Python Setup and iPython Notebook Examples.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# First steps with Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This document is written for people who had little or no experience with Python yet and are looking for a simple entry point to install and use the programming language. After the setup introduction you will find some short examples which aim to show what can be done with a few lines of code. A small collection of further sources for learning the language or specific packages are listed at the end.\n",
    "\n",
    "Before using Python for the first time everything has to be set up correctly. While Python is preinstalled on many operating systems, most of the time you will need additional python-packages. Installing those packages by hand can be exhausting and time intensive especially for Windows users, which is why the use of a collective distribution is recommended. \n",
    "\n",
    "[Anaconda](https://store.continuum.io/cshop/anaconda/) is one example for a very good and widely used Python distribution.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Anaconda"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Anaconda includes the most important Python packages within one single installer which is available for all popular operating systems and automatically sets up <a href=\"http://en.wikipedia.org/wiki/Environment_variable\" target=\"_blank\">environment variables</a>, so that python can be started via the command line entry ```python``` in the <a href=\"http://en.wikipedia.org/wiki/Shell_(computing) \"target=\"_blank\">system-shell</a>."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Alt Text](https://dl.dropboxusercontent.com/u/22352379/python3shell.PNG)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Selecting the proper version"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At the moment two independent Python versions are available: Python 2 und Python 3. Python 3 offers some new features and did not inherit certain weaknesses of the old version (like <a href =\"http://en.wikipedia.org/wiki/Unicode\"target=\"_blank\">unicode</a> compability). By now most of the important python 2 packages have already been converted to Python 3. For this reason I would recommend you to install the Python 3 64-bit version of Anaconda."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### After the installation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you allowed Anaconda to set up environment variables, Python and the corresponding packages can now be loaded via the system-shell. In case you need a package which is not included in Anaconda, you can install this package with using the paket manager <a href= \"https://pypi.python.org/pypi/pip\"target=\"_blank\">pip</a>. You only need to know under which name the package is registered. The following command installs the graphic package <a href =\"http://stanford.edu/~mwaskom/software/seaborn/\"target=\"_blank\">seaborn</a>."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```markdown\n",
    "pip install seaborn\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While it is possible to work with python only by using the system shell, most people prefer specific development environments. One of these <a href =\"http://en.wikipedia.org/wiki/Integrated_development_environment\"target=\"_blank\">IDE</a>'s is included in the standard installation of Python, another one called Spyder is integrated in Anaconda. The IDE which most certainly contributed to a rising <a href =\"http://cacm.acm.org/blogs/blog-cacm/176450-python-is-now-the-most-popular-introductory-teaching-language-at-top-us-universities/fulltext\"target=\"_blank\">popularity</a> for Python within programming-courses is called <a href =\"http://ipython.org/notebook.html\"target=\"_blank\">iPython-Notebook</a>. At the moment the notebook environment is also being developed for other languages like R in the process of the <a href =\"https://jupyter.org/\"target=\"_blank\">Jupyter</a> project. iPython is now a part of this project and therefore notebooks are also often refered to as *jupyter notebooks*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## iPython/Jupyter Notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The iPython Notebook is a browser based, interactive programming interface for Python (this document is an example). Within iPython Notebooks you can execute code in real time, write text, show plots any many more things. Notebooks can then be saved and converted to different formats (<a href =\"http://nbconvert.readthedocs.org/en/latest/index.html#\"target=\"_blank\">e.g. Latex and PDF</a>). With only a little effort you are able to create publication-ready documents.    \n",
    "\n",
    "The display possibilities  are indeed so rich that entire interactive books have been written as iPython Notebooks. One example is the Introduction to Bayes-Statistics <a href =\"http://nbviewer.ipython.org/github/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/blob/master/Prologue/Prologue.ipynb\"target=\"_blank\">Probabilistic Programming and\n",
    "Bayesian Methods for Hackers</a>. The link is refering to an HTML version of the notebook generated by the <a href = \"http://nbviewer.jupyter.org/\"target=\"_blank\">notebook-viewer</a>, but you can always download the notebook and add it to your own programming environment.    \n",
    "\n",
    "iPython and its notebook feature are already integrated in Anaconda. The next shell command will launch the Notebook:\n",
    "\n",
    "        jupyter notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  \n",
    "  ![Alt Text](https://dl.dropboxusercontent.com/u/22352379/IpythonNotebook.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notebook files with the tag *.ipynb* can then be drawn into the environment oder simply be uploaded via **'click here'**. A new notebook can be started via **'New Notebook'**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using Notebooks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the next part you will find some examples for using and shaping iPython Notebooks. However, the possibilities reach far beyond this short introduction. You can find additional options in the <a href =\"http://ipython.readthedocs.org/en/stable/\"target=\"_blank\">iPython documentation</a>."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cells"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notebooks are organized in successive cells. Depending on the type of the cell (which can be changed via the dropdown menu at the top) either text/media or code will be executed.    \n",
    "\n",
    "To navigate between cells you can use various keyboard-shortcuts, which can be found via the **help** slider.   \n",
    "\n",
    "Within a <a href =\"http://daringfireball.net/projects/markdown/\"target=\"_blank\">markdown</a> cell a ton of display forms can be implemented. Double-clicking a markdown cell enables to see and edit the corresponding code."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### There are different font styles..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    " - *Italic*\n",
    " - _Italic_\n",
    " - **Bold**\n",
    " - __Bold__\n",
    " - ~~Throught~~"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Lists.."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* One\n",
    "    - Sublist\n",
    "        - This\n",
    "  - Sublist\n",
    "        - That\n",
    "        - The other thing\n",
    "* Two\n",
    "  - Sublist\n",
    "* Three\n",
    "  - Sublist"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Quotations.."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> [Beautiful is better than ugly](http://legacy.python.org/dev/peps/pep-0020/).\n",
    "> Explicit is better than implicit.\n",
    "> Simple is better than complex.\n",
    "> Complex is better than complicated.\n",
    "> Flat is better than nested.\n",
    "> Sparse is better than dense.\n",
    "> Readability counts.\n",
    "> Special cases aren't special enough to break the rules.\n",
    "> Although practicality beats purity.\n",
    "> Errors should never pass silently.\n",
    "> Unless explicitly silenced.\n",
    "> In the face of ambiguity, refuse the temptation to guess.\n",
    "> There should be one-- and preferably only one --obvious way to do it.\n",
    "> Although that way may not be obvious at first unless you're Dutch.\n",
    "> Now is better than never.\n",
    "> Although never is often better than *right* now.\n",
    "> If the implementation is hard to explain, it's a bad idea.\n",
    "> If the implementation is easy to explain, it may be a good idea.\n",
    "> Namespaces are one honking great idea -- let's do more of those!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### HTML.."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>Almost any  <strong>HTML</strong> code can be <br /><i>displayed</i> flawlessly</p> \n",
    "\n",
    "\n",
    "<table>\n",
    "<tr>\n",
    "<th>Header 1</th>\n",
    "<th>Header 2</th>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>row 1, cell 1</td>\n",
    "<td>row 1, cell 2</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>row 2, cell 1</td>\n",
    "<td>row 2, cell 2</td>\n",
    "</tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Latex.."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "$e^{i\\pi} + 1 = 0$ \n",
    "\n",
    "\n",
    "$$e^x=\\sum_{i=0}^\\infty \\frac{1}{i!}x^i$$\n",
    "\n",
    "- Alpha: $\\alpha A$\n",
    "- Beta: $\\beta B$\n",
    "- Gamma: $\\gamma \\Gamma$\n",
    "\n",
    "\n",
    "- $y = x + 5$\n",
    "- $y = x \\times 5$\n",
    "- $y = \\frac{x}{5}$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Links.."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://www.google.com\n",
    "\n",
    "[Google](https://www.google.com)\n",
    "\n",
    "[Google with tooltip](https://www.google.com \"Google's Homepage\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Images.."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Alt Text](https://www.python.org/static/community_logos/python-logo-master-v3-TM.png \"Python Logo\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### External pages.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe src=https://en.wikipedia.org/wiki/Main_Page width=800 height=400></iframe>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This cell is not a markdown cell but a code cell in which python code can be executed.\n",
    "# Code cells can be commented with the use of '#'.\n",
    "\n",
    "from IPython.display import HTML\n",
    "# use import to load python packages\n",
    "HTML('<iframe src=https://en.wikipedia.org/wiki/Main_Page width=800 height=400></iframe>')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### And videos.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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5IrUWhtpBX/D3h5NGGI9UDGG28zzVeDfb9yZvPqFN25iANpwZiVad1sOWXDEnOZaMMari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\nTqA5CLyMzp0wguQMzuczp02lg2DDBsidOin/2Q==\n",
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"400\"\n",
       "            height=\"300\"\n",
       "            src=\"https://www.youtube.com/embed/cFrtpT1mKy8\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x296f23ed668>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# You can not only load youtube videos but also locally stored media files.\n",
    "\n",
    "from IPython.display import YouTubeVideo \n",
    "YouTubeVideo('cFrtpT1mKy8')  \n",
    "\n",
    "# By the way: Python is not named after the famous snake!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So you can use iPython-Notebooks to display texual elements, images and so on."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The most important feature however is the interactive use of program code within code cells:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# The next part contains some small examples to show what is possible with just a few lines of Python code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Numbers\n",
    "\n",
    "10 / 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1337"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "34**2 + 181"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'This is a not a number.'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Strings\n",
    "\n",
    "\"This is a not a number.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'hello there'"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lists\n",
    "\n",
    "l = ['hello there', 1,5, 'spam', 94]\n",
    "l[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dictionaries\n",
    "\n",
    "types = { 'string': '1', 'integer': 1, 'float': 1.0}\n",
    "types['float']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1444.45"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Variables\n",
    "\n",
    "x = 2000.0\n",
    "x - 555.55"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16\n"
     ]
    }
   ],
   "source": [
    "# Functions\n",
    "\n",
    "g = lambda x: x**2\n",
    "print(g(4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584, 4181, 6765]\n"
     ]
    }
   ],
   "source": [
    "# Fibonacci sequence as a function\n",
    "\n",
    "def fibonacci(z):\n",
    "    x,y = 0,1\n",
    "    l = []\n",
    "    while x < z:\n",
    "        l.append(x)\n",
    "        x, y = y, x + y     \n",
    "    return l\n",
    "\n",
    "print(fibonacci(10000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Plots\n",
    "\n",
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "sns.set(style=\"white\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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kU9eowzv78nDifFt3zNAYL7w4NQwervyrSWQPLB1kqluqsTlrPS7V/CyuDfWL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\niIhI0hhmiIiISNIYZoiIiEjS/h8/4EVM/dESNgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f568e1dea20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = sns.load_dataset(\"anscombe\")\n",
    "sns.lmplot(x=\"x\", y=\"y\", col=\"dataset\", hue=\"dataset\", data=df,\n",
    "           col_wrap=2, ci=None, palette=\"muted\", size=4,\n",
    "           scatter_kws={\"s\": 50, \"alpha\": 1});"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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vv37ON9522221HgYAAH9XbTF7uxQnxQwAQO2rtpgzMzOrfdOrr75qSxgAAPyd16Oyt2/f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\ncHVtN96C/wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f568e1de198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "titanic = sns.load_dataset(\"titanic\")\n",
    "g = sns.factorplot(x=\"class\", y=\"survived\", hue=\"sex\", data=titanic,\n",
    "                   size=6, kind=\"bar\", palette=\"muted\")\n",
    "g.despine(left=True)\n",
    "g.set_ylabels(\"titanic - survival probability\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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0mshMSepHVGZhbJI7ISHEeVbMLwbotyaUaJRSNDa34fJpQp79NphEWn+TICSE\nOI9Wq0noNSCPx0NdUzs6gxl9FLLfdAm0KCRBSAghzuLocNJq78YwitpvoyV3QkIIkWDOnn4zRHj6\n7Vw6nQQhIYRIGF6vl9rGtqhNv51LF6EEiFggQUgIkdBiYfrtXHInJIQQcS6Wpt/OJWtCQggRx6Kd\n/TYYnUay44QQIi7ZHZ20Olyj6nwabhq5ExJCiPgSnH7zRn7z6XAl0DYhCUJCiPjndntoaLajNSRF\npPbbaOnkTkgIIeKD3dFJW4c7rK0XQi1SNepigQQhIURcimTrhaGMZTgS6U4ogWYehRCJwu32cLq2\nGb/GiN5giNo4XB4fr2w+xr/+8qNhvU4rd0JCCDE2RaLz6WCUUuwub+KlTUdp63AP+/WyT0gIIcaY\n3uk3b0AX1em3xrYuXth4lINnOtICpKcObzyBQOL09454EOrq6uLf//3fsdvteL1e7rnnHq644opI\nD0MIEUfcbg/1zXZ0hiR0Udp86vX5eXdbJes/rcTnDwA9dzQr5xdz7ZLxwzpX7+sTQcSD0Lp16ygt\nLeW+++6jsbGRu+66i3feeSfSwxBCxIlYmH47cKKZFzYepam9O3hs8rg01qwuoyA75cyRoQcWv9wJ\nhU96ejrl5eUA2O12MjIyIj0EIUQciIXpt1aHi5c2HWV3eVPwWKrFwC1XT2bhjLwRp1rLnVAYXXvt\ntbz66qusXr0ah8PBE088EekhCCHGOJfLTUOLI2rTb35/gE2fneZvH1fg9voB0ABXziviS1eWYkka\nXUaeBKEweuONNygoKODJJ5/kyJEj/OAHP+CVV16J9DCEEGNUtKffjla18dyGcuqancFjJflWvnpN\nGSV51pBco6vLFZLzjAURD0Kff/45S5cuBWDq1Kk0NjailEqoHcJCiOFTSlHX2IovStNvDqebV94/\nzvaD9cFjliQ9N101iSWzC0K6t8cvd0LhU1JSwp49e1i1ahU1NTUkJydLABJCDCia02+BgOKjPTW8\n/uEJut2+4PHLZ+Zz0/JJpFqMIb+mP3HyEiIfhG6//Xa+//3vs3btWvx+Pw899FCkhyCEGEPa7Q7a\nOjwYTZGffjtV5+DZ9Ueoqu8IHivMTmHN6jImjUsL23X9CRSFIh6ELBYLv/zlLyN9WSHEGHP29Fuk\nWy84XV5e//AEW3bX0BsOTEYdN1xRyvJLi9DpwlvxzCvTcUIIET0ul5v6Zjt6ozmi029KKbbtr2Pd\nB8fp6PLPwh8rAAAgAElEQVQGj186NYdbV0wmPTUywVCy44QQIkra2h3YnV4MEe58WtPYyXMbjnC8\n2h48lpNu5o7VZUyfkBnRsXh9EoSEECKiAoEA9Y2t+JQ+otlvLrePtz6u4P3PThM403LBoNfyxcXj\nWbWwBIM+8s0GJAgJIaIiEFBs2lnFqXoH4/OsrJhfnBAVlXun3wwmC7oIXVMpxa4jjby06Rj2zr5K\n1zMnZnL7qjKy0qJXBkiCkBAiKjbtrOLtrRUAHDpThXnVwpJoDinsojH91tDaxfMbyjl8qjV4LMOa\nxG0rpzB7clbUt41IEBJCRMWpeseAj+NJNKbfPF4/7247xYbtlfjOpEHrtBpWLSzmi4snYDJG6j5s\nYF5J0RZCRMP4PGvwDqj3cTzqdrlpiPD02/4TzbywoZxme19JnLLidO5YXUZ+VnKERjE0fn+0RxA5\nEoSEiCEr5hcD9FsTijeRnn5rsXfz4nvH2Husr9K1NdnIrVdPZv703KhPvV2IN4GikAQhIWKIVquJ\n2zWg3uk3P4aITL/5/AHe29Gzxubx9qyxaDRw1bwiblw6EXNS7L79ebwShIQQImS6ul00tXagN5qJ\nRMJzeWUrz20op76lK3hsQoGVNaunUpyXGoERjE5v0EwEEoSEEGHV2u7A4fRGpPWCvdPNy+8fY+eh\nhuCx5CQ9Ny2fxOWzQlvpOpy63T58/gD6MJcHigUShIQQYREIBKiL0PSbPxDgo89reH3LCVzuvqms\nJbMLuGnZRFLCUOk63OydbjJt0durFCkShIQQIdfV1U1jaycGkzns2W8VtXaeXV/O6Ya+StfjclJY\nc81USgttYb56+LR1SBASQohha25tp7M7gCHMrRc6u7289sFxPt5bGzyWZNRx45UTWTavEJ12bE9l\nnV3FIZ5JEBJChITP56OusQ10JgzG8L21BJRi2746Xv3gOM7uvkrXl03L5SsrJmNLiXzX1XBo75Ag\nJIQQQ9Lp7KKpzRn2xnPVDR08u6GckzV9la5zMyysWV3G1PEZYb32aHjcLqzm4f1sJAgJIcQQNDa3\n0e0hrAGo2+3jzS0n2bzrNGcKXWPQa7n28gmsXFAclUrXQ+FxuzAbtRTl2jAYDMN6bUOLA7vdjtVq\njckNtaEiQUgIMSJer5e6xnY0ehN6Q3iCgFKKzw438PL7x7B3eoLHZ0/O4isrpkS10vVAPK5uLEk6\n8vLT0elGlppRXtnGGx8c4sarpmOzjd0Ei8FIEBJCDJujw0mrvTusyQf1LU6e31DOkcq24LFMWxK3\nr5zCrMnZYbvuSCml8LpdJJv15Bdmoh1lYoTHr8Fiia2aduEgQUgIMWRKKRqb23D5NBhM4Wl17fH6\neXvrKTZur8Qf6Jl70+s0rF5YwhcWj8doiI1K172UUng9LlLNBgpCEHwA9Dot3W5fCEYX+yQICSGG\nxOPxUNfYjs5oRq8PzxrF3mNNvLDxKK2OvkrXU8dnsGZ1GbkZkW33PZjeO5/UZAMFWaEJPr3MJh1d\nLglCQogEdW6H18vK0mnv9GAMU+Xr5vZuXnzvKPuONweP2VKMfGXFFC6dmhNTC/NKKXweFykWA4XZ\n4WmAZzbpaXf7gu3G45kEISFGIV7bcfd2eFVKsetAJS3tpSydF/q2El5fgI07Knln66lgN1GtRsPy\ny4q4/opSzKbYeYsKBAIEfG5Sk02khyn49DKZ9Cg3uD3xX8g0dn7DQoxB8dqO+1S9A5/Xg8fjQWdI\noq61O+TXOHyqlec3lNPQ2lfpemKRjTWryijKjZ1K14FAAL/XhS0libScyLT+Np/Z7OvyxH9LBwlC\nQoxCvLbjzkrR4vX50Z+pfF2YnRKyc7d3uHn5/aN8drgxeCzFbODm5ZNYNDM/Zipd+/1+lN+DLcVE\nWm5ORK9tNGgBhTsB+gpJEBJiFOKtHXdv47m5UwvwKj01TZ0UZqeweGbBqM/tDwTY/Fk1b318MvgJ\nXwNcMaeQLy+bSLJ5eJs5w6U3+KSlmrFZo5MK3pMB6EuIvkIShIQYhXhqx93tctPQbMdgsmDQwZLZ\nhSE79/Hqdp5bX05NU2fwWHFuKmuuKWNCQWxsxPT7fBDwYoti8OllOhOE3BKEhIiOUC74hzN5IF7a\ncQcbz4U4+62zy8OrHxxn67664LEkk44vXTmRZXOLYiKJw+/zgfKSbrWQmpIW7eEAvUEIPD4JQkJE\nRSgX/OM1eSAUwtV4LqAUn+yt5bUPjuM8a7/Lwhl53Lx8UkxUuvZ5vWjxk2E1kxIjwadXwNezT8rZ\n5RrkmWOfBCERk0K54B+vyQOjFa7Gc1X1Dp7bUE5Fbd/POT8rmTtWlVFWkh7CK42Mz+tFp/GTZbOQ\nnBxbG2B7XTLByrbj7RTmpmO1ju11xsFIEBIxKZQL/vGWPBAK4Wg81+Xy8sZHJ/lwd3Ww0rXRoOW6\nJaWsmD8OvS66la69Xg96TYCstGSSLbFZ+LRXVoYNaMfr18TURt1wkCAkYlIoF/zjKXlgtMLReE4p\nxY5DDbzy/jEczr5K13OmZHPbiilk2MJTY26oPB43Rh3kpCdjMUd3LEPVu0m386ymffFKgpCISaFc\n8I+X5IHRCkfjudrmTp7fUM7Rqvbgsaw0M3esmsIlE7NCdp2R8Ho8GPWK/MwUkpKivwY1HCaDFp1W\nQ2eXZ/Anj3EShISIc0opmlraQ9p4zuXx8fYnp3hvZxWBYKVrLdcsKuGaRSVRrXTtcbsxGSBvDAaf\nXhqNhhSLgY4uuRMSQoxhoW48p5Riz9EmXtx0lDZHX/vp6RMyuGN1GTnp0Vvo93rcmPRQmJOK0WiM\n2jhCJcVspLNb7oTC5o033uAPf/gDer2eb33rWyxbtixaQxEiLoW68VxTezcvbCznwIm+JI+0VBNf\nWTGZeWXRq3Td20I7Ozs+gk+vVIuB+hYnSqm4Tk6IShBqb2/n8ccf57XXXsPpdPLrX/9agpAQIRLq\nxnNen58N26t4d1v/Stcr5o/juiUTSIpSpWuP24XFpCUn14bBEBslf0IpxWLEH1B0u31YkuLv++sV\nlX89W7duZcmSJZjNZsxmMw899FA0hiFE3Al147lDFS08t6Gcpra+KtqTitJYc01ZSIuaDtXZLbTz\n8tPR6WKry2oopVp6Ao/D6ZEgFGo1NTV0d3fz9a9/nY6ODu655x4WL14cjaEIMaix0jPI7uig1eEO\nSeO5NoeLlzYd4/PyvkrXqRYDNy+fzKJL8iI+PRTsYmoJXQvtWJeV1jON2tjWRV5mcpRHEz5RCUJK\nKdrb2/ntb39LdXU1X/va19i8eXM0hiISzEgCSqyX/VFKUd/YijegwzjK6Te/P8D7n53mrY8rgm0E\nNMCV8wq58cqJJEf4E3lvI7kUizFsXUxjUUdHB2nJPZuq65qdzJoU3YKq4RSVIJSVlcXcuXPRaDSM\nGzeO5ORkWltbycjIiMZwRAIZSUCJ5bI/LpebhhYHOkMSulFOvx0/3c6zG45Q2+QMHivJt/LV1WWU\n5Ee2yoTf7yfgc0e0kVws2XvCjkfbk/peWdsGjI/qeMIpKkFoyZIlfP/73+df/uVfaG9vp6urSwKQ\nGFQopsVGElBitexPu91BW4dn1Ht/HE4Pr24+zqcH+ipdW0x6vnzVRK6YXRjRqUe/3w9+z5l2CpFt\nJBdLLMmppKdkAPXUn9V5Nh5FJQjl5uZyzTXXcNttt6HRaHjggQeiMQwxxoRiWmwkASXWyv4opahr\nbMU3yum3QEDx8d4aXvvgBF3uvkrXi2fmc9NVk7AmRy7dubedQlqqGWtq/E49DUdykp4ko47j1Y64\nTtOO2j6h2267jdtuuy1alxdjUCimxUYSUGKp7E+opt8q6xw8u/4IlfUdwWMF2cmsWV3G5HGRq3Qd\ni718YoVGoyE33URlQxdNbd3kZMRmxe/RkooJYswIxbRYLAWU4bI7Omjr8GAwjnz6zeny8vqHJ9iy\nu4Yzha4xGXRcf8UErr5sHLoIVbru7eWTbjVL8BlATloSlQ1dHDrVKkFIiEi60PpPrE2LRcrZ2W8j\nbTynlOLTA/W8uvlYv3pk86bm8JWrJ5NujUx1aY/HjUGrxkQ7hViQk97z+z5wopmr5hVFeTThIUFI\nxKSLrf+E4y4mlvcBud0e6pvto5p+q2nq5Ln15Ryv7qt0nZ1u5o5VZcwozQzVUAfkcbtIMmjGZEXr\naMq0mbAlG9i2v46v3zwrYneqkSRBSMSkSKZFx+o+oNFOv7k8Pv72cQWbdp4mcKbLnEGv5QuLx7N6\nYTEGffirDbhd3VhMOoritLROuGk1GhZMz2bjzlr2Hm9mXln8ZQxKEBIxKZJp0bG2D6h3+s3j145o\n+k0pxe7yJl7adJS2jr5K15dMzOT2VWVkp4V/Gqy3rlteQUZcl9aJhIXTc9i4s5aP99RIEBIiUiK5\n/hNL+4A8Hg/1TXa0hiT0huFPvzW0dvHCxnIOVbQGj6VbTdy+sozZk8O76VMphdfjIsVsiPu6bpE0\npdhGpi2Jrftq+V83zSQpRB1xY0V8fTcibkQyiy1WEh7sjk5aHa4RbT71eP2s/7SS9Z9W4vOfqXSt\n1bByfjHXLZmAyRi+gKCUwudxkWIxUJCVGHXdwq2ttQWFlu5uJx2ONK6YmcPrH1exYetxls3NH/C1\nVqt1TO0pkiAkEl6007bPbr0wks2n+08088LGozS391W6nlKcxh2ryyjICl+l6966btZkE2kJVNct\nEgIBH4GAF5PJyI4jbRh0Cg3w6ocVeDzui/6su7qc3HjVdGw2W2QHPAoShISIIo/HQ11TOzrD8Fsv\ntNpdvLjpKHuONgWPWZON3LJ8EgtmhK/Std/vR/k9pKUmYbNKdYNwyMzKJTM7L/g4JRXGF9ipqHXg\n9BrIjaM9QxKEhIiSvs6nw3tD8fkDbNp5mr99chKPt2fqTaOBq+YVccPS0rD1npHSOtE1c2IWFbUO\nDpxoJjcjfvbISRASIsJG0/m0vLKN5zYcob6lr6jlhAIra1aXURymhAqf14tO4yfTaiE5WaobREtR\nTgrWZCPHq+0snePHaIiPxA8JQkKEwFA3vHq9Xmob24Y9/eZwunnl/eNsP1gfPGZJ0nPTVZNYMrsA\nbRim3rxeD3pNgOz0FCzmyFRUEBen0WiYWpLBjkP1HK9uZ/qEyGw0DjcJQmNALO/oj7ZY+dm8t6OS\nF947itvr51ODDqUUqxeN7/eckUy/BQKKD3dX88ZHJ+k+q9L15bN6Kl2nWkJf6drjdmMyQF6GVDeI\nNVPHp7PjUD2HK1olCInIidUd/bFgND+bUAawLXtrsHd6AHC5/WzZWxMMQkopGppacfu1w5p+q6i1\n89z6cqoa+ipdF2ansOaaMiYVhX5arLe0TmFOKkZj5No4iKFLtRgpzk2lqqGD9k43aSlj/0PCkIKQ\n3W6nsrISgNLSUlJSwpf2Kc4Xazv6Y8lofjbv7ajihffKz7p7gdWLRhjcleaCj91uD/VN7eiMQ59+\nc3Z7ee3D43y8pzZY6TrJqOOGpaVcdWkRuhDvw5HSOmPLxCIbVQ0dVNY5SJs89hNEBg1Cf/7zn/nd\n737HhAkTCAQCVFVV8a1vfYuvfvWrkRifILZ29Mea0fxstuw55+5lT82Ig9DSOYXUNnfi9voxGXQs\nnVNIW7uD9k4PxiFOvwWU4tP9dby6+Tid3X2Vri+blsOtV08hLTW0n3qltM7YVHLm33hVfQezEyEI\nrVu3jvfee4/U1FSg567oa1/7mgShCIqVHf2hcLEpsOFOjfU+v6LOTkmeFYvZwIR8K8svHcfG7ZVD\nO49GXfDxhcYCDDi+lQuK0Wigos5Bp9PF3vIq2uxdXD5naOX3qxs6eHZDOSdr7MFjuRkW1qwuY+r4\njOC4tu2vpaapk8LsFBbPLAiOYaCvnSsYfC5SWidW1tnEhSWbDdhSjDS0dsVFx9VBg1BWVlYwAAHY\nbDaKiuKzr0WsivaO/lC62BrOcNd2zn4+wLWXT2DVwhI2bq8c8nmWzi6ktsnZd/cyu/CiYwQGPG/v\n7+iND8r528Eq9IYkTjXUotFpWXLmvBfS7fbx5paTfLCrul+l62svn8DKBcUY9H1Tb9v21/Lh59UA\nHD/d05ah99wDfa3XYMGnl6xBRl9v2R6AJLMJDf0DTUaqgYpOD/VNrVgtfVOoXV3OiI4zFAYNQuPG\njeMb3/gGS5YsQSnF9u3bSUtL4+WXXwbg1ltvDfsgRfy42BrOcNd2QnGelQtK0Gg0593xDOUc5x7r\n3ftTXt2O3tCXfFDT1HnBayul2HWkkZc2HQ1OCQLMmpTFbSunkHWBStfnnuvsxwN9bajB52Lfm6xB\nRl5v2Z7uri6unFN2Xhkee1eAijonU4ozuKQ0o9/XrNaxNV0/aBByu93YbDYOHDgAQEpKCoFAgF27\ndgEShMTwXGwNZ7hrO6E4z8XuMC92joud1+Vy09DiQGdIoiQvjVO1fQGgMPv8JJ6G1i6e21DOkVN9\nla4zrEncvmrKgHP8hdkpwbucc899oa953S7Mwwg+Z39vsgYZXb1le5ydDmw223lBaFxeOlBJl0c7\npurEXcigQeinP/1pJMYhEsTF1reGu+4VqvMM59wXOtbc2k5ntz/YeG7xzAKAfmszvTxeP+9uO8WG\n7ZX4/D1Tb7ozgfDay8cPugN+oHOf/bUcq55ls3PIzrSNqKJ1PK1Bxqvs9J5/b01nFa0dqy4ahL79\n7W/zy1/+kmXLlvVb+FJK4XK52L59e0QGKOLLxe4+hrvuFarzDOfcZx/z+XzU1LehtMZ+jee0Ws0F\n14D2HWvihfeO0mJ3BY+VlaSzZnUZeZnJQx7XxdaXNBqYX5bB8rm5ZKZbR9VOIZ7WIONVTnpPxmVj\nW9cgz4x9Fw1CP/zhDwGYNGkSDz/8cDALQynFzTffHLEBCnEh0czg6nR20dTmHFLfn+b2bl587yj7\njjcHj9lSjNx69WQum5Y7YGbTUDLelFJ43Wd6+RRKL59EkXlmzbA5nu+Etm7dyuOPP05dXV2/dGyf\nz0d+/sBNlYQIt2hkcPUmH3R7GTQAebx+/vjmQfYdbyYQ6Jl602hg+aXjuGFpKWbT4PvEB8p46+1i\nmmqW4JOITAYdaSkmGtviOAjdeOONXHfddfzgBz/gm9/8ZvC4VqslJyf++pyLsSXSGVxnFx41DNJ2\n+8ipVv745kEczr6st+w0M//rppmMy00d4JX9XSjjrV/wKZDgk8iy0s1U1jkIBNSY3sc14McxnU7H\no48+GqmxCDFkkczgGmrhUXunm5ffP8bOQw3BY1oNpKWauGRixrACEPTPeFNKkWvVk6TzS/ARAOSk\nmzl+up22DheZtuG3hI8VUsBUjEmRyOAaat8ffyDAh5/X8MZHJ3B5/MHjyWY9aSkmdFoNRTnDC0DQ\nk/GmlKKqtoXSAhtfvno6Op0EH9GjtMDG1n11HK1qZ/FMCUJCRFS4M7jcbg+vbjpIXbufopzUi5bB\nOVlj59n1R6hu7Js6G5eTwu2rymhodV4wnXoolFL4vS5WXJpH5sqyMV+aRYRebzmnAyebWTxz7K7T\nSxAS4hyODidvbTnBJwd6NpOeqO6p53Z2enRnl4d1H57gk721wWNJJh1fWjqRK+cVotNqmTRu+O0W\nAoEAfq+b1GQjGdlZEnwSVG/Znu5uJ3b7hf8dFWboMZt0bN1bw1eWjbvovxWr1RrT/44kCAlxhlKK\n+sZWPH4tDXZvv6/1JgkElGLrvlrWbT6O09XXZG7BjDxuWT4J2wj7u/j9fpTfgy3FhC1Hgk+i6y3b\nYzIZ2XGkDY2m/YLPy89M4mStk2fXHyU34/wp464uJzdeNT2mqypIEBJjVij3Cp1dekdv0FywDE5V\nfQfPbThCRW1fJl5eZk+l67KSjAuddlB+nw+Ul7RUM9bUsV+WX4RGb9mewcyerOVk7QnKq7soLR6b\nWcsShMSQhWOD6EjO2fuaj3bXUNvcSarFMKq9Qn19f/oWd88ug5OdZuZ0YydPv3uEM4WuMRq0XLdk\nAivmF6MfQbKAz+tFi58Mq5mUlNB3SRWJIT8rmZx0Mydr7bTYu8dklpwEITFk4dggOpJz9r6mqb0b\nl7snG82abBz2XqFAIEBdYysBDBjPyX7TajVcPquAzw438NKmY/32/MyenM3tK6eQYRt6q+5eHo8b\ng1aRlZZMsmXsvWGI2KLRaJg/PY+/fVLB1n113LC0NNpDGjYJQmLIwrFBdCTn7H2OyaDD5fbj9vYE\nouHsFXI6u2hs6WTn0bYLlsWpa3by/IZyyqvagq/JSjNz+6opzJyYNeTr9PJ43Bh1kJ+ZQlJSaDuk\nisRWkpdKUU4KVQ0dnKy1U1oQu+s/FyJBSAzZSDeIDjTlNpJz9r4mNdkIQEF2MlfOKRrSXiGlFE0t\n7XS5FTuPtp1XFueyaXm8s62Cjdur8J8pt6PXaVi9sIQvLB680vW5vF4PBq2S4CPCRqPRsHROIS+8\nd5SPPq+mMCsFk3HstGuXICSGbKQbRAeachvJOS/0mqGsTXm9Xuoa29EakjAYNf3K4iil2HO0ib99\ncopWR1+l6+kTMrh9VRm5GQNXSzj/Wj3BJzc9GbN5+NN2QgxHhjWJ+dNy2X6wno/2VLNyfvGYybCM\nWhByu91cf/313HPPPXz5y1+O1jDEMFxsg+hgyQXnTrFV1DnYuL1yxAkOI9moand00Opw90s+6M2A\n8/kCtHa4Od3Y1xrZlmLiKysmc+nUnGH9z+z1etBrAuSkp2CR4CMiaG5ZDhW1Do5WtVOUk8q08SPL\n2Iy0qAWh3/72t6SlSVZQPBgsuaB3+kwBHU4Pe8ob2X6gbtRZbUPRu/fHG9Cdl3xw2bQ89h1vZv/x\nZs7MvKHVaLh6/jiuXzKBpCFUuu4lwUdEm06r4ZpFxT3TcrtryM2wYBoDVZ6iEoROnjzJyZMnWbZs\nWTQuH5dClT492HkCAcV7O6rYsqcGNIqlswuDdzpKKTq6vLz58QkOnmzBYjYwId/K8kvHAfDh59U0\ntnbR3uEmoBRKKWwppvPulIb6vVzseT5fgMde2sOx083kWo187ca5GM5ZyzlU0cLzG8r7lcKfVGRj\nzeqpFOac35L7YrweDwbdyKbdotkTScQna7KJqy8dx7ufVrL+00quXZgb7SENKipB6L/+67944IEH\nWLduXTQuH5dClT492Hk27azihffKsXf2pCzXNjmD2WIdXV7snR663T5qm2qxpRg5XNF3jo/2VOPx\nBggohT+g6Oz2YksxnZeMMNTv5WLPe+ylPWzeeRINUNdiQr/+CHddPwOAtg4XL286xq4jjcHzpJgN\n3HL1ZBZdkjfkqTevx41JD3mjSDiIRk8kMTb0lu0ZCZsJJhZYOFHbxZa99VyzaHxoBxdiEQ9Cr732\nGnPnzqWwsK85lxi9UKVPD3aeU/WOYEo0gNvrx2I2cO3lE3jrk5PBY2f/HTyH6nmD12m1QIBks4GS\nPCsVdXY2bq8M3gkM9Xu50POUUhw+UYNGq0Or6/nnfbqxA78/wOZd1bz58UncZypda4Ar5hTy5WUT\nSTYbhvLjweN2kWTUUpCditFoHNJrLibSPZHE2NFbtmekZk8w09zuorLRzfu7m7h5eewufUQ8CH34\n4YdUV1ezefNm6uvrMZlM5OXlsXjx4kgPJa6Eqr/OYOcZn2fl0zP7c6Bnr86EfGvwE/zbWytwOD24\n3H5MZ6bAes+xdE4htc2duL1+TAYjMydmUnnmjfdwRU+x0FULS4b8vZz7vKJMM1W1TRQXZNPU0RQ8\nbks28ZM/7+yXDVecl8pXr5nK+Pyh/Zw8bhcWk5acXBsGw9AC1mAi2RNJjC1DLdszkNUGCxs/a+Av\nfzvE+Hwb86bGZlmfiAehX/ziF8H/fuyxxygqKpIAFAKh6q8z2HlWzC9GKfqtCfU+p/fvijo7Xd2+\n4JpQ7/GVC4rRaPrOXVFn73fu3juBoX4vZz8vx6qnrMSG3mjmzi9OR6M5zKl6Bz6f4uCZAAdgNun5\n0pWlXDm3aND1F6UUXrcLS5KOvPx0dLrQ7r2IRE8kkbiSk/R8+7ZL+PFf9/Czp3by399eRmH20Nc7\nI0X2CcWJUPXXGew8Wq2G1YtKWL3owqnaB0+2UFFnZ0K+jX+8fgZ6vbbfa88+98btlcE7IOi7Exhs\nDGcv6JfkpnL9okLcfjAYjMHXTyi0sfd4M11nVbpedEkeNy+fhDV54DWcfi20C8PXxTTcPZGEmFRk\n5d6vzOEXz33Ofz75Kf/3m0tHXOk9XKIahO69995oXl4M02DZXI+9tIePz/TXqTmz5+bba+Zd9Hyj\n3fzq9/n4bP8p2hZO5oo5RQBU1jt4dn05lXV96yv5WcmsWV3GlOL0Ac+rlMLncZFiMVAovXxEnLj6\nsnFUN3bw0qZjPPyH7Tzy9ctJMsbO/UfsjETEvMGyuc6dXjv38blGeidwqt6B1+PC6wugN1qobXbS\n5fLy+kcn+ejzanpTXUwGHdddMYEVl40bsC12IBAg4HNjTTaRNoLgI6nWItat/eI0mtu72byrmv/7\n1C6+//fzY6ZVvAQhMWSDZXNNyLcF74B6H4eaUgqbWeELgN5gQimF2+PnwSe20dHVl000tyybr6yY\nQob14nt3zm4kl2YbeS8fSbUWsU6j0fDN2+bS5nCz41A9/7NuP9+4ZVZM3O1LEBJDNlg2171fmQMQ\nXBPqfTxavXcax6qaaWp1kJySQnFeOv6AorLewSf76oLPzU4zc8fqMmaUZl70fL29fNKtZlJTRh8o\nJdVajAUGvZb7/34+9z/+Ce9uO0VWWhK3ryyL9rAkCImhG2wNR6/XDrgGNFKbdlbx2uZD2Ds9dHm1\npJhd+Pw9m117t5npdVq+sLiEaxaVYNBfOIutt7pBVpolpL18SnKtfLq/7kzquY6SXEm1FrHJkmTg\ngX9eyHd+s4Wn3zlCls0c9axMCUIJbjjrGWev4fSU76nkoz3VtNrdZFiTyEoz90vL1mo1Fzx/IKB4\n7DIDoA0AACAASURBVKU9nKy1k2TUMbEojdICG8vmFvHbV/b2u5PSamFv+WkCSodX6fD7/bR1ePqN\na0ZpJrevmkJO+oUrXYeiusHAzt1wHXsbsGXdSvTKtJn5z39ZzHd/s4XfvLiHrDQzsydHr7W8BKEE\nN9L1jJ7yPUdpdbjwBxQ1TZ1otRoyrEn9SvVc6PwHT7bw8d5a/IEA/oCiurGTI6da2bijkpM1PVNZ\nNY1OvB43t62cRHFBNkerq3B7AsFCowBGvZZ/uGEGc6ZkX3Bu2+vxYNKrkFQ3GEhlQwfWZGO/x7FG\n1q3GltGU7enV3e3Ebr9wpQRrEnz7thn89Km9/OTPO/jRP86lICt5VNcDsFqtw15nkiCUAAb6FDyS\n9YxAQPHRnmraO93Bxm9K9fw5t1TPhc7fmzXXO5XW+5qapr6kBq+7i+M1OpTWSIvdRUNbN4GzIlCS\nUcf86bnMLevbBR4IKLbtr+VUbQvjc5O5fmkZFkv4K1qPhcoHsm41toy2bA+AyWRkx5E2NJr2iz7n\n8hmZbNnfzMN/2s11iwtIGkUzvK4uJzdeNR2bbXjrrBKEEsBAn4JH8ga6aWcVtU1OAgEVDCRaDWg0\nnFeq59zzl+RaOVbVhs8f6JdKDVCYncyxqlb8Xjc6YxLWlGQeenI7Te19la5NBi3pVhNGve68kjtb\ndp1ky+5qDCYTdS0e0tMaIvJpfyxUPhgLgVL0CUXZnqGYVWal26fjs8MNfLi3mRuvnIg+wqnbEoQS\nwECfgkfyBnqq3kFqsvFM6wYPep2OiYW289aELnR+pVRP0dMkPW6Pn0ybmUun5VJaYGN2qZXfvbqf\n6hYXHm+AI5VtwWsa9FrmT8tlQoGVuhYnhdkpLJ5ZAPTVdetwKZIsyf3GGQljofLBWAiUIjoWTM/F\n3unm2Ol2Ptlby7J5RRG9vgShBDDQp+CRvIH2ns+WYsKWYuKLiyf0qwl39nTfuef//ev70Wo0wXbZ\n00sz+cfrp1Pf1IbTrSguzGR/RUW/St0pZgNpKUaqGzuYWGTjtjNppW5XN8ln1XUrLezgSGV7v3GK\nHmMhUIro0Gg0XH3ZOFrsLg6cbKE4L5UJBaHf43cxEoQSwHA/BQ+0htQ7BZdiNgYLmILi7a2nUErx\n6f46Ptpdw5VzCy/YEM/Z5aWpvRuTQUeqxUCezcjpujZONXTz3IZyapv71oUybUmMz7fS1NYVPFbT\n1InH7cJs1FKcn45e3/dPWD7tCzEyep2W1QuLeWnTMTbvqqYwOwWjIbQFey967YhcRUTVcD8Fn7uG\npJRCo+np8+Ps8nKq3kFvaOk9Dn1N7dxeO53dPWnUqxaWBIPaR3uqqW1yYjLocHv9TEg1UZxv4+kN\nJ9h+sL5vvBqwpZiwmHQYzyqA6vd5KMwwUHSRdgqh/rQvac0ikWTazFw6NZcdh+rZdaSRxTPzI3Jd\nCULiPOeupWzZW0PnmZI4vXcxvSnJvW/Qh062BKfQehMNes+zaWcVf/vkJLXNTjzeACYDWJMUrU4z\nP/7LLrrdfZWu8zMtaLUadGfe7JNMepbMzKG+xcG08SVcc/nEAQNBb2vvs/canV3JezgkrVkkmjlT\nsjlU0cLeY03MmZKN2RT+ECFBSJzn3DWk3o6oQPAuRgEdTg9V9Q6Kc1IpyUulvdNNIKBItRiD54Ge\nYNTR5cXrC+Bxu/B6NLi9JurtfQVOC7NTWHNNGQ0tTj78vBoAv99LYYaem64uG/I+n+FW8h5O+npv\nB1i5MxLxyqDXMntyNp/sq6W8so05U8K/iVWCkADO7dFj5YuLx1PZ0HEmow3e2dZzR5BqMTAzP4sW\nu4sO5//f3p3HR1Xm+R7/nNpTSSpkXyDsEBQQcAVBRBS6tR1tbRccR6/3Zeu0a2/Ttsttu6dfd8YR\n++V0T2tf2wGn8eWCYqu40ILSirYsQWmRRUAgJCELJKGSSu1Vp577RyWVPalgkqokv/d/Vk5OPSTx\nfOt5zu/8niDNniCvbj4EQEaqhWYtRLrdErsnBNEw+ujzCsJBL0azFTDEni+yWoz8w6LJXHLuOIwG\nA5OLMtDDYapONlIyYRzfuWh6vy70/e3k3Z/yda8vLDMjMeLNmJDJtr01HKyQEBJDqHXJrNkbYsuu\n45w7I58zJmby0a4KKmrdLUtmGrljbJwxMYtjtU1U17upa/ThC4TRNA1vIIw/EKbJE8AfDHO0qglf\nIExV7Sn8/iAYO/ZrS7ebycuyY7MY0dCiXa3DAdJsJjIyHNhs/X/QtLtO3p0DFlQsYMtq4i9f7+3Y\n/uq8MR9olJ/ovhBE7kuJoWSzmsjPslNb7yEY0ge9QEFCSABtS2ZN7mhBwdY9NZTur8XjC3VolVNd\n7+HVzYfIzrDR5A6iRyKEdYWmKYKhCABhXXGg3ElZlROfz4fBZAOt7b6MpoEGeP0hKmqbOdXoRQ/5\n+M6iqZR+5eOvf492xT6d2UZ3nbzbz3a274me25FqYf/RBiZ0KuPurXw9uhPswDzw2duYoO3fLPel\nRqeBaNvTE1uKFY3eP8hkp5uoqYfKmnoKsuJr9uv1evo+qBsSQgKIXlBb78W0CoT0WEeEVq2tefxB\nnYw0C43uQOyPKKy3HRwO+YlETBjMHf+ALWYDZpMBnz8cLfcO+whoJtxBAxmONI7VlnU4/litq1+z\nge46ebefsbR//gjAnmLiigsnxVXWPZAl4L2Nqf3XpN3O6DQQbXu64/N6WTy3pO/WOgYTe8uamDw2\nkwtn58d9foej/x/MJIQEEL3A7jvawGcHTsSq23yBMOFwpEMQtbbmmVyUQXnLBbHJHcRiNuDxhwmH\nQkQiOkaTtUMjQ00Dm9lIii36J+dudgMKqy2VtDQrEwscvL+jnIqaZlyeIOl2M5qmMbHA8Y1nA+3v\n7Vg7LS1MKsyI+1wDWQLe25jaz7Ck3c7oNFhtezxuFxkZGX2GUEFudFYTihj73QuuvySEBBC9wN5/\n47wO906UivDxF8eprHUT0hVmk4Hi/DQunjeOpeeO58PPKymracLrC5NiNVJRXc9XFW5CetuflcVs\nIDcjhTS7icx0K3VON0ZNcdaUyTibg2gGWh541diwtSzWTy491cLiueO49LzxrH57b4ex9nc20H4G\n0/meUKIeaO04pq73hLo7Th7AFUPF3NI/rv3qxmCREBIx3X3S/9aCST0e33qss7GZlzYdZH+Fh3DL\nypLRoLHsgvFcvmASVouRLZ8d4+Nd5VgsVjSTmbOm5XVp5wPRe0WOVAvjCxzfqMlqX/+uRIt3TMk4\ndjHytS53q87r8YNAQmgUa92Y7pPdVaA0Lpo7lsvOj7/6KhAI8fDTH3GkxoseaXu9ZEImK5aVUJiT\nSigUBD1MozuAzZ5KsydIwO3j479Xdbi301vQyGxAiKHVupIekRASg6l1Y7rWirjqejeaFt/9lsMV\nJ/nXVZ/R6Gm7eWo2Gbj1ijM494x8IpEIeshHXmY69hQb08a7+fxgQ4f32ryzIvZevQWNzAaEGFqG\nlhTSIxJCYhAdq3V1qMwKhPQ+77c0u328+sFBNmyvjJVkt0q1mTi7JI9QwE+mw8qYjLYH3S49bzwf\nf3GcQEiPNS9t/14SNEIkj9SUaG9Gj2/gK/Q6kxAaYfpTzjyxwME2kwGPL4RSEIkYWm7cd3feCJ/s\nOsYLGw9Te8rX7TE+n5ede8q55tKZGAwdn3EwGDQWzx2H29tWgi2VXkIkp9bekI3uwKC/l4TQCNOf\ncuZLzxvP3iP1bNsbfVjSZjUBXaffx6rqWLPhaz47UBd7LdVm4urFU/hgZwUnGppA6aSmp9PgoUsA\ntX8/6P+9nYHoGiCdB4SIX26mHavFSHnN4D+XJiE0wsTTdDMcjvB//vgpVXUeDFp06h0KR0DBGx8d\n5t2tZUwqzODWb0/lnU+P8fanFfiDbct2GWkWcjNTQEWYkGPmVKMRoykFj19xuNLJf6/f0+2F/nSX\n3Aaia8Bo6zwgoSu+CaNBY2Khg8OVjXj9Iey2rlunDBQJoREmnqab75eWx7bOVipaCWMxGWn2hkAp\nDOgcrahj255qvIG28MlMtxLWI7i9QZyNLo7XNDB9Yj5ZY9IIhHQiEUVVnZtQONLjhf50Lo4D0TVg\ntHUeGG2hO9IMVtsen89DU9OYuI6dOTGDg+VOtnxexoWz4u+a0J7D4ejw0Hp3JIRGmHiablbVde3x\nZLMaCbqDhINBTBYrGK2xALJZjFy1eAonnR4+/aKSgD+AyWIlFDHidAVi68etew21f6/OTufiOBBd\nA0Zb54HRFrojzWC17bFaLZQecKJpjX0eq4ej77/+42N4vf5+v5fX6+GqJWf22XFBQmiEiafp5tjc\nVA6UB2OvWYyQZtbxaBrKktLhrtB5Z+Zz3dJppNvN/O3zo6DAbI32g9M0yMqwcfG8cW27rrbbOqG7\nC/3pXBwH4jmh0fas0WgL3ZFmsNr29EdqGhRmO6mq9xKIWMhy9L+rfTwkhEa41otta3udshoXS88p\nBuD4yWayUzWmT8hh+1cNhFXb0pvNYuSO785m5uRsggE/dovixm/P4USTztaWrs9pKWYWz43uG7R5\nZwVlNU1MKHDQ4PKjobVU3KkOy22nc3EciPLt0VYCPtpCVwyOudPzqNl2jC8O1bH03OJBeQ8JoRGu\n9eL7/o7ytmWwI3VcdFYut10xjfd2VPP+58djTUrNJgPfWTgpeq+GCOh+xuVnYDZHb0zee/08QItt\nlbD03PEdlthcnugMy5Fq4S/byro8/CoXx6Ex2kJXDI5JRQ4y0iwcrHBywcyC2PNDA0lCaJQ4VutC\nKUXA70OPRNhxwMkrH1a0bFYXddbUHG64bDo5Y1IIBfxkOGxkOMYQiSg2bT/GJ7uraGj0EwjppKda\nKK918eHnlXFvSyCEGF40TWPu9Dy27DrOl4frWTC7cMDfQ0JoFFBKkWXX8Ho9eAIGPP4INU5n7OtG\ng8bieWO5cVkJejiMCvk4WOmm4mRNy/beKtbeJ6xHYn2lHKmW2Iwmnm0JoO/CBCktFiK5zJiQSem+\nWvYdbeCcGXkDvtOqhNAIFolEOOWM7pg6Z8ZY3v+8FpezY2WcI9WMI9WCpkEw4CPLkULpV028t70c\niAZFmt0cm+FoWtvGdkCHJbW+tiVoPaa3/5bSYiGSi8loYPaUbEr3n+DrykZmTs4e2PMP6Nn6YeXK\nlezatQtd17nzzjtZtmxZooYy4sTCxxfCbLHxVaWTV94/xClXW5mlyahhMRsZk2YlEtEpHGOkuCAT\nk8nEsdrovj7NniCBkE4gqGMxGfAHdAyahtVqZFJhBovnjY3NVOINir4KE6S0WIjkM318JqX7T3Cs\nxjUyQmjHjh0cOXKEtWvX0tjYyDXXXCMhNACUUjQ4m3B7w5gsVly+EK++/SVfHq6PHWM0aIxJt5Bi\nNVGcl47ZGGFqkYOrl57ZYVuF7XtqYh2vLWads6bm0ODyn9aWD+31VZgwWKXFsswnxOnLSLOSkWah\nqs494OdOSAidf/75zJkzB4g+Uevz+VBK9flk7WjW+SJ6yTnFsaKACfnpnD09E7c3CAYLL2z8mt2H\nGzq02tGI7vNz1tQcvjxczymXnwNHq3F5I3zy5Ume33SEVJuJsK6IKBVt4wNkpJpBKY5WNeELhqlz\n+th7tJ43tnzNdxdPjc2AervAR/ctquCTL6pAU1w0Zyy3/8OsbkPgknOK2Xe0IVZ9d8k5xR2+v6HJ\nR1aGlcVzx3HZ+RO6PUd3gdPTMl93Y+vpvEKMZul2C03uILoewWgcuG4OCQkhTdOw2aIPPq1bt46L\nL75YAqgPnS+i+442cKymiWDAx2d7FM7mKSyaO47frd3FV8ecXb7fZjHi8YX47MAJKmqcBPxBDGYb\nmmYCFd3Gt8nT9QntRncQTdNobA4Siu1cpzh+0sOaDftjF+ve7uNE9y062LaXUJ0HTet+Ce/Dzysp\nr3Vh0LRY9R3AKx8c5JTLjx5R1DR4qKn39niO7gKnp2W+/oxNiKEyWG17OrOlWNGI79obiUQ/1Lpc\nLizmvsfm9XbtzNKdhBYmfPDBB7z++uusXr06kcMYFtpfRJVSfFVWjdsTRtdMWM0mjla5OFC+p9sA\ngmghQUOTD6/XDZoJoyUlrvfVI2AyQjgS6fK1nvYf6u6CH+++RT2FRSCkx55lai2M6M85elrmO509\nlYQYbIPVtqc9n9fL4rklfbbVgegOq69tqSQnw8oVCyfF/R4OR9/L6QkLoU8++YRnn32W1atXk5aW\nlqhhDBsTCxzsO1JPMOBDjyis1hTqmrwoFcEXCLJjX22vuyCqSBiv10daWjr+oE5Yj2/HRJvFiFKg\nVAS901a/VrMxdjHv7T7OxAIH281G/C296Np/X3f/zu7Otd1sxKOFoKXhan/P0dO9qP6MTYihMhRt\nezxuFxkZGXGF0K4DJ3H7wpw/szCu4/sjISHkdrt54okn+NOf/kR6enoihjCsKKU4e9oY6hoyqXGm\nUZzv4PhJN/VNATy+EO2zJ8thBcAf0FFKEY5E0CJBjEYDE8cWEQiGafaFafYEsJoN+AIRQnoEo0Hr\ncE/I2LLMljvGhsevU5ht54TTS53Th6ZpFGTb+e7iqR0KC3oqNrj0vPEoRYf7Lj11SugpLFq/v/09\nof6co6cKvv6MTYjRKKxHWPXWXjQNrrpo8oCfPyEhtGHDBhobG/nRj34UK0hYuXIlBQWJbdiXbJRS\nnGp00ewJYrLYuPi8qQC4vUG27qmJbr3QwmwyMG96LjX1bjRNIy3FzKI5RaD7afQppo7L5pJzivnr\nZxV8srsKctJ6rXLrbzVZb/dQDAaN5fMnsHx+3/dZegqLeL+/t3N807EJMdoopVi9fi+VJ5r51vwJ\nTBkX3zYQ/ZGQELrhhhu44YYbEvHWw0Ln8GntWh1Rik93V/PmR4fx+Nva7UwqcnDH1bN5/i/7cbqD\nWEwGUsyK2vpGfnjT/FiYRG/ya7hbwqu73m6t+nMhl/JnIUamdZu/5p1Py5hY6OB/XzlzUN5DOiYk\nkZ7CB6Ci1sXLmw5SVt1207wwJ5UVy0oomZDJp7urOHnKSyCo0+xyYbFYmDEpnw9KK/jLtrZKsbQU\nS4f37G7n1f4GiHQ5EGJkiUQUz2/Yz58/PExuZgq/umP+oDQvBQmhpNBb+Hj9Id76+Chb/t7W6dpi\nNvCdhZO59LxiTC31+lV1blKsBjxuP5rJiq4MfPplFYcqnFgsRlCKZm+IJncATdNIt5vRNK3bnVf7\n28tNuhwIMXL4A2GefHkX2/bUMDY3lUe/P5/sjPiqaU+HhFACKaVobGqmyR3oEj5KKUr3n+DPf/06\ntj0CwLySXK6/dHqXDabyx1j4+lgYg9kOgTARBf5ghOp6N1kOG5qm0eQOkpEWnQmlp1pYPHccR6ub\ncLW057GajZS125QO4pvlyAZqQowMR6uaeOKFzzh+0s3sKTk8dNt5pNstfX/jNyAhlACt4ePyBDCY\nrB3CB6Cm3sPLmw5wqKJtC96cMSmsWDadWVNyupxLD/n57sXTyMl08D/v7Ovw9YgCm9WE2RSdMaWn\nWtCA8QUOll0wgd++vCv2oKY/oOP1hTt8f1mNq1NIdZ3lyB5BQgxvkYji7b8d5U/v7CesR7jqosnc\nduXM2HVjMEkIDaHO4WPq9MBoIKizYWsZ75dWEGmpuzYZNb41fyLfmj8h1kI9ElFs21NNeW0jE/JS\n+N6lszAaDWga2G0m3L6OD7m5vUFSUyw0e4P4AmE0DcwVBp59cw8NTT4y0iyxkLF3Wvf1+kI0NgeI\nKIXbG+JwpbPLbqndFTEMdbGCFEcIcXoqTzTz+1e/4Ktjp8hIs/CjFWdz7hn5Q/b+EkJDoK/wUUrx\nxaE6Xt18CKcrEHv9zElZ3LishPwse4fjt+2pZvOOw/gCEXZ/baK6IcyZk7J4t2XZLM1ujlXAKQWN\nzUFcniAKDX8wGjZHjjfR0NTWVTt3THRMkwo7LqXZU0xYLUa8/mh4Vdd72Lyzos/Cg6EuVpDiCDGS\n+LxuPO7Bvbfa7Hbz+pZjvPW3CsJ6hAvPKuQH15xFZqel/sEmITSI2hccGM1dwwegrtHHK+8fZO+R\ntnsqY9KtXH/pNM4uyYv11ItEFFu/rKZ0fzU1tafQNTNhXUPTdLbuqWbPkWin7CZ3EEeqmUAwHOuK\noFR0Wc6gRfv2RVqezQqEdHIybKSnWhhf4Oh2KW1SYQYfG6piBRBWszGuwoOhLlaQ4ggxkgR9ToLe\n09s8zm7WWLJwXq/HHKpsYtXbJ6iuryM7w8YPrj2L+bMGftfUeEgIDYL2m8mZrbYu93wAQmGdTTsq\neG/bsVjHaoNB49Jzi/nOoknYLB1/Ndv2VPPOloPR2ZTZih5RGA1gbAmp1kCB6EzIZjFx0hndyrs1\njFp7xFrNRoKhCFazEU3TWDx3XI+zhkvPG8++ow18duAEVrOR9FRLr4UHrctiFTXNuDzBWBXeYBcr\nSHGEGEkysovJPM22PYaQs8fWOl5/iDXv7ucv246hFFxx4UT+13fOxG4bnPLreEgIDSBd12lwuvD4\nwpitNiy27n+8+442sPb9g9Q5fbHXpo4bw03fKmFsbtc+ekopyqvqCCkVCzRNU2gaZKRZUC21283e\nEIGQzuzCHGZMzGLd5kMEQjqRiCIjzUpORgrZGVZSbCZ8fh17iplJhb0XEhgMGvffOK/L/ZaetC6L\ntXYSaq3CG+xiBSmOEKJ32/fW8MzrX9LQ5Kc4P417r5/LmZMGdoO60yEhNAB0Xaf+VBPeQASL1Yal\nh08VTpefdZu/ZtfBk7HX0u1mvnfJNC6YVdDtdhZ6OIzZoDOnZBwHKr2xSrbMdCuzp+SQajczId/B\nvqP1fH7wZGy57MxJWaxYVjIgN+r70z2hdRlMAxwty3xDcW+mP2MUYjRxuvz88Y09fPplNSajxj8u\nL+G6S6dhNp3ect9AkxD6BsLhMA1OV1v4WLs/TtcjbP6sknf/VhbbNkADFp89lqsWTyG1h9AKBQNk\nplvIcIwhPzcLINr3TWksnFOEQYPyE81oGqTazeRk2Gj2hqh3+njz4yPMnZ7LpMKMIa0Uk2UxIZKD\nUootu47zxzf24PaFOGNiFvfdMJfi/ORqGi0hdBpCoRANzma8AR2rLaXH8AH4utLJy5sOUl3XtsHT\nhEIH/7i8hAmF3V+glVJEQn4KczKwWqMPikUbbU5k+fyJALy/o7xDNdiEAkdLR4QgeiSCxx8iENT5\nquwUMHSVYrIsJkTinXL5+cNru9mxrxabxcgPrpnN5RdOSsrHFiSE+iEYDNLQ6MYfjM58rL1UMro8\nQV7/8DDb99bEXrNbTXx3yRQWzRnb4x9DKBQkxQx5RTm97jbbufrLnmKmKCeNQKiJYEgjElGxWddQ\nVoqN5GUxeRZJJDulFAcrPfzPpr/i9oWYPSWH+2+cS0F2aqKH1iMJoTj4/QGcTW78IbBYrb3OfCIR\nxSdfVLF+yxG8gbbuA/NnFXLtJVNxpPbcAiMY8JEzxk56Wt9/MJ2XvSYVOphU6MDtiz4T1OQOYm15\nuLW7JTG5oPafPIskkpnHH+Kjz49zrMaV9LOf9iSEeuH3BzjV5CYY1jBbeg8fgPIaFy9tPEB5bXPs\ntaLcVG5aXsK04swevy8SiYAeoLggE5Mpvl9Jb8teZTUuvL4Q9hRT7J5QZ3JB7T95Fkkkq8oTzbxf\nWoEvEGZsjpVf3XlRUs9+2pMQ6obX5+dUo5tQRMNisWLuo3+fxx9i/ZYjfPL3qlhpstVi5MqFk1h6\nbjGapvHp7iqq6tyMzU1jweyi2KeTUDBAWoqRnPxcIhHV7bYKPc1auguNeIIkElF8/MVx6hp90Wd/\n7OZ+X1BH40xKii5EsolEFKX7a/n8wEkMmsaiOUXMnWAcNgEEEkIdeDxenC4vYWXAbLbRV+9YpRTb\n99by+odfd9jl9OwZeVy/dFqs/cWnu6vYsus4AIcro01JF84ZSyjgJS/bgT0lelxPs5OBnrVs3llB\ndZ0Hf0DHH4jeN+rvBXU0zqSk6EIkk0BIZ9P2cipONONItbD8ggnkZ9nRQs5ED61fJIQAV7ObxmYf\naGaMJhvxPDtcVefm5Y0HOXy8rdN1bmYKK5aVMHNydpdj26s40cQiPZvxRTkYDG1danta7hnoZaBj\ntS7SW+5NBUI6RTlp/b6gjsalqZFcdCGSyxirnyybt8evn2oO8uZHNdQ3BZlaZOeaRQXYLABe0rKG\n1wx9VIeQq9lNo8uHZrRgNMe3aZM/EOadT8v4685KIi2dCswmA99eMJHlF4zv9gGwsblpsRmQHg4y\npSCVooKcLsf1tNwz0MtAredrLZJYPK/nar2+zjFQYxJCtJkzcyrjxo3r9mtl1U38/s1tNLqDfPfi\nKdx25UyMw3gpfFSGULPbg9Pljc58umkq2h2lFLsOnmTd5q9pbG7rdD1rSjY3LiuJdaHuzoLZRdHW\nO9V1zJoynm9fOLXb43pa7hnoZaCBOJ8sTQkx9A6Wn+KX/70drz/ED649i+8snJToIX1joyqE3G4P\np1rDJ86ZD8BJp5e1mw6yv+XBT4BMh5UbLythzrTen+cB0PUQS+bmkXdZSa/H9rTcM9DLQANxPlma\nEmJolVU38ctnt+EL6vz4prO55JziRA9pQIyKEOow8+lH+ARDOhu3l7Nx+7FYJ2qDQWPZ+eO54sJJ\nWC19914KBfzkZNpJS7X3eawQQnTnxCkvv3x2Gx5/mJ/efA5Lzu5+qW44GtEh1KHgoB/hA7D3SD1r\n3z9EfWNbp+vp48ewYnkJRTldO113pus6BhWiuDATozE5GgUKIYafQEjn/z63A2dzgDuunjWiAghG\nYAgppWhyNdPkDvSr4KDVqSY/r24+xBeH6mKvOVItXLd0Guedmd/n0htEn/1xpJrJGtO1+EAIIfrj\nj69/ybEaF99eMJGrFk9J9HAG3IgJodZdTN3eYLdbaPclrEfYvLOCdz8tIxiKbjKnabDk7HFcdHlI\nrwAAD/JJREFUddEUUnrYG6jzGPSQn4JsBzZbH+0VhBCiDzv31/J+aQWTx2Zwx9WzEj2cQTHsQ0gp\nRYOziWZPdBfT/oYPwMFyJy9vOkBtQ1td/qQiBzctn8H4gvjanodDISzGCGP7aDwqhBDx8AfDPPPG\nHowGjZ/849lYzCNzWX/YhlDn8OlpF9PeNLkD/PnDw5Tuq429ZreZuGbJ1Jb9euILk1DAT6bDRoaj\n73tFQggRj3f+VsbJU16uXTKVCSP4ObxhF0IDET6RiGLL34+z/uMjsbY1ABeeVcg1S6aSbu+rYU/b\nWPSgj7H5mZjNidujXQgxsgTDEdZvOYLdZuL6y6YnejiDatiEkFKK+lON3yh8IFpr/9LGg1SeaOt0\nPTY3jZu+VcLUcWPiPk/rtttjx+bK8psQYkCV7q2h0R3g+kunkZYysj/gDpsQOl7rpHjCmNMOH48v\nxJtbDvO3L6o7dLq+6qLJLDlnHMZ2Pdz6EgoGyEg1kzkm/tASQoh4bd1bA2h8u2Un5ZFs2ISQxWo7\nrRlHRCm276nh9Q8P4/a1dbo+94x8rls6jTHp/atiCwV85GWnxzpfCyHEQDtU7mTurGnkZY38h9yH\nTQidjuMnm3lp40GOVjXFXsvPsrNieQlnTMzq17liG88VZsnDp0KIQXf+mfmJHsKQGJEh5AuEeedv\nR/nws+MdOl1fceEkLjt/PGZT/EtvAKFgkDSbgZz83MEYrhBCdHHW1NFxvRlRIaSU4vMDJ1m3+RBN\n7mDs9dlTcrhx2XRyeul03ZNgwEdeZiqp0vtNCDFEjEYDE4tGbll2eyMmhGobPKx9/xAHjrV1us5y\n2Lhx2XTmTOv/J4rY8ltBJibTiPkxCSGGgaIcOyZj/1ZshquEXF0fe+wxdu/ejaZpPPzww8yePfu0\nzxUM6fxl2zE2bS9Hj0SX3owGjeXzJ3D5gomn9ZRxKBggLcUoy29CiITIzuj/qs1wNeQhtHPnTsrL\ny1m7di1HjhzhkUceYe3atad1ri+/ruOVDw7R0OSPvTZjQiYrlpdQkJ16WucMBbzkZTuk+k0IkTD9\nrdodzoY8hLZt28Zll10GwJQpU3C5XHg8HlJT4w+N+kYfr35wiC8P18dey0iLdro+94z4Ol13pofD\nmAw644tyMPTjmSEhhBhoY9IkhAZNfX09s2a1dYPNzMykvr4+rhAKhSN8UFrBhq1lhMJtna4vOaeY\nf7hoMinW0/vnBAN+shxWMhzy8KkQIvEcqRJCQ0Yp1fdBwJHjjfy/t8s5caqt0/XksRnctLyE4vz4\nOl1399560EdR7his1vj6xQkhxGCzxbFr80gx5CGUl5dHfX3bMtrJkyfJze27AOC5t/dhtkcfME1N\nMXPtkqksOKsw7k7XnUnvNyFEsjL181nG4WzI/6ULFy5k48aNAOzbt4/8/Hzs9viewdGARXOK+Nc7\nF/Rrq4XOor3fjBTmZ0sACSGSjtUkM6FBM2/ePGbOnMmKFSswGo08+uijcX1fYU4at193LpOKMk77\nvWXnUyHEcGAyj56ZUELuCf3kJz/p9/fc/b2zKPoGAdRa/SY7nwohkp15lDyoCklQmBAvg+H0g0O2\nXhBCDCdp9pG9h1B7wyaETlco4KUgJ0OW34QQw0Ze5ujpVTliQyii6xgJycOnQgiRxEZkCLUtv+Uk\neihCCCF6MeJCSHq/CSHE8DFiQkiW34QQYvgZESEky29CCDE8DesQUkoRDvqk+k0IIYapYRtCsvWC\nEEIMf8MyhGTrBSGEGBmGVQi19n4rzJHeb0IIMRIMmxDSw2GMKii934QQYgQZNiGUbo9uvSCEEGLk\nGDZ39NPT+t7+WwghxPAybEJICCHEyCMhJIQQImEkhIQQQiSMhJAQQoiEkRASQgiRMBJCQgghEkZC\nSAghRMJICAkhhEgYCSEhhBAJIyEkhBAiYSSEhBBCJIyEkBBCiISREBJCCJEwEkJCCCESRkJICCFE\nwkgICSGESBgJISGEEAkjISSEECJhJISEEEIkjISQEEKIhJEQEkIIkTCmoX5DXdd55JFHqKioIBKJ\n8MADD3D22WcP9TCEEEIkgSEPofXr12O323nppZc4fPgwDz30EOvWrRvqYQghhEgCQx5CV199NVde\neSUAWVlZNDU1DfUQhBBCJIkhDyGj0YjRaARgzZo1sUASQggx+gxqCK1bt47XXnsNTdNQSqFpGvfd\ndx8LFy7kxRdfZP/+/TzzzDO9nkPXdQBqa2sHc6hCCDHoCgoKMJmG/LN/UhvUn8b111/P9ddf3+X1\ndevW8dFHH/GHP/whNivqSV1dHQA333zzoIxRCCGGyubNmxk3blyih5FUNKWUGso3rKys5Mc//jEv\nvvgiVqu1z+P9fj979+4lNze3z8ASQohk1tdMKBwOU1tbO6pmTEMeQv/5n//Jhg0bKCwsjC3RPffc\nc6PmBy6EEKLNkIeQEEII0Uo6JgghhEgYCSEhhBAJIyEkhBAiYZK6GqC0tJQf/ehHPPbYY1x88cUA\nHDhwgF/96lcYDAZKSkr45S9/mZCxPfbYY+zevRtN03j44YeZPXt2QsYBcOjQIe655x5uu+02br75\nZmpra/nZz36GUorc3FxWrlyJ2Wwe8nGtXLmSXbt2oes6d955J7Nnz074uPx+Pw8++CANDQ0Eg0Hu\nuusuZsyYkfBxtRcIBLjyyiu55557mD9/fsLHVlpayg9/+EOmTZuGUoqSkhK+//3vJ3xcAG+99Rar\nV6/GZDJx//33U1JSkhTjeu2111i/fn3sGcl9+/axYcOGpBhb0lFJqqKiQt11113q3nvvVR999FHs\n9VtuuUXt3btXKaXUT37yE/Xxxx8P+dhKS0vVP//zPyullDp8+LC68cYbh3wMrbxer7rlllvUL37x\nC/XCCy8opZR68MEH1caNG5VSSj355JPq5ZdfHvJxbd++Xd15551KKaWcTqdasmSJevDBB9V7772X\n0HG9++67atWqVUoppaqqqtTy5cuTYlztPfnkk+q6665Tb7zxRlL8Lnfs2KHuv//+Dq8lw7icTqda\nvny58nq9qq6uTv3iF79IinF1Vlpaqn79618n5diSQdIux+Xl5fH000+TlpYWey0UClFVVcXMmTMB\nWLp0KVu3bh3ysW3bto3LLrsMgClTpuByufB4PEM+DgCr1cqqVavIy8uLvVZaWsoll1wCwCWXXJKQ\nn9H555/P7373OwAcDgder5edO3eydOnShI7riiuu4PbbbwegurqawsLCpBhXq6NHj3L06FEuvvhi\nlFLs3Lkz4b9LANWpiDYZ/sa2bt3KwoULSUlJIScnh1//+tdJMa7Onn76ae6+++6kHFsySNoQslqt\naJrW4TWn00lGRkbsv7OysmIdFYZSfX09WVlZsf/OzMykvr5+yMcBYDAYsFgsHV7z+XyxaX52dnZC\nfkaapmGz2YDo0sSSJUuSYlytVqxYwQMPPMBDDz2UVON6/PHHefDBB2P/nSxjO3LkCHfffTc333wz\nW7duxe/3J3xcVVVV+Hw+7rrrLv7pn/6Jbdu2JcW42tuzZw+FhYVkZ2cnze8y2STFPaHeeswNB50/\nJSaTRI/tgw8+4M9//jOrV69m+fLlsdcTPa61a9dy4MAB/uVf/qXDWBI5rjfffJN58+YxduzYbr+e\nqLFNmDCBe++9l8svv5zKykpuvfVWwuFwwsellKKxsZGnn36aqqoqbr311qT5XbZat24d1157bZfX\nk2FsySIpQqinHnOdZWVl4XQ6Y/994sSJDstQQyUvL6/DzOfkyZPk5uYO+Th6kpqaSjAYxGKxJOxn\nBPDJJ5/w7LPPsnr1atLS0pJiXPv27SM7O5uCggJmzJhBJBJJinEBbNmyhePHj/Phhx9y4sQJzGYz\ndrs94WPLz8/n8ssvB6C4uJicnBz27t2b8HHl5OQwb948DAYDxcXFpKamYjKZEj6u9kpLS3n00UeB\n5Pn/Mtkk7XJce62fGkwmE5MnT2bXrl0AbNq0iYsuumjIx7Nw4UI2btwIRC9q+fn52O32IR9HTxYs\nWBAb38aNGxPyM3K73TzxxBM888wzpKenJ824du7cyXPPPQdEl1W9Xi8LFizgvffeS+i4INrSat26\ndbzyyitcd9113HPPPUkxtrfffjv2M6urq6OhoYFrr7024eNauHAhO3bsQCmF0+lMqt8lRD+ctgYj\nJMfffzJK2rY9W7ZsYdWqVZSVlZGVlUVubi6rV6/myJEjPProoyilmDNnDj//+c8TMr4nn3yS0tJS\njEYjjz76KCUlJQkZx759+/iP//gPqqurMZlM5Ofn85vf/IYHH3yQYDBIUVERjz322JA3f3311Vd5\n6qmnmDhxYmyJ9fHHH+eRRx5J6LgCgQAPP/wwtbW1BAIB7rvvPmbOnMkDDzyQ0HF19tRTTzFu3DgW\nLVqU8LF5PB5++tOf0tzcTDgc5t5772XGjBn8/Oc/T/jP7NVXX2XdunVomsbdd9/NrFmzEv7zarVv\n3z5+97vf8eyzzwLRAE+Gn1mySdoQEkIIMfINi+U4IYQQI5OEkBBCiISREBJCCJEwEkJCCCESRkJI\nCCFEwkgICSGESBgJITFsvfXWW71+fcuWLbhcrl6PueWWW9i2bVuPX6+qqoptI9JZa5uYp556Ktas\ntbULgxAiPhJCYljSdZ2nn36612PWrFlDY2PjN36vzo10Wz3//PNdvtbTsUKI7iVF7zgh+uuRRx6h\nurqa22+/nSuuuIK1a9d2aOn/9ttv89lnn/Gzn/2Mf//3f6esrIxVq1ZhtVrRdZ2VK1dSVFQU13sp\npXjsscfYs2cPAL/97W/Jy8tjxowZ7N+/v8uxQoj4yUxIDEv33Xcf2dnZ/Nu//Ru///3vef7553n+\n+efJz89nzZo13HTTTeTk5PCb3/yGKVOm0NzczG9/+1vWrFnD4sWLeeGFF+J+rxMnTnDVVVfx0ksv\nccEFF/CnP/0JkFmPEANBZkJiWNu7dy+zZs0iJSUFgAsuuIC1a9d2OS4rK4sHHngApRT19fXMnTs3\n7vdwOByxjRTnzZvXrwATQvROQkgMawaDocseMp1nKOFwmB//+MesX7+e4uJiXnzxRfbu3Rv3e7Q/\nX3fnF0KcPlmOE8OSwWAgHA4zc+ZM9u/fj9frBaJbPs+bNy92TCgUwuPxYDQaKSoqIhAIsHnzZoLB\nYNzv1dTUxFdffQXArl27mD59OiD3f4QYCDITEsNSXl4eOTk53H333dxxxx3cdtttWK1W8vPz+elP\nfwrAokWLuOuuu3j88ce58sor+d73vsfYsWP5/ve/zwMPPMDGjRvjmtUUFxfz5ptv8vjjjxMKhfiv\n//ovoPt7QjJLEqJ/ZCsHIYQQCSMzITHqHT9+nIceeqjbez8PP/wwM2bMSODohBjZZCYkhBAiYaQw\nQQghRMJICAkhhEgYCSEhhBAJIyEkhBAiYSSEhBBCJIyEkBBCiIT5/3KdVKAapncDAAAAAElFTkSu\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f568b27e6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tips = sns.load_dataset(\"tips\")\n",
    "sns.jointplot(x=\"total_bill\", y=\"tip\", data=tips, kind=\"reg\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-01</th>\n",
       "      <td>-0.096645</td>\n",
       "      <td>0.235097</td>\n",
       "      <td>0.763849</td>\n",
       "      <td>0.038892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02</th>\n",
       "      <td>0.051448</td>\n",
       "      <td>0.653894</td>\n",
       "      <td>0.961519</td>\n",
       "      <td>-1.222823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03</th>\n",
       "      <td>-0.659892</td>\n",
       "      <td>-0.208087</td>\n",
       "      <td>1.178758</td>\n",
       "      <td>0.048706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-04</th>\n",
       "      <td>1.044162</td>\n",
       "      <td>-1.517962</td>\n",
       "      <td>0.520855</td>\n",
       "      <td>-0.029094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-05</th>\n",
       "      <td>-0.105185</td>\n",
       "      <td>-1.318362</td>\n",
       "      <td>-2.239050</td>\n",
       "      <td>-0.224289</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2014-01-01 -0.096645  0.235097  0.763849  0.038892\n",
       "2014-01-02  0.051448  0.653894  0.961519 -1.222823\n",
       "2014-01-03 -0.659892 -0.208087  1.178758  0.048706\n",
       "2014-01-04  1.044162 -1.517962  0.520855 -0.029094\n",
       "2014-01-05 -0.105185 -1.318362 -2.239050 -0.224289"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data-Frames\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "dates = pd.date_range('20140101',periods=5)\n",
    "df = pd.DataFrame(np.random.randn(5,4),index=dates,columns=list('ABCD'))\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td> 5.000000</td>\n",
       "      <td> 5.000000</td>\n",
       "      <td> 5.000000</td>\n",
       "      <td> 5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-0.113749</td>\n",
       "      <td> 0.200367</td>\n",
       "      <td>-0.656165</td>\n",
       "      <td> 0.234242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td> 0.778772</td>\n",
       "      <td> 2.033275</td>\n",
       "      <td> 0.873535</td>\n",
       "      <td> 0.872042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.776214</td>\n",
       "      <td>-2.524699</td>\n",
       "      <td>-2.063224</td>\n",
       "      <td>-1.004033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.485327</td>\n",
       "      <td>-1.354499</td>\n",
       "      <td>-0.676476</td>\n",
       "      <td>-0.282626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.423582</td>\n",
       "      <td> 1.255635</td>\n",
       "      <td>-0.515791</td>\n",
       "      <td> 0.461763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-0.093735</td>\n",
       "      <td> 1.376570</td>\n",
       "      <td>-0.347435</td>\n",
       "      <td> 0.891361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td> 1.210115</td>\n",
       "      <td> 2.248828</td>\n",
       "      <td> 0.322101</td>\n",
       "      <td> 1.104748</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              A         B         C         D\n",
       "count  5.000000  5.000000  5.000000  5.000000\n",
       "mean  -0.113749  0.200367 -0.656165  0.234242\n",
       "std    0.778772  2.033275  0.873535  0.872042\n",
       "min   -0.776214 -2.524699 -2.063224 -1.004033\n",
       "25%   -0.485327 -1.354499 -0.676476 -0.282626\n",
       "50%   -0.423582  1.255635 -0.515791  0.461763\n",
       "75%   -0.093735  1.376570 -0.347435  0.891361\n",
       "max    1.210115  2.248828  0.322101  1.104748"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Lottery</th>\n",
       "      <th>Literacy</th>\n",
       "      <th>Wealth</th>\n",
       "      <th>Region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>41</td>\n",
       "      <td>37</td>\n",
       "      <td>73</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38</td>\n",
       "      <td>51</td>\n",
       "      <td>22</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>66</td>\n",
       "      <td>13</td>\n",
       "      <td>61</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>80</td>\n",
       "      <td>46</td>\n",
       "      <td>76</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>79</td>\n",
       "      <td>69</td>\n",
       "      <td>83</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Lottery  Literacy  Wealth Region\n",
       "0       41        37      73      E\n",
       "1       38        51      22      N\n",
       "2       66        13      61      C\n",
       "3       80        46      76      E\n",
       "4       79        69      83      E"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.formula.api as sm\n",
    "\n",
    "url = \"http://vincentarelbundock.github.com/Rdatasets/csv/HistData/Guerry.csv\"\n",
    "df = pd.read_csv(url)\n",
    "df = df[['Lottery', 'Literacy', 'Wealth', 'Region']].dropna()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                Lottery   R-squared:                       0.338\n",
      "Model:                            OLS   Adj. R-squared:                  0.287\n",
      "Method:                 Least Squares   F-statistic:                     6.636\n",
      "Date:                Mon, 22 Feb 2016   Prob (F-statistic):           1.07e-05\n",
      "Time:                        16:29:38   Log-Likelihood:                -375.30\n",
      "No. Observations:                  85   AIC:                             764.6\n",
      "Df Residuals:                      78   BIC:                             781.7\n",
      "Df Model:                           6                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===============================================================================\n",
      "                  coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
      "-------------------------------------------------------------------------------\n",
      "Intercept      38.6517      9.456      4.087      0.000        19.826    57.478\n",
      "Region[T.E]   -15.4278      9.727     -1.586      0.117       -34.793     3.938\n",
      "Region[T.N]   -10.0170      9.260     -1.082      0.283       -28.453     8.419\n",
      "Region[T.S]    -4.5483      7.279     -0.625      0.534       -19.039     9.943\n",
      "Region[T.W]   -10.0913      7.196     -1.402      0.165       -24.418     4.235\n",
      "Literacy       -0.1858      0.210     -0.886      0.378        -0.603     0.232\n",
      "Wealth          0.4515      0.103      4.390      0.000         0.247     0.656\n",
      "==============================================================================\n",
      "Omnibus:                        3.049   Durbin-Watson:                   1.785\n",
      "Prob(Omnibus):                  0.218   Jarque-Bera (JB):                2.694\n",
      "Skew:                          -0.340   Prob(JB):                        0.260\n",
      "Kurtosis:                       2.454   Cond. No.                         371.\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# Statistical models used in the Social Sciences like OLS and Logit Regressions\n",
    "\n",
    "model = sm.ols(formula='Lottery ~ Literacy + Wealth + Region', data=df)\n",
    "res = model.fit()\n",
    "print(res.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Text Analysis (on archived TED-Talks: https://www.ted.com/talks)\n",
    "\n",
    "import urllib\n",
    "import nltk\n",
    "import xml.etree.ElementTree as ET\n",
    "tedtalks =\"https://www.dropbox.com/s/9bl9b3m4yu726zq/ted_transcripts_en_jan2014.xml?dl=1\"\n",
    "with urllib.request.urlopen(tedtalks) as response, open('ted.xml', 'wb') as out_file:\n",
    "    data = response.read()\n",
    "    out_file.write(data)\n",
    "root = ET.parse('ted.xml').getroot()\n",
    "text = [file[1].text for file in root]\n",
    "string = \"\".join(text).lower()\n",
    "tokens = nltk.Text(nltk.word_tokenize(string))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Displaying 15 of 407 matches:\n",
      "ong the way , we became a city of innovation . the shopping cart was invented \n",
      "is courageous , and brimming with innovation and scientific discovery , as is \n",
      "as experienced two major waves of innovation . first , the industrial revoluti\n",
      "ry day , because this new wave of innovation is bringing about new tools and a\n",
      "mportant point . this new wave of innovation is fundamentally changing the way\n",
      "e concerned about the impact that innovation might have on jobs . unemployment\n",
      ", and there is always a fear that innovation will destroy jobs . and innovatio\n",
      "nnovation will destroy jobs . and innovation is disruptive . but let me stress\n",
      " employment has gone up , because innovation is fundamentally about growth . i\n",
      "re people who argue that today 's innovation is all about social media and sil\n",
      " ever seen . human creativity and innovation have always propelled us forward \n",
      "e rewarding . and the new wave of innovation which is beginning to sweep throu\n",
      " color , both hotbeds of cultural innovation : think hip-hop and jazz . both a\n",
      "ented . in fact , he says , it 's innovation that will keep the west ahead of \n",
      "e a source , or a global hub , of innovation , just like it 's become a global\n"
     ]
    }
   ],
   "source": [
    "tokens.concordance(\"innovation\", lines=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "years ago; little bit; 've got; united states; 're going; new york;\n",
      "n't know; looks like; every single; even though; years old; could n't;\n",
      "every day; last year; n't want; climate change; human beings; first\n",
      "time; high school; san francisco\n"
     ]
    }
   ],
   "source": [
    "tokens.collocations()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Rg8cARwZBCIGdI0ZgVi22ANg5fDhmHTjA4VQtCSHwwQcf4LvvvsO+ffvw1FNPSV0S6VhZ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\nOCIiIiKJYYEjIiIikpj/B528dhzfIXd4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe20819dac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Widgets in which program code can by changed via browser elements.\n",
    "# Widgets will only be displayed in an iPython Notebook and not in HTML versions of the notebook viewer.\n",
    "\n",
    "from IPython.html.widgets import interact\n",
    "import matplotlib.pyplot as plt\n",
    "import networkx as nx\n",
    "\n",
    "def random_lobster(n, m, k, p):\n",
    "    return nx.random_lobster(n, p, p / m)\n",
    "\n",
    "def powerlaw_cluster(n, m, k, p):\n",
    "    return nx.powerlaw_cluster_graph(n, m, p)\n",
    "\n",
    "def erdos_renyi(n, m, k, p):\n",
    "    return nx.erdos_renyi_graph(n, p)\n",
    "\n",
    "def newman_watts_strogatz(n, m, k, p):\n",
    "    return nx.newman_watts_strogatz_graph(n, k, p)\n",
    "\n",
    "def plot_random_graph(n, m, k, p, generator):\n",
    "    g = generator(n, m, k, p)\n",
    "    nx.draw(g)\n",
    "    plt.show()\n",
    "    \n",
    "interact(plot_random_graph, n=(2,30), m=(1,10), k=(1,10), p=(0.0, 1.0, 0.001),\n",
    "        generator={'lobster': random_lobster,\n",
    "                   'power law': powerlaw_cluster,\n",
    "                   'Newman-Watts-Strogatz': newman_watts_strogatz,\n",
    "                   u'ErdÅ‘s-RÃ©nyi': erdos_renyi,\n",
    "                   });"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href = \"https://github.com/ipython/ipython/wiki/A-gallery-of-interesting-IPython-Notebooks\"target=\"_blank\">and much more..</a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Learning Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By now a huge selection of introduction literature for Python can be found. Many sources are available online for free - you don't necessarily have to buy books or video courses. Depending on your prefered way of learning you can take a look at a small collection of introductory material below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### E-Docs:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Think Python - How to think like a Computer Scientist](http://www.greenteapress.com/thinkpython/). \n",
    "\n",
    "An introductory book for python which also teaches basic knowledge in Computer Science."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[The Python Tutorial](https://docs.python.org/3/tutorial/index.html)\n",
    "\n",
    "The official Python tutorial."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### MOOC's:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Intro to Computer Science - \n",
    "Build a Search Engine & a Social Network](https://www.udacity.com/course/cs101)\n",
    "\n",
    "A excellent, hands-on orientated video course for Python and Computer Science."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[An Introduction to Interactive Programming in Python](https://www.coursera.org/course/interactivepython)\n",
    "\n",
    "The Coursera pendant for *Intro to Computer Science*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Other Sources:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Code-Academy](http://www.codecademy.com/)\n",
    "\n",
    "Interactive learning platform for different programming languages."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[The Python Challenge](http://www.pythonchallenge.com/)\n",
    "\n",
    "Online puzzle which has to be solved by using increasingly difficult tasks with Python."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Kaggle - From Big Data to Big Analytics](http://www.kaggle.com/c/titanic-gettingStarted) \n",
    "\n",
    "Data Science portal which includes an introduction to machine learning. The site hosts (sometimes highly priced) online competitions for data analytic problems."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Finally, some links to popular Python packages for specific application areas:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "http://www.nltk.org/ - Natural Language Processing with Python.\n",
    "\n",
    "http://pandas.pydata.org/ - The Python alternative for statistic programs like R, Stata und SPSS.\n",
    "\n",
    "http://statsmodels.sourceforge.net/ - Another statistic packages which includes regression models.\n",
    "\n",
    "http://scikit-learn.org/stable/ - Machine Learning in Python. \n",
    "\n",
    "https://github.com/CamDavidsonPilon/lifelines - Survival Analysis in Python.\n",
    "\n",
    "https://networkx.github.io/ - (Social) Network Analysis in Python.\n",
    "\n",
    "http://www.crummy.com/software/BeautifulSoup/ - Web Scraping  in Python.\n",
    "\n",
    "http://www.pygame.org/news.html - Game Development in Python.\n",
    "\n",
    "http://kivy.org/#home - A Python package for programming multitouch-applications."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>\n",
    "<br>\n",
    "<br>\n",
    "<br>\n",
    "<br> \n",
    "                \n",
    "                                                                 contact: carsten.schwemmer@uni-bamberg.de"
   ]
  }
 ],
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