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 "metadata": {
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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "# Python para an\u00e1lisis de datos\n",
      "\n",
      "![](Python-logo-notext.png)\n",
      "\n",
      "## Juan Luis Cano Rodr\u00edguez < <juanlu001@gmail.com> >\n",
      "## Data Science Spain, Madrid 2014-07-15"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "## \u00cdndice\n",
      "\n",
      "1. Introducci\u00f3n\n",
      "2. Bibliotecas: numpy, matplotlib, pandas\n",
      "3. IPython y su filosof\u00eda\n",
      "4. Instalaci\u00f3n: Anaconda\n",
      "5. Python en Espa\u00f1a"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "## \u00bfQui\u00e9n soy yo?\n",
      "\n",
      "* _Casi_ ingeniero aeron\u00e1utico por la UPM\n",
      "* Fortran ~~77~~ 90, ~~Excel~~, MATLAB y **Python**\n",
      "* **Pybonacci**: blog sobre Python cient\u00edfico en espa\u00f1ol <http://pybonacci.wordpress.com/>\n",
      "* Presidente de la **Asociaci\u00f3n Python Espa\u00f1a** <http://www.es.python.org/>\n",
      "* Becario en Airbus"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "## \u00bfQu\u00e9 es Python?\n",
      "\n",
      "* Lenguaje de prop\u00f3sito general din\u00e1mico y f\u00e1cil de aprender*\n",
      "* Desarrollado por voluntarios, libre (estilo BSD) y mantenido por una fundaci\u00f3n (PSF)\n",
      "* Usos: lenguaje de scripting, servidores web, computaci\u00f3n cient\u00edfica...\n",
      "* A partir de ~2010, **an\u00e1lisis de datos**\n",
      "\n",
      "<span class=\"small\">* \"Python es ya el lenguaje de introducci\u00f3n m\u00e1s popular en las universidades norteamericanas\" http://www.genbetadev.com/formacion/python-es-ya-el-lenguaje-de-introduccion-mas-popular-en-las-universidades-norteamericanas</span>"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(\"Hello, world!\")"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Hello, world!\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def fib(N):\n",
      "    a, b = 0, 1\n",
      "    for ii in range(N):\n",
      "        a, b = b, a + b\n",
      "    return b\n",
      "\n",
      "for ii in range(8):\n",
      "    print(fib(ii))"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1\n",
        "1\n",
        "2\n",
        "3\n",
        "5\n",
        "8\n",
        "13\n",
        "21\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "### Lenguaje de prop\u00f3sito general\n",
      "\n",
      "* Sintaxis cuidadosamente dise\u00f1ada\n",
      "* Interfaz con multitud de bibliotecas diferentes\n",
      "* Mayor variedad de herramientas de desarrollo"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "## NumPy\n",
      "\n",
      "* **NumPy es el pilar fundamental** de todo el ecosistema num\u00e9rico en Python <http://www.numpy.org/>\n",
      "* Arrays N-dimensionales: almacenamiento en memoria eficiente\n",
      "* Funciones para operar eficientemente con ellos: operaciones **vectorizadas**\n",
      "* Adem\u00e1s: \u00e1lgebra lineal, FFTs, n\u00fameros aleatorios, funciones financieras"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "source": [
      "Motivaci\u00f3n:\n",
      "\n",
      "> \"Make [Python] equivalent to a basic scientific calculator.\""
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.array([\n",
      "    [1, 2, 3],\n",
      "    [4, 5, 6]\n",
      "])"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "array([[1, 2, 3],\n",
        "       [4, 5, 6]])"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.linspace(0, 10, 5)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "array([  0. ,   2.5,   5. ,   7.5,  10. ])"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "_.mean()"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "5.0"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "### Desventajas\n",
      "\n",
      "* Pensado para conjuntos de datos de tama\u00f1o _fijo_\n",
      "* No optimizado para datos heterog\u00e9neos (n\u00fameros, texto, fechas)\n",
      "* Manejo de datos textuales engorroso"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "source": [
      "Soluci\u00f3n: **pandas**"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "## pandas\n",
      "\n",
      "* Estructura de datos de alto nivel y optimizada para an\u00e1lisis de datos: **DataFrame** <http://pandas.pydata.org/>\n",
      "* Herramientas para leer y escribir datos en diversos formatos: CSV y texto, Excel, bases de datos SQL, HDF5\n",
      "* Manejo de series temporales\n",
      "* Tama\u00f1o flexible, _merge_ y _join_ de varios DataFrames...\n",
      "* Tutorial en espa\u00f1ol: <http://pybonacci.wordpress.com/2014/05/30/pandas-i/>\n",
      "\n",
      "![](pydata_cover.jpg)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pd.Series([1, 3, 5, np.nan, 6, 8])"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "0     1\n",
        "1     3\n",
        "2     5\n",
        "3   NaN\n",
        "4     6\n",
        "5     8\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dates = pd.date_range('20140701', periods=6)\n",
      "dates"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "<class 'pandas.tseries.index.DatetimeIndex'>\n",
        "[2014-07-01, ..., 2014-07-06]\n",
        "Length: 6, Freq: D, Timezone: None"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "datos = pd.DataFrame(np.random.randn(6,4), index=dates,\n",
      "                     columns=list('ABCD'))\n",
      "datos"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "outputs": [
      {
       "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>2014-07-01</th>\n",
        "      <td>-0.278300</td>\n",
        "      <td> 0.424450</td>\n",
        "      <td>-1.508621</td>\n",
        "      <td>-1.746290</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-07-02</th>\n",
        "      <td> 0.941110</td>\n",
        "      <td> 0.050437</td>\n",
        "      <td> 0.371443</td>\n",
        "      <td>-0.508548</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-07-03</th>\n",
        "      <td> 0.761886</td>\n",
        "      <td> 1.308292</td>\n",
        "      <td> 0.871853</td>\n",
        "      <td> 0.548142</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-07-04</th>\n",
        "      <td> 1.972977</td>\n",
        "      <td> 0.213835</td>\n",
        "      <td>-1.778572</td>\n",
        "      <td>-0.165594</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-07-05</th>\n",
        "      <td>-0.783794</td>\n",
        "      <td>-1.057578</td>\n",
        "      <td> 0.067174</td>\n",
        "      <td>-0.141001</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-07-06</th>\n",
        "      <td> 0.326622</td>\n",
        "      <td>-1.085902</td>\n",
        "      <td>-0.236260</td>\n",
        "      <td>-0.403086</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>6 rows \u00d7 4 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "                   A         B         C         D\n",
        "2014-07-01 -0.278300  0.424450 -1.508621 -1.746290\n",
        "2014-07-02  0.941110  0.050437  0.371443 -0.508548\n",
        "2014-07-03  0.761886  1.308292  0.871853  0.548142\n",
        "2014-07-04  1.972977  0.213835 -1.778572 -0.165594\n",
        "2014-07-05 -0.783794 -1.057578  0.067174 -0.141001\n",
        "2014-07-06  0.326622 -1.085902 -0.236260 -0.403086\n",
        "\n",
        "[6 rows x 4 columns]"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "datos.describe()"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "outputs": [
      {
       "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> 6.000000</td>\n",
        "      <td> 6.000000</td>\n",
        "      <td> 6.000000</td>\n",
        "      <td> 6.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
        "      <td> 0.490083</td>\n",
        "      <td>-0.024411</td>\n",
        "      <td>-0.368831</td>\n",
        "      <td>-0.402730</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
        "      <td> 0.971202</td>\n",
        "      <td> 0.920232</td>\n",
        "      <td> 1.056548</td>\n",
        "      <td> 0.754353</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td>-0.783794</td>\n",
        "      <td>-1.085902</td>\n",
        "      <td>-1.778572</td>\n",
        "      <td>-1.746290</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td>-0.127069</td>\n",
        "      <td>-0.780575</td>\n",
        "      <td>-1.190531</td>\n",
        "      <td>-0.482183</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td> 0.544254</td>\n",
        "      <td> 0.132136</td>\n",
        "      <td>-0.084543</td>\n",
        "      <td>-0.284340</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
        "      <td> 0.896304</td>\n",
        "      <td> 0.371796</td>\n",
        "      <td> 0.295376</td>\n",
        "      <td>-0.147149</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
        "      <td> 1.972977</td>\n",
        "      <td> 1.308292</td>\n",
        "      <td> 0.871853</td>\n",
        "      <td> 0.548142</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>8 rows \u00d7 4 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "              A         B         C         D\n",
        "count  6.000000  6.000000  6.000000  6.000000\n",
        "mean   0.490083 -0.024411 -0.368831 -0.402730\n",
        "std    0.971202  0.920232  1.056548  0.754353\n",
        "min   -0.783794 -1.085902 -1.778572 -1.746290\n",
        "25%   -0.127069 -0.780575 -1.190531 -0.482183\n",
        "50%    0.544254  0.132136 -0.084543 -0.284340\n",
        "75%    0.896304  0.371796  0.295376 -0.147149\n",
        "max    1.972977  1.308292  0.871853  0.548142\n",
        "\n",
        "[8 rows x 4 columns]"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "## matplotlib\n",
      "\n",
      "* El **est\u00e1ndar de visualizaci\u00f3n 2D** en Python <http://matplotlib.org/>\n",
      "* Inspirada en MATLAB\n",
      "* Aprendizaje costoso, pero extremadamente vers\u00e1til"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt\n",
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "datos[\"A\"].plot()"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x7fd6a471ed10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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WUXExE19PZmDP1lw7pLNbj22p4r/i933MW7qFyfcNIzBQ+vxCeFJCXMkc/8XF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\nlwNtAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fd6a4727e10>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "datos[\"A\"].plot()\n",
      "plt.xlabel(\"Fecha\")\n",
      "plt.ylabel(\"Columna A\")\n",
      "plt.legend([\"Datos\"])\n",
      "plt.title(\"Gr\u00e1fica 1\")"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "<matplotlib.text.Text at 0x7fd6a4189090>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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xbhJ9OrVg5CUdjA5HCLcIDgpkaL9Ilqzfw+1X9jI6HFENjtzHMQropLU+6+5g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\nEEIIIYQQQgghhBBCCOHH/h8tAgLaJUc8OwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fd6a41d7a10>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "## Otras bibliotecas\n",
      "\n",
      "* Computaci\u00f3n cient\u00edfica general: **SciPy** <http://www.scipy.org/>\n",
      "* ggplot2 en Python: **ggplot** <http://ggplot.yhathq.com/>\n",
      "* Aprendizaje autom\u00e1tico (_machine learning_): **scikit-learn** <http://scikit-learn.org>\n",
      "* Modelos y tests estad\u00edsticos: **StatsModels** <http://statsmodels.sourceforge.net/>\n",
      "* Manejo de vol\u00famenes grandes de datos: **PyTables** <http://www.pytables.org/>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "**\u00a1Y mucho m\u00e1s!**\n",
      "\n",
      "![](scipy_eco.png)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "## IPython y el notebook\n",
      "\n",
      "* IPython es un **int\u00e9rprete de Python mejorado** <http://ipython.org/>\n",
      "* Inspirado en el notebook de Mathematica\n",
      "* Ayuda en l\u00ednea, autocompletado, mejoras en la depuraci\u00f3n..."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "source": [
      "La joya de la corona: **el notebook**"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "* Documento interactivo dividido en celdas\n",
      "* Mezcla de c\u00f3digo con texto, HTML, v\u00eddeo, im\u00e1genes...\n",
      "* Gr\u00e1ficos incrustados, animaciones...\n",
      "* Formato f\u00e1cil de exportar y compartir: <http://nbviewer.ipython.org/>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "source": [
      "> **\u00bfPor qu\u00e9 usar solo Python?**"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X = np.array([0,1,2,3,4])\n",
      "Y = np.array([3,5,4,6,7])"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%load_ext rpy2.ipython"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%Rpush X Y\n",
      "%R lm(Y~X)$coef"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "<FloatVector - Python:0x7fd6a2fdf5f0 / R:0x2d86ec8>\n",
        "[3.200000, 0.900000]"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b = %R a=resid(lm(Y~X))\n",
      "%Rpull a\n",
      "print(a)\n",
      "%R -o a"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   1    2    3    4    5 \n",
        "-0.2  0.9 -1.0  0.1  0.2 \n",
        "\n"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%R -i X,Y -o XYcoef\n",
      "XYlm = lm(Y~X)\n",
      "XYcoef = coef(XYlm)\n",
      "print(summary(XYlm))\n",
      "par(mfrow=c(2,2))\n",
      "plot(XYlm)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "\n",
        "Call:\n",
        "lm(formula = Y ~ X)\n",
        "\n",
        "Residuals:\n",
        "   1    2    3    4    5 \n",
        "-0.2  0.9 -1.0  0.1  0.2 \n",
        "\n",
        "Coefficients:\n",
        "            Estimate Std. Error t value Pr(>|t|)  \n",
        "(Intercept)   3.2000     0.6164   5.191   0.0139 *\n",
        "X             0.9000     0.2517   3.576   0.0374 *\n",
        "---\n",
        "Signif. codes:  0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n",
        "\n",
        "Residual standard error: 0.7958 on 3 degrees of freedom\n",
        "Multiple R-squared:   0.81,\tAdjusted R-squared:  0.7467 \n",
        "F-statistic: 12.79 on 1 and 3 DF,  p-value: 0.03739\n",
        "\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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DRddkZ2d3nDkJHz58mJGRMWXKFOeO15NGETFdHNHR0Q0NDTY2Nn5+fpGRkRJ0\nH8tweMaGhoZr167xeDzPCxfUIyLk4xhQXR2srSEtDf49F2FSfT2sWgUnTzIdB2M0NDROnDixZs2a\nr776SvqDD9RcUVHRlStX/Pz8rK2tFf7BVJqJuUgYExMzY8aMI0eOJCUl7dmzh/6YhOrr68eMGXP7\n9u3GGzdunT6d2bMng8G0T1AQsGRw+i1bYPp0MDVlOg4mKSkpbdy4sWfPnn/++SfTsSgOHo8XGxtb\nWlpqYmIyffp0+ZrrT16ISdDa2tpGRkaVlZX29vadGJ3GKTEx0cvLK2LFikm3b5ufObNt2zaCIB4+\nfCgHM/IOHQp37wKfz3AYjx7BH39AaCjDYTBK2OkcGBhYU1PDbDCK4fz589nZ2RoaGtOnTycHZkAU\nEZOg+Xz+9evXu3XrVllZWVFRQX9MQiUlJRYWFlBeDl98YTZwYElJSVlZ2ZUrV8hHQtlu5Ei4fJnJ\nAAgCwsIgMhI66iDIHA6ntrYWx4OWlZSUlJs3bwKAl5cX+fiJSoecYYdOYhL0vHnzJk6cOHXq1FWr\nVoWFhdEfk9CIESMOHjz4WkWlYcyYjRs3jhs3ztDQcMmSJQyG1A4zZwKz8yTt2weurtCB+wQJglBX\nV2/y7CzTQcmfrKysK1euAMDAgQNdXFwA8zKNxHQbhYaGhoaGAsCwYcNoj+cdffr0Wblypb+/P5/P\nnzBhAoXTslDB0hIqKqC0FBg5B3z1CuLjO9pT3UiGCgsLb9y44evr261bt549ewKAlpYW00F1OGKO\noF+8eOHu7q6npxcbG3uNwZlYAQDA3d394sWLSUlJixcvlr/zUz8/+OknZqpesAAiI6FjX7fBKa8k\nwOVyz58/X1NTo6OjM3bsWA6Ho6Ghwey1qI5MTIImB5d58+aNu7t7ZGQk/TG9l9ycqPr4MDNR4enT\nYGUFijuTZhuRfRo45VVbEARx+fLlly9fqqqqDh8+XEtLS0NDAw+ZGScmQePgMjLTpQt07QqPH9Na\naXk5REXBxo20VspiOOVV6+7fv//w4UMA+OijjywsLJSVlXV1dZkOCr0lJkHj4DKyFBgIR4/SWmN4\nOKxZw9LxTpmAU16JlZOTQ94VbmRk1LdvXw6HY2Fh0WFnlmIt8Q+qfPPNN+TgMtHR0fTHpFA8PODa\nNWh1UglZSkoCLhfGjKGpOnnQqVOn9evXG2dwmLAAACAASURBVBoabtq0ad++fRKUsGbNGgBIT093\ndnbW0tJydXV98uSJrMOkyevXr8mZoFVVVT/66CMA6Nq1Kz5jwlpiEnSvXr2SkpJqamoyMjKcOnw/\nprSUlODjj4Gea608HqxdCzt30lGX/Bg5cuTq1avfvHkj8ZRXcXFxABAcHOzt7Z2fn79kyZJ58+bJ\nOkxq8Xi8W7du8fl8giCsrKwKCgqePHlSWlpKvio3z391PO8k6N27d+vo6Dg5OSUlJfXu3btTp06S\nTQ6mSEccMkDbY98bNsCcOdCBB7cUa+PGjZMnT1ZTU5PyLo6srKz58+fr6Oj4+PiQ0+ixn0AguHfv\nXnl5OZ/PNzQ0VFZWNjAwuHnz5tSpU2/durV48eItW7YAgDw9/9XBvJOgN2/e/Oeff3711VceHh47\nduzg8/nFxcUSFKoARxyy9OGH8M8/QPVP+u+/4f59wFF3m5H+QZWioqLw8HArKytyWPPTp0937txZ\n1mHK2PPnzwsKChobGwmC0NLS6tKlS58+fci/T7t377506dLq1asTExMvX75cXV0tT89/dTDvJGhD\nQ8MPP/xw7NixADB27FgprxjI4xEHVai+366xEcLCgNGRrdiPIIioqCgJdszIyPD09Jw/fz55e0NK\nSgrVA/9LrLS09NmzZwBQXl6upaWloqIycOBAVZEJLgiCUFZWFs663aNHDzxwZrN3UjCZkaW/mV8e\njzio5e8PsppTRqzdu2HsWLC0pLAKuRUREaGkpMThcJSUlM6cOSNBCZaWliNHjpwzZ46vry8AREdH\nW7Lso66trSXz8qtXr8jk6+Dg0EXc2LwcDkdHR+ePP/4AgOfPnz9+/NjKyormaFHbvXP19v79+8Ls\nLFyQ4KwwIyMjOzvbysqK/UccNNHXB01NyMmBHj1kX/jz53D2LPz2m+xLVgjXrl3LycnZs2fPlClT\n4mQxXWRdXZ2trW1mZqb0RUmpoaHh2bNnffr0efXqVUNDAwDY2dm9d6/9+/cvWbIkIiJCW1s7NjZW\neKKM9yCykJgZVaRnaWlJHnSQ/8R79QAAAgPh+HFYvVr2JS9ZArt3Az6M2wKBQGBpacnn8x0cHGTy\nJKGamhrj2Tk/P9/Q0LCkpKSkpKRPnz7k2HJtZGRkdPz4cepiQzJEx/2P7DniYNLYsbB1K0REyHjw\nz+PHwdoabGxkWaZi4XK5ycnJpaWlmZmZ9fX1lNaVlpbWZMqrx48f28tuYp2ysjIVFRUlJaVHjx65\nurqam5ubm5vLqnDEQnQk6FaOOKhu0CyiogKOjvDHH+DiIrMyi4vh++8hKUlmBSqihQsXJiYm+vv7\nu7q6Ll++XIIS8vPzIyIikpKSyDk2R40atXnzZrF3Ddvb25MjcwqFhoZKPydhXV1dbW1tly5dbt68\n6ezsbGRk5OHhIWWZSC4w/AQRRQ2apQIDISZGlgl6xQrYuBH+vSKPxJo9eza5IHwuo72CgoImTJiw\nefNmfX19cpb6kJCQX375RXYxitfY2PjmzRsDA4M///yze/fuOjo6EyZMoLpSxCqUPHqfn58fHBzc\nvXt3NTU1c3PzGTNmFBQUUFGRnHF0hIcPgceTTWmXLoGKCnz8sWxKU0SyGm60vLx80aJFpqamqqqq\nJiYmc+bM4XK5Mo9WFHlbampqKnlvxscff9yDisvLiPUoSdBBQUH29va3b9+uqqpKS0tzdnaWs7H2\nqTNuHCQmyqCc6mpYvx62b5dBUYpLVsONGhgY7N69u7CwkHx0a//+/RoaGjKPFgCqqqoIgnj+/Pmt\nW7cAYPDgwYMGDaKiIiQvKEnQ9B9xyI2AAJDJBfSvvoIlS0BHRwZFKTrphxuNjY29e/euk5OTlpaW\nnZ1dSkpKTEyMDCNsaGhobGwsLS29cOFCXV2dlZXV6NGjZVg+kl+UJGjajjjkT9eu0NAAUj679ccf\n8OIF+PrKKCYFJ/1wo127dj106NA///xTX19fUFBw5MiRrl27Sh+YQCDg8/kNDQ2nT59+/fq1oaHh\n1KlT1dXVpS8ZKQxKEjTVRxzybdo0OHFC8t35fAgPh927ZReQgtu3b98333wjzXCjMkdeBj9z5syz\nZ8+UlZWnTJliZGTEdFCIjSi5i4M84iCXORzOkSNHqKhFXk2aBKNGwbJlEu6+bRv4+YEsjuA6iIED\nB969e1dWpT2Wen6clJQUgiCGDRvm4+Mjk5CQAsMJFGinoQF9+8K9e5Lsm5kJSUkwd66sY1Jky5cv\n79Spk6wmje3Xr59kOz548CAhIQEAXF1dhw0bJmUYqIOgPEFLf8ShgIKCQIKzCoKAJUtgzx7AeYna\n49q1a0VFRdIMNyolY2PjxsbGAQMGeHt70187kmuU/9QlPuJQZMOGwR9/AJ/fvr0OHABnZ+jbl5qY\nFJaHh8eDBw/47f20Zae4uLgTjpSCJILHYkzgcMDTEy5dascueXkQGwurVlEWk8LaunXriBEjVFVV\nZdLFgRCdMEEzZMaM9s32HRYGUVEgMvI6aiPpZ1RBiCmYoBnSowe8fg3l5W3aOCEBDA1h8GCKY1Jw\nEs+oghBTMEEz57PP4Kef3r/ZmzeweTNs3kx9QIpJ+hlVEGIKJmjmTJkCP//8/s0iIuCrr6CDzxkm\nBXJGlRUrVqSmpg4ZMoTpcBBqB0zQzOnSBYyNofV5DFJS4PVr+OQTumJSQKIzqqSlpTEdDkLtgAma\nUYGBcOxYi6/W1sKKFbBjB40BKSA6Z1RBSLYwQTPK0xOuXoWWJij49lsIDQUTE3pjUjSiM6qMGzeO\n6XAQageGZ1Tp6Dp1gmHD4Pp1cHdv+tLDh3DnDnz1FRNhKZSxY8eSE/eVlJTk5uZSWtfjx493vHvG\nk5KSgs9qIYlRkqDbPocbghkzIDKyaYJubITPP4eYGBnPMNvB7NixY+nSpaJrBg8e/Oeff1JXY69e\nvZrMsVlZWYlj7SKJ4YwqTLO2hufPobr6nZXffw+enoCzHElnyZIlBEFMnjxZ+JQKpdkZAFRUVKze\npa2tjY8vIonhjCosMHkynD373z//+QdOnYKwMOYCUiinTp1iOgSEJIQzqrCAv/87Q/gvXAg7d4Iy\nXh6Q1u+//96jR49Xr16lpqb27t27Z8+e165dYzoohNoBZ1RhASMjUFeHly8BAH76Cfr0AVtbpmNS\nBIsXL46Pjzc3Nw8PD9+0aVNsbOznn3/OdFAItQPlM6qgtsiws8v09r7Rr9/mBw+0/vqL6XAUhEAg\nGDx4cGVl5atXr3x8fDgcDg6WhOQLHfdB19XV4Z1Grbhx48baO3fGczjra2vXqqg8pvhWsA4lNTX1\n+++/HzVqVENDw9mzZ3VwHnQkV+jo6FRTU8ts4YHmFy9e/PTugEHp6emWlpY0RMWsW7duHT16tLq6\n2sLCoqKi4pstW9R271arqhqzZcuZM2ciIiKYDlARbNu2beLEiZ07d7506dLJkyfXr19/8uRJpoNC\nqB0YvhLVpUsXBwcH0TWvXr2SyZz2LFdRUbFp06b8/PxRo0b5+/tzuVxYtw7U1GquX9fU1GQ6OgUx\nevTovLw8ctnS0nLatGnMxoNQezGcoPX19T08PETX5OXlCVp69FmBeHl5XbhwYfz48fv373d1dZ05\nc+bq1avr6uq2bNly7tw5pqNDCLEC3svFjOvXr48ePfrvv//29PQsLi6Oj4+Pi4tTVVVNTEw0NjZm\nOjqEECtQkqAtLCyEp5ZCeAFdVGVl5fTp0zU0NObNmwcAlpaWK1euZDoohBC7UHIXx9OnT83Nzfl8\nPs4F15Lx48f/9NNPhw4d+vrrr5mOBb3HmjVrACA9Pd3Z2VlLS8vV1fXJkydMB4U6BEoStIaGxubN\nm3EIAqQY4uLiACA4ONjb2zs/P3/JkiXkeY8EGhoarly5cv78+devXwPArVu35s2bFxAQsArna0fi\nUHUfdEBAQKdOnQAA0zRSDFlZWfPnz9fR0fHx8amqqpKghPr6+tGjR//+++/Z2dmjR4/OzMwkb+ZZ\nuXLl4cOHZR4wUgA4YD9C71FUVBQeHm5lZRUfHw8Ap0+f7izRFJGJiYmffPLJxo0bly1bdujQoW3b\ntnl5ed28ebN///7Y04XEovwujsePH1NdBUKUysjIyM7OtrKy0tXVBYCUlJTY2FgJyikpKbGwsCCX\nu3XrVlJSInozT2hoqCyDRgqB8gSND3kjeWdpaWlpaTly5Ejyn9HR0ZKVM2LEiEWLFo0aNapLly4b\nN24cN25ck5t5EGoC74NGqH3q6upsbW1bGr2gFX369Fm5cqW/vz+fz58wYUJISAiHwxk/fjwVQSLF\ngAkaofZpZWyZysrKS5cuia7JyckxNTUV/tPd3d29+fyTCLWAjQk6NTVVW1u7vXs9f/48NzdXXV1d\n4npfv36tr68v8e6lpaWGhoYS715TU9OpUye5jt/a2lqCmSczMjLs7e0lrpdVysvLnz9/LrpGXV29\npqbm559/blc5Dx8+fP36tTKVkzZI+XW/l/Tt+b2ofgu1tbWWlpZWVlbt2ku27Zl1I+S+evXq119/\nlWDHxMTEysrK7t27S1Yvj8e7e/euq6urZLsDwJUrV0aNGiXx7k+ePNHQ0JAm/tTU1GHDhkkcgJTx\nP336tE+fPhJ8gMrKyt7e3tL8aWEzydrzsWPHNDU1jYyMqAiJJOXX/V5Stue2oPotvHz5skuXLu3t\ng5JxeyYUxa5du06dOiXx7gUFBX5+ftIE4ObmJs3uu3fv/vnnnyXevbCwcMqUKdIEIGX833333cmT\nJ6UpAQl9+eWXN2/epLQKKb/u95KyPbcF1W/h9OnTO3fupLSK98L7oBF6DwsLC04zTAeFOgRM0Ai9\nB44tg5iCCRqh98CxZRBT2HgXB0JsExAQQC7gzLOITorT2nJzczU0NCQe7b6hoeHBgwcDBw6UOIA7\nd+44OTlJvDt5j6CJiYlkuzc0NKSnp0tzf4+U8b98+VJNTU3i+OUFPQn6yZMnZmZmXbp0oa4KKb/u\n95KyPbcF1W+huLiYx+MxO0Wq4iRohGiQmZmJoxcg2mCCRgghlsKLhAghxFKYoBFCiKUwQSOEEEth\ngkYIIZbCBI0QQiyFCRohhFhKLhO0QCCYOXOmo6Njv379moy0m5ub27lz5169evXq1WvNmjWUhvH3\n339raWk1WUlPAK3UQkMAJSUlvr6+Dg4OQ4cOffXqFc21KyoejyecsVBUfn7+6NGjHR0dPT098/Pz\nJSu8lUKk/8paKVwmwVMdvxClX4GEGBhBT2pXr1797LPPCIJIT083NDRs8tIXX3xBQwyFhYXe3t7N\nP0B6AmilFhoCCAoK+uGHHwiCiIyMnDFjBs21K6QjR46QqaH5S0FBQeSgl7t27WryabddK4VI/5W1\nUrhMgm+9HFk1Oaq/AsnIZYIuLCzMzc3l8Xi//PLLgAEDRF/64YcfbGxstLW1HR0d09PTKQqgtrbW\n29s7Nze3+ddJTwCt1EJDAKampmFhYXp6etbW1k3G/KXn7SuehoYGPp8vNjtYWFgUFBQQBFFQUNCt\nWzfJym+lEOm/slYKl0nwVMdPovorkIxcdnGYmJh0797dx8dn7Nix27dvF31JX19/wYIF+fn5kydP\nnjNnDkUBLFmyZPHixWJni6AngFZqoSGAsrIyAwOD3NxcPz+/HTt20Fy7QurUqVNLE1wVFRUZGBgA\ngL6+flFRkWTlt1KI9F9ZK4XLJHiq4ydR/RVIiM6/BrLS0NDQ2NjY0NBw6tQpKysrsdu8efNGU1OT\nogDIb0uIx+PRHEBbaqEuADMzs6qqKoIgCgoK6K9dkTx+/Fj0Zyj292hmZlZUVEQQRGFhYdeuXSUr\nvy2FSPyVtVK4NMG3txyZNDmZfwVSkssj6P3794eHh3fq1Klnz55cLpdcWVdXBwBLly49ffo0AFy5\nckWaoelaV1paKvp1kjNj0hmA2FpoC8DDw+PKlSsAkJSURH/tiqRfv37ChtQc+ZF6eHicO3cOAM6d\nO9feKfiE5YstRFZfWSuFSxN8G6ugtMnJ9l1Igs6/BrJSUVHx6aefDhgwoF+/fufPnydX9u3blyCI\nFy9euLu7Ozg4uLm5PX78mOpIRD9AOgMQWwttAeTl5Y0ePXrQoEFubm5PnjyhuXYF1uT3SH6keXl5\nY8aMcXNz8/Lyys/Pl6xksYXI6itrpXCZBE91/KKo+wokg6PZIYQQS8llFwdCCHUEmKARQoilMEEj\nhBBLYYJGCCGWwgSNEEIshQkaIYRYChM0QgixFCZohBBiKUzQCCHEUpigEUKIpTBBI4QQS2GCRggh\nlsIEjRBCLIUJGiGEWAoTNEIIsRQmaElwOBw7EeQaABBO0Ndkpr6WCpGg3vbugjqIixcvkq1R2Div\nXLki8wYjbNhtLFl0M4IgYmNjHRwcbG1tHRwcYmNjpRyMvkkwCvnrwAH7JcHhNP3camtr1dXVheub\nb9CWQiSoF6EmRBuJzBuMsECywbcrmIMHDx44cODs2bPGxsbFxcWTJk0KCQkJDg6WVTAK+evAI2jZ\n0NDQ8PHxAYC8vDzhQnZ2tru7u5OT06hRo168eAEA9+7dGzx48JAhQxYsWNCkBE9Pz/v37wPAiRMn\nvvjiix9//NHGxmbw4MEuLi4RERGiWwqPFMiF5rXExMQ4OjoOHjx4wIABbTmWRwps6dKlgwcPDgsL\nA3FN5d69e0OHDnVxcXF1db137x65C4fDWb16taenZ5ONhQ0bADQ0NDIyMoYPHz5o0CBvb+/q6upW\nWiwpKipq165dxsbGAGBsbLxr166oqChhjaILYovicDhff/21o6PjihUrmgdDbqOAvwU659dSGKIf\nYEREBPHvVGbw7vTMI0aMuHbtGkEQd+7ccXNzIwjCwcHh7NmzBEEcO3asyYd/+PBhsigvL6/MzMwV\nK1bcuHGDIIicnBzy0KDttejo6Fy/fp0giOLi4q1bt1L6USC2EW1XAJCcnFxUVEQ2oeZNxdHRMS4u\njiCICxcuODk5Cfc6deqUu7t7k42Jd9vekCFDTp48SRDE7t27k5OTW2mxJE1NzZqaGuE/q6qqtLS0\nmhdLEERLRV2/fr2kpERJSUnsXgCgeL8FBTwpoEHzkylyTZMuDi0tLeGk47q6uuXl5ZqamqWlpZqa\nmiUlJcbGxqKFVFVVDR8+/NKlS35+fr/99tvFixePHz9uaGiooqISGRkpWiz5/4aGBhUVFbG13Lhx\n48SJE3l5eTo6OoGBgZ6enjR9LogFmnRx8Pl8ZWXllhqkhobGP//8Y2hoWFFRYWZmRr7K4XDq6ur0\n9PSabCxaOIfDIfc1MDAgt2mlxZIb9O/f/+jRo+TE23w+/969e3Pnzr17965wM2GTbqmo+vp6FRWV\n5h2Jwt+Fpqamov0WaP1zoCiaf27w799wgUAgXLC2tv7rr78IgsjJyTl48CBBEE5OTi0dQRMEMXXq\n1FmzZh0+fJggCAMDA3KK4tTUVNFiCYLgcDilpaXXrl0j/9m8lvnz51dUVBAEcffuXV1dXQo/CMQ+\n8O4RtOhC86Zib28fHx9PEMSJEyccHR1b35h4t4Xb29ufOXOGIIjIyMiTJ0+20mJJBw4ccHV1LSkp\nEQgEkyZNGjhw4IULF8iXmjTp1osSXRB9CQAU77eACVoSLSXoQYMG5eTkCBdu377t4uIydOjQMWPG\nJCUlEQTx999/Ozk5DRkyhOxHa1LIL7/80qVLF/I0cNOmTUOHDnV3dw8LCxs6dOj27duFtSxbtsze\n3n7x4sXkP5vXsnfv3p49ezo4ONjZ2e3fv5/iDwOxSysJunlTSUtLc3FxcXZ2Hjp06L1791rfmBBp\n4QBw9+5dZ2dnBwcHLy+vqqqqVlosSSAQHDhwwN7e3traesCAARMmTFixYgX5UpMm3XpRwgXRYMj/\nK95vAbs4EEIMIAji6dOnffr0YToQVsMEjRBCLIW32SGEEEthgkYIIZbCBI0QQiyFCRohhFgKEzRC\nCLEUJmiEEGIpTNAIIcRSmKARQoilMEEjhBBLYYJGCCGWwgSNEEIshQkaIYRYChM0QgixFCZohBBi\nKUzQCCHEUpigEUKIpTBBI4QQS2GCRgghlsIEjRBCLIUJGiGEWAoTNEIIsRQmaIQQYilM0AghxFKY\noBFCiKUwQSOEEEthgkYIIZbCBI0QQiyFCRohhFgKEzRCCLEUJmiEEGIpTNAIIcRSmKARQoilMEEj\nhBBLYYJGCCGWwgSNEEIshQkaIYRYChM0QgixFCZohBBiKUzQCCHEUpigEUKIpTBBI4QQS2GCRggh\nlsIEjRBCLIUJGiGEWAoTNEIIsRQmaIQQYilM0AghxFKYoBFCiKUwQSOEEEthghbj5cuXEydONDQ0\nNDExmTJlSn5+frt253A4Mt+yvagrGdGMw+Eo/0tNTc3R0fGvv/5qbwltXClBOdLAVvpemKDFmDFj\nxpAhQ54+ffrixQtLS8vQ0FCmI2orDw8PciE8PJzZSJAMNfyroqLCz89v1qxZ7dodG4P84hAEwXQM\nrKOjo5OVlWViYgIA1dXV8+fPP3LkSNt353Da+qm2fUuZV43kRZPvtLq6umvXrpWVlbItlqJdaC5Q\n8eARtBju7u6ffvrp2bNnq6urtbW1yexcWlrq4+NjZGTUt2/f06dPk1ueP3/ezs5OV1fXzMxs27Zt\nooUUFhb6+voaGxtbWVkFBgYWFBS0perXr18HBgaamZl17do1KCjo9evXba/a29sbAOzs7ODfk0ex\npZGvxsfH29raGhgY7NixQxafGaIDl8v98ccfR4wYAS03sIMHD5qZmRkaGu7atYtcI2wM06ZNMzAw\n6NWrl/AleLefQbjcSsMWWwVpwoQJMTEx5PLnn3/+5ZdftrKxWGLfVPNiW3rvHA7n6NGjpqamYt9C\nZWXlrFmzTE1N+/bte/jwYfLNSvY7pQ+Bmqmtrd22bdugQYPU1dWnTp1aUFBAEMT06dNXrFjB5/Nv\n3Lihp6dXW1tLEISNjU1kZGRDQ0NaWpqqqiq5O/mpjhs3Lj4+nsvllpeXr1u3buzYsc0rav75BwQE\nzJo1i8vlcrnckJCQGTNmSFC1cEFsaeSrW7ZsEQgESUlJGhoaMv74kEw1+cEqKSndunWLaLmB6erq\npqenP3nyxMPDQ1gCQRDTp0/39/evqKiorq4OCgpq0lSaLLfeuppXQfrpp5/INXw+38TEJDs7u5WN\nCXHtX+ybal5sS+8dABYsWJCRkSH2LcyePXvmzJk1NTU8Hm/27Nlt/50yCBO0GHw+XyAQEATx6tWr\ngIAABwcHgiAMDAxKS0vJDcrKyhoaGgiCaGxs/PPPPw8cOBAYGNikxWtpaYn+royMjAiC6Nu3r+jf\nxeYN1MDAoKioiFwuLCw0NjaWoGrhgtjSyFcrKytbigGxiugXxOPxoqKi7O3tiRYaGEEQEyZMGDdu\n3MmTJ/l8vmgJ+vr65KEGQRDkcWLz8oXLrbeu5lWQuFyunp5eUVHRr7/+OnLkyNY3JsS1PbFvqnmx\nLb13ACguLm7pLRgbG4v+HFr5nbIH/jjF0NbWzszMJJeLi4u1tbUJgtDV1X39+jW5MiMjo6amhiCI\nyZMnT5069fz583l5eU3asYWFxbNnz8g11dXVubm5zSsSm6CFLayoqMjAwECCqgmRBN28NKKF3yRi\npyZfEI/H09LSIlpuYAKB4OLFi35+fk2OoA0NDYUJWpieRMsn+7XJ5dZbV/MqhIKDg/fu3evv7x8X\nF/fejZu3vZbeVJNiW9pMtMDmb0H051BSUtL23ymD8McpxmeffTZlypSioqL8/PzVq1dPmDCBIAgf\nH59Vq1Y1NDTcunVLW1u7oqKCIIguXbpkZGQIBIIDBw4AAHmMQH7xn3/+eUhISE1NTVFR0ciRI+fO\nndu8ouYNdPr06aGhoTwej+yUCAgIaG/V9fX1wpLFlkZggpYrzb8gJSUlouUGZmlp+eTJk0ePHunp\n6YmWEBgYOG3atMrKyurq6uDgYGGxampqv/32m0Ag2LJli3BlKw1bbBVCSUlJTk5O3bt3JzviWt+4\n+Vtr6U01KbalzUQLbP4WgoKCQkJCuFwuj8ebN29e23+nDMIfpxg1NTUzZ840MzPT1dWdOHFiXl4e\nQRAFBQXjxo3T19e3srI6deoUueV3331naGhobW29devW0aNHBwYGEv+2ksrKyuDgYGNjY0NDw1mz\nZlVXVzevCAA6iSAIorS0dPr06SYmJqampoGBgWVlZe2q+pNPPunWrZswBrGlEZig5UrzL8jMzIxo\nuYHt2bNHX1/f2Ng4JiZGtISysrKpU6eSrejQoUPCYiMjI/X09Pr37y+6spWGLbYKoYaGBnNz86VL\nlwrXtLJx8/bf0ptqUmxLm4l+Vs3fwuvXr6dPn25gYPDRRx8dPXpUR0enlaJYAm9zQQh1COfPn7e1\ntbW0tASAJ0+ejB8/Pisri+mg3gNvs0MIdQgpKSkLFy4sLi7Oy8sLDw/38fFhOqL3oyRBJycn9+zZ\ns3fv3mfPniXX9OrVi4qKEEKojdauXaurq9unTx9HR0cTExPyNm2Wo6SLw9raeteuXd26dfP19Y2L\ni/voo4969eqVnZ0t84oQQkiBKVNRqKam5siRIwFg7969y5cv/+WXX9q+76tXr3799VcqokKs5ePj\no6+vz3QUlMD23AHJsD1TkqAtLS3Xrl07c+bMoUOHWllZhYWF1dbWtnHfpKSk+/fvu7m5UREYYqGE\nhARLS8vRo0czHQglmrdnZWVlVVVVLpfLXFAyoPPype3hwwWOjtmjRxNK9F3K6pOYqF1c/Njbm2dg\nQFul7SLb9kxJgj548GB8fHxOTs4HH3ywc+fO7777rn///m3f3dHR0dfXl4rA2OPWrVtHjx6trq62\nsLDYvHkz0+Ewif19X2vWrPnmm2/S09PnzJmTnp4+cODAAwcOCB8Kfa8m7bmhoaG+vl5TU5OaYGm0\ndKnJDz/YmZuDqyt9lfr6wu+/99y8ePkjjgAAIABJREFUGdzdYeVK+uptM9m2Z0r+9HXp0mX27Nnu\n7u4A0KlTp8WLF1+8eFHslgKBoPxd1dXVAoGAiqhYpaKiYtOmTStXrjx8+DAAxMXFjR8/ftKkSS19\nUIhBcXFxABAcHOzt7Z2fn79kyRLyMQfJKCsra2hoyC465igpwfz5b7NzYiK8eUNTvW5ucOkSzJkD\nAFBTAy9f0lQvEyg5gm6irq7O1tY2MzOz+Uvp6embNm0SXZOWlmZjY9PeEW/ljpeX14ULF8aPH79/\n//7//e9/v/7666lTp+rq6vz9/U1MTAYOHMh0gKiprKys+fPnd+7c2cfHh3zoTjKVlZVlZWU9evSQ\nYWzM09ICb2+YMgVCQ0GZjqwCenoAAOXlsGABGBtDRARYWdFRL73o+CjV1NTEZmcAsLOzO3nypOia\n0NBQxo+gr169mpaW9uGHH44fPx4ALl68eOTIkdLSUi8vr6VLl0pbemUlvHlzPSlpdI8ef+/c6bl8\n+S4bm2gnJ7W1a9WmTw8LC0tMTMQEzSpFRUXh4eFWVlbx8fGhoaGnT5/u3LmzxKVpamoq05PC6DRi\nBAwbBjt3ws8/w9Sp9NVrYQGJiXD7Nnz1FezeDbq69FVNC4VrKFLbsGFDfn7++PHjr1+/fuXKlV27\ndqWlpR09erSoqMjW1rZpgubz4c0bqKiAigooL/9vWXShuvrtxhwOEATo6ICOTmVZ2fRnzzQ0Nee5\nufE7dcrr1s3www9hzZqCzz7TVbhGJu8yMjKys7OtrKzIryYlJSU2Nlbi0pSVlTt16iS76FhDRQWW\nL3+7nJwM+vpgbU1T1YMGwdGjb5fXrYPBg2HMGFCI+bQwQb9DIBBcunQpOTkZHjzwGj58y6pVtdHR\nXyopvVi4cPmlSxs/+AC8vIDP/28HFRXQ1QUdHdDVffufjg507Upm4bf/1NZuXtF4gPH/Lr98+XLq\n1KlBQUHu//xzZfv2qKtX6XirqM0sLS0tLS3JO0cBIDo6WprSFLOLowlTU1i5EtTV4Ztv6O55CA2F\nvXvh229h9WoYNYrWqimACfodjY2NmpqakJoKy5bBpEk6amr1uro3iouvcrm7jh/v2rs36OrKvIut\ne/fuiYmJFy5cyJ48+cf795XJzjXEVq1cU0lLS2syAeDTp0+dnZ3J0eNIitnF0UTv3nD6NKSmQkIC\nLFtGa9Xm5rBxI1RXQ1ERAMDr11BfD6amtMYgO8oAsGHDhtmzZxsbG8uqUAsLC3IMVlFyMSqTioqK\npZ5e6dSpKr/8kvL06QVj47nBwVs9PAwMDJbt3AkA8fHxVNSrr69PDisO3t6QlQV9+lBRC5KJVq6p\n2NvbX7lyRXRN82sqCtvF0ZyjIzg6AgC8fAmRkfDll2BmRlPV2tpvz1zz82HuXLCygsWLwcGBptpl\nRxkAcnJyqqurz58//8MPPzR5uXPnzhcuXGgy6cB7PX36tHfv3i9evJDHI4W9KipnBg06vHSpnZ3d\n0aNHAeAqnX0Oq1bB1q3w7wxsSPF0iC6OJrp3h08/haAg6NkTIiPFdvpRpX9/SEmBW7dg3z7YuRPk\n7QbH/xJoSEhISEgIAERHRzc0NNjY2Pj5+UVGRrY3OwOAhobG5s2bOfLYSR8fr6yh8dn+/Z8xFcDg\nwbB6Nbx8Cd27MxUColRH6OLIy8s7deqUsrIyOQL12/ugALzU1ZeSZw9VVSDFnTDt5uICLi5vl+fN\nAwMDmD1bLn5iYh5UiYmJmTFjxpEjR5KSkvbs2SNZuQEBAfJ3HpebCzt2gHSXgGRg+XLmY0CUUZwH\nVVqQlZU1ZcoUc3NzHR2d8ePHFxcXk/dBHTp0aNOxY2+PYb/6CsaMAUauh+/eDXZ2MGsW7N/PQO3t\nJOYvuba2tpGRUWVlpb29vfwlWYk1NsKsWbB3L7T/jEHGRo+GTZugpASMjBiORCE0OSMkTxPbRbbX\nVBS+i2P//v2RkZHOzs4AoK6ufvz48S+//PLFixfLly/fuHHj242iouD5c9izB549e/tMIG2UlcHH\nB4SDQWdlwS+/QEAAGBrSGkbbiDmC5vP5169f79atW2VlZUVFBf0xMSMyEkaOBHt7puMAAIBFi+C7\n75gOQkFIf0b49OlTc3PzJpNSSxyPpqamiYmJxLuzHzkJN7msp6fH5XIvX768d+/eXbt2zZ49+7/t\nrKxg+/a32Tk9Hdavh8JCBsK1soJu3SA4GHx8oKyMgQBaJSZBz5s3b+LEiVOnTl21alVYWBj9MTHg\n9m24dg3evUGKSZ9+CklJUFXFdByKQPozQtleU1H4Lg5fX99Vq1YVFhbm5uZu3rzZ29t769atubm5\ny5Yt8/PzE79P//5gbw9z58KUKXQ3e/KAOjERtm9/2y2enAz5+bTG0LJ3ujhEm+Dw4cPJhblz59Ia\nEf2qq2HRIjh9GmgcNfE9lJRg5kzYt++/R7OQpGRyRhgQECCreBS+i8Pd3b22tnbRokWqqqrr16+3\ntrZ+/31QSkowbhyMGwd5eUCO8/fbb2BnB3SOEm5p+XaBIGDxYmhshDlzgOlRcN9J0HJxq7LsLV8O\nX3wBFhZMx/GugABwdYWFC0FdnelQ5Bt5RpiYmMiSM8KOcBeHl5eXl5eXJHuam79dqKuDoCDo0gXm\nzaN1OFMAGD4chg+HkhJ4/BgAQCCAx4/pe2z9Xa01FIIgoqOjl9H8IBDNfv4ZeDyYPJnpOJpRUQF/\nfzh8mO5LKAqEnWeEHehBFWl4eYGXF7x4AeTwyvX18PIl0Dm1qZHR26v09fXwww/w118wbhzMm0fz\npXsxJ/URERFKSkocDkdJSenMmTN0RkO3/HyIjgZJbyWkXGgoxMZCQwPTccgrQhymg4LKysoXL14w\nHYWc+OAD8PAAAKiqgtWrYcwYOHAAaJ6MRl0ddu+G5GT48MO3lzHLy6Gykp7KxSToa9eu5eTkrFix\nIjU1dciQIfTEwQCCgNBQ2LmT1uea2kVTE8aOhZ9/ZjoOBUEQRFRUFNNRKP5dHJQwMID4eDh1CtTU\n3l7BKysDHo++ANTUwNcXBgwAAHj8+O3g1//7H9XViknQAoHA0tKSz+c7ODikpaVRHQFjdu4EJydw\ncmI6jlZ9/jl8/z2w4LhPfrHtjFDh7+KgkLY2TJ/+tqMjLQ1GjgQ/P0hMpDsMFxf47TeIjIScnLcn\nuPn54n+kBAEPH0ozAomYBM3lcpOTk0tLSzMzM+vr6yUumtXu34dz52D1aqbjeB8dHRgyBNozLTpq\ngm1nhNjFIRujRsGtW7Bu3X9TXj16BHTmq+7dYf78t2NbHj8OQ4bAsmXw4ME725SVwZUr0tzfLSZB\nL1y4MDEx0d/f39XVddy4cRIXzV48HsydCwcP0jQ3j5TCwmD7dqaDkGNsOyPELg5Z6tcPFix4u5yU\nBG5uEBgIt27RHcaKFXDrFowfD9nZV69eTU1Nfbve0BCWLJGmYDEZSvi0T2lpqTRFt0V6evrMmTNF\n17x8+dJFOKwJRVauhLlzQV5uRDUxgX794MYN+Pc+BNQubDsjxLs4qLJoESxaBA8eQE0NAEB1NZw/\nD6NHg4EBHbV36gTu7gCg/MMPMhwnTkyCblI6pRe+bWxs/vtrAwA0zEl44QKUlEBQEIVVyNwXX8Dn\nn2OClozoGeFyFjz4o/APqjCMvI4HACoqUFQE/v6gpASbNoF8zvMpJkGTGbmhoSElJSUhIYH2kKhU\nWAjr18Ply0zH0U49eoCODty9K48jjjOOzjNCACgvLxf9Z11dnYqKiuiajvCgCiuoqcHSpbB0KVRV\nvX1IOCUFLl+GTz6BQYNofWxYimPcFhuKsrLygAED2HDEITMEASEhsH27XE79++WXsH49UDOfi2Kj\n84wwPT29ycOKmZmZDu/+WcUuDroJB552dgYeD44fh5Ur4cwZWp8jl1RrXRwcDkehHiP84QcYMACG\nDWM6Dol89BHU1eFsWBKg84zQxsbmvVNeYRcHYzp1glGj3plJdssWuHQJ3Nxg7Fh2np6KOc4XPnMl\nEAi2bdtGf0yUePAAfvoJvvmG6TiksHIlbN3KdBDyijwjTElJYToQvIuDTcLDITERHBz+mzrg4kX2\nDGUHYhO0AqqvhwULICZGPu6ra8ngwZCb+99dn6htOP8yMjJyc3NjOhx8UIVltLRg7Nj/hhouLYUF\nC8DVFZpN0MqIFocbFWLD8AXSWr36vweQ5Bo5GxZOiNUebGvA2MXBatOnw/Tp0NAA5MVeHg/8/GDQ\nIHB3h0GD6D/Ce+cImuzZmD9//rFjx4qLi48dO7ZixQqaA5K9pCR4+RJEp3KQX6NHQ1oalJQwHQeS\nHHZxyAFl5bej1mlowJEjYGsLp07BhQsAANXVcO8ebaMviOniSEtLmzZtmpGR0bRp05KTk+mJgypl\nZbBypUJNH4WzYbUZRxymg8IuDnmjowPjxkFUFHh7AwBUVsKhQ+DlBZ9+Ck+evGff8+dh3Dj45BM4\nf/7tmt27YexY8PODmJi2VC7miL2xsTEhIcHT0/Py5ctsOz1stzlzYNMmdk4HKaFPP4UdO2D5clpn\nrZdPZOtdsGCBi4sL2Z7v37/PdFDYxSHnunaFHTsAAMrL385QfvEi7N0Lbm7g5tZ0UtPly+HMGSAI\nmDwZJkwAAMjMhOfPoboaAgPbUpuYI+h9+/Z98803hoaGmzZt2rdvn5Rvh0kHD0L37u/cVaMAhLNh\nobZh2xkhdnEoCD29t7MdkaNUd+8OR4/C69fl5eVv3rx5u01uLvToAT16/Hdt380NLl+Gb775bwiR\nVr2ToDkcTm1trb29fVpaGo/Hu3Pnjp2dnezeEL2ysuDgQdi8mek4KBAQAD//DLW1TMchH8gzQi6X\nm5CQwIYzwo7SxZGUBJs2dZSbjoyMwMcHoqNBX79Lly6dhWe33bvDixeQkwPdu79do6oKFhYwYgRU\nV7el4KYXCdXV1dk2A4Uk+HwICYH9+0FNjelQKCCcDQu1AdvOCDvKcKPffw96ejBvHnh6wuHDbcxH\nCqBTp07/PSm6fTusWQNr174dkHLIECgvh6AgCA2F779vS2liujiio6MjIyMvXbqkp6cX07ae7CZO\nnDhhZGRka2ubnJzcv39/Y2Pjn376SYJyJLd+Pfj6wkcf0VopnXA2rDZg5xlhh+jiqKmB8nKYNw9+\n+QVOnoS6Opg4ESZMgJ9/Bj7//+2deTxV6RvAH0zWRNEktLjatVqSpSJbWk20TIstRtvEFGlqaoow\noz01RTVKTZSKqEYkKW1CJSbaZH7ZwhDZl/f3x6nbxaXr3HvPPff2fj/zmc+57zn3eR+n9zz3fZ7z\nvO8jaOUoZM4cuHgRoqJg9mwAgPv3wdERwsLg2jWws+NEABsDfezYMUdHx7CwsMTExEOk6vVt27bt\nzp07O3bsmDJlip+f3+3bt7ds2UJCDklu3oTMTFizhroeqUdWFmbNwtWwuoaeHuFXEeK4dg2mT/94\nrKgIP/wAiYmwcydkZ4OBAbi5AQ2WdAoFbAx0z549+/btW1VVpa2tTXpXF9Z8JkrTmyoqwMsLjh4F\nGiRU8Zcff8TVsDiBe4+QmF5kZmYaGBjIyckZGxvnfjG/qnO+ihBHVBR89137xuHDYds2SE2FZcvg\n1CmYNAm2bYP8fEHoJzSwMdBNTU3JyckDBgyoqqp6//49CaHbt283MDD45ZdfkpKSNm3aZGRktH37\ndq5V5Yy1a8HXF779lqLuBAiuhsUZ3HuE4eHhAODk5GRjY1NYWOjh4bFy5UrS+oh+iKOxEfLyOl24\nKy4OxsYQHAw3b4KWFri7g7U1hIV93GUf0xY2edArV66cO3dubGzszz//3G7vRA75/vvvv//+e+I4\nOzubKwW7xV9/Qa9en30rkWf9eli0CESyLBnv4IlHCADPnz9ftWqVvLy8nZ3d77//TlqO6G83eusW\nUVvkC0hLw/z5MH8+FBZCZCTMmAGDBoG9PZiZib77yzFsZtCurq6VlZWTJ08+dOjQihUrqNeJJG/e\nwJEjX1f5PmY1LEzncO8RlpSUeHt7MxiMiIgIALhw4YI8F6uERD/EwTa+0QWqquDuDsnJ4O0N16+D\nnh5s3AgvXvBNP2GCzQz6zZs3Tk5Ojx8/3rNnz+DBg005+THskoaGhnHjxuXk5HQ8VVhYeOrUKdaW\nJ0+eMBiMbvfR0gIuLnD48MfU8a8HXA3rS3DvEWZlZb18+ZLBYCgqKgJASkpKaGgoaX1EvKIKQvDk\nSfsFdRyipQW//QYtLZCUBL6+8OYN2NrCkiUitRK4m7AZKC4uLjt37tTT0zM1NV21ahX3BlpKSoqt\ndQYAaWnpdvUmkpOTyTiAAQEwfTqMHk1OQyEGV8P6Eq6urq6urgAwmWythkGDBg0aNMjMzIz4uLfz\n3QTr6ur++ecf1paysrJevXqxtoh4iOPhQ26HooQEmJuDuTlUVkJMDDg6grg4LFsGNjbQtnjY1wAb\nA/3+/XtdXV0AGDx4cFVVFV+779Onj7m5OWvL2bNnu1009v59SEmBq1d5qZkQgathdQmVHmF+fn5k\n29zHvLy8dh6hiO/F0d34RhcoKoK9PdjbQ24uhIfDzp0wYQL88MNXNRdhY6Dl5ORu3rwJAKmpqeSE\nFhYWbt68OTExsaSkRFlZ2cLCIiAgoH///two2inV1bB2LURFUVoFklbgalhdQqVHOGLEiN9++421\npby8vN2EQ8RDHLduwY4dPJZJ5Odt3Qp370JICGRmgo0NODiAigqPO/oSQUFBcXFx8vLy5ubmLi4u\nt2/fjo+P79Onj6ura8+ePfnRIxujpqam5uvrKysr6+zs3IU31wUODg7a2tqpqanV1dUZGRkGBgYu\nLi5cq9oJHh7w88+gpsYv+UIBrobVOVR6hJwgygtVnj2DYcOATwEcZn5eUhIwGLBiBfX5eTk5Oa9f\nv75z546qqurZs2eDgoKmT5+urKw8Z86choYGfvTIxkBraGisXr26oqIiKytLT0+PhNCKiooff/xR\nRUVFUlKyX79+bm5utbW1XKvKjrNnQUyMZy6V8IKrYXUOTzxCJyengQMHSklJqampOTo6FhUVkdZH\nlLM4oqOpeBiJ/LzoaDh+HCoqYNYssLeH69cpWLRlYmISHx/v6+u7evXq48ePnzx50sjIaNmyZVOm\nTOFTuUs2BtrPz8/W1lZKSor0CkAlJaWgoKDi4uKmpqZ3796FhITwZcrw77+wdy/s3897ycIIUQ0L\n0wFJSUkfHx/6eISivFAlIYHS3X2J/LykpI/5efr6sHEjvHzJvw4lJSXV1dWnTZv24cMHhJCkpCTR\nLi0t3dLSwo8eu6rqTXrvgtDQ0PT0dD09PTk5ufHjx6ekpJBbYtsVLS2wfDkcOgRycjyWLKTgalid\nMHHixDVr1tDHIxTZEMf//gdKSiCQP43Iz7t3D8zNwccHpkyB/fuhvJzn/VRUVDg4OLi6uh4+fNjO\nzm7NmjWvXr1KSkqKjY01MjLieXfA9iUh96iqqp44cYIfkj+zZw9MnfpVvc/9MkQ1LMpW1QsJfn5+\nrB9JzDkIj3D+/PlKSkoVFRXR0dHcWFiRzeKIiYG5cwWpADM/7/17uHQJHB1BVhaWLgVra14Ve3V0\ndHR0dCSOEUIXLlzw8fFRU1OLjIyU489MUTgzHx4+hIQE2LRJ0HrQjHnz4MYNqK4WtB70gm4eociG\nOK5cocuuAwoKYG8PsbGwcyf88w9Mmwbu7vDoEW87ERMTs7OzO3nypL+/v6qqKm+FMxHCdJ/aWvDw\ngLNnv968us5gVsPy9BS0KiIFbz1C0Vyo8t9/AACKioLWoy0DB4K3N3h7Q3o6nDgB7u4wcyY4OoLw\n/EAKoY3z9ISffgJ1dUHrQUuWLoXISKirE7QemE4RzSyOy5fpMn1mi44O7N8P8fHAYICbG8yYAWFh\nwKfUMp7SviYhDcvUtyEmBqqrOSxG8DVCVMMKCxO0HphOEc0QR0wM2NgwP7W2tmZnZ3OzazZfaJef\nN3s2Zfl5pGkT4qBnmfrPFBXBjh2QkCBoPeiNqytMmwbLl/PqxQiGt4hgiKOmBv77Dz7FYd+/fz97\n9uxRo0bV19eXlpZGRUUx09HoQv/+4O4O7u6QnQ2nTsHmzWBqCq6uoKkpaM3awybEQbcy9R9BCFxc\nYO9eUFAQtCr0BlfD+gQ9PUIRDHEkJIClJfNTUFDQunXrjhw5cuLECQsLizNnzghQtS9A5OfdvQvm\n5uDrC1ZWEBJCq9fsbAw0rcrUNzY2njt37uTJk1V+fqCjgwwNs7Oz+bWth8hA12pYr169Cg0NvXTp\nUrf3wyIFkbaxatWq06dPv3v37vTp015eXhT02zUiGOKIjgZbW+anvLy8MWPGEMc6OjqvX78WkFoc\nQ+TnnTgB586BtDQsXgwLF0JsLB2KMrMx0PQpU19XVzdz5sx///1XIT//1a5d2ba25eXlCQkJxcXF\nAtRKCKBlNaykpCQ3N7dvvvnm8ePHdnZ21NhooJ9HKGoLVZqb4dUrGDqU2TB58uSwsDAAaG1tDQsL\nI73LqwBg5uft2gX//ANmZuDuDo8fC1AjNgZ6woQJ6enptbW1qamp48aNo14nJjExMTNmzPBcu9Ym\nIUHhzJmde/cqKyt7eHgIUCWhwd0dDhwQtBJtCAwMjIyMXGZt/evWrQwG4+7du9T0SyuPEEQvxHHr\nVrt6EQ4ODgihyZMnT548edy4cRZULv7mFQMGgLc3JCeDvT2EhsLUqfD77/DuHfWKUFFRhTTv37/v\n378//PcfbNigZGT0/uhRQWkifKipwaBBcPcuGBoKWpWPNDU0KJ4+DTExEBWlqqpaWVlJTb/BwcEu\nLi6LFy8ePXr0URoMIVHbbjQ6GpYtY20QExPz8fERlDo8RkcHdHSgoQHi48HdHd6/h0WLYP58yla0\ns5lBE/vnVlZWmpqa7ty5kxo92GJhYRESElIqIdFsbb1161YbljwezJfZtAkCAgStxCfS0o7n51+5\nfRtdu5ZfXh4VFcWnvQs6Qh+PkECkQhwIQVoa6OoKWg8+IyUFs2dDeDicPg319bBgAbi5QUoKBa95\n2BhoKvfPbWpqet2W6upq5r5QGhoaPj4+Li4uFhYWo0aNcnBwINrp4KgKAcxqWIKlpgbc3WH3brXk\n5H90dadMnert7X3o0KHevXtT0/+bN29MTU179+4dGhqalJTE177KyspC2pKTkzNgwACEUGNjY3Fx\nMUKovLw8KyuLtUWIDx4/btTXLy4poYs+/D5obESuro0XLhTb26OIiMY5c4rDwlBTE+s1vN2Ugy8V\nVTgnJyfn4MGDrC1PnjwZPnw486OxsbGxsTG/1RBZNm+G7dsFWQ3r+nXw9QVPT5g9+xuADRs2bNiw\ngWIVeF5RpQuam5vb/fBISUk1NjYS+SQSEhIIIWlp6d69e7O2CPFBdDSaMUPwalB/MGwYOnAA1dZK\n3LuHmpuRuDjzFOLt9BEh5Ozs/OrVK6b0Fy9eTJs2TVZWVktLKzU1FVGLi4uLs7MzxZ2KMjY2KCdH\nAP2WlSF7e+Tmhioqur7Q398/Li6Of4ro6uoihIihbmRkxL+O2MJ2PLe2tlKsBr8wNkZNTYJWgl5Y\nWlr6+fnxShqbEIeWltbKlStramqysrJ+/vlnXv4aYKhHINWwIiPB1hZcXODIEYFvoEOlR8gJopPF\nkZsLDAZesMpX2BhoXV3d8+fP//HHH9Rrg+E9+vrwv/9RVw3r7VuwtYXsbPj7b6BHAuyxY8e4rLHJ\nW0RnoUp0NOD39nyGjYGWkZE5c+ZMQUHBr7/+ivDrOBGAmmpYra2wfz84OsLWrbBtm2Aqa7CDbh6h\n6GRxxMeDlZWglRBx2G83Ki4u7ufnp6mpef/+fYoVwvAeS0u+V8PKyYGZMwEA4uKABqlsrNDNIxSR\nEMfbt6CgALKygtZDxGFjoK9fv04c2Nvb11BY0hzDR378EYKC+CK5sRG2bQMvLzh0CNzdaRiRpJtH\nKCIhjthYHN+ggPb7QdfX19Nt9y8MD5g3D5KSeL9N18OHMH06MBgQEwMMBo+F8w5aeYQiEuKg+Q79\nokIbA40QkpaWbpfnISjNMLyEWQ2LVxDLT/bsgTNnwN4eaPxDTjePUBRCHBUV0NICffoIWg/RRwhL\nXmHIwcNqWNevw4wZYG4O4eGgosIDgfyBnh6hKIQ4rl7F02dqaBMxZDt88SRaRGBWw3JzIy+kvBzW\nrwdFRYiJoX/lBPRpfYqgFWmDKFRUiY6GffsErcRXQfsQB6LfBucYnuHqCqGh5LchDwsDW1tYvhz2\n7aO/deYhW7ZsAYDMzEwDAwM5OTljY2Nuqu0JfYijrg7KykBNTdB6fBUIT8krDPeQrob1v/+BrS28\nfg1xcTRZfsIJvCp5FR4eDgBOTk42NjaFhYUeHh4rV64krZXQhziuXwdh3OKZD8TGxq5Zs2br1q3l\n5eV86oLvJa/q6+u5lIDhJd2thkUsP3F2hl9/hW3bQFqan8rxGN56hM+fP1+1apWCgoKdnV01F/kw\nQp/FERUF330naCUEz59//hkVFbVq1aqJEyd+9913tbW1/OiFjYEePHgwD0teCfdYFD0UFMDAgNNq\nWDk5MGMGAMDff8PYsXzVi39w7xGWlJR4e3szGIyIiAgAuHDhgry8PGl9hDvE0dwMubkwcqSg9RA8\nZ86cOXLkyKhRo2bNmmVlZZWSksKPXtgsKxg2bNiiRYtmzZpFuli6rKxsHUu2AOFX0u1dzdfLunWw\naNEX3sI3NoK/P2RkwB9/0DnBmRMIj9DS0jI+Pp7cIMzKynr58iWDwVBUVASAlJSU0NBQ0voId0WV\nO3eEKMbFV8TFP89uW1tb+fTil80M2s/Pz9bWVkpKinTM7s6dO3Pnzs3Pz2e+RsfWmUb06wcjRkBy\ncqcXpKZ+XH5y6ZKwW2cAkJCQ8PHx4cYjHDRokJmZmZub2/z58wFg7969gwYNIq2PcIc4cHzjE/b2\n9kuXLr1z586pU6fi4+P5VCHmm0YCAAAdR0lEQVSIzS8598Z0woQJwcHBK1ascHd351IUhi9s2AA/\n/ghTp7Zv//AB1q+HhgYIDwehfpHFgpmZmba2NjceYTsaGhrGjRuXk5ND7utVVVXl5eUaGho8UYZS\niAJXNNgRkA4sXbq0f//+cXFxffr0iY2NlebP65muXC2E0N69e9etW0dCbr9+/SIiIjw8PJSUlMjq\nhuEbGhqgqAjp6aCj87kxIQF27AAvLxFbg+Dn58f6kfv5h5SUVGfW+cWLF8ePH2dtefjwoaamJmuL\nEIc4Hj2CsWPpvGqUYszMzMzMzPjaBZuBsnnz5oCAAGIcGxkZkTPQACAlJXX48OHDhw9zpSCGPxQv\nX/6/BQu2DB1qbGy8wdlZct066NdPKJafdBcqw2sqKipEGIRJbm6ubNst34R4oUp0NI5vUAybGHRS\nUlJeXp6Xl1daWtqkSZO476OhoWHEiBFsT2VkZFi05e+//y7l68aYGIDGxsb5W7cOGTDg8u7dhi9f\n/jtxIqxZA/v3i551ZgUhtGfPHhJfLCwsdHJyGjhwoJSUlJqamqOjY1FREdsr5eXlddqirKzcLrQi\nxFkcyclgYiJoJb4u2Bjo1tbWQYMGNTU16ejoZGRkcN9HFy6htrZ2Qlusra379u3LfaeYLsjOztbT\n0+vt6/vNzJnTevdeo6EBoluZd/PmzeLi4mJiYuLi4hcvXiQhwcHBQVtbOzU1tbq6OiMjw8DAwMXF\nhbQ+wrpQ5flzGDgQevQQtB5fF2wMdG1t7e3bt8vKynJychobG6nXCcNvFBQUysvLYfJkuHWrKTCw\nQUhDopzBvUdYUVHx448/qqioSEpK9uvXz83NjZtVCcKaxRETgzeAph42BnrNmjWxsbGLFy82Njae\nRep9EecuIUYgMBiMpqam7du3Rz54sGDBguXLlwtaIz7CvUeopKQUFBRUXFzc1NT07t27kJAQbiys\nsIY4rl0Da2tBK/HVwWbq9MMPPxAHZWVl5IQ6ODjMmTMnICCgT58+FRUV0dHRLi4uVzhcvYahhL/+\n+uvq1asvXrzw8fEZM2aMoNXhI9x7hKGhoZs2bQoMDCwpKVFWVjY3Nz927BhpfYQyi6OoCOTkcIEr\n6uHLdqOES0gcEy4hsUYWQx/ExMRmElUERR1Wj9DT05OEBFVV1RMnThDHYmJiYWFh3OgjlFkcMTEw\nd66glfgaaWOgCVu8evVqQ0NDYmnskydPSAglXML58+crKSkRM2ihDLphRALuPULeIpQLVS5fhk8/\nURgq4ct2o6Ghoenp6Xp6enJycuPHj09JSeHGJcRgyMGr7UZZefbsGZcShC+Lo7ISGhsBrzgjQUwM\nzJoFM2ZATMzHlqAgmDkTFi0Czkwim1gY95vLsLqEGIyg4JVHyEpnGf2cI3whjr///ripIYYDWltb\nP5tNT0+4eBEQAltbmDMHACAnB16/hg8fwN6eE2lsZtDBwcE83G4UgxEsdCtAIXxZHJcu4QS7L1Ba\nChcvwtOnAFBZWVlRUfGxPT8fNDRAQwP+/fdji4kJxMeDry+sXs2JYDYz6AkTJqSnp/NEbQxG4HDv\nEfIWIcviqK+H4mLgYvc+Eae0FObNg969wcQEdHUBoE+fPn2Y9c4HDoQ3b6C1FQYO/NgiKQnq6jBt\nGnBWO4LNQPH09Ny7d29rayvxkQ5jGoMhTXBwsIuLy+LFi0ePHn306FFBqyNsIY7ERODzfkBCRnw8\nxMVBWhrY2cHatdC3L3Thlu3eDVu2AEKwezcAwKRJsGIFODhASQlwtkkRGwOdlJRE5HuS/AMwGDpB\nN49QyLI4oqPhK980uKEBUlKgsBCWLQMAeP4c7OwgMBA4cYPmzPkYeia4fx8AwNGR887ZxKDNzc2f\nPn3a1NTEuRQMhrZ4enpKSEjwKouDe4Qpi6OlBXJyYPRoQeshODw9wcICEhNh+PCPLWvWgKEhR9aZ\nF7DpJjAwMDAwkPkRhzgwQg3dPEJhCnHcvQsGBoJWgkLevYOYGEhOhtpaOH8exMRg1y7BasRmBo3a\nQr1OGAwPoZtHKExZHNHRop+/QUQwPnwAAEhLA4TAzw8uXKBJXQI2BpoJ6f1zMRj6EBgYOG3aNElJ\nSRzi6Db37gEvdoSnKc+ewbx5YG4O164B8fs9Ywa4un7OuKABfKyogsHQASq9wIyMDG9vb9aWZ8+e\naWtrs7YITYjjyRMYOxbEu5rDCRktLZCWBlFRYGYGFhagpgZBQaCmJmi1uoJ9FkdeXt6hQ4cWLlwY\nHh5OvU4YDD/gpsYmhxAFKFhbXF1dmRmrBEKTxREVJVLxjepqmDMHRo+GuXM/1oXp1Qt69RKwVl+C\niooqGIwA4b6iCm8RmhDHzZvCnQFdVgbHjoG1NWzdCgAgLw9JSRAUBObmlOVgAEKQnQ39+5MWwEZR\nKiuq5Ofnt9uJ9MmTJ4PwsiUM76CbRygcIY7Xr0FVVSgLXFVXg7w8AMCePaCuDqGhoKIiMGXKyyEh\nAYqLSQtgY6C53z+Xc3r37q2jo8Pacvv2bWlpab52ivmqYPUIvThbX8tXhCPEIXQFvEtKICICrl2D\nb7+F0FAQEwN/f6p1ePwYwsPHpaVJjxv3sUVZGTw84KefSItkY6BnzpyppqYGAKWlpfn5+aRFc0Kv\nXr3Mzc1ZW86ePdsuZofBcAPdamwKx14ccXFAg3DQlykogF69QF4ecnNh8GC4eBGon94Rc3aEICIC\nvvuut4KCVttJJze0iUHv27dPTExMXV2dSEgSFxdftGgRr3rCYAQC9zU2eYsQFI0tLgZpaejZU9B6\ndE59PRw+DDNngpcXEHUYpkyBuXMptc7FxbBnD1hZwY4dAABiYvDbbzBpEm8TqNv8knt4eHh4eNjZ\n2Z0/f56HfWAwAoRKj5AThCDEcfkyTQtc1dRASQkwGFBdDTIycPasAH5FqquhZ08QE4Nbt0BFBaKj\n4Ys/t1wkerLJ4sDWGSMa0NMjFIIsjpgYmD1b0Eq05cEDcHQEMzNISwMA6NsXHB0ptc4IQWIiLF0K\ns2dDQQEAwIIFsHjxl60zd7Qx0Ddv3tTQ0Hj79m1aWtrQoUM1NTWTkpL42j0Gwz88PDwQQra2tsx9\nC+4T24kJFLqHOKqqoK4Ovv1W0HoAAEB19ceDf/6BNWvg/n1YsIBqHZqbAQBycuDOHfD1hZs3QV2d\nss7bhDjc3d0jIiLU1NQcHBz8/f379eu3evXqp0+fUqYNBsNz6OYR0j3EERcn+AJXTU0QFwenTn18\n8yYhAU5OVOtQVganT8PZs+DsDK6uMHLkx3xqamkzg25tbdXX16+urn779q2dnd2UKVPwZkkY4YWe\nHiHdQxx0SLC7cQPS0mDPHoiMBEHljG/cCHJycO0auLoKRgEA6BiDTktLO3z4sIWFRXNzc1RUlIKC\ngkDUwmC4h+kRent7+/v7h4aGrl27loScLVu2AEBmZqaBgYGcnJyxsXFubi5prWgd4mhshIICGDxY\nAF3fvQsLF8KFCwAAVlawfTuVkQQAgMJC8PEBK6uPH48dA1dXga8FbxPi2LVr19y5c+Xl5a9du3bu\n3DkfH59z584JSjMMhksIj7CqqorwCMXExMh5hOHh4b6+vk5OTgsWLIiLi0tISFi5cuWNGzfIaUXP\nEEdZWdnvv/+u9PChmby8dksL1WsdZ8yA4cNhxw4YOpTSfplER8Nff4GDA2zaJBgFOuHzDLqxsXHi\nxIlZWVn37t3r1avXjBkz7t+/P1ggv6UYDI/goUf4/PnzVatWKSgo2NnZVTNfXnUfvoY4Wltbf//9\n97Fjx2pqahoYGGRmZnLyraampr59+1paWroPHPhYQ2Pr1q2c78vKvJLtV/bt29fpN4uK4ODBj8dX\nr8LevVRb53//hW3bPlYUtLGByEiYNYu6bTo447M2586di4mJaXdaXFz8+PHjcnJy1GqFwfAAXnmE\nJSUl3t7eDAYjIiLC1dX1woUL8sRuD6Tg614cR48eTUlJuX//voyMTFxc3JIlSzIzM79obbOzswHA\nwtwctm1zvXPHxMRk8ODBK1eu/PDhg7q6ekBAACdd19XVdWz86aefPDw82re+fQvbtkFRkcCqHdbX\ng4MDNDSAgwPNS8Z8AwDi4uIHDx6cOnXqrl27BtJps2oMhhusrKwKiJRVgEGDBi1ZsoScnKysrJcv\nXzIYDEVFRQBISUkJDQ0lrdXnEEd5ORBxEjExsLSEXr04aumSgwcPRkREyMrKAoC1tbWqqmpra+vT\np09Xr16NECKe9PHjxz9+/Ji15WNMXEzsLxub6PnzEUKrV69evnx5UFDQjh07bty4UVtbu3z5cqap\nffz4sZubm5iYGOtGOjIyMkePHj1y5IiEhARxfUpKCgAUFBRcvXo1KChIRkZGQkLC1NTUb9EicHMT\nmzhx28SJsZs2mZqa7ty5Mysra9WqVfX19aqqqqdPny4uLnZ1df3w4YOiouLRo0d548pXVcH9+2Bp\nCVJScOgQ0KYKWlcghKqqqhITE3ft2rVkyZLJkydbWVl5eXmdOXPm2bNnLS0tiFpcXFycnZ0p7hQj\nQPz9/ePi4gStBb/oOJ6bmppqamoQQig3F/3228f/3rzhtKVLZGVla2tr2zXq6uqGh4cjhC5fvqyn\np9exhdj9ZsmSJRMnTly8ePHhw4cRQrGxsQDg5eWFEHr37l1gYCBToI6OTlRUFELo9OnT8KlIHgAo\nKCgkJyezXk+c9fLyuhUcjObMydu3j3gTQJxKTk4uLS0VFxdHCE2aNOncuXMIoaCgoNu3b0+bNi0p\nKQkh9PDhQxMTE85vOHtev0Zr16Jp09D589yK+hK8Hc/tKxAihGpqau7du+fp6SktLT18+PAPHz50\nV2hBQYGjo+OAAQMkJSVVVVUdHBwKCws5/C420F8bQmeg6+vrhw8fzuHFbMdza2srr5X6iJaW1tOn\nT5kf169fX1hYKC0tXVpaihCqrKyUkZFBCHVsAYDJkyf37ds3Pj4eIXTz5s3Gxsbjx49LS0vPmjVr\nyZIl165dY4qVkZEhfmPevXvHaqCTk5Pd3NxYryfO/m1puZTB8HByInYTZF7f2NjIvEZGRqasrIzZ\nBeEEECgqKnJ7X+Lj0Z073ArhDN6OZ/ZFY3/55ZfS0tLY2Njs7GwSAWgHBwdtbe3U1NTq6uqMjAwD\nAwMXF5fuCsFg6ImUlFROTg7bU5mZmbptuXz5ckNDA+s1fC0au2LFCi8vrw8fPgDAzZs3L1261K9f\nv1GjRiUmJgLA1atXtbS0AKBjCwAkJyf//PPPV65cIZRcunRpYGCgu7t7bGzsunXrFi5cyOxl9OjR\n8fHxAED8n8nZs2cDAwM/X5+TAwAIoaXp6ZuvXNn755+EEPQpl6YHy37TI0eOvHXrFgDs2rUrMjJS\nQ0PjwYMHCKG8vDwylVE/fIAjR2DqVEhNBQCwsABDw24LoQNszbaVldWLFy9IW30dHZ12LZw7KXgG\n/bUhdDPobnHixIk///yTteVziIMPtLS0+Pj4jBgxYtSoUUZGRo8ePUIIZWRkGBoaGhgYGBkZPX78\nmG0LYQqam5v19fUJy4gQ+uOPPzQ1NXV0dMaPHx8SEsLs5dGjR3p6epMmTWo3I/58/ahRIfr6aMGC\niTo6eXl5/v7+RkZGpqam69evNzIy2r17N7NH5kF6erqBgYGOjo61tXV1dXVqaqqhoaGRkdH06dMT\nExO7dxdqapCZGTpwAHXf++ce3o5n9pmhRUVF0tLSvXv3Jmf0raysZs2aNX/+fCUlpYqKiujo6Ojo\n6KtXr3LyXaKG2/Hjx8l1jRE6AgICtLW1rZgLBESLkydPtra2OrVdqYwQokN9cX5RWgqOjrB1K+jr\nU9pvXh5cuwYrVlDaaQd4O57Zl+zt378/aesMAKGhoenp6Xp6enJycuPHj09JSTl27BhpaRiMKMHX\nEIcgITbQAIC+feHKFUqt88uXsGQJeHrCmDHUdUoJfMnKVlVVPXHiBCdXNjU1tculLysrw+vLMbRC\nXV2dma7HhK3ryQnCUVGlu1y/Dlu2wOLFMH26AHqvrISNG0XPOgOfDHQ7Ghoaxo0bx/a9ysuXL0NC\nQlhbCgsLie3VMRia8OLFi6FDh75584YnhlU4isZ2i5QUOH0aoqOByk2g7t2DHTt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      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Puedo usar esta t\u00e9cnica con multitud de lenguajes:\n",
      "\n",
      "* MATLAB <https://github.com/arokem/python-matlab-bridge>\n",
      "* Octave <http://blink1073.github.io/oct2py/docs/>\n",
      "* \u00a1Fortran! <http://nbviewer.ipython.org/github/mgaitan/fortran_magic/blob/master/documentation.ipynb>\n",
      "\n",
      "\u00a1y m\u00e1s! <https://github.com/ipython/ipython/wiki/Extensions-Index>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "> **P**: \u00bfPor qu\u00e9 lo llaman IPython si puede iteractuar con multitud de lenguajes diferentes?\n",
      "\n",
      "> **R**: <https://speakerdeck.com/fperez/project-jupyter>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "## Distribuciones de Python: Anaconda\n",
      "\n",
      "* En Windows especialmente, instalar individualmente cada una de las bibliotecas es una pesadilla\n",
      "* Incluso en Linux o OS X el problema se complica si necesitamos _versiones diferentes_\n",
      "* Soluci\u00f3n: **distribuciones monol\u00edticas**\n",
      "  * Anaconda, de Continuum Analytics <https://store.continuum.io/cshop/anaconda>\n",
      "  * Otras: Pyzo, WinPython, Canopy, Python(x,y)..."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "## La comunidad Python espa\u00f1ola\n",
      "\n",
      "* Python Madrid <http://www.meetup.com/Madrid-Python-Meetup/>\n",
      "* Asociaci\u00f3n Python Espa\u00f1a <http://www.es.python.org/>\n",
      "  * Calendario de eventos y meetups nacionales <http://calendario.es.python.org/>\n",
      "  * \u00a1Hazte socio! <http://www.es.python.org/page/quiero-ser-socio>\n",
      "* Primera conferencia nacional: PyCon Espa\u00f1a 2013 <http://2013.es.pycon.org/>\n",
      "  * \u00a1Estamos preparando la segunda en Zaragoza! <http://2014.es.pycon.org/>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "<p style=\"text-align: center\">**Muchas gracias :)**</p>"
     ]
    }
   ],
   "metadata": {}
  }
 ]
}