Spike Number and Frequency Contrast Plots.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting Manually-defined Spike Number and Frequency\n",
    "\n",
    "Tong-Wey performed manual spike analysis. Here we plot them in with contrasts and CIs.\n",
    "\n",
    "- 500 ms pulses of green light (horizontal lines) inhibit action potentials in segmental nerves of larvae with pan-neuronal expression of dGtACR1, but not in control larvae (UAS-dGtACR1 with no driver).\n",
    "\n",
    "- Action potential frequencies 500 ms before and 500 ms during the pulses of green light, were significantly reduced by pan-neuronal expression of GtACR1 (P < 0.01, One-tailed paired T-test).\n",
    "\n",
    "- __Recordings were from four segmental nerves from three nSyb>dGtACR1 larvae and three segmental nerves from three control larvae.__\n",
    "\n",
    "- The action potentials were heterogeneous, likely reflecting the activities of both motor and sensory neurons."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-15T17:33:33.797840",
     "start_time": "2016-09-15T17:33:33.704992"
    }
   },
   "source": [
    "#### Load Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-22T17:53:14.493971",
     "start_time": "2016-09-22T17:53:14.374442"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Thu Sep 22 17:53:14 SGT 2016\r\n"
     ]
    }
   ],
   "source": [
    "!date\n",
    "import os\n",
    "import seaborn as sb\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import MultipleLocator, AutoLocator\n",
    "import bootstrapContrast as bs\n",
    "%matplotlib inline\n",
    "sb.set_style('ticks')\n",
    "plt.rcParams['svg.fonttype'] = 'none'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-22T17:53:14.498176",
     "start_time": "2016-09-22T17:53:14.495143"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/jacuzzijo/Google Drive/Data+Scripts/Python/optoElectro'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print the current directory.\n",
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-15T17:33:33.797840",
     "start_time": "2016-09-15T17:33:33.704992"
    }
   },
   "source": [
    "#### Read in data and munge it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-22T17:53:14.591749",
     "start_time": "2016-09-22T17:53:14.499348"
    },
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Genotype</th>\n",
       "      <th>NerveID</th>\n",
       "      <th>PulseNumber</th>\n",
       "      <th>PulseEpoch</th>\n",
       "      <th>EpochLength</th>\n",
       "      <th>SpikeNumber</th>\n",
       "      <th>GenotypePulseEpoch</th>\n",
       "      <th>GenotypePulseEpochPulseNum</th>\n",
       "      <th>SpikeFrequencyHz</th>\n",
       "      <th>NervePulseID</th>\n",
       "      <th>NerveEpoch</th>\n",
       "      <th>GenotypeNervePulse</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>UAS-dGtACR1</td>\n",
       "      <td>16719045</td>\n",
       "      <td>1</td>\n",
       "      <td>before</td>\n",
       "      <td>500</td>\n",
       "      <td>68</td>\n",
       "      <td>UAS-dGtACR1 before</td>\n",
       "      <td>UAS-dGtACR1 before_1</td>\n",
       "      <td>136</td>\n",
       "      <td>16719045 1</td>\n",
       "      <td>16719045 before</td>\n",
       "      <td>UAS-dGtACR1 16719045 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>UAS-dGtACR1</td>\n",
       "      <td>16719045</td>\n",
       "      <td>2</td>\n",
       "      <td>before</td>\n",
       "      <td>500</td>\n",
       "      <td>43</td>\n",
       "      <td>UAS-dGtACR1 before</td>\n",
       "      <td>UAS-dGtACR1 before_2</td>\n",
       "      <td>86</td>\n",
       "      <td>16719045 2</td>\n",
       "      <td>16719045 before</td>\n",
       "      <td>UAS-dGtACR1 16719045 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>UAS-dGtACR1</td>\n",
       "      <td>16719045</td>\n",
       "      <td>3</td>\n",
       "      <td>before</td>\n",
       "      <td>500</td>\n",
       "      <td>48</td>\n",
       "      <td>UAS-dGtACR1 before</td>\n",
       "      <td>UAS-dGtACR1 before_3</td>\n",
       "      <td>96</td>\n",
       "      <td>16719045 3</td>\n",
       "      <td>16719045 before</td>\n",
       "      <td>UAS-dGtACR1 16719045 3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>UAS-dGtACR1</td>\n",
       "      <td>16719043</td>\n",
       "      <td>1</td>\n",
       "      <td>before</td>\n",
       "      <td>500</td>\n",
       "      <td>34</td>\n",
       "      <td>UAS-dGtACR1 before</td>\n",
       "      <td>UAS-dGtACR1 before_1</td>\n",
       "      <td>68</td>\n",
       "      <td>16719043 1</td>\n",
       "      <td>16719043 before</td>\n",
       "      <td>UAS-dGtACR1 16719043 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>UAS-dGtACR1</td>\n",
       "      <td>16719043</td>\n",
       "      <td>2</td>\n",
       "      <td>before</td>\n",
       "      <td>500</td>\n",
       "      <td>54</td>\n",
       "      <td>UAS-dGtACR1 before</td>\n",
       "      <td>UAS-dGtACR1 before_2</td>\n",
       "      <td>108</td>\n",
       "      <td>16719043 2</td>\n",
       "      <td>16719043 before</td>\n",
       "      <td>UAS-dGtACR1 16719043 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>UAS-dGtACR1</td>\n",
       "      <td>16719043</td>\n",
       "      <td>3</td>\n",
       "      <td>before</td>\n",
       "      <td>500</td>\n",
       "      <td>45</td>\n",
       "      <td>UAS-dGtACR1 before</td>\n",
       "      <td>UAS-dGtACR1 before_3</td>\n",
       "      <td>90</td>\n",
       "      <td>16719043 3</td>\n",
       "      <td>16719043 before</td>\n",
       "      <td>UAS-dGtACR1 16719043 3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>UAS-dGtACR1</td>\n",
       "      <td>16719044</td>\n",
       "      <td>1</td>\n",
       "      <td>before</td>\n",
       "      <td>500</td>\n",
       "      <td>42</td>\n",
       "      <td>UAS-dGtACR1 before</td>\n",
       "      <td>UAS-dGtACR1 before_1</td>\n",
       "      <td>84</td>\n",
       "      <td>16719044 1</td>\n",
       "      <td>16719044 before</td>\n",
       "      <td>UAS-dGtACR1 16719044 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>UAS-dGtACR1</td>\n",
       "      <td>16719044</td>\n",
       "      <td>2</td>\n",
       "      <td>before</td>\n",
       "      <td>500</td>\n",
       "      <td>64</td>\n",
       "      <td>UAS-dGtACR1 before</td>\n",
       "      <td>UAS-dGtACR1 before_2</td>\n",
       "      <td>128</td>\n",
       "      <td>16719044 2</td>\n",
       "      <td>16719044 before</td>\n",
       "      <td>UAS-dGtACR1 16719044 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>UAS-dGtACR1</td>\n",
       "      <td>16719044</td>\n",
       "      <td>3</td>\n",
       "      <td>before</td>\n",
       "      <td>500</td>\n",
       "      <td>55</td>\n",
       "      <td>UAS-dGtACR1 before</td>\n",
       "      <td>UAS-dGtACR1 before_3</td>\n",
       "      <td>110</td>\n",
       "      <td>16719044 3</td>\n",
       "      <td>16719044 before</td>\n",
       "      <td>UAS-dGtACR1 16719044 3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>UAS-dGtACR1</td>\n",
       "      <td>16719045</td>\n",
       "      <td>1</td>\n",
       "      <td>during</td>\n",
       "      <td>500</td>\n",
       "      <td>55</td>\n",
       "      <td>UAS-dGtACR1 during</td>\n",
       "      <td>UAS-dGtACR1 during_1</td>\n",
       "      <td>110</td>\n",
       "      <td>16719045 1</td>\n",
       "      <td>16719045 during</td>\n",
       "      <td>UAS-dGtACR1 16719045 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>UAS-dGtACR1</td>\n",
       "      <td>16719045</td>\n",
       "      <td>2</td>\n",
       "      <td>during</td>\n",
       "      <td>500</td>\n",
       "      <td>72</td>\n",
       "      <td>UAS-dGtACR1 during</td>\n",
       "      <td>UAS-dGtACR1 during_2</td>\n",
       "      <td>144</td>\n",
       "      <td>16719045 2</td>\n",
       "      <td>16719045 during</td>\n",
       "      <td>UAS-dGtACR1 16719045 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>UAS-dGtACR1</td>\n",
       "      <td>16719045</td>\n",
       "      <td>3</td>\n",
       "      <td>during</td>\n",
       "      <td>500</td>\n",
       "      <td>49</td>\n",
       "      <td>UAS-dGtACR1 during</td>\n",
       "      <td>UAS-dGtACR1 during_3</td>\n",
       "      <td>98</td>\n",
       "      <td>16719045 3</td>\n",
       "      <td>16719045 during</td>\n",
       "      <td>UAS-dGtACR1 16719045 3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>UAS-dGtACR1</td>\n",
       "      <td>16719043</td>\n",
       "      <td>1</td>\n",
       "      <td>during</td>\n",
       "      <td>500</td>\n",
       "      <td>46</td>\n",
       "      <td>UAS-dGtACR1 during</td>\n",
       "      <td>UAS-dGtACR1 during_1</td>\n",
       "      <td>92</td>\n",
       "      <td>16719043 1</td>\n",
       "      <td>16719043 during</td>\n",
       "      <td>UAS-dGtACR1 16719043 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>UAS-dGtACR1</td>\n",
       "      <td>16719043</td>\n",
       "      <td>2</td>\n",
       "      <td>during</td>\n",
       "      <td>500</td>\n",
       "      <td>51</td>\n",
       "      <td>UAS-dGtACR1 during</td>\n",
       "      <td>UAS-dGtACR1 during_2</td>\n",
       "      <td>102</td>\n",
       "      <td>16719043 2</td>\n",
       "      <td>16719043 during</td>\n",
       "      <td>UAS-dGtACR1 16719043 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>UAS-dGtACR1</td>\n",
       "      <td>16719043</td>\n",
       "      <td>3</td>\n",
       "      <td>during</td>\n",
       "      <td>500</td>\n",
       "      <td>38</td>\n",
       "      <td>UAS-dGtACR1 during</td>\n",
       "      <td>UAS-dGtACR1 during_3</td>\n",
       "      <td>76</td>\n",
       "      <td>16719043 3</td>\n",
       "      <td>16719043 during</td>\n",
       "      <td>UAS-dGtACR1 16719043 3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>UAS-dGtACR1</td>\n",
       "      <td>16719044</td>\n",
       "      <td>1</td>\n",
       "      <td>during</td>\n",
       "      <td>500</td>\n",
       "      <td>59</td>\n",
       "      <td>UAS-dGtACR1 during</td>\n",
       "      <td>UAS-dGtACR1 during_1</td>\n",
       "      <td>118</td>\n",
       "      <td>16719044 1</td>\n",
       "      <td>16719044 during</td>\n",
       "      <td>UAS-dGtACR1 16719044 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>UAS-dGtACR1</td>\n",
       "      <td>16719044</td>\n",
       "      <td>2</td>\n",
       "      <td>during</td>\n",
       "      <td>500</td>\n",
       "      <td>78</td>\n",
       "      <td>UAS-dGtACR1 during</td>\n",
       "      <td>UAS-dGtACR1 during_2</td>\n",
       "      <td>156</td>\n",
       "      <td>16719044 2</td>\n",
       "      <td>16719044 during</td>\n",
       "      <td>UAS-dGtACR1 16719044 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>UAS-dGtACR1</td>\n",
       "      <td>16719044</td>\n",
       "      <td>3</td>\n",
       "      <td>during</td>\n",
       "      <td>500</td>\n",
       "      <td>66</td>\n",
       "      <td>UAS-dGtACR1 during</td>\n",
       "      <td>UAS-dGtACR1 during_3</td>\n",
       "      <td>132</td>\n",
       "      <td>16719044 3</td>\n",
       "      <td>16719044 during</td>\n",
       "      <td>UAS-dGtACR1 16719044 3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1</td>\n",
       "      <td>16719023</td>\n",
       "      <td>1</td>\n",
       "      <td>before</td>\n",
       "      <td>500</td>\n",
       "      <td>125</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 before</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 before_1</td>\n",
       "      <td>250</td>\n",
       "      <td>16719023 1</td>\n",
       "      <td>16719023 before</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 16719023 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1</td>\n",
       "      <td>16719023</td>\n",
       "      <td>2</td>\n",
       "      <td>before</td>\n",
       "      <td>500</td>\n",
       "      <td>53</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 before</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 before_2</td>\n",
       "      <td>106</td>\n",
       "      <td>16719023 2</td>\n",
       "      <td>16719023 before</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 16719023 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1</td>\n",
       "      <td>16719023</td>\n",
       "      <td>3</td>\n",
       "      <td>before</td>\n",
       "      <td>500</td>\n",
       "      <td>121</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 before</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 before_3</td>\n",
       "      <td>242</td>\n",
       "      <td>16719023 3</td>\n",
       "      <td>16719023 before</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 16719023 3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1</td>\n",
       "      <td>16719022</td>\n",
       "      <td>1</td>\n",
       "      <td>before</td>\n",
       "      <td>500</td>\n",
       "      <td>96</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 before</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 before_1</td>\n",
       "      <td>192</td>\n",
       "      <td>16719022 1</td>\n",
       "      <td>16719022 before</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 16719022 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1</td>\n",
       "      <td>16719022</td>\n",
       "      <td>2</td>\n",
       "      <td>before</td>\n",
       "      <td>500</td>\n",
       "      <td>98</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 before</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 before_2</td>\n",
       "      <td>196</td>\n",
       "      <td>16719022 2</td>\n",
       "      <td>16719022 before</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 16719022 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1</td>\n",
       "      <td>16719022</td>\n",
       "      <td>3</td>\n",
       "      <td>before</td>\n",
       "      <td>500</td>\n",
       "      <td>70</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 before</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 before_3</td>\n",
       "      <td>140</td>\n",
       "      <td>16719022 3</td>\n",
       "      <td>16719022 before</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 16719022 3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1</td>\n",
       "      <td>16717002</td>\n",
       "      <td>1</td>\n",
       "      <td>before</td>\n",
       "      <td>500</td>\n",
       "      <td>56</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 before</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 before_1</td>\n",
       "      <td>112</td>\n",
       "      <td>16717002 1</td>\n",
       "      <td>16717002 before</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 16717002 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1</td>\n",
       "      <td>16717002</td>\n",
       "      <td>2</td>\n",
       "      <td>before</td>\n",
       "      <td>500</td>\n",
       "      <td>36</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 before</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 before_2</td>\n",
       "      <td>72</td>\n",
       "      <td>16717002 2</td>\n",
       "      <td>16717002 before</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 16717002 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1</td>\n",
       "      <td>16717002</td>\n",
       "      <td>3</td>\n",
       "      <td>before</td>\n",
       "      <td>500</td>\n",
       "      <td>45</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 before</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 before_3</td>\n",
       "      <td>90</td>\n",
       "      <td>16717002 3</td>\n",
       "      <td>16717002 before</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 16717002 3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1</td>\n",
       "      <td>16717001</td>\n",
       "      <td>1</td>\n",
       "      <td>before</td>\n",
       "      <td>500</td>\n",
       "      <td>20</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 before</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 before_1</td>\n",
       "      <td>40</td>\n",
       "      <td>16717001 1</td>\n",
       "      <td>16717001 before</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 16717001 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1</td>\n",
       "      <td>16717001</td>\n",
       "      <td>2</td>\n",
       "      <td>before</td>\n",
       "      <td>500</td>\n",
       "      <td>48</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 before</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 before_2</td>\n",
       "      <td>96</td>\n",
       "      <td>16717001 2</td>\n",
       "      <td>16717001 before</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 16717001 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1</td>\n",
       "      <td>16717001</td>\n",
       "      <td>3</td>\n",
       "      <td>before</td>\n",
       "      <td>500</td>\n",
       "      <td>43</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 before</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 before_3</td>\n",
       "      <td>86</td>\n",
       "      <td>16717001 3</td>\n",
       "      <td>16717001 before</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 16717001 3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1</td>\n",
       "      <td>16719022</td>\n",
       "      <td>1</td>\n",
       "      <td>during</td>\n",
       "      <td>500</td>\n",
       "      <td>5</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 during</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 during_1</td>\n",
       "      <td>10</td>\n",
       "      <td>16719022 1</td>\n",
       "      <td>16719022 during</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 16719022 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1</td>\n",
       "      <td>16719022</td>\n",
       "      <td>2</td>\n",
       "      <td>during</td>\n",
       "      <td>500</td>\n",
       "      <td>6</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 during</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 during_2</td>\n",
       "      <td>12</td>\n",
       "      <td>16719022 2</td>\n",
       "      <td>16719022 during</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 16719022 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1</td>\n",
       "      <td>16719022</td>\n",
       "      <td>3</td>\n",
       "      <td>during</td>\n",
       "      <td>500</td>\n",
       "      <td>5</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 during</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 during_3</td>\n",
       "      <td>10</td>\n",
       "      <td>16719022 3</td>\n",
       "      <td>16719022 during</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 16719022 3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1</td>\n",
       "      <td>16717002</td>\n",
       "      <td>1</td>\n",
       "      <td>during</td>\n",
       "      <td>500</td>\n",
       "      <td>2</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 during</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 during_1</td>\n",
       "      <td>4</td>\n",
       "      <td>16717002 1</td>\n",
       "      <td>16717002 during</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 16717002 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1</td>\n",
       "      <td>16717002</td>\n",
       "      <td>2</td>\n",
       "      <td>during</td>\n",
       "      <td>500</td>\n",
       "      <td>0</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 during</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 during_2</td>\n",
       "      <td>0</td>\n",
       "      <td>16717002 2</td>\n",
       "      <td>16717002 during</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 16717002 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1</td>\n",
       "      <td>16717002</td>\n",
       "      <td>3</td>\n",
       "      <td>during</td>\n",
       "      <td>500</td>\n",
       "      <td>0</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 during</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 during_3</td>\n",
       "      <td>0</td>\n",
       "      <td>16717002 3</td>\n",
       "      <td>16717002 during</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 16717002 3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1</td>\n",
       "      <td>16717001</td>\n",
       "      <td>1</td>\n",
       "      <td>during</td>\n",
       "      <td>500</td>\n",
       "      <td>0</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 during</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 during_1</td>\n",
       "      <td>0</td>\n",
       "      <td>16717001 1</td>\n",
       "      <td>16717001 during</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 16717001 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1</td>\n",
       "      <td>16717001</td>\n",
       "      <td>2</td>\n",
       "      <td>during</td>\n",
       "      <td>500</td>\n",
       "      <td>0</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 during</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 during_2</td>\n",
       "      <td>0</td>\n",
       "      <td>16717001 2</td>\n",
       "      <td>16717001 during</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 16717001 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1</td>\n",
       "      <td>16717001</td>\n",
       "      <td>3</td>\n",
       "      <td>during</td>\n",
       "      <td>500</td>\n",
       "      <td>0</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 during</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 during_3</td>\n",
       "      <td>0</td>\n",
       "      <td>16717001 3</td>\n",
       "      <td>16717001 during</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 16717001 3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>UAS-dGtACR1</td>\n",
       "      <td>16719045</td>\n",
       "      <td>1</td>\n",
       "      <td>after</td>\n",
       "      <td>500</td>\n",
       "      <td>68</td>\n",
       "      <td>UAS-dGtACR1 after</td>\n",
       "      <td>UAS-dGtACR1 after_1</td>\n",
       "      <td>136</td>\n",
       "      <td>16719045 1</td>\n",
       "      <td>16719045 after</td>\n",
       "      <td>UAS-dGtACR1 16719045 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>UAS-dGtACR1</td>\n",
       "      <td>16719045</td>\n",
       "      <td>2</td>\n",
       "      <td>after</td>\n",
       "      <td>500</td>\n",
       "      <td>60</td>\n",
       "      <td>UAS-dGtACR1 after</td>\n",
       "      <td>UAS-dGtACR1 after_2</td>\n",
       "      <td>120</td>\n",
       "      <td>16719045 2</td>\n",
       "      <td>16719045 after</td>\n",
       "      <td>UAS-dGtACR1 16719045 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>UAS-dGtACR1</td>\n",
       "      <td>16719045</td>\n",
       "      <td>3</td>\n",
       "      <td>after</td>\n",
       "      <td>500</td>\n",
       "      <td>52</td>\n",
       "      <td>UAS-dGtACR1 after</td>\n",
       "      <td>UAS-dGtACR1 after_3</td>\n",
       "      <td>104</td>\n",
       "      <td>16719045 3</td>\n",
       "      <td>16719045 after</td>\n",
       "      <td>UAS-dGtACR1 16719045 3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>UAS-dGtACR1</td>\n",
       "      <td>16719043</td>\n",
       "      <td>1</td>\n",
       "      <td>after</td>\n",
       "      <td>500</td>\n",
       "      <td>61</td>\n",
       "      <td>UAS-dGtACR1 after</td>\n",
       "      <td>UAS-dGtACR1 after_1</td>\n",
       "      <td>122</td>\n",
       "      <td>16719043 1</td>\n",
       "      <td>16719043 after</td>\n",
       "      <td>UAS-dGtACR1 16719043 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>UAS-dGtACR1</td>\n",
       "      <td>16719043</td>\n",
       "      <td>2</td>\n",
       "      <td>after</td>\n",
       "      <td>500</td>\n",
       "      <td>65</td>\n",
       "      <td>UAS-dGtACR1 after</td>\n",
       "      <td>UAS-dGtACR1 after_2</td>\n",
       "      <td>130</td>\n",
       "      <td>16719043 2</td>\n",
       "      <td>16719043 after</td>\n",
       "      <td>UAS-dGtACR1 16719043 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>UAS-dGtACR1</td>\n",
       "      <td>16719043</td>\n",
       "      <td>3</td>\n",
       "      <td>after</td>\n",
       "      <td>500</td>\n",
       "      <td>50</td>\n",
       "      <td>UAS-dGtACR1 after</td>\n",
       "      <td>UAS-dGtACR1 after_3</td>\n",
       "      <td>100</td>\n",
       "      <td>16719043 3</td>\n",
       "      <td>16719043 after</td>\n",
       "      <td>UAS-dGtACR1 16719043 3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>UAS-dGtACR1</td>\n",
       "      <td>16719044</td>\n",
       "      <td>1</td>\n",
       "      <td>after</td>\n",
       "      <td>500</td>\n",
       "      <td>52</td>\n",
       "      <td>UAS-dGtACR1 after</td>\n",
       "      <td>UAS-dGtACR1 after_1</td>\n",
       "      <td>104</td>\n",
       "      <td>16719044 1</td>\n",
       "      <td>16719044 after</td>\n",
       "      <td>UAS-dGtACR1 16719044 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>UAS-dGtACR1</td>\n",
       "      <td>16719044</td>\n",
       "      <td>2</td>\n",
       "      <td>after</td>\n",
       "      <td>500</td>\n",
       "      <td>51</td>\n",
       "      <td>UAS-dGtACR1 after</td>\n",
       "      <td>UAS-dGtACR1 after_2</td>\n",
       "      <td>102</td>\n",
       "      <td>16719044 2</td>\n",
       "      <td>16719044 after</td>\n",
       "      <td>UAS-dGtACR1 16719044 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>UAS-dGtACR1</td>\n",
       "      <td>16719044</td>\n",
       "      <td>3</td>\n",
       "      <td>after</td>\n",
       "      <td>500</td>\n",
       "      <td>51</td>\n",
       "      <td>UAS-dGtACR1 after</td>\n",
       "      <td>UAS-dGtACR1 after_3</td>\n",
       "      <td>102</td>\n",
       "      <td>16719044 3</td>\n",
       "      <td>16719044 after</td>\n",
       "      <td>UAS-dGtACR1 16719044 3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1</td>\n",
       "      <td>16719023</td>\n",
       "      <td>1</td>\n",
       "      <td>after</td>\n",
       "      <td>500</td>\n",
       "      <td>17</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 after</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 after_1</td>\n",
       "      <td>34</td>\n",
       "      <td>16719023 1</td>\n",
       "      <td>16719023 after</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 16719023 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1</td>\n",
       "      <td>16719023</td>\n",
       "      <td>2</td>\n",
       "      <td>after</td>\n",
       "      <td>500</td>\n",
       "      <td>26</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 after</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 after_2</td>\n",
       "      <td>52</td>\n",
       "      <td>16719023 2</td>\n",
       "      <td>16719023 after</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 16719023 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1</td>\n",
       "      <td>16719023</td>\n",
       "      <td>3</td>\n",
       "      <td>after</td>\n",
       "      <td>500</td>\n",
       "      <td>25</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 after</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 after_3</td>\n",
       "      <td>50</td>\n",
       "      <td>16719023 3</td>\n",
       "      <td>16719023 after</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 16719023 3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1</td>\n",
       "      <td>16719022</td>\n",
       "      <td>1</td>\n",
       "      <td>after</td>\n",
       "      <td>500</td>\n",
       "      <td>27</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 after</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 after_1</td>\n",
       "      <td>54</td>\n",
       "      <td>16719022 1</td>\n",
       "      <td>16719022 after</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 16719022 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1</td>\n",
       "      <td>16719022</td>\n",
       "      <td>2</td>\n",
       "      <td>after</td>\n",
       "      <td>500</td>\n",
       "      <td>26</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 after</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 after_2</td>\n",
       "      <td>52</td>\n",
       "      <td>16719022 2</td>\n",
       "      <td>16719022 after</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 16719022 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1</td>\n",
       "      <td>16719022</td>\n",
       "      <td>3</td>\n",
       "      <td>after</td>\n",
       "      <td>500</td>\n",
       "      <td>32</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 after</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 after_3</td>\n",
       "      <td>64</td>\n",
       "      <td>16719022 3</td>\n",
       "      <td>16719022 after</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 16719022 3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1</td>\n",
       "      <td>16717002</td>\n",
       "      <td>1</td>\n",
       "      <td>after</td>\n",
       "      <td>500</td>\n",
       "      <td>28</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 after</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 after_1</td>\n",
       "      <td>56</td>\n",
       "      <td>16717002 1</td>\n",
       "      <td>16717002 after</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 16717002 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1</td>\n",
       "      <td>16717002</td>\n",
       "      <td>2</td>\n",
       "      <td>after</td>\n",
       "      <td>500</td>\n",
       "      <td>35</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 after</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 after_2</td>\n",
       "      <td>70</td>\n",
       "      <td>16717002 2</td>\n",
       "      <td>16717002 after</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 16717002 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1</td>\n",
       "      <td>16717002</td>\n",
       "      <td>3</td>\n",
       "      <td>after</td>\n",
       "      <td>500</td>\n",
       "      <td>30</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 after</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 after_3</td>\n",
       "      <td>60</td>\n",
       "      <td>16717002 3</td>\n",
       "      <td>16717002 after</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 16717002 3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1</td>\n",
       "      <td>16717001</td>\n",
       "      <td>1</td>\n",
       "      <td>after</td>\n",
       "      <td>500</td>\n",
       "      <td>13</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 after</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 after_1</td>\n",
       "      <td>26</td>\n",
       "      <td>16717001 1</td>\n",
       "      <td>16717001 after</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 16717001 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1</td>\n",
       "      <td>16717001</td>\n",
       "      <td>2</td>\n",
       "      <td>after</td>\n",
       "      <td>500</td>\n",
       "      <td>15</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 after</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 after_2</td>\n",
       "      <td>30</td>\n",
       "      <td>16717001 2</td>\n",
       "      <td>16717001 after</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 16717001 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1</td>\n",
       "      <td>16717001</td>\n",
       "      <td>3</td>\n",
       "      <td>after</td>\n",
       "      <td>500</td>\n",
       "      <td>16</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 after</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 after_3</td>\n",
       "      <td>32</td>\n",
       "      <td>16717001 3</td>\n",
       "      <td>16717001 after</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 16717001 3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>63 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Genotype   NerveID PulseNumber PulseEpoch  EpochLength  \\\n",
       "0             UAS-dGtACR1  16719045           1     before          500   \n",
       "1             UAS-dGtACR1  16719045           2     before          500   \n",
       "2             UAS-dGtACR1  16719045           3     before          500   \n",
       "3             UAS-dGtACR1  16719043           1     before          500   \n",
       "4             UAS-dGtACR1  16719043           2     before          500   \n",
       "5             UAS-dGtACR1  16719043           3     before          500   \n",
       "6             UAS-dGtACR1  16719044           1     before          500   \n",
       "7             UAS-dGtACR1  16719044           2     before          500   \n",
       "8             UAS-dGtACR1  16719044           3     before          500   \n",
       "9             UAS-dGtACR1  16719045           1     during          500   \n",
       "10            UAS-dGtACR1  16719045           2     during          500   \n",
       "11            UAS-dGtACR1  16719045           3     during          500   \n",
       "12            UAS-dGtACR1  16719043           1     during          500   \n",
       "13            UAS-dGtACR1  16719043           2     during          500   \n",
       "14            UAS-dGtACR1  16719043           3     during          500   \n",
       "15            UAS-dGtACR1  16719044           1     during          500   \n",
       "16            UAS-dGtACR1  16719044           2     during          500   \n",
       "17            UAS-dGtACR1  16719044           3     during          500   \n",
       "18  nSyb-GAL4>UAS-dGtACR1  16719023           1     before          500   \n",
       "19  nSyb-GAL4>UAS-dGtACR1  16719023           2     before          500   \n",
       "20  nSyb-GAL4>UAS-dGtACR1  16719023           3     before          500   \n",
       "21  nSyb-GAL4>UAS-dGtACR1  16719022           1     before          500   \n",
       "22  nSyb-GAL4>UAS-dGtACR1  16719022           2     before          500   \n",
       "23  nSyb-GAL4>UAS-dGtACR1  16719022           3     before          500   \n",
       "24  nSyb-GAL4>UAS-dGtACR1  16717002           1     before          500   \n",
       "25  nSyb-GAL4>UAS-dGtACR1  16717002           2     before          500   \n",
       "26  nSyb-GAL4>UAS-dGtACR1  16717002           3     before          500   \n",
       "27  nSyb-GAL4>UAS-dGtACR1  16717001           1     before          500   \n",
       "28  nSyb-GAL4>UAS-dGtACR1  16717001           2     before          500   \n",
       "29  nSyb-GAL4>UAS-dGtACR1  16717001           3     before          500   \n",
       "..                    ...       ...         ...        ...          ...   \n",
       "33  nSyb-GAL4>UAS-dGtACR1  16719022           1     during          500   \n",
       "34  nSyb-GAL4>UAS-dGtACR1  16719022           2     during          500   \n",
       "35  nSyb-GAL4>UAS-dGtACR1  16719022           3     during          500   \n",
       "36  nSyb-GAL4>UAS-dGtACR1  16717002           1     during          500   \n",
       "37  nSyb-GAL4>UAS-dGtACR1  16717002           2     during          500   \n",
       "38  nSyb-GAL4>UAS-dGtACR1  16717002           3     during          500   \n",
       "39  nSyb-GAL4>UAS-dGtACR1  16717001           1     during          500   \n",
       "40  nSyb-GAL4>UAS-dGtACR1  16717001           2     during          500   \n",
       "41  nSyb-GAL4>UAS-dGtACR1  16717001           3     during          500   \n",
       "42            UAS-dGtACR1  16719045           1      after          500   \n",
       "43            UAS-dGtACR1  16719045           2      after          500   \n",
       "44            UAS-dGtACR1  16719045           3      after          500   \n",
       "45            UAS-dGtACR1  16719043           1      after          500   \n",
       "46            UAS-dGtACR1  16719043           2      after          500   \n",
       "47            UAS-dGtACR1  16719043           3      after          500   \n",
       "48            UAS-dGtACR1  16719044           1      after          500   \n",
       "49            UAS-dGtACR1  16719044           2      after          500   \n",
       "50            UAS-dGtACR1  16719044           3      after          500   \n",
       "51  nSyb-GAL4>UAS-dGtACR1  16719023           1      after          500   \n",
       "52  nSyb-GAL4>UAS-dGtACR1  16719023           2      after          500   \n",
       "53  nSyb-GAL4>UAS-dGtACR1  16719023           3      after          500   \n",
       "54  nSyb-GAL4>UAS-dGtACR1  16719022           1      after          500   \n",
       "55  nSyb-GAL4>UAS-dGtACR1  16719022           2      after          500   \n",
       "56  nSyb-GAL4>UAS-dGtACR1  16719022           3      after          500   \n",
       "57  nSyb-GAL4>UAS-dGtACR1  16717002           1      after          500   \n",
       "58  nSyb-GAL4>UAS-dGtACR1  16717002           2      after          500   \n",
       "59  nSyb-GAL4>UAS-dGtACR1  16717002           3      after          500   \n",
       "60  nSyb-GAL4>UAS-dGtACR1  16717001           1      after          500   \n",
       "61  nSyb-GAL4>UAS-dGtACR1  16717001           2      after          500   \n",
       "62  nSyb-GAL4>UAS-dGtACR1  16717001           3      after          500   \n",
       "\n",
       "    SpikeNumber            GenotypePulseEpoch      GenotypePulseEpochPulseNum  \\\n",
       "0            68            UAS-dGtACR1 before            UAS-dGtACR1 before_1   \n",
       "1            43            UAS-dGtACR1 before            UAS-dGtACR1 before_2   \n",
       "2            48            UAS-dGtACR1 before            UAS-dGtACR1 before_3   \n",
       "3            34            UAS-dGtACR1 before            UAS-dGtACR1 before_1   \n",
       "4            54            UAS-dGtACR1 before            UAS-dGtACR1 before_2   \n",
       "5            45            UAS-dGtACR1 before            UAS-dGtACR1 before_3   \n",
       "6            42            UAS-dGtACR1 before            UAS-dGtACR1 before_1   \n",
       "7            64            UAS-dGtACR1 before            UAS-dGtACR1 before_2   \n",
       "8            55            UAS-dGtACR1 before            UAS-dGtACR1 before_3   \n",
       "9            55            UAS-dGtACR1 during            UAS-dGtACR1 during_1   \n",
       "10           72            UAS-dGtACR1 during            UAS-dGtACR1 during_2   \n",
       "11           49            UAS-dGtACR1 during            UAS-dGtACR1 during_3   \n",
       "12           46            UAS-dGtACR1 during            UAS-dGtACR1 during_1   \n",
       "13           51            UAS-dGtACR1 during            UAS-dGtACR1 during_2   \n",
       "14           38            UAS-dGtACR1 during            UAS-dGtACR1 during_3   \n",
       "15           59            UAS-dGtACR1 during            UAS-dGtACR1 during_1   \n",
       "16           78            UAS-dGtACR1 during            UAS-dGtACR1 during_2   \n",
       "17           66            UAS-dGtACR1 during            UAS-dGtACR1 during_3   \n",
       "18          125  nSyb-GAL4>UAS-dGtACR1 before  nSyb-GAL4>UAS-dGtACR1 before_1   \n",
       "19           53  nSyb-GAL4>UAS-dGtACR1 before  nSyb-GAL4>UAS-dGtACR1 before_2   \n",
       "20          121  nSyb-GAL4>UAS-dGtACR1 before  nSyb-GAL4>UAS-dGtACR1 before_3   \n",
       "21           96  nSyb-GAL4>UAS-dGtACR1 before  nSyb-GAL4>UAS-dGtACR1 before_1   \n",
       "22           98  nSyb-GAL4>UAS-dGtACR1 before  nSyb-GAL4>UAS-dGtACR1 before_2   \n",
       "23           70  nSyb-GAL4>UAS-dGtACR1 before  nSyb-GAL4>UAS-dGtACR1 before_3   \n",
       "24           56  nSyb-GAL4>UAS-dGtACR1 before  nSyb-GAL4>UAS-dGtACR1 before_1   \n",
       "25           36  nSyb-GAL4>UAS-dGtACR1 before  nSyb-GAL4>UAS-dGtACR1 before_2   \n",
       "26           45  nSyb-GAL4>UAS-dGtACR1 before  nSyb-GAL4>UAS-dGtACR1 before_3   \n",
       "27           20  nSyb-GAL4>UAS-dGtACR1 before  nSyb-GAL4>UAS-dGtACR1 before_1   \n",
       "28           48  nSyb-GAL4>UAS-dGtACR1 before  nSyb-GAL4>UAS-dGtACR1 before_2   \n",
       "29           43  nSyb-GAL4>UAS-dGtACR1 before  nSyb-GAL4>UAS-dGtACR1 before_3   \n",
       "..          ...                           ...                             ...   \n",
       "33            5  nSyb-GAL4>UAS-dGtACR1 during  nSyb-GAL4>UAS-dGtACR1 during_1   \n",
       "34            6  nSyb-GAL4>UAS-dGtACR1 during  nSyb-GAL4>UAS-dGtACR1 during_2   \n",
       "35            5  nSyb-GAL4>UAS-dGtACR1 during  nSyb-GAL4>UAS-dGtACR1 during_3   \n",
       "36            2  nSyb-GAL4>UAS-dGtACR1 during  nSyb-GAL4>UAS-dGtACR1 during_1   \n",
       "37            0  nSyb-GAL4>UAS-dGtACR1 during  nSyb-GAL4>UAS-dGtACR1 during_2   \n",
       "38            0  nSyb-GAL4>UAS-dGtACR1 during  nSyb-GAL4>UAS-dGtACR1 during_3   \n",
       "39            0  nSyb-GAL4>UAS-dGtACR1 during  nSyb-GAL4>UAS-dGtACR1 during_1   \n",
       "40            0  nSyb-GAL4>UAS-dGtACR1 during  nSyb-GAL4>UAS-dGtACR1 during_2   \n",
       "41            0  nSyb-GAL4>UAS-dGtACR1 during  nSyb-GAL4>UAS-dGtACR1 during_3   \n",
       "42           68             UAS-dGtACR1 after             UAS-dGtACR1 after_1   \n",
       "43           60             UAS-dGtACR1 after             UAS-dGtACR1 after_2   \n",
       "44           52             UAS-dGtACR1 after             UAS-dGtACR1 after_3   \n",
       "45           61             UAS-dGtACR1 after             UAS-dGtACR1 after_1   \n",
       "46           65             UAS-dGtACR1 after             UAS-dGtACR1 after_2   \n",
       "47           50             UAS-dGtACR1 after             UAS-dGtACR1 after_3   \n",
       "48           52             UAS-dGtACR1 after             UAS-dGtACR1 after_1   \n",
       "49           51             UAS-dGtACR1 after             UAS-dGtACR1 after_2   \n",
       "50           51             UAS-dGtACR1 after             UAS-dGtACR1 after_3   \n",
       "51           17   nSyb-GAL4>UAS-dGtACR1 after   nSyb-GAL4>UAS-dGtACR1 after_1   \n",
       "52           26   nSyb-GAL4>UAS-dGtACR1 after   nSyb-GAL4>UAS-dGtACR1 after_2   \n",
       "53           25   nSyb-GAL4>UAS-dGtACR1 after   nSyb-GAL4>UAS-dGtACR1 after_3   \n",
       "54           27   nSyb-GAL4>UAS-dGtACR1 after   nSyb-GAL4>UAS-dGtACR1 after_1   \n",
       "55           26   nSyb-GAL4>UAS-dGtACR1 after   nSyb-GAL4>UAS-dGtACR1 after_2   \n",
       "56           32   nSyb-GAL4>UAS-dGtACR1 after   nSyb-GAL4>UAS-dGtACR1 after_3   \n",
       "57           28   nSyb-GAL4>UAS-dGtACR1 after   nSyb-GAL4>UAS-dGtACR1 after_1   \n",
       "58           35   nSyb-GAL4>UAS-dGtACR1 after   nSyb-GAL4>UAS-dGtACR1 after_2   \n",
       "59           30   nSyb-GAL4>UAS-dGtACR1 after   nSyb-GAL4>UAS-dGtACR1 after_3   \n",
       "60           13   nSyb-GAL4>UAS-dGtACR1 after   nSyb-GAL4>UAS-dGtACR1 after_1   \n",
       "61           15   nSyb-GAL4>UAS-dGtACR1 after   nSyb-GAL4>UAS-dGtACR1 after_2   \n",
       "62           16   nSyb-GAL4>UAS-dGtACR1 after   nSyb-GAL4>UAS-dGtACR1 after_3   \n",
       "\n",
       "    SpikeFrequencyHz NervePulseID       NerveEpoch  \\\n",
       "0                136   16719045 1  16719045 before   \n",
       "1                 86   16719045 2  16719045 before   \n",
       "2                 96   16719045 3  16719045 before   \n",
       "3                 68   16719043 1  16719043 before   \n",
       "4                108   16719043 2  16719043 before   \n",
       "5                 90   16719043 3  16719043 before   \n",
       "6                 84   16719044 1  16719044 before   \n",
       "7                128   16719044 2  16719044 before   \n",
       "8                110   16719044 3  16719044 before   \n",
       "9                110   16719045 1  16719045 during   \n",
       "10               144   16719045 2  16719045 during   \n",
       "11                98   16719045 3  16719045 during   \n",
       "12                92   16719043 1  16719043 during   \n",
       "13               102   16719043 2  16719043 during   \n",
       "14                76   16719043 3  16719043 during   \n",
       "15               118   16719044 1  16719044 during   \n",
       "16               156   16719044 2  16719044 during   \n",
       "17               132   16719044 3  16719044 during   \n",
       "18               250   16719023 1  16719023 before   \n",
       "19               106   16719023 2  16719023 before   \n",
       "20               242   16719023 3  16719023 before   \n",
       "21               192   16719022 1  16719022 before   \n",
       "22               196   16719022 2  16719022 before   \n",
       "23               140   16719022 3  16719022 before   \n",
       "24               112   16717002 1  16717002 before   \n",
       "25                72   16717002 2  16717002 before   \n",
       "26                90   16717002 3  16717002 before   \n",
       "27                40   16717001 1  16717001 before   \n",
       "28                96   16717001 2  16717001 before   \n",
       "29                86   16717001 3  16717001 before   \n",
       "..               ...          ...              ...   \n",
       "33                10   16719022 1  16719022 during   \n",
       "34                12   16719022 2  16719022 during   \n",
       "35                10   16719022 3  16719022 during   \n",
       "36                 4   16717002 1  16717002 during   \n",
       "37                 0   16717002 2  16717002 during   \n",
       "38                 0   16717002 3  16717002 during   \n",
       "39                 0   16717001 1  16717001 during   \n",
       "40                 0   16717001 2  16717001 during   \n",
       "41                 0   16717001 3  16717001 during   \n",
       "42               136   16719045 1   16719045 after   \n",
       "43               120   16719045 2   16719045 after   \n",
       "44               104   16719045 3   16719045 after   \n",
       "45               122   16719043 1   16719043 after   \n",
       "46               130   16719043 2   16719043 after   \n",
       "47               100   16719043 3   16719043 after   \n",
       "48               104   16719044 1   16719044 after   \n",
       "49               102   16719044 2   16719044 after   \n",
       "50               102   16719044 3   16719044 after   \n",
       "51                34   16719023 1   16719023 after   \n",
       "52                52   16719023 2   16719023 after   \n",
       "53                50   16719023 3   16719023 after   \n",
       "54                54   16719022 1   16719022 after   \n",
       "55                52   16719022 2   16719022 after   \n",
       "56                64   16719022 3   16719022 after   \n",
       "57                56   16717002 1   16717002 after   \n",
       "58                70   16717002 2   16717002 after   \n",
       "59                60   16717002 3   16717002 after   \n",
       "60                26   16717001 1   16717001 after   \n",
       "61                30   16717001 2   16717001 after   \n",
       "62                32   16717001 3   16717001 after   \n",
       "\n",
       "                  GenotypeNervePulse  \n",
       "0             UAS-dGtACR1 16719045 1  \n",
       "1             UAS-dGtACR1 16719045 2  \n",
       "2             UAS-dGtACR1 16719045 3  \n",
       "3             UAS-dGtACR1 16719043 1  \n",
       "4             UAS-dGtACR1 16719043 2  \n",
       "5             UAS-dGtACR1 16719043 3  \n",
       "6             UAS-dGtACR1 16719044 1  \n",
       "7             UAS-dGtACR1 16719044 2  \n",
       "8             UAS-dGtACR1 16719044 3  \n",
       "9             UAS-dGtACR1 16719045 1  \n",
       "10            UAS-dGtACR1 16719045 2  \n",
       "11            UAS-dGtACR1 16719045 3  \n",
       "12            UAS-dGtACR1 16719043 1  \n",
       "13            UAS-dGtACR1 16719043 2  \n",
       "14            UAS-dGtACR1 16719043 3  \n",
       "15            UAS-dGtACR1 16719044 1  \n",
       "16            UAS-dGtACR1 16719044 2  \n",
       "17            UAS-dGtACR1 16719044 3  \n",
       "18  nSyb-GAL4>UAS-dGtACR1 16719023 1  \n",
       "19  nSyb-GAL4>UAS-dGtACR1 16719023 2  \n",
       "20  nSyb-GAL4>UAS-dGtACR1 16719023 3  \n",
       "21  nSyb-GAL4>UAS-dGtACR1 16719022 1  \n",
       "22  nSyb-GAL4>UAS-dGtACR1 16719022 2  \n",
       "23  nSyb-GAL4>UAS-dGtACR1 16719022 3  \n",
       "24  nSyb-GAL4>UAS-dGtACR1 16717002 1  \n",
       "25  nSyb-GAL4>UAS-dGtACR1 16717002 2  \n",
       "26  nSyb-GAL4>UAS-dGtACR1 16717002 3  \n",
       "27  nSyb-GAL4>UAS-dGtACR1 16717001 1  \n",
       "28  nSyb-GAL4>UAS-dGtACR1 16717001 2  \n",
       "29  nSyb-GAL4>UAS-dGtACR1 16717001 3  \n",
       "..                               ...  \n",
       "33  nSyb-GAL4>UAS-dGtACR1 16719022 1  \n",
       "34  nSyb-GAL4>UAS-dGtACR1 16719022 2  \n",
       "35  nSyb-GAL4>UAS-dGtACR1 16719022 3  \n",
       "36  nSyb-GAL4>UAS-dGtACR1 16717002 1  \n",
       "37  nSyb-GAL4>UAS-dGtACR1 16717002 2  \n",
       "38  nSyb-GAL4>UAS-dGtACR1 16717002 3  \n",
       "39  nSyb-GAL4>UAS-dGtACR1 16717001 1  \n",
       "40  nSyb-GAL4>UAS-dGtACR1 16717001 2  \n",
       "41  nSyb-GAL4>UAS-dGtACR1 16717001 3  \n",
       "42            UAS-dGtACR1 16719045 1  \n",
       "43            UAS-dGtACR1 16719045 2  \n",
       "44            UAS-dGtACR1 16719045 3  \n",
       "45            UAS-dGtACR1 16719043 1  \n",
       "46            UAS-dGtACR1 16719043 2  \n",
       "47            UAS-dGtACR1 16719043 3  \n",
       "48            UAS-dGtACR1 16719044 1  \n",
       "49            UAS-dGtACR1 16719044 2  \n",
       "50            UAS-dGtACR1 16719044 3  \n",
       "51  nSyb-GAL4>UAS-dGtACR1 16719023 1  \n",
       "52  nSyb-GAL4>UAS-dGtACR1 16719023 2  \n",
       "53  nSyb-GAL4>UAS-dGtACR1 16719023 3  \n",
       "54  nSyb-GAL4>UAS-dGtACR1 16719022 1  \n",
       "55  nSyb-GAL4>UAS-dGtACR1 16719022 2  \n",
       "56  nSyb-GAL4>UAS-dGtACR1 16719022 3  \n",
       "57  nSyb-GAL4>UAS-dGtACR1 16717002 1  \n",
       "58  nSyb-GAL4>UAS-dGtACR1 16717002 2  \n",
       "59  nSyb-GAL4>UAS-dGtACR1 16717002 3  \n",
       "60  nSyb-GAL4>UAS-dGtACR1 16717001 1  \n",
       "61  nSyb-GAL4>UAS-dGtACR1 16717001 2  \n",
       "62  nSyb-GAL4>UAS-dGtACR1 16717001 3  \n",
       "\n",
       "[63 rows x 12 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel('spreadsheets/Larval nerve nSyb-dACR1 spike analysis MELTED.xlsx')\n",
    "# Make sure NerveID and PulseNumber columns are strings and not numericals.\n",
    "df['NerveID'] = df['NerveID'].astype('str') \n",
    "df['PulseNumber'] = df['PulseNumber'].astype('str')\n",
    "# Create new categorical columns for proper plotting and grouping.\n",
    "df['GenotypePulseEpoch'] = df['Genotype'] + ' ' + df['PulseEpoch']\n",
    "df['GenotypePulseEpochPulseNum'] = df['GenotypePulseEpoch'] + '_' + df['PulseNumber']\n",
    "# Create SpikeFrequency column.\n",
    "df['SpikeFrequencyHz'] = df['SpikeNumber'] / (df['EpochLength']/1000)\n",
    "# Create ID columns\n",
    "df['NervePulseID'] = df['NerveID'] + ' ' + df['PulseNumber']\n",
    "df['NerveEpoch']  = df['NerveID'] + ' ' + df['PulseEpoch']\n",
    "df['GenotypeNervePulse']  = df['Genotype'] + ' ' + df['NerveID'] + ' ' + df['PulseNumber']\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-22T17:53:14.598674",
     "start_time": "2016-09-22T17:53:14.593057"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Create palette for nice plotting.\n",
    "myPal = dict()\n",
    "palKeys = list()\n",
    "for i in df['PulseEpoch'].unique().tolist():\n",
    "    palKeys.append(i)\n",
    "for i in df['NerveID'].unique().tolist():\n",
    "    palKeys.append(i)\n",
    "palItems = sb.color_palette(\"muted\", len(palKeys))\n",
    "\n",
    "# code adapted from\n",
    "# http://stackoverflow.com/questions/26424452/python-dictionary-key-assign\n",
    "for i in range(len(palKeys)):\n",
    "    myPal.setdefault(palKeys[i], palItems[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Ungrouped Paired Contrast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-22T17:55:16.612561",
     "start_time": "2016-09-22T17:55:15.646343"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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p1q2b2nDoVZcvXwagbt262Wo5nJG6devi4eGhBtzWrVvj4eHB999/n25ZbS6gc+fOFC1a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\n97Bq1SpKS0ud29RqNQMGDHDZ9Kct1fjx47HZbDz66KPYbDYGDhzIE08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RCW4hhBDCi0hw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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fcb3cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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sy9y7dw9XV1d8fX2Jioqifv36eZoJp6Boer6o1KpViwsXLiiDnO7cuaMsmzJl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PDjT/FMSqRsyzZ8H38UjX3v9Y2njxZSmFPNo89ORjHImodCjDSZ8RPDRdb4jWIXL15k165d7N27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\niIiIg/g/NIs9aefWUpcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1101eb908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for geno in df['Genotype'].unique():\n",
    "    fig, contrast = bs.pairedcontrast(df[df['Genotype'] == geno], \n",
    "                                      x = 'PulseEpoch', \n",
    "                                      y = 'SpikeFrequencyHz', \n",
    "                                      idcol = 'NervePulseID',\n",
    "                                      hue = 'NerveID',\n",
    "                                      pal = myPal, # Does not seem to be working....\n",
    "                                      #summaryBar = True,\n",
    "                                      swarmYlim = (-40, 300),\n",
    "                                      idx = ('before', 'during') )\n",
    "    # Aesthetic tweaks \n",
    "    # Make sure legend does not overlap with plot.\n",
    "    fig.get_axes()[0].legend(loc = 0, title = \"NerveID\")\n",
    "    # Figure title.\n",
    "    title = geno + ' befVsDuring All Recordings'\n",
    "    fig.suptitle(title, fontsize = 25)\n",
    "    # Save figure\n",
    "    filename = os.getcwd() + \"/images/\" + title + \".svg\" \n",
    "    fig.savefig(filename, dpi = 5000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-22T17:55:25.730832",
     "start_time": "2016-09-22T17:55:24.831203"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5evfuzZIlS/D29qZNmzZaY/eMZCrg67rQ+Pj4pPvh6UbK043glpLurbRkyZJ6\nI66lRdegKiYmRu8kdG8kbm5u6eYD4MCBA8CLAT4yW+xSsWJFLaCl1ZhGd0OkxcjIiLlz5wIvhrtd\nunRphg35goODuXTpUpppuvrll9809+7dS2JiIoUKFUrVOv9luoYoumuWMq8zZ85EVVU8PT1TNdxK\nSVVV5s6dy7NnzzA1Nc1WkXz58uWZMWMGqqoSFhbGd999l+V9ZFWXLl20bjNpva0bCii6BjhRUVFp\nFu+lNxLkm2JnZ0epUqVISEgweJNnJmDa2tryzjvv8PTpU/744w9tECdDDVOTkpLo06cPo0aNSnNo\n2FKlSmk/aro2BykfUDMasa9+/frUr18fVVVxd3fPVENYMHyv6Abz8vT0NFhUO3v2bAYNGsS0adMy\ndayM/PXXX9pvU0Y9QTp16qT16sntkfcWLFigVVcuWLBA76G8Xbt2GBkZcevWLYOllz///DPDhw+n\nb9++WilomzZtUBSFoKAgrTFeSjExMVoXSXhRnN2sWTNUVWXz5s1pHufOnTv8/vvvKIqSpWqfzp07\na6Upab3J79y5M9Pft+zq0KEDxsbG3L17N1X3RXhReqxrm5XSjBkzGDRoUJpzIRgbG2tjimSmi2em\nAn737t1p3LgxiYmJjB49Gm9v71Q3l7+/Px9//DFPnjzBxsZGr+g3ISGBAwcOoCgKvXr1yrCfetu2\nbbU3tJRDbn788cfY2Njw/PlzPvroI1xcXPRaL8OLehJHR0c2btyImZlZlm/kadOmoaoqW7ZsYfPm\nzdp5xsbGMmfOHL2iype1a9eOWbNmoSgKmzdvZujQoVrLzJRu3LjB4sWL6dGjh9Yn/uXGJLrin5en\nGNZVi3To0EEbnc+QWrVqYW9vr33RUxZhd+vWTWs4OWPGDBYtWpRq5LWbN29qjbQURWH+/PkZznJn\niG5UKV1L7IxGSnxVY8aMoXjx4ly5ciVVqcLWrVsNTqrUqFEjjI2NUVWVJUuWaD8OiYmJbN68WRvo\nJjeZmJgwfvx4VFVl2bJlem9kMTExzJ07N9261pT69eunfSbnzp2jVKlSdOzYMc11CxUqRI8ePVBV\nlcWLF6e6F44cOaL9eOtGKty9ezeJiYkYGxtn+IAK/3vYDw8PT9UK29B1N3SvdOvWjdq1axMVFcWY\nMWP03vSfPXvG/PnzOX36tNa9KSfoXgjKlCmjVfMZouv1oxu/ITPD2b6u716ZMmWYPn06qqry4MED\nvYfyypXFLf7QAAAgAElEQVQra9+TyZMn6z3sJScn4+bmxvr161EUheHDh2tVKVWrVtVG/pw+fbre\nMOdRUVF88cUXREREULlyZa1r6cSJEylUqBA+Pj7Mnz9f7769evUq48aNIyEhgXr16qVq+J3etRk0\naBCVKlXi3r17TJw4Ue8B8NChQ6xatSrbMxFmVtmyZRk8eDCqqjJjxgy9l73IyEgmT56c5pTyuh4Q\nrq6u2ouszo0bN3B1ddWGqc9IphrtKYrCmjVr+Pzzz/nzzz8ZP348xYsX16ayvXfvHo8fP9ZaV/73\nv//Ve6s+evSolp6Z/s+KojB48GBWrlyp1bs3adKE4sWLs3nzZqZMmcKlS5dYtGgRy5Yto1KlShQr\nVoyoqChtTPrSpUvz7bffZnke41atWvHFF1/g6OjI0qVL2bhxIxUqVCAwMJDnz5+nO6gJwKhRo7Cx\nsWHhwoVcunSJTz/9FDMzMypUqICpqSlhYWE8fPhQ+7LY29szbdq0VOPG60oa/Pz86Nq1KzVr1mTs\n2LHcunUr09cRXvyAXrhwgUePHnH48GFtxC+A7777jjJlyrBp0yZcXV1xdXXFysqKsmXLEhERwb17\n91AUBQsLC77++us0e1ZkxTfffEPv3r2Jj49n7ty57N27N8PSl+zODlamTBlWrVrFZ599xr59+zhy\n5Ag1a9YkNDSUiIgIrbj15af6YsWK8cknn/DDDz9w4MABTp48SaVKlQgJCSEqKoohQ4Zw7NixXC/a\nHz58OJcuXcLT05P//Oc/WFtbU7p0aQICAoiJiaFRo0ZcunQpw4frXr16sXz5cm3Aj969e6e7zeef\nf46fnx/Xrl1jwIABWFtbU6pUKR48eKDNbjh06FCt3YauOL9Dhw6ZGpOge/fufPvttzx58gQ3Nze9\nAXoMfRfSulecnJwwNjZm3bp1jBs3juvXr9OjRw+qVauGmZkZt2/f1qaE/vLLL7X8voq4uDit6qtP\nnz6Zmgp54MCBbNy4URtWXFefbkh274fMGDhwIPv27ePChQvs2LGDHj16YG9vD7xoB/HgwQNOnDjB\n//3f/1G+fHnKli1LSEgIjx490kZP1A3eo7NgwQIePHjAhQsXGDhwIDY2NpiZmfHvv/8SGxtL6dKl\ncXJy0lqZ29vba5N5bd++nT179lCjRg2ePXvGv//+q1Ur6j7fzF4bMzMznJycGDduHL6+vrRv355a\ntWrx6NEj7t27R+PGjQkJCcnU4DWZOZ4h06dP58aNG1y4cIFBgwZRtWpVzM3NuXXrFsnJyTRo0IDL\nly/r9XDr2rUrvr6+eHh4MH36dJYsWUKFChV4+vSpVlXbpEmTTA2Tnul++OXKlWPLli2sXr2aLl26\nULJkSYKCgrh9+zbFixenffv2rFq1ih07dqR6C9TVadWvX99gN6mX9e/fX/sSpCyOtrKyws3NjXXr\n1tGvXz9sbGx4/PgxV69e5dmzZzRt2pQvvvgCLy+vbN/EY8aMwdnZWXvT+eeff6hRowYrV65k5MiR\nGc6P3KFDB44cOYKjoyPdu3enXLly3L9/n1u3bmFkZIS9vT3/93//x+7du3FxcUlzkpg+ffowduxY\n7aa6fv26dh0z8/ag07lzZ63YMK2R92bOnImHhwfDhg3ThrC8evUq0dHRNG7cmEmTJnH48OF0g31m\nn3SrVavG+PHjURSF27dvpxo97lX2nRYHBwc8PDwYOHAgpUqV4ubNm5ibm/PZZ5+xZs0ag/ufMmUK\nK1aswN7ensTERP7991+qVavGihUrtAmP0touo+9FdrdLK11RFBwdHVmyZAlNmjThyZMn/PPPP9St\nWxcnJydtPPmMWplbWlrSuXNn7RgZFbubm5vj4uLCZ599Rv369Xny5AnXr19HVVU6derEDz/8oFVt\n/fXXXwQGBmbYCDAlU1NT+vTpg6IonDlzRq/tiaHr9PK9cuPGDS2tcuXK7N69mxkzZtC4cWMiIiK4\nefMmlpaWdO3aFRcXFz766KNU+8zMZ/myI0eO8OzZsyw9kFesWFErmj5w4IDWKNTQ8bOTr6xs8803\n31CkSBGtKk/XRqRIkSL88MMPODo60qZNGxISErh+/TrJyck4ODjw3XffsWLFilT7s7CwYNOmTSxY\nsAA7OzsiIyMJCAigXLlyjBw5kn379qXqtvfhhx+yd+9e+vfvT5kyZbh16xZRUVHY29szf/58tm/f\nnma36YzOs27duuzevZtRo0ZRoUIFrcRnzJgx/Prrr5iYmGTpmmfmvn2Zqakpv/76KzNnzuSdd94h\nLCyMoKAgWrRogbOzs1bd/XJPscWLF7N48WJatGhBcnIyN27c0LsmLi4u6fYu0/Kkvs5HRiFErnB1\ndWXhwoW0atWKn3/+ObezI4TIhKVLl2rVwbqXi5yUa9PjCiGyJz4+ntatWzNs2DCDVQu6hnLpDWAi\nhHhzAgICaN++PWPGjDE4yunJkyez3RsqMyTgC/GWKVy4MOXKlcPPz49ly5bp9VmOj49n7dq1nDhx\nAlNT00wXpQshXi8bGxtiY2P5448/cHR01OstEB0dzdy5cwkMDKRs2bJ682PkJCnSF+It5Ofnx9ix\nY4mJidGmqTUyMiI4OFjrQrlkyZI0h14WQuQOT09PZsyYQVJSEubm5lSpUoXExESCg4OJjY2lZMmS\nODk5aY0lc5oEfCHeUmFhYTg7O3Pq1Cnu379PQkIC5cqVo3Xr1gwZMiRTk2kIId6swMBANm/ejJ+f\nH/fv39dGZOzYsSNDhw7NdtfnzJCAL4QQQhQAUocvhBBCFAAS8IUQQogCQAK+EEIIUQBIwBdCCCEK\ngHwf8MPCwvjss89o0aIFzZo1Y+rUqXqDlfj6+tKnTx8aNWpE7969OXHihN72ukkNmjVrRsuWLVmx\nYsVrn1VJCCGEyGn5PuCPGzeO6OhotmzZgouLC+Hh4Xz66afAizHy//Of/9CtWzf27NlDx44dmTBh\nAgEBAdr2upmVXF1dWbZsGR4eHto47EIIIcTbIl8H/IiICGrWrMmiRYuoXbs2derUYdSoUVy9epWn\nT5/i7OxM48aNGTduHNWqVWPy5Mk0adJEm4vZ398ff39/vv32W2rXrk3btm2ZMWMGLi4uelPNCiGE\nEHldvg74ZcqUwdHREWtrawBCQ0Nxc3OjYcOGWFpa4ufnR/PmzfW2ad68uTZXu5+fH9bW1tr2uvTo\n6GiuXbv25k5ECCGEeEXpT5adj0yYMIFjx45RvHhxnJ2dgRcPAC+PalS+fHnu379vML1cuXJaWsOG\nDd9AzoUQQohXl6/f8FOaMmUK7u7uNG3alI8//piwsDBiY2MpUqSI3nomJibaHNBppRsbG6Moit7E\nB0IIIUReV2De8GvVqgXA999/T/v27dmzZw+mpqZacNdJSEjAzMwMIM30xMREVFXV1jHEycmJtWvX\nplqenJzMrVu3XuVUhBBCiCzL12/4Dx8+xNPTU2+ZqakplStX5sGDB1SoUIHw8HC99LCwMK0Y38rK\nKlW6rktfRhMcTJo0iRs3bqT6q1ix4quelhBCCJFl+Trgh4SEMHXqVK5cuaIte/r0Kbdv36ZGjRo0\nbdqU8+fP621z9uxZbWpCOzs7goODCQsL09LPnDmDhYUFdevWfTMnIYQQQuSAfB3wGzRoQLNmzZgz\nZw5//fUXV69eZcqUKZQuXZoPP/yQjz76iPPnz+Pk5ERgYCCrV6/m8uXLjBgxAoAmTZrQqFEjpkyZ\nwtWrV/Hx8WHFihWMHj0aY+MCUxsihBAiH8j30+M+fvyYb7/9lhMnThAXF0ebNm2YPXu21tpeF8SD\ngoKoXr06M2fO5N1339W2f/jwIfPnz8fX15eiRYvSv39/pkyZku38tG/fHm9v71c9LSGEECJL8n3A\nz2sk4AshhMgN+bpIXwghhBAvSMAXQgghCgAJ+EIIIUQBIAFfCCGEKAAk4AshxFvi3Llz2Nvb640N\n4ujoyJ49e3L0OGvXrmX79u0A1K9fnxEjRjB8+HAGDRqEk5NTjh5LvDkS8IUQ4i1SuHBhZs+e/caO\nV6JECZydndmyZQvbt28nPDwcFxeXN3Z8kXMk4AshxFvk3XffpXjx4ri6uuotd3FxYfDgwQwZMkQL\nyLNnz2b8+PEMGTKEL7/8UisJiIiIoG/fvgCsXLmSYcOGMXjwYA4fPpzh8T/++ONUQ5aLt4MMFyeE\nEG8RRVH4+uuvGTBgAG3atAHg+fPneHp6sm3bNlRVZfTo0bRq1QoABwcHRo4cSUBAAN988w19+vRh\n79699OvXjxMnTnD37l1cXV2Jj49n4MCB2naGlClThsePH7/28xQ5TwK+EEK8ZYoXL87s2bOZOXMm\ndnZ2PH/+nHv37jFy5EhUVeXp06cEBQUBUK1aNQBq1KhBcnIy9+7dw9PTk82bN+Pm5saVK1cYMWIE\nqqqSlJTE3bt30z12SEgIVlZWr/0cRc6TIn0hhHgLdejQgWrVquHh4UGRIkWoVauWVtf+4YcfUqdO\nHQCMjP73M9+vXz++++47atWqhYWFBdWrV6dFixY4Ozvj7OzMBx98QJUqVQweMzk5mV9++YVu3bq9\n9vMTOU/e8IUQ4i315ZdfcubMGSwtLXFwcGDIkCHEx8fTqFEjbb6QlD744AOWLFnC+vXrAejYsSPn\nzp1j2LBhxMTE8P7772Nubq63TVRUFCNGjEBRFJKSkmjZsiX9+/d/I+cncpaMpf+GyVj6QgghcoMU\n6QshhBAFgAR8IYQQogCQgC+EEEIUABLwhRBCiAJAAr4QQghRAEjAF0KIXKSqKqEPnxHxOCa3syLy\nOemHL4QQueTI2X85fPoOASFRFDJSqG1Tkg/b1aB5vQqvvO9Lly6xYsUKtmzZQmRkJHPmzOHp06ck\nJSXx7bff8uzZMxYvXoyiKKiqyqVLl/jvf/9L69atATh69CiHDh3C0dERAF9fXxwdHTE3N6dNmzaM\nHz8eeDGzno+PD8bGxsyePZuGDRtqeTh37hwzZszQuiIfPnyYjRs3YmRkRI8ePRgxYsQrn6fIPAn4\nQgiRC46dD+LH3X8Tl5AEQFKyyt8BDwkOi2bmcBMa1CyT7X3/9NNP7N27l6JFiwLw3Xff0atXLz74\n4APOnj1LYGAg7dq1Y8uWLQAcOnQIKysrLdgvXryYU6dOUbduXeBFKcTcuXNxcXGhYsWKfPHFF/z5\n55+Ymppy4cIF3N3duX//PpMmTWLnzp0AhIaGsmnTJhITE4EXo/StXLkSDw8PzMzM6NatG7169aJE\niRLZPk+RNVKkL4QQb5iqqhw5c0cL9ilFRcdx8NTtV9q/jY0N69at0/7/559/EhoayujRozlw4AAt\nWrTQ0mJiYnBycuKrr77SljVt2pT58+dr/3/06BHFixenYsWKWvqFCxfw8/PTJtupUKECycnJPHr0\niPj4eObPn6+3DyMjI7y8vChatCiPHj1CVVVMTExe6TxF1kjAF0KINyw6JoE7YU8Mpt8JNZyWGZ06\ndaJQoULa/0NCQihRogS//vorVlZW/Pjjj1razp076dq1q96bdteuXfX2V6pUKWJiYrh9+zZJSUn4\n+PgQGxtLdHQ0lpaW2npFixYlOjqab775ho8//jjV8L5GRkYcPXqU3r1707x581TD+IrXSwK+EEK8\nYcaFjDApZPjnN7207ChRogQdOnQAXoyff+XKFS1t//79DBgwIMN9LF++nK+//prx48dTvXp1SpYs\niaWlJc+ePdPWiY6OxsTEhAsXLrB27VqGDx/O48ePmTZtmrZOp06d8PX1JT4+nj179uTgWYqMSMAX\nQog3zKyIMXVsShlMt61qOC077Ozs8PHxAeD8+fPUrFkTeBGgExISKF++fIb7OHnyJL/88gsbN24k\nKCiIli1b0qRJE3x9fVFVlXv37qGqKlZWVhw6dEibua9EiRI4OjoSHR3N8OHDiY+PB8DMzAxFUXL0\nPEX6pNGeEELkgoHv1+bO/SeERj7XW17NuhgD36+do8eaOXMmc+bMYdu2bVhaWmot72/fvq3Vy2ek\nfPny9O/fH1NTU3r27EmNGjWAFw8TgwYNQlVV5s2bZ3B7CwsLevXqxUcffYSJiQl16tShd+/er35y\nItNktrw3TGbLE0LoBIU+Ye/JQALvPqZQISNqVylJ/w41KVXcLLezJvIhecMXQohcUsWqGJMGNM7t\nbIgCQurwhRBCiAJAAr4QQghRAEjAF0IIIQoACfhCCCFEASABXwghcpGqqjyIjuDh80e5nRWRz0kr\nfSGEyCXHA09xNOAk/z66i5GREbVKVaNnnfewq9gw440zkBdny9OZN28eJUqUYOrUqa98niLzJOAL\nIUQu8Ll9hl//3EFc0ouR55KSkrgafpO7T+7zuckY6pWrk+1958XZ8nTc3Ny4efMmzZs3z/b5ieyR\nIn0hhHjDVFXlWKCvFuxTehL3lMO3TrzS/vPibHkA/v7+XL58mcGDB7/S+YnskYAvhBBv2LP45wRF\n3TOYHhwV8kr7z4uz5T148IC1a9cyb948ZIDX3CFF+kII8YYZGxXCxMjwz69xOmnZ8fJseatWrdLS\n9u/fj5OTU4b70M2WV6RIEWrVqkXJkiVRFMXgbHlBQUGoqkpUVBTTpk2jcePGPH78mLFjxxIeHk5c\nXBzVq1enT58+OXquwrB8HfAfPnzI8uXLOXXqFHFxcTRs2JBZs2ZRq1YtAPr378/ff/+tra8oCv37\n92fhwoUAREZGsmDBAv744w9MTEzo27cvU6dOxchICkaEENlnamJK7dLVOH/vrzTTa5epnqPH082W\n16tXr1eeLc/Y2JiJEyfSr18/YmNjWbFiBR9//DH379/Xmy1Pp3Xr1lrDv+HDhwOwe/dubt++LcH+\nDcu3AV9VVSZMmADAhg0bMDMzw8nJiVGjRuHp6Unx4sUJCAhg5cqVevVZpqam2r8nTpxIoUKFcHV1\nJTQ0lFmzZmFsbMyUKVPe+PkIIfKXD9/pyp2oezx4FqG33KZ4Rfq+09XAVtmTF2bLE7kv386Wd+3a\nNfr27YunpyfVqlUDID4+nhYtWjB//nyaNm1Kp06dOHbsWJpfeH9/f4YOHcqxY8ewtrYGYM+ePSxa\ntIjTp09jYmKSrXzJbHlCCJ3gqHt43jzO7UfBFFIKUat0VXrX7UxJsxIZbyxEFuXbN/wKFSqwYcMG\nLdgDWlH8kydPuHnzJmZmZgafbv38/LC2ttaCPUDz5s2Jjo7m2rVren1NhRAiOyoXt+b/mn2U29kQ\nBUS+rYwuUaIE7dq101vm7OxMXFwcrVq14tatW1hYWDBt2jTatGlDz5492bRpk7ZuaGhoqnotXYvT\n0NDQ155/IYQQIifl2zf8lx07doyVK1cyevRoqlevzq1bt4iJidFGjPrzzz/59ttviY6OZuLEicTG\nxlKkSBG9fRgbG6MoCnFxcbl0FkIIIUT2FIiA7+Hhwbx58+jRowdffPEF8GLkqefPn2NhYQFArVq1\nePLkCT/88AMTJ07E1NSU+Hj9QTESExNRVRUzM7M3fg5CCCHEq8j3AX/9+vWsXr2a4cOH640kZWRk\npAV7ndq1a/Ps2TOio6OxsrLixAn90a4ePHgAkKkuLE5OTqxduzbV8uTk5OychhBCCPFK8m0dPsDG\njRtZs2YNU6ZM0Qv2AIMGDWLx4sV6yy5fvky5cuWwsLDAzs6O4OBgwsLCtPQzZ85gYWGhjS+dnkmT\nJnHjxo1Uf5ntAiOEKBhUVSU2LIy4iIe5nRWRz+XbN/zr16+zatUq+vXrR//+/YmI+F9f16JFi9Kp\nUyecnJyoV68ednZ2nDlzhp9//pk5c+YA0KRJExo1asSUKVOYO3cu4eHhrFixgtGjR2NsnG8vmxDi\nDQo9+hthh48SHXgbpVAhLGvXomKfXpRqZv/K+86Ls+Vt2rSJnTt3UqpUKQC++eYbqlat+srnKjIn\n30YuLy8vkpOT2bVrF7t27dJLmzx5MuPHj8fY2JgNGzZw//59KlSowJdffkm/fv209datW8f8+fMZ\nNmwYRYsWZeDAgdpgPkII8SoeHPfm9sZfSP7/jYDVpCSe/H2F58F3qfPFNEo0qJftfefV2fKuXLnC\n8uXLeeedd7J9biL78m3A//zzz/n888/TXWfUqFGMGjXKYHrp0qUzNca0EEJkhaqqhB45qgX7lBKj\nogj18nqlgK+bLW/GjBnAi9ny6tSpw+jRo6lUqZJeFadutjxXV1dtmW5gsu3btwOGZ8szMzNLc7a8\nokWLMn/+fL755hv69u2r7ffKlSv88MMPhIeH0759e8aNG5ftcxRZl6/r8IUQIi9KjI7meVCQwfTn\n/xpOy4y8OFseQPfu3VmwYAHOzs74+fnh4+PzSucpsibfvuELIUReZWRsjJGJCUkG0hWT/DNb3uPH\nj5k2bRqOjo6MHDlS6x3Vrl07rl69mmqANPH6yBu+EEK8YYXMzLCsU9tgejHbOjl6PN1secArz5a3\nceNGgoKCaNmyJU2aNMHX1xdVVbl3757ebHnOzs5s2bKFEiVK4OjoSHR0ND169CAmJgZVVTlz5gz1\n6mW/2kJknbzhCyFELqjUvz/P7wQRGxqmt9y8WlUqDuiX9kbZlBdmy7OwsGDq1KkMHz6cIkWK4ODg\nQNu2bV/95ESm5dvZ8vIqmS1PCKHzLCiYe/sP8CzwNoqREZa1a1Op34cULlUyt7Mm8iF5wxdCiFxS\ntEplak34NLezIQoIqcMXQgghCgAJ+EIIIUQBIAFfCCGEKAAk4AshhBAFgAR8IYQQogCQgC+EEEIU\nABLwhRBCiAJAAr4QQghRAEjAF0IIIQoACfhCCCFEASABXwghhCgAJOALIYQQBYAEfCGEEKIAkIAv\nhBBCFAAS8IUQQogCQAK+EEIIUQBIwBdCCCEKAAn4QgghRAEgAV8IIYQoACTgCyGEEAWABHwhhBCi\nAJCAL4QQQhQAEvCFEEKIAkACvhBCCFEASMAXQgghCgAJ+EIIIUQBIAFfCCGEKAAk4AshhBAFgAR8\nIYQQogCQgC+EEEIUABLwhRBCiAIgXwf8hw8fMnPmTFq3bk2zZs0YM2YMt27d0tJ9fX3p06cPjRo1\nonfv3pw4cUJv+8jISCZPnkyzZs1o2bIlK1asIDk5+U2fhhBCCPHK3qqAHxsby7179zK1rqqqTJgw\ngTt37rBhwwbc3NywtLRk1KhRREVF8c8///Cf//yHbt26sWfPHjp27MiECRMICAjQ9jFx4kQiIyNx\ndXVl2bJleHh4sGbNmtd1ekIIIcRrk6cC/nvvvcfixYtJSkpKM/3IkSO89957mdrX9evXuXTpEkuX\nLqV+/frUqFGD5cuX8/z5c7y9vXF2dqZx48aMGzeOatWqMXnyZJo0acLmzZsB8Pf3x9/fn2+//Zba\ntWvTtm1bZsyYgYuLCwkJCTl2zkIIIcSbkKcCfkhICC4uLgwfPpyIiIhX2leFChXYsGED1apV05YZ\nGb043SdPnuDn50fz5s31tmnevDl+fn4A+Pn5YW1tjbW1tV56dHQ0165de6W8CSGEEG9angr4AH36\n9OGff/6hT58+WvDNjhIlStCuXTu9Zc7OzsTFxdGqVStCQ0MpX768Xnr58uW5f/8+QJrp5cqV09KE\nEEKIt0meC/gtW7bE3d2dYsWKMXLkSJydnXNkv8eOHWPlypWMHj2a6tWrExsbS5EiRfTWMTExIT4+\nHiDNdGNjYxRFIS4uLkfyJIQQQrwpeS7gA9jY2LBjxw5at27N0qVLmTZtGrGxsRgbG2drfx4eHkye\nPJkePXrwxRdfAFCkSBEtuOskJCRgZmYGgKmpaar0xMREVFXV1hFCCCHeFtmLoG+AhYUFGzZs4Pvv\nv+eHH34gICCAAQMGZHk/69evZ/Xq1QwfPpyvvvpKW16hQgXCw8P11g0LC9OK8a2srFJ103vw4AFA\nqqL+tDg5ObF27dpUy6VbnxBCiNyQJ9/wU/r8889ZtWoVd+7cYenSpVnaduPGjaxZs4YpU6boBXsA\nOzs7zp8/r7fs7Nmz2Nvba+nBwcGEhYVp6WfOnMHCwoK6detmeOxJkyZx48aNVH8VK1bM0jkIIYQQ\nOSFPBfxmzZpRpkyZVMs/+OADtm3blqk3a53r16+zatUq+vXrR//+/YmIiND+YmJi+Oijjzh//jxO\nTk4EBgayevVqLl++zIgRIwBo0qQJjRo1YsqUKVy9ehUfHx9WrFjB6NGjs121IIQQQuQWRVVVNbcz\nkVkxMTFERkZm6i35+++/58cff0wzbfLkyYwfP14L4kFBQVSvXp2ZM2fy7rvvaus9fPiQ+fPn4+vr\nS9GiRenfvz9Tpkx5pXNo37493t7er7QPIYQQIqveqoCfH0jAF0IIkRtytWza1tYWRVGytI2iKFy9\nevU15UgIIYTIn3I14Ddr1izVssTERPz9/alTpw7FihXLhVwJIYQQ+U+uBvwtW7akWhYZGUnLli2Z\nNWsWDg4OuZArIYQQIv/JU630gSwX8QshhBAiY3ku4AshhBAi50nAF0IIIQoACfhCCCFEASABXwgh\nhCgAcrWV/uzZs1Mti4+PR1EUfv75Z/bt25cqXVEUlixZ8iayJ4QQQuQbuTrSnq2tbZa3URSFa9eu\nvYbcvBky0p4QQojckKtv+MeOHcvNwwshhBAFRq4G/JcnwXn69CmWlpa5lBshhBAi/8pTjfZatmzJ\nxIkTOXToEHFxcbmdHSGEECLfyFMTu48cORIvLy9+++03ihYtyvvvv0/37t1p1aoVhQoVyu3sCSGE\nEG+tPDk9rr+/PwcPHuTw4cOEh4dTsmRJunTpQo8ePbC3t8/t7L0SabQnhBAiN+TJgK+jqirnzp3j\n+PHj+Pj4cOfOHaysrOjZsyd9+vShevXquZ3FLJOAL4QQIjfkqTr8lymKgpmZmfanqipPnz5l27Zt\ndO/enQkTJhAZGZnb2RRCCCHyvDxVh69z+fJlvLy8OHz4MPfu3cPExIR27doxfvx4OnToAMDBgweZ\nP239FfgAACAASURBVH8+U6dOZdOmTbmbYSGEECKPy1MBf8WKFRw6dIiQkBAURaF58+Z8+umndOnS\nJVV3vQ8//JBjx45x6tSpXMqtEEII8fbIUwH/p59+4p133uGjjz6iW7dulCtXLt317ezsaNy48RvK\nnRBCCPH2ylON9m7fvk21atVSLY+MjKRkyZIoipILucpZ0mhPCCFEbshTjfaqVauGi4sLbdq0ITg4\nWFu+fPlyWrZsyZ49e3Ixd0IIIcTbK08FfHd3dxYtWoSNjQ3Gxv+rbejWrRt169Zl9uzZHDlyJBdz\nKIQQQryd8lSRfs+ePalSpQrr1q1LM338+PGEh4eza9euN5yznCNF+kIIIXJDnnrDDw4Opl27dgbT\nO3ToQGBg4BvMkRBCCJE/5KmAX7JkSW7cuGEw/fbt21hYWLzBHAkhhBD5Q54K+J06dWL79u14enqm\nSvvtt9/YunUrnTp1yoWcCSGEEG+3PFWHHx0dzbBhw7h58yalSpWicuXKKIpCcHAwDx8+pFatWri4\nuFCsWLHczmq2SR2+EEKI3JCnBt6xsLBg586duLu74+PjQ0hICElJSdSpU4e2bdsyePBgihQpktvZ\nFEIIId46eeoNvyCQN3whhBC5IU+94evExcXx+PFjkpKS0ky3trZ+wzkSQggh3m55KuA/fvyYBQsW\ncPToUYPBHuDatWtvMFdCCCHE2y9PBfxly5bh5eVFmzZtqFu3LoULF87tLAkhhBD5Qp4K+MePH2fA\ngAEsXLgwt7MihBBC5Ct5qh9+YmIiDRo0yO1sCCGEEPlOngr4zZo14+zZs7mdDSGEECLfyVPd8u7c\nucOIESPo3r07H3zwAaVKlcLIKPUzydvcSl+65QkhhMgNeSrgN2zYkOTkZBITE1EUxeB62W2lP2/e\nPFRV1Wsj0L9/f/7++2/t/4qi0L9/f22dyMhIFixYwB9//IGJiQl9+/Zl6tSpaT6IZIYE/BdUVUVV\n1WxfRyGEEFmTpxrtjR07Nt1A/ypWr17Njh07GDBggN7ygIAAVq5cSYsWLbRlpqam2r8nTpxIoUKF\ncHV1JTQ0lFmzZmFsbMyUKVNeSz4LiqjYJ+y66kWlYhVoal2fskVL53aWhBAiX8tTAX/SpEk5vs/g\n4GC++uor/vnnn1RVAcHBwcTGxtKoUSNKl04dcPz9/fH39+fYsWNYW1tTu3ZtZsyYwaJFi5gwYQIm\nJiY5nt+CooRZcT5uOog7j+9y4t+zPIt/TqMK7/BO2VqYFJLrKoQQOS1PlqfeuHGDH374ga+//pqA\ngADu37+Pj49Ptvbl7++PtbU1+/fvp2LFinppN2/exNTUNNVyHT8/P6ytrfUeFJo3b050dLQM/pMD\nFEWhasnK9KvXjf71uvM8IQbni7vwuvk7YdHhuZ09IYTIV/LUGz7AwoUL2bp1K6qqoigKH3zwAU+e\nPGHy5Mm0b9+e1atXZ2kCnV69etGrV680027duoWFhQXTpk3j3LlzlChRgn79+jFq1CgAQkNDKV++\nvN425cqV09IaNmyYvZMUqZgXNsOhsh3vVmpKcNQ9/gjy40lcNA2tbKlXrg6F5a1fCCFeSZ56w3d2\ndsbV1ZVx48axY8cOdO0JHRwcGDVqFN7e3mzcuDHHjnfr1i1iYmJo06YNv/zyCx999BFr1qxh7dq1\nAMTGxqZ6uDA2NkZRFOLi4nIsH+J/FEWhSomKfPjOBwys34P4pARcLnpw8MYxQp8+yO3sCSHEWytP\nveG7ubnxwQcf8Pnnn/Po0SNtebFixZg1axaRkZEcOHCAiRMn5sjxvvvuO54//3/s3Xd40/Xe//Fn\nkiZp06a7TXehhdKyCpSpLEHP7cKDniNy1B8Hjop6QMDj0cPRo+IEjjgQDnrAgSAoeOvBgd6iHEVw\nMWRKgbI7ku69sr6/P0pja9m0Tdq8H9fVS/v9Js27UPrKZ9cQEBAAQPfu3amoqODf//4306dPx9fX\nF6vV2uw5drsdRVHw8/NrlRrEmflpfRkS15/Bsf3IrbDwY84uSmvL6BOVRu/IHuh9ZOtlIYQ4Xx4V\n+NnZ2UyaNOmM9wcOHMjnn3/eaq+nVqtdYd8oJSWF6upqqqqqiIqK4ptvvml2v6CgoZX5667+X1u0\naJGrp6App9N5iVV7H5VKRVxQNHFB0dTZ69mbf4DVe9YRbgglI6Y30UZTm63uEEKIzsKjuvRDQkKw\nWCxnvJ+VlUVQUFCrvd4tt9zCM8880+za3r17iYyMJCAggIyMDLKzs8nPz3fd/+GHHwgICCAtLe2s\nX/u+++7j4MGDLT7ONEFQnB9fHz2DYtOZ3P9mBsT0ZkfePpbvfI/tubupt1vP/QWEEMJLeVTgX3XV\nVaxevZrDhw+7rjW23DZt2sSaNWu44oorWvX11q5dy7p168jOzua9997j9ddfZ8aMGQD079+f9PR0\nZs2axf79+9m0aRMLFixgypQp+Ph4VOeI11GpVMQGRjEu9Upu7TselUrNO3vW8WHmBnIqzHjQflJC\nCOERPGqnvYqKCm677TZOnDhB9+7d2b9/PxkZGVRXV3PgwAFiY2NZu3YtoaGhF/X1J02aRGJiYrOd\n9pYvX867776L2WwmOjqaO+64o9nmPMXFxcyZM4ctW7bg7+/P73//+0vadEd22mtb5soCduTtpaCq\niN6mHvQ1peKr9T33E4UQopPzqMAHqK2t5bXXXmPDhg1kZ2fjcDiIjY1lzJgx3H333a3ape8OEvjt\nw2q38nPhIfZYDhCoDyAjpg/xQTEy1i+E8FoeF/idnQR+A6fNRn1BIfrICNRtvGNhflUhO/L2Yq4s\noFdkCn2j0jBoZZWFEMK7eNRAdF5e3nk9riOflicaOOutlO3aTV1BAYrNjsbfgK/JhG+0CV9TFLrQ\nEFStdLCOKSCCa1PGYHPY2F+Yxf/uW4+/zkBGTF8Sg2Ol1S+E8Aoe1cJPTU09r1++HXlbW2nhn569\nqpq6/HzqLPnU5edjLS4GQBcaim+UqeHNQJQJn18to7xYBdXF/JS3l9wKC2kR3UiP6om/ztAqX1sI\nITyRR7Xwp02b1iLwHQ4HRUVFbNq0CX9//zY5YEe4n0+APwEBSQQkJ7muKU4n1pJS6vLzqTl5kuIf\nt+KorkGl9cE3MgLfqCj0pkh8TaYLHhaI9A/j6u6jsTvs7C/M4oP9n+Gn9WVAdB+6hsRLq18I0el4\nVAv/bKqqqpg4cSLXXXcd9957r7vLuWjSwr90TpuNuvwC6vPzqcsvoC4/v1WGBYpqSvgpby8ny/NI\nDU+mX1QvAvT+bfidCHF233zzDZdffjkajcbdpYhOwKNa+GcTEBDAzTffzJtvvtmhA19cOrVWiyEu\nFkNc802Mmg4LVB7MuuBhgXBDKL/pNgq708GBwsOsO/A5Oo2OAdG9SQ5NlFa/aHc2m42ioqJz7uwp\nxPnoMIEPDT/8TffYF6Kp1hoW8FFr6G3qQW9TD0pqyvjJvJevj39PSlgS/aN7YdS3zjwCIc5Fp9Od\n9tROIS6GRwX+mWbpW61WMjMzeeONN865pa0QTanUavThYejDw6BXT9f1psMC5Xv2kZ+/8bTDAiGh\nIVyZPAKH08HBoqN8dOALfNQ+9I/uRbewLqhVHrVZpeiELBYL6enp7i5DdAIeFfhjxow5Y7epoijo\n9XoeeOCBdq5KdEYXMywQGWXit6a+1AX5safiGN8c/5FuYV3oH92LIN9Ad3wbnYaiKNjKK6gzm/GL\njUUbaHR3SR6jurqanJwc4uLi3F2K6OA8KvBPN0sfGk61i4iIYOzYsRe9ra4Q5+N8hgVqzRa6VNfQ\nxUdDuX8W/9XsxB4SQJ+eQ0gxdUfdSvsHdFaK00l9URF1Zgu1eWbqLBYUmw1tUBC+0dH4xckBU01p\ntVr27dtHTEyM/GyJS9JhZul3FjJLv/NoOixQmnOSnGOZlFeVERgSTkJSGqEJXVt9E6GOpvHPqM5s\nps5soa6gABQFXVgYfjHR+MVEozeZ0Oj17i7VI23evJmysjIcDgeJiYn079/f3SWJDsyjWvjnu9Pe\nr8nOe8Idmg4LhGQMIInxOJ1ODuVk8lPmVjS7j5Bg9yeoruF0v7baRMhTOGprqTVbXOFuLSlBpdGg\nN5nwi4kmZNBAfCMjUMkSswum0Wg4fvw4wcHBdO3a1d3liA7KowL/bGP4Z9ORd97zVtXWGg6XHCfW\nGEWYIaTTLHlTq9WkJvQiNaEXFXWV7DT/zE/Fx+gSFEcfv1i05XWtvolQe1MUBXtlFXVmM7V5edTm\nmXFUV6PW6xu65GOiMab2QBca2mn+Xj2Bj48PO3fuxOl0kpyc7O5yRAfkUYE/b948Fi9eTGlpKePH\njycpKQm9Xs/Jkyf54IMPqKqq4vbbb8fXV4477ei0Gi12p4NvT26nuKYUvY+OuMBo4oKiiQ2Mwten\n43fxBvoaGdV1KCO6DOZoyUk2mfdiddjp37sXqaNHolFrznu1gLuGBRRFwVpc0hDup1ruzvp6fIzG\nU+EeQ8jADLRGmWTXHrRaLbt376aiooJ+/frJGypxQTxqDH/+/Pl8/vnnrFmzhoiIiGb3qqqq+MMf\n/sCgQYN47LHH3FThpZMx/NOrtdWRW2Ehp8JMboWFeruVcP+QU28CYogwdI7WYmV9FbvM+zlYdISE\n4FgyYvoQZghp8bj2PlsAQHE4Gl7TbKHWbKE+Px/F4UQXGoLvqfF236goNG3whtvhcFJQWktuYRVJ\nsUGEBsqb+ldffZXDhw8zatSoFvccDgf+/v4MHjy4wx8ZLtqPRwX+0KFDueuuu7jjjjtOe/+tt97i\nX//6F1u3bm3nylqPBP75URSFopoScirM5JRbKKwpRqfREmuMIi4omrjAaPy0HTcUFEXhWOlJduTt\npc5eT7/oXqSFd8NHc+ZOt6arBerz86k1Wy56WMBRX98wic5sptZswVpUjEqjRh8Z4eqW10dGovZp\n/U5ARVEoLKslp6CK3IIqLCXVoEBkqIHYiAB6JYXhp/eozke3mDhxIoWFhcyaNeuMj7HZbHTp0oX0\n9HTZfleck0f9q3I4HNTW1p7xfnFxcado5YlzU6lURPiHEeEfRv/o3gDU263kVpjJqbCwLXc3tbY6\nwgwhxAVGERcUQ6QhrMMsW1KpVCSFJpIUmki1tYZdlp9Zvus94gNjGBDTmwj/sJbPuchNhLRBQSgo\nOKprqLNYsJWXo9Hr8Y2KwjcmmogRw9GFhbbJkIGiKJRV1pNTUEVOYRV5hVXYHU7Cg/yIiwxgQGok\nplADPpqO8ffmabRaLTk5OeTk5JCUlERaWpoEvzgjjwr8oUOHsnz5coYOHcrAgQOb3duyZQsrV67k\n2muvdVN1wt30PjpXSEJDmJTUlpFTYWZH7h4KqovRqDXEGk2uXoCOcOStv87A5QmDuCx+ICfKcvjm\n+I9UW2tIj+5Jz4juaDVnb603rhbwi43BVlp2aqzdTM2Jk5Tt3InT4WwIc0XBJ8AffZQJv6ioNhkW\nqKi2kltQRW5hJTkFVdRbHQQH6omLMNKzSyhXDopH6yOB1JoaG0FHjhzhyJEjJCYmkpaWhl6WOopf\n8agu/ezsbCZOnEhJSQk9evQgISEBRVE4fvw4hw8fplu3bqxcuZLg4GB3l3rRpEu/bVntVvIq8xuG\nAirMVFtrCfELck0INPmHo1F7fuDUWGvZnb+f/QVZxBhNDIjpjSngl3ktisNBfWFhwzK4PHPDiYF2\nB9qQ4Iax9ugofKOi8TH4Nfu6rTksUFNnI7ewqqH1XlBFda0No0FHXGQAsZEBxEYESNf8JTifLv3T\nURQFu91OTEwMqampHfr3pWhdHhX4ACUlJSxdupRNmzZhNpsBSExM5De/+Q1/+tOf8PPzO8dX8GwS\n+O1LURTK6irIqTCTXZ5HflURapWaGKOJuKAo4gNjPPoIXEVROFF0gj37vqfeYiHRZiCkTo1GrUEf\nEY5fdDS+MdGXvJzvXEcOq8PDKdcaMdf7kFNUQ3lVPQZfH2IjAk4FvJEAP89eTtjRXGzgN2W1WomI\niCAtLa3FRGjhfTwu8Ds7CXz3szlsmCsLyK7II6fcQpW1miDfwFNzAaKJDoh0Wy+Avabmly1nzWZs\nZWWotNqG0I0M45i2iv1WC6bASDJi+hBljGz1Gmx2J5bianIKKsnNLqQ614KhtpwwZzVGew0BfloC\nosLbbFhANGiNwG9ks9kICwujV69ehIeHt0J1oiPyyP62gwcP8vXXX2M2m/l//+//YTAYOHTo0GmX\npwhxobQaLQnBsSQEN+zZrigKFfWV5FRY2Jd/kC+PbAEgOiCSuKBo4gOjCfRt3XXmTQ+Lqc3Loy7P\njL2mBh+DAd/ohi75oN490QYHN5uoagKGKAo5FWZ+yNlJWW05faLS6B3ZA72P7oLraFwOl1PQMOZe\nWFaLWqUiOtyfuIgArrg8hdDAvs1quJgjh4V7abVaKioq+OabbwgPD6d3795yLokX8rgW/lNPPcXq\n1atRFAWVSsUbb7xBRUUFM2fOZPTo0SxcuLBDT0aRFn7HYHc6sLh6AcxU1Fdh1AcQFxhN/KlegLMt\noWvqXIfF+EZH4RcdjU/AhQ8t1Nnq2FtwkL35B4gwhJER05too+m0q1nOtRwuLjKAyBADavXFrYQ5\n17DA2TYRqqmzkZ1fSXZ+Jb2Tw4kK89xhlvbSmi38X7NarURGRtKnTx8Z4/ciHtXCX7FiBatWreLu\nu+9m7NixTJgwAYBhw4YxefJkli9fzrJly5g+fbqbKxWdnY9a0zDTPyga4huuVdRXkVNuJrPwMP89\n+h2K4sQUEEH8qRUBQb6BKHb7WQ+LCeqVRuSY0a12WIyv1pdBsekMjOlLXmU+2/P2UlzzDb0iU0gw\ndKWwxHbG5XBRoQY0rbgc7nyPHK4vKqamzk6Vjx9Fan9KNP5owiOITYikS0wQEcEde55OR6DT6Sgr\nK2Pjxo1ERUXRt29fjLJbYqfnUYH/7rvvcvXVV3P//fdTWlrquh4YGMjs2bMpKSnhk08+kcAXbhGo\nD6BnZHd6RnYHwFpdTe7R/Vh+3MEP2Sexl5bho9VhjIkhPLEbMRnpREfFtMthMZU1NiqKdRgqUijM\nL+WLoyep0v5IVGAYw+LTGTswFZ3WPf/c6zQ6stXBnNRoyNUGYo9MxhTiS5yvk3Rq0FeVUWc5hGPH\nLlRaH6xX/w9+0VFuqdXb6HQ6SkpK+OKLL4iPj6dfv35oO+EwjNPp5MUXX+Q///kP1dXVjBgxgscf\nf5ywsJb7XXRmHhX42dnZTJo06Yz3Bw4cyOeff96OFQlx9sNi/KKj6ZnUD9/Lr0EXGkK1rYaccgvZ\nFWZ+LNyKI99BpH8Y8UExxAVGE+IXdMmbR/16OVxNrY2AU8vhusYEcXl6LH76fgCYKwvYkbeXt/f8\nRC9TCn1NaW26Q6HD4cRSUsNJS0P3fGlFHQY/LfEmIykJwVyREYdOe+Y3QE6bTU7To2GTsdzcXEpK\nSvjhhx/o2bMngYGBbfZ6Wq0Ws9mMxWKhb9++JCYmttlrucPLL7/Mhx9+yHPPPUdwcDBz5sxhxowZ\nrFq1yt2ltSuPCvyQkBAsFssZ72dlZcm+0Z1EWV0FXx39DqPeH6M+AKMugEB9AEa9PwE6f7fNkr/U\nw2ICdP6kRiSTGtFwmpnT6SS/uoicCjObjv9ASW0ZBq3fLwcFGU3ozjLZrs5qx1xU7Qr3Xy+HG9Qz\n6qzL4aKNkVzfYyxWu5V9BYdYs+9jgvRGMmL6EB8Uc8lvPiprrJy0VHIyv5K8wiocToXoMH8STEau\nHJxAiFF/Qa/h7ZP8ysrKWLhwIZs3b+bgwYPYbDY+/vhj1q9fT8+ePRk3bhwBbbQiovHvaceOHeTl\n5TF48OBOsWufzWZj5cqVPProowwbNgyAF154gbFjx7Jr1y769evn5grbj0cF/lVXXcXq1au5/vrr\nXV0tjT+EmzZtYs2aNdx4443uLFG0kiC9kSu6DqPSWk1lfRUV9ZXkVliotFZRVV+NQ3ECoNNoCdQb\nm7wxaPhvoD4Ag9bvkgLrXIfFGFO6EzFyxCUdFqNWq4k2RhJtjGRQbDrQsKlOToWZY6Un2XJiKzan\nnUj/MKL8TegcwVSVq8ktrKakvA6dVkPMqXDvkxxOUIDuor5nnY+OATG9GRDTm/yqQnbk7eWLI5vp\nGdmd9KieGLTnHjd3OJzkFVVz8tTkurLKeowGHQkm2UWvNZSVlTF9+nRyc3Nb3HM6nezbt4+8vDzu\nvvvuNgt9aGjtFxYW8sUXXzBy5EgMBs/frfJsMjMzqampYfDgwa5rsbGxxMbGsn37dgl8d5k5cyZb\nt27lpptuonv37qhUKhYvXsz8+fM5cOAAsbGxzJw5091lilagUqkI9gsi2O/sPTb1diuV1ioq6xve\nGBTWFHOk5ASV1mpqrDU0LjExaP0w6v1P9RL88sbAqA9Ar9HhtFrPelhMyIB+bXZYzK8ZdH4kh3Qh\ngEiMdclk51dy+EQ+e5UTqA0/o9LWEWr0p0dSHPFBMa1+XLApIIJrU8Zgc9j4uSCL9/atJ0BnICOm\nL4nBsa43FOVV9a7Wu7moGoDo8IbW+9VDu1z0mw9xegsXLjxt2DdVUlLCxx9/zB/+8Ic2rUWlUmGz\n2fjqq68YM2ZMh97wLD8/HwCTydTsemRk5Fl7lDsjjwr8wMBA1q5dy2uvvcaGDRvQ6/Xs3r2b2NhY\npkyZwt133y1d+l5G76ND7xNKuOHMa4YVRaHWVkeFtaqht6C0iOzcg9SZzdgthVBVg1Prgz3UiI8p\nHN/4aPwz0gnyC0Sj80elD0Cr80d9nsvsLtTZlsPFRQZwWd8Yxod0a7YcrvG44OzyPH7I/qlNjgvW\narT0i+5Jv+ie5JUX8vXhHaws+j/01kj8rNGEBxhJiAqkT3IY/zM0UQ64aUPFxcVs3rz5vB67f/9+\nKioq2nRMv5HT6WTr1q1u3wPFbrdjsViIiorC5wLflNfW1qJWq1sMT+h0Ourr61uzTI/nUYH//vvv\nk5GRwX333cd9993n7nJEG1u+fDnLly+/pK+h2O046utxWq04rVZwOkGtQaPXodY1fKhO/YJQUHA6\nndgVBw5nw4fd6cBx6vPG3gIVKjRqDT5qDRqVusn/a85rboHd4aTe6qDe1vChKKD1UaPXatDrNOh8\nNFxMVtscNuodVurtVmxOGyrUDW+INDr0PjrUqvMPZLvDSZ3VQb3VgdXmAECv06DXqnGobNTaa1Cr\n1Bj1Aa3auyBOr6ioiJycnGbXampqUBSFo0ePtnj8ww8/3C6BDw2hHx4e7tbeHLvdTmFh4WnvTZ8+\n/ax54evri9PpxOl0NjtN02q1duiei4vhUYH/zDPPcMcddzBt2jR3lyLaweTJk5k8efJ5PfZiD4u5\nGDaHrWEIobHH4NRwQqW1mmprDc5T8wt8ffToVH7Y6jTUVKuoKAfsOsIDg+gSGUp8pJGYCP82G9du\nOC7Y4jooqM5WT6gh2DUhMNI/DLVKjc3uIKegiuz8Sk5aKqmqtRFi1BNvMpIQZSQ6zP+06/GLqkv4\nybyXk+V5pIYn0y+qV7ucO9AabwQ7GofDcUGPdzqdbVSJZ9u4cSNxcXEX9JyoqIYlnoWFhc269QsK\nClp083d2HhX4BoOhQ++iJ1qH02Y7NZGuYZZ8fWEhqFSuw2KC0vtgMl3ZZjO6tRotoYZgQg3NdyBr\nuhwuO7+SktoadH5OgoMhKkyhS5yTWkcNldZjZNkyyTKDyqLCX+vnmmj46/kFunMcfXs2DccFJ5AU\nmgA0DB0U15SSaTnJh0e/I6esgLp6JwHqELqGxtA7tivjRyUTYDi/LXjD/UP5TbdR2J0ODhQeZt2B\nz9FptAyI7kNyaGKbtfgu5I1gZ/Hhhx/y0ksvNbv2888/Y7PZiI+Pb/H4cePGMXTo0HapLTg4mBEj\nRrTLa51JTk4OY8eOvajnpqamYjAY2Lp1K+PGjXN9vdzcXAYNGtSaZXo8jwr8OXPm8Pjjj6NSqRg4\ncCChoaGnXRYSExPjhupEW7DX1FCX1zCR7teHxfjFRBM2bCj68LB2X5t9puVwcaeOfT3XcrhGTsVJ\njbXWNb+gsr6aouoSKq0NPQc2hw0AjVrT5I3AqcmHunMvU6yz2sltbL3nV1JbZyc0yEC6aTDj0oyE\nBGnJryogp8LMoYrt7My88OOCfdQaept60NvUg5KaMn4y7+XrY9+TEp5E/+heGPVycM6lGj58OIsW\nLTqvlr5araZnz57tUFWDjIyMdnuttqDT6bj11luZP38+wcHBhIaG8uSTTzJkyBD69u3r7vLalUcF\n/l/+8hfsdjsLFiw46+MyMzPbqSLRVqylpeS89wEaP99T69ujT3tYTHtoejpcTkFVi+VwfbuFE+h/\ncTPS1So1AXr/hq5w45m7D+1OB1Wnhg0q6qsor6skp/zXyxQV7HY19jofamvU1Far8NP4kxAeRnJ0\nBDeldTtt671LSDxdQhpaiU2PC95t3o+lqvCCjgsONQRzZfIIHE4HB4uO8tGBL9CoNQyI7k23sC4X\nNI9A/CIsLIwRI0ac1zkbbb0JTyNFUbj88ss7/LI8gFmzZmG323nooYew2+2MHDmSRx991N1ltTuP\nOjxn0aJF5/VLtSNvrSuH57iXw+Ekv7SG3FMt98KyWjRqFVFhDafDxZkCCA309YjlZnX1drILKl1j\n7/VWB8FBPoSFaQgKUqH1tVNl/WV+wa+XKTZuZGRs0lsQqDei02ibfX+NxwXnVJjJLjc3Oy44PiiG\nqICIs/YClNaWs9O8jyMlJ+ge1pX+0b0I8m2fCWWdya/X4Z+uSz80NLTN1+ErioKPjw+XX365x6yK\nauzSv5gxfPELjwp8byCB337OtRwuNuLSTodr7VoLSms5aakgO7+SwtJa9DoN8Saj68NPf56n8ykK\nNbZa16ZGjUMJFfVVVFqrqLdbgVO9DzpDi/kFATp/FEXBUl1IdnkelqpCVKiIanJQ0OmOC3Y4SNvY\n6wAAIABJREFUHWQVH2On+WcA+kf3JiWsa7OZ0eLsmu60t2fPHlfgN3bjt+VOe9Awcz0hIYEBAwZ4\n1C57Evitw62BP3fuXH7729+263iUu0ngtw1FUSitrG9ouZ86Hc7hVAgP8m3YhtZkbPXT4S5F0+Ng\ns/OrsNodRIYYGmbOm4xEhFzaLoLnw+F0UG2taTa/oLG3oLK+CruzYTxZq/HBX2vAoTips9dRWV+N\n3WkjxDeYpNAE4oNiWhwXXFZXwc68fRwuOU5yaBcGRPc65yZL4hfFxcWMHz+ekpISxo8f3+bd+Dab\njdDQUPr27Uto6Jn3vHAXCfzW4dYx/LfeeovevXs3C/yamhqeeuop7rzzTpKTk1v19R577DEUReGp\np55yXduyZQsLFizg2LFjdOnShQceeICRI0e67peUlPDEE0/w3XffodVquemmm/jLX/4irRY3q6i2\nkltQRW5hw7h7vdVBcKCeuEjP2+bV4VQoKKnhpKWCk/mVFJfXnZoAaKRLTBDD+8Xiq2v/f4oatYZA\nX+NpW+tNWR02qk4tU6xo8sagqKaEH7J38sXhzVTbatCoNYT5hRBjNBEf3NATkBbRjcLqEj7L+hqn\n4mRATG9SwpLcdlZCRxEWFkZsbCw6na5NZ+NbrVbCwsJIS0vzuiVq3sijJu0B1NfXs27dOm644YZW\nDfyFCxeydu1abr75Zte1w4cP8+c//5np06dz1VVX8dFHHzFt2jTWrVvneu3p06ej0WhYtWoVFouF\n2bNn4+Pjw6xZs1qtNnF2pzsdzuivIzai4XS44emx+J5nd3d7qKq1kX1qS9qcgkocTgVTaEPr/YqM\neMKCPGOOwPnSnWGZYlOKolBtreFY6UmOlmaz25zJ13U/oAL0Pnp0Gh1atQ/v7/+Mqvpqoo0m0iKS\niTaamswvCEB7CcsUxfmz2WyYTCbS0tI8skUv2obn/JZsojVHGbKzs3nkkUc4fPhwi+V8K1asoF+/\nfkydOhVo2Mt/x44dvPXWWzz55JPs3LmTnTt3snHjRmJiYkhJSeGhhx7i6aefZtq0aZ3y3Gh3a63l\ncO2l6XGwJ/MrKK2ox//UcbDd44MZnRGH/izHwXZ0iqKgOBw4rTZ0Vjvd1WEkGwNRfFNwWq1U11RQ\nWJZPYWEhZZWFRNqsGNCiWC2UVh2ixG4jUO2HFjU2pwOn00HfCbeT0L23u7+1TslmsxETE0OvXr0w\nnuHER9F5eWTgt6adO3cSExPDiy++2KJVvmPHDq655ppm1wYPHsxnn33muh8TE9PsjcLgwYOpqqoi\nMzPT69Zwtra2XA7XViqqra4177kFVTiVX46DvWpw4gUfB9teFKcTp82Gs75hC2KnzYrTanNtSdzw\n0fC5ctp7Vpx2+6kvduoNuUoFioLKx6dhG2Ot1rWdccOHFq1OR1xQDAnhiah1OtBqKbVXYakvxVZb\nTEF9GSedNdQ7bSSHdeGKrpcRbYx03x9UJ9XYok9PT2/TSX/Cs3X6wL/hhhu44YYbTnvPYrG0GLcy\nmUyYzeYz3o+MjHTdk8C/eKUVdXzw9WHXcrgxA+M9Zjlco6bHwZ60VFJeVU+AQUuiKZC0NpgnoCgK\nit1+jlA+dc3WPKQbPxSbDaVx29XGP0tFQaVWN5wr0CKUG4JZrdOh8dWjDTSi1utQa5vc0+pQ6xvO\nJGiNvx8jkNDk8xprLdkVZnaZ97Fk61v8vtf1pEelXfLriIY96AMCAhg2bBjh4eHuLke4WacP/LOp\nq6trsZWvVqvFarWe8b7PqV963nbKUmsLCfTljhs8q9u26XGweUVVAMSE+ZMQFcg1w345DlZxOHDU\nW3FWVVFvs+Kobwja8w1lp82G0thaPtVKbvx/tVZ7mpZyQ/CqtDo0eh0ag6FlYJ8KZbVWi6qDTSg1\n6PzoEZ5Ej/AkbnF3MZ2E0+nEx8eH9PR0kpKS3F2O8BBuD/y1a9fy3XffuT63Wq2oVCpef/11Pvro\noxaPV6lUPPvss63y2nq93hXujWw2m+sEJV9f3xb37XY7iqKc85SlRYsWsXjx4hbXvfXQi/bWrAv7\nNMFrq62nqLiCwsIKSoorqa+pw6BRCPZTk6BXk6ZVgd0GhSrIVCgBSgBUKlQazRm7sBuD1yfAH7Uu\npElrWXuqhd0Q2u29VbAncjgVistryS+uwVJcjaWkhpo6G+OGJxETId3OF8PpdKJSqUhOTqZnz56y\nmkg04/bA37ZtG9u2bWtxfcuWLad9fGsGfnR0dIsjF/Pz813d+FFRUXzzzTfN7hcUFACccwnLmY74\nHT169CVU3LmcsQvbZm1yrWlot+y+dlqtDZM8fz2u3LQLW6ulXlFTWuukuMZOSa0TlY+W0NAATNEm\nuvdLITDI3xXKrd2F7e0URaGyxkZ+STWWU+FeWlmPCggL8iUqzJ+k2CAu6xuDvwdNyOxIFEVBURSS\nk5NJS0vzqE1zhOdwa+Bv3LjRnS9PRkYG27Zt495773Vd+/HHHxk4cKDr/vPPP9/sTcAPP/xAQEAA\naWkyxngprGVl5Pzvf1BrfU7fUj7VGlbrtGiDg1rcO1sXttXmILewiuOWho1tqmpthATqiU8x0uss\nx8GKS1dvc1BQcqrFXlxDQWkNdocTo0FHVJgBU6g/PbuGetx8jY7MbrfTpUsX+vTpg4+P29twwoO5\n9acjNjbWnS/P7bffzu9+9zsWLVrEddddx8cff8zevXt54oknAOjfvz/p6enMmjWLRx99lMLCQhYs\nWMCUKVPkH9Yl0gUHk3TnlEv+OorS0C3cOPaeX1KDRq0iLjKAeJORjNTI8z4OVpy/M3XH63w0mMIM\nRIX6k5EaSUSIn8dsgNTZKIqCXq9n5MiRHrPnvfBsHplaBw8e5OuvvyYvL49JkyZhMBg4dOgQo0aN\nuqSv++sWRUpKCosXL2bBggW89tprJCUl8eqrrzab5PKvf/2LOXPmcNttt+Hv78+ECROYNm3aJdUh\nLl7jcbAnT21LW1NnJyzIl3iTkUFpJkxh/mg8YG/8zkK64z2ToigEBAQwatQo6b4X583jAv+pp55i\n9erVKIqCSqXi6quvpqKigpkzZzJ69GgWLlzYYub8+VqxYkWLa6NGjTrrG4mwsDAWLVp0Ua8nLk3j\n4TeNp8UVlNSg1WqIiwwgIcrI4J5REjKtSLrjOw6dTidhLy6YRwX+ihUrWLVqFXfffTdjx45lwoQJ\nAAwbNozJkyezfPlyli1b1qGPxxVnVltvJ+dXx8GGB/sRH2VkWJ9ojznZrqOT7viOTVEUhg8fLmEv\nLphHBf67777L1Vdfzf33309paanremBgILNnz6akpIRPPvlEAr8TUBSF/JIaV7gXltXiq9MQF2kk\nIcrI0N7RGHyl9X4ppDu+87HZbAwbNkx2yxMXxaMCPzs7m0mTJp3x/sCBA/n888/bsSLRVsoq6/l+\nr5l4k5ER/WLb5TjYzky64zs/u91Ojx49iI6OdncpooPyqMAPCQnBYrGc8X5WVpbMRu0kQgJ9uXF0\nN3eX0eFId7z3GD16NIcPH3Z9HhISQq9evdxYkejoPCrwr7rqKlavXs31119PWFgY8MvM+k2bNrFm\nzRpuvPFGd5YoRLuQ7nhxzz33sHnzZsrKyrDZbK79QYS4WB4V+DNnzmTr1q3cdNNNdO/eHZVKxeLF\ni5k/fz4HDhwgNjaWmTNnurtMIVqVdMeLc4mJiZFxe3HJPCrwAwMDWbt2La+99hobNmxAr9eze/du\nYmNjmTJlCnfffbd06YsOS7rjxcWwWq306NHD3WWITsCjAh/Az8/vjPvQA1RVVck7XeHRmnXHF9Vg\nKZHueHHxjEYjISEh7i5DdAIeFfgrVqw46yz9Tz/9lLlz57J58+Z2rEqIMzuv7vgk6Y4XFy8qKsrd\nJYhOwqMC/9lnn6Wuro6pU6c2u56Tk8MTTzzB5s2bCQ8Pd1N1wptJd7xwh/r6erefOSI6D48K/IkT\nJ/Liiy9SV1fHjBkzcDgcvP7667zyyitYrVZuu+02Zs2a5e4yRScm3fHCkyiKIt35otV4VODPmTOH\n4OBglixZgsViYe/evWRlZdGvXz8ef/xxOZJWtCrpjheezsfHB7VajnIWrcOjAh9g1qxZhIaGMnfu\nXNRqNU899RQ333yzu8sSHZh0x4uOSvbLF63JrYGfl5d32utXXnkl1dXVLFy4kB07djB8+HAURXHd\nj4mJaa8SRQci3fGis5GeJdGa3Br4Y8aMOecP9Lp16/jwww+bXcvMzGzLskQHIN3xwhvIz65oTW4N\n/GnTpskPtDgr6Y4X3mzQoEHuLkF0Im4N/DNtriO8j3THC9GSXq93dwmiE/G4SXvCO9TbHOzJKpTu\neCGEaCduDfy0tDT++c9/Mm7cOABSU1PP+QtepVKxf//+9ihPtCG73YlGo5bueCGEaCduDfzx48eT\nkJDQ7HNp0XkHfz8tA3pEursMIYTwGm4N/Llz5zb7fN68eW6qRAghhOjcPHYMf9++feTm5qLRaEhM\nTKR79+7uLkkIIYTosDwu8NevX88///lPCgoKXJvtqFQqunTpwmOPPcawYcPcXKEQQgjR8XhU4G/Y\nsIEHHniAxMRE/va3v5GYmIjD4eD48eO88847TJ06leXLl5ORkeHuUoUQQnQSq1ev5sknn0SlUrka\nmj4+Puzbt8/1mOXLl7NixQpKSkoYMGAAjz/+OImJie4q+aJ4VOC/8sor9OrVi9WrV7dYf3rrrbcy\nceJEXnrpJVauXOmmCoUQQnQ2hw4dYuzYsTz11FPNepYbvffeeyxevJi5c+fSpUsXXnjhBe68804+\n/fRTtNqOsy+IRx3DdPToUW688cbTbjZhMBj4/e9/3+wdlxBCCHGpsrKySEtLIzQ0lLCwMMLCwggN\nDXXdf/3115kyZQpXXXUV3bt35/nnn6e4uJgNGza4seoL51GBHxcXx9GjR894v6CggIiIiHasSAgh\nRGd3+PBhkpKSTnuvpKSE48ePM3jwYNc1g8FA79692b59e3uV2Co8KvD/+te/8r//+7+sXLkSu93e\n7N6nn37KqlWr+Nvf/uam6oQQQnQ2+fn5lJeX880333DNNdcwevRoHnzwQQoKCgCwWCyoVCpMJlOz\n50VGRmI2m91R8kXzqDH8FStWEBQUxLPPPsuiRYuIj49Hq9WSk5NDcXExGo2GZ599lmeffdb1HJVK\nxZdffunGqoUQQniq3Nxcxo4d22xCXiO9Xs+SJUtQqVTodDpefPFFSktLeeGFF5gyZQr/+c9/qK2t\ndT22KZ1Oh9VqbbfvozV4VODb7Xa6dOlCly5dml3v2rUrXbt2dU9RQgghOiyTycRnn3122ntqtZrE\nxES+//57goODXdeXLFnCyJEj2bRpEzExMQAtwt1qteLn59d2hbcBjwr8082+Ly8vx9fXV06NEkII\nLzd27NgW16ZPn37Wk1d9fHzO2WBsGvYAERERhISEYDabycjIQFEUCgoKiI+Pdz2moKCAbt26XeB3\n4F5uH8O32Wy8++67/P3vf292ffv27Vx33XUMHTqU/v37c+edd5Kdne2mKoUQQrhLVFQUGzdu5Oef\nf+bgwYPNPi71mPWVK1cyYsQIHA6H61pubi4lJSV0796d0NBQEhMT2bZtm+t+dXU1+/btY9CgQZf0\n2u3NrYFvtVqZPHkyc+bM4ZNPPnFN1Dt+/Dh33HEHR48eZcSIEUyePJljx45xyy23UFRU5M6ShRBC\ntDMfHx/i4uLw8Wn9TunRo0dTU1PDI488wtGjR9mxYwczZsxg0KBBrp1dp0yZwtKlS/n00085dOgQ\nDzzwACaTiauuuqrV62lLbg38FStW8NNPP/Hggw+ydetW11/mokWLqK+v57rrrmPp0qU89NBDvP/+\n+2g0Gl599VV3liyEEKITiY+P580338RsNjNhwgSmTZtGWloaS5YscT1m4sSJ3HPPPcybN4+JEyfi\ndDpZtmxZm7wBaUsq5dfTFtvRTTfdREJCAi+99JLrmtVqZciQIdTV1fHBBx+Qlpbmuvfiiy+yfv36\nDj0rf/To0Xz99dfuLkMIIYSXcWsL/8SJEwwcOLDZtV27dlFbW0tERESzsAdISEhwrY0UQgghxPlz\na+A7nU40Gk2za99//z0Al112WYvHV1ZWdrhlEEIIIYQncGvgJyQkkJmZ2ezal19+iUqlYvTo0S0e\nv2XLFhISElq1hiNHjpCamkpaWhqpqamu///pp59crzl+/HjS09P57W9/yzfffNOqry+EEEK0B7cG\n/nXXXceHH37Il19+SW1tLcuXLycrK4uwsDDGjBnT7LEfffQR33777WnXYV6KgwcPEhoayrfffuv6\n2LJlC+np6Rw+fJg///nPXHvttaxbt44xY8Ywbdo0jhw50qo1CCGEEG3NrVMMJ0+ezObNm5k+fbpr\n20OtVsszzzyDTqcD4IsvvuDtt99m69atdO3alcmTJ7dqDVlZWSQnJzc7GanRihUr6NevH1OnTgVg\n5syZ7Nixg7feeosnn3yyVesQQggh2pJbA1+n07F8+XI+/fRTdu3ahb+/PzfccEOz3Yv27dvHTz/9\nxA033MDs2bPx9fVt1RoaA/90duzYwTXXXNPs2uDBg8+4TaMQQgjhqdy+iFCj0TBu3DjGjRt32vv3\n3HMPM2fORK1um9GHrKws6uvrueWWW8jNzaV79+7cf//99O3bF4vF0uKEJJPJ1OFOSBJCCCHcvrXu\nufj5+bVZ2NfX15OdnU11dTUPPfQQr7zyCpGRkUyaNIkjR45QV1fXYg9/rVbb4U5IEkIIIdzewncn\nvV7Ptm3b0Ol0aLVaAObNm8f+/ft555138PX1bRHuNptNlgYKIYTocLw68AH8/f2bfa5SqUhOTsZs\nNhMdHU1hYWGz+/n5+S26+U9n0aJFLF68uMV1p9N5aQULIYQQF8Hju/Tb0s8//0xGRgb79+93XXM6\nnWRmZtK9e3cGDBjA1q1bmz3nxx9/bLE74Oncd999LU51OnjwILGxsa3+fQghhBDn4tWBn5qaSlxc\nHI899hh79uwhKyuL2bNnU1ZWxqRJk7j99tvZvn07ixYt4ujRoyxcuJC9e/cyadIkd5cuhBBCXBCv\nDnyNRsOyZcvo2rUr9957LxMmTKC4uJhVq1YRGhpKSkoKixcvZsOGDdx44418/fXXvPrqqyQlJbm7\ndCGEEOKCuPW0PG8kp+UJIYRwB69u4QshhBDeQgJfCCGE8AIS+EIIIYQXkMAXQgghvIAEvhBCCOEF\nJPCFEEIILyCBL4QQQngBCXwhhBDCC0jgCyGEEF5AAl8IIYTwAhL4QgghhBeQwBdCCCG8gAS+EEII\n4QUk8IUQQggvIIEvhBBCeAEJfCGEEMILSOALIYQQXkACXwghhPACEvhCCCGEF5DAF0IIIbyABL4Q\nQgjhBSTwhRBCCC8ggS+EEEJ4AQl8IYQQwgtI4AshhBBeQAJfCCGE8AIS+EIIIYQXkMAXQgghvIAE\nvhBCCOEFJPCFEEIILyCBL4QQQngBCXwhhBDCC0jgCyGEEF5AAl8IIYTwAhL4QgghhBeQwBdCCCG8\ngAS+EEII4QUk8M/B6XTy/PPPM3z4cPr378+MGTMoLi52d1lCCCHEBZHAP4eXX36ZDz/8kOeee47V\nq1eTn5/PjBkz3F2WEEIIcUEk8M/CZrOxcuVK/vKXvzBs2DDS0tJ44YUX2LFjB7t27XJ3eUIIIcR5\nk8A/i8zMTGpqahg8eLDrWmxsLLGxsWzfvt2NlQkhhBAXRgL/LPLz8wEwmUzNrkdGRmKxWNxRkhBC\nCHFRJPDPora2FrVajUajaXZdp9NRX1/vpqqEEEKICyeBfxa+vr44nU6cTmez61arFT8/PzdVJYQQ\nQlw4H3cX4MmioqIAKCwsbNatX1BQ0KKb/9cWLVrE4sWLW1xvOh9ACCGEaC8qRVEUdxfhqaxWK8OG\nDWPOnDmMGzcOgJycHK688krWrl1L37593VyhEEIIcX6khX8WOp2OW2+9lfnz5xMcHExoaChPPvkk\nQ4YMkbAXQgjRoUgL/xwcDgcLFixg3bp12O12Ro4cyaOPPkpwcLC7SxNCCCHOmwS+EEII4QVklr4Q\nQgjhBSTwhRBCCC8ggS+EEEJ4AQl8IYQQwgtI4AshhBBeQAJfCCGE8AIS+EIIIYQXkMAXQgghvIAE\nvhBCCOEFJPCFEEIILyCBL4QQQngBCXwhhBDCC0jgCyGEEF5AAl8IIYTwAhL4QgghhBeQwBdCCCG8\ngAS+EEII4QUk8IUQQggvIIEvhBBCeAEJfCGEEMILSOALIYQQXkACXwghhPACEvhCCCGEF5DAF0II\nIbyABL4QQniwMWPGkJqa6vro06cPV155JfPnz6e6uhqArVu3kpaWRn5+PgB5eXn8/ve/p0+fPtx/\n//3Y7Xbuv/9++vXrx8iRI9357Qg38nF3AUIIIc7u7rvvZtKkSQDU1NSwb98+5s2bx+7du1mxYgUD\nBgxgy5YthIWFAbBq1SosFgsfffQRQUFBfPvtt3z22WcsXbqUHj16uPNbEW4kgS+EEB7Oz8/PFeZh\nYWHEx8eTmJjI7373O95//31uueUW132AiooKunbtSteuXQEoLy9HpVJJ697LSeALIUQH1LNnTzIy\nMli/fj1du3Zl0qRJbNq0ib/+9a9s27YNgLS0NAYOHNjs82nTpjF9+nS2b9/OggULyMzMJDIykmuu\nuYbp06ej0+kASE1N5c9//jPvv/8+KpWK999/H61Wy9y5c/nvf/8LQHp6On//+99dbyz+/ve/o1ar\n8fPz4+OPP8ZqtTJmzBieeuopDAYDACdOnGDu3Lls27YNvV7PmDFjePjhhzEYDCiKwr///W/Wrl1L\naWkp3bp1Y/r06YwaNaq9/3g7JRnDF0KIDiolJYVDhw4BoFKpAPjXv/7F9ddfT//+/fn222959dVX\nefTRR1GpVHz77bfccccdZGZmcuedd3L11VfzySef8PTTT/PVV1/x+OOPN/v67733HkuXLmXRokWE\nhoZy1113UVxczJtvvsnq1auJjY3ltttuo7y83PWcjz76CEVRWLt2LQsXLuS///0vK1asAKCyspLb\nb78djUbDO++8w6uvvsrOnTtdr7tgwQL+85//8PTTT/PRRx8xfvx4ZsyY4XrDIi6NtPCFEKKDCgwM\npKqqCgBFUVzXfH190Wq1hIaGAmA0GgFcn7/xxhuMHj2ayZMnAxAfH88TTzzBrbfeygMPPEB4eDgA\nN910k2vM/7vvvuPnn3/mxx9/xN/fH4DHH3+c7777jjVr1jB16lQAQkJC+Mc//oFKpSIxMZHLL7+c\nXbt2AbB+/Xpqa2t57rnnXC3+Z555hu+++46amhpWrlzJokWLuOyyywC47bbbOHDgAK+++iqDBg1q\nuz9ILyGBL4QQHVRVVRWBgYEX/LzMzExOnDhB//79m11Xq9UcOXLEFfhxcXHNnmO32xk+fHiz59hs\nNo4dO+b6PCEhwdXbAA1vNgoKCgDIysoiKSnJFfYA/fr1o1+/fuzduxer1cqsWbOafX2Hw+GqR1wa\nCXwhhOig9u/fT1paGkCzkD0XrVbLjTfeyF133dXiXkREhOv/fX19mz0nODiY9957r8VzmgZ44xyA\nphp7H3x8zhw5Wq0WgMWLF5OQkNDsnloto8+tQf4UhRCiAzpw4AA7d+7khhtuuODnduvWjSNHjhAf\nH+/6KCoqYt68ea61/b/WvXt311h943NiY2N58cUXz3uMPTk5mWPHjlFbW+u6tnnzZq644gq6du2K\nj48PZrO5WV0ffvghH3zwwQV/j6IlaeELt7Db7VgsFneXITxUVFTUWVuD3qampoaioiIAamtr2bNn\nD88//zyDBw/mhhtuYNu2ba5W9Pm46667uOmmm5g3bx4TJkygqKiIRx99lKioqGbL+5oaNmwY6enp\nzJw5k4cffpiwsDCWLl3KV199xbRp087rdceNG8eSJUuYPXs206dPp6qqinnz5jF06FD0ej1Tpkzh\nhRdewN/fn969e/PVV1/xyiuv8Mwzz5z39ybOTP5FCbewWCyMHTvW3WUID7Vx48Zm48febtmyZSxb\ntgwAf39/YmJimDhxIn/84x9dXfkX0qWfkpLC0qVLWbhwIe+88w5Go5ExY8bw4IMPuh5zuq+3ZMkS\n5s+fz7Rp07BarfTs2ZPXX3+d5OTk83pdPz8/Xn/9dZ599lkmTJiAv78/V199NX/9618BmDVrFjqd\njueee46ioiLi4+N58sknGT9+/Hl/b+LMVMqFvC0UopXY7XZ69erFxo0b3V2K8DBjx47l559/lha+\nEK1M/kUJt2j8ZS6tOHE6EvZCtD6ZtCeEEEJ4AQl8IYQQwgtI4AshhBBeQAJfuM306dPdXYLwQPJz\nIUTbkFn6QgghhBeQFr4QQgjhBSTwhRBCCC8ggS+EEEJ4AQl8IYQQwgtI4AshhBBeQAJfCCGE8AIS\n+EIIIYQXkMAXQgghvIAEvmg1v97Dyel0uqkS4Wlkfy8h3E8CX7QKRVFQqVQArFmzhiNHjqBWqyX0\nBQAqlYoNGzawePFid5cihNeSwBeXzOl0usI+MzOTd999lwceeICcnBwJfS/X2LI/fvw4jz32GL6+\nvthsNjdXJYR3kr30Rat56aWX+P777yktLSU7O5uUlBQWLlxIly5dcDqdqNXy/tIbbdmyhbfffhtf\nX19eeukld5cjhNeS38CiVaxdu5ZVq1Zx//3388Ybb/D8888TERHBfffdx4kTJ6Sl78V0Oh1ff/01\nn3/+OTt27HB3OUJ4LQl80SoOHDjAddddx9ChQ4mLi+Paa6/l3nvvxWg0MmvWLHJzcyX0vdTgwYN5\n55130Gg0vPbaaxw7dszdJQnhlSTwxQU73ShQTU0Nhw8fbnYvIyOD4cOHk5mZyYwZMzh58iRqtVpm\nbHdijX+3WVlZfPXVV6xbt44TJ07Qv39/3nrrLTZv3szLL7/MyZMn3VypEN5HM2fOnDleFHo7AAAK\nGklEQVTuLkJ0HE3H4q1WKxqNBoCSkhK2bNlCbGwssbGxruuFhYUUFhYSGBjI999/z6hRo9Dr9W6r\nX7Stxtn4Dz/8MLt37+bgwYM899xzREREcNVVVzF48GBeeOEFioqK6N69OyEhIe4uWQivIS18cd4U\nRXGF/apVq3jooYd47rnnOHbsGDfffDORkZHMnz+fTz/9lMLCQkpLS1m3bh09e/bk6quv5sCBAxQW\nFrr5uxBtac+ePTzyyCNMnTqVDz74gGeffRYAu91OWVkZAwcO5I033mD9+vUsW7ZMZuwL0Y4k8MV5\nabrO/sUXX2TBggUoisLbb7/N3//+d37++WfeeustkpOT+fe//83//M//8Mc//pGcnBz+8Y9/MGLE\nCHQ6HXa73c3fiWgt5eXlQMPPRmNXfk5ODkOGDGHChAlkZ2czefJkJk6cyG9+8xuefvppjh496hrT\nv/POO9Fqte78FoTwKj7uLkB4vqZhf+jQIXbt2sXy5ctJT08nNzeXe+65h7lz5/K3v/2NJUuWcODA\nAQ4ePIjRaGTEiBEALF++HD8/P8LDw935rYhW9Morr5Camsr48eMxm82EhYVhNpvJy8vjxIkTTJ48\nmZEjRzJnzhyKi4vZtm0b3333HUlJSfTv39/d5QvhdaSFL85o8uTJ7Nu3zxX2q1ev5umnn6auro4u\nXboAEBsby0svvUR5eTn//Oc/+eGHH0hNTeW3v/0tYWFhzJs3jwcffJD33nuPZ599VsZsO4FFixbx\n5ZdfotfrmT17No899hhTp06lsLCQPn36oCgKv/vd7xg6dChPPvmk63kBAQHExMS4sXIhvJsEvjit\n8vJyevXqRUpKiutajx49MJvNHDhwoNl66uTkZF5++WUqKyt55pln2L17NwDHjh2jpKQEg8HAmjVr\nSEtLa/fvQ7Su5cuX89Zbb9G1a1emTp3K5Zdfztq1a+nVqxexsbGkp6eTlJSETqcjOTmZ6upqSktL\nefvtt6mtrZWfASHcSHbaE6fVtBt/6dKl9OjRg1GjRnHgwAFmzpxJTEwM99xzD0OGDHE959ChQyxb\ntoy5c+fi4/PLaJHsstd5zJ49m2PHjrFmzRry8/NZsGAB+fn5bN26laeeeoqbb76Zuro6HnnkEQ4c\nOEBOTg4pKSmYzWaWLl1Kz5493f0tCOG1JPBFC00DOjs7m5dffpnPPvuM1157jaFDh7Jnzx4efPBB\nEhISuOuuuxg8eHCLr2Gz2VwTspq+eRAd2/vvv88LL7xAamoq+/btY+3atRgMBpYuXcrKlStdoW+1\nWjl69Cj79+8nKCiItLQ06c4Xws1kHb5opmnYz58/nw8++ID777+f8vJyFi1aRN++fRk4cCD9+vXj\nvffe49ixYwQHB5OYmNjs6zSuwwck7DuRnj17sn//fjZu3Ejfvn2ZMmUK/v7+dOvWDUVReOmll4iO\njqZPnz6YzWaGDBlCSkoKRqPR3aUL4fUk8EUzjeG8d+9e1q9fz4MPPkhKSgoDBgwgJyeHf/3rX81C\nf8mSJRiNRoYPH+7mykVbauylKSoq4oMPPqBPnz5kZWVx8uRJRo0ahdFoJDk5GYAXXniBr776ig0b\nNnD99dfj5+fn5uqFECCBL07j888/509/+hMGg4Gbb76ZgIAADAYDGRkZrtBPT08nIyODK6+8kuuu\nu07G6DuxxrDfs2cPP/zwA9HR0fzlL3/B4XCwfv36ZqGfkpLimqw3e/Zs4uPj3V2+EOIUCXzRYow9\nODiYvLw8duzYwbBhw1zd9Y2hn5eXx3PPPceoUaNITU1FrVbjcDgk9Duhxp+N//u//+Pee+8lOzub\nTz75hJKSEq699loCAwP55JNPXKEfEBBAr169GDt2LJGRke4uXwjRhAS+cIX92rVr2bBhA2PHjmXw\n4MEcPHiQlStXMmTIEEwmE9AQ+v369cNgMHDNNde4Ql7CvnNSqVT8+OOP/PWvf+WRRx7h6aefJikp\niRdffBGdTsd1111HQEAAn332GZmZmVx55ZWA/DwI4Ykk8AUA1dXVvPPOO3z//fdUV1czYsQIRowY\nwe7du3nttdcYNmyYq8Xm7+/PkCFDUKvV2O12+eXeya1du5aoqChmzJhBUVERCxYsoHfv3nz++efU\n19czbtw41Go1P/30E2PHjsVgMLi7ZCHEaUjgCwB0Oh19+/aloKCAr776ivLyckaMGMHIkSPZs2cP\nb775JgMGDCA6OrrZ8yTsOzen08myZcsICQnhiiuu4JVXXsHX15e5c+cSHh7O4sWLKSsro3fv3sya\nNYuwsDB3lyyEOAP5be1l3nzzTfLz812fN+6KB2AymZg6dSoDBw7k008/5d///jfBwcH885//JDo6\nmiVLlrijZOFGarWa6dOnM2jQIA4cOMD+/fu5/PLLXRsrdenSheLiYi677DLZNlkIDyctfC+SlZXF\ntGnTKCwsZPDgwfz44488/PDD2Gw2BgwYADTsd96jRw8OHz7Mp59+ilqtZtiwYVx77bXcdNNNsqbe\nC8XExNCjRw8++eQTtm/fzmOPPQbARx99RFpaGk888QShoaFurvL/t3e/Lu3EcRzHX6dzoE4FdUPc\nH7AVgzIQDcLKQGEOsWgQNJoXjBbjEPEHaHeMWeetCIJgsVu0CIIsTIeHHk7B2zeIx9ekTbzP8xGP\nC9eefLj35/MB8B2Cb5CBgQGNj49rb29PtVpNIyMjenp60sXFhVzX9W8w6+np0fDwsMrlsi4vLxWJ\nRDQ6OirLsuR5HtE31O3trY6PjyVJtm2rUqlobW1NQ0NDv/xlAH6C4BviM9TxeFyJREKbm5uKxWKa\nnp5WvV7X+fm5XNf1V/qO46hWq2l5eVm5XM6PPLE3VzQaVaPR0OnpqVzX1dbWlhKJxG9/FoAfCn3/\nCoLgc7iuUCioXq+ru7tbBwcHchxH8/PzsixLlUpFj4+PymazKhQK6uvr08zMjCzL0vv7+5fjcmGe\n/v5+ra+v6+XlRa1Wi+NygT+Gy3MMUiwWtbu7q52dHYVCITUaDeXzeWWzWeVyOVWrVdm2rXA4rFgs\nplKppI6ODi6/AYAAYIVvkKurK6XTaaVSKf9ZqVTS4uKiwuGwFhYWtLS0pPv7e42Njfn77P+/6hYA\n8DexLc8ArVZLnufp7u5OjuP4z9/e3pRMJrW6uqrDw0Ntb28rGo0qlUr5x+USewAIBoJvAMuy1NbW\nprm5OZ2dncm2bUkfh+1IUm9vryYmJvT8/PzlZjP+2QNAcLB8M0g6ndbs7Kw2Njb0+vqqTCajZrOp\nk5MTTU1NaWVlRdLHRD8n6AFAsDC0Z5iHhwft7++rWCxqcHBQnZ2d6urq0tHREQN6ABBgBN9Anufp\n+vpaNzc3ikQimpycVHt7OwN6ABBgBB+SxD57AAg4gg8AgAGYzAIAwAAEHwAAAxB8AAAMQPABADAA\nwQcAwAAEHwAAAxB8AAAMQPABADDAP3hEgPRPlO8mAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ed048d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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eBFBrscmpTp06OvfXp08flixZQmBgoFrlPCAggIyMDBo3bqwxb3bSpEmcPn2a\nu3fvMm7cOAwNDWnYsCEtWrSgdevWWtMcVBWcWrVqadyuKs/w7Nmz3L59W2MZ/aZUn9PWrVtzBRcq\nqp4BTb8nZmZmb5wDraKqGLdt2zZXxdjY2JiOHTvi7+9PUFAQ7u7ueaYtvAvV1EjZ52F88eIFfn5+\nLF26lBcvXmBoaEibNm00rq/6HC9evMjAgQM1vkeVq/fy5UseP36MhYUFUVFReaa+ZJ92TFX+mpqa\n5qoEZ1e7dm0uX76sMfdU0/2XlJTE48ePC6U8UKUa5KRqKcx53+b1edSpUyfXxO79+/fHz8+PyMhI\n5s6dy7fffoudnR3NmzenVatWODg45Du3PN8BY36mI8jrUWUqpqamyjQeU6ZMYdy4cZw+fZrg4GA8\nPDzUArTevXvzyy+/4O/vrwSMqilVtLUu6hrxq1qm6j4PDw9n3rx5GoM6Z2dnevfurVaw3rp1S2Mg\n0qJFC1q0aKH22oMHD7Q+EkjVHZ2enq4zsFF1sb5rwJicnKy0zsyfP/+tJ1VNTEzE39+fLVu2KBew\niYkJ/fr1w8XFJVdBnX0/mro3NR1nzvVUVD8asiwzZswYndv5+++/iY+Pz3e3qaWlJU5OTjg5OSkT\nyT969IhNmzYpAePEiRPV0hNULCwsWLp0qdKKU6xYMa37UaUa5FSjRg0eP36sFK6arF+/PtdrQ4YM\nyXOez5xUrReSJPHdd9/pnJD3ypUrhIeHqxWI1tbW3LlzR+ex5nTv3j2KFy+utVsue9khSRLVq1fn\nl19+wd3dnZ07dzJ58mQ8PT01ttwWtOytcdq+S23f47t62/syr/I5++erCvR1Xad5BSC9evVi6dKl\nXLp0iXv37inzzKnKZm1zL9rZ2eHv78/KlSvZv38/z58/5/Tp05w6dYolS5Zga2vL3Llz1Srfz58/\nV7pnr127lmfu7JYtW5g1a5bO9+SHqiIWHR2tc1YFSZI09q697XcZHh5OeHg4kiQRFBSk81GxKSkp\n7Nq1i8GDB7/Vvt5WiRIlGD58ODY2Nnz11VdKy9WyZctyvVf12cTHx2tMYVBR9fC8fPkSCwsLpYtU\n13Waner7yuvaVS3XNBWZpi7d/JQHRkZGGBoavnGvy/u4b83MzPD19WX16tXs2bOH6OhowsPDCQsL\nY9WqVZQpU4YpU6bkawaLQpt+fMyYMdy6dYsJEybo7N83MzNj3rx5dO7cmaysLKKjo9UCxl69erFs\n2TLOnTtEr7cAAAAgAElEQVRHbGwshoaGHDt2DAMDA62z3evqwlVdVKofroSEBK3DylUJymXKlKFW\nrVqEhYURHByc5xx5eblx4wZhYWFIkkTp0qW1zgKfmprKs2fPuHHjBhcvXnzr7g14/cN///59JEnS\nWbioEpPhdSqAai67a9eusXnzZvbs2UNycrJS23JxcaFnz55aL2Jzc3PlRtLVeqaiei5szppeRkYG\ngYGBSJJE8eLFteZeyrJMXFwcGRkZbN++Xcl3e/TokZJns2HDBp0TqVavXp1x48YxZ84ctWmYrl69\nysOHD3O9X5V/qspP1DUnorapMNq0acPRo0cJCwvjwYMHajmtBU3VelGsWDGd81E+evRImWJn3rx5\nyuvt27cnJCSE8PBwXrx4ka/crG+++Ybjx4/j5OTEjz/+mO9j/fbbb7l27RoRERHMmjWLGjVqFEjr\nkS7Z80wTExM15hHmVfnRVnnOmef9IajuHV3XaV7zelpYWNCqVSslWX/s2LFERERw/fp1zMzMlLlR\nNfnkk0+YN28e8+bN4+rVq5w+fZoTJ05w8uRJIiIiGDVqFMHBwUqrqb+/P+np6ejr6+tsgU1ISCAp\nKYmAgACmT5+u8cf/TRgbG5OYmMiKFSu0tp4VBlXFuEiRIjorvM+fPyclJYWtW7e+94BRpWXLlri6\nuuLt7c3+/ftZvnw5rq6uau8pVqwYkiTx5Zdf5usJUtnXg7yvRRVVY1BePS6qHGxdDUvZZS8PtB1L\nVlZWoT9KVnXf6jo/bcdnYmLCxIkTmThxItHR0Zw6dYqTJ09y5MgRnj59ioeHB6VKldLYpZ1doQWM\nSUlJPHjwgIMHD+aZEJq9EMh5g1haWtKiRQuOHj3K33//jbGxMRkZGXTs2FFrQviTJ09ITEzUGG1f\nu3YN+Kcpu0mTJoSHh+d5PoMHD2bmzJmcO3eOEydOvNOjrlQFQokSJQgNDdUaMD5+/Ji2bduSmZnJ\nli1b3ilgNDMzy9Vlnt3Dhw+JiYnBzMxMGaSkKnCDgoKYOnUqkiShr69Pp06dGDRokPJUmrzY2tpy\n7do1ZeoPXVRdzjlbEUJDQ3n69CmSJLF27VqNqQgq3bt35+bNm2zbtk0JGEuVKsXNmzfJzMzkyJEj\nWrtGVFTXZPZrLK+nSai6yu7evasMzMlJ2xMaevbsydKlS0lKSmLZsmUsXLhQ577e1vXr15XWix9/\n/FHnPJNjxowhNDSU3bt34+bmphSw7dq1w9jYmJSUFFavXp3nj0BkZKQyFY+u700TQ0NDFi5cSN++\nfUlJSeHrr79m+/bthfq0DisrK4yMjEhLSyM8PFzjyFZtrauqFAxtk+6rKkQfkqrs0zW5cn5mZ3B2\ndiY0NJSgoCDGjh2rjJru1q2b1mDtwYMHREdHK+VnnTp1qFOnDiNGjCAqKoo+ffqQlJTEX3/9pcxf\nqxpE07p1a7VBETnt27ePSZMmKbm37zphuLW1NVeuXOHmzZtaA8aoqCgSEhKwsrIqkEFgaWlp7N69\nWxm4oStnd+XKlSxZsoRbt25x9uxZtcFL75OrqysHDx7k2rVr+Pj40KZNG7X73NramtDQUJ09EvHx\n8dy9excrKyslB7JKlSp5rjdz5kzi4+MZPHiwkteXmJhIdHS01m7pq1evIklSng/fUDE2NsbS0pJH\njx4RFhamsYX79u3byLJcqI8irVGjBqGhoTpnl1ENcMkuPj6eO3fuYGNjQ4kSJahUqRKVKlWib9++\nvHr1isGDBxMeHo6/v3+eAWOhlbqqJNb9+/dz8uRJne/dvXs38M/cdzmpJovdv3+/MlBEV2GgmmIh\np6SkJKXw0dZVrE2vXr2oX78+WVlZuLm55Wsme00DBtLT05UCoUePHjqfMWlhYaE8ySA4OFipGb2N\nmjVrsmnTJq3/VJ+n6n0bN25URuympqZSpkwZxo4dS0hICEuXLs13sAivJ4VWPb1GVyvjrVu3lOkJ\ncrZQqLrwa9SokWfQoRqQ8ODBA2UOPQMDA2U6kJUrV+b5w626Jh0dHXW+L7vGjRtTunRpMjIyNI4U\nT0lJUb77nMzMzJg6dary6Ls//vgjz/2FhYVpnQNOFVDlbOlSVVZKliyZZ+GgSoNITk5Wm6nA3Nyc\nsWPHIssy69at03l/p6am4u7uTlZWFhYWFvnOB82uZs2ajBo1ClmWCQ8PZ/Xq1W+8jTdhZGSkzIWq\nbVCBarqNnFQVjHv37mmcpqIgR/G+LUdHR4oUKcLDhw81zgualpamdC3r0rZtW8qUKcPt27e5c+cO\n+/bt0zovLrxuEevYsSMjRoxQKu7ZqR4QAP/kboWFhSnBa15dZp999plS0XuTwSDa7hXVJPa+vr4a\nKwCZmZm4urri7OzMokWL8r0/XUJCQpRyPq+A19nZWenSfF+DXzTR19fnhx9+wMDAgMzMTDw8PNQG\n8anKmaNHjxIVFaVxG4sWLWLgwIFqMxKogvTTp09rnNj78ePHBAYGcujQIYoVK0a1atWUIFHbBOln\nzpwhLCwM4I2m4erYsaPOGTjexxRHqsr9uXPnNKZIPHnyRONclkOHDmXw4MEaZ5spVqwY9erVQ5bl\nfA28LLSAsWfPnjRo0ICsrCzGjBmDj49PrvyvpKQk1qxZw/fff4++vr7ak16ya9euHSVLluTs2bMc\nO3aM0qVL5/llL1myhH379il/P336lPHjxxMXF0elSpXe+IkTqkk9ra2tefz4MX379sXb2zvXxM9J\nSUns3r2bnj17KvO4lS9fXmntPHDggJKLkJ8asOpHOzU1VePkmmlpaTx79kznv3d9xKBqnqgJEyZo\nTa7XZejQoVSsWJGEhAQGDRqUK+dONTpu9OjRZGVl0bhxYzp16qQsf/LkCUeOHNH5Y5Sdk5OT0nyf\nfS7BSZMmUapUKWJjY+nbty/+/v65ughjY2OZOXMme/bsoUyZMnnmSmanp6fHpEmTkGWZxYsXq+Ue\nPXv2jEmTJumc53HgwIFKq8qPP/7IV199xcmTJ3P9kF26dIkZM2bQt29fnjx5gr6+fq5Rc8WKFUOW\nZWWeT3izygq8LlBVKQk5C8Thw4fTpEkT0tLSGDVqFF5eXrnyky5cuMDAgQO5ePEiRYoUYdGiRW+d\n1zV27FhltJ+Pj4/OnLKCMGHCBIoUKcLff/+Np6en0t2UkZHB0qVLtU5arWrFf/nyJUuWLFECn5SU\nFBYvXqxxTlmVt22deNP1SpcuzcCBA5FlGTc3Ny5cuKAsU12nmp6akpO+vr4yYnnZsmVER0dTo0YN\nrQNmzM3NlUDc3d1dbaCILMts2rSJmzdvoqenR6tWrYB/Kjj56S7T19enb9++yLLM1atX89VzBJrv\nFXg904Zq8MWYMWPU0lHi4+OZNGkSd+7cwcjIiJEjR+ZrX3lRVYxr166d6znyOZUqVUoJZPbv31+g\ng9DelK2tLSNHjtRYqWvSpAnNmzcnPT2dUaNGqT0COC0tDW9vb6UhJ/uUWc2aNaNhw4ZkZGQwduxY\ntcpxbGwskyZNIj09HXt7e2V0s6r83bx5Mz4+PmrdxCdOnGDy5MnKYKI3afT48ssvKV68OJcvX8bd\n3V0t7W3jxo3Kk+AKU506dXB0dFQ+j+z3z/3793F1ddWYjqfq1fPy8spVQTx9+rTym5CftItC65Iu\nUqQIq1atYvr06Rw6dAgvLy+8vb355JNPKFmyJElJSURFRZGRkYGJiYnyyDlt2+revTvr168nKysL\nJycnnV1SVlZWlC5dmkmTJmFlZUXJkiW5efMm6enpWFpa4uPj81Y/XBYWFmzbto0FCxbg7++Pj48P\nPj4+WFhYULZsWRITE3nw4AHp6elKrt3AgQMZM2aMsj9VAVizZs18TXz86aefYm1tTWRkJFu3blWr\ngcmyzJ49e/J8hODMmTO1TjOQH/mdC06bokWLsnLlSiZOnMitW7cYPHgwpUuXply5cmRlZXHv3j0S\nExORJIkWLVrkmgR+586dZGRkYGhoqDVvNTtTU1O6d+/Otm3blOk4LC0tqVixImvXrmXatGncvn0b\nNzc3Zs+eTcWKFZUJjlV5npUrV+aXX37JNUVEXvr160dERASbNm1i6tSpeHp6Kt3h6enpfPbZZ1qD\nDYBZs2ZhZ2fHokWLOHz4MIcOHcLExAQrKysMDAyIiYnhxYsXSoK4o6MjX3/9da68vlq1anHw4EEC\nAgK4fv06jRs3xsHBgefPn+fZQq8iSRIDBgxgyZIlREREcOHCBRo2bAi8brFdvXo1M2bMICgoCB8f\nH1asWEH58uUpVaoUMTExPHnyBEmSsLCw4KefftI5uCsvhoaGzJs3j6FDh5KSkoKHh4fGQUAFxcbG\nhgULFjBr1ixWrVqFr6+vMmPDixcv6Nixo8bWQhsbG3r06EFgYCBr1qzB398fS0tLoqOjSUpKYsqU\nKfz8888a95nfQYMFsd7UqVMJDw/nzJkzuLi4ULlyZUxNTZW0jbp163LlypU8KxXOzs6sWbNG65Nd\ncvr222+5fv06N2/epFu3bnzyySeYmZkRExPDs2fPkCSJqVOnUq1aNbXu2R49euRrxgXV08iysrL4\n888/dQ7oUtF0r8yZM4fixYsrOXknTpygffv2VKtWDT09PSIjI0lLS8PAwIDFixcXyAj+uLg4jh8/\nrnPQUE4uLi7s2bOH9PR0duzYwahRo975OHLK7/U1btw4goODiYqKwsfHh44dOyrdvp6enowZM4Yr\nV67Qv39/KlasSPHixYmOjiYhIQFJkhg1alSucumXX35h1KhR3Lp1i65du1KtWjUkSSIyMpLMzEwq\nV66sNrPK559/TnR0NF5eXnh5efHHH39QpUoV4uPjefDgAZIk8emnn75xyk/ZsmX5+eefmTRpErt2\n7WLfvn1Uq1aNhw8f8vTpUzp06PBeeg8WLFjAkCFDuH37Np9//jnVq1dHX1+fmzdvYmhoSI0aNYiI\niFC7V4YPH86JEyc4fvw4I0aMoGzZspQtW5b4+HhiYmKUR09qmzIou3y1MKp+nPJ6T06mpqasWLGC\ndevWMWjQIGxtbXn16hXh4eHEx8dTs2ZNJkyYwN69e3PNrZVT9gtJ14+dJEkULVqUdevWMWLECOB1\nvlHZsmUZNmwYu3bteqeb28zMjB9++IG9e/fi6upKw4YNSU9P5/r16zx9+pQKFSrQvXt3FixYwOHD\nh5k8ebISLD569Ijjx4+jp6eX7wIBXnexSpJEVFSUWvdf9rkDtf17k1yv/HzPb8va2prt27fzww8/\n4OjoiImJCZGRkdy7dw9LS0u6dOnC77//zqpVq3Llnqpqn+3atcv3o+gGDhyIJEnK9DgqNWrUICAg\ngJ9++onPP/+c8uXL8+TJE8LDw0lLS6NFixbMmTOHwMDAt54qwcPDA29vb5o1a8arV6+4c+cO9erV\nY/Xq1XTo0CHPz7lPnz6Ehoby3Xff8dlnn2Fubk50dDR37tzB2NiY5s2bM3HiRIKDg1mxYoXGQSBf\nfvkl/fr1o2TJkkRFRREREaF8jnXq1Mmz9ULF2dlZmRok55NfihQpwuLFi1m/fj3Ozs5YW1sTHx9P\nWFgYGRkZODg44Obmxt69e7Xm/L7JNde0aVOcnZ2RJIkzZ84U6gTxgFLpUE0yHRERgYWFBbNnz2bm\nzJmA5nJv4cKFzJ49mzp16vDq1Svu379P/fr1WbVqlfKDrmm9vD4LbcvfZj0jIyPWrl2Lm5sbtWrV\n4vHjx0RFReHg4MC6deuU7q+8KtZVq1bF3t5eKXfzKsctLCzYvn07I0eOpHr16jx+/JiIiAiKFi1K\nt27dlPl64fVMB6pgIr89QuXKlVNaIoOCgvI1YCLnvZI9Z65OnTrs3r2bcePGYWdnR0xMDHfu3KFM\nmTL06tULPz8/5bnW2b1NWbpz505kWVY+i/xo3LixMtVL9vuhIMvy/G7L0NCQb7/9FkmSSE1NVXtc\nXqlSpfjzzz/59ttvcXBw4OXLl9y4cQNDQ0Patm3LihUrmDZtWq5tWlpa4uvry/Tp06lduzYPHz4k\nOjqaKlWqMHbsWPz8/HJV6l1dXdmyZQvdunXDxMSE69evk5qaSosWLVi0aBFr167VOdhPmxYtWuDn\n50ffvn0pWbIkERERmJiYqFUC3+b+1EbTeqVKlWL79u2MHTuWqlWrcu/ePR4+fEj79u3Ztm2b0rqf\nfVCovr4+K1asYMaMGTRs2JCUlBTlM2ndujWLFy9m2bJl+YoVJPltq7Xv2cGDB3F1daV+/fofzSOR\nBEEQ/o1++ukn1qxZQ79+/dRGyAuC8PEaP348ISEhTJ8+vcDSJLIrvKGGBWzr1q356vIQBEEQtLt7\n9y6Ojo6MGDFC61Qgqpzh2rVrv+ejEwRBkyNHjtChQweNLbHwenDimTNngDefjSK/PtqAMSsrS5mT\nzsvLi9DQUEqXLp1nl4cgCIKgXcWKFUlLS+PEiRN4enqqzSmZmJjI3LlzuXnzJqVLl9b4FCpBEN6/\nWrVq8fDhQ4KCgli3bp3aU2AeP37MlClTePHiBTY2Nu+UL67LR90lXbduXaUGLEkSixcvfi9PehAE\nQfg327dvH9OmTSMzM5NixYpRqVIlMjIyuHfvHikpKZQoUQJvb+93ft68IAgFZ82aNSxatAhZlilR\nogSffPIJycnJREdHk5mZiZWVFStXriy0hxt81AHj8OHDOXfuHFZWVnz11Vf5GsUjCIIg5O3u3bv8\n8ccfnDt3Tpkypnz58jg6OjJo0KA3niFAEITCd+XKFdavX8/Vq1eVp99VqFCBjh07MnDgwEJ7bCl8\n5AGjIAiCIAiC8OF9tDmMgiAIgiAIwsdBBIyCIAiCIAiCTiJgFARBEARBEHQSAaMgCIIgCIKgkwgY\n/+Pi4uKYOHEiTZs2xcHBgalTp/Lo0SNl+dGjR+nZsyf169fHycmJw4cPq60fHx/PpEmTcHBwoHnz\n5nh6epKVlfW+T0MQBEEQhEIkAsb/uNGjR5OYmMiGDRvYuHEjjx8/xtXVFYBbt24xduxYunbtyq5d\nu2jXrh3jxo3j9u3byvrjx48nPj6eTZs28eOPP+Ln58eyZcs+1OkIgiAIglAIRMD4H/bkyROqV6/O\n999/j62tLTVq1GDYsGGEhYWRkJDA+vXradCgAaNHj8ba2ppJkybRsGFD1q1bB8CFCxe4cOECCxcu\nxNbWltatW/O///2PjRs3an3kmCAIgiAI//+IgPE/rEyZMixevBgrKysAYmNj2bJlC/Xq1cPMzIxz\n587RpEkTtXWaNGnCuXPnAJRJ1VXrq5YnJiYSHh7+/k5EEARBEIRCZfChD0D4OIwbN46QkBBKlCjB\n+vXrgdcBpKWlpdr7LC0tladCaFpetmxZZVm9evXew5ELgiAIglDYRAujAMDkyZPx9fXF3t6eESNG\nEBcXR0pKCkZGRmrvK1KkCGlpaQAalxsYGCBJEqmpqe/t2AVBEARBKFyihVEAwMbGBoCff/4ZR0dH\ndu3aRdGiRZXgUCU9PR1jY2MAjcszMjKQZVl5jzZeXl54e3vnej0rK4ubN2++y6kIgiAIglDARAvj\nf9jTp08JCgpSe61o0aJUrFiRR48eUb58eR4/fqy2PC4uTumGLleuXK7lqil5cnZV5zRhwgRu3LiR\n61+FChXe9bQEQRAEQShgImD8D3vw4AFTp07l2rVrymsJCQlERkZSrVo17O3tOXPmjNo6p06donHj\nxgA0atSIe/fuERcXpyw/efIkpqam1KxZ8/2chCAIgiAIhU4EjP9hdevWxcHBAQ8PDy5fvkxYWBiT\nJ0+mdOnS9OrVi8GDB3PmzBm8vLy4c+cOS5cu5cqVK3zxxRcANGzYkPr16zN58mTCwsI4dOgQnp6e\nDB8+HAMDke0gCIIgCP8WkizL8oc+COHDef78OQsXLuTw4cOkpqbSqlUr3N3dldHOqiAwOjqaqlWr\n4ubmxqeffqqs//TpU7755huOHj2KiYkJzs7OTJ48+a2Px9HRkdDQ0Hc9LUEQBEEQCpAIGIWPiggY\nBUEQBOHjI7qkBUEQBEEQBJ1EwCgIgiAIgiDoJAJGQRAEQRAEQScRMAqCIAiCIAg6iYBREARB+Fc4\nffo0jRs3VpsbdvHixezatatA9+Pt7c3WrVsBqFOnDl988QVDhgyhf//+eHl5Fei+BOFjIQJGQRAE\n4V/D0NAQd3f397Y/c3Nz1q9fz4YNG9i6dSuPHz9m48aN723/gvC+iIBREARB+Nf49NNPKVGiBJs2\nbVJ7fePGjQwYMAAXFxcloHN3d2fMmDG4uLgwc+ZMpSXyyZMn9O7dG4AlS5YwaNAgBgwYwL59+/Lc\n/4gRI3I9clUQ/g3E4zgEQRCEfw1Jkpg7dy59+/alVatWALx69YqgoCA2b96MLMsMHz6cFi1aANCs\nWTOGDh3K7du3mTdvHj179sTf358+ffpw+PBh7t+/z6ZNm0hLS6Nfv37KetqUKVOG58+fF/p5CsL7\nJgJGQRAE4V+lRIkSuLu74+bmRqNGjXj16hUxMTEMHToUWZZJSEggOjoaAGtrawCqVatGVlYWMTEx\nBAUFsW7dOrZs2cK1a9f44osvkGWZzMxM7t+/r3PfDx48oFy5coV+joLwvokuaUEQBOFfp23btlhb\nW+Pn54eRkRE2NjZKrmGvXr2oUaMGAHp6//wM9unTh0WLFmFjY4OpqSlVq1aladOmrF+/nvXr19O5\nc2cqVaqkdZ9ZWVmsWbOGrl27Fvr5CcL7JloYBUEQhH+lmTNncvLkSczMzGjWrBkuLi6kpaVRv359\nypYtm+v9nTt3ZsGCBSxfvhyAdu3acfr0aQYNGkRycjKfffYZxYoVU1vnxYsXfPHFF0iSRGZmJs2b\nN8fZ2fm9nJ8gvE/iWdLCR0U8S1oQBEEQPj6iS1oQBEEQBEHQSQSMgiAIgiAIgk4iYBQEQRAEQRB0\nEgGjIAiCIAiCoJMIGAVBEARBEASdRMAoCIIgfLSysmRinyYR/zLlQx+KIPyniXkYBUEQhI9S4JE7\n/H0mmqiHLzAw0Meuckn6trehvk3uORTf1KVLl/D09GTDhg3Ex8fj4eFBQkICmZmZLFy4kKSkJObP\nn48kSciyzKVLl/j1119p2bIlAPv37yc4OJjFixcDMGTIEOW9kZGR9O7dm6lTp+Lt7c2hQ4cwMDDA\n3d2devXq8ezZM6ZPn05qaiply5blhx9+wMjICIDk5GRGjBjBggULlKfQCMLHQASMgiAIwkcn8Mgd\n1gZeJT3z9VTBmWmZXLr5hPuPEvEY0YTqn5R8622vWrUKf39/TExMAFi0aBE9evSgc+fOnDp1ijt3\n7tCmTRs2bNgAQHBwMOXKlVOCxfnz53Ps2DFq1qypbFP13nv37jFlyhRcXV0JCwvj7Nmz+Pr68vDh\nQyZMmMD27dvx8fGhe/fu9OzZk5UrV7J582aGDRvG1atXmTt3LnFxcW99boJQWESXtCAIgvBRycqS\nCTkTrQSL2T19kcLuo5HvtP3KlSvj4+Oj/H3+/HliY2MZPnw4u3fvpmnTpsqy5ORkvLy8mDVrlvKa\nvb0933zzjcZtL1iwgK+//hpjY2POnTtHixYtAChfvjxZWVnEx8dz/vx5WrVqBUDr1q05efIkAOnp\n6fz6669UrVr1nc5PEAqDCBgFQRCEj8qjZ6+4+/CF1uVRD1++0/Y7dOiAvr6+8veDBw8wNzdn7dq1\nlCtXjpUrVyrLtm/fTpcuXTA3N1de69Kli8bt3rhxg6SkJCXgTExMxMzMTFluYmJCYmIiSUlJyusm\nJiYkJCQA0LBhQywtLREPYBM+RiJgFARBED4qhkX0KWKgr3W5gX7B/nSZm5vTtm1b4PXzo69du6Ys\nCwwMpG/fvvnaTkBAAP369VP+NjU1JSkpSfk7MTGR4sWLK4EjoBY8CsLHTASMgiAIwkelVPGi2FUp\npXW5nbX2ZW+jUaNGHDp0CIAzZ85QvXp14HWAl56ejqWlZb62c+LECaWrGV53XR89ehRZlomJiUGW\nZczNzbG3t+fw4cMAHD58mMaNGxfo+QhCYRCDXgRBEISPjnM7G+7HJfDkhfp0OjaVzOnbzrZA9+Xm\n5oaHhwebN2/GzMxMGfkcGRlJhQoV8r2dp0+fUqJECeXv2rVr06hRI/r3748sy8yZMwcAV1dX3Nzc\n2LZtGyVLllT2pyJJUgGclSAULEkWyRLCR8TR0ZHQ0NAPfRiCIHwEbt1/xu6jkUQ9fImBvh521qXo\n286W4iaGH/rQBOE/R7QwCoIgCB+l6p+UZPKAt58+RxCEgiNyGAVBEARBEASdRMAoCIIgCIIg6CQC\nRkEQBEEQBEEnETAKgiAIgiAIOomAURAEQfhoZclZxCU+5lmy9ie/CIJQ+MQoaUEQBOGjtDfiIKF3\nTxD1/AFF9AywLV2VnrU6UdfS7p23fenSJTw9PdmwYQPx8fF4eHiQkJBAZmYmCxcuJCkpifnz5yNJ\nErIsc+nSJX799VdatmwJwP79+wkODlbmUBwyZIjy3sjISHr37s3UqVPx9vbm0KFDGBgY4O7uTr16\n9Xj27BnTp08nNTWVsmXL8sMPP2BkZMTu3btZv349BgYG2Nraan1etSB8CCJgFARBED46eyMOsuGS\nHxlZGQCkZqZx5dF1HiTE8r+WY6haqvJbb3vVqlX4+/tjYmICwKJFi+jRowedO3fm1KlT3LlzhzZt\n2rBhwwYAgoODKVeunBIszp8/n2PHjlGzZk1lm6r33rt3jylTpuDq6kpYWBhnz57F19eXhw8fMmHC\nBLZv346Pjw/du3enZ8+erFy5ki1btjBgwACWLVvG7t27MTQ0ZNq0aRw8eFB5ZKEgfGiiS1oQBEH4\nqGTJWYRGnlCCxezik5+z92boO22/cuXK+Pj4KH+fP3+e2NhYhg8fzu7du2natKmyLDk5GS8vL2bN\nmqW8Zm9vr7X1b8GCBXz99dcYGxtz7tw5WrRoAUD58uXJysoiPj6e8+fPK48QbN26NSdOnMDIyIgt\nW0VpSq0AACAASURBVLZgaPh6UvKMjAyMjIze6TwFoSCJgFEQBEH4qDxOekrUiwdal997EfNO2+/Q\noQP6+vrK3w8ePMDc3Jy1a9dSrlw5Vq5cqSzbvn07Xbp0wdzcXHmtS5cuGrd748YNkpKSlIAzMTER\nMzMzZbmJiQmJiYkkJSUpr5uYmJCQkABAqVKvn5G9YcMGkpOTad68+TudpyAUJBEwCoIgCB8VQ31D\niugV0brcQK9gs6nMzc2Vrt927dpx7do1ZVlgYCB9+/bN13YCAgLo16+f8repqSlJSUnK34mJiRQv\nXlwJHAG14FGWZRYuXMiJEyfw9vZ+5/MShIIkAsb/sKdPn+Lm5kbLli1xcHBg5MiR3Lx5U1nu7OyM\nnZ2d8q9mzZrMnj1bWR4fH8+kSZNwcHCgefPmeHp6kpWV9SFORRCEf5GSxiWwLWOtdbltmaoFur9G\njRpx6NAhAM6cOUP16tWB1wFeeno6lpaW+drOiRMnlK5meN11ffToUWRZJiYmBlmWMTc3x97ensOH\nDwNw+PBhGjduDMDs2bNJT0/n119/VbqmBeFjIQa9/EfJssy4ceMAWLFiBcbGxnh5eTFs2DCCgoIo\nUaIEt2/fZsmSJWr5PEWLFlX+P378ePT19dm0aROxsbHMmDEDAwMDJk+e/N7PRxCEf5deNTvz4GUs\n8cnP1V6vVqoyvWp2KtB9ubm54eHhwebNmzEzM1NGPkdGRlKhQoV8b+fp06eUKFFC+bt27do0atSI\n/v37I8syc+bMAcDV1RU3Nze2bdtGyZIlWbx4MWFhYfj5+dGoUSNlxPUXX3zBZ599VqDnKghvS5Jl\nWf7QByG8f+Hh4fTu3ZugoCCsrV/X5NPS0mjatCnffPMN9vb2dOjQgZCQEI0F5oULFxg4cCAhISFY\nWVkBsGvXLr7//ntOnDhBkSLau5N0cXR0JDQ09K3PSxCEf4878VEE3wwl+kUM+nr61ChTjV41O2Fm\nZPqhD00Q/nNEC+N/VPny5VmxYoUSLALo6b3OUHj58iUREREYGxtrrV2fO3cOKysrJVgEaNKkCYmJ\niYSHh1OvXr3CPQFBEP71qpaqzNimQz/0YQiCgMhh/M8yNzenTZs2aq+tX7+e1NRUWrRowc2bNzE1\nNWXatGm0atWK7t2788cffyjvjY2NzZXXU7ZsWWWZIAiCIAj/HqKFUQAgJCSEJUuWMHz4cKpWrcrN\nmzdJTk6mVatWjBkzhvPnz7Nw4UISExMZP348KSkpueYIMzAwQJIkUlNTP9BZCIIgCIJQGETAKODn\n58ecOXPo1q0bX3/9NfD6yQevXr3C1PR1rpCNjQ0vX77kt99+Y/z48RQtWpS0tDS17WRkZCDLMsbG\nxu/9HARBEARBKDwiYPyPW758OUuXLmXIkCFqTzLQ09NTgkUVW1tbkpKSSExMpFy5csq0ECqPHj0C\nyNcUFF5eXhrnGRPT8giCIAjCx0fkMP6H/f777yxbtozJkyerBYsA/fv3Z/78+WqvXblyhbJly2Jq\nakqjRo24d+8ecXFxyvKTJ09iamqq9nxVbSZMmMCNGzdy/XuTKSwEQfj3k7OySImNJS3+2Yc+FEH4\nTxMtjP9R169f55dffqFPnz44Ozvz5MkTZZmJiQkdOnTAy8tLmUfs5MmTrF69Gg8PDwAaNmxI/fr1\nmTx5MrNnz+bx48d4enoyfPhwDAzEZSUIwruL2R3Eo5ADJEVFo2dggJldDT5x7o15vbrvvO1Lly7h\n6enJhg0biI+Px8PDg4SEBDIzM1m4cCFJSUnMnz8fSZKQZZlLly7x66+/0rJlSwD2799PcHCwMmfj\n0aNHWbx4McWKFVNyvwG8vb05dOgQBv/H3n2HR13mex9/T3qvQDpJCC0ozQBSRBDkCCLIKq56UITH\nI+su8KDrse2ugg/HssJxRXCPK66rFBUsxxUU+4JGBSEgUgMhEFImvUwmZTLt+QOZNRuCCjEzEz6v\n68p1mfv+zW++AyZ8rvt3Fz8/HnzwQQYNGoTRaOR3v/sdNtups7KXLl1KWloamzdvZs2aNfj5+dG3\nb992z6sWcQf9y36B2rJlCw6HgzfffJM333yzVd+iRYu488478fPz47nnnsNoNJKQkMDvfvc7rr/+\netd1zz77LEuWLGHWrFmEhobyy1/+0rUZuIjI+SjZ/B4nXlqD02oFwGG3U7f3W5qKiuj/+wcIz8g4\n53u/8MIL/P3vfyc0NBQ4NWd7+vTpTJ48mR07dpCfn8+4ceNYu3YtAO+//z7x8fGusPjoo4/yxRdf\nuJ6mOJ1OHnroIdatW0dSUhL33nsvu3fvJigoiF27dvH6669jNBpZuHAhb7zxhmsa0IQJE8jOzuap\np55i2bJlPPPMM2zevJmAgADuuece/vGPf7iOLBRxNwXGC9Tdd9/N3XfffdZr5syZw5w5c9rtj42N\nZeXKlR1cmYhc6JwOB+WffOoKi9/XUlVN6eYthC9acM73T01N5dlnn+W+++4DYPfu3fTr14+5c+eS\nnJzcaopOU1MTK1euZP369a620wcbbNiwAYCamhoiIyNdU2ouueQSdu3aRXBwMGPGjAFO7X3rcDio\nqanhgQcecJ0fbbPZCAgIIDAwkNdee811JKDNZmuzE4WIO2kOo4iIeBRLeTkNBSfb7W8oKDiv+0+a\nNAlfX1/X98XFxURFRfG3v/2N+Ph4nn/+eVffG2+8wZQpU4iKinK1TZkypdX9YmJiaGpq4vjx49jt\ndrZt20ZzczNms9kVDAFCQkIwm81ERUXh6+tLfn4+y5YtY8GCBa77AKxdu5ampiZGjx59Xp9TpCNp\nhFFERDyKT0AgPv5+OOz2M/ef49Gj7YmKinI9+p0wYQJPP/20q2/Tpk0/6knKk08+yeLFiwkMDKRP\nnz5ER0djMBhoaGhwXdPQ0OAKkNu3b2fp0qUsW7aMtLQ04NSj7SeffJKCgoIz7iIh4k4aYRQREY8S\nEBNNeL9+7faH92+/71xkZWWxbds2AHbu3Env3r0BMJvNWK3WH7VV2Oeff86LL77I6tWrOXnyJKNH\nj2bo0KFkZ2fjdDopKSnB6XQSFRXF9u3beeyxx3jhhRcYMGCA6x4PPfQQVquVP//5z65H0yKeQiOM\nIiLicZJnXkdTUTEtVVWt2sP69CZ55nUd+l73338/f/jDH3j11VcJDw93rXw+fvz4j97qKy4ujpkz\nZxIUFMS0adPI+G5RTlZWFjfeeCNOp5PFixcD8Pjjj2Oz2bj//vtxOp306tWLG2+8kbfeeousrCxu\nvfVWDAYDs2fP5sorr+zQzypyrgxOp9Pp7iJEThs/fjxbt251dxki4gHqjx2jdPMWGgoKTm2rk9mf\n5JnX4f+9eYEi0jk0wigiIh4pPCPjvFZDi0jH0RxGERERETkrBUYREREROSsFRhERERE5KwVGERER\nETkrBUYREfFYToeTmqoG6k3N7i5F5IKmVdIiIuKRvv48n292FlFmrMPPz5fk1Ggum9iH9D7dzvve\ne/fuZfny5axdu5bq6mr+8Ic/UF9fj91u549//CMNDQ08+uijGAwGnE4ne/fu5c9//jOXXXYZAB99\n9BHvv/++a8/G7Oxs/vu//5uQkBDGjh3LnXfeCcCqVavYtm0bfn5+PPjggwwaNAij0cjvfvc7bDYb\nAEuXLiUtLY0PPviA1atX4+PjwzXXXMPs2bPP+3OKdBQFRhER8Thff57PR5sOYref2irY2mLn+NFK\nKsvN3PR/hpOQHPUDd2jfCy+8wN///ndCQ0MBWLZsGdOnT2fy5Mns2LGD/Px8xo0bx9q1awF4//33\niY+Pd4XFRx99lC+++ILMzEzg1JF+Dz30EOvWrSMpKYl7772X3bt3ExQUxK5du3j99dcxGo0sXLiQ\nN954gxUrVnDrrbcyYcIEsrOzeeqpp3j66ad56qmneOuttwgODubqq69m+vTprc6wFnEnPZIWERGP\n4nQ4+WZnoSssfl99XTM7so+f1/1TU1N59tlnXd/v3r2b0tJS5s6dy+bNm7n00ktdfU1NTaxcuZLf\n//73rrZLLrmEJUuWuL6vqakhMjLSdSrMJZdcwq5du8jJyWHMmDEAJCQk4HA4qKmp4YEHHmDcuHEA\n2Gw2AgIC8PHxYcuWLYSGhlJTU4PT6cS/g8/MFjkfCowiIuJRamsaKTOa2u2vMNaf1/0nTZqEr6+v\n6/vi4mKioqL429/+Rnx8PM8//7yr74033mDKlCmtRvqmTJnS6n4xMTE0NTVx/Phx7HY727Zto7m5\nGbPZTPj3TqUJCQnBbDYTFRWFr68v+fn5LFu2jAULTm1O7uPjw0cffcS1117LiBEjCAkJOa/PKdKR\nFBhFRMSj+Pn74ufn226/r2/H/tMVFRXFFVdcAcCECRM4cOCAq2/Tpk3ccMMNP3iPJ598ksWLF3Pn\nnXfSq1cvoqOjCQ8Pp6GhwXVNQ0ODK0Bu376dhQsXsmzZMtLS0lzXTJo0iezsbFpaWnj77bc76BOK\nnD8FRhER8SjhEUGkpEW325+S3n7fucjKymLbtm0A7Ny5k969ewNgNpuxWq3ExcX94D0+//xzXnzx\nRVavXs3JkycZPXo0Q4cOJTs7G6fTSUlJCU6nk6ioKLZv385jjz3GCy+8wIABA1zvdeutt9LS0gJA\ncHAwBoOhQz+nyPnQohcREfE4Yyb0obLMjKmu9XY6iT0juWxCnw59r/vvv58//OEPvPrqq4SHh7tW\nPh8/ftw1L/GHxMXFMXPmTIKCgpg2bRoZGRnAqTB644034nQ6Wbx4MQCPP/44NpuN+++/H6fTSa9e\nvXjkkUeYPn06t9xyC/7+/vTr149rr722Qz+nyPkwOJ3OtrOKRdxk/PjxbN261d1liIgHMBbV8nX2\nCcqNJnx9fUhJj+ayCX0IDg1wd2kiFxyNMIqIiEdKSI7i2puGuLsMEUFzGEVERETkBygwioiIiMhZ\nKTCKiIiIyFkpMIqIiIjIWSkwioiIiMhZKTCKiIiIyFkpMIqIiIjIWSkwioiIiMhZKTCKiIiIyFkp\nMIqIiIjIWSkwioiIiMhZKTCKiIiIyFkpMIqIiIjIWSkwioiIiMhZKTCKiIiIyFkpMIqIiIjIWSkw\nioiIiMhZKTBewKqqqrj//vu57LLLGD58OLfffjtHjx519WdnZzNjxgwGDx7Mtddey2effdbq9dXV\n1SxatIjhw4czevRoli9fjsPh6OyPISIiIj8zBcYupLm5mZKSkh91rdPpZP78+RQUFPDcc8/x2muv\nER4ezpw5c6irqyMvL4/f/OY3XH311bz99ttMmDCB+fPnc+zYMdc9FixYQHV1NevXr+eJJ57grbfe\n4plnnvm5Pp6IiIi4iQKjF5k4cSKPPvoodrv9jP0ffvghEydO/FH3Onz4MHv37uXxxx/n4osvJiMj\ngyeffJLGxka2bt3KmjVrGDJkCPPmzSM9PZ1FixYxdOhQXn75ZQD27NnDnj17+OMf/0jfvn25/PLL\nue+++1i3bh1Wq7XDPrOIiIi4nwKjFykuLmbdunXceuutVFZWnte9EhISeO6550hPT3e1+fic+t/B\nZDKRk5PDiBEjWr1mxIgR5OTkAJCTk0NiYiKJiYmt+s1mM4cOHTqv2kRERMSzKDB6mRkzZpCXl8eM\nGTNc4e1cREVFMW7cuFZta9aswWKxMGbMGEpLS4mLi2vVHxcXh9FoBDhjf48ePVx9IiIi0nUoMHqZ\n0aNH8/rrrxMREcFtt93GmjVrOuS+n3zyCU899RRz586lV69eNDc3ExgY2Ooaf39/WlpaAM7Y7+fn\nh8FgwGKxdEhNIiIi4hkUGL1QamoqGzdu5LLLLuPxxx/nnnvuobm5GT8/v3O631tvvcWiRYu45ppr\nuPfeewEIDAx0hcPTrFYrwcHBAAQFBbXpt9lsOJ1O1zUiIiLSNZxbwhC3CwsL47nnnuNPf/oTf/nL\nXzh27Bg33HDDT77P//zP/7BixQpuvfVWfv/737vaExISqKioaHVtWVmZ6zF0fHx8m212ysvLAdo8\nqj6TlStXsmrVqjbt2pZHRETE82iE0cvdfffdPP300xQUFPD444//pNeuXr2aZ555hrvuuqtVWATI\nyspi586drdp27NjBsGHDXP2FhYWUlZW5+rdv305YWBiZmZk/+N4LFy4kNze3zVdSUtJP+gwiIiLy\n81Ng9CLDhw+nW7dubdonT57Mq6+++qNG9k47fPgwTz/9NNdffz0zZ86ksrLS9dXU1MQtt9zCzp07\nWblyJfn5+axYsYJ9+/Yxe/ZsAIYOHcrgwYO56667OHjwINu2bWP58uXMnTv3nB+Ni4iIiGcyOJ1O\np7uLkI7R1NREdXX1jxql+9Of/sTzzz9/xr5FixZx5513ukLgyZMn6dWrF/fffz8jR450XVdVVcWS\nJUvIzs4mNDSUmTNnctddd53XZxg/fjxbt249r3uIiIhIx1JgFI+iwCgiIuJ59OzQg/Xv3x+DwfCT\nXmMwGDh48ODPVJGIiIhciBQYPdjw4cPbtNlsNvbs2UO/fv2IiIhwQ1UiIiJyoVFg9GBr165t01Zd\nXc3o0aN54IEHGDVqlBuqEhERkQuNVkl7mZ/6iFpERETkfCkwioiIiMhZKTCKiIiIyFkpMIqIiIjI\nWSkwioiIiMhZaZW0B3vwwQfbtLW0tGAwGPjrX//KO++806bfYDDw2GOPdUZ5IiIicoHQSS8erH//\n/j/5NQaDgUOHDv0M1XQOnfQiIiLieTTC6ME++eQTd5cgIiIiosDoyZKSklp9X19fT3h4uJuqERER\nkQuVFr14kdGjR7NgwQLef/99LBaLu8sRERGRC4RGGL3IbbfdxpYtW/j4448JDQ3lyiuvZOrUqYwZ\nMwZfX193lyciIiJdlBa9eKE9e/bw7rvv8sEHH1BRUUF0dDRXXXUV11xzDcOGDXN3eedFi15EREQ8\njwKjF3M6nXz99dd8+umnbNu2jYKCAuLj45k2bRozZsygV69e7i7xJ1NgFBER8Tyaw+jFDAYDwcHB\nri+n00l9fT2vvvoqU6dOZf78+VRXV7u7TBEREfFymsPohfbt28eWLVv44IMPKCkpwd/fn3HjxnHn\nnXdyxRVXAPDuu++yZMkSfvvb3/LSSy+5t2ARERHxagqMXmT58uW8//77FBcXYzAYGDFiBL/+9a+5\n6qqr2my384tf/IJPPvmEL774wk3VioiISFehwOhFXnjhBQYMGMAtt9zC1VdfTY8ePc56fVZWFkOG\nDOmk6kRERKSr0qIXL3L8+HHS09PbtFdXVxMdHY3BYHBDVR1Li15EREQ8jxa9eJH09HTWrVvH2LFj\nKSwsdLU/+eSTjB49mrffftuN1YmIiEhXpcDoRV5//XX+67/+i9TUVPz8/jmb4OqrryYzM5MHH3yQ\nDz/80I0VioiISFekR9JeZNq0afTs2ZNnn332jP133nknFRUVvPnmm51cWcfRI2kRERHPoxFGL1JY\nWMi4cePa7b/iiivIz8/vxIpERETkQqDA6EWio6PJzc1tt//48eOEhYV1YkUiIiJyIVBg9CKTJk1i\nw4YNvPfee236Pv74Y1555RUmTZrkhspERESkK9McRi9iNpuZNWsWR44cISYmhpSUFAwGA4WFhVRV\nVdGnTx/WrVtHRESEu0s9Z5rDKCIi4nm0cbcXCQsL44033uD1119n27ZtFBcXY7fb6devH5dffjk3\n3XQTgYGB7i5TREREuhiNMIpH0QijiIiI59EIoxeyWCzU1tZit9vP2J+YmNjJFYmIiEhXpsDoRWpr\na3nkkUf46KOP2g2LAIcOHerEqkRERKSrU2D0Ik888QRbtmxh7NixZGZmEhAQ4O6SRERE5AKgwOhF\nPv30U2644QaWLl3q7lJERETkAqJ9GL2IzWZj4MCB7i5DRERELjAKjF5k+PDh7Nixw91liIiIyAVG\n2+p4kYKCAmbPns3UqVOZPHkyMTEx+Pi0zfzevEpa2+qIiIh4HgVGLzJo0CAcDgc2mw2DwdDudee6\nSvrhhx/G6XS2miM5c+ZM9u/f7/reYDAwc+ZM1zXV1dU88sgjfPnll/j7+3Pdddfx29/+9oxB9sdQ\nYBQREfE8WvTiRe64446zBsXzsWLFCjZu3MgNN9zQqv3YsWM89dRTXHrppa62oKAg138vWLAAX19f\n1q9fT2lpKQ888AB+fn7cddddP0udIiIi0vkUGL3IwoULO/yehYWF/P73vycvL6/No+zCwkKam5sZ\nPHgwsbGxbV67Z88e9uzZwyeffEJiYiJ9+/blvvvu47/+67+YP38+/v7+HV6viIiIdD4tevFCubm5\n/OUvf2Hx4sUcO3YMo9HItm3bzulee/bsITExkU2bNpGUlNSq78iRIwQFBbVpPy0nJ4fExMRWQXPE\niBGYzWZtHi4iItKFaITRyyxdupRXXnkFp9OJwWBg8uTJmEwmFi1axPjx41mxYgWBgYE/+n7Tp09n\n+vTpZ+w7evQoYWFh3HPPPXz99ddERUVx/fXXM2fOHABKS0uJi4tr9ZoePXq4+gYNGnRuH1JEREQ8\nikYYvciaNWtYv3498+bNY+PGjZxerzRq1CjmzJnD1q1bWb16dYe939GjR2lqamLs2LG8+OKL3HLL\nLTzzzDOsWrUKgObm5jbh1M/PD4PBgMVi6bA6RERExL00wuhFXnvtNSZPnszdd99NTU2Nqz0iIoIH\nHniA6upqNm/ezIIFCzrk/ZYtW0ZjYyNhYWEA9OnTB5PJxF/+8hcWLFhAUFAQLS0trV5js9lwOp0E\nBwd3SA0iIiLifhph9CKFhYWMHDmy3f5hw4ZhNBo77P18fHxcYfG0vn370tDQgNlsJj4+noqKilb9\n5eXlAG0eVf+rlStX0q9fvzZfxcXFHVa/iIiIdAwFRi8SHR1NaWlpu/1Hjx4lMjKyw97vxhtv5NFH\nH23Vtm/fPnr06EFYWBhZWVkUFhZSVlbm6t++fTthYWFkZmae9d4LFy4kNze3zVd7C2xERETEfRQY\nvcikSZN45ZVXyMvLc7Wd3pdx27ZtbNiwgSuuuKJD32/jxo28/fbbFBYW8vrrr/PXv/6V//t//y8A\nQ4cOZfDgwdx1110cPHiQbdu2sXz5cubOnYufn2Y7iIiIdBU66cWLmEwmZs2aRUFBAX369OHgwYNk\nZWXR0NDA4cOHSUpKYuPGjcTExJzT/WfPnk1qamqrk15eeuklXnvtNYxGIwkJCdx+++2tNveuqqpi\nyZIlZGdnExoaysyZM89r026d9CIiIuJ5FBi9TFNTEy+88AIffvghhYWF2O12kpKSmDBhAr/61a86\n9JG0OygwioiIeB4FRvEoCowiIiKeRxPNvEhJScmPuu5fj/gTEREROR8KjF5kwoQJrkUuZ6Nj+URE\nRKQjKTB6kfnz57cJjHa7ncrKSrZt20ZoaCgLFy50U3UiIiLSVSkwepGzhUGz2cxNN91EQUFBJ1Yk\nIiIiFwLtw9hFhIWFccMNN7BhwwZ3lyIiIiJdjAJjF2K1WludMS0iIiLSEfRI2ou0t0q6paWFQ4cO\n8eKLL/7gkXwiIiIiP5UCoxc52yppp9NJYGAg99xzTydXJSIiIl2dAqMXOdMqaQAfHx+6d+/OxIkT\nz/lYQBEREZH2KDB6EW2ZIyIiIu6gwOhFfuxJL/9KJ7+IiIjI+VBg9CI/9qSXf6WTX0REROR8KDB6\nkSeeeIJVq1ZRU1PDjBkz6NWrF4GBgZw8eZK33noLs9nMLbfcQlBQkLtLFRERkS5EgdGL5Obm4nA4\neP/99+nevXurvnnz5nHzzTfT2NjIf/7nf7qpQhEREemKtHG3F/nf//1fZs2a1SYswqmTXmbOnMnm\nzZvdUJmIiIh0ZQqMXsRut9PU1NRuf1VV1TnNcRQRERE5GwVGLzJy5Eheeukldu3a1aYvOzubtWvX\ncuWVV7qhMhEREenKDE6n0+nuIuTHKSws5KabbqK6upp+/frRs2dPnE4nJ06cIC8vj969e7N27Vqi\noqLcXeo5Gz9+PFu3bnV3GSIiIvI9GmH0IikpKWzatInbbrsNi8XCZ599xueff46Pjw8LFixg48aN\nXh0WRURExDNphFE8ikYYRUREPI+21fFCubm5bN26FaPRyK233kpISAhHjhxh3Lhx7i5NREREuiAF\nRi+zdOlSXnnlFZxOJwaDgauuugqTycSiRYsYP348K1asIDAw0N1lioiISBeiOYxeZM2aNaxfv555\n8+axceNGTs8mGDVqFHPmzGHr1q2sXr3azVWKiIhIV6PA6EVee+01Jk+ezN13301KSoqrPSIiggce\neIDp06dr424RERHpcAqMXqSwsJCRI0e22z9s2DCMRmMnViQiIiIXAgVGLxIdHU1paWm7/UePHiUy\nMrITKxIREZELgQKjF5k0aRKvvPIKeXl5rrbTRwFu27aNDRs2cMUVV7irPBEREemitA+jFzGZTMya\nNYuCggL69OnDwYMHycrKoqGhgcOHD5OUlMTGjRuJiYlxd6nnTPswioiIeB6NMHqRiIgINm7cyB13\n3EFLSwuBgYHs3buXpqYm5s6dy5tvvunVYVFEREQ8k0YYvcibb75JVlYWaWlp7i7lZ6MRRhEREc+j\nEUYv8uijj/Luu++6uwwRERG5wCgwepGQkBCd4iIiIiKdTkcDepElS5awePFiDAYDw4YNIyYmBl9f\n3zbXJSYmuqE6ERER6ao0h9GLDBo0CJvNxg/9lR06dKiTKup4msMoIiLieTTC6EXuuOMO176LIiIi\nIp1FgdGLLFy40N0liIiIyAVIi1482OOPP87BgwfdXYaIiIhc4BQYPdjLL7/MsWPHWrU1Njby4IMP\ntmnvCA8//DAPPfRQq7bs7GxmzJjB4MGDufbaa/nss89a9VdXV7No0SKGDx/O6NGjWb58OQ6Ho8Nr\nExEREfdRYPQyFouFt99+m/Ly8g6974oVK9i4cWOrtry8PH7zm99w9dVX8/bbbzNhwgTmz5/fa+Nx\nGgAAIABJREFUKqwuWLCA6upq1q9fzxNPPMFbb73FM88806G1iYiIiHspMHqhjlzYXlhYyOzZs9mw\nYUOb7XjWrFnDkCFDmDdvHunp6SxatIihQ4fy8ssvA7Bnzx727NnDH//4R/r27cvll1/Offfdx7p1\n67BarR1Wo4iIiLiXAuMFbs+ePSQmJrJp0yaSkpJa9eXk5DBixIhWbSNGjCAnJ8fVn5iY2Cpojhgx\nArPZ7NVb+4iIiEhrWiV9gZs+fTrTp08/Y19paSlxcXGt2uLi4jAaje329+jRw9U3aNCgn6FiERER\n6WwaYZR2NTc3tzmK0N/fn5aWlnb7/fz8MBgMWCyWTqtTREREfl4aYfRwGzdu5Msvv3R939LSgsFg\n4K9//SvvvPNOm+sNBgOPPfZYh7x3YGCgKxyeZrVaCQ4OBiAoKKhN/+mTaE5f056VK1eyatWqNu1a\nYS0iIuJ5FBg93M6dO9m5c2eb9uzs7DNe35GBMSEhgYqKilZtZWVlrsfQ8fHxbbbZOb16+18fVf+r\nhQsXnnEj8vHjx59HxSIiIvJzUGD0YJ988olb3z8rK4udO3fy61//2tW2Y8cOhg0b5ur/7//+71Yh\ncvv27YSFhZGZmemWmkVERKTjKTB6sH9dtdzZbrnlFq6//npWrlzJ1KlT2bRpE/v27eORRx4BYOjQ\noQwePJi77rqLhx56iIqKCpYvX87cuXPx89P/WiIiIl2F/lX3Qrm5uWzdupWSkhJmz55NSEgIR44c\nYdy4ced1X4PB0Or7vn37smrVKpYvX84LL7xAr169eO655+jVq5frmmeffZYlS5Ywa9YsQkND+eUv\nf8n8+fPPqw4RERHxLAZnR+4CLT+7pUuX8sorr+B0OjEYDLz44ouYTCYWLVrE+PHjWbFiRZuVy95k\n/PjxbN261d1liIiIyPdoWx0vsmbNGtavX8+8efPYuHGj68SXUaNGMWfOHLZu3crq1avdXKWIiIh0\nNQqMXuS1115j8uTJ3H333aSkpLjaIyIieOCBB5g+fTqbN292Y4UiIiLSFSkwepHCwkJGjhzZbv+w\nYcNcp7CIiIiIdBQFRi8SHR1NaWlpu/1Hjx4lMjKyEysSERGRC4ECoxeZNGkSr7zyCnl5ea620yub\nt23bxoYNG7jiiivcVZ6IiIh0UVol7UVMJhOzZs2ioKCAPn36cPDgQbKysmhoaODw4cMkJSWxceNG\nYmJi3F3qOdMqaREREc+jEUYvEhERwcaNG7njjjtoaWkhMDCQvXv30tTUxNy5c3nzzTe9OiyKiIiI\nZ9IIYxdjNpsJCwtzdxnnTCOMIiIinkcjjF5kzZo1Z+1/7733mDJlSidVIyIiIhcKBUYv8thjj/H8\n88+3aS8qKuKOO+7gt7/9LRowFhERkY6mwOhFbrrpJv70pz/xzDPPAGC323n++eeZNm0aX375JbNm\nzWLLli1urlJERES6Gj93FyA/3pIlS4iKiuLPf/4zpaWl7Nu3j6NHjzJkyBAWL15MZmamu0sUERGR\nLkiB0cvcddddxMTE8Pjjj+Pj48PSpUu54YYb3F2WiIiIdGEKjB6spKTkjO1XXnklDQ0NrFixgpyc\nHC677LJWcxcTExM7q0QRERG5AGhbHQ/Wv39/10kuZ3L6r+5frzl06NDPWtfPSdvqiIiIeB6NMHqw\n+fPnnzUwioiIiHQGBUYPtnDhQneXICIiIqJtdURERETk7DTC6MEyMzN58sknmTZtGvDDcxrh1HzG\ngwcPdkZ5IiIicoFQYPRgM2bMoGfPnq2+15xGERER6WxaJS0eRaukRUREPI9GGL3U/v37KS4uxtfX\nl9TUVPr06ePukkRERKSLUmD0Mu+++y5PPvkk5eXlrfZhTEtL4+GHH2bUqFFurlBERES6GgVGL/Lh\nhx9yzz33kJqayv33309qaip2u50TJ07w6quvMm/ePF566SWysrLcXaqI/AwKy+qJjggiLNjf3aWI\nyAVGcxi9yC9+8Qt8fHx45ZVXCAwMbNXX2NjITTfdRGRkJGvXrnVThedPcxhF2iqpMPPxzpOEBPlz\n9eg0QoIUGEWkc2mE0Yvk5+dz7733tgmLACEhIcycOZM//elPbqhMRH4OpVUNfLqrEF8fA1NGpdM9\nOtjdJYnIBUqB0YskJyeTn5/fbn95eTndu3fvxIpE5OdQWdvEJztPYrM7mTg8hfjYUHeXJCIXOAVG\nL/Kf//mfLFq0iPT0dG6++Wb8/P751/fee++xfv16li9f7sYKReR81Jia+XRXIQ3NViYO70lS9zB3\nlyQiAmgOo1eZO3cueXl5VFZWEh4eTkpKCv7+/hQVFVFVVYWvry9xcXGtXmMwGPj444/dVPFPpzmM\nciGqM1vYuruIGlMzVwxLITU+wt0liYi0ohFGL2Kz2UhLSyMtLa1Ve3p6Ounp6e4pSkTOmbmxhW27\niyitbmT8JclkJEe1e63DasV06DChqT3xj4zsxCo9l9Pp1OlXIp1EgdGLnGn1c11dHUFBQWdcCCMi\nnqmx2cpne4opLKtn3CXJXD0mvd3g01xeTs2u3TSePEnEgAH49tfCl9O+/fZbBg8e7O4yRC4ICowe\nzmq18uabb7J3714ef/xxV/uuXbtYvHgx+fn5GAwGRo8ezeLFi0lJSXFjtSJyNs0WG9l7SzhWXMtl\ng5O4amTqGYOiw2qlbv8Bavd8g19oKNHDs4ifcpVG0/6FZlSJdB4FRg/W0tLC3LlzycnJwd/fn6VL\nl+Ln58eJEye4/fbbaWlpYezYsfTu3ZsPPviAG2+8kXfeeYdu3bq5u3QR+R6L1c5X+4wcPlHN6EEJ\nTByecsbw11xaSvWu3TQVFRM58CJSbrwBv1CtkG6PAmPHOHbsGFOnTsVgMLQ6QWz9+vVccsklZGdn\ns3z5co4fP05aWhr33HMPl19+uZurls6mwOjB1qxZw+7du7n33nv593//d9eq6JUrV2KxWLjmmmtc\nq6LnzZvHtGnTeO655/jDH/7gzrJF5DtWm50dB0rZl1fJpRclcMeMgfj6tA6KjpYW6vbtp/abvfhF\nRBAzPIuEqVPaBEqrzc7eo5VkJEUSHRHUmR/DY7W0tLi7hC4hNzeXmJgYNm/e3CqER0VFkZeXx29+\n8xsWLFjApEmTeOedd5g/fz5vv/02GRkZbqxaOpsCowd77733uOqqq7j99ttdbS0tLXz66acYDIZW\n7VFRUVx33XW8++67Cowibma3O9h5qIzdueUMy4xj3oyB+Pr6tLqmyWikZmcOTUYjUYMGknLzTfiF\ntJ2faGpoYft+I8eKahncpzthIQGd9TE8Xk1NDTabrdUWY/LTHT16lIyMDGJiYtr0rVmzhiFDhjBv\n3jwAFi1aRE5ODi+//DL/7//9v84uVdxIP2UerKCggOuuu65V2zfffENTUxM9evQgMzOzVV/Pnj0p\nLy/vzBJF5HvsDid7csvZebCUwX26c8e1A/H3+2dQtFss1H27j9q93xIQHX1qNHHa1DM+ni6pMPPF\ntyXUN1oZeXE8Vw7viY+P5jB+X0BAAAcOHNDCl/N0OjCeSU5ODlOmTGnVNmLECLZs2dIZpYkHUWD0\nYA6HA19f31ZtX331FQCjR49uc319fT3BwVpBKdLZHA4n+/Iq+Wq/kQHpMdw+/WIC/E/97DqdTpqK\nS6jZlYOlvILIQReTOutmfM/ws+pwODlcUM32/aWEh/gzZlAiidq8+4yee+45cnNzGT9+PMnJycTG\nxrq7JK919OhRLBYLN954I8XFxfTp04e7776bQYMGUVpa2mZ/37i4OIxGo5uqFXdRYPRgPXv25NCh\nQ63aPv74YwwGA+PHj29zfXZ2Nj179uzQGjQZWqR9TqeTA/lVZO8toW/PKOZcM4CggFO/Vu0WC7Xf\nfEvdt98SEBtLzIhhBCclnXE00WK1k3OojL1HK8hIjmLmhD5EhLZ+9NxotpB/pJLUjFjCIzWHcevW\nrZSXlzNhwgQ+++wzEhMT6devH1FR7e9lKW1ZLBYKCwuJjY3lvvvuIyAggHXr1jF79mzefPNNmpub\n22zb5u/vr/mjFyAFRg82depUnn32WS6//HLGjBnDhg0bOHr0KN26dWPChAmtrn3nnXf44osvWLRo\nUYfWoMnQIm05nU5yT9bw2Z5i0hIimH11JiFB/jidThoLi6jeuYuWqiqiBg8idfYt+LazT2ptvYWv\n9pVQUFrPJf178B/XXoy/3z+fKtjtDgpPVJOfW4ETyOjbndBw7bn6r/z8/CgvL6eoqIjIyEjS0tLo\n1asXPj4+P/ziLsBms1FaWkp8fPxPns8ZGBjIzp07CQgIwN/fH4AnnniCgwcP8uqrrxIUFNQmHFqt\nVj3NugApMHqwOXPm8Pnnn7NgwQLXCJ+/vz+PPvooAQGnRh8++ugj1q1bx9dff016ejpz5szp0Bo0\nGVrkn5xOJ8eK6ti6u4jE7qH8+1X9CQv2x97URNVXu6nbt4/AuB7EXjqC4KTEdu9TUGriy70ltNgc\njBqYwORRaa1GHmuqGjh6qJzKMjMpadGMGJuOT10V9Ye+wRo9lMAL/PFrVVUVxcXFVFVVsX37dgYM\nGEBERAQBAQE0NTWxf/9+cnNzSUlJISMjg9AuvjVRaWkpEydOPGPfggULWLhw4Vlf/69/PgaDgYyM\nDIxGIwkJCVRUVLTqLysra/OYWro+BUYPFhAQwEsvvcR7773HN998Q2hoKNOnT6d3796ua/bv38/u\n3buZPn06DzzwAEFBHfuoSpOhRU45XlLHP3KK6BYVxC+v7Et4iD+NJwsp2rmLltpaooYMJvW2W9sd\nTbQ7nBzIr2THgVK6RQYzYXhP4mJCXP2WZhsn8io5nldJWHggvfv3YECyD/UH91O28yRBCQlEXjSA\ngOjozvrIHqe2tpYVK1bw+eefk5ubi9VqZdOmTbz77rsMGDCAadOmERYWhq+vLw6Hg4KCAo4cOUJo\naCjR0dGEh4fTvXt3YmJiuuTK6k8++YTk5OSf9JoDBw4we/Zs1q5dy4ABA4BT8+cPHTrElClTiI2N\n5euvv+bXv/616zU7duxg2LBhHVq7eL6u9xPTxfj6+jJt2jSmTZt2xv4777yTRYsW/WyPXjQZWi50\nhWX1fLqrkPCQAH4xPoNwXwe1e3LI37+f4MQEYseMJjghvt3XN1ls7DxYyv78KvqnxnDzv50alYRT\nI5bGojryDpfT1GglvXcMI/sH0Hgkl4ZN/8CZkkzERQOIuvxyCnbncfDjwwz5t2C692p/9LKrqq2t\nZcGCBRQXF7fpczgc7N+/n5KSEn71q18RFvbPhUKBgYHYbDYqKiqoqKggNzcXh8NBcHAwYWFhhIaG\nEhYWRrdu3YiOjm6z0LCr69+/P8nJyTz88MM8/PDDBAcHs3r1ampra5k9ezaVlZVcf/31rFy5kqlT\np7Jp0yb27dvHI4884u7SpZMpMHq5n3MeiSZDy4XMWNnAJztPEhjgy9QxaQTXlFO9ZTO19fVEDR1C\n+v+Zg893c77OpKquiS++LaG0qtG1F6Pfd3sx1puaOXa4nJLCWnrEhdInuhl7fT6WzytoTk8lfPBg\n7P0u4dg3eRg35GCwWUlIjiJzVF9i09oPp13ZihUrzhgWv6+6uppNmzZx8803t3vN6Xl6DocDk8mE\nyWQCTo20OZ1OV4CMjIwkNjaW+Pj4Lh0ifX19Wb16NcuWLePXv/41jY2NXHLJJaxfv56YmBhiYmJY\ntWoVy5cv54UXXqBXr14899xz9OrVy92lSydTYJR2aTK0XIjKqxv5ZFchOJ1MHNgNv+NHqNuwDWty\nEt3HXU5QXI92X+t0OjleYuLLb0sAGD0okWmXRWAwGLDZ7OQfqeBYbgV+Pg6SA81cbC3EdrAOQ3o6\nPv0HUxtez4EjJTTv/JLoMB96Dkhm2KQJBERGUlpiovB4NREmCxFRF9bPWFVVFZ9//vmPuvbgwYOY\nTCYiIiJ+0nucnhdus9mora2ltraWY8eO4XA4iIiIIDo6mh49epCUlNTlAmSPHj1YtmxZu/3jxo1j\n3LhxnViReCIFRjmrn2sy9MqVK1m1alWbdofDcX4Fi5yjqromPt1ViMViY0ycA9+jB2n+qInoHzGa\naLc72JtXya5DZcTHhjB5VBrdooJxOp1UlZvJO1xOTVktiX4mMsxGsDRhSMmgLukiSqxVmPZUEbLr\nCxKSoxgzrjfR/TJw+vhiLKoj55sq6mqKiE+KIKN/jwtyS53s7GzsdvuPutbhcHDw4EFGjhx53u97\nep5jc3MzRqORoqIidu3aRUxMDHFxcWRkZLiCpkhXZ3Dq9HZpR3uToadMmcKUKVOorq6msLCQv/3t\nb67X3HrrrWRkZLBkyZJzes/x48ezdevWDqhe5Meprbfwj5xC6qtqGRFQg3/xCUJSU4gZlkVg9+5n\nfW1Dk5UdB0o5fKKaizNiGZYZR0iQP81NVvKPVFBwqJjI5nKim8sx+Bho7p5GrT2Y6rI6/Mw1xATb\nSclMJm5wfwK7d8duc1B8spaC/Coa6i0kJEeRmhFDVEyIaxX1Sy+9xEsvvdQJfzKeo6ysrM3c6MbG\nRhwOxxkX+kVHR3fKfoxOp5PAwEDCw8N/9vc6m9NzNM9l0YvIj6XAKO2y2+1cd911+Pv7t5oMvW3b\nNrZs2eKaDD1v3jzXZOi//e1vvPXWW+c8v0WBUTqLqaGFrTknMeflc5HNSGSAgahLhhIxIBOfH1hB\nW1bdyBd7S6gyNXHpRfFclB6LwWCg+GQNed+cwFl0nMiWSuwBoTRGJVLX5ANNZiIs1cQnhBE/sDcR\nffvgGxyMtcVG4YkaTuZXY2m2ktgzitResRfcY+ez+fvf/87TTz/dqu3AgQNYrVZSUlLaXD9t2rQO\nGWH8IRaLhcTExDOevNWZioqKmDhxogKj/Kz0SFrapcnQ0hWZm6xkf5mL6Zu99HZWk3BxX2KGXUNg\nt7Pvbeh0OjlaWMuX35YQGODHmEEJ9IyPoK6miT3/OEjV3v34NdRiD42iKSSG+uAIwqy1RNeepF+f\nJCIzBxOcnITBxwdLs5X849WcPH4Cu81Bcmo0w8akEaZNuc/osssuY+XKlT/qsbSPj4/ricjPwWKx\nEBoaSmJiIn369CEkJOSHXyTSBSgwyllpMrR0FQ2NFr56fwfmb/aQ0i2Ekf82isgBmRh+YAGD1WZn\nz5EKdh8uJzU+nGvHZRAW6EdezlHeXfselpo6rMFR+EdEEBkfQVRzBRmhlURn9iW87wj8IyMBaGxo\n4cjBck4erwagZ3oMo8ZlEBKqOXA/JDY2lrFjx/6opw+nN/E+Xw6Hg5aWFnx9fQkNDSUiIoKIiAiS\nkpJ0/KBckBQYRaRLq6+oIuedrZgO55I0ZABjF91KYGzbk4v+lamhhe37jeQV1TKkT3duu7o/xd8e\n5/P/2URtRR3O4DC6dQshKc2fcGs1UT3DCO/fj9C0Sa4FMuZ6C0f3llB4ogY/fx96pscw9so+BAW3\nv4BGzmzRokUcPXr0rFvrxMTEtLtn7dnYbDYcDgehoaGEh4e7vrp3705oaOgZz/8WudAoMIpIl+O0\n26k5lMvB9z+noqaBnpePZOzsa/H1/+FfeSUVZr74toT6RitZfbuR0OxL7pavOPByPUFBviT1CKBX\nhj8hARbCeycT3r8vgd27u0KFqbaJgvwySk7WEhjkT89eMVwxuR8Bgfp1ez6ioqJYtWqV66SX7zv9\nGPr0SS9nc3rkMCgoiIiICKKioujWrRtxcXFd8vQXkY6inw4R6TJaamqo3JnD8Z37OOkXQ+8rJjB9\nWG98fc4+QuRwODlcUM1X3xoJsNvpYWvCesxIzmc7CPe1kRjjw6BedkLiuhPery9hvTPw/W6/UafT\nSW11IwXHqjEW1RIaHkhqRiyZAxPw8+9a+/W5W1RUFIsXL6aqqooZM2ZQVVXFtGnTzvoY2mq14uvr\nS1RUlGs/xbi4OO0XK/ITKTCKiFdz2u2YDudSvSuH0ppmcoMS6Dftl9xwUYLrZJX2NLfYyP76JPv2\nGYmwNBLcZMa/tgyLvYleYXai08KJ6ptBeL9+rgUrcCokVpbVU5BfTVmJicjoYFIzYhmYlYTvD7yn\nnL/Y2FhmzZpFbm7uGVdDWywWwsLCiIuLIzk5mW7duumxssh5UmAUEa/UUl1D9a4czMfyqY6M5xu/\nDAZckcqsi+Lx92t/ZK/e1MzRIxXs3lNEvbGKeB8zmQ2l+FnMBPtDVFoS8VlZRPTr61qwAqdCYpnR\nxMljVVSU1RPTPYye6TEMHZGCj0Jip7vzzjv5xz/+gdlsBqClpYWwsDASEhJIS0vrkIUvIvJPCowi\n4jWcdjumg4eo2b0Hg58f1QkZ7OhxKf3TYrhtUCKBZ3gE3NxkxVhcR2lRHSdPVlNjLCe0voSM5lL8\n7DbsBh+iMvuSOmYykX0yWp3o4nA4KS2u4+TxaqorzHSPC6dnRizDRqdh+IHH3NI5bDYbsbGxDBgw\ngG7durm7HJEuS4FRRDyepbKKml05NJw4QXj/fpizLueLo3VkhEVy25gkgr+3oMRmtVNmNGEsqqOy\n3IyvD1iqymjK/ZYYcxmJvj5Y/cMIzbyY9Muz6NY7pdXjSrvdgbGojoJjVZhqm4hLjKB3/x7Ejk3X\nY00PY7PZ6Nu3LxdddJG7SxHp8hQYRcQjOaxW12iib3Aw0VlDqcscxt/3FJMS6OTWKZmEBPnjcDgp\nL63HWFRLWYkJgG6xQQTXGQne/QVNpeUE+/kSFJOA39DL6TlmCCl94ls9RrZZ7W2O5BswOKHVkXzi\neRwOx8+6SbeI/JMCo4h4lObycmp27abxZCERAzJJueF6TtbZ2ZhTSFxMLTdO6ovDYqfgSCXG4jpa\nmm3E9gije4w/3UMqqfjqK2qKy2ggAEdSL3xHDCM+qx8Z/eNa7X94piP5hgxP0ZF8XqR3794K9CKd\nRIFRRNzOYbVSt/8AtXu+wS8sjOhhlxA/5SpOltXz7tYCwgL8GN4zGlNVI198cISIqGASkiPJGhyL\nedcOaj7cTVl1HdV+YZTH9sIvazTpfRLpmxlHbI8wV6iwNFs5ebyak8erdSRfF5CRkeHuEkQuGAqM\nIuI2zWVlVO/Moam4mMiLLyLlxhvwCw2loLiOV17djbPJRo/QQKKj/Aj082XQsGR866up3LaNug/2\nY7LaaOqexL6IizAnxJEWG85lgxJJzYjB77uV0o0NLRR+FxLBu47kszvslJorKDIZKaozUtdcz7WZ\n/0ZsSLS7SxORC4wCo4h0KkdLC3X79lP7zV78IiKIGZ5Fj8lXUVlmJnt7CXv3l+LjayBrcBL9+ncn\nKjqYhiNHqPjsHfJzj+ATGEjIgIsoGjmNncU2ovx9GXlxAgMGJhAeEQScOpLvZH6Z1x3JV28xU1hn\npMhkxFhfjtPpIC6sOymRCUzsNYbIoAg9ghURt1BgFJFO0WQ0UrMzhyajkciBFxP6b9Mpr2wmd78J\n8459lDW04B8WwMybhxAfHUTN17uofPl/OVlQSEBsDNHDh+E/6Rre31XGyeI6+gcE8X+mpZGcEoXB\nx4Cptol9u4u85kg+m8NOaX05haYSiuqMmCxmwgPDSI5IILN7b65IH4WvwY+y6kZOGOvYsrcUk7mA\n6yf0oZvmWYpIJ/PM36Qi0iXYLRbXaKItNBpLQh+qAvtwpLSFaFsdYdHBVAcYaPYJYPLwOPwPf0PN\n/2ymvKqK4JRkuo0aRcbC+ezcX8HbOwpw5OVx+SXJ3HbtQAICfamtbmTvrqJ/HsnXK5bMqZ53JJ/T\n6cRkqafIVEphXQml5goMGIj/bvTwot59iQgKx2qzU1RuJi/PxCfGI1isduJjQ0mLj2DKqDQiwwI0\nwigibqHAKCIdrqm4BONXuygtqqUxOgVLxFAiYkKJj4tkeHIUNgNszSnk2J5DDLQUYsg/Ss3HFsJ6\n9yb5+l8QMSATk8nCh58dY/czX9IzPpx/n5pJWs9oqsrN7N9T7NFH8lntVoyu0cNSzC0NRAZFkBwR\nz8C4/lyZMRY/H18am60UGOv54lgVReUnMGAgJS6M1IQILr0oHl8/J8UmI0WmfN47bqTZZuHa/prD\nKCKdT4FRRDqExdzIsc/2cHJ/AWafMMJT4kken0VmSjRRMcEYDAbqqk1k//0ftOz7hgRLNUFB/kQM\nyKTbr/6D0NRUHA4nhw+X8fGLX1Nuamb4xQn8Yf5ozHUWTh6r4tCuIo87ks/pdFLbbKLIZKSwroQy\ncyW+Bh8SwuNIjoxnUFwm4YFhp66rt3DcaOJr43HKqxsJDvKjZ3wEmWkxTByWQl1LLUUmI0fr8vjy\nYBUBvgEkhceREpnI8KTBBPsHufvjisgFSoFRRM6Jw+Gkoqyek98ep3D/CexNzST2TWDwjf9GXHIM\nPr4+OJ1OWiqrKP54B3mfZNNSVUV0TASJl15C7KhLCYqPx2AwUFPVwPsf5JKTW05AaAATR6YSHxFE\n4Ykatm3J9agj+VrsVkpMpadWLpuMNLQ0ER0cSXJEAkMSLiIutBu+Pr44HE7KqhvZf6SOE8Zi6swW\nosMDSU2IYNTABCLCfSipL6PIVMgX5aVYjC10C4khOSKBkSlD6RYSo8fPIuIxFBhF5EdxOp3U1TRh\nLKqj5EQlpuIygkzlxCeGc+XMLCJSkwFw2Gw05OdjOnCQmn0HqK6sw+zwI37UcAZMGkNQ91Pn/Vqa\nbRzeX8r23UUUmS0kxUcw9fJeNNY0U36kEkNiBL37dXfrkXxOp5PqplrXtjblDVX4+fiSGB5HcmQC\nQxMuJjQgBMA1//DzIyWcMJpazT+8amRPrD5mikylFNXt4+CJGoL8A0kKjyclMpGRKZcQ5PfDe0Ha\nHXZ8DD4KkiLS6RQYRaRdjWYLxuI6jEV1mGqbCDa0EFpTSJK9jqHDBxI5cCy+gYFYTfVU78rBdOAg\nTUXF2Gw2amx+FEWlMmDOpYwd0guDwYDT6aSksJZDB0rJLa6jzu4gIy6Mkd3CsFlsGKwkErB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CEJsWH0HZBEWmQKqZEDiLfFfOuo3pH9hztLqikqr6GFRkxWNxFRfnoNiMRusWEyRNPQYqamqY6i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\n+gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10eb6d5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for geno in df['Genotype'].unique():\n",
    "    fig, contrast = bs.pairedcontrast(df[df['Genotype'] == geno], \n",
    "                                      x = 'PulseEpoch', \n",
    "                                      y = 'SpikeFrequencyHz', \n",
    "                                      idcol = 'NervePulseID',\n",
    "                                      hue = 'NerveID',\n",
    "                                      pal = myPal, # Does not seem to be working....\n",
    "                                      #summaryBar = True,\n",
    "                                      swarmYlim = (-40, 300),\n",
    "                                      idx = ('during', 'after') )\n",
    "    # Aesthetic tweaks \n",
    "    # Make sure legend does not overlap with plot.\n",
    "    fig.get_axes()[0].legend(loc = 0, title = \"NerveID\")\n",
    "    # Figure title.\n",
    "    title = geno + ' duringVsAfter All Recordings'\n",
    "    fig.suptitle(title, fontsize = 25)\n",
    "    # Save figure\n",
    "    filename = os.getcwd() + \"/images/\" + title + \".svg\" \n",
    "    fig.savefig(filename, dpi = 5000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Grouped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-22T17:53:15.628409",
     "start_time": "2016-09-22T17:53:15.611114"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>PulseEpoch</th>\n",
       "      <th>after</th>\n",
       "      <th>before</th>\n",
       "      <th>during</th>\n",
       "      <th>deltaSpikeFrequencyHz</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GenotypeNervePulse</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>UAS-dGtACR1 16719043 1</th>\n",
       "      <td>122</td>\n",
       "      <td>68</td>\n",
       "      <td>92</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UAS-dGtACR1 16719043 2</th>\n",
       "      <td>130</td>\n",
       "      <td>108</td>\n",
       "      <td>102</td>\n",
       "      <td>-6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UAS-dGtACR1 16719043 3</th>\n",
       "      <td>100</td>\n",
       "      <td>90</td>\n",
       "      <td>76</td>\n",
       "      <td>-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UAS-dGtACR1 16719044 1</th>\n",
       "      <td>104</td>\n",
       "      <td>84</td>\n",
       "      <td>118</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UAS-dGtACR1 16719044 2</th>\n",
       "      <td>102</td>\n",
       "      <td>128</td>\n",
       "      <td>156</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UAS-dGtACR1 16719044 3</th>\n",
       "      <td>102</td>\n",
       "      <td>110</td>\n",
       "      <td>132</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UAS-dGtACR1 16719045 1</th>\n",
       "      <td>136</td>\n",
       "      <td>136</td>\n",
       "      <td>110</td>\n",
       "      <td>-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UAS-dGtACR1 16719045 2</th>\n",
       "      <td>120</td>\n",
       "      <td>86</td>\n",
       "      <td>144</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UAS-dGtACR1 16719045 3</th>\n",
       "      <td>104</td>\n",
       "      <td>96</td>\n",
       "      <td>98</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nSyb-GAL4&gt;UAS-dGtACR1 16717001 1</th>\n",
       "      <td>26</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>-40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nSyb-GAL4&gt;UAS-dGtACR1 16717001 2</th>\n",
       "      <td>30</td>\n",
       "      <td>96</td>\n",
       "      <td>0</td>\n",
       "      <td>-96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nSyb-GAL4&gt;UAS-dGtACR1 16717001 3</th>\n",
       "      <td>32</td>\n",
       "      <td>86</td>\n",
       "      <td>0</td>\n",
       "      <td>-86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nSyb-GAL4&gt;UAS-dGtACR1 16717002 1</th>\n",
       "      <td>56</td>\n",
       "      <td>112</td>\n",
       "      <td>4</td>\n",
       "      <td>-108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nSyb-GAL4&gt;UAS-dGtACR1 16717002 2</th>\n",
       "      <td>70</td>\n",
       "      <td>72</td>\n",
       "      <td>0</td>\n",
       "      <td>-72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nSyb-GAL4&gt;UAS-dGtACR1 16717002 3</th>\n",
       "      <td>60</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>-90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nSyb-GAL4&gt;UAS-dGtACR1 16719022 1</th>\n",
       "      <td>54</td>\n",
       "      <td>192</td>\n",
       "      <td>10</td>\n",
       "      <td>-182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nSyb-GAL4&gt;UAS-dGtACR1 16719022 2</th>\n",
       "      <td>52</td>\n",
       "      <td>196</td>\n",
       "      <td>12</td>\n",
       "      <td>-184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nSyb-GAL4&gt;UAS-dGtACR1 16719022 3</th>\n",
       "      <td>64</td>\n",
       "      <td>140</td>\n",
       "      <td>10</td>\n",
       "      <td>-130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nSyb-GAL4&gt;UAS-dGtACR1 16719023 1</th>\n",
       "      <td>34</td>\n",
       "      <td>250</td>\n",
       "      <td>4</td>\n",
       "      <td>-246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nSyb-GAL4&gt;UAS-dGtACR1 16719023 2</th>\n",
       "      <td>52</td>\n",
       "      <td>106</td>\n",
       "      <td>16</td>\n",
       "      <td>-90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nSyb-GAL4&gt;UAS-dGtACR1 16719023 3</th>\n",
       "      <td>50</td>\n",
       "      <td>242</td>\n",
       "      <td>6</td>\n",
       "      <td>-236</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "PulseEpoch                        after  before  during  deltaSpikeFrequencyHz\n",
       "GenotypeNervePulse                                                            \n",
       "UAS-dGtACR1 16719043 1              122      68      92                     24\n",
       "UAS-dGtACR1 16719043 2              130     108     102                     -6\n",
       "UAS-dGtACR1 16719043 3              100      90      76                    -14\n",
       "UAS-dGtACR1 16719044 1              104      84     118                     34\n",
       "UAS-dGtACR1 16719044 2              102     128     156                     28\n",
       "UAS-dGtACR1 16719044 3              102     110     132                     22\n",
       "UAS-dGtACR1 16719045 1              136     136     110                    -26\n",
       "UAS-dGtACR1 16719045 2              120      86     144                     58\n",
       "UAS-dGtACR1 16719045 3              104      96      98                      2\n",
       "nSyb-GAL4>UAS-dGtACR1 16717001 1     26      40       0                    -40\n",
       "nSyb-GAL4>UAS-dGtACR1 16717001 2     30      96       0                    -96\n",
       "nSyb-GAL4>UAS-dGtACR1 16717001 3     32      86       0                    -86\n",
       "nSyb-GAL4>UAS-dGtACR1 16717002 1     56     112       4                   -108\n",
       "nSyb-GAL4>UAS-dGtACR1 16717002 2     70      72       0                    -72\n",
       "nSyb-GAL4>UAS-dGtACR1 16717002 3     60      90       0                    -90\n",
       "nSyb-GAL4>UAS-dGtACR1 16719022 1     54     192      10                   -182\n",
       "nSyb-GAL4>UAS-dGtACR1 16719022 2     52     196      12                   -184\n",
       "nSyb-GAL4>UAS-dGtACR1 16719022 3     64     140      10                   -130\n",
       "nSyb-GAL4>UAS-dGtACR1 16719023 1     34     250       4                   -246\n",
       "nSyb-GAL4>UAS-dGtACR1 16719023 2     52     106      16                    -90\n",
       "nSyb-GAL4>UAS-dGtACR1 16719023 3     50     242       6                   -236"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_delta_pivot = df.pivot(index='GenotypeNervePulse', columns='PulseEpoch', values='SpikeFrequencyHz')\n",
    "df_delta_pivot['deltaSpikeFrequencyHz'] = df_delta_pivot['during'] - df_delta_pivot['before']\n",
    "df_delta_pivot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-22T17:53:15.658539",
     "start_time": "2016-09-22T17:53:15.630138"
    },
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Pivot `df` to enable delta calculation.\n",
    "df_delta_pivot = df.pivot(index='GenotypeNervePulse', columns='PulseEpoch', values='SpikeFrequencyHz')\n",
    "# Then calculate the delta.\n",
    "df_delta_pivot['deltaSpikeFrequencyHz'] = df_delta_pivot['during'] - df_delta_pivot['before']\n",
    "# Reset index.\n",
    "df_delta_pivot.reset_index(inplace = True)\n",
    "# Split up the index.\n",
    "df_delta_pivot['Genotype'] = df_delta_pivot['GenotypeNervePulse'].str.split(' ').str.get(0)\n",
    "df_delta_pivot['NerveID'] = df_delta_pivot['GenotypeNervePulse'].str.split(' ').str.get(1)\n",
    "df_delta_pivot['Pulse'] = df_delta_pivot['GenotypeNervePulse'].str.split(' ').str.get(2)\n",
    "df_delta_pivot['GenotypeNerveID'] = df_delta_pivot['Genotype'] + \" \" + df_delta_pivot['NerveID']\n",
    "\n",
    "# Group by GenotypeNerveID\n",
    "df_delta_pivot_grouped = df_delta_pivot.groupby('GenotypeNerveID').mean()\n",
    "df_delta_pivot_grouped.reset_index(inplace = True)\n",
    "# Create ID columns again.\n",
    "df_delta_pivot_grouped['Genotype'] = df_delta_pivot_grouped['GenotypeNerveID'].str.split(' ').str.get(0)\n",
    "df_delta_pivot_grouped['NerveID'] = df_delta_pivot_grouped['GenotypeNerveID'].str.split(' ').str.get(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-22T17:53:15.677256",
     "start_time": "2016-09-22T17:53:15.659572"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>PulseEpoch</th>\n",
       "      <th>GenotypeNerveID</th>\n",
       "      <th>after</th>\n",
       "      <th>before</th>\n",
       "      <th>during</th>\n",
       "      <th>deltaSpikeFrequencyHz</th>\n",
       "      <th>Genotype</th>\n",
       "      <th>NerveID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>UAS-dGtACR1 16719043</td>\n",
       "      <td>117.333333</td>\n",
       "      <td>88.666667</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>UAS-dGtACR1</td>\n",
       "      <td>16719043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>UAS-dGtACR1 16719044</td>\n",
       "      <td>102.666667</td>\n",
       "      <td>107.333333</td>\n",
       "      <td>135.333333</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>UAS-dGtACR1</td>\n",
       "      <td>16719044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>UAS-dGtACR1 16719045</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>106.000000</td>\n",
       "      <td>117.333333</td>\n",
       "      <td>11.333333</td>\n",
       "      <td>UAS-dGtACR1</td>\n",
       "      <td>16719045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 16717001</td>\n",
       "      <td>29.333333</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-74.000000</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1</td>\n",
       "      <td>16717001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 16717002</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>91.333333</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>-90.000000</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1</td>\n",
       "      <td>16717002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 16719022</td>\n",
       "      <td>56.666667</td>\n",
       "      <td>176.000000</td>\n",
       "      <td>10.666667</td>\n",
       "      <td>-165.333333</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1</td>\n",
       "      <td>16719022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1 16719023</td>\n",
       "      <td>45.333333</td>\n",
       "      <td>199.333333</td>\n",
       "      <td>8.666667</td>\n",
       "      <td>-190.666667</td>\n",
       "      <td>nSyb-GAL4&gt;UAS-dGtACR1</td>\n",
       "      <td>16719023</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "PulseEpoch                 GenotypeNerveID       after      before  \\\n",
       "0                     UAS-dGtACR1 16719043  117.333333   88.666667   \n",
       "1                     UAS-dGtACR1 16719044  102.666667  107.333333   \n",
       "2                     UAS-dGtACR1 16719045  120.000000  106.000000   \n",
       "3           nSyb-GAL4>UAS-dGtACR1 16717001   29.333333   74.000000   \n",
       "4           nSyb-GAL4>UAS-dGtACR1 16717002   62.000000   91.333333   \n",
       "5           nSyb-GAL4>UAS-dGtACR1 16719022   56.666667  176.000000   \n",
       "6           nSyb-GAL4>UAS-dGtACR1 16719023   45.333333  199.333333   \n",
       "\n",
       "PulseEpoch      during  deltaSpikeFrequencyHz               Genotype   NerveID  \n",
       "0            90.000000               1.333333            UAS-dGtACR1  16719043  \n",
       "1           135.333333              28.000000            UAS-dGtACR1  16719044  \n",
       "2           117.333333              11.333333            UAS-dGtACR1  16719045  \n",
       "3             0.000000             -74.000000  nSyb-GAL4>UAS-dGtACR1  16717001  \n",
       "4             1.333333             -90.000000  nSyb-GAL4>UAS-dGtACR1  16717002  \n",
       "5            10.666667            -165.333333  nSyb-GAL4>UAS-dGtACR1  16719022  \n",
       "6             8.666667            -190.666667  nSyb-GAL4>UAS-dGtACR1  16719023  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Take a quick look at the dataframe we want to plot.\n",
    "df_delta_pivot_grouped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-22T17:53:55.643203",
     "start_time": "2016-09-22T17:53:55.171651"
    },
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jacuzzijo/anaconda/lib/python3.4/site-packages/matplotlib/axes/_axes.py:519: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.\n",
      "  warnings.warn(\"No labelled objects found. \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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E4P3799DR0cGkSZMwZ84ciS0jPjfFpQL8E/0aCa+yoczlwMJQF3yD2j8nVY01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\nIUdERERKpP8DH1Iw4tN9FkwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f9dd198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig2, contrast2 = bs.contrastplot(df_delta_pivot_grouped, x = 'Genotype', y = 'deltaSpikeFrequencyHz', \n",
    "                                  idx = ('UAS-dGtACR1', 'nSyb-GAL4>UAS-dGtACR1'),\n",
    "                                  #floatContrast = False,\n",
    "                                  showAllYAxes = False,\n",
    "                                  #summaryBar = True,\n",
    "                                  #pal = myPal,\n",
    "                                  size = 7\n",
    "                                 )\n",
    "\n",
    "fig2.suptitle('Average Delta Spike Frequency per Nerve', fontsize = 20)\n",
    "fname = os.getcwd() + \"/images/\" + 'Average Delta Spike Frequency per Nerve' + \".svg\" \n",
    "fig2.savefig(fname, dpi = 5000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-22T17:53:59.474765",
     "start_time": "2016-09-22T17:53:59.464868"
    },
    "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>nSyb-GAL4&gt;UAS-dGtACR1 v.s UAS-dGtACR1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bca_ci_high</th>\n",
       "      <td>-95.5556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bca_ci_low</th>\n",
       "      <td>-194.889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bca_low_high_indices</th>\n",
       "      <td>[68, 2916]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bootstrap_reps</th>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>diffarray</th>\n",
       "      <td>[-213.111111111, -213.111111111, -212.33333333...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_ci_high</th>\n",
       "      <td>-94.8889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_ci_low</th>\n",
       "      <td>-194.556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_low_high_indices</th>\n",
       "      <td>[75, 2924]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pvalue_mannWhitney</th>\n",
       "      <td>0.0518299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pvalue_ttest</th>\n",
       "      <td>0.00851384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ref_input</th>\n",
       "      <td>[1.33333333333, 28.0, 11.3333333333]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>statistic</th>\n",
       "      <td>&lt;function mean at 0x106902ea0&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>statistic_exp</th>\n",
       "      <td>-130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>statistic_ref</th>\n",
       "      <td>13.5556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>summary</th>\n",
       "      <td>-143.556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test_input</th>\n",
       "      <td>[-74.0, -90.0, -165.333333333, -190.666666667]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  nSyb-GAL4>UAS-dGtACR1 v.s UAS-dGtACR1\n",
       "bca_ci_high                                                    -95.5556\n",
       "bca_ci_low                                                     -194.889\n",
       "bca_low_high_indices                                         [68, 2916]\n",
       "bootstrap_reps                                                     3000\n",
       "diffarray             [-213.111111111, -213.111111111, -212.33333333...\n",
       "pct_ci_high                                                    -94.8889\n",
       "pct_ci_low                                                     -194.556\n",
       "pct_low_high_indices                                         [75, 2924]\n",
       "pvalue_mannWhitney                                            0.0518299\n",
       "pvalue_ttest                                                 0.00851384\n",
       "ref_input                          [1.33333333333, 28.0, 11.3333333333]\n",
       "statistic                                <function mean at 0x106902ea0>\n",
       "statistic_exp                                                      -130\n",
       "statistic_ref                                                   13.5556\n",
       "summary                                                        -143.556\n",
       "test_input               [-74.0, -90.0, -165.333333333, -190.666666667]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
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    "contrast2"
   ]
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   "cell_type": "code",
   "execution_count": null,
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   },
   "outputs": [],
   "source": []
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