02_snapshots_per_hour.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as patches\n",
    "import os\n",
    "import itertools\n",
    "\n",
    "from multiprocessing import Pool"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%run config.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "valid = pd.read_pickle(PATH_VALID_DEVICES, compression='gzip')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Snapshots per hour"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**RQ:** How many snapshots did we collect per hour of the day?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def worker_snapshots_per_hour(uuid):\n",
    "    df = pd.read_pickle('%s%s.pkl.gz' % (PATH_DEVICES_DIR, uuid), compression='gzip')\n",
    "    return pd.DataFrame(columns=[h for h in range(0,24)]).append(df.LocalTime.dt.hour.value_counts().sort_index().T, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3948</th>\n",
       "      <td>61</td>\n",
       "      <td>51</td>\n",
       "      <td>40</td>\n",
       "      <td>38</td>\n",
       "      <td>31</td>\n",
       "      <td>32</td>\n",
       "      <td>64</td>\n",
       "      <td>81</td>\n",
       "      <td>92</td>\n",
       "      <td>78</td>\n",
       "      <td>...</td>\n",
       "      <td>77</td>\n",
       "      <td>87</td>\n",
       "      <td>77</td>\n",
       "      <td>75</td>\n",
       "      <td>88</td>\n",
       "      <td>79</td>\n",
       "      <td>84</td>\n",
       "      <td>85</td>\n",
       "      <td>80</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3949</th>\n",
       "      <td>33</td>\n",
       "      <td>36</td>\n",
       "      <td>34</td>\n",
       "      <td>40</td>\n",
       "      <td>34</td>\n",
       "      <td>41</td>\n",
       "      <td>37</td>\n",
       "      <td>43</td>\n",
       "      <td>42</td>\n",
       "      <td>35</td>\n",
       "      <td>...</td>\n",
       "      <td>40</td>\n",
       "      <td>37</td>\n",
       "      <td>36</td>\n",
       "      <td>32</td>\n",
       "      <td>42</td>\n",
       "      <td>49</td>\n",
       "      <td>44</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3950</th>\n",
       "      <td>151</td>\n",
       "      <td>157</td>\n",
       "      <td>154</td>\n",
       "      <td>154</td>\n",
       "      <td>148</td>\n",
       "      <td>149</td>\n",
       "      <td>146</td>\n",
       "      <td>146</td>\n",
       "      <td>152</td>\n",
       "      <td>146</td>\n",
       "      <td>...</td>\n",
       "      <td>159</td>\n",
       "      <td>168</td>\n",
       "      <td>167</td>\n",
       "      <td>172</td>\n",
       "      <td>165</td>\n",
       "      <td>171</td>\n",
       "      <td>162</td>\n",
       "      <td>157</td>\n",
       "      <td>154</td>\n",
       "      <td>147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3951</th>\n",
       "      <td>80</td>\n",
       "      <td>81</td>\n",
       "      <td>74</td>\n",
       "      <td>73</td>\n",
       "      <td>73</td>\n",
       "      <td>72</td>\n",
       "      <td>75</td>\n",
       "      <td>71</td>\n",
       "      <td>71</td>\n",
       "      <td>66</td>\n",
       "      <td>...</td>\n",
       "      <td>74</td>\n",
       "      <td>73</td>\n",
       "      <td>80</td>\n",
       "      <td>83</td>\n",
       "      <td>78</td>\n",
       "      <td>74</td>\n",
       "      <td>76</td>\n",
       "      <td>82</td>\n",
       "      <td>80</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3952</th>\n",
       "      <td>44</td>\n",
       "      <td>48</td>\n",
       "      <td>46</td>\n",
       "      <td>47</td>\n",
       "      <td>31</td>\n",
       "      <td>29</td>\n",
       "      <td>31</td>\n",
       "      <td>34</td>\n",
       "      <td>29</td>\n",
       "      <td>31</td>\n",
       "      <td>...</td>\n",
       "      <td>44</td>\n",
       "      <td>50</td>\n",
       "      <td>48</td>\n",
       "      <td>50</td>\n",
       "      <td>45</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "      <td>48</td>\n",
       "      <td>42</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       0    1    2    3    4    5    6    7    8    9   ...   14   15   16  \\\n",
       "3948   61   51   40   38   31   32   64   81   92   78  ...   77   87   77   \n",
       "3949   33   36   34   40   34   41   37   43   42   35  ...   40   37   36   \n",
       "3950  151  157  154  154  148  149  146  146  152  146  ...  159  168  167   \n",
       "3951   80   81   74   73   73   72   75   71   71   66  ...   74   73   80   \n",
       "3952   44   48   46   47   31   29   31   34   29   31  ...   44   50   48   \n",
       "\n",
       "       17   18   19   20   21   22   23  \n",
       "3948   75   88   79   84   85   80   75  \n",
       "3949   32   42   49   44   45   45   42  \n",
       "3950  172  165  171  162  157  154  147  \n",
       "3951   83   78   74   76   82   80   86  \n",
       "3952   50   45   44   44   48   42   43  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uuids = valid.UUID.tolist()\n",
    "\n",
    "p = Pool(NUM_CORES)\n",
    "lst = p.map(worker_snapshots_per_hour, uuids)\n",
    "\n",
    "hours = pd.concat(lst)\n",
    "hours = hours.reset_index(drop=True)\n",
    "hours.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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QUFBQqAmlIBQUFBQUakIpCAUFBQWFmlAKQkFBQUGhJpSCUFBQUFCoCaUgFBQUFBRqQikIBQUFBYWamNexmPr7++WaNWvayimlRIj2xurrBGeneBWn4lScred85JFHjkkpa6ZlHY95rSBWrVrFww8/3FbOsbExurq6TnnOTvEqTsWpOFvPKYTYW89589rE1IlRdSaTOS04O8WrOBWn4pw7nPNaQXQCcdz+vB6d4OwUr+JUnIpz7nDOawXRiVDlnuedFpyd4lWcilNxzh3Oea0gFBQUFBRah3mtIDrhg7As67Tg7BSv4lScinPucCoF0SAMo/0LvzrB2Slexak4Fefc4ZzXCqITjqFSqXRacHaKV3EqTsU5dzhbpiCEEF8VQhwRQjwx7th5QoiHhBC/EUI8LIS4MD0uhBC3CCH2CCF+K4Q4v1XlUlBQUFCoD62co3wN+Dxw67hjfwn8Nynl/xZCXJl+fxXwOmB9+ncR8MX0/5yDruunBWeneBWn4qwgiiUgiWWyYlEy4X/1M0hk9X/JDwmKZeL098TQEBMDMk7OiWVyflU+iTwkIAAhkn8ACIQ48XP6qXrcdV1KUqBrWvon0MSEv3HHmoF2tGfLFISU8qdCiDUTDwPd6ece4ED6+RrgVpmsW31ICNErhFgmpTw4FYemtd9Cls1mTwvOTvEqztOHM4pjwjgmiJL/XhDihhFuEOIGUbXTBwkTO9VKR55+lgI0CTESgYCyf1KHTuWStPNPPybnjBM3nkKe8A0mrqyvfpWSsbFSqnjkcY6JK/FThWKkisTQBKaukTEtMpaOpetYhl6XEmlHe7bbs/I+4G4hxF+TmLcuTo+vAF4cd96+9NhJCkIIcSNwE9C7cOFCxsbGALBtG03TKJfLQOLAcRyHQqFQvbarq4tSqUQURUBSwWEY4vt+XTKEEEgp0TSt6v/I5XL4vk8QBAA4jgMkIwoA0zSxLItisQgkSi2Xy1EsFuuW4XkefX19J8koFArVvSD5fB7XdQnDEEh2WcZxXF0rbVkWhmFU7Za6rpPNZqv1V0tGFEVks9mGZMy2jovFIkuXLp11O+Xz+brruFgs0tvbO+t2mqyta7XTyMgIuVyuKe1Ur4xDhw6Ry+Wa0k71yDAti5GxMRAaYSyJpEAaOkMjo3hRTCQl2UyGctlN3isB+WyWOAoRcYwmBJZtA+Cn92YYBqZpVsslNI1MJkO5XEbGMToQS4llmtX6mUqGTGU4mQzlUqnaTplsFt/3iMPk3mzHJo4lQeBX21rX9RPe0QV9fZTSth8vI5ogw/c8IgkYBrGuMTxWJowkaALbsXFLZTKmQdY0WLSglyjw0QFT106o42KxSF9f34zaqV6IVm42S2cQ35NSnp1+vwV4QEr5bSHEHwI3SilfI4T4HnCzlPLn6Xk/Aj4kpZwy0NL5558vt2/f3rLy18J8i7ky33gV5/zilFISRDFBFBHEMeUgpByEuH6IF0WUSmWy2Uw6CRAYukAXGoauNc3UMhGlYpFsqgjbhWZySimPz6yiOJlySImuaWQtk5xtkjVNvHKJ/gW9M6pHIcQjUsqXTndeu2cQNwB/ln6+E/iH9PN+4Ixx561MjykoKHQYlQ7LTzssNwgoByElP8QLo3GmnmR2baR/XYaOEYVkHbuj5Z9vEEJg6jrmBB9DLCV+FFEcC4hjSalc5mA5YGHOYUHGIWOZTVe67VYQB4BXAj8BLgN2p8e/C/ypEOIOEuf0yHT+B+iMD6ITI/lOcHaKV3G2nzOMk44/8QkknZAbJJ2/F4b4YYys2PYBTUucsaam0eXoU+5HavdI/lTm1ITANgzstNfuzthEccxg0eXoWBldFyzMZujNOmRNoyn7xFqmIIQQt5OsUOoXQuwDPg68E/hbIYQBuMCN6ek/AK4E9gAl4I/r4ejUPoh2O/s6wdkp3tOJ08lkkHL8Kp1xn9PjcXpcysSpG6UrckBLVtCI6qKbdFXN8U6hcqyCcrmMaVt4QYwbpQogCBPHa2rGSK5LVuHomsDQdWxj5p2N67pVX027cLpx5uxkR3UUxxwrljk8VsTSdfpyGXozNplZKItWrmK6fpKfLqhxrgT+pFVlaSYqjp5TnbNTvKcqZxTHuEFIKQgZLrscGxrFyWQgHZnL8e9vZeVOelyky3QShXD8xOoKn/GXVEXIZIWOhMrSmXKpRCabTZZhikQB5GyrZb4AgLgD7Xm6cuqaRj5VFmEcc2SsxOHRIqausSifoduxcRpUFvM6YZCCwlxFECUKoej7jJQ9Sn5AZcmlZejkLINcpr22eeUPOH1gaBpdTqosophDo0UOjBSxDI3+XC6ZJtYjp6WlbDHUPohTj3e+cnrp+v2ClyiEivNWEwJL1+ly7BNGbmYHEsy02wSiOOcGp6Fr5PVkYBBEEQdHxzCdTF1Ok3mtIDqRDyIMw7bvSO0EZ6d45wunH0aUgoDRss+o6xHEMVJKDE3DMnS6p5kdRFGE1ub7VJyKs7o6qk47k1IQDcL3fWy7vdP0TnB2ineuckaxxA0CxjyfwZKLG4RAsgvWNnQymtkQZxAEmG0OEa04FWejmNcKQkGhlfDCkKIXMOx6jJY9ZLrZyzF1ejLtNykoKLQb81pBdCIfRCdG8p3g7BRvJzmjdCfwqOszVCrjhzEgMQ2j6at92j3aVJyKcyaYlwpCCPEG4A3r1q1rO3cnHOOd4OwUb7s5gyhiuOwxNlJkzPOJ05AGjmHgZBozGzUCTWv/4EZxKs6GOVrO0AJIKe+SUt7Y3d09/clNRiUo1qnO2SnednBKKSl4Ps8PDPPEgWPsOXwML4zI2xY9GYe8bWHorX01PLcDSe4Vp+JsEPNyBlFBmG4+csx5fRsKbUIQxYy4LodHi/hhjKEna8WNOMRWz5CCwkmY129FFEueOjzAWf0LqptCWo1TNffsXOFtNqeUklIQMlAoM1gsI5FkLJPuceYj3ehAIh3FqTjnAee8VhBCExiaxtNHBlnZ283irkzLHddzeUPMqcDbLM4wjhktexwaK+L6IYahk3NqO5otq/2OccWpOOcD57z0QVQg4xhD0+jO2OwbHuWFwVGiFgfwG5+Io13oBGeneGfLWfID9g2N8sSBo+wdHEUg6Mk65KYIhVzuQMJ5xak45wPnvJ5BVKAJQU/GZqjsUg4C1vYvwO7AlE+h/ZBS4oURJT/gSKFE0Q8wNK3lQegUFE4HnBIKApI9EV2OTckP2HV4gDP7e6uRDRVOHUgpccOIsh8w6nqMuj6hjBESHNOkV21gU1BoGlpmYhJCfFUIcUQI8cSE4/+3EGKnEOJJIcRfjjv+ESHEHiHELiHE5fVw1Fozn7VMTF1n15FBjhVKTQ/HMZcSvZyKvBM5pZSUg5DBYpnnBob57f6j7Dw0wN6hEQp+gGMa9DgO3RkHa4azxlM1wYziVJyzRStnEF8DPg/cWjkghLgUuAY4V0rpCSEWp8e3ANcBW4HlwH1CiA1SyimDrMdx7c7fMnR0TbB3cJRyELK8pwu9SZtKTpeENp3iLRSL6KZFOQgZdj3Gyn41aY5lGGTt5qdVPN0SzChOxVkvWpkw6KdCiDUTDr8buFlK6aXnHEmPXwPckR5/TgixB7gQ+LdpWCb9Rdc0ejI2xwolykHImr6eGY8wx+NUTWjTSV4pJSU/YLDosn9gENtJQmGbht4ShTARcyHZi+JUnHORs90+iA3Ay4UQnyZJOfpBKeWvgRXAQ+PO25ceOwlCiBuBm4DehYsXUy4VCXUd07LQNFHdXagbOpZlY8QRQ6OjDI2OcvaqFYgwqHZ82WyWMAzxfR9IYvJomlbdzWsYBo7jVFfWVJbQFovFarrTXC6H7/sEQQAcX6bpui4ApmliWRbFYhFIzGK5XK4hGZ7nVa8ZL6NQKFRNaPl8Htd1CcMQgEwmQxzHeF5SH5ZlYRgGpXTlg67rZLNZxsbGqnU7UUYURfi+35CMrq4uSqVSXXXshRHFIGQ0iBkrFtGFBlFAd6YX13WJfQ/XT+ojiqJq/UzW1pVVHUIIMtks5XIZmdZxJpMhCILqvVlp/CXf8yi7ZQzDwDTNatsLTSOTydQto/K81JRROm7qzGSz+H7CCWA7NnEsCdL6MU0TXderba/pOo7jUErbfryMKIwaklHhhMQ84bputZNptI7rlRFFUbXss22nKet4nIxYSnzPa0o71VvHnueRzeWa0k7jZUxVx2W3PON2qheilSGz0xnE96SUZ6ffnwB+DLwX+B3gG8CZwP8AHpJS3pae9xXgf0spvzWV/LPPPU/eefeP6poZ+GFEOQhY09dDX27myVqiKGp7voJOcLaKN4hixlyPo4USRT9EE5CxTIzUnxR3IK6+4lScpxvn5tUrd/vFwobpzmv3DGIf8M9pDupfCSFioB/YD5wx7ryV6bFpUL9yq/glnhsYoRSELO/Jz8h0MV8S2swl3ihOTEjHiiWGS8lIxzENemok1ZkviVcUp+I8HTjbvVHuX4BLAYQQGwALOAZ8F7hOCGELIdYC64FfTSes0dmPnm6qOzJa5JmjQ2nSl8ZQMZW0E53gnC1vxa+wf3iMJw4eZc/RQQpeQJdj0Z2xJ531VcwT7YTiVJyKszZaNoMQQtwOvAroF0LsAz4OfBX4ampq8oEb0tnEk0KIbwI7gBD4k+lWMM0Umkh21pb8gKcODbC8p4tFXRm1qapJiKVksFTmyGgJN4zQNUHGNNA7FLJcQUFh5mjlKqbrJ/npP0xy/qeBTzfCMZu4S1nLJIpj9o+MMlQqsaqvh6w1ffz/0yWJzkx4S37A3sERykFI1jJrmpCmw6maeEVxKs75yDnPh3WzG/UnS2EdIgk7Dw9wYKQwbSyn0yGJTqO8URxzYKTAU4cHiCX0ZJwkMfqMOE/NxCuKU3HOR855rSCkbE5gPsc06HJsDo8W2HV4kII3ue39VE2iM1Peguez6/Agh0cLdDv2rHNznKqJVxSn4pyPnPNaQTQTmhB0ZxyEEDx9eJB9Q6OELY4MO58RxjH7hsZ4+vAgCOjOOMqPo6BwimGeB+trfodkGTqmrnG0UGao7LK6r4du57gt/VRIojNb3lHX44XBUYIopitjN1UxnKqJVxSn4pyPnPNaQYgW2eCEEHRnbIIoYs+RIfpyGVb05jHTnY7txlxJGBREEQdGChwrlMnZJpk6nPqN4lRNvKI4Fed85JzXJibZYhOQqet0Z2xGyh47Dh5jsFg+IaxEu9DphEFSSoZLLk8dGmC45NGTsWfshJ4Op2riFcWpOOcj57ycQQgh3gC8YdXaM9vBRd6xCKOY5wZGMOOQ9U6mJaPnuQgvjDgwMsZgsUzetjH0eT2mUFBQaADz8m2XUt4lpbwxn8+3jdPQNXqzDm4Y8dThAfYcHaLg+U3PN1ELrc6zXQtSSobKHk8dOsaY69ObzbRFOXTiXhWn4lSctTEvZxAVdGLt8cLeHgDKQcDTRwbJWibLuvN0Oa1LcdlORSilpOAFHBgZo+CF5B2rGkivHch0IO+F4lScirM25uUMooLJEga1EpW9ARnTpCfjEEvJM0eH2HHwGAPF8rQb7WaC4rjwv62ClJIxz+fpo0M8fXSQSEpsIduqHOD02WeiOBXnfOCc1zOIRqK5No1xggKwDQPbMAiiiL2Do+zTBEu7svTlMk1z5MYtdsYXPJ+DIwXGXB/bNKp5nUte+zf/tHrhgeJUnIqzfsxzBTF3YOo6PRk9DTtR5OBIkf6uLIvyGewO7WOYDsVUMYykiqEn25nltAoKCnMTc7PnqhNCtN9ClslMnWyoElI8lpKBQpkjY0X6sg6LunLkZrjyKdfk5OQlP+DgaJHhsott6PROohimu9dWQHEqTsU5dzjntQ+iHSuIJqLeGOxaujy227EZ8wJ2HR5g1+GBNJNaQNxA2ZuVD6LsBzx7bJidhwYo+QG9GYeMObnSOlVj3CtOxak460PLFIQQ4qtCiCNp7oeJv/0/QggphOhPvwshxC1CiD1CiN8KIc6vj6X9CqKS57ZeCCHS0NcOsYT9w2PsOjzAb/cf4dmBYQaLZdwgnFLZzfZBKAchzw8Ms+PwAEU/oDtj1xXavNF7bQYUp+JUnHOHs5Umpq8BnwduHX9QCHEG8FrghXGHX0eSRW5dcn1CAAAgAElEQVQ9cBHwxfT/KQXL0KuZ1GIpKfkhI2UXCZiaTq9j052xyZhGXXm2JyKKY8I4JoxiwljiRxEFz2e45KHrGj2O3ZH12goKCvMTrUwY9FMhxJoaP/0N8J+Bfx137Brg1jS73ENCiF4hxDIp5cGpODrhg7CalLxHE0mmtUwaHjuKY4Zdj6PFZPu8Yxj0Zh26HAvTsoilTBRA2vmHcYwbhnhBiBdGeGGULLGtKACZfDR0jS7HmpFiaNa9Kk7FqTjnJ2dbndRCiGuA/VLKxyZ0WCuAF8d935ceO0lBCCFuBG4CehcuXkK5VCTUdUzLQtNENUa6buhYln1CvJJsLofrusRRks3UcRyiKKqacKaTIYTAtCzK5XJ1iVkmkyEIgup0r9JofrpE1DAMTNOsrlkWmkYmkzlJRhQEEIaYqYwginnhyDEiKRFC4Ng2rutWy+E4GXzPRRcCXQhy+Rxh4BOl5bAdmziWBL5P2fcxTRNd16sytDTwYGncHotMNovve0RhVL1/KRMZQF0yZlvHURTR1d0963bKZLN1t1MURdi2XVc7zaitS6WqCbFSx77no+v6Ce1Ubx1PbKd6ZZRLJfR06fVs26leGXEcVetntu00ZR2Pk2GaJr7nNaWd6q3jWEry+XxT2qne9ymKIpxMZkbtVC9EKx296Qzie1LKs4UQWeDHwGullCNCiOeBl0opjwkhvgfcLKX8eXrdj4APSSkfnkr+1nPOkd+658czMsfMFKVikWyTVxVNh2KhQDaXa7t5qBP3qjgVp+JsPefm1St3+8XChunOa+cMYh2wFqjMHlYC24UQFwL7gTPGnbsyPaZAMtJSvgMFBYV2o21GfCnl41LKxVLKNVLKNSRmpPOllIeA7wJvS1czvQwYmc7/kKD9naZKGKQ4FafiPF04W7nM9Xbg34CNQoh9Qoi3T3H6D4BngT3Al4H31Mkx63I2CnOKfQOnEmeneBWn4lScc4ezZQpCSnm9lHKZlNKUUq6UUn5lwu9rpJTH0s9SSvknUsp1Uspt0/kejstof/yTUzUo11zhVZyKU3HOHc55vZNaQUFBQaF1mOcKogNJOtoc/rpTnJ3iVZyKU3HOHc55rSA6kTDoVA3KNVd4FafiVJxzh3NeK4hOJgw61Tk7xas4FafinDuc81pBzIWEQacqZ6d4FafiVJxzh3OeKwgFBQUFhVZhXioIIcQbhBBfKhZL05/cZJyqtsa5wqs4FafinDuc81JBSCnvklLe2OxMa/XgVE0MMld4FafiVJxzh3NeKojjmPsJg+YrZ6d4FafiVJxzh3OeKwgFBQUFhVZhXiuI+ZwwaK5zdopXcSpOxTl3OOe1glBQUFBQaB3mtYLoRLC+SlaqU52zU7yKU3EqzrnDOa8VhIKCgoJC69CyjBNCiK8CVwFHpJRnp8f+CngD4APPAH8spRxOf/sI8HYgAt4rpby7DpaWlH0qnKqJQeYKr+JUnI1CSkk87i+UUA7au6ooBPwwQhMCTRNobchV0466bSXD14DPA7eOO3Yv8BEpZSiE+CzwEeBDQogtwHXAVmA5cJ8QYoOUMpqKQCUMOvV4FafiBAjjmDiWRJWOP5ZEcUwQx0RRTBjHhLEkjGOiOEZyfLgoYwlFd4K8iJIfkrUMDK35OezDMKQcjpK1DHRNQxNgaBq6pmHqyX9DE5i6nigRIdDHKRNtBmmF29GeLVMQUsqfCiHWTDh2z7ivDwF/kH6+BrhDSukBzwkh9gAXkmSkm4KjMwmD2p2cvBOcneJVnKcPZyzTTj+K8cMQL4xxwxA/jIhlssdp/E4nISqd6fHPlqEj0E/oXN1yGcc+vsvYDyPKQUh/LotlNF85+GHESKHIop7uqvzjsxpwg4hYhsRSIqWs3tNEdVBRIsl/DUNP/1eUyDhlogmB14b27IztIsF/Ar6Rfl5BojAq2JceU1BQmOeIpSSIIsIoxo9i3CDEC0O8MCKIJUXPI2uaOJaJoWnYptE0E40fRoy4Hj2O3Trl4Hp02eYJ8kU6Q2iEsaJAwjjGjyJiH7wgZMz1yTsmpq6foFR8z6MviMhZFo5pYBl6001bHVEQQoj/QmK2+/oMrr0RuAnoXbh4CeVSkVDXMS0LTRN4buLZ1w0dy7Ipl47Ha8rmcriuSxwllivHcYiiqLplfToZQgiEplEul6uRFDOZDEEQVHc1VtYmV1YYGIaBaZrV0LxC08hkMg3JCMMQGccnyyiVkOlIK5PN4vseUZjcm+3YxLEk8P3k3kwTXddx3WTqrek6juNQKhar9TNRhkQSBEFDMmZbx57nNaWdMtls3XXseV5T2mlSGTXayUuvaUY71SvDG7fqZbo6FoZBGMcMjxYphSGarmMYJr533HRjOxl830fGiQzLsojjuFo/hmlSdj2iY8NJO+k6hmkS+T66JhLFgUa3ZWAKIAwwbJswCPDTcpmWBVC9N13XMQyjei9C07BtG8/zTohuGvg+Zd9nzAvoy+fQRTKzmFKG61bbyXYcwsAniuLj9yYlYVo/hmEQxZJjhSJdtolIr3PHheCuR4auaXjpvWmahmXbBKmMIIoI0FjgGBgCiCNsyyJK61iLI4quz2jZw/d9BJBzLHrzOQgCrNS0Vaut64WoVEgrkJqYvldxUqfH/iPwfwGvllKW0mMfAZBSfib9fjfwCSnllCambeedJ7/5wx+1ZGSgoHA6IYgivDCi5AcU/ADXT0eutknWNmfs7xMkppOJaNfI/nSSL6UkSE12UkoEYBs6edsiY5nYuo6hJ22xefXK3X6xsGE6mW2dQQghrgD+M/DKinJI8V3g/xdC/H8kTur1wK+mk9eRhEGlEpls9pTn7BSv4mw9p5SSIIrxwpCiH1DwAsI4Bgm6roGUeFHEoq7Z2+w910WfMGJtdec6ViziStHWzttzXewGRuaNyq+FiZxCCCxdx9KPXxNGMUMll2PFZFZi6TpZq/5uv5XLXG8HXgX0CyH2AR8nWbVkA/emI5KHpJTvklI+KYT4JrCDxPT0J9OtYErQgYRBLZxxzSXOTvGeypwV+3IYxxS9gNhobwTQsbLLoJ/MEipO4GSVjY5jJl2BH0aMeH7TOteJdduOkfeo65/gMG62/Frlb9Yz1Ej91MNp6Fp11gBQ9gN2Hx2quzwNKwghxALgDCnlb6c6T0p5fY3DX5ni/E8Dn260PAoKcw2xlIRRsiQzCCPcMMQNIrwoBJkMazzXxfYbW6sfxhFFLyBnmzNaqum7Lrnc5E7guWg2mYn8iQ7jZsufz/VT8AN6G5jp1KUghBA/Aa5Oz38EOCKE+IWU8gMzKWizIGrYNluNTph6OsHZKd75xBnFcVUReGFIwQ3wo5BIHl/CKEiWJxqaRtY8bsvPWY3Z9StLNRflczPuPOQUnK3qnComkHZ2rqbe/H5huvLP1rw0k/pphHO8/MoCgHpQ7wyiR0o5KoR4B3CrlPLjQogpZxDtgOyAD8L3PWy7ObbGuczZKd65zFl15AaJ3d4PIwQQhDFjvs+CjEMm3Sg1HcLAx7Tqi8bZrM51Ms5Wdt5h4CM1o60j78D36q7bmcivhUbacybyZ8M5UX4rFIQhhFgG/CHwX+qW3nK0X0FEYZR4UU5xzk7xzhXO447ciKLvU/CC6o7dyu7YvG3hhxGlIGRJV2Mj+yiKqWcfbDM771qcrR7Zl/0Aj6itZpl663am8mthppyzqf96OGfbvvUqiP8G3A38XEr5ayHEmcDuhtmaDLO7DzcI1TJXhVkjlhI/jJKVPV5A0Q+I4sRWZExw5FZwKtikWy1/zAva7jBW8psnv14FcVBKeU7li5Ty2XRJakdh9y/mhcERco5FX9Yhb1t1Te9nxem0fyjfCc5O8baLM0p3q7pBxGjZxyt4VIIgGLo+7W7e2b58VroBrFXyp+NsV+e0cBY+k3rk1yr/dHU7W/m10ChnM+p/Ks5mtW+9CuJ/AOfXcaytkFFIzrEAwcHRIoIiPRmbnoyDY+gtCeYXxxK9zROWTnB2irdVnGEcJ+YgP5kduEGIBDQEgpiMZdX9vDTj5YulnDTWfqs67wpnO0euWgvMwNOVf6q6bYb8WmiEs1n1PxlnM9t3SgUhhPh3wMXAIiHE+BVL3dBQmJGmQgjxBuANZ192BQCmrmHqFlJKxryA4bKHbegsSGcVRhNnFYHvtz0qZic4O8XbLM6JO4P9NCyFJpKImtlxK3rccrmtygEgDIKa4Zpb6zAOiBFtNWu45XJTw1LX5zCuXbfNkj8bzma2by3OZj8/092RBeTT87rGHR/leCTWtkNKeRdw1+9c9e/fOf64EIJMaicOophDJ8wqbBzD6EiIcIXWorLvwAsjRsoupSBASpDpzuCKQ3m2mA824ynlRxFeNI/Lr+S3Xf6UCkJK+QDwgBDia1LKvUKIfHq80BT2WWKqzn7irGKgkIwSl/fmyVvWCbsLG8F8iqk/H3mn4ozSXcgnRwWNCaOIUc+nN2OTtSx0rf6BQLtHfrU429F5lMKYvnymrZ1Ts2YPjdTPTDhnvdpnGs6WdN7jOFv1/NRbk11CiEeBPgAhxDHgBinlE00ryYwwfSdQCbvrxzE50+TwaJHDokhf1mFBNtOw+UnvgDOgE5yd4tV1vaoEKvGCvCCkHEZEabROSdKuRroBzdAEpSBueKlplXOaZ6AVL994znaNLBdknLaPXJuxaKTR+mmUsxn1PxVnq9q3wtnK56deBfEl4ANSyh8DCCFelR67uKmlaRD1JAyqVXmxlAwWXYZLHv35LD0Zu+446q7rtj3pSic4m8l7QkrI8VnCKnkC4uOJY0YLxWoYbUh8BhPjBY2HHyYzh9m8HJ7v42QyNX9r1ctX4Wyn2SEO/JbKr1X+qeq2GfJroRHOZtX/ZJytbF/P99FMq6XPT70KIldRDgBSyp8IIdrfYzWIyRpHE4KcbRHFksOjRYZKZRbls+Tt+leyKJwMKSVFP2DM9Y+vGgrCJHHKOJPP+Ixax7OEJSkXbUMnW6e/YD7adDsp321ybMBTrX7mnfwW+5SgfgXxrBDivwL/lH7/D8CzLSlRQ5jdOnVdE+QdiyCK2T9cIGMZLO7KkpnCDq51wOzSCc5GectByNGxIiU/xDQ0wihmzPPpzTg1R/6TQdbJ2cyXT+tAvoJQSspt7pxq3Wcz5dfCTDlnU//1cDa7fSdytkP5FP2Qhd1dLd0oXG/r/SdgEfDP6d+i9FhHMZlZqNHGMXWNvGMRxpK9AyMcHBmrLouciEayMTULneCsl9cPIw6OjLF3YJggluQdC4GgFIT0ZTMNKQfgBPPSVJzNfPkmcrbj5S7HtH3kWk/dzkZ+LcyEc9abEKfhbMkmxHGcbduE2GLlAHXOIKSUQ8B7GxEshPgqcBVwpJJRTgjRR5KHeg3wPPCHUsohkdh1/ha4EigB/1FKuX06jriGD2I2jWMbOpauUfBDRtxhFuZOdmSXisW2+wM6wTkdbxjHDKeJSHRNI5ea52b7crjl8pT241a8fOM52/Vy28RtN2tMV7ezlV8LjXL66VJlxzTxI0nJ9wkjSRRLgjhZzhxGVPNqhBEEcUwUScJYEkaSsueBZqTf4+rxZOFDRBBHRLEgiiCoXpecJ9Nw7JVcCxO3+SWHZfWzrB6XmLqGoQtsU6JhJN81gWEcX0xh6KJ63NST4xlbp8cx6M6YdGcMuh0D26xdtyf5lIzZted0qDfc9wbggyQde/UaKeVlU1z2NeDzwK3jjn0Y+JGU8mYhxIfT7x8CXkeSRW49cBHwxfR/Q2jGy13ZSzEbR/apjFhKRsoexwolYinJWma1Xua9TfcUdxi3S34QRgwWAwYKPgNjZWJRxAsjvCDGDePkfxCPO5b896OIWEYUXUkkm/OuGRpph6xhGhLLECC1amduaBqOqWHYSTrO464yQeV1T3xl44SKE43bAkEYhsQIAhkShYIwFvhRTMmPCeNkX1aihDhBaU22z9w2RKIwHJOeTKI88o6GZUJ/zqGcg4wWYTuypX7Teuf/dwJ/B/wDUFesWCnlT9Oc1ONxDUmWOYD/BfyEREFcQxJGXAIPCSF6hRDLpJQH6yxf822KExzZxwpFlnTl0DuU3a3TkFJS8HyOFkr4YXxSWOu50jnNF/nz2WEMcHDEZaDgM1jwGSj6DBQCBosexwoBY27tREgCsE0N20j/TA3H0OnJmFh5gWlIMpZJ1jSwDA3LSDp2Q+eEEXhyrPJZJFnTNJGOyjVC3yOfy6Brotp5trp+RgsFPLSG5EuZzIxKfsRoOWS0HDDiJv9H3ZDRcshIOeDAsMvOQ6NEMiYIIR6nPHsyBmv6s6ztz7K2P8fqhZlJZx8zQb0KIpRSfrEJfEvGdfqHgCXp5xXAi+PO25cem1JBCNH6dcC6JrAMnYFSGTeIyNkmCzSXnNWarFW10OmEQeUg5MhYkbIfYpsGead1m7xqJUFp9cstDLPtyqdZ+Ysnk18L9XAGUcxIKWCkHDJcChgpBwwWXQaKHkPFiMFCyJh3ogLQBfTlLRbmLLatdOjP2fTlTPryFj2OgWPp2GmHX2u02+z2lfaJcdjaoTw9KejJNCZfiETJdWc0ujMmMPVS66xp4gVxqkRCBgoeewfKPHesxGMvjqYyYXmvw9r+LGv6s5zZn2Npjz3jWcZ0sZj60o93CSHeA3wH8Cq/SykHZ8SaXCuFEA0Px4UQNwI3Ab1nXXwphWKRUhCxIJfFEInNE5IQC4Zp4blu9Vonk8H3POJ0w5VtWcnu3DB54A0zMZf4vl+VEQudY6NjdNkmti4Ig4B9Ay4yjjE0ncW9XVga6DKZ6lWcVb6XVJNhGJimSTktl9A0MpkM5XIZmZYjk8kQBEG1HBNlRHFMPpc7WUapVLWVZrJZfN9LchuQREWNY0mQ3otpmui6jpvWh6brOI5DqVis1s9EGUEUMRpEDBRKWJpG1rHRkdU61jQNdIOjI6N02WbVJtpoHY9vpyAM6erqqspIlvJBzjaIAx83mF6GEALbcfA8r1rHtm0ThiFRmizFTCNhFstlhgpl+nvymJqo3pvQNGzbrktGUC2HjmEYeGm7CU1D6AbHRsbIp+WXukMY+Liuh2maWJaVhAoJgurzomsanu8jpUQKDaGbFEtloigmkhLbcYjCkDjdQ+IjyZsmo4UyQoCZyghSmbqu4dgWx4bGKASS0XJIKdIYGHMZLvnJaNWNGS4FFP0TDQSakDgW5C2ThXmH5cttFmRN+nIGSxfk6bKgy0qXKNdo6+S+QmQIoTy5nYRhcmx0jLyV1o/WeB1PbCehaWhCEEURfhRRjqDbsarPz6QyXLf6PtlO0k5RlMicrJ3GymXGvICcoVdjT1VQrwwvvTdN07Bs+wQZmmkxMDpGzjLQ45AFGYtuWyPMaQQ9gles70MTgsGxMi8Ourww5PHCkMsjzw/z891J9+yYGmcscFjdl/yt6KvfbyGmSnwthHiOdONqjZ+llPLMKYUnJqbvjXNS7wJeJaU8mCYg+omUcqMQ4u/Tz7dPPG8q+Rde/QfyAx//FAtz7QsfMN7pVlnrH0uJoWl0Zyy6bAvbmDpUdKNop5NaSokXRox5HgcGhslms5NGxj2VHMbd+XzL5Fu6wWAx4NCIy8Fhj0MjLgU3IJJUbdJRnDhLg0imqUwl0TTDJ01ITIOTzA71QgBdGYPejEFv1qQ3a9GTMenNGvRkTHK2hqZJlnRlsBtcjVbBVE7qVrVvhbPdPqXZLgCYSn6t8k9Vt1JKjox6PHusxPPHSjx3rMi+IRcpwdAlOz97/W6/WNgwXRmmi8W0tt6bqRPfBW4Abk7//+u4438qhLiDxDk9Uo//wcjk6HasjtmkDU3DsBIzVxRLRkoeg0UXXQi6HIsux8Yx9JbnqJgtYilxg5CC7zNa9gmjGF1LnPWZSTqGU8kn0CyHccELOTTscXDE5cBwiSOjZQ6N+gwWj4/KDQ0WdzvkLUHOMDAMgS4SO7upa+jiRNu6rokTbe/poxTEMW4YYmkGuhDEHF99UxnzxdXPkliCJiMW9eSqCqAnY6JNErPqVGrf09GnJIRgSY/Dkh6Hf7euryrzhQGXpw+PsrNOOfWuYroW+KGUckwI8VGSPBCfklI+OsU1t5M4pPuFEPuAj5Mohm8KId4O7CVJYQrwA5IlrntIlrn+cT3litxy2x+uyZJ06JogYyUb7OJxYcc1BHnbpDuNJjuTIIGtSKITxcnqkTHXY9T1iaVEFwLLMKp7F+JJytrKl8+yrLa/3LFoLNJrwQs5MORyYMTl4LDLwRGXQyNe1TmrCUnWFvRlbdYv6WJZj8OyHoelPQ79eQtNE8RRNOMNkDOtn3o5m1n/td6XVrcvmt525dOMJEVTya+FRjktQ+esJTkW5nX+ps5r6p03/lcp5Z1CiEuA1wB/RbKqadKlqFLK6yf56dU1zpXAn9RZluPXxbVXS8wG0zVOPYlBtHFhx6WUlMOQ0eHUhioga1mJQ0vXMfXajrsTOJuURCeMkwioo2WPghcQI9NlfrVNYrXutdUvtxuEFMP25jCerE2DMOLgiMf+YZcDw2UODLnsH3YZKR9/7jKmxtJeh20rE0XQ32XRZcPKBfkpzTIzTWozm/qvh7PZ7TuRsx3Kf9j16Mtl2zp4nG2Sounk18JMOP3UfFwv6lUQlTny64EvSSm/L4T4iwbL1nR4wfTB+hpBPY3TaDKSJL6QgW0k8gdLZYIoWTIKSUTGnGWSs0xsU8fST7b3zyaJThBFlP2QUdej4AcgwTQ0Mtb0uTEm3ms7Xu6hYqntOYx9z2ewFLF/KFEEiUJwOTLmVc01pi5Y2mOzeVkXy3sdVi5wWNbr0JMxZ7SUciZJbWZb/9NxtqJ9x3O2a2aY1UXbLQuzSVJUj/xaaJSzIr+rgd3t9UrfnzqSfw/4rBDCpv4wHS3DQDHg73/yPFeft4xVC2e3FLRdD29fNnPSyKMUBIy66YoKIchZBlnLwjEN7AllqUREjeW4qKhpNFQvDInS38JYVnebAli6Tm5cFrWZlr/V9dNlt2b58PjyhxHsPDTCnsMF9hwpsm+oTJAOgQTQ32WxYoHDBWt6WdHrsKLXYVGXPam9fqL8U8GmPp/ln8qbEJsh34/q2soG1K8g/hC4AvhrKeVwugLpphmUs6nocnSePVriMz/YzTlndHPVOUs4o69xRdFI4zQ7GYk2boYBiQLwwpiCV1l+KtCJ0cpJhNRYnrikTJLMEsa8gB7HxjH1aoRUy9BxxMxHNe0e+fU4NlodIdwbxUDBZcfBEfYN+jxztMSBIRdJ4jBevTDHJev6WLkwy/LexFfQ6EajmdTPTEZ+s63/yThb2b6GYbS9cw1l81Kc1pJfC7OZPcy0furlnCi/6QpCSlkSQjwPvE4IcQXwCynlPXWztAhdjsnHrtnIz3YPcd+OI/y/L45y3qpuXn/OUlYuaE08+FYnI6l07JVzpZSEYYiW5kSYOAPww4hyELJ0hslypoKuaW1/uWX9z25NSCkZLPrsPlxk95ECzxwuMOz6BCEYusa6RTkuOHcpZy3JsaY/i6lryChCtNlhXO9z1Mz6r8XZ6vaNYslo0N6RdzNXDdZbPzPlnE3918M52/atdxXTx4BrSSK5AvyjEOJOKWVH/RBSxjiWzpXnLOHSjf38aOdR7n/qKL954WlesqqHq85ZwvIpFMVMKq/dyUiEEERhWNMH0eqXe6xcbjh8QCOoVf5G6lfKZNPXQNFn/5DLniMFdh8uMFRKnMhZS7BuscPL1vWxaVkXZyzIoNdYmTXTpDazqf96OJvdvhM526H8jxWKbfcpzTZJ0XTya2EmnLOt/+k4m9G+9c6L3gKcK6V0AYQQNwO/ATruqK4gY+tcde5SLtu0iB89dYT7dx7jNy+McP7qHl5/zlKW9Z4YZuBUsim2Sv6YF7T95R4PKWUS6qEQMFD0OVbwGSz6DBSSz0NFn3CcRarLMVi/OMdrl+RZ3e+QtwU9HUizqeS316ek5LdOfr0K4gDgAJW4FTawf8ass4QQ4g3AG86+7IqTfsvaOm84bxmXbV7EfTuO8uOdR9m+d4SXrunlynOWsLTHmVXlnS7JSEZcr+HYMg3LT8vvBRHPDyQ7Pg+PuAyXw5oKAKDLNujLm5yxIMN5Z/SwMG+xMG+yuMthUdfMQo43mtSmGfU/FWer2rfC2c7Oiaj9S9FnmxhpJvXTCGez6n8yzma275ShNqonCfEvwO8A95L4RX8P+BVJUD2klA3limgWLnrD78tP//XnMKewHxfckPt2HOHHu44RRJKXru7md9cvYG1/a5JtzJeRQafke0HI3sECx8ZC9g64PHe0yL5ht7qctMsxWJgzk44/Z7EwbyWB4PIWfVlzWgfyfK8fJV/Jb7X8gufz7y88d/ahNsbhO+lfBT+p87qWolbCoInIOwZvPH85r96ymLufOMQvnx1g+wsjnHdGL5dtWsTaRdmGln6eLrFlLENvWoKZvQNlnj1a5JljRfYPFRkuRsRSYBuCtf05Xnf2YtYuyrG2P4seBzPmnGn91Hufzaz/Wpytbt+ZhKRuBLXK34xnaCr5tTBTztnUfz2czW7fiZyteH7qXcX0v5rC1kHYhuCVm/q4bFM/D+wa5Ge7B3hk7wir+zJctrmf81f3zigMRgVzYWQwF+QXvJCnDo7x3JESzx4t8uJQOYkDJCSLe0zOWpTnrC1drF2cY3mPc9LeArc8s6A286V+Oim/0z4lJX/+ya93FdN64DPAFhJfBADTRXOdK5hYef/+guVcec4SHnp2iB/vPMY//uJF/nn7QV65oZ9LNiyky2nvDtdp5UcRXjS3H67Bgsd9O47xiz0D+JHE0gWrF2b5va2LWbXQYWHOYFlPe8MfKPknylcOYyW/UdTbE/4jSbC9vwEuJQmm1/Gd1JqYucPYNnVeubGfV2xYyI4DY9z/1FG++9ghfvD4IS5cu4BLNy1iZY246e02C/hh1Dqw4gkAACAASURBVHazQAX1TNP3DZW598kj/Pr5YQRw4doFvGLjQlalS0obrZ92LxWcjrNV7dvukOadkt/uHNiNcjarftptdq5wtrp961UQGSnlj4QQQkq5F/iEEOIR4GNNL1EDiKdxsNdTeUIItq7oZuuKbg6OuPxk5zEeemaQB58ZYv2SHJdtWsQ5K7urphDf844n9GnTy5fR6MjLPf5ex0NKyZ4jRe5+4ghPHhjDMgSXburnNZsXsSB3PMLkTOpnMs6ZlL9eTMbZyvb1PQ90o62ddyN1OxP5Nc+ZBedM679ezma2by3OVvcPhVKJckzL5EP9CsITSX7P3UKIPyVZ4tr8DCsNY3IFMZPGWdbjcP1FK7nmvGX8Ys8xfrxzgL9/4Hn68yav2tjPxWctRKSxjdo5MutUbJlKVrgKpJQ89uIo9zx5mOeOlemyDa4+dymv2LiQnH3iozTT+pnIOZvyz4az1e3rBgFe0OaotXXW7Uzl18JMOWdT//VwNrt9J3K2pX8oey3zKVVQr4L4MyALvBf4FHAZScKfGUEI8X7gHSQ9/OMkJqtlwB3AQuAR4K1Syhn1jLNtnKyt83tbl/DqzYv5zYsj3P/UMb71yEHueuwQL13VwwVrF7Awr9Obbc8mrE4nIwmimF89N8S9Tx7l8KhHf97i+gtX8O/WLcCsNfM4xc0mzZCvHMZK/mzlt8qnNB71rmL6dfqxQJ3JfCaDEGIFiaLZIqUsCyG+CVxHkjDob6SUdwgh/g54O/DFqWW1NraMpgnOX93L+at7eWGgxP07j/KrvUM8/OIQtmFw7hk9vOSMHjYsydcM4TAT1Cq/3YFkJACx0LnnycPc/9QxRsohZyxwePvLV3H+qt6WZSKb7l5b8fKN52zXy92fb37srPHya5W/Gc9Ro/XTKGcz6n8qzla1b4WzncrHbF5W40lR7yqmDSTRW1ePv0ZKedkseDNCiIBkZnKQZFby5vT3/wV8gmkUxEQTUysbZ9XCLG++aCWXbezh0GjMb/eN8stnh/jZ04NkTI1zzujm/FW9bF7ehTlDZTFZ+aM4xmhCxqB66+fgsMtDzw7y06cHcIOYTUvz3HDxYjYty0+5Z6QZ9T/VvbaqfSuc7Xy5WxG1drryz/Y5mkn9NMLZrPqfjLOV7RulkZbbOTMJg6Ap/cJUqNfEdCdJBrkvczx50IwgpdwvhPhr4AWgDNxDYlIallJW9uXvA1bUIav6uV0vd69jsKo/z4Vn9hGEEU8dLLD9hWF+++Iov3x2GNsQnL2im5es6mXrii6cOkNHT1X+MAwxZpgwqB75kCiFR/YOs33vMAdHPISAc1bked05S1m9MDdr+fVisnttZfuGYUgstDZvQvRn3aZTya+F2TxHM63/ejmb2b61OFvdP5Q8r+2rDZvRL0yHehVEKKWcZjRfH4QQC4BrgLXAMInyOTmo0uTX30gym+k96+JX4bkuZSkpRZLe1KHrBqDrGoZp4blu9Vonk8H3vKpDybYsojgmDBO9ZJgmmhD4fprtLZUxViwy5gV0OxampuN5HjKVsW1FF5uXZLj2vEXsPlriiQMlfrt/lEf2jmBosHlpnpes6WVTv03G0hGahm3bJ8gQusFgsUTO1IkDnyjNkRyk5QjCEDuO8dJUgVUZrltVkrbjEAY+UZQmCLKsJHFQEOBHEaUwZkHGqdaPpmkMlCW/fOYov3mxwOExHwGcuSjDm85bxLkrusjbGpZt45bLSf0YBrqm4aXl0rTk99FCgTEvqNpEZ1LHlXbyfO+kdhKazlDZJWtoxIFPyNQyhBDYjnNCHdu2TRiGRGksfNM6XseFcpnA9enLZav1U6udppKRlEPHMIyT2mmsWGTU9emyTUxdI/A9PN87qZ2mquNKG9RqazSdYTfJpBYHPqGsLaPC2ei74EcRxSCkL5+v1k+9MuI4rpZ9snYaK5UYLSc2dVMT/J/2zj1Yvqyq7591nt19H78nMwzMwA8ENI9SHuOUEx8gY0WjIGqIpmLIoKSmyj8onxjQqoRUaYmaiJpUaSYSM1EiQUABU0lQdIx5gJmBQdFBQRiGxwwzODO/3723+7xX/jjn9O1f/053n+4+5/TtO/tbdasf9/T6nrP2Y+299t5rxVG0tI4ny0lVr5Khls2VIKJvQxZHxNkMGTXb03Q5RWnK44dDbrh44Sr9LCNjVln3+n0Oh0Mul/qRPJNckiSEUbiwPVWVk2p931TdWExvBB4hD7cxrmWq+lhtpmNZ/wD4JlV9TfH5nwC3kocTf6qqJiJyK/BGVf3GebJuefl36Bvf9LMM46S7aV2SLEzUkWXKXz16xH0PXubDDz7B48MES+CZFwY888KAZ1zoc+nCgOv3fZIsqzfyWzEhyfT9XzNTAJ5z3Q4vvHSWF9x0hjOD4xFJHd6mR2bTnF3MDB87Gnaew3idMq0jvwqrcK6r/0WcbZTvJGdXnoUdx2bQa24L8bT8mZ6FFepQG7GYyh1LPzL1/SonqR8EvkpEBuQuptuAe4A/AF5JvpPpduDdiwSJ5XAliLiw0++scVs14jZZlvDc63d57vW7vPLmp/Hpvx5y34OX+eSjQ/7vX/01d/9FMUpx4KbzPW48N+DSxR2eWRiNaT9/Hc559z8K85nCvQ88wcNXjo3Cd91y8RqjsAxvG41vkrM7t2H3u1lWLdO68quwLGcT+p/H2Vb5WivkCF8Fk/KdFhaMF91/E3VoEeYaCBH5SuAzqvqs4vPtwN8HHiBfRF4aqvpBEXkH8CEgAT4M3An8V+BtIvITxXdvWSTL7vXZ73mdNu5oycQgIsKliztcupj78bNM+cJByKceOeRTjx3y0OMR//sTj3P3X+STsZ5rcdO5Ps+82OcZFwZcOj9g10np9fvEaUYYZwRJRphkRHFGmKQEsRImKVFSfE6UURQzimMeeDTk4SvR2Ci8+MvmG4WrdDDnWdtqfCXnaT9nsmw9WlZ+5W86TnY1j7PVQ4hRhOV6Ha8pNReUsEp+5TUNJUaah0UziH8HfAOAiHwdeTym1wLPJ+/UX7kKqar+C/LQHZP4JHDLMnKS0dHW7WO2LOHCjotzw4Cbn3UOz7HJMuXhywGffmzIp7844sHHhtz9sS+OcyE4FqTZvGOBUxyiDHzBEpvr93u8+MuewgufcYb9fjMLWqdlH/lJOWdi5Dcsfwtil21SfjydZGUOFhkIe2Kd4buAO1X1ncA7ReS+Fe+vMWiDJ0NLLCqcdc87VLqtLOFp5/o87VyfW78kvy5NMz5/OeDBx0Z8/rEhPc/Bcyx818Z3LHqOhT/x2XfzzyJKEMeNZFKreta2K2+KctBx42vqDMss+VVYlXMd/dfhbLp8pzm76FyP4oQLe93me2nzHNQsrMKZH9IMF19YYKGBEBGn2H56G3DHEr9tHcvkcaiDOoXjuKsfNlq28G86P+Cm8wNUz9d61rH8htJsTj9rF417mGjnaULXKdM68quwCue6+l/E2Ub5TnJ2NfI+v7vb+ci+iTq0rH6W5Tw+gV1/MX2RCfoN4A9F5N3kC8p/BCAizwEuL3V3LUAbPGxUt3Amt4+1IX9VzjYa3yRvV43bF+28ca9apnXlV2FZzib0P4+zrfItObt0y2jSsM+Qxfe/bh1aRT/LcF51AtupP/OYOwtQ1Z8UkfeTx0l6nx7vibXI1yJOBbbdp3ia5G8qMKGRf3rkmzWl+fKjtP5Z54VuIlX9QMV3f7nkPZ5YnLbC33b5pnGfMvlmwXir5W886c86qJMwaB5WUd6TJRkJ0PlWQVg/wcwi+VXoOgd2Xc6my/fJkuyq6xzYq3Kuq/8ucmBvpYEQkZeLyJ3D4XBlGasqL6q5A6DJxlfF2UXj/usrB5037rr6XVV+5bUrcK6r/0WcbZTvJGdXI9dNJLtqog4tq59lOZvQ/zzOpsp3Kw2Eqr5XVe/o9XuLL67AOsp70iQjCUJ2PKfzxt1EUptl9bMsZxP6n8fZVvmWnN2eMG7+tO+i+1+3Dq2in2U4m9L/LM4my3crDcQ6OOk+vxMlv4VQwqdKP0a+kX/K5W/8LMM6qEoYNA9NKO/JkozEc2xUmktUVCW/CusktVlVP3U5m9R/FWfb5SuW3XnntIlkV6tyrqP/OpxNl+80Zxv1Z8tnEHWDTzSnvLSDaV0V5yZGHrOetSn5VViVcx391OFsWv/TnF2U7+OjoPORa1N1aBn9rMK5rv4XcbZRvpOcbdWfrTYQdUKVQ7PKK2PdtyW/CsMw3Mi0tOpZm5RfhVU419X/Is42yneSsyvjP3Cszt0aTdShZfWzLGcT+p/H2Vb5lpxt1p+tNhB1sG0+vyr5B2G81fdv5J8Q+WZNychfElu+BtF9voLJFH9dFf65jpPZlGg7zWkVluFsSv+zONssX8d1O+88EppNT1nn/tepQ6vqpy5nk/rvOk0uQCZWq4EtYUMzCBE5KyLvEJGPicj9InKriJwXkd8VkY8Xr+dqSJr5n9Z8chtIRtJzm7fjde5/nYQkq+qnLmejW/kqONsu3yRdnElwHVTdf5MJZurqZ91kV6vopw5n0+U7zdlF/3DYomehxKZcTL8A/HdV/TLgK4D7gdcD71fV5wLvLz7PxaxgfW0WThRFnY/8ynyzbcmfed2KvOvopw5n0/qf5uzkEOLhUfeHEBuqR0sdQlyBc+1DiAs4WzmEOMHZVf/Qs9s5hDiJzg2EiJwBvo4iY5yqRqr6BPAK4K7isruAb1tFfuuFk263T9HIPxny93x3q+/fyD8B8ltYU5rGJmYQzwIeBX5VRD4sIr8iIjvA9ar6UHHNw8D1i0V1P607ipPOK9cmkpGswtuE/udxtlW+JWeXjbvvNbseMC2/6v7bSHa1CMtwNqX/WZxtlq9tW50bnyYTXc3CJhapHeCFwGuL/NS/wJQ7SVVVRCr3sIrIHcDrgLPP+eqvJwwCUssiE4vDMKZnQxZHxJmF43pXxUzv9ftEYTg+ou57HmmWjbeLOa6LJTKeLtr2sYwoTTkMEy7s76JpQlCEpfZ9nyRJSIsQum5xeCUey7BxHIewiJsiloXv+4RhOM6I5/s+wzDkiWHAnu9iC6RpOiHDQrPsWhlBMN7q6/d6JHFEmuYyPc8jUyWJ8/ComQhHYYJf6CdKLTzfJxiNxvqZluF6HkmSjGU4joNtWYTFfVnWsYwozXdbPeXMPkzqZwkdl3DhmnIaxTGPH43Y810slCxNZ8oQEfxe7xodzyonRYlGI4Zxim/l+gnT6nJaqayDgDBJOAhjLu7vIVlCkqako9E15TRPx7PKyfM8gjjh8aNhrh/N0JRrZJScsHxbSFGGieKLksURQVxThu2M731eOYVxnOtnbxfJUoJRtJyOJ8vJ84ij6KpyipKUxw6P2PNdJEvRTNZqT9PlFKsSZCk+2Vg/y8qoKutSx0Gpn90dLM0IRnm9lSRZ2J6my0m1/rqQ1D1L0BRE5KnAB1T1UvH5a8kNxHOAl6jqQyJyA3C3qn7pPFlf+bJv15/6129Glc4sdxY3nyh80chj3YToq45s6vI2OXKq4mx7ZHbl8LDzqKNNJrmvq59VOdfRfx3Opst3mrOLkf2jl6/wlDP7nXoWVi3PwzDi22/5io9HR4fPW3Rt5y4mVX0Y+IyIlJ3/bcCfA+8Bbi++ux14dx15p8qnaORvRL45Z2Lkryt/W9eUFmFT5yBeC7xVRDzgk8D3kBurt4vIa4BPA9+5SIhYDleCiAs7/c4Kp8k82HULf1XOdSvXJs6ZTHJ21bj3e17njbuJerSsfpblbEL/8zjbKl/ZwFb0TaQ5bbIvmoWNGAhVvQ+4ueJfty0jx93Z6bxx+73VQozXlV+FVTibaBzzeNtqfCXnaRlZzpK/bj1a5f6X4WxKP7M429S/3+t1X75Nb6evcf9N9UXzsNWhNuKjo84bd7iBZCTLcjbVOGbxttn4wjDsvHE3Uabz5FdhHc5V9VOXs0n9V3G2Xb4Hw2Hnxr/JOlRXP6tyxkn9YIZbbSA0ayaY3CQWFY5uIBnJMpxNNr4q3rYbdxjHnTfudct0kfwqrMq5jv7rcDZdvtOcXRj/K6PuZ4ZN1aFl9LMKZ77mVt+wbLWBaBqbdjsY+WbB2MhfX/62Lhh3px+/9m+2PFhfc/atbuH4Syh3FfmrcrZRuSZ5u6q8F/d2O298q5ZpXflVWJazkTWlOZytrSkVnF12rq7VfZrTdevQSmtKS3BOyo+K8yF1sNUziKbOcCxTOE+WfAWTvF02bmuJJFCryK+6/3VzFqyin2U4m9L/LM42yzdJks5H3k3lMZklvwrrcK6qn7qc6+h/qw3EMhnlZmFZ5aVLWN9V5C/L2WbjS9O088a9rH6XlV+FdThX1U9dzib1X8XZdvmOoqhzt0yTdaiuflblXEf/dTjXLd+tNBAi8nIRuTMYBYsvnoPT4lNsTb4JTGjkrynfrCltt/ytNBCq+l5VvaM/GKwsY1XluRtIcF/F2UXlGqV0Xnnr6ndV+VVYhXNd/S/ibKN8Jzm76pzO7+503vk1UYeW1c+ynE3ofx5nU+W71YvUq2IbLPdJkL+JE8ZG/smXr6ookKmSqaKqZJp/RpUkSxlGCQPXJc4ysnD1PBQKiORvRAQRIUkzDsOIs30fp0ZE0/L+tLjfyb8Sk8vacaYEcUzPdYnTlLiGKyfNMmyrnptpnvz8eQVLBEsm3wtCvdPTTdafrTYQsxIGzcO6youjCHtOgKw2GvckZ5edRxY3m6hoWn7V/S/S77ryq7AMZ1P6n8XZZvnGUUTqeteeA7mq02Tc6VdtAlHyztS2LBzLwresPIJo8dm2LCwr79BsEUbDIYOd1Wb643vK8tckU6I0JYwTBq5DhhLGKdlUPxAGQX7KeGxYGN+bb9s4luBYFq5tj+913AmvuAFqdDSkv+JzqkI68axplpGUf2k2fu40y8b6v+o5qTaebgPhwLfaQCyLkz4yO2nyg4bDy5w2/ZxE+VWj5fK7wyAkCWN2Pe+qkaslMu7ke5aFbQlO8bnsOO1rOtJ6PWli5x1xm7jKwGXK0dBhd2c3v9fivttG3PVzqnJ46OD3+4XxzIiTjCBJ6LkOihLEyXiWJCJjg58ssaC+5QaifsE31bjtGZWgzc7Dtu2NdE6znrUp+VVYlXMd/dThXOsE89TIPFMlzhSNj7cpxknKlTBk3/dJVRnFs7cwZlOj+6tagYAtxejetnHsfLTs2BZp36PX8487+olOvy04TvtdjBSzFRvABun1Wmkj89D5cwLS7+H589c+pmdh0661RdhqA1F3FNOoT66iIrTdeWcIVzYwcm2q0i+jn1U419X/Is5p+VnF6Lx8P10jFbAmXBxO4eIQz0Ws3AWQqZJ5yoWd/uLOupDlWNd28vlIf7ZbQf1szNkVXLf5zHmGsz7neACwYrFvtYHIooCjMMa1UzzHqfS5Nd15h2HYeTKSLx4cdp6MBK591qblV2FZziYWXIejAK/XQ1VJp/zvSZoxjGP6nkdUuGVK/7tXjMxn+d/nuTiGR0cr++ZXxWg0YrCzYzgNZ21szECIiA3cA3xOVV8mIs8C3gZcAO4FXqWqc1dJRw99hkvnzxBnGZdHAUfFbgnXtnFtizjNTrzPuI58E1tmffmlnzYtFgGB8a6QTBXbElzLrva/T43Uu4jDb2BwErDJGcT3A/cD+8XnnwberKpvE5FfBl4D/NJ8EYLn2Ow6HucGPeI0ZRjFXAkirowiDsKQM/1eI6v5Y0ar+wT3mnYftRZYyx2xqn7qcs6Sn+8AyY1AOQtQcreM79js+Q49x8axbdxiQTYIAvoNp5FdhK5dPYbTcK6CjRgIEbkR+BbgJ4EfknxI9lLgHxWX3AW8kQUGwpoKyuXaNmf6Nmf6PZL9jFGccCUIOAzjcSfhOfZai3K+73c/Mm6YY9sCE5b+/dL9E8THM6soTYnStNzRiOfYDFwH37XxCiPg2NZc33zXxsFwGs5t4dzUDOLngR8F9orPF4AnVLUcKn8WePoiIVk2ezXesS32bI+9nkeaZQRxwkGYzywyVSxLcMsFwyUMxsFwSJB1e8I4DMNGIo7Okj8Ly/KmWcZhEHEQRtdspayLMIrwp06I2sWirFu4fQaeyw37O7h24QayLGzJXUOrGP/RaNR5AzechnMbODs3ECLyMuARVb1XRF6ywu/vAF4HnL1w3XWMhkckto3reViWEAZ5MgzbsfE8n9FwmP8OeOr+LvvuiFEYEcQJkWZcOYpI0wQhH736jk0c5wcAbNvCcT3CII/5FGcZV4KI/X5+iCyI898kSTIOnFUef4+jqJBh4zjOOPuTWBa+7xOG4TjhRyljFEUchDHnd3ewBYLRKJeVJHiue62MIBi7UfxejySOSNNcpud5ud+9eBbHcUgz5YuHR+z5LqQJOPaYo0pGlmUktn2VDNuyCItnsywLy3E5ODwkAyyE68+d4am7PbJCH77vk6UZcZGz13U9xBKi8LicXNcjGI0QIMTi4vkzRFEEWYolQr/fJ03TcblMl7Xl2LiTZS1CfzBgNBqNddzv94njeBwB0yvDUIcho2CEbVm4rsuo0IdYFv1+v7aMUj+VMobDcTn1BwOiKGQ4PEKzDL/nk2U6ri+u62LbNkFR5yzbptfrMTw6GpdTKSNNCh3XlVFwAgx2dgiCYFxOvV5vro6n21NdGUkcMyw4r2mTS5bTXB1PyMhUicKwkXKqq+MwDOn3+82U04SMeToeBSMcx1mpnOpCmgqZXZtQ5KeAVwEJ0CNfg/gt4BuBp6pqIiK3Am9U1W+cJ+tvffmX6zve9wdrjeTTLCNKU4I45TCMGEUJGfl2RadY7LZExiNvn4z93d2V+WZh3sg+GI063000jzcuorwq4Ds2Z/o+O26+kL7uAm6+u6fb3SCG03A+2Tj/xjNv/Hh0dPi8Rdd1PoNQ1TcAbwAoZhA/oqrfLSK/CbySfCfT7cC7F8lqImGQbVn0LYu+63Ju0MsPMSUpQZJwFMYcRTFhknIQRJzp+wzcZoLJTeIkJiOZ5FVV4jQbJxrpuw7X7e2w4zW/u+q0+nINp+HcRs6TdA7inwFvE5GfAD4MvGXRD9qY/Vgi+K6D7zqc6ffGnWOYpBxFEZePhgRpNl4UFQTHzg8o2StsgaybjGTVCJWrGgdV5SgIwbJBYNdzubjTp+85rYYUiON47BLoCobTcBrOamzUQKjq3cDdxftPArcsKaHpW7oGIvlWWs+x2et57NtCbzAgSTPiLCNKUoI4IUwShlPhEaxicdW2qhdPl0lGsso5zTrytQiClmbZVcHABMG14Ppze/RcB6ejbXxJknTe0Ayn4TSc1ThJM4itgVUaDWx2vOOuW8ugWWkeiTGIE4Ikjz6ZTezJhzz2zkEUc7bno+R+/fIQVhOxcarCQ1QdFCufZeC79Fyn2NmVbw0NhkMGC2K9GBgYnF5stYFoYg1iWcyz2CJSnOLOR+v7veNrk2KEPhk4K04zUtUipG/+/zjNxh14iVSFZImY+kmacRTF7Hp5vPkoTeceFJvlFut6RGQ4DafhPFmcW20gtgllvJ46mA5fHCXJylFOy/MB8w6KGRgYGFRhqw3EKgmD1kUUhq2H9p0OX5xGIYN+/b3LTaGLZzWchtNwnlxOM6w0MDAwMKjElhuI7qNqdj1K2BTnpngNp+E0nCeHcysNhIi8XETuPJo4kt4VTmpikNPCazgNp+E8OZxbaSBU9b2qesdOxwlXgHH8ltPOuSlew2k4DefJ4dxKA2FgYGBg0D623EB0vwZxWhODnBRew2k4DefJ4dxqAzGdMKgLnNagXCeF13AaTsN5cji32kDMSxjUFibjrJ9mzk3xGk7DaThPDudWG4gugvVdw9hx/oxNcW6K13AaTsN5cji33EAYGBgYGLSFrQ61sYmFof6g+621m+DcFK/hNJxNc6oqWr4qKMVr+f3U5/w3FP/hKkeFFtkmBcl/KyCqYNlcGYXj60Rm+DfmjfqLoJlSXCbHd4CW/xv/XNGC07LyCNC2JdhijT83gU3kpL4J+E/A9eQ6vFNVf0FEzgP/BbgEPAB8p6o+Pk+WbmANIopCfL/buEib4NwUr+HcHs4yOnGaKakWYeTHPWreQYVRiO/5eYdZ/m+68zq+HDTvdC0tO0cZd8IikrtVJntfuVZOGEb4njfuaUXynCwCRW4WsG3BpuxM86jGliWFS8XCsoq8KJIbgzLicXnrwtWfgyCYuWg8q6ue/l6nXss3YyMx1d2NRiP8Xo8oSQiTPONjnGWEUUKqOiHjWMGWlRuSutjEDCIBflhVPyQie8C9IvK7wKuB96vqm0Tk9cDrybPMzUH3BiJNUug4su8mODfFazg3y1lGEk6LkPSpKlmWHXfM4wvBsS0822LgOfi2g+dYRT6RIsOiZTE6OmRvb4+sauR+1ci+fD9xHVp0zkWHLGWnLcUoHijfjztyODw4ZH9/7/i6hkbT8yBxxF7HuVOsJGJvUG3w8/LLJpKBKUmWEqUZcZKSJUmt/AGbyEn9EPBQ8f5ARO4Hng68AnhJcdld5JnmFhiIPM+Cq1YnlcDAoCmUHfH0d1d9nnqjaw6I4jTPfnhsALLxSHicQxdwi46/XySQ8hwbtwgZ70yEj6/jxgiKJFi2lCTtw4S3J3c3YePOyBKQRmFQR85G1yBE5BLwAuCDwPWF8QB4mNwFVfWbO4DXAWcvPuU6kjDgkcMEx/WwREjjCNe28D0X3+9dtRVssLNDEARkaQpAr9cjTVPiOAbA9TwsSwiD3JdoOzae549liAh+z2c0GqFFUp9+v08cxyRJnm60TOIRhbkMx3FwXXd8LF4si36/v5QMEUGz7FoZw+G4U+kPBkRRmI8SAb/nk2VKHOUDBdd1sW2bIMjrhWXb9Ho9hhPxrKZlOI5DHMdLyVhXx2WypHXLqT8Y1NZxmqZEYbh+OVk2tuNwNBzmuTwQfN8nCEaFMRD6/R5xFBHHMcO4SBmpuLqBUgAADGZJREFUShzHCOC6Do7jEAQhInna2n6vz3A4HI+U+/0+URSRFvrxfB/NlDiuLifbtvF9Px+lRyG+Y3P2zD5JFCKq2JawuzNAs4w0jvN67vtYlpXrQ1MchJ7ncnh4SJlYd29vj+FwOL6PwWBAkiRERX3xfR/XdTk4OBjXp16vx+Hh4bicdnd3OTo6Iit0vLOzQ1Topyz7sj6Uz+Z5HmUcNsuy2NnZuUpGrvNgaRmHh4fj9rS7u0sQBOOy7vf7ZFlGWJS153k4jsOwqHPl78pnXUWGbdsMBoOrZMzTcfk6LqcKHc+SUReyqS2UIrIL/CHwk6r6LhF5QlXPTvz/cVU9N0/Gi170Ir333ntJMyVKU6Ik5TCMOAyjPD908Wi2lafVdKz1ZxpxHHcemGsTnJviPQmcZbImLbP/UWYBnBjllyPuiZG3Y1v56NuycB0bz7bwHAdLBMeScY5ySyzSJMbzunVJRFFkOA0nACJyr6revOi6jcwgRMQF3gm8VVXfVXz9BRG5QVUfEpEbgEcWySkbq20Jfcuh7zqc6eejutJohEnCURhzGEYchNGx0Sim0e6SmdriKOq+A9sA56Z4m+acTPGaTmTpU9Xx4udodPUCoyWCYwuOZRXpWS0cyx4bAEvyBU272DliSd7xLzP4GIZh5x1KaDgN55LYxC4mAd4C3K+qPzfxr/cAtwNvKl7fvQ7PpNE4W2RjmzYal4Mw35pWLH55to3r2I1tETOoRnWnne+IGYURiVVUy6IY6s5xxwuXHG/ecGxr3LEPbAvXsnGdPP2rXXTyw6Mj9vf3i86+uS2CBgbbjk3MIL4aeBXwpyJyX/Hdj5EbhreLyGuATwPfuUjQsu6iaaPxdPZIsowgThjFCVdGIYdhTIZO7NLIR44lTmvc96Z4kzQjSJLjMCgVrhjbFlzbruy0k3iA583fajOr2K0iVas10fnXqSMOiu+slvN7VXQ92jSchnMVbGIX0/9i9naG25aR1cTOJcey2PU9dn2Pp+wOUFXCJCVIEg6CiIMgYhjFQL6f2hHBVu10lGkv6Qbrkjcr9BWXi+OOzVN2BvQ9Z+LwTv1OO03dzp/3tGYDM5yGc22O1hlaRLlroUmICD3XoTfhmorTrHBLJXzh8ccZat4xlq4px84XJNvaWhcEAYOdnVZkr8Kbb5dMUVVE4Ezf59zZXfquu/ZIfDgcsre3t5YMw2k4DWcz2GoD0RVc28K181nGQFJ2d3eJ0vzkYhAnHIURh2FMnObb6hQdu1Ca2Dm1aWSqBHFCnGWgSt9zuWF/wG7Pp+86xmdvYHBKYQzEkrBtO98n7tj4js1e4ZqC/NBemBxvtx1Gcb5zitz9bhdH/h1LljIcVsculzTLSNKMYZKSBCEWwtmBz9l+j77r4LXor9+EO81wGk7DWY2tNhDWBk5Lzjtk4lgWjmex47mcK47AZ6pESZqva8QxozhhVBgQ1eNYMxYUxiPfdTM5Ki8P+jSJrIidE6cZSRlDp6B0bYuB43Dxuovs+i69DmcJyxziMZyG03C2i602EG2sQSzCwcHBUn4/a2JNozyjAfkZjqTooOPCXTWKktyIRDFpsQtIUYJgxM5g56rYM/nbyZg0x7Fo8o8y7vDTNCPOskJfgpIvsvddhzM9P4+l4ziFK80eB/M6ODhg4HVb8ZfVr+E0nIazPWy1gdhmiJRbPauniaXxSNKMxy5fptfvk5FHsM2KRXJl4sRvGeQsUzIYny0A6DsO5/zecWydU7I2YmBg0C620kCIyMuBlz/72c/e9K20BqdwN+ECcY+9vd1N35KBgcGTDBuLxdQEbr75Zr3nnns65dQivvxp59wUr+E0nIazfc66sZi2OibuJtYgyoiQp51zU7yG03AazpPDudUGYhMoQ/eeds5N8RpOw2k4Tw6nMRAGBgYGBpXYagOxCb/8rLyzp41zU7yG03AazpPDudUGYhPYxLrHJjg3xWs4DafhPDmcW20gNrEDq0wXeNo5N8VrOA2n4Tw5nFttIAwMDAwM2sNWn4MQkRHwZyv+/AxweYXfPQN48EnAuQ6v4TxdnOvwGs6TyflcVT2z8CotQjVs4x/w6Bq/vdNwtsNrOE8X5zq8hnO7ObfdxfTEGr99r+Fsjddwni7OdXgN5xZzbruBWHWqjaquWihPFs6VeQ3n6eJck9dwbjHnthuIOw3nqeM1nIbTcJ4Qzq1epDYwMDAwaA/bPoMwMDAwMGgJW2sgROSbROQvROQTIvL6DvhuEpE/EJE/F5E/E5Hvb5tzgtsWkQ+LyO90xHdWRN4hIh8TkftF5NYOOH+w0OtHReQ3RKT5PKs5z38QkUdE5KMT350Xkd8VkY8Xr+c64PzZQr9/IiK/JSJn2+ac+N8Pi4iKyMUuOEXktcWz/pmI/EzbnCLyfBH5gIjcJyL3iMgtDXNW9gVt1qM5nK3Wo5W2SG36D7CBvwKeDXjAR4C/2TLnDcALi/d7wF+2zTnB/UPAfwZ+pyO+u4B/Wrz3gLMt8z0d+BTQLz6/HXh1S1xfB7wQ+OjEdz8DvL54/3rgpzvg/LuAU7z/6S44i+9vAv4H8GngYgfP+fXA7wF+8fm6DjjfB/y94v03A3c3zFnZF7RZj+ZwtlqPtnUGcQvwCVX9pKpGwNuAV7RJqKoPqeqHivcHwP3kHVurEJEbgW8BfqVtroLvDHmjewuAqkaqus52urpwgL6IOMAA+HwbJKr6P4HHpr5+BblRpHj9trY5VfV9qlrGa/4AcGPbnAXeDPwo0Pji4wzO7wPepKphcc0jHXAqsF+8P0PDdWlOX9BaPZrF2XY92lYD8XTgMxOfP0sHnXUJEbkEvAD4YAd0P0/eoLuKBvYs4FHgVwu31q+IyE6bhKr6OeBfkZ8KfQi4rKrva5NzCter6kPF+4eB6zvkBvhe4L+1TSIirwA+p6ofaZtrAs8DvlZEPigifygiX9kB5w8APysinyGvV29oi2iqL+ikHs3pfxqvR9tqIDYGEdkF3gn8gKpeaZnrZcAjqnpvmzxTcMin7L+kqi8Ajsiny62h8NW+gtw4PQ3YEZF/3CbnLGg+V+9sa5+I/DiQAG9tmWcA/Bjwz9vkqYADnAe+Cngd8HZpP07/9wE/qKo3AT9IMRtuGvP6grbq0SzOturRthqIz5H7UkvcWHzXKkTEJS+ct6rqu9rmA74a+FYReYDcjfZSEfn1ljk/C3xWVcvRyTvIDUab+AbgU6r6qKrGwLuAv9My5yS+ICI3ABSvjbpBZkFEXg28DPjuokNpE19CboA/UtSnG4EPichTW+b9LPAuzfHH5DPhRhfHK3A7eR0C+E1yl3SjmNEXtFqPZvU/bdajbTUQ/w94rog8S0Q84B8C72mTsBj1vAW4X1V/rk2uEqr6BlW9UVUvkT/j76tqqyNrVX0Y+IyIfGnx1W3An7fJSe5a+ioRGRR6vo3cx9oV3kPeqVC8vrttQhH5JnLX4beq6rBtPlX9U1W9TlUvFfXps+SLng+3TP3b5AvViMjzyDc9fLFlzs8DLy7evxT4eJPC5/QFrdWjWZyt16MmV7y7/CPfnfCX5LuZfrwDvq8hnzL+CXBf8ffNHT7vS+huF9PzgXuKZ/1t4FwHnP8S+BjwUeDXKHa9tMDzG+TrHDF5J/ka4ALwfvKO5PeA8x1wfoJ8Ha2sS7/cNufU/x+g+V1MVc/pAb9elOuHgJd2wPk1wL3kuxs/CLyoYc7KvqDNejSHs9V6ZE5SGxgYGBhUYltdTAYGBgYGLcMYCAMDAwODShgDYWBgYGBQCWMgDAwMDAwqYQyEgYGBgUEljIEwMFgCInI49fnVIvJvN3U/BgZtwhgIA4MTgCJIoYHBiYIxEAYGDUFELonI7xex+d8vIs8ovv+PIvLKiesOi9eXiMgfich7aP+0uoHB0jCjFgOD5dAXkfsmPp/nOMzLvwHuUtW7ROR7gV9kccjnFwJ/W1U/1fytGhisB2MgDAyWw0hVn19+KAKl3Vx8vBX4juL9r5EnkFmEPzbGweCkwriYDAzaR0LR1kTEIo9PVOJoI3dkYFADxkAYGDSH/0MedRfgu4E/Kt4/ALyoeP+tgNvtbRkYrAZjIAwMmsNrge8RkT8BXgV8f/H9vwdeLCIfIXdDmVmDwVbARHM1MDAwMKiEmUEYGBgYGFTCGAgDAwMDg0oYA2FgYGBgUAljIAwMDAwMKmEMhIGBgYFBJYyBMDAwMDCohDEQBgYGBgaVMAbCwMDAwKAS/x+aPNBd4F18lwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = hours.fillna(0).mean(axis=0).plot(\n",
    "    kind='line',\n",
    "    xlim=(0, 23),\n",
    "    xticks=range(0, 24, 1),\n",
    "    yticks=range(0, 190, 10),\n",
    "    figsize=(6, 4),\n",
    "    legend=True,\n",
    "    grid=True)\n",
    "\n",
    "# Ticks\n",
    "ax.set_xticks(range(0, 24, 2))\n",
    "ax.set_xticks(range(0, 24, 1), minor=True)\n",
    "ax.set_yticks(range(0, 190, 20))\n",
    "ax.set_yticks(range(0, 190, 10), minor=True)\n",
    "\n",
    "# Grid\n",
    "ax.set_axisbelow(True)\n",
    "ax.xaxis.grid(alpha=0.2, linestyle='dashed')\n",
    "ax.yaxis.grid(alpha=0.2, linestyle='dashed')\n",
    "\n",
    "# Tweak font sizes\n",
    "ax.set_xlabel('Hour', fontsize=10)\n",
    "ax.set_ylabel('Snapshots', fontsize=10)\n",
    "ax.tick_params(labelsize=10)\n",
    "\n",
    "# Quantiles\n",
    "ax.fill_between(range(0, 24),\n",
    "                [hours.fillna(0)[x].quantile(.25) for x in hours.fillna(0).columns],\n",
    "                [hours.fillna(0)[x].quantile(.75) for x in hours.fillna(0).columns],\n",
    "                color='#539caf',\n",
    "                alpha=0.2,\n",
    "                zorder=100,\n",
    "                hatch='/')\n",
    "\n",
    "# Std\n",
    "ax.fill_between(range(0, 24),\n",
    "                [hours.fillna(0)[x].mean() - hours.fillna(0)[x].std() for x in hours.fillna(0).columns],\n",
    "                [hours.fillna(0)[x].mean() + hours.fillna(0)[x].std() for x in hours.fillna(0).columns],\n",
    "                color='#539caf',\n",
    "                alpha=0.2)\n",
    "\n",
    "# Overall mean\n",
    "#ax.axhline(hours.mean().mean(), color='r', linestyle='--')\n",
    "\n",
    "# Legend\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles=handles+[patches.Patch(color='#539caf', alpha=0.2), patches.Patch(color='#539caf', hatch='///', alpha=0.4)],\n",
    "          labels=['Mean', 'Std', '25th/75th Percentile'],\n",
    "          loc='center',\n",
    "          bbox_to_anchor=(0.5, 1.1),\n",
    "          ncol=3, \n",
    "          prop={'size': 10})\n",
    "\n",
    "plt.savefig('figures/snapshots_per_hour.png', bbox_inches='tight', pad_inches=0, dpi=300)\n",
    "plt.savefig('figures/snapshots_per_hour.pdf', bbox_inches='tight', pad_inches=0)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "93.13721832299193"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hours.mean().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.931816134384421"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hours.mean().std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}