officers_and_counts.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[View in Colaboratory](https://colab.research.google.com/github/jdungan/oscn-notebooks/blob/master/criminal_justice_summit.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis of Counts by Officers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting oscn==0.0.0.20\n",
      "  Using cached https://files.pythonhosted.org/packages/1c/3b/5d19b769d859a53d079a482b8731042071fe0c77e52a506e65f534419177/oscn-0.0.0.20-py3-none-any.whl\n",
      "Requirement already satisfied: requests in /Users/owner/projects/python_envs/oscn/lib/python3.6/site-packages (from oscn==0.0.0.20) (2.19.1)\n",
      "Requirement already satisfied: beautifulsoup4 in /Users/owner/projects/python_envs/oscn/lib/python3.6/site-packages (from oscn==0.0.0.20) (4.6.0)\n",
      "Requirement already satisfied: urllib3<1.24,>=1.21.1 in /Users/owner/projects/python_envs/oscn/lib/python3.6/site-packages (from requests->oscn==0.0.0.20) (1.23)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /Users/owner/projects/python_envs/oscn/lib/python3.6/site-packages (from requests->oscn==0.0.0.20) (2018.4.16)\n",
      "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /Users/owner/projects/python_envs/oscn/lib/python3.6/site-packages (from requests->oscn==0.0.0.20) (3.0.4)\n",
      "Requirement already satisfied: idna<2.8,>=2.5 in /Users/owner/projects/python_envs/oscn/lib/python3.6/site-packages (from requests->oscn==0.0.0.20) (2.7)\n",
      "Installing collected packages: oscn\n",
      "Successfully installed oscn-0.0.0.20\n"
     ]
    }
   ],
   "source": [
    "# !pip uninstall -y oscn\n",
    "!pip install oscn==0.0.0.20\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Request and save cases from Rogers and Love \n",
    "\n",
    "\n",
    "### Step 1. Define the data to request"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "import oscn\n",
    "cases = oscn.request.CaseList(county='adair', year='2017', type='CF', stop=50)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2.  Request and save the cases\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CF-2017-1\n",
      "CF-2017-2\n",
      "CF-2017-3\n",
      "CF-2017-4\n",
      "CF-2017-5\n",
      "CF-2017-6\n",
      "CF-2017-7\n",
      "CF-2017-8\n",
      "CF-2017-9\n",
      "CF-2017-10\n",
      "CF-2017-11\n",
      "CF-2017-12\n",
      "CF-2017-13\n",
      "CF-2017-14\n",
      "CF-2017-15\n",
      "CF-2017-16\n",
      "CF-2017-17\n",
      "CF-2017-18\n",
      "CF-2017-19\n",
      "CF-2017-20\n",
      "CF-2017-21\n",
      "CF-2017-22\n",
      "CF-2017-23\n",
      "CF-2017-24\n",
      "CF-2017-25\n",
      "CF-2017-26\n",
      "CF-2017-27\n",
      "CF-2017-28\n",
      "CF-2017-29\n",
      "CF-2017-30\n",
      "CF-2017-31\n",
      "CF-2017-32\n",
      "CF-2017-33\n",
      "CF-2017-34\n",
      "CF-2017-35\n",
      "CF-2017-36\n",
      "CF-2017-37\n",
      "CF-2017-38\n",
      "CF-2017-39\n",
      "CF-2017-40\n",
      "CF-2017-41\n",
      "CF-2017-42\n",
      "CF-2017-43\n",
      "CF-2017-44\n",
      "CF-2017-45\n",
      "CF-2017-46\n",
      "CF-2017-47\n",
      "CF-2017-48\n",
      "CF-2017-49\n",
      "CF-2017-50\n",
      "CF.adair.2017.pckl                criminal_justice_summit.ipynb\n",
      "CF.harper.2017.pckl               \u001b[34mdata\u001b[m\u001b[m\n",
      "README.md                         delaware_county_pro_se.ipynb\n",
      "Searching_count_description.ipynb officers_and_counts.ipynb\n",
      "arresting_agency-Copy1.ipynb      oscn_example.ipynb\n",
      "arresting_agency.ipynb\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "\n",
    "all_cases = [c for c in cases]\n",
    "f = open(f'CF.adair.2017.pckl', 'wb')\n",
    "pickle.dump(all_cases, f)\n",
    "f.close()\n",
    "!ls"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2.  Retrieve the data from saved files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CF.adair.2017.pckl\n",
      " added 50\n",
      "counted case: 50 length saved 50\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "import pickle\n",
    "\n",
    "\n",
    "saved_cases = []\n",
    "case_count =0 \n",
    "file_name = f'CF.adair.2017.pckl'\n",
    "print(file_name)\n",
    "new_cases = pickle.load(open(file_name, 'rb'))\n",
    "new_case_count = len(new_cases)\n",
    "case_count += new_case_count\n",
    "print(f' added {new_case_count}')\n",
    "saved_cases += new_cases\n",
    "        \n",
    "print(f'counted case: {case_count} length saved {len(saved_cases)}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3.  Load the data for analysis\n",
    "This takes about 8 minutes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.08 ms, sys: 181 µs, total: 1.26 ms\n",
      "Wall time: 1.27 ms\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "columns = ['Year','County','Type','Agency','Case Number', 'Count']\n",
    "\n",
    "\n",
    "\n",
    "def agency(case):\n",
    "  agency_desc = 'ARRESTING AGENCY'\n",
    "  for p in case.parties:\n",
    "    if agency_desc in p['type']:\n",
    "      return p['name']\n",
    "  return 'UNKNOWN'\n",
    "\n",
    "  \n",
    "count_data = lambda c, desc: [c.year, c.county, c.type, agency(c), c.case_number, desc ]\n",
    "\n",
    "panda_data = []\n",
    "for case in saved_cases:\n",
    "    for count in case.counts:\n",
    "#         print(count['description'])\n",
    "        panda_data.append(count_data(case, count['description']))\n",
    "\n",
    "%time df = pd.DataFrame(panda_data, columns = columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11421d630>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PAWgDb5pmObzX+YO+7fHe4wMANOTIb1rSjbxFRKR2CiIheNPcfgpvdbR6YYbKM0sf3bEFHVOeeim1thTAt0semJzom3oRkcCQ/Au8FV63BR1LU6IETkREaq4gkgfvIvjTGqL55yrOnf79iq/FrkIoDefvAO4oeWBysmX7RUQkYErgREQkdQWR1gB+DG8VswZbov6QZa0aUvpkKstZS/3ZD++5/U3JA5PLk1UWEZFgaBETERFJTUHks/Cm3N2JBkzeAKAlywaN4srlDdmHHKcNvKXEi3LzC8cnqywiIsHQCJyIiFSvINId3ip0VzZmt7MrT3l3Svk9E5LXlAZg8J7z75c8MPlg0MGIiMhRSuBERCS+ggjhLUn+AIBIY3cfNW4bVPp0h0pkaMXk4KwGcH3JA5NnBx2IiIh4NIVSRESOVxDpA2AGvGWfGz15A4AQrcvnM2bonnDBGghgRm5+4U9y8wuVSIuINAFK4ERE5FgFkXMBzAdwdtCh3JLxcjR5LWlgGQDuAfBebn5hbsCxiIic8JTAiYjIUQWROwC8CaBz0KEAQB9uHRXB/t1BxyEAvFtGzM/NL9Rd1kVEAqRr4EREBCiItALwRwBTgg4l1u8rJr97f8W1Wsyk6TAAPwHw45IHJmuEVESkkWkETkTkRFcQ6QfgPTTB5A0Arsl4u1PQMcgxCOBHAF7PzS/UcyMi0siUwImInMgKIp8CMA/AiKBDSaQtD506mOvWBh2HHOcz8KZUDgs6EBGRE4kSOBGRE1VB5LsA3gDQMehQkrk7PHVd0DFIXH0BzMzNLzw/6EBERE4UugZOROREUxBpDeDPaOQbc9dFhYU2Dip9urshpC8em6ZyAF8veWDyU0EHIiLS3Ok/QhGRE0lBpAuAmUij5A0Awoz2+ExoblHQcUhCmQCezM0vvDfoQEREmjuNwImInCgKIj0A/BfAkKBDqY0V0V6zPlP2i8DvTSdJPQHgxpIHJlcGHYiISHOkETgRkRNBQaQvgHeRpskbAJzE9XmtcWh/0HFIUl8B8FxufmE46EBERJojJXAiIs1dQWQAvORtQNCh1AWJ1l/JeGNR0HFISq4C8FclcSIi9U8JnIhIc1YQGQwveesTdCj14Ybwv1sFHYOk7AoAU3PzCzODDkREpDlRAici0lwVRPoDeAtAj6BDqS8dsS+vD7esDzoOSdllAJ7PzS/MCjoQEZHmQgmciEhzVBDpDS956xl0KPWJBO8MP7866DikRi6BNxKXEXQgIiLNgRI4EZHmpiCSAy95yw04kgZxYWhObtAxSI1dAuD3QQchItIcKIETEWlOCiKd4d0qYFDQoTSULFbkjgstWRJ0HFJjX83NL/xJ0EGIiKQ7JXAiIs1FQSQLwCsATgk6lIZ2Z3jqrqBjkFq5Jze/8BtBByEiks6UwImINB+/BXBW0EE0hjwWj8hG2eGg45BaeTQ3v/CyoIMQEUlXSuBERJqDgshXAdwUdBiNhURkSsbbC4OOQ2olBO9G36cHHYiISDpSAiciku4KIqcD+F3QYTS2m8Kv6SbR6asFgBdy8wu7Bh2IiEi6UQInIpLOCiJdAbwAIDvoUBpbd+wc2RW7tgUdh9RaL3j3iFMiLiJSA0rgRETSVUEkDOB5eB+ETzgkwreGX1wWdBxSJxMAPBh0ECIi6UQJnIhI+noQ3gfgE9blGTNygo5B6uzW3PzC64IOQkQkXSiBExFJRwWRLwK4NegwgtaSZSeN5KqVQcchdfb73PzCYUEHISKSDpTAiYikm4LISACPBx1GU3FXeOqmoGOQOmsJ4Nnc/MIT7lpOEZGaUgInIpJOCiIdAbwI7wOvADgrtOyUDFRWBB2H1NkwAD8POggRkaZOCZyISHp5EEBu0EE0JSFal8syZi4IOg6pF7fn5heeG3QQIiJNmRI4EZF0URCZCOCGoMNoim7JeDkadAxSLwjgqdz8wvZBByIi0lQpgRMRSQcFkSwA/xd0GE1VX24ZGcH+3UHHIfWiF4DHgg5CRKSpUgInIpIe8gGcHHQQTRWJ7G+EX1kcdBxSb76Qm184OeggRESaIppZ0DGIiEh1CiKDACwBoBX6qrHXWn44vPRPQ4OOQ+rNWgCnljww+VDQgYiINCUagRMRafoeg5K3pNrx0NCT+MnaoOOQetMPwD1BByEi0tQogRMRacoKItcBOC/oMNLFXeGpHwcdg9Sru3LzC4cEHYSISFOiBE5EpKny7vn2YNBhpJNzQwsHAbo2oBnJAvC/QQchItKUKIETEWm6fgGgS9BBpJMwoz0/E5pbFHQcUq8m5eYXXhN0ECIiTYUSOBGRpqggMg7AV4IOIx3dHn7hQNAxSL37eW5+oa4DFRGBEjgRkabqEXg3NZYaGsxPRrbCYSVxzUtfAN8OOggRkaZACZyISFNTELkIQF7QYaQrEq2/nPGvhUHHIfXu+7n5hZGggxARCZoSOBGRpud7QQeQ7r4SfqN10DFIvesA4K6ggxARCZoSOBGRpqQgMgHA2KDDSHcdsS+vN7duCDoOqXf/k5tfqIV9ROSEpgRORKRp+X7QATQHJHhH+PnVQcch9a4NgDuDDkJEJEg03S5HRKRpKIiMAjA/6DCaizILrz2p9Ol+Qcch9W4PgN4lD0zeF3QgIiJB0AiciEjTkR90AM1JFiv6nR368MOg45B6FwHw9aCDEBEJihI4EZGmoCByEoDLgw6jubkzPHVn0DFIg/if3PzCcNBBiIgEQQmciEjT8F3o3+R6l8fVw7NQXhp0HFLvegP4QtBBiIgEQR8W6gHJXJIfxpQVkLzL/f0kyQ0ks93jziRLEu3rayNMchvJB3xll5B82ff4eyRX+x5fTPIV93cJySUki9zPWJKTSL4W08+TJK9wf08jOSbJsR5y7S0j+X8kQ27bqSTfJrmC5CqSPyRJt60byddILnL7ve7KQyR/S/JDF+tckv0SxP9bV34myQ9c2UckC5L0ccw5JjmO5BySy93PjTHP20GSXX1l+xOciwjJp0muJlns/o7EOU9VP1kx+08iuYfkQnfO3iV5UUwsG3zneopv2zHPU5xjPN3VWUVyAclCksPiHMMN7jVW1cfXY7a/TPL9mLKEcbntx71ufTGvcM/PLJKDkx2L7xz5z+P5blule/whyVdJtvftbyS/7WvzUZI3pBjjmJiyeO+Z485LnRREegO4rt7akyNCRPsvZLy9IOg4pEHcHXQAIiJBUALXeCoBfKWG+3wKwEoAV1YlQgDeA3Cmr85ZAPb6Eo6xrk6Vc8wsz/34y+ui2MzyAAwHcAqAS0m2BPAKgAfMbDCAES6Wb7p97gPwHzMbYWan4Oi1PlcD6AFguJkNA3AZgN0J4r/VlT0F4EYXw1AAU5P0cQTJ7gCeA3CzmZ0MYByAm0hO9lXbjtRWOfsTgDVmNtDMBgBYC+CPsefJ91MWp40ZZjbSnbNbATxK8jzf9ofccV4C4PckM5MFRbIbvHPyfTMbZGajANwPYECCXf7u+pgE4Oduf7iEaDSACMn+MftUF1e8122Va81sBLzn8JfJjsWZEXMe/+vKD7nHQwHsBPAt3z5bAdwWmzSnGGO1kpyX2roTQNLnVmrn5vCrGUHHIA1ieG5+4aSggxARaWxK4BrPwwBuJ1mTOftTAPwGwDp4iRrMbBu8hG2gq9MTwAs4et+osQBm1UvESZhZBbxkcSCAawDMMrM33baDAG7B0SQqB8B6376LfeWbzCzqyteb2a4kXXcFsMnVrzSzZUn68PsWgCfNbIGrsx3Ad3BssvcEgKtJdkwUgDv/owH8xFd8H4AxJBMlStUysyLXxi1xtq0CcBDejWyTuQXAU/6E3cxmmtnL1ewDM9sKoBhAX1f0eQCvAvgbEkxVShDXca/bON6F97qpL7PhvReqbAPwFoAvJaifSoyJJD0vNVIQ0YIMDSwHO0d1xu5tQcchDeKrQQcgItLYlMA1nnUAZiLFaVIkWwA4H94Hxb/C+8BZZRaAsW4K2ioA77vHYXgjX3N9dd9x08w+qPshHBdjKwDnAVgC4FTELH9uZsUA2pBsB+B3AP5E8h2SPyDZw1WbCuBiF+ODJEfGdFMVfxHJ213ZQwBWkHyJ5E3uXKGaPvyOixPAPFdeZT+8JO62ag7/FABFZlbpO95KAEW+tgb4Yv9dNW35LQBwcmwhyVEAVrkkK5lTXTs14kaT+gOompI7Bd5rL/b1lzCuJK9bv4vhvW6qPFt1rgC8HlN3fMwUymMSZJIZ8F6Hr8Ts9/8A3OW2++unGmMiSc9LDV0JoFU9tCMJkAjfFn5xWfKakoYuz80vjAQdhIhIY1ICVz8S3Uwvtvx+eHP2UznvFwF4x8wOwRthu9T3QfQ9eCNtY+GNPMwBcAaAkQCWm9lhXztVUxDPqGGs1RngPmjPAlBoZm8k28HM/g0vOfgDvARlIckuZrYewGAA3wMQBfBWzBRC/xTKh1xb9wEYA+BNeCN//6qujxocl99vAXyJZNta7g8cO4XyW8mrAwBip/PdTnIpgA8A/MxXHu/5ivsc0rte8COSv0nQ59Xu+fwrgJvMbKebRjkIwEwzWwmgnOTQFOKq7nULuEQNwNkA7vKVX1t1rgB8Nia+2CmUxa68pWtrM4BuAP5zzMkwW+PiuyamvWQxJpTCeakNXfvWCC7PeDcn6BikQbRE/XyRIiKSNpTA1Y8dOH5qW0d411Id4aabFQG4KoU2pwA4n95iJ/MBdAJwrts2C74Ezsz2AWgB7xqmZNe5pRRrElWJyUgzK3Bly+BNKTzCjejsN7O9AGBmO83sOTO7Dt4o4QRXXmpmb5jZ3QB+DuDSZAGYWbGZPQZv5GUEyU7V9eFzXJzu8dKY9nfDu1YuUeK1DEAe3QIu7nhDAPLcttoaCeAj3+OHzOxUeMvL/8k32hj7PPqfw6UARlVtcMn7D+HdOymev1cl+Wb2kiu7yrW/1r0Gc3Hsh6REcVX3ugWOJmqXmtkniU9DSg65hK8vvMQ33nP1c3irO/oT42QxVifZeamZgkhfAONrvb+krBXLThrB1SuDjkMahKZRisgJRQlcPTCz/QA2kTwXANy1UxfAmzIZ62c4duThOG7K4XgAfcws18xy4X04rfqg+BG8hT/GAVjoyooA3Izk17+tAtCD5BDXV1940y6LkuyXzLMAxvlWCGwJbxTrF+7xuW7KJdyo1gAA60iOqprq6BKg4QA+rq4jkpN9C08MgrdAzO5EfcTs/jsAN5DMc/U6wZtq94s4Xf0awE0Ajrtu0cxWwzv39/iK7wGwwG2rMZLD4SVax023NLNX4E31rLqmaxqAL/rOw5cAvOP+rjrGsb4majpFbwqAC3yvv9GIc72XP64UXrcNwl1veSuAO2OvMTWz5fAS6ouBlN5bySQ8LyQvI3l/DcO/FsePukoDuTs8dVPQMUiDGJObX3jcKrsiIs2VErj6cz2AH7opXW8D+LFvqtcRZrYUx1+fNJjk+qofeCsxvm1m/nsX/RPetWLZZmbwpobtMLNyt302vOmD1Y7AuTa/CODPLtZ/APiame3xVSv0xfN8KgfvpqNdAuAekivgXd80F8CjrspoAPNILnax/tHM5sJbkORVesvGLwZQ4dsHOPYauKdd2XXwroErAvAMvFGdymr68Me5yR3/H0gud+frCTN7Nc4xbQfwEoDsBIf9VQAn0buFQDGAk1Dzb4LH091GAF7idauZvZWg7n0A7nCJ7uMA9gFYRHIRgDYAfuXi3gxvdc/76d3i4D0AV+DY85oQyVx4o1pHlsk3s7UA9pA8I84u9wG4A0let6n0nUDsNXBXxFYws4XwXj/xErGfAejl/k4lxriv/xTOywAAe2t4bF+sYX2pg7NCS0/JQGVF0HFIg7g+6ABERBoLvVxARETqguRfANzuVopNriBSHyPfUkN3lN0898XohNOCjkPqXXHJA5Prc2VbEZEmSyNwIiL1wMy+mHLy5rm8wYKRhG4Jv6wRuOZpQG5+4fCggxARaQxK4EREgnFZ0AGciPpx86h22L8neU1JQ3pPicgJQQmciEhjK4gMAlDX2w9ILZDI/kb41UVBxyENQgmciJwQlMCJiDQ+fdAM0LUZb3UMOgZpECNy8wv7BR2EiEhDUwInItL4lMAx7qA5AAAgAElEQVQFqB0PDh3E9SVBxyEN4pKgAxARaWhK4EREGlNBpD2AeLdjkEZ0V3hqSdAxSIM4N+gAREQamhI4EZHGdTp08+7AnRdaMBDQfXSaofG5+YX6bCMizZr+kRMRaVwafWsCwoz2+nRonu7D1/y0B5AXdBAiIg1JCZyISONSAtdE/E/4hYNBxyANYlLQAYiINCQlcCIijUsJXBMxhOtGtMLhA0HHIfVuUtABiIg0JCVwIiKNpSDSH0DnoMMQD4k2N2T8W9Mom58Jug5ORJoz/QMnItJ4NPrWxHwl/EbLoGOQehcBMCjoIEREGooSOBGRxqMEronphL15vbhtY9BxSL3TQiYi0mwpgRMRaTxK4JoYEqHbw/9YGXQcUu9GBB2AiEhDUQInItIYCiJZAEYGHYYc76LQ7L5BxyD1TiNwItJsKYETEWkcIwBkBx2EHC+bFf3Ghj5cGnQcUq+UwIlIs6UETkSkcZwedACS2J3h53cEHYPUq5zc/MKuQQchItIQlMCJiDSOYUEHIImN5KphmagoCzoOqVenBB2AiEhDUAInItI4egQdgCQWIjp8IePtBUHHIfUqN+gAREQaghI4EZHG0T3oAKR6N4df1f+JzYsWpxGRZkn/WYlI2iN5diplAcsJOgCpXg/sGNUZu7cFHYfUm9ygAxARaQhK4ESkOXgkxbJgFEQIoFvQYUj1SIS/HX7po6DjkHqjETgRaZbCQQcgIlJbJM8CMBZAF5J3+Da1A5ARTFRxdQKQGXQQktyVGe92vbfiy0GHIfUjN+gAREQagkbgRCSdZQFoA+/LqLa+n70Arggwrli6/i1NtGLpycNZvCroOKRe9M7NL2TQQYiI1DeNwIlI2jKz6QCmk3zSzD4OOp5q6Pq3NHJ3eOrG68q/NyjoOKTOwvBG4/cEHYiISH1SAicizUE2ycfhTZk68u+amZ0bWETH0ghcGhkb+nBICNHKKEJNaRqu1E57KIETkWZGCZyINAfPA/g/AH8EUBlwLPFoBC6NZNC6fi703ryXo+PGBB2L1Fl7AE15dF5EpMaUwIlIc1BhZo8FHUQ1NAKXZm4Nv1T2ctm4oMOQumsfdAAiIvVNi5iISHPwKslvkswh2bHqJ+igfDQCl2b6cdOodtivqXfpTwmciDQ7SuBEpDn4EoC7AbwHYL77mRdoRMfqFHQAUjMkWtwULlwcdBxSZ5GgAxARqW9K4EQk7ZlZvzg//YOOy6cpXpcnSXwx4z/68J/+WgcdgIhIfdM1cCKS9kheH6/czJ5u7FgSKA86AKm5CA8OH8ANHxdbz75BxyK1ppVERaTZ0QiciDQHp/l+xgMoAPC5IAOKoQQuTd0dnro26BikTpTAiUizoxE4EUl7ZvZt/2OS7QH8LaBw4lECl6ZCpTv3n9ur+MXi7u2y14YHtiwPZWUGHZOkjocqDwQdg4hIfVMCJyLN0QEA/YIOwkcJXJr5OBz+5MbOXTfd9OrpWZftLefeQ2szsgb9IbSrbZsOizhy9yKMtE/Qp0spWgwAqaSuibIIpgYdg4hIfVMCJyJpj+SrAMw9zAAwBGhSH9zKgg5AUhMFoj/v1GHG39u2Oe3Ol6JrGMrJXr1/fdant0zI3LD35IyVLRZHzuq/YMdl3f8xkLSeZcg8vMKGrFiA03YuxbCMLejeswLhviAZ9LEIAH15IiLNkBI4EWkOfuX7uwLAx2a2Pqhg4tCHyDSwNCtr1VdzupYdCIUm9t9kq05fYWMXjOwxO9yif9e3Nj7T89K+t+7ufbBTxssrW7dfveqMbj16rpjdt++idsPCi4cOw9E7DuxHm91LbMSaBRizbwWGtNyJTv2MoS4BHtqJrCLoAERE6psSOBFJe2Y2nWQ3eIuYAMCqIOOJQwlcE1YGlOV37Tz7P61ajgWZSbPoj56rLCOQeahF51ahcGTIYSvfOH3z1H2Tun9h8LWlE7pNy1w6c8360LiNG4ZktG+/ccnAQXP2t2ix73QSGW2wv/1ZmDXqLMw60sdW67qxCKPWLcSY0jUY0H4/2g4A2SbAwz5R6L0nIs2OEjgRSXskrwLwSwDTABDAIyTvNrN/BBrYUfoQ2US93yL7w29169qyLMSJVWU3/Cc6s1UZJgBAeWbrrgDAjJzVWw+vm/DxgWXTc9ucOvHc8qETB4VyFr+Zuajj7t09hs2beylatNi3fuCg94vbt988kkQ7fz9dsbXHp/GvHp/GvwAAUTBaYv1WL8SYzYsw0tajT5dSZA8Eqf+X65feeyLS7Og/ChFpDn4A4DQz2woAJLsA+C8AJXAS10HywC3dusyb2yJ7PMgjt9TptsvWXzDfRgNAlKEKY0YPAAi3GNmm/MBGfLDttfE5LfsVZWe0yusd7TT8i6Xj9/wza+7svaFDZx0+3LbXh0s+1Ssjo3xfbu7C6d1zVvYLhaxPvP5DsFB/rBnYH2sGXu4u13TX0y1319OFN6N7z0pm6h50daNVKEWk2VECJyLNQagqeXN2oGnd51KLmDQhb7RuNf97XTp1q+TRUbcqP/5L5WYCvQDgcIvOG0H2AYBQ5sBhAPYAiLy58aluF/W6eRfJDtnIjFxVNvas+eHiGQszSkaDaFVZmdm2uPj0icXFp0W756x6Pzd3YcvMzLIRyeLKQnmLYTj2erp91nbXEoxYswBj9q/AkFa70DFX19PVyJagAxARqW9N6QOOiEht/Yvkv0neQPIGAIUAXg84Jj8lcE3A7lBo1+d7dp/1na6dR1eSvWK3XzI7OqvjfoyperyvTa9tVX+TGZkMdVwKAAcr9ubM3/HmCv++oysGjP982RlbwhZafrSUoc2bTjrz/dlXj1hU9OmPDhyIzDKr2WhsW+zrMBYzR9+Chyc+gptO+wuu7PJr++aG6+2P7w+zhdPb2L5FMNMoU2I1SuBIVpIsIvkhyVfdPSVBchLJ12LqPknyCvf3NJLzfNvGkJzm29dIXuzb/hrJSb59x7i/S0i+4Kt3BcknfY8vIDmH5HIX59/pvmRIcDxFJBPeE5NkAckNrt4yklN820jyHpKrSK4k+Q7JU2P2z3PHdkGC87iU5CKSd9KNdLvzscdtX0zyvyS7um03kHw0TpwRkk+TXE2y2P0d8W0/ieTrLtYFJKeS7Bb7vJH8Kcl/kcxOdE5E0oESOBFJWyQHkjzbzO4G8HsAw93PbACPBxrcsTYHHcCJ7i/t2sye0KdnxaqsrLPjbY8csO1TpkWH+Mv2te1zTGKUkT3syN/F+4rO3HF447v+7R2tTb/rSif2z6nsMB125LYWAIC9e7sNWTD/c2fPnfP57Tt29Jxmht21PZZu2NLzM3jjzHz8dOLvccOIZ3BVi5/Yd1Z93v4+Y4CtnJFth5fDTKsvemo6AnfIzPLMbCiAnQC+VYN9u5K8MMG29fCmeqdiNMlTYgtJDgXwCIAvmdnJZpYH4FkAufEaITkE3m1VxpNsXU1/D7m2LgHwex69r+G3AIwFMMLMTgJwP4BXSLbw7TsFwEz326/qPJ4K4FMALgRwr2/7DLd9OIC5SH6e/wRgjZkNNLMBANYC+KM7zhbwvrR7zMwGmdkoAP8L4JiRapL3ADgbwGVmVpqkP5EmTVMoRSSdPQzgewBgZi8CeBEASA5z2y5OvGujWhd0ACeqzRkZm6/P6fbxpszwWdXVu/fZyhUh78PdEfvb9DomCcvIHjq04tD0MgBZAPDO5r+edlmf24ozQuEBR+oglDW5fNTE1aFN86ZlLusLHvshsrS0dc6ypefmhEIVB/v2XfRuj57Le4dC0TrddD6EaEZ/FA/qj+JBVdfTlSLrkP96ui3o3quS4YQjNc3U3s3n5B2qw/6z4X0hlKpfwkvS3oizbRGATJKfMrP/JGnnQdfOtTHl3wXwczP7qKrAzF6ppp0pAJ6Bd1/MSwA8V12nZraK5EEAHQBsdf1NNLODbvubJN9zcf2J3r0Or4SXoM0g2cLMDsdpdyvJGwHMJVng3+baaAtgdaK4SA4EMBrA1b7i+wCsJjkAwEQAs83sVV+f09y+k9zvO+ElkZ8xs7q8JkSaBCVwIpLOupnZkthCM1tCMrfxw0lICVwjM8Ae6RCZ+YdIu+Egz6iu7oQl0bm9duC4kbmDLbv6RxpAZrcDW8+DHRgDAJVW0fLtTc9Vnt/jutLYKVkDozljcko7bn05e878QywbHdt2NBputXbt6Alr146yrt3WzO3Xb344K6t0ZO2O9njZKGs5HIuGDseiI2V7re3OJRixdgFO278SJ58I19PV+vo3khkAzoM38pOq2QAuI3kOgH1xtv8MwE8AJEvgpgL4pktc/E7Fsfe8TOZqeMnVyQC+jSQJHMlRAFa5hKsdgNZmtiam2jwXB+CNzq01s2I3XXQygBcQh5mtcee0qysaT7IIQCd4C818v5rQTgFQZGaVvvYq3f6nAhgKYH41+58NYDCA0Wa2v5p6ImlDCZyIpLP21Wxr2WhRJKcErhGtzQx//KWcbjt3ZWSMT1a3RZnt/0ZhtEe8bWVZbTvGlmVkDTlYWXrkUifsLNt00qq986efFBlz3IIorZHd9ZrScV1mhZdPX56x8SzQG7k7Frl1y4DTtm4ZgLZtt60YOOiDba1b7zqdcevWTTvs63g2ZnY8GzOPlG227huKMGpdEUaXrcGADgfQZgCqn26XTj6pxT4tXWLQE8BHOJpsWYL6seU/BXAPvNGrYyuavUsSJMcliaES3mje9xB/NA8kOwF4C0ArAI+b2a9ito8BsN3M1pHcAOAJkh3NbGec5m4n+WUAJ6FmsxamAKi6vu5vAK5HggQujhlmdpGL9bsAfgHg5hr0XROr4Y0qfgqpxyfSpOkaOBFJZ/NIfj22kOTXUP03so2rYM8BeNfTSAOqBCrv7dxx+ud65nTdlZGR0mhW/tTK+RmGnvG2RUOZxyV24RZ5gxDzoX3hzrcmHqzYNzdeGwQ5rmLIxIvKRheHjCXVxbJvX5fBCxdcNO6DDy7fs31bn+lm2J7KMdRFd2zueQFePysfP5n4OG4Y/gyuanGffXfVpfb8zP62aka2HV6RxtfTldRin0PuerC+8O4pWXVt1g54SYBfR+DY58jM3ob35dGZCdr/GbwEL5lnAEwA0NtXthTAKNfPDhfn4wDi3RB+CoCTSZYAKAbQDsDlCfp6yF2rdjm8qZEtzGwvgAMk+8fUHQ1gqRtNuxzAj1wfjwC4gGTbeB24dirhTc2M9Yo71kSWAcij73Yf7u88t22piyuRLQA+C+BhNzoqkvaUwIlIOvsfAF92q7g96H6mA/gqgNsCji3Wx0EH0JwtycpaObZvrxUvtm0zEWRKo6/D1kaXDPkEcUfpSrMiW+ONRDHULgfI/Ci2/M0NT/Yzi8b7cAoA6G7th1xXOqFzx2ibWcniKi9r1eWjjyZOnDVrSpt1Hw+bUVmZkfD6oPoWQjRjAFYPuhJ/G/cT5I9/AtcOfgLXlH/HfrLkfPvX9Bxb/16GVaTLiPLa2u7orvu6FcCd9G6uvgpAD7cwCEj2BTACQFGc3X8K4DsJ2n0TXiJY7bV1ZlYO4CEAt/uKfwHgB1UxOK1i93XJzVUAhplZrpnlwrsGLnahkdg+X4E3RfJLruiXAH5L934ieT6AcfCmYp4HYLGZ9XZ99IU3unVZnHi6APg/AI+aWbyRzHHwksxEca0GsBDHJr73AFjgtj0HYCzJyb4+J7hFX6raWAng8wD+QjLP1bmf5HHxiqQDTaEUkbRlZlvg/cd9DrzrIACg0H0L3tSsBlBv1ziJpwwovatr59nvtGp5No6unpdUuNLKvvt8tBUTfJG5v03PLTh6vc4xQlkDtkXLlh9TVho92Hn2tlfnn9Xlc13cwgzHyUS4zefLzjh7ccbH780Jrx4Kol11MVo03OLjj/PGf/xxHjp3KZnfv/88y8o6NJpE3PYbSjbKWo5A0bARvlxlr7XduRh5axbgtAMrcXKr3ejQzxjq3JhxpaDWCRwAmNlCkosBTDGzZ0h+EcCf3aqH5QC+ZmZ74uz3OsltseU+PwPwzxRC+BN8SYu7tvc2AE+7a9S2w5uefW/MfuMBbDCzjb6ydwGcQjLHzDZV0+d9AJ4j+Qd4o2odACwhWQlvNd1LzOwQvdsNvBSz7wsAvgHgaRydipoJoALeiOKv/TG67YR3f8Wv+bbdQPJS3+Mz4X0p9wjJqkRvtiuDi+cieCNsD8N7bhbD+xLvyGvSzOa6qaKvuP8zhsEb/RNJO4z/ZYiIiNSrgsh9AH4YdBjNycyWLRbf1q1Lm7Ljp3kldftLldPPWm7HXbdWZW3fC2au7Xdx3GuVohVbVpXte3ZQvG0Tu181vXvLfgnbrbKHBz95OWvOnnJWDk1W1691653Fgwa9v7FN2x1jyCZ1nSc2I2f9Qoz6pAijy9aif4cDaDMQ5HEjRI1o3OZz8pKOeMqJieS/zewzQcchUhtK4EREGkNB5Bp492ySOtpP7vtm9y4LF2Znj0eC0a7q5G624v/358reROKFQpac+vVp27rkTUq0/fCuh9cB0eOW5Q8hVHZp39uKM0NZQ+Lt5xdFtOK/mUtmrgttnwDW7JKGzMzDO/oPmLukS5eSU8j4I4VBiyJUuRb9ixdgzNbFyLP16N2tDNkD4F0/1Rh6bD4nr7rRJhGRtKQETkSkMRRERgJYEHQY6e611q3m3dOlU04lGXfhkWRoFn3iocqlrUsxrLp67592z6yDrXPi3vQbAMr2PT89WvFJ3JG2dpmd117Q8ytdk9w8+YiS0NaFb2Uu6W5ETir1/cjKsl69ls3p3WdJl4yMysE13b+xlSL74Ec4pXgBTtu5DEOztqJbr0qGeyffs8a2bj4nr1sDtCsiEjhdAyci0jhWwFu9sFGvX2oudoVCO7+c03V5cVbW2Lq0c/1/ozNbl1a74h0AoDS7fXW3qEBG9qj20Yr4q9TvLd/eb+nuWTOGdhiX9DYGAJAb7TrymtLxO/+ZPfeD/Txc7T3rYpllZH3yybBxn3wyDJ06rSvqP2BuWXb2wdMa+zq5VGWjtFUeFg7Lw8IjZXut3Y5FyFu7EKcdWInBrd31dJ3q2FXTWYVWRKSeaQRORKSxFERK4C1PLjXwVLu27z3Ysf1JRtZpkYyuu23DI49VRhh/2fVjvD3xkR2oJokwi1aW7n54D7yl5OP6bK+vz26b2fGsmsT4QXjVu0sy1p0OokXy2vG1arW7ZOCg99e1a7dtNIm0vKfbJuR8shCj1xdhVFlJ7a6n+8nmc/J+1GABiogESAmciEhjKYhMBXBl0GGki43hjE3X53T7ZEs4fHp9tPfYIxVzO+3HacnqlWe03DNj/K8iyeqV7n16plVuT3hT5sxQ9p5L+3x7X4gZvWoS5zbuXfVa1nxWMjqwJvvFCodLd/frP7+oW7c1g0mr8fTMpiSKUOUaDChegDFbFiMPG9C7Wxmyqrue7pLN5+RphUERaZaUwImINJaCyM0AHgs6jKbOAHuoQ/sZf460zYO3XHqdXfx+dNZ170QTXtPmt7td/48WjLoz6SIkFaWL3q84+FaiGzYDALq37L94QrcrTmUNF+6oQOWh17MWzt0a2pN0umdy0YqePZfP6dN3UftwuOKUurfXNJQi++AynLp6AU7btQxDs7aha+9KhquSZS1gIiLNlhI4EZHGUhA5Cd61cJJAcWa45Es53fbsycgYUV9ttjtgOx7/bSVCQErXVX3Sc+LsVYOuSjr10az8YOnuRwhUv5z/2K6XTOvd+uRJqUV7rOUZGz6YGV5+EogOtdk/VocOGxYPGDjnYIsW+08j0VirQTaaPYhsL8Loefed+4cLg45FRKSh1GjZYhERqYOCPSsBrA86jKaoAqi4p3PH6Zf2zOlen8kbAPzoucrlqSZvALCvbZ/SVOqRma3AtkuS1Zu99ZXxZZWHF6fav9/JlT3P+ELp2YezLbMoee3kdu3qOXze3MvOnDfvko27d3Wfboa99dFuUxHBns4T8fauoOMQEWlISuBERBrX20EH0NQUZWctH9u31+p/tm0zEWStF++I5+yl0Xl9tiOlqZNVDrTOSXlkKiP71KTJnsEy3tz4VEcz21OTOKq0QYucL5aOHz6gsts0GCpq00asw4fa9V6y5FMTZ793NTZuPGl6NMrm9MXCjKADEBFpSErgREQalxI4p5Q4/M1uXaZdl9Nt4KFQ6OT6bj+7zA7c8lq0e033O5zdqW2qdcPZI04GEE1W70DF7l4Ld761rKaxVCEYOqd86KQLyvM+otVfslVZmdWuePUZE2fNvCZn9arT3y8vz6rVSGET827QAYiINCQlcCIijUsJHIDpLVssOqtv700zWrWcBLJB7kma/3x0XkYUNVoBEgDKM1t2TbUuQ627gNlLU6m7au/8s3aVbplZ03j8ekU7Dfti6fi2kWir9+rSzvFCGZs2DT7z/dlXD1+86FPLDh5s957V02hfI9sOoNaJsohIOlACJyLSmAr2fAJgddBhBGUfufeanG4zbunWZXg52a+h+hlaEl16yjpL6UbaflGGS4FQjUbtMjIH7Ui17lubnh1ZaRVraxqXXzYyI1eWnTV2VHm/mTAcqEtb8ezZ0/2U+fMuGTt3zmVbd+7sMc0MtZr6GZCZ551brNXZRKRZUwInItL4TshRuJfbtJ4zrm+vA0taZI8HyYbqJ6PSyr/7fDSLtfg/7mCrrhtA1mi/jBajUr45e6WVt5626W+HzaysprHFGlXZf9zny87YGraMj+raVjylpW16LP3wvEnvzfpCeP36Ie9Go6GShuinnv076ABERBqaEjgRkcZ3QiVwO0Kh7Rf3zHnvh106nR4lG/yG0re8Gn0vuwKDarPvvja9Ux5NqxLK6NwPyFiTav3tpRuGrNm3aHZN+4mno7Xpd13phAE9KztOh6FBRp6i0czWa9eMmTBr5jV9Vq48c055eXa9rIjZAKIAXg46CBGRhqYETkSk8b0DNMyH7abmT5F2s87p0zNUkpU5tjH667vFisd+ZEnv4ZbIvrZ9DtVmv1C4z7qa1J+3498TDlXsn1+bvmJlIJR1YfnIieeUnzofhm310WZ8DG3ZPOj092dflVdU9JnlB/a3n2mGOo8k1qP3zzu3eHPQQYiINDQlcCIija1gz1YAHwYdRkNaH87YcG7vHvMe7tj+bCM7NkqnZnbvs5UHCGTVton9bXrWKrHOaDGqcw134Zsbn+ptZttr0188A6Ldx1xTOg6tLGtefbWZyL69XU9esODicXM+uHzXju29pplhZ0P3mYIXgw5ARKQxKIETEQnG34MOoCEYYP+vY/vpF/bqEdkWDo9pzL6vezs6o00phteljUMtu7SqzX6hcJ9TAW6tyT6HK/d3nbP99TotaBKrFbK7TCkdN3pIRc/paITRsbKyVt2WLTtn0nuzprT8ZN3QGdFoqLih+6xGygkcyUqSRSSXklxE8k66ax9JTiK5x21fTvJXvv1uIPkoyYkkZ8e0GSa5hWQPkk+SvMKVTyM5z1dvDMlpvsenuzqrSC4gWUhyWEzbJLmdZAf3OIekkRznq7ONZCeSBSTvIvk7dwzLSB5yfxeRvMIfn2///QnO1XHlVX34Ht/lzlURybkkr/cd+wpf3//w7X+QZNd4/ZD8gXtuFrv9zqiuvTjxXUhynjv2hSQfjNleRPJvMWVPktxAMts97kyyxLf9JJKv+56nqSS7xbxeqn7OT/Xc+corY9rIjznmxe4cP0qyfcy+/0fybJJnkvzA7f8RyYJEfZIsIdk5xfO9iOQskoPr+Lym+r6LPY8J9/O1PZ9ktjuuJe5YppPsG6f/I+eY5L0k749pK4/kR+7vCMmnSa4mWez+jrhtuSQ/9O33dRdHB/d6Wuvr6z1X5wZ679WF7rX0b5I1nqHSIEs3i4hIUk8CuA/N6Iu0FZmZa76c023/vozQxMbuu8tu23jRHBtZ13bKMlvXdCQNgPuAndF1hVVuSfkWBABQsv/D0/q1GfZu15Z9JtSm37ixgDy74uSJAyu7Ly/MWpAdpTXYap9VotFwy5KSkeNLSvKsS5eSef0HzGNW1uHRDd2vz8Lzzi2uSTJ8yMzyAMB92HwOQDsA97rtM8zsIpItASwk+ZKZzfLtPwNAL5J9zexjV3Y+gKVmtpHHr9HTleSFZvaGv5BkNwBTAVxjZlUf8MYBGABgSVU9MzOS7wM4C8DrAMYCWOh+z3QfrHeY2Y6qvs3sW669XACvVR2vK7uoBueqWiRvBvApAKeb2V6S7QBc5qtyrZnFGxXeDuBOAN+Nae8sABcBGGVmpS7J8I+qJ2qvav+hAB4FMNnMlpPMAHCjb/sQABkAxpNsbWb+lVwrAXwFwGMxbbYAUAjgDjN71ZVNAtDFVZlhZnU9p4f8z1GMa81sHsksAPcD+CcA/7+zZwL4FrxbaFxlZovccQ9O1mmq55vkjQB+CeBz/vI4TcZ9XmOPsbr3XU33o7ei8QYXPwCcY2bbSf4YwD0Avh7bju/4TwLwLwDf8xV/AcBf3d9/AvChmVV9KfFjAH8EcGVMO9cB+DaAc81sl4vjbjOL9yXD383sFrffOQBeJHmOmaW8IFWz+eAgIpJWCvZsQDNZMa8cKP9ul07Tr+jZvee+jFCdRsBq676/VG4gkPINuOMxMGoM96zt/uHsvBa12W/65qlnVETLV9a230S6WfuTryud0LVTtO2M+m47MXLbtn5jPnj/ytELFnx29b59HWea4XAjdPxcbXc0s63wPuDfwpjMy8wOASgC0DOmPAov8fqCr9j/oS/WLwH8IE75LQCeqkreXNszzSzeYizvwUvY4H4/BC+hq3o8K84+jeH7AL5hZnsBwMz2mtlTKez3BICrefwU6xwA282s1LW33cw21iCe7wD4mZktd/tXmpk/IZsC4BkAbwK4JGbfhwHczuPvTXkNgNlVyZtrd5qZNepUeLd67XcA9CE5AjiSkK40sy4Z0GYAACAASURBVEoAXQFscnUrzSyVeyKmer7fBTAwhfYSPa+xx5LwfVeL/S6Al4TFmo2Y926c9lYC2FU16uhcBeCvJAcCGA3gJ75t9wEYQ3JAVQHJqwDkA/h0TafFm9k7AB6H70uGVCiBExEJzp+CDqCu5rXIXja2b6+1r7dpPRFu6lFjmzwnOrvTPpxW13YOt+i0Cd433LUSyho8DEDcaWjViaIy+61Nf8lwyUK9ykS49WVlp48/s3zQe2jk+7kd2N9pYNHCyeM+eP+K/du29p1mDbfAShR1SOAAwMzWwBuVOWYEld6UxUHwPrzG+itcAkfvtf9ZAC8k6GI2gDL3bbvfqQAWpBjmLBxN4E4H8BKA3u7xWHgJXk380j+drIb7AgDcaFtbd/4SedbXzy995fvhfdi/Lab+mwB6k1xJ8n9Jxo7oJ2qvylAA1S0QdDWAv8F7/qbEbFsHYCaA62rY5viYqXkDqqmbSMuYNq6OV8klaosAnOyKLsTR5OUhACtIvkTyJjdymEyy813lYvhGhVHz5zXescS+71I6j3H2S5TAXYBjV6ZNdI797+UzAew0s1UATgFQ5M55Vd+V8L7UOdUV9YU34vtpM4tdRMn/Hnu2mlOxAEefz5RoCqWISHBeAbANR6fhpI3D5KHbunae817LFuPgTdUJRJuDtuu6t6OpfCuc1L42Pbciybe11SHDLRiKvG/RPWfWdN/dZVsHLN/zwbtD2p9Zb1Mp/YZW9hnbO9p5/T+z5q4rY8Ww5HvUn/Lylp2XL58wiSsqS3v1Xjqzd+8Pu/7/9s47zqrq2uPf38zAUFUUGw7FDiKIJfbB3l40lmiUaILpvthiNC8xmpfRWEiMJYlGfUlssZEYKyl2ERtiAUaKBVEQG4p0GGBmvT/2vnC43Hvn3pl7587o+n4+9zNz99llnX3O3eesvddau7KycbsiNvHkQQfOKGSFJh9qJU0iKG/XZHgxI5qV9Yjmi4OA8WaWK5jLJQRzrkymZQBIGk8wDXvEzNJfgCcAO0vqDnQys8WS3o6rBHsDV1IYa5l3KYsPXBHIZfL4e2CiEn6G8bx2BWqBA4DRkn5mZrfkUV9OJO1GWG2aJWkOcJOkDdOuW8pE8Z8FVF1qE8p0kitWhwHfAjCzi6OicChh1XAEsD/Zox5bHv19h6RlwDsEE8EUBV3XPCm4H6NZaU3aJMKTcQVwMfCLRHq2Ph4NPCfpXHKvpGdiLjCPsGp3ddqxbCaU6RS8L6qvwDmO45SLugUrCaY8HYrHu3V9da/+NR8/163rfuVU3gB+eWfj1AorjgK8qGe/Vr/AVnbesbH5XJmZ/NnY4YtXzn+htTJkY33rVnNKw/Ad+jduPBajqVTtZMOssnr2rKH7Pvfs17ebOnX4Kw0N3SZYcfauu721FUjaiuD/lApEM87MdiLMsn9HUrYX69TMfbMvfWb2BNCV4K+UYgqwSyLPHoQXzvUzlF8KvEnw0Uqt2r1AWPnbBHg9V/ulIJpNLo7915Ly8wmrp6enpTdGE8VfEsxMv1pAtVMIZm+ZGAEMVAhOMoOgLK9Vd1x5mUh4Ic+nzjYl+rYNAaZJ6gZskDR5NLMZ0WT0IGAnSRsBnwK90qrqCcyPZXL198lmNszMjjGz2fnImO26ZjiX9N9dXqSVqyWsmiY5gLAyNhG4KA95ZwMzCX6FX2VNkLGpwDAlAqbE/4fFYwBLCb/B0ySdXMh5JNgZyNv/DVyBcxzHKTc3lVuAfFlYoQUn9dl03I826T1sVSKyV7nYe2rTy/3nsk+x6lvco6bFyleKyuqhOwCrWlr+0fdv3b7Jmj5orRzZqECVh6wcut8hK4dOllGydprj00/67/Li+K9+6ZWXj3xn4cLeT5uxtIVVLSO72WJeSNoYuAG41szWUijNbCYwiuyrZncBpwAHElZtmuMSgg9TiuuAU7V2FLpckVCfA35EMMkk/j0beCFd9jbkcuC6aE5JXJX8ZgHlrwJ+QLQKk7S9pG0Tx4cB72YqmIUrgJ/H4BRIqpB0Wnzx/howxMwGmNkAgg9cuhklwKVAMmrjncDekr6cSpA0XCFgSpshqROhv2eb2WSCovJk4viXE35h2xKUnPkEE+CvSOoZ8x0HTDKzxiL0dzbWuq4ZziXr7y4XGcodDvw7PZ+ZrSL8Vr7ZnD9e5C7CCtrbZvZerOMtQrCgCxP5LgReicdSbX0c5bhM0mH5nks8n/0I/m9/it/PkHRGc+VcgXMcxykndQumAOPLLUZz3NOj+/jafjXLplRX11KAw3mpqF5hS854qGnTYta5tNumrfbhU0XXXqhriwMbrGha3uvZj+/7KAbJKBn9mzYednJDbZceTV1KtuKXD0uX9tpy0sQjhr/w/AkNH3201VgzFboR910HHThjUQuaTvnCTAEeI/gBZZupvwEYrhDNcS1i1LglwBNp0QwzYmb/gjW+gNE080TgcoUw5c8BxxN8ajLxLLAVaxS4V4AaCvd/K4Rukt5LfH6cdvx6ghIxQSGk+jhYa4U36Sv1WHrlMejDfUDq99cDuFVhC4DJBD+kugLqm0x4cb9LIRT8a4Q+qyVEKkya2z4N7CBp87Q6ppDwTYz+qUcCZyqEfp8K/JA11zLddyu1RUO2vrswmR7T0v2zRqWd8+R4Lt1ZE3wl6f8GwXfvdQWfxr8SVs8aY59cS4hYOhE4Dfhunv2djUKva/Ics/3usvVjrnL7A2MzCWhmHxAUs9RKYK4+/jthxT19Jf07wHYKWwjMALaLaeltzSRE6LxJ0u4x+Yq09lI+1ifG728QggB9NRGBciBhxTQnKt+EjeM4jgNA3frfI0ShanfMrayYe+rmm741q1OnvZrP3Xb84s7GsUPetaJuV/BU7VXTmyqrC3Ikz8TKpU+MbWyY2CrZ9t30uLFbdNu2TbZjeLHqrXGTK9/dDdG1LdrLhdS0costpr7Yr3/9hpWVqwblUWTHgw6cMaXkgjlOO0TSK8AeZray3LKUA0k1wJ/M7Ihyy1IsJI0BjosRR7PncwXOcRynzNSt35MQ+rl7uUVJcuMG6z1z3QbrD7a4eXB7YfC7TVP+986mQSqyFckT+127kGgG1hqaGj97b8XCm2taU4fQqmP7n/16p4rqwc3nbj2faOFbD3V+2RrVtG3zuduGXhu+N2mbrScsq+6yeHcp47V+5KADZxRkruQ4jvN5wE0oHcdxyk3dgkUE8412wayqqvcO6LvFy9f22mDf9qa8VTbayp/9ralTsZW3FZ16flIM5Q2gorJXDVS1al83w6oenXNrz9TeWqWmt623zTcahvfdtGn9jKZI5eCzeTU7TZhw7J4vv/SV9+bP3/Rps3W2aEiP+OY4jvOFwBU4x3Gc9sEfyy1AEzRdtlGvsV+u2bzXJ1WV7SLiWjo//GfTs9WrKGYIegAWd+9TqO9VTio6DWh1gJBFqz7rN/mzsW22UXAVlV2OWrHbfrUrB72IkSscfpuybNn6/eonHzr8+edObPzgg23HNjXpPWDKQQfOyLTvk+M4zuceV+Acx3HaA3ULJhD2hSsL0zp3mrFP/5qpd63Xcz/CPlPtjr4f28x9p1hJfPEW9exb1E2uq7rsuknzuZpn+oLxey9YMffZYtSVL9s39tn9pIZ9VnSxTq+2ZbvN0djYef233txzv2ef+frm06fvc0m55XEcxykXrsA5juO0Hy6Ett2fayWsPG/jjcZ+rc9mfRdXVLRpSOyCMLO6OxoXau2IZkVjUc9+RQ0CUFG1xSBQUTaXfuz924c2WeOsYtSVLz3ostnJDbU7bdu42Vis5dsilIaK9+Z+vFWrtg5wHMfpyLgC5ziO016oW1AP3N1Wzb3YpXrKXv1r3nm4R/f9WBPeuF1y8pNNz/Rczk6lqn9Jt806FbtOVW7+VvO5mmeVreg59sO/LYr7GrUZQhX7rRy833+t3Pn1ClObKpDNMKquru4LGXXPcRwHXIFzHMdpb/wvUNKX02XS0u9utsnY72y2yaCGiop2E3UwGxstsA++Mt5KprwBNFT3KkoAkyRVXXbuUay6Pl4+a/C7i6e0qSllij5NGw4+pWH4Bhs0dStL+2nMAW4utxCO4zjlxBU4x3Gc9kTdghnATaWq/tFuXV/Zu3/Np+O7dtkPqUM8Ay6+vXG2oOgKVpJVVV02K3adFZ22GQIUzbdu/Cf/rF3euLQsfmmdqVrv+BV77bPbyq2eYd1okG3JFXV1dQ1lbN9xHKfsdIiHt+M4zheMi4HlxaxwQYUWnNBns2d+vOnGu6yS+haz7lJyxISm5zdeyO6lbGNVZfViVLFxseuVKjupYsNibjJd8eicWzYzs7JFiBzWuOW+X12x56edrHJqGZr/gHa64b3jOE5b4gqc4zhOe6NuwfvAdcWq7u6ePV4Y3q+mYXp1532LVWdb0GOpfTby8aZtSt3O0m6bFSXYSCYqq4cUtb6ljYs2f+nTh98saqUF0su69/9Gw/Btaxo3HIthbdj0+XV1dcvasD3HcZx2iStwjuM47ZPLgVZt4vxRZeVHh9f0eeHS3hvu2SQVJax9W/K/dzVOqTCKvjKWzsKe/T4rVd2V1TvuCKwoZp1vL5q0xyfL5zxdzDoLpYKKToev3Hm/A1fu+IqMj9ugyReB29qgHcdxnHaPK3CO4zjtkboFnwJXtbT4tRusP+7gvn26zOlUtWcRpWoz9pzW9MqAj2mTFcNFPfuWbFVHql4Pdasvdr1PfXj3lxqbVs0odr2FslXTprt+vaG2ortVTyhhMwacWVdX15arfY7jOO0WV+Acx3HaL1cBnxRS4J2qqln79dvilRt7rV+LtH6J5CopnVfa0rMebOrdVu0t6d6npM/Cys47LCl2nY22qusTH9zZaGZlD+jRlc69RzTs+6UdVtU8jVEKef5aV1f3YgnqdRzH6ZC4Auc4jtNeqVuwCPhlPlkbofHijXqNPapm897zKit3KbFkJeUn/2iaUNVEv7Zqb1mXjYoW7j8TVV2GbQvF9xWbt+KD7d5c+PILxa63pey9avvhR63Y7Z0K09tFrHYR8LMi1uc4jtPhcQXOcRynfXM9MC5Xhtc6d35zn/41r/99vZ77IXVrI7lKwqBZNnXoTGvTYCurqrqX1M9OFettDp2mlaLuV+c9vt/SVQtLab5YEJva+tt/o2G/zXo39cx5zxbAz+vq6j7IN7OkGkkPSHpT0gxJv1PcpF7S/pIWSJooabKkxxR9QyVtKmmMpEmSpkr6l6QhMe9ESfMkzYz/PyZpgKRl8ftUSbdJ6hTrOlXStWlyPSVpt/h/D0k3Rvlejsf2SLT1oaQ5ie+dJS2OZQdIMklnJuq+VtKpie8/ljRdUn08n6tSsqXJ1FnSNZLeiv31gKSaxPHGhAwTY9vJPpwo6bGYt07SeYmy50UZJkqaIOmbiX54PVH+nkT51DlPlTQi32vuOF9EXIFzHMdpz9QtMOC7ZNhWYAWs+NEmvZ8a0WfTAUsqKnZoe+GKS0WTrfr56MZKQWVbtdmkipWmij6lbqei89ZzS1X3I3Nu3dKsqS0CieRFJyq7HbNi99q9Vm73PNaqffDGA3/MN7MkAfcC95vZtsB2QA/g0kS2cWY2zMyGAhOA02P6xcCjZraTme0A/MzM6mPeYcCDwE/i94NjmRnx2BCgBvhanqL+GZgHbGtmuwLfAnon2roBuDr13czSg+B8DJydUkzT+uA04FBgTzMbAnwp5u+aQY7LgJ7A9rG/7gfujf0IsCwhwzAzeyetD5N9kS7DIcDu8XwOApTIcnKi/PGJ9Ktj/qOBGzMpnY7jBFyBcxzHae/ULXiDNFPK57p0qd+rf9/Zj3fvtj+fkxed//5n0zPVq9i+Ldtc1nWTOUglVxirqnctmZLY0LS09/NzH5ptZu0qyMfgxr57fW3F3os7W9XkFhRfBXyvrq6uqYAyBwLLzexmADNrBM4Bvq20lemopPQEUhFINwfeSx03s7xlju28CGzRXF5JWwN7ABeaWVMsP9PM/plve8Bc4HFgZIZjFwD/bWbzY90rzGyUma0V0Tb2x7eAc6L8xH5rIPRja/h5lGFhrHehmd2ab2EzexNYCvRqpRyO87nFFTjHcZyOwZXAS0ulJadutsnTP9hs48ErKrR1uYUqFjVzbebw16zNI2Yu6lFTUJCYllJRtem2UDGrVPXPXjJ91w+XvVPWrQUysZ513eKUhuGDt2zc5CmMxgKKXllXV1do9M7BwMvJhKhEzAJS+wnWSpoY0w4Gborp1wF/kfSkpAsk5a1wS+pCUMr+k6eME1NKUyv4NXCeEpMPktYDepjZzDzKbwPMSlfsgJeijABdE6aO9yXy1CbSL0gWjjL0NLNcfpB3JMpfkX5Q0i7Am2bWblaVHae94Qqc4zhOR6BuQeOsqqpT9+lf89HLXbsMR/r8jN9mdtHtjQsFXdq66UU9+y1tq7YqqrbI58W6xYz76J69VzatKImvXWuoQJUHrRyy/2Erd5oi05w8irwJXFQicVLmf32Bm4HfAJjZw8BWwJ+AgcCrkprzjdw6KoMfAR8kVu2yrYQWbYU0Kkjjga9nyyPpsKgkvSNp7xY0kzShPDaRnjShvDRr6ewkTSh/kkg/R9IUwnm1pF7H+cLw+XkBcBzH+ZzT78JPp6ySriu3HMVmxNimZ3ouZ6dytL24R02bmR1WVu+yQSnrN5o6Pfb+X7uYWdG3LSgGfZt6Dz25obZ7z6auuSJnrgBOqqura8nefFOBXZMJcUWoH/BWhvwPAsNTX8xsnpndaWbfIPjHDc9QJknKB25rYFdJX4npn7Ku+d+GhC1BpgA7qThmu5cBPyX6l8XVtMWStozfH47yvQak+8vNAPpJ6pmWvmuUsUUkZNiqBcWvNrPBwFcJq6FtPqHjOB0FV+Acx3E6FlcDj5VbiGKx0UL78JjnbWi52l/adeNMwR1KQkWnLXckBK8oGQtXfrLllPnPvlrKNlpDFzptcOKKvfcctmrAOIxMq5//U1dX90oLq38c6JaIeFhJMD2+xcwytbUvQZFB0oEpP7mo1GxNMLNsFjP7hLDVwfkxaQKwj6TNYn27AdXAbDObQTBTvCgVLCRGd/xyoSdrZtMJSutRieTLgeslbRDrFhlWtqOSfytwVUqZjP3WDXiiUFnSuBy4LirPqaib38y3sJk9SOijkbH8GZLOaKVMjvO5whU4x3GcDkT9yHojvNiUVBFoKy66vfFdQdk2HF/ZqedGbdWWVFGpyt5TS93OlPnP7rtw5bznSt1Oa9ht1da1x67Y/YMqq3g9kfxQXV3d71paZwzicixwgqQ3gTcI0Vt/nsiW8t+aBHwDODem7wq8JGky8DzwZzMrZHuG+wnKY62ZfQScDfwrmlheA4xIBS0hRJXdFHhL0mvALYRIkS3hUkIEzBTXExTZ8fFcngVejZ90zif0zxuxv04Aji1CMJzrgSeBCfH8xgHJYDRJH7hsk1EXAz9WMBUfSFjVdBwnonYWtMpxHMfJgyG3DjkO+Ee55WgNh73c9MJ3Hmlq88AlKQzsyf2ubaANTbVWNUx6YdXSx0t+zp0qqhcc0+/MRRWqrGk+d/lopKnh351fHf9hxfwtgZ3r6ur8Rd1ZC0ljgOMybKfgOF9YfAXOcRynA1I/sv5e4NpmM7ZTeiyz+d96tKklfjJFo6G614dtqbwBVHbeYSjQEv+ugljZ1LD+uI/+Ma8I0Q5LSiUV1Ueu2HXvI1bsfKIrb04mzOxIV94cZ21cgXMcx+m4/Ij8Qpe3Oy68q/G1CmOTcsqwuMcWbR6mXOrUDfUsNDx+i/hw2cyhs5dMH9cWbbWS8/a47Ojnyy2E4zhOR8EVOMdxnA5K/cj6RuBEQpS5DsPurze9utVH7FtuORb16LeoHO1WVg9uaKu2Xpj7UO2KxmUt2Ui7rbirZlRti/3eHMdxvoi4Auc4jtOBqR9ZvxA4krAXVbun80pb9qP7mzYstxwAi3r2LYt5YVX1TgNZO6hDyTCs8pH3b93IzBa0RXsFMpkQ0MNxHMcpAFfgHMdxOjj1I+vfBY6mDXyrWsu59za9WNVE/3LLAbCk26adytGuKrpvjKpbvNdWoSxZtWCLV+c9VvLolwUyG/ivmlG1bbaRuuM4zucFV+Acx3E+B9SPrB9P2F6g3YYW3n62TRv2tpXddDLFis7rl3Rj7VxUdtq2TbeBeHPhK3vNa/jwmbZsMwfzgMNqRtXOKbcgjuM4HRFX4BzHcT4n1I+s/ztwYbnlyERFk626YHSjBJXlliVFY2V1n3K1Xdlll35t3eYTH9y5c6OtmtnW7aaxDDiqZlTttDLL4TiO02FxBc5xHOdzRP3I+ssIGwO3K37w76Znu6xkYLnlSLGyqtt8pLKtwFVU9t4SKt9uyzYbbWX3pz64e3kZQ7I3AiNqRtW2603GHcdx2juuwDmO43z++D4wttxCpNjiE3t3/8m2e7nlSLK4e5/3yy1DRVW/WW3d5icNcwa9vWhSuUL2/7BmVO0DZWrbcRznc4MrcI7jOJ8z6kfWrwSOBV4utyyY2UW3N34m6FpuUZIs6tlvfrllqOyyS+9ytPvSpw8PX7Zq8Utt3OxFNaNq/6+N23Qcx/lc4gqc4zjO55D6kfWfAQcDE8opx4lPNz2z3jKGlVOGTCzq2bdcZoSrqajqNxjU5puJA3rk/Vv6mzXNbaP2rqkZVVvXRm05juN87nEFznEc53NK/cj6+cAhwAvlaH/DhfbRcc/Z0HK03RxLum1eVW4ZJEmVm7xejraXNy7ZePwn/3rHzEodtfTXNaNqzylxG47jOF8oXIFzHMf5HFM/sn4BcCjwbFu3fdEdjTMF67d1u/mwvMuGPcstA0BV9bAu5Wr73cVTvjR3+exxJWziVzWjan9Wwvodx3G+kLgC5ziO8zmnfmT9IuBw4PG2avOQV5rGbzqfPduqvUJZVdV1k3LLAFDRefshwOJytT/2w7/tsappZSlWAX9eM6r2f0tQr+M4zhceV+Acx3G+ANSPrF8M/Bfw91K31W25LfjOI00DSt1OS2ms6LQMtFm55QCQqrqoYv3XytV+E43Vj39we5WZLStalfCDmlG1lxepPsdxHCcNV+Acx3G+INSPrF8BnATcUMp2Lry7cXKFsWkp22gNS7ttNgdJ5ZYjRWXnHRvL2f78FR9vPX3B+GIEu1kBnOTRJh3HcUqLK3COkyeSGiVNlPSapL9L6hbTayQ9IOlNSTMk/U5S53ism6Q7JNXHcs9I6hGPXSBpiqTJsd49YvpTkl6PaRMl3RPTt4/HJkqaJun/8mhjcUL+wZKeiHW/KekXii+xkk6V1CRpaCL/a5IGZOiHzpKukfRWrOcBSTUZ+in1GZBWvkLS72P99ZImSNpS0viYf5akucnyktaXdFtsc0b8f/1Y3wBJ66xgSLpF0sxEPc8Vcq6Sfinp8rS0YZKmxf/fifKn6v99ot3j08otziSrpN0lPR2vyauS/hyv56mSrs1wThnbLIT6kfVN9SPr/xu4uNCy+bDbG00Tt/6AfUtRd7FY1KNmXrllSFJZPXQHYFU5ZZj82djhi1fOb02wm3nAETWjavNa4ZW0UeI+/lDSnMR3S4y1Dyltw3VJP5K0PDUGxLT9Y7mjEmljJO0f/z8y/sYmSZoq6QcxvS7R9muSvpIo/31J0+PnRUn7Jo6lxulJcQwbFtNzjWPvSOod85mkKxP1nSepLvH9FIVnw5TYxp/T+yHmk6QLFcbiNyQ9KWlw4nhqzJgs6REprDw3M37NkVQdv/eW9E78f4CkZVp7fP9mWn31sX8vkZTRv1PSZpLuVhjLX5b0L0nbxWMtek7l6veYb1js88PTZEmOzSbpzMSxayWdmuiXbM+TVHvTJWUN2CPpmHgdpsV+OiZxLL3+szKUT74bTJP0/QzXOdP1bJXcTvuk7FG4HKcDsczMUg/pO4DTJF0N3Atcb2ZHS6oE/g+4FPgJcDbwkZkNieW2B1ZK2gs4EtjFzBriQ71zoq2TzSx9n6bfA1eb2QOxriExPWMbyYKSugIPAv9tZo8oKJ//AH4IXBezvQdcAJzYTD9cBvQEtjezRknfAu6VtEeMaLe6n7JwItAHGGpmTQrK3xIzSymwpwK7mdkZCfnvAV4zs9TLwkXAn4ETmpH1J2Z2T4b0fM71LuA/wPmJtJNieooDzOyTZmTIiKRNCeaMJ5nZ8zHteELf5qLFbSapH1n/yyG3DpkFXAsUJZBGp1W2/Jz7mjYQtJvVrUws6tmvWOaCRUEVXXuhrhOxZWXdbuHR92/d/uh+Z35QoYrNCyxaDxxdM6p2Zr4FzOxTIDWe1gGLzey38fvixFh7K3A6YUxNMYKwPcZxwM2J9NTv+qFkW5I6Ecbl3c3svaicDEhkudrMfitpEDBO0iYEc+MfAPua2SeSdgHul7S7mX0Yy51sZi/FMfAK4JBmxrGkWA3AcZIuT/89RyXjHOAIM5sTnysjgU2B9P0LTwf2BnYys6WSDgUelDTYzJbHPAfEc7gM+DlwVjKddWkEvg1cn+HYjBzje6qdHoT+vjHKnTw3AfcBt5rZSTFtJ2BTSbNp4XMqV79HRgDPxL//ySL/x8DZkm40s0zbjGR7now2szMkbQS8LukeM5uddt47Ab8l3CMzJW0JPCrpbTOb3Ez9SVL33IbADEm3JGTNdj1bLLfTfvEVOMdpGeOAbYADgeVmdjOAmTUSHrzfjg+fzYE5qUJm9rqZNcT0T+L/mNknZvZ+M21uTnh4peqqT6RnaiPJ14FnzeyRmGcpcAaQjBA3BhgcFcCMxHP6FnBOPFfiuTfEvsiHzYEPzKwpln/PzD7L0eY2wK7ArxLJFwO7Sdo6zzbTafZczewN4DPFldHI11hbgWsNpxNeYp5PtHmPmX1UpPqbpX5k/V8IL39vF6O+c+9tGt+paa0X43bJ4h5btDsFs7Lz9gvKLcOKpuW9nv34vo9Sv808WbL4RgAAIABJREFUuQfYqxDlrUCeB7ZIfYm/+R7AhYSX8SSTgAWSDklL70mYsP4UwMwazGydwC1mNo2wEtob+CnhxfeTeOwVIKVM5pQxT1YRlJxMKx8XAOeZ2ZzYdqOZ3ZRJ5ijnGXFMJ47xzwEnZ8j7NOG51RzXAOdIatEkv5ktBk4DjomKRpIDgJVmdkMi/yQzG0eRnlPpRKXxBOBU4BBlWRkE5hICPY3McjwncVLiLcIzLp3zgMvMbGbMOxO4nDDR2xJ6AEsIynaraEZup53iCpzjFEh8qB1BmHUeDLycPG5mC4FZhAflTcBPJT2vYFKybcz2CNBXweTlj5L2S2vmjoTJwxUx7WrgCUn/lnSO1pjTZGsjSSY5ZwA9JK0Xk5qA3xBmaLOxDTArnmOSl2IbAF0Tst+XoY6/AUfF41dK2jlHewA7ABNTCmOUvRGYmGgzG1ckZLkjkZ7PuUJQ1lKzxHsC88zszcTxJxP1J1/Eku1OzFL3jqRdkzzJ1maLqB9Z/ypBQX6gNfVsO8de33mG7dNaedqCZV16dy+3DOlUVu/c0smIovL+0reGvb/0rXy2FmgCLqwZVXtCzajaJaWQJa48HURYlUlxEnA3YRJt+7iSneRSgnK3GjObF+t4V9Jdkk6WtM77T5ysaSK8yK8zZrL2OJfkcOD+fM8rwXXAyUqYgkYGA680VziO3d3NLH0CJpucRxKeWymyjSWzCKtV38hQx9bJsU1SbSbZ4jNiJpD+PMo17hXrOZXO3sDMWNdTwJdz5P01cF6899LJ9jwBQFI/gjXD5HWL5nU/JesfQmbukDQZeB34VfK5SH7Po0LldtopbkLpOPnTNfEyPg74C2GWMStmNlHSVoR9uA4GJkjay8ymSdoVqCXMSI6W9DMzuyUWXceE0sxulvQw4WXhaOAHknbK1UYLzvFO4IJo3tFScppQRhOm7QkrdgcCj0s6wcxKEeI+l0lKPuc6GnhO0rmsaz4JeZqsKOGLWASKYkKZJG74fcyQW4ecR5gVLujZUNFkjRfe1dikDvJMWdmpe+9yy5BORWWvGqh6A1ZtV25Znv34vn2O6X/Wa50ruuyYJctC4OSaUbVjSiRCaqzdApgGPJo4NgI4Nppf/4OwsrLaX9TMnpaEEv5qMf278aX4YMJqyCGEFRkIq02nAIuAE83MlF+MmzsU/J17EM1BC8HMFkq6jWDSmNGsN8r8V8Iq4s/NbHSh7RBe7BsJL+hJ5TbXWHI5YVLnn2npuUwo0ynVSnehz6kRBKWf+PebBNPMdTCztyWNJ6wGppPteXKipOHAQMJq6PIMefKhEBPKjQnPpv+Y2bvxWKEmlMWS2ykDvgLnOPmzzMyGxc+Z0e58KmH1YjVxprAfwSQBM1tsZvea2Q+B2wm+FSmzmKfM7JcEM5GvNieAmb0fTWmOJpjg7JirjQSZ5NyK4HeyejXNzFYBVxLMcjIxA+gnKd1Pa1dgSnPyJ9ppMLN/m9lPCD51x+TIPhUYlpwxj/8Pi8daRB7nSvQHmAnsR7g+LXl5ysYU0q5JuakfWf9bwoRCc+a8a/G9/zQ903Ulg0ojVXEx1Giq7FNuOTJR0WlAQf1eKgyrenTObetnWGmHMPO/RwmVN1gzCdSfoAScDquVmW0JvkPvECZV0s0oIcMqHASzczO7mqC8Jcfbq+O4XhtN+SDDmMm649zJwFYE08o/FHSGa7gG+A6QXBWeAuySkHkY8G+ga9r5LASWxLE8l5wHxPP7ppml+9BlJFoaTCSYjRdMfEYMAN5IO5Rr3CvWcypZvpJwrf833jN/AA7P8AxLclmsO18FdLSZDSWs9I2SMm5Rks/9lDdmNpewSrtHc3lzkI/cTjvFFTjHaR2PA920JhJXJeHBcosFh/J9JPWKxzoTzAHfVYgomTQtGQa8Sw4kHa7gjE8caDcC5mRrI634HcC+kg6O+boSgqL8JkNTtxBmqTdOP2BmSwgvK1elTEziuXcDnsglf+I8dpHUJ/5fAQzNIG+yzbeAV1n7hexC4JV4rDXcQpZzTXAXwXz1bTN7L0e+QrkWGKmEj52k4zKYhDWLpDMkpTvtt4j6kfXPADuT56bffT61dw+cZLsXo+22YFnX3u8Tf0ftjarqXdrNC9TiVZ/1nfTZU/VpyX8Cdq0ZVTu9LWSIPlBnAecqmK6PAOrMbED89AH6SOqfVu4RoBdhbEFSD8VolJFmx1vC2PhrhQAPKESZPBX4Y1pbBvwC2FPSwBac4zyCWfl3EsmXA79VIrovacpbgiuA38cxnTjG70tYpWotlxJWKwtCIYjJH4H7M/g3PwFUa+0IikOjKWZRnlNpHARMNrO+8Z7pT1h9OzZbATObTlC4jsqWJ0u5lwirpWdnOPxb4HytiYo5gGAGemWGvM2i4I++M2FStVWkyy3pWKVFYHbaH67AOU4riA/vY4ETJL1JmG1czhr7/K2BsZLqCUrIS4SHRw/gVoVwy5MJSlddouqkD9xjMe1Q4DVJk4CHCWYRH+ZoIynnMoLZ5YWSXif4QUwgYXqUyLuC8NDcJMtpnx/P8Y14zicQTJqs2Q4LbAI8pBBOfzJhJXEdOdL4DrCdQtjpGcB2rP3Cs72k9xKfVHTKtXzRooJbyLlCiBQ5mMzBS5I+B7c1cw5rYSFYyUmEF7XXFbYnOIxgxgVwato5pV7mMrU5kBigoRjUj6z/mHC/XQLkvK4X3d44T9lfLtsdi3vUzC23DNmo6FQzENQuVuEAXl/w4j7zV8x9luATdnTNqNrvl8rfLRtm9iphnBhB+L2k+9XeF9PTuRToG/8X8D/xdzYRuIg15pPZ2n2Q4F/8nKTpBOX1FDP7IEPeZYQX8ZYGpLiSEDglVd+/COPSv+Mz4jlCsIqHM5T9A2Esr49j+y+Aoy2/jdlzjl9mNoV1ffHSfeCS4e6fjOP6iwQ/uh9kqDP1zDw4judTCArrh0V+TqUYwbr3zD/IvHKb5FKgJi0t5/Mk8mvgW+krfGY2kbCq91C8nx4C/iemF8Id8R5+mTBRnPSry3Y9C5V7a4KZtNOOUf7vXI7jOE57RNIY4DjLHPq6VQy5dchhhJfXvunHThjXOO6EZyxjEIP2yltbHT1uVr9D263MDQvvftoa3x9ebjlSdK7oOvrY/medXTOqts2iozqOUz4k3U6INN1uJ7scX4FzHMfp8JjZkaVQ3gDqR9Y/TFgh/j0hAhwAvRbZx8c/Y9kipbVbFveoKSREfptT1WXn9hIhcx5wypl3/f0kV94c54uDmZ3iylv7xxU4x3EcJyf1I+sX14+sPxvYixhquu6OxrcFG+Qu2f5Y2nWT6nLLkIuKTtsMBcq9J9z9wOBzR49ZJ+S44ziOU35cgXMcx3Hyon5k/YvArru90XTG5p/R4VbfAFZ0Xi99Y+F2hVTZSRUbtigyXRGYBhx+7ugxx547esyHZZLBcRzHaQZX4BzHcZy8qR9Zv+rmS6dcB2xP5sAu7Zqmik7tcguBJJXVba4bzyNEexx67ugxmQJlOI7jOO0ID2LiOI7jtJhpAwfVEvzjCt7IuK1p6Lz+3Gf3vqy5sONlx6xhYcP867oAmaLFFZNVwA3AL88dPWZeidtyHMdxioSvwDmO4zgtZtD0aeMIG9KeBswuszg5Wdy9zzoh4NsjUvV6qFv6HmzF5mFgp3NHjznTlTfHcZyORVW5BXAcx3E6NoOmT2sCbpw2cNBNwCmE/Y62L69U67KoZ78Os7dRZecdljQ2vFSKqp8HLnJTScdxnI6LK3CO4zhOURg0fdpK4OZpAwfdStis93zC6ly7YFHPvqvKLUO+VHUZtm1jw0tG2IS6GIwFfnXu6DGPF6k+x3Ecp0y4Auc4juMUlbgi9w/gH9MGDjqUoMjtX1ahgCXdNutUbhnyRRXrbQ6dpsLKHVpRjQFjgN+cO3rMM0USzXEcxykzrsA5juM4JWPQ9GmPAI9MGzhoT4IidxTFW1UqiIbqDdYvR7stpaLz1nObVkxvSdEG4HbgynNHj5lWXKkcx3GccuMKnOM4jlNyBk2f9gJw9LSBg3YEzgOOB7q3pQyNldWbt2V7raWqetc+KwpT4OqBm4Hbzx09Zm5ppHIcx3HKjW8j4DiO47Q50wYO6kFQ4kYC+1HiVblVlV0WPl175XqlbKMULP/smlnQ1C9HlvnAncDN544eU5KoJ47jOE77wlfgHMdxnDZn0PRpi4FbgFumDRw0APgG8HVgYCnaW9x98/eBDqfAVVRtMbNp1ex0Ba4JeJyw2nbfuaPHLG97yRzHcZxy4fvAOY7jOGVl0PRp7wyaPu1Xg6ZPGwQMAS4Giuq7tbhn38+KWV9bUVm9ywbx31XAE8DZwIBzR4859NzRY+5qrfImaTNJd0uaIellSf+StJ2kAZKWSZooaaqk2yR1imVOlXRtWj1PSdot/v+OpPpYdqKk38f0WyQdnygzLJFnnqSZ8f9HJb0haYdE3vMlXZf4vkmi7IeS5iS+V0rqK+lBSW/Gc7sqJX+WfjhP0lJJPRNpB0sySUck0v4jad/4/zOShsX/35P0ZFqdr0mamJZ2raRZkpRI+66kazLI9L3Yj5Pi3yOzye84zhcLX4FzHMdx2g2Dpk97DXgN+OW0gYMGA18FDgD2ALq2tN5FPfo1FEfCNuXjik4DJgO/Bv5z7ugxRVVCoxJxH3CrmZ0U03YCNiVsyj7DzIZJqgQeBb4G3JFn9QeY2Se5MpjZRCClAN0O3GNm98fvRwLXAQdI6gd8F9glUfbjRNlLgE/M7JrEeT0AXG1mf5VUBfyFMDFwfhZxRgAvA8cAf02kzwYuAP6dxzlvIKmPmb0vaQhB6V5N7MejgfeBfYFx2SqS1B/4CbCrmS2KiuVGecjgOM4XAFfgHMdxnHbJoOnTpgBTgIunDRzUmbCnXG387AP0yreuxd37VJZEyOKyAngWeAR4GJh4xo2HGBxSqvYOAFaa2Q2pBDObBCBpQCKtUdKLwBalEiQdMxsj6duSTiYo8Rea2YI8ix8KzDezv8a6Vkk6G5gh6SIzW2vVUtJ2hPehOuBc1lbgXgF6SjrAzNZaYcvA3wlK7jUEhfCu+DfFQcCrBOVyBDkUOIISvRBYEs9hEbComfYdx/mC4Aqc4ziO0+4ZNH3aCuD5+PnNtIGDBOzIGoWulhwKxvIuG/ZoCzkLYBEwGZgETIx/60+/4cBlbSjDjoRVp5xI6kJYAT27gLqflNQY/7/VzK5ugXxnA+OBKWZ2VwHlBpN2XmY2X9L7wFbA1LT8I4C7gaeAmyX1Tls9vBS4EMhHgbuZoMB9GTiZtRW4lFL3H+AiSWeZWbbN5V8hBKiZKelx4F4zG9NM+47jfEFwBc5xHMfpcAyaPs0IYfPrgT8CTBs4aEvgS8B2aZ9eq6q6bVImUQ14h6CgJT8zT7/hwPYeBnrr6MO1JfBPM5sc07PJnUxv1oSyOcxstqSngHtaU08enAT8V1xpvJ8QHTW5KvmEpEsk7dlMPXOBJZJOIlzj1St9kqqBw4AzzGyJpFeAgwnK3DrEVcNDCIrzgcDvJQ0zs0tafpqO43xeaFaBizNo9Ymku81sVBxUe5hZyml5N+C3Zra/pP0JJgJvA92Aj4DfpM8exQfD9JTtfUy7hWAvspWZNUjqDbxkZgPi8W2Bq4FBhNmphcAvzexpSacCVwBzEs18HVhKcIh/HegMvAR8x8xWRlnPM7MjY/mbgGGpB5Wk14AjzewdST1i/YcCCwgPqxvM7E8Z+q2GYL+/AyFYzBiCPfsBBH8GgG2irMsIM7E3pWRJ648xZnZP7PPNY36At8zseEl1wPcID4/OwK9Ss5XxgfM7oDp+RptZXZqsrwLfMrOJ0VdgPnCamd0ej78c6x8K7GZmZyTKPhVlfknSO/H4J5IWm1mPtHaScqbY38zmJ/IMYM21SnGVmd2WrD+tzsVm9ttEWlKO1P3bieCPcBvBL6Kp2Nde0r+B75nZe/G+/QA4M2WeJGl8vAYbEnx5Uvfp5jFvevoxhBnhRUBqJvtpMzsr3hdfAzaNpjVEJ/izgY3Tzr0q9umPgH/GejaLdaauxe7APDPrEa/BTOAsM/tDrPtawu/wlvj9x8D3gZWsiYj3UzNbmeiP+wgvfj2AjWOdAD8k/AZ/AxwZ+3IqcLqZvUca8Xpm64MxZnZPIu/+5PcbaiD8Vh4jmGbNj3kzjndp8txCCHu/IJ776Wb2fPS7uYAQFt8I1/EMM5uSOI/0+/dUEr8pSd8E/ieWXwXcYWa/TWsTYKmZ7Z2hr/YFrmJNtMWrzOz/4rE61v79/cfMfpZhXLkk9tXq33A0MbsG2JZwLd4CziSMww+w5tpC6P/H0u6/mcA3kr/1UjBo+rSZabIAMG3goI0QA4D+QF+gJv7tSzDDrCbcD9Vp/+fa2qCBMFYuiH8/JvyOP0z8fR+YdvoNBy5s/dmVhCkEZSUbKR+43sCzkr5iZg8Cn7Ku+eqGQKsUtiw0xU8hTCWMLauRtAFhhfbttPSdCatyT8a4ItXAGyQUuMglhFW45hhNePafkpb+X8D6wJTYTnfgM7IocAAW9nl6AXhB0hPA9VEOx3G+4OSzArfMzIZlObaJpCPMLJNz77jUS5RClKb7JS0zs8dj2iCgEqiV1N3MliTKNgLfJgxWq4lmHP8kvCA8GNN2BHYDno7ZRicVjJhnAPk7Y79HeAk7McOxPxMG/22jArBxlHMt4ovcvcD1ZnZ0bPP/gEvN7CcE34a1lJ/4ff8MbaZzcip/GlfHF71tgZcl3RNfpm8FvmZmk6Ic22co+yywN8GMZyfCw2tv4HZJ3YGtCbOJQ/OQrzmuTipbWZiR454rlNX3r6RNCPslrQf8MkPeFl97SV2BjRIKyAmEB+8I4ouAme0R855KmiKcLT0+6LPNZL9FcIi/XVIFYZY2OXmRPPc7gBMT3+tYV/FN1v0xcLakG81sRZqcpxEU2T2jWVJn4McE5XO1Amdmx8b8+7OuUvVboCewfZz1/hZwr6Q94ktLOq2ezU9wcpxw6AxcTlBA9ovHco13SX4SlZxDgRsJv43TCb+bncxsaTz2oKTB6T43mVCIdPcj4FALQRCqgW+mt5mj/GaE+/sYM3slvnQ/LGmOmaUU92y/v2zjSnLc/bGZPRTT9ico5ZAY69NI3n+3Evrn0mzyl5JB06d9OigoHc2aCya57rQnOrFGmUv9XQ7MP/2GAztiUJR0ngAuk/T9hKI/lKBozE5lihNCPyMEAHkQmABcK2kzM/swTuBWJ8uUmUeAyyWdbGZ3xInJq4A/ZfgtjiBM4lwBq5/f78ZJ2NWY2b8kXUzwTcvFPwi/jUeBAWntnGpmf4/t9CT45HXJVElsv7eFQC8QAra8G4+dDTRYwnfRcZwvFq01obyCPKIzxZWdi4EzCDP1EAazvxJmcI8mvHikuAY4R1L6ytbJwPMp5S3WnYpYlhfWvDP2GGC4pO3NbPUqkKStCasUXzezpljXXNaspiU5EFhuZjcn2jyHYMv+SzNbmq+8hWJmb0paSpgd/RjYhDAbjJk1sq7tP8BzhNnBPxJeQG8ATo3HdgdejudQKrHbBDP7WNL3gQlRgUmnNdd+f8JqWYoRBGf4OyXVZFpZKgJ3E5TN22P7zwJHZMk7jsIU8LmxvpFA+u/wAmB4ajUlKnijyBNJ3YBvAVvGexIzu1nStwm/ncdzlS8WZrZC0v8Ab0nayWLwhgJ5mrCSDvBTYL/U79vMHpH0HGHc+ksedZ1PUHTfj+UbWLfvc3E6cIuZvRLLfxLPr441K68t4euEcfehVIKZPQV5TzpB8FsrxgRQm3L6DQeuJExKLGkub0fEzEzSscA1kn5KUE7fIUwkpHM/UCep1szGRSXiX3HyaDEwIjU+RpI+cJPNLDUZcaPWhMyfbWZ7leC8miQdA1wXx/qUFcwvkvmisnYiIbhIqqwpmFGeSJi4THIZQUHL1fYC4rMh9cxUsOA4mMSkn4XIki8QfOUAvqPEFguEScDLJKUsBj4CfhCPDaKNxknHcdon+ShwXbX2PiaXm9no+P/zwLGSDqD56EivEEwIU5xIMJUcSDDFSSpws4BnCBu7PpRIHxzrycWJ0YwoxVoPBzXvjN1EMO36OeHlNdn2pLQHVDYyOVAvlDSL8LI3OWOpQG1af/cjPHhS3CEpZer0aFzRW42kXYA3LYRYhmBu+npc7fsPwZk8fQbyWdaYZewNXASMiDOEexMUvBTp/bsNhXGOpJRpyWdmdkCGPFun9cGZZpYrWlfemNnbcSUykz9Ma679EYQXHCT1BTY3sxcl/Y1wr1/ZCrGzBQN4A/iKpF4EhfF2Mihwcfb5CHKY6mTh18C/Jd2UqGs9gun0OmZqBbANMMvM0s3KXiL0daYXk2IERFiHODExiTAOTSL3eJeJo4D62C/dzezttOOpc8qH5gJKXCEpZcI1xcxOTjs+mLDinqv95O/vp2b2cPw/Oa4cZGafFiBX+pj1VTObkfoSf28HkZ8S67QxccLga1kO75jIZwQLjdT3Bwir15nqHJAl/dQccqSbHOZMT8uzjmmjmb1LmhllhjxGMKtNTz8r8fWxRPq9JMxqzWzfxP9rrdjFtLeIWx0QTEzTj38l8fXPGUTM9HyEYPp7X5ZjjuN8AWitCSWssQv/aTP1JDet3I2wZ8ssSXOAmyRtaGbzEvlTpk1ZZ44VfGy2Bd4ws+NiciYTSsjujJ2JO4ELJG2Zo+0LCDNkm5hZnxx1Fcpa5kgKvi9Jspk6nRPN0LYjvFQCYGYXR/O5Qwkz6SMIqzUk8rwrqXM0wRpI8D+bQFB09wb+kMi+Vv9GxbAQim1CmY8zfSG09NrvA5wX/z8R+Fv8/26Cb11rFLhc5oP3Ehzw92DN7GyKpDIyjgJfoKOyO55w32RE0mEERW8Dwgrlc9nytpJ8TShbcj8kl5bzNaFMKVNzge/kkb8Y5DShzJOCTSjzIJsJZer+24Lgg/loC+t3HCeBmX25+VyO43yeqWhtBWb2BMH3pbnoTDsTHuIQlIiBCk79Mwg+SV9Nq/dNgk9WcmZwCmtv5HkswdRvnZmtDKSUgq2BXSV9JVtGC2F9r2RtpXQqsFM0F8HMLo31rZehiqmE/YpWE2fo+xH8lkrB1WaW2vT2L0m7ejObYWbXE2bBd5KUaTPQ5whKyQdxVvIFglKyO2Gltb2SyZm+JyGowDpI2orgY/lxpuMtufaxztm2xldsBHBqvL8fBIYq+CaWgtHArwirsekrhMvMbFj8nGlpvmx5chmhLwRhJRlYnFJwzezh2BevEXyE8mEG0C+u8CbZlfAbbw0FBVeIq0NDWDM25ctPYr8eYmavxX5ZEu+FJIWc0xTSxo0CWWfcKbD9bLRUrpQy3J9w/5zeSjkcx3Ecx6EIClzkEkLktIxEp+hfEOzRKwhK2RAzGxBNLY5m7b1SUlzKmlUNCKsj+6QpX90KETTO4qecsXNxC8FmfeNY7i2COdIl8aUvZY6ZyTHscaCbQkS51EvilQT/lJL5v0U5H4xyjoxtf1lrnNe2JSgvmZSb5wh+Dyll7XlCAIUPLf/NU8vB0wQzwp4Ako4jmDs2pmdUCDxyA3BtVFKzcQuFXfvV5okK0fp6mNkWifv7cjLf360mmgldQAyjXoL6pxMUg6MSyZcD1ytEdUv5kWR0xM9S5xKCqd9Vif78JuG3/EQrRX4T6KMQJAlJ/QlmXxPTM0rqRDiX2c2syOfLFYRQ311j/QcD+7K2eXguLies7G0Wy3eW9N0C2r+OMHGQChyyEWF19DcF1JGJO4G9Ja2e9Zc0XCGAVLPEMe8s4Nxozouk6a2UyXEcx3G+sOSjwHWVNDHxWSdYgZn9i7VDw0Pwi3hV0uuEF4uzLESgrAXmRLv7FE8DO0Rn3WS9U0j4vJnZMoJN+2mS3pb0PMF8MxlW98Q0edcJtU3wVeomqTbbScfVit+ztq/Ud4GNCEEPXiKYBK2juEbl4FjgBElvEnyVlhN8q1rLHYlzeyxLnouBH0dl+RsEH7iJhKAxJ2dSbgh+cFsRFTgz+4AQJbSlJnHdJL2X+Pw4pp+Tdn0GZCi7dVqepD/C5ESdV8UX72uBZ+I5nka4TilS9+8Ugi/DIwQfv6y04Nofzhr/shGs65vwD1qnwD2Z6IvbMsh7Y9LnqARcSgi7nuJ6wiTFeEmTCffOq/GTL+cTfhNvxN/ICcCxORTrbH1wY+J+eN5C4I9TCJvxTiTsH/XdtEmIO6LcrxFCeR+dONbseJeDPxBMj+vjuPcL4Og4bqVY6/5NFo7j6LXAY/F+fYW1V/ivSJOtc1r5D+K5/ykqSM8BN1ki+EhLSIy7Z0p6U9JUwlYQqTG/Nk2udcLSm9mrBN/fEQrRMTt2RKQcSGqM/TBJ0iupZ5Ck/SWlb6VzS6q/JD0l6fVYbkJKEY/Heki6XtKMWOfLkr4Xjw1Q2PIkkyxVkuam38fZ2pJ0h6T/TuTbQ9LkONmRXj61hdA7kv6ROHa80kz/JR2WuD8Wx7YnSrqtgH5JlV/HjFjSppLGxPOZKulfib5ZlnZ/piZWe0i6Mfbpy7GdVKTgGkkPxPt9hqTfpX5vUV6TdFSi/TGKAX0S8k6WNF3StYqTXRnkfkdSffxMVdhrrkuesue6H1LlpsY+7pSQfUFanQfHY41p6T/LJLPjOO0EM/OPf/zTwg8hdPZL5ZbDP/7pKB+CMnhWueUo4fktTvx/GDA2/r8/YT/CZN5bgOPj/08RthGBEKX10US+uwnmzBXx+8aEIDQQQtW/lkWWIwgTLDMAJdIztkUIkf92rL+CMCGxb4Z6k+XfiZ8d4vfjCdYm2fpnddlC+yVHnTcCZye+D82jb+4mrHqn+nRLQkRIAS8S9kaFMJH5F+CKhLyzgRcSdY0h7Gma3jedCdY3Y7PI8A5hqwAI+2XeSQjSlI/szd4PUfYnCBO3Gfs6033rH//4p/1/imVC6ThfSMysweK8tR06AAAEg0lEQVRm9o7jNI+ZjTGz35dbjjZiPcJmzYXyPHGrG63ZxuRCS2xjYmaZtrBJZwTwO0Jk52zh+le3ZWYfAb8lmN2eRgj//0we7VxJMOUuF5sT9vEEwJoxiY59ugdr9+lMC/slrrMNEHAO8G2FLVAgRKxdIOmQXO1YsOb4H4LP707N5F1M6PNjJGX16y/kfoiy59o2yXGcDkpr94FzHMdxHGcNqeibXQiKxYEtqONw4rYkFLaFzWqiKd7BhOi0GxCUuUwm8cm2IPgJjySs1uQ7OfU34IeSCt1WJkWrts8huGmMlnQGwVT+ZlvjprHOtjSEQEcTLbM7QXPbAKW4lBhAKteJ2bpbleTKu1DSTIK/+kc5ZM/rflDmbZOybftR6BYqjuOUEVfgHMdxHKd4rN6KQtJewG0KAV/y2eLijuhr1YM1+4ethfLfwuZI4EkzWxZ91H4h6UcJpSVjWxY2wb6RYAb46brVZqSREMTnfODfeZZJ0tLtc4AQDVchAuzhBLPRV7UmyM4629IoRxTqfDGzpyWhtfdFzUYhPp/JvM3KnuV+yLVtUrZtP/LdQsVxnHaAm1A6juM4Tgkws+eB3gQfpXy2uDiZEEzqVtbsv1nIFjZJRgAHK2xn8jIhCFNyNTBTWyma4qcQ/goMJ2wy3eaY2Twzu9PMvkHw3RueI/sUQp9WZjhWyDZAlxICqWVFBWxVohBNeQAh8Fk28rkf8t42yXGcjokrcI7jOI5TAiQNJASS+JQ8t7gwMyNEMN1T0kArbAubVLvrESI+97M125mcTlo03PS2WnOuZrYSuJrgL9amSDow5Z8WlaCtCX5/GYkmgy8BF0lhm50YvfHLFLANkJk9QlDKh2aRK++tSiT1IGwHc7+ZZfWbLOR+sPy3Tcol1+WSjm1pecdxSoMrcI7jOI5TPFZvRQGMBkaaWaPlt8UFsHrrhiuBlK9Xc1vYbK/Eti2EbWyeiG2meAA4SlJ1M221hr9QGteM5rbP2RV4SWF7kOeBP5vZhHgs27Y03yVE3XxLYRuGW4CPo1JbyDZAl7LuqmOurUrSeTK2/yJB6fxB4lgu2Zvd0iiSvm1Stm0/sm2hMgT4MIf8juOUAYWxynEcx3Ecx3HWIOlhMzus3HI4jrM2rsA5juM4juM4juN0ENyE0nEcx3Ecx3Ecp4PgCpzjOI7jOI7jOE4HwRU4x3Ecx3Ecx3GcDoIrcI7jOI7jOI7jOB0EV+Acx3Ecx3Ecx3E6CK7AOY7jOI7jOI7jdBBcgXMcx3Ecx3Ecx+kguALnOI7jOI7jOI7TQXAFznEcx3Ecx3Ecp4PgCpzjOI7jOI7jOE4HwRU4x3Ecx3Ecx3GcDoIrcI7jOI7jOI7jOB0EV+Acx3Ecx3Ecx3E6CK7AOY7jOI7jOI7jdBBcgXMcx3Ecx3Ecx+kguALnOI7jOI7jOI7TQXAFznEcx3Ecx3Ecp4PgCpzjOI7jOI7jOE4HwRU4x3Ecx3Ecx3GcDoIrcI7jOI7jOI7jOB0EV+Acx3Ecx3Ecx3E6CK7AOY7jOI7jOI7jdBD+HwTSSTcFuoT/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "\n",
    "df['Year'].value_counts()\n",
    "# df['Type'].value_counts()\n",
    "# df['Agency'].value_counts()\n",
    "# df.filter(like='LIGHTHORSE',axis=0)\n",
    "# qf = df.query(\"Type == 'CF'\")\n",
    "\n",
    "top_ten = df['Count'].value_counts().head(10)\n",
    "\n",
    "\n",
    "top_ten.plot.pie(y='Count', figsize=(5, 5))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  }
 ],
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