ipynb/publication.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Sebastian Raschka](http://www.sebastianraschka.com)\n",
    "\n",
    "[back](https://github.com/rasbt/matplotlib-gallery) to the `matplotlib-gallery` at [https://github.com/rasbt/matplotlib-gallery](https://github.com/rasbt/matplotlib-gallery)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%load_ext watermark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last updated: 09/10/2014 \n",
      "\n",
      "CPython 3.4.1\n",
      "IPython 2.2.0\n",
      "\n",
      "matplotlib 1.4.0\n",
      "numpy 1.9.0\n",
      "scipy 0.14.0\n"
     ]
    }
   ],
   "source": [
    "%watermark -u -v -d -p matplotlib,numpy,scipy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"1.5em\">[More info](http://nbviewer.ipython.org/github/rasbt/watermark) about the `%watermark` extension</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preparing Plots for Publication via matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sometimes, we are not quite happy with matplotlib's default designs. Here are a few suggestions to adjust matplotlib's default plots so that they are more pleasing in the viewer's eye.  \n",
    "\n",
    "In this section, I will re-use certain settings for different types of plots, but if you'd like to change the settings globally (for the active session or even in the matplotlib settings) please see the section [Applying customization and settings globally](http://nbviewer.ipython.org/github/rasbt/matplotlib-gallery/blob/master/ipynb/formatting.ipynb#Applying-customization-and-settings-globally)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sections"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- [Errorbar Plots](#Errorbar-Plots)\n",
    "- [Boxplots](#Boxplots)\n",
    "- [Barplots](#Barplots)\n",
    "- [Histograms](#Histograms)\n",
    "- [Pie charts](#Pie-charts)\n",
    "- [Violin plots](#Violin-plots)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Errorbar Plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#Sections)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Default Errorbar Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def errorbar_default():\n",
    "\n",
    "    # Data\n",
    "    data = [1, 1.5, 1.2]\n",
    "    std_devs = [0.15, 0.25, 0.12]\n",
    "\n",
    "    # X axis positions\n",
    "    x_pos = range(len(data))\n",
    "\n",
    "    for d, std, x in zip(data, std_devs, x_pos):\n",
    "        plt.errorbar(x=x, y=d, yerr=std, fmt='o')\n",
    "\n",
    "    # setting axis limits   \n",
    "    plt.xlim([min(x_pos)-1, max(x_pos)+1])    \n",
    "    plt.ylim([min(data)*0.7, max(data)*1.3])  \n",
    "\n",
    "    # setting labels and titles   \n",
    "    plt.ylabel('x label')\n",
    "    plt.title('Matplotlib default')\n",
    "\n",
    "    plt.legend(['X1', 'X2', 'X3'], loc='upper right')\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modified Errorbar Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "def errorbar_modified():\n",
    "\n",
    "    # Data\n",
    "    data = [1, 1.5, 1.2]\n",
    "    std_devs = [0.15, 0.25, 0.12]\n",
    "\n",
    "    # X axis positions\n",
    "    x_pos = range(len(data))\n",
    "\n",
    "    colors = ['lightblue', 'pink', 'lightgreen']\n",
    "\n",
    "    fig = plt.gca()\n",
    "    ax = plt.subplot(111)  \n",
    "\n",
    "    # draw plots\n",
    "    for d, std, col, x in zip(data, std_devs, colors, x_pos):\n",
    "        plt.errorbar(x=x, y=d, yerr=std, fmt='o', color=col, ecolor='black')\n",
    "    \n",
    "    # setting axis limits    \n",
    "    plt.xlim([min(x_pos)-1, max(x_pos)+1])    \n",
    "    plt.ylim([min(data)*0.7, max(data)*1.3])  \n",
    "    \n",
    "    # setting labels and titles    \n",
    "    plt.ylabel('x label')\n",
    "    plt.text(1, 2, 'Modified',\n",
    "         horizontalalignment='center',\n",
    "         fontsize=14)\n",
    "\n",
    "    # remove axis spines\n",
    "    ax.spines[\"top\"].set_visible(False)  \n",
    "    ax.spines[\"right\"].set_visible(False) \n",
    "    ax.spines[\"bottom\"].set_visible(False) \n",
    "    ax.spines[\"left\"].set_visible(False)\n",
    "\n",
    "    # hiding axis ticks\n",
    "    plt.tick_params(axis=\"both\", which=\"both\", bottom=\"off\", top=\"off\",  \n",
    "                labelbottom=\"off\", left=\"off\", right=\"off\", labelleft=\"on\")\n",
    "\n",
    "    # adding horizontal grid lines \n",
    "    ax.yaxis.grid(True)\n",
    "\n",
    "    plt.legend(['X1', 'X2', 'X3'], loc='upper right', fancybox=True, numpoints=1)\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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L/9+0sRt2c/QRGxuLcePGITU1FbNnz8bWrVtRVFQEAAgICNC1a2pqgoODA7y8vIzq1yyE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\nMBARkQiDgYiIRBgMREQk8v9hdiJhAo3rrQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x106464780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "errorbar_default()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10640e4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "errorbar_modified()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Boxplots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#Sections)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = [np.random.normal(0, std, 50) for std in range(1, 4)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Default Boxplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "def boxplot_default():\n",
    "\n",
    "    fig = plt.figure(figsize=(8,6))\n",
    "\n",
    "    plt.boxplot(data, \n",
    "            notch=False, # box instead of notch shape \n",
    "            sym='rs',    # red squares for outliers\n",
    "            vert=True)   # vertical box aligmnent\n",
    "\n",
    "    plt.xticks([y+1 for y in range(len(data))], ['x1', 'x2', 'x3'])\n",
    "    plt.title('Matplotlib default')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modified Boxplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def boxplot_modified():\n",
    "    \n",
    "    fig = plt.figure(figsize=(8,6))\n",
    "    ax = plt.subplot(111) \n",
    "\n",
    "    bplot = plt.boxplot(data, \n",
    "            notch=True,          # notch shape \n",
    "            vert=True,           # vertical box aligmnent\n",
    "            sym='ko',            # red circle for outliers\n",
    "            patch_artist=True,   # fill with color\n",
    "            )   \n",
    "\n",
    "    # choosing custom colors to fill the boxes\n",
    "    colors = ['pink', 'lightblue', 'lightgreen']\n",
    "    for patch, color in zip(bplot['boxes'], colors):\n",
    "        patch.set_facecolor(color)\n",
    "\n",
    "    # modifying the whiskers: straight lines, black, wider\n",
    "    for whisker in bplot['whiskers']:\n",
    "        whisker.set(color='black', linewidth=1.2, linestyle='-')    \n",
    "    \n",
    "    # making the caps a little bit wider \n",
    "    for cap in bplot['caps']:\n",
    "        cap.set(linewidth=1.2)\n",
    "    \n",
    "    # hiding axis ticks\n",
    "    plt.tick_params(axis=\"both\", which=\"both\", bottom=\"off\", top=\"off\",  \n",
    "            labelbottom=\"on\", left=\"off\", right=\"off\", labelleft=\"on\")\n",
    "\n",
    "    # adding horizontal grid lines \n",
    "    ax.yaxis.grid(True) \n",
    "    \n",
    "    # remove axis spines\n",
    "    ax.spines[\"top\"].set_visible(False)  \n",
    "    ax.spines[\"right\"].set_visible(False) \n",
    "    ax.spines[\"bottom\"].set_visible(False) \n",
    "    ax.spines[\"left\"].set_visible(False)\n",
    "\n",
    "    plt.xticks([y+1 for y in range(len(data))], ['x1', 'x2', 'x3'])\n",
    "\n",
    "    # raised title\n",
    "    plt.text(2, 9, 'Modified',\n",
    "         horizontalalignment='center',\n",
    "         fontsize=18)\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x106510f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "boxplot_default()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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jJ47X/v37bf/++cK4OG6dotqyZYteeOGFolNUiYmJCgwM1KhRo1x9SuMcO3ZM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\nLoIQpAQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10648d940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "boxplot_modified()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Barplots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#Sections)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Default Barplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def barplot_default():   \n",
    "\n",
    "    # input data\n",
    "    mean_values = [1, 2, 3]\n",
    "    variance = [0.2, 0.4, 0.5]\n",
    "    bar_labels = ['bar 1', 'bar 2', 'bar 3']\n",
    "\n",
    "    fig = plt.figure(figsize=(6,4))\n",
    "\n",
    "    # plot bars\n",
    "    x_pos = list(range(len(bar_labels)))\n",
    "    plt.bar(x_pos, mean_values, yerr=variance, align='center')\n",
    "\n",
    "\n",
    "    # set axes labels and title\n",
    "    plt.ylabel('variable y')\n",
    "    plt.xticks(x_pos, bar_labels)\n",
    "    plt.title('Matplotlib default')\n",
    "\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modified Barplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def barplot_modified():\n",
    "\n",
    "    # input data\n",
    "    mean_values = [1, 2, 3]\n",
    "    variance = [0.2, 0.4, 0.5]\n",
    "    bar_labels = ['bar 1', 'bar 2', 'bar 3']\n",
    "\n",
    "    fig = plt.figure(figsize=(6,4))\n",
    "    ax = plt.subplot(111) \n",
    "    \n",
    "    # plot bars\n",
    "    x_pos = list(range(len(bar_labels)))\n",
    "    plt.bar(x_pos, mean_values, yerr=variance, \n",
    "            ecolor='black', # black error bar color\n",
    "            alpha=0.5,      # transparency\n",
    "            width=0.5,      # smaller bar width\n",
    "            align='center')\n",
    "\n",
    "    # set height of the y-axis\n",
    "    max_y = max(zip(mean_values, variance)) # returns a tuple, here: (3, 5)\n",
    "    plt.ylim([0, (max_y[0] + max_y[1]) * 1.1])\n",
    "\n",
    "    # hiding axis ticks\n",
    "    plt.tick_params(axis=\"both\", which=\"both\", bottom=\"off\", top=\"off\",  \n",
    "            labelbottom=\"on\", left=\"off\", right=\"off\", labelleft=\"on\")\n",
    "\n",
    "    # adding horizontal grid lines \n",
    "    ax.yaxis.grid(True) \n",
    "    \n",
    "    # remove axis spines\n",
    "    ax.spines[\"top\"].set_visible(False)  \n",
    "    ax.spines[\"right\"].set_visible(False) \n",
    "    ax.spines[\"bottom\"].set_visible(False) \n",
    "    ax.spines[\"left\"].set_visible(False)\n",
    "\n",
    "    # set axes labels and title\n",
    "    plt.ylabel('variable y')\n",
    "    plt.xticks(x_pos, bar_labels)\n",
    "    \n",
    "    plt.text(1, 4, 'Modified',\n",
    "         horizontalalignment='center',\n",
    "         fontsize=18)\n",
    "\n",
    "    \n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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rF5Wg0Fcv3r59G3v27EF6ejpefPFFvPzyyzh37hymT5+O1NRUm68zGAwYP348hg4dimHD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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x106495668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "barplot_default()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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hlZWVpWHDhunw4cOSztyBYsGCBbY3BgBAkyFVWVmpe+65R23atJEkhYSEKDiYX+gLALBf\nkyEVERGhr776yjcuLi5WZGSkrU0BACBdxPek5s+fr9GjR2vfvn0aMGCAvvzyS61Zs8YfvQEAWrkm\nQ6pfv34qKirSZ599Jkm68cYbFRIS0uSOy8rKdP/99+vIkSNyOBx66KGHNGPGjAZ1M2bM0Lp16xQW\nFqb8/HylpqZextsAAFyJGg2pN998Uw6HQ5Zl+f6WpN27d0uSxowZc8Edh4SEaMGCBUpJSVF1dbX6\n9eunoUOHKjEx0VdTWFiovXv3as+ePfroo480depUFRcXN8f7AgBcARoNqXfeeUcOh6PRJzYVUtHR\n0YqOjpZ05rxWYmKiDh8+XC+kCgoKNGHCBElSWlqaqqqqVFFRoaioqEt6EwCAK1OjIZWfn99sL+L1\nerV9+3alpaXVe/zQoUOKjY31jbt166aDBw8SUgAASRdxTqqyslK5ubnasmWLHA6HBg0apN/97nfq\n2rXrRb1AdXW17rrrLi1atEgRERENtp9dRjzrfEdvHo9HkuRyuRgz9sv4pZdWqm3bH0iSysu9kqTo\naKdfx1lZ7oC8/smTh/XQQ//PqPnw59jrPTN2OlvX+KxAf/7ncljnpsQ57rjjDg0ePFj33XefLMvS\nypUr5fF4tGHDhgs9TZJ06tQpjRo1SiNHjtQjjzzSYPuUKVPkcrk0duxYSVJCQoKKioo4kkLAZWW5\n5XS6A/b6ubkOzZp1wR9N23i9buXnuwPy2oEW6HkPJFPn/aJui/Tb3/5WPXr00PXXX68nn3xSFRUV\nTe7YsixNnjxZSUlJ5w0oScrIyNDy5cslnfn+VadOnQgoAIBPk8t9w4YN06pVq3TPPfdIkt544w0N\nGzasyR1/+OGHWrFihfr06eO7rPypp57SgQMHJEnZ2dlKT09XYWGh4uLiFB4ermXLlv0v7wUAcIVp\nNKQiIiJ854cWLlyo8ePHS5Lq6uoUHh6u+fPnX3DHAwcOVF1dXZMNLFmy5FL6BQC0Io2GVHV1tT/7\nAACggYu6U+zRo0e1Z88e1dTU+B677bbbbGsKAADpIkLq5Zdf1nPPPaeysjKlpqaquLhY/fv318aN\nG/3RHwCgFWvy6r5FixappKRETqdTmzZt0vbt27kLOgDAL5oMqdDQULVv316SVFNTo4SEBN/NZgEA\nsFOTy32xsbE6evSoMjMzNXToUHXu3FlOp9MPrQEAWrsmQ+qtt96SJLndbrlcLh07dkwjRoywvTEA\nABoNqWPHjqljx476+uuvfY/16dNH0pnL07t06WJ/dwCAVq3RkBo3bpzeffdd9e3bt8FNXx0Oh/bt\n22d7cwCA1q3RkHr33XdlWZY2b96s7t27+7MnAAAkXcTVfenp6f7oAwCABi4YUg6HQ/369VNJSYm/\n+gEAwKfJq/uKi4u1YsUKXXfddQoPD5d0Jrw+/fRT25sDALRuTYbU3//+d3/0AQBAA02G1Nkv7h45\ncqTeDWYBALBbkxdOFBQUKD4+Xj169NDgwYPldDo1cuRIf/QGAGjlmgypJ598Uv/4xz/Us2dP7d+/\nXx988IHS0tL80RsAoJVrMqRCQkJ01VVXqa6uTrW1tRoyZIj+9a9/+aM3AEAr1+Q5qc6dO+v48eMa\nNGiQ7r33Xl1zzTWKiIjwR28AgFauySOptWvXKiwsTAsWLNCIESMUFxend955xx+9AQBauSaPpF58\n8UWNHTtWMTExysrK8kNLAACc0WRIHT9+XMOGDVPnzp01duxY3X333YqKivJHb0Cr4vV65PV6JEnX\nXTdYHo9bkuR0uuR0ugLWFxBITYaU2+2W2+3WJ598or/85S+67bbb1K1bN33wwQf+6A9oNQgjoKEm\nz0mddc011yg6Olpdu3bVl19+aWdPAABIuoiQeuGFF+RyuXT77bersrJSf/rTny76vn2TJk1SVFSU\nevfufd7tHo9HkZGRSk1NVWpqqubMmXNp3QMArmhNLveVlZVp4cKFSklJueSdT5w4UdOnT9f999/f\naM3gwYNVUFBwyfsGAFz5mgypuXPnXvbOBw0aJK/Xe8Eay7Iue/8AgCvbRZ+TsoPD4dDWrVuVnJys\n9PR0lZaWBrIdAIBhAhpSffv2VVlZmT755BNNnz5dmZmZ563zeDzyeDyMGfttXF7u9Y2/f2l4axiX\nl3sD/vkHchzozz/Q40B//udyWDavt3m9Xo0ePVo7d+5ssrZHjx7atm2bunTpYmdLQJOystxyOt2B\nbiMgvF638vPdgW4jIJh3d6DbaCCgR1IVFRW+c1IlJSWyLIuAAgD4NHnhxP9i3LhxKioqUmVlpWJj\nY5Wbm6tTp05JkrKzs7VmzRotXbpUwcHBCgsL0+rVq+1sBwDQwtgaUqtWrbrg9pycHOXk5NjZAgCg\nBQvoch8AABdCSAEAjEVIAQCMRUgBAIxFSAEAjEVIAQCMRUgBAIxFSAEAjEVIAQCMRUgBAIxFSAEA\njEVIAQCMRUgBAIxFSAEAjEVIAQCMZevvk8Ll8Xg88ng8vn+7XC5Jksvl8v0bAFoDQspA3w8jh8Ph\nCywAaG1Y7gMAGIuQAgAYi5ACABiLkAIAGIuQAgAYi5ACABiLkAIAGMvWkJo0aZKioqLUu3fvRmtm\nzJih+Ph4JScna/v27Xa2AwBoYWwNqYkTJ2r9+vWNbi8sLNTevXu1Z88evfTSS5o6daqd7QAAWhhb\nQ2rQoEHq3Llzo9sLCgo0YcIESVJaWpqqqqpUUVFhZ0sAgBYkoOekDh06pNjYWN+4W7duOnjwYAA7\nAgCYJOD37rMsq97Y4XA0qDl777qz97Pz5/jXv56nHTt2SZKio52SpPJyr1/HI0ZkBeT1U1IS9fTT\nvwro5x+ocXm5V84zH4e83jPbnU5XqxiXl3vr3djYhPnw5zjQn3+gxmcF+vM/l8M6NyWamdfr1ejR\no7Vz584G26ZMmSKXy6WxY8dKkhISElRUVKSoqCg7W7okWVluOZ3ugL1+bq5Ds2bZOkWN8nrdys93\nB+S1Ay3Q8x5IzLs70G0EhKnzHtDlvoyMDC1fvlySVFxcrE6dOhkVUACAwLJ1uW/cuHEqKipSZWWl\nYmNjlZubq1OnTkmSsrOzlZ6ersLCQsXFxSk8PFzLli2zsx0AQAtja0itWrWqyZolS5bY2QIAoAXj\njhMAAGMRUgAAYxFSAABjEVIAAGMRUgAAYxFSAABjEVIAAGMRUgAAYwX8BrNoyOv1+G76eN11g+Xx\nuCWduSHk2ZtCAkBrQEgZiDACgDNY7gMAGIuQAgAYi5ACABiLkAIAGIuQAgAYi5ACABiLkAIAGIuQ\nAgAYi5ACABiLkAIAGIuQAgAYi5ACABiLkAIAGIuQAgAYy9aQWr9+vRISEhQfH6958+Y12O7xeBQZ\nGanU1FSlpqZqzpw5drYDAGhhbPt9UrW1tZo2bZo2bNigmJgY3XLLLcrIyFBiYmK9usGDB6ugoMCu\nNgAALZhtR1IlJSWKi4uT0+lUSEiIxo4dq7Vr1zaosyzLrhYAAC2cbSF16NAhxcbG+sbdunXToUOH\n6tU4HA5t3bpVycnJSk9PV2lpqV3tAABaINuW+xwOR5M1ffv2VVlZmcLCwrRu3TplZmZq9+7dDeo8\nHo8kyeVyBWTs9Z4Zn/2V7q1lfFagP/9AjMvLvXI6z7x/U+bDX+Pycq88Ho9R8+HPcaA//9b+834u\nh2XTeltxcbHcbrfWr18vSZo7d66CgoL0q1/9qtHn9OjRQ9u2bVOXLl3saOmyZGW55XS6A91GQHi9\nbuXnuwPdRkAw7+5AtxEQzLs70G00YNty380336w9e/bI6/Xq5MmTev3115WRkVGvpqKiwndOqqSk\nRJZlGRVQAIDAsm25Lzg4WEuWLNHw4cNVW1uryZMnKzExUXl5eZKk7OxsrVmzRkuXLlVwcLDCwsK0\nevVqu9oBALRAtoWUJI0cOVIjR46s91h2drbv3zk5OcrJybGzBQBAC8YdJwAAxiKkAADGIqQAAMYi\npAAAxiKkAADGIqQAAMYipAAAxiKkAADGIqQAAMYipAAAxiKkAADGIqQAAMYipAAAxiKkAADGIqQA\nAMYipAAAxiKkAADGIqQAAMYipAAAxiKkAADGIqQAAMYipAAAxiKkAADGsjWk1q9fr4SEBMXHx2ve\nvHnnrZkxY4bi4+OVnJys7du329kOAKCFsS2kamtrNW3aNK1fv16lpaVatWqVdu3aVa+msLBQe/fu\n1Z49e/TSSy9p6tSpdrUDAGiBbAupkpISxcXFyel0KiQkRGPHjtXatWvr1RQUFGjChAmSpLS0NFVV\nVamiosKulgAALYxtIXXo0CHFxsb6xt26ddOhQ4earDl48KBdLQEAWhjbQsrhcFxUnWVZl/U8AMCV\nL9iuHcfExKisrMw3LisrU7du3S5Yc/DgQcXExNjV0mXJz3cHuoUAcge6gYBh3lsn5t08th1J3Xzz\nzdqzZ4+8Xq9Onjyp119/XRkZGfVqMjIytHz5cklScXGxOnXqpKioKLtaAgC0MLYdSQUHB2vJkiUa\nPny4amtrNXnyZCUmJiovL0+SlJ2drfT0dBUWFiouLk7h4eFatmyZXe0AAFogh3XuSSEAAAzBHSf8\nyOv1qnfv3s22v//85z/q37+/QkNDNX/+/GbbL5pXc8/7n//8ZyUnJ6tPnz669dZb9emnnzbbvtF8\nmnve165dq+TkZKWmpqpfv37auHFjs+3bZLYt96H51dbWqk2bNr5x165dtXjxYr399tsB7Ap2O3fe\nr7/+em3evFmRkZFav369HnroIRUXFwewQ9jh3Hm/44479NOf/lSStHPnTt15553au3dvoNrzG46k\n/Oz06dO67777lJSUpLvvvlsnTpyQJM2ePVs//OEP1bt3b2VnZ/vqXS6XHn30Ud1yyy167rnn6u3r\n6quv1s0336yQkBC/vgdcuuac9/79+ysyMlLSmS/B891CczXnvIeHh/v+XV1drauuuso/byLACCk/\n++yzz5STk6PS0lJ17NhRL7zwgiRp2rRpKikp0c6dO3XixAn97W9/k3Tme2OnTp3SP//5Tz366KOB\nbB3/A7vm/ZVXXlF6erpf3gMuXXPP+9tvv63ExESNHDmyQYhdqQgpP4uNjVX//v0lSffdd5+2bNki\nSdq4caN+9KMfqU+fPtq4caNKS0t9z7nnnnsC0iuajx3zvmnTJr366quN3rwZgdfc856Zmaldu3bp\nnXfe0fjx4+1t3hCck/Kz799Rw7IsORwOfffdd/r5z3+ujz/+WDExMcrNzVVNTY2v7vuH+WiZmnve\nP/30Uz344INav369OnfubGvvuHx2/bwPGjRIp0+f1ldffaWuXbva0rspOJLyswMHDvhOcq9cuVKD\nBg1STU2NHA6Hunbtqurqar3xxhuXtE++RWC+5pz3AwcOaMyYMVqxYoXi4uLsbBv/o+ac988//9z3\ns/7xxx9L0hUfUBJHUn7lcDh044036vnnn9ekSZPUq1cvTZ06VaGhoXrwwQd10003KTo6WmlpaRe1\nv/Lyct1yyy06duyYgoKCtGjRIpWWlioiIsLmd4JL0dzzPnv2bB09etT3q21CQkJUUlJi51vAZWju\neX/zzTe1fPlyhYSEKCIiQqtXr7b5HZiBL/MCAIzFch8AwFiEFADAWIQUAMBYhBQAwFiEFADAWIQU\nAMBYhBQAwFiEFADAWP8fh4tr9rkIbHkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x106598cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "barplot_modified()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Histograms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#Sections)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import random\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "data1 = [random.gauss(15,10) for i in range(500)]  \n",
    "data2 = [random.gauss(5,5) for i in range(500)]  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Default Histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def histogram_default():\n",
    "\n",
    "    fig = plt.figure(figsize=(8,6))\n",
    "    \n",
    "    bins = np.arange(-60, 60, 2.5)\n",
    "\n",
    "    # plot histograms\n",
    "    plt.hist(data1, bins=bins, label='class 1')\n",
    "    plt.hist(data2, bins=bins, label='class 2')\n",
    "    \n",
    "    # labels\n",
    "    plt.title('Matplotlib default')\n",
    "    plt.xlabel('variable X')\n",
    "    plt.ylabel('count')\n",
    "    plt.legend(loc='upper right')\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modified Histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def histogram_modified():\n",
    "\n",
    "    bins = np.arange(-60, 60, 2.5)\n",
    "    \n",
    "    fig = plt.figure(figsize=(8,6))\n",
    "    ax = plt.subplot(111) \n",
    "    \n",
    "    # plot histograms\n",
    "    plt.hist(data1, bins=bins, \n",
    "             alpha=0.3,        # transparency\n",
    "             label='class 1')\n",
    "    plt.hist(data2, bins=bins, \n",
    "             alpha=0.3,        # transparency\n",
    "             label='class 2')\n",
    "    \n",
    "    # axis formatting\n",
    "    plt.ylim([0, 110])\n",
    "    plt.xlim([min(data1+data2)-5, max(data1+data2)+5])\n",
    "\n",
    "    # hiding axis ticks\n",
    "    plt.tick_params(axis=\"both\", which=\"both\", bottom=\"off\", top=\"off\",  \n",
    "            labelbottom=\"on\", left=\"off\", right=\"off\", labelleft=\"on\")\n",
    "\n",
    "    # adding horizontal grid lines \n",
    "    ax.yaxis.grid(True) \n",
    "    \n",
    "    # remove axis spines\n",
    "    ax.spines[\"top\"].set_visible(False)  \n",
    "    ax.spines[\"right\"].set_visible(False) \n",
    "    ax.spines[\"bottom\"].set_visible(False) \n",
    "    ax.spines[\"left\"].set_visible(False)    \n",
    "    \n",
    "    # labels    \n",
    "    plt.xlabel('variable X')\n",
    "    plt.ylabel('count')\n",
    "    plt.legend(loc='upper right', fancybox=True)\n",
    "\n",
    "    # raised title\n",
    "    plt.text(15, 120, 'Modified',\n",
    "         horizontalalignment='center',\n",
    "         fontsize=18)    \n",
    "    \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Tpk2rt210dLR2796tkpISTZ8+XZMnT1ZlZaWk86E/f/58lZWV6eDBg5oyZYokadWqVSov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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x106590ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "histogram_default()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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HCv9ZT1a6srLSL6ldUJDrdv+CgtxLPp8n277++02bdk3a5+Mw50vSWvLUU09p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\nSxHiAABYihAHAMBShDgAAJb6fzOrJB2A/nTBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x106437940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "histogram_modified()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pie charts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#Sections)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Default pie chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "def piechart_default():\n",
    "\n",
    "    plt.pie(\n",
    "        (10,5),\n",
    "        labels=('spam','ham'))\n",
    "    plt.legend()\n",
    "    plt.title('Matplotlib default')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modified pie chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def piechart_modified():\n",
    "\n",
    "    plt.pie(\n",
    "        (10,5),\n",
    "        labels=('spam','ham'),\n",
    "        shadow=True,\n",
    "        colors=('lightskyblue', 'yellowgreen'),\n",
    "        explode=(0,0.15), # space between slices \n",
    "        startangle=90,    # rotate conter-clockwise by 90 degrees\n",
    "        autopct='%1.1f%%',# display fraction as percentage\n",
    "        )\n",
    "    plt.legend(fancybox=True)\n",
    "    plt.axis('equal')     # plot pyplot as circle\n",
    "    plt.tight_layout()\n",
    "    plt.title('Modified')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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d5JrcvnQPHz6M2dwMMKgdRQjhxkpLE0lJSVE7xjW5felmZGQADdWOIYRwc3Z7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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10652fc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "piechart_default()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SXu1IopIJ7+xCjTA71j/3Fr8e+JlNn36Om5ub2rEqhUWLFqkdodTkbYkotUuXLtGj98OM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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x106578e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "piechart_modified()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Violin plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#Sections)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "data = [np.random.normal(0, std, 50) for std in range(1, 4)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Default violin plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def violin_default():\n",
    "\n",
    "    fig = plt.figure(figsize=(8,6))\n",
    "\n",
    "    plt.violinplot(data)\n",
    "\n",
    "    plt.xticks([y+1 for y in range(len(data))], ['x1', 'x2', 'x3'])\n",
    "    plt.title('Matplotlib default')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modified violin plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def violin_modified():\n",
    "    \n",
    "    fig = plt.figure(figsize=(8,6))\n",
    "    ax = plt.subplot(111) \n",
    "\n",
    "    vplot = plt.violinplot(data,\n",
    "               showmeans=False,\n",
    "               showmedians=True,\n",
    "               showextrema=False\n",
    "               )\n",
    "\n",
    "    # choosing custom colors to fill the boxes\n",
    "    colors = ['red', 'blue', 'green']\n",
    "    for patch, color in zip(vplot['bodies'], colors):\n",
    "        patch.set_facecolor(color)\n",
    "\n",
    "\n",
    "    # hiding axis ticks\n",
    "    plt.tick_params(axis=\"both\", which=\"both\", bottom=\"off\", top=\"off\",  \n",
    "            labelbottom=\"on\", left=\"off\", right=\"off\", labelleft=\"on\")\n",
    "\n",
    "    # adding horizontal grid lines \n",
    "    ax.yaxis.grid(True) \n",
    "    \n",
    "    # remove axis spines\n",
    "    ax.spines[\"top\"].set_visible(False)  \n",
    "    ax.spines[\"right\"].set_visible(False) \n",
    "    ax.spines[\"bottom\"].set_visible(False) \n",
    "    ax.spines[\"left\"].set_visible(False)\n",
    "\n",
    "    plt.xticks([y+1 for y in range(len(data))], ['x1', 'x2', 'x3'])\n",
    "\n",
    "    # raised title\n",
    "    plt.text(2, 9, 'Modified',\n",
    "         horizontalalignment='center',\n",
    "         fontsize=18)\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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6S3EspRQ4hmpvRNbfpNB+ZTLLrh25vC4ahe1HMDN09yCTyeDixe/gvvtGbTlC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\nYegSERF1CUOXiIioS/5/0Sa5FEPmv84AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10570f6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "violin_default()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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JCVwYH0etVmvLfam9MpkMqlUPz55qAZ8viOlpcx5iqDfJZBLSxWX7m7E77CjU\nWju/U29YcBSbefwY73z963g2EEDI72/rvYUQOD0yAvfkJM59//uoVCptvT+1XiqVgpStHRE0K7fb\nj6WlLEdAdWAhugC7x646hu4JlzDVyeIsOAo9fPAAN7/5TbwQCqHLq2ZynBACTw4Po/vBA5x99VWU\ny2UlOag1YrE0rNb2jAqaTePwTQ+y2ezWD6aWml2ahcfPHYy3IlwCsXhMdYy2YcFR5O7kJO595zt4\nIRyGTwerMI4PDqJvdhY//va3USgUVMchjSwtZeF2t3dk0Fx8LDiK1Wo1LCYWeYL4Nnj8HsxGzbOj\nMQuOAreuXcPj738fL/T1wePUz94kh/v7sTcex5lvfctU79MaWTyeg8vFpbOtIqUPuVxOdQxTy2Qy\nkA7J1aDb4PF5MLc0pzpG2/Aros2uX7mCxfFxvDAwAJdDf+cC7YtEcDCTwZlvfIM/mXY4KSXS6SLs\ndpfqKIZlt7uRSPCHAZVSqRSkkxOMt8PusKMoi6b5AZYFp43euXgRiTffxPNDQ3DY9Tshbk84jCPF\nIs7+x/+IdDqtOg7tUqlUgpQO/mTbQg6HG+l0UXUMU1uKLcHm5irB7RJOgVQqpTpGW/A7X5tcfftt\nZM+dw3PDw7Db9P+XcbinB8crFZxjyelYjXNn9PMWqBHZ7U4WHMVmo7Pw+vk27LYt72hsBiw4bfDO\nxYvInT+PZ4eGYOugjagGe3pwsl7HuW98A5lMRnUc2qHGijj9vQ1qJDabA8Uit1dQRUqJxcQi3D71\nCzU6hdvnxnxsXnWMtmDBabHrV64gffYsnhka6shdNgdCIRyvVnHum9/kZMoO09jXSL9vhRqBzWZn\nwVEom82iZuEOxjvh8XmwEF9QHaMtWHBa6O7kJKJvvtlxIzdrDfX04HCxiHPf+c7y2x7UCarVKnjc\nXGtZrTZUKtwFXJVUKgVwDv2OOF1OZIoZU3wvZ8FpkZnHj/Hwtdfw3OBgR8y52cqecBh7kklceO01\nHuvQIRqfp84t1p1CSgv/TiiSSCYgXEJ1jI4jnMIUcytZcFogmUzi+n/6T3impwdOHa+W2qmD/f3w\nP3yIK+fOqY5C2yClhJT85t96AlJymbIKc7E5uL2cf7NT0ilZcGjnyuUy3v7Lv8QTDgf8HuPtrPnE\n4CDyly9j6u5d1VFoC1JKCMGC02pCsOCoshhfZMHZBYfHgcXYouoYLceCo7F3LlzAQDKJ/mBQdZSW\nsFgs+ECKFJMHAAAgAElEQVR/P+7+8Iem+AmAiPSpXC4jW8rC6eJWCDvl9rpNMdGYBUdD8/PzyL7z\nDo7296uO0lJupxNHbTZc/dGP+JOrjjVGb/j5aT2OlKmQyWS4C8Iuub1uRFNRw3//ZsHRSL1ex/U3\n3sDJri5T7Bw70tsL68OHmH70SHUU2kDj67CuOobhSVk3xd95vUmn09zHcpesVitqlprht/7g30qN\nTD96BF80ip5AQHWUtjkaDOLOuXOo1/kiqkdWqxVScnVPKzXmOfGgRxXiyTisLq4S3DUnDH/eIP9W\namTq4kUc7O5WHaOtgj4f3LEY5ufNsStmp7FarRCCBaeV6vUa7Ha+yKqwmFiEx2u8hRztIu3GX0nF\ngqOBZDIJLC0h5PerjtJ2ez0ePJ6cVB2D1mGz2QBUVccwtFqtCqez8/e56kSLCa6gaobT48RSYkl1\njJZiwdHA4vw8+k06yTDS1YXY1BTfptIhFpzWaxQc4+x11SnK5TIKlQLsDv7Z75bL42LBoa3Fp6cR\ncpvzJwm7zQZvtWr4oc5OZLfbAZRVxzC0arXMERwFstkshMOcP1Rqxe1xI56Oq47RUiw4GsguLRly\nU7/t8kvJ08Z1yOFwAOBBkK1UrVbg9XKtcrtls1lIh7GXOLea1WZFBRUUCgXVUVqGBUcDpWwWLgMd\nybBTLsAUB7d1Ghac1qtWy/B4WHDaLZ1JcwRHC3Zjr6RiwWlSvV4H6ubeB8MmBKplvhWiN0IIuFw2\nVCr83LRKpVKCz8fNWNptKbkEt8ec0wI05YSh98Ix76uyRqSUMPvPEQIw/I6YncrrdaJS4ehaq0hZ\ngtvNEZx2i6VicLpZLJtlcViQSCVUx2gZFpwmWSwW1E3+4l6XEhYbJ1rqkd/vRLXKgtMqQpTgcrlU\nxzAVKSWSmSQLjgacbidiqZjqGC3DgtMkIQQsdjtqNfNuqFYVYnlJMumNz8cRnNYqwunkC207FYtF\n1EQNVis3WGyWy+0y9EoqFhwN2JxOVE28D0xFyuUlyaQ3gYAL5XJRdQwD4whOu+VyOYDfbjThdDuR\nyqYMO8WABUcDdqcTlap5N1TjCI5+eb0u1OvGXQaqXpEFp81yuRyk3ZgvyO1msVhQt9YNu1ScBUcD\nNqcTNROP4FQBFhydcrvdEIIjOK1QrVbgdLLct1smm+EScQ0Ju0A+n1cdoyVYcDRgsVpNPdG4Dph6\nmbyeNUYXjPnTmWrlcgF+P0dv2i2RScDp4rwnrUi7ZMGhjRn1/cvt4jJx/XK73QA4gtMK5XIRXV3c\ni6XdEmkWHE3ZgGzOmJv9seBooFouw2biEQwbgKqJ5yDpmdPphBBlHobaAuVyAd3dLDjtlsxyibiW\nnG4nEmlj7oVj3ldlDRUzGThNvIrIKSWPatApIcTySiq+TaW1SqXAEZw2q9VqyJfyPEVcQw6nA4kM\nCw6to1QqQRSLcJi44PisVmQTxvwLYgTBoIcFpwWEyMNj4kN2VSgUChA2TjDWksPlQCqbUh2jJTj9\nv0mJRALdwtx/4bq8XjyengZOnVIdhdbR1eXGzIy2kwgPfeWLOPynv6npc7ba5Gd/A7df+aKGz5hf\nnuNE7VIoFLgHjsYcTgdi+RjqBjxTkQWnSYszM+g12BfFTnV7vcjNzaFUKnFXVx1qjOBoW3Buv/JF\njctC55GSIzjtVigUIG1c0KAlIQSkrTHNwGiF3dyvzE2q1+uYv3EDA8Gg6ihKWSwW9EmJ2ZkZ1VFo\nHV6vB4Axl4GqUqvVYLNVuclfm+XzeQi7uUfMW0HYhCE3+2PBacLs7CwCmQw8HLXA3kAADy5fVh2D\n1uHxeCAEC46WSqU8JxgrkMwkOcG4FexgwaG/IqXE3bfewv5AQHUUXQj5/bAvLGB2dlZ1FFrD6/VC\nShYcLZVKeQSDfHuq3dL5NBwuh+oYxmNrHGJqNCw4uzT96BEcc3MId3WpjqIbhwMB3Dpzhnuu6IzT\n6YTdXkWtxr2KtFIq5dDT41Udw3TSuTRHcFrAYrcgk8uojqG5pguOEOJjQohbQog7Qoh/pEUovSuX\ny7j1xhs4HgqpjqIr4a4u+BcXce/2bdVRaI1g0INiMac6hmHU63l0d7PgtFsml4HDyREcrdkddkMu\nFW+q4AghrAD+JYCPATgG4L8QQhzVIpieXXv7bQzn8+jy8hvcWiciEdz/0Y+QyRjvp4FOFgr5UCrx\nbSrt5ODl3/+2qtVqKFVLsNm5+FdrDqcD2YLxjmtodgTnGQB3pZQPpJQVAH8K4Oeaj6VfMzMzSF+5\ngsN9faqj6JLb6cRRmw2Xxsf5VpWO9PRwBEdbOS4Rb7NSqQRYVacwJrvDbsi3qJqtwkMAplddPwbw\nbJPPqVvZbBbXX30Vz/X0wGrdxd+0r3wF+NM/1T5YK332s8Arr+zoPxnp7cXi9DSuX7mCk6dPtygY\n7URXlw8Ad5vWQmNDtCJHcNqsWCy2ZIn4od/5Cg7/bmd9X578lc/i9q/u7PvyZuxOO9KFtGbPpxfN\nFpwtd1waHx8HALz88ssdff3iiy/i4g9+gPjNm7jU3Y2XT55sfHxiovH47Vy/8grGl39/V/+9quuJ\niR3/9x8+dgxvnD2L//fePYTDYeWfP7Nfnzx5EsA0JiZWrhsf5/XOr0ulAo4fd0EIoZvPrxmuS6US\nJm9MIpaJ4eSzje83E+cb32+auZ545iROLpcFLZ6vU68r9Qpee+01WK1WXXy+t3u98u/rEVLufldI\nIcRzAL4opfzY8vUXANSllP/HqocZYtvJS2fPwnLlCp4aHlYdpWOk83mcy2bx3Kc/jQCX0ytVKpXw\n5S+/jqGhn1EdpeMlEgsYHn6In/zJZ1RHMZWHDx/iu9e+i6EDQ6qjGNLsO7P43Mc/14kjkxsO6zU7\nB+dtAAeFEKNCCAeAzwD4RpPPqTtTd+8ie/EiTg4MqI7SUQIeD45brXj7e99DpVJRHcfUnE4nHI4a\nqlV+HppVLGa5RFyBYrHIOTitZFue52QgTRUcKWUVwH8H4C8B3ADwVSnlTS2C6UU8Hsfd117DB/v7\ndzfvxuSGenrQF43i8pkzqqOYXm+vD8Wi8VZKtFu9nkUw6Fcdw3RyhRxXULWSpbEFipE0vQ+OlPK7\nUsrDUsoDUsrf0iKUXpTLZVx69VWc8nrh5nEMu3ZsYACViQncnZxUHcXUent9KBhwKWi7CZHtxGH8\njpcrsuC0lI0Fx1SunD2L4XSauxU3SQiBpwcGMDU+jmQyqTqOafX2+lAus+A0S8os/H6O4LRbvphn\nwWkhaZEsOGYx/egRiteu4XB/v+oohuByOHDS5cIV7o+jjN/vgxAsOM2oVMpwuwGHg7vpthsLTmsJ\nq0ChaKwDN1lw1lGpVHDz9dfxZE8PhNB+3wWzGgiF4J2ZwdTdu6qjmFJj1IEFpxmFQga9vT7VMUyp\nUCqw4LSQ3W5Hvmis3c5ZcNYxdecOItksj2JogWORCO6dPYtqlQc/tpvX64XFUuAIWhOKxSzCYRYc\nFYqlImw2FpxWsdqsKJaNdaI4C84aUko8vHQJB3p6VEcxJK/Lhd58Ho8fP1YdxXSEEAiFeGRDM6rV\nLHp7Of+m3aSUKFfLsNq4krVVbHYbC47RxeNxuLNZ+Nxu1VEMa9jrxTxPHFeisZLKeGfOtE8GPh9H\ncNqtWq3y1arFrDYriiUWHENLpVIIqg5hcEGfD8nZWdUxTCkS8aNU4jyc3eMKKhVYcFrPYrGgVOFG\nf4ZWKZfB9RGt5bDbUSka6yeFThEI+ABwBGc3arUq7PYK3BzdbbtqtQph4YKPVrLarKgYbKdzFpw1\nHE4nylw51VKlSgUOvkgo0Rh9YMHZDa6gUqdWq/HVqsWsVitKZY7gGFp3dzeiXGXSUrF0Gt0jI6pj\nmFJj/kgezRyya1YsOOpwBKf1LFYLR3CMLhgMQkYiiKbTqqMY1v18HiNHjqiOYUoWiwXBoIsrqXah\nUskiEuH8GxXq9TokWMpbSQgBCWmoH35YcNZx5IUXcC2ZbAyLkqamo1HU9uzBAE9mVyYc9vPQzV3J\ncIKxIvV6na9WbSAswlCve/ySWcfAwAACp0/jHa700VQql8PNeh1PvfQSd4hWKBLxo1jkPJydkjLL\nJeKK1Go1gN8yWk5CGmojUBacDTz5zDPI79+PazMzqqMYQjqfx4VkEk984hMIBAKq45haV5cP9ToL\nzk7UajXYbCV4PB7VUUxJSsm3qNpAWATfojIDq9WKZ37yJ5Hatw+XpqcN1WrbLZZO41wqheOf+hT6\neXipcn6/n4du7lCxmEUo5OXIoyJSSo7gtImRXutYcDZht9vx3E/9FHDqFH48PY1CyVhL6NphanER\nF2s1PP03/gYGBwdVxyE0VlJJmTXUT2qtVihk0NfH+Teq8Gu1TQxWInly2RasVitOP/88psJh/OgH\nP8BxhwNDPKdqS6VKBVfn51EaHcWLH/kIN0fTEavViq4uJ8rlApxOvuWyHaVSlkvEFePoWetJaaxV\nVCw427TvwAH0hMO4/MMfYn56Gif6++G021XH0qWZWAzXSyXseeklHD5+nN+YdCgc9mFuLsuCs01C\nZBAIDKuOQdRyRvp+zbeodqCrqwsvfepT8Lz4Il6PRvFoaUl1JF3Jl0o4Pz2Nu6EQnvnMZ3DkxAlD\n/WUxknCYh27uDFdQqSSEAOcYt56AMNT3bI7g7JDFYsHRkycxtHcvJs6exaO7d3EiFEK3ib/51Wo1\n3Ftawn2rFfs/+lHsP3jQUH9JjKi724d6Pak6Rkeo1+sQogCv16s6imkJYazVPdQeLDi7FAgE8KGf\n/VlMHzuGt954A5HHj3Gkr890b1vNxeO4kcuh+8kn8dLp05xr0yEaK6keq47REYrFHLq73bBYOOCt\nisVigZD8oanlJAz1dc6C06SRkREMfOYzuH3jBsbPncN+IbAvEjHUF8l6UrkcrsfjqAwP46lPfhI9\nnHjdURorqfgW1XYUi1ns2WPeEVo9MPr3U72QdQmr1ao6hmZYcDRgs9lw7IknsHf/fty8fBk/fOcd\nHPV4MGjAF/1iuYxbi4tYCgRw+BOfwMiePXw7qgM5HA643QKVSgl2u1N1HF0rlbIIh1lwVLJarZyD\n0w4SLDi0Pq/Xiw98+MOIHT2K62fP4v6jRzgeDBpifs6782yEwN4XX8RHjhyBzcYvn07W2+tDMpll\nwdlCvZ5Fd3dYdQxTs1qtgHGOSNKlWq0Gm9VY39ON9X+jEz09PXjxE5/A48eP8daPfoTex49xNBKB\ny+FQHW1XHkejuFUqIfTEE3jp1CnOszGIvj4/5uYyCASMN9KoJSGy8PnGVMcwNZvNBhhng11dqtfq\ncBrshx0WnBYRQmBkZASDn/kM7k5O4vUzZzAmJfaHwx0zBJjMZnEtkYAcHcXTzz+PYDCoOhJpKBTy\noVrlkQ1b4xJx1ex2OwtOi1UrVTgdLDi0A1arFYePHcOesTHcuHQJ41ev4rjPh34dl4VypYKbCwtY\nDARw9FOfwvAwNzgzosZKqgXVMXStVCrA77fz7VjF7HY7ZI2TcFqpVq3B6zDWVgj8W9smbrcbT3/o\nQ4gePoxrb76JR9PTOBGJwOPUV2N+uLSEyWoVwy+8gI8cP85v7AbWGJXgCM5mCoUMBgZ4BpVqNpsN\nVmFFvV7niqoWqVQq8LmNNVLJV6826+3txUuf+hSm7t7Fj954AwcA7ItElK9EyhYKuLq0BBw4gOdf\neAF+P7+pG53b7YbdXkW1WoHNZq79m7arUMhwBZVOeN1eVMoVOF36+qHQKKrlKrx+juBQkywWCw4c\nOoTB4WFcPXMGs7du4VQkAp+CybtSStxbWMA9mw2HP/5xjI5xMqWZhMN+ZDIZ+P0h1VF0qV7PIBjk\nn40eeF1eFCoFFpwWqVQq8HmMVeY51qeQx+PB8x/9KPZ88pP4cTaL+4uLbb1/vlTCjx89QnT/frz0\n2c+y3JhQX5+fZ1JtQogMRzN1wu/xo1wsq45hWLIq4XEb6/BdjuDowN7RUfR+5jO49PrrmL9/H4E2\nzHup1+uYs9lw8K/9NYzt39/y+5E+hcMBVCpp1TF0SUoJKVlw9KLb342p2JTqGIYlqsJwW4Cw4OiE\n1+vFhz72MczMzKBarbblns/19CAQCLTlXqRPjZVUc6pj6FKxmEMw6OREe50I+AKozrXne6MpVcCC\nQ61jsVgwMjKiOgaZSKPgcgRnPfl8GmNjXapj0DK32w1R4bEwLVM1XsHhHBwiE3M4HPD7bSiV8qqj\n6E6xmEZ/P0c49cLr9QIV1SmMqVKuwGlxwtGhu+1vhAWHyOQGBrqQy6VUx9AdIVLo7uYIjl74fD4W\nnBYpFUro6TbekS0sOEQmNzjYhWKRBWctKVPo6mLB0QubzQaPw4NyiSuptFYsFNHTxYJDRAYTDHYD\nSKqOoSuNIxoEXC6X6ii0SiQUQSFXUB3DcEr5Enq7e1XH0BwLDpHJdXd3Q0oWnNVyuSQGB7tVx6A1\n+kJ9yGc5X0xroiQMOVrJgkNkcg6HA8GgHYUCz6VaUSwmMDys3wNxzaon2IN6kceKa02WpCG3DGHB\nISKMjISQzcZVx9ANKeMIhVhw9CYQCECUuFRcS5VyBW6r23BLxAEWHCICMDQUQrnMggMAtVoNVmsG\n3d18i0pv/H4/rDUrarWa6iiGkU1nMRQZUh2jJVhwiAihUAgACw4A5HIJDAz4YbVaVUehNSwWC/p7\n+pFL51RHMYx8Os+CQ0TG5ff74fVWUSpxhUo2G8PoqPGWzBrFSN8IJxprSJQEekLG/HpnwSEiAMDY\nWA/S6ajqGMpJGUVfn/GWzBpFuCeMeo4TjbUgpQSKMOzbsSw4RAQA2LMnjHJ5SXUMpWq1Kmy29PJb\ndqRHPT09QHH5xZmaksvkEOmOGO6IhhUsOEQEAAiHwwCipn7hSKejGBsLcf6NjjmdTvT4erjhnway\nqSxGB0ZVx2iZpgqOEOKfCiFuCiGuCiG+JoQw3k5BRCbhdrsRiTiQy5l3079cbgH79kVUx6AtjA2O\nIZ1Iq47R8WrZGvoj/apjtEyzIzivAjgupXwSwG0AX2g+EhGpcvhwP9LpBdUxFFpAX1+f6hC0hcH+\nQdSyXCrejHq9DlEU6O017nyzpgqOlPJ7UsqV2V7nAQw3H4mIVBkc7EO9Pq86hhLZbAJ9fQ54PB7V\nUWgLvb29sBQt3A+nCdlUFiPhEdjtdtVRWkbLOTh/C8B3NHw+ImqzYDCIQKBiymMb0uk5HDkyoDoG\nbYPdbsdweBjZlPm+TrWSTWSxb3if6hgttWXBEUJ8Twgxsc6vv77qMf8YQFlK+ZWWpiWiljt2bADJ\n5JzqGG1Xr89heJgFp1Mc3HMQ2QQLzm7JrER/n3Hn3wCAbasHSCl/erOPCyF+GcDHAfzUeh8fHx8H\nALz88su85jWvO+A6nU6jVgsAOIiJicbHT55sfNyo12NjTyISseDSpUvv+/PgtT6v+/v7cfuPbiMe\njePksycBABPnJwCA11tcHzhxAH67H1euXNnwz7dTrlf+fT2imSWhQoiPAfhtAD8hpdxohzDzrjkl\n6lBf/eprAD4Ij8d4JwyvZ2bmGl5+2YEjRw6pjkI78Gff+jPIAQmPj/OmdmJ+eh5P9z6N00+dVh1F\nCxuevtrsHJzfAeAD8D0hxGUhxL9q8vmISAdOnBhCMvlYdYy2qNfrAGaxZw/XSHSaI6NHkFwy77YG\nu1VL1zAyNKI6Rss1u4rqoJRyr5Ty1PKvv6tVMCJSZ+/eYdTrj02x6V8qtYjRUR9XT3WgkaERyIzx\nv0a1VCqW4Jbuxo7QBsedjInofXw+H8bGfEgmjb8nTi73CCdO7FEdg3YhGAyi29HNwzd3IBlN4tjo\nMVgsxn/5N/7/IRHtyokTe5DNPlQdo6VKpQLc7gQGBrh6qlMd238MqWhKdYyOUU1VMbpnVHWMtmDB\nIaJ1DQwMwOtNolQy7k/H8fgjPPnkEM+e6mB7hvegluKGf9tRKpTghdfQuxevxoJDROuyWq04dWoE\nsdgD1VFaol6vo15/hIMHR1VHoSZ0d3cj4osgm+aeOFuJL8ZxYv8JCLHhwiNDYcEhog0dODAKYNqQ\nW+LH47M4eNAPn8+nOgo16cSBE0gt8W2qrdSSNdO8PQWw4BDRJjweD44d60EsNq06iuYKhSmcPGns\nrerNYmR4BMisLPmn9WTTWfR6ehEMBlVHaRsWHCLa1PHj+1AuTxlqyXg6HcXAQB2RSER1FNKAx+PB\naGQUqRhHcTaSWkzhiUNPqI7RViw4RLSpUCiE0VEnEgnjnDKeSt3D009z9MZIjh84jlw0pzqGLtVq\nNYiswJ4Rc22HwIJDRFs6ffoAcrk7qmNoIpdLIRTKYHiYOxcbyeDgIJwVJyrliuooupOMJrG/fz/c\nbrfqKG3FgkNEW+rr68PQkEQyuag6StPi8Tt45pn9ptjozEysVitO7juJ2HxMdRTdKUQLOHbwmOoY\nbce/4US0LR/84EFkMrdVx2hKPp9BIBDH3r3mGqo3i4P7D6KaqKqOoSulQgk++NDf3686Stux4BDR\ntgwMDGBgoIpUakl1lF2Lx2/j2Wf3c2M/g+ru7sZgYBDpRFp1FN2Iz8fx5KEnTTliab7/YyLaFSEE\nnn32ENLpSdVRdiWfTyMQiGPfvlHVUaiFTh46ifQCCw6wvJllqo59o+acUM+CQ0TbNjg4iKGhGhKJ\nzjuEMxabxPPPH+DojcGNjIzAWeZkY6AxuXgsMmbazSxZcIhoR5577giy2VuqY+xINptAKJTC6Ohe\n1VGoxex2OycbL8sv5XHy8EnVMZRhwSGiHenr68O+fTbEYjOqo2xbInETL7xwyJTzEMzo0IFDqMar\nhtqccqcKuQL8wm/KycUr+LediHbsmWeOIp+/1RFb46dSSxgYKGNkZER1FGqTrq4ujIRGkIqbd2fj\nxEICpw6fMnWpN+//ORHtWigUwrFjfiwtPVQdZVNSSqTTN/D880dMc4IyNTx55ElkF815wnitVgPS\nwL4xc04uXsGCQ0S78vTTR1Gt3kG1qt/JnNHoY+zbZzP1ML1ZDQ4Owlv3olQoqY7SdvGFOI6MHDHd\nzsVrseAQ0a74/X48/XQfFhfvqo6yrlqthlJpEs8+a74dXKmxs/FTh55CbM58k42LsaIpdy5eiwWH\niHbt5MnDsNsfoVQqqI7yPktL9/HEE0EEg0HVUUiR/WP7IdOyI+aKaSWbyiLsCiMcDquOohwLDhHt\nmsvlwnPPjWJpSV/LxiuVEoB7OHXqqOoopJDX68WhwUOIL8ZVR2mb1EIKp4+eVh1DF2yqAxBRZzt8\n+ABu3RrHzMy3VUd5l5QSH/7wAXg8HtVRSLHjh47j1o9uASaYhlUpV2Av2LFnD89aA1hwiKhJVqsV\nP//zP6W7PUe4aooAIBKJoMfZg1wmB6/fqzpOS8XmY3hq/1Ow2+2qo+gC36IiIk0IIXT1iwhofF2e\nOnIKifmE6igtJaVELVHDoQOHVEfRDRYcIiIytNG9o7Dn7IY+nyoZTWJPzx50dXWpjqIbLDhERGRo\nDocDJ/adMPRk49xSDk8cfkJ1DF1hwSEiIsM7fOAwKjFjjuAU80V4pReDg4Oqo+gKCw4RERleMBjE\nUNeQIc+nis/H8dShp0x97tR6+KdBRESm8OSRJ5FZzKiOoal6vQ6kG5sa0nux4BARkSkMDw/DVXGh\nXCqrjqKZxFICBwYOwOs19hL43WDBISIiU7BarXhi/xOIzRvnfKpCtMBzpzbAgkNERKZxYN8B1JN1\n3W1MuRuFXAEBSwD9/SbYpnkXWHCIiMg0urq6MBwcNsRk45XJxdzYcn0sOEREZConD51EdimrOkZT\n6vU6kGlsYkjrY8EhIiJTGRoagrPs7OidjZPRJPb17ePk4k2w4BARkanYbDYcHzuO2ELnTjbOR/M4\nfvC46hi6xoJDRESmc3DfQVQTVdUxdqVcKsNT83By8RZYcIiIyHRCoRB63b3IZXKqo+xYfCGOE/tP\nwGq1qo6iayw4RERkSicPnkRqsfNWU1UTVewb3ac6hu6x4BARkSntGdkDmZaNFUkdIpvOIuKLIBgM\nqo6ieyw4RERkSh6PB3sjeztqT5zUUgrH93Ny8Xaw4BARkWkd238M+VhedYxtkVICGWBkeER1lI7A\ngkNERKY1MDAAa8GKWrWmOsqW0ok0RnpGuPfNNrHgEBGRaTkcDhwYOoBkLKk6ypay0SyO7juqOkbH\nYMEhIiJTOzh6EMVEUXWMTdXrdYi8wODgoOooHYMFh4iITK2/vx/WohXVin43/kvFU9gb2QuXy6U6\nSsdouuAIIf6BEKIuhAhpEYiIiKidbDYbDg4f1PXbVPl4HodHD6uO0VGaKjhCiBEAPw3goTZxiIiI\n2m//nv0oJUuqY6xLSgmRFxgYGFAdpaM0O4LzJQC/rkUQIiIiVfr6+mApWlCr6W81VSaZwVDPEN+e\n2qFdFxwhxM8BeCylfEfDPERERG3ncDgw1j+GdDytOsr7ZGIZHN7Lt6d2yrbZB4UQ3wOw3nGl/xjA\nFwD8zOqHa5iLiIiorQ7sOYB71+8hGNbZMQhZ8O2pXdh0BEdK+dNSypNrfwGYAjAG4KoQ4j6AYQAX\nhRCRtc8xPj6O8fFxXvOa17zmNa91fT05OYnJC5PvXk+cn8DE+Qml12+Nv4WQNwSfz6f8z0eP15sR\nUspNH7AdyyXnaSllfJ0PN38DIiKiNvjad7+GUm8JvoBPdRQAwNzDOTzT/wyeeuIp1VH0asN3j7Ta\nB4clhoiIOt6hPYeQjulnHk49W8fQwJDqGB1Jk4Ijpdy3wegNERFRx+jv64fM6eNn9mqlCnvVjp6e\nHtVROhJ3MiYiIloWCoXgqDl0satxOpHG2MAYLBa+VO8G/9SIiIiWWSwWjA2OIRVPqY6CfCqPseEx\n1TE6FgsOERHRKqNDoyhm1B++KfIC4XBYdYyOxYJDRES0Sm9vL5BXm6FUKMHv8MPn08dqrk7EgkNE\nRFyY6wIAAAdrSURBVLSK3++Hz+ZDqajubKp0Io2xQb491QwWHCIiojVGB0aRTqhbLl7OljHcP6zs\n/kbAgkNERLTGcP8wytmyugAFcHl4k1hwiIiI1ujp6VE2D6dUbMy/8Xq9agIYBAsOERHRGn6/H26r\nG+VS+0dxMskM9vbvbft9jYYFh4iIaB17+vYgm8q2/b6lbAlDfTyeoVksOEREROsY7htGMdv+/XBE\nQSAUCrX9vkbDgkNERLSOUCgEFNp7z1q1BlvdhkAg0N4bGxALDhER0Tq6u7uBElCv19t2z1wmh8He\nQQgh2nZPo2LBISIiWofVakUkGEE+277lVLlMDsMR7n+jBRYcIiKiDYxERpBL59p2P5mX6Alx/xst\nsOAQERFtINIbQbVQbdv9ZFE23hqjprHgEBERbaCrqwui2J75MJVyBW6bGx6Ppy33MzoWHCIiog34\n/X5YqhbUarWW3yubzmKwd7Dl9zELFhwiIqINWCwW9IX6UMi2fr14IVvAYJgFRyssOERERJsY7B1E\nLtOGicYlINgdbP19TIIFh4iIaBO9oV5Ui62faCyLEl1dXS2/j1mw4BAREW0iEAi0fKJxtVKFAw6e\nIK4hFhwiIqJNBAIBoAJIKVt2j3w2j76evpY9vxmx4BAREW3CZrOh29ONYr51B2/mc3n0h/pb9vxm\nxIJDRES0hUhPBIVc61ZS1Yo19AS5g7GWWHCIiIi20N/Tj2KudSM4oiR4grjGWHCIiIi20BXoAsqt\ne35ZkvD7/a27gQmx4BAREW3B7/cDpdY8d6lYgt/th91ub80NTIoFh4iIaAterxeWmgX1el3z5y7k\nCggHw5o/r9mx4BAREW3BYrGgp6unJRONC7kC+kJcIq41FhwiIqJtCAfDLVkqXi/VEeziEQ1aY8Eh\nIiLahkgw0pKVVKIs4PP5NH9es2PBISIi2ga/3w9R0f7IBq6gag0WHCIiom3w+/2aLxUvl8rwOr1c\nQdUCLDhERETb4PV6ISpC0zOpCrkCIqGIZs9Hf4UFh4iIaBssFgu6fdqeSVXIFxDu5hLxVmDBISIi\n2qbe7l5NC06tWOMKqhZhwSEiItqmcDCMQl67vXC4gqp1WHCIiIi2qSvQBVnWbg6OLEsWnBZhwSEi\nItomn88HUdZmqXitWoNDOOB2uzV5PnovFhwiIqJt8vl8mo3gFPIFhAIhTZ6L3o8Fh4iIaJscDgfc\ndjcq5UrTz1UqlNAb7NUgFa3n/2/vbl7jrMIwjF936Id1QmNjCraiC7FSFKSbCuIHtuBG0KW4quJG\nupAudKVuBK0U/wI3XSiIbqr4AUKhIkIX2uJnFcRCRcGiqCAap4n0cZEJVpvETmbGNzNz/SCbzJvJ\nDXmSPO857znHBkeSpC5Mb57uy0qqc7PnmJmywRkUGxxJkrqw9Yo+Hbo5h0c0DJANjiRJXZjZMsNc\nuw9nNszjCqoBssGRJKkLrVarPyup5hfeS4NhgyNJUhcmJyd7PnRz7twck5dNsm7duv6E0kV6anCS\nPJrkyySfJznUr1CSJK1VrVYL/qSnQzfbs22XiA/YqlvHJHuA+4Cbq2o+iaeFSZJG3sTEBFOtKeba\nc2zctHFV79GebXvI5oD1MoKzH3iuquYBqurH/kSSJGltm56aZvb3WapqVR/z7XkP2RywXib/dgB3\nJjkItIHHq+pEf2JJkrR2bd+6nTMfn+HsN2dX9fUbJjYwNTXV51S6UFaaQ0xyFLhqiZeeBJ4FjlXV\ngSS7gVer6rolru3fqWSSJEl/W3Y524ojOFV197LvmOwHjnSu+zDJ+SRXVtVPl/rNJUmSBqGXZ3Be\nB/YCJLkB2LBEcyNJkvS/6+UZnMPA4SSfsbAjwL7+RJIkSerNis/gSJIkDSN3Mh4RSd5J8kuSN5vO\nIq1Wkl1Jjnc2D/0kyf1NZ5JWI8m1SU4m+SjJqSQHms40bhzBGRFJ9gKXA49U1b1N55FWI8kO4HxV\nnU6yDTgJ7KyqXxuOJnUlyXqAzka4LeAUcHtVfddssvHhCM6QSbK7c2e7MUmrc6d7Y1UdA35rOp90\nqZaqZWB9VZ0GqKrvgR8At3vVmrZMLe9Y3AgX2ATMA7PNpRw/nvI1ZDpL8t8AnmHhl+alqvqi4VhS\n1/6rlpPcwgUNj7RWLVfLSa4B3gauZ2Ez3J+bzDlunKIaQp2hzxPAH8Ct1fkhJrkLeMwpKg2LFWp5\nG/AusK+qPmgwonRJlqvlzmvbgPeAe6rq64Yijh2nqIbTDNACJlm4W1hkt6phc1EtJ9kMvAU8YXOj\nIbLc3+XF6db3gV0N5BpbNjjD6QXgKeBl4NAFn3fXaA2bf9Ry5y74NeDFqjrSaDKpO/+u5auTLDbt\nW4DbgE8bzDd2fAZnyCTZB5yrqleSTADHk+wBngZ2ApNJvgUerqqjTWaVVrJULQMPAHcA00ke6lz6\nYFX5j0Fr1jK1fBPwfJJiYXT9YFV91WTOceMzOJIkaeQ4RSVJkkaODY4kSRo5NjiSJGnk2OBIkqSR\nY4MjSZJGjg2OJEkaOTY4kiRp5NjgSJKkkfMXxWhvBHyPic8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10589a0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "violin_modified()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}