{
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"![http://ipython.org/_static/IPy_header.png](http://ipython.org/_static/IPy_header.png)\n",
"\n",
"[IPython](http://ipython.org/) in action creating reproducible and publishable interactive work.\n",
"\n",
"What is this?\n",
"------\n",
"\n",
"This `repo` contains the complete [talk](http://za.pycon.org/talks/10/) I intend to deliver (have delivered) at [PyConZA2013](http://za.pycon.org/). It contains all the files needed to build a final publishable PDF document from an interactive notebook and even adds a custom front page.\n",
"\n",
"[The Complete Talk GitHub Website can be accessed here][6]\n",
"\n",
"Background\n",
"-------------\n",
"IPython had become a popular choice for doing interactive scientific work. It extends the standard Python interpreter and adds many useful new futures. There is really no need to use the standard Python interpreter anymore.\n",
"In addition to this IPython offers a web based Notebook that makes interactive work much easier, and have been used to write repeatable scientific papers and more recently a book has been written using this platform, the online Notebook Viewer and GitHub. The development of this material and tool chain to compile the notebook to a publishable PDF, has inspired me to maybe even try and turn this into a complete (free) book. Let\u2019s see what happens.\n",
"\n",
"Combining the most common scientific packages with IPython makes it a formidable tool and serious competition to R. ( _R is still awesome!_ )\n",
"\n",
"![http://ipython.org/_static/ipy_0.13.png](http://ipython.org/_static/ipy_0.13.png)\n",
"\n",
"As a matter of fact you can run R in the notebook session, embed YouTube Videos, Images and lots more but let me not get ahead of myself....\n",
"\n",
"The science stack consists of (but not limited to):\n",
"\n",
"package | description\n",
"--- | ---\n",
"[pandas][1] | `dataframe` implementation (based on numpy)\n",
"[scipy][2] | efficient numerical routines\n",
"[sympy][3] | `symbolic mathematics`\n",
"[matplotlib][4] | python standard `plotting` package\n",
"[sci-kit learn][5] | machine learning and `well documented!`\n",
"\n",
"Talk contents\n",
"--------\n",
"The talk will aim to introduce these tools and explore some practical interactive examples. Once completed it will be shown how easy it is to publish your work to various formats. Some of the topics covered in the talk are listed below:\n",
"\n",
"item | description\n",
"---- | -------\n",
"ipython | quick intro to ipython and the notebook\n",
"setup | set up your environment / get the talk files\n",
"notebook basics | navigate the notebook\n",
"notebook magic\u2019s | special notebook commands that can be very useful\n",
"getting input | as from IPython 1.00 getting input from sdtin is possible\n",
"local files | how to link to local files in the notebook directory\n",
"plotting\t| how to create beautiful inline plots\n",
"symbolic math | quick demo of sympy model\n",
"pandas | quick intro to pandas dataframe\n",
"typesetting | include markdown, Latex via MathJax\n",
"loading code | how to load a remote .py code file\n",
"gist | paste some of your work to gist for sharing\n",
"js\t\t| some javascript examples\n",
"customising | loading a customer css and custom matplotlib config file\n",
"git cell | add code to a special cell that would commit to git\n",
"output formats | how to publish your work to html, pdf or jeveal.js presentation\n",
"\n",
"\n",
"Get the processed presentation files here:\n",
"------\n",
"format | description\n",
"------- | ------------\n",
"[IPython notebook](https://github.com/Tooblippe/zapycon2013_ipython_science/blob/master/src/pycon13_ipython.ipynb) | .ipynb file to run in browser\n",
"[IPython html notebook](http://htmlpreview.github.io/?https://github.com/Tooblippe/zapycon2013_ipython_science/blob/master/src/output/pycon13_ipython.html) | converted to HTML and served online\n",
"[IPython pdf notebook](https://github.com/Tooblippe/zapycon2013_ipython_science/blob/master/src/output/pycon13_ipython_pdf.pdf?raw=true) | converted to PDF for download (to be added, needs pandoc)\n",
"[IPython pdf book](https://github.com/Tooblippe/zapycon2013_ipython_science/blob/master/src/output/pycon13_ipython_complete.pdf?raw=true) | converted to pdf and a front-page stitched to it)\n",
"[Ipython reveal.js presentation](http://htmlpreview.github.io/?https://github.com/Tooblippe/zapycon2013_ipython_science/blob/master/src/output/pycon13_ipython.slides.html#/) | converted to a reveal.js presentation and served online\n",
"[Online IPython NBveiwer](http://nbviewer.ipython.org/urls/raw.github.com/Tooblippe/zapycon2013_ipython_science/master/src/pycon13_ipython.ipynb) | view on the ipython notebook viewer\n",
"\n",
"\n",
"Dependencies\n",
"-------------\n",
"I was given the challenge to develop all of this on a Windows machine as some of my sponsors want to demonstrate that this stuff can not only be done on GNU/Linux/OSX. So all the tool chains are Windows based. If you know Linux, then you are the type of person that would easily port this. That being said the Windows GitHub client is refreshing. I have also added a MacBook Air to my arsenal and have been porting the toolchain to Mac aswell and it seems to be working fine. \n",
"\n",
"package | description\n",
"-------- | ------------\n",
"IPython | To use NBConvert you need V1.00. If you only want to use the interactive notebook then v0.13 will be ok.\n",
"pandoc \t | The document converter used by IPythonr\n",
"MikeTex | If you want to do a TEX to PDF transform. I had so many issues with the TEX to PDF conversion by NBConvert, so settled for wkhtmltopdf(below) to convert HTML to PDF rather. (Convert notebook to HTML with NBconvert and then from HTML to PDF with wkhtmltopdf\n",
"wkhtmltopdf | Convert HTML to PDF (i could only install this on windows)\n",
"wkpdf\t\t| I couldn't get wkhtmltopdf to work on os x so i installed wkpdf for handling the HTML to PDF conversion on my Mac. It's a Ruby Gem install and painless.\n",
"pdftk | Can be used to combine PDF's. In this case add a frontpage to the generated IPython notebook PDF. Only available for Windows.\n",
"*ImageMagick | for compressing the PDF. Still experimenting with this.(have not got this working yet so not needed)\n",
"*GhostScript | needed by ImageMagick(not needed as PDF compression is not functional yet)\n",
"anaconda | install anaconda from Continuum Analytics. Almost all the Python packages are included and it has a virtual environment manager via it's console application `conda'\n",
"\n",
" \n",
"How to run the Interactive Notebook\n",
"--------\n",
"Navigate to the `src` directory and run from the command line:\n",
"\n",
" ```python\n",
" ipython notebook\n",
"```\n",
"\n",
"If everything works your browser should open and you can select the `notebook` and start experimenting!\n",
" \n",
"PDF, HTML, Slideshow Build Script\n",
"------------\n",
"There is a build script in the `src` directory. It is an IPython file. You can basically build shell scripts this way. To use the power of IPython commands save the file with the `.ipy` extension and call it with IPython. Even the magic\u2019s work. To build the document use `ipython builddocs.ipy` You will have to change the paths to the software however. Currently I can use the build script on Windows and on my Mac but it is a bit of a hack.\n",
"\n",
"Cross Platform Output Rendering\n",
"--------\n",
"I have tested the HTML outputs on my Galaxy S3 and S4, IPAD and Nexus7. They render very well. Even the downloaded PDF was easily readable on the NEXUS 7 in landscape mode. In conclusion the produces work is really very well packaged and easily consumed on most platforms. This is not bad, and all done with open source software.\n",
"\n",
"Some interesting links\n",
"-----------------------\n",
"* [A book written with IPython Notebook][7]\n",
"* [Notebook Viewer][8]\n",
"* [Anaconda - Installing almost everything you need][9]\n",
"\n",
"\n",
"About the presenter\n",
"----------\n",
"* I am an Electrical Engineer and is currently working for a [consulting firm][10] where I manage the Business Analytics and Quantitative Decision Support Services division.\n",
"* I use python in my day to day work as a practical alternative to the limitations of EXCEL in using large data sets.\n",
"* [LinkedIn][11]\n",
"* I am also a co-founder at [House4Hack][12]\n",
"\n",
"\n",
"\n",
" [1]: http://pandas.pydata.org/\n",
" [2]: http://www.scipy.org/\n",
" [3]: http://sympy.org/en/index.html\n",
" [4]: http://matplotlib.org/\n",
" [5]: http://scikit-learn.org/\n",
" [6]: http://tooblippe.github.io/zapycon2013_ipython_science\n",
" [7]: http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/\n",
" [8]: http://nbviewer.ipython.org/\n",
" [9]: http://www.continuum.io/downloads\n",
" [10]: http://www.eon.co.za/index.php/our-services-main/our-services/business-analytics\n",
" [11]: http://www.linkedin.com/in/tobienortje\n",
" [12]: http://www.house4hack.co.za/\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# The IPython notebook\n",
"\n",
"The IPython notebook is part of the IPython project. The IPython project is one of the packacges making up the python scientific stack called SciPi.\n",
"SciPy (pronounced \u201cSigh Pie\u201d) is a Python-based ecosystem of open-source software for mathematics, science, and engineering. In particular, these are some of the core packages:\n",
"\n",
"![SciPy](https://raw.github.com/Tooblippe/zapycon2013_ipython_science/master/src/static/scistack.png)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Quick IPython introdution\n",
"\n",
"IPython provides a rich architecture for interactive computing with:\n",
"\n",
"* Powerful `interactive shells` (terminal and Qt-based).\n",
"* A `browser-based notebook` with support for code, text, mathematical expressions, inline plots and other rich media.\n",
"* Support for `interactive data visualization` and use of GUI toolkits.\n",
"* Flexible, embeddable interpreters to load into your own projects.\n",
"* Easy to use, high performance tools for parallel computing.\n",
"\n",
"The main reasons I have been using it includes:\n",
"\n",
"* A `superior` shell\n",
"* Plotting is possible in the QT console or the Notebook\n",
"* the `magic` functions makes life easier (magics gets called with a %, use %-tab to see them all)\n",
"* I also use it as a `replacement shell` for Windows Shell or Terminal\n",
"* `Code Completion`\n",
"* `GNU Readline` based editing and command history"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Some helpfull commands\n",
"\n",
"The four most helpful commands, as well as their brief description, is shown\n",
"to you in a banner, every time you start IPython:\n",
"\n",
"\n",
"`command` | `description`\n",
"------------|----------- \n",
"? | Introduction and overview of IPython's features.\n",
"%quickref | Quick reference.\n",
"help | Python's own help system.\n",
"object? | Details about 'object', use 'object??' for extra details.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Some imports and settings\n",
"The following code cells make sure that plotting is enabled and also loads a customised matplotlib confirguration file that spices up the inline plots. The custom matplotlib file has been taken from the [Bayesian Methods for Hackers Project](http://nbviewer.ipython.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter1_Introduction/Chapter1_Introduction.ipynb)"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# makes sure inline plotting is enabled\n",
"%pylab --no-import-all inline "
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#loads a customer marplotlib configuration file\n",
"def CustomPlot():\n",
" import json\n",
" s = json.load( open(\"static/matplotlibrc.json\") )\n",
" matplotlib.rcParams.update(s)\n",
" figsize(18, 6) "
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Changing the notebook layout\n",
"The code cell below is an example of how you should not be chaning the layout and css of the notebook. From IPython V1.00 it is possible to include custom css by creating IPython profiles. Since this file needs to be distributable I have opted for the hack below as used by the `Bayesian Methods for Hackers Team`"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from IPython.core.display import HTML\n",
"def css_styling():\n",
" styles = open(\"static/custom.css\", \"r\").read()\n",
" return HTML(styles)\n",
"css_styling()"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"html": [
"\n",
"\n",
""
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 3,
"text": [
""
]
}
],
"prompt_number": 3
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Notebook basics\n",
"The IPython Notebook is a web-based interactive computational environment where you can combine code execution, text, mathematics, plots and rich media into a single document.\n",
"\n",
"* Code Completion\n",
"* Help\n",
"* Docstrings\n",
"* Markdown cells\n",
"* Running a Code cell (Shift+Enter)\n",
"* Setting a cell to be included in the presentation"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Run the contents of a cell\n",
"\n",
"* SHIFT+ENTER will run the contents of a cell and move to the next one\n",
"\n",
"* CTRL+ENTER run the cell in place and don't move to the next cell. (best for presenting)\n",
"\n",
"* CTRL-m h show keyboard shorcuts"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# press shift-enter to run code\n",
"print \"Hallo Pycon\""
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Hallo Pycon\n"
]
}
],
"prompt_number": 4
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Save the notebook\n",
"CTRL-S will save the notebook"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Lets get some help\n",
"The `%quickref` commmand can be used to obtain a bit more information"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#IPython -- An enhanced Interactive Python - Quick Reference Card\n",
"%quickref # now press shift-ender"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Code completion and introspection\n",
"The cell below defines a function with a bit of a long name. By using the `?` command the docstring can we viewed. `??` will open up the source code. The autocomplete function is also demostrated, and for fun the function is called and the output displayed"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# lets degine a function with a long name.\n",
"def long_silly_dummy_name(a, b):\n",
" \"\"\"\n",
" This is the docstring for dummy. \n",
" It takes two arguments a and b\n",
" It returns the sum of a and b\n",
" No error checking is done!\n",
" \"\"\"\n",
" return a+b"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# lets get the docstring or some help\n",
"long_silly_dummy_name?"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"long_silly_dummy_name??"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": []
},
{
"cell_type": "code",
"collapsed": true,
"input": [
"#press tab to autocplete\n",
"long_si"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# press shift-enter to run\n",
"long_silly_dummy_name(5,6)"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 8,
"text": [
"11"
]
}
],
"prompt_number": 8
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Setting up the notebook to enable a slideshow view\n",
"\n",
"You need to activate the Cell Toolbar in the Toolbar above. Here you can set if this cell should be compiled as a slide or not. The options are given below:\n",
"\n",
"* slide\n",
"* sub slide\n",
"* fragment\n",
"* skip\n",
"* notes"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Using markdown\n",
"You can set the contents type of a cell in the toolbar above. When Markdown is selected you can enter markdown in a cell and it's contents will be rendered as HTML. The markdown syntax can by [found here](http://daringfireball.net/projects/markdown/)\n",
"\n",
"# This is heading 1\n",
"## This is heading 2\n",
"### This is heading 5\n",
"\n",
"> Beautiful is better than ugly.\n",
" Explicit is better than implicit.\n",
" Simple is better than complex.\n",
" Complex is better than complicated."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Notebook magics\n",
"IPython has a set of predefined \u2018magic functions\u2019 that you can call with a command line style syntax. There are two kinds of magics, line-oriented and cell-oriented. Line magics are prefixed with the % character and work much like OS command-line calls: they get as an argument the rest of the line, where arguments are passed without parentheses or quotes. Cell magics are prefixed with a double %%, and they are functions that get as an argument not only the rest of the line, but also the lines below it in a separate argument."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Timeit magic\n",
"The timeit magic can be used to evaluate the average time your loop or piece of code is taking to complete it's run."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%timeit \n",
"x = 0 # setup\n",
"for i in range(100000): #lets use range here\n",
" x = x + i**2"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"100 loops, best of 3: 12.2 ms per loop\n"
]
}
],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%timeit \n",
"x = 0 # setup\n",
"for i in xrange(100000): #replace range with slightly improved xrange\n",
" x += i**2"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"100 loops, best of 3: 10.7 ms per loop\n"
]
}
],
"prompt_number": 17
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Know when the kernel is busy\n",
"Have a look at the top right hand side of the notebook and run the code cell above again. This shows that the kernel is busy running the current cell."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## User input\n",
"In the snippet below it the `raw_input()` function is used to read some user input to a variable `raw` and printed to stdout."
]
},
{
"cell_type": "code",
"collapsed": true,
"input": [
"from IPython.display import HTML\n",
"\n",
"raw = raw_input(\"enter your input here >>> \")\n",
"\n",
"print \"Hallo, \",raw"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"stream": "stdout",
"text": [
"enter your input here >>> World!\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Hallo, World!\n"
]
}
],
"prompt_number": 18
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## How to link to the filesystem"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from IPython.display import FileLink, FileLinks\n",
"FileLinks('.', notebook_display_formatter=True)"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 11,
"text": [
"./\n",
" .DS_Store\n",
" builddocs.ipy\n",
" calling_r_example.ipynb\n",
" calling_ruby_example.ipynb\n",
" pycon13_ipython.ipynb\n",
" README.md\n",
"./.ipynb_checkpoints/\n",
" calling_r_example-checkpoint.ipynb\n",
" calling_ruby_example-checkpoint.ipynb\n",
" pycon13_ipython-checkpoint.ipynb\n",
"./data/\n",
" CapeTown_2009_Temperatures.csv\n",
" READEME.md\n",
"./output/\n",
" .DS_Store\n",
" pycon13_ipython.html\n",
" pycon13_ipython.slides.html\n",
" pycon13_ipython_complete.pdf\n",
" pycon13_ipython_pdf.pdf\n",
"./static/\n",
" .DS_Store\n",
" custom.css\n",
" frontpage.docx\n",
" frontpage.pdf\n",
" ip.png\n",
" ip2.png\n",
" matplotlibrc.json\n",
" python-vs-java.jpg\n",
" scistack.png"
]
}
],
"prompt_number": 11
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Running shell commands\n",
"I now use ipython as my default shell scripting language. lets put the contents of the current directory into a list. by using the `!` before a command indicates that you want to run a system command.\n",
"\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"filelist = !ls #read the current directory into variable\n",
"for x,i in enumerate(filelist):\n",
" print '#',x, '--->', i"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"# 0 ---> README.md\n",
"# 1 ---> builddocs.ipy\n",
"# 2 ---> calling_r_example.ipynb\n",
"# 3 ---> calling_ruby_example.ipynb\n",
"# 4 ---> data\n",
"# 5 ---> output\n",
"# 6 ---> pycon13_ipython.ipynb\n",
"# 7 ---> static\n"
]
}
],
"prompt_number": 20
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Embedding Images\n",
"\n",
"[Image](http://askrahul.com/blog/from-funny-moments-to-emails-to-sms/python-vs-java/) released under [CC BY-NC-ND 2.5 IN)](http://creativecommons.org/licenses/by-nc-nd/2.5/in/) by [Rhul Singh](http://askrahul.com/blog/about-rahul-singhs-blog/)"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from IPython.display import Image\n",
"Image('static/python-vs-java.jpg')"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
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Q2cQU9kQL+pBNed/tUjb+xz4++Zv+QYf4j/fSvYkHNeP/ALVn/JnHxA/7Bh/9GJWFV3TY\n6as0fjMete8fscn/AIzQ8F/9dpv/AEnkrwfvXvH7HH/J6Hgv/rtN/wCk8ldNH4vkyKmx+wIX/e/7\n6Nfl/wD8FDv+TmtL/wCwDD/6Nlr9QR9wV+X3/BQ//k5rSf8AsAw/+jJa55br1/Q2hs/T9UfIwr9J\nf2XD/wAayvGH/XDWf/RFfm3/AAmvRPDfxx+KvhD4c3XgLw54vubDw7dLMs9ikELK4lXEnzMhYZHH\nB+mK0lrFruQviT7M89b79ek/s+f8nReAv+w5bf8AodeaZyK9L/Z8/wCTovAX/Yctv/Q6qn8SJq/C\nz9Hf25riSD9jPXURuJbu0jb6ecD/ADUV+Szda/Vz9vX/AJM+vv8AsKWn/oTV+UbferGO7NX8KPrD\n/gn0kbftSXjsvzLodwVb+788Yrn/ANun/k83Xv8Ar0tP/RK10f8AwT3/AOTpLz/sBz/+hx1zn7dP\n/J5uvf8AXpaf+iVq6nxRM6f2vkfOA6/jX6pfCO7+1/8ABK4ytuwnhbUov++VmXt9K/Kwd6/Uv4K/\n8op5v+xd1X+c9OX8OQR/iR9T8tDTkP7ymmkpIbPtr9oHx5/wsX/gnD8L/EUswkvE1ZbG9Pfz4beW\nNs+mQoP0YHvXxQK7qLx1v/ZvufhvcM+U8RRa1begBtpIZR+flED61wg60+rfca+FLsfsF+xt/wAm\nX+C/+uU//o+SveRXg37G3/Jl/gv/AK5T/wDo+SveRVVPiZENhaKKKgoKKKKACiiigAooooAKKKKA\nCiiigAprdKdTW6UAfkD+2h/yen4w/wB63/8ASeOvAl6177+2h/yen4w/3rf/ANJ468CXrU0/hRc9\nz9kv2Uv+TNPAX/YOP/o16/KP4v60fEfx88Za23S61i6kXvx5rAfoBX6jfs86nHon7AXhrWJXEcdl\noM9wz/3drSnP6V+RV3PJdX011M26SVzI7erMck/macv4rM6f8NHpf7P3hL/hNvjjZ6E6b43sL+Vh\n/u2cu3/x7bXmDf6yvrL/AIJ+aF/av7SGqag6K0djoc/3vWRkj/kSK+X/ABHp50fxlq2ktnNpezW/\np9x2X+lVLRpeRSej9T7k/wCCbOt/6X4+8OM/zMtpexr9DIjnH4pXmn/BQTRv7P8A2rotSSHaupaL\nbzs/ZnRnjP5KiD8qZ+wBrcmm/tXNp2/bHqej3Nvtz94oVmH1P7s/rXqf/BSPw/8A8iN4qRf+frT5\nD9dki/oH/OlV+KLFT+0j4B71+ivwR8Qm3/4JM+LZUlffp9tqlup/u7juH/oyvzq719cfCnxHJaf8\nEvPi3YLL8yatBFtz91JxAv6lW/WnL4JAvij6nyQfvGvU/wBm3R/7e/az8A6f0X+2IbhvpETKR+IT\nFeWHrX03+wdow1X9sPTrt41YaZpt3ec/wkoIgfr+9/nV0/8Agkz2Z7n/AMFJNZ8rwd4E8Pr/AMvF\n5c3rf9s0RB/6NP5V+eI619lf8FF9Z+1/Hbw3oaTblsNF8xk/uvLM5/VUT8q+Nl+9WVFX+80nsvQ9\nQ+LXg/8A4Rfwn8NbwJsGreFo71v9pjcTHP8A3yUrkPAuq/2H8UPDms7tostTt7gt04WVSf0FfUf7\nb/hP/hHPBvwbRUWNbTw6NLYL0zEkZ6/VjXx0vWrhJKV+zIavFX7H76wSxzQpLE26N1Dq3seRU9cP\n8JNc/wCEj+BHg7XS25r3R7WdjnPJiXP61269KUlZ2FF3VxaKKKRQUUUUAFFFFABRRRQAUh60tIet\nAC0UUUAFFFFACHtXy3+35/yaHP8A9hez/m1fUh7V8t/t+f8AJoc//YXs/wCbVE9vmiobn5T9q0NO\n0fVdYuWt9J068vplXe0dpA0rAdM4UE45HPvWf3r68/4J2n/jJrWfmb/kX5u+P+W8NbRjf8SG7Hzl\npnwv+IusapBpumeBfEdzdzNtjiTTpgWOM91A6Anr2r7V/ZG/ZD8V+GPiDYfFL4m2iaW1ipk0zR3Y\nNP5rLgSTAZCABiVXO7dgkLgZ+9tn+dxpdvHWpTsDVxu3jpX43/tV+B/+EC/au8WaZFF5dnd3P9p2\nnp5c/wC8wPYMXX/gNfslg5Ffmp/wUbgiX45+F7hYkWR9D2s4XBYCeTAJ74ycfU1k9JIuGzR8Xetf\npD/wTq8C/wBm/DTxL4/urdlk1a7Wytnb+KGEEsR7F3IP+4K/OBeor9hf2PYIof2NPBPlRLHvt5pG\n2rjcTM+SfUn1reOzZlLdI92xXjn7Vn/JnHxA/wCwYf8A0YleyV43+1Z/yZx8QP8AsGH/ANGJWFT4\nTWHxI/GbvXvH7HH/ACeh4L/67Tf+k8leD/xV7x+xx/yeh4L/AOu03/pPJXTR+L5MyqbH7Bj7wr8v\nf+Ch/wDyc1pP/YBh/wDRktfqEPvCvy9/4KH/APJzWk/9gGH/ANGS1zS3Xr+htDZ+n6o+R88EV6l4\nR/Z7+KPjj4WXvxF8O6Ha3Xh6yWdp7lr+GNl8ld0n7tmDHA9Bz2rywd6/SX9l0f8AGsrxf/1w1n/0\nQK1m7Rb7IhfEl3Z+beOtel/s+/8AJ0fgL/sOW3/odeat/rDXpX7Pv/J0fgL/ALDlt/6HTpP3kKr8\nL9D9D/29v+TP77/sJ2f/AKE1flI33q/WD9u+Lf8Asc6mzfwajaOv/fZH9a/J9utZR3Zo/gR9Zf8A\nBPf/AJOkvf8AsBz/APocdc7+3P8A8nna7/16Wn/ola6L/gnv/wAnSXv/AGA5/wD0OOue/bo/5PQ1\n7/r0tP8A0QtXU3iRT+18v0Pm/vX6k/Bb/lFNN/2Lmq/znr8tu9fqV8GY5Iv+CVMu9dufDmqN+BMx\nB/KnP+FIUf4kfU/LQ1qQaFqNx4Xv9fii3WNjPBbzt3VphIU/A+S/P09azK+l/wBm/wAD/wDCwP2e\nPjnoEVp9ovE0qxvbRR97zoZJ5Fx7naR9GI700tG+yGz5mpV60MMGhetSgP2D/Y2/5Mv8F/8AXKf/\nANHyV7yK8G/Y2/5Mv8F/9cp//R8le8irqfEyYbC0UUVBQUUUUAFFFFABRRRQAUUUUAFFFFABTW6U\nuaRugoA/IH9tD/k9Pxh/vW//AKTx14EvWvff20P+T0/GH+9b/wDpPHXgQqaXwoue5+n2jax/YX/B\nIP8AtDzFjY+F5rdT7yyvEPxy4r8wjX3t8RNZ/sv/AII6eELH5g2pva2g/C4kmP6RfrXwS3WnJ3qS\nfmTH+Gj7x/4Jt6Tv1jx5rrfwRWlovT+JpHPv/CK+Wf2gNH/sH9qDx5pnO1NcuZFzj7sjmQdPZxXP\neE/iT8QPAtpc23g3xprugQ3LB50029kt1lYAgFgpGSATjNY2t67rPiXXrnW9f1S71PUrlt893dyt\nLJKwAGWZiSTgAc+gqpatPysKOia7npn7L/iD/hG/2uPA2oNL5cUmpJaSN/sygxn/ANCr7o/4KA6F\n/aX7Ldrqv3m0rWIJW9g6vGf1I/OvzT8J6p/YXjrR9bzt+wX0N1n/AHJFb+lfrv8AtM6dH4p/Yz8a\nLCnmB9K+3xEDP+rKzA/kp/ClWXuJ9mFP47H43969k8Ea/wDZf2N/inoTP/x96popVfo87N+ka142\na1rPWpbTwlqOhIv7u+mgmc/9chJj/wBGH8qroxpamQOgr7e/4Jw6LHN8S/GXiBomzaadDbK//XSQ\nlh+UYNfEIr9Jf+CculG3+D/i3WGX/j81WOBT3xHEM/q9VHZvyIl0XmfMP7bWsDVv2y/Eio6stjFb\nWfHYrCpI+uWNeK+DNM/tr4jaDo23d9t1G3tscH78qr3+tdN8dNb/AOEj/aQ8b6v8wWbWbnb9FkKD\n/wBBrh9Pv7zStVtdT027ltLy1lWeG4gco8UinKspHIYEAgjoaypaJM0qLVo/Rb/goro3mfBjwfrK\nf8uWqPbduksP/wBqr84OwrsPFHxW+JnjXRl0jxf498Ra5p6yCZbXUL+SeMSAEBtrEjIDHn3Ncbjp\nRFWuK+iXY/Xn9i/xD/wkH7GfhTe4aXT/AD9Pk/2fLmbYP++Chr6DHWvjL/gnRrwu/gT4j8Ps2ZNP\n1jz8f7E0S4/WJq+zO9aVPiM47DqKKKgsKKKKACiiigAooooAKQ9aWkPWgBaKbu9qXNAC0UmaM0AB\n7V8t/t+f8mhz/wDYXs/5tX1GTXzJ+3Xp+oar+yhJaaZY3V5P/a9o3lW8TSvgF8nCgnA9aznt80VD\nc/J71r69/wCCdn/Jzes/9i/N/wCj4a+Yv+EG8af9Cjr3/gvm/wDia+sP+Cf/AId1/R/2ldXuNW0L\nUrKFtBmVZbq1kiTPnQ8ZYAZ9vY+ldNPf5P8AIzn8PzX5n6WjrS0gPFGayKF7mvzV/wCCj3/JbvCf\n/YGP/o96/SnPNfnP/wAFC/D+vax8aPC82k6JqV9GmjFWe1tZJQp85zglQQD7VnLdev6Fw2fofDg6\n1+xX7IH/ACZp4H/69Jf/AEc9fkoPA3jQ/wDMo69/4L5v/ia/XD9ky0urD9kDwVaX1rPa3EdtKrQz\no0br++fqrAH9K6F8D+RlLdHtncV45+1Z/wAmcfED/sGH/wBGJXseeRXkH7UFtc3v7Inju0s7eW4n\nl04iOKFGd2O9eAqgk/hXPU+FmkN0fjCOte8fscf8nn+Cv+u03/oiSvKP+EG8aZ/5FLXP/BfN/wDE\n17h+yN4V8T6b+2B4Nu9Q8O6ta26TzBpZrKVEXMEg5YqAOeOTXTS3+8yqfCfrUPuCvy+/4KH/APJz\nWk/9gGH/ANGS1+oIPIr80/2/fDuv6x+0jpdxpWhapfQjQ4UaW1tZJUz5kpxlQRn2rnluvX9DaGz9\nP1R8ZD+tfpJ+y5/yjF8X/wDXDWf/AERX59DwN40/6FHXv/BfN/8AE1+iP7NGk6rYf8E3PFun3mmX\nttdmDVwtvLbukjboBtwpAJz2wOaqfwS9DNfHH1PzPf8A1pr0v9nv/k6HwF/2HLb/ANDrlG8DeNM/\n8ilr3/gum/8Aia9E+A/hDxXZftLeB7i68M6xBCmtWztLLYTKqjzBySVwB71pT+JCq/Cz9Jv2t/Dd\n54o/Y68Z2ljE81xb2yX6oP7sEiyP+SK598V+OpPNfvpNFHcWrwyoskbqUZGXIYHggjuCK/NX9o39\nibxf4c8UXvij4S6TLrnhu5kaZtKtfmurAlhlFTrLHk5UrkgAhhhQxx2ZondWPLv2Rvif4Z+FH7RU\nXiDxhqD6fo09jPZzXKxPKIywDKSqAsRlccAnmsf9p34h6D8T/wBprxD4s8MXBudGk8mC0uGjaPzk\njiVS21sEAsGIyAcYyAeK8r1DSdT0i7+yarYXVjOV3eVdRNE+MkZwwBxkHn2NSaXoeta3d/ZdF0q9\n1KYAHyrOBpm5OBkKCRyQK0etvIlaX8zPT71frn4b8OXvhP8A4Jtf2HqEXk3UPgy4eeLGCrvbvIQf\ncbsV82/sz/sT+JrjxdZeOPjBpv8AZel2conttBmYefdSKcqZlH3IwRnaTubHQLyfuH4txSy/ALxr\nb28TySPod6ipEpYsTA4AAAySfQUqrtBoIfGmfh2eor7s/wCCb0ccus/EaGVN8b2liGTruBa4BH4j\nivjM+BvGmf8AkUde/wDBdN/8TX3B/wAE69C1rR9f8f8A9saRf2Pm29js+1W7xbsNPnG4DOMjp61p\nDZ+hMj5A+NvguT4eftA+K/CPkvHDZ6jILYNxmBjvjP0KMpFefL1r7f8A+CgPwt1V/jHoXjfQNDvb\nxNWsDb3ZtLd5ts0DAAttBwSjqBnrsNfIA8DeNM/8ilr3/gum/wDiawpvQ0lufrH+xt/yZf4L/wCu\nU/8A6Pkr3kV4b+yHZ3un/se+ELTUbSe1uEjnDQzxNG6/v36qwBH5V7kOtbVPiZnDYWikzRmoKFop\nu72pQeKAFopM0ZoAWikzRmgBaKTNGaAFopM0ZoAD1rw39oj9ojTfgBp2g3d/4Xutc/taSeJRb3Sw\neV5aoTncGznf6cYNe4k18bft7fDrx58QtA8EQ+B/Ceqa9JaXN29yunwmXygyxBS2Omdpx9DUSb0s\nNH5/fE/x9qHxP+LWt+O9UtYLO41OfzTbwEskQChVUE8nAUcnqc9K44da9U/4Zp+Pv/RJPFn/AIAP\nXrXwg/YZ+KXi7xLYXvj/AE0+FvDW4S3P2iVftcqhsGNIVJKMQOr4AB3DPANpdBNmj+0DqEmm/wDB\nPn4FeGH/AHL3Ucl+0Xsq/Kx+vnE/ia+QK+/v22fhP8QPFPi3wXoPw3+Hus6loOh6ObeL+zbUyQwk\nvhYwR0wiKPyr5W/4Zq+Pn/RJPFn/AIAPSTu2+7G1ZJdj0P8AZb/Zl0b4+6N4jvdY8S6jo66VNBDH\n9lgSQSeYrk53njG0dPWvNvj78Lrb4OfHPVPAllqVxqVtaRwyRXNwio8gdAxyFOOCSOPSvvv9hL4c\neK/h98J/EsXjPw1qWg395qqssN/EYneNYQAwB525Zhn1BrxP9tH4K/E7xl+00+v+DfAuva1p82l2\nyNc2Vq0qLIu4MuR3Axx706mklbYUNU7nxEvWv2i+HzR/EP8AY40KGVyy6z4WW2lPXl7fy2z7gk1+\nWI/Zr+PuP+SS+LP/AAAev0z/AGStK8X+H/2UfD/h3xroV/o+qabJcW/2a9QxyeWZndDg9trgD/dp\ny1g0TtJM/H66hkt76W3mXbJGxR16YIOCP0qAdK+gvif+zX8Z2+N3i19F+GXiK+02TWLqS0ubWyZo\n5ImmYoVI4I2kVyX/AAzV8fc/8kk8Wf8AgA9SnoXLdnlXOa/Vr9jWy/4RT9haz1u5KQrcNfaoz/7I\nYqCfoIq/Pr/hmr4+Z/5JJ4s/8AHr9JdM8J+I/CP/AAToTwlpWi383iRPCTQLpyRZnW5miJZNvXcr\nyNkexpzf7uVtyVrJH5JarfS6nrV3qMx3SXMzzsfdmLH9TWj4M0H/AISj4jaD4aeV4V1PUbeyaRFD\nMolkVCQDwSA2cH0rt/8Ahmv4+/8ARJfFn/gA9ehfAv8AZ3+MWm/tH+C9T8R/DTxHp+l2urQXFxdX\nFoyRxIjbssT0GVFVTSuk9gm3Zs7H9oT9jTw78GvgbeeO9I8X6tqk9tcwQtb3VtEibJG2k5U5yCRX\nxuelfsn+1R4T1rxr+yZ4s8P+HtMudS1SaOB7a0t03ySlLiNiFHUnarH8K/ME/s1fH3H/ACSTxb/4\nAPWcW7sp7I+jP+Cb+teV8RfGmgO+PtNhBdqu7qY5Cp/HElfo2Otfmt+x38Kfi/8AD39qCw1bxH8O\n/Eel6TcWVxaXN3dWrRxx7k3KWJ/2lA/Gv0nFaz2XoRHdj6KTNGagoWikzRmgBaKTNGaAFopM0ZoA\nWkPWjNJQB8UeAvj3+1z8V/C7+L/Angv4ZLokl3NbwpfyypMuwjhs3C7iAwGQBnngV1H/AAmf7dP/\nAEJvwh/8CpP/AJJrN/Yf/wCTRbX/ALDV9/KKvo+uadZqVjWNNNHgf/CZft1f9Cb8IP8AwKk/+SaP\n+Ey/bq/6E34Qf+BUn/yTXvlFT7dj9mjwP/hMv26v+hN+EH/gVJ/8k0n/AAmX7dX/AEJnwj/8CpP/\nAJJr1LxH8S/h14Puvs/ijxx4f0e4/wCeF7fxxyf98Fs/jiuZf9o/4FpK6P8AFLw5uXj5Jiw49CFI\nP1FP2s+xPIjkv+Ez/bp/6E/4Rf8AgVJ/8k0o8Y/t1Z/5E/4R/wDgVIf/AG5rqv8AhpP4Ff8ARUvD\n/wD39b/4mj/hpP4Ff9FS8P8A/f1v/iaPa1OwckTlv+Ey/bq/6E34Qf8AgVJ/8k0f8Jl+3V/0Jvwg\n/wDAqT/5Jrqf+Gk/gV/0VLw//wB/W/8AiaP+Gk/gV/0VLw//AN/W/wDiaPa1OwciOW/4TL9ur/oT\nfhB/4FSf/JNJ/wAJl+3V/wBCf8Iv/AqQf+3NdV/w0n8Cv+ipeH/+/rf/ABNH/DSfwK/6Kl4f/wC/\nrf8AxNHtanYORHKf8Jn+3T/0J/wi/wDAqT/5Jpf+Ey/bq/6Ez4Rf+BUn/wAk11X/AA0n8Cv+ipeH\n/wDv63/xNH/DSfwK/wCipeH/APv63/xNHtanYOSJy3/CZft1f9Cb8IP/AAKk/wDkmk/4TP8Abp/6\nE34Qf+BUn/yTXVf8NJ/Ar/oqXh//AL+t/wDE0f8ADSfwK/6Kl4f/AO/rf/E0e1qdg5EcqPGf7dWf\n+RP+EX/gVJ/8k0n/AAmX7dP/AEJ/wi/8C5P/AJJrq/8AhpP4Ff8ARUvD/wD39b/4mj/hpP4Ff9FS\n8P8A/f1v/iaPa1OwckTlf+Ez/bp/6E34Qf8AgVJ/8k0n/CZ/t0/9Cf8ACL/wKk/+Sa6v/hpP4Ff9\nFS8P/wDf1v8A4mj/AIaT+BX/AEVLw/8A9/W/+Jo9rU7ByROU/wCEz/bp/wChP+EX/gVJ/wDJNH/C\nZ/t0/wDQn/CL/wACpP8A5Jrq/wDhpP4Ff9FS8P8A/f1v/iaP+Gk/gV/0VLw//wB/W/8AiaPaz7By\nROV/4TL9ur/oT/hF/wCBUv8A8k0n/CZ/t0/9Cf8ACL/wKk/+Sa6v/hpP4Ff9FS8P/wDf1v8A4mj/\nAIaT+BX/AEVLw/8A9/W/+Jo9rPsHJE5b/hMv26f+hN+EP/gVJ/8AJNJ/wmf7dP8A0Jvwh/8AAqT/\nAOSa6r/hpP4Ff9FS8P8A/f1v/iaP+Gk/gV/0VLw//wB/W/8AiaPaz7Byo4a91f8AbP1OZZtQ+G/w\nUvJAuxXuHaQqAScZa5Jxknj3p1jrX7aemSO2n/Dr4LWbPwxgdoi2PXbcjNdv/wANJ/Ar/oqXh/8A\n7+t/8TR/w0n8Cv8AoqXh/wD7+t/8TR7WfYOWJyo8Y/t05/5E34Qf+BUn/wAk0f8ACZ/t0/8AQm/C\nD/wKk/8Akmuq/wCGk/gV/wBFS8P/APf1v/iaP+Gk/gV/0VLw/wD9/W/+Jo9rU7ByROU/4TP9un/o\nT/hF/wCBUn/yTS/8Jl+3V/0Jnwj/APAqT/5Jrqv+Gk/gV/0VLw//AN/W/wDiafH+0d8C5ZURfil4\nc3Nx88xUfiSoA/Gj2s+wckTkv+Ey/bq/6Ez4R/8AgVJ/8k0f8Jn+3T/0KHwi/wDAuT/5Jr1bw58S\nPh94vl8rwr418P6xNz+5sr+OWTj/AGA2R+VdPS9tIfs0eBf8Jn+3V/0Jnwj/APAqT/5Jpf8AhMv2\n6v8AoTfhB/4FSf8AyTXvlFH1hj9mjwL/AITT9un/AKE34Q/+BUn/AMk1zfjP48fta/C/TLDxB498\nG/DL+xZtRgsZFsJZXmYyE8DFwccK3JUgHHBr6hr5y/bP/wCTftJ/7GWw/lJThWcpconTSR9eJ80W\n78fz5r5Hvvjp+0n4q+N3jzwl8KPCvgGfS/C2o/2e761LKszfeAYt5yA5KscAccDnrX1xF/qU/wB0\nfyr5H+Av/Jz/AO0D/wBjKn/oUtbznZXM0rsuf8Jp+3T/ANCb8IP/AAKk/wDkml/4TL9ur/oTfhB/\n4FSf/JNe9jpRmuf6wzX2aPBP+E0/bp/6E34Qf+BUn/yTSf8ACZ/t0/8AQm/CD/wKk/8Akmu6+IHx\nx+Fvwyl+yeMvGFnZ323f/Z8W64ucHpmOMErnsWwD615o/wC218F/+WVv4vuv9qLSAdv/AH1KKpVK\nj6E8iNL/AITL9un/AKE34Qf+BUn/AMk0f8Jl+3T/ANCb8IP/AAKk/wDkmsv/AIbc+Df/AECfHP8A\n4Jk/+P0f8NufBv8A6BPjn/wTJ/8AH6Oep2CyNT/hMv26f+hN+EH/AIFSf/JNH/CZft0/9Cb8IP8A\nwKk/+Say/wDhtz4N/wDQJ8c/+CZP/j9H/Dbnwb/6BPjn/wAEyf8Ax+jnqdgsjU/4TL9un/oTfhB/\n4FSf/JNNPjD9ulv+ZN+EH/gRJ/8AJNZv/Dbnwb/6BPjn/wAEyf8Ax+j/AIbc+Df/AECfHP8A4Jk/\n+P0c9TsFkaP/AAl37cv/AEJXwg/8CX/+SacPGP7dK/8AMm/CD/wKk/8Akmsz/htz4N/9Anxz/wCC\nZP8A4/R/w258G/8AoE+Of/BMn/x+jnqdgsjRPjD9uVvveDPhB/4ESf8AyTR/wmH7c3/QlfCD/wAC\nZP8A5JrO/wCG3Pg3/wBAnxz/AOCZP/j9H/Dbnwb/AOgT45/8Eyf/AB+jnqdgsjTHjH9unP8AyJvw\ng/8AAqT/AOSaafGH7c2f+RJ+EH/gRJ/8k1nf8NufBv8A6BPjn/wTJ/8AH6P+G3Pg3/0CfHP/AIJk\n/wDj9HPU7BZGj/wmH7c3/QlfCD/wJk/+SacPGP7dOf8AkTfhD/4FSf8AyTWZ/wANufBv/oE+Of8A\nwTJ/8fo/4bc+Df8A0CfHP/gmT/4/Rz1OwcsTR/4TD9ub/oSvhB/4Eyf/ACTR/wAJh+3N/wBCV8IP\n/AmT/wCSazv+G3Pg3/0CfHP/AIJk/wDj9H/Dbnwb/wCgT45/8Eyf/H6Oep2CyNH/AITD9ub/AKEr\n4Qf+BMn/AMk07/hMf26cf8ib8Idv/X1J/wDJNZn/AA258G/+gT45/wDBMn/x+j/htz4N/wDQJ8c/\n+CZP/j9HPU7BZGj/AMJd+3L/ANCV8IP/AAJf/wCSaP8AhMP25v8AoSvhB/4Eyf8AyTWd/wANufBv\n/oE+Of8AwTJ/8fo/4bc+Df8A0CfHP/gmT/4/Rz1OwWiaZ8Y/t05/5E34Qf8AgVJ/8k03/hMP25v+\nhK+EH/gTJ/8AJNZ3/Dbnwb/6BPjn/wAEyf8Ax+j/AIbc+Df/AECfHP8A4Jk/+P0c9TsFkaf/AAmH\n7cv/AEJPwg/8CJP/AJJpf+Ey/bp/6E34Qf8AgVJ/8k1l/wDDbnwb/wCgT45/8Eyf/H6P+G3Pg3/0\nCfHP/gmT/wCP0c9TsFkan/CZft0/9Cb8IP8AwKk/+SaP+Ey/bp/6E34Qf+BUn/yTWX/w258G/wDo\nE+Of/BMn/wAfo/4bc+Df/QJ8c/8AgmT/AOP0c9TsFkan/CZft0/9Cb8IP/AqT/5Jo/4TL9un/oTf\nhB/4FSf/ACTWX/w258G/+gT45/8ABMn/AMfpyfttfBd/9baeMrdf70ujLhvb5ZTzRz1OwWRpf8Jl\n+3V/0Jvwg/8AAqT/AOSaP+Ey/bq/6E34Qf8AgVJ/8k11/gH9oD4SfEq/TT/C/jC1k1JuF029R7Wd\nj6Kjgbz7KSa9MzUutKO4/Zo8E/4TL9ur/oTfhB/4FSf/ACTR/wAJp+3T/wBCb8IP/AqT/wCSa98o\no+sMfs0fNnin4qftr+EPBuqeKNY8H/ChdP0y2ku7kxSyyP5aLubCi5yTjsOtfSHwl8Z3PxD+CPhj\nxre2VvZ3OrafHdy28BJSNmzkLnnGR3rzz4+/8mt/EH/sA3X/AKLNbn7Mf/JoHw8/7AsP9a2pT50Z\nzVjxr9h7/k0a1/7DV9/KKvo+vnD9h7/k0a1/7DV9/KKvo+uWp8bNYbBXz1+0V458aS+J/CXwQ+Ft\n39h8T+MJG8/UEPzWVmuQzAjlSQsjEjkKh28tX0Ge9fPtjHHN/wAFYbNrhNxi8EmSH5f9WxbBP5Mw\n49adFXmOb0Ox8D/safBDwvpSjWvDn/CXas/z3Op647SvNIeWYJnauSenJ6ZJOTXdRfs9/A+KFIk+\nFXhXai4XOnxn+YzXpa9KdXcc55t/wz98E/8AolnhP/wXR/4Uf8M/fBP/AKJZ4T/8F0f+Fek0UAeb\nf8M/fBP/AKJZ4T/8F0f+FH/DP3wT/wCiWeE//BdH/hXpNFAHmv8Awz98E/8AolnhL/wXR/4Uf8M/\nfBP/AKJZ4S/8F0f+FdprWvaL4d0s6n4g1nT9JswwT7Rf3CQR7j0G5yBk+mea4lPj58H5PG1r4St/\niJoV5rN3KkUFpZ3H2kyO/wB1Q0YK5PpnjvigB3/DP3wT/wCiWeEv/BdH/hR/wz98E/8AolnhL/wX\nR/4V19x4m0C38Mf8JFNq9ouklVZL5ZQYmDMFXDDIOWIAxnORWB4i+LXw58KaNd6r4j8aaTptraXr\nafLJcT423C7d0YXBLMNykgA4BycCgDP/AOGfvgn/ANEs8Jf+C6P/AAo/4Z++Cf8A0Szwl/4Lo/8A\nCul8I+NfDHjzwtF4k8Ia7a6xpUjNGt3atld6nDA5AII9CBxg9DXNeIPjv8IPCul6ZqGv/ELRLGHV\nYftFkXmJM8R6SKqgttPQMQAe1AB/wz98E/8AolnhL/wXR/4Uf8M/fBP/AKJZ4S/8F0f+FdBf+PvB\n+mfDr/hPr7xHYw+G/IS5/tTzC0LRvjYwIByDuAGBnmsnwr8Zfhb43sL278KeOtG1SOxjea5WKf54\no0XczlGAbaAeSAR75oAq/wDCgPgl/wBEs8Jf+C6P/Cj/AIZ++Cf/AESzwl/4Lo/8K7rTtRstY0a1\n1XTbpbizu4UuIJk+7JGygqwzzggg1m23jLwvdeEp/FFv4i01tFt/NE2pfaFEEflOUk3OSANrKQSe\n4oA5f/hn74J/9Es8Jf8Aguj/AMKP+Gfvgn/0Szwl/wCC6P8AwrR0L4tfDnxL4IuvF+heMtLvtEtJ\nRDc3sUuUhcsAFYEAgksoAI53DGa37rxNoNlrz6Jd6rax6ktk+otaM/7z7OjBWk29doJAz60Acf8A\n8M/fBP8A6JZ4S/8ABdH/AIUf8M/fBP8A6JZ4S/8ABdH/AIVZ8F/Gn4W/EPUG0/wZ460bVrxf+XaC\n42zN8pbIjYBmAAJJAOMHOKm8R/F/4beEtHl1XxH410nTbOK9k09nuJsH7RGcSRhQCWZSRuwCB3xQ\nBQ/4Z++Cf/RLPCX/AILo/wDCj/hn74J/9Es8Jf8Aguj/AMK19M+KPgLWPBum+LdM8WaXcaLqN2mn\n2l6suEmuHk8tYlyAdxYYwQD36c10EOtabceIrjQotQgbUraCO5ntFbLxxuzqjEdgTG4HrtNAHE/8\nM/fBP/olnhP/AMF0f+FH/DP3wT/6JZ4T/wDBdH/hXpNFAHm3/DP3wT/6JZ4T/wDBdH/hR/wz98E/\n+iWeE/8AwXR/4V6TRQB5t/wz98E/+iWeE/8AwXR/4VHN+z18D7iF4pPhT4VKtw2NOjH8hXptFAHz\n142/Y5+CviPRX/sDw6vg/WovmstW0N3he3kA+VigbDAHkjg9cEHmsP8AZ5+IXifXtP8AEHw4+IXz\neNvBd3/Z9/N1+1xciK4z3J24J/iyrdzX0+3SvkvSF+yf8FRfiDFb/u1u/DVpNOP+ejqsSg/gAPyr\nKtG8S4PU+gR0paQdKWuI3CvnP9tH/k3zS/8AsZbH+UlfRlfOf7aP/Jvml/8AYy2P8pKun8aFPY+u\nov8AUp/uj+VfJHwG/wCTn/2gf+xlT/0KWvreL/Up/uj+VfJHwG/5Of8A2gf+xlT/ANClrprfAzCG\n59E15Z+0H8Sbn4W/AfV/EWlfNrUzJp+mDaGP2iU4DAd9oBbHqBXqdfOf7XS79A+FsT/NG/jqxVk7\nMCr8H1rmpK8jeex2nwF/Zi8IeBfCNn4j8a6VB4k8eaiq3mp6nqqi6aGdwGZI9+QNpOC+MsQTkcAf\nQkNtbQQ7LeFIV/uxqF6fSpQP8/jT67zmG4+v50Y+v506sHxZ4t8O+B/CV14l8V6vBpOk2u3zru4y\nETcwVQcAnlmA6HrQBuY+v50Y+v51yHhn4meBvGFvpdx4a8S2epR6qlxJZNFuH2gQMqzFdwHCF1B9\nz9avf8Jn4Y+ya7df2/YeToDvHqsvmjbZOsayMJD2IRgSPQ/hQB0OPr+dGPr+dUJ9W0200Z9Yu7+3\nt9PSLz3u5ZRHGseM7izEALg5yTivPtc/aG+Cfhy4t4NS+KHhlZLjJjW3vVuehx/yy3AcnvjPPpQB\n6hj6/nRj6/nXOL458JtoM2tf8JFYLp8d7/Zz3DSYRbnzBH5RJ/i3kLj1IFN1nx54P8P22s3Wt+Ir\nCxh0SOOXUXuH2rbCQEx7j6sFOAMk8ccjIB0uPr+dGPr+dct4K+IXgz4i6C+teB/EthrlkkhgkltH\nJ8txztYEAg4OcEDI5Fc2n7QHwcfxxL4Q/wCFkaDHrUU72slrNceVtlQkMhZgFDAgjGeSMDJoA9Nx\n9fzox9fzrJfxDosXim38OPqtqurXFs95DZbx5kkKMqu4X+6CyjPvWDrnxU+HvhnxxYeD/EHjLSNP\n1zUMC2sLi4CySZ4XI6Lk8DdjJ6ZoA7TH1/OjH1/OuK174p+C/DXiOXQtT1pzqUUaTTWtrZz3Twq+\ndhfyY32ZwSA2CQMgYrM1z47fCDw54yg8K618QtEs9YlZE+xyzHepdtqq+AQjE9QxBHBIAoA9Ix9f\nzox9fzrL1PXdJ0c2S6rfw2v22cWtt5rY82Uoz7V9TtRz9FNU7Pxl4X1DS9E1Gy12yurPW38vTJ4p\nNyXbbGfEZHU7Uc/8BNAHQY+v50Y+v51la7r+k+GtBl1jxBqVvpunwMokubh9qLuYIuT7sQPxpJPE\nGiw+J4PDj6larq1xbNdw2TOBJJErBWcDuoLAE+9AGtj6/nRj6/nXm/iX47fCXwhaWF14j8e6Tp8O\noGYWzSsx83yX8uTG1T0YFTnHIIGcV2mja7pPiPQrfW/D+q2upabdLvgu7WVZYpADg7WUkHBBB9CC\nDgipv1A1MfX86MfX864Xwv8AF74deNPF2o+FfC/i2w1TWNO3/a7W33kw7H2NklQOG44P0zVTxR8c\n/hL4K8Wp4a8UfEDRNN1Y7f8ARJZ8vHuOBv2ghCfRscc9OaoD0XH1/OjH1/OuL8QfFn4a+FBeL4i8\nf+HNPktF3zwz6jCJowQCMxhi+SCMAAk5FUtE+Nfws8R6D/bGi+ONLvLH/SB56M4X/R41lm6gH5EZ\nWb0DD1oA9Bx9fzox9fzrIuvEmi2XhGXxRd6lBDo8Vp9ta8fPlrBt3bycZ27eelaVvNFcWqXET+ZG\n6h1b+8CMg/kaAJcfX86jlgili2SxJIvXa67h+RqaigDwr41fsyfD74qeF7qax0Wz0PxZEpl07W7B\nBbyJOOU8zYAHUtgEkEjqCCK5n9mb4i618QPg21v4r3/8JR4evX0bVt/32kj+47/7RAIb1ZCe9fTW\nK+RP2eUEX7QXx/ih+WP/AIS1n2Lwu4vMSfqSTWVZe6XTep9DjpS0UVxG55z8ff8Ak1v4g/8AYBu/\n/RZrc/Zj/wCTQPh3/wBgWH+tYfx9/wCTW/iD/wBgG7/9Fmtz9mP/AJNA+Hf/AGBYf6110PhMJ7nj\nX7D3/Jo1r/2Gr7+UVfR9fOH7D3/Jo1r/ANhq+/lFX0fXPU+NmkNhD0r5/wBO/wCUr1r/ANiOf/Qx\nX0AelfP+nf8AKV61/wCxHP8A6GKqh8Yqmx9ajpRUE08VvCZZ5UjjXHzuwUc8dTwKR7mCKVIpJkWS\nTIVWYBmwMnA74HPFdpiWKKox6rp02oy2EN/ayXUXMlukqs8Y45ZQcjqOo7j1qb7VB5Usv2iLy4s7\n23DC465OeMd89KALFFRq6NEHVgysMjHNV7TULLULX7RZXcF1CGK74pFcZHUZUkZHpQBJcQRXEWye\nJJF4+V1DD8jXm/wbtItK/Zv8OSvaLDNFpvmt+4COpO5jxgHNeirc20uwJcQt5i7o8OD5gHceo9xS\nG7tvl/0iLmTyv9YPv9NvXluOnWlJXVgPlXQJfis37NPgvwx4g8D6Ha6He3Og29ve2Wqs86wvdwMT\nLbPEMMV5IVvlyfTnN1280Hwpc6z8TdQ8YL4R1S38W67Y2F1qXh+bVrGcSNGGV1jjLxMTECrqykhX\nX5lBWvri91HTdNhWXUL23s42barXEqxBj1wCxGelTQTRXFqlxbypNC67llRwysD0wRwRTe9w8meM\nfAzxna+KvhL4m8QWuhaba2v9qXcv9p6XFcR2usOUV3ukW4USj5iYyCCFMZVWZVFeHWviLw58JPA/\ng3XX8ead4b1jXvDGmC90zxJ4bvL2C7hijb/VXMEe+JsPhlBYLndtBPP2vJcW8W/fKi7VMjbnA2gd\nTyeAPXpStLEhiV5VXzW2r8+NxwTx6nAJ49KT3+78AR5Do+syS/sSvrWk+Gn8MSJ4auHtNNtd6/Zi\nsTiJoi4D4OFdSwDEMCQCTXA+IfAPxB8VeCJfiBqvhnRtL1rR/Cl3bacmnytd6nqTy2DR7biYIo2B\npJHWIBiXIOQc5+k11fSW1T+zE1K1a8Gf9GWdTJwMn5M56c9KL7VtN00J/aWoWtmHzt+1SrFux1xu\nIzjI6UPqEdLHzrqv7R3hC5+Bn9mfCqHxLrGoS2C6TZXVloN40FpdsqRojuYhmRQxcIoYkRt7Z840\nifQfB1zdeD9Y0TX7j4W+HPE8GuXf23RryELbT2beTJNBJGrywpexO7jaRuKNggGvtlrq2W0+1PdR\nLBt3+bvATGM53ZxjHfNMjvrWUIIr2CRpN23bKDu2nDYwecHg+nfFHW4Lax8ran8RI/iR8JfG91o/\ngOCPTb3VNPitvFOkQSRwa0BfwRxqRLFHMZkThvlZFKMofgVlXvib7V+0PYfFhNP8Q7rvxBc+GN39\njXfkf2Q8BggcTmIRFWu183IcnDgdjj7AW6tmiilW4iaOXAjfeCGz0wc859qrjW9J/tT+zf7VsmvN\n237N9oTzM4zjbnOcc4xTutgPlTTdM1LUtM+DOn+O/h/pOg2Xl2ItPFkWbzUFukEcsVsGSJTaCcjB\nLMV+8p5INUtW1HQfBlrcfE278axeEdafxH4hsrKbVfD1xq1jPHJe5ZWEab4mPlgqVYFgCCGAIH15\nfajp2mRLcajfWtnGW2b7iVYwxIzjLEAnjOKZdavpOnzRQ32q2VrJNzEs1wqGToOAxBPJHT1FAHyF\nLBH8d/hnpHhrStCs9Lhv9W1rWYdR0qO5itr+5ggzDfJ5ypKAbi5Aw3QxFRlVGOh+F134qi+L/hn4\nteMNMvNPuPH2nXtlqNpLGyiwFnEktvuXHGViujggffHWvqj/AL6z/uk1Si1jSZ9TfT4NVspbxMhr\ndLhGkXHXKg5GO+RxQtHoJq4/SNWsdd0Wz1jTLjzrG8hW4hl2MnmIwyp2sARkdiAa0axr/wASaDo9\n2tvquu6ZYTOu9Yrq6jidhnGQGIJGQRmrN7rOk6eUXUNTs7VpBlBcTrHu+m4jNAzQoqhfaxpWmBW1\nLUbWzV87TcSrFux1xuIzj2qx9pg/dfvU/e/6v5h83GePXjnigCeiqv2y2+zPc/aIvJTdul3jYu0k\nNk5wMEEH0wadNd21vD501xFHGcDe7hRz05J70ATnrXyXY/8AKUvxv/2K1t/KOvq+KWK4iSWF1kjc\nBldSCGB5BBHUV8o2P/KUnxv/ANitbfyjrOp8DKhue/jpS0UVwnQFfOf7aP8Ayb5pf/Yy2P8AKSvo\nyvnP9tH/AJN80v8A7GWx/lJV0/jQp7H11F/qU/3R/Kvkj4Df8nP/ALQP/Yyp/wChS19bxf6lP90f\nyr5I+A3/ACc/+0D/ANjKn/oUtdNb4GYQ3PomvnT9rf8A5BXwq/7H2x/9BavouvnT9rf/AJBXwq/7\nH2x/9Bauej8RrPY+vV6/n/OnU0f4/wA6aZI1HLD+VdxgSVwHxm17SvDXwD8W6rrF2tpbppdxGsrI\nz/vJEMcYCgEkl2UDAPX0BrujNF5nk+anmbd2zd82Omcdce9RC6tpRH5VxE3mKWXa4O4DqRg8gZ5x\nSYHnPwnl8IeLfhX4I8YaO6ag2n6T9gtr7ypYfLykaTgI4HBaEDJH8PBwTn52sPA3ibX/ABHFp9rp\n97/YHxf+1XHiO4TePsn2e8kk3EnhDLbssagYyVHXv9g6j4i0HRwqarrenWLN91bq6SLd/wB9EGrU\nV9ZXGnpqEN1BJauokW4SVWRh6hgcEe+aTQHBfA6+1LWP2ePDqeILV11C0gfS71J1/wBZJbSPbsxB\nAyGMWenem/C/TraLVPHjtZRR7/FNyV/cKvAhgUY45HHX616H58XneT5qiRlLqmRnGcZx6DI56VDF\nqenXGoS6fDqFrJdRcyW6SqzxgY6qDkdR1HcVbd3cS0Vj5X8YfDjxF41+NHxD+F9pBdWegzKPGlpe\no5RP7RkthBAmQMYFxE8xByCUyc1laP4ouH0Xw18b/iX4a1CPw/ceILq41pPsr3A02eG2isba5lhU\nFjGkkE/zAHa0itjJFfX91fWVjGHurqC3UKWzLKqcDGTyQMDIye2RTbPUbHUoTcWF9b3UW7ZvglWR\ncgZxlSRnnp1qVohs8S+Ft94Y8b/tA+IPib8N7SZfDN7osNje6mtlJaQ6pfLM5VlWRVLtFH8jPgD5\nwuTjjO8QaH40+Ivij4keBYfDfh++8O3erRWjaj4hlMyWKCwgJNtaBPncSNvUl1UMSeoJPu1v4l8O\n3Orf2VBrumzXwZk+ypdRtLuXO4bAScjByMcYq+Jo3laJZUZkxvXcCVzyMjtmmC0PjWx1SPT/AI3e\nF/Httb+JrpdN8QQ+DoLi40m7kik0UQfZBObnyhEd92TKSHJwVHbFXrf4jaL4PtbrwPqXgKDxR8TN\nT8RT3OveHdSsp3utUAndoJ7WYwvC6pGkLRh2VVRCcqVOfrr7Vb4lP2iLETbZDvHy8A4PPBwQcH1H\nrS+fH5/k+enmBd+zeM4JwDj0yCM0EnytqPjy58NftS+I7jUvEvi/QdQuJLO/g8Gab4Z/tOfX7ZLO\nPKrKivgqyzIQrKFIY7iDxzfjrxx4U+HuveIfBOma7pPiKPUr+XUbnwH4h8NX0F7c3E8iSiGK8giI\nl3v93zFYNlVLFcFfs8yRpKiNKis+do3YLY5OB3xSCWJ5HVZQzJgMqtkrkZ5A6cHPNJFM8d+Nd5Kl\nr4BuPsU6yLq0140OPmi8vS7t8HGQMEgHtXknw58F+K/A/jb4PeFLXSry68G3WPEVtcurt/ZNy2mS\nLdWkhOcI8kolTOMFpF5xx9Z32r6TppX+0NStbPfnb9omWLdjGcbiM9asCeM+V/pEX73/AFQ3D95x\nnjnngE8duaE7O/n+gntbyPnv9pH7H4w1PTfhbe2urTabNYXeq376fpd1fbZFjaKyRxbxyFQZmMnI\nAPkn1ri7SLxX8UL/AMG/EXQre9tfG3hrwXbahbJcK8C3N4LqSG6s5VOMLKIZE55XcrDoa+uRPEbp\n7dJUaZFDtFuGVByAcdQDg4Psar22qabdyzxWmoWtw1vxMsUyv5fUfMATt5BHOOhoW34lXPmDwzrH\n/CP/AAq+CmseJdC1a1kuNc1O6bTUsJbm6UywXzpEYkQuWzIByoGeSVHI9g+EXgz/AIRa18R6nb+G\noPCtnruojULbw9bsoWyQRJHl1QmNJXKl2VCVUkDJIJPoxuLfyYpfPi8uXaFfzBhs9MHPOe2OtVJ9\nc0a3kSK41iwikdiiq9wiliDggAnkg8EevFO4jxv4P+PPB/iD4+fFXSdF1iK8vhqkcvlRWsiBYoYI\n4HO8oEYCVXHBOcEjI5ry34o+MPCnw08ZeLfCll4r0TUl1u7n1DUfBniHw9fLNeyzIoEcF/bxfMrl\ncKXDjou7b0+vby+stPsWvtQu4rW3j5aW4lEaLk45LEAckDmo9N1fSdYtnuNJ1K1voUbYz2s6yopA\nBwSpIBwRxSirWA811m0j1D4mfCrUH0JdPaRb2ee1e3XfATY4EbkDgqWx+GK4v9pG31GTU9NtNCsJ\nbi6Twx4hlWK1iYtgwQI2Ao5YhmwByTgCvfX1fSV1Qaa2pWa3p4+ytOok5GR8mc9OenSrE13bQB/O\nuoo9imRt7hdqDqTk9B69KH/mNPY+dPGfxh8IfEP9n3xJpvwytPEOtWUOks8l9b6NdR2scUTxiaDz\nXRQZhEzEIuSQp5r07wD8YfBnxI1T7L4GXWdS01LZpV1f+yLm3sMq4QwCaVFBlAIOwA8ZOe1d+80U\nIiSWdVZ22LuYAs2CcDPU4BOBzwaIbq3m/wBTcRSfLu+Rw3GSM8HpkEZ9QaZKVkWF6U6qj6hZxaf9\ntkurdLULv89pFCY9d2cAe+alhninhSWGVJI3UOrowYMD0II6g+tAyavkT9n3/k4b4/8A/Y1n/wBC\nlr67r5E/Z9/5OG+P/wD2NZ/9ClrKp8BUNz6HoooriOg85+Pv/JrfxB/7AN3/AOizW5+zH/yaB8O/\n+wLD/WsP4+/8mt/EH/sA3f8A6LNbn7Mf/JoHw7/7AsP9a66HwmE9zxr9h7/k0a1/7DV9/KKvo+vn\nD9h7/k0a1/7DV9/KKvo+uep8bNIbCHpXz/p3/KV61/7Ec/8AoYr6APSvnuKaOy/4KvaM9x+7W+8G\nvBbN/wA9HUszD8AjflVUPjFU2PevjH4Zk8WfBXXtEt9VstLuGhS4hu71tsEbwyJMvmnIxGWjAbno\nTXzH43+KHxK8VeKfC+t2ll4Qt9S0e01i5ttM8N62muXW8afIDdlowFRAWULGQSWPJIwK+1JIo54W\nimRJI3XDI6ghgeCCD1Fc94a+HvgPwbdXFx4P8FeH/D81yoWeXStOhtWlAJIDGNQTgkkZ9a7Opjc+\na7zRvgfbaX4G8VfB3WtLm8fS6xYfYr2wv/N1DVhNKn2oXoBLyK0XnPIXA27Sfl6VvaF8QvAdv+yX\n4q0LxH4l06z15IdXt9Y0y9uFivftkjTMyeUxDuzb0ClQdwK4zXu2k/DzwHoHiO68RaF4K8P6XrF3\nv+0ajZadDDcTb23PvkVQzZbk5PJ606++H3gTU/FsHirUvBfh+81632eTqlxp0Ml1FtOV2yspYYJy\nMHih66AeU+OLy50T9nPwDonirULrRdN1J9M0fxFqMUrQNaQNDh1aQHMQdwkTPkYEh5Gcjl9f1T4Q\nfDz4feKNC+FXibS9LXxNqNhpEi2tyi6ZpktwpjkljkGERxbpJIwDE5RCQu4Z+lb/AEzTtW0q40zV\nbK3vrG5jaKe1uolljlRhgqyMCGUg4IIINZeneBvBmj6ZZadpXhHQ7GzsWke0t7WwijjgL/fKKqgK\nWyckAZ70NXv5gtLHxx4al1rxBYeCNM+HmuxX3iL4Y/2+LRLe4WaHVIILi1jjt3dSQRNbTAAgkBip\n7EV7L8FLHQfiz8Jf+Eonhv4bJ/G2o+ILKKUBJNwuJVVJQQSMb2BAwcjr6+2WPhjw5pusz6tp+hab\nZ6hcKVmu4LVI5JB8owzKAT9xOv8AdX0FXraytrKHybW3igjLM+yJAoyxLMcDjJJJJ7k1VwPBNF+C\n/wALdZ+M/jXStX8BaJqFhp8enfZLa8gM6QGSKRpGVXJCliFyRgnaM13vxC8Y6T8G/hppc2naRp1v\nY/a7bR7SOWX7HY2COdqtLIiN5UShcDCnkqvGcjvYbCzt7+4vYbWKO5uNvnTKgDy7Rhdx6nAOBnoK\nbfabY6npc+nanaQXlnOpjmt7iJZI5VIwVZSCCpHGCKmV+g3qz5e+M/iLRfEHi7UNY8L6nBrVjZeF\nxB4ln0iUXaQWb6lbOQWjJBby47o7epjVyRgDPXfETxd4Q+IGsfDfTvAvjPS77U5Nc+2Qy6VdJM9t\naixuRLOyqTtVVkT7+BllHfFex+HfCHhXwlpcmmeFPDWkaFYySGVrXTLOO2jZyACxVFAJIAGcdAKr\naD4A8D+FdTutR8MeDfD+i3l5/wAfVxp2nQ28k/zbvnZFBbk55PWgR8l+FNG+Fvh/xx4S0fxL4Q8F\n6pfRahb2lv4u8HeIYru41K4mR4S17CzfaQjtIN2GkBYjcdmQfWPCvwU+FOu+PfHlvrfgDQtSt9O1\niK0sor2Az/ZozYWsjKm8kKCzsxAxkkmvU9O+Fvw20fxQPEukfD3wvp+tb3k/tO10qCK53uCHbzFU\nNkhmBOecn1rpILGztZriW2tIoJLmTzZ3iRVMr7VXcxHVsKoycnCgdAKYGT4j8JaT4l+H2o+DbpHt\ndLvrJ7B1tNqGOJl24TIIGBwOCB6V8+/ETRtJ+Fd1o2j6BNd+TpXhDxJc2zTyB5GkuJ7UDkAc75uD\njsO+TX1Hism/8O6Dqt2LjVdFsL6YReR5txbpIfL3K+zLAnbuRGx0yoPUClYE7HxO9z4v8GaDF4Fe\n4v5LX4KanFrNzdPgm9s5HjFuhwADi3nujjAA8lfWu58a+EfCHiX40+PNFtfgmfEmqS/YZLTVtHgj\n0+S0nmgaZ55tQ3qyZYR5xklTgKwJB+pLnRdKvYL2G802zuI76PyrtJYFYXCYI2yAj51wSMHPBNPt\n9L061u7i7tbK3hnuNvnSxRKrybV2ruI5OBwM9BxTvcS0PmLT9G+H2oePNU8FftF6xo2taxoOk6fZ\n6cniKdYIJ4DbIJ7y3EjAM7zh1aQfMNgHHOeR0jRfCmsSeF9P8ReCtS+JPhe0tPEEXh/Zafb7qSwS\n9gS2cO7KQm0sqSZ+7sOcc19a+JvAXgfxqLUeMPB+g+IBa7vs/wDathFdeTuxu2eYpxnaM464Fa0W\nl6fDLFLFY2qyQwm3hdIlBjjO0lFIGQuVU4HHyj0FKwz5X0rwz4d1b9jLxb4lVHutNggv7zw/Y3+q\nPfXWgAQhWhecNuEolV5ChLGNm25OK968JfCL4a+CtZ/t3wx4F0HSdWMJia+tbVUmYNguC/U5Iycn\nJ710y+HtBWxv7JdE05bfUZHnvYRbIEu5HADtIuMOWCgEnJIAzWoEGadyT5Q1XxHoum/tL+Oda8Ta\nl8K7HQ7TVLa31VPFO59U+zpYwGM2iEYClpHIOG3EkYGMnL+K/hzwZafELxbf+MNC8DeO7bVsyebc\na9b2mu6HAbUKkVvBckxHBw8YUxklssrHBP1LceC/CF14vi8WXfhXR59fij8iPVpbKJ7qNOflEpXc\nF+Y8Z7mqPiD4X/DfxbrH9reKfh/4X1zUAgj+16npUFzNsGcLvdScDJwM45qUrWLe58y+MNK8D3uv\nW/jC70fwT450XW9J037BpXifX4rLV9Ftjb43Qpds0TlgFbJKMzn5mZQCvN678UfBdx/wg3iXwr4m\nOn6H8OINIkg0zVdRRby5Ny4iuQ6lyZTFbbeUBBMjYOBX2D4i+Gvw78YXkN14s8CeGtduII/Khl1P\nS4Ll40znapdSQPYcVcXwZ4QTTbjTl8L6MtncZE1v9ii8uXKhTuXbg5UAc9gBTT/Mg+SvHuv+L/BX\nwg8f/CG01X4eTaOY9Tng1m419GvmgumkuPsp08Au9yxmMasSFO5Wweh+ir3wVo3xI+Ddh4N8S3cs\nbW8Nkb2KymUTQTxLHJsbIO05A4IBwffNdDc/Dn4fXviuHxPd+BfDdxrcJQxapLpsLXMe0YTbKV3D\nA4GDx2rbs9K0+wubq4srG3t5LuXz7l4olRp5NoXe5AG5tqgZOTgAdqfQNb3IPD+iWfhrwnpnh+w8\nz7HptrFZweawLbI0CLk4GTgDnAr5hsf+UpPjf/sVrb+UdfWLnivknQ5Y9Q/4Kf8AxGuLX95Fp/hy\n0trg9lkZYiB+R/nWdX4GaQ3PoWikHSlrhOgK+c/20f8Ak3zS/wDsZbH+UlfRlfOf7aP/ACb5pf8A\n2Mtj/KSrp/GhT2PrqL/Up/uj+VfJHwG/5Of/AGgf+xlT/wBClr63i/1Kf7o/lXyR8Bv+Tn/2gf8A\nsZU/9ClrprfAzCG59E186ftb/wDIK+FX/Y+2P/oLV9F186ftjLLY/CXwr4q2O1v4e8V2Wo3Pf5Bu\nX+ZxXPS+JG0tj687/nXkPxx1y90K6+H17p+havr10niUSLpmlMgnuQtldEhQ7BSB94gkcL64r1Wy\nvLbUNPt761lWWC4jWWN0bcrKwBBBHUEHOaSews7q7tp7i0gmmtnMkMroGaJipUlSeVJDMMjsSO9d\nrRgj5n8YfFK5t9Z8W+Kzo+qeFdRXw1pmi2UXiFFs3hur28uVVi28oUTbuLhsfI3NcP4W1XTvB/w+\n03W9K8RW+qWfwm8YTadd3trdC5W40K/YEyM6Ehtvmq2ATgwkcYr7EuPDPh671H+0LvQtOuLvcj/a\nJbVHfcqsqHcQTkK7gHsGPqaa3hXwzLDfxS+H9LePUFWO9R7SPbcqoIUSDHzgAkAHOMmmI+QLl9Rf\n48N4zv5fhzYatruhxarInxD3LHDYSXMixQQKAAJEhhiLZI2tIchs5Ht/xZvvDniP9jzWZfDV1bto\nOq2EVtaTaaoRPKmmSMGMYAAG7jj8K9L1rwX4Q8R39he+IvDGjatdaa/mWM1/ZRTvaNkHdEXUlDlV\nORjlR6Vo3mlafqGniyv7G2ubbereTLErplWDKdpGMggEehAPaptpbz/AWu58jR+JfiDoXjbxN4Yv\nY7+78YeBvAGo28GopFvOoRNPE1peRr/E5jXDrj/WRsOc4F690T4H2lh4G8UfBjW9Lk8eS6xYi0u9\nPv8Azr7VkmkT7WL1QxeRTD5zuXA2lDyvQ/WH2O2+3/bvs8X2ny/K8/YN+zOdu7rtzzjpnmsDSfh7\n4D8P+JLrxFoXgrw/perXe/7TqFlp0ME829tz75FUMcnk5PJ5NNPVNgzx349abpOt+O/7P1i1t7y3\ng8B6/eGCdBIjMr2hjYr6hlBBx1UelYHw70DwXpXi7Q/EHwdsXgisdDdvFP8AYsp/su5f7KrRRFQT\nG915mDmLLKFO4jIFfS15oOi6hPJPqGk2N1JLbPZSPPArloHILxEkcoxAJXocDIqex02x0vS4NP0y\nyt7Ozt1EcNvbxLHHEo6BVAAAHoBRYpu58t/s8eKPDOi6D4fh8Ra58Jla80lbjR00PdJq+Vgaa6N2\nzLwwXeTgLggqd3UxfAfx3b6h+0leanL4lsL6T4jaO+s/Yor5J2spbWdlhtyisTGfsksZIIBJRh/C\ncfSGn/D/AMD6VDqUWleDfD9jHqjM1+lrp0MQuy2dxlCqN+cnOc5yfWr6eHNBiv7S+h0TTo7i03fZ\npktUDwbl2tsIGVyvBx1HFC3+VhM+YfiToOta38bvGXwP0S7vdPtfHdtb+I/tsS4S3EMLxT4bqC8s\nNnnGOCR3rJsvGmq+LPhJ4j+Id3o+o3Grf2j4b8Oy6da4S5e6tbyFrmGPcQNzTO4AJAPGTzX16dM0\n86yuq/Ybf7csJt1u/KXzBGWDFN/XaSAcZxkZqtF4c0CKF4IdE06ON7v7e6JbIA1zuDecQBgybgDv\n+9kA5pRuv68wZ40vj8678afD2seIPCXiPwnDonh7V9Tu4tct1jKor2qEo6OyPgMx4ORkdMkDgfgX\n4i03XvjB4gsNZ1vS9aj+I2hp4kuLGK/S5FpKsrxtaEIxKbbeS3BBwco3ocfUd94e0LVDK+paPYXh\nlga0kNxAsm+FmDNGcg5UkAlehIHpSp4f0JNQt9QTR7Bbu3VkhuFt0EkQYAMFYDIBAAIB5wKaVmD2\nPEfAvwU+FHiDXfGn9vfD3w9qn2HxFNZ2n223+0eRCtvbsEXeTtXczHA4yx9a6L42Wmq6PF4S+IHh\n+50FLzwvfu66drmorp1rexzwPA0Ynb5Y5AGypII4IxzXq1tYWdmbh7S0gtzcSmeYxIF8yQgAs2Or\nEAAk88Cq+taDoviPRp9H8RaRYatp0+BJZ39uk8MmDkbkcEHBGeRQwe58fT+OPGep+Ifif46v9Q0m\nzhbQ9Ftru48I3v28aPbNeuJlN0ow8yQNNKzoAEDqR0yYfi3onwx0DQNL1D9m+XS/+EmvLe4tL2y8\nMXgne90k2zm4knRGJZkHlsruQxdgOScV9gaD4T8MeFdG/sfwx4f0vRdP3M/2TTbSO3i3N947EAGT\n3OKp+HPAPgfwdPc3HhDwb4f8PzXQAuJdK0+G1abBJG8oo3YJJ5z1NIDx/wAbeNvh94u8EeAdJ+Hv\nijRtUupfEukSaVZ2F0jyLHFKHYmJTvRUhSQtuA27ecVjfETwB8P9O+Kuk6Vrfw3v/G+n3Gna3qv2\nS3sFu7mO4uL2GVivKCNcyuFbqCRk5ya940j4feBNA8SXXiLQvBXh/S9XuldbnUbLToYLiYMwZg8i\nqGYFlBOTyQDW21hZtqCag1rC1ykZiW4KDeqEglQ3UKSASOhIB7VQLsfG7vGvwD8IPpWq6Guhat4x\n+06PZeMdUe9tdHSO1mZLW9m+YlvPUsYix2sQm7+IfRnwk1rRdY8BS2+k6l4NuNSspvs2rSeDk22K\n3e0MfLyOflZDyT6E8V09/wCB/Bmq6Le6PqXhLQ7zTr65N5d2VxYRSQ3MxIJlkQqQ7kqp3EE/KPSt\nOw0zT9J0+HT9KsbWxtIUEcdvaxLFHGoGAqqoAAAAAAHAqbbgfGFnpPw68FeKNGh8W6F4O8R3VlrE\nFx/wnHhbxDBJq81w15tj+028refgFkEgid+PlVQuVWt8Tdd07x78adSu08e6Tp9vrVzffDuO3lvU\nP2aJYfMjuZIQ27Y97E6MSANmPUZ+uI/hf8NovF3/AAlUXw98Lx695xuP7VTSrcXXmH+Pzdu7d75z\nWiPB/hNLCCyTw1o629u6yQQ/YotkTq25WUbcBgeQRyDzTT2A+YG+IXjj4gfF/wCFVvq6+A7VdP14\nzy2WgeIF1ee522U/mXeUAEECKWG1iWJdRnA55LwRc614XOkfFjQri9vLXwz4SsY9c0mLn7Xp91dX\n0krxjnMsW1JB6hWHevsDQfh18P8AwrqF1f8AhjwR4d0W7ulKXE+m6bDbvMpOSHZFBIJ5wa09P8Oa\nDpETxaVothYxPEkDJa26Rq0aAhEIUDKgMwA6AE+tPoC0Pk7wtdeFda8G/DTw18TLm1fwvB4JTW7D\nR9SuI7W01q8L7SJGkZUdo0KFY3IAMu8g4BX2r4F+LPhtqfw1tdN+H9re6HYw3M9vFpWpM2+KUEvL\nHAzMyyxoWIBiZkUDAIAxXea54I8GeKNBt9F8S+FNE1jTbZleCy1CwinhiKrtUqjqQpCkgYAwDirl\nv4e0K1OnfZdH0+H+zIjBYeVbIv2SMqFKxYHyKVUAhcAgAdqCTVHWvkX9n3/k4b4//wDY1n/0KWvr\ncvs+/XyF+y3cR+IPFvxk8e2nzafrPi+Y2UvaSNDI24fhIn51lV+E0p/EfRtFFFcR0HnPx9/5Nb+I\nP/YBu/8A0Wa3P2Y/+TQPh3/2BYf61h/H3/k1v4g/9gG7/wDRZrc/Zj/5NA+Hf/YFh/rXXQ+Ewnue\nNfsPf8mjWv8A2Gr7+UVfR9fOH7D3/Jo1r/2Gr7+UVfR9c9T42aQ2CvBP2i/hx4v1W68NfFj4Xr5n\njbwdOZ7a025N7blsvEBkbjy3y9WV2Uc4r3uilB8r5ipRueN+Cf22fhBrelrbeOL288EeIYVxd6Zq\ntrLtRxw2yRVIIz0DYb24zXXj9rT9nX/oqWkf98S//EVs634M8KeJZUl8R+F9G1aRfuy3tmkr/wDf\nTLk/iayf+FRfC3/onXhf/wAF0f8AhXT7eJn7Mj/4a0/Z1/6KnpP/AHxL/wDEUf8ADWn7Ov8A0VPS\nf++Jf/iKk/4VF8Lf+ideF/8AwXR/4Uf8Ki+Fv/ROvC//AILo/wDCl7ddhchH/wANafs6/wDRU9J/\n74l/+Io/4a0/Z1/6KnpP/fEv/wARUn/Covhb/wBE68L/APguj/wo/wCFRfC3/onXhf8A8F0f+FHt\n12DkI/8AhrT9nX/oqek/98S//EUf8Nafs6/9FT0n/viX/wCIqT/hUXwt/wCideF//BdH/hR/wqL4\nW/8AROvC/wD4Lo/8KPbrsHIR/wDDWn7Ov/RU9J/74l/+Io/4a0/Z1/6KnpP/AHxL/wDEVJ/wqL4W\n/wDROvC//guj/wAKP+FRfC3/AKJ14X/8F0f+FHt12DkI/wDhrT9nX/oqek/98S//ABFH/DWn7Ov/\nAEVPSf8AviX/AOIqT/hUXwt/6J14X/8ABdH/AIUf8Ki+Fv8A0Trwv/4Lo/8ACj267B7MZ/w1p+zr\n/wBFT0n/AL9y/wDxFWNO/aj+AWrarbadY/E/RpLq5kWGFG8xAzscAZZQBk8ZJFRf8Ki+Fv8A0Trw\nv/4Lo/8ACvn39sXwR4L8K/s5rrHhnwjoml6hFrFqFuLKzSJ8fOcZUA4JA4qo1lJ8oOnY+5ieK898\ncfG/4U/DXW4NG8b+N9O0e/mgFxHazbmfyySAxCqcAkEDOM4Nd3Ac2ETfdzGP5CvjP4Z6LoPxt/au\n+KvxQ8QaPZaxoNlPH4e0lL2BZY28rG9wGyM4RSCO0hrSUuVXIUbntf8Aw1p+zr/0VPSf++Jf/iKP\n+GtP2df+ip6T/wB8S/8AxFSf8Ki+Fv8A0Trwv/4Lo/8ACj/hUXwt/wCideF//BdH/hWPt12L5CP/\nAIa0/Z1/6KnpP/fEv/xFH/DWn7Ov/RU9J/74l/8AiKk/4VF8Lf8AonXhf/wXR/4Uf8Ki+Fv/AETr\nwv8A+C6P/Cj267ByEf8Aw1p+zr/0VPSf++Jf/iKP+GtP2df+ip6T/wB8S/8AxFSf8Ki+Fv8A0Trw\nv/4Lo/8ACj/hUXwt/wCideF//BdH/hR7ddg5CP8A4a0/Z1/6KnpP/fEv/wARR/w1p+zr/wBFT0n/\nAL4l/wDiKk/4VF8Lf+ideF//AAXR/wCFH/Covhb/ANE68L/+C6P/AAo9uuwchH/w1p+zr/0VPSf+\n+Jf/AIij/hrT9nX/AKKnpP8A3xL/APEVJ/wqL4W/9E68L/8Aguj/AMKP+FRfC3/onXhf/wAF0f8A\nhR7ddg5CP/hrT9nX/oqekf8AfEv/AMRQf2tP2df+ip6R/wB8S/8AxFSf8Ki+Fv8A0Trwv/4Lo/8A\nCj/hUXwt/wCideF//BdH/hR9YXYXszh/Gv7Z/wAPooX0L4SW9/8AELxbdL5dlZ6baSiBZG4VpHYD\nKjOSFBzjBK9Q79nz4VeI/AWla94q+IF7FfeOPFd6b7VZYm3CEZJWIMODgsScfKMgDhcn1LRPC/hz\nw1E8Phzw/pekxvnctlapDuz1yVAJHsTitas6lXn90uELBRRRWJoFfOf7aP8Ayb5pf/Yy2P8AKSvo\nyvnP9tH/AJN80v8A7GWx/lJV0/jQp7H11F/qU/3R/Kvkj4Df8nP/ALQP/Yyp/wChS19bxf6lP90f\nyr5I+A3/ACc/+0D/ANjKn/oUtdNb4GYQ3PomsLxh4V0Xxx4D1Twlrtv52m6jbNbzBMBlzyGUnowb\nBB9QO2a3aTFcaOg+ZfC3xN+KX7MFhF4E+JXhLVvGvgPT8RaT4r0OAPNDbj7sc0ZOPl4ABZSAMAsA\nMdov7dnwKxkv4oHt/ZD8frXsw/8Arfgarf2Zp3/QPsv/AAHT/CuiNfuZOmeSf8N2/Ab/AJ6+J/8A\nwUP/AI0f8N2/Ab/nr4n/APBQ/wDjXrn9mab/ANA+y/78J/8AE0f2Zpv/AED7L/vwn/xNP2/kHszy\nP/hu34Df89fE/wD4KH/xo/4bt+A3/PXxP/4KH/xr1z+zNN/6B9l/34T/AOJo/szTf+gfZf8AfhP/\nAImj2/kHszyP/hu34Df89fE//gof/Gj/AIbt+A3/AD18T/8Agof/ABr1z+zNN/6B9l/34T/4mj+z\nNN/6B9l/34T/AOJo9v5B7M8j/wCG7fgN/wA9fE//AIKH/wAaP+G7fgN/z18T/wDgof8Axr1z+zNN\n/wCgfZf9+E/+Jo/szTf+gfZf9+E/+Jo9v5B7M8j/AOG7fgN/z18T/wDgof8Axo/4bt+A3/PXxP8A\n+Ch/8a9c/szTf+gfZf8AfhP/AImj+zNN/wCgfZf9+E/+Jo9v5B7M8j/4bt+A3/PXxP8A+Ch/8aP+\nG7fgN/z18T/+Ch/8a9c/szTf+gfZf9+E/wDiaP7M03/oH2X/AH4T/wCJo9v5B7M8iP7dnwGI3GXx\nRxz/AMgh/wDGvdvA/jXQPiH4C03xn4Vu2utI1KMyW8roUPDFWBU8hgysCPUVx3irS9OfwHryPZWq\nq2mXY3LAg25gfnOOMda4f9huWSX9iTwuj/diub5F+n2qQ/zY1rTqc5MoWPcfFHibRvBvg3U/FXiC\n7W00rTLZ7q7mKltsajJwBkknoAASSQB1rwFf27vgMw3JceJXU910h/8AGsz9tfxNe6h4N8L/AAS8\nPvv1jxvqkNvIi9VtkkUknuAZNhz0wjV7DonhfRfD/hyw0LT9PtfstjbR2kbPbplkRAoJ4PJ25PuT\nRUqcgQhc8y/4bt+A3/PXxP8A+Ch/8aP+G7fgN/z18T/+Ch/8a9b/ALN07/oH2f8A4DJ/8TS/2Zpv\n/QPsv+/Cf/E1l7fyK9meR/8ADdvwG/56+J//AAUP/jR/w3b8Bv8Anr4n/wDBQ/8AjXrn9mab/wBA\n+y/78J/8TR/Zmm/9A+y/78J/8TR7fyD2Z5H/AMN2/Ab/AJ6+J/8AwUP/AI0f8N2/Ab/nr4n/APBQ\n/wDjXrn9mab/ANA+y/78J/8AE0f2Zpv/AED7L/vwn/xNHt/IPZnkf/DdvwG/56+J/wDwUP8A40f8\nN2/Ab/nr4n/8FD/4165/Zmm/9A+y/wC/Cf8AxNH9mab/ANA+y/78J/8AE0e38g9meR/8N2/Ab/nr\n4n/8FD/40f8ADdvwG/56+J//AAUP/jXrn9mab/0D7L/vwn/xNH9mab/0D7L/AL8J/wDE0e38g9me\nR/8ADdvwG/56+J//AAUP/jSH9u34FY+/4o/8FD/4167/AGZpv/QPsv8Avwn/AMTSf2bp3/QPs/8A\nwGT/AOJo9v5B7M+c/E3xw+JX7QljL4H+CfgvWPDnh++zbal4z16LyVihYEOIUBxvIJAIYsc8BfvD\n2r4b+AdF+GXwv0jwV4fR2s7GPDTOoDzyscyStjjcx59hgdAK6sD7ifwrwq/3R6D0oxWVSo5lxhYW\niiisyjzn4+/8mt/EH/sA3f8A6LNbn7Mf/JoHw7/7AsP9aw/j7/ya38Qf+wDd/wDos1ufsx/8mgfD\nv/sCw/1rrofCYT3PGv2Hv+TRrX/sNX38oq+j6+cP2Hv+TRrX/sNX38oq+j656nxs0hsFFFFQWFFF\nFABRRRQAUUmaF+b7nzf7nNAC0UuP9h/++TTA9ADqKTNLQAUUUUAFfNP7XTSa9F8NPhva72uvEPie\nDdEmTuiQhWbA7AyZ/A19K5/z9a+V/DGu6T8UP22/EHxa1XUIrf4e/C6we2t9Rlb9w1yQweQHvyZC\nMZJCR9yM60Y3kTUeh7f+078U/wDhVf7P+ozaU7t4j1j/AIk+iwpzI1xKNu9R1OxSW6H5to71U+Bv\nw5j+FXwH0Hwg6J9uiiNxft13XMh3SDPfBIUdeF44NeSfD9dS/aR/aIPx08QWk9r4H8OM1r4Q026X\nDTyA/NdMvTIPOf72xQTsJP09/n/P+fWrrT+yRTXUdRRRXOahRRRQAUUUmaAFopM0tABRRRQAUUUU\nAFFFFABRRRQAV85/to/8m+aX/wBjLY/ykr6Mr5z/AG0f+TfNL/7GWx/lJV0/jQp7H11F/qU/3R/K\nvkj4Df8AJz/7QP8A2Mqf+hS19bxf6lP90fyr5I+A3/Jz/wC0D/2Mqf8AoUtdNb4GYQ3PomiiiuM6\nAooooAKKKKACikzS0AFFAO/7nzfrS4k/uP8A98mgBKKbupc0ALRSDpS0AFFFIelAHF/F7XI/DnwC\n8Za1LsVbfR7n7/8AEWjMYHvktjFZn7KGn2/g79hrwbNq0sVnD9gn1WeeXCKkcsskwdiegEbA59BX\nmf7Vur3niiHwr8AvDT+ZrnjHUYvtKr1gs43yXb0BIzz2jasr42+Mr34i69pv7JfwSuUW1tIYrbxF\nrEDZgsraEAGDcvXGBvGeThP72OyirQuY1NyT4NSXHx0/ak8T/tD6hbyr4f0rdovhZJVxwAQ8oB6H\nazE+hlI/hr6kHSsHwZ4R0XwL4D0vwf4ft/J03ToRBGrffbHLOx7szEsT6k1vDpXNUnzu5qlZC0UU\nVAwoopM0ALRSZpaACiiigAooooAKKKKACiiigAooooA85+Pv/JrfxB/7AN3/AOizW5+zH/yaB8O/\n+wLD/WsP4+/8mt/EH/sA3f8A6LNbn7Mf/JoHw7/7AsP9a66HwmE9zxr9h7/k0a1/7DV9/KKvo+vn\nD9h7/k0a1/7DV9/KKvo+uep8bNIbBRRRUFhRRRQAma8q+Jn7QXgP4a6gmiXFxda94ml+WHQNFT7R\ndM56BgOEz6H5vauP+InxN8aePfihL8EvgLcQLq0K7vEHinduh0WPdgqhGQZeoOMkE7V5DMvqPwi+\nAvgf4QWDy6PbvqniC5y1/wCItQ/eXdy5OWO452KTztB/3ix5pVKkKest+wt9jyaG4/bB+KB+0aTp\nXhz4UaLLzG2pf6XfMvbI2sQccj5U+tXB+y18StVAl8VftP8Aja8uPSyi8hFPoMynI/AfSvp8CgCu\nOWNqfZ0DkPmH/hkvxXb/AL3T/wBpT4gw3C/6t3bzAv4bxn8xVaf4c/tfeBN9x4V+Kug/ES1Tn7D4\nhtfInkHoHJOOn/PUV9UUmKUcbU6j5D5h8O/tPWWn+I7fwl8avB+qfDXxBL8kct+pfT5z6pNjgdOT\nlRkDca9+ililiSWKVJIXUOrowYMCMggjggg5BHBp3i7wX4U8d+F5/DvjDQrPWNNmXDW90mdp/vIR\nyjDsykEV8tXtn4w/ZC16K4S9v/FHwVvLhYmW4bzLrw87nAIIHzRkkdBhumA33uulWhU02YtUfU1J\nmqml6pp2taNa6xo97b32n3caz21zbuHSaNhlWU9wf8aff28l3pd1aW97LZzSxvHHdRYL25KkB1zx\nuBIIz6VYz5f/AGtv2jbbwJ4buPhv4Q1BG8ValGY724ibP9l27DByRyJXBIAHIXLHkqD578IPhD4r\n+LXgPQ/DN7ZXnhP4O6dKLyW3bMV74nuuC08vfaSuFP3UVVVdzDdXs/w4/ZD+HXg/X28UeJ7i98b+\nInmNx9r1lQYVkJzv8nJ3Nnnc7HnkAEZr6CCbPkT5VXjb7DtW/tFBWgZ8t3qVdL0zTtH0a10fSrK3\nsbG0jWC2tbddscMajCqo7Af555NykHSlrA0CiikPSgAzTZJI4onlldI40UuzuwUKB1JJ4A9zXEfF\nP4seFPhF4N/t3xRdu0krGOy023w1zeyj+CNfxGScKuRnkgHyPTfhP8Wv2hZE13406xf+C/BcreZZ\n+CdLk8qeaPjabqTHUjsQW9FTNN2SvLYVzq/GX7VPwg8Jao+lW+tXHibVvuLp/h2A3ZY9xvGEyPQE\n1zqftE/FbWP3vhL9l7xjqFr1V9Qn+yHHrjy255HGfWvfPBHwu+H3w30tLLwV4P0vR41wrSxRBppM\nd3lbLsfck11pXf8Af+b/AH+a5ni4L4V94csj5Z/4Xp8fVG+f9kvxGsK8sU1Te2PYeRzTbb9rzwxp\nV+lp8Svh/wCNPA0jNt+0X9gZIM9gGXBP4LxX1R5f+zUN5ZWWp2r2moWtveW78NDcRiVGB7ENkUlj\nY9YByyOK8KeN/B/jjRv7V8H+I9N1q1/ie1nDmP2deqHtggVvZrxTxp+yb4Vl1T/hLfg/qd18NvFs\nXzw3OkSMtpKf7ssA4CnvswOuVbNZ/wAPfjd4i0rx5F8Jfj1pUHhvxkygWGoq2LHWgeA0bdFcnIwC\nFJ4wrfLXTGUKnwfd1Dm7nvY6UtNp1AwooooAKKKKACvnP9tH/k3zS/8AsZbH+UlfRlfOf7aP/Jvm\nl/8AYy2P8pKun8aFPY+uov8AUp/uj+VfJHwG/wCTn/2gf+xlT/0KWvreL/Up/uj+VfJHwG/5Of8A\n2gf+xlT/ANClrprfAzCG59E0UUVxnQFFFFABSZoJrxz4yfGLUfCms6b8Ovhxp8WvfEXXfksLHdlL\nKMg/6TP2CgAkAkZ2kngcuMbgzqPiX8YPAXwn0ZLvxhraQ3Eq5ttNt1826uc8DZGOcE9zhfevKYPH\nH7T/AMWj5vw4+H+neAPD8v8AqtY8V/PcyIejLDtOPoEbBxhq7r4Q/s3aL4N1T/hOviBff8Jp8Q7v\n99c6zf8A71LZzyVt0YYUDoHxn+6FHy17riuepilDSGvmKzZ8vr+zR8Y9diEvjX9qHxRJIfma10i1\nMEUZ/wBk+YOP+ACl/wCGRvESHfF+0f8AENZF+6fMB5+m/mvp8dKWsPrlXuPlR8tTfBr9qTwf/pHg\nr4/2viqNF/48fFNkRux/CGJkxnpnK/UVDa/tH+K/AWqRaJ+0P8NL/wAJs7CNfEGmq11p0hPGSQWK\nAn0Zj34xX1XVPUtL07WNKn0rVdPtb6xuFMc1tdRLJHIDwQysCDn6VpDGv7auLl7HO6Nrek+INGt9\nY0LVbXUtPuF3wXdrKskcg9iOPqDyO9aFfNHjD4T+L/2ddUvfiV8CPP1DwnuNzr3gW4dnj8sAbpbU\nkkhgMnH3lx/Evyj2/wCH/j7w78SvAdl4t8K3q3FjdL8y8B4JABujkHZ1JwR0PBHBFdaamuaGwJnU\nV578XPjF4P8Ag74Il13xLdpJdOp+waVEw8+9kHQKOygkbnPAHqeK9A+/XjXif9mD4Y+Nfi/dfELx\neus61dXGw/2ddXp+yR7VAAVFAYJxnbu25J4OcU4W+0Er9D47+HGqfG/43/FvxL4j8IWP2XXNbzaX\nfiqUsItDsSADDbtj92xXC5XLlRhcbmJ+5Pg58GfCnwY8E/2L4fR7q+utsmo6rcKPOvZB3P8AdQEn\nameMknJJNdzpOjaT4f0a30fRdMtdN0+3UJDa2sSxRxgeiqAB+VXcVVSpf3UJQsA6UtFFZlBSHpQe\nlY3ivxXoPgrwle+KPFGq2+m6XaLvkuJW9eAqgcsxOAAASScYoQGyOleV+Pf2i/hB8Orp7LWvFUF1\nqXT+zdKU3c+/srBMhSTxyRzXmtgvxf8A2pR9qtL2/wDhr8JpWKxyxcanrMYOMg/wIfrt/wB+vdPh\nz8B/hR8LLRE8JeErKO824k1O9UXN3J6kyuCRnuFAX2FRUqwp/Fq+yFq9jxuP9pnxx4gO7wF+zh48\n1iH/AJZzXv8Aoat054Rhjr39Kf8A8Lx/aG/6NK8Qbf8AsLZ/9oV9VEf3/mpnlx/3E/75FY/XY/yB\nyPufKn/DWMfh+XZ8Svg1488Ix/xXb2v2iBB6s2FJ/AGvXvAvxW+HXxKtfN8FeKrDVG25a1V/Lnj7\n/NE2GHHJwCB616bJHFLF5UqJJG38D4YfkeK8R+In7LHwu8b3X9taPp8vg7xOjeZBrXh5vs0iydQX\njUhG55Jwrf7VXDFU5bqwWkepA0tfNel/FX4i/A3xTZ+DP2h9moaHeyiDS/H1qhEMhxnbdADKt6kg\nMOSdwBavpCGWO4tYri3lSSGVQ6ujAhgRkEEdQQcg966OX7QJklFFFSMKKKKACiiigDzn4+/8mt/E\nH/sA3f8A6LNbn7Mf/JoHw7/7AsP9aw/j7/ya38Qf+wDd/wDos1ufsx/8mgfDv/sCw/1rrofCYT3P\nGv2Hv+TRrX/sNX38oq+j6+cP2Hv+TRrX/sNX38oq+j656nxs0hsFFFFQWFeK/tB/EnXvDumaR8Pf\nh6PO8e+Lp/sWmKn/AC6RniS5PpgHgnpy38Ne0D+5/e49OvFfOv7P8H/C1f2m/iH8cr1DNp+nTf8A\nCNeH96/LHEgzI6+hIwfrK1DfInOXQUux7N8G/hLoPwd+Glr4Y0jbcXj/AL/UdSdf3l/ckfPIxPOM\n5Cg5wvuST6HijFLXkzm5y5pFJWCiiipGFFFFABVPU9M07WNGutK1Wyt7yxu42gubW4Tek0bDDKyn\nggg/5xVykIppgfI3gg6j+zb+0Mvwa1i7lm+H/imR7nwpe3D7vsVwSC9ozHsWIHXqUbqzCvpqvNf2\npvh5/wAJ/wDs6az9i3R61oSnW9MuF4dJYQWYKRyCUDDjvtq58EfH3/CzfgP4a8ZTOv2y5tvLvdn/\nAD8Rny5eOgyy7gOwcV6tOftIc/XqQtPdPQKKKKoYUUUUAFc1488baD8Ovh9qnjLxLceTp+nx72VM\nb5nPCRpnqzNgAe+egNdIelfNnjCzHx3/AGy9L+GE377wZ4FjXV9ei/gu7xgDHCw6EDKjHTHmDqac\nbfExNmp8D/htr3xC8ZxftD/GC03apdLnwxoMvMOj2p+aOTaf+WhByCRkbi5+Yrt+nB2oUbfkRNqr\nxt7fh7U6vLrVnUldjSsFFFFZFBRRRQAmK4H4u/Cbwz8Yfh9L4Y19DDOrebYanCoM9jMOVkjPX/eG\nfmHvgjv6Q9KqE5QlzRFKJ81fBL4jeK7XxbqXwN+LTbfG+hR77S+/g1qzH3ZkPG5gvJPUjOcMrCvd\nx0rxT9q7wLqN34IsPi/4NTyfGHgaT+04ZYl5ntQczRt3Khcvj+6HH8VeleBfGGnePfhzo3jLSv8A\njz1W0S5VOpjJ4dD7qwYfhXrRkqkOePzJX8p0VFFFAwooooAK+c/20f8Ak3zS/wDsZbH+UlfRlfOf\n7aP/ACb5pf8A2Mtj/KSrp/GhT2PrqL/Up/uj+VfJHwG/5Of/AGgf+xlT/wBClr63i/1Kf7o/lXyR\n8Bv+Tn/2gf8AsZU/9ClrprfAzCG59E0UUVxnQFIelLTWoA8++M/xPsvhL8Jb3xRLEl1qDsLTS7Hk\nm7un4jTA5KjBLY5wD3IrL/Z0+Dt74K0W8+IHj2X+0viN4o/0zVb24+Z7ZGwVtU/ugDG4DjICj5VW\nuAvLX/hcv/BQ210K4T7R4Z+GlkLyaJ87JNQkKlQw5BIbaMHtC3rX1kO1c+Kqci5I/MS1dxR0paKK\n88sKKKKACiiigBCK+RvH+lSfswfHi3+J/h+J1+Gfiu7W08RaXEv7vTLpj8l0ijhVPJ7dGX+JQPrq\nuV+IvgnTviL8Ltc8Faqitb6raNb7m/5ZydY3HurhTn2rfD1fZz8upM0WIpY7iJJYpUkhdQ6ujZDA\njIIPcEHI9qeOleCfsk+MNR174EN4V1xnbWvB9/Jodzv+95aEmLPfgbk5/wCeea97HSvTasxRdxaK\nKKkYUUUh6UAV76/stM0u61PULuK1s7WNp57iVtqQxqpLMx7AAE180eENC1H9q/4lp498VRXFr8JN\nBuyND0aVSv8AbU6HaZ5h3QHt77B/ETqftHahqvjrxl4S/Z18NXUtvdeJpRea1dRdbbTY2y2fTcVY\n88HYFPWvpfQdC0nwz4YsPD+g2KWem2EKW1vbouAkajAH9Se5JJ5NZ1qvs4abv8id2XooY4YlhhiW\nONFCKiqFCgDAAA4AA4xUmKB0pa8s0CiiigAooooAxfFXhbQfGvhK/wDC/iXTYtQ0u+jMU9vL0YHo\nQeoYHBUjkEAivmT4e6rr37PXxftfgV491OXUPCOrZPg3Xrhh+7webOU9AQSAB2Yrj5WAH1pivMPj\n38Krf4u/BbUfDqJ5esQ/6bo9zna8F4gymG6gN9wn0bPaurDV+R8stmRKPU7KivKv2eviTcfFD4G6\ndrGq/LrlizaZqyPw32mLALEdt6lWx6lh2r1QdK75KwxaKKKQBRRRQB5z8ff+TW/iD/2Abv8A9Fmt\nz9mP/k0D4d/9gWH+tYfx9/5Nb+IP/YBu/wD0Wa3P2Y/+TQPh3/2BYf6110PhMJ7njX7D3/Jo1r/2\nGr7+UVfR9fOH7D3/ACaNa/8AYavv5RV9H1z1PjZpDYKKKKgs5X4k63/wjnwb8V66nytZaTczq390\niJgD+BINeVfsPeKfBl7+zNp3hfRdYt5vEFjJcXOq2TttnV5JmIfaeWTbsG8ZHGDg12n7Qgkb9lH4\ngKv3v7Dn/muf0zXj3h/9mjTvFv7Onw6+I/wr1r/hC/iHbaDaTx6paMUhvZPKBZZwo4YnILgH0ZWF\nKpGMqdpu2pDvc+yR0pa+bfhT+0pe/wDCZH4UfHnSk8HeO4sRxXEuI7LVM8K0b/dVmxxglWz8pB+W\nvpKvNqUZU3Zlp3CikzRmsihaKTNGaAFopM0ZosAjpG4KSqrK3ysG5DA8EEdxjrXyj+yPDL4fi+Jv\nw6d28nw54tngtl/uxvuXI+vlKce9fV1fK/wE/e/tS/H24h+aH+3I03pwMhpNw+vB/Wu/BP3ZIze6\nPosdKWkHSlrpKCiiigCveXltp9hcahdvtt7WN55G/uoilmP5Ka8L/YusZNT+F/iX4oamj/2p4v8A\nEFzeSO3/ADzRsIo77QzScew9K9M+LFzJafAfxrcRf6xNDvdv427j+RrE/ZMto7X9jPwIsX8VlLI3\n1a4lY/zx+VZ13al8xfaPaB0paKK8ssKKKKACiiigAooooAhurS2vrCWxu4lmt7iNopImXIZGBBBH\ncEE18tfslS3GhaD48+Ft077vCfiK4t7ZXbLfZ5CSh9eSpP419UvXyx8Jj9k/b0+PVlF/qZWsbhl6\nfPtJ4/76Nd+Cl7somct0fRdFFFdJQUUUUAFfOf7aP/Jvml/9jLY/ykr6Mr5z/bR/5N80v/sZbH+U\nlXT+NCnsfXUX+pT/AHR/Kvkj4Df8nP8A7QP/AGMqf+hS19bxf6lP90fyr5I+A3/Jz/7QP/Yyp/6F\nLXTW+BmENz6JooorjOgKQvGnzv8A6teW+g5P6UtVtRH/ABJr3Z9420oX6lGoQHy3+xl4+8Ian48+\nJtvqGuwR+Mte8QSXkdtcNse5tk3bPLY8OwLyZUcgYOCM4+yR2r4Q+A37PHgz4w/sh2t9PLPofjKy\n1e8Np4hssrcQSLICqyYILqMA4yGXkqwyc+geEfj344+DXjG3+Gf7TkDRwyt5eleObdS9reIOB5xA\n69MvgMP41x81YYml7STcN+3+RMXZan1jRUFrd213aRXdpcRXFvMokjmicOkiEZDKw4II5yKmzXnW\nZoLRSZozQAtFIDQDQAtIelGaD0oA+U/h9B/wiX/BRb4s+Got62et2FtrkadvNOxnb/vqSUV9HDpX\nzxcfN/wVXumiw3leCQJNn8JLHGfQ4I6+or6HHSvZ6L0RCFooopDCkPSlp8P/AB9xf9dF/wDQhQB8\n6fAeP/hOP2zPjB8SrjZNDpM8XhrTn6iNEyJCh9/KBP8A10PrX1KOlfMP7D0Zl+EHjXVX/wCPi78Z\nX3mN/e2xwkc9Ty7da+nh0rhxcv3rQqewtFFFcpYUUUUAFFFFABTadRQB8q/DOD/hBf27/iv4Ai/d\n6frkEPia0h6BXYjzWH1aRx9MV9FjpXz34sH2L/gqJ4XuIfvXvhKWKf8A2grSEfyHWvoVeteze6T8\niEFFFFIYUUUUAec/H3/k1v4g/wDYBu//AEWa3P2Y/wDk0D4d/wDYFh/rWH8ff+TW/iD/ANgG7/8A\nRZrc/Zj/AOTQPh3/ANgWH+tddD4TCe541+w9/wAmjWv/AGGr7+UVfR9fOH7D3/Jo1r/2Gr7+UVfR\n9c9T42aQ2CiiioLOd8e6H/wkvwq8R+HNm5tQ0u5tlXrudo22D8WwK4b9jvxD/bv7IXhq3Zz9q0lr\njSblP+ebxSkqP+/bR/nXrf8Atp95ed30r5y+EU3/AAp39szxl8Jbv/R9D8Yt/wAJF4fZ+E80gmWF\ne2cbhjriNfUVFaHPTcfmJ6M9s+Kvwe8D/GXwk+heMtM8xkz9k1C3wtzaOf4o3wce6nKnuD1r45+L\nHiX9pf8AZ4+GF18Pb7W7rVvDtxJFFovja1ZlubSMOCYJG5IJUEYY5H8LEcV+gdfP/wC2bo+ra1+y\nhqVpomlXupXCX9nK1vZRNLJsWXk7VBJAz6cdeK58LVd1CWqCUepzU/7PHxnTRrW9tf2lfH+oNOqu\n0NvHGhj3LnOZLhQfTjmso/BD46LKUb42/Fn/AHkgtmH14vOfwresf2yLK00u3t2+B/xSZoo0jz/Z\nf91QP6VwXxo+P1n8YPhXP4Ni+HXxl8Ms88dx9rsNLz5m3OUkUMC6HOcZHzKp5xg+hyO/T8DPmRq+\nNfg78X/CXwg8S+Nf+GkviC0mjWEt6tpdWvk+aUXdt3CZsZxjIBxXpVj458V/8O6F+IDarcSeJP8A\nhEmvP7RdQX84IR5nTBbvnHUZryPxR+0Smq/s06l8L7T4UfFa6vJtBOkR6nqWlktI/lbBJLjJ5Iyc\nZ/GuUtv2YPFf/DI6+Ik+J3ja31Z/D5u/+EPZSEbKEm28oyBsEDGNuf8AZJ4qalODtz23BHofww+C\n3xf+IXwc8OeOJf2n/GmnyaxZJeNapEZFiLZ43eaM4x1wK2L34CfE7T7r7K/7VnxDmm/552ukyTf+\ngy4rnPhJ+1FH8P8A4I+F/BWpfBr4lXV5pNhHayTW+lny5Cuclc4OOe4q98Qf2odP8e/C/WfBp+FH\nxg0f+07Zrf7dZaaBJDnnI55GRgjIyCRkdatwd+n4CLlt8Cvi/d39vFb/ALRXxU8uVgrXFxpfkJHz\njLCS4DY78A1nfsO2OozfCXxV4w1i7lvr7W/EEhkupW3SSPEg3sxPJJaUmsX4ZftX+Hfg78KvDXw9\n13wJ8TdSvolkjhudStEjku2aVmKxq7ZKqXCqBnAAFd3+xnpOraP+zIlvrGlXum3EutXsyw3sLwuy\nMIgG2sAcEqwzjsaGrRY4/EfQQ6UtFFYmoUUUUAc9480z+2/hV4m0dU8yS70m7t1T+87QOF/8eINe\nYfst+PNB0T9gvQvEXiLUorHTdBjube9uZckRbLhyOAMk4kQADJJI9a9v/wCWtfN/7O1hp3hr4mfF\nn9nXxFaW91pv2uTV9PsbpA0dzY3ACuu0/eUKYgfcGlUjem/LUl7n0V4D8e+GPiV4ItfFvg/UPt2l\n3DPGsrRNEVdGwysrAEEHsfaulrF8K+E/DvgrwvB4d8K6Pa6TpdvuaO0tV2opY5Y+pJJySea2q8qd\nub3di0FITS0h6VIzzH4m/HbwR8KPFvhrw14kXVJtQ8Q3Cw2y2VvvWMFwnmOxIG0MQCFy3OduK9Pr\nO1HQtF1i6srjVdHsL6SxnFxaPdQLIYJRwHjLDKsPUe1aAFaSceVcoC0UUVmA1l3/ACf3uPzr5Y+B\nbx63+1n8evFsX7y3fVLfTY368wh1cA/8BHGK+hPH/jHTvh/8L9e8Zam6Lb6TZSXLK7Y8xwPkQH+8\nzFVA9WFeI/sm+F77Q/2eLfXdZX/ibeKb2bXrs9z5rYjJz0JUbvowr0MErRcvkQ/iPdaKKK6BhRRR\nQAV85/to/wDJvml/9jLY/wApK+jK+c/20f8Ak3zS/wDsZbH+UlXT+NCnsfXUX+pT/dH8q+SPgN/y\nc/8AtA/9jKn/AKFLX1vF/qU/3R/Kvkj4Df8AJz/7QP8A2Mqf+hS101vgZhDc+iaKKK4zoCkPl/8A\nLX5o/wCJfY9f0paQ9KAPn/8AY8lk0KX4pfDK7+W88P8AimWdU/6ZTAhMeozEx/4EK9/8YeC/DHj/\nAMJXXhjxfo8GraXcf6yGdc7SOjK3VHHZgQRXzf4ruT8Ff26tB+Icv7nwr4+tl0PVn6JDerjypG7c\n7YznrjzPx+rh/wDW/KuTFqUanPHqKP8AKfEvizw78cf2TvDmt3Hw41K68XfDae3lWO3ujvuvD0jK\nQsy4B+VSQcgbTj5lU4ar/wAM/hL8Z/iF8CNB+IaftH+N47rVbf7T/ZtuqsVJdgQHeZQemecV9NfF\nqyvdT+AXjXT9PtJbq8uNDvIoLeJdzyOYWAVR3JPGO/SvmH4OftM/8K3+BHhrwPqvwX+JV1eaXafZ\n5JrfSz5ch3s2V3YPfuK6qFR1Ic1lchqxtH4F/HBAUf46/FVf92C3kDfit2a0tP8A2efi/qEuyX9p\nj4kWLbc/6VZADtxkTkE8/oaz/Gn7Vdn4w+H2s+F1+FHxj0dtStHtv7Q0/TQk9vuH3kOR+WRkEjIz\nkYPwf/aGs/hV8INN8FS/DL4weIJLRpWa+utLwW3uW2qCxwozgDJ7+tbcjt0/AV0eofsgeKfFfir4\nN6z/AMJh4gvNfvNN8R3OnR3t780jRII8AnqeWY85xuxXkvwW8H/GT49eFte8a3H7RPi3w+0Wv3Wn\nrZWsXmRqqCNgVxIoUfvcYAwAornPgd+z54r8e+CNX8Uah8SfHPw5W41y5ktNKRGtg2SreeQ0iEnJ\nAJA/g6ntb/Z3+Mmo/AP4daz4E8T/AAn+IerX39vXV59qstLYowZI4+d3ViYyeMjkcmphTheTja/5\nBroepaj+z78TtKKfa/2sfH24rlVi06SU+n8MprLb4JfFp4i9l+0V8Wbzt/yBmg7ZzmWdcj6VuXP7\nZNlcWE9v/wAKU+LMPmxmPfFpu148jGVPZhnIPrXj3wf/AGgtG/Z+8Ha3b+JfDXxc1e11LUjcx3et\nWaxJECu0LmRyN5wSxBG4gccctQb7fcHMjp/2efDGvaf+2t8Ul8S+K7/xRfaDZQ6YuqX/ABNLvdCM\nglsAKpGMmvrYdK+Yf2bdT1Lxb+0P8XPiFN4Y1zQ9L1yS0ltE1W1aF2wDxyMEgDPBIAI9RX08OlKp\nuXHYWiiioKCnIdkqP/dYH8iDTaQ9KAPnv9jiT+xLD4r+CrpvLk0TxfcSMjceWjrtB9h+4POT0r2v\n4dfFz4f/ABVtdUuPAuu/2pHplyLa5byHi2kglSu8DcpAOGHBxXhmgPH8MP8Ago3qlhdfudJ+JOmL\ncWxdiE+2w/fX0LEqx/7aAV9D+FfAfgvwQb9fB/h2w0VdQuftV2llFsWWTGMkdB16DA5rlxcY8zcu\ntrCidJRRRXCWFef/ABf+L3hj4K/D5PF/iq01K6tXu0s44bCJZJJJGDMB8zBVAVWOSQOABkkCvQKp\naro+k67pcula3plrqVjNjzLW6iWWOTByMqwIPIz9aqFub3thMZomsW3iDwvpuu6f5v2XULaO7g81\nCj7HUMNynkHB6VoU1Y40iRETaq4CqnAUDgCnUmMKbTqiuJo7e0luLiVIYYlLySM2AigZJJ7AAHJ9\nKSA+YdQP9u/8FSFVfmj0Hwd+8PXy5ZG4X2JEme9fQg6V84/s0GTx18Qfib8dLiJ1h8R6sbDSy6/e\ns4OAR6ceWOO6n0r6OHSvZtZKPZGcRaKKKRQUUUUAec/H3/k1v4g/9gG7/wDRZrc/Zj/5NA+Hf/YF\nh/rWH8ff+TW/iD/2Abv/ANFmtz9mP/k0D4d/9gWH+tddD4TCe541+w9/yaNa/wDYavv5RV9H184f\nsPf8mjWv/Yavv5RV9H1z1PjZpDYKKKKgsK8k+PnwtvfiL4ItdQ8KXH2Hxt4emGpaDeowU+auCYi3\nQB9oHPG4LnjNet0h6U1KwmrnD/Ar40ad8X/AbS3dv/ZfirS2+ya5o0vySW1wp2swQ8hGIJBPQ5U8\njn1YdK+afi78E9Zu/GUXxf8Ag3fpoPxFsf8AWx7gttq0YGDFMvTcRxk8NwGxgMur8JP2otB8W37+\nCviVZf8ACCePbT91c6XqRMMM7j+KB39euxjn0LDmuSthvtw2/IE7fEfQe7/bryv46fEe58AeA91l\ndPa3V1vP2vbuFtEi5kf/AHgOn4mvUAa+R/2tbzUvFD6n4L0eI/bIdOWGDe6RI0kk0budzHAHlK6d\nvvY71z0o3mlIuKPlfxB+0j4svdUuotHt7e3s3bEd/qRmuLl8HhyxbAJ64wQOmT1qjD8VPirPErW/\njazjj6r5Wm27e+dzRls+5Oa3rP4Q/Ea70ZILm+8A2UUcYVYrjW4pWcY/uoXwfrisab4G+P8ARvOu\nrPW/Bl55jc2trqgJXvxvCgenWu93a92yfydz36EcsoVF7VupF76OLT+/VDbD4hfHfxD4jXQ9C8Y3\nl7cqguJilvDEII1ZfndwgKqCV6HJJAGSRX6O+CPHd7ov7MNh46+K+tWMc1vYNc399bqUjZA5CYUn\nlyAAAPvMfevz58CeJLb4T+EfGMfjSyis9W1mS2jgWKVJpGiiLOVVQThSxQ7iQPl6mvfvht4K8T/t\nHaP4f8Q/EAjTPhdpChdF8K21yXOoyISpmumBzjcG4wDjKgAZLVOnKdlPZbvueJipUnUk6Ksruy10\nXTc3vg5oPif40fGif9pL4hWktrp6K1v4P0e4XiC3ywE5B6HGSD/EzM33QK+najhijt4lt7eJIY4l\nCKiLtCgDAAA4AA4wOlSVcpGEVYKKKKkYUUUUAIRXz5+0T4f17wr4k8PftD+CLX7RrXhNtmrWi8fb\ntNYkSKcddgZvorbv4K+hKZJHHLE8UqJJG6lGR1BDA8EEHqCOMU4sTQ7wZ4w0Hx74D0vxh4au0utL\n1GETwv3XPBRh2ZW+UjsRW8OlfIV1aeJv2S/G974q8NWF1rfwe1a5E+p6PB88+hSscGaEHrH264xh\nWwQrH6f8JeMfDHjrwla+JfCWsWuraXcf6u4t2zz3Vh1Rh3UgEelefiMO6b5o7DizfopB0pa5igoo\nooAKbRu96+Zfit+0HqviDxi3wZ/Z9WPV/GFxuiv9dQhrPRo+jv5gyC6jvyFPA3Nha1o0ZVHZClKx\nl/GrVZPj18d9O+AHhq4aTw3os6an4xv7flF2H5LQMOC2Tgjszf7BFfRkEFtaWsVpbxJDbxKsccSc\nCNFACgD0AGPauF+EXwp0H4R/D9NA02Vr6+mb7Tqeqyr++v7g/ekYkkgAkhQTwPUkk+gV6iUVHliS\ngooopDCiiigAr5z/AG0f+TfNL/7GWx/lJX0ZXzn+2j/yb5pf/Yy2P8pKun8aFPY+uov9Sn+6P5V8\nkfAb/k5/9oH/ALGVP/Qpa+t4v9Sn+6P5V8kfAb/k5/8AaB/7GVP/AEKWumt8DMIbn0TRRRXGdAUU\nUUAcb8Uvh1ovxV+FWqeCta+WO7XfBcbcm2nXmORfo3UDqpYd64r9nf4r61cXV18Evil/ofxB8OL5\nStK3GrWq/cniP8bBcZx1GG/vAez15T8afgpp3xS0u11LTNQfw/400n99o+v27FJInByEcryYyfTJ\nUnIzypJRVSPJIT/mPcqeT/t/+PV8s/Dz9prWfCniiH4ZftKab/wjHiJPltPEO3Gn6ko4DlhwrH+8\nPlJ67Dwfp21u7a9sIruyuIri3lXMc0Tq6SD1DKSCPcGvNq0J03qOMkznPiH4uk8GfD+/1pFeS4GI\nLZduR5r8IW/2QeT9Md6/N3xz+0n4nfxZdJo6LqXls0U2p6o00wkk/i8tFYBFB4GM5xnHNfav7R3i\nH7JYaNoESOryyG/Z0wPufKvPqC2enQV8M+Hfg/8AEuPR101rnwVp8asWZ7/XICeueURmP/jtdOHV\noc1k35ux3YOlQqStiJuKs9Ur3fRaGTb/ABa+KN+jy2ni6xiU8+WNPgl6DHWVGIHtnHtTv+Fl/HC8\n1Oz0XS/Gd5f6heyCG3tLe0gUsT6HYAoHrwAATwBmr0vwC8bxao2pQ+JfAUJ2/NFBqTYP/Adnf2q/\n4BW9+E/xF1LxR4+j0yOA6Jd2Vlc2t1HOqzSgJuUKSSdpkGAM89q6IqXPdWt2tr6HdWq5d9VcYU37\nS9k7u1u//APt79lzWPiJL8EZ5vibrVhef2fctDbXK/K0cKJl/OkOAcE9ewH0ryeS7vf2t/2i4rwp\nOvwg8F3JaFuVTWr1e+O4+mdsfoZK5H4a/wDCb/tG+EX8D6Fey+FfhhZXZOtXsUoN7q0jYPkKAfkQ\nqBwfl7tuOFH2R4X8MaD4M8JWXhrw1pkWn6XYx+XBbxdF5yST1LEkkk8kkk0RhyNzlv8AkeE7N6bG\nt/nb/np6UtFFMYUUUUAFIelLRQB5B+0P8MtR+IXw1ivfDDPD4w8OXK6tocy/faWPkxA/7YAwP7yr\nXY/BD4t6d8YvhVa+IrdUtdWg/wBF1jTuj2V0o+dSp5Ck5K57ZHUGutxXzx8SPAPjT4ZfFCf46/BW\nyS6uJl/4qfwtyqatEDuaWMDpMOvA3Zyw3ZZWmpT9rDl69Bban1NRXAfCv4v+C/i/4STW/CWofvk4\nvdMuGCXVlIOqSp1HPAYZU9j2rvh0ry5wlB8sioyuLRRRUjCiikJoAQmvnL9p/wAearfRab8A/AEv\nmeMPGP7i5KN/yD7An97LJg/LuUMOf4Q3qM7fxt/aI0r4dSxeCvB9ovij4kajiGw0O1UyeS7dHn2n\n5QBzsyGbvhcsKPwR+EOo+Cv7R8dePb3+2viN4hbzdW1BmDi2Q4ItoccBRgAkYBwAPlVRXfhqHL+8\nn8iG76HofgjwfpPgL4faT4P0KLbY6ZbLbxtt5kI5Z2/2mYsx92NdAOlA6UtdIwooooAKKKKAPOfj\n7/ya38Qf+wDd/wDos1ufsx/8mgfDv/sCw/1rD+Pv/JrfxB/7AN3/AOizW5+zH/yaB8O/+wLD/Wuu\nh8JhPc8a/Ye/5NGtf+w1ffyir6Pr5w/Ye/5NGtf+w1ffyir6PrnqfGzSGwUUUVBYUUUUAIelfIXx\nD0PRf2if2w38K3Vr53hHwNZNDqNzbsI3ubuXpF5q84UgADOB5T4+9X15IN0Tpvddykbk6rkdR7jr\nXwfqHhX4v/Aj9oG1+Hfwr8ZWviCTxPDPrrW2s2ka7mUyBjM/UsRExBBA9hya3oWuYYi/J7p6ba/C\n346fDWJIvhB8Z7i60mLiPQvFcQuY4xknarlWAHPUBKy9f1f9oHVdQ+1eMv2evC/iTUFjEX23SNXk\ntQwHQkCU59ien04pf+GifiJ4HAi+L3wU1vToUYI2qaM3nwfXqV/AOK7/AMJftCfBzxkIotK8aWFt\ndP8A8umpZtJMnt8+AT7KxraVGm9WjzViMRT8zwDxr4n8e+CPCU/iLxF+z7Bo+nxMkf2iXX3kRWY4\nUbVOTk9gar/DDTPil8eNGutV0TxF4V8E6PFcm2uRptu899GcA9ZGZ0yvQh1Dc+hxd/a48YW3iX4k\n+FfhhaXUTWNp/wATW/2uCrSMp8teOCRGGPv5vtXC/BPxL4w0X4+X8/wr0S18Rw3GnGXVdMfUIbKF\ngr4D+ZKyqrKxUjBydzDoTXoU8qh9UeL0smkl1fdr0On6zWnC3U+ovh9+zV8MfARe9l09vEmsSqUk\n1LWsTnkYOxPupkZGeWwcbq4b4eatJ+zn+1T/AMKgluvtngfxfMLjSIt4eTTbiRtqqy5yFJG0kjBG\n1h0YHa+B/gz4rfHL4Xv8Qrv44+IPDv2vUbqGPTrWygmjijSTChWOMgcjI9OtbelfsOXukePX8b2X\nxx15fEUjM/8AaculwzSgt1IZ2ODjjIwQOBgcVxyjdWFRhUjLmmz6NHalzXkw/Z4+K/8A0c/4t/8A\nBXb0v/DPHxX/AOjoPF3/AIK7aub6uzu9oesZpa8mP7PPxX/6Oh8Xf+Cu2rB+Bt940g+MfxX8F+KP\nHeo+LLXw3dWFtbXd/EkUm+SKR3wqDAGVx1P3V6c1E6LSuJVLnu9FIOlLWRoFFFFADJI45YniliSS\nN1KMjrkMCMEEHggjgg8EV8RfE/wvrvwl/a10XSv2edXfwte69p0mo3emtKW0+RkdwA0TBlCkKeDk\nKem2vuCvlP4snzf+Chfg6JPvQ+FJpW7fIXnH4/dPFNy5It+TOfF1HTpOa3SNbR/2wta8JlNM+Ofw\nw1bQph8jazoyfabST/a2k5XPoGY9+9elad+1n+z1qVrFMvxJ061Z/wDllewTwuvs2UIH51gFN8Wx\n0Rl/uvyPTp7iuavfhz8PtQl8298FeH5pOrO1hFls+pCgn8c146xlGXxw+5ngUuINPfj9x6jJ+098\nA0hZ2+Kfh3aP7jyMfwAQk/lXE6/+2t8JrSZ9P8F2PiDxvqnSK30mwdUY/wC+4B29OimudT4VfDaK\nVHXwF4c3Dn/jwjP6EYNdFp+l6dpNr9n0rT7LT4f7lrAkQ/JQKUsXQj8MG/mXLiGNvcieW2njr41/\ntP8AxG1f4a6hqafC7Q9PtEudTsNPVpr25ic4CGUkdQ3Iyq4PzBiAK+kfhp8KvA/wo8Jf2F4N0pbW\nN8PcXTt5k92443SSEZJ7gDCr2ArwX4Xyf2f/AMFFfEVpxt1Dwkk3/A1eH8uA36V9XjpXsRnzU1y6\nXSZ7mFqe1pqo+qAdKWiig6AooooAKKKKACvnP9tH/k3zS/8AsZbH+UlfRlfOf7aP/Jvml/8AYy2P\n8pKun8aFPY+uov8AUp/uj+VfJHwG/wCTn/2gf+xlT/0KWvreL/Up/uj+VfJHwG/5Of8A2gf+xlT/\nANClrprfAzCG59E0UUVxnQFFFFABRRTSKAPmD9p64/4WP8QfBvwA0pIpLjULkavq9zsDPZWSZPB6\noXAY8YzhB3qGL4DeOPh1dy3fwF+LWr+Gbd2L/wBhar/pllk/7wYfiUJH97vXJ/tC+FfHHwk+McXx\nd8BeOnbVvGGqR6S2m3tokm3coKoJCCBGCqjAAPI5OOdtvjP8dPABdfif8GZdSs4siTVvDL70wP4i\no3AD67TXbTjFwsedinVUvcZZ17Wv2mtQigtfHXwd8F+NvsmfIudKv3s5GzgEkrIOuMkYA6ccVxGv\nXvxS0rQL3WLv9m+DS7Oyga4muH8Qs0cMajLMQWyQAD3/ADr1rwp+0/8ABjxXKtu3if8AsO8PDW2u\nRG1Kn03nMf8A48D7Vw/7XfxEtofhNpfgvQtTtZrjxTMu6a3nVkW1jZSzbgcYd9o9CFYd6cMJTnJR\nS3Ip4yunyM8q+G3if4ifG/xPf6L4MXwX4P8As0KzyS3Uct1P5ZON0aylw2DjJVQRuHPOa948F/ss\n+C9H15PEXjfUL/xzr27zPO1f/UK/qIQTn/gbEdDgGvk7wze6t4f+NvhD/hWSRXnimC4FkliZ1hW5\nUghopWZgoDAsCSRjPsK+nvhpY/F/46/E/wAfQ3vxF1T4droM9pDHo+lfZtRhj8yNiR5ykhz8ucgn\nliOMYHfj8vjgK7oxaduqHUlVrbPQqfEG4j/Zt+PFh8XdC8mPwr4mmWw8RaJEyo277wuIY8jLAZOA\nMA5BwJM19b2V5bahpdvqFpL51vdRpPBLtI8yN1DKeeeVIPPrXzzq/wCw/qOv+N7Xxbrvxy17VNWt\nSphuL3S4JhHt5G1HYoAOuNuM89a7Ufs8/Fr/AKOi8W/+Cu3rzqlLnOmi3CPLLU9ZpM15P/wzx8V/\n+joPF3/grtqD+zz8V8/8nQ+Lf/BXb1n9XZv7Q9Zor558LQ/EXwR+2lB8Ndd+KWs+MNJl8KSaxMt/\nbxQqshn8tNoUc42g5yPvEV9CDtWUocjsUncdRRRUDCkP+e1LSHpQB8g/tY+DbLwLrPhf4ofDW4uv\nCvjjVtcj02S802UwpcI6OS8iKMFtypk/xBjuB4xvaV+078VfhsE0342fDqXWLGH93/wkvhnDLIAc\nbpIvujjkkbOeAtS/tjt5un/DCyQfvpfFUTKv+6pB5/4EK7R/9a/+0x/nXLjcTGkoqSv+Z4uY5hLC\nVFZaM09H/a//AGe9XsFn/wCE+j01j/y76jazxSL9cKw/8erZP7TnwD/6Kt4d/wC/r/8AxPNeY6n4\nF8F6xM82q+D9DvJjy0txZRs7fVsZ/Ws//hVHwy+9/wAIB4c/8AE/wriWKw/ZnOuIYdYs7HxF+2j8\nENHP2fRdX1LxTfN/q7bRrB38w+gaQKM/nXkmsftCfHT4r/FDRPhj4S0iH4WQa+s0kOpX6ma9aFFY\nsw4/dthWAAUHPRh1r0nTNA0HRItuiaJpumr/AHbW1SEcdPuqM15x4om/s/8Aba+DWqt8qvJdWe7r\n99XTv1/1ldGExFGpU5Ix776mmGzf6xVVO1ke2fCX4B+C/hL5up2Xn614mu8/bfEGpN5lzKScsFzk\nRqT1AJJ7s2K9VHSkp1ehJ3PeCiiikAUUUUAFFFFAHnPx9/5Nb+IP/YBu/wD0Wa3P2Y/+TQPh3/2B\nYf61h/H3/k1v4g/9gG7/APRZrc/Zj/5NA+Hf/YFh/rXXQ+EwnueNfsPf8mjWv/Yavv5RV9H184fs\nPf8AJo1r/wBhq+/lFX0fXPU+NmkNgoooqCwooooAQ9K+ZfHbfaP+Cl/gZYs77XwldmT/AIEbvGPX\n74r6aPSvkHx/4gjsv+CpvhKB3XD6Gun7f9qaOYqD65ZgR+Fa0PjMMT/DZ9K/7H4enXsfY15/4s+B\n/wAKfGkTHX/BGltM/wDy9WifZZv++o8c+5Br0AdqdXafPRnKPwnwx+0X8Avh98IvhBFrvhr+1JNS\nvdaht4Zbq53eRH5cjFECgZB25ycngc15Po+m219efDbS9T0+CS3vrazjmRE2efG+s3IJkK43kqdu\nTyFCjsK+mv24Z93wu8J6b/DNrTS+3yQsP/Z6+atNWOLxb4FSKVpFisNOkkH8SkSy3Bx9FyfwHerv\n7tj18O3KF5H6H/sU2kdv+xl4X2M372a8kx/d/wBJkGPyWvoWvDf2PYZIP2KPAiOm1mtJ5P8Avq6m\nYH8QQa90HSoNwooooAa3+ravlv4Lk3H7R/x+vX/1jeJLaDP8OI4ZFX8cHmvqNjXy1+z7+9+Ivxwv\nU/1cvjy6RW/i+Vef/Qh+tZVfhKp/Ee5jpS0g6UtcR0BRRRQAV8nfEWQ3H/BSTSV+79m8GMvruy85\n/D736V9Y18keLD9o/wCCkt/v/eC08KRIv/TPdzg/XeevrUVXalP0Zw5jK2Gl6Hpw6UtIOlLXyx8C\nIeleefEPx5qPhPxt4H0jT7W3kj13U/sdyZckrH8o+XB4OXzk56V6GeleJ/HVfL+IPwkul/1n/CTJ\nF7YLRZ/HmurBwU6ln2f5HVg6anUs+z/I3tLJ0/8A4KNeDpecan4duoPX7iTH/wBpj9PWvrdetfH3\nig/2f+218GdS/huJ7mx/76BX6f8ALWvr9TXu4d3ox9D7HK5Xw0R1FFFanohRRRQAUUUUAFfOf7aP\n/Jvml/8AYy2P8pK+jK+c/wBtH/k3zS/+xlsf5SVdP40Kex9dRf6lP90fyr5I+A3/ACc/+0D/ANjK\nn/oUtfW8X+pT/dH8q+SPgN/yc/8AtA/9jKn/AKFLXTW+BmENz6JooorjOgKKKKACiiigD5o/a1T7\nX4j+Delf8/Pi+M/Jy/y+WOB0P3/T0r27P73f/tE//q/xr5w/bG8Rf2F8UPgzd+ai/Ytakv8AvlcS\nW4z9Pl/Q19IkbJXT+6xH5Gu2j8B4+Y/Gjj/Ffwu+H3jgv/wlHg/SdSkOf9Ia3VJue/mJhifqTXz5\n8U/2XvhL4L+E3jDxhpsGqeda6ZI9la3F4Xiglyu11ONxOeMEkHJr6xPSvHv2pbz7D+yZ4vdH+aWO\n3g+9jdvuY1OPXgnj0rU5aNSfOo3PgC4i3fs+6fqEtpDNcXPiC9ja4ZczMIrS2P3+u1fNzjpluc9v\n0C/Yp0PTtC134qafpVutva22q2VssS5x8ls2W5JOSWJPb0r4B1Bf+LBeF7Xd5LPrWszZ/vE2+noE\nH12d/Wv0f/ZHjD6n8X72NP3Evja4jjPThY049gARxTk3f3j2D6ZA4paKKQBTW6U6kPWgD5fuW+1/\n8FO9U83P+hfD6MR7f9q9TOf++jxXtg6V4jpH+kf8FIviDKn3bfwpp8DfVnRxj2wPzr2+uKt8Z0U/\nhCiiisigpD0paQ9KAPmL9rb994x+Dmn52+b4iZ9/XG3yh0991dt/y0/OuH/ahP2j47/AvT3fdG+r\n3UjRfT7Ng+w6/rXbL0ryc13h6fqfJZ//ABY+g6kPSlpD0ryTwDgPjJ441H4efCefxPpEEE90l3bw\nKlwpKYcnceCOcDA9K5r4xXH9n/FT4NeIPu+T4rgWRv8AYZ4ifbpuGaP2oE3/ALNupt/cvbVvx3kf\n1rO/aLllT4Q+FfEHHnWWtWNx8uRyUY59hlRXsYKMV7OXW7X4HsZeoxdKfW7X4I+3Mf4flTqRnjeV\n3ifcrMSrf3gTkH8aWvUPtAooooAKKKKACiiigDzn4+/8mt/EH/sA3f8A6LNbn7Mf/JoHw7/7AsP9\naw/j7/ya38Qf+wDd/wDos1ufsx/8mgfDv/sCw/1rrofCYT3PGv2Hv+TRrX/sNX38oq+j6+cP2Hv+\nTRrX/sNX38oq+j656nxs0hsFFFFQWFFFFACHpX51/tJarqWlft33vjWx3fZ/C39kTTv/AM802xA4\nA6gsxH/AhX6JvXxvH4Ot/iZ+0z+0X4fulTy5re202KVlyIZQCY2yehDxD2wD6Vvh9zDEyShdn05b\nXNte2sV3aPut5lWWNtwO5GGV6cdDU1eCfsu+PrnWPh1cfDjxFvt/FPhBjp1xay8O1urFY3x3Cn92\nevRSfvV7yO1dZ8/UhySsfIn7cV1H5vw+0/f96a+lb1UBYQOOmDk/lXh3lyf8LC0SJ0SFrbw5p86+\njAaI0wJ+ucf/AF69X/bduS/xE8HWqf8ALvpV5c47ctj/ANk/lXkU01vF8S41muHkjh8PWke7BZl2\n6Ay446YOB7VrJfu0/N/oerhv4aP0w/Zct/sv7Hvw8iD7t2jxyZ/3mZsfhuxXsFeZ/s/W72v7Knw3\niZQrf8I5YPj/AHrdG/XOfxr0ysjoCiiigBnf/PrXyv8AsyN9ri+Kupy/664+IOp7mTp8qREYH/Aj\nX1M/+H8xXxb+xn4o/tWw+JXh1xuax8TS3yvtwWFwXBz9Gh/8eFZVfhKp/EfUQ6UtIOlLXEdAUUUU\nAFfI95/pH/BRrx8/3fsvh+yT/e3R25/D73f0r63PSvj/AEZ/tf7eXxeuvn/dW9rbfP8Ae4WIce3y\nfyrHEfwZ+h52au2GkesDpS0g6UtfMnwgh6V4n8f3+z+Ivhbdv8yw+Koiy/jEf/Za9sPSvD/2jz5N\nt8P73732fxLCdn97O0/h92uzA/xV8/yOzAfxl8/yL/xkn/sz4s/B/Xfu/Y/FCLn+7uaI/T+CvsnG\nz5P7vH5V8X/tLDyvBHhrVfutY+J7SXd/dHzj6dcdfSvtIyebK0qfddi6/QnI/Q17OEf7hfP8z6nJ\npf7OvViUUUVueuFFFFABRRRQAV85/to/8m+aX/2Mtj/KSvoyvnP9tH/k3zS/+xlsf5SVdP40Kex9\ndRf6lP8AdH8q+SPgN/yc/wDtA/8AYyp/6FLX1vF/qU/3R/Kvkj4Df8nP/tA/9jKn/oUtdNb4GYQ3\nPomiiiuM6AooooAKKKKAPhT9vK01LWPiX4I03TNzTW+i32o7Om1I3eR2B7kLCxx7D1r6X+GniaPx\nl8IfDXiWJ932zToXl77ZAoVwfcMpyO2a8v8AiDa23iD/AIKL+GtHu4kmt7Xwlc+dF975JVlU5B6Z\nEnX/ABrB/Z11m5+HXxK8S/s8eIpXWaxu5L7QXl4+027/ADMq+pK4kHr8/pXdS+BHk41c9121Ppiv\nBP2xLnyv2X7q3/5+tTs4Omf4i/4fc6173XzV+2xc/wDFlvD2n/xXHiCE/wDfMMnT0Hz1rFXaRw4f\n+Ij5L1O0kf4feA7Lbujlu9Rf18sPdRwiTnvuXHr8or9Ev2Nv3vgf4jXrf6ybx5qW4dvlSEcd+9fA\npgF3J8JbUOWa48yTy/8Anq7a5ImzHcYUHHrX6AfsYRf8WH1y62YS68V6nMrd2HmKuT3B+XHPPApy\n3Z7R9H0UUVIBTH6U+mSf6qgD5i8JATf8FA/jDM33odK0eFf90xZOfU5UfrXtlfMfw88Xi3/4KXfG\nHwoz7l1O2glVmXB8y1RPlHttlkOf9kV9NDtXFX+M2p/COooorI0CkPSlpD0oA+V/2i2+0ftffBe0\nxt8sXc2715X5f/HevvXfr0rzz42j7X+3r8MrXZu8jQbq5+fp9+46e/yfyr0Qda8jNfjj6fqfIZ8/\n3y9BaQ9KWkPSvKPCPIf2movN/Zt1jZ/BPbSfh5n/ANes34+p/aH7IL3qI37lbC8X/Z5Rf/Z8Vuft\nGQed+zZ4i+bbsEMn5Srx+v6VV+IsH9q/sXX/AMv3/D9tcY/3Vjk/TbXqYZ2hSf8Ae/yPWw0rQpP+\n9/kfWHha8/tDwHoeob932rTrWfdz/HAjZ/8AHu9a9cH8FNQ/tX9nPwNqH/PXQ7Ve/wDCgTvz/DXe\nV7UviPtUFFFFSMKKKKACiiigDzn4+/8AJrfxB/7AN3/6LNbn7Mf/ACaB8O/+wLD/AFrD+Pv/ACa3\n8Qf+wDd/+izW5+zH/wAmgfDv/sCw/wBa66HwmE9zxr9h7/k0a1/7DV9/KKvo+vnD9h7/AJNGtf8A\nsNX38oq+j656nxs0hsFFFFQWFFFFACgbtif3sD86+Vv2fpP7T+NPxt8Rfe+0+Ixb7+u7yzJxuPPG\n704r6nMnlfvf7nz/AJc/0r5X/ZOH2nwH401v+LU/Fd5cbu+MKBz1xknrXRh+pxY9/ug+M3wf8T/8\nJ3b/ABk+DsqWfjOy/wCPuy4CapGFxggnBbaNpBxuGOQwBo8F/tXeB9QH9i/ES3uvA3iKH5Lm11KB\n/I3gdVfaSoPYMAR6kc17+e9YfiPwV4Q8XxeV4q8L6TrCr91r21SVl+jMCR9QRXUeVGtGStNHwz+1\nb428M+K/jTYXvh7XbDVLO38OvbfaLSXzE8xnlbaT2b5xx9K8w8TeK9P/AOFj6xqGlP8AboZLQ28T\n26lk4sRblucfLlmOe23pXZ/tK+FPCvhL4/axo3h/Q7PSbGLRreWG3iTYjSMqlmTJ5PJyfUMO1Zeu\n6bpNp8RfiRaaVaxQ2dtbaktsixhVVFa3ChVAABAJ5wOvetp/w4/M9OjblVj9If2Xde8a63+z7olv\n4t8Cv4Xt9NsLOw0tpboTPf20duirOV2jZnAIHfPtz7eOlc/4Lt5LX4c+H7aRBG0WmW0bKv8ACREo\nI/SugHSsTUKKKKAK9zJ5NrLM+1VRS+76DP8ASvir9ijwRe6V4S8VfEO7uopI/EmoultEn8McE0qs\nxPTl2OB2CZ7ivsfxFP8AZ/COqXHy/urSZ/m6cIx/pXzr+yUmz9jzwkz/AHpPtk3p9+7lbj254rGs\n/cNKfxHtY6UtIOlLXGbBRRRQA1z+6evj7wEn2j9rL446hz8mtpa7u/ytKPy+T+VfYQG+VP8AaYD8\nzivjH4JS/wBofEX4t6x8u268USjcvzfdaQ/e7/erHFO2HmeVnDthmezDpS0g6UtfMnxAV4d+08PK\n+Gug3v3Gttft383/AJ55Vuf/AB39K9xrxb9qOPf+z9LL/wA++p2kv6uuff73SurB/wAaPqdeB/jR\n9S3+07b7v2dNXul4ktLu2mXvz5yr/wCzHrX1p4duvtvhLSLtH3edYW8u7j+KJD24718u/GuP+2P2\nZfEsv3t2nLd/d9Ckn4dM1798HtQ/tL9n3wRqX3vO0O1bPXpGB/SvYwP8FrzZ9Lkkv3Uo+bO2ooor\nqPcCiiigAooooAK+c/20f+TfNL/7GWx/lJX0ZXzn+2j/AMm+aX/2Mtj/ACkq6fxoU9j66i/1Kf7o\n/lXyR8Bv+Tn/ANoH/sZU/wDQpa+t4v8AUp/uj+VfJHwG/wCTn/2gf+xlT/0KWumt8DMIbn0TRRRX\nGdAUUUUAFIelLTWoA+Yk/wCJh/wU48QXH3l07whDb9c7XYxnt06twfWtP46fBiT4j2Fh4l8MXq6P\n430RhJpmobinmYbcIXYdBu5VudpJHQmsr4f/APEz/b0+NGpb9y2UdnZRnrwVGR6cFDwa93rvjseF\niqjVa6Pm7wh+1B/YVynhD486Ff8AhLxHD+7a+a2Y2tzjjfheVz3K5XuCM4HCftYfEfwP4w0XwJb+\nGvFul6tDFqc1xc/ZZt3kpsjCl1IBGctjPoa+utb8PaD4lsPsXiLRNN1a1/543tukyfgHBx+FfFX7\nVvgLwP4N8beC7fw14X03R7e7iupLloItkcxUjaGOcbh6cDketdGHV6kV5hRlCU721PGLbxdpul6j\n8Ori3/06TSLSA3MNuCz+Yupvc7FHALFSBjOMnrX3j+wt4n8X6r8NL/T7vwVLp/hcXd3e2muS3GTd\n3ElwxeFYsZAQZBOTyp9ePinTdL0mx+I/w88m1ihilstLuLhkQAyFjLK7MVGS2UXnrhQK/Rj9jWMp\n+xb4Nd/vStfy7v7wa/uCp/EEGok9T0z3delOooqQCo5f9S9SU1ulAHxB8LvAmpa1/wAFEvir8Snu\nolsdGv59OVOrzSyxqoGcYCqikk9ckD1r6rHavE/gEftHjz406hF/qZfHFxGu/g5WJM8dvvCvbq4a\nr946KfwhRRRWZQUh6UtIelAHyZ8SG+1/8FG9Ei/58vCjn52/vGb7vv8AvP516WOteX63/pv/AAUk\n8Syp8y2Pha2jz15YQ5BPY/OenYV6gOteNmj/AHq9EfF5274n5C0h6UtIeleYeOedfHeCOX9nPxbu\nXdts1dfqJYz/ACzUEUf9qfserCreY1x4QVM/7f2QDv3BH51rfF+2+1fAnxfB97OmSt97H3QG/wDZ\nao/Cdv7V/Zn8Oo3zLJo3kN26K6Y/8dxXoU3ahHykehSdqEX2kemfstah/aH7I/ghv+eNk9t6fcmk\nH9etew188/sV3Xnfskada/eks9SvYW/GQMP/AELvX0NXv1N2fdU/hCiiioKCiiigAooooA85+Pv/\nACa38Qf+wDd/+izW5+zH/wAmgfDv/sCw/wBaw/j7/wAmt/EH/sA3f/os1ufsx/8AJoHw7/7AsP8A\nWuuh8JhPc8a/Ye/5NGtf+w1ffyir6Pr5w/Ye/wCTRrX/ALDV9/KKvo+uep8bNIbBRRRUFhRRRQBl\neJb+PSvBus6nL921sJ52/wCAxsa+ef2RrL7L+yjokr/6y5u7q4Y/WUgH8hXq3x71T+x/2X/Hl791\nv7FuYVb3kQxqfzYVynwC0/8As39mXwRa7drf2YkzD3dmf+TCurD7M83MZe4onpFFFFdB458//tbe\nDNB174GPrt3p6f21p97awWV8nyvGJp1jdSR1Uhs4P8QyMHOflOLTtf8AFHj34lQaHpl1rl69vqMn\nl2UTSPsa5t08wKvJzsYfhX3l8W/Al98RvhhceGNM1tNJumure6juZYfOTMUgcBl7jIB+orxTwf8A\nCX48fCL42XXj/wAG/wDCB+JrvULKS3uftEX9lxh3k3sRFHtBbIByDjBxgYFB6OFrQULNntdh+1lo\ntlpVrZf8KQ+NLeTEkW7/AIR2L5tqgZ/4+ParP/DXmjf9EN+NP/hOxf8AyRWJ/wALM/a+/wChC+Fv\n/gyn/wDjlUtZ+MH7WuheHL/W7v4ffDV7aytnupVhv53fYiknaPM5OB0oOr20O6Op/wCGvdF/6Ib8\naf8AwnYv/kij/hr3Rf8Aohvxp/8ACdi/+SK5DwL8a/2rPiJ8PtN8ZeGfAvwwl0vUFZoDNqE8b/K7\nIwZTJwQykYrof+E3/bL/AOhA+FX/AINJ/wD45U867m1mZPjT9sTww/he60K4+EvxXsb7WLeaxsEv\ndEiiE8zxsqqP35J5IJABOOxrr/2etGvfD/7MPgvStTtJ7W8i05WkhnjKSRlmZtrA8gjPQ1wt94f/\nAGiviL8Zfh5rfxF0LwRpOi+FtWbU5P7I1F5XkJXA+VmbJBXjGMbiTxX0KBXPWmnojSmhR0paQdKW\nuc1CiiigBI32SxO/3VYFvoCDXxZ+zWPO8G+LNVf7154mvJPTgBO3blj+VfY2sz/ZPDmpXf8Azysp\n5P8AvmNj/SvkD9mCHb+zza3rp817f3Vx/wCRNv4/drlx7/2d+qPFzx/7P80eyDpS0g6UtfOnxoV5\nb+0TYi+/Zs8SZ+9DHFOv/AZo/wCjGvUq53x3ov8AwkXww8QaHCm6a806eGJf9vYdv/jwBrbDT5as\nX5o2w0+WrF+aOY1AnX/2UZynzG78LD/a3N9nXPfnkGvV/wBmXUP7S/ZH8DXG/d5Vh9mzuz/q3dMf\nhjp2968K+B3iHTvFH7NcWirPG97pthPYXlp/Gg2yBCy+hB4/3TXqX7Gt79p/ZD0S1fiSzu7y2bnJ\n/wBezYPpgMBj8a9zCR5faQ7M+pyhck6sH3PfB0paQdKWuo90KKKKACiiigAr5z/bR/5N80v/ALGW\nx/lJX0ZXzn+2j/yb5pf/AGMtj/KSrp/GhT2PrqL/AFKf7o/lXyR8Bv8Ak5/9oH/sZU/9Clr63i/1\nKf7o/lXyR8Bv+Tn/ANoH/sZU/wDQpa6a3wMwhufRNFFFcZ0BRRRQAUq/61P94UlNY7d7v90ZP5c0\nAfLX7PhOpfF/41+Ij/y18TNZZ6/6syDjnPQfSvfh0r59/ZEH2v4S+IPEux/+J14ju7nd/ext/wDi\nvSvoIdK9FHzmJd6jA9K89+NvhLw74t+BniG18S2UU0NlZTXttM/yvbSohKurdV6YPYjIOa9DrF8X\naH/wlHgPXPDX2j7P/adhNZ+dt3eXvQruxxnGc4pmUJcsuY+AdDjudQ+N3gG10yJ7q8bR9Hjggbgv\nINPlwgzjqcAY65HrX1H8Cvj5F8Lf2evDXgPW/g18X7rUNKt3jnlsvD6PEzNK8h2FpVJA345APFed\nw/s7/GLwb488HeNfD+seDvE+oeHGhSCG6tBp37uGPZEJJFw0uBgctu4Gc17cPiV+17/0IXwv/wDB\nlP8A/HKD2fb0+5t/8Ne6L/0Qz40/+E7F/wDJFH/DXui/9EN+NP8A4TsX/wAkVij4lftfOdn/AAgf\nwt+bj/kJTd/+2lcz8Pv2hf2n/iba6zceGvAfw3P9j6i+mXaXd7PC6yqATgGTleeD7Gk3YuMlLY9A\n/wCGvdF/6IZ8af8AwnYv/kiqGrftp+END0t9S1v4RfF7TrJGUPc3ehQxRqWICgs1wACSQB6kil/4\nTb9sv/oQvhT/AODSf/45XGfE7R/2rPi74D/4QfxL4Y+Hmm6XcXtvcXM9hqcpk2RSrJjDMwIyoJGM\n8DFT7SPcvlZ0n7MC6rdeF/HXiPVdC1TRf7e8W3ep21tqUBhk8pkjCEqSewwcEjIODxXutI3+tfZ8\nq7jtX+6M8D8BgUtcU3zO50JWQUUUVIwpr06kxv2J/e4/OgD5A0d/7Q/by+Ll7977FHbWKtnPQLx6\nfwfpXq46V5B8K7j+1fj58aNd3bvtHiAQbuv+rMo+nevYK8PM5fvn8vyPhs3lfEv5BSHpS0V555hh\n+MLP+0Ph94hstm77Rp1zHt92hcD9TXCfs33n9ofs4eHYv+eJntG/CZzz+DV6qRG/yv8AMrcMvtXh\nX7N+o2+hQ+J/hpezpFqujazPLFAww0kJKgyAdwCq/gw9a7aPvUJLs0zvo+9QkuzT/Q9I/Ywk8r4c\n+OdHeTP9n+LbmJRnsUU59cEg9a+l6+Xv2UD9i+J/xl8P/d+z63Fd7ef+Wvm84Prt9a+oa99u+vof\ncYed6afkFFFFBqFFFFABRRRQB5z8ff8Ak1v4g/8AYBu//RZrc/Zj/wCTQPh3/wBgWH+tYfx9/wCT\nW/iD/wBgG7/9Fmtz9mP/AJNA+Hf/AGBYf6110PhMJ7njX7D3/Jo1r/2Gr7+UVfR9fOH7D3/Jo1r/\nANhq+/lFX0fXPU+NmkNgoooqCwpD0paKAPEP2unvU/Y58Y/ZEZty2okK9VjN1FuP07H2NdL4DFsn\nwq8M/YnRrX+x7Ty2Tow8hOfzz+Oa7LxX4a07xh4I1fwprCbrHU7SS0n2dVDqRkejDOR7gV8oeBvi\nBq37PF+vwg+NUNxb6TbyMnh/xTFEz2s9uTlUcgZGMn1K8qwwAa6cPJWsefj6TmrxPp+isnSPE3hz\nxBYRXuia7pepW8vKy2t0kgbPToc/mBWuUkT76Ov/AAEiuk8ZxaG4oxS0oSR/uI7f7ik0CsJUFzaR\n3trLZXH+puI2gk/3HUq36Maoa54l8O+F7B7rxHrum6PCn3nvblYf0Y5/Svn/AMcfHzUfiLc3Hwz/\nAGfdKvPEWtXytb3OtojR2thGwId1dgOQCfnICgcjccYDajRnOWh1X7EuoyTfs/avoWd8Oj+ILu0t\nn6jy2CSAf99Mx/4EK+k8V538EfhdbfCD4Oad4NhuEurpGa5v7lFIE9w+N5GedoCqozzhBmvRa4Jy\nvLQ+jhG0RMUDpS0VAwooooAKKKKAOY+Ir3KfCDxW9km64XR7vyl6c+S39M18z/s3mP8A4Zn8N+Vt\nzm4Hy+vnyZzX1zNBHcWstvcReZDKpjkT+8jAhh+IJH418O6dLffsw/EvUvh/40huF8Eandvd6Drn\nls8ce7rG+OhAwGHUFd2CrZrDFUnWouMd9zyc4w861H3Omp7+OlLWVYeJfDuqxLLpviDS7yN1DqYL\nyNtwPQj5smtJZI3O9HRl/vIwIr5105LdHxjpyW8R9FRvJGnzvKir/tsBWRf+L/Cmlf8AIT8S6Ra9\n9st5GpwP+BURhJ/CgVOT+FFnT9A0HTb+8uNP0qzsZNQk8y9lt4lQysQRufA5OCeT6n1NYv7FE274\nG+JbRWzHaeK7yCLuNhjhbgdBy3QfXvXnHjv432+sF/AXwet7jxX4p1FTbQvpsZeGDdwX3dGIB4P3\nR1J4wfo34B/C+T4R/A3S/Cd1LFNqjM17qMkTZRriTBYA9wqhUB7hc9697AUqkKbdTrbffQ+qyTD1\nYJyqde56aOlLSDpS11nvhRRRQAUUUUAFfOf7aP8Ayb5pf/Yy2P8AKSvoyvnP9tH/AJN80v8A7GWx\n/lJV0/jQp7H11F/qU/3R/Kvkj4Df8nP/ALQP/Yyp/wChS19bxf6lP90fyr5I+A3/ACc/+0D/ANjK\nn/oUtdNb4GYQ3PomiiiuM6AooooAK53x9Pe2/wAKvFFxp6O15Fo948Co2C0gt3Kj/voCuiprDeXR\n03K2Qy/3geoNOIHzb+yhHZRfso+Gv7PlVtzXLz/7MpmbcPY4C/mK9rHSvlqGbWv2SfiNqmmavpV/\nqHwm1u7N1Yajao0p0mVuPLcegGAQeWCqy5OQfoHw14/8D+MNLTUPDXirSdShbH+quEDrnsyMQwPs\nQCK74yufP4mjOM2zpaQ9KMSeVv2PtbndtOPwPQ/hS1Ry2ExS0ASP9xHb/gJNU9T1TTtEtXu9Y1C1\n0+FF3tLdSrCFHr8xGfrQFiyTs+f+7zXhX7Otx/ZX7V3xy8JWn/Hmt/Dfx/NxG5Zww9MncB/wGofH\nP7Tvh22uv+EV+E1pL478W3X7u0h02NpbaNzwGdx9/Gc4XIyOWWu4/Zw+D2tfDLwnq+t+Mrtbzxl4\nmuRe6rKrbxDjcVi3dCQXcsRxlsDIANZVpRsergKU4u8j23FGKB0pa4j1RMUtFFABRRRQAU6L/j6i\n/wB5f5im0h6UAfGPwB+S++JiS/8AH0PF935i98Z4z7Zz+tezZryX4q6VrPwG/aA1T4n2um3F94A8\nVuh1f7Km59Nuv+ehXspJLA9DuZc5C57nRfHXgzxFYRXui+J9Juo5OV23ShvoVYggjPQjNeRmWHn7\nXnitGfF5thaka7nbRnRUVGksUvzxSpIv95GDfyNKX/20/wB7dj/P1ry+SR5HJIfWcND0aLxFLr6a\nVZrqskP2eS+WJRK0fXaXxkjjpTL7xDoOmRb9T13S7Nf71xdRr1+rV5z4y/aB8EaDD9g8MXH/AAlu\nv3DeXaadpeZt0h4G51BGAewyT6dxvRoVpu0E9Tejh605csE9TZ/Z9mNv+2h8YrKJt0dxBaXEm1j8\nrA9CPX5z9Pxr6prwP9mP4U+J/BGg69418fqY/Fviq4W6urfqbaJclI27BssxIB4GFPINe+V9LayU\neySPvsNB06ahLogooopGwUUUUAFFFFAHnPx9/wCTW/iD/wBgG7/9Fmtz9mP/AJNA+Hf/AGBYf61h\n/H3/AJNb+IP/AGAbv/0Wa3P2Y/8Ak0D4d/8AYFh/rXXQ+EwnueNfsPf8mjWv/Yavv5RV9H184fsP\nf8mjWv8A2Gr7+UVfR9c9T42aQ2CiiioLCiiigArN1zw/oPiXRpdH8R6PZatp8v8ArLW9gWWNvwYc\nH3GDWlSHpQB8O2n7PXw28Qft1+NPAUVlf6Lodlotvf20Ol3TRGOV1i3EE5+XLsQOg4r0wfsj3umf\n8iv8d/iDpcK/dSWdZ9vr90oP0rG8aa14n+D/AO2t4l+Jt78OPEviDwvq2j29kt7osXm+SVWPczcH\nGChGDjtg17H8K/jr4C+MEt7b+Eri/jvtPjWS7stQtTBJCGOM9SCAeOD+AreU52vEy5IPc83/AOGX\n/Hn/AEcv45/79f8A22o2/ZH1HUDv8QfH34g30nRTFOIfzBZs19MDpQelR7Wfcfsodj4W/aL/AGZ/\nh/8ADL9n6/8AF2n6h4j1jWor22gW71e/Exw8mG+VVUHIGOQcfrX2P4H8PaD4a8Eadp/h3RbDSbVr\nSGRobKBYgxKKxJ29Tkk5Oa5342/DH/hbvwbv/A8WsLpM1xPDcR3bReaivG+QGUEHByRxzXnGh2X7\nX3g/VbDT7u48DeONFSSOJpn/ANDnWIEDPCrjAHXaTwKpy51uCjys+jKKKKxNAooooAKKKKACiiig\nArN1vw1oPi3Rn0LxFotrrGmzMvmWl1EJUbsDjHBwcZGDzWieleF+Ov2cLjx/48v9a1j4yeO4dLu5\nN8eh2VwIra2GB8ifMQFyM/dz6knmqh8QmeHfsz/s6/Cr4o/AafX/ABTpF6dWTWru2S9s76SErHGs\nRUbQdvBYnJGa9Vb9in4W/wDLLxH45hX+GJNUXC/TMZOK9g+Gfw08MfCf4fReEvCiXv2FJ5LlnvZR\nLNJI+AzMwUDOFUAAAAKO+SexqpzbZHso/aPm9P2KvhT5v+keIPG1xH/zyl1VcN+SA8VPrH7J/wAC\nPDnw5169tPB0lxeW2nTzJcXt9NMd6xswbG7AwR2HOOlfRB6V8++Nvg18cPiH4x1dNV+OT6H4QuJX\nS20vQ7Ro5Ps7cBJGyu47eGyxB5wAOKIN9w5IrZD/ANjnRdJsv2VtB1W00yzt769ef7XdRRKsk+JW\nA3vjJAAAAPAr38dK5X4beAdK+GXw00vwVotxdXFnp6sFmumBkkLOWYnGAMk9B0rq6mbu7lx+EKKK\nKkYUUUUAFFFFABXzn+2j/wAm+aX/ANjLY/ykr6Mr5z/bR/5N80v/ALGWx/lJV0/jQp7H11F/qU/3\nR/Kvkj4Df8nP/tA/9jKn/oUtfW8X+pT/AHR/Kvkj4Df8nP8A7QP/AGMqf+hS101vgZhDc+iaKKK4\nzoCiiigAooooAr3VpbXtrLZXtpFcW8y7JIZUDpID2ZWBBHsRXxf8XPgR8MX/AGzPhv4S03w//Yel\n+ILe5lv4tKdoNzIW2lOoT7vYfhX2xXzN+0HB4s8NftGfDn4r6N4I1vxRpOg21zHexaQnmSKzE4GA\nCRw2c4I4PStKUtSJxHN+x/ZafLv8JfGf4g6GvVYnvFnRT7AbOPrk+9O/4Zg8ef8ARy/jn/v1/wDb\na7H4aftK/Dr4leLU8H2UWuaP4iKu39marZGI/KMsAwJBIHOCAa9jHSqdScdyfZwfQ+aW/ZO17UCF\n8QftCePr6FfurE4hK/iXbv7VznxB/ZH+HXhv4N+KvE1/4j8X+IdU0/Sbm7gfVNRDIsqRsVO1VBID\nDOCT719dVheM/DcfjD4c694Ulu3tV1WwmsvtCKGMPmIV3YPXGc470lVfcfs4LoeZ/ss+HNB0n9mD\nwhqumaLYWd9qGnebe3cECrNcuZHGXfqeFHU44HFe0DpXy1oHgL9rX4S+F7Pw/wCDfEfgnxhoenR+\nVbWV5AbeRY852qcKScseSxPvX0roE+tXHhewuPEVlb2OrPbI97a28vmxwykfMqvgbgD3xU1N7lo0\naKKKgYUUUUAFFFFABRRSHpQBFc21td2stpd28VxDKpSSKVA6SAjBBUggg+hBFfFfw3+AXwq8dftI\nfGbQfEHhgrYaLqscWnQWU8lqtorGTIUIwGPlGAcgYr3z4p/BHUfib4oivf8AhbXjLw7paQLBJo+k\nSiOCQgnLnDD5iGwcqfbHStX4TfBLwh8Hb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"metadata": {},
"output_type": "pyout",
"prompt_number": 21,
"text": [
""
]
}
],
"prompt_number": 21
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Adding YouYube videos\n",
"\n",
"I am making the video small as it does not embed into the final output pdf."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from IPython.display import YouTubeVideo\n",
"YouTubeVideo('iwVvqwLDsJo', width=200, height=200)"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"html": [
"\n",
" \n",
" "
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 22,
"text": [
""
]
}
],
"prompt_number": 22
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Plotting with Matplotlib\n",
"matplotlib is a python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. matplotlib can be used in python scripts, the python and ipython shell, web application servers, and six graphical user interface toolkits."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from matplotlib.pylab import xkcd\n",
"#xkcd()\n",
"CustomPlot()\n",
"from numpy import *\n",
"\n",
"#generate some data\n",
"n = array([0,1,2,3,4,5])\n",
"xx = np.linspace(-0.75, 1., 100)\n",
"x = linspace(0, 5, 10)\n",
"y = x ** 2\n",
"\n",
"fig, axes = plt.subplots(1, 4, figsize=(12,3))\n",
"\n",
"axes[0].scatter(xx, xx + 0.25*randn(len(xx)))\n",
"axes[0].set_title('scatter')\n",
"axes[1].step(n, n**2, lw=2)\n",
"axes[1].set_title('step')\n",
"axes[2].bar(n, n**2, align=\"center\", width=0.5, alpha=0.5)\n",
"axes[2].set_title('bar')\n",
"axes[3].fill_between(x, x**2, x**3, color=\"green\", alpha=0.5);\n",
"axes[3].set_title('fill')\n",
"\n",
"for i in range(4):\n",
" axes[i].set_xlabel('x')\n",
"axes[0].set_ylabel('y')\n",
"show()"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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EEKIBmjx5MkFBQURGRpbvu3TpEoMGDaJDhw5ERUWRlZVV/tqCBQto37494eHh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\ndJc6L3fiLudUarRdl9Ro10xqtF2Hu1x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Og6Oqsz2Af29v6eyEkHQ2JGwhCyGLQIBHhuRh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\nOSl0/omRa0Lm1ZUrVxASEoIXXngBqampeOmll2C1WnmN8TAl5a07aN7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"text": [
""
]
}
],
"prompt_number": 23
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Combined plots"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"CustomPlot()\n",
"font_size = 20\n",
"figsize(11.5, 6) \n",
"fig, ax = plt.subplots()\n",
"\n",
"ax.plot(xx, xx**2, xx, xx**3)\n",
"ax.set_title(r\"Combined Plot $y=x^2$ vs. $y=x^3$\", fontsize = font_size)\n",
"ax.set_xlabel(r'$x$', fontsize = font_size)\n",
"ax.set_ylabel(r'$y$', fontsize = font_size)\n",
"fig.tight_layout()\n",
"# inset\n",
"inset_ax = fig.add_axes([0.29, 0.45, 0.35, 0.35]) # X, Y, width, height\n",
"inset_ax.plot(xx, xx**2, xx, xx**3)\n",
"inset_ax.set_title(r'zoom $x=0$',fontsize=font_size)\n",
"# set axis range\n",
"inset_ax.set_xlim(-.2, .2)\n",
"inset_ax.set_ylim(-.005, .01)\n",
"# set axis tick locations\n",
"inset_ax.set_yticks([0, 0.005, 0.01])\n",
"inset_ax.set_xticks([-0.1,0,.1]);\n",
"show()"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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isRh9+/ZFq1ateKnpTRwdHXHixAmsXLkS9+/fR25uLjIzM3Ho0CHe8iCEEKIe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\nagAAAABENZoaAAAAAFHt/wMZR+luC0uV9gAAAABJRU5ErkJggg==\n",
"text": [
""
]
}
],
"prompt_number": 24
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Adding text to a plot"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"CustomPlot()\n",
"figsize(11.5, 6) \n",
"font_size = 20\n",
"fig, ax = plt.subplots()\n",
"ax.plot(xx, xx**2, xx, xx**3)\n",
"ax.set_xlabel(r'$x$', fontsize = font_size)\n",
"ax.set_ylabel(r'$y$', fontsize = font_size)\n",
"ax.set_title(r\"Adding Text $y=x^2$ vs. $y=x^3$\", fontsize = font_size)\n",
"ax.text(0.15, 0.2, r\"$y=x^2$\", fontsize=font_size, color=\"blue\")\n",
"ax.text(0.65, 0.1, r\"$y=x^3$\", fontsize=font_size, color=\"green\");"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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7wlIAAJOD3TGsZcMnem1xbh5CX3sPmvxCuI0cAPfRz1ZnqTrFmWTijJcWzEYe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\n",
"text": [
""
]
}
],
"prompt_number": 25
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## xkcd style plotting\n",
"[matplolib v1.3](http://matplotlib.org/xkcd/examples/showcase/xkcd.html) now includes a setting to make plots resemple xkcd styles."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from matplotlib import pyplot as plt\n",
"import numpy as np\n",
"\n",
"plt.xkcd()\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.add_subplot(1, 1, 1)\n",
"ax.spines['right'].set_color('none')\n",
"ax.spines['top'].set_color('none')\n",
"plt.xticks([])\n",
"plt.yticks([])\n",
"ax.set_ylim([-30, 10])\n",
"\n",
"data = np.ones(100)\n",
"data[70:] -= np.arange(30)\n",
"\n",
"plt.annotate(\n",
" 'THE DAY I REALIZED\\nI COULD COOK BACON\\nWHENEVER I WANTED',\n",
" xy=(70, 1), arrowprops=dict(arrowstyle='->'), xytext=(15, -10))\n",
"\n",
"plt.plot(data)\n",
"\n",
"plt.xlabel('time')\n",
"plt.ylabel('my overall health')\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.add_subplot(1, 1, 1)\n",
"ax.bar([-0.125, 1.0-0.125], [0, 100], 0.25)\n",
"ax.spines['right'].set_color('none')\n",
"ax.spines['top'].set_color('none')\n",
"ax.xaxis.set_ticks_position('bottom')\n",
"ax.set_xticks([0, 1])\n",
"ax.set_xlim([-0.5, 1.5])\n",
"ax.set_ylim([0, 110])\n",
"ax.set_xticklabels(['CONFIRMED BY\\nEXPERIMENT', 'REFUTED BY\\nEXPERIMENT'])\n",
"plt.yticks([])\n",
"\n",
"plt.title(\"CLAIMS OF SUPERNATURAL POWERS\")\n",
"\n",
"plt.show()"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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mzcumd29YsgRmzHAwcaJPhpcSh0yddYjKyspi4MCBnHXWWT9rgVQaN5FISE9B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ABJRU5ErkJggg==\n",
"text": [
""
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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vJm9ceumlrsdiWZY9fMM5TlYFYalDByzLkiVLlsiDDz7YYdxkKhVA1tfXZ3ye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\nsrOzKTc3l2xsbCgqKopUKpVB323bthEA+vHHH0mlUhEA+uyzz2jZsmX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"text": [
""
]
}
],
"prompt_number": 26
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Symbolic math using SymPy\n",
"\n",
"SymPy is a Python library for symbolic mathematics. It aims to become a full-featured computer algebra system (CAS) while keeping the code as simple as possible in order to be comprehensible and easily extensible. SymPy is written entirely in Python and does not require any external libraries.\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from sympy import *\n",
"init_printing(use_latex=True)\n",
"x = Symbol('x')\n",
"y = Symbol('y')\n",
"series(exp(x), x, 1, 5)"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"latex": [
"$$e + e x + \\frac{1}{2} e x^{2} + \\frac{1}{6} e x^{3} + \\frac{1}{24} e x^{4} + \\mathcal{O}\\left(x^{5}\\right)$$"
],
"metadata": {},
"output_type": "pyout",
"png": "iVBORw0KGgoAAAANSUhEUgAAATkAAAAfCAYAAACcY7puAAAABHNCSVQICAgIfAhkiAAAB6JJREFU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\nampqampqampqSvF//lWFTKLgGsQAAAAASUVORK5CYII=\n",
"prompt_number": 27,
"text": [
" 2 3 4 \n",
" \u212f\u22c5x \u212f\u22c5x \u212f\u22c5x \u239b 5\u239e\n",
"\u212f + \u212f\u22c5x + \u2500\u2500\u2500\u2500 + \u2500\u2500\u2500\u2500 + \u2500\u2500\u2500\u2500 + O\u239dx \u23a0\n",
" 2 6 24 "
]
}
],
"prompt_number": 27
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"eq = ((x+y)**2 * (x+1))\n",
"eq"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"latex": [
"$$\\left(x + 1\\right) \\left(x + y\\right)^{2}$$"
],
"metadata": {},
"output_type": "pyout",
"png": "iVBORw0KGgoAAAANSUhEUgAAAHQAAAAbCAYAAACtOKuoAAAABHNCSVQICAgIfAhkiAAAA+lJREFU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\n",
"prompt_number": 28,
"text": [
" 2\n",
"(x + 1)\u22c5(x + y) "
]
}
],
"prompt_number": 28
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"expand(eq)"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"latex": [
"$$x^{3} + 2 x^{2} y + x^{2} + x y^{2} + 2 x y + y^{2}$$"
],
"metadata": {},
"output_type": "pyout",
"png": "iVBORw0KGgoAAAANSUhEUgAAAQ0AAAAbCAYAAABm6to6AAAABHNCSVQICAgIfAhkiAAABP5JREFU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\n7l6NeSaTyWQymUwmk8lkMpke518QvOZokU+z1gAAAABJRU5ErkJggg==\n",
"prompt_number": 29,
"text": [
" 3 2 2 2 2\n",
"x + 2\u22c5x \u22c5y + x + x\u22c5y + 2\u22c5x\u22c5y + y "
]
}
],
"prompt_number": 29
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a = 1/x + (x*sin(x) - 1)/x\n",
"a"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"latex": [
"$$\\frac{x \\sin{\\left (x \\right )} -1}{x} + \\frac{1}{x}$$"
],
"metadata": {},
"output_type": "pyout",
"png": "iVBORw0KGgoAAAANSUhEUgAAAFkAAAAbCAYAAAAXmyPIAAAABHNCSVQICAgIfAhkiAAAAoNJREFU\naIHt2U2IjVEYwPHfGB/jW9kQBtNQMkg+BhMrUmoiWzKjUSMlkYWFhSIfG8XCemRFJMrKhkYZNJRC\ngzTFgo1YqCFicc6t2637MXfuve81c//19j73Pc89z8d7nnPOPZcaVc9zdCbtRBrTcRONo8luO+aV\no+MiOIBT+ItFY8BuoiQVbFa749PkNizBKvRjKrbjQmz7iAnxWQe24ixuRZ12nBPKZjAa3YW9+BFt\nLMXbHLaOox7vRhZvdTIDXVHeiSdRvoqTaW1wOE0+LZRKijO4l/b5LnZHeYLwYnLZmhPbNxQXBqp4\nJP/CtShvxO0od2AZ7qMbj3Ax7ft/Mvr7LSyGKb4KSYNWvMpjK8UC9OEQmrJFhWe4nqO9WFbitRDP\niEkleSjt2TYcjPJM1GExNmEHHmA5fmbpMzPxKRrxOKM909b3KDfE+5V8AZSJY0KFDpais1SS29Es\njKoWvBCS24lZuIOH8WrGJNmTnI3vmIYtOWxdirrZXtR/SX28twmjczZ6sR6rcSM+bxFKuFVYuPqE\nUd0tJOxN1OkWFrdPsY99WIj3eIkVmJ/D1g8h4U0YGGYse4TpZS3mCvP702H2kWKXULHfKmy3JGwu\nQGcNppTbkTz0KOHiOT6/SknpLUCnv+xeVJhxSTswFqhL2oGEuSr8IMqkEZ+F7WYmXYqstr9j4BoO\nPQqbkwuynZqTx/qILpaC8labkytAJXcXuQ6FPlTQj1JTNXHlOxSqNnoUNidXVVwNmBjl8zhRaQeG\nyWWFJalq4+rHuijPTNKREpN4XO04KpTgT2EtqMORJJwpIQXFVamt237hTGIAk4VTtiHhX5QvFfKh\nHIzWuGrUqFFj1PIPLG3KJDnKTjUAAAAASUVORK5CYII=\n",
"prompt_number": 30,
"text": [
"x\u22c5sin(x) - 1 1\n",
"\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 + \u2500\n",
" x x"
]
}
],
"prompt_number": 30
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"simplify(a)"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"latex": [
"$$\\sin{\\left (x \\right )}$$"
],
"metadata": {},
"output_type": "pyout",
"png": "iVBORw0KGgoAAAANSUhEUgAAACwAAAASCAYAAAAg9DzcAAAABHNCSVQICAgIfAhkiAAAAjFJREFU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\nRU5ErkJggg==\n",
"prompt_number": 31,
"text": [
"sin(x)"
]
}
],
"prompt_number": 31
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Data analysis using the Pandas library\n",
"pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with \u201crelational\u201d or \u201clabeled\u201d data both easy and intuitive\n",
"\n",
"The two primary data structures of pandas, Series (1-dimensional) and DataFrame (2-dimensional), handle the vast majority of typical use cases in finance, statistics, social science, and many areas of engineering. \n",
"\n",
"> For R users, DataFrame provides everything that R\u2019s data.frame provides and much more. pandas is built on top of NumPy and is intended to integrate well within a scientific computing environment with many other 3rd party libraries.\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from pandas import DataFrame, read_csv\n",
"\n",
"Cape_Weather = DataFrame( read_csv('data/CapeTown_2009_Temperatures.csv' ))\n",
"Cape_Weather.head()"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"html": [
"
\n",
"\n",
"\n"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 6,
"text": [
""
]
}
],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"qwerty\n"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 7,
"text": [
"'foo'"
]
}
],
"prompt_number": 7
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Saving a Gist\n",
"It is possible to save spesific lines of code to a GitHub gist. This is achieved with the `pastebin` magic as demonstrated below."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%pastebin \"cell one\" 0-10"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 8,
"text": [
"u'https://gist.github.com/6651917'"
]
}
],
"prompt_number": 8
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Connect to this kernel remotely\n",
"Using the `%connect_info` magic you can obtain the connection info to connect to this workbook from another ipython console or qtconsole using :\n",
"\n",
" ipython qtconsole --existing"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%connect_info"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"{\n",
" \"stdin_port\": 55291, \n",
" \"ip\": \"127.0.0.1\", \n",
" \"control_port\": 55292, \n",
" \"hb_port\": 55293, \n",
" \"signature_scheme\": \"hmac-sha256\", \n",
" \"key\": \"dcc990e7-2eeb-4c41-8099-a25cf2308be4\", \n",
" \"shell_port\": 55289, \n",
" \"transport\": \"tcp\", \n",
" \"iopub_port\": 55290\n",
"}\n",
"\n",
"Paste the above JSON into a file, and connect with:\n",
" $> ipython --existing \n",
"or, if you are local, you can connect with just:\n",
" $> ipython --existing kernel-5db61520-8ecd-492b-9afb-5c8e65985a19.json \n",
"or even just:\n",
" $> ipython --existing \n",
"if this is the most recent IPython session you have started.\n"
]
}
],
"prompt_number": 9
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Publishing your Work\n",
"Newly added in the 1.0 release of IPython is the nbconvert tool, which allows you to convert an `.ipynb` notebook document file into various static formats.\n",
"\n",
"Currently, nbconvert is provided as a command line tool, run as a script using IPython. A direct export capability from within the IPython Notebook web app is planned.\n",
"\n",
"The command-line syntax to run the nbconvert script is: [MORE OPTIONS](http://ipython.org/ipython-doc/rel-1.0.0/interactive/nbconvert.html)\n",
"\n",
" ipython nbconvert --to FORMAT notebook.ipynb\n",
"\n",
"This page is converted and published to the following formats using this tool:\n",
"\n",
"* HTML\n",
"\n",
"* PDF (the PDF is created using `wkhtml2pdf` that takes the html file as an input)\n",
"\n",
"* LATEX\n",
"\n",
"* `Reveal.js` slideshow\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Building(exporting) from within the notebook\n",
"You can even call the build script from the notebook. The script will convert this page to an html and slide file. It will also compile to PDF and stitch a front page to it. Some of the last text in the building process wont appear as this notebook is being updated as it is being compile. Maybe not the best idea but saved a lot of time..."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!ipython builddocs.ipy;\n",
"print \"Done\""
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## File links to exported content\n",
"The links below can be used to verify the output from the convertion process. This saved me a lot of time as I could just click below and have a look at the files without exiting the notebook."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"FileLinks('output/')"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"html": [
"output/ \n",
" .DS_Store \n",
" pycon13_ipython.html \n",
" pycon13_ipython.slides.html \n",
" pycon13_ipython_complete.pdf \n",
" pycon13_ipython_pdf.pdf "
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 12,
"text": [
"output/\n",
" .DS_Store\n",
" pycon13_ipython.html\n",
" pycon13_ipython.slides.html\n",
" pycon13_ipython_complete.pdf\n",
" pycon13_ipython_pdf.pdf"
]
}
],
"prompt_number": 12
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Links to some interesting notebooks\n",
"The following notebooks showcase multiple aspects of IPython, from its basic\n",
"use to more advanced scenarios. They introduce you to the use of the Notebook\n",
"and also cover aspects of IPython that are available in other clients, such as\n",
"the cell magics for multi-language integration or our extended display\n",
"protocol.\n",
"\n",
"For beginners, we recommend that you start with the 5-part series that\n",
"introduces the system, and later read others as the topics interest you.\n",
"\n",
"Once you are familiar with the notebook system, we encourage you to visit our\n",
"[gallery](https://github.com/ipython/ipython/wiki/A-gallery-of-interesting-IPython-Notebooks)\n",
"where you will find many more examples that cover areas from basic Python\n",
"programming to advanced topics in scientific computing.\n",
"\n",
"* [Animations Using clear_output](http://nbviewer.ipython.org/url/github.com/ipython/ipython/raw/master/examples/notebooks/Animations%20Using%20clear_output.ipynb)\n",
"* [Cell Magics](http://nbviewer.ipython.org/url/github.com/ipython/ipython/raw/master/examples/notebooks/Cell%20Magics.ipynb)\n",
"* [Custom Display Logic](http://nbviewer.ipython.org/url/github.com/ipython/ipython/raw/master/examples/notebooks/Custom%20Display%20Logic.ipynb)\n",
"* [Cython Magics](http://nbviewer.ipython.org/url/github.com/ipython/ipython/raw/master/examples/notebooks/Cython%20Magics.ipynb)\n",
"* [Data Publication API](http://nbviewer.ipython.org/url/github.com/ipython/ipython/raw/master/examples/notebooks/Data%20Publication%20API.ipynb)\n",
"* [Frontend-Kernel Model](http://nbviewer.ipython.org/url/github.com/ipython/ipython/raw/master/examples/notebooks/Frontend-Kernel%20Model.ipynb)\n",
"* [Octave Magic](http://nbviewer.ipython.org/url/github.com/ipython/ipython/raw/master/examples/notebooks/Octave%20Magic.ipynb)\n",
"* [Part 1 - Running Code](http://nbviewer.ipython.org/url/github.com/ipython/ipython/raw/master/examples/notebooks/Part%201%20-%20Running%20Code.ipynb)\n",
"* [Part 2 - Basic Output](http://nbviewer.ipython.org/url/github.com/ipython/ipython/raw/master/examples/notebooks/Part%202%20-%20Basic%20Output.ipynb)\n",
"* [Part 3 - Pylab and Matplotlib](http://nbviewer.ipython.org/url/github.com/ipython/ipython/raw/master/examples/notebooks/Part%203%20-%20Pylab%20and%20Matplotlib.ipynb)\n",
"* [Part 4 - Markdown Cells](http://nbviewer.ipython.org/url/github.com/ipython/ipython/raw/master/examples/notebooks/Part%204%20-%20Markdown%20Cells.ipynb)\n",
"* [Part 5 - Rich Display System](http://nbviewer.ipython.org/url/github.com/ipython/ipython/raw/master/examples/notebooks/Part%205%20-%20Rich%20Display%20System.ipynb)\n",
"* [Progress Bars](http://nbviewer.ipython.org/url/github.com/ipython/ipython/raw/master/examples/notebooks/Progress%20Bars.ipynb)\n",
"* [R Magics](http://nbviewer.ipython.org/url/github.com/ipython/ipython/raw/master/examples/notebooks/R%20Magics.ipynb)\n",
"* [Script Magics](http://nbviewer.ipython.org/url/github.com/ipython/ipython/raw/master/examples/notebooks/Script%20Magics.ipynb)\n",
"* [SymPy Examples](http://nbviewer.ipython.org/url/github.com/ipython/ipython/raw/master/examples/notebooks/SymPy%20Examples.ipynb)\n",
"* [Trapezoid Rule](http://nbviewer.ipython.org/url/github.com/ipython/ipython/raw/master/examples/notebooks/Trapezoid%20Rule.ipynb)\n",
"* [Typesetting Math Using MathJax](http://nbviewer.ipython.org/url/github.com/ipython/ipython/raw/master/examples/notebooks/Typesetting%20Math%20Using%20MathJax.ipynb)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Sources / References \n",
"Since this talk focussed on the life cycle of the analysis to publication many of the code examples were taken from their respective websites. If I have not given credit at any point please let me know and I will make sure that the work is updated\n",
"\n",
"1. [SciPy](http://www.scipy.org/)\n",
"\n",
"2. Fernando P\u00e9rez, Brian E. Granger, IPython: A System for Interactive Scientific Computing, omputing in Science and Engineering, vol. 9, no. 3, pp. 21-29, May/June 2007, doi:10.1109/MCSE.2007.53. URL: http://ipython.org3\n",
"\n",
"3. [Hunter, J. D.Matplotlib: A 2D graphics environment](http://matplotlib.org/)\n",
"\n",
"4. [Sympy]()\n",
"\n",
"5. [Bayesian Methods for Hackers, the use of the custom css and also the custom matplotlib skin](http://nbviewer.ipython.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter1_Introduction/Chapter1_Introduction.ipynb)\n",
"\n",
"6. [Custom Stylesheets](http://python.6.x6.nabble.com/Style-Rules-for-Notebook-td4985853.html) and [here](http://zulko.wordpress.com/2013/04/14/customize-your-ipython-notebook-with-css/)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Embedding the final presentation into the notebook! \n",
"The build script generates a slideshow version of this notebook and saves it in the output directory. You can also use normal HTML in a cell and using the `iframe` tag the slideshow was embedded to a cell below. Since this document has not been build yet...we are editing it now, the slideshow below is linked to the previous saved version of this notebook. So if we did not make to many changes it should be pretty close to being the same thing."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
""
]
}
],
"metadata": {}
}
]
}