ConvertCSV-Cams46.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import glob\n",
    "import pandas as pd\n",
    "import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "cameras = [\n",
    "    [\"CAM02009CCTV7.jpg\", \"I-80 Eastbound Exit 161 Bellefonte Interchange (I-80 & I-99)\",40.944571,-77.720918],\n",
    "    [\"CAM02001CCTV2.jpg\", \"Port Matilda US-322 By-Pass Westbound\",40.810961,-78.075259],\n",
    "    [\"CAM02010CCTV11.jpg\", \"Port Matilda I-99 Exit 61 Median\",40.803880,-78.063664],\n",
    "    [\"CAM02005CCTV9.jpg\", \"Milesburg Interchange West I-80 Exit 158 Eastbound / Alt US-220\",40.955477,-77.773749],\n",
    "    [\"CAM02006CCTV10.jpg\", \"Milesburg Interchange East I-80 Exit 158 Westbound / PA-150\",40.956061,-77.766277],\n",
    "    [\"CAM02028CCTV32.jpg\",\"Atherton and Valley Vista\",40.812073,-77.922500],\n",
    "    [\"CAM02037CCTV43.jpg\",\"Atherton and Vairo\",40.807936,-77.895204],\n",
    "    [\"CAM02038CCTV44.jpg\",\"Atherton and North Hills\",40.805031,-77.886886],\n",
    "    [\"CAM02039CCTV45.jpg\",\"Atherton and Park\",40.796522,-77.872549],\n",
    "    [\"CAM02040CCTV46.jpg\",\"Atherton and W.College\",40.791787,-77.864971],\n",
    "    [\"CAM02042CCTV49.jpg\",\"Atherton and W.Beaver\",40.790894,-77.863812],\n",
    "    [\"CAM02003CCTV4.jpg\",\"I-99/US-322 Interchange\",40.828613,-77.840339],\n",
    "    [\"CAM02020CCTV24.jpg\",\"Benner Pike\",40.829048,-77.804817],\n",
    "    [\"CAM02002CCTV3.jpg\",\"Grays Woods\",40.817144,-77.939841],\n",
    "    [\"CAM02007CCTV13.jpg\",\"US-322 E/O PA-26\",40.795773,-77.820937],\n",
    "    [\"CAM02046CCTV52.jpg\",\"I-99 at Exit 71 (Toftrees)\",40.817953,-77.900271],\n",
    "    [\"CAM02033CCTV38.jpg\",\"Snowshoe I-80 Exit 147\",41.022703,-77.933981],\n",
    "    [\"collegeTwp.jpg\",\"College Township Traffic camera\",40.811564,-77.830719],\n",
    "    [\"parkArboretum.jpg\",\"PSU Park Ave.\",40.804483,-77.864219]\n",
    "    ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "camdir = \"cams46_csvs\"\n",
    "\n",
    "for c in cameras:\n",
    "    x = glob.glob(f\"/home/ubuntu/nitelite/{camdir}/*vehicles*{c[0].split('.')[0]}*.csv\")\n",
    "    k = pd.read_csv(x[0])\n",
    "    for i in range(0,len(x)): # 1\n",
    "        x2 = pd.read_csv(x[i])\n",
    "        k = pd.concat([k,x2])\n",
    "    k['time'] =  pd.to_datetime(k['time'])\n",
    "    k.to_csv(f\"xfull_vehicles_{camdir}_{c[0].split('.')[0]}.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1728x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "x = glob.glob(f\"/home/ubuntu/nitelite/xfull_vehicles_{camdir}_*.csv\")\n",
    "k = pd.read_csv(x[0])\n",
    "for i in range(0,len(x)): # 2\n",
    "    x2 = pd.read_csv(x[i])\n",
    "    k = pd.concat([k,x2])\n",
    "k['time'] =  pd.to_datetime(k['time'])\n",
    "k.to_csv(f\"full_vehicles_{camdir}.csv\",index=False)\n",
    "ax = k.plot(x ='time', y='vehicle', kind = 'line',figsize=(24,3),title=f\"Cameras\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>time</th>\n",
       "      <th>file</th>\n",
       "      <th>vehicle</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-02-13 13:00:23</td>\n",
       "      <td>/home/ubuntu/cams46/CAM02007CCTV13/20210213/20...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-02-13 13:01:04</td>\n",
       "      <td>/home/ubuntu/cams46/CAM02007CCTV13/20210213/20...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-02-13 13:01:45</td>\n",
       "      <td>/home/ubuntu/cams46/CAM02007CCTV13/20210213/20...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-02-13 13:02:26</td>\n",
       "      <td>/home/ubuntu/cams46/CAM02007CCTV13/20210213/20...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-02-13 13:03:07</td>\n",
       "      <td>/home/ubuntu/cams46/CAM02007CCTV13/20210213/20...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14535</th>\n",
       "      <td>2021-02-10 09:57:01</td>\n",
       "      <td>/home/ubuntu/cams46/collegeTwp/20210210/202102...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14536</th>\n",
       "      <td>2021-02-10 09:57:42</td>\n",
       "      <td>/home/ubuntu/cams46/collegeTwp/20210210/202102...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14537</th>\n",
       "      <td>2021-02-10 09:58:22</td>\n",
       "      <td>/home/ubuntu/cams46/collegeTwp/20210210/202102...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14538</th>\n",
       "      <td>2021-02-10 09:59:03</td>\n",
       "      <td>/home/ubuntu/cams46/collegeTwp/20210210/202102...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14539</th>\n",
       "      <td>2021-02-10 09:59:43</td>\n",
       "      <td>/home/ubuntu/cams46/collegeTwp/20210210/202102...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>264637 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     time                                               file  \\\n",
       "0     2021-02-13 13:00:23  /home/ubuntu/cams46/CAM02007CCTV13/20210213/20...   \n",
       "1     2021-02-13 13:01:04  /home/ubuntu/cams46/CAM02007CCTV13/20210213/20...   \n",
       "2     2021-02-13 13:01:45  /home/ubuntu/cams46/CAM02007CCTV13/20210213/20...   \n",
       "3     2021-02-13 13:02:26  /home/ubuntu/cams46/CAM02007CCTV13/20210213/20...   \n",
       "4     2021-02-13 13:03:07  /home/ubuntu/cams46/CAM02007CCTV13/20210213/20...   \n",
       "...                   ...                                                ...   \n",
       "14535 2021-02-10 09:57:01  /home/ubuntu/cams46/collegeTwp/20210210/202102...   \n",
       "14536 2021-02-10 09:57:42  /home/ubuntu/cams46/collegeTwp/20210210/202102...   \n",
       "14537 2021-02-10 09:58:22  /home/ubuntu/cams46/collegeTwp/20210210/202102...   \n",
       "14538 2021-02-10 09:59:03  /home/ubuntu/cams46/collegeTwp/20210210/202102...   \n",
       "14539 2021-02-10 09:59:43  /home/ubuntu/cams46/collegeTwp/20210210/202102...   \n",
       "\n",
       "       vehicle  \n",
       "0            0  \n",
       "1            0  \n",
       "2            0  \n",
       "3            0  \n",
       "4            0  \n",
       "...        ...  \n",
       "14535        0  \n",
       "14536        0  \n",
       "14537        0  \n",
       "14538        0  \n",
       "14539        0  \n",
       "\n",
       "[264637 rows x 3 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfcam = pd.read_csv(f\"full_vehicles_{camdir}.csv\")\n",
    "\n",
    "dfcam['time'] =  pd.to_datetime(dfcam['time'])\n",
    "dfcam = dfcam.sort_values(by='time')\n",
    "\n",
    "dfcam = dfcam.rename(columns={\"time\": \"datetime_UTC\", \"file\": \"image_file\", \"vehicle\": \"vehicle_count\"})\n",
    "dfcam['datetime_EST'] = pd.to_datetime(dfcam['datetime_UTC']) - pd.Timedelta(hours=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime_UTC</th>\n",
       "      <th>image_file</th>\n",
       "      <th>vehicle_count</th>\n",
       "      <th>datetime_EST</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>252870</th>\n",
       "      <td>2021-02-08 17:42:15</td>\n",
       "      <td>/home/ubuntu/cams46/collegeTwp/20210208/202102...</td>\n",
       "      <td>5</td>\n",
       "      <td>2021-02-08 13:42:15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204977</th>\n",
       "      <td>2021-02-08 17:42:19</td>\n",
       "      <td>/home/ubuntu/cams46/CAM02028CCTV32/20210208/20...</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-02-08 13:42:19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124072</th>\n",
       "      <td>2021-02-08 17:42:30</td>\n",
       "      <td>/home/ubuntu/cams46/CAM02042CCTV49/20210208/20...</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-02-08 13:42:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188052</th>\n",
       "      <td>2021-02-08 17:42:32</td>\n",
       "      <td>/home/ubuntu/cams46/CAM02003CCTV4/20210208/202...</td>\n",
       "      <td>0</td>\n",
       "      <td>2021-02-08 13:42:32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69931</th>\n",
       "      <td>2021-02-08 17:42:34</td>\n",
       "      <td>/home/ubuntu/cams46/parkArboretum/20210208/202...</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-02-08 13:42:34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>226157</th>\n",
       "      <td>2021-02-15 16:35:44</td>\n",
       "      <td>/home/ubuntu/cams46/CAM02037CCTV43/20210215/20...</td>\n",
       "      <td>0</td>\n",
       "      <td>2021-02-15 12:35:44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216128</th>\n",
       "      <td>2021-02-15 16:35:48</td>\n",
       "      <td>/home/ubuntu/cams46/CAM02039CCTV45/20210215/20...</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-02-15 12:35:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113883</th>\n",
       "      <td>2021-02-15 16:35:51</td>\n",
       "      <td>/home/ubuntu/cams46/CAM02040CCTV46/20210215/20...</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-02-15 12:35:51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120470</th>\n",
       "      <td>2021-02-15 16:35:53</td>\n",
       "      <td>/home/ubuntu/cams46/CAM02042CCTV49/20210215/20...</td>\n",
       "      <td>0</td>\n",
       "      <td>2021-02-15 12:35:53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183558</th>\n",
       "      <td>2021-02-15 16:35:55</td>\n",
       "      <td>/home/ubuntu/cams46/CAM02003CCTV4/20210215/202...</td>\n",
       "      <td>0</td>\n",
       "      <td>2021-02-15 12:35:55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>264637 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              datetime_UTC                                         image_file  \\\n",
       "252870 2021-02-08 17:42:15  /home/ubuntu/cams46/collegeTwp/20210208/202102...   \n",
       "204977 2021-02-08 17:42:19  /home/ubuntu/cams46/CAM02028CCTV32/20210208/20...   \n",
       "124072 2021-02-08 17:42:30  /home/ubuntu/cams46/CAM02042CCTV49/20210208/20...   \n",
       "188052 2021-02-08 17:42:32  /home/ubuntu/cams46/CAM02003CCTV4/20210208/202...   \n",
       "69931  2021-02-08 17:42:34  /home/ubuntu/cams46/parkArboretum/20210208/202...   \n",
       "...                    ...                                                ...   \n",
       "226157 2021-02-15 16:35:44  /home/ubuntu/cams46/CAM02037CCTV43/20210215/20...   \n",
       "216128 2021-02-15 16:35:48  /home/ubuntu/cams46/CAM02039CCTV45/20210215/20...   \n",
       "113883 2021-02-15 16:35:51  /home/ubuntu/cams46/CAM02040CCTV46/20210215/20...   \n",
       "120470 2021-02-15 16:35:53  /home/ubuntu/cams46/CAM02042CCTV49/20210215/20...   \n",
       "183558 2021-02-15 16:35:55  /home/ubuntu/cams46/CAM02003CCTV4/20210215/202...   \n",
       "\n",
       "        vehicle_count        datetime_EST  \n",
       "252870              5 2021-02-08 13:42:15  \n",
       "204977              1 2021-02-08 13:42:19  \n",
       "124072              1 2021-02-08 13:42:30  \n",
       "188052              0 2021-02-08 13:42:32  \n",
       "69931               1 2021-02-08 13:42:34  \n",
       "...               ...                 ...  \n",
       "226157              0 2021-02-15 12:35:44  \n",
       "216128              1 2021-02-15 12:35:48  \n",
       "113883              1 2021-02-15 12:35:51  \n",
       "120470              0 2021-02-15 12:35:53  \n",
       "183558              0 2021-02-15 12:35:55  \n",
       "\n",
       "[264637 rows x 4 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfcam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfcam.to_csv(f\"dfull_vehicles_{camdir}_020821-021521-partial.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#full = pd.read_csv(\"full_vehicles_427-1128-partial-all-cameras.csv\")\n",
    "full = pd.read_csv(\"full_vehicles_427-020821-partial-all-cameras.csv\")\n",
    "#full = pd.read_csv(\"all_traffic_cameras_426-601.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        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>datetime_UTC</th>\n",
       "      <th>image_file</th>\n",
       "      <th>vehicle_count</th>\n",
       "      <th>datetime_EST</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-04-27 00:00:02</td>\n",
       "      <td>/home/ubuntu/cams1/20200427_000002_CAM02033CCT...</td>\n",
       "      <td>0</td>\n",
       "      <td>2020-04-26 20:00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-04-27 00:00:05</td>\n",
       "      <td>/home/ubuntu/cams1/20200427_000005_collegeTwp.jpg</td>\n",
       "      <td>2</td>\n",
       "      <td>2020-04-26 20:00:05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-04-27 00:00:08</td>\n",
       "      <td>/home/ubuntu/cams1/20200427_000008_CAM02028CCT...</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-04-26 20:00:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-04-27 00:00:10</td>\n",
       "      <td>/home/ubuntu/cams1/20200427_000010_CAM02037CCT...</td>\n",
       "      <td>3</td>\n",
       "      <td>2020-04-26 20:00:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-04-27 00:00:12</td>\n",
       "      <td>/home/ubuntu/cams1/20200427_000012_CAM02038CCT...</td>\n",
       "      <td>0</td>\n",
       "      <td>2020-04-26 20:00:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>10544170</th>\n",
       "      <td>2021-02-08 17:41:55</td>\n",
       "      <td>/home/ubuntu/cams45/CAM02010CCTV11/20210208/20...</td>\n",
       "      <td>0</td>\n",
       "      <td>2021-02-08 13:41:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10544171</th>\n",
       "      <td>2021-02-08 17:41:57</td>\n",
       "      <td>/home/ubuntu/cams45/CAM02005CCTV9/20210208/202...</td>\n",
       "      <td>0</td>\n",
       "      <td>2021-02-08 13:41:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10544172</th>\n",
       "      <td>2021-02-08 17:42:00</td>\n",
       "      <td>/home/ubuntu/cams45/CAM02006CCTV10/20210208/20...</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-02-08 13:42:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10544173</th>\n",
       "      <td>2021-02-08 17:42:02</td>\n",
       "      <td>/home/ubuntu/cams45/CAM02033CCTV38/20210208/20...</td>\n",
       "      <td>0</td>\n",
       "      <td>2021-02-08 13:42:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10544174</th>\n",
       "      <td>2021-02-08 17:42:04</td>\n",
       "      <td>/home/ubuntu/cams45/collegeTwp/20210208/202102...</td>\n",
       "      <td>3</td>\n",
       "      <td>2021-02-08 13:42:04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10544175 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 datetime_UTC  \\\n",
       "0         2020-04-27 00:00:02   \n",
       "1         2020-04-27 00:00:05   \n",
       "2         2020-04-27 00:00:08   \n",
       "3         2020-04-27 00:00:10   \n",
       "4         2020-04-27 00:00:12   \n",
       "...                       ...   \n",
       "10544170  2021-02-08 17:41:55   \n",
       "10544171  2021-02-08 17:41:57   \n",
       "10544172  2021-02-08 17:42:00   \n",
       "10544173  2021-02-08 17:42:02   \n",
       "10544174  2021-02-08 17:42:04   \n",
       "\n",
       "                                                 image_file  vehicle_count  \\\n",
       "0         /home/ubuntu/cams1/20200427_000002_CAM02033CCT...              0   \n",
       "1         /home/ubuntu/cams1/20200427_000005_collegeTwp.jpg              2   \n",
       "2         /home/ubuntu/cams1/20200427_000008_CAM02028CCT...              1   \n",
       "3         /home/ubuntu/cams1/20200427_000010_CAM02037CCT...              3   \n",
       "4         /home/ubuntu/cams1/20200427_000012_CAM02038CCT...              0   \n",
       "...                                                     ...            ...   \n",
       "10544170  /home/ubuntu/cams45/CAM02010CCTV11/20210208/20...              0   \n",
       "10544171  /home/ubuntu/cams45/CAM02005CCTV9/20210208/202...              0   \n",
       "10544172  /home/ubuntu/cams45/CAM02006CCTV10/20210208/20...              1   \n",
       "10544173  /home/ubuntu/cams45/CAM02033CCTV38/20210208/20...              0   \n",
       "10544174  /home/ubuntu/cams45/collegeTwp/20210208/202102...              3   \n",
       "\n",
       "                 datetime_EST  \n",
       "0         2020-04-26 20:00:02  \n",
       "1         2020-04-26 20:00:05  \n",
       "2         2020-04-26 20:00:08  \n",
       "3         2020-04-26 20:00:10  \n",
       "4         2020-04-26 20:00:12  \n",
       "...                       ...  \n",
       "10544170  2021-02-08 13:41:55  \n",
       "10544171  2021-02-08 13:41:57  \n",
       "10544172  2021-02-08 13:42:00  \n",
       "10544173  2021-02-08 13:42:02  \n",
       "10544174  2021-02-08 13:42:04  \n",
       "\n",
       "[10544175 rows x 4 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1728x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "newfull = pd.concat([full,dfcam])\n",
    "newfull['datetime_EST'] =  pd.to_datetime(newfull['datetime_EST'])\n",
    "newfull = newfull.sort_values(by='datetime_EST')\n",
    "ax = newfull.plot(x ='datetime_EST', y='vehicle_count', kind = 'line',figsize=(24,3),title=f\"Cameras\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "newfull.to_csv(\"full_vehicles_427-021521-partial-all-cameras.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f4582c62780>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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yI8lJSS5PckWSk0opO0oph7XbuGgqrw5gC3OnZGCZGPMAANbmfGnrG3JF7mp+J8lbSyl/mORjSf60Xf+nSd5cStmVZG+axGxqrZ8upVyY5DNJbk/y4lrrHUlSSnlJkkuSHJLkDbXWTx9EvQAAAAAAtpQDSuTWWj+Y5IPt359PckZPmX1Jzl7l+a9I8oqe9e9J8p4DqQtbg68lAsDW4bgOAACzczBX5AIAsGDMYby8JNoBABabRC7AFiE5AwAAAFvXujc7AwAAAABgviRyAWDBuBstAADA8pHIZWnMO/Ex7+0DAAAAsLgkcgGYKx9yAAAAwPrc7AxgztykDAAAAFiPRC4AjMhKYl9Sf7n4QAcAAFiPRC6bxj+pAAAAHCwffAPLSiIXloAkOgDAdEggAQDzIpELwD34JxUAYPpcYAHAwbjXvCsAME3bd1683wkyALB8nA/Mj7YHgNmRyAVYMv7BAgAAgMVjagUAAABYIKZoAFhOErkAAAAAwIb5gGlzmFoBAGCKTF8CAADMgkQuAGxBkomwdXg/AwCQSOQCAAAAAIyeOXIBZsg8QfOj7QEAANhKXJELHBRf9wQAAACYPYlcAOCg+EAHAABg9iRyAQAAAABGzhy5ALl7PtXNmEt1M7e1VZn/FmDxOP6xjPR7AKZJIhcAAADWICELLDMX0oyHqRUAAGBOzDENAMBQErnAKPhHFmDcjNMAADBfplYAAACYIV/LB2CsTJuwWFyRCwAAsEW4eh6my3sKGBNX5DIqi/pJ0CJeZbGIdR6bRe2vAEyfYwJbjT7NovP/DrAVuSIXAACW1LSuNHPFGgDA7Lkil6nwif3isw8BAGD+nJcDsBpX5AIAAAAAjJxELjPjK3aAcQCAaXNsAQCWlakVWFq+sgTT5YYSAAAAMDuuyIUtyJUqAAAAAFuLRC4AAAAAwMhJ5AIAAIMt0zd/lum1AgDjJ5ELAAAAjIIPUABW52ZnAGyIm5sBAACMl5u8bz0SuQAAAMDCkqxiLFzswqxJ5AJbnoMpwPT5pxkAADaXOXKBmTPPFQAAAMDBcUUuAMAI+TYBi2wz+6+rwwGAZSGRCywdyRGAcTEuH7xpteEy74tlSggv02sFgK3E1AoAAAAjZHoqgHEzTrPZJHIBpsiBHAAYwjkDAHCgJHK5ByeV42FfwOLy/gWA+XM8BmArkcgFYF3+CQIAAID5crMzWHBuVgEAAAAwP5t1w9h1E7mllCOSfCjJ4W35t9dazy+l7Ejy1iTHJrkqya/VWm8rpRye5E1JHpvkliTn1Fp3t7FeluT5Se5I8s9rrZe065+W5NVJDknyn2utF0z1VQKbZkhiWfIZAAAA4MAMuSL3+0meXGv9dinl0CT/s5Ty3iT/V5I/qrW+tZTyH9MkaF/X/v56rfXBpZRnJ/k3Sc4ppTw8ybOTPCLJjyZ5XynlIe02/jjJU5LsSXJFKeWiWutnpvg6AWBmfDgxG9oVWASbdQUOAMC6c+TWxrfbxUPbn5rkyUne3q5/Y5JntX8/s11O+/gvlFJKu/6ttdbv11r/LsmuJGe0P7tqrZ+vtd6W5irfZx70KwM4COaEBQAAAMZk0M3OSimHlFKuTnJTksuSfC7JrbXW29sie5Kc0P59QpIvJUn7+DfSTL9w1/rOc1Zb31eP80opV5ZSrrz55puHVB0AGMgHGAAAAOM1KJFba72j1npKkhPTXEH792Zaq9Xr8fpa62m11tOOO+64eVQBAAAAAGDTDZkj9y611ltLKR9I8oQkR5VStrVX3Z6Y5Ia22A1JHpRkTyllW5L7p7np2cr6FZPPWW39QjO3HwAAACwG/8MzL+ZbZ6h1r8gtpRxXSjmq/fvINDcluybJB5L8o7bYuUne1f59Ubuc9vG/qrXWdv2zSymHl1J2JDkpyeVJrkhyUillRynlsDQ3RLtoGi8OAAAAGAfTOI2b/QP9xvTeGHJF7vFJ3lhKOSRN4vfCWuu7SymfSfLWUsofJvlYkj9ty/9pkjeXUnYl2ZsmMZta66dLKRcm+UyS25O8uNZ6R5KUUl6S5JIkhyR5Q63101N7hcCW5pNLAGArcE4DAKxn3URurfUTSU7tWf/5NPPldtfvS3L2KrFekeQVPevfk+Q9A+oLAMyRrxwCrE9SFgCYhUE3OwMAYOsa09fFAACAfgd0szOYB1c0AADztKhXojuHWpv2AeBAOG4wBq7IBQAAAAAYOYlcAAAAmGDKGQDGSCIXgNHzzxQAAADLzhy5AAAArMv8kAAwzKzuseCKXAAAAACAkZPIBYARM60EAAAAiakVtrQhX33y9SgAoGtWXwUDYHn4XxNg+iRyAQBYWpLWALB1TPO47sMIxsjUCgAAAADsxxRfMD6uyO3hygyA5WVaGgAYJ8dfAJadK3KB0fIJMAAAAEDDFbkAAAAcMN9kBFifbxMwTa7IBQBgZny7AgAApsMVucBgrroAAAAAmA+JXAAAAAAOmIt9GItlmcLC1AoAAAAsHFO3LAf7GeBuErkAAAAAACMnkQsAAAAAMHISucCqfI0JAAAAYBzc7AwAAADmZMgNepblJj6z5KZcwFYgkQvrcNIEm8t7DlhmQxMNxkoA5sHxB+bL1AqwYEx3AAAAALB8JHKnaN4JtnlvHwAAAACYDYlcAAAAAGAwFxPOhzlyAQYyHxQAsFXN6jzHDaaWh3NlgNmTyAUAAIAtRhIdYOuRyAVYAE7EAQAAZsMV5SwKiVwAAABGRVJl3Owf5sHFLeBmZwAAAACwKdwkjIPhilwAAAAAZsKVtDA9S5fIXfYBxFdgAAAAgLGTv4B7MrUCAAAAAMDISeSyLvO3AAAAAMB8SeQCAAAAS8dFS8CikcgFFoYTLQAAYJn5nwiW29Ld7AxgEZjYHwAAWBbLfmP6Lv8PshpX5AIAAAAAS2/sV727Incgn4YAAAAAwHKb5xXkErkAAAAAsEBMR7GcTK0AAAAAACydsU+l0CWRCwAAExbthB44MN7jACyqLT+1grltl5OvGAAAAIyb/9cBDsyWT+SOnYQjAAAAAJvBByiLzdQKI+SrPgAAAAAsMvmt6dtSiVwdBAAAAADYikytsEGmRJgfbQ8AAADAsln3itxSyoNKKR8opXymlPLpUspvteuPKaVcVkq5vv19dLu+lFJeU0rZVUr5RCnlMROxzm3LX19KOXdi/WNLKZ9sn/OaUkqZxYsFAAAAVuebrgDjNWRqhduT/N+11ocneXySF5dSHp5kZ5L311pPSvL+djlJnp7kpPbnvCSvS5rEb5LzkzwuyRlJzl9J/rZlXjDxvKcd/EsDAAAAANga1k3k1lq/Umv9aPv3t5Jck+SEJM9M8sa22BuTPKv9+5lJ3lQbH0lyVCnl+CRnJrms1rq31vr1JJcleVr72P1qrR+ptdYkb5qIBaPjE2oAAAAANtsB3eyslLI9yalJ/jbJA2utX2kf+mqSB7Z/n5DkSxNP29OuW2v9np71fds/r5RyZSnlyptvvvlAqg4AAAAAsLAGJ3JLKfdJ8o4kv11r/ebkY+2VtHXKdbuHWuvra62n1VpPO+6442a9OQAAAKbMN9wAxs04PV6DErmllEPTJHH/S631v7Wrb2ynRUj7+6Z2/Q1JHjTx9BPbdWutP7FnPQAAAMyUhAUAi2LdRG4ppST50yTX1Fr/3cRDFyU5t/373CTvmlj/nNJ4fJJvtFMwXJLkqaWUo9ubnD01ySXtY98spTy+3dZzJmIBAAAAACy9bQPK/HSSX0vyyVLK1e26f5XkgiQXllKen+QLSX65few9SX4pya4k303y60lSa91bSvmDJFe05V5ea93b/v2iJH+e5Mgk721/mLDyCfHuC86ac00AAABYJv4fBRiHdRO5tdb/maSs8vAv9JSvSV68Sqw3JHlDz/orkzxyvboAAAAAACyjIVfkAsDSmpwzz1UoAAAwHst0tfgyvVZWN+hmZwAAAAAAzI9ELgAAAAAwStt3XrzfNyWXmakVAAAAAKbE1FxAn2lMj+GKXABgVT79BgAAGAeJXABYcJKtAABbk/M8YJJELgAAAADAyEnkAgAAAGxhruyFrUEiFwAAAABg5LbNuwIAAAAsr8mrBA/mTt4AsNW5IhcAAAAAYOQkcgEAAACAUTCn8+okcgEAAAAARk4id5P5VAEAAAAAOFASuWyIhDQAAAAAbB6JXAAAAACAkZPIBQAAAAAWxpBvim/Fb5NL5LIlbMU3JwAAAACs2DbvCgAAAAAsgskLiHZfcNYcawIsI1fkAgAAwILzLUWArU8iFwAAAGDOJOOB9UjkAgAAAACMnEQuAAAAAFuSK53ZSiRyAQAAAABGTiJ3QflECQAAAACWh0QuAAAAwAyN7WKssdUHGEYiFwAAAABg5CRyAQAAAABGTiIXAAAAAGDkJHIBAAAAAEZOIjcm+QYAAAAAxk0iFwAAAABg5CRyAQAAAABGTiIXAAAAAGDkJHIBAAAAAEZOIhcAAAAAYOQkcgEAAAAARk4iFwAAAABg5CRyAQAAAIBNt33nxdm+8+J5V2NhSOQCAAAAAIzctnlXAAAAANh8k1fB7b7grDnWBIAhXJELAAAAS8BXmIF5Mw4dHIlcAAAAAICRk8gFAGBUXKkBAAD3JJELAAAAADByErkAAAAAACMnkQsAAAAAbGlbYfouiVwAAAAAgJFbN5FbSnlDKeWmUsqnJtYdU0q5rJRyffv76HZ9KaW8ppSyq5TyiVLKYyaec25b/vpSyrkT6x9bSvlk+5zXlFLKtF8kAAAAACRb48pMltOQK3L/PMnTOut2Jnl/rfWkJO9vl5Pk6UlOan/OS/K6pEn8Jjk/yeOSnJHk/JXkb1vmBRPP624LAAAAAGCprZvIrbV+KMnezupnJnlj+/cbkzxrYv2bauMjSY4qpRyf5Mwkl9Va99Zav57ksiRPax+7X631I7XWmuRNE7EAAAAAAMjG58h9YK31K+3fX03ywPbvE5J8aaLcnnbdWuv39KwHAAAAAKB10Dc7a6+krVOoy7pKKeeVUq4spVx58803b8YmAQAAAADmbqOJ3BvbaRHS/r6pXX9DkgdNlDuxXbfW+hN71veqtb6+1nparfW04447boNVBwAAAABYLBtN5F6U5Nz273OTvGti/XNK4/FJvtFOwXBJkqeWUo5ub3L21CSXtI99s5Ty+FJKSfKciVgAAAAAACTZtl6BUsr/l+RJSR5QStmT5PwkFyS5sJTy/CRfSPLLbfH3JPmlJLuSfDfJrydJrXVvKeUPklzRlnt5rXXlBmovSvLnSY5M8t72BwAAAACA1rqJ3Frrr6zy0C/0lK1JXrxKnDckeUPP+iuTPHK9egAAAAAALKuDvtkZAAAAAAy1fefF2b7z4nlXAxbOulfkAgAAADA9k0nM3RecNceaAIvEFbkAAAAAACMnkQsAAAAAMHISuQAAAAAAIyeRCwAAAAAwchK5AAAAAAAjJ5ELAAAAsKC277w423dePO9qAJtAIhcAAABggyRSgc0ikQsAAAAAMHISuQAAAABMhSuUYXYkcgEAAAAARk4iFwAAAABg5CRyAQAAAABGTiIXAAAAAGDkts27AgAAAAAAy2TypoC7Lzhr0HNckQsAAAAAMHISuQAAAAAAIyeRCwAAAAAwchK5AAAAAAAjJ5ELAAAAADByErkAAAAAACMnkQsAAAAAMHISuQAAAAAAIyeRCwAAAAAwchK5AAAAAAAjJ5ELAAAAsEVs33lxtu+8eN7VAGZAIhcAAAAAYOQkcgEAAAAARk4iFwAAAABg5CRyAQAAAABGTiIXAAAAAGDkJHIBAAAAAEZOIhcAAAAAYOQkcgEAAAAARk4iFwAAAABg5CRyAQAAAABGTiIXAAAAAGDkJHIBAAAAAEZOIhcAAAAAYOQkcgEAAAAARk4iFwAAAABg5CRyAQAAAABGTiIXAAAAAGDkJHIBAAAAAEZOIhcAAAAAYOQkcgEAAAAARk4iFwAAAABg5EaTyC2lPK2Ucm0pZVcpZee86wMAAAAAMBajSOSWUg5J8sdJnp7k4Ul+pZTy8PnWCgAAAABgHEaRyE1yRpJdtdbP11pvS/LWJM+cc50AAAAAAEZhLIncE5J8aWJ5T7tuTdt3XpztOy+eWaUAAHDH9SgAACAASURBVAAAAMag1FrnXYeUUv5RkqfVWn+jXf61JI+rtb6kU+68JOe1iw9Ncm2SByT52kSx7nLfummVmWVsZdYuM+/tL3OZeW9/mcvMe/tbtcy8t7/MZea9/WUuM+/tL3OZeW9/mcvMe/tbtcy8t7/MZea9/WUuM+/tL3OZeW9/q5aZ9/aXuczkuh+vtR6XtdRa5/6T5AlJLplYflmSlw187pVrLc+yzLy3v8xl5r39ZS4z7+0vc5l5b3+rlpn39pe5zLy3v8xl5r39ZS4z7+0vc5l5b3+rlpn39pe5zLy3v8xl5r39ZS4z7+1v1TLz3v4yl1lt3Wo/Y5la4YokJ5VSdpRSDkvy7CQXzblOAAAAAACjsG3eFUiSWuvtpZSXJLkkySFJ3lBr/fScqwUAAAAAMAqjSOQmSa31PUnes4Gnvn6d5VmWmff2l7nMvLe/zGXmvf1lLjPv7W/VMvPe/jKXmff2l7nMvLe/zGXmvf1lLjPv7W/VMvPe/jKXmff2l7nMvLe/zGXmvf2tWmbe21/mMqut6zWKm50BAAAAALC6scyRCwAAAADAKiRyAQAAAABGbjRz5A5VSnlgkhPaxRva33ct11pvbMsdkyS11r3d56yUWSturfXG1Z43GXuV5XXruMr2DihOkpuSnLFOmcuT/PCU4nTL7B4QZ0iZdevc12ZJtg+o45D2uEecWmtdrw9tpC+usjzq/rLZbT+wzJr9bBH6S98+TBavfxxEHOPJRH2S9ceKoYbEGRJ7GmPXkO1Pa3wd0q7Tem/Ous5di9j2G3yP7Z5SnPSUmefxZ5bj4uVJjk4Ovn9s0ePPIvSPIXE2VOf1jj9ZkONoJ86tSY7rlPlwkid01n0tyf0PMs7eJCXte6wtc0mSh64TZ0iZjdT5hiSfTfK0deo4JHY3zg1JPprkvhPLi3Bu2I0zpH26+2dInEXoH7Pu04d31m3P+vswPWXmPZYbT+5eN8bxZFTH0cymv6zWZpfUWm/NAAszR24p5ZQk/zFNg9yQ5D5JHt0+/Ikk30rT8EclOSRN4x2e5Pgk303yyST7kvxYknsnua2Nda+23HeSfCrJ7Ul+MskD2hifa7fRjX1okh9pH/tKkjuSHJPkzjQ7ePcqdezGPrwtc2Qb57aBcR6SpuNdleSaNB3g59oyH2rb6GFJHpvmTXPdQcR5dPtzdfu8Y5I8OckPtW22tyfOkDJD6pyeNjsmySPT7NcPJLmlp45DYvfFeXBb5o4kN6e/D3XrM6QvLmJ/2ey2H1JmSF8ce3/p24dHpRmD7pO7D6Bj7x/Gk+n0j+1Zf6y4/8TzV06s7kzTT+7V/r03yQ/aNvn6KnG6/eyQJA9sH7uz/T0kzpA+1X1dpWf70xpfh7TrtN6bs6xz9/xkZf8e2tanLkjbb+Q9Nu+xfFbHn2R24+K9kzypXffVJN/LxvrHIp6fDImTnnYdW/+Y9/FnEY6j3TgPTvPP8A+SvDPJrradf7aN+4G2zD9M07cvb8tsJM4pSZ7S1vmy9rmPT3Jmks8kuWKVOEPKbKTOaeM8LMlfJvnbVeo4JHY3zo8kOTvNOcZ1acaUsZ8b9sUZ0j7d/TMkTnradWz9Y5Z9+tFJ/n67Hz6T6b3nN3ssN56MezwZ23F0Vv2lr+2T5MS2/X+/1vqmrKfWuhA/7Qt/XHc5Tef+eLvuw0n+n4nlq9Nkx5+d5CPtukuS/Ickn54o8/Qkv5Pk0ol1z1uJs0rsD7dxf2Ui9tVJ/t/OcreO+8Vu45yT5Kc6ZdaLc03bSSaXtyfZkeSaiec9q7O8kTjXJPmZnjjdMpNxhpRZt86rtNnK87qv42c621qvPfrifDjJSzrP6fahbn2G9MWF6y9zaPshZYb0xbH3l759+OEkr155ziL0j4OIYzzZv38MGSsuSfIXSa6aWP6D9mfluHVlkv8ysdwXZ79+1sbZmeSFE+uGxBnSp/Z7Xatsf1rj65B2ncp7c8Z17p6ffDjJee0+unSB2n4j77F5j+UzOf7MeFxc6a8/2XneAfWPLOD5yZA4i9A/BsaZ5fFnEY6j3TjXpvkgYkdn3Y8nua4T5+jOugONs1KmG+fknjLd9+p6ZQ64zhPPO7nndRzd2d56sbtxVvprd/tjPjfsizOkfbr7Z904i9A/BsbZaJ2vTvKLPfU52Pf8Zo/lxpNxjydjO47OpL+sNsa06+963no/c0/QDv1Jcv1qy0l2Ta7rLnceu3aNMtf2xTmA2L2/D6CO3TJrxkkzNUZ3+bDOusPW29bAOEf21K9bZtuBllmvzqu1WZJtPeuOPJDYa8RZrc26+2XIPlzY/jKGth+yrb5+tsD9Zd1+Mpb+cZBxjCfD+kf3uLXf8lqPrbOtvji9jw2Ms27fHPC8WY2vU31vzrjOB3J+slBtnwUcy6dR506dNmVcPND+sc4+XNjjzyL0j6FxplXnvv18oG0/p9cxGee6NN9YOKyz7kE9r+v+nXUHGmelTDfOMT1luq9jvTIHXOeJ5z2g53Xcv7O99WJ346ys725/1OeGPXGGtE93/6wbZxH6x8A4B1Pne/SNabzn5zCWG0/GPZ6M7Tg69f6y2hjTbbP1fhZpjtz3llIuTvKmJF9K8rFSykfbx64upfxUkptKKbuTXFFK+dEkHyql/K80Xyf8YltmXynlmiR/PRH3sjSXVX+zLbOrlHJTko+2y+mJ/elSytvbx65p1320LfOV9nl9dezG3l1KuTTNV+Q+dABxrkhzyfenSyn/OM1l6l9oy/xNu+6mNF+nuvgg4+xqy/x1u/y5tg2PSPKXq8QZUmZIndPTZu9Lcn0pZV+7nb46DondF+c7aS63/69r9KFufYb0xUXsL5vd9kPKDOmLY+8vffvw70opn0ryhXY5C9A/jCfT6R9DxopbSyn/O83XqtL2k5en+cr8l9p1nyqlfDjJN9aI0+1nXy2lvDbNV9s+dgBxhvSp7utKz/anNb4OaddpvTdnWefu+clVpZQ/a8vcuEBtv5H32LzH8lkdf5LZjYs3t+/VY5JceBD9YxHPT4bEyQL0j3kffxbhONqN8/4kn08zZcdHSyn/Ksmedt3ftMufT/NV3tK+/o3G+XiSL7Z1vqxdtzfN1CHvWiPOkDIbqXPSHKtvSPMeWK2OQ2J349xcSvlCmik4/vOM35vTGgP74gxpn+7+GRInPe06tv4xyz69L8mXk7xxyu/5zR7LjSfjHk/GdhydVX/pa/ukmWLtKWm+cbmuhZkjN0lKKU9P8szcPSHwvdI00h3t8sqcXA+eKPP9NPOGfTNNo9+cZtLlR6WZGzBp5mO5JcmNaea8uKH9OWEiTjd2aX+SZt662j5nV5os/fGr1LEbuyS5X5q5Pw6fKDMkzsfSXH69UscftL8PnSjz9SSnrtFmQ+N8OskjJsrcu/35oTXiDCkzpM59++O77c+316jjkNjdOF9N0+6Py+p9qK8+6/XFRe0vm9n2Q8sM6Ytj7i99+/DLaQ7UxyT50Yntj71/GE8Ovn8MGStubMv+eJr5pUqa+dxLmnkua7utL6b5FPiHV4nT7Wf3SvMJ8WR/GRJnSJ/qOx739fNpjK9D2nXItoa+N2dV577zk31tmX1trEVo+42+x+Y5ls/y+DOrcfFeafblA9J81W+lzIH2j0U9PxkSZxH6x7zPT8Z+HO2Ls7d9DZko85E0X4k9YWL9/dKMeQcT59b29+R7bHea+anXijOkzEbqfEOSD2b/G/L01XFI7G6cB6Q59hw3UWbs54Z9cbqva8g+HBJnEfrHLPv0HUl+IdN/z2/2WG48Gfd4Mqbj6Cz7y2pjzCW11q9ngIVK5NKvjPxO55tZ543G3uj2F9Eitv1G++I867yojCf6B8zKIo7l06rz0Dou8xiziGP5IvbpIc8bY9tPriudO5bXnruYt+sOOk77e78yQ+LMqs7t8rp1HBJ7tXp3jf3ccCPts5E4i9A/Zl3nrmm952cZ23iyeOPJ2I6jm9X2q7VZbz3rgiRySyn3T/KyNFfkrlypcmf7+5D275vSXDa9cifu7tVGtS3ziTSZ9uPSXHX0I2k+NbhPW+ZrufvKleMmnjcZO238yXrcmObTpB9fo459sW9Lk/3/XltmSJy9aT5ZOC7NJyeHZvU7nR+Wuz8R2Eico9J8FeveaT65WGmD29v639ETZ0iZIXXu2x+lbfttaa44qD11HBK7L8790uzn77Rl+vpQtz597boV+stmt/2QMkP64tj7S98+/HKabwUcm7uvlht7/9hoHOPJPY9J640VN6SZX2nlruN9Zb6cZt+flP2vcuiW6faz76fpL99s6zMkzpA+1fe6utuf1vg6pF2n9d6cdZ0nz0/6rnBchLbfyHts3mP5rI4/NbMbF0uaq+a/neQb7WMb7R+Ldn4yJE7padex9Y95H38W4TjajbNyHPxumvHy+9n/Lua70nwN99Ht6125An0jce7bxkmarxx/O83VU0e19bxllThDymykzqVtpzvb/fV3q9RxSOxunG1pbrzzQ+3jt2f854Z9cYa0T3f/DIlTetp1bP1jln36x9rt3JZmn0/rPZ9s7lhuPBn3eDK24+is+ktf25ckJ7bLL6q1fjTruNd6BUbkwjQv/OdrrcekmWfjdUn+Y5Iraq3HJvlsmoa6X5JfStOg701zJ+jL2jJ/k6ZxT0/yb9P8U/PZNBMOv7kt8+U0jfiVWusxq8T+fJq7zV2b5nL3X0rT+e+f5k5zq9Vxv9hJLm3r95dp3pxD43wjzd32PlZrPSlNR3lukl9PcmOt9cFJPtqW+cZBxrk+yX9PMxnzSe3yHyT5l0k+u0qcIWXWrfMqbfbZJC9N8odJPrdKHYe0R1+cv2q39YA1+lC3PkP64sL1lzm0/ZAyQ/ri2PtL3z68V5pxaVu7PPr+cRBxjCf7948hY8U30nyI+ag0x619ufsrRd9v1z2qLfPNNeJ0+9nlaU6qbktzsjU0zpA+1X1dfduf1vg6pF2n9d6cZZ275ydHtPvmB23MRWn7jbzH5j2Wz+T4M+Nx8Yokf5HkT9IkTjfaPxbu/GRInEXoHwPjzPL4swjH0W6cvUl+LskLkxxaa/3F9nX8VpJv1lqfkmace0GSJyb5+kHEOSLN15l/PsmRbZmbk/xRki+tEWdImQOuc7vui0n+fZKvrVHHIbG7cW5Lc9z5P5PsXpBzw744Q9qnu3/WjbMI/WNgnI3W+e+SvDPJd6f8nt/ssdx4Mu7xZGzH0Zn0l9XGmFrr30vy20n+LEPUAXdEG8NPJu6u3V3O3Xdzvq6zPFnmurXKpMmCd+8c3ff8/ba11mOr1LH7+7o1yqwVp1vXte6Aff1Bxun+HnKn843eDb3vsW67HOidvFdrj7441/U9Z52+M6QvLlx/mUPbDykzpC+Ovb/07cPrBjw2qv5xEHGMJz37fJV92C3T/T153Lquuzw0TnfdgDhD+lTfY72veZ33y1SP9Qf73tzkOvft50Vo+428x+Y9ls/k+LNWPdaq/yrP7+0Daz02cB8u3PnJkDiL0D8Gxpnl8WcRjqND4qz83tVTpnun82nF6SszJM6G6zzgdQyp42rtce0aZcZ6bjgkzsHuw77HRtk/phBnrfpcu0aZg3nPb/ZYbjwZ93gyZBxY+OPPauu6z1vvZ1sWxxdKKf8yyRtrM3fEF8o979r9rVLKWyaWv1BK+Z00V/KuTBp8e2nu0v2lTtxPpLnKKWnurvueNF8JW9GNvbeU8stprnL5+kSst6S5edrKcreO3dh7Sym/keYT8y8dQJwhdxaf553ON3o39G6d+9qs707e3ToOid0X57ZSyiUTde7rQ936DOmLi9hfNrvth5QZ0hfH3l/69uHeUsofTTxnJdaY+4fxZDr9Y8hYsa+U8rLO8ult268ct/Ym2Tm53BOn28/2lVLOSLJjssyAOEP6VPd19W1/WuPrkHad1ntzlnXunp/sLaWcneZT+30Tzxt722/kPTbvsXxWx59kduPit9px4UHZf4w50P6xiOcnQ+Kkp13H1j/mffxZhONoN86HSin/qy3zxdJ/F/PdpZRL03yN9UMHEedjpZSVr7de3a67qZSyO8kVa8QZUmYjdU5bfne7D1ar45DY3Ti3tu/VB6a5qn6j/WOj7/GNjIF9cYa0T3f/DImTBegfs+zT+0op1yT56/a503rPD+kv2WBs48nijSdjO47Oqr/0tX3SnM89Z6LN1rRIc+Qeneafy2fm7nm27kjuumt3d06VvWlOSo9vy9X29zfSzG/xzdx9Z+6T0zT2D9LMwXFzmpOc1eY429s+b2VHfLl93tFpvro0Oe9Lt47d2JPzcXy5LTMkzo25553F0/6u7c/K/B4/3pbZaJwbJur8o+mfZ6sbZ0iZIXWuq+yPlTvvTt7Je7KOQ2N349yS5quAP9bG6+tD3fr0tetW6C+b3fZDygzpi2PvL3378Jg08+3cu42ZjL9/bDSO8WT//jFkrHhA7j4O/V0b85R23cfTJEl+Is3XpQ9pX2NfnG4/O7wtszLX0/cGxhnSp7qvKz3bn9b4OqRdp/XenGWdu+cnR6TZz0e29fn+grT9Rt5j8x7LZ3n8mdW4eGiaaQvuM1H/jfSPRTw/GRInPe06xv4xz+PPIhxHu3HSPrYvzVh5Z+55F/OSe84vvpE4aduttHVJ7p7z8METZbpxhpTZaJ33JPlcmvfU5Jz5k3UcErsb59A0/19P3jNm7OeGfXGGtE93/wyNswj9Y1Z9+uY048mj0vSTab3nk80dy40n4x5PxnYcnWV/WW2MuajW+p4MsDCJ3ANVSjk2SWqtt6zy+I9k/zvEfXWjsdfb1rTqzOaa1X7VX7amjeyPvufoH1vTkP2x3ljRPW61v+9xHBsy5kyuW+14OK2xa979fNHem337Y1Hbns0xrf6hb2xN9gcAbC2LdLOzu5RSHtNZ/sc9jx/a+cflKZ0wP1prvar9+erE8/aL07euG7uNNbmtHxlSx26ZJI/ZYJxndNb9fmf5GVOM0y3zWwPiDCkzpM59+6Mbu6+OQ2LfI07W6UOr1GfdvpjF7C+b2vYb3FZfPxt1f0nPPuyJswj9w3iyTpwM6B/dMumMFUkyedxqf/Y7jrXWHXMmy7TPvWEjcTYyBq7Sz6cyvg5p1ym+N2dS5/Scn/Q8ZyHafhHH8hnWeWbjYpKnT6N/ZDHPT4bEWYT+Me/zk9EfR3vivLazfF4p5bx1ymw0zpt7ygyJM6s6P2NgHYfE7pb5w87yIpwb9sXZyD4cEmcR+scs+/Ss3vObPZYvYtsv03gyquPoJrf9fsurWchEbpJ/2ln+vZ7H/7Szrrv8T8vdc4CsuLQnTndbfbHf37OtIXXslumr85A4p3fWPbGzfPoU43TLPGdAnCFlhtS5r826sfvqOCR2X5x1+1BPnCF9cRH7y2a3/Ua21dfPxt5f+p6z0f1qPFl7W/Pu00P6x7pjRfe4VUr5bmf5o0Pi9JS5doNxNno8ntX4OqRdp/neXC/OtM5Pru0sL0rbL+JYPqs6z3Jc7O6fjfaPRTw/GRJnEfrHvI8/i3Ac7cbZ0Vku7c9aZTYap295SJxZ1fn0gXUcErtbprt/FuHcsC/ORvbh0Dhj7x+z7NOzes9v9li+iG2/TOPJ2I6j82z7Xlt2agUAAAAAgK1i27wrcCBKKfdP8rTcPXfc3jQZ66Pb5RuSXJLkoRNlbk0zEfIJnTKHT6z7ZpLHdsp8OMkTep730KwxR2GSzw6oY1/sr6W5acWBxPl8kidPlLmt/X3YRJm/SnPzmoON88k0E5yvlLkld9+FeLU4Q8oMqXNfm9U0E0kfu0Ydh8Tui3NRu26tPtStz5C+uIj9ZbPbfkiZIX1x7P2lbx9enuSMLFb/MJ5Mp38MGSsuTzMZ/1plbso9+1BfnG6Z3RuIM6RP9b2u7vanNb4OaddpvTdnXefDO+u2Z/HafiPvsXmP5bM6/sxyXFx5//5gYnmj/SM58H429uPPIvSPeR9/FuE42o3zQ2lu3HjviTJ7kpw4UeY7aW7w+J2DjLPy7dU7J8pc1dZvrThDymykzjek6VeT/7f21XFI7G6cm9PckOhrE2XGfm7YF2dI+3T3z5A4i9A/ZtmnL0tzE6tpv+c3eyw3nox7PBnbcXRW/WW1MeZdtda/zAALc0VuKeU5Sc5PM/3BDWnu5PyU9uHLklyd5PFJzkzymSRXpLm73xlpTnDfmWRXkkcn+ftpBo3PJPmRJA9Jcye5t6e5M+CTk/xskg8l+UC7jW7sE5L8XPvYh9o6nZHkYUn+MsnfrlLHbuwHJ/mHaQbGy9s6DonzjCSnJvnvSf5HuzxZn3e3r/NZST7WLm80zq8meVKSDyb5L0l+JsnZaTr7VUn+pifOkDJD6pyeNvvZNJ2+JPmvSf5nTx2HxO6L8/NJ/kmaf4z+Jv19qFufIX1xEfvLZrf9kDJD+uLY+0vfPnx0+3N1kk+09Rh7/zCeTKd/DBkrHtbGvinJdWnuAPvotswn0pzgPCRNoveqJNesEqfbz45p2+OHknwqzXFxSJwhfar7utKz/WmNr0PadVrvzVnWuXt+ckySR6Y5GfxAmhO+RWj7jbzH5j2Wz+r4k8xuXHxkkt9I80/JZ9Ps1430j0U8PxkSJz3tOrb+Me/jzyIcR7tx/kmaD7eOSPLedt2/SnJa286vaJ/z9DR3Ft+d5C0bjPPPk5zU1nlXklcneVH7Wj6Z5HWrxBlSZiN1TpLfTHJy215/skodh8TuxjkzyXltmRuSvCfjPzfsizOkfbr7Z0ic9LTr2PrHLPv0ryR5bpLPJbkw03vPb/ZYbjwZ93gytuPorPpLX9snTTL4OUmur7XuN09vr1rrQvykmR/uqO5ymk9vrmvXXdM2yHWdMjuSXNOuuzrJL/aUeXySj0+s+/GVMqvEvqbdkZOxr23LXLNGHfeLPRGnW2a9ONel+VR9cvnQNCf0108877jO8kbiXJfmH/1unG6ZyThDyqxb51XabOV53dfxQ51trdcefXGuSTOAdV9rdz9P1mdIX1y4/jKHth9SZkhfHHt/6duH16Q5WF3TafvR9o+DiGM82b9/DBkrrk5zsjG5/Ljsf9y6Jk0S5eNrxNmvn03E6ZZZL86QPrXf61pl+9MaX4e061TemzOuc/f8ZKU+3fOTsbf9Rt5j8x7LZ3L8mfG4uNI/uts/oP6Rjb/HR338WYT+MTDOLI8/i3AcvUec9nf3vVF64nTXHXCc9ndf7PXiDClzQHWeWHdYT+zu84bE7u6Lo3rKjPnccLU4Q9rn0J7lVeMsQv8YGmeDdV7ZHwf0Xu1p13mP5caTcY8nYzuOzqS/rDbGdNt+vZ9FmlqhpMnGd5dr7p4QeFuaS5tLp8wNaRo0aS6DvqJbptb6kVLKvSfW3TlRpi/2yvK9JmKXJF/uLHfr2I29EufenTLrxbkzyQM7yz868ffK8x7YWd5InDvTfGLWjXN8p8xknCFlhtR5Zd2dPc+7tbNuso5DYvfF2Zbkjp7XOtmHuvUZ0hcXsb/0bX+WbT+kzJC+OPb+0rcPt6X5JG/lOSvPG3P/MJ5Mp38MGSvunebT7FetLNda/zZJJo5b29J8wv3KNeJ0+9m9a61/W0o5rFNmvThD+lT3dfVtf1rj65B2ndZ7c5Z17p6fbEuyp9a6u3N+Mva238h7bN5j+ayOPyvPm8W4uLIvTug870D7xyKenwyJs7JuzP1j3sefRTiOduPsK6WcnuYbKnetS3MV075OnAd21h1onJUypRPn6T1lJuMMKbOROq8878ye1zFZxyGxu3FW+sfpneeN+dywL86Q9ununyFxVtaNuX/Msk+XNFdTdutzsO/5zR7LjSfjHk/GdhydVX9ZeV53jEn2b7M1LdLUCuemuZvhpUm+lGYweWr78GVJPprkmWka6V1pLtv+2SS/nKaBPprkr9N8NeAnkrwxzaXOT0/ygiTfT3Oy+T/SXBHzs2ku335fu41u7H/Qlklb7qK2zMol2hetUsdu7Cdm/8u4PzQwzk+3665J8pE0l2I/sS3z12lOph+f5ittlyT53wcR5zFprqj4eFunk9JcxfWDJP8tyfU9cYaUGVLn9LTZSUn+j9z99b3reuo4JHZfnCel+ZTnLWnu3tzXh7r1GdIXF7G/bHbbDykzpC+Ovb/07cNfa9f/dZI3t/UYe/8wnkynfwwZK16c5mvUFyf5D0l+O83XZJNmvql/l+QlSc5K8um2TF+cbj97Tlu/I9J89fnNA+MM6VPd15We7U9rfB3SrtN6b86yzt3zk19LM7foviRfbNf//+2dedgdRZX/PyckEUJYEkBWJYgoMKCIgLiCooIOCi4orsM4Cu6OO6OOguM+o4L6UwcXFNRxV3BhU9mUPRshhCSEJSEhBLKQBMhCUr8/TjVvv5W+99Z739u3um+f7/P0c2/V/fY5p0+dPlW3l6o6+L6bcyx1Li+r/4Hy8uIhwP7ovG/fR6dW6CY+6jg+iZFDgV+rFh+p+5869KOhnJ2AY9Cnm2ahU87sB+yMzsl4u+ccjE5L8zd0jsZu5GyHnivO77cGPeey6Ydmt5ATw+nGZtCxwKHo3JNzW9gYIzuUszs6b/ZK9PxdQvXHhkVyYvwTtk+MHAr8WrX4KDOmD0Kne/pDrn16cc73O5dbPql2PqlaP1pWvBT5HuAJwIPAe5xzU+mA2lzIBRCRSeiV6z191Sr/uaP/XIzORXE0wyfQXp0Tsxi9Q30MwydjXuY/M851aAPvmasLZW/0n+NynCuAKR1sDGWDTqw8eYRyLkXnM8w4S9AB9O45znx07pnRyrkJnf8j46xGg3aPNnJiODE2F/lsCXpibN/GxhjZRXLWonOptIuh0J6YWKxjvPTb9zGcmFiserzAlm04G/gnhvv+CqodH5ZPehMfd9E5V6xEB7wZZ4y3Z1OOMx19JaidnDDOsgn7J4xQzhV0jqnwuIr09yq/xvi1Q6eNLgAAIABJREFUV+dmmTaH45OH/bY2x7mC6vu+m3MsdS4vq/8pMy+OQ1+Vz3OuYOTxUcfxSYycOsRH6v6nDv1oKGcxenPrMTnOuaUislsgZ2/ax0KMnMX+M18uWhB0mJwYzihsvpEtF60bZmOk7FDOWoaPM+owNiyS09E/bNk+UXJqEh9lxfR6hl+H6dU53+9cbvmk2vmkav1omfFSmGOIRJ2mVsA5t1JELqd9oN0HXCcik/0+K0Rk1wLOr/Kyizgi8kgn2X7fsHxnJxuLZI9UDvrHPnsdAYYezXa5zxXoRNCjlbMp4KxGn/5oJyeGE2Vzkc/QPwbbtbEx1h+hnNnOuVs6xVCBPYQcBiBe+uz7GE7HWKxDvBS1IfCHusVHl3Isn3SRK2IRIWeLOOtGTkxsFh1XqL9X+TXGr706N0u2edj4xPu6dr7v4hxLncvL7H/Kyot3Ar/Ar/Y92vgYwP6nLvHRSU5p/Q/16EdDORPQJ7Ie20dEwlXMF6N/yNvFQoycFeif/0k5uZd0khPD6dJmhy54elQ7GyNlh3IWo0/9zcuVqz42LJLT0T9s2T4xcuoQH2XH9EjP1Srmcssn1c4nVetHy4qXVjnmEudcdnOjLWrzRK6IHAJ8F9gBfRx6O4ZW7Z6JXvWfgt7NGYM+6vw49Gr5w2gArAeeiD59tMHLGuN5D6GPSG8C9kWv/D+APiItBbLHAbt5/ff6/SajQbIKHVwX2RjK3tpztvFyNkTKeQqdVxbPr3Q+dxRy2q10Pgs9KUM5MZwYm6XAZzux5UreoY0xsovkPNlzNqFPaBfFUGhPTCzWMV767fsYTkwsVj1eitpwRzQHbeuPQah+fFg+6U18TKFzrtjB7z+eoSfdsgHJVv77CvSpul3Qti6SE8bZVugcTo8yNMiIkRMTU+FxSYH+XuXXGL/26tws0+ZwfCKePxZY6tuoDr7v5hxLncvL6n+E8vLituhTtbugr+Y9QnfxUcfxSYwcCvxatfhI3f/UoR8N5TwZfTJtI/A7rz9cxfzJ6KKd49BXZG/vUs4h6NNaoK99z0CfAjsWuBV9mqpITgynG5vxcg5Ap0O6voWNMbJDObuhq8NPQi+8LKX6Y8MiOTH+CdsnRg4Ffq1afJQZ008HXoGOEbPX+3txzvc7l1s+qXY+qVo/Wla8FPkedAqIlwBnOufOoxNiVkSrwuYP/FlhmeGrOV8LfDJXnoFeHT8ZuM7XXYLO+zc7x3kZcDpwaa7ubZmcFrKv9XLfkJM9A/hUUA5tHCbby3k98JyA00nOHDqvLD6DdCudx3A62tzCZ9l+4XE8L9DVyR9Fcq5F54jM7xPGUGhPTCzWLl4S+D6GExOLVY+Xoja8Fjg726cO8TEKOZZPhsdHTK64BPgZMDVX/i/gcwz1Wzd5zqVt5AyLMy/ndOC0XF2MnJiYGnZcLfT3Kr/G+LUn52bJNofjk2uBU4H/YPj4pOq+7+YcS53LS+l/Ss6LWbzuG+w3ovighuOTGDl1iI9IOWX2P3XoR0M5c9EbEfsEdXsztKJ4JmdSUDdSORknlPO0Ak54rnbijNjm3H5PKziOSYG+TrJDOVm8hvqrPDYskhPjn7B9OsqpQ3xEyunW5hno3KShPaM95/udyy2fVDufVK0fLSVeWuUYX//Yfp225BdoYzdgfqsycHu+LiwHv81tw5lbJGcEsgs/R2BjyGkrB31KJyyPD+rGd9IVKWebAvtCztiRcjrZ3MpnwNiCum1GIruNnFY+C9slpg1rGy9V8H2MrqI4q3G8dIyTqsTHKOVYPomLj7DfGlZu91sHXUVyCn+LlNMxNiP2Kyu/9vTcLNnmkYxPauV7apjLe2FzYFNf8uJI46NDG9a2/6lDfMTK6ZXNRe08Ut8nOo68nHnoGwvjg7onFBzXDkHdSOVknFDO5AJOeBydOCO2ObffzgXHsUOgr5PsUE5WH+qv9NiwQE6Mf8L26SinDvERKWc0Nm8RG7045xPkcssn1c4nVetHex4vrXJM6LNOW53myL1IRP4EnAcsAqaLyDT/2wwReQ6wTETuAm4UkT2Aq0TkH+jrhAs9Z52IzEFXrMvkXoY+Vr3ac24XkWXANF+mQPZsEfm1/22Or5vmOff6/YpsDGXfJSKXoq/IXTUCOTeij3zPFpE3oo+p3+05V/u6bAG3P41Szu2ec6UvL/A+3Bq4uIWcGE6MzRT47C/AfBFZ5/UU2Rgju0jOQ+jj9r9qE0OhPTGxWMd46bfvYzgxsVj1eClqwztF5Bbgbl+mBvFh+aQ38RGTK1aJyDXoa1X4OPks+orrIl93i+gcTQ+2kRPG2VIR+SY6R9P0EciJianwuCjQ36v8GuPXXp2bZdocjk+misi5nnNfjXzfzTmWOpeX1f9AeXnxfn+uTgZ+OYr4qOP4JEYONYiP1P1PHfrRUM5fgTvQKTumicgn0Cn37vByP+G/L0X7yF+NQs5MdFEbgMt83Qp06pAL2siJ4XRjM2hfvRg9B1rZGCM7lHO/iNyNTsHx/ZLPzV7lwCI5Mf4J2ydGDgV+rVp8lBnT69AFnH7c43O+37nc8km180nV+tGy4qXI96BTrL0EfeuyI2ozRy6AiLwMOIGhCYHDVbuzObmenOOs89tq1On3o/OxHIzODejQ+ViWo3OMbUSdutjL2LOFbMnMYmji4nuABehV+t1b2BjKFnRlvAlosDMCOdMZvrL4Bs8Zl+OsZOQrnRfJuQWdNyTjxKx0HsOJsbmoPR5C54PLr+Qd2hgjO5SzFPX7s2gdQ0X2dIrFusZLP30fy4mJxSrHS1EbZromM7QqZx3iw/LJ6OMjJldk86Pujc4vBepzYWiO3PvQwddYzymSE8bZGPQOcRYvLlJOTEwV9cdFcd6L/Brj1xhdsedmWTYXjU/WozG1zsuqg++7PcdS5vIy+5+y8uIYtC13Zmi1727io67jkxg5dYiP1OOTqvejRXJW+GMgxwlXMXfok02Tc5xu5KzynzvmOHcCL+wgJ4bTjc2LgSvQV3fb2RgjO5SzM9r37JLjVH1sWCQnxj9h+8TIqUN8lBnTm4Bj6P053+9cbvmk2vmkSv1omfHSKsdc4pxbSQRqdSHXYDAYDAaDwWAwGAwGg8FgMBiaiDGpDegGInJqUD4//F1Ejg/qvlnAOTSo+1wBJ9RVJPvMoHx8pI0hJ7QxVs4ZQd0VQfmMHsoJOX+IkBPDibG5yGeh7CIbY2QXyYmJoW5isY7x0m/fd6OrKM6qHi9FbRjKqUN8WD7pLCcmPmJyRdhvvTEoHxopJ+R8oEs53eTAIv29yq8xfu3VuVmmzWE7h+1TF9/XMZeXZXOZefFnQbnb+Kjj+CRGTh3iI3X/042ufh9HKGdqUD5HRM7pwOlWzpwCToycsmw+I9LGGNkh56qgXIexYZGcbtowRk4d4qPMmC7rnO93Lg/l1MH3ZdlcxXwSclL3o6GcMn0/rNwKdZojNw+JKB8O/DFXt08B513AO3J1R3WQ20p2uN/h6DwynWwMEdoYK2dqUHdVUJ7K0KuUo5UTYmFQLpITw+nG5iLZRTbGyC6SExNDIWJicRDiBcr1fQynkz11iJeiNiSQU8f4sHyyJScmPmJyxR4M77c+DeQv4rwLbedOcgg4bwXO7kJOTEyFx1Wkv1f5NcavvTo3y7Q5HJ+E7VMX34eoQy4vq/8pMy+G+rqNj0EYnxTJqUN8pO5/6tCPhjg/KP9vBKdbOZ8r4IQ2F8mJ4XSyp8jmqcAfgroiG2Nkh3KuDsp1GBsWyYnxT2hzjJw6xEeZMX1YUNerc77fuTxEHXzfpHwSInU/GqJM3xfJ2gI2tYLBYDAYDAaDwWAwGAwGg8FgMFQctXoiV0SOBU5k+OTYoBP2g04QPBXYKceZwNAkyRnnMnSi44yzE7AbwydjvgfYi+GTD4eyN/jP8TnOcuCZHWwMZT8EPOw/Y+Us8fqfwtBdhKLJwueji07kF5kYqZwl3p6dc3LWe5tXoxM6F8mJ4cTYHPpM0AU1tmXI90U2dpJdJCebsHpDTk5RDIVtGBOLdYyXfvu+EycmFusQL0VtOAtd5Cifc6oeH5ZPehMfMbnib8CTGD7ZvqCLBmScO4AXdZATxtlyYA1DiJUTE5vhcRXp71V+jfFrr87NMm0OxycOjaudcpw6+L6bcyx1Li+j/8n2KysvrgIe8L9li1d1Ex91HJ/EyKl6fKTuf+rQj4ZyskU6M45Dz4EVaEzv7Os2oIvWPIzGSDdy8PuK529GVzq/m6EFQYvkxHC6sRl0QdJN6AI52eKnoY0xskM5Dj1vH0AXynuUeowNQzkx/gnbJ0ZOHeKj7Ji+GV0Eahd6d85nnMyuTE5ZudzySbXzSdX60bLipcj3WVtfAHzJOZctOtcSYzoRqgIROQv4AHAl8BV01dVstbr1vm4H4GvAK4Eb0EB5HvBstLG+gjrzfOCT6IDmtcCbgeOBmZ6zt9c1xZeLZD8eeKPfHu/rXuE5O7SxMZS92Nv3XG9vrJyDgROAB4GXo3+85wK3AXf5ugc95+BRyhkDPB1dHf3l3saNaJBOaCEnhhNjc5HPtvZyNwA3trAxRnaRnAXA+4CTaR1DoT0xsVjHeOm372M4MbFY9XgpasOjgd/4zxtG0K6WT6od0zHxEZMrDgX+DnzQH/9z0Nfsz/LfJ/jf/u65reQczfA4Gw+8H/gEcOwI5MTEVHhcRfp7lV9j/Nqrc7NMm8PxybH++/t9W9XF992cY6lzeVn9T5l58Q/oQwh7oK+6dhsfdRyfxMipQ3yk7n/q0I+Gch4ALgIuBi5zzu2E/kleCSxxzk0GLgUuAf6M/nnuVs6NwHeAbwM3es5taDxv10ZODKcbm1/uZe4AzG1jY4zsUM75wDxv+86jiI9+jg2L5MT4J2yfjnJqEh9lxvTVXtfh9Pac73cut3xS7XxStX60rHhplWNe6GX9khg452qxAfOKyujV8/lZHfoky/yAMz5XNxfYsYAzKfd9Xl5uK9m+PD6oG18gO7SxyOawbsRyCnzT6rMncsK6GDkj1dVuvxZ1bW2M9Me4Ak7Yzh3toebxksL33XJi4qwsm+kiXlq04YSsXIf46LWchueTTrliLvoERNiPhf3WLkG5KOdMKJATcmLkxMTUY8fVQn/P8muMX3txbpZs8xbjE79P2M518H1PzrFeyYk5V7vltLO57LwIjCvgjCg+cuXajE9i5NQhPkYjJ9IfXY1nY+zo53EEn3ML5MwNPvO65o5Cztw2coo4hTp6ZXM7O9rZ32L/KB0jjY9WbditnJjzp4Vf2/mnkBvxW6XjI1JOr2wuO3f1K5fX0feNyydhXYs2rU3/04of/tZpq9PUCutE5HDn3I35Mursdb5uM/CyXDnjLGPocX0BnlHAyYIC/9tbc5wi2ZsZeix8c26/Y/PlAhtD2ZmcXQNOJzkrgNPzZRF5HXrFf2Vuv/8Iyt3IWSEiXw/kHIEulrCyhZwYTozNRT5bISInoRNR5+vyNsbILpKzGTi1wGf5GArtiYnFOsZLkf4yfR/DiYnFqsdLURtuBg7J7ZPtV+X4sHzSm/iIyRWCtmm+7PwmOe6uQTmUE8ZZJmf3gNNJTkxMhcdVpL9X+TXGr706N8u0ORyfZPasYvj4pOq+7+YcS53Ly+p/oLy8uBnYU0SeFegfaXzUcXwSIyeTVeX4SN3/1KEfDeXcLSIf9+Ws7l4R+TP6qmq2z9vRJ8EWjULO3SLyWTTOMjlrROQnuXKRnBhONzZnsn7C0JRIRTbGyA7lrBOdwvCQvH6qPTYskhPjn7B9YuRA9eOjzJh+VES+GejuxTnf71xu+aTa+aRq/WhZ8VLkM0RkV+CU3H5tUZvFzkTkUPRR7+3QeTG2Q4PDoVMirAH2R18LmwrMRueqOAZ9CmUW+njzQcBk9JW06eig8yT8Ey/o49H7oc6/H7jdmxDK3ouh1X2v9DYdhL6Oeh/6ZE2RjaHsndBHuh9G50B8IFLOk9And7byssYxNOfGEnTOjZ3RwdoK4M5RyJmMznOyrffh1p7zMDpfziMFcmI4MTZT4LOt/XFsw9B8K6GNMbKL5Ozi7ZuKxkNRDIX2xMRiHeOl376P4cTEYtXjpagNDwWe5ttrurej6vFh+aQ38RGTK44EDkBf1bkGvdj3Us+5DJiGvs78UmAOOi9ikZwwzvYDnoW+tvRbYH6knJiYCo+LAv29yq8xfu3VuVmmzeH4ZD/g1Wj83YDGUB183805ljqXl9X/QHl5cVt0aoVlwBW+fbqJjzqOT2LkSIFfqxYfqfufOvSjoZytvJxH0fbfjJ4DKz13F4bmQhzr5bgu5Yj/XTzf+X03+t9XtJATw+nGZkH/r96PPlk9uYWNMbJDOVv5NnoIjddHqf7YsEhOjH/C9omRQ4FfqxYfZcb0KnQu0dXoha5enfP9zuWWT6qdT6rWj5YVL0W+B51P+ELgy865FXRAbS7kZhCR3Ri+eAdBeRlwRFC3kOET9K9HnxDIc6YDE7Oyc25pga5Q9hK0IfKyb0QbsaWNBbKXoPN97DlSOd4nOwE455a3KLf1WaycsC6UWyQnhhNjc1F7+Lq2No5Adt7mMXSIoRbxAQMYL/30fSwnlF3DeClqw5vQeQ5H3K6WT6ob0zHxQVyumA+8JMfJJsHfMce5FJ0HsZ2cMM5Wo4OZPUYop2NMFRxXqzjvRX6N8Wsvz82ybA7HJ0vQC2Lb5ziV932359gg9z8xNsbIznPQPw9hW3QTH7Ucn8TK6UZ2k8Yno9DVt3OzaL8Y9EpOrOxuODH79MvmXo2z/GdfxoYxxxXrj175OVbWIMRHL855/9m3XB5zrC2O3/LJCOwpcyzU9P4HqNXUCojIDuhTA5mzi1btvgQee+UU9M7z8xmeLC4BLs/VrWb4irqLReRadEGAcL+87OzVJJf7nNjJxhayH+hCzh3kVhYXkQ3+t/G+vBj4K7BvD+TcjD7Nk3GWe79JGzkxnBibi3zmROSxlbxb2Bgju0jOBXSIoQJ7YmKxjvHSb9/HcGJiserxUtSGmwI5dYgPyyc9iA/icsUKhvdbRRdnVkbICeNstT/WkcrpGFMFx1Wkv1f5NcavvTo3y7Y5bOcp6FMrmT2V932X51jqXF5W/1NaXkTb40706RDoPj7qOD6JkVOH+Eg9PqlDPxrKmYA+2bRtZg/DVzEHffrpYf85Gjlj/OfmHGeqt6+dnBhONzZn51j+plyRjTGyQzn3o0/PP5DJqcHYsEhOjH/C9omRU4f4KDOmL0OfbOz1Od/vXG75pNr5pGr9aFnx0irHXOCcu5gI1OaJXBF5K/AZ9Emhxeijzy/xP18GzEBfPz0WuBV9AuDJ6J3ojcDv0Mevn46uorsCfaVsd/TJo5XAr4F70QB7PnAV+oeKAtl7Ai/wv13lbToCff31YuD6FjaGsp8MvIqhVydvj5RzPPp67e/R1zCPD+z5oz/OE9Gnjf84CjlvQle6vgL4Kbrq7UnowHAqupJlKCeGE2MzBT57PnrhXYBfoSushzbGyC6S80LgzeiKhldTHEOhPTGxWMd46bfvYzgxsVj1eClqw6f7bQaa6KH68WH5pDfxEZMrDvCylzH0evLTPWcmsBbtx7LXpee0kBPG2WTvj+xV25WRcmJiKjwuCvT3Kr/G+LVX52aZNofjk53QV9Mf9nKX18T33ZxjqXN5Wf0PlJcXDwLejv4puQ1t127io47jkxg5FPi1avGRuv+pQz8aynkzenNra3T18J8Cn0D/DE8FPu/3yeaBvgv4SZdy3o++xov319nAu/2xzEKn/CuSE8PpxmaAd6IXA/6IrixfZGOM7FDOseicyuvQHPBnqj82LJIT45+wfWLkUODXqsVHmTH9BnTuzgXAL+ndOd/vXG75pNr5pGr9aFnxUuR70IvBb0UXW/sAneAiVkSrwoZfzTksM3w15zneIfMCzj7AHF83A3hxAedIYGaubm+GrywXyp7jGzIve67nzGlj4zDZOTkhp5OceXReWXwu6VY6j+F0tLmFz1qt5D2BLVfrbeePIjlz0AQWHmvYznl7YmKxdvGSwPcxnJhYrHq8FLXhHLSzmhP4vrLxMQo5lk+Gx0dMrpiBDjby5WcxvN+ag15EmdlGzrA4y8kJOZ3kxMTUsONqob9X+TXGrz05N0u2ORyfZPaE45Oq+76bcyx1Li+l/yk5L2bxEeofUXzQ/Tle6f6nDvERKafM/qcO/egWcvxneG5IgZywbsRy/GeR7E5yYjgjsjlXN75AdrhfjOywLXYs4FR5bNhKTox/xhWUW8qpQ3zEyunS5qw9RnSuFvg1dS63fFLtfFK1frSUeGmVY0Lfd9rqNLWCMPRqUL7sGHrUeiz6aLMEnMWoQ0Efg74x5DjnrhORbXN1m3OcItlZeUxOtqDzg+XLoY2h7EzOtgGnk5zNdF5ZXEi30nkMJ8bmrG5zwX6rgrq8jTGyi+SMRV8/DY81H0OhPTGxWMd4KdJfpu9jODGxWPV4KWrDseidvGyfbL8qx4flk97ER0yu2Ba9m/0/Wdk5dz1Art8ai97h/u82csI429Y5d72IjA84neTExFR4XEX6e5VfY/zaq3OzTJvD8clY4B7n3F3B+KTqvu/mHEudy8vqf7L9ysiLWVvsGew30vio4/gkRk5WV+X4SN3/1KEfDeVkq9wvy9cxfBXzTM6uQd1I5WQcCeS8rICTlxPD6cbmbL9jC44jb2OM7FBOFh+HB/tVeWxYJCfGP2H7xMjJ6qocH2XGtKBPU4b2jPac73cut3xS7XxStX60rHjJ9gtzDAz3WVvUaWqFfwE+jU6tsIjiVbtPQJ10AfrY9vOB16EOmoauxvsGdNW8H6OPOr8MeAe6wMgS9A/si/2+VwN/8TpC2a/0HDzvQs/JHtG+sIWNoeyjGP4Y91WRcp5L55XFjyTdSucxnBibKfDZfmy5kndoY4zsIjlHo3d5foLOm1IUQ6E9MbFYx3jpt+9jODGxWPV4KWrDt/j6K4HzvR1Vjw/LJ72Jj5hc8R70Neo/Ad8C/h19TRbgDuBrwHuBf0Zfyf9WCzlhnL3V27c1+urz+ZFyYmIqPC4K9Pcqv8b4tVfnZpk2h+OTtwDHoQO6hb6+Dr7v5hxLncvL6n+gvLx4CLA/urLy99GpFbqJjzqOT2LkUODXqsVH6v6nDv1oKGcn4BiGpgRazparmO8EHIxOS/M3dB7CbuRsh54rzu+3Bj3nsumHZreQE8PpxmbQscCh6NyTc1vYGCM7lLM78FR0iqVfof+Jqz42LJIT45+wfWLkUODXqsVHmTF9EDod1x9y7dOLc77fudzySbXzSdX60bLipcj3AE8AHgTe45ybSgfU5kIugIhMYvhqzqv8547+czG66MMLGT6B9uqcmMXoHepjGD4Z8zL/mXGuQxt4z1xdKHsDOmAdl+NcAUzpYGMo2wE7oAlyJHIupfPK4vNJt9J5DCfG5iKfLWHLlbxDG2NkF8lZg86b0i6GQntiYrGO8dJv38dwYmKx6vFS1Ia3oEk97/srqHZ8WD7pTXzE5IqV6IA344zxnE05znT0laB2csI4yybsnzBCOVfQOabC4yrS36v8GuPXXp2bZdocjk8eAh5B50HOOFdQfd93c46lzuVl9T9l5sVx6Kvyec4VjDw+6jg+iZFTh/hI3f/UoR8N5SxGb249JsdtuYr5EvRCdrtYiJGz2H/my8vQ+YZbyonhjMLmG9FXglvaGCk7lLOW4eOMOowNi+R09A9btk+UnJrER1kxvZ7h12F6dc73O5dbPql2PqlaP1pmvBTmGCJRqwu5BoPBYDAYDAaDwWAwGAwGg8HQRIxJbYDBYDAYDAaDwWAwGAwGg8FgMBjawy7kGgwGg8FgMBgMBoPBYDAYDAZDxVHrC7kislNqG/IQkcdHcEZls4hMFpHJnZnR8g7t8Pv2IvJMPz9xO97Oue+TRGT7XLmnNnuZPbU7tLkfqGO8eJml+77qcd6PeKljfFg+SZdP6op+xHmvUUebi2A5pp79j/9eao4ZxNjwnIGPj1T9T9nHISK7isihftu1xb4T28jdos1E5JXtOCLyDBF5jYgcGNSPzesUkcNEZD/Pf1pmRz9sDnki8mQReUukzZNFZJcCuysd4/77Y/ER42fPi/Z1Jz/7cuXjo9c2ZzJ7GR8Fx1B6Lq+j7y2ftEavc0yKeGnFawvnXC024EvAzv77YeiE28vRiYKPytVfjq7Q+gR0pbh16EIha9DFzK4DfoSu0H2z3y4C3gmM83K2B77oZb/R1+0GfAddoW4n4MvAreiKvwf4uvvQBWImo4s9zERXF/wZukrfHd6eVcAbWth8Gbpa3WJggf++HJ14eTU6KfPt6KTXPwemeDlP8OWrgaXoghX7+2NbCuyLrhi8Bl3w5NXo6sIr0ImoD0VX9LvX67wGeBs6mfNGv9+ZgAAvQxfK+LvfdzZwF7ooy1p0sZbFvvzQKG3+UWDzod6Oe7zu49DVeDegKww+BZ2IPbPhPuAkry+0e66XvdnzFwKL/O+3ed8sR1dX/BKwYxCTFwXxcj4wY4Dipd++zxbjuT/C7oeAT6AL91wE/AmdQL3fNneMF4J8wlBs/D/gB8AZDI+NydQjPiyflJNPtgJOA/4LuMDXTQA+hvZbWwOneP9/BZjoOXflZI1DVxa+GPgCep5c5Y/rduAIz3sS8EPgc8BE4HveNzei/edMYAYanwtbHQNxOfAMtozzk3xbT0IXBfiBb9ffALsyur7+RjQv9PrcLNPmjuOTXBufU2HfTwOup165vKz+p8y8uMjLeYDRnZt1HJ/E9D91iI+q9T9V7Efv9bLnoCuU/8Uf03XAoV7Owb78KHCOt2EOGrOz0Vhcisbe6d7un/m6VwPvR1cof8TrfBbwFnTxvsVe/8e8rlO8L+d5+xd5/270+q/3+9zvj6eXNr/G++fVfjsQXRn9bjQ/nOHt2ozmiZ9kxOtlAAAgAElEQVSiC0iFNt8BXIuew0t9W033+6xDz9c6jA3vRfup+9GYa+fnRX6/ScBzva8f9m09M/B1kZ/rEB9lxvRC3x4P0CIH5vLJrArncssnFcknHWKmKv1oWfHSKsfc6X3/rKjro6kv0MZuWQP775f7xvgfdFW4h4AP+qB5GfAG9ES4wQfb671T/hP9czvXB8Je3tn/AvwKuMQHxF+Bc9ET6UL0j80lwPu83pt9Iy9EA/kh73jnP+8Avu8Ddm9v2/3A4f73H6Adzw3oyfKmnM2vBS5AB9ZTgQ95PR8AzgO+4H3wWuCr/lhejXZC3wU+jCaOa/z2Cn/sd3ubr/H2r/B+3Ow//wb80gfrGOBV6AB9itfxcX+cWTI5GXi2P/4j/f5vRf/Avdq3y1uBzzP0h7Mbm0/2Ns7J2bzG8y9HT8hT0T8kr/Jtdw2aNN6LJsCNwNnoCXJAzu6bgKN93R3A19ET9K/A+d7mQ9HVDM9Gk9Sh6B/+N3k/5OPlw2jCGpR46bfvrwd+C3zP2/zqFnb/r7fnm+hA8I3eHxsS2BwTL2E+Werb+HRv18e9zfnYqEN8WD7pTXwcja4evxS9aHI+8Gt0ALMB+Jr35VcZ+rOywftvPUN/fpz/XO25K4GjvB33+ONZDFzp9/klOsB4LxqLt3jf/QKNmanAWf7Yv4deCH6fP45WMd4uBxbFeWb/nd7uz6EDpA+iF5VG09df7Y/h7B6fm2XaHI5PDkIvmv0Q+B0aH0ej8XJfhX0/HY2zL9Yol5fV/5SZF+8CPuXtP2cU52Ydxycx/U8d4iN1//Nh4CNof1HVfvRmNAdmMf4hv52NxvCHPPccfxwfQS9GvMzLfRR4ntd5lbfvXK9vjf++yPNvQVeGvwa9GXgrcDx6YeFRNJ4Xovl5HzR3Tgeeit4Amw/8GL0B+gXg1z22+Vz0AsO53id/wt/E9XavQW/EzEIvFKz0x7kaeAd6EW01egHtOuA53r9HoOf4633b/bomY8Nl6DjncwyPj9DP/wt80vt4tj+Og73OB7y//+jbN/N16Oc6xEeZMX2t9+Ov2+TAL6MPGDxIdXO55ZPq5JMvUxwzVepHy4qXwhyT8/01g3Yhdw4w1n+/DpiW+20B8G3vpMt9Ay4EZuY409GTaB7aedzm6zehieJy7/gsIC4HHvGcT6J3FXZCO4+FaFK6GO0Isqcc1uf0zQhsfsh/TssuTAPPRzuqpV7fcv/bzMxm/7nOf+bt3oj+2XuQoQSSBcYa4M1oUO/rbb4dPQmv9ME1rYXNs4HtfXmt15lxZwNP9IE7zQfu6sDmGXmb/feubfb7ZXZn5WnAnYG+2eiTJ9PROyv5+JiHPs32sLf5C2iSnRkc+1RgbmBzUXw49GTdxPB4udzbPxDxksD381vEy7kFdq/xnLu8/n1zx121eAnzyRLgH2h8bPB1WXzcltNR6fjA8kkv88kj6MDkTnSgckfu+znoH53HeXuWAt9AL4TsCtzcwveZbgEeDmLgFvTpgAfRmDkXWOZ/uzngZnH7OGBOcBxhjLfMgUVx3sL3c4Cx/hhG09fPZeR9fcdzs2Sbi8YnWSxkF13z8VJV38+nOL9UOZeX0v8kyIsjPjep4fiEiP6nDvFB+v5nM/rE0aOxvq/AubkOfWPlM97ez6Bx9xlglefM99uRDOX3w9E/9QsLbM7iczqwp/+8PFe/FXoR5nVof7wcfQJvSWDzzV5uZvOcXtpcEOfTM5t9eQ361s40b/NsYBs0Fn/rdWfjgezczPr8fD7J7K7j2DDzdejnz/i2e6HnHOnlz8n5+V0tbK5FfNDfmC7KgZu9jRupTy6vo+8HJZ9k8VIUM1XpR0uJl1Y5plW51Zb8Am3shj49cCnwIvRR7+wu3JkM3W2+Fn2K6XL0Ls9t6FXwo7xDL0H/nJzE0ADnFvSx69cD12cBh3Yqi3L6F/qguBv4nK/bC32S935gO3Qg9CF0UHxHYPN9DF39D20+Dn3SbC1wInrn4L3onYFXooOsb6OP5t8O7OFt/Dnwy1zAbu2/L/Kfd3j+vcC7fd1E9A7Dg2hHuQm9KPBNtNN8gw/ct/nj+o0/5h8BX/Uy/oa++vsVhp5guhq963aDP+5FOZsXjMbmnN0PeH8/4O3PbB6HJsOp6F3eL6OJ8V8K7P40Ggc3+7Z4H/p0yAJ/rJeiJ/eCXHwcid4l/kuubr+czXOAMcFxDES89Nn3P/d2L/Q234zG47dDu3M23wy82Nv9YEXjZVg+Qe82nuKPZVXO5r28HV+jJvGB5ZNexMd877vM5vzF/KzuXvQC43zgh77umd6GxWh8bUTvPr/G++bz3t4n+f3/3ev/V+CPXsZU9M7vF9GLxof5uhf5YzoUvWn1MfSi8a25mP4KQYx3yoEFcb4YHQhlMS4MxfkCRtfXZ76/vcfnZpk2h+OT+ehTkPnxSVf9T5+PI8vld1OfXF5W/1NmXryPoXNz3mjOTWo4PqFz/1OH+Ejd/0xFn/xfNBLf9/k4voE+rbQEfeJrFvrWxJ+Ab+Vy9w4Mz+VPQ3PS6pzNY/xxXI5epHgIfRPifnQKo6O9D5YB30Lj+DPoq7Ef8TIuRHPoX7z989G3Vr6PXnT4obf5zyXYfAQa5xfm7D7W2/xZNKdf4/mhzV9Ebwzfij4VdwV6YeF6X74DjefnUJ+x4e3ep69Hz83noDdxrw79HNg81/t5OXBizs8f8G1e5Oc6xEeZMf1z7/t2/XpX+YT+5nLLJ9XJJ1OBg4L4qFo/Wla8FOaYHPeW7Hu7LfkF2pFs6AnxC/Tq+io0qE9laG7bp/vGuAh93Psn6GB0k3fqU9DXN36PnkzzvENXeLn7eDlf8UFzYk73Z9FB6fzApiejf7CvQ0/iz+S2XdA7fxd4HbN8IJzWxuaz0cfUH/by/o4mxXehJ8gSL+c69MmKx3k5HyQ3h53/PM0H3WUFNv/UB9JqH9jZNsn//i20o/uD992xuf2fgD72/h10rrUPeu4SH7Q/QRNxZvPdPbL5LHQOkzmB3ZM850j0j8Tv0BPwOx3sPhM98degJ9Lu/vjPRk/8Ff63BWgSmOzlvBZ97eHEfLz47ycOaLz0w/fj0VcSpnubF6Ax+u7Q7sDmid62yyoaL8PyiY+Niegf3vmBzb8GTqB+8WH5pPv4eI9vtyw+fgIc579nUxl83/M2FhzHAvRPy8MMPflwLnpx5xT0TvUGhgaZX2DoT80x3tdz0Itxv0EHQZvQvvFONHa/7NviEX8c96ADzWExPoIc+Gv0os4ihsf5Lp6TvTY+mr5+EjoAu6+N70d6bpZt8xSGj0+WseX4JOt/3jcK358wguO4iPY5JjyOp6K5/KPoAL8uubys/qfMvLg/Gs/z/LH04tys4/ikZf9Tg/hI3f88H+1fT+zS92ehN/xaHcezI4/ju62Ow/Neh8bWH9A/3z8CXp6T80bvs119+ZXohZQn4qfD8PX7ohe39/Dtt8S32VEMzTf/ZPRiw9fRmP04sH9OxvbAf6B/5ieib7fcjubNs4HtPO816FNrvbJ5T/Q187zNR3kbdvCcv6EXM/7UwebXoE/hT0Mv0HwenbrnXegFpdvROL+e8saG+xGXA7PYKMqBu6NPcp6Lvg3yB+D/gJNDP/vveV8/Je/nnK8/18rPNYiP0mIazYEfRM/nLAdewfAcmOWTw3odL5ZPBjKfPB94YhAfI+1H69r/tMsxuwLvyZ8frTbxOzQS4lfcdc4t74GsbYB9nXO3jNqwPkFEBE0Qq1PbMhLU1e486hgvUE/f19Tm2sVHHf0M9bU7g7d/N+AZzrk/90DezuhTuJOccw+MVl4HXXWM877Y3MvxSQv5dfR97c7VmtpssdEn1NXuEINyHIbew2LDMBJYvBhGiibHTK0u5IrI/ugdhOudc2tz5e2cc7/PcU5EH3duxTkCvTv3DRE5EH2Eek90rg4YWlFwL1/fqm6M/2y3X7ecReidgrz+C5xzFwc++bRz7rPt6lJxRORYf5wHOuc+nKs7GTjTOXdXjjPZOfffRZwu9jvDOXd3jOyQ45PBSejT3+9BXyP8sPf/aegTKSegr6GOpT+xULl46Xa/ThzfHu8CPtBtG3bB6XW8vA+9E7kq1xbdtHNt48PyyYjzyUr0adc9/KFv8J/j/edi9G72wQy1YZmcm9HXgvLxcqFzbg45iMi/OufObVcXw+l2v04c3/+/DW2fh0YxZugnJxyfvAOd+ysrH4fe6d+TdPkkhrNFfvHHV4l8UlRXVv/T7X6d8hL6KvFJ6LzJ26FvKzR+fFLWGGZAxidh/3MiOl/yZIb6nzFoDG3y5SXoK7Y7o09AtqoL2z7j7BQhO+Tk5SxGnwT/gXNuY9A+5zjnTm1VTsURka2At6N+zhbYeTvaPrsGnJPRRQuv68Rxzv2jw36Pd86d1oXsT6JzQr4XzSfbom/kvBYdG4xBnxKDuPjopp1jY+oBz8nkFMZHP2NhpPuVFR8RnE92oSuLqQne1heheeMt6JRdy9A4eQX6ZGZZ8WL5pP755HX++2Xo9AtvRK/BbYe+ubOZkcXHQPY/reqKUJsLuSLyfnQgMgc4BA2CF/jyy9Dg2NtzdkUvohRxDvHfD0Zfu8wm3t6Evp56Hjr/WzZX4Oe9CWHd+9HXQkAfGT+7RA7oyfJW9FW4D+T8stA598TAV8PqUnBE5Avoq7rT0Db5EHqyPQ995S57xSubRPtd6GtrewDPzXHOityvG87t6KTdec4BwOPR123/iD4qvwL9A7IGvQiyHXqRYyb9j4VKxEu3+7Xj5GLmEHSumKL2CeOjV7HQq3g5Au2MHkaT/2jasLbxYflkRPlkEzon1a3oaz/Ho/0W6Aqnf0RXgj8afY3tp33mgMbLycDPnXNfCtuHHCqUT7IxwxPQPxrdjhn6yQnHJ69H5/raEY2rTWgc7o/Of/kt0ueTIk5hfsm3T6tyKk6J/c9ZBfvFyC7ihLKXoFOHjPf1f8XGJ6XknAEZnxT1P3uhf4anoRd5v8XQn9xx6GvTP/THthSdroeCupj9uuUciPY/k4B/Q/Mh6B/yq9B8uWNBOSXnbLQ9Xoy233jv7xvQ6Y2+i76ePAF9vXhZJOfKLveL4UxE42Qb9L/xeWh87YxOtfSOFu3Tz1gIOTv6chgfZbQzPZRdVnyUydkLvSn3VvRm/xPRvP5U9K2w31BevNDlfpZPqpVPDkTHKNujN4GfgP73Wet5HyBtPulnvNCiLttvpnNuLzrBRcy/UIUNDZhs7ogp6IWSj/rybHQhhSVo4EzvwNkKvVOwmqGFULZhaKW9ed6J+TnyhtUxtLDEsLqSOKtz2yb/6XJbWC6q6zcns3MNOn/en9G7qGN9++zofz/LH+PNLTjd7tc1x5eno3dpbvHlrf1xjPftM5ZcvPQxFvoZL/2OoU0MxUvp7dwrThAvWfsMi4+RtmFN4sPySe/yydYMj5dx/rd8G04Iyv3i3Oy3WcB6//0Rv20OykV1m3MyutlvNJzZ3uYpjG7M0E9Ofnwy25cno+fA9r59ho1XEueTIk67/FKFfNLP/qfsnPN19Bxu2vgkhlOH+KhC/7NFvATtOa8Xv42CswldOGcj+lSX8983+u/5urCcirPef27w/l2Ozm/5ON8e56BzMWblWE63+8VwHshxNqLnbXb+RsdHnzlZbNzJlvHR63auQ3yUyVmVKy8FZuTyfanxYvlkYPLJDP95MxpD+TFDOGZJkU/6GS+t6rJ8tiGzpd2WPT5cB4xxzq0FcPqq2t3AMSLyNdQJR6NX+T8LSBvOROC/vcwFwMMicrhzLvsDCLAOveO0Lqc/rFsnIocDh+frSuKsQp9yeDG6avj26F2x3YB78mXnnIR1iTjznHPbO+e2Q+/MvQI9sf8PGO+cW4U+sbidiPwK/bNaxOl2v2454svj0YnzNwI459YBDzvnNvh2eQa5eOljLPQtXhLE0LwsXvrQzmXFyzqfTx6ldT6JacNu97N8Ur98shl9SjOLl83oHeLdg7pDEnF2RePnNPQO+ivQu+evQZ9My8qvRmPq/hacbvfrlrMI+Gdg+SjHDP3khOOTDc65Tc65FcB6p/N/rUMXjBpJ/1NUV2aftAp9wvjWiuaTfvY/ZeacBejF/Z8BjzRpfBLDqUl8pO5/VgCvYiheVojI60TkZHTKn6zu67lyUV3Mft1y7gD+B5jmnNsHffp4X+fcON8+j9WF5YScO/3nfX48eD96EeNv6ENJp6IXZbJyFKfb/SI59+Q4DzvnnI+P16IXGspu5244d6BThJwexkev27kO8VEy55GsjN4oAsDHyZqS46Xb/SyfVCufZPHi0BhaISInoRdy8zGTKp/0LV5a1TnnnpS1NRGo04Xc+0TkkFz5XnQOjp2Bg51e5L0JHSAe3IYzB/1jejDwTPyj1CJyG/AkEbkU/cP1LWBbEbm0qA5d/fsq9PXTtSVzHkXnI/kGQ68YnIe+qvmzoExBXQrOAhE5KuM45zahqzU+gr7uBfon5Dx0FdIDijjd7jcKzg3oSXuAc+44YKmITBSR3YDb/H6noKscPjVBLPQzXrrdr1tOFjOPlSmvncuKl1PQfDIX2KfbNqxJfFg+6U0++Xe0vaaIyDmef6vfFvi6tZ6zOgFnJfra1HnoKqp3o6u5rgb+miuvcc79I6h7jNPtfqPg3IM+qXaF93O3Y4Z+csLxyQYRmSAiOzC8//kOI+h/EvRJE9AL0qd4m6uWT/rW/5Scc5airyLOReedg+aMT/o9hhmE8UlR/3My+trrP4nIPHQqhvPQVbl38XX7oTfOniwi84rqYvYbBWc3dBXwk73NZ6GvuQJ8JagLy6k4N4nIcfky2h7nom9kgK6CfkOuHMPpdr9Yztc8Z4KvOxldBf7APrRzN5zt0ScAX82W8dHrdu6l7LLio0zOn9D8N8U59zZ/DBNFZF90DAklxYvlk4HJJzeJyMHoeP1taBu8Fr0w/JQK5JN+xkurugxFdVugTnPk7gU86pxbGpZF5LlOJ2zeCx0k7psvB5zHOefWZ+Wc/APRP0+3A4v9PruRW5yhqM5/9oWTHXsdILrqMU6fdB5Why7EsDjPEZE983UZp9v9uuXkbA3L2wLbOueW5cpT0FfUoM+xEMOpU7zAljFTZjv3IV72Qe+yrWSUbWjx0Yh8MhGdVyob7CxB71Bnk/gvRgdFhzHUhv3kLAZu9BcHaoEejhn6yRk2PsmVdwZ2d87N8seyM/pU7hp/uMnzSREntq2qgLL6n7yskewXKztn/545fTY+6TEGbHxSNJ7dFj83oHNuua/fKV+OrSuTY0gDERF0ip++tHM3HEM6iIg4N3RBqcx46XY/i5fqIIwXX1epfFKbeHER8y9UdQNObVceBeeMAk7HOuP0h5Naf5M5qfU3mZNa/6ByUutvwSmrb6sUJ7X+AeIMStw3gpNaf5M5qfXXhHNOu3JsnXH6w0mtv8mc1PqbzEmtf1A5qfU3mdOqrt0WTazihs5X0bJcJie1/iZzUutvMie1/iZzUusfVE5q/U3mpNbfZE5q/U3mpNbfZE5q/YPKSa2/yZzU+pvMSa2/yZzU+geVk1p/kzmt6tptdZojtwjSoVwmJ7X+JnNS628yJ7X+JnNS6x9UTmr9Teak1t9kTmr9Teak1t9kTmr9deAs61COrTNOfzip9TeZk1p/kzmp9Q8qJ7X+JnNa1bVEbebILYKI7OWcu6dVeRScMc65zQGnY51x+sNJrb8FZ4v5gfLlQeGk1l9TziDHfe05qfW34JTVt1WKk1r/AHEGJe4bwUmtv8mc1PrrwDEYDAaDwVB91P1C7t+ccy9qVY7hiC4a8susTkTeDLwAXezle84516LuncCBwC3A94A3GacUThV9fzLwF+BsdMXxX6Irjc8CPg98BDgKmAa8D1hfU85Xa2Bj1ThXAo9DY+jDzrl7RORrwG+cX1wxLMfWGWf0nNT6W3AmA+9FFx37AfBfwEnAncAbgHejeWlbNAa/WVPOJ2pgYx04zwbWogudPR5doGid/9zef85D89SzgSe0qYvZzzij5/RT/3w0Np4H7IFiDCD+d9Bcsxwdv+w+4JzU+qvOWQxcAPzAObeRHETkHOfcqa3KsXXG6Q+nDNkishXwduA0dJx7nS/vBewacE4GPjWoHKcLkFbaxtScXPx8yjn3uaKYalXullOm7CZz+qE/F1N7AU9zzp1QtZjuZz4J/htucQ4VoTYXckXk5qBqP/SCyXpfvh34p1x5/gg4Y4C56J+imcBTgRuBe9BB0PML6j6GdmrHo4PltcYphVNF3x8EXOM5e6AX8w4DXgx8Cfgy8C7gC+iF3xU15VxXAxurxvlnYAG6Ovda4FXApcDdwC7AL4B3eE5W/r8CTlFdzH7GqZ9fP49enNkeOACYAvwDzUVTPOcwYDOab+6uKWdWDWysA2dH4GD0Qsyd6EBxE5pz7gX+jo5zngX81Mu6s6AuZj/jjJ7Tb98fBewDfB04D/gWQxftxqE3A37o918KnDLAHGpgY2rOgeifyEnAv6H5BfRi71VortkxKFNQF7OfcUbP6bfvzwa2Qce9C4DxaF9+Azr2/S46dpmA/l9bNsCcK2tgY2pOduHpcuCF1Dfum8JJ7dcsv0wDzkD7qKrFdD/zyZXOuQ8BiMg059yhdIIbwYS6KTfgQuAnwP7A3sBlwEPAi4DnoH9yHgF+5+tiObOBRf77LejTMNPRQc4sNLiK6mZ6u8Z5mcYph5NafxFnbo4zFZibi9OH/edc/zmjrpzU+mvKme7LT0H/WM9Gn5j6DPBS4D/ROLoN+AZwVgtOt/sZp75+fQr6dNQMH0MCbMjiKxdnteSk1j9AnFk5zljgIV+ehI5h/oH2TVl5bIu6mP2MM3pOv/XPy8r+t3m5fmteu89B46TWXxPOJuAOYCN608H57xv993xdVi6qi9nPOKPn9Fv/ev+5Ac05y4Hfog9JTQfOAR7MlQeZk1p/HTgOzSdZ3NQ17pvCSa1/Axov2fcqxnQ/88k5+f2y/rrdVpvFzpxzrwR+gx7k051zL0FfHzsD2Nk5dxd64eQ8XxfFAbZC/yTtDGxyzj2E3h04CL2ivlVRHfA4EXkG8DRvn3FK4KTW34JzBXpRZrP/fpeIfFZEjgWWi8irgCtE5Mdo/NWSk1p/TTnbo9gT/aP0T2gntTXwbefcfwFzgNehN5mOL+J0u59xaunXhehd6kuBiWjOmYTerd5KRKYAY0TkSeid21pyUusfIM5mEdkX7aP2ABCRyc65lQz1W5vRcY14TlFdzH7GGT2n3/pXoE/PCYoVIvI6ETkZWJmr+3pQHkROav114NwB/A+6UvY+6JuL+zrnxgH35OuyclFdzH7GGT0ngf47/ed9zrlHgfvRm4h/AyY6fV16Q648sJzU+mvCWYFexFpY87hvBCe1fuAO59yTcjmmijHdt3zi6x7bjxi4iKu9VdrQJyK/hs7pdE9Y7oJzP/pE1OV+291/Xg2sblP3j6DOOOVwUusv4oxDX6tfj16E2ey/P4Q+RbUZvbA3y5frzEmtv24c5+t+BjzR55rpQQ7b4i5bTJ1xRs9Jrb8F5w3AfX57jY+n9eiTUt9B5+Oe5cura8xJrX+QOI8Ct6I550voNAxXoLnpn4HXo28a3es5RXUx+xln9Jx+658C/B4d185DL9St89sdvu4Bv//yAeek1l8HzhrgYmAf3x+9B30QBuB9+bqsXFQXs59xRs9JoP8nwHEF5bcDG3N1Xw/KA8dJrb8mnPegD8rly3WM+0ZwUuvHx1BBzqlSTPctn+Tq3x7WtdqSX5jtdvOB8M5W5W45ufqtgAkjrTNOfzip9WdlYAdgp1z9sHJRXR05qfXXiPPEfJ2vn9iuHFtnnMH0q6/fChjrv48FjgD2ypUPQ5/yrjsntf5B4RzgP3f09ZPz5dg64/SH02/9Oe5ODO+3hpWbxEmtvw4c22yzzTbbbLOtPlttFjsDEJEd0Cvce/qqFejrY5N8eTFwLbqib685Zco2jvm+qpzU+pvMSa1/UDmp9TeZk1p/kzmp9TeZ02/9lzjnVhFARF7inLusVblJnNT6B5WTWn+TOan1N5mTWn+VOCKyP/BhdE0I0NfHQaf/Ae2jZqFTRu05Ak63+xnH/FpVTqu6C51zc4hAbS7kishb0QVhLkUP8hDgJf7ny9A5JV6Ervx2Ffo6fK84lCjbOOb7qnLM9+b7QeOYX833TeSY75vj+7183ZnOufPIQUQWOuee2KrcJE5q/YPKSa2/yZzU+pvMSa2/KhwR+Tg6XdgU4L3A8cALPO0q4I/Am4Cj0SmBfhrJocv9jNOeQw1sHFQOLer2Ak4Gfu6c+xKd4CrwWHDMBsxl+Otlc9EFYiYxtPrqXGDvoDxqTpmyjWO+ryontf4mc1LrH1ROav1N5qTW32ROav1N5iTQfyFwEbDWf1+a2x4N6sLyoHFS6x9UTmr9Teak1t9kTmr9deCsRS9aPeT7o3noujLjgfm5uglBuS2n2/2MY36tKqdVna8fH9a12sZQHwi6kFBY3uy/Z3VhuRecMmUbx3xfVU5q/U3mpNY/qJzU+pvMSa2/yZzU+pvM6bf+5wM/BFYBX0UX9v068EV0Eb2s7qygPIic1PoHlZNaf5M5qfU3mZNafx04y4Hz0Qu6oH3THuhC4ZtzdYcE5U6cbvczjvm1qpxWdQT7tUWdLuR+HpgmIt8RkU8AM9HVWBcCM33dPehKrIt6ySlTtnHM91XlpNbfZE5q/YPKSa2/yZzU+pvMSa2/yZwE+tegf7Y/5Zy7Evg7cINz7mzgllzd9UF54Dip9Q8qJ7X+JnNS628yJ7X+mnBOQ6/XjBORc9D+6la/LfB1a9FXyVfHcrrdzzjm16pyWtWJyMXAX4EPEIHazJELICKTgGMZmhB4lf/c0X8uBq4DjiyBU6Zs45jvq8pJrb/JnNT6B5WTWn+TOan1N5mTWt3G4+wAAA5bSURBVH+TOf3Wf4lzbiUGg8FgMPQRIjIGOIKhPmoJ+sbI7r68GLgJOGyEnG73M475taqcVnU3Ouc2EQMXMf9CVTfg+HblMjmp9TeZk1p/kzmp9TeZk1r/oHJS628yJ7X+JnNS628yJ7X+JnNS6x9UTmr9Teak1t9kTmr9NeGc2q7cLadM2U3mpNbfZE6runZbNLGKGzCtXblMTmr9Teak1t9kTmr9Teak1j+onNT6m8xJrb/JnNT6m8xJrb/JnNT6B5WTWn+TOan1N5mTWn+TOan1Dyontf4mc1rVtdvGUG9Ih3KZnNT6m8xJrb/JnNT6m8xJrX9QOan1N5mTWn+TOan1N5mTWn+TOan1Dyontf4mc1LrbzIntf4mc1LrH1ROav1N5rSqa42RXPWt2gYc0a5cJie1/iZzUutvMie1/iZzUusfVE5q/U3mpNbfZE5q/U3mpNbfZE5q/YPKSa2/yZzU+pvMSa2/Jpy92pW75ZQpu8mc1PqbzGlV126r22Jn+wMnMDQh8Ab/Od5/LgZmAQeXwClTtnHM91XlpNbfZE5q/YPKSa2/yZzU+pvMSa2/yZzU+pvMSa1/UDmp9TeZk1p/kzmp9deBc6Fzbg4BRORfnXPntip3yylTdpM5qfU3mdOqrhAjueqbcgM+DswATgfeDPwcXRFuif/+ZuAi4BH/2UtOmbKNY76vKie1/iZzUusfVE5q/U3mpNbfZE5q/U3mpNbfZE5q/YPKSa2/yZzU+pvMSa2/DpzT8ddqCq7jLGxX7pZTpuwmc1LrbzKnVV3R1pFQlQ2YB4wLy+hdoPm5uglBedScMmUbx3xfVU5q/U3mpNY/qJzU+pvMSa2/yZzU+pvMSa2/yZzU+geVk1p/kzmp9TeZk1p/TTg3o0/trvffH8ltm4O6sNyO0+1+xjG/VpXTqu6xcyjLO+22Oi12thnYo6C8u/+e1R0SlHvBKVO2ccz3VeWk1t9kTmr9g8pJrb/JnNT6m8xJrb/JnNT6m8xJrX9QOan1N5mTWn+TOan114GzK/Ax4B7gFcBa4NXAi4H7c3WvCcqdON3uZxzza1U5reqybTkRqM0cuSJyHPAtYD6wCNgLOMr/fCWaNA4FngbMBKb3kEOJso1jvq8qx3xvvh80jvnVfN9EjvnefN9Ejvne/DpoHPO9+b7KnJf78tudcxeLyA+Ac51zfxeRnznn3pjVAe/Ol9txALrZzzjm16pyinxPDtl+dEBtLuQCiMgY4AiGJtleAgh6Nwh0ku2bgMNK4JQp2zjm+6pyUutvMie1/kHlpNbfZE5q/U3mpNbfZE5q/U3mpNY/qJzU+pvMSa2/yZzU+uvAudE5twmDwVA6anUh12AwGAwGg8FgMBgMBoPBYDAYGomYiXSrsKGP81+HTqtwDvDcXHkZMCnHWd9jTpmyjWO+ryontf4mc1LrH1ROav1N5qTW32ROav1N5qTW32ROav2Dykmtv8mc1PqbzEmtv8mc1PoHlZNaf5M5hXW56543RF0fTX2BdgQXcv8OHAfsCHwEeAj4V19eDMxGH/k/Dl3xrZecMmUbx3xfVU5q/U3mpNY/qJzU+pvMSa2/yZzU+pvMSa2/yZzU+geVk1p/kzmp9TeZk1p/kzmp9Q8qJ7X+JnNa1e3rr3tOH7QLuTOD8u3owmdHAtOAF6JXtI8EpvWYU6Zs45jvq8pJrb/JnNT6B5WTWn+TOan1N5mTWn+TOan1N5mTWv+gclLrbzIntf4mc1LrbzIntf5B5aTW32ROq7r5+bpOW/ILtLEbukriDkH5Of6Al/u6ucCCXLknnDJlG8d8X1VOav1N5qTWP6ic1PqbzEmtv8mc1PqbzEmtv8mc1PoHlZNaf5M5qfU3mZNaf5M5qfUPKie1/iZz2tQ9Lb9fp20s9cGXgQPQuSSy8mbgGOA/fd2Z6OPMx/eYU6Zs45jvq8pJrb/JnNT6B5WTWn+TOan1N5mTWn+TOan1N5mTWv+gclLrbzIntf4mc1LrbzIntf5B5aTW32ROYZ1z7mYRye/XFuKv/hoMBoPBYDAYDAaDwWAwGAwGg6GiqM0TuSKyA/AfwInA4331ZkCAMf77A8AKYCdg5x5yXImyjWO+ryrHfG++HzSO+dV830SO+d5830SO+d78Omgc8735vokc8735ddA4Rb53wDLgAuBLzrlVdEBtLuQCvwT+BhztnFsqIpcAN/jfnuWce6mIXA48Aqx3zj2lVxyAsmQbx3xfVY753nw/aBzzq/m+iRzzvfm+iRzzvfl10Djme/N9Eznme/ProHGKfO/rdgP+Bb3u+VI6wUVMpFuFDZjbqpx9b/U5Wk6Zso1jvq8qJ7X+JnNS6x9UTmr9Teak1t9kTmr9Teak1t9kTmr9g8pJrb/JnNT6m8xJrb/JnNT6B5WTWn+TOa3qwt86bXV6IvduEfkY8GPn3H2+/Fn0seVFnnOviPwZuC+3Ty84Zco2jvm+qpzU+pvMSa1/UDmp9TeZk1p/kzmp9TeZk1p/kzmp9Q8qJ7X+JnNS628yJ7X+JnNS6x9UTmr9TeYU1onIrsApuf3aojaLnYnIJOB04AR0jlwBNvnPMei8EvcDK4HJ6FwTveJQomzjmO+ryjHfm+8HjWN+Nd83kWO+N983kWO+N78OGsd8b75vIsd8b34dNE6R70Ev6l4IfNk5t4IOqM2FXIPBYDAYDAaDwWAwGAwGg8FgaCxi5l+oygbsDxwDbBuUTwg4p/eaU6Zs45jvq8pJrb/JnNT6B5WTWn+TOan1N5mTWn+TOan1N5mTWv+gclLrbzIntf4mc1LrbzIntf5B5aTW32ROqzpff1y+3GrrSKjKBrwfmAv8HrgL+F6uvB44IcdZ1WNOmbKNY76vKie1/iZzUusfVE5q/U3mpNbfZE5q/U3mpNbfZE5q/YPKSa2/yZzU+pvMSa2/yZzU+geVk1p/kzmFdbnrntMG7ULuLGCi/z4FeBj4qC/PBm4ClgATgek95pQp2zjm+6pyUutvMie1/kHlpNbfZE5q/U3mpNbfZE5q/U3mpNY/qJzU+pvMSa2/yZzU+pvMSa1/UDmp9TeZ06ruA36/6c51vj46lvpgjHNuLYBz7i4RuRs4RkR2BzYCRwP3Ap9F5/7tJadM2cYx31eVk1p/kzmp9Q8qJ7X+JnNS628yJ7X+JnNS628yJ7X+QeWk1t9kTmr9Teak1t9kTmr9g8pJrb/JnFZ1vxaRvQEhAmNiSBXBfSJySK58L/BJdJW3g51e5L0J2A84uJecMmUbx3xfVU5q/U3mpNY/qJzU+pvMSa2/yZzU+pvMSa2/yZzU+geVk1p/kzmp9TeZk1p/kzmp9Q8qJ7X+JnPa1B2f7UcMXMRju1XYgL2A3YrKwHPzdWF5tJwyZRvHfF9VTmr9Teak1j+onNT6m8xJrb/JnNT6m8xJrb/JnNT6B5WTWn+TOan1N5mTWn+TOan1Dyontf4mc1rV5X7boq5oE082GAwGg8FgMBgMBoPBYDAYDAZDRVGnqRUMBoPBYDAYDAaDwWAwGAwGg6GRsAu5BoPBYDAYDAaDwWAwGAwGg8FQcdiFXIPBYDAYDAaDwWAwGAwGg8FgqDjsQq7BYDAYDAaDofIQkTNE5CNtfj9RRA6MkDOMJyKfFZEX98rOAn2bRGRGbjvd1x8vItNFZKaI3Coip4nIJ3O8/H7vL8s+g8FgMBgMBkN9MDa1AQaDwWAwGAwGQw9wIvBH4NaR8Jxzny7Zrkecc4fkK0RkHHAOcIRz7h4ReRwwxTk3F/i856wN9zMYDAaDwWAwNBv2RK7BYDAYDAaDoZLwT6jOE5G/A0/1de8QkRv9k6y/EZEJIvIc4JXAf/snWPf128UiMlVErhaR/VvwfiQir/Wy7xKRL/rfbhKRQ0XkEhFZICLvzNn1UW/DzSJyZheHth36QMVyAOfcen8R12AwGAwGg8FgaAm7kGswGAwGg8FgqBxE5JnAycAhwMuBw/1Pv3XOHe6cezowB/g359w1wIXAR51zhzjnFqBPvL7POfdM4CPAt1vwQiz0T8JeDfwIeC1wJHCmt+ulwH7AEd62Z4rIC9ocyjbB1Aqvd86t8HbcLSL/JyJvEhEblxsMBoPBYDAY2sKmVjAYDAaDwWAwVBHPB37nnHsYQEQu9PUHicjngB2BicAl4Y4iMhF4DvArEcmqHxepN9MzC5jonFsDrBGR9SKyI/BSv033vInohd2rWsjbYmoFAOfc20XkYODF6IXmlwCnRNpoMBgMBoPBYGgg7EKuwWAwGAwGg6FO+BFwonNupoicAhxdwBkDrOpyjtn1/nNz7ntWHgsI8EXn3P92IXsYnHOzgFkicj5wJ3Yh12AwGAwGg8HQBvYKl8FgMBgMBoOhirgKOFFEthGR7YBX+PrtgHv9gmFvyvHX+N9wzq0G7hSRkwBE8fSQ1yUuAd7mn/pFRPYUkcePRICITBSRo3NVhwB3j8Img8FgMBgMBkMDYBdyDQaDwWAwGAyVg3NuGvALYCZwEXCj/+k/geuBfwC35Xb5OfBREZkuIvuiF3n/TURmArOBE1rwRmrXpcDPgGtFZBbwa9pfGA7nyP0S+lTvx0RkrojMQOffPWWkthgMBoPBYDAYmgVxzqW2wWAwGAwGg8FgMBgMBoPBYDAYDG1gT+QaDAaDwWAwGAwGg8FgMBgMBkPFYYudGQwGg8FgMBgMo4CI7AT8teCnY5xzy/ttj8FgMBgMBoNhMGFTKxgMBoPBYDAYDAaDwWAwGAwGQ8VhUysYDAaDwWAwGAwGg8FgMBgMBkPFYRdyDQaDwWAwGAwGg8FgMBgMBoOh4rALuQaDwWAwGAwGg8FgMBgMBoPBUHHYhVyDwWAwGAwGg8FgMBgMBoPBYKg47EKuwWAwGAwGg8FgMBgMBoPBYDBUHP8fnNFayqsslsYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1728x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "newfull.set_index('datetime_EST', drop=False, inplace=True)\n",
    "newfull = newfull.set_index('datetime_EST').resample('D').sum().reset_index()\n",
    "newfull.plot(x ='datetime_EST', y='vehicle_count', kind='bar',figsize=(24,6))\n",
    "# this retains artificial bump on the benner pike"
   ]
  },
  {
   "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": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}