partial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Partials, pipes, assignments, factors\n",
    "### imports, etc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.simplefilter('ignore')\n",
    "from functools import partial\n",
    "\n",
    "import numpy as np\n",
    "import pandas\n",
    "import seaborn\n",
    "seaborn.set(style='ticks')\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## function to classify (bin) values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<5\n",
      "20 - 25\n",
      ">30\n"
     ]
    }
   ],
   "source": [
    "def _classifier(val, bins, units=None):\n",
    "    units = units or ''\n",
    "    if val <= min(bins):\n",
    "        output = '<{}'.format(min(bins))\n",
    "\n",
    "    elif val > max(bins):\n",
    "        output = '>{}'.format(max(bins))\n",
    "\n",
    "    else:\n",
    "        for left, right in zip(bins[:-1], bins[1:]):\n",
    "            if left < val <= right:\n",
    "                output = '{} - {}'.format(left, right)\n",
    "                break\n",
    "\n",
    "    return '{} {}'.format(output, units or '').strip()\n",
    "\n",
    "def _unique_categories(classifier, bins):\n",
    "    midpoints = 0.5 * (bins[:-1] + bins[1:])\n",
    "    all_bins = [min(bins) * 0.5] + list(midpoints) + [max(bins) * 2]\n",
    "    return [classifier(val) for val in all_bins]\n",
    "\n",
    "bins = np.arange(5, 31, 5)\n",
    "print(_classifier(3, bins))\n",
    "print(_classifier(23, bins))\n",
    "print(_classifier(33, bins))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## function to make (categorical) histograms with `seaborn.factorplot`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def storm_histogram(df, valuecol, bins, classifier=None,\n",
    "                    filename=None, **factoropts):\n",
    "\n",
    "    if classifier is None:\n",
    "        classifier = partial(_classifier, bins=bins, units='mm')\n",
    "        \n",
    "    cats = _unique_categories(classifier, bins)\n",
    "\n",
    "    fig = (\n",
    "        df.assign(storm_size=df[valuecol].apply(classifier).astype(\"category\", categories=cats, ordered=True))\n",
    "          .rename(columns=lambda c: c.replace('_', ' ').title())\n",
    "          .pipe((seaborn.factorplot, 'data'), x='Storm Size', kind='count', **factoropts)\n",
    "          .set_ylabels(\"Occurences\")\n",
    "    )\n",
    "\n",
    "    if filename is not None:\n",
    "        fig.savefig(filename, dpi=300)\n",
    "\n",
    "    return fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## use it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7d059b0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhAAAAEZCAYAAADG5xzcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJFX2/wBOVAGTH6LTawAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7d05908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "excel = 'C:/Users/phobson/sources/stormwater/pycvc/examples/output/xlsx/CVCHydro_StormInfo.xlsx'\n",
    "storms = pandas.read_excel(excel, sheetname='ED-1').dropna(subset=['total_precip_depth'])\n",
    "storm_histogram(storms, 'total_precip_depth', np.arange(5, 31, 5), aspect=1.6, hue='Has Outflow')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x85595c0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhAAAAI6CAYAAAB2N3HZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XmYXGWZ9/Fv0gFG0nQbCBCCSyAzud1ww33YwVFER1xG\nnFccBFSURdwREBPGnV0FHEQURERwg1ccQQUE9B1REBVUbqBsRQbIwhLSCbIk/f5xTmvRdtJ1uqq6\nqtPfz3VxUXXqOefcT1V36tdneZ5pQ0NDSJIkVTG90wVIkqTJxwAhSZIqM0BIkqTKDBCSJKkyA4Qk\nSarMACFJkiqb0ekCpPVFRDwZ+HFmbjNi+ZrMbDqsR8SbgA8APcAa4BvAJzJzzTrW6QPOyczXlM+P\nBd4EnAac2Iq6qoiIAeC8zPxw3bIvA1dm5lcmshZJzfEIhNRaow2s0vRgKxHxForwsHdmbge8BHgO\n8IUxVt0UeHbd832Bl2fmya2oa5wOj4jndGjfklrEIxDSBImITYCzgK2BucDVmblfRGwNnAdsTHFk\n4V2Z+fMRqy8E/iMz/wiQmSsj4kDgf8ujCrsBu2Tm/uW+rizXeR+wVUR8C1gCPAG4qDyaMVzX44Az\ngWcBq4ETgK8BdwHblvv6CXBxZh4fEfsAO2bmoXXbOBh464iar8zM943yVnwCOCcinpuZj454j14J\nfBSYBvwBOCgzl67jbZXUIQYIqbW2johfUnwBDpX/H7YXcENmviEiNgB+V/4l/irgu5l5YkTsBOwA\n/DVARMRs4EnAL+p3lJn3R8TvgO3LRaMdUXgXxRf568ptvRzYMzP/HBHDbY4FlmXmdhGxWbnvXwOX\nAztHxFXAPGBn4HhgT+DrI2o5HTi9gfdniCIsPR9YBNSfytgc+C/gxWV976c41fKGBrYraYIZIKTW\n+t/MfG79gohYDZCZX4+I50fE4cBTKU4v9AI/Ar4dEc8FvgecOmKbw8FgtN/XDcdR47QRz3cFDihr\nvCciLqIIC98D9ij3/1Vgn4iYAewIvH1EHw8G3jZiu1eMcgRieN/vBH4VEd+ue+0FwLWZ+efy+ReA\nIyv2TdIEMUBIEyQiDgNeC5wB/BB4BjAtM/9fRDwVeCXFX9tvAf5leL3yS70GvLhcb3h7s4Ftgeso\nTmHUB4MNKpQ28lqo6RT/NlwGvB94hOJoRAAHAjdm5sP1K1Q4AjHcfnFEvBc4G/hN3X7r+zCd4oJR\nSV3Iiyil1hr51339sj2AMzLz6+WyZwM9EfFpiusbzgUOo7g4cqRjgFMiYhuAiOiluG7h/My8A1hG\ncVSDss0zy/UeZe1hYriuKyiCwXAo2ZvitMcy4EGKUyw/Aa4s67hkjPegIZl5PlADXl8uuhZ4YUQ8\nqXz+9nKfkrqQAUJqrXXdhXEKsCgirqM4TfFTYBvgs8DrIuIG4NvAO0ZuIDMvoPjyvjAifkPxZXtd\nXdsfAXdExM3AycA15fLFwO0Rcfko9Q0//k9gs3K7PwY+mpm/Ll/7b+D+zFxFETS2oji1MV4j3593\nACvKPi6hCA0XRcSNwE6M8l5I6g7TnM5bkiRV1bZrIMqLrb5EcfX2hsDHgT9THP68pWz2+cz8Rrtq\nkCRJ7dHOiyj3pbg17D8iYhbwK4rbxU4sB7GRJEmTVDsDxIUUQ+1Cca3FIxT3qz8lIvYGbgUOz8yV\nbaxBkiS1QduvgShH37uY4p7ujYDfZOYNEXEUMCszPzCObc6gGFHvjpEj2UmSpPZr6zgQEfFEiqvK\nTy0H0enPzOXly9+huPp8rG0sohiS9+9cfvnloy1um1tuuYULDj6UOb29DbW/e3CQfU4/lQULFrS5\nMknSBBvtlu0ppZ0XUW5JMRDNIZk5fC/3ZRFxaGZeB+wOXD/WdjJzEcWQt/XbngcMtLLeRs3p7WXr\nvv5O7FqSpK7RziMQRwKPB46JiI9Q3P/9HorBcB4G7mbEcLiSJGlyaFuAyMx3A+8e5aUd2rVPSZI0\nMRyJUpIkVWaAkCRJlRkgJElSZQYISZJUWVvHgZjq1gwNMTBQ7W7T+fPn09PT06aKJElqDQNEGy1d\nOcjPzr6aWf03N9T+vuVLOPLY/Rx4SpLU9QwQbTarfwtmbza302VIktRSXgMhSZIqM0BIkqTKDBCS\nJKkyA4QkSarMACFJkiozQEiSpMoMEJIkqTIDhCRJqswAIUmSKjNASJKkygwQkiSpMgOEJEmqzAAh\nSZIqM0BIkqTKDBCSJKkyA4QkSapsRqcL0Ppj9erV1Gq1htvPnz+fnp6eNlYkSWoXA4Raplar8cmF\n5zCrf4sx2963fAlHHrsfCxYsmIDKJEmtZoBQS83q34LZm83tdBmSpDbzGghJklSZAUKSJFVmgJAk\nSZUZICRJUmUGCEmSVJkBQpIkVWaAkCRJlTkOhNaq6siSAwMDbaxGktRNDBBaq1qtxgUHH8qc3t6G\n2t+0ZDE9z3xDm6uSJHUDA4TWaU5vL1v39TfUdvHgCu5tcz2SpO7gNRCSJKkyA4QkSarMACFJkioz\nQEiSpMradhFlRMwAvgTMAzYEPg78DjgbWAPclJmHtGv/kiSpfdp5BGJfYFlm7gS8HDgVOAk4KjN3\nBqZHxKvbuH9JktQm7QwQFwLHlI97gEeB52bmNeWy7wN7tHH/kiSpTdp2CiMzVwFExCbAN4CjgRPq\nmqwAxhxgICIWAQvbUKIkSRqntl5EGRFPBK4AzsnMr1Nc+zBsE+D+sbaRmYsyc1r9f8A27alYkiQ1\nom0BIiK2BC4DPpiZ55SLb4iIncrHewLXjLqyJEnqau0cyvpI4PHAMRHxEWAIOBz4XERsAPwe+GYb\n9y9JktqknddAvBt49ygv7dKufUqSpInhQFKSJKkyA4QkSarMACFJkiozQEiSpMraeReGuszq1aup\n1WoNtx8YGGhjNZKkycwAMYXUajUOOeOD9M7ua6j94lvvZH8aaytJmloMEFNM7+w++ufMaqjt4LIH\n4N42FyRJmpS8BkKSJFVmgJAkSZUZICRJUmUGCEmSVJkBQpIkVWaAkCRJlRkgJElSZQYISZJUmQFC\nkiRVZoCQJEmVGSAkSVJlBghJklSZAUKSJFVmgJAkSZUZICRJUmUGCEmSVNmMThfQSatXr6ZWqzXc\nfmBgoI3VSJI0eUzpAFGr1TjkjA/SO7uvofaLb72T/WmsrSRJ67MpHSAAemf30T9nVkNtB5c9APe2\nuSBJkiYBr4GQJEmVGSAkSVJlBghJklSZAUKSJFVmgJAkSZUZICRJUmUGCEmSVJkBQpIkVWaAkCRJ\nlRkgJElSZQYISZJUmQFCkiRVZoCQJEmVtX02zoh4IfCpzNw1Ip4NXALcUr78+cz8RrtrkCRJrVU5\nQEREH/DEzPxtA20/ALwZGCwXbQ+cmJknV92vJEnqHg0FiIh4K/AS4AjgBmBFRHwrMz88xqq3Aa8B\nzi2fbw8siIi9gVuBwzNz5bgqlyRJHdPoNRDvBN4P/DtwMbAd8PKxVsrM7wCP1i26FvhAZu4M/AFY\nVKVYSZLUHRo+hZGZ90bEK4DPZuajEfG4cezvosxcXj7+DvDZsVaIiEXAwnHsS5IktUmjRyB+GxGX\nANsCP4qIC4FfjGN/l0XE88rHuwPXj7VCZi7KzGn1/wHbjGPfkiSpRRo9AnEAxTUQN2bmwxFxLvD9\ncezvncDnIuJh4G7g7ePYhiRJ6rBGA8R0YEfgwIg4DHgOcFkjK2bmnyjCB5l5A7DDOOqUJEldpNFT\nGKcBMynuongU+EfgrHYVJUmSulujAWL7zDwKeCQzVwH7URyFkCRJU1CjAWIoIjYEhsrns+seS5Kk\nKabRAHEK8CNgTkScQnH3hKNJSpI0RTV0EWVmnhsR1wO7Aj3AXpl5Y1srkyRJXavRoay3A47OzDdG\nxFOBMyLibZmZ7S2vmtWrV1Or1RpuPzAw0MZqJElafzV6G+eZlMNOZ+bvI+KjFHdhdNUtmbVajYOO\nPouZ/Zs31H7pHcncndtclCRJ66FGA8TMzLx0+Elm/jAijmtTTU2Z2b85fZtu1VDbweVLgbvaW5Ak\nSeuhRgPEkoh4B/DV8vkbgcXtKUmSJHW7Ru/C2B94JcWf67cDewFvbVdRkiSpuzV6F8btFAFCkiSp\n4bswXgZ8DNgUmDa8PDO3bVNdkiSpizV6DcTngPcCN+EIlJIkTXmNBohlmXlJWyuRJEmTRqMB4pqI\nOAm4FPjL8MLMvLotVUmSpK7WaIB4Qfn/+hk4h4DdWluOJEmaDBq9C2PXdhciSZImj0bvwngy8EVg\nHrAj8DXggMz8Y9sqkyRJXavRgaTOAI4HBilGoDwf+Eq7ipIkSd2t0QAxOzN/AJCZQ5l5JtDXvrIk\nSVI3azRAPBgRT6AcAyIidgAealtVkiSpqzV6F8Z7gEuA+RHxK4oRKf+tbVVJkqSu1miA2BJ4PrAA\n6AFuzsyH21aVJEnqao0GiOMy83vAb9tZjCRJmhwaDRC1iPgScC3w4PDCzPRODEmSpqBGA8Q9FLNw\nvqhu2RDeyilJ0pTU6EiU+7e7EEmSNHk0OhLlAKNM452Z27a8IkmS1PUaPYWxS93jDYDXABu1vBpJ\nkjQpNHoK408jFh0fEdcBH2t9SZIkqds1egpjp7qn04CnA49rS0WSJKnrNXoK49i6x0PAMmC/1pcj\nSZImg0ZPYewaEVtk5pKI2BiYm5m3tbk2SZLUpRqaTCsiDgMuLZ9uDnw3It7etqokSVJXa3Q2zoOA\nHeGvF1RuDxzWrqIkSVJ3azRAbMBjp+9+mFHGhZAkSVNDoxdRXgRcEREXls9fC1zcnpIkSVK3a+gI\nRGYeAXwWCGBb4DOZeUw7C5MkSd2r0Yso5wLPz8zDgM8Br4uILdtamSRJ6lqNXgNxHvCH8vGdwDXA\nuW2pSJIkdb1Gr4HYNDPPAMjMh4AzI+KdjawYES8EPlWOJTEfOBtYA9yUmYeMo2ZJktRhjR6BeDAi\n9hx+EhG7AyvHWikiPgCcyd8m3joJOCozdwamR8SrK9YrSZK6QKNHIA4CvhoRw6ct/gy8uYH1bqOY\nuXN4ve0z85ry8feBl+LdHJIkTTpjHoEoJ9I6EXgSxQRavwEOz8ybxlo3M78DPFq3aFrd4xVAf6Vq\nJUlSV1jnEYiI2I3i6MHHgMOBDYEXA+dHxJsy88cV97em7vEmwP1jrRARi4CFFfcjSZLaaKxTGAuB\nvTLzV3XLboiIa4GTgZ1GX22tfhkRO2Xm1cCewBVjrZCZi4BF9csiYh4wUHHfkiSpRcY6hdE3IjwA\nkJnXA5uOY3/vB/4zIn5KMTz2N8exDUmS1GFjHYHojYgZmVl/HQMRMaOBdYG/Tr71kvLxrcAu46hT\nkiR1kbGOQFwGfLp+QUT0UJy++F67ipIkSd1trKMIRwDfjYjbgOvK9s8DfksxoZYkSZqC1hkgMnMl\nsFtE7Aw8n2IK71My8ycTUZwkSepOjV7HcBVwVZtrkSRJk0SjQ1lLkiT9VaNDWUuTyurVq6nVapXW\nmT9/Pj09PW2qSJLWLwYIrZdqtRqHnPFBemf3NdR+cNkDnHbQcSxYsKDNlUnS+sEAofVW7+w++ufM\n6nQZkrRe8hoISZJUmQFCkiRVZoCQJEmVGSAkSVJlBghJklSZAUKSJFXmbZyTWNXBkgYGBtpYjSRp\nKjFATGK1Wo2Djj6Lmf2bN9R+6R3J3J3bXJQkaUowQExyM/s3p2/TrRpqO7h8KXBXewuSJE0JXgMh\nSZIqM0BIkqTKDBCSJKkyA4QkSarMACFJkiozQEiSpMoMEJIkqTLHgZDGoeoooADz58+np6enTRVJ\n0sQyQEjjUKvV+OTCc5jVv0VD7e9bvoQjj92PBQsWtLkySZoYBghpnGb1b8HszeZ2ugxJ6givgZAk\nSZUZICRJUmUGCEmSVJkBQpIkVWaAkCRJlRkgJElSZQYISZJUmeNASMDQmjUMDAw03L5KW0laHxkg\nJGDlvYP8/PgTub23t6H2Ny1ZTM8z39DmqiSpexkgpNKc3l627utvqO3iwRXc2+Z6JKmbeQ2EJEmq\nzAAhSZIqM0BIkqTKOnINRERcDywvnw5k5oGdqEOSJI3PhAeIiNgIIDN3m+h9S5Kk1ujEEYhnATMj\n4jKgBzg6M6/tQB2SJGmcOhEgVgHHZ+ZZEfFPwPcjYkFmrulALZokVq9eTa1Wa7i9Az1JUnt1IkDc\nAtwGkJm3RsQ9wFbA/47WOCIWAQsnrDp1pVqtxkFHn8XM/s0bar/0jmTuzm0uSpKmsE4EiAOA7YBD\nImIusAlw19oaZ+YiYFH9soiYB/gn5hQzs39z+jbdqqG2g8uXso4fK0lSkzoRIM4CvhwR1wBrgAM8\nfSFJ0uQy4QEiMx8B9p3o/UqSpNZxIClJklSZAUKSJFVmgJAkSZUZICRJUmUGCEmSVJkBQpIkVWaA\nkCRJlRkgJElSZQYISZJUmQFCkiRVZoCQJEmVGSAkSVJlBghJklSZAUKSJFU24dN5Sxrb6tWrqdVq\nldoD9PT0tKX9/PnzG2472VV972FqvT/SMAOE1IVqtRoHHX0WM/s3b6j90juSvqcvpXd2X0PtF996\nJ3vePp05vb1jtr17cJB9Tj+VBQsWNLTtya5Wq/HJhecwq3+Lhtrft3wJRx6735R5f6RhBgipS83s\n35y+TbdqqO3g8qX0zn6I/jmzGmu/7AHm3Dudrfv6mylxvTWrfwtmbza302VIXc1rICRJUmUGCEmS\nVJkBQpIkVWaAkCRJlRkgJElSZQYISZJUmQFCkiRV5jgQktZpzdAQAwMDldapMjKjIz9Kk5MBQtI6\nLV05yM/OvppZ/Tc31L7qyIxVR91cuXwpZ3z8QEd+lDrMACFpTO0embHKqJuSuoPXQEiSpMoMEJIk\nqTIDhCRJqswAIUmSKjNASJKkygwQkiSpMm/jlDSpDK1Z09aBraaSyT6I12Svf7IzQEiaVFauuIcT\nfvB5emf3NdR+cNkDnHbQcQ48NYparcYnF57DrP4tGmpfdZCwdqvValxw8KHM6e1tqP3dg4Psc/qp\nXVP/ZGeAkDTp9M7uo3/OrE6XsV5o9yBh7Tant5et+/o7XcaU5DUQkiSpMgOEJEmqzAAhSVIXiIhz\nIuKwuucviIhrI6Irv6u9BkKSpO7wXuB/IuJCYCnwWWD/zFzT2bJGN+EBIiKmAacDzwL+Arw1M/8w\n0XVIktRNMvOeiFgInAj8DPhuZv4uIo4AXlU2W5SZP4qIfYH9gX8AbsrMgyLiy8CmwPTMfNVo+2il\nThwW2RvYKDNfAhwJnNSBGiRJ6jqZeT4wC3gd8MmIeAawY2buALwMOL5suhWwB7AD8M8R8bhy+bcn\nIjxAZwLEDsClAJl5LfC8DtQgSVK3uhD4fnnq4qnA0yLiCuASYKOI2BRYCZwHnAFsAmxQrpsTVWQn\nroHoA5bXPX80IqZXPMfTA3D33Xc/ZuHixYtZvvSPPPKXFQ1tZPDeO5l2+z08vOKhhto/cNf93Hb/\ndJY//HBD7f+0YgWDSwZY9eADDbVfvuIeFi9ezMYbb9xQ+8nc36nUV5jc/Z3sfV1134rK9dzVxv52\nk8ne18WLF3Pb/fc3/Hu7dNUqntKi+nffffd5wB2Z+WjTG1u3W4H/ycw3RcQM4Ohy+UGZ+awyTPwr\nMK1cPmHXS0wbGhqaqH0BEBEnUrwZ3yyf356ZT1pH+0XAwgkqT5KkRm2TmX9s9UYjYj9gy8w8rnz+\nYeBfgJnAlzLztIj4FvBE4EFgI+AtwAeB/8rMn7e6ptF0IkC8FnhlZh4QES8CjsnMvSpuYyPg+cBd\nwOo2lLkuA8A2E7zPTppK/Z1KfYWp1V/7uv7qZH8n4ghE1+pEgBi+C+OZ5aL9M/OWCS2iCRExlJnT\nxm65fphK/Z1KfYWp1V/7uv6aav3tJhN+DURmDgHvnOj9SpKk1unK0a0kSVJ3M0BIkqTKHMq6umM7\nXcAEm0r9nUp9hanVX/u6/uqa/k6bNq0HmN/izdaGhoYm+maBhkz4RZSSJK2Ppk2btmCX138wZ/Zv\n3pLtrVy+lB9/87gYGhrqyhsNPAIhSVKLzOzfnL5Nt5qw/ZWDS30JmAdsCHwc+B1wNsWgUjdl5iF1\n7TcHfgJsl5kP1y1/DfD6zHxTo/v2GghJkiavfYFlmbkT8HLgVIo5po7KzJ2B6RHxaoCI+BfgMmDL\n+g1ExCkUwaPS7bAGCEmSJq8LgWPKxz3Ao8BzM/Oactn3KSbdgmLgxd2Be0ds46eMY3gFA4QkSZNU\nZq7KzJURsQnwDYq5MuqPJKwA+su2l2fmfSNeJzO/MZ59GyAkSZrEIuKJwBXAOZn5dR47odYmwP0j\nVmnJ3RNeRClJUousXL50QrcVEVtSXNdwSGZeWS6+ISJ2ysyrgT0pwkW9lgz9bYCQJKk1aj/+5nHR\n6m2O8fqRwOOBYyLiIxRHFw4HPhcRGwC/B745Yp2WHIFwHAipSRHxZODHmbnNiOVrMrPp04QR8Sbg\nAxQXSK2hOM/5icxcs451+igOZ76mfH4s8CbgNODEVtTVqIh4HXAC8LTMfLBctiXwa+DlmfmriapF\nUut4DYTUGqMl8abTeUS8hSI87J2Z2wEvAZ4DfGGMVTcFnl33fF+KL+uTW1FXFZn5LeAXwMfqFn8O\n+JzhQZq8PIUhtVl5dfRZwNbAXODqzNwvIrYGzgM2pjiy8K7M/PmI1RcC/5GZfwQor7Y+EPjf8qjC\nbsAumbl/ua8ry3XeB2wVEd8ClgBPAC4qj2YM1/U44EzgWRS3d50AfA24C9i23NdPgIsz8/iI2AfY\nMTMPrdvGwcBbR9R8ZWa+b8SyQ4FfR8RXgCcBTwbeWG5jS+CM8v1ZDRyRmVeV96x/onxv7gH+PTNH\nXgwmqUMMEFJrbB0Rv6S4OGmIx16ktBdwQ2a+oTwn+buIeA7wKuC7mXliROwE7AD8NUBExGyKL9tf\n1O8oM++PiN8B25eLRjui8C6KL/LXldt6ObBnZv454q+naI+lGIBmu4jYrNz3r4HLgZ0j4iqK0e12\nBo6nuBjr6yNqOR04faw3JzOXRMR7gc8DfcBr607BnAr8V2ZeGhFzgasjYjvgw8CBmfnriDic4sjL\nlaNtX9LEM0BIrfG/mfnc+gURsRogM78eEc8vvwSfSnF6oRf4EfDtiHgu8D2KL9J6w8FgtN/TDcdR\n48grr3cFDihrvCciLqIIC9+jGHhmCPgqsE85XO6OwNtH9PFg4G0jtnvFKEcgyMzzI+JtwKWZWT+2\n/x7A/Ij4ZPl8BrANcDHw3bKui+quMJfUBQwQUptFxGHAaykO0/8QeAYwLTP/X0Q8FXgl8AbgLcC/\nDK9XfqnXgBeX6w1vbzawLXAdxSmM+mCwQYXSRl4DNZ3i34TLgPcDj1AcjQjgQODG+rHzyxobOgJR\n50/AH0csm0ZxamQlQERslZl3URypuYji/TkpIs7LzOMr7EuaUFNtNk4DhNQao91XPbxsD+CM8kjE\n0ykubuyJiE8Dd2bmZyLix8AvR9nGMcApEfHKzByIiF6K6xbOz8w7ImIZxVENImIb4Jnleo+y9jAx\nXNcVFMHg3WUo2ZviYs1lEfEgxSmWY4GnlHUsauSNGIcrKa6R+HR56uLy8s6Wa4C3lO/PA9SFK6lL\nzd/jva/O3tl9LdnY4LIH+NFJFwfgbJzSemxdd2GcAnw+It5PMazsTykO0X8WOL+80+JR4B0jN5CZ\nF0TEI8CFEbERxa2cXwOGD/f/CDggIm4Gbqb40gVYDNweEZdn5u4j6ht+/J/A6RHxG4qjDx/NzF+X\nr/038IrMXBURVwAnU5zaaNZo79MhwBci4v+Ur/97Zj4YER8Cziv7vxI4qAX7l9qqd3Yf/XNmTdj+\nmp2Ns7zl+6sU1yZtALwvM3/WyL4dB0KSpBaYNm3agr0/8eZsVYBYfvd9XHTUuTE0NLTWIxDlHyDP\nzMz3RsTjKS6E/hVwQmZeExGfp7ju6OLyzqZPUZwC3aIMEIuAezPzsxGxgOLo5vaj7+2xJvwIRJmW\nzqFIS48CbxtxQZUkSWrMhRSDy8HaZ+N8KcVFycOzcV5ft/5JwEPl4w2ABxvdcSdOYbwC6MnMf46I\nPSju8359B+qQJGlSy8xV8NfxZoZn4zyhrsljZuMs206rW/+Bctkc4FyKW8Ab0omRKG8BZpQd6Ace\nHqP934mIGRExrzyaIUnSlNXsbJzlxcs/BD6UmT9pdL+d+AIepLiA7GZgM4pbtNaqPD+zcLTXLr/8\n8lbXJkmaJG655RYuOPhQ5vT2NtT+7sFB9jn9VBYsWNCK3Y86o+Xgsgdase2Gt9XsbJwR8TSK0yBv\nyMwbq9TXiQDxHooLOo4uh/K9MiKeMfL+8mGZuYgRt49FxDxgoM11SpK63JzeXrbu6+90GcNq5W2X\nLd3mGK83OxvnJ4CNgM+UZwbuH56EbyydCBD3UgxQA8VhlRkUF35IkjRplQM+TehNAZn5buDdo7y0\nyzrW2bbu8d7j3XcnAsQpwJci4mqKKz6PHJ7iV5IkTQ4THiDK4Wr3mej9SpKk1unEXRiSJGmSM0BI\nkqTKHEdBkqQWcDZOSZI0HvP/c/eXZqPjUozl7sFBPnL5D52NU5Kk9d1Ej0vRgtk4N6aY4XcWxZwY\n+2XmXY3s22sgJEmavPYFlmXmTsDLgVMpJsg6KjN3BqZHxKsBytk4LwO2rFv/bcB1ZdvzgCMa3bEB\nQpKkyetC4Jjy8dpm49yjfDw8G+e9wytn5mcojloAPAm4r9EdewpDkqRJqtnZOMvlQxFxOfAMiqm/\nG+IRCEmSJrFmZ+MEyMzdgZ2Abze6X49ASJLUIncPDk7otlowG+eHgDsy86vASopTIA0xQEiS1Bq1\n8rbLlm5zjNebnY3zS8A5EXEgxVmJ/RstzAAhSVILTNLZOJdQHKWozGsgJElSZQYISZJUmQFCkiRV\nZoCQJEmVeRGlJEkt4GyckiRpPOa/5Y1H5az+LVqysfuWL+Hsr3/C2TglSVrfzerfgtmbzZ2w/TU7\nG2fd8qcAPwO2qF++Ll4DIUnS5NXsbJzD82icAPylyo4NEJIkTV5NzcZZ+gLFiJarqux4wk9hRMR+\nwFsohtKCYpqTAAAaZElEQVR8HPAsYE5mPjDRtUiSNJk1OxtnRCwELsnMG0fO0jmWCT8CkZnnZOau\nmbkbcD1wmOFBkqTxaXI2zn2BAyPiSmAO8ING99uxiygj4nnA0zLz0E7VIElSK923fMmEbqvZ2Tgz\n85/qtjUAvLTR+jp5F8aRwLEd3L8kSa1UK2+7bOk2x3i92dk4Ry5v+DRGRwJERPQDCzLzqgbaLgIW\ntr0oSZKaMBln42xk+dp06gjETsDljTTMzEXAovplETEPGGh1UZIkqTGduo0zgD90aN+SJKlJHTkC\nkZknjN1KkiR1KweSkiRJlRkgJElSZQYISZJUmQFCkiRVZoCQJEmVGSAkSVJlBghJklSZAUKSJFVm\ngJAkSZUZICRJUmUGCEmSVJkBQpIkVWaAkCRJlRkgJElSZQYISZJUmQFCkiRVZoCQJEmVGSAkSVJl\nBghJklSZAUKSJFVmgJAkSZUZICRJUmUzOrHTiPgQ8K/ABsDpmfnlTtQhSZLGZ8KPQETEzsCLM/Ml\nwC7AEye6BkmS1JxOHIF4GXBTRFwEbAJ8oAM1SJKkJnQiQMwGngS8EtgW+L/AUzpQhyRJGqdOBIh7\ngN9n5qPALRHxl4iYnZnLRmscEYuAhRNZoCRJWrdOBIifAO8CTo6IucDGFKFiVJm5CFhUvywi5gED\nbatQkiSt04RfRJmZ3wNuiIifAxcDB2fm0ETXIUmSxq8jt3Fm5oc6sV9JktQaDiQlSZIqM0BIkqTK\nDBCSJKkyA4QkSarMACFJkiozQEiSpMoMEJIkqTIDhCRJqswAIUmSKjNASJKkygwQkiSpMgOEJEmq\nzAAhSZIqM0BIkqTKDBCSJKkyA4QkSarMACFJkiozQEiSpMoMEJIkqTIDhCRJqswAIUmSKjNASJKk\nymZ0YqcRcT2wvHw6kJkHdqIOSZI0PhMeICJiI4DM3G2i9y1JklqjE0cgngXMjIjLgB7g6My8tgN1\nSJKkcerENRCrgOMz82XAO4HzIsJrMSRJmkQ6cQTiFuA2gMy8NSLuAbYC/ne0xhGxCFg4YdVJkqQx\ndSJAHABsBxwSEXOBTYC71tY4MxcBi+qXRcQ8YKBtFUqSpHXqRIA4C/hyRFwDrAEOyMw1HahDkiSN\nU0sCRET0AU/MzN+O1TYzHwH2bcV+JUlSZ4w7QETEW4GXAEcANwArIuJbmfnhVhUnSZK6UzN3P7wT\neD/w78DFFNc1vLwVRUmSpO7W1O2TmXkv8Arge5n5KPC4llQlSZK6WjMB4rcRcQmwLfCjiLgQ+EVr\nypIkSd2smQBxAHAc8MLMfBg4F3hrS6qSJEldrZkAMR3YETilvAvjOU1uT5IkTRLNfOGfBswEtgce\nBf6RYowHSZK0nmsmQGyfmUcBj2TmKmA/iqMQkiRpPddMgBiKiA2BofL57LrHkiRpPdZMgDgF+BEw\nJyJOAa4HTm5JVZIkqauNeyTKzDw3Iq4HdgV6gL0y88aWVSZJkrrWuI9ARMR2wEcy8zTgh8BpEREt\nq0ySJHWtZk5hnAmcDZCZvwc+indhSJI0JTQTIGZm5qXDTzLzhxS3dUqSpPVcM9N5L4mIdwBfLZ+/\nEVjcfEmSJKnbNXMEYn/glcBdwO3AXjiUtSRJU0Izd2HcThEgJEnSFDPuABERLwM+BmwKTBtenpnb\ntqAuSZLUxZq5BuJzwHuBm3AESkmSppRmAsSyzLykZZVIkqRJo5kAcU1EnARcCvxleGFmXt10VZIk\nqas1EyBeUP6/fgbOIWC3JrYpSZImgWbuwti1mR1HxBbAdcAemXlLM9uSJEkTq5m7MJ4MfBGYB+wI\nfA04IDP/2MC6M4D/AlaNd/+SJKlzmhlI6gzgeGCQYgTK84GvNLjuCcDngTub2L8kSeqQZq6BmJ2Z\nP4iIT2fmEHBmRBwy1koR8RZgSWb+MCKOamL/UsesXr2aWq1WaZ358+fT09PTpoqk7lP192T16tUA\nDf+eDAwMjKsutUYzAeLBiHgC5RgQEbED8FAD6+0PrImIlwLPBr4SEf+amUtGaxwRi4CFTdQptVyt\nVuOTC89hVv8WDbW/b/kSjjx2PxYsWNDmyqTuUavVOOjos5jZv3lD7ZfekfQ9fSm9s/saar/41jvZ\nn8baqvWaCRDvAS4B5kfEryhGpPy3sVbKzJ2HH0fElcBBawsPZftFwKL6ZRExDzB6qqNm9W/B7M3m\ndroMqavN7N+cvk23aqjt4PKl9M5+iP45sxprv+wBuLeZ6tSMZgLElsDzgQVAD3BzZj5ccRuOYClJ\n0iTUTIA4LjO/B/x2vBvITMeMkCRpEmomQNQi4kvAtcCDwwszs9E7MSRJ0iTVTIC4h2IWzhfVLRui\n8Vs5JUnSJNXMSJT7t7IQSZI0eTQzEuUAo1wEmZnbNlWRJEnqes2cwtil7vEGwGuAjZqqRpIkTQrN\nnML404hFx0fEdcDHmitJkiR1u2ZOYexU93Qa8HTgcU1XJEmSul4zpzCOrXs8BCwD9muuHEmSNBk0\ncwpj14jYIjOXRMTGwNzMvK2FtUmSpC417um8I+Iw4NLy6ebAdyPi7S2pSpIkdbVxBwjgIGBH+OsF\nldsDh7WiKEmS1N2aCRAb8Njpux/GybEkSZoSmrmI8iLgioi4sHz+WuDi5kuSJEndbtxHIDLzCOCz\nQADbAp/JzGNaVZgkSepezVxEORd4fmYeBnwOeF1EbNmyyiRJUtdq5hqI84A/lI/vBK4Bzm26IkmS\n1PWaCRCbZuYZAJn5UGaeCcxuTVmSJKmbNRMgHoyIPYefRMTuwMrmS5IkSd2umbswDgK+GhHDpy3+\nDLy5+ZIkSVK3G9cRiHIirROBJ1FMoPUb4PDMvKmFtUmSpC5VOUBExG7A+cC3gJcAOwDfBM6PiF1a\nWp0kSepK4zmFsRDYKzN/Vbfshoi4FjgZ2Gn01SRJ0vpiPAGib0R4ACAzr4+ITcdaOSKmA2dSDEC1\nBnhHZv5uHHVIkqQOGc81EL0R8XfBo1zWSCB5FTCUmTsAxwCfGEcNkiSpg8YTIC4DPl2/ICJ6KE5f\nfG+slTPzYmB42u95wH3jqEGSJHXQeE5hHAF8NyJuA64rt/E84LcUE2qNKTPXRMTZwN7A68dRg6Qm\nrF69mlqt1nD7+fPn09PT08aK2qdqX6G7+juVPqt2WzM0xMDAQKV1fD/XrnKAyMyVwG4RsTPwfIop\nvE/JzJ9U3M5bImIL4OcR8dTMfHC0dhGxiOLCTUktUqvVuODgQ5nT2ztm27sHB9nn9FNZsGDBBFTW\nelX6Ct3X31qtxiFnfJDe2X1jth1c9gCnHXRc19TebZauHORnZ1/NrP6bG2p/3/IlHHnsfr6fazHu\ngaQy8yrgqqrrRcS+wBMy81PAX4DVFBdTrm0/i4BFI7YxD6gWIyU9xpzeXrbu6+90GRNisve1d3Yf\n/XNmdbqM9cKs/i2YvdncTpexXmhmJMrx+jbw5Yi4qtz/4Zn5UAfqkCRJ4zThASIzVwH7TPR+JUlS\n6zQzmZYkSZqiDBCSJKkyA4QkSarMACFJkiozQEiSpMoMEJIkqTIDhCRJqswAIUmSKjNASJKkygwQ\nkiSpMgOEJEmqzAAhSZIqM0BIkqTKDBCSJKkyA4QkSarMACFJkiozQEiSpMoMEJIkqTIDhCRJqswA\nIUmSKjNASJKkygwQkiSpshkTvcOImAF8CZgHbAh8PDO/O9F1SJKk8evEEYh9gWWZuROwJ3BqB2qQ\nJElNmPAjEMCFwDfKx9OBRzpQgyRJasKEB4jMXAUQEZtQBImjJ7oGaaTVq1dTq9Uabj8wMNDGaqpr\nZ/1rhoYq93f+/Pn09PRUWqdbTOb+Dq1ZM2lr1+TTiSMQRMQTgW8Dp2bmBWO0XQQsnIi6NHXVajUu\nOPhQ5vT2NtT+piWL6XnmG9pcVeNqtRqHnPFBemf3NdR+8a13sj+NtV26cpCfnX01s/pvbqj9fcuX\ncOSx+7FgwYKG2nebydzflfcO8vPjT+T2Bn+O7x4cZJ/TT+2K2jX5dOIiyi2By4BDMvPKsdpn5iJg\n0YhtzAO6609ATXpzenvZuq+/obaLB1dwb5vrqap3dh/9c2Y11HZw2QNU6cCs/i2YvdnccVY2+Uzm\n/lb5OZaa0YkjEEcCjweOiYiPAEPAnpn5UAdqkSRJ49CJayDeDbx7ovcrSZJax4GkJElSZQYISZJU\nmQFCkiRVZoCQJEmVGSAkSVJlBghJklSZAUKSJFVmgJAkSZUZICRJUmUGCEmSVJkBQpIkVWaAkCRJ\nlRkgJElSZQYISZJUmQFCkiRVZoCQJEmVGSAkSVJlBghJklSZAUKSJFVmgJAkSZUZICRJUmUGCEmS\nVFnHAkREvDAiruzU/iVJ0vjN6MROI+IDwJuBwU7sX5IkNadTRyBuA17ToX1LkqQmdeQIRGZ+JyKe\n3Il9q3GrV6+mVqtVWmf+/Pn09PS0qaLGVa19YGCgjdVIf9NNP5trhoYqb79bfsfVeR0JEFVExCJg\nYafrmIpqtRoXHHwoc3p7G2p/9+Ag+5x+KgsWLGhzZWOr1WoccsYH6Z3d11D7xbfeyf401lZqRq1W\n46Cjz2Jm/+YNtV96RzJ35/bUsnTlID87+2pm9d/cUPv7li/hyGP364rfcXVepwPEtLEaZOYiYFH9\nsoiYB/gn4wSY09vL1n39nS5jXHpn99E/Z1ZDbQeXPQD3trkgqTSzf3P6Nt2qobaDy5cCd7Wtlln9\nWzB7s7lt277WX52+jXOow/uXJEnj0LEjEJn5J+Alndq/JEkav04fgZAkSZOQAUKSJFVmgJAkSZUZ\nICRJUmUGCEmSVJkBQpIkVWaAkCRJlRkgJElSZQYISZJUmQFCkiRVZoCQJEmVGSAkSVJlBghJklSZ\nAUKSJFVmgJAkSZUZICRJUmUGCEmSVJkBQpIkVWaAkCRJlRkgJElSZQYISZJUmQFCkiRVNmOidxgR\n04DTgWcBfwHempl/mOg6JEnS+HXiCMTewEaZ+RLgSOCkDtQgSZKa0IkAsQNwKUBmXgs8rwM1SJKk\nJkz4KQygD1he9/zRiJiemWsqbKMH4O67725pYXqsxYsXc9v997P84Ycbar901SqesngxG2+8cZsr\nG9vixYu5//ZlPLzioYbaP3DX/dx2//SG+/qnFSsYXDLAqgcfaKj98hX3sLiN7007+9ttfa2q6s9x\nu/u7ePFili/9I4/8ZUVD7QfvvZNpt9/T0GfbbT/H7ewrdLa/u++++zzgjsx8tKGNrYemDQ0NTegO\nI+JE4H8y85vl89sz80nraL8IWDhB5UmS1KhtMvOPnS6iUzpxBOKnwCuBb0bEi4Ab19U4MxcBi+qX\nRcRGwPOBu4DVbaly7QaAbSZ4n500lfo7lfoKU6u/9nX91cn+3tGh/XaFThyBGL4L45nlov0z85YJ\nLaIJETGUmdM6XcdEmUr9nUp9hanVX/u6/ppq/e0mE34EIjOHgHdO9H4lSVLrOJCUJEmqzAAhSZIq\nM0BUd2ynC5hgU6m/U6mvMLX6a1/XX1Otv11jwi+ilCRJk59HICRJUmUGCEmSVJkBQpIkVWaAkCRJ\nlRkgJElSZZ2YC6NrRcQs4Bb+Nj/HdzLzcx0sqWERcT1/m+V0IDMPrLBuD/B14MzM/EG57CPAXsAj\nwHsy8xctLnlcIuKFwKcyc9fy+XzgbGANcFNmHlJxe/8IfDszn1k+75qfgVH6+mzgkrI+gM9n5jca\n3NZxwA4UM9memZlfjIjNgK8B/wDcSTGs/F9a3I1GapsBfAmYB2wIfDwzv9vMZ7uW/nb8s11HX5v5\nbD8O7E7xPh2ZmVd1y2c7HhExB/gqsAFwL7BvZq6MiFcBx1D8m/TlzPxiB8sUHoEgImZExGsjYlvg\nucDXMnO38r/JEh42Aqiru0p42Ba4Cnhe3bLnADtl5guBfwdOa3HJ4xIRHwDOBDaqW3wScFRm7gxM\nj4hXV9jevsD5wOy6xV3xM7CWvm4PnFhXW6NfMLsA8zPzJcCOwBER0Q98BDivfO9+BbyjlX2oYF9g\nWWbuBOwJnFouH9dnu47+dsNnu7a+jvezfTbwgsx8EcXv6mfKl7rls12riNitnFBxpCMoAsJw7W8t\ng9dJwB7ALsDbI2LzCStWo5qyRyDKL863AjsD3wd+BLweeF5E/BhYDByemXfXrbMzcCTwEPAE4Axg\nN4qJwT6TmWdExG+Aq8tlN5fb2Qn4C/CKzGzH7KHPAmZGxGUUf3EdnZnXNrjuTOBAil/aYTsAPwDI\nzD9HRE9EbJaZ9ww3GKufwIeBf6T4ct6MIoS8DvgnYL/M/Pk4+nkb8Brg3Lpl22fmNeXj7wMvBS5u\ncHv3ljXX6rdHd/wMjNpXYEFE7A3cWta2soFt/T/ghrrn0yn+itsB+Hi57Pvl41M60NcLgeEvzOHa\nYPyf7dr62w2f7Vr7yjg+28z8VUS8rHw6D7ivfNwtn+26/Bl4T0R8muJ9OS8z78/M95Q1TgeeCPwR\neCpwa2Y+UL72k7KWb9X16csU7+eTKYL314FXldt4NfCksfrcZH+mnCl5BCIi3knxV/fVmfnPmfmx\n8gfz98AxmbkLxT9Uo/2FsjXFP+wHA0cDb6L4wjyofH0T4KvlXxg7Aj8pk/RGwNPb1KVVwPGZ+TKK\nicrOK3/5xpSZN2ZmAvWz2fXxt9MhAINA/4hV19bPDflbP1dl5p4Uv+R7Zua/Ap8G3lipd3+r9TvA\no+tosmKUOte1vf/OzAdHLO6Kn4G19PVa4APltv7AiGnu17GthzNzeflX3NnAGZm5isd+zmt77yai\nr6vKQ9SbUHy5Hl2+VP8z2fBnu47+dvyzXUdfx/XZlttcExEfA/4v8OVycVd8tmPUfWtmHgy8jOIP\nnz+UR1SGT/XcSHG04Qr+/t+ktfVpoPx38PfAvMzcC/g2RZBYV5+77gjNZDAlAwRFMj0JODIiPh0R\nC8rlVwI/Lh9/B3j2KOvelJlrgPuBWpnC76M41zhs+K+f+yl+kBmlTSvdApwHxS8lcA+w1fCLEfHP\nEXFlRFwREXs2sL0HKP7xGLYJRV/qDTF6P+/nb/38Zd2y35WPW/0+rFlXnRHx0bq+NzLlbzf/DFyU\nmcPb/bva1tXXiHg8cGlZ+3Hl4uX87XMe7TOGCeprRDyR4ovinMy8oFxc/xdupc92Lf3tis92LX0d\n92cLkJkfBuYCHyyPrnbNZ7su5ZGQM4GXU3yJ31T259HMfDpFmDm37E9f3apr69NY/+asrc8bocqm\n5CmMzLwPOBk4ufwB/khEnAx8gOKv5W9QnGu7fpTV68f+XtsX0kSPD34AsB1wSETMpfjlumv4xcz8\nKbBrhe39FPh0RJxIcfhvWmbeO6LNNMbuZ7veh/r3/YaI2Ckzr6Y4p3xFfcPMPKbi9r5Id/0M1G//\nsog4NDOvo7ho7jG1ra2vEfEPwOXACZl5ft1LP6X46+srFO/dNaOs3va+RsSWwGXAIZl5Zd1L4/ps\n19Hfjn+26+jreD/bXYHXZeahwMPlf6spPtu9gHPo4Ge7LhHxNmAB8J/lHz7Dy08DvpGZP6Y4+rma\n4hTKP5bBcBXF6Yvjx1Hv2vrcyB8XGmFKBoh6mXkVxekMIuII4MvlKY6VFNdIrMtoP6xDDTxutbMo\n6r6G4i/yA8qUXcVf68vMX5bb+h+KX6zRrn4fq5/t7G/9tt8PnBkRG1D8hfTNJrfXbT8D9eu8Azg1\nIh4G7gbe3uA23gFsA7wtIt5ebnN/ivPi55T/kC8D/k+FWkZbNt6+Hgk8HjimvPtniOJLb7yf7dr6\n2w2f7dr6Ot7P9irg38prAqYDp2Xmn8o7M86JiLfS2c92rTLzzLW89FngvyLiGIp/zw7OzEcj4r0U\n12ZNA76YmXeNWK9qjRP17/N6y8m0JElSZVP1GghJktQEA4QkSarMACFJkiozQEiSpMoMEJIkqTID\nhCRJqmzKjwMhdZOIeD3wIYrfzWnAuZl5QvnaIuCH5cBg7dr/hhSjtO5McQ/+fcD7M/O6iNgeOCgz\nGx2jQNJ6zCMQUpcoRxE9AdgjM58NvBjYJyJeWTbZmWLOgHZ6N8XIo9tl5rOAo4CLI6InM683PEga\n5kBSUpeIiGcC/w28KDPvKJc9jWL2w38GTqcYovw1FEMWfwHYlGK433dl5vXljISbAfMpRl78HHAB\n8EqKmQqPBt5HMVPq+zLzMaM7RsRJFEOhH5yZj5TL/oViDokXU0zy9FLgFxSj902jGPXxK5n5rnI0\n1zdQ/HFyWWZ+qKVvkqSu4REIqUtk5m8oZlT8Q0RcGxGfAmZk5h8y81zgOuDAzPwt8FXglPIowXuB\nb5VDPgMsy8ynZ+Yl5fM7MvMZFBMjHUERAN5McXRhpM9QBIUlEXFRRBwG/CwzHy5fHyonOnpOZj6X\nYgjmu4BF5bTS2wPPA54LPCEixhpCWdIkZYCQukg5vfGTKY42PBn4n4jYu67JtIiYCczPzIvLda6l\nmIE1yjbXjtjspeX//wRcVc6T8ieKORlG7v9PZdh4KfAz4D8oJrXqG9k2IrammAV2n3KytT2AF1BM\nBPVLijDRrinsJXWYF1FKXSIiXgH0ZuaFFLMoDk+GdCBwUV3T6fz97IHT+dvv84MjXnu47vGjY9Tw\ncYoJma6jOOLxqXKippdSTMo03G4jimmnjymPnEBxfcYpmXlK2aZvrP1Jmrw8AiF1j1XAJyLiyQAR\nMQ14GsVf81B8Gc/IzBVAbfjIRES8CNgSuKni/kabwnhr4MPDp0MiYlNgNnDjiHZfojiaUT9d9hX/\nv727R2kwiKIw/BbpXMjZgmncg2CRQldgYWkt7sT0gmAhpHEFKSIoN2uxSDFfqgTJ7Qy8D0w1UwwD\nA4f54QJ3SS6SzIA34KY5J0lnwgAh/RNV9Qk8Ae9JfoBvxh59noZ8MMocXwK3wEOSDaP88XVV/XJY\nlvivV9LH+u4ZJwnbJF/ACnisqu1+QJI5sACukqyntpzeXLwyrlA2wLqqXk5fAUnnxF8YkiSpzRMI\nSZLUZoCQJEltBghJktRmgJAkSW0GCEmS1GaAkCRJbQYISZLUZoCQJEltO29MP/XkApTrAAAAAElF\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ded668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "storm_histogram(storms, 'total_precip_depth', np.arange(5, 31, 5), \n",
    "                aspect=1.6, hue='Year', row='Has Outflow', sharey=False)"
   ]
  }
 ],
 "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
}