ConvertCSV-Cams45.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "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": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "camdir = \"cams45_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": 4,
   "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": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-02-03 04:00:38</td>\n",
       "      <td>/home/ubuntu/cams45/CAM02020CCTV24/20210203/20...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-02-03 04:01:18</td>\n",
       "      <td>/home/ubuntu/cams45/CAM02020CCTV24/20210203/20...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-02-03 04:01:58</td>\n",
       "      <td>/home/ubuntu/cams45/CAM02020CCTV24/20210203/20...</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-02-03 04:02:38</td>\n",
       "      <td>/home/ubuntu/cams45/CAM02020CCTV24/20210203/20...</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-02-03 04:03:18</td>\n",
       "      <td>/home/ubuntu/cams45/CAM02020CCTV24/20210203/20...</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>12059</th>\n",
       "      <td>2021-02-02 13:56:07</td>\n",
       "      <td>/home/ubuntu/cams45/CAM02046CCTV52/20210202/20...</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>12060</th>\n",
       "      <td>2021-02-02 13:56:48</td>\n",
       "      <td>/home/ubuntu/cams45/CAM02046CCTV52/20210202/20...</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>12061</th>\n",
       "      <td>2021-02-02 13:57:29</td>\n",
       "      <td>/home/ubuntu/cams45/CAM02046CCTV52/20210202/20...</td>\n",
       "      <td>0</td>\n",
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       "      <th>12062</th>\n",
       "      <td>2021-02-02 13:58:51</td>\n",
       "      <td>/home/ubuntu/cams45/CAM02046CCTV52/20210202/20...</td>\n",
       "      <td>0</td>\n",
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       "      <th>12063</th>\n",
       "      <td>2021-02-02 13:59:32</td>\n",
       "      <td>/home/ubuntu/cams45/CAM02046CCTV52/20210202/20...</td>\n",
       "      <td>0</td>\n",
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       "</table>\n",
       "<p>255581 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     time                                               file  \\\n",
       "0     2021-02-03 04:00:38  /home/ubuntu/cams45/CAM02020CCTV24/20210203/20...   \n",
       "1     2021-02-03 04:01:18  /home/ubuntu/cams45/CAM02020CCTV24/20210203/20...   \n",
       "2     2021-02-03 04:01:58  /home/ubuntu/cams45/CAM02020CCTV24/20210203/20...   \n",
       "3     2021-02-03 04:02:38  /home/ubuntu/cams45/CAM02020CCTV24/20210203/20...   \n",
       "4     2021-02-03 04:03:18  /home/ubuntu/cams45/CAM02020CCTV24/20210203/20...   \n",
       "...                   ...                                                ...   \n",
       "12059 2021-02-02 13:56:07  /home/ubuntu/cams45/CAM02046CCTV52/20210202/20...   \n",
       "12060 2021-02-02 13:56:48  /home/ubuntu/cams45/CAM02046CCTV52/20210202/20...   \n",
       "12061 2021-02-02 13:57:29  /home/ubuntu/cams45/CAM02046CCTV52/20210202/20...   \n",
       "12062 2021-02-02 13:58:51  /home/ubuntu/cams45/CAM02046CCTV52/20210202/20...   \n",
       "12063 2021-02-02 13:59:32  /home/ubuntu/cams45/CAM02046CCTV52/20210202/20...   \n",
       "\n",
       "       vehicle  \n",
       "0            0  \n",
       "1            0  \n",
       "2            0  \n",
       "3            0  \n",
       "4            0  \n",
       "...        ...  \n",
       "12059        0  \n",
       "12060        0  \n",
       "12061        0  \n",
       "12062        0  \n",
       "12063        0  \n",
       "\n",
       "[255581 rows x 3 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</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>81486</th>\n",
       "      <td>2021-02-02 02:21:09</td>\n",
       "      <td>/home/ubuntu/cams45/collegeTwp/20210202/202102...</td>\n",
       "      <td>0</td>\n",
       "      <td>2021-02-01 22:21:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68457</th>\n",
       "      <td>2021-02-02 02:21:12</td>\n",
       "      <td>/home/ubuntu/cams45/CAM02028CCTV32/20210202/20...</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-02-01 22:21:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52335</th>\n",
       "      <td>2021-02-02 02:21:14</td>\n",
       "      <td>/home/ubuntu/cams45/CAM02037CCTV43/20210202/20...</td>\n",
       "      <td>2</td>\n",
       "      <td>2021-02-01 22:21:14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216095</th>\n",
       "      <td>2021-02-02 02:21:16</td>\n",
       "      <td>/home/ubuntu/cams45/CAM02038CCTV44/20210202/20...</td>\n",
       "      <td>0</td>\n",
       "      <td>2021-02-01 22:21:16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129740</th>\n",
       "      <td>2021-02-02 02:21:18</td>\n",
       "      <td>/home/ubuntu/cams45/CAM02039CCTV45/20210202/20...</td>\n",
       "      <td>0</td>\n",
       "      <td>2021-02-01 22:21:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230245</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>117260</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>188333</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>204478</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>86406</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>255581 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              datetime_UTC                                         image_file  \\\n",
       "81486  2021-02-02 02:21:09  /home/ubuntu/cams45/collegeTwp/20210202/202102...   \n",
       "68457  2021-02-02 02:21:12  /home/ubuntu/cams45/CAM02028CCTV32/20210202/20...   \n",
       "52335  2021-02-02 02:21:14  /home/ubuntu/cams45/CAM02037CCTV43/20210202/20...   \n",
       "216095 2021-02-02 02:21:16  /home/ubuntu/cams45/CAM02038CCTV44/20210202/20...   \n",
       "129740 2021-02-02 02:21:18  /home/ubuntu/cams45/CAM02039CCTV45/20210202/20...   \n",
       "...                    ...                                                ...   \n",
       "230245 2021-02-08 17:41:55  /home/ubuntu/cams45/CAM02010CCTV11/20210208/20...   \n",
       "117260 2021-02-08 17:41:57  /home/ubuntu/cams45/CAM02005CCTV9/20210208/202...   \n",
       "188333 2021-02-08 17:42:00  /home/ubuntu/cams45/CAM02006CCTV10/20210208/20...   \n",
       "204478 2021-02-08 17:42:02  /home/ubuntu/cams45/CAM02033CCTV38/20210208/20...   \n",
       "86406  2021-02-08 17:42:04  /home/ubuntu/cams45/collegeTwp/20210208/202102...   \n",
       "\n",
       "        vehicle_count        datetime_EST  \n",
       "81486               0 2021-02-01 22:21:09  \n",
       "68457               1 2021-02-01 22:21:12  \n",
       "52335               2 2021-02-01 22:21:14  \n",
       "216095              0 2021-02-01 22:21:16  \n",
       "129740              0 2021-02-01 22:21:18  \n",
       "...               ...                 ...  \n",
       "230245              0 2021-02-08 13:41:55  \n",
       "117260              0 2021-02-08 13:41:57  \n",
       "188333              1 2021-02-08 13:42:00  \n",
       "204478              0 2021-02-08 13:42:02  \n",
       "86406               3 2021-02-08 13:42:04  \n",
       "\n",
       "[255581 rows x 4 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfcam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfcam.to_csv(f\"dfull_vehicles_{camdir}_020121-020821-partial.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#full = pd.read_csv(\"full_vehicles_427-1128-partial-all-cameras.csv\")\n",
    "full = pd.read_csv(\"full_vehicles_427-020221-partial-all-cameras.csv\")\n",
    "#full = pd.read_csv(\"all_traffic_cameras_426-601.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</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",
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       "      <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",
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       "    <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",
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       "      <th>10288589</th>\n",
       "      <td>2021-02-02 02:20:54</td>\n",
       "      <td>/home/ubuntu/cams44/CAM02028CCTV32/20210202/20...</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-02-01 22:20:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10288590</th>\n",
       "      <td>2021-02-02 02:20:56</td>\n",
       "      <td>/home/ubuntu/cams44/CAM02037CCTV43/20210202/20...</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-02-01 22:20:56</td>\n",
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       "    <tr>\n",
       "      <th>10288591</th>\n",
       "      <td>2021-02-02 02:20:58</td>\n",
       "      <td>/home/ubuntu/cams44/CAM02038CCTV44/20210202/20...</td>\n",
       "      <td>0</td>\n",
       "      <td>2021-02-01 22:20:58</td>\n",
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       "    <tr>\n",
       "      <th>10288592</th>\n",
       "      <td>2021-02-02 02:20:58</td>\n",
       "      <td>/home/ubuntu/cams44/CAM02038CCTV44/20210202/20...</td>\n",
       "      <td>0</td>\n",
       "      <td>2021-02-01 22:20:58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10288593</th>\n",
       "      <td>2021-02-02 02:21:00</td>\n",
       "      <td>/home/ubuntu/cams44/CAM02039CCTV45/20210202/20...</td>\n",
       "      <td>0</td>\n",
       "      <td>2021-02-01 22:21:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10288594 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",
       "10288589  2021-02-02 02:20:54   \n",
       "10288590  2021-02-02 02:20:56   \n",
       "10288591  2021-02-02 02:20:58   \n",
       "10288592  2021-02-02 02:20:58   \n",
       "10288593  2021-02-02 02:21:00   \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",
       "10288589  /home/ubuntu/cams44/CAM02028CCTV32/20210202/20...              1   \n",
       "10288590  /home/ubuntu/cams44/CAM02037CCTV43/20210202/20...              1   \n",
       "10288591  /home/ubuntu/cams44/CAM02038CCTV44/20210202/20...              0   \n",
       "10288592  /home/ubuntu/cams44/CAM02038CCTV44/20210202/20...              0   \n",
       "10288593  /home/ubuntu/cams44/CAM02039CCTV45/20210202/20...              0   \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",
       "10288589  2021-02-01 22:20:54  \n",
       "10288590  2021-02-01 22:20:56  \n",
       "10288591  2021-02-01 22:20:58  \n",
       "10288592  2021-02-01 22:20:58  \n",
       "10288593  2021-02-01 22:21:00  \n",
       "\n",
       "[10288594 rows x 4 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "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": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "newfull.to_csv(\"full_vehicles_427-020821-partial-all-cameras.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fad5b077a58>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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11rb8V0spB5dStiU5LsklSS5NclwpZVsp5aB2Heevy9YBbGKedgzQcDwEANh3XGvtP0PuyJ3md5O8r5Tyr5NckeTP2vI/S/KeUsqOJHekGZhNrfWaUsp5Sb6Y5J4kr6213pskpZTXJbkwyQFJ3lVrvWYv4gIAAAAA2FDWNJBba/1kkk+2P385yck9y+xOcsaU+m9K8qae8o8m+ehaYmHj8RFSANi8XAcAAMDKVp1aAQAAEh+boyEPAADmY2+mVgBggXiwDgAAACwud+QCAAAAAIycO3IBYINyFzYAAMDGYSCXTWkMgxse6gIAAADAUKZWAGD0PFgHAACAzc5ALsDIGcQEAAAATK0AAAtoDFPEMH+m6QEAgM3DHbnMhTsMAQAAGCt/swJj5I5c2KTczQcAMF/uqgcA1sJALgBr5g9PAIBxcaMGwMZnagVg0/DxKABgrVw/zJ8+AICGgVwAlvHHEgAAAIyPgVwAAADgfv6xDzBOBnIBAAAAgIW0mf75ZCAXAGDONtPFJwAAMBsDuQCwyRlEhI3P7zkAwOIzkAsAAAAAMHIGcgHmyB1S86cPAAAAWARb5h0AsPFMDortPOf0OUYCAAAAsDG4IxcA2K/cBQ0AALB2BnIBAAAAAEbO1AoAK9jf00SYlmJ9Le1P+xJg43Bsh4bfBYDNxx25AAAAMCemHAKYzjFyOQO5AACwwPyBAwCwORjIBUbNH6cAi8+xHAAA9p45cgEAABaIuVEBWHSeDzMbA7kAAABMZeAY9i0DWsBQBnIZrUU+mS3qxe6ixj1Gi5y/AMyH8zAbnRxno5HTwP5mjlwAAGBm6zkHsvmUAQCmc0cu685/JTcW/QkAABuDT40BLDZ35AIAAAAAjJw7ctnn/NcXmMbxAYB5cQ4CABaNO3KhZU422Pf8ngEAAMBsDOTCJmEADQAAAGBxGcgFAAAAABg5c+QCAAD73dInhTbL/LTm5AUA9paBXAAAAGBT8E8VYJGZWgGAfcK8zAAAAJuPvwX3HXfkAgAAAKzAnbyM1WabqmizM5ALbFpOeADz4Y9hAABYO1MrAHPj4xYAAAAAw7gjFwBgA/FpAzaS/X33tt8fAGDMDOQCTPAHHMBicdxeP+s5aKpfNt8+2GzbCwDzYCAXAABgEzDYCrDYPGcAA7kA+4k/ngCAfcEf9gCwORjIZSqDTuOkX2Bj8cc3ACwe1+QAzMOD5h0AABvD1u0XLBuUBAAAANaPgVzYgAyoAQAAAGwsqw7kllIOKaVcUkq5qpRyTSnlD9vybaWUz5ZSdpRS3l9KOagtP7h9vaN9f+tEW29oy68vpZw6UX5aW7ajlLJ9/TcTmLchg8tLyxiEBgAAAFhuyB25P0jy7FrrU5KckOS0Usozk/ybJH9ca31skm8leWW7/CuTfKst/+N2uZRSnpDkV5M8MclpSd5eSjmglHJAkj9N8oIkT0jy0nZZANhw/LNi37J/gUXnOAYATLPqQG5tfLd9eWD7VZM8O8kH2vJzk7y4/flF7eu07z+nlFLa8vfVWn9Qa/37JDuSnNx+7ai1frnW+sMk72uXBRglf2ABAAAA+9ugOXLbO2evTHJrkouTfCnJnbXWe9pFdiU5pv35mCRfTZL2/W8nOWKyvFNnWnlfHGeVUi4rpVx22223DQkdANhH/FMDAABg/xk0kFtrvbfWekKSY9PcQfsP9mlU0+N4Z631pFrrSUceeeQ8QgAAAAAA2O+2rGXhWuudpZRPJHlWkkeUUra0d90em+TmdrGbkzw6ya5SypYkD09y+0T5ksk608oX3uSdSjvPOX2OkQAAAADztjROYIyAeZKHi2nVO3JLKUeWUh7R/nxokucluTbJJ5L803axM5N8uP35/PZ12vf/ptZa2/JfLaUcXErZluS4JJckuTTJcaWUbaWUg9I8EO389dg4AAAAYHMw7dPi0FcwmyF35B6d5NxSygFpBn7Pq7V+pJTyxSTvK6X86yRXJPmzdvk/S/KeUsqOJHekGZhNrfWaUsp5Sb6Y5J4kr6213pskpZTXJbkwyQFJ3lVrvWbdthDYlPx3EQDYzFwLAcDGs+pAbq3180lO7Cn/cpr5crvlu5OcMaWtNyV5U0/5R5N8dEC8AMACMZAAsHdM1QYALFnTHLkAALDEABMAAOw/q86RC2NiHh0AYBEt6jXMosa9v9lPAOwLzi90GcgFAAAAABg5A7kAAACwH7i7DoC9YSAXgA3BH0YAAABsZAZyAQAAmAv/iAWA4QzkAgAAAACMnIFcANgg3NUEAACwcW2ZdwDse0t/1O885/S9WgYAYDWuKQDYHyb/ee2cA2wWBnIBAGBGBhIAYHNbr39iu6ZgCAO5AAAAAMyNQUwYxkDuFD4WCMBKTFsDAIvP4BEAi8TDzoCF44FOAAAAwGbjjlwAAABGwR2yAHvPJwM3LnfkAgAwSj6BAQAAexjIBfaaP7QBAAAA9i1TKwAAAAAweqYMYIz257RA7sgFAACAlk+bbT76HFgUBnIBAAAAAEbOQC4AAAAAwMgZyAXWxMeOAAAAAPY/A7kAAACwAQ25CcONGuvL/gT2pS3zDgAWyf58EiEwnd9FgOmGHiM9+RuAMXOeggcykAsLzoAWAAAAwMZnIHedzfs/Rgb1AAAAAGDjMZALAAAAAIzeZr+B0cPOANaBhxoAACy3L6+PXHttPvocwEAuAAAAsEYGVgH2PwO5ABuAC2kAAIDF4W84ZmGOXAAAADaFeT+cmmE2+xyYjIM8ZIwM5AIAAADABmVQeuMwtQIAAAAAG4IpC9jINuVArl9q+wAAAABgiXESFsGmHMgFAAAAAFgk5shlMHOqAAAAAMB8uCMXAAAAYB/wcX1gPbkjF1h47hYHAABYX0t/Z/kbC8bDQC7AgjFwDQAAMDuD1P3sl/EztQIAAAAAwMgZyF0Dc9sAAAAAAPNgIBcAAAAAWJWbHOfLQC4AAAAAwMgZyAUAgHXkThXYnPzuA7CvbfiBXCdTEnkAAACwGflbENhINvxA7qJwcgEAAABgIzLutT62zDsA+k0m985zTp9jJAAAAACwOJbG1TbamNqGGsg1+AkAAAAAbEQbaiB3f9uoo/uLxOA9AAAAAJvBqnPkllIeXUr5RCnli6WUa0opv92WH15KubiUcmP7/bC2vJRS3lpK2VFK+Xwp5akTbZ3ZLn9jKeXMifKnlVKubuu8tZRS9sXGAgAAAPNjnkyA2Q152Nk9Sf6vWusTkjwzyWtLKU9Isj3Jx2utxyX5ePs6SV6Q5Lj266wk70iagd8kZyd5RpKTk5y9NPjbLvOqiXqn7f2mAQAAAABsDKsO5NZav15r/Vz7811Jrk1yTJIXJTm3XezcJC9uf35RknfXxmeSPKKUcnSSU5NcXGu9o9b6rSQXJzmtfe9htdbP1FprkndPtAWj5z/KAAAAAOxrQ+7IvV8pZWuSE5N8NslRtdavt2/dkuSo9udjknx1otqutmyl8l095X3rP6uUclkp5bLbbrttLaEDAAAAACyswQ87K6U8JMkHk/xOrfU7k9PY1lprKaXug/iWqbW+M8k7k+Skk07a5+sDAABg/DwEGWDxLR3LHcenGzSQW0o5MM0g7n+utf63tvgbpZSja61fb6dHuLUtvznJoyeqH9uW3ZzklE75J9vyY3uWBwAAgIVkcBmA9bbq1AqlufX2z5JcW2v9txNvnZ/kzPbnM5N8eKL8ZaXxzCTfbqdguDDJ80sph7UPOXt+kgvb975TSnlmu66XTbQFAAAAALDpDbkj9+eS/HqSq0spV7Zlv5fknCTnlVJemeSmJL/SvvfRJL+cZEeS7yX5jSSptd5RSvmjJJe2y72x1npH+/NrkvxFkkOT/FX7RYdbzAEAANho/K0LMMyqA7m11v+ZpEx5+zk9y9ckr53S1ruSvKun/LIkT1otFgAAAACAzWjww84AgOnMgwcAABvbZrt73N8447PqHLkAAAAAAMyXgVwAAAAAgI6t2y9YdmfyvJlaAQAAAGDB+Ng7bD4GcgGAdeGPCQAAgH3H1AoAsImM7aNBAADMn2tEWAwGcgEAAAAARs7UCgAAAADsNVNtwb7ljlwAAAAAgJEzkAsAAMCGY85PADYaA7kAAAAAACNnjlwAAAAAYFNbhDme3ZELAAAAADByBnLnwFxNAAAAAMBaGMhlrxiUBgAAAIB9z0AuAAAAAMDIGcgFAAAAABhgyKfT99Un2A3ksuGY7gEAAACAjcZALgAAAMCIuEEJ6LNl3gEAAAAA4zU5oLjznNPnGAnA5mYgFwAAAGCTMDAPi8vUCgAAAAAAI+eOXAAAAABYB+54Zl9yRy4AAAAAwMgZyF1wnmQJAAAAABufgVwAAACADWCMN3uNMSZYVAZyAQAAAABGzkAuAAAAAMDIGcgFAAAAABg5A7kAAAAAACO3Zd4BjMHkpNs7zzl9jpEAAAAAADyQO3IBAAAAAEbOQC4AAAAAwMgZyAUAAAAAGDkDuQAAAAAAI2cgFwAAAABg5AzkAgAAAACMnIFcAAAAAICRM5ALAAAAAGwqW7dfkK3bL5h3GGtiIBcAAAAAYOS2zDsAAAAAYOOZvNNt5zmnzzESgI3BQC4AAACw15YGbg3aAmOzUf6xZGoFAAAAAICRc0cuAAAbxka52wIAALrckQsAAAAAMHIGcgEAAAAARs5ALgAAAADAPrR1+wXLpgGbhYFcAAAAAICRW3Ugt5TyrlLKraWUL0yUHV5KubiUcmP7/bC2vJRS3lpK2VFK+Xwp5akTdc5sl7+xlHLmRPnTSilXt3XeWkop672RAAAAADAP63EnJiTD7sj9iySndcq2J/l4rfW4JB9vXyfJC5Ic136dleQdSTPwm+TsJM9IcnKSs5cGf9tlXjVRr7suAAAAAIBNbdWB3Frrp5Lc0Sl+UZJz25/PTfLiifJ318ZnkjyilHJ0klOTXFxrvaPW+q0kFyc5rX3vYbXWz9Raa5J3T7QFAAAAAEBmnyP3qFrr19ufb0lyVPvzMUm+OrHcrrZspfJdPeUAAAAAALT2+mFn7Z20dR1iWVUp5axSymWllMtuu+22/bFKAAAAAIC5m3Ug9xvttAhpv9/alt+c5NETyx3blq1UfmxPea9a6ztrrSfVWk868sgjZwwdAAAAAGCxzDqQe36SM9ufz0zy4Ynyl5XGM5N8u52C4cIkzy+lHNY+5Oz5SS5s3/tOKeWZpZSS5GUTbQEAAAAAkGTLaguUUv5LklOSPLKUsivJ2UnOSXJeKeWVSW5K8ivt4h9N8stJdiT5XpLfSJJa6x2llD9Kcmm73BtrrUsPUHtNkr9IcmiSv2q/AAAAAAA2pK3bL7j/553nnD6ozqoDubXWl0556zk9y9Ykr53SzruSvKun/LIkT1otDgAAAACAzWrVgVwAAAAAWESz3PUIYzXrHLkAAAAAjNzW7RcsG8wEFpeBXAAAAACAkTOQCwAAAAAwcgZyAQAAAABGzkAuAAAAAMDIGcgFAAAAABg5A7kAAAAArGjr9guydfsF8w4DNjUDuQAAAAAjZyAVMJALAAAAADByW+YdAAAAAACs1eQdyjvPOX2OkcD+4Y5cAAAAAICRM5ALAAAAADByBnIBAAAAAEbOQC4AAAAAwMgZyAUAAAAAGDkDuQAAAAAAI2cgFwAAAABg5AzkAgAAAACMnIFcAAAAAICRM5ALAAAAADByBnIBAAAAAEbOQC4AAAAAwMgZyAUAAAAAGDkDuQAAAAAAI2cgFwAAAABg5AzkAgAAALBmW7dfkK3bL5h3GLBpGMgFAAAAABg5A7kAAAAAACNnIBcAAAAAYOQM5AIAAAAAjJyBXAAAAACAkTOQCwAAAAAwcgZyAQAAAABGzkAuAAAAAMDIGcgFAAAAABg5A7kAAAAAACNnIBcAAAAAYOQM5AIAAAAAjJyBXAAAAACAkTOQCwAAAAAwcgZyAQAAAABGzkAuAAAAAMDIGcgFAAAAABg5A7kAAAAAACNnIBcAAAAAYOQM5AIAAAAAjJyBXAAAAACAkRvNQG4p5bRSyvWllB2llO3zjgcAAAAAYCxGMZBbSjkgyZ8meUGSJyR5aSnlCfONCgAAAABgHEYxkJvk5CQ7aq1frrX+MMn7krxozjEBAAAAAANME1cAACAASURBVIzCWAZyj0ny1YnXu9qyFW3dfkG2br9gnwUFAAAAADAGpdY67xhSSvmnSU6rtf5m+/rXkzyj1vq6znJnJTmrffm4JNcneWSSb3aa7Jat1zL7u94YY5q13hhjmrXeGGOatZ6YbMsY2t7f9cRkW8bQ9kaKadZ6Y4xp1npjjGnWemKyLWNoe3/XE5NtGUPbGymmWeuNMaZZ640xplnriWn/bctjaq1HZiW11rl/JXlWkgsnXr8hyRsG1r1stbL1WmZ/1xtjTLZlnDFtpG0ZY0y2ZZwxbaRtGWNMtkVMtmUcbduWccZkW8YZ00baljHGZFvEZFvG0bZtGWdM67ktK32NZWqFS5McV0rZVko5KMmvJjl/zjEBAAAAAIzClnkHkCS11ntKKa9LcmGSA5K8q9Z6zZzDAgAAAAAYhVEM5CZJrfWjST46Q9V3Dihbr2X2d70xxjRrvTHGNGu9McY0az0x2ZYxtL2/64nJtoyh7Y0U06z1xhjTrPXGGNOs9cRkW8bQ9v6uJybbMoa2N1JMs9YbY0yz1htjTLPWE9P+35apRvGwMwAAAAAAphvLHLkAAAAAAExhIBcAAAAAYORGM0fuUKWUo5Ic0768udb6jSllhydJrfWOafXWq+227orra7+vS9w9bW/tvL4kyck9ZY9aKaYkt06p1y3r1utruy+m7jJ961u17b3cB92ynQP3wWHJmvsl3WXauivmzxrycM35041phe1dcT9N2d6h/TlrHwzJsdHlfa21znJ8aJeTK+tzrOlbZiFzZeg5aKiB57NV216v8+KQtvZl3s96Hh7DtUF6LOq2uM5ZjOscuSJXkt7+XZRt6cZ0XZLTOmWfTvKsTtk3kzx8lfV327ojSUm779bQdl9MFyZ53ETZnUmOHBB3t96s+6BvW4bEdGGSgyNX5Mq+zZUxHu9n6fO+Pui2NbQ/Z82fIcuMNe+7+bM1w3JlbmMg067lp1mYOXJLKSck+Q9pOm5p5/x0kkem6dAvpemwpyQ5NMnXk/wwyeFJ7kuT6DuTHJDk+CQ/1r5/T5Jvt3W/m+Sra2j7EUnuTvKQ7Em+7voe0tZLks8nuWvGuPtiOjzJk5J8L8knktzetvOUJFe260uSxyd5Wpoku2FKTD+T5kB0eZJr2/e6bR2T5Bfa9z7V9kO37b6YusukZ31D2t6bfdAtOzzJs9PkwRfS9F83pgcnOaUtuyXJ9zOsX/r279Y0+XJAu1zJA/Pn4CRHt9tydZLdPW33rW9I/sza5337qS+mIf05Sx8kw3JsjHn/2HaZe5PcluHHh62RK0P6c8ixZiPlytBz0MPbNg7Onouh+9r3HtT+fEeSH7Xb863059iBSX68rf/1NrZu20ttHtguU6fEtAh5P+t5eAzXBj+Z5nz1w7aP+vplUbbFdc5iXOfIFbkyLVcWYVv6Yjq5jeuvk3y2fe/ZSX6+XeYTac7V/yTNsfWSJDumxNRt64Qkz2vfu7hddkjbfTE9M8mpSb6Y5NK23slpzukfaut12+6rN+s+6NuWITE9Jck/avvji219uSJX1jNXxni8n7XPu/upL6ah/TlL/gzNsTHmfTd/1vP4sC/HQI5Ncy59Ta31cxmi1roQX+2GP6On7BVJrmpffzrJS5L87ETZlUn+nySfaV9fmOR3k/zyxDJfSPLvk1y0xrY/neRPltqesr4rkzwjzS/kVbPGPaXetWn++H1mp+wfJrm2U+/FS2VTYro2zS/HVRP1lrU1sb5tnbZe3LNMd3vvX6ZvfUPa3st90N2WpX0wub5uTEv98tOdeqv1S9/+/XSS/7uzf5flT1vvWUl+tVP2ik69WfJ+1j7v2099MQ3pzzX3wRpybIx5/+kkr+uJabV+kSvD+nPVY82UZRY1V4aegy5M8pdJLp94/Uft10Vt2WVJ/nOWn/O6OfbpNPn10iw/f062/ekkZyXZPtH2rOfFeef9rOfhMVwbXJjmGuaaFfplUbbFdc5iXOfIFbkyrV8WYVv6Yro+zY0+k21fn+QxSW7o1DusU9aNaVlb7etHdOoNabsvpmvbshs6bXe35f62p9SbdR/0bcuQmK5M8tyemOSKXFmvXBnj8X7WPl+2n6bENLQ/15w/Q5YZcd4vy5819Mtcx0Da8mdOtr3a19wHaAcHmtw4rSzJju4y3bKJ79evsMz1e9P2lPWtVG/Wtu9fJsmWnrJDl15PlB202vrSTLXRrXdozzIHdcq6r/tiOqin7S1raXsd9sGybWm/PyD21ZYZ0i+r9fmAXOm+t+I+GJD3M/f5Cvtglv5cUx+sMccWIu/XenyQK2vOg82YK5PvXd957/qB7w3JsW79lda7V+fFseT9Cusd07XB9SvUG9IvY9oW1zl7tw/GcJ0jVzZ5rizCtvTEdEOaO60m274hyaMnt7Ot9/BOWTemZW21rx/eqTek7b6YbkxzN9eNnba723J/21PqzboP+rZlSEw3TtaRK3JlH+XKGI/3s/T5sv00Jaah/bnm/BmyzMjzvrv+dTs+TLYza272td3NyyFfizRH7l+VUi5I8u7smf5gRynl1iSfK6X8bJKdpZSL0nz851OllJ9o39uZ5OvtMneWUt6b5Kg0t2EnySdLKdcm+WG7zNC2/76U8oUkN7Wv07O+K0opS7dHX9mWzRJ3X0wfS3JjKWV3ki+VUv5Zkh1pbhn/2/Z10tzyf1uSC1aI6dK23jUT9bptXZ7kpva9v2vLum33xdRdJj3rG9L23uyDbtmX2j4/JMlfT9kHt5VSPp3mQHreGvqlb//e2ta7dCJXuvnzqVLK/0rz0cSvTMmVvvUNyZ9Z+7xvP/XFNKQ/Z+mD9OTBouT93Wk+TvFf13h8kCvD+nPIsWYj5crQc9CdpZT/L3s+jnZTKeWNaT7mvHTu/EJ7bPv2Cjl2TSnlA+1717Zl3bYvL6X8eRv/N1aIaRHyftbz8BiuDXa3v3t/u0K/LMq2uM5ZjOscuSJXpvXLImxLX0xXpDkPX1FK+b32vV1Jvtwu83vtz7ekOZ/+1xVi6rZ1VZKvtO9d3JYNabsvpjvSTDny4XaZj7d172v3XV/bffVm3Qd92zIkpt1JvpbkXLkiV1aJadZcGePxftY+7+6nvpiG9ucs+TM0x8aY9938Wc/jw74cA3l0kpdlz/jkqhZmjtwkKaW8IMmLsnyS4Jvb18ek6diHpZlv4uCJZXakGfE+Os28GkelGb1/SLvMLWnm27u7LR/a9tfS/KIdnuQnpqwvaeaqK2nmGJw17r56STPfx/fSzO+7tMw1SZ7Y2U/fSnLiRFlfTFekuV39mBXa+lH7/cAV2u6LqbtM3/qGtL03+6Bb9uD268emxPSgNP36yDS3+y8tM6Rfuvt3aZ63x04s05c/P0gz79h30hyY+9qeNX9m7fO+/dQX05D+XGsfLJUNybGx5f0tafb/Mzptr9YvcmV4fw451myUXBl6DvpGmnx4TJo5m0qaueBLmvlna7u+r6T5j/GjprRV2q+0dWpP20lzwfT99vu9U2JahLyf9Tw8hmuD25I8NMmT01zfJA/sl0XZFtc5i3GdI1cacqW/X8a+LdNi+mQe+ECcz6T5uOtSWdLk5+GrxNRt6872++S+G9J2X0w708xXvFR2R5pzYiaW6bbdV2/WfdC3LUNiujfJc7JYeT8tpk9GrvStb965Msbj/ax9vjPL91NfW0P7c5b8GbLMWPO+mz/reXzoHntmzc2+ts+vtX40Ay3UQC7TlQV5ivmQ5fZl3Osd+0axL/NnX/bBIuf9opIrcgXmYZF/h13n7F9yZRy5sgjb0vP6qEz8gV57nlDelg3Ji2X12u9rbrtvme76hsa9Xvugb1uGxJQp5Ipc6Vumz3r97TCk7aHrm7GdNe+nvrbWkIcz5c8i533Xeh4fhrS/nm33rq8uyEBuKeXhSd6Q5o7co9LcHfTN7Pnv/5Ft2Q/TjLh/P83dOt9IMyr/mIl6d7d1b0lzp9Jtae7OPX6NbX8tzW3SR2T53VCT60u7bNLcDXXfXsTdrVfS/FdpS7stNXueGvzg7Hka+cPbbTwoe/6D0Y3pjjT/PTgyzX9ESk9bB2b6U8yX2u6LqbtM7VnfkLb3Zh90yw5o9+s9bZ17p+yDR6X578232/eG9Evf/r01zW32S+tNHpg/D0ozf8rSXSjT8nCW/OmLaUif9+2nvvUP6c9Z+2BIjo0x7x+Wps/vnlj/kH6RK8P6c8ixZiPlytBz0M1p5mI6OMvvgJvcpq+1bR2XPRc+fW1163XbnnZX3iznxXnn/azn4TFcG9ya5im4382e82K3XxZlW1znLMZ1jlyRK9NyZRG2pS+mw9P0/Z1J/r5te/IJ5TvSfAz2KWnOQ0ufIumLqdvWQ9t6SfMR4u8ObLsvpq3tOh+U5vdt6Xz8vTTngR/0tN1Xb9Z90LctQ2L6yXYf/TBN/8sVubLeuTLG4/2sfd7dT30xDe3PWfJnaI6NMe+7+bOex4d9OQZya5IPJzmn1rp0d/KKHrT6IqNxXpoN/6Va6+G11iPSXDTemeTrtdbDk1yU5unNf50m6X85TWI8PM2T6w5P8p40EyNflyY5/t8kJ6VJjP+5xrYf1Nbb0r7uW9+lSd6R5D8kuXTWuKfUuy7J65P86yRfqrU+Ns0f2f89zUTJx7Vln0vzdLxvrxDTt9tlrpiot6ytNMn48iS/keQbU9rui2nZMn3rG9L2Xu6D7rbcmOYp7v8yyXVTYro0zRPa357mj4mh/dK3f69L80v8sIlc6ebPN5P8VZsLF/e1vRd5P2uf9+2nvpiG9Oea+2ANOTbGvP+bdj89Mms7PsiVYf256rFmg+XK0HPQt9P8w/PJac5vu7PnI0A/aMue3C7znRVy7MtpngZ7fZqPd/W1fUiaC6Uftftsb86L8877Wc/DY7g2+Lt2fU9foV8WZVtc5yzGdY5ckSvT+mURtqUvpq8k+XdJvllrfV6t9bltvd9O8p1a6/PS5Parkvxikm+tENOyttIck09J8ktJDl1D230x3Zbkj5N8td2WO5L8QpJXJzmwr+0p9WbdB33bMiSmv0/yoSTfkytyZR/lyhiP97P2+bL9NCWmof255vxZQ46NMe+X5c8a+mXeYyC/1LZ9XoaqA5+KNu+vTDxdu1s28f2G1d6btkyakfi+Jz2v1PZKbd7QjbunzcFxT3lv6JOp5/0U8754e+sPeG/WfdDdlpW2s2/fr/Reb78OyIcbBry3UptryftZ+7zvvb6YhvTnmvtghtweU97fMOA9uTIgV9awzzdyrgw9B3VzZel7mYjhhjzwnNdbb5U2e9seEO8Y837W8/AYrg26+6KvXxZlW1znLMZ1jlyRK0PfG+O2DL3uWCpb8Yn3a2yz9+nnM7TdV6+73h0z1pt1H6zU9vUrxCRX5Mp65MoYj/fr0udriGmt/dmbP0OWGXHeX79Cvb09PqzL37V99bsxDvnaksVxUynlXyY5t+6Z9+LrpZSPpvloT5LcUUr5zTR3NX11ot57k9zVvt5dSjk1yQmTyyR5a5rbm5cMafuOUsofp7lTeDLOyfXdVB74xPBZ4u6rN/SJ4fN+inl3mb71DWl7b/ZBt+yWUsrb0kypccWUmO4qpbwhzVMEl5YZ0i99+/eutt5XJ+p18+emUsrvtq+Xyrpt95UNyZ9Z+7xvP/XFNKQ/Z+mDZFiOjTHvf1hKuXBi24b2i1wZ1p9DjjUbKVeGnoN2t8etb028fnq7LbuX6iXZPvG6r607Sim/kuYuu8m23tBZ5ow0/xHfPaWdvm0ZY97Peh4ew7XBPe3v3uT6u/2yKNviOmcxrnPkilyZ1i+LsC19MX2ulLKzjeNn2/e6TyjfWUq5KM3HYz+1Qkzdtq4opXyufe/K0v/08762+2K6tS27tF3mU6WU/9Xup69Mabuv3qz7oG9bhsS0uzRPaf/biT6XK3JlPXNljMf7Wfu8u5/6Yhran7Pkz9AcG2Ped/NnPY8P+2wMpDTz/L48y/8OWtEizZF7WJo/PF+UPfMw3ZbmgrFvjpyvpZkz47A0H8dcmr/kQe3Pd6eZT+SeNHM7PSR75vEY2vbhaeYleXCaj3+lZ31JM+9FyZ65WGaJuy+mmj1P8J58MvXSMkuJuTQfyGOy50nj3Zi+kQc+xbzbVlnqjra92tN2X0x9y3TXN7TtWfdBt6xvrsVuTAem+cjYQyZiGdIvfft3cv6Upcmtu/nzoDRz3dybPfOl9G3vLPnTF9OQPu/bT30xDenPWfogGZZjY8z729N87PAns+cP2yH9IleG9efQY81GyZWh56BHZs+cTX/ftntCW3ZVmj+4fyrNue6ANH2Unra2TOyfr6U5V3bbPqRt+9B2mR9MiWkR8n7W8/AYrg2+neZ37jtpcrSvXxZlW1znLMZ1jlyRK9NyZRG2pS+mXUm+1G7L0RNtTz6hvOSB84/3xdTX1oPa+veuoe2+dpbmhnxs9sxxv7v9+k6a/O17snq33qz7oG9bhsR0W5rrkSdnz9/xckWu9MU0a66M8Xg/a5/37ae+tob05yz5MzTHxpj3ffmzXseHfT0Gcn6Sf1MHPghtYQZy16qUckSS1Fpvn/L+j2f50+1u2Zu2V1vf3rTN/K1nv8ifzWfWfpErm8+QfhmSF91zXPv9Aee8gW2t2Hat9Zb1zNV55/0Cxz2oX2Zdn2PPxiZXGEq/AMB8LdLDzu5XSnlq93W3LMlTOxeVPz65TPtHbK21Xj45iDtr22kmmZ66vrbsn+1t3NPqlVJ+u/P6haWUF/aUDYmpr1637A8HtN0XU9+2zNL23uyDblm33gNiSvKCGfvlAfs3PbnSU/a8AW3Pmj+z9nnffpq1P9fcB2vIsdHlfWY8PvTV6ynbbLky67Fmw+RKBpyDkqQ9v11ea72l/bq8e87rW19PWz+xUttD21mUvB+yzNC2Z90HM8b9E0P6ZRG2ZUp/LsTv8F4c6xbyOidyRa5M75fRb8uUmM7qlJ3VU/a2gTF1671nxrb7Yuqur1uvr+2+erPug75tGRKTXJEr+zpXxni8n7XPh8Q0tD9nyZ+hOTbGvN+Xx4d9NgaSgRZyIDfJ/9Hzulv2Zz2vu8tcNPmiNHNzzNr2kPX9wYxtD4npZZ3XT2+/umVDYuqr1y37xQFt98XUty2ztL03+6Bb1q3XF1N3Pw3tl779OzR/Vmt71vyZtc/79tOs/TlLHwzNsTHm/az9Ildmz4ONnCuD8qLsmW8qSVJK+V7n9ecGtvXx1dpOcv2QmLIYeT/reXju1wYD+2UhtqWnbFF+hzfbdY5ckSvT+mURtqUvptIpKz1l2wbG1NfWLG331euur1uvr+2+erPug1ljkityZV/nyhiP97P2+ZCYhvbnLPkzNMfGmPf78viwL8dABtmwUysAAAAAAGwUW+YdwFqUUh6e5LQsn/fv00me1Sn7ZpoJ+ZdeX9dT73NpJkJeer17xrYvSXJyZ5nu+u5I81+Bw9Yh7m69mmbi5yMmlrk6zQTPk/X+Js0DblaK6ctJnt2p123rh+33g1Zouy+m7jJ96xvS9t7sg27Z7Vn+pN1p+2BnmofwLL0e0i99+/fCJI/r1Ovmz51pJs9eLQ9nyZ9Z+7xvP/Wtf0h/ztoHQ3JsjHl/flu21uODXBnWn0OONRspV4aegy5JM+H+1Dlyk9w6pV63rFuvr+2tA9pZhLyf9Tw8hmuDC9M8MGGlflmUbXGdsxjXOXJFrkzrl0XYlr6Ybk/ytE7bu5IcO1F2d5qH+t29SkzdtpY+BXvfGtvui+nydp8slf1YmocMPniFtvvqzboP+rZlSEwXp3lonlyRK/sqV8Z4vJ+1z7v7qa+tof05S/4MzbEx5n03f9bz+LAvx0AurLXemYEW5o7cUsrLkpydZjqEm9viZyf5+SSfSvKJNE/2+ydpOu6SJDvSXFA+PslfJ/lskh9PckaaX94b0jxx8AlpniL3P9I81Xto209pv65M8vm2Xnd9JyR5Xvvexe2ys8TdF9PPp0nckuS/JvmfSX4tySlJPpnkP7f1/lGSFye5IslHpsT0wiQnJvnv7X5IT1svTPIL7Xufatvqtt0XU3eZ9KxvSNt7sw+6Zf8wTR7UNAfKv+uJ6UlJfjPNL+F1afp4SL/07d9nJjk1yReTXNq+182fx7bt/yjJh9LkQbftvvUNyZ9Z+7xvP/XFNKQ/Z+mDZFiOjTHvfynJ/57mj56/a+sP6Re5Mqw/hxxrNlKuDD0HPb5t/9Y057iHtMukXeauJD+TZjD28iTXtu912zqmsy0397R9eJrj5PfS9O/tU2JahLyf9Tw8hmuDp6TJszva/dfXL4uyLa5zFuM6R67IlWm5sgjb0hfTbyU5vv357e17v5fkpLadN7XrekGam392JnnvlJi6bf2fSY5r39uR5E8Gtt0X02va+K9O8o401w5bkxyS5K/aGLpt99WbdR/0bcuQmF6a5OVpnjB/XltfrsiV9cyVMR7vZ+3z7n7qi2lof86SP0NzbIx5382f9Tw+7MsxkGPT5OYf1lrfnSFqrQvxlWaet0f0lD0myQ3t62vTJPRhE2XXtx18bfv6yiTP6FnmuUmuWmPb17YdcW2n3uT6rk/yiJ71rSnuKfVuSHOhe1in7MeS3Nipd+RS2ZSYbmiXuWGi3rK2JtZ3UKetI3uW6W7L/cv0rW9I23u5D7rbsrQPJtfXjWmpX7oxrdYvffv32rbe5P5dlj8T9bZ1yh7TqTdL3s/a5337qS+mIf255j5YQ46NMe+vTXNS6sa0Wr/IlWH9ueqxZsoyi5orQ89BV6a5sOye856Z9hzX1vsnWX7O6+bY0vq2ddp6cc8y3bZnOS/OO+9nPQ+P4drgyjTXMN16k/2yKNviOmcxrnPkilyZ1i+LsC3TrjsO6mm79PRnt6wvpmVtt9+79Ya03RfTgT1td7elrFZv1n0wZVtWi2mpXxYt7+XK4uTKGI/3e9PnB/bU+7GeekP6c035M2SZEef9svxZY7/MbQykLT9ssu3VvhZpaoWSZtS6W3Zf9kxuvCXNLdgPnigrSb6WZocmyYNrrZ9tp2mYXObS7LktfWjbW9KMoh/YqTe5vqW4a2d9a427r959SX4izUdNJ8tOyJ7b0pfqHTVR1hfTfe0ykxNFd9taWl+yvK2jepaZjKm7TN/6hrS9N/ugW7a0D47uxD4Z01K/HNOpt1q/9O3fpbYm9283f5bq3dwpu69Tb5b8mbXP+/ZTX0xD+nOWPlhabrUcG2Peb0lyb09Mq/WLXBnWn0OONRspV4aegx6c5KNJ3rL0utb62SQppTx4ot7/SPLmiXrdtpbW96CJsm7bW5LsqrXu7LQ9y3lx3nk/63l4DNcGD05zDbOsXqdfFmVbXOcsxnWOXJErS/W6/bII29IX0+40nwqZbHt3mgfR7O7UO6pT1o2p29buUsrSw3h2TyyzWtt9Md2XPXelTbZ9a2dbJtvuqzfrPujbliExlTR3rXVjkityZb1yZYzH+1n7vLuf+toa2p+z5M/QHBtj3nfzZz2PD+v1d21f20vLTba9okWaWuHMNE8XvCjJV9vi56a5vfnvknwszZPhJm+d/lSSF2XPbe/np7nd+Zg0c8n9pzR/lP5amluwv5zkv6yh7V9vy/82yXvaet31nZjk+e17F6eZm3eWuPtiOi7J/5Y9Hz+7IclT0/x3/Kq2btLclfP4NHPo/X9TYvq5tuzaJJ9p3+u2dWz2PH3vb9Nc0HXb7oupu0x61jek7b3ZB92y49LcpfajJP8tyY09MZ2Q5B+kmefkP6X5GNmQfunbvy9q2/twmlvvkwfmz88n+ZU0v8Sfa8u7bfetb0j+zNrnffupL6Yh/TlLHyTDcmyMeX9Kmv/avTfJx9v6Q/pFrgzrzyHHmo2UK0PPQa9N8xHYC5L8+yS/k+ajyklzjvu3SV6X5PQk17TLpKetf5ym75Km/87vafvX08yNuDvJV5KcOyWmRcj7Wc/DY7g2eGmaOdrOTZNDff2yKNviOmcxrnPkilyZliuLsC19MT2pbesbae6WStvWI5PclubjvkekmePwe2nmxvzmlJi6bT203X+1Xe6ugW33xfQPsmdqpGvaes9Jc7fX1Wmm0um23Vdv1n3Qty1DYnpS9kxjuLSf5IpcWc9cGePxftY+///bO/OwuYoq/39OSEBDWMK+RBbBBQRBBESUEXf0h4DiAjoq4yjuMjruOAq4O+M2MqPiCK64b6DIouyyJwFCDEkICYEEAiTEhCUJ5K3fH6cu7+16q7urby9v3c75Pk8//d7qOud+T53ve291dd2qsJ1inFLzWUU/qRrLUfehfnp5fejnGMgu6NIKn3XO/YAE1GYgF0BEpqKj7uXFha9B/5F2LlXdHE1gUedSYLdSnW3QkfNtS3UuRmcedep7NvCMwC4830r/vmWXvGN2S1Ehb16qcwO6pkjZbj4qjlacLkTXTizbhb6Wop3rHVv4jnEK68TOl+K7mzYIy1ahWtipVCfkNAl9jLdc51La5yXWvovQAZuyXaifFZ4XpTqh76r6qZrzWDvFOKXks0oOUjWWo+4fZPTHo6LsUtrnZRGmlZR8plxrhkkrkHYPegDtrBZlEzyn9aU6M9FHeFpp7FH/PqmF74f968E2nMJYctR9Sp1c+wbr0Q59q7zUJRbr59SjnwOmFdNKPC91iCXG6XqCzTSdc/eIyA6B3a5tzh/15d879R3zE9usdHE5lojvmF3VNhgTSwonYC1jv8ebVkwrvdRKjtf7Sjlv0k4xX23zWVE/SRrLVPehfnp5fejnGMgFzrkHSESdllbAOfeAiFxCY4KXicgjkbKtvM0KABFZGNYJ/YvI9lV8A+e2O59/7wnv0A7tQG1WNBP6ha54bKAoW4EuytzqgvFAxC70NVL6rJXvkFOsTni+JN9dtEFYtorRTX2acVoI/AK/s2WHeSGsA1zTTj8d6LBj/YScIvEmm9Kx5gAAIABJREFUtVOTeFPyWSUHqRrLUfeznXO3VLk+YFpJzedutL/WhHVqq5XUe1AqEu9nbX336r7IOOu+6n04h74BOnuxAXWNxfo59ejnmFZMKxDNbx1iiXGags6ketxORGI7lN/f5vwxXyvQL/ZTO/Q9hhM6A7B8vsnoLLF2vEO7qm0wJpYUTr5OHXVvWqmPVnK83lfKOWPbKeYrKZ9d6KdtnYx1XyUv4zoG0skgLtRoRq6I7A98B9iC0XX09kBHsu9Hp1w/Ad0x94nA3ejjP1uhjbkS7YBMRKc2T/afPwb8A11q4UH0V41U31sCD6HreD3g7cLzbcbojuE3+XNU4R3jtDVpO4aXdxovpq+HnJ5K97uYF1PxQ05hHYmcL8V3N20Qlm2F7jxYPI7wQITTpujssG3RKfuPJOYl1r67oXqZ4DkKY/WzCfrLzcOe49qI76q6r5rzWDvFOKXks0oOIE1jOep+T19nPfpYSOr1YTdMKyn5TLnWDJNWUu9BW3gfGzM6m63oUGzk/16BzrbdltE1o0Jfk4AdvN3dnlvoW1CdTgTuQTsnVe+LuzG+uq96H86hb7CLP9c6n6NYXuoSi/Vz6tHPMa2YVppppQ6xxDgd7HmdD1zrPwt3KN8T3Sh0EvqI7m1NOIW+9kdn7YE+zn1jou8Yp0PQmWZ/R2ei7enrPQr8ztvFdlYP7aq2QSyWFE77Aa9C+x+zMa2YVnqvlRyv91VzHrZTjFNqPqvoJ1VjOeo+1E8vrw/9HAOZht5L3+Ocm0EKUndFG++XD/w5kbK3Mbor89XAG4BDS2U3Ap8CrvHHFwAfA15ZqnML8D/AhR36vhr4ZuG7yfluZOyO4R3zbmI3B/3ye0hQltsu5g11YudL8d1lG4SxFG1QPl/IqcjLHoFdu7zE2vdq4OSgfRv04+2eCxwXlL0tsKui+6o5j7VTjFNKPjvOQQcay1H3V6NrkYac2uXFtJKWz7bXmiZ16qqV1HvQBcDZwPTS8WeBz+HvcejjPWfTeM8LNXY1qq/jabx/ln1fDZwIfKLku+p9cbx1X/U+nEPf4AJ0zeLZLfJSl1isn1OPfo5pxbTSLC91iCXGaS460afsey6lHcpLdlODspBTgy9Gdygv26X4jnGa48vmBb7DWB733cSuahvEYknhdCO6vmTIybRiWumVVnK83lfNeUM7NeGUms+O9ZNSJ2PdN+ing7yM6xiILz+k7Lvda9wHaJOJwvxmZcBtYZ2wrPQ+t0Wdud34bnK+VnZVfT9eB5gYKXticVwq27jd+dDZO6HdEyN1Ng7KwuMYp40jvid24rsHbdAQi38fw71dnZS8tMt5glbCz1q2QYLuK+e8RRtUyWdHOehQY7XQfafXB9NKxzrYELVS/mxu8NncxM9SNBbatzpvV/fFXHTf4rw59Q3mtrBLyUtOsVg/p7s2yKGfY1rZwLVSh1ginOahM63KvucBTyrH6e22CMpCTg2+/PEWgV2K7xin+ehsrvmB7zCWx303savaBrFYUjjNL9uYVkwrfdJKjtf7KjlvaKcmnFLz2bF+Uupkrvvw/D27PpT9VNVmzHeoy5RXndbI/bOI/An4EXCnL7tNRO4FZojIocAiEbkQffznchHZyX+2CLjb11kpIj9BNzs73/u5VETmAOt8nVTfC0XkFuAOf0zkfDNFpJgefaMvq8I7xukvwHwRWQMsEJE3ArehU8Yv88egU/7vA/7UgtP13m52yS70NR24w392hS8Lfcc4hXWInC/FdzdtEJYt8Dl/AnB+kza4z6/NshXwyw7yEmvfe73d9SWthPq5XET+hj6auLiJVmLnS9FP1ZzH2inGKSWfVXJARAd10f1D6OMUv+rw+mBaSctnyrVmmLSSeg9aKSJXoY9j4T87DX38p7h33uKvbf9oobHZIvJr/9kcXxb6ni4iZ3n+y1pwqoPuq96Hc+gbrPH/e5e1yEtdYrF+Tj36OaYV00qzvNQhlhinmeh9eKaIfNJ/dhdwu6/zSf/3Pej99FctOIW+bkKX7gO4yJel+I5xWoEuOfIHX+ev3nbEt13Md8yuahvEYknhtAbdAOiHphXTShtOVbWS4/W+as7DdopxSs1nFf2kaixH3Yf66eX1oZ9jIE8C3sLo+GRb1GaNXAAReQVwNI2LCy/xxzujid0cXW9iE1/nLmABOuK9I7quxvbo6P0UdO24Zej6FA/58lTfxeLGWzG641x4PojvGN4p75gdnvMjNO5MfQu6Fki5nR6g2i7moa913m5SC98xTmGd2PlSfHfTBmHZpv41uQmnCWhet2F0Z8vUvITtW6zztidj81LWzxr/WoVemGO+q+qnas5j7RTjlJLPTnNQbqd2GstN9/eg7f+cUp2UvJhW0vOZcq0ZFq2k3oOKNVF3RddswscgjK6RuwztME0s1Ql9iS8XRhfnD307dJ3aR1Atrm/CqQ66r3ofzqFvcB+67tu+aP8mlpe6xGL9nHr0c0wrCtNKPC+5x9KM06XoI7Fl3+EO5Q6d7bVVqU6MU+hrpX/fslQnxXeM00LghaWyFeg9kVKd2M7qoV3VNojFksJpPfBi6qX7ZpwuxbSS0gaD1kqO1/uqOQ/bKeYrNZ9V9JOqsRx1H+qnl9eH2HeOXo2BnOOcO49E1Gog12AwGAwGg8FgMBgMBoPBYDAYNkRMGG8CVSAiJ4bHkbJvBcdHRup8Ljg+oAvfRyac78c94h2zOzc4PkVETomUpXCK2YVllyb4jnGKxVLFdzdtEJaFdjFOZwfHqXmJtW9MK2FZqIOY76r6qZrzWDtVzWeK7xinuuq+6vXBtFJdB8OsldR70AFB2RuD4wMSNXZqgu+TEjnVQfdV78M59A1S8lKXWOr6P7yh9XNMK6aVZnmpQywxTmcEZWdEyqYncgrt5lT0HeMUni+0i/mO2V2aeL6UWFI4mVZMK/3WSlgnh+t9zHeVdor5Ss1nFf2kaixH3ffz+tC3MRASUac1csuQNscAuwfHB6HrZZTxguD43ehu3lV8A/yxzfl6xTtmtzg4nh6pM53Rx91acQptY74uT/Ad4xQ7f+g/xXc3bdDOLsYpPF9qXmLtexBjtUJQFuogxXfMLlWHKTmPtVOMU0o+U3zHOKVoLEQOuo/lPCUvppWxqHqtGSatpN6DdgLeUSr7NFD+Av5u77udxsJ7Zcz3W9Ad69txqoPuq96Hc+gbvJv2ealLLCHq8j+8ofVzTCumlWZ5qUMsMU7nBmXfjfj+cXDcjFPo63PBcarvGKew7UK7mO+YXdU2iMWSwunAoMy0YlppxqmqVkLkcL2PcUrJeeg75is1n+3sYpxSNZbiO0S/dR/qp5fXhxC9HANJgi2tYDAYDAaDwWAwGAwGg8FgMBgMmaNWM3JF5OXAMTQuLnwXMI3GxYwf9u9FneXAs0t17kM3e7m/VOdR4KUVfM9CNxgp24XnK5awGOmSd2hXbAixKbrRAOgv4svRzQjKGw/MRzeNKG82Uea0FF2Y+amM/vIT8xVbILzsO8Ypdv7Y+dr57qYNYmVr0TZfhS6eHeO0EtXJUkY31mmXlyIWaMz5dGDrwC7Uz2RGF79u5ruqfqrmPGyn2PlT8lk1Bykay1X313j/ZU4p1wfTSvt8plxrhk0rqfegi4Enl8pW+PNOLdW5HXgRrTW2zr+XeYa+nY9n6zac6qD7KvfhZnaD7htchG6q0CovdYnF+jn16OeYVkwrsbzUJZaQ0zL/926MbuZ5P3r/3Nr7d97PI97/SJPzh77wdcWfdyTRd4zTveiO6MVmpROAJ5baqRnv0K5qG8RiSeF0L3AzutnQtt6PacW00kut5Hi9r5rzWA5CX6n5rKqflDq56r6sn15eH/o5BrIE+INz7nwSUZs1ckXkG8BJwGXAV/xrV1+2mz9eAjwXeB7amNcBrwK+hu5w9xXgJuBI4E3Aa9EvhSejU7RHOvR9OPAb/35dk/OtYXRnu7Vd8I7ZPQEVzjrgeuCVaE73Q3cnf6V//QM4Gu1cN+O0r6/zj5Jd6Ot2YC5wK7Coie8Yp7BO7Hwpvrtpg7DsOnTwfh2qgRinc4Ed0H/KAzvIS6x9t/B2RzGqlcNp1M9TgeejWljSxHdV/VTNeaydYpxS8lklB6kay1H3C4D3A8fR2fXBtJKWz5RrzTBpJfUedABwJfBBn4dD0Ufrv+H/nuw/u9LXbaax7YA3+td2TXy/HL1/fgDtHDXjVAfdV70P59A3GEH7MCe3yEtdYrF+Tj36OaYV00qzvNQhlhinzXwcc51zWznntka/fD8ALHXObQVcCFwAnId+KW/GqcGX5/9t4H+B6zvwHeN0qy/bzNe5H/gzcD5wUcx3E7uqbRCLJYXTFb6dDjKtmFb6pJUcr/dVcx62U8xXaj471k9KnYx1H+qnl9eHfo6BXAZ8QETKy6G1hnOuFi9gXqwMHe2eXzqeFCnbuHQ8F9gyUmdq+Rwd+J5cHDc53zz/Htp1xLuZXcx32F5hWQqnVnZtfCZzitVJOW8v2yAsi+UlUqdSXnyOQ7vJEbuNaZOXJr5b6b6nOU/hlKqVFN9VNTbeuvd5CfWbkhfTSsV8DrNWmuQuvAfNRX+BDu95U2nMy7aR802OnC+8f24bqRP6rnpfHFfdt6vToe9B9g2KHLfLSx1isX5Oj9sgLIvlJVLHtGJa6WmfOLdYInXmRj6bm/BZ7LzhZ3Nb+Oz0vCHfuRU/q9oGsVhSzhtrC9OKaaXvWmnyPp7X+5ScR9sg4bNWbZ+sn5Q6NdJ9364PVbUZqxNyTHnVaWmFNSJykHPu+nIZupHHGn88gv5SvH2pbA06O6WYPi/o9OWDgjr7+c868T0C7F/yHTvfGhE5yPsun69T3jG7FSLyOnSR5HLZ19FfMB63E5FPlMpinFYAHy8dx3ytEJHXo78qlH19IqgTcgrrxM6X4rubNgjL1ojIwehGGo9zDziNADuLyHPKdrTPS6x9R4BX0Ni+oX4Ku3vLZYHvWFmKfqrmPNZOMU4p+aySg6JeO43lqPsR4MSQE+3zYlpJy2fKtWaYtJJ6DxJfJ7znOUbvcSO+TvmeF/oqzgeNvraP1FkZ+K5yXxxv3Ve9D+fQNxDgWRG7cl7qEov1c+rRzzGtmFaK84d5qUMsMU53iMhPgNUl33eLyHno47uF3dvRx2PvbMEp9HWHiJyGaqywS/Ed47Tal5XrfMyfv+AQ+o7ZVW2DWCwpnB4TkW+V6oBpJebLtFJdKzle76vmPGynmK/UfFbRT6rGctR9qJ9eXh/6OQYCjeOTbVGbzc5E5AB0yvVm6NoUAE9Bk3kfcBu6hsa+6HoTF6NTzvdBHwddhs5a2RF4Gtpov0KnbR+ALrewHJjdge8DgGeiyzXM9Hbh+TZDO7bO11tdkXeM0xO87ycyuj7HVuiaYpv6eMTbjKBCW9iE05PRGT0bef8S8TWJxnVE1kd8xziFdYicL8V3N20Qlj3Bn+9hdB2VRyKcNkUfI7sXuNS3U0peYu37dPTx5OmMaizUz9bAi9EZLbM8z9B37Hwp+qma81g7xTil5LNKDmL6rYvut/V+p6O/1pGYF9NKWj5TrjXDpJXUe9AhwF7o40dXoQN8L/OfXQTMQB9ZfhkwB12bl4ivacAL/GeXoffd0PdTgNf4uK/z3KveF8db91Xvwzn0DfZBtXiuP18sL3WJxfo59ejnmFZMK820UodYYpym+s829j7xsT7gj7dldN3Did7ONeEU+hLgMf++EaPrOLbzHeO0HH0kd1vffhv5WB7zNiMR3zG7qm0QiyWF00p0LdFVjO7SbloxrfRSKzle76vmPGynGKfUfFbRT6rGctR9qJ9eXh/6OQbyJHR5hvc656aTgNoM5BYQkR0obXTgnLsnKFuKrl9R3gzhelQARdmD6Jfbcp0L0KR36vsGdK2oVudb4t+75R2zK8q2BnDOLS+1VUNZaBfjFLNr4qul7xinWJ0ufFdqg7CsHSf0AnIwFfISti96QQp9xfSzmNIi7S3i7Vg/IafUnKfmIOar2xx0orEUu6ptUIWTiEwgQT8hJ0wryflMvdYMiVZS70Hzady8c6V/37JU50J0/clWvpainZsdS3VC30vRgZbN23Cqg+6r3ofHvW+ArvX2clrnpRaxWD+nNv0c04pppWleco+lVb8jBamcqiDVT6+494p3iq866t60Ui+tNKkzrtf7lHibYbz1U2Pefbs+9Eqbzfykok5LKyAiW6AzhB4PWESuRjdFKP9z3o+O0uPfp4R2NM58WoL+ilHF93r/3up8Y3YMr8o7YudE5PGdqUVkCTqy/8yiji/7K7BHG063U9rFvImvdb7+xi18xziFdWLnS/HdTRuEZcvRX2uk5LuBE5qXheivRJCel9gu8RcwViuhfiYDh7XxXVU/VXMea6cYp5R8dpyDDjSWo+7/QIXrA6aV1HymXGuGSSup96AVwCWBHcHxAxG70NdI6bNWvndDZzm04lQH3Ve9D49738C3Xbu81CIW6+fUpp9jWjGtNMtL9rE04XQ/Y3+ICHcofwidRfVQG06hrwn+vbivpvqOcZru26Q432R0ptemLXzH7Kq2QSyWFE4XobPVTCumlX5pJcfrfdWch+0U85Wazyr6SdVYjroP9dPL60M/x0AucM4Vk2/aojYzckXkLcBn0FlExZfSF6Ff6C5Hv8DsCbya0ccJb0N/Od4L3dHvWvSxoNfhNwEB7gH2ZvSxxJs68L2ff92IJofI+fZHZy+BiurGirxjnA4Dno0K4FfobuJvQncMvhT4qbd7FXAM+qjbH5twOhKdpfx73w5EfB0J/JP/7HLvK/Qd4xTWIXK+FN/dtEFY9nxUBw69UF4R4bQP8Hb0n/BWNMcpeYm17yHobKm/o7MXYKx+9vT+HwV+h+og9B07X4p+quY81k4xTin5rJIDSNNYjrp/IfDP6I6VV3j7lLyYVtLymXKtGSatpN6D9vL+72X0Ueb9/Gc3oU+kPJXRZQzm+M9CXzsHsSyJ+N4avU4+jOZ3eRNOddB91ftwDn2D/VCdrUCXpYjlpS6xWD+nHv0c04pppZlW6hBLjNO70C/Zf0R3Tgf4JDpAMB34vD9XsZb7IuAnTTiFvj6APpILqudvJvqOcXqP5z8LXXLwn9Ef7Z6A7mD/04jvmF3VNojFksLpeOAEYAHwS29vWjGt9FIrOV7vq+Y8bKcYp9R8VtFPqsZy1H2on15eH/o5BjIN1eapzrkfkQKXuCvaeL/wuzJHynZldAe4Oaigyzs1z/UJnuOPbwSeE6nzEuCmDn3P8YmYE9iVzzeXsTuGd8y7iV2znalz28W8oU7sfCm+u2yDMJaiDcrnCzkVeQk5tctLrH3neLty+zbop2S3e1C2a2BXRfdVcx5rpxinlHx2nIMONJaj7uegN6WQU7u8mFbS8tn2WtOkTl21knoPuhHtWIb3vEPw9zhv92oa73mhxorz7R74OiZSJ/Rd5b443rqveh/OoW9wI9qHCe3KealLLNbPqUc/x7RiWmmWlzrE0qzfsXHEt0TyGZbFODX49u+hXYrvGKdJEd9hLNLOrmobNImlHaciL3XTvWmlPlrJ8XrfTc4nRewmR+xS8tmRflLqZKz7Bv10mJdxGwPx5VPLvtu96rS0gjD6iFW5bITRKckT0SnYm5bKBF1ra5I/3tQ5d63oMg3lOtczOi091fdEdBR9UmBXPl/B2wXn65R3zC62M3Vs12BhfHcxD+vEzpfiu5s2CMuKNtgx4F7mVORl58CuXV5i7Vv4KrdvqJ/CbklQNhLYVdFP1ZzH2inGKSWfVXJQ1GunsRx1PxF9zDTk1C4vppW0fKZca4ZJK6n3oE2B84D/Ko6dc9cCiMimJbtzgf8s2YW+ivNNKJWFvicCdznnFgW+q9wXx1v3Ve/DOfQNNkX7MA12QV7qEov1c+rRzzGtmFYKuzAvdYglxmkN+lRI2Xe4Q3lht31QFnIKfa0RkYP8+deU6rTzHeM0wuistLLve4NYyr5jdlXbIBZLCidBZ62FnEwrppVeaSXH633VnIftFPOVms8q+knVWI66D/XTy+tDr77XxnwX9cq+W6JOSyu8Ffg0urTCnb74Jej05iuAv6BrNJWnTl8OHM3otPdz0OnOO6Mbm/0f+qX0TegU7NuBn3Xg+82+/DLgx94uPN+zGLtjeBXeMU5PIW3H8EMY313MwzpEzpfiu5s2CMuegs5SexT4LTA/wml/dCf1dahWbiYtL7H2Pdr7+wM69R7G6ucw4PXoP/EMXx76jp0vRT9Vcx5rpxinlHxWyQGkaSxH3R+O/mr3E3QNJUjLi2klLZ8p15ph0krqPei96COwfwJOB/4NfVQZ9B73NeB9wP9DH8M/3X8W+joKzR1o/s6J+H4zcATaWVoM/LAJpzrovup9OIe+wfHorrk/RDUUy0tdYrF+Tj36OaYV00ozrdQhlhinfbyvZehsKRi7Q/nWwL7osjUXo2szxjiFvjbz7ed8vdWJvmOcns7o0kjFUjovRmd7zUKX0gl9x+yqtkEslhRO+zC6jGHRTqYV00ovtZLj9b5qzsN2inFKzWcV/aRqLEfdh/rp5fWhn2Mgu6BLK3zWOfcDElCbgVwAEZlK467MS9AGO6RU5oAt0AQWdS4FdivV2QYdOd+2VOdidOZRp75vQQVTtgvPt9K/b9kl75jdUtJ2DJ/P+O5iHtaJnS/FdzdtEJatQrWwU6lOyGkS+hhvuc6ltM9LrH0Xomthlu1C/azwvCjVCX1X1U/VnMfaKcYpJZ9VcpCqsRx1vxpdQ6nT64NpJS2fKdeaYdJK6j3oAbSzWpRN8JzWl+rMRB/haaWxdd5uUgvfDwGPoGvvtuIUxpKj7qveh3PoG6xHO/St8lKXWKyfU49+jmnFtNIsL3WIJcbpevS74eO+3dgdypeiP7y2On/Ul3/v1HfMz73o+sTlssXlWCK+Y3ZV22BMLCmcgLWM/R5vWjGt9FIrOV7vK+W8STvFfLXNZ0X9JGksU92H+unl9aGfYyAXOOceIBG1Gsg1GAwGg8FgMBgMBoPBYDAYDIYNERPGm4DBYDAYDAaDwWAwGAwGg8FgMBhawwZyDQaDwWAwGAwGg8FgMBgMBoMhc9R6IFdEth5vDiFEZLuEOl3zFpGtRGSr9jWT/R2QUGdzEXm2X6u4Vb1t/PtUEdk8+KynvL3Pltw75e3/HsN9EBiEfuqcg7rqvh/Y0LXi6w7sWlNnrQwTBnWP7TXqyjsG6+dYPycVphXTSjfodywisr2IHOBf27ewn9LiszH5E5GjWtUTkT1F5M0isndQZ2L5nCJyoNfItiLyLBF5ZsElhXsr3jHu7Xj742eJyLGJ3J8S8i581k33ppXh0UqEd9+v94PSTyQHXemnzrovfI63fqpce5LhnKvFC/gSsI3/+0B09+016ILVx5fKL0F3bX4SuhvhP9DFgxf4v+/ztrPQHVdvBv4MvAuYVDrf5sAXve0bgR2AbwP/A3wfOAX4O7q79l7ohgxbo7vmTfXHW6C7080GzkZ3tOuG9wzgWv/3fHQHv3uBnwO7eV9P8scPAZ9ENyZ4uo/xHmAPdGfZ1eimFK9BdwdegW5gcwC6295f0Y1urkMXb345upnKEh/j6/z5XoFuUHOlt5/rfY/4+osZ3QAjhfcVnmeM9w8C3gcAp6K7AT4L3aU7xnsxuivgal9fIrxnA4t8uz3oud/p2/5+3z7L0R0JvwRsGdHonwPt/Bi40ZcV+pmF6uQUxurndZ7DVHQh7+/7dvoNujlfN7pf7uNY1SwHTfSzj8/Bn9CFvGM5iOnnbn/eq4C3dZGDTvWTk+7v9J/fShP9YFpJ0kqfrjW10kqTa81GwDvxu+76ssnAR4HzgScAJ/h4vgJM8XUWlXxNAj6F3gu/7u339Bp4CN1EYF9f98nAmcDngCnA99Dr792oRmegGjy7VSzkqfv7/fHdrXJAnn2Da9D/t/Np0a/xPs/IPBbr52TYz8G0Ylqp3ifOMZa70c1w7vN+/4Les64BDijFsK8veww4w/OY433ORrV5j2+Tj3vux3o+r/GvvdFd1u/wPE5Bd00f8Xx+im66dIJv03k+htuBq9H/j3t87Nei+rkP1dZfYtwjvKeiO6vP8Vyeg/4vlrnHeM9DN81c7G3ejG5EuMTn5qP+fCH3O1GNPOo5XOt9LPTnq5PuTSsZaiW41szK+Hp/t49/TkIO7vR5juUgpp+zfdlrgA80yUFV/cxi9N58SYx3xrpP1g9eO5mMgSxAtfLi5PHR8R6gTSba2NCXAAf5Bvg+eoG9zgvgTcDx6D/Da4E/AF8GpgMfQr/gnO2T8i3f4G8FfgVc4JNwAPpPfJZPzjleIB/0InwE+JgX2HKf8IX+5fz77cD/eeHs6m3v64L3f6C7jF8GfNG3w2t83a+iF4/XoBfb73re3/IiugrdDX2RP9eIL7vZ17vEl10CXAz8EjgR/Sd+tW+Lq7zA3of+kzwKfBP9Z9oLeK5vixuAw33Z7ejAwLXAb4HvlXIY4/0d4N99+8Z4H+d5zinxXu3rX4JeXGO8d/Pn+ZjPVXFjO67E+xAf+1vQLwev8ef9lI/hDM/7pT7uqxnVyht9/u6jUTv/jl5cfoNq6/3+3DcT189a364L0UG0z6EXzQ+iX4660f1i4CTgR8AXmuQgpp9/+PiORy+QsRzE9HMXOuP/1ehNoGoOxuiH+uj+L97Hj0u6D/VjWknTSqVrDcOllcP964Xo/WgrdCD0177dpgNf89y+yuiXjnW+HQvNrEbvU6tR7XwV7Zws9Fx+hN4fl3vfq9DO0KvRL1fvRu+Dt6Ca+ho6wHMncJqPbz7wyZrpfj462P0LRnUfu97n2Dc4F9X3D9A+zT7oINmZwO9QrRyOamdZ5rFYPyfPfo5pxbSyiDSt/DvwYeCBjGO5F3gBeg8pYvmQj2WJ//tDPmdn+Fg+jA42vML7fgx4Ppr7yz1N6HZbAAAgAElEQVTHs/xrvX8/E72fLvLnONjHtbWP5Vjvezl6T3sHOvC0Ch10ugY4FNXmwcAPgRuBLwC/LvEOuYe8Z/s87Ovb637P/4/o/1bBPeT9CvRef7Bv7+vRH2yORAc8HvP2i9F7z+6e+0zgaegPrPM976vR/8Vf10z3ppU8tfJl//oK+v0j1+v9zb6Nyj+ix3LwXeBkVDexHMT0sxzVyFnovTKWg6r6WYEOBr+DRv3UQfcN+iHeNwi1M+5jIJ7rXsCMYRzInQNM9H9f499nFIO8wGG+Ue7xjbncf3aTf5/p39f49wnArf7v9b7xHy4lZbV/f8TXWQr8zQtrnS/7d3QGzK0lnmtLf98Y8H6oC97Xoxe4Mu9H0S9uZ3mxFf/UZwGrfZ1/Rm9ue6A3pdvQf4rL0H+0GTHe/n02OgtjJvqLwYxSnXnorK+H0X/iL6AXkZuC+KcD8/1xuZ0K7iHvs3zbj+Ht7QruxfEMYGEb3hNKcc4GdkH/iWZ47quCNi/8rCnxbaUVh15k1tOonUt8DCejvyht7c+5OKafJrHMASb6tuy17sMcxPSzyLfZHiUfDTloovvZwOb++MEucjBGP9RH93Mjug/1Y1pJ00qlaw3DpRXneT6CdhoWogMfC9EOxkS0M7US2MRzugf4b3Rwdnvg5iY5mIR2MgTtDF1f0sPmaMf7PN+eZ6GDhIsLf4V+0A7VXH/+OTXTfXGvr2PfYF6E9+2oNoqB70I/RYcz11isn5NnP8e0YlpJ1coIOiPpsZrFsgb4rOf7Gf+6x7+v9HXm+9chjP4vHIQO9ixuovuZ/rWzP16NPi0zA32qZjbwRLQP9Vt//ocD3jeX2qTQz5yA92dK3EPeL/T1DvE+5pR4v5u47meW+O/s3y8plW+EDtS9Hu13LEcnSi2lUfc3x3jXXPemlfHXykipjYu+aV2u96F+ihx8xrfPmBzE9EOj7pvloKp+yroP9VM33cf6BoV+Cu3E9LOIAY6BhBpMeY37AG0yUZ2pcyHwInTKdfFLyKn4WUzoCPwRwOd9wx2D/iLxPvRXkqPQf5DX+UYtvjzfgs5Au7YsWl/nzkIo6JTu2YVYffk07/NrwGZox+VDaIf29oD3si54X4D+ovW/6C9KO6EXvON92S9LAnlCwduX3Y5evO4G3uPLpqC/FP3DC2o9+oX/W15ck9AL7nT0l4Uv+zjfiv4zfNX7uRj4tOd3s4/t/b49F6AzrQreiz3vnXz7/jzk7f++sxnvEvf70VnU9/sYWvH+jW+zkPc70V9jipklV6C/BF/nY1iGXqi3B+aVdPEV4C8lPrcATynxngNMCGJZ7GO8A/hcE/0sQS8ihXaEUf0soDvd3+1z8BzfpmNyENOPz+lLvM0/muQgpp/jfQ7ehn4pq5qDmH7qovsLUf0sCK4rj+sH00qSVrq41gyTVuYDuwRaubV87P++Gx1gmQ+c6cue7XksQe9rj6K/FB/LaEfn8573HPSRojvRmXP/Avyx1EH5NDqIcr+vuwe6FMM89Bf3Ipa5NdP9/Yxe7+fGeGfcN7gG7dfMLWllV+AN+H4NwX0q41isn5NnP8e0YlpJ1cp09KmAOzOO5TZ0FtgbfB4ORX+EvAI4vcTnJnRJkLLun4leY1eV6k3wsVyCzgpbj85cPxft17zct8Np6D3sKl//IuDD3sc56NMtZ6CDXl8FLkUHQK71x2eiujwPHQQ7FP0B5OM+ntNjvH3ZXM97OXBMifdJ6P9ZjPdkdIbhbHRm6um+7mci3L+GPl10sT/P99BZ+Is875/7HBT/r3XRvWklT61MB/YJtJLj9f6/fXsXORijnyIHQSxjchDRz2p0hmerHFTVz2zP+40+D3XSfaifWN+g4T4V0w+DHwPZGdXYlcW52r3GfYC2k5cX5y/QUftZ6D/Fifg14ID90H+mP6PTrr+JTr1+GBX7leivHL/wCbjXC+0f6IVu99K5vuKTV1zATvOJOwI/0u/L90QfPz0a/TK1mtFfKT4DbOvP+Qd0GnbB+50d8n4asDHwES/eWehF4hLgPcAm3tcH0UdADixxfCf6D3hR0J57ouuOXOLP99bSa6qvcwjaqf6dP9+3gZeXfDwJnYb+bXTNtFN9e65G/+F29Lw/XMrbLN9WJ4e8/d8HJvD+BrqmyJyAe4z3ueiFpRXvD6IXqKVeEz/xufgyemFe7fN3F3rD2ark67U+P8eUteP/LuvnGEraiejnThr1s62vUzzSVtZ9p/rZB/0l9a/efkwOYvrx55ni2/qiJjmI6sfXOd23a9UcxPRTF91P9bm4C9XOGP1gWknSShfXmmHSynuB/QKt/AS9J72/dL7/83UfjcSyAO2EPczoL9BnAdv7OicxugbiY+i95guMdjBf7HM0B31c6nJG1zVbhg7+T0U7NMtbxFJF90fRme5T+warUN0fiF7v56Md42bX+077BkfRvG9wOGP7Bp32aZ6KPrb1e/RL0zy0b7MC7evsHtyn3t9FLNbP2XD7OaYV00qqVg5Dv9we00UsB7SI5bmJsXynRSw7ojPyzkJ/mDwX+BlwXMDnjb7tinvkUehgyS40Lo2xBzrIvRP6aO1SVHfFawo62PFR9Av8t9BBkqeXfGwOfAIdaJuC/tB6Pjpr6yL0h43NfN1j0Zlp53p/PwBe2Yx3iftTy7xL3D/XjHcpLz9HB7+uRB8RbsX9zejgxyL0/3czVPcf9LkrdH8t3evetLJhauUwRic3HFjWD7273pf10+p6X+hnzPXe13s9ek87l4h+ihz4v7dPyEGhn0uCPMRyUFk/qO7PRL839Er3O9Nn3TNWPwvQAd9y36C4T5W10un32gNo/r32KaSNgZSvPbf4mPcqt3GrV7GJzAYJ8TvlOueW98jfE4E9nHO39MLfoCAigl7wV403l05RZ+4hTD+DRV15g2ll0Kgr7xA+jh2AZznnzuuRz23Q2bX39sJfm3PVVfcD493rfk3Ef11zUNv/4bpyN60MHnXmHmKYYjH0F6YVQ1WYdgzdYLz1U6uBXBF5OjqSf61z7sFS2THoNO8HS3U2c879vkmdg9H14rZGH5M4Al17Y5q3hdFdFstlE/z7SIs6Ve1ide5ER+zLvv/gnDs/0jafds6d1uw4taxfdiLycnSW30nOuTtKZccBpzrnFvnjaegv+/8Zq1PB7pSK5zsF1cbrUK1shk6LPxr9pelCdEr9i3yZoOs0DkIrPdVPblppctygnz5pJWZ3Sur5wjr+4v46dNbdezGtDMRuyLQSXmtOQH8dvtOHvgT9tXlfGnMXlq3z7xv3wS5W5xzn3BwCiMi/OOfOalWWUqdfdqX+w57Oue+Wyh7vP8TKUvodVe068H0wOqPjv0Vkb3RDiIWl4yNQPe2M9XOsn9O+nxPep0wrPbQbMq2E96lj0O9XW6GzxkDzKegjqKCzsZYD26AzHpuVhToo6mxd8p1qd7+vU9gtQWeGf98596gvQ0TOcM6dSAkpZf20Kx+LyEbA29HZYsXmS29Hc7e9c+7EUp3jgE855/4WlqGzwDu1OznlfIHv7Zxz7xSRyZ5voZE3oE+KbIzma3sfrmmlC7sh1srrgU3RmZdfRWeCnoReix5G89eJVmIai2mlXJZiF7t3jdFOmKtmZeOlsVKepgHPdM4d3UetNNNBp75Pds5dFdHPm4F/Q59Wey26+d2xjL32dKKVmMZa6SDl2rMEfbruzFArzVCbgVwR+QD6BXcOsD/6z7urL9seXbvkIuCffJ1XoP/0YZ2rgCejHdRp6CNZT0AfGboQnQYNukbggegaF58HPoBOkwZ9BOCbkTpV7VJ9T0N30pzvnDspaJ/Fzrldmh2nlvXDTkS+gD6Guz+6Vsg3UBE/H2331ei092Jh63ejj53tBDyvVCfVLqzzjYiv29DB+1Z2S9HHhTf25X9Fp8s/DX2c+Cp0U5/N0PVgbqL/WunGLqqfnLQSO47oJ5a7mFZSct4rjcV87wVshz6O+kdMK323GzKthNeaZ6KPHa1E1436Cbqz++HoI0M/9c0Rlh2J3hdBl0L4Yw/tYnWmoR2qnzvnvkQJOWklPA76GEeinbyw/1C131HVLtX3/v7zfdFlLd6ArvG2Jaqz9Vg/x/o56f2c8D4FppWe2Q2ZVmJ94mnol9UZ6NrIpzP6JXgS+njrmZ7DPegPlETK+mm3pT8+zsfwr75M0HvZvt4+LNvSl4dlvbBL9f1NNFcvQfO9MfoD63XoUkjfQR9Bnow+PnwvujlPWNZPu1idaeiPMW9B156cg2prG7RP8w5MK6aV5lrZG703bY7+8PckdAznQR/jSYy/VmJ19qZROzBYrVS1K7QzA/0h73Ty1ErMd6ifXdC+w9PQpxJ/w9hrD4y/VqahSzNs5Zx7AylwiWswjPfLJ65Y+2M3dMODpejspJm+7GHgI77O7BZ1/g1N+npUAPNQsZZ34J6HCrrY+a5Y2L+hrHxc1a4D36v8a71/d8ErLFvVpKyfdrE669FO4GPoheI89NfOiT4vW/p63/Bx3tykzqDtVqHry0zy3IsLyERvs9yXzSvK+q2VLjS2itb6yUUrMd+hfnLVypg6/ngmppVB2Q2TVsJrzTz0WtSgH/ReFub88bKS3cZBWdd2Terc7Lmv9X/fjO4a/Aj6q3P5OFY2Mk52j6DrUxXcY/2Hqv2Obvorqb43QjejW+XrboTOiLN+jvVzWtnF+jkN9ynTimmFHvSJg9zOG8/PfA5uBxaig9ALfX4e9e8Lm5QVx7Gybu1Sfa/17+t8Oy9H10DdxOfqDHSdzuJ4YpOyftrF6qwsHd+D/j8X/9emFdNKO63c6N9v9mXl+0T4nWq8tBKrU+in0M6gtVLVbh2jul/X45z3Uisx36F+bixp5VEi154ctNLsuNWrmNJbB0xw/hFCp48THY7+MnMaIL7sDuDFIvI1NFGxOvPRRww/B6x1uqbFGnSDnxFGsQYdyV9THIvIQcBB5bKgTlW7VN8r0Rk5f3fObY7+2rCDc06Au8plxXGsrJ92TerMc85tBtztnFuJTmmfiC6avbEvWwJsJiK/Qr+AxuoM2m4B+gX4bOAR59w64DHn3GOoVq73ZWvQhbAL/fRTK1XtVqIzXF5CoB/y0krMd6ifHLUSqyP+eGNMK4OyGyatNFxr/PtOpTL8+/403rvCshH0V+Ad+2AXq7M9ulnBXT62V6EzJo5FZ58Vx69BNXZfkzqDtrsT+H/+7+XE+w+xspR+R1W7VN9TgP/07b8AWOecW++cW4H1c6yf03k/J7xPdZNP08pwayXWJ14BvJpR/awQkdeLyHHAA6Wyr5eOY2X9tLsd3XTv48AM59zu6KzkPXyedo+VFcfOuUlhWbd2Hfhe6N+X+ba/Dx3kuhid8HQiOvBSHD8WK+unXZM6jxTHwHnOOYdq5bXo4FG/c17VzrSSh1bw7w79oWmFiLwOHYwr62c8tRKrczvwX3jtDForXWjsdufck0v6yVUrMd8N+ikS6vWzusm1p2rOq9qNqSMiE0TkDYHvlpiYWjEDLBOR/Z1zNwI4XTvuBnTAoZg6fje6JstJwL5N6qxB13j6H3S0HnSK8xnA00TkQl+2GTrt+T5f9hA63dwBN/mysE5Vu1Tfk9Evayf44x+hj1kuQztW5bLiOFbWT7tYnQUi8oLi2Dm3XkSuRP/R9irqeNuXAsfG6gzaDv0V5yT05n1sUSYiT0a/KB/hy05Ad4p/2gC0UtXuMXRtmWWM1U9OWon5btAPeWolVuc69AZ+rHPuGb7eCZhW+mk3TFppuNaIyBHAX0XkDmAnETkDHYy8FM3lGd4uLLsdffwM4LIe28XqPIruYv52N7oW4znobK6/Ol1H+By0M/U3ESmXPV5n0HYiche6U/GNInJprP9Qtd/RRX8l1fccdAB9X3S23NWia4RNAm71vk7A+jnWz0nr55xA430KTCtV7YZdK7E+8XHoY7nPEJFidu6P/GdLfVmxluCm/phIWT/tNkdna13k+YIuOTEV+AqjCMuK48WRsm7tUn3f4PsDRdkN6KPGS4Fv+7IL0EGP95XswrJ+2sXq/Alt828454qlU45D/6/3Nq2YVmitlRtEZF+0L/c2EdkN+DL6eP3kTLQSq7MDcBSj2oHBaqWq3Q0icoTTNeiLshy1EvPdoB8R+T8RmYL2k4vvLOG1B8ZfK1PRAeiyVlqiTmvkTkN/+b0nLEN/VfhbuY6IPK9cVqqziXNurbd/nnPub/7vbdDZKqu9+yXezw40bqxA+ThWp6pdqu8Om27cIbpzMc65R8IydB2QJeU6IrJzuayoMx52Jb47l3xtCmzqSru0+7Ld0LXJoM9a2ZD1k6NWYnVK/MNj00qfMKRaefxaIyIT0DVTd0cHIZegHZsDacxvWLYUnbWwY4s6Ve1ida53zhVrP9UCKX2MWFlKv6OqXQe+N3HOrS3VKY63AXZ0zs3y9ayf0ycMaT9nN0r3KdNKbzCkWon1iTfFrwHonFvuy7cuH6eW9dvOML4QEUGXAjKtGFpCRMS5xsGrHLVi2skToX5i1x5fXg+tuMQ1GHJ8ASe2K+uizintylLqVLXrp2+LxWIxThZLLr4tlq58t72/xcr6aZcjp2GKpce+66r77Oxy5DRMseTIyWLpyPcZrY5Ty/ppZ5wslhx8DxMniyVPTsMUS46cehlLu1dHlXN7oeuNtCzrVZ1B2+XIyWLJk9MwxZIjJ4slT07DFEuOnCwW42Sx5OHbYsmTk8WSJ6dhiiVHThaLcbJY8vBtseTJqZextHvVabOzGCShrFd1Bm2XI6eqdjlyqmqXI6eqdsbJYsnB96DtjJPFkoPvYeJU1S5HTlXtcuRU1c44WSy99n1vm+PUsn7aGSeLJQffw8Spql2OnKra5cipqp1xGnwsLVGbNXJjEJFpzrm7WpV1UWeCc26kVVlKnap2/fRtsQwkljFrsJSPU8v6aWecsollmHSfvd2QcWp7f4uV9dMuR07DFEuPfddV99nZ5chpmGLJkZPFku7bYDAYDAZDb1H3gdyLnXMvalWWWOcK59xhpeN/RjeUuQH4nnPOici7gL2BW9Adud8UqVPVrp++LZb+x3Ic8Bd0l95tgF+iOyPOAj4PfBh4ATADeD+wFvhqpKyfdsYpj1heAdwMvNU5d5eIfA34jfObFgGklPXTLkdOwxRLF763QndlXQp8H/gkej3aFNXft5qUfRZ4HbAQOB54Tw/t+unbYumt7+cCD6IbnW2HbkI0D71mPRd4ki9b4983b1Gnql0/fVssvYtlPqqT56O7LINed5ajfZxiY8MJgHibZnWq2vXTt8XS31iWAH8Avu+ce5QSROQM59yJzY5Ty/ppZ5wGF4uIbAS8HXgn2le+xh9PA7Z3zp1YqnMc8CmnG3o2lPXTzjjlGUvQX/6Uc+5zzTSXWmb/w+Pve1CcwmtPDrovcWzQcyvUZiBXRG4Oi4BnoAMkoB3PpwCblMpu66DOLf74l8BhwNOA64G70E7KR9GbzJFox/bBSJ2qdv30bbH0P5Z9gKt82U7oAN6BwEuALwFfBt4NfAEdDF6B/gOHZf20M055xPIpYBWwAzrg8g5gAbAt8AvgZ8CFwB1tyvpplyOnYYqlqu9Dgb+hgy17oYMxBwIj6HXnjiZlu3m7p/m/f9ZDu376tlh663tLYF90AGahfz0DeA7wU287zX++M3A3cGWkTlW7fvq2WHobywuA3YGvAz9Ccaavfw9wAnA6o4N5k9AfC8I6Ve366dti6X8se6NfVqcC/4pee0C/t12OXoe2DI6JlPXTzjjlEcs3gSeifecFwMboPe06tP/8HbTPMxn9vnUvcFmkrJ92xinPWK5Fv1MBXAK8kProvg52w84pvPaMu+6dcx8CEJEZzrkDSIHrcFHd8XoB5wA/AZ4O7Ip+OXkE+B3wIl92EfCQPz60gzrr/Ge7ojPoNgVmoh2VWb7sJs9jkvcZq1PVrp++LZb+xzK3VDYdmFvS7cP+fa5/vxGYHivrp51xyiaWmf59DvAfqK5uBf4b+AYwG50d9RngZb5OrKyfdjlyGqZYuvX9GWBZoa2SxpbEykrHAqzrpV0/fVssPfc9q1RnIjrQOwsdbLnFlz3k6xRlsTpV7frp22LpbSzziuPSfW1e7L0fn/XTt8UykFjWA7cDj6I/MDj/96P+73JZcRwr66edccojlrX+fR16PVoO/BadcDUTOAP4R+l4YpOyftoZpzxjGUHHc8o6qovu62A37JzCa08Oun/8uLiftnvVZrMz59xRwG/QQPdzzi1CZw/8CDjFl70UfRzsFGCbDuoAbIU+GrSRc+4hdJR+H3TEfCNgExF5FvBMz2dMnap2/fRtsQwklkvRgZYR//ciETlNRF4OLBeRVwOXisgPUe1dGivrp51xyiYWJyIvBO51zn0WHdB9PdoZOdI59wz0C9ATgP91zn02VtZPuxw5DVMsXfiej85u2g7YRkR2AyaIyJPRX3mnxMrQ69hU9BfojXpp10/fFkvPfY+IyB7ovWsn9B43gg72ii9DRLZyzj3A6H0wrFPVrp++LZbexrICnaUijGKFiHwdeKB0/HoROS4oK9epatdP3xZL/2O5HfgvdAfu3dEnH/dwzk0C7iqXFcexsn7aGadsYlno35c55x4D7kN/bLwYmOL0ceN1pePHYmX9tDNO2cayAJ3puBRYXDPdZ2+3AXBquPZkovvHj0mFSxzxzeWFzoD8Grr+0l2xsgp11qDT8ovXjv79CvQx6EvQWQrlslidqnb99G2x9D+WSeij9GuBxegXpbXoQM0Sf7wanQmzpFQnVtZPO+M0/rGsB84GdvHXpTG/uqWU9dMuR07DFEsXvo8HlvnXsei63LO8pla1KJvltbke+HaP7frp22Lpve/HgL+j16P/B7wBuBP9wXsxuizMHeiPUw81qVPVrp++LZbexrIb8Hu0bzzPv+739Zf749v952v837E6Ve366dti6X8sq4Hzgd39veu96EQa0LUIHy8rjmNl/bQzTtnE8hPgiFKd4vjtwKOlsq8Xx7GyftoZp2xjKfT09khZ7rrP3m4D4NRw7clB97684bjdq9Jgag4vn6B3tSqrWqf02UbA5FZlKXWq2vXTt8XSn1iALYCtS+UNx6ll/bQzTuMXC/qrW7m84Ti1rJ92OXIapliq+vZlGwET/d8T0bVRd04oOxiY1ie7fvq2WHrrey//vmVJU1uVy8Lj1LJe1amLXY6cehlLqe7WjL2fNZSl1Klq10/fFkv/Y7GXvexlL3vZy179edVmszMAEdkCHb3e2RctAa5Gd98tylagj4NN7bLOoO1y5GSx5MlpmGLJkZPFkienYYolR04Wi3GyWCyWos4FzrmVBBCRlzrnLmpVllKnql0/fVssFotxslhy8T1MnPoVi4g8HTgaOAR90mQJ+vTRvjTeF8Oydf594xZ1qtr107fFkienXsZyjnNuDomozUCuiLwF3eTlQjRQ0A3LDkN3oLsE2B94qf/sInStiSp1qvoeJk4WS56chimWHDlZLHlyGqZYcuRksRgni8ViKepM82WnOud+RAkistg5t0urspQ6Ve366dtisViMk8WSi+9h4tSPWETkY+iSYz8HPgz8G/Am4HB0qaCfepOw7Ejgn/xnlwN/7KFdP31bLHly6mUs04DjgJ87575EClwG04JTXsBcgse+fNmujO6aOhfYEp1VMK9qnUHb5cjJYsmT0zDFkiMniyVPTsMUS46cLBbjZLFYLKWyc4A/Aw/6v88B7vGvx4LjWNljXdr107fFYrEYJ4ul7rHkyGnQsTwInOtfD/l71zx0mcP5pftiQ5k/noTOgpzfS7t++rZY8uTUy1h8ecNxu9cE6gMBXKRsxL+X64RlndYZtF2OnCyWPDkNUyw5crJY8uQ0TLHkyMliMU4Wi8VSlB0GnAmsBL7qX5sC30A31CuOvw58MSgr16lq10/fFovFYpwslrrHkiOnQceyHPix//tBFCPoUyYjjCIsGwF2QjcrH+mxXT99Wyx5cuplLESOW2JiasUM8HlghohciK6DAnAXunPqFSLySeAmdPdUgIt8WZU6VX0PEyeLJU9OwxRLjpwsljw5DVMsOXKyWIyTxWKxFHVWo1+YT3bOXQYgIlcC1wK3OOcu88fXOecuEZFjS2WP16lq10/fFovFYpwslrrHkiOncYjlncDpwHxgRETOQAd0LwVu8sdEym4H/u4/u6zHdv30bbHkyamXsewC7Am8j0TUZo1cABGZCrycxsWFr0EXuS7KVvr3LbusM2i7HDlZLHlyGqZYcuRkseTJaZhiyZGTxWKcLBaLpahzgXPuAQwGg8FgyBAiMgE4mMZ74A3AgW3KlqJPoOzYB7t++rZY8uTUy1iud86tJxUucQ2GHF/Ake3KelVn0HY5crJY8uQ0TLHkyMliyZPTMMWSIyeLxThZLHn4tljy5GSx5MlpmGLJkZPFYpwyjuXESJ22Zf20y5HTMMWSI6dextLu1VHl3F7AjHZlvaozaLscOVkseXIaplhy5GSx5MlpmGLJkZPFYpwsljx8Wyx5crJY8uQ0TLHkyMliMU4WSx6+LZY8OfUylnavOm12FoMklPWqzqDtcuRU1S5HTlXtcuRU1c44WSw5+B60nXGyWHLwPUycqtrlyKmqXY6cqtoZJ4slB9+DtjNOFksOvoeJU1W7HDlVtcuRU1U74zT4WFqj05HfnF7Awe3KelVn0HY5crJY8uQ0TLHkyMliyZPTMMWSIyeLxThZLHn4tljy5GSx5MlpmGLJkZPFYpwyjmVapE7bsn7a5chpmGLJkVMvY2n3qttmZ08HjqZxceFZwL6lsnX+feMu6wzaLkdOFkuenIYplhw5WSx5chqmWHLkZLEYJ4vFYjFOFovFYpwsFuNUh1iWAOc45+ZQgoj8i3PurE7L+mmXI6dhiiVHTr2MpRlqs7SCiHwM+JY0HQ4AAAuhSURBVDk67fg6/zoc+I1/vw7YDnijf23XRZ1B2+XIyWLJk9MwxZIjJ4slT07DFEuOnCwW42SxWCzGyWKxWIyTxWKc6hDLdeiY0M9E5OM04lTGIqWsn3Y5cqpqZ5wGH0scnU7hHa8XMA+YFCmbDMwv10F/0Zlftc6g7XLkZLHkyWmYYsmRk8WSJ6dhiiVHThaLcbJYLBbjZLFYLMbJYjFONYnlZv+aBaz1fz/iXyOlz8OyR1qUdWvXT98WS56cehnL43oudN/uNYH6YATYKVK2v38v19kxKOu0zqDtcuRkseTJaZhiyZGTxZInp2GKJUdOFotxslgsFuNksVgsxsliMU51iGV74C3AO4G7gFcBDwLHAvf541jZg8BrgJcEZb2w66dviyVPTr2MpXgtJxG1WSNXRI4ATgfmA3f64gOAZwI3ATOBacAL/GeXof/YVepU9T1MnCyWPDkNUyw5crJY8uQ0TLHkyMliMU4Wi8VinCwWi8U4WSzGqQ6x/BOwEtgGeJ9z7nwR+T5wFvAe59wbAcKy4tg5d6WInF0u69aun74tljw59TIWr3PC41aozUAugIhMAA6mcZHrG4ADS2VL0TVTduyyzqDtcuRkseTJaZhiyZGTxZInp2GKJUdOFotxslgsFuNksVgsxsliMU51iGUJcL1zbj0GwwaIWg3kGgwGg8FgMBgMBoPBYDAYDAbDBonUxXTH+4VOp78GXVbhDGBqqWytL3teqc69XdQZtF2OnCyWPDkNUyw5crJY8uQ0TLHkyMliMU4Wi8VinCwWi8U4WSzGyWKxWIzTgGMpjXlelzw+Ot4DtB0M5F4JHAFsCXwYmI1OsT8C3eXtw8BDwL/4Oku6qDNouxw5WSx5chqmWHLkZLHkyWmYYsmRk8VinCwWi8U4WSwWi3GyWIyTxWKxGKfBx7KHH/OcOYwDuTcFxy9ER7UPAWb4stvQzdAOAWZ0UWfQdjlysljy5DRMseTIyWLJk9MwxZIjJ4vFOFksFotxslgsFuNksRgni8ViMU6Dj2V+uU7Ka9wHaJOJ6i6FWwRlc4EFwPJSnUN9QyyvWmfQdjlysljy5DRMseTIyWLJk9MwxZIjJ4vFOFksFotxslgsFuNksRgni8ViMU7jEsszy8cpr4nUB18G9kLXkyhwKjo1+chSnRHgxcB/dFFn0HY5crJY8uQ0TLHkyMliyZPTMMWSIyeLxThZLBaLcbJYLBbjZLEYJ4vFYjFOA47FOXeziJRt2kL8CLDBYDAYDAaDwWAwGAwGg8FgMBgyRW1m5IrIFsAngGOA7QAH3A+sALYGtvFVRwABJvi/q9Sp6nuYOFkseXIaplhy5GSx5MlpmGLJkZPFYpwsFovFOFksFotxsliMk8VisRinwcdyL/AH4EvOuZUkoDYDucAvgYuBw51z9wCIyCXAI8Ba59xTReQC4Dpf/znOuZdVqVPV9zBxsljy5DRMseTIyWLJk9MwxZIjJ4vFOFksFotxslgsFuNksRgni8ViMU7jEssOwFvRMc+XkQKXuJjueL+Auc3KwvdWn6XUGfRnOXKyWPLkNEyx5MjJYsmT0zDFkiMni8U4WSwWSy6f5cjJYsmT0zDFkiMni8U4WSwWSy6f5cipl7E0O271qtOM3DtE5KPAD51zy3zZ3SJyHrCsVOc0dJrynV3UGbRdjpwsljw5DVMsOXKyWPLkNEyx5MjJYjFOFovFYpwsFovFOFksxslisViM04BjEZHtgRNKNm1Rm83ORGQq8HHgaHSNXID7gAeArdD1JgRY798noOtWVKlT1fcwcbJY8uQ0TLHkyMliyZPTMMWSIyeLxThZLBaLcbJYLBbjZLEYJ4vFYjFOg49lGXAO8GXn3AoSUJuBXIPBYDAYDAaDwWAwGAwGg8Fg2GCRugZDDi/g6cCLgU2Dso8XZaU6R3dTZ9B2OXKyWPLkNEyx5MjJYsmT0zDFkiMni8U4WSwWi3GyWCwW42SxGCeLxWIxToOPxZcfUT5u9UqqlMML+AAwF/g9sAhdYqEoW+nLvleqs7aLOoO2y5GTxZInp2GKJUdOFkuenIYplhw5WSzGyWKxWIyTxWKxGCeLxThZLBaLcRpwLKUxzxnDOJA7C5ji/94NuAFYCkwBZvqyh4GP+Dqzu6gzaLscOVkseXIaplhy5GSx5MlpmGLJkZPFYpwsFovFOFksFotxsliMk8VisRinwcdykj+emTo+OpH6YIJz7kEA59wiETkcuBs4DV3rd5GI3AG8WER2BB4FqtYZtF2OnCyWPDkNUyw5crJY8uQ0TLHkyMliMU4Wi8VinCwWi8U4WSzGyWKxWIzT4GP5tYjsCgiJmJBaMQMsE5H9iwOng7o3AE8B9vXFdwMno7vB7Vu1zqDtcuRkseTJaZhiyZGTxZInp2GKJUdOFotxslgsFuNksVgsxsliMU4Wi8VinMYlliOLY1LhEqfujvcLmAbsECsDnhfWCcs6qTNouxw5WSx5chqmWHLkZLHkyWmYYsmRk8VinCwWi8U4WSwWi3GyWIyTxWKxGKfBx1Ly13Dc6iXewGAwGAwGg8FgMBgMBoPBYDAYDJmiTksrGAwGg8FgMBgMBoPBYDAYDAbDBgkbyDUYDAaDwWAwGAwGg8FgMBgMhsxhA7kGg8FgMBgMBoPBYDAYDAaDwZA5bCDXYDAYDAaDwZA9ROQUEflwi8+PEZG9E/w01BOR00TkJb3iGTnfehG5sfT6uC8/UkRmishNIvJ3EXmniJxcqle2+0C/+BkMBoPBYDAY6oOJ403AYDAYDAaDwWDoAY4B/gj8vZN6zrlP95nXI865/csFIjIJOAM42Dl3l4hsAuzmnJsLfN7XeTC0MxgMBoPBYDBs2LAZuQaDwWAwGAyGLOFnqM4TkSuBp/myd4jI9X4m629EZLKIHAocBfynn8G6h3+dLyLTReQKEXl6k3o/EJHXet+LROSL/rMbROQAEblARBaIyLtKvD7iOdwsIqdWCG0zdELFcgDn3Fo/iGswGAwGg8FgMDSFDeQaDAaDwWAwGLKDiDwbOA7YH3glcJD/6LfOuYOcc/sBc4B/dc5dBZwDfMQ5t79zbgE64/X9zrlnAx8G/rdJvRCL/UzYK4AfAK8FDgFO9bxeBjwFONhze7aI/FOLUJ4YLK3wBufcCs/jDhH5mYi8SUSsX24wGAwGg8FgaAlbWsFgMBgMBoPBkCMOA37nnHsYQETO8eX7iMjngC2BKcAFoaGITAEOBX4lIkXxJonnLc4zC5jinFsNrBaRtSKyJfAy/5rp601BB3Yvb+JvzNIKAM65t4vIvsBL0IHmlwInJHI0GAwGg8FgMGyAsIFcg8FgMBgMBkOd8APgGOfcTSJyAnB4pM4EYGXFNWbX+veR0t/F8URAgC86575bwXcDnHOzgFki8mNgITaQazAYDAaDwWBoAXuEy2AwGAwGg8GQIy4HjhGRJ4rIZsCrfPlmwN1+w7A3leqv9p/hnFsFLBSR1wGIYr+wXkVcALzNz/pFRHYWke06cSAiU0Tk8FLR/sAdXXAyGAwGg8FgMGwAsIFcg8FgMBgMBkN2cM7NAH4B3AT8Gbjef/QfwLXA34BbSyY/Bz4iIjNFZA90kPdfReQmYDZwdJN6nfK6EDgbuFpEZgG/pvXAcLhG7pfQWb0fFZG5InIjuv7uCZ1yMRgMBoPBYDBsWBDn3HhzMBgMBoPBYDAYDAaDwWAwGAwGQwvYjFyDwWAwGAwGg8FgMBgMBoPBYMgcttmZwWAwGAwGg8HQBURka+CvkY9e7JxbPmg+BoPBYDAYDIbhhC2tYDAYDAaDwWAwGAwGg8FgMBgMmcOWVjAYDAaDwWAwGAwGg8FgMBgMhsxhA7kGg8FgMBgMBoPBYDAYDAaDwZA5bCDXYDAYDAaDwWAwGAwGg8FgMBgyhw3kGgwGg8FgMBgMBoPBYDAYDAZD5rCBXIPBYDAYDAaDwWAwGAwGg8FgyBz/H2zoFpSqtkamAAAAAElFTkSuQmCC\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
}