwI2/gqCSeEqSmzdupWePXty%0A8tQ5QBpOa3iMCSNoPQ6iSsPax0AouGkKtOgDibvgrd6QcgzuewM632p+m85UeOAa2LuZyDemEH5j%0A74zX0W437vvG4Zu3DHy9oNlxaLsGUuJhzWWwqy7orJ93B4Z+0wOTJLcHKUujVEOML3FBv7EfMVz1%0A2WSKqzTGr/Ud4Jy/SL2Z/C9YdwGTMPZa5QEnUo5HqblAf0zUbLBIQ8o3UOoDBg/uzzPPTMzz2G3T%0AvexxfH4XL4XxVU1OTs7omBZ0zrA7psVtaWVZFmXKlAl5sOaPULEaQsmGZVn07t2bIUOGcPXVV+da%0AXtyK5x07dtC+fUfS020PxLMYxesvmGSqswiRgJRN/aO4yhi+WR+CtXoR4ltgOVqPIv/IRjAjwOMY%0A4dNGoCxS+vxCjVJIWQvLqoHhqwWKW/Ug5WvABZgUqKwG+78gxGq0PooQsUA7tG6N4dXZOIHJEm+G%0AUvcDvyHEbLTe77f06ocRUuUshk4AzwM/ImVtlBqD4bJ+jZTDUGoisBEp70epncjoPqjIsRDmNyZX%0ACtKfQHjfAkccuuozUPZmM8Z17UMc7I9O+wl5QX9Uw/EQUxWOLkP+MgLlc0K9XpB8BE5uMQbyPjeE%0AhxuBjVIm3lNKCA+DsDCEz0tYpXL4Es8i0FS+owuuXYdJ/v43Gt3Vhv0LtxAZqanZsgK7vzrN29Nm%0A0qlTpxJfDP4dARt/FcXtqZwXVq9eTe/evUl3+UBG+JOQJTQfCckHYf8SiK8IfV+HBlfDmpnwyUNQ%0AvQ48Ngtq+acCS96FN+7F0aEtUdNeQZTPHKF7FyzGPfxBSG8PoiI0+AXa7wSHD7E+Cn4F7bPFlg7/%0A7SLM5CWhUJ9HyleBun6h1UaEeBs4i9ZXYI5PwVrnjQWuRakeCDEIIaIL2U3VGK/kJ5CyFEoNonTp%0At9izZxsejyfP/cW2tPJ6vQXyUoMpFL1eb8b+GayIqrgtreziOSYmJmRplT9CxWoIJR9nz56lS5cu%0AzJo1i1q1cqtk3W43Ho+n2BTPL744mRdeWITTOY7c+4wX08XYgJQHUOoMZmQXiZR1ESIWraNQKgpT%0AiAa+mdQpH1q3A05i0qtScDhcaO32d2uN+EOIWIQo5be/OoJJs2lI8IKrNIR4HSEuQan6wLeYfPMw%0AhLAL1OoBPquNU5iC3IGx4uqNSaIKJKzYihBT0HoXDseVWNYoshv8b0eIW9A6BXAhYu5HR90P0j9G%0AVT5wPozwvQdh5dBVJ0GZ600R4TmKODAAnboWWeMmVKOJEFsDzm5GbuqPSt2H6DAO3XI0pBxDLr4J%0AdXon4qYJ6C4jYeFTsOJVZMsrUWNfg3dfQHw2i5gBNxJ2fVfSB95LeHwk1R/vy8GHZxJVKpzzOl3I%0AH+9upGmP80k+4iHBqsQH731E1apVza/hX1AMut1uvF7v314MBgvbU1kIUWQOAYXF7Nmzueee+7Cs%0AdNNldUSZEALtg4hYOK8xdBwNpavB8mdg9yrodScMfRpi4iD5DNzbGY7tJuqdNwm7NnMcr3btIb13%0AH/SxUwh3PEJWQNUQ0O4YlHfChhbwUzvwxPgnIaDU3QQXPJIVh4GpQHmESPIXqbcQbJGaiY2YdKww%0ATFjIA4V47l6kHIfWf6D1YMAIVuPiRjF16hh69uyZ5/5iC648Hk9QllZ2oRiIOmDTBeygjMKIqIrL%0A0sp2NIiNjSU1NZXY2FiioqKKZUr4H0CoWA3h34EtW7YwYsQIPv3001zcpOKwv8kKn89H69ZXsGNH%0AS79StiDsB97GnCzqYsbdHsCLlBZCWJjC1AIstLb8//YC4HBUB0pjWaUw3ZR4zCgvHiOqyvx8QnyA%0AEUfcReBo1ZxwYkIOfsVY3WhMOldrDA0hv+9us1+pfwgTzWo4sOaEWDPHusuQ8h2USkTKfig1jNzF%0A7EdIOQWlUoFOCLkKRDQ65iUI7wZp9yF8/4PIauiqz/oN/gV4T8GBQZDyLbJaN1TjSRB/ITiPIjb2%0AQ5/aiLzkTlSH8Wak+9lA2PU5st0tqL7PwZHtyGm3o6VGPzUTwsJxjL0VERdJ3OwpuN6cjWfxcmo+%0AfDPuU0mcmPkFjYe35fjafSTtPEaHu+rx0+xDDLh1EOPHTch1Yizui6e/iuLeX4oCNqcvIiLiH03h%0AOnPmDKNGjWbx4s/td2bs0RwxEOYPkvB5wOeCyGhza3YllC4PpcvB5lWw62cc13YhvM8NGdvV6el4%0Axj+DPukEZ3egtllQ5Si0Ww0X7IEfL4WNlyLT3wEaotQN5A83pjj8A623oXUS5phgAdPIjDQOBhr4%0AHSmXoNRvQBhCXI3WLwX5fKffem4uZsI0nuwX09/RpMlS1q//Jt/9xS5YXS4XDoeD2Nj8P0NehaLd%0AVU1ISMh4jcKIqIra0kprTVJSUkaiVVZLq5J6EfkPI1SshlC0GDx4MEuXLqVixYr8+uuvRbrtDz/8%0AkKVLlzJ9+vSAV+HFeXLbvn07HTp0ykIHKAgpGK/B9hjP0WCwD5iJyeYOVqCjkPJNoApK2fGm2Zcb%0A/tvPSHkcpVKQsjJaN0TrCsBihOju90YNhGSMef+vKOX12051xtjuALwCbMCM+FsCMxDiM7S2EOJu%0Af4hBTkPyWf4TmQcT33gH5kSmgInAZBBeEBFwwUeQcK0pUn3JcGAIpCxHVr4S1eQ5SLjIZMRvGgxH%0AlyDrXou66gUoXRPWvoDY+BzivHqoIdOh3PmIV29E71qPuPNRdN97EI/0gx++JW7cSGTr5qT3G0V4%0A2RhqvTiY/aOnI9zpNB7dgS2TvqJ60zJUblCa3xYeZ+b0WXTqFDilyC4Gtdb/7+yiihLFzUkvLBYt%0AWsSAAUNRymfoJ9EtTCiFdx9U7g6Vu8KRxXDya1AeqNjUUEu8aZB+FBzadGkj7O9bmLPpmTPg64kZ%0A9/tR5gy0WQuNt8K2urBuO+JsL//Uw4YCDiPEToT43e9JHOf3dG6KSZ+LQMopQGOUuiOIT+nBOAR8%0AihFfXYShDLiApzHUnfy49RpYAYz3j/yfwFyw54SP6Og+fPvtJzRu3Djf/SWrpVUwvFSn05mNOmBz%0AQ6OiorKdGworoipKS6v09PSMYjbr9nO+xxAyECpWQyharF69mri4OPr371/kxarWmvvuu4/zzz+f%0AYcOG5Vpe3BGTzz//IpMnf0pa2mPk34G0sQVj0p3T3zRvmFjT1Wg9muAcBQCcCPEGQrRFqSswY/qN%0AOBx7sayzmBNWA//IvzbZLa8OA28jxHVobY8qbYHVlyh13P/cbhhFf6DvdQHwEYbOUBkT+diD7J1e%0ABUxHyumYoKYnMSk5EVmWP26EY7I6yGZIvkYpJ1QaCa59kPwpskJLVJMXoWxzwzXd8jBi39uISo1R%0AnV+DKs1h7zfI5UNMMTz4Tbj0Olj0NCx7CXlxO9T4afDDKuSLowlvWIeYmc+T/vhkPEu/pua4viil%0AOPLsPOrddglep4d9CzfTcUxDDm5IIt5biQ9mfUiVKll5vLkRKgaLBv90ClegpLGTJ0/SsWMnjh8/%0ADiIawiuBdoJKgboPQO0RsOk2OLMO2o2DVg+AIxxWPQ4/ToFOI+GGpyDM/53v+xEm9wRnA7CuIBvP%0APDYVWm6AFuthvwfWXgtHo3A4tmNZuxEiDCHKoVRdoDWBk6pOA68B92McTQLhDEJ8idYr/Ar/Dhgn%0Agcz9XYjXEKIsSr2dxzb2IeXjaL0DrQdixFt5w+GYzQ03pDNr1lsF2qv9GUsrMIWiTc0JxHstrIjK%0Atpz6K5ZWNr82a5fWrrlCIqs8ESpWQyh67N+/nx49ehR5sQpmnHPttdfy8MMP07Zt24DLi4sz6PP5%0AaNasFQcOVECpizBj7Wrkp8yV8h3gR78tUzDvRyHlu4ALpYKJY03GiK22YTqzURgRVU2/crguRsWb%0A3wFwP8a0/xpMp2YHWjsQopvf6D+vQvsPhJiJ1nsxvqmHkLKxX3xhP0dhEqveAyLR+hmMX2vW4mgK%0AQj4HohQ66mUI756p/E+7HqyvADci5jx0o4lQvTfsm4PY/iREl0Z3eQNqd4akg4jFN6MTf0P0fgzd%0A7T7Y+xNyWj9TuE58B+o3xzGyO+rATuJfn4ioVgXnrfcQVTmBC9+8m70jp+I+dJy2L/Vg89Nf4Tqd%0AQusBF/LL/w5zx8A7GTf28aAvhELFYNHg73AIyJmEFyieOWdEsxCC119/nUceeQSk3wJLCuNM0egZ%0AiKsDPw6AyBjoMQeqtYETW2F+V4gvA6M/gcp+v+HkRJjcDY7tQnoBtJ8e5AMsiPCZsLs2wJkwWHMB%0A7OlC8BOYbzATkFfItJzTwC7/qP8XpKyKUteRt090KsZ6biZwSZbHnX4LutmYpLsnCI4/f5aoqH78%0A8cdvlCtXrsDp2J+1tPJ6vURHR+dZ4BZGRPVXLa3s52f1ebXjZiMiIkrsPlgCECpWQyh6FGexCnD8%0A+HF69OjBvHnzqFy5cq7lxakm/v7777n22u5AWYTw+vmWEUhZDaiFUjUwJ5DqmC6HByHGoHVNMm2m%0ACkIaxs7qEoyPIhhawS7gIEKcQso0LMsJ+BCiDFJWxrLCMVzUoeTmkOaFM8B3CLEdk6qVgOkENyHv%0A4vorpFyEUqeQspuffnAe4ELKe1HKdGvhe4SYC5RC60kYL8esB+P3kY5HURqIfhHCbzHCKQDPQqRn%0AFFqEoatNhZhL4fjTiNQFaHciaAuqtoBrpkGFRrD0TvhjIbLl9ah+L0JkLOLVm9A7ViMHP4i64xF4%0A+znEnJeI6t6RmJcfJ23E43i+/I5aE27DUSaO/ffP4LzLL6B8i2pseeEbPGk+HBGSyJgInn3yee64%0AI5gxanaEisGiQVHs03ZRkDOaOWdRmjNpLJjXO3XqFO3atefw0XOmw+qIhYjS0PRVOLkSDsyC+jdD%0Ap8kmLevTW2DvMrh1Mlxxp5/m4oU5o2DdAvBchXHhyCrMDAPHOmi0DNqWAh0Bay+HbU1AFfzbMu4A%0AF6LUncAGhPgUrU9hAkFuIbjpz2ykPIVSn/j//xVm5B+HUhMIPPLPCz4cjpH07duc6dOnAZn2agVZ%0AWrnd7gLH8bavKpBnWlXGOymEiOqvWFrZ4sGs3FmlFA6Hg/Dw8FBXNW+EitUQih7FXawCrFu3jnHj%0AxrFo0aKAOdNOpxMpZbEk9kye/DLPPfchTqed2LIfk6G9D4fDeLEaA/5wpKyK1hFovQu4DBMdqrLc%0ArFz3RoB1GK0PIEQMWhtxlilKq2BZlTD+pRUx/qRZD9hfAT9j0mTK5PEJTIEq5W6USsbhqINltcQI%0AMGYhxE1onTNm1gXMQYg1aC0Qoh9adyN3V9mDiZw9gDlUzMZw3rK+x8+QjjEolQTRT0PEHUa0AuDb%0AinT3Q1kHEVWfQZcbZpb5khEHb0anrobaw0AL5OlvUWn7wJsKykLUbYPu0B/OHIav3kDUqod+fi6k%0AO3Hc1xu8acS/PwWtFM7bR6G9Xs5/tA9nPt/AuTW/g9ZIh0SESeLPL4OICCcqRbBk0WfUr1+YjPUc%0A34jHg9vtJjY29j9dDBYnCuMQYHMcA3VLhRC5ClK7S/pXP7fdNXvvvfd45JGxIKJAOiC2JlS72RSs%0AvrPQ5U1o2Bd2fQ7LBkLtlnDXBxBf3mzo+1kw517w9CIbj9UPKb9E6Q1wYU9otwkSzsL6y+CXFuAN%0A1D30ADsxMa47AAdSRvtFlT0onDuADyEeRuvhSLkSrX/3j/z7FGIbGliFEK8DHuLiJAcO7MroahZ0%0AgWc7BPh8vnwtrbTWnDt3DiCoIrQwIiq7Y2oLpAqCMBoQkgAAIABJREFU7VSQ873YF1ChUIACESpW%0AQyh6/B3FKsC0adPYunUrL730UkAuUmpqarEk9liWRfv2Hfntt9oo1TGPtYz4wRSxe/3/PusvOB1o%0ALQCB1qC1xOyLEtN5tO/TgWMYwVUVgqMRgBDzgESyR7KeBr5Fyr3+ArWev0BtRHYO6wG/rVUPlLoV%0A4+k6A6MMrolSt2N8VHMezF3AqwixEqiK1n2RciZaO9B6HkZ8tQYph6L0EUT0OHTESMP5A1BnEOm3%0Aon3fIyvciar4BIT5i+3jkxCnnkeUa41q/hbE1gLXSeSGHqjk7dB2rKEMHPgWTmwENISFgccFHo8Z%0AzSplPFS1BqWQkeHI6EjQGq008R2a4Nz0OxHRDq5eche/P/01Uft8fDJvIRUrBiOoyx/p6elYllXi%0Ai8HiusArCuQcE2ctSnOO74UQuQpS+1acyEr9SEpKYsSIu1m6dAk44gEfWOkQHmvEV93ehdgq8L+r%0AIXk3DP8IGvvdRvb+AJN7QXpjsK4k+76vkfJjYK/xOj7vOLRfCefvh00t4IcEcB5EyhNACko5ESIO%0AKathWQpzPHoKc+FcGPgwdKN5mGNZC7R+muAt8wB+QohXgVNo3QO4idjYZ3jhhYEMHDgwY638uv32%0A393tdgPk6brhdrtxu91ERUUFXYT+GUurgigJNmxP1dKlS/tDZkyhGh4eXmJ9mUsQQsVqCEWPv6tY%0A1VozZMgQ2rRpQ79+/XItL87Enj179tCqVQfS0x/ABAEUBIWUk9Ha5/cbDAYaKT8CTpF3Tnher/UO%0AWoejdXmk3JejQG1M/ieYo8BLGBrDOaS8DKX6YsaFOZEKTAHWIuWFfm5ua/97VZj0m/kgzgN9FBFz%0ALzriQRB+g3OlwDUavO8jS12GqvoqRPptfFLXI4/0QykXXDwDqvojabc/C7ueRdbujLp6KsRWhA0v%0AwfonkZdej+r3OqSnIF/qhPKmwNhpUKYiYlwfhEMRtWAWastveMeMpfJdPSk/qAs7O95HuYsq03Z6%0AH9b3/4jmVRowa/rMIivc/o3FYEmBXZxYloVlWXg8ngyVdyAuqT2+/6cQqDM4d+5c7rzzLsx+5wRH%0ANOaiKgoqNob0M5C8F9oNgFtfNo4BSSf8PNaD4KlEBhWAcMzF4g/+/1dDytOosknQxgUNBeLXcuh1%0AjeHchZjjU+bfU4j3MdZ5D1Owd6sG9vjT7DYhZRRKXYgRU/VGqUFBfis7kfI1lNqNcUcZQmZHdytV%0Aq85ix45fshWT+XX77d9Eenp6LgGTvTwpKYnY2FjCw8MLVYQWRkQVbDfWpgJERERkbBtACEFERESJ%0AvIAtYQgVqyEULfr27cuqVas4ffo0FStWZOLEiQwaFOwBrfBwuVx07tyZ5557jmbNmuVaXpyCq2nT%0A3mL8+LdwOu8nOMPuJOBxDA+1TZCv4kaIN9C6OvlzXhWG0/o7DsdxLCsJ49vqAPpRcIEKpvu6GCF2%0AobUCQMomKDWJ3B6uSZiY1E3+5KoxGLeArDiKEA+i9VYQ8YAFMa9CeD/DTXXPQHjGQXh5dLW3IO4y%0A8zRfin/kvwpZ7wFUvUeNKXvKH8iNPVGes9D9Pah9DaSeQC7oiko5BHfOgabXwMp3YN59yI43oB5+%0AA9Z9gXh6MJE9OhPxxvO47nkE3ydLuHD2wyAE+wY8S/2h7agzpDWres6kf+9bmfjEk0X+eynObn9R%0A4Z+MZA2kvLdvWQtRIINWUVI7UnlRPw4ePEjLlq1ISVGYU6nGFK+lTcdVJ4HlhTLnQcXaUKkObPoY%0AvF6EpzJSRmP2ax/gxbJOYsb8XTC88UoQlw6t18HFP8LuOrC2A5zI6l7hQ8qX0bo9Wl+Xxyc4jhAb%0A0HoNQngxcc1dyPCDZQ8wHfiA/C/WDyHlNJTahBFf3U2mwMuGJjb2Yd5+ezy9evXKfLQAP2D795Ke%0Anp5LHOVyufB6vdmsoYL1VbX3UyllUNZzLpcLt9tNfHx8wGOGLfayLwLT0tIybLqioqJKLDWohCFU%0ArIbw78f+/fu56aabWLRoEeXK5RYJFBcfTylFx47X8NNPlbCsrkE+61fMQf5ugh/DncSYel+L8U4E%0AI8L6FaPmPePnyEbhcNTEsmph4lbjEeJNhGiBUjcSeH/3ASv9nZPTOBxNsKyOGHpAKlJOACqj1IsY%0ATuspjB3XL0h5KUqNJrcdzjngEWA9DkdPLOtJ//t5ByGfRItKCOkypuXnTYEyt2UKq44/6x/5X4pq%0ANh3iLjDd1833wKH3kU0HoS5/DiLi4Kc34fuxyGbdUP2nQng04pVu6P0/woT34Ipe8ORA+G4hMa89%0Ah6NHZ1xX9kSmJtHgi+dJnL2CxFcX0G7aLcSeX5o1fWbz7IRnGDhgYJB/l8LjnywGg4XdGSwuwVVe%0AfFK7UxpofJ9zv/2384B9Ph/t27fn11/3AakgYyG8GlSfAaffh3MfQERZKNUIvMcN19V5BtQtZE+A%0AcyLEqwgRhlJDyUYXiHTBJT+YwjWxMqzpAPtrYY4DxzAiyHuABv4nJAM/IMT3aH0KKav4LaxaEIiC%0AJMRUhCjjPzbkxCmkfBulvkaIi9B6DIZfnxfWUb/+l/z44+ps31VBFnBZHQJsLqjNVc05ni+Mkr8o%0ALa3cbndGV9eeCKSmphIXF1dibe1KIELFagj/DXz11VdMmTKFefPmBYzaK64R7KFDh2jSpAWW1QTL%0AKotR05f23ydgRunZ34+UHwJb/YVefidaL6YoTcOIpn5EiPIIkYZSTqSsAFyAUjWxi9PcOIMQ0xDC%0ANvO3ccBv3r8fIeIxyVztAmzDh5RPoJTP76G6DSk7oNQoctMCXJikmq+Qsp2/I9swy/JEjG3VDyAi%0AkLFNUFWnQGwbSNuIPHyr8VW9eAZU7WGecmoN8sdb0OHR6J5zoWpLcJ4x3dSzu2HoLLi4F+xcg5h6%0AA6JGHdRz80AI5F2XI6SP6EXvo5OTcfe+nVJtGnDBB2PZ22ciaT9up8uy4SRtT2TLQ0v44N3ZXHnl%0Alfn8PYoG/5ZI1r+SwmXz8Qqyg/ozynsb/xYecH6iMMuy6NmzJytX/gCkgYiB+Kug/Ag4PMxwr1t+%0ADGVawNmfYP114K0PqhuZ05w0hHgFIaJRajC5jikOHzTZDO3WgDvSdFq3NwS9GlgL3IyUG1BqN1KW%0AR6kWmFF9Qd1/JybEYyKG+gOQgpRzUGohUtbyH+POC+LbsoiJGcXChTO47LLLsi2xecBRUVEBJxL2%0Ab8v2UrXFV1m7qjYKU4QWRkSVVzfWLpxziqog5KlaSISK1RD+O3j++ec5c+YM48ePz3UQKOiA91cw%0AadKzPPPMM0BNHA4P4EYpt1/F78GY5ccjZWmgLJYVD3yLOYhfhBCpSJmCEUOkonUapvBTQDhCmJtS%0AboxrQD9McRqsd+cR4F3gOuA0Uv6CSbJqi1JXkn/M6laEmIfWh/2v/SLQK8c6CngBIRYgRF2Uegkj%0AqMq6/F7gI2RYV5R+GXQC6OEgPoOw8uA7gqj3ALr+OMPnUx7Y2AdOrEC2ewTV6hFjrL55JmLl/YiG%0AV6IGvW0U1O8Ph3WzEcMmoG+7H75dZMb+vboS8fpzeN56H8/TL1D98dspP+QadrS+m8hoQecvRrDr%0ArXUc+2Arny34hAYNGvB3we124/V6S3Sh5XK5UErlOwrNaQf1dynv7df+L/GAe/fuzYoVqwGvccAo%0APxKs03DuQ7jgbmj4NFhpsPFGOLsVrLoYi7yqQAJCTAXis3DiUzH7/gkgEcRZZINzqLapEK1gHbBF%0Agi8cY+LajcAXvPlhOUL8iNaz/VZY7yNlRZS6h8Ac9/ywhPr1f+Cnn9bkWlLQREIphdfrxePxoJSi%0AVKlSeU4uCuOrWhgRVaBC2B75x8XFZawTElX9KYSK1RD+O1BK0adPH2688UZ69OiRa7l9wCtqz0ut%0ANb163cTq1RYeT/ccS32Yk8UxzDj/NHAOIZxofRwzWi+PsYAqhenGlsV4HsaTvUviQ8rXgXoo1TPI%0Ad3cKWOc3+k/3v87NmGIyrwO1Ar5Ayq9QKhUTx9oT+AxYAkwAevvXfRshZgLl0fpFoBPZjytzEHIc%0AUAEt3gaRJchBzQD9EDjiECId7YhE1BmNjqiI+P1BKFMb3X0OlKsLrmTEgmvQp7bB4Leh5U1w+iDy%0ApU5o7UG/tBjqNoUn+sOqxcS8/jyOvtfj7n071vpN1Fs0ARkfw65rHuK8K+vS9u1b+HHEIiL3uPlk%0A3qIiUfwXBgXx8UoCso5gIyMjg1Le5xzf/x3vsSSKwrKisOEQ8+fPZ9DgO9HaATIKyg+Hs7MhLAJa%0AzYeEZrBjIuyajFBR/v3aiZngOMi0wtMIEYcQCQhRFssqg5n6lILzndB+C1Q5htjkgx+bodNvLewn%0AAw4Bb2GCSMr5qQiXFnI7iX7f5q+RMpxvv13KpZfm3kZ+E4mskaxa66B9VYMpQgtraWULu6SUpKSk%0AkJCQkPF+bf51SFRVaISK1RD+W0hOTqZLly5MnTqVevVyX9nbXLc/O97MCydPnqRp0xYkJd0KXBjk%0AszYCizD81bxTsLLjHEK8BXRB60AnBdte5mekPIlSThyOC7GsRpgT2DIMT615gOc6gQ8R4icgGq1v%0AxojBsvKq1mESqdohxFa0lmj9HEb8lfUEsgUpB6HUcXBMAQZk8lLVPqTsZcIDKrwFcTcZS6nkt+Ds%0AI6BdEB4NV74I9XrD3hWIb+5B1GmNGvIeJFSCb6bCgoeRnW9BPfAqpJ5DDrsc4fARvXg2olQ8rit7%0AEBEfQb2lkzi3fBMH73uT5uO6UmdIa1b3nkXTSvV5b8a7/1hXzi4Gw8PDS0yhFWh07/P5ALLZP+Uc%0A3/+T+DfxgAtzoZycnEzbtm3Zd+CkScdCm1N27ZHQ8Ck4tQo23mqsrXRHTCf1DMYr2ULr4RQ4fal4%0AAtp+A3V/h831YMOtkJyXPzOYcJIdOBy/YlnbEcKB1vGYi/BXMBOfYLEfKT9GqU0IUQOt+yPEXlq1%0A2ss33ywL+AyXy4XH4yE6OjpgsEPWC6eCphaFKUJtEVV+vq427EJYSklkZGQGL9XuqkZGRpZY+k8J%0ARqhYDeG/hx07djBw4EA+/fTTgLyl9PT0AsebfwbLly+nf/8RfneA4AogKd8HDqHUiEK80i7gY4z/%0Aak2MoGk9DsduLOssQsQgRGNM3OoFZOfMbgI+AcaQKYw6ghCz0Xo3UtZBqZsxY8FAB9QVCDHHT1WI%0AwhSvNbMsT0KIAWi9Bhk2HKXHg/DnlSsFejQwC5nQF1XmRaOCBkj5GHFmOKJUc1Tt5+HYbOS5pai0%0Ag6B9ULUh3PQs1LgYMaMf+tBmmDgbLu8JX32MeOYOIntdQ8Trz+JbtQ73gOGUv74DNaeNZv/dr3Fm%0A3jdc8b9BJNSpwMpu73D7dX2LRfFfWPxTkax5JTnlVN7b3096enqJVt//l5PClFLcf//9zJjxjvEl%0AlhoiK8PFMyGmBqy/EZwSrNsxSvt0P4c1GaVsu6wCUOoXaLMYmoXDjmaw7io4WQXTPT2AEL8DW9D6%0AFA5HApZ1PoanWtO/gQ8RIhmtp5C/M4oGtiHlRyj1B0LU94cKVPAv9xET8wiffDKbNm3aBHSHsOuT%0A8PDwgBdNdoc1MjKywAvRgpT8Ge/aTzlRSgXV6EhLS8sobu19RilFWFhYiY1eLuEIFash/DexaNEi%0A5s6dy/vvvx9wZJSfwjRYBDIlHzFiNJ9/vh+X65Ygt+JGiGfRuhYmTaYgpAH7gDXASYSIRes0v2F/%0AY4yyt3wB21iD6bD29AsrTvi9VG8AauTxnC+Q8n8o5QGGAd39STa/YfiwXYEJIGYgZRsUb4DI0mFW%0A3yJFf7QjGl1xDkT5BRnKiTjRE+3aCPVehSqDTPTkufWIbdeh42vAeZ3h5FpE0nZ0qukyiRr1EM3a%0Aoc4kwvrlhHXrQsTQ2/EtXYF35hzKdGtNuVuu4vgLH5Hy0y7KNqoKGlL2nebllyYzaEDx2akVFsVZ%0AaOXFJy2M8h7+f4jC/g78VWeS5cuXM2DAHaSlJUFYnOm4lm0LSb+A5QKrLIYrWh8hvsUk4d1FcFzU%0AbyB6A7RoAa1+gKMRsDodcTgCQ/NpjBnxBzpm+hDiBeCGPOywFCbi9X9onYiZ7PTH0KByYhVNm27h%0Aiy8+DfgbBXPxJKUMmPxk/+btfSo/jYJdhAbjqxqspZVNBYiIiMhllRUSVf1phIrVEP6b0Fozbtw4%0AYmJiGDNmTJ6Cq2A6WnmpmrN2oeyDqdPp5OKLW3PiREeMEj4c06HM7wB1BHgZuBGTre0FDvpvx5Ey%0AGXAac3y8CFEKKStgWWnAWeAhDN81GBwBVmC6s16gFTAKw5UNhGVIOQ+lfJgitRfZVcLzgVeBGOMq%0AIGaAvDpzsUpFcIPxayw3Hp1wX2a0aspCxJk7EfFNUA3nQFQ18/jO0XDsHUSLceimD5nIyp+fhV+e%0AgasehJodYPd38P1kCAvDUb4ioLCSzyB8XsLiYhFhYfhSkhFaE9G4LloIvJt+4+23ptOnT2GiIf8e%0A/BV6SmGV9/kVpfmhpEeyQvFNTYoKhYmNzQ9btmzhttsGs3fvbkAZUaL2gfJCRDyUT4FwBV4JpyR4%0AyuJwSOxYZ+OjbPnvFVrbcc8KEIiIOHSTeGibBKllYO0V8Ed90PldqOwC5mCOB1X9j3mBbzGpeh60%0Abo/hzOfXobeIiXmU+fNncMUVVwRcoyCuclZLq4J4qYXxVbVFVFnH+zmRlpYGQExMTEYhHBsbG/JU%0A/WsIFash/Hdh28KMGDEioCVRzo5WsF2onMrmnPjuu+/o3r03mQd/jRmN2bcwhAjz34cD4Sh1kkwT%0Afw8QjcNRHq0rolR5jOCqHKaotA94CilnAPF+YUNeXbkzwJdIuRul0nA4mmNZrTDiiOWYoIKLczxn%0AiZ9PpoC7gJ7k5r9tQ8oJhpcq4oAIkB+BaO9/e1MRPIaIvhhVfiaE1/Q/7vJ3U9dD3Zeh6hDTTXUd%0ARW7phNYp6C6LoWIL8HkQyzqjz26FgYuh9uVwZDPinc6Iei1Qj84DrZFjLkVHh6E/WgoeD46e7Ylp%0A05hK857H+cVaku94ijlvv8PVV19dIosYKLjQykt5b/5GBPyNFpXy3n79f4sozOFw/CccAgrCsWPH%0AuPLKjhw6lAg4IKIO1PkNbvJmrjQ/GnZ5wNMYqI/Zj+1bBKZwjPD/PwwhPgd2o/UDIMKg4a/QbhWE%0Ae2HtZfBrc7DyKv7eRwgfWk9AiC/QeiFSRqNUF0yoQLDF2hoaNfqRDRtW5tvBzK/hoJTC5/NleJzm%0AN7UIpgi1YadRBera2nxVW1Rl/61jY2NL7O/xX4JQsRrCfxunT5/mmmuuYfbs2Zx//vkZtiVxcXFY%0AloXX682w2cnahQpmNJofJkyYyJtvfo7TeTtG9OQB0gF3gJvXf78LIc6h9QgK9ji04UHKN4BG/jG+%0ADSfG7/R3lDqHlPVRqg1wUY5tr8JwWB/BdFk/RcqFKKWB4UB3chepxxHiMbTe4RdR3YPp7E4CZiMc%0AfRBiE0odgYozIPYGU4wCpCxGnBmKiL8I1fADiKpuHj8yC3aPRta+DtV+KoTHwenfkMs7Q7maqAGL%0AoFRl2PgufDYKeeP9qH5PwJFdiIcuQzS/BDV9LuzYhrytGwm3Xkv51x8mde4ynA++wufzF2ZYU5X0%0AQssWZuQsSP8OO6hg32NJEoXlxL8hKayoucppaWk0bHgRpyJOw50q9woflYW0FDjZGtw5HUtywkLK%0A94BzKHUvpsDUUGsPtFtpRFnrO8DPLcFtF3YujLDzd2A3xrmkAiaMpNWf+ERJSPkAU6Y8y9ChQ/Nc%0AqyAKjVIKj8eD1+slISEh330kvyI00OvmdBOwC96oqKiMfcO+wAxEVwihUAgVqyH8N6G15tChQ2zf%0Avp0VK1awbNky4uPj2b17N5UrV2blypUZhajP58sYyxXVmMbn89G27RVs314dpdoF+SwPQryC1ueR%0Af7RqTpxDiOlo3RFzovkFpU4hZQ2Uags0JXfEYVasBeb7+a8OTJHajdxFajrGtmotUnZGqUfJHPeB%0AKcqHAd8DLqP0L3WnKVSVC5F4HTp9DaLuFHTVof7HPYitPdBJ6+HKWVDbX3D/+gZsegTZYSSq69OG%0ACjBvMGz9GB6eC217wQ/L4bk+yH5DUOMmwYqlyNEDKDduKKUfGkTK1I/xPvseX376GQ0aNChxNkeB%0AOM+B7KBKkvIe/jlRWGHwX3UIKAid7ujE+nrrcy/4HLg4Aip4wBUGibXgZCVIrGTuT1YEd9bOn8fv%0A2xrl57xmQeWD0G4p1D4MP0fBBgWpLoQojZQ1sKxITIjJUwQXCGBDYeKiv8KytiJlAlWrluL33zfn%0A+/0EY2ll+68WxEstrKWV0+mkVKlSSCkznArs1wh5qhYpQsVqCP8trF+/nlGjRrFjxw7i4+Np0KAB%0ADRo0yPDfGzduHJUqVcp2UCuuImbfvn20bNkOp3Mg2Yu6/HASeA3T0WxSwLqngd+A/QhxEq1dGBeC%0ALsAl5M1DtXECY521GyOacCLESLTum2M9BbyOEIsRogFKPUX2ZCqAzxHiCaASWr8HbEA4noDwmui4%0A/oikiYi4BqiL5kKU394maSPit15Qqia683yIqw7Kh/iyF/r4Grh9HtTvCh4ncloHdHoi+pkvoUZD%0AWDgF5jyOmDgZfesgeO8txDNjqTRtHKX6dydp0kzkO5/y9ZJl1KxZM/OT+Autv7OICYbznLUgtXmN%0A/98KraLGv0EU9mcdAvJCz+E9+eaCb3IvmAEcrQfiKCS4oSJQMRLKK6jog/IeSA+DkzGQWApOloGT%0A8XByI7jPB85DiAMIkYRSqUA0slxlVCsPND4B2y6CdZfBGVvcuRAhjqH1JAqeEp1FiFVo/ZXfcqsu%0Axse5LLGxr/Pcc/cweHD+gkiXy4XX6w3I+bb3P5fLhcPhIDY2kKgrEzmL0PyQnp6eIepLTk7OVuSG%0APFWLFKFiNYT/Fk6ePMnu3btp0KABpUtnZlFrrRk5ciT16tVjyJAhuZ5XXEXMBx/M5d57n8TpDMLz%0AMANbgQUYrqjteegGtgN/IOVJw+vUPqSsjNa10Lo6xsLqa2AkUDuPbStgI1J+7e++tvDzyWpjRniv%0AImVflBqOOT4s8vu6xqP1U5gYxqw4hpRDUWov8AJwJ5m8tGSzXZEKEWWg+QqIa2QW/XE/HH0LeclY%0AVLOxpnOatAe55Ep0fFn0oM+hTHU4sR05/Sqo1QD1+CKIKw2TB8G6hfDufGh3BTw9FjlnOlUWTSGm%0AU2uSHnmN2CXr+OqzJVSpUiXXN2AXWkVdxBQV5xn+PYWW2+3OMEAvifj/4BCQFcu/Xc5DMx5i7yV7%0AMx9cIOAPbZhIAPRBiES0XoextquOkOmIMklQIRldIRVdPh0quKG8zwxUEiWcOg8Sq0FiHThZAzz+%0AC/uYNGi5AS7dCAdqwZoOcLQqUr6GCq8G5SzDd/WGw6nO4GmOEXptQcoVKLUTKSuh1FWYsJKsv6UD%0AJCS8y86dvwa0IbRh86m11gE531lDA6KiogrkpdpFaEG+qvaFpcfjITw8PFdSVchTtcgQKlZD+P8D%0Aj8dD165defzxx2nVKjePqjgKBK01ffrcxtdfn8LtzmpNpcnksnqz3Oz/f4npnJZGyhSUSkOIBKSs%0AiWXVAKph2iM53+d3mLH+Q0DlLI+nYgrPbWjtQIiuaH05uS1tDiHEJOBihNiLUinAOIxTQdYOmsII%0AsxYi5fUoNYXsllkLEXIYwtEAFTYFfE+C9R2i3BXg2gsqBd11MVT0BxvseB/W3YNsORDVfbJJ6/n5%0AQ1g0DNljBGrgJCOkeuhy1Kn98PEXcGFdxPDbkKtXcN7XM4hsWpdzI56lwk+7+WLxp5QrVy7Pv8tf%0AKWLyGt0XJecZ/j3qe1vtXBLfY0FFTElAUQvXln2zjOkLppNupRMpIxnScwitL27N7bf3Z926tf61%0ABGZ/PY0RV95GQPGTUFB6B1R4FypWggploWIilD8FaTF+CkEFSKwI58pA9YNwyQ9wtiysrARxG+Cm%0ALNubXw521QbP70jpQKkGGIeRvKdA0dFzGD68PU89NSHfz12QuK6wllZZo1ILChewOdJ21zbkqVrk%0ACBWrIfz/wtGjR+nVqxfz5s2jcuXKuZYXR2b7uXPnqFOnIU6nF1Og+jDFnvTfHAhh/i2EI+PesuwI%0AxZsw3K9gKQqLgT+AscBRpFyCUkeRsi5KXYMJA8irGP8BIT5C63MYSsE3QKUc66xAykfROsE/8m+T%0AZZkTIXug1SaImgxhQzPFVe7XwPMgOCSi9IXo5o9Crd7w7e1w+Au45X1o4ufqLrobfnof7nsXLr8Z%0Akk4ZxX+FcugPPoXSZZE3dsJxZA/nrXqX8OqVONt/PDWPJLFk/sJ8uzBQcIGQU3lfkB1UUSvv7fdQ%0AFDZHxQn7PUopS6zauah8lYsTf+Y9ZuU857x4yisCF+CBBx5g+vQZZJ7KoxAiEq1HYi6AA+EoxpKq%0AGdDNX8SeNYVrhcTM+/KnID0K4lPhWw0dA2xqRhQc7e/fVjA4Q3T0C2ze/APVqlXLd02lFGlpaXmK%0A6wpraZWSkkJYWBgxMYE5/1ldBFwuVzZxVchTtUgRKlZD+P+H1atXM2HCBBYtWpTryre4Tr5ff/01%0AN97YB6+3D+aEEEX+SS9gog2nYtS0nQrxanuAedieiVJ2RKmOZKbEBMJKpPzM38G9Ea2vRson0Tod%0ArT/CJNUkIsSdaL0TIZ5C63vI7pf4EULcjQhrhop4H6Rf6a88CE8vtLUWqr0D8T3gxBOItDlo92lQ%0APugzE1r0B2Uh37oclbQfnvkSLmgCuzcjHuuEuOxK1CszwbJwdGtDeKSi6jczkHExnL3pIZqoKObP%0AmRv0383mKoeFhREWFhbwhP9PKu+zvseSIgoLhH+T+j4qKqrEv8ecwrXiEuL9/PPP3H//o2zatNr/%0AiLHTczhi0ToGpUpjpjPnY+gCyZiCtQUmBMTsOxMpAAAgAElEQVSGCzgMHAFxAsokIis5UeHJgXWi%0Asy6EA/cV6rsJC1tCt26xfPjhewWuW5C4zra0shOm8pui2e4xeVEHsoqq7HVjYmJKNN/8X4pQsRpC%0AycT/tXff8U3V++PHX+ek6S57U7Qtu6js9ZONtICAgEyVIVMUEGUq3i+i4AbFLV5BAUVlK1IUUbgO%0AoBdEQaFlCRaBUi4IdDc55/dHmtA2aZu2SZuU9/Px4HEvOenJpyEm73zOeyQkJDB69GguXryIoihM%0AmjSJ6dOnu+z8b775JvHx8bzwwgsOd9Xc8eH77LOLef31daSmjsT5foMJwCrgPqBhPvdJxpKHegxN%0A+x+WALUJmpaAogSj60/heOqMhqXp/zdomjn7MaLIvYP7IpbK3igsvVp7o2lvkDvF4BqK0h+dg+C3%0ADHzG3thNNcWiZt2D7lcPPXQ9+FqLq7agnBuDXrWDJZhNOYRuNoGvr2Wc5Eu7IKwZ7P4cXhuPOuUx%0AtMeehEsXMfTtgH+TW6j9xWug6VweMINONW5h1fJ/53vZraAPfLD0KPXx8bFrnO8JpPreNTx9jbqu%0A21KRjEZjrteso0K84qaX5BUXF8fixUvYuPGT7FsCga6oahqKcgFNu5h9pcXSh9VyZcgn+zJ+BpbU%0ApUBUtQqKUg2zuQpQBersgUln7R9weVM4N83J1SWiqr8SGHgIP79UTp8+4dR/l862tDKZTIXmpVpb%0AWgUHB+f67886qSpnD9fMzExbGoLsqrqUBKvCM124cIELFy7QokULkpOTad26NZs3b7b1yiwpTdMY%0AO3YsPXr0YNiwYXbH3fHBZjab6d49it9+C8Jk6uz0zynKAWAHuj4dSz9TDctl/v2o6gU07TqqWgdd%0Ab4auN8ESSCpYeh2+AVRE0+ZyoyrXhKVadzfgh66PwlI45ej3jAH+nf0zg4BPyf2+8RGKMgPF2A7N%0AuBLUHF0P0meB+R3UGvPQqj0JSvaHxt9T4OoquP0NqDfOctuFbfDLUAiuhWrU0K5egKAKkHwZmreG%0AidMhKBjDo2MJ7tWeGqsXoV1L4X+9p9KveVvefvU1WyW9s5X31v+fM4/NUyvbpfreNTxhjfmll1hf%0Ao4qiYDab8ff3x8fHx2VBaWHOnDnDvHn/4osvNmTf0g24P/v/a1gKOJOwdBFZj+VKzRAs+aYOXpO+%0A8dDwKxh6+cZtn4fAiQeyBxQ4ogNnMRgOERBwGIMhjYED72HYsEF06tSpSO/FBRUAWt8nMjIyAArN%0AS83KyiI5OTlX6kDOqVfWc0pRldtIsCqKT9d1unTpwvz58+nd23JZaN26daxYsYKYmBiXPtbAgQOZ%0ANm0aPXs6SoIqntTUVKKioli6dCm33Xab3XF3fLCdO3eOVq3ac/36QCyX1wpjxvIB8QVwGVWthKZd%0AwbKzEZldoFAfxzunYAlYXwXqZjfvX4uixAKVs4PUjjhOR9iHqr6HZcTrdOAWFGUOitIaTfsUUFHU%0Au9H138H/bTDcd2M3VbuAmtkTjX/glk0Q2C57KddQ/+qCbr6E3u4rqNjccnv8M3DqRZQer6M3y+7U%0AEHM/nNoCjbtB2mWUf86gX7+MgoaelQUGA4qqMO7Bcbzw7KIS70KVZNxpafGGynZZ4w3OdodwVIhX%0AlsV1Fy5cIDq6DydOHENVg9G0AUADLHnz1vfBC8DLKEoddP2B/E/mGw/V9mZ3A0iDS5cg8wlyt/LT%0AgD8xGg/j63uIoCAjQ4YMZMiQQbRt27ZE770FFQBa3zOsO9n55aVaZWRkkJaWRoUKFWybGTkHDUhR%0AlVtJsCpK5o8//mDo0KEcPHiQrKwsWrVqxddff014eLjLHuP06dN07dqVP/74w9YaxFVOnTrFiBEj%0A2LRpE5UrV7Y77o4PjZiYGEaNeoi0tFHAVSw7FZew9BtMQVUz0PUMNC0TyyU23+zL+clYRq6OwJL3%0A6ux64gDLJT5VvQVNG42lAtjRzx9DVV9D0y6iKBPQ9eHcCIRTUdUpaNp5UEyoxs5oxn+DmqMAK/Mj%0AlKzpKJX6odV6FwzZhU4pP6GcvQelSnu0Fp+AsSJoGsovg9Av/wADt0Kd/2e5bWNX9Oun4PFdULMh%0A/PYlrLwPZeJC9OGPQ9I5/KZ2ZuyAPixa+LRLKu/B8+fKg+ev0Zuq7121Rme6Q+RNLynsMT1htO2x%0AY8dYv3498fF/8uOPP3P58iX8/euTnByGptUHKqMoy4BAdH0CzqU2bUFRTqDrTwBn8fM7hKoeonr1%0AqgwfPpjBgwdy++23u+z3LaxI0fqFIi0tjYCAgELzwlNTU8nKykLTtFwdBXRdR1EU6anqPhKsipKb%0AO3cuQUFBJCcnU7FiRebPn++ycycnJ9OtWzeeeuopBg4c6LLz5hQTE8Nbb73F2rVr7S6xuqvgasSI%0A+/jyyy1AAKoagqJUQtcro2kVsFzqt/4J4cbl+UTgAyyX3poX8giJWMatnsnOK2uOosShKC3QtNnY%0A76YmoiivoOsnUdWhaNq47MfP6SSqOg9NOweA6j8DzWchKL65i6jqvg+Vhuc49UL430sojZ9Gj5hl%0A2YHN/Ad1b0d0g44+6BuocAtkXEP9rA16YCD6ozsgpDrs+Qg+fRhmvgV9x0LS3wQ+2oNHR4/gqXlz%0Ai/ScF6a8Vo2XtvK4xoK6QwAOd/NLWojnac9jUlISsbGx/PDDT+zc+SPHj/+BwVCB9PSLqGoNNK07%0AN9rvZaAoWRiNJgyGLAwGE6qahaJkcvXqScBMo0a3c999gxk48B4aNswvH7/kCnsec7a0ypuX6ui+%0A165dQ9M0KlasiKqqcvm/dEiwKkouNTWVli1b4u/vz/79+112GSQrK4t+/frRp08fZsyY4ZJzOqLr%0AOs899xypqak8+eSTdh8w7qgkzsjIoGPHLhw/HoqmdSjCTx7GMjtxMpZeqzn9A+xEVY+jacmo6m1o%0AWjugCZbgNBlVfQm4I0fAmgwsBX7FYOiJ2fwI9q2qMoAFwA+o6v1o2izgDKo6Dl2pgO7zFKp5ln0R%0AlZaJ8lcv9PTfoe1mqJqdp3v1V5TYXiih/w+t9ydgDIKrZ1DWtUcJa402aR34BsKOpbD1/+DpT6Dz%0AALjwFwEzejB34lhmzyxaNbGzvKlqXNZYMo7WmDcoLevuEJ78PGZkZHDw4EF27drFypWrCQurT7Vq%0AValQIYSKFYOpVKkCQUFBBAcHExgYSHBwMEFBQQQFBVG1alUiIiJKba2FPY/Wf2PrZf788sLNZjNX%0Ar17FYDDYUgfk8n+pkGBVuMaCBQsICQlh1qxZLjmfruuMGTOGqlWr8uqrr7rknAXRNI2hQ4cycuRI%0A+vbta3fcmqPkygKX06dP067dnaSkDME+8CzIDuAg8BiWCt1dqOrvaNo/qGoDNK09cBuO+7JaAlZd%0Avx1dD8ASgLZA0x7H0p4mrw0oytsoSjia9jLQOMcxM3AnKP8D33CoHwuG7DSN9KOof/WEoDC0NpvA%0ALzsATlgNfzyM2mYmWvsFll3Wv39G+bIvSof70Ya9bplmtelJ2P0GvPgFtO4O508T8GgPnpo6mRnT%0Ana0kLh53/Fu7mqyxZKz5iiaTyTaG03qbo3ZQZdkdwtO7GED5WKOmaWRlZdkmVzn697b2XfXz87O1%0AtPLz85Oequ7n8Mn1zFea8Giurlj96aefWLNmDXfccQctW7YE4Pnnn7cVcrmaqqqsWLGC6OhoGjZs%0AaHdZymAw4O/vb7tU5IrfNSwsjH//+x3GjZtGWto4LC1jCvMPlupbHXgVyERV62b3UW2BphU89xrS%0A0bRawN7sc7yNprV2cL9T2Zf8L6Hri9D1e8j9fvEbqjoZXQ9A15ejmp9GO94I6vwbshIgcSZEPIzW%0A6DlQs99Sfp8GZ1dC9Cq0htkNGOM+gZ2T4J6FaHfNtNy2eiL8ug7e+B6atoGzJwl4rCdPPzaNqQ9P%0AceI5Khnrv3VKSorHVrZb2+PIGgvmTI9SHx8fTCaTLYjxtKDD+jy6Y0Swq5SHNSqKgtFotO3ChoSE%0A5HotZGZmYjabbe//wcHBpKamemxu9s1AdlZFkS1cuJDg4GBmzpxZ1kspkT/++IMJEyawZcsWh8Vc%0A7ihwmTFjJh9//AOpqUO4ERBmAX9iafB/HoMhGbM5BUt1f1WgNpp2CkWpha4/QuHFDb+gqjvQtIvZ%0AO6ldUNVVQDiWUanWfNxMblzyH5l9yT/nNCgNyyjXL1GUx9D1/+NGS6xFwGLLOZo+Bw2yc0o1E8q+%0A7uipx2Hw11A9O9927zNw4EV48CNoNQQA5b3B6Kd+gLf+A2FNIeE4ATN6snjeLCZPnFDk57YkvGHc%0AqazRoigtyxx1h/CW5zErK0s6LZRQQWu0poBkZGSgqqrt9aDrOlevXiUwMNCWRmD9wiO7qqVC0gCE%0AayxcuJCQkBAef9w9uYSlad26daxfv54PPvjAYX8+Vxc9ZGZm0qFDF44du4KqZqJpKeh6GhCIwVAb%0As7kOljzSmkBlbgSmGajqm0BrNM3RqJh0YCuq+huaZkJReqHr3bgxhzsdVX0GXa+Crr8N7EBR3gTC%0A0PWXseS65vRr9m5qMLr+GbnHJR5CVfugUdXSa1X/AbV2P7RbZ6D+NgI9uCb6PV9BYPYUre2j4fQX%0AMG0b1M/uArCsB1w+if7Oj1DrVjgTR8Bjd/HSv55k3INjS/gsF523jBK1rtHf398jPzRdOTbWUT5p%0AzqC0oHZQhZ23rKvvC3MzdlpwB13XSU9Pz3fTIWdLK19fXwICAkhLS8NkMtnGOEtRVamTYFWIvHRd%0AZ+7cuVSrVo2pU6faHXfHRKHDhw/TqVM3TKamQGssDbedGa95BUV5D7gbXe+afdtfwBYsRVD10LTe%0AQEsc91M1oSj/h66nYHk/eBYYSO73Bg2YBXyFqs5G0+ZjyZW1WgzK86h+09B8ngXFB7SzkN4btONg%0A9Ich30HN1tmtqbqhXz95ozWVyYT6UlvLaNe3/wOVa8CpPwiYGcXSZxcw+oEC+ji6mTeMEvWmkazO%0ArjG/mffO9CgtyRo9qfreEW9ao/Wyuycq7Itozg4B/v7+pKen5yq8kqKqUifBqhCOmEwm+vXrx4wZ%0AM+jSpYvD466eKLRjxw5GjnwwO381pND733AaSx/VdlhGrv6Dqv4/NO0uLI288/MHqroWTUvCsmOr%0AAZ9jaQBu9Quq+hC6Xgld/xS4I8exZFS1J5p+EvzXgU/3G4cy/gWmpVD9CZS0H9HTfkSp3hzSLkBQ%0AsKU1VYUakJmKurgFeuVK6K/tgOCKcOIQATOjeePFxYwcMaIIz4N7lIfiEU+Q90uedXfK2R6lrmgH%0AVdQ1eiJP7hBgpWkaKSkpXv0lL+f4Wz8/P4KCgmy3A3L5v3RJsCpEfpKSkujbty+ffPIJdevaB33u%0AyM965plFvPHG56Sm3ofjnVArM3AE+B2DIQmz+Z/s2zthGRpQ0I7Gr6jq52jaFRTlHnR9AJbg+HUs%0AhVcrgXbA40AMqjoXTXuC3LupO1DUkSg+bdCMa0CtZrlZM6Fk9kLXDkPYVgjMbsuV9iuc7Ai+RlBV%0A1I6j0Frci/rRaAhvjPbSF+AXAMd+JWB2b9555SWGDh1SpOfOnTxhTGdhrF+gPG2NOdtBmUwmMjMz%0Abf0pAYcjcN0dlBbEG0bbesOXE29YY87A38fHx+EXJytrhwBd1/H19fXY10Y5JcGqEAWJjY1l9uzZ%0AbNq0ye6ymzvy3DRNo2/fe9i3L4PMzF45jpiBo8BhVDUJTbuGogShKA3QtAZAGPBfYA/wFLnHGVr9%0AF1XdkP2zg9H1fkDe7gEbgU+BEBSlevZuas453jqWHq8fo/i/gO4zNceY1T9RM7qg+9VGr/clGLPb%0AVV3/BuXsEJTwCWjNXoHEHRA3H67/DqYM1D6j0boMgpDKBCwYxvLXljJ48KCSPI1u4Q1FOGVZ4OJs%0Aj1Jd123PY2nNvS+qzMxM0tPTPS7wz8mbvkB5SuDvaDffZDLZglJHu/nWHdasrCxCQkJsl/898XVb%0AjkmwKkRhPvjgA/bs2cOyZcscJuOnpKRgNBpdki+o6zqXLl2ibds7SUoKBy5l75xeRVECUZSGaFoE%0Alp6ojlIFtmAZr7oAy2hWgJ9R1c1oWgqKMhRd74vjndfzqOoSNO1PwBdVHZfdKcC6K3IWVe2Bjgnd%0AfzMYcqQEZK2HzHGoVceg1VwKSvYubNJrkDQf5Y6l6GGTLbddOYiypwdEjkSvdxf8sRLlUix62jU+%0AXP4uQ4cOLdFz6C5ShHPjMQprB+Xo8n1Onj42FuTLiauUxRqLOtzBbDaTnJyMpmlUr17d7lyaptmu%0ACFSqVMljn+tyTIJVIQqj6zpTpkzhjjvuYOzYsXbHrZeSinK5K783UmsByYEDB+jXrz+WyvzWQDj2%0A408dU5SPgSR0vVd2u6oMFGU4uh6N46KtVOA1LOkBvdC0h4BUVHUq0AhN2wxsAeVRVN+haMY3QMnR%0AEzb9ETB/BHWXQ6X7btx+djxcXwftN0GNnpbbEnfA/ntR289Bazc/eyjATwR8NYhVH7xN586dvTrP%0AzRO4qginKO2g8gtKCzq3t3RacEUXA3fxhup7sHw5MZvNLg/8c35xchSU5n19FjTcYcWKFaxcuZLt%0A27fj6+vLqVOniIuLs/35+++/ee2112jTpo3L1i+cJsGqEM7IyMggOjqaZ555xuGbVX75gvntQOUs%0AIMmvqvnjjz/h0UefIi1tAgXnoOYUjyUV4AyWXq1jgP7c6IWakwZ8COxAVZugaTPJPcUqHUV5GF0/%0Aa7mv/4dgvDfHj6eiZnVB08/DrTEQkL3TqplQ/uqOnnUS7twJFZpabv/rYzg0GaX7UvQ7JmXftovA%0Ar4exdtW/ueuuu7wqz80b1uhMoVBJe5QWl3RacA1v6RBQkhZrrtjNd3TOzMxMTp48ydGjRzl69Cg/%0A/PADCQkJhIaGUr9+fSIjI2nWrBmRkZHccsstRfpCJlxKglUhnHX27FkGDRrE+vXrc10qsl5ysl42%0AtCbqu6Kq2TIwYDepqSNx3Phfw1JotQ9FSUTXyW76fweq+kV2T9TF2O+o7kRRVgFB6PpsoL2Dc29B%0AUd7KHst6HcV/EbrPDMtuqOkQSlYUSkAztHrrwVDZ8iOmy6in26L7V0LvuB38sp+n40sgfgH0XQ0N%0As/NRz3xL4NcjWbf2I7p162Z7VG/KxfOGNVrzBQvbzXdHO6jCeNOXE+kQUDLOBP4FBaXF3c23vjcf%0AP36co0ePEhcXR3x8PImJifj6+tKgQQNbUBoREcHYsWPp2bMnzz77rDueBlE8EqwK4SxN01i7di1v%0Av/02vXr1Ij4+nhMnTrBhwwZ8fX1RVdX2TT8gIMD2YV+SD3yTycRdd/Xht998ycy8y7oS4FcUZT9w%0AEV33QVVboWnNgVu4EdSaUNWlQHU0bSGWav6jqOobaNo14BGgH/ZdBxJR1Tlo2nngGWAAsAdFnY5i%0AaIWmRIPpadTqM9CqLwQl+/HSDqP81QOlRje0VqvBkL3Lc3gmnFkOg7dCvexesKdiCNo5hk3rPubO%0AO++0+70lX7D4cn7YZ2Vl2S6JOrObXxa86cuJpxQKOeJNgb+/v78tV9RVu/nWHeb4+HiOHj1KfHw8%0AcXFxXLlyBT8/Pxo1apRrp7RWrVoOX28XL16kQ4cOLFy4kFGjRrnrqRBFI8GqEPm5ePEiy5cv5+jR%0Aoxw5coT4+HiqVatGSEgIDRo0oHv37jRt2pT27dvbdjPccWnz0qVLtGnTkaSkeqjq32haEoriD7RG%0A1+8AQsnnv2UgE1Vdgq6HAhno+p8oykh0fRQQmOe+GvAWsMkyjUp7CqiU4/g/QDfgOlSdBnVeu3Ho%0A6kY4Nwa14WNojRfe6BCw/z5I2g7DvoMa2ROvTnxB0K4JfLnxM9q3d7Sj6z05jWVVcFWUHqXWamdr%0A9b0n8tTAP6fMzEwyMjI8+nn0tMA/526+9TXqaDe/qEHptWvXcuWTxsXFcf36dQIDA2nSpAmRkZG2%0AwLRatWpFfk0dOXKE77//nkceeaQkv75wHQlWhchPYmIir776Kk2bNiUyMpImTZoQEhKCpmmMGjWK%0APn36MHiw/ZhTd+xw7Nu3j549ewG3o+t3AbXIP0DN6QCK8h26/j8seaurcdzW6ndU9V/ouoquL8XS%0AZzWn/SjKI0A9S6GW+iZqxSi0Wu/C/96F/z0PLd+FetnTpjQNdW8UWsoRGPEDVKpvuf3YOoJ/mErM%0AFxto1apVgSv3lpxGd+YLFrWq2dFuvjcF/p5eKCQ7/o4VNcXEZDKRmJiIn58fNWvWzPecV65cyXXp%0APi4ujtTUVEJCQmzvy9agVKr0yzUJVoUojpSUFHr16sXrr79OZGSk3XF37HBs3ryZCROmk5b2CFCx%0AgHteBbaiKMfQdQVF6YGut0ZVX8dS3f8iNxr8ZwL/B+xFVSejaVPInd+qYenbugVFeRJdn4klbeAi%0AiuEedO0IKBrc+S1U65T9I1moP7RDJw19+C4IqmW5Pe4TKvw8k+1bN9K8eXOnfmdvurRZkpxGR7l6%0Axa1qzu/8N3vg7wo3e4eA/F6jjnLzC0sxWbZsGRs2bGD79u0kJycTFxdnu3x/7Ngx0tPTqVy5cq6g%0ANDIykpCQEI983oVbSbAqRHEdP36c+++/n82bN1OpUiW74+7YhVm8+Hlee+1jUlMnYV/hvx9V3Y2m%0AJWVX9/cAIrmRw5qKqi4C6qNpLwHfoSivoShhaNoScncCADiDqo7J3m39DGiR49h5VLUHmpaK4pMO%0AIRHoLd6HoAao/2mBHlwd/d6vwc8SVCt/fEiF2CfZEbOFZs2aFel39rRLm444m9PojqpmZ90sgb+7%0AeVOHAIPBUOTddHeNwdU0jQsXLuQKSg8dOsT58+dp2bJlrl3SJk2aePQOuyh1EqwKURJbt27l/fff%0AZ82aNXZBijsuv+q6zn33jeabb/4iPX0Ell3Ur7J3UdXsXdQ7yZ1rmlM6ivI0lv/EM7Dsqg7B/r3g%0AHeAtVHV09k5szp2uL1CU8Sg+96AZ3gXdAKbxoG8AYwBKzebog7eBj+Vn1MPvU/GXhez8+ksaN25c%0ArN/bGy6/WnMag4ODAcqkHVRhvCHwtwbVnlzM5A1BtaZppKSk5Lubnl+KiXWakzMpJvk9bkJCQq58%0A0j///BOz2Uzt2rVtOaXNmjWjXr169OnTh379+vGvf/3LLc+DKBckWBWiJHRd55lnnkHTNObMmWP3%0ARl7YB0ZxpKenc+ed3Th27Byadg1VbYqmdSf3LqojJ1HVtWjaOSAARQlD19eQexLWP9kB6nlgDdAj%0AzzmmA6vA9w0wjLtxs3k3ZPYHYxDoV1FvG4PW4V+oJzdR+fCLfPf1Vho0aFDs39lT8y7zFpBkZmbm%0AmnlfVkFpQbylmMnTx516S4cAa1CtKEqx857zsu68nj59OldQeubMGXRdJzQ0NNdOaf369fH19XV4%0AzvPnz9OhQwdefvllhg0b5s6nQ3gvCVZF+Zeenk7Xrl1tH9L33HMPzz//vMvObzabGTx4MOPGjaNX%0Ar14Oj7t6p+jEiRPceWd3UlJ6outRhdz7EKq6AU37X/aEqruBkOyCKh90fS2W0aybUZSnUZTuaNp7%0AQOUc57iGqvZE0y+B71eg5sg5zXoLzHNRqj2HXnE6ZBxGvTweLeN3KlaqxM+7vyUsLKzEv3NZ5l06%0AW0CiqioZGRn4+Ph4VFCdkzeMjQXv200v6zUWlPcMlrn31j85c0oLO6fJZLKb5nT27FkUReHWW2/N%0AFZRGREQUK3Xlt99+IyEhgX79+hX79xflmgSr4uaQmppKYGAgJpOJTp068corr9CpUyeXnf/KlStE%0AR0ezYsUKIiLy5n6650PtyJEjdOsWRUrKOKCJg3v8jKpuRdOSUZR+2eNWg3Mc11CUxej6ZRQlAl3/%0ADUvrqpF251GUe1F82qMZPgElR3FX5gTQP4Oa6yEo2nKbruN7fQ7VfDfzxeZPadq0qUt+X3Bv3qWr%0AcvW8pUG7FDO5RlpaGpqmlVqOZXHynjMzM/n9999p0KABFSvaF2fmneZkrb4/d+4cBoOBiIiIXD1K%0Ab731Vo/dTRblkgSr4uaSmppK165d+eijjxxW8ZfEoUOHmDJlCps3byYoKMjuuDs+1Hbt2sW9995P%0AevpjWFpSaVjGp+5E00woymB0vTu5c06tNGAjsAXLaNaNWIYE5LQYeBnV92k0ddaN/qmaCdXcFY3T%0AUOdb8M0OSPUs/K9OpH7to8R8tZ6qVau65PfMqaR5l3lz9XJ+2IPjy/dFHe4geZeu4S3FTO5IUXHl%0AGFxd15k1axYnT55k9erVnDp1ylbkFB8fz8WLFzEajbmmOUVGRhIaGuqxaRjuNnv2bLZu3Yqvry/1%0A69dn5cqVDgP9sLAwKlSogMFgwGg0EhsbWwarLfckWBU3B03TaNWqFSdPnmTKlCm89NJLbnmctWvX%0A8uWXX7J8+XK7N3l37WatWfMxjz46n/T05ihKLGBE14cAnYH8dh9/RFU/yU4DeAT4Dfga+AToA2Si%0AKH3R+R2Mm8HQ+caPahdQzR3QjTXQa20DQ7Xs21MI+GcobW7X2LButcOA3VWcuUTsih6lJSF5l65h%0ADao9uYtBSYJqVwalOc+Zd5qTdeJeZmYmUVFRuYLSmjVreuxrtKzs2LGDnj17oqoq8+bNA+CFF16w%0Au194eDgHDhygSpUqpb3Em4kEq+LmcvXqVaKjo3nhhRdyzaN3FV3XmTlzJqGhoTz00EN2x921mzVp%0A0kN8/PFq4CGgC/kXWsWjqsvRtH+A8VgCU2sA8BWwHJiBoq5EUW5BM24GpdaNHzfvQzH3RQnug1Zt%0ABSjZl7nNlwm8cje9e0aw4oO33b5Tl/MSsb+/f74f+I4uixa1R2lJSN6la3hDUF1Yikp+lffWoNTR%0A69TV05xUVaVjx47MnTuX8ePHu+upKHc2bdrEhg0bWLNmjd2x8PBw9u/f75arSMJGglVx83n22WcJ%0ACAhg1qxZbjl/VlYWd999N7Nnz3Y49xbwqOYAABzjSURBVN4dH7y6rjN58lQ2bfqF1NTZ3Gj6b3UR%0ARXkTXT+DogxB14fheNzqAuBXS4DqdwSUHJc1TSvBNA2l6r/QK865kRKQlUDglWjGjormpRcXuS3g%0AcRSQmkwmgFxBqDt6lJZkzZ7YxSCv0s67LA5vCapTUlJs/9bOTHNyNii9fPmyLRgtyTSn+Ph4unTp%0Awueff07Xrl1d/hyUR/3792fkyJHcd999dsciIiKoWLEiBoOByZMnM3HixDJYYbknwaoo/y5duoSP%0Ajw+VKlUiLS2N6OhoFixYQM+ePd32mImJifTr149PP/2U2rVr2x13R/sgs9nM0KH385///I+0tKlY%0AdldTsfRMPYSqdkXTHgSqOfjpQ6jqi+i6L7o+A1Vdiq7URjdutQSumdNAWwm1PoGgATd+LPMoAZd7%0AM2/OZGbNnOGS36Mol0XBEmgFBQV5/CViT58eJUF10eRX5GT9/DQajUXOe9Z1naSkJLdPc/rPf/5D%0ArVq1aNSoUbF+9/KiV69eXLhwwe725557jv79+wOwePFifvnlFzZs2ODwHOfPn6d27dokJSXRq1cv%0A3njjDTp37uzwvqLYJFgV5d/hw4cZM2aM7cNl1KhRzJ492+2Pu3fvXp544gk2btxol8fmrvZB6enp%0AREX15/DhEDIzFeA/qGpjNO1hIMzRT6Aoi9D131CUiej6GCy7siYUZQq6fgIMDYFTUGcH+OVoWZW2%0Ah4B/BrHs1UXcf7/9jkNhijpPPL8dKG9qdO/JeZfu6AnsaiWZzFTcxytOh4j09HS+++47oqOjHf57%0Aa5rG+fPnbUHpsWPHOHHiBFlZWVSvXj1XUCrTnMrOhx9+yPvvv8/OnTudqjNYuHAhwcHBzJw5sxRW%0Ad1ORYFUId3rvvfc4ePAgS5YssfuwsX7wGo1Gl1Y6X716ldtua8nly9eBheQek5rTNhTlAxSlIZq2%0AEKiX5/gRLHmtWShVHkev/Bwo2cFgyjYCr41hzerlREdHF7ienLl5rrosmldGRgZZWVkenRsqQbVr%0AuCOodnUxntls5u677+b2229n6tSpufJJT506hdlspk6dOragtFmzZjRq1Ag/Pz+Pff26m7PV99u3%0Ab2fGjBmYzWYmTJjA3Llz3bKe7du3M3PmTHbv3k21ao6uRlm6y5jNZkJCQkhJSSEqKooFCxYQFVVY%0A72tRRBKsCuFOuq4zYcIE2rdvzwMPPGB33F2VzufOnaNbt95cuNADsznvVJiLqOoCNC0ReBJLkVXe%0A94KlwDpU9RE0rROKYTyK/+1o1T9FSY8hOHUuX2z5lHbt2tl+T3fME3eWNLp3nfIcVBcnKHWmcb6j%0AaU7nz5/n0KFDREREcPfddzs1zelm5kz1vdlspnHjxnz77bfUrVuXtm3bsnbtWpf2crZq2LAhmZmZ%0Atir/jh078vbbb3Pu3DkmTpzIV199xalTpxg8eDBgyVe+//77eeKJJ1y+FiHBqhBuZ7k0H8Xzzz9P%0Ay5Yt7Y5bC65cHRz8/fffdOp0F5cu3YOmDcBSQLUc2IaqRqFps4AKeX4qEVWdgq6nousfAm2yb09F%0AUYegc4RKFUP4evsmGjZsWOA8cXcEpQXxpp6cnt7o3tuD6uI0zncmn7So05yOHz9O165d2bhxo0uH%0AkJR3+VXf79mzh4ULF7J9+3bgRjBrDW5FueXwP07PvPYjhJfy9/dn9erVDBkyhI0bN9q1OPHx8cHP%0Az8/WIcBVwUHdunX57rttdOnSi8uXL6Gq32X3VX0HTbMPmuFzYBnQH11/gdzTrkz4+VanevVafPjh%0Au4SFhaFpGgaDAV9fX5f3KC0ORVEICgoiOTkZg8HgkZexFUUhMDCQ5ORkMjMzPTao9vPzQ9M00tLS%0APDaoNhqNth1W63rzC0p9fHzw9fV1OijNb5qTj48P4eHhREZG0rZtW8aMGVPgNKemTZuyatUqhg4d%0Ayr59+7jlllvc8VSUOytWrGDkyLyT9CxfwOvVu5GuFBoayr59+0pzacKDeN47vBBe7tZbb+X5559n%0A4sSJfP7553aBlK+vL2az2eXBQXh4ON9++xXt23fBZGqOri/Dvq1VGooyFV0/BryDpvXNczyOgIDR%0ADBrUhVdeWY7BYPDYHTdrNbs7dqpdxVuC6oCAAI8JqgvqEAGWnWCj0Wj74udsO6j09HSOHTtmN83J%0A19fXNs2pS5cuPPTQQ9StW7dYr6fevXuzYsUKqlevXqzfvTxxtvre19fXYZsoT3zPEWXH8945hSgH%0A7rrrLg4ePMizzz7L008/neuN153BQePGjfn55++5665+XLu2HV3vn+PoTyjKfBSlGbq+F6iZ56c3%0AEhAwj6VLFzN69Chbbqin77hZi3A8tSenqqoEBgZ6TVCtqmqpjGR1tm1ZzrZQYGlPt3PnTgYNGuTw%0AnHmnOcXFxXHlyhX8/f1p1KgRkZGRREVFMWPGDLdMc+rTp49Lz+etduzYUeDxDz/8kG3btrFz506H%0Ax+vWrUtCQoLt7wkJCYSGhrp0jcJ7SM6qEG6iaRojR45k0KBBDBgwwO54SauxC/qwP378OAMGDOPa%0AtWno+t1Yiqt2oygL0PXx5E4LysLX92kqVdrOpk0f06JFi1yP4Q25od5QcOWOfruu5q4hFq5oW2Z1%0A7tw5unbtyqJFiwgLC3NqmlO1atU89jkvDevWrePpp58mLi6O//73v7Rq1crh/cLCwqhQoYLtS0Js%0AbKxb1uNM9b3JZKJx48bs3LmTOnXq0K5dO7cVWAmPIgVWQpS269evEx0dzZtvvkmTJk3sjjtTjV3c%0AD/v4+Hi6d+/NtWs6UBld/wjI2xj8AoGB42ndugJr166gcuXKdo/vDS2OJKh2neJOjyqobVneQryS%0ATnMyGo3s3r2bwYMH06VLF6emOd3M4uLiUFWVyZMns2TJknyD1fDwcA4cOGCrincXZ6rvAWJiYmyt%0Aq8aPHy/V9zcHCVaFKAtxcXGMHTuWzZs3U6FC3or8G9XYAQEBuQJTV3zY79mzh/79h5GR8Vj2sICc%0AfiYgYBLTp4/jqafmFXg51BtaHLmrNZgrWS9T+/j4ONV4vKzkNz3KXW3L8pvmlJGRYTfNqWnTpoSE%0AhLB27VqeeuopYmNj892dE7l179690GB1//79doWhQpQiCVaFKCubNm1izZo1rFy5ksTERI4ePUqD%0ABg2oWbMmZrMZs9kM4JYepWfOnKFHj7u5dGkEJtPM7Md5l4CAZaxevdzpptbe0OLIXa3BXMkaVAcE%0ABJRKbmhxWPOADQYDBoMhV1AK2H1xcvZ1mneaU3x8vG2aU40aNXI1zm/cuHGh05yeeOIJ9uzZw3ff%0Afeex/96epLBgNSIigooVK2IwGJg8eTITJ04s5RUKIa2rhCg1mqaRkJDAkSNHbH/27dtHaGgovr6+%0ANG7cmPnz51O7dm2MRiOKopCSkoKvr6/Lx1/eeuut/PjjDnr1uoe//76CwXCBevVOs2nTLsLCwpw+%0Aj5+fn62LQWBgoEvX6CrWCnFvKbiyBnplpbDG+VlZWWiahtFoxGg0Ot22zPr6L2yaU+/evUs0zWnR%0AokV8//33EqjiXPV9YX766Sdq165NUlISvXr1okmTJnTu3NnVSxWiyGRnVQg3aNCgAenp6bbLlpGR%0AkTRu3JilS5cyadIkevToYfcz1txQVxa35HT58mUGDBhB48YNeeutJcW6DG3NDfX0mfLlOTe0OHI2%0AzncUlOaXZmI2m/n5558JCgqy243Lb5rTmTNn0HWd0NDQXEVODRo0sH0xE2WjsJ3VnBYuXEhwcDAz%0AZ84shZUJYSM7q0KUlsOHDxMQEGB3++23306fPn2oX78+t956a65jBoMBf39/WzW2q3eLqlSpwo8/%0AflOic1gb3aekpNgasHsaa2uwlJQUj+gbmh9rv93U1NRCL3c7y9lpTj4+Pk5NczIYDCQmJvLkk0+y%0AatUqEhMT853mdNtttzF8+HAiIiKcashfnjlbfb99+3ZbAdGECROYO3eu29eW3wZVamoqZrOZkJAQ%0AUlJS+Oabb1iwYIHb1yOEM2RnVYhSdvDgQaZNm8aWLVscBrT5Fbd4Em8quPLk3NDiFlzlLXLK+f8d%0A7ZCWdJqTqqqcPHmSadOm0bx5cyIjI7nlllvKNIXBkzlTfW82m2ncuDHffvstdevWpW3btm5rzbRp%0A0yamT5/OpUuXqFixIi1btiQmJiZX9f2pU6cYPHgwYMn9vv/++6X6XpQFKbASwlOsXr2aHTt28Pbb%0Abzucde4NFeNScOUa1qDa39/fLrXC2cb5Of+3qNOcrEHpxYsX8fPzs01zatasGZGRkdStWxeAIUOG%0AULVqVZYvX+6x/96epqDL7nv27GHhwoVs374dgBdeeAGAefPmleoahfAwkgYghKd44IEHiI2NZcWK%0AFUyYMCHXsZwz5a3NuT2RteAqPT3d4Q6xJ/CmgquUlBRbtX3eoNQaiOac5uRMUOrMNKfo6Ggee+yx%0AQqc5rVq1io4dO/L+++8zadIklz4HN6O///6bevXq2f4eGhrKvn37ynBFQnguCVaFKITZbKZNmzaE%0Ahoby5ZdfuuSciqKwZMkS+vTpw2233UaHDh1yHfekivH85AyqMzMzPbbgypob6gljYwsa8KAoChkZ%0AGbaOEEVpnH/t2rVcRU6Opjn179+fefPmFXuaU3BwMF9++aVHvhbLQkmr7z3xi5MQnkqCVSEKsWzZ%0AMiIjI7l+/bpLz+vr68uaNWsYMGAAn332GbVq1cp13JoGYL2M7Ykfbt5WcJWRkVEqqRV580gdDXgw%0AGAy2QidrUJqSksKKFSuYOHGiXVCY3zSntLQ0QkJCaNq0KU2bNmXo0KFum+ZUlFZn5d2OHTtK9PN1%0A69YlISHB9veEhARCQ0NLuiwhyiXP+2QRwoOcPXuWbdu2MX/+fJYuXery89euXZtXX32VCRMmsHHj%0ARrvdSV9fX1vepacWXBkMBgICAjw6N9QdqRVFmebk4+PjVON8Pz8/vvnmG44cOcLw4cMLnOY0atQo%0A2zQnT3xdlKbLly8zfPhwzpw5Q1hYGJ9//jmVKlWyu19YWBgVKlSwvQZiY2Pdvrb86kLatGnD8ePH%0AOX36NHXq1OGzzz5j7dq1bl+PEN5ICqyEKMDQoUN58sknuXbtGq+88orL0gDyevPNN4mLi+PFF1+0%0ACzysuYdGo9Fj2zCBdxVcFaWXbX6N83NOc3JU5FSUaU7WPydOnEBRFH7//XfatWvHyJEjnZ7mdDOb%0AM2cO1apVY86cObz44otcuXLFVrCUU3h4OAcOHLDNpHcXZ6rvAWJiYmytq8aPHy/V90JINwAhimbr%0A1q3ExMTw1ltvsWvXLpYsWeK2YFXTNB588EG6du3KiBEjHB73hrn31hxbTy24AsjIyCAzM9MutaKw%0AaU4lCUqdmebUrFkz2zSn+Ph4unTpwpYtW+jYsaO7nxKv16RJE3bv3k3NmjW5cOEC3bp1Iy4uzu5+%0A4eHh7N+/n6pVq5bBKoUQTpBgVYiiePLJJ1m9ejU+Pj6kp6dz7do17r33XlatWuWWx0tLSyMqKoqX%0AX36ZO+64w+64N7Rh8oYJV5qm2XrZGo3GXDmlORvn5+xTWtjz7Y5pTlu3bmXy5MkcOnRIgqtCVK5c%0AmStXrgCWf4sqVarY/p5TREQEFStWxGAwMHnyZCZOnFjaSxVCFEyCVSGKa/fu3W5NA7D6888/GT58%0AOJs2baJy5cp2x/PbFfQk7h4b66zCpjlZ80qtlffONs43mUycOnUqV1B69uxZVFW1TXOyBqXh4eEl%0AmuYUGxtL27ZtPfbfujTlV32/ePFixowZkys4rVKlCpcvX7a77/nz56lduzZJSUn06tWLN954g86d%0AO7t13UKIIpE+q0KURGkEDOHh4Tz77LNMnjyZtWvX2gV7ntSGKT/Wgitrb1N37wI72zjf2nPVevle%0A0zT27dvH9evXiYqKsjuno2lO58+fx2AwEBERQWRkJO3atWPs2LFum+bUrl07l5/TWxVUfW+9/F+r%0AVi3Onz9PjRo1HN6vdu3aAFSvXp1BgwYRGxsrwaoQXkB2VoXwMLqu8/zzz5OcnMz8+fMdFlwlJyfj%0A6+vr0QVXaWlpmM1mlxVcuWOa048//sh9993Hu+++a+tVWtg0J09NwXA3Z+bYT58+nZiYGAIDA/nw%0Aww9p2bJlqaxtzpw5VK1alblz5/LCCy/wzz//2BVYpaamYjabCQkJISUlhaioKBYsWGD3RUUIUaYk%0ADUAIb6FpGkOHDmX48OH069fP7rj1Unt5LLgqqHG+o4C0pNOcAgMD+eWXX5g3bx6tW7cmMjKy0GlO%0ANxtn5thv27aNN998k23btrFv3z4effRR9u7dWyrru3z5MsOGDeOvv/7K1boqZ/X9qVOnGDx4MGDJ%0A/77//vul+l4IzyPBqhDe5OrVq/Tu3Zt3332Xhg0b2h3PysoiLS3NowuuCpt7746g1NE0J2snBes0%0Ap6ZNm9KsWTPbNKcpU6Zw7tw5Nm3a5LHPZVlyZo79Qw89RPfu3Rk+fDiQu0JfCCGcJDmrQniTihUr%0A8sEHHzB+/Hi2bNlCcHBwruNGoxGz2WzrG+qJ+at5597nDFCL2zgfnJvmFBkZybBhw4iMjCx0mtOy%0AZcvo0aMHL7/8ssPL2zc7Z+bYO7rP2bNnJVgVQpSYBKtCeLDIyEgef/xxHnnkEVauXGm36+fn54fZ%0AbCY9Pb1Me5sWNs1JVVUyMjLw8/PDz8+vSEFpUlIScXFxhU5zioyMLHaXBF9fX9avX09WVlZxn4Jy%0AzdnnNO+VOk/8AiWE8D4SrArh4YYMGcKBAwd44403ePTRR3MdyzlGNDMz0+29TYvSON9oNOZqnH/1%0A6lXeffddpk6dahd05zfNKSsrixo1atiC0q5du7ptmlOtWrVcer7yxJk59nnvc/bsWerWrVvix05I%0ASKBr164cOHDA1k+1devW7Nq1i1tuuaXE5xdCeD7JWRXCC5hMJgYMGMD06dPp0qWL3XFX9zYtzjSn%0AwnI9s7Ky6NevH7fddhtRUVG2oPTPP/+0TXOy5pTmnOZ0s+7OFVZ9v2vXLu655x4iIiIAuPfee3nq%0AqafcshaTyUTjxo3ZuXMnderUoV27dgUWWO3du5cZM2a4rMDq5Zdf5sSJE7z33ntMnjyZiIgISdcQ%0AonySAishvNmlS5fo06cPH3/8sd2uFkBmZibp6elFKrgqrHF+3oDU2cb5+U1z8vf3Z//+/URHRzN0%0A6FCaNWtG/fr1C53mdLNxpvp+165dLF26lC+++KJU1uRojv17770HwOTJkwGYOnUq27dvJygoiJUr%0AV9KqVSuXPLbJZKJ169Y8+OCDfPDBB/z6669lOnBCCOE2EqwK4e3++9//MnPmTDZv3oy/v7/dcesY%0A0byXyd0VlBZnmtPBgweJjo5m165dNGvWzOXPUXngTPX9rl27WLJkidunqnmKr7/+mj59+rBjxw56%0A9uxZ1ssRQriHdAMQwtu1bduWBx98kNmzZ/P666/bBZR+fn6kpKSQmpqKwWBweppTQazTnE6cOJFr%0AmtOFCxeKNc2pVatWLF26lEGDBnHo0CGHQffNzpnqe0VR+Pnnn2nevDl169bllVdeITIysrSXWmpi%0AYmKoU6cOhw8flmBViJuMBKtCeJmxY8fy008/8dJLL1G7dm3i4uIwGAzMmTPHFpSaTCbA0t7K2WlO%0Auq6Tnp7OsWPHcgWlSUlJGI1GGjZsaCtymjJlSommOY0aNYqmTZtKoJoPZ1IiWrVqRUJCAoGBgcTE%0AxDBw4ECOHTtWCqsrfb/++ivffvste/bsoVOnTowYMUIK4oS4iUiwKoSHO3XqFLt37+bIkSO2P4mJ%0AiYSEhNCmTRuaN29Oq1atCAwMtAWlJpOJTZs2cdttt+XKc4T8pzn9888/+Pv706hRIyIjI+nduzeP%0AP/44NWvWdEs+aZs2bVx+zvLCmer7kJAQ2//v06cPDz/8MJcvX6ZKlSqlts7SoOs6U6ZMYdmyZdSr%0AV4/Zs2cza9Ys1qxZU9ZLE0KUEglWhfBwBw4c4PvvvycyMpLJkycTGRlJeHg4Fy5cYODAgUyaNIka%0ANWrk+hkfHx+uXr3KiBEjeP3113MVO+Wd5jRgwACeeOIJqlatetMWOY0bN46vvvqKGjVqcPjwYYf3%0AKc25923atOH48eOcPn2aOnXq8Nlnn7F27dpc90lMTKRGjRooikJsbCy6rpe7QBXg/fffJywszHbp%0A/+GHH2blypX88MMPdO7cuYxXJ4QoDVJgJYQX2717N4sWLWL58uWcOHHCbppTWloa169fZ/bs2TRr%0A1sypaU43ox9++IHg4GBGjx7tMFgti7n3hVXfv/XWW7zzzjv4+PgQGBjI0qVL6dChg1vXJIQQbibd%0AAIQojyZNmsShQ4fo3LmzrfreOs0pMzOTLl26MHjwYOlLWYjTp0/Tv39/h8GqzL0XQohSId0AhCiP%0Ali9fnu8xPz8/NmzYQLt27ejYsaPDgQKicDL3Xgghyo4Eq0KUorCwMCpUqIDBYMBoNBIbG+v2xwwN%0ADeXrr7+mfv36bn+s8kzm3gshRNmQYFWIUqQoCrt27Sr1Qpjbb7+9VB+vvHHX3HshhBCFK16TRCFE%0AsRWSJ35TGDduHDVr1sw3iN61axcVK1akZcuWtGzZkkWLFpXyCnMbMGAAq1atAmDv3r1UqlRJUgCE%0AEKKUyM6qEKVIURTuuusuDAYDkydPZuLEiWW9pDLx4IMPMm3aNEaPHp3vfbp27Vpqc+9HjhzJ7t27%0AuXTpEvXq1WPhwoVkZWUBlsr7vn37sm3bNho0aGCbey+EEKJ0SLAqRCn66aefqF27NklJSfTq1Ysm%0ATZrclL0iO3fuzOnTpwu8T2nuQOftYerIm2++WQorEUIIkZekAQhRimrXrg1A9erVGTRoUKkUWHmj%0AnHPv+/bty5EjR8p6SUIIIcqIBKtClJLU1FSuX78OQEpKCt98840UPuXDOvf+t99+Y9q0aQwcOLCs%0AlySEEKKMSLAqRClJTEykc+fOtGjRgvbt29OvXz+ioqLKelkeKSQkhMDAQMAy9z4rK4vLly+X8aqE%0AEEKUBclZFaKUhIeH8+uvv5b64yYkJDB69GguXryIoihMmjSJ6dOn291v+vTpxMTEEBgYyIcffkjL%0Ali1Lfa1WN8vceyGEEIWTYFWIcs5oNPLqq6/SokULkpOTad26Nb169aJp06a2+2zbto0TJ05w/Phx%0A9u3bx5QpU9i7d6/b1lRY9f369etzzb3/9NNP3bYWIYQQnk0ppOJWGkIKUc4MHDiQadOm0bNnT9tt%0ADz30EN27d2f48OEANGnShN27d0svUSGEEKXJ4WhAyVkV4iZy+vRpDh48SPv27XPd/vfff1OvXj3b%0A30NDQzl79mxpL08IIYSwI8GqEDeJ5ORkhgwZwrJlywgODrY7nvcqi6I4/IIrhBBClCoJVoW4CWRl%0AZXHvvffywAMPOGwDVbduXRISEmx/P3v2LHXr1i3NJQohhBAOSbAqRDmn6zrjx48nMjKSGTNmOLzP%0AgAEDWLVqFQB79+6lUqVKkq8qhBDCI0iBlRDl3I8//kiXLl244447bJf2n3vuOf766y/AUn0PMHXq%0AVLZv305QUBArV66kVatWZbZmIYQQNyWH+WcSrAohhBBCCE8g3QCEEEIIIYR3kWBVCCGEEEJ4LAlW%0AhRBCCCGEx5JgVQghhBBCeCwJVoUQQgghhMeSYFUIIYQQQngsCVaFEEIIIYTHkmBVCCGEEEJ4LAlW%0AhRBCCCGEx5JgVQghhBBCeCwJVoUQQgghhMeSYFUIIYQQQngsCVaFEEIIIYTHkmBVCCGEEEJ4LJ9C%0AjiulsgohhBBCCCEckJ1VIYQQQgjhsSRYFUIIIYQQHkuCVSGEEEII4bEkWBVCCCGEEB5LglUhhBBC%0ACOGxJFgVQgghhBAe6/8D6bfN7+5DVO4AAAAASUVORK5CYII=%0A
-
-Nelder-Mead Simplex 算法
------------------------------
-
-
-改变 minimize 使用的算法，使用 Nelder–Mead 单纯形算法：
-
-
-
-
-
-::
-
-    xi = [x0]
-    result = minimize(rosen, x0, method="nelder-mead", callback =     xi.append)
-    xi = np.asarray(xi)
-    print xi.shape
-    print "Solved the Nelder-Mead Simplex method with {} function evaluations.".format(result.nfev)
-
-
-结果:
-::
-
-    (120L, 2L)
-
-    Solved the Nelder-Mead Simplex method with 226 function evaluations.
-
-
-
-::
-
-    x, y = meshgrid(np.linspace(-1.9,1.75,25), np.linspace(-0.5,4.5,25))
-    z = rosen([x,y])
-    fig = figure(figsize=(12,5.5))
-    ax = fig.gca(projection="3d"); ax.azim = 70; ax.elev = 75
-    ax.set_xlabel("X"); ax.set_ylabel("Y"); ax.set_zlim((0,1000))
-    p = ax.plot_surface(x,y,z,rstride=1, cstride=1, cmap=cm.jet)
-    intermed = ax.plot(xi[:,0], xi[:,1], rosen(xi.T), "g-o")
-    rosen_min = ax.plot([1],[1],[0],"ro")
-
-
-
-.. image:: 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ukXxbu3OBkz8Dz3fdCcG29JF8U3NijOvl3LcTgcREZmbMtqNBozlLQym81CsAquS8RdLxAIcpVA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%0AEEIIIYTP+v/YLseLtZ7EnQAAAABJRU5ErkJggg==%0A
-
-Powell 算法
-------------------
-
-
-使用 Powell 算法
-
-
-
-
-
-::
-
-    xi = [x0]
-    result = minimize(rosen, x0, method="powell", callback=xi.append)
-    xi = np.asarray(xi)
-    print xi.shape
-    print "Solved Powell's method with {} function evaluations.".format(result.nfev)
-
-结果:
-::
-
-    (31L, 2L)
-
-
-    Solved Powell's method with 855 function evaluations.
-
-
-
-
-::
-
-    x, y = meshgrid(np.linspace(-2.3,1.75,25), np.linspace(-0.5,4.5,25))
-    z = rosen([x,y])
-    fig = figure(figsize=(12,5.5))
-    ax = fig.gca(projection="3d"); ax.azim = 70; ax.elev = 75
-    ax.set_xlabel("X"); ax.set_ylabel("Y"); ax.set_zlim((0,1000))
-    p = ax.plot_surface(x,y,z,rstride=1, cstride=1, cmap=cm.jet)
-    intermed = ax.plot(xi[:,0], xi[:,1], rosen(xi.T), "g-o")
-    rosen_min = ax.plot([1],[1],[0],"ro")
-
-
-.. image:: 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IWlO2wc269qekaPk/ArrqPlR+o6Z+1YlcqRnemMinuOyA2Xidl7Rt2FBAIPR4hVgUAg+A+TX5Sq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%0AQgghhBBeS4JVIYQQQgjhtf4/QWwBBPzDxt8AAAAASUVORK5CYII=%0A
-
-
-作者 & 更新时间
-------------------------------------
-作者:`李金  <lijinwithyou@gmail.com>`
-
-提交: 2017/12/14
-
diff --git a/docs/sources/aipy-scipy/cn/7integral.rst.txt b/docs/sources/aipy-scipy/cn/7integral.rst.txt
deleted file mode 100644
index cc5019f..0000000
--- a/docs/sources/aipy-scipy/cn/7integral.rst.txt
+++ /dev/null
@@ -1,876 +0,0 @@
-
-积分
-=======
-符号积分
------------------
-积分与求导的关系：
-
-**\frac{d}{dx} F(x) = f(x)
-\Rightarrow F(x) = \int f(x) dx**
-
-
-符号运算可以用 sympy 模块完成。
-
-先导入 init_printing 模块方便其显示：
-
-
-
-
-::
-
-    from sympy import init_printing
-    init_printing()
-
-
-
-
-::
-
-    from sympy import symbols, integrate
-    import sympy
-
-
-产生 x 和 y 两个符号变量，并进行运算：
-
-
-
-
-::
-
-    x, y = symbols('x y')
-    sympy.sqrt(x ** 2 + y ** 2)
-
-
-
-结果:
-::
-
-    sqrt{x^{2} + y^{2}}
-
-
-对于生成的符号变量 z，我们将其中的 x 利用 subs 方法替换为 3：
-
-
-
-
-::
-
-    z = sympy.sqrt(x ** 2 + y ** 2)
-    z.subs(x, 3)
-
-
-
-结果:
-::
-
-    sqrt{y^{2} + 9}
-
-
-再替换 y：
-
-
-
-
-::
-
-    z.subs(x, 3).subs(y, 4)
-
-
-结果:
-::
-
-    5
-
-
-还可以从 sympy.abc 中导入现成的符号变量：
-
-
-
-
-::
-
-    from sympy.abc import theta
-    y = sympy.sin(theta) ** 2
-    y
-
-
-结果:
-::
-
-    sin^{2}{\left (\theta \right )}
-
-对 y 进行积分：
-
-
-
-
-::
-
-    Y = integrate(y)
-    Y
-
-
-
-结果:
-::
-
-    frac{\theta}{2} - \frac{1}{2} \sin{\left (\theta \right )} \cos{\left (\theta \right )}
-
-
-计算 *Y(\pi) - Y(0)* ：
-
-
-
-
-::
-
-    import numpy as np
-    np.set_printoptions(precision=3)
-
-    Y.subs(theta, np.pi) - Y.subs(theta, 0)
-
-
-结果:
-::
-
-    1.5707963267949
-
-计算 int_0^\pi y d\theta ：
-
-
-
-
-::
-
-    integrate(y, (theta, 0, sympy.pi))
-
-
-结果:
-::
-
-     frac{\pi}{2} 
-
-
-显示的是字符表达式，查看具体数值可以使用 evalf() 方法，或者传入 numpy.pi，而不是 sympy.pi ：
-
-
-
-
-::
-
-    integrate(y, (theta, 0, sympy.pi)).evalf()
-
-
-结果:
-::
-
-     1.5707963267949 
-
-
-
-::
-
-    integrate(y, (theta, 0, np.pi))
-
-
-结果:
-::
-
-     1.5707963267949 
-
-
-根据牛顿莱布尼兹公式，这两个数值应该相等。
-产生不定积分对象：
-
-
-
-
-::
-
-    Y_indef = sympy.Integral(y)
-    Y_indef
-
-
-
-结果:
-::
-
-     int \sin^{2}{\left (\theta \right )}\, d\theta 
-
-
-
-
-::
-
-    print type(Y_indef)
-
-
-<class 'sympy.integrals.integrals.Integral'>
-
-
-定积分：
-
-
-
-
-::
-
-    Y_def = sympy.Integral(y, (theta, 0, sympy.pi))
-    Y_def
-
-
-
-
-
-结果:
-::
-
-     int_{0}^{\pi} \sin^{2}{\left (\theta \right )}\, d\theta 
-
-产生函数 *Y(x) = \int_0^x sin^2(\theta) d\theta*，并将其向量化：
-
-
-
-
-::
-
-    Y_raw = lambda x: integrate(y, (theta, 0, x))
-    Y = np.vectorize(Y_raw)
-
-
-
-
-::
-
-    %matplotlib inline
-    import matplotlib.pyplot as plt
-
-    x = np.linspace(0, 2 * np.pi)
-    p = plt.plot(x, Y(x))
-    t = plt.title(r'$Y(x) = \int_0^x sin^2(\theta) d\theta$')
-
-
-.. 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caV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%0Agg==%0A
-
-数值积分
------------
-
-数值积分：
-
- F(x) = \lim_{n \rightarrow \infty} \sum_{i=0}^{n-1} f(x_i)(x_{i+1}-x_i) 
-\Rightarrow F(x) = \int_{x_0}^{x_n} f(x) dx 
-
-
-导入贝塞尔函数：
-
-
-
-
-::
-
-    from scipy.special import jv
-
-
-
-
-::
-
-    def f(x):
-        ^4$return jv(2.5, x)
-
-
-
-
-::
-
-    x = np.linspace(0, 10)
-    p = plt.plot(x, f(x), 'k-')
-
-
-.. image::data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAX0AAAEACAYAAABfxaZOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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%0AUn7k/wDY8//eOSbGSgAAAABJRU5ErkJggg==%0A
-
-quad 函数
---------------
-
-Quadrature 积分的原理参见：
-
-
-http://en.wikipedia.org/wiki/Numerical_integration#Quadrature_rules_based_on_interpolating_functions
-
-
-quad 返回一个 (积分值，误差) 组成的元组：
-
-
-
-
-::
-
-    from scipy.integrate import quad
-    interval = [0, 6.5]
-    value, max_err = quad(f, *interval)
-
-
-积分值：
-
-
-
-
-::
-
-    print value
-
-结果:
-::
-
-    1.28474297234
-
-
-最大误差：
-
-
-
-
-::
-
-    print max_err
-
-
-结果:
-::
-
-    2.34181853668e-09
-
-
-积分区间图示，蓝色为正，红色为负：
-
-
-
-
-
-::
-
-    print "integral = {:.9f}".format(value)
-    print "upper bound on error: {:.2e}".format(max_err)
-    x = np.linspace(0, 10, 100)
-    p = plt.plot(x, f(x), 'k-')
-    x = np.linspace(0, 6.5, 45)
-    p = plt.fill_between(x, f(x), where=f(x)>0, color="blue")
-    p = plt.fill_between(x, f(x), where=f(x)<0, color="red",     interpolate=True)
-
-
-结果:
-::
-
-    integral = 1.284742972
-
-
-    upper bound on error: 2.34e-09
-
-
-
-.. image:: 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ufZQ86jy2rgMjZvXkvZsie5DmPikKpy1113sWnTJiZOnBjWMf35QFURqSIiJwC3AH97orSIVCKz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%0AR6zoG2NMHLGib4wxceT/AZhppPNX9o1QAAAAAElFTkSuQmCC%0A
-
-积分到无穷
---------------
-
-
-
-
-::
-
-    from numpy import inf
-    interval = [0., inf]
-
-    def g(x):
-        ^4$return np.exp(-x ** 1/2)
-
-
-
-
-
-
-::
-
-    value, max_err = quad(g, *interval)
-    x = np.linspace(0, 10, 50)
-    fig = plt.figure(figsize=(10,3))
-    p = plt.plot(x, g(x), 'k-')
-    p = plt.fill_between(x, g(x))
-    plt.annotate(r"$\int_0^{\infty}e^{-x^1/2}dx = $" + "{}".format    (value), (4, 0.6),
-             ^4$fontsize=16)
-    print "upper bound on error: {:.1e}".format(max_err)
-
-
-
-结果:
-::
-    
-    upper bound on error: 7.2e-11
-
-
-.. image:: 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QMmTJiA7/vFeruefPJJrr/+epKTkxk6dChPP/00EyZM4L777mP06NHFAlVBQQHXXHMN+/btK/Ha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%0ATkSuQmCC%0A
-
-
-双重积分
----------------
-
-
-假设我们要进行如下的积分：
-
-
-  I_n = \int \limits_0^{\infty} \int \limits_1^{\infty} \frac{e^{-xt}}{t^n}dt dx = \frac{1}{n} 
-
-
-
-
-
-::
-
-    def h(x, t, n):
-        ^4$"""core function, takes x, t, n"""
-        ^4$return np.exp(-x * t) / (t ** n)
-
-
-一种方式是调用两次 quad 函数，不过这里 quad 的返回值不能向量化，所以使用了修饰符 vectorize 将其向量化：
-
-
-
-
-
-::
-
-    from numpy import vectorize
-    @vectorize
-    def int_h_dx(t, n):
-        ^4$"""Time integrand of h(x)."""
-        ^4$return quad(h, 0, np.inf, args=(t, n))[0]
-
-
-
-
-
-::
-
-    @vectorize
-    def I_n(n):
-        ^4$return quad(int_h_dx, 1, np.inf, args=(n))
-
-
-
-
-::
-
-    I_n([0.5, 1.0, 2.0, 5])
-
-
-
-结果:
-::
-
-    (array([ 1.97,  1.  ,  0.5 ,  0.2 ]),
-
-
-    array([  9.804e-13,   1.110e-14,   5.551e-15,   2.220e-15]))
-
-
-或者直接调用 dblquad 函数，并将积分参数传入，传入方式有多种，后传入的先进行积分：
-
-
-
-
-
-::
-
-    from scipy.integrate import dblquad
-    @vectorize
-    def I(n):
-        ^4$"""Same as I_n, but using the built-in dblquad"""
-        ^4$x_lower = 0
-        ^4$x_upper = np.inf
-        ^4$return dblquad(h,
-                       ^4$^4$lambda t_lower: 1, lambda t_upper: np.inf,
-                       ^4$^4$x_lower, x_upper, args=(n,))
-
-
-
-
-::
-
-    I_n([0.5, 1.0, 2.0, 5])
-
-
-
-结果:
-::
-
-    (array([ 1.97,  1.  ,  0.5 ,  0.2 ]),
-
-    array([  9.804e-13,   1.110e-14,   5.551e-15,   2.220e-15]))
-
-
-
-采样点积分
-------------------------
-
-**trapz** 方法 和 **simps** 方法
-
-
-
-
-
-::
-
-    from scipy.integrate import trapz, simps
-
-
-sin 函数， 100 个采样点和 5 个采样点：
-
-
-
-
-::
-
-    x_s = np.linspace(0, np.pi, 5)
-    y_s = np.sin(x_s)
-    x = np.linspace(0, np.pi, 100)
-    y = np.sin(x)
-
-
-
-
-::
-
-    p = plt.plot(x, y, 'k:')
-    p = plt.plot(x_s, y_s, 'k+-')
-    p = plt.fill_between(x_s, y_s, color="gray")
-
-
-.. image:: 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wCxwFFgrdY6USn1tFLq6aLN3gH6KKUOAFuBV7TW6dYMLf5Pz549adOmTanfy8vLY+jQoWzYsIGI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%0AQmCC%0A
-
-
-采用 trapezoidal 方法 和 simpson 方法 对这些采样点进行积分（函数积分为 2）：
-
-
-
-
-::
-
-    result_s = trapz(y_s, x_s)
-    result_s_s = simps(y_s, x_s)
-    result = trapz(y, x)
-    print "Trapezoidal Integration over 5 points : {:.3f}".format(result_s)
-    print "Simpson Integration over 5 points : {:.3f}".format(result_s_s)
-    print "Trapezoidal Integration over 100 points : {:.3f}".format(result)
-
-
-
-Trapezoidal Integration over 5 points : 1.896
-
-
-Simpson Integration over 5 points : 2.005
-
-
-Trapezoidal Integration over 100 points : 2.000
-
-
-使用 ufunc 进行积分
----------------------------
-
-
-Numpy 中有很多 ufunc 对象：
-
-
-
-
-::
-
-    type(np.add)
-
-
-
-结果:
-::
-
-    numpy.ufunc
-
-
-
-
-::
-
-    np.info(np.add.accumulate)
-
-
-
-    accumulate(array, axis=0, dtype=None, out=None)
-
-
-Accumulate the result of applying the operator to all elements.
-
-
-For a one-dimensional array, accumulate produces results equivalent 
-
-to::
-
-
-  r = np.empty(len(A))
-
-  t = op.identity        # op = the ufunc being applied to A's  
-
-elements
-
-  for i in range(len(A)):
-
-      t = op(t, A[i])
-
-      r[i] = t
-
-  return r
-
-
-For example, add.accumulate() is equivalent to np.cumsum().
-
-
-For a multi-dimensional array, accumulate is applied along only one
-
-axis (axis zero by default; see Examples below) so repeated use is
-
-necessary if one wants to accumulate over multiple axes.
-
-
-Parameters
-
-array : array_like
-
-    The array to act on.
-
-axis : int, optional
-
-    The axis along which to apply the accumulation; default is zero.
-
-dtype : data-type code, optional
-
-    The data-type used to represent the intermediate results. Defaults
-
-    to the data-type of the output array if such is provided, or the
-
-    the data-type of the input array if no output array is provided.
-
-out : ndarray, optional
-
-    A location into which the result is stored. If not provided a
-
-    freshly-allocated array is returned.
-
-
-Returns
-
-r : ndarray
-
-    The accumulated values. If `out` was supplied, `r` is a reference 
-
-to
-
-    `out`.
-
-
-Examples
-
-
-1-D array examples:
-
-
-
-::
-
-
-    np.add.accumulate([2, 3, 5])
-    array([ 2,  5, 10])
-    np.multiply.accumulate([2, 3, 5])
-    array([ 2,  6, 30])
-
-
-
-2-D array examples:
-
-
-
-::
-
-    I = np.eye(2)
-    I
-    array([[ 1.,  0.],
-           ^4$[ 0.,  1.]])
-
-
-
-
-Accumulate along axis 0 (rows), down columns:
-
-
-::
-
-    np.add.accumulate(I, 0)
-    array([[ 1.,  0.],
-           ^4$[ 1.,  1.]])
-    np.add.accumulate(I) # no axis specified = axis zero
-    array([[ 1.,  0.],
-           ^4$[ 1.,  1.]])
-
-Accumulate along axis 1 (columns), through rows:
-
-
-
-::
-
-    np.add.accumulate(I, 1)
-    array([[ 1.,  1.],
-           ^4$[ 0.,  1.]])
-
-
-np.add.accumulate 相当于 cumsum ：
-
-
-
-
-::
-
-    result_np = np.add.accumulate(y) * (x[1] - x[0]) - (x[1] - x[0]) / 2
-
-
-
-
-::
-
-    p = plt.plot(x, - np.cos(x) + np.cos(0), 'rx')
-    p = plt.plot(x, result_np)
-
-
-速度比较
-------------
-
-
-计算积分： int_0^x sin \theta d\theta 
-
-
-
-
-
-::
-
-    import sympy
-    from sympy.abc import x, theta
-    sympy_x = x
-
-
-
-
-
-::
-
-    x = np.linspace(0, 20 * np.pi, 1e+4)
-    y = np.sin(x)
-    sympy_y = vectorize(lambda x: sympy.integrate(sympy.sin(theta), (theta, 0, x)))
-
-
-numpy 方法：
-
-
-
-
-::
-
-    %timeit np.add.accumulate(y) * (x[1] - x[0])
-    y0 = np.add.accumulate(y) * (x[1] - x[0])
-    print y0[-1]
-
-
-The slowest run took 4.32 times longer than the fastest. This could 
-
-mean that an intermediate result is being cached 
-
-10000 loops, best of 3: 56.2 μs per loop
-
--2.34138044756e-17
-
-
-
-quad 方法：
-
-
-
-
-::
-
-    %timeit quad(np.sin, 0, 20 * np.pi)
-    y2 = quad(np.sin, 0, 20 * np.pi, full_output=True)
-    print "result = ", y2[0]
-    print "number of evaluations", y2[-1]['neval']
-
-
-
-10000 loops, best of 3: 40.5 μs per loop
-
-result =  3.43781337153e-15
-
-number of evaluations 21
-
-
-trapz 方法：
-
-
-
-
-::
-
-    %timeit trapz(y, x)
-    y1 = trapz(y, x)
-    print y1
-
-
-10000 loops, best of 3: 105 μs per loop
-
-
--4.4408920985e-16
-
-
-simps 方法：
-
-
-
-
-::
-
-    %timeit simps(y, x)
-    y3 = simps(y, x)
-    print y3
-
-
-1000 loops, best of 3: 801 μs per loop
-
-
-3.28428554968e-16
-
-
-sympy 积分方法：
-
-
-
-
-::
-
-    %timeit sympy_y(20 * np.pi)
-    y4 = sympy_y(20 * np.pi)
-    print y4
-
-
-100 loops, best of 3: 6.86 ms per loop
-
-
-0
-
-作者 & 更新时间
-------------------------------------
-作者:`李金  <lijinwithyou@gmail.com>`
-
-提交: 2017/12/14
\ No newline at end of file
diff --git a/docs/sources/aipy-scipy/cn/8differential-equation.rst.txt b/docs/sources/aipy-scipy/cn/8differential-equation.rst.txt
deleted file mode 100644
index cefebcb..0000000
--- a/docs/sources/aipy-scipy/cn/8differential-equation.rst.txt
+++ /dev/null
@@ -1,162 +0,0 @@
-
-解微分方程
----------------
-
-
-
-
-::
-
-    %pylab inline
-
-
-Populating the interactive namespace from numpy and matplotlib
-
-
-积分求解
--------------
-
-简单的例子
------------------------
-
-frac{dy}{dt} = sin(t)
- 
-
-
-
-::
-
-    def dy_dt(y, t):
-        ^4$return np.sin(t)
-
-
-积分求解：
-
-
-
-
-::
-
-    from scipy.integrate import odeint
-
-    t = np.linspace(0, 2*pi, 100)
-
-    result = odeint(dy_dt, 0, t)
-
-
-
-
-::
-
-    fig = figure(figsize=(12,4))
-    p = plot(t, result, "rx", label=r"$\int_{0}^{x}sin(t) dt $")
-    p = plot(t, -cos(t) + cos(0), label=r"$cos(0) - cos(t)$")
-    p = plot(t, dy_dt(0, t), "g-", label=r"$\frac{dy}{dt}(t)$")
-    l = legend(loc="upper right")
-    xl = xlabel("t")
-
-
-
-.. image:: data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAsgAAAEPCAYAAABfrjLnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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%0A
-
-
-高阶微分方程
-------------------
-
-
-抛物运动（竖直方向）：
-
-
-**
-frac{d^2x}{dt^2} = g - \frac{D}{m}\frac{dx}{dt}
-**
-
-
-
-改写成如下形式：
-
-
-**
-y = \left[x, \frac{dx}{dt}\right] ****begin{aligned}
-\frac{dy_0}{dt} &amp;= y_1 \\\
-\frac{dy_1}{dt} &amp;= -g - \frac{D}{m} y_1 \\\
-\end{aligned}
-**
-
-
-
-
-
-::
-
-    def dy_dt(y, t):
-        ^4$"""Governing equations for projectile motion with drag.
-        ^4$y[0] = position
-        ^4$y[1] = velocity
-        ^4$g = gravity (m/s2)
-        ^4$D = drag (1/s) = force/velocity
-        ^4$m = mass (kg)
-        ^4$"""
-        ^4$g = -9.8
-        ^4$D = 0.1
-        ^4$m = 0.15
-        ^4$dy1 = g - (D/m) * y[1]
-        ^4$dy0 = y[1] if y[0] >= 0 else 0.
-        ^4$return [dy0, dy1]
-
-
-
-
-
-::
-
-    position_0 = 0.
-    velocity_0 = 100
-    t = linspace(0, 12, 100)
-    y = odeint(dy_dt, [position_0, velocity_0], t)
-
-
-
-
-
-::
-
-    p = plot(t, y[:,0])
-    yl = ylabel("Height (m)")
-    xl = xlabel("Time (s)")
-
-
-
-.. image:: 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QtiRyT5bqNdTGbW3t3nbvLNKZyTCZnuYlqzJkxrXbw4lDwQyTf33RdWXd92W2hViID2g0iL++6D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%0ASUoJQkREklKCEBGRpP4fPzntqp99LaMAAAAASUVORK5CYII=%0A
-
-
-
-
-::
-
-    y, infodict = odeint(dy_dt, [position_0, velocity_0], t,     full_output=True, printmessg=True, )
-    print sorted(infodict.keys())
-    print "cumulative number of function evaluations at each calculated     point:", infodict['nfe']
-    print "cumulative number of time steps", infodict['nst']
-    Integration successful.
-    ['hu', 'imxer', 'leniw', 'lenrw', 'message', 'mused', 'nfe', 'nje',     'nqu', 'nst', 'tcur', 'tolsf', 'tsw']
-    cumulative number of function evaluations at each calculated point:     [ 45  49  51  53  55  59  61  61  63  65  67  67  69  71  73  73  75  77
-    77  79  79  81  81  83  85  85  87  87  89  89  91  91  93  95  95  97
-    97  99  99 101 101 103 103 105 107 107 109 109 111 111 113 113 115 115
-    117 117 119 119 121 121 123 123 123 125 125 127 127 129 129 131 131 131
-    133 133 135 135 135 137 137 139 139 139 141 141 143 143 143 145 145 147
-    147 149 149 149 154 158 274 280 280]
-    cumulative number of time steps [ 20  22  23  24  25  27  28  28      29  30  31  31  32  33  34  34  35  36
-    36  37  37  38  38  39  40  40  41  41  42  42  43  43  44  45  45  46
-    46  47  47  48  48  49  49  50  51  51  52  52  53  53  54  54  55  55
-    56  56  57  57  58  58  59  59  59  60  60  61  61  62  62  63  63  63
-    64  64  65  65  65  66  66  67  67  67  68  68  69  69  69  70  70  71
-    71  72  72  72  73  75 130 133 133]
-
-
-
-作者 & 更新时间
-------------------------------------
-作者:`李金  <lijinwithyou@gmail.com>`
-
-提交: 2017/12/14
\ No newline at end of file
diff --git a/docs/sources/aipy-scipy/cn/9sparse.rst.txt b/docs/sources/aipy-scipy/cn/9sparse.rst.txt
deleted file mode 100644
index b60e5a9..0000000
--- a/docs/sources/aipy-scipy/cn/9sparse.rst.txt
+++ /dev/null
@@ -1,406 +0,0 @@
-稀疏矩阵
--------------------
-
-
-Scipy 提供了稀疏矩阵的支持（scipy.sparse）。
-
-
-稀疏矩阵主要使用 位置 + 值 的方法来存储矩阵的非零元素，根据存储和使用方式的不同，有如下几种类型的稀疏矩阵：
-
-	
-	
-+----------------------------------------------------+---------------------------------------------------+
-|Type                                                | Description                                       |
-+====================================================+===================================================+
-|bsr_matrix(arg1[, shape, dtype, copy, blocksize])   | Block Sparse Row matrix                           |    
-+----------------------------------------------------+---------------------------------------------------+
-| coo_matrix(arg1[, shape, dtype, copy])             |A sparse matrix in COOrdinate format.              |
-+----------------------------------------------------+---------------------------------------------------+
-| csc_matrix(arg1[, shape, dtype, copy])             |Compressed Sparse Column matrix                    |
-+----------------------------------------------------+---------------------------------------------------+
-| csr_matrix(arg1[, shape, dtype, copy])             |Compressed Sparse Row matrix                       |         
-+----------------------------------------------------+---------------------------------------------------+
-| dia_matrix(arg1[, shape, dtype, copy])             |Sparse matrix with DIAgonal storage                |
-+----------------------------------------------------+---------------------------------------------------+
-|dok_matrix(arg1[, shape, dtype, copy])              |Dictionary Of Keys based sparse matrix.            |
-+----------------------------------------------------+---------------------------------------------------+
-| lil_matrix(arg1[, shape, dtype, copy])             |Row-based linked list sparse matrix                |
-+----------------------------------------------------+---------------------------------------------------+
-
-
-在这些存储格式中：
-
-- COO 格式在构建矩阵时比较高效
-- CSC 和 CSR 格式在乘法计算时比较高效
-
-
-构建稀疏矩阵
---------------
-
-
-
-::
-
-    from scipy.sparse import *
-    import numpy as np
-
-
-创建一个空的稀疏矩阵：
-
-
-
-
-::
-
-    coo_matrix((2,3))
-
-
-
-结果:
-::
-
-    <2x3 sparse matrix of type '<type 'numpy.float64'>'
-    with 0 stored elements in COOrdinate format>
-
-
-也可以使用一个已有的矩阵或数组或列表中创建新矩阵：
-
-
-
-::
-
-    A = coo_matrix([[1,2,0],[0,0,3],[4,0,5]])
-    print A
-
-
-结果:
-::
-
-    (0, 0)	1
-
-    (0, 1)	2
-
-    (1, 2)	3
-
-    (2, 0)	4
-
-    (2, 2)	5
-
-不同格式的稀疏矩阵可以相互转化：
-
-
-
-
-::
-
-    type(A)
-
-
-
-结果:
-::
-
-    scipy.sparse.coo.coo_matrix
-
-
-
-
-::
-
-    B = A.tocsr()
-
-
-    type(B)
-
-
-
-结果:
-::
-
-    scipy.sparse.csr.csr_matrix
-
-
-可以转化为普通矩阵：
-
-
-
-
-::
-
-    C = A.todense()
-    C
-
-
-
-结果:
-::
-
-    matrix([[1, 2, 0],
-
-               [0, 0, 3],
-
-               [4, 0, 5]])
-
-与向量的乘法：
-
-
-
-
-::
-
-    v = np.array([1,0,-1])
-    A.dot(v)
-
-
-
-结果:
-::
-
-    array([ 1, -3, -1])
-
-
-还可以传入一个 (data, (row, col)) 的元组来构建稀疏矩阵：
-
-
-
-
-::
-
-    I = np.array([0,3,1,0])
-    J = np.array([0,3,1,2])
-    V = np.array([4,5,7,9])
-    A = coo_matrix((V,(I,J)),shape=(4,4))
-
-
-
-
-
-::
-
-  print A
-
-结果:
-::
-
-    (0, 0)	4
-
-    (3, 3)	5
-
-    (1, 1)	7
-
-    (0, 2)	9
-
-COO 格式的稀疏矩阵在构建的时候只是简单的将坐标和值加到后面，对于重复的坐标不进行处理：
-
-
-
-::
-
-    I = np.array([0,0,1,3,1,0,0])
-    J = np.array([0,2,1,3,1,0,0])
-    V = np.array([1,1,1,1,1,1,1])
-    B = coo_matrix((V,(I,J)),shape=(4,4))
-
-
-    print B
-
-
-结果:
-::
-
-  (0, 0)	1
-
-  (0, 2)	1
-
-  (1, 1)	1
-
-  (3, 3)	1
-
-  (1, 1)	1
-
-  (0, 0)	1
-
-  (0, 0)	1
-
-
-转换成 CSR 格式会自动将相同坐标的值合并：
-
-
-
-
-::
-
-    C = B.tocsr()
-    print C
-
-
-结果:
-::
-
-  (0, 0)	3
-
-  (0, 2)	1
-
-  (1, 1)	2
-
-  (3, 3)	1
-
-求解微分方程
---------------
-
-
-
-
-
-::
-
-    from scipy.sparse import lil_matrix
-    from scipy.sparse.linalg import spsolve
-    from numpy.linalg import solve, norm
-    from numpy.random import rand
-
-
-构建 1000 x 1000 的稀疏矩阵：
-
-
-
-
-::
-
-    A = lil_matrix((1000, 1000))
-    A[0, :100] = rand(100)
-    A[1, 100:200] = A[0, :100]
-    A.setdiag(rand(1000))
-
-
-转化为 CSR 之后，用 spsolve 求解 $Ax=b$：
-
-
-
-
-
-::
-
-    A = A.tocsr()
-    b = rand(1000)
-    x = spsolve(A, b)
-
-
-转化成正常数组之后求解：
-
-
-
-
-::
-
-    x_ = solve(A.toarray(), b)
-
-
-查看误差：
-
-
-
-
-::
-
-    err = norm(x-x_)
-    err
-
-
-结果:
-::
-
-    6.4310987107687431e-13
-
-sparse.find 函数
---------------------
-
-
-返回一个三元组，表示稀疏矩阵中非零元素的 (row, col, value)：
-
-
-
-
-::
-
-    from scipy import sparse
-
-    row, col, val = sparse.find(C)
-    print row, col, val
-
-
-结果:
-::
-
-    [0 0 1 3] [0 2 1 3] [3 1 2 1]
-
-
-sparse.issparse 函数
----------------------------
-
-
-查看一个对象是否为稀疏矩阵：
-
-
-
-
-::
-
-    sparse.issparse(B)
-
-
-结果:
-::
-
-    True
-
-或者
-
-
-
-
-::
-
-    sparse.isspmatrix(B.todense())
-
-
-结果:
-::
-
-    False
-
-还可以查询是否为指定格式的稀疏矩阵：
-
-
-
-
-::
-
-    sparse.isspmatrix_coo(B)
-
-
-结果:
-::
-
-    True
-
-
-
-
-::
-
-    sparse.isspmatrix_csr(B)
-
-
-结果:
-::
-
-    False
-
-
-
-作者 & 更新时间
-------------------------------------
-作者:`李金  <lijinwithyou@gmail.com>`
-
-提交: 2017/12/14
diff --git a/docs/sources/aipy-scipy/index.rst.txt b/docs/sources/aipy-scipy/index.rst.txt
deleted file mode 100644
index 3d4bdc7..0000000
--- a/docs/sources/aipy-scipy/index.rst.txt
+++ /dev/null
@@ -1,19 +0,0 @@
-Scipy Programming
-=======================
-The courses are still in the planning. Welcome to reward us. 
-
-1. **Primary course**
-
-If the total number of people who reward us is more than 100, we promise to complete the primary course within 1 week, so that, after finishing your study, you can master the basic concepts and write simple scripts.

-
-2. **Intermediate course**
-
-If the total number of people who reward us is more than 500, we will launch the intermediate course within 2 weeks, so that, after finishing your study, you will be familiar with the knowledge and framework of the course and apply it to practical development.
-
-3. **Advanced course**
-
-If the total number of people who reward us is more than 2,000, we will launch the advanced course in a month, so that, after finishing your study, you will have a better understanding of the principles of the framework and be able to do advanced development.
-
-
-**The primary course is free to all, and the intermediate and advanced courses are only open to those who have rewarded us.**
-
diff --git a/docs/sources/aipy-tensorflow/index.rst.txt b/docs/sources/aipy-tensorflow/index.rst.txt
deleted file mode 100644
index 1bcbd65..0000000
--- a/docs/sources/aipy-tensorflow/index.rst.txt
+++ /dev/null
@@ -1,19 +0,0 @@
-Tensorflow Programming
-=======================
-The courses are still in the planning. Welcome to reward us. 
-
-1. **Primary course**
-
-If the total number of people who reward us is more than 100, we promise to complete the primary course within 1 week, so that, after finishing your study, you can master the basic concepts and write simple scripts.

-
-2. **Intermediate course**
-
-If the total number of people who reward us is more than 500, we will launch the intermediate course within 2 weeks, so that, after finishing your study, you will be familiar with the knowledge and framework of the course and apply it to practical development.
-
-3. **Advanced course**
-
-If the total number of people who reward us is more than 2,000, we will launch the advanced course in a month, so that, after finishing your study, you will have a better understanding of the principles of the framework and be able to do advanced development.
-
-
-**The primary course is free to all, and the intermediate and advanced courses are only open to those who have rewarded us.**
-
diff --git a/docs/sources/aipy-theano/index.rst.txt b/docs/sources/aipy-theano/index.rst.txt
deleted file mode 100644
index 1490255..0000000
--- a/docs/sources/aipy-theano/index.rst.txt
+++ /dev/null
@@ -1,19 +0,0 @@
-Theano Programming
-=======================
-The courses are still in the planning. Welcome to reward us. 
-
-1. **Primary course**
-
-If the total number of people who reward us is more than 100, we promise to complete the primary course within 1 week, so that, after finishing your study, you can master the basic concepts and write simple scripts.

-
-2. **Intermediate course**
-
-If the total number of people who reward us is more than 500, we will launch the intermediate course within 2 weeks, so that, after finishing your study, you will be familiar with the knowledge and framework of the course and apply it to practical development.
-
-3. **Advanced course**
-
-If the total number of people who reward us is more than 2,000, we will launch the advanced course in a month, so that, after finishing your study, you will have a better understanding of the principles of the framework and be able to do advanced development.
-
-
-**The primary course is free to all, and the intermediate and advanced courses are only open to those who have rewarded us.**
-
diff --git a/docs/sources/ar/index.rst.txt b/docs/sources/ar/index.rst.txt
deleted file mode 100644
index aa27495..0000000
--- a/docs/sources/ar/index.rst.txt
+++ /dev/null
@@ -1,19 +0,0 @@
-AR Programming with Python
-=============================
-The courses are still in the planning. Welcome to reward us. 
-
-1. **Primary course**
-
-If the total number of people who reward us is more than 100, we promise to complete the primary course within 1 week, so that, after finishing your study, you can master the basic concepts and write simple scripts.

-
-2. **Intermediate course**
-
-If the total number of people who reward us is more than 500, we will launch the intermediate course within 2 weeks, so that, after finishing your study, you will be familiar with the knowledge and framework of the course and apply it to practical development.
-
-3. **Advanced course**
-
-If the total number of people who reward us is more than 2,000, we will launch the advanced course in a month, so that, after finishing your study, you will have a better understanding of the principles of the framework and be able to do advanced development.
-
-
-**The primary course is free to all, and the intermediate and advanced courses are only open to those who have rewarded us.**
-
diff --git a/docs/sources/blockchain/index.rst.txt b/docs/sources/blockchain/index.rst.txt
deleted file mode 100644
index 2995cc5..0000000
--- a/docs/sources/blockchain/index.rst.txt
+++ /dev/null
@@ -1,19 +0,0 @@
-Blockchain with Python
-=======================
-The courses are still in the planning. Welcome to reward us. 
-
-1. **Primary course**
-
-If the total number of people who reward us is more than 100, we promise to complete the primary course within 1 week, so that, after finishing your study, you can master the basic concepts and write simple scripts.

-
-2. **Intermediate course**
-
-If the total number of people who reward us is more than 500, we will launch the intermediate course within 2 weeks, so that, after finishing your study, you will be familiar with the knowledge and framework of the course and apply it to practical development.
-
-3. **Advanced course**
-
-If the total number of people who reward us is more than 2,000, we will launch the advanced course in a month, so that, after finishing your study, you will have a better understanding of the principles of the framework and be able to do advanced development.
-
-
-**The primary course is free to all, and the intermediate and advanced courses are only open to those who have rewarded us.**
-
diff --git a/docs/sources/bottle-web/index.rst.txt b/docs/sources/bottle-web/index.rst.txt
deleted file mode 100644
index d358d7b..0000000
--- a/docs/sources/bottle-web/index.rst.txt
+++ /dev/null
@@ -1,19 +0,0 @@
-Bottle Web
-=======================
-The courses are still in the planning. Welcome to reward us. 
-
-1. **Primary course**
-
-If the total number of people who reward us is more than 100, we promise to complete the primary course within 1 week, so that, after finishing your study, you can master the basic concepts and write simple scripts.
-
-2. **Intermediate course**
-
-If the total number of people who reward us is more than 500, we will launch the intermediate course within 2 weeks, so that, after finishing your study, you will be familiar with the knowledge and framework of the course and apply it to practical development.
-
-3. **Advanced course**
-
-If the total number of people who reward us is more than 2,000, we will launch the advanced course in a month, so that, after finishing your study, you will have a better understanding of the principles of the framework and be able to do advanced development.
-
-
-**The primary course is free to all, and the intermediate and advanced courses are only open to those who have rewarded us.**
-
diff --git a/docs/sources/bottle-web/tutorial.rst.txt b/docs/sources/bottle-web/tutorial.rst.txt
deleted file mode 100644
index e13ab34..0000000
--- a/docs/sources/bottle-web/tutorial.rst.txt
+++ /dev/null
@@ -1,1073 +0,0 @@
-pache Server:
-.. _Apache: http://www.apache.org/
-.. _cherrypy: http://www.cherrypy.org/
-.. _decorator: http://docs.python.org/glossary.html#term-decorator
-.. _flup: http://trac.saddi.com/flup
-.. _http_code: http://www.w3.org/Protocols/rfc2616/rfc2616-sec10.html
-.. _http_method: http://www.w3.org/Protocols/rfc2616/rfc2616-sec9.html
-.. _json: http://de.wikipedia.org/wiki/JavaScript_Object_Notation
-.. _lighttpd: http://www.lighttpd.net/
-.. _mako: http://www.makotemplates.org/
-.. _mod_wsgi: http://code.google.com/p/modwsgi/
-.. _Paste: http://pythonpaste.org/
-.. _Pound: http://www.apsis.ch/pound/
-.. _`WSGI Specification`: http://www.wsgi.org/
-.. _issue: http://github.com/bottlepy/bottle/issues
-.. _Python: http://python.org/
-.. _SimpleCookie: http://docs.python.org/library/cookie.html#morsel-objects
-.. _testing: http://github.com/bottlepy/bottle/raw/master/bottle.py
-
-========
-Tutorial
-========
-
-This tutorial introduces you to the concepts and features of the Bottle web framework and covers basic and advanced topics alike. You can read it from start to end, or use it as a reference later on. The automatically generated :doc:`api` may be interesting for you, too. It covers more details, but explains less than this tutorial. Solutions for the most common questions can be found in our :doc:`recipes` collection or on the :doc:`faq` page. If you need any help, join our `mailing list <mailto:bottlepy@googlegroups.com>`_ or visit us in our `IRC channel <http://webchat.freenode.net/?channels=bottlepy>`_.
-
-.. _installation:
-
-Installation
-==============================================================================
-
-Bottle does not depend on any external libraries. You can just download `bottle.py </bottle.py>`_ into your project directory and start coding:
-
-.. code-block:: bash
-
-    $ wget https://bottlepy.org/bottle.py
-
-This will get you the latest development snapshot that includes all the new features. If you prefer a more stable environment, you should stick with the stable releases. These are available on `PyPI <http://pypi.python.org/pypi/bottle>`_ and can be installed via :command:`pip` (recommended), :command:`easy_install` or your package manager:
-
-.. code-block:: bash
-
-    $ sudo pip install bottle              # recommended
-    $ sudo easy_install bottle             # alternative without pip
-    $ sudo apt-get install python-bottle   # works for debian, ubuntu, ...
-
-Either way, you'll need Python 2.7 or newer (including 3.2+) to run bottle applications. If you do not have permissions to install packages system-wide or simply don't want to, create a `virtualenv <http://pypi.python.org/pypi/virtualenv>`_ first:
-
-.. code-block:: bash
-
-    $ virtualenv develop              # Create virtual environment
-    $ source develop/bin/activate     # Change default python to virtual one
-    (develop)$ pip install -U bottle  # Install bottle to virtual environment
-
-Or, if virtualenv is not installed on your system:
-
-.. code-block:: bash
-
-    $ wget https://raw.github.com/pypa/virtualenv/master/virtualenv.py
-    $ python virtualenv.py develop    # Create virtual environment
-    $ source develop/bin/activate     # Change default python to virtual one
-    (develop)$ pip install -U bottle  # Install bottle to virtual environment
-
-
-
-Quickstart: "Hello World"
-==============================================================================
-
-This tutorial assumes you have Bottle either :ref:`installed <installation>` or copied into your project directory. Let's start with a very basic "Hello World" example::
-
-    from bottle import route, run
-
-    @route('/hello')
-    def hello():
-        return "Hello World!"
-
-    run(host='localhost', port=8080, debug=True)
-
-This is it. Run this script, visit http://localhost:8080/hello and you will see "Hello World!" in your browser. Here is how it works:
-
-The :func:`route` decorator binds a piece of code to an URL path. In this case, we link the ``/hello`` path to the ``hello()`` function. This is called a `route` (hence the decorator name) and is the most important concept of this framework. You can define as many routes as you want. Whenever a browser requests a URL, the associated function is called and the return value is sent back to the browser. It's as simple as that.
-
-The :func:`run` call in the last line starts a built-in development server. It runs on ``localhost`` port ``8080`` and serves requests until you hit :kbd:`Control-c`. You can switch the server backend later, but for now a development server is all we need. It requires no setup at all and is an incredibly painless way to get your application up and running for local tests.
-
-The :ref:`tutorial-debugging` is very helpful during early development, but should be switched off for public applications. Keep that in mind.
-
-This is just a demonstration of the basic concept of how applications are built with Bottle. Continue reading and you'll see what else is possible.
-
-.. _tutorial-default:
-
-The Default Application
-------------------------------------------------------------------------------
-
-For the sake of simplicity, most examples in this tutorial use a module-level :func:`route` decorator to define routes. This adds routes to a global "default application", an instance of :class:`Bottle` that is automatically created the first time you call :func:`route`. Several other module-level decorators and functions relate to this default application, but if you prefer a more object oriented approach and don't mind the extra typing, you can create a separate application object and use that instead of the global one::
-
-    from bottle import Bottle, run
-
-    app = Bottle()
-
-    @app.route('/hello')
-    def hello():
-        return "Hello World!"
-
-    run(app, host='localhost', port=8080)
-
-The object-oriented approach is further described in the :ref:`default-app` section. Just keep in mind that you have a choice.
-
-
-
-
-.. _tutorial-routing:
-
-Request Routing
-==============================================================================
-
-In the last chapter we built a very simple web application with only a single route. Here is the routing part of the "Hello World" example again::
-
-    @route('/hello')
-    def hello():
-        return "Hello World!"
-
-The :func:`route` decorator links an URL path to a callback function, and adds a new route to the :ref:`default application <tutorial-default>`. An application with just one route is kind of boring, though. Let's add some more (don't forget ``from bottle import template``)::
-
-    @route('/')
-    @route('/hello/<name>')
-    def greet(name='Stranger'):
-        return template('Hello {{name}}, how are you?', name=name)
-
-This example demonstrates two things: You can bind more than one route to a single callback, and you can add wildcards to URLs and access them via keyword arguments.
-
-
-
-.. _tutorial-dynamic-routes:
-
-Dynamic Routes
--------------------------------------------------------------------------------
-
-Routes that contain wildcards are called `dynamic routes` (as opposed to `static routes`) and match more than one URL at the same time. A simple wildcard consists of a name enclosed in angle brackets (e.g. ``<name>``) and accepts one or more characters up to the next slash (``/``). For example, the route ``/hello/<name>`` accepts requests for ``/hello/alice`` as well as ``/hello/bob``, but not for ``/hello``, ``/hello/`` or ``/hello/mr/smith``.
-
-Each wildcard passes the covered part of the URL as a keyword argument to the request callback. You can use them right away and implement RESTful, nice-looking and meaningful URLs with ease. Here are some other examples along with the URLs they'd match::
-
-    @route('/wiki/<pagename>')            # matches /wiki/Learning_Python
-    def show_wiki_page(pagename):
-        ...
-
-    @route('/<action>/<user>')            # matches /follow/defnull
-    def user_api(action, user):
-        ...
-
-Filters can be used to define more specific wildcards, and/or transform the covered part of the URL before it is passed to the callback. A filtered wildcard is declared as ``<name:filter>`` or ``<name:filter:config>``. The syntax for the optional config part depends on the filter used.
-
-The following filters are implemented by default and more may be added:
-
-* **:int** matches (signed) digits only and converts the value to integer.
-* **:float** similar to :int but for decimal numbers.
-* **:path** matches all characters including the slash character in a non-greedy way and can be used to match more than one path segment.
-* **:re** allows you to specify a custom regular expression in the config field. The matched value is not modified.
-
-Let's have a look at some practical examples::
-
-    @route('/object/<id:int>')
-    def callback(id):
-        assert isinstance(id, int)
-
-    @route('/show/<name:re:[a-z]+>')
-    def callback(name):
-        assert name.isalpha()
-
-    @route('/static/<path:path>')
-    def callback(path):
-        return static_file(path, ...)
-
-You can add your own filters as well. See :doc:`routing` for details.
-
-
-HTTP Request Methods
-------------------------------------------------------------------------------
-
-.. __: http_method_
-
-The HTTP protocol defines several `request methods`__ (sometimes referred to as "verbs") for different tasks. GET is the default for all routes with no other method specified. These routes will match GET requests only. To handle other methods such as POST, PUT, DELETE or PATCH, add a ``method`` keyword argument to the :func:`route` decorator or use one of the five alternative decorators: :func:`get`, :func:`post`, :func:`put`, :func:`delete` or :func:`patch`.
-
-The POST method is commonly used for HTML form submission. This example shows how to handle a login form using POST::
-
-    from bottle import get, post, request # or route
-
-    @get('/login') # or @route('/login')
-    def login():
-        return '''
-            <form action="/login" method="post">
-                Username: <input name="username" type="text" />
-                Password: <input name="password" type="password" />
-                <input value="Login" type="submit" />
-            </form>
-        '''
-
-    @post('/login') # or @route('/login', method='POST')
-    def do_login():
-        username = request.forms.get('username')
-        password = request.forms.get('password')
-        if check_login(username, password):
-            return "<p>Your login information was correct.</p>"
-        else:
-            return "<p>Login failed.</p>"
-
-In this example the ``/login`` URL is linked to two distinct callbacks, one for GET requests and another for POST requests. The first one displays a HTML form to the user. The second callback is invoked on a form submission and checks the login credentials the user entered into the form. The use of :attr:`Request.forms` is further described in the :ref:`tutorial-request` section.
-
-.. rubric:: Special Methods: HEAD and ANY
-
-The HEAD method is used to ask for the response identical to the one that would correspond to a GET request, but without the response body. This is useful for retrieving meta-information about a resource without having to download the entire document. Bottle handles these requests automatically by falling back to the corresponding GET route and cutting off the request body, if present. You don't have to specify any HEAD routes yourself.
-
-Additionally, the non-standard ANY method works as a low priority fallback: Routes that listen to ANY will match requests regardless of their HTTP method but only if no other more specific route is defined. This is helpful for *proxy-routes* that redirect requests to more specific sub-applications.
-
-To sum it up: HEAD requests fall back to GET routes and all requests fall back to ANY routes, but only if there is no matching route for the original request method. It's as simple as that.
-
-
-
-Routing Static Files
-------------------------------------------------------------------------------
-
-Static files such as images or CSS files are not served automatically. You have to add a route and a callback to control which files get served and where to find them::
-
-  from bottle import static_file
-  @route('/static/<filename>')
-  def server_static(filename):
-      return static_file(filename, root='/path/to/your/static/files')
-
-The :func:`static_file` function is a helper to serve files in a safe and convenient way (see :ref:`tutorial-static-files`). This example is limited to files directly within the ``/path/to/your/static/files`` directory because the ``<filename>`` wildcard won't match a path with a slash in it. To serve files in subdirectories, change the wildcard to use the `path` filter::
-
-  @route('/static/<filepath:path>')
-  def server_static(filepath):
-      return static_file(filepath, root='/path/to/your/static/files')
-
-Be careful when specifying a relative root-path such as ``root='./static/files'``. The working directory (``./``) and the project directory are not always the same.
-
-
-
-
-.. _tutorial-errorhandling:
-
-Error Pages
-------------------------------------------------------------------------------
-
-If anything goes wrong, Bottle displays an informative but fairly plain error page. You can override the default for a specific HTTP status code with the :func:`error` decorator::
-
-  from bottle import error
-  @error(404)
-  def error404(error):
-      return 'Nothing here, sorry'
-
-From now on, `404 File not Found` errors will display a custom error page to the user. The only parameter passed to the error-handler is an instance of :exc:`HTTPError`. Apart from that, an error-handler is quite similar to a regular request callback. You can read from :data:`request`, write to :data:`response` and return any supported data-type except for :exc:`HTTPError` instances.
-
-Error handlers are used only if your application returns or raises an :exc:`HTTPError` exception (:func:`abort` does just that). Changing :attr:`Request.status` or returning :exc:`HTTPResponse` won't trigger the error handler.
-
-
-
-
-
-
-.. _tutorial-output:
-
-Generating content
-==============================================================================
-
-In pure WSGI, the range of types you may return from your application is very limited. Applications must return an iterable yielding byte strings. You may return a string (because strings are iterable) but this causes most servers to transmit your content char by char. Unicode strings are not allowed at all. This is not very practical.
-
-Bottle is much more flexible and supports a wide range of types. It even adds a ``Content-Length`` header if possible and encodes unicode automatically, so you don't have to. What follows is a list of data types you may return from your application callbacks and a short description of how these are handled by the framework:
-
-Dictionaries
-    As mentioned above, Python dictionaries (or subclasses thereof) are automatically transformed into JSON strings and returned to the browser with the ``Content-Type`` header set to ``application/json``. This makes it easy to implement json-based APIs. Data formats other than json are supported too. See the :ref:`tutorial-output-filter` to learn more.
-
-Empty Strings, ``False``, ``None`` or other non-true values:
-    These produce an empty output with the ``Content-Length`` header set to 0.
-
-Unicode strings
-    Unicode strings (or iterables yielding unicode strings) are automatically encoded with the codec specified in the ``Content-Type`` header (utf8 by default) and then treated as normal byte strings (see below).
-
-Byte strings
-    Bottle returns strings as a whole (instead of iterating over each char) and adds a ``Content-Length`` header based on the string length. Lists of byte strings are joined first. Other iterables yielding byte strings are not joined because they may grow too big to fit into memory. The ``Content-Length`` header is not set in this case.
-
-Instances of :exc:`HTTPError` or :exc:`HTTPResponse`
-    Returning these has the same effect as when raising them as an exception. In case of an :exc:`HTTPError`, the error handler is applied. See :ref:`tutorial-errorhandling` for details.
-
-File objects
-    Everything that has a ``.read()`` method is treated as a file or file-like object and passed to the ``wsgi.file_wrapper`` callable defined by the WSGI server framework. Some WSGI server implementations can make use of optimized system calls (sendfile) to transmit files more efficiently. In other cases this just iterates over chunks that fit into memory. Optional headers such as ``Content-Length`` or ``Content-Type`` are *not* set automatically. Use :func:`send_file` if possible. See :ref:`tutorial-static-files` for details.
-
-Iterables and generators
-    You are allowed to use ``yield`` within your callbacks or return an iterable, as long as the iterable yields byte strings, unicode strings, :exc:`HTTPError` or :exc:`HTTPResponse` instances. Nested iterables are not supported, sorry. Please note that the HTTP status code and the headers are sent to the browser as soon as the iterable yields its first non-empty value. Changing these later has no effect.
-
-The ordering of this list is significant. You may for example return a subclass of :class:`str` with a ``read()`` method. It is still treated as a string instead of a file, because strings are handled first.
-
-.. rubric:: Changing the Default Encoding
-
-Bottle uses the `charset` parameter of the ``Content-Type`` header to decide how to encode unicode strings. This header defaults to ``text/html; charset=UTF8`` and can be changed using the :attr:`Response.content_type` attribute or by setting the :attr:`Response.charset` attribute directly. (The :class:`Response` object is described in the section :ref:`tutorial-response`.)
-
-::
-
-    from bottle import response
-    @route('/iso')
-    def get_iso():
-        response.charset = 'ISO-8859-15'
-        return u'This will be sent with ISO-8859-15 encoding.'
-
-    @route('/latin9')
-    def get_latin():
-        response.content_type = 'text/html; charset=latin9'
-        return u'ISO-8859-15 is also known as latin9.'
-
-In some rare cases the Python encoding names differ from the names supported by the HTTP specification. Then, you have to do both: first set the :attr:`Response.content_type` header (which is sent to the client unchanged) and then set the :attr:`Response.charset` attribute (which is used to encode unicode).
-
-.. _tutorial-static-files:
-
-Static Files
---------------------------------------------------------------------------------
-
-You can directly return file objects, but :func:`static_file` is the recommended way to serve static files. It automatically guesses a mime-type, adds a ``Last-Modified`` header, restricts paths to a ``root`` directory for security reasons and generates appropriate error responses (403 on permission errors, 404 on missing files). It even supports the ``If-Modified-Since`` header and eventually generates a ``304 Not Modified`` response. You can pass a custom MIME type to disable guessing.
-
-::
-
-    from bottle import static_file
-    @route('/images/<filename:re:.*\.png>')
-    def send_image(filename):
-        return static_file(filename, root='/path/to/image/files', mimetype='image/png')
-
-    @route('/static/<filename:path>')
-    def send_static(filename):
-        return static_file(filename, root='/path/to/static/files')
-
-You can raise the return value of :func:`static_file` as an exception if you really need to.
-
-.. rubric:: Forced Download
-
-Most browsers try to open downloaded files if the MIME type is known and assigned to an application (e.g. PDF files). If this is not what you want, you can force a download dialog and even suggest a filename to the user::
-
-    @route('/download/<filename:path>')
-    def download(filename):
-        return static_file(filename, root='/path/to/static/files', download=filename)
-
-If the ``download`` parameter is just ``True``, the original filename is used.
-
-.. _tutorial-error:
-
-HTTP Errors and Redirects
---------------------------------------------------------------------------------
-
-The :func:`abort` function is a shortcut for generating HTTP error pages.
-
-::
-
-    from bottle import route, abort
-    @route('/restricted')
-    def restricted():
-        abort(401, "Sorry, access denied.")
-
-To redirect a client to a different URL, you can send a ``303 See Other`` response with the ``Location`` header set to the new URL. :func:`redirect` does that for you::
-
-    from bottle import redirect
-    @route('/wrong/url')
-    def wrong():
-        redirect("/right/url")
-
-You may provide a different HTTP status code as a second parameter.
-
-.. note::
-    Both functions will interrupt your callback code by raising an :exc:`HTTPError` exception.
-
-.. rubric:: Other Exceptions
-
-All exceptions other than :exc:`HTTPResponse` or :exc:`HTTPError` will result in a ``500 Internal Server Error`` response, so they won't crash your WSGI server. You can turn off this behavior to handle exceptions in your middleware by setting ``bottle.app().catchall`` to ``False``.
-
-
-.. _tutorial-response:
-
-The :class:`Response` Object
---------------------------------------------------------------------------------
-
-Response metadata such as the HTTP status code, response headers and cookies are stored in an object called :data:`response` up to the point where they are transmitted to the browser. You can manipulate these metadata directly or use the predefined helper methods to do so. The full API and feature list is described in the API section (see :class:`Response`), but the most common use cases and features are covered here, too.
-
-.. rubric:: Status Code
-
-The `HTTP status code <http_code>`_ controls the behavior of the browser and defaults to ``200 OK``. In most scenarios you won't need to set the :attr:`Response.status` attribute manually, but use the :func:`abort` helper or return an :exc:`HTTPResponse` instance with the appropriate status code. Any integer is allowed, but codes other than the ones defined by the `HTTP specification <http_code>`_ will only confuse the browser and break standards.
-
-.. rubric:: Response Header
-
-Response headers such as ``Cache-Control`` or ``Location`` are defined via :meth:`Response.set_header`. This method takes two parameters, a header name and a value. The name part is case-insensitive::
-
-  @route('/wiki/<page>')
-  def wiki(page):
-      response.set_header('Content-Language', 'en')
-      ...
-
-Most headers are unique, meaning that only one header per name is send to the client. Some special headers however are allowed to appear more than once in a response. To add an additional header, use :meth:`Response.add_header` instead of :meth:`Response.set_header`::
-
-    response.set_header('Set-Cookie', 'name=value')
-    response.add_header('Set-Cookie', 'name2=value2')
-
-Please note that this is just an example. If you want to work with cookies, read :ref:`ahead <tutorial-cookies>`.
-
-
-.. _tutorial-cookies:
-
-Cookies
--------------------------------------------------------------------------------
-
-A cookie is a named piece of text stored in the user's browser profile. You can access previously defined cookies via :meth:`Request.get_cookie` and set new cookies with :meth:`Response.set_cookie`::
-
-    @route('/hello')
-    def hello_again():
-        if request.get_cookie("visited"):
-            return "Welcome back! Nice to see you again"
-        else:
-            response.set_cookie("visited", "yes")
-            return "Hello there! Nice to meet you"
-
-The :meth:`Response.set_cookie` method accepts a number of additional keyword arguments that control the cookies lifetime and behavior. Some of the most common settings are described here:
-
-* **max_age:**    Maximum age in seconds. (default: ``None``)
-* **expires:**    A datetime object or UNIX timestamp. (default: ``None``)
-* **domain:**     The domain that is allowed to read the cookie. (default: current domain)
-* **path:**       Limit the cookie to a given path (default: ``/``)
-* **secure:**     Limit the cookie to HTTPS connections (default: off).
-* **httponly:**   Prevent client-side javascript to read this cookie (default: off, requires Python 2.7 or newer).
-* **same_site:**  Disables third-party use for a cookie. Allowed attributes: `lax` and `strict`. In strict mode the cookie will never be sent. In lax mode the cookie is only sent with a top-level GET request.
-
-If neither `expires` nor `max_age` is set, the cookie expires at the end of the browser session or as soon as the browser window is closed. There are some other gotchas you should consider when using cookies:
-
-* Cookies are limited to 4 KB of text in most browsers.
-* Some users configure their browsers to not accept cookies at all. Most search engines ignore cookies too. Make sure that your application still works without cookies.
-* Cookies are stored at client side and are not encrypted in any way. Whatever you store in a cookie, the user can read it. Worse than that, an attacker might be able to steal a user's cookies through `XSS <http://en.wikipedia.org/wiki/HTTP_cookie#Cookie_theft_and_session_hijacking>`_ vulnerabilities on your side. Some viruses are known to read the browser cookies, too. Thus, never store confidential information in cookies.
-* Cookies are easily forged by malicious clients. Do not trust cookies.
-
-.. _tutorial-signed-cookies:
-
-.. rubric:: Signed Cookies
-
-As mentioned above, cookies are easily forged by malicious clients. Bottle can cryptographically sign your cookies to prevent this kind of manipulation. All you have to do is to provide a signature key via the `secret` keyword argument whenever you read or set a cookie and keep that key a secret. As a result, :meth:`Request.get_cookie` will return ``None`` if the cookie is not signed or the signature keys don't match::
-
-    @route('/login')
-    def do_login():
-        username = request.forms.get('username')
-        password = request.forms.get('password')
-        if check_login(username, password):
-            response.set_cookie("account", username, secret='some-secret-key')
-            return template("<p>Welcome {{name}}! You are now logged in.</p>", name=username)
-        else:
-            return "<p>Login failed.</p>"
-
-    @route('/restricted')
-    def restricted_area():
-        username = request.get_cookie("account", secret='some-secret-key')
-        if username:
-            return template("Hello {{name}}. Welcome back.", name=username)
-        else:
-            return "You are not logged in. Access denied."
-
-In addition, Bottle automatically pickles and unpickles any data stored to signed cookies. This allows you to store any pickle-able object (not only strings) to cookies, as long as the pickled data does not exceed the 4 KB limit.
-
-.. warning:: Signed cookies are not encrypted (the client can still see the content) and not copy-protected (the client can restore an old cookie). The main intention is to make pickling and unpickling safe and prevent manipulation, not to store secret information at client side.
-
-
-
-
-
-
-
-
-
-.. _tutorial-request:
-
-Request Data
-==============================================================================
-
-Cookies, HTTP header, HTML ``<form>`` fields and other request data is available through the global :data:`request` object. This special object always refers to the *current* request, even in multi-threaded environments where multiple client connections are handled at the same time::
-
-  from bottle import request, route, template
-
-  @route('/hello')
-  def hello():
-      name = request.cookies.username or 'Guest'
-      return template('Hello {{name}}', name=name)
-
-The :data:`request` object is a subclass of :class:`BaseRequest` and has a very rich API to access data. We only cover the most commonly used features here, but it should be enough to get started.
-
-
-
-Introducing :class:`FormsDict`
---------------------------------------------------------------------------------
-
-Bottle uses a special type of dictionary to store form data and cookies. :class:`FormsDict` behaves like a normal dictionary, but has some additional features to make your life easier.
-
-**Attribute access**: All values in the dictionary are also accessible as attributes. These virtual attributes return unicode strings, even if the value is missing or unicode decoding fails. In that case, the string is empty, but still present::
-
-  name = request.cookies.name
-
-  # is a shortcut for:
-
-  name = request.cookies.getunicode('name') # encoding='utf-8' (default)
-
-  # which basically does this:
-
-  try:
-      name = request.cookies.get('name', '').decode('utf-8')
-  except UnicodeError:
-      name = u''
-
-**Multiple values per key:** :class:`FormsDict` is a subclass of :class:`MultiDict` and can store more than one value per key. The standard dictionary access methods will only return a single value, but the :meth:`~MultiDict.getall` method returns a (possibly empty) list of all values for a specific key::
-
-  for choice in request.forms.getall('multiple_choice'):
-      do_something(choice)
-
-**WTForms support:** Some libraries (e.g. `WTForms <http://wtforms.simplecodes.com/>`_) want all-unicode dictionaries as input. :meth:`FormsDict.decode` does that for you. It decodes all values and returns a copy of itself, while preserving multiple values per key and all the other features.
-
-.. note::
-
-    In **Python 2** all keys and values are byte-strings. If you need unicode, you can call :meth:`FormsDict.getunicode` or fetch values via attribute access. Both methods try to decode the string (default: utf8) and return an empty string if that fails. No need to catch :exc:`UnicodeError`::
-
-      >>> request.query['city']
-      'G\xc3\xb6ttingen'  # A utf8 byte string
-      >>> request.query.city
-      u'Göttingen'        # The same string as unicode
-
-    In **Python 3** all strings are unicode, but HTTP is a byte-based wire protocol. The server has to decode the byte strings somehow before they are passed to the application. To be on the safe side, WSGI suggests ISO-8859-1 (aka latin1), a reversible single-byte codec that can be re-encoded with a different encoding later. Bottle does that for :meth:`FormsDict.getunicode` and attribute access, but not for the dict-access methods. These return the unchanged values as provided by the server implementation, which is probably not what you want.
-
-      >>> request.query['city']
-      'Göttingen' # An utf8 string provisionally decoded as ISO-8859-1 by the server
-      >>> request.query.city
-      'Göttingen'  # The same string correctly re-encoded as utf8 by bottle
-
-    If you need the whole dictionary with correctly decoded values (e.g. for WTForms), you can call :meth:`FormsDict.decode` to get a re-encoded copy.
-
-
-Cookies
---------------------------------------------------------------------------------
-
-Cookies are small pieces of text stored in the clients browser and sent back to the server with each request. They are useful to keep some state around for more than one request (HTTP itself is stateless), but should not be used for security related stuff. They can be easily forged by the client.
-
-All cookies sent by the client are available through :attr:`BaseRequest.cookies` (a :class:`FormsDict`). This example shows a simple cookie-based view counter::
-
-  from bottle import route, request, response
-  @route('/counter')
-  def counter():
-      count = int( request.cookies.get('counter', '0') )
-      count += 1
-      response.set_cookie('counter', str(count))
-      return 'You visited this page %d times' % count
-
-The :meth:`BaseRequest.get_cookie` method is a different way do access cookies. It supports decoding :ref:`signed cookies <tutorial-signed-cookies>` as described in a separate section.
-
-HTTP Headers
---------------------------------------------------------------------------------
-
-All HTTP headers sent by the client (e.g. ``Referer``, ``Agent`` or ``Accept-Language``) are stored in a :class:`WSGIHeaderDict` and accessible through the :attr:`BaseRequest.headers` attribute. A :class:`WSGIHeaderDict` is basically a dictionary with case-insensitive keys::
-
-  from bottle import route, request
-  @route('/is_ajax')
-  def is_ajax():
-      if request.headers.get('X-Requested-With') == 'XMLHttpRequest':
-          return 'This is an AJAX request'
-      else:
-          return 'This is a normal request'
-
-
-Query Variables
---------------------------------------------------------------------------------
-
-The query string (as in ``/forum?id=1&page=5``) is commonly used to transmit a small number of key/value pairs to the server. You can use the :attr:`BaseRequest.query` attribute (a :class:`FormsDict`) to access these values and the :attr:`BaseRequest.query_string` attribute to get the whole string.
-
-::
-
-  from bottle import route, request, response, template
-  @route('/forum')
-  def display_forum():
-      forum_id = request.query.id
-      page = request.query.page or '1'
-      return template('Forum ID: {{id}} (page {{page}})', id=forum_id, page=page)
-
-
-HTML `<form>` Handling
-----------------------
-
-Let us start from the beginning. In HTML, a typical ``<form>`` looks something like this:
-
-.. code-block:: html
-
-    <form action="/login" method="post">
-        Username: <input name="username" type="text" />
-        Password: <input name="password" type="password" />
-        <input value="Login" type="submit" />
-    </form>
-
-The ``action`` attribute specifies the URL that will receive the form data. ``method`` defines the HTTP method to use (``GET`` or ``POST``). With ``method="get"`` the form values are appended to the URL and available through :attr:`BaseRequest.query` as described above. This is considered insecure and has other limitations, so we use ``method="post"`` here. If in doubt, use ``POST`` forms.
-
-Form fields transmitted via ``POST`` are stored in :attr:`BaseRequest.forms` as a :class:`FormsDict`. The server side code may look like this::
-
-    from bottle import route, request
-
-    @route('/login')
-    def login():
-        return '''
-            <form action="/login" method="post">
-                Username: <input name="username" type="text" />
-                Password: <input name="password" type="password" />
-                <input value="Login" type="submit" />
-            </form>
-        '''
-
-    @route('/login', method='POST')
-    def do_login():
-        username = request.forms.get('username')
-        password = request.forms.get('password')
-        if check_login(username, password):
-            return "<p>Your login information was correct.</p>"
-        else:
-            return "<p>Login failed.</p>"
-
-There are several other attributes used to access form data. Some of them combine values from different sources for easier access. The following table should give you a decent overview.
-
-==============================   ===============   ================   ============
-Attribute                        GET Form fields   POST Form fields   File Uploads
-==============================   ===============   ================   ============
-:attr:`BaseRequest.query`        yes               no                 no
-:attr:`BaseRequest.forms`        no                yes                no
-:attr:`BaseRequest.files`        no                no                 yes
-:attr:`BaseRequest.params`       yes               yes                no
-:attr:`BaseRequest.GET`          yes               no                 no
-:attr:`BaseRequest.POST`         no                yes                yes
-==============================   ===============   ================   ============
-
-
-File uploads
-------------
-
-To support file uploads, we have to change the ``<form>`` tag a bit. First, we tell the browser to encode the form data in a different way by adding an ``enctype="multipart/form-data"`` attribute to the ``<form>`` tag. Then, we add ``<input type="file" />`` tags to allow the user to select a file. Here is an example:
-
-.. code-block:: html
-
-    <form action="/upload" method="post" enctype="multipart/form-data">
-      Category:      <input type="text" name="category" />
-      Select a file: <input type="file" name="upload" />
-      <input type="submit" value="Start upload" />
-    </form>
-
-Bottle stores file uploads in :attr:`BaseRequest.files` as :class:`FileUpload` instances, along with some metadata about the upload. Let us assume you just want to save the file to disk::
-
-    @route('/upload', method='POST')
-    def do_upload():
-        category   = request.forms.get('category')
-        upload     = request.files.get('upload')
-        name, ext = os.path.splitext(upload.filename)
-        if ext not in ('.png','.jpg','.jpeg'):
-            return 'File extension not allowed.'
-
-        save_path = get_save_path_for_category(category)
-        upload.save(save_path) # appends upload.filename automatically
-        return 'OK'
-
-:attr:`FileUpload.filename` contains the name of the file on the clients file system, but is cleaned up and normalized to prevent bugs caused by unsupported characters or path segments in the filename. If you need the unmodified name as sent by the client, have a look at :attr:`FileUpload.raw_filename`.
-
-The :attr:`FileUpload.save` method is highly recommended if you want to store the file to disk. It prevents some common errors (e.g. it does not overwrite existing files unless you tell it to) and stores the file in a memory efficient way. You can access the file object directly via :attr:`FileUpload.file`. Just be careful.
-
-
-JSON Content
---------------------
-
-Some JavaScript or REST clients send ``application/json`` content to the server. The :attr:`BaseRequest.json` attribute contains the parsed data structure, if available.
-
-
-The raw request body
---------------------
-
-You can access the raw body data as a file-like object via :attr:`BaseRequest.body`. This is a :class:`BytesIO` buffer or a temporary file depending on the content length and :attr:`BaseRequest.MEMFILE_MAX` setting. In both cases the body is completely buffered before you can access the attribute. If you expect huge amounts of data and want to get direct unbuffered access to the stream, have a look at ``request['wsgi.input']``.
-
-
-
-WSGI Environment
---------------------------------------------------------------------------------
-
-Each :class:`BaseRequest` instance wraps a WSGI environment dictionary. The original is stored in :attr:`BaseRequest.environ`, but the request object itself behaves like a dictionary, too. Most of the interesting data is exposed through special methods or attributes, but if you want to access `WSGI environ variables <WSGI specification>`_ directly, you can do so::
-
-  @route('/my_ip')
-  def show_ip():
-      ip = request.environ.get('REMOTE_ADDR')
-      # or ip = request.get('REMOTE_ADDR')
-      # or ip = request['REMOTE_ADDR']
-      return template("Your IP is: {{ip}}", ip=ip)
-
-
-
-
-
-
-
-.. _tutorial-templates:
-
-Templates
-================================================================================
-
-Bottle comes with a fast and powerful built-in template engine called :doc:`stpl`. To render a template you can use the :func:`template` function or the :func:`view` decorator. All you have to do is to provide the name of the template and the variables you want to pass to the template as keyword arguments. Here’s a simple example of how to render a template::
-
-    @route('/hello')
-    @route('/hello/<name>')
-    def hello(name='World'):
-        return template('hello_template', name=name)
-
-This will load the template file ``hello_template.tpl`` and render it with the ``name`` variable set. Bottle will look for templates in the ``./views/`` folder or any folder specified in the ``bottle.TEMPLATE_PATH`` list.
-
-The :func:`view` decorator allows you to return a dictionary with the template variables instead of calling :func:`template`::
-
-    @route('/hello')
-    @route('/hello/<name>')
-    @view('hello_template')
-    def hello(name='World'):
-        return dict(name=name)
-
-.. rubric:: Syntax
-
-.. highlight:: html+django
-
-The template syntax is a very thin layer around the Python language. Its main purpose is to ensure correct indentation of blocks, so you can format your template without worrying about indentation. Follow the link for a full syntax description: :doc:`stpl`
-
-Here is an example template::
-
-    %if name == 'World':
-        <h1>Hello {{name}}!</h1>
-        <p>This is a test.</p>
-    %else:
-        <h1>Hello {{name.title()}}!</h1>
-        <p>How are you?</p>
-    %end
-
-.. rubric:: Caching
-
-Templates are cached in memory after compilation. Modifications made to the template files will have no affect until you clear the template cache. Call ``bottle.TEMPLATES.clear()`` to do so. Caching is disabled in debug mode.
-
-.. highlight:: python
-
-
-
-
-.. _plugins:
-
-Plugins
-================================================================================
-
-.. versionadded:: 0.9
-
-Bottle's core features cover most common use-cases, but as a micro-framework it has its limits. This is where "Plugins" come into play. Plugins add missing functionality to the framework, integrate third party libraries, or just automate some repetitive work.
-
-We have a growing :doc:`/plugins/index` and most plugins are designed to be portable and re-usable across applications. The chances are high that your problem has already been solved and a ready-to-use plugin exists. If not, the :doc:`/plugindev` may help you.
-
-The effects and APIs of plugins are manifold and depend on the specific plugin. The ``SQLitePlugin`` plugin for example detects callbacks that require a ``db`` keyword argument and creates a fresh database connection object every time the callback is called. This makes it very convenient to use a database::
-
-    from bottle import route, install, template
-    from bottle_sqlite import SQLitePlugin
-
-    install(SQLitePlugin(dbfile='/tmp/test.db'))
-
-    @route('/show/<post_id:int>')
-    def show(db, post_id):
-        c = db.execute('SELECT title, content FROM posts WHERE id = ?', (post_id,))
-        row = c.fetchone()
-        return template('show_post', title=row['title'], text=row['content'])
-
-    @route('/contact')
-    def contact_page():
-        ''' This callback does not need a db connection. Because the 'db'
-            keyword argument is missing, the sqlite plugin ignores this callback
-            completely. '''
-        return template('contact')
-
-Other plugin may populate the thread-safe :data:`local` object, change details of the :data:`request` object, filter the data returned by the callback or bypass the callback completely. An "auth" plugin for example could check for a valid session and return a login page instead of calling the original callback. What happens exactly depends on the plugin.
-
-
-Application-wide Installation
---------------------------------------------------------------------------------
-
-Plugins can be installed application-wide or just to some specific routes that need additional functionality. Most plugins can safely be installed to all routes and are smart enough to not add overhead to callbacks that do not need their functionality.
-
-Let us take the ``SQLitePlugin`` plugin for example. It only affects route callbacks that need a database connection. Other routes are left alone. Because of this, we can install the plugin application-wide with no additional overhead.
-
-To install a plugin, just call :func:`install` with the plugin as first argument::
-
-    from bottle_sqlite import SQLitePlugin
-    install(SQLitePlugin(dbfile='/tmp/test.db'))
-
-The plugin is not applied to the route callbacks yet. This is delayed to make sure no routes are missed. You can install plugins first and add routes later, if you want to. The order of installed plugins is significant, though. If a plugin requires a database connection, you need to install the database plugin first.
-
-
-.. rubric:: Uninstall Plugins
-
-You can use a name, class or instance to :func:`uninstall` a previously installed plugin::
-
-    sqlite_plugin = SQLitePlugin(dbfile='/tmp/test.db')
-    install(sqlite_plugin)
-
-    uninstall(sqlite_plugin) # uninstall a specific plugin
-    uninstall(SQLitePlugin)  # uninstall all plugins of that type
-    uninstall('sqlite')      # uninstall all plugins with that name
-    uninstall(True)          # uninstall all plugins at once
-
-Plugins can be installed and removed at any time, even at runtime while serving requests. This enables some neat tricks (installing slow debugging or profiling plugins only when needed) but should not be overused. Each time the list of plugins changes, the route cache is flushed and all plugins are re-applied.
-
-.. note::
-    The module-level :func:`install` and :func:`uninstall` functions affect the :ref:`default-app`. To manage plugins for a specific application, use the corresponding methods on the :class:`Bottle` application object.
-
-
-Route-specific Installation
---------------------------------------------------------------------------------
-
-The ``apply`` parameter of the :func:`route` decorator comes in handy if you want to install plugins to only a small number of routes::
-
-    sqlite_plugin = SQLitePlugin(dbfile='/tmp/test.db')
-
-    @route('/create', apply=[sqlite_plugin])
-    def create(db):
-        db.execute('INSERT INTO ...')
-
-
-Blacklisting Plugins
---------------------------------------------------------------------------------
-
-You may want to explicitly disable a plugin for a number of routes. The :func:`route` decorator has a ``skip`` parameter for this purpose::
-
-    sqlite_plugin = SQLitePlugin(dbfile='/tmp/test1.db')
-    install(sqlite_plugin)
-
-    dbfile1 = '/tmp/test1.db'
-    dbfile2 = '/tmp/test2.db'
-
-    @route('/open/<db>', skip=[sqlite_plugin])
-    def open_db(db):
-        # The 'db' keyword argument is not touched by the plugin this time.
-
-        # The plugin handle can be used for runtime configuration, too.
-        if db == 'test1':
-            sqlite_plugin.dbfile = dbfile1
-        elif db == 'test2':
-            sqlite_plugin.dbfile = dbfile2
-        else:
-            abort(404, "No such database.")
-
-        return "Database File switched to: " + sqlite_plugin.dbfile
-
-The ``skip`` parameter accepts a single value or a list of values. You can use a name, class or instance to identify the plugin that is to be skipped. Set ``skip=True`` to skip all plugins at once.
-
-Plugins and Sub-Applications
---------------------------------------------------------------------------------
-
-Most plugins are specific to the application they were installed to. Consequently, they should not affect sub-applications mounted with :meth:`Bottle.mount`. Here is an example::
-
-    root = Bottle()
-    root.mount('/blog', apps.blog)
-
-    @root.route('/contact', template='contact')
-    def contact():
-        return {'email': 'contact@example.com'}
-
-    root.install(plugins.WTForms())
-
-Whenever you mount an application, Bottle creates a proxy-route on the main-application that forwards all requests to the sub-application. Plugins are disabled for this kind of proxy-route by default. As a result, our (fictional) `WTForms` plugin affects the ``/contact`` route, but does not affect the routes of the ``/blog`` sub-application.
-
-This behavior is intended as a sane default, but can be overridden. The following example re-activates all plugins for a specific proxy-route::
-
-    root.mount('/blog', apps.blog, skip=None)
-
-But there is a snag: The plugin sees the whole sub-application as a single route, namely the proxy-route mentioned above. In order to affect each individual route of the sub-application, you have to install the plugin to the mounted application explicitly.
-
-
-
-Development
-================================================================================
-
-So you have learned the basics and want to write your own application? Here are
-some tips that might help you being more productive.
-
-.. _default-app:
-
-Default Application
---------------------------------------------------------------------------------
-
-Bottle maintains a global stack of :class:`Bottle` instances and uses the top of the stack as a default for some of the module-level functions and decorators. The :func:`route` decorator, for example, is a shortcut for calling :meth:`Bottle.route` on the default application::
-
-    @route('/')
-    def hello():
-        return 'Hello World'
-
-    run()
-
-This is very convenient for small applications and saves you some typing, but also means that, as soon as your module is imported, routes are installed to the global default application. To avoid this kind of import side-effects, Bottle offers a second, more explicit way to build applications::
-
-    app = Bottle()
-
-    @app.route('/')
-    def hello():
-        return 'Hello World'
-
-    app.run()
-
-Separating the application object improves re-usability a lot, too. Other developers can safely import the ``app`` object from your module and use :meth:`Bottle.mount` to merge applications together.
-
-
-.. versionadded:: 0.13
-
-Starting with bottle-0.13 you can use :class:`Bottle` instances as context managers::
-
-    app = Bottle()
-
-    with app:
-
-        # Our application object is now the default
-        # for all shortcut functions and decorators
-
-        assert my_app is default_app()
-
-        @route('/')
-        def hello():
-            return 'Hello World'
-
-        # Also useful to capture routes defined in other modules
-        import some_package.more_routes
-
-
-
-
-.. _tutorial-debugging:
-
-
-Debug Mode
---------------------------------------------------------------------------------
-
-During early development, the debug mode can be very helpful.
-
-.. highlight:: python
-
-::
-
-    bottle.debug(True)
-
-In this mode, Bottle is much more verbose and provides helpful debugging information whenever an error occurs. It also disables some optimisations that might get in your way and adds some checks that warn you about possible misconfiguration.
-
-Here is an incomplete list of things that change in debug mode:
-
-* The default error page shows a traceback.
-* Templates are not cached.
-* Plugins are applied immediately.
-
-Just make sure not to use the debug mode on a production server.
-
-Auto Reloading
---------------------------------------------------------------------------------
-
-During development, you have to restart the server a lot to test your
-recent changes. The auto reloader can do this for you. Every time you
-edit a module file, the reloader restarts the server process and loads
-the newest version of your code.
-
-::
-
-    from bottle import run
-    run(reloader=True)
-
-How it works: the main process will not start a server, but spawn a new
-child process using the same command line arguments used to start the
-main process. All module-level code is executed at least twice! Be
-careful.
-
-The child process will have ``os.environ['BOTTLE_CHILD']`` set to ``True``
-and start as a normal non-reloading app server. As soon as any of the
-loaded modules changes, the child process is terminated and re-spawned by
-the main process. Changes in template files will not trigger a reload.
-Please use debug mode to deactivate template caching.
-
-The reloading depends on the ability to stop the child process. If you are
-running on Windows or any other operating system not supporting
-``signal.SIGINT`` (which raises ``KeyboardInterrupt`` in Python),
-``signal.SIGTERM`` is used to kill the child. Note that exit handlers and
-finally clauses, etc., are not executed after a ``SIGTERM``.
-
-
-Command Line Interface
---------------------------------------------------------------------------------
-
-.. versionadded: 0.10
-
-Starting with version 0.10 you can use bottle as a command-line tool:
-
-.. code-block:: console
-
-    $ python -m bottle
-
-    Usage: bottle.py [options] package.module:app
-
-    Options:
-      -h, --help            show this help message and exit
-      --version             show version number.
-      -b ADDRESS, --bind=ADDRESS
-                            bind socket to ADDRESS.
-      -s SERVER, --server=SERVER
-                            use SERVER as backend.
-      -p PLUGIN, --plugin=PLUGIN
-                            install additional plugin/s.
-      -c FILE, --conf=FILE  load config values from FILE.
-      -C NAME=VALUE, --param=NAME=VALUE
-                            override config values.
-      --debug               start server in debug mode.
-      --reload              auto-reload on file changes.
-
-
-The `ADDRESS` field takes an IP address or an IP:PORT pair and defaults to ``localhost:8080``. The other parameters should be self-explanatory.
-
-Both plugins and applications are specified via import expressions. These consist of an import path (e.g. ``package.module``) and an expression to be evaluated in the namespace of that module, separated by a colon. See :func:`load` for details. Here are some examples:
-
-.. code-block:: console
-
-    # Grab the 'app' object from the 'myapp.controller' module and
-    # start a paste server on port 80 on all interfaces.
-    python -m bottle -server paste -bind 0.0.0.0:80 myapp.controller:app
-
-    # Start a self-reloading development server and serve the global
-    # default application. The routes are defined in 'test.py'
-    python -m bottle --debug --reload test
-
-    # Install a custom debug plugin with some parameters
-    python -m bottle --debug --reload --plugin 'utils:DebugPlugin(exc=True)'' test
-
-    # Serve an application that is created with 'myapp.controller.make_app()'
-    # on demand.
-    python -m bottle 'myapp.controller:make_app()''
-
-
-Deployment
-================================================================================
-
-Bottle runs on the built-in `wsgiref WSGIServer <http://docs.python.org/library/wsgiref.html#module-wsgiref.simple_server>`_  by default. This non-threading HTTP server is perfectly fine for development, but may become a performance bottleneck when server load increases.
-
-The easiest way to increase performance is to install a multi-threaded server library like paste_ or cherrypy_ and tell Bottle to use that instead of the single-threaded server::
-
-    bottle.run(server='paste')
-
-This, and many other deployment options are described in a separate article: :doc:`deployment`
-
-
-
-
-.. _tutorial-glossary:
-
-Glossary
-========
-
-.. glossary::
-
-   callback
-      Programmer code that is to be called when some external action happens.
-      In the context of web frameworks, the mapping between URL paths and
-      application code is often achieved by specifying a callback function
-      for each URL.
-
-   decorator
-      A function returning another function, usually applied as a function transformation using the ``@decorator`` syntax. See `python documentation for function definition  <http://docs.python.org/reference/compound_stmts.html#function>`_ for more about decorators.
-
-   environ
-      A structure where information about all documents under the root is
-      saved, and used for cross-referencing.  The environment is pickled
-      after the parsing stage, so that successive runs only need to read
-      and parse new and changed documents.
-
-   handler function
-      A function to handle some specific event or situation. In a web
-      framework, the application is developed by attaching a handler function
-      as callback for each specific URL comprising the application.
-
-   source directory
-      The directory which, including its subdirectories, contains all
-      source files for one Sphinx project.
-
diff --git a/docs/sources/data-analytics/index.rst.txt b/docs/sources/data-analytics/index.rst.txt
deleted file mode 100644
index dd04a53..0000000
--- a/docs/sources/data-analytics/index.rst.txt
+++ /dev/null
@@ -1,19 +0,0 @@
-Data Analytics
-=======================
-The courses are still in the planning. Welcome to reward us. 
-
-1. **Primary course**
-
-If the total number of people who reward us is more than 100, we promise to complete the primary course within 1 week, so that, after finishing your study, you can master the basic concepts and write simple scripts.

-
-2. **Intermediate course**
-
-If the total number of people who reward us is more than 500, we will launch the intermediate course within 2 weeks, so that, after finishing your study, you will be familiar with the knowledge and framework of the course and apply it to practical development.
-
-3. **Advanced course**
-
-If the total number of people who reward us is more than 2,000, we will launch the advanced course in a month, so that, after finishing your study, you will have a better understanding of the principles of the framework and be able to do advanced development.
-
-
-**The primary course is free to all, and the intermediate and advanced courses are only open to those who have rewarded us.**
-
diff --git a/docs/sources/dive-into-python/cn/actionscope.rst.txt b/docs/sources/dive-into-python/cn/actionscope.rst.txt
deleted file mode 100644
index f18db1f..0000000
--- a/docs/sources/dive-into-python/cn/actionscope.rst.txt
+++ /dev/null
@@ -1,399 +0,0 @@
-作用域
--------
-
-
-在函数中，*Python* 从命名空间中寻找变量的顺序如下：
-
-
-- local function scope
-- enclosing scope
-- global scope
-- builtin scope
-
-
-例子：
-
-local 作用域
----------------
-
-
- 
-
-::
-
-
-    def foo(a,b): 
-       ^4$c = 1 
-       ^4$d = a + b + c
-
-
-
-这里所有的变量都在 *local* 作用域。
-
-
-
-global 作用域
----------------
-
- 
-
-::
-
-
-     c = 1
-     def foo(a,b): 
-       ^4$d = a + b + c
-
-
-这里的 *c* 就在 *global* 作用域。
-
-
-
-
-global 关键词
------------------
-
-
-使用 *global* 关键词可以在 *local* 作用域中修改 *global* 作用域的值。
-
-
-
- 
-
-::
-
-   c = 1
-   def foo(): 
-      ^4$global c 
-      ^4$c = 2
-    
-   print c
-   foo()
-   print c
-
-结果:
-
-::
-
-  1
-  2
-
-
-
-其作用是将 *c* 指向 *global* 中的 *c*
-
-
-
-如果不加关键词，那么 *local* 作用域的 *c* 不会影响 *globa* 作用域中的值：
-
-
-
- 
-
-::
-
-   c = 1
-   def foo(): 
-       ^4$c = 2
-    
-   print c
-   foo()
-   print c
-
-结果:
-
-::
-
-  1
-  1
-
-
-
-built-in 作用域
------------------
-
-::
-
-
-
-    def list_length(a): 
-       ^4$return len(a)
-
-    a = [1,2,3]
-    print list_length(a)
-
-
-
-结果:
-
-::
-
-  3
-
-
-这里函数 *len *就是在* built-in* 作用域中：
-
-
-
-
- 
-
-::
-
-    import __builtin__
-
-    __builtin__.len
-
-
-
-
-class 中的作用域
-----------------------
-
-
-+------------+------------+
-|Global      |	MyClass   |
-+============+============+
-|var = 0     |            | 
-+------------+------------+
-|MyClass     |    var = 1 |
-+------------+------------+
-|access_class|access_class|
-+------------+------------+
-
- 
-
-
-::
-
-    # global
-    var = 0
-
-    class MyClass(object): 
-       ^4$# class variable 
-       ^4$var = 1
-     
-       ^4$def access_class_c(self): 
-       ^4$^4$    print 'class var:', self.var
-     
-       ^4$def write_class_c(self): 
-       ^4$ ^4$    MyClass.var = 2 
-       ^4$ ^4$    print 'class var:', self.var 
-       ^4$def access_global_c(self): 
-       ^4$ ^4$    print 'global var:', var
-     
-       ^4$def write_instance_c(self): 
-       ^4$ ^4$    self.var = 3 
-       ^4$ ^4$    print 'instance var:', self.var
-
-
-
-::
-
-  Global	 MyClass	obj
-  var = 0 
-  MyClass 
-  [access_class] 
-  obj	         var = 1
-  access_class	
-
-
- 
-
-::
-
-
-   obj = MyClass()
-
-
-
-查询 *self.var* 时，由于 *obj* 不存在 *var*，所以跳到 **MyClass** 中：
-
-::
-
-   Global	 MyClass	obj
-   var = 0 
-   MyClass 
-   [access_class 
-   self] 
-   obj	         var = 1
-   access_class	
-
-
- 
-
-::
-
-   obj.access_class_c()
-
-
-class var: 1
-
-
-查询 *var* 直接跳到 *global* 作用域：
-
-::
-
-   Global	MyClass	obj
-   var = 0 
-   MyClass 
-   [access_class 
-   self] 
-   obj	       var = 1
-   access_class	
-
-
-
- 
-
-::
-
-
-    obj.access_global_c()
-
-
-global var: 0
-
-
-修改类中的 *MyClass.var*：
-
-
-::
-
-   Global	MyClass	obj
-   var = 0 
-   MyClass 
-   [access_class 
-   self] 
-   obj	      var = 2
-   access_class	
-
-
-
- 
-
-::
-
-   obj.write_class_c()
-   class var: 2
-
-修改实例中的 *var* 时，会直接在 *obj* 域中创建一个：
-
-
-::
-
-  Global	MyClass	obj
-  var = 0 
-  MyClass 
-  [access_class 
-  self] 
-  obj	var = 2
-  access_class	var = 3
-
-
-
-
- 
-
-::
-
-   obj.write_instance_c()
-
-
-instance var: 3
-
-
- 
-
-::
-
-    MyClass.var
-
-
-结果:
-
-::
-
-  2
-
-
-
-*MyClass* 中的 *var* 并没有改变。
-
-
-
-词法作用域
--------------
-
-
-
-对于嵌套函数：
-
-
-
- 
-
-::
-
-    def outer(): 
-      ^4$a = 1 
-      ^4$def inner():
- 
-       ^4$ ^4$    print "a =", a
- 
-       ^4$ ^4$inner()
-    
-    outer() 
-
-
-结果:
-
-::
-
-  a = 1
-
-
-
-
-如果里面的函数没有找到变量，那么会向外一层寻找变量，如果再找不到，则到 global 作用域。
-
-返回的是函数的情况：
-
-
-
- 
-
-::
-
-    def outer(): 
-       ^4$a = 1 
-       ^4$def inner(): 
-       ^4$ ^4$   return a 
-       ^4$return inner
-    
-    func = outer()
-
-    print 'a (1):', func()
-
-
-
-结果:
-
-::
-
-  a (1): 1
-
-
-*func()* 函数中调用的* a *要从它定义的地方开始寻找，而不是在 *func* 所在的作用域寻找。
-
-
-
-
-
-作者 & 更新时间
-------------------------------------
-作者:李金  lijinwithyou@gmail.com
-
-提交: 2017/12/6
-
-   
\ No newline at end of file
diff --git a/docs/sources/dive-into-python/cn/builder.rst.txt b/docs/sources/dive-into-python/cn/builder.rst.txt
deleted file mode 100644
index 1fcbf3c..0000000
--- a/docs/sources/dive-into-python/cn/builder.rst.txt
+++ /dev/null
@@ -1,280 +0,0 @@
-生成器 &  迭代器
-================
-
-*while* 循环通常有这样的形式：
-
-
-::
-
-    <do setup>
-    result = []
-    while True:
-        ^4$<generate value>
-        ^4$result.append(value)
-        ^4$if <done>:
-             ^8$break
-
-
-
-使用迭代器实现这样的循环：
-
-
-::
-
-
-    class GenericIterator(object):
-        ^4$def __init__(self, ...):
-            ^4$<do setup>
-            ^4$# 需要额外储存状态
-            ^4$<store state>
-        ^4$def next(self): 
-            ^4$<load state>
-            ^4$<generate value>
-            ^4$if <done>:
-                ^8$raise StopIteration()
-            ^4$<store state>
-            ^4$return value
-
-
-更简单的，可以使用生成器：
-
-
-::
-
-
-    def generator(...):
-        ^4$<do setup>
-        ^4$while True:
-            ^8$<generate value>
-            ^8$# yield 说明这个函数可以返回多个值！
-            ^8$yield value
-            ^8$if <done>:
-                ^12$break
-
-生成器使用 *yield* 关键字将值输出，而迭代器则通过 *next* 的 *return* 将值返回；与迭代器不同的是，生成器会自动记录当前的状态，而迭代器则需要进行额外的操作来记录当前的状态。
-
-对于之前的 *collatz* 猜想，简单循环的实现如下：
-
-::
-
-
-    def collatz(n):
-        ^4$sequence = []
-        ^4$while n != 1:
-            ^8$if n % 2 == 0:
-                ^12$n /= 2
-            ^8$else:
-                ^12$n = 3*n + 1
-            ^8$sequence.append(n)
-        ^4$return sequence
-
-    for x in collatz(7):
-        ^4$print x,
-
-
-<button>Run ...</button>
-
-
-运行的结果会是：
-
-::
-
-    22 11 34 17 52 26 13 40 20 10 5 16 8 4 2 1
-
-
-迭代器的版本如下：
-
-::
-
-    class Collatz(object):
-        ^4$def __init__(self, start):
-            ^8$self.value = start
-
-        ^4$def __iter__(self):
-            ^8$return self
-
-        ^4$def next(self):
-            ^8$if self.value == 1:
-                ^12$raise StopIteration()
-            ^8$elif self.value % 2 == 0:
-                ^12$self.value = self.value/2
-            ^8$else:
-                ^12$self.value = 3*self.value + 1
-            ^8$return self.value
-
-    for x in Collatz(7):
-        ^4$print x,
-
-<button>Run ...</button>
-
-
-
-生成器的版本如下：
-
-::
-
-    def collatz(n):
-        ^4$while n != 1:
-            ^8$if n % 2 == 0:
-                ^12$n /= 2
-            ^8$else:
-                ^12$n = 3*n + 1
-            ^8$yield n
-
-    for x in collatz(7):
-        ^4$print x,
-
-<button>Run ...</button>
-
-
-
-事实上，生成器也是一种迭代器：
-
-::
-
-    x = collatz(7)
-    print x
-
-<button>Run ...</button>
-
-
-输出如下
-::
-
-    <generator object collatz at 0x0000000003B63750>
-
-
-它支持 *next* 方法，返回下一个 *yield* 的值：
-
-::
-
-    x = collatz(7)
-    print x.next()
-    print x.next()
-
-<button>Run ...</button>
-
-
-*__iter__* 方法返回的是它本身：
-
-
-::
-
-    x = collatz(7)
-    print x.__iter__()
-
-输出结果：
-::
-
-    <generator object collatz at 0x0000000003B63750>
-
-
-之前的二叉树迭代器可以改写为更简单的生成器模式来进行中序遍历：
-
-::
-
-    class BinaryTree(object):
-        ^4$def __init__(self, value, left=None, right=None):
-            ^8$self.value = value
-            ^8$self.left = left
-            ^8$self.right = right
-
-        ^4$def __iter__(self):
-            ^8$# 将迭代器设为生成器方法
-            ^8$return self.inorder()
-
-        ^4$def inorder(self):
-            ^4$# traverse the left branch
-            ^4$if self.left is not None:
-                ^8$for value in self.left:
-                    ^12$yield value
-
-            ^4$# yield node's value
-            ^4$yield self.value
-
-            ^4$# traverse the right branch
-            ^4$if self.right is not None:
-                ^8$for value in self.right:
-                    ^12$yield value
-
-
-非递归的实现：
-
-::
-
-    def inorder(self):
-        ^4$node = self
-        ^4$stack = []
-        ^4$while len(stack) > 0 or node is not None:
-            ^8$while node is not None:
-                ^12$stack.append(node)
-                ^12$node = node.left
-            ^8$node = stack.pop()
-            ^8$yield node.value
-            ^8$node = node.right
-
-<button>Run ...</button>
-
-
-
-::
-
-    class BinaryTree(object):
-        ^4$def __init__(self, value, left=None, right=None):
-            ^8$self.value = value
-            ^8$self.left = left
-            ^8$self.right = right
-
-        ^4$def __iter__(self):
-            ^8$# 将迭代器设为生成器方法
-            ^8$return self.inorder()
-
-        ^4$def inorder(self):
-            ^4$# traverse the left branch
-            ^4$if self.left is not None:
-                ^8$for value in self.left:
-                    ^12$yield value
-
-            ^4$# yield node's value
-            ^4$yield self.value
-
-            ^4$# traverse the right branch
-            ^4$if self.right is not None:
-                ^8$for value in self.right:
-                    ^12$yield value
-
-
-    tree = BinaryTree(
-        ^4$left=BinaryTree(
-            ^8$left=BinaryTree(1),
-            ^8$value=2,
-            ^8$right=BinaryTree(
-                ^12$left=BinaryTree(3),
-                ^12$value=4,
-                ^12$right=BinaryTree(5)
-            ^8$),
-        ^4$),
-        ^4$value=6,
-        ^4$right=BinaryTree(
-            ^8$value=7,
-            ^8$right=BinaryTree(8)
-        ^4$)
-    )
-    for value in tree:
-        ^4$print value,
-
-<button>Run ...</button>
-
-
-正确应输出：
-
-::
-
-    1 2 3 4 5 6 7 8
-
-
-作者 & 更新时间
-------------------------------------
-作者:李金  lijinwithyou@gmail.com
-
-提交: 2017/12/6
diff --git a/docs/sources/dive-into-python/cn/csv.rst.txt b/docs/sources/dive-into-python/cn/csv.rst.txt
deleted file mode 100644
index abcd72b..0000000
--- a/docs/sources/dive-into-python/cn/csv.rst.txt
+++ /dev/null
@@ -1,178 +0,0 @@
-CSV 文件和 csv 模块
-=======================
-标准库中有自带的 csv (逗号分隔值) 模块处理 csv 格式的文件：
-
-::
-
-    import csv
-    dir(csv)
-
-读 csv 文件
-----------------------------------------------------------
-假设我们有这样的一个文件：
-
-::
-
-    #file data.csv, 内容如下
-
-    "alpha 1",  100, -1.443
-    "beat  3",   12, -0.0934
-    "gamma 3a", 192, -0.6621
-    "delta 2a",  15, -4.515
-
-
-打开这个文件，并产生一个文件 reader：
-
-::
-
-    fp = open("data.csv")
-    r = csv.reader(fp)
-
-
-
-可以按行迭代数据：
-
-::
-
-    fp = open("data.csv")
-    r = csv.reader(fp)
-
-
-    for row in r:
-        ^4$print row
-
-    fp.close()
-
-<button>Run ...</button>
-
-
-::
-
-    #如运行正确，内容将会如下
-
-    ['alpha 1', '  100', ' -1.443']
-    ['beat  3', '   12', ' -0.0934']
-    ['gamma 3a', ' 192', ' -0.6621']
-    ['delta 2a', '  15', ' -4.515']
-
-
-默认数据内容都被当作字符串处理，不过可以自己进行处理：
-
-::
-
-    data = []
-
-    with open('data.csv') as fp:
-        ^4$r = csv.reader(fp)
-        ^4$for row in r:
-            ^8$data.append([row[0], int(row[1]), float(row[2])])
-
-    print(data)
-
-<button>Run ...</button>
-
-
-
-写 csv 文件
--------------------------------------------------------------
-可以使用 *csv.writer* 写入文件，不过相应地，传入的应该是以写方式打开的文件，不过一般要用 *'wb'* 即二进制写入方式，防止出现换行不正确的问题：
-In [7]:
-
-::
-
-    data = [('one', 1, 1.5), ('two', 2, 8.0)]
-    with open('out.csv', 'wb') as fp:
-        ^4$w = csv.writer(fp)
-        ^4$w.writerows(data)
-
-<button>Run ...</button>
-
-
-如果运行正确，结果将会如下：
-
-::
-
-    one,1,1.5
-    two,2,8.0
-
-
-更换分隔符
-----------------------------------------------------
-默认情况下，*csv* 模块默认 *csv* 文件都是由 *excel* 产生的，实际中可能会遇到这样的问题：
-
-::
-
-    data = [('one, \"real\" string', 1, 1.5), ('two', 2, 8.0)]
-    with open('out.csv', 'wb') as fp:
-        ^4$w = csv.writer(fp)
-        ^4$w.writerows(data)
-
-<button>Run ...</button>
-
-
-可以修改分隔符来处理这组数据：
-
-::
-
-    data = [('one, \"real\" string', 1, 1.5), ('two', 2, 8.0)]
-    with open('out.psv', 'wb') as fp:
-        ^4$w = csv.writer(fp, delimiter="|")
-        ^4$w.writerows(data)
-
-
-<button>Run ...</button>
-
-
-其他选项
--------------------------------------------------------------------------
-
-*numpy.loadtxt()* 和 *pandas.read_csv()* 可以用来读写包含很多数值数据的*csv*文件：
-
-
-::
-    #有文件 trades.csv,内容如下
-
-    Order,Date,Stock,Quantity,Price
-    A0001,2013-12-01,AAPL,1000,203.4
-    A0002,2013-12-01,MSFT,1500,167.5
-    A0003,2013-12-02,GOOG,1500,167.5
-
-
-使用 pandas 进行处理，生成一个 DataFrame 对象：
-
-::
-
-    import pandas
-    df = pandas.read_csv('trades.csv', index_col=0)
-    print df
-
-
-::
-                 Date Stock  Quantity  Price
-    Order                                   
-    A0001  2013-12-01  AAPL      1000  203.4
-    A0002  2013-12-01  MSFT      1500  167.5
-    A0003  2013-12-02  GOOG      1500  167.5
-
-
-通过名字进行索引：
-
-::
-
-    df['Quantity'] * df['Price']
-
-
-将会输出如下内容：
-
-::
-
-    Order
-    A0001    203400
-    A0002    251250
-    A0003    251250
-    dtype: float64
-
-
-作者 & 更新时间
-------------------------------------
-作者:`李金  <lijinwithyou@gmail.com>`
diff --git a/docs/sources/dive-into-python/cn/datetime.rst.txt b/docs/sources/dive-into-python/cn/datetime.rst.txt
deleted file mode 100644
index 166da4f..0000000
--- a/docs/sources/dive-into-python/cn/datetime.rst.txt
+++ /dev/null
@@ -1,185 +0,0 @@
-datetime 模块
-==============================
-
-datetime 提供了基础时间和日期的处理。
-
-::
-
-    import datetime as dt
-    dir(dt)
-
-<button>Run ...</button>
-
-
-date 对象
-----------------------------------------------------------
-可以使用 date(year, month, day) 产生一个 date 对象：
-
-::
-
-    import datetime as dt
-    d1 = dt.date(2007, 9, 25)
-    d2 = dt.date(2008, 9, 25)
-
-
-可以格式化 date 对象的输出：
-
-::
-
-    import datetime as dt
-    d1 = dt.date(2007, 9, 25)
-    d2 = dt.date(2008, 9, 25)
-
-
-    print d1
-    print d1.strftime('%A, %m/%d/%y')
-    print d1.strftime('%a, %m-%d-%Y')
-
-<button>Run ...</button>
-
-
-可以看两个日期相差多久：
-
-::
-
-    import datetime as dt
-    d1 = dt.date(2007, 9, 25)
-    d2 = dt.date(2008, 9, 25)
-
-    print d2 - d1
-
-<button>Run ...</button>
-
-返回的是一个 timedelta 对象：
-::
-
-    import datetime as dt
-    d1 = dt.date(2007, 9, 25)
-    d2 = dt.date(2008, 9, 25)
-
-    d = d2 - d1
-    print d.days
-    print d.seconds
-
-
-查看今天的日期：
-
-
-::
-
-    import datetime as dt
-    print dt.date.today()
-
-<button>Run ...</button>
-
-
-time 对象
-------------------------------------------------------------------------
-可以使用 time(hour, min, sec, us) 产生一个 time 对象：
-
-::
-
-    import datetime as dt
-    t1 = dt.time(15, 38)
-    t2 = dt.time(18)
-
-<button>Run ...</button>
-
-改变显示格式：
-
-::
-
-    import datetime as dt
-    t1 = dt.time(15, 38)
-    print t1
-    print t1.strftime('%I:%M, %p')
-    print t1.strftime('%H:%M:%S, %p')
-
-<button>Run ...</button>
-
-因为没有具体的日期信息，所以 time 对象不支持减法操作。
-
-datetime 对象
--------------------------------------------------------------------------
-可以使用 datetime(year, month, day, hr, min, sec, us) 来创建一个 datetime 对象。
-
-
-
-获得当前时间：
-
-::
-
-    import datetime as dt
-    d1 = dt.datetime.now()
-    print d1
-
-<button>Run ...</button>
-
-
-给当前的时间加上 30 天，timedelta 的参数是 timedelta(day, hr, min, sec, us)：
-
-::
-
-    import datetime as dt
-    d1 = dt.datetime.now()
-    d2 = d1 + dt.timedelta(30)
-    print d2
-
-<button>Run ...</button>
-
-
-除此之外，我们还可以通过一些指定格式的字符串来创建 datetime 对象：
-
-::
-
-    import datetime as dt
-    print dt.datetime.strptime('2/10/01', '%m/%d/%y')
-
-<button>Run ...</button>
-
-
-+------------+------------------------------------------------------------------------------------------+
-| 子表达式   | 匹配内容                                                                                 |
-+============+==========================================================================================+
-|%a          | 星期英文缩写                                                                             |    
-+------------+------------------------------------------------------------------------------------------+
-| %A         |星期英文                                                                                  |
-+------------+------------------------------------------------------------------------------------------+
-| %w         |一星期的第几天，[0(sun),6]                                                                |
-+------------+------------------------------------------------------------------------------------------+
-|%b          | 月份英文缩写                                                                             |         
-+------------+------------------------------------------------------------------------------------------+
-| %B         | 月份英文                                                                                 |
-+------------+------------------------------------------------------------------------------------------+
-|%d          |日期，[01,31]                                                                             |
-+------------+------------------------------------------------------------------------------------------+
-| %H         |小时，[00,23]                                                                             |
-+------------+------------------------------------------------------------------------------------------+
-| %I         |小时，[01,12]                                                                             |
-+------------+------------------------------------------------------------------------------------------+
-|%j          |      一年的第几天，[001,366]                                                             |
-+------------+------------------------------------------------------------------------------------------+
-|%m          |	月份，[01,12]                                                                           |
-+------------+------------------------------------------------------------------------------------------+
-|%M 	     | 分钟，[00,59]                                                                            |
-+------------+------------------------------------------------------------------------------------------+
-|%p 	     | AM 和 PM                                                                                 |
-+------------+------------------------------------------------------------------------------------------+
-|%S 	     |秒钟，[00,61] （大概是有闰秒的存在）                                                      |
-+------------+------------------------------------------------------------------------------------------+
-|%U          |       一年中的第几个星期，星期日为第一天，[00,53]                                        |
-+------------+------------------------------------------------------------------------------------------+
-|%W          |	一年中的第几个星期，星期一为第一天，[00,53]                                             |
-+------------+------------------------------------------------------------------------------------------+
-|%y  	     |没有世纪的年份                                                                            |
-+------------+------------------------------------------------------------------------------------------+
-|%Y  	     |完整的年份                                                                                |
-+------------+------------------------------------------------------------------------------------------+
-
-
-
-作者 & 更新时间
-------------------------------------
-作者:`李金  <lijinwithyou@gmail.com>`
-
-提交: 2017/12/6
diff --git a/docs/sources/dive-into-python/cn/dynamiccompiling.rst.txt b/docs/sources/dive-into-python/cn/dynamiccompiling.rst.txt
deleted file mode 100644
index 7559e48..0000000
--- a/docs/sources/dive-into-python/cn/dynamiccompiling.rst.txt
+++ /dev/null
@@ -1,301 +0,0 @@
-动态编译
-================
-
-
-标准编程语言
----------------
-
-
-对于 **C** 语言，代码一般要先编译，再执行。
-
-::
-
-   .c -> .exe
-
-
-解释器语言
---------------
-
-
-::
-
-    shell 脚本
-
-       .sh -> interpreter
-
-
-Byte Code 编译
-------------------
-
-
-**Python**, **Java** 等语言先将代码编译为 byte code（不是机器码），然后再处理：
-
-
-::
-
-    .py -> .pyc -> interpreter
-
-
-
-eval 函数
---------------
-
-::
- 
-    eval(statement, glob, local)
-
-
-
-使用 *eval* 函数动态执行代码，返回执行的值：
-
-
-
-
-::
- 
-   a = 1
-
-   eval("a+1")
-
-
-
-结果:
-::
-
-    2
-
-
-
-可以接收明明空间参数：
-
-
-
-
-
-::
-
-
-   local = dict(a=2)
-   glob = {}
-   eval("a+1", glob, local)
-
-
-
-结果:
-::
-
-    3
-
-
-
-这里 *local* 中的 *a* 先被找到。
-
-
-
-exec 函数
---------------
-
-::
-
-   exec(statement, glob, local)
-
-
-
-使用 *exec* 可以添加修改原有的变量。
-
-
-
-
-
-
-::
-
-   a = 1
-
-   exec("b = a+1")
-
-   print b
-
-结果:
-::
-
-  2
-
-
-
-
-::
-
-
-   local = dict(a=2)
-   glob = {}
-   exec("b = a+1", glob, local)
-
-   print local
-
-
-
-结果:
-::
-
-  {'a': 2, 'b': 3}
-
-
-执行之后，*b* 在 *local* 命名空间中。
-
-
-
-警告
---------
-
-
-动态执行的时候要注意，不要执行不信任的用户输入，因为它们拥有 *Python* 的全部权限。
-
-
-
-compile 函数生成 byte code
-------------------------------
-
-
-     compile(str, filename, mode)
-
-
-
-
-
-::
-
-
-   a = 1
-   c = compile("a+2", "", 'eval')
-
-   eval(c)
-
-
-
-结果:
-::
-
-    3
-
-
-
-
-
-
-::
-
-
-   a = 1
-   c = compile("b=a+2", "", 'exec')
-
-   exec(c)
-   b
-
-
-结果:
-::
-
-    3
-
-
-
-abstract syntax trees
------------------------
-
-
-
-
-
-::
-
-    import ast
-
-
-
-
-
-::
-
-
-    tree = ast.parse("a+2", "", "eval")
-
-    ast.dump(tree)
-
-
-
-结果:
-::
-
-    "Expression(body=BinOp(left=Name(id='a', ctx=Load()), op=Add(), right=Num(n=2)))"
-
-
-
-改变常数的值：
-
-
-
-
-
-
-::
-
-
-    tree.body.right.n = 3
-
-    ast.dump(tree)
-
-
-
-结果:
-::
-
-    "Expression(body=BinOp(left=Name(id='a', ctx=Load()), op=Add(), right=Num(n=3)))"
-
-
-
-
-
-
-
-::
-
-   a = 1
-   c = compile(tree, '', 'eval')
-
-   eval(c)
-
-
-
-结果:
-::
-
-    4
-
-
-
-
-安全的使用方法 *literal_eval* ，只支持基本值的操作：
-
-
-
-
-
-::
-
-
-   ast.literal_eval("[10.0, 2, True, 'foo']")
-
-
-结果:
-::
-
-    [10.0, 2, True, 'foo']
-
-
-
-作者 & 更新时间
-------------------------------------
-作者:李金  lijinwithyou@gmail.com
-
-提交: 2017/12/6
diff --git a/docs/sources/dive-into-python/cn/index.rst.txt b/docs/sources/dive-into-python/cn/index.rst.txt
deleted file mode 100644
index d971f49..0000000
--- a/docs/sources/dive-into-python/cn/index.rst.txt
+++ /dev/null
@@ -1,19 +0,0 @@
-Dive into Python
-=======================
-The courses are still in the planning. Welcome to reward us. 
-
-1. **Primary course**
-
-If the total number of people who reward us is more than 100, we promise to complete the primary course within 1 week, so that, after finishing your study, you can master the basic concepts and write simple scripts.
-
-2. **Intermediate course**
-
-If the total number of people who reward us is more than 500, we will launch the intermediate course within 2 weeks, so that, after finishing your study, you will be familiar with the knowledge and framework of the course and apply it to practical development.
-
-3. **Advanced course**
-
-If the total number of people who reward us is more than 2,000, we will launch the advanced course in a month, so that, after finishing your study, you will have a better understanding of the principles of the framework and be able to do advanced development.
-
-
-**The primary course is free to all, and the intermediate and advanced courses are only open to those who have rewarded us.**
-
diff --git a/docs/sources/dive-into-python/cn/modifier.rst.txt b/docs/sources/dive-into-python/cn/modifier.rst.txt
deleted file mode 100644
index ff5642b..0000000
--- a/docs/sources/dive-into-python/cn/modifier.rst.txt
+++ /dev/null
@@ -1,462 +0,0 @@
-修饰符
-================
-
-
-函数是一种对象
-----------------
-
-
-
-在 *Python* 中，函数是也是一种对象。
-
-
-
-::
-
-    def foo(x):
-       print x
-    
-    print(type(foo))
-
-结果:
-
-::
-
-    type 'function'
-
-
-
-查看函数拥有的方法：
-
-
-
-
-::
-
-   dir(foo)
-   Out[2]:
-   ['__call__',
-    '__class__',
-    '__closure__',
-    '__code__',
-    '__defaults__',
-    '__delattr__',
-    '__dict__',
-    '__doc__',
-    '__format__',
-    '__get__',
-    '__getattribute__',
-    '__globals__',
-    '__hash__',
-    '__init__',
-    '__module__',
-    '__name__',
-    '__new__',
-    '__reduce__',
-    '__reduce_ex__',
-    '__repr__',
-    '__setattr__',
-    '__sizeof__',
-    '__str__',
-    '__subclasshook__',
-    'func_closure',
-    'func_code',
-    'func_defaults',
-    'func_dict',
-    'func_doc',
-    'func_globals',
-    'func_name']
-
-
-
-在这些方法中，*__call__* 是最重要的一种方法：
-
-
-
-
-::
-
-   foo.__call__(42)
-
-结果:
-::
-
-    42
-
-
-相当于：
-
-::
-
-
-   foo(42)
-
-结果:
-::
-
-    42
-
-
-因为函数是对象，所以函数可以作为参数传入另一个函数：
-
-
-::
-
-   def bar(f, x):
-       x += 1
-       f(x)
-
-
-::
-
-   bar(foo, 4)
-
-结果:
-::
-
-    5
-
-
-修饰符
---------
-
-
-修饰符是这样的一种函数，它接受一个函数作为输入，通常输出也是一个函数：
-
-
-
-
-::
-
-    def dec(f):
-        print 'I am decorating function', id(f)
-        return f
-
-
-将 *len* 函数作为参数传入这个修饰符函数：
-
-
-
-::
-
-
-   declen = dec(len)
-
-
-结果:
-::
-
-    I am decorating function 33716168
-
-
-使用这个新生成的函数：
-
-
-::
-
-
-    declen([10,20,30])
-
-
-
-结果:
-::
-
-    3
-
-
-
-上面的例子中，我们仅仅返回了函数的本身，也可以利用这个函数生成一个新的函数，看一个新的例子：
-
-
-
-
-::
-
-
-    def loud(f):
-        def new_func(*args, **kw):
-            print 'calling with', args, kw
-            rtn = f(*args, **kw)
-            print 'return value is', rtn
-            return rtn
-        return new_func
-
-
-<button>Run ...</button>
-
-
-
-
-::
-
-    loudlen = loud(len)
-
-    loudlen([10, 20, 30])
-    calling with ([10, 20, 30],) {}
-    return value is 3
-
-<button>Run ...</button>
-
-
-结果:
-::
-
-    3
-
-
-用 @ 来使用修饰符
----------------------
-
-
-
-*Python* 使用 @ 符号来将某个函数替换为修饰符之后的函数：
-
-
-
-例如这个函数：
-
-
-
-::
-
-    def foo(x):
-        print x
-    
-    foo = dec(foo)
-
-
-
-结果:
-::
-
-    I am decorating function 64021672
-
-
-可以替换为：
-
-
-
-
-::
-
-
-    @dec
-    def foo(x):
-        print x
-
-
-结果:
-::
-
-    I am decorating function 64021112
-
-
-
-事实上，如果修饰符返回的是一个函数，那么可以链式的使用修饰符：
-
-
-
-    @dec1
-
-
-    @dec2
-
-    def foo(x):
-
-        print x
-
-
-
-
-
-使用修饰符 *loud* 来定义这个函数：
-
-
-
-
-
-
-::
-
-
-    @loud
-    def foo(x):
-       print x
-
-
-
-
-::
-
-    foo(42)
-    calling with (42,) {}
-    42
-    return value is None
-
-
-例子
-----------
-
-
-
-定义两个修饰器函数，一个将原来的函数值加一，另一个乘二：
-
-
-
-
-
-
-::
-
-    def plus_one(f):
-        def new_func(x):
-            return f(x) + 1
-        return new_func
-
-    def times_two(f):
-        def new_func(x):
-            return f(x) * 2
-        return new_func
-
-<button>Run ...</button>
-
-
-定义函数，先乘二再加一：
-
-
-
-
-::
-
-
-
-    @plus_one
-    @times_two
-    def foo(x):
-       return int(x)
-
-
-
-
-::
-
-   foo(13)
-
-
-
-
-结果:
-::
-
-    27
-
-
-
-
-修饰器工厂
--------------
-
-
-*decorators factories* 是返回修饰器的函数，例如：
-
-
-
-
-
-::
-
-
-    def super_dec(x, y, z):
-        def dec(f):
-            def new_func(*args, **kw):
-                ^12$print x + y + z
-                ^12$return f(*args, **kw)
-            return new_func
-        return dec
-
-
-
-它的作用在于产生一个可以接受参数的修饰器，例如我们想将 *loud* 输出的内容写入一个文件去，可以这样做：
-
-
-
-
-
-::
-
-    def super_loud(filename):
-        fp = open(filename, 'w')
-        def loud(f):
-            def new_func(*args, **kw):
-                ^12$fp.write('calling with' + str(args) + str(kw))
-                ^12$# 确保内容被写入
-                ^12$fp.flush()
-                ^12$fp.close()
-                ^12$rtn = f(*args, **kw)
-                ^12$return rtn
-            return new_func
-        return loud
-
-
-
-
-可以这样使用这个修饰器工厂：
-
-
-
-
-
-::
-
-
-    @super_loud('test.txt')
-    def foo(x):
-        print x
-
-
-
-
-
-调用 *foo* 就会在文件中写入内容：
-
-
-
-
-::
-    
-    foo(12)
-
-
-结果:
-::
-
-    12
-
-
-
-查看文件内容：
-
-
-
-::
-
-    with open('test.txt') as fp:
-        print fp.read()
-    calling with(12,){}
-
-
-
-
-
-::
-
-    import os
-    os.remove('test.txt')
-
-
-作者 & 更新时间
-------------------------------------
-作者:李金  lijinwithyou@gmail.com
-
-提交: 2017/12/6
diff --git a/docs/sources/dive-into-python/cn/operator.rst.txt b/docs/sources/dive-into-python/cn/operator.rst.txt
deleted file mode 100644
index b83f39b..0000000
--- a/docs/sources/dive-into-python/cn/operator.rst.txt
+++ /dev/null
@@ -1,240 +0,0 @@
-operator, functools, itertools, toolz, fn, funcy 模块
-================================================================
-
-
-operator 模块
----------------
-
-::
-
-    import operator as op
-
-
-*operator* 模块提供了各种操作符（*+,*,[]*）的函数版本方便使用：
-
-
-
-加法：
-
-
-
-::
-
-
-    print reduce(op.add, range(10))
-
-结果:
-
-::
-
-  45
-
-
-乘法：
-
-
-
-
-
-::
-
-
-    print reduce(op.mul, range(1,10))
-
-
-
-结果:
-
-::
-
-  362880
-
-
-
-
-::
-
-    my_list = [('a', 1), ('bb', 4), ('ccc', 2), ('dddd', 3)]
-
-   # 标准排序
-    print sorted(my_list)
-
-   # 使用元素的第二个元素排序
-    print sorted(my_list, key=op.itemgetter(1))
-
-   # 使用第一个元素的长度进行排序：
-    print sorted(my_list, key=lambda x: len(x[0]))
-
-
-
-结果:
-
-::
-
-  [('a', 1), ('bb', 4), ('ccc', 2), ('dddd', 3)]
-  [('a', 1), ('ccc', 2), ('dddd', 3), ('bb', 4)]
-  [('a', 1), ('bb', 4), ('ccc', 2), ('dddd', 3)]
-
-
-
-functools 模块
-----------------
-
-
-
-*functools* 包含很多跟函数相关的工具，比如之前看到的 *wraps* 函数，不过最常用的是 *partial* 函数，这个函数允许我们使用一个函数中生成一个新函数，这个函数使用原来的函数，不过某些参数被指定了：
-
-
-
-
-
-::
-
-    from functools import partial
-
-    # 将 reduce 的第一个参数指定为加法，得到的是类似求和的函数
-    sum_ = partial(reduce, op.add)
-
-    # 将 reduce 的第一个参数指定为乘法，得到的是类似求连乘的函数
-    prod_ = partial(reduce, op.mul)
-
-    print sum_([1,2,3,4])
-    print prod_([1,2,3,4])
-
-结果:
-
-::
-
-  10
-  24
-
-
-*partial* 函数还可以按照键值对传入固定参数。
-
-
-
-
-
-itertools 模块
-------------------
-
-
-
-*itertools* 包含很多与迭代器对象相关的工具，其中比较常用的是排列组合生成器* permutations *和* combinations*，还有在数据分析中常用的 *groupby* 生成器：
-
-
-
-
-
-::
-
-    from itertools import cycle, groupby, islice, permutations, combinations
-
-
-
-*cycle* 返回一个无限的迭代器，按照顺序重复输出输入迭代器中的内容，*islice* 则返回一个迭代器中的一段内容：
-
-
-
-
-
-
-::
-
-    print list(islice(cycle('abcd'), 0, 10))
-
-
-
-结果:
-
-::
-
-  ['a', 'b', 'c', 'd', 'a', 'b', 'c', 'd', 'a', 'b']
-
-
-
-*groupby* 返回一个字典，按照指定的 *key* 对一组数据进行分组，字典的键是 *key*，值是一个迭代器：
-
-
-
-
-
-::
-
-    animals = sorted(['pig', 'cow', 'giraffe', 'elephant',
-                  'dog', 'cat', 'hippo', 'lion', 'tiger'], key=len)
-
-    # 按照长度进行分组
-    for k, g in groupby(animals, key=len):
-        ^4$print k, list(g)
-    print
-
-
-
-
-结果:
-
-::
-  
-  3 ['pig', 'cow', 'dog', 'cat']
-  4 ['lion']
-  5 ['hippo', 'tiger']
-  7 ['giraffe']
-  8 ['elephant']
-
-
-
-
-排列：
-
-
-
-
-
-::
-
-
-    print [''.join(p) for p in permutations('abc')]
-
-
-结果:
-
-::
-
-  ['abc', 'acb', 'bac', 'bca', 'cab', 'cba']
-
-
-
-组合：
-
-
-
-
-
-::
-
-    print [list(c) for c in combinations([1,2,3,4], r=2)]
-
-
-结果:
-
-::
-
-  [[1, 2], [1, 3], [1, 4], [2, 3], [2, 4], [3, 4]]
-
-
-
-toolz, fn 和 funcy 模块
---------------------------
-
-
-这三个模块的作用是方便我们在编程的时候使用函数式编程的风格。
-
-
-
-作者 & 更新时间
-------------------------------------
-作者:李金  lijinwithyou@gmail.com
-
-提交: 2017/12/6
-
diff --git a/docs/sources/dive-into-python/cn/orm.rst.txt b/docs/sources/dive-into-python/cn/orm.rst.txt
deleted file mode 100644
index 8b30c5c..0000000
--- a/docs/sources/dive-into-python/cn/orm.rst.txt
+++ /dev/null
@@ -1,140 +0,0 @@
-对象关系映射
-=================================
-
-数据库中的记录可以与一个 Python 对象对应。
-
-
-例如对于上一节中的数据库：
-
-
-+------------+--------------------------------+-------------------+-----------------------+----------------------+
-| Order      | Date                           |          Stock    |             Quantity  |        Price         |
-+============+================================+==================================================================+
-|A0001       |2013-12-01                      |         AAPL      |              1000     |        203.4         |    
-+------------+--------------------------------+-------------------+-----------------------+----------------------+
-|A0002       |2013-12-01                      |         MSFT      |              1500     |        167.5         |
-+------------+--------------------------------+-------------------+-----------------------+----------------------+
-|A0003       |2013-12-02                      |           GOOG    |              150      |        167.5         | 
-+------------+--------------------------------+-------------------+-----------------------+----------------------+
-
-
-
-可以用一个类来描述：
-
-
-+------------+--------------------------------+
-| Attr.      |  Method                        |          
-+============+================================+
-|Order id    |  Cost                          |      
-+------------+--------------------------------+
-|Stock       |                                |       
-+------------+--------------------------------+
-|Quant.      |                                |           
-+------------+--------------------------------+
-|Price       |                                |
-+------------+--------------------------------+
-
-可以使用 sqlalchemy 来实现这种对应：
-
-::
-
-    from sqlalchemy.ext.declarative import declarative_base
-    from sqlalchemy import Column, Date, Float, Integer, String
-
-    Base = declarative_base()
-
-    class Order(Base):
-        ^4$__tablename__ = 'orders'
-        ^4$order_id = Column(String, primary_key=True)
-        ^4$date = Column(Date)
-        ^4$symbol = Column(String)
-        ^4$quantity = Column(Integer)
-        ^4$price = Column(Float)
-
-        ^4$def get_cost(self):
-            ^8$return self.quantity*self.price
-
-
-生成一个 Order 对象：
-
-::
-
-    import datetime
-    order = Order(order_id='A0004', date=datetime.date.today(), symbol='MSFT', quantity=-1000, price=187.54)
-
-
-调用方法：
-
-::
-
-    order.get_cost()
-
-
-使用上一节生成的数据库产生一个 session：
-
-::
-
-    from sqlalchemy import create_engine
-    from sqlalchemy.orm import sessionmaker
-
-    engine = create_engine("sqlite:///my_database.sqlite")   # 相当于 connection
-    Session = sessionmaker(bind=engine) # 相当于 cursor
-    session = Session()
-
-
-使用这个 session 向数据库中添加刚才生成的对象：
-
-::
-
-    session.add(order)
-    session.commit()
-
-显示是否添加成功：
-
-::
-
-    for row in engine.execute("SELECT * FROM orders"):
-        ^4$print row
-
-
-成功则会输出以下内容
-::
-
-    (u'A0001', u'2013-12-01', u'AAPL', 1000, 203.4)
-    (u'A0002', u'2013-12-01', u'MSFT', 1500, 167.5)
-    (u'A0003', u'2013-12-02', u'GOOG', 1500, 167.5)
-    (u'A0004', u'2015-09-10', u'MSFT', -1000, 187.54)
-
-使用 filter 进行查询，返回的是 Order 对象的列表：
-
-::
-
-    for order in session.query(Order).filter(Order.symbol=="AAPL"):
-        ^4$print order.order_id, order.date, order.get_cost()
-
-成功则会输出以下内容
-
-
-::
-
-    A0001 2013-12-01 203400.0
-
-返回列表的第一个：
-
-::
-
-    order_2 = session.query(Order).filter(Order.order_id=='A0002').first()
-
-::
-
-    order_2.symbol
-
-
-输出： u'MSFT'
-
-
-作者 & 更新时间
-------------------------------------
-作者:`李金  <lijinwithyou@gmail.com>`
-
-提交: 2017/12/6
diff --git a/docs/sources/dive-into-python/cn/os.rst.txt b/docs/sources/dive-into-python/cn/os.rst.txt
deleted file mode 100644
index bbf12df..0000000
--- a/docs/sources/dive-into-python/cn/os.rst.txt
+++ /dev/null
@@ -1,149 +0,0 @@
-与操作系统进行交互：os 模块
-======================================================
-os 模块提供了对系统文件进行操作的方法：
-
-::
-
-    import os
-    dir(os)
-
-<button>Run ...</button>
-
-
-文件路径操作
-----------------------------------------------------------
-- *os.remove(path)* 或 *os.unlink(path)* ：删除指定路径的文件。路径可以是全名，也可以是当前工作目录下的路径。
-- *os.removedirs*：删除文件，并删除中间路径中的空文件夹
-- *os.chdir(path)*：将当前工作目录改变为指定的路径
-- *os.getcwd()*：返回当前的工作目录
-- *os.curdir*：表示当前目录的符号
-- *os.rename(old, new)*：重命名文件
-- *os.renames(old, new)*：重命名文件，如果中间路径的文件夹不存在，则创建文件夹
-- *os.listdir(path)*：返回给定目录下的所有文件夹和文件名，不包括 *'.'* 和 *'..'* 以及子文件夹下的目录。（*'.'* 和* '..'* 分别指当前目录和父目录）
-- *os.mkdir(name)*：产生新文件夹
-- *os.makedirs(name)*：产生新文件夹，如果中间路径的文件夹不存在，则创建文件夹
-
-获得当前目录：
-::
-
-    import os
-    os.getcwd()
-
-<button>Run ...</button>
-
-
-获得当前目录下的文件：
-
-::
-
-    import os
-    os.listdir(os.curdir)
-
-
-
-写文件：
-
-::
-
-    import os
-    f = open("test.file", "w")
-    f.close()
-    print "test.file" in os.listdir(os.curdir)
-
-
-<button>Run ...</button>
-
-重命名文件：
-
-::
-
-    import os
-    os.rename("test.file", "test.new.file")
-    print "test.file" in os.listdir(os.curdir)
-    print "test.new.file" in os.listdir(os.curdir)
-
-<button>Run ...</button>
-
-删除文件：
-
-::
-
-    import os
-    os.remove("test.new.file")
-
-<button>Run ...</button>
-
-
-系统常量
--------------------------------------------------------------
-当前操作系统的换行符：
-
-::
-
-    import os
-    os.linesep
-
-<button>Run ...</button>
-
-
-当前操作系统的路径分隔符：
-
-::
-
-    import os
-    os.sep
-
-<button>Run ...</button>
-
-当前操作系统的环境变量中的分隔符（*';'* 或 *':'*）：
-
-
-::
-
-    import os
-    os.pathsep
-
-<button>Run ...</button>
-
-
-其他
-----------------------------------------------------
-*os.environ* 是一个存储所有环境变量的值的字典，可以修改。
-
-::
-
-    import os
-    os.environ["USER"]
-
-<button>Run ...</button>
-
-
-*os.urandom(len)* 返回指定长度的随机字节。
-
-os.path 模块
-------------------
-不同的操作系统使用不同的路径规范，这样当我们在不同的操作系统下进行操作时，可能会带来一定的麻烦，而 *os.path* 模块则帮我们解决了这个问题。
-
-- os.path.isfile(path) ：检测一个路径是否为普通文件
-- os.path.isdir(path)：检测一个路径是否为文件夹
-- os.path.exists(path)：检测路径是否存在
-- os.path.isabs(path)：检测路径是否为绝对路径
-
-split 和 join
-----------------
-- os.path.split(path)：拆分一个路径为 (head, tail) 两部分
-- os.path.join(a, * p)：使用系统的路径分隔符，将各个部分合成一个路径
-
-其他
-----------------------
-- os.path.abspath()：返回路径的绝对路径
-- os.path.dirname(path)：返回路径中的文件夹部分
-- os.path.basename(path)：返回路径中的文件部分
-- os.path.splitext(path)：将路径与扩展名分开
-- os.path.expanduser(path)：展开 '~' 和 '~user'
-
-作者 & 更新时间
-------------------------------------
-作者:`李金  <lijinwithyou@gmail.com>`
-
-提交: 2017/12/6
diff --git a/docs/sources/dive-into-python/cn/parameterpassing.rst.txt b/docs/sources/dive-into-python/cn/parameterpassing.rst.txt
deleted file mode 100644
index d6c8b5c..0000000
--- a/docs/sources/dive-into-python/cn/parameterpassing.rst.txt
+++ /dev/null
@@ -1,552 +0,0 @@
-函数进阶
-========
-本章内容包括参数传递，高阶函数，lambda 匿名函数，global 变量，递归
-
-
-
-函数是基本类型
-----------------
-
-在 Python 中，函数是一种基本类型的对象，这意味着
-
-- 可以将函数作为参数传给另一个函数
-- 将函数作为字典的值储存
-- 将函数作为另一个函数的返回值
-
-::
-
-    def square(x):
-        ^4$"""Square of x."""
-        ^4$return x*x
-
-    def cube(x):
-        ^4$"""Cube of x."""
-        ^4$return x*x*x
-
-<button>Run ...</button>
-
-
-作为字典的值：
-
-
-::
-
-    funcs = {
-        'square': square,
-        'cube': cube,
-    }
-
-
-例子：
-
-
-::
-    def square(x):
-        ^4$"""Square of x."""
-        ^4$return x*x
-
-    def cube(x):
-        ^4$"""Cube of x."""
-        ^4$return x*x*x
-
-    funcs = {
-        'square': square,
-        'cube': cube,
-    }
-
-
-
-    x = 2
-
-    print square(x)
-    print cube(x)
-
-
-    func in sorted(funcs):
-        ^4$print func, funcs[func](x)
-
-<button>Run ...</button>
-
-
-函数参数
-================
-
-引用传递
-------------
-
-
-Python 中的函数传递方式是 call by reference 即引用传递，例如，对于这样的用法：
-
-::
-
-    x = [10, 11, 12]
-    f(x)
-
-
-
-传递给函数 f 的是一个指向 x 所包含内容的引用，如果我们修改了这个引用所指向内容的值（例如 x[0]=999），那么外面的 x 的值也会被改变。不过如果我们在函数中赋给 x 一个新的值（例如另一个列表），那么在函数外面的 x 的值不会改变：
-
-::
-
-    def mod_f(x):
-        ^4$x[0] = 999
-        ^4$return x
-
-    x = [1, 2, 3]
-
-    print x
-    print mod_f(x)
-    print x
-
-<button>Run ...</button>
-
-
-::
-
-    def no_mod_f(x):
-        ^4$x = [4, 5, 6]
-        ^4$return x
-
-    x = [1,2,3]
-
-    print x
-    print no_mod_f(x)
-    print x
-
-<button>Run ...</button>
-
-
-默认参数是可变的
-------------------
-
-函数可以传递默认参数，默认参数的绑定发生在函数定义的时候，以后每次调用默认参数时都会使用同一个引用。
-
-这样的机制会导致这种情况的发生：
-
-::
-
-    def f(x = []):
-        ^4$x.append(1)
-        ^4$return x
-
-
-理论上说，我们希望调用 f() 时返回的是 [1]， 但事实上：
-
-
-::
-    def f(x = []):
-        ^4$x.append(1)
-        ^4$return x
-
-    print f()
-    print f()
-    print f()
-    print f(x = [9,9,9])
-    print f()
-    print f()
-
-<button>Run ...</button>
-
-将会得到以下结果
-::
-
-    [1]
-    [1, 1]
-    [1, 1, 1]
-    [9, 9, 9, 1]
-    [1, 1, 1, 1]
-    [1, 1, 1, 1, 1]
-
-
-而我们希望看到的应该是这样：
-
-
-::
-
-    def f(x = None):
-        if x is None:
-            x = []
-        x.append(1)
-        return x
-
-    print f()
-    print f()
-    print f()
-    print f(x = [9,9,9])
-    print f()
-    print f()
-
-<button>Run ...</button>
-
-
-输出应该是
-::
-
-    [1]
-
-    [1]
-
-    [1]
-
-    [9, 9, 9, 1]
-
-    [1]
-
-    [1]
-
-
-
-高阶函数
---------------
-
-
-以函数作为参数，或者返回一个函数的函数是高阶函数，常用的例子有 *map* 和 *filter* 函数：
-
-*map(f, sq)* 函数将 *f* 作用到*sq* 的每个元素上去，并返回结果组成的列表，相当于：
-
-
-*[f(s) for s in sq]*
-
-
-::
-
-    map(square, range(5))
-
-
-输入：
-::
-
-    [0, 1, 4, 9, 16]
-
-
-*filter(f, sq)* 函数的作用相当于，对于 *sq* 的每个元素 *s*，返回所有 *f(s)* 为 *True* 的 *s* 组成的列表，相当于：
-
-*[s for s in sq if f(s)]*
-
-
-::
-
-    def is_even(x):
-        return x % 2 == 0
-
-    filter(is_even, range(5))
-
-<button>Run ...</button>
-
-
-
-输出：
-
-::
-
-    [0, 2, 4]
-
-
-一起使用：
-
-
-::
-
-    map(square, filter(is_even, range(5)))
-
-
-
-输出：
-
-::
-
-    [0, 4, 16]
-
-
-*reduce(f, sq)* 函数接受一个二元操作函数 *f(x,y)*，并对于序列 *sq* 每次合并两个元素：
-
-
-::
-
-    def my_add(x, y):
-        ^4$return x + y
-
-    reduce(my_add, [1,2,3,4,5])
-
-
-Out[12]:
-15
-
-
-传入加法函数，相当于对序列求和。
-
-返回一个函数：
-
-In [13]:
-
-::
-    def make_logger(target):
-        def logger(data):
-            with open(target, 'a') as f:
-                f.write(data + '\n')
-
-        return logger
-
-    foo_logger = make_logger('foo.txt')
-    foo_logger('Hello')
-    foo_logger('World')
-
-<button>Run ...</button>
-
-
-In [14]:
-
-::
-
-   !cat foo.txt
-   Hello
-   World
-
-<button>Run ...</button>
-
-
-In [15]:
-
-::
-
-   import os
-   os.remove('foo.txt')
-
-<button>Run ...</button>
-
-
-匿名函数
-----------
-
-
-在使用 *map*， *filter*，*reduce* 等函数的时候，为了方便，对一些简单的函数，我们通常使用匿名函数的方式进行处理，其基本形式是：
-
-lambda <variables>: <expression>
-
-例如，我们可以将这个：
-
-In [16]:
-
-
-::
-
-   print map(square, range(5))
-   [0, 1, 4, 9, 16]
-
-<button>Run ...</button>
-
-用匿名函数替换为：
-
-In [17]:
-
-::
-
-    print map(lambda x: x * x, range(5))
-    [0, 1, 4, 9, 16]
-
-
-<button>Run ...</button>
-
-
-匿名函数虽然写起来比较方便（省去了定义函数的烦恼），但是有时候会比较难于阅读：
-
-In [18]:
-
-::
-
-
-   s1 = reduce(lambda x, y: x+y, map(lambda x: x**2, range(1,10)))
-   print(s1)
-   285
-
-<button>Run ...</button>
-
-
-当然，更简单地，我们可以写成这样：
-
-In [19]:
-
-::
-
-   s2 = sum(x**2 for x in range(1, 10))
-   print s2
-   285
-
-<button>Run ...</button>
-
-
-
-global 变量
----------------
-
-
-一般来说，函数中是可以直接使用全局变量的值的：
-
-In [20]:
-
-::
-
-   x = 15
-
-   def print_x():
-       print x
-    
-   print_x()
-   15
-
-<button>Run ...</button>
-
-
-但是要在函数中修改全局变量的值，需要加上 *global* 关键字：
-
-In [21]:
-
-::
-
-
-   x = 15
-
-   def print_newx():
-       global x
-       x = 18
-       print x
-    
-   print_newx()
-
-   print x
-   18
-   18
-
-<button>Run ...</button>
-
-
-如果不加上这句 global 那么全局变量的值不会改变：
-
-In [22]:
-
-::
-
-
-   x = 15
-
-   def print_newx():
-       x = 18
-       print x
-    
-   print_newx()
-
-   print x
-   18
-   15
-
-<button>Run ...</button>
-
-
-递归
---------
-
-
-递归是指函数在执行的过程中调用了本身，一般用于分治法，不过在 *Python* 中这样的用法十分地小，所以一般不怎么使用：
-
-Fibocacci 数列：
-
-In [23]:
-
-
-::
-
-
-   def fib1(n):
-       """Fib with recursion."""
-
-       # base case
-       if n==0 or n==1:
-           return 1
-       # recurssive caae
-       else:
-           return fib1(n-1) + fib1(n-2)
-
-   print [fib1(i) for i in range(10)]
-   [1, 1, 2, 3, 5, 8, 13, 21, 34, 55]
-
-
-<button>Run ...</button>
-
-
-一个更高效的非递归版本：
-
-In [24]:
-
-::
-
-
-    def fib2(n):
-         """Fib without recursion."""
-        a, b = 0, 1
-        for i in range(1, n+1):
-           a, b = b, a+b
-        return b
-
-    print [fib2(i) for i in range(10)]
-    [1, 1, 2, 3, 5, 8, 13, 21, 34, 55]
-
-<button>Run ...</button>
-
-
-速度比较：
-
-In [25]:
-
-::
-
-
-
-   %timeit fib1(20)
-   %timeit fib2(20)
-   100 loops, best of 3: 5.35 ms per loop
-   100000 loops, best of 3: 2.2 μs per loop
-
-<button>Run ...</button>
-
-对于第一个递归函数来说，调用 *fib(n+2)* 的时候计算 *fib(n+1)*, *fib(n)*，调用 *fib(n+1)* 的时候也计算了一次 *fib(n)*，这样造成了重复计算。
-
-使用缓存机制的递归版本，这里利用了默认参数可变的性质，构造了一个缓存：
-
-In [26]:
-
-::
-
-
-    def fib3(n, cache={0: 1, 1: 1}):
-         """Fib with recursion and caching."""
-
-        try:
-            return cache[n]
-        except KeyError:
-            cache[n] = fib3(n-1) + fib3(n-2)
-            return cache[n]
-
-    print [fib3(i) for i in range(10)]
-
-    %timeit fib1(20)
-    %timeit fib2(20)
-    %timeit fib3(20)
-
-
-
-<button>Run ...</button>
-
-
-    [1, 1, 2, 3, 5, 8, 13, 21, 34, 55]
-    100 loops, best of 3: 5.37 ms per loop
-    100000 loops, best of 3: 2.19 μs per loop
-    The slowest run took 150.16 times longer than the fastest. This could mean that an intermediate result is being cached 
-    1000000 loops, best of 3: 230 ns per loop
-
-
-作者 & 更新时间
-------------------------------------
-作者:李金  lijinwithyou@gmail.com
-
-提交: 2017/12/6
-
diff --git a/docs/sources/dive-into-python/cn/re.rst.txt b/docs/sources/dive-into-python/cn/re.rst.txt
deleted file mode 100644
index 305869a..0000000
--- a/docs/sources/dive-into-python/cn/re.rst.txt
+++ /dev/null
@@ -1,240 +0,0 @@
-正则表达式和 re 模块
-==============================
-正则表达式
--------------
-正则表达式是用来匹配字符串或者子串的一种模式，匹配的字符串可以很具体，也可以很一般化。
-
-Python 标准库提供了 re 模块。
-
-::
-
-    import re
-    dir(re)
-
-<button>Run ...</button>
-
-
-
-re.match & re.search
-----------------------------------------------------------
-在 re 模块中， re.match 和 re.search 是常用的两个方法：
-re.match(pattern, string[, flags])
-re.search(pattern, string[, flags])
-
-
-两者都寻找第一个匹配成功的部分，成功则返回一个 match 对象，不成功则返回 None，不同之处在于 re.match 只匹配字符串的开头部分，而 re.search 匹配的则是整个字符串中的子串。
-
-re.findall & re.finditer
------------------------------------------------------------------
-re.findall(pattern, string) 返回所有匹配的对象,
-re.finditer 则返回一个迭代器。
-
-re.split
------------
-re.split(pattern, string[, maxsplit]) 按照 pattern 指定的内容对字符串进行分割。
-
-re.sub
-------------------------------------------------------------------------
-re.sub(pattern, repl, string[, count]) 将 pattern 匹配的内容进行替换。
-
-re.compile
-------------------------------------------------------------------------
-re.compile(pattern) 生成一个 pattern 对象，这个对象有匹配，替换，分割字符串的方法。
-
-正则表达式规则
--------------------------------------------------------------------------
-
-正则表达式由一些普通字符和一些元字符（metacharacters）组成。普通字符包括大小写的字母和数字，而元字符则具有特殊的含义：
-
-
-+------------+------------------------------------------------------------------------------------------+
-| 子表达式   | 匹配内容                                                                                 |
-+============+==========================================================================================+
-|.           | 匹配除了换行符之外的内容                                                                 |    
-+------------+------------------------------------------------------------------------------------------+
-| \w         |匹配所有字母和数字字符                                                                    |
-+------------+------------------------------------------------------------------------------------------+
-| \d         |匹配所有数字，相当于 [0-9]                                                                |
-+------------+------------------------------------------------------------------------------------------+
-| \s         |        匹配空白，相当于 [\t\n\t\f\v]                                                     |         
-+------------+------------------------------------------------------------------------------------------+
-| \W,\D,\S   |   匹配对应小写字母形式的补                                                               |
-+------------+------------------------------------------------------------------------------------------+
-|[...]       |表示可以匹配的集合支持范围表示如 a-z, 0-9 等                                              |
-+------------+------------------------------------------------------------------------------------------+
-| (...)      |表示作为一个整体进行匹配                                                                  |
-+------------+------------------------------------------------------------------------------------------+
-| ^          |表示匹配后面的子表达式的补                                                                |
-+------------+------------------------------------------------------------------------------------------+
-|*           |      表示匹配前面的子表达式 0 次或更多次                                                 |
-+------------+------------------------------------------------------------------------------------------+
-|?           |	表示匹配前面的子表达式 1 次或更多次                                                     |
-+------------+------------------------------------------------------------------------------------------+
-|{m} 	     | 表示匹配前面的子表达式 m 次                                                              |
-+------------+------------------------------------------------------------------------------------------+
-|{m,} 	     |表示匹配前面的子表达式至少 m 次                                                           |
-+------------+------------------------------------------------------------------------------------------+
-|{m,n} 	     |表示匹配前面的子表达式至少 m 次，至多 n 次                                                |
-+------------+------------------------------------------------------------------------------------------+
-
-例如：
-- ca*t 匹配： ct, cat, caaaat, ...
-- ab\d|ac\d 匹配： ab1, ac9, ...
-- ([^a-q]bd) 匹配： rbd, 5bd, ...
-
-例子
---------------------------------------------------------------------
-
-假设我们要匹配这样的字符串：
-
-::
-
-    string = 'hello world'
-    pattern = 'hello (\w+)'
-
-    match = re.match(pattern, string)
-    print match
-
-<button>Run ...</button>
-
-
-一旦找到了符合条件的部分，我们便可以使用 group 方法查看匹配的部分：
-::
-
-    string = 'hello world'
-    pattern = 'hello (\w+)'
-
-    match = re.match(pattern, string)
-
-    if match is not None:
-        ^4$print match.group(0)
-
-<button>Run ...</button>
-
-
-获得第二个匹配的部分
-
-::
-
-    string = 'hello world'
-    pattern = 'hello (\w+)'
-
-    match = re.match(pattern, string)
-
-    if match is not None:
-        ^4$print match.group(1)
-
-<button>Run ...</button>
-
-
-我们可以改变 string 的内容：
-
-::
-
-    string = 'hello there'
-    pattern = 'hello (\w+)'
-
-    = re.match(pattern, string)
-    if match is not None:
-        ^4$print match.group(0)
-
-    print match.group(1)
-
-<button>Run ...</button>
-
-通常，match.group(0) 匹配整个返回的内容，之后的 1,2,3,... 返回规则中每个括号（按照括号的位置排序）匹配的部分。
-
-如果某个 pattern 需要反复使用，那么我们可以将它预先编译：
-
-::
-
-    pattern1 = re.compile('hello (\w+)')
-
-    match = pattern1.match(string)
-    if match is not None:
-        ^4$print match.group(1)
-
-<button>Run ...</button>
-
-
-由于元字符的存在，所以对于一些特殊字符，我们需要使用 '\' 进行逃逸字符的处理，使用表达式 '\\' 来匹配 '\' 。
-但事实上，Python 本身对逃逸字符也是这样处理的：
-
-::
-
-    pattern = '\\'
-    print pattern
-
-<button>Run ...</button>
-
-
-因为逃逸字符的问题，我们需要使用四个 '\\\\' 来匹配一个单独的 '\'：
-
-::
-
-    pattern = '\\\\'
-    path = "C:\\foo\\bar\\baz.txt"
-    print re.split(pattern, path)
-
-<button>Run ...</button>
-
-
-
-这样看起来十分麻烦，好在 Python 提供了 raw string 来忽略对逃逸字符串的处理，从而可以这样进行匹配：
-
-
-::
-
-    pattern = r'\\'
-    path = r"C:\foo\bar\baz.txt"
-    print re.split(pattern, path)
-
-<button>Run ...</button>
-
-
-如果规则太多复杂，正则表达式不一定是个好选择。
-
-Numpy 的 fromregex()
-----------------------------------------------------------
-
-::
-
-    # 有 test.dat 文件，内容如下
-
-    1312 foo
-    1534    bar
-    444  qux
-
-<button>Run ...</button>
-
-numpy.fromregex(file, pattern, dtype) 中，dtype 中的内容与 pattern 的括号一一对应：
-
-::
-
-    pattern = "(\d+)\s+(...)"
-    dt = [('num', 'int64'), ('key', 'S3')]
-
-    from numpy import fromregex
-    output = fromregex('test.dat', pattern, dt)
-    print output
-
-<button>Run ...</button>
-
-
-显示 num 项：
-::
-
-    pattern = "(\d+)\s+(...)"
-    dt = [('num', 'int64'), ('key', 'S3')]
-
-    from numpy import fromregex
-    output = fromregex('test.dat', pattern, dt)
-    print output['num']
-
-<button>Run ...</button>
-
-作者 & 更新时间
-------------------------------------
-作者:`李金  <lijinwithyou@gmail.com>`
-
-提交: 2017/12/6
diff --git a/docs/sources/dive-into-python/cn/sql.rst.txt b/docs/sources/dive-into-python/cn/sql.rst.txt
deleted file mode 100644
index de089a8..0000000
--- a/docs/sources/dive-into-python/cn/sql.rst.txt
+++ /dev/null
@@ -1,141 +0,0 @@
-SQL 数据库
-===============================
-Python 提供了一系列标准的数据库的 API，这里我们介绍 sqlite 数据库的用法，其他的数据库的用法大同小异：
-
-::
-
-    import sqlite3 as db
-
-
-首先我们要建立或者连接到一个数据库上：
-
-::
-
-    import sqlite3 as db
-    connection = db.connect("my_database.sqlite")
-
-不同的数据库有着不同的连接方法，例如 cx-oracle 数据库的链接方式为：
-
-
-::
-
-    connection = db.connect(username, password, host, port,  'XE')
-
-
-一旦建立连接，我们可以利用它的 cursor() 来执行 SQL 语句：
-
-::
-
-    import sqlite3 as db
-    connection = db.connect("my_database.sqlite")
-    cursor = connection.cursor()
-    cursor.execute("""CREATE TABLE IF NOT EXISTS orders(
-          order_id TEXT PRIMARY KEY,
-          date TEXT,
-          symbol TEXT,
-          quantity INTEGER,
-          price NUMBER)""")
-
-    cursor.execute("""INSERT INTO orders VALUES ('A0001', '2013-12-01', 'AAPL', 1000, 203.4)""")
-    connection.commit()
-
-<button>Run ...</button>
-
-
-
-不过为了安全起见，一般不将数据内容写入字符串再传入，而是使用这样的方式：
-
-::
-
-    import sqlite3 as db
-    connection = db.connect("my_database.sqlite")
-    cursor = connection.cursor()
-    orders = [
-        ("A0002","2013-12-01","MSFT",1500,167.5),
-        ("A0003","2013-12-02","GOOG",1500,167.5)
-    ]
-    cursor.executemany("""INSERT INTO orders VALUES
-        (?, ?, ?, ?, ?)""", orders)
-    connection.commit()
-
-<button>Run ...</button>
-
-cx-oracle 数据库使用不同的方式：
-
-::
-
-    cursor.executemany("""INSERT INTO orders VALUES
-        (:order_id, :date, :symbol, :quantity, :price)""",
-    orders)
-
-
-查看支持的数据库格式：
-
-::
-
-    import sqlite3 as db
-    print(db.paramstyle)
-
-
-<button>Run ...</button>
-
-在 query 语句执行之后，我们需要进行 commit，否则数据库将不会接受这些变化，如果想撤销某个 commit，可以使用 rollback() 方法撤销到上一次 commit() 的结果：
-
-::
-    try:
-        ^4$... # perform some operations
-    except:
-        ^4$connection.rollback()
-        ^4$raise
-    else:
-        ^4$connection.commit()
-
-
-使用 SELECT 语句对数据库进行查询：
-
-::
-
-    import sqlite3 as db
-    connection = db.connect("my_database.sqlite")
-    cursor = connection.cursor()
-
-    stock = 'MSFT'
-    cursor.execute("""SELECT *
-        FROM orders
-        WHERE symbol=?
-        ORDER BY quantity""", (stock,))
-    for row in cursor:
-        print row
-
-<button>Run ...</button>
-
-cursor.fetchone() 返回下一条内容， cursor.fetchall() 返回所有查询到的内容组成的列表（可能非常大）：
-
-::
-
-    import sqlite3 as db
-    connection = db.connect("my_database.sqlite")
-    cursor = connection.cursor()
-    stock = 'AAPL'
-    cursor.execute("""SELECT *
-    FROM orders
-    WHERE symbol=?
-    ORDER BY quantity""", (stock,))
-    cursor.fetchall()
-
-<button>Run ...</button>
-
-
-读写完毕数据库之后需要关闭数据库：
-
-::
-
-    cursor.close()
-    connection.close()
-
-
-作者 & 更新时间
-------------------------------------
-作者:`李金  <lijinwithyou@gmail.com>`
-
-提交: 2017/12/6
diff --git a/docs/sources/dive-into-python/cn/sys.rst.txt b/docs/sources/dive-into-python/cn/sys.rst.txt
deleted file mode 100644
index 94169d7..0000000
--- a/docs/sources/dive-into-python/cn/sys.rst.txt
+++ /dev/null
@@ -1,145 +0,0 @@
-sys 模块简介
-=======================
-
-通过以下方式引入sys模块
-::
-
-    import sys
-
-
-命令行参数
-----------------------------------------------------------
-*sys.argv* 显示传入的参数：
-
-假设print_args.py的内容如下：
-
-::
-
-    import sys
-    print sys.argv
-
-
-用以下方法运行这个程序
-
-::
-
-    python print_args.py 1 foo
-
-将会得到以下的输出
-
-::
-
-    ['print_args.py', '1', 'foo']
-
-
-第一个参数 （*sys.args[0]*） 表示的始终是执行的文件名，然后依次显示传入的参数。
-
-可以用os.remove删除文件：
-
-::
-
-    import os
-    os.remove('print_args.py')
-
-
-异常消息
--------------------------------------------------------------
-*sys.exc_info() *可以显示 *Exception* 的信息，返回一个 *(type, value, traceback)* 组成的三元组，可以与 *try/catch* 块一起使用：
-
-::
-
-    try:
-        ^4$x = 1/0
-
-    except Exception:
-        ^4$print sys.exc_info()
-
-
-运行上述代码，将会得到：
-
-::
-
-    (<type 'exceptions.ZeroDivisionError'>, ZeroDivisionError('integer division or modulo by zero',), <traceback object at 0x0000000003C6FA08>)
-
-*sys.exc_clear()* 用于清除所有的异常消息。
-
-
-
-标准输入输出流
-----------------------------------------------------
-
-以下属性代表python的标准输入输出流操作句柄
-
-- sys.stdin
-- ys.stdout
-- sys.stderr
-
-退出Python
-------------------------------------------------------
-*sys.exit(arg=0)* 用于退出 Python。*0* 或者 *None* 表示正常退出，其他值表示异常。
-
-Python Path
---------------------------------------------------------
-*sys.path* 表示 Python 搜索模块的路径和查找顺序：
-
-::
-
-    import sys
-    print sys.path
-
-<button>Run ...</button>
-
-在程序中可以通过sys.append, sys.insert 等方式添加，修改新的路径。
-
-
-操作系统信息
-----------------------------------------------------------------
-*sys.platform* 显示当前操作系统信息：
-
-- Windows: win32
-- Mac OSX: darwin
-- Linux: linux2
-
-::
-
-    import sys
-    sys.platform
-
-<button>Run ...</button>
-
-'win32'
-返回*Windows* 操作系统的版本：
-
-::
-
-    import sys
-    sys.getwindowsversion()
-
-
-将会输出
-::
-
-    sys.getwindowsversion(major=6, minor=2, build=9200, platform=2, service_pack='')
-
-标准库中有 *planform* 模块提供更详细的信息。
-
-Python 版本信息
-------------------------------------------------------------
-::
-
-    import sys
-    sys.version
-
-::
-
-    import sys
-    sys.version_info
-
-
-
-作者 & 更新时间
-------------------------------------
-作者:`李金  <lijinwithyou@gmail.com>`
-
-提交: 2017/12/6
-
diff --git a/docs/sources/dive-into-python/cn/the-use-of-modifier.rst.txt b/docs/sources/dive-into-python/cn/the-use-of-modifier.rst.txt
deleted file mode 100644
index 8bb9fb6..0000000
--- a/docs/sources/dive-into-python/cn/the-use-of-modifier.rst.txt
+++ /dev/null
@@ -1,361 +0,0 @@
-修饰符的使用
-================
-
-
-
-@classmethod 修饰符
---------------------
-
-
-在 *Python* 标准库中，有很多自带的修饰符，例如 *classmethod* 将一个对象方法转换了类方法：
-
-
- 
-
-::
-
-    class Foo(object):
-        @classmethod
-        ^4$def bar(cls, x):
-            ^8$print 'the input is', x
-        
-        ^4$def __init__(self):
-            ^8$pass
-
-
-
-类方法可以通过 **类名.方法** 来调用：
-
-
-
- 
-
-::
-
-   Foo.bar(12)
-
-
-结果:
-::
-    12
-
-
-@property 修饰符
------------------
-
-
-有时候，我们希望像 **Java** 一样支持 *getters* 和 *setters* 的方法，这时候就可以使用 *property* 修饰符：
-
-
-
- 
-
-::
-
-
-    class Foo(object):
-        ^4$def __init__(self, data):
-            ^8$self.data = data
-    
-        ^4$@property
-        ^4$def x(self):
-            ^8$return self.data
-
-
-
-此时可以使用* .x* 这个属性查看数据（不需要加上括号）：
-
-
-
- 
-
-::
-
-   foo = Foo(23)
-   foo.x
-
-结果: 
-::
-
-    23
-
-
-这样做的好处在于，这个属性是只读的：
-
-::
-
-
-   foo.x = 1
-
-
-
-如果想让它变成可读写，可以加上一个修饰符 *@x.setter*：
-
-
-
-
- 
-
-
-::
-
-    class Foo(object):
-    def __init__(self, data):
-        ^4$self.data = data
-    
-    @property
-    def x(self):
-        ^4$return self.data
-    
-    @x.setter
-    def x(self, value):
-        ^4$self.data = value
-
-
-
- 
-
-
-::
-
-    foo = Foo(23)
-    print foo.x
-
-结果:
-::
-
-    23
-
-
-可以通过属性改变它的值：
-
-
-
- 
-
-::
-
-
-   foo.x = 1
-   print foo.x
-
-
-结果:
-::
-
-    1
-
-
-Numpy 的 @vectorize 修饰符
----------------------------
-
-*numpy* 的 *vectorize* 函数讲一个函数转换为 *ufunc*，事实上它也是一个修饰符：
-
-
-
- 
-
-
-::
-     
-    from numpy import vectorize, arange
-
-    @vectorize
-    def f(x):
-        ^4$if x <= 0:
-            ^8$return x
-        ^4$else:
-            ^8$return 0
-
-    f(arange(-10.0,10.0))
-
-
-
-
-结果: 
-::
-
-    
-    array([-10.,  -9.,  -8.,  -7.,  -6.,  -5.,  -4.,  -3.,  -2.,  -1.,   0.,0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.])
-
-
-
-注册一个函数
-------------------
-
-
-来看这样的一个例子，定义一个类：
-
-
-
- 
-
-::
-
-
-    class Registry(object):
-    def __init__(self):
-        ^4$self._data = {}
-    def register(self, f, name=None):
-        ^4$if name == None:
-            ^8$name = f.__name__
-        ^4$self._data[name] = f
-        ^4$setattr(self, name, f)
-
-
-*register* 方法接受一个函数，将这个函数名作为属性注册到对象中。
-
-
-
-
-产生该类的一个对象：
-
-
-
- 
-
-::
-
-    registry = Registry()
-
-
-
-使用该对象的 *register* 方法作为修饰符：
-
-
-
- 
-
-::
-
-    @registry.register
-    def greeting():
-        ^4$print "hello world"
-
-
-这样这个函数就被注册到 *registry* 这个对象中去了：
-
-
-
- 
-
-::
-
-
-    registry._data
-
-
-结果: 
-::
-
-    {'greeting': <function __main__.greeting>}
-
-
-
- 
-
-::
-
-
-    registry.greeting
-
-
-结果: 
-::
-
-    <function __main__.greeting>
-
-
-
-*flask* ，一个常用的网络应用，处理 url 的机制跟这个类似。
-
-
-
-
-使用 @wraps
-----------------
-
-
-一个通常的问题在于：
-
-
-
- 
-
-::
-
-     def logging_call(f):
-        ^4$def wrapper(*a, **kw):
-            ^8$print 'calling {}'.format(f.__name__)
-            ^8$return f(*a, **kw)
-        ^4$return wrapper
-
-    @logging_call
-    def square(x):
-        '''
-       square function.
-        '''
-       return x ** 2
-
-    print square.__doc__, square.__name__
-
-
-
-None wrapper
-
-
-
-我们使用修饰符之后，*square* 的 *metadata* 完全丢失了，返回的函数名与函数的 *docstring* 都不对。
-
-一个解决的方法是从 *functools* 模块导入 *wraps* 修饰符来修饰我们的修饰符：
-
-
-
- 
-
-::
-
-     import functools
-
-    def logging_call(f):
-        @functools.wraps(f)
-        ^4$def wrapper(*a, **kw):
-            ^8$print 'calling {}'.format(f.__name__)
-            ^8$return f(*a, **kw)
-        ^4$return wrapper
-
-    @logging_call
-    def square(x):
-       '''
-       square function.
-       '''
-       return x ** 2
-
-    print square.__doc__, square.__name__
- 
-
-<button>Run ...</button>
-
-
-    square function.
-     square
-
-
-
-现在这个问题解决了，所以在自定义修饰符方法的时候为了避免出现不必要的麻烦，尽量使用* wraps* 来修饰修饰符！
-
-Class 修饰符
----------------
-
-
-
-与函数修饰符类似，类修饰符是这样一类函数，接受一个类作为参数，通常返回一个新的类。
-
-
-
-作者 & 更新时间
-------------------------------------
-作者:李金  lijinwithyou@gmail.com
-
-提交: 2017/12/6
-
diff --git a/docs/sources/dive-into-python/cn/with.rst.txt b/docs/sources/dive-into-python/cn/with.rst.txt
deleted file mode 100644
index 0a9b610..0000000
--- a/docs/sources/dive-into-python/cn/with.rst.txt
+++ /dev/null
@@ -1,504 +0,0 @@
-with 语句和上下文管理器
-================================
-
-
-::
-
-    # create/aquire some resource
-    ...
-    try:
-        # do something with the resource
-        ...
-    finally:
-        # destroy/release the resource
-        ...
-
-
-处理文件，线程，数据库，网络编程等等资源的时候，我们经常需要使用上面这样的代码形式，以确保资源的正常使用和释放。
-
-好在Python 提供了 with 语句帮我们自动进行这样的处理，例如之前在打开文件时我们使用：
-
-
-
-::
-
-    with open('my_file', 'w') as fp:
-        ^4$# do stuff with fp
-        ^4$data = fp.write("Hello world")
-
-<button>Run ...</button>
-
-
-这等效于下面的代码，但是要更简便：
-
-::
-
-    fp = open('my_file', 'w')
-    try:
-        ^4$# do stuff with f
-        ^4$data = fp.write("Hello world")
-    finally:
-        ^4$fp.close()
-
-<button>Run ...</button>
-
-上下文管理器
----------------
-
-
-其基本用法如下：
-
-::
-
-    with <expression>:
-         ^4$<block>
-
-
-*<expression>* 执行的结果应当返回一个实现了上下文管理器的对象，即实现这样两个方法，*__enter__* 和 *__exit__*：
-
-
-::
-
-    with open('my_file', 'w') as fp:
-        ^4$# do stuff with fp
-        ^4$data = fp.write("Hello world")
-
-        ^4$print fp.__enter__
-        ^4$print fp.__exit__
-
-输出如下：
-::
-
-    <built-in method __enter__ of file object at 0x0000000003C1D540>
-    <built-in method __exit__ of file object at 0x0000000003C1D540>
-
-
-
-*__enter__ *方法在 *<block>* 执行前执行，而 *__exit__ *在 *<block>* 执行结束后执行：
-
-
-比如可以这样定义一个简单的上下文管理器：
-
-
-
-::
-
-    class ContextManager(object):
-        ^4$def __enter__(self):
-            ^8$print "Entering"
-
-        ^4$def __exit__(self, exc_type, exc_value, traceback):
-            ^4$print "Exiting"
-
-
-
-    with ContextManager():
-        ^4$print "  Inside the with statement"
-
-
-<button>Run ...</button>
-
-
-即使 *<block>* 中执行的内容出错，*__exit__* 也会被执行：
-
-
-
-::
-
-    class ContextManager(object):
-        ^4$def __enter__(self):
-            ^8$print "Entering"
-
-        ^4$def __exit__(self, exc_type, exc_value, traceback):
-            ^4$print "Exiting"
-
-
-    with ContextManager():
-        ^4$print 1/0
-
-<button>Run ...</button>
-
-输出会是：
-
-::
-
-    Entering
-    Exiting
-
-    ZeroDivisionError Traceback (most recent call last)
-
-    ZeroDivisionError: integer division or modulo by zero
-
-
-*__enter__* 的返回值
----------------------
-
-如果在 *__enter__ *方法下添加了返回值，那么我们可以使用 *as* 把这个返回值传给某个参数：
-
-
-
-::
-
-    class ContextManager(object):
-        ^4$def __enter__(self):
-            ^8$print "Entering"
-            ^8$return "my value"
-
-        ^4$def __exit__(self, exc_type, exc_value, traceback):
-            ^8$print "Exiting"
-
-
-    #将 *__enter__*返回的值传给 *value* 变量：
-    with ContextManager() as value:
-        ^4$print value
-
-<button>Run ...</button>
-
-输出应该是：
-
-::
-
-    Entering
-    my value
-    Exiting
-
-
-一个通常的做法是将 *__enter__* 的返回值设为这个上下文管理器对象本身，文件对象就是这样做的：
-
-
-
-::
-
-
-    fp = open('my_file', 'r')
-    print fp.__enter__()
-    fp.close()
-    import os
-    os.remove('my_file')
-
-
-
-<button>Run ...</button>
-
-
-实现方法非常简单：
-
-
-::
-
-    class ContextManager(object):
-        ^4$def __enter__(self):
-            ^8$print "Entering"
-            ^8$return self
-
-        ^4$def __exit__(self, exc_type, exc_value, traceback):
-            ^8$print "Exiting"
-
-<button>Run ...</button>
-
-
-
-::
-
-    with ContextManager() as value:
-        ^4$print value
-
-
-输出是：
-::
-
-    Entering
-    <__main__.ContextManager object at 0x0000000003D48828>
-    Exiting
-
-
-错误处理
--------------
-
-上下文管理器对象将错误处理交给 *__exit__* 进行，可以将错误类型，错误值和 *traceback* 等内容作为参数传递给 *__exit__* 函数：
-
-
-::
-
-    class ContextManager(object):
-        ^4$def __enter__(self):
-            ^8$print "Entering"
-
-        ^4$def __exit__(self, exc_type, exc_value, traceback):
-            ^8$print "Exiting"
-            ^4$if exc_type is not None:
-                ^8$print "  Exception:", exc_value
-
-    #如果没有错误，这些值都将是 *None*, 当有错误发生的时候：
-
-    with ContextManager():
-        ^4$print 1/0
-
-
-<button>Run ...</button>
-
-输出是：
-::
-
-    Entering
-    Exiting
-    Exception: integer division or modulo by zero
-
-    ZeroDivisionError Traceback (most recent call last)
-    ZeroDivisionError: integer division or modulo by zero
-
-
-在这个例子中，我们只是简单的显示了错误的值，并没有对错误进行处理，所以错误被向上抛出了，如果不想让错误抛出，只需要将 *__exit__* 的返回值设为* True*：
-
-
-
-::
-
-    class ContextManager(object):
-        ^4$def __enter__(self):
-            ^8$print "Entering"
-
-        ^4$def __exit__(self, exc_type, exc_value, traceback):
-            ^8$print "Exiting"
-            ^4$if exc_type is not None:
-                ^8$print " Exception suppresed:", exc_value
-                ^8$return True
-
-    with ContextManager():
-        ^4$print 1/0
-
-
-<button>Run ...</button>
-
-
-输出是：
-::
-
-    Entering
-    Exiting
-    Exception suppresed: integer division or modulo by zero
-
-
-在这种情况下，错误就不会被向上抛出。
-
-
-数据库的例子
--------------
-
-对于数据库的 *transaction* 来说，如果没有错误，我们就将其 *commit* 进行保存，如果有错误，那么我们将其回滚到上一次成功的状态。
-
-
-::
-
-    class Transaction(object):
-        ^4$def __init__(self, connection):
-            ^8$self.connection = connection
-
-        ^4$def __enter__(self):
-            ^8$return self.connection.cursor()
-
-        ^4$def __exit__(self, exc_type, exc_value, traceback):
-            ^8$if exc_value is None:
-                ^12$# transaction was OK, so commit
-                ^12$self.connection.commit()
-            ^8$else:
-                ^12$# transaction had a problem, so rollback
-                self.connection.rollback()
-
-    #建立一个数据库，保存一个地址表：
-
-    import sqlite3 as db
-    connection = db.connect(":memory:")
-
-    with Transaction(connection) as cursor:
-        ^4$cursor.execute("""CREATE TABLE IF NOT EXISTS addresses (
-            ^8$address_id INTEGER PRIMARY KEY,
-            ^8$street_address TEXT,
-            ^8$city TEXT,
-            ^8$state TEXT,
-            ^8$country TEXT,
-            ^8$postal_code TEXT
-        ^4$)""")
-
-
-    #插入数据：
-
-    with Transaction(connection) as cursor:
-        ^4$cursor.executemany("""INSERT OR REPLACE INTO addresses VALUES (?, ?, ?, ?, ?, ?)""", [
-            ^8$(0, '515 Congress Ave', 'Austin', 'Texas', 'USA', '78701'),
-            ^8$(1, '245 Park Avenue', 'New York', 'New York', 'USA', '10167'),
-            ^8$(2, '21 J.J. Thompson Ave.', 'Cambridge', None, 'UK', 'CB3 0FA'),
-            ^8$(3, 'Supreme Business Park', 'Hiranandani Gardens, Powai, Mumbai', 'Maharashtra', 'India', '400076'),
-        ^4$])
-
-    #假设插入数据之后出现了问题：
-    with Transaction(connection) as cursor:
-        ^4$cursor.execute("""INSERT OR REPLACE INTO addresses VALUES (?, ?, ?, ?, ?, ?)""",
-            ^8$(4, '2100 Pennsylvania Ave', 'Washington', 'DC', 'USA', '78701'),
-        ^4$)
-        ^4$raise Exception("out of addresses")
-
-<button>Run ...</button>
-
-
-输出为：
-
-::
-
-    Exception Traceback (most recent call last)
-    ...
-    Exception: out of addresses
-
-
-最新的一次插入将不会被保存，而是返回上一次 commit 成功的状态：
-
-
-
-如果继续执行：
-
-::
-
-    cursor.execute("SELECT * FROM addresses")
-    for row in cursor:
-        ^4$print row
-
-
-输出为：
-::
-
-    (0, u'515 Congress Ave', u'Austin', u'Texas', u'USA', u'78701')
-
-    (1, u'245 Park Avenue', u'New York', u'New York', u'USA', u'10167')
-
-    (2, u'21 J.J. Thompson Ave.', u'Cambridge', None, u'UK', u'CB3 0FA')
-
-    (3, u'Supreme Business Park', u'Hiranandani Gardens, Powai, Mumbai', u'Maharashtra', u'India', u'400076')
-
-
-contextlib 模块
-----------------
-
-很多的上下文管理器有很多相似的地方，为了防止写入很多重复的模式，可以使用 *contextlib* 模块来进行处理。
-
-最简单的处理方式是使用 *closing* 函数确保对象的 *close()* 方法始终被调用：
-
-::
-
-    from contextlib import closing
-    import urllib
-
-    with closing(urllib.urlopen('http://www.qpython.com')) as url:
-        ^4$html = url.read()
-
-    print html[:100]
-
-
-<button>Run ...</button>
-
-
-另一个有用的方法是使用修饰符 *@contextlib*：
-
-
-::
-
-    from contextlib import contextmanager
-
-    @contextmanager
-    def my_contextmanager():
-        ^4$print "Enter"
-        ^4$yield
-        ^4$print "Exit"
-
-    with my_contextmanager():
-        ^4$print "  Inside the with statement"
-
-<button>Run ...</button>
-
-输出应是：
-::
-
-    Enter
-    Inside the with statement
-    Exit
-
-
-*yield *之前的部分可以看成是* __enter__* 的部分，*yield* 的值可以看成是 *__enter__* 返回的值，*yield *之后的部分可以看成是* __exit__* 的部分。
-
-使用 *yield* 的值：
-
-
-
-::
-
-
-    @contextmanager
-    def my_contextmanager():
-        print "Enter"
-        yield "my value"
-        print "Exit"
-
-    with my_contextmanager() as value:
-        print value
-
-
-输出是：
-::
-
-    Enter
-    my value
-    Exit
-
-
-错误处理可以用 *try* 块来完成：
-
-::
-
-    @contextmanager
-    def my_contextmanager():
-        ^4$print "Enter"
-        ^4$try:
-            ^8$yield
-        except Exception as exc:
-            ^8$print "   Error:", exc
-        finally:
-            ^4$print "Exit"
-
-
-    with my_contextmanager():
-        ^4$print 1/0
-
-<button>Run ...</button>
-
-输出为：
-::
-
-    Enter
-    Error: integer division or modulo by zero
-    Exit
-
-
-对于之前的数据库 *transaction* 我们可以这样定义：
-
-
-::
-
-    @contextmanager
-    def transaction(connection):
-        ^4$cursor = connection.cursor()
-        ^4$try:
-            ^8$yield cursor
-        ^4$except:
-            ^8$connection.rollback()
-            ^8$raise
-        ^4$else:
-            ^8$connection.commit()
-
-<button>Run ...</button>
-
-
-作者 & 更新时间
-------------------------------------
-作者:`李金  <lijinwithyou@gmail.com>`
-
-提交: 2017/12/6
-
diff --git a/docs/sources/dive-into-python/index.rst.txt b/docs/sources/dive-into-python/index.rst.txt
deleted file mode 100644
index d971f49..0000000
--- a/docs/sources/dive-into-python/index.rst.txt
+++ /dev/null
@@ -1,19 +0,0 @@
-Dive into Python
-=======================
-The courses are still in the planning. Welcome to reward us. 
-
-1. **Primary course**
-
-If the total number of people who reward us is more than 100, we promise to complete the primary course within 1 week, so that, after finishing your study, you can master the basic concepts and write simple scripts.
-
-2. **Intermediate course**
-
-If the total number of people who reward us is more than 500, we will launch the intermediate course within 2 weeks, so that, after finishing your study, you will be familiar with the knowledge and framework of the course and apply it to practical development.
-
-3. **Advanced course**
-
-If the total number of people who reward us is more than 2,000, we will launch the advanced course in a month, so that, after finishing your study, you will have a better understanding of the principles of the framework and be able to do advanced development.
-
-
-**The primary course is free to all, and the intermediate and advanced courses are only open to those who have rewarded us.**
-
diff --git a/docs/sources/django/index.rst.txt b/docs/sources/django/index.rst.txt
deleted file mode 100644
index 49feed4..0000000
--- a/docs/sources/django/index.rst.txt
+++ /dev/null
@@ -1,19 +0,0 @@
-Django Programming
-=======================
-The courses are still in the planning. Welcome to reward us. 
-
-1. **Primary course**
-
-If the total number of people who reward us is more than 100, we promise to complete the primary course within 1 week, so that, after finishing your study, you can master the basic concepts and write simple scripts.
-
-2. **Intermediate course**
-
-If the total number of people who reward us is more than 500, we will launch the intermediate course within 2 weeks, so that, after finishing your study, you will be familiar with the knowledge and framework of the course and apply it to practical development.
-
-3. **Advanced course**
-
-If the total number of people who reward us is more than 2,000, we will launch the advanced course in a month, so that, after finishing your study, you will have a better understanding of the principles of the framework and be able to do advanced development.
-
-
-**The primary course is free to all, and the intermediate and advanced courses are only open to those who have rewarded us.**
-
diff --git a/docs/sources/featured/featured_2017_0821.rst.txt b/docs/sources/featured/featured_2017_0821.rst.txt
deleted file mode 100644
index e09e226..0000000
--- a/docs/sources/featured/featured_2017_0821.rst.txt
+++ /dev/null
@@ -1,13 +0,0 @@
-QPython Daily Featured
-========================
-
-How to help with translate QPython to other languages?
-------------------------------------------------------
-Thank you very much for being willing to contribute, pelase see `QPython localization project <https://github.com/qpython-android/localization>`_ for more detail.  by *Railson Oliveira ( from facebook )*
-
-Run Kivy app don't show log information
-----------------------------------------
-How to avoid the qpython miss important log in kivy project ? Just see  `an example of changing qpython log behavior <https://github.com/qpython-android/qpython/issues/24>`_ by  *yingshaoxo ( from github )*
-
-
-*If you have any question, please leave a message to us, we will answer in next featured.*
diff --git a/docs/sources/featured/featured_2017_0822.rst.txt b/docs/sources/featured/featured_2017_0822.rst.txt
deleted file mode 100644
index ffec3a6..0000000
--- a/docs/sources/featured/featured_2017_0822.rst.txt
+++ /dev/null
@@ -1,10 +0,0 @@
-QPython Daily Featured
-========================
-
-How to copy and paste text in terminal?
-----------------------------------------
-*By Neelu from Facebook*
-
-By long touching on the terminal, the menu shows, you can copy text through "Select text"  and paste text through "Paste".
-
-.. image:: http://edu.qpython.org/static/terminal-howto-select-text.png
diff --git a/docs/sources/featured/featured_2017_0923.rst.txt b/docs/sources/featured/featured_2017_0923.rst.txt
deleted file mode 100644
index 3a9274a..0000000
--- a/docs/sources/featured/featured_2017_0923.rst.txt
+++ /dev/null
@@ -1,23 +0,0 @@
-QPython Featured
-========================
-
-How to close the push notification
-------------------------------------------------------
-Last time, someone asked how to close the push notification. The fact is QPython didn't support that function, so we stopped sending push notifications. But now QPython supports closing push notifications. Besides, QPython supports closing log notifications too. You can find these options in your QPython's setting page.
-
-
-We don't recommend to close notifications because you may lost some amazing featured contents.
-
-What's new recently
---------------------
-We published a stable version (v2.0.5) a few days ago, which supports Russian language and fixes some bugs like FTP issue.
-
-We published a BETA version (v2.0.6) yesterday which fixes sqlite3 error (get it from https://github.com/qpython-android/qpython/releases) and allows users to call QPython API with service.
-
-
-The QPython community
--------------------------
-More and more talent people join the QPython community (https://www.facebook.com/groups/qpython)  recently.  And we hope that we could learn from each other.
-
-
-*If you want to talk to us, please leave a message to us through twitter ( @qpython ), and we will reply you as soon as possible.*
diff --git a/docs/sources/featured/featured_2017_1013.rst.txt b/docs/sources/featured/featured_2017_1013.rst.txt
deleted file mode 100644
index b256bd9..0000000
--- a/docs/sources/featured/featured_2017_1013.rst.txt
+++ /dev/null
@@ -1,18 +0,0 @@
-Hello
-========
-
-
-What's new
------------------------------------------------
-
-We are working on the AIPY branch which offers data analysis and deeplearning features. It mainly includes numpy, scipy, matplitlib, pandas, theano, scikit-learn and keras etc. Please follow us on facebook (http://www.facebook.com/qpython) to get it's progress.
-
-A Chinese qpython users group is going to launching a QPython Programming contest, if you want to join this contest, please follow wechat GONGZHONGHAO qpython to get the detail information.
-
-
-Saving scripts to cloud
------------------------
-Newest QPython (2.0.6) has support saving script to cloud (require google login), Would you like give a try ?
-
-
-*If you want to talk to us, please leave a message to us through twitter ( @qpython ), and we will reply you as soon as possible.*
diff --git a/docs/sources/featured/featured_2018_0116.rst.txt b/docs/sources/featured/featured_2018_0116.rst.txt
deleted file mode 100644
index 7a0c174..0000000
--- a/docs/sources/featured/featured_2018_0116.rst.txt
+++ /dev/null
@@ -1,52 +0,0 @@
-QPY Programming Challenge 0x00
-================================================
-
-.. image:: http://edu.qpython.org/static/qpyprogrammingchanlleage-0x00.png
-    :alt: QPython - QPY Programming Challenge 0x00
-
-
-Did you learn the word "Pythonic" when you first write Python?
-
-Have you read the P2P transmission of 15 lines, neural network of 11 lines or block chain python code of 50 lines?
-
-Even if you have written countless Bugs, do you still remember the heart that you wanted to write Simple and elegant code at first?
-
-Why not try to put down the impetuous and try to revisit Pythonic?
-
-
-QPY Programming Challenge
------------------------------------------------
-Since 2018, we plan to start QPython Challenge once a month.
-
-
-
-How to submit
------------------------
-You can send your entries to us via email (support@qpython.org)，note QPyC20181 participate in this event and your nickname.
-
-You can submit multiple times, we will take the final manuscript as your final work. The event at the end of the month, the highest percentage of votes is the champion.
-
-
-Evaluation methods
------------------------
-At the end of the month, we will issue a document on QPython community to list the entries, Each contest will voted the user selected the most Pythonic code winner, Domestic winners receive QPython T-Shirt or themed souvenirs.
-
-The 0x00 issue of the challenge
---------------------------------
-#The first topic
-The title of Challenge 2 is already in the article, please find it.
-
-
-# The second topic
-100 * 100 a chessboard, the first piece at the position [0, 0],Every second piece random walk horizontal or vertical step, At the same time there are n traps on the board, traps arranged randomly. Known traps abscissa i(nt[]) coordx and ordinate (int[]) coordy,how many steps can a player move without falling into the trap?
-
-
-Take out QPython in your pocket and write out the Pythonic code comments on the subject two subject names and send them to us, Compared to other QPythoners, see who is more Pythonic?
-
-
-Join the QPython community
-----------------------------
-
-* `QPython@Facebook <https://www.facebook.com/groups/qpython>`_
-* `QPython@G+ <https://plus.google.com/u/2/communities/111759148772865961493>`_
-
diff --git a/docs/sources/featured/featured_2018_0301_notebook.rst.txt b/docs/sources/featured/featured_2018_0301_notebook.rst.txt
deleted file mode 100644
index adeb72e..0000000
--- a/docs/sources/featured/featured_2018_0301_notebook.rst.txt
+++ /dev/null
@@ -1,74 +0,0 @@
-Share a live documents with QPyNotebook
-========================================
-
-Do you want some application which helps you create and share documents that contain live Python code, equations, visualizations and narrative text?
-
-Now, QPyNotebook for QPython is published, which is developed based on the great opensource project jupyter notebook, it will help you do the following jobs easily like data cleaning and transformation, numerical simulation, statistical modeling, data visualization, machine learning and much more.
-
-Before Use
------------
-**1. Make sure you have installed the newest QPython (>=2.2.0)**
-
-**2. Download QPyNotebook**
-
-- Click the QPyNotebook App button to download QPyNotebook, the button will take you to the Google Play App Store.
-
-- If you could not connect to Google you can download directly from our `Github page <https://github.com/qpython-android/notebook/releases/>`_ *(please check the "Trust unknow source" in system's setting if you install it with APK downloaded from github)*
-
-.. image:: http://edu.qpython.org/static/notebook-setting.png
-    :alt: Enable QPyNotebook in QPython Setting
-
-
-
-**3. Download related libraries**
-
-Click the QPyNotebook Libraries button and there'll be a terminal window to download those libraries. It may take a while, and after downloading finish, the terminal window will be closed automatically.
-
-.. image:: http://edu.qpython.org/static/notebook-lib.png
-    :alt: Enable QPyNotebook in QPython Setting
-
-
-
-.. image:: http://edu.qpython.org/static/notebook-download.png
-    :alt: Download libraries
-
-
-**Now all set!**
-
-.. image:: http://edu.qpython.org/static/notebook-downloaded.png
-    :alt: all set
-
-
-How to use
------------------------
-Open the Explorer from QPython dashboard, find some *.ipynb files (For example notebooks/Welcome.ipynb) and open it, then you will start the QPyNotebook.
-
-- You could start the QPyNotebook from QPython's explorer*
-
-.. image:: http://edu.qpython.org/static/notebook-explorer.png
-    :alt: You could start QPyNotebook from QPython's explorer
-
-- Or from QPyNotebook App
-
-.. image:: http://edu.qpython.org/static/notebook-app.png
-    :alt: Start QPyNotebook
-
-*QPyNotebook's live documentation*
-
-.. image:: http://edu.qpython.org/static/notebook-page.png
-    :alt: Start to access Welcome.ipynb
-
-
-Feedback
------------------------
-Feel free to give us any feedback please.
-
-* `@qpython <http://twitter.com/qpython>`_
-* `support@qpython.org <support@qpython.org>`_
-
-Join the community
-----------------------------
-
-* `QPython@G+ <https://plus.google.com/u/2/communities/111759148772865961493>`_
-* `QPython@Facebook <https://www.facebook.com/groups/qpython>`_
-
diff --git a/docs/sources/featured/index.rst.txt b/docs/sources/featured/index.rst.txt
deleted file mode 100644
index 0a0ec80..0000000
--- a/docs/sources/featured/index.rst.txt
+++ /dev/null
@@ -1,8 +0,0 @@
-QPython Featured
-========================
-
-How to help with translate QPython to other language?
-------------------------------------------------------
-
-
-.. image:: http://dl.qpy.io/assets/banners/course-qpython-quick-start.png
diff --git a/docs/sources/featured/tpl.rst.txt b/docs/sources/featured/tpl.rst.txt
deleted file mode 100644
index bb656e9..0000000
--- a/docs/sources/featured/tpl.rst.txt
+++ /dev/null
@@ -1,8 +0,0 @@
-QPython Featured
-========================
-
-Question
-------------------------------------------------------
-Answer
-
-.. image:: http://dl.qpy.io/assets/banners/course-qpython-quick-start.png
diff --git a/docs/sources/full-text-search/index.rst.txt b/docs/sources/full-text-search/index.rst.txt
deleted file mode 100644
index 54e949b..0000000
--- a/docs/sources/full-text-search/index.rst.txt
+++ /dev/null
@@ -1,19 +0,0 @@
-Full Text Search with Python
-=============================
-The courses are still in the planning. Welcome to reward us. 
-
-1. **Primary course**
-
-If the total number of people who reward us is more than 100, we promise to complete the primary course within 1 week, so that, after finishing your study, you can master the basic concepts and write simple scripts.

-
-2. **Intermediate course**
-
-If the total number of people who reward us is more than 500, we will launch the intermediate course within 2 weeks, so that, after finishing your study, you will be familiar with the knowledge and framework of the course and apply it to practical development.
-
-3. **Advanced course**
-
-If the total number of people who reward us is more than 2,000, we will launch the advanced course in a month, so that, after finishing your study, you will have a better understanding of the principles of the framework and be able to do advanced development.
-
-
-**The primary course is free to all, and the intermediate and advanced courses are only open to those who have rewarded us.**
-
diff --git a/docs/sources/interesting-3rd-packages/index.rst.txt b/docs/sources/interesting-3rd-packages/index.rst.txt
deleted file mode 100644
index 0654a8b..0000000
--- a/docs/sources/interesting-3rd-packages/index.rst.txt
+++ /dev/null
@@ -1,19 +0,0 @@
-Python Advanced
-=======================
-The courses are still in the planning. Welcome to reward us. 
-
-1. **Primary course**
-
-If the total number of people who reward us is more than 100, we promise to complete the primary course within 1 week, so that, after finishing your study, you can master the basic concepts and write simple scripts.

-
-2. **Intermediate course**
-
-If the total number of people who reward us is more than 500, we will launch the intermediate course within 2 weeks, so that, after finishing your study, you will be familiar with the knowledge and framework of the course and apply it to practical development.
-
-3. **Advanced course**
-
-If the total number of people who reward us is more than 2,000, we will launch the advanced course in a month, so that, after finishing your study, you will have a better understanding of the principles of the framework and be able to do advanced development.
-
-
-**The primary course is free to all, and the intermediate and advanced courses are only open to those who have rewarded us.**
-
diff --git a/docs/sources/kivy-qpython/cn/index.rst.txt b/docs/sources/kivy-qpython/cn/index.rst.txt
deleted file mode 100644
index bd4b423..0000000
--- a/docs/sources/kivy-qpython/cn/index.rst.txt
+++ /dev/null
@@ -1,53 +0,0 @@
-QPython的KivyApp
-==========================
-Kivy是Python上一个支持多点触摸的跨平台图形界面开发框架，使用Kivy可以方便地开发包括游戏在内的图形界面程序。QPython能较好地支持Kivy。
-
-Kivy的安装
-----------
-在使用Kivy之前，您需要先安装Kivy。您可以在QPYPI中安装及升级kivy相关的库，QPYPI能自动安装依赖库。如果Kivy有更新，也可以通过QPYPI来进行升级操作。
-
-.. image:: http://edu.qpython.org/static/qpypi-kivy.png
-    :alt: 在QPYPI中管理kivy库
-
-
-KivyApp声明
------------
-在QPython中，通过头部声明来区分程序类型，当头部有以下定义时，我们知道它是一个KivyApp程序。
-
-::
-
-    #qpy:kivy
-
-此外，如果想要让你的Kivy程序按照全屏模式运行，您需要增加下面声明。
-
-::
-
-    #qpy:fullscreen
-
-
-KivyApp运行流程
---------------
-在程序被识别为KivyApp运行后，QPython会启动OpenGL引擎，新建一个SDL视图，并在其中完成渲染，输出画面。
-
-
-KivyApp退出流程
---------------
-按退出退出时，QPython会结束Kivy程序，进行清理，结束OpenGL引擎，同时退出SDL视图。 
-
-例子
---------
-让我们来看一个最简单的KivyApp示例，这个是安装完QPython后默认带的示例程序。
-
-::
-
-    #qpy:kivy
-    from kivy.app import App
-    from kivy.uix.button import Button
-
-    class TestApp(App):
-        ^4$def build(self):
-            ^8$return Button(text='Hello World')
-
-    TestApp().run()
-
-<button>Run ...</button>
diff --git a/docs/sources/kivy-qpython/cn/your-first-kivyapp.rst.txt b/docs/sources/kivy-qpython/cn/your-first-kivyapp.rst.txt
deleted file mode 100644
index 8c97eca..0000000
--- a/docs/sources/kivy-qpython/cn/your-first-kivyapp.rst.txt
+++ /dev/null
@@ -1,309 +0,0 @@
-Kivy App 教程>>一个简单的绘画应用程序
-============================================
-
-贡献者
-------------------------------------------------------
-作者: `Kivy <https://kivy.org/>`_
-
-
-
-在接下来的教程中, 您将需要创建一个新的小部件作为引导。 这将在编写Kivy应用程序时提供了丰富并且重要的知识，它可以根据您想要达到的目的让您创建全新的用户界面与自定义元素。 
-
-基本事项
----------------------
-在创建应用程序时，你必须问自己三个重要的问题：
-
-- 我的应用程序要处理什么数据？
-- 我该如何直观地表示这些数据？
-- 用户如何与这些数据交互？
-
-
-举个例子，如果您想写一个非常简单的线画应用程序，让用户用手指在屏幕上画画， 这就是用户和您的应用程序的交互方式。 当用户这么做时, 应用程序将会记住用户手指的位置， 以便稍后您可以在这些位置进行画线。所以手指所在的点将是您的数据，你在它们之间画的线将是您的视觉表现。
-
-
-在Kivy上, 应用程序的用户界面由小部件组成。 您在屏幕上所看的的东西都是由这个小部件绘制而成。 通常情况下，你会想要重写之前在不同背景下的写的代码，这就是为什么小部件典型地代表了一个回答上述三个问题的具体实例。 小部件封装数据，定义用户与数据的交互并绘制其可视化表示。 您可以通过嵌套小部件来构建从简单到复杂的用户界面。 这里有很多内置的小部件，如按钮，滑块和其他常见的东西。在很多情况下， 你可能你需要一个超出Kivy附带范围的自定义小部件 (例如：一个医学可视化部件)。
-
-
-所以，在设计小部件的时候请记住这三个问题。 尝试以最小及可重用的方式写它们 (即一个小部件完全是它应该做的，只不过是其他事情而已。 如果您需要更多，可以编写更多的小部件或编写更小的小部件的其他小部件。 我们试图坚持单一责任原则).
-
-
-
-绘制小部件
-------------
-
-我们确信您从童年开始就梦想创造属于自己的多点触控画图程序。而我们，可以助您圆梦 。在下面的章节中，您将会学习如何使用Kivy来编写这样的程序。 在此之前，请确保您已经阅读和理解创建一个应用程序。准备好了吗？让我们开始吧！ 
-
-
-**初始结构**
-
-
-先来写一个我们需要的基础代码框架。顺便提一句，本节中使用的所有不同的代码片段也可以在Kivy附带的examples / guide / firstwidget目录中找到，所以您可以不用每次都要复制和粘贴 。下面是我们所需要的一个基础代码框架 ：
-
-
-::
-
-    #qpy:kivy
-    from kivy.app import App
-    from kivy.uix.widget import Widget
-
-
-    class MyPaintWidget(Widget):
-        ^4$pass
-
-
-    class MyPaintApp(App):
-        ^4$def build(self):
-            ^8$return MyPaintWidget()
-
-
-    if __name__ == '__main__':
-        ^4$MyPaintApp().run()
-
-<button>Run ...</button>
-
-
-说实话，这很简单。保存为paint.py. 如果你运行它，你应该只会看见黑屏。这时你所看到，并不是使用像Button这样的内置小部件（请参阅创建应用程序），我们将继续编写小部件来完成绘图。我们通过一个从小部件(line 5-6)继承的类来实现这一点，虽然这个类暂时还没什么用，但我们还能像正常的Kivy部件一样对待它（line 11）。if __name__ ...结构（第14行）是一种Python机制，可防止在从文件导入时执行if语句中的代码， 即：如果您编写导入绘图，它不会执行一些计划之外的事情，但可以很好地提供文件中定义的类。
-
-*注意: 您可能想知道为什么您必须单独导入App和Widget， 而不是从kivy导入 *. 尽管时间较短，但这会使你的命名造成污染，并导致应用程序的启动速度变慢。它也可能引入歧义类和变量命名，所以通常在Python社区中被人们所诟病。 我们这样做的方式更快，更全面。*
-
-
-**添加行为**
-
-现在就让我们开始添加一些实际行为，即：即使其对用户输入做出反应。
-
-像这样改编代码：
-
-
-::
-    from kivy.app import App
-    from kivy.uix.widget import Widget
-
-
-    class MyPaintWidget(Widget):
-        ^4$def on_touch_down(self, touch):
-            ^8$print(touch)
-
-
-    class MyPaintApp(App):
-        ^4$def build(self):
-            ^8$return MyPaintWidget()
-
-
-    if __name__ == '__main__':
-        ^4$MyPaintApp().run()
-
-
-<button>Run ...</button>
-
-这只是为了显示对用户输入做出反应的容易程度。当发生MotionEvent（即触摸，点击等）时，我们将会把关于触摸对象的信息打印6给控制台。 在屏幕上您不会看到任何东西，但是如果您观察从中运行程序的命令行，你会看到每一个触摸的消息。这也表明一个小部件不必具有可视化表示。
-
-这些用户体验并不会让人无所适从。接下来让我们在绘制窗口中添加一些实际的代码：
-
-
-::
-
-    #qpy:kivy
-    from kivy.app import App
-    from kivy.uix.widget import Widget
-    from kivy.graphics import Color, Ellipse
-
-    class MyPaintWidget(Widget):
-
-        ^4$def on_touch_down(self, touch):
-            ^8$with self.canvas:
-                ^12$Color(1, 1, 0)
-                ^12$d = 30.
-                ^12$Ellipse(pos=(touch.x - d / 2, touch.y - d / 2), size=(d, d))
-
-
-    class MyPaintApp(App):
-
-        ^4$def build(self):
-            ^8$return MyPaintWidget()
-
-
-    if __name__ == '__main__':
-        ^4$MyPaintApp().run()
-
-<button>Run ...</button>
-
-.. image:: http://edu.qpython.org/static/kivy-guide-3.jpg
-    :alt: screenshot of pong game
-
-
-如果您通过这些修改运行代码，您会看到每次触摸时，你会触摸到一个小的黄色圆圈。 
-
-它是如何工作的？
-
-- Line 9: 我们使用Python的语句和小部件的Canvas对象 这就像是小部件可以在屏幕上绘制事物来表示自己的一个区域。通过使用with语句，所有正确缩进的连续绘图命令都将修改此画布。 with声明还确保在我们绘制之后，内部状态可以被妥善地清理。
-- Line 10: 您可能已经猜到了：这将连续绘制操作的颜色设置为黄色（默认颜色格式为RGB，所以（1,1,0）为黄色）。直到另一个颜色设置。 然后将该颜色当作是您的画笔， 您可以使用它在画布上绘画，直到将画笔切换成另一种颜色。
-- Line 11: 我们需要先确定即将绘制的圆的直径。 使用一个可取的变量，因为我们需要参考该值多次，我们并不希望在哪些地方会改变它，假如我们想圆更大或更小
-- Line 12: 想要绘制一个圆，我们只需绘制一个宽度和高度相等的椭圆。 由于我们希望在用户触摸的位置绘制圆，我们会将触摸的位置传递给椭圆。 请注意，我们需要在x和y方向上（即向左和向下）将椭圆移动到-d / 2，因为位置指定了椭圆边界框的左下角，我们希望它以我们的触摸为中心。
-
-
-那很简单，不是吗？ 它变得更好了！ 更新代码如下所示：
-
-::
-
-    #qpy:kivy
-    from kivy.app import App
-    from kivy.uix.widget import Widget
-    from kivy.graphics import Color, Ellipse, Line
-
-    class MyPaintWidget(Widget):
-
-        ^4$def on_touch_down(self, touch):
-            ^8$with self.canvas:
-                ^12$Color(1, 1, 0)
-                ^12$d = 30.
-                ^12$Ellipse(pos=(touch.x - d / 2, touch.y - d / 2), size=(d, d))
-                ^12$touch.ud['line'] = Line(points=(touch.x, touch.y))
-
-        ^4$def on_touch_move(self, touch):
-            ^8$touch.ud['line'].points += [touch.x, touch.y]
-
-    class MyPaintApp(App):
-
-        ^4$def build(self):
-            ^8$return MyPaintWidget()
-
-    if __name__ == '__main__':
-        ^4$MyPaintApp().run()
-
-<button>Run ...</button>
-
-.. image:: http://edu.qpython.org/static/kivy-guide-4.jpg
-    :alt: screenshot of pong game
-
-
-
-发生了以下的变化：
-
-- Line 3: 我们现在不仅导入了椭圆绘图指令，还导入了线绘图指令如果您查看Line的文档，您将看到它接受的点参数必须是二维点坐标列表，比如 (x1, y1, x2, y2, ..., xN, yN).
-- Line 13: 这是它变得有趣的地方。 touch.ud是一个Python字典（键入<dict>），它允许我们存储触摸的自定义属性。
-- Line 13:我们利用我们导入的Line指令，并设置一个Line来绘制。由于这是在on_touch_down中完成的，所以每次新的触摸都会有一个新的线。 通过在with块内创建行， 画布会感应到该线，并将绘制它。 我们只是想稍后修改这行，所以我们在touch.ud字典下面存储了一个对它的引用，这个字典在任意选择的，但是恰当的命名键'line'下面。 通过我们创建初始触摸位置的线，这是我们的线开始的地方。
-- Lines 15: 我们添加一个新的方法到我们的小部件。这与on_touch_down方法类似，但不是在发生新的触摸时被调用，当现有的触摸（已经调用on_touch_down）移动，即其位置改变时，他的方法被调用。 请注意，这是具有更新属性的相同MotionEvent对象。 这会让人觉得十分方便，你很快就会明白为什么会有这样的感觉了。
-- Line 16: 请记住：这是我们在on_touch_down中获得的触摸对象，因此我们可以简单地访问我们存储在touch.ud字典中的数据！早些时候我们为这条线设置了触摸点，我们现在在当前位置添加新的触摸点。我们需要扩展这条线，因为这发生在on_touch_move上，这只是在触摸移动时才被调用，这正是我们要更新这一行的原因。为了使我们不必将这些触摸点记录到在线簿记中，在touch.ud中存储该行使我们在使用时可以更方便一些。
-
-到目前为止一切还不错，虽然还不算漂亮。这让它看上去有点像意大利面。如何让触摸点有自己的颜色？让我们接着往下看：
-
-::
-
-    #qpy:kivy
-    from random import random
-    from kivy.app import App
-    from kivy.uix.widget import Widget
-    from kivy.graphics import Color, Ellipse, Line
-
-    class MyPaintWidget(Widget):
-
-        ^4$def on_touch_down(self, touch):
-            ^8$color = (random(), random(), random())
-            ^8$with self.canvas:
-                ^12$Color(*color)
-                ^12$d = 30.
-                ^12$Ellipse(pos=(touch.x - d / 2, touch.y - d / 2), size=(d, d))
-                ^12$touch.ud['line'] = Line(points=(touch.x, touch.y))
-
-        ^4$def on_touch_move(self, touch):
-            ^8$touch.ud['line'].points += [touch.x, touch.y]
-
-
-    class MyPaintApp(App):
-
-        ^4$def build(self):
-            ^8$return MyPaintWidget()
-
-
-    if __name__ == '__main__':
-        ^4$MyPaintApp().run()
-
-<button>Run ...</button>
-
-.. image:: http://edu.qpython.org/static/kivy-guide-5.jpg
-    :alt: Screenshot of pong game
-
-Here are the changes:
-
-- Line 1: 我们导入Python的random（）函数，它会给我们在[0.，1）范围内的随机值。
-- Line 10: 在这种情况下，我们只需创建一个包含3个随机浮点值的新元组，它们将代表随机的RGB颜色。这样，每一个新的触摸点都将有属于自己的颜色。 不要被使用元组困惑。我们只是将这个元组绑定在颜色上作为这个方法的一个快捷方式。
-- Line 12: 和上面一样，我们设置画布的颜色。这一次，我们使用我们生成的随机值，并使用Python的元组解压缩语法将它们提供给颜色类（因为Color类需要三个单独的颜色分量，而不是一个。如果我们要直接传递元组，1值被传递，而不管元组本身包含3个值的事实）。
-
-这看起来好多了！假如你有更多 的技能和耐心，您甚至可以创建一个漂亮的小绘图！
-
-
-*注意：由于默认情况下，颜色指令采用RGB模式，我们正在向它提供一个带有三个随机浮点值的元组，如果运气不好，很可能会得到黑色。 这并不是好的情况，因为默认情况下，背景颜色也很暗， 这样的话，你会很难看到你自己画的线。有一个很好的技巧来防止这种情况：而不是创建一个具有三个随机值的元组，像这样创建一个元组：（random（），1.，1）。然后，将它传递给颜色指令时，将模式设置为HSV颜色空间：颜色（* color，mode ='hsv'）。这样可能出现的颜色将会变少，但是你得到的颜色将永远是同样明亮的：只有色调会改变。
-
-
-**奖励积分**
-
-
-在这一点上，我们可以说我们已经完成了。小部件做了它应该做的事：跟踪触摸并画线。在一条线开始的位置绘制圆圈。
-
-但是，如果用户想要开始一个新的绘图呢？使用当前的代码，清除窗口的唯一方法是重新启动整个应用程序。不过，我们可以做得更好。现在让我们添加一个清除按钮，删除迄今为止绘制的所有线条和圆圈。 有两种选择：
-
-- 我们可以创建按钮作为我们的小部件的附件。这意味着如果您创建多个小部件，则每个小部件都有自己的按钮。如果你不小心，这也将允许用户在按钮上绘制，这可能不是你想要的得到的结果。
-- 如果你不小心，这也将允许用户在按钮上绘制，这可能不是你想要的。或者我们只在我们的应用程序类中设置按钮一次，当它被按下时，我们清除这个小部件。
-
-就我们这个简单的例子来说，这并不重要。对于较大的应用程序，您应该考虑一下谁在你的应用程序中做什么。 我们将在这里介绍第二个选项，以便您了解如何在应用程序类的build（）方法中构建应用程序的构件树。我们也将更改为HSV色彩空间（参见前面的注释）：
-
-::
-
-    #qpy:kivy
-    from random import random
-    from kivy.app import App
-    from kivy.uix.widget import Widget
-    from kivy.uix.button import Button
-    from kivy.graphics import Color, Ellipse, Line
-
-    class MyPaintWidget(Widget):
-
-        ^4$def on_touch_down(self, touch):
-            ^8$color = (random(), 1, 1)
-            ^8$with self.canvas:
-                ^12$Color(*color, mode='hsv')
-                ^12$d = 30.
-                ^12$Ellipse(pos=(touch.x - d / 2, touch.y - d / 2), size=(d, d))
-                ^12$touch.ud['line'] = Line(points=(touch.x, touch.y))
-
-        ^4$def on_touch_move(self, touch):
-            ^8$touch.ud['line'].points += [touch.x, touch.y]
-
-
-    class MyPaintApp(App):
-
-        ^4$def build(self):
-            ^8$parent = Widget()
-            ^8$self.painter = MyPaintWidget()
-            ^8$clearbtn = Button(text='Clear')
-            ^8$clearbtn.bind(on_release=self.clear_canvas)
-            ^8$parent.add_widget(self.painter)
-            ^8$parent.add_widget(clearbtn)
-            ^8$return parent
-
-        ^4$def clear_canvas(self, obj):
-            ^8$self.painter.canvas.clear()
-
-
-    if __name__ == '__main__':
-        ^4$MyPaintApp().run()
-
-<button>Run ...</button>
-
-.. image:: http://edu.qpython.org/static/kivy-guide-6.jpg
-    :alt: screenshot of pong game
-
-
-将会发生以下的事情：
-
-- Line 4: 我们添加了一个导入语句，以便能够使用Button类。
-- Line 25: 我们创建一个虚拟的Widget（）对象作为我们的绘画控件和我们即将添加的按钮的父对象。 这只是一个比较简陋的方法来设置一个小部件树层次结构。 我们也可以使用布局或做其他一些丰富的东西。再一次：这个小部件除了保存我们现在添加的两个小部件以外，完全没有任何功能。
-- Line 26:我们像往常一样创建我们的MyPaintWidget（），只是这次我们不直接返回它，而是将它绑定到一个变量名。
-- Line 27: 我们创建一个按钮小部件。 它上面会有一个标签，显示文本“清除”。
-- Line 28: 然后，我们将按钮的on_release事件（当按钮被按下然后释放时触发）绑定到第33行和第34行上定义的回调函数clear_canvas。
-- Line 29 & 30: 我们通过制作虚拟父窗口小部件的画家和clearbtn子元素来设置窗口小部件层次结构。 这意味着画家和clearbtn在通常的计算机科学树术语中是兄弟姐妹。
-- Line 33 & 34: 到目前为止，按钮什么也没做。 它在那里，你可以看见它，也可以按它，但什么都不会发生。我们在这里改变：我们创建了一个小的抛弃函数，当按钮被按下时，它将成为我们的回调函数。该功能只是清除画家的画布内容，使其再次变黑。
-
-*注意：Kivy Widget类在设计上保持简单。没有一般属性，如背景颜色和边框颜色。相反，这些示例和文档说明了如何轻松地处理这些简单的事情，就像我们在这里完成的那样，为画布设置颜色并绘制形状。从一个简单的开始，你可以逐渐使它成为更精细的定制。从Widget派生的更高级别的内置小部件（如Button）确实具有诸如background_color之类的便利属性，但这些属性因widget而异。如果您需要添加更多功能，请使用API文档查看小部件提供的内容以及子类。*
-
-恭喜！ 你已经写了你的第一个Kivy部件。很明显，这只是一个快速介绍。还会有更多的东西等着你去发现。 不过我们建议您短暂休息一下，让刚刚学到的东西沉淀一下。 不如先画一些不错的图片让自己放松一下？如果您觉得您已经理解了所有内容，并准备好学习更多，请继续往下阅读。
diff --git a/docs/sources/kivy-qpython/index.rst.txt b/docs/sources/kivy-qpython/index.rst.txt
deleted file mode 100644
index c8829d1..0000000
--- a/docs/sources/kivy-qpython/index.rst.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-QPython's KivyApp
-==========================
-Kivy is a support multi-touch cross-platform graphical interface development framework in python.Use Kivy can easily develop GUI applications including games.QPython can support Kivy better.
-
-Kivy's installation
-----------
-Before using Kivy, you need to have Kivy installed.You can install and upgrade kivy related libraries in QPYPI,QPYPI can automatically install dependent libraries.If Kivy needs to be updated, You can also upgrade operated by QPYPI.
-
-.. image:: http://edu.qpython.org/static/qpypi-kivy.png
-    :alt: Manage kivy libraries in QPYPI
-
-
-KivyApp'sstatement
------------
-In QPython, the program type distinguished by head declarations, When the header has the following definition, we know it is a KivyApp program.
-
-::
-
-    #qpy:kivy
-
-In addition, If you want to make your Kivy program run in full-screen mode, You need to add the following statement.
-
-::
-
-    #qpy:fullscreen
-
-
-KivyApp's Run Process
---------------
-After the program is identified as KivyApp operation, QPython will start the OpenGL engine at the same time, Create a new SDL view, And in which to complete the rendering, output screen.
-
-
-Exit
------------------------------
-Press quit exiting, QPython will finish the Kivy program, clean up, end OpenGL engine, and exit SDL view.
-
-E.g
---------
-Let's look at a simple example about KivyApp,This is the default sample program installed after installing QPython.
-
-::
-
-    #qpy:kivy
-    from kivy.app import App
-    from kivy.uix.button import Button
-
-    class TestApp(App):
-        ^4$def build(self):
-            ^8$return Button(text='Hello World')
-
-    TestApp().run()
-
-<button>Run ...</button>
-
diff --git a/docs/sources/kivy-qpython/your-first-kivyapp.rst.txt b/docs/sources/kivy-qpython/your-first-kivyapp.rst.txt
deleted file mode 100644
index 5af8812..0000000
--- a/docs/sources/kivy-qpython/your-first-kivyapp.rst.txt
+++ /dev/null
@@ -1,315 +0,0 @@
-Kivy App Tutorials >> A Simple Paint App
-============================================
-
-
-In the following tutorial, you will be guided through the creation of your first widget. This provides powerful and important knowledge when programming Kivy applications, as it lets you create completely new user interfaces with custom elements for your specific purpose.
-
-Basic Considerations
----------------------
-When creating an application, you have to ask yourself three important questions:
-
-- What data does my application process?
-- How do I visually represent that data?
-- How does the user interact with that data?
-
-
-If you want to write a very simple line drawing application for example, you most likely want the user to just draw on the screen with his/her fingers. That’s how the user interacts with your application. While doing so, your application would memorize the positions where the user’s finger were, so that you can later draw lines between those positions. So the points where the fingers were would be your data and the lines that you draw between them would be your visual representation.
-
-
-In Kivy, an application’s user interface is composed of Widgets. Everything that you see on the screen is somehow drawn by a widget. Often you would like to be able to reuse code that you already wrote in a different context, which is why widgets typically represent one specific instance that answers the three questions above. A widget encapsulates data, defines the user’s interaction with that data and draws its visual representation. You can build anything from simple to complex user interfaces by nesting widgets. There are many widgets built in, such as buttons, sliders and other common stuff. In many cases, however, you need a custom widget that is beyond the scope of what is shipped with Kivy (e.g. a medical visualization widget).
-
-
-So keep these three questions in mind when you design your widgets. Try to write them in a minimal and reusable manner (i.e. a widget does exactly what its supposed to do and nothing more. If you need more, write more widgets or compose other widgets of smaller widgets. We try to adhere to the Single Responsibility Principle).
-
-
-
-Paint Widget
-------------
-
-We’re sure one of your childhood dreams has always been creating your own multitouch paint program. Allow us to help you achieve that. In the following sections you will successively learn how to write a program like that using Kivy. Make sure that you have read and understood Create an application. You have? Great! Let’s get started!
-
-
-**Initial Structure**
-
-
-Let’s start by writing the very basic code structure that we need. By the way, all the different pieces of code that are used in this section are also available in the examples/guide/firstwidget directory that comes with Kivy, so you don’t need to copy & paste it all the time. Here is the basic code skeleton that we will need:
-
-
-::
-
-    #qpy:kivy
-    from kivy.app import App
-    from kivy.uix.widget import Widget
-
-
-    class MyPaintWidget(Widget):
-        ^4$pass
-
-
-    class MyPaintApp(App):
-        ^4$def build(self):
-            ^8$return MyPaintWidget()
-
-
-    if __name__ == '__main__':
-        ^4$MyPaintApp().run()
-
-<button>Run ...</button>
-
-
-This is actually really simple. Save it as paint.py. If you run it, you should only see a black screen. As you can see, instead of using a built-in widget such as a Button (see Create an application), we are going to write our own widget to do the drawing. We do that by creating a class that inherits from Widget (line 5-6) and although that class does nothing yet, we can still treat it like a normal Kivy widget (line 11). The if __name__ ... construct (line 14) is a Python mechanism that prevents you from executing the code in the if-statement when importing from the file, i.e. if you write import paint, it won’t do something unexpected but just nicely provide the classes defined in the file.
-
-
-*Note: You may be wondering why you have to import App and Widget separately, instead of doing something like from kivy import *. While shorter, this would have the disadvantage of polluting your namespace and make the start of the application potentially much slower. It can also introduce ambiguity into class and variable naming, so is generally frowned upon in the Python community. The way we do it is faster and cleaner.*
-
-
-**Adding Behaviour**
-
-Let’s now add some actual behaviour to the widget, i.e. make it react to user input. Change the code like so:
-
-
-::
-
-    #qpy:kivy
-    from kivy.app import App
-    from kivy.uix.widget import Widget
-
-
-    class MyPaintWidget(Widget):
-        ^4$def on_touch_down(self, touch):
-            ^8$print(touch)
-
-
-    class MyPaintApp(App):
-        ^4$def build(self):
-            ^8$return MyPaintWidget()
-
-
-    if __name__ == '__main__':
-        ^4$MyPaintApp().run()
-
-<button>Run ...</button>
-
-
-This is just to show how easy it is to react to user input. When a MotionEvent (i.e. a touch, click, etc.) occurs, we simply print the information about the touch object to the console. You won’t see anything on the screen, but if you observe the command-line from which you are running the program, you will see a message for every touch. This also demonstrates that a widget does not have to have a visual representation.
-
-Now that’s not really an overwhelming user experience. Let’s add some code that actually draws something into our window:
-
-
-::
-
-    #qpy:kivy
-    from kivy.app import App
-    from kivy.uix.widget import Widget
-    from kivy.graphics import Color, Ellipse
-
-    class MyPaintWidget(Widget):
-
-        ^4$def on_touch_down(self, touch):
-            ^8$with self.canvas:
-                ^12$Color(1, 1, 0)
-                ^12$d = 30.
-                ^12$Ellipse(pos=(touch.x - d / 2, touch.y - d / 2), size=(d, d))
-
-
-    class MyPaintApp(App):
-
-        ^4$def build(self):
-            ^8$return MyPaintWidget()
-
-
-    if __name__ == '__main__':
-        ^4$MyPaintApp().run()
-
-<button>Run ...</button>
-
-
-.. image:: http://edu.qpython.org/static/kivy-guide-3.jpg
-    :alt: screenshot of pong game
-    :align: center
-
-
-If you run your code with these modifications, you will see that every time you touch, there will be a small yellow circle drawn where you touched. How does it work?
-
-- Line 9: We use Python’s with statement with the widget’s Canvas object. This is like an area in which the widget can draw things to represent itself on the screen. By using the with statement with it, all successive drawing commands that are properly indented will modify this canvas. The with statement also makes sure that after our drawing, internal state can be cleaned up properly.
-- Line 10: You might have guessed it already: This sets the Color for successive drawing operations to yellow (default color format is RGB, so (1, 1, 0) is yellow). This is true until another Color is set. Think of this as dipping your brushes in that color, which you can then use to draw on a canvas until you dip the brushes into another color.
-- Line 11: We specify the diameter for the circle that we are about to draw. Using a variable for that is preferable since we need to refer to that value multiple times and we don’t want to have to change it in several places if we want the circle bigger or smaller.
-- Line 12: To draw a circle, we simply draw an Ellipse with equal width and height. Since we want the circle to be drawn where the user touches, we pass the touch’s position to the ellipse. Note that we need to shift the ellipse by -d/2 in the x and y directions (i.e. left and downwards) because the position specifies the bottom left corner of the ellipse’s bounding box, and we want it to be centered around our touch.
-
-
-That was easy, wasn’t it? It gets better! Update the code to look like this:
-
-::
-
-    #qpy:kivy
-    from kivy.app import App
-    from kivy.uix.widget import Widget
-    from kivy.graphics import Color, Ellipse, Line
-
-    class MyPaintWidget(Widget):
-
-        ^4$def on_touch_down(self, touch):
-            ^8$with self.canvas:
-                ^12$Color(1, 1, 0)
-                ^12$d = 30.
-                ^12$Ellipse(pos=(touch.x - d / 2, touch.y - d / 2), size=(d, d))
-                ^12$touch.ud['line'] = Line(points=(touch.x, touch.y))
-
-        ^4$def on_touch_move(self, touch):
-            ^8$touch.ud['line'].points += [touch.x, touch.y]
-
-    class MyPaintApp(App):
-
-        ^4$def build(self):
-            ^8$return MyPaintWidget()
-
-    if __name__ == '__main__':
-        ^4$MyPaintApp().run()
-
-<button>Run ...</button>
-
-
-.. image:: http://edu.qpython.org/static/kivy-guide-4.jpg
-    :alt: screenshot of pong game
-    :align: center
-
-
-
-This is what has changed:
-
-- Line 3: We now not only import the Ellipse drawing instruction, but also the Line drawing instruction. If you look at the documentation for Line, you will see that it accepts a points argument that has to be a list of 2D point coordinates, like (x1, y1, x2, y2, ..., xN, yN).
-- Line 13: This is where it gets interesting. touch.ud is a Python dictionary (type <dict>) that allows us to store custom attributes for a touch.
-- Line 13: We make use of the Line instruction that we imported and set a Line up for drawing. Since this is done in on_touch_down, there will be a new line for every new touch. By creating the line inside the with block, the canvas automatically knows about the line and will draw it. We just want to modify the line later, so we store a reference to it in the touch.ud dictionary under the arbitrarily chosen but aptly named key ‘line’. We pass the line that we’re creating the initial touch position because that’s where our line will begin.
-- Lines 15: We add a new method to our widget. This is similar to the on_touch_down method, but instead of being called when a new touch occurs, this method is being called when an existing touch (for which on_touch_down was already called) moves, i.e. its position changes. Note that this is the same MotionEvent object with updated attributes. This is something we found incredibly handy and you will shortly see why.
-- Line 16: Remember: This is the same touch object that we got in on_touch_down, so we can simply access the data we stored away in the touch.ud dictionary! To the line we set up for this touch earlier, we now add the current position of the touch as a new point. We know that we need to extend the line because this happens in on_touch_move, which is only called when the touch has moved, which is exactly why we want to update the line. Storing the line in the touch.ud makes it a whole lot easier for us as we don’t have to maintain our own touch-to-line bookkeeping.
-
-So far so good. This isn’t exactly beautiful yet, though. It looks a bit like spaghetti bolognese. How about giving each touch its own color? Great, let’s do it:
-
-::
-
-    #qpy:kivy
-    from random import random
-    from kivy.app import App
-    from kivy.uix.widget import Widget
-    from kivy.graphics import Color, Ellipse, Line
-
-    class MyPaintWidget(Widget):
-
-        ^4$def on_touch_down(self, touch):
-            ^8$color = (random(), random(), random())
-            ^8$with self.canvas:
-                ^12$Color(*color)
-                ^12$d = 30.
-                ^12$Ellipse(pos=(touch.x - d / 2, touch.y - d / 2), size=(d, d))
-                ^12$touch.ud['line'] = Line(points=(touch.x, touch.y))
-
-        ^4$def on_touch_move(self, touch):
-            ^8$touch.ud['line'].points += [touch.x, touch.y]
-
-
-    class MyPaintApp(App):
-
-        ^4$def build(self):
-            ^8$return MyPaintWidget()
-
-
-    if __name__ == '__main__':
-        ^4$MyPaintApp().run()
-
-<button>Run ...</button>
-
-
-.. image:: http://edu.qpython.org/static/kivy-guide-5.jpg
-    :alt: Screenshot of pong game
-    :align: center
-
-Here are the changes:
-
-- Line 1: We import Python’s random() function that will give us random values in the range of [0., 1.).
-- Line 10: In this case we simply create a new tuple of 3 random float values that will represent a random RGB color. Since we do this in on_touch_down, every new touch will get its own color. Don’t get confused by the use of tuples. We’re just binding the tuple to color for use as a shortcut within this method because we’re lazy.
-- Line 12: As before, we set the color for the canvas. Only this time we use the random values we generated and feed them to the color class using Python’s tuple unpacking syntax (since the Color class expects three individual color components instead of just 1. If we were to pass the tuple directly, that would be just 1 value being passed, regardless of the fact that the tuple itself contains 3 values).
-
-This looks a lot nicer already! With a lot of skill and patience, you might even be able to create a nice little drawing!
-
-
-*Note: Since by default the Color instructions assume RGB mode and we’re feeding a tuple with three random float values to it, it might very well happen that we end up with a lot of dark or even black colors if we are unlucky. That would be bad because by default the background color is dark as well, so you wouldn’t be able to (easily) see the lines you draw. There is a nice trick to prevent this: Instead of creating a tuple with three random values, create a tuple like this: (random(), 1., 1.). Then, when passing it to the color instruction, set the mode to HSV color space: Color(*color, mode='hsv'). This way you will have a smaller number of possible colors, but the colors that you get will always be equally bright: only the hue changes.*
-
-
-**Bonus Points**
-
-
-At this point, we could say we are done. The widget does what it’s supposed to do: it traces the touches and draws lines. It even draws circles at the positions where a line begins.
-
-But what if the user wants to start a new drawing? With the current code, the only way to clear the window would be to restart the entire application. Luckily, we can do better. Let us add a Clear button that erases all the lines and circles that have been drawn so far. There are two options now:
-
-- We could either create the button as a child of our widget. That would imply that if you create more than one widget, every widget gets its own button. If you’re not careful, this will also allow users to draw on top of the button, which might not be what you want.
-- Or we set up the button only once, initially, in our app class and when it’s pressed we clear the widget.
-
-For our simple example, it doesn’t really matter that much. For larger applications you should give some thought to who does what in your app. We’ll go with the second option here so that you see how you can build up your application’s widget tree in your app class’s build() method. We’ll also change to the HSV color space (see preceding note):
-
-::
-
-    #qpy:kivy
-    from random import random
-    from kivy.app import App
-    from kivy.uix.widget import Widget
-    from kivy.uix.button import Button
-    from kivy.graphics import Color, Ellipse, Line
-
-    class MyPaintWidget(Widget):
-
-        ^4$def on_touch_down(self, touch):
-            ^8$color = (random(), 1, 1)
-            ^8$with self.canvas:
-                ^12$Color(*color, mode='hsv')
-                ^12$d = 30.
-                ^12$Ellipse(pos=(touch.x - d / 2, touch.y - d / 2), size=(d, d))
-                ^12$touch.ud['line'] = Line(points=(touch.x, touch.y))
-
-        ^4$def on_touch_move(self, touch):
-            ^8$touch.ud['line'].points += [touch.x, touch.y]
-
-
-    class MyPaintApp(App):
-
-        ^4$def build(self):
-            ^8$parent = Widget()
-            ^8$self.painter = MyPaintWidget()
-            ^8$clearbtn = Button(text='Clear')
-            ^8$clearbtn.bind(on_release=self.clear_canvas)
-            ^8$parent.add_widget(self.painter)
-            ^8$parent.add_widget(clearbtn)
-            ^8$return parent
-
-        ^4$def clear_canvas(self, obj):
-            ^8$self.painter.canvas.clear()
-
-    if __name__ == '__main__':
-        ^4$MyPaintApp().run()
-
-<button>Run ...</button>
-
-
-.. image:: http://edu.qpython.org/static/kivy-guide-6.jpg
-    :alt: screenshot of pong game
-    :align: center
-
-
-Here’s what happens:
-
-- Line 4: We added an import statement to be able to use the Button class.
-- Line 25: We create a dummy Widget() object as a parent for both our painting widget and the button we’re about to add. This is just a poor-man’s approach to setting up a widget tree hierarchy. We could just as well use a layout or do some other fancy stuff. Again: this widget does absolutely nothing except holding the two widgets we will now add to it as children.
-- Line 26: We create our MyPaintWidget() as usual, only this time we don’t return it directly but bind it to a variable name.
-- Line 27: We create a button widget. It will have a label on it that displays the text ‘Clear’.
-- Line 28: We then bind the button’s on_release event (which is fired when the button is pressed and then released) to the callback function clear_canvas defined on below on Lines 33 & 34.
-- Line 29 & 30: We set up the widget hierarchy by making both the painter and the clearbtn children of the dummy parent widget. That means painter and clearbtn are now siblings in the usual computer science tree terminology.
-- Line 33 & 34: Up to now, the button did nothing. It was there, visible, and you could press it, but nothing would happen. We change that here: we create a small, throw-away function that is going to be our callback function when the button is pressed. The function just clears the painter’s canvas’ contents, making it black again.
-
-*Note: The Kivy Widget class, by design, is kept simple. There are no general properties such as background color and border color. Instead, the examples and documentation illustrate how to easily handle such simple things yourself, as we have done here, setting the color for the canvas, and drawing the shape. From a simple start, you can move to more elaborate customization. Higher-level built-in widgets, deriving from Widget, such as Button, do have convenience properties such as background_color, but these vary by widget. Use the API docs to see what is offered by a widget, and subclass if you need to add more functionality.*
-
-Congratulations! You’ve written your first Kivy widget. Obviously this was just a quick introduction. There is much more to discover. We suggest taking a short break to let what you just learned sink in. Maybe draw some nice pictures to relax? If you feel like you’ve understood everything and are ready for more, we encourage you to read on.
-
-Chapter Author, contributor
-------------------------------------------------------
-Author: `Kivy <https://kivy.org/>`_
-
diff --git a/docs/sources/opencv/index.rst.txt b/docs/sources/opencv/index.rst.txt
deleted file mode 100644
index 7bb2868..0000000
--- a/docs/sources/opencv/index.rst.txt
+++ /dev/null
@@ -1,19 +0,0 @@
-OpenCV Programming
-=======================
-The courses are still in the planning. Welcome to reward us. 
-
-1. **Primary course**
-
-If the total number of people who reward us is more than 100, we promise to complete the primary course within 1 week, so that, after finishing your study, you can master the basic concepts and write simple scripts.

-
-2. **Intermediate course**
-
-If the total number of people who reward us is more than 500, we will launch the intermediate course within 2 weeks, so that, after finishing your study, you will be familiar with the knowledge and framework of the course and apply it to practical development.
-
-3. **Advanced course**
-
-If the total number of people who reward us is more than 2,000, we will launch the advanced course in a month, so that, after finishing your study, you will have a better understanding of the principles of the framework and be able to do advanced development.
-
-
-**The primary course is free to all, and the intermediate and advanced courses are only open to those who have rewarded us.**
-
diff --git a/docs/sources/python-base/cn/index.rst.txt b/docs/sources/python-base/cn/index.rst.txt
deleted file mode 100644
index a4ce056..0000000
--- a/docs/sources/python-base/cn/index.rst.txt
+++ /dev/null
@@ -1,16 +0,0 @@
-学习 Python 的由来
-==============================
-
-
-第一次接触 Python 时，是在刚毕业不久，那时公司在做一个网盘客户端，需要调研一些 GUI 框架。由于当时 Python 很火（当然，现在也一样），便尝试了一下 PyQt（Python 语言和 Qt 库的融合）。
-
-很长时间里，我对 Python 的认知停留在“Life is short, You need Python ”上，就像“PHP 是世界上最好的语言”一样。直到去年的一次“机缘巧合”，要做一个邮件收发组件，我发现 Python 简直太不可思议了，它提供了很多方便的模块（例如：email、smtplib、poplib），使用起来十分简单，能够让我在很短的时间里出色地完成任务。
-
-在此以后，我几乎每天都要面对 Python，很高兴，我又迈出了新的一步！开始认真研究，包括它自带的一些文档、教程，里面的很多内容确实很好，但大部分都是概念相关的，不够全面，我觉得不太适合新手。所以，是时候该做笔记了，学习 -> 编写 -> 改进 -> 学习… 这是一个漫长的过程，希望在多次的改进和重写后，它能成为了有用的 Python 学习指南。
-
-
-作者 & 更新时间
-------------------------------------
-作者: `一去丶二三里 <http://blog.csdn.net/liang19890820>`_
-
-提交: 2017/12/6
diff --git a/docs/sources/python-base/cn/python-base-annotation.rst.txt b/docs/sources/python-base/cn/python-base-annotation.rst.txt
deleted file mode 100644
index d0a7b50..0000000
--- a/docs/sources/python-base/cn/python-base-annotation.rst.txt
+++ /dev/null
@@ -1,95 +0,0 @@
-简述
-================
-
-对于任何编程语言来说，都可以在代码中包含注释。这不仅有助于解释代码以提高可读性，也便于日后自己参考或者他人阅读，有时对跟踪问题也非常有用。
-
-添加注释很有必要，好的开发人员基本都会大量地使用注释。
-
-
-注释风格
----------
-关键字是预先保留的标识符，每个关键字都有特殊的含义。编程语言众多，但每种语言都有相应的关键字，Python 也不例外，它自带了一个 *keyword* 模块，用于检测关键字。
-
-在 Python 中，注释的风格与其他语言相比有很大的不同，但使用起来也很简单，主要包含两种：
-  - 单行注释
-  - 多行注释
-
-单行注释适用于简短、快速的注释（或者用于调试），而多行注释常用于描述一段内容，或屏蔽整个代码块。
-
-**PS**： 注释是写给其他开发人员（包括自己）看的，Python 解释器会忽略注释。
-
-单行注释
--------------
-# 被用作单行注释符号，**使用 # 时，其右侧的任何数据都会被忽略，被当做是注释。**
-
-例如，在代码的上面或下面添加注释：
-
-::
-
-
-    #print("这是单行注释")
-
-    print("Hello, World!")
-
-    #print("这也是单行注释")
-
-这将产生以下结果：
-
-::
-
-    Hello, World!
-
-也可以在代码的后面添加注释：
-
-::
-
-
-    print("Hello, World!")  #这是单行注释
-
-总之，# 号右侧的内容是不会被执行的。
-
-
-
-多行注释
-----------------
-
-
-相比单行注释，多行注释略有不同，需要在想要注释的部分之前和之后添加 三个单引号（'''）或三个双引号（"""）。
-
-例如，描述一段内容，或屏蔽整个代码块：
-
-::
-
-   '''
-   这在多行注释中
-   这也在多行注释中
-   这仍然在多行注释中
-   '''
-
-   """
-   if True:
-       print("Hello")
-   else:
-       print("Python")
-   """
-
-   print("Hello, World!")
-
-除此之外，还有一种方式，使用多个单行注释来表示多行注释：
-
-::
-
-   #print("这是注释")
-
-   print("Hello, World!")
-
-
-这种方式适用于简短的代码注释。大多数情况下，相比三引号形式还是略显复杂，一般使用率不高。
-
-**建议**： 注释是我们的好伙伴，应尽量多写，尤其是复杂的 Python 代码。
-
-作者 & 更新时间
-------------------------------------
-作者: `一去丶二三里 <http://blog.csdn.net/liang19890820>`_
-
-提交: 2017/12/6
diff --git a/docs/sources/python-base/cn/python-base-keyword.rst.txt b/docs/sources/python-base/cn/python-base-keyword.rst.txt
deleted file mode 100644
index 2d304d4..0000000
--- a/docs/sources/python-base/cn/python-base-keyword.rst.txt
+++ /dev/null
@@ -1,124 +0,0 @@
-Python关键词
-================
-
-
-
-简述
------
-关键字是预先保留的标识符，每个关键字都有特殊的含义。编程语言众多，但每种语言都有相应的关键字，Python 也不例外，它自带了一个 *keyword* 模块，用于检测关键字。
-
-
-关键词列表
--------------
-进入 Python 交互模式，获取关键字列表：
-
-
-::
-
-    import keyword
-    keyword.kwlist
-
-<button>Run ...</button>
-
-共 33 个关键字，除 True、False 和 None 外，其他关键字均为小写形式。
-
-**注意**： Python 是一种动态语言，根据时间在不断变化，关键字列表将来有可能会更改。
-
-
-关键词判断
-----------------
-除此之外，keyword 模块还提供了关键字的判断功能：
-
-::
-
-    import keyword
-    keyword.iskeyword('and')
-    keyword.iskeyword('has')
-
-<button>Run ...</button>
-
-如果是关键字，返回 True；否则，返回 False。
-
-
-
-关键词含义
-------------------------
-
-
-
-+------------+------------------------------------------------------------------------------------------+
-| 关键字     | 含义                                                                                     |
-+============+==========================================================================================+
-|False       | 布尔类型的值，表示假，与 True 相反                                                       |    
-+------------+------------------------------------------------------------------------------------------+
-| None       |None 比较特殊，表示什么也没有，它有自己的数据类型 - NoneType                              |
-+------------+------------------------------------------------------------------------------------------+
-| True       |布尔类型的值，表示真，与 False 相反                                                       |
-+------------+------------------------------------------------------------------------------------------+
-| and        |        用于表达式运算，逻辑与操作                                                        |         
-+------------+------------------------------------------------------------------------------------------+
-| as         |用于类型转换                                                                              |
-+------------+------------------------------------------------------------------------------------------+
-|assert      |断言，用于判断变量或者条件表达式的值是否为真                                              |
-+------------+------------------------------------------------------------------------------------------+
-| break      |中断循环语句的执行                                                                        |
-+------------+------------------------------------------------------------------------------------------+
-| class      |用于定义类                                                                                |
-+------------+------------------------------------------------------------------------------------------+
-| continue   |      跳出本次循环，继续执行下一次循环                                                    |
-+------------+------------------------------------------------------------------------------------------+
-|def         |	用于定义函数或方法                                                                      |
-+------------+------------------------------------------------------------------------------------------+
-|del	     |删除变量或序列的值                                                                        |
-+------------+------------------------------------------------------------------------------------------+
-|elif	     |条件语句，与 if、else 结合使用                                                            |
-+------------+------------------------------------------------------------------------------------------+
-|else	     |条件语句，与 if、elif 结合使用。也可用于异常和循环语句                                    |
-+------------+------------------------------------------------------------------------------------------+
-|except	     |except 包含捕获异常后的操作代码块，与 try、finally 结合使用                               |
-+------------+------------------------------------------------------------------------------------------+
-|finally     |	用于异常语句，出现异常后，始终要执行 finally 包含的代码块。与 try、except 结合使用      |
-+------------+------------------------------------------------------------------------------------------+
-|for	     |for 循环语句                                                                              |
-+------------+------------------------------------------------------------------------------------------+
-|from	     |用于导入模块，与 import 结合使用                                                          |
-+------------+------------------------------------------------------------------------------------------+
-|global	     |定义全局变量                                                                              |
-+------------+------------------------------------------------------------------------------------------+
-|if	         |条件语句，与 else、elif 结合使用                                                          |
-+------------+------------------------------------------------------------------------------------------+
-|import	     |用于导入模块，与 from 结合使用                                                            |
-+------------+------------------------------------------------------------------------------------------+
-|in	         |判断变量是否在序列中                                                                      |
-+------------+------------------------------------------------------------------------------------------+
-|is	         |判断变量是否为某个类的实例                                                                |
-+------------+------------------------------------------------------------------------------------------+
-|lambda      |定义匿名函数                                                                              |
-+------------+------------------------------------------------------------------------------------------+
-|nonlocal    |	用于标识外部作用域的变量                                                                |
-+------------+------------------------------------------------------------------------------------------+
-|not	     |用于表达式运算，逻辑非操作                                                                |
-+------------+------------------------------------------------------------------------------------------+
-|or	         |用于表达式运算，逻辑或操作                                                                |
-+------------+------------------------------------------------------------------------------------------+
-|pass	     |空的类、方法或函数的占位符                                                                |
-+------------+------------------------------------------------------------------------------------------+
-|raise	     |异常抛出操作                                                                              |
-+------------+------------------------------------------------------------------------------------------+
-|return      |用于从函数返回计算结果                                                                    |
-+------------+------------------------------------------------------------------------------------------+
-|try	     |try 包含可能会出现异常的语句，与 except、finally 结合使用                                 |
-+------------+------------------------------------------------------------------------------------------+
-|while	     |while 循环语句                                                                            |
-+------------+------------------------------------------------------------------------------------------+
-|with	     |简化 Python 的语句                                                                        |
-+------------+------------------------------------------------------------------------------------------+
-|yield       |用于从函数依次返回值                                                                      |
-+------------+------------------------------------------------------------------------------------------+
-
-
-作者 & 更新时间
-------------------------------------
-作者: `一去丶二三里 <http://blog.csdn.net/liang19890820>`_
-
-提交: 2017/12/6
diff --git a/docs/sources/python-base/cn/python-base-operator.rst.txt b/docs/sources/python-base/cn/python-base-operator.rst.txt
deleted file mode 100644
index e3ee414..0000000
--- a/docs/sources/python-base/cn/python-base-operator.rst.txt
+++ /dev/null
@@ -1,319 +0,0 @@
-简述
-================
-
-在 Python 中，运算符是执行算术或逻辑运算的特殊符号，操作的值被称为操作数。例如：
-
-::
-
-    5 + 6
-
-<button>Run ...</button>
-
-
-这里，+ 是执行加法的运算符，5 和 6 是操作数，11 是操作的输出。
-
-
-
-运算符种类
-
-----------------
-
-在 Python 中，支持以下类型的运算符：
-
-
-
-- 算术运算符
-- 比较（关系）运算符
-- 逻辑（布尔）运算符
-- 位运算符
-- 赋值运算符
-- 特殊运算符
-   - 成员运算符
-   - 身份运算符
-- 算术运算符
-
-----------------
-
-算术运算符用于执行加、减、乘、除等数学运算。
-
-
-+------------+-----------------------------------------------------------+-------------------------------+
-|  运算符    |                  含义                                     |          示例                 |
-+============+===========================================================+===============================+
-|   +        | 	        加 - 两个操作数相加，或者一元加                  |          x + y                |
-+------------+-----------------------------------------------------------+-------------------------------+
-|   -        | 	        减 - 两个操作数相减，或者一元减   	           |          x - y                |
-+------------+-----------------------------------------------------------+-------------------------------+
-|   *        | 	   乘 - 两个操作数相乘，或是返回一个被重复若干次的字符串 | 	    x * y                   |
-+------------+-----------------------------------------------------------+-------------------------------+
-|   /	     |         除 - 两个操作数相除（总是浮点数）                 |          x / y                |
-+------------+-----------------------------------------------------------+-------------------------------+	
-|   %        | 	        取模 - 返回除法（/）的余数	                      |          x % y（x/y 的余数）  |
-+------------+-----------------------------------------------------------+-------------------------------+
-|  //	     |         取整除 - 返回商的整数部分                         | 	     x // y               |
-+------------+-----------------------------------------------------------+-------------------------------+
-|   **       | 	        幂 - 返回 x 的 y 次幂	                           |           x ** y              |
-+------------+-----------------------------------------------------------+-------------------------------+
-
-
-
-示例：
-
-::
-
-    x = 8
-    y = 3
-    x + y
-    x - y
-    x * y
-    x / y  # 总是浮点数
-    x // y
-    x ** y
-
-<button>Run ...</button>
-
-
-**注意**： 除法 / 运算的结果总是浮点数。例如：4 / 2 返回的是 2.0，而不是 2。
-
-比较运算符
-
-----------------
-
-比较运算符又称关系运算符，用于比较值，根据条件返回 True 或 False。
-
-+------------+-----------------------------------------------------------+-------------------------------+
-|   运算符   | 	                    含义                              | 	          示例              |                  
-+============+===========================================================+===============================+
-|    >       | 	        大于 - 如果左操作数大于右操作数，则为 True       | 	          x > y             | 
-+------------+-----------------------------------------------------------+-------------------------------+
-|    < 	     |   小于 - 如果左操作数小于右操作数，则为 True	            |                 x < y         | 
-+------------+-----------------------------------------------------------+-------------------------------+
-|    ==	     |      等于 - 如果两个操作数相等，则为 True	               |              x == y           | 
-+------------+-----------------------------------------------------------+-------------------------------+
-|    !=	     |         不等于 - 如果两个操作数不相等，则为 True          |              x != y           | 
-+------------+-----------------------------------------------------------+-------------------------------+	
-|    >=	     |    大于等于 - 如果左操作数大于或等于右操作数，则为 True   |               x >= y          | 
-+------------+-----------------------------------------------------------+-------------------------------+
-|    <=	     |   小于等于 - 如果左操作数小于或等于右操作数，则为 True    |                x <= y         | 
-+------------+-----------------------------------------------------------+-------------------------------+
-
-示例：
-
-::
-
-    x = 6
-    y = 10
-    x > y
-    x < y
-    x == y
-    x != y
-    x >= y
-    x <= y
-
-<button>Run ...</button>
-
-
-逻辑运算符
-----------------
-
-逻辑运算符又称布尔运算符，通常用于测试真假值。
-
-
-+------------+-----------------------------------------------------------+-------------------------------+
-|    运算符  |     含义	                                                |        示例                   |
-+============+===========================================================+===============================+
-|    and     |    逻辑与 - 如果两个操作数都为 True，则为 True	          |     x and y                   |
-+------------+-----------------------------------------------------------+-------------------------------+
-|    or	     |       逻辑或 - 如果任一操作数为 True，则为 True	       |      x or y                   |
-+------------+-----------------------------------------------------------+-------------------------------+
-|   not	     |     逻辑非 - 如果操作数为 False，则为 True（补数）	     |               not x           |
-+------------+-----------------------------------------------------------+-------------------------------+
-
-示例：
-
-
-::
-
-    x = True
-    y = False
-    x and y
-    not x
-    not y
-
-
-<button>Run ...</button>
-
-
-位运算符
-----------------
-
-位运算符作用于操作数，就像它们是二进制数字的字符串一样，它一点点地运行，因此而得名。
-
-+------------+-----------------------------------------------------------+-------------------------------+
-|运算符      |	        含义                                              |	示例                        |
-+============+===========================================================+===============================+
-|  &         |	     按位与（AND）                                       |	x & y                       |  
-+------------+-----------------------------------------------------------+-------------------------------+
-|  |         |	     按位或（OR）                                        |	x | y                       |
-+------------+-----------------------------------------------------------+-------------------------------+
-|  ~         |	按位翻转/取反（NOT）                                     |	  ~x                         |
-+------------+-----------------------------------------------------------+-------------------------------+
-|   ^        |	按位异或（XOR）                                          |	x ^ y                      |
-+------------+-----------------------------------------------------------+-------------------------------+
-|   >>       |	   按位右移                                              |	x >> 2                       |     
-+------------+-----------------------------------------------------------+-------------------------------+
-|   <<       |	   按位左移                                              |	x << 2                       | 
-+------------+-----------------------------------------------------------+-------------------------------+
-
-例如：2 是二进制 10，7 是 111。
-
-令 x = 10（二进制 0000 1010），y = 4（二进制 0000 0100），进行位运算：
-
-::
-
-    x = 10  # 二进制 0000 1010
-    y = 4  # 二进制 0000 0100
-    x & y  # 0000 0000
-    x | y  # 0000 1110
-    ~x  # 1111 0101
-    x ^ y  # 0000 1110
-    x >> 2  # 0000 0010
-    x << 2  # 0010 1000
-
-<button>Run ...</button>
-
-
-赋值运算符
-----------------
-
-赋值运算符用于为变量赋值。
-
-例如：x = 5，是一个简单的赋值运算符，它将右侧的值 5 分配给左侧的变量 x。
-
-在 Python 中，有各种各样的复合运算符，例如：x += 5，先将变量 x 的值与 5 相加，再将最终结果分配给变量 x，等价于：x = x + 5。
-
-
-+------------+-----------------------------------------------------------+-------------------------------+
-| 运算符     | 	      示例                                            | 	等价于                      | 
-+============+===========================================================+===============================+
-| =          | 	x = 5                                                    | 	x = 5（相同）                | 
-+------------+-----------------------------------------------------------+-------------------------------+
-| +=         | 	x += 5                                                   | 	x = x + 5                    | 
-+------------+-----------------------------------------------------------+-------------------------------+
-| -=         | 	x -= 5                                                   | 	x = x - 5                    | 
-+------------+-----------------------------------------------------------+-------------------------------+
-| *=         | 	x *= 5                                                   | 	x = x * 5                    | 
-+------------+-----------------------------------------------------------+-------------------------------+
-| /=         | 	x /= 5                                                   | 	x = x / 5                    |  
-+------------+-----------------------------------------------------------+-------------------------------+
-| %=         | 	x %= 5                                                   | 	x = x % 5                    | 
-+------------+-----------------------------------------------------------+-------------------------------+
-| //=        | 	x //= 5	                                                 |      x = x // 5               | 
-+------------+-----------------------------------------------------------+-------------------------------+
-| **=        |  x **= 5                                                  | 	x = x ** 5                   |
-+------------+-----------------------------------------------------------+-------------------------------+
-| &=         | 	x &= 5                                                   |      x = x & 5                | 
-+------------+-----------------------------------------------------------+-------------------------------+
-| =          | 	x = 5                                                    | 	x = x | 5                    | 
-+------------+-----------------------------------------------------------+-------------------------------+
-| ^=         | 	x ^= 5	                                                 |      x = x ^ 5                | 
-+------------+-----------------------------------------------------------+-------------------------------+
-| >>=        | 	x >>= 5                                                  |       x = x >>                | 
-+------------+-----------------------------------------------------------+-------------------------------+
-| <<=        | 	x <<= 5                                                  | 	x = x << |                   |
-+------------+-----------------------------------------------------------+-------------------------------+
-
-特殊运算符
-----------------
-
-除以上运算符外，Python 还提供了一些特殊类型的运算符：
-
-- 身份运算符
-- 成员运算符
-
-
-身份运算符
-----------------
-
-身份运算符用于检查两个值（或变量）是否位于存储器的同一部分。
-
-**注意**： 两个变量相等，并不意味着它们也相同。
-
-
-+------------+-----------------------------------------------------------+-------------------------------+
-|   运算符   |	含义                                                      |	示例                        |
-+============+===========================================================+===============================+
-|    is      |	如果操作数相同，则为 True（引用同一个对象）              |	x is True                     |
-+------------+-----------------------------------------------------------+-------------------------------+
-|   is not   |	如果操作数不相同，则为 True（引用不同的对象）            |	x is not True                |
-+------------+-----------------------------------------------------------+-------------------------------+
-
-示例：
-
-::
-
-    x1 = 5
-    y1 = 5
-
-    x2 = 'Python'
-    y2 = 'Python'
-
-    x3 = [1, 2, 3]
-    y3 = [1, 2, 3]
-    x1 is not y1
-
-
-    x2 is y2
-
-    x3 is y3
-
-<button>Run ...</button>
-
-
-可以看到 x1 和 y1 是相同值的整数，所以它们相等且相同。x2 和 y2（字符串）同样是这样。
-
-但 x3 和 y3 是列表，它们相等，但不相同。由于列表是可变的（可以更改），即使它们相等，解释器也会将它们分别存储在内存中。
-
-成员运算符
-----------------
-
-成员运算符用于测试在序列（字符串、列表、元组、集合和字典）中是否可以找到一个值/变量。
-
-**注意**： 在字典中，只能检测 key 是否存在，而不能检测 value。
-
-+------------+-----------------------------------------------------------+-------------------------------+
-|   运算符   |	含义                                                      |	示例                        |
-+============+===========================================================+===============================+
-|    in      |	如果在序列中找到值/变量，则为 True                       |	5 in x                     |
-+------------+-----------------------------------------------------------+-------------------------------+
-|   not in   |	如果在序列中没有找到值/变量，则为 True                   |	5 not in x                   |
-+------------+-----------------------------------------------------------+-------------------------------+
-
-
-示例：
-
-
-::
-
-    x = 'Hello, World!'  # 字符串
-    y = {1:'Java', 2:'Python'}  # 字典
-
-    'H' in x
-
-    'hello' not in x
-
-    1 in y
-
-    'Python' in y
-
-<button>Run ...</button>
-
-1这里，'H' 在 x 中，但 'hello' 不存在于 x 中（切记，Python 是区分大小写的）。同样地，1 是 key，'Python' 是字典 y 中的值。因此，'Python' in y 返回 False。
-
-
-作者 & 更新时间
-------------------------------------
-作者: `一去丶二三里 <http://blog.csdn.net/liang19890820>`_
-
-提交: 2017/12/6
-
diff --git a/docs/sources/python-base/cn/python-base-python-datatype-number.rst.txt b/docs/sources/python-base/cn/python-base-python-datatype-number.rst.txt
deleted file mode 100644
index 536bb5a..0000000
--- a/docs/sources/python-base/cn/python-base-python-datatype-number.rst.txt
+++ /dev/null
@@ -1,397 +0,0 @@
-简述
-================
-
-Python 中的每个值都有一个数据类型。
-
-在 Python 编程中，**一切（万物）皆对象**，数据类型实际上是类，变量是这些类的实例（对象）。
-
-
-数据类型
--------------
-
-Python 中有各种数据类型，下面列出了一些重要类型：
-
-  - Number（数字）
-  - String（字符串）
-  - List（列表）
-  - Tuple（元组）
-  - Set（集合）
-  - Dictionary（字典）
-
-Number（数字）
-----------------
-  
-
-Python 支持三种不同的数字类型：
-
-   - int（整型）
-   - float（浮点型）
-   - complex（复数）
-
-
-**注意**： Py3.x 去除了 long 类型，现在只有一种整型 - int，表示为长整型。
-
-可以使用 type() 函数获取变量或值的类型，使用 isinstance() 函数来检查一个对象是否属于一个特定的类。
-
-::
-
-    i = 5  # 整型
-    type(i)
-
-    f = 2.5  # 浮点型
-    type(f)
-    type(f)
-
-    c = 1+2j  # 复数
-    type(c)
-
-<button>Run ...</button>
-
-
-
-整数可以是任何长度，只受到可用内存的限制。
-
-浮点数精确到 15 位小数。
-
-复数以 x + yj 的形式写成，其中 x 是实部，y 是虚部。
-
-::
-
-    i = 2 ** 500  # 2 的 500 次幂
-    f = 0.12345678901234567890
-    c = 5+6j
-
-<button>Run ...</button>
-
-String（字符串）
------------------
-
-
-字符串是 Unicode 字符的序列。
-
-可以使用单引号（''）或双引号（""）来表示字符串，多行字符串可以使用三重引号 ''' 或 """ 来表示。
-
-::
-
-    s = 'Hello, Python!'
-    type(s)
-
-<button>Run ...</button>
-
-各种表示方式：
-
-::
-
-    s = 'Hello, Python!'  # 单引号
-    s = "Hello, World!"  # 双引号
-    s = '''Hello,  # 三重单引号
-    World! '''
-
-    s = """  # 三重双引号
-    Hello,
-    World!
-    """
-
-
-
-字符串可以被索引和截取，截取的语法格式如下：
-
-变量[头下标:尾下标]
-
-索引值以 0 开始，-1 为从末尾的开始位置。
-
-::
-
-    s = "Hello, World!"
-    s[4]  # 第五个字符
-    s[7:10]  # 第八个开始到第十个的字符
-    s[-4:-1]  # 倒数第四个开始到倒数第一个的字符
-    s[5] = 'p'  # 生成错误，字符串是不可变的
-
-<button>Run ...</button>
-
-加号（+）是字符串的连接符， 星号（*） 表示复制当前字符串，紧跟的数字为复制的次数。
-
-::
-
-    s1 = "Hello,"
-    s2 = " World!"
-    s1 + s2  # 连接字符串
-    (s1 + s2) * 3  # 复制 3 次字符串
-
-<button>Run ...</button>
-
-List（列表）
--------------
-
-列表是有序的元素序列，它是 Python 中使用最频繁的数据类型，非常灵活。
-
-列表中元素的类型可以不同，支持数字、字符串，甚至可以包含列表（所谓的嵌套）。
-
-列表用 [] 标识，内部元素用逗号分隔。
-
-::
-
-    l = [1, 5.5, 'Python']
-    type(l)
-
-<button>Run ...</button>
-
-和字符串一样，列表同样可以被索引和截取，列表被截取后返回一个包含所需元素的新列表。
-
-::
-
-    l = [3, 2, 5, 4, 1]
-    l
-    l[2]  # 第三个元素
-    l[0:3] # 从第一个元素到第三个元素
-    l[3:] # 从第三个元素开始的所有元素
-
-<button>Run ...</button>
-
-列表是可变的，意思是说，列表中元素的值可以被改变。
-
-::
-
-    l = [1, 2, 3]
-    l[2] = 5  # 将第三个元素 3 变为 5
-    l
-
-<button>Run ...</button>
-
-Tuple（元组）
--------------
-
-元组与列表相同，也是有序序列，唯一的区别是元组是不可变的。
-
-元组适用于保护性的数据，通常比列表快，因为它不能动态更改。
-
-元组用 () 标识，内部元素用逗号分隔。
-
-::
-
-    t = (5, 'Python', 1+2j)
-    type(t)
-
-<button>Run ...</button>
-
-元组也可以被索引和截取，但是不能被更改。
-
-::
-
-    t = (3, 2, 5, 4, 1)
-    t
-    t[1]  # 第二个元素
-    t[0:2] # 从第一个元素到第二个元素
-    t[0] = 10  # 生成错误，元组是不可变的
-
-<button>Run ...</button>
-
-虽然元组中的元素不可变，但它可以包含可变的对象，例如：List（列表）。
-
-构造一个空的或者包含一个元素的元组比较特殊，所以要采用一些额外的语法规则：
-
-::
-
-    tup1 = ()  # 空元组
-    tup2 = (5, )  # 一个元素，需要在元素后添加逗号
-
-<button>Run ...</button>
-
-Set（集合）
--------------
-
-
-集合是一个无序不重复元素集。
-
-集合用 {} 标识，内部元素用逗号分隔。
-
-可以使用大括号 {} 或者 set() 函数创建集合，注意： 要创建一个空集合，必须使用 set() 而不是 {}，因为 {} 用于创建一个空字典。
-
-::
-
-    s = {5, 'Python', 1+2j}
-    type(s)
-
-<button>Run ...</button>
-
-
-既然集合是无序的，那么索引就没有任何意义，也就是说，切片操作符 [] 不起作用。
-
-
-::
-
-    s = {"Java", "Python", "PHP"}
-    s[1]  # 生成错误，不支持索引
-
-<button>Run ...</button>
-
-
-不重复，是指集合中相同的元素会被自动过滤掉，只保留一份。
-
-
-::
-
-    s = {"PHP", "Python", "Java", "Python", "PHP"}
-    s
-
-<button>Run ...</button>
-
-
-除了去重之外，还常用于成员关系的测试。
-
-::
-
-    if ('Python' in s) :
-        ^4$print('Python 在集合中')
-    else :
-        ^4$print('Python 不在集合中')
-
-<button>Run ...</button>
-
-
-
-集合之间也可执行运算，例如：并集、差集、交集。
-
-::
-
-    a = set('abcdefg')
-    b = set('abghijk')
-    a
-    >>>{'b', 'f', 'e', 'd', 'a', 'c', 'g'}
-    b
-    >>>{'b', 'k', 'h', 'i', 'j', 'a', 'g'}
-
-    a - b  # 差集
-    >>>{'f', 'c', 'd', 'e'}
-
-    a | b  # 并集
-    >>>{'b', 'k', 'f', 'h', 'i', 'e', 'j', 'd', 'a', 'c', 'g'}
-
-    a & b  # 交集
-    >>>{'b', 'a', 'g'}
-
-    a ^ b  # 对称差 - 不同时存在的元素
-    >>>{'e', 'c', 'k', 'f', 'h', 'i', 'j', 'd'}
-
-
-
-对称差公式：A Δ B = (A ? B) ∪(B ? A)。也可表示为两个集合的并集减去它们的交集：A Δ B = (A ∪B) ? (A ∩B)。
-
-
-Dictionary（字典）
----------------------
-
-字典是键值对的无序集合。
-
-通常在有大量的数据时会使用，在检索数据时字典做了优化，必须知道要检索的 value 所对应的 key。
-
-字典用 {} 标识，其中的每个元素都以 key:value 对的形式出现，key 和 value 可以是任何类型。
-
-**注意**： 字典中的 key 必须是唯一的。
-
-::
-
-    d = {1:'value', 'key':2}
-    type(d)
-
-<button>Run ...</button>
-
-可以用 key 来检索相应的 value，但相反则不行。
-
-::
-
-    d = {}  # 创建空字典
-    d['name'] = 'Python'  # 增加新的键/值对
-    d['site'] = 'www.python.org'
-    d
-    d['site']  # 键为 'site' 的值
-    d['Python']  # 生成错误，不能用 value 检索 key
-
-<button>Run ...</button>
-
-
-字典有一些内置的函数，例如：keys()、values()、clear() 等。
-
-::
-
-    d.keys()  # 所有键
-    dict_keys(['name', 'site'])
-
-    d.values()  # 所有值
-    dict_values(['Python', 'www.python.org'])
-
-    d.clear()  # 清空字典
-    d
-
-<button>Run ...</button>
-
-
-数据类型之间的转换
----------------------
-
-可以使用不同的类型转换函数来转换不同的数据类型，例如：int()、float()、str() 等。
-
-从 int 转换为 float：
-
-::
-
-     float(5)
-
-<button>Run ...</button>
-
-
-从 float 到 int 的转换，值将会被截断（使其接近零）：
-
-::
-
-     int(10.8)
-     int(-10.8)
-
-<button>Run ...</button>
-
-字符串的转换必须包含兼容的值：
-
-::
-
-    float('2.5')
-    >>>2.5
-
-    str(25)
-    >>>'25'
-
-    int('str')
-    >>>Traceback (most recent call last):
-     File "<stdin>", line 1, in <module>
-     ValueError: invalid literal for int() with base 10: 'str'
-
-
-
-甚至可以将一个序列转换为另一个序列：
-
-::
-
-    set([1, 2, 3])
-
-    tuple({4, 5, 6})
-
-    list('hello')
-
-<button>Run ...</button>
-
-
-要转换为字典，每个元素必须是一对：
-
-::
-
-    dict([[1, 'value'], ['key', 2]])
-
-<button>Run ...</button>
-
-
-
-作者 & 更新时间
-------------------------------------
-作者: `一去丶二三里 <http://blog.csdn.net/liang19890820>`_
-
-提交: 2017/12/6
diff --git a/docs/sources/python-base/cn/python-base-variable.rst.txt b/docs/sources/python-base/cn/python-base-variable.rst.txt
deleted file mode 100644
index b0f9ba0..0000000
--- a/docs/sources/python-base/cn/python-base-variable.rst.txt
+++ /dev/null
@@ -1,184 +0,0 @@
-简述
-================
-
-变量只不过是保留的内存位置，用于存储规定范围内的值。这意味着，在创建变量时会在内存中开辟一个空间。
-
-基于变量的数据类型，解释器会分配指定内存，并决定什么数据可以被存储在内存中。因此，变量可以指定不同的数据类型，例如：整数、小数或字符串等。
-
-
-何为变量？
------------
-
-简单地说，变量就是给数据起一个名字。
-
-变量更多情况下是一种引用，对应的只是内存当中的一个值，该值可以根据需要存储不同的数据类型。
-
-
-如下等式，一个名为 age 的变量被赋值为 18 。正如每个人都有姓名一样，变量的名字叫做标识符。
-
-.. image:: http://img.blog.csdn.net/20170331192942534
-
-
-
-
-
-**注意**： 在很多语言中，声明变量必须要声明变量的类型。例如，要在 C++ 中声明一个整形变量，必须先声明变量的类型为 int，然后使用变量名以及变量对应的值。而在 Python 中，
-
-变量命名
--------------
-
-变量可以任意取名，但必须遵循以下命名规则：
-
-   - 由字母、数字、下划线（_）组成
-   - 不能以数字开头
-   - 不能使用 Python 关键字
-   - 不能使用特殊符号，例如：!、@、#、$、% 等
-
-
-例如，定义以下变量：
-
-::
-
-   age = 18  // 正确
-   _age = 18  // 正确
-   age_18 = 18  // 正确
-   18_age = 18  // 错误，以数字开头
-   global = 18  // 错误，使用关键字 global
-   a@e = 18  // 错误，使用特殊符号 @
-
-除此之外，还需要注意：
-
-- 变量对大小写敏感（区分大小写）
-
-例如，定义以下变量：
-
-::
-
-    AGE = 18  // 正确
-    age = 18  // 正确
-
-AGE 和 age 是两个不同的变量，而非同一个。
-
-**建议**： 变量名应做到见明知义，避免晦涩难懂。虽然可以是任意长度，但一般应确保精简、无歧义。
-
-
-变量赋值
-================
-
-变量的基本赋值
-----------------
-
-变量是数据的引用，更像是标签。
-
-
-例如，有一个 teacher 叫 Jack Ma，相当于将 teacher 这个标签拴在了 Jack Ma 上。通过这个 teacher，就顺延到了 Jack Ma，于是当我们叫 teacher 时，实际是在叫 Jack Ma，给人的
-
-感觉似乎 teacher 就是 Jack Ma，而事实上是 teacher 这个标签贴在了 Jack Ma 上。
-
-::
-
-    teacher = "Jack Ma"
-    teacher
-
-<button>Run ...</button>
-
-在这里，teacher 标签就是所谓的变量，Jack Ma 就是该变量所对应的值，存储于内存中。
-
-在 Python 中，有非常重要的一句话：**对象有类型，变量无类型。** 可以这样理解，就是标签不仅可以贴在整形类型的对象上，还可以贴在其他类型的对象上（例如：字符串）。变量的这个特点（随意贴标签）非常重要，它没有类型。
-
-
-变量的重新赋值
-----------------
-
-变量，从字面意思理解就是变化的量，也就是说，数值是可变的。将变量从一个存储空间移至另一个存储空间中（也就是将标签移走，换到另一个位置），被称作变量的重新赋值。
-
-例如，原来 teacher 叫 Jack Ma，后来年龄大退休了，换了一个老师叫 Pony，当我们再叫 teacher 时，其实叫的是 Pony，而非 Jack Ma。
-
-::
-
-    teacher = "Jack Ma"
-    teacher
-    teacher = "Pony"
-    teacher
-
-<button>Run ...</button>
-
-一开始，相当于将 teacher 这个标签拴在了 Jack Ma 上，后来标签换了位置，拴在了 Pony 上。
-
-如果学过其他编程语言，例如：C++，很容易想到的是 teacher 开辟了内存空间，里面存了 Jack Ma，然后这个空间当中又换为 Pony。就好比 teacher 是一个容器，里面放着值。实际在 Python 当中，正好相反，Python 是以数据为主，Jack Ma、Pony 都存储在内存当中，teacher 无非是对数据的一个引用。所以，当重新赋值后，查看 teacher 所对应的地址时，会发生变化。
-
-每个对象在内存中都有对应的地址，这就是它的“身份”，使用 Python 的内建函数 id() 来查看：
-
-::
-
-    teacher = "JacK Ma"
-    id(teacher)
-    teacher = "Pony"
-    id(teacher)
-
-<button>Run ...</button>
-
-可以看到，重新赋值后，teacher 对应的内存空间已经变了。所以，JacK Ma 和 Pony 实际上存储在两个空间中。
-
-为多个变量指定同一个值
------------------------
-
-同一个内存地址，可以有多个标签。这个很容易理解，一个人可以有多重身份，JacK Ma 既是 teacher，又是 ceo。
-
-::
-
-    ceo = teacher = "Jack Ma"
-
-或者：
-
-::
-
-   teacher = "Jack Ma"
-   ceo = teacher
-
-<button>Run ...</button>
-
-看似不相关的变量，但他们指的都是同一个人 Jack Ma，不妨看一下：
-
-::
-
-    teacher = "Jack Ma"
-    ceo = teacher
-    id(teacher)
-    id(ceo)
-
-<button>Run ...</button>
-
-显然，对应的是同一个内存地址。也就是说，teacher、ceo 是不同的标签，但是所引用的数据是同一地址上的。
-
-将多个值分配给多个变量
-
-要将多个值分配给多个变量，可以单独分开来写：
-
-::
-
-    age = 18
-    teacher = "Jack Ma"
-
-<button>Run ...</button>
-
-也可以连在一起：
-
-::
-
-    age, teacher = 18, "Jack Ma"
-    age
-    teacher
-
-<button>Run ...</button>
-
-
-这里，值为 18 的整形分配给了变量 age，值为 “Jack Ma” 的字符串分配给了变量 teacher。
-
-
-
-作者 & 更新时间
-------------------------------------
-作者: `一去丶二三里 <http://blog.csdn.net/liang19890820>`_
-
-提交: 2017/12/6
diff --git a/docs/sources/python-base/cn/python-controller-break.rst.txt b/docs/sources/python-base/cn/python-controller-break.rst.txt
deleted file mode 100644
index 92eb4a6..0000000
--- a/docs/sources/python-base/cn/python-controller-break.rst.txt
+++ /dev/null
@@ -1,98 +0,0 @@
-Python break 和 continue 语句
-======================================
-
-简述
---------------------------
-在 Python 中，break 和 continue 语句用于改变普通循环的流程。
-
-通常情况下，循环遍历一段代码，直到判断条件为 False。但有时，可能会希望不检测判断条件就可以终止当前迭代，甚至是整个循环。这种情况下，就需要使用 break 和 continue 语句。
-
-break 语句
-----------------------------------
-break 用于终止循环语句。即使循环条件不是 False 或者序列还没被完全递归完，也会终止。
-
-**注意：** 如果 break 语句在嵌套循环内，break 将终止最内层循环。
-
-语法格式：
-
-::
-
-    break
-
-
-流程图：
-
-.. image:: http://img.blog.csdn.net/20170416165752222
-
-当我们陶醉在单曲循环的世界中时，突然一声：老师来啦，^_^以迅雷不及掩耳之势关闭歌曲吧：
-
-::
-
-    i = 0
-    while i < 3:
-        ^4$if i == 1:
-            ^8$print('老师来啦')
-            ^8$print('关闭歌曲')
-            ^8$break
-
-        ^4$print('正在播放：双节棍')
-        ^4$i += 1
-
-<button>Run ...</button>
-
-运行程序，输出如下：
-
-::
-
-    正在播放：双节棍
-    老师来啦
-    关闭歌曲
-
-continue 语句
---------------------------------
-语法格式：
-
-::
-
-   continue
-
-
-流程图：
-
-.. image:: http://img.blog.csdn.net/20170416170554216
-
-列表播放时，遇到不喜欢的歌曲经常会选择下一曲，直接跳过当前歌曲：
-
-::
-
-    songs = ['安静', '蜗牛', '稻香']
-    # 通过索引遍历列表
-    for i in range(len(songs)):
-        ^4$if i == 1:
-            ^8$print('不想听', songs[i])
-            ^8$print('快进，下一曲')
-            ^8$continue
-        ^4$print("正在播放：", songs[i])
-
-<button>Run ...</button>
-
-运行程序，输出如下：
-
-::
-
-   正在播放： 安静
-   不想听 蜗牛
-   快进，下一曲
-   正在播放： 稻香
-
-遍历歌曲列表，当播放到“蜗牛”时，发现这首歌曲太煽情了，直接进入下一曲。
-
-**两者的根本区别：** break 用于终止整个循环；continue 用于跳出本次循环，还会继续下一次循环。
-
-
-
-作者 & 更新时间
-------------------------------------
-作者: `一去丶二三里 <http://blog.csdn.net/liang19890820>`_
-
-提交: 2017/12/6
diff --git a/docs/sources/python-base/cn/python-controller-for.rst.txt b/docs/sources/python-base/cn/python-controller-for.rst.txt
deleted file mode 100644
index f0c73ba..0000000
--- a/docs/sources/python-base/cn/python-controller-for.rst.txt
+++ /dev/null
@@ -1,134 +0,0 @@
-Python 运算符
-================
-
-简述
------
-在 Python 中，for 循环用于迭代序列（例如：列表、元组）或其他可迭代对象，迭代序列称为遍历。
-
-for 循环
-----------
-语法格式：
-
-::
-
-    for <val> in <序列>:
-        ^4$<循环体>
-
-
-val 是一个变量，在每次迭代中，用于接收将序列中元素的值。
-
-循环会一直继续，直到到达序列的最后一项。循环体与其余的代码使用缩进分隔。
-
-流程图：
-
-.. image:: http://img.blog.csdn.net/20170416152544984
-
-
-::
-
-    for letter in 'Jay':
-        ^4$print('当前字母：', letter)
-
-    songs = ['安静', '蜗牛', '稻香']   
-    for song in songs:
-        ^4$print('正在播放：', song)
-
-<button>Run ...</button>
-
-运行程序，输出如下：
-
-::
-
-    当前字母： J
-    当前字母： a
-    当前字母： y
-    正在播放： 安静
-    正在播放： 蜗牛
-    正在播放： 稻香
-
-这里，可以将 for 循环视为歌曲中的顺序播放。
-
-通过索引遍历
----------------
-
-可以使用 range() 函数生成一个数字序列。
-
-还可以定义 range(start, stop[, step]) 中的 start、stop 和 step，如果没有提供 step，步长默认为 1。
-
-要强制该函数输出所有元素，可以使用函数 list()：
-
-::
-
-    # 输出：range(0, 10)
-    print(range(10))
-
-    # 输出：[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
-    print(list(range(10)))
-
-    # 输出：[2, 3, 4, 5, 6, 7]
-    print(list(range(2, 8)))
-
-    # 输出：[2, 5, 8, 11, 14, 17]
-    print(list(range(2, 20, 3)))    
-
-<button>Run ...</button>
-
-可以在 for 循环中使用 range() 来遍历一个数字序列，它可以与内置函数 len() 结合，使用索引进行序列迭代。
-
-len() 返回列表的长度，即：元素的个数。
-
-::
-
-    songs = ['安静', '蜗牛', '稻香']
-
-    # 通过索引遍历列表
-    for i in range(len(songs)):
-        ^4$print("正在播放：", songs[i])
-
-<button>Run ...</button>
-
-运行程序，输出如下：
-
-::
-
-    正在播放： 安静
-    正在播放： 蜗牛
-    正在播放： 稻香
-
-
-for … else
-------------------
-
-for 循环也可以有一个可选的 else 块，如果循环正常执行完（即：不是通过 break 跳出而中断的），则执行 else 部分。
-
-**注意：** break 语句可以用来跳出 for 循环，在这种情况下，else 部分会被忽略。
-
-::
-
-    songs = ['安静', '蜗牛', '稻香']
-    for song in songs:
-        ^4$print('正在播放：', song)
-    else:
-        ^4$print('播放结束')
-
-<button>Run ...</button>
-
-运行程序，输出如下：
-
-::
-
-    正在播放： 安静
-    正在播放： 蜗牛
-    正在播放： 稻香
-    播放结束
-
-
-可以看到，当 for 循环结束时，执行 else 中的代码块。
-
-
-
-作者 & 更新时间
-------------------------------------
-作者: `一去丶二三里 <http://blog.csdn.net/liang19890820>`_
-
-提交: 2017/12/6
diff --git a/docs/sources/python-base/cn/python-controller-if.rst.txt b/docs/sources/python-base/cn/python-controller-if.rst.txt
deleted file mode 100644
index c7fb4c6..0000000
--- a/docs/sources/python-base/cn/python-controller-if.rst.txt
+++ /dev/null
@@ -1,191 +0,0 @@
-简述
-================
-
-当仅在满足某个条件才会执行相应的代码时，需要进行决策。在 Python 中，由 if … elif … else 语句来实现。
-
-
-if 语句
----------------
-
-
-语法格式：
-
-if <判断条件>：
-    <执行语句……>
-
-
-当“判断条件”成立（True）时，才执行语句；反之，则不执行。
-
-执行语句可以为多行，以缩进来区分表示同一范围。
-
-**注意**： 在 Python 中，非零值表示 True；None 和 0 表示 False。
-
-流程图：
-
- .. image:: http://img.blog.csdn.net/20170416144600526
-
-
-以购物为例（预算 500），进入商场，价格都是 1000+。
-
-
-
-::
-
-    price = 1000
-    print('纳尼，居然', price)
-    print('简直太贵了！')
-    print("货比三家，再转转。")
-
-<button>Run ...</button>
-
-执行程序，输出如下：
-
-::
-
-    纳尼，居然 1000
-    简直太贵了！
-    货比三家，再转转。
-
-
-if…else 语句
------------------------
-
-语法格式：
-
-::
-
-    if <判断条件>:
-        ^4$<执行语句1……>
-    else:
-        ^4$<执行语句2……>
-
-
-当“判断条件”为 True 时，执行语句1；否则，执行语句2。
-
-流程图：
-
-.. image:: http://img.blog.csdn.net/20170416145014731
-
-
-既然 1000 贵了，好吧！开始讨价还价，降价到 300：
-
-::
-    price = 300
-    if price > 500:
-        print('纳尼，居然', price)
-        print('简直太贵了！')
-    else:
-        print(price, '还算地道')
-        print('来一打！')
-        print("合适才买")
-
-<button>Run ...</button>
-
-执行程序，输出如下：
-
-::
-    300 还算地道
-    来一打！
-    合适才买
-
-
-if…elif…else 语句
--------------------------------------
-
-语法格式：
-
-::
-
-    if <判断条件1>:
-        ^4$<执行语句1……>
-    elif <判断条件2>:
-        ^4$<执行语句2……>
-    else:
-        ^4$<执行语句3……>
-
-elif 是 else if 的缩写，允许我们检查多个表达式。
-
-如果 if 的条件为 False，则检查下一个 elif 的状态，依次进行。倘若所有条件都为 False，则执行 else 中的语句（语句3）。
-
-**注意**： if 和 else 只能有一个，但 elif 可以有多个，if … elif … else 中只有一个语句块可以根据条件来执行。
-
-流程图：
-
-.. image:: http://img.blog.csdn.net/20170416145420452
-
-一般，大部分顾客买东西都要讨价还价。当然，其中不乏土豪，只管买买买。
-
-::
-
-    price = 2000
-    if price > 1000:
-        ^4$print('这么贵，肯定是好东西。')
-        ^4$print('买、买、买！')
-    elif price > 500:
-        ^4$print('价格还行，值得拥有。')
-        ^4$print('买买买！')
-    elif price > 200:
-        ^4$print('先入手，合不合适再说。')
-        ^4$print('买买买。')
-    else:
-        ^4$print('这么便宜，赶紧抢。')
-        ^4$print('买买买')
-
-    print('管它多钱，反正我要买买买。')
-
-<button>Run ...</button>
-
-
-执行程序，输出如下：
-
-::
-
-    这么贵，肯定是好东西。
-    买、买、买！
-    管它多钱，反正我要买买买。
-
-
-嵌套 if 语句
--------------------------------
-
-可以将一个 if … elif … else 语句加入至另一个 if … elif … else 语句中，这被称为嵌套。
-
-任何数量的这些语句都可以嵌套在一起，要找出嵌套级别，缩进是唯一的方法。
-
-买东西，追求的是性价比，所以既要价格适中，又要质量有保证：
-
-::
-
-    price = 300
-    quality = True
-    if price > 500:
-        ^4$print('纳尼，居然', price)
-        ^4$print('简直太贵了！')
-    else:
-        ^4$print(price, '价钱合适')
-
-        ^4$if quality:
-            ^8$print('质量也还不错')
-            ^8$print('来一打')
-         ^4$else:
-            ^8$print('质量不行')
-            ^8$print('算了，不要')
-         ^4$print('性价比较高再买')
-
-<button>Run ...</button>
-
-输出如下：
-
-::
-
-    300 价钱合适
-    质量也还不错
-    来一打
-    性价比较高再买
-
-
-作者 & 更新时间
-------------------------------------
-作者: `一去丶二三里 <http://blog.csdn.net/liang19890820>`_
-
-提交: 2017/12/6
diff --git a/docs/sources/python-base/cn/python-controller-pass.rst.txt b/docs/sources/python-base/cn/python-controller-pass.rst.txt
deleted file mode 100644
index 6ad0570..0000000
--- a/docs/sources/python-base/cn/python-controller-pass.rst.txt
+++ /dev/null
@@ -1,73 +0,0 @@
-Python pass 语句
-================
-
-简述
------------------------------------
-在 Python 中，pass 是一个空语句，为了保持程序结构的完整性。一般情况下，pass 被用作占位符。
-
-pass 和注释之间的区别在于：解释器会完全忽略注释，但不会忽略 pass。
-
-然而，执行 pass 时什么都不会发生，导致无操作（NOP）。
-
-pass 语句
-------------------------------------
-
-语法格式：
-
-::
-
-   pass
-
-假设，欧阳锋写了一个循环或者函数，尚未实现（暂未想好如何实现或者出差交付给其他人），但是会在将来的某个时候实现。这时，如果循环体或者函数体为空，解释器就会报错。所以，可以使用 pass 语句构造一个不做任何事情的主体。
-
-上课了，开始听歌，随机播放、顺序播放、单曲循环、列表循环。。。反复思量着，到底选哪一个呢？没想好，那么就先纠结着，什么都不干！
-
-::
-
-    songs = ['安静', '蜗牛', '稻香']
-    for song in songs:
-
-<button>Run ...</button>
-
-执行语句，输出如下：
-
-::
-
-    SyntaxError: unexpected EOF while parsing
-
-
-报错了，所以呢？还是乖乖的加上 pass 吧！
-
-
-::
-
-    songs = ['安静', '蜗牛', '稻香']
-    for song in songs:
-        ^4$pass
-
-<button>Run ...</button>
-
-相应的，也可以在空函数或类中做同样的事情：
-
-::
-    def function(args):
-        ^4$pass
-
-<button>Run ...</button>
-
-::
-
-    class example:
-        ^4$pass
-
-<button>Run ...</button>
-
-总之，pass 什么也不做，就是为了占位，防止语法错误。
-
-
-
-作者 & 更新时间
-------------------------------------
-作者: `一去丶二三里 <http://blog.csdn.net/liang19890820>`_
-
-提交: 2017/12/6
diff --git a/docs/sources/python-base/cn/python-controller-while.rst.txt b/docs/sources/python-base/cn/python-controller-while.rst.txt
deleted file mode 100644
index 24f48d9..0000000
--- a/docs/sources/python-base/cn/python-controller-while.rst.txt
+++ /dev/null
@@ -1,88 +0,0 @@
-Python while 循环
-============================
-
-简述
----------------------------------
-在 Python 中，while 循环用于遍历代码块，只要判断条件为 True，就会一直不停地循环执行。
-
-通常，在事先不知道迭代次数的情况下使用 while 循环。
-
-while 循环
----------------------------------
-语法格式：
-
-::
-
-    while <判断条件>:
-        ^4$<循环体>
-
-
-进入 while 循环，首先检测判断条件，只有当其为 True 时，才会进入循环体。一次迭代后，再次检测判断条件，此过程一直持续到判断条件为 False。
-
-和 for 循环一样，while 的循环体也通过缩进来确定。
-
-流程图：
-
-.. image:: http://img.blog.csdn.net/20170416154810480
-
-如果说 for 循环是**顺序播放**，那 while 循环可以视为**单曲循环**。
-
-
-::
-
-    i = 0
-    while i < 3:
-        ^4$print('正在播放：双节棍')
-        ^4$i += 1
-
-<button>Run ...</button>
-
-运行程序，输出如下：
-
-::
-
-    正在播放：双节棍
-    正在播放：双节棍
-    正在播放：双节棍
-
-这里，只要计数器 i 小于 3（单曲循环 3 次），判断条件将为 True。
-
-**注意**：要在循环体中增加计数器的值，这非常重要（很容易忽视），否则将导致无限循环。
-
-while … else
----------------------------------
-与 for 循环相同，在 while 循环中也可以有一个可选的 else 块。
-
-如果 while 循环中的判断条件为 False，则 else 部分被执行。
-
-**注意** ：while 循环可以用 break 语句终止，在这种情况下，else 部分被忽略。
-
-::
-
-    i = 0
-    while i < 3:
-        ^4$print('正在播放：双节棍')
-
-        ^4$i += 1
-    else:
-        print('播放结束')
-
-<button>Run ...</button>
-
-运行程序，输出如下：
-
-::
-    正在播放：双节棍
-    正在播放：双节棍
-    正在播放：双节棍
-    播放结束
-
-可以看到，在第四次迭代中，while 中的条件变为 False。因此，else 部分被执行。
-
-
-
-作者 & 更新时间
-------------------------------------
-作者: `一去丶二三里 <http://blog.csdn.net/liang19890820>`_
-
-提交: 2017/12/6
diff --git a/docs/sources/python-base/cn/python-datatype-character-string.rst.txt b/docs/sources/python-base/cn/python-datatype-character-string.rst.txt
deleted file mode 100644
index f909a97..0000000
--- a/docs/sources/python-base/cn/python-datatype-character-string.rst.txt
+++ /dev/null
@@ -1,445 +0,0 @@
-简述
-================
-
-字符串（String），是由零个或多个字符组成的有限序列，在 Python 中表示文本的数据类型。
-
-字符只是一个符号，例如：字母（A-Z）、数字（0-9）、特殊符号（~!@#￥%…）等。
-
-
-字符串
-----------
-
-
-在 Python 中，字符串由内置的 str 类型定义。
-
-::
-
-    s = 'Hello, Python!'
-    type(s)
-    <class 'str'>
-
-
-
-可以使用单引号（''）或双引号（""）来表示字符串。
-
-**PS**： 单引号比双引号更常用。
-
-::
-
-    s = 'Hello'  # 单引号
-    s
-
-    s = "Hello"  # 双引号
-    s
-
-
-<button>Run ...</button>
-
-
-字符串可以跨多行，但是每行末尾必须添加反斜杠（\）以转义换行符。
-
-::
-
-    s = 'Hello \
-    World'
-
-    s
-
-<button>Run ...</button>
-
-三重引号（''' 或 """）内的字符串可以跨多个文本行。
-
-::
-
-
-    s = '''Hello
-    Python
-    '''
-
-    s
-
-    s = """
-    Hello
-    Python
-    """
-
-    s
-
-<button>Run ...</button>
-
-
-**注意**： 三重引号通常用于表示多行字符串和 docstring。
-
-访问字符串
-================
-
-
-索引
--------------
-
-
-字符串是字符的有序序列，可以通过其位置来获取具体的字符。在 Python 中，位置有一个优雅的称呼 - 索引（index）。
-
-索引分为两种形式：
-
-- 正向索引（从左到右）：索引从 0 开始，直到 length - 1，可以很方便地访问字符串开头附近的字符。
-- 反向索引（从右到左）：索引从 -1 开始，直到 -length，可以很方便地访问字符串末尾附近的字符。
-
-其中，length 为字符串的长度。
-
-为了更清晰以上概念，来看一个直观地索引图。假设，有一个字符串 s = "Hello"：
-
-.. image:: http://img.blog.csdn.net/20170510181247783
-
-
-使用 [index] 的语法形式来访问指定索引处的字符：
-
-::
-
-    s = 'Hello'
-    s[0]  # 第一个字符
-    s[4]  # 最后一个字符
-
-<button>Run ...</button>
-
-
-负索引（反向索引）从字符串的末尾开始计数，也可以起到同样的效果：
-
-::
-
-    s = 'Hello'
-    s[-5]  # 第一个字符
-    s[-1]  # 最后一个字符
-
-<button>Run ...</button>
-
-
-试图访问索引范围外的字符会引发 IndexError。索引必须是一个整数，不能使用 float 或其他类型，这将引发 TypeError。
-
-::
-
-    s = 'Hello'
-    s[10]
-    s[1.5]
-
-
-<button>Run ...</button>
-
-
-
-切片
-------------------------
-
-
-切片操作（slice）是一种很方便的方法，用于引用序列（通常是字符串和列表）的子部分。
-
-切片操作的语法格式：
-
-[start:stop:step]
-
-
-- start（开始索引）：第一个索引的值是 0，最后一个是 -1。
-- stop（结束索引）：切片操作符将取到该索引为止，但不包含该索引的值。
-- step（步长）：默认是 1，也就是说，一个接一个切取。如果为 2，则表示隔一取一。步长为正数时，表示从左向右取；如果为负数，表示从右向左取；步长不能为 0。
-
-
-通过上图，其实也可以很直观地看到切片。
-
-::
-
-    s = 'Hello'
-    s[1:4]  # 第 2 个到第 4 个字符
-    s[1:]  # 第 2 个到最后一个字符（将索引默认设置为字符串的开始或结束）
-    s[1:100]  # 索引太大，被截断至字符串长度处
-    s[:]  # 所有字符（从开始到结束）
-
-<button>Run ...</button>
-
-**PS**： s[:] 形式会忽略开始和结束索引，总是获得一个完整的副本。是 pythonic 使用的方式，用于复制序列（例如：字符串或列表）。
-
-和提取字符类似，切片操作也可以使用反向索引：
-
-::
-
-    s[-4:-1]  # 第 2 个到第 4 个字符
-    s[:-3]  # 开始到第 2 个字符
-    s[-3:]  # 第 3 个到最后一个字符
-
-<button>Run ...</button>
-
-
-对于任何索引 n，s[:n] + s[n:] == s 是一个整齐的切片，甚至对于负数或超出界限的值也是如此。换一种说法，s[:n] 和 s[n:] 总是将字符串分成两部分，来保存所有的字符。
-
-
-更改或删除字符串
-------------------
-
-
-字符串是不可变的，也就是说，一旦分配了字符串的元素就不能被更改。
-
-但是，可以将不同的字符串重新分配给同一个变量。
-
-::
-
-    s = 'Hello'
-    s[2] = 'a'
-
-    s = 'Python'
-    s
-
-<button>Run ...</button>
-
-无法从字符串中删除字符，但是可以使用关键字 del 完全删除字符串。
-
-::
-
-    s = 'Hello'
-    del s[2]
-    del s
-    s
-
-
-<button>Run ...</button>
-
-基本操作
--------------
-
-
-字符串能成为 Python 中最常用的数据类型之一，很大原因是因为它使用非常方便，提供了大量的字符串操作，例如：连接字符串、遍历字符串…
-
-所有的序列（例如：字符串、列表）都可以进行以下基本操作：
-
-- +：连接两个序列
-- *：重复序列元素
-- in：判断元素是否在序列中
-- min()：返回最小值
-- max()：返回最大值
-- len()：返回序列长度
-- enumerate()：返回一个枚举对象，包含字符串中所有元素的索引和值（作为一对）
-
-
-连接字符串
--------------
-
-连接是指将多个字符串合并为一个单独的字符串。
-
-::
-
-    s1 = "Hello,"
-    s2 = " World!"
-
-    s = s1 + s2  # 连接字符串
-    s
-
-
-<button>Run ...</button>
-
-如果想在不同的行中连接字符串，可以使用括号。
-
-::
-
-    # 两个字符串一起
-    'Hello,' ' World!'  
-
-
-    # 使用括号
-    s = ('Hello,'  
-    s
-
-<button>Run ...</button>
-
-
-操作符 + 不会将数字或其他类型自动转换为字符串形式，需要通过 str() 函数将值转换为字符串形式，以便它们可以与其他字符串组合。
-
-::
-
-    age = 18
-    s = 'My age is ' + age
-    s = 'My age is ' + str(age)
-    s
-
-
-<button>Run ...</button>
-
-重复字符串
--------------
-
-
-操作符 * 用于以指定的次数来重复字符串。有时很好用，比如打印一条华丽的分割线：
-
-::
-
-    s = 'Hello'
-    s * 3  # 重复字符串 3 次
-    print('-' * 20)  # 无需输入很多 -
-
-
-<button>Run ...</button>
-
-
-
-最大值/最小值
--------------
-
-
-在一个字符串中，每个字符在计算机中都有对应的 ASCII 码。min() 和 max() 就是根据对应的 ASCII 码进行比较的，从而获取最小值和最大值对应的字符。
-
-Python 提供了两个内置函数 ord() 和 chr()，用于字符和 ASCII 码之间的转换。
-
-::
-
-    ord('H')  # 字符转 ASCII 码
-    chr(72)  # ASCII 码转字符
-
-<button>Run ...</button>
-
-别犹豫啦，开始比较吧！
-
-::
-
-    s = 'Hello'
-
-    min(s)  # 最小字符
-
-    max(s)  # 最大字符
-
-<button>Run ...</button>
-
-
-字符串成员测试
-------------------------
-
-
-使用关键字 in，可以测试字符串中是否包含指定的子串。
-
-::
-
-    't' in 'python'
-
-    'th' not in 'python'
-
-
-<button>Run ...</button>
-
-
-
-字符串长度
-------------------------
-
-
-要获取一个字符串的长度，可以使用内置函数 len()：
-
-::
-
-    s = 'Hello'
-
-    len(s)  # 字符数
-
-<button>Run ...</button>
-
-
-迭代字符串
-------------------------
-
-
-
-使用 for 循环，可以遍历一个字符串。
-
-::
-
-     # 查找字符串中 l 的数量
-     count = 0
-     for letter in 'Hello':
-         ^4$if (letter == 'l'):
-            ^8$count += 1
-     print(count, 'letters found')
-
-<button>Run ...</button>
-
-
-还可使用 enumerate()，它会返回一个枚举对象，包含字符串中所有元素的索引和值（作为一对），这对于迭代很有用。
-
-::
-
-    s = 'Hello'
-    e = list(enumerate(s))
-    print(e)
-
-    for index, item in e:
-        ^4$print(index, item)
-
-
-<button>Run ...</button>
-
-
-运行程序，输出如下：
-
-::
-
-[(0, ‘H’), (1, ‘e’), (2, ‘l’), (3, ‘l’), (4, ‘o’)]
-0 H
-1 e
-2 l
-3 l
-4 o
-
-
-字符串的方法
-------------------------
-
-
-字符串有许多方法，可以通过 dir() 来查看方法列表：
-
-::
-
-     dir(str)
-
-
-这么多，当然不需要逐一介绍了，重在掌握如何使用（授人以鱼不如授人以渔），能做到随用随查就好。
-
-利用 help() 函数，可以查看函数或模块用途的详细说明：
-
-::
-
-    help(str.lower)
-    >>>Help on method_descriptor:
-
-    lower(...)
-        S.lower() -> str
-
-        Return a copy of the string S converted to lowercase.
-    (END)
-
-
-**注意**： 要终止查询，使用 q 键。
-
-按照说明，可以在交互模式下进行实验，这里仅列举一些比较常用的方法。
-
-::
-
-    s = 'Hello'
-
-    s.lower()  # 转换所有字符为小写
-    s.upper()  # 转换所有字符为大写
-
-    s.find('ll')  # 返回子串的开始索引
-
-    s.endswith('llo')  # 检查字符串是否以指定的子串结束
-
-    s.isdigit()  # 检查字符串是否只包含数字
-
-    s.replace('ll', 'r')  # 替换字符串中的子串
-
-    s = 'I like Python'
-    s.split()  # 分割字符串
-
-<button>Run ...</button>
-
-
-
-作者 & 更新时间
-------------------------------------
-作者: `一去丶二三里 <http://blog.csdn.net/liang19890820>`_
-
-提交: 2017/12/6
-
diff --git a/docs/sources/python-base/cn/python-datatype-dictionaries.rst.txt b/docs/sources/python-base/cn/python-datatype-dictionaries.rst.txt
deleted file mode 100644
index 616c234..0000000
--- a/docs/sources/python-base/cn/python-datatype-dictionaries.rst.txt
+++ /dev/null
@@ -1,322 +0,0 @@
-Python pass 语句
-================
-
-
-
-简述
------
-记得上学时，《新华字典》绝对堪称神器，检索内容相当给力。但随着网络的发展，现在几乎很少有人在用了。
-
-如何使用《新华字典》呢？先查索引，然后通过索引查找相应的内容，简单、方便、快捷。
-
-出于这种需求，Python 也提供了这样一种数据类型 - dict，翻译为中文就是“字典”。
-
-字典的特点
------
-像列表和字典，可以很容易地被改变 - 在运行时可以随意地变小/变大（无需复制）。所以，没有列表和字典的 Python 程序或脚本，几乎是无法想象的。
-
-词典可以包含在列表中，反之亦然。但字典和列表（或其它序列）之间有什么区别呢？
-
-- 列表是对象的有序集合，而字典是无序集。
-- 对于列表（其他复合数据类型也一样）来说，元素只有值；而对于字典，元素由 key:value - 对的形式组成。
-- 字典中的元素通过 key 来访问，而非通过他们的位置来访问（主要区别）。
-
-字典是一个关联数组（也称为哈希），其中的任何 key 都与 value 相关联（或映射），value 可以是 Python 中的任何数据类型，所以字典是无序的 key:value 对。
-
-字典不支持序列数据类型（例如：字符串、元组和列表）的序列操作（例如：索引 
-切片、连接 +、重复 *），字典属于内置映射类型，同时也是该类型的唯一代表！
-
-虽然没有报错，但前面的值被后面的覆盖了，这样做毫无意义。
-在 Python 中，字典由内置的 dict 类型定义。
-
-要创建字典，需要将所有项（元素）放在花括号（{}）内，以逗号（,）分隔。其中，每个元素都以 key:value 对的形式出现，key 和 value 可以是任何数据类型。
-
-**注意：** 字典中的 key 必须是唯一的。
-
-:: 
-    d = {'name':'Python', 'site':'www.python.org'}
-    type(d)
-    <class 'dict'>
-
-<button>Run ...</button>
-
-字典的创建方式很多，还可以使用 dict()：
-
-由于列表是可变的，不可 hash，因此不能作为字典的 key。
-
-key 唯一，意思是说：不要有重复的 key 出现。
-
-虽然 value 可以是任何数据类型，并且可以重复，但 key 必须是不可变类型（例如：字符串、数字）或具有不可变元素的元组。
-
-::
-     d = {'name':'python', (1, 2):'C++'}  # 具有不可变元素的元组
-     d
-    {(1, 2): 'C++', 'name': 'python'}
-    
-     d = {'name':'python', [1, 2]:'C++'}  # 列表
-    ...
-    TypeError: unhashable type: 'list'
-
-<button>Run ...</button>
-
-由于列表是可变的，不可 hash，因此不能作为字典的 key。
-
-key 唯一，意思是说：不要有重复的 key 出现。
-
-::
-     d = {'name':'Python', 'name':'C++'}  # key 都是 'name'
-     d
-    {'name': 'C++'}
-
-<button>Run ...</button>
-
-虽然没有报错，但前面的值被后面的覆盖了，这样做毫无意义。
-
-
-从字典访问元素
------
-
-字典通过 key 来检索 value，有两种方式：
-
-- 在方括号（[]）内使用 key
-- key 与 get() 方法与一起使用
-
-区别在于：使用 get()，如果没有找到 key，则返回 None，而不是 KeyError。
-
-::
-    d = {'name':'Python', 'site':'www.python.org'}
-    
-    print(d['name'])
-   Python
-    
-    print(d.get('site'))
-   www.python.org
-    
-    print(d['version'])  # 没有找到 key，引发 KeyError
-   ...
-   KeyError: 'version'
-    
-    print(d.get('version'))  # 没有找到 key，返回 None
-   None
-    print(d.get('version', 3.5))  # 没有找到 key，返回默认值
-   3.5
-   1
-
-
-<button>Run ...</button>
-
-更改或添加元素
------
-
-字典是可变的，可以添加新元素，也可以使用赋值运算符（=）更改现有元素的值。
-
-如果 key 存在，value 将被更新；否则，新的 key:value 对会被添加到字典中。
-
-::
-     d = {}
-     
-     d['name'] = 'Python'  # 添加
-     d
-    {'name': 'Python'}
-     
-     d['site'] = 'www.python.org'  # 添加
-     d
-    {'site': 'www.python.org', 'name': 'Python'}
-     
-     d['name'] = 'Py'  #  更新
-     d
-    {'site': 'www.python.org', 'name': 'Py'}
-
-<button>Run ...</button>
-
-删除字典或元素
------
-
-通过使用 pop() 方法，可以删除指定 key 对应的元素，并返回其 value。
-
-popitem() 方法可以用来删除并返回一个任意元素 (key, value)，clear() 方法则会一次性删除所有元素。
-
-还可以使用 del 关键字来删除单个元素或整个字典本身。
-
-::
-     d = {'name':'Python', 'site':'www.python.org', 'version':3.5, 'from':1991}
-    >>> 
-     d.pop('site')  # 删除某一项
-    'www.python.org'
-     d
-    {'from': 1991, 'version': 3.5, 'name': 'Python'}
-     
-     d.popitem()  # 删除任意项
-    ('from', 1991)
-     d
-    {'version': 3.5, 'name': 'Python'}
-     
-     del d['version']  # 删除某一项
-     d
-    {'name': 'Python'}
-     
-     d.clear()  # 清空 - 删除所有项
-     d
-    {}
-     
-     del d  # 删除字典本身
-     d
-    ...
-    NameError: name 'd' is not defined
-
-<button>Run ...</button>
-
-基本操作
------
-
-字典不支持序列数据类型（例如：字符串、元组和列表）的序列操作，例如：索引 
-切片、连接（+）、重复（*）等。
-
-成员测试
->>>>>>>>>>
-
-可以测试一个 key 是否存在于字典中，使用关键字 in。
-
-注意： 成员测试仅适用于 key，不适用于 value。
-
-::
-     d = {'name':'Python', 'site':'www.python.org'}
-    
-     'name' in d
-    True
-     
-     'version' not in d
-    True
-     
-     'Python' in d  # 不能检测 value
-    False
-
-<button>Run ...</button>
-
-遍历字典
->>>>>>>>>>
-
-使用 for 循环，可以遍历字典中的每个 key。
-
-::
-     d = {'name':'Python', 'site':'www.python.org'}
-     
-     for key in d:
-    ...     print(key)
-    ... 
-    name
-    site
-
-<button>Run ...</button>
-
-字典的方法
------
-
-字典提供了许多方法，可以通过 dir() 来查看方法列表：
-
-::
-     dir(dict)
-    ['__class__', '__contains__', '__delattr__', '__delitem__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__getitem__', '__gt__', '__hash__', '__init__', '__iter__', '__le__', '__len__', '__lt__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__setitem__', '__sizeof__', '__str__', '__subclasshook__', 'clear', 'copy', 'fromkeys', 'get', 'items', 'keys', 'pop', 'popitem', 'setdefault', 'update', 'values']
-1
-2
-可以看到，有以下方法可用：
-+-----------------------+----------------------------------------------------------------------------------------------------------------+
-| 方法	                |         描述                                                                                                   | 
-+=======================+================================================================================================================+
-|clear()                | 	删除字典中的所有元素                                                                                     |
-+=======================+================================================================================================================+
-|copy()	                |           返回字典的浅拷贝                                                                                     |
-+=======================+================================================================================================================+
-|fromkeys(seq[, v])	|从 seq 返回一个新的字典，值等于 v（默认为 None）                                                                |
-+=======================+================================================================================================================+
-|get(key[,d])	        |返回 key 的值。如果 key 不存在，返回 d（默认为 None）。                                                         |
-+=======================+================================================================================================================+
-|items()	        |以列表形式，返回可遍历的 (key, value) 元组数组。                                                                |
-+=======================+================================================================================================================+
-|keys()	                |以列表的形式，返回字典中所有的 key。                                                                            |
-+=======================+================================================================================================================+
-|pop(key[,d])	        |用 key 删除元素并返回其 value。如果没有找到 key，则返回 d；如果没|有提供 d 并且没有找到 key，则会引发 KeyError。|
-+=======================+================================================================================================================+
-|popitem()	        |删除并返回一个任意元素，如果字典为空，则会引发 KeyError。                                                       |
-+=======================+================================================================================================================+
-|setdefault(key[,d])	|如果 key 在字典中，则返回对应的 value；否则，插入 key，将其值设为 d，并返回 d（默认为 None）。                  |
-+=======================+================================================================================================================+
-|update([other])	|将 other 字典中的键/值对更新到字典中，覆盖现有键。                                                              |
-+=======================+================================================================================================================+
-|values()	        |以列表的形式，返回字典中所有的 value                                                                            |
-+-----------------------+----------------------------------------------------------------------------------------------------------------+
-
-其中一些在上述示例中已经被使用过了。
-
-::
-    all_stars = {}.fromkeys(['Lebron', 'Kobe', 'Curry'], 'NBA')
-     
-    all_stars
-    {'Lebron': 'NBA', 'Curry': 'NBA', 'Kobe': 'NBA'}
-    
-    all_stars.keys()  # 返回所有 key
-    dict_keys(['Lebron', 'Curry', 'Kobe'])
-         
-    all_stars.values()  # 返回所有 value
-    dict_values(['NBA', 'NBA', 'NBA'])
-     
-    for item in all_stars.items():
-   ...     print(item)
-   ... 
-   ('Lebron', 'NBA')
-   ('Curry', 'NBA')
-   ('Kobe', 'NBA')
-
-<button>Run ...</button>
-
-字典推导式
------
-
-
-字典推导式（Dict Comprehensions）是一种优雅、简洁的方式，可以从一个 iterable 中创建新的字典。
-
-字典推导式由花括号标识，其中包含一个表达式对（key:value），紧随其后的是 for 语句。
-
-来看一个例子，NBA All-Star。
-
-::
-     all_stars = ['Lebron', 'Kobe', 'Curry']
-     
-     d = {key: value for key, value in enumerate(all_stars)}  
-     d
-    {0: 'Lebron', 1: 'Kobe', 2: 'Curry'}
-
-<button>Run ...</button>
-
-列表推导式可以可选地包含更多的 for 或 if 语句，if 语句可以过滤出新字典的元素。
-
-::
-    # value 的长度需要大于 5
-     d = {key: value for key, value in enumerate(all_stars) if len(value) > 5}  
-     d
-   {0: 'Lebron'}
-
-<button>Run ...</button>
-
-666，老詹独一档。。。
-
-字典与内置函数
------
-
-下述内置函数通常与字典一起使用，来执行不同的任务。
-+--------+--------------------------------------------------------------------------------------+
-函数     |描述                                                                                  |   
-+======+========================================================================================+
-all()	 |如果字典的所有 key 都是 True（或者字典为空），返回 True。                             |   
-+======+========================================================================================+
-any()	 |如果字典的所有 key 都是 True，返回 True；如果字典为空，返回 False。                   |   
-+======+========================================================================================+
-len()	 |返回字典的长度（元素个数）                                                            |  
-+======+========================================================================================+
-sorted() |	返回一个新的排序字典（不排序字典本身）                                          |
-+------+----------------------------------------------------------------------------------------+
-
-
-作者 & 更新时间
-------------------------------------
-作者: `CSDN <http://blog.csdn.net/liang19890820>`_
diff --git a/docs/sources/python-base/cn/python-datatype-escape-sequence.rst.txt b/docs/sources/python-base/cn/python-datatype-escape-sequence.rst.txt
deleted file mode 100644
index 79a7922..0000000
--- a/docs/sources/python-base/cn/python-datatype-escape-sequence.rst.txt
+++ /dev/null
@@ -1,127 +0,0 @@
-简述
-================
-
-在字符串中，有时需要包含一些特殊的符号，但是有些符号不能直接输出，就需要使用转义序列。
-
-关于转义，维基百科这样定义：
-
-转义是当由于技术等原因、无法直接在代码中写出所要的字符时采用的，以多个字符的有序组合来表示原本需要的字符的手段，而转义序列（escape sequence）指在转义时使用的有序字符组合。
-
-
-
-转义序列
--------------
-
-
-作为一名伟大的程序员，在向别人介绍自己的职业时，往往会很自豪的说：Hi, "I'm a Pythoner"。如果要在 Python 中表示这句话，不能直接使用单引号或双引号。
-
-
-::
-
-    print('Hi, "I'm a Pythoner"')
-    print("Hi, "I'm a Pythoner"")
-
-<button>Run ...</button>
-
-
-要解决这个问题，有两种方式：
-
-- 使用三重引号
-- 使用转义序列
-
-转义序列以反斜杠（\）开头，并以不同的方式解释。如果使用单引号来表示字符串，那么字符串内的所有单引号都必须转义，双引号也不例外。
-
-::
-
-    print('''Hi, "I'm a Pythoner"''')  # 使用三重引号
-
-    print('Hi, "I\'m a Pythoner"')  # 转义单引号
-
-    print("Hi, \"I'm a Pythoner\"")  # 转义双引号
-
-
-<button>Run ...</button>
-
-
-
-以下是 Python 支持的所有转义序列的列表：
-
-+------------+------------------------------------------------------------------------------------------+
-| 转义序列   | 说明                                                                                     |
-+============+==========================================================================================+
-|\（行尾）   | 续行符                                                                                   |    
-+------------+------------------------------------------------------------------------------------------+
-| \\         |反斜杠                                                                                    |
-+------------+------------------------------------------------------------------------------------------+
-| \'         |单引号                                                                                    |
-+------------+------------------------------------------------------------------------------------------+
-| \"         |       双引号                                                                             |         
-+------------+------------------------------------------------------------------------------------------+
-| \a         |响铃                                                                                      |
-+------------+------------------------------------------------------------------------------------------+
-|\b          |退格（Backspace）                                                                         |
-+------------+------------------------------------------------------------------------------------------+
-| \f         |换页                                                                                      |
-+------------+------------------------------------------------------------------------------------------+
-| \n         |换行                                                                                      |
-+------------+------------------------------------------------------------------------------------------+
-| \r         |      回车                                                                                |
-+------------+------------------------------------------------------------------------------------------+
-|\t          |	水平制表符                                                                              |
-+------------+------------------------------------------------------------------------------------------+
-|\v	         |垂直制表符                                                                                |
-+------------+------------------------------------------------------------------------------------------+
-|\ooo	     |值为八进制 ooo 的字符                                                                     |
-+------------+------------------------------------------------------------------------------------------+
-|\xhh	     |值为十六进制 hh 的字符                                                                    |
-+------------+------------------------------------------------------------------------------------------+
-
-
-测试其中的转义序列，感受一下实际的意思：
-
-::
-
-    print('C:\\Windows\\System32')
-
-    print('I like \nPython')
-
-    print('This is \x48\x45\x58')
-
-
-<button>Run ...</button>
-
-
-原始字符串
-----------------
-
-
-原始字符串（Raw String），简单理解就是：字符串中的每个字符都表示原始含义。
-
-有时，可能希望忽略字符串中的转义序列：
-
-::
-
-    print('This is \x48\x45\x58')
-
-
-<button>Run ...</button>
-
-
-遗憾的是，这里的反斜杠并不是原始符号的含义，而是和后面的字符一起表示十六进制（被转义了）。
-
-如何避免呢？很 easy，为字符串加上前缀 r 或者 R（大小写均可）：
-
-::
-
-    print(r'This is \x48\x45\x58')
-
-
-<button>Run ...</button>
-
-R 是 Raw 的缩写，以其开头的字符串被称作原始字符串，并将反斜杠视为原义字符。因此，其中的任何转义序列都不会被特别处理，将会被忽略。
-
-作者 & 更新时间
-------------------------------------
-作者: `一去丶二三里 <http://blog.csdn.net/liang19890820>`_
-
-提交: 2017/12/6
diff --git a/docs/sources/python-base/cn/python-datatype-gather.rst.txt b/docs/sources/python-base/cn/python-datatype-gather.rst.txt
deleted file mode 100644
index aa8ad50..0000000
--- a/docs/sources/python-base/cn/python-datatype-gather.rst.txt
+++ /dev/null
@@ -1,396 +0,0 @@
-Python 集合
-================
-
-
-
-简述
------
-数学上有一个基础概念 -集合，上高一的时候学过。集合的作用大吗？高考必考，你说呢？
-
-关于集合，维基百科这样描述：
-
-.. 集合是基本的数学概念，它是集合论的研究对象，指具有某种特定性质的事物的总体，（在最原始的集合论─朴素集合论─中的定义，集合就是“一堆东西”。）集合里的事物（“东西”），叫作元素。若然 x 是集合 A 的元素，记作 x ∈ A。
-
-在 Python 中，集合分为两类：
-
-- set：可变集合
-- frozenset：不可变集合
-set 可以原地修改，或者说是可变的，也可以说是 unhashable（不可哈希）的。
-
-frozenset，顾名思义，是一个被“冻结”的集合，不能原地修改，是 hashable（可哈希）的。
-
-集合
------
-在 Python 中，集合由内置的 set 类型定义。
-
-要创建集合，需要将所有项（元素）放在花括号（{}）内，以逗号（,）分隔。
-
-:: 
-    d = {'name':'Python', 'site':'www.python.org'}
-    type(d)
-    <class 'dict'>
-
-<button>Run ...</button>
-
-字典的创建方式很多，还可以使用 dict()：
-
-由于列表是可变的，不可 hash，因此不能作为字典的 key。
-
-key 唯一，意思是说：不要有重复的 key 出现。
-
-虽然 value 可以是任何数据类型，并且可以重复，但 key 必须是不可变类型（例如：字符串、数字）或具有不可变元素的元组。
-
-::
-     s = {'P', 'y', 't', 'h', 'o', 'n'}
-     type(s)
-    <class 'set'>
-
-<button>Run ...</button>
-
-集合可以有任意数量的元素，它们可以是不同的类型（例如：数字、元组、字符串等）。但是，集合不能有可变元素（例如：列表、集合或字典）。
-
-::
-     s = {1, 2, 3}  # 整形的集合
-     
-     s = {1.0, 'Python', (1, 2, 3)}  # 混合类型的集合
-      
-     s = set(['P', 'y'])  # 从列表创建
-     
-     s = {1, 2, [3, 4]}  # 不能有可变元素
-    ...
-    TypeError: unhashable type: 'list'
-
-<button>Run ...</button>
-
-创建空集合比较特殊。在 Python 中，空花括号（{}）用于创建空字典。要创建一个没有任何元素的集合，使用 set() 函数（不要包含任何参数）。
-
-::
-     d = {}  # 空字典
-     type(d)
-    <class 'dict'>
-         
-     s = set()  # 空集合
-    type(s)
-    <class 'set'>
-
-<button>Run ...</button>
-
-集合的特性
------
-
-.. 子曰：“温故而知新，可以为师矣。” 
-–《论语》
-
-回顾数学相关知识，集合有以下特性：
-
-- 无序性：一个集合中，每个元素的地位都是相同的，元素之间是无序的。 
-集合上可以定义序关系，定义了序关系后，元素之间就可以按照序关系排序。但就集合本身的特性而言，元素之间没有必然的序。
-- 互异性：一个集合中，任何两个元素都认为是不相同的，即每个元素只能出现一次。 
-有时需要对同一元素出现多次的情形进行刻画，可以使用多重集，其中的元素允许出现多次。
-- 确定性：给定一个集合，任给一个元素，该元素或者属于或者不属于该集合，二者必居其一，不允许有模棱两可的情况出现。
-
-当然，Python 中的集合也具备这些特性：
-
-::
-    # 无序性
-     s = set('Python')
-     s
-    {'y', 'n', 'h', 'o', 'P', 't'}
-    
-     s[0]  # 不支持索引
-    ...
-    TypeError: 'set' object does not support indexing
-
-    # 互异性
-     s = set('Hello')
-     s
-    {'e', 'H', 'l', 'o'}
-
-    # 确定性
-     'l' in s
-    True
-     
-     'P' not in s
-    True
-
-<button>Run ...</button>
-
-**注意：** 由于集合是无序的，所以索引没有任何意义。也就是说，无法使用索引或切片访问或更改集合元素。
-
-
-集合运算
------
-
-集合之间也可进行数学集合运算（例如：并集、交集等），可用相应的操作符或方法来实现。
-
-考虑 A、B 两个集合，进行以下操作。
-
-::
-     A = set('abcd')
-     B = set('cdef')
-
-<button>Run ...</button>
-
-子集
------
-
-子集，为某个集合中一部分的集合，故亦称部分集合。
-
-使用操作符 < 执行子集操作，同样地，也可使用方法 issubset() 完成。
-
-::
-     C = set('ab')
-     
-     C < A
-    True
-    
-     C < B
-     False
-     
-     C.issubset(A)
-     True
-
-<button>Run ...</button>
-
-并集
------
-
-一组集合的并集是这些集合的所有元素构成的集合，而不包含其他元素。
-
-使用操作符 | 执行并集操作，同样地，也可使用方法 union() 完成。
-
-::
-     A | B
-    {'e', 'f', 'd', 'c', 'b', 'a'}
-  
-     A.union(B)
-    {'e', 'f', 'd', 'c', 'b', 'a'}
-
-<button>Run ...</button>
-
-交集
------
-
-两个集合 A 和 B 的交集是含有所有既属于 A 又属于 B 的元素，而没有其他元素的集合。
-
-使用 & 操作符执行交集操作，同样地，也可使用方法 intersection() 完成。
-
-::
-     A & B
-    {'d', 'c'}
-    
-     A.intersection(B)
-    {'d', 'c'}
-
-<button>Run ...</button>
-
-差集
------
-A 与 B 的差集是所有属于 A 且不属于 B 的元素构成的集合
-
-使用操作符 - 执行差集操作，同样地，也可使用方法 difference() 完成。
-
-::
-     A - B
-    {'b', 'a'}
-     
-     A.difference(B)
-    {'b', 'a'}
-
-<button>Run ...</button>
-
-对称差
------
-
-两个集合的对称差是只属于其中一个集合，而不属于另一个集合的元素组成的集合。
-
-使用 ^ 操作符执行差集操作，同样地，也可使用方法 symmetric_difference() 完成。
-
-::
-     A ^ B
-    {'b', 'e', 'f', 'a'}
-     
-     A.symmetric_difference(B)
-    {'b', 'e', 'f', 'a'}
-
-<button>Run ...</button>
-
-更改集合
------
-
-虽然集合不能有可变元素，但是集合本身是可变的。也就是说，可以添加或删除其中的元素。
-
-可以使用 add() 方法添加单个元素，使用 update() 方法添加多个元素，update() 可以使用元组、列表、字符串或其他集合作为参数。
-
-::
-     s = {'P', 'y'}
-     
-     s.add('t')  # 添加一个元素
-     s
-    {'P', 'y', 't'}
-     
-     s.update(['h', 'o', 'n'])  # 添加多个元素
-    s
-    {'y', 'o', 'n', 't', 'P', 'h'}
-     
-     s.update(['H', 'e'], {'l', 'l', 'o'})  # 添加列表和集合
-     s
-    {'H', 'y', 'e', 'o', 'n', 't', 'l', 'P', 'h'}
-
-<button>Run ...</button>
-
-在所有情况下，元素都不会重复。
-
-从集合中删除元素
------
-
-可以使用 discard() 和 remove() 方法删除集合中特定的元素。
-
-两者之间唯一的区别在于：如果集合中不存在指定的元素，使用 discard() 保持不变；但在这种情况下，remove() 会引发 KeyError。
-
-::
-     s = {'P', 'y', 't', 'h', 'o', 'n'}
-     
-     s.discard('t')  # 去掉一个存在的元素
-     s
-    {'y', 'o', 'n', 'P', 'h'}
-     s.remove('h')  # 删除一个存在的元素
-     s
-    {'y', 'o', 'n', 'P'}
-     
-     s.discard('w')  # 去掉一个不存在的元素（正常）
-     s
-    {'y', 'o', 'n', 'P'}
-        
-     s.remove('w')  # 删除一个不存在的元素（引发错误）
-    ...
-    KeyError: 'w'
-
-<button>Run ...</button>
-
-类似地，可以使用 pop() 方法删除和返回一个项目。
-
-还可以使用 clear() 删除集合中的所有元素。
-
-::
-     s = set('Python')
-     
-     s.pop()  # 随机返回一个元素
-    'y'
-     
-     s.clear()  # 清空集合
-     s
-    set()
-
-<button>Run ...</button>
-
-**注意：** 集合是无序的，所以无法确定哪个元素将被 pop，完全随机。
-
-集合的方法
------
-
-老规矩，利用 dir() 来查看方法列表：
-
-::
-     dir(set)
-    ['__and__', '__class__', '__contains__', '__delattr__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__gt__', '__hash__', '__iand__', '__init__', '__ior__', '__isub__', '__iter__', '__ixor__', '__le__', '__len__', '__lt__', '__ne__', '__new__', '__or__', '__rand__', '__reduce__', '__reduce_ex__', '__repr__', '__ror__', '__rsub__', '__rxor__', '__setattr__', '__sizeof__', '__str__', '__sub__', '__subclasshook__', '__xor__', 'add', 'clear', 'copy', 'difference', 'difference_update', 'discard', 'intersection', 'intersection_update', 'isdisjoint', 'issubset', 'issuperset', 'pop', 'remove', 'symmetric_difference', 'symmetric_difference_update', 'union', 'update']
-
-<button>Run ...</button>
-
-可以看到，有以下方法可用：
-
-::
-
-可以看到，有以下方法可用：
-+-----------------------------+--------------------------------------------------------------------------------------+
-| 方法	                      |                                描述                                                  | 
-+=============================+======================================================================================+
-|add()	                      |            将元素添加到集合中                                                        |
-+=============================+======================================================================================+
-clear()                       |	    删除集合中的所有元素                                                             |
-+=============================+======================================================================================+
-copy()	                      |             返回集合的浅拷贝                                                         |
-+=============================+======================================================================================+
-difference                    |          将两个或多个集合的差集作为一个新集合返回                                    |
-+=============================+======================================================================================+
-difference_update()	      |            从这个集合中删除另一个集合的所有元素                                      |
-+=============================+======================================================================================+
-discard()	              |         删除集合中的一个元素（如果元素不存在，则不执行任何操作）                     |
-+=============================+======================================================================================+
-intersection()	              |            将两个集合的交集作为一个新集合返回                                        |
-+=============================+======================================================================================+
-intersection_update()	      |              用自己和另一个的交集来更新这个集合                                      |
-+=============================+======================================================================================+
-isdisjoint()	              |             如果两个集合有一个空交集，返回 True                                      |
-+=============================+======================================================================================+
-issubset()	              |          如果另一个集合包含这个集合，返回 True                                       |
-+=============================+======================================================================================+
-issuperset()	              |         如果这个集合包含另一个集合，返回 True                                        |
-+=============================+======================================================================================+
-pop()	                      |          删除并返回任意的集合元素（如果集合为空，会引发 KeyError）                   |
-+=============================+======================================================================================+
-remove()	              |        删除集合中的一个元素（如果元素不存在，会引发 KeyError）                       |
-+=============================+======================================================================================+
-symmetric_difference()	      |             将两个集合的对称差作为一个新集合返回                                     |
-+=============================+======================================================================================+
-symmetric_difference_update() |	          用自己和另一个的对称差来更新这个集合                                       |
-+=============================+======================================================================================+
-union()	                      |           将集合的并集作为一个新集合返回                                             |        
-+=============================+======================================================================================+
-update()	              |           用自己和另一个的并集来更新这个集合                                         |
-+-----------------------------+--------------------------------------------------------------------------------------+
-
-其中一些方法在上述示例中已经被使用过了，如果有方法不会用，可利用 help() 函数，查看用途及详细说明。
-
-集合与内置函数
------
-下述内置函数通常作用于集合，来执行不同的任务。
-
-+-----------+----------------------------------------------------------------------------------------+
-函数        |描述                                                                                    |   
-+===========+========================================================================================+
-all()	    |如果集合中的所有元素都是 True（或者集合为空），则返回 True。                            | 
-+===========+========================================================================================+
-any()	    |如果集合中的所有元素都是 True，则返回 True；如果集合为空，则返回 False。                | 
-+===========+========================================================================================+
-enumerate() |	返回一个枚举对象，其中包含了集合中所有元素的索引和值（配对）。                       | 
-+===========+========================================================================================+
-len()	    |返回集合的长度（元素个数）                                                              | 
-+===========+========================================================================================+
-max()	    |返回集合中的最大项                                                                      | 
-+===========+========================================================================================+
-min()	    |返回集合中的最小项                                                                      | 
-+===========+========================================================================================+
-sorted()    |	从集合中的元素返回新的排序列表（不排序集合本身）                                     | 
-+===========+========================================================================================+
-sum()	    |返回集合的所有元素之和                                                                  |
-+-----------+----------------------------------------------------------------------------------------+
-
-不可变集合
------
-frozenset 是一个具有集合特征的新类，但是一旦分配，它里面的元素就不能更改。这一点和元组非常类似：元组是不可变的列表，frozenset 是不可变的集合。
-
-集合是 unhashable 的，因此不能用作字典的 key；而 frozensets 是 hashable 的，可以用作字典的 key。
-
-可以使用函数 frozenset() 创建 frozenset。
-
-::
-     s = frozenset('Python')
-     type(s)
-    <class 'frozenset'>
-
-<button>Run ...</button>
-
-frozenset 也提供了一些列方法，和 set 中的类似。
-::
-     dir(frozenset)
-    ['__and__', '__class__', '__contains__', '__delattr__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__gt__', '__hash__', '__init__', '__iter__', '__le__', '__len__', '__lt__', '__ne__', '__new__', '__or__', '__rand__', '__reduce__', '__reduce_ex__', '__repr__', '__ror__', '__rsub__', '__rxor__', '__setattr__', '__sizeof__', '__str__', '__sub__', '__subclasshook__', '__xor__', 'copy', 'difference', 'intersection', 'isdisjoint', 'issubset', 'issuperset', 'symmetric_difference', 'union']
-
-<button>Run ...</button>
-
-由于 frozenset 是不可变的，所以没有添加或删除元素的方法。
-
-
-作者 & 更新时间
-------------------------------------
-作者: `CSDN <http://blog.csdn.net/liang19890820>`_
diff --git a/docs/sources/python-base/cn/python-datatype-list.rst.txt b/docs/sources/python-base/cn/python-datatype-list.rst.txt
deleted file mode 100644
index 4f46390..0000000
--- a/docs/sources/python-base/cn/python-datatype-list.rst.txt
+++ /dev/null
@@ -1,530 +0,0 @@
-简述
-================
-
-
-Python 提供了一系列复合数据类型，通常被称为序列，列表（List）就是其中的一种。
-
-在 Python 中，列表是使用最频繁、用途最广泛的数据类型之一，非常灵活。
-
-
-列表
--------------
-
-
-列表是有序的元素序列，在 Python 中，列表由内置的 list 类型定义。
-
-要创建列表，需要将所有项（元素）放在方括号（[]）内，以逗号（,）分隔。
-
-::
-
-
-    l = ['P', 'y', 't', 'h', 'o', 'n']
-    type(l)
-
-<button>Run ...</button>
-
-
-列表可以有任意数量（n >= 0）的元素，当 n 为 0 时，表示一个空列表。
-
-列表中元素的类型可以不同，支持数字、字符串，甚至可以包含列表（所谓的嵌套）。
-
-::
-
-    l = []  # 空列表
- 
-    l = [1, 2, 3]  # 整形列表
-
-    l = [1, 5.5, 'Python']  # 混合类型列表
-
-    l = [6, ['Python'], ['P', 'y', 't', 'h', 'o', 'n']]  # 嵌套列表
-
-<button>Run ...</button>
-
-
-访问列表中的元素
-================
-
-
-
-列表索引
--------------
-
-
-和字符串类似，可以使用索引操作符（[]）访问列表中的一个元素。
-
-.. image:: http://img.blog.csdn.net/20170516213732766
-
-
-::
-
-    l = ['P', 'y', 't', 'h', 'o', 'n']
-
-    l[0]  # 第 1 个元素
- 
-    l[4]  # 第 5 个元素
-
-
-<button>Run ...</button>
-
-序列也可以反向索引，-1 表示最后一个元素，-2 表示倒数第二个元素，依此类推。
-
-::
-
-    l[-6]  # 第 1 个元素
-
-
-    l[-2]  # 第 5 个元素
-
-<button>Run ...</button>
-
-
-同样地，嵌套的列表可以使用嵌套索引来访问。
-
-::
-
-    l = [6, ['Python'], ['P', 'y', 't', 'h', 'o', 'n']]  # 嵌套列表
-
-    l[0]
-
-    l[2][3]  # 使用嵌套索引
-
-<button>Run ...</button>
-
-
-对列表进行切片
-------------------------
-
-
-可以使用切片操作符（:）来访问列表中的一系列元素。
-
-**PS**： 通过上图，可以很直观地看到切片。
-
-::
-
-    l = ['P', 'y', 't', 'h', 'o', 'n']
-
-    l[1:4]  #  第 2 个到第 4 个元素
-
-    l[1:]  # 第 2 个到最后一个元素（将索引默认设置为列表的开始或结束）
-
-    l[1:100]  # 索引太大，被截断至列表长度处
-
-    l[:]  # 所有元素（从开始到结束）
-
-
-<button>Run ...</button>
-
-
-
-和索引类似，切片操作也可以使用反向索引：
-
-::
-
-    l[-5:-2]  # 第 2 个到第 5 个元素
-
-    l[:-3]  # 开始到第 3 个元素
- 
-    l[-3:]  # 第 4 个到最后一个元素
-
-<button>Run ...</button>
-
-
-更改及添加元素
-----------------
-
-
-列表是可变的，也就是说，其中的元素可以被改变（不像字符串或元组）。
-
-可以使用赋值运算符（=）改变一个或一系列元素。
-
-::
-
-    l = ['P', 'y', 't', 'h', 'o', 'n']
-
-    l[0] = 'H'  # 更改第 1 个元素
-    l
-
-    l[1:4] = ['e', 'l', 'l']  # 更改第 2 个到第四个元素
-    l
-
-
-<button>Run ...</button>
-
-
-可以使用 append() 方法将一个元素添加到列表中，或者使用 extend() 方法添加多个元素。
-
-::
-
-    l = ['P', 'y']
- 
-    l.append('t')  # 添加一个元素
-    l
-
-    l.extend(['h', 'o', 'n'])  # 添加多个元素
-    l
-
-
-<button>Run ...</button>
-
-
-此外，可以使用 insert() 方法将一个元素插入到所需的位置。要插入多个元素，可以将其挤压进到一个列表的空切片中。
-
-::
-
-    l = ['P', 'n']
-
-    l.insert(1, 'y')  # 在指定索引处插入一个元素
-    l
-
-
-    l[2:2] = ['t', 'h', 'o']  # 插入多个元素
-    l
-
-<button>Run ...</button>
-
-
-从列表中删除元素
-----------------
-
-可以使用关键字 del，从列表中删除一个或多个元素，甚至可以完全删除列表。
-
-::
-
-    l = ['P', 'y', 't', 'h', 'o', 'n']
- 
-    del l[2]  # 删除第 3 个元素
-    l
-
- 
-    del l[1:3]  # 删除第 2 个到第 3 个元素
-    l
-
- 
-    del l  # 删除列表
-    l
-
-<button>Run ...</button>
-
-
-可以使用 remove() 方法删除指定的元素，或用 pop() 方法删除一个给定索引处的元素。
-
-如果没有提供索引，pop() 方法将删除并返回最后一个元素。这有助于我们将列表实现为栈（先进后出数据结构）。
-
-还可以使用 clear() 方法清空一个列表。
-
-::
-
-    l = ['P', 'y', 't', 'h', 'o', 'n']
- 
-    l.remove('h')  # 删除指定的元素
-    l
-
-
-    l.pop(2)  # 删除并返回第 3 个元素
-
-    l
-
- 
-    l.pop()  # 删除并返回最后一个元素
-
-    l
-
- 
-    l.clear()  # 清空列表
-    l
-
-<button>Run ...</button>
-
-
-最后，还可以通过将一个空列表分配给一个元素切片的方式来删除列表中的项目。
-
-::
-
-    l = ['P', 'y', 't', 'h', 'o', 'n']
- 
-    l[1:3] = []
-    l
- 
-    l[1:4] = []
-    l
-
-
-<button>Run ...</button>
-
-基本操作
-----------------
-
-
-在字符串一节中已经提过，所有的序列都有几种基本操作。列表是序列的一种，固然也适用。
-
-连接列表
-
-操作符 + 用于组合列表，也被称为连接。
-
-::
-
-
-    l1 = ['P', 'y']
-    l2 = ['t', 'h', 'o', 'n']
-
-    l1 + l2
-
-
-<button>Run ...</button>
-
-重复列表
-----------------
-
-
-操作符 * 会以给定次数重复一个列表。
-
-
-::
-
-    l = ['P', 'y', 't', 'h', 'o', 'n']
- 
-    l * 2  # 重复列表 2 次
-
-<button>Run ...</button>
-
-最小值/最大值
-----------------
-
-
-min() 和 max() 用于获取最小值和最大值。
-
-::
-
-
-    l = ['P', 'y', 't', 'h', 'o', 'n']
- 
-    min(l)  # 最小值
-
-    max(l)  # 最大值
-
-
-<button>Run ...</button>
-
-列表长度
-----------------
-
-
-要获取一个列表的长度，可以使用内置函数 len()。
-
-::
-
-    l = ['P', 'y', 't', 'h', 'o', 'n']
-
-    len(l)  # 元素个数
-
-<button>Run ...</button>
-
-成员测试
-----------------
-
-
-使用关键字 in，可以测试一个元素是否存在于列表中。
-
-::
-
-    l = ['P', 'y', 't', 'h', 'o', 'n']
-
-    't' in l
-
-    'o' not in l
-
-
-<button>Run ...</button>
-
-
-
-遍历列表
-----------------
-
-
-使用 for 循环，可以遍历列表中的每个元素。
-
-::
-
-
-    l = ['P', 'y', 't', 'h', 'o', 'n']
-    for letter in l:
-        ^4$print(letter)
-
-<button>Run ...</button>
-
-
-列表的方法
-------------
-
-列表中有许多方法，可以通过 dir() 来查看方法列表：
-
-
-::
-
-     dir(list)
-
-<button>Run ...</button>
-
-
-
-以双下划线开始和结尾的（例如：__add__）暂不考虑，后面讨论。剩下以下几个：
-
-+------------+------------------------------------------------------------------------------------------+
-| 方法       | 描述                                                                                     |
-+============+==========================================================================================+
-|append()    | 将元素添加到列表的末尾                                                                   |    
-+------------+------------------------------------------------------------------------------------------+
-| extend()   |将列表的所有元素添加到另一个列表                                                          |
-+------------+------------------------------------------------------------------------------------------+
-| insert()   |在定义的索引处插入一个元素                                                                |
-+------------+------------------------------------------------------------------------------------------+
-| remove()   |       从列表中删除一个元素                                                               |         
-+------------+------------------------------------------------------------------------------------------+
-| pop()      |从列表中删除所有元素                                                                      |
-+------------+------------------------------------------------------------------------------------------+
-|clear()     |断言，用于判断变量或者条件表达式的值是否为真                                              |
-+------------+------------------------------------------------------------------------------------------+
-|index()     |返回第一个匹配项的索引                                                                    |
-+------------+------------------------------------------------------------------------------------------+
-| count()    |统计某个元素在列表中出现的次数                                                            |
-+------------+------------------------------------------------------------------------------------------+
-| sort()     |      按升序排列列表中的元素                                                              |
-+------------+------------------------------------------------------------------------------------------+
-|reverse()   |	反转列表中元素的顺序                                                                    |
-+------------+------------------------------------------------------------------------------------------+
-|copy()	     |返回一个列表的浅拷贝                                                                      |
-+------------+------------------------------------------------------------------------------------------+
-
-
-按照惯例，在使用不熟悉的方法时，先看官方文档描述：
-
-
-::
-
-
-    help(list.append)
-
-    >>>Help on method_descriptor:
-
-    append(...)
-        L.append(object) -> None -- append object to end
-
-
-
-可以使用 list.method() 的方式来访问方法，上面已经列举了一些。
-
-::
-
-
-    l = ['P', 'y', 't', 'h', 'o', 'n']
- 
-    l.index('h')  # 返回第一个匹配 'h' 的索引
-
-
-    l.count('o')  # 统计 'o' 在列表中出现的次数
-
-
-    l.sort()  # 按升序排列列表中的元素
-    l
-
-
-    l.reverse()  # 反转列表中元素的顺序
-    l
-
-
-<button>Run ...</button>
-
-列表推导式
-----------------
-
-
-列表推导式（List Comprehension）是一种优雅、简洁的方式，通过现有列表来创建一个新列表。
-
-列表推导式由方括号标识，其中包含一个表达式，紧随其后的是 for 语句。
-
-假设，要创建一个正方形：
-
-::
-
-    squares = []
-    for x in range(10):
-        ^4$squares.append(x**2)
- 
-    square
-
-<button>Run ...</button>
-
-
-
-用列表推导式，可以获得相同的结果：
-
-::
-
-    squares = [x**2 for x in range(10)]
-
-<button>Run ...</button>
-
-这也相当于：squares = map(lambda x: x**2, range(10))，但它更简洁可读。
-
-列表推导式可以可选地包含更多的 for 或 if 语句，if 语句可以过滤出新列表的元素。
-
-::
-
-    squares = [x**2 for x in range(10) if x > 5]
-    squares
-
-<button>Run ...</button>
-
-::
-
-    odd = [x for x in range(10) if x % 2 == 1]
-    odd
-
-<button>Run ...</button>
-
-
-::
-
-    [x+y for x in ['I love ','I like '] for y in ['C++','Python']]
-    ['I love C++', 'I love Python', 'I like C++', 'I like Python']
-
-<button>Run ...</button>
-
-
-
-列表与内置函数
----------------
-
-
-下述内置函数通常与列表一起使用，来执行不同的任务。
-
-
-+------------+------------------------------------------------------------------------------------------+
-|函数        |描述                                                                                      |
-+============+==========================================================================================+
-|all()       |	如果列表中的所有元素都是 True（或者列表为空），则返回 True。                            |
-+------------+------------------------------------------------------------------------------------------+
-|any()	     |如果列表中的所有元素都是 True，则返回 True；如果列表为空，则返回 False。                  |
-+------------+------------------------------------------------------------------------------------------+
-|enumerate() |	返回一个枚举对象，其中包含了列表中所有元素的索引和值（配对）。                          |
-+------------+------------------------------------------------------------------------------------------+
-|len()	     |返回列表的长度（元素个数）。                                                              |
-+------------+------------------------------------------------------------------------------------------+
-|max()       |	返回列表的最大项                                                                        |
-+------------+------------------------------------------------------------------------------------------+
-|min()       |	返回列表的最小项                                                                        |
-+------------+------------------------------------------------------------------------------------------+
-|sorted()    |	返回一个排序的新列表（不排序列表本身）                                                  |
-+------------+------------------------------------------------------------------------------------------+
-|sum()       |	返回列表的所有元素之和                                                                  |
-+------------+------------------------------------------------------------------------------------------+
-|list()	     |将 iterable（元组、字符串、集合、字典）转换为列表                                         |
-+------------+------------------------------------------------------------------------------------------+
-
-
-作者 & 更新时间
-------------------------------------
-作者: `一去丶二三里 <http://blog.csdn.net/liang19890820>`_
-
-提交: 2017/12/6
diff --git a/docs/sources/python-base/cn/python-datatype-number.rst.txt b/docs/sources/python-base/cn/python-datatype-number.rst.txt
deleted file mode 100644
index fe84be4..0000000
--- a/docs/sources/python-base/cn/python-datatype-number.rst.txt
+++ /dev/null
@@ -1,309 +0,0 @@
-简述
-================
-
-
-在 Python 中，对于数字的定义很简单 - 整数、浮点数和复数，分别被定义为 int、float 和 complex 类。只要数学不是体育老师教的，绝大部分人学起来都不会有任何问题。
-
-对于数字而言，除了进行基本的运算（例如：算术运算、比较运算）之外，Python 还提供了类型间的相互转换功能，以及其他的高级操作。
-
-
-
-数字类型
----------
-
-
-在 Python 中，支持三种不同的数字类型：
-
-- int（整型）：也被称为整数，是正或负整数，不带小数点。
-- float（浮点型）：由整数部分与小数部分组成，浮点型也可以使用科学计数法表示。
-- complex（复数）：以 x + yj 的形式构成，其中 x 是实部，y 是虚部。
-
-**注意**： Py3.x 去除了 long 类型，现在只有一种整型 - int，表示为长整型。
-
-
-::
-
-    type(5)  # 整型
-    type(2.5)  # 浮点型
-
-    c = 2.5 + 5.0j  # 复数
-    type(c)
-
-
-<button>Run ...</button>
-
-
-整数溢出？
--------------
-
-
-天文数字：形容非常大的数字，已经无法用一个确切的数来形容。
-对于很多编程语言来说，天文数字是需要被正视的，因为往往会引发整数溢出问题。但是，在 Python 中无需忧虑，因为它支持“无限精度”的整数。
-
-来感受一下：
-
-::
-
-    i = 2 ** 500  # 2 的 500 次幂
-    i
-
-<button>Run ...</button>
-
-
-哇，多么幸运，Python 做出了如此精妙的安排。
-
-虽然整数可以是任意长度，但是浮点数只能精确到 15 位小数（16 位不准确）。
-
-::
-
-    f = 0.12345678901234567890  # 值被截断
-    f
-
-<button>Run ...</button>
-
-**注意**： f 的值被截断。
-
-数字系统
-------------------------
-
-
-对于绝大部分人来说，每天处理的数字都是十进制（以 10 为基数）数字系统。但是程序员（尤其是搞嵌入式的），往往需要使用二进制（以 2 为基数）、八进制（以 8 为基数）和十六进制（以 16 为基数）数字系统。
-
-在 Python 中，可以在数字前面放置相应的前缀来表示这些数字。
-
-+--------------------------------+-----------------------------------------------------------------------+
-| 数字系统                       | 前缀                                                                  |
-+================================+=======================================================================+
-|十进制（Decimal）               |                                                                       |    
-+--------------------------------+-----------------------------------------------------------------------+
-| 二进制（Binary）               |‘0b’ 或者 ‘0B’                                                     |
-+--------------------------------+-----------------------------------------------------------------------+
-| 八进制（Octal）                |‘0o’ 或者 ‘0O’                                                     |
-+--------------------------------+-----------------------------------------------------------------------+
-| 十六进制（Hexadecimal）        |       ‘0x’ 或者 ‘0X’                                              |         
-+--------------------------------+-----------------------------------------------------------------------+
-
-来看一些示例：
-
-::
-
-
-   5  # 十进制
-   0b1010  # 二进制
-   0o15  # 八进制
-   0xFB  # 十六进制
-
-
-
-十进制还可以转换为二进制、八进制、十六进制：
-
-::
-
-   dec = 20  # 十进制
-   bin(dec)  # 十进制转二进制
-   oct(dec)  # 十进制转八进制
-   hex(dec)  # 十进制转十六进制
-
-
-<button>Run ...</button>
-
-类型转换
-------------------------
-
-
-一般情况下，数据的类型的转换通常是由编译系统自动进行的，不需要人工干预，所以被称为隐式类型转换。但如果程序要求一定要将某一类型的数据转换为另外一种类型，则可以利用强制类型转换运算符进行转换，这种强制转换过程称为**显式类型转换**。
-
-在进行数字运算（例如：加法、减法）时，如果其中的一个操作数是浮点数，则会将整数隐式（自动）转换为浮点数。
-
-::
-
-   2 + 3.0
-
-
-<button>Run ...</button>
-
-可以看到，2（整数）被转换为 2.0（浮点数），计算结果（5.0）也是一个浮点数。
-
-除此之外，还可以使用 int()、float() 和 complex() 等内置函数来**显式转换**类型，这些函数甚至可以将字符串转换为数字。
-
-
-::
-
-    int(2.3)
-    int(-2.8)
-    float(5)
-    complex('3+5j')
-
-<button>Run ...</button>
-
-从 float 转换为 int 时，数字将被截断（整数更接近零）。
-
-Decimal
-
-----------------
-
-通常，在使用内置类 float 时，会执行一些“匪夷所思”的计算。
-
-我们知道 1/3 等于 0.333…（无限循环），0.1 + 0.2 等于 0.3，但 Python 似乎不这么认为。
-
-::
-
-   1/3
-   0.1 + 0.2
-
-
-<button>Run ...</button>
-
-What？难道是眼花了？和数学知识不一样？
-
-事实证明，浮点数在计算机硬件中以二进制小数来表示，因为计算机只能理解二进制（0 和 1）。出于这个原因，大部分十进制小数都不能准确地存储在计算机中。
-
-例如，十进制小数 0.1，将转换为无限长的二进制小数 0.000110011001100110011…，而计算机存储的位数是有限的。也就是说，转换为二进制后，只会接近十进制的 0.1，但永远不会相等。
-
-因此，**这是计算机硬件的局限性，而不是 Python 中的错误**。
-
-为了克服这个问题，可以使用 Python 自带的 decimal 模块。虽然浮点数的精度最高可达 15 位，但 decimal 模块可自定义精度。
-
-
-::
-
-
-    import decimal
-    0.1
-    decimal.Decimal(0.1)
-    decimal.getcontext()  # 当前上下文
-    decimal.getcontext().prec  # 精度默认 28 位
-
-    d = decimal.Decimal(1) / decimal.Decimal(9)
-    d
-    decimal.getcontext().prec = 3  # 将精度修改为 3 位
-
-    d = decimal.Decimal(1) / decimal.Decimal(9)
-    d
-
-<button>Run ...</button>
-
-除此之外，它也保留了重要意义。我们知道 25.50 比 25.5 更精确，因为 25.50 有两个有效的小数位。
-
-
-::
-
-    from decimal import Decimal as D
-    D('0.1') + D('0.2')
-    D('1.2') * D('2.50')
-
-
-
-<button>Run ...</button>
-
-**注意**： 计算结果末尾的 0
-
-有人可能会问：既然 Decimal 这么棒，为什么每次不使用 Decimal 来代替 float 呢？主要原因是效率，float 运算要比 Decimal 运算更快。
-
-何时使用 Decimal，而不是 float 呢？在以下情况下，通常使用 Decimal。
-
-- 当在做金融应用时，需要精确表示。
-- 当想要控制所需的精度级别时
-- 当想要实现有效的小数位概念时
-- 当我们想要像在学校里学到的那样进行小数运算时
-
-
-fractions（有理数）
---------------------
-
-fractions 模块提供了涉及分数的操作，该模块支持有理数运算。
-
-一个 Fraction 实例可以由一对整数、另一个有理数、或者一个字符串来构造。
-
-
-::
-
-
-   import fractions
-   fractions.Fraction(1.5)
-   Fraction(3, 2)
-
-   fractions.Fraction(10)
-   Fraction(10, 1)
-   fractions.Fraction(3, 5)
-   Fraction(3, 5)
-
-   print(fractions.Fraction(1.5))
-
-<button>Run ...</button>
-
-
-当从 float 创建 Fraction 时，可能会得到一些不寻常的结果，这是由于之前讨论的不完美的二进制浮点数造成的。
-
-幸运的是，fraction 也允许用字符串进行实例化，这是使用十进制数时的首选方案。
-
-::
-
-    from fractions import Fraction
-    print(Fraction(1.1))  # 浮点数 1.1
-    print(Fraction('1.1'))  # 字符串 '1.1'
-
-<button>Run ...</button>
-
-
-此外，该数据类型也支持所有基本操作。
-
-::
-
-    from fractions import Fraction as F
-
-    F(1,3) + F(1,3)
-    Fraction(2, 3)
-
-    1 / F(2,3)
-    Fraction(3, 2)
-
-    F(-2,5) < 0
-
-<button>Run ...</button>
-
-
-math（数学函数）
-----------------
-
-
-math 模块用于处理数学相关的运算，例如：三角学、对数等。
-
-::
-
-    import math
-    math.pi  # 圆周率 π
-    math.e  # 自然常数
-
-    math.cos(math.pi)  # 余弦
-    math.log10(100)  # 以 10 为基数的 100 的对数
-
-    math.pow(2, 5)  # 2**5（2 的 5 次方） 运算后的值
-    math.sqrt(9)  # 9 的平方根
-    math.factorial(5)  # 5!（5 的阶乘）
-
-<button>Run ...</button>
-
-random（生成伪随机数）
-----------------------
-
-
-random 模块用于生成伪随机数。随机数可用于数学、游戏、安全等领域，还经常被嵌入到算法中，用以提高算法效率，并提高程序的安全性。
-
-::
-
-    import random
-    l = ['p', 'y', 't', 'h', 'o', 'n']
-    random.choice(l)  # 从序列 l 中随机挑选一个元素
-    random.shuffle(l)  # 将序列 l 的所有元素随机排序
-    random.randrange(0, 10)  # 从指定范围内，按指定基数（缺省值为 1）递增的集合中获取一个随机数
-    random.random()  # 随机生成一个实数，范围 [0,1)。
-
-<button>Run ...</button>
-
-
-作者 & 更新时间
-------------------------------------
-作者: `一去丶二三里 <http://blog.csdn.net/liang19890820>`_
-
-提交: 2017/12/6
diff --git a/docs/sources/python-base/cn/python-datatype-tuple.rst.txt b/docs/sources/python-base/cn/python-datatype-tuple.rst.txt
deleted file mode 100644
index 7fee742..0000000
--- a/docs/sources/python-base/cn/python-datatype-tuple.rst.txt
+++ /dev/null
@@ -1,279 +0,0 @@
-简述
-================
-
-
-在 Python 中，元组（Tuple）与列表类似。两者之间的区别在于：元组不能更改，列表可以。也就是说，一旦被分配，就不能从元组中添加、更改或删除元素。
-
-
-元组的优点
--------------
-
-既然和列表类似，那元组的作用到底是什么呢？用列表代替不就可以了？
-
-与列表相比，元组有很多优点：
-
-- 通常将元组用于不同的数据类型，将列表用于相同（或相似）的数据类型。
-- 由于元组不可变，所以遍历元组比列表要快（较小的性能提升）。
-- 元组可以用作字典的 key，而列表不行。因为字典的 key 必须是不可变的，元组本身就是不可变的。
-- 如果数据不需要更改，将其作为元组来实现，可以确保“写保护”。
-- 元组可以被用在字符串格式化中。
-
-
-元组
--------
-
-
-在 Python 中，元组由内置的 tuple 类型定义。
-
-要创建元组，需要将所有项（元素）放在括号（()）内，以逗号（,）分隔。
-
-**注意**： 括号是可选的，但还是建议写上。
-
-::
-
-
-    tup = ('P', 'y', 't', 'h', 'o', 'n')
-    type(tup)
-    <class 'tuple'>
-
-<button>Run ...</button>
-
-
-元组中的元素可以是任意数量，还可以是不同类型（例如：数字、字符串、列表等）。
-
-::
-
-    tup = ()  # 空元组
- 
-    tup = ('P', 'y', 't', 'h', 'o', 'n')   # 字符串类型元组
-
-    tup = (1, 5.5, 'Python')   # 混合类型元组
-
-    tup = (6, 'Python', ('P', 'y', 't', 'h', 'o', 'n'))  # 嵌套元组
-
-    tup = 1, 5.5, 'Python'   # 创建元组时可以没有括号，也称为元组包装
-    tup
-    (1, 5.5, 'Python')
- 
-    a, b, c = tup  # 元组解包
-    a
-    1
-    b
-    5.5
-    c
-   'Python'
-
-<button>Run ...</button>
-
-
-构造包含一个元素的元组比较特殊，括号内只包含一个元素是不够的，还需要在元素后添加一个逗号。
-
-::
-
-    tup = ('Python')  # 只有括号
-    type(tup)
-    <class 'str'>
- 
-    tup = ('Python',)  # 在元素后面添加逗号
-    type(tup)
-    <class 'tuple'>
-
-<button>Run ...</button>
-
-可以看出，没有逗号就不是元组，加了逗号才是。
-
-索引和切片
------------
-
-有了前面关于字符串和列表的基础知识，元组很容易掌握。因为它们都属于序列，因此，基本操作是一样的。
-
-可以使用索引操作符（[]） 来访问元组中的一个元素，使用切片操作符（:）来访问元组中的一系列元素。
-
-.. image:: http://img.blog.csdn.net/20170517202258988?watermark/2/text/aHR0cDovL2Jsb2cuY3Nkbi5uZXQvbGlhbmcxOTg5MDgyMA==/font/5a6L5L2T/fontsize/400/fill/I0JBQkFCMA==/dissolve/70/gravity/SouthEast
-
-::
-
-
-    tup = ('P', 'y', 't', 'h', 'o', 'n')
-
-    tup[0]  # 第 1 个元素
-    'P'
-
-    tup[-2]  # 第 5 个元素（反向索引）
-    'o'
-
-   tup = (6, 'Python', ('P', 'y', 't', 'h', 'o', 'n'))  # 嵌套元组
- 
-   tup[2][3]  # 嵌套索引
-   'h'
-
-   tup = ('P', 'y', 't', 'h', 'o', 'n')
-
-   tup[1:4]  #  第 2 个到第 4 个元素
-   ('y', 't', 'h')
- 
-   tup[:-3]  # 开始到第 3 个元素
-   ('P', 'y', 't')
-
-<button>Run ...</button>
-
-
-
-其他的一些基本操作，例如：连接（+）、重复（*）、成员测试（in）、遍历（for）等，就不再一一展示了，请参考：Python 列表
-
-更改元组
-
-元组是不可变的，也就是说，元组中的元素在被赋值后不能改变。
-
-但是，如果元素本身是一个可变数据类型的列表，那么其嵌套项可以被改变。
-
-::
-
-    tup = (6, 'Python', ['P', 'y', 't', 'h', 'o', 'n'])
- 
-    tup[0] = 8  # 不能改变元素
-
-
-   tup[2][3] = 's'  # 可变的元素可以被改变
-   tup
-   (6, 'Python', ['P', 'y', 't', 's', 'o', 'n'])
-
-<button>Run ...</button>
-
-删除元组
----------
-
-如上所述，不能更改元组中的元素，这也意味着无法删除元组中的元素。
-
-但是，使用关键字 del 可以删除整个元组。
-
-
-::
-
-
-    tup = ('P', 'y', 't', 'h', 'o', 'n')
-
-    del tup[2]  # 无法删除元素
-
-
-    del tup  # 可以删除整个元组
-    tup
-
-<button>Run ...</button>
-
-
-列表和元组互转
-----------------
-
-列表和元组之间可以进行相互转换，分别使用 list() 和 tuple() 实现：
-
-::
-
-    tup = ('P', 'y', 't', 'h', 'o', 'n')
-    listx = list(tup)
-    listx
-    ['P', 'y', 't', 'h', 'o', 'n']
- 
-    l = ['H', 'e', 'l', 'l', 'o']
-    tupx = tuple(l)
-    tupx
-   ('H', 'e', 'l', 'l', 'o')
-
-<button>Run ...</button>
-
-
-既然可以互转，那么要改变元组，可以先将其转化为列表，对列表进行更改，然后再将列表转换为元组。
-
-例如，删除元组中的元素：
-
-::
-
-    tup = ('P', 'y', 't', 'h', 'o', 'n')
-    listx = list(tup)  # 将元组转换为列表
-    listx.remove('h')  # 删除列表中的元素
-    tup = tuple(listx)  # 再将列表转换为元组
-    tup
-   ('P', 'y', 't', 'o', 'n')
-
-<button>Run ...</button>
-
-**注意**： 元组本身是不可变的。这里说的并不是传统意义的改变，相当于元组的重新赋值。
-
-
-元组的方法
------------
-
-元组中的方法相对较少，可以通过 dir() 来查看方法列表：
-
-::
-
-     dir(tuple)
-     ['__add__', '__class__', '__contains__', '__delattr__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__getitem__',      '__getnewargs__', '__gt__', '__hash__', '__init__', '__iter__', '__le__', '__len__', '__lt__', '__mul__', '__ne__', '__new__', '__reduce__', '__reduce_ex__',      '__repr__', '__rmul__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', 'count', 'index']
-
-
-<button>Run ...</button>
-
-
-可以看到，有两个可用的方法：：
-
-+------------+------------------------------------------------------------------------------------------+
-| 方法       | 描述                                                                                     |
-+============+==========================================================================================+
-|index()     | 返回第一个匹配项的索引                                                                   |    
-+------------+------------------------------------------------------------------------------------------+
-| count()    |统计某个元素在列表中出现的次数                                                            |
-+------------+------------------------------------------------------------------------------------------+
-
-
-使用很简单，也非常好记。
-
-::
-
-    tup = ('P', 'y', 't', 'h', 'o', 'n')
- 
-    tup.index('h')  # 返回第一个匹配 'h' 的索引
-
- 
-    tup.count('o')  # 统计 'o' 在元组中出现的次数
-
-<button>Run ...</button>
-
-
-
-**注意**： 元组不可变，所以添加或删除元素的方法不适用于元组。
-
-元组与内置函数
---------------
-
-
-下述内置函数通常与元组一起使用，来执行不同的任务。
-
-+------------+------------------------------------------------------------------------------------------+
-|函数	       |描述                                                                                      |      
-+============+==========================================================================================+
-|all()       |	如果元组中的所有元素都是 True（或者元组为空），则返回 True。                            |
-+------------+------------------------------------------------------------------------------------------+
-|any()	     |如果元组中的所有元素都是 True，则返回 True；如果元组为空，则返回 False。                  |
-+------------+------------------------------------------------------------------------------------------+
-|enumerate() |	返回一个枚举对象，其中包含了元组中所有元素的索引和值（配对）。                          |
-+------------+------------------------------------------------------------------------------------------+
-|len()	     |返回元组的长度（元素个数）                                                                |
-+------------+------------------------------------------------------------------------------------------+
-|max()	     |返回元组中的最大项                                                                        |
-+------------+------------------------------------------------------------------------------------------+
-|min()	     |返回元组中的最小项                                                                        |
-+------------+------------------------------------------------------------------------------------------+
-|sorted()    |	返回一个新的排序元组（不排序元组本身）                                                  |
-+------------+------------------------------------------------------------------------------------------+
-|sum()	     |返回元组的所有元素之和                                                                    |
-+------------+------------------------------------------------------------------------------------------+
-|tuple()     |	将 iterable（字符串、列表、集合、字典）转换为元组                                       |
-+------------+------------------------------------------------------------------------------------------+
-
-
-作者 & 更新时间
-------------------------------------
-作者: `CSDN <http://blog.csdn.net/liang19890820>`_
-
-提交: 2017/12/6
-
diff --git a/docs/sources/python-base/index.rst.txt b/docs/sources/python-base/index.rst.txt
deleted file mode 100644
index 8f0d55c..0000000
--- a/docs/sources/python-base/index.rst.txt
+++ /dev/null
@@ -1,19 +0,0 @@
-Python Basics
-=======================
-The courses are still in the planning. Welcome to reward us. 
-
-1. **Primary course**
-
-If the total number of people who reward us is more than 100, we promise to complete the primary course within 1 week, so that, after finishing your study, you can master the basic concepts and write simple scripts.
-
-2. **Intermediate course**
-
-If the total number of people who reward us is more than 500, we will launch the intermediate course within 2 weeks, so that, after finishing your study, you will be familiar with the knowledge and framework of the course and apply it to practical development.
-
-3. **Advanced course**
-
-If the total number of people who reward us is more than 2,000, we will launch the advanced course in a month, so that, after finishing your study, you will have a better understanding of the principles of the framework and be able to do advanced development.
-
-
-**The primary course is free to all, and the intermediate and advanced courses are only open to those who have rewarded us.**
-
diff --git a/docs/sources/qpy101/index.rst.txt b/docs/sources/qpy101/index.rst.txt
deleted file mode 100644
index c33a08b..0000000
--- a/docs/sources/qpy101/index.rst.txt
+++ /dev/null
@@ -1,82 +0,0 @@
-QPython 101
-=========================================
-
-    QPython 系列入门教程
-
-用短视频形式, 引导学习者快速进入编程状态, 可以利用 QPython 移动编程环境进行各种实用软件的开发和探索.
-
-
-
-
-
-
-
-内容规划
------------------------------------------
-
-:发布状态:
-    - <+ 完成并发布
-    - <- 完成未发布
-    - <| 筹备完成中
-    - <? 规划未决定
-
-
-
-====  ============================  ===============  =================
-状态              编号                   幻灯              视频
-====  ============================  ===============  =================
-<+    起源综述                      和River对谈      视频:|QR2QPyC100|
-<+    101:语法/关键字               幻灯:`QPyC101`_  视频:|QR2QPyC101|
-<+    102:语法/数据类型,运算符      幻灯:`QPyC102`_  视频:|QR2QPyC102|
-<+    103:语法/变量,集合            幻灯:`QPyC103`_  视频:|QR2QPyC103|
-<-    104:语法/流程控制,if..else    幻灯:`QPyC104`_  ...
-<-    105:语法/流程控制,for..in     幻灯:`QPyC105`_  ...
-<-    106:语法/流程控制,while       幻灯:`QPyC106`_  ...
-<-    107:语法/流程控制,try:except  幻灯:`QPyC107`_  ...
-<-    108:语法/函式,创建            幻灯:`QPyC108`_  ...
-<|    109:语法/函式,可变参数        TBD              ...
-<?    110:语法/函式,作用域          TBD              ...
-<?    111:语法/函式,匿名函式        TBD              ...
-<?    112:语法/函式,高阶函式        TBD              ...
-<|    113:游戏/猜数字,伪代码        TBD              ...
-<|    114:游戏/猜数字,接受输入      TBD              ...
-<|    115:游戏/猜数字,随机数字      TBD              ...
-<|    116:游戏/猜数字,整体可用      TBD              ...
-====  ============================  ===============  =================
-
-
-
-PS:
------------------------------------------
-
-官方视频号是 **QPYIO**
-
-|QPyIOQA|
-
-已经开通客服, 可以随时通过微信和我们交流.
-
-
-
-.. |QPyIOQA| image:: https://ipic.zoomquiet.top/2022-07-02-QPyIOQA.jpeg
-             :width: 360px
-
-.. |QR2QPyC100| image:: https://ipic.zoomquiet.top/2022-07-02-QPyC100.jpg
-             :width: 200px
-
-.. _QPyC101: https://slides.101.camp/QPyCamp101
-.. |QR2QPyC101| image:: https://ipic.zoomquiet.top/2022-07-02-QPyC101.jpg
-             :width: 200px
-
-.. _QPyC102: https://slides.101.camp/QPyCamp102
-.. |QR2QPyC102| image:: https://ipic.zoomquiet.top/2022-07-02-QPyC102.jpg
-             :width: 200px
-
-.. _QPyC103: https://slides.101.camp/QPyCamp103
-.. |QR2QPyC103| image:: https://ipic.zoomquiet.top/2022-07-02-QPyC103.jpg
-             :width: 200px
-
-.. _QPyC104: https://slides.101.camp/QPyCamp104
-.. _QPyC105: https://slides.101.camp/QPyCamp105
-.. _QPyC106: https://slides.101.camp/QPyCamp106
-.. _QPyC107: https://slides.101.camp/QPyCamp107
-.. _QPyC108: https://slides.101.camp/QPyCamp108
diff --git a/docs/sources/qpython-quick-start/cn/community-howto.rst.txt b/docs/sources/qpython-quick-start/cn/community-howto.rst.txt
deleted file mode 100644
index f2eabf8..0000000
--- a/docs/sources/qpython-quick-start/cn/community-howto.rst.txt
+++ /dev/null
@@ -1,58 +0,0 @@
-社区指南
-================
-很多QPython用户喜欢在QPython各类社区里交流分享编程经验，你将有机会在这里遇见像你一样聪明的人，互相学习，获得成长。
-
-获得QPython的新消息和提示
---------------------------
-我们衷心希望你可以在微信和微博上关注我们，关注后可以及时获取QPython的新闻。
-QPython的微信会及时发布我们的最新消息。
-QPython的微博会分享一些QPython的小贴士，回答你的问题等。
-
-* 在微信搜索并关注我们: qpython
-
-* `在微博关注我们<http://weibo.com/qpython>`_
-
-
-和其他开发者一起聊聊QPython
-------------------------------------
-如果你想和其他人一起学习Python编程，可以考虑加入以下学习小组
-
-* `QPython 百度贴吧<https://tieba.baidu.com/f?kw=qpython>`_
-
-* 还可以在QQ中搜索QPython编程交流QQ群，群号：540717901
-
-* QPython还有几个微信交流群，请添加优趣小言微信quseitlab,留言qpython,它会把你加到QPython社区中
-
-第三社区
---------------
-这里有一些讨论QPython的帖子，像StackOverfolow或Reddit
-
-* `Stackoverflow上的QPython <http://stackoverflow.com/questions/tagged/qpython>`_
-* `Reddit上的QPython <https://www.reddit.com/search?q=qpython>`_
-
-
-贡献
--------------------
-如果你想快人一步获得新版本，并帮助测试，请加入QPython测试社区:
-
-* `加入QPython测试员社区 <https://plus.google.com/communities/111759148772865961493>`_
-
-
-在QPython里课程是非常重要的一个部分。如果您想为课程社区做出一些贡献，例如发布您自己的课程或帮助将当前课程翻译成其他语言，欢迎加入课程社区：
-
-* `加入QPython课程贡献社区 <https://plus.google.com/u/1/communities/111340957575273631204>`_
-
-
-如果您在使用QPython时遇到任何问题，请随时与我们联系，我们将尽快解决
-
-* `报告一个问题 <https://github.com/qpython-android/qpython/issues>`_
-* `报告QPython3的问题 <https://github.com/qpython-android/qpython3/issues>`_
-* `希望能支持一个库 <https://github.com/qpython-android/QPYPI/issues>`_
-* `直接给我们写信 <mailto:support@qpython.org>`_
-
-作者 & 更新时间
-------------------------------------
-作者: `River <https://github.com/riverfor>`_
-
-提交: 2017/8/6
-
diff --git a/docs/sources/qpython-quick-start/cn/editor-howto.rst.txt b/docs/sources/qpython-quick-start/cn/editor-howto.rst.txt
deleted file mode 100644
index 8cd7dfe..0000000
--- a/docs/sources/qpython-quick-start/cn/editor-howto.rst.txt
+++ /dev/null
@@ -1,81 +0,0 @@
-编辑器指南
-==============
-这是Python开发者最经常用的工具了 - 编辑器。
-
-.. image:: http://edu.qpython.org/static/editor-1.png
-    :alt: QPython - 编辑器
-
-
-特性
----------
-在QPython编辑器里你可以编写或运行你的QPython脚本程序或项目。 QPython编辑器支持Python语法高亮显示，并显示行号。
-
-编辑器顶部右上角分别是打开、新建、编辑器设置，分别让你打开脚本或项目，新建脚本或项目，以及设置编辑器属性（主题，默认字体等）。
-
-在文件比较大时，语法高亮功能在某些设备上会让打开大文件的速度变慢，因此当总行数低于某个限制（默认值为300）时，编辑器才会开启语法高亮，当然，您可以根据需要在辑器设置中对高亮行数数值进行调整。
-
-在编辑器底部，工具栏上的按钮分别对应切换到热键工具栏，锁定键盘，跳转，保存，运行，查找，撤销，重做，最近打开，代码片段。
-
-.. image:: http://edu.qpython.org/static/editor-toolbar-1.png
-    :alt: QPython - 编辑器开发工具栏
-
-
-切换到热键助手之后，工具栏的按钮分别为切换为开发工具栏, 反TAB, TAB，DEF(def), IF(if), EF(elif), EL(else), FOR(for), IMT(import), IMF(from import), CLZ(class), RET(return), @, :, =, ", ', ;, ,, +, -, *, /, <, >, (, ), [, ], {, }, #.
-
-.. image:: http://edu.qpython.org/static/editor-toolbar-2.png
-    :alt: QPython - 编辑器热键工具栏
-
-
-熟练使用工具栏能够大大加快你的开发。
-
-保存新文件时，不要忘记添加.py的后缀因为编辑器不会自动添加后缀。
-
-当你打开一个QPython项目时，你还可以划出侧栏，让你更容易编辑相同项目中的其他文件。
-
-.. image:: http://edu.qpython.org/static/editor-left.png
-    :alt: QPython - 项目结构树
-
-
-其他
--------
-在手机上高效输入是一个比较有挑战的事情，为了加快输入效率，QPython团队提供了各种辅助工具。
-
-**扫描二维码**
-在QPython首页，还有一个二维码扫描功能，能够方便地扫描二维码，将里面的内容读取到编辑器中。
-
-**QEditor WebApp**
-在QPYPI的工具集中，有一个QEditor WebApp程序，在安装和运行之后，它会在手机上开启一项WebApp服务，允许用户从相同局域网的电脑用户通过访问指定地址来打开一个在线的编辑器，可以通过电脑浏览器来直接在手机上进行编程。
-
-.. image:: http://edu.qpython.org/static/qeditor.png
-    :alt: QPython - QEditor
-
-.. image:: http://edu.qpython.org/static/qeditor-run.png
-    :alt: QPython - 启动QEditor
-
-*如上图启动后，你可以在电脑上输入http://192.168.1.191:10000即可在浏览器上进行开发*
-
-
-在编辑器中试试运行以下代码？
----------------------------
-
-..  code-block :: none
-
-    class TestC():
-        ^4$def ask_age(self):
-            ^8$v = raw_input("> How old are you ?")
-            ^8$return v
-
-    tc = TestC()
-    tc.ask_age()
-
-<button>Run ...</button>
-
-.. image:: http://edu.qpython.org/static/course-index-principle.png
-    :target: data-video: "http://player.youku.com/embed/XMzE0OTI4NDgyMA=="
-    :alt: QPython - 编辑器帮助
-
-作者 & 更新时间
-------------------------------------
-作者: `River <https://github.com/riverfor>`_
-
-提交: 2017/8/6
diff --git a/docs/sources/qpython-quick-start/cn/index.rst.txt b/docs/sources/qpython-quick-start/cn/index.rst.txt
deleted file mode 100644
index 8066366..0000000
--- a/docs/sources/qpython-quick-start/cn/index.rst.txt
+++ /dev/null
@@ -1,74 +0,0 @@
-QPython是什么
-================
-
-
-
-关于
------
-QPython (for Android)是安卓上的Python脚本引擎，它能让你在安卓设备上运行Python程序，它包含了Python解析器、控制台、编辑器。除了集成了基础的Python库之外，它还集成了支持WebApp开发的bottle库、支持调用安卓API的SL4A库等常见有用的库。
-
-针对iOS用户的QPython (for iOS)应用也已在appstore上线。
-
-QPython历史
--------------
-QPython诞生于2012年, 最初是其作者River为了实现能在安卓程序中实现热更新的一个Python插件SDK，后来作为安卓上的Python App独立发布，一经发布后便引起了广大Python开发者的喜爱，在很短的时间内，在没有任何推广的前提下，有数万的Python用户安装和评论，在15年，其安装使用用户量超过了2,000,000+，它也是目前安卓上最受欢迎的Python集成开发环境。
-
-
-支持的程序类型
-----------------
-目前QPython能够运行控制台程序、WebApp程序，后台应用程序，基于图形界面的应用程序等，各个类型的程序适合不同的应用场景，QPython通过特定的头部声明来确定如何运行应用程序，如"#qpy:webapp"表明该程序将会按照WebApp方式发布，"#qpy:kivy"表明该程序是一个kivy程序，"#qpy:qpyapp"声明该程序将会是QPYApp（适合于后台应用场景），而不带头部声明则默认会按照控制台的形式运行。
-
-
-*WebApp头部声明*
-::
-
-    #qpy:webapp
-
-
-*QPYApp头部声明*
-::
-
-    #qpy:qpyapp
-
-
-*Kivy程序头部声明*
-::
-
-    #qpy:kivy
-
-
-*控制台程序头部声明*
-::
-
-    #qpy:console
-
-
-如何安装第三方库
------------------
-QPython内置了PC版Python的绝大部分库。由于安卓里本身不支持编译工具链，因此你无法像PC上的Python一样能够安装所有的库，但是你可以通过QPYPI安装一些预编译的库，目前已经支持包括kivy, numpy, opencv, pycrypto, tornado 在内的库。此外你还可以用pip控制台从官方或者你指定的第三方QPYPI来安装大多数Python实现的库。
-
-
-QPython媒体、社区
---------------------
-QPython是一个更新迅速，注重倾听用户反馈的项目，包括facebook fan page, twitter, weibo, facebook群组，G+, Wechat/QQ group 等在内的媒体、社群是QPython的重要组成部分。我们鼓励你广泛参加到我们的社群讨论、活动以及QPython的建设当中，这样才能了解和掌握QPython最新的发展动态。
-
-
-QPython的社区版和商业版
-------------------------
-QPython分为社区开源版和商业版。社区版提供了基础的Python运行环境（会与商业版保持一致）,使用Apache开源协议，欢迎感兴趣的个人和团队在此基础上定制自己的业务，QPython开发团队同样会积极响应社区版用户的各种问题。
-QPython的商业版则致力于提供独特便利的功能和服务，包括在线教程，在线开发，代码分享等。
-
-无论是社区版或者商业版，我们希望都能让大家真正享受到在手机进行Python编程的快乐。
-
-
-
-.. image:: http://edu.qpython.org/static/codeanywhere.png
-    :target: data-video: "http://player.youku.com/embed/XMzAzMjE3NjI1Ng=="
-    :alt: QPython - 随时随地编程
-
-作者 & 更新时间
-------------------------------------
-作者: `River <https://github.com/riverfor>`_
-
-提交: 2017/8/6
-
diff --git a/docs/sources/qpython-quick-start/cn/principle.rst.txt b/docs/sources/qpython-quick-start/cn/principle.rst.txt
deleted file mode 100644
index 6246f18..0000000
--- a/docs/sources/qpython-quick-start/cn/principle.rst.txt
+++ /dev/null
@@ -1,31 +0,0 @@
-随时随地学习编程
-====================================
-
-QPython专注为Python用户提供方便易用的移动Python IDE工具和编程学习服务。
-
-
-支持Python2和Python3
----------------------
-QPython支持最新的Python2和Python3，满足用户对于不同Python版本的要求
-
-
-移动IDE和的学习社区
---------------------
-QPython的控制台，编辑器，开发快捷工具集（QRCode代码读取器、本地文件管理器）、QPYPI、课程系统、代码分享社区，QPY.IO在线开发服务，为Python用户提供便捷的掌上编程学习环境和交流社区。
-
-
-强大、开源
-----------
-除了可以在安卓上学习Python编程外，QPython还允许你编程控制你的安卓设备。
-此外开源版QPython方便你在APP开发中使用Python引擎。
-
-.. image:: http://edu.qpython.org/static/course-index-principle.png
-    :target: data-video: "http://player.youku.com/embed/XMzA1NzI1NTE5Ng=="
-    :alt: QPython - 专注的掌上开发环境和编程学习服务
-
-作者 & 更新时间
-------------------------------------
-作者: `River <https://github.com/riverfor>`_
-
-提交: 2017/8/7
-
diff --git a/docs/sources/qpython-quick-start/cn/qpypi-howto.rst.txt b/docs/sources/qpython-quick-start/cn/qpypi-howto.rst.txt
deleted file mode 100644
index 6a6ef7a..0000000
--- a/docs/sources/qpython-quick-start/cn/qpypi-howto.rst.txt
+++ /dev/null
@@ -1,52 +0,0 @@
-QPYPI指南
-=============
-第三方库可以快速扩展你的编程能力。 QPython主要通过QPYPI来管理第三方模块，你可以采用以下方法来更好地使用QPYPI。
-
-工具
-----
-工具栏目提供了常用的QPython工具，它能帮助你快速安装某些QPython上的应用（如SQLMap tool, Fabric tool等），为编程工作提供快捷助手（如Django manage script, QEditor WebApp, Scapy tool等)，并且我们还会陆续完善更多工具。
-
-.. image:: http://edu.qpython.org/static/qpypi-tools.png
-    :alt: QPYPI - 工具
-
-
-PIP控制台
-------------------------------------
-INSTALL WITH PYTHON'S PYPI
-
-.. image:: http://edu.qpython.org/static/qpypi-pip.png
-    :alt: QPYPI - PIP控制台
-
-
-通过该工具，你可以使用QPython内置的pip工具来安装很多Python库，它能帮你安装大部分由纯Python语言开发（包括依赖库）的Python库。
-
-
-注意：一些与c / c++文件混合的库不能通过pip控制台安装，这是由于安卓本身缺少开发编译环境支持，您可以向QPython团队寻求帮助。
-
-
-库
--------
-在库中，你可以安装一些由QPython团队整理和维护的Python的库。它们主要是由PIP控制台无法安装的Python库，或者由混合c/c++开发的但无法在QPython中编译的Python库，如kivy, Paramiko, PIL, Tornado等等。 你可以从这里方便地安装它们。
-
-.. image:: http://edu.qpython.org/static/qpypi-libraries.png
-    :alt: QPYPI - 库管理
-
-
-AIPY库
---------
-在AIPY库栏目中，QPython团队专门将与AI相关（包括神经网络、深度学习、数据统计和数学运算等等）的库整理进来，以便用户能够借助QPython在手机上进行AI相关编程学习。
-
-.. image:: http://edu.qpython.org/static/qpypi-aipy.png
-    :alt: QPYPI - AIPY
-
-
-手动管理
-------------
-如果通过QPYPI无法安装你希望安装的库，你可以将你所要的安装的库复制到设备的 /sdcard/qpython/lib/python2.7/site-packages 目录，这样你就可以在代码中使用 import <module> 的方式来让QPython加载对应的库。
-
-作者 & 更新时间
-------------------------------------
-作者: `River <https://github.com/riverfor>`_
-
-提交: 2017/11/6
-
diff --git a/docs/sources/qpython-quick-start/cn/terminal-howto.rst.txt b/docs/sources/qpython-quick-start/cn/terminal-howto.rst.txt
deleted file mode 100644
index e4201ac..0000000
--- a/docs/sources/qpython-quick-start/cn/terminal-howto.rst.txt
+++ /dev/null
@@ -1,32 +0,0 @@
-终端使用帮助
-=======================
-是的, 这是一个可以直接输入Python指令的终端。
-
-.. image:: http://edu.qpython.org/static/terminal.png
-    :alt: QPython - 终端
-
-
------------------------
-它与电脑上的Python终端无异, 大多数人通常使用它来探索对象的属性, 测试语法或实现他们的想法. 你可以直接输入命令, Python解释器会帮你运行它们. QPython的终端有以下便捷功能:
-
-* 点击右上角的加号按钮新建终端
-* 点击左上交“终端 ?"，在下拉列表切换不同的终端，也可以点"X"关闭终端
-* 在终端窗口长按，可以通过弹出菜单来完成类似选择文本，拷贝所有内容，粘贴，发送control键，发送fn键等复杂的操作
-
-.. image:: http://edu.qpython.org/static/terminal-menu.png
-    :alt: QPython - Terminal 
-
-
-注意
--------
-除非您已完全关闭终端, 否则在通知栏中会有通知. 您可以在任何界面通过点击通知回到已打开的终端.
-
-.. image:: http://edu.qpython.org/static/course-index-principle.png
-    :target: data-video: "https://v.qq.com/iframe/player.html?vid=j0569yn4knh"
-    :alt: QPython - 终端帮助
-
-作者 & 更新时间
-------------------------------------
-作者: `River <https://github.com/riverfor>`_
-
-提交: 2017/10/7
diff --git a/docs/sources/qpython-quick-start/community-howto.rst.txt b/docs/sources/qpython-quick-start/community-howto.rst.txt
deleted file mode 100644
index d7eee98..0000000
--- a/docs/sources/qpython-quick-start/community-howto.rst.txt
+++ /dev/null
@@ -1,56 +0,0 @@
-QPython Communities
-================
-Many QPython users like to exchange and share programming experience in QPython community, you have a great chance to meet smart and pythonic guys like you there, learn from each other and gain experience and growth.
-
-
-Get QPython news & tips
---------------------------
-We recommend you follow us on facebook and twitter to get QPython's news.
-QPython facebook page publishes official news and alerts.
-QPython twitter shares QPython tips and an active response to your question.
-
-* `Like us on facebook <https://www.facebook.com/QPython>`_
-
-* `Follow us on twitter <https://twitter.com/qpython>`_
-
-
-Talk about QPython with other guys
-------------------------------------
-If you want to learn Python programming with other guys, please, join our facebook group where many python users are there to help each other and the QPython google group maillist is a good way if you like communicate with email.
-
-* `Join Facebook community <https://www.facebook.com/groups/qpython>`_
-
-* `Join Google group <https://groups.google.com/forum/#!forum/qpython>`_
-
-Third community
---------------
-There are some communities which like to talk about QPython like, stackoverfolow and reddit
-
-* `QPython on Stackoverflow <http://stackoverflow.com/questions/tagged/qpython>`_
-* `QPython on Reddit <https://www.reddit.com/search?q=qpython>`_
-
-
-Contribute
--------------------
-
-If you want to checkout the latest version and want to help test beta, please join the Beta Tester community:
-
-* `Join QPython testers G+ community <https://plus.google.com/communities/111759148772865961493>`_
-
-The course is a very important part to QPython as it helps new users to learn more about QPython, If you want to contribute on course, like post your own course or help translate current courses to different languages, please join the course community:
-
-* `Join QPython course contributors G+ community <https://plus.google.com/u/1/communities/111340957575273631204>`_
-
-Feel free to contact us if you encounter any problem with QPython, we will try our best to solve it.
-
-* `Report QPython's issue <https://github.com/qpython-android/qpython/issues>`_
-* `Report QPython3's issue <https://github.com/qpython-android/qpython3/issues>`_
-* `I want QPython add a library <https://github.com/qpython-android/QPYPI/issues>`_
-* `Email us <mailto:support@qpython.org>`_
-
-Author & updated time
-------------------------------------------------------
-Author: `River <https://github.com/riverfor>`_
-
-Submitted: 2017/10/7
-Updated: 2018/04/20
diff --git a/docs/sources/qpython-quick-start/editor-howto.rst.txt b/docs/sources/qpython-quick-start/editor-howto.rst.txt
deleted file mode 100644
index dc14964..0000000
--- a/docs/sources/qpython-quick-start/editor-howto.rst.txt
+++ /dev/null
@@ -1,82 +0,0 @@
-Editor
-==============
-This is the most used tool by Python developers - editor.
-
-.. image:: http://edu.qpython.org/static/editor-main.png
-    :alt: QPython - editor
-
-
-
-Features
----------
-The editor allows you to edit or run your QPython script program or project. The QPython editor supports Python syntax highlighting and shows line numbers (there is no ability to go to the line by number).
-
-There are the open(folder icon) and new(+ icon), setting on the editor's right-top area, which allow you to open script or project, create script or project, and set the editor's behaviors(like theme, font etc.).
-
-Syntax highlight feature makes the editor slow down for a bit when opening a large file, so the editor's highlight is enable when the total lines are lower than the limit(default is 300), and you can adjust it to the amount you want in editor's setting page.
-
-
-In the editor's bottom, the icons are switched to hotkeys, lock keyboard, goto, save, run, find, undo, redo, recent open, snippets.
-
-.. image:: http://edu.qpython.org/static/editor-toolbar-1.png
-    :alt: QPython - editor's toolbar
-
-When you switch to the hot keys toolbar, the toolbar shows the following icons: switch to develop toolbar, untab, tab, TAB，DEF(def), IF(if), EF(elif), EL(else), FOR(for), IMT(import), IMF(from import), CLZ(class), RET(return), @, :, =, ", ', ;, ,, +, -, *, /, <, >, (, ), [, ], {, }, #.
-
-.. image:: http://edu.qpython.org/static/editor-toolbar-2.png
-    :alt: QPython - editor's hotkey toolbar
-
-
-Skilled using the toolbar can greatly accelerate your development
-
-When saving the script, don’t forget to add .py extension to the file name since the editor don’t do it for you.
-
-When you are opening a QPython project, you could swipe on the editor's left to unfold the project's tree drawer, which allow you edit other files in the same project easily.
-
-.. image:: http://edu.qpython.org/static/editor-left.png
-    :alt: QPython - project tree
-
-Other
--------
-It's a big challenge for mobile inputting, the QPython team offers many kinds of tools for improving development efficiency.
-
-**Scan**
-In QPython's dashboard, there is a QRcode scanner, which can scan the QRcode and read the code into editor.
-
-
-**QEditor WebApp**
-In QPYPI's tools, there is a QEditor WebApp. After installing and running, it starts a WebApp service, allow user edit files from the computer's browser in the same lAN by visiting some online editors.
-
-
-.. image:: http://edu.qpython.org/static/qeditor.png
-    :alt: QPython - QEditor
-
-.. image:: http://edu.qpython.org/static/qeditor-run.png
-    :alt: QPython - Start QEditor
-
-
-*As above shows, you could start to develop by pressing enter http://192.168.1.191:10000 on your computer browser*
-
-
-Give a try to run the code in editor ?
-------------------------------------------------
-
-..  code-block :: none
-
-    class TestC():
-        ^4$def ask_age(self):
-            ^8$v = raw_input("> How old are you ?")
-            ^8$return v
-
-    tc = TestC()
-    tc.ask_age()
-
-<button>Run ...</button>
-
-
-Author & updated time
-------------------------------------------------------
-Author: `River <https://github.com/riverfor>`_
-
-Submitted: 2017/10/7
-Updated: 2018/04/20
diff --git a/docs/sources/qpython-quick-start/in/principle.rst.txt b/docs/sources/qpython-quick-start/in/principle.rst.txt
deleted file mode 100644
index b56cd71..0000000
--- a/docs/sources/qpython-quick-start/in/principle.rst.txt
+++ /dev/null
@@ -1,29 +0,0 @@
-Dasar - dasar QPython
-======================
-
-Bab Penulis, Kontributor dan updated time
-------------------------------------------------------
-Bab Penulis: Muhammad Khoiruddin Harahap
-
-About
-----------
-QPython menyediakan Python IDE mobile yang mudah digunakan dan layanan pengembangan untuk pengguna Python.
-
-
-
-Supports Python2 dan Python3
--------------------------------------------------
-QPython mendukung program Python2 dan Python3 yang terbaru, memenuhi persyaratan pengguna versi Python yang berbeda.
-
-
-
-Mobile IDE dan Komunitas Pembelajaran
------------------------------------------------------------------
-QPython memiliki konsol, editor, toolkit pengembangan (QRCode Code reader, local file manager), QPYPI, kursus, komunitas untuk berbagi kode dan layanan pengembangan online QPYIO, menyediakan IDE Python versi mobile yang mudah digunakan untuk  komunitas pengguna Python.
-
-
-
-Powerful dan open source
------------------------------------------
-Selain belajar pemrograman Python di Android, QPython membantu mengendalikan perangkat android Anda dengan program.
-Versi open source QPython mudah bagi untuk menanamkan Python SDK ke dalam pengembangan aplikasi Anda.
diff --git a/docs/sources/qpython-quick-start/index.rst.txt b/docs/sources/qpython-quick-start/index.rst.txt
deleted file mode 100644
index 9f75be8..0000000
--- a/docs/sources/qpython-quick-start/index.rst.txt
+++ /dev/null
@@ -1,77 +0,0 @@
-What is QPython
-========================
-
-About
---------
-QPython(for Android) is a Python script engine for Android system, which runs Python programs on any Android device. It contains a Python interpreter, a console, and an editor. Besides a basic Python library, QPython also integrates a bottle library that supports WebApp development, and an SL4A library that supports invoking Android APIs.
-
-A QPython (iOS) application designed for iOS users is also released in Appstore.
-
-
-History of QPython
-------------------------------------------------------
-Born in 2012, QPython at first was a Python script SDK created by River for the rapid release of hotfix on Android programs, and was later independently released as a Python App on Android system. Ever since its release, QPython has been widely favored by Python developers, and received hundreds of thousands of installations and reviews in a very short time even in the circumstances of none market promotion. In 2015, the total installations exceeded more than 2,000,000. Until now, it is the most popular Python integrated development environment on Android.
-
-Supported program types
-------------------------------------------------------
-Currently, QPython can run console applications, WebApp applications, backend applications, graphic interface-based applications, and so on. Each type of program fits with a different application scenario, Python determines how to  run an application according to a unique head statement. For example, #qpy:webapp indicates that the program will be released in the WebApp manner, #qpy:kivy indicates that the program is a ivy program, #qpy:qpyapp indicates that the program is a QPYApp (fits with the backend application scenario), and a program without head statement will by default run in the console manner (for easy debugging).
-
-How to install a third party library
-------------------------------------------------------
-QPython is imbedded with most of the Python libraries for PCs. As Android system itself does not support compilation tool links, you cannot install all libraries like Python on PCs. However, you can install some pre-compiled libraries via QPYPI, which supports libraries including kivy, numpy, opencv, pycrypto, tornado and so on . In addition, you can use the pip client to install most libraries implemented by Python from the official website or from a third party Pypi designated by you.
-
-
-*WebApp header declaration*
-::
-
-    #qpy:webapp
-
-
-*QPYApp header declaration*
-::
-
-    #qpy:qpyapp
-
-
-*Kivy header declaration*
-::
-
-    #qpy:kivy
-
-*Console header declaration*
-::
-
-    #qpy:console
-
-
-
-
-QPython media and community
-------------------------------------------------------
-QPython is a rapidly updated program which pays attention to user feedbacks, including feedbacks from Facebook fan page, twitter, weibo, Facebook group, G+, WeChat/QQ group, and other medias. Social groups are a significant part of QPython. We welcome you to join in our social group discussion, activities, and QPython construction, so as to learn and understand the latest development state of QPython.
-
-
-Open source branch and business branch of QPython
-------------------------------------------------------
-QPython has two branches: the open source branch and the business branch. The open source branch provides a basic Python operating environment (which is consistent with the business branch), and uses the Apache license. We welcome any person or company of interest to customize your own service on this basis. Our QPython development team will also actively respond to any kinds of questions from users of the open source branch.
-The business branch of QPython, on the other hand, is dedicated to providing unique and convenient features and services, including online tutoring, online development, code sharing, etc.
-
-No matter which branch you are using, we hope everyone can really enjoy Python programing on cell phones.
-
-
-
-*Video: QPython - Code Anywhere，Study Anytime*
-
-
-.. image:: http://edu.qpython.org/static/codeanywhere.png
-    :target: data-video: "https://youtu.be/HqfUUiftrRw"
-    :alt: QPython - Code Anywhere，Study Anytime
-
-Author & updated time
-------------------------------------------------------
-Author: `River <https://github.com/riverfor>`_
-
-Translator: `TGJ <http://quseit.com/>`_
-
-Submitted: 2017/8/6
-Updated: 2018/04/25
diff --git a/docs/sources/qpython-quick-start/notebook.rst.txt b/docs/sources/qpython-quick-start/notebook.rst.txt
deleted file mode 100644
index ef8028c..0000000
--- a/docs/sources/qpython-quick-start/notebook.rst.txt
+++ /dev/null
@@ -1,74 +0,0 @@
-Share a live documents with QPyNotebook
-========================================
-
-Do you want an application which helps you create and share documents that contain live Python code, equations, visualizations and narrative text?
-
-Now, QPyNotebook for QPython is published, which is developed based on the great opensource project jupyter notebook, it will help you do the following jobs easily like data cleaning and transformation, numerical simulation, statistical modeling, data visualization, machine learning and much more.
-
-Before Use
------------
-**1. Make sure you have installed the newest QPython (>=2.2.0)**
-
-**2. Download QPyNotebook**
-
-- Click the QPyNotebook App button to download QPyNotebook, the button will take you to the Google Play App Store.
-
-- If you could not connect to Google you can download directly from our `Github page <https://github.com/qpython-android/notebook/releases/>`_ *(please check the "Trust unknown source" in system's setting if you want to install it with APK downloaded from github)*
-
-.. image:: http://edu.qpython.org/static/notebook-setting.png
-    :alt: Enable QPyNotebook in QPython Setting
-
-
-
-**3. Download related libraries**
-
-Click the QPyNotebook Libraries button and there'll be a terminal window to download those libraries. It may take a while, and after downloading is finished, the terminal window will be closed automatically.
-
-.. image:: http://edu.qpython.org/static/notebook-lib.png
-    :alt: Enable QPyNotebook in QPython Setting
-
-
-
-.. image:: http://edu.qpython.org/static/notebook-download.png
-    :alt: Download libraries
-
-
-**Now all set!**
-
-.. image:: http://edu.qpython.org/static/notebook-downloaded.png
-    :alt: all set
-
-
-How to use
------------------------
-Open the Explorer from QPython dashboard, find some *.ipynb files (For example notebooks/Welcome.ipynb) and open it, then you will start the QPyNotebook.
-
-- You could start the QPyNotebook from QPython's explorer*
-
-.. image:: http://edu.qpython.org/static/notebook-explorer.png
-    :alt: You can start QPyNotebook from QPython's explorer
-
-- Or from QPyNotebook App
-
-.. image:: http://edu.qpython.org/static/notebook-app.png
-    :alt: Start QPyNotebook
-
-*QPyNotebook's live documentation*
-
-.. image:: http://edu.qpython.org/static/notebook-page.png
-    :alt: Start to access Welcome.ipynb
-
-
-Feedback
------------------------
-Feel free to give us any feedback.
-
-* `@qpython <http://twitter.com/qpython>`_
-* `support@qpython.org <support@qpython.org>`_
-
-Join the community
-----------------------------
-
-* `QPython@G+ <https://plus.google.com/u/2/communities/111759148772865961493>`_
-* `QPython@Facebook <https://www.facebook.com/groups/qpython>`_
-
diff --git a/docs/sources/qpython-quick-start/principle.rst.txt b/docs/sources/qpython-quick-start/principle.rst.txt
deleted file mode 100644
index 9ef1015..0000000
--- a/docs/sources/qpython-quick-start/principle.rst.txt
+++ /dev/null
@@ -1,31 +0,0 @@
-Principle
-====================================
-
-
-About
---------
-QPython is focused on providing the convenient mobile Python IDE and learning service for Python users.
-
-
-Supports Python2 and Python3
-----------------------------
-QPython supports the newest Python2 and Python3, which satisfy users requirements for different Python versions.
-
-
-Mobile IDE and learning community
----------------------------------------
-QPython has a console, an editor, a develop toolkit(QRCode Code reader, local file manager), QPYPI, courses, a code share community and QPYIO online development service, which provide a convenient mobile Python IDE and learning community for Python users.
-
-
-Powerful and open source
-------------------------
-Besides learning Python programing on Android, QPython helps you control your android device with programs.
-The open source version of QPython is convenient for you to embed Python SDK into your app development.
-
-
-Author & updated time
-------------------------------------------------------
-Author: `River <https://github.com/riverfor>`_
-
-Submitted: 2017/8/7
-Updated: 2018/04/25
diff --git a/docs/sources/qpython-quick-start/qpypi-howto.rst.txt b/docs/sources/qpython-quick-start/qpypi-howto.rst.txt
deleted file mode 100644
index 8b3afc3..0000000
--- a/docs/sources/qpython-quick-start/qpypi-howto.rst.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-QPYPI howto
-=============
-Third party libraries could extend your programming abilities upto a greater extent, QPython manages libraries through QPYPI, There are some ways to use QPYPI as well.
-
-Tools
--------
-Tools category contains the useful tools for QPython, which could help you install QPython applications (Like SQLMap tool, Fabric tool and so on) easily, offer help for programming (Like django manage script, QEditor WebApp, Scapy tool and so on), and we will keep to improve more tools.
-
-.. image:: http://edu.qpython.org/static/qpypi-tools.png
-    :alt: QPYPI - Tools
-
-PIP Console
-------------------------------------
-INSTALL WITH PYTHON'S PYPI
-
-.. image:: http://edu.qpython.org/static/qpypi-pip.png
-    :alt: QPYPI - PIP Console
-
-
-By using QPython's built in pip console, you could install many Python libraries, it can help you install many pure-python libraries (including the dependencies).
-
-Notice: Some libraries mixed with c/c++ files can not be install through pip console for the android lacks the compiler environment, you could ask for help from qpython developer team.
-
-Libraries
-----------
-In libraries, you can manage some Python libraries which are directly maintained by QPython team. There are some libraries which you can not install with pip console, or that are written in c/c++ code that can not be compiled on android. Like kivy, paramiko, PIL, tornado etc. You can install them easily from here.
-
-.. image:: http://edu.qpython.org/static/qpypi-libraries.png
-    :alt: QPYPI - Libraries manage
-
-
-
-
-AIPY Libraries
-------------------
-In AIPY libraries, QPython team has added the AI related programming libraries (like neural network, deeplearnning, data analysis and mathematical operation etc.), which help users to start the AI program learning with QPython's help easliy.
-
-
-.. image:: http://edu.qpython.org/static/qpypi-aipy.png
-    :alt: QPYPI - AIPY
-
-
-
-Manage Manually
------------------
-Besides these ways above, you could copy libraries into the /sdcard/qpython/lib/python2.7/site-packages in your device to install them.
-
-
-Author & updated time
-------------------------------------------------------
-Author: `River <https://github.com/riverfor>`_
-
-Submitted: 2017/10/7
-Updated: 2018/04/25
diff --git a/docs/sources/qpython-quick-start/terminal-howto.rst.txt b/docs/sources/qpython-quick-start/terminal-howto.rst.txt
deleted file mode 100644
index 1c35e18..0000000
--- a/docs/sources/qpython-quick-start/terminal-howto.rst.txt
+++ /dev/null
@@ -1,31 +0,0 @@
-Terminal howto
-=================
-Yes, it’s regular Python terminal, feel free to comunicate with interpreter directly.
-
-.. image:: http://edu.qpython.org/static/terminal.png
-    :alt: QPython - Terminal
-
-Features
----------
-There is an ordinary Python terminal. Many people usually use it to explore objects’ properties, consult about syntax and test their ideas. You can type your commands directly and Python interpreter will execute them.
-
-QPython's terminal has the following features:
-
-* open additional terminals by tapping the plus button 
-* Switch terminal by click the drop-down list on the upper left corner, click the "X" to close the terminal
-* Long clicking on the terminal window, you could complete the complex operations like select text, copy all, paste, send control key, send fn key from the popup menu.
-
-.. image:: http://edu.qpython.org/static/terminal-menu.png
-    :alt: QPython - Terminal popup menu
-
-
-Note
-------
-There will be notification in the notification bar unless you explicitly close the terminal and you always can reach the open terminal by tapping the notification.
-
-Author & updated time
-------------------------------------------------------
-Author: `River <https://github.com/riverfor>`_
-
-Submitted: 2017/10/7
-Updated: 2018/04/25
diff --git a/docs/sources/qpython-webapp/cn/django-howto.rst.txt b/docs/sources/qpython-webapp/cn/django-howto.rst.txt
deleted file mode 100644
index 20f4742..0000000
--- a/docs/sources/qpython-webapp/cn/django-howto.rst.txt
+++ /dev/null
@@ -1,15 +0,0 @@
-
-.. image:: http://edu.qpython.org/static/djangoman-installdjango.png
-    :alt: djangoman.py
-
-*2 运行Django manage script，根据提示安装Django库*
-
-.. image:: http://edu.qpython.org/static/djangoman-createproject.png
-    :alt: djangoman.py
-
-*3 运行Django manage script，创建新项目*
-
-.. image:: http://edu.qpython.org/static/djangoman-startserver.png
-    :alt: djangoman.py
-
-*3 运行完syncdb, createapp之后，运行startserver，启动服务*
diff --git a/docs/sources/qpython-webapp/cn/index.rst.txt b/docs/sources/qpython-webapp/cn/index.rst.txt
deleted file mode 100644
index 166c18c..0000000
--- a/docs/sources/qpython-webapp/cn/index.rst.txt
+++ /dev/null
@@ -1,39 +0,0 @@
-QPython WebApp Principle
-====================================
-
-
-关于
---------
-QPython支持WebApp，能让所有的Web开发者马上开始快速移动开发。
-
-优势
-----------
-移动开发领域已经有了很多WebApp解决方案，比如cordova, phonegap等等，和它们对比，QPython的WebApp方案有下面的优势：
-
-*QPython是一个强大的Python引擎，能够处理许多逻辑事务，能有效地减少数据传输时间由此极大地提升应用的用户使用体验*
-
-*QPython支持大量的Python库，安装之后，你可以很方便地在项目中使用它们，由此大大节省开发时间*
-
-*使用QPython开发非常方便，你可以在掌上就完成开发任务，同时你也可以在PC上开发然后传输到移动设备上运行*
-
-QPython中的WebApp是如何工作的
-----------------------------
-QPython的WebApp包含以下几个模块： WebApp服务和Webview。WebApp服务在后台运行并且负责响应用户的输入， WebView是一个前端组件，它负责显示用户交互界面和负责响应用户的交互，比如界面展示，界面滚动，点击等等。
-
-**1** 当你从QPython运行一个WebApp程序时，QPython在后台启动WebApp服务，同时，QPython会在前端启动一个Webview去载入程序头部声明的WebApp入口链接。
-
-::
-
-#qpy:webapp:<webapp-title>
-#qpy://<webapp-address>:<webapp-port>/<webapp-path>
-
-**2** 然后用户能够开始用WEB的模式和应用进行交互，因此开发者能够用Web开发的模式去编写代码。
-
-**3** 当用户靠点击左上角的箭头退出应用时，程序将会请求 **http://<webapp-address><:webapp-port>/__exit** 这个URL来退出程序，开发能够重写其对应的逻辑函数 __exit 来定义退出行为。
-
-作者
-------------------------------------------------------
-作者: `River <https://github.com/riverfor>`_
-
-更新: 2017/10/16
-
diff --git a/docs/sources/qpython-webapp/cn/web-frameworks.rst.txt b/docs/sources/qpython-webapp/cn/web-frameworks.rst.txt
deleted file mode 100644
index 9cb2e6b..0000000
--- a/docs/sources/qpython-webapp/cn/web-frameworks.rst.txt
+++ /dev/null
@@ -1,133 +0,0 @@
-QPython中的WEB框架
-==========================
-使用WEB开发框架，开发者能够高效迅速地开发出WEB应用。同样，在QPython中，你能用下列WEB框架来快速开发WebApp。
-
-Bottle
-----------
-Bottle是QPython内置的WebApp开发框架，您无需额外安装，只需要遵循WebApp的规范，参考bottle开发的方法就可以进行。
-
-来看一个最简单的bottle的Hello world。
-
-
-::
-
-    #qpy:webapp:Hello Qpython
-    #qpy://127.0.0.1:8080/
-    """
-    This is a sample for qpython webapp
-    """
-    from bottle import Bottle, ServerAdapter
-    from bottle import run, debug, route, error, static_file, template
-
-    ######### QPYTHON WEB SERVER ###############
-    class MyWSGIRefServer(ServerAdapter):
-        ^4$server = None
-        ^4$def run(self, handler):
-            ^8$from wsgiref.simple_server import make_server, WSGIRequestHandler
-            ^8$if self.quiet:
-                ^12$class QuietHandler(WSGIRequestHandler):
-                    ^12$def log_request(*args, **kw): pass
-                ^8$self.options['handler_class'] = QuietHandler
-            ^4$self.server = make_server(self.host, self.port, handler, **self.options)
-            ^4$self.server.serve_forever()
-
-        ^4$def stop(self):
-            ^8$import threading
-            ^8$threading.Thread(target=self.server.shutdown).start()
-            ^8$self.server.server_close()
-
-    ######### BUILT-IN ROUTERS ###############
-    @route('/__exit', method=['GET','HEAD'])
-    def __exit():
-        ^4$global server
-        ^4$server.stop()
-
-    @route('/assets/<filepath:path>')
-    def server_static(filepath):
-        ^4$return static_file(filepath, root='/sdcard')
-
-
-    ######### WEBAPP ROUTERS WRITE YOUR CODE BELOW###############
-    @route('/')
-    def home():
-        ^4$return template('<h1>Hello {{name}} !</h1><a href="/assets/qpython/projects/WebAppSample/main.py">View source</a><br /><br /> <a href="http://edu.qpython.org/qpython-webapp/index.html">>> About QPython Web App</a>',name='QPython')
-
-    ######### WEBAPP ROUTERS ###############
-    app = Bottle()
-    app.route('/', method='GET')(home)
-    app.route('/__exit', method=['GET','HEAD'])(__exit)
-    app.route('/assets/<filepath:path>', method='GET')(server_static)
-
-    try:
-        ^4$server = MyWSGIRefServer(host="127.0.0.1", port="8080")
-        ^4$app.run(server=server,reloader=False)
-    except Exception,ex:
-        ^4$print "Exception: %s" % repr(ex)
-
-
-<button>Run ...</button>
-
-
-点击运行，将代码复制到编辑器中点击运行即可看见效果。
-
-
-Tornado
-----------
-QPython同样支持异步通信Web开发框架Tornado，您可以通过QPYPI来安装它。
-
-
-.. image:: http://edu.qpython.org/static/qpypi-tornado.png
-    :alt: tornado library in QPYPI
-
-*通过QPYPI来安装tornado库*
-
-安装完毕之后，点击以下代码底部的运行即可将代码拷贝到编辑器中，保存运行即可看到基于Tornado的WebApp示例效果。
-
-
-::
-
-    #qpy:webapp:Tornado Sample
-    #qpy://127.0.0.1:8080/
-
-    import tornado
-    import tornado.web
-
-    class MainHandler(tornado.web.RequestHandler):
-        ^4$def get(self):
-            ^8$self.write("Hello, world")
-
-    class ExitHandler(tornado.web.RequestHandler): 
-        ^4$def get(self):
-            ^8$import os,signal
-            ^8$os.kill(os.getpid(), signal.SIGKILL)
-
-    def make_app():
-        ^4$return tornado.web.Application([
-            ^8$(r"/", MainHandler),
-            ^8$(r"/__exit", ExitHandler),
-        ^4$])
-
-    if __name__ == "__main__":
-        ^4$app = make_app()
-        ^4$app.listen(8080)
-        ^4$tornado.ioloop.IOLoop.current().start()
-
-<button>Run ...</button>
-
-此外值得一提的是，在QPython的AIPY扩展插件中，matlibplot也是默认支持了用Tornado作为绘图展示支持后端。
-
-Django
-----------
-作为Python WEB开发的最常用框架，Django同样获得了QPython的支持, QPython提供了一个djangoman.py脚本来帮助您快速安装django库和初始化django项目，通过运行该工具，你可以快速创建或者管理django项目。
-
-.. image:: http://edu.qpython.org/static/djangoman.png
-    :alt: djangoman.py
-
-*可以通过Django manage script来管理Django项目*
-
-通过djangoman.py来完成项目的初始化之后，您就可以按照WebApp的规范来开发您的WebApp并在QPython上运行。
-
-
-其他
-------
-除了上述框架，诸如其他框架如Flask,cherrypy等也有可能被QPython支持，你若感兴趣可以通过QPYPI中的INSTALL FROM PYTHON'S PYPI来探索。
diff --git a/docs/sources/qpython-webapp/cn/your-first-webapp.rst.txt b/docs/sources/qpython-webapp/cn/your-first-webapp.rst.txt
deleted file mode 100644
index f187e1a..0000000
--- a/docs/sources/qpython-webapp/cn/your-first-webapp.rst.txt
+++ /dev/null
@@ -1,96 +0,0 @@
-QPython的WebApp
-==========================
-WebApp是一种Web开发者能够快速上手开发的应用，QPython的WebApp支持常见WebAApp的优势。此外，QPython让手机本身就能成为WebApp的运行容器，支持处理复杂的程序逻辑，因此能极大地提高WebApp使用体验，同时也增加了开发的灵活度。
-
-WebApp声明
------------
-在QPython中，通过头部声明来区分程序类型，当头部有以下定义时，我们知道它是一个WebApp程序。
-
-::
-
-    #qpy:webapp:<WebApp程序名称>
-    #qpy://<WebApp运行所在的IP，一般为127.0.0.1>:<WebApp运行所在的端口>/<初次载入时对应的WebApp Url路径>
-
-
-WebApp运行流程
---------------
-在程序被识别为WebApp运行后，QPython会让程序作为后台服务的方式启动，同时在前端会启动一个WebView组件，载入你在程序头部声明所对应的URL（由IP、端口、URL路径组合而成）
-
-如，如果IP为127.0.0.1，端口为8080，URL路径为hello/test，则WebView载入的URL为：http://127.0.0.1:8080/hello/test
-
-
-WebApp退出原理
---------------
-当你点了后退键退出WebView时，WebApp后台进程也需要退出，由于一开始QPython让其按照后台服务的方式启动，所以我们需要通知其退出，QPython是在WebView退出时，发出一个http://IP:端口/__exit 的GET请求到后台服务，由其处理结束后台进程，退出服务的工作，因此，作为一个完成的流程，您需要在自己的WebApp中定义/__exit 对应的退出服务操作，我们在结尾示例中附有示例。
-
-
-
-例子
---------
-作为QPython内嵌的Web开发库，来看一个最简单的bottle的Hello world
-
-
-::
-
-    #qpy:webapp:Hello Qpython
-    #qpy://127.0.0.1:8080/
-    """
-    This is a sample for qpython webapp
-    """
-    from bottle import Bottle, ServerAdapter
-    from bottle import run, debug, route, error, static_file, template
-
-    ######### QPYTHON WEB SERVER ###############
-    class MyWSGIRefServer(ServerAdapter):
-        ^4$server = None
-        ^4$def run(self, handler):
-            ^8$from wsgiref.simple_server import make_server, WSGIRequestHandler
-            ^8$if self.quiet:
-                ^12$class QuietHandler(WSGIRequestHandler):
-                    ^12$def log_request(*args, **kw): pass
-                ^8$self.options['handler_class'] = QuietHandler
-            ^4$self.server = make_server(self.host, self.port, handler, **self.options)
-            ^4$self.server.serve_forever()
-
-        ^4$def stop(self):
-            ^8$import threading
-            ^8$threading.Thread(target=self.server.shutdown).start()
-            ^8$self.server.server_close()
-
-    ######### BUILT-IN ROUTERS ###############
-    @route('/__exit', method=['GET','HEAD'])
-    def __exit():
-        ^4$global server
-        ^4$server.stop()
-
-    @route('/assets/<filepath:path>')
-    def server_static(filepath):
-        ^4$return static_file(filepath, root='/sdcard')
-
-
-    ######### WEBAPP ROUTERS WRITE YOUR CODE BELOW###############
-    @route('/')
-    def home():
-        ^4$return template('<h1>Hello {{name}} !</h1><a href="/assets/qpython/projects/WebAppSample/main.py">View source</a><br /><br /> <a href="http://edu.qpython.org/qpython-webapp/index.html">>> About QPython Web App</a>',name='QPython')
-
-    ######### WEBAPP ROUTERS ###############
-    app = Bottle()
-    app.route('/', method='GET')(home)
-    app.route('/__exit', method=['GET','HEAD'])(__exit)
-    app.route('/assets/<filepath:path>', method='GET')(server_static)
-
-    try:
-        ^4$server = MyWSGIRefServer(host="127.0.0.1", port="8080")
-        ^4$app.run(server=server,reloader=False)
-    except Exception,ex:
-        ^4$print "Exception: %s" % repr(ex)
-
-
-
-<button>Run ...</button>
-
-
-点击运行，将代码复制到编辑器中点击运行即可看见效果。
-
-当然，在QPython中，还提供了其他Web开发框架的支持来帮助你简化工作。
-
diff --git a/docs/sources/qpython-webapp/index.rst.txt b/docs/sources/qpython-webapp/index.rst.txt
deleted file mode 100644
index 267c733..0000000
--- a/docs/sources/qpython-webapp/index.rst.txt
+++ /dev/null
@@ -1,40 +0,0 @@
-QPython WebApp Principle
-====================================
-
-
-About
---------
-QPython supports WebApp, the goal is to enable all WEB developers to the fastest development of mobile applications.
-
-Advantages
-----------
-There are lots of mobile WebApp solutions, like cordova, phonegap etc, and compared to them, QPython's WebApp has the following advantages.
-
-*QPython is a powerful Python engine that can handle many logical jobs, effectively reducing data transfer time and enhancing your app's user experience*
-
-*QPython supports massive Python libraries, you could import them in your project, so that you could save much develop time.*
-
-*It is very convenient for development with QPython, you can complete your development just in palm. And you could develop from PC then transfer to your mobile to run*
-
-How does WebApp work in QPython
-----------------------------
-QPython WebApp contains the following components: WebApp service and Webview. The WebApp service runs in the background and is responsible for responding to user's input. WebView is a front component, which shows the user interface and and is responsible for responding to render user interactions, such as scrolling, click the button and so on.
-
-**1** When you run a WebApp program from QPython, QPython launch the WebApp service in the background, at the same time, QPython will start a webview which loads the entry link declared in the program header in the front.
-
-::
-
-#qpy:webapp:<webapp-title>
-#qpy://<webapp-address>:<webapp-port>/<webapp-path>
-
-**2** Then the user can start to interact with the applicaton with the web mode. So the developer could write the logic code with the web development way.
-
-**3** When the user exit by clicking the left arrow, the application will request the **http://<webapp-address><:webapp-port>/__exit** to exit the application
-The developer could rewrite the __exit function to defines the exit behaviors.
-
-Chapter Author, contributor and updated time
-------------------------------------------------------
-Author: `River <https://github.com/riverfor>`_
-
-Submitted: 2017/10/16
-
diff --git a/docs/sources/qpython-webapp/web-frameworks.rst.txt b/docs/sources/qpython-webapp/web-frameworks.rst.txt
deleted file mode 100644
index 3d643d6..0000000
--- a/docs/sources/qpython-webapp/web-frameworks.rst.txt
+++ /dev/null
@@ -1,132 +0,0 @@
-WEB framework in QPython
-==========================
-By using the WEB development framework, developers can efficiently and quickly develop Web applications. Similarly, in QPython, you can quickly develop WebApp with the following web frameworks.
-
-Bottle
-----------
-Bottle is a QPython built-in WebApp development framework, you do not need to install additional, just follow the WebApp's specifications, reference bottle development methods can be carried out.
-
-Look at one of the most simple Hello world with bottle
-
-::
-
-    #qpy:webapp:Hello Qpython
-    #qpy://127.0.0.1:8080/
-    """
-    This is a sample for qpython webapp
-    """
-    from bottle import Bottle, ServerAdapter
-    from bottle import run, debug, route, error, static_file, template
-
-    ######### QPYTHON WEB SERVER ###############
-    class MyWSGIRefServer(ServerAdapter):
-        ^4$server = None
-        ^4$def run(self, handler):
-            ^8$from wsgiref.simple_server import make_server, WSGIRequestHandler
-            ^8$if self.quiet:
-                ^12$class QuietHandler(WSGIRequestHandler):
-                    ^12$def log_request(*args, **kw): pass
-                ^8$self.options['handler_class'] = QuietHandler
-            ^4$self.server = make_server(self.host, self.port, handler, **self.options)
-            ^4$self.server.serve_forever()
-
-        ^4$def stop(self):
-            ^8$import threading
-            ^8$threading.Thread(target=self.server.shutdown).start()
-            ^8$self.server.server_close()
-
-    ######### BUILT-IN ROUTERS ###############
-    @route('/__exit', method=['GET','HEAD'])
-    def __exit():
-        ^4$global server
-        ^4$server.stop()
-
-    @route('/assets/<filepath:path>')
-    def server_static(filepath):
-        ^4$return static_file(filepath, root='/sdcard')
-
-
-    ######### WEBAPP ROUTERS WRITE YOUR CODE BELOW###############
-    @route('/')
-    def home():
-        ^4$return template('<h1>Hello {{name}} !</h1><a href="/assets/qpython/projects/WebAppSample/main.py">View source</a><br /><br /> <a href="http://edu.qpython.org/qpython-webapp/index.html">>> About QPython Web App</a>',name='QPython')
-
-    ######### WEBAPP ROUTERS ###############
-    app = Bottle()
-    app.route('/', method='GET')(home)
-    app.route('/__exit', method=['GET','HEAD'])(__exit)
-    app.route('/assets/<filepath:path>', method='GET')(server_static)
-
-    try:
-        ^4$server = MyWSGIRefServer(host="127.0.0.1", port="8080")
-        ^4$app.run(server=server,reloader=False)
-    except Exception,ex:
-        ^4$print "Exception: %s" % repr(ex)
-
-
-
-
-<button>Run ...</button>
-
-
-Click Run, copy the code to the editor and Run to check out the result.
-
-
-Tornado
-----------
-QPython also supports the asynchronous Communication Web Development Framework - Tornado, which you can install with QPYPI.
-
-.. image:: http://edu.qpython.org/static/qpypi-tornado.png
-    :alt: tornado library in QPYPI
-
-*To install tornado library by QPYPI*
-
-After the installation is complete, click on the bottom of the following code to run the code can be copied to the editor,save the run to check out Tornado-based WebApp sample effects.
-
-
-::
-
-    #qpy:webapp:Tornado Sample
-    #qpy://127.0.0.1:8080/
-
-    import tornado
-    import tornado.web
-
-    class MainHandler(tornado.web.RequestHandler):
-        ^4$def get(self):
-            ^8$self.write("Hello, world")
-
-    class ExitHandler(tornado.web.RequestHandler): 
-        ^4$def get(self):
-            ^8$import os,signal
-            ^8$os.kill(os.getpid(), signal.SIGKILL)
-
-    def make_app():
-        ^4$return tornado.web.Application([
-            ^8$(r"/", MainHandler),
-            ^8$(r"/__exit", ExitHandler),
-        ^4$])
-
-    if __name__ == "__main__":
-        ^4$app = make_app()
-        ^4$app.listen(8080)
-        ^4$tornado.ioloop.IOLoop.current().start()
-
-<button>Run ...</button>
-
-Also worth mentioning is that in QPython's AIPY extension,matlibplot also supports Tornado as the default drawing support backend by default.
-
-Django
-----------
-As the most commonly used framework for Python Web development, Django also received QPython support, QPython provides a djangoman.py scripts to help you quickly install and initialize django library project,By running this tool,You can quickly create or manage django projects.
-
-.. image:: http://edu.qpython.org/static/djangoman.png
-    :alt: djangoman.py
-
-*Django projects can be managed by Django manage script*
-
-After djangoman.py to complete the project's initialization,You can follow the WebApp's specifications to develop your WebApp and run it on QPython.
-
-Other
-------
-In addition to the above framework,Other frameworks such as Flask, Cherrypy, etc. are also likely to be supported by QPython,If you are interested, you can explore through INSTALL FROM PYTHON'S PYPI in QPYPI.
diff --git a/docs/sources/qpython-webapp/your-first-webapp.rst.txt b/docs/sources/qpython-webapp/your-first-webapp.rst.txt
deleted file mode 100644
index 0e7228b..0000000
--- a/docs/sources/qpython-webapp/your-first-webapp.rst.txt
+++ /dev/null
@@ -1,86 +0,0 @@
-QPython’s WebApp
-==========================
-WebApp is an app allows developers to quickly get started application development, QPython's WebApp has the advantage of supporting common WebApps.Moreover, QPython lets the phone itself become a WebApp's run container, support for handling complex program logic, therefore, it can greatly improve the WebApp user experience, while also increasing the flexibility of the development.WebApp statement
-
------------
-In QPython, through the header statement to distinguish between program types, When the header has the following definition, we know that it is a WebApp program.
-
-::
-
-    #qpy:webapp:<WebApp Program name>
-    #qpy://<WebApp runs on which the IP，generally for this 127.0.0.1>:<The port on which WebApp is running>/<Corresponding WebApp URL path when Initializing>
-
-
-WebApp's Run process
----------------------
-After the program is recognized as a WebApp run，QPython will let the program starts as a background service，At the same time in the front end will start a WebView component，Load the URL you specified in the program header(By the IP, port, URL path combination)For example, If the IP is 127.0.0.1, the port is 8080 and the URL path is hello / test, then the URL that WebView loads is：http://127.0.0.1:8080/hello/test Web application exit principle
-
-Exit the WebApp
------------------
-When you click the Back button to exit WebView, the WebApp daemon also needs to quit. Since QPython started it as a background service in the first place, we need to tell it to quit,QPython is when WebView exits,Issue a http://IP:port/__exit GET request to the background service, By its end of background process, exit the service of work, Therefore, as a completed process, you need to define it in your own WebApp/__exit The corresponding exit service operation, we have an example in the end.
-
-E.g
---------
-As QPython embedded Web development library, Let's look at one of the easiest bottles - Hello world
-
-::
-
-    #qpy:webapp:Hello Qpython
-    #qpy://127.0.0.1:8080/
-    """
-    This is a sample for qpython webapp
-    """
-    from bottle import Bottle, ServerAdapter
-    from bottle import run, debug, route, error, static_file, template
-
-    ######### QPYTHON WEB SERVER ###############
-    class MyWSGIRefServer(ServerAdapter):
-        ^4$server = None
-        ^4$def run(self, handler):
-            ^8$from wsgiref.simple_server import make_server, WSGIRequestHandler
-            ^8$if self.quiet:
-                ^12$class QuietHandler(WSGIRequestHandler):
-                    ^12$def log_request(*args, **kw): pass
-                ^8$self.options['handler_class'] = QuietHandler
-            ^4$self.server = make_server(self.host, self.port, handler, **self.options)
-            ^4$self.server.serve_forever()
-
-        ^4$def stop(self):
-            ^8$import threading
-            ^8$threading.Thread(target=self.server.shutdown).start()
-            ^8$self.server.server_close()
-
-    ######### BUILT-IN ROUTERS ###############
-    @route('/__exit', method=['GET','HEAD'])
-    def __exit():
-        ^4$global server
-        ^4$server.stop()
-
-    @route('/assets/<filepath:path>')
-    def server_static(filepath):
-        ^4$return static_file(filepath, root='/sdcard')
-
-
-    ######### WEBAPP ROUTERS WRITE YOUR CODE BELOW###############
-    @route('/')
-    def home():
-        ^4$return template('<h1>Hello {{name}} !</h1><a href="/assets/qpython/projects/WebAppSample/main.py">View source</a><br /><br /> <a href="http://edu.qpython.org/qpython-webapp/index.html">>> About QPython Web App</a>',name='QPython')
-
-    ######### WEBAPP ROUTERS ###############
-    app = Bottle()
-    app.route('/', method='GET')(home)
-    app.route('/__exit', method=['GET','HEAD'])(__exit)
-    app.route('/assets/<filepath:path>', method='GET')(server_static)
-
-    try:
-        ^4$server = MyWSGIRefServer(host="127.0.0.1", port="8080")
-        ^4$app.run(server=server,reloader=False)
-    except Exception,ex:
-        ^4$print "Exception: %s" % repr(ex)
-
-<button>Run ...</button>
-
-
-Click Run, copy the code to the editor and click Run to see the result.
-
-Of course, in QPython, It also provides support for other web development frameworks to help you streamline your work.
diff --git a/docs/sources/qsl4a-develop/cn/index.rst.txt b/docs/sources/qsl4a-develop/cn/index.rst.txt
deleted file mode 100644
index 7ce0e3d..0000000
--- a/docs/sources/qsl4a-develop/cn/index.rst.txt
+++ /dev/null
@@ -1,71 +0,0 @@
-QSL4A - QPython的安卓脚本层
-==============================================
-SL4A是Scripting Layer for Android 的缩写，中文直译为“安卓的脚本层”，QSL4A是QPython的安卓脚本层。
-
-
-什么是SL4A
-----------
-SL4A的全称为Scripting Layer for Android, 顾名思义就是Android的脚本架构层，它的目的就是可以用熟知的脚本开发语言来开发Android应用程序。其工作原理基于RPC远程调用，通过本地的脚本解析器和远端的原生态Android Server层的APK进行信息交互，即实现一个远程代理，把本地脚本的函数调用通过json格式的封装，传递给远程原生态Server APK进行实际的android系统函数呼叫，最后将操作的执行结果反馈给本地脚本解析器，然后再在终端显示出运行结果。
-
-QPython中集成了SL4A的支持，通过SL4A，用户可以很方便地通过Python代码来调用安卓特性。
-
-
-QSL4A支持的接口
--------------------
-
-您需要在安卓系统设置中开启QPython对应的权限设置才能使用相关的SL4A接口。
-
-**系统应用接口**
-
-* Android：包括剪贴板、Intent对象、广播、震动、网络状态、版本号、发送邮件、提示、输入框等在内的接口 `(接口文档) <http://www.qpython.org/en/guide_androidhelpers.html>`_
-* 应用：主要为应用管理接口 `(接口文档) <http://www.qpython.org/en/guide_androidhelpers.html#applicationmanagerfacade>`_
-* ActivityResult：安卓里控制ActivityResult行为的接口 `(接口文档) <http://www.qpython.org/en/guide_androidhelpers.html#activityresultfacade>`_
-* 通用Intent：访问二维码，用XX应用查看的接口 `(接口文档) <http://www.qpython.org/en/guide_androidhelpers.html#commonintentsfacade>`_
-
-**安卓相关设备接口**
-
-* 相机：访问&调用设备的相机接口 `(接口文档) <http://www.qpython.org/en/guide_androidhelpers.html#camerafacade>`_
-* 联系人：联系人访问接口 `(接口文档) <http://www.qpython.org/en/guide_androidhelpers.html#contactsfacade>`_
-* 地理位置：访问地理位置接口 `(接口文档) <http://www.qpython.org/en/guide_androidhelpers.html#locationfacade>`_
-* 电话：访问电话接口 `(接口文档) <http://www.qpython.org/en/guide_androidhelpers.html#phonefacade>`_
-* 媒体录制：能够控制录音或者视频 `(接口文档) <http://www.qpython.org/en/guide_androidhelpers.html#mediarecorderfacade>`_
-* 感应器管理：访问&控制设备的传感器 `(接口文档) <http://www.qpython.org/en/guide_androidhelpers.html#sensormanagerfacade>`_
-* 设置：能够设置设备的屏幕、飞行模式、铃声模式、震动模式、音量、亮度等 `(接口文档) <http://www.qpython.org/en/guide_androidhelpers.html#settingsfacade>`_
-* 短信：能够访问设备的短信 `(接口文档) <http://www.qpython.org/en/guide_androidhelpers.html#smsfacade>`_
-* 语音识别：能够识别用户的讲话并返回最可能的结果 `(接口文档) <http://www.qpython.org/en/guide_androidhelpers.html#speechrecognitionfacade>`_
-* 音调生成器：能够由给定的电话号码生成DTMF的语音 `(接口文档) <http://www.qpython.org/en/guide_androidhelpers.html#tonegeneratorfacade>`_
-* 唤醒锁：设备的唤醒锁接口 `(接口文档) <http://www.qpython.org/en/guide_androidhelpers.html#wakelockfacade>`_
-* WIFI：设备的WIFI管理 `(接口文档) <http://www.qpython.org/en/guide_androidhelpers.html#wififacade>`_
-* 电池管理：提供设备的电池管理接口 `(接口文档) <http://www.qpython.org/en/guide_androidhelpers.html#batterymanagerfacade>`_
-* 媒体播放器：媒体播放器设置 `(接口文档) <http://www.qpython.org/en/guide_androidhelpers.html#mediaplayerfacade>`_
-* 偏好设置：访问／控制偏好设置 `(接口文档) <http://www.qpython.org/en/guide_androidhelpers.html#preferencesfacade>`_
-* 文字语音：控制文字到语音输出 `(接口文档) <http://www.qpython.org/en/guide_androidhelpers.html#texttospeechfacade>`_
-* 蓝牙：控制手机上的蓝牙设备 `(接口文档) <http://www.qpython.org/en/guide_androidhelpers.html#bluetoothfacade>`_
-* 信号强度：访问手机信号强度信息 `(接口文档) <http://www.qpython.org/en/guide_androidhelpers.html#signalstrengthfacade>`_
-* 网络摄像头：能够控制手机的摄像机 `(接口文档) <http://www.qpython.org/en/guide_androidhelpers.html#webcamfacade>`_
-* 用户交互界面：用于控制UI界面相关的展现，如文本对话框，密码输入框、状态栏、进度条、日期选择器等 `(接口文档) <http://www.qpython.org/en/guide_androidhelpers.html#uifacade>`_
-* NFC：NFC相关的控制，主要提供了NFC中的master/slave交换信息的方式 `(接口文档) <http://www.qpython.org/en/guide_androidhelpers.html#nfc>`_
-* USB宿主序列：用于控制来自安卓的拥有USB主机控制器的类USB序列的设备 `(接口文档) <http://www.qpython.org/en/guide_androidhelpers.html#usb-host-serial-facade>`_
-
-
-**QPython相关的接口**
-
-* QPy接口：在手机上运行QPython脚本 `(接口文档) <http://www.qpython.org/en/guide_androidhelpers.html#qpyinterfacefacade>`_
-
-
-示例代码
------------
-在安卓中，可以引入androidhelper模块来载入QSL4A的支持。下面我们来运行一个简单的QSL4A脚本来体验下：
-
-
-::
-
-    ^0$import androidhelper
-    ^0$droid = androidhelper.Android()
-    ^0$line = droid.dialogGetInput()
-    ^0$s = "Hello, %s" % (line.result,)
-    ^0$droid.makeToast(line)
-
-
-<button>Run ...</button>
-
diff --git a/docs/sources/qsl4a-develop/index.rst.txt b/docs/sources/qsl4a-develop/index.rst.txt
deleted file mode 100644
index e17d40f..0000000
--- a/docs/sources/qsl4a-develop/index.rst.txt
+++ /dev/null
@@ -1,71 +0,0 @@
-QSL4A - QPython's Android script layer
-==============================================
-SL4A is an acronym for Android's script layer, QSL4A is QPython's Android script layer.
-
-
-What is SL4A?
-----------
-The full name of SL4A Scripting Layer for Android, as the name suggests is Android's script architecture layer, Its purpose is to develop Android applications using well-known scripting languages.Its working principle is based on RPC remote call, through the local script parser and the remote native Android server layer APK information exchange,That is to achieve a remote agent,The local script function calls is encapsulated by JSON format,pass to the Remote ECS APK and proceed to the actual android system function call,and the results of the operation will be fed back to the local script parser,finally the terminal will show the results of the operation.
-
-QPython integrated support of SL4A, users can easily be invoked by Andrews characteristics Python code, by SL4A.
-
-
-QSL4A supported interfaces
--------------------
-
-You need to turn on the QPython permission settings in your Android system settings to use the associated SL4A interface.
-
-**System application interface**
-
-* Including clipboard, Intent object, broadcast, vibration, network status, version number, send mail, prompts, Input boxes, etc. `(APIs Document) <http://www.qpython.org/en/guide_androidhelpers.html>`_
-* Application: Mainly as an application management interface `(APIs Document) <http://www.qpython.org/en/guide_androidhelpers.html#applicationmanagerfacade>`_
-* ActivityResult��In Android control ActivityResult the interface of behavior `(APIs Document) <http://www.qpython.org/en/guide_androidhelpers.html#activityresultfacade>`_
-* Common Intent��Access two-dimensional code, Use XX application to view the interface `(APIs Document) <http://www.qpython.org/en/guide_androidhelpers.html#commonintentsfacade>`_
-
-**Android related device interface**
-
-* Camera��Access & recall device's camera interface `(APIs Document) <http://www.qpython.org/en/guide_androidhelpers.html#camerafacade>`_
-* Contact��Contact access interface `(APIs Document) <http://www.qpython.org/en/guide_androidhelpers.html#contactsfacade>`_
-* Location��Access location interface `(APIs Document) <http://www.qpython.org/en/guide_androidhelpers.html#locationfacade>`_
-* Phone: Access the phone interface `(APIs Document) <http://www.qpython.org/en/guide_androidhelpers.html#phonefacade>`_
-* 
-* Media recording: able to control recording or video `(APIs Document) <http://www.qpython.org/en/guide_androidhelpers.html#mediarecorderfacade>`_
-* Sensor Management: Access Control & Sensor device `(APIs Document) <http://www.qpython.org/en/guide_androidhelpers.html#sensormanagerfacade>`_
-* Settings: Ability to set device screen, flight mode, ringtone mode, vibration mode, volume, brightness, etc. `(APIs Document) <http://www.qpython.org/en/guide_androidhelpers.html#settingsfacade>`_
-* SMS: equipment can be access to SMS `(APIs Document) <http://www.qpython.org/en/guide_androidhelpers.html#smsfacade>`_
-* Speech recognition: Can recognize the user's speech and returns the most likely outcome `(APIs Document) <http://www.qpython.org/en/guide_androidhelpers.html#speechrecognitionfacade>`_
-* Tone Generator: The ability to generate DTMF speech from a given phone number `(APIs Document) <http://www.qpython.org/en/guide_androidhelpers.html#tonegeneratorfacade>`_
-* Wake lock: The device's wake lock interface `(APIs Document) <http://www.qpython.org/en/guide_androidhelpers.html#wakelockfacade>`_
-* WIFI: Device WIFI Management `(APIs Document) <http://www.qpython.org/en/guide_androidhelpers.html#wififacade>`_
-* Battery Management: Provides the device's battery management interface `(APIs Document) <http://www.qpython.org/en/guide_androidhelpers.html#batterymanagerfacade>`_
-* Media Player: Media Player Settings `(APIs Document) <http://www.qpython.org/en/guide_androidhelpers.html#mediaplayerfacade>`_
-* Preferences: Access / Control Preferences `(APIs Document) <http://www.qpython.org/en/guide_androidhelpers.html#preferencesfacade>`_
-* Text Voice: Control text to voice output `(APIs Document) <http://www.qpython.org/en/guide_androidhelpers.html#texttospeechfacade>`_
-* Bluetooth: Control Bluetooth devices on your phone `(APIs Document) <http://www.qpython.org/en/guide_androidhelpers.html#bluetoothfacade>`_
-* Signal strength: access phone signal strength information `(APIs Document) <http://www.qpython.org/en/guide_androidhelpers.html#signalstrengthfacade>`_
-* Webcam: A camera that controls the phone `(APIs Document) <http://www.qpython.org/en/guide_androidhelpers.html#webcamfacade>`_
-* User interface: UI interface is used to control related exhibits, such as a text box, the password input box, the status bar, progress bar, date picker, etc. `(APIs Document) <http://www.qpython.org/en/guide_androidhelpers.html#uifacade>`_
-* NFC: NFC related control, mainly provided in the NFC mode master/slave exchange information `(APIs Document) <http://www.qpython.org/en/guide_androidhelpers.html#nfc>`_
-* USB Host Sequence: A device used to control USB-like sequences from Android that has a USB host controller `(APIs Document) <http://www.qpython.org/en/guide_androidhelpers.html#usb-host-serial-facade>`_
-
-
-**QPython relevant interface**
-
-* QPy interface: Run QPython script on your phone `(APIs Document) <http://www.qpython.org/en/guide_androidhelpers.html#qpyinterfacefacade>`_
-
-
-Sample Code
------------
-In Android, you can import the androidhelper module to load QSL4A support.Let's run a simple QSL4A script to experience the following:
-
-
-::
-
-    ^0$import androidhelper
-    ^0$droid = androidhelper.Android()
-    ^0$line = droid.dialogGetInput()
-    ^0$s = "Hello, %s" % (line.result,)
-    ^0$droid.makeToast(line)
-
-
-<button>Run ...</button>
diff --git a/docs/sources/sample.rst.txt b/docs/sources/sample.rst.txt
deleted file mode 100644
index 2f4ffb4..0000000
--- a/docs/sources/sample.rst.txt
+++ /dev/null
@@ -1,78 +0,0 @@
-A hands-on introduction to Python for beginning programmers
-===========================================================
-
-If you are a new guy to QPython, you should watch the tutorial video and just follow the steps to learn how to run script with QPython
-
-<jumpbutton data-url="http\://www.baidu.com">Watch Tutorial</jumpbutton>
-
-Running with Console
------------------------------------
-
-Running QPython’s simple script with console model
-
-..  code-block :: none
-
-    qpy:2
-    #qpy:console
-    #qpy:http://qpython.com/s/qpy-sample.py
-    class Calculator(object):
-        ^4$#define class to simulate a simple calculator
-        ^4$def __init__ (self):
-            ^8$#start with zero
-            ^8$self.current = 0
-        ^4$def add(self, amount):
-            ^8$#add number to current
-            ^8$self.current += amount
-        ^4$def getCurrent(self):
-            ^8$return self.current
-    myBuddy = Calculator() # make myBuddy into a Calculator object
-    myBuddy.add(2) #use myBuddy's new add method derived from the Calculator class
-    print(myBuddy.getCurrent()) #print myBuddy's current instance variable
-
-<button>Run ...</button>
-
-Running with Kivy
----------------------------
-
-Running QPython’s script with GUI model ( Kivy library )
-
-..  code-block :: none
-
-    #qpy:2
-    #qpy:kivy
-    #qpy:http://qpython.com/s/qpy-guisample.py
-
-    from kivy.app import App
-    from kivy.uix.button import Button
-
-    class TestApp(App):
-        ^4$def build(self):
-            ^8$# display a button with the text : Hello QPython 
-            ^8$return Button(text='Hello QPython')
-
-    TestApp().run()
-
-<button>Run ...</button>
-
-Running as Web App
----------------------------------
-
-Running Web App with QPython
-
-..  code-block :: none
-
-    #qpy:2
-    #qpy:webapp:Hello Qpython
-    #qpy://localhost:8080/hello
-    """
-    This is a sample for qpython webapp
-    """
-    from bottle import route, run
-
-    @route('/hello')
-    def hello():
-        ^4$return "Hello World!"
-
-    run(host='localhost', port=8080)
-
-<button>Run ...</button>
diff --git a/docs/sources/unity3d/index.rst.txt b/docs/sources/unity3d/index.rst.txt
deleted file mode 100644
index 9bd34fb..0000000
--- a/docs/sources/unity3d/index.rst.txt
+++ /dev/null
@@ -1,19 +0,0 @@
-Unity 3D with IronPython
-=======================
-The courses are still in the planning. Welcome to reward us. 
-
-1. **Primary course**
-
-If the total number of people who reward us is more than 100, we promise to complete the primary course within 1 week, so that, after finishing your study, you can master the basic concepts and write simple scripts.

-
-2. **Intermediate course**
-
-If the total number of people who reward us is more than 500, we will launch the intermediate course within 2 weeks, so that, after finishing your study, you will be familiar with the knowledge and framework of the course and apply it to practical development.
-
-3. **Advanced course**
-
-If the total number of people who reward us is more than 2,000, we will launch the advanced course in a month, so that, after finishing your study, you will have a better understanding of the principles of the framework and be able to do advanced development.
-
-
-**The primary course is free to all, and the intermediate and advanced courses are only open to those who have rewarded us.**
-
diff --git a/docs/sources/vr/index.rst.txt b/docs/sources/vr/index.rst.txt
deleted file mode 100644
index 56c1176..0000000
--- a/docs/sources/vr/index.rst.txt
+++ /dev/null
@@ -1,19 +0,0 @@
-VR Programming with Python
-==============================
-The courses are still in the planning. Welcome to reward us. 
-
-1. **Primary course**
-
-If the total number of people who reward us is more than 100, we promise to complete the primary course within 1 week, so that, after finishing your study, you can master the basic concepts and write simple scripts.

-
-2. **Intermediate course**
-
-If the total number of people who reward us is more than 500, we will launch the intermediate course within 2 weeks, so that, after finishing your study, you will be familiar with the knowledge and framework of the course and apply it to practical development.
-
-3. **Advanced course**
-
-If the total number of people who reward us is more than 2,000, we will launch the advanced course in a month, so that, after finishing your study, you will have a better understanding of the principles of the framework and be able to do advanced development.
-
-
-**The primary course is free to all, and the intermediate and advanced courses are only open to those who have rewarded us.**
-
diff --git a/docs/sources/wego/index.rst.txt b/docs/sources/wego/index.rst.txt
deleted file mode 100644
index 80db98f..0000000
--- a/docs/sources/wego/index.rst.txt
+++ /dev/null
@@ -1,19 +0,0 @@
-WegoX (Wechat openplatform SDK)
-==============================================
-The courses are still in the planning. Welcome to reward us. 
-
-1. **Primary course**
-
-If the total number of people who reward us is more than 100, we promise to complete the primary course within 1 week, so that, after finishing your study, you can master the basic concepts and write simple scripts.
-
-2. **Intermediate course**
-
-If the total number of people who reward us is more than 500, we will launch the intermediate course within 2 weeks, so that, after finishing your study, you will be familiar with the knowledge and framework of the course and apply it to practical development.
-
-3. **Advanced course**
-
-If the total number of people who reward us is more than 2,000, we will launch the advanced course in a month, so that, after finishing your study, you will have a better understanding of the principles of the framework and be able to do advanced development.
-
-
-**The primary course is free to all, and the intermediate and advanced courses are only open to those who have rewarded us.**
-
diff --git a/docs/static/QPy_meta_barnners.001.jpeg b/docs/static/QPy_meta_barnners.001.jpeg
deleted file mode 100644
index f7e51b2..0000000
Binary files a/docs/static/QPy_meta_barnners.001.jpeg and /dev/null differ
diff --git a/docs/static/_sphinx_javascript_frameworks_compat.js b/docs/static/_sphinx_javascript_frameworks_compat.js
deleted file mode 100644
index 8549469..0000000
--- a/docs/static/_sphinx_javascript_frameworks_compat.js
+++ /dev/null
@@ -1,134 +0,0 @@
-/*
- * _sphinx_javascript_frameworks_compat.js
- * ~~~~~~~~~~
- *
- * Compatability shim for jQuery and underscores.js.
- *
- * WILL BE REMOVED IN Sphinx 6.0
- * xref RemovedInSphinx60Warning
- *
- */
-
-/**
- * select a different prefix for underscore
- */
-$u = _.noConflict();
-
-
-/**
- * small helper function to urldecode strings
- *
- * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL
- */
-jQuery.urldecode = function(x) {
-    if (!x) {
-        return x
-    }
-    return decodeURIComponent(x.replace(/\+/g, ' '));
-};
-
-/**
- * small helper function to urlencode strings
- */
-jQuery.urlencode = encodeURIComponent;
-
-/**
- * This function returns the parsed url parameters of the
- * current request. Multiple values per key are supported,
- * it will always return arrays of strings for the value parts.
- */
-jQuery.getQueryParameters = function(s) {
-    if (typeof s === 'undefined')
-        s = document.location.search;
-    var parts = s.substr(s.indexOf('?') + 1).split('&');
-    var result = {};
-    for (var i = 0; i < parts.length; i++) {
-        var tmp = parts[i].split('=', 2);
-        var key = jQuery.urldecode(tmp[0]);
-        var value = jQuery.urldecode(tmp[1]);
-        if (key in result)
-            result[key].push(value);
-        else
-            result[key] = [value];
-    }
-    return result;
-};
-
-/**
- * highlight a given string on a jquery object by wrapping it in
- * span elements with the given class name.
- */
-jQuery.fn.highlightText = function(text, className) {
-    function highlight(node, addItems) {
-        if (node.nodeType === 3) {
-            var val = node.nodeValue;
-            var pos = val.toLowerCase().indexOf(text);
-            if (pos >= 0 &&
-                !jQuery(node.parentNode).hasClass(className) &&
-                !jQuery(node.parentNode).hasClass("nohighlight")) {
-                var span;
-                var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg");
-                if (isInSVG) {
-                    span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
-                } else {
-                    span = document.createElement("span");
-                    span.className = className;
-                }
-                span.appendChild(document.createTextNode(val.substr(pos, text.length)));
-                node.parentNode.insertBefore(span, node.parentNode.insertBefore(
-                    document.createTextNode(val.substr(pos + text.length)),
-                    node.nextSibling));
-                node.nodeValue = val.substr(0, pos);
-                if (isInSVG) {
-                    var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect");
-                    var bbox = node.parentElement.getBBox();
-                    rect.x.baseVal.value = bbox.x;
-                    rect.y.baseVal.value = bbox.y;
-                    rect.width.baseVal.value = bbox.width;
-                    rect.height.baseVal.value = bbox.height;
-                    rect.setAttribute('class', className);
-                    addItems.push({
-                        "parent": node.parentNode,
-                        "target": rect});
-                }
-            }
-        }
-        else if (!jQuery(node).is("button, select, textarea")) {
-            jQuery.each(node.childNodes, function() {
-                highlight(this, addItems);
-            });
-        }
-    }
-    var addItems = [];
-    var result = this.each(function() {
-        highlight(this, addItems);
-    });
-    for (var i = 0; i < addItems.length; ++i) {
-        jQuery(addItems[i].parent).before(addItems[i].target);
-    }
-    return result;
-};
-
-/*
- * backward compatibility for jQuery.browser
- * This will be supported until firefox bug is fixed.
- */
-if (!jQuery.browser) {
-    jQuery.uaMatch = function(ua) {
-        ua = ua.toLowerCase();
-
-        var match = /(chrome)[ \/]([\w.]+)/.exec(ua) ||
-            /(webkit)[ \/]([\w.]+)/.exec(ua) ||
-            /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) ||
-            /(msie) ([\w.]+)/.exec(ua) ||
-            ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) ||
-            [];
-
-        return {
-            browser: match[ 1 ] || "",
-            version: match[ 2 ] || "0"
-        };
-    };
-    jQuery.browser = {};
-    jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true;
-}
diff --git a/docs/static/activity-qpython-edu.png b/docs/static/activity-qpython-edu.png
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index 52f6436..0000000
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diff --git a/docs/static/activity-qpython-qpypi.png b/docs/static/activity-qpython-qpypi.png
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index ec4cd3b..0000000
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diff --git a/docs/static/aipy.png b/docs/static/aipy.png
deleted file mode 100644
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diff --git a/docs/static/basic.css b/docs/static/basic.css
deleted file mode 100644
index 9039e02..0000000
--- a/docs/static/basic.css
+++ /dev/null
@@ -1,932 +0,0 @@
-/*
- * basic.css
- * ~~~~~~~~~
- *
- * Sphinx stylesheet -- basic theme.
- *
- * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
- * :license: BSD, see LICENSE for details.
- *
- */
-
-/* -- main layout ----------------------------------------------------------- */
-
-div.clearer {
-    clear: both;
-}
-
-div.section::after {
-    display: block;
-    content: '';
-    clear: left;
-}
-
-/* -- relbar ---------------------------------------------------------------- */
-
-div.related {
-    width: 100%;
-    font-size: 90%;
-}
-
-div.related h3 {
-    display: none;
-}
-
-div.related ul {
-    margin: 0;
-    padding: 0 0 0 10px;
-    list-style: none;
-}
-
-div.related li {
-    display: inline;
-}
-
-div.related li.right {
-    float: right;
-    margin-right: 5px;
-}
-
-/* -- sidebar --------------------------------------------------------------- */
-
-div.sphinxsidebarwrapper {
-    padding: 10px 5px 0 10px;
-}
-
-div.sphinxsidebar {
-    float: left;
-    width: 230px;
-    margin-left: -100%;
-    font-size: 90%;
-    word-wrap: break-word;
-    overflow-wrap : break-word;
-}
-
-div.sphinxsidebar ul {
-    list-style: none;
-}
-
-div.sphinxsidebar ul ul,
-div.sphinxsidebar ul.want-points {
-    margin-left: 20px;
-    list-style: square;
-}
-
-div.sphinxsidebar ul ul {
-    margin-top: 0;
-    margin-bottom: 0;
-}
-
-div.sphinxsidebar form {
-    margin-top: 10px;
-}
-
-div.sphinxsidebar input {
-    border: 1px solid #98dbcc;
-    font-family: sans-serif;
-    font-size: 1em;
-}
-
-div.sphinxsidebar #searchbox form.search {
-    overflow: hidden;
-}
-
-div.sphinxsidebar #searchbox input[type="text"] {
-    float: left;
-    width: 80%;
-    padding: 0.25em;
-    box-sizing: border-box;
-}
-
-div.sphinxsidebar #searchbox input[type="submit"] {
-    float: left;
-    width: 20%;
-    border-left: none;
-    padding: 0.25em;
-    box-sizing: border-box;
-}
-
-
-img {
-    border: 0;
-    max-width: 100%;
-}
-
-/* -- search page ----------------------------------------------------------- */
-
-ul.search {
-    margin: 10px 0 0 20px;
-    padding: 0;
-}
-
-ul.search li {
-    padding: 5px 0 5px 20px;
-    background-image: url(file.png);
-    background-repeat: no-repeat;
-    background-position: 0 7px;
-}
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index d25eed3..0000000
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diff --git a/docs/static/course-wego.png b/docs/static/course-wego.png
deleted file mode 100644
index b9c286c..0000000
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diff --git a/docs/static/course_ic_like@2x.png b/docs/static/course_ic_like@2x.png
deleted file mode 100644
index 073825e..0000000
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diff --git a/docs/static/course_ic_play@2x.png b/docs/static/course_ic_play@2x.png
deleted file mode 100644
index 692ea88..0000000
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diff --git a/docs/static/course_web.css b/docs/static/course_web.css
deleted file mode 100644
index f88af58..0000000
--- a/docs/static/course_web.css
+++ /dev/null
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-    float: left;
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-    margin-right: 20px;
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-  .sc-course-item a:first-child {
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-    white-space: nowrap;
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-    color: #fff;
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diff --git a/docs/static/course_web.js b/docs/static/course_web.js
deleted file mode 100644
index 6a8dcf2..0000000
--- a/docs/static/course_web.js
+++ /dev/null
@@ -1,147 +0,0 @@
-var STATUS = false;
-
-var vm = new Vue({
-  el: '#app',
-  data: {
-    content: {
-      'new': {},
-      'featured': {}
-    },
-    tag: 'new',
-    c_page: {
-      new: 1,
-      featured: 1
-    },
-    page_size:12,
-    new_page: false,
-    new_page_data: {},
-    is_zh: navigator.language == 'zh-CN'  ? true : navigator.language == 'zh' ? true : false
-  },
-  created: function () {
-    var that = this;
-    var _url = this.is_zh === true ? '/index/zh.json' :  '/index/default.json';
-    $.get(_url, function(data){
-      var _new = [];
-        var _featured = [];
-        $.each(data, function(k, v){
-          v.is_open = false;
-          if (v.cat == 'new') {
-            _new.push(v);
-          }else if (v.cat == 'featured') {
-            _featured.push(v);
-          }
-        })
-        that.content = {
-          new: _new,
-          featured: _featured
-        };
-    })
-  },
-  methods: {
-    toggle_tag: function(tag) {
-      this.tag = tag;
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-    change_page: function(p) {
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-      }else if (this.tag == 'new') {
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-      return this.content[this.tag] ? this.content[this.tag].length/this.page_size > this.c_page[this.tag] : false;
-    },
-    if_has_many_page:  function() {
-      return this.content[this.tag].length/this.page_size >1 ? true : false;
-    },
-    course_desc: function(data) {
-      this.new_page_data = data;
-      this.new_page = true;
-      window.STATUS = true;
-    },
-    go_back: function () {
-      this.new_page = false;
-      window.STATUS = false;
-    }
-  }
-})
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-window.onload = function (){
-  var h = $(window).height();
-  $('#app').css("minHeight", h+'px');
-}
-window.onresize = function (){
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-  $('#app').css("minHeight", h+'px');
-}
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-history.pushState(null, null, document.URL);
-window.addEventListener('popstate', function () {
-  if (window.STATUS){
-    history.pushState(null, null, document.URL);
-    vm.go_back();
-  }else {
-    history.go(-1);
-  }
-});
-
-function setSearchHistory (t) {
-  if (!t)
-        return;
-  if (localStorage.course_search_list) {
-      var list = localStorage.course_search_list.split('%,%');
-      var i = list.indexOf(t);
-      if (i !== -1) {
-        list.splice(i, 1);
-        list.push(t);
-        localStorage.course_search_list = list.join('%,%');
-      }else {
-        localStorage.course_search_list += '%,%'+t;
-      }
-    }else {
-      localStorage.course_search_list = t;
-    }
-}
-
-$(function(){
-  $('.hot-search a').bind('click', function(){
-    var t = $(this).text();
-    $('#index_search').val(t);
-    $('#index_search').parent().submit();
-  })
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-  $('#index_search').bind('focus', function() {
-    var lists = localStorage.course_search_list;
-    if (lists) {
-      var list = lists.split('%,%');
-      var content = '';
-      list.reverse();
-      for (var i=0; i<list.length && i<5; i++) {
-        if (list[i]) {
-          content += `<li>${list[i]}</li>`;
-        }
-        $('.history-list').html(content);
-        $('.history-list li').click(function() {
-          var val = $(this).text();
-          $('#index_search').val(val);
-          $('#index_search').parent().submit();
-        })
-        $('.search-history').show();
-      }
-    }
-  })
-  $('#index_search').bind('blur', function() {
-    setTimeout(function (){
-      $('.search-history').hide();
-    }, 200)
-  })
-  $('.index-search-box form').submit(function(){
-    setSearchHistory($('#index_search').val());
-  })
-
-  $('#clear_history').click(function() {
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-    $('.history-list').html('');
-    $('.search-history').hide();
-  })
-})
diff --git a/docs/static/djangoman-createproject.png b/docs/static/djangoman-createproject.png
deleted file mode 100644
index c45d076..0000000
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diff --git a/docs/static/djangoman-installdjango.png b/docs/static/djangoman-installdjango.png
deleted file mode 100644
index 6bf05c5..0000000
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diff --git a/docs/static/djangoman-startserver.png b/docs/static/djangoman-startserver.png
deleted file mode 100644
index ac19899..0000000
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diff --git a/docs/static/djangoman.png b/docs/static/djangoman.png
deleted file mode 100644
index b5049f4..0000000
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diff --git a/docs/static/doctools.js b/docs/static/doctools.js
deleted file mode 100644
index c3db08d..0000000
--- a/docs/static/doctools.js
+++ /dev/null
@@ -1,264 +0,0 @@
-/*
- * doctools.js
- * ~~~~~~~~~~~
- *
- * Base JavaScript utilities for all Sphinx HTML documentation.
- *
- * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
- * :license: BSD, see LICENSE for details.
- *
- */
-"use strict";
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-const _ready = (callback) => {
-  if (document.readyState !== "loading") {
-    callback();
-  } else {
-    document.addEventListener("DOMContentLoaded", callback);
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-
-/**
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-  if (node.nodeType === Node.TEXT_NODE) {
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-    const pos = val.toLowerCase().indexOf(text);
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-      const closestNode = parent.closest("body, svg, foreignObject");
-      const isInSVG = closestNode && closestNode.matches("svg");
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-      node.nodeValue = val.substr(0, pos);
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-        rect.width.baseVal.value = bbox.width;
-        rect.height.baseVal.value = bbox.height;
-        rect.setAttribute("class", className);
-        addItems.push({ parent: parent, target: rect });
-      }
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-  } else if (node.matches && !node.matches("button, select, textarea")) {
-    node.childNodes.forEach((el) => _highlight(el, addItems, text, className));
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-};
-const _highlightText = (thisNode, text, className) => {
-  let addItems = [];
-  _highlight(thisNode, addItems, text, className);
-  addItems.forEach((obj) =>
-    obj.parent.insertAdjacentElement("beforebegin", obj.target)
-  );
-};
-
-/**
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- */
-const Documentation = {
-  init: () => {
-    Documentation.highlightSearchWords();
-    Documentation.initDomainIndexTable();
-    Documentation.initOnKeyListeners();
-  },
-
-  /**
-   * i18n support
-   */
-  TRANSLATIONS: {},
-  PLURAL_EXPR: (n) => (n === 1 ? 0 : 1),
-  LOCALE: "unknown",
-
-  // gettext and ngettext don't access this so that the functions
-  // can safely bound to a different name (_ = Documentation.gettext)
-  gettext: (string) => {
-    const translated = Documentation.TRANSLATIONS[string];
-    switch (typeof translated) {
-      case "undefined":
-        return string; // no translation
-      case "string":
-        return translated; // translation exists
-      default:
-        return translated[0]; // (singular, plural) translation tuple exists
-    }
-  },
-
-  ngettext: (singular, plural, n) => {
-    const translated = Documentation.TRANSLATIONS[singular];
-    if (typeof translated !== "undefined")
-      return translated[Documentation.PLURAL_EXPR(n)];
-    return n === 1 ? singular : plural;
-  },
-
-  addTranslations: (catalog) => {
-    Object.assign(Documentation.TRANSLATIONS, catalog.messages);
-    Documentation.PLURAL_EXPR = new Function(
-      "n",
-      `return (${catalog.plural_expr})`
-    );
-    Documentation.LOCALE = catalog.locale;
-  },
-
-  /**
-   * highlight the search words provided in the url in the text
-   */
-  highlightSearchWords: () => {
-    const highlight =
-      new URLSearchParams(window.location.search).get("highlight") || "";
-    const terms = highlight.toLowerCase().split(/\s+/).filter(x => x);
-    if (terms.length === 0) return; // nothing to do
-
-    // There should never be more than one element matching "div.body"
-    const divBody = document.querySelectorAll("div.body");
-    const body = divBody.length ? divBody[0] : document.querySelector("body");
-    window.setTimeout(() => {
-      terms.forEach((term) => _highlightText(body, term, "highlighted"));
-    }, 10);
-
-    const searchBox = document.getElementById("searchbox");
-    if (searchBox === null) return;
-    searchBox.appendChild(
-      document
-        .createRange()
-        .createContextualFragment(
-          '<p class="highlight-link">' +
-            '<a href="javascript:Documentation.hideSearchWords()">' +
-            Documentation.gettext("Hide Search Matches") +
-            "</a></p>"
-        )
-    );
-  },
-
-  /**
-   * helper function to hide the search marks again
-   */
-  hideSearchWords: () => {
-    document
-      .querySelectorAll("#searchbox .highlight-link")
-      .forEach((el) => el.remove());
-    document
-      .querySelectorAll("span.highlighted")
-      .forEach((el) => el.classList.remove("highlighted"));
-    const url = new URL(window.location);
-    url.searchParams.delete("highlight");
-    window.history.replaceState({}, "", url);
-  },
-
-  /**
-   * helper function to focus on search bar
-   */
-  focusSearchBar: () => {
-    document.querySelectorAll("input[name=q]")[0]?.focus();
-  },
-
-  /**
-   * Initialise the domain index toggle buttons
-   */
-  initDomainIndexTable: () => {
-    const toggler = (el) => {
-      const idNumber = el.id.substr(7);
-      const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`);
-      if (el.src.substr(-9) === "minus.png") {
-        el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`;
-        toggledRows.forEach((el) => (el.style.display = "none"));
-      } else {
-        el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`;
-        toggledRows.forEach((el) => (el.style.display = ""));
-      }
-    };
-
-    const togglerElements = document.querySelectorAll("img.toggler");
-    togglerElements.forEach((el) =>
-      el.addEventListener("click", (event) => toggler(event.currentTarget))
-    );
-    togglerElements.forEach((el) => (el.style.display = ""));
-    if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler);
-  },
-
-  initOnKeyListeners: () => {
-    // only install a listener if it is really needed
-    if (
-      !DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS &&
-      !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS
-    )
-      return;
-
-    const blacklistedElements = new Set([
-      "TEXTAREA",
-      "INPUT",
-      "SELECT",
-      "BUTTON",
-    ]);
-    document.addEventListener("keydown", (event) => {
-      if (blacklistedElements.has(document.activeElement.tagName)) return; // bail for input elements
-      if (event.altKey || event.ctrlKey || event.metaKey) return; // bail with special keys
-
-      if (!event.shiftKey) {
-        switch (event.key) {
-          case "ArrowLeft":
-            if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break;
-
-            const prevLink = document.querySelector('link[rel="prev"]');
-            if (prevLink && prevLink.href) {
-              window.location.href = prevLink.href;
-              event.preventDefault();
-            }
-            break;
-          case "ArrowRight":
-            if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break;
-
-            const nextLink = document.querySelector('link[rel="next"]');
-            if (nextLink && nextLink.href) {
-              window.location.href = nextLink.href;
-              event.preventDefault();
-            }
-            break;
-          case "Escape":
-            if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break;
-            Documentation.hideSearchWords();
-            event.preventDefault();
-        }
-      }
-
-      // some keyboard layouts may need Shift to get /
-      switch (event.key) {
-        case "/":
-          if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break;
-          Documentation.focusSearchBar();
-          event.preventDefault();
-      }
-    });
-  },
-};
-
-// quick alias for translations
-const _ = Documentation.gettext;
-
-_ready(Documentation.init);
diff --git a/docs/static/documentation_options.js b/docs/static/documentation_options.js
deleted file mode 100644
index 82cedc5..0000000
--- a/docs/static/documentation_options.js
+++ /dev/null
@@ -1,14 +0,0 @@
-var DOCUMENTATION_OPTIONS = {
-    URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'),
-    VERSION: '0.9',
-    LANGUAGE: 'en',
-    COLLAPSE_INDEX: false,
-    BUILDER: 'html',
-    FILE_SUFFIX: '.html',
-    LINK_SUFFIX: '.html',
-    HAS_SOURCE: true,
-    SOURCELINK_SUFFIX: '.txt',
-    NAVIGATION_WITH_KEYS: false,
-    SHOW_SEARCH_SUMMARY: true,
-    ENABLE_SEARCH_SHORTCUTS: false,
-};
\ No newline at end of file
diff --git a/docs/static/editor-1.png b/docs/static/editor-1.png
deleted file mode 100644
index ea32b79..0000000
Binary files a/docs/static/editor-1.png and /dev/null differ
diff --git a/docs/static/editor-2.png b/docs/static/editor-2.png
deleted file mode 100644
index 982e3f5..0000000
Binary files a/docs/static/editor-2.png and /dev/null differ
diff --git a/docs/static/editor-left.png b/docs/static/editor-left.png
deleted file mode 100644
index 8e88faa..0000000
Binary files a/docs/static/editor-left.png and /dev/null differ
diff --git a/docs/static/editor-main.png b/docs/static/editor-main.png
deleted file mode 100644
index d7757e6..0000000
Binary files a/docs/static/editor-main.png and /dev/null differ
diff --git a/docs/static/editor-toolbar-1.png b/docs/static/editor-toolbar-1.png
deleted file mode 100644
index 146ce26..0000000
Binary files a/docs/static/editor-toolbar-1.png and /dev/null differ
diff --git a/docs/static/editor-toolbar-2.png b/docs/static/editor-toolbar-2.png
deleted file mode 100644
index ebcd777..0000000
Binary files a/docs/static/editor-toolbar-2.png and /dev/null differ
diff --git a/docs/static/editor-toolbar.png b/docs/static/editor-toolbar.png
deleted file mode 100644
index 40dbcc8..0000000
Binary files a/docs/static/editor-toolbar.png and /dev/null differ
diff --git a/docs/static/file.png b/docs/static/file.png
deleted file mode 100644
index a858a41..0000000
Binary files a/docs/static/file.png and /dev/null differ
diff --git a/docs/static/format_800_width.sh b/docs/static/format_800_width.sh
deleted file mode 100644
index a9f8295..0000000
--- a/docs/static/format_800_width.sh
+++ /dev/null
@@ -1,26 +0,0 @@
-#!/bin/bash
-
-{
-	F_NAME="${1}"
-
-	if test -f "${F_NAME}"
-	then
-		convert "${F_NAME}" -resize 800 "${F_NAME}"
-		echo 'resize finish'
-	elif test -d "${F_NAME}"
-	then
-		find $1 -type f -name "*.png" | while read png; do
-			convert "$png" -resize 800 "$png"
-		done
-		echo 'resize finish'
-	else                                   
-	   echo "${F_NAME} is not valid"
-	fi
-} || {
-	echo 'please install imagemagick by'
-	echo '1. sudo apt-get install imagemagick'
-	echo '2. brew install imagemagick'
-	echo 'or any package manager you have'
-}
-exit 0
-
diff --git a/docs/static/ic_search_1.png b/docs/static/ic_search_1.png
deleted file mode 100644
index 07a4a46..0000000
Binary files a/docs/static/ic_search_1.png and /dev/null differ
diff --git a/docs/static/jquery-3.6.0.js b/docs/static/jquery-3.6.0.js
deleted file mode 100644
index fc6c299..0000000
--- a/docs/static/jquery-3.6.0.js
+++ /dev/null
@@ -1,10881 +0,0 @@
-/*!
- * jQuery JavaScript Library v3.6.0
- * https://jquery.com/
- *
- * Includes Sizzle.js
- * https://sizzlejs.com/
- *
- * Copyright OpenJS Foundation and other contributors
- * Released under the MIT license
- * https://jquery.org/license
- *
- * Date: 2021-03-02T17:08Z
- */
-( function( global, factory ) {
-
-	"use strict";
-
-	if ( typeof module === "object" && typeof module.exports === "object" ) {
-
-		// For CommonJS and CommonJS-like environments where a proper `window`
-		// is present, execute the factory and get jQuery.
-		// For environments that do not have a `window` with a `document`
-		// (such as Node.js), expose a factory as module.exports.
-		// This accentuates the need for the creation of a real `window`.
-		// e.g. var jQuery = require("jquery")(window);
-		// See ticket #14549 for more info.
-		module.exports = global.document ?
-			factory( global, true ) :
-			function( w ) {
-				if ( !w.document ) {
-					throw new Error( "jQuery requires a window with a document" );
-				}
-				return factory( w );
-			};
-	} else {
-		factory( global );
-	}
-
-// Pass this if window is not defined yet
-} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) {
-
-// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1
-// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode
-// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common
-// enough that all such attempts are guarded in a try block.
-"use strict";
-
-var arr = [];
-
-var getProto = Object.getPrototypeOf;
-
-var slice = arr.slice;
-
-var flat = arr.flat ? function( array ) {
-	return arr.flat.call( array );
-} : function( array ) {
-	return arr.concat.apply( [], array );
-};
-
-
-var push = arr.push;
-
-var indexOf = arr.indexOf;
-
-var class2type = {};
-
-var toString = class2type.toString;
-
-var hasOwn = class2type.hasOwnProperty;
-
-var fnToString = hasOwn.toString;
-
-var ObjectFunctionString = fnToString.call( Object );
-
-var support = {};
-
-var isFunction = function isFunction( obj ) {
-
-		// Support: Chrome <=57, Firefox <=52
-		// In some browsers, typeof returns "function" for HTML <object> elements
-		// (i.e., `typeof document.createElement( "object" ) === "function"`).
-		// We don't want to classify *any* DOM node as a function.
-		// Support: QtWeb <=3.8.5, WebKit <=534.34, wkhtmltopdf tool <=0.12.5
-		// Plus for old WebKit, typeof returns "function" for HTML collections
-		// (e.g., `typeof document.getElementsByTagName("div") === "function"`). (gh-4756)
-		return typeof obj === "function" && typeof obj.nodeType !== "number" &&
-			typeof obj.item !== "function";
-	};
-
-
-var isWindow = function isWindow( obj ) {
-		return obj != null && obj === obj.window;
-	};
-
-
-var document = window.document;
-
-
-
-	var preservedScriptAttributes = {
-		type: true,
-		src: true,
-		nonce: true,
-		noModule: true
-	};
-
-	function DOMEval( code, node, doc ) {
-		doc = doc || document;
-
-		var i, val,
-			script = doc.createElement( "script" );
-
-		script.text = code;
-		if ( node ) {
-			for ( i in preservedScriptAttributes ) {
-
-				// Support: Firefox 64+, Edge 18+
-				// Some browsers don't support the "nonce" property on scripts.
-				// On the other hand, just using `getAttribute` is not enough as
-				// the `nonce` attribute is reset to an empty string whenever it
-				// becomes browsing-context connected.
-				// See https://github.com/whatwg/html/issues/2369
-				// See https://html.spec.whatwg.org/#nonce-attributes
-				// The `node.getAttribute` check was added for the sake of
-				// `jQuery.globalEval` so that it can fake a nonce-containing node
-				// via an object.
-				val = node[ i ] || node.getAttribute && node.getAttribute( i );
-				if ( val ) {
-					script.setAttribute( i, val );
-				}
-			}
-		}
-		doc.head.appendChild( script ).parentNode.removeChild( script );
-	}
-
-
-function toType( obj ) {
-	if ( obj == null ) {
-		return obj + "";
-	}
-
-	// Support: Android <=2.3 only (functionish RegExp)
-	return typeof obj === "object" || typeof obj === "function" ?
-		class2type[ toString.call( obj ) ] || "object" :
-		typeof obj;
-}
-/* global Symbol */
-// Defining this global in .eslintrc.json would create a danger of using the global
-// unguarded in another place, it seems safer to define global only for this module
-
-
-
-var
-	version = "3.6.0",
-
-	// Define a local copy of jQuery
-	jQuery = function( selector, context ) {
-
-		// The jQuery object is actually just the init constructor 'enhanced'
-		// Need init if jQuery is called (just allow error to be thrown if not included)
-		return new jQuery.fn.init( selector, context );
-	};
-
-jQuery.fn = jQuery.prototype = {
-
-	// The current version of jQuery being used
-	jquery: version,
-
-	constructor: jQuery,
-
-	// The default length of a jQuery object is 0
-	length: 0,
-
-	toArray: function() {
-		return slice.call( this );
-	},
-
-	// Get the Nth element in the matched element set OR
-	// Get the whole matched element set as a clean array
-	get: function( num ) {
-
-		// Return all the elements in a clean array
-		if ( num == null ) {
-			return slice.call( this );
-		}
-
-		// Return just the one element from the set
-		return num < 0 ? this[ num + this.length ] : this[ num ];
-	},
-
-	// Take an array of elements and push it onto the stack
-	// (returning the new matched element set)
-	pushStack: function( elems ) {
-
-		// Build a new jQuery matched element set
-		var ret = jQuery.merge( this.constructor(), elems );
-
-		// Add the old object onto the stack (as a reference)
-		ret.prevObject = this;
-
-		// Return the newly-formed element set
-		return ret;
-	},
-
-	// Execute a callback for every element in the matched set.
-	each: function( callback ) {
-		return jQuery.each( this, callback );
-	},
-
-	map: function( callback ) {
-		return this.pushStack( jQuery.map( this, function( elem, i ) {
-			return callback.call( elem, i, elem );
-		} ) );
-	},
-
-	slice: function() {
-		return this.pushStack( slice.apply( this, arguments ) );
-	},
-
-	first: function() {
-		return this.eq( 0 );
-	},
-
-	last: function() {
-		return this.eq( -1 );
-	},
-
-	even: function() {
-		return this.pushStack( jQuery.grep( this, function( _elem, i ) {
-			return ( i + 1 ) % 2;
-		} ) );
-	},
-
-	odd: function() {
-		return this.pushStack( jQuery.grep( this, function( _elem, i ) {
-			return i % 2;
-		} ) );
-	},
-
-	eq: function( i ) {
-		var len = this.length,
-			j = +i + ( i < 0 ? len : 0 );
-		return this.pushStack( j >= 0 && j < len ? [ this[ j ] ] : [] );
-	},
-
-	end: function() {
-		return this.prevObject || this.constructor();
-	},
-
-	// For internal use only.
-	// Behaves like an Array's method, not like a jQuery method.
-	push: push,
-	sort: arr.sort,
-	splice: arr.splice
-};
-
-jQuery.extend = jQuery.fn.extend = function() {
-	var options, name, src, copy, copyIsArray, clone,
-		target = arguments[ 0 ] || {},
-		i = 1,
-		length = arguments.length,
-		deep = false;
-
-	// Handle a deep copy situation
-	if ( typeof target === "boolean" ) {
-		deep = target;
-
-		// Skip the boolean and the target
-		target = arguments[ i ] || {};
-		i++;
-	}
-
-	// Handle case when target is a string or something (possible in deep copy)
-	if ( typeof target !== "object" && !isFunction( target ) ) {
-		target = {};
-	}
-
-	// Extend jQuery itself if only one argument is passed
-	if ( i === length ) {
-		target = this;
-		i--;
-	}
-
-	for ( ; i < length; i++ ) {
-
-		// Only deal with non-null/undefined values
-		if ( ( options = arguments[ i ] ) != null ) {
-
-			// Extend the base object
-			for ( name in options ) {
-				copy = options[ name ];
-
-				// Prevent Object.prototype pollution
-				// Prevent never-ending loop
-				if ( name === "__proto__" || target === copy ) {
-					continue;
-				}
-
-				// Recurse if we're merging plain objects or arrays
-				if ( deep && copy && ( jQuery.isPlainObject( copy ) ||
-					( copyIsArray = Array.isArray( copy ) ) ) ) {
-					src = target[ name ];
-
-					// Ensure proper type for the source value
-					if ( copyIsArray && !Array.isArray( src ) ) {
-						clone = [];
-					} else if ( !copyIsArray && !jQuery.isPlainObject( src ) ) {
-						clone = {};
-					} else {
-						clone = src;
-					}
-					copyIsArray = false;
-
-					// Never move original objects, clone them
-					target[ name ] = jQuery.extend( deep, clone, copy );
-
-				// Don't bring in undefined values
-				} else if ( copy !== undefined ) {
-					target[ name ] = copy;
-				}
-			}
-		}
-	}
-
-	// Return the modified object
-	return target;
-};
-
-jQuery.extend( {
-
-	// Unique for each copy of jQuery on the page
-	expando: "jQuery" + ( version + Math.random() ).replace( /\D/g, "" ),
-
-	// Assume jQuery is ready without the ready module
-	isReady: true,
-
-	error: function( msg ) {
-		throw new Error( msg );
-	},
-
-	noop: function() {},
-
-	isPlainObject: function( obj ) {
-		var proto, Ctor;
-
-		// Detect obvious negatives
-		// Use toString instead of jQuery.type to catch host objects
-		if ( !obj || toString.call( obj ) !== "[object Object]" ) {
-			return false;
-		}
-
-		proto = getProto( obj );
-
-		// Objects with no prototype (e.g., `Object.create( null )`) are plain
-		if ( !proto ) {
-			return true;
-		}
-
-		// Objects with prototype are plain iff they were constructed by a global Object function
-		Ctor = hasOwn.call( proto, "constructor" ) && proto.constructor;
-		return typeof Ctor === "function" && fnToString.call( Ctor ) === ObjectFunctionString;
-	},
-
-	isEmptyObject: function( obj ) {
-		var name;
-
-		for ( name in obj ) {
-			return false;
-		}
-		return true;
-	},
-
-	// Evaluates a script in a provided context; falls back to the global one
-	// if not specified.
-	globalEval: function( code, options, doc ) {
-		DOMEval( code, { nonce: options && options.nonce }, doc );
-	},
-
-	each: function( obj, callback ) {
-		var length, i = 0;
-
-		if ( isArrayLike( obj ) ) {
-			length = obj.length;
-			for ( ; i < length; i++ ) {
-				if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {
-					break;
-				}
-			}
-		} else {
-			for ( i in obj ) {
-				if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {
-					break;
-				}
-			}
-		}
-
-		return obj;
-	},
-
-	// results is for internal usage only
-	makeArray: function( arr, results ) {
-		var ret = results || [];
-
-		if ( arr != null ) {
-			if ( isArrayLike( Object( arr ) ) ) {
-				jQuery.merge( ret,
-					typeof arr === "string" ?
-						[ arr ] : arr
-				);
-			} else {
-				push.call( ret, arr );
-			}
-		}
-
-		return ret;
-	},
-
-	inArray: function( elem, arr, i ) {
-		return arr == null ? -1 : indexOf.call( arr, elem, i );
-	},
-
-	// Support: Android <=4.0 only, PhantomJS 1 only
-	// push.apply(_, arraylike) throws on ancient WebKit
-	merge: function( first, second ) {
-		var len = +second.length,
-			j = 0,
-			i = first.length;
-
-		for ( ; j < len; j++ ) {
-			first[ i++ ] = second[ j ];
-		}
-
-		first.length = i;
-
-		return first;
-	},
-
-	grep: function( elems, callback, invert ) {
-		var callbackInverse,
-			matches = [],
-			i = 0,
-			length = elems.length,
-			callbackExpect = !invert;
-
-		// Go through the array, only saving the items
-		// that pass the validator function
-		for ( ; i < length; i++ ) {
-			callbackInverse = !callback( elems[ i ], i );
-			if ( callbackInverse !== callbackExpect ) {
-				matches.push( elems[ i ] );
-			}
-		}
-
-		return matches;
-	},
-
-	// arg is for internal usage only
-	map: function( elems, callback, arg ) {
-		var length, value,
-			i = 0,
-			ret = [];
-
-		// Go through the array, translating each of the items to their new values
-		if ( isArrayLike( elems ) ) {
-			length = elems.length;
-			for ( ; i < length; i++ ) {
-				value = callback( elems[ i ], i, arg );
-
-				if ( value != null ) {
-					ret.push( value );
-				}
-			}
-
-		// Go through every key on the object,
-		} else {
-			for ( i in elems ) {
-				value = callback( elems[ i ], i, arg );
-
-				if ( value != null ) {
-					ret.push( value );
-				}
-			}
-		}
-
-		// Flatten any nested arrays
-		return flat( ret );
-	},
-
-	// A global GUID counter for objects
-	guid: 1,
-
-	// jQuery.support is not used in Core but other projects attach their
-	// properties to it so it needs to exist.
-	support: support
-} );
-
-if ( typeof Symbol === "function" ) {
-	jQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ];
-}
-
-// Populate the class2type map
-jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ),
-	function( _i, name ) {
-		class2type[ "[object " + name + "]" ] = name.toLowerCase();
-	} );
-
-function isArrayLike( obj ) {
-
-	// Support: real iOS 8.2 only (not reproducible in simulator)
-	// `in` check used to prevent JIT error (gh-2145)
-	// hasOwn isn't used here due to false negatives
-	// regarding Nodelist length in IE
-	var length = !!obj && "length" in obj && obj.length,
-		type = toType( obj );
-
-	if ( isFunction( obj ) || isWindow( obj ) ) {
-		return false;
-	}
-
-	return type === "array" || length === 0 ||
-		typeof length === "number" && length > 0 && ( length - 1 ) in obj;
-}
-var Sizzle =
-/*!
- * Sizzle CSS Selector Engine v2.3.6
- * https://sizzlejs.com/
- *
- * Copyright JS Foundation and other contributors
- * Released under the MIT license
- * https://js.foundation/
- *
- * Date: 2021-02-16
- */
-( function( window ) {
-var i,
-	support,
-	Expr,
-	getText,
-	isXML,
-	tokenize,
-	compile,
-	select,
-	outermostContext,
-	sortInput,
-	hasDuplicate,
-
-	// Local document vars
-	setDocument,
-	document,
-	docElem,
-	documentIsHTML,
-	rbuggyQSA,
-	rbuggyMatches,
-	matches,
-	contains,
-
-	// Instance-specific data
-	expando = "sizzle" + 1 * new Date(),
-	preferredDoc = window.document,
-	dirruns = 0,
-	done = 0,
-	classCache = createCache(),
-	tokenCache = createCache(),
-	compilerCache = createCache(),
-	nonnativeSelectorCache = createCache(),
-	sortOrder = function( a, b ) {
-		if ( a === b ) {
-			hasDuplicate = true;
-		}
-		return 0;
-	},
-
-	// Instance methods
-	hasOwn = ( {} ).hasOwnProperty,
-	arr = [],
-	pop = arr.pop,
-	pushNative = arr.push,
-	push = arr.push,
-	slice = arr.slice,
-
-	// Use a stripped-down indexOf as it's faster than native
-	// https://jsperf.com/thor-indexof-vs-for/5
-	indexOf = function( list, elem ) {
-		var i = 0,
-			len = list.length;
-		for ( ; i < len; i++ ) {
-			if ( list[ i ] === elem ) {
-				return i;
-			}
-		}
-		return -1;
-	},
-
-	booleans = "checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|" +
-		"ismap|loop|multiple|open|readonly|required|scoped",
-
-	// Regular expressions
-
-	// http://www.w3.org/TR/css3-selectors/#whitespace
-	whitespace = "[\\x20\\t\\r\\n\\f]",
-
-	// https://www.w3.org/TR/css-syntax-3/#ident-token-diagram
-	identifier = "(?:\\\\[\\da-fA-F]{1,6}" + whitespace +
-		"?|\\\\[^\\r\\n\\f]|[\\w-]|[^\0-\\x7f])+",
-
-	// Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors
-	attributes = "\\[" + whitespace + "*(" + identifier + ")(?:" + whitespace +
-
-		// Operator (capture 2)
-		"*([*^$|!~]?=)" + whitespace +
-
-		// "Attribute values must be CSS identifiers [capture 5]
-		// or strings [capture 3 or capture 4]"
-		"*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" +
-		whitespace + "*\\]",
-
-	pseudos = ":(" + identifier + ")(?:\\((" +
-
-		// To reduce the number of selectors needing tokenize in the preFilter, prefer arguments:
-		// 1. quoted (capture 3; capture 4 or capture 5)
-		"('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" +
-
-		// 2. simple (capture 6)
-		"((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" +
-
-		// 3. anything else (capture 2)
-		".*" +
-		")\\)|)",
-
-	// Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter
-	rwhitespace = new RegExp( whitespace + "+", "g" ),
-	rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" +
-		whitespace + "+$", "g" ),
-
-	rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ),
-	rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace +
-		"*" ),
-	rdescend = new RegExp( whitespace + "|>" ),
-
-	rpseudo = new RegExp( pseudos ),
-	ridentifier = new RegExp( "^" + identifier + "$" ),
-
-	matchExpr = {
-		"ID": new RegExp( "^#(" + identifier + ")" ),
-		"CLASS": new RegExp( "^\\.(" + identifier + ")" ),
-		"TAG": new RegExp( "^(" + identifier + "|[*])" ),
-		"ATTR": new RegExp( "^" + attributes ),
-		"PSEUDO": new RegExp( "^" + pseudos ),
-		"CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" +
-			whitespace + "*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" +
-			whitespace + "*(\\d+)|))" + whitespace + "*\\)|)", "i" ),
-		"bool": new RegExp( "^(?:" + booleans + ")$", "i" ),
-
-		// For use in libraries implementing .is()
-		// We use this for POS matching in `select`
-		"needsContext": new RegExp( "^" + whitespace +
-			"*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" + whitespace +
-			"*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" )
-	},
-
-	rhtml = /HTML$/i,
-	rinputs = /^(?:input|select|textarea|button)$/i,
-	rheader = /^h\d$/i,
-
-	rnative = /^[^{]+\{\s*\[native \w/,
-
-	// Easily-parseable/retrievable ID or TAG or CLASS selectors
-	rquickExpr = /^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/,
-
-	rsibling = /[+~]/,
-
-	// CSS escapes
-	// http://www.w3.org/TR/CSS21/syndata.html#escaped-characters
-	runescape = new RegExp( "\\\\[\\da-fA-F]{1,6}" + whitespace + "?|\\\\([^\\r\\n\\f])", "g" ),
-	funescape = function( escape, nonHex ) {
-		var high = "0x" + escape.slice( 1 ) - 0x10000;
-
-		return nonHex ?
-
-			// Strip the backslash prefix from a non-hex escape sequence
-			nonHex :
-
-			// Replace a hexadecimal escape sequence with the encoded Unicode code point
-			// Support: IE <=11+
-			// For values outside the Basic Multilingual Plane (BMP), manually construct a
-			// surrogate pair
-			high < 0 ?
-				String.fromCharCode( high + 0x10000 ) :
-				String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 );
-	},
-
-	// CSS string/identifier serialization
-	// https://drafts.csswg.org/cssom/#common-serializing-idioms
-	rcssescape = /([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g,
-	fcssescape = function( ch, asCodePoint ) {
-		if ( asCodePoint ) {
-
-			// U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER
-			if ( ch === "\0" ) {
-				return "\uFFFD";
-			}
-
-			// Control characters and (dependent upon position) numbers get escaped as code points
-			return ch.slice( 0, -1 ) + "\\" +
-				ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " ";
-		}
-
-		// Other potentially-special ASCII characters get backslash-escaped
-		return "\\" + ch;
-	},
-
-	// Used for iframes
-	// See setDocument()
-	// Removing the function wrapper causes a "Permission Denied"
-	// error in IE
-	unloadHandler = function() {
-		setDocument();
-	},
-
-	inDisabledFieldset = addCombinator(
-		function( elem ) {
-			return elem.disabled === true && elem.nodeName.toLowerCase() === "fieldset";
-		},
-		{ dir: "parentNode", next: "legend" }
-	);
-
-// Optimize for push.apply( _, NodeList )
-try {
-	push.apply(
-		( arr = slice.call( preferredDoc.childNodes ) ),
-		preferredDoc.childNodes
-	);
-
-	// Support: Android<4.0
-	// Detect silently failing push.apply
-	// eslint-disable-next-line no-unused-expressions
-	arr[ preferredDoc.childNodes.length ].nodeType;
-} catch ( e ) {
-	push = { apply: arr.length ?
-
-		// Leverage slice if possible
-		function( target, els ) {
-			pushNative.apply( target, slice.call( els ) );
-		} :
-
-		// Support: IE<9
-		// Otherwise append directly
-		function( target, els ) {
-			var j = target.length,
-				i = 0;
-
-			// Can't trust NodeList.length
-			while ( ( target[ j++ ] = els[ i++ ] ) ) {}
-			target.length = j - 1;
-		}
-	};
-}
-
-function Sizzle( selector, context, results, seed ) {
-	var m, i, elem, nid, match, groups, newSelector,
-		newContext = context && context.ownerDocument,
-
-		// nodeType defaults to 9, since context defaults to document
-		nodeType = context ? context.nodeType : 9;
-
-	results = results || [];
-
-	// Return early from calls with invalid selector or context
-	if ( typeof selector !== "string" || !selector ||
-		nodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) {
-
-		return results;
-	}
-
-	// Try to shortcut find operations (as opposed to filters) in HTML documents
-	if ( !seed ) {
-		setDocument( context );
-		context = context || document;
-
-		if ( documentIsHTML ) {
-
-			// If the selector is sufficiently simple, try using a "get*By*" DOM method
-			// (excepting DocumentFragment context, where the methods don't exist)
-			if ( nodeType !== 11 && ( match = rquickExpr.exec( selector ) ) ) {
-
-				// ID selector
-				if ( ( m = match[ 1 ] ) ) {
-
-					// Document context
-					if ( nodeType === 9 ) {
-						if ( ( elem = context.getElementById( m ) ) ) {
-
-							// Support: IE, Opera, Webkit
-							// TODO: identify versions
-							// getElementById can match elements by name instead of ID
-							if ( elem.id === m ) {
-								results.push( elem );
-								return results;
-							}
-						} else {
-							return results;
-						}
-
-					// Element context
-					} else {
-
-						// Support: IE, Opera, Webkit
-						// TODO: identify versions
-						// getElementById can match elements by name instead of ID
-						if ( newContext && ( elem = newContext.getElementById( m ) ) &&
-							contains( context, elem ) &&
-							elem.id === m ) {
-
-							results.push( elem );
-							return results;
-						}
-					}
-
-				// Type selector
-				} else if ( match[ 2 ] ) {
-					push.apply( results, context.getElementsByTagName( selector ) );
-					return results;
-
-				// Class selector
-				} else if ( ( m = match[ 3 ] ) && support.getElementsByClassName &&
-					context.getElementsByClassName ) {
-
-					push.apply( results, context.getElementsByClassName( m ) );
-					return results;
-				}
-			}
-
-			// Take advantage of querySelectorAll
-			if ( support.qsa &&
-				!nonnativeSelectorCache[ selector + " " ] &&
-				( !rbuggyQSA || !rbuggyQSA.test( selector ) ) &&
-
-				// Support: IE 8 only
-				// Exclude object elements
-				( nodeType !== 1 || context.nodeName.toLowerCase() !== "object" ) ) {
-
-				newSelector = selector;
-				newContext = context;
-
-				// qSA considers elements outside a scoping root when evaluating child or
-				// descendant combinators, which is not what we want.
-				// In such cases, we work around the behavior by prefixing every selector in the
-				// list with an ID selector referencing the scope context.
-				// The technique has to be used as well when a leading combinator is used
-				// as such selectors are not recognized by querySelectorAll.
-				// Thanks to Andrew Dupont for this technique.
-				if ( nodeType === 1 &&
-					( rdescend.test( selector ) || rcombinators.test( selector ) ) ) {
-
-					// Expand context for sibling selectors
-					newContext = rsibling.test( selector ) && testContext( context.parentNode ) ||
-						context;
-
-					// We can use :scope instead of the ID hack if the browser
-					// supports it & if we're not changing the context.
-					if ( newContext !== context || !support.scope ) {
-
-						// Capture the context ID, setting it first if necessary
-						if ( ( nid = context.getAttribute( "id" ) ) ) {
-							nid = nid.replace( rcssescape, fcssescape );
-						} else {
-							context.setAttribute( "id", ( nid = expando ) );
-						}
-					}
-
-					// Prefix every selector in the list
-					groups = tokenize( selector );
-					i = groups.length;
-					while ( i-- ) {
-						groups[ i ] = ( nid ? "#" + nid : ":scope" ) + " " +
-							toSelector( groups[ i ] );
-					}
-					newSelector = groups.join( "," );
-				}
-
-				try {
-					push.apply( results,
-						newContext.querySelectorAll( newSelector )
-					);
-					return results;
-				} catch ( qsaError ) {
-					nonnativeSelectorCache( selector, true );
-				} finally {
-					if ( nid === expando ) {
-						context.removeAttribute( "id" );
-					}
-				}
-			}
-		}
-	}
-
-	// All others
-	return select( selector.replace( rtrim, "$1" ), context, results, seed );
-}
-
-/**
- * Create key-value caches of limited size
- * @returns {function(string, object)} Returns the Object data after storing it on itself with
- *	property name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength)
- *	deleting the oldest entry
- */
-function createCache() {
-	var keys = [];
-
-	function cache( key, value ) {
-
-		// Use (key + " ") to avoid collision with native prototype properties (see Issue #157)
-		if ( keys.push( key + " " ) > Expr.cacheLength ) {
-
-			// Only keep the most recent entries
-			delete cache[ keys.shift() ];
-		}
-		return ( cache[ key + " " ] = value );
-	}
-	return cache;
-}
-
-/**
- * Mark a function for special use by Sizzle
- * @param {Function} fn The function to mark
- */
-function markFunction( fn ) {
-	fn[ expando ] = true;
-	return fn;
-}
-
-/**
- * Support testing using an element
- * @param {Function} fn Passed the created element and returns a boolean result
- */
-function assert( fn ) {
-	var el = document.createElement( "fieldset" );
-
-	try {
-		return !!fn( el );
-	} catch ( e ) {
-		return false;
-	} finally {
-
-		// Remove from its parent by default
-		if ( el.parentNode ) {
-			el.parentNode.removeChild( el );
-		}
-
-		// release memory in IE
-		el = null;
-	}
-}
-
-/**
- * Adds the same handler for all of the specified attrs
- * @param {String} attrs Pipe-separated list of attributes
- * @param {Function} handler The method that will be applied
- */
-function addHandle( attrs, handler ) {
-	var arr = attrs.split( "|" ),
-		i = arr.length;
-
-	while ( i-- ) {
-		Expr.attrHandle[ arr[ i ] ] = handler;
-	}
-}
-
-/**
- * Checks document order of two siblings
- * @param {Element} a
- * @param {Element} b
- * @returns {Number} Returns less than 0 if a precedes b, greater than 0 if a follows b
- */
-function siblingCheck( a, b ) {
-	var cur = b && a,
-		diff = cur && a.nodeType === 1 && b.nodeType === 1 &&
-			a.sourceIndex - b.sourceIndex;
-
-	// Use IE sourceIndex if available on both nodes
-	if ( diff ) {
-		return diff;
-	}
-
-	// Check if b follows a
-	if ( cur ) {
-		while ( ( cur = cur.nextSibling ) ) {
-			if ( cur === b ) {
-				return -1;
-			}
-		}
-	}
-
-	return a ? 1 : -1;
-}
-
-/**
- * Returns a function to use in pseudos for input types
- * @param {String} type
- */
-function createInputPseudo( type ) {
-	return function( elem ) {
-		var name = elem.nodeName.toLowerCase();
-		return name === "input" && elem.type === type;
-	};
-}
-
-/**
- * Returns a function to use in pseudos for buttons
- * @param {String} type
- */
-function createButtonPseudo( type ) {
-	return function( elem ) {
-		var name = elem.nodeName.toLowerCase();
-		return ( name === "input" || name === "button" ) && elem.type === type;
-	};
-}
-
-/**
- * Returns a function to use in pseudos for :enabled/:disabled
- * @param {Boolean} disabled true for :disabled; false for :enabled
- */
-function createDisabledPseudo( disabled ) {
-
-	// Known :disabled false positives: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable
-	return function( elem ) {
-
-		// Only certain elements can match :enabled or :disabled
-		// https://html.spec.whatwg.org/multipage/scripting.html#selector-enabled
-		// https://html.spec.whatwg.org/multipage/scripting.html#selector-disabled
-		if ( "form" in elem ) {
-
-			// Check for inherited disabledness on relevant non-disabled elements:
-			// * listed form-associated elements in a disabled fieldset
-			//   https://html.spec.whatwg.org/multipage/forms.html#category-listed
-			//   https://html.spec.whatwg.org/multipage/forms.html#concept-fe-disabled
-			// * option elements in a disabled optgroup
-			//   https://html.spec.whatwg.org/multipage/forms.html#concept-option-disabled
-			// All such elements have a "form" property.
-			if ( elem.parentNode && elem.disabled === false ) {
-
-				// Option elements defer to a parent optgroup if present
-				if ( "label" in elem ) {
-					if ( "label" in elem.parentNode ) {
-						return elem.parentNode.disabled === disabled;
-					} else {
-						return elem.disabled === disabled;
-					}
-				}
-
-				// Support: IE 6 - 11
-				// Use the isDisabled shortcut property to check for disabled fieldset ancestors
-				return elem.isDisabled === disabled ||
-
-					// Where there is no isDisabled, check manually
-					/* jshint -W018 */
-					elem.isDisabled !== !disabled &&
-					inDisabledFieldset( elem ) === disabled;
-			}
-
-			return elem.disabled === disabled;
-
-		// Try to winnow out elements that can't be disabled before trusting the disabled property.
-		// Some victims get caught in our net (label, legend, menu, track), but it shouldn't
-		// even exist on them, let alone have a boolean value.
-		} else if ( "label" in elem ) {
-			return elem.disabled === disabled;
-		}
-
-		// Remaining elements are neither :enabled nor :disabled
-		return false;
-	};
-}
-
-/**
- * Returns a function to use in pseudos for positionals
- * @param {Function} fn
- */
-function createPositionalPseudo( fn ) {
-	return markFunction( function( argument ) {
-		argument = +argument;
-		return markFunction( function( seed, matches ) {
-			var j,
-				matchIndexes = fn( [], seed.length, argument ),
-				i = matchIndexes.length;
-
-			// Match elements found at the specified indexes
-			while ( i-- ) {
-				if ( seed[ ( j = matchIndexes[ i ] ) ] ) {
-					seed[ j ] = !( matches[ j ] = seed[ j ] );
-				}
-			}
-		} );
-	} );
-}
-
-/**
- * Checks a node for validity as a Sizzle context
- * @param {Element|Object=} context
- * @returns {Element|Object|Boolean} The input node if acceptable, otherwise a falsy value
- */
-function testContext( context ) {
-	return context && typeof context.getElementsByTagName !== "undefined" && context;
-}
-
-// Expose support vars for convenience
-support = Sizzle.support = {};
-
-/**
- * Detects XML nodes
- * @param {Element|Object} elem An element or a document
- * @returns {Boolean} True iff elem is a non-HTML XML node
- */
-isXML = Sizzle.isXML = function( elem ) {
-	var namespace = elem && elem.namespaceURI,
-		docElem = elem && ( elem.ownerDocument || elem ).documentElement;
-
-	// Support: IE <=8
-	// Assume HTML when documentElement doesn't yet exist, such as inside loading iframes
-	// https://bugs.jquery.com/ticket/4833
-	return !rhtml.test( namespace || docElem && docElem.nodeName || "HTML" );
-};
-
-/**
- * Sets document-related variables once based on the current document
- * @param {Element|Object} [doc] An element or document object to use to set the document
- * @returns {Object} Returns the current document
- */
-setDocument = Sizzle.setDocument = function( node ) {
-	var hasCompare, subWindow,
-		doc = node ? node.ownerDocument || node : preferredDoc;
-
-	// Return early if doc is invalid or already selected
-	// Support: IE 11+, Edge 17 - 18+
-	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-	// two documents; shallow comparisons work.
-	// eslint-disable-next-line eqeqeq
-	if ( doc == document || doc.nodeType !== 9 || !doc.documentElement ) {
-		return document;
-	}
-
-	// Update global variables
-	document = doc;
-	docElem = document.documentElement;
-	documentIsHTML = !isXML( document );
-
-	// Support: IE 9 - 11+, Edge 12 - 18+
-	// Accessing iframe documents after unload throws "permission denied" errors (jQuery #13936)
-	// Support: IE 11+, Edge 17 - 18+
-	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-	// two documents; shallow comparisons work.
-	// eslint-disable-next-line eqeqeq
-	if ( preferredDoc != document &&
-		( subWindow = document.defaultView ) && subWindow.top !== subWindow ) {
-
-		// Support: IE 11, Edge
-		if ( subWindow.addEventListener ) {
-			subWindow.addEventListener( "unload", unloadHandler, false );
-
-		// Support: IE 9 - 10 only
-		} else if ( subWindow.attachEvent ) {
-			subWindow.attachEvent( "onunload", unloadHandler );
-		}
-	}
-
-	// Support: IE 8 - 11+, Edge 12 - 18+, Chrome <=16 - 25 only, Firefox <=3.6 - 31 only,
-	// Safari 4 - 5 only, Opera <=11.6 - 12.x only
-	// IE/Edge & older browsers don't support the :scope pseudo-class.
-	// Support: Safari 6.0 only
-	// Safari 6.0 supports :scope but it's an alias of :root there.
-	support.scope = assert( function( el ) {
-		docElem.appendChild( el ).appendChild( document.createElement( "div" ) );
-		return typeof el.querySelectorAll !== "undefined" &&
-			!el.querySelectorAll( ":scope fieldset div" ).length;
-	} );
-
-	/* Attributes
-	---------------------------------------------------------------------- */
-
-	// Support: IE<8
-	// Verify that getAttribute really returns attributes and not properties
-	// (excepting IE8 booleans)
-	support.attributes = assert( function( el ) {
-		el.className = "i";
-		return !el.getAttribute( "className" );
-	} );
-
-	/* getElement(s)By*
-	---------------------------------------------------------------------- */
-
-	// Check if getElementsByTagName("*") returns only elements
-	support.getElementsByTagName = assert( function( el ) {
-		el.appendChild( document.createComment( "" ) );
-		return !el.getElementsByTagName( "*" ).length;
-	} );
-
-	// Support: IE<9
-	support.getElementsByClassName = rnative.test( document.getElementsByClassName );
-
-	// Support: IE<10
-	// Check if getElementById returns elements by name
-	// The broken getElementById methods don't pick up programmatically-set names,
-	// so use a roundabout getElementsByName test
-	support.getById = assert( function( el ) {
-		docElem.appendChild( el ).id = expando;
-		return !document.getElementsByName || !document.getElementsByName( expando ).length;
-	} );
-
-	// ID filter and find
-	if ( support.getById ) {
-		Expr.filter[ "ID" ] = function( id ) {
-			var attrId = id.replace( runescape, funescape );
-			return function( elem ) {
-				return elem.getAttribute( "id" ) === attrId;
-			};
-		};
-		Expr.find[ "ID" ] = function( id, context ) {
-			if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
-				var elem = context.getElementById( id );
-				return elem ? [ elem ] : [];
-			}
-		};
-	} else {
-		Expr.filter[ "ID" ] =  function( id ) {
-			var attrId = id.replace( runescape, funescape );
-			return function( elem ) {
-				var node = typeof elem.getAttributeNode !== "undefined" &&
-					elem.getAttributeNode( "id" );
-				return node && node.value === attrId;
-			};
-		};
-
-		// Support: IE 6 - 7 only
-		// getElementById is not reliable as a find shortcut
-		Expr.find[ "ID" ] = function( id, context ) {
-			if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
-				var node, i, elems,
-					elem = context.getElementById( id );
-
-				if ( elem ) {
-
-					// Verify the id attribute
-					node = elem.getAttributeNode( "id" );
-					if ( node && node.value === id ) {
-						return [ elem ];
-					}
-
-					// Fall back on getElementsByName
-					elems = context.getElementsByName( id );
-					i = 0;
-					while ( ( elem = elems[ i++ ] ) ) {
-						node = elem.getAttributeNode( "id" );
-						if ( node && node.value === id ) {
-							return [ elem ];
-						}
-					}
-				}
-
-				return [];
-			}
-		};
-	}
-
-	// Tag
-	Expr.find[ "TAG" ] = support.getElementsByTagName ?
-		function( tag, context ) {
-			if ( typeof context.getElementsByTagName !== "undefined" ) {
-				return context.getElementsByTagName( tag );
-
-			// DocumentFragment nodes don't have gEBTN
-			} else if ( support.qsa ) {
-				return context.querySelectorAll( tag );
-			}
-		} :
-
-		function( tag, context ) {
-			var elem,
-				tmp = [],
-				i = 0,
-
-				// By happy coincidence, a (broken) gEBTN appears on DocumentFragment nodes too
-				results = context.getElementsByTagName( tag );
-
-			// Filter out possible comments
-			if ( tag === "*" ) {
-				while ( ( elem = results[ i++ ] ) ) {
-					if ( elem.nodeType === 1 ) {
-						tmp.push( elem );
-					}
-				}
-
-				return tmp;
-			}
-			return results;
-		};
-
-	// Class
-	Expr.find[ "CLASS" ] = support.getElementsByClassName && function( className, context ) {
-		if ( typeof context.getElementsByClassName !== "undefined" && documentIsHTML ) {
-			return context.getElementsByClassName( className );
-		}
-	};
-
-	/* QSA/matchesSelector
-	---------------------------------------------------------------------- */
-
-	// QSA and matchesSelector support
-
-	// matchesSelector(:active) reports false when true (IE9/Opera 11.5)
-	rbuggyMatches = [];
-
-	// qSa(:focus) reports false when true (Chrome 21)
-	// We allow this because of a bug in IE8/9 that throws an error
-	// whenever `document.activeElement` is accessed on an iframe
-	// So, we allow :focus to pass through QSA all the time to avoid the IE error
-	// See https://bugs.jquery.com/ticket/13378
-	rbuggyQSA = [];
-
-	if ( ( support.qsa = rnative.test( document.querySelectorAll ) ) ) {
-
-		// Build QSA regex
-		// Regex strategy adopted from Diego Perini
-		assert( function( el ) {
-
-			var input;
-
-			// Select is set to empty string on purpose
-			// This is to test IE's treatment of not explicitly
-			// setting a boolean content attribute,
-			// since its presence should be enough
-			// https://bugs.jquery.com/ticket/12359
-			docElem.appendChild( el ).innerHTML = "<a id='" + expando + "'></a>" +
-				"<select id='" + expando + "-\r\\' msallowcapture=''>" +
-				"<option selected=''></option></select>";
-
-			// Support: IE8, Opera 11-12.16
-			// Nothing should be selected when empty strings follow ^= or $= or *=
-			// The test attribute must be unknown in Opera but "safe" for WinRT
-			// https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section
-			if ( el.querySelectorAll( "[msallowcapture^='']" ).length ) {
-				rbuggyQSA.push( "[*^$]=" + whitespace + "*(?:''|\"\")" );
-			}
-
-			// Support: IE8
-			// Boolean attributes and "value" are not treated correctly
-			if ( !el.querySelectorAll( "[selected]" ).length ) {
-				rbuggyQSA.push( "\\[" + whitespace + "*(?:value|" + booleans + ")" );
-			}
-
-			// Support: Chrome<29, Android<4.4, Safari<7.0+, iOS<7.0+, PhantomJS<1.9.8+
-			if ( !el.querySelectorAll( "[id~=" + expando + "-]" ).length ) {
-				rbuggyQSA.push( "~=" );
-			}
-
-			// Support: IE 11+, Edge 15 - 18+
-			// IE 11/Edge don't find elements on a `[name='']` query in some cases.
-			// Adding a temporary attribute to the document before the selection works
-			// around the issue.
-			// Interestingly, IE 10 & older don't seem to have the issue.
-			input = document.createElement( "input" );
-			input.setAttribute( "name", "" );
-			el.appendChild( input );
-			if ( !el.querySelectorAll( "[name='']" ).length ) {
-				rbuggyQSA.push( "\\[" + whitespace + "*name" + whitespace + "*=" +
-					whitespace + "*(?:''|\"\")" );
-			}
-
-			// Webkit/Opera - :checked should return selected option elements
-			// http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked
-			// IE8 throws error here and will not see later tests
-			if ( !el.querySelectorAll( ":checked" ).length ) {
-				rbuggyQSA.push( ":checked" );
-			}
-
-			// Support: Safari 8+, iOS 8+
-			// https://bugs.webkit.org/show_bug.cgi?id=136851
-			// In-page `selector#id sibling-combinator selector` fails
-			if ( !el.querySelectorAll( "a#" + expando + "+*" ).length ) {
-				rbuggyQSA.push( ".#.+[+~]" );
-			}
-
-			// Support: Firefox <=3.6 - 5 only
-			// Old Firefox doesn't throw on a badly-escaped identifier.
-			el.querySelectorAll( "\\\f" );
-			rbuggyQSA.push( "[\\r\\n\\f]" );
-		} );
-
-		assert( function( el ) {
-			el.innerHTML = "<a href='' disabled='disabled'></a>" +
-				"<select disabled='disabled'><option/></select>";
-
-			// Support: Windows 8 Native Apps
-			// The type and name attributes are restricted during .innerHTML assignment
-			var input = document.createElement( "input" );
-			input.setAttribute( "type", "hidden" );
-			el.appendChild( input ).setAttribute( "name", "D" );
-
-			// Support: IE8
-			// Enforce case-sensitivity of name attribute
-			if ( el.querySelectorAll( "[name=d]" ).length ) {
-				rbuggyQSA.push( "name" + whitespace + "*[*^$|!~]?=" );
-			}
-
-			// FF 3.5 - :enabled/:disabled and hidden elements (hidden elements are still enabled)
-			// IE8 throws error here and will not see later tests
-			if ( el.querySelectorAll( ":enabled" ).length !== 2 ) {
-				rbuggyQSA.push( ":enabled", ":disabled" );
-			}
-
-			// Support: IE9-11+
-			// IE's :disabled selector does not pick up the children of disabled fieldsets
-			docElem.appendChild( el ).disabled = true;
-			if ( el.querySelectorAll( ":disabled" ).length !== 2 ) {
-				rbuggyQSA.push( ":enabled", ":disabled" );
-			}
-
-			// Support: Opera 10 - 11 only
-			// Opera 10-11 does not throw on post-comma invalid pseudos
-			el.querySelectorAll( "*,:x" );
-			rbuggyQSA.push( ",.*:" );
-		} );
-	}
-
-	if ( ( support.matchesSelector = rnative.test( ( matches = docElem.matches ||
-		docElem.webkitMatchesSelector ||
-		docElem.mozMatchesSelector ||
-		docElem.oMatchesSelector ||
-		docElem.msMatchesSelector ) ) ) ) {
-
-		assert( function( el ) {
-
-			// Check to see if it's possible to do matchesSelector
-			// on a disconnected node (IE 9)
-			support.disconnectedMatch = matches.call( el, "*" );
-
-			// This should fail with an exception
-			// Gecko does not error, returns false instead
-			matches.call( el, "[s!='']:x" );
-			rbuggyMatches.push( "!=", pseudos );
-		} );
-	}
-
-	rbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join( "|" ) );
-	rbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join( "|" ) );
-
-	/* Contains
-	---------------------------------------------------------------------- */
-	hasCompare = rnative.test( docElem.compareDocumentPosition );
-
-	// Element contains another
-	// Purposefully self-exclusive
-	// As in, an element does not contain itself
-	contains = hasCompare || rnative.test( docElem.contains ) ?
-		function( a, b ) {
-			var adown = a.nodeType === 9 ? a.documentElement : a,
-				bup = b && b.parentNode;
-			return a === bup || !!( bup && bup.nodeType === 1 && (
-				adown.contains ?
-					adown.contains( bup ) :
-					a.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16
-			) );
-		} :
-		function( a, b ) {
-			if ( b ) {
-				while ( ( b = b.parentNode ) ) {
-					if ( b === a ) {
-						return true;
-					}
-				}
-			}
-			return false;
-		};
-
-	/* Sorting
-	---------------------------------------------------------------------- */
-
-	// Document order sorting
-	sortOrder = hasCompare ?
-	function( a, b ) {
-
-		// Flag for duplicate removal
-		if ( a === b ) {
-			hasDuplicate = true;
-			return 0;
-		}
-
-		// Sort on method existence if only one input has compareDocumentPosition
-		var compare = !a.compareDocumentPosition - !b.compareDocumentPosition;
-		if ( compare ) {
-			return compare;
-		}
-
-		// Calculate position if both inputs belong to the same document
-		// Support: IE 11+, Edge 17 - 18+
-		// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-		// two documents; shallow comparisons work.
-		// eslint-disable-next-line eqeqeq
-		compare = ( a.ownerDocument || a ) == ( b.ownerDocument || b ) ?
-			a.compareDocumentPosition( b ) :
-
-			// Otherwise we know they are disconnected
-			1;
-
-		// Disconnected nodes
-		if ( compare & 1 ||
-			( !support.sortDetached && b.compareDocumentPosition( a ) === compare ) ) {
-
-			// Choose the first element that is related to our preferred document
-			// Support: IE 11+, Edge 17 - 18+
-			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-			// two documents; shallow comparisons work.
-			// eslint-disable-next-line eqeqeq
-			if ( a == document || a.ownerDocument == preferredDoc &&
-				contains( preferredDoc, a ) ) {
-				return -1;
-			}
-
-			// Support: IE 11+, Edge 17 - 18+
-			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-			// two documents; shallow comparisons work.
-			// eslint-disable-next-line eqeqeq
-			if ( b == document || b.ownerDocument == preferredDoc &&
-				contains( preferredDoc, b ) ) {
-				return 1;
-			}
-
-			// Maintain original order
-			return sortInput ?
-				( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :
-				0;
-		}
-
-		return compare & 4 ? -1 : 1;
-	} :
-	function( a, b ) {
-
-		// Exit early if the nodes are identical
-		if ( a === b ) {
-			hasDuplicate = true;
-			return 0;
-		}
-
-		var cur,
-			i = 0,
-			aup = a.parentNode,
-			bup = b.parentNode,
-			ap = [ a ],
-			bp = [ b ];
-
-		// Parentless nodes are either documents or disconnected
-		if ( !aup || !bup ) {
-
-			// Support: IE 11+, Edge 17 - 18+
-			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-			// two documents; shallow comparisons work.
-			/* eslint-disable eqeqeq */
-			return a == document ? -1 :
-				b == document ? 1 :
-				/* eslint-enable eqeqeq */
-				aup ? -1 :
-				bup ? 1 :
-				sortInput ?
-				( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :
-				0;
-
-		// If the nodes are siblings, we can do a quick check
-		} else if ( aup === bup ) {
-			return siblingCheck( a, b );
-		}
-
-		// Otherwise we need full lists of their ancestors for comparison
-		cur = a;
-		while ( ( cur = cur.parentNode ) ) {
-			ap.unshift( cur );
-		}
-		cur = b;
-		while ( ( cur = cur.parentNode ) ) {
-			bp.unshift( cur );
-		}
-
-		// Walk down the tree looking for a discrepancy
-		while ( ap[ i ] === bp[ i ] ) {
-			i++;
-		}
-
-		return i ?
-
-			// Do a sibling check if the nodes have a common ancestor
-			siblingCheck( ap[ i ], bp[ i ] ) :
-
-			// Otherwise nodes in our document sort first
-			// Support: IE 11+, Edge 17 - 18+
-			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-			// two documents; shallow comparisons work.
-			/* eslint-disable eqeqeq */
-			ap[ i ] == preferredDoc ? -1 :
-			bp[ i ] == preferredDoc ? 1 :
-			/* eslint-enable eqeqeq */
-			0;
-	};
-
-	return document;
-};
-
-Sizzle.matches = function( expr, elements ) {
-	return Sizzle( expr, null, null, elements );
-};
-
-Sizzle.matchesSelector = function( elem, expr ) {
-	setDocument( elem );
-
-	if ( support.matchesSelector && documentIsHTML &&
-		!nonnativeSelectorCache[ expr + " " ] &&
-		( !rbuggyMatches || !rbuggyMatches.test( expr ) ) &&
-		( !rbuggyQSA     || !rbuggyQSA.test( expr ) ) ) {
-
-		try {
-			var ret = matches.call( elem, expr );
-
-			// IE 9's matchesSelector returns false on disconnected nodes
-			if ( ret || support.disconnectedMatch ||
-
-				// As well, disconnected nodes are said to be in a document
-				// fragment in IE 9
-				elem.document && elem.document.nodeType !== 11 ) {
-				return ret;
-			}
-		} catch ( e ) {
-			nonnativeSelectorCache( expr, true );
-		}
-	}
-
-	return Sizzle( expr, document, null, [ elem ] ).length > 0;
-};
-
-Sizzle.contains = function( context, elem ) {
-
-	// Set document vars if needed
-	// Support: IE 11+, Edge 17 - 18+
-	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-	// two documents; shallow comparisons work.
-	// eslint-disable-next-line eqeqeq
-	if ( ( context.ownerDocument || context ) != document ) {
-		setDocument( context );
-	}
-	return contains( context, elem );
-};
-
-Sizzle.attr = function( elem, name ) {
-
-	// Set document vars if needed
-	// Support: IE 11+, Edge 17 - 18+
-	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-	// two documents; shallow comparisons work.
-	// eslint-disable-next-line eqeqeq
-	if ( ( elem.ownerDocument || elem ) != document ) {
-		setDocument( elem );
-	}
-
-	var fn = Expr.attrHandle[ name.toLowerCase() ],
-
-		// Don't get fooled by Object.prototype properties (jQuery #13807)
-		val = fn && hasOwn.call( Expr.attrHandle, name.toLowerCase() ) ?
-			fn( elem, name, !documentIsHTML ) :
-			undefined;
-
-	return val !== undefined ?
-		val :
-		support.attributes || !documentIsHTML ?
-			elem.getAttribute( name ) :
-			( val = elem.getAttributeNode( name ) ) && val.specified ?
-				val.value :
-				null;
-};
-
-Sizzle.escape = function( sel ) {
-	return ( sel + "" ).replace( rcssescape, fcssescape );
-};
-
-Sizzle.error = function( msg ) {
-	throw new Error( "Syntax error, unrecognized expression: " + msg );
-};
-
-/**
- * Document sorting and removing duplicates
- * @param {ArrayLike} results
- */
-Sizzle.uniqueSort = function( results ) {
-	var elem,
-		duplicates = [],
-		j = 0,
-		i = 0;
-
-	// Unless we *know* we can detect duplicates, assume their presence
-	hasDuplicate = !support.detectDuplicates;
-	sortInput = !support.sortStable && results.slice( 0 );
-	results.sort( sortOrder );
-
-	if ( hasDuplicate ) {
-		while ( ( elem = results[ i++ ] ) ) {
-			if ( elem === results[ i ] ) {
-				j = duplicates.push( i );
-			}
-		}
-		while ( j-- ) {
-			results.splice( duplicates[ j ], 1 );
-		}
-	}
-
-	// Clear input after sorting to release objects
-	// See https://github.com/jquery/sizzle/pull/225
-	sortInput = null;
-
-	return results;
-};
-
-/**
- * Utility function for retrieving the text value of an array of DOM nodes
- * @param {Array|Element} elem
- */
-getText = Sizzle.getText = function( elem ) {
-	var node,
-		ret = "",
-		i = 0,
-		nodeType = elem.nodeType;
-
-	if ( !nodeType ) {
-
-		// If no nodeType, this is expected to be an array
-		while ( ( node = elem[ i++ ] ) ) {
-
-			// Do not traverse comment nodes
-			ret += getText( node );
-		}
-	} else if ( nodeType === 1 || nodeType === 9 || nodeType === 11 ) {
-
-		// Use textContent for elements
-		// innerText usage removed for consistency of new lines (jQuery #11153)
-		if ( typeof elem.textContent === "string" ) {
-			return elem.textContent;
-		} else {
-
-			// Traverse its children
-			for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {
-				ret += getText( elem );
-			}
-		}
-	} else if ( nodeType === 3 || nodeType === 4 ) {
-		return elem.nodeValue;
-	}
-
-	// Do not include comment or processing instruction nodes
-
-	return ret;
-};
-
-Expr = Sizzle.selectors = {
-
-	// Can be adjusted by the user
-	cacheLength: 50,
-
-	createPseudo: markFunction,
-
-	match: matchExpr,
-
-	attrHandle: {},
-
-	find: {},
-
-	relative: {
-		">": { dir: "parentNode", first: true },
-		" ": { dir: "parentNode" },
-		"+": { dir: "previousSibling", first: true },
-		"~": { dir: "previousSibling" }
-	},
-
-	preFilter: {
-		"ATTR": function( match ) {
-			match[ 1 ] = match[ 1 ].replace( runescape, funescape );
-
-			// Move the given value to match[3] whether quoted or unquoted
-			match[ 3 ] = ( match[ 3 ] || match[ 4 ] ||
-				match[ 5 ] || "" ).replace( runescape, funescape );
-
-			if ( match[ 2 ] === "~=" ) {
-				match[ 3 ] = " " + match[ 3 ] + " ";
-			}
-
-			return match.slice( 0, 4 );
-		},
-
-		"CHILD": function( match ) {
-
-			/* matches from matchExpr["CHILD"]
-				1 type (only|nth|...)
-				2 what (child|of-type)
-				3 argument (even|odd|\d*|\d*n([+-]\d+)?|...)
-				4 xn-component of xn+y argument ([+-]?\d*n|)
-				5 sign of xn-component
-				6 x of xn-component
-				7 sign of y-component
-				8 y of y-component
-			*/
-			match[ 1 ] = match[ 1 ].toLowerCase();
-
-			if ( match[ 1 ].slice( 0, 3 ) === "nth" ) {
-
-				// nth-* requires argument
-				if ( !match[ 3 ] ) {
-					Sizzle.error( match[ 0 ] );
-				}
-
-				// numeric x and y parameters for Expr.filter.CHILD
-				// remember that false/true cast respectively to 0/1
-				match[ 4 ] = +( match[ 4 ] ?
-					match[ 5 ] + ( match[ 6 ] || 1 ) :
-					2 * ( match[ 3 ] === "even" || match[ 3 ] === "odd" ) );
-				match[ 5 ] = +( ( match[ 7 ] + match[ 8 ] ) || match[ 3 ] === "odd" );
-
-				// other types prohibit arguments
-			} else if ( match[ 3 ] ) {
-				Sizzle.error( match[ 0 ] );
-			}
-
-			return match;
-		},
-
-		"PSEUDO": function( match ) {
-			var excess,
-				unquoted = !match[ 6 ] && match[ 2 ];
-
-			if ( matchExpr[ "CHILD" ].test( match[ 0 ] ) ) {
-				return null;
-			}
-
-			// Accept quoted arguments as-is
-			if ( match[ 3 ] ) {
-				match[ 2 ] = match[ 4 ] || match[ 5 ] || "";
-
-			// Strip excess characters from unquoted arguments
-			} else if ( unquoted && rpseudo.test( unquoted ) &&
-
-				// Get excess from tokenize (recursively)
-				( excess = tokenize( unquoted, true ) ) &&
-
-				// advance to the next closing parenthesis
-				( excess = unquoted.indexOf( ")", unquoted.length - excess ) - unquoted.length ) ) {
-
-				// excess is a negative index
-				match[ 0 ] = match[ 0 ].slice( 0, excess );
-				match[ 2 ] = unquoted.slice( 0, excess );
-			}
-
-			// Return only captures needed by the pseudo filter method (type and argument)
-			return match.slice( 0, 3 );
-		}
-	},
-
-	filter: {
-
-		"TAG": function( nodeNameSelector ) {
-			var nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase();
-			return nodeNameSelector === "*" ?
-				function() {
-					return true;
-				} :
-				function( elem ) {
-					return elem.nodeName && elem.nodeName.toLowerCase() === nodeName;
-				};
-		},
-
-		"CLASS": function( className ) {
-			var pattern = classCache[ className + " " ];
-
-			return pattern ||
-				( pattern = new RegExp( "(^|" + whitespace +
-					")" + className + "(" + whitespace + "|$)" ) ) && classCache(
-						className, function( elem ) {
-							return pattern.test(
-								typeof elem.className === "string" && elem.className ||
-								typeof elem.getAttribute !== "undefined" &&
-									elem.getAttribute( "class" ) ||
-								""
-							);
-				} );
-		},
-
-		"ATTR": function( name, operator, check ) {
-			return function( elem ) {
-				var result = Sizzle.attr( elem, name );
-
-				if ( result == null ) {
-					return operator === "!=";
-				}
-				if ( !operator ) {
-					return true;
-				}
-
-				result += "";
-
-				/* eslint-disable max-len */
-
-				return operator === "=" ? result === check :
-					operator === "!=" ? result !== check :
-					operator === "^=" ? check && result.indexOf( check ) === 0 :
-					operator === "*=" ? check && result.indexOf( check ) > -1 :
-					operator === "$=" ? check && result.slice( -check.length ) === check :
-					operator === "~=" ? ( " " + result.replace( rwhitespace, " " ) + " " ).indexOf( check ) > -1 :
-					operator === "|=" ? result === check || result.slice( 0, check.length + 1 ) === check + "-" :
-					false;
-				/* eslint-enable max-len */
-
-			};
-		},
-
-		"CHILD": function( type, what, _argument, first, last ) {
-			var simple = type.slice( 0, 3 ) !== "nth",
-				forward = type.slice( -4 ) !== "last",
-				ofType = what === "of-type";
-
-			return first === 1 && last === 0 ?
-
-				// Shortcut for :nth-*(n)
-				function( elem ) {
-					return !!elem.parentNode;
-				} :
-
-				function( elem, _context, xml ) {
-					var cache, uniqueCache, outerCache, node, nodeIndex, start,
-						dir = simple !== forward ? "nextSibling" : "previousSibling",
-						parent = elem.parentNode,
-						name = ofType && elem.nodeName.toLowerCase(),
-						useCache = !xml && !ofType,
-						diff = false;
-
-					if ( parent ) {
-
-						// :(first|last|only)-(child|of-type)
-						if ( simple ) {
-							while ( dir ) {
-								node = elem;
-								while ( ( node = node[ dir ] ) ) {
-									if ( ofType ?
-										node.nodeName.toLowerCase() === name :
-										node.nodeType === 1 ) {
-
-										return false;
-									}
-								}
-
-								// Reverse direction for :only-* (if we haven't yet done so)
-								start = dir = type === "only" && !start && "nextSibling";
-							}
-							return true;
-						}
-
-						start = [ forward ? parent.firstChild : parent.lastChild ];
-
-						// non-xml :nth-child(...) stores cache data on `parent`
-						if ( forward && useCache ) {
-
-							// Seek `elem` from a previously-cached index
-
-							// ...in a gzip-friendly way
-							node = parent;
-							outerCache = node[ expando ] || ( node[ expando ] = {} );
-
-							// Support: IE <9 only
-							// Defend against cloned attroperties (jQuery gh-1709)
-							uniqueCache = outerCache[ node.uniqueID ] ||
-								( outerCache[ node.uniqueID ] = {} );
-
-							cache = uniqueCache[ type ] || [];
-							nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
-							diff = nodeIndex && cache[ 2 ];
-							node = nodeIndex && parent.childNodes[ nodeIndex ];
-
-							while ( ( node = ++nodeIndex && node && node[ dir ] ||
-
-								// Fallback to seeking `elem` from the start
-								( diff = nodeIndex = 0 ) || start.pop() ) ) {
-
-								// When found, cache indexes on `parent` and break
-								if ( node.nodeType === 1 && ++diff && node === elem ) {
-									uniqueCache[ type ] = [ dirruns, nodeIndex, diff ];
-									break;
-								}
-							}
-
-						} else {
-
-							// Use previously-cached element index if available
-							if ( useCache ) {
-
-								// ...in a gzip-friendly way
-								node = elem;
-								outerCache = node[ expando ] || ( node[ expando ] = {} );
-
-								// Support: IE <9 only
-								// Defend against cloned attroperties (jQuery gh-1709)
-								uniqueCache = outerCache[ node.uniqueID ] ||
-									( outerCache[ node.uniqueID ] = {} );
-
-								cache = uniqueCache[ type ] || [];
-								nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
-								diff = nodeIndex;
-							}
-
-							// xml :nth-child(...)
-							// or :nth-last-child(...) or :nth(-last)?-of-type(...)
-							if ( diff === false ) {
-
-								// Use the same loop as above to seek `elem` from the start
-								while ( ( node = ++nodeIndex && node && node[ dir ] ||
-									( diff = nodeIndex = 0 ) || start.pop() ) ) {
-
-									if ( ( ofType ?
-										node.nodeName.toLowerCase() === name :
-										node.nodeType === 1 ) &&
-										++diff ) {
-
-										// Cache the index of each encountered element
-										if ( useCache ) {
-											outerCache = node[ expando ] ||
-												( node[ expando ] = {} );
-
-											// Support: IE <9 only
-											// Defend against cloned attroperties (jQuery gh-1709)
-											uniqueCache = outerCache[ node.uniqueID ] ||
-												( outerCache[ node.uniqueID ] = {} );
-
-											uniqueCache[ type ] = [ dirruns, diff ];
-										}
-
-										if ( node === elem ) {
-											break;
-										}
-									}
-								}
-							}
-						}
-
-						// Incorporate the offset, then check against cycle size
-						diff -= last;
-						return diff === first || ( diff % first === 0 && diff / first >= 0 );
-					}
-				};
-		},
-
-		"PSEUDO": function( pseudo, argument ) {
-
-			// pseudo-class names are case-insensitive
-			// http://www.w3.org/TR/selectors/#pseudo-classes
-			// Prioritize by case sensitivity in case custom pseudos are added with uppercase letters
-			// Remember that setFilters inherits from pseudos
-			var args,
-				fn = Expr.pseudos[ pseudo ] || Expr.setFilters[ pseudo.toLowerCase() ] ||
-					Sizzle.error( "unsupported pseudo: " + pseudo );
-
-			// The user may use createPseudo to indicate that
-			// arguments are needed to create the filter function
-			// just as Sizzle does
-			if ( fn[ expando ] ) {
-				return fn( argument );
-			}
-
-			// But maintain support for old signatures
-			if ( fn.length > 1 ) {
-				args = [ pseudo, pseudo, "", argument ];
-				return Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ?
-					markFunction( function( seed, matches ) {
-						var idx,
-							matched = fn( seed, argument ),
-							i = matched.length;
-						while ( i-- ) {
-							idx = indexOf( seed, matched[ i ] );
-							seed[ idx ] = !( matches[ idx ] = matched[ i ] );
-						}
-					} ) :
-					function( elem ) {
-						return fn( elem, 0, args );
-					};
-			}
-
-			return fn;
-		}
-	},
-
-	pseudos: {
-
-		// Potentially complex pseudos
-		"not": markFunction( function( selector ) {
-
-			// Trim the selector passed to compile
-			// to avoid treating leading and trailing
-			// spaces as combinators
-			var input = [],
-				results = [],
-				matcher = compile( selector.replace( rtrim, "$1" ) );
-
-			return matcher[ expando ] ?
-				markFunction( function( seed, matches, _context, xml ) {
-					var elem,
-						unmatched = matcher( seed, null, xml, [] ),
-						i = seed.length;
-
-					// Match elements unmatched by `matcher`
-					while ( i-- ) {
-						if ( ( elem = unmatched[ i ] ) ) {
-							seed[ i ] = !( matches[ i ] = elem );
-						}
-					}
-				} ) :
-				function( elem, _context, xml ) {
-					input[ 0 ] = elem;
-					matcher( input, null, xml, results );
-
-					// Don't keep the element (issue #299)
-					input[ 0 ] = null;
-					return !results.pop();
-				};
-		} ),
-
-		"has": markFunction( function( selector ) {
-			return function( elem ) {
-				return Sizzle( selector, elem ).length > 0;
-			};
-		} ),
-
-		"contains": markFunction( function( text ) {
-			text = text.replace( runescape, funescape );
-			return function( elem ) {
-				return ( elem.textContent || getText( elem ) ).indexOf( text ) > -1;
-			};
-		} ),
-
-		// "Whether an element is represented by a :lang() selector
-		// is based solely on the element's language value
-		// being equal to the identifier C,
-		// or beginning with the identifier C immediately followed by "-".
-		// The matching of C against the element's language value is performed case-insensitively.
-		// The identifier C does not have to be a valid language name."
-		// http://www.w3.org/TR/selectors/#lang-pseudo
-		"lang": markFunction( function( lang ) {
-
-			// lang value must be a valid identifier
-			if ( !ridentifier.test( lang || "" ) ) {
-				Sizzle.error( "unsupported lang: " + lang );
-			}
-			lang = lang.replace( runescape, funescape ).toLowerCase();
-			return function( elem ) {
-				var elemLang;
-				do {
-					if ( ( elemLang = documentIsHTML ?
-						elem.lang :
-						elem.getAttribute( "xml:lang" ) || elem.getAttribute( "lang" ) ) ) {
-
-						elemLang = elemLang.toLowerCase();
-						return elemLang === lang || elemLang.indexOf( lang + "-" ) === 0;
-					}
-				} while ( ( elem = elem.parentNode ) && elem.nodeType === 1 );
-				return false;
-			};
-		} ),
-
-		// Miscellaneous
-		"target": function( elem ) {
-			var hash = window.location && window.location.hash;
-			return hash && hash.slice( 1 ) === elem.id;
-		},
-
-		"root": function( elem ) {
-			return elem === docElem;
-		},
-
-		"focus": function( elem ) {
-			return elem === document.activeElement &&
-				( !document.hasFocus || document.hasFocus() ) &&
-				!!( elem.type || elem.href || ~elem.tabIndex );
-		},
-
-		// Boolean properties
-		"enabled": createDisabledPseudo( false ),
-		"disabled": createDisabledPseudo( true ),
-
-		"checked": function( elem ) {
-
-			// In CSS3, :checked should return both checked and selected elements
-			// http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked
-			var nodeName = elem.nodeName.toLowerCase();
-			return ( nodeName === "input" && !!elem.checked ) ||
-				( nodeName === "option" && !!elem.selected );
-		},
-
-		"selected": function( elem ) {
-
-			// Accessing this property makes selected-by-default
-			// options in Safari work properly
-			if ( elem.parentNode ) {
-				// eslint-disable-next-line no-unused-expressions
-				elem.parentNode.selectedIndex;
-			}
-
-			return elem.selected === true;
-		},
-
-		// Contents
-		"empty": function( elem ) {
-
-			// http://www.w3.org/TR/selectors/#empty-pseudo
-			// :empty is negated by element (1) or content nodes (text: 3; cdata: 4; entity ref: 5),
-			//   but not by others (comment: 8; processing instruction: 7; etc.)
-			// nodeType < 6 works because attributes (2) do not appear as children
-			for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {
-				if ( elem.nodeType < 6 ) {
-					return false;
-				}
-			}
-			return true;
-		},
-
-		"parent": function( elem ) {
-			return !Expr.pseudos[ "empty" ]( elem );
-		},
-
-		// Element/input types
-		"header": function( elem ) {
-			return rheader.test( elem.nodeName );
-		},
-
-		"input": function( elem ) {
-			return rinputs.test( elem.nodeName );
-		},
-
-		"button": function( elem ) {
-			var name = elem.nodeName.toLowerCase();
-			return name === "input" && elem.type === "button" || name === "button";
-		},
-
-		"text": function( elem ) {
-			var attr;
-			return elem.nodeName.toLowerCase() === "input" &&
-				elem.type === "text" &&
-
-				// Support: IE<8
-				// New HTML5 attribute values (e.g., "search") appear with elem.type === "text"
-				( ( attr = elem.getAttribute( "type" ) ) == null ||
-					attr.toLowerCase() === "text" );
-		},
-
-		// Position-in-collection
-		"first": createPositionalPseudo( function() {
-			return [ 0 ];
-		} ),
-
-		"last": createPositionalPseudo( function( _matchIndexes, length ) {
-			return [ length - 1 ];
-		} ),
-
-		"eq": createPositionalPseudo( function( _matchIndexes, length, argument ) {
-			return [ argument < 0 ? argument + length : argument ];
-		} ),
-
-		"even": createPositionalPseudo( function( matchIndexes, length ) {
-			var i = 0;
-			for ( ; i < length; i += 2 ) {
-				matchIndexes.push( i );
-			}
-			return matchIndexes;
-		} ),
-
-		"odd": createPositionalPseudo( function( matchIndexes, length ) {
-			var i = 1;
-			for ( ; i < length; i += 2 ) {
-				matchIndexes.push( i );
-			}
-			return matchIndexes;
-		} ),
-
-		"lt": createPositionalPseudo( function( matchIndexes, length, argument ) {
-			var i = argument < 0 ?
-				argument + length :
-				argument > length ?
-					length :
-					argument;
-			for ( ; --i >= 0; ) {
-				matchIndexes.push( i );
-			}
-			return matchIndexes;
-		} ),
-
-		"gt": createPositionalPseudo( function( matchIndexes, length, argument ) {
-			var i = argument < 0 ? argument + length : argument;
-			for ( ; ++i < length; ) {
-				matchIndexes.push( i );
-			}
-			return matchIndexes;
-		} )
-	}
-};
-
-Expr.pseudos[ "nth" ] = Expr.pseudos[ "eq" ];
-
-// Add button/input type pseudos
-for ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) {
-	Expr.pseudos[ i ] = createInputPseudo( i );
-}
-for ( i in { submit: true, reset: true } ) {
-	Expr.pseudos[ i ] = createButtonPseudo( i );
-}
-
-// Easy API for creating new setFilters
-function setFilters() {}
-setFilters.prototype = Expr.filters = Expr.pseudos;
-Expr.setFilters = new setFilters();
-
-tokenize = Sizzle.tokenize = function( selector, parseOnly ) {
-	var matched, match, tokens, type,
-		soFar, groups, preFilters,
-		cached = tokenCache[ selector + " " ];
-
-	if ( cached ) {
-		return parseOnly ? 0 : cached.slice( 0 );
-	}
-
-	soFar = selector;
-	groups = [];
-	preFilters = Expr.preFilter;
-
-	while ( soFar ) {
-
-		// Comma and first run
-		if ( !matched || ( match = rcomma.exec( soFar ) ) ) {
-			if ( match ) {
-
-				// Don't consume trailing commas as valid
-				soFar = soFar.slice( match[ 0 ].length ) || soFar;
-			}
-			groups.push( ( tokens = [] ) );
-		}
-
-		matched = false;
-
-		// Combinators
-		if ( ( match = rcombinators.exec( soFar ) ) ) {
-			matched = match.shift();
-			tokens.push( {
-				value: matched,
-
-				// Cast descendant combinators to space
-				type: match[ 0 ].replace( rtrim, " " )
-			} );
-			soFar = soFar.slice( matched.length );
-		}
-
-		// Filters
-		for ( type in Expr.filter ) {
-			if ( ( match = matchExpr[ type ].exec( soFar ) ) && ( !preFilters[ type ] ||
-				( match = preFilters[ type ]( match ) ) ) ) {
-				matched = match.shift();
-				tokens.push( {
-					value: matched,
-					type: type,
-					matches: match
-				} );
-				soFar = soFar.slice( matched.length );
-			}
-		}
-
-		if ( !matched ) {
-			break;
-		}
-	}
-
-	// Return the length of the invalid excess
-	// if we're just parsing
-	// Otherwise, throw an error or return tokens
-	return parseOnly ?
-		soFar.length :
-		soFar ?
-			Sizzle.error( selector ) :
-
-			// Cache the tokens
-			tokenCache( selector, groups ).slice( 0 );
-};
-
-function toSelector( tokens ) {
-	var i = 0,
-		len = tokens.length,
-		selector = "";
-	for ( ; i < len; i++ ) {
-		selector += tokens[ i ].value;
-	}
-	return selector;
-}
-
-function addCombinator( matcher, combinator, base ) {
-	var dir = combinator.dir,
-		skip = combinator.next,
-		key = skip || dir,
-		checkNonElements = base && key === "parentNode",
-		doneName = done++;
-
-	return combinator.first ?
-
-		// Check against closest ancestor/preceding element
-		function( elem, context, xml ) {
-			while ( ( elem = elem[ dir ] ) ) {
-				if ( elem.nodeType === 1 || checkNonElements ) {
-					return matcher( elem, context, xml );
-				}
-			}
-			return false;
-		} :
-
-		// Check against all ancestor/preceding elements
-		function( elem, context, xml ) {
-			var oldCache, uniqueCache, outerCache,
-				newCache = [ dirruns, doneName ];
-
-			// We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching
-			if ( xml ) {
-				while ( ( elem = elem[ dir ] ) ) {
-					if ( elem.nodeType === 1 || checkNonElements ) {
-						if ( matcher( elem, context, xml ) ) {
-							return true;
-						}
-					}
-				}
-			} else {
-				while ( ( elem = elem[ dir ] ) ) {
-					if ( elem.nodeType === 1 || checkNonElements ) {
-						outerCache = elem[ expando ] || ( elem[ expando ] = {} );
-
-						// Support: IE <9 only
-						// Defend against cloned attroperties (jQuery gh-1709)
-						uniqueCache = outerCache[ elem.uniqueID ] ||
-							( outerCache[ elem.uniqueID ] = {} );
-
-						if ( skip && skip === elem.nodeName.toLowerCase() ) {
-							elem = elem[ dir ] || elem;
-						} else if ( ( oldCache = uniqueCache[ key ] ) &&
-							oldCache[ 0 ] === dirruns && oldCache[ 1 ] === doneName ) {
-
-							// Assign to newCache so results back-propagate to previous elements
-							return ( newCache[ 2 ] = oldCache[ 2 ] );
-						} else {
-
-							// Reuse newcache so results back-propagate to previous elements
-							uniqueCache[ key ] = newCache;
-
-							// A match means we're done; a fail means we have to keep checking
-							if ( ( newCache[ 2 ] = matcher( elem, context, xml ) ) ) {
-								return true;
-							}
-						}
-					}
-				}
-			}
-			return false;
-		};
-}
-
-function elementMatcher( matchers ) {
-	return matchers.length > 1 ?
-		function( elem, context, xml ) {
-			var i = matchers.length;
-			while ( i-- ) {
-				if ( !matchers[ i ]( elem, context, xml ) ) {
-					return false;
-				}
-			}
-			return true;
-		} :
-		matchers[ 0 ];
-}
-
-function multipleContexts( selector, contexts, results ) {
-	var i = 0,
-		len = contexts.length;
-	for ( ; i < len; i++ ) {
-		Sizzle( selector, contexts[ i ], results );
-	}
-	return results;
-}
-
-function condense( unmatched, map, filter, context, xml ) {
-	var elem,
-		newUnmatched = [],
-		i = 0,
-		len = unmatched.length,
-		mapped = map != null;
-
-	for ( ; i < len; i++ ) {
-		if ( ( elem = unmatched[ i ] ) ) {
-			if ( !filter || filter( elem, context, xml ) ) {
-				newUnmatched.push( elem );
-				if ( mapped ) {
-					map.push( i );
-				}
-			}
-		}
-	}
-
-	return newUnmatched;
-}
-
-function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postSelector ) {
-	if ( postFilter && !postFilter[ expando ] ) {
-		postFilter = setMatcher( postFilter );
-	}
-	if ( postFinder && !postFinder[ expando ] ) {
-		postFinder = setMatcher( postFinder, postSelector );
-	}
-	return markFunction( function( seed, results, context, xml ) {
-		var temp, i, elem,
-			preMap = [],
-			postMap = [],
-			preexisting = results.length,
-
-			// Get initial elements from seed or context
-			elems = seed || multipleContexts(
-				selector || "*",
-				context.nodeType ? [ context ] : context,
-				[]
-			),
-
-			// Prefilter to get matcher input, preserving a map for seed-results synchronization
-			matcherIn = preFilter && ( seed || !selector ) ?
-				condense( elems, preMap, preFilter, context, xml ) :
-				elems,
-
-			matcherOut = matcher ?
-
-				// If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results,
-				postFinder || ( seed ? preFilter : preexisting || postFilter ) ?
-
-					// ...intermediate processing is necessary
-					[] :
-
-					// ...otherwise use results directly
-					results :
-				matcherIn;
-
-		// Find primary matches
-		if ( matcher ) {
-			matcher( matcherIn, matcherOut, context, xml );
-		}
-
-		// Apply postFilter
-		if ( postFilter ) {
-			temp = condense( matcherOut, postMap );
-			postFilter( temp, [], context, xml );
-
-			// Un-match failing elements by moving them back to matcherIn
-			i = temp.length;
-			while ( i-- ) {
-				if ( ( elem = temp[ i ] ) ) {
-					matcherOut[ postMap[ i ] ] = !( matcherIn[ postMap[ i ] ] = elem );
-				}
-			}
-		}
-
-		if ( seed ) {
-			if ( postFinder || preFilter ) {
-				if ( postFinder ) {
-
-					// Get the final matcherOut by condensing this intermediate into postFinder contexts
-					temp = [];
-					i = matcherOut.length;
-					while ( i-- ) {
-						if ( ( elem = matcherOut[ i ] ) ) {
-
-							// Restore matcherIn since elem is not yet a final match
-							temp.push( ( matcherIn[ i ] = elem ) );
-						}
-					}
-					postFinder( null, ( matcherOut = [] ), temp, xml );
-				}
-
-				// Move matched elements from seed to results to keep them synchronized
-				i = matcherOut.length;
-				while ( i-- ) {
-					if ( ( elem = matcherOut[ i ] ) &&
-						( temp = postFinder ? indexOf( seed, elem ) : preMap[ i ] ) > -1 ) {
-
-						seed[ temp ] = !( results[ temp ] = elem );
-					}
-				}
-			}
-
-		// Add elements to results, through postFinder if defined
-		} else {
-			matcherOut = condense(
-				matcherOut === results ?
-					matcherOut.splice( preexisting, matcherOut.length ) :
-					matcherOut
-			);
-			if ( postFinder ) {
-				postFinder( null, results, matcherOut, xml );
-			} else {
-				push.apply( results, matcherOut );
-			}
-		}
-	} );
-}
-
-function matcherFromTokens( tokens ) {
-	var checkContext, matcher, j,
-		len = tokens.length,
-		leadingRelative = Expr.relative[ tokens[ 0 ].type ],
-		implicitRelative = leadingRelative || Expr.relative[ " " ],
-		i = leadingRelative ? 1 : 0,
-
-		// The foundational matcher ensures that elements are reachable from top-level context(s)
-		matchContext = addCombinator( function( elem ) {
-			return elem === checkContext;
-		}, implicitRelative, true ),
-		matchAnyContext = addCombinator( function( elem ) {
-			return indexOf( checkContext, elem ) > -1;
-		}, implicitRelative, true ),
-		matchers = [ function( elem, context, xml ) {
-			var ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || (
-				( checkContext = context ).nodeType ?
-					matchContext( elem, context, xml ) :
-					matchAnyContext( elem, context, xml ) );
-
-			// Avoid hanging onto element (issue #299)
-			checkContext = null;
-			return ret;
-		} ];
-
-	for ( ; i < len; i++ ) {
-		if ( ( matcher = Expr.relative[ tokens[ i ].type ] ) ) {
-			matchers = [ addCombinator( elementMatcher( matchers ), matcher ) ];
-		} else {
-			matcher = Expr.filter[ tokens[ i ].type ].apply( null, tokens[ i ].matches );
-
-			// Return special upon seeing a positional matcher
-			if ( matcher[ expando ] ) {
-
-				// Find the next relative operator (if any) for proper handling
-				j = ++i;
-				for ( ; j < len; j++ ) {
-					if ( Expr.relative[ tokens[ j ].type ] ) {
-						break;
-					}
-				}
-				return setMatcher(
-					i > 1 && elementMatcher( matchers ),
-					i > 1 && toSelector(
-
-					// If the preceding token was a descendant combinator, insert an implicit any-element `*`
-					tokens
-						.slice( 0, i - 1 )
-						.concat( { value: tokens[ i - 2 ].type === " " ? "*" : "" } )
-					).replace( rtrim, "$1" ),
-					matcher,
-					i < j && matcherFromTokens( tokens.slice( i, j ) ),
-					j < len && matcherFromTokens( ( tokens = tokens.slice( j ) ) ),
-					j < len && toSelector( tokens )
-				);
-			}
-			matchers.push( matcher );
-		}
-	}
-
-	return elementMatcher( matchers );
-}
-
-function matcherFromGroupMatchers( elementMatchers, setMatchers ) {
-	var bySet = setMatchers.length > 0,
-		byElement = elementMatchers.length > 0,
-		superMatcher = function( seed, context, xml, results, outermost ) {
-			var elem, j, matcher,
-				matchedCount = 0,
-				i = "0",
-				unmatched = seed && [],
-				setMatched = [],
-				contextBackup = outermostContext,
-
-				// We must always have either seed elements or outermost context
-				elems = seed || byElement && Expr.find[ "TAG" ]( "*", outermost ),
-
-				// Use integer dirruns iff this is the outermost matcher
-				dirrunsUnique = ( dirruns += contextBackup == null ? 1 : Math.random() || 0.1 ),
-				len = elems.length;
-
-			if ( outermost ) {
-
-				// Support: IE 11+, Edge 17 - 18+
-				// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-				// two documents; shallow comparisons work.
-				// eslint-disable-next-line eqeqeq
-				outermostContext = context == document || context || outermost;
-			}
-
-			// Add elements passing elementMatchers directly to results
-			// Support: IE<9, Safari
-			// Tolerate NodeList properties (IE: "length"; Safari: <number>) matching elements by id
-			for ( ; i !== len && ( elem = elems[ i ] ) != null; i++ ) {
-				if ( byElement && elem ) {
-					j = 0;
-
-					// Support: IE 11+, Edge 17 - 18+
-					// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-					// two documents; shallow comparisons work.
-					// eslint-disable-next-line eqeqeq
-					if ( !context && elem.ownerDocument != document ) {
-						setDocument( elem );
-						xml = !documentIsHTML;
-					}
-					while ( ( matcher = elementMatchers[ j++ ] ) ) {
-						if ( matcher( elem, context || document, xml ) ) {
-							results.push( elem );
-							break;
-						}
-					}
-					if ( outermost ) {
-						dirruns = dirrunsUnique;
-					}
-				}
-
-				// Track unmatched elements for set filters
-				if ( bySet ) {
-
-					// They will have gone through all possible matchers
-					if ( ( elem = !matcher && elem ) ) {
-						matchedCount--;
-					}
-
-					// Lengthen the array for every element, matched or not
-					if ( seed ) {
-						unmatched.push( elem );
-					}
-				}
-			}
-
-			// `i` is now the count of elements visited above, and adding it to `matchedCount`
-			// makes the latter nonnegative.
-			matchedCount += i;
-
-			// Apply set filters to unmatched elements
-			// NOTE: This can be skipped if there are no unmatched elements (i.e., `matchedCount`
-			// equals `i`), unless we didn't visit _any_ elements in the above loop because we have
-			// no element matchers and no seed.
-			// Incrementing an initially-string "0" `i` allows `i` to remain a string only in that
-			// case, which will result in a "00" `matchedCount` that differs from `i` but is also
-			// numerically zero.
-			if ( bySet && i !== matchedCount ) {
-				j = 0;
-				while ( ( matcher = setMatchers[ j++ ] ) ) {
-					matcher( unmatched, setMatched, context, xml );
-				}
-
-				if ( seed ) {
-
-					// Reintegrate element matches to eliminate the need for sorting
-					if ( matchedCount > 0 ) {
-						while ( i-- ) {
-							if ( !( unmatched[ i ] || setMatched[ i ] ) ) {
-								setMatched[ i ] = pop.call( results );
-							}
-						}
-					}
-
-					// Discard index placeholder values to get only actual matches
-					setMatched = condense( setMatched );
-				}
-
-				// Add matches to results
-				push.apply( results, setMatched );
-
-				// Seedless set matches succeeding multiple successful matchers stipulate sorting
-				if ( outermost && !seed && setMatched.length > 0 &&
-					( matchedCount + setMatchers.length ) > 1 ) {
-
-					Sizzle.uniqueSort( results );
-				}
-			}
-
-			// Override manipulation of globals by nested matchers
-			if ( outermost ) {
-				dirruns = dirrunsUnique;
-				outermostContext = contextBackup;
-			}
-
-			return unmatched;
-		};
-
-	return bySet ?
-		markFunction( superMatcher ) :
-		superMatcher;
-}
-
-compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) {
-	var i,
-		setMatchers = [],
-		elementMatchers = [],
-		cached = compilerCache[ selector + " " ];
-
-	if ( !cached ) {
-
-		// Generate a function of recursive functions that can be used to check each element
-		if ( !match ) {
-			match = tokenize( selector );
-		}
-		i = match.length;
-		while ( i-- ) {
-			cached = matcherFromTokens( match[ i ] );
-			if ( cached[ expando ] ) {
-				setMatchers.push( cached );
-			} else {
-				elementMatchers.push( cached );
-			}
-		}
-
-		// Cache the compiled function
-		cached = compilerCache(
-			selector,
-			matcherFromGroupMatchers( elementMatchers, setMatchers )
-		);
-
-		// Save selector and tokenization
-		cached.selector = selector;
-	}
-	return cached;
-};
-
-/**
- * A low-level selection function that works with Sizzle's compiled
- *  selector functions
- * @param {String|Function} selector A selector or a pre-compiled
- *  selector function built with Sizzle.compile
- * @param {Element} context
- * @param {Array} [results]
- * @param {Array} [seed] A set of elements to match against
- */
-select = Sizzle.select = function( selector, context, results, seed ) {
-	var i, tokens, token, type, find,
-		compiled = typeof selector === "function" && selector,
-		match = !seed && tokenize( ( selector = compiled.selector || selector ) );
-
-	results = results || [];
-
-	// Try to minimize operations if there is only one selector in the list and no seed
-	// (the latter of which guarantees us context)
-	if ( match.length === 1 ) {
-
-		// Reduce context if the leading compound selector is an ID
-		tokens = match[ 0 ] = match[ 0 ].slice( 0 );
-		if ( tokens.length > 2 && ( token = tokens[ 0 ] ).type === "ID" &&
-			context.nodeType === 9 && documentIsHTML && Expr.relative[ tokens[ 1 ].type ] ) {
-
-			context = ( Expr.find[ "ID" ]( token.matches[ 0 ]
-				.replace( runescape, funescape ), context ) || [] )[ 0 ];
-			if ( !context ) {
-				return results;
-
-			// Precompiled matchers will still verify ancestry, so step up a level
-			} else if ( compiled ) {
-				context = context.parentNode;
-			}
-
-			selector = selector.slice( tokens.shift().value.length );
-		}
-
-		// Fetch a seed set for right-to-left matching
-		i = matchExpr[ "needsContext" ].test( selector ) ? 0 : tokens.length;
-		while ( i-- ) {
-			token = tokens[ i ];
-
-			// Abort if we hit a combinator
-			if ( Expr.relative[ ( type = token.type ) ] ) {
-				break;
-			}
-			if ( ( find = Expr.find[ type ] ) ) {
-
-				// Search, expanding context for leading sibling combinators
-				if ( ( seed = find(
-					token.matches[ 0 ].replace( runescape, funescape ),
-					rsibling.test( tokens[ 0 ].type ) && testContext( context.parentNode ) ||
-						context
-				) ) ) {
-
-					// If seed is empty or no tokens remain, we can return early
-					tokens.splice( i, 1 );
-					selector = seed.length && toSelector( tokens );
-					if ( !selector ) {
-						push.apply( results, seed );
-						return results;
-					}
-
-					break;
-				}
-			}
-		}
-	}
-
-	// Compile and execute a filtering function if one is not provided
-	// Provide `match` to avoid retokenization if we modified the selector above
-	( compiled || compile( selector, match ) )(
-		seed,
-		context,
-		!documentIsHTML,
-		results,
-		!context || rsibling.test( selector ) && testContext( context.parentNode ) || context
-	);
-	return results;
-};
-
-// One-time assignments
-
-// Sort stability
-support.sortStable = expando.split( "" ).sort( sortOrder ).join( "" ) === expando;
-
-// Support: Chrome 14-35+
-// Always assume duplicates if they aren't passed to the comparison function
-support.detectDuplicates = !!hasDuplicate;
-
-// Initialize against the default document
-setDocument();
-
-// Support: Webkit<537.32 - Safari 6.0.3/Chrome 25 (fixed in Chrome 27)
-// Detached nodes confoundingly follow *each other*
-support.sortDetached = assert( function( el ) {
-
-	// Should return 1, but returns 4 (following)
-	return el.compareDocumentPosition( document.createElement( "fieldset" ) ) & 1;
-} );
-
-// Support: IE<8
-// Prevent attribute/property "interpolation"
-// https://msdn.microsoft.com/en-us/library/ms536429%28VS.85%29.aspx
-if ( !assert( function( el ) {
-	el.innerHTML = "<a href='#'></a>";
-	return el.firstChild.getAttribute( "href" ) === "#";
-} ) ) {
-	addHandle( "type|href|height|width", function( elem, name, isXML ) {
-		if ( !isXML ) {
-			return elem.getAttribute( name, name.toLowerCase() === "type" ? 1 : 2 );
-		}
-	} );
-}
-
-// Support: IE<9
-// Use defaultValue in place of getAttribute("value")
-if ( !support.attributes || !assert( function( el ) {
-	el.innerHTML = "<input/>";
-	el.firstChild.setAttribute( "value", "" );
-	return el.firstChild.getAttribute( "value" ) === "";
-} ) ) {
-	addHandle( "value", function( elem, _name, isXML ) {
-		if ( !isXML && elem.nodeName.toLowerCase() === "input" ) {
-			return elem.defaultValue;
-		}
-	} );
-}
-
-// Support: IE<9
-// Use getAttributeNode to fetch booleans when getAttribute lies
-if ( !assert( function( el ) {
-	return el.getAttribute( "disabled" ) == null;
-} ) ) {
-	addHandle( booleans, function( elem, name, isXML ) {
-		var val;
-		if ( !isXML ) {
-			return elem[ name ] === true ? name.toLowerCase() :
-				( val = elem.getAttributeNode( name ) ) && val.specified ?
-					val.value :
-					null;
-		}
-	} );
-}
-
-return Sizzle;
-
-} )( window );
-
-
-
-jQuery.find = Sizzle;
-jQuery.expr = Sizzle.selectors;
-
-// Deprecated
-jQuery.expr[ ":" ] = jQuery.expr.pseudos;
-jQuery.uniqueSort = jQuery.unique = Sizzle.uniqueSort;
-jQuery.text = Sizzle.getText;
-jQuery.isXMLDoc = Sizzle.isXML;
-jQuery.contains = Sizzle.contains;
-jQuery.escapeSelector = Sizzle.escape;
-
-
-
-
-var dir = function( elem, dir, until ) {
-	var matched = [],
-		truncate = until !== undefined;
-
-	while ( ( elem = elem[ dir ] ) && elem.nodeType !== 9 ) {
-		if ( elem.nodeType === 1 ) {
-			if ( truncate && jQuery( elem ).is( until ) ) {
-				break;
-			}
-			matched.push( elem );
-		}
-	}
-	return matched;
-};
-
-
-var siblings = function( n, elem ) {
-	var matched = [];
-
-	for ( ; n; n = n.nextSibling ) {
-		if ( n.nodeType === 1 && n !== elem ) {
-			matched.push( n );
-		}
-	}
-
-	return matched;
-};
-
-
-var rneedsContext = jQuery.expr.match.needsContext;
-
-
-
-function nodeName( elem, name ) {
-
-	return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase();
-
-}
-var rsingleTag = ( /^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i );
-
-
-
-// Implement the identical functionality for filter and not
-function winnow( elements, qualifier, not ) {
-	if ( isFunction( qualifier ) ) {
-		return jQuery.grep( elements, function( elem, i ) {
-			return !!qualifier.call( elem, i, elem ) !== not;
-		} );
-	}
-
-	// Single element
-	if ( qualifier.nodeType ) {
-		return jQuery.grep( elements, function( elem ) {
-			return ( elem === qualifier ) !== not;
-		} );
-	}
-
-	// Arraylike of elements (jQuery, arguments, Array)
-	if ( typeof qualifier !== "string" ) {
-		return jQuery.grep( elements, function( elem ) {
-			return ( indexOf.call( qualifier, elem ) > -1 ) !== not;
-		} );
-	}
-
-	// Filtered directly for both simple and complex selectors
-	return jQuery.filter( qualifier, elements, not );
-}
-
-jQuery.filter = function( expr, elems, not ) {
-	var elem = elems[ 0 ];
-
-	if ( not ) {
-		expr = ":not(" + expr + ")";
-	}
-
-	if ( elems.length === 1 && elem.nodeType === 1 ) {
-		return jQuery.find.matchesSelector( elem, expr ) ? [ elem ] : [];
-	}
-
-	return jQuery.find.matches( expr, jQuery.grep( elems, function( elem ) {
-		return elem.nodeType === 1;
-	} ) );
-};
-
-jQuery.fn.extend( {
-	find: function( selector ) {
-		var i, ret,
-			len = this.length,
-			self = this;
-
-		if ( typeof selector !== "string" ) {
-			return this.pushStack( jQuery( selector ).filter( function() {
-				for ( i = 0; i < len; i++ ) {
-					if ( jQuery.contains( self[ i ], this ) ) {
-						return true;
-					}
-				}
-			} ) );
-		}
-
-		ret = this.pushStack( [] );
-
-		for ( i = 0; i < len; i++ ) {
-			jQuery.find( selector, self[ i ], ret );
-		}
-
-		return len > 1 ? jQuery.uniqueSort( ret ) : ret;
-	},
-	filter: function( selector ) {
-		return this.pushStack( winnow( this, selector || [], false ) );
-	},
-	not: function( selector ) {
-		return this.pushStack( winnow( this, selector || [], true ) );
-	},
-	is: function( selector ) {
-		return !!winnow(
-			this,
-
-			// If this is a positional/relative selector, check membership in the returned set
-			// so $("p:first").is("p:last") won't return true for a doc with two "p".
-			typeof selector === "string" && rneedsContext.test( selector ) ?
-				jQuery( selector ) :
-				selector || [],
-			false
-		).length;
-	}
-} );
-
-
-// Initialize a jQuery object
-
-
-// A central reference to the root jQuery(document)
-var rootjQuery,
-
-	// A simple way to check for HTML strings
-	// Prioritize #id over <tag> to avoid XSS via location.hash (#9521)
-	// Strict HTML recognition (#11290: must start with <)
-	// Shortcut simple #id case for speed
-	rquickExpr = /^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/,
-
-	init = jQuery.fn.init = function( selector, context, root ) {
-		var match, elem;
-
-		// HANDLE: $(""), $(null), $(undefined), $(false)
-		if ( !selector ) {
-			return this;
-		}
-
-		// Method init() accepts an alternate rootjQuery
-		// so migrate can support jQuery.sub (gh-2101)
-		root = root || rootjQuery;
-
-		// Handle HTML strings
-		if ( typeof selector === "string" ) {
-			if ( selector[ 0 ] === "<" &&
-				selector[ selector.length - 1 ] === ">" &&
-				selector.length >= 3 ) {
-
-				// Assume that strings that start and end with <> are HTML and skip the regex check
-				match = [ null, selector, null ];
-
-			} else {
-				match = rquickExpr.exec( selector );
-			}
-
-			// Match html or make sure no context is specified for #id
-			if ( match && ( match[ 1 ] || !context ) ) {
-
-				// HANDLE: $(html) -> $(array)
-				if ( match[ 1 ] ) {
-					context = context instanceof jQuery ? context[ 0 ] : context;
-
-					// Option to run scripts is true for back-compat
-					// Intentionally let the error be thrown if parseHTML is not present
-					jQuery.merge( this, jQuery.parseHTML(
-						match[ 1 ],
-						context && context.nodeType ? context.ownerDocument || context : document,
-						true
-					) );
-
-					// HANDLE: $(html, props)
-					if ( rsingleTag.test( match[ 1 ] ) && jQuery.isPlainObject( context ) ) {
-						for ( match in context ) {
-
-							// Properties of context are called as methods if possible
-							if ( isFunction( this[ match ] ) ) {
-								this[ match ]( context[ match ] );
-
-							// ...and otherwise set as attributes
-							} else {
-								this.attr( match, context[ match ] );
-							}
-						}
-					}
-
-					return this;
-
-				// HANDLE: $(#id)
-				} else {
-					elem = document.getElementById( match[ 2 ] );
-
-					if ( elem ) {
-
-						// Inject the element directly into the jQuery object
-						this[ 0 ] = elem;
-						this.length = 1;
-					}
-					return this;
-				}
-
-			// HANDLE: $(expr, $(...))
-			} else if ( !context || context.jquery ) {
-				return ( context || root ).find( selector );
-
-			// HANDLE: $(expr, context)
-			// (which is just equivalent to: $(context).find(expr)
-			} else {
-				return this.constructor( context ).find( selector );
-			}
-
-		// HANDLE: $(DOMElement)
-		} else if ( selector.nodeType ) {
-			this[ 0 ] = selector;
-			this.length = 1;
-			return this;
-
-		// HANDLE: $(function)
-		// Shortcut for document ready
-		} else if ( isFunction( selector ) ) {
-			return root.ready !== undefined ?
-				root.ready( selector ) :
-
-				// Execute immediately if ready is not present
-				selector( jQuery );
-		}
-
-		return jQuery.makeArray( selector, this );
-	};
-
-// Give the init function the jQuery prototype for later instantiation
-init.prototype = jQuery.fn;
-
-// Initialize central reference
-rootjQuery = jQuery( document );
-
-
-var rparentsprev = /^(?:parents|prev(?:Until|All))/,
-
-	// Methods guaranteed to produce a unique set when starting from a unique set
-	guaranteedUnique = {
-		children: true,
-		contents: true,
-		next: true,
-		prev: true
-	};
-
-jQuery.fn.extend( {
-	has: function( target ) {
-		var targets = jQuery( target, this ),
-			l = targets.length;
-
-		return this.filter( function() {
-			var i = 0;
-			for ( ; i < l; i++ ) {
-				if ( jQuery.contains( this, targets[ i ] ) ) {
-					return true;
-				}
-			}
-		} );
-	},
-
-	closest: function( selectors, context ) {
-		var cur,
-			i = 0,
-			l = this.length,
-			matched = [],
-			targets = typeof selectors !== "string" && jQuery( selectors );
-
-		// Positional selectors never match, since there's no _selection_ context
-		if ( !rneedsContext.test( selectors ) ) {
-			for ( ; i < l; i++ ) {
-				for ( cur = this[ i ]; cur && cur !== context; cur = cur.parentNode ) {
-
-					// Always skip document fragments
-					if ( cur.nodeType < 11 && ( targets ?
-						targets.index( cur ) > -1 :
-
-						// Don't pass non-elements to Sizzle
-						cur.nodeType === 1 &&
-							jQuery.find.matchesSelector( cur, selectors ) ) ) {
-
-						matched.push( cur );
-						break;
-					}
-				}
-			}
-		}
-
-		return this.pushStack( matched.length > 1 ? jQuery.uniqueSort( matched ) : matched );
-	},
-
-	// Determine the position of an element within the set
-	index: function( elem ) {
-
-		// No argument, return index in parent
-		if ( !elem ) {
-			return ( this[ 0 ] && this[ 0 ].parentNode ) ? this.first().prevAll().length : -1;
-		}
-
-		// Index in selector
-		if ( typeof elem === "string" ) {
-			return indexOf.call( jQuery( elem ), this[ 0 ] );
-		}
-
-		// Locate the position of the desired element
-		return indexOf.call( this,
-
-			// If it receives a jQuery object, the first element is used
-			elem.jquery ? elem[ 0 ] : elem
-		);
-	},
-
-	add: function( selector, context ) {
-		return this.pushStack(
-			jQuery.uniqueSort(
-				jQuery.merge( this.get(), jQuery( selector, context ) )
-			)
-		);
-	},
-
-	addBack: function( selector ) {
-		return this.add( selector == null ?
-			this.prevObject : this.prevObject.filter( selector )
-		);
-	}
-} );
-
-function sibling( cur, dir ) {
-	while ( ( cur = cur[ dir ] ) && cur.nodeType !== 1 ) {}
-	return cur;
-}
-
-jQuery.each( {
-	parent: function( elem ) {
-		var parent = elem.parentNode;
-		return parent && parent.nodeType !== 11 ? parent : null;
-	},
-	parents: function( elem ) {
-		return dir( elem, "parentNode" );
-	},
-	parentsUntil: function( elem, _i, until ) {
-		return dir( elem, "parentNode", until );
-	},
-	next: function( elem ) {
-		return sibling( elem, "nextSibling" );
-	},
-	prev: function( elem ) {
-		return sibling( elem, "previousSibling" );
-	},
-	nextAll: function( elem ) {
-		return dir( elem, "nextSibling" );
-	},
-	prevAll: function( elem ) {
-		return dir( elem, "previousSibling" );
-	},
-	nextUntil: function( elem, _i, until ) {
-		return dir( elem, "nextSibling", until );
-	},
-	prevUntil: function( elem, _i, until ) {
-		return dir( elem, "previousSibling", until );
-	},
-	siblings: function( elem ) {
-		return siblings( ( elem.parentNode || {} ).firstChild, elem );
-	},
-	children: function( elem ) {
-		return siblings( elem.firstChild );
-	},
-	contents: function( elem ) {
-		if ( elem.contentDocument != null &&
-
-			// Support: IE 11+
-			// <object> elements with no `data` attribute has an object
-			// `contentDocument` with a `null` prototype.
-			getProto( elem.contentDocument ) ) {
-
-			return elem.contentDocument;
-		}
-
-		// Support: IE 9 - 11 only, iOS 7 only, Android Browser <=4.3 only
-		// Treat the template element as a regular one in browsers that
-		// don't support it.
-		if ( nodeName( elem, "template" ) ) {
-			elem = elem.content || elem;
-		}
-
-		return jQuery.merge( [], elem.childNodes );
-	}
-}, function( name, fn ) {
-	jQuery.fn[ name ] = function( until, selector ) {
-		var matched = jQuery.map( this, fn, until );
-
-		if ( name.slice( -5 ) !== "Until" ) {
-			selector = until;
-		}
-
-		if ( selector && typeof selector === "string" ) {
-			matched = jQuery.filter( selector, matched );
-		}
-
-		if ( this.length > 1 ) {
-
-			// Remove duplicates
-			if ( !guaranteedUnique[ name ] ) {
-				jQuery.uniqueSort( matched );
-			}
-
-			// Reverse order for parents* and prev-derivatives
-			if ( rparentsprev.test( name ) ) {
-				matched.reverse();
-			}
-		}
-
-		return this.pushStack( matched );
-	};
-} );
-var rnothtmlwhite = ( /[^\x20\t\r\n\f]+/g );
-
-
-
-// Convert String-formatted options into Object-formatted ones
-function createOptions( options ) {
-	var object = {};
-	jQuery.each( options.match( rnothtmlwhite ) || [], function( _, flag ) {
-		object[ flag ] = true;
-	} );
-	return object;
-}
-
-/*
- * Create a callback list using the following parameters:
- *
- *	options: an optional list of space-separated options that will change how
- *			the callback list behaves or a more traditional option object
- *
- * By default a callback list will act like an event callback list and can be
- * "fired" multiple times.
- *
- * Possible options:
- *
- *	once:			will ensure the callback list can only be fired once (like a Deferred)
- *
- *	memory:			will keep track of previous values and will call any callback added
- *					after the list has been fired right away with the latest "memorized"
- *					values (like a Deferred)
- *
- *	unique:			will ensure a callback can only be added once (no duplicate in the list)
- *
- *	stopOnFalse:	interrupt callings when a callback returns false
- *
- */
-jQuery.Callbacks = function( options ) {
-
-	// Convert options from String-formatted to Object-formatted if needed
-	// (we check in cache first)
-	options = typeof options === "string" ?
-		createOptions( options ) :
-		jQuery.extend( {}, options );
-
-	var // Flag to know if list is currently firing
-		firing,
-
-		// Last fire value for non-forgettable lists
-		memory,
-
-		// Flag to know if list was already fired
-		fired,
-
-		// Flag to prevent firing
-		locked,
-
-		// Actual callback list
-		list = [],
-
-		// Queue of execution data for repeatable lists
-		queue = [],
-
-		// Index of currently firing callback (modified by add/remove as needed)
-		firingIndex = -1,
-
-		// Fire callbacks
-		fire = function() {
-
-			// Enforce single-firing
-			locked = locked || options.once;
-
-			// Execute callbacks for all pending executions,
-			// respecting firingIndex overrides and runtime changes
-			fired = firing = true;
-			for ( ; queue.length; firingIndex = -1 ) {
-				memory = queue.shift();
-				while ( ++firingIndex < list.length ) {
-
-					// Run callback and check for early termination
-					if ( list[ firingIndex ].apply( memory[ 0 ], memory[ 1 ] ) === false &&
-						options.stopOnFalse ) {
-
-						// Jump to end and forget the data so .add doesn't re-fire
-						firingIndex = list.length;
-						memory = false;
-					}
-				}
-			}
-
-			// Forget the data if we're done with it
-			if ( !options.memory ) {
-				memory = false;
-			}
-
-			firing = false;
-
-			// Clean up if we're done firing for good
-			if ( locked ) {
-
-				// Keep an empty list if we have data for future add calls
-				if ( memory ) {
-					list = [];
-
-				// Otherwise, this object is spent
-				} else {
-					list = "";
-				}
-			}
-		},
-
-		// Actual Callbacks object
-		self = {
-
-			// Add a callback or a collection of callbacks to the list
-			add: function() {
-				if ( list ) {
-
-					// If we have memory from a past run, we should fire after adding
-					if ( memory && !firing ) {
-						firingIndex = list.length - 1;
-						queue.push( memory );
-					}
-
-					( function add( args ) {
-						jQuery.each( args, function( _, arg ) {
-							if ( isFunction( arg ) ) {
-								if ( !options.unique || !self.has( arg ) ) {
-									list.push( arg );
-								}
-							} else if ( arg && arg.length && toType( arg ) !== "string" ) {
-
-								// Inspect recursively
-								add( arg );
-							}
-						} );
-					} )( arguments );
-
-					if ( memory && !firing ) {
-						fire();
-					}
-				}
-				return this;
-			},
-
-			// Remove a callback from the list
-			remove: function() {
-				jQuery.each( arguments, function( _, arg ) {
-					var index;
-					while ( ( index = jQuery.inArray( arg, list, index ) ) > -1 ) {
-						list.splice( index, 1 );
-
-						// Handle firing indexes
-						if ( index <= firingIndex ) {
-							firingIndex--;
-						}
-					}
-				} );
-				return this;
-			},
-
-			// Check if a given callback is in the list.
-			// If no argument is given, return whether or not list has callbacks attached.
-			has: function( fn ) {
-				return fn ?
-					jQuery.inArray( fn, list ) > -1 :
-					list.length > 0;
-			},
-
-			// Remove all callbacks from the list
-			empty: function() {
-				if ( list ) {
-					list = [];
-				}
-				return this;
-			},
-
-			// Disable .fire and .add
-			// Abort any current/pending executions
-			// Clear all callbacks and values
-			disable: function() {
-				locked = queue = [];
-				list = memory = "";
-				return this;
-			},
-			disabled: function() {
-				return !list;
-			},
-
-			// Disable .fire
-			// Also disable .add unless we have memory (since it would have no effect)
-			// Abort any pending executions
-			lock: function() {
-				locked = queue = [];
-				if ( !memory && !firing ) {
-					list = memory = "";
-				}
-				return this;
-			},
-			locked: function() {
-				return !!locked;
-			},
-
-			// Call all callbacks with the given context and arguments
-			fireWith: function( context, args ) {
-				if ( !locked ) {
-					args = args || [];
-					args = [ context, args.slice ? args.slice() : args ];
-					queue.push( args );
-					if ( !firing ) {
-						fire();
-					}
-				}
-				return this;
-			},
-
-			// Call all the callbacks with the given arguments
-			fire: function() {
-				self.fireWith( this, arguments );
-				return this;
-			},
-
-			// To know if the callbacks have already been called at least once
-			fired: function() {
-				return !!fired;
-			}
-		};
-
-	return self;
-};
-
-
-function Identity( v ) {
-	return v;
-}
-function Thrower( ex ) {
-	throw ex;
-}
-
-function adoptValue( value, resolve, reject, noValue ) {
-	var method;
-
-	try {
-
-		// Check for promise aspect first to privilege synchronous behavior
-		if ( value && isFunction( ( method = value.promise ) ) ) {
-			method.call( value ).done( resolve ).fail( reject );
-
-		// Other thenables
-		} else if ( value && isFunction( ( method = value.then ) ) ) {
-			method.call( value, resolve, reject );
-
-		// Other non-thenables
-		} else {
-
-			// Control `resolve` arguments by letting Array#slice cast boolean `noValue` to integer:
-			// * false: [ value ].slice( 0 ) => resolve( value )
-			// * true: [ value ].slice( 1 ) => resolve()
-			resolve.apply( undefined, [ value ].slice( noValue ) );
-		}
-
-	// For Promises/A+, convert exceptions into rejections
-	// Since jQuery.when doesn't unwrap thenables, we can skip the extra checks appearing in
-	// Deferred#then to conditionally suppress rejection.
-	} catch ( value ) {
-
-		// Support: Android 4.0 only
-		// Strict mode functions invoked without .call/.apply get global-object context
-		reject.apply( undefined, [ value ] );
-	}
-}
-
-jQuery.extend( {
-
-	Deferred: function( func ) {
-		var tuples = [
-
-				// action, add listener, callbacks,
-				// ... .then handlers, argument index, [final state]
-				[ "notify", "progress", jQuery.Callbacks( "memory" ),
-					jQuery.Callbacks( "memory" ), 2 ],
-				[ "resolve", "done", jQuery.Callbacks( "once memory" ),
-					jQuery.Callbacks( "once memory" ), 0, "resolved" ],
-				[ "reject", "fail", jQuery.Callbacks( "once memory" ),
-					jQuery.Callbacks( "once memory" ), 1, "rejected" ]
-			],
-			state = "pending",
-			promise = {
-				state: function() {
-					return state;
-				},
-				always: function() {
-					deferred.done( arguments ).fail( arguments );
-					return this;
-				},
-				"catch": function( fn ) {
-					return promise.then( null, fn );
-				},
-
-				// Keep pipe for back-compat
-				pipe: function( /* fnDone, fnFail, fnProgress */ ) {
-					var fns = arguments;
-
-					return jQuery.Deferred( function( newDefer ) {
-						jQuery.each( tuples, function( _i, tuple ) {
-
-							// Map tuples (progress, done, fail) to arguments (done, fail, progress)
-							var fn = isFunction( fns[ tuple[ 4 ] ] ) && fns[ tuple[ 4 ] ];
-
-							// deferred.progress(function() { bind to newDefer or newDefer.notify })
-							// deferred.done(function() { bind to newDefer or newDefer.resolve })
-							// deferred.fail(function() { bind to newDefer or newDefer.reject })
-							deferred[ tuple[ 1 ] ]( function() {
-								var returned = fn && fn.apply( this, arguments );
-								if ( returned && isFunction( returned.promise ) ) {
-									returned.promise()
-										.progress( newDefer.notify )
-										.done( newDefer.resolve )
-										.fail( newDefer.reject );
-								} else {
-									newDefer[ tuple[ 0 ] + "With" ](
-										this,
-										fn ? [ returned ] : arguments
-									);
-								}
-							} );
-						} );
-						fns = null;
-					} ).promise();
-				},
-				then: function( onFulfilled, onRejected, onProgress ) {
-					var maxDepth = 0;
-					function resolve( depth, deferred, handler, special ) {
-						return function() {
-							var that = this,
-								args = arguments,
-								mightThrow = function() {
-									var returned, then;
-
-									// Support: Promises/A+ section 2.3.3.3.3
-									// https://promisesaplus.com/#point-59
-									// Ignore double-resolution attempts
-									if ( depth < maxDepth ) {
-										return;
-									}
-
-									returned = handler.apply( that, args );
-
-									// Support: Promises/A+ section 2.3.1
-									// https://promisesaplus.com/#point-48
-									if ( returned === deferred.promise() ) {
-										throw new TypeError( "Thenable self-resolution" );
-									}
-
-									// Support: Promises/A+ sections 2.3.3.1, 3.5
-									// https://promisesaplus.com/#point-54
-									// https://promisesaplus.com/#point-75
-									// Retrieve `then` only once
-									then = returned &&
-
-										// Support: Promises/A+ section 2.3.4
-										// https://promisesaplus.com/#point-64
-										// Only check objects and functions for thenability
-										( typeof returned === "object" ||
-											typeof returned === "function" ) &&
-										returned.then;
-
-									// Handle a returned thenable
-									if ( isFunction( then ) ) {
-
-										// Special processors (notify) just wait for resolution
-										if ( special ) {
-											then.call(
-												returned,
-												resolve( maxDepth, deferred, Identity, special ),
-												resolve( maxDepth, deferred, Thrower, special )
-											);
-
-										// Normal processors (resolve) also hook into progress
-										} else {
-
-											// ...and disregard older resolution values
-											maxDepth++;
-
-											then.call(
-												returned,
-												resolve( maxDepth, deferred, Identity, special ),
-												resolve( maxDepth, deferred, Thrower, special ),
-												resolve( maxDepth, deferred, Identity,
-													deferred.notifyWith )
-											);
-										}
-
-									// Handle all other returned values
-									} else {
-
-										// Only substitute handlers pass on context
-										// and multiple values (non-spec behavior)
-										if ( handler !== Identity ) {
-											that = undefined;
-											args = [ returned ];
-										}
-
-										// Process the value(s)
-										// Default process is resolve
-										( special || deferred.resolveWith )( that, args );
-									}
-								},
-
-								// Only normal processors (resolve) catch and reject exceptions
-								process = special ?
-									mightThrow :
-									function() {
-										try {
-											mightThrow();
-										} catch ( e ) {
-
-											if ( jQuery.Deferred.exceptionHook ) {
-												jQuery.Deferred.exceptionHook( e,
-													process.stackTrace );
-											}
-
-											// Support: Promises/A+ section 2.3.3.3.4.1
-											// https://promisesaplus.com/#point-61
-											// Ignore post-resolution exceptions
-											if ( depth + 1 >= maxDepth ) {
-
-												// Only substitute handlers pass on context
-												// and multiple values (non-spec behavior)
-												if ( handler !== Thrower ) {
-													that = undefined;
-													args = [ e ];
-												}
-
-												deferred.rejectWith( that, args );
-											}
-										}
-									};
-
-							// Support: Promises/A+ section 2.3.3.3.1
-							// https://promisesaplus.com/#point-57
-							// Re-resolve promises immediately to dodge false rejection from
-							// subsequent errors
-							if ( depth ) {
-								process();
-							} else {
-
-								// Call an optional hook to record the stack, in case of exception
-								// since it's otherwise lost when execution goes async
-								if ( jQuery.Deferred.getStackHook ) {
-									process.stackTrace = jQuery.Deferred.getStackHook();
-								}
-								window.setTimeout( process );
-							}
-						};
-					}
-
-					return jQuery.Deferred( function( newDefer ) {
-
-						// progress_handlers.add( ... )
-						tuples[ 0 ][ 3 ].add(
-							resolve(
-								0,
-								newDefer,
-								isFunction( onProgress ) ?
-									onProgress :
-									Identity,
-								newDefer.notifyWith
-							)
-						);
-
-						// fulfilled_handlers.add( ... )
-						tuples[ 1 ][ 3 ].add(
-							resolve(
-								0,
-								newDefer,
-								isFunction( onFulfilled ) ?
-									onFulfilled :
-									Identity
-							)
-						);
-
-						// rejected_handlers.add( ... )
-						tuples[ 2 ][ 3 ].add(
-							resolve(
-								0,
-								newDefer,
-								isFunction( onRejected ) ?
-									onRejected :
-									Thrower
-							)
-						);
-					} ).promise();
-				},
-
-				// Get a promise for this deferred
-				// If obj is provided, the promise aspect is added to the object
-				promise: function( obj ) {
-					return obj != null ? jQuery.extend( obj, promise ) : promise;
-				}
-			},
-			deferred = {};
-
-		// Add list-specific methods
-		jQuery.each( tuples, function( i, tuple ) {
-			var list = tuple[ 2 ],
-				stateString = tuple[ 5 ];
-
-			// promise.progress = list.add
-			// promise.done = list.add
-			// promise.fail = list.add
-			promise[ tuple[ 1 ] ] = list.add;
-
-			// Handle state
-			if ( stateString ) {
-				list.add(
-					function() {
-
-						// state = "resolved" (i.e., fulfilled)
-						// state = "rejected"
-						state = stateString;
-					},
-
-					// rejected_callbacks.disable
-					// fulfilled_callbacks.disable
-					tuples[ 3 - i ][ 2 ].disable,
-
-					// rejected_handlers.disable
-					// fulfilled_handlers.disable
-					tuples[ 3 - i ][ 3 ].disable,
-
-					// progress_callbacks.lock
-					tuples[ 0 ][ 2 ].lock,
-
-					// progress_handlers.lock
-					tuples[ 0 ][ 3 ].lock
-				);
-			}
-
-			// progress_handlers.fire
-			// fulfilled_handlers.fire
-			// rejected_handlers.fire
-			list.add( tuple[ 3 ].fire );
-
-			// deferred.notify = function() { deferred.notifyWith(...) }
-			// deferred.resolve = function() { deferred.resolveWith(...) }
-			// deferred.reject = function() { deferred.rejectWith(...) }
-			deferred[ tuple[ 0 ] ] = function() {
-				deferred[ tuple[ 0 ] + "With" ]( this === deferred ? undefined : this, arguments );
-				return this;
-			};
-
-			// deferred.notifyWith = list.fireWith
-			// deferred.resolveWith = list.fireWith
-			// deferred.rejectWith = list.fireWith
-			deferred[ tuple[ 0 ] + "With" ] = list.fireWith;
-		} );
-
-		// Make the deferred a promise
-		promise.promise( deferred );
-
-		// Call given func if any
-		if ( func ) {
-			func.call( deferred, deferred );
-		}
-
-		// All done!
-		return deferred;
-	},
-
-	// Deferred helper
-	when: function( singleValue ) {
-		var
-
-			// count of uncompleted subordinates
-			remaining = arguments.length,
-
-			// count of unprocessed arguments
-			i = remaining,
-
-			// subordinate fulfillment data
-			resolveContexts = Array( i ),
-			resolveValues = slice.call( arguments ),
-
-			// the primary Deferred
-			primary = jQuery.Deferred(),
-
-			// subordinate callback factory
-			updateFunc = function( i ) {
-				return function( value ) {
-					resolveContexts[ i ] = this;
-					resolveValues[ i ] = arguments.length > 1 ? slice.call( arguments ) : value;
-					if ( !( --remaining ) ) {
-						primary.resolveWith( resolveContexts, resolveValues );
-					}
-				};
-			};
-
-		// Single- and empty arguments are adopted like Promise.resolve
-		if ( remaining <= 1 ) {
-			adoptValue( singleValue, primary.done( updateFunc( i ) ).resolve, primary.reject,
-				!remaining );
-
-			// Use .then() to unwrap secondary thenables (cf. gh-3000)
-			if ( primary.state() === "pending" ||
-				isFunction( resolveValues[ i ] && resolveValues[ i ].then ) ) {
-
-				return primary.then();
-			}
-		}
-
-		// Multiple arguments are aggregated like Promise.all array elements
-		while ( i-- ) {
-			adoptValue( resolveValues[ i ], updateFunc( i ), primary.reject );
-		}
-
-		return primary.promise();
-	}
-} );
-
-
-// These usually indicate a programmer mistake during development,
-// warn about them ASAP rather than swallowing them by default.
-var rerrorNames = /^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/;
-
-jQuery.Deferred.exceptionHook = function( error, stack ) {
-
-	// Support: IE 8 - 9 only
-	// Console exists when dev tools are open, which can happen at any time
-	if ( window.console && window.console.warn && error && rerrorNames.test( error.name ) ) {
-		window.console.warn( "jQuery.Deferred exception: " + error.message, error.stack, stack );
-	}
-};
-
-
-
-
-jQuery.readyException = function( error ) {
-	window.setTimeout( function() {
-		throw error;
-	} );
-};
-
-
-
-
-// The deferred used on DOM ready
-var readyList = jQuery.Deferred();
-
-jQuery.fn.ready = function( fn ) {
-
-	readyList
-		.then( fn )
-
-		// Wrap jQuery.readyException in a function so that the lookup
-		// happens at the time of error handling instead of callback
-		// registration.
-		.catch( function( error ) {
-			jQuery.readyException( error );
-		} );
-
-	return this;
-};
-
-jQuery.extend( {
-
-	// Is the DOM ready to be used? Set to true once it occurs.
-	isReady: false,
-
-	// A counter to track how many items to wait for before
-	// the ready event fires. See #6781
-	readyWait: 1,
-
-	// Handle when the DOM is ready
-	ready: function( wait ) {
-
-		// Abort if there are pending holds or we're already ready
-		if ( wait === true ? --jQuery.readyWait : jQuery.isReady ) {
-			return;
-		}
-
-		// Remember that the DOM is ready
-		jQuery.isReady = true;
-
-		// If a normal DOM Ready event fired, decrement, and wait if need be
-		if ( wait !== true && --jQuery.readyWait > 0 ) {
-			return;
-		}
-
-		// If there are functions bound, to execute
-		readyList.resolveWith( document, [ jQuery ] );
-	}
-} );
-
-jQuery.ready.then = readyList.then;
-
-// The ready event handler and self cleanup method
-function completed() {
-	document.removeEventListener( "DOMContentLoaded", completed );
-	window.removeEventListener( "load", completed );
-	jQuery.ready();
-}
-
-// Catch cases where $(document).ready() is called
-// after the browser event has already occurred.
-// Support: IE <=9 - 10 only
-// Older IE sometimes signals "interactive" too soon
-if ( document.readyState === "complete" ||
-	( document.readyState !== "loading" && !document.documentElement.doScroll ) ) {
-
-	// Handle it asynchronously to allow scripts the opportunity to delay ready
-	window.setTimeout( jQuery.ready );
-
-} else {
-
-	// Use the handy event callback
-	document.addEventListener( "DOMContentLoaded", completed );
-
-	// A fallback to window.onload, that will always work
-	window.addEventListener( "load", completed );
-}
-
-
-
-
-// Multifunctional method to get and set values of a collection
-// The value/s can optionally be executed if it's a function
-var access = function( elems, fn, key, value, chainable, emptyGet, raw ) {
-	var i = 0,
-		len = elems.length,
-		bulk = key == null;
-
-	// Sets many values
-	if ( toType( key ) === "object" ) {
-		chainable = true;
-		for ( i in key ) {
-			access( elems, fn, i, key[ i ], true, emptyGet, raw );
-		}
-
-	// Sets one value
-	} else if ( value !== undefined ) {
-		chainable = true;
-
-		if ( !isFunction( value ) ) {
-			raw = true;
-		}
-
-		if ( bulk ) {
-
-			// Bulk operations run against the entire set
-			if ( raw ) {
-				fn.call( elems, value );
-				fn = null;
-
-			// ...except when executing function values
-			} else {
-				bulk = fn;
-				fn = function( elem, _key, value ) {
-					return bulk.call( jQuery( elem ), value );
-				};
-			}
-		}
-
-		if ( fn ) {
-			for ( ; i < len; i++ ) {
-				fn(
-					elems[ i ], key, raw ?
-						value :
-						value.call( elems[ i ], i, fn( elems[ i ], key ) )
-				);
-			}
-		}
-	}
-
-	if ( chainable ) {
-		return elems;
-	}
-
-	// Gets
-	if ( bulk ) {
-		return fn.call( elems );
-	}
-
-	return len ? fn( elems[ 0 ], key ) : emptyGet;
-};
-
-
-// Matches dashed string for camelizing
-var rmsPrefix = /^-ms-/,
-	rdashAlpha = /-([a-z])/g;
-
-// Used by camelCase as callback to replace()
-function fcamelCase( _all, letter ) {
-	return letter.toUpperCase();
-}
-
-// Convert dashed to camelCase; used by the css and data modules
-// Support: IE <=9 - 11, Edge 12 - 15
-// Microsoft forgot to hump their vendor prefix (#9572)
-function camelCase( string ) {
-	return string.replace( rmsPrefix, "ms-" ).replace( rdashAlpha, fcamelCase );
-}
-var acceptData = function( owner ) {
-
-	// Accepts only:
-	//  - Node
-	//    - Node.ELEMENT_NODE
-	//    - Node.DOCUMENT_NODE
-	//  - Object
-	//    - Any
-	return owner.nodeType === 1 || owner.nodeType === 9 || !( +owner.nodeType );
-};
-
-
-
-
-function Data() {
-	this.expando = jQuery.expando + Data.uid++;
-}
-
-Data.uid = 1;
-
-Data.prototype = {
-
-	cache: function( owner ) {
-
-		// Check if the owner object already has a cache
-		var value = owner[ this.expando ];
-
-		// If not, create one
-		if ( !value ) {
-			value = {};
-
-			// We can accept data for non-element nodes in modern browsers,
-			// but we should not, see #8335.
-			// Always return an empty object.
-			if ( acceptData( owner ) ) {
-
-				// If it is a node unlikely to be stringify-ed or looped over
-				// use plain assignment
-				if ( owner.nodeType ) {
-					owner[ this.expando ] = value;
-
-				// Otherwise secure it in a non-enumerable property
-				// configurable must be true to allow the property to be
-				// deleted when data is removed
-				} else {
-					Object.defineProperty( owner, this.expando, {
-						value: value,
-						configurable: true
-					} );
-				}
-			}
-		}
-
-		return value;
-	},
-	set: function( owner, data, value ) {
-		var prop,
-			cache = this.cache( owner );
-
-		// Handle: [ owner, key, value ] args
-		// Always use camelCase key (gh-2257)
-		if ( typeof data === "string" ) {
-			cache[ camelCase( data ) ] = value;
-
-		// Handle: [ owner, { properties } ] args
-		} else {
-
-			// Copy the properties one-by-one to the cache object
-			for ( prop in data ) {
-				cache[ camelCase( prop ) ] = data[ prop ];
-			}
-		}
-		return cache;
-	},
-	get: function( owner, key ) {
-		return key === undefined ?
-			this.cache( owner ) :
-
-			// Always use camelCase key (gh-2257)
-			owner[ this.expando ] && owner[ this.expando ][ camelCase( key ) ];
-	},
-	access: function( owner, key, value ) {
-
-		// In cases where either:
-		//
-		//   1. No key was specified
-		//   2. A string key was specified, but no value provided
-		//
-		// Take the "read" path and allow the get method to determine
-		// which value to return, respectively either:
-		//
-		//   1. The entire cache object
-		//   2. The data stored at the key
-		//
-		if ( key === undefined ||
-				( ( key && typeof key === "string" ) && value === undefined ) ) {
-
-			return this.get( owner, key );
-		}
-
-		// When the key is not a string, or both a key and value
-		// are specified, set or extend (existing objects) with either:
-		//
-		//   1. An object of properties
-		//   2. A key and value
-		//
-		this.set( owner, key, value );
-
-		// Since the "set" path can have two possible entry points
-		// return the expected data based on which path was taken[*]
-		return value !== undefined ? value : key;
-	},
-	remove: function( owner, key ) {
-		var i,
-			cache = owner[ this.expando ];
-
-		if ( cache === undefined ) {
-			return;
-		}
-
-		if ( key !== undefined ) {
-
-			// Support array or space separated string of keys
-			if ( Array.isArray( key ) ) {
-
-				// If key is an array of keys...
-				// We always set camelCase keys, so remove that.
-				key = key.map( camelCase );
-			} else {
-				key = camelCase( key );
-
-				// If a key with the spaces exists, use it.
-				// Otherwise, create an array by matching non-whitespace
-				key = key in cache ?
-					[ key ] :
-					( key.match( rnothtmlwhite ) || [] );
-			}
-
-			i = key.length;
-
-			while ( i-- ) {
-				delete cache[ key[ i ] ];
-			}
-		}
-
-		// Remove the expando if there's no more data
-		if ( key === undefined || jQuery.isEmptyObject( cache ) ) {
-
-			// Support: Chrome <=35 - 45
-			// Webkit & Blink performance suffers when deleting properties
-			// from DOM nodes, so set to undefined instead
-			// https://bugs.chromium.org/p/chromium/issues/detail?id=378607 (bug restricted)
-			if ( owner.nodeType ) {
-				owner[ this.expando ] = undefined;
-			} else {
-				delete owner[ this.expando ];
-			}
-		}
-	},
-	hasData: function( owner ) {
-		var cache = owner[ this.expando ];
-		return cache !== undefined && !jQuery.isEmptyObject( cache );
-	}
-};
-var dataPriv = new Data();
-
-var dataUser = new Data();
-
-
-
-//	Implementation Summary
-//
-//	1. Enforce API surface and semantic compatibility with 1.9.x branch
-//	2. Improve the module's maintainability by reducing the storage
-//		paths to a single mechanism.
-//	3. Use the same single mechanism to support "private" and "user" data.
-//	4. _Never_ expose "private" data to user code (TODO: Drop _data, _removeData)
-//	5. Avoid exposing implementation details on user objects (eg. expando properties)
-//	6. Provide a clear path for implementation upgrade to WeakMap in 2014
-
-var rbrace = /^(?:\{[\w\W]*\}|\[[\w\W]*\])$/,
-	rmultiDash = /[A-Z]/g;
-
-function getData( data ) {
-	if ( data === "true" ) {
-		return true;
-	}
-
-	if ( data === "false" ) {
-		return false;
-	}
-
-	if ( data === "null" ) {
-		return null;
-	}
-
-	// Only convert to a number if it doesn't change the string
-	if ( data === +data + "" ) {
-		return +data;
-	}
-
-	if ( rbrace.test( data ) ) {
-		return JSON.parse( data );
-	}
-
-	return data;
-}
-
-function dataAttr( elem, key, data ) {
-	var name;
-
-	// If nothing was found internally, try to fetch any
-	// data from the HTML5 data-* attribute
-	if ( data === undefined && elem.nodeType === 1 ) {
-		name = "data-" + key.replace( rmultiDash, "-$&" ).toLowerCase();
-		data = elem.getAttribute( name );
-
-		if ( typeof data === "string" ) {
-			try {
-				data = getData( data );
-			} catch ( e ) {}
-
-			// Make sure we set the data so it isn't changed later
-			dataUser.set( elem, key, data );
-		} else {
-			data = undefined;
-		}
-	}
-	return data;
-}
-
-jQuery.extend( {
-	hasData: function( elem ) {
-		return dataUser.hasData( elem ) || dataPriv.hasData( elem );
-	},
-
-	data: function( elem, name, data ) {
-		return dataUser.access( elem, name, data );
-	},
-
-	removeData: function( elem, name ) {
-		dataUser.remove( elem, name );
-	},
-
-	// TODO: Now that all calls to _data and _removeData have been replaced
-	// with direct calls to dataPriv methods, these can be deprecated.
-	_data: function( elem, name, data ) {
-		return dataPriv.access( elem, name, data );
-	},
-
-	_removeData: function( elem, name ) {
-		dataPriv.remove( elem, name );
-	}
-} );
-
-jQuery.fn.extend( {
-	data: function( key, value ) {
-		var i, name, data,
-			elem = this[ 0 ],
-			attrs = elem && elem.attributes;
-
-		// Gets all values
-		if ( key === undefined ) {
-			if ( this.length ) {
-				data = dataUser.get( elem );
-
-				if ( elem.nodeType === 1 && !dataPriv.get( elem, "hasDataAttrs" ) ) {
-					i = attrs.length;
-					while ( i-- ) {
-
-						// Support: IE 11 only
-						// The attrs elements can be null (#14894)
-						if ( attrs[ i ] ) {
-							name = attrs[ i ].name;
-							if ( name.indexOf( "data-" ) === 0 ) {
-								name = camelCase( name.slice( 5 ) );
-								dataAttr( elem, name, data[ name ] );
-							}
-						}
-					}
-					dataPriv.set( elem, "hasDataAttrs", true );
-				}
-			}
-
-			return data;
-		}
-
-		// Sets multiple values
-		if ( typeof key === "object" ) {
-			return this.each( function() {
-				dataUser.set( this, key );
-			} );
-		}
-
-		return access( this, function( value ) {
-			var data;
-
-			// The calling jQuery object (element matches) is not empty
-			// (and therefore has an element appears at this[ 0 ]) and the
-			// `value` parameter was not undefined. An empty jQuery object
-			// will result in `undefined` for elem = this[ 0 ] which will
-			// throw an exception if an attempt to read a data cache is made.
-			if ( elem && value === undefined ) {
-
-				// Attempt to get data from the cache
-				// The key will always be camelCased in Data
-				data = dataUser.get( elem, key );
-				if ( data !== undefined ) {
-					return data;
-				}
-
-				// Attempt to "discover" the data in
-				// HTML5 custom data-* attrs
-				data = dataAttr( elem, key );
-				if ( data !== undefined ) {
-					return data;
-				}
-
-				// We tried really hard, but the data doesn't exist.
-				return;
-			}
-
-			// Set the data...
-			this.each( function() {
-
-				// We always store the camelCased key
-				dataUser.set( this, key, value );
-			} );
-		}, null, value, arguments.length > 1, null, true );
-	},
-
-	removeData: function( key ) {
-		return this.each( function() {
-			dataUser.remove( this, key );
-		} );
-	}
-} );
-
-
-jQuery.extend( {
-	queue: function( elem, type, data ) {
-		var queue;
-
-		if ( elem ) {
-			type = ( type || "fx" ) + "queue";
-			queue = dataPriv.get( elem, type );
-
-			// Speed up dequeue by getting out quickly if this is just a lookup
-			if ( data ) {
-				if ( !queue || Array.isArray( data ) ) {
-					queue = dataPriv.access( elem, type, jQuery.makeArray( data ) );
-				} else {
-					queue.push( data );
-				}
-			}
-			return queue || [];
-		}
-	},
-
-	dequeue: function( elem, type ) {
-		type = type || "fx";
-
-		var queue = jQuery.queue( elem, type ),
-			startLength = queue.length,
-			fn = queue.shift(),
-			hooks = jQuery._queueHooks( elem, type ),
-			next = function() {
-				jQuery.dequeue( elem, type );
-			};
-
-		// If the fx queue is dequeued, always remove the progress sentinel
-		if ( fn === "inprogress" ) {
-			fn = queue.shift();
-			startLength--;
-		}
-
-		if ( fn ) {
-
-			// Add a progress sentinel to prevent the fx queue from being
-			// automatically dequeued
-			if ( type === "fx" ) {
-				queue.unshift( "inprogress" );
-			}
-
-			// Clear up the last queue stop function
-			delete hooks.stop;
-			fn.call( elem, next, hooks );
-		}
-
-		if ( !startLength && hooks ) {
-			hooks.empty.fire();
-		}
-	},
-
-	// Not public - generate a queueHooks object, or return the current one
-	_queueHooks: function( elem, type ) {
-		var key = type + "queueHooks";
-		return dataPriv.get( elem, key ) || dataPriv.access( elem, key, {
-			empty: jQuery.Callbacks( "once memory" ).add( function() {
-				dataPriv.remove( elem, [ type + "queue", key ] );
-			} )
-		} );
-	}
-} );
-
-jQuery.fn.extend( {
-	queue: function( type, data ) {
-		var setter = 2;
-
-		if ( typeof type !== "string" ) {
-			data = type;
-			type = "fx";
-			setter--;
-		}
-
-		if ( arguments.length < setter ) {
-			return jQuery.queue( this[ 0 ], type );
-		}
-
-		return data === undefined ?
-			this :
-			this.each( function() {
-				var queue = jQuery.queue( this, type, data );
-
-				// Ensure a hooks for this queue
-				jQuery._queueHooks( this, type );
-
-				if ( type === "fx" && queue[ 0 ] !== "inprogress" ) {
-					jQuery.dequeue( this, type );
-				}
-			} );
-	},
-	dequeue: function( type ) {
-		return this.each( function() {
-			jQuery.dequeue( this, type );
-		} );
-	},
-	clearQueue: function( type ) {
-		return this.queue( type || "fx", [] );
-	},
-
-	// Get a promise resolved when queues of a certain type
-	// are emptied (fx is the type by default)
-	promise: function( type, obj ) {
-		var tmp,
-			count = 1,
-			defer = jQuery.Deferred(),
-			elements = this,
-			i = this.length,
-			resolve = function() {
-				if ( !( --count ) ) {
-					defer.resolveWith( elements, [ elements ] );
-				}
-			};
-
-		if ( typeof type !== "string" ) {
-			obj = type;
-			type = undefined;
-		}
-		type = type || "fx";
-
-		while ( i-- ) {
-			tmp = dataPriv.get( elements[ i ], type + "queueHooks" );
-			if ( tmp && tmp.empty ) {
-				count++;
-				tmp.empty.add( resolve );
-			}
-		}
-		resolve();
-		return defer.promise( obj );
-	}
-} );
-var pnum = ( /[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/ ).source;
-
-var rcssNum = new RegExp( "^(?:([+-])=|)(" + pnum + ")([a-z%]*)$", "i" );
-
-
-var cssExpand = [ "Top", "Right", "Bottom", "Left" ];
-
-var documentElement = document.documentElement;
-
-
-
-	var isAttached = function( elem ) {
-			return jQuery.contains( elem.ownerDocument, elem );
-		},
-		composed = { composed: true };
-
-	// Support: IE 9 - 11+, Edge 12 - 18+, iOS 10.0 - 10.2 only
-	// Check attachment across shadow DOM boundaries when possible (gh-3504)
-	// Support: iOS 10.0-10.2 only
-	// Early iOS 10 versions support `attachShadow` but not `getRootNode`,
-	// leading to errors. We need to check for `getRootNode`.
-	if ( documentElement.getRootNode ) {
-		isAttached = function( elem ) {
-			return jQuery.contains( elem.ownerDocument, elem ) ||
-				elem.getRootNode( composed ) === elem.ownerDocument;
-		};
-	}
-var isHiddenWithinTree = function( elem, el ) {
-
-		// isHiddenWithinTree might be called from jQuery#filter function;
-		// in that case, element will be second argument
-		elem = el || elem;
-
-		// Inline style trumps all
-		return elem.style.display === "none" ||
-			elem.style.display === "" &&
-
-			// Otherwise, check computed style
-			// Support: Firefox <=43 - 45
-			// Disconnected elements can have computed display: none, so first confirm that elem is
-			// in the document.
-			isAttached( elem ) &&
-
-			jQuery.css( elem, "display" ) === "none";
-	};
-
-
-
-function adjustCSS( elem, prop, valueParts, tween ) {
-	var adjusted, scale,
-		maxIterations = 20,
-		currentValue = tween ?
-			function() {
-				return tween.cur();
-			} :
-			function() {
-				return jQuery.css( elem, prop, "" );
-			},
-		initial = currentValue(),
-		unit = valueParts && valueParts[ 3 ] || ( jQuery.cssNumber[ prop ] ? "" : "px" ),
-
-		// Starting value computation is required for potential unit mismatches
-		initialInUnit = elem.nodeType &&
-			( jQuery.cssNumber[ prop ] || unit !== "px" && +initial ) &&
-			rcssNum.exec( jQuery.css( elem, prop ) );
-
-	if ( initialInUnit && initialInUnit[ 3 ] !== unit ) {
-
-		// Support: Firefox <=54
-		// Halve the iteration target value to prevent interference from CSS upper bounds (gh-2144)
-		initial = initial / 2;
-
-		// Trust units reported by jQuery.css
-		unit = unit || initialInUnit[ 3 ];
-
-		// Iteratively approximate from a nonzero starting point
-		initialInUnit = +initial || 1;
-
-		while ( maxIterations-- ) {
-
-			// Evaluate and update our best guess (doubling guesses that zero out).
-			// Finish if the scale equals or crosses 1 (making the old*new product non-positive).
-			jQuery.style( elem, prop, initialInUnit + unit );
-			if ( ( 1 - scale ) * ( 1 - ( scale = currentValue() / initial || 0.5 ) ) <= 0 ) {
-				maxIterations = 0;
-			}
-			initialInUnit = initialInUnit / scale;
-
-		}
-
-		initialInUnit = initialInUnit * 2;
-		jQuery.style( elem, prop, initialInUnit + unit );
-
-		// Make sure we update the tween properties later on
-		valueParts = valueParts || [];
-	}
-
-	if ( valueParts ) {
-		initialInUnit = +initialInUnit || +initial || 0;
-
-		// Apply relative offset (+=/-=) if specified
-		adjusted = valueParts[ 1 ] ?
-			initialInUnit + ( valueParts[ 1 ] + 1 ) * valueParts[ 2 ] :
-			+valueParts[ 2 ];
-		if ( tween ) {
-			tween.unit = unit;
-			tween.start = initialInUnit;
-			tween.end = adjusted;
-		}
-	}
-	return adjusted;
-}
-
-
-var defaultDisplayMap = {};
-
-function getDefaultDisplay( elem ) {
-	var temp,
-		doc = elem.ownerDocument,
-		nodeName = elem.nodeName,
-		display = defaultDisplayMap[ nodeName ];
-
-	if ( display ) {
-		return display;
-	}
-
-	temp = doc.body.appendChild( doc.createElement( nodeName ) );
-	display = jQuery.css( temp, "display" );
-
-	temp.parentNode.removeChild( temp );
-
-	if ( display === "none" ) {
-		display = "block";
-	}
-	defaultDisplayMap[ nodeName ] = display;
-
-	return display;
-}
-
-function showHide( elements, show ) {
-	var display, elem,
-		values = [],
-		index = 0,
-		length = elements.length;
-
-	// Determine new display value for elements that need to change
-	for ( ; index < length; index++ ) {
-		elem = elements[ index ];
-		if ( !elem.style ) {
-			continue;
-		}
-
-		display = elem.style.display;
-		if ( show ) {
-
-			// Since we force visibility upon cascade-hidden elements, an immediate (and slow)
-			// check is required in this first loop unless we have a nonempty display value (either
-			// inline or about-to-be-restored)
-			if ( display === "none" ) {
-				values[ index ] = dataPriv.get( elem, "display" ) || null;
-				if ( !values[ index ] ) {
-					elem.style.display = "";
-				}
-			}
-			if ( elem.style.display === "" && isHiddenWithinTree( elem ) ) {
-				values[ index ] = getDefaultDisplay( elem );
-			}
-		} else {
-			if ( display !== "none" ) {
-				values[ index ] = "none";
-
-				// Remember what we're overwriting
-				dataPriv.set( elem, "display", display );
-			}
-		}
-	}
-
-	// Set the display of the elements in a second loop to avoid constant reflow
-	for ( index = 0; index < length; index++ ) {
-		if ( values[ index ] != null ) {
-			elements[ index ].style.display = values[ index ];
-		}
-	}
-
-	return elements;
-}
-
-jQuery.fn.extend( {
-	show: function() {
-		return showHide( this, true );
-	},
-	hide: function() {
-		return showHide( this );
-	},
-	toggle: function( state ) {
-		if ( typeof state === "boolean" ) {
-			return state ? this.show() : this.hide();
-		}
-
-		return this.each( function() {
-			if ( isHiddenWithinTree( this ) ) {
-				jQuery( this ).show();
-			} else {
-				jQuery( this ).hide();
-			}
-		} );
-	}
-} );
-var rcheckableType = ( /^(?:checkbox|radio)$/i );
-
-var rtagName = ( /<([a-z][^\/\0>\x20\t\r\n\f]*)/i );
-
-var rscriptType = ( /^$|^module$|\/(?:java|ecma)script/i );
-
-
-
-( function() {
-	var fragment = document.createDocumentFragment(),
-		div = fragment.appendChild( document.createElement( "div" ) ),
-		input = document.createElement( "input" );
-
-	// Support: Android 4.0 - 4.3 only
-	// Check state lost if the name is set (#11217)
-	// Support: Windows Web Apps (WWA)
-	// `name` and `type` must use .setAttribute for WWA (#14901)
-	input.setAttribute( "type", "radio" );
-	input.setAttribute( "checked", "checked" );
-	input.setAttribute( "name", "t" );
-
-	div.appendChild( input );
-
-	// Support: Android <=4.1 only
-	// Older WebKit doesn't clone checked state correctly in fragments
-	support.checkClone = div.cloneNode( true ).cloneNode( true ).lastChild.checked;
-
-	// Support: IE <=11 only
-	// Make sure textarea (and checkbox) defaultValue is properly cloned
-	div.innerHTML = "<textarea>x</textarea>";
-	support.noCloneChecked = !!div.cloneNode( true ).lastChild.defaultValue;
-
-	// Support: IE <=9 only
-	// IE <=9 replaces <option> tags with their contents when inserted outside of
-	// the select element.
-	div.innerHTML = "<option></option>";
-	support.option = !!div.lastChild;
-} )();
-
-
-// We have to close these tags to support XHTML (#13200)
-var wrapMap = {
-
-	// XHTML parsers do not magically insert elements in the
-	// same way that tag soup parsers do. So we cannot shorten
-	// this by omitting <tbody> or other required elements.
-	thead: [ 1, "<table>", "</table>" ],
-	col: [ 2, "<table><colgroup>", "</colgroup></table>" ],
-	tr: [ 2, "<table><tbody>", "</tbody></table>" ],
-	td: [ 3, "<table><tbody><tr>", "</tr></tbody></table>" ],
-
-	_default: [ 0, "", "" ]
-};
-
-wrapMap.tbody = wrapMap.tfoot = wrapMap.colgroup = wrapMap.caption = wrapMap.thead;
-wrapMap.th = wrapMap.td;
-
-// Support: IE <=9 only
-if ( !support.option ) {
-	wrapMap.optgroup = wrapMap.option = [ 1, "<select multiple='multiple'>", "</select>" ];
-}
-
-
-function getAll( context, tag ) {
-
-	// Support: IE <=9 - 11 only
-	// Use typeof to avoid zero-argument method invocation on host objects (#15151)
-	var ret;
-
-	if ( typeof context.getElementsByTagName !== "undefined" ) {
-		ret = context.getElementsByTagName( tag || "*" );
-
-	} else if ( typeof context.querySelectorAll !== "undefined" ) {
-		ret = context.querySelectorAll( tag || "*" );
-
-	} else {
-		ret = [];
-	}
-
-	if ( tag === undefined || tag && nodeName( context, tag ) ) {
-		return jQuery.merge( [ context ], ret );
-	}
-
-	return ret;
-}
-
-
-// Mark scripts as having already been evaluated
-function setGlobalEval( elems, refElements ) {
-	var i = 0,
-		l = elems.length;
-
-	for ( ; i < l; i++ ) {
-		dataPriv.set(
-			elems[ i ],
-			"globalEval",
-			!refElements || dataPriv.get( refElements[ i ], "globalEval" )
-		);
-	}
-}
-
-
-var rhtml = /<|&#?\w+;/;
-
-function buildFragment( elems, context, scripts, selection, ignored ) {
-	var elem, tmp, tag, wrap, attached, j,
-		fragment = context.createDocumentFragment(),
-		nodes = [],
-		i = 0,
-		l = elems.length;
-
-	for ( ; i < l; i++ ) {
-		elem = elems[ i ];
-
-		if ( elem || elem === 0 ) {
-
-			// Add nodes directly
-			if ( toType( elem ) === "object" ) {
-
-				// Support: Android <=4.0 only, PhantomJS 1 only
-				// push.apply(_, arraylike) throws on ancient WebKit
-				jQuery.merge( nodes, elem.nodeType ? [ elem ] : elem );
-
-			// Convert non-html into a text node
-			} else if ( !rhtml.test( elem ) ) {
-				nodes.push( context.createTextNode( elem ) );
-
-			// Convert html into DOM nodes
-			} else {
-				tmp = tmp || fragment.appendChild( context.createElement( "div" ) );
-
-				// Deserialize a standard representation
-				tag = ( rtagName.exec( elem ) || [ "", "" ] )[ 1 ].toLowerCase();
-				wrap = wrapMap[ tag ] || wrapMap._default;
-				tmp.innerHTML = wrap[ 1 ] + jQuery.htmlPrefilter( elem ) + wrap[ 2 ];
-
-				// Descend through wrappers to the right content
-				j = wrap[ 0 ];
-				while ( j-- ) {
-					tmp = tmp.lastChild;
-				}
-
-				// Support: Android <=4.0 only, PhantomJS 1 only
-				// push.apply(_, arraylike) throws on ancient WebKit
-				jQuery.merge( nodes, tmp.childNodes );
-
-				// Remember the top-level container
-				tmp = fragment.firstChild;
-
-				// Ensure the created nodes are orphaned (#12392)
-				tmp.textContent = "";
-			}
-		}
-	}
-
-	// Remove wrapper from fragment
-	fragment.textContent = "";
-
-	i = 0;
-	while ( ( elem = nodes[ i++ ] ) ) {
-
-		// Skip elements already in the context collection (trac-4087)
-		if ( selection && jQuery.inArray( elem, selection ) > -1 ) {
-			if ( ignored ) {
-				ignored.push( elem );
-			}
-			continue;
-		}
-
-		attached = isAttached( elem );
-
-		// Append to fragment
-		tmp = getAll( fragment.appendChild( elem ), "script" );
-
-		// Preserve script evaluation history
-		if ( attached ) {
-			setGlobalEval( tmp );
-		}
-
-		// Capture executables
-		if ( scripts ) {
-			j = 0;
-			while ( ( elem = tmp[ j++ ] ) ) {
-				if ( rscriptType.test( elem.type || "" ) ) {
-					scripts.push( elem );
-				}
-			}
-		}
-	}
-
-	return fragment;
-}
-
-
-var rtypenamespace = /^([^.]*)(?:\.(.+)|)/;
-
-function returnTrue() {
-	return true;
-}
-
-function returnFalse() {
-	return false;
-}
-
-// Support: IE <=9 - 11+
-// focus() and blur() are asynchronous, except when they are no-op.
-// So expect focus to be synchronous when the element is already active,
-// and blur to be synchronous when the element is not already active.
-// (focus and blur are always synchronous in other supported browsers,
-// this just defines when we can count on it).
-function expectSync( elem, type ) {
-	return ( elem === safeActiveElement() ) === ( type === "focus" );
-}
-
-// Support: IE <=9 only
-// Accessing document.activeElement can throw unexpectedly
-// https://bugs.jquery.com/ticket/13393
-function safeActiveElement() {
-	try {
-		return document.activeElement;
-	} catch ( err ) { }
-}
-
-function on( elem, types, selector, data, fn, one ) {
-	var origFn, type;
-
-	// Types can be a map of types/handlers
-	if ( typeof types === "object" ) {
-
-		// ( types-Object, selector, data )
-		if ( typeof selector !== "string" ) {
-
-			// ( types-Object, data )
-			data = data || selector;
-			selector = undefined;
-		}
-		for ( type in types ) {
-			on( elem, type, selector, data, types[ type ], one );
-		}
-		return elem;
-	}
-
-	if ( data == null && fn == null ) {
-
-		// ( types, fn )
-		fn = selector;
-		data = selector = undefined;
-	} else if ( fn == null ) {
-		if ( typeof selector === "string" ) {
-
-			// ( types, selector, fn )
-			fn = data;
-			data = undefined;
-		} else {
-
-			// ( types, data, fn )
-			fn = data;
-			data = selector;
-			selector = undefined;
-		}
-	}
-	if ( fn === false ) {
-		fn = returnFalse;
-	} else if ( !fn ) {
-		return elem;
-	}
-
-	if ( one === 1 ) {
-		origFn = fn;
-		fn = function( event ) {
-
-			// Can use an empty set, since event contains the info
-			jQuery().off( event );
-			return origFn.apply( this, arguments );
-		};
-
-		// Use same guid so caller can remove using origFn
-		fn.guid = origFn.guid || ( origFn.guid = jQuery.guid++ );
-	}
-	return elem.each( function() {
-		jQuery.event.add( this, types, fn, data, selector );
-	} );
-}
-
-/*
- * Helper functions for managing events -- not part of the public interface.
- * Props to Dean Edwards' addEvent library for many of the ideas.
- */
-jQuery.event = {
-
-	global: {},
-
-	add: function( elem, types, handler, data, selector ) {
-
-		var handleObjIn, eventHandle, tmp,
-			events, t, handleObj,
-			special, handlers, type, namespaces, origType,
-			elemData = dataPriv.get( elem );
-
-		// Only attach events to objects that accept data
-		if ( !acceptData( elem ) ) {
-			return;
-		}
-
-		// Caller can pass in an object of custom data in lieu of the handler
-		if ( handler.handler ) {
-			handleObjIn = handler;
-			handler = handleObjIn.handler;
-			selector = handleObjIn.selector;
-		}
-
-		// Ensure that invalid selectors throw exceptions at attach time
-		// Evaluate against documentElement in case elem is a non-element node (e.g., document)
-		if ( selector ) {
-			jQuery.find.matchesSelector( documentElement, selector );
-		}
-
-		// Make sure that the handler has a unique ID, used to find/remove it later
-		if ( !handler.guid ) {
-			handler.guid = jQuery.guid++;
-		}
-
-		// Init the element's event structure and main handler, if this is the first
-		if ( !( events = elemData.events ) ) {
-			events = elemData.events = Object.create( null );
-		}
-		if ( !( eventHandle = elemData.handle ) ) {
-			eventHandle = elemData.handle = function( e ) {
-
-				// Discard the second event of a jQuery.event.trigger() and
-				// when an event is called after a page has unloaded
-				return typeof jQuery !== "undefined" && jQuery.event.triggered !== e.type ?
-					jQuery.event.dispatch.apply( elem, arguments ) : undefined;
-			};
-		}
-
-		// Handle multiple events separated by a space
-		types = ( types || "" ).match( rnothtmlwhite ) || [ "" ];
-		t = types.length;
-		while ( t-- ) {
-			tmp = rtypenamespace.exec( types[ t ] ) || [];
-			type = origType = tmp[ 1 ];
-			namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort();
-
-			// There *must* be a type, no attaching namespace-only handlers
-			if ( !type ) {
-				continue;
-			}
-
-			// If event changes its type, use the special event handlers for the changed type
-			special = jQuery.event.special[ type ] || {};
-
-			// If selector defined, determine special event api type, otherwise given type
-			type = ( selector ? special.delegateType : special.bindType ) || type;
-
-			// Update special based on newly reset type
-			special = jQuery.event.special[ type ] || {};
-
-			// handleObj is passed to all event handlers
-			handleObj = jQuery.extend( {
-				type: type,
-				origType: origType,
-				data: data,
-				handler: handler,
-				guid: handler.guid,
-				selector: selector,
-				needsContext: selector && jQuery.expr.match.needsContext.test( selector ),
-				namespace: namespaces.join( "." )
-			}, handleObjIn );
-
-			// Init the event handler queue if we're the first
-			if ( !( handlers = events[ type ] ) ) {
-				handlers = events[ type ] = [];
-				handlers.delegateCount = 0;
-
-				// Only use addEventListener if the special events handler returns false
-				if ( !special.setup ||
-					special.setup.call( elem, data, namespaces, eventHandle ) === false ) {
-
-					if ( elem.addEventListener ) {
-						elem.addEventListener( type, eventHandle );
-					}
-				}
-			}
-
-			if ( special.add ) {
-				special.add.call( elem, handleObj );
-
-				if ( !handleObj.handler.guid ) {
-					handleObj.handler.guid = handler.guid;
-				}
-			}
-
-			// Add to the element's handler list, delegates in front
-			if ( selector ) {
-				handlers.splice( handlers.delegateCount++, 0, handleObj );
-			} else {
-				handlers.push( handleObj );
-			}
-
-			// Keep track of which events have ever been used, for event optimization
-			jQuery.event.global[ type ] = true;
-		}
-
-	},
-
-	// Detach an event or set of events from an element
-	remove: function( elem, types, handler, selector, mappedTypes ) {
-
-		var j, origCount, tmp,
-			events, t, handleObj,
-			special, handlers, type, namespaces, origType,
-			elemData = dataPriv.hasData( elem ) && dataPriv.get( elem );
-
-		if ( !elemData || !( events = elemData.events ) ) {
-			return;
-		}
-
-		// Once for each type.namespace in types; type may be omitted
-		types = ( types || "" ).match( rnothtmlwhite ) || [ "" ];
-		t = types.length;
-		while ( t-- ) {
-			tmp = rtypenamespace.exec( types[ t ] ) || [];
-			type = origType = tmp[ 1 ];
-			namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort();
-
-			// Unbind all events (on this namespace, if provided) for the element
-			if ( !type ) {
-				for ( type in events ) {
-					jQuery.event.remove( elem, type + types[ t ], handler, selector, true );
-				}
-				continue;
-			}
-
-			special = jQuery.event.special[ type ] || {};
-			type = ( selector ? special.delegateType : special.bindType ) || type;
-			handlers = events[ type ] || [];
-			tmp = tmp[ 2 ] &&
-				new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" );
-
-			// Remove matching events
-			origCount = j = handlers.length;
-			while ( j-- ) {
-				handleObj = handlers[ j ];
-
-				if ( ( mappedTypes || origType === handleObj.origType ) &&
-					( !handler || handler.guid === handleObj.guid ) &&
-					( !tmp || tmp.test( handleObj.namespace ) ) &&
-					( !selector || selector === handleObj.selector ||
-						selector === "**" && handleObj.selector ) ) {
-					handlers.splice( j, 1 );
-
-					if ( handleObj.selector ) {
-						handlers.delegateCount--;
-					}
-					if ( special.remove ) {
-						special.remove.call( elem, handleObj );
-					}
-				}
-			}
-
-			// Remove generic event handler if we removed something and no more handlers exist
-			// (avoids potential for endless recursion during removal of special event handlers)
-			if ( origCount && !handlers.length ) {
-				if ( !special.teardown ||
-					special.teardown.call( elem, namespaces, elemData.handle ) === false ) {
-
-					jQuery.removeEvent( elem, type, elemData.handle );
-				}
-
-				delete events[ type ];
-			}
-		}
-
-		// Remove data and the expando if it's no longer used
-		if ( jQuery.isEmptyObject( events ) ) {
-			dataPriv.remove( elem, "handle events" );
-		}
-	},
-
-	dispatch: function( nativeEvent ) {
-
-		var i, j, ret, matched, handleObj, handlerQueue,
-			args = new Array( arguments.length ),
-
-			// Make a writable jQuery.Event from the native event object
-			event = jQuery.event.fix( nativeEvent ),
-
-			handlers = (
-				dataPriv.get( this, "events" ) || Object.create( null )
-			)[ event.type ] || [],
-			special = jQuery.event.special[ event.type ] || {};
-
-		// Use the fix-ed jQuery.Event rather than the (read-only) native event
-		args[ 0 ] = event;
-
-		for ( i = 1; i < arguments.length; i++ ) {
-			args[ i ] = arguments[ i ];
-		}
-
-		event.delegateTarget = this;
-
-		// Call the preDispatch hook for the mapped type, and let it bail if desired
-		if ( special.preDispatch && special.preDispatch.call( this, event ) === false ) {
-			return;
-		}
-
-		// Determine handlers
-		handlerQueue = jQuery.event.handlers.call( this, event, handlers );
-
-		// Run delegates first; they may want to stop propagation beneath us
-		i = 0;
-		while ( ( matched = handlerQueue[ i++ ] ) && !event.isPropagationStopped() ) {
-			event.currentTarget = matched.elem;
-
-			j = 0;
-			while ( ( handleObj = matched.handlers[ j++ ] ) &&
-				!event.isImmediatePropagationStopped() ) {
-
-				// If the event is namespaced, then each handler is only invoked if it is
-				// specially universal or its namespaces are a superset of the event's.
-				if ( !event.rnamespace || handleObj.namespace === false ||
-					event.rnamespace.test( handleObj.namespace ) ) {
-
-					event.handleObj = handleObj;
-					event.data = handleObj.data;
-
-					ret = ( ( jQuery.event.special[ handleObj.origType ] || {} ).handle ||
-						handleObj.handler ).apply( matched.elem, args );
-
-					if ( ret !== undefined ) {
-						if ( ( event.result = ret ) === false ) {
-							event.preventDefault();
-							event.stopPropagation();
-						}
-					}
-				}
-			}
-		}
-
-		// Call the postDispatch hook for the mapped type
-		if ( special.postDispatch ) {
-			special.postDispatch.call( this, event );
-		}
-
-		return event.result;
-	},
-
-	handlers: function( event, handlers ) {
-		var i, handleObj, sel, matchedHandlers, matchedSelectors,
-			handlerQueue = [],
-			delegateCount = handlers.delegateCount,
-			cur = event.target;
-
-		// Find delegate handlers
-		if ( delegateCount &&
-
-			// Support: IE <=9
-			// Black-hole SVG <use> instance trees (trac-13180)
-			cur.nodeType &&
-
-			// Support: Firefox <=42
-			// Suppress spec-violating clicks indicating a non-primary pointer button (trac-3861)
-			// https://www.w3.org/TR/DOM-Level-3-Events/#event-type-click
-			// Support: IE 11 only
-			// ...but not arrow key "clicks" of radio inputs, which can have `button` -1 (gh-2343)
-			!( event.type === "click" && event.button >= 1 ) ) {
-
-			for ( ; cur !== this; cur = cur.parentNode || this ) {
-
-				// Don't check non-elements (#13208)
-				// Don't process clicks on disabled elements (#6911, #8165, #11382, #11764)
-				if ( cur.nodeType === 1 && !( event.type === "click" && cur.disabled === true ) ) {
-					matchedHandlers = [];
-					matchedSelectors = {};
-					for ( i = 0; i < delegateCount; i++ ) {
-						handleObj = handlers[ i ];
-
-						// Don't conflict with Object.prototype properties (#13203)
-						sel = handleObj.selector + " ";
-
-						if ( matchedSelectors[ sel ] === undefined ) {
-							matchedSelectors[ sel ] = handleObj.needsContext ?
-								jQuery( sel, this ).index( cur ) > -1 :
-								jQuery.find( sel, this, null, [ cur ] ).length;
-						}
-						if ( matchedSelectors[ sel ] ) {
-							matchedHandlers.push( handleObj );
-						}
-					}
-					if ( matchedHandlers.length ) {
-						handlerQueue.push( { elem: cur, handlers: matchedHandlers } );
-					}
-				}
-			}
-		}
-
-		// Add the remaining (directly-bound) handlers
-		cur = this;
-		if ( delegateCount < handlers.length ) {
-			handlerQueue.push( { elem: cur, handlers: handlers.slice( delegateCount ) } );
-		}
-
-		return handlerQueue;
-	},
-
-	addProp: function( name, hook ) {
-		Object.defineProperty( jQuery.Event.prototype, name, {
-			enumerable: true,
-			configurable: true,
-
-			get: isFunction( hook ) ?
-				function() {
-					if ( this.originalEvent ) {
-						return hook( this.originalEvent );
-					}
-				} :
-				function() {
-					if ( this.originalEvent ) {
-						return this.originalEvent[ name ];
-					}
-				},
-
-			set: function( value ) {
-				Object.defineProperty( this, name, {
-					enumerable: true,
-					configurable: true,
-					writable: true,
-					value: value
-				} );
-			}
-		} );
-	},
-
-	fix: function( originalEvent ) {
-		return originalEvent[ jQuery.expando ] ?
-			originalEvent :
-			new jQuery.Event( originalEvent );
-	},
-
-	special: {
-		load: {
-
-			// Prevent triggered image.load events from bubbling to window.load
-			noBubble: true
-		},
-		click: {
-
-			// Utilize native event to ensure correct state for checkable inputs
-			setup: function( data ) {
-
-				// For mutual compressibility with _default, replace `this` access with a local var.
-				// `|| data` is dead code meant only to preserve the variable through minification.
-				var el = this || data;
-
-				// Claim the first handler
-				if ( rcheckableType.test( el.type ) &&
-					el.click && nodeName( el, "input" ) ) {
-
-					// dataPriv.set( el, "click", ... )
-					leverageNative( el, "click", returnTrue );
-				}
-
-				// Return false to allow normal processing in the caller
-				return false;
-			},
-			trigger: function( data ) {
-
-				// For mutual compressibility with _default, replace `this` access with a local var.
-				// `|| data` is dead code meant only to preserve the variable through minification.
-				var el = this || data;
-
-				// Force setup before triggering a click
-				if ( rcheckableType.test( el.type ) &&
-					el.click && nodeName( el, "input" ) ) {
-
-					leverageNative( el, "click" );
-				}
-
-				// Return non-false to allow normal event-path propagation
-				return true;
-			},
-
-			// For cross-browser consistency, suppress native .click() on links
-			// Also prevent it if we're currently inside a leveraged native-event stack
-			_default: function( event ) {
-				var target = event.target;
-				return rcheckableType.test( target.type ) &&
-					target.click && nodeName( target, "input" ) &&
-					dataPriv.get( target, "click" ) ||
-					nodeName( target, "a" );
-			}
-		},
-
-		beforeunload: {
-			postDispatch: function( event ) {
-
-				// Support: Firefox 20+
-				// Firefox doesn't alert if the returnValue field is not set.
-				if ( event.result !== undefined && event.originalEvent ) {
-					event.originalEvent.returnValue = event.result;
-				}
-			}
-		}
-	}
-};
-
-// Ensure the presence of an event listener that handles manually-triggered
-// synthetic events by interrupting progress until reinvoked in response to
-// *native* events that it fires directly, ensuring that state changes have
-// already occurred before other listeners are invoked.
-function leverageNative( el, type, expectSync ) {
-
-	// Missing expectSync indicates a trigger call, which must force setup through jQuery.event.add
-	if ( !expectSync ) {
-		if ( dataPriv.get( el, type ) === undefined ) {
-			jQuery.event.add( el, type, returnTrue );
-		}
-		return;
-	}
-
-	// Register the controller as a special universal handler for all event namespaces
-	dataPriv.set( el, type, false );
-	jQuery.event.add( el, type, {
-		namespace: false,
-		handler: function( event ) {
-			var notAsync, result,
-				saved = dataPriv.get( this, type );
-
-			if ( ( event.isTrigger & 1 ) && this[ type ] ) {
-
-				// Interrupt processing of the outer synthetic .trigger()ed event
-				// Saved data should be false in such cases, but might be a leftover capture object
-				// from an async native handler (gh-4350)
-				if ( !saved.length ) {
-
-					// Store arguments for use when handling the inner native event
-					// There will always be at least one argument (an event object), so this array
-					// will not be confused with a leftover capture object.
-					saved = slice.call( arguments );
-					dataPriv.set( this, type, saved );
-
-					// Trigger the native event and capture its result
-					// Support: IE <=9 - 11+
-					// focus() and blur() are asynchronous
-					notAsync = expectSync( this, type );
-					this[ type ]();
-					result = dataPriv.get( this, type );
-					if ( saved !== result || notAsync ) {
-						dataPriv.set( this, type, false );
-					} else {
-						result = {};
-					}
-					if ( saved !== result ) {
-
-						// Cancel the outer synthetic event
-						event.stopImmediatePropagation();
-						event.preventDefault();
-
-						// Support: Chrome 86+
-						// In Chrome, if an element having a focusout handler is blurred by
-						// clicking outside of it, it invokes the handler synchronously. If
-						// that handler calls `.remove()` on the element, the data is cleared,
-						// leaving `result` undefined. We need to guard against this.
-						return result && result.value;
-					}
-
-				// If this is an inner synthetic event for an event with a bubbling surrogate
-				// (focus or blur), assume that the surrogate already propagated from triggering the
-				// native event and prevent that from happening again here.
-				// This technically gets the ordering wrong w.r.t. to `.trigger()` (in which the
-				// bubbling surrogate propagates *after* the non-bubbling base), but that seems
-				// less bad than duplication.
-				} else if ( ( jQuery.event.special[ type ] || {} ).delegateType ) {
-					event.stopPropagation();
-				}
-
-			// If this is a native event triggered above, everything is now in order
-			// Fire an inner synthetic event with the original arguments
-			} else if ( saved.length ) {
-
-				// ...and capture the result
-				dataPriv.set( this, type, {
-					value: jQuery.event.trigger(
-
-						// Support: IE <=9 - 11+
-						// Extend with the prototype to reset the above stopImmediatePropagation()
-						jQuery.extend( saved[ 0 ], jQuery.Event.prototype ),
-						saved.slice( 1 ),
-						this
-					)
-				} );
-
-				// Abort handling of the native event
-				event.stopImmediatePropagation();
-			}
-		}
-	} );
-}
-
-jQuery.removeEvent = function( elem, type, handle ) {
-
-	// This "if" is needed for plain objects
-	if ( elem.removeEventListener ) {
-		elem.removeEventListener( type, handle );
-	}
-};
-
-jQuery.Event = function( src, props ) {
-
-	// Allow instantiation without the 'new' keyword
-	if ( !( this instanceof jQuery.Event ) ) {
-		return new jQuery.Event( src, props );
-	}
-
-	// Event object
-	if ( src && src.type ) {
-		this.originalEvent = src;
-		this.type = src.type;
-
-		// Events bubbling up the document may have been marked as prevented
-		// by a handler lower down the tree; reflect the correct value.
-		this.isDefaultPrevented = src.defaultPrevented ||
-				src.defaultPrevented === undefined &&
-
-				// Support: Android <=2.3 only
-				src.returnValue === false ?
-			returnTrue :
-			returnFalse;
-
-		// Create target properties
-		// Support: Safari <=6 - 7 only
-		// Target should not be a text node (#504, #13143)
-		this.target = ( src.target && src.target.nodeType === 3 ) ?
-			src.target.parentNode :
-			src.target;
-
-		this.currentTarget = src.currentTarget;
-		this.relatedTarget = src.relatedTarget;
-
-	// Event type
-	} else {
-		this.type = src;
-	}
-
-	// Put explicitly provided properties onto the event object
-	if ( props ) {
-		jQuery.extend( this, props );
-	}
-
-	// Create a timestamp if incoming event doesn't have one
-	this.timeStamp = src && src.timeStamp || Date.now();
-
-	// Mark it as fixed
-	this[ jQuery.expando ] = true;
-};
-
-// jQuery.Event is based on DOM3 Events as specified by the ECMAScript Language Binding
-// https://www.w3.org/TR/2003/WD-DOM-Level-3-Events-20030331/ecma-script-binding.html
-jQuery.Event.prototype = {
-	constructor: jQuery.Event,
-	isDefaultPrevented: returnFalse,
-	isPropagationStopped: returnFalse,
-	isImmediatePropagationStopped: returnFalse,
-	isSimulated: false,
-
-	preventDefault: function() {
-		var e = this.originalEvent;
-
-		this.isDefaultPrevented = returnTrue;
-
-		if ( e && !this.isSimulated ) {
-			e.preventDefault();
-		}
-	},
-	stopPropagation: function() {
-		var e = this.originalEvent;
-
-		this.isPropagationStopped = returnTrue;
-
-		if ( e && !this.isSimulated ) {
-			e.stopPropagation();
-		}
-	},
-	stopImmediatePropagation: function() {
-		var e = this.originalEvent;
-
-		this.isImmediatePropagationStopped = returnTrue;
-
-		if ( e && !this.isSimulated ) {
-			e.stopImmediatePropagation();
-		}
-
-		this.stopPropagation();
-	}
-};
-
-// Includes all common event props including KeyEvent and MouseEvent specific props
-jQuery.each( {
-	altKey: true,
-	bubbles: true,
-	cancelable: true,
-	changedTouches: true,
-	ctrlKey: true,
-	detail: true,
-	eventPhase: true,
-	metaKey: true,
-	pageX: true,
-	pageY: true,
-	shiftKey: true,
-	view: true,
-	"char": true,
-	code: true,
-	charCode: true,
-	key: true,
-	keyCode: true,
-	button: true,
-	buttons: true,
-	clientX: true,
-	clientY: true,
-	offsetX: true,
-	offsetY: true,
-	pointerId: true,
-	pointerType: true,
-	screenX: true,
-	screenY: true,
-	targetTouches: true,
-	toElement: true,
-	touches: true,
-	which: true
-}, jQuery.event.addProp );
-
-jQuery.each( { focus: "focusin", blur: "focusout" }, function( type, delegateType ) {
-	jQuery.event.special[ type ] = {
-
-		// Utilize native event if possible so blur/focus sequence is correct
-		setup: function() {
-
-			// Claim the first handler
-			// dataPriv.set( this, "focus", ... )
-			// dataPriv.set( this, "blur", ... )
-			leverageNative( this, type, expectSync );
-
-			// Return false to allow normal processing in the caller
-			return false;
-		},
-		trigger: function() {
-
-			// Force setup before trigger
-			leverageNative( this, type );
-
-			// Return non-false to allow normal event-path propagation
-			return true;
-		},
-
-		// Suppress native focus or blur as it's already being fired
-		// in leverageNative.
-		_default: function() {
-			return true;
-		},
-
-		delegateType: delegateType
-	};
-} );
-
-// Create mouseenter/leave events using mouseover/out and event-time checks
-// so that event delegation works in jQuery.
-// Do the same for pointerenter/pointerleave and pointerover/pointerout
-//
-// Support: Safari 7 only
-// Safari sends mouseenter too often; see:
-// https://bugs.chromium.org/p/chromium/issues/detail?id=470258
-// for the description of the bug (it existed in older Chrome versions as well).
-jQuery.each( {
-	mouseenter: "mouseover",
-	mouseleave: "mouseout",
-	pointerenter: "pointerover",
-	pointerleave: "pointerout"
-}, function( orig, fix ) {
-	jQuery.event.special[ orig ] = {
-		delegateType: fix,
-		bindType: fix,
-
-		handle: function( event ) {
-			var ret,
-				target = this,
-				related = event.relatedTarget,
-				handleObj = event.handleObj;
-
-			// For mouseenter/leave call the handler if related is outside the target.
-			// NB: No relatedTarget if the mouse left/entered the browser window
-			if ( !related || ( related !== target && !jQuery.contains( target, related ) ) ) {
-				event.type = handleObj.origType;
-				ret = handleObj.handler.apply( this, arguments );
-				event.type = fix;
-			}
-			return ret;
-		}
-	};
-} );
-
-jQuery.fn.extend( {
-
-	on: function( types, selector, data, fn ) {
-		return on( this, types, selector, data, fn );
-	},
-	one: function( types, selector, data, fn ) {
-		return on( this, types, selector, data, fn, 1 );
-	},
-	off: function( types, selector, fn ) {
-		var handleObj, type;
-		if ( types && types.preventDefault && types.handleObj ) {
-
-			// ( event )  dispatched jQuery.Event
-			handleObj = types.handleObj;
-			jQuery( types.delegateTarget ).off(
-				handleObj.namespace ?
-					handleObj.origType + "." + handleObj.namespace :
-					handleObj.origType,
-				handleObj.selector,
-				handleObj.handler
-			);
-			return this;
-		}
-		if ( typeof types === "object" ) {
-
-			// ( types-object [, selector] )
-			for ( type in types ) {
-				this.off( type, selector, types[ type ] );
-			}
-			return this;
-		}
-		if ( selector === false || typeof selector === "function" ) {
-
-			// ( types [, fn] )
-			fn = selector;
-			selector = undefined;
-		}
-		if ( fn === false ) {
-			fn = returnFalse;
-		}
-		return this.each( function() {
-			jQuery.event.remove( this, types, fn, selector );
-		} );
-	}
-} );
-
-
-var
-
-	// Support: IE <=10 - 11, Edge 12 - 13 only
-	// In IE/Edge using regex groups here causes severe slowdowns.
-	// See https://connect.microsoft.com/IE/feedback/details/1736512/
-	rnoInnerhtml = /<script|<style|<link/i,
-
-	// checked="checked" or checked
-	rchecked = /checked\s*(?:[^=]|=\s*.checked.)/i,
-	rcleanScript = /^\s*<!(?:\[CDATA\[|--)|(?:\]\]|--)>\s*$/g;
-
-// Prefer a tbody over its parent table for containing new rows
-function manipulationTarget( elem, content ) {
-	if ( nodeName( elem, "table" ) &&
-		nodeName( content.nodeType !== 11 ? content : content.firstChild, "tr" ) ) {
-
-		return jQuery( elem ).children( "tbody" )[ 0 ] || elem;
-	}
-
-	return elem;
-}
-
-// Replace/restore the type attribute of script elements for safe DOM manipulation
-function disableScript( elem ) {
-	elem.type = ( elem.getAttribute( "type" ) !== null ) + "/" + elem.type;
-	return elem;
-}
-function restoreScript( elem ) {
-	if ( ( elem.type || "" ).slice( 0, 5 ) === "true/" ) {
-		elem.type = elem.type.slice( 5 );
-	} else {
-		elem.removeAttribute( "type" );
-	}
-
-	return elem;
-}
-
-function cloneCopyEvent( src, dest ) {
-	var i, l, type, pdataOld, udataOld, udataCur, events;
-
-	if ( dest.nodeType !== 1 ) {
-		return;
-	}
-
-	// 1. Copy private data: events, handlers, etc.
-	if ( dataPriv.hasData( src ) ) {
-		pdataOld = dataPriv.get( src );
-		events = pdataOld.events;
-
-		if ( events ) {
-			dataPriv.remove( dest, "handle events" );
-
-			for ( type in events ) {
-				for ( i = 0, l = events[ type ].length; i < l; i++ ) {
-					jQuery.event.add( dest, type, events[ type ][ i ] );
-				}
-			}
-		}
-	}
-
-	// 2. Copy user data
-	if ( dataUser.hasData( src ) ) {
-		udataOld = dataUser.access( src );
-		udataCur = jQuery.extend( {}, udataOld );
-
-		dataUser.set( dest, udataCur );
-	}
-}
-
-// Fix IE bugs, see support tests
-function fixInput( src, dest ) {
-	var nodeName = dest.nodeName.toLowerCase();
-
-	// Fails to persist the checked state of a cloned checkbox or radio button.
-	if ( nodeName === "input" && rcheckableType.test( src.type ) ) {
-		dest.checked = src.checked;
-
-	// Fails to return the selected option to the default selected state when cloning options
-	} else if ( nodeName === "input" || nodeName === "textarea" ) {
-		dest.defaultValue = src.defaultValue;
-	}
-}
-
-function domManip( collection, args, callback, ignored ) {
-
-	// Flatten any nested arrays
-	args = flat( args );
-
-	var fragment, first, scripts, hasScripts, node, doc,
-		i = 0,
-		l = collection.length,
-		iNoClone = l - 1,
-		value = args[ 0 ],
-		valueIsFunction = isFunction( value );
-
-	// We can't cloneNode fragments that contain checked, in WebKit
-	if ( valueIsFunction ||
-			( l > 1 && typeof value === "string" &&
-				!support.checkClone && rchecked.test( value ) ) ) {
-		return collection.each( function( index ) {
-			var self = collection.eq( index );
-			if ( valueIsFunction ) {
-				args[ 0 ] = value.call( this, index, self.html() );
-			}
-			domManip( self, args, callback, ignored );
-		} );
-	}
-
-	if ( l ) {
-		fragment = buildFragment( args, collection[ 0 ].ownerDocument, false, collection, ignored );
-		first = fragment.firstChild;
-
-		if ( fragment.childNodes.length === 1 ) {
-			fragment = first;
-		}
-
-		// Require either new content or an interest in ignored elements to invoke the callback
-		if ( first || ignored ) {
-			scripts = jQuery.map( getAll( fragment, "script" ), disableScript );
-			hasScripts = scripts.length;
-
-			// Use the original fragment for the last item
-			// instead of the first because it can end up
-			// being emptied incorrectly in certain situations (#8070).
-			for ( ; i < l; i++ ) {
-				node = fragment;
-
-				if ( i !== iNoClone ) {
-					node = jQuery.clone( node, true, true );
-
-					// Keep references to cloned scripts for later restoration
-					if ( hasScripts ) {
-
-						// Support: Android <=4.0 only, PhantomJS 1 only
-						// push.apply(_, arraylike) throws on ancient WebKit
-						jQuery.merge( scripts, getAll( node, "script" ) );
-					}
-				}
-
-				callback.call( collection[ i ], node, i );
-			}
-
-			if ( hasScripts ) {
-				doc = scripts[ scripts.length - 1 ].ownerDocument;
-
-				// Reenable scripts
-				jQuery.map( scripts, restoreScript );
-
-				// Evaluate executable scripts on first document insertion
-				for ( i = 0; i < hasScripts; i++ ) {
-					node = scripts[ i ];
-					if ( rscriptType.test( node.type || "" ) &&
-						!dataPriv.access( node, "globalEval" ) &&
-						jQuery.contains( doc, node ) ) {
-
-						if ( node.src && ( node.type || "" ).toLowerCase()  !== "module" ) {
-
-							// Optional AJAX dependency, but won't run scripts if not present
-							if ( jQuery._evalUrl && !node.noModule ) {
-								jQuery._evalUrl( node.src, {
-									nonce: node.nonce || node.getAttribute( "nonce" )
-								}, doc );
-							}
-						} else {
-							DOMEval( node.textContent.replace( rcleanScript, "" ), node, doc );
-						}
-					}
-				}
-			}
-		}
-	}
-
-	return collection;
-}
-
-function remove( elem, selector, keepData ) {
-	var node,
-		nodes = selector ? jQuery.filter( selector, elem ) : elem,
-		i = 0;
-
-	for ( ; ( node = nodes[ i ] ) != null; i++ ) {
-		if ( !keepData && node.nodeType === 1 ) {
-			jQuery.cleanData( getAll( node ) );
-		}
-
-		if ( node.parentNode ) {
-			if ( keepData && isAttached( node ) ) {
-				setGlobalEval( getAll( node, "script" ) );
-			}
-			node.parentNode.removeChild( node );
-		}
-	}
-
-	return elem;
-}
-
-jQuery.extend( {
-	htmlPrefilter: function( html ) {
-		return html;
-	},
-
-	clone: function( elem, dataAndEvents, deepDataAndEvents ) {
-		var i, l, srcElements, destElements,
-			clone = elem.cloneNode( true ),
-			inPage = isAttached( elem );
-
-		// Fix IE cloning issues
-		if ( !support.noCloneChecked && ( elem.nodeType === 1 || elem.nodeType === 11 ) &&
-				!jQuery.isXMLDoc( elem ) ) {
-
-			// We eschew Sizzle here for performance reasons: https://jsperf.com/getall-vs-sizzle/2
-			destElements = getAll( clone );
-			srcElements = getAll( elem );
-
-			for ( i = 0, l = srcElements.length; i < l; i++ ) {
-				fixInput( srcElements[ i ], destElements[ i ] );
-			}
-		}
-
-		// Copy the events from the original to the clone
-		if ( dataAndEvents ) {
-			if ( deepDataAndEvents ) {
-				srcElements = srcElements || getAll( elem );
-				destElements = destElements || getAll( clone );
-
-				for ( i = 0, l = srcElements.length; i < l; i++ ) {
-					cloneCopyEvent( srcElements[ i ], destElements[ i ] );
-				}
-			} else {
-				cloneCopyEvent( elem, clone );
-			}
-		}
-
-		// Preserve script evaluation history
-		destElements = getAll( clone, "script" );
-		if ( destElements.length > 0 ) {
-			setGlobalEval( destElements, !inPage && getAll( elem, "script" ) );
-		}
-
-		// Return the cloned set
-		return clone;
-	},
-
-	cleanData: function( elems ) {
-		var data, elem, type,
-			special = jQuery.event.special,
-			i = 0;
-
-		for ( ; ( elem = elems[ i ] ) !== undefined; i++ ) {
-			if ( acceptData( elem ) ) {
-				if ( ( data = elem[ dataPriv.expando ] ) ) {
-					if ( data.events ) {
-						for ( type in data.events ) {
-							if ( special[ type ] ) {
-								jQuery.event.remove( elem, type );
-
-							// This is a shortcut to avoid jQuery.event.remove's overhead
-							} else {
-								jQuery.removeEvent( elem, type, data.handle );
-							}
-						}
-					}
-
-					// Support: Chrome <=35 - 45+
-					// Assign undefined instead of using delete, see Data#remove
-					elem[ dataPriv.expando ] = undefined;
-				}
-				if ( elem[ dataUser.expando ] ) {
-
-					// Support: Chrome <=35 - 45+
-					// Assign undefined instead of using delete, see Data#remove
-					elem[ dataUser.expando ] = undefined;
-				}
-			}
-		}
-	}
-} );
-
-jQuery.fn.extend( {
-	detach: function( selector ) {
-		return remove( this, selector, true );
-	},
-
-	remove: function( selector ) {
-		return remove( this, selector );
-	},
-
-	text: function( value ) {
-		return access( this, function( value ) {
-			return value === undefined ?
-				jQuery.text( this ) :
-				this.empty().each( function() {
-					if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
-						this.textContent = value;
-					}
-				} );
-		}, null, value, arguments.length );
-	},
-
-	append: function() {
-		return domManip( this, arguments, function( elem ) {
-			if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
-				var target = manipulationTarget( this, elem );
-				target.appendChild( elem );
-			}
-		} );
-	},
-
-	prepend: function() {
-		return domManip( this, arguments, function( elem ) {
-			if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
-				var target = manipulationTarget( this, elem );
-				target.insertBefore( elem, target.firstChild );
-			}
-		} );
-	},
-
-	before: function() {
-		return domManip( this, arguments, function( elem ) {
-			if ( this.parentNode ) {
-				this.parentNode.insertBefore( elem, this );
-			}
-		} );
-	},
-
-	after: function() {
-		return domManip( this, arguments, function( elem ) {
-			if ( this.parentNode ) {
-				this.parentNode.insertBefore( elem, this.nextSibling );
-			}
-		} );
-	},
-
-	empty: function() {
-		var elem,
-			i = 0;
-
-		for ( ; ( elem = this[ i ] ) != null; i++ ) {
-			if ( elem.nodeType === 1 ) {
-
-				// Prevent memory leaks
-				jQuery.cleanData( getAll( elem, false ) );
-
-				// Remove any remaining nodes
-				elem.textContent = "";
-			}
-		}
-
-		return this;
-	},
-
-	clone: function( dataAndEvents, deepDataAndEvents ) {
-		dataAndEvents = dataAndEvents == null ? false : dataAndEvents;
-		deepDataAndEvents = deepDataAndEvents == null ? dataAndEvents : deepDataAndEvents;
-
-		return this.map( function() {
-			return jQuery.clone( this, dataAndEvents, deepDataAndEvents );
-		} );
-	},
-
-	html: function( value ) {
-		return access( this, function( value ) {
-			var elem = this[ 0 ] || {},
-				i = 0,
-				l = this.length;
-
-			if ( value === undefined && elem.nodeType === 1 ) {
-				return elem.innerHTML;
-			}
-
-			// See if we can take a shortcut and just use innerHTML
-			if ( typeof value === "string" && !rnoInnerhtml.test( value ) &&
-				!wrapMap[ ( rtagName.exec( value ) || [ "", "" ] )[ 1 ].toLowerCase() ] ) {
-
-				value = jQuery.htmlPrefilter( value );
-
-				try {
-					for ( ; i < l; i++ ) {
-						elem = this[ i ] || {};
-
-						// Remove element nodes and prevent memory leaks
-						if ( elem.nodeType === 1 ) {
-							jQuery.cleanData( getAll( elem, false ) );
-							elem.innerHTML = value;
-						}
-					}
-
-					elem = 0;
-
-				// If using innerHTML throws an exception, use the fallback method
-				} catch ( e ) {}
-			}
-
-			if ( elem ) {
-				this.empty().append( value );
-			}
-		}, null, value, arguments.length );
-	},
-
-	replaceWith: function() {
-		var ignored = [];
-
-		// Make the changes, replacing each non-ignored context element with the new content
-		return domManip( this, arguments, function( elem ) {
-			var parent = this.parentNode;
-
-			if ( jQuery.inArray( this, ignored ) < 0 ) {
-				jQuery.cleanData( getAll( this ) );
-				if ( parent ) {
-					parent.replaceChild( elem, this );
-				}
-			}
-
-		// Force callback invocation
-		}, ignored );
-	}
-} );
-
-jQuery.each( {
-	appendTo: "append",
-	prependTo: "prepend",
-	insertBefore: "before",
-	insertAfter: "after",
-	replaceAll: "replaceWith"
-}, function( name, original ) {
-	jQuery.fn[ name ] = function( selector ) {
-		var elems,
-			ret = [],
-			insert = jQuery( selector ),
-			last = insert.length - 1,
-			i = 0;
-
-		for ( ; i <= last; i++ ) {
-			elems = i === last ? this : this.clone( true );
-			jQuery( insert[ i ] )[ original ]( elems );
-
-			// Support: Android <=4.0 only, PhantomJS 1 only
-			// .get() because push.apply(_, arraylike) throws on ancient WebKit
-			push.apply( ret, elems.get() );
-		}
-
-		return this.pushStack( ret );
-	};
-} );
-var rnumnonpx = new RegExp( "^(" + pnum + ")(?!px)[a-z%]+$", "i" );
-
-var getStyles = function( elem ) {
-
-		// Support: IE <=11 only, Firefox <=30 (#15098, #14150)
-		// IE throws on elements created in popups
-		// FF meanwhile throws on frame elements through "defaultView.getComputedStyle"
-		var view = elem.ownerDocument.defaultView;
-
-		if ( !view || !view.opener ) {
-			view = window;
-		}
-
-		return view.getComputedStyle( elem );
-	};
-
-var swap = function( elem, options, callback ) {
-	var ret, name,
-		old = {};
-
-	// Remember the old values, and insert the new ones
-	for ( name in options ) {
-		old[ name ] = elem.style[ name ];
-		elem.style[ name ] = options[ name ];
-	}
-
-	ret = callback.call( elem );
-
-	// Revert the old values
-	for ( name in options ) {
-		elem.style[ name ] = old[ name ];
-	}
-
-	return ret;
-};
-
-
-var rboxStyle = new RegExp( cssExpand.join( "|" ), "i" );
-
-
-
-( function() {
-
-	// Executing both pixelPosition & boxSizingReliable tests require only one layout
-	// so they're executed at the same time to save the second computation.
-	function computeStyleTests() {
-
-		// This is a singleton, we need to execute it only once
-		if ( !div ) {
-			return;
-		}
-
-		container.style.cssText = "position:absolute;left:-11111px;width:60px;" +
-			"margin-top:1px;padding:0;border:0";
-		div.style.cssText =
-			"position:relative;display:block;box-sizing:border-box;overflow:scroll;" +
-			"margin:auto;border:1px;padding:1px;" +
-			"width:60%;top:1%";
-		documentElement.appendChild( container ).appendChild( div );
-
-		var divStyle = window.getComputedStyle( div );
-		pixelPositionVal = divStyle.top !== "1%";
-
-		// Support: Android 4.0 - 4.3 only, Firefox <=3 - 44
-		reliableMarginLeftVal = roundPixelMeasures( divStyle.marginLeft ) === 12;
-
-		// Support: Android 4.0 - 4.3 only, Safari <=9.1 - 10.1, iOS <=7.0 - 9.3
-		// Some styles come back with percentage values, even though they shouldn't
-		div.style.right = "60%";
-		pixelBoxStylesVal = roundPixelMeasures( divStyle.right ) === 36;
-
-		// Support: IE 9 - 11 only
-		// Detect misreporting of content dimensions for box-sizing:border-box elements
-		boxSizingReliableVal = roundPixelMeasures( divStyle.width ) === 36;
-
-		// Support: IE 9 only
-		// Detect overflow:scroll screwiness (gh-3699)
-		// Support: Chrome <=64
-		// Don't get tricked when zoom affects offsetWidth (gh-4029)
-		div.style.position = "absolute";
-		scrollboxSizeVal = roundPixelMeasures( div.offsetWidth / 3 ) === 12;
-
-		documentElement.removeChild( container );
-
-		// Nullify the div so it wouldn't be stored in the memory and
-		// it will also be a sign that checks already performed
-		div = null;
-	}
-
-	function roundPixelMeasures( measure ) {
-		return Math.round( parseFloat( measure ) );
-	}
-
-	var pixelPositionVal, boxSizingReliableVal, scrollboxSizeVal, pixelBoxStylesVal,
-		reliableTrDimensionsVal, reliableMarginLeftVal,
-		container = document.createElement( "div" ),
-		div = document.createElement( "div" );
-
-	// Finish early in limited (non-browser) environments
-	if ( !div.style ) {
-		return;
-	}
-
-	// Support: IE <=9 - 11 only
-	// Style of cloned element affects source element cloned (#8908)
-	div.style.backgroundClip = "content-box";
-	div.cloneNode( true ).style.backgroundClip = "";
-	support.clearCloneStyle = div.style.backgroundClip === "content-box";
-
-	jQuery.extend( support, {
-		boxSizingReliable: function() {
-			computeStyleTests();
-			return boxSizingReliableVal;
-		},
-		pixelBoxStyles: function() {
-			computeStyleTests();
-			return pixelBoxStylesVal;
-		},
-		pixelPosition: function() {
-			computeStyleTests();
-			return pixelPositionVal;
-		},
-		reliableMarginLeft: function() {
-			computeStyleTests();
-			return reliableMarginLeftVal;
-		},
-		scrollboxSize: function() {
-			computeStyleTests();
-			return scrollboxSizeVal;
-		},
-
-		// Support: IE 9 - 11+, Edge 15 - 18+
-		// IE/Edge misreport `getComputedStyle` of table rows with width/height
-		// set in CSS while `offset*` properties report correct values.
-		// Behavior in IE 9 is more subtle than in newer versions & it passes
-		// some versions of this test; make sure not to make it pass there!
-		//
-		// Support: Firefox 70+
-		// Only Firefox includes border widths
-		// in computed dimensions. (gh-4529)
-		reliableTrDimensions: function() {
-			var table, tr, trChild, trStyle;
-			if ( reliableTrDimensionsVal == null ) {
-				table = document.createElement( "table" );
-				tr = document.createElement( "tr" );
-				trChild = document.createElement( "div" );
-
-				table.style.cssText = "position:absolute;left:-11111px;border-collapse:separate";
-				tr.style.cssText = "border:1px solid";
-
-				// Support: Chrome 86+
-				// Height set through cssText does not get applied.
-				// Computed height then comes back as 0.
-				tr.style.height = "1px";
-				trChild.style.height = "9px";
-
-				// Support: Android 8 Chrome 86+
-				// In our bodyBackground.html iframe,
-				// display for all div elements is set to "inline",
-				// which causes a problem only in Android 8 Chrome 86.
-				// Ensuring the div is display: block
-				// gets around this issue.
-				trChild.style.display = "block";
-
-				documentElement
-					.appendChild( table )
-					.appendChild( tr )
-					.appendChild( trChild );
-
-				trStyle = window.getComputedStyle( tr );
-				reliableTrDimensionsVal = ( parseInt( trStyle.height, 10 ) +
-					parseInt( trStyle.borderTopWidth, 10 ) +
-					parseInt( trStyle.borderBottomWidth, 10 ) ) === tr.offsetHeight;
-
-				documentElement.removeChild( table );
-			}
-			return reliableTrDimensionsVal;
-		}
-	} );
-} )();
-
-
-function curCSS( elem, name, computed ) {
-	var width, minWidth, maxWidth, ret,
-
-		// Support: Firefox 51+
-		// Retrieving style before computed somehow
-		// fixes an issue with getting wrong values
-		// on detached elements
-		style = elem.style;
-
-	computed = computed || getStyles( elem );
-
-	// getPropertyValue is needed for:
-	//   .css('filter') (IE 9 only, #12537)
-	//   .css('--customProperty) (#3144)
-	if ( computed ) {
-		ret = computed.getPropertyValue( name ) || computed[ name ];
-
-		if ( ret === "" && !isAttached( elem ) ) {
-			ret = jQuery.style( elem, name );
-		}
-
-		// A tribute to the "awesome hack by Dean Edwards"
-		// Android Browser returns percentage for some values,
-		// but width seems to be reliably pixels.
-		// This is against the CSSOM draft spec:
-		// https://drafts.csswg.org/cssom/#resolved-values
-		if ( !support.pixelBoxStyles() && rnumnonpx.test( ret ) && rboxStyle.test( name ) ) {
-
-			// Remember the original values
-			width = style.width;
-			minWidth = style.minWidth;
-			maxWidth = style.maxWidth;
-
-			// Put in the new values to get a computed value out
-			style.minWidth = style.maxWidth = style.width = ret;
-			ret = computed.width;
-
-			// Revert the changed values
-			style.width = width;
-			style.minWidth = minWidth;
-			style.maxWidth = maxWidth;
-		}
-	}
-
-	return ret !== undefined ?
-
-		// Support: IE <=9 - 11 only
-		// IE returns zIndex value as an integer.
-		ret + "" :
-		ret;
-}
-
-
-function addGetHookIf( conditionFn, hookFn ) {
-
-	// Define the hook, we'll check on the first run if it's really needed.
-	return {
-		get: function() {
-			if ( conditionFn() ) {
-
-				// Hook not needed (or it's not possible to use it due
-				// to missing dependency), remove it.
-				delete this.get;
-				return;
-			}
-
-			// Hook needed; redefine it so that the support test is not executed again.
-			return ( this.get = hookFn ).apply( this, arguments );
-		}
-	};
-}
-
-
-var cssPrefixes = [ "Webkit", "Moz", "ms" ],
-	emptyStyle = document.createElement( "div" ).style,
-	vendorProps = {};
-
-// Return a vendor-prefixed property or undefined
-function vendorPropName( name ) {
-
-	// Check for vendor prefixed names
-	var capName = name[ 0 ].toUpperCase() + name.slice( 1 ),
-		i = cssPrefixes.length;
-
-	while ( i-- ) {
-		name = cssPrefixes[ i ] + capName;
-		if ( name in emptyStyle ) {
-			return name;
-		}
-	}
-}
-
-// Return a potentially-mapped jQuery.cssProps or vendor prefixed property
-function finalPropName( name ) {
-	var final = jQuery.cssProps[ name ] || vendorProps[ name ];
-
-	if ( final ) {
-		return final;
-	}
-	if ( name in emptyStyle ) {
-		return name;
-	}
-	return vendorProps[ name ] = vendorPropName( name ) || name;
-}
-
-
-var
-
-	// Swappable if display is none or starts with table
-	// except "table", "table-cell", or "table-caption"
-	// See here for display values: https://developer.mozilla.org/en-US/docs/CSS/display
-	rdisplayswap = /^(none|table(?!-c[ea]).+)/,
-	rcustomProp = /^--/,
-	cssShow = { position: "absolute", visibility: "hidden", display: "block" },
-	cssNormalTransform = {
-		letterSpacing: "0",
-		fontWeight: "400"
-	};
-
-function setPositiveNumber( _elem, value, subtract ) {
-
-	// Any relative (+/-) values have already been
-	// normalized at this point
-	var matches = rcssNum.exec( value );
-	return matches ?
-
-		// Guard against undefined "subtract", e.g., when used as in cssHooks
-		Math.max( 0, matches[ 2 ] - ( subtract || 0 ) ) + ( matches[ 3 ] || "px" ) :
-		value;
-}
-
-function boxModelAdjustment( elem, dimension, box, isBorderBox, styles, computedVal ) {
-	var i = dimension === "width" ? 1 : 0,
-		extra = 0,
-		delta = 0;
-
-	// Adjustment may not be necessary
-	if ( box === ( isBorderBox ? "border" : "content" ) ) {
-		return 0;
-	}
-
-	for ( ; i < 4; i += 2 ) {
-
-		// Both box models exclude margin
-		if ( box === "margin" ) {
-			delta += jQuery.css( elem, box + cssExpand[ i ], true, styles );
-		}
-
-		// If we get here with a content-box, we're seeking "padding" or "border" or "margin"
-		if ( !isBorderBox ) {
-
-			// Add padding
-			delta += jQuery.css( elem, "padding" + cssExpand[ i ], true, styles );
-
-			// For "border" or "margin", add border
-			if ( box !== "padding" ) {
-				delta += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
-
-			// But still keep track of it otherwise
-			} else {
-				extra += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
-			}
-
-		// If we get here with a border-box (content + padding + border), we're seeking "content" or
-		// "padding" or "margin"
-		} else {
-
-			// For "content", subtract padding
-			if ( box === "content" ) {
-				delta -= jQuery.css( elem, "padding" + cssExpand[ i ], true, styles );
-			}
-
-			// For "content" or "padding", subtract border
-			if ( box !== "margin" ) {
-				delta -= jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
-			}
-		}
-	}
-
-	// Account for positive content-box scroll gutter when requested by providing computedVal
-	if ( !isBorderBox && computedVal >= 0 ) {
-
-		// offsetWidth/offsetHeight is a rounded sum of content, padding, scroll gutter, and border
-		// Assuming integer scroll gutter, subtract the rest and round down
-		delta += Math.max( 0, Math.ceil(
-			elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] -
-			computedVal -
-			delta -
-			extra -
-			0.5
-
-		// If offsetWidth/offsetHeight is unknown, then we can't determine content-box scroll gutter
-		// Use an explicit zero to avoid NaN (gh-3964)
-		) ) || 0;
-	}
-
-	return delta;
-}
-
-function getWidthOrHeight( elem, dimension, extra ) {
-
-	// Start with computed style
-	var styles = getStyles( elem ),
-
-		// To avoid forcing a reflow, only fetch boxSizing if we need it (gh-4322).
-		// Fake content-box until we know it's needed to know the true value.
-		boxSizingNeeded = !support.boxSizingReliable() || extra,
-		isBorderBox = boxSizingNeeded &&
-			jQuery.css( elem, "boxSizing", false, styles ) === "border-box",
-		valueIsBorderBox = isBorderBox,
-
-		val = curCSS( elem, dimension, styles ),
-		offsetProp = "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 );
-
-	// Support: Firefox <=54
-	// Return a confounding non-pixel value or feign ignorance, as appropriate.
-	if ( rnumnonpx.test( val ) ) {
-		if ( !extra ) {
-			return val;
-		}
-		val = "auto";
-	}
-
-
-	// Support: IE 9 - 11 only
-	// Use offsetWidth/offsetHeight for when box sizing is unreliable.
-	// In those cases, the computed value can be trusted to be border-box.
-	if ( ( !support.boxSizingReliable() && isBorderBox ||
-
-		// Support: IE 10 - 11+, Edge 15 - 18+
-		// IE/Edge misreport `getComputedStyle` of table rows with width/height
-		// set in CSS while `offset*` properties report correct values.
-		// Interestingly, in some cases IE 9 doesn't suffer from this issue.
-		!support.reliableTrDimensions() && nodeName( elem, "tr" ) ||
-
-		// Fall back to offsetWidth/offsetHeight when value is "auto"
-		// This happens for inline elements with no explicit setting (gh-3571)
-		val === "auto" ||
-
-		// Support: Android <=4.1 - 4.3 only
-		// Also use offsetWidth/offsetHeight for misreported inline dimensions (gh-3602)
-		!parseFloat( val ) && jQuery.css( elem, "display", false, styles ) === "inline" ) &&
-
-		// Make sure the element is visible & connected
-		elem.getClientRects().length ) {
-
-		isBorderBox = jQuery.css( elem, "boxSizing", false, styles ) === "border-box";
-
-		// Where available, offsetWidth/offsetHeight approximate border box dimensions.
-		// Where not available (e.g., SVG), assume unreliable box-sizing and interpret the
-		// retrieved value as a content box dimension.
-		valueIsBorderBox = offsetProp in elem;
-		if ( valueIsBorderBox ) {
-			val = elem[ offsetProp ];
-		}
-	}
-
-	// Normalize "" and auto
-	val = parseFloat( val ) || 0;
-
-	// Adjust for the element's box model
-	return ( val +
-		boxModelAdjustment(
-			elem,
-			dimension,
-			extra || ( isBorderBox ? "border" : "content" ),
-			valueIsBorderBox,
-			styles,
-
-			// Provide the current computed size to request scroll gutter calculation (gh-3589)
-			val
-		)
-	) + "px";
-}
-
-jQuery.extend( {
-
-	// Add in style property hooks for overriding the default
-	// behavior of getting and setting a style property
-	cssHooks: {
-		opacity: {
-			get: function( elem, computed ) {
-				if ( computed ) {
-
-					// We should always get a number back from opacity
-					var ret = curCSS( elem, "opacity" );
-					return ret === "" ? "1" : ret;
-				}
-			}
-		}
-	},
-
-	// Don't automatically add "px" to these possibly-unitless properties
-	cssNumber: {
-		"animationIterationCount": true,
-		"columnCount": true,
-		"fillOpacity": true,
-		"flexGrow": true,
-		"flexShrink": true,
-		"fontWeight": true,
-		"gridArea": true,
-		"gridColumn": true,
-		"gridColumnEnd": true,
-		"gridColumnStart": true,
-		"gridRow": true,
-		"gridRowEnd": true,
-		"gridRowStart": true,
-		"lineHeight": true,
-		"opacity": true,
-		"order": true,
-		"orphans": true,
-		"widows": true,
-		"zIndex": true,
-		"zoom": true
-	},
-
-	// Add in properties whose names you wish to fix before
-	// setting or getting the value
-	cssProps: {},
-
-	// Get and set the style property on a DOM Node
-	style: function( elem, name, value, extra ) {
-
-		// Don't set styles on text and comment nodes
-		if ( !elem || elem.nodeType === 3 || elem.nodeType === 8 || !elem.style ) {
-			return;
-		}
-
-		// Make sure that we're working with the right name
-		var ret, type, hooks,
-			origName = camelCase( name ),
-			isCustomProp = rcustomProp.test( name ),
-			style = elem.style;
-
-		// Make sure that we're working with the right name. We don't
-		// want to query the value if it is a CSS custom property
-		// since they are user-defined.
-		if ( !isCustomProp ) {
-			name = finalPropName( origName );
-		}
-
-		// Gets hook for the prefixed version, then unprefixed version
-		hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];
-
-		// Check if we're setting a value
-		if ( value !== undefined ) {
-			type = typeof value;
-
-			// Convert "+=" or "-=" to relative numbers (#7345)
-			if ( type === "string" && ( ret = rcssNum.exec( value ) ) && ret[ 1 ] ) {
-				value = adjustCSS( elem, name, ret );
-
-				// Fixes bug #9237
-				type = "number";
-			}
-
-			// Make sure that null and NaN values aren't set (#7116)
-			if ( value == null || value !== value ) {
-				return;
-			}
-
-			// If a number was passed in, add the unit (except for certain CSS properties)
-			// The isCustomProp check can be removed in jQuery 4.0 when we only auto-append
-			// "px" to a few hardcoded values.
-			if ( type === "number" && !isCustomProp ) {
-				value += ret && ret[ 3 ] || ( jQuery.cssNumber[ origName ] ? "" : "px" );
-			}
-
-			// background-* props affect original clone's values
-			if ( !support.clearCloneStyle && value === "" && name.indexOf( "background" ) === 0 ) {
-				style[ name ] = "inherit";
-			}
-
-			// If a hook was provided, use that value, otherwise just set the specified value
-			if ( !hooks || !( "set" in hooks ) ||
-				( value = hooks.set( elem, value, extra ) ) !== undefined ) {
-
-				if ( isCustomProp ) {
-					style.setProperty( name, value );
-				} else {
-					style[ name ] = value;
-				}
-			}
-
-		} else {
-
-			// If a hook was provided get the non-computed value from there
-			if ( hooks && "get" in hooks &&
-				( ret = hooks.get( elem, false, extra ) ) !== undefined ) {
-
-				return ret;
-			}
-
-			// Otherwise just get the value from the style object
-			return style[ name ];
-		}
-	},
-
-	css: function( elem, name, extra, styles ) {
-		var val, num, hooks,
-			origName = camelCase( name ),
-			isCustomProp = rcustomProp.test( name );
-
-		// Make sure that we're working with the right name. We don't
-		// want to modify the value if it is a CSS custom property
-		// since they are user-defined.
-		if ( !isCustomProp ) {
-			name = finalPropName( origName );
-		}
-
-		// Try prefixed name followed by the unprefixed name
-		hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];
-
-		// If a hook was provided get the computed value from there
-		if ( hooks && "get" in hooks ) {
-			val = hooks.get( elem, true, extra );
-		}
-
-		// Otherwise, if a way to get the computed value exists, use that
-		if ( val === undefined ) {
-			val = curCSS( elem, name, styles );
-		}
-
-		// Convert "normal" to computed value
-		if ( val === "normal" && name in cssNormalTransform ) {
-			val = cssNormalTransform[ name ];
-		}
-
-		// Make numeric if forced or a qualifier was provided and val looks numeric
-		if ( extra === "" || extra ) {
-			num = parseFloat( val );
-			return extra === true || isFinite( num ) ? num || 0 : val;
-		}
-
-		return val;
-	}
-} );
-
-jQuery.each( [ "height", "width" ], function( _i, dimension ) {
-	jQuery.cssHooks[ dimension ] = {
-		get: function( elem, computed, extra ) {
-			if ( computed ) {
-
-				// Certain elements can have dimension info if we invisibly show them
-				// but it must have a current display style that would benefit
-				return rdisplayswap.test( jQuery.css( elem, "display" ) ) &&
-
-					// Support: Safari 8+
-					// Table columns in Safari have non-zero offsetWidth & zero
-					// getBoundingClientRect().width unless display is changed.
-					// Support: IE <=11 only
-					// Running getBoundingClientRect on a disconnected node
-					// in IE throws an error.
-					( !elem.getClientRects().length || !elem.getBoundingClientRect().width ) ?
-					swap( elem, cssShow, function() {
-						return getWidthOrHeight( elem, dimension, extra );
-					} ) :
-					getWidthOrHeight( elem, dimension, extra );
-			}
-		},
-
-		set: function( elem, value, extra ) {
-			var matches,
-				styles = getStyles( elem ),
-
-				// Only read styles.position if the test has a chance to fail
-				// to avoid forcing a reflow.
-				scrollboxSizeBuggy = !support.scrollboxSize() &&
-					styles.position === "absolute",
-
-				// To avoid forcing a reflow, only fetch boxSizing if we need it (gh-3991)
-				boxSizingNeeded = scrollboxSizeBuggy || extra,
-				isBorderBox = boxSizingNeeded &&
-					jQuery.css( elem, "boxSizing", false, styles ) === "border-box",
-				subtract = extra ?
-					boxModelAdjustment(
-						elem,
-						dimension,
-						extra,
-						isBorderBox,
-						styles
-					) :
-					0;
-
-			// Account for unreliable border-box dimensions by comparing offset* to computed and
-			// faking a content-box to get border and padding (gh-3699)
-			if ( isBorderBox && scrollboxSizeBuggy ) {
-				subtract -= Math.ceil(
-					elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] -
-					parseFloat( styles[ dimension ] ) -
-					boxModelAdjustment( elem, dimension, "border", false, styles ) -
-					0.5
-				);
-			}
-
-			// Convert to pixels if value adjustment is needed
-			if ( subtract && ( matches = rcssNum.exec( value ) ) &&
-				( matches[ 3 ] || "px" ) !== "px" ) {
-
-				elem.style[ dimension ] = value;
-				value = jQuery.css( elem, dimension );
-			}
-
-			return setPositiveNumber( elem, value, subtract );
-		}
-	};
-} );
-
-jQuery.cssHooks.marginLeft = addGetHookIf( support.reliableMarginLeft,
-	function( elem, computed ) {
-		if ( computed ) {
-			return ( parseFloat( curCSS( elem, "marginLeft" ) ) ||
-				elem.getBoundingClientRect().left -
-					swap( elem, { marginLeft: 0 }, function() {
-						return elem.getBoundingClientRect().left;
-					} )
-			) + "px";
-		}
-	}
-);
-
-// These hooks are used by animate to expand properties
-jQuery.each( {
-	margin: "",
-	padding: "",
-	border: "Width"
-}, function( prefix, suffix ) {
-	jQuery.cssHooks[ prefix + suffix ] = {
-		expand: function( value ) {
-			var i = 0,
-				expanded = {},
-
-				// Assumes a single number if not a string
-				parts = typeof value === "string" ? value.split( " " ) : [ value ];
-
-			for ( ; i < 4; i++ ) {
-				expanded[ prefix + cssExpand[ i ] + suffix ] =
-					parts[ i ] || parts[ i - 2 ] || parts[ 0 ];
-			}
-
-			return expanded;
-		}
-	};
-
-	if ( prefix !== "margin" ) {
-		jQuery.cssHooks[ prefix + suffix ].set = setPositiveNumber;
-	}
-} );
-
-jQuery.fn.extend( {
-	css: function( name, value ) {
-		return access( this, function( elem, name, value ) {
-			var styles, len,
-				map = {},
-				i = 0;
-
-			if ( Array.isArray( name ) ) {
-				styles = getStyles( elem );
-				len = name.length;
-
-				for ( ; i < len; i++ ) {
-					map[ name[ i ] ] = jQuery.css( elem, name[ i ], false, styles );
-				}
-
-				return map;
-			}
-
-			return value !== undefined ?
-				jQuery.style( elem, name, value ) :
-				jQuery.css( elem, name );
-		}, name, value, arguments.length > 1 );
-	}
-} );
-
-
-function Tween( elem, options, prop, end, easing ) {
-	return new Tween.prototype.init( elem, options, prop, end, easing );
-}
-jQuery.Tween = Tween;
-
-Tween.prototype = {
-	constructor: Tween,
-	init: function( elem, options, prop, end, easing, unit ) {
-		this.elem = elem;
-		this.prop = prop;
-		this.easing = easing || jQuery.easing._default;
-		this.options = options;
-		this.start = this.now = this.cur();
-		this.end = end;
-		this.unit = unit || ( jQuery.cssNumber[ prop ] ? "" : "px" );
-	},
-	cur: function() {
-		var hooks = Tween.propHooks[ this.prop ];
-
-		return hooks && hooks.get ?
-			hooks.get( this ) :
-			Tween.propHooks._default.get( this );
-	},
-	run: function( percent ) {
-		var eased,
-			hooks = Tween.propHooks[ this.prop ];
-
-		if ( this.options.duration ) {
-			this.pos = eased = jQuery.easing[ this.easing ](
-				percent, this.options.duration * percent, 0, 1, this.options.duration
-			);
-		} else {
-			this.pos = eased = percent;
-		}
-		this.now = ( this.end - this.start ) * eased + this.start;
-
-		if ( this.options.step ) {
-			this.options.step.call( this.elem, this.now, this );
-		}
-
-		if ( hooks && hooks.set ) {
-			hooks.set( this );
-		} else {
-			Tween.propHooks._default.set( this );
-		}
-		return this;
-	}
-};
-
-Tween.prototype.init.prototype = Tween.prototype;
-
-Tween.propHooks = {
-	_default: {
-		get: function( tween ) {
-			var result;
-
-			// Use a property on the element directly when it is not a DOM element,
-			// or when there is no matching style property that exists.
-			if ( tween.elem.nodeType !== 1 ||
-				tween.elem[ tween.prop ] != null && tween.elem.style[ tween.prop ] == null ) {
-				return tween.elem[ tween.prop ];
-			}
-
-			// Passing an empty string as a 3rd parameter to .css will automatically
-			// attempt a parseFloat and fallback to a string if the parse fails.
-			// Simple values such as "10px" are parsed to Float;
-			// complex values such as "rotate(1rad)" are returned as-is.
-			result = jQuery.css( tween.elem, tween.prop, "" );
-
-			// Empty strings, null, undefined and "auto" are converted to 0.
-			return !result || result === "auto" ? 0 : result;
-		},
-		set: function( tween ) {
-
-			// Use step hook for back compat.
-			// Use cssHook if its there.
-			// Use .style if available and use plain properties where available.
-			if ( jQuery.fx.step[ tween.prop ] ) {
-				jQuery.fx.step[ tween.prop ]( tween );
-			} else if ( tween.elem.nodeType === 1 && (
-				jQuery.cssHooks[ tween.prop ] ||
-					tween.elem.style[ finalPropName( tween.prop ) ] != null ) ) {
-				jQuery.style( tween.elem, tween.prop, tween.now + tween.unit );
-			} else {
-				tween.elem[ tween.prop ] = tween.now;
-			}
-		}
-	}
-};
-
-// Support: IE <=9 only
-// Panic based approach to setting things on disconnected nodes
-Tween.propHooks.scrollTop = Tween.propHooks.scrollLeft = {
-	set: function( tween ) {
-		if ( tween.elem.nodeType && tween.elem.parentNode ) {
-			tween.elem[ tween.prop ] = tween.now;
-		}
-	}
-};
-
-jQuery.easing = {
-	linear: function( p ) {
-		return p;
-	},
-	swing: function( p ) {
-		return 0.5 - Math.cos( p * Math.PI ) / 2;
-	},
-	_default: "swing"
-};
-
-jQuery.fx = Tween.prototype.init;
-
-// Back compat <1.8 extension point
-jQuery.fx.step = {};
-
-
-
-
-var
-	fxNow, inProgress,
-	rfxtypes = /^(?:toggle|show|hide)$/,
-	rrun = /queueHooks$/;
-
-function schedule() {
-	if ( inProgress ) {
-		if ( document.hidden === false && window.requestAnimationFrame ) {
-			window.requestAnimationFrame( schedule );
-		} else {
-			window.setTimeout( schedule, jQuery.fx.interval );
-		}
-
-		jQuery.fx.tick();
-	}
-}
-
-// Animations created synchronously will run synchronously
-function createFxNow() {
-	window.setTimeout( function() {
-		fxNow = undefined;
-	} );
-	return ( fxNow = Date.now() );
-}
-
-// Generate parameters to create a standard animation
-function genFx( type, includeWidth ) {
-	var which,
-		i = 0,
-		attrs = { height: type };
-
-	// If we include width, step value is 1 to do all cssExpand values,
-	// otherwise step value is 2 to skip over Left and Right
-	includeWidth = includeWidth ? 1 : 0;
-	for ( ; i < 4; i += 2 - includeWidth ) {
-		which = cssExpand[ i ];
-		attrs[ "margin" + which ] = attrs[ "padding" + which ] = type;
-	}
-
-	if ( includeWidth ) {
-		attrs.opacity = attrs.width = type;
-	}
-
-	return attrs;
-}
-
-function createTween( value, prop, animation ) {
-	var tween,
-		collection = ( Animation.tweeners[ prop ] || [] ).concat( Animation.tweeners[ "*" ] ),
-		index = 0,
-		length = collection.length;
-	for ( ; index < length; index++ ) {
-		if ( ( tween = collection[ index ].call( animation, prop, value ) ) ) {
-
-			// We're done with this property
-			return tween;
-		}
-	}
-}
-
-function defaultPrefilter( elem, props, opts ) {
-	var prop, value, toggle, hooks, oldfire, propTween, restoreDisplay, display,
-		isBox = "width" in props || "height" in props,
-		anim = this,
-		orig = {},
-		style = elem.style,
-		hidden = elem.nodeType && isHiddenWithinTree( elem ),
-		dataShow = dataPriv.get( elem, "fxshow" );
-
-	// Queue-skipping animations hijack the fx hooks
-	if ( !opts.queue ) {
-		hooks = jQuery._queueHooks( elem, "fx" );
-		if ( hooks.unqueued == null ) {
-			hooks.unqueued = 0;
-			oldfire = hooks.empty.fire;
-			hooks.empty.fire = function() {
-				if ( !hooks.unqueued ) {
-					oldfire();
-				}
-			};
-		}
-		hooks.unqueued++;
-
-		anim.always( function() {
-
-			// Ensure the complete handler is called before this completes
-			anim.always( function() {
-				hooks.unqueued--;
-				if ( !jQuery.queue( elem, "fx" ).length ) {
-					hooks.empty.fire();
-				}
-			} );
-		} );
-	}
-
-	// Detect show/hide animations
-	for ( prop in props ) {
-		value = props[ prop ];
-		if ( rfxtypes.test( value ) ) {
-			delete props[ prop ];
-			toggle = toggle || value === "toggle";
-			if ( value === ( hidden ? "hide" : "show" ) ) {
-
-				// Pretend to be hidden if this is a "show" and
-				// there is still data from a stopped show/hide
-				if ( value === "show" && dataShow && dataShow[ prop ] !== undefined ) {
-					hidden = true;
-
-				// Ignore all other no-op show/hide data
-				} else {
-					continue;
-				}
-			}
-			orig[ prop ] = dataShow && dataShow[ prop ] || jQuery.style( elem, prop );
-		}
-	}
-
-	// Bail out if this is a no-op like .hide().hide()
-	propTween = !jQuery.isEmptyObject( props );
-	if ( !propTween && jQuery.isEmptyObject( orig ) ) {
-		return;
-	}
-
-	// Restrict "overflow" and "display" styles during box animations
-	if ( isBox && elem.nodeType === 1 ) {
-
-		// Support: IE <=9 - 11, Edge 12 - 15
-		// Record all 3 overflow attributes because IE does not infer the shorthand
-		// from identically-valued overflowX and overflowY and Edge just mirrors
-		// the overflowX value there.
-		opts.overflow = [ style.overflow, style.overflowX, style.overflowY ];
-
-		// Identify a display type, preferring old show/hide data over the CSS cascade
-		restoreDisplay = dataShow && dataShow.display;
-		if ( restoreDisplay == null ) {
-			restoreDisplay = dataPriv.get( elem, "display" );
-		}
-		display = jQuery.css( elem, "display" );
-		if ( display === "none" ) {
-			if ( restoreDisplay ) {
-				display = restoreDisplay;
-			} else {
-
-				// Get nonempty value(s) by temporarily forcing visibility
-				showHide( [ elem ], true );
-				restoreDisplay = elem.style.display || restoreDisplay;
-				display = jQuery.css( elem, "display" );
-				showHide( [ elem ] );
-			}
-		}
-
-		// Animate inline elements as inline-block
-		if ( display === "inline" || display === "inline-block" && restoreDisplay != null ) {
-			if ( jQuery.css( elem, "float" ) === "none" ) {
-
-				// Restore the original display value at the end of pure show/hide animations
-				if ( !propTween ) {
-					anim.done( function() {
-						style.display = restoreDisplay;
-					} );
-					if ( restoreDisplay == null ) {
-						display = style.display;
-						restoreDisplay = display === "none" ? "" : display;
-					}
-				}
-				style.display = "inline-block";
-			}
-		}
-	}
-
-	if ( opts.overflow ) {
-		style.overflow = "hidden";
-		anim.always( function() {
-			style.overflow = opts.overflow[ 0 ];
-			style.overflowX = opts.overflow[ 1 ];
-			style.overflowY = opts.overflow[ 2 ];
-		} );
-	}
-
-	// Implement show/hide animations
-	propTween = false;
-	for ( prop in orig ) {
-
-		// General show/hide setup for this element animation
-		if ( !propTween ) {
-			if ( dataShow ) {
-				if ( "hidden" in dataShow ) {
-					hidden = dataShow.hidden;
-				}
-			} else {
-				dataShow = dataPriv.access( elem, "fxshow", { display: restoreDisplay } );
-			}
-
-			// Store hidden/visible for toggle so `.stop().toggle()` "reverses"
-			if ( toggle ) {
-				dataShow.hidden = !hidden;
-			}
-
-			// Show elements before animating them
-			if ( hidden ) {
-				showHide( [ elem ], true );
-			}
-
-			/* eslint-disable no-loop-func */
-
-			anim.done( function() {
-
-				/* eslint-enable no-loop-func */
-
-				// The final step of a "hide" animation is actually hiding the element
-				if ( !hidden ) {
-					showHide( [ elem ] );
-				}
-				dataPriv.remove( elem, "fxshow" );
-				for ( prop in orig ) {
-					jQuery.style( elem, prop, orig[ prop ] );
-				}
-			} );
-		}
-
-		// Per-property setup
-		propTween = createTween( hidden ? dataShow[ prop ] : 0, prop, anim );
-		if ( !( prop in dataShow ) ) {
-			dataShow[ prop ] = propTween.start;
-			if ( hidden ) {
-				propTween.end = propTween.start;
-				propTween.start = 0;
-			}
-		}
-	}
-}
-
-function propFilter( props, specialEasing ) {
-	var index, name, easing, value, hooks;
-
-	// camelCase, specialEasing and expand cssHook pass
-	for ( index in props ) {
-		name = camelCase( index );
-		easing = specialEasing[ name ];
-		value = props[ index ];
-		if ( Array.isArray( value ) ) {
-			easing = value[ 1 ];
-			value = props[ index ] = value[ 0 ];
-		}
-
-		if ( index !== name ) {
-			props[ name ] = value;
-			delete props[ index ];
-		}
-
-		hooks = jQuery.cssHooks[ name ];
-		if ( hooks && "expand" in hooks ) {
-			value = hooks.expand( value );
-			delete props[ name ];
-
-			// Not quite $.extend, this won't overwrite existing keys.
-			// Reusing 'index' because we have the correct "name"
-			for ( index in value ) {
-				if ( !( index in props ) ) {
-					props[ index ] = value[ index ];
-					specialEasing[ index ] = easing;
-				}
-			}
-		} else {
-			specialEasing[ name ] = easing;
-		}
-	}
-}
-
-function Animation( elem, properties, options ) {
-	var result,
-		stopped,
-		index = 0,
-		length = Animation.prefilters.length,
-		deferred = jQuery.Deferred().always( function() {
-
-			// Don't match elem in the :animated selector
-			delete tick.elem;
-		} ),
-		tick = function() {
-			if ( stopped ) {
-				return false;
-			}
-			var currentTime = fxNow || createFxNow(),
-				remaining = Math.max( 0, animation.startTime + animation.duration - currentTime ),
-
-				// Support: Android 2.3 only
-				// Archaic crash bug won't allow us to use `1 - ( 0.5 || 0 )` (#12497)
-				temp = remaining / animation.duration || 0,
-				percent = 1 - temp,
-				index = 0,
-				length = animation.tweens.length;
-
-			for ( ; index < length; index++ ) {
-				animation.tweens[ index ].run( percent );
-			}
-
-			deferred.notifyWith( elem, [ animation, percent, remaining ] );
-
-			// If there's more to do, yield
-			if ( percent < 1 && length ) {
-				return remaining;
-			}
-
-			// If this was an empty animation, synthesize a final progress notification
-			if ( !length ) {
-				deferred.notifyWith( elem, [ animation, 1, 0 ] );
-			}
-
-			// Resolve the animation and report its conclusion
-			deferred.resolveWith( elem, [ animation ] );
-			return false;
-		},
-		animation = deferred.promise( {
-			elem: elem,
-			props: jQuery.extend( {}, properties ),
-			opts: jQuery.extend( true, {
-				specialEasing: {},
-				easing: jQuery.easing._default
-			}, options ),
-			originalProperties: properties,
-			originalOptions: options,
-			startTime: fxNow || createFxNow(),
-			duration: options.duration,
-			tweens: [],
-			createTween: function( prop, end ) {
-				var tween = jQuery.Tween( elem, animation.opts, prop, end,
-					animation.opts.specialEasing[ prop ] || animation.opts.easing );
-				animation.tweens.push( tween );
-				return tween;
-			},
-			stop: function( gotoEnd ) {
-				var index = 0,
-
-					// If we are going to the end, we want to run all the tweens
-					// otherwise we skip this part
-					length = gotoEnd ? animation.tweens.length : 0;
-				if ( stopped ) {
-					return this;
-				}
-				stopped = true;
-				for ( ; index < length; index++ ) {
-					animation.tweens[ index ].run( 1 );
-				}
-
-				// Resolve when we played the last frame; otherwise, reject
-				if ( gotoEnd ) {
-					deferred.notifyWith( elem, [ animation, 1, 0 ] );
-					deferred.resolveWith( elem, [ animation, gotoEnd ] );
-				} else {
-					deferred.rejectWith( elem, [ animation, gotoEnd ] );
-				}
-				return this;
-			}
-		} ),
-		props = animation.props;
-
-	propFilter( props, animation.opts.specialEasing );
-
-	for ( ; index < length; index++ ) {
-		result = Animation.prefilters[ index ].call( animation, elem, props, animation.opts );
-		if ( result ) {
-			if ( isFunction( result.stop ) ) {
-				jQuery._queueHooks( animation.elem, animation.opts.queue ).stop =
-					result.stop.bind( result );
-			}
-			return result;
-		}
-	}
-
-	jQuery.map( props, createTween, animation );
-
-	if ( isFunction( animation.opts.start ) ) {
-		animation.opts.start.call( elem, animation );
-	}
-
-	// Attach callbacks from options
-	animation
-		.progress( animation.opts.progress )
-		.done( animation.opts.done, animation.opts.complete )
-		.fail( animation.opts.fail )
-		.always( animation.opts.always );
-
-	jQuery.fx.timer(
-		jQuery.extend( tick, {
-			elem: elem,
-			anim: animation,
-			queue: animation.opts.queue
-		} )
-	);
-
-	return animation;
-}
-
-jQuery.Animation = jQuery.extend( Animation, {
-
-	tweeners: {
-		"*": [ function( prop, value ) {
-			var tween = this.createTween( prop, value );
-			adjustCSS( tween.elem, prop, rcssNum.exec( value ), tween );
-			return tween;
-		} ]
-	},
-
-	tweener: function( props, callback ) {
-		if ( isFunction( props ) ) {
-			callback = props;
-			props = [ "*" ];
-		} else {
-			props = props.match( rnothtmlwhite );
-		}
-
-		var prop,
-			index = 0,
-			length = props.length;
-
-		for ( ; index < length; index++ ) {
-			prop = props[ index ];
-			Animation.tweeners[ prop ] = Animation.tweeners[ prop ] || [];
-			Animation.tweeners[ prop ].unshift( callback );
-		}
-	},
-
-	prefilters: [ defaultPrefilter ],
-
-	prefilter: function( callback, prepend ) {
-		if ( prepend ) {
-			Animation.prefilters.unshift( callback );
-		} else {
-			Animation.prefilters.push( callback );
-		}
-	}
-} );
-
-jQuery.speed = function( speed, easing, fn ) {
-	var opt = speed && typeof speed === "object" ? jQuery.extend( {}, speed ) : {
-		complete: fn || !fn && easing ||
-			isFunction( speed ) && speed,
-		duration: speed,
-		easing: fn && easing || easing && !isFunction( easing ) && easing
-	};
-
-	// Go to the end state if fx are off
-	if ( jQuery.fx.off ) {
-		opt.duration = 0;
-
-	} else {
-		if ( typeof opt.duration !== "number" ) {
-			if ( opt.duration in jQuery.fx.speeds ) {
-				opt.duration = jQuery.fx.speeds[ opt.duration ];
-
-			} else {
-				opt.duration = jQuery.fx.speeds._default;
-			}
-		}
-	}
-
-	// Normalize opt.queue - true/undefined/null -> "fx"
-	if ( opt.queue == null || opt.queue === true ) {
-		opt.queue = "fx";
-	}
-
-	// Queueing
-	opt.old = opt.complete;
-
-	opt.complete = function() {
-		if ( isFunction( opt.old ) ) {
-			opt.old.call( this );
-		}
-
-		if ( opt.queue ) {
-			jQuery.dequeue( this, opt.queue );
-		}
-	};
-
-	return opt;
-};
-
-jQuery.fn.extend( {
-	fadeTo: function( speed, to, easing, callback ) {
-
-		// Show any hidden elements after setting opacity to 0
-		return this.filter( isHiddenWithinTree ).css( "opacity", 0 ).show()
-
-			// Animate to the value specified
-			.end().animate( { opacity: to }, speed, easing, callback );
-	},
-	animate: function( prop, speed, easing, callback ) {
-		var empty = jQuery.isEmptyObject( prop ),
-			optall = jQuery.speed( speed, easing, callback ),
-			doAnimation = function() {
-
-				// Operate on a copy of prop so per-property easing won't be lost
-				var anim = Animation( this, jQuery.extend( {}, prop ), optall );
-
-				// Empty animations, or finishing resolves immediately
-				if ( empty || dataPriv.get( this, "finish" ) ) {
-					anim.stop( true );
-				}
-			};
-
-		doAnimation.finish = doAnimation;
-
-		return empty || optall.queue === false ?
-			this.each( doAnimation ) :
-			this.queue( optall.queue, doAnimation );
-	},
-	stop: function( type, clearQueue, gotoEnd ) {
-		var stopQueue = function( hooks ) {
-			var stop = hooks.stop;
-			delete hooks.stop;
-			stop( gotoEnd );
-		};
-
-		if ( typeof type !== "string" ) {
-			gotoEnd = clearQueue;
-			clearQueue = type;
-			type = undefined;
-		}
-		if ( clearQueue ) {
-			this.queue( type || "fx", [] );
-		}
-
-		return this.each( function() {
-			var dequeue = true,
-				index = type != null && type + "queueHooks",
-				timers = jQuery.timers,
-				data = dataPriv.get( this );
-
-			if ( index ) {
-				if ( data[ index ] && data[ index ].stop ) {
-					stopQueue( data[ index ] );
-				}
-			} else {
-				for ( index in data ) {
-					if ( data[ index ] && data[ index ].stop && rrun.test( index ) ) {
-						stopQueue( data[ index ] );
-					}
-				}
-			}
-
-			for ( index = timers.length; index--; ) {
-				if ( timers[ index ].elem === this &&
-					( type == null || timers[ index ].queue === type ) ) {
-
-					timers[ index ].anim.stop( gotoEnd );
-					dequeue = false;
-					timers.splice( index, 1 );
-				}
-			}
-
-			// Start the next in the queue if the last step wasn't forced.
-			// Timers currently will call their complete callbacks, which
-			// will dequeue but only if they were gotoEnd.
-			if ( dequeue || !gotoEnd ) {
-				jQuery.dequeue( this, type );
-			}
-		} );
-	},
-	finish: function( type ) {
-		if ( type !== false ) {
-			type = type || "fx";
-		}
-		return this.each( function() {
-			var index,
-				data = dataPriv.get( this ),
-				queue = data[ type + "queue" ],
-				hooks = data[ type + "queueHooks" ],
-				timers = jQuery.timers,
-				length = queue ? queue.length : 0;
-
-			// Enable finishing flag on private data
-			data.finish = true;
-
-			// Empty the queue first
-			jQuery.queue( this, type, [] );
-
-			if ( hooks && hooks.stop ) {
-				hooks.stop.call( this, true );
-			}
-
-			// Look for any active animations, and finish them
-			for ( index = timers.length; index--; ) {
-				if ( timers[ index ].elem === this && timers[ index ].queue === type ) {
-					timers[ index ].anim.stop( true );
-					timers.splice( index, 1 );
-				}
-			}
-
-			// Look for any animations in the old queue and finish them
-			for ( index = 0; index < length; index++ ) {
-				if ( queue[ index ] && queue[ index ].finish ) {
-					queue[ index ].finish.call( this );
-				}
-			}
-
-			// Turn off finishing flag
-			delete data.finish;
-		} );
-	}
-} );
-
-jQuery.each( [ "toggle", "show", "hide" ], function( _i, name ) {
-	var cssFn = jQuery.fn[ name ];
-	jQuery.fn[ name ] = function( speed, easing, callback ) {
-		return speed == null || typeof speed === "boolean" ?
-			cssFn.apply( this, arguments ) :
-			this.animate( genFx( name, true ), speed, easing, callback );
-	};
-} );
-
-// Generate shortcuts for custom animations
-jQuery.each( {
-	slideDown: genFx( "show" ),
-	slideUp: genFx( "hide" ),
-	slideToggle: genFx( "toggle" ),
-	fadeIn: { opacity: "show" },
-	fadeOut: { opacity: "hide" },
-	fadeToggle: { opacity: "toggle" }
-}, function( name, props ) {
-	jQuery.fn[ name ] = function( speed, easing, callback ) {
-		return this.animate( props, speed, easing, callback );
-	};
-} );
-
-jQuery.timers = [];
-jQuery.fx.tick = function() {
-	var timer,
-		i = 0,
-		timers = jQuery.timers;
-
-	fxNow = Date.now();
-
-	for ( ; i < timers.length; i++ ) {
-		timer = timers[ i ];
-
-		// Run the timer and safely remove it when done (allowing for external removal)
-		if ( !timer() && timers[ i ] === timer ) {
-			timers.splice( i--, 1 );
-		}
-	}
-
-	if ( !timers.length ) {
-		jQuery.fx.stop();
-	}
-	fxNow = undefined;
-};
-
-jQuery.fx.timer = function( timer ) {
-	jQuery.timers.push( timer );
-	jQuery.fx.start();
-};
-
-jQuery.fx.interval = 13;
-jQuery.fx.start = function() {
-	if ( inProgress ) {
-		return;
-	}
-
-	inProgress = true;
-	schedule();
-};
-
-jQuery.fx.stop = function() {
-	inProgress = null;
-};
-
-jQuery.fx.speeds = {
-	slow: 600,
-	fast: 200,
-
-	// Default speed
-	_default: 400
-};
-
-
-// Based off of the plugin by Clint Helfers, with permission.
-// https://web.archive.org/web/20100324014747/http://blindsignals.com/index.php/2009/07/jquery-delay/
-jQuery.fn.delay = function( time, type ) {
-	time = jQuery.fx ? jQuery.fx.speeds[ time ] || time : time;
-	type = type || "fx";
-
-	return this.queue( type, function( next, hooks ) {
-		var timeout = window.setTimeout( next, time );
-		hooks.stop = function() {
-			window.clearTimeout( timeout );
-		};
-	} );
-};
-
-
-( function() {
-	var input = document.createElement( "input" ),
-		select = document.createElement( "select" ),
-		opt = select.appendChild( document.createElement( "option" ) );
-
-	input.type = "checkbox";
-
-	// Support: Android <=4.3 only
-	// Default value for a checkbox should be "on"
-	support.checkOn = input.value !== "";
-
-	// Support: IE <=11 only
-	// Must access selectedIndex to make default options select
-	support.optSelected = opt.selected;
-
-	// Support: IE <=11 only
-	// An input loses its value after becoming a radio
-	input = document.createElement( "input" );
-	input.value = "t";
-	input.type = "radio";
-	support.radioValue = input.value === "t";
-} )();
-
-
-var boolHook,
-	attrHandle = jQuery.expr.attrHandle;
-
-jQuery.fn.extend( {
-	attr: function( name, value ) {
-		return access( this, jQuery.attr, name, value, arguments.length > 1 );
-	},
-
-	removeAttr: function( name ) {
-		return this.each( function() {
-			jQuery.removeAttr( this, name );
-		} );
-	}
-} );
-
-jQuery.extend( {
-	attr: function( elem, name, value ) {
-		var ret, hooks,
-			nType = elem.nodeType;
-
-		// Don't get/set attributes on text, comment and attribute nodes
-		if ( nType === 3 || nType === 8 || nType === 2 ) {
-			return;
-		}
-
-		// Fallback to prop when attributes are not supported
-		if ( typeof elem.getAttribute === "undefined" ) {
-			return jQuery.prop( elem, name, value );
-		}
-
-		// Attribute hooks are determined by the lowercase version
-		// Grab necessary hook if one is defined
-		if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {
-			hooks = jQuery.attrHooks[ name.toLowerCase() ] ||
-				( jQuery.expr.match.bool.test( name ) ? boolHook : undefined );
-		}
-
-		if ( value !== undefined ) {
-			if ( value === null ) {
-				jQuery.removeAttr( elem, name );
-				return;
-			}
-
-			if ( hooks && "set" in hooks &&
-				( ret = hooks.set( elem, value, name ) ) !== undefined ) {
-				return ret;
-			}
-
-			elem.setAttribute( name, value + "" );
-			return value;
-		}
-
-		if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {
-			return ret;
-		}
-
-		ret = jQuery.find.attr( elem, name );
-
-		// Non-existent attributes return null, we normalize to undefined
-		return ret == null ? undefined : ret;
-	},
-
-	attrHooks: {
-		type: {
-			set: function( elem, value ) {
-				if ( !support.radioValue && value === "radio" &&
-					nodeName( elem, "input" ) ) {
-					var val = elem.value;
-					elem.setAttribute( "type", value );
-					if ( val ) {
-						elem.value = val;
-					}
-					return value;
-				}
-			}
-		}
-	},
-
-	removeAttr: function( elem, value ) {
-		var name,
-			i = 0,
-
-			// Attribute names can contain non-HTML whitespace characters
-			// https://html.spec.whatwg.org/multipage/syntax.html#attributes-2
-			attrNames = value && value.match( rnothtmlwhite );
-
-		if ( attrNames && elem.nodeType === 1 ) {
-			while ( ( name = attrNames[ i++ ] ) ) {
-				elem.removeAttribute( name );
-			}
-		}
-	}
-} );
-
-// Hooks for boolean attributes
-boolHook = {
-	set: function( elem, value, name ) {
-		if ( value === false ) {
-
-			// Remove boolean attributes when set to false
-			jQuery.removeAttr( elem, name );
-		} else {
-			elem.setAttribute( name, name );
-		}
-		return name;
-	}
-};
-
-jQuery.each( jQuery.expr.match.bool.source.match( /\w+/g ), function( _i, name ) {
-	var getter = attrHandle[ name ] || jQuery.find.attr;
-
-	attrHandle[ name ] = function( elem, name, isXML ) {
-		var ret, handle,
-			lowercaseName = name.toLowerCase();
-
-		if ( !isXML ) {
-
-			// Avoid an infinite loop by temporarily removing this function from the getter
-			handle = attrHandle[ lowercaseName ];
-			attrHandle[ lowercaseName ] = ret;
-			ret = getter( elem, name, isXML ) != null ?
-				lowercaseName :
-				null;
-			attrHandle[ lowercaseName ] = handle;
-		}
-		return ret;
-	};
-} );
-
-
-
-
-var rfocusable = /^(?:input|select|textarea|button)$/i,
-	rclickable = /^(?:a|area)$/i;
-
-jQuery.fn.extend( {
-	prop: function( name, value ) {
-		return access( this, jQuery.prop, name, value, arguments.length > 1 );
-	},
-
-	removeProp: function( name ) {
-		return this.each( function() {
-			delete this[ jQuery.propFix[ name ] || name ];
-		} );
-	}
-} );
-
-jQuery.extend( {
-	prop: function( elem, name, value ) {
-		var ret, hooks,
-			nType = elem.nodeType;
-
-		// Don't get/set properties on text, comment and attribute nodes
-		if ( nType === 3 || nType === 8 || nType === 2 ) {
-			return;
-		}
-
-		if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {
-
-			// Fix name and attach hooks
-			name = jQuery.propFix[ name ] || name;
-			hooks = jQuery.propHooks[ name ];
-		}
-
-		if ( value !== undefined ) {
-			if ( hooks && "set" in hooks &&
-				( ret = hooks.set( elem, value, name ) ) !== undefined ) {
-				return ret;
-			}
-
-			return ( elem[ name ] = value );
-		}
-
-		if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {
-			return ret;
-		}
-
-		return elem[ name ];
-	},
-
-	propHooks: {
-		tabIndex: {
-			get: function( elem ) {
-
-				// Support: IE <=9 - 11 only
-				// elem.tabIndex doesn't always return the
-				// correct value when it hasn't been explicitly set
-				// https://web.archive.org/web/20141116233347/http://fluidproject.org/blog/2008/01/09/getting-setting-and-removing-tabindex-values-with-javascript/
-				// Use proper attribute retrieval(#12072)
-				var tabindex = jQuery.find.attr( elem, "tabindex" );
-
-				if ( tabindex ) {
-					return parseInt( tabindex, 10 );
-				}
-
-				if (
-					rfocusable.test( elem.nodeName ) ||
-					rclickable.test( elem.nodeName ) &&
-					elem.href
-				) {
-					return 0;
-				}
-
-				return -1;
-			}
-		}
-	},
-
-	propFix: {
-		"for": "htmlFor",
-		"class": "className"
-	}
-} );
-
-// Support: IE <=11 only
-// Accessing the selectedIndex property
-// forces the browser to respect setting selected
-// on the option
-// The getter ensures a default option is selected
-// when in an optgroup
-// eslint rule "no-unused-expressions" is disabled for this code
-// since it considers such accessions noop
-if ( !support.optSelected ) {
-	jQuery.propHooks.selected = {
-		get: function( elem ) {
-
-			/* eslint no-unused-expressions: "off" */
-
-			var parent = elem.parentNode;
-			if ( parent && parent.parentNode ) {
-				parent.parentNode.selectedIndex;
-			}
-			return null;
-		},
-		set: function( elem ) {
-
-			/* eslint no-unused-expressions: "off" */
-
-			var parent = elem.parentNode;
-			if ( parent ) {
-				parent.selectedIndex;
-
-				if ( parent.parentNode ) {
-					parent.parentNode.selectedIndex;
-				}
-			}
-		}
-	};
-}
-
-jQuery.each( [
-	"tabIndex",
-	"readOnly",
-	"maxLength",
-	"cellSpacing",
-	"cellPadding",
-	"rowSpan",
-	"colSpan",
-	"useMap",
-	"frameBorder",
-	"contentEditable"
-], function() {
-	jQuery.propFix[ this.toLowerCase() ] = this;
-} );
-
-
-
-
-	// Strip and collapse whitespace according to HTML spec
-	// https://infra.spec.whatwg.org/#strip-and-collapse-ascii-whitespace
-	function stripAndCollapse( value ) {
-		var tokens = value.match( rnothtmlwhite ) || [];
-		return tokens.join( " " );
-	}
-
-
-function getClass( elem ) {
-	return elem.getAttribute && elem.getAttribute( "class" ) || "";
-}
-
-function classesToArray( value ) {
-	if ( Array.isArray( value ) ) {
-		return value;
-	}
-	if ( typeof value === "string" ) {
-		return value.match( rnothtmlwhite ) || [];
-	}
-	return [];
-}
-
-jQuery.fn.extend( {
-	addClass: function( value ) {
-		var classes, elem, cur, curValue, clazz, j, finalValue,
-			i = 0;
-
-		if ( isFunction( value ) ) {
-			return this.each( function( j ) {
-				jQuery( this ).addClass( value.call( this, j, getClass( this ) ) );
-			} );
-		}
-
-		classes = classesToArray( value );
-
-		if ( classes.length ) {
-			while ( ( elem = this[ i++ ] ) ) {
-				curValue = getClass( elem );
-				cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " );
-
-				if ( cur ) {
-					j = 0;
-					while ( ( clazz = classes[ j++ ] ) ) {
-						if ( cur.indexOf( " " + clazz + " " ) < 0 ) {
-							cur += clazz + " ";
-						}
-					}
-
-					// Only assign if different to avoid unneeded rendering.
-					finalValue = stripAndCollapse( cur );
-					if ( curValue !== finalValue ) {
-						elem.setAttribute( "class", finalValue );
-					}
-				}
-			}
-		}
-
-		return this;
-	},
-
-	removeClass: function( value ) {
-		var classes, elem, cur, curValue, clazz, j, finalValue,
-			i = 0;
-
-		if ( isFunction( value ) ) {
-			return this.each( function( j ) {
-				jQuery( this ).removeClass( value.call( this, j, getClass( this ) ) );
-			} );
-		}
-
-		if ( !arguments.length ) {
-			return this.attr( "class", "" );
-		}
-
-		classes = classesToArray( value );
-
-		if ( classes.length ) {
-			while ( ( elem = this[ i++ ] ) ) {
-				curValue = getClass( elem );
-
-				// This expression is here for better compressibility (see addClass)
-				cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " );
-
-				if ( cur ) {
-					j = 0;
-					while ( ( clazz = classes[ j++ ] ) ) {
-
-						// Remove *all* instances
-						while ( cur.indexOf( " " + clazz + " " ) > -1 ) {
-							cur = cur.replace( " " + clazz + " ", " " );
-						}
-					}
-
-					// Only assign if different to avoid unneeded rendering.
-					finalValue = stripAndCollapse( cur );
-					if ( curValue !== finalValue ) {
-						elem.setAttribute( "class", finalValue );
-					}
-				}
-			}
-		}
-
-		return this;
-	},
-
-	toggleClass: function( value, stateVal ) {
-		var type = typeof value,
-			isValidValue = type === "string" || Array.isArray( value );
-
-		if ( typeof stateVal === "boolean" && isValidValue ) {
-			return stateVal ? this.addClass( value ) : this.removeClass( value );
-		}
-
-		if ( isFunction( value ) ) {
-			return this.each( function( i ) {
-				jQuery( this ).toggleClass(
-					value.call( this, i, getClass( this ), stateVal ),
-					stateVal
-				);
-			} );
-		}
-
-		return this.each( function() {
-			var className, i, self, classNames;
-
-			if ( isValidValue ) {
-
-				// Toggle individual class names
-				i = 0;
-				self = jQuery( this );
-				classNames = classesToArray( value );
-
-				while ( ( className = classNames[ i++ ] ) ) {
-
-					// Check each className given, space separated list
-					if ( self.hasClass( className ) ) {
-						self.removeClass( className );
-					} else {
-						self.addClass( className );
-					}
-				}
-
-			// Toggle whole class name
-			} else if ( value === undefined || type === "boolean" ) {
-				className = getClass( this );
-				if ( className ) {
-
-					// Store className if set
-					dataPriv.set( this, "__className__", className );
-				}
-
-				// If the element has a class name or if we're passed `false`,
-				// then remove the whole classname (if there was one, the above saved it).
-				// Otherwise bring back whatever was previously saved (if anything),
-				// falling back to the empty string if nothing was stored.
-				if ( this.setAttribute ) {
-					this.setAttribute( "class",
-						className || value === false ?
-							"" :
-							dataPriv.get( this, "__className__" ) || ""
-					);
-				}
-			}
-		} );
-	},
-
-	hasClass: function( selector ) {
-		var className, elem,
-			i = 0;
-
-		className = " " + selector + " ";
-		while ( ( elem = this[ i++ ] ) ) {
-			if ( elem.nodeType === 1 &&
-				( " " + stripAndCollapse( getClass( elem ) ) + " " ).indexOf( className ) > -1 ) {
-				return true;
-			}
-		}
-
-		return false;
-	}
-} );
-
-
-
-
-var rreturn = /\r/g;
-
-jQuery.fn.extend( {
-	val: function( value ) {
-		var hooks, ret, valueIsFunction,
-			elem = this[ 0 ];
-
-		if ( !arguments.length ) {
-			if ( elem ) {
-				hooks = jQuery.valHooks[ elem.type ] ||
-					jQuery.valHooks[ elem.nodeName.toLowerCase() ];
-
-				if ( hooks &&
-					"get" in hooks &&
-					( ret = hooks.get( elem, "value" ) ) !== undefined
-				) {
-					return ret;
-				}
-
-				ret = elem.value;
-
-				// Handle most common string cases
-				if ( typeof ret === "string" ) {
-					return ret.replace( rreturn, "" );
-				}
-
-				// Handle cases where value is null/undef or number
-				return ret == null ? "" : ret;
-			}
-
-			return;
-		}
-
-		valueIsFunction = isFunction( value );
-
-		return this.each( function( i ) {
-			var val;
-
-			if ( this.nodeType !== 1 ) {
-				return;
-			}
-
-			if ( valueIsFunction ) {
-				val = value.call( this, i, jQuery( this ).val() );
-			} else {
-				val = value;
-			}
-
-			// Treat null/undefined as ""; convert numbers to string
-			if ( val == null ) {
-				val = "";
-
-			} else if ( typeof val === "number" ) {
-				val += "";
-
-			} else if ( Array.isArray( val ) ) {
-				val = jQuery.map( val, function( value ) {
-					return value == null ? "" : value + "";
-				} );
-			}
-
-			hooks = jQuery.valHooks[ this.type ] || jQuery.valHooks[ this.nodeName.toLowerCase() ];
-
-			// If set returns undefined, fall back to normal setting
-			if ( !hooks || !( "set" in hooks ) || hooks.set( this, val, "value" ) === undefined ) {
-				this.value = val;
-			}
-		} );
-	}
-} );
-
-jQuery.extend( {
-	valHooks: {
-		option: {
-			get: function( elem ) {
-
-				var val = jQuery.find.attr( elem, "value" );
-				return val != null ?
-					val :
-
-					// Support: IE <=10 - 11 only
-					// option.text throws exceptions (#14686, #14858)
-					// Strip and collapse whitespace
-					// https://html.spec.whatwg.org/#strip-and-collapse-whitespace
-					stripAndCollapse( jQuery.text( elem ) );
-			}
-		},
-		select: {
-			get: function( elem ) {
-				var value, option, i,
-					options = elem.options,
-					index = elem.selectedIndex,
-					one = elem.type === "select-one",
-					values = one ? null : [],
-					max = one ? index + 1 : options.length;
-
-				if ( index < 0 ) {
-					i = max;
-
-				} else {
-					i = one ? index : 0;
-				}
-
-				// Loop through all the selected options
-				for ( ; i < max; i++ ) {
-					option = options[ i ];
-
-					// Support: IE <=9 only
-					// IE8-9 doesn't update selected after form reset (#2551)
-					if ( ( option.selected || i === index ) &&
-
-							// Don't return options that are disabled or in a disabled optgroup
-							!option.disabled &&
-							( !option.parentNode.disabled ||
-								!nodeName( option.parentNode, "optgroup" ) ) ) {
-
-						// Get the specific value for the option
-						value = jQuery( option ).val();
-
-						// We don't need an array for one selects
-						if ( one ) {
-							return value;
-						}
-
-						// Multi-Selects return an array
-						values.push( value );
-					}
-				}
-
-				return values;
-			},
-
-			set: function( elem, value ) {
-				var optionSet, option,
-					options = elem.options,
-					values = jQuery.makeArray( value ),
-					i = options.length;
-
-				while ( i-- ) {
-					option = options[ i ];
-
-					/* eslint-disable no-cond-assign */
-
-					if ( option.selected =
-						jQuery.inArray( jQuery.valHooks.option.get( option ), values ) > -1
-					) {
-						optionSet = true;
-					}
-
-					/* eslint-enable no-cond-assign */
-				}
-
-				// Force browsers to behave consistently when non-matching value is set
-				if ( !optionSet ) {
-					elem.selectedIndex = -1;
-				}
-				return values;
-			}
-		}
-	}
-} );
-
-// Radios and checkboxes getter/setter
-jQuery.each( [ "radio", "checkbox" ], function() {
-	jQuery.valHooks[ this ] = {
-		set: function( elem, value ) {
-			if ( Array.isArray( value ) ) {
-				return ( elem.checked = jQuery.inArray( jQuery( elem ).val(), value ) > -1 );
-			}
-		}
-	};
-	if ( !support.checkOn ) {
-		jQuery.valHooks[ this ].get = function( elem ) {
-			return elem.getAttribute( "value" ) === null ? "on" : elem.value;
-		};
-	}
-} );
-
-
-
-
-// Return jQuery for attributes-only inclusion
-
-
-support.focusin = "onfocusin" in window;
-
-
-var rfocusMorph = /^(?:focusinfocus|focusoutblur)$/,
-	stopPropagationCallback = function( e ) {
-		e.stopPropagation();
-	};
-
-jQuery.extend( jQuery.event, {
-
-	trigger: function( event, data, elem, onlyHandlers ) {
-
-		var i, cur, tmp, bubbleType, ontype, handle, special, lastElement,
-			eventPath = [ elem || document ],
-			type = hasOwn.call( event, "type" ) ? event.type : event,
-			namespaces = hasOwn.call( event, "namespace" ) ? event.namespace.split( "." ) : [];
-
-		cur = lastElement = tmp = elem = elem || document;
-
-		// Don't do events on text and comment nodes
-		if ( elem.nodeType === 3 || elem.nodeType === 8 ) {
-			return;
-		}
-
-		// focus/blur morphs to focusin/out; ensure we're not firing them right now
-		if ( rfocusMorph.test( type + jQuery.event.triggered ) ) {
-			return;
-		}
-
-		if ( type.indexOf( "." ) > -1 ) {
-
-			// Namespaced trigger; create a regexp to match event type in handle()
-			namespaces = type.split( "." );
-			type = namespaces.shift();
-			namespaces.sort();
-		}
-		ontype = type.indexOf( ":" ) < 0 && "on" + type;
-
-		// Caller can pass in a jQuery.Event object, Object, or just an event type string
-		event = event[ jQuery.expando ] ?
-			event :
-			new jQuery.Event( type, typeof event === "object" && event );
-
-		// Trigger bitmask: & 1 for native handlers; & 2 for jQuery (always true)
-		event.isTrigger = onlyHandlers ? 2 : 3;
-		event.namespace = namespaces.join( "." );
-		event.rnamespace = event.namespace ?
-			new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ) :
-			null;
-
-		// Clean up the event in case it is being reused
-		event.result = undefined;
-		if ( !event.target ) {
-			event.target = elem;
-		}
-
-		// Clone any incoming data and prepend the event, creating the handler arg list
-		data = data == null ?
-			[ event ] :
-			jQuery.makeArray( data, [ event ] );
-
-		// Allow special events to draw outside the lines
-		special = jQuery.event.special[ type ] || {};
-		if ( !onlyHandlers && special.trigger && special.trigger.apply( elem, data ) === false ) {
-			return;
-		}
-
-		// Determine event propagation path in advance, per W3C events spec (#9951)
-		// Bubble up to document, then to window; watch for a global ownerDocument var (#9724)
-		if ( !onlyHandlers && !special.noBubble && !isWindow( elem ) ) {
-
-			bubbleType = special.delegateType || type;
-			if ( !rfocusMorph.test( bubbleType + type ) ) {
-				cur = cur.parentNode;
-			}
-			for ( ; cur; cur = cur.parentNode ) {
-				eventPath.push( cur );
-				tmp = cur;
-			}
-
-			// Only add window if we got to document (e.g., not plain obj or detached DOM)
-			if ( tmp === ( elem.ownerDocument || document ) ) {
-				eventPath.push( tmp.defaultView || tmp.parentWindow || window );
-			}
-		}
-
-		// Fire handlers on the event path
-		i = 0;
-		while ( ( cur = eventPath[ i++ ] ) && !event.isPropagationStopped() ) {
-			lastElement = cur;
-			event.type = i > 1 ?
-				bubbleType :
-				special.bindType || type;
-
-			// jQuery handler
-			handle = ( dataPriv.get( cur, "events" ) || Object.create( null ) )[ event.type ] &&
-				dataPriv.get( cur, "handle" );
-			if ( handle ) {
-				handle.apply( cur, data );
-			}
-
-			// Native handler
-			handle = ontype && cur[ ontype ];
-			if ( handle && handle.apply && acceptData( cur ) ) {
-				event.result = handle.apply( cur, data );
-				if ( event.result === false ) {
-					event.preventDefault();
-				}
-			}
-		}
-		event.type = type;
-
-		// If nobody prevented the default action, do it now
-		if ( !onlyHandlers && !event.isDefaultPrevented() ) {
-
-			if ( ( !special._default ||
-				special._default.apply( eventPath.pop(), data ) === false ) &&
-				acceptData( elem ) ) {
-
-				// Call a native DOM method on the target with the same name as the event.
-				// Don't do default actions on window, that's where global variables be (#6170)
-				if ( ontype && isFunction( elem[ type ] ) && !isWindow( elem ) ) {
-
-					// Don't re-trigger an onFOO event when we call its FOO() method
-					tmp = elem[ ontype ];
-
-					if ( tmp ) {
-						elem[ ontype ] = null;
-					}
-
-					// Prevent re-triggering of the same event, since we already bubbled it above
-					jQuery.event.triggered = type;
-
-					if ( event.isPropagationStopped() ) {
-						lastElement.addEventListener( type, stopPropagationCallback );
-					}
-
-					elem[ type ]();
-
-					if ( event.isPropagationStopped() ) {
-						lastElement.removeEventListener( type, stopPropagationCallback );
-					}
-
-					jQuery.event.triggered = undefined;
-
-					if ( tmp ) {
-						elem[ ontype ] = tmp;
-					}
-				}
-			}
-		}
-
-		return event.result;
-	},
-
-	// Piggyback on a donor event to simulate a different one
-	// Used only for `focus(in | out)` events
-	simulate: function( type, elem, event ) {
-		var e = jQuery.extend(
-			new jQuery.Event(),
-			event,
-			{
-				type: type,
-				isSimulated: true
-			}
-		);
-
-		jQuery.event.trigger( e, null, elem );
-	}
-
-} );
-
-jQuery.fn.extend( {
-
-	trigger: function( type, data ) {
-		return this.each( function() {
-			jQuery.event.trigger( type, data, this );
-		} );
-	},
-	triggerHandler: function( type, data ) {
-		var elem = this[ 0 ];
-		if ( elem ) {
-			return jQuery.event.trigger( type, data, elem, true );
-		}
-	}
-} );
-
-
-// Support: Firefox <=44
-// Firefox doesn't have focus(in | out) events
-// Related ticket - https://bugzilla.mozilla.org/show_bug.cgi?id=687787
-//
-// Support: Chrome <=48 - 49, Safari <=9.0 - 9.1
-// focus(in | out) events fire after focus & blur events,
-// which is spec violation - http://www.w3.org/TR/DOM-Level-3-Events/#events-focusevent-event-order
-// Related ticket - https://bugs.chromium.org/p/chromium/issues/detail?id=449857
-if ( !support.focusin ) {
-	jQuery.each( { focus: "focusin", blur: "focusout" }, function( orig, fix ) {
-
-		// Attach a single capturing handler on the document while someone wants focusin/focusout
-		var handler = function( event ) {
-			jQuery.event.simulate( fix, event.target, jQuery.event.fix( event ) );
-		};
-
-		jQuery.event.special[ fix ] = {
-			setup: function() {
-
-				// Handle: regular nodes (via `this.ownerDocument`), window
-				// (via `this.document`) & document (via `this`).
-				var doc = this.ownerDocument || this.document || this,
-					attaches = dataPriv.access( doc, fix );
-
-				if ( !attaches ) {
-					doc.addEventListener( orig, handler, true );
-				}
-				dataPriv.access( doc, fix, ( attaches || 0 ) + 1 );
-			},
-			teardown: function() {
-				var doc = this.ownerDocument || this.document || this,
-					attaches = dataPriv.access( doc, fix ) - 1;
-
-				if ( !attaches ) {
-					doc.removeEventListener( orig, handler, true );
-					dataPriv.remove( doc, fix );
-
-				} else {
-					dataPriv.access( doc, fix, attaches );
-				}
-			}
-		};
-	} );
-}
-var location = window.location;
-
-var nonce = { guid: Date.now() };
-
-var rquery = ( /\?/ );
-
-
-
-// Cross-browser xml parsing
-jQuery.parseXML = function( data ) {
-	var xml, parserErrorElem;
-	if ( !data || typeof data !== "string" ) {
-		return null;
-	}
-
-	// Support: IE 9 - 11 only
-	// IE throws on parseFromString with invalid input.
-	try {
-		xml = ( new window.DOMParser() ).parseFromString( data, "text/xml" );
-	} catch ( e ) {}
-
-	parserErrorElem = xml && xml.getElementsByTagName( "parsererror" )[ 0 ];
-	if ( !xml || parserErrorElem ) {
-		jQuery.error( "Invalid XML: " + (
-			parserErrorElem ?
-				jQuery.map( parserErrorElem.childNodes, function( el ) {
-					return el.textContent;
-				} ).join( "\n" ) :
-				data
-		) );
-	}
-	return xml;
-};
-
-
-var
-	rbracket = /\[\]$/,
-	rCRLF = /\r?\n/g,
-	rsubmitterTypes = /^(?:submit|button|image|reset|file)$/i,
-	rsubmittable = /^(?:input|select|textarea|keygen)/i;
-
-function buildParams( prefix, obj, traditional, add ) {
-	var name;
-
-	if ( Array.isArray( obj ) ) {
-
-		// Serialize array item.
-		jQuery.each( obj, function( i, v ) {
-			if ( traditional || rbracket.test( prefix ) ) {
-
-				// Treat each array item as a scalar.
-				add( prefix, v );
-
-			} else {
-
-				// Item is non-scalar (array or object), encode its numeric index.
-				buildParams(
-					prefix + "[" + ( typeof v === "object" && v != null ? i : "" ) + "]",
-					v,
-					traditional,
-					add
-				);
-			}
-		} );
-
-	} else if ( !traditional && toType( obj ) === "object" ) {
-
-		// Serialize object item.
-		for ( name in obj ) {
-			buildParams( prefix + "[" + name + "]", obj[ name ], traditional, add );
-		}
-
-	} else {
-
-		// Serialize scalar item.
-		add( prefix, obj );
-	}
-}
-
-// Serialize an array of form elements or a set of
-// key/values into a query string
-jQuery.param = function( a, traditional ) {
-	var prefix,
-		s = [],
-		add = function( key, valueOrFunction ) {
-
-			// If value is a function, invoke it and use its return value
-			var value = isFunction( valueOrFunction ) ?
-				valueOrFunction() :
-				valueOrFunction;
-
-			s[ s.length ] = encodeURIComponent( key ) + "=" +
-				encodeURIComponent( value == null ? "" : value );
-		};
-
-	if ( a == null ) {
-		return "";
-	}
-
-	// If an array was passed in, assume that it is an array of form elements.
-	if ( Array.isArray( a ) || ( a.jquery && !jQuery.isPlainObject( a ) ) ) {
-
-		// Serialize the form elements
-		jQuery.each( a, function() {
-			add( this.name, this.value );
-		} );
-
-	} else {
-
-		// If traditional, encode the "old" way (the way 1.3.2 or older
-		// did it), otherwise encode params recursively.
-		for ( prefix in a ) {
-			buildParams( prefix, a[ prefix ], traditional, add );
-		}
-	}
-
-	// Return the resulting serialization
-	return s.join( "&" );
-};
-
-jQuery.fn.extend( {
-	serialize: function() {
-		return jQuery.param( this.serializeArray() );
-	},
-	serializeArray: function() {
-		return this.map( function() {
-
-			// Can add propHook for "elements" to filter or add form elements
-			var elements = jQuery.prop( this, "elements" );
-			return elements ? jQuery.makeArray( elements ) : this;
-		} ).filter( function() {
-			var type = this.type;
-
-			// Use .is( ":disabled" ) so that fieldset[disabled] works
-			return this.name && !jQuery( this ).is( ":disabled" ) &&
-				rsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) &&
-				( this.checked || !rcheckableType.test( type ) );
-		} ).map( function( _i, elem ) {
-			var val = jQuery( this ).val();
-
-			if ( val == null ) {
-				return null;
-			}
-
-			if ( Array.isArray( val ) ) {
-				return jQuery.map( val, function( val ) {
-					return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) };
-				} );
-			}
-
-			return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) };
-		} ).get();
-	}
-} );
-
-
-var
-	r20 = /%20/g,
-	rhash = /#.*$/,
-	rantiCache = /([?&])_=[^&]*/,
-	rheaders = /^(.*?):[ \t]*([^\r\n]*)$/mg,
-
-	// #7653, #8125, #8152: local protocol detection
-	rlocalProtocol = /^(?:about|app|app-storage|.+-extension|file|res|widget):$/,
-	rnoContent = /^(?:GET|HEAD)$/,
-	rprotocol = /^\/\//,
-
-	/* Prefilters
-	 * 1) They are useful to introduce custom dataTypes (see ajax/jsonp.js for an example)
-	 * 2) These are called:
-	 *    - BEFORE asking for a transport
-	 *    - AFTER param serialization (s.data is a string if s.processData is true)
-	 * 3) key is the dataType
-	 * 4) the catchall symbol "*" can be used
-	 * 5) execution will start with transport dataType and THEN continue down to "*" if needed
-	 */
-	prefilters = {},
-
-	/* Transports bindings
-	 * 1) key is the dataType
-	 * 2) the catchall symbol "*" can be used
-	 * 3) selection will start with transport dataType and THEN go to "*" if needed
-	 */
-	transports = {},
-
-	// Avoid comment-prolog char sequence (#10098); must appease lint and evade compression
-	allTypes = "*/".concat( "*" ),
-
-	// Anchor tag for parsing the document origin
-	originAnchor = document.createElement( "a" );
-
-originAnchor.href = location.href;
-
-// Base "constructor" for jQuery.ajaxPrefilter and jQuery.ajaxTransport
-function addToPrefiltersOrTransports( structure ) {
-
-	// dataTypeExpression is optional and defaults to "*"
-	return function( dataTypeExpression, func ) {
-
-		if ( typeof dataTypeExpression !== "string" ) {
-			func = dataTypeExpression;
-			dataTypeExpression = "*";
-		}
-
-		var dataType,
-			i = 0,
-			dataTypes = dataTypeExpression.toLowerCase().match( rnothtmlwhite ) || [];
-
-		if ( isFunction( func ) ) {
-
-			// For each dataType in the dataTypeExpression
-			while ( ( dataType = dataTypes[ i++ ] ) ) {
-
-				// Prepend if requested
-				if ( dataType[ 0 ] === "+" ) {
-					dataType = dataType.slice( 1 ) || "*";
-					( structure[ dataType ] = structure[ dataType ] || [] ).unshift( func );
-
-				// Otherwise append
-				} else {
-					( structure[ dataType ] = structure[ dataType ] || [] ).push( func );
-				}
-			}
-		}
-	};
-}
-
-// Base inspection function for prefilters and transports
-function inspectPrefiltersOrTransports( structure, options, originalOptions, jqXHR ) {
-
-	var inspected = {},
-		seekingTransport = ( structure === transports );
-
-	function inspect( dataType ) {
-		var selected;
-		inspected[ dataType ] = true;
-		jQuery.each( structure[ dataType ] || [], function( _, prefilterOrFactory ) {
-			var dataTypeOrTransport = prefilterOrFactory( options, originalOptions, jqXHR );
-			if ( typeof dataTypeOrTransport === "string" &&
-				!seekingTransport && !inspected[ dataTypeOrTransport ] ) {
-
-				options.dataTypes.unshift( dataTypeOrTransport );
-				inspect( dataTypeOrTransport );
-				return false;
-			} else if ( seekingTransport ) {
-				return !( selected = dataTypeOrTransport );
-			}
-		} );
-		return selected;
-	}
-
-	return inspect( options.dataTypes[ 0 ] ) || !inspected[ "*" ] && inspect( "*" );
-}
-
-// A special extend for ajax options
-// that takes "flat" options (not to be deep extended)
-// Fixes #9887
-function ajaxExtend( target, src ) {
-	var key, deep,
-		flatOptions = jQuery.ajaxSettings.flatOptions || {};
-
-	for ( key in src ) {
-		if ( src[ key ] !== undefined ) {
-			( flatOptions[ key ] ? target : ( deep || ( deep = {} ) ) )[ key ] = src[ key ];
-		}
-	}
-	if ( deep ) {
-		jQuery.extend( true, target, deep );
-	}
-
-	return target;
-}
-
-/* Handles responses to an ajax request:
- * - finds the right dataType (mediates between content-type and expected dataType)
- * - returns the corresponding response
- */
-function ajaxHandleResponses( s, jqXHR, responses ) {
-
-	var ct, type, finalDataType, firstDataType,
-		contents = s.contents,
-		dataTypes = s.dataTypes;
-
-	// Remove auto dataType and get content-type in the process
-	while ( dataTypes[ 0 ] === "*" ) {
-		dataTypes.shift();
-		if ( ct === undefined ) {
-			ct = s.mimeType || jqXHR.getResponseHeader( "Content-Type" );
-		}
-	}
-
-	// Check if we're dealing with a known content-type
-	if ( ct ) {
-		for ( type in contents ) {
-			if ( contents[ type ] && contents[ type ].test( ct ) ) {
-				dataTypes.unshift( type );
-				break;
-			}
-		}
-	}
-
-	// Check to see if we have a response for the expected dataType
-	if ( dataTypes[ 0 ] in responses ) {
-		finalDataType = dataTypes[ 0 ];
-	} else {
-
-		// Try convertible dataTypes
-		for ( type in responses ) {
-			if ( !dataTypes[ 0 ] || s.converters[ type + " " + dataTypes[ 0 ] ] ) {
-				finalDataType = type;
-				break;
-			}
-			if ( !firstDataType ) {
-				firstDataType = type;
-			}
-		}
-
-		// Or just use first one
-		finalDataType = finalDataType || firstDataType;
-	}
-
-	// If we found a dataType
-	// We add the dataType to the list if needed
-	// and return the corresponding response
-	if ( finalDataType ) {
-		if ( finalDataType !== dataTypes[ 0 ] ) {
-			dataTypes.unshift( finalDataType );
-		}
-		return responses[ finalDataType ];
-	}
-}
-
-/* Chain conversions given the request and the original response
- * Also sets the responseXXX fields on the jqXHR instance
- */
-function ajaxConvert( s, response, jqXHR, isSuccess ) {
-	var conv2, current, conv, tmp, prev,
-		converters = {},
-
-		// Work with a copy of dataTypes in case we need to modify it for conversion
-		dataTypes = s.dataTypes.slice();
-
-	// Create converters map with lowercased keys
-	if ( dataTypes[ 1 ] ) {
-		for ( conv in s.converters ) {
-			converters[ conv.toLowerCase() ] = s.converters[ conv ];
-		}
-	}
-
-	current = dataTypes.shift();
-
-	// Convert to each sequential dataType
-	while ( current ) {
-
-		if ( s.responseFields[ current ] ) {
-			jqXHR[ s.responseFields[ current ] ] = response;
-		}
-
-		// Apply the dataFilter if provided
-		if ( !prev && isSuccess && s.dataFilter ) {
-			response = s.dataFilter( response, s.dataType );
-		}
-
-		prev = current;
-		current = dataTypes.shift();
-
-		if ( current ) {
-
-			// There's only work to do if current dataType is non-auto
-			if ( current === "*" ) {
-
-				current = prev;
-
-			// Convert response if prev dataType is non-auto and differs from current
-			} else if ( prev !== "*" && prev !== current ) {
-
-				// Seek a direct converter
-				conv = converters[ prev + " " + current ] || converters[ "* " + current ];
-
-				// If none found, seek a pair
-				if ( !conv ) {
-					for ( conv2 in converters ) {
-
-						// If conv2 outputs current
-						tmp = conv2.split( " " );
-						if ( tmp[ 1 ] === current ) {
-
-							// If prev can be converted to accepted input
-							conv = converters[ prev + " " + tmp[ 0 ] ] ||
-								converters[ "* " + tmp[ 0 ] ];
-							if ( conv ) {
-
-								// Condense equivalence converters
-								if ( conv === true ) {
-									conv = converters[ conv2 ];
-
-								// Otherwise, insert the intermediate dataType
-								} else if ( converters[ conv2 ] !== true ) {
-									current = tmp[ 0 ];
-									dataTypes.unshift( tmp[ 1 ] );
-								}
-								break;
-							}
-						}
-					}
-				}
-
-				// Apply converter (if not an equivalence)
-				if ( conv !== true ) {
-
-					// Unless errors are allowed to bubble, catch and return them
-					if ( conv && s.throws ) {
-						response = conv( response );
-					} else {
-						try {
-							response = conv( response );
-						} catch ( e ) {
-							return {
-								state: "parsererror",
-								error: conv ? e : "No conversion from " + prev + " to " + current
-							};
-						}
-					}
-				}
-			}
-		}
-	}
-
-	return { state: "success", data: response };
-}
-
-jQuery.extend( {
-
-	// Counter for holding the number of active queries
-	active: 0,
-
-	// Last-Modified header cache for next request
-	lastModified: {},
-	etag: {},
-
-	ajaxSettings: {
-		url: location.href,
-		type: "GET",
-		isLocal: rlocalProtocol.test( location.protocol ),
-		global: true,
-		processData: true,
-		async: true,
-		contentType: "application/x-www-form-urlencoded; charset=UTF-8",
-
-		/*
-		timeout: 0,
-		data: null,
-		dataType: null,
-		username: null,
-		password: null,
-		cache: null,
-		throws: false,
-		traditional: false,
-		headers: {},
-		*/
-
-		accepts: {
-			"*": allTypes,
-			text: "text/plain",
-			html: "text/html",
-			xml: "application/xml, text/xml",
-			json: "application/json, text/javascript"
-		},
-
-		contents: {
-			xml: /\bxml\b/,
-			html: /\bhtml/,
-			json: /\bjson\b/
-		},
-
-		responseFields: {
-			xml: "responseXML",
-			text: "responseText",
-			json: "responseJSON"
-		},
-
-		// Data converters
-		// Keys separate source (or catchall "*") and destination types with a single space
-		converters: {
-
-			// Convert anything to text
-			"* text": String,
-
-			// Text to html (true = no transformation)
-			"text html": true,
-
-			// Evaluate text as a json expression
-			"text json": JSON.parse,
-
-			// Parse text as xml
-			"text xml": jQuery.parseXML
-		},
-
-		// For options that shouldn't be deep extended:
-		// you can add your own custom options here if
-		// and when you create one that shouldn't be
-		// deep extended (see ajaxExtend)
-		flatOptions: {
-			url: true,
-			context: true
-		}
-	},
-
-	// Creates a full fledged settings object into target
-	// with both ajaxSettings and settings fields.
-	// If target is omitted, writes into ajaxSettings.
-	ajaxSetup: function( target, settings ) {
-		return settings ?
-
-			// Building a settings object
-			ajaxExtend( ajaxExtend( target, jQuery.ajaxSettings ), settings ) :
-
-			// Extending ajaxSettings
-			ajaxExtend( jQuery.ajaxSettings, target );
-	},
-
-	ajaxPrefilter: addToPrefiltersOrTransports( prefilters ),
-	ajaxTransport: addToPrefiltersOrTransports( transports ),
-
-	// Main method
-	ajax: function( url, options ) {
-
-		// If url is an object, simulate pre-1.5 signature
-		if ( typeof url === "object" ) {
-			options = url;
-			url = undefined;
-		}
-
-		// Force options to be an object
-		options = options || {};
-
-		var transport,
-
-			// URL without anti-cache param
-			cacheURL,
-
-			// Response headers
-			responseHeadersString,
-			responseHeaders,
-
-			// timeout handle
-			timeoutTimer,
-
-			// Url cleanup var
-			urlAnchor,
-
-			// Request state (becomes false upon send and true upon completion)
-			completed,
-
-			// To know if global events are to be dispatched
-			fireGlobals,
-
-			// Loop variable
-			i,
-
-			// uncached part of the url
-			uncached,
-
-			// Create the final options object
-			s = jQuery.ajaxSetup( {}, options ),
-
-			// Callbacks context
-			callbackContext = s.context || s,
-
-			// Context for global events is callbackContext if it is a DOM node or jQuery collection
-			globalEventContext = s.context &&
-				( callbackContext.nodeType || callbackContext.jquery ) ?
-				jQuery( callbackContext ) :
-				jQuery.event,
-
-			// Deferreds
-			deferred = jQuery.Deferred(),
-			completeDeferred = jQuery.Callbacks( "once memory" ),
-
-			// Status-dependent callbacks
-			statusCode = s.statusCode || {},
-
-			// Headers (they are sent all at once)
-			requestHeaders = {},
-			requestHeadersNames = {},
-
-			// Default abort message
-			strAbort = "canceled",
-
-			// Fake xhr
-			jqXHR = {
-				readyState: 0,
-
-				// Builds headers hashtable if needed
-				getResponseHeader: function( key ) {
-					var match;
-					if ( completed ) {
-						if ( !responseHeaders ) {
-							responseHeaders = {};
-							while ( ( match = rheaders.exec( responseHeadersString ) ) ) {
-								responseHeaders[ match[ 1 ].toLowerCase() + " " ] =
-									( responseHeaders[ match[ 1 ].toLowerCase() + " " ] || [] )
-										.concat( match[ 2 ] );
-							}
-						}
-						match = responseHeaders[ key.toLowerCase() + " " ];
-					}
-					return match == null ? null : match.join( ", " );
-				},
-
-				// Raw string
-				getAllResponseHeaders: function() {
-					return completed ? responseHeadersString : null;
-				},
-
-				// Caches the header
-				setRequestHeader: function( name, value ) {
-					if ( completed == null ) {
-						name = requestHeadersNames[ name.toLowerCase() ] =
-							requestHeadersNames[ name.toLowerCase() ] || name;
-						requestHeaders[ name ] = value;
-					}
-					return this;
-				},
-
-				// Overrides response content-type header
-				overrideMimeType: function( type ) {
-					if ( completed == null ) {
-						s.mimeType = type;
-					}
-					return this;
-				},
-
-				// Status-dependent callbacks
-				statusCode: function( map ) {
-					var code;
-					if ( map ) {
-						if ( completed ) {
-
-							// Execute the appropriate callbacks
-							jqXHR.always( map[ jqXHR.status ] );
-						} else {
-
-							// Lazy-add the new callbacks in a way that preserves old ones
-							for ( code in map ) {
-								statusCode[ code ] = [ statusCode[ code ], map[ code ] ];
-							}
-						}
-					}
-					return this;
-				},
-
-				// Cancel the request
-				abort: function( statusText ) {
-					var finalText = statusText || strAbort;
-					if ( transport ) {
-						transport.abort( finalText );
-					}
-					done( 0, finalText );
-					return this;
-				}
-			};
-
-		// Attach deferreds
-		deferred.promise( jqXHR );
-
-		// Add protocol if not provided (prefilters might expect it)
-		// Handle falsy url in the settings object (#10093: consistency with old signature)
-		// We also use the url parameter if available
-		s.url = ( ( url || s.url || location.href ) + "" )
-			.replace( rprotocol, location.protocol + "//" );
-
-		// Alias method option to type as per ticket #12004
-		s.type = options.method || options.type || s.method || s.type;
-
-		// Extract dataTypes list
-		s.dataTypes = ( s.dataType || "*" ).toLowerCase().match( rnothtmlwhite ) || [ "" ];
-
-		// A cross-domain request is in order when the origin doesn't match the current origin.
-		if ( s.crossDomain == null ) {
-			urlAnchor = document.createElement( "a" );
-
-			// Support: IE <=8 - 11, Edge 12 - 15
-			// IE throws exception on accessing the href property if url is malformed,
-			// e.g. http://example.com:80x/
-			try {
-				urlAnchor.href = s.url;
-
-				// Support: IE <=8 - 11 only
-				// Anchor's host property isn't correctly set when s.url is relative
-				urlAnchor.href = urlAnchor.href;
-				s.crossDomain = originAnchor.protocol + "//" + originAnchor.host !==
-					urlAnchor.protocol + "//" + urlAnchor.host;
-			} catch ( e ) {
-
-				// If there is an error parsing the URL, assume it is crossDomain,
-				// it can be rejected by the transport if it is invalid
-				s.crossDomain = true;
-			}
-		}
-
-		// Convert data if not already a string
-		if ( s.data && s.processData && typeof s.data !== "string" ) {
-			s.data = jQuery.param( s.data, s.traditional );
-		}
-
-		// Apply prefilters
-		inspectPrefiltersOrTransports( prefilters, s, options, jqXHR );
-
-		// If request was aborted inside a prefilter, stop there
-		if ( completed ) {
-			return jqXHR;
-		}
-
-		// We can fire global events as of now if asked to
-		// Don't fire events if jQuery.event is undefined in an AMD-usage scenario (#15118)
-		fireGlobals = jQuery.event && s.global;
-
-		// Watch for a new set of requests
-		if ( fireGlobals && jQuery.active++ === 0 ) {
-			jQuery.event.trigger( "ajaxStart" );
-		}
-
-		// Uppercase the type
-		s.type = s.type.toUpperCase();
-
-		// Determine if request has content
-		s.hasContent = !rnoContent.test( s.type );
-
-		// Save the URL in case we're toying with the If-Modified-Since
-		// and/or If-None-Match header later on
-		// Remove hash to simplify url manipulation
-		cacheURL = s.url.replace( rhash, "" );
-
-		// More options handling for requests with no content
-		if ( !s.hasContent ) {
-
-			// Remember the hash so we can put it back
-			uncached = s.url.slice( cacheURL.length );
-
-			// If data is available and should be processed, append data to url
-			if ( s.data && ( s.processData || typeof s.data === "string" ) ) {
-				cacheURL += ( rquery.test( cacheURL ) ? "&" : "?" ) + s.data;
-
-				// #9682: remove data so that it's not used in an eventual retry
-				delete s.data;
-			}
-
-			// Add or update anti-cache param if needed
-			if ( s.cache === false ) {
-				cacheURL = cacheURL.replace( rantiCache, "$1" );
-				uncached = ( rquery.test( cacheURL ) ? "&" : "?" ) + "_=" + ( nonce.guid++ ) +
-					uncached;
-			}
-
-			// Put hash and anti-cache on the URL that will be requested (gh-1732)
-			s.url = cacheURL + uncached;
-
-		// Change '%20' to '+' if this is encoded form body content (gh-2658)
-		} else if ( s.data && s.processData &&
-			( s.contentType || "" ).indexOf( "application/x-www-form-urlencoded" ) === 0 ) {
-			s.data = s.data.replace( r20, "+" );
-		}
-
-		// Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.
-		if ( s.ifModified ) {
-			if ( jQuery.lastModified[ cacheURL ] ) {
-				jqXHR.setRequestHeader( "If-Modified-Since", jQuery.lastModified[ cacheURL ] );
-			}
-			if ( jQuery.etag[ cacheURL ] ) {
-				jqXHR.setRequestHeader( "If-None-Match", jQuery.etag[ cacheURL ] );
-			}
-		}
-
-		// Set the correct header, if data is being sent
-		if ( s.data && s.hasContent && s.contentType !== false || options.contentType ) {
-			jqXHR.setRequestHeader( "Content-Type", s.contentType );
-		}
-
-		// Set the Accepts header for the server, depending on the dataType
-		jqXHR.setRequestHeader(
-			"Accept",
-			s.dataTypes[ 0 ] && s.accepts[ s.dataTypes[ 0 ] ] ?
-				s.accepts[ s.dataTypes[ 0 ] ] +
-					( s.dataTypes[ 0 ] !== "*" ? ", " + allTypes + "; q=0.01" : "" ) :
-				s.accepts[ "*" ]
-		);
-
-		// Check for headers option
-		for ( i in s.headers ) {
-			jqXHR.setRequestHeader( i, s.headers[ i ] );
-		}
-
-		// Allow custom headers/mimetypes and early abort
-		if ( s.beforeSend &&
-			( s.beforeSend.call( callbackContext, jqXHR, s ) === false || completed ) ) {
-
-			// Abort if not done already and return
-			return jqXHR.abort();
-		}
-
-		// Aborting is no longer a cancellation
-		strAbort = "abort";
-
-		// Install callbacks on deferreds
-		completeDeferred.add( s.complete );
-		jqXHR.done( s.success );
-		jqXHR.fail( s.error );
-
-		// Get transport
-		transport = inspectPrefiltersOrTransports( transports, s, options, jqXHR );
-
-		// If no transport, we auto-abort
-		if ( !transport ) {
-			done( -1, "No Transport" );
-		} else {
-			jqXHR.readyState = 1;
-
-			// Send global event
-			if ( fireGlobals ) {
-				globalEventContext.trigger( "ajaxSend", [ jqXHR, s ] );
-			}
-
-			// If request was aborted inside ajaxSend, stop there
-			if ( completed ) {
-				return jqXHR;
-			}
-
-			// Timeout
-			if ( s.async && s.timeout > 0 ) {
-				timeoutTimer = window.setTimeout( function() {
-					jqXHR.abort( "timeout" );
-				}, s.timeout );
-			}
-
-			try {
-				completed = false;
-				transport.send( requestHeaders, done );
-			} catch ( e ) {
-
-				// Rethrow post-completion exceptions
-				if ( completed ) {
-					throw e;
-				}
-
-				// Propagate others as results
-				done( -1, e );
-			}
-		}
-
-		// Callback for when everything is done
-		function done( status, nativeStatusText, responses, headers ) {
-			var isSuccess, success, error, response, modified,
-				statusText = nativeStatusText;
-
-			// Ignore repeat invocations
-			if ( completed ) {
-				return;
-			}
-
-			completed = true;
-
-			// Clear timeout if it exists
-			if ( timeoutTimer ) {
-				window.clearTimeout( timeoutTimer );
-			}
-
-			// Dereference transport for early garbage collection
-			// (no matter how long the jqXHR object will be used)
-			transport = undefined;
-
-			// Cache response headers
-			responseHeadersString = headers || "";
-
-			// Set readyState
-			jqXHR.readyState = status > 0 ? 4 : 0;
-
-			// Determine if successful
-			isSuccess = status >= 200 && status < 300 || status === 304;
-
-			// Get response data
-			if ( responses ) {
-				response = ajaxHandleResponses( s, jqXHR, responses );
-			}
-
-			// Use a noop converter for missing script but not if jsonp
-			if ( !isSuccess &&
-				jQuery.inArray( "script", s.dataTypes ) > -1 &&
-				jQuery.inArray( "json", s.dataTypes ) < 0 ) {
-				s.converters[ "text script" ] = function() {};
-			}
-
-			// Convert no matter what (that way responseXXX fields are always set)
-			response = ajaxConvert( s, response, jqXHR, isSuccess );
-
-			// If successful, handle type chaining
-			if ( isSuccess ) {
-
-				// Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.
-				if ( s.ifModified ) {
-					modified = jqXHR.getResponseHeader( "Last-Modified" );
-					if ( modified ) {
-						jQuery.lastModified[ cacheURL ] = modified;
-					}
-					modified = jqXHR.getResponseHeader( "etag" );
-					if ( modified ) {
-						jQuery.etag[ cacheURL ] = modified;
-					}
-				}
-
-				// if no content
-				if ( status === 204 || s.type === "HEAD" ) {
-					statusText = "nocontent";
-
-				// if not modified
-				} else if ( status === 304 ) {
-					statusText = "notmodified";
-
-				// If we have data, let's convert it
-				} else {
-					statusText = response.state;
-					success = response.data;
-					error = response.error;
-					isSuccess = !error;
-				}
-			} else {
-
-				// Extract error from statusText and normalize for non-aborts
-				error = statusText;
-				if ( status || !statusText ) {
-					statusText = "error";
-					if ( status < 0 ) {
-						status = 0;
-					}
-				}
-			}
-
-			// Set data for the fake xhr object
-			jqXHR.status = status;
-			jqXHR.statusText = ( nativeStatusText || statusText ) + "";
-
-			// Success/Error
-			if ( isSuccess ) {
-				deferred.resolveWith( callbackContext, [ success, statusText, jqXHR ] );
-			} else {
-				deferred.rejectWith( callbackContext, [ jqXHR, statusText, error ] );
-			}
-
-			// Status-dependent callbacks
-			jqXHR.statusCode( statusCode );
-			statusCode = undefined;
-
-			if ( fireGlobals ) {
-				globalEventContext.trigger( isSuccess ? "ajaxSuccess" : "ajaxError",
-					[ jqXHR, s, isSuccess ? success : error ] );
-			}
-
-			// Complete
-			completeDeferred.fireWith( callbackContext, [ jqXHR, statusText ] );
-
-			if ( fireGlobals ) {
-				globalEventContext.trigger( "ajaxComplete", [ jqXHR, s ] );
-
-				// Handle the global AJAX counter
-				if ( !( --jQuery.active ) ) {
-					jQuery.event.trigger( "ajaxStop" );
-				}
-			}
-		}
-
-		return jqXHR;
-	},
-
-	getJSON: function( url, data, callback ) {
-		return jQuery.get( url, data, callback, "json" );
-	},
-
-	getScript: function( url, callback ) {
-		return jQuery.get( url, undefined, callback, "script" );
-	}
-} );
-
-jQuery.each( [ "get", "post" ], function( _i, method ) {
-	jQuery[ method ] = function( url, data, callback, type ) {
-
-		// Shift arguments if data argument was omitted
-		if ( isFunction( data ) ) {
-			type = type || callback;
-			callback = data;
-			data = undefined;
-		}
-
-		// The url can be an options object (which then must have .url)
-		return jQuery.ajax( jQuery.extend( {
-			url: url,
-			type: method,
-			dataType: type,
-			data: data,
-			success: callback
-		}, jQuery.isPlainObject( url ) && url ) );
-	};
-} );
-
-jQuery.ajaxPrefilter( function( s ) {
-	var i;
-	for ( i in s.headers ) {
-		if ( i.toLowerCase() === "content-type" ) {
-			s.contentType = s.headers[ i ] || "";
-		}
-	}
-} );
-
-
-jQuery._evalUrl = function( url, options, doc ) {
-	return jQuery.ajax( {
-		url: url,
-
-		// Make this explicit, since user can override this through ajaxSetup (#11264)
-		type: "GET",
-		dataType: "script",
-		cache: true,
-		async: false,
-		global: false,
-
-		// Only evaluate the response if it is successful (gh-4126)
-		// dataFilter is not invoked for failure responses, so using it instead
-		// of the default converter is kludgy but it works.
-		converters: {
-			"text script": function() {}
-		},
-		dataFilter: function( response ) {
-			jQuery.globalEval( response, options, doc );
-		}
-	} );
-};
-
-
-jQuery.fn.extend( {
-	wrapAll: function( html ) {
-		var wrap;
-
-		if ( this[ 0 ] ) {
-			if ( isFunction( html ) ) {
-				html = html.call( this[ 0 ] );
-			}
-
-			// The elements to wrap the target around
-			wrap = jQuery( html, this[ 0 ].ownerDocument ).eq( 0 ).clone( true );
-
-			if ( this[ 0 ].parentNode ) {
-				wrap.insertBefore( this[ 0 ] );
-			}
-
-			wrap.map( function() {
-				var elem = this;
-
-				while ( elem.firstElementChild ) {
-					elem = elem.firstElementChild;
-				}
-
-				return elem;
-			} ).append( this );
-		}
-
-		return this;
-	},
-
-	wrapInner: function( html ) {
-		if ( isFunction( html ) ) {
-			return this.each( function( i ) {
-				jQuery( this ).wrapInner( html.call( this, i ) );
-			} );
-		}
-
-		return this.each( function() {
-			var self = jQuery( this ),
-				contents = self.contents();
-
-			if ( contents.length ) {
-				contents.wrapAll( html );
-
-			} else {
-				self.append( html );
-			}
-		} );
-	},
-
-	wrap: function( html ) {
-		var htmlIsFunction = isFunction( html );
-
-		return this.each( function( i ) {
-			jQuery( this ).wrapAll( htmlIsFunction ? html.call( this, i ) : html );
-		} );
-	},
-
-	unwrap: function( selector ) {
-		this.parent( selector ).not( "body" ).each( function() {
-			jQuery( this ).replaceWith( this.childNodes );
-		} );
-		return this;
-	}
-} );
-
-
-jQuery.expr.pseudos.hidden = function( elem ) {
-	return !jQuery.expr.pseudos.visible( elem );
-};
-jQuery.expr.pseudos.visible = function( elem ) {
-	return !!( elem.offsetWidth || elem.offsetHeight || elem.getClientRects().length );
-};
-
-
-
-
-jQuery.ajaxSettings.xhr = function() {
-	try {
-		return new window.XMLHttpRequest();
-	} catch ( e ) {}
-};
-
-var xhrSuccessStatus = {
-
-		// File protocol always yields status code 0, assume 200
-		0: 200,
-
-		// Support: IE <=9 only
-		// #1450: sometimes IE returns 1223 when it should be 204
-		1223: 204
-	},
-	xhrSupported = jQuery.ajaxSettings.xhr();
-
-support.cors = !!xhrSupported && ( "withCredentials" in xhrSupported );
-support.ajax = xhrSupported = !!xhrSupported;
-
-jQuery.ajaxTransport( function( options ) {
-	var callback, errorCallback;
-
-	// Cross domain only allowed if supported through XMLHttpRequest
-	if ( support.cors || xhrSupported && !options.crossDomain ) {
-		return {
-			send: function( headers, complete ) {
-				var i,
-					xhr = options.xhr();
-
-				xhr.open(
-					options.type,
-					options.url,
-					options.async,
-					options.username,
-					options.password
-				);
-
-				// Apply custom fields if provided
-				if ( options.xhrFields ) {
-					for ( i in options.xhrFields ) {
-						xhr[ i ] = options.xhrFields[ i ];
-					}
-				}
-
-				// Override mime type if needed
-				if ( options.mimeType && xhr.overrideMimeType ) {
-					xhr.overrideMimeType( options.mimeType );
-				}
-
-				// X-Requested-With header
-				// For cross-domain requests, seeing as conditions for a preflight are
-				// akin to a jigsaw puzzle, we simply never set it to be sure.
-				// (it can always be set on a per-request basis or even using ajaxSetup)
-				// For same-domain requests, won't change header if already provided.
-				if ( !options.crossDomain && !headers[ "X-Requested-With" ] ) {
-					headers[ "X-Requested-With" ] = "XMLHttpRequest";
-				}
-
-				// Set headers
-				for ( i in headers ) {
-					xhr.setRequestHeader( i, headers[ i ] );
-				}
-
-				// Callback
-				callback = function( type ) {
-					return function() {
-						if ( callback ) {
-							callback = errorCallback = xhr.onload =
-								xhr.onerror = xhr.onabort = xhr.ontimeout =
-									xhr.onreadystatechange = null;
-
-							if ( type === "abort" ) {
-								xhr.abort();
-							} else if ( type === "error" ) {
-
-								// Support: IE <=9 only
-								// On a manual native abort, IE9 throws
-								// errors on any property access that is not readyState
-								if ( typeof xhr.status !== "number" ) {
-									complete( 0, "error" );
-								} else {
-									complete(
-
-										// File: protocol always yields status 0; see #8605, #14207
-										xhr.status,
-										xhr.statusText
-									);
-								}
-							} else {
-								complete(
-									xhrSuccessStatus[ xhr.status ] || xhr.status,
-									xhr.statusText,
-
-									// Support: IE <=9 only
-									// IE9 has no XHR2 but throws on binary (trac-11426)
-									// For XHR2 non-text, let the caller handle it (gh-2498)
-									( xhr.responseType || "text" ) !== "text"  ||
-									typeof xhr.responseText !== "string" ?
-										{ binary: xhr.response } :
-										{ text: xhr.responseText },
-									xhr.getAllResponseHeaders()
-								);
-							}
-						}
-					};
-				};
-
-				// Listen to events
-				xhr.onload = callback();
-				errorCallback = xhr.onerror = xhr.ontimeout = callback( "error" );
-
-				// Support: IE 9 only
-				// Use onreadystatechange to replace onabort
-				// to handle uncaught aborts
-				if ( xhr.onabort !== undefined ) {
-					xhr.onabort = errorCallback;
-				} else {
-					xhr.onreadystatechange = function() {
-
-						// Check readyState before timeout as it changes
-						if ( xhr.readyState === 4 ) {
-
-							// Allow onerror to be called first,
-							// but that will not handle a native abort
-							// Also, save errorCallback to a variable
-							// as xhr.onerror cannot be accessed
-							window.setTimeout( function() {
-								if ( callback ) {
-									errorCallback();
-								}
-							} );
-						}
-					};
-				}
-
-				// Create the abort callback
-				callback = callback( "abort" );
-
-				try {
-
-					// Do send the request (this may raise an exception)
-					xhr.send( options.hasContent && options.data || null );
-				} catch ( e ) {
-
-					// #14683: Only rethrow if this hasn't been notified as an error yet
-					if ( callback ) {
-						throw e;
-					}
-				}
-			},
-
-			abort: function() {
-				if ( callback ) {
-					callback();
-				}
-			}
-		};
-	}
-} );
-
-
-
-
-// Prevent auto-execution of scripts when no explicit dataType was provided (See gh-2432)
-jQuery.ajaxPrefilter( function( s ) {
-	if ( s.crossDomain ) {
-		s.contents.script = false;
-	}
-} );
-
-// Install script dataType
-jQuery.ajaxSetup( {
-	accepts: {
-		script: "text/javascript, application/javascript, " +
-			"application/ecmascript, application/x-ecmascript"
-	},
-	contents: {
-		script: /\b(?:java|ecma)script\b/
-	},
-	converters: {
-		"text script": function( text ) {
-			jQuery.globalEval( text );
-			return text;
-		}
-	}
-} );
-
-// Handle cache's special case and crossDomain
-jQuery.ajaxPrefilter( "script", function( s ) {
-	if ( s.cache === undefined ) {
-		s.cache = false;
-	}
-	if ( s.crossDomain ) {
-		s.type = "GET";
-	}
-} );
-
-// Bind script tag hack transport
-jQuery.ajaxTransport( "script", function( s ) {
-
-	// This transport only deals with cross domain or forced-by-attrs requests
-	if ( s.crossDomain || s.scriptAttrs ) {
-		var script, callback;
-		return {
-			send: function( _, complete ) {
-				script = jQuery( "<script>" )
-					.attr( s.scriptAttrs || {} )
-					.prop( { charset: s.scriptCharset, src: s.url } )
-					.on( "load error", callback = function( evt ) {
-						script.remove();
-						callback = null;
-						if ( evt ) {
-							complete( evt.type === "error" ? 404 : 200, evt.type );
-						}
-					} );
-
-				// Use native DOM manipulation to avoid our domManip AJAX trickery
-				document.head.appendChild( script[ 0 ] );
-			},
-			abort: function() {
-				if ( callback ) {
-					callback();
-				}
-			}
-		};
-	}
-} );
-
-
-
-
-var oldCallbacks = [],
-	rjsonp = /(=)\?(?=&|$)|\?\?/;
-
-// Default jsonp settings
-jQuery.ajaxSetup( {
-	jsonp: "callback",
-	jsonpCallback: function() {
-		var callback = oldCallbacks.pop() || ( jQuery.expando + "_" + ( nonce.guid++ ) );
-		this[ callback ] = true;
-		return callback;
-	}
-} );
-
-// Detect, normalize options and install callbacks for jsonp requests
-jQuery.ajaxPrefilter( "json jsonp", function( s, originalSettings, jqXHR ) {
-
-	var callbackName, overwritten, responseContainer,
-		jsonProp = s.jsonp !== false && ( rjsonp.test( s.url ) ?
-			"url" :
-			typeof s.data === "string" &&
-				( s.contentType || "" )
-					.indexOf( "application/x-www-form-urlencoded" ) === 0 &&
-				rjsonp.test( s.data ) && "data"
-		);
-
-	// Handle iff the expected data type is "jsonp" or we have a parameter to set
-	if ( jsonProp || s.dataTypes[ 0 ] === "jsonp" ) {
-
-		// Get callback name, remembering preexisting value associated with it
-		callbackName = s.jsonpCallback = isFunction( s.jsonpCallback ) ?
-			s.jsonpCallback() :
-			s.jsonpCallback;
-
-		// Insert callback into url or form data
-		if ( jsonProp ) {
-			s[ jsonProp ] = s[ jsonProp ].replace( rjsonp, "$1" + callbackName );
-		} else if ( s.jsonp !== false ) {
-			s.url += ( rquery.test( s.url ) ? "&" : "?" ) + s.jsonp + "=" + callbackName;
-		}
-
-		// Use data converter to retrieve json after script execution
-		s.converters[ "script json" ] = function() {
-			if ( !responseContainer ) {
-				jQuery.error( callbackName + " was not called" );
-			}
-			return responseContainer[ 0 ];
-		};
-
-		// Force json dataType
-		s.dataTypes[ 0 ] = "json";
-
-		// Install callback
-		overwritten = window[ callbackName ];
-		window[ callbackName ] = function() {
-			responseContainer = arguments;
-		};
-
-		// Clean-up function (fires after converters)
-		jqXHR.always( function() {
-
-			// If previous value didn't exist - remove it
-			if ( overwritten === undefined ) {
-				jQuery( window ).removeProp( callbackName );
-
-			// Otherwise restore preexisting value
-			} else {
-				window[ callbackName ] = overwritten;
-			}
-
-			// Save back as free
-			if ( s[ callbackName ] ) {
-
-				// Make sure that re-using the options doesn't screw things around
-				s.jsonpCallback = originalSettings.jsonpCallback;
-
-				// Save the callback name for future use
-				oldCallbacks.push( callbackName );
-			}
-
-			// Call if it was a function and we have a response
-			if ( responseContainer && isFunction( overwritten ) ) {
-				overwritten( responseContainer[ 0 ] );
-			}
-
-			responseContainer = overwritten = undefined;
-		} );
-
-		// Delegate to script
-		return "script";
-	}
-} );
-
-
-
-
-// Support: Safari 8 only
-// In Safari 8 documents created via document.implementation.createHTMLDocument
-// collapse sibling forms: the second one becomes a child of the first one.
-// Because of that, this security measure has to be disabled in Safari 8.
-// https://bugs.webkit.org/show_bug.cgi?id=137337
-support.createHTMLDocument = ( function() {
-	var body = document.implementation.createHTMLDocument( "" ).body;
-	body.innerHTML = "<form></form><form></form>";
-	return body.childNodes.length === 2;
-} )();
-
-
-// Argument "data" should be string of html
-// context (optional): If specified, the fragment will be created in this context,
-// defaults to document
-// keepScripts (optional): If true, will include scripts passed in the html string
-jQuery.parseHTML = function( data, context, keepScripts ) {
-	if ( typeof data !== "string" ) {
-		return [];
-	}
-	if ( typeof context === "boolean" ) {
-		keepScripts = context;
-		context = false;
-	}
-
-	var base, parsed, scripts;
-
-	if ( !context ) {
-
-		// Stop scripts or inline event handlers from being executed immediately
-		// by using document.implementation
-		if ( support.createHTMLDocument ) {
-			context = document.implementation.createHTMLDocument( "" );
-
-			// Set the base href for the created document
-			// so any parsed elements with URLs
-			// are based on the document's URL (gh-2965)
-			base = context.createElement( "base" );
-			base.href = document.location.href;
-			context.head.appendChild( base );
-		} else {
-			context = document;
-		}
-	}
-
-	parsed = rsingleTag.exec( data );
-	scripts = !keepScripts && [];
-
-	// Single tag
-	if ( parsed ) {
-		return [ context.createElement( parsed[ 1 ] ) ];
-	}
-
-	parsed = buildFragment( [ data ], context, scripts );
-
-	if ( scripts && scripts.length ) {
-		jQuery( scripts ).remove();
-	}
-
-	return jQuery.merge( [], parsed.childNodes );
-};
-
-
-/**
- * Load a url into a page
- */
-jQuery.fn.load = function( url, params, callback ) {
-	var selector, type, response,
-		self = this,
-		off = url.indexOf( " " );
-
-	if ( off > -1 ) {
-		selector = stripAndCollapse( url.slice( off ) );
-		url = url.slice( 0, off );
-	}
-
-	// If it's a function
-	if ( isFunction( params ) ) {
-
-		// We assume that it's the callback
-		callback = params;
-		params = undefined;
-
-	// Otherwise, build a param string
-	} else if ( params && typeof params === "object" ) {
-		type = "POST";
-	}
-
-	// If we have elements to modify, make the request
-	if ( self.length > 0 ) {
-		jQuery.ajax( {
-			url: url,
-
-			// If "type" variable is undefined, then "GET" method will be used.
-			// Make value of this field explicit since
-			// user can override it through ajaxSetup method
-			type: type || "GET",
-			dataType: "html",
-			data: params
-		} ).done( function( responseText ) {
-
-			// Save response for use in complete callback
-			response = arguments;
-
-			self.html( selector ?
-
-				// If a selector was specified, locate the right elements in a dummy div
-				// Exclude scripts to avoid IE 'Permission Denied' errors
-				jQuery( "<div>" ).append( jQuery.parseHTML( responseText ) ).find( selector ) :
-
-				// Otherwise use the full result
-				responseText );
-
-		// If the request succeeds, this function gets "data", "status", "jqXHR"
-		// but they are ignored because response was set above.
-		// If it fails, this function gets "jqXHR", "status", "error"
-		} ).always( callback && function( jqXHR, status ) {
-			self.each( function() {
-				callback.apply( this, response || [ jqXHR.responseText, status, jqXHR ] );
-			} );
-		} );
-	}
-
-	return this;
-};
-
-
-
-
-jQuery.expr.pseudos.animated = function( elem ) {
-	return jQuery.grep( jQuery.timers, function( fn ) {
-		return elem === fn.elem;
-	} ).length;
-};
-
-
-
-
-jQuery.offset = {
-	setOffset: function( elem, options, i ) {
-		var curPosition, curLeft, curCSSTop, curTop, curOffset, curCSSLeft, calculatePosition,
-			position = jQuery.css( elem, "position" ),
-			curElem = jQuery( elem ),
-			props = {};
-
-		// Set position first, in-case top/left are set even on static elem
-		if ( position === "static" ) {
-			elem.style.position = "relative";
-		}
-
-		curOffset = curElem.offset();
-		curCSSTop = jQuery.css( elem, "top" );
-		curCSSLeft = jQuery.css( elem, "left" );
-		calculatePosition = ( position === "absolute" || position === "fixed" ) &&
-			( curCSSTop + curCSSLeft ).indexOf( "auto" ) > -1;
-
-		// Need to be able to calculate position if either
-		// top or left is auto and position is either absolute or fixed
-		if ( calculatePosition ) {
-			curPosition = curElem.position();
-			curTop = curPosition.top;
-			curLeft = curPosition.left;
-
-		} else {
-			curTop = parseFloat( curCSSTop ) || 0;
-			curLeft = parseFloat( curCSSLeft ) || 0;
-		}
-
-		if ( isFunction( options ) ) {
-
-			// Use jQuery.extend here to allow modification of coordinates argument (gh-1848)
-			options = options.call( elem, i, jQuery.extend( {}, curOffset ) );
-		}
-
-		if ( options.top != null ) {
-			props.top = ( options.top - curOffset.top ) + curTop;
-		}
-		if ( options.left != null ) {
-			props.left = ( options.left - curOffset.left ) + curLeft;
-		}
-
-		if ( "using" in options ) {
-			options.using.call( elem, props );
-
-		} else {
-			curElem.css( props );
-		}
-	}
-};
-
-jQuery.fn.extend( {
-
-	// offset() relates an element's border box to the document origin
-	offset: function( options ) {
-
-		// Preserve chaining for setter
-		if ( arguments.length ) {
-			return options === undefined ?
-				this :
-				this.each( function( i ) {
-					jQuery.offset.setOffset( this, options, i );
-				} );
-		}
-
-		var rect, win,
-			elem = this[ 0 ];
-
-		if ( !elem ) {
-			return;
-		}
-
-		// Return zeros for disconnected and hidden (display: none) elements (gh-2310)
-		// Support: IE <=11 only
-		// Running getBoundingClientRect on a
-		// disconnected node in IE throws an error
-		if ( !elem.getClientRects().length ) {
-			return { top: 0, left: 0 };
-		}
-
-		// Get document-relative position by adding viewport scroll to viewport-relative gBCR
-		rect = elem.getBoundingClientRect();
-		win = elem.ownerDocument.defaultView;
-		return {
-			top: rect.top + win.pageYOffset,
-			left: rect.left + win.pageXOffset
-		};
-	},
-
-	// position() relates an element's margin box to its offset parent's padding box
-	// This corresponds to the behavior of CSS absolute positioning
-	position: function() {
-		if ( !this[ 0 ] ) {
-			return;
-		}
-
-		var offsetParent, offset, doc,
-			elem = this[ 0 ],
-			parentOffset = { top: 0, left: 0 };
-
-		// position:fixed elements are offset from the viewport, which itself always has zero offset
-		if ( jQuery.css( elem, "position" ) === "fixed" ) {
-
-			// Assume position:fixed implies availability of getBoundingClientRect
-			offset = elem.getBoundingClientRect();
-
-		} else {
-			offset = this.offset();
-
-			// Account for the *real* offset parent, which can be the document or its root element
-			// when a statically positioned element is identified
-			doc = elem.ownerDocument;
-			offsetParent = elem.offsetParent || doc.documentElement;
-			while ( offsetParent &&
-				( offsetParent === doc.body || offsetParent === doc.documentElement ) &&
-				jQuery.css( offsetParent, "position" ) === "static" ) {
-
-				offsetParent = offsetParent.parentNode;
-			}
-			if ( offsetParent && offsetParent !== elem && offsetParent.nodeType === 1 ) {
-
-				// Incorporate borders into its offset, since they are outside its content origin
-				parentOffset = jQuery( offsetParent ).offset();
-				parentOffset.top += jQuery.css( offsetParent, "borderTopWidth", true );
-				parentOffset.left += jQuery.css( offsetParent, "borderLeftWidth", true );
-			}
-		}
-
-		// Subtract parent offsets and element margins
-		return {
-			top: offset.top - parentOffset.top - jQuery.css( elem, "marginTop", true ),
-			left: offset.left - parentOffset.left - jQuery.css( elem, "marginLeft", true )
-		};
-	},
-
-	// This method will return documentElement in the following cases:
-	// 1) For the element inside the iframe without offsetParent, this method will return
-	//    documentElement of the parent window
-	// 2) For the hidden or detached element
-	// 3) For body or html element, i.e. in case of the html node - it will return itself
-	//
-	// but those exceptions were never presented as a real life use-cases
-	// and might be considered as more preferable results.
-	//
-	// This logic, however, is not guaranteed and can change at any point in the future
-	offsetParent: function() {
-		return this.map( function() {
-			var offsetParent = this.offsetParent;
-
-			while ( offsetParent && jQuery.css( offsetParent, "position" ) === "static" ) {
-				offsetParent = offsetParent.offsetParent;
-			}
-
-			return offsetParent || documentElement;
-		} );
-	}
-} );
-
-// Create scrollLeft and scrollTop methods
-jQuery.each( { scrollLeft: "pageXOffset", scrollTop: "pageYOffset" }, function( method, prop ) {
-	var top = "pageYOffset" === prop;
-
-	jQuery.fn[ method ] = function( val ) {
-		return access( this, function( elem, method, val ) {
-
-			// Coalesce documents and windows
-			var win;
-			if ( isWindow( elem ) ) {
-				win = elem;
-			} else if ( elem.nodeType === 9 ) {
-				win = elem.defaultView;
-			}
-
-			if ( val === undefined ) {
-				return win ? win[ prop ] : elem[ method ];
-			}
-
-			if ( win ) {
-				win.scrollTo(
-					!top ? val : win.pageXOffset,
-					top ? val : win.pageYOffset
-				);
-
-			} else {
-				elem[ method ] = val;
-			}
-		}, method, val, arguments.length );
-	};
-} );
-
-// Support: Safari <=7 - 9.1, Chrome <=37 - 49
-// Add the top/left cssHooks using jQuery.fn.position
-// Webkit bug: https://bugs.webkit.org/show_bug.cgi?id=29084
-// Blink bug: https://bugs.chromium.org/p/chromium/issues/detail?id=589347
-// getComputedStyle returns percent when specified for top/left/bottom/right;
-// rather than make the css module depend on the offset module, just check for it here
-jQuery.each( [ "top", "left" ], function( _i, prop ) {
-	jQuery.cssHooks[ prop ] = addGetHookIf( support.pixelPosition,
-		function( elem, computed ) {
-			if ( computed ) {
-				computed = curCSS( elem, prop );
-
-				// If curCSS returns percentage, fallback to offset
-				return rnumnonpx.test( computed ) ?
-					jQuery( elem ).position()[ prop ] + "px" :
-					computed;
-			}
-		}
-	);
-} );
-
-
-// Create innerHeight, innerWidth, height, width, outerHeight and outerWidth methods
-jQuery.each( { Height: "height", Width: "width" }, function( name, type ) {
-	jQuery.each( {
-		padding: "inner" + name,
-		content: type,
-		"": "outer" + name
-	}, function( defaultExtra, funcName ) {
-
-		// Margin is only for outerHeight, outerWidth
-		jQuery.fn[ funcName ] = function( margin, value ) {
-			var chainable = arguments.length && ( defaultExtra || typeof margin !== "boolean" ),
-				extra = defaultExtra || ( margin === true || value === true ? "margin" : "border" );
-
-			return access( this, function( elem, type, value ) {
-				var doc;
-
-				if ( isWindow( elem ) ) {
-
-					// $( window ).outerWidth/Height return w/h including scrollbars (gh-1729)
-					return funcName.indexOf( "outer" ) === 0 ?
-						elem[ "inner" + name ] :
-						elem.document.documentElement[ "client" + name ];
-				}
-
-				// Get document width or height
-				if ( elem.nodeType === 9 ) {
-					doc = elem.documentElement;
-
-					// Either scroll[Width/Height] or offset[Width/Height] or client[Width/Height],
-					// whichever is greatest
-					return Math.max(
-						elem.body[ "scroll" + name ], doc[ "scroll" + name ],
-						elem.body[ "offset" + name ], doc[ "offset" + name ],
-						doc[ "client" + name ]
-					);
-				}
-
-				return value === undefined ?
-
-					// Get width or height on the element, requesting but not forcing parseFloat
-					jQuery.css( elem, type, extra ) :
-
-					// Set width or height on the element
-					jQuery.style( elem, type, value, extra );
-			}, type, chainable ? margin : undefined, chainable );
-		};
-	} );
-} );
-
-
-jQuery.each( [
-	"ajaxStart",
-	"ajaxStop",
-	"ajaxComplete",
-	"ajaxError",
-	"ajaxSuccess",
-	"ajaxSend"
-], function( _i, type ) {
-	jQuery.fn[ type ] = function( fn ) {
-		return this.on( type, fn );
-	};
-} );
-
-
-
-
-jQuery.fn.extend( {
-
-	bind: function( types, data, fn ) {
-		return this.on( types, null, data, fn );
-	},
-	unbind: function( types, fn ) {
-		return this.off( types, null, fn );
-	},
-
-	delegate: function( selector, types, data, fn ) {
-		return this.on( types, selector, data, fn );
-	},
-	undelegate: function( selector, types, fn ) {
-
-		// ( namespace ) or ( selector, types [, fn] )
-		return arguments.length === 1 ?
-			this.off( selector, "**" ) :
-			this.off( types, selector || "**", fn );
-	},
-
-	hover: function( fnOver, fnOut ) {
-		return this.mouseenter( fnOver ).mouseleave( fnOut || fnOver );
-	}
-} );
-
-jQuery.each(
-	( "blur focus focusin focusout resize scroll click dblclick " +
-	"mousedown mouseup mousemove mouseover mouseout mouseenter mouseleave " +
-	"change select submit keydown keypress keyup contextmenu" ).split( " " ),
-	function( _i, name ) {
-
-		// Handle event binding
-		jQuery.fn[ name ] = function( data, fn ) {
-			return arguments.length > 0 ?
-				this.on( name, null, data, fn ) :
-				this.trigger( name );
-		};
-	}
-);
-
-
-
-
-// Support: Android <=4.0 only
-// Make sure we trim BOM and NBSP
-var rtrim = /^[\s\uFEFF\xA0]+|[\s\uFEFF\xA0]+$/g;
-
-// Bind a function to a context, optionally partially applying any
-// arguments.
-// jQuery.proxy is deprecated to promote standards (specifically Function#bind)
-// However, it is not slated for removal any time soon
-jQuery.proxy = function( fn, context ) {
-	var tmp, args, proxy;
-
-	if ( typeof context === "string" ) {
-		tmp = fn[ context ];
-		context = fn;
-		fn = tmp;
-	}
-
-	// Quick check to determine if target is callable, in the spec
-	// this throws a TypeError, but we will just return undefined.
-	if ( !isFunction( fn ) ) {
-		return undefined;
-	}
-
-	// Simulated bind
-	args = slice.call( arguments, 2 );
-	proxy = function() {
-		return fn.apply( context || this, args.concat( slice.call( arguments ) ) );
-	};
-
-	// Set the guid of unique handler to the same of original handler, so it can be removed
-	proxy.guid = fn.guid = fn.guid || jQuery.guid++;
-
-	return proxy;
-};
-
-jQuery.holdReady = function( hold ) {
-	if ( hold ) {
-		jQuery.readyWait++;
-	} else {
-		jQuery.ready( true );
-	}
-};
-jQuery.isArray = Array.isArray;
-jQuery.parseJSON = JSON.parse;
-jQuery.nodeName = nodeName;
-jQuery.isFunction = isFunction;
-jQuery.isWindow = isWindow;
-jQuery.camelCase = camelCase;
-jQuery.type = toType;
-
-jQuery.now = Date.now;
-
-jQuery.isNumeric = function( obj ) {
-
-	// As of jQuery 3.0, isNumeric is limited to
-	// strings and numbers (primitives or objects)
-	// that can be coerced to finite numbers (gh-2662)
-	var type = jQuery.type( obj );
-	return ( type === "number" || type === "string" ) &&
-
-		// parseFloat NaNs numeric-cast false positives ("")
-		// ...but misinterprets leading-number strings, particularly hex literals ("0x...")
-		// subtraction forces infinities to NaN
-		!isNaN( obj - parseFloat( obj ) );
-};
-
-jQuery.trim = function( text ) {
-	return text == null ?
-		"" :
-		( text + "" ).replace( rtrim, "" );
-};
-
-
-
-// Register as a named AMD module, since jQuery can be concatenated with other
-// files that may use define, but not via a proper concatenation script that
-// understands anonymous AMD modules. A named AMD is safest and most robust
-// way to register. Lowercase jquery is used because AMD module names are
-// derived from file names, and jQuery is normally delivered in a lowercase
-// file name. Do this after creating the global so that if an AMD module wants
-// to call noConflict to hide this version of jQuery, it will work.
-
-// Note that for maximum portability, libraries that are not jQuery should
-// declare themselves as anonymous modules, and avoid setting a global if an
-// AMD loader is present. jQuery is a special case. For more information, see
-// https://github.com/jrburke/requirejs/wiki/Updating-existing-libraries#wiki-anon
-
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diff --git a/docs/static/jquery.js b/docs/static/jquery.js
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index c4c6022..0000000
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diff --git a/docs/static/kivy-sample.png b/docs/static/kivy-sample.png
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diff --git a/docs/static/language_data.js b/docs/static/language_data.js
deleted file mode 100644
index 2e22b06..0000000
--- a/docs/static/language_data.js
+++ /dev/null
@@ -1,199 +0,0 @@
-/*
- * language_data.js
- * ~~~~~~~~~~~~~~~~
- *
- * This script contains the language-specific data used by searchtools.js,
- * namely the list of stopwords, stemmer, scorer and splitter.
- *
- * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
- * :license: BSD, see LICENSE for details.
- *
- */
-
-var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"];
-
-
-/* Non-minified version is copied as a separate JS file, is available */
-
-/**
- * Porter Stemmer
- */
-var Stemmer = function() {
-
-  var step2list = {
-    ational: 'ate',
-    tional: 'tion',
-    enci: 'ence',
-    anci: 'ance',
-    izer: 'ize',
-    bli: 'ble',
-    alli: 'al',
-    entli: 'ent',
-    eli: 'e',
-    ousli: 'ous',
-    ization: 'ize',
-    ation: 'ate',
-    ator: 'ate',
-    alism: 'al',
-    iveness: 'ive',
-    fulness: 'ful',
-    ousness: 'ous',
-    aliti: 'al',
-    iviti: 'ive',
-    biliti: 'ble',
-    logi: 'log'
-  };
-
-  var step3list = {
-    icate: 'ic',
-    ative: '',
-    alize: 'al',
-    iciti: 'ic',
-    ical: 'ic',
-    ful: '',
-    ness: ''
-  };
-
-  var c = "[^aeiou]";          // consonant
-  var v = "[aeiouy]";          // vowel
-  var C = c + "[^aeiouy]*";    // consonant sequence
-  var V = v + "[aeiou]*";      // vowel sequence
-
-  var mgr0 = "^(" + C + ")?" + V + C;                      // [C]VC... is m>0
-  var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$";    // [C]VC[V] is m=1
-  var mgr1 = "^(" + C + ")?" + V + C + V + C;              // [C]VCVC... is m>1
-  var s_v   = "^(" + C + ")?" + v;                         // vowel in stem
-
-  this.stemWord = function (w) {
-    var stem;
-    var suffix;
-    var firstch;
-    var origword = w;
-
-    if (w.length < 3)
-      return w;
-
-    var re;
-    var re2;
-    var re3;
-    var re4;
-
-    firstch = w.substr(0,1);
-    if (firstch == "y")
-      w = firstch.toUpperCase() + w.substr(1);
-
-    // Step 1a
-    re = /^(.+?)(ss|i)es$/;
-    re2 = /^(.+?)([^s])s$/;
-
-    if (re.test(w))
-      w = w.replace(re,"$1$2");
-    else if (re2.test(w))
-      w = w.replace(re2,"$1$2");
-
-    // Step 1b
-    re = /^(.+?)eed$/;
-    re2 = /^(.+?)(ed|ing)$/;
-    if (re.test(w)) {
-      var fp = re.exec(w);
-      re = new RegExp(mgr0);
-      if (re.test(fp[1])) {
-        re = /.$/;
-        w = w.replace(re,"");
-      }
-    }
-    else if (re2.test(w)) {
-      var fp = re2.exec(w);
-      stem = fp[1];
-      re2 = new RegExp(s_v);
-      if (re2.test(stem)) {
-        w = stem;
-        re2 = /(at|bl|iz)$/;
-        re3 = new RegExp("([^aeiouylsz])\\1$");
-        re4 = new RegExp("^" + C + v + "[^aeiouwxy]$");
-        if (re2.test(w))
-          w = w + "e";
-        else if (re3.test(w)) {
-          re = /.$/;
-          w = w.replace(re,"");
-        }
-        else if (re4.test(w))
-          w = w + "e";
-      }
-    }
-
-    // Step 1c
-    re = /^(.+?)y$/;
-    if (re.test(w)) {
-      var fp = re.exec(w);
-      stem = fp[1];
-      re = new RegExp(s_v);
-      if (re.test(stem))
-        w = stem + "i";
-    }
-
-    // Step 2
-    re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;
-    if (re.test(w)) {
-      var fp = re.exec(w);
-      stem = fp[1];
-      suffix = fp[2];
-      re = new RegExp(mgr0);
-      if (re.test(stem))
-        w = stem + step2list[suffix];
-    }
-
-    // Step 3
-    re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;
-    if (re.test(w)) {
-      var fp = re.exec(w);
-      stem = fp[1];
-      suffix = fp[2];
-      re = new RegExp(mgr0);
-      if (re.test(stem))
-        w = stem + step3list[suffix];
-    }
-
-    // Step 4
-    re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;
-    re2 = /^(.+?)(s|t)(ion)$/;
-    if (re.test(w)) {
-      var fp = re.exec(w);
-      stem = fp[1];
-      re = new RegExp(mgr1);
-      if (re.test(stem))
-        w = stem;
-    }
-    else if (re2.test(w)) {
-      var fp = re2.exec(w);
-      stem = fp[1] + fp[2];
-      re2 = new RegExp(mgr1);
-      if (re2.test(stem))
-        w = stem;
-    }
-
-    // Step 5
-    re = /^(.+?)e$/;
-    if (re.test(w)) {
-      var fp = re.exec(w);
-      stem = fp[1];
-      re = new RegExp(mgr1);
-      re2 = new RegExp(meq1);
-      re3 = new RegExp("^" + C + v + "[^aeiouwxy]$");
-      if (re.test(stem) || (re2.test(stem) && !(re3.test(stem))))
-        w = stem;
-    }
-    re = /ll$/;
-    re2 = new RegExp(mgr1);
-    if (re.test(w) && re2.test(w)) {
-      re = /.$/;
-      w = w.replace(re,"");
-    }
-
-    // and turn initial Y back to y
-    if (firstch == "y")
-      w = firstch.toLowerCase() + w.substr(1);
-    return w;
-  }
-}
-
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diff --git a/docs/static/searchtools.js b/docs/static/searchtools.js
deleted file mode 100644
index ac4d586..0000000
--- a/docs/static/searchtools.js
+++ /dev/null
@@ -1,531 +0,0 @@
-/*
- * searchtools.js
- * ~~~~~~~~~~~~~~~~
- *
- * Sphinx JavaScript utilities for the full-text search.
- *
- * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
- * :license: BSD, see LICENSE for details.
- *
- */
-"use strict";
-
-/**
- * Simple result scoring code.
- */
-if (typeof Scorer === "undefined") {
-  var Scorer = {
-    // Implement the following function to further tweak the score for each result
-    // The function takes a result array [docname, title, anchor, descr, score, filename]
-    // and returns the new score.
-    /*
-    score: result => {
-      const [docname, title, anchor, descr, score, filename] = result
-      return score
-    },
-    */
-
-    // query matches the full name of an object
-    objNameMatch: 11,
-    // or matches in the last dotted part of the object name
-    objPartialMatch: 6,
-    // Additive scores depending on the priority of the object
-    objPrio: {
-      0: 15, // used to be importantResults
-      1: 5, // used to be objectResults
-      2: -5, // used to be unimportantResults
-    },
-    //  Used when the priority is not in the mapping.
-    objPrioDefault: 0,
-
-    // query found in title
-    title: 15,
-    partialTitle: 7,
-    // query found in terms
-    term: 5,
-    partialTerm: 2,
-  };
-}
-
-const _removeChildren = (element) => {
-  while (element && element.lastChild) element.removeChild(element.lastChild);
-};
-
-/**
- * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping
- */
-const _escapeRegExp = (string) =>
-  string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string
-
-const _displayItem = (item, highlightTerms, searchTerms) => {
-  const docBuilder = DOCUMENTATION_OPTIONS.BUILDER;
-  const docUrlRoot = DOCUMENTATION_OPTIONS.URL_ROOT;
-  const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX;
-  const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX;
-  const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY;
-
-  const [docName, title, anchor, descr] = item;
-
-  let listItem = document.createElement("li");
-  let requestUrl;
-  let linkUrl;
-  if (docBuilder === "dirhtml") {
-    // dirhtml builder
-    let dirname = docName + "/";
-    if (dirname.match(/\/index\/$/))
-      dirname = dirname.substring(0, dirname.length - 6);
-    else if (dirname === "index/") dirname = "";
-    requestUrl = docUrlRoot + dirname;
-    linkUrl = requestUrl;
-  } else {
-    // normal html builders
-    requestUrl = docUrlRoot + docName + docFileSuffix;
-    linkUrl = docName + docLinkSuffix;
-  }
-  const params = new URLSearchParams();
-  params.set("highlight", [...highlightTerms].join(" "));
-  let linkEl = listItem.appendChild(document.createElement("a"));
-  linkEl.href = linkUrl + "?" + params.toString() + anchor;
-  linkEl.innerHTML = title;
-  if (descr)
-    listItem.appendChild(document.createElement("span")).innerText =
-      " (" + descr + ")";
-  else if (showSearchSummary)
-    fetch(requestUrl)
-      .then((responseData) => responseData.text())
-      .then((data) => {
-        if (data)
-          listItem.appendChild(
-            Search.makeSearchSummary(data, searchTerms, highlightTerms)
-          );
-      });
-  Search.output.appendChild(listItem);
-};
-const _finishSearch = (resultCount) => {
-  Search.stopPulse();
-  Search.title.innerText = _("Search Results");
-  if (!resultCount)
-    Search.status.innerText = Documentation.gettext(
-      "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories."
-    );
-  else
-    Search.status.innerText = _(
-      `Search finished, found ${resultCount} page(s) matching the search query.`
-    );
-};
-const _displayNextItem = (
-  results,
-  resultCount,
-  highlightTerms,
-  searchTerms
-) => {
-  // results left, load the summary and display it
-  // this is intended to be dynamic (don't sub resultsCount)
-  if (results.length) {
-    _displayItem(results.pop(), highlightTerms, searchTerms);
-    setTimeout(
-      () => _displayNextItem(results, resultCount, highlightTerms, searchTerms),
-      5
-    );
-  }
-  // search finished, update title and status message
-  else _finishSearch(resultCount);
-};
-
-/**
- * Default splitQuery function. Can be overridden in ``sphinx.search`` with a
- * custom function per language.
- *
- * The regular expression works by splitting the string on consecutive characters
- * that are not Unicode letters, numbers, underscores, or emoji characters.
- * This is the same as ``\W+`` in Python, preserving the surrogate pair area.
- */
-if (typeof splitQuery === "undefined") {
-  var splitQuery = (query) => query
-      .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu)
-      .filter(term => term)  // remove remaining empty strings
-}
-
-/**
- * Search Module
- */
-const Search = {
-  _index: null,
-  _queued_query: null,
-  _pulse_status: -1,
-
-  htmlToText: (htmlString) => {
-    const htmlElement = document
-      .createRange()
-      .createContextualFragment(htmlString);
-    _removeChildren(htmlElement.querySelectorAll(".headerlink"));
-    const docContent = htmlElement.querySelector('[role="main"]');
-    if (docContent !== undefined) return docContent.textContent;
-    console.warn(
-      "Content block not found. Sphinx search tries to obtain it via '[role=main]'. Could you check your theme or template."
-    );
-    return "";
-  },
-
-  init: () => {
-    const query = new URLSearchParams(window.location.search).get("q");
-    document
-      .querySelectorAll('input[name="q"]')
-      .forEach((el) => (el.value = query));
-    if (query) Search.performSearch(query);
-  },
-
-  loadIndex: (url) =>
-    (document.body.appendChild(document.createElement("script")).src = url),
-
-  setIndex: (index) => {
-    Search._index = index;
-    if (Search._queued_query !== null) {
-      const query = Search._queued_query;
-      Search._queued_query = null;
-      Search.query(query);
-    }
-  },
-
-  hasIndex: () => Search._index !== null,
-
-  deferQuery: (query) => (Search._queued_query = query),
-
-  stopPulse: () => (Search._pulse_status = -1),
-
-  startPulse: () => {
-    if (Search._pulse_status >= 0) return;
-
-    const pulse = () => {
-      Search._pulse_status = (Search._pulse_status + 1) % 4;
-      Search.dots.innerText = ".".repeat(Search._pulse_status);
-      if (Search._pulse_status >= 0) window.setTimeout(pulse, 500);
-    };
-    pulse();
-  },
-
-  /**
-   * perform a search for something (or wait until index is loaded)
-   */
-  performSearch: (query) => {
-    // create the required interface elements
-    const searchText = document.createElement("h2");
-    searchText.textContent = _("Searching");
-    const searchSummary = document.createElement("p");
-    searchSummary.classList.add("search-summary");
-    searchSummary.innerText = "";
-    const searchList = document.createElement("ul");
-    searchList.classList.add("search");
-
-    const out = document.getElementById("search-results");
-    Search.title = out.appendChild(searchText);
-    Search.dots = Search.title.appendChild(document.createElement("span"));
-    Search.status = out.appendChild(searchSummary);
-    Search.output = out.appendChild(searchList);
-
-    const searchProgress = document.getElementById("search-progress");
-    // Some themes don't use the search progress node
-    if (searchProgress) {
-      searchProgress.innerText = _("Preparing search...");
-    }
-    Search.startPulse();
-
-    // index already loaded, the browser was quick!
-    if (Search.hasIndex()) Search.query(query);
-    else Search.deferQuery(query);
-  },
-
-  /**
-   * execute search (requires search index to be loaded)
-   */
-  query: (query) => {
-    // stem the search terms and add them to the correct list
-    const stemmer = new Stemmer();
-    const searchTerms = new Set();
-    const excludedTerms = new Set();
-    const highlightTerms = new Set();
-    const objectTerms = new Set(splitQuery(query.toLowerCase().trim()));
-    splitQuery(query.trim()).forEach((queryTerm) => {
-      const queryTermLower = queryTerm.toLowerCase();
-
-      // maybe skip this "word"
-      // stopwords array is from language_data.js
-      if (
-        stopwords.indexOf(queryTermLower) !== -1 ||
-        queryTerm.match(/^\d+$/)
-      )
-        return;
-
-      // stem the word
-      let word = stemmer.stemWord(queryTermLower);
-      // select the correct list
-      if (word[0] === "-") excludedTerms.add(word.substr(1));
-      else {
-        searchTerms.add(word);
-        highlightTerms.add(queryTermLower);
-      }
-    });
-
-    // console.debug("SEARCH: searching for:");
-    // console.info("required: ", [...searchTerms]);
-    // console.info("excluded: ", [...excludedTerms]);
-
-    // array of [docname, title, anchor, descr, score, filename]
-    let results = [];
-    _removeChildren(document.getElementById("search-progress"));
-
-    // lookup as object
-    objectTerms.forEach((term) =>
-      results.push(...Search.performObjectSearch(term, objectTerms))
-    );
-
-    // lookup as search terms in fulltext
-    results.push(...Search.performTermsSearch(searchTerms, excludedTerms));
-
-    // let the scorer override scores with a custom scoring function
-    if (Scorer.score) results.forEach((item) => (item[4] = Scorer.score(item)));
-
-    // now sort the results by score (in opposite order of appearance, since the
-    // display function below uses pop() to retrieve items) and then
-    // alphabetically
-    results.sort((a, b) => {
-      const leftScore = a[4];
-      const rightScore = b[4];
-      if (leftScore === rightScore) {
-        // same score: sort alphabetically
-        const leftTitle = a[1].toLowerCase();
-        const rightTitle = b[1].toLowerCase();
-        if (leftTitle === rightTitle) return 0;
-        return leftTitle > rightTitle ? -1 : 1; // inverted is intentional
-      }
-      return leftScore > rightScore ? 1 : -1;
-    });
-
-    // remove duplicate search results
-    // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept
-    let seen = new Set();
-    results = results.reverse().reduce((acc, result) => {
-      let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(',');
-      if (!seen.has(resultStr)) {
-        acc.push(result);
-        seen.add(resultStr);
-      }
-      return acc;
-    }, []);
-
-    results = results.reverse();
-
-    // for debugging
-    //Search.lastresults = results.slice();  // a copy
-    // console.info("search results:", Search.lastresults);
-
-    // print the results
-    _displayNextItem(results, results.length, highlightTerms, searchTerms);
-  },
-
-  /**
-   * search for object names
-   */
-  performObjectSearch: (object, objectTerms) => {
-    const filenames = Search._index.filenames;
-    const docNames = Search._index.docnames;
-    const objects = Search._index.objects;
-    const objNames = Search._index.objnames;
-    const titles = Search._index.titles;
-
-    const results = [];
-
-    const objectSearchCallback = (prefix, match) => {
-      const name = match[4]
-      const fullname = (prefix ? prefix + "." : "") + name;
-      const fullnameLower = fullname.toLowerCase();
-      if (fullnameLower.indexOf(object) < 0) return;
-
-      let score = 0;
-      const parts = fullnameLower.split(".");
-
-      // check for different match types: exact matches of full name or
-      // "last name" (i.e. last dotted part)
-      if (fullnameLower === object || parts.slice(-1)[0] === object)
-        score += Scorer.objNameMatch;
-      else if (parts.slice(-1)[0].indexOf(object) > -1)
-        score += Scorer.objPartialMatch; // matches in last name
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-      const objName = objNames[match[1]][2];
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-      // add custom score for some objects according to scorer
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-    // perform the search on the required terms
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-            arr.push({ files: titleTerms[word], score: Scorer.partialTitle });
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-      // no match but word was a required one
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-      )
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-      // ensure that none of the excluded terms is in the search result
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-        [...excludedTerms].some(
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-            terms[term] === file ||
-            titleTerms[term] === file ||
-            (terms[term] || []).includes(file) ||
-            (titleTerms[term] || []).includes(file)
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-
-      // select one (max) score for the file.
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-      // add result to the result list
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-        docNames[file],
-        titles[file],
-        "",
-        null,
-        score,
-        filenames[file],
-      ]);
-    }
-    return results;
-  },
-
-  /**
-   * helper function to return a node containing the
-   * search summary for a given text. keywords is a list
-   * of stemmed words, highlightWords is the list of normal, unstemmed
-   * words. the first one is used to find the occurrence, the
-   * latter for highlighting it.
-   */
-  makeSearchSummary: (htmlText, keywords, highlightWords) => {
-    const text = Search.htmlToText(htmlText).toLowerCase();
-    if (text === "") return null;
-
-    const actualStartPosition = [...keywords]
-      .map((k) => text.indexOf(k.toLowerCase()))
-      .filter((i) => i > -1)
-      .slice(-1)[0];
-    const startWithContext = Math.max(actualStartPosition - 120, 0);
-
-    const top = startWithContext === 0 ? "" : "...";
-    const tail = startWithContext + 240 < text.length ? "..." : "";
-
-    let summary = document.createElement("div");
-    summary.classList.add("context");
-    summary.innerText = top + text.substr(startWithContext, 240).trim() + tail;
-
-    highlightWords.forEach((highlightWord) =>
-      _highlightText(summary, highlightWord, "highlighted")
-    );
-
-    return summary;
-  },
-};
-
-_ready(Search.init);
diff --git a/docs/static/star.jpg b/docs/static/star.jpg
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Binary files a/docs/static/star.jpg and /dev/null differ
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deleted file mode 100644
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\ No newline at end of file
diff --git a/docs/static/terminal-howto-select-text.png b/docs/static/terminal-howto-select-text.png
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diff --git a/docs/static/terminal-menu.png b/docs/static/terminal-menu.png
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diff --git a/docs/static/terminal.png b/docs/static/terminal.png
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Binary files a/docs/static/terminal.png and /dev/null differ
diff --git a/docs/static/underscore-1.13.1.js b/docs/static/underscore-1.13.1.js
deleted file mode 100644
index ffd77af..0000000
--- a/docs/static/underscore-1.13.1.js
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-  // Modern feature detection.
-  var supportsArrayBuffer = typeof ArrayBuffer !== 'undefined',
-      supportsDataView = typeof DataView !== 'undefined';
-
-  // All **ECMAScript 5+** native function implementations that we hope to use
-  // are declared here.
-  var nativeIsArray = Array.isArray,
-      nativeKeys = Object.keys,
-      nativeCreate = Object.create,
-      nativeIsView = supportsArrayBuffer && ArrayBuffer.isView;
-
-  // Create references to these builtin functions because we override them.
-  var _isNaN = isNaN,
-      _isFinite = isFinite;
-
-  // Keys in IE < 9 that won't be iterated by `for key in ...` and thus missed.
-  var hasEnumBug = !{toString: null}.propertyIsEnumerable('toString');
-  var nonEnumerableProps = ['valueOf', 'isPrototypeOf', 'toString',
-    'propertyIsEnumerable', 'hasOwnProperty', 'toLocaleString'];
-
-  // The largest integer that can be represented exactly.
-  var MAX_ARRAY_INDEX = Math.pow(2, 53) - 1;
-
-  // Some functions take a variable number of arguments, or a few expected
-  // arguments at the beginning and then a variable number of values to operate
-  // on. This helper accumulates all remaining arguments past the function’s
-  // argument length (or an explicit `startIndex`), into an array that becomes
-  // the last argument. Similar to ES6’s "rest parameter".
-  function restArguments(func, startIndex) {
-    startIndex = startIndex == null ? func.length - 1 : +startIndex;
-    return function() {
-      var length = Math.max(arguments.length - startIndex, 0),
-          rest = Array(length),
-          index = 0;
-      for (; index < length; index++) {
-        rest[index] = arguments[index + startIndex];
-      }
-      switch (startIndex) {
-        case 0: return func.call(this, rest);
-        case 1: return func.call(this, arguments[0], rest);
-        case 2: return func.call(this, arguments[0], arguments[1], rest);
-      }
-      var args = Array(startIndex + 1);
-      for (index = 0; index < startIndex; index++) {
-        args[index] = arguments[index];
-      }
-      args[startIndex] = rest;
-      return func.apply(this, args);
-    };
-  }
-
-  // Is a given variable an object?
-  function isObject(obj) {
-    var type = typeof obj;
-    return type === 'function' || type === 'object' && !!obj;
-  }
-
-  // Is a given value equal to null?
-  function isNull(obj) {
-    return obj === null;
-  }
-
-  // Is a given variable undefined?
-  function isUndefined(obj) {
-    return obj === void 0;
-  }
-
-  // Is a given value a boolean?
-  function isBoolean(obj) {
-    return obj === true || obj === false || toString.call(obj) === '[object Boolean]';
-  }
-
-  // Is a given value a DOM element?
-  function isElement(obj) {
-    return !!(obj && obj.nodeType === 1);
-  }
-
-  // Internal function for creating a `toString`-based type tester.
-  function tagTester(name) {
-    var tag = '[object ' + name + ']';
-    return function(obj) {
-      return toString.call(obj) === tag;
-    };
-  }
-
-  var isString = tagTester('String');
-
-  var isNumber = tagTester('Number');
-
-  var isDate = tagTester('Date');
-
-  var isRegExp = tagTester('RegExp');
-
-  var isError = tagTester('Error');
-
-  var isSymbol = tagTester('Symbol');
-
-  var isArrayBuffer = tagTester('ArrayBuffer');
-
-  var isFunction = tagTester('Function');
-
-  // Optimize `isFunction` if appropriate. Work around some `typeof` bugs in old
-  // v8, IE 11 (#1621), Safari 8 (#1929), and PhantomJS (#2236).
-  var nodelist = root.document && root.document.childNodes;
-  if (typeof /./ != 'function' && typeof Int8Array != 'object' && typeof nodelist != 'function') {
-    isFunction = function(obj) {
-      return typeof obj == 'function' || false;
-    };
-  }
-
-  var isFunction$1 = isFunction;
-
-  var hasObjectTag = tagTester('Object');
-
-  // In IE 10 - Edge 13, `DataView` has string tag `'[object Object]'`.
-  // In IE 11, the most common among them, this problem also applies to
-  // `Map`, `WeakMap` and `Set`.
-  var hasStringTagBug = (
-        supportsDataView && hasObjectTag(new DataView(new ArrayBuffer(8)))
-      ),
-      isIE11 = (typeof Map !== 'undefined' && hasObjectTag(new Map));
-
-  var isDataView = tagTester('DataView');
-
-  // In IE 10 - Edge 13, we need a different heuristic
-  // to determine whether an object is a `DataView`.
-  function ie10IsDataView(obj) {
-    return obj != null && isFunction$1(obj.getInt8) && isArrayBuffer(obj.buffer);
-  }
-
-  var isDataView$1 = (hasStringTagBug ? ie10IsDataView : isDataView);
-
-  // Is a given value an array?
-  // Delegates to ECMA5's native `Array.isArray`.
-  var isArray = nativeIsArray || tagTester('Array');
-
-  // Internal function to check whether `key` is an own property name of `obj`.
-  function has$1(obj, key) {
-    return obj != null && hasOwnProperty.call(obj, key);
-  }
-
-  var isArguments = tagTester('Arguments');
-
-  // Define a fallback version of the method in browsers (ahem, IE < 9), where
-  // there isn't any inspectable "Arguments" type.
-  (function() {
-    if (!isArguments(arguments)) {
-      isArguments = function(obj) {
-        return has$1(obj, 'callee');
-      };
-    }
-  }());
-
-  var isArguments$1 = isArguments;
-
-  // Is a given object a finite number?
-  function isFinite$1(obj) {
-    return !isSymbol(obj) && _isFinite(obj) && !isNaN(parseFloat(obj));
-  }
-
-  // Is the given value `NaN`?
-  function isNaN$1(obj) {
-    return isNumber(obj) && _isNaN(obj);
-  }
-
-  // Predicate-generating function. Often useful outside of Underscore.
-  function constant(value) {
-    return function() {
-      return value;
-    };
-  }
-
-  // Common internal logic for `isArrayLike` and `isBufferLike`.
-  function createSizePropertyCheck(getSizeProperty) {
-    return function(collection) {
-      var sizeProperty = getSizeProperty(collection);
-      return typeof sizeProperty == 'number' && sizeProperty >= 0 && sizeProperty <= MAX_ARRAY_INDEX;
-    }
-  }
-
-  // Internal helper to generate a function to obtain property `key` from `obj`.
-  function shallowProperty(key) {
-    return function(obj) {
-      return obj == null ? void 0 : obj[key];
-    };
-  }
-
-  // Internal helper to obtain the `byteLength` property of an object.
-  var getByteLength = shallowProperty('byteLength');
-
-  // Internal helper to determine whether we should spend extensive checks against
-  // `ArrayBuffer` et al.
-  var isBufferLike = createSizePropertyCheck(getByteLength);
-
-  // Is a given value a typed array?
-  var typedArrayPattern = /\[object ((I|Ui)nt(8|16|32)|Float(32|64)|Uint8Clamped|Big(I|Ui)nt64)Array\]/;
-  function isTypedArray(obj) {
-    // `ArrayBuffer.isView` is the most future-proof, so use it when available.
-    // Otherwise, fall back on the above regular expression.
-    return nativeIsView ? (nativeIsView(obj) && !isDataView$1(obj)) :
-                  isBufferLike(obj) && typedArrayPattern.test(toString.call(obj));
-  }
-
-  var isTypedArray$1 = supportsArrayBuffer ? isTypedArray : constant(false);
-
-  // Internal helper to obtain the `length` property of an object.
-  var getLength = shallowProperty('length');
-
-  // Internal helper to create a simple lookup structure.
-  // `collectNonEnumProps` used to depend on `_.contains`, but this led to
-  // circular imports. `emulatedSet` is a one-off solution that only works for
-  // arrays of strings.
-  function emulatedSet(keys) {
-    var hash = {};
-    for (var l = keys.length, i = 0; i < l; ++i) hash[keys[i]] = true;
-    return {
-      contains: function(key) { return hash[key]; },
-      push: function(key) {
-        hash[key] = true;
-        return keys.push(key);
-      }
-    };
-  }
-
-  // Internal helper. Checks `keys` for the presence of keys in IE < 9 that won't
-  // be iterated by `for key in ...` and thus missed. Extends `keys` in place if
-  // needed.
-  function collectNonEnumProps(obj, keys) {
-    keys = emulatedSet(keys);
-    var nonEnumIdx = nonEnumerableProps.length;
-    var constructor = obj.constructor;
-    var proto = isFunction$1(constructor) && constructor.prototype || ObjProto;
-
-    // Constructor is a special case.
-    var prop = 'constructor';
-    if (has$1(obj, prop) && !keys.contains(prop)) keys.push(prop);
-
-    while (nonEnumIdx--) {
-      prop = nonEnumerableProps[nonEnumIdx];
-      if (prop in obj && obj[prop] !== proto[prop] && !keys.contains(prop)) {
-        keys.push(prop);
-      }
-    }
-  }
-
-  // Retrieve the names of an object's own properties.
-  // Delegates to **ECMAScript 5**'s native `Object.keys`.
-  function keys(obj) {
-    if (!isObject(obj)) return [];
-    if (nativeKeys) return nativeKeys(obj);
-    var keys = [];
-    for (var key in obj) if (has$1(obj, key)) keys.push(key);
-    // Ahem, IE < 9.
-    if (hasEnumBug) collectNonEnumProps(obj, keys);
-    return keys;
-  }
-
-  // Is a given array, string, or object empty?
-  // An "empty" object has no enumerable own-properties.
-  function isEmpty(obj) {
-    if (obj == null) return true;
-    // Skip the more expensive `toString`-based type checks if `obj` has no
-    // `.length`.
-    var length = getLength(obj);
-    if (typeof length == 'number' && (
-      isArray(obj) || isString(obj) || isArguments$1(obj)
-    )) return length === 0;
-    return getLength(keys(obj)) === 0;
-  }
-
-  // Returns whether an object has a given set of `key:value` pairs.
-  function isMatch(object, attrs) {
-    var _keys = keys(attrs), length = _keys.length;
-    if (object == null) return !length;
-    var obj = Object(object);
-    for (var i = 0; i < length; i++) {
-      var key = _keys[i];
-      if (attrs[key] !== obj[key] || !(key in obj)) return false;
-    }
-    return true;
-  }
-
-  // If Underscore is called as a function, it returns a wrapped object that can
-  // be used OO-style. This wrapper holds altered versions of all functions added
-  // through `_.mixin`. Wrapped objects may be chained.
-  function _$1(obj) {
-    if (obj instanceof _$1) return obj;
-    if (!(this instanceof _$1)) return new _$1(obj);
-    this._wrapped = obj;
-  }
-
-  _$1.VERSION = VERSION;
-
-  // Extracts the result from a wrapped and chained object.
-  _$1.prototype.value = function() {
-    return this._wrapped;
-  };
-
-  // Provide unwrapping proxies for some methods used in engine operations
-  // such as arithmetic and JSON stringification.
-  _$1.prototype.valueOf = _$1.prototype.toJSON = _$1.prototype.value;
-
-  _$1.prototype.toString = function() {
-    return String(this._wrapped);
-  };
-
-  // Internal function to wrap or shallow-copy an ArrayBuffer,
-  // typed array or DataView to a new view, reusing the buffer.
-  function toBufferView(bufferSource) {
-    return new Uint8Array(
-      bufferSource.buffer || bufferSource,
-      bufferSource.byteOffset || 0,
-      getByteLength(bufferSource)
-    );
-  }
-
-  // We use this string twice, so give it a name for minification.
-  var tagDataView = '[object DataView]';
-
-  // Internal recursive comparison function for `_.isEqual`.
-  function eq(a, b, aStack, bStack) {
-    // Identical objects are equal. `0 === -0`, but they aren't identical.
-    // See the [Harmony `egal` proposal](https://wiki.ecmascript.org/doku.php?id=harmony:egal).
-    if (a === b) return a !== 0 || 1 / a === 1 / b;
-    // `null` or `undefined` only equal to itself (strict comparison).
-    if (a == null || b == null) return false;
-    // `NaN`s are equivalent, but non-reflexive.
-    if (a !== a) return b !== b;
-    // Exhaust primitive checks
-    var type = typeof a;
-    if (type !== 'function' && type !== 'object' && typeof b != 'object') return false;
-    return deepEq(a, b, aStack, bStack);
-  }
-
-  // Internal recursive comparison function for `_.isEqual`.
-  function deepEq(a, b, aStack, bStack) {
-    // Unwrap any wrapped objects.
-    if (a instanceof _$1) a = a._wrapped;
-    if (b instanceof _$1) b = b._wrapped;
-    // Compare `[[Class]]` names.
-    var className = toString.call(a);
-    if (className !== toString.call(b)) return false;
-    // Work around a bug in IE 10 - Edge 13.
-    if (hasStringTagBug && className == '[object Object]' && isDataView$1(a)) {
-      if (!isDataView$1(b)) return false;
-      className = tagDataView;
-    }
-    switch (className) {
-      // These types are compared by value.
-      case '[object RegExp]':
-        // RegExps are coerced to strings for comparison (Note: '' + /a/i === '/a/i')
-      case '[object String]':
-        // Primitives and their corresponding object wrappers are equivalent; thus, `"5"` is
-        // equivalent to `new String("5")`.
-        return '' + a === '' + b;
-      case '[object Number]':
-        // `NaN`s are equivalent, but non-reflexive.
-        // Object(NaN) is equivalent to NaN.
-        if (+a !== +a) return +b !== +b;
-        // An `egal` comparison is performed for other numeric values.
-        return +a === 0 ? 1 / +a === 1 / b : +a === +b;
-      case '[object Date]':
-      case '[object Boolean]':
-        // Coerce dates and booleans to numeric primitive values. Dates are compared by their
-        // millisecond representations. Note that invalid dates with millisecond representations
-        // of `NaN` are not equivalent.
-        return +a === +b;
-      case '[object Symbol]':
-        return SymbolProto.valueOf.call(a) === SymbolProto.valueOf.call(b);
-      case '[object ArrayBuffer]':
-      case tagDataView:
-        // Coerce to typed array so we can fall through.
-        return deepEq(toBufferView(a), toBufferView(b), aStack, bStack);
-    }
-
-    var areArrays = className === '[object Array]';
-    if (!areArrays && isTypedArray$1(a)) {
-        var byteLength = getByteLength(a);
-        if (byteLength !== getByteLength(b)) return false;
-        if (a.buffer === b.buffer && a.byteOffset === b.byteOffset) return true;
-        areArrays = true;
-    }
-    if (!areArrays) {
-      if (typeof a != 'object' || typeof b != 'object') return false;
-
-      // Objects with different constructors are not equivalent, but `Object`s or `Array`s
-      // from different frames are.
-      var aCtor = a.constructor, bCtor = b.constructor;
-      if (aCtor !== bCtor && !(isFunction$1(aCtor) && aCtor instanceof aCtor &&
-                               isFunction$1(bCtor) && bCtor instanceof bCtor)
-                          && ('constructor' in a && 'constructor' in b)) {
-        return false;
-      }
-    }
-    // Assume equality for cyclic structures. The algorithm for detecting cyclic
-    // structures is adapted from ES 5.1 section 15.12.3, abstract operation `JO`.
-
-    // Initializing stack of traversed objects.
-    // It's done here since we only need them for objects and arrays comparison.
-    aStack = aStack || [];
-    bStack = bStack || [];
-    var length = aStack.length;
-    while (length--) {
-      // Linear search. Performance is inversely proportional to the number of
-      // unique nested structures.
-      if (aStack[length] === a) return bStack[length] === b;
-    }
-
-    // Add the first object to the stack of traversed objects.
-    aStack.push(a);
-    bStack.push(b);
-
-    // Recursively compare objects and arrays.
-    if (areArrays) {
-      // Compare array lengths to determine if a deep comparison is necessary.
-      length = a.length;
-      if (length !== b.length) return false;
-      // Deep compare the contents, ignoring non-numeric properties.
-      while (length--) {
-        if (!eq(a[length], b[length], aStack, bStack)) return false;
-      }
-    } else {
-      // Deep compare objects.
-      var _keys = keys(a), key;
-      length = _keys.length;
-      // Ensure that both objects contain the same number of properties before comparing deep equality.
-      if (keys(b).length !== length) return false;
-      while (length--) {
-        // Deep compare each member
-        key = _keys[length];
-        if (!(has$1(b, key) && eq(a[key], b[key], aStack, bStack))) return false;
-      }
-    }
-    // Remove the first object from the stack of traversed objects.
-    aStack.pop();
-    bStack.pop();
-    return true;
-  }
-
-  // Perform a deep comparison to check if two objects are equal.
-  function isEqual(a, b) {
-    return eq(a, b);
-  }
-
-  // Retrieve all the enumerable property names of an object.
-  function allKeys(obj) {
-    if (!isObject(obj)) return [];
-    var keys = [];
-    for (var key in obj) keys.push(key);
-    // Ahem, IE < 9.
-    if (hasEnumBug) collectNonEnumProps(obj, keys);
-    return keys;
-  }
-
-  // Since the regular `Object.prototype.toString` type tests don't work for
-  // some types in IE 11, we use a fingerprinting heuristic instead, based
-  // on the methods. It's not great, but it's the best we got.
-  // The fingerprint method lists are defined below.
-  function ie11fingerprint(methods) {
-    var length = getLength(methods);
-    return function(obj) {
-      if (obj == null) return false;
-      // `Map`, `WeakMap` and `Set` have no enumerable keys.
-      var keys = allKeys(obj);
-      if (getLength(keys)) return false;
-      for (var i = 0; i < length; i++) {
-        if (!isFunction$1(obj[methods[i]])) return false;
-      }
-      // If we are testing against `WeakMap`, we need to ensure that
-      // `obj` doesn't have a `forEach` method in order to distinguish
-      // it from a regular `Map`.
-      return methods !== weakMapMethods || !isFunction$1(obj[forEachName]);
-    };
-  }
-
-  // In the interest of compact minification, we write
-  // each string in the fingerprints only once.
-  var forEachName = 'forEach',
-      hasName = 'has',
-      commonInit = ['clear', 'delete'],
-      mapTail = ['get', hasName, 'set'];
-
-  // `Map`, `WeakMap` and `Set` each have slightly different
-  // combinations of the above sublists.
-  var mapMethods = commonInit.concat(forEachName, mapTail),
-      weakMapMethods = commonInit.concat(mapTail),
-      setMethods = ['add'].concat(commonInit, forEachName, hasName);
-
-  var isMap = isIE11 ? ie11fingerprint(mapMethods) : tagTester('Map');
-
-  var isWeakMap = isIE11 ? ie11fingerprint(weakMapMethods) : tagTester('WeakMap');
-
-  var isSet = isIE11 ? ie11fingerprint(setMethods) : tagTester('Set');
-
-  var isWeakSet = tagTester('WeakSet');
-
-  // Retrieve the values of an object's properties.
-  function values(obj) {
-    var _keys = keys(obj);
-    var length = _keys.length;
-    var values = Array(length);
-    for (var i = 0; i < length; i++) {
-      values[i] = obj[_keys[i]];
-    }
-    return values;
-  }
-
-  // Convert an object into a list of `[key, value]` pairs.
-  // The opposite of `_.object` with one argument.
-  function pairs(obj) {
-    var _keys = keys(obj);
-    var length = _keys.length;
-    var pairs = Array(length);
-    for (var i = 0; i < length; i++) {
-      pairs[i] = [_keys[i], obj[_keys[i]]];
-    }
-    return pairs;
-  }
-
-  // Invert the keys and values of an object. The values must be serializable.
-  function invert(obj) {
-    var result = {};
-    var _keys = keys(obj);
-    for (var i = 0, length = _keys.length; i < length; i++) {
-      result[obj[_keys[i]]] = _keys[i];
-    }
-    return result;
-  }
-
-  // Return a sorted list of the function names available on the object.
-  function functions(obj) {
-    var names = [];
-    for (var key in obj) {
-      if (isFunction$1(obj[key])) names.push(key);
-    }
-    return names.sort();
-  }
-
-  // An internal function for creating assigner functions.
-  function createAssigner(keysFunc, defaults) {
-    return function(obj) {
-      var length = arguments.length;
-      if (defaults) obj = Object(obj);
-      if (length < 2 || obj == null) return obj;
-      for (var index = 1; index < length; index++) {
-        var source = arguments[index],
-            keys = keysFunc(source),
-            l = keys.length;
-        for (var i = 0; i < l; i++) {
-          var key = keys[i];
-          if (!defaults || obj[key] === void 0) obj[key] = source[key];
-        }
-      }
-      return obj;
-    };
-  }
-
-  // Extend a given object with all the properties in passed-in object(s).
-  var extend = createAssigner(allKeys);
-
-  // Assigns a given object with all the own properties in the passed-in
-  // object(s).
-  // (https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Object/assign)
-  var extendOwn = createAssigner(keys);
-
-  // Fill in a given object with default properties.
-  var defaults = createAssigner(allKeys, true);
-
-  // Create a naked function reference for surrogate-prototype-swapping.
-  function ctor() {
-    return function(){};
-  }
-
-  // An internal function for creating a new object that inherits from another.
-  function baseCreate(prototype) {
-    if (!isObject(prototype)) return {};
-    if (nativeCreate) return nativeCreate(prototype);
-    var Ctor = ctor();
-    Ctor.prototype = prototype;
-    var result = new Ctor;
-    Ctor.prototype = null;
-    return result;
-  }
-
-  // Creates an object that inherits from the given prototype object.
-  // If additional properties are provided then they will be added to the
-  // created object.
-  function create(prototype, props) {
-    var result = baseCreate(prototype);
-    if (props) extendOwn(result, props);
-    return result;
-  }
-
-  // Create a (shallow-cloned) duplicate of an object.
-  function clone(obj) {
-    if (!isObject(obj)) return obj;
-    return isArray(obj) ? obj.slice() : extend({}, obj);
-  }
-
-  // Invokes `interceptor` with the `obj` and then returns `obj`.
-  // The primary purpose of this method is to "tap into" a method chain, in
-  // order to perform operations on intermediate results within the chain.
-  function tap(obj, interceptor) {
-    interceptor(obj);
-    return obj;
-  }
-
-  // Normalize a (deep) property `path` to array.
-  // Like `_.iteratee`, this function can be customized.
-  function toPath$1(path) {
-    return isArray(path) ? path : [path];
-  }
-  _$1.toPath = toPath$1;
-
-  // Internal wrapper for `_.toPath` to enable minification.
-  // Similar to `cb` for `_.iteratee`.
-  function toPath(path) {
-    return _$1.toPath(path);
-  }
-
-  // Internal function to obtain a nested property in `obj` along `path`.
-  function deepGet(obj, path) {
-    var length = path.length;
-    for (var i = 0; i < length; i++) {
-      if (obj == null) return void 0;
-      obj = obj[path[i]];
-    }
-    return length ? obj : void 0;
-  }
-
-  // Get the value of the (deep) property on `path` from `object`.
-  // If any property in `path` does not exist or if the value is
-  // `undefined`, return `defaultValue` instead.
-  // The `path` is normalized through `_.toPath`.
-  function get(object, path, defaultValue) {
-    var value = deepGet(object, toPath(path));
-    return isUndefined(value) ? defaultValue : value;
-  }
-
-  // Shortcut function for checking if an object has a given property directly on
-  // itself (in other words, not on a prototype). Unlike the internal `has`
-  // function, this public version can also traverse nested properties.
-  function has(obj, path) {
-    path = toPath(path);
-    var length = path.length;
-    for (var i = 0; i < length; i++) {
-      var key = path[i];
-      if (!has$1(obj, key)) return false;
-      obj = obj[key];
-    }
-    return !!length;
-  }
-
-  // Keep the identity function around for default iteratees.
-  function identity(value) {
-    return value;
-  }
-
-  // Returns a predicate for checking whether an object has a given set of
-  // `key:value` pairs.
-  function matcher(attrs) {
-    attrs = extendOwn({}, attrs);
-    return function(obj) {
-      return isMatch(obj, attrs);
-    };
-  }
-
-  // Creates a function that, when passed an object, will traverse that object’s
-  // properties down the given `path`, specified as an array of keys or indices.
-  function property(path) {
-    path = toPath(path);
-    return function(obj) {
-      return deepGet(obj, path);
-    };
-  }
-
-  // Internal function that returns an efficient (for current engines) version
-  // of the passed-in callback, to be repeatedly applied in other Underscore
-  // functions.
-  function optimizeCb(func, context, argCount) {
-    if (context === void 0) return func;
-    switch (argCount == null ? 3 : argCount) {
-      case 1: return function(value) {
-        return func.call(context, value);
-      };
-      // The 2-argument case is omitted because we’re not using it.
-      case 3: return function(value, index, collection) {
-        return func.call(context, value, index, collection);
-      };
-      case 4: return function(accumulator, value, index, collection) {
-        return func.call(context, accumulator, value, index, collection);
-      };
-    }
-    return function() {
-      return func.apply(context, arguments);
-    };
-  }
-
-  // An internal function to generate callbacks that can be applied to each
-  // element in a collection, returning the desired result — either `_.identity`,
-  // an arbitrary callback, a property matcher, or a property accessor.
-  function baseIteratee(value, context, argCount) {
-    if (value == null) return identity;
-    if (isFunction$1(value)) return optimizeCb(value, context, argCount);
-    if (isObject(value) && !isArray(value)) return matcher(value);
-    return property(value);
-  }
-
-  // External wrapper for our callback generator. Users may customize
-  // `_.iteratee` if they want additional predicate/iteratee shorthand styles.
-  // This abstraction hides the internal-only `argCount` argument.
-  function iteratee(value, context) {
-    return baseIteratee(value, context, Infinity);
-  }
-  _$1.iteratee = iteratee;
-
-  // The function we call internally to generate a callback. It invokes
-  // `_.iteratee` if overridden, otherwise `baseIteratee`.
-  function cb(value, context, argCount) {
-    if (_$1.iteratee !== iteratee) return _$1.iteratee(value, context);
-    return baseIteratee(value, context, argCount);
-  }
-
-  // Returns the results of applying the `iteratee` to each element of `obj`.
-  // In contrast to `_.map` it returns an object.
-  function mapObject(obj, iteratee, context) {
-    iteratee = cb(iteratee, context);
-    var _keys = keys(obj),
-        length = _keys.length,
-        results = {};
-    for (var index = 0; index < length; index++) {
-      var currentKey = _keys[index];
-      results[currentKey] = iteratee(obj[currentKey], currentKey, obj);
-    }
-    return results;
-  }
-
-  // Predicate-generating function. Often useful outside of Underscore.
-  function noop(){}
-
-  // Generates a function for a given object that returns a given property.
-  function propertyOf(obj) {
-    if (obj == null) return noop;
-    return function(path) {
-      return get(obj, path);
-    };
-  }
-
-  // Run a function **n** times.
-  function times(n, iteratee, context) {
-    var accum = Array(Math.max(0, n));
-    iteratee = optimizeCb(iteratee, context, 1);
-    for (var i = 0; i < n; i++) accum[i] = iteratee(i);
-    return accum;
-  }
-
-  // Return a random integer between `min` and `max` (inclusive).
-  function random(min, max) {
-    if (max == null) {
-      max = min;
-      min = 0;
-    }
-    return min + Math.floor(Math.random() * (max - min + 1));
-  }
-
-  // A (possibly faster) way to get the current timestamp as an integer.
-  var now = Date.now || function() {
-    return new Date().getTime();
-  };
-
-  // Internal helper to generate functions for escaping and unescaping strings
-  // to/from HTML interpolation.
-  function createEscaper(map) {
-    var escaper = function(match) {
-      return map[match];
-    };
-    // Regexes for identifying a key that needs to be escaped.
-    var source = '(?:' + keys(map).join('|') + ')';
-    var testRegexp = RegExp(source);
-    var replaceRegexp = RegExp(source, 'g');
-    return function(string) {
-      string = string == null ? '' : '' + string;
-      return testRegexp.test(string) ? string.replace(replaceRegexp, escaper) : string;
-    };
-  }
-
-  // Internal list of HTML entities for escaping.
-  var escapeMap = {
-    '&': '&amp;',
-    '<': '&lt;',
-    '>': '&gt;',
-    '"': '&quot;',
-    "'": '&#x27;',
-    '`': '&#x60;'
-  };
-
-  // Function for escaping strings to HTML interpolation.
-  var _escape = createEscaper(escapeMap);
-
-  // Internal list of HTML entities for unescaping.
-  var unescapeMap = invert(escapeMap);
-
-  // Function for unescaping strings from HTML interpolation.
-  var _unescape = createEscaper(unescapeMap);
-
-  // By default, Underscore uses ERB-style template delimiters. Change the
-  // following template settings to use alternative delimiters.
-  var templateSettings = _$1.templateSettings = {
-    evaluate: /<%([\s\S]+?)%>/g,
-    interpolate: /<%=([\s\S]+?)%>/g,
-    escape: /<%-([\s\S]+?)%>/g
-  };
-
-  // When customizing `_.templateSettings`, if you don't want to define an
-  // interpolation, evaluation or escaping regex, we need one that is
-  // guaranteed not to match.
-  var noMatch = /(.)^/;
-
-  // Certain characters need to be escaped so that they can be put into a
-  // string literal.
-  var escapes = {
-    "'": "'",
-    '\\': '\\',
-    '\r': 'r',
-    '\n': 'n',
-    '\u2028': 'u2028',
-    '\u2029': 'u2029'
-  };
-
-  var escapeRegExp = /\\|'|\r|\n|\u2028|\u2029/g;
-
-  function escapeChar(match) {
-    return '\\' + escapes[match];
-  }
-
-  // In order to prevent third-party code injection through
-  // `_.templateSettings.variable`, we test it against the following regular
-  // expression. It is intentionally a bit more liberal than just matching valid
-  // identifiers, but still prevents possible loopholes through defaults or
-  // destructuring assignment.
-  var bareIdentifier = /^\s*(\w|\$)+\s*$/;
-
-  // JavaScript micro-templating, similar to John Resig's implementation.
-  // Underscore templating handles arbitrary delimiters, preserves whitespace,
-  // and correctly escapes quotes within interpolated code.
-  // NB: `oldSettings` only exists for backwards compatibility.
-  function template(text, settings, oldSettings) {
-    if (!settings && oldSettings) settings = oldSettings;
-    settings = defaults({}, settings, _$1.templateSettings);
-
-    // Combine delimiters into one regular expression via alternation.
-    var matcher = RegExp([
-      (settings.escape || noMatch).source,
-      (settings.interpolate || noMatch).source,
-      (settings.evaluate || noMatch).source
-    ].join('|') + '|$', 'g');
-
-    // Compile the template source, escaping string literals appropriately.
-    var index = 0;
-    var source = "__p+='";
-    text.replace(matcher, function(match, escape, interpolate, evaluate, offset) {
-      source += text.slice(index, offset).replace(escapeRegExp, escapeChar);
-      index = offset + match.length;
-
-      if (escape) {
-        source += "'+\n((__t=(" + escape + "))==null?'':_.escape(__t))+\n'";
-      } else if (interpolate) {
-        source += "'+\n((__t=(" + interpolate + "))==null?'':__t)+\n'";
-      } else if (evaluate) {
-        source += "';\n" + evaluate + "\n__p+='";
-      }
-
-      // Adobe VMs need the match returned to produce the correct offset.
-      return match;
-    });
-    source += "';\n";
-
-    var argument = settings.variable;
-    if (argument) {
-      // Insure against third-party code injection. (CVE-2021-23358)
-      if (!bareIdentifier.test(argument)) throw new Error(
-        'variable is not a bare identifier: ' + argument
-      );
-    } else {
-      // If a variable is not specified, place data values in local scope.
-      source = 'with(obj||{}){\n' + source + '}\n';
-      argument = 'obj';
-    }
-
-    source = "var __t,__p='',__j=Array.prototype.join," +
-      "print=function(){__p+=__j.call(arguments,'');};\n" +
-      source + 'return __p;\n';
-
-    var render;
-    try {
-      render = new Function(argument, '_', source);
-    } catch (e) {
-      e.source = source;
-      throw e;
-    }
-
-    var template = function(data) {
-      return render.call(this, data, _$1);
-    };
-
-    // Provide the compiled source as a convenience for precompilation.
-    template.source = 'function(' + argument + '){\n' + source + '}';
-
-    return template;
-  }
-
-  // Traverses the children of `obj` along `path`. If a child is a function, it
-  // is invoked with its parent as context. Returns the value of the final
-  // child, or `fallback` if any child is undefined.
-  function result(obj, path, fallback) {
-    path = toPath(path);
-    var length = path.length;
-    if (!length) {
-      return isFunction$1(fallback) ? fallback.call(obj) : fallback;
-    }
-    for (var i = 0; i < length; i++) {
-      var prop = obj == null ? void 0 : obj[path[i]];
-      if (prop === void 0) {
-        prop = fallback;
-        i = length; // Ensure we don't continue iterating.
-      }
-      obj = isFunction$1(prop) ? prop.call(obj) : prop;
-    }
-    return obj;
-  }
-
-  // Generate a unique integer id (unique within the entire client session).
-  // Useful for temporary DOM ids.
-  var idCounter = 0;
-  function uniqueId(prefix) {
-    var id = ++idCounter + '';
-    return prefix ? prefix + id : id;
-  }
-
-  // Start chaining a wrapped Underscore object.
-  function chain(obj) {
-    var instance = _$1(obj);
-    instance._chain = true;
-    return instance;
-  }
-
-  // Internal function to execute `sourceFunc` bound to `context` with optional
-  // `args`. Determines whether to execute a function as a constructor or as a
-  // normal function.
-  function executeBound(sourceFunc, boundFunc, context, callingContext, args) {
-    if (!(callingContext instanceof boundFunc)) return sourceFunc.apply(context, args);
-    var self = baseCreate(sourceFunc.prototype);
-    var result = sourceFunc.apply(self, args);
-    if (isObject(result)) return result;
-    return self;
-  }
-
-  // Partially apply a function by creating a version that has had some of its
-  // arguments pre-filled, without changing its dynamic `this` context. `_` acts
-  // as a placeholder by default, allowing any combination of arguments to be
-  // pre-filled. Set `_.partial.placeholder` for a custom placeholder argument.
-  var partial = restArguments(function(func, boundArgs) {
-    var placeholder = partial.placeholder;
-    var bound = function() {
-      var position = 0, length = boundArgs.length;
-      var args = Array(length);
-      for (var i = 0; i < length; i++) {
-        args[i] = boundArgs[i] === placeholder ? arguments[position++] : boundArgs[i];
-      }
-      while (position < arguments.length) args.push(arguments[position++]);
-      return executeBound(func, bound, this, this, args);
-    };
-    return bound;
-  });
-
-  partial.placeholder = _$1;
-
-  // Create a function bound to a given object (assigning `this`, and arguments,
-  // optionally).
-  var bind = restArguments(function(func, context, args) {
-    if (!isFunction$1(func)) throw new TypeError('Bind must be called on a function');
-    var bound = restArguments(function(callArgs) {
-      return executeBound(func, bound, context, this, args.concat(callArgs));
-    });
-    return bound;
-  });
-
-  // Internal helper for collection methods to determine whether a collection
-  // should be iterated as an array or as an object.
-  // Related: https://people.mozilla.org/~jorendorff/es6-draft.html#sec-tolength
-  // Avoids a very nasty iOS 8 JIT bug on ARM-64. #2094
-  var isArrayLike = createSizePropertyCheck(getLength);
-
-  // Internal implementation of a recursive `flatten` function.
-  function flatten$1(input, depth, strict, output) {
-    output = output || [];
-    if (!depth && depth !== 0) {
-      depth = Infinity;
-    } else if (depth <= 0) {
-      return output.concat(input);
-    }
-    var idx = output.length;
-    for (var i = 0, length = getLength(input); i < length; i++) {
-      var value = input[i];
-      if (isArrayLike(value) && (isArray(value) || isArguments$1(value))) {
-        // Flatten current level of array or arguments object.
-        if (depth > 1) {
-          flatten$1(value, depth - 1, strict, output);
-          idx = output.length;
-        } else {
-          var j = 0, len = value.length;
-          while (j < len) output[idx++] = value[j++];
-        }
-      } else if (!strict) {
-        output[idx++] = value;
-      }
-    }
-    return output;
-  }
-
-  // Bind a number of an object's methods to that object. Remaining arguments
-  // are the method names to be bound. Useful for ensuring that all callbacks
-  // defined on an object belong to it.
-  var bindAll = restArguments(function(obj, keys) {
-    keys = flatten$1(keys, false, false);
-    var index = keys.length;
-    if (index < 1) throw new Error('bindAll must be passed function names');
-    while (index--) {
-      var key = keys[index];
-      obj[key] = bind(obj[key], obj);
-    }
-    return obj;
-  });
-
-  // Memoize an expensive function by storing its results.
-  function memoize(func, hasher) {
-    var memoize = function(key) {
-      var cache = memoize.cache;
-      var address = '' + (hasher ? hasher.apply(this, arguments) : key);
-      if (!has$1(cache, address)) cache[address] = func.apply(this, arguments);
-      return cache[address];
-    };
-    memoize.cache = {};
-    return memoize;
-  }
-
-  // Delays a function for the given number of milliseconds, and then calls
-  // it with the arguments supplied.
-  var delay = restArguments(function(func, wait, args) {
-    return setTimeout(function() {
-      return func.apply(null, args);
-    }, wait);
-  });
-
-  // Defers a function, scheduling it to run after the current call stack has
-  // cleared.
-  var defer = partial(delay, _$1, 1);
-
-  // Returns a function, that, when invoked, will only be triggered at most once
-  // during a given window of time. Normally, the throttled function will run
-  // as much as it can, without ever going more than once per `wait` duration;
-  // but if you'd like to disable the execution on the leading edge, pass
-  // `{leading: false}`. To disable execution on the trailing edge, ditto.
-  function throttle(func, wait, options) {
-    var timeout, context, args, result;
-    var previous = 0;
-    if (!options) options = {};
-
-    var later = function() {
-      previous = options.leading === false ? 0 : now();
-      timeout = null;
-      result = func.apply(context, args);
-      if (!timeout) context = args = null;
-    };
-
-    var throttled = function() {
-      var _now = now();
-      if (!previous && options.leading === false) previous = _now;
-      var remaining = wait - (_now - previous);
-      context = this;
-      args = arguments;
-      if (remaining <= 0 || remaining > wait) {
-        if (timeout) {
-          clearTimeout(timeout);
-          timeout = null;
-        }
-        previous = _now;
-        result = func.apply(context, args);
-        if (!timeout) context = args = null;
-      } else if (!timeout && options.trailing !== false) {
-        timeout = setTimeout(later, remaining);
-      }
-      return result;
-    };
-
-    throttled.cancel = function() {
-      clearTimeout(timeout);
-      previous = 0;
-      timeout = context = args = null;
-    };
-
-    return throttled;
-  }
-
-  // When a sequence of calls of the returned function ends, the argument
-  // function is triggered. The end of a sequence is defined by the `wait`
-  // parameter. If `immediate` is passed, the argument function will be
-  // triggered at the beginning of the sequence instead of at the end.
-  function debounce(func, wait, immediate) {
-    var timeout, previous, args, result, context;
-
-    var later = function() {
-      var passed = now() - previous;
-      if (wait > passed) {
-        timeout = setTimeout(later, wait - passed);
-      } else {
-        timeout = null;
-        if (!immediate) result = func.apply(context, args);
-        // This check is needed because `func` can recursively invoke `debounced`.
-        if (!timeout) args = context = null;
-      }
-    };
-
-    var debounced = restArguments(function(_args) {
-      context = this;
-      args = _args;
-      previous = now();
-      if (!timeout) {
-        timeout = setTimeout(later, wait);
-        if (immediate) result = func.apply(context, args);
-      }
-      return result;
-    });
-
-    debounced.cancel = function() {
-      clearTimeout(timeout);
-      timeout = args = context = null;
-    };
-
-    return debounced;
-  }
-
-  // Returns the first function passed as an argument to the second,
-  // allowing you to adjust arguments, run code before and after, and
-  // conditionally execute the original function.
-  function wrap(func, wrapper) {
-    return partial(wrapper, func);
-  }
-
-  // Returns a negated version of the passed-in predicate.
-  function negate(predicate) {
-    return function() {
-      return !predicate.apply(this, arguments);
-    };
-  }
-
-  // Returns a function that is the composition of a list of functions, each
-  // consuming the return value of the function that follows.
-  function compose() {
-    var args = arguments;
-    var start = args.length - 1;
-    return function() {
-      var i = start;
-      var result = args[start].apply(this, arguments);
-      while (i--) result = args[i].call(this, result);
-      return result;
-    };
-  }
-
-  // Returns a function that will only be executed on and after the Nth call.
-  function after(times, func) {
-    return function() {
-      if (--times < 1) {
-        return func.apply(this, arguments);
-      }
-    };
-  }
-
-  // Returns a function that will only be executed up to (but not including) the
-  // Nth call.
-  function before(times, func) {
-    var memo;
-    return function() {
-      if (--times > 0) {
-        memo = func.apply(this, arguments);
-      }
-      if (times <= 1) func = null;
-      return memo;
-    };
-  }
-
-  // Returns a function that will be executed at most one time, no matter how
-  // often you call it. Useful for lazy initialization.
-  var once = partial(before, 2);
-
-  // Returns the first key on an object that passes a truth test.
-  function findKey(obj, predicate, context) {
-    predicate = cb(predicate, context);
-    var _keys = keys(obj), key;
-    for (var i = 0, length = _keys.length; i < length; i++) {
-      key = _keys[i];
-      if (predicate(obj[key], key, obj)) return key;
-    }
-  }
-
-  // Internal function to generate `_.findIndex` and `_.findLastIndex`.
-  function createPredicateIndexFinder(dir) {
-    return function(array, predicate, context) {
-      predicate = cb(predicate, context);
-      var length = getLength(array);
-      var index = dir > 0 ? 0 : length - 1;
-      for (; index >= 0 && index < length; index += dir) {
-        if (predicate(array[index], index, array)) return index;
-      }
-      return -1;
-    };
-  }
-
-  // Returns the first index on an array-like that passes a truth test.
-  var findIndex = createPredicateIndexFinder(1);
-
-  // Returns the last index on an array-like that passes a truth test.
-  var findLastIndex = createPredicateIndexFinder(-1);
-
-  // Use a comparator function to figure out the smallest index at which
-  // an object should be inserted so as to maintain order. Uses binary search.
-  function sortedIndex(array, obj, iteratee, context) {
-    iteratee = cb(iteratee, context, 1);
-    var value = iteratee(obj);
-    var low = 0, high = getLength(array);
-    while (low < high) {
-      var mid = Math.floor((low + high) / 2);
-      if (iteratee(array[mid]) < value) low = mid + 1; else high = mid;
-    }
-    return low;
-  }
-
-  // Internal function to generate the `_.indexOf` and `_.lastIndexOf` functions.
-  function createIndexFinder(dir, predicateFind, sortedIndex) {
-    return function(array, item, idx) {
-      var i = 0, length = getLength(array);
-      if (typeof idx == 'number') {
-        if (dir > 0) {
-          i = idx >= 0 ? idx : Math.max(idx + length, i);
-        } else {
-          length = idx >= 0 ? Math.min(idx + 1, length) : idx + length + 1;
-        }
-      } else if (sortedIndex && idx && length) {
-        idx = sortedIndex(array, item);
-        return array[idx] === item ? idx : -1;
-      }
-      if (item !== item) {
-        idx = predicateFind(slice.call(array, i, length), isNaN$1);
-        return idx >= 0 ? idx + i : -1;
-      }
-      for (idx = dir > 0 ? i : length - 1; idx >= 0 && idx < length; idx += dir) {
-        if (array[idx] === item) return idx;
-      }
-      return -1;
-    };
-  }
-
-  // Return the position of the first occurrence of an item in an array,
-  // or -1 if the item is not included in the array.
-  // If the array is large and already in sort order, pass `true`
-  // for **isSorted** to use binary search.
-  var indexOf = createIndexFinder(1, findIndex, sortedIndex);
-
-  // Return the position of the last occurrence of an item in an array,
-  // or -1 if the item is not included in the array.
-  var lastIndexOf = createIndexFinder(-1, findLastIndex);
-
-  // Return the first value which passes a truth test.
-  function find(obj, predicate, context) {
-    var keyFinder = isArrayLike(obj) ? findIndex : findKey;
-    var key = keyFinder(obj, predicate, context);
-    if (key !== void 0 && key !== -1) return obj[key];
-  }
-
-  // Convenience version of a common use case of `_.find`: getting the first
-  // object containing specific `key:value` pairs.
-  function findWhere(obj, attrs) {
-    return find(obj, matcher(attrs));
-  }
-
-  // The cornerstone for collection functions, an `each`
-  // implementation, aka `forEach`.
-  // Handles raw objects in addition to array-likes. Treats all
-  // sparse array-likes as if they were dense.
-  function each(obj, iteratee, context) {
-    iteratee = optimizeCb(iteratee, context);
-    var i, length;
-    if (isArrayLike(obj)) {
-      for (i = 0, length = obj.length; i < length; i++) {
-        iteratee(obj[i], i, obj);
-      }
-    } else {
-      var _keys = keys(obj);
-      for (i = 0, length = _keys.length; i < length; i++) {
-        iteratee(obj[_keys[i]], _keys[i], obj);
-      }
-    }
-    return obj;
-  }
-
-  // Return the results of applying the iteratee to each element.
-  function map(obj, iteratee, context) {
-    iteratee = cb(iteratee, context);
-    var _keys = !isArrayLike(obj) && keys(obj),
-        length = (_keys || obj).length,
-        results = Array(length);
-    for (var index = 0; index < length; index++) {
-      var currentKey = _keys ? _keys[index] : index;
-      results[index] = iteratee(obj[currentKey], currentKey, obj);
-    }
-    return results;
-  }
-
-  // Internal helper to create a reducing function, iterating left or right.
-  function createReduce(dir) {
-    // Wrap code that reassigns argument variables in a separate function than
-    // the one that accesses `arguments.length` to avoid a perf hit. (#1991)
-    var reducer = function(obj, iteratee, memo, initial) {
-      var _keys = !isArrayLike(obj) && keys(obj),
-          length = (_keys || obj).length,
-          index = dir > 0 ? 0 : length - 1;
-      if (!initial) {
-        memo = obj[_keys ? _keys[index] : index];
-        index += dir;
-      }
-      for (; index >= 0 && index < length; index += dir) {
-        var currentKey = _keys ? _keys[index] : index;
-        memo = iteratee(memo, obj[currentKey], currentKey, obj);
-      }
-      return memo;
-    };
-
-    return function(obj, iteratee, memo, context) {
-      var initial = arguments.length >= 3;
-      return reducer(obj, optimizeCb(iteratee, context, 4), memo, initial);
-    };
-  }
-
-  // **Reduce** builds up a single result from a list of values, aka `inject`,
-  // or `foldl`.
-  var reduce = createReduce(1);
-
-  // The right-associative version of reduce, also known as `foldr`.
-  var reduceRight = createReduce(-1);
-
-  // Return all the elements that pass a truth test.
-  function filter(obj, predicate, context) {
-    var results = [];
-    predicate = cb(predicate, context);
-    each(obj, function(value, index, list) {
-      if (predicate(value, index, list)) results.push(value);
-    });
-    return results;
-  }
-
-  // Return all the elements for which a truth test fails.
-  function reject(obj, predicate, context) {
-    return filter(obj, negate(cb(predicate)), context);
-  }
-
-  // Determine whether all of the elements pass a truth test.
-  function every(obj, predicate, context) {
-    predicate = cb(predicate, context);
-    var _keys = !isArrayLike(obj) && keys(obj),
-        length = (_keys || obj).length;
-    for (var index = 0; index < length; index++) {
-      var currentKey = _keys ? _keys[index] : index;
-      if (!predicate(obj[currentKey], currentKey, obj)) return false;
-    }
-    return true;
-  }
-
-  // Determine if at least one element in the object passes a truth test.
-  function some(obj, predicate, context) {
-    predicate = cb(predicate, context);
-    var _keys = !isArrayLike(obj) && keys(obj),
-        length = (_keys || obj).length;
-    for (var index = 0; index < length; index++) {
-      var currentKey = _keys ? _keys[index] : index;
-      if (predicate(obj[currentKey], currentKey, obj)) return true;
-    }
-    return false;
-  }
-
-  // Determine if the array or object contains a given item (using `===`).
-  function contains(obj, item, fromIndex, guard) {
-    if (!isArrayLike(obj)) obj = values(obj);
-    if (typeof fromIndex != 'number' || guard) fromIndex = 0;
-    return indexOf(obj, item, fromIndex) >= 0;
-  }
-
-  // Invoke a method (with arguments) on every item in a collection.
-  var invoke = restArguments(function(obj, path, args) {
-    var contextPath, func;
-    if (isFunction$1(path)) {
-      func = path;
-    } else {
-      path = toPath(path);
-      contextPath = path.slice(0, -1);
-      path = path[path.length - 1];
-    }
-    return map(obj, function(context) {
-      var method = func;
-      if (!method) {
-        if (contextPath && contextPath.length) {
-          context = deepGet(context, contextPath);
-        }
-        if (context == null) return void 0;
-        method = context[path];
-      }
-      return method == null ? method : method.apply(context, args);
-    });
-  });
-
-  // Convenience version of a common use case of `_.map`: fetching a property.
-  function pluck(obj, key) {
-    return map(obj, property(key));
-  }
-
-  // Convenience version of a common use case of `_.filter`: selecting only
-  // objects containing specific `key:value` pairs.
-  function where(obj, attrs) {
-    return filter(obj, matcher(attrs));
-  }
-
-  // Return the maximum element (or element-based computation).
-  function max(obj, iteratee, context) {
-    var result = -Infinity, lastComputed = -Infinity,
-        value, computed;
-    if (iteratee == null || typeof iteratee == 'number' && typeof obj[0] != 'object' && obj != null) {
-      obj = isArrayLike(obj) ? obj : values(obj);
-      for (var i = 0, length = obj.length; i < length; i++) {
-        value = obj[i];
-        if (value != null && value > result) {
-          result = value;
-        }
-      }
-    } else {
-      iteratee = cb(iteratee, context);
-      each(obj, function(v, index, list) {
-        computed = iteratee(v, index, list);
-        if (computed > lastComputed || computed === -Infinity && result === -Infinity) {
-          result = v;
-          lastComputed = computed;
-        }
-      });
-    }
-    return result;
-  }
-
-  // Return the minimum element (or element-based computation).
-  function min(obj, iteratee, context) {
-    var result = Infinity, lastComputed = Infinity,
-        value, computed;
-    if (iteratee == null || typeof iteratee == 'number' && typeof obj[0] != 'object' && obj != null) {
-      obj = isArrayLike(obj) ? obj : values(obj);
-      for (var i = 0, length = obj.length; i < length; i++) {
-        value = obj[i];
-        if (value != null && value < result) {
-          result = value;
-        }
-      }
-    } else {
-      iteratee = cb(iteratee, context);
-      each(obj, function(v, index, list) {
-        computed = iteratee(v, index, list);
-        if (computed < lastComputed || computed === Infinity && result === Infinity) {
-          result = v;
-          lastComputed = computed;
-        }
-      });
-    }
-    return result;
-  }
-
-  // Sample **n** random values from a collection using the modern version of the
-  // [Fisher-Yates shuffle](https://en.wikipedia.org/wiki/Fisher–Yates_shuffle).
-  // If **n** is not specified, returns a single random element.
-  // The internal `guard` argument allows it to work with `_.map`.
-  function sample(obj, n, guard) {
-    if (n == null || guard) {
-      if (!isArrayLike(obj)) obj = values(obj);
-      return obj[random(obj.length - 1)];
-    }
-    var sample = isArrayLike(obj) ? clone(obj) : values(obj);
-    var length = getLength(sample);
-    n = Math.max(Math.min(n, length), 0);
-    var last = length - 1;
-    for (var index = 0; index < n; index++) {
-      var rand = random(index, last);
-      var temp = sample[index];
-      sample[index] = sample[rand];
-      sample[rand] = temp;
-    }
-    return sample.slice(0, n);
-  }
-
-  // Shuffle a collection.
-  function shuffle(obj) {
-    return sample(obj, Infinity);
-  }
-
-  // Sort the object's values by a criterion produced by an iteratee.
-  function sortBy(obj, iteratee, context) {
-    var index = 0;
-    iteratee = cb(iteratee, context);
-    return pluck(map(obj, function(value, key, list) {
-      return {
-        value: value,
-        index: index++,
-        criteria: iteratee(value, key, list)
-      };
-    }).sort(function(left, right) {
-      var a = left.criteria;
-      var b = right.criteria;
-      if (a !== b) {
-        if (a > b || a === void 0) return 1;
-        if (a < b || b === void 0) return -1;
-      }
-      return left.index - right.index;
-    }), 'value');
-  }
-
-  // An internal function used for aggregate "group by" operations.
-  function group(behavior, partition) {
-    return function(obj, iteratee, context) {
-      var result = partition ? [[], []] : {};
-      iteratee = cb(iteratee, context);
-      each(obj, function(value, index) {
-        var key = iteratee(value, index, obj);
-        behavior(result, value, key);
-      });
-      return result;
-    };
-  }
-
-  // Groups the object's values by a criterion. Pass either a string attribute
-  // to group by, or a function that returns the criterion.
-  var groupBy = group(function(result, value, key) {
-    if (has$1(result, key)) result[key].push(value); else result[key] = [value];
-  });
-
-  // Indexes the object's values by a criterion, similar to `_.groupBy`, but for
-  // when you know that your index values will be unique.
-  var indexBy = group(function(result, value, key) {
-    result[key] = value;
-  });
-
-  // Counts instances of an object that group by a certain criterion. Pass
-  // either a string attribute to count by, or a function that returns the
-  // criterion.
-  var countBy = group(function(result, value, key) {
-    if (has$1(result, key)) result[key]++; else result[key] = 1;
-  });
-
-  // Split a collection into two arrays: one whose elements all pass the given
-  // truth test, and one whose elements all do not pass the truth test.
-  var partition = group(function(result, value, pass) {
-    result[pass ? 0 : 1].push(value);
-  }, true);
-
-  // Safely create a real, live array from anything iterable.
-  var reStrSymbol = /[^\ud800-\udfff]|[\ud800-\udbff][\udc00-\udfff]|[\ud800-\udfff]/g;
-  function toArray(obj) {
-    if (!obj) return [];
-    if (isArray(obj)) return slice.call(obj);
-    if (isString(obj)) {
-      // Keep surrogate pair characters together.
-      return obj.match(reStrSymbol);
-    }
-    if (isArrayLike(obj)) return map(obj, identity);
-    return values(obj);
-  }
-
-  // Return the number of elements in a collection.
-  function size(obj) {
-    if (obj == null) return 0;
-    return isArrayLike(obj) ? obj.length : keys(obj).length;
-  }
-
-  // Internal `_.pick` helper function to determine whether `key` is an enumerable
-  // property name of `obj`.
-  function keyInObj(value, key, obj) {
-    return key in obj;
-  }
-
-  // Return a copy of the object only containing the allowed properties.
-  var pick = restArguments(function(obj, keys) {
-    var result = {}, iteratee = keys[0];
-    if (obj == null) return result;
-    if (isFunction$1(iteratee)) {
-      if (keys.length > 1) iteratee = optimizeCb(iteratee, keys[1]);
-      keys = allKeys(obj);
-    } else {
-      iteratee = keyInObj;
-      keys = flatten$1(keys, false, false);
-      obj = Object(obj);
-    }
-    for (var i = 0, length = keys.length; i < length; i++) {
-      var key = keys[i];
-      var value = obj[key];
-      if (iteratee(value, key, obj)) result[key] = value;
-    }
-    return result;
-  });
-
-  // Return a copy of the object without the disallowed properties.
-  var omit = restArguments(function(obj, keys) {
-    var iteratee = keys[0], context;
-    if (isFunction$1(iteratee)) {
-      iteratee = negate(iteratee);
-      if (keys.length > 1) context = keys[1];
-    } else {
-      keys = map(flatten$1(keys, false, false), String);
-      iteratee = function(value, key) {
-        return !contains(keys, key);
-      };
-    }
-    return pick(obj, iteratee, context);
-  });
-
-  // Returns everything but the last entry of the array. Especially useful on
-  // the arguments object. Passing **n** will return all the values in
-  // the array, excluding the last N.
-  function initial(array, n, guard) {
-    return slice.call(array, 0, Math.max(0, array.length - (n == null || guard ? 1 : n)));
-  }
-
-  // Get the first element of an array. Passing **n** will return the first N
-  // values in the array. The **guard** check allows it to work with `_.map`.
-  function first(array, n, guard) {
-    if (array == null || array.length < 1) return n == null || guard ? void 0 : [];
-    if (n == null || guard) return array[0];
-    return initial(array, array.length - n);
-  }
-
-  // Returns everything but the first entry of the `array`. Especially useful on
-  // the `arguments` object. Passing an **n** will return the rest N values in the
-  // `array`.
-  function rest(array, n, guard) {
-    return slice.call(array, n == null || guard ? 1 : n);
-  }
-
-  // Get the last element of an array. Passing **n** will return the last N
-  // values in the array.
-  function last(array, n, guard) {
-    if (array == null || array.length < 1) return n == null || guard ? void 0 : [];
-    if (n == null || guard) return array[array.length - 1];
-    return rest(array, Math.max(0, array.length - n));
-  }
-
-  // Trim out all falsy values from an array.
-  function compact(array) {
-    return filter(array, Boolean);
-  }
-
-  // Flatten out an array, either recursively (by default), or up to `depth`.
-  // Passing `true` or `false` as `depth` means `1` or `Infinity`, respectively.
-  function flatten(array, depth) {
-    return flatten$1(array, depth, false);
-  }
-
-  // Take the difference between one array and a number of other arrays.
-  // Only the elements present in just the first array will remain.
-  var difference = restArguments(function(array, rest) {
-    rest = flatten$1(rest, true, true);
-    return filter(array, function(value){
-      return !contains(rest, value);
-    });
-  });
-
-  // Return a version of the array that does not contain the specified value(s).
-  var without = restArguments(function(array, otherArrays) {
-    return difference(array, otherArrays);
-  });
-
-  // Produce a duplicate-free version of the array. If the array has already
-  // been sorted, you have the option of using a faster algorithm.
-  // The faster algorithm will not work with an iteratee if the iteratee
-  // is not a one-to-one function, so providing an iteratee will disable
-  // the faster algorithm.
-  function uniq(array, isSorted, iteratee, context) {
-    if (!isBoolean(isSorted)) {
-      context = iteratee;
-      iteratee = isSorted;
-      isSorted = false;
-    }
-    if (iteratee != null) iteratee = cb(iteratee, context);
-    var result = [];
-    var seen = [];
-    for (var i = 0, length = getLength(array); i < length; i++) {
-      var value = array[i],
-          computed = iteratee ? iteratee(value, i, array) : value;
-      if (isSorted && !iteratee) {
-        if (!i || seen !== computed) result.push(value);
-        seen = computed;
-      } else if (iteratee) {
-        if (!contains(seen, computed)) {
-          seen.push(computed);
-          result.push(value);
-        }
-      } else if (!contains(result, value)) {
-        result.push(value);
-      }
-    }
-    return result;
-  }
-
-  // Produce an array that contains the union: each distinct element from all of
-  // the passed-in arrays.
-  var union = restArguments(function(arrays) {
-    return uniq(flatten$1(arrays, true, true));
-  });
-
-  // Produce an array that contains every item shared between all the
-  // passed-in arrays.
-  function intersection(array) {
-    var result = [];
-    var argsLength = arguments.length;
-    for (var i = 0, length = getLength(array); i < length; i++) {
-      var item = array[i];
-      if (contains(result, item)) continue;
-      var j;
-      for (j = 1; j < argsLength; j++) {
-        if (!contains(arguments[j], item)) break;
-      }
-      if (j === argsLength) result.push(item);
-    }
-    return result;
-  }
-
-  // Complement of zip. Unzip accepts an array of arrays and groups
-  // each array's elements on shared indices.
-  function unzip(array) {
-    var length = array && max(array, getLength).length || 0;
-    var result = Array(length);
-
-    for (var index = 0; index < length; index++) {
-      result[index] = pluck(array, index);
-    }
-    return result;
-  }
-
-  // Zip together multiple lists into a single array -- elements that share
-  // an index go together.
-  var zip = restArguments(unzip);
-
-  // Converts lists into objects. Pass either a single array of `[key, value]`
-  // pairs, or two parallel arrays of the same length -- one of keys, and one of
-  // the corresponding values. Passing by pairs is the reverse of `_.pairs`.
-  function object(list, values) {
-    var result = {};
-    for (var i = 0, length = getLength(list); i < length; i++) {
-      if (values) {
-        result[list[i]] = values[i];
-      } else {
-        result[list[i][0]] = list[i][1];
-      }
-    }
-    return result;
-  }
-
-  // Generate an integer Array containing an arithmetic progression. A port of
-  // the native Python `range()` function. See
-  // [the Python documentation](https://docs.python.org/library/functions.html#range).
-  function range(start, stop, step) {
-    if (stop == null) {
-      stop = start || 0;
-      start = 0;
-    }
-    if (!step) {
-      step = stop < start ? -1 : 1;
-    }
-
-    var length = Math.max(Math.ceil((stop - start) / step), 0);
-    var range = Array(length);
-
-    for (var idx = 0; idx < length; idx++, start += step) {
-      range[idx] = start;
-    }
-
-    return range;
-  }
-
-  // Chunk a single array into multiple arrays, each containing `count` or fewer
-  // items.
-  function chunk(array, count) {
-    if (count == null || count < 1) return [];
-    var result = [];
-    var i = 0, length = array.length;
-    while (i < length) {
-      result.push(slice.call(array, i, i += count));
-    }
-    return result;
-  }
-
-  // Helper function to continue chaining intermediate results.
-  function chainResult(instance, obj) {
-    return instance._chain ? _$1(obj).chain() : obj;
-  }
-
-  // Add your own custom functions to the Underscore object.
-  function mixin(obj) {
-    each(functions(obj), function(name) {
-      var func = _$1[name] = obj[name];
-      _$1.prototype[name] = function() {
-        var args = [this._wrapped];
-        push.apply(args, arguments);
-        return chainResult(this, func.apply(_$1, args));
-      };
-    });
-    return _$1;
-  }
-
-  // Add all mutator `Array` functions to the wrapper.
-  each(['pop', 'push', 'reverse', 'shift', 'sort', 'splice', 'unshift'], function(name) {
-    var method = ArrayProto[name];
-    _$1.prototype[name] = function() {
-      var obj = this._wrapped;
-      if (obj != null) {
-        method.apply(obj, arguments);
-        if ((name === 'shift' || name === 'splice') && obj.length === 0) {
-          delete obj[0];
-        }
-      }
-      return chainResult(this, obj);
-    };
-  });
-
-  // Add all accessor `Array` functions to the wrapper.
-  each(['concat', 'join', 'slice'], function(name) {
-    var method = ArrayProto[name];
-    _$1.prototype[name] = function() {
-      var obj = this._wrapped;
-      if (obj != null) obj = method.apply(obj, arguments);
-      return chainResult(this, obj);
-    };
-  });
-
-  // Named Exports
-
-  var allExports = {
-    __proto__: null,
-    VERSION: VERSION,
-    restArguments: restArguments,
-    isObject: isObject,
-    isNull: isNull,
-    isUndefined: isUndefined,
-    isBoolean: isBoolean,
-    isElement: isElement,
-    isString: isString,
-    isNumber: isNumber,
-    isDate: isDate,
-    isRegExp: isRegExp,
-    isError: isError,
-    isSymbol: isSymbol,
-    isArrayBuffer: isArrayBuffer,
-    isDataView: isDataView$1,
-    isArray: isArray,
-    isFunction: isFunction$1,
-    isArguments: isArguments$1,
-    isFinite: isFinite$1,
-    isNaN: isNaN$1,
-    isTypedArray: isTypedArray$1,
-    isEmpty: isEmpty,
-    isMatch: isMatch,
-    isEqual: isEqual,
-    isMap: isMap,
-    isWeakMap: isWeakMap,
-    isSet: isSet,
-    isWeakSet: isWeakSet,
-    keys: keys,
-    allKeys: allKeys,
-    values: values,
-    pairs: pairs,
-    invert: invert,
-    functions: functions,
-    methods: functions,
-    extend: extend,
-    extendOwn: extendOwn,
-    assign: extendOwn,
-    defaults: defaults,
-    create: create,
-    clone: clone,
-    tap: tap,
-    get: get,
-    has: has,
-    mapObject: mapObject,
-    identity: identity,
-    constant: constant,
-    noop: noop,
-    toPath: toPath$1,
-    property: property,
-    propertyOf: propertyOf,
-    matcher: matcher,
-    matches: matcher,
-    times: times,
-    random: random,
-    now: now,
-    escape: _escape,
-    unescape: _unescape,
-    templateSettings: templateSettings,
-    template: template,
-    result: result,
-    uniqueId: uniqueId,
-    chain: chain,
-    iteratee: iteratee,
-    partial: partial,
-    bind: bind,
-    bindAll: bindAll,
-    memoize: memoize,
-    delay: delay,
-    defer: defer,
-    throttle: throttle,
-    debounce: debounce,
-    wrap: wrap,
-    negate: negate,
-    compose: compose,
-    after: after,
-    before: before,
-    once: once,
-    findKey: findKey,
-    findIndex: findIndex,
-    findLastIndex: findLastIndex,
-    sortedIndex: sortedIndex,
-    indexOf: indexOf,
-    lastIndexOf: lastIndexOf,
-    find: find,
-    detect: find,
-    findWhere: findWhere,
-    each: each,
-    forEach: each,
-    map: map,
-    collect: map,
-    reduce: reduce,
-    foldl: reduce,
-    inject: reduce,
-    reduceRight: reduceRight,
-    foldr: reduceRight,
-    filter: filter,
-    select: filter,
-    reject: reject,
-    every: every,
-    all: every,
-    some: some,
-    any: some,
-    contains: contains,
-    includes: contains,
-    include: contains,
-    invoke: invoke,
-    pluck: pluck,
-    where: where,
-    max: max,
-    min: min,
-    shuffle: shuffle,
-    sample: sample,
-    sortBy: sortBy,
-    groupBy: groupBy,
-    indexBy: indexBy,
-    countBy: countBy,
-    partition: partition,
-    toArray: toArray,
-    size: size,
-    pick: pick,
-    omit: omit,
-    first: first,
-    head: first,
-    take: first,
-    initial: initial,
-    last: last,
-    rest: rest,
-    tail: rest,
-    drop: rest,
-    compact: compact,
-    flatten: flatten,
-    without: without,
-    uniq: uniq,
-    unique: uniq,
-    union: union,
-    intersection: intersection,
-    difference: difference,
-    unzip: unzip,
-    transpose: unzip,
-    zip: zip,
-    object: object,
-    range: range,
-    chunk: chunk,
-    mixin: mixin,
-    'default': _$1
-  };
-
-  // Default Export
-
-  // Add all of the Underscore functions to the wrapper object.
-  var _ = mixin(allExports);
-  // Legacy Node.js API.
-  _._ = _;
-
-  return _;
-
-})));
-//# sourceMappingURL=underscore-umd.js.map
diff --git a/docs/static/underscore.js b/docs/static/underscore.js
deleted file mode 100644
index cf177d4..0000000
--- a/docs/static/underscore.js
+++ /dev/null
@@ -1,6 +0,0 @@
-!function(n,r){"object"==typeof exports&&"undefined"!=typeof module?module.exports=r():"function"==typeof define&&define.amd?define("underscore",r):(n="undefined"!=typeof globalThis?globalThis:n||self,function(){var t=n._,e=n._=r();e.noConflict=function(){return n._=t,e}}())}(this,(function(){
-//     Underscore.js 1.13.1
-//     https://underscorejs.org
-//     (c) 2009-2021 Jeremy Ashkenas, Julian Gonggrijp, and DocumentCloud and Investigative Reporters & Editors
-//     Underscore may be freely distributed under the MIT license.
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diff --git a/docs/vr/index.html b/docs/vr/index.html
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-<li><p><strong>Advanced course</strong></p></li>
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-<p>If the total number of people who reward us is more than 2,000, we will launch the advanced course in a month, so that, after finishing your study, you will have a better understanding of the principles of the framework and be able to do advanced development.</p>
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-</div>
-</div>
-</div>
-<div class="clearer"></div>
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-<img onclick="openPurchase()" src="../static/star.jpg">
-<div id="praise"><span>0</span> persons have rewarded</div>
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-<div class="foo"></div>
-<footer class="clearfix fixfooter web-show">
-<div class="col-sm-offset-2 col-sm-6 footer-div1">
-<div class="footer-block-item">
-<span class="footer-item1">Built with Sphinx using a theme provid by QPython.</span>
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-<div class="en-thanks-sel">
-<select name="os0">
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-temp_data = temp_data.replace(/\^10\$/g, spaceString(10));
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diff --git a/docs/wego/index.html b/docs/wego/index.html
deleted file mode 100644
index e70ca8d..0000000
--- a/docs/wego/index.html
+++ /dev/null
@@ -1,291 +0,0 @@
-<!DOCTYPE html>
-<html lang="en-US">
-<head>
-<meta charset="utf-8" />
-<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
-<link href="https://cdn.bootcss.com/bootstrap/3.3.7/css/bootstrap.min.css" rel="stylesheet">
-<title>WegoX (Wechat openplatform SDK)</title>
-<link rel="stylesheet" type="text/css" href="../static/pygments.css" />
-<link rel="stylesheet" type="text/css" href="../static/style.css" />
-<meta charset="UTF-8">
-<meta name="viewport" content="width=device-width, initial-scale=1,maximum-scale=1, user-scalable=no">
-<script src="https://cdn.bootcss.com/bootstrap/3.3.7/js/bootstrap.min.js"></script>
-</head><body>
-<div class="header doc-header web-show">
-<div class="col-sm-offset-2 col-sm-8  index-header">
-<div class="navbar-header">
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-<span class="sr-only">切换导航</span>
-<span class="icon-bar"></span>
-<span class="icon-bar"></span>
-<span class="icon-bar"></span>
-</button>
-<a href="/index.html" class="logo"><img src="http://www.qpython.org/static/img_logo.png"></a>
-</div>
-<ul class="header-title collapse navbar-collapse" id="example-navbar-collapse" >
-<li>
-<a href="http://www.qpython.org/index.html">Home</a>
-</li>
-<li>
-<a href="http://www.qpython.org/document.html">Guide</a>
-</li>
-<li class="header-phone-selected">
-<a href="/index.html" class="header-selected">Course</a>
-<span class="header-title-selected"></span>
-</li>
-<li>
-<a href="http://www.aipy.org">AIPY</a>
-</li>
-<li>
-<a target="_blank" href="https://github.com/qpython-android/qpython">Github</a>
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-<div class="search-box hidden-xs">
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-<input type="search" name="q" placeholder="keyword">
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-<main class="contain web-content">
-<div class="document">
-<div class="documentwrapper">
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-<div class="body" role="main">
-<section id="wegox-wechat-openplatform-sdk">
-<h1>WegoX (Wechat openplatform SDK)<a class="headerlink" href="#wegox-wechat-openplatform-sdk" title="Permalink to this heading">¶</a></h1>
-<p>The courses are still in the planning. Welcome to reward us.</p>
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-<li><p><strong>Primary course</strong></p></li>
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-<ol class="arabic simple" start="2">
-<li><p><strong>Intermediate course</strong></p></li>
-</ol>
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-<ol class="arabic simple" start="3">
-<li><p><strong>Advanced course</strong></p></li>
-</ol>
-<p>If the total number of people who reward us is more than 2,000, we will launch the advanced course in a month, so that, after finishing your study, you will have a better understanding of the principles of the framework and be able to do advanced development.</p>
-<p><strong>The primary course is free to all, and the intermediate and advanced courses are only open to those who have rewarded us.</strong></p>
-</section>
-<div class="clearer"></div>
-</div>
-</div>
-</div>
-<div class="clearer"></div>
-</div>
-</main>
-<div class="star-footer">
-<img onclick="openPurchase()" src="../static/star.jpg">
-<div id="praise"><span>0</span> persons have rewarded</div>
-</div>
-<div class="foo"></div>
-<footer class="clearfix fixfooter web-show">
-<div class="col-sm-offset-2 col-sm-6 footer-div1">
-<div class="footer-block-item">
-<span class="footer-item1">Built with Sphinx using a theme provid by QPython.</span>
-<span class="footer-item2 visible-xs">Copyight 2017, QPython.</span>
-</div>
-</div>
-<div class="col-sm-2 footer-div2">
-<a href="/index.html" class="pull-right"><img src="http://www.qpython.org/static/img_logo.png"></a>
-</div>
-</footer>
-<div id="purchase-box">
-<div class="zh-img purchase-img"></div>
-<div class="en-img purchase-img">
-<form action="https://www.paypal.com/cgi-bin/webscr" method="post" target="_top">
-<input type="hidden" name="cmd" value="_s-xclick">
-<input type="hidden" name="hosted_button_id" value="ST6KWAK8R63JW">
-<div class="en-thanks-tit">
-<input type="hidden" name="on0" value="Thanks">
-Thanks
-</div>
-<div class="en-thanks-sel">
-<select name="os0">
-<option value="Reward this course">Reward this course $ 0.99 USD</option>
-<option value="Reward this course">Reward this course $ 1.49 USD</option>
-<option value="Reward this course">Reward this course $ 1.99 USD</option>
-<option value="Reward this course">Reward this course $ 2.99 USD</option>
-<option value="Reward this course">Reward this course $ 3.49 USD</option>
-</select>
-</div>
-<input type="hidden" name="currency_code" value="USD">
-<input type="image" class="pay_img" src="https://www.paypalobjects.com/en_GB/i/btn/btn_paynow_LG.gif" border="0" name="submit" alt="PayPal – The safer, easier way to pay online!">
-<img alt="" border="0" src="https://www.paypalobjects.com/zh_XC/i/scr/pixel.gif" width="1" height="1">
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-window.open(url)
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diff --git a/index.html b/index.html
new file mode 100644
index 0000000..334658f
--- /dev/null
+++ b/index.html
@@ -0,0 +1,21 @@
+<!DOCTYPE html>
+<html lang="en">
+  <head>
+    <meta charset="UTF-8" />
+    <link rel="icon" type="image/svg+xml" href="/vite.svg" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>QPython+ - Personalized Programming Learning Community</title>
+    <!-- Google tag (gtag.js) -->
+    <script async src="https://www.googletagmanager.com/gtag/js?id=G-JH6WGHX4M2"></script>
+    <script>
+      window.dataLayer = window.dataLayer || [];
+      function gtag(){dataLayer.push(arguments);}
+      gtag('js', new Date());
+      gtag('config', 'G-JH6WGHX4M2');
+    </script>
+  </head>
+  <body>
+    <div id="root"></div>
+    <script type="module" src="/src/main.tsx"></script>
+  </body>
+</html>
diff --git a/package-lock.json b/package-lock.json
new file mode 100644
index 0000000..157d312
--- /dev/null
+++ b/package-lock.json
@@ -0,0 +1,2786 @@
+{
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+  "lockfileVersion": 3,
+  "requires": true,
+  "packages": {
+    "": {
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+      "version": "0.0.1",
+      "dependencies": {
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+        "lucide-react": "^0.363.0",
+        "react": "^18.2.0",
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+        "react-router-dom": "^6.8.1",
+        "tailwind-merge": "^2.2.2",
+        "zustand": "^4.3.2"
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+      "devDependencies": {
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+        "@types/react-dom": "^18.2.19",
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+        "postcss": "^8.4.35",
+        "tailwindcss": "^3.4.1",
+        "typescript": "^5.2.2",
+        "vite": "^5.1.4"
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+    "node_modules/@alloc/quick-lru": {
+      "version": "5.2.0",
+      "resolved": "https://registry.npmjs.org/@alloc/quick-lru/-/quick-lru-5.2.0.tgz",
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+      "dev": true,
+      "license": "MIT",
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+}
diff --git a/package.json b/package.json
new file mode 100644
index 0000000..4fee348
--- /dev/null
+++ b/package.json
@@ -0,0 +1,30 @@
+{
+  "name": "qpython-edu",
+  "private": true,
+  "version": "0.0.1",
+  "type": "module",
+  "scripts": {
+    "dev": "vite",
+    "build": "cp docs/CNAME /tmp/qpy-CNAME && cp docs/courses-cn.html /tmp/qpy-courses-cn.html && cp docs/courses.html /tmp/qpy-courses.html && tsc && vite build && cp /tmp/qpy-CNAME docs/CNAME && cp /tmp/qpy-courses-cn.html docs/courses-cn.html && cp /tmp/qpy-courses.html docs/courses.html",
+    "preview": "vite preview"
+  },
+  "dependencies": {
+    "react": "^18.2.0",
+    "react-dom": "^18.2.0",
+    "react-router-dom": "^6.8.1",
+    "lucide-react": "^0.363.0",
+    "clsx": "^2.1.0",
+    "tailwind-merge": "^2.2.2",
+    "zustand": "^4.3.2"
+  },
+  "devDependencies": {
+    "@types/react": "^18.2.56",
+    "@types/react-dom": "^18.2.19",
+    "@vitejs/plugin-react": "^4.2.1",
+    "autoprefixer": "^10.4.18",
+    "postcss": "^8.4.35",
+    "tailwindcss": "^3.4.1",
+    "typescript": "^5.2.2",
+    "vite": "^5.1.4"
+  }
+}
diff --git a/postcss.config.js b/postcss.config.js
new file mode 100644
index 0000000..2e7af2b
--- /dev/null
+++ b/postcss.config.js
@@ -0,0 +1,6 @@
+export default {
+  plugins: {
+    tailwindcss: {},
+    autoprefixer: {},
+  },
+}
diff --git a/public/assets/qpy-community.png b/public/assets/qpy-community.png
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+      "url": "https://www.qpython.com.cn/course/flask-web-development-course/",
+      "tags": [
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+      "url": "https://www.qpython.com.cn/course/qpy-numpy-basic/",
+      "tags": [
+        "数据分析",
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+      "title": "Pandas数据分析和处理编程",
+      "description": "关于Pandas\nPandas是Python中一个强大且易用的库，专门用于处理和分析结构化数据。它能让您像操作Excel表格一样轻松处理数据表，适合整理、清洗、筛选和分析各类数据集。\n通过Pandas，您可以快速执行数据清洗、缺失值处理、数据分组、聚合等常见任务。Pandas是数据分析的必备工具，它能大大简化处理大量数据的工作流程。\n学习Pandas的好处：\n提升效率：掌握Pandas可以显著提高您处理大量数据的效率。\n增强洞察力：通过Pandas，您可以快速从数据中提取有价值的信息和洞察。",
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+      "url": "https://www.qpython.com.cn/course/qpy-pandas-basic/",
+      "tags": [
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+        "Pandas",
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+      "price": "¥9",
+      "status": "publish",
+      "catalog": "数据分析与处理",
+      "slug": "qpy-pandas-basic"
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+      "title": "使用Beautiful Soup 4解析网页",
+      "description": "关于Beautiful Soup\nBeautiful Soup是一个Python库，用于从HTML和XML文档中提取数据。Beautiful Soup的魅力在于它的简单和灵活性。它提供了一个直观的API，让你能够轻松地解析网页，就像浏览自己的书架一样轻松。\n无论是遇到格式不规范的HTML文档，还是需要在复杂的数据结构中找到特定的信息，Beautiful Soup都能帮你轻松应对。\n使用Beautiful Soup，你可以快速定位到网页中的特定元素，提取出你需要的文本或属性，甚至还能进行数据的清洗和整理。",
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+      "url": "https://www.qpython.com.cn/course/qpy-bs4-basics/",
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+        "数据采集",
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+      "status": "publish",
+      "catalog": "数据分析与处理",
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+      "title": "Pillow图像处理编程",
+      "description": "关于Pillow\n在当今的编程世界中，图像处理是一个不可或缺的技能，而Pillow库正是这一领域的佼佼者。Pillow，作为Python Imaging Library（PIL）的现代分支，已经成为Python中最流行的图像处理库。\n想象一下，你想要编辑一张图片，无论是进行简单的操作，比如打开和保存图像、剪切图像的一部分、或者旋转图像，还是进行更复杂的处理，比如应用各种滤镜、合成多个图像、甚至改变图像的颜色模式，Pillow都能帮你高效地完成这些任务。\n它的易用性使得即使是初学者也能快速上手，而其强大的功能也能满足专业开发者的需求。",
+      "image": "/assets/udemy_12.png",
+      "url": "https://www.qpython.com.cn/course/py-pillow-started/",
+      "tags": [
+        "图像处理",
+        "Python库",
+        "中级"
+      ],
+      "difficulty": "advanced",
+      "price": "¥9",
+      "status": "publish",
+      "catalog": "深入Python开发",
+      "slug": "py-pillow-started"
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+      "title": "使用QPython的QSL4A编程",
+      "description": "关于QSL4A\nQSL4A（QPython Scripting Layer for Android）是QPython团队在SL4A项目基础上开发的扩展库，旨在为Android开发者提供更灵活的脚本编程环境。通过QSL4A，开发者不仅可以使用Python快速实现Android应用的原型，还可以通过调用Android API，实现设备控制和自动化任务。\nQSL4A的功能亮点：\n轻量级用户界面（UI）支持：提供基础的用户界面功能，开发者可以快速创建简单的交互式应用程序。\nHTML和JavaScript集成：支持通过HTML和JavaScript构建移动应用界面。\n传感器和硬件控制：能够访问Android设备的传感器、相机、电话等功能。",
+      "image": "/assets/udemy_13.png",
+      "url": "https://www.qpython.com.cn/course/qpy-qsl4a/",
+      "tags": [
+        "QPython",
+        "Android",
+        "中级"
+      ],
+      "difficulty": "intermediate",
+      "price": "¥9",
+      "status": "publish",
+      "catalog": "移动应用开发",
+      "slug": "qpy-qsl4a"
+    },
+    {
+      "id": "9562",
+      "title": "OpenPyXL数据表格编程",
+      "description": "关于OpenPyXL\nOpenPyXL是一个功能强大的Python库，专门用于处理Excel文件，特别是.xlsx格式的文件。它允许开发者创建、修改、读取和保存Excel文件，支持复杂的操作，如添加图表、设置格式、处理公式、合并单元格、设置单元格样式等。\nOpenPyXL被广泛应用于数据处理、自动化办公以及生成报告等场景。\n课程介绍\n本课程将带您深入学习如何使用OpenPyXL来处理Excel文件。无论您是数据分析师、开发者，还是需要自动化生成报告的用户，都能通过本课程掌握使用OpenPyXL的核心技能。课程特别加入了在QPython中进行实践的内容。",
+      "image": "/assets/udemy_14.png",
+      "url": "https://www.qpython.com.cn/course/qpy-openpyxl/",
+      "tags": [
+        "数据分析",
+        "Excel",
+        "中级"
+      ],
+      "difficulty": "advanced",
+      "price": "¥9",
+      "status": "publish",
+      "catalog": "深入Python开发",
+      "slug": "qpy-openpyxl"
+    },
+    {
+      "id": "9799",
+      "title": "Scikit-learn机器学习入门实践",
+      "description": "关于Scikit-learn\nScikit-learn是一个开源的Python库，广泛应用于机器学习和数据科学领域。它提供了简单而高效的工具，适用于数据挖掘、数据分析以及构建机器学习模型。Scikit-learn建立在NumPy、SciPy和matplotlib等科学计算库的基础之上，专注于机器学习模型的实现，既适合初学者，也适合有经验的开发者。\n主要特点：\n简单易用：Scikit-learn提供了一致的接口来调用不同的机器学习算法，使用起来十分直观。\n广泛的算法支持：包含了众多经典的机器学习算法，如分类、回归、聚类、降维等。\n丰富的文档和示例：拥有详尽的文档和大量示例，帮助开发者快速上手。",
+      "image": "/assets/udemy_15.png",
+      "url": "https://www.qpython.com.cn/course/scikit-learn-startup/",
+      "tags": [
+        "机器学习",
+        "AI",
+        "入门",
+        "初级"
+      ],
+      "difficulty": "beginner",
+      "price": "¥19",
+      "status": "publish",
+      "catalog": "人工智能",
+      "slug": "scikit-learn-startup"
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+    {
+      "id": "10416",
+      "title": "PyTorch 从入门到实践",
+      "description": "关于PyTorch\nPyTorch是一个广泛使用的深度学习框架，它提供了动态计算图和强大的GPU加速功能，使得研究人员和开发人员能够快速实现和测试复杂的神经网络模型。它的张量计算和自动微分机制为深度学习提供了高效且灵活的支持。\nPyTorch通过简洁的API使得深度学习的学习和实践变得更加容易，特别适合用于学术研究和工业应用。\n课程介绍\n本课程旨在帮助你快速入门PyTorch，全面涵盖PyTorch的基础概念，包括张量操作、自动微分，以及构建和训练简单神经网络。",
+      "image": "/assets/udemy_17.png",
+      "url": "https://www.qpython.com.cn/course/pytorch-startup/",
+      "tags": [
+        "深度学习",
+        "PyTorch",
+        "入门",
+        "初级"
+      ],
+      "difficulty": "beginner",
+      "price": "¥29",
+      "status": "publish",
+      "catalog": "人工智能",
+      "slug": "pytorch-startup"
+    },
+    {
+      "id": "11000",
+      "title": "AI素养基础",
+      "description": "关于AI\n人工智能（Artificial Intelligence, AI）是一门研究如何让机器具备人类智能的技术和学科。AI技术已经广泛应用于日常生活，从语音助手到推荐算法，再到自动驾驶汽车。\nAI的核心是让计算机能够模仿人类的思考、学习和解决问题的能力，这包括理解自然语言、分析数据、识别图像、规划决策等。\n在本课程中，我们将通过生动易懂的方式向您介绍AI的基本概念和实际应用。无论您是否拥有技术背景，这门课程都能帮助您迈出学习AI的第一步。",
+      "image": "/assets/udemy_16.png",
+      "url": "https://www.qpython.com.cn/course/qpai-artificial-intelligence-open-course-ai-literacy-foundation/",
+      "tags": [
+        "AI基础",
+        "通识",
+        "入门"
+      ],
+      "difficulty": "beginner",
+      "price": "¥5",
+      "status": "publish",
+      "catalog": "人工智能",
+      "slug": "qpai-artificial-intelligence-open-course-ai-literacy-foundation"
+    },
+    {
+      "id": "11564",
+      "title": "OpenCV入门课程",
+      "description": "关于OpenCV\nOpenCV（Open Source Computer Vision Library）是一个开源的计算机视觉库，广泛应用于图像处理、视频分析、机器学习等领域。它提供了丰富的工具，可以让开发者处理图像、视频，甚至进行实时计算机视觉应用开发。\n本课程特别为Python编程新手设计，帮助你通过简单易懂的步骤掌握OpenCV库的基础知识和实用技巧，轻松开启计算机视觉的世界。\n课程内容：\n在本教程中，我们将从最基础的内容开始，逐步引导你学习如何使用OpenCV进行图像和视频处理。",
+      "image": "/assets/udemy_18.png",
+      "url": "https://www.qpython.com.cn/course/opencv-started-tutorial/",
+      "tags": [
+        "计算机视觉",
+        "OpenCV",
+        "入门",
+        "初级"
+      ],
+      "difficulty": "beginner",
+      "price": "¥29",
+      "status": "publish",
+      "catalog": "人工智能",
+      "slug": "opencv-started-tutorial"
+    },
+    {
+      "id": "12497",
+      "title": "OpenAI 入门实践",
+      "description": "关于OpenAI库\nOpenAI是一个人工智能研究实验室，即鼎鼎大名的ChatGPT背后的实验室，它致力于开发对全人类有益的AI技术。在这门课程中，我们将学习如何使用OpenAI提供的Python库，借助该库与PGPT对接的AI模型进行交互。\nOpenAI Python库使得与强大的AI模型（如GPT系列、DALL·E等）进行沟通和使用变得简单而高效。通过这个库，你可以轻松地向AI发送请求并获取回答，生成文本、图像，甚至代码！\n课程内容：\n本课程将帮助你理解如何通过OpenAI调用PGPT服务与AI模型进行交互，并通过实际案例来掌握常见的使用场景。",
+      "image": "/assets/udemy_19.png",
+      "url": "https://www.qpython.com.cn/course/pgpt-openai-introduction/",
+      "tags": [
+        "AI应用",
+        "OpenAI",
+        "入门",
+        "初级"
+      ],
+      "difficulty": "beginner",
+      "price": "¥9",
+      "status": "publish",
+      "catalog": "人工智能",
+      "slug": "pgpt-openai-introduction"
+    },
+    {
+      "id": "12759",
+      "title": "DeepSeek入门实践",
+      "description": "关于DeepSeek\n在人工智能浪潮席卷全球的今天，Deepseek如同一颗冉冉升起的新星，以其对通用人工智能（AGI）的执着追求和创新精神，吸引着全球的目光。\nDeepseek成立于2023年，是一家专注实现AGI的中国公司。AGI，即能够像人类一样思考、学习、推理和解决问题的通用人工智能，是人工智能领域的终极目标。\nDeepSeek模型支持多种功能，包括智能对话、准确翻译、创意写作、高效编程、智能解题和文件解读等。其“深度思考”和“联网搜索”功能使得DeepSeek能够更全面地理解用户问题并提供准确答案。",
+      "image": "/assets/udemy_20.png",
+      "url": "https://www.qpython.com.cn/course/aipy-deepseek-intro/",
+      "tags": [
+        "AI应用",
+        "DeepSeek",
+        "入门",
+        "初级"
+      ],
+      "difficulty": "beginner",
+      "price": "¥9",
+      "status": "publish",
+      "catalog": "人工智能",
+      "slug": "aipy-deepseek-intro"
+    },
+    {
+      "id": "13083",
+      "title": "Twisted入门篇",
+      "description": "关于Twisted\nTwisted是一个强大的Python异步编程框架，专为事件驱动的网络应用程序设计。它支持多种协议的开发，如TCP、UDP、HTTP、FTP等，适用于构建高性能的网络服务器和客户端应用。\nTwisted的核心思想是基于事件循环的异步编程，这使得它能够高效地处理大量并发连接，而无需使用传统的线程或进程。\n它的Deferred和Callback机制使得异步操作变得更加简单易懂，避免了回调地狱的问题。",
+      "image": "/assets/udemy_21.png",
+      "url": "https://www.qpython.com.cn/course/python-twisted-intro/",
+      "tags": [
+        "深入Python开发",
+        "网络编程",
+        "中级"
+      ],
+      "difficulty": "beginner",
+      "price": "¥19",
+      "status": "publish",
+      "catalog": "深入Python开发",
+      "slug": "python-twisted-intro"
+    },
+    {
+      "id": "13724",
+      "title": "提示词工程入门",
+      "description": "关于提示词工程\n大语言模型的强大能力来源于海量的数据训练，但它们对提示词的解读极为敏感，甚至是微小的变化都可能导致完全不同的结果。因此，精准设计有效的提示词，不仅能显著提升模型表现，还能避免浪费计算资源和减少错误输出。\n随着人工智能技术，尤其是大语言模型（如GPT和Deepseek）在各行各业的广泛应用，提示词工程（Prompt Engineering）已经成为提高人机交互效率的关键技能。掌握如何设计、优化和评估提示词，能够帮助我们更好地与这些强大的语言模型进行高效对话。\n在这门课程中，我们将深入探索提示词工程的基本概念、设计原理和优化技巧。",
+      "image": "/assets/udemy_22.png",
+      "url": "https://www.qpython.com.cn/course/qpy-prompt-engineering/",
+      "tags": [
+        "AI应用",
+        "提示词工程",
+        "入门",
+        "初级"
+      ],
+      "difficulty": "beginner",
+      "price": "¥9",
+      "status": "publish",
+      "catalog": "人工智能",
+      "slug": "qpy-prompt-engineering"
+    },
+    {
+      "id": "13934",
+      "title": "Langchain开发入门",
+      "description": "关于Langchain\nLangchain是一个开源框架，旨在帮助开发者与大语言模型（如GPT、LLMs等）进行高效的交互与应用开发。它提供了一套强大的工具，使得开发者能够在处理自然语言任务时，轻松构建复杂的应用程序。\nLangchain通过简化数据流、管理多步任务、与外部API集成以及提供内存和状态管理等功能，帮助开发者充分利用语言模型的潜力，解决实际问题。\n课程简介\n本课程将深入讲解如何使用Langchain框架开发AI应用，从基础知识到高级应用，帮助你掌握Langchain的核心用法。",
+      "image": "/assets/udemy_23.png",
+      "url": "https://www.qpython.com.cn/course/ai-development-getting-started-with-langchain/",
+      "tags": [
+        "AI开发",
+        "LangChain",
+        "中级"
+      ],
+      "difficulty": "beginner",
+      "price": "¥19",
+      "status": "publish",
+      "catalog": "人工智能",
+      "slug": "ai-development-getting-started-with-langchain"
+    },
+    {
+      "id": "14285",
+      "title": "LangGraph开发入门",
+      "description": "关于LangGraph\nLangGraph是一个基于LangChain的库，专门用于构建可靠且可扩展的语言模型应用程序的状态管理和工作流编排工具。它不仅仅是一个简单的编程库，而是一个革命性的平台，使开发者能够创建复杂、动态且智能的AI应用程序。\n在传统的AI应用开发中，开发者常常受限于线性、静态的工作流程。LangGraph的出现彻底改变了这一局面。LangGraph允许开发者创建复杂的、有状态的AI工作流，可以精确控制语言模型代理的执行过程。\n它特别适合需要多步骤、有状态交互的AI应用程序。",
+      "image": "/assets/udemy_24.png",
+      "url": "https://www.qpython.com.cn/course/ai-course-langgraph/",
+      "tags": [
+        "AI开发",
+        "LangGraph",
+        "中级"
+      ],
+      "difficulty": "beginner",
+      "price": "¥19",
+      "status": "publish",
+      "catalog": "人工智能",
+      "slug": "ai-course-langgraph"
+    }
+  ]
+}
\ No newline at end of file
diff --git a/public/data/courses_en.json b/public/data/courses_en.json
new file mode 100644
index 0000000..0c26044
--- /dev/null
+++ b/public/data/courses_en.json
@@ -0,0 +1,91 @@
+{
+  "courses": [
+    {
+      "id": "6492",
+      "title": "Python Syntax Practice",
+      "description": "About Python Programming\nPython: A Simple Yet Powerful Programming Language\nImagine a programming language that is not only easy to learn but also powerful - that's Python. Just as we can express complex ideas with simple words, Python lets us accomplish a lot with simple code.\nPython is designed to be human-friendly, its code reads like a story, easy to understand and write. And Python checks for errors better than some older programming languages. It comes with many useful tools built-in, like having a versatile toolkit at your side that you can use anytime to solve problems.",
+      "image": "/assets/udemy_en_06.png",
+      "url": "https://www.qpython.com/course/python-basic-syntax/",
+      "tags": [
+        "Python Syntax",
+        "Getting Started",
+        "Beginner"
+      ],
+      "difficulty": "advanced",
+      "price": "¥5",
+      "status": "publish",
+      "catalog": "Advanced Python Development",
+      "slug": "python-basic-syntax"
+    },
+    {
+      "id": "3995",
+      "title": "QPython Getting Started",
+      "description": "About QPython\nQPython is a Python programming engine designed specifically for Android devices. It integrates an interpreter, console, and editor, along with the QSL4A library and APIGPT, fully supporting web development, scientific computing, and AI extensions. Whether you are a Python beginner or an experienced developer, QPython provides you with a powerful mobile programming platform.\nCore Features:\nComplete Python Environment: Built-in Python interpreter allows you to write and execute code anytime, anywhere.\nEfficient Code Editor: QEditor provides a convenient editor for developing Python projects on your phone.\nJupyter Notebook Compatibility: Through QNotebook browser, you can easily learn and run Notebook files.\nExtension Libraries and PIP Support: Easily install and manage third-party libraries, greatly expanding your programming possibilities.\nCore Libraries: QPython's exclusive QSL4A and APIGPT libraries bring unique features and powerful support for your Android or AI projects.\nLearning Resources and Community: Built-in curated courses and community links to help you quickly improve your programming skills.",
+      "image": "/assets/udemy_en_01.png",
+      "url": "https://www.qpython.com/course/qpython-basic/",
+      "tags": [
+        "QPython",
+        "Getting Started",
+        "Beginner"
+      ],
+      "difficulty": "beginner",
+      "price": "¥9",
+      "status": "publish",
+      "catalog": "Mobile App Development",
+      "slug": "qpython-basic"
+    },
+    {
+      "id": "4198",
+      "title": "Generative AI Programming Getting Started",
+      "description": "Course Overview\nWith the rise of Generative AI (AIGC), especially the widespread adoption of ChatGPT, learning how to integrate AIGC technology into your applications has become a trend. The QPai Generative AI Development Series - Getting Started course aims to provide Python developers with a quick start path, enabling you to master AIGC application skills in program development.\nCourse Features\nDeep Understanding of AIGC: We will explore the fundamental principles of generative AI and its applications in artistic creation, image generation, dialogue generation, and text content generation.\nMaster PGPT Services: PGPT, as a cloud API platform that aggregates multiple large AI services, provides a lightweight AIGC development library to help developers quickly build applications.\nSpecial QPython Support: The course specifically uses the method of calling PGPT services in QPython, allowing learners to conveniently experience the charm of generative AI on mobile devices.",
+      "image": "/assets/udemy_en_02.png",
+      "url": "https://www.qpython.com/course/getting-started-with-qpy-generative-ai-development/",
+      "tags": [
+        "AI Application",
+        "Generative AI",
+        "Getting Started",
+        "Beginner"
+      ],
+      "difficulty": "beginner",
+      "price": "¥19",
+      "status": "publish",
+      "catalog": "Artificial Intelligence",
+      "slug": "getting-started-with-qpy-generative-ai-development"
+    },
+    {
+      "id": "6948",
+      "title": "Pandas Data Analysis and Processing Programming",
+      "description": "About Pandas\nPandas is a powerful and easy-to-use library in Python specifically for processing and analyzing structured data. It allows you to handle data tables as easily as operating Excel spreadsheets, suitable for organizing, cleaning, filtering, and analyzing various datasets.\nWith Pandas, you can quickly perform common tasks such as data cleaning, missing value handling, data grouping, and aggregation. Pandas is an essential tool for data analysis that can greatly simplify the workflow of processing large amounts of data.\nBenefits of Learning Pandas:\nImprove Efficiency: Mastering Pandas can significantly improve your efficiency in processing large amounts of data.\nEnhanced Insight: With Pandas, you can quickly extract valuable information and insights from data.",
+      "image": "/assets/udemy_en_09.png",
+      "url": "https://www.qpython.com/course/qpy-pandas-basic/",
+      "tags": [
+        "Data Analysis",
+        "Pandas",
+        "Intermediate"
+      ],
+      "difficulty": "intermediate",
+      "price": "¥9",
+      "status": "publish",
+      "catalog": "Data Analysis and Processing",
+      "slug": "qpy-pandas-basic"
+    },
+    {
+      "id": "12759",
+      "title": "DeepSeek Getting Started Practice",
+      "description": "About DeepSeek\nIn today's wave of artificial intelligence sweeping the globe, DeepSeek shines like a rising star, attracting global attention with its pursuit and innovative spirit toward Artificial General Intelligence (AGI).\nDeepSeek was established in 2023 and is a company focused on achieving AGI. AGI, the ability to think, learn, reason, and solve problems like humans, is the ultimate goal of artificial intelligence.\nDeepSeek models support multiple functions including intelligent dialogue, accurate translation, creative writing, efficient programming, intelligent problem solving, and file interpretation. Its \"deep thinking\" and \"web search\" features enable DeepSeek to more comprehensively understand user questions and provide accurate answers.",
+      "image": "/assets/udemy_en_20.png",
+      "url": "https://www.qpython.com/course/aipy-deepseek-intro/",
+      "tags": [
+        "AI Application",
+        "DeepSeek",
+        "Getting Started",
+        "Beginner"
+      ],
+      "difficulty": "beginner",
+      "price": "¥9",
+      "status": "publish",
+      "catalog": "Artificial Intelligence",
+      "slug": "aipy-deepseek-intro"
+    }
+  ]
+}
diff --git a/public/data/courses_en_backup.json b/public/data/courses_en_backup.json
new file mode 100644
index 0000000..577434a
--- /dev/null
+++ b/public/data/courses_en_backup.json
@@ -0,0 +1,424 @@
+{
+  "courses": [
+    {
+      "id": "6492",
+      "title": "Python Syntax Practice",
+      "description": "About Python Programming\nPython: A Simple Yet Powerful Programming Language\nImagine a programming language that is not only easy to learn but also powerful - that's Python. Just as we can express complex ideas with simple words, Python lets us accomplish a lot with simple code.\nPython is designed to be human-friendly, its code reads like a story, easy to understand and write. And Python checks for errors better than some older programming languages. It comes with many useful tools built-in, like having a versatile toolkit at your side that you can use anytime to solve problems.",
+      "image": "/assets/udemy_en_06.png",
+      "url": "https://www.qpython.com/course/python-basic-syntax/",
+      "tags": [
+        "Python Syntax",
+        "Getting Started",
+        "Beginner"
+      ],
+      "difficulty": "advanced",
+      "price": "¥5",
+      "status": "publish",
+      "catalog": "Advanced Python Development",
+      "slug": "python-basic-syntax"
+    },
+    {
+      "id": "3995",
+      "title": "QPython Getting Started",
+      "description": "About QPython\nQPython is a Python programming engine designed specifically for Android devices. It integrates an interpreter, console, and editor, along with the QSL4A library and APIGPT, fully supporting web development, scientific computing, and AI extensions. Whether you are a Python beginner or an experienced developer, QPython provides you with a powerful mobile programming platform.\nCore Features:\nComplete Python Environment: Built-in Python interpreter allows you to write and execute code anytime, anywhere.\nEfficient Code Editor: QEditor provides a convenient editor for developing Python projects on your phone.\nJupyter Notebook Compatibility: Through QNotebook browser, you can easily learn and run Notebook files.\nExtension Libraries and PIP Support: Easily install and manage third-party libraries, greatly expanding your programming possibilities.\nCore Libraries: QPython's exclusive QSL4A and APIGPT libraries bring unique features and powerful support for your Android or AI projects.\nLearning Resources and Community: Built-in curated courses and community links to help you quickly improve your programming skills.",
+      "image": "/assets/udemy_en_01.png",
+      "url": "https://www.qpython.com/course/qpython-basic/",
+      "tags": [
+        "QPython",
+        "Getting Started",
+        "Beginner"
+      ],
+      "difficulty": "beginner",
+      "price": "¥9",
+      "status": "publish",
+      "catalog": "Mobile App Development",
+      "slug": "qpython-basic"
+    },
+    {
+      "id": "4198",
+      "title": "Generative AI Programming Getting Started",
+      "description": "Course Overview\nWith the rise of Generative AI (AIGC), especially the widespread adoption of ChatGPT, learning how to integrate AIGC technology into your applications has become a trend. The QPai Generative AI Development Series - Getting Started course aims to provide Python developers with a quick start path, enabling you to master AIGC application skills in program development.\nCourse Features\nDeep Understanding of AIGC: We will explore the fundamental principles of generative AI and its applications in artistic creation, image generation, dialogue generation, and text content generation.\nMaster PGPT Services: PGPT, as a cloud API platform that aggregates multiple large AI services, provides a lightweight AIGC development library to help developers quickly build applications.\nSpecial QPython Support: The course specifically uses the method of calling PGPT services in QPython, allowing learners to conveniently experience the charm of generative AI on mobile devices.",
+      "image": "/assets/udemy_en_02.png",
+      "url": "https://www.qpython.com/course/getting-started-with-qpy-generative-ai-development/",
+      "tags": [
+        "AI Application",
+        "Generative AI",
+        "Getting Started",
+        "Beginner"
+      ],
+      "difficulty": "beginner",
+      "price": "¥19",
+      "status": "publish",
+      "catalog": "Artificial Intelligence",
+      "slug": "getting-started-with-qpy-generative-ai-development"
+    },
+    // {
+    //   "id": "7229",
+    //   "title": "Scrapy Web Crawler Programming",
+    //   "description": "About Scrapy\nScrapy is a powerful and efficient Python web crawling framework that helps developers quickly crawl and extract data from websites. Scrapy was designed from the ground up for crawling large-scale websites and storing crawled data in a structured way for data analysis, machine learning model training, content aggregation, and more.\nScrapy provides a flexible API and rich features, including support for handling various page structures, easily defining data extraction logic, configuring proxies, handling redirects, and page caching.\nAdditionally, it has excellent extensibility, allowing users to write custom components to meet various complex needs.",
+    //   "image": "/assets/udemy_en_11.png",
+    //   "url": "https://www.qpython.com/course/scrapy-spider/",
+    //   "tags": [
+    //     "Data Collection",
+    //     "Web Crawler",
+    //     "Intermediate"
+    //   ],
+    //   "difficulty": "intermediate",
+    //   "price": "¥9",
+    //   "status": "publish",
+    //   "catalog": "Data Analysis and Processing",
+    //   "slug": "scrapy-spider"
+    // },
+    // {
+    //   "id": "4200",
+    //   "title": "Pico Car Basic Motion Programming",
+    //   "description": "What is QPai Pico Smart Car\nThe QPai Pico Smart Car is a mini car programming platform based on the Raspberry Pi Pico microcontroller, designed specifically for programming education and maker enthusiasts. Using MicroPython tools, it allows programming enthusiasts to interact and explore through various sensors in a flat, compact car design, learning programming while understanding the basic principles of physical computing and robot control.\nStrong Interactivity: The Pico Smart Car allows learners to directly interact with hardware through programming, enhancing the fun and practicality of learning.\nMultiple Sensor Support: Including infrared sensors, ultrasonic sensors, etc., enabling functions like automatic obstacle avoidance and line tracking.\nQPython Environment Support: Programming experiments can be conducted in the QPython mobile environment, allowing learning and creating anytime, anywhere.",
+    //   "image": "/assets/udemy_en_03.png",
+    //   "url": "https://www.qpython.com/course/picocar-basic/",
+    //   "tags": [
+    //     "Smart Hardware",
+    //     "Embedded",
+    //     "Intermediate"
+    //   ],
+    //   "difficulty": "intermediate",
+    //   "price": "¥29",
+    //   "status": "publish",
+    //   "catalog": "Smart Hardware",
+    //   "slug": "picocar-basic"
+    // },
+    // {
+    //   "id": "4218",
+    //   "title": "Flask Web Development Getting Started",
+    //   "description": "What is Flask\nFlask is a simple, flexible, and easy-to-use web framework in Python, suitable for beginners. It provides rich features and extensibility, helping developers quickly build fully functional web applications.\nSimple and Easy to Use: Flask's design philosophy is to keep things simple and user-friendly. Its API is straightforward and easy to learn and understand.\nRouting System: Flask provides a powerful routing system that can match different handler functions based on URL paths.\nTemplate Engine: Flask comes with the Jinja2 template engine, making it easy to separate dynamic content from static HTML pages.\nDatabase Integration: Flask supports various database extensions for easy database operations.",
+    //   "image": "/assets/udemy_en_04.png",
+    //   "url": "https://www.qpython.com/course/flask-web-development-course/",
+    //   "tags": [
+    //     "Web Development",
+    //     "Flask",
+    //     "Getting Started",
+    //     "Beginner"
+    //   ],
+    //   "difficulty": "beginner",
+    //   "price": "¥9",
+    //   "status": "publish",
+    //   "catalog": "Web Development",
+    //   "slug": "flask-web-development-course"
+    // },
+    // {
+    //   "id": "6457",
+    //   "title": "Poetry Q&A Project Practice",
+    //   "description": "This course aims to help students master basic Python programming skills through actual project development, and further learn how to apply programming technology in the field of culture and art. Through developing an interactive poetry Q&A system, students will learn the entire process from conception, design to implementing a complete application.\nCourse Content:\n1. Python Programming Basics\n2. Design and Implementation of Q&A Logic\n3. Application of Generative AI in Poetry\n4. Interface Design and User Experience Optimization\nExpected Outcomes:\nAfter completing this course, students will master practical skills for developing complex projects using Python.",
+    //   "image": "/assets/udemy_en_05.png",
+    //   "url": "https://www.qpython.com/course/poetry-you-ask-and-i-answer/",
+    //   "tags": [
+    //     "AI Application",
+    //     "NLP",
+    //     "Project Practice",
+    //     "Advanced"
+    //   ],
+    //   "difficulty": "advanced",
+    //   "price": "¥19",
+    //   "status": "publish",
+    //   "catalog": "Artificial Intelligence",
+    //   "slug": "poetry-you-ask-and-i-answer"
+    // },
+    // {
+    //   "id": "6705",
+    //   "title": "Django Web Development Getting Started",
+    //   "description": "About Django\nImagine you are building a house. Django is like a set of pre-designed blueprints and tools that help you quickly build a sturdy, beautiful, and powerful website.\nDjango is an open-source web framework written in Python. It helps you handle many tedious tasks in website development, such as user authentication, database management, URL routing, and more.\nRapid Development: Django's design philosophy is rapid development. It provides a large number of built-in features that allow you to build fully functional websites in a short time.\nSecurity: Django has many built-in security mechanisms that can effectively protect your website from common network attacks.",
+    //   "image": "/assets/udemy_en_07.png",
+    //   "url": "https://www.qpython.com/course/django-basics/",
+    //   "tags": [
+    //     "Web Development",
+    //     "Django",
+    //     "Getting Started",
+    //     "Beginner"
+    //   ],
+    //   "difficulty": "beginner",
+    //   "price": "¥9",
+    //   "status": "publish",
+    //   "catalog": "Web Development",
+    //   "slug": "django-basics"
+    // },
+    // {
+    //   "id": "6898",
+    //   "title": "NumPy Scientific Computing Programming",
+    //   "description": "About NumPy\nNumPy (Numerical Python) is a library specially designed for Python to help us handle large amounts of data more easily. Think of it as a \"computing assistant\" in Python that allows us to complete complex mathematical operations and data processing with less code.\nWith NumPy, you can:\nCreate Arrays: Not only can it handle one-dimensional data like lists, but also easily handle two-dimensional, three-dimensional, and even higher-dimensional arrays.\nFast Computation: NumPy provides a complete set of built-in functions for quick arithmetic, statistical analysis, and other operations.\nTime-Saving: Due to its efficient computation method, NumPy is much faster than traditional Python methods.",
+    //   "image": "/assets/udemy_en_08.png",
+    //   "url": "https://www.qpython.com/course/qpy-numpy-basic/",
+    //   "tags": [
+    //     "Data Analysis",
+    //     "NumPy",
+    //     "Getting Started",
+    //     "Beginner"
+    //   ],
+    //   "difficulty": "intermediate",
+    //   "price": "¥9",
+    //   "status": "publish",
+    //   "catalog": "Data Analysis and Processing",
+    //   "slug": "qpy-numpy-basic"
+    // },
+    {
+      "id": "6948",
+      "title": "Pandas Data Analysis and Processing Programming",
+      "description": "About Pandas\nPandas is a powerful and easy-to-use library in Python specifically for processing and analyzing structured data. It allows you to handle data tables as easily as operating Excel spreadsheets, suitable for organizing, cleaning, filtering, and analyzing various datasets.\nWith Pandas, you can quickly perform common tasks such as data cleaning, missing value handling, data grouping, and aggregation. Pandas is an essential tool for data analysis that can greatly simplify the workflow of processing large amounts of data.\nBenefits of Learning Pandas:\nImprove Efficiency: Mastering Pandas can significantly improve your efficiency in processing large amounts of data.\nEnhanced Insight: With Pandas, you can quickly extract valuable information and insights from data.",
+      "image": "/assets/udemy_en_09.png",
+      "url": "https://www.qpython.com/course/qpy-pandas-basic/",
+      "tags": [
+        "Data Analysis",
+        "Pandas",
+        "Intermediate"
+      ],
+      "difficulty": "intermediate",
+      "price": "¥9",
+      "status": "publish",
+      "catalog": "Data Analysis and Processing",
+      "slug": "qpy-pandas-basic"
+    },
+    // {
+    //   "id": "7168",
+    //   "title": "Web Scraping with Beautiful Soup 4",
+    //   "description": "About Beautiful Soup\nBeautiful Soup is a Python library for extracting data from HTML and XML documents. The charm of Beautiful Soup lies in its simplicity and flexibility. It provides an intuitive API that allows you to easily parse web pages, just like easily browsing your own bookshelf.\nWhether encountering non-standardized HTML documents or needing to find specific information in complex data structures, Beautiful Soup can help you easily handle it.\nWith Beautiful Soup, you can quickly locate specific elements in web pages, extract the text or attributes you need, and even clean and organize data.",
+    //   "image": "/assets/udemy_en_10.png",
+    //   "url": "https://www.qpython.com/course/qpy-bs4-basics/",
+    //   "tags": [
+    //     "Data Collection",
+    //     "Web Scraper",
+    //     "Getting Started",
+    //     "Beginner"
+    //   ],
+    //   "difficulty": "beginner",
+    //   "price": "¥9",
+    //   "status": "publish",
+    //   "catalog": "Data Analysis and Processing",
+    //   "slug": "qpy-bs4-basics"
+    // },
+    // {
+    //   "id": "7552",
+    //   "title": "Pillow Image Processing Programming",
+    //   "description": "About Pillow\nIn today's programming world, image processing is an indispensable skill, and the Pillow library is a leader in this field. Pillow, as the modern branch of the Python Imaging Library (PIL), has become the most popular image processing library in Python.\nImagine you want to edit an image, whether for simple operations like opening and saving images, cropping part of an image, or rotating an image, or for more complex processing like applying various filters, compositing multiple images, or even changing the color mode of an image, Pillow can help you complete these tasks efficiently.\nIts ease of use allows even beginners to get started quickly, while its powerful features can meet the needs of professional developers.",
+    //   "image": "/assets/udemy_en_12.png",
+    //   "url": "https://www.qpython.com/course/py-pillow-started/",
+    //   "tags": [
+    //     "Image Processing",
+    //     "Python Library",
+    //     "Intermediate"
+    //   ],
+    //   "difficulty": "advanced",
+    //   "price": "¥9",
+    //   "status": "publish",
+    //   "catalog": "Advanced Python Development",
+    //   "slug": "py-pillow-started"
+    // },
+    // {
+    //   "id": "8878",
+    //   "title": "QSL4A Programming with QPython",
+    //   "description": "About QSL4A\nQSL4A (QPython Scripting Layer for Android) is an extension library developed by the QPython team based on the SL4A project, designed to provide a more flexible scripting programming environment for Android developers. Through QSL4A, developers can not only quickly implement prototypes of Android applications using Python, but also achieve device control and automation tasks by calling Android APIs.\nQSL4A Feature Highlights:\nLightweight User Interface (UI) Support: Provides basic user interface functionality, allowing developers to quickly create simple interactive applications.\nHTML and JavaScript Integration: Supports building mobile app interfaces through HTML and JavaScript.\nSensor and Hardware Control: Can access Android device sensors, cameras, phone, and other features.",
+    //   "image": "/assets/udemy_en_13.png",
+    //   "url": "https://www.qpython.com/course/qpy-qsl4a/",
+    //   "tags": [
+    //     "QPython",
+    //     "Android",
+    //     "Intermediate"
+    //   ],
+    //   "difficulty": "intermediate",
+    //   "price": "¥9",
+    //   "status": "publish",
+    //   "catalog": "Mobile App Development",
+    //   "slug": "qpy-qsl4a"
+    // },
+    // {
+    //   "id": "9562",
+    //   "title": "OpenPyXL Spreadsheet Programming",
+    //   "description": "About OpenPyXL\nOpenPyXL is a powerful Python library specifically for handling Excel files, especially .xlsx format files. It allows developers to create, modify, read, and save Excel files, supporting complex operations such as adding charts, setting formats, handling formulas, merging cells, and setting cell styles.\nOpenPyXL is widely used in data processing, automated office work, and report generation scenarios.\nCourse Introduction\nThis course will take you to deeply learn how to use OpenPyXL to process Excel files. Whether you are a data analyst, developer, or need to automate report generation, you can master the core skills of using OpenPyXL through this course. The course specifically includes practical content in QPython.",
+    //   "image": "/assets/udemy_en_14.png",
+    //   "url": "https://www.qpython.com/course/qpy-openpyxl/",
+    //   "tags": [
+    //     "Data Analysis",
+    //     "Excel",
+    //     "Intermediate"
+    //   ],
+    //   "difficulty": "advanced",
+    //   "price": "¥9",
+    //   "status": "publish",
+    //   "catalog": "Advanced Python Development",
+    //   "slug": "qpy-openpyxl"
+    // },
+    // {
+    //   "id": "9799",
+    //   "title": "Scikit-learn Machine Learning Getting Started",
+    //   "description": "About Scikit-learn\nScikit-learn is an open-source Python library widely used in machine learning and data science. It provides simple and efficient tools for data mining, data analysis, and building machine learning models. Scikit-learn is built on NumPy, SciPy, and matplotlib, focusing on machine learning model implementation, suitable for both beginners and experienced developers.\nMain Features:\nEasy to Use: Scikit-learn provides a consistent interface to call different machine learning algorithms, making it very intuitive to use.\nWide Algorithm Support: Includes many classic machine learning algorithms such as classification, regression, clustering, dimensionality reduction, etc.\nRich Documentation and Examples: With detailed documentation and a large number of examples to help developers get started quickly.",
+    //   "image": "/assets/udemy_en_15.png",
+    //   "url": "https://www.qpython.com/course/scikit-learn-startup/",
+    //   "tags": [
+    //     "Machine Learning",
+    //     "AI",
+    //     "Getting Started",
+    //     "Beginner"
+    //   ],
+    //   "difficulty": "beginner",
+    //   "price": "¥19",
+    //   "status": "publish",
+    //   "catalog": "Artificial Intelligence",
+    //   "slug": "scikit-learn-startup"
+    // },
+    // {
+    //   "id": "10416",
+    //   "title": "PyTorch From Beginner to Practice",
+    //   "description": "About PyTorch\nPyTorch is a widely used deep learning framework that provides dynamic computation graphs and powerful GPU acceleration, enabling researchers and developers to quickly implement and test complex neural network models. Its tensor computation and automatic differentiation mechanisms provide efficient and flexible support for deep learning.\nPyTorch's concise API makes learning and practicing deep learning easier, especially suitable for academic research and industrial applications.\nCourse Introduction\nThis course aims to help you quickly get started with PyTorch, comprehensively covering the basic concepts of PyTorch, including tensor operations, automatic differentiation, and building and training simple neural networks.",
+    //   "image": "/assets/udemy_en_17.png",
+    //   "url": "https://www.qpython.com/course/pytorch-startup/",
+    //   "tags": [
+    //     "Deep Learning",
+    //     "PyTorch",
+    //     "Getting Started",
+    //     "Beginner"
+    //   ],
+    //   "difficulty": "beginner",
+    //   "price": "¥29",
+    //   "status": "publish",
+    //   "catalog": "Artificial Intelligence",
+    //   "slug": "pytorch-startup"
+    // },
+    // {
+    //   "id": "11000",
+    //   "title": "AI Literacy Fundamentals",
+    //   "description": "About AI\nArtificial Intelligence (AI) is a technology and discipline that studies how to make machines possess human intelligence. AI technology has been widely used in daily life, from voice assistants to recommendation algorithms, and self-driving cars.\nThe core of AI is to enable computers to imitate human thinking, learning, and problem-solving abilities, including understanding natural language, analyzing data, recognizing images, planning decisions, and more.\nIn this course, we will introduce you to the basic concepts and practical applications of AI in an vivid and easy-to-understand way. Whether you have a technical background or not, this course can help you take your first step in learning AI.",
+    //   "image": "/assets/udemy_en_16.png",
+    //   "url": "https://www.qpython.com/course/qpai-artificial-intelligence-open-course-ai-literacy-foundation/",
+    //   "tags": [
+    //     "AI Fundamentals",
+    //     "General Knowledge",
+    //     "Getting Started"
+    //   ],
+    //   "difficulty": "beginner",
+    //   "price": "¥5",
+    //   "status": "publish",
+    //   "catalog": "Artificial Intelligence",
+    //   "slug": "qpai-artificial-intelligence-open-course-ai-literacy-foundation"
+    // },
+    // {
+    //   "id": "11564",
+    //   "title": "OpenCV Getting Started Course",
+    //   "description": "About OpenCV\nOpenCV (Open Source Computer Vision Library) is an open-source computer vision library widely used in image processing, video analysis, machine learning, and other fields. It provides rich tools that allow developers to process images and videos, and even develop real-time computer vision applications.\nThis course is specially designed for Python programming beginners, helping you master the basic knowledge and practical skills of the OpenCV library through simple and easy-to-understand steps, easily starting your journey in the world of computer vision.\nCourse Content:\nIn this tutorial, we will start from the most basic content and gradually guide you through learning how to use OpenCV for image and video processing.",
+    //   "image": "/assets/udemy_en_18.png",
+    //   "url": "https://www.qpython.com/course/opencv-started-tutorial/",
+    //   "tags": [
+    //     "Computer Vision",
+    //     "OpenCV",
+    //     "Getting Started",
+    //     "Beginner"
+    //   ],
+    //   "difficulty": "beginner",
+    //   "price": "¥29",
+    //   "status": "publish",
+    //   "catalog": "Artificial Intelligence",
+    //   "slug": "opencv-started-tutorial"
+    // },
+    // {
+    //   "id": "12497",
+    //   "title": "OpenAI Getting Started Practice",
+    //   "description": "About the OpenAI Library\nOpenAI is an artificial intelligence research laboratory, the lab behind the famous ChatGPT, dedicated to developing AI technology that benefits all of humanity. In this course, we will learn how to use the Python library provided by OpenAI to interact with AI models connected through PGPT.\nThe OpenAI Python library makes it simple and efficient to communicate with and use powerful AI models (such as the GPT series, DALL·E, etc.). Through this library, you can easily send requests to AI and get responses, generate text, images, and even code!\nCourse Content:\nThis course will help you understand how to interact with AI models by calling PGPT services through OpenAI, and master common usage scenarios through practical cases.",
+    //   "image": "/assets/udemy_en_19.png",
+    //   "url": "https://www.qpython.com/course/pgpt-openai-introduction/",
+    //   "tags": [
+    //     "AI Application",
+    //     "OpenAI",
+    //     "Getting Started",
+    //     "Beginner"
+    //   ],
+    //   "difficulty": "beginner",
+    //   "price": "¥9",
+    //   "status": "publish",
+    //   "catalog": "Artificial Intelligence",
+    //   "slug": "pgpt-openai-introduction"
+    // },
+    {
+      "id": "12759",
+      "title": "DeepSeek Getting Started Practice",
+      "description": "About DeepSeek\nIn today's wave of artificial intelligence sweeping the globe, DeepSeek shines like a rising star, attracting global attention with its pursuit and innovative spirit toward Artificial General Intelligence (AGI).\nDeepSeek was established in 2023 and is a company focused on achieving AGI. AGI, the ability to think, learn, reason, and solve problems like humans, is the ultimate goal of artificial intelligence.\nDeepSeek models support multiple functions including intelligent dialogue, accurate translation, creative writing, efficient programming, intelligent problem solving, and file interpretation. Its \"deep thinking\" and \"web search\" features enable DeepSeek to more comprehensively understand user questions and provide accurate answers.",
+      "image": "/assets/udemy_en_20.png",
+      "url": "https://www.qpython.com/course/aipy-deepseek-intro/",
+      "tags": [
+        "AI Application",
+        "DeepSeek",
+        "Getting Started",
+        "Beginner"
+      ],
+      "difficulty": "beginner",
+      "price": "¥9",
+      "status": "publish",
+      "catalog": "Artificial Intelligence",
+      "slug": "aipy-deepseek-intro"
+    },
+    // {
+    //   "id": "13083",
+    //   "title": "Twisted Getting Started",
+    //   "description": "About Twisted\nTwisted is a powerful Python asynchronous programming framework designed for event-driven network applications. It supports the development of multiple protocols such as TCP, UDP, HTTP, FTP, etc., suitable for building high-performance network server and client applications.\nTwisted's core idea is event loop-based asynchronous programming, which allows it to efficiently handle a large number of concurrent connections without using traditional threads or processes.\nIts Deferred and Callback mechanisms make asynchronous operations simpler and easier to understand, avoiding callback hell issues.",
+    //   "image": "/assets/udemy_en_21.png",
+    //   "url": "https://www.qpython.com/course/python-twisted-intro/",
+    //   "tags": [
+    //     "Advanced Python Development",
+    //     "Network Programming",
+    //     "Intermediate"
+    //   ],
+    //   "difficulty": "beginner",
+    //   "price": "¥19",
+    //   "status": "publish",
+    //   "catalog": "Advanced Python Development",
+    //   "slug": "python-twisted-intro"
+    // },
+    // {
+    //   "id": "13724",
+    //   "title": "Prompt Engineering Getting Started",
+    //   "description": "About Prompt Engineering\nThe powerful capabilities of large language models come from massive data training, but they are extremely sensitive to prompt interpretation - even tiny changes can lead to completely different results. Therefore, precisely designing effective prompts can not only significantly improve model performance but also avoid wasting computing resources and reduce errors.\nAs AI technology, especially large language models (like GPT and DeepSeek), are widely used in various industries, Prompt Engineering has become a key skill for improving human-computer interaction efficiency. Mastering how to design, optimize, and evaluate prompts can help us have more efficient conversations with these powerful language models.\nIn this course, we will deeply explore the basic concepts, design principles, and optimization techniques of prompt engineering.",
+    //   "image": "/assets/udemy_en_22.png",
+    //   "url": "https://www.qpython.com/course/qpy-prompt-engineering/",
+    //   "tags": [
+    //     "AI Application",
+    //     "Prompt Engineering",
+    //     "Getting Started",
+    //     "Beginner"
+    //   ],
+    //   "difficulty": "beginner",
+    //   "price": "¥9",
+    //   "status": "publish",
+    //   "catalog": "Artificial Intelligence",
+    //   "slug": "qpy-prompt-engineering"
+    // },
+    // {
+    //   "id": "13934",
+    //   "title": "LangChain Development Getting Started",
+    //   "description": "About LangChain\nLangChain is an open-source framework designed to help developers efficiently interact with and develop applications using large language models (such as GPT, LLMs, etc.). It provides a powerful set of tools that enables developers to easily build complex applications when processing natural language tasks.\nLangChain helps developers fully leverage the potential of language models to solve practical problems by simplifying data flow, managing multi-step tasks, integrating with external APIs, and providing memory and state management.\nCourse Introduction\nThis course will deeply explain how to develop AI applications using the LangChain framework, from basic knowledge to advanced applications, helping you master the core usage of LangChain.",
+    //   "image": "/assets/udemy_en_23.png",
+    //   "url": "https://www.qpython.com/course/ai-development-getting-started-with-langchain/",
+    //   "tags": [
+    //     "AI Development",
+    //     "LangChain",
+    //     "Intermediate"
+    //   ],
+    //   "difficulty": "beginner",
+    //   "price": "¥19",
+    //   "status": "publish",
+    //   "catalog": "Artificial Intelligence",
+    //   "slug": "ai-development-getting-started-with-langchain"
+    // },
+    // {
+    //   "id": "14285",
+    //   "title": "LangGraph Development Getting Started",
+    //   "description": "About LangGraph\nLangGraph is a library based on LangChain, specifically designed for building reliable and scalable state management and workflow orchestration tools for language model applications. It is not just a simple programming library, but a revolutionary platform that enables developers to create complex, dynamic, and intelligent AI applications.\nIn traditional AI application development, developers are often limited by linear, static workflows. LangGraph has completely changed this situation. LangGraph allows developers to create complex, stateful AI workflows with precise control over the execution process of language model agents.\nIt is especially suitable for AI applications that require multi-step, stateful interactions.",
+    //   "image": "/assets/udemy_en_24.png",
+    //   "url": "https://www.qpython.com/course/ai-course-langgraph/",
+    //   "tags": [
+    //     "AI Development",
+    //     "LangGraph",
+    //     "Intermediate"
+    //   ],
+    //   "difficulty": "beginner",
+    //   "price": "¥19",
+    //   "status": "publish",
+    //   "catalog": "Artificial Intelligence",
+    //   "slug": "ai-course-langgraph"
+    // }
+  ]
+}
diff --git a/src/App.tsx b/src/App.tsx
new file mode 100644
index 0000000..1b008bb
--- /dev/null
+++ b/src/App.tsx
@@ -0,0 +1,15 @@
+import { Routes, Route } from 'react-router-dom'
+import { LanguageProvider } from '@/context/LanguageContext'
+import { CoursesPage } from '@/pages/CoursesPage'
+
+function App() {
+  return (
+    <LanguageProvider>
+      <Routes>
+        <Route path="/" element={<CoursesPage />} />
+      </Routes>
+    </LanguageProvider>
+  )
+}
+
+export default App
diff --git a/src/components/LanguageSwitch.tsx b/src/components/LanguageSwitch.tsx
new file mode 100644
index 0000000..daaf8ad
--- /dev/null
+++ b/src/components/LanguageSwitch.tsx
@@ -0,0 +1,33 @@
+import { useLanguage } from '@/context/LanguageContext'
+import { cn } from '@/lib/utils'
+
+export function LanguageSwitch() {
+  const { language, setLanguage } = useLanguage()
+
+  return (
+    <div className="flex items-center gap-1 bg-gray-100 rounded-md p-0.5">
+      <button
+        onClick={() => setLanguage('zh')}
+        className={cn(
+          'px-2 py-1 text-xs rounded transition-colors',
+          language === 'zh'
+            ? 'bg-white text-black shadow-sm font-medium'
+            : 'text-gray-600 hover:text-gray-900'
+        )}
+      >
+        ZH
+      </button>
+      <button
+        onClick={() => setLanguage('en')}
+        className={cn(
+          'px-2 py-1 text-xs rounded transition-colors',
+          language === 'en'
+            ? 'bg-white text-black shadow-sm font-medium'
+            : 'text-gray-600 hover:text-gray-900'
+        )}
+      >
+        EN
+      </button>
+    </div>
+  )
+}
diff --git a/src/context/LanguageContext.tsx b/src/context/LanguageContext.tsx
new file mode 100644
index 0000000..cd2c690
--- /dev/null
+++ b/src/context/LanguageContext.tsx
@@ -0,0 +1,49 @@
+import { createContext, useContext, useState, useCallback, ReactNode } from 'react'
+import translations from '@/i18n/translations.json'
+
+type Language = 'zh' | 'en'
+
+interface LanguageContextType {
+  language: Language
+  setLanguage: (lang: Language) => void
+  t: (key: string, params?: Record<string, string | number>) => string
+}
+
+const LanguageContext = createContext<LanguageContextType | null>(null)
+
+export function LanguageProvider({ children }: { children: ReactNode }) {
+  const getInitialLanguage = (): Language => {
+    const params = new URLSearchParams(window.location.search)
+    const langParam = params.get('lang')
+    if (langParam === 'zh' || langParam === 'en') {
+      return langParam
+    }
+    return 'en'
+  }
+
+  const [language, setLanguage] = useState<Language>(getInitialLanguage)
+
+  const t = useCallback((key: string, params?: Record<string, string | number>) => {
+    const langData = translations[language] as Record<string, string>
+    const enData = translations.en as Record<string, string>
+    let text = langData[key] || enData[key] || key
+    if (params) {
+      Object.entries(params).forEach(([k, v]) => {
+        text = text.replace(`{${k}}`, String(v))
+      })
+    }
+    return text
+  }, [language])
+
+  return (
+    <LanguageContext.Provider value={{ language, setLanguage, t }}>
+      {children}
+    </LanguageContext.Provider>
+  )
+}
+
+export function useLanguage() {
+  const context = useContext(LanguageContext)
+  if (!context) throw new Error('useLanguage must be used within LanguageProvider')
+  return context
+}
diff --git a/src/i18n/translations.json b/src/i18n/translations.json
new file mode 100644
index 0000000..cba8792
--- /dev/null
+++ b/src/i18n/translations.json
@@ -0,0 +1,48 @@
+{
+  "zh": {
+    "discoverCourses": "QPY精选课程",
+    "totalCourses": "共 {count} 门课程",
+    "loading": "加载中...",
+    "noResults": "没有找到符合条件的课程",
+    "clearFilters": "清除筛选",
+    "filter": "筛选",
+    "difficulty": "难度",
+    "tags": "标签",
+    "all": "全部分类",
+    "beginner": "初级",
+    "intermediate": "中级",
+    "advanced": "高级",
+    "catalog": "分类",
+    "人工智能": "人工智能",
+    "深入Python开发": "深入Python开发",
+    "数据分析与处理": "数据分析与处理",
+    "移动应用开发": "移动应用开发",
+    "WEB开发": "WEB开发",
+    "智能硬件": "智能硬件",
+    "viewDetails": "查看详情",
+    "filterByTag": "Tags"
+  },
+  "en": {
+    "discoverCourses": "QPY Courses",
+    "totalCourses": "{count} courses",
+    "loading": "Loading...",
+    "noResults": "No courses found",
+    "clearFilters": "Clear filters",
+    "filter": "Filter",
+    "difficulty": "Difficulty",
+    "tags": "Tags",
+    "all": "All",
+    "beginner": "Beginner",
+    "intermediate": "Intermediate",
+    "advanced": "Advanced",
+    "catalog": "Category",
+    "人工智能": "Artificial Intelligence",
+    "深入Python开发": "Advanced Python",
+    "数据分析与处理": "Data Analysis",
+    "移动应用开发": "Mobile Development",
+    "WEB开发": "Web Development",
+    "智能硬件": "Smart Hardware",
+    "viewDetails": "View Details",
+    "filterByTag": "Tags"
+  }
+}
diff --git a/src/index.css b/src/index.css
new file mode 100644
index 0000000..b5c61c9
--- /dev/null
+++ b/src/index.css
@@ -0,0 +1,3 @@
+@tailwind base;
+@tailwind components;
+@tailwind utilities;
diff --git a/src/lib/utils.ts b/src/lib/utils.ts
new file mode 100644
index 0000000..fed2fe9
--- /dev/null
+++ b/src/lib/utils.ts
@@ -0,0 +1,6 @@
+import { clsx, type ClassValue } from 'clsx'
+import { twMerge } from 'tailwind-merge'
+
+export function cn(...inputs: ClassValue[]) {
+  return twMerge(clsx(inputs))
+}
diff --git a/src/main.tsx b/src/main.tsx
new file mode 100644
index 0000000..fa94fac
--- /dev/null
+++ b/src/main.tsx
@@ -0,0 +1,13 @@
+import React from 'react'
+import ReactDOM from 'react-dom/client'
+import { BrowserRouter } from 'react-router-dom'
+import App from './App'
+import './index.css'
+
+ReactDOM.createRoot(document.getElementById('root')!).render(
+  <React.StrictMode>
+    <BrowserRouter>
+      <App />
+    </BrowserRouter>
+  </React.StrictMode>,
+)
diff --git a/src/pages/CoursesPage.tsx b/src/pages/CoursesPage.tsx
new file mode 100644
index 0000000..86ae87d
--- /dev/null
+++ b/src/pages/CoursesPage.tsx
@@ -0,0 +1,337 @@
+import { useState, useEffect, useMemo } from 'react'
+import { BookOpen } from 'lucide-react'
+import { useLanguage } from '@/context/LanguageContext'
+import { Course, CourseData } from '@/types/course'
+import { cn } from '@/lib/utils'
+import { LanguageSwitch } from '@/components/LanguageSwitch'
+
+const CATALOG_ORDER = [
+  '人工智能',
+  '深入Python开发',
+  '数据分析与处理',
+  '移动应用开发',
+  'WEB开发',
+  '智能硬件',
+]
+
+export function CoursesPage() {
+  const { t, language } = useLanguage()
+  const [courses, setCourses] = useState<Course[]>([])
+  const [isLoading, setIsLoading] = useState(true)
+  const [selectedCatalog, setSelectedCatalog] = useState<string>('all')
+  const [selectedTags, setSelectedTags] = useState<string[]>([])
+  const [tagModalOpen, setTagModalOpen] = useState(false)
+  const showLangSwitch = new URLSearchParams(window.location.search).get('lang') === 'true'
+
+  useEffect(() => {
+    const url = language === 'zh' ? '/data/courses.json' : '/data/courses_en.json'
+    fetch(url)
+      .then(res => res.json())
+      .then((data: CourseData) => {
+        setCourses(data.courses)
+      })
+      .catch(console.error)
+      .finally(() => setIsLoading(false))
+  }, [language])
+
+  const availableCatalogs = useMemo(() => {
+    const cats = [...new Set(courses.map(c => c.catalog).filter(Boolean))]
+    return cats.sort((a, b) => {
+      const ai = CATALOG_ORDER.indexOf(a)
+      const bi = CATALOG_ORDER.indexOf(b)
+      if (ai === -1 && bi === -1) return a.localeCompare(b)
+      if (ai === -1) return 1
+      if (bi === -1) return -1
+      return ai - bi
+    })
+  }, [courses])
+
+  const availableTags = useMemo(() => {
+    const tags = new Set<string>()
+    courses.forEach(c => c.tags.forEach(tag => tags.add(tag)))
+    return [...tags].sort()
+  }, [courses])
+
+  const filteredCourses = useMemo(() => {
+    let result = courses
+    if (selectedCatalog !== 'all') {
+      result = result.filter(course => course.catalog === selectedCatalog)
+    }
+    if (selectedTags.length > 0) {
+      result = result.filter(course =>
+        selectedTags.some(tag => course.tags.includes(tag))
+      )
+    }
+    return result
+  }, [courses, selectedCatalog, selectedTags])
+
+  const toggleTag = (tag: string) => {
+    setSelectedTags(prev =>
+      prev.includes(tag) ? prev.filter(t => t !== tag) : [...prev, tag]
+    )
+  }
+
+  const clearAll = () => {
+    setSelectedCatalog('all')
+    setSelectedTags([])
+  }
+
+  const hasFilters = selectedCatalog !== 'all' || selectedTags.length > 0
+
+  return (
+    <div className="min-h-screen bg-gray-100">
+      {/* Tag filter modal */}
+      {tagModalOpen && (
+        <div className="fixed inset-0 z-50 lg:hidden">
+          <div
+            className="absolute inset-0 bg-black/40"
+            onClick={() => setTagModalOpen(false)}
+          />
+          <div className="absolute bottom-0 left-0 right-0 bg-white rounded-t-2xl p-4 max-h-[70vh] overflow-y-auto">
+            <div className="flex items-center justify-between mb-4">
+              <h3 className="font-semibold text-base">{t('filterByTag')}</h3>
+              <div className="flex gap-2">
+                {selectedTags.length > 0 && (
+                  <button
+                    onClick={() => setSelectedTags([])}
+                    className="text-xs text-gray-500 hover:text-gray-700"
+                  >
+                    {t('clearFilters')}
+                  </button>
+                )}
+                <button
+                  onClick={() => setTagModalOpen(false)}
+                  className="text-gray-500 hover:text-gray-700 p-1"
+                >
+                  ✕
+                </button>
+              </div>
+            </div>
+            <div className="flex flex-wrap gap-2">
+              {availableTags.map(tag => (
+                <button
+                  key={tag}
+                  onClick={() => toggleTag(tag)}
+                  className={cn(
+                    'px-3 py-1.5 text-sm rounded-full border transition-colors',
+                    selectedTags.includes(tag)
+                      ? 'bg-black text-white border-black'
+                      : 'bg-white text-gray-600 border-gray-300 hover:border-gray-700'
+                  )}
+                >
+                  {tag}
+                </button>
+              ))}
+            </div>
+          </div>
+        </div>
+      )}
+
+      {/* Header */}
+      <header className="bg-white border-b">
+        <div className="max-w-7xl mx-auto px-4 sm:px-6 lg:px-8 py-3 flex items-center justify-between">
+          <div className="flex items-center gap-4">
+            <div>
+              <h1 className="text-xl font-bold">{t('discoverCourses')}</h1>
+              {!isLoading && (
+                <div className="flex items-center gap-2 mt-0.5">
+                  <span className="text-gray-600 text-sm">
+                    {t('totalCourses', { count: filteredCourses.length })}
+                  </span>
+                  {showLangSwitch && <LanguageSwitch />}
+                </div>
+              )}
+            </div>
+          </div>
+          {/* Mobile controls */}
+          <div className="flex items-center gap-2 lg:hidden">
+            {/* Catalog select */}
+            <select
+              value={selectedCatalog}
+              onChange={e => setSelectedCatalog(e.target.value)}
+              className="text-sm border border-gray-300 rounded-md px-2 py-1.5 bg-white text-gray-700 focus:outline-none focus:border-gray-700 w-4/5"
+            >
+              <option value="all">{t('all')}</option>
+              {availableCatalogs.map(cat => (
+                <option key={cat} value={cat}>{t(cat)}</option>
+              ))}
+            </select>
+            {/* Tag filter button */}
+            <button
+              onClick={() => setTagModalOpen(true)}
+              className={cn(
+                'px-3 py-1.5 text-xs rounded-full border transition-colors',
+                selectedTags.length > 0
+                  ? 'bg-black text-white border-black'
+                  : 'bg-white text-gray-600 border-gray-300 hover:border-gray-700'
+              )}
+            >
+              {t('filterByTag')}{selectedTags.length > 0 ? ` (${selectedTags.length})` : ''}
+            </button>
+          </div>
+        </div>
+      </header>
+
+      {/* Main content */}
+      <main className="max-w-7xl mx-auto px-4 sm:px-6 lg:px-8 py-8">
+        <div className="flex gap-8">
+          {/* Sidebar */}
+          <aside className="hidden lg:block w-60 flex-shrink-0">
+            <div className="sticky top-8 space-y-6">
+              {hasFilters && (
+                <div className="flex items-center justify-between">
+                  <span className="font-semibold flex items-center gap-2 text-sm">
+                    {t('filter')}
+                  </span>
+                  <button
+                    onClick={clearAll}
+                    className="text-xs text-gray-500 hover:text-gray-700"
+                  >
+                    {t('clearFilters')}
+                  </button>
+                </div>
+              )}
+
+              {/* Catalog */}
+              <div>
+                <p className="text-xs font-medium mb-2 text-gray-500 uppercase tracking-wide">
+                  {t('catalog')}
+                </p>
+                <div className="space-y-0.5">
+                  <button
+                    key="all"
+                    onClick={() => setSelectedCatalog('all')}
+                    className={cn(
+                      'w-full text-left text-sm px-3 py-1.5 rounded-md transition-colors',
+                      selectedCatalog === 'all'
+                        ? 'bg-black text-white'
+                        : 'hover:bg-gray-200'
+                    )}
+                  >
+                    {t('all')}
+                  </button>
+                  {availableCatalogs.map(cat => (
+                    <button
+                      key={cat}
+                      onClick={() => setSelectedCatalog(cat)}
+                      className={cn(
+                        'w-full text-left text-sm px-3 py-1.5 rounded-md transition-colors',
+                        selectedCatalog === cat
+                          ? 'bg-black text-white'
+                          : 'hover:bg-gray-200'
+                      )}
+                    >
+                      {t(cat)}
+                    </button>
+                  ))}
+                </div>
+              </div>
+
+              {/* Tags */}
+              <div>
+                <p className="text-xs font-medium mb-2 text-gray-500 uppercase tracking-wide">
+                  {t('tags')}
+                </p>
+                <div className="space-y-0.5 max-h-64 overflow-y-auto">
+                  {availableTags.map(tag => (
+                    <button
+                      key={tag}
+                      onClick={() => toggleTag(tag)}
+                      className={cn(
+                        'w-full text-left text-sm px-3 py-1.5 rounded-md transition-colors flex items-center gap-2',
+                        selectedTags.includes(tag)
+                          ? 'bg-black text-white'
+                          : 'hover:bg-gray-200'
+                      )}
+                    >
+                      <span className={cn(
+                        'w-3.5 h-3.5 rounded border flex items-center justify-center shrink-0',
+                        selectedTags.includes(tag)
+                          ? 'bg-white border-black'
+                          : 'border-gray-400'
+                      )}>
+                        {selectedTags.includes(tag) && (
+                          <span className="w-2 h-2 rounded-full bg-black" />
+                        )}
+                      </span>
+                      {tag}
+                    </button>
+                  ))}
+                </div>
+              </div>
+            </div>
+          </aside>
+
+          {/* Course grid */}
+          <div className="flex-1 min-w-0">
+            {/* Tip banner */}
+            <div className="mb-6 p-4 bg-blue-50 border border-blue-200 rounded-lg">
+              <p className="text-sm text-blue-800">
+                {language === 'zh'
+                  ? '需要选课帮助，请加微信助手 learnwithqpy'
+                  : 'For course selection assistance, please add our WhatsApp assistant: +85262684559'}
+              </p>
+            </div>
+            {isLoading ? (
+              <div className="flex items-center justify-center py-20">
+                <span className="ml-3 text-lg text-gray-600">{t('loading')}</span>
+              </div>
+            ) : filteredCourses.length === 0 ? (
+              <div className="text-center py-20">
+                <BookOpen className="h-16 w-16 mx-auto text-gray-400 mb-4" />
+                <p className="text-lg text-gray-600">{t('noResults')}</p>
+              </div>
+            ) : (
+              <div className="grid grid-cols-1 md:grid-cols-2 xl:grid-cols-3 gap-6">
+                {filteredCourses.map((course) => (
+                  <a
+                    key={course.id}
+                    href={course.url}
+                    target="_blank"
+                    rel="noopener noreferrer"
+                    className="block bg-white rounded-lg shadow-lg overflow-hidden hover:shadow-xl transition-shadow"
+                    onClick={(e) => {
+                      if (typeof window.gtag !== 'undefined') {
+                        window.gtag('event', 'select_content', {
+                          content_type: 'course',
+                          item_id: course.id,
+                        })
+                      }
+                      if (typeof window !== 'undefined' && (window as any).milib) {
+                        e.preventDefault()
+                        ;(window as any).milib.openUrl(course.url)
+                      }
+                    }}
+                  >
+                    <div className="aspect-video w-full overflow-hidden bg-gray-200">
+                      <img
+                        src={course.image}
+                        alt={course.title}
+                        className="w-full h-full object-cover"
+                      />
+                    </div>
+                    <div className="p-4">
+                      <p className="text-xs text-black mb-1 font-medium">{t(course.catalog)}</p>
+                      <h3 className="text-lg font-semibold mb-2 line-clamp-1">{course.title}</h3>
+                      <p className="text-sm text-gray-600 mb-3 line-clamp-2">{course.description}</p>
+                      <div className="flex flex-wrap gap-1">
+                        {course.tags.slice(0, 3).map(tag => (
+                          <span
+                            key={tag}
+                            className="text-xs bg-gray-100 text-gray-700 px-2 py-1 rounded"
+                          >
+                            {tag}
+                          </span>
+                        ))}
+                      </div>
+                    </div>
+                  </a>
+                ))}
+              </div>
+            )}
+          </div>
+        </div>
+      </main>
+    </div>
+  )
+}
diff --git a/src/types/course.ts b/src/types/course.ts
new file mode 100644
index 0000000..a7cf35d
--- /dev/null
+++ b/src/types/course.ts
@@ -0,0 +1,17 @@
+export interface Course {
+  id: string
+  title: string
+  description: string
+  image: string
+  url: string
+  tags: string[]
+  slug: string
+  catalog: string
+  difficulty: 'beginner' | 'intermediate' | 'advanced'
+  price?: string
+  status?: string
+}
+
+export interface CourseData {
+  courses: Course[]
+}
diff --git a/src/types/global.d.ts b/src/types/global.d.ts
new file mode 100644
index 0000000..877c154
--- /dev/null
+++ b/src/types/global.d.ts
@@ -0,0 +1,3 @@
+interface Window {
+  gtag: (...args: any[]) => void
+}
diff --git a/src/vite-env.d.ts b/src/vite-env.d.ts
new file mode 100644
index 0000000..11f02fe
--- /dev/null
+++ b/src/vite-env.d.ts
@@ -0,0 +1 @@
+/// <reference types="vite/client" />
diff --git a/tailwind.config.js b/tailwind.config.js
new file mode 100644
index 0000000..dca8ba0
--- /dev/null
+++ b/tailwind.config.js
@@ -0,0 +1,11 @@
+/** @type {import('tailwindcss').Config} */
+export default {
+  content: [
+    "./index.html",
+    "./src/**/*.{js,ts,jsx,tsx}",
+  ],
+  theme: {
+    extend: {},
+  },
+  plugins: [],
+}
diff --git a/tsconfig.json b/tsconfig.json
new file mode 100644
index 0000000..c20738e
--- /dev/null
+++ b/tsconfig.json
@@ -0,0 +1,25 @@
+{
+  "compilerOptions": {
+    "target": "ES2020",
+    "useDefineForClassFields": true,
+    "lib": ["ES2020", "DOM", "DOM.Iterable"],
+    "module": "ESNext",
+    "skipLibCheck": true,
+    "moduleResolution": "bundler",
+    "allowImportingTsExtensions": true,
+    "resolveJsonModule": true,
+    "isolatedModules": true,
+    "noEmit": true,
+    "jsx": "react-jsx",
+    "strict": true,
+    "noUnusedLocals": true,
+    "noUnusedParameters": true,
+    "noFallthroughCasesInSwitch": true,
+    "baseUrl": ".",
+    "paths": {
+      "@/*": ["./src/*"]
+    }
+  },
+  "include": ["src"],
+  "references": [{ "path": "./tsconfig.node.json" }]
+}
diff --git a/tsconfig.node.json b/tsconfig.node.json
new file mode 100644
index 0000000..97ede7e
--- /dev/null
+++ b/tsconfig.node.json
@@ -0,0 +1,11 @@
+{
+  "compilerOptions": {
+    "composite": true,
+    "skipLibCheck": true,
+    "module": "ESNext",
+    "moduleResolution": "bundler",
+    "allowSyntheticDefaultImports": true,
+    "strict": true
+  },
+  "include": ["vite.config.ts"]
+}
diff --git a/vite.config.ts b/vite.config.ts
new file mode 100644
index 0000000..17b52ab
--- /dev/null
+++ b/vite.config.ts
@@ -0,0 +1,15 @@
+import { defineConfig } from 'vite'
+import react from '@vitejs/plugin-react'
+import path from 'path'
+
+export default defineConfig({
+  plugins: [react()],
+  resolve: {
+    alias: {
+      '@': path.resolve(__dirname, './src'),
+    },
+  },
+  build: {
+    outDir: 'docs',
+  },
+})