{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# (Simple) Data Visualization tools for Data Science\n",
    "\n",
    "##  [*Romain Vuillemot*](http://romain.vuillemot.net/) ([@romsson](https://twitter.com/romsson))\n",
    "\n",
    "\n",
    "<a href='http://romain.vuillemot.net/'>\n",
    "  <img src='simple-dataviz-datascience/img/teaser.png' style='width:600px'>\n",
    "</a>\n",
    "\n",
    "[Mix-IT'16 Workshop](https://www.mix-it.fr/session/2802/)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A Swiss Army Knife..\n",
    "\n",
    "\n",
    "<a href='http://www2.knifecenter.com/knifecenter/sak/'>\n",
    "  <img src='simple-dataviz-datascience/img/swiss-army-knife.jpg' style='width:400px'>\n",
    "</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ..To stay in the flow\n",
    "\n",
    "<img src='https://upload.wikimedia.org/wikipedia/commons/8/8f/South-Goodwin.jpg' style='width:600px'>\n",
    "\n",
    "Source: [Flow theory](https://en.wikipedia.org/wiki/Flow_(psychology) on Wikipedia"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataviz Resources\n",
    "\n",
    "Jobs\n",
    "* [`<data-vis-jobs@googlegroups.com>`](https://groups.google.com/forum/#!forum/data-vis-jobs)\n",
    "\n",
    "Social media\n",
    "* Twitter [#dataviz](https://twitter.com/search?q=%23dataviz&src=typd) \n",
    "* Twitter lists https://twitter.com/infobeautyaward/lists/data-visualisation-people\n",
    "* LinkedIn topic https://www.linkedin.com/topic/dataviz\n",
    "* Reddit data is beautiful (top 50 sub-reddit), internet is beautiful, some random [discussion thread](https://www.reddit.com/r/dataisbeautiful/comments/435g7b/i_love_live_data_visualizations_heres_every_one/)\n",
    "\n",
    "Learn more\n",
    "* Books: [Design for Information](http://isabelmeirelles.com/book-design-for-information/), [Visualization Analysis and Design](https://www.cs.ubc.ca/~tmm/vadbook/).\n",
    "* Design processes explained http://animateddata.co.uk/articles/f1-timeline-design/\n",
    "* Mooc https://twitter.com/YuriEngelhardt/status/697349217027780609\n",
    "* Color http://colorbrewer2.org/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src='data-science-dataviz/nytimes-2016-primaries-new-hampshire.png' style='height: 500px;'>\n",
    "\n",
    "\n",
    "New-York Times elections 2016 [Iowa](http://www.nytimes.com/elections/2016/primaries/iowa) & [New Hampshire Results Exit Polls](http://www.nytimes.com/elections/2016/primaries/new-hampshire)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Four Ways to Slice Obama’s 2013 Budget Proposal\n",
    "\n",
    "<img src='data-science-dataviz/nytimes-budget-proposal.png' style='width: 700px'>\n",
    "\n",
    "http://www.nytimes.com/interactive/2012/02/13/us/politics/2013-budget-proposal-graphic.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How the U.S. and OPEC Drive Oil Prices\n",
    "\n",
    "<img src='data-science-dataviz/nytimes-oil-prices.png' style='width: 600px'>\n",
    "\n",
    "http://www.nytimes.com/interactive/2015/09/30/business/how-the-us-and-opec-drive-oil-prices.html?_r=0\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A Visual Introduction to Machine Learning\n",
    "\n",
    "<img src='data-science-dataviz/r2d3-intro-ml.png' style='width: 600px'>\n",
    "\n",
    "A [Visual Introduction to Machine Learning](http://www.r2d3.us/visual-intro-to-machine-learning-part-1/) by R2D3\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Climate Change Calculator\n",
    "\n",
    "<img src='data-science-dataviz/ft-climate-change-calculator.png' style='height: 600px'>\n",
    "\n",
    "http://ig.ft.com/sites/climate-change-calculator/\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## El Atlas de Complejidad Económica de México<br>*Mexican Atlas of Economic Complexity*\n",
    "\n",
    "\n",
    "<div style='text-align: center'>\n",
    "  <a href='http://complejidad.datos.gob.mx/'>\n",
    "  <img src='data-science-dataviz/atlas-complejidad-mexico.png' style='height: 500px'></a>\n",
    "</div>\n",
    "\n",
    "*The Mexican Atlas of Economic Complexity is a diagnostic tool that firms, investors and policymakers can use to improve the productivity of states, cities and municipalities. Check it out: [http://complejidad.datos.gob.mx/](http://complejidad.datos.gob.mx/)*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Why Visualize?\n",
    "\n",
    "Can you spot any interesting patterns in those **4 datasets** with eleven (x,y) points each? ([source](http://nbviewer.jupyter.org/github/psychemedia/ou-tm351/blob/master/notebooks-RFC/Anscombe's%20Quartet%20%5Bopen%5D.ipynb))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>group</th>\n",
       "      <th colspan=\"2\" halign=\"left\">I</th>\n",
       "      <th colspan=\"2\" halign=\"left\">II</th>\n",
       "      <th colspan=\"2\" halign=\"left\">III</th>\n",
       "      <th colspan=\"2\" halign=\"left\">IV</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>var</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>8.04</td>\n",
       "      <td>10</td>\n",
       "      <td>9.14</td>\n",
       "      <td>10</td>\n",
       "      <td>7.46</td>\n",
       "      <td>8</td>\n",
       "      <td>6.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>6.95</td>\n",
       "      <td>8</td>\n",
       "      <td>8.14</td>\n",
       "      <td>8</td>\n",
       "      <td>6.77</td>\n",
       "      <td>8</td>\n",
       "      <td>5.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13</td>\n",
       "      <td>7.58</td>\n",
       "      <td>13</td>\n",
       "      <td>8.74</td>\n",
       "      <td>13</td>\n",
       "      <td>12.74</td>\n",
       "      <td>8</td>\n",
       "      <td>7.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>8.81</td>\n",
       "      <td>9</td>\n",
       "      <td>8.77</td>\n",
       "      <td>9</td>\n",
       "      <td>7.11</td>\n",
       "      <td>8</td>\n",
       "      <td>8.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11</td>\n",
       "      <td>8.33</td>\n",
       "      <td>11</td>\n",
       "      <td>9.26</td>\n",
       "      <td>11</td>\n",
       "      <td>7.81</td>\n",
       "      <td>8</td>\n",
       "      <td>8.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>14</td>\n",
       "      <td>9.96</td>\n",
       "      <td>14</td>\n",
       "      <td>8.10</td>\n",
       "      <td>14</td>\n",
       "      <td>8.84</td>\n",
       "      <td>8</td>\n",
       "      <td>7.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>7.24</td>\n",
       "      <td>6</td>\n",
       "      <td>6.13</td>\n",
       "      <td>6</td>\n",
       "      <td>6.08</td>\n",
       "      <td>8</td>\n",
       "      <td>5.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4</td>\n",
       "      <td>4.26</td>\n",
       "      <td>4</td>\n",
       "      <td>3.10</td>\n",
       "      <td>4</td>\n",
       "      <td>5.39</td>\n",
       "      <td>19</td>\n",
       "      <td>12.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>12</td>\n",
       "      <td>10.84</td>\n",
       "      <td>12</td>\n",
       "      <td>9.13</td>\n",
       "      <td>12</td>\n",
       "      <td>8.15</td>\n",
       "      <td>8</td>\n",
       "      <td>5.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7</td>\n",
       "      <td>4.82</td>\n",
       "      <td>7</td>\n",
       "      <td>7.26</td>\n",
       "      <td>7</td>\n",
       "      <td>6.42</td>\n",
       "      <td>8</td>\n",
       "      <td>7.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5</td>\n",
       "      <td>5.68</td>\n",
       "      <td>5</td>\n",
       "      <td>4.74</td>\n",
       "      <td>5</td>\n",
       "      <td>5.73</td>\n",
       "      <td>8</td>\n",
       "      <td>6.89</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "group   I         II       III         IV       \n",
       "var     x      y   x     y   x      y   x      y\n",
       "0      10   8.04  10  9.14  10   7.46   8   6.58\n",
       "1       8   6.95   8  8.14   8   6.77   8   5.76\n",
       "2      13   7.58  13  8.74  13  12.74   8   7.71\n",
       "3       9   8.81   9  8.77   9   7.11   8   8.84\n",
       "4      11   8.33  11  9.26  11   7.81   8   8.47\n",
       "5      14   9.96  14  8.10  14   8.84   8   7.04\n",
       "6       6   7.24   6  6.13   6   6.08   8   5.25\n",
       "7       4   4.26   4  3.10   4   5.39  19  12.50\n",
       "8      12  10.84  12  9.13  12   8.15   8   5.56\n",
       "9       7   4.82   7  7.26   7   6.42   8   7.91\n",
       "10      5   5.68   5  4.74   5   5.73   8   6.89"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "anscombe = pd.read_csv('simple-dataviz-datascience/data/anscombes_quartet.csv', header=[0,1], index_col=[0])\n",
    "anscombe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "group  var\n",
       "I      x      9.000000\n",
       "       y      7.500909\n",
       "II     x      9.000000\n",
       "       y      7.500909\n",
       "III    x      9.000000\n",
       "       y      7.500000\n",
       "IV     x      9.000000\n",
       "       y      7.500909\n",
       "dtype: float64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anscombe.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "group  var\n",
       "I      x      11.000000\n",
       "       y       4.127269\n",
       "II     x      11.000000\n",
       "       y       4.127629\n",
       "III    x      11.000000\n",
       "       y       4.122620\n",
       "IV     x      11.000000\n",
       "       y       4.123249\n",
       "dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anscombe.var()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Summary\n",
    "\n",
    "\n",
    "| Property\t            | Value                |\n",
    "|-----------------------|----------------------|\n",
    "| Mean of x             |\t9                  |\n",
    "| Sample variance of x  |\t11                 |\n",
    "| Mean of y             |\t7.50               |\n",
    "| Sample variance of y  |   4.122              |\n",
    "| Correlation           |   0.816              |\n",
    "| Linear regression line|   y = 3.00 + 0.500x  |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>group</th>\n",
       "      <th>var</th>\n",
       "      <th>value</th>\n",
       "      <th>index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>I</td>\n",
       "      <td>x</td>\n",
       "      <td>12.00</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>I</td>\n",
       "      <td>x</td>\n",
       "      <td>7.00</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>I</td>\n",
       "      <td>x</td>\n",
       "      <td>5.00</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>I</td>\n",
       "      <td>y</td>\n",
       "      <td>8.04</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>I</td>\n",
       "      <td>y</td>\n",
       "      <td>6.95</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>I</td>\n",
       "      <td>y</td>\n",
       "      <td>7.58</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>I</td>\n",
       "      <td>y</td>\n",
       "      <td>8.81</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   group var  value  index\n",
       "8      I   x  12.00      8\n",
       "9      I   x   7.00      9\n",
       "10     I   x   5.00     10\n",
       "11     I   y   8.04      0\n",
       "12     I   y   6.95      1\n",
       "13     I   y   7.58      2\n",
       "14     I   y   8.81      3"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp = pd.melt(anscombe) # http://pandas.pydata.org/pandas-docs/stable/generated/pandas.melt.html\n",
    "tmp['index'] = list(range(int(len(tmp)/8)))*8\n",
    "tmp[8:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group</th>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">I</th>\n",
       "      <th>7</th>\n",
       "      <td>4</td>\n",
       "      <td>4.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>12</td>\n",
       "      <td>10.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7</td>\n",
       "      <td>4.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5</td>\n",
       "      <td>5.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">II</th>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>9.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>8.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13</td>\n",
       "      <td>8.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>8.77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              x      y\n",
       "group index           \n",
       "I     7       4   4.26\n",
       "      8      12  10.84\n",
       "      9       7   4.82\n",
       "      10      5   5.68\n",
       "II    0      10   9.14\n",
       "      1       8   8.14\n",
       "      2      13   8.74\n",
       "      3       9   8.77"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = tmp.set_index(['group','index', 'var']).unstack()\n",
    "df.columns = [col[1].strip() for col in df.columns.values]\n",
    "df[7:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>group</th>\n",
       "      <th>index</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>I</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>4.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>I</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>10.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>I</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>4.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>I</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>5.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>II</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>9.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>II</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>8.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>II</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>8.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>II</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>8.77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   group  index   x      y\n",
       "7      I      7   4   4.26\n",
       "8      I      8  12  10.84\n",
       "9      I      9   7   4.82\n",
       "10     I     10   5   5.68\n",
       "11    II      0  10   9.14\n",
       "12    II      1   8   8.14\n",
       "13    II      2  13   8.74\n",
       "14    II      3   9   8.77"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.reset_index(inplace=True)\n",
    "df[7:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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16WSnXPT29lZ9llr49wL1wCF14IxzqBKA41nhBM64yUOVLovETMrlcmzfvn1qldMfJDD/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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12281d940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (-9223372036550156989)>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from ggplot import *\n",
    "%matplotlib inline\n",
    "ggplot(aes(x='x', y='y'), data=df)+geom_point()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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4LQEaeWiIiIhIL/gUujiBQABz587Fyy+/jMmTJ+P3v/89TCZT9vddLhcaGxuxZMkSVFdX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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1201e03c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (302107312)>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from ggplot import *\n",
    "ggplot(aes(x='x', y='y'), data=df[df['group'] == \"I\"])+stat_smooth(method='lm',se=True)+geom_point()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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18ssv67zzztMPfvADnX766aqqqjJyQo/dbld5eflBH8aBXEG4zJLEsYrhcDjZ\nIYbD4ax1iAmHOwGipKSk37D4cIfgE/MoE0M3bH0BYCgSIzp9P4jnwhGg+/bt08qVK9XS0qLS0lI1\nNjbqu9/9riorKyWl/iTRarUm+04+jCPXES4zJPFUMtEhZmol92AN5gSIRYsWJRf0XHbZZclOczD6\nzqPkpAcARxKPxw96KmlyiNuEWCymV155RS6XS5s2bdL555+vH//4xzrppJOMhL8D51GyMAf5gn/l\n0yQej2vfvn3at2+f/H5/VodpDidxAoTT6VRPT88RT4Corq7WjTfeqMrKykEN7VgsFlVUVCRDJQAc\nSTwe1549e5J9ZyQSybm+s6OjQ62trWppaVF1dbUaGhr0gx/8QOXl5Uaun5gyVFZWxrA38hLhMo08\nHo/cbndOPaE81AkQ119/vdETIBLzgcrKytg+CMCQdXV1yev15lTfGY1G9dJLL8npdOqvf/2rvvCF\nL+juu+/WlClTjN3D4XAkQyVPKZHPCJcjRN8TIPx+vxoaGoyfAJHYOqi0tJS5lAByWnd396B22vjo\no4/U2tqq1tZWHXXUUWpsbNRdd92lsrIyI+1IDH2Xl5erpKSEuZQoCITLApaJEyASC3TKy8uZZA4g\nLxy4b+8zzzzT75zwSCSiF154QU6nU3/72980e/Zs/eIXv9AJJ5xgrA0MfaOQES4LkNvtTu6tlq4T\nIGw2myorKxn6BpB3lixZ0m/f3sQ54Q0NDXK5XFq5cqXGjRunxsZG/exnPzM6Z9zhcCQXODL0jUJF\nuCwQ8XhcmzdvltPp1AsvvJCWEyAkqbi4WHV1dYpEIjmzYhMAUrVu3TqtWLFCF110kX71q1/pU5/6\nlNHrl5aWasyYMQqHwzk1lxRIB8JlnkucANHS0qKioiI1NDToO9/5jkaNGmX0Pon5lFVVVTrqqKPU\n0dFBuASQl+bPn6/169frgw8+kLS/f7vyyit1ySWXqLi42Oi9ysvLVVFRoYqKCtXW1qqjo4NwiYJn\niefaHg+HsW/fPiPzBIuKijIWijo6OuTxeAb9/u7ubj366KOSpK997WuHnWAei8X02muvafny5Xrx\nxRd1/vnna+7cuTrttNOMz3ksKSlRZWWlysvLVVRUJIvFkjwNKBPfOpn6elGXGflYl8npIrkoH/vO\nDz74wPiJY5IUCoX07LPPavny5Wpvb9f48eN14okn6sYbbzR+dG55ebkqKytVWloqi8XCz6Ih1JWa\nTPWdefPNOFAlAAAgAElEQVTkMhgMGrlOaWmpent7jVxrIDabTQ6HQ9FodFCfUo80wVySOjs7tXLl\nSrlcLpWUlGjOnDm6/fbbVVlZafz4x9LSUlVWViZXLyb+/u12u6qrq+Xz+TJyKkamvl7UZUY+1lXo\n4TIf+0673a7e3l5jT/h27twpl8ul1atXa9KkSWpsbNR5550nh8OR7DtN9J+JBY4VFRXJJ6CBQEAS\nP4umUFdqMtV35k24LHSHm2De1NR00AkQd911l04++eS0rMwuKytLhkoAyFfBYFDPPvusnE6n3nvv\nPV1yySVavHixxo8fb/xeiQMjKioqWPkNiHCZ07Zs2aIrrrhClZWVamxsNHoCxIESn7YJlQDy2bvv\nviuXy6Unn3xSU6ZM0RVXXKEZM2akZVcLi8WSnDZEqAT+iXCZI+bPn6+2tjbt2LFD0v75F2PHjtW3\nvvUtoydAHKiioiK5eS8A5KNAIKCnn35aTqdTu3fvVn19vR544AGNGzcuLfezWCyqqqpSeXk5W7EB\nh0C4zAF79uxRa2urfD6fRo8ercmTJ+v2229PW8co/XOyuemVkQCQKe3t7XK5XFq3bp1OOeUUXXnl\nlTrnnHNks6Xnn7bEk8qKigpCJTAAwmWWRCIRvfjii3I6nXrzzTd1wQUX6Be/+IUmT56c1vuWlpaq\nqqqKJ5UA8pLf79dTTz0ll8ulvXv36tJLL9VDDz2kMWPGpPW+FRUVycWTAAZGuMyw3bt3q6WlRa2t\nrRo3bpwaGhr0k5/8RKWlpWm9b2JLocS2GACQT95++225XC6tX79eZ5xxhq699lpNnz497afcMMoD\nDB3hMgMikYiee+45OZ1Ovf3227rwwgt13333adKkSWm/t8PhUFVVlcrKygiVAPKKz+fTunXr5HQ6\n5Xa7demll+qRRx5RXV1d2u/NKA8wfITLNNq5c6ceeOABrVy5Uscdd5waGhp0zz33ZOQTsN1uT4bK\nQ22g3NnZqebmZklSU1OTamtr094mADiSeDyuv/71r3r00Uf1zDPPaNq0abrhhht01llnGdkM/kgY\n5QFSR7hMk7a2Nt1444266KKL9Otf/1oTJ07MyH2tVqtGjRqVPFHnUDo7OzVv3jxt3bpVkrRmzRot\nW7aMgAkg6xYtWqSHHnpIl156qR577LEj9kvd3d1asmSJpP27bgz3pB1GeQBzCJdpcvbZZ+v5559X\nT09PRs6RtVgsqq6uVkVFxRHnIDU3NyeDpSRt3bpVzc3NuvPOO9PdTAAY0LXXXquvfOUr8nq9R+w7\nDzzZbMOGDQedbHYkNptNlZWVhx3lATB0/CSlicPhyMjwt8Vi0ahRo3Tsscdq1KhRaZ/cDgDpNJSQ\nd7iTzQbDarWqpqZGxxxzjCoqKgiWgEH8NOWxiooKjR07VtXV1UPaHqOpqanflkeTJ09WU1NTOpoI\nADkl8YF87NixqqqqStuemMBIxk9VHkp1FWNtba2WLVvGgh4AeW3+/PnasGFD8unlJz/5Sc2fP/+w\n72evSiAzCJdp0NnZqd///vcKh8NqaGhQZWWlkeuanHBeW1vLHEsAea26ulqLFi064oIethUCMotw\nadiBK7FXrVo15AnmB7JaraqqqmJeEAAcoLq6Wtdff/0hf48V4EB25ERSicViuv/++/Xoo49muykp\nO3Al9lAmmB/owLlBBEsAOLLEYp0xY8aovLycYAlkWE48uXzppZdUV1enYDCY7abkjIqKClVVVclu\nt2e7KQCQFywWS3KUh4U6QPZk/VFYT0+P2tvbdeaZZ2a7KUYcuBL7SBPMD1RaWqoxY8Zo9OjRBEsA\nGKSKigqNGzdO1dXVBEsgy7L+E/jkk0/qwgsv7PfU0u12y+v19ntfKBRSeXl5yvezWq1pDW1jxoyR\n0+lUc3OzgsGg5s6dO6gFPXa7XdXV1cPeyDfddSUkOu1Mdd7UlRrqGlnyue/se5++vx5JSUmJRo0a\nNezjGgv1e5a6UkNdqbHE4/F4Wu8wgHfeeUf/+Mc/9KUvfUnbt2/Xiy++qK997Wt65pln1NbW1u+9\nM2fO1KxZs7LU0qGLx+PauXOnent7B3xf4mSd2tpatscAkJJC6Duj0aja29t1pH+aEvMqa2trOTwC\nyDFZDZdPPfWUtmzZoqKiIkUiEQWDQZ100km64IIL0vbpu7i4OCNzO61Wq9xut/bt23fYI8wqKys1\natQo2Wy2lCecZ6oum82mmpoadXV1KRKJpP1+1JUa6jqyuro6Q63KvnQ+ucxk3/nxxx/L5/Mdsu8s\nKipSVVWVKisrjTx9ycfv2cGgrtRQ15EN1HdmdUzpggsu0AUXXCBJ2rFjh1544QVddtllkqSqqqp+\n7921a5fC4XDK97TZbEaucySJzB6NRg/qIA/cc83EN26m6kqIRCIZuR91mUFdI0NVVVVB9519FzrG\n4/G8qiuBn8XUUJcZ6a6LCUsZxJ5rADB0bIIO5JecCZcTJ07UxIkTs92MtGATdAAYOrvdrlGjRvGB\nHMgzORMuC1VlZaXKysrYVggABqmoqEg1NTUqKSlhsQ6QhwiXaVRXVyefz5eRycAAUCiOPfZY+k4g\njxEu08Risai8vFx+vz/bTQGAvNDZ2akHHnhAZWVlWrBgwUGLkwDkB8IlACDrOjs7NW/ePG3dulWS\ntHTpUi1btky1tbVZbhmAoWJ1CQAg65qbm5PBUpK2bt2q5ubmLLYIwHARLgEAAGAM4RIAkHVNTU2a\nPHly8v9PnjxZTU1NWWwRgOFiziUAIOtqa2u1bNkyFvQABYBwCQDICbW1tfre976nuro6dXR0cLQn\nkKcYFgcAAIAxhEsAAAAYQ7gEAACAMYRLAAAAGEO4BAAAgDGESwAAABhDuAQAAIAxhEsAAAAYY4nH\n4/FsN2Iw9u3bp6Ki1LNwUVGRYrGYgRYNzGKxyOFwKBQKKRN/xdSVGuoyIx/rqqmpMdSq3ETfOTDq\nSg11mZGPdQ3Ud+bNCT3BYNDIdUpLS9Xb22vkWgOx2+2qrq6Wz+fLyCkT1JUa6jIjH+sq9HBJ3zkw\n6koNdZmRj3UN1HcyLA4AAABjCJcAAAAwhnAJAAAAYwiXAAAAMIZwCQAAAGMIlwAAADCGcAkAAABj\nCJcAAAAwhnAJAAAAYwiXAAAAMIZwCQAAAGMIlwAAADDGEo/H49luRCFyu93atGmTpk2bpqqqqmw3\nxxjqyi/UhXxTqF9b6sov1JUanlymidfrVVtbm7xeb7abYhR15RfqQr4p1K8tdeUX6koN4RIAAADG\nEC4BAABgDOESAAAAxljvuuuuu7LdiEIUj8flcDg0ceJEFRcXZ7s5xlBXfqEu5JtC/dpSV36hrtSw\nWhwAAADGMCwOAAAAYwiXAAAAMIZwCQAAAGMIlwAAADCGcAkAAABjCJcAAAAwhnAJAAAAYwiXAAAA\nMIZwiYKxbds2jR49Wps3b5Yk7dq1S3V1ddqwYUOWWwYAuYl+E+nACT0oKL/73e9033336bXXXtOc\nOXN0+umn6+c//3m2mwUAOYt+E6YRLlFwGhsb9e6778pqterVV1+V3W7PdpMAIKfRb8IkhsVRcL7x\njW/ob3/7m771rW/RQQLAINBvwiSeXKKgeL1enX766frCF76gVatW6Y033lBNTU22mwUAOYt+E6YR\nLlFQrrvuOvn9fj322GO6/vrr1d3drSVLlmS7WQCQs+g3YRrD4igYTqdTa9eu1X//939Lku699169\n/vrreuyxx7LcMgDITfSbSAeeXAIAAMAYnlwCAADAGMIlAAAAjCFcAgAAwBjCJQAAAIwhXAIAAMAY\nwiUAAACMIVwCAADAGMIlAAAAjCFcAgAAwBjCJQAAAIwhXAIAAMAYwiUAAACMIVwCAADAGMIlAAAA\njCFcAgAAwBjCJQAAAIwhXAIAAMAYwiUAAACMIVwCAADAGMIlAAAAjCFcAgAAwBjCJQAAAIwhXAIA\nAMAYwiUAAACMIVwCAADAGMIlAAAAjCFcAgAAwBjCJQAAAIwhXAIAAMAYwiUAAACMIVwCAADAGMIl\nAAAAjCFcAgAAwBjCJQAAAIwhXAIAAMAYwiUAAACMIVwCAADAGMIlAAAAjCFcAgAAwBjCJQAAAIwh\nXAIAAMAYwiUAAACMIVwCAADAGMIlAAAAjCFcAgAAwBjCJQAAAIwhXAIAAMAYwiUAAACMIVwCAADA\nGMIlAAAAjCFcAgAAwBjCJQAAAIwhXAIAAMAYwiUAAACMsWW7AYP1/e9/X6FQKNvNAFBgHA6H7r77\n7mw3I23oOwGkw0B9Z96Ey1AopGuuuSbbzQBQYB588MFsNyGt6DsBpMNAfSfD4igIF110kV566SWt\nWLFCV1999UGvAwD+6aKLLtLixYv12c9+Vn6//6Dfv+KKK/SnP/0pCy1DISBcoiBYLJbkf4d6HQDw\nTxaLRaeddprGjBmjdevW9fu99vZ2vfvuu/riF7+YpdYh3xEuUVDi8Xi2mwAAeaOxsVEtLS39Xmtp\nadF5552nUaNGZalVyHeESwAARiCLxaL6+npt2rRJH330kSQpFotp1apVamhoyHLrkM8IlwAAjFBj\nx47VZz7zGbW2tkqSXnrpJYXDYZ133nlZbhnyGeESAIARrKGhITk03traqi9+8YuyWq1ZbhXyGeES\nAIAR7Atf+IL27NmjV155RevXr2dIHCkjXKJgsJgHAIaurKxMs2fP1g9/+EMdc8wxOvnkk7PdJOQ5\nwiUKBtsOAcDwNDQ0aPfu3Ty1hBF5c0IPMJA1a9Yk/3djY+MhXwcA7Hdg33jWWWdpy5YtWWoNCk3e\nhMtIJKLFixenfJ05c+ZoxYoVBlo0sOOOO0533HGHfvGLX+j9999P+/2oKzXUZUY+1lVRUWGoVbmJ\nvnNg1JUa6jIjH+saqO+0xPNkotquXbuMXKe0tFS9vb1GrjUQu92uuro6dXR0KBwOp/1+1JUa6jIj\nH+s65phjDLUqN9F3Doy6UkNdZuRjXQP1ncy5BAAAgDGESwAAABhDuAQAAIAxhEsAAAAYQ7gEAACA\nMYRLAAAAGEO4BAAAgDGESwAAABiTkRN6VqxYofb2dpWXl+umm25Kvv7yyy/r1VdflcVi0eTJkzV7\n9uxMNAcAAABpkpFwOXXqVE2fPl3Lly9PvrZ9+3a98847uvHGG2W1WuXz+TLRFAAAAKRRRobFJ0yY\noJKSkn6vvfrqq5oxY4asVqskqby8PBNNAQAAQBpl5MnloXR2dmrnzp1av369bDabLrzwQh177LHZ\nag4AAAAMyFq4jMViCgQCampq0ocffqilS5fqtttukyS53W55vd5+7w+FQkaeblqtVtnt9pSvcyQ2\nm63fr+lGXamhLjMKta58Qd85dNSVGuoyo9DqylrPXFVVpZNOOkmSdOyxx8piscjv96usrEybNm1S\nW1tbv/fPnDlTs2bNykZTU1JTU5PtJqQFdeUX6hoZ6DtzH3XlF+oanqyFyylTpmj79u2aOHGi9u7d\nq2g0qrKyMknStGnTdOKJJ/Z7fygUUkdHR8r3LS4uVjAYTPk6R2Kz2VRTU6Ouri5FIpG034+6UkNd\nZuRjXXV1dYZalX30nUNHXamhLjPysa6B+s6MhMsnnnhCO3bsUG9vr+69917NmjVLU6dOldPp1G9/\n+1tZrVbNnTs3+f6qqipVVVX1u8auXbsUDodTbovNZjNyncGKRCIZuR91mUFdqSnUuvIFfefQUZcZ\n1JWaQqsrI+Hy8ssvP+Trl112WSZuDwAAgAzhhB4AAAAYQ7gEAACAMYRLAAAAGEO4BAAAyBPxeFx+\nv1+9vb3ZbsphsQMxAABAHggEAnK73ert7VVtbW22m3NYhEsAAIAcFgwG5fF45PP5st2UQSFcAgAA\n5KBwOCyPxyOv16t4PJ7t5gwa4RIAACCHhMNh+Xw+ud3uvAqVCYRLAACAHBCNRuX1euXxeBSNRrPd\nnGEjXAIAAGRRNBqVx+OR2+3OyFnm6Ua4BAAAyILEtkIdHR3y+/3Zbo4xhEsAAIAMisfj6u3tlcfj\nUSAQkMPhyHaTjCJcAgAAZEjfvSoLFeESAAAgzfJtr8pUEC4BAADSJF/3qkwF4RIAAMCwSCQir9eb\nt3tVpoJwCQAAYEg0Gk1ugJ7Pe1WmwhLPkzi9b98+FRUVpXydoqIixWIxAy0amMVikcPhUCgUysgn\nFupKDXWZkY911dTUGGpVbqLvHBh1pYa6/imVvSotFsuQ//5qa2tVXV095Ptkou/MmyeXwWDQyHVK\nS0szskLLbrerurpaPp9P4XA47fejrtRQlxn5WFehh0v6zoFRV2qo6597VbrdboVCoWHdLxH4hiIU\nCg357yJTfWfehEsAAIBc0tvbK7fbrUAgkO2m5BTCJQAAwBAEAgF5PJ6COlXHJMIlAADAIIRCoeS2\nQjg8wiUAAMAAwuGwvF6vPB7PiNtWaDgIlwAAAAeIx+MKh8Pq6emR2+3OyCr1QkG4BAAA6CMSiejj\njz/W7t27h70CfCQjXAIAAEiKxWLy+/3yer0qLS0dsZugp4pwCQAARrQD96q0Wq3ZblJeI1wCw9TZ\n2anm5mZJUlNTk2pra7PcIgDAUPX29srj8WRkc/aRgnAJDENnZ6fmzZunrVu3SpLWrFmjZcuWETAB\nIE+wV2X6pH7gLDACNTc3J4OlJG3dujX5FBMAkLvC4bD27dunPXv2ECzThCeXAACg4BXSXpXd3d36\n4x//KIfDkZPTsgiXwDA0NTVpzZo1yaeXkydPVlNTU5ZbBQA4UDQaldfrLZi9Kru7u3XzzTdr+/bt\nknJzWhbhEhiG2tpaLVu2jAU9AJCjYrGYfD6fenp6CmpLoSVLliSDpfTPaVl33nlnFlvVH+ESGKba\n2tqc+mEGABy8rVChCIfD2rhxo9atW5ftphxRxsLlihUr1N7ervLyct10002SpLVr12rr1q2yWq2q\nqanRnDlzVFJSkqkmAQCAAhGPx9Xb2yu3261gMJjt5hjzwQcfyOVyadWqVZowYYKuvPJKPf7449q5\nc6ek3JyWlbFwOXXqVE2fPl3Lly9PvjZp0iRdcMEFKioq0rp167Rx40bNnj07U00CAAAFIBAIyO12\nF8xelaFQSG1tbXK5XNq2bZsuueQSLVq0SBMmTJAkzZo1Sy6XiwU9EyZMUFdXV7/XJk2alPzf48eP\n19///vdMNQcAAOS5YDAoj8cjn8+X7aYYsWPHDrlcLq1Zs0bHH3+85syZo3PPPVcOh6Pf+6qrq3Xb\nbbepsrIySy0dWM7Mudy8ebNOPfVUSZLb7ZbX6+33+6FQSOXl5Snfx2q1ym63p3ydI7HZbP1+TTfq\nSg11mVGodeUL+s6ho67UZKMum82mUCgkj8cjj8eTfN30fdJx3UMJBAJav369li1bpvfee0/19fX6\n/e9/r/Hjxx+xjUP9Gmfq65UTPfOGDRtktVp12mmnSZI2bdqktra2fu+ZOXOmZs2alY3mpaSmpibb\nTUgL6sov1DUy0HfmPuoavsScykgkkgyV6X5yV1ZWlrZrt7e3a+nSpWptbdWpp56qhQsX6vzzzx90\nYKyqqlJdXd2w7p3ur1fWw+XmzZvV3t6ur3/968nXpk2bphNPPLHf+0KhkDo6OlK+X3FxcUYm+tps\nNtXU1Kirq0uRSCTt96Ou1FCXGflY13A751xE3zl01JWaTNUViUTk9Xrl8/kysgLcarWqrKxMfr/f\n6DZGgUBATz31lFasWKGPPvpI9fX1+sMf/qBPfOITikQiCgQCCgQCg7qW3W6XxWIZ0v0z1XdmNVy2\nt7frhRde0DXXXNMvqVdVVamqqqrfe3ft2qVwOJzyPW02m5HrDFYkEsnI/ajLDOpKTaHWlS/oO4eO\nusxIV12JvSrdbrcikYgcDkdG96yMRqNG7rd161a5XC6tW7dOp512mhYsWKCzzz47OTwdj8eHfJ9o\nNDrsv/N0fx9mLFw+8cQT2rFjh/x+v+69916df/75eu655xSNRvXwww9L2r+op76+PlNNAgAAOagQ\n9qr0+/166qmn5HQ61dnZqfr6ev3xj3/UmDFjst20tMtYuLz88ssPeu3MM8/M1O2BnJLv59oCQDok\n5lV6PJ5BDw/nkng8rrffflsul0tPP/20pk6dquuuu07Tp0/PyOKgXJH1OZfASBIOh+X1enNuTzIA\nyLZ83qvS6/Vq7dq1crlc8ng8amho0COPPFJQc7qHgnAJZEAsFpPX65Xb7VY0GiVcAsD/CYVCcrvd\nebdXZTwe19/+9je5XC61tbXpM5/5jG688UadddZZKioqynbzsopwCaRRIcwbAoB0SIzkeDyevJoq\n5Ha79eSTT8rlcikYDKqhoUGPPfYYDw36IFwCaZLPQzwAkC6RSEQ+n089PT15Eyrj8bi2bNkip9Op\n5557Tp/73Od02223aerUqSP+KeWhEC4Bw8LhsDwej7xeb950nACQbtFoNLmtUCa3E0pFT0+PWltb\n5XK5FIvF1NjYqFtvvVXV1dXZblpOI1wChhw4rxIAsL9vTEwPyod9aePxuF577TWtWrVKbW1t+vzn\nP6/vfve7Ov3004e8aflIRbgEUsS8SgA4WL71jZ2dnVq9erVcLpccDoe+/OUv69vf/rYqKiqy3bS8\nQ7gEUsC8SgA4WG9vr9xud87vVRmLxfTaa6/J5XLplVde0cyZM/Wv//qvOv3001VVVSWPx8NI1DAQ\nLoFhCIfDcrvd8nq92W4KAOSMQCAgj8cjv9+f7aYMaO/evVq1apVcLpfKy8vV0NCg//f//l/yKSXD\n36khXAJDEIlEkvMqWawDAPuHv4PBoLq6unL6A3c0GtUrr7wil8ul119/XbNmzdJPfvITTZkyhTBp\nGOESGIRYLJbcOoMhEgDYLxwO68MPP9Tu3bsViUSy3ZxD+vjjj9Xa2qqWlhbV1NSosbFR//qv/6ry\n8vJsN61gES6BAeTbhHQAyITEXpU+n0/l5eU5N5ITiUT00ksvyel0asuWLbrgggt0zz336MQTT8x2\n00YEwiVwGCzWAYD+Dtyr0mq1ZrtJ/ezevVutra1qbW3V0UcfrcbGRv3bv/2bSktLs920EYVwCfQR\nj8cVCoWSm6ADAP45itPT05Nze1VGIhE9//zzcjqdeuuttzR79mzde++9mjRpUrabNmIRLgHt7zh7\ne3vV1dWlrq6unBviAYBsyOWpQR9++KFaWlq0cuVKjR8/Xg0NDfr3f/93FRcXZ7tpIx7hEiNeYpin\nq6srr866BYB0ysWpQeFwWOvXr5fL5VJ7e7suuugi/frXv9bEiROz3TT0QbjEiJVYAZ7YVqiysjLt\n9+zp6dHq1atVXFys2267Le33A4ChCgaD8ng88vl82W5K0nvvvaeWlhatXr1aEydOVENDg2bOnMlT\nyhxliefJY5p9+/apqKgo5esUFRUpFosZaNHALBaLHA6HQqFQRp6EUdfgxWIxeTwe9fT09Ns6w2az\npWUrjXg8rk2bNmn58uV6/vnnde655+qmm27S5z//eeP3OlAhfL0OxWRdNTU1hlqVm+g7B0Zd+yX2\nqhzO4RDp6juDwaCeeeYZLV++XNu3b1d9fb3mzp2r4447zvi9DiVddR2KxWIZ8vdFbW2tqqurh3yf\nTPSdefPkMhgMGrlOaWlpRh7x2+12VVdXy+fzZWTyM3UdWWJepdvtPuj7yWq1qqysTMFg0Ng+lp2d\nnVq1apVaWlpkt9vV0NCg73znO6qqqtLEiRP5eqXAZF2FHi7pOwdGXfuHmvuO4gxFOvrOHTt2yOl0\n6sknn9QJJ5yguXPn6txzz5Xdbk8Go3RLR10DGU5doVBoyN+7meo78yZcAqno7e2Vx+NJ+z8iiXNq\nnU6nXn31Vc2cOVM//OEPdcopp3ACBICcEo1G5fV6c+L87GAwqKeffloul0sffPCBvvSlL6m5uVnH\nHntsVtuF4SFcoqBl6pzbvXv3auXKlWppaVF5ebkaGxv1ve99L3lOLQDkir7zzbN9qs62bdvkdDq1\ndu1anXLKKfrKV76iz3/+87LZiCcHstlsKikpUXFxsRwOh+x2e7abdFh89VCQMrFXZeKcWqfTqc2b\nN3NOLYCclivbCvX29mr9+vVyOp36+OOPdemll+oPf/iDxo0bl7U25SKr1ZoMk3a7XcXFxXnzbwvh\nEgUlFArJ6/XK6/WmbdL8oc6p/eEPf8g5tQByUmK+ucfjUSAQyFo73nnnHblcLj311FM6/fTTdc01\n12j69Ok8pfw/RUVF/Z5MOhwOI4vxsoGvKApCMBiU1+uVz+dLS6iMRCJ68cUX5XK5tGXLFs2ePVv/\n8R//ocmTJxu/FwCYku29Kn0+n9atWyeXy6Wuri41NDTo4Ycf1tFHH52V9uQSi8WSDJOJKVS5dpzm\ncBEukdcCgUAyVKbD7t271dLSotbWVo0dO5ZzagHkhd7eXu3duzcre1XG43G99dZbcjqdevbZZ3Xm\nmWfqm9/8ps4666yCCU/DYbFYVFxc3O/JZOKpbaZ2LcgUwiXyUjpDZSQS0XPPPSen06m3335bF154\noe677z7OqQWQ88LhsDwej4LBYMbnVXq9Xj355JNyuVzy+XxqaGjQ//7v/+qoo47KaDtyhcVikd1u\nV2lpaXIbpVxehGPSoMLlbbfdpquvvlpTp05Nd3uAw4rH48lQmY7V3zt37tRjjz2mlStX6rjjjlND\nQ4PuueceoydAdHd3a8mSJaqurtY3vvEN1dbWGrs28hv9bOHq7OzUAw88oLKyMi1YsEBVVVXG7xGJ\nROT1epN7VTocDuP3OJR4PK7Nmzfr0UcfVVtbm6ZPn65bbrlF06ZNy9v5gqmw2+0qKSmRw+FILsQZ\niQYVLmOxmC6++GLV1dXpqquu0pVXXqnx48enu22ApH9ORvd6vcaHDUKhkDZs2KCWlhZt27ZNF198\ncdrOqe3u7tbNN9+s7du3S5JWr16tZcuWETAhiX62UHV2dmrevHnaunWrJGnp0qVGf+6ztVel2+3W\n6gye84QAAB4FSURBVNWr1dLSomg0qoaGBt14440jrj871PZA+bKiO50G9bHiv/7rv/Thhx/qnnvu\n0ebNm3XSSSfpggsu0EMPPZTWrV4wssXjcXk8HnV0dKijo8NosNy5c6d+/etfa86cOXI6nWpsbNTT\nTz+tb3/722kJlpK0ZMmSZLCUpK1bt6q5uTkt90L+oZ8tTM3NzclgKZn7uU8cY/vRRx+pu7s7I8Ey\n8ZTyxz/+sS6//HK99dZbuv3227Vq1SotWLBgRARLq9Wq8vJy1dbWauzYsTrmmGM0evRoVVRUyOFw\nECz/z6DnXNpsNtXX16u+vl5vvvmmvva1r2nhwoW66aab9NWvflU//vGP2UkfRkSjUfn9/uQ/qKbm\nDQWDQT377LNyOp167733dMkll2jx4sUaP368rFarHA6HsaPygOGgn8WRZGOvyu7ubq1atUoul0tF\nRUVqbGzUbbfdplGjRslqtRZ0oCoqKjpoEc5IHO4fqkGHy56eHi1dulSPPPKItmzZonnz5um3v/2t\nJkyYoF/+8pe6+OKL9cYbb6SzrShwoVAoGSoTn8JNzBt699135XK59OSTT2rKlCm64oorNGPGjIzP\nhZk/f742bNiQfHo5efJkNTU1ZbQNyG30s4WnqalJK1eu1LZt2yRJkyZNGvbPfW9vr9xud0b2qozF\nYnr99dfldDr18ssv69xzz9X3v/99ffrTny7oMJnYHqisrEx1dXUKBAJp2zO5kA0qXF5++eVas2aN\nzj33XN1www1qbGzstxXLvffem5YJyih8ifmUPp/P6CKdQCCgp59+Wk6nU7t371Z9fb0eeOCBrJ4A\nUV1drUWLFrGgB4dEP4vDydQxttL+OaIrV66Uy+VSSUmJGhsbdccddxTs917f7YESTyetVqvsdrtq\namrU0dGhcDic7WbmnUGFy+nTp+s3v/mNxo4de8jfLyoq0p49e4w2DIWt79C3yaGd9vZ2uVwurVu3\nTqeeeqquvPJKnXPOOTlzAkR1dbWuv/56TZw4kU/D6Id+tjA1Nzcnn1pK+8/Sbm5u1p133nnEP5uJ\nY2yl/U8pX3nlFblcLm3atEkzZ87UXXfdpZNPPrngnlL23R7owL0mYc6g/kbvuOOOI76Ho+8wGIca\n+k6V3+/XU089JZfLpb1796q+vl4PPvjgYf+RzqbEp+SioqKMruxE7qOfRUI4HE6uAE/nh9COjg6t\nXLlSLS0tqqysVGNjo37wgx8U3PfZgWFypG4PlElZj+sbN27Uli1bZLFYdPTRR2vOnDl8iigwsVhM\ngUBAfr9ffr/fWGf59ttvy+l06umnn9YZZ5yhhQsX6nOf+9yQT4Do7u7W448/ruLiYl122WWqrKw0\n0j5p/wKNvhPB7XZ7cvFQIZ3GAODQmpqatGbNmuSK8YHmWkciEfl8PrndbsVisbS0JxqN6qWXXpLL\n5dJf/vIX/cu//It+9rOfacqUKWm5Xzb03WuS7YGyI6sprqurS5s2bdItt9wim82mpUuX6s0339QZ\nZ5yRzWbBgHg8rmAwqEAgIJ/Pp0gkYuS6Pp9Pa9eulcvlUk9PjxoaGvTII4+orq5uWNc7cO/JtWvX\natGiRaqurh7ytSwWS7+Nc+nUANTW1mrZsmUDbqIejUaToTJdIxp79uxRa2urWltbNXr0aDU0NOhH\nP/qRysrK0nK/TLJarf2eTLIlUPZlNVwWFxfLarUqHA7LYrEoHA4bfWqEzAuHw8kFOqbmUsbjcf39\n73+X0+lUW1ubpk2bphtuuEFnnXVWyltCHLj35Pbt27VkyRJdf/31R/yzVqs1+enYbrfLbrfz1B3A\noMViseS2QulYNBKJRPTCCy/I5XLpzTff1OzZs/Xzn/9cJ5xwgvF7ZVLfvpftgXJTVv8lLCsr09ln\nn6377rtPNptNxx9/vCZNmiS3233QBOZQKGRkHkhiFVi6JUJGpsJGtuqKx+OKRCLJJ5R9h3qHOjx9\nII/HoxUrVujPf/6zAoGAGhsbtWTJEo0ePfqwf6a7u1uP/f/27j82yruOA/j7ud+/e9fR0g6QQs2g\nC6MFImyuWutq4yZruwlBkaCiZBnBZMmcbgkmyEJcjBmJ0xnBQfyRkdKC3FELzGHTQhjSjQ7mzGpH\noWUpCO2V/u799g9yZw/6+77PPfcc71eyLDTXe74f7vbZ53m+3+/ne/AgAODb3/72lE8gx7u7lSRp\n3LFHE9rd/c5mc4fM72Fikh2XWjB3zpzccfX09MSd0FNbW4sjR47AZDLF9apMNF+O1dXVhbq6Ong8\nHuTm5qKqqgq/+MUvYDKZhF0jKjpukeO/W7Q9kNFohM1mg0ajmXXuna50+x5GJSsuKaLgllWv14u3\n334bW7ZsgdFoRE1NDR5++GH09PSgsbEx7rUlJSUoLS1VaKQ0VrSg7O/vx9DQEIaGhoS+94ULF1Bb\nW4t//OMfKC4uxvr167F69eop70x7e3uxefPmuH5yf/rTn+ByuWb1O9GplmhSs1gsad8wmNStoaGB\nuTPF7NixA7t374772bZt27B9+3ah1wkEAmhoaEBtbS3+9a9/4emnn8a6detU+5TSYDDAbDbH/jEa\njcy9KqLobX9XVxcWLFgQW/NRUFCAa9eu4Utf+hKWLFkS91q/349bt24lfE2j0ZiUU1h0Oh1cLhd6\ne3uFrTecjNxxRSIRhMNhBINBaDQa3Lx5U2gLob6+PtTX1+Po0aOIRCKorKxETU0NsrKyYovcpzJR\ny4/nn39+wt/R6XR48803UV1dDaPRiM2bNyM7O/ue9ZI+n0/o3y+/h4kRGdds1+umolWrVjF3zpDc\ncXV3d9/zs97eXgwMDAh5/2vXrsHj8aCurg4LFy5EVVUVdu/eDZvNhmAwKOw6E9FqtbBYLBgeHp71\netHoevXoVLfRaIROp4MkSbEYonHwe5iYZOVORYvLOXPmoLGxEYFAADqdDu3t7Zg3bx4cDsc9C567\nurqErEnR6XRJbYgaDAaTcj254gqFQhgdHYXP54s18LXb7fD7/QkvPI+eU+t2u/Hee+/h8ccfx0sv\nvYSioqJYUReJRKZ9nfEewk/2+9EdhU6nE7t27UJubi66u7tj/8HJ2SqI30MxkhWXWjB3zpzccY23\n6zscDieUX/x+P5qamuB2u3H58mU8+eST+M1vfoO8vLzYa2aSO0UIhUIzut7YYnK89kATFT78Hooh\nd1yKFpc5OTkoLCzE3r17IUkScnNzsWrVKiWHRLgzvRLd6T0yMhKXHEWsq/F6vTh+/Dg8Hg/0ej0q\nKirw4osvxv1P8fbt26iuroZWq8W6deumtXv77uMVFy1ahA0bNsSNPTq9cveTSb1eL/saHiK6/4w9\nZSlqtmsfOzo64PF4cPz4ceTn56OyshJf/vKXhRyTK7ex7YGiHTUofSm+Gr64uBjFxcVKD+O+FolE\n4Pf74fP5MDIyAp/PJ7xxbzgcxvvvvw+Px4Pz58+jpKQEO3bswLJly+4p6O5uD9TQ0DCt9kBjj1cE\n7mzomTt3btzdMXcUElEy3d3n8u6b3qn4fD40NDTA4/Ggs7MTTz31FPbu3Yv58+fLNWQhdDpd3AZI\ntmW7vyheXJIyQqFQXEEpcv3kWN3d3bETIKxWKyoqKvDyyy/DZrNN+DuJtAfKzs7GT37yk7gzYomI\nlBLtc/nWW29hZGRk2gc1tLe3w+Px4OTJk1i6dCnWr1+P4uLilH3iN7abhl6v5wac+xyLy/tEtKl5\ntKAcHR2V9QSI8+fPw+12o6WlBaWlpdi1axcKCgqEJ5toi4qxd8hMaESUSjIzM/HKK6/A6/VO2ih9\ndHQUp06dgsfjwfXr17F27Vrs378fubm5SR7x1DQaDUwmEywWC7Kzs+9ZQkX3NxaXaSoSiSAQCMQ9\nnZR7cffNmzdRV1eHY8eOweVyobKyEj/72c8QCARQXV2N06dPY8OGDVNOb0+1dlKj0cS1CErVO3ki\nouloa2uDx+PB3//+dyxbtgzf+c538MUvfjGl+riO7TUZXWYU7c2YkZEBv9/P4pJiUuebSwkZW0z6\n/X6MjIwkZedZMBjEuXPn4Ha7cenSJZSVleG1116LtUO5e/1kU1PTlOsnx66djG7omTNnTlxByelu\nIlILr9d7z7T48PAw3n33XXg8HnR3d+Ppp5/GH//4R8ydO1fp4QK4U0wajca4YjKVil1KbfymqFgg\nEIgVlOFwGIODg8I34kzk+vXrcLvdqKurQ3Z2NiorK7Fr1657dkbOdv2k0+nE9u3b4XQ6odFouBmH\niFTJ6/Wiqqoq1oPX7XZjxYoVOHPmDIqKirBlyxasWbNG8RtmSZKg1+thNpuh1+vHbQ9ENF0sLlUi\neiqOz+dDIBDA6OgoAoFArJg0GAyyF5bd3d3Ys2cPPv30U/T19aG8vByvv/468vPzhV1Dq9XCarXG\nWgZZLJa4IyWJiNTk17/+ddzhDjdu3MBnn32Gv/zlL4o38Gd7IJILi8sUFW0PFH0yGS0mlfDZZ5+h\npqYGR44cia3bXLhwIbZs2ZLw+kngzh2zxWKJTXsrfQdPRCTKhx9+OO7PlSgs2R6IkoXFZYoIhUJx\n09yjo6NJOXJqIn6/H6dPn4bb7cann36KBx98MG5DUEdHx7Snt8f2nhy7oSe609BkMvGOmYjS0ooV\nK9Dc3Bz3s4cffjgp1x7bHii6bpLFJCUDi0sFRKe4xz6Z9Pl8KbHTrrOzEx6PB/X19Vi8eDEqKytR\nUlKCAwcO4N///ves3tPpdMaKUL1eD6vVGkt4RETpbNOmTThw4EBs5kmn06GqqkqWa0XbAxmNRtjt\ndkQiEa5VJ0WwuJRZJBJBOBy+p5AMBoNJ23wznujxigDwzDPPoKWlBR6PBx0dHXjqqafw+9//HgsW\nLIi9fjrT2xORJAk2mw0Wi4WNdYnovlJbWxu3pCkYDOLEiRPTOhRiKhqNJrajO7pmMrqsyGw2c706\nKYbFpUwikQj++9//4ubNmxgeHk6Jp5JRd7cH+vOf/4yioiKsW7duwhMgotPbhw4dgtFonNYpE9Gn\nlBaLhdPeREQJGtseiKeQUSpjcSmjkZGRlDu1YHR0FLt3745rDxQOh/HII4+gtLR00t91Op14/vnn\nYbfbMTAwMGFTdrPZHNvxzSkZIrqfJXK2+Nj2QOw1SWrCb+l9YuwJEA6HQ/j7j536NplMwt+fiEiN\nZnq2+N3FJGd9SI1YXKpcdO1k9CSbsa2BJjoBwmg0xk2Lz+RO+m6c+iYimtxkZ4vrdLp7ikmuSye1\nY3GpYnevnWxoaMBvf/tb3LhxAx6PB6dOnUJRURG+//3v49FHH41bmzNRe6DpMplMMJvNnPomIpoB\nrVYbV0yyPRClIxaXKjbe0YqbNm2CwWBARUXFpCdAjG0PNBNWqxUPPvhgbMc7ERFNT25ubsqtwyeS\nA4tLFRuvldHKlSuxc+dOoU8TJUmC1WqFzWaD1WpFRkYGbt26Jez9iYjSmdfrxf79+2GxWLBp0yZZ\n1r0TpRIpomSzxRno6ekRUjBpNJqk3TXeunULAwMD03797du38fbbbwMANm7cOOFU9cDAAOrr63H4\n8GF0dnbGniAuWrQI+/btm/EU90Sim3RsNhtMJhMkSYIkSTAYDPD7/Unp05msz4txiaHGuFwul6BR\npSa15c50+8729PRg7dq1aG1tBQAsWbIEdXV1eOCBB2S7JsDPK1GMa2qT5U7VPLn0+XxC3idZjWV1\nOh0MBgNCodCELXvGmmj9ZLRQjEQi+Oijj+B2u3H69GmsWbMGL7zwAhYvXoyamprYhh6LxQK/35/Q\n2CVJgt1uh81mi23SGR0dBXBnA4/T6cTQ0FBSzjpP1ufFuMRQY1zpXlyqLXem23f2jTfeiBWWANDa\n2oo33ngDP/3pT2W7JsDPK1GMa2ppUVymu/HWT1ZXV+Nb3/oWTpw4AY/Hg1AohIqKCmzfvj3uQ33u\nuedidyKJ0Gg0sNvtsFqt3PlNRCTAeAUDT86hdMfiUgZerxdvvfUWAoEAKioqpjzJZiJNTU04fPgw\nHn/8cfz4xz9GUVGRLLsKtVot7HY72wkREQk2Xs7m7nBKdywuBfN6vfjmN78ZO42hvr4+bnp7Ihs2\nbEBDQwM6OzsB3Hl0XV5ejmeeeUa2xd/RotJqtfLUByIiGYx3qAQPmqB0xwaFgu3bty9WWAL/n96e\nSDgcRnNzM371q1/B6/UiPz8fTz75JI4ePYrvfve7shSWGo0GTqcTOTk5yMjIYGFJRCSTrVu3Ij8/\nP/bn/Px8bN26VcEREcmPVYVCenp68Le//Q0ejwdWqxUVFRV4+eWXYbPZZLvmeBt1iIiIiERicSnY\n1q1bceLEidjTy7FHK4ZCIZw/fx4ejwcXLlxAaWkpXn31VSxdulT2NTg2mw12ux0Gg0HW6xAR0f/t\n27cPly9fjv358uXL2Ldvn+y7xYmUxOJSsMzMTBw+fDhuQ8/IyAj279+PY8eOweVyobKyEjt27IDV\napV9PFarFXa7HUajUfZrEREREbG4lEFmZiZeeukl1NfX49VXX8XFixdRVlaG1157DUuWLEnKGMxm\nMxwOBxeOExEp6O7ZrIceeohrLintsbiUyZkzZ/Dmm2/iG9/4Bn7+85/DbDYn5bomkwl2ux1ms5nt\nLoiIFBadzeLxj3Q/YXEpk5KSEqxevRo3b96c1gk9idLr9XA4HLBYLCwqiYhSSGZmJl555RVkZWXh\n1q1bSTnxhUhJLC5lkqwCT6fTISMjA5mZmQmf0ENERESUKBaXKqXRaOBwOGCz2aDVaqHVapUeEhER\nERGLS7WRJCnWVoi9KomIiCjVsLhUEYvFAofDwbZCRERElLJYXKqA0WiMbdYhIiIiSmUpUVyGw2Hs\n3bsXDocDGzduVHo4KSO6A9xqtXIHOBEREalCShSX586dQ1ZWFnw+n9JDSQkajQYZGRmwWq3cqENE\nRESqolF6AH19fWhra8PKlSuVHoriJEmCw+FATk4OHA4HC0siIiJSHcWfXJ48eRLl5eVxTy37+/sx\nODgY9zq/3y/kLG6tVpuUXdbRwnC6BaLZbEZGRgZMJtOspsCTFZdOp4v7t9wYV2IY1/0lHXInv7Ni\nMK7EMK4EryPru0+htbUVVqsVubm5uHLlSuznH3zwARobG+NeW1JSgtLS0mQPcdYikQj6+/un3ISj\n1+vhcrngdDpV9aTS5XIpPQRZMC51Sde4ZisdcmdUun62jEtdGNfsSJFIJCLrFSbx7rvv4tKlS9Bo\nNAgGg/D5fCgoKEBZWZlsd99GozEpazu1Wi36+/vR09Mz7vGPWq0WDocDdrtdSFGZrLh0Oh1cLhd6\ne3sRDAZlvx7jSgzjmlpWVpagUSlPzieXavxsp4NxJYZxiaHGuCbLnYo+uSwrK0NZWRkA4OrVqzh7\n9iyeffZZAIDD4Yh7bVdXl5DzWHU6XVLOdY3W7KFQKK64lCQJdrsddrsdOp0O4XAY4XA44eslK66o\nYDCYlOsxLjEY1/3B4XCoPndG8TubGMYlBuOaHS5YSiI2QSciIqJ0lzLFZV5eHvLy8pQehiwMBgMy\nMjJgNpvZr5KIiIjSWsoUl+lIkiRkZmbCZDJBo1G86xMRERGR7FjxyCjar5KFJREREd0vWPXIRJIk\nGI1GToMTERHRfYXFJREREREJw+KSiIiIiIRhcUlEREREwrC4JCIiIiJhWFwSERERkTAsLomIiIhI\nGBaXRERERCQMi0siIiIiEobFJREREREJw+KSiIiIiIRhcUlEREREwrC4JCIiIiJhWFwSERERkTAs\nLomIiIhIGCkSiUSUHsR09PT0QKNJvBbWaDQIh8MCRjQ5SZJgMBjg9/uRjL9ixpUYxiWGGuNyuVyC\nRpWamDsnx7gSw7jEUGNck+VOXULvnEQ+n0/I+5jNZoyMjAh5r8no9Xo4nU4MDQ0hEAjIfj3GlRjG\nJYYa40r34pK5c3KMKzGMSww1xjVZ7uS0OBEREREJw+KSiIiIiIRhcUlEREREwrC4JCIiIiJhWFwS\nERERkTAsLomIiIhIGBaXRERERCQMi0siIiIiEobFJREREREJw+KSiIiIiIRhcUlEREREwrC4JCIi\nIiJhpEgkElF6EOmov78fH3zwAVatWgWHw6H0cIRhXOrCuEht0vWzZVzqwrgSwyeXMhkcHERjYyMG\nBweVHopQjEtdGBepTbp+toxLXRhXYlhcEhEREZEwLC6JiIiISBgWl0REREQkjHbnzp07lR5EOopE\nIjAYDMjLy4PRaFR6OMIwLnVhXKQ26frZMi51YVyJ4W5xIiIiIhJGp/QA0lk4HMbevXvhcDiwceNG\npYcjxOnTp3Hp0iVIkoTs7GxUVVVBp1Pf1+jo0aNoa2uD1WrFtm3bAADvvPMO/vOf/0Cr1cLlcqGq\nqgomk0nhkc7MeHEBwD//+U80NzdDkiQ89NBD+NrXvqbgKGeur68Pf/3rXzE0NAQAWLVqFR599FEM\nDw+jtrYWt2/fhtPpxPr162E2mxUeLSWCeTO1pWPuZN4Unzc5LS6j9957D+FwGKFQCI888ojSw0lY\nb28vjh8/jm3btmHNmjX4+OOPEQqFkJOTo/TQZsxsNmPFihX45JNP8IUvfCH28/LycqxevRrXr19H\nZ2cn8vPzFRzlzI0X15UrV3DhwgVs2bIFa9asQU5ODgwGg8IjnZlAIIDPfe5z+OpXv4rCwkIcO3YM\nixcvxvnz55GdnY3169djYGAA7e3tqvvMKB7zZmpLx9zJvCk+b3JDj0z6+vrQ1taGlStXKj0UYYxG\nI7RaLQKBAEKhEAKBAOx2u9LDmpWFCxfec2edn58PjebOfxLz589Hf3+/EkNLyHhxNTc3o7i4GFqt\nFgBgtVqVGFpC7HY7cnNzAdz5Hs6ZMwf9/f1obW1FUVERAKCwsBCffPKJksOkBDFvpr50zJ3Mm+Lz\npjqfy6vAyZMnUV5eDp/Pp/RQhLFYLHjsscewZ88e6HQ6fP7zn1fV3elMtLS0YNmyZUoPQwiv14uO\njg6cOnUKOp0O5eXlmDdvntLDmrXe3l7cuHED8+fPx9DQEGw2GwDAZrPFpn9InZg31S9dcifzZmL4\n5FIGra2tsFqtyM3NRTrtl/J6vTh37hxeeOEFvPjii/D7/bh06ZLSwxKuqakJWq0Wy5cvV3ooQoTD\nYYyOjmLr1q0oLy9HTU2N0kOaNZ/Ph0OHDuHrX//6PTsdJUlSaFQkAvOm+qVT7mTeTAyfXMrg2rVr\naG1tRVtbG4LBIHw+H44cOYJnn31W6aElpKurCwsWLIDFYgEAFBQU4Nq1a2mRSKJaWlrQ1taGzZs3\nKz0UYRwOBwoKCgAA8+bNgyRJGB4ejn2OahEKhXDo0CEsX748Fo/VasXAwADsdjsGBgZUOXVFdzBv\nqlu65U7mzcSwuJRBWVkZysrKAABXr17F2bNnVZ8gAWDOnDlobGxEIBCATqdDe3u7qqcJ7tbW1oaz\nZ8/ie9/7HvR6vdLDEWbp0qW4cuUK8vLy0N3djVAopLoEGYlE4Ha7kZWVhcceeyz28yVLluDixYso\nLi7Ghx9+iKVLlyo4SkoE86Z6pWPuZN5MDPtcyiyaJNOlpcaZM2dw8eJFSJKE3NxcVFRUxBY8q0lt\nbS2uXr2K4eFh2Gw2fOUrX8GZM2cQCoViLRnmz5+PtWvXKjzSmYnGNTIyAqvVitLSUixfvhxutxs3\nbtyAVqtFeXk5Fi1apPRQZ6SjowMHDhzA3LlzY9M4TzzxBObNm4eamhr09fWxFVEaYd5MXemYO5k3\nxedNFpdEREREJAw39BARERGRMCwuiYiIiEgYFpdEREREJAyLSyIiIiIShsUlEREREQnD4pKIiIiI\nhGFxSURERETCsLgkIiIiImFYXBIRERGRMCwuKW1cvnwZDzzwAFpaWgAAXV1dyMrKQlNTk8IjIyJK\nTcybJAce/0hp5Q9/+AP27NmD999/H1VVVSgsLMQvf/lLpYdFRJSymDdJNBaXlHYqKyvR3t4OrVaL\n5uZm6PV6pYdERJTSmDdJJE6LU9r54Q9/iI8//hg/+tGPmCCJiKaBeZNE4pNLSiuDg4MoLCzEE088\ngfr6enz00UdwuVxKD4uIKGUxb5JoLC4prfzgBz/A8PAwDh48iOeeew63b99GdXW10sMiIkpZzJsk\nGqfFKW243W688847+N3vfgcAeP3113HhwgUcPHhQ4ZEREaUm5k2SA59cEhEREZEwfHJJRERERMKw\nuCQiIiIiYVhcEhEREZEwLC6JiIiISBgWl0REREQkDItLIiIiIhKGxSURERERCcPikoiIiIiE+R9s\nFSBBgRraKgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1202578d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (-9223372036565091780)>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from ggplot import *\n",
    "ggplot(aes(x='x', y='y'), data=df)+facet_wrap('group',scales='fixed')+stat_smooth(method='lm',se=True)+geom_point()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A couple of lessons (already) learned\n",
    "\n",
    "* Data preview can be done as table and simple statistic but visualizing is imporant\n",
    "* Data massage is (alread) required to fit into visualization libraries\n",
    "* Black & white visualization are good enough\n",
    "* Visual composition (points, lines, area) reinforces the message with derived data (regression, confidence interval)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Basics of Visualization / Perception"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Humans visual system has a natural hability to generate similarity, continuation, closure, etc. among visual elements.\n",
    "\n",
    "<img src='http://www.fusioncharts.com/blog/wp-content/uploads/2014/03/597x323xillustrations.jpg.pagespeed.ic.E4Yr9kUmb6.webp'>\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Marks and properties list\n",
    "\n",
    "<div style='text-align: center'>\n",
    "  <img src='http://www.infovis-wiki.net/images/8/89/VisualVariables.png'>\n",
    "</div>\n",
    "\n",
    "Bertin, Jacques. \"Semiology of graphics: diagrams, networks, maps.\" (1967)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Marks and properties variations\n",
    "\n",
    "\n",
    "<img src='data-science-dataviz/bertin-semiology.jpg' style='width: 500px'>\n",
    "\n",
    "Bertin, Jacques. \"Semiology of graphics: diagrams, networks, maps.\" (1967)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization Templates (or Charts)\n",
    "\n",
    "\n",
    "<a href='http://www.excelcharts.com/blog/wp-content/uploads/2013/11/classification-chart-types.png'>\n",
    "    <img src='simple-dataviz-datascience/img/classification-chart-types.png' style='height: 500px'>\n",
    "</a>\n",
    "\n",
    "\n",
    "Visualization templates can be seen as pre-defined and (usually) well accepted combinations. See The Data Visualisation Catalogue http://www.datavizcatalogue.com/search.html. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Design Mantra\n",
    "\n",
    "> \"Show data variations, not design variations\"\n",
    "\n",
    "* Edward Tufte http://www.edwardtufte.com/tufte/\n",
    "* More mantras http://datavis.ca/papers/friendly-scat.pdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizations Libraries & Languages\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Default values\n",
    "\n",
    "\n",
    "> [..] possible to omit parts from the specification and rely on defaults: if the stat is omitted,\n",
    "the geom will supply a default; if the geom is omitted, the stat will supply a default; if the\n",
    "mapping is omitted, the plot default will be used\n",
    "\n",
    "\n",
    "* See styles changes for [Matlplotlib 2.0](http://matplotlib.org/style_changes.html) \n",
    "* [Comparing ggplot2 and R Base Graphics](http://flowingdata.com/2016/03/22/comparing-ggplot2-and-r-base-graphics/)\n",
    "* Improving defaults example ([video](https://www.youtube.com/watch?v=xAoljeRJ3lU))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from numpy.random import randn\n",
    "import pandas as pd\n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Matlibplot\n",
    "\n",
    "* Created by the late John Hunter\n",
    "* Can generate plots, histograms, power spectra, bar charts, error-charts, scatterplots, and etc, "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "from numpy.random import randn\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  4.,   4.,   3.,   7.,   9.,  11.,  11.,  13.,   6.,   7.]),\n",
       " array([-2.43257245, -1.99644337, -1.56031428, -1.1241852 , -0.68805611,\n",
       "        -0.25192703,  0.18420205,  0.62033114,  1.05646022,  1.49258931,\n",
       "         1.92871839]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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56jJTX9fAycyHMvO7Xe7et5/GdZmr3+sFXQXc2rl9K/D6VfZtcq66ed8/yZqZ9wDPjoiz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112bda780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = randn(75)\n",
    "plt.hist(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 2.,  2.,  2.,  2.,  2.,  1.,  5.,  2.,  6.,  3.,  6.,  5.,  7.,\n",
       "         4.,  6.,  7.,  4.,  2.,  5.,  2.]),\n",
       " array([-2.43257245, -2.21450791, -1.99644337, -1.77837883, -1.56031428,\n",
       "        -1.34224974, -1.1241852 , -0.90612066, -0.68805611, -0.46999157,\n",
       "        -0.25192703, -0.03386249,  0.18420205,  0.4022666 ,  0.62033114,\n",
       "         0.83839568,  1.05646022,  1.27452476,  1.49258931,  1.71065385,\n",
       "         1.92871839]),\n",
       " <a list of 20 Patch objects>)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x12076e630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(data, bins=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distributions visualization\n",
    "\n",
    "* Example http://stanford.edu/~mwaskom/software/seaborn-dev/tutorial/distributions.html\n",
    "* Binning\n",
    "* Marginal distributions\n",
    "* Overplotting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Iris Dataset\n",
    "\n",
    "* Three species of iris, total 150 flowers\n",
    "* Meticulously collected by American botanist Edgar Anderson  in the early 1930s\n",
    "* \"Classic\" [dataset](https://en.wikipedia.org/wiki/Data_set#Classic_datasets)\n",
    "* More information on this dataset https://en.wikipedia.org/wiki/Iris_flower_data_set\n",
    "* Browse [Google image](https://www.google.fr/search?site=&tbm=isch&source=hp&biw=1160&bih=885&q=iris+dataset&oq=iris+dataset&gs_l=img.3..0j0i24l9.2204.4044.0.4181.12.10.0.2.2.0.140.801.8j2.10.0....0...1ac.1.64.img..0.12.801.lHBOCGBUmg4) as source of inspiration/examples\n",
    "* List of [+200 publications](http://archive.ics.uci.edu/ml/datasets/Iris) using this dataset\n",
    "* [Many](https://gist.github.com/jobliz/2903500) ways to plot it\n",
    "\n",
    "\n",
    "The data dimensions are as follows:\n",
    "\n",
    "* 4 attributes\n",
    " * sepal length in cm\n",
    " * sepal width in cm\n",
    " * petal length in cm\n",
    " * petal width in cm\n",
    "\n",
    "* 3 classes:\n",
    " * Iris Setosa\n",
    " * Iris Versicolour\n",
    " * Iris Virginica"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href='http://blog.kaggle.com/2015/04/22/scikit-learn-video-3-machine-learning-first-steps-with-the-iris-dataset/'>\n",
    "    <img src='simple-dataviz-datascience/img/iris_petal_sepal.png' style='height: 500px'>\n",
    "</a>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scatterplot\n",
    "\n",
    "* Bivariate data display\n",
    "* Details [on scatterplot](https://en.wikipedia.org/wiki/Scatter_plot)\n",
    "* Article on [its history](http://www.datavis.ca/papers/friendly-scat.pdf)\n",
    "* Plotting categorical data [as scatterplot](https://stanford.edu/~mwaskom/software/seaborn/tutorial/categorical.html \n",
    "\n",
    "<a href='http://www.datavis.ca/papers/friendly-scat.pdf'>\n",
    "    <img src='simple-dataviz-datascience/img/history_scatterplot.png' style='height: 400px'>\n",
    "</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href='http://museum-of-artifacts.blogspot.fr/2015/10/swiss-knife-victorinox-wast-first.html'>\n",
    "    <img src='simple-dataviz-datascience/img/swiss-army-knife-first.jpg' style='height: 400px'>\n",
    "</a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## (Pretty) Tables can be useful!\n",
    "* https://pypi.python.org/pypi/PrettyTable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_json(\"simple-dataviz-datascience/data/iris.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>petalLength</th>\n",
       "      <th>petalWidth</th>\n",
       "      <th>sepalLength</th>\n",
       "      <th>sepalWidth</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>5.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>4.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>4.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>5.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>5.7</td>\n",
       "      <td>4.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1.7</td>\n",
       "      <td>0.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>5.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.7</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.6</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>5.2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>5.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>5.7</td>\n",
       "      <td>2.3</td>\n",
       "      <td>6.9</td>\n",
       "      <td>3.2</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>4.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>6.7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2.7</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>5.7</td>\n",
       "      <td>2.1</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>6.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>7.2</td>\n",
       "      <td>3.2</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>6.2</td>\n",
       "      <td>2.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>6.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>5.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>7.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>6.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>7.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>3.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>5.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>5.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>7.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>6.3</td>\n",
       "      <td>3.4</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>5.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>6.4</td>\n",
       "      <td>3.1</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>5.4</td>\n",
       "      <td>2.1</td>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>5.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>5.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>5.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.2</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>5.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     petalLength  petalWidth  sepalLength  sepalWidth    species\n",
       "0            1.4         0.2          5.1         3.5     setosa\n",
       "1            1.4         0.2          4.9         3.0     setosa\n",
       "2            1.3         0.2          4.7         3.2     setosa\n",
       "3            1.5         0.2          4.6         3.1     setosa\n",
       "4            1.4         0.2          5.0         3.6     setosa\n",
       "5            1.7         0.4          5.4         3.9     setosa\n",
       "6            1.4         0.3          4.6         3.4     setosa\n",
       "7            1.5         0.2          5.0         3.4     setosa\n",
       "8            1.4         0.2          4.4         2.9     setosa\n",
       "9            1.5         0.1          4.9         3.1     setosa\n",
       "10           1.5         0.2          5.4         3.7     setosa\n",
       "11           1.6         0.2          4.8         3.4     setosa\n",
       "12           1.4         0.1          4.8         3.0     setosa\n",
       "13           1.1         0.1          4.3         3.0     setosa\n",
       "14           1.2         0.2          5.8         4.0     setosa\n",
       "15           1.5         0.4          5.7         4.4     setosa\n",
       "16           1.3         0.4          5.4         3.9     setosa\n",
       "17           1.4         0.3          5.1         3.5     setosa\n",
       "18           1.7         0.3          5.7         3.8     setosa\n",
       "19           1.5         0.3          5.1         3.8     setosa\n",
       "20           1.7         0.2          5.4         3.4     setosa\n",
       "21           1.5         0.4          5.1         3.7     setosa\n",
       "22           1.0         0.2          4.6         3.6     setosa\n",
       "23           1.7         0.5          5.1         3.3     setosa\n",
       "24           1.9         0.2          4.8         3.4     setosa\n",
       "25           1.6         0.2          5.0         3.0     setosa\n",
       "26           1.6         0.4          5.0         3.4     setosa\n",
       "27           1.5         0.2          5.2         3.5     setosa\n",
       "28           1.4         0.2          5.2         3.4     setosa\n",
       "29           1.6         0.2          4.7         3.2     setosa\n",
       "..           ...         ...          ...         ...        ...\n",
       "120          5.7         2.3          6.9         3.2  virginica\n",
       "121          4.9         2.0          5.6         2.8  virginica\n",
       "122          6.7         2.0          7.7         2.8  virginica\n",
       "123          4.9         1.8          6.3         2.7  virginica\n",
       "124          5.7         2.1          6.7         3.3  virginica\n",
       "125          6.0         1.8          7.2         3.2  virginica\n",
       "126          4.8         1.8          6.2         2.8  virginica\n",
       "127          4.9         1.8          6.1         3.0  virginica\n",
       "128          5.6         2.1          6.4         2.8  virginica\n",
       "129          5.8         1.6          7.2         3.0  virginica\n",
       "130          6.1         1.9          7.4         2.8  virginica\n",
       "131          6.4         2.0          7.9         3.8  virginica\n",
       "132          5.6         2.2          6.4         2.8  virginica\n",
       "133          5.1         1.5          6.3         2.8  virginica\n",
       "134          5.6         1.4          6.1         2.6  virginica\n",
       "135          6.1         2.3          7.7         3.0  virginica\n",
       "136          5.6         2.4          6.3         3.4  virginica\n",
       "137          5.5         1.8          6.4         3.1  virginica\n",
       "138          4.8         1.8          6.0         3.0  virginica\n",
       "139          5.4         2.1          6.9         3.1  virginica\n",
       "140          5.6         2.4          6.7         3.1  virginica\n",
       "141          5.1         2.3          6.9         3.1  virginica\n",
       "142          5.1         1.9          5.8         2.7  virginica\n",
       "143          5.9         2.3          6.8         3.2  virginica\n",
       "144          5.7         2.5          6.7         3.3  virginica\n",
       "145          5.2         2.3          6.7         3.0  virginica\n",
       "146          5.0         1.9          6.3         2.5  virginica\n",
       "147          5.2         2.0          6.5         3.0  virginica\n",
       "148          5.4         2.3          6.2         3.4  virginica\n",
       "149          5.1         1.8          5.9         3.0  virginica\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" cellpadding=\"3\" cellspacing=\"0\"  style=\"border:1px solid black;border-collapse:collapse;\"><tr><td  style=\"background-color:LightGray;\"><b>Sepal&nbsplength</b></td><td  style=\"background-color:LightGray;\"><b>Sepal&nbspwidth</b></td><td  style=\"background-color:LightGray;\"><b>Petal&nbsplength</b></td><td  style=\"background-color:LightGray;\"><b>Petal&nbspwidth</b></td><td  style=\"background-color:LightGray;\"><b>Species</b></td></tr><tr><td  style=\"background-color:yellow;\">1.4</td><td  style=\"background-color:yellow;\">0.2</td><td  style=\"background-color:yellow;\">5.1</td><td  style=\"background-color:yellow;\">3.5</td><td  style=\"background-color:yellow;\">setosa</td></tr><tr><td  style=\"background-color:AliceBlue;\">1.4</td><td  style=\"background-color:AliceBlue;\">0.2</td><td  style=\"background-color:AliceBlue;\">4.9</td><td  style=\"background-color:AliceBlue;\">3.0</td><td  style=\"background-color:AliceBlue;\">setosa</td></tr><tr><td  style=\"background-color:Ivory;\">1.3</td><td  style=\"background-color:Ivory;\">0.2</td><td  style=\"background-color:Ivory;\">4.7</td><td  style=\"background-color:Ivory;\">3.2</td><td  style=\"background-color:Ivory;\">setosa</td></tr><tr><td  style=\"background-color:AliceBlue;\">1.5</td><td  style=\"background-color:AliceBlue;\">0.2</td><td  style=\"background-color:AliceBlue;\">4.6</td><td  style=\"background-color:AliceBlue;\">3.1</td><td  style=\"background-color:AliceBlue;\">setosa</td></tr><tr><td  style=\"background-color:Ivory;\">1.4</td><td  style=\"background-color:Ivory;\">0.2</td><td  style=\"background-color:Ivory;\">5.0</td><td  style=\"background-color:Ivory;\">3.6</td><td  style=\"background-color:Ivory;\">setosa</td></tr></table>"
      ],
      "text/plain": [
       "<ipy_table.IpyTable at 0x120ac2be0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate a pretty table\n",
    "from ipy_table import *\n",
    "import numpy as np\n",
    "\n",
    "table = df.head().as_matrix()\n",
    "\n",
    "header = ['Sepal length', 'Sepal width', 'Petal length', 'Petal width', 'Species']\n",
    "\n",
    "# df.rename(columns=lambda x: x[1:], inplace=True)\n",
    "table_with_header = np.concatenate(([header], table))\n",
    "\n",
    "# Basic themes\n",
    "# Detais http://nbviewer.ipython.org/github/epmoyer/ipy_table/blob/master/ipy_table-Introduction.ipynb\n",
    "make_table(table_with_header)\n",
    "apply_theme('basic')\n",
    "# Only show the top-10\n",
    "set_row_style(1, color='yellow')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "\n",
    "# Load iris dataset\n",
    "iris = datasets.load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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eXo21uKiA5a/9iY6VZ8nTBdHy+vvoP869Ows//+dfCE9dT2yIke+LbNz5zjxi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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x121413cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "data = iris.data[:, :2]\n",
    "cat = iris.target\n",
    "\n",
    "# Padding calculation\n",
    "x_min, x_max = data[:, 0].min() - .5, data[:, 0].max() + .5\n",
    "y_min, y_max = data[:, 1].min() - .5, data[:, 1].max() + .5\n",
    "\n",
    "# Plot the points\n",
    "plt.scatter(data[:, 0], data[:, 1], c=cat, cmap=plt.cm.Paired)\n",
    "\n",
    "plt.xlabel(iris.feature_names[0])\n",
    "plt.ylabel(iris.feature_names[1])\n",
    "\n",
    "# Enabling padding\n",
    "plt.xlim(x_min, x_max)\n",
    "plt.ylim(y_min, y_max)\n",
    "\n",
    "# Removing ticks\n",
    "plt.xticks(())\n",
    "plt.yticks(())\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "from sklearn import linear_model\n",
    "clf = linear_model.LinearRegression()\n",
    "X=data[:, 0]\n",
    "Y=data[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x12243be80>]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UGoCISI5SAxARyVFqACIiOUoNQEQkR6kBiIjkKDUAEZEcpQYgIpKjClraaWYD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\nEZEc9X9IfNMMnKZAyQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12140b630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clf.fit(X[:, np.newaxis],Y)\n",
    "plt.plot(X, clf.predict(X[:, np.newaxis]), color='blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1224542b0>]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WreD666d6JJto2fyu0HuSbq/oNpeY5GDKTyy0T1rMU/fc6dbFIu/O/Iouo6su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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x122454080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data[:, 0], data[:, 1], c=cat, cmap=plt.cm.Paired)\n",
    "plt.plot(X, clf.predict(X[:, np.newaxis]), color='blue')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### More visualization\n",
    "\n",
    "* Visualize first three PCA dimensions http://scikit-learn.org/stable/auto_examples/datasets/plot_iris_dataset.html\n",
    "* More matliplot visualizations http://pandas.pydata.org/pandas-docs/stable/visualization.html\n",
    "* Visualize distributions http://stanford.edu/~mwaskom/software/seaborn-dev/tutorial/distributions.html\n",
    "* Machine learning http://www.scipy-lectures.org/advanced/scikit-learn/\n",
    "* Analysis of the Fisher Iris Dataset http://pythonhosted.org/bob.db.iris/example.html\n",
    "* http://nbviewer.jupyter.org/github/rhiever/Data-Analysis-and-Machine-Learning-Projects/blob/master/example-data-science-notebook/Example%20Machine%20Learning%20Notebook.ipynb#Bonus:-Testing-our-data\n",
    "* "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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E1+kxJEeCZDma97jtjsWo6QpDlqNQVZWKxqsEEk4uU1s7AgMD5SqcyiXqVb6p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\n5tZ2jp08RRNLDNwaxa8G6O9soc4m89q5y8TqW5kYvUPX+i1pOsALJ48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obKpf8R5a5oba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\nkVMOzAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x120797128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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9xQ5atAhvpFEbd/3kURbsPVbZ3erzA8WMv+7OOqtV5Cu1OWe7FbjZNM0Nx7eH\nA/8wTbPG/mI6Z3tmtpsm67bvgFCQqRPG06JF5F2avF4v78/9mIDloFv7dpw/YkS142Z/PIe5yz8H\nK8SUsaOZPvWyiPcpTdeC918nd982klp1YtqNd9TJ7edO58kHbqckcxPlzgQeenE2zZqFt4msrc8/\nW8JbT/wQl8NiyPRbuenO++qwUmlqzriD1FcMw1hrmuaw033tZApbERFpaiK+eTywwjCM54B/AEHg\nZmDvV4eSTdP8os6qFBERaYRqE7YDqOiJ/Ofj247j208c325YndNFRETOMd08XkREpI5EfBjZMIwu\nVKxE7gqMA14DbjNNM7MuC6xP8vPzWPnFF7Rs2ZJRI0698HrX7t1s32kyoG9funapX/dm+N9bb5K5\nfz9XTb+cPr17VzvG7/ezZNkyACZccMEpbxGYffQoX6xdQ/u2bRk62N77kJ6pfbtNdm1YTfcBQ+lm\n9D2ruQoK8nnjmSdwxSZy2wO/wF3NJVMARw7uZ8uqz+jQozd9BtW4dOG0PB4PLzz9W0LBALfe/3NS\nU1OrHZebc5RVS+fRql1HRowef8r5Fsx5j3XLFnDBpTM4f9yks6qtrj3+0A84lLmTW+75KSNHV9+E\nzuv1snLBRzhdLkZPuvSUP4MtG9by8Zsv0b3/EK66+bZT7nPLxjXsM7cx5PzxtD+LW+Kdid3mNnZs\nXEOfwSPo3iv8HrpQcdXByk8XUJiXw+gLp5BWzW0Jo8Xv97N03myskMX4S6YTGxtb7bic7CxWfTqf\ndp26MnTk2HNcZcNTmwVSnwBPA48Dw6jojfxN0zTH1fS4hvrJ9suDB3hv6QqM0RdxLPconswtfOu6\n68LGLfz0U/Z7oEPvAXy5dT1G8wQuGHV+FCoO98snn6TXmMm0at+J1QvncH73jlw04cSj/T6fj7+/\n/Aq9xk3Fsix2L5vLvd/6Zljg7ty9m3nrNtNr5HhyDn2JO2c/119xxbl8Oqe0esEcYua/yIhUB+uL\nLUomfINRxztinamcnBze+t4Mvtu3FR5/kGd2F/ODV+aFvdhvXfM5hW89zfjUEGZJkP2DpzPxhlO/\n2NfE4/HwyI3jmNQ2iMvhYP7BEA/+dxFpaWknjNu3Zycf/+F+BrhzyfM7Ket/OTfeG96A5B+P/ICU\n3Yvo0jyWHbk+EkbfxE33/iSi2uraPZcNY1LrABlJsSzdV0TfG3/KjJu+fcIYj8fD+49+j2vicgmG\n4D2rPdfu8RS+AAAgAElEQVT86s9hP4O5777OzjceY2jrOLJK/GSm9OPBv74Wts/Zrz5P+bKXaBcb\nYJc/laG3P8qgEWNsfZ5L5rzDwQ/+TNc4D5neBDrNeIBx1fxOvvTUz8nYPY/UWAcbgq259pfP06Zt\ne1trqw2/389zP7udfmVbcTocbI7vzXceezEscPeY21jw5wfo584j1+fAO/hqrv9u/fhdi7az6SCV\nbprmJwCmaYZM03wBiHydfT239Is19Bs/BXdMDC3btqc8sTm5ublh43YczqZT30E4nU66DBjG1v2H\nolBtuIKCfJLadqZt52643G5GXXIFi9dsCBv3ycIF9Jk4nfjEJBKSkjEunMa8RQvDxq3YsIneoyfi\ndLlo3akr2d5Qjf2Wz6XcZR8wspkTp8PBsFQnhcs/jHiuNx7/Gff3b0OC20XzhFju6pbMa/8I793y\n5aJ3mJhm4XI66Jvqxr/mEyI9FfPPpx5mUtsgiTEu4txOpnRy8fxvHwwbt/y9/zA4Ng+X00GrOIvS\ndXMoKgpv4lC8eTHdWsThdDjomxHHwc/ejaiuurZzp0nvxDLapMThcjqY2K0Zy14L/94u++BNbkrI\nJynGTWqcm6udh/jsk/B7nmx493lGtI3H5XTQPjUW16FNYb+TlmVx8LN36RgfxOV00DuumPUfhQdy\nXdu16C26x3txOhx0j/ewc2F4G8kDB74kZvt8WsQ7cTsdDHNns2Tmi7bXVhsLZr/NwPJtJMa4iHc7\nGezdwbz3/xc2buWslxgYm4/L6aB1PBxb8yFlZWoGUpPahG2ZYRgdvtowDGMs4KlhfIN28jWCTpe7\n2lZwDoezxu1oCQSCuE4+9OYMry0YCuFyVY1zudwEqmt5d/L3w+kiFArVSa1n6+Quzo6zWH9ghQI4\nv/ZUY91OAt7wjlrOk/bhtCL/XgT9Ptxf26nTAVZ1v2snbbuwqv2dPPmn7Kgnx5a8Xi/uk/4+nIQX\nFwoGcH3t+xHjdBIK+MPGuRzh24FAIHzHJ/9sQufgJgMn77Oa349AIICLqq87HI6z+t2tS8GA/4Sf\ngdPhIFiL762TUL15XaivapMQDwBzgB6GYWwEXgfut7WqKDpvQH92fvEpAGXFRQRzDtCqVXgHpg7N\nkjj6ZcVp66y9O+mS0fyc1nkqGRkZHN21laL8PAA2rljC0J5dwsZNnnAhmxfMIhQMEgoG2bJgFpMn\nhC8sH9yrO3s2rAKguCCPxEAZ8fHxdj6FWosfMoHtJRUvoLtKg8QOqvHMRo2m3/1z/rU1G8uy8AVD\nPLcjj+vv+n7YuPRRl7CmqOKF8VBZgECfMRE3cfj2A79i/oEgwZBFyLKYv8/LN374SNi4IRdfw3Zv\nClDRpSnYcyzNqzvH12kIWcUV4bS/0EfqgMi/H3VpwICBbCyAYm/Fi/YXB4sxJl0fNm70Zdfw+rFE\nQpZFIBTizfI0Rl9yedi4LhOuZntuxRuhwvIAhSmdSUxMPGGMw+Gg5ZDJ5B1/v5TpTcAYH9kphjPR\nfuRUjvgq3sQe9sXQYdSlYWO6dOlKYcfzKfdXhNNmbxrnXRr+/YiGidOuZa2jc+Xv5Fo6cdHlN4SN\nGzT5WnZ4K27WUOS3cBnjdfOG06jVamTDMGKBXlSE8w7TNE97HLGhnrMF2H/gS1Zv2EhSQjyXTLwI\nZzWfDAFWrV3DgcNH6N6pI0MGDT7HVZ5aKBTib88/R0FxGReOHsn4sdW/6JaUlDBvyWIALp5w4Sn/\nWHbv3cOGrdtIS0nmovETzkmHoNra/MVyDu/YTOsefRhcw8Kh2tifuYcPnnkMy+Xm1l88ecrFSjs3\nrydz/Sqate/MqIumntU+Cwry+edvHoRQgJt/+CgdTrGIZ9+enaxbNo+U5hlMuvy6U/4MXn3ujxza\nvh5j5ESurGHh0LkWDAb58Z3X4C3M5aJrbgs7X/uV4qJjrJj9Ng6nk7FX3BAWol9Z/NH7fDH3HdLa\ndebOHz96yr/RZfPnkHNwL32GjaHPQPtbSQKsWbGEL3dsomvfwQwZVf3fXjAY5JP3XsdTUsjIi6af\ns8VbtVFaWsr8d17BAibNuImUlOr/Dvbu3M6G5QtoltGWiZddXa9eF6Ipog5ShmFMB7aZprnHMIwZ\nwO3AOuBR0zSrObZQpSGHrYiISCTOeIGUYRg/ouLetfGGYQyk4pKf94EU4A92FCkiItIY1XTO9pvA\neNM0twI3AbOOr0R+AJhyLooTERFpDGoK25BpmqXH/30h8NXlPxZUs5RQREREqlVTB6mAYRjNgSRg\nCMfD1jCMTkD4evwmaOny5WTl5dGxdWtGjxwZ7XLkLGUfOcT6ue+Cy83Yq75xygVjm79YzuEta0jM\naMfYadeck4Uhu7duZM+qpbhSmnHhVbfgcp184VOFlZ98QNGhTFr1GsCQsROrHePxeFj6zsvg89J3\nwlQ6du1R7bj9mbtZPX8Wzth4Lr3h9lOuQl//2SKO7txMavuunF/N6uEzkZdzlNVz3sLCwZgZN5Pa\nLO30D2rEDh3Yz8pP3sHhdDP1hjtOuWBM6r9TLpAyDOMaKs7NxlBxCPluwzCuBR6jYoHUyzVN3NgX\nSM2c/SGhNt1o0bY9OQf3kVx4hOlTdHS9oTqadZi1f/wRVzb3EwxZvFacwhWPPBcWMKvmzSZt0X8Y\nmOoi1xNgUasRXHHvz22tbeuazyl/+w+Maeag1B/gnZieXPezJ8JC/uP//I0RexbRPtHNrtIgmcOv\nZvyMm08YEwgEmPnw97gp7igxLidzC510u+s3dO7e64Rx+/bsZN5T9zIgrpBAyGJ9XB/ufvzFsG5O\nn77/Op2/eIteyW4Olwf5ousEpt4W2f1gC/JyWfb4/VyT5sUCXj+WyJRfPdtkLyk5dGA/H/z+LgbH\n5hMMWaxz9+Cux18iLi4u2qVJDc54gZRpmjOBMcClpmneffzLZcAdpwvapiC7zEuL4+3VMjp04XBx\neZQrkrOx7pP3uSKt4oo2l9PBjIRCVi+dHzaucN0SBqZWfKpMj3eTmLk+4g5StXVwxSeMaVbx95sU\n46Zn3g7y8/PDxrl3rqZ9YkUY9kxy4d28PGzM9s0bGO8/QIyr4k9/SlqI7YvCuzStnvcuA+IKK+Z1\nOuhSuJktm9aHjfNs/oxeyRX7bJfgwr0z8jtufvHJ+1yT5sXhcOB0OLgupZRV8yPvCtbQrZg7k8Gx\nFT9nl9OBUb6TNSuWRrkqiVSNNyIwTfMQcOhr23Nsr6ihOKlbihWs8UooqeecsXEEQhYxx9sTFfuD\nxCeFf6IKOl18rfkPQafL9sPIQceJh4zLQk7i4sKbw/tPGhdwhB9qTkxKpuRrjZRCloXlrOZlwBVD\nyLJwHn9uHstFYjXfj8BJnaH81c1VS664BLzBEPHuirpL/CFiEpruYVOnO5ZgyKrs6OQJQqvklChX\nJZGqHz0GG6CBXTuwZ/3nlBUXsWvNcob26h7tkuQsjJtxM//zpJNb5mN/sZeFKX0ZPmZC2Liel97E\nR4Uujnn9rC4IkDzG/psyDL3qm8wsjKXQ42dzoY/SQZNIruZFt+X4GSwvCFHkDbCw0EHHi8NvoNG9\nV2+2dhrJ7iIv+R4frxWlMPbaW8PGXXLDHaxxdKHIG+RIWYiy3pfQywi/g03Hi69nQYGDY14/ywuC\ntBw/I+LnOf7y63gz0JbsUi8Hi718ENONsRdPi3i+hm7q9bez1t2DIm+A7LIgOV0vZPDwUdEuSyKk\n+9mehcOHD7Fzzx769DJo3bp1tMuRs+T3+9mw6jNi4xIYOHzkKT+xFuTnsX39ajp070WnLt3OSW0l\nJcVsWb2S9Lbt6dG73ynHHT74JfvMrfQcMJSMVqf+ndy6cR0lxwoZdN7oUy588ng8rFu1jJRmzRkw\nePgp58o5ms2uzevpYvSlXYdOtX9S1QgGg6xftRyXy8WgEeefsjNUU+Hz+Vjz+TISk5IZNPQ8dWlq\nACLqIHU2mkLYioiIfN3Z3GJPREREzoLCVkRExGYKWxEREZtFvk5fpJHZsn4Vmxa8h+VwMu6aO+h4\nisVPLzzyAO7DuymOSeZbv/9HtbfiKy8v5/1//4lQaQFteg/noivsv1+px+PhhZ/+H8nlhXhatOf/\nfvdMtQuMjmYfYf5rz+AI+jHGTGHY6PD7GAOsXbEYc/lcLFcMk26+h9at24aNCYVCPPngHfiz92Kl\nZPDjP71S7YKrkuIilrzyLLH+ctL6jeC8SfavMvb5fCx8+VlcpQXEd+7NuCtvtH2f0WBZFnPffon8\nzG3EpLVixm3fJyYmxvb9Lv3oXQ5t+Rzik7ni9h+SlJRk+z4bMi2QEgH27NjKZ3/5Hr1iSwBY78/g\npt+9QtpJN2n/x8++wzUcpH1KAr5giD/tLOb7r8wLm+/ZX9zFgKJ1uJ0Ocr0OYibdw5Rrvmnrc/jT\nbdO5r3MMiTEu8sq8vFSUyr1/ffWEMeXl5fz7xzcx1HEQh8PBXk88A+58goHDzz9h3Oa1n7Ppnz+m\nW7wHy7JYZ3Xg9if/R0JCwgnjHvnONYy09tIs3o03EOKT/FQee33RCWMsy+KtX36XWxJycTocmCUB\njl54O+dNtjdwZz72Y64N7CHW5eRweYAN/adz0fX15x6/dWXWf58h9vOXSIt14AuG2Nn6Au785Z9s\n3efiD2dSMPsPtI0PEQxZbEjoxz1P/EerpdECKZEabfhsXmXQAvR1ZLP6s8Vh41KOZtI+pSJwYl1O\nerg9hE5qcOLz+XBlbcd9vBlBepzF0e2rbKy+QhermMSYioYQLRPjyCjJChuzfctGOnv2Vb4odov3\nsGPVorBxO75YRLd4DwAOh4Munn1s2xzeQcqdVxG0AHFuJ818OWFjcnNzMcoPVTbIMJLdFG5bHeGz\nrB3LsmiWu5fY452y2iW4Ce7dYus+o6Vg11rSYiu+t7EuJ8GD9j/Pw5tX0Da+4vfe5XSQlGtSVHTM\n9v02ZApbESAxLZ2yQFVo5vqdtKnmmtEi68SuTEUBwg7VxsTE4I+pOqRmWRZWjP2H2IqDJ76hLg6F\n/3lntG5LYaiqt64vGMKd1CxsnDsxDV+w6vtREIojo3W7sHFex4mdrDxWeNeqlJQUcqyqcSHLwhdT\n/bW9dcXhcOCNObH7lN37jBYr9sTfLSvO/i5TVlwSoa8dFS13JZKYqMPINXE9/PDDtkxcVuazZ2IR\nG3TvPYCF63dQkH2ILJ+b2GEzmDg9vANTQseevPP+u1gBP58eKiLmgivpM+zEQ7AOhwN/fBobN62n\npNxLZnw3ZnzvUZJsbqh/uDzEqpXL8foDfPRlIcatD9KpW88TxjRrlsb+Ij97dm7nmM/iQPoQbrrv\nV2F3EerRbzBzV22kOC+bw/5YWo67mZHjLw7bp7t5Oz6Z9zGBQICNOT4GXvU9+gwaduIYt5ujQTdb\ntmyisNzHQqsVk+/+OXFx9oZfeUIz1qxfR3FZOZ8G0hh5+4Mkp4a/sWjoMrr0YfGKVZSVFLGfFpx3\n0w9o16mrrfvs1Hswc5etoqyogAPBZPpdeTfdeg+wdZ8NRVJS3CPVfV3nbEW+pqAgH7fbTUpK+KKn\nrwQCAUxzB507d6nxjjRer5djx46Rnp5+zjohlZWVkZm5l549exEbG94/+SulpaV4PB5atGhxyvNs\nlmWRn59PfHx8jYtffD4fu3btpGvXbjXeAs7j8VBcXEx6evo5O7fn8/koKCggIyOjUXejCgaD5Obm\n0rx58xp/7nXJsixycnJITU09ZReypkgdpERERGymBVIiIiJRorAVERGxmcJWRETEZuogJefU9nWr\nyJz7Bm4rSNKgcYyZdk20S6q0cu77FK9dRMDhouOkqxlw3tiI55r97usseOZXpMU5OeJ18dc56yJe\nRGJZFvNf/QeOL7fhdSUw+Ia76NDF/vsnz535MlkblmA5Yxh1zf/RZ+CwsDE+n483//5bArn7ISWD\nq+/5JamNcMWvyNnSAik5Z/Jyc9jy5L1cnFZx/eaOkiBF0+5n0OjxUa4MtqxeSdy7f6B/SsXBnkWF\n0OMHf6Z12/YRzffApO5c07dipW+pL8Dbu/08P39zRHMtmfkKgze+S+uEivfGbx5LZPrvXwy7XKcu\nrVj4EdlvP0qbuIqf1SZfC2584s2wIH3ljw/ROfMTYlwOQpbFtuYjuPORZ22rS6S+0wIpibodG1Yz\nMsFbud072UXWjg1RrKjKoa1rK4MWYExyiK1rP49orqysLDqkxlZe3pIU6ybd7Yu4Nv/h3ZVBC9A9\nkE9ubninprp0yNxQGbQAHYJH2bkt/M2C/2gmMa6K5+l0OAjl7be1LpGGSmEr50znnn3ZVlb1pi+r\nPEBym45RrKhKs/ZdOVQWqNzeUmLRxegX0Vxt2rQhr9xfuR0MWeR5a3jAaQRS0ikPBCu3DzqSaH5S\nz+a6ltK6I0X+qoNT2VYynbr1ChtnJbfkhKNjSem21iXSUOmcrZwzHTp34cCF32DWp+/jCvnx9RjF\npVNnRLssAEZNvoyPD+xh/Y6VBB0uksdMo3/P3hHP12LIVGavm0tKrJMDxQF++M+PI55r0i3f4b0/\nZ5N6dA/emAQ6X/tt2xsXXHLVLbx2YC/7dn6O5Yql79W30qpVq7BxV373l7z99M9wFByAlFZM+c4v\nba1LpKHSOVsREZE6onO2IiIiUaKwFRERsZnCVkRExGYKWxEREZtpNbI0eptWLOXAnJeJDXgobdeb\ny7/3i4hvt1ZaWsrcv/ya1GOHKY9PZcBN99HV6Bs2bveWDWx76zkSvCUUt+jIpfc/bPttyA7uz+TD\n5x7BUXQUWnTgugceo3mLlrbuU5omv9/Py0/+lMDh7VhxKYy95QH6Dx0Z7bLqNX2ylUatrKyM7Hef\n5aqkIqY18zG1YD0LX/93xPMt/PcfuT60j2nN/Fwbl8emV56udtzWV//EtfH5TGvm4xrfLha8+OeI\n91lbc557lEHl2xgYk8uAovW8+8yjtu9Tmqb3XniaHkeWMtCdy6BgJkte+A2hUOj0D2zCFLbSqGVn\nZ9GVksrt1FgXoYKsiOeLLy3E5axa2Z9UVsDJl8/5fD7SvMcqt2NcTmJL8iPeZ62VHK38p8PhwFFs\nb5cpabr8hVnEuqriI9mTy7FjhVGsqP5T2Eqj1q5de0xn88rtI2V+Ejr0jHg+f4t2lPkrujlZlkVR\nSuvKtoxfiY2NJS8xo3K72BcgmN4h4n3WWvOOlcEfCFm40jvZv09pkpLb96Tkax3GSpLbk5bWvIZH\niJpaSKO3d8dWtr37b+KCXqwuA5h8y11hAVlbgUCAj//5B+Jzv6Q8LoXzb/0+Ga3bho3LPnyAVS//\nlQRfKb5W3Zhy5w9svXEAwLHCAt75+6NQnIMrvRPXf+9Xtp8nlqbJsizefv4pSvdtJhSXzKRbf0jn\nrj2iXVa9cKqmFgpbERGROqIOUiIiIlGisBUREbGZwlZERMRmCtsmrrS0lNLS0jqZKxgMkp+fF3Yp\nTH3g8/lqdWlCSUkx5eXlNY6xLIuCgnyCwWCN4xqDw4cPUVhY/y7p+OpnEAgETj9YpB5QB6kmyrIs\nXv3LI5RvWgBA0uCLufm+X0U835bVy9n35t9pGyplSVw659/9a9p27FxX5Z6VFR++RenCN0lzBNiT\n2okrfvIkCQkJJ4yxLIt3n/417Q5uwIcLz6CLuOTWe8Pmys46zFuP/4CUov2UxjVn1DceZNjoiefq\nqZwzHo+H33xjEp1dxygLWNB1JN9/KvJmIHUpPy+X1353H8mFmZTFpDDs+h8wauLUaJclUiN9sm2i\nPl0wh3RzDgNSfAxI8ZG27UM+Wxj5Dc4z33uBq1sEGJ0ex40pxax5/dk6rDZyRUXHCC5+nWkZTsam\nx3KT6zCLX/1H2LilH7zF1KLNTMiI4+IMNz23z2fbxrVh4z769x8YFtqLkRJiaGweq17/67l4Gufc\n337+XSa38jCgdQIj2yeSdng1a1Z/Hu2yAJj9wlMMC+yid0qQofGFrH37b/XyaIrI1ylsm6iCrIM0\ni6nabh5rkZt1MOL54v1lJ23XfCj2XCkoKKCNw1e5HeNy4vKFHzb3FubSPK7qQE+XJBfZB/aHjXN6\nS064RtfhKWqcL/RlhcS5q14e0hNd7Nm+OYoFVXH6Sk/4GcT4ivH5fDU8QiT6FLZN1PBxl7Ddn1a5\nvc3fnPPGXxzxfEXpXfEGKnqj5noChDr0Pusa60L79h3YGNO6MhC3FQdJ7z8ibFzPkeNZUVS1Pb8k\nniFjJoSNa95zCIW+qi5Nzra9I26QUZ/1HHMpu/OqAmxjbohJl82IYkVV0nsPJ9db8e9gyCLQyiAu\nLi66RYmchppaNGHbN61n7dz/gQXDL7uZ3v0HRzyXz+djwX//jrskH3e77lx43a31JoQK8nJZ/r9/\nEBv0ktZ/JOdNmlbtuK1rPufgZx8RxEn/y2+mU7fwto6WZTH3rZfI37sZZ3ILZtz5o0bbpen1557i\n0Oef4MPJ9Pt/w4B6dFeXee+8Ss7OdTiS0rjyjh+RmJgY7ZJEAHWQEhERsZ06SImIiESJwlZERMRm\nClsRERGbKWybsFAoxOb1q9m8fg2hUCja5dhq6aJ5vP7CsxQUnIObuIuInEQLpJqoYDDI27//ERNK\nd2NhsTTJ4LpfPIXT2fjefz1z/y1MdBylXVI8b2cWcOHD/6J7r/pxaZKINC5aICUnWPrhTK4O7qNz\najxdUhOYEcxk6UfvRrusOrdzp8kQ72EGt2pGq6Q47u7Xmo+ffijaZYlIE6OwbaL8ZWUkxbgqt5Nj\nnATKymp4RMN07FjhCZ2hHA4H8Q4ddBGRc0th20SNnHIFbx+Lx7IsLMti5rEEzru4+mYPDdmwYSP4\n6GigsrvVoi/z6TixfnRCEpGmQ+dsm7C8nKOs/uB1LOC8y2+kZUaraJdki5KSEv7z6+8T4yvDuPgq\nLpx+TbRLEpFGSh2kREREbKYFUiIiIlGisBUREbGZwlZERMRm7tMPkbMRCoX4ZNECyr0+xow4j9at\nGt4ipC/37mLH50tJzmjL+ZMurTe3zqsty7L49KP38B4rYOC4ybTp0CnaJTUIeTlHWbtgNu6EJMZP\nvxaXy3X6B4lItbRAykaWZfHMS/+l8+jJJCQls+3TecwYfz4d23eIdmm1tn39agrfeIoJzSyyygMs\nazOSy+/5abTLqjXLsnjnqYe4tHQbzePcfFToovudD9OlpzpI1ST78AHW/OknzEjzURYIMtPRhese\n+mOj7DAmUpe0QCoK9uzZTXK3viQkJQPQd9zFLPtiTZSrOjP7l3zAhGYV75vaJLhJ270Kj8cT5apq\nLyvrCEb2psrGFpemBdkxr/F1yqpr6z9+h6ua+3E4HCTFuBlXuovtWzZGuyyRBkthayOn04EVrGrw\nb1kW2HQkwTYnvUcLQoM6jOx0Ogmd9C1vYD+BqPn6Ua+gBU6nDiOLREpha6OuXbvjPbSL4oJ8QsEg\nW5d8zMQx50e7rDPSfdLVzCt0YlkW+0v9lPe9gLi4uGiXVWutW7dhT6fhZJUHCFkWswpiGDjtxmiX\nVe+NuPwm3ipMIBiyKPAGWNm8L737DYh2WSINls7Z2syyLJZ+9inFJaWMHTWK5s1bRLukM5Z9+AAb\nP1tMy/adGDZmQrTLOWOWZbFq8TyK844ydMIljbZTVl0rOlbAqgUfEZ+cypjJl+l8rUgtqIOUiIiI\nzbRASkREJEoUtiIiIjZT2IqIiNhMHaREGoB3XnqWPcs/JhiXzD2/f57U1NSI5yopLmLZm//GGQzQ\nbcxkevYfXIeVikh1tEBKpJ574/k/EvjsZbq3iMMftPj4sJPfzVwZ0epgn8/HrF/fzU3JhTgdDpYW\nQvq3HqJHv0E2VC7S9GiBlEgDdeDzT+jeouLa5hiXg14JZezevSuiucytGxlvZeE83phkfBrsWbGg\nzmoVkeopbEXqOS+uE7o5FfusiK/XTklrQW6g6o13IBQiFNNwmpSINFQKW5F67vqf/IFP9vs5Uuxj\n69FyQl1HkZGREdFcXbp2Z78xkdV55ewuLOe18nQmXH97HVcsIifTOVuRBqCoqIjPli6gc7de9OvX\n/6znO3TwAKXFRXTraeB2a52kSF1RBykRERGbaYGUiIhIlChsRUREbKawFRERsZnCVkRExGYKWxER\nEZspbEVERGymsBUREbGZwlZERMRmClsRERGbKWxFRERsprAVERGxmcJWRETEZgpbERERm+neWlKj\nYDDInOeeJOHoHjyxiQy4/rt06dkn2mWJiDQo+mQrNZr/ynNMzf2CafHHuMZ5hI0vPoFdt2UUEWms\nFLZSI2feYZJjqw6AtPEWUFpaEsWKREQaHoWt1CiU1poyf7ByOyumGUlJyVGsSESk4dE5W6nRpG/d\nw+xnCknK2YcnJpEBt34Hh8MR7bJERBoUh13n33JyinViT0REmpSMjJRqP43oMLKIiIjNFLYiIiI2\nU9iKiIjYTGErIiJiM4WtiIiIzRS2IiIiNlPYioiI2ExhKyIiYjOFrYiIiM0UtiIiIjZT2IqIiNhM\nYSsiImIzha2IiIjNFLYiIiI2U9iKiIjYTGErIiJiM4WtiIiIzRS2IiIiNlPYioiI2ExhKyIiYjOF\nrYiIiM0UtiIiIjZT2IqIiNhMYSsiImIzha2IiIjNFLYiIiI2U9iKiIjYTGErIiJiM4WtiIiIzRS2\nIiIiNlPYioiI2ExhKyIiYjOFrYiIiM0UtiIiIjZT2IqIiNhMYSsiImIzha2IiIjNFLYiIiI2U9iK\niIjYTGErIiJiM4WtiIiIzRS2IiIiNlPYioiI2ExhKyIiYjOFrYiIiM0UtiIiIjZzWJYV7RpEREQa\nNX2yFRERsZnCVkRExGYKWxEREZspbEVERGymsBWxkWEY1xiGscYwjA2GYWwyDONHNuzjYcMwfl3N\n10M27GuxnfOLNFYKWxGbGIbRHvgDMNk0zcHA+cANhmFMr+NdnctLCsafw32JNBruaBcg0oilAzFA\nEtjuu3EAAAMuSURBVFBgmmapYRjfAjwAhmGMAJ4GEoFc4C7TNPcZhrEE2AyMBuKB75umOd8wjP7A\nX4FkoBXwR9M0/3a6IgzDSAaeAfoBLuAJ0zTfMAzjVmAK0BzoBswzTfOe4495DLj6eF1HgA+Aocf/\n20rTNM8//u/nqHgTAXC1aZp7IvxeiTRq+mQrYhPTNDcCs4C9hmGsMgzjccBlmuYewzBigReAG03T\nHEZF6P7r+EMtwH386zcD/zUMIwa4HfiNaZrnAROB3x0f7zhNKQ8Ba0zTHE7FJ9NfGIbR9fh/Ox+4\nChgITDcMo//xT95jgL7ApcAQwDJN8/7jz+v8r809//in9vnAXWf6PRJpKhS2IjYyTfNuoDPw3PH/\n/9wwjBlALyo+Tc42DGM98DjQ9WsP/cfxx2+g4pPlAOCHQKJhGD+lImiTalnGJOA7x/ezlIpP0v2o\nCPUVpmmWmqZZDuwF/r+9O2atIgijMPzeG0S0EEkVCwVBPLaChYWFhV1KkaCCYLDyByjYqGms/A9B\nEJU0SgIWokgwKYRoJEo8TUo7wUYEMWoxE7yEbCLIENHzVLs738xud/hm93KHa/0D299sfwIebrL2\n2tg7SicfERvINnJEI5JGgd22p4BJYFLSJUqHeg1YsX201vaBkYHpqwPH/Xo+BXwEpoH7wFgd3+qd\nbR84X4MbSSN1nXPULe2BdXr1XkMD1zs7Z9vff6cu4n+Xzjainc/ALUkHACT1KB3lK+A9MCzpRK0d\nB+7W4x5l+xhJx4C9lHe4p4DrtqeBk3W8z9Yh9wy4XOv3Aa+B/ZvMewKclrRD0h5glF+BvippqGNe\nRHRI2EY0Yvs5MAHMSFoGlikBN2H7K3AGuC3pDXCBErhQgu2QpAXKdvJY7SBvAC8kzQFH6noHa/1G\n3e3atZvALklLwFPgiu2Vjnk/bD8GZimhPAN8AL7U8UfAoqSd6+Z2PUNEkD8iiPjr1N+yXrX9cpvu\nfxw4bPtO/TBrHrho++12PE/EvyCdbUSsZ+CspEVgAbiXoI34M+lsIyIiGktnGxER0VjCNiIiorGE\nbURERGMJ24iIiMYSthEREY0lbCMiIhr7CexuFJNeazceAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x122514e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# http://scikit-learn.org/stable/auto_examples/datasets/plot_iris_dataset.html\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from sklearn import datasets\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "# import some data to play with\n",
    "iris = datasets.load_iris()\n",
    "X = iris.data[:, :2]  # we only take the first two features.\n",
    "Y = iris.target\n",
    "\n",
    "x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\n",
    "y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\n",
    "\n",
    "plt.figure(2, figsize=(8, 6))\n",
    "plt.clf()\n",
    "\n",
    "# Plot the training points\n",
    "plt.scatter(X[:, 0], X[:, 1], c=Y, cmap=plt.cm.Paired)\n",
    "plt.xlabel('Sepal length')\n",
    "plt.ylabel('Sepal width')\n",
    "\n",
    "plt.xlim(x_min, x_max)\n",
    "plt.ylim(y_min, y_max)\n",
    "plt.xticks(())\n",
    "plt.yticks(())\n",
    "\n",
    "# To getter a better understanding of interaction of the dimensions\n",
    "# plot the first three PCA dimensions\n",
    "fig = plt.figure(1, figsize=(8, 6))\n",
    "ax = Axes3D(fig, elev=-150, azim=110)\n",
    "X_reduced = PCA(n_components=3).fit_transform(iris.data)\n",
    "ax.scatter(X_reduced[:, 0], X_reduced[:, 1], X_reduced[:, 2], c=Y,\n",
    "           cmap=plt.cm.Paired)\n",
    "ax.set_title(\"First three PCA directions\")\n",
    "ax.set_xlabel(\"1st eigenvector\")\n",
    "ax.w_xaxis.set_ticklabels([])\n",
    "ax.set_ylabel(\"2nd eigenvector\")\n",
    "ax.w_yaxis.set_ticklabels([])\n",
    "ax.set_zlabel(\"3rd eigenvector\")\n",
    "ax.w_zaxis.set_ticklabels([])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Seaborn\n",
    "\n",
    "* Based on matplotlib\n",
    "* Themes & styles\n",
    "* Nice color palettes https://stanford.edu/~mwaskom/software/seaborn/tutorial/color_palettes.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "sns.set(style=\"whitegrid\", color_codes=True)\n",
    "iris = sns.load_dataset(\"iris\")\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width species\n",
       "0           5.1          3.5           1.4          0.2  setosa\n",
       "1           4.9          3.0           1.4          0.2  setosa\n",
       "2           4.7          3.2           1.3          0.2  setosa\n",
       "3           4.6          3.1           1.5          0.2  setosa\n",
       "4           5.0          3.6           1.4          0.2  setosa"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.7.0'"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sns.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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YsWMHXnrpJd3bJSqGt68vYwIep4RC8Pb1lXRd3/BwxkQ4To11U6kkZoyZiIio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\nIQR4AAAAwEL+P0mf6q47s8ydAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x122477320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# styling\n",
    "sns.set(style=\"ticks\")\n",
    "sns.despine() # remove the top and right line in graph\n",
    "\n",
    "# regplot https://stanford.edu/~mwaskom/software/seaborn/generated/seaborn.regplot.html#seaborn.regplot\n",
    "g = sns.regplot(x=\"sepal_length\", y=\"sepal_width\",fit_reg=True, color='r', data=iris, ci=False, \n",
    "    scatter_kws={\"color\":\"darkred\",\"alpha\":0.3,\"s\":90}, marker= \"o\",\n",
    "    line_kws={\"color\":\"g\",\"alpha\":0.5,\"lw\":4})\n",
    "\n",
    "g.figure.set_size_inches(12,8)\n",
    "g.axes.set_title('Iris', fontsize=30, color=\"r\",alpha=0.5)\n",
    "\n",
    "g.set_xlabel(\"Sepal Length\", size = 25,color=\"r\",alpha=0.5)\n",
    "g.set_ylabel(\"Sepal Width\" ,size = 25,color=\"r\",alpha=0.5)\n",
    "g.tick_params(labelsize=14,labelcolor=\"black\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bokeh\n",
    "\n",
    "* http://bokeh.pydata.org/en/0.10.0/docs/user_guide/plotting.html\n",
    "* Server app examples http://bokeh.pydata.org/en/0.11.1/docs/gallery.html#gallery-server-examples\n",
    "    \n",
    "<img src='simple-dataviz-datascience/img/bokeh_scatterplot.png' style='width: 400px'>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from bokeh.plotting import figure, show, output_file\n",
    "from bokeh.sampledata.iris import flowers\n",
    "\n",
    "colormap = {'setosa': 'red', 'versicolor': 'green', 'virginica': 'blue'}\n",
    "colors = [colormap[x] for x in flowers['species']]\n",
    "\n",
    "p = figure(title = \"Iris Morphology\")\n",
    "p.xaxis.axis_label = 'Petal Length'\n",
    "p.yaxis.axis_label = 'Petal Width'\n",
    "\n",
    "p.circle(flowers[\"petal_length\"], flowers[\"petal_width\"],\n",
    "         color=colors, fill_alpha=0.2, size=10)\n",
    "\n",
    "output_file(\"iris.html\", title=\"iris.py example\")\n",
    "\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from bokeh.charts import Scatter, output_file, show\n",
    "from bokeh.sampledata.iris import flowers as data\n",
    "\n",
    "scatter = Scatter(data, x='petal_length', y='petal_width',\n",
    "                  color='species', marker='species',\n",
    "                  title='Iris Dataset Color and Marker by Species',\n",
    "                  legend=True)\n",
    "\n",
    "output_file(\"iris_simple.html\", title=\"iris_simple.py example\")\n",
    "\n",
    "show(scatter)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ggplot\n",
    "\n",
    "* [Grammar of graphics](http://byrneslab.net/classes/biol607/readings/wickham_layered-grammar.pdf) based\n",
    "* Simple [example](http://ggplot.yhathq.com/how-it-works.html)\n",
    "* Using R with python http://nbviewer.jupyter.org/gist/ramnathv/9334834/example.ipynb \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ZNiIiAoslOAZom81mrFZroMuozEO5VKVcfAu2XEDZ1ES5+KZcfAu2XKRhCsh3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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12466af60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (-9223372036550393456)>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from ggplot import *\n",
    "ggplot(iris,aes(x='petal_width', y='sepal_length', colour='species')) + \\\n",
    "    geom_point() + \\\n",
    "    geom_abline(intercept = 4.7776, slope = 0.88858)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pandas plotting tools\n",
    "\n",
    "* http://pandas.pydata.org/pandas-docs/version/0.16.0/visualization.html\n",
    "* http://pandas.pydata.org/pandas-docs/version/0.16.0/cookbook.html#cookbook-plotting\n",
    "* Making matplotlib graphs look like R by default? http://stackoverflow.com/questions/14349055/making-matplotlib-graphs-look-like-r-by-default\n",
    "* e.g. matplotlib.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>petalLength</th>\n",
       "      <th>petalWidth</th>\n",
       "      <th>sepalLength</th>\n",
       "      <th>sepalWidth</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     petalLength  petalWidth  sepalLength  sepalWidth    species\n",
       "145          5.2         2.3          6.7         3.0  virginica\n",
       "146          5.0         1.9          6.3         2.5  virginica\n",
       "147          5.2         2.0          6.5         3.0  virginica\n",
       "148          5.4         2.3          6.2         3.4  virginica\n",
       "149          5.1         1.8          5.9         3.0  virginica"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x124730c88>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MmvNVU1ecQz+0nvzGyPotXoUkY/5Oah5LRldkZ0BRrZL7ZwOJiUfkON7pyFrn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T6fRcUEUb6KAgm1e8DnBcRr3GyzlHD04EXEIhncsm89tu6STWSZZa58yS7fSuRr5ViqVMlFuxyc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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x124738d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Axes(0.125,0.125;0.775x0.775)\n",
       "1    Axes(0.125,0.125;0.775x0.775)\n",
       "2    Axes(0.125,0.125;0.775x0.775)\n",
       "dtype: object"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DHl9otvVdwJVjyVz1KUU7dxF6y6zOTzCTuvx8as/lohiV0CWWjoqEeLI+/Zyy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\n+zjT3tJvNa9waHCEPdsLsXdQ8PAPlnH7vXPx8HIa81iZXEZ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j1RaJboyoNHR2DOAf5D4j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ppYGtsH+OUbmQwOq9l1oprHfrefj/aX4+Ziz4/umcdLP1g5Ywp5xsNQDXvYhAHgeZOc3yuwPfqQ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\nVfHyyIV4K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YTQMUm+QgghhI5pPflGRkYyadIkJkyYwBdffKHtw3Uqr732GgEBAUyfPr32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\nTJKvEEIIoWOSfIUQQggd+3808kE3C/fGwgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1246f4a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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UVHDw4EGuvPJKt+MEzz/fUa8wbdo0mpqaaGpqQq/Xc+ONN7Jw4UIeeeQRDrY9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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x121413ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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PidVqzP/lPSiaNxcT23bg2K/+D4fuugeOrm6Unb8Ss368NuZDBduuS/cewc6Y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\nTlRl4lrL5NjIiapz6Bnpw7KANK1XoUnuM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lpzZM3122+/jWXLlmH16tXYunUrBgYGpO+R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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12469e5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby([\"species\"], as_index=False).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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779V3330X8hoABFkg7q9vOwPw83WetuvP/goLzwQAyfW+sNDjffeF6wq5Fx8Rp9MzAU1N\nTXrzzTdDUUsHgwYN0ieffKL4+HhVVlZq9uzZ+vDDD8/6ndLSUpWVlYWoQgAAwldWVlaHdUVFRSou\nLnYtGJ148MEHjT179nQ27JwdOHDAyMnJ8Wns9ddfbxw5cuSc9nHZZZcZBw4c8Pu7AILsxAnDyMw0\nDMn1ysx0rfPHt9+e+X7ba/duz9v1Z39HjhhGYuKZsYmJhnHkiHGi+YSR+VqmoQUytEBG5muZxpET\nRzqsO9Hs53EAAeLr757XMwFjx46VJJ08eVIbNmxQcnKyYmNjJblO01dUVAQhs7TX2NioXr16yWaz\nac+ePZKkpP+flAMgSgTi/voBA6Rvv+04MdDbdn3dX1KSVF3dYWJgnOTxvnvuxUeksRmG54v9bRP2\nbDZbh/kANptNF110UZd3PnfuXG3fvl0Oh0O9evVScXGxTp8+LUnKy8vTm2++qZUrVyo2Nlbx8fF6\n7LHHdNVVV/m9n9raWmVlZamiokJpaWldrhsAgHDm6++e1zMBbV8qLi5WaWlpu8/uvvtuvf76610u\ncvHixWf9fNq0aZo2bVqX9wMAADryGgJmz56tb775Rg0NDe5LA5LU0tKiCy+8MCTFAQCA4PEaAn7/\n+9/L4XBo4cKFmj9/vvuSgN1uV+/evUNWIIAwF4hGP/7w0rzHr9o8bSPUxxEANCdCV3kNAfv27ZPN\nZtP06dP1ww8/tPvswIEDGj58eNCLAxDmAtHoxx9emvd4DALeanM6O27j22+lvLzQHUcAOE87OzQs\nojkR/OU1BLz66quy2WxqaGhQdXW1RowYIbvdrqqqKg0YMEDLly8PZZ0AwpG3xjvBeoiOl+Y9WrnS\n99oqKztuIydH+uKLjmPD9GFAkjw2LFq2axkPCYJfvIaAl156SZI0Y8YMvfjii+6Jgg0NDfrtb38b\nmuoAAEDQdNo2+Icffmh3e0GfPn108ODBoBYFIEIUFEiZmWeWMzNd64KlvFxKTDyznJjoWudPbZ62\nsW5daI8jAAqGFiiz35maM/tlqmBogXkFISJ12jb4yiuv1MMPP6wJEyaotbVVa9asUUZGRihqAxDu\nAtHoxx9emvf4VVtcnOdthPI4AoDmRAgEr82C2pw6dUorVqzQ9u3bJUmjR4/W1KlTZbeb+hRiv9As\nCABgJV1uFnTo0CH16dNHjY2NGjdunMaNG+f+rKGhQf/8z/8c2IoBAEBIeQ0BJSUlWrp0qfLz8z1+\nvnnz5qAVBQAAgs9rCHjyyScl8WMPRL1gNcmpr3fdeie5Jt6lpnpv9OPPem/1elofgQ2AgFDyGgKm\nTp2qf/qnf9Lo0aM1evRoZWRkqEePHqGsDUCwBavZT329lJYmtbS4ltPSpL17pYyMjo1+JM8NgDyt\n99bUR+p4HKtXS7m5EdUACAg1ryHg008/VU1NjXbu3KmPP/5Yf/jDH5SUlKTRo0drzJgx5/Q0PwBh\nJljNfnJyzgQAyfV+xAjPjX7a3vuy3ltTn7b3P11fWBjaRkZABDrrFP9+/fqpX79+mjx5spqamvTx\nxx/rtdde05IlS/T111+HqkYAABAEXkNAc3OzvvjiC3322Wf67LPPdOLECY0aNUr3338/fQKAaFFQ\n4DpN3vYv5kA1yVm3rv3lgNhYadu29pcDftroZ8MG39avW9f+csBP6/35cZSXS3V1gT82IIp4DQG/\n/vWvNXToUI0bN05lZWXcXw9Eo2A1+0lNlWprO04M9Nbox5/13ur1tD7CGgABoea1WdDvf/97bd26\nVeeff77GjBmj0aNHa+jQoYqJ6bTTcNihWRAAwEq63Czo0UcflSQdPHhQn332md588009+uijGjBg\ngEaPHq077rgj8FUDAICQ6bT3b0pKim6++Wb1799fAwcO1Jo1a7R7925CAAAAEc5rCPj444/15Zdf\n6ssvv9SBAwc0ZMgQjRw5Ui+88IJ+9atfhbJGAOHMn+Y9/m6jq2ODuQ0gCngNAX/+8581YsQIPfHE\nExo8eLBiY2NDWReASOCt2ZDkexMifxoWBaK5UbAaJAERyOssv1mzZumqq67SyZMn9eWXX2rHjh3t\nXgDgtdmQt/X+bKOrY/2tGbAgr2cCSktLz/rFN954I+DFAACA0PF6i2A04RZBIEh+fmo9M9Pz5YC2\n9b6c4g/UWH9r5nIAokiXbxFss3PnTr3yyis6ceKEWltb1draqrq6Op4uCODsDXl8bdTjT1OfQDQA\nookQ4NZpCCgpKdG9996r1atX684771RlZaWys7NDURuASBAX5/mhPN7W+7ONro4N5jaAKNBp+7+4\nuDhNmTJFw4cPV0JCghYuXKgPP/wwFLUBAIAg8ikEOBwOXXLJJdq9e7dsNpsOHz4citqA6OZ0SkuW\nuF5Op9nV+MZTzZF4HAAk+XA5oKCgQA888IDKyso0efJkrV27VoMHDw5FbUD0isR71T3VvHq1lJsb\nWccBwK3TEDBy5EiNGzdOMTExevfdd1VdXa2EhIRQ1AZEL2/3qofzdWpPNRcWRt5xAHDzejmgrq5O\n//u//6v8/HzV19frhx9+0NGjR3XBBRfo3nvvDWWNAAAgCLyeCXjxxRdVVVWlhoYG5efnn/mC3a7r\nrrsuFLUB0augwHXq/Kf3qhcUmFlR5zzVXF4u1dVF1nEAcPMaAhYtWiRJWrp0qWbOnBmyggBLiMR7\n1b3VHGnHAcDNp4mB5eXl+v777zVv3jwtX75cM2fO1HnnnReK+oDoFYn3qnuqORKPA4AkH24RfOqp\np3T8+HHt3btXsbGxqqmpUUlJSShqAwAAQdRpCNi7d68eeughdevWTd27d9fzzz+vffv2BWTnjz/+\nuEaNGqWbb77Z65iFCxcqOztbEydODNh+AQCADyEgJiZGp06dci8fOXJEMTGdfs0nkydP1iuvvOL1\n88rKStXU1GjTpk165plntGDBgoDsF4gaDoc0darr5XAEbmwgGgDRWAgIe53OCbjrrrt0zz33qLGx\nUb/73e/00Ucfafbs2QHZ+bBhw1RbW+v184qKCk2aNEmSNGTIEDU1NamxsVG9e/cOyP6BiOZwSP37\nS0ePupY3bJCqq6WkpK6NDUQjIxoLARGh03/S/8u//IvGjBmjw4cP64033tD06dM1efLkUNSmhoYG\npaamupdTU1NVX18fkn0DYa+w8MyPuuR6X1jY9bHeGhn5w5/GQgBM0+mZgHnz5unkyZMqKytTa2ur\n1qxZo7/97W+aN29eKOqTYRjtlm0221nHl5aWqqysLJglAQAQEbKysjqsKyoqUnFxsWvB6MS4ceOM\n1tZW93JLS4sxfvz4zr7mswMHDhg5OTkeP5s/f76xbt26drUcOnTonPZx2WWXGQcOHDjnOoGwc+SI\nYSQmGobkeiUmutZ1deyJE4aRmXlmbGama50/PG3jyJGubxeAT3z93ev0TEBqaqoOHDigvn37SpJ+\n/PFHJScnBzaqeJGVlaUVK1ZowoQJ+uqrr5SQkMB8AKBNUpLrun7baf3ycs/X+P0dG4gGQDQWAiKC\nzTB+dr79ZwoKCrR7926NHDlSdrtdVVVVSk5OVkpKimw2m15++eVz3vncuXO1fft2ORwO9erVS8XF\nxTp9+rQkKS8vT5L09NNP69NPP1V8fLwWLVp0Tk8wrK2tVVZWlioqKpSWlnbO9QIAEAl8/d3r9ExA\n4c8mD02bNs39vrPr851ZvHhxp2OefPLJLu0DAAB41mkIyMjICEUdAAAgxALT9QcAAEQcQgAAABZF\nCAAAwKIIAQAAWBQhAAAAiyIEAABgUYQAAAAsihAAAIBFEQIAALAoQgAAABZFCAAAwKIIAQAAWBQh\nAAAAiyIEAABgUYQAAAAsihAAAIBFEQIAALAoQgAAABZFCAAAwKIIAQAAWBQhAAAAiyIEAABgUYQA\nAAAsihAAAIBFEQIAALAoQgAAABZlN7sARAGnU1q2zPW+oECKizOzGgCAjwgB6BqnUxo/XqqsdC2v\nWiVt3EgQAIAIwOUAdM2yZWcCgOR633ZWAAAQ1ggBAABYFCEAXVNQIGVmnlnOzHStAwCEPeYEoGvi\n4lxzAJgYCAARhxCArouLk2bNMrsKAICfTL0csGXLFo0fP17Z2dlaunRph8+rqqp09dVXKzc3V7m5\nufrTn/5kQpUAAEQn084EtLS06JlnntFrr72mlJQUTZkyRVlZWUpPT283bvjw4VqyZIlJVQIAEL1M\nOxOwZ88e9e3bV2lpaerWrZsmTJigiooKs8pBoDmd0pIlrpfTaXY1AAAPTAsBBw8e1IUXXuheTklJ\n0cGDB9uNsdls2rVrlyZOnKh7771X3333XajLxLloayBUWOh6jR9PEACAMGRaCLDZbJ2OGTRokD75\n5BOtXbtW+fn5mj17dggqQ5fRQAgAIoJpcwJSUlJUV1fnXq6vr1dKSkq7MT169HC/z8zM1FNPPSWH\nw6GkpCSv2y0tLVVZWVngCwYAIMJkZWV1WFdUVKTi4mJJJoaAK664QjU1NaqtrVVycrLWr1+vxYsX\ntxvT2NioXr16yWazac+ePZJ01gAgScXFxe6Da1NbW+vxD4EgKShwPUOg7WwADYQAwBQVFRVKS0vz\n+rlpIcBut2v+/PmaMWOGWltbNWXKFKWnp2vVqlWSpLy8PH344YdauXKlYmNjFR8f3yEkIEzRQAgA\nIoLNMAzD7CKCre1MQGeJCACAaODr7x7PDgAAwKIIAQAAWBTPDrA6p9P3a/f19VJOjuv9unVSaqr3\nbfizXX/rAAAEBCHAytqa+rTN4l+1yjWhz9MPcH29lJYmtbS4ltPSpNpaKSmp4zZWr5Zyc33brr91\nAAAChssBVuZPU5+cnDMBQHK9z8nxvI3CQv+aBdFcCABMQQgAAMCiCAFWVlDgauTT5mxNfdatk2Jj\nzyzHxrrWedpGebnv2/W3DgBAwDAnwMr8aeqTmuqaA+BpYqCnbfjTLIjmQgBgCkKA1cXFSbNm+TY2\nNVXaudO3bfiz3XMZDwDoMi4HAABgUYQAAAAsihBgdQ6HNHWq6+VwnFlfXy8NG+Z61de71jmd0pIl\nrpfTefbt+jMWAGAK5gRYmcMh9e8vHT3qWt6wQaqudv1o/7wx0HffuSbs+dLQh+Y/ABAROBNgZYWF\nZwKA5HpfWOi5MdC11/re0IfmPwAQEQgBAABYFCHAysrLpcTEM8uJia51nhoDbdnie0Mfmv8AQERg\nToCVJSW55gAUFrqWy8td6yTPjYF8behD8x8AiAiEAKtLSpJWruy43lNjIH8a+tD8BwDCHpcDAACw\nKEJAJPP3XnxPPQGqq6W+fV2v6uozYz/7zDUXIDbW9V6SduyQunVzvXbsODN2/37pF79wvfbvP7fa\n6CsAACHH5YBI5e+9+J56AmzZIg0ZcmbMJZdI33/vmg9wzTVn1l9zjfTSS9J9951Z9+tfS9u3SwkJ\n0sCBZ9YPHCjt3i3NmeN7bfQVAABTcCYgUvl7L76nngAjRnQcd+217Wf2t/lpAGgzapTnbYwY4V9t\n9BUAAFMQAgAAsChCQKTy9158Tz0Btm3rOG7Llvb/Km/z0ksd123d6nkb27b5Vxt9BQDAFMwJiFT+\n3ovvrSfA99+7LgFIrgDQv7/r9emnZ36YKyulMWOkoUNdlwAkVwAYPtz1/ttvz1wW2LZNGjDAv9ro\nKwAAprAZhmGYXUSw1dbWKisrSxUVFUpLSzO7HAAAgsrX3z0uBwAAYFGEAAAALIoQEI0C0XjHU2Mh\nT+sAABGLiYHRJhCNdzw1FvrqK+mqq9qvq64+88AhAEDE4UxAtAlE4x1PjYWuvbbjurY7DQAAEYkQ\nAACARRECok0gGu94aiy0ZUvHdeXlXakUAGAy5gREm0A03vHWWMjTOgBAxCIERKO4OGnWrK5tIylJ\nWrmy83UAgIhl6uWALVu2aPz48crOztbSpUs9jlm4cKGys7M1ceJE7du3L8QVAgAQvUwLAS0tLXrm\nmWf0yiuv6IMPPtAHH3yg//7v/243prKyUjU1Ndq0aZOeeeYZLViwwJxiAQCIQqaFgD179qhv375K\nS0tTt27dNGHCBFVUVLQbU1FRoUmTJkmShgwZoqamJjU2NppRLgAAUce0EHDw4EFdeOGF7uWUlBQd\nPHiw3ZhhRd+8AAAOAElEQVSGhgalpqa6l1NTU1VfXx+yGgEAiGamhQCbzebTuJ8/5NDX7wEAgLMz\n7e6AlJQU1dXVuZfr6+uVkpLSbkxycnK7f/l7GvNzpaWlKisrC2yxAABEoKysrA7rioqKVFxcLMnE\nEHDFFVeopqZGtbW1Sk5O1vr167V48eJ2Y7KysrRixQpNmDBBX331lRISEtS7d++zbre4uNh9cG3a\nnqsMAICVVFRUKC0tzevnpoUAu92u+fPna8aMGWptbdWUKVOUnp6uVatWSZLy8vKUmZmpyspK3Xjj\njYqPj9eiRYvMKhcAgKhjarOgzMxMZf60xa1cP/4/9eSTT4ayJAAALINnBwAAYFGEAAAALIoQAACA\nRRECAACwKEIAAAAWRQgAAMCiCAEAAFgUIQAAAIsiBAAAYFGEAAAALIoQAACARRECAACwKEIAAAAW\nRQgAAMCiCAEAAFgUIQAAAIsiBAAAYFGEAAAALIoQAACARRECAACwKEIAAAAWRQgAAMCiCAEAAFgU\nIQAAAIsiBAAAYFGEAAAALIoQAACARRECAACwKEIAAAAWRQgAAMCiCAEAAFgUIQAAAIsiBAAAYFGE\nAAAALIoQAACARdnN2KnD4dCDDz6oH374QRdddJFeeOEFJSQkdBg3duxYde/eXbGxsbLb7XrnnXdM\nqBYAgOhkypmApUuXatSoUfrwww81YsQILV261OvYN954Q6tXryYAAAAQYKaEgM2bN2vSpEmSpEmT\nJunjjz/2OtYwjFCVBQCApZgSAn788Uf17t1bktS7d2/9+OOPHsfZbDbdc889uvXWW/XWW2+FskQA\nAKJe0OYE3HPPPWpsbOyw/oEHHmi3bLPZZLPZPG5j5cqVSk5O1uHDh3XPPffol7/8pYYNG+Z3LS0t\nLZKk+vp6v78LAECkafu9a/v98yZoIeC1117z+lmvXr106NAh9enTRw0NDerZs6fHccnJyZKknj17\n6sYbb9SePXs6DQGlpaUqKyvz+Nm0adN8rB4AgMiXnZ3dYV1RUZGKi4slSTbDhIvuzz//vJKSkjRz\n5kwtXbpUTU1Nevjhh9uNOXHihFpaWtSjRw8dP35c06dPV1FRkcaMGeP3/pxOp4YMGaJNmzYpNjY2\nUIcRNrKyslRRUWF2GUHD8UU2ji9yRfOxSdF9fC0tLcrOztbu3bsVFxfndZwptwjOnDlTDzzwgP7y\nl7+4bxGUpIMHD2r+/PlaunSpGhsbVVRUJMl1MDfffPM5BQBJ7j9Av379AnMAYSgtLc3sEoKK44ts\nHF/kiuZjk6L/+M4WACSTQkBSUpKWLVvWYX1KSor7dsGLL75Ya9asCXFlAABYBx0DAQCwKEIAAAAW\nFbtgwYIFZhcRKhkZGWaXEDTRfGwSxxfpOL7IFc3HJnF8ptwdAAAAzMflAAAALIoQAACARRECAACw\nKEIAAAAWRQgAAMCiTOkYGEpbtmzRs88+q9bWVk2ZMkUzZ840u6SAefzxx1VZWalevXrp/fffN7uc\ngKurq9Mjjzyiw4cPy2az6fbbb9ddd91ldlkBc/LkSeXn5+vUqVNqbm5WVlaWHnroIbPLCqiWlhZN\nnjxZqampWrJkidnlBNTYsWPVvXt3xcbGym6365133jG7pIBqamrSvHnz9F//9V+y2Wx69tlnddVV\nV5ldVkD8z//8j+bOnetePnDggO6///6o+v+Xl156SWvXrlVMTIwuu+wyLVq0SOedd17HgUYUO336\ntHHDDTcYBw4cME6dOmVMnDjR+O6778wuK2B27Nhh7N2718jJyTG7lKBoaGgw9u3bZxiGYfzjH/8w\nsrOzo+o/P8MwjOPHjxuGYRjNzc3GbbfdZuzYscPkigLr3//93425c+ca9913n9mlBNz1119vHDly\nxOwyguaRRx4x3n77bcMwXP/9bGpqMrmi4GhpaTFGjx5t/PDDD2aXEjAHDhwwxo4da5w8edIwDMO4\n//77jXfffdfj2Ki+HLBnzx717dtXaWlp6tatmyZMmBBVT4waNmyYEhISzC4jaPr06aPLL79cktS9\ne3elp6eroaHB5KoCKz4+XpLU3NyslpYWJSUlmVxR4NTX16uyslK33Xab2aUEjRGlbVb+/ve/a+fO\nnZoyZYokyW6364ILLjC5quDYunWrLr74Yl144YVmlxIwPXr0kN1u14kTJ3T69Gk5nU6lpKR4HBvV\nIeDgwYPt/oNNSUnRwYMHTawI56q2tlbffPONrrzySrNLCajW1lbdcsstGjVqlDIyMnTppZeaXVLA\nPPvss3rkkUcUExOd/zdjs9l0zz336NZbb9Vbb71ldjkBVVtbq549e+rxxx/XpEmTNG/ePJ04ccLs\nsoLigw8+UE5OjtllBFRSUpKmT5+u6667Ttdcc40uuOACjRo1yuPY6Pxf5/+z2Wxml4AAOHbsmObM\nmaOSkhJ1797d7HICKiYmRmvWrNGWLVu0c+dOVVVVmV1SQPz1r39Vr169NGjQoKj91/LKlSu1evVq\nvfLKK3rzzTe1c+dOs0sKmNOnT2vfvn2aOnWq3nvvPcXHx7uf8BpNTp06pb/+9a+66aabzC4loP72\nt7/p9ddf1+bNm/Xpp5/q+PHjWrt2rcexUR0CUlJSVFdX516ur6/3ekoE4am5uVlz5szRxIkTdcMN\nN5hdTtBccMEFyszM1Ndff212KQGxa9cubd68WWPHjtVDDz2kbdu26ZFHHjG7rIBKTk6WJPXs2VM3\n3nij9uzZY3JFgZOamqqUlBT3mbdx48Zp3759JlcVeFu2bNHgwYPVs2dPs0sJqK+//lpDhw7VL37x\nC9ntdt14443atWuXx7FRHQKuuOIK1dTUqLa2VqdOndL69euVlZVldlnwkWEYKikpUXp6ugoKCswu\nJ+AOHz6spqYmSZLT6dTWrVs1aNAgk6sKjLlz56qyslKbN2/W4sWLNWLECD3//PNmlxUwJ06c0D/+\n8Q9J0vHjx/XZZ5/psssuM7mqwOnTp48uvPBCff/995Kkzz//PKouVbWJxksBkvTLX/5Su3fvltPp\nlGEYZ/3PL6pvEbTb7Zo/f75mzJjhvkUwPT3d7LICZu7cudq+fbscDocyMzM1Z84cTZ482eyyAuaL\nL77Q2rVrNWDAAOXm5kpyHfO1115rcmWBcejQIT322GNqbW11zw0YOXKk2WXBB42NjSoqKpLkug3y\n5ptv1pgxY0yuKrDmz5+vhx9+WM3Nzerbt68WLVpkdkkBdfz4cW3dulXPPPOM2aUE3MCBA3XLLbdo\n8uTJiomJ0aBBg3T77bd7HMtTBAEAsKiovhwAAAC8IwQAAGBRhAAAACyKEAAAgEURAgAAsChCAAAA\nFkUIAHBWjz/+eLvOm57ceeed2r59u6qqqnTnnXcGtZ4XX3zR3aK3bb8Azg0hAMBZVVVVqbW1tdNx\nNpstJM/r2LFjh0/1AOhcVHcMBNBRVVWVysvLJbmep3HllVdq4cKFWr9+vZYvX67W1lYNHjxY//qv\n/6ply5apoaFB9913n1asWKHPP/9cy5Ytk9PplNPp1O9+9zsNGzbMve2z9R5bunSpNm7cqJaWFo0Z\nM0a//e1vVVtbq6KiIl122WX65ptv1KtXL/3bv/2bEhMTtX79epWWlio+Pl6DBg1SS0uLMjIy9PXX\nX2v+/PkqLS2VJL399tt67rnn1NTUpJKSEl1//fXB/QMCUYQzAYAF7d69W08//bQ2bNigkydP6tVX\nX9Xbb7+tVatWafXq1erZs6deffVVzZw5U8nJyVq6dKkSEhL0H//xH3rppZe0Zs0a3XvvvXrllVd8\n2t+WLVu0d+9evfPOO3rvvfdUX1/vfqrZ/v37NX36dL3//vtKSEjQ+++/r8OHD2vRokV6/fXX9Ze/\n/EVHjx6VJOXm5uqKK67QwoUL3b36ExMT9e6772revHn64x//GJw/GBClOBMAWNDIkSPVt29fSdIt\nt9yioqIi9ezZ091fvLm5WYMHD273nZiYGJWVlWnz5s36/vvvtWPHDsXGxvq0v88//1x79uzRrbfe\nKkk6efKk0tLSdPXVV6tXr14aOHCgJOlXv/qVHA6HvvjiCw0dOtT9pL7c3Fx9/PHHHrfd9lCw9PR0\nHTlyxM+/BGBthADAguz2M//Tb3uA0U033aSSkhJJ0rFjx9TS0tLuO8eOHdPkyZM1adIk/frXv9bA\ngQO1YsUKn/bX2tqqu+++2/00yKNHj8put+vIkSM677zz3OPa5hTExMS0u+5/tssMbcdis9nOOg5A\nR1wOACyoqqpKhw4dUmtrq9asWaMnnnhCH330kQ4fPizDMLRgwQItX75ckutH9vTp06qurlZsbKzu\nu+8+ZWRkqLKy0ucf6hEjRmjNmjU6fvy4Tp8+raKiIn300Udexw8dOlT/+Z//qUOHDskwDK1fv94d\nENrqAdB1nAkALCg5OVkPP/ywGhoaNHr0aOXn5ys+Pl533323WltbNWjQIM2cOVOSdN1112nmzJl6\n+eWXdfnll+umm25Sz549NW7cOG3bts29zbYf6bZT+W1uueUWLViwQN9++61uv/12tbS06Nprr1Vu\nbq5qa2s93lHQs2dPzZs3T9OnT9d5552niy66SHFxcZKka665RgsWLNBzzz3X4XuhuDsBiCY8Shiw\nmKqqKr388ss+T+ozg8Ph0BtvvKGioiLZbDYtXLhQl1xyiaZNm2Z2aUBU4UwAYDGhup+/K5KSktTU\n1KScnBzFxsZq8ODBuu2228wuC4g6nAkAAMCimBgIAIBFEQIAALAoQgAAABZFCAAAwKIIAQAAWBQh\nAAAAi/o/dyUhuvUyjrkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12464e780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df[df['species'] == 'setosa'].plot(kind='scatter', x='petalLength', y='petalWidth', color='red');\n",
    "df[df['species'] == 'versicolor'].plot(kind='scatter', x='petalLength', y='petalWidth', color='red', ax=ax);\n",
    "df[df['species'] == 'virginica'].plot(kind='scatter', x='petalLength', y='petalWidth', color='green', ax=ax);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interaction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "petalWidth petalWidth\n"
     ]
    },
    {
     "data": {
      "image/png": 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ScXGxEhISfFEbAHjmEnz6KZMPAR9pNxisXbtWRUVF+u1vfytJSkpK0lNPPdXrhQGAYdIk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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1249929e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ipywidgets import interact, interactive, fixed\n",
    "import ipywidgets as widgets\n",
    "\n",
    "list_dimensions = ['petalWidth', 'petalLength', 'sepalWidth', 'sepalLength']# list(set(df['species']))\n",
    "\n",
    "x_axis = 'petalWidth'\n",
    "y_axis = 'petalLength'\n",
    "\n",
    "def f(x, y):\n",
    "    print(x, y)\n",
    "    df.plot(kind='scatter', x=x, y=y, color='red');\n",
    "    \n",
    "interact(f, x=list_dimensions, y=list_dimensions);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizing Models\n",
    "\n",
    "* Not visualizing the data, but behavior of mathematical functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Function visualization\n",
    "\n",
    "Find the minimal value for the following equation:\n",
    "\n",
    "$1.3 x^2 + 4 x + 0.6$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: -2.476923\n",
      "         Iterations: 20\n",
      "         Function evaluations: 40\n",
      "solved: x=[-1.53845215]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import scipy.optimize as opt\n",
    "\n",
    "objective = np.poly1d([1.3, 4.0, 0.6])\n",
    "x_ = opt.fmin(objective, [3])\n",
    "print(\"solved: x={}\".format(x_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Unz8/VdLtlmXdIilOUk/Lsp6ybftjl3uxZVmLJH3Xz/e8ZjNG99UfX9uv5VtP6J65g+UM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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12253e2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pylab as plt\n",
    "from ipywidgets import widgets\n",
    "from IPython.display import display\n",
    "\n",
    "x = np.linspace(-4, 1, 101.)\n",
    "fig = plt.figure(figsize=(10, 8))\n",
    "plt.plot(x, objective(x))\n",
    "plt.plot(x_, objective(x_), 'ro')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot different SVM classifiers in the iris dataset\n",
    "\n",
    "From: http://scikit-learn.org/stable/auto_examples/svm/plot_iris.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Automatically created module for IPython interactive environment\n"
     ]
    },
    {
     "data": {
      "image/png": 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V9bskdz70SPe+Ta+/SM3ZKiKhdrx+c3mlc5UnWPHpJ4mGw9SfOcWk2QvY9PpL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\njP8PeeV5YP8xviUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12140b588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(__doc__)\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import svm, datasets\n",
    "\n",
    "# import some data to play with\n",
    "iris = datasets.load_iris()\n",
    "X = iris.data[:, :2]  # we only take the first two features. We could\n",
    "                      # avoid this ugly slicing by using a two-dim dataset\n",
    "y = iris.target\n",
    "\n",
    "h = .02  # step size in the mesh\n",
    "\n",
    "# we create an instance of SVM and fit out data. We do not scale our\n",
    "# data since we want to plot the support vectors\n",
    "C = 1.0  # SVM regularization parameter\n",
    "svc = svm.SVC(kernel='linear', C=C).fit(X, y)\n",
    "rbf_svc = svm.SVC(kernel='rbf', gamma=0.7, C=C).fit(X, y)\n",
    "poly_svc = svm.SVC(kernel='poly', degree=3, C=C).fit(X, y)\n",
    "lin_svc = svm.LinearSVC(C=C).fit(X, y)\n",
    "\n",
    "# create a mesh to plot in\n",
    "x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
    "y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
    "xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n",
    "                     np.arange(y_min, y_max, h))\n",
    "\n",
    "# title for the plots\n",
    "titles = ['SVC with linear kernel',\n",
    "          'LinearSVC (linear kernel)',\n",
    "          'SVC with RBF kernel',\n",
    "          'SVC with polynomial (degree 3) kernel']\n",
    "\n",
    "\n",
    "for i, clf in enumerate((svc, lin_svc, rbf_svc, poly_svc)):\n",
    "    # Plot the decision boundary. For that, we will assign a color to each\n",
    "    # point in the mesh [x_min, m_max]x[y_min, y_max].\n",
    "    plt.subplot(2, 2, i + 1)\n",
    "    plt.subplots_adjust(wspace=0.4, hspace=0.4)\n",
    "\n",
    "    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "\n",
    "    # Put the result into a color plot\n",
    "    Z = Z.reshape(xx.shape)\n",
    "    plt.contourf(xx, yy, Z, cmap=plt.cm.Paired, alpha=0.8)\n",
    "\n",
    "    # Plot also the training points\n",
    "    plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Paired)\n",
    "    plt.xlabel('Sepal length')\n",
    "    plt.ylabel('Sepal width')\n",
    "    plt.xlim(xx.min(), xx.max())\n",
    "    plt.ylim(yy.min(), yy.max())\n",
    "    plt.xticks(())\n",
    "    plt.yticks(())\n",
    "    plt.title(titles[i])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Machine Learning classifier comparison\n",
    "\n",
    "http://nbviewer.jupyter.org/github/etpinard/plotly-misc-nbs/blob/master/ml-classifier-comp/ml-classifier-comp.ipynb\n",
    "\n",
    "\n",
    "<img src='simple-dataviz-datascience/img/machine-learning-classifier-comparison.png'>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Other python libraries\n",
    "\n",
    "* http://pandas.pydata.org/pandas-docs/stable/visualization.html\n",
    "* http://pandas.pydata.org/pandas-docs/stable/visualization.html\n",
    "* Vicent (Vega) http://vincent.readthedocs.org/en/latest/quickstart.html\n",
    "* http://pandas.pydata.org/pandas-docs/stable/ecosystem.html#ecosystem-visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ordinary Least Squares Regression\n",
    "\n",
    "* http://setosa.io/ev/ordinary-least-squares-regression/\n",
    "\n",
    "<img src='simple-dataviz-datascience/img/regression-model.png'>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NN Visualization\n",
    "\n",
    "<img src='simple-dataviz-datascience/img/ann-model-visualization.png' style='width: 400px'>\n",
    "\n",
    "http://playground.tensorflow.org/#activation=tanh&batchSize=10&dataset=circle&regDataset=reg-plane&learningRate=0.03&regularizationRate=0&noise=0&networkShape=4,2&seed=0.53606&showTestData=false&discretize=false&percTrainData=50&x=true&y=true&xTimesY=false&xSquared=false&ySquared=false&cosX=false&sinX=false&cosY=false&sinY=false&collectStats=false&problem=classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Others\n",
    "\n",
    "* http://multithreaded.stitchfix.com/blog/2016/04/19/agent-based-modeling/\n",
    "* How to select SVM Kernels? https://www.quora.com/How-do-I-select-SVM-kernels  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tools\n",
    "\n",
    "<img src='http://uncrate.com/p/2010/07/giant-swiss-army-knife-xl.jpg' style='width: 600px'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tableau Software\n",
    "\n",
    "* Drag and drop for visual mapping\n",
    "\n",
    "\n",
    "<img src='simple-dataviz-datascience/img/tableau_scatterplot.png' style='width: 500px'>\n",
    "\n",
    "Tableau has a 10-12 year jump on Microsoft here and is a swiss army knife when it comes to data sources. Tableau does monthly updates as well with a big release or 2 every year. Still a much easier product to use and deploy at scale.\n",
    "\n",
    "\n",
    "* https://www.tableau.com/products/desktop\n",
    "* Free trial (14 days)\n",
    "* Personal Edition ($999), Professional Edition ($1,999)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Trifacta Visual Profiler\n",
    "\n",
    "* 80% of the work in data science is data manipulation\n",
    "* Trifacta data wrangler, shows most frequent values, outliers or potential variable transformations. http://vis.stanford.edu/papers/wrangler.\n",
    "* Ex-Data Wrangler\n",
    "* See also Google Open Refine http://openrefine.org/.\n",
    "* https://www.trifacta.com/support/articles/article/626991/\n",
    "* https://www.trifacta.com/visual-profiling-for-data-transformation/\n",
    "    \n",
    "<img src='simple-dataviz-datascience/img/trifacta.png' style='height:500px'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Raw\n",
    "\n",
    "* http://app.raw.densitydesign.org/#%2F\n",
    "    \n",
    "<img src='simple-dataviz-datascience/img/raw_scatterplot.png' style='width: 500px'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Microsoft Excel / Google Docs\n",
    "\n",
    "* https://docs.google.com/spreadsheets/d/1s4ewB7XJc-YraQ2j850BwIBb0eUGL9eb1Qylr3SU6Uw/edit#gid=0\n",
    "\n",
    "<img src='simple-dataviz-datascience/img/google_spreadhseet_scatterplot.png'>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Online tools"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Google Visualization\n",
    "\n",
    "<div style='text-align: center'>\n",
    "  <img src='data-science-dataviz/google-visualization.png'>\n",
    "</div>\n",
    "\n",
    "https://developers.google.com/chart/interactive/docs/gallery"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Vega\n",
    "\n",
    "\n",
    "<img src='simple-dataviz-datascience/img/vega_scatterplot.png' style='width: 500px'>\n",
    "\n",
    "\n",
    "* IRIS http://vega.github.io/vega-editor/index.html?mode=vega&spec=scatter_matrix\n",
    "* Iris linking http://vega.github.io/vega-editor/index.html?spec=linking\n",
    "* Live editor http://vega.github.io/vega-editor/index.html?mode=vega\n",
    "* Example Vega scatterplot http://bl.ocks.org/iaindillingham/8bac462308a66341c217\n",
    "\n",
    "* Configuration from [existing templates](https://github.com/vega/vega/wiki/Marks). \n",
    "* Extend them or override. \n",
    "* JSON objects as configurations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Voyager\n",
    "\n",
    "<!-- <iframe src='http://vega.github.io/voyager/' width='800' height='500'> -->\n",
    "\n",
    "<img src='simple-dataviz-datascience/img/voyageur_scatterplot.png'>\n",
    "\n",
    "[Data Voyager](http://vega.github.io/voyager/) automatically generates charts with no input by default (besides loading the dataset). You can interact to get more details and bookmark them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## plot.ly\n",
    "\n",
    "* Python API, MATLAB API, R API, JavaScript Graphing Library\n",
    "\n",
    "<img src='simple-dataviz-datascience/img/plotly_scatterplot.png' style='width: 800px'>\n",
    "\n",
    "\n",
    "* https://plot.ly/matplotlib/histograms/\n",
    "* Use the [web editor](https://plot.ly/how-to-create-a-line-of-best-fits/)\n",
    "* Plot.ly2 [is coming](https://plot.ly/plotly2/)\n",
    "* https://plot.ly/plot/?plot_type=Bar+chart#\n",
    "* Offline mode https://plot.ly/python/offline/\n",
    "* https://plot.ly/~romsson/18/\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# D3 & D3-based libraries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## D3\n",
    "\n",
    "* https://d3js.org/\n",
    "* LOTS of examples https://github.com/mbostock/d3/wiki/Gallery\n",
    "* [Live editor](http://tributary.io/inlet/4653053). \n",
    "* [Block builder](http://blockbuilder.org/)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<img src='simple-dataviz-datascience/img/d3_scatterplot.png' style='width: 600px'>\n",
    "\n",
    "* Scatterplot example https://bl.ocks.org/mbostock/3887118\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NVD3 \n",
    "\n",
    "<img src='simple-dataviz-datascience/img/nvd3_scatter.png' style='width: 600px'>\n",
    "\n",
    "* http://nvd3.org/\n",
    "* GitHub https://github.com/novus/nvd3\n",
    "* Live coding http://nvd3.org/livecode/index.html\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Highchart.js\n",
    "\n",
    "<img src='simple-dataviz-datascience/img/highchart_combo_regression.png' style='width: 600px'>\n",
    "\n",
    "\n",
    "* http://www.highcharts.com/\n",
    "* http://www.highcharts.com/demo/combo-regression\n",
    "* http://www.highcharts.com/demo/scatter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## VisTK.js\n",
    "\n",
    "\n",
    "<img src='simple-dataviz-datascience/img/vistk_scatterplot.png' style='width: 600px'>\n",
    "\n",
    "* https://github.com/cid-harvard/vis-toolkit\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Brunel\n",
    "\n",
    "* https://github.com/Brunel-Visualization/Brunel\n",
    "* Succint pipe commands\n",
    "* Examples http://brunel.mybluemix.net/docs/\n",
    "\n",
    "<img src='simple-dataviz-datascience/img/brunel_scatterplot.png' style='width: 600px'>\n",
    "\n",
    "http://brunelvis.org/\n",
    "\n",
    "> `data('https://raw.githubusercontent.com/uiuc-cse/data-fa14/gh-pages/data/iris.csv') x(sepal_width) y(sepal_length) color(species`)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Wrapper for Notebooks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append(\"./simple-dataviz-datascience/modules\")\n",
    "import vistk\n",
    "import pandas as pd\n",
    "from linnaeus import classification\n",
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = pd.read_json(\"simple-dataviz-datascience/data/iris.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>petalLength</th>\n",
       "      <th>petalWidth</th>\n",
       "      <th>sepalLength</th>\n",
       "      <th>sepalWidth</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   petalLength  petalWidth  sepalLength  sepalWidth species\n",
       "0          1.4         0.2          5.1         3.5  setosa\n",
       "1          1.4         0.2          4.9         3.0  setosa\n",
       "2          1.3         0.2          4.7         3.2  setosa\n",
       "3          1.5         0.2          4.6         3.1  setosa\n",
       "4          1.4         0.2          5.0         3.6  setosa"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div id='vistk_div_67577' style='height:auto;'></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div id='vistk_div_75388' style='height:auto;'></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "          <link href='https://cid-harvard.github.io/vis-toolkit/css/vistk.css' rel='stylesheet'>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "$.getScript(\"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.6/d3.min.js\", function () {\n",
       "$.getScript(\"http://cid-harvard.github.io/vis-toolkit/js/topojson.js\", function () {\n",
       "$.getScript(\"http://cid-harvard.github.io/vis-toolkit/build/vistk.js\", function () {\n",
       "\n",
       "        (function (){\n",
       "\n",
       "          var viz_data = 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Length\":5.8,\"sepalWidth\":2.7,\"species\":\"virginica\"},{\"petalLength\":5.9,\"petalWidth\":2.3,\"sepalLength\":6.8,\"sepalWidth\":3.2,\"species\":\"virginica\"},{\"petalLength\":5.7,\"petalWidth\":2.5,\"sepalLength\":6.7,\"sepalWidth\":3.3,\"species\":\"virginica\"},{\"petalLength\":5.2,\"petalWidth\":2.3,\"sepalLength\":6.7,\"sepalWidth\":3.0,\"species\":\"virginica\"},{\"petalLength\":5.0,\"petalWidth\":1.9,\"sepalLength\":6.3,\"sepalWidth\":2.5,\"species\":\"virginica\"},{\"petalLength\":5.2,\"petalWidth\":2.0,\"sepalLength\":6.5,\"sepalWidth\":3.0,\"species\":\"virginica\"},{\"petalLength\":5.4,\"petalWidth\":2.3,\"sepalLength\":6.2,\"sepalWidth\":3.4,\"species\":\"virginica\"},{\"petalLength\":5.1,\"petalWidth\":1.8,\"sepalLength\":5.9,\"sepalWidth\":3.0,\"species\":\"virginica\"}];\n",
       "          var viz_container = '#vistk_div_67577';\n",
       "\n",
       "          var params = {\n",
       "                type: 'scatterplot',\n",
       "                width: 800,\n",
       "                height: 600,\n",
       "                margin: {top: 10, right: 10, bottom: 30, left: 30},\n",
       "                container: viz_container,\n",
       "                data: viz_data,\n",
       "                var_id: '__id',\n",
       "                var_group: '__id',\n",
       "                var_color: 'species',\n",
       "                color: d3.scale.category10(),\n",
       "                radius_min: 5,\n",
       "                radius_max: 10,\n",
       "                var_x: 'petalLength',\n",
       "                var_y: 'petalWidth',\n",
       "                var_r: 'petalWidth',\n",
       "                var_text: 'petalWidth',\n",
       "                items: [{\n",
       "                  marks: [{\n",
       "                    type: 'circle'\n",
       "                  }, {\n",
       "                    var_mark: '__selected',\n",
       "                    type: d3.scale.ordinal().domain([true, false]).range([\"text\", \"none\"]),\n",
       "                    translate: [10, 10]\n",
       "                  }, {\n",
       "                    var_mark: '__highlighted',\n",
       "                    type: d3.scale.ordinal().domain([true, false]).range([\"text\", \"none\"]),\n",
       "                    translate: [10, 10]\n",
       "                  }]\n",
       "                }]\n",
       "              };\n",
       "\n",
       "          var visualization = vistk.viz()\n",
       "            .params(params);\n",
       "\n",
       "          d3.select(viz_container).call(visualization);\n",
       "\n",
       "        })();\n",
       "        });\n",
       "});\n",
       "});\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatterplot = vistk.Scatterplot(id='__id', name='petalWidth', color='species', x='petalLength', \n",
    "                                y='petalWidth', r='petalWidth')\n",
    "scatterplot.draw(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Can be embeded [within notebooks](http://nbviewer.jupyter.org/github/cid-harvard/visualization-notebook-templates/blob/master/demo-VisTK.ipynb) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Other tools & Libraries\n",
    "\n",
    "* Rickshaw, [crossfilter](http://square.github.io/crossfilter/)\n",
    "* Framweork-specific charts (React, Ember, Angular, ..)\n",
    "* Quadrigramm http://www.quadrigram.com/\n",
    "* Exhaustive list on http://keshif.me/demo/VisTools or http://www.jsgraphs.com/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Custom Tools\n",
    "\n",
    "Design is a Search Problem\n",
    "\n",
    "<img src='https://d262ilb51hltx0.cloudfront.net/max/1600/1*NYCujc1c1gD9Ta5VrX0D7w.gif'>\n",
    "    \n",
    "\n",
    "[Design is a Search Problem](https://youtu.be/fThhbt23SGM?t=32m50s) OpenVis 2014. \n",
    "Animated Gif from [The Journalist-Engineer](https://d262ilb51hltx0.cloudfront.net/max/1600/1*NYCujc1c1gD9Ta5VrX0D7w.gif)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scatterplot Matrix\n",
    "\n",
    "\n",
    "<img src='data-science-dataviz/d3-scatterplot-matrix.png' style='height: 600px'>\n",
    "\n",
    "[D3 Scatterplot Matrix](http://bl.ocks.org/mbostock/raw/4063663/)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scatterplot Matrix Navigation \n",
    "\n",
    "<img src='http://romain.vuillemot.net/img/teaser-french-unemployment-data-exploration.png' style='height: 600px'>\n",
    "\n",
    "INSEE unemployement data exploration using INRIA ScatterDice http://labs.data-publica.com/emploi/. Multidimensional *Grand Tour* visualization."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Upset\n",
    "\n",
    "* Available [online](http://vcg.github.io/upset/?dataset=2&duration=1000&orderBy=subsetSize&grouping=groupByIntersectionSize&selection=)\n",
    "\n",
    "<img src='simple-dataviz-datascience/img/upset-movies.png' style='height: 600px'>\n"
   ]
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