Tutorials/wk4-FEMA/project_description.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import requests\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "import folium\n",
    "from folium.plugins import HeatMap\n",
    "import json\n",
    "from geopy.geocoders import GoogleV3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " # Individuals and Households Program - Valid Registrations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\simpson xu\\AppData\\Local\\Temp\\ipykernel_6296\\1336325672.py:1: DtypeWarning: Columns (4) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  dataowner = pd.read_csv(\"HousingAssistanceOwners.csv\")\n"
     ]
    }
   ],
   "source": [
    "dataowner = pd.read_csv(\"HousingAssistanceOwners.csv\")\n",
    "disasterSummary = pd.read_csv(\"DisasterDeclarationsSummaries.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>disasterNumber</th>\n",
       "      <th>state</th>\n",
       "      <th>county</th>\n",
       "      <th>city</th>\n",
       "      <th>zipCode</th>\n",
       "      <th>validRegistrations</th>\n",
       "      <th>averageFemaInspectedDamage</th>\n",
       "      <th>totalInspected</th>\n",
       "      <th>totalDamage</th>\n",
       "      <th>noFemaInspectedDamage</th>\n",
       "      <th>...</th>\n",
       "      <th>totalApprovedIhpAmount</th>\n",
       "      <th>repairReplaceAmount</th>\n",
       "      <th>rentalAmount</th>\n",
       "      <th>otherNeedsAmount</th>\n",
       "      <th>approvedBetween1And10000</th>\n",
       "      <th>approvedBetween10001And25000</th>\n",
       "      <th>approvedBetween25001AndMax</th>\n",
       "      <th>totalMaxGrants</th>\n",
       "      <th>id</th>\n",
       "      <th>Region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1439</td>\n",
       "      <td>TX</td>\n",
       "      <td>Aransas (County)</td>\n",
       "      <td>ARANSAS PASS</td>\n",
       "      <td>78335</td>\n",
       "      <td>4</td>\n",
       "      <td>1345.01</td>\n",
       "      <td>3</td>\n",
       "      <td>5380.02</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>5915.91</td>\n",
       "      <td>3573.02</td>\n",
       "      <td>970.0</td>\n",
       "      <td>1372.89</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>064e6e4f-a4c9-46b3-af5e-1d84cc552b24</td>\n",
       "      <td>Southwest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1439</td>\n",
       "      <td>TX</td>\n",
       "      <td>Aransas (County)</td>\n",
       "      <td>ARANSAS PASS</td>\n",
       "      <td>78336</td>\n",
       "      <td>68</td>\n",
       "      <td>3082.82</td>\n",
       "      <td>63</td>\n",
       "      <td>209632.06</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>180717.57</td>\n",
       "      <td>131097.36</td>\n",
       "      <td>23946.0</td>\n",
       "      <td>25674.21</td>\n",
       "      <td>27</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>942a9af8-155b-4d2f-ac54-b96baddb42b0</td>\n",
       "      <td>Southwest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1439</td>\n",
       "      <td>TX</td>\n",
       "      <td>Aransas (County)</td>\n",
       "      <td>FULTON</td>\n",
       "      <td>78358</td>\n",
       "      <td>20</td>\n",
       "      <td>4722.34</td>\n",
       "      <td>18</td>\n",
       "      <td>94446.72</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>94240.88</td>\n",
       "      <td>58754.70</td>\n",
       "      <td>6784.0</td>\n",
       "      <td>28702.18</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>8bad7f2e-6706-4d6f-a39e-c07596139758</td>\n",
       "      <td>Southwest</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   disasterNumber state            county          city zipCode  \\\n",
       "0            1439    TX  Aransas (County)  ARANSAS PASS   78335   \n",
       "1            1439    TX  Aransas (County)  ARANSAS PASS   78336   \n",
       "2            1439    TX  Aransas (County)        FULTON   78358   \n",
       "\n",
       "   validRegistrations  averageFemaInspectedDamage  totalInspected  \\\n",
       "0                   4                     1345.01               3   \n",
       "1                  68                     3082.82              63   \n",
       "2                  20                     4722.34              18   \n",
       "\n",
       "   totalDamage  noFemaInspectedDamage  ...  totalApprovedIhpAmount  \\\n",
       "0      5380.02                      0  ...                 5915.91   \n",
       "1    209632.06                      4  ...               180717.57   \n",
       "2     94446.72                      0  ...                94240.88   \n",
       "\n",
       "   repairReplaceAmount  rentalAmount  otherNeedsAmount  \\\n",
       "0              3573.02         970.0           1372.89   \n",
       "1            131097.36       23946.0          25674.21   \n",
       "2             58754.70        6784.0          28702.18   \n",
       "\n",
       "   approvedBetween1And10000  approvedBetween10001And25000  \\\n",
       "0                         3                             0   \n",
       "1                        27                            26   \n",
       "2                         3                            11   \n",
       "\n",
       "   approvedBetween25001AndMax  totalMaxGrants  \\\n",
       "0                           0               0   \n",
       "1                           1               0   \n",
       "2                           3               0   \n",
       "\n",
       "                                     id     Region  \n",
       "0  064e6e4f-a4c9-46b3-af5e-1d84cc552b24  Southwest  \n",
       "1  942a9af8-155b-4d2f-ac54-b96baddb42b0  Southwest  \n",
       "2  8bad7f2e-6706-4d6f-a39e-c07596139758  Southwest  \n",
       "\n",
       "[3 rows x 25 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# geographic groups\n",
    "labels = {\n",
    "    'Northeast': ['CT', 'ME', 'MA', 'NH', 'RI', 'VT', 'NJ', 'NY', 'PA', \"DE\"],\n",
    "    'Southeast': ['FL', 'GA', 'MD', 'NC', 'SC', 'VA', 'DC', 'WV', \"TN\", \"AL\", \"KY\", \"MS\", 'VI'],\n",
    "    'Midwest': ['IL', 'IN', 'IA', 'KS', 'MI', 'MN', 'MO', 'NE', 'ND', 'OH', 'SD', 'WI'],\n",
    "    'Southwest': ['AR', 'AZ', 'LA', 'NM', 'OK', 'TX'],\n",
    "    'West': ['AK', 'CA', 'CO', 'HI', 'ID', 'MT', 'NV', 'OR', 'UT', 'WA', 'WY'],\n",
    "    'Territories': ['AS', 'GU', 'MP', 'PR', \"FM\"]\n",
    "}\n",
    "\n",
    "# Add \"Region\" column to the dataframe\n",
    "dataowner['Region'] = dataowner['state'].map(\n",
    "    {state: region for region, states in labels.items() for state in states})\n",
    "\n",
    "dataowner.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(137256, 25) (64528, 25)\n"
     ]
    }
   ],
   "source": [
    "print(dataowner.shape, disasterSummary.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>femaDeclarationString</th>\n",
       "      <th>disasterNumber</th>\n",
       "      <th>state</th>\n",
       "      <th>declarationType</th>\n",
       "      <th>declarationDate</th>\n",
       "      <th>fyDeclared</th>\n",
       "      <th>incidentType</th>\n",
       "      <th>declarationTitle</th>\n",
       "      <th>ihProgramDeclared</th>\n",
       "      <th>iaProgramDeclared</th>\n",
       "      <th>...</th>\n",
       "      <th>tribalRequest</th>\n",
       "      <th>fipsStateCode</th>\n",
       "      <th>fipsCountyCode</th>\n",
       "      <th>placeCode</th>\n",
       "      <th>designatedArea</th>\n",
       "      <th>declarationRequestNumber</th>\n",
       "      <th>lastIAFilingDate</th>\n",
       "      <th>lastRefresh</th>\n",
       "      <th>hash</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FM-5465-NM</td>\n",
       "      <td>5465</td>\n",
       "      <td>NM</td>\n",
       "      <td>FM</td>\n",
       "      <td>2023-05-10T00:00:00.000Z</td>\n",
       "      <td>2023</td>\n",
       "      <td>Fire</td>\n",
       "      <td>LAS TUSAS FIRE</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>33</td>\n",
       "      <td>99033</td>\n",
       "      <td>Mora (County)</td>\n",
       "      <td>23050</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023-05-22T03:41:22.800Z</td>\n",
       "      <td>56e2cdd7de53fd6bd0cbe22cd681d0d87708d0ac</td>\n",
       "      <td>e46df225-24a5-4178-9368-0f1c753f8748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FM-5465-NM</td>\n",
       "      <td>5465</td>\n",
       "      <td>NM</td>\n",
       "      <td>FM</td>\n",
       "      <td>2023-05-10T00:00:00.000Z</td>\n",
       "      <td>2023</td>\n",
       "      <td>Fire</td>\n",
       "      <td>LAS TUSAS FIRE</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>47</td>\n",
       "      <td>99047</td>\n",
       "      <td>San Miguel (County)</td>\n",
       "      <td>23050</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023-05-22T03:41:22.800Z</td>\n",
       "      <td>33f747f70112aed4df749cfb1dd58f1d50f029ff</td>\n",
       "      <td>c8660535-8995-4187-af20-771b5669d4b7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FM-5464-RI</td>\n",
       "      <td>5464</td>\n",
       "      <td>RI</td>\n",
       "      <td>FM</td>\n",
       "      <td>2023-04-14T00:00:00.000Z</td>\n",
       "      <td>2023</td>\n",
       "      <td>Fire</td>\n",
       "      <td>QUEENS RIVER FIRE</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>44</td>\n",
       "      <td>9</td>\n",
       "      <td>99009</td>\n",
       "      <td>Washington (County)(in (P)MSA 5520,6480)</td>\n",
       "      <td>23042</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023-05-22T03:41:22.800Z</td>\n",
       "      <td>1111d4d4a33f8a6e4d383ac5bd0e60b9bf142da0</td>\n",
       "      <td>9b9a9d9c-8b03-4049-9b16-a3c0d581556c</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  femaDeclarationString  disasterNumber state declarationType  \\\n",
       "0            FM-5465-NM            5465    NM              FM   \n",
       "1            FM-5465-NM            5465    NM              FM   \n",
       "2            FM-5464-RI            5464    RI              FM   \n",
       "\n",
       "            declarationDate  fyDeclared incidentType   declarationTitle  \\\n",
       "0  2023-05-10T00:00:00.000Z        2023         Fire     LAS TUSAS FIRE   \n",
       "1  2023-05-10T00:00:00.000Z        2023         Fire     LAS TUSAS FIRE   \n",
       "2  2023-04-14T00:00:00.000Z        2023         Fire  QUEENS RIVER FIRE   \n",
       "\n",
       "   ihProgramDeclared  iaProgramDeclared  ...  tribalRequest  fipsStateCode  \\\n",
       "0                  0                  0  ...              0             35   \n",
       "1                  0                  0  ...              0             35   \n",
       "2                  0                  0  ...              0             44   \n",
       "\n",
       "  fipsCountyCode placeCode                            designatedArea  \\\n",
       "0             33     99033                             Mora (County)   \n",
       "1             47     99047                       San Miguel (County)   \n",
       "2              9     99009  Washington (County)(in (P)MSA 5520,6480)   \n",
       "\n",
       "   declarationRequestNumber  lastIAFilingDate               lastRefresh  \\\n",
       "0                     23050               NaN  2023-05-22T03:41:22.800Z   \n",
       "1                     23050               NaN  2023-05-22T03:41:22.800Z   \n",
       "2                     23042               NaN  2023-05-22T03:41:22.800Z   \n",
       "\n",
       "                                       hash  \\\n",
       "0  56e2cdd7de53fd6bd0cbe22cd681d0d87708d0ac   \n",
       "1  33f747f70112aed4df749cfb1dd58f1d50f029ff   \n",
       "2  1111d4d4a33f8a6e4d383ac5bd0e60b9bf142da0   \n",
       "\n",
       "                                     id  \n",
       "0  e46df225-24a5-4178-9368-0f1c753f8748  \n",
       "1  c8660535-8995-4187-af20-771b5669d4b7  \n",
       "2  9b9a9d9c-8b03-4049-9b16-a3c0d581556c  \n",
       "\n",
       "[3 rows x 25 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "disasterSummary.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(137256, 26)\n"
     ]
    }
   ],
   "source": [
    "# Group by disasterNumber\n",
    "# Aggregate unique incidentType values\n",
    "agg_disasterSummary = (disasterSummary.groupby('disasterNumber')['incidentType']\n",
    "                       .apply(lambda x: ','.join(x.unique()))\n",
    "                       .reset_index())\n",
    "\n",
    "# Merge\n",
    "merged_data = dataowner.merge(agg_disasterSummary, on='disasterNumber', how='left')\n",
    "\n",
    "print(merged_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>disasterNumber</th>\n",
       "      <th>state</th>\n",
       "      <th>county</th>\n",
       "      <th>city</th>\n",
       "      <th>zipCode</th>\n",
       "      <th>validRegistrations</th>\n",
       "      <th>averageFemaInspectedDamage</th>\n",
       "      <th>totalInspected</th>\n",
       "      <th>totalDamage</th>\n",
       "      <th>noFemaInspectedDamage</th>\n",
       "      <th>...</th>\n",
       "      <th>repairReplaceAmount</th>\n",
       "      <th>rentalAmount</th>\n",
       "      <th>otherNeedsAmount</th>\n",
       "      <th>approvedBetween1And10000</th>\n",
       "      <th>approvedBetween10001And25000</th>\n",
       "      <th>approvedBetween25001AndMax</th>\n",
       "      <th>totalMaxGrants</th>\n",
       "      <th>id</th>\n",
       "      <th>Region</th>\n",
       "      <th>incidentType</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1439</td>\n",
       "      <td>TX</td>\n",
       "      <td>Aransas (County)</td>\n",
       "      <td>ARANSAS PASS</td>\n",
       "      <td>78335</td>\n",
       "      <td>4</td>\n",
       "      <td>1345.01</td>\n",
       "      <td>3</td>\n",
       "      <td>5380.02</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3573.02</td>\n",
       "      <td>970.0</td>\n",
       "      <td>1372.89</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>064e6e4f-a4c9-46b3-af5e-1d84cc552b24</td>\n",
       "      <td>Southwest</td>\n",
       "      <td>Severe Storm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1439</td>\n",
       "      <td>TX</td>\n",
       "      <td>Aransas (County)</td>\n",
       "      <td>ARANSAS PASS</td>\n",
       "      <td>78336</td>\n",
       "      <td>68</td>\n",
       "      <td>3082.82</td>\n",
       "      <td>63</td>\n",
       "      <td>209632.06</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>131097.36</td>\n",
       "      <td>23946.0</td>\n",
       "      <td>25674.21</td>\n",
       "      <td>27</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>942a9af8-155b-4d2f-ac54-b96baddb42b0</td>\n",
       "      <td>Southwest</td>\n",
       "      <td>Severe Storm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1439</td>\n",
       "      <td>TX</td>\n",
       "      <td>Aransas (County)</td>\n",
       "      <td>FULTON</td>\n",
       "      <td>78358</td>\n",
       "      <td>20</td>\n",
       "      <td>4722.34</td>\n",
       "      <td>18</td>\n",
       "      <td>94446.72</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>58754.70</td>\n",
       "      <td>6784.0</td>\n",
       "      <td>28702.18</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>8bad7f2e-6706-4d6f-a39e-c07596139758</td>\n",
       "      <td>Southwest</td>\n",
       "      <td>Severe Storm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1439</td>\n",
       "      <td>TX</td>\n",
       "      <td>Aransas (County)</td>\n",
       "      <td>FULTON</td>\n",
       "      <td>78381</td>\n",
       "      <td>1</td>\n",
       "      <td>2578.30</td>\n",
       "      <td>1</td>\n",
       "      <td>2578.30</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1407.96</td>\n",
       "      <td>726.0</td>\n",
       "      <td>1170.34</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>d3ce0948-ab68-4115-875d-a0fda9390c99</td>\n",
       "      <td>Southwest</td>\n",
       "      <td>Severe Storm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1439</td>\n",
       "      <td>TX</td>\n",
       "      <td>Aransas (County)</td>\n",
       "      <td>ROCKPORT</td>\n",
       "      <td>78331</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>67dd11a2-9248-4f36-bc69-4a3e03750083</td>\n",
       "      <td>Southwest</td>\n",
       "      <td>Severe Storm</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   disasterNumber state            county          city zipCode  \\\n",
       "0            1439    TX  Aransas (County)  ARANSAS PASS   78335   \n",
       "1            1439    TX  Aransas (County)  ARANSAS PASS   78336   \n",
       "2            1439    TX  Aransas (County)        FULTON   78358   \n",
       "3            1439    TX  Aransas (County)        FULTON   78381   \n",
       "4            1439    TX  Aransas (County)      ROCKPORT   78331   \n",
       "\n",
       "   validRegistrations  averageFemaInspectedDamage  totalInspected  \\\n",
       "0                   4                     1345.01               3   \n",
       "1                  68                     3082.82              63   \n",
       "2                  20                     4722.34              18   \n",
       "3                   1                     2578.30               1   \n",
       "4                   1                        0.00               0   \n",
       "\n",
       "   totalDamage  noFemaInspectedDamage  ...  repairReplaceAmount  rentalAmount  \\\n",
       "0      5380.02                      0  ...              3573.02         970.0   \n",
       "1    209632.06                      4  ...            131097.36       23946.0   \n",
       "2     94446.72                      0  ...             58754.70        6784.0   \n",
       "3      2578.30                      0  ...              1407.96         726.0   \n",
       "4         0.00                      0  ...                 0.00           0.0   \n",
       "\n",
       "   otherNeedsAmount  approvedBetween1And10000  approvedBetween10001And25000  \\\n",
       "0           1372.89                         3                             0   \n",
       "1          25674.21                        27                            26   \n",
       "2          28702.18                         3                            11   \n",
       "3           1170.34                         1                             0   \n",
       "4              0.00                         0                             0   \n",
       "\n",
       "   approvedBetween25001AndMax  totalMaxGrants  \\\n",
       "0                           0               0   \n",
       "1                           1               0   \n",
       "2                           3               0   \n",
       "3                           0               0   \n",
       "4                           0               0   \n",
       "\n",
       "                                     id     Region  incidentType  \n",
       "0  064e6e4f-a4c9-46b3-af5e-1d84cc552b24  Southwest  Severe Storm  \n",
       "1  942a9af8-155b-4d2f-ac54-b96baddb42b0  Southwest  Severe Storm  \n",
       "2  8bad7f2e-6706-4d6f-a39e-c07596139758  Southwest  Severe Storm  \n",
       "3  d3ce0948-ab68-4115-875d-a0fda9390c99  Southwest  Severe Storm  \n",
       "4  67dd11a2-9248-4f36-bc69-4a3e03750083  Southwest  Severe Storm  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "incidentType\n",
      "Hurricane            60411\n",
      "Severe Storm         44119\n",
      "Biological           10892\n",
      "Flood                10632\n",
      "Severe Ice Storm      3406\n",
      "Fire                  3239\n",
      "Typhoon               2056\n",
      "Tornado               1353\n",
      "Earthquake             950\n",
      "Dam/Levee Break        107\n",
      "Volcanic Eruption       27\n",
      "Mud/Landslide           26\n",
      "Winter Storm            19\n",
      "Other                    8\n",
      "Name: count, dtype: int64\n",
      "Region\n",
      "Southeast      45065\n",
      "Southwest      25104\n",
      "Midwest        20726\n",
      "Northeast      20101\n",
      "Territories    18063\n",
      "West            8197\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(merged_data['incidentType'].value_counts())\n",
    "print(merged_data['Region'].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DATA DESCRIPTION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of disaster types: 14\n"
     ]
    }
   ],
   "source": [
    "# Number of disaster types\n",
    "num_disasters = merged_data['incidentType'].nunique()\n",
    "print(\"Number of disaster types:\", num_disasters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counts of claims per disaster:\n",
      "incidentType\n",
      "Hurricane            60411\n",
      "Severe Storm         44119\n",
      "Biological           10892\n",
      "Flood                10632\n",
      "Severe Ice Storm      3406\n",
      "Fire                  3239\n",
      "Typhoon               2056\n",
      "Tornado               1353\n",
      "Earthquake             950\n",
      "Dam/Levee Break        107\n",
      "Volcanic Eruption       27\n",
      "Mud/Landslide           26\n",
      "Winter Storm            19\n",
      "Other                    8\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Counts of claims per disaster\n",
    "claims_per_disaster = merged_data['incidentType'].value_counts()\n",
    "print(\"Counts of claims per disaster:\")\n",
    "print(claims_per_disaster)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Counts of claims per state\n",
    "# U.S. territories or associated states, including Purto Rico ('PR'), District of Columbia ('DC'), and etc.\n",
    "claims_per_state = merged_data.groupby('state')['id'].count().sort_values(ascending=False)\n",
    "# print(\"Counts of claims per state:\")\n",
    "# print(claims_per_state)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counts of claims per state:\n",
      "Region\n",
      "Southeast      45065\n",
      "Southwest      25104\n",
      "Midwest        20726\n",
      "Northeast      20101\n",
      "Territories    18063\n",
      "West            8197\n",
      "Name: id, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Counts of claims per region\n",
    "claims_per_region = merged_data.groupby('Region')['id'].count().sort_values(ascending=False)\n",
    "print(\"Counts of claims per state:\")\n",
    "print(claims_per_region)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  state  count       Region\n",
      "0    FL  17477    Southeast\n",
      "1    PR  15172  Territories\n",
      "2    TX  11926    Southwest\n",
      "3    LA   8296    Southwest\n",
      "4    NY   5870    Northeast\n"
     ]
    }
   ],
   "source": [
    "claims_per_state_df = claims_per_state.to_frame().reset_index()\n",
    "\n",
    "# Rename the columns\n",
    "claims_per_state_df.columns = ['state', 'count']\n",
    "\n",
    "# Add a new column 'Region' to the dataframe using labels\n",
    "claims_per_state_df['Region'] = claims_per_state_df['state'].map(\n",
    "    {state: region for region, states in labels.items() for state in states})\n",
    "\n",
    "print(claims_per_state_df.head(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Region  count\n",
      "0      Midwest  20726\n",
      "1    Northeast  20101\n",
      "2    Southeast  45065\n",
      "3    Southwest  25104\n",
      "4  Territories  18063\n",
      "5         West   8197\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Group the dataframe by 'Region'\n",
    "# Calculate the sum of 'count' for each region\n",
    "region_counts_df = claims_per_state_df.groupby('Region')['count'].sum().reset_index()\n",
    "print(region_counts_df)\n",
    "\n",
    "# Create bar plot\n",
    "plt.bar(region_counts_df['Region'], region_counts_df['count'])\n",
    "plt.xlabel('Region')\n",
    "plt.ylabel('Total Counts')\n",
    "plt.title('Total Counts of Claims per Region')\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average amount claimed: 238310.83427449438\n",
      "        Region  Average amount claimed\n",
      "0      Midwest           109652.332407\n",
      "1    Northeast           205237.203024\n",
      "2    Southeast           230523.244771\n",
      "3    Southwest           481748.257091\n",
      "4  Territories           137734.563785\n",
      "5         West           163624.120101\n",
      "Average amount paid: 141135.00891917292\n",
      "        Region  Average amount paid\n",
      "0      Midwest         85283.390457\n",
      "1    Northeast        122131.748502\n",
      "2    Southeast        116277.260844\n",
      "3    Southwest        287281.435838\n",
      "4  Territories        124635.954926\n",
      "5         West         54388.895683\n"
     ]
    },
    {
     "data": {
      "image/png": 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TERQUhICAAOVUT926dVGgQAGN0/4BAQGIjo7WODWzfv16pKenK8f848ePsWfPHrRu3RpPnz5V2vDRo0fw8/PD1atXM8XZo0cP6Ovra5RlPN5TUlLw6NEjuLq6wtraOtMx7+PjgwoVKihltra2ygUBaiEhIYiJiUHbtm01Xlt9fX1UqVIl0+dQVtasWQM9PT20aNFCKWvbti127NiBJ0+eZKpfr149jVNw6quoW7RooXGMqMvV78Vjx44hOjoaffr0gYmJiVLP398fpUuXznTa/20EBATAxsZGeV6jRg2Nfb+rS5cuwd7eHvb29nBzc8Ps2bPh7++vDKfI7uv5MnVb9OrVC0ZGRkq5+tR7bsOkKg9KS0vDmjVrUKdOHYSHh+PatWu4du0aqlSpgqioKISGhgIADAwM0KJFC/z111/K2KiNGzciJSVFI6m6evUqzp8/rxxw6sdnn30GAIiOjtbYv4uLS5Zxbdu2DVWrVoWJiQlsbW1hb2+P+fPnIzY2Vqlz69Yt6OnpZdqGq6urxvMHDx4gJiYGixYtyhSXepzYy3G9LCgoCCkpKfD09FTa6PHjx6hSpUqWV06ZmJjA3t5eo8zKygpFihTJNGbByspK40P61q1bKFWqVKZturm5KcvfRVb7trGxyfILIjssLS0BAE+fPn2n9YEXUwb8+uuvKFmyJIyNjWFnZwd7e3ucOXNG47V+lfdp56tXr2Lnzp2Z3hO+vr4A/veeuHXrFgoWLAhzc3ON7WX1Gr1KmzZtoK+vr4xbev78OTZt2oRGjRppfOm9TE9PD3379sXx48fx8OFD/PXXX2jUqBH27NmDNm3aZHv/Gf3999948OABvL29lfdyeHg46tSpgz///FMZT9awYUNYWVlp/JAKCgpChQoVlOP52rVrEBF89913mdpx3LhxALJ3zCcmJmLs2LHK2Db1+yAmJibTMf/y8Q1kPuavXr0K4EWy+HJcf//99xuPdwD4448/4O3tjUePHint5OnpieTkZKxbty5T/aJFi2o8VycBTk5OWZar34vq4zmr91Pp0qXf+XjPKib1e+1dj3k1Z2dnhISEYPfu3fj3338RGRmJbdu2wc7ODkD2X8+Xqf/WkiVLapQbGhqiePHi7xWzLhjoOgD6+NRTBKxZswZr1qzJtHzVqlVo0KABgBdfDAsXLsSOHTvQrFkzrF27FqVLl4aHh4dSPz09HeXKlcP06dOz3N/LHzAZf9Go/fPPP2jatClq1qyJefPmoWDBgjA0NMTSpUtfe2n6q6i/JDp06IDAwMAs62R1OXtG6sQpq0HDwItffhkP+pd/ib+pXEReu/+svGpAqbp38UPuG3iRVBUqVEhjwPLbmjBhAr777jt07doVP/74I2xtbaGnp4dBgwZla86b92nn9PR01K9fH8OGDcuyrjpx0IYCBQqgfv362LBhA+bOnYutW7fi6dOnmXpYXid//vxo2rQpmjZtitq1a2P//v24desWihUr9laxqN/LGXvyMtq/fz/q1KkDY2NjNGvWDJs2bcK8efMQFRWF//77DxMmTFDqql+joUOHws/PL8vtvZzwZHXM9+/fH0uXLsWgQYPg4+MDKysrqFQqtGnT5p3mPlKvs3LlSjg6OmZabmDw+q879XQTQOYveOBFG/bs2VOj7GMd81mt97GOeTUzMzPlx0dWtP165lZMqvKgVatWoUCBAsoVIBlt3LgRmzZtwoIFC2BqaoqaNWuiYMGCCAoKwueff449e/Zg9OjRGuuUKFECp0+fRr169d7pKhIA2LBhA0xMTLBr1y4YGxsr5UuXLtWoV6xYMaSnpyM8PFzjg+/lObbs7e1hYWGBtLS0134QvEp4eDgOHjyIfv36oVatWhrL0tPT0bFjR6xevRpjxox5621npVixYrh8+XKm8kuXLinLgf/96nz5KrT3+WX7tq9ZkyZNsGjRIoSFhcHHx+et97d+/XrUqVMHixcv1iiPiYlRfvV+KCVKlEB8fPwb3xPFihVDaGgo4uPjNXqrsnqNXqd9+/bYuXMnduzYgdWrV8PS0hJffPHFO8Xu5eWF/fv34/79+2+VVCUkJOCvv/5CQEAAWrZsmWn5gAEDsGrVKmUoQEBAAJYvX47Q0FBcvHgRIqLRM63+IWFoaPhOx5ba+vXrERgYiGnTpillz58/z/TeLlasWJZz6L1cVqJECQAvktl3iWvVqlUwNDTEypUrMyUm//77L2bNmoWIiIhMPUHvQv36Xb58WRkmoXb58mWN19fGxibLU3cf85jPjuy+ni9T/61Xr17VaIuUlBSEh4dr/IDPDXj6L49JTEzExo0b0aRJE7Rs2TLTo1+/fnj69KlyabSenh5atmyJrVu3YuXKlUhNTdX4gAVe/Pq9e/cufvvttyz3l5CQ8Ma49PX1oVKpNH593bx5E5s3b9aop/5lPG/ePI3y2bNnZ9peixYtsGHDhix7VR48ePDaeNS/7IcNG5apjVq3bo1atWppdfLExo0b48iRIwgLC1PKEhISsGjRIjg7Oytj0NRfHBnH6aSlpWHRokXvvG8zMzMAmRO1Vxk2bBjMzMzQvXt3REVFZVp+/fp1zJw585Xr6+vrZ/rVvG7dunceL/Q2WrdujbCwMOzatSvTspiYGKSmpgJ48XqkpqZqzDqelpaW6X32Js2aNUO+fPkwb9487NixA82bN9cYQ3P9+nVcv35deR4ZGZnl+Lnk5GSEhoZCT09P6QVKSUnBpUuXcP/+/dfGsGnTJiQkJKBv375ZHvNNmjTBhg0blFP8vr6+sLW1RVBQEIKCguDt7a1x+q5AgQKoXbs2Fi5cmOW+33RsqWX1Ppg9e3amHhg/Pz+EhYVpTE77+PHjTMefn58fLC0tMWHCBKSkpLx1XKtWrUKNGjWU5DPj49tvvwXwYtJWbfDy8kKBAgWwYMECjWlnduzYgYsXL8Lf318pK1GiBC5duqQR/+nTp/Hff/+98/7f9pjPjuy+ni/z8vKCvb09FixYgOTkZKV82bJlWo3vY2FPVR6zZcsWPH36FE2bNs1yedWqVWFvb49Vq1YpyVNAQABmz56NcePGoVy5cso4H7WOHTti7dq16NWrF/bu3Yvq1asjLS0Nly5dwtq1a7Fr1y54eXm9Ni5/f39Mnz4dDRs2RLt27RAdHY25c+fC1dVV41YelSpVQosWLTBjxgw8evQIVatWxf79+3HlyhUAmr/AfvnlF+zduxdVqlRBjx494O7ujsePH+PEiRPYvXs3Hj9+/Mp4Vq1ahQoVKmQ6danWtGlT9O/fHydOnEDFihVf+7dlx4gRI/Dnn3+iUaNGGDBgAGxtbbF8+XKEh4djw4YNysDuMmXKoGrVqhg5ciQeP34MW1tbrFmzRkkG3kWJEiVgbW2NBQsWwMLCAmZmZqhSpcorx76VKFECq1evRkBAANzc3NCpUyeULVsWycnJOHjwINatW/fae/01adIE48ePR5cuXVCtWjWcPXsWq1at+ijjJ7799lts2bIFTZo0QefOnVGpUiUkJCTg7NmzWL9+PW7evAk7Ozt88cUXqF69OkaMGIGbN2/C3d0dGzduzNaYr4zMzc3RrFkz5RT2y6f+6tWrBwDKXD537tyBt7c36tati3r16sHR0RHR0dH4888/cfr0aQwaNEjpzbt79y7c3NwQGBj42nnFVq1ahfz586NatWpZLm/atCl+++03BAcHo3nz5jA0NETz5s2xZs0aJCQkYOrUqZnWmTt3Lj7//HOUK1cOPXr0QPHixREVFYWwsDDcuXMn05xjWWnSpAlWrlwJKysruLu7IywsDLt370b+/Pk16g0bNgx//PEH6tevj/79+8PMzAy///47ihYtisePHyvHvKWlJebPn4+OHTuiYsWKaNOmDezt7REREYHg4GBUr14dc+bMyTKWw4cP49q1a+jXr1+WywsXLoyKFSti1apVGD58+Bv/tjcxNDTEpEmT0KVLF9SqVQtt27ZFVFQUZs6cCWdnZwwePFip27VrV0yfPh1+fn7o1q0boqOjsWDBApQpU0a5cORtVapUCcCLXko/Pz/o6+u/83g9tey+ni8zNDTETz/9hK+//hp169ZFQEAAwsPDsXTp0lw5popTKuQxX3zxhZiYmLx2DpzOnTuLoaGhMhVBenq6ODk5CQD56aefslwnOTlZJk2aJGXKlBFjY2OxsbGRSpUqyQ8//CCxsbFKPQDSt2/fLLexePFiKVmypBgbG0vp0qVl6dKlMm7cuEyX/iYkJEjfvn3F1tZWzM3NpVmzZnL58uUsZ6mOioqSvn37ipOTkxgaGoqjo6PUq1dPFi1a9Mq/Xz1tw3fffffKOjdv3hQAMnjwYBF5cXmzmZlZpnqvmt+lWLFi4u/vr1F2/fp1admypVhbW4uJiYl4e3tnmqNLXc/X11eMjY3FwcFBRo0aJSEhIVlOqZDVvl++FFvkxWXX7u7uYmBgkO0pA65cuSI9evQQZ2dnMTIyEgsLC6levbrMnj1b41L7rKZU+Oabb6RgwYJiamoq1atXl7CwsEyXjr9qSoX3beenT5/KyJEjxdXVVYyMjMTOzk6qVasmU6dO1ZgZ+tGjR9KxY0extLQUKysr6dixo5w8efKtZp8XEQkODhYAUrBgwUxTSRQrVkzjtYiLi5OZM2eKn5+fFClSRAwNDcXCwkJ8fHzkt99+U6ZDyNg+r5utXj1FQMeOHV9Z59mzZ5IvXz756quvlDL1+0mlUsnt27ezXO/69evSqVMncXR0FENDQylcuLA0adJE1q9fr9RRT6mgntMuoydPnkiXLl3Ezs5OzM3Nxc/PTy5dupTlDPwnT56UGjVqiLGxsRQpUkQmTpwos2bNEgASGRmpUXfv3r3i5+cnVlZWYmJiIiVKlJDOnTvLsWPHXtkG/fv3FwCvnZ/t+++/FwBy+vRpEcn6s0z9mkyZMiVTTABk3bp1GuVBQUHi6ekpxsbGYmtrK+3bt5c7d+5k2vcff/whxYsXFyMjI6lQoYLs2rXrlVMqvLxvdawZpxZJTU2V/v37i729vahUqjdOr/Cq4yuj7L6eWU3/IiIyb948cXFxEWNjY/Hy8pIDBw7kyhnVVSLvOXqNKAc4deoUPD098ccff7zVQGAiyp0GDRqEhQsXIj4+/pWDs4k+No6polwnMTExU9mMGTOgp6f36d6kkygPe/mYf/ToEVauXInPP/+cCRXlKBxTRbnO5MmTcfz4cdSpUwcGBgbYsWMHduzYgZ49e75yDBQR5V4+Pj6oXbs23NzcEBUVhcWLFyMuLg7fffedrkMj0sDTf5TrhISE4IcffsCFCxcQHx+PokWLomPHjhg9evQb56Ihotxn1KhRWL9+Pe7cuQOVSoWKFSti3Lhx7zWlA9GHwKSKiIiISAs4poqIiIhIC5hUEREREWkBB6B8ROnp6bh37x4sLCw+yG0CiIiISPtEBE+fPkWhQoWUyZhfVVFn1BM7ZnyUKlVKWZ6YmCh9+vQRW1tbMTMzk+bNm2ea6O3WrVvSuHFjMTU1FXt7exk6dKikpKRo1Nm7d694enqKkZGRlChRIsuJ++bMmSPFihUTY2Nj8fb2lsOHD2ssz04sb3L79u1Mfy8ffPDBBx988JE7Hq+aDFdN5z1VZcqUwe7du5XnGa/eGjx4MIKDg7Fu3TpYWVmhX79+aN68uXLPo7S0NPj7+8PR0REHDx7E/fv30alTJxgaGip3VQ8PD4e/vz969eqFVatWITQ0FN27d0fBggWV+8gFBQVhyJAhWLBgAapUqYIZM2bAz88Ply9fRoECBbIVS3ZYWFgAAG7fvg1LS8v3azgiIiL6KOLi4uDk5KR8j7/SW3W1aNm4cePEw8Mjy2UxMTFiaGioMa3/xYsXBYCEhYWJiMj27dtFT09Po8do/vz5YmlpKUlJSSIiMmzYsEzT6wcEBIifn5/y3NvbW+N2A2lpaVKoUCGZOHFitmPJjtjYWAGgcdsWIiIiytmy+/2t84HqV69eRaFChVC8eHG0b98eERERAIDjx48jJSVFYx6S0qVLo2jRoggLCwMAhIWFoVy5cnBwcFDq+Pn5IS4uDufPn1fqvDyXifqu58CLu78fP35co46enh58fX2VOtmJJStJSUmIi4vTeBAREdGnSadJVZUqVbBs2TLs3LkT8+fPR3h4OGrUqIGnT58iMjISRkZGsLa21ljHwcEBkZGRAIDIyEiNhEq9XL3sdXXi4uKQmJiIhw8fIi0tLcs6GbfxpliyMnHiRFhZWSkPzvZNRET06dLpmKpGjRop/y9fvjyqVKmCYsWKYe3atTA1NdVhZNoxcuRIDBkyRHmuPidLREREnx6dD1TPyNraGp999hmuXbuG+vXrIzk5GTExMRo9RFFRUXB0dAQAODo64siRIxrbiIqKUpap/1WXZaxjaWkJU1NT6OvrQ19fP8s6GbfxpliyYmxsDGNj47drBLwYgJ+SkvLW6xHR+zMyMnr9JdNERK+Qo5Kq+Ph4XL9+HR07dkSlSpVgaGiI0NBQtGjRAgBw+fJlREREwMfHB8CLm2z+/PPPiI6OVq7SCwkJgaWlJdzd3ZU627dv19hPSEiIsg0jIyNUqlQJoaGhaNasGYAX80mFhoaiX79+AJCtWLRBRBAZGYmYmBitbZOI3o6enh5cXFxgZGSk61CIKJfRaVI1dOhQfPHFFyhWrBju3buHcePGQV9fH23btoWVlRW6deuGIUOGwNbWFpaWlujfvz98fHxQtWpVAECDBg3g7u6Ojh07YvLkyYiMjMSYMWPQt29fpYeoV69emDNnDoYNG4auXbtiz549WLt2LYKDg5U4hgwZgsDAQHh5ecHb2xszZsxAQkICunTpAgDZikUb1AlVgQIFkC9fPk4QSvSRqSfovX//PooWLcpjkIjezse5GDFrAQEBUrBgQTEyMpLChQtLQECAXLt2TVmunnDTxsZG8uXLJ1999ZXcv39fYxs3b96URo0aiampqdjZ2ck333yT5eSfFSpUECMjIylevHiWk3/Onj1bihYtKkZGRuLt7S2HDh3SWJ6dWN7kdZdkpqamyoULF+Thw4dvtU0i0q6YmBi5cOGCJCcn6zoUIsohsjulgkpERNeJXV4RFxcHKysrxMbGZpr88/nz5wgPD4ezs/MnMUifKLdKTEzEzZs34eLiAhMTE12HQ0Q5wOu+vzPiaMwchqcbiHSLxyARvSsmVURERERawKSKiN7b999/jwoVKrzVOiqVCps3b/4g8bzOzZs3oVKpcOrUqY++byL6tOWoKRUoM+cRwW+upEU3f/F/p/XCwsLw+eefo2HDhhpXVn7qvv76a/z+++9Ys2YNWrVqpetwsk09ZujkyZPZSoY2bNiA2bNn4+TJk0hLS0Px4sXRsmVL9OvXD7a2tu8Uw/3792FjY/NO6xIR5UTsqSKtWLx4Mfr3748DBw7g3r17H3RfIoLU1NQPuo/sePbsGdasWYNhw4ZhyZIlug7ngxk9ejQCAgJQuXJl7NixA+fOncO0adNw+vRprFy58p236+jo+E6T4xIR5VRMqui9xcfHIygoCL1794a/vz+WLVumLGvXrh0CAgI06qekpMDOzg4rVqwA8GJuoIkTJ8LFxQWmpqbw8PDA+vXrlfr79u2DSqXCjh07UKlSJRgbG+Pff//F9evX8eWXX8LBwQHm5uaoXLkydu/erbGv+/fvw9/fH6ampnBxccHq1avh7OyMGTNmKHViYmLQvXt32Nvbw9LSEnXr1sXp06ff+HevW7cO7u7uGDFiBA4cOIDbt29rLO/cuTOaNWuGCRMmwMHBAdbW1hg/fjxSU1Px7bffwtbWFkWKFMHSpUs11jt79izq1q0LU1NT5M+fHz179kR8fLyyvHbt2hg0aJDGOs2aNUPnzp2V587OzpgwYQK6du0KCwsLFC1aFIsWLVKWu7i4AAA8PT2hUqlQu3btLP/GI0eOYMKECZg2bRqmTJmCatWqwdnZGfXr18eGDRsQGBiY5XpHjx5F/fr1YWdnBysrK9SqVQsnTpzQqJPx9J/6lNzatWtRo0YNmJqaonLlyrhy5QqOHj0KLy8vmJubo1GjRnjw4IHGdn7//Xe4ubnBxMQEpUuXxrx58zL9DZ6enjAxMYGXlxdOnjyZZcxERO+Lp//ova1duxalS5dGqVKl0KFDBwwaNAgjR46ESqVC+/bt0apVK8THx8Pc3BwAsGvXLjx79gxfffUVgBc3nv7jjz+wYMEClCxZEgcOHECHDh1gb2+PWrVqKfsZMWIEpk6diuLFi8PGxga3b99G48aN8fPPP8PY2BgrVqzAF198gcuXL6No0aIAgE6dOuHhw4fYt28fDA0NMWTIEERHR2vE36pVK5iammLHjh2wsrLCwoULUa9ePVy5cuW1p7YWL16MDh06wMrKCo0aNcKyZcvw3XffadTZs2cPihQpggMHDuC///5Dt27dcPDgQdSsWROHDx9GUFAQvv76a9SvXx9FihRBQkIC/Pz84OPjg6NHjyI6Ohrdu3dHv379NJLV7Jg2bRp+/PFHjBo1CuvXr0fv3r1Rq1YtlCpVCkeOHIG3tzd2796NMmXKvHL28FWrVsHc3Bx9+vTJcvnLNxlXe/r0KQIDAzF79myICKZNm4bGjRvj6tWrsLCweGXM48aNw4wZM1C0aFF07doV7dq1g4WFBWbOnIl8+fKhdevWGDt2LObPn6/EN3bsWMyZMweenp44efIkevToATMzMwQGBiI+Ph5NmjRB/fr18ccffyA8PBwDBw58q3b8kD7m6f13PbVPRNnHnip6b+rkAgAaNmyI2NhY7N+/HwDg5+cHMzMzbNq0Sam/evVqNG3aFBYWFkhKSsKECROwZMkS+Pn5oXjx4ujcuTM6dOiAhQsXauxn/PjxqF+/PkqUKAFbW1t4eHjg66+/RtmyZVGyZEn8+OOPKFGiBLZs2QIAuHTpEnbv3o3ffvsNVapUQcWKFfH7778jMTFR2ea///6LI0eOYN26dfDy8kLJkiUxdepUWFtba/SWvezq1as4dOiQ0gvXoUMHLF26FC9P+2Zra4tZs2ahVKlS6Nq1K0qVKoVnz55h1KhRKFmyJEaOHAkjIyP8+++/Sts8f/4cK1asQNmyZVG3bl3MmTMHK1euzHR/yjdp3Lgx+vTpA1dXVwwfPhx2dnbYu3cvAMDe3h4AkD9/fjg6Or4yebx69SqKFy8OQ0PDt9p33bp10aFDB5QuXRpubm5YtGgRnj17prwvXmXo0KHw8/ODm5sbBg4ciOPHj+O7775D9erV4enpiW7duil/A/AiCZs2bRqaN28OFxcXNG/eHIMHD1beO6tXr0Z6ejoWL16MMmXKoEmTJvj222/f6m8hIsouJlX0Xi5fvowjR46gbdu2AAADAwMEBARg8eLFyvPWrVtj1apVAICEhAT89ddfaN++PQDg2rVrePbsGerXrw9zc3PlsWLFCly/fl1jX15eXhrP4+PjMXToULi5ucHa2hrm5ua4ePEiIiIilNgMDAxQsWJFZR1XV1eNwdGnT59GfHw88ufPr7H/8PDwTPvPSJ0E2tnZAXiRwMTGxmLPnj0a9cqUKaNxc14HBweUK1dOea6vr4/8+fMrvWcXL16Eh4cHzMzMlDrVq1dHeno6Ll++/Mp4slK+fHnl/yqVCo6Ojpl66d7kXecGjoqKQo8ePVCyZElYWVnB0tIS8fHxymuTnZgdHBwAQKO9HBwclL8hISEB169fR7du3TReu59++kl57S5evIjy5ctrTOKpzft1EhFlxNN/9F4WL16M1NRUFCpUSCkTERgbG2POnDmwsrJC+/btUatWLURHRyMkJASmpqZo2LAhAChjhYKDg1G4cGGNbb88iDljogG86NUICQnB1KlT4erqClNTU7Rs2RLJycnZjj8+Ph4FCxbEvn37Mi171amttLQ0LF++HJGRkTAwMNAoX7JkCerVq6eUvdzDo1KpsixLT0/Pdsx6enqZkp2UlJRM9d53PwDw2Wef4d9//0VKSspb9VYFBgbi0aNHmDlzJooVKwZjY2P4+Pi88bXJuA/1JJwvl6n/BvV7R90TmZG+vn62YyUi0hYmVfTOUlNTsWLFCkybNg0NGjTQWNasWTP8+eef6NWrF6pVqwYnJycEBQVhx44daNWqlfJF6e7uDmNjY0RERGiMn8qO//77D507d1bGZsXHx+PmzZvK8lKlSiE1NRUnT55EpUqVALzoGXvy5IlSp2LFikpy5OzsnK39bt++HU+fPsXJkyc1vrzPnTuHLl26ICYm5pUJ2Zu4ublh2bJlSEhIUJLI//77D3p6eihVqhSAF6fu7t+/r6yTlpaGc+fOoU6dOtnej3oMVVpa2mvrtWvXDrNmzcK8efOyHIv0qr/1v//+w7x589C4cWMAwO3bt/Hw4cNsx5cdDg4OKFSoEG7cuKH0fL7Mzc0NK1euxPPnz5XeqkOHDmk1DiIiNZ7+o3e2bds2PHnyBN26dUPZsmU1Hi1atFBOAQIvvpwXLFiAkJAQjS9ACwsLDB06FIMHD8by5ctx/fp1nDhxArNnz8by5ctfu/+SJUti48aNOHXqFE6fPo127dpp9MSULl0avr6+6NmzJ44cOYKTJ0+iZ8+eMDU1VXpBfH194ePjg2bNmuHvv//GzZs3cfDgQYwePRrHjh3Lcr+LFy+Gv78/PDw8NP7m1q1bw9raWjnV+S7at28PExMTBAYG4ty5c9i7dy/69++Pjh07KqfD6tati+DgYAQHB+PSpUvo3bs3YmJi3mo/BQoUgKmpKXbu3ImoqCjExsZmWa9KlSoYNmwYvvnmGwwbNgxhYWG4desWQkND0apVq1e+RiVLlsTKlStx8eJFHD58GO3bt/8g97T84YcfMHHiRMyaNQtXrlzB2bNnsXTpUkyfPh3Ai/edSqVCjx49cOHCBWzfvh1Tp07VehxERAB7qnK8nHzFzuLFi+Hr6wsrK6tMy1q0aIHJkyfjzJkzKF++PNq3b4+ff/4ZxYoVQ/Xq1TXq/vjjj7C3t8fEiRNx48YNWFtbo2LFihg1atRr9z99+nR07doV1apVg52dHYYPH464uDiNOitWrEC3bt1Qs2ZNODo6YuLEiTh//rzSa6FSqbB9+3aMHj0aXbp0wYMHD+Do6IiaNWsqSUxGUVFRCA4OxurVqzMt09PTw1dffYXFixejb9++b2y/rOTLlw+7du3CwIEDUblyZeTLlw8tWrRQkgQA6Nq1K06fPo1OnTrBwMAAgwcPfqteKuDFWLdZs2Zh/PjxGDt2LGrUqJHlKVAAmDRpEipVqoS5c+diwYIFSE9PR4kSJdCyZctXTqmwePFi9OzZExUrVoSTkxMmTJiAoUOHvlWM2dG9e3fky5cPU6ZMwbfffgszMzOUK1dOmXLC3NwcW7duRa9eveDp6Ql3d3dMmjQJLVq00HosREQqedeRqPTWXneX6+fPnyM8PBwuLi4ag2pJu+7cuQMnJyfs3r1bY+wTkdrHPBY5pQJR7vC67++M2FNFn7Q9e/YgPj4e5cqVw/379zFs2DA4OzujZs2aug6NiIg+MUyq6JOWkpKCUaNG4caNG7CwsEC1atWwatWqt553iYiI6E2YVNEnzc/PD35+froOg4iI8gBe/UdERESkBUyqiIiIiLSASRURERGRFjCpIiIiItICJlVEREREWsCkioiIiEgLmFQR0Qfl7OyMGTNmvLaOSqXC5s2bP0o8REQfCuepyum+z3xfvQ+7v6xvrPsmYWFh+Pzzz9GwYUMEB3+8W2/o2tdff43ff/8da9asQatWrXQdTrbdvHkTLi4uOHnyJCpUqPDGemq2traoVKkSJk2aBE9Pz2zt6+jRozAzM3vfkImIcjz2VJFWLF68GP3798eBAwdw7969D7ovEUFqauoH3Ud2PHv2DGvWrMGwYcOwZMkSXYfzQe3evRv379/Hrl27EB8fj0aNGiEmJiZb69rb2yNfvnwfNkAiohyASRW9t/j4eAQFBaF3797w9/fHsmXLlGXt2rVDQECARv2UlBTY2dlhxYoVAID09HRMnDgRLi4uMDU1hYeHB9avX6/U37dvH1QqFXbs2IFKlSrB2NgY//77L65fv44vv/wSDg4OMDc3R+XKlbF7926Nfd2/fx/+/v4wNTWFi4sLVq9enel0VExMDLp37w57e3tYWlqibt26OH369Bv/7nXr1sHd3R0jRozAgQMHcPv2bY3lnTt3RrNmzTBhwgQ4ODjA2toa48ePR2pqKr799lvY2tqiSJEiWLp0qcZ6Z8+eRd26dWFqaor8+fOjZ8+eiI+PV5bXrl0bgwYN0linWbNm6Ny5s/Lc2dkZEyZMQNeuXWFhYYGiRYti0aJFynJ175OnpydUKhVq16792r81f/78cHR0hJeXF6ZOnYqoqCgcPnw4W6/By+199epV1KxZEyYmJnB3d0dISMhr901ElFswqaL3tnbtWpQuXRqlSpVChw4dsGTJEogIAKB9+/bYunWrRlKwa9cuPHv2DF999RUAYOLEiVixYgUWLFiA8+fPY/DgwejQoQP279+vsZ8RI0bgl19+wcWLF1G+fHnEx8ejcePGCA0NxcmTJ9GwYUN88cUXiIiIUNbp1KkT7t27h3379mHDhg1YtGgRoqOjNbbbqlUrREdHY8eOHTh+/DgqVqyIevXq4fHjx6/9uxcvXowOHTrAysoKjRo10kgm1fbs2YN79+7hwIEDmD59OsaNG4cmTZrAxsYGhw8fRq9evfD111/jzp07AICEhAT4+fnBxsYGR48exbp167B7927069cv+y/I/5s2bRq8vLxw8uRJ9OnTB71798bly5cBAEeOHAHwvx6ojRs3Znu7pqamAIDk5ORsvQYZpaeno3nz5jAyMsLhw4exYMECDB8+/K3/NiKinIhJFb03dXIBAA0bNkRsbKySEPn5+cHMzAybNm1S6q9evRpNmzaFhYUFkpKSMGHCBCxZsgR+fn4oXrw4OnfujA4dOmDhwoUa+xk/fjzq16+PEiVKwNbWFh4eHvj6669RtmxZlCxZEj/++CNKlCiBLVu2AAAuXbqE3bt347fffkOVKlVQsWJF/P7770hMTFS2+e+//+LIkSNYt24dvLy8ULJkSUydOhXW1tYavWUvu3r1Kg4dOqT0wnXo0AFLly5Vkkk1W1tbzJo1C6VKlULXrl1RqlQpPHv2DKNGjULJkiUxcuRIGBkZ4d9//1Xa5vnz51ixYgXKli2LunXrYs6cOVi5ciWioqLe6nVp3Lgx+vTpA1dXVwwfPhx2dnbYu3cvgBen5ID/9UDZ2tpma5sxMTH48ccfYW5uDm9v7ze+Bi/bvXs3Ll26hBUrVsDDwwM1a9bEhAkT3urvIiLKqZhU0Xu5fPkyjhw5grZt2wIADAwMEBAQgMWLFyvPW7dujVWrVgF40RPz119/oX379gCAa9eu4dmzZ6hfvz7Mzc2Vx4oVK3D9+nWNfXl5eWk8j4+Px9ChQ+Hm5gZra2uYm5vj4sWLSi/J5cuXYWBggIoVKyrruLq6wsbGRnl++vRpxMfHI3/+/Br7Dw8Pz7T/jNRJoJ2dHYAXCUxsbCz27NmjUa9MmTLQ0/vfYebg4IBy5copz/X19ZE/f36l9+zixYvw8PDQGNhdvXp1pKenK71M2VW+fHnl/yqVCo6Ojpl66bKrWrVqMDc3h42NDU6fPo2goCA4ODi88TV42cWLF+Hk5IRChQopZT4+Pu8UExFRTsOr/+i9LF68GKmpqRpfkiICY2NjzJkzB1ZWVmjfvj1q1aqF6OhohISEwNTUFA0bNgQA5bRgcHAwChcurLFtY2NjjecvX0E2dOhQhISEYOrUqXB1dYWpqSlatmyJ5OTkbMcfHx+PggULYt++fZmWWVtbZ7lOWloali9fjsjISBgYGGiUL1myBPXq1VPKDA0NNdZVqVRZlqWnp2c7Zj09vUw9YikpKZnqve9+MgoKCoK7uzvy58+v0S7aeA2IiD4VTKronaWmpmLFihWYNm0aGjRooLGsWbNm+PPPP9GrVy9Uq1YNTk5OCAoKwo4dO9CqVSvlC9/d3R3GxsaIiIhArVq13mr///33Hzp37qyMzYqPj8fNmzeV5aVKlUJqaipOnjyJSpUqAXjRM/bkyROlTsWKFZXkyNnZOVv73b59O54+fYqTJ09CX19fKT937hy6dOmCmJiYVyZkb+Lm5oZly5YhISFBSSL/++8/6OnpoVSpUgBenLq7f/++sk5aWhrOnTuHOnXqZHs/RkZGyrrZ4eTkhBIlSmQqf9Nr8DI3Nzfcvn0b9+/fR8GCBQEAhw4dynbcREQ5GU//0Tvbtm0bnjx5gm7duqFs2bIajxYtWiinAIEXVwEuWLAAISEhyqk/ALCwsMDQoUMxePBgLF++HNevX8eJEycwe/ZsLF++/LX7L1myJDZu3IhTp07h9OnTaNeunUZPTOnSpeHr64uePXviyJEjOHnyJHr27AlTU1OoVCoAgK+vL3x8fNCsWTP8/fffuHnzJg4ePIjRo0fj2LFjWe538eLF8Pf3h4eHh8bf3Lp1a1hbWyunOt9F+/btYWJigsDAQJw7dw579+5F//790bFjRzg4OAAA6tati+DgYAQHB+PSpUvo3bt3tqc3UCtQoABMTU2xc+dOREVFITb23eYne9Nr8DJfX1989tlnCAwMxOnTp/HPP/9g9OjR77RvIqKchj1VOd07Tsb5MSxevBi+vr6wsso8QWmLFi0wefJknDlzBuXLl0f79u3x888/o1ixYqhevbpG3R9//BH29vaYOHEibty4AWtra1SsWBGjRo167f6nT5+Orl27olq1arCzs8Pw4cMRFxenUWfFihXo1q0batasCUdHR0ycOBHnz5+HiYkJgBenxLZv347Ro0ejS5cuePDgARwdHVGzZk0lickoKioKwcHBWL16daZlenp6+Oqrr7B48WL07dv3je2XlXz58mHXrl0YOHAgKleujHz58qFFixaYPn26Uqdr1644ffo0OnXqBAMDAwwePPiteqmAF2PdZs2ahfHjx2Ps2LGoUaNGlqdA3yQ7r0FGenp62LRpE7p16wZvb284Oztj1qxZyulgIqLcTCUvD86gDyYuLg5WVlaIjY2FpaWlxrLnz58jPDwcLi4uyhc+ad+dO3fg5OSE3bt3a4x9IlL7mMei84iPd/eBm7/4f7R9EX1qXvf9nRF7quiTtmfPHsTHx6NcuXK4f/8+hg0bBmdnZ9SsWVPXoRER0SeGSRV90lJSUjBq1CjcuHEDFhYWqFatGlatWpXpyjgiIqL3xaSKPml+fn7w8/PTdRhERJQH8Oo/IiIiIi1gUpXD8LoBIt3iMUhE74pJVQ6hHuPz7NkzHUdClLepZ4PPOLErEVF2cExVDqGvrw9ra2vl3mz58uVTJqgkoo8jPT0dDx48QL58+TRuQURElB381MhBHB0dAeCdb3pLRO9PT08PRYsW5Y8aInprTKpyEJVKhYIFC6JAgQJZ3iCXiD48IyMj6OlxZAQRvT0mVTmQvr4+x3MQERHlMvw5RkRERKQFTKqIiIiItIBJFREREZEWMKkiIiIi0gImVURERERawKSKiIiISAuYVBERERFpAZMqIiIiIi1gUkVERESkBUyqiIiIiLSASRURERGRFjCpIiIiItICJlVEREREWsCkioiIiEgLmFQRERERaQGTKiIiIiItYFJFREREpAU5Jqn65ZdfoFKpMGjQIKXs+fPn6Nu3L/Lnzw9zc3O0aNECUVFRGutFRETA398f+fLlQ4ECBfDtt98iNTVVo86+fftQsWJFGBsbw9XVFcuWLcu0/7lz58LZ2RkmJiaoUqUKjhw5orE8O7EQERFR3pUjkqqjR49i4cKFKF++vEb54MGDsXXrVqxbtw779+/HvXv30Lx5c2V5Wloa/P39kZycjIMHD2L58uVYtmwZxo4dq9QJDw+Hv78/6tSpg1OnTmHQoEHo3r07du3apdQJCgrCkCFDMG7cOJw4cQIeHh7w8/NDdHR0tmMhIiKivE0lIqLLAOLj41GxYkXMmzcPP/30EypUqIAZM2YgNjYW9vb2WL16NVq2bAkAuHTpEtzc3BAWFoaqVatix44daNKkCe7duwcHBwcAwIIFCzB8+HA8ePAARkZGGD58OIKDg3Hu3Dlln23atEFMTAx27twJAKhSpQoqV66MOXPmAADS09Ph5OSE/v37Y8SIEdmKJTvi4uJgZWWF2NhYWFpaaq0NiSh3ch4R/NH2dfMX/4+2L6JPTXa/v3XeU9W3b1/4+/vD19dXo/z48eNISUnRKC9dujSKFi2KsLAwAEBYWBjKlSunJFQA4Ofnh7i4OJw/f16p8/K2/fz8lG0kJyfj+PHjGnX09PTg6+ur1MlOLFlJSkpCXFycxoOIiIg+TQa63PmaNWtw4sQJHD16NNOyyMhIGBkZwdraWqPcwcEBkZGRSp2MCZV6uXrZ6+rExcUhMTERT548QVpaWpZ1Ll26lO1YsjJx4kT88MMPr1xOREREnw6d9VTdvn0bAwcOxKpVq2BiYqKrMD6okSNHIjY2Vnncvn1b1yERERHRB6KzpOr48eOIjo5GxYoVYWBgAAMDA+zfvx+zZs2CgYEBHBwckJycjJiYGI31oqKi4OjoCABwdHTMdAWe+vmb6lhaWsLU1BR2dnbQ19fPsk7GbbwplqwYGxvD0tJS40FERESfJp0lVfXq1cPZs2dx6tQp5eHl5YX27dsr/zc0NERoaKiyzuXLlxEREQEfHx8AgI+PD86ePatxlV5ISAgsLS3h7u6u1Mm4DXUd9TaMjIxQqVIljTrp6ekIDQ1V6lSqVOmNsRAREVHeprMxVRYWFihbtqxGmZmZGfLnz6+Ud+vWDUOGDIGtrS0sLS3Rv39/+Pj4KFfbNWjQAO7u7ujYsSMmT56MyMhIjBkzBn379oWxsTEAoFevXpgzZw6GDRuGrl27Ys+ePVi7di2Cg/931c2QIUMQGBgILy8veHt7Y8aMGUhISECXLl0AAFZWVm+MhYiIiPI2nQ5Uf5Nff/0Venp6aNGiBZKSkuDn54d58+Ypy/X19bFt2zb07t0bPj4+MDMzQ2BgIMaPH6/UcXFxQXBwMAYPHoyZM2eiSJEi+P333+Hn56fUCQgIwIMHDzB27FhERkaiQoUK2Llzp8bg9TfFQkRERHmbzuepyks4TxURZcR5qohyh1wzTxURERHRp4BJFREREZEWMKkiIiIi0gImVURERERawKSKiIiISAuYVBERERFpAZMqIiIiIi1gUkVERESkBUyqiIiIiLSASRURERGRFjCpIiIiItICJlVEREREWsCkioiIiEgLmFQRERERaQGTKiIiIiItYFJFREREpAVMqoiIiIi0gEkVERERkRYwqSIiIiLSAiZVRERERFrApIqIiIhIC5hUEREREWkBkyoiIiIiLWBSRURERKQFTKqIiIiItIBJFREREZEWMKkiIiIi0gImVURERERawKSKiIiISAuYVBERERFpAZMqIiIiIi1gUkVERESkBUyqiIiIiLSASRURERGRFjCpIiIiItICJlVEREREWsCkioiIiEgLmFQRERERaQGTKiIiIiItYFJFREREpAVMqoiIiIi0gEkVERERkRYwqSIiIiLSAiZVRERERFrApIqIiIhIC5hUEREREWkBkyoiIiIiLWBSRURERKQFTKqIiIiItIBJFREREZEWMKkiIiIi0gImVURERERawKSKiIiISAuYVBERERFpAZMqIiIiIi1gUkVERESkBUyqiIiIiLSASRURERGRFjCpIiIiItICJlVEREREWsCkioiIiEgLmFQRERERaYFOk6r58+ejfPnysLS0hKWlJXx8fLBjxw5l+fPnz9G3b1/kz58f5ubmaNGiBaKiojS2ERERAX9/f+TLlw8FChTAt99+i9TUVI06+/btQ8WKFWFsbAxXV1csW7YsUyxz586Fs7MzTExMUKVKFRw5ckRjeXZiISIiorxLp0lVkSJF8Msvv+D48eM4duwY6tatiy+//BLnz58HAAwePBhbt27FunXrsH//fty7dw/NmzdX1k9LS4O/vz+Sk5Nx8OBBLF++HMuWLcPYsWOVOuHh4fD390edOnVw6tQpDBo0CN27d8euXbuUOkFBQRgyZAjGjRuHEydOwMPDA35+foiOjlbqvCkWIiIiyttUIiK6DiIjW1tbTJkyBS1btoS9vT1Wr16Nli1bAgAuXboENzc3hIWFoWrVqtixYweaNGmCe/fuwcHBAQCwYMECDB8+HA8ePICRkRGGDx+O4OBgnDt3TtlHmzZtEBMTg507dwIAqlSpgsqVK2POnDkAgPT0dDg5OaF///4YMWIEYmNj3xhLdsTFxcHKygqxsbGwtLTUWpsRUe7kPCL4o+3r5i/+H21fRJ+a7H5/55gxVWlpaVizZg0SEhLg4+OD48ePIyUlBb6+vkqd0qVLo2jRoggLCwMAhIWFoVy5ckpCBQB+fn6Ii4tTervCwsI0tqGuo95GcnIyjh8/rlFHT08Pvr6+Sp3sxJKVpKQkxMXFaTyIiIjo06TzpOrs2bMwNzeHsbExevXqhU2bNsHd3R2RkZEwMjKCtbW1Rn0HBwdERkYCACIjIzUSKvVy9bLX1YmLi0NiYiIePnyItLS0LOtk3MabYsnKxIkTYWVlpTycnJyy1yhERESU6+g8qSpVqhROnTqFw4cPo3fv3ggMDMSFCxd0HZZWjBw5ErGxscrj9u3bug6JiIiIPhADXQdgZGQEV1dXAEClSpVw9OhRzJw5EwEBAUhOTkZMTIxGD1FUVBQcHR0BAI6Ojpmu0lNfkZexzstX6UVFRcHS0hKmpqbQ19eHvr5+lnUybuNNsWTF2NgYxsbGb9EaRERElFvpvKfqZenp6UhKSkKlSpVgaGiI0NBQZdnly5cREREBHx8fAICPjw/Onj2rcZVeSEgILC0t4e7urtTJuA11HfU2jIyMUKlSJY066enpCA0NVepkJxYiIiLK23TaUzVy5Eg0atQIRYsWxdOnT7F69Wrs27cPu3btgpWVFbp164YhQ4bA1tYWlpaW6N+/P3x8fJSr7Ro0aAB3d3d07NgRkydPRmRkJMaMGYO+ffsqPUS9evXCnDlzMGzYMHTt2hV79uzB2rVrERz8v6tuhgwZgsDAQHh5ecHb2xszZsxAQkICunTpAgDZioWIiIjyNp0mVdHR0ejUqRPu378PKysrlC9fHrt27UL9+vUBAL/++iv09PTQokULJCUlwc/PD/PmzVPW19fXx7Zt29C7d2/4+PjAzMwMgYGBGD9+vFLHxcUFwcHBGDx4MGbOnIkiRYrg999/h5+fn1InICAADx48wNixYxEZGYkKFSpg586dGoPX3xQLERER5W05bp6qTxnnqSKijDhPFVHukOvmqSIiIiLKzZhUEREREWmBzqdUICKij+B7q4+8v9iPuz+iHIA9VURERERawKSKiIiISAuYVBERERFpAZMqIiIiIi14p6Rq586d+Pfff5Xnc+fORYUKFdCuXTs8efJEa8ERERER5RbvlFR9++23iIuLAwCcPXsW33zzDRo3bozw8HAMGTJEqwESERER5QbvNKVCeHi4csPiDRs2oEmTJpgwYQJOnDiBxo0bazVAIiIiotzgnXqqjIyM8OzZMwDA7t270aBBAwCAra2t0oNFRERElJe8U09V9erVMWTIEFSvXh1HjhxBUFAQAODKlSsoUqSIVgMkIiIiyg3eqadq7ty5MDQ0xPr16zF//nwULlwYALBjxw40bNhQqwESERER5QZv3VOVmpqKffv24bfffoOjo6PGsl9//VVrgRERERHlJm/dU2VgYIBevXohKSnpQ8RDRERElCu90+k/b29vnDx5UtuxEBEREeVa7zRQvU+fPvjmm29w584dVKpUCWZmZhrLy5cvr5XgiIiIiHKLd0qq2rRpAwAYMGCAUqZSqSAiUKlUSEtL0050RERERLnEO0/+SURERET/805JVbFixbQdBxEREVGu9k5JldqFCxcQERGB5ORkjfKmTZu+V1BEREREuc07JVU3btzAV199hbNnzypjqYAX46oAcEwVERER5TnvNKXCwIED4eLigujoaOTLlw/nz5/HgQMH4OXlhX379mk5RCIiIqKc7516qsLCwrBnzx7Y2dlBT08Penp6+PzzzzFx4kQMGDCAc1gRERFRnvNOPVVpaWmwsLAAANjZ2eHevXsAXgxgv3z5svaiIyIiIsol3qmnqmzZsjh9+jRcXFxQpUoVTJ48GUZGRli0aBGKFy+u7RiJiIiIcrx3SqrGjBmDhIQEAMD48ePRpEkT1KhRA/nz50dQUJBWAyQiIiLKDd4pqfLz81P+7+rqikuXLuHx48ewsbFRrgAkIiIiykvea56qjGxtbbW1KSIiIqJcJ9tJVfPmzbO90Y0bN75TMERERES5VbaTKisrqw8ZBxEREVGulu2kaunSpR8yDiIiIqJc7Z3mqSIiIiIiTdnuqfL09Mz2lX0nTpx454CIiIiIcqNsJ1XNmjX7gGEQERER5W7ZTqrGjRv3IeMgIiIiytXea56q48eP4+LFiwCAMmXKwNPTUytBEREREeU275RURUdHo02bNti3bx+sra0BADExMahTpw7WrFkDe3t7bcZIRERElOO909V//fv3x9OnT3H+/Hk8fvwYjx8/xrlz5xAXF4cBAwZoO0YiIiKiHO+deqp27tyJ3bt3w83NTSlzd3fH3Llz0aBBA60FR0RERJRbvFNPVXp6OgwNDTOVGxoaIj09/b2DIiIiIspt3impqlu3LgYOHIh79+4pZXfv3sXgwYNRr149rQVHRERElFu8U1I1Z84cxMXFwdnZGSVKlECJEiXg4uKCuLg4zJ49W9sxEhEREeV47zSmysnJCSdOnEBoaKgypYKbmxt8fX21GhwRERFRbvHWSVV6ejqWLVuGjRs34ubNm1CpVHBxcYGVlRVEJNu3siGiT4PziOCPtq+bv/h/tH0REb2ttzr9JyJo2rQpunfvjrt376JcuXIoU6YMbt26hc6dO+Orr776UHESERER5Whv1VO1bNkyHDhwAKGhoahTp47Gsj179qBZs2ZYsWIFOnXqpNUgiYiIiHK6t+qp+vPPPzFq1KhMCRXw4orAESNGYNWqVVoLjoiIiCi3eKuk6syZM2jYsOErlzdq1AinT59+76CIiIiIcpu3SqoeP34MBweHVy53cHDAkydP3jsoIiIiotzmrZKqtLQ0GBi8ehiWvr4+UlNT3zsoIiIiotzmrQaqiwg6d+4MY2PjLJcnJSVpJSgiIiKi3OatkqrAwMA31uGVf0RERJQXvVVStXTp0g8VBxEREVGu9k73/iMiIiIiTUyqiIiIiLSASRURERGRFjCpIiIiItICJlVEREREWsCkioiIiEgLmFQRERERaQGTKiIiIiIteKvJP7Vt4sSJ2LhxIy5dugRTU1NUq1YNkyZNQqlSpZQ6z58/xzfffIM1a9YgKSkJfn5+mDdvnsaNnSMiItC7d2/s3bsX5ubmCAwMxMSJEzXuU7hv3z4MGTIE58+fh5OTE8aMGYPOnTtrxDN37lxMmTIFkZGR8PDwwOzZs+Ht7f1WsVDu5Dwi+KPt6+Yv/h9tX0RE9PHotKdq//796Nu3Lw4dOoSQkBCkpKSgQYMGSEhIUOoMHjwYW7duxbp167B//37cu3cPzZs3V5anpaXB398fycnJOHjwIJYvX45ly5Zh7NixSp3w8HD4+/ujTp06OHXqFAYNGoTu3btj165dSp2goCAMGTIE48aNw4kTJ+Dh4QE/Pz9ER0dnOxYiIiLKu1QiIroOQu3BgwcoUKAA9u/fj5o1ayI2Nhb29vZYvXo1WrZsCQC4dOkS3NzcEBYWhqpVq2LHjh1o0qQJ7t27p/QYLViwAMOHD8eDBw9gZGSE4cOHIzg4GOfOnVP21aZNG8TExGDnzp0AgCpVqqBy5cqYM2cOACA9PR1OTk7o378/RowYka1Y3iQuLg5WVlaIjY2FpaWlVtuO3g97qt4d2+7dfdS2M2n30fYFAPg+9uPuj+gDyu73d44aUxUb++IgtLW1BQAcP34cKSkp8PX1VeqULl0aRYsWRVhYGAAgLCwM5cqV0zgF5+fnh7i4OJw/f16pk3Eb6jrqbSQnJ+P48eMadfT09ODr66vUyU4sL0tKSkJcXJzGg4iIiD5NOSapSk9Px6BBg1C9enWULVsWABAZGQkjIyNYW1tr1HVwcEBkZKRS5+UxTernb6oTFxeHxMREPHz4EGlpaVnWybiNN8XysokTJ8LKykp5ODk5ZbM1iIiIKLfJMUlV3759ce7cOaxZs0bXoWjNyJEjERsbqzxu376t65CIiIjoA9Hp1X9q/fr1w7Zt23DgwAEUKVJEKXd0dERycjJiYmI0eoiioqLg6Oio1Dly5IjG9qKiopRl6n/VZRnrWFpawtTUFPr6+tDX18+yTsZtvCmWlxkbG8PY2PgtWoKIiIhyK532VIkI+vXrh02bNmHPnj1wcXHRWF6pUiUYGhoiNDRUKbt8+TIiIiLg4+MDAPDx8cHZs2c1rtILCQmBpaUl3N3dlToZt6Guo96GkZERKlWqpFEnPT0doaGhSp3sxEJERER5l057qvr27YvVq1fjr7/+goWFhTI2ycrKCqamprCyskK3bt0wZMgQ2NrawtLSEv3794ePj49ytV2DBg3g7u6Ojh07YvLkyYiMjMSYMWPQt29fpZeoV69emDNnDoYNG4auXbtiz549WLt2LYKD/3flzZAhQxAYGAgvLy94e3tjxowZSEhIQJcuXZSY3hQLERER5V06Tarmz58PAKhdu7ZG+dKlS5WJOX/99Vfo6emhRYsWGhNuqunr62Pbtm3o3bs3fHx8YGZmhsDAQIwfP16p4+LiguDgYAwePBgzZ85EkSJF8Pvvv8PPz0+pExAQgAcPHmDs2LGIjIxEhQoVsHPnTo3B62+KhYiIiPKuHDVP1aeO81TlXJxr6d2x7d4d56kiyh1y5TxVRERERLkVkyoiIiIiLWBSRURERKQFTKqIiIiItIBJFREREZEWMKkiIiIi0gImVURERERawKSKiIiISAuYVBERERFpAZMqIiIiIi1gUkVERESkBUyqiIiIiLSASRURERGRFjCpIiIiItICJlVEREREWsCkioiIiEgLmFQRERERaQGTKiIiIiItYFJFREREpAVMqoiIiIi0wEDXARAREdHH4zwi+KPu7+Yv/h91f7rEnioiIiIiLWBSRURERKQFTKqIiIiItIBJFREREZEWMKkiIiIi0gImVURERERawKSKiIiISAuYVBERERFpAZMqIiIiIi1gUkVERESkBUyqiIiIiLSASRURERGRFjCpIiIiItICJlVEREREWsCkioiIiEgLmFQRERERaQGTKiIiIiItYFJFREREpAUGug6AiIjobTmPCP6o+7v5i/9H3R/lTuypIiIiItICJlVEREREWsCkioiIiEgLmFQRERERaQGTKiIiIiItYFJFREREpAVMqoiIiIi0gEkVERERkRZw8k8iIqI3+d7qI+4r9uPti7SKPVVEREREWsCeKqKP7WP+4gX4q5eI6CNhTxURERGRFjCpIiIiItICJlVEREREWsAxVUSUe3A8GhHlYOypIiIiItICJlVEREREWsCkioiIiEgLmFQRERERaQEHqn8inEcEf9T93fzF/6Puj4iIKKdjTxURERGRFjCpIiIiItICnSZVBw4cwBdffIFChQpBpVJh8+bNGstFBGPHjkXBggVhamoKX19fXL16VaPO48eP0b59e1haWsLa2hrdunVDfHy8Rp0zZ86gRo0aMDExgZOTEyZPnpwplnXr1qF06dIwMTFBuXLlsH379reOhYiIiPIunSZVCQkJ8PDwwNy5c7NcPnnyZMyaNQsLFizA4cOHYWZmBj8/Pzx//lyp0759e5w/fx4hISHYtm0bDhw4gJ49eyrL4+Li0KBBAxQrVgzHjx/HlClT8P3332PRokVKnYMHD6Jt27bo1q0bTp48iWbNmqFZs2Y4d+7cW8VCREREeZdOB6o3atQIjRo1ynKZiGDGjBkYM2YMvvzySwDAihUr4ODggM2bN6NNmza4ePEidu7ciaNHj8LLywsAMHv2bDRu3BhTp05FoUKFsGrVKiQnJ2PJkiUwMjJCmTJlcOrUKUyfPl1JvmbOnImGDRvi22+/BQD8+OOPCAkJwZw5c7BgwYJsxUJERER5W44dUxUeHo7IyEj4+voqZVZWVqhSpQrCwsIAAGFhYbC2tlYSKgDw9fWFnp4eDh8+rNSpWbMmjIyMlDp+fn64fPkynjx5otTJuB91HfV+shNLVpKSkhAXF6fxICIiok9Tjk2qIiMjAQAODg4a5Q4ODsqyyMhIFChQQGO5gYEBbG1tNepktY2M+3hVnYzL3xRLViZOnAgrKyvl4eTk9Ia/moiIiHKrHJtUfQpGjhyJ2NhY5XH79m1dh0REREQfSI5NqhwdHQEAUVFRGuVRUVHKMkdHR0RHR2ssT01NxePHjzXqZLWNjPt4VZ2My98US1aMjY1haWmp8SAiIqJPU45NqlxcXODo6IjQ0FClLC4uDocPH4aPjw8AwMfHBzExMTh+/LhSZ8+ePUhPT0eVKlWUOgcOHEBKSopSJyQkBKVKlYKNjY1SJ+N+1HXU+8lOLERERJS36TSpio+Px6lTp3Dq1CkALwaEnzp1ChEREVCpVBg0aBB++uknbNmyBWfPnkWnTp1QqFAhNGvWDADg5uaGhg0bokePHjhy5Aj+++8/9OvXD23atEGhQoUAAO3atYORkRG6deuG8+fPIygoCDNnzsSQIUOUOAYOHIidO3di2rRpuHTpEr7//nscO3YM/fr1A4BsxUJERER5m06nVDh27Bjq1KmjPFcnOoGBgVi2bBmGDRuGhIQE9OzZEzExMfj888+xc+dOmJiYKOusWrUK/fr1Q7169aCnp4cWLVpg1qxZynIrKyv8/fff6Nu3LypVqgQ7OzuMHTtWYy6ratWqYfXq1RgzZgxGjRqFkiVLYvPmzShbtqxSJzuxEBERUd6l06Sqdu3aEJFXLlepVBg/fjzGjx//yjq2trZYvXr1a/dTvnx5/PPPP6+t06pVK7Rq1eq9YiEiIqK8K8eOqSIiIiLKTZhUEREREWkBkyoiIiIiLWBSRURERKQFOh2oTrnY91YfeX+xH3d/REREb4k9VURERERawKSKiIiISAuYVBERERFpAZMqIiIiIi1gUkVERESkBUyqiIiIiLSASRURERGRFjCpIiIiItICJlVEREREWsCkioiIiEgLmFQRERERaQGTKiIiIiItYFJFREREpAVMqoiIiIi0gEkVERERkRYwqSIiIiLSAiZVRERERFrApIqIiIhIC5hUEREREWkBkyoiIiIiLWBSRURERKQFTKqIiIiItMBA1wEQERHRJ+x7q4+4r9iPt68ssKeKiIiISAuYVBERERFpAZMqIiIiIi1gUkVERESkBUyqiIiIiLSASRURERGRFjCpIiIiItICJlVEREREWsCkioiIiEgLmFQRERERaQGTKiIiIiItYFJFREREpAVMqoiIiIi0gEkVERERkRYwqSIiIiLSAiZVRERERFrApIqIiIhIC5hUEREREWkBkyoiIiIiLWBSRURERKQFTKqIiIiItIBJFREREZEWMKkiIiIi0gImVURERERawKSKiIiISAuYVBERERFpAZMqIiIiIi1gUkVERESkBUyqiIiIiLSASRURERGRFjCpIiIiItICJlVEREREWsCkioiIiEgLmFQRERERaQGTqrc0d+5cODs7w8TEBFWqVMGRI0d0HRIRERHlAEyq3kJQUBCGDBmCcePG4cSJE/Dw8ICfnx+io6N1HRoRERHpGJOqtzB9+nT06NEDXbp0gbu7OxYsWIB8+fJhyZIlug6NiIiIdMxA1wHkFsnJyTh+/DhGjhyplOnp6cHX1xdhYWFZrpOUlISkpCTleWxsLAAgLi5O6/GlJz3T+jZfJ04lH3V/+ABtltHHbD+23btj2707tt37+ajtx7Z7j519mLZTf2+LvOFvEcqWu3fvCgA5ePCgRvm3334r3t7eWa4zbtw4AcAHH3zwwQcffHwCj9u3b782V2BP1Qc0cuRIDBkyRHmenp6Ox48fI3/+/FCpVDqM7P3ExcXByckJt2/fhqWlpa7DyVXYdu+Obffu2Hbvh+337j6VthMRPH36FIUKFXptPSZV2WRnZwd9fX1ERUVplEdFRcHR0THLdYyNjWFsbKxRZm1t/aFC/OgsLS1z9UGiS2y7d8e2e3dsu/fD9nt3n0LbWVlZvbEOB6pnk5GRESpVqoTQ0FClLD09HaGhofDx8dFhZERERJQTsKfqLQwZMgSBgYHw8vKCt7c3ZsyYgYSEBHTp0kXXoREREZGOMal6CwEBAXjw4AHGjh2LyMhIVKhQATt37oSDg4OuQ/uojI2NMW7cuEynNunN2Hbvjm337th274ft9+7yWtupRN50fSARERERvQnHVBERERFpAZMqIiIiIi1gUkVERESkBUyqiIiIiLSASRURERGRFjCpIiIi0gFefP/pYVJFOsEPk/fHNnx76enpyv9TU1MBAMnJyboKJ9dh+70f9TF7584dAMjV94DVhoyfYZ/K5xmTKvrg1B/EaWlpSll0dLSuwsnVMral+gM5Ojpa+YKj19PT00N4eDgePHgAAwMDbN68GcOHD0dSUpKuQ8sV9PT0EBERgRs3bijtt2jRIiZW2SAiUKlU2LZtG5o2bYoVK1boOiSdSUhIAPAiqdy/fz8uX778ySSYTKrog9PT08Ply5cxd+5cAMC6devg6emJ+/fv6ziy3Ef9pTZ06FAAwMaNG1GnTh08ePBAx5HlDsnJyejZsyc8PDywePFiNG/eHN7e3nlmtuf3FR8fj8GDB6N169aYM2cOmjdvDjs7OxgZGek6tBxPpVJh06ZNCAgIQGBgICpUqKDrkHTi/v37KF++PE6dOoV169bBz88PERERug5Le4ToI5gzZ46oVCrp1KmT6Ovry7Jly3QdUq41e/Zs8fT0FD8/PzE0NJSVK1fqOqRc5dGjR+Li4iLGxsYye/ZsERFJTU3VcVS5R2hoqJQtW1YMDAxkypQpIiKSkpKi46hyvnv37kmFChVk1qxZIvLiPff8+XPZsWOHPHz4MM+8B1NSUqRDhw5iY2Mj+vr6snz5cl2HpFXsqaKPom/fvujQoQP++OMPtGzZEoGBgboOKdfq168fKleujL///huff/452rRpA0Dz9CplJhnGbKSkpMDW1hbz5s3DgwcPoK+vz/Z7A/Wp55IlSyItLQ0uLi4IDg7GtWvXYGBgwPZ7g4SEBDx8+BDVqlVDSkoKJk2ahHr16qFp06bw8fHBhQsXdB3iR2FgYID27dsjJiYGpqamKF26tK5D0iomVfTRmJiYoHnz5li7di0mTZqE2NhYXYeUq2RMCgoUKIBOnTohNTUVffr0waNHj5gYvIb8/3iW8PBwpKWl4cyZMzh27BjMzc1Ro0YNREdHQ19fX0kcnj17puOIcxYRgZ6eHu7cuQMTExNs374d8+fPh4GBAbp06YJr165pvP8SExN1HHHO4+rqCjc3NzRr1gwuLi44cuQImjZtitjYWCQlJWHVqlW6DvGDUn9+JScno2bNmtiyZQtat24NPz8/hIaGvnadXEWX3WT0aUtPTxcRkSNHjsiePXuU8qlTp4pKpZJffvlF4uLilPLr169/9BhzC3Vb/vPPP7Jv3z6lfMqUKeLj4yM9evSQR48eKeU3btzgKZn/p267TZs2Sbly5WTJkiXy+PFjERG5dOmSVK5cWdzc3CQqKkpERH799VcZOnRonjkd8ybq9vvrr7/Ezc1N1q5dq5Rt2bJFfH19pWbNmnLt2jUReXF8z5s3L0+3n7p9Ll26JAcPHpSQkBAREYmMjJQZM2bI7Nmz5eHDh8ox2rJlS5k+fbrO4v3Q1O2xc+dO6devn5w4cUJEXpwCbd++vVhbW8vevXuV+itXrpTLly/rItT3xqSKPgj1QbRhwwZxdHSUsWPHyvnz55XlU6ZMURKrmzdvyk8//SRFixaVuLg4ZV16IWNb2tnZSa9evSQ8PFxZNnnyZKlWrZp069ZN7ty5I+PGjRMPDw+NhDWv27Jli5iZmcm0adPk/v37GsuuXbsmlStXFisrK2nRooUYGBjIqVOndBRpzrR582YxMzOTqVOnypUrVzSWbd++XerXry9FihSRjh07ikqlktOnT+soUt3LeLx+9tln4ubmJu7u7lKhQoVMPxxjYmLku+++Ezs7u1ybRGTXhg0bJF++fPLTTz/JhQsXNJa1adNGrKysZN68eTJgwACxsLDI9D7LLZhU0Qeze/duMTc3l/nz52f5q3XatGmSL18+qVixotja2srRo0d1EGXusHv3bjEzM5OlS5dKUlJSpuXz5s2TihUrSpEiRaRw4cJy6NAhHUSZMz18+FC8vb1l4sSJIiKSmJgoDx48kKCgIAkNDVXKRowYIYMGDdJI/knkwYMHUrFiRfnll19ERCQ5OVni4+Nl06ZNSiJw4sQJGT58uLRp00bOnTuny3BzhAMHDoiFhYX89ttvkpKSIv/995+oVCqZN2+eUmfnzp0SEBAgTk5OSs/Np+rkyZNSqFAhWbp0qUb5zZs3lf/36NFDSpUqJRUrVpTjx49/5Ai1RyWSG09aUk4mIkhPT8fXX38NAwMDLFiwAHFxcbh06RLWrl2LuLg4TJ48GdbW1tizZw/i4uJQoUIFODs76zr0HCk9PR2jR4/G48ePsXDhQsTFxeHs2bNYvnw5zMzM0K5dO1SuXBlnz57FrVu3ULZsWbZlBk+fPsUXX3yBVq1aoWnTpliwYAEOHjyIM2fOwMbGBr1798Y333wD4MWElgYGBjqOOGe5c+cOfH19MWvWLHh4eGDBggXYu3cvDh48iAoVKmD48OFo0aIFgBcXABgaGuo44o/r1q1bKFasmEbZrFmzcOHCBSxYsADh4eGoU6cOGjdujHnz5il1zp8/j927d8Pf3x+urq4fO+yPKjg4GKNHj8ahQ4egr6+P1atXY8WKFbh79y4qVKiANWvWAAAiIiJgZWUFKysrHUf87vjpQVol/z8gWF9fHxYWFjh06BAOHDiA33//HdHR0Xjy5AmePXuG2rVr49SpU6hbt66uQ87x9PT08PDhQ+zduxcXLlzADz/8gMePHyMlJQVRUVE4f/48tm3bhnLlyqFcuXK6DjfHMTExgYWFBZYtW4ZvvvkGTZo0Qbt27bB06VIMHToUN2/eVOoyocqsSJEicHR0RNeuXZGUlIQaNWqgRYsW+PPPP9G4cWMcOXJESaryWkK1cuVKDBs2DDdu3ICJiYkygeWZM2fw/PlzPHz4ELVq1UKjRo2UefpWrFiBR48eYfDgwXBzc4Oe3qd/vZiBgQGeP3+OQYMG4dixYyhcuDBcXV3RoUMHDB48GJs2bcJXX32FokWL6jrU98ZPENIKdTKVcVZcX19fnD9/Hg0aNMBXX32FPn36oHHjxti0aRN+/fVXxMXFwdLSUodR5x5jxozB4cOHUb16dfj5+WHQoEHw9/fH33//jcGDB+PJkydwcHDQdZg6p34fRkVFwcjICM+ePUPhwoWxZs0abN++Henp6WjevDkMDAygUqlgaGgIPT09Zb28Tt0O586dw5MnT3D//n20bt0ae/bswdKlS6Gvr48WLVrA1NQUBgYGcHNzg4GBgXKVVl5rw4YNG6JmzZowNTVFbGys0sPy5ZdfYvbs2crVfgsXLkR6ejpEBEePHkVycjISExNhamqq479AuzK+D2JjY5GYmAhHR0f4+fmhT58+OHDgAGrVqoXOnTujXLlyePLkCRYsWABbW1sdR65FOjrtSJ8Q9cDMffv2yejRo6V79+7y22+/SXJysqSmpmYa9DtgwACpV6+eJCQk6CLcHE3dlufPn5etW7fKvn375N69e8qyM2fOaNQfOnSo1KlTh4PS5X9tt3XrVvHx8RE3Nzfx8vKSVatWZar7+PFjGTFihNjY2MjFixc/dqg5UsYB1k5OTuLt7S0ODg7i7e0tu3bt0qgbGxsro0ePFhsbG7l06ZIuws1Rzp07JyYmJvLvv/+KiMjVq1elTp064uLiIlu3bhWRF++5UaNGiYODwyf7nlO/hzZv3iy1a9eWwoULyxdffCGTJk3SWK42btw4KVGihERERHz0WD8UJlWkFRs2bBBzc3Pp3bu3dO7cWTw8PKR69eqSnJys1Dl79qwMGjRIrK2t8/TVQa/y8hWTHh4eUqhQIWndurXs3LlTo+4///wj33zzjVhZWfFKtQy2bt2qXKW2e/duGThwoKhUKlmyZIlSJygoSPz9/cXV1fWTHyD8tg4ePCg2NjbKHQ8uXbokKpVKFixYoNTZsWOHNGzYUFxcXPJ0+6mP1zNnzsipU6ekVatWYmNjI//995+IiJw+fVq8vLykXLlyUrx4calXr54ULlz4k2yz58+fK//fvn27mJqayrRp0+TkyZPSr18/MTAwkG3btil11q9fL3369JH8+fN/cu3BpIre261bt8Td3V3mzp0rIiLh4eFiZ2cnffv2VeqcOnVKunfvLlWqVGFC9Rq7d++W/PnzK225Zs0asbS0lBo1aigfStevX5cBAwZI5cqV2ZYZRERESN26dWXmzJkiInL37l1xdnYWDw8PUalU8ttvv4mIyLNnz2T+/Ply48YNXYabIy1cuFCaN28uIi8SqhIlSkj37t2V5c+ePZOoqCiZO3euMi9VXrZlyxYpWrSo7N+/X+7fvy9t27YVMzMzJbG6fv26bN++Xb777jtZt26dMhXKp+T27dvi7u4ut27dEhGR1q1byw8//CAiL3rnChcuLP3799dYZ+7cuRIYGJhpaoVPAZMqem8nTpyQkiVLSnJysty6dUucnJykZ8+eynL1ZJVnzpyRyMhIXYWZ4z179kx69eolQ4cOFZEXyWrx4sXF399fatWqJd7e3sokqhEREcpklfRCZGSkfPfddxIVFSV3794VNzc36dmzpzx69EhatWolKpVKudcfvZDxdEx6eroMGDBAOnToIGlpaVKkSBHp2bOnUmflypXKfevyMnV7PH78WDp16qQxaWd0dHSmxOpTFxERIcWLF5fAwEBJSUkRf39/Wb9+vdy+fVsKFy6s8V2wadMmOXz4sIiIxMfH6yrkD4pJFb019YeKehzPlStXpF69erJv3z4loVLPFHzu3Dnp0aNHprFAlLWTJ0/KiRMnJCYmRjw9PaVr164iIrJu3ToxNTWVsmXLSnBwsI6jzLmio6NFRGTMmDHSuHFjiYmJERGR4cOHS6FChcTW1lYePXrECWYzCA0NVebr2r9/vxQvXlzMzc01eppFRPr06SPt2rX7ZL8M38aBAwekfPnyUr16dWUclVpUVJS0bdtWbGxs5J9//tFRhB9PamqqTJo0ScqWLSvr16+Xpk2bSo8ePZReTvWx9vDhQ+nQoYPMnz//kz7+Pv1rOUnrVCoV/vvvP4wYMQIpKSmws7PDvXv3UKdOHTRo0AALFy5ULk1funQpzp8/zyvTsiBZTBFXoUIFeHp6IjQ0FIaGhhg/fjwAwNraGhUqVICPjw/Kli37sUPNcdRtd+XKFezfvx/79+9HXFwc7O3tkZycjLNnz8Le3l65Guv58+eYMGECbty4AVtb2zx3ldqrJCUlYd26dRg7dizu3r2LMmXKwNfXFwUKFICPjw8AICoqCqNHj8a6deswZswYmJmZ6Thq3fPy8kJSUhIOHjyIq1evaiwrUKAAZs6cierVq6Nt27Z4/vy5jqL8cJ48eaL8X19fH19//TVUKhW2bt2KUaNGYfXq1bCwsMBvv/2mHGvTp09HWFgYGjRo8Gkff7rO6ih32r59u6hUKtm8ebOIiFy8eFHs7Ozkyy+/lODgYNmzZ48MHDhQLC0tOe4nCxmvmJwyZYp07dpVQkND5e7duyIisnr1anFycpKDBw+KiMioUaNk8ODB8uTJE12FnGNkHNBfsmRJKV26tPj4+Iirq6tyG5DJkyeLkZGRjB8/Xrp06SJ2dna59rYX2vZyL8HWrVulevXqylWSYWFh0rFjR7GxsZGSJUtK5cqVpVixYp/cgOL3lZiYKGXKlJHSpUtneTeIBw8eyJ07d3QQ2Yd17do15bM+KipKuYr70KFDYmBgIL/++qusXbtWVCqVtG7dWjp37iwdO3YUKyurPPEeYlJF2aL+IE5MTJS0tDQRefFFX6pUKeW2HkeOHJGyZcuKi4uLlCpVSmrUqMEr015jw4YNYmlpKV27dhV/f3/x9PSUgIAAefr0qRw6dEhq1qwpZcuWlc8//1zMzc15CjWDf/75RywsLGThwoUiIrJr1y7lXpIiLwapDxs2TMqUKSN169aVkydP6jDanGffvn2yaNEi5fmgQYOkcOHCyhfk7du3JSwsTH755Rf566+/lEHIeZH6s+/y5csSGhoqJ06cUG6vkpCQICVLlpQKFSrk6lurvI0rV66ItbW1qFQqadCggcyYMUPOnj0rIiKDBw+WypUry5EjR+TAgQMSEBAgX375pQwdOvSTnUbiZUyqKNv+/vtv8fPzk7Vr10piYqLcvXtXGjZsKD/++KMyziI+Pl5u3LghEREREhsbq+OIc64rV65IyZIllSvSHj16JMbGxjJ69Gilzt69e+Xnn3+WwYMH55kPpOyaOXOm9OjRQ0REuTgi4xgg9SXejx8/5higDNLS0uThw4diYmIiKpVKevfuLcePH5eEhATx8/OTli1b6jrEHEWdUK1fv14cHR2lZMmSYmdnJz4+PrJ27VoReZFYffbZZ+Ll5aUMwv7UqNtBPVZ25syZMnjwYBk9erT06tVLKleuLDt27JDDhw9L6dKlZdy4cSLy4uIbEVF+iOcFTKrojdLT0yU5OVnGjh2r/Dpp3LixPHjwQCZNmiTly5dXLk/PSwfP+zh69KiUL19e0tPT5cqVK1K0aFElSRB5cUVlVjehphe++eYbad++vURERGS6Sm3Dhg3y888/Z3njaXph/vz5UqZMGalatap07txZevfuLTNnzpRWrVrJpk2bdB1ejnLkyBGxsLCQuXPnSlRUlISEhEiPHj2kcOHCsn79ehF5kTwUKFBAatasqTFn06fi6dOnGs/37dsnDRs2lO3bt8uzZ89k9uzZYm1tLdOnTxc/Pz+xsrLSGPbxKQ9MfxkHqtMrpaWlAYByO48vv/wS7u7uqFmzJsqVK4fq1avD0dERERERGDZsGADkiftYvQ/5/wHWT58+hampKe7du4f69eujQYMGWLBgAQAgLCwMS5Yswa1bt3QZao4gr7jfe8WKFXH37l1UqVIFfn5+WLhwIYAX79nQ0FDcvXsXqampHzPUHO/UqVN48OAB0tPT0axZMzRo0ABt2rRB06ZNkZ6ejm+++Qb79u3D6tWrX9nueYn68+/48ePw9PRE7969UaBAAfj6+uLbb79FgwYNMHfuXNy/fx+mpqa4desWlixZAmNjYx1Hrl2RkZFwd3fH6NGjERERAQCoVasWqlevjk6dOuHx48fo168ftm7dinPnzsHAwABxcXEYM2aMxndInqHjpI5ymL/++ktj0OWhQ4dk4MCByszoixcvlsKFC8uTJ09k1apV0r17d3FzcxOVSiUrVqzQVdg5Vmpqapa/0lJSUsTV1VVUKpUMHDhQY9nQoUOlZs2a8uDBg48UZc6Umpqq9HxeuXJFzp8/rww2f/z4sdSoUUMsLS0lNDRU0tLSJDY2VkaOHPlJ3wbkXSUkJEiBAgWkevXqMmfOHElJSZEVK1ZIvXr15PHjxyLyYkJGZ2dnsbS0zHPvPfX7LOOxqu5xWrBggRQuXFi5iERt8+bNYmNjI5cvX/54gerAkydP5IcffhArKyupW7eu/Prrr8qywMBACQwMVKYuiYyMlD179oi/v3+eHQPKpIpE5MWHyc2bN8XCwkLatm2rDOydPn26lClTRooXLy4hISESHR0to0aNkqFDh0paWppcv35dli5dKs7OznL16lXd/hE5iPpeVuoP63/++Ue+//57Wbp0qdK2e/fuVSb3vHLliuzfv1+GDh0qlpaWefYDSURk4sSJsmbNGuX5+vXrJX/+/FK8eHGxsbGRpUuXisiLeW/c3d2lXLlyUrRoUalfv/4nexuQt5VVIn/v3j0ZMGCAfP7551K7dm25c+eO1KpVSwICApQ6p06dypQ85BXXrl2Tv//+W0RE1q5dKzVq1JDnz5/L3r17xdXVVRYtWqRxj81r165JqVKl5NixY7oK+aM6f/68tGzZUlxdXaV27dpy6dIlWbt2rQQGBkpISIhG3bx0uu9lTKpIQ0hIiJQoUUI6dOig/AKLiYmRDh06SKlSpaRnz54yaNAg6dOnj8Y5809xHMG7WrNmjVSpUkWZSf6vv/4SExMTqVatmhQqVEh8fX2Vm6zu2LFDSpYsKY6OjlKqVCmpWrVqnr5SLSkpSXr06KFM15GYmCjFixeX33//Xfbs2aOM65syZYqIvHhvbtq0SX766SdZv369clVWXpZVD5/6diDPnj2T//77Txo1aiTFihWTbt26ibOzs6xbt06XIecIbdu2FQMDAxk1apTo6+srybuISL9+/cTBwUHmzZsn169fl4SEBBk2bJiUKFEiT93Z4NGjR7Jt2zbx9PSU4sWLy4gRI6RSpUoas6bndUyqSERe9Kior+zYuXOnODs7S5cuXTS+4JctWybdunUTe3t7UalU4u/vr6Noc7bNmzdL/fr1pWHDhrJjxw4ZPHiwcvn6/v37pW3btuLl5SVbtmwRkRdfgmFhYRIeHi6PHj3SZeg5QmxsrAwdOlT09fVl9uzZMmjQIOWXb3p6ukyfPl1UKpVMmzZNx5HmLG/q4VPfJFlt2rRpUq9ePVGpVNKhQ4c8ObB/3bp1GqfvvLy8RF9fX4YMGZKp7oABA6R06dJiY2Mj3t7eUqBAgTzdKzpo0CBp2LChFC5cWOPemnkdkyoSkf91127evFl+/PFHKVmypOjr60vLli015l+5ffu2bNiwQczNzcXCwiJP/Up7G9u3b5dGjRpJo0aN5PPPP9c4nXf48GFp166deHl5KVcP5WUZTxWoe1iePn0qgwcPFpVKJZUqVdK4qjQ9PV2mTZsmxsbGMnHixI8eb06U3R6+GTNmaKx37tw5mTZtWp4bg5aeni6nTp2Szz77TG7fvq2UVaxYUcqWLSsODg4SHBysjCVVO3LkiPz5558SFBSUZ3tFMx6ve/fuleHDh4uFhUWeew+9CpOqPC7jARIaGioGBgayYMEC2bJliyxbtkysrKykTZs2mU5JXb16NU9PCJhRxi/8jNMgHDx4UHx9fcXU1DTT/fqOHDkinTp1EldXV+VUYF6kfv89ePBAuW9fcHCwnDlzRmJiYuS7774TPT09CQoK0qgvIvLTTz+Jra2tMtA6r8tuD9/MmTM11svL41/UA6zPnDkjDx8+VMr9/f3F3t4+U2Klnhw1r3v5PcM5Cf+HSVUe9c8//yin+9QGDhwofn5+GmW7d+8WCwsLadmyZZ4e6/Mm165dU66aXLt2rXz11Vci8mKMWu3ataVGjRqyf/9+jXX+++8/6dGjhzLHV14VHR0tderUkR9++EEWL14sKpVKNmzYICIvvvQGDx4s+vr6snHjRhHR/EDPy6dL36eHTz3zfF6V8arcmJgYKVCggHTq1EmjR9nf318cHBxk27Zt8vz5c/npp5+kTp06kpKSkqcTUXo9JlV50IoVK6Ru3boav8xEXowZaNCggYi8+NBR/0KbP3++mJqaSuvWrXnbmSykpaVJu3btxNjYWCZMmCAqlUpj/MrOnTulUaNG0qBBAzlw4IDGuomJiR873Bxp9OjRUqJECdHX15c5c+aIyP+ShtjYWCWxUt9rMq9jD592bNq0SS5duiSbN28WZ2dn6d27t3LLFRGRL7/8UmxtbaVatWpiZWUlR44c0WG0lBswqcpD1L9a4+LilMumb926pfRYrVq1SlQqlfLFrz6VtXLlSilbtqxUqFAhz15unR1eXl5iYGAgY8eOFRHN04LqMVaNGzeW0NBQXYWY46jfYydPnhRra2txcnKSn3/+WUkUMiZWQ4cOFZVKJdu2bdNZvDkJe/jez7Fjx0RPT0/mzZsnIi96mIsUKZIpsZo/f77MnDnzk5+PirSDSVUeof6Cv3btmvKldOHCBalUqZJMmzZN6ZXq1KmTWFlZKdMBiIiMHDlSpk2bpjFHC72gbtdDhw6JjY2NlC9fXgoWLChhYWEiovlFtmPHDqlatao0b95cuScWvUgOnj17JkePHpXvvvtOPD09ZcyYMUpipfb06VMZOXKkMj0AsYfvXV24cEF++ukn+eGHHzTKMyZW586d01F0lJsxqcpD7t69K3Z2duLu7i5BQUGSlJQkbdq0kWrVqsncuXMlLS1NIiMjpXPnzqKnpyfVqlWTKlWqSL58+TTmpCJNW7ZsEU9PTyVZ/eKLL8TR0THLxOr48eMc4J/BmTNnxMPDQ/7880+lbNiwYeLp6Snjxo1TEqvZs2dr9B7kdezhezvp6elKm1y+fFn8/PzE3t5exo8fLyIiycnJyvK1a9eKs7OzdOzYkb1T9NYMdH2bHPp4rly5gsePH8PFxQUrVqyAqakpli9fjl69emHp0qXQ09NDjx49sHTpUjRp0gTnzp2DiGD58uUoVaqUrsPPUUQEKpUKUVFRWLt2LQIDA+Hv7w8AWLduHVq3bo3mzZtjw4YN8PHxwcSJE3HixAmsWbMG+vr6Oo4+50hOToarqytmzpyJ9PR0tGvXDpMmTYJKpcL27dtx8eJF5M+fHwsWLMDZs2d1HW6Ooa+vjwcPHqBUqVIICQnBli1bsH79eiQmJmLAgAGwt7cHAFhaWmLcuHEwNDRE8eLFdRy17qjvPff3338jMjISPj4+uHHjBrZs2YLevXvDzs4OqampMDAwQKtWrZCcnIyJEyfC0tJSx5FTrqPrrI4+rq5du0qFChWkRYsWUrNmTdm2bZskJSVJly5dxMvLS+bMmZNpbhbK2r///iuNGjWSKlWqKL1S6h6EpKQkad68uRgYGEi9evXE1NRUY74v+p8TJ05Ip06dxMvLS/744w+lfPLkyRIQECC1atViT+lL2MP39o4eParM45WSkiJz584VT09P6dq1q9JeGa+I5nAHehfsqfpEpaenQ09PT3melJQEY2NjtGjRAunp6Wjbti0WLlyICRMmQKVSYcGCBejduzdWr16NxMREDBo0CAYGfHu8jqOjI8LDw3H58mWcPHkSVatWhb6+PtLT02FkZIQNGzZg9uzZePbsGebOncvevv937NgxxMbGol69egAAT09PDBw4ELNmzcK0adNgZGSEVq1a4dtvv0VSUhLS0tKQL18+HUeds7CH7+2cOXMGN2/exOjRo/Hll18CAPr06YOkpCSsX78eI0eOxMSJE2Fvb6/0WJmbm+s4asqVdJ3VkfapB09HREQoV/6oRUdHS+nSpWXOnDkSHR0tzZs3l88//1yCg4MlKSlJWrVqpXHnenq9mzdviqenp1SvXl3jqr6Mv3g5p83/PHr0SBo1aiQ+Pj6yd+9ejWXHjh2TcuXKSZkyZTR6rChr7OHLnvj4eClYsKCoVCrp1KlTpuXTp0+XmjVrSuvWrTNNM0P0tphUfaIiIiIkf/78olKppHHjxhIUFKQMutyyZYvUqFFDoqOj5cKFC9K8eXOpXbu2bNy4UZKTk+XevXs6jj7nUSdGly5dkpCQEDl69Khye4srV65IuXLlpEGDBhqJQsYpFfK6jInl1q1bpWnTpuLr65tpeokuXbpI4cKFpX79+pyl+SVHjx6V3bt3a5QdP35cAgMDxdPTU9auXauUP3/+nLN/Z3D27FkpV66clC1bVsLDw0VE8z35888/i5+fHz/76L2pRER03VtG2nfr1i20bNkShoaGSEpKQsWKFRESEoJRo0bB2toaK1euRJ8+fdCoUSNcuHABAwcOhKGhIdatWwczMzNdh5+jyP8PSt+wYYPSTiICExMTLFq0CDVr1sSVK1fQsmVLODk5YeDAgWjQoIGuw84R1G2XlJQEfX195ZTynj17MG3aNKSkpGDkyJGoU6cOAGDw4MEoWrQoOnTooAy2JuDx48fo0KEDYmJiMGHCBNSuXVtZdvz4cXTp0gXp6ekYOXIk2rdvr7tAdUxedBRAT08PiYmJMDIyQmpqKoyNjXHu3Dk0aNAA5cuXx6pVq5A/f37l/Qm8aGNbW1sd/wWU2zGp+oRdvXoVI0aMQHp6Ojp16gSVSoWZM2fC2toaf/31F7y9vXHgwAEYGRnh8uXLMDMzQ5EiRXQdts5lHI+mHl9x5MgR+Pr6YsqUKWjSpAmuXbuG33//HevXr8fff/+NGjVq4Nq1a6hbty4qV66MlStX5vlxQOovrB07dmDevHmIi4uDjY0NfvjhB3h4eOCff/7BlClTEBERgTp16uD58+fYtGkTjh49CicnJ12HnyNk/NLftm0bfvvtNzx79gwjR45E3bp1lXpdu3bF33//DXd3d6xfvz5PXbWmPl5TUlJgaGgIANixYwdWrlyJq1evwtvbG40bN4a/vz/OnTsHPz8/lC9fHn/88UemxIrovemkf4w+mkuXLim3SLl8+bLEx8dLWFiYNGnSRFauXCkiHPOTlZs3byrtkpqaKr///rvUqVNH45Te/fv3pV27duLp6Sn3798XEZHw8HC5fv26TmLOSdRtt3XrVjEyMpLBgwfLuHHjpGrVqlK0aFFZs2aNiLw4pTV69Gjl9CnvL/mCuv2eP3+uMT4vNDRUGjduLPXr15c9e/Yo5YMGDZLp06dnmjD1U6c+Hs+dO6dM5Ll582YxMTGRn376SebPny8BAQGip6enTBp77tw5KVq0qPj4+HBmedI6JlV5wJUrV6RBgwbSoEED+ffff3UdTo73/PlzqVq1qjg7OytfbtOnTxcbGxt58uSJiPzvS2/btm3i5OSU52f5Vn+5ZUw64+LipHbt2jJ69GiNum3bttVos/T0dHn27JnEx8d/vIBzMPV7a/v27dKkSROpWbOmfPnll8p9Nw8cOCBffPGFeHh4yKBBg6RXr17i4OAgERERugz7o1O/106dOiUqlUomTJggCQkJ4uvrK7/++quIvLgwp1ChQtK3b1+NdU+dOiVubm6ciJe0Tu/NfVmU25UsWRJz5syBnp4efvzxR/z777+6DilHMzIywpQpU2Bubo6KFStCRPDll1+iYMGCWLp0KWJiYpTTBSVLloShoSGePn2q46h1R3365ebNm/j9999x7NgxAIChoSFiYmJQqFAhAC+m9QCA1atXw97eHhMmTADw4hSXqakpx/Lhf6f7tm3bhmbNmqFkyZKoU6cOoqKi0LRpUwQFBaFGjRoYO3YsmjRpgtDQUNy4cQM7d+7MU6dM1e+5CxcuwMfHB2PHjsXIkSORmJiI8PBw1KxZE/fu3YOnpyf8/f0xZ84cAMCGDRtw9epVeHh44NSpUyhatKiO/xL65Og4qaOP6MqVK9KkSROpWrWqMlklZX2VXlpamoSFhUmpUqWkcuXKIiLKaarJkydLZGSkPH36VIYPHy6urq4SFRX1scPOEdRtd+bMGfnss8/kq6++kuDgYGV5tWrV5Msvv1SeP3/+XERE+vXrJ82aNfuoseZE7OF7e+q2Onv2rNjZ2Ymbm5uy7OHDh9KkSRP57bffxNnZWXr06KFMyHv79m3p0qWLbN68mUMe6INhT1UeUrJkSUyZMgVFihRReg/yOvUv3sjISBw6dEgp19PTQ6VKlbBixQo8fPgQtWrVwk8//YRmzZphxYoVcHZ2Rv369bFkyRKsXbsWBQoU0OFfoTt6enq4dOkSatWqhebNm2POnDlo3Lixsnz06NE4d+4cBg8eDAAwNjYG8OJKKwsLC6SlpUHy6LUy7OF7e+o2O336NKpUqYKyZcsiNjYWAwcOBADkz58fRYoUQc+ePeHp6YmFCxcqt4WaO3cuDh8+jIoVK3JgOn0wvPovD0pOToaRkZGuw8gxbt++DU9PTzx+/Bi1atWCj48PfH194eXlBUtLSxw9ehTdunWDpaUl/v33X0RGRmL79u2wsbFBxYoVUaxYMV3/CTrz/PlzdOrUCQUKFFBOsQBASkoKHj9+jJs3b+LgwYNYvnw57OzsUKtWLYSHh2Pt2rU4fPgwypQpo8PodUedHJw9exYtW7ZEmTJl0L17dyUhrV69Ouzt7bF582YA/7sjQv/+/XHnzh1s2rRJh9Hr1rFjx1CtWjWMHj0aY8aMweLFizF69GgEBAQo78GWLVti//79GDJkCAwMDHDt2jX8+eef+Oeff+Dh4aHjv4A+ZeypyoOYUGlKT0+Hk5MTPvvsM8THx+PevXvw9/dHrVq10KlTJ4SHh+O7775DZGQkGjRoAAcHB3Tt2hVfffVVnk6oAMDAwACRkZEoXbq0UrZr1y4MGzYMrq6u6NatG1avXo0pU6bAysoKu3fvxpMnTxAWFpZnEyqAPXzv49mzZ+jduzfGjRsHfX19BAQE4Oeff0ZQUBD69esHAFi/fj0CAgIQEhKCP//8E4mJiTh48CATKvrg2FNFBODatWsYNmyYMoFiwYIFcfDgQcyZMwcpKSk4d+4cSpQogXPnzuHLL7/Epk2bOL8NgLi4OFSp8n/t3V9IU/0fB/D3TJ1zofZHCMRkpIlhicX6pSVmGYV0YaTL/sAKyRFYosuGLoo0G6UkI8gFzj/lRYVYKGVRZF3MGwkqlJqDhLwbslQqZMa+v4vnaTx78BGyM4+y9+vK8z3/Pt+h8uZzzs75H3JycmA0GtHT04POzk6kp6cjJycHK1euRFNTE4qKilBfXw8hBGZnZ0M+2LPDJ41ff4PT09O4f/8+zGYzSkpKcOvWLQDA5OQkoqKiEBYWFvK/c7Q4GKqI/uZ0OlFRUQGfz4eGhgZotVoAf/1j7uvrw6dPn9Df3w+73Y7MzEyZq106Xr16hf379yMhIQEejweNjY3Yu3cvkpOTMTs7i4MHDyI+Ph5dXV1yl7pk/Pz5E3v27IFOp/N3V54/f45nz56htbUVSUlJUKlUuHbtGmw2GyYmJrB69WrU1dVh8+bNMle/NP0zWB07dgxWq1XukigEMVQR/YPL5cLZs2cBADU1NcjNzQ1Y/+sJ6xRofHwcbrcbSUlJWLt2rX/c5/OhpKQEqampqKurA4CQ7+4B7PAFy/T0NB4+fIiysjKYTCZYLBa5S6IQw1BF9C8ulwvnzp2DEAKXLl1Cdna23CUtS16vF/X19Whra8Pr16+RkpIid0lLCjt8wTE1NYXHjx8jKysLGzdulLscCjEMVURzcLlcqKqqwsTEBJqbm7Fjxw65S1pWurq6MDQ0hAcPHqC/v5+XS/8DO3zBwfsdSS789h/RHPhMr4VzOp2w2+0YHx/HwMAAA9U8EhMTsW3btoBA5fV6cfnyZTgcDv+L0BkQfg8/L5ILO1VE8+AzvRbG7XZDqVQiNjZW7lKWFXb4iJY33nFLNA8GqoUJ1SfM/4lfHb5Vq1ZhYGAAaWlpcpdERL+JnSoioiWCHT6i5Y2hioiIiEgCvFGdiIiISAIMVUREREQSYKgiIiIikgBDFREREZEEGKqIiIiIJMBQRURERCQBhioiIiIiCTBUEREFQUdHB+Li4uQug4gWEUMVEYWckydP+l9UHBERAY1GgwsXLmBmZkaycxw5cgSjo6OSHY+Ilj6++4+IQtKBAwfQ3t6O2dlZvH37Fnq9HgqFAtevX5fk+CqVCiqVSpJjEdHywE4VEYUkpVKJdevWITExEYWFhcjPz8eLFy8AAD6fDxaLBRqNBiqVChkZGeju7g7Yv7e3FykpKYiKikJeXh46OzuhUCgwOTkJYO7Lfy0tLdiwYQMiIyORmpqKe/fuBaxXKBRobW3FoUOHEB0djZSUFPT29gbtMyAiaTFUEVHIGx4exuDgICIjIwEAFosFd+/ehc1mw8jICCorK3HixAm8efMGADA2NoaioiIUFhbi/fv3MBgMMJvN857j0aNHqKiogNFoxPDwMAwGA06dOoWBgYGA7a5cuQKdTocPHz6goKAAx48fh8fjCc7EiUhagogoxOj1erFixQqhVquFUqkUAERYWJjo7u4WMzMzIjo6WgwODgbsU1paKo4ePSqEEMJkMon09PSA9WazWQAQX79+FUII0d7eLmJjY/3rs7OzxenTpwP2KS4uFgUFBf5lAOLixYv+5W/fvgkAor+/X4ppE1GQ8Z4qIgpJeXl5aGlpwffv39Hc3Izw8HAcPnwYIyMj+PHjB/bt2xewvdfrRWZmJgDA6XRCq9UGrN++ffu85/v48SPKysoCxnbu3Amr1RowtmXLFv/ParUaMTExcLvdvz0/Ilp8DFVEFJLUajWSk5MBAG1tbcjIyIDdbkd6ejoA4MmTJ0hISAjYR6lUBr2uiIiIgGWFQgGfzxf08xLRn2OoIqKQFxYWhtraWlRVVWF0dBRKpRJfvnxBbm7unNunpqbi6dOnAWNDQ0PzniMtLQ0OhwN6vd4/5nA4sGnTpj+fABEtCQxVREQAiouLUV1djTt37uD8+fOorKyEz+fDrl27MDU1BYfDgZiYGOj1ehgMBty8eRMmkwmlpaV49+4dOjo6APzVWZpLdXU1dDodMjMzkZ+fj76+PvT09ODly5eLOEsiCiaGKiIiAOHh4SgvL8eNGzcwNjaG+Ph4WCwWfP78GXFxcdi6dStqa2sBABqNBt3d3TAajbBarcjKyoLZbMaZM2f+8xJhYWEhrFYrmpqaUFFRAY1Gg/b2duzevXsRZ0lEwaQQQgi5iyAiWu4aGhpgs9kwPj4udylEJBN2qoiIFuD27dvQarVYs2YNHA4HGhsbUV5eLndZRCQjhioiogVwuVy4evUqPB4P1q9fD6PRiJqaGrnLIiIZ8fIfERERkQT4mhoiIiIiCTBUEREREUmAoYqIiIhIAgxVRERERBJgqCIiIiKSAEMVERERkQQYqoiIiIgkwFBFREREJIH/A+BD9QgGDO8AAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Average amount claimed\n",
    "avg_amount_claimed = dataowner['totalDamage'].mean()\n",
    "print(\"Average amount claimed:\", avg_amount_claimed)\n",
    "# By Region\n",
    "avg_amount_claimed_region = dataowner.groupby('Region')['totalDamage'].mean().reset_index()\n",
    "avg_amount_claimed_region = avg_amount_claimed_region.rename(columns={'totalDamage': 'Average amount claimed'})\n",
    "print(avg_amount_claimed_region)\n",
    "\n",
    "# Average amount paid\n",
    "avg_amount_paid = dataowner['totalApprovedIhpAmount'].mean()\n",
    "print(\"Average amount paid:\", avg_amount_paid)\n",
    "# By Region\n",
    "avg_amount_paid_region = dataowner.groupby('Region')['totalApprovedIhpAmount'].mean().reset_index()\n",
    "avg_amount_paid_region = avg_amount_paid_region.rename(columns={'totalApprovedIhpAmount': 'Average amount paid'})\n",
    "print(avg_amount_paid_region)\n",
    "\n",
    "# Create the bar plot\n",
    "x = np.arange(len(avg_amount_claimed_region['Region']))\n",
    "plt.bar(x, avg_amount_claimed_region['Average amount claimed'], width=0.35, label='Average Amount Claimed')\n",
    "plt.bar(x + 0.35, avg_amount_paid_region['Average amount paid'], width=0.35, label='Average Amount Paid')\n",
    "\n",
    "plt.xlabel('Region')\n",
    "plt.ylabel('Dollars')\n",
    "plt.title('Average Amount Claimed V.S. Average Amount Paid')\n",
    "plt.xticks(x + 0.35 / 2, avg_amount_paid_region['Region'], rotation=45)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Counts of Inspected Damage Between 1 and 10k: 4805783\n",
      "        Region  Sum of Counts\n",
      "0      Midwest         580176\n",
      "1    Northeast         470358\n",
      "2    Southeast        1918652\n",
      "3    Southwest        1130734\n",
      "4  Territories         635320\n",
      "5         West          70543\n",
      "Total Counts of Approved Between 1 and 10k: 4069874\n",
      "        Region  Sum of Counts\n",
      "0      Midwest         373172\n",
      "1    Northeast         286921\n",
      "2    Southeast        1495049\n",
      "3    Southwest         998813\n",
      "4  Territories         871591\n",
      "5         West          44328\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Between 1 and 10k\n",
    "num_1_to_10k = dataowner['femaInspectedDamageBetween1And10000'].sum()\n",
    "print(\"Total Counts of Inspected Damage Between 1 and 10k:\", num_1_to_10k)\n",
    "# By Region\n",
    "num_1_to_10k_region = dataowner.groupby('Region')['femaInspectedDamageBetween1And10000'].sum().reset_index()\n",
    "num_1_to_10k_region = num_1_to_10k_region.rename(columns={'femaInspectedDamageBetween1And10000': 'Sum of Counts'})\n",
    "print(num_1_to_10k_region)\n",
    "\n",
    "# Approved\n",
    "num_1_to_10k_approved = dataowner['approvedBetween1And10000'].sum()\n",
    "print(\"Total Counts of Approved Between 1 and 10k:\", num_1_to_10k_approved)\n",
    "# By Region\n",
    "num_1_to_10k_region_approved = dataowner.groupby('Region')['approvedBetween1And10000'].sum().reset_index()\n",
    "num_1_to_10k_region_approved = num_1_to_10k_region_approved.rename(columns={'approvedBetween1And10000': 'Sum of Counts'})\n",
    "print(num_1_to_10k_region_approved)\n",
    "\n",
    "# Create the bar plot\n",
    "x = np.arange(len(num_1_to_10k_region['Region']))\n",
    "plt.bar(x, num_1_to_10k_region['Sum of Counts'], width=0.35, label='Total Damage')\n",
    "plt.bar(x + 0.35, num_1_to_10k_region_approved['Sum of Counts'], width=0.35, label='Approved Damage')\n",
    "\n",
    "plt.xlabel('Region')\n",
    "plt.ylabel('Sum of Counts')\n",
    "plt.title('Total Counts of Inspected and Approved Damage Between 1 and 10k')\n",
    "plt.xticks(x + 0.35 / 2, num_1_to_10k_region['Region'], rotation=45)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Counts of Inspected Damage Between 10k and 20k: 363767\n",
      "        Region  Sum of Counts\n",
      "0      Midwest          19128\n",
      "1    Northeast          58144\n",
      "2    Southeast         105567\n",
      "3    Southwest         145648\n",
      "4  Territories          29871\n",
      "5         West           5409\n",
      "Total Counts of Inspected Damage Between 20k and 30k: 152281\n",
      "        Region  Sum of Counts\n",
      "0      Midwest           8932\n",
      "1    Northeast          31412\n",
      "2    Southeast          38211\n",
      "3    Southwest          66099\n",
      "4  Territories           5346\n",
      "5         West           2281\n"
     ]
    },
    {
     "data": {
      "image/png": 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7mjVrhoEDB+LChQuwtrbGihUrkJ2djRkzZjxzH97e3li1ahX69+8PMzMzfPPNN/mqrV+/fhg6dCju3LmDwMDAXOvT0tLQvHlzNGvWDB07doS9vT0SExMREhKC33//Hd7e3nB1dX3m9q9fv67Mxt69e3ds2bJFY329evVQr149AEDlypXh7++PefPmISsrC40bN1b2s2HDBujr6+e5j+rVq2Pfvn1o06YNPD09ceDAAZiZmeXr+Bs2bIhNmzZh9OjRaNy4McqUKYN3330XQ4cOxTfffIMBAwYgIiICDg4O2Lp1K44ePYpFixahbNmy+do+ANStWxft27eHi4sLLC0tceXKFfzwww/IysrK1bs6b948vPfee/Dw8ECvXr0QFRWFZcuWYfDgwbmmmlAzNTXFrl270K5dO3Tq1AmHDx9GnTp18l0f0TPpOtURUd6qVq2q3E7+31dMTIxG26ioKPHw8JBSpUqJhYWF9O3bV2JjYzXa5DWjenZ2tvTu3VsMDAwkJCQk37X98ssvUqtWLTEwMMjVi7Zp0yZxdXUVY2NjsbKykr59+8qtW7cKdOzTpk175rEDkIMHD2q0T0hIEF9fXylXrpyUKlVKWrdunWvm+GfNqL5ixQoBIGPHjs1XbQkJCWJsbCwANHpC1LKysuS7774Tb29vqVq1qhgbG0upUqXE1dVV5s2bJxkZGc/d/sGDB5977NOmTdNon52dLV999ZVUrVpVjIyMpHbt2hrTGqjlNaP68ePHpWzZstKqVStJS0vL1/GnpKRInz59xMLCQgBoTK8QFxcnAwcOFGtrazEyMpK6deu+1Mz706ZNk0aNGomlpaUYGBiInZ2d9OrVS86ePZtn++3bt4uLi4sYGxtL5cqVZfLkyZKZmanRJq8Z1e/fvy+1atUSW1tbjZnqiV6WSuQ/IxyJiIiIqMA4UJ2IiIhICzimiogAPJkU80WPVTE3N881909R2we9nISEBGRmZj5zvb6+PsqXL1/k90GkS7z8R0QAnjwsuW3bts9ts2rVKgwYMKBI74NeTps2bZ4763nVqlVx7dq1Ir8PIl1iqCIiAMCDBw8QERHx3Da1a9dWJlwsqvuglxMREYEHDx48c72pqSlatGhR5PdBpEsMVURERERawIHqRERERFrAgeqFKCcnB3fu3EHZsmWf+agHIiIiKlpEBA8fPoSdnV2uh4Y/jaGqEN25cwf29va6LoOIiIhews2bN1G5cuVnrmeoKkTqxzTcvHkz34+EICIiIt1KTk6Gvb39Cx+3xFBViNSX/MzMzBiqiIiIipkXDd3hQHUiIiIiLWCoIiIiItIChioiIiIiLeCYKiIiohfIzs5GVlaWrsug18TQ0BD6+vqvvB2GKiIiomcQEcTGxiIxMVHXpdBrZmFhAVtb21eaR5KhioiI6BnUgapChQooVaoUJ25+A4kI0tLSEB8fDwCv9OxRhioiIqI8ZGdnK4GqXLlyui6HXiNTU1MAQHx8PCpUqPDSlwI5UJ2IiCgP6jFUpUqV0nElVBjU/8+vMnaOoYqIiOg5eMmvZNDG/zNDFREREZEWMFQRERGRhmvXrkGlUiEyMlLXpby0Nm3awN/fv1D3yYHqREREBeAwMbRQ93dttleB2h85cgTz5s1DREQE7t69i+3bt8Pb21ujjYhg2rRp+O6775CYmIgWLVpg5cqVcHJyeuk6BwwYgMTERISEhLz0NgDgr7/+wuzZs/HHH3/g/v37cHBwwLBhwzBq1CiNdocOHcLo0aNx/vx52NvbY/LkyRgwYMAr7ftVsaeKiIjoDZKamor69etj+fLlz2wzd+5cLFmyBEFBQTh+/DhKly4NT09PpKenF2KleYuIiECFChWwfv16nD9/HoGBgZg0aRKWLVumtImJiYGXlxfatm2LyMhI+Pv7Y/Dgwfj11191WDlDFRER0RulU6dOmDlzJj744IM814sIFi1ahMmTJ+P9999HvXr1sHbtWty5c+eZvUzZ2dkYNGgQatasiRs3buRaP336dKxZswa//PILVCoVVCoVDh06BAA4d+4c2rVrB1NTU5QrVw5Dhw5FSkrKM+sfNGgQFi9ejNatW6NatWro168fBg4ciJ9//llpExQUBEdHR8yfPx/Ozs7w8/ND9+7dsXDhwmduNzQ0FObm5tiwYcMz27wqXv4jItKRwryMVNBLSPTmiomJQWxsLNzd3ZVl5ubmaNq0KcLDw9GrVy+N9hkZGejduzeuXbuG33//HeXLl8+1zbFjx+LixYtITk7GqlWrAABWVlZITU2Fp6cn3NzccPLkScTHx2Pw4MHw8/PD6tWr811zUlISrKyslK/Dw8M16gcAT0/PZ46hCg4OxrBhwxAcHIwuXbrke78FxVBFRERUgsTGxgIAbGxsNJbb2Ngo69RSUlLg5eWFjIwMHDx4EObm5nlus0yZMjA1NUVGRgZsbW2V5WvWrEF6ejrWrl2L0qVLAwCWLVuGd999F3PmzMlVQ16OHTuGTZs2ITT0f3+ExMbG5ll/cnIyHj16pEzmCQDLly9HYGAgdu7cidatW79wf6+CoYqIiIjy1Lt3b1SuXBkHDhzQCCr5dfHiRdSvX18JVADQokUL5OTkIDo6+oWhKioqCu+//z6mTZsGDw+PAu9/69atiI+Px9GjR9G4ceMCv7+gOKaKiIioBFH3JMXFxWksj4uL0+hlAoDOnTvj7NmzCA8PL7T61C5cuID27dtj6NChmDx5ssY6W1vbPOs3MzPTCH+urq4oX748fvzxR4jIa6+ZoYqIiKgEcXR0hK2tLfbv368sS05OxvHjx+Hm5qbRdvjw4Zg9ezbee+89HD58+LnbNTIyQnZ2tsYyZ2dn/PXXX0hNTVWWHT16FHp6eqhRo8Yzt3X+/Hm0bdsWPj4++PLLL3Otd3Nz06gfAMLCwnLV/9Zbb+HgwYP45ZdfMHLkyOfWrw0MVURERG+QlJQUREZGKhN3xsTEIDIyUrlrT6VSwd/fHzNnzsSOHTtw7tw59O/fH3Z2drnmswKAkSNHYubMmejSpQv++OOPZ+7XwcEBZ8+eRXR0NO7fv4+srCz07dsXJiYm8PHxQVRUFA4ePIiRI0fio48+eualv6ioKLRt2xYeHh4YPXo0YmNjERsbi3v37ilthg0bhn/++Qfjx4/HpUuXsGLFCmzevBkBAQG5tvf222/j4MGD2LZt22ufDJRjqoiIiAqgqN9JeerUKbRt21b5evTo0QAAHx8f5Y678ePHIzU1FUOHDkViYiLeeecd7N27FyYmJnlu09/fHzk5OejcuTP27t2L5s2b52ozZMgQHDp0CI0aNUJKSgoOHjyINm3a4Ndff8WoUaPQuHFjlCpVCt26dcOCBQueWf/WrVtx7949rF+/HuvXr1eWV61aFdeuXQPwpLctNDQUAQEBWLx4MSpXrozvv/8enp6eeW6zRo0aOHDgANq0aQN9fX3Mnz//uefwZamkMC4yEoAn3avm5uZISkqCmZmZrsshIh3jlApFW3p6OmJiYuDo6PjMsEFvjuf9f+f385uX/4iIiIi0gKGKiIiISAt0GqqOHDmCd999F3Z2dlCpVM99COOwYcOgUqmwaNEijeUJCQno27cvzMzMYGFhAV9f31zT3589exYtW7aEiYkJ7O3tMXfu3Fzb37JlC2rWrAkTExPUrVsXu3fv1lgvIpg6dSoqVqwIU1NTuLu748qVKy997ERERPRm0Wmoys9DHwFg+/bt+PPPP2FnZ5drXd++fXH+/HmEhYVh165dOHLkCIYOHaqsT05OhoeHB6pWrYqIiAjMmzcP06dPx7fffqu0OXbsGHr37g1fX1+cOXMG3t7e8Pb2RlRUlNKmKD98koiIiHRPp3f/derUCZ06dXpum9u3b2PkyJH49ddf4eWlOdDy4sWL2Lt3L06ePIlGjRoBAJYuXYrOnTvj66+/hp2dHTZs2IDMzEz8+OOPMDIyQu3atREZGYkFCxYo4Wvx4sXo2LEjxo0bBwD44osvEBYWhmXLliEoKCjXwycBYO3atbCxsUFISEiu5yQRERFRyVOkx1Tl5OTgo48+wrhx41C7du1c68PDw2FhYaEEKgBwd3eHnp4ejh8/rrRp1aoVjIyMlDaenp6Ijo7GgwcPlDZ5PZhRPYPsix4++SwZGRlITk7WeBEREdGbqUiHqjlz5sDAwACffvppnutjY2NRoUIFjWUGBgawsrJSHgr5rIcuqtc9r83T659+X15t8jJr1iyYm5srL3t7++ceLxERERVfRTZURUREYPHixVi9ejVUKpWuy3kpkyZNQlJSkvK6efOmrksiIiKi16TIhqrff/8d8fHxqFKlCgwMDGBgYIDr169jzJgxcHBwAPDkgYrx8fEa73v8+DESEhKUh0I+66GL6nXPa/P0+qffl1ebvBgbG8PMzEzjRURERG+mIhuqPvroI5w9e1Z5flFkZCTs7Owwbtw4/PrrrwCePFAxMTERERERyvsOHDiAnJwcNG3aVGlz5MgRZGVlKW3CwsJQo0YNWFpaKm2e92DGgjx8koiIqLg7dOgQVCoVEhMTdV3KS3NwcMg1DdPrptO7/1JSUnD16lXla/VDH62srFClShWUK1dOo72hoSFsbW2VJ1s7OzujY8eOGDJkCIKCgpCVlQU/Pz/06tVLmX6hT58+mDFjBnx9fTFhwgRERUVh8eLFWLhwobLdUaNGoXXr1pg/fz68vLywceNGnDp1Spl24emHTzo5OcHR0RFTpkx55sMniYjoDTbdvJD3l1Sg5rNmzcLPP/+MS5cuwdTUFM2bN8ecOXOUz07gySNZxowZg40bNyIjIwOenp5YsWLFMx9ynB9t2rSBi4vLKweZ6OhoDBs2DBcuXEBSUhLs7OzQp08fTJs2DYaGhkq7LVu2YMqUKbh27RqcnJwwZ84cdO7c+ZX2/ap02lN16tQpuLq6wtXVFcCThz66urpi6tSp+d7Ghg0bULNmTbRv3x6dO3fGO++8ozEHlbm5Ofbt24eYmBg0bNgQY8aMwdSpUzXmsmrevDmCg4Px7bffon79+ti6dStCQkJQp04dpc348eMxcuRIDB06FI0bN0ZKSspzHz5JRESkC4cPH8aIESPw559/IiwsDFlZWfDw8EBqaqrSJiAgADt37sSWLVtw+PBh3LlzB127dtVh1f9jaGiI/v37Y9++fYiOjsaiRYvw3XffYdq0aUqb/MwvqQt8oHIh4gOViehpfKBy0fbMB+wW8Z6q/7p37x4qVKiAw4cPo1WrVkhKSkL58uURHByM7t27AwAuXboEZ2dnhIeHo1mzZjh06BDatm2LBw8ewMLCAmlpaejWrRuSk5MRGhoKCwsLjX0MGDAAa9as0VgWExMDBwcHHD58GOPGjcNff/0FKysr+Pj4YObMmTAwyP/FstGjR+PkyZP4/fffAQA9e/ZEamoqdu3apbRp1qwZXFxcEBQUBODJ5T9/f3/4+/sDAL7//nuMHTsW27ZtQ/v27XPtgw9UJiIioudKSnoSyqysrAA8ubs+KytLY+7FmjVrokqVKnnOvZiYmIgOHTogJycHYWFhuQIV8GQSbTc3NwwZMgR3797F3bt3YW9vj9u3b6Nz585o3Lgx/vrrL6xcuRI//PADZs6cme/6r169ir1796J169bKshfNL/lfc+fOxcSJE7Fv3748A5W26HRMFREREb0+OTk58Pf3R4sWLZQhLbGxsTAyMsoVjvKaezE2NhY9e/aEk5MTgoODNSbSfpq5uTmMjIxQqlQpjbviV6xYAXt7eyxbtgwqlQo1a9bEnTt3MGHCBEydOhV6es/u22nevDlOnz6NjIwMDB06FJ9//rlGXfmdO3LChAlYt24dDh8+nOdE4trEUEVERPSGGjFiBKKiovDHH3+81Ps7dOiAJk2aYNOmTdDX1y/w+y9evAg3NzeN+SZbtGiBlJQU3Lp1C1WqVHnmezdt2oSHDx/ir7/+wrhx4/D1119j/PjxBdr//PnzkZqailOnTqFatWoFrr+gePmPiIjoDeTn54ddu3bh4MGDqFy5srLc1tYWmZmZuaZLyGvuRS8vLxw5cgQXLlwojJI12Nvbo1atWujduzdmz56N6dOnIzs7G8CL55dUa9myJbKzs7F58+ZCqZmhioiI6A0iIvDz88P27dtx4MABODo6aqxv2LAhDA0NNeZejI6Oxo0bN3LNvTh79mz4+Pigffv2LwxWRkZGSuhRUw9+f/qeuKNHj6Js2bIaQe9FcnJykJWVhZycHAAvnl9SrUmTJtizZw+++uorfP311/ne38vi5T8iIqI3yIgRIxAcHIxffvkFZcuWVcYZmZubw9TUFObm5vD19cXo0aNhZWUFMzMzjBw5Em5ubmjWrFmu7X399dfIzs5Gu3btcOjQIdSsWTPP/To4OOD48eO4du0aypQpAysrK3zyySdYtGgRRo4cCT8/P0RHR2PatGkYPXr0M8dTbdiwAYaGhqhbty6MjY1x6tQpTJo0CT179lTmqXrR/JJPa968OXbv3o1OnTrBwMBAuRvwdWCoIiIiKohXnOLgdVu5ciWAJ5NxPm3VqlUYMGAAAGDhwoXQ09NDt27dNCb/fJaFCxdqBKu33347V5uxY8fCx8cHtWrVwqNHj5QpFXbv3o1x48ahfv36sLKygq+vLyZPnvzMfRkYGGDOnDm4fPkyRARVq1aFn58fAgIClDbq+SUnT56Mzz77DE5OTrnml3zaO++8g9DQUHTu3Bn6+voYOXLkM/f/KjhPVSHiPFVE9DTOU1W0PW/eInrzcJ4qIiIioiKCoYqIiIhICxiqiIiIiLSAoYqIiIhICxiqiIiInoP3c5UM2vh/ZqgiIiLKg3pOpLS0NB1XQoVB/f+s/n9/GZynioiIKA/6+vqwsLBAfHw8AKBUqVIaz7CjN4OIIC0tDfHx8bCwsHipZxyqMVQRERE9g/pZcupgRW8uCwuLXM8OLCiGKiIiomdQqVSoWLEiKlSogKysLF2XQ6+JoaHhK/VQqTFUERERvYC+vr5WPnTpzcaB6kRERERawFBFREREpAUMVURERERawFBFREREpAUMVURERERawFBFREREpAUMVURERERawFBFREREpAUMVURERERawFBFREREpAUMVURERERawFBFREREpAUMVURERERawFBFREREpAUMVURERERawFBFREREpAUMVURERERawFBFREREpAUMVURERERaoNNQdeTIEbz77ruws7ODSqVCSEiIsi4rKwsTJkxA3bp1Ubp0adjZ2aF///64c+eOxjYSEhLQt29fmJmZwcLCAr6+vkhJSdFoc/bsWbRs2RImJiawt7fH3Llzc9WyZcsW1KxZEyYmJqhbty52796tsV5EMHXqVFSsWBGmpqZwd3fHlStXtHcyiIiIqFjTaahKTU1F/fr1sXz58lzr0tLScPr0aUyZMgWnT5/Gzz//jOjoaLz33nsa7fr27Yvz588jLCwMu3btwpEjRzB06FBlfXJyMjw8PFC1alVERERg3rx5mD59Or799lulzbFjx9C7d2/4+vrizJkz8Pb2hre3N6KiopQ2c+fOxZIlSxAUFITjx4+jdOnS8PT0RHp6+ms4M0RERFTcqEREdF0EAKhUKmzfvh3e3t7PbHPy5Ek0adIE169fR5UqVXDx4kXUqlULJ0+eRKNGjQAAe/fuRefOnXHr1i3Y2dlh5cqVCAwMRGxsLIyMjAAAEydOREhICC5dugQA6NmzJ1JTU7Fr1y5lX82aNYOLiwuCgoIgIrCzs8OYMWMwduxYAEBSUhJsbGywevVq9OrVK1/HmJycDHNzcyQlJcHMzOxlThMRvUEcJoYW2r6uzfYqtH0RvWny+/ldrMZUJSUlQaVSwcLCAgAQHh4OCwsLJVABgLu7O/T09HD8+HGlTatWrZRABQCenp6Ijo7GgwcPlDbu7u4a+/L09ER4eDgAICYmBrGxsRptzM3N0bRpU6VNXjIyMpCcnKzxIiIiojdTsQlV6enpmDBhAnr37q2kxNjYWFSoUEGjnYGBAaysrBAbG6u0sbGx0Wij/vpFbZ5e//T78mqTl1mzZsHc3Fx52dvbF+iYiYiIqPgoFqEqKysLPXr0gIhg5cqVui4n3yZNmoSkpCTldfPmTV2XRERERK+Jga4LeBF1oLp+/ToOHDigcS3T1tYW8fHxGu0fP36MhIQE2NraKm3i4uI02qi/flGbp9erl1WsWFGjjYuLyzNrNzY2hrGxcUEOl4iIiIqpIt1TpQ5UV65cwW+//YZy5cpprHdzc0NiYiIiIiKUZQcOHEBOTg6aNm2qtDly5AiysrKUNmFhYahRowYsLS2VNvv379fYdlhYGNzc3AAAjo6OsLW11WiTnJyM48ePK22IiIioZNNpqEpJSUFkZCQiIyMBPBkQHhkZiRs3biArKwvdu3fHqVOnsGHDBmRnZyM2NhaxsbHIzMwEADg7O6Njx44YMmQITpw4gaNHj8LPzw+9evWCnZ0dAKBPnz4wMjKCr68vzp8/j02bNmHx4sUYPXq0UseoUaOwd+9ezJ8/H5cuXcL06dNx6tQp+Pn5AXhyZ6K/vz9mzpyJHTt24Ny5c+jfvz/s7Oyee7ciERERlRw6nVLh0KFDaNu2ba7lPj4+mD59OhwdHfN838GDB9GmTRsATyb/9PPzw86dO6Gnp4du3bphyZIlKFOmjNL+7NmzGDFiBE6ePAlra2uMHDkSEyZM0Njmli1bMHnyZFy7dg1OTk6YO3cuOnfurKwXEUybNg3ffvstEhMT8c4772DFihV4++238328nFKBiJ7GKRWIiof8fn4XmXmqSgKGKiJ6GkMVUfHwRs5TRURERFRUMVQRERERaQFDFREREZEWMFQRERERaQFDFREREZEWMFQRERERaQFDFREREZEWMFQRERERaQFDFREREZEWMFQRERERaQFDFREREZEWMFQRERERaQFDFREREZEWMFQRERERaQFDFREREZEWMFQRERERaQFDFREREZEWMFQRERERaQFDFREREZEWMFQRERERaQFDFREREZEWMFQRERERaQFDFREREZEWMFQRERERaQFDFREREZEWMFQRERERaQFDFREREZEWMFQRERERaYGBrgsgouLNYWJooe3r2myvQtsXEVFBsaeKiIiISAsYqoiIiIi0gKGKiIiISAsYqoiIiIi0gKGKiIiISAsYqoiIiIi0gKGKiIiISAsYqoiIiIi0gKGKiIiISAt0GqqOHDmCd999F3Z2dlCpVAgJCdFYLyKYOnUqKlasCFNTU7i7u+PKlSsabRISEtC3b1+YmZnBwsICvr6+SElJ0Whz9uxZtGzZEiYmJrC3t8fcuXNz1bJlyxbUrFkTJiYmqFu3Lnbv3l3gWoiIiKjk0mmoSk1NRf369bF8+fI818+dOxdLlixBUFAQjh8/jtKlS8PT0xPp6elKm759++L8+fMICwvDrl27cOTIEQwdOlRZn5ycDA8PD1StWhURERGYN28epk+fjm+//VZpc+zYMfTu3Ru+vr44c+YMvL294e3tjaioqALVQkRERCWXSkRE10UAgEqlwvbt2+Ht7Q3gSc+QnZ0dxowZg7FjxwIAkpKSYGNjg9WrV6NXr164ePEiatWqhZMnT6JRo0YAgL1796Jz5864desW7OzssHLlSgQGBiI2NhZGRkYAgIkTJyIkJASXLl0CAPTs2ROpqanYtWuXUk+zZs3g4uKCoKCgfNWSH8nJyTA3N0dSUhLMzMy0ct6IdI3P/nt5PHdExUN+P7+L7JiqmJgYxMbGwt3dXVlmbm6Opk2bIjw8HAAQHh4OCwsLJVABgLu7O/T09HD8+HGlTatWrZRABQCenp6Ijo7GgwcPlDZP70fdRr2f/NSSl4yMDCQnJ2u8iIiI6M1UZENVbGwsAMDGxkZjuY2NjbIuNjYWFSpU0FhvYGAAKysrjTZ5bePpfTyrzdPrX1RLXmbNmgVzc3PlZW9v/4KjJiIiouKqyIaqN8GkSZOQlJSkvG7evKnrkoiIiOg1KbKhytbWFgAQFxensTwuLk5ZZ2tri/j4eI31jx8/RkJCgkabvLbx9D6e1ebp9S+qJS/GxsYwMzPTeBEREdGbqciGKkdHR9ja2mL//v3KsuTkZBw/fhxubm4AADc3NyQmJiIiIkJpc+DAAeTk5KBp06ZKmyNHjiArK0tpExYWhho1asDS0lJp8/R+1G3U+8lPLURERFSy6TRUpaSkIDIyEpGRkQCeDAiPjIzEjRs3oFKp4O/vj5kzZ2LHjh04d+4c+vfvDzs7O+UOQWdnZ3Ts2BFDhgzBiRMncPToUfj5+aFXr16ws7MDAPTp0wdGRkbw9fXF+fPnsWnTJixevBijR49W6hg1ahT27t2L+fPn49KlS5g+fTpOnToFPz8/AMhXLURERFSyGehy56dOnULbtm2Vr9VBx8fHB6tXr8b48eORmpqKoUOHIjExEe+88w727t0LExMT5T0bNmyAn58f2rdvDz09PXTr1g1LlixR1pubm2Pfvn0YMWIEGjZsCGtra0ydOlVjLqvmzZsjODgYkydPxmeffQYnJyeEhISgTp06Spv81EJEREQlV5GZp6ok4DxV9CbiXEsvj+eOqHgo9vNUERERERUnBQ5Va9asQWjo//66Gj9+PCwsLNC8eXNcv35dq8URERERFRcFDlVfffUVTE1NATyZiXz58uWYO3curK2tERAQoPUCiYiIiIqDAg9Uv3nzJqpXrw4ACAkJQbdu3TB06FC0aNECbdq00XZ9RERERMVCgXuqypQpg3///RcAsG/fPnTo0AEAYGJigkePHmm3OiIiIqJiosA9VR06dMDgwYPh6uqKy5cvo3PnzgCA8+fPw8HBQdv1ERERERULBe6pWr58Odzc3HDv3j1s27YN5cqVAwBERESgd+/eWi+QiIiIqDgocE9VcnIylixZAj09zTw2ffp0PjCYiIiISqwC91Q5Ojri/v37uZYnJCTA0dFRK0URERERFTcFDlXPmoA9JSWFj2whIiKiEivfl//Uz+VTqVSYOnUqSpUqpazLzs7G8ePH4eLiovUCiYiIiIqDfIeqM2fOAHjSU3Xu3DkYGRkp64yMjFC/fn2MHTtW+xUSERERFQP5DlUHDx4EAAwcOBCLFy/mA4GJiIiInlLgu/9WrVr1OuogIiIiKtYKHKpSU1Mxe/Zs7N+/H/Hx8cjJydFY/88//2itOCIiIqLiosChavDgwTh8+DA++ugjVKxYESqV6nXURURERFSsFDhU7dmzB6GhoWjRosXrqIeIiIioWCrwPFWWlpawsrJ6HbUQERERFVsFDlVffPEFpk6dirS0tNdRDxEREVGxVODLf/Pnz8fff/8NGxsbODg4wNDQUGP96dOntVYcERERUXFR4FDl7e39GsogIiIiKt4KHKqmTZv2OuogIiIiKtYKPKaKiIiIiHIrcE+Vnp7ec+emys7OfqWCiIiIiIqjAoeq7du3a3ydlZWFM2fOYM2aNZgxY4bWCiMiIiIqTgocqt5///1cy7p3747atWtj06ZN8PX11UphRERERMWJ1sZUNWvWDPv379fW5oiIiIiKFa2EqkePHmHJkiWoVKmSNjZHREREVOwU+PKfpaWlxkB1EcHDhw9RqlQprF+/XqvFERERERUXBQ5VixYt0vhaT08P5cuXR9OmTWFpaamtuoiIiIiKlQKHKh8fn9dRBxEREVGxVuBQBQCJiYn44YcfcPHiRQBA7dq1MWjQIJibm2u1OCIiIqLiosAD1U+dOoW33noLCxcuREJCAhISErBgwQK89dZbfJgyERERlVgF7qkKCAjAe++9h++++w4GBk/e/vjxYwwePBj+/v44cuSI1oskIiIiKuoKHKpOnTqlEagAwMDAAOPHj0ejRo20WhwRERFRcVHgy39mZma4ceNGruU3b95E2bJltVIUERERUXFT4FDVs2dP+Pr6YtOmTbh58yZu3ryJjRs3YvDgwejdu/frqJGIiIioyCtwqPr666/RtWtX9O/fHw4ODnBwcMCAAQPQvXt3zJkzR6vFZWdnY8qUKXB0dISpqSneeustfPHFFxARpY2IYOrUqahYsSJMTU3h7u6OK1euaGwnISEBffv2hZmZGSwsLODr64uUlBSNNmfPnkXLli1hYmICe3t7zJ07N1c9W7ZsQc2aNWFiYoK6deti9+7dWj1eIiIiKr4KHKqMjIywePFiPHjwAJGRkYiMjERCQgIWLlwIY2NjrRY3Z84crFy5EsuWLcPFixcxZ84czJ07F0uXLlXazJ07F0uWLEFQUBCOHz+O0qVLw9PTE+np6Uqbvn374vz58wgLC8OuXbtw5MgRDB06VFmfnJwMDw8PVK1aFREREZg3bx6mT5+Ob7/9Vmlz7Ngx9O7dG76+vjhz5gy8vb3h7e2NqKgorR4zERERFU8qebrb5zmys7Nx/vx5ODk5wdTUVGPdo0ePcOXKFdSpUwd6elp7RjO6dOkCGxsb/PDDD8qybt26wdTUFOvXr4eIwM7ODmPGjMHYsWMBAElJSbCxscHq1avRq1cvXLx4EbVq1cLJkyeVgfR79+5F586dcevWLdjZ2WHlypUIDAxEbGwsjIyMAAATJ05ESEgILl26BODJZc/U1FTs2rVLqaVZs2ZwcXFBUFBQvo4nOTkZ5ubmSEpKgpmZmVbOEZGuOUwMLbR9XZvtVWj7Kgw8d0TFQ34/v/OdgNatW4dBgwYpoeNphoaGGDRoEIKDg1+u2mdo3rw59u/fj8uXLwMA/vrrL/zxxx/o1KkTACAmJgaxsbFwd3dX3mNubo6mTZsiPDwcABAeHg4LCwuNOxPd3d2hp6eH48ePK21atWqlcWyenp6Ijo7GgwcPlDZP70fdRr2fvGRkZCA5OVnjRURERG+mfIeqH374AWPHjoW+vn6udeopFZ6+XKYNEydORK9evVCzZk0YGhrC1dUV/v7+6Nu3LwAgNjYWAGBjY6PxPhsbG2VdbGwsKlSokKteKysrjTZ5bePpfTyrjXp9XmbNmgVzc3PlZW9vX6DjJyIiouIj36EqOjoazZo1e+b6xo0bK4+t0ZbNmzdjw4YNCA4OxunTp7FmzRp8/fXXWLNmjVb387pMmjQJSUlJyuvmzZu6LomIiIhek3xP/pmamvrcy1cPHz5EWlqaVopSGzdunNJbBQB169bF9evXMWvWLPj4+MDW1hYAEBcXh4oVKyrvi4uLg4uLCwDA1tYW8fHxGtt9/PgxEhISlPfb2toiLi5Oo4366xe1Ua/Pi7GxsdYH7xMREVHRlO+eKicnJxw7duyZ6//44w84OTlppSi1tLS0XAPf9fX1kZOTAwBwdHSEra0t9u/fr6xPTk7G8ePH4ebmBgBwc3NDYmIiIiIilDYHDhxATk4OmjZtqrQ5cuQIsrKylDZhYWGoUaMGLC0tlTZP70fdRr0fIiIiKtnyHar69OmDyZMn4+zZs7nW/fXXX5g6dSr69Omj1eLeffddfPnllwgNDcW1a9ewfft2LFiwAB988AEAQKVSwd/fHzNnzsSOHTtw7tw59O/fH3Z2dvD29gYAODs7o2PHjhgyZAhOnDiBo0ePws/PD7169YKdnZ1ybEZGRvD19cX58+exadMmLF68GKNHj1ZqGTVqFPbu3Yv58+fj0qVLmD59Ok6dOgU/Pz+tHjMREREVT/m+/BcQEIA9e/agYcOGcHd3R82aNQEAly5dwm+//YYWLVogICBAq8UtXboUU6ZMwSeffIL4+HjY2dnh448/xtSpU5U248ePR2pqKoYOHYrExES888472Lt3L0xMTJQ2GzZsgJ+fH9q3bw89PT1069YNS5YsUdabm5tj3759GDFiBBo2bAhra2tMnTpVYy6r5s2bIzg4GJMnT8Znn30GJycnhISEoE6dOlo9ZiIiIiqe8j1PFQBkZWVh4cKFCA4OxpUrVyAiePvtt9GnTx/4+/vnOd0C/Q/nqaI3Eedaenk8d0TFQ34/v/PdUwU8mY9q/PjxGD9+/CsXSERERPQm0d7050REREQlGEMVERERkRYwVBERERFpQb5CFZ9ZR0RERPR8+QpVlpaWyqzk7dq1Q2Ji4uusiYiIiKjYyVeoKlOmDP79918AwKFDhzRmHiciIiKifE6p4O7ujrZt28LZ2RkA8MEHHzxzTqoDBw5orzoiIiKiYiJfoWr9+vVYs2YN/v77bxw+fBi1a9dGqVKlXndtRERERMVGvkKVqakphg0bBgA4deoU5syZAwsLi9dZFxEREVGxUqAZ1QHg4MGDyr/VT7hRqVTaq4iIiIioGHqpearWrl2LunXrwtTUFKampqhXrx7WrVun7dqIiIiIio0C91QtWLAAU6ZMgZ+fH1q0aAEA+OOPPzBs2DDcv38fAQEBWi+SiIiIqKgrcKhaunQpVq5cif79+yvL3nvvPdSuXRvTp09nqCIiIqISqcCX/+7evYvmzZvnWt68eXPcvXtXK0URERERFTcFDlXVq1fH5s2bcy3ftGkTnJyctFIUERERUXFT4Mt/M2bMQM+ePXHkyBFlTNXRo0exf//+PMMWERERUUlQ4J6qbt264fjx47C2tkZISAhCQkJgbW2NEydO4IMPPngdNRIREREVeQXuqQKAhg0bYv369dquhYiIiKjYeql5qoiIiIhIE0MVERERkRYwVBERERFpAUMVERERkRYwVBERERFpQYHv/ktPT8fSpUtx8OBBxMfHIycnR2P96dOntVYcERERUXFR4FDl6+uLffv2oXv37mjSpAlUKtXrqIuIiIioWClwqNq1axd2796tzKZORERERC8RqipVqoSyZcu+jlqIiOh1mW5eyPtLKtz9ERUBBR6oPn/+fEyYMAHXr19/HfUQERERFUsF7qlq1KgR0tPTUa1aNZQqVQqGhoYa6xMSErRWHBEREVFxUeBQ1bt3b9y+fRtfffUVbGxsOFCdiIiICC8Rqo4dO4bw8HDUr1//ddRDREREVCwVeExVzZo18ejRo9dRCxEREVGxVeBQNXv2bIwZMwaHDh3Cv//+i+TkZI0XERERUUlU4Mt/HTt2BAC0b99eY7mIQKVSITs7WzuVERERERUjBQ5VBw8efB11EBERERVrBQ5VrVu3fh11EBERERVrBR5TdeTIkee+tO327dvo168fypUrB1NTU9StWxenTp1S1osIpk6diooVK8LU1BTu7u64cuWKxjYSEhLQt29fmJmZwcLCAr6+vkhJSdFoc/bsWbRs2RImJiawt7fH3Llzc9WyZcsW1KxZEyYmJqhbty52796t9eMlIiKi4qnAPVVt2rTJtezpuaq0OabqwYMHaNGiBdq2bYs9e/agfPnyuHLlCiwtLZU2c+fOxZIlS7BmzRo4OjpiypQp8PT0xIULF2BiYgIA6Nu3L+7evYuwsDBkZWVh4MCBGDp0KIKDgwEAycnJ8PDwgLu7O4KCgnDu3DkMGjQIFhYWGDp0KIAnU0n07t0bs2bNQpcuXRAcHAxvb2+cPn0aderU0doxExERUfGkEhEpyBuSkjSf55SVlYUzZ85gypQp+PLLL3MNYH8VEydOxNGjR/H777/nuV5EYGdnhzFjxmDs2LFKfTY2Nli9ejV69eqFixcvolatWjh58iQaNWoEANi7dy86d+6MW7duwc7ODitXrkRgYCBiY2NhZGSk7DskJASXLl0CAPTs2ROpqanYtWuXsv9mzZrBxcUFQUFB+Tqe5ORkmJubIykpCWZmZi99XoiKEoeJoYW2r2uzvQptX4WhUM+dSZ9C2xcAPvuP3ij5/fwu8OU/c3NzjZe1tTU6dOiAOXPmYPz48a9U9H/t2LEDjRo1wocffogKFSrA1dUV3333nbI+JiYGsbGxcHd316ivadOmCA8PBwCEh4fDwsJCCVQA4O7uDj09PRw/flxp06pVKyVQAYCnpyeio6Px4MEDpc3T+1G3Ue8nLxkZGZxygoiIqIQocKh6FhsbG0RHR2trcwCAf/75BytXroSTkxN+/fVXDB8+HJ9++inWrFkDAIiNjVX2/d9a1OtiY2NRoUIFjfUGBgawsrLSaJPXNp7ex7PaqNfnZdasWRoB1N7evkDHT0RERMVHgcdUnT17VuNrEcHdu3cxe/ZsuLi4aKsuAEBOTg4aNWqEr776CgDg6uqKqKgoBAUFwcfHR6v7eh0mTZqE0aNHK18nJyczWBEREb2hChyqXFxcoFKp8N+hWM2aNcOPP/6otcIAoGLFiqhVq5bGMmdnZ2zbtg0AYGtrCwCIi4tDxYoVlTZxcXFKwLO1tUV8fLzGNh4/foyEhATl/ba2toiLi9Noo/76RW3U6/NibGwMY2PjfB0rERERFW8FvvwXExODf/75BzExMYiJicH169eRlpaGY8eOoWbNmlotrkWLFrkuKV6+fBlVq1YFADg6OsLW1hb79+9X1icnJ+P48eNwc3MDALi5uSExMRERERFKmwMHDiAnJwdNmzZV2hw5cgRZWVlKm7CwMNSoUUO509DNzU1jP+o26v0QERFRyVbgnip1oCkMAQEBaN68Ob766iv06NEDJ06cwLfffotvv/0WwJOpHPz9/TFz5kw4OTkpUyrY2dnB29sbwJOerY4dO2LIkCEICgpCVlYW/Pz80KtXL9jZ2QEA+vTpgxkzZsDX1xcTJkxAVFQUFi9ejIULFyq1jBo1Cq1bt8b8+fPh5eWFjRs34tSpU0otREREVLLlO1SFh4fj33//RZcuXZRla9euxbRp05Camgpvb28sXbpUq5e7GjdujO3bt2PSpEn4/PPP4ejoiEWLFqFv375Km/HjxyM1NRVDhw5FYmIi3nnnHezdu1eZowoANmzYAD8/P7Rv3x56enro1q0blixZoqw3NzfHvn37MGLECDRs2BDW1taYOnWqMkcVADRv3hzBwcGYPHkyPvvsMzg5OSEkJIRzVL0hOC0AERG9qnzPU9WpUye0adMGEyZMAACcO3cODRo0wIABA+Ds7Ix58+bh448/xvTp019nvcUa56kquhiqXh7P3cvjPFVExYPW56mKjIzUmNhz48aNaNq0Kb777juMHj0aS5YswebNm1+taiIiIqJiKt+h6sGDBxrzNB0+fBidOnVSvm7cuDFu3ryp3eqIiIiIiol8hyobGxvExMQAADIzM3H69Gk0a9ZMWf/w4UMYGhpqv0IiIiKiYiDfoapz586YOHEifv/9d0yaNAmlSpVCy5YtlfVnz57FW2+99VqKJCIiIirq8n333xdffIGuXbuidevWKFOmDNasWaPxrLwff/wRHh4er6VIIiIioqIu36HK2toaR44cQVJSEsqUKQN9fX2N9Vu2bEGZMmW0XiARERFRcVDgyT/Nzc3zXG5lZfXKxRAREREVVwV+TA0RERER5cZQRURERKQFDFVEREREWsBQRURERKQFDFVEREREWsBQRURERKQFDFVEREREWsBQRURERKQFDFVEREREWsBQRURERKQFDFVEREREWsBQRURERKQFDFVEREREWsBQRURERKQFDFVEREREWsBQRURERKQFDFVEREREWsBQRURERKQFDFVEREREWsBQRURERKQFDFVEREREWsBQRURERKQFDFVEREREWsBQRURERKQFDFVEREREWsBQRURERKQFDFVEREREWsBQRURERKQFDFVEREREWsBQRURERKQFDFVEREREWlCsQtXs2bOhUqng7++vLEtPT8eIESNQrlw5lClTBt26dUNcXJzG+27cuAEvLy+UKlUKFSpUwLhx4/D48WONNocOHUKDBg1gbGyM6tWrY/Xq1bn2v3z5cjg4OMDExARNmzbFiRMnXsdhEhERUTFUbELVyZMn8c0336BevXoaywMCArBz505s2bIFhw8fxp07d9C1a1dlfXZ2Nry8vJCZmYljx45hzZo1WL16NaZOnaq0iYmJgZeXF9q2bYvIyEj4+/tj8ODB+PXXX5U2mzZtwujRozFt2jScPn0a9evXh6enJ+Lj41//wRMREVGRVyxCVUpKCvr27YvvvvsOlpaWyvKkpCT88MMPWLBgAdq1a4eGDRti1apVOHbsGP78808AwL59+3DhwgWsX78eLi4u6NSpE7744gssX74cmZmZAICgoCA4Ojpi/vz5cHZ2hp+fH7p3746FCxcq+1qwYAGGDBmCgQMHolatWggKCkKpUqXw448/Fu7JICIioiKpWISqESNGwMvLC+7u7hrLIyIikJWVpbG8Zs2aqFKlCsLDwwEA4eHhqFu3LmxsbJQ2np6eSE5Oxvnz55U2/922p6enso3MzExERERotNHT04O7u7vSJi8ZGRlITk7WeBEREdGbyUDXBbzIxo0bcfr0aZw8eTLXutjYWBgZGcHCwkJjuY2NDWJjY5U2Twcq9Xr1uue1SU5OxqNHj/DgwQNkZ2fn2ebSpUvPrH3WrFmYMWNG/g6UiIiIirUi3VN18+ZNjBo1Chs2bICJiYmuyymwSZMmISkpSXndvHlT1yURERHRa1KkQ1VERATi4+PRoEEDGBgYwMDAAIcPH8aSJUtgYGAAGxsbZGZmIjExUeN9cXFxsLW1BQDY2trmuhtQ/fWL2piZmcHU1BTW1tbQ19fPs416G3kxNjaGmZmZxouIiIjeTEU6VLVv3x7nzp1DZGSk8mrUqBH69u2r/NvQ0BD79+9X3hMdHY0bN27Azc0NAODm5oZz585p3KUXFhYGMzMz1KpVS2nz9DbUbdTbMDIyQsOGDTXa5OTkYP/+/UobIiIiKtmK9JiqsmXLok6dOhrLSpcujXLlyinLfX19MXr0aFhZWcHMzAwjR46Em5sbmjVrBgDw8PBArVq18NFHH2Hu3LmIjY3F5MmTMWLECBgbGwMAhg0bhmXLlmH8+PEYNGgQDhw4gM2bNyM0NFTZ7+jRo+Hj44NGjRqhSZMmWLRoEVJTUzFw4MBCOhtEhOnmhby/pMLdHxEVa0U6VOXHwoULoaenh27duiEjIwOenp5YsWKFsl5fXx+7du3C8OHD4ebmhtKlS8PHxweff/650sbR0RGhoaEICAjA4sWLUblyZXz//ffw9PRU2vTs2RP37t3D1KlTERsbCxcXF+zduzfX4HUiIiIqmVQiIrouoqRITk6Gubk5kpKSOL6qiHGYGPriRlpybbZXoe2rMBTquTPpU2j7AvDae6p47oiKh/x+fhfpMVVERERExQVDFREREZEWMFQRERERaQFDFREREZEWMFQRERERaQFDFREREZEWMFQRERERaQFDFREREZEWMFQRERERaQFDFREREZEWMFQRERERaQFDFREREZEWMFQRERERaYGBrgsgKnGmmxfy/pIKd39ERCUUe6qIiIiItIChioiIiEgLGKqIiIiItIBjqoiIqNhxmBhaqPu7NturUPdHxRN7qoiIiIi0gKGKiIiISAsYqoiIiIi0gKGKiIiISAsYqoiIiIi0gKGKiIiISAsYqoiIiIi0gKGKiIiISAsYqoiIiIi0gKGKiIiISAsYqoiIiIi0gKGKiIiISAsYqoiIiIi0gKGKiIiISAsYqoiIiIi0gKGKiIiISAsYqoiIiIi0gKGKiIiISAsYqoiIiIi0oEiHqlmzZqFx48YoW7YsKlSoAG9vb0RHR2u0SU9Px4gRI1CuXDmUKVMG3bp1Q1xcnEabGzduwMvLC6VKlUKFChUwbtw4PH78WKPNoUOH0KBBAxgbG6N69epYvXp1rnqWL18OBwcHmJiYoGnTpjhx4oTWj5mIiIiKpyIdqg4fPowRI0bgzz//RFhYGLKysuDh4YHU1FSlTUBAAHbu3IktW7bg8OHDuHPnDrp27aqsz87OhpeXFzIzM3Hs2DGsWbMGq1evxtSpU5U2MTEx8PLyQtu2bREZGQl/f38MHjwYv/76q9Jm06ZNGD16NKZNm4bTp0+jfv368PT0RHx8fOGcDCIiIirSVCIiui4iv+7du4cKFSrg8OHDaNWqFZKSklC+fHkEBweje/fuAIBLly7B2dkZ4eHhaNasGfbs2YMuXbrgzp07sLGxAQAEBQVhwoQJuHfvHoyMjDBhwgSEhoYiKipK2VevXr2QmJiIvXv3AgCaNm2Kxo0bY9myZQCAnJwc2NvbY+TIkZg4cWK+6k9OToa5uTmSkpJgZmamzVNDr8hhYmih7euaSZ9C2xcAYHrSa908z93L47l7eYV57gDg2myvQt0fFS35/fwu0j1V/5WU9OSH1MrKCgAQERGBrKwsuLu7K21q1qyJKlWqIDw8HAAQHh6OunXrKoEKADw9PZGcnIzz588rbZ7ehrqNehuZmZmIiIjQaKOnpwd3d3elTV4yMjKQnJys8SIiIqI3U7EJVTk5OfD390eLFi1Qp04dAEBsbCyMjIxgYWGh0dbGxgaxsbFKm6cDlXq9et3z2iQnJ+PRo0e4f/8+srOz82yj3kZeZs2aBXNzc+Vlb29f8AMnIiKiYqHYhKoRI0YgKioKGzdu1HUp+TZp0iQkJSUpr5s3b+q6JCIiInpNDHRdQH74+flh165dOHLkCCpXrqwst7W1RWZmJhITEzV6q+Li4mBra6u0+e9deuq7A59u8987BuPi4mBmZgZTU1Po6+tDX18/zzbqbeTF2NgYxsbGBT/gl8DxBURERLpVpHuqRAR+fn7Yvn07Dhw4AEdHR431DRs2hKGhIfbv368si46Oxo0bN+Dm5gYAcHNzw7lz5zTu0gsLC4OZmRlq1aqltHl6G+o26m0YGRmhYcOGGm1ycnKwf/9+pQ0RERGVbEW6p2rEiBEIDg7GL7/8grJlyyrjl8zNzWFqagpzc3P4+vpi9OjRsLKygpmZGUaOHAk3Nzc0a9YMAODh4YFatWrho48+wty5cxEbG4vJkydjxIgRSi/SsGHDsGzZMowfPx6DBg3CgQMHsHnzZoSG/q/3Z/To0fDx8UGjRo3QpEkTLFq0CKmpqRg4cGDhnxgiIiIqcop0qFq5ciUAoE2bNhrLV61ahQEDBgAAFi5cCD09PXTr1g0ZGRnw9PTEihUrlLb6+vrYtWsXhg8fDjc3N5QuXRo+Pj74/PPPlTaOjo4IDQ1FQEAAFi9ejMqVK+P777+Hp6en0qZnz564d+8epk6ditjYWLi4uGDv3r25Bq8TERFRyVSkQ1V+ptAyMTHB8uXLsXz58me2qVq1Knbv3v3c7bRp0wZnzpx5bhs/Pz/4+fm9sCYiIiIqeYr0mCoiIiKi4oKhioiIiEgLGKqIiIiItIChioiIiEgLGKqIiIiItIChioiIiEgLGKqIiIiItIChioiIiEgLGKqIiIiItIChioiIiEgLGKqIiIiItIChioiIiEgLGKqIiIiItIChioiIiEgLGKqIiIiItIChioiIiEgLGKqIiIiItIChioiIiEgLGKqIiIiItIChioiIiEgLDHRdABVT080LeX9Jhbs/IiKiAmJPFREREZEWMFQRERERaQFDFREREZEWMFQRERERaQFDFREREZEWMFQRERERaQFDFREREZEWcJ4qIiKiFynMufle87x8DhNDX+v2/+vabK9C3Z8usaeKiIiISAsYqoiIiIi0gKGKiIiISAsYqoiIiIi0gKGKiIiISAsYqoiIiIi0gKGKiIiISAs4TxURERG9Pm/QHF8vwp6qAlq+fDkcHBxgYmKCpk2b4sSJE7ouiYiIiIoAhqoC2LRpE0aPHo1p06bh9OnTqF+/Pjw9PREfH6/r0oiIiEjHGKoKYMGCBRgyZAgGDhyIWrVqISgoCKVKlcKPP/6o69KIiIhIxzimKp8yMzMRERGBSZMmKcv09PTg7u6O8PDwPN+TkZGBjIwM5eukpCfXepOTk7VeX05Gmta3+TzJKinU/eE1nLOnFeb547l7eTx3L4/n7tUU6vnjuXuFnb2ec6f+3BZ5wbEI5cvt27cFgBw7dkxj+bhx46RJkyZ5vmfatGkCgC+++OKLL774egNeN2/efG5WYE/VazRp0iSMHj1a+TonJwcJCQkoV64cVCqVDit7NcnJybC3t8fNmzdhZmam63KKFZ67l8dz9/J47l4Nz9/Le1POnYjg4cOHsLOze247hqp8sra2hr6+PuLi4jSWx8XFwdbWNs/3GBsbw9jYWGOZhYXF6yqx0JmZmRXrHxJd4rl7eTx3L4/n7tXw/L28N+HcmZubv7ANB6rnk5GRERo2bIj9+/cry3JycrB//364ubnpsDIiIiIqCthTVQCjR4+Gj48PGjVqhCZNmmDRokVITU3FwIEDdV0aERER6RhDVQH07NkT9+7dw9SpUxEbGwsXFxfs3bsXNjY2ui6tUBkbG2PatGm5Lm3Si/HcvTyeu5fHc/dqeP5eXkk7dyqRF90fSEREREQvwjFVRERERFrAUEVERESkBQxVRERERFrAUEVERESkBQxVRERERFrAUEVERKQDvPn+zcNQRTrBXyavjuew4HJycpR/P378GACQmZmpq3KKHZ6/V6P+mb116xYAFOtnwGrD07/D3pTfZwxV9NqpfxFnZ2cry+Lj43VVTrH29LlU/0KOj49XPuDo+fT09BATE4N79+7BwMAAISEhmDBhAjIyMnRdWrGgp6eHGzdu4J9//lHO37fffstglQ8iApVKhV27duG9997D2rVrdV2SzqSmpgJ4EioPHz6M6OjoNyZgMlTRa6enp4fo6GgsX74cALBlyxa4urri7t27Oq6s+FF/qI0dOxYA8PPPP6Nt27a4d++ejisrHjIzMzF06FDUr18fP/zwA7p27YomTZqUmNmeX1VKSgoCAgLQo0cPLFu2DF27doW1tTWMjIx0XVqRp1KpsH37dvTs2RM+Pj5wcXHRdUk6cffuXdSrVw+RkZHYsmULPD09cePGDV2XpT1CVAiWLVsmKpVK+vfvL/r6+rJ69Wpdl1RsLV26VFxdXcXT01MMDQ1l3bp1ui6pWPn333/F0dFRjI2NZenSpSIi8vjxYx1XVXzs379f6tSpIwYGBjJv3jwREcnKytJxVUXfnTt3xMXFRZYsWSIiT77n0tPTZc+ePXL//v0S8z2YlZUl/fr1E0tLS9HX15c1a9bouiStYk8VFYoRI0agX79+WL9+Pbp37w4fHx9dl1Rs+fn5oXHjxti3bx/eeecd9OrVC4Dm5VXKTZ4as5GVlQUrKyusWLEC9+7dg76+Ps/fC6gvPTs5OSE7OxuOjo4IDQ3F1atXYWBgwPP3Aqmpqbh//z6aN2+OrKwszJkzB+3bt8d7770HNzc3XLhwQdclFgoDAwP07dsXiYmJMDU1Rc2aNXVdklYxVFGhMTExQdeuXbF582bMmTMHSUlJui6pWHk6FFSoUAH9+/fH48eP8cknn+Dff/9lMHgO+f/xLDExMcjOzsbZs2dx6tQplClTBi1btkR8fDz09fWV4JCWlqbjiosWEYGenh5u3boFExMT7N69GytXroSBgQEGDhyIq1evanz/PXr0SMcVFz3Vq1eHs7MzvL294ejoiBMnTuC9995DUlISMjIysGHDBl2X+Fqpf39lZmaiVatW2LFjB3r06AFPT0/s37//ue8pVnTZTUZvtpycHBEROXHihBw4cEBZ/vXXX4tKpZLZs2dLcnKysvzvv/8u9BqLC/W5/P333+XQoUPK8nnz5ombm5sMGTJE/v33X2X5P//8w0sy/0997rZv3y5169aVH3/8URISEkRE5NKlS9K4cWNxdnaWuLg4ERFZuHChjB07tsRcjnkR9fn75ZdfxNnZWTZv3qws27Fjh7i7u0urVq3k6tWrIvLk53vFihUl+vypz8+lS5fk2LFjEhYWJiIisbGxsmjRIlm6dKncv39f+Rnt3r27LFiwQGf1vm7q87F3717x8/OT06dPi8iTS6B9+/YVCwsLOXjwoNJ+3bp1Eh0drYtSXxlDFb0W6h+ibdu2ia2trUydOlXOnz+vrJ83b54SrK5duyYzZ86UKlWqSHJysvJeeuLpc2ltbS3Dhg2TmJgYZd3cuXOlefPm4uvrK7du3ZJp06ZJ/fr1NQJrSbdjxw4pXbq0zJ8/X+7evaux7urVq9K4cWMxNzeXbt26iYGBgURGRuqo0qIpJCRESpcuLV9//bVcvnxZY93u3bulQ4cOUrlyZfnoo49EpVLJX3/9paNKde/pn9e3335bnJ2dpVatWuLi4pLrD8fExESZMmWKWFtbF9sQkV/btm2TUqVKycyZM+XChQsa63r16iXm5uayYsUK+fTTT6Vs2bK5vs+KC4Yqem1+++03KVOmjKxcuTLPv1rnz58vpUqVkgYNGoiVlZWcPHlSB1UWD7/99puULl1aVq1aJRkZGbnWr1ixQho0aCCVK1eWSpUqyZ9//qmDKoum+/fvS5MmTWTWrFkiIvLo0SO5d++ebNq0Sfbv368smzhxovj7+2uEfxK5d++eNGjQQGbPni0iIpmZmZKSkiLbt29XgsDp06dlwoQJ0qtXL4mKitJluUXCkSNHpGzZsvLdd99JVlaWHD16VFQqlaxYsUJps3fvXunZs6fY29srPTdvqjNnzoidnZ2sWrVKY/m1a9eUfw8ZMkRq1KghDRo0kIiIiEKuUHtUIsXxoiUVZSKCnJwcfPzxxzAwMEBQUBCSk5Nx6dIlbN68GcnJyZg7dy4sLCxw4MABJCcnw8XFBQ4ODrouvUjKyclBYGAgEhIS8M033yA5ORnnzp3DmjVrULp0afTp0weNGzfGuXPncP36ddSpU4fn8ikPHz7Eu+++iw8//BDvvfcegoKCcOzYMZw9exaWlpYYPnw4xowZA+DJhJYGBgY6rrhouXXrFtzd3bFkyRLUr18fQUFBOHjwII4dOwYXFxdMmDAB3bp1A/DkBgBDQ0MdV1y4rl+/jqpVq2osW7JkCS5cuICgoCDExMSgbdu26Ny5M1asWKG0OX/+PH777Td4eXmhevXqhV12oQoNDUVgYCD+/PNP6OvrIzg4GGvXrsXt27fh4uKCjRs3AgBu3LgBc3NzmJub67jil8ffHqRV8v8DgvX19VG2bFn8+eefOHLkCL7//nvEx8fjwYMHSEtLQ5s2bRAZGYl27drpuuQiT09PD/fv38fBgwdx4cIFzJgxAwkJCcjKykJcXBzOnz+PXbt2oW7duqhbt66uyy1yTExMULZsWaxevRpjxoxBly5d0KdPH6xatQpjx47FtWvXlLYMVLlVrlwZtra2GDRoEDIyMtCyZUt069YNP/30Ezp37owTJ04ooaqkBap169Zh/Pjx+Oeff2BiYqJMYHn27Fmkp6fj/v37aN26NTp16qTM07d27Vr8+++/CAgIgLOzM/T03vz7xQwMDJCeng5/f3+cOnUKlSpVQvXq1dGvXz8EBARg+/bt+OCDD1ClShVdl/rK+BuEtEIdpp6eFdfd3R3nz5+Hh4cHPvjgA3zyySfo3Lkztm/fjoULFyI5ORlmZmY6rLr4mDx5Mo4fP44WLVrA09MT/v7+8PLywr59+xAQEIAHDx7AxsZG12XqnPr7MC4uDkZGRkhLS0OlSpWwceNG7N69Gzk5OejatSsMDAygUqlgaGgIPT095X0lnfo8REVF4cGDB7h79y569OiBAwcOYNWqVdDX10e3bt1gamoKAwMDODs7w8DAQLlLq6Sdw44dO6JVq1YwNTVFUlKS0sPy/vvvY+nSpcrdft988w1ycnIgIjh58iQyMzPx6NEjmJqa6vgItOvp74OkpCQ8evQItra28PT0xCeffIIjR46gdevWGDBgAOrWrYsHDx4gKCgIVlZWOq5ci3R02ZHeIOqBmYcOHZLAwEAZPHiwfPfdd5KZmSmPHz/ONej3008/lfbt20tqaqouyi3S1Ofy/PnzsnPnTjl06JDcuXNHWXf27FmN9mPHjpW2bdtyULr879zt3LlT3NzcxNnZWRo1aiQbNmzI1TYhIUEmTpwolpaWcvHixcIutUh6eoC1vb29NGnSRGxsbKRJkyby66+/arRNSkqSwMBAsbS0lEuXLumi3CIlKipKTExM5I8//hARkStXrkjbtm3F0dFRdu7cKSJPvuc+++wzsbGxeWO/59TfQyEhIdKmTRupVKmSvPvuuzJnzhyN9WrTpk2Tt956S27cuFHotb4uDFWkFdu2bZMyZcrI8OHDZcCAAVK/fn1p0aKFZGZmKm3OnTsn/v7+YmFhUaLvDnqW/94xWb9+fbGzs5MePXrI3r17Ndr+/vvvMmbMGDE3N+edak/ZuXOncpfab7/9JqNGjRKVSiU//vij0mbTpk3i5eUl1atXf+MHCBfUsWPHxNLSUnniwaVLl0SlUklQUJDSZs+ePdKxY0dxdHQs0edP/fN69uxZiYyMlA8//FAsLS3l6NGjIiLy119/SaNGjaRu3bpSrVo1ad++vVSqVOmNPGfp6enKv3fv3i2mpqYyf/58OXPmjPj5+YmBgYHs2rVLabN161b55JNPpFy5cm/c+WCoold2/fp1qVWrlixfvlxERGJiYsTa2lpGjBihtImMjJTBgwdL06ZNGaie47fffpNy5cop53Ljxo1iZmYmLVu2VH4p/f333/Lpp59K48aNeS6fcuPGDWnXrp0sXrxYRERu374tDg4OUr9+fVGpVPLdd9+JiEhaWpqsXLlS/vnnH12WWyR988030rVrVxF5EqjeeustGTx4sLI+LS1N4uLiZPny5cq8VCXZjh07pEqVKnL48GG5e/eu9O7dW0qXLq0Eq7///lt2794tU6ZMkS1btihTobxJbt68KbVq1ZLr16+LiEiPHj1kxowZIvKkd65SpUoycuRIjfcsX75cfHx8ck2t8CZgqKJXdvr0aXFycpLMzEy5fv262Nvby9ChQ5X16skqz549K7Gxsboqs8hLS0uTYcOGydixY0XkSVitVq2aeHl5SevWraVJkybKJKo3btxQJqukJ2JjY2XKlCkSFxcnt2/fFmdnZxk6dKj8+++/8uGHH4pKpVKe9UdPPH05JicnRz799FPp16+fZGdnS+XKlWXo0KFKm3Xr1inPrSvJ1OcjISFB+vfvrzFpZ3x8fK5g9aa7ceOGVKtWTXx8fCQrK0u8vLxk69atcvPmTalUqZLGZ8H27dvl+PHjIiKSkpKiq5JfK4YqKjD1LxX1OJ7Lly9L+/bt5dChQ0qgUs8UHBUVJUOGDMk1FojydubMGTl9+rQkJiaKq6urDBo0SEREtmzZIqamplKnTh0JDQ3VcZVFV3x8vIiITJ48WTp37iyJiYkiIjJhwgSxs7MTKysr+ffffznB7FP279+vzNd1+PBhqVatmpQpU0ajp1lE5JNPPpE+ffq8sR+GBXHkyBGpV6+etGjRQhlHpRYXFye9e/cWS0tL+f3333VUYeF5/PixzJkzR+rUqSNbt26V9957T4YMGaL0cqp/1u7fvy/9+vWTlStXvtE/f2/+vZykdSqVCkePHsXEiRORlZUFa2tr3LlzB23btoWHhwe++eYb5db0VatW4fz587wzLQ+SxxRxLi4ucHV1xf79+2FoaIjPP/8cAGBhYQEXFxe4ubmhTp06hV1qkaM+d5cvX8bhw4dx+PBhJCcno3z58sjMzMS5c+dQvnx55W6s9PR0fPXVV/jnn39gZWVV4u5Se5aMjAxs2bIFU6dOxe3bt1G7dm24u7ujQoUKcHNzAwDExcUhMDAQW7ZsweTJk1G6dGkdV617jRo1QkZGBo4dO4YrV65orKtQoQIWL16MFi1aoHfv3khPT9dRla/PgwcPlH/r6+vj448/hkqlws6dO/HZZ58hODgYZcuWxXfffaf8rC1YsADh4eHw8PB4s3/+dJ3qqHjavXu3qFQqCQkJERGRixcvirW1tbz//vsSGhoqBw4ckFGjRomZmRnH/eTh6Tsm582bJ4MGDZL9+/fL7du3RUQkODhY7O3t5dixYyIi8tlnn0lAQIA8ePBAVyUXGU8P6HdycpKaNWuKm5ubVK9eXXkMyNy5c8XIyEg+//xzGThwoFhbWxfbx15o2397CXbu3CktWrRQ7pIMDw+Xjz76SCwtLcXJyUkaN24sVatWfeMGFL+qR48eSe3ataVmzZp5Pg3i3r17cuvWLR1U9npdvXpV+V0fFxen3MX9559/ioGBgSxcuFA2b94sKpVKevToIQMGDJCPPvpIzM3NS8T3EEMV5Yv6F/GjR48kOztbRJ580NeoUUN5rMeJEyekTp064ujoKDVq1JCWLVvyzrTn2LZtm5iZmcmgQYPEy8tLXF1dpWfPnvLw4UP5888/pVWrVlKnTh155513pEyZMryE+pTff/9dypYtK998842IiPz666/KsyRFngxSHz9+vNSuXVvatWsnZ86c0WG1Rc+hQ4fk22+/Vb729/eXSpUqKR+QN2/elPDwcJk9e7b88ssvyiDkkkj9uy86Olr2798vp0+fVh6vkpqaKk5OTuLi4lKsH61SEJcvXxYLCwtRqVTi4eEhixYtknPnzomISEBAgDRu3FhOnDghR44ckZ49e8r7778vY8eOfWOnkfgvhirKt3379omnp6ds3rxZHj16JLdv35aOHTvKF198oYyzSElJkX/++Udu3LghSUlJOq646Lp8+bI4OTkpd6T9+++/YmxsLIGBgUqbgwcPypdffikBAQEl5hdSfi1evFiGDBkiIqLcHPH0GCD1Ld4JCQkcA/SU7OxsuX//vpiYmIhKpZLhw4dLRESEpKamiqenp3Tv3l3XJRYp6kC1detWsbW1FScnJ7G2thY3NzfZvHmziDwJVm+//bY0atRIGYT9plGfB/VY2cWLF0tAQIAEBgbKsGHDpHHjxrJnzx45fvy41KxZU6ZNmyYiT26+ERHlD/GSgKGKXignJ0cyMzNl6tSpyl8nnTt3lnv37smcOXOkXr16yu3pJemH51WcPHlS6tWrJzk5OXL58mWpUqWKEhJEntxRmddDqOmJMWPGSN++feXGjRu57lLbtm2bfPnll3k+eJqeWLlypdSuXVuaNWsmAwYMkOHDh8vixYvlww8/lO3bt+u6vCLlxIkTUrZsWVm+fLnExcVJWFiYDBkyRCpVqiRbt24VkSfhoUKFCtKqVSuNOZveFA8fPtT4+tChQ9KxY0fZvXu3pKWlydKlS8XCwkIWLFggnp6eYm5urjHs400emP5fHKhOz5SdnQ0AyuM83n//fdSqVQutWrVC3bp10aJFC9ja2uLGjRsYP348AJSI51i9Cvn/AdYPHz6Eqakp7ty5gw4dOsDDwwNBQUEAgPDwcPz444+4fv26LkstEuQZz3tv0KABbt++jaZNm8LT0xPffPMNgCffs/v378ft27fx+PHjwiy1yIuMjMS9e/eQk5MDb29veHh4oFevXnjvvfeQk5ODMWPG4NChQwgODn7meS9J1L//IiIi4OrqiuHDh6NChQpwd3fHuHHj4OHhgeXLl+Pu3bswNTXF9evX8eOPP8LY2FjHlWtXbGwsatWqhcDAQNy4cQMA0Lp1a7Ro0QL9+/dHQkIC/Pz8sHPnTkRFRcHAwADJycmYPHmyxmdIiaHjUEdFzC+//KIx6PLPP/+UUaNGKTOj//DDD1KpUiV58OCBbNiwQQYPHizOzs6iUqlk7dq1uiq7yHr8+HGef6VlZWVJ9erVRaVSyahRozTWjR07Vlq1aiX37t0rpCqLpsePHys9n5cvX5bz588rg80TEhKkZcuWYmZmJvv375fs7GxJSkqSSZMmvdGPAXlZqampUqFCBWnRooUsW7ZMsrKyZO3atdK+fXtJSEgQkScTMjo4OIiZmVmJ+95Tf589/bOq7nEKCgqSSpUqKTeRqIWEhIilpaVER0cXXqE68ODBA5kxY4aYm5tLu3btZOHChco6Hx8f8fHxUaYuiY2NlQMHDoiXl1eJHQPKUEUi8uSXybVr16Rs2bLSu3dvZWDvggULpHbt2lKtWjUJCwuT+Ph4+eyzz2Ts2LGSnZ0tf//9t6xatUocHBzkypUruj2IIkT9LCv1L+vff/9dpk+fLqtWrVLO7cGDB5XJPS9fviyHDx+WsWPHipmZWYn9hSQiMmvWLNm4caPy9datW6VcuXJSrVo1sbS0lFWrVonIk3lvatWqJXXr1pUqVapIhw4d3tjHgBRUXkH+zp078umnn8o777wjbdq0kVu3bknr1q2lZ8+eSpvIyMhc4aGkuHr1quzbt09ERDZv3iwtW7aU9PR0OXjwoFSvXl2+/fZbjWdsXr16VWrUqCGnTp3SVcmF6vz589K9e3epXr26tGnTRi5duiSbN28WHx8fCQsL02hbki73/RdDFWkICwuTt956S/r166f8BZaYmCj9+vWTGjVqyNChQ8Xf318++eQTjWvmb+I4gpe1ceNGadq0qTKT/C+//CImJibSvHlzsbOzE3d3d+Uhq3v27BEnJyextbWVGjVqSLNmzUr0nWoZGRkyZMgQZbqOR48eSbVq1eT777+XAwcOKOP65s2bJyJPvje3b98uM2fOlK1btyp3ZZVkefXwqR8HkpaWJkePHpVOnTpJ1apVxdfXVxwcHGTLli26LLlI6N27txgYGMhnn30m+vr6SngXEfHz8xMbGxtZsWKF/P3335Kamirjx4+Xt956q0Q92eDff/+VXbt2iaurq1SrVk0mTpwoDRs21Jg1vaRjqCIRedKjor6zY+/eveLg4CADBw7U+IBfvXq1+Pr6Svny5UWlUomXl5eOqi3aQkJCpEOHDtKxY0fZs2ePBAQEKLevHz58WHr37i2NGjWSHTt2iMiTD8Hw8HCJiYmRf//9V5elFwlJSUkyduxY0dfXl6VLl4q/v7/yl29OTo4sWLBAVCqVzJ8/X8eVFi0v6uFTPyRZbf78+dK+fXtRqVTSr1+/Ejmwf8uWLRqX7xo1aiT6+voyevToXG0//fRTqVmzplhaWkqTJk2kQoUKJbpX1N/fXzp27CiVKlXSeLZmScdQRSLyv+7akJAQ+eKLL8TJyUn09fWle/fuGvOv3Lx5U7Zt2yZlypSRsmXLlqi/0gpi9+7d0qlTJ+nUqZO88847Gpfzjh8/Ln369JFGjRopdw+VZE9fKlD3sDx8+FACAgJEpVJJw4YNNe4qzcnJkfnz54uxsbHMmjWr0OstivLbw7do0SKN90VFRcn8+fNL3Bi0nJwciYyMlLfffltu3rypLGvQoIHUqVNHbGxsJDQ0VBlLqnbixAn56aefZNOmTSW2V/Tpn9eDBw/KhAkTpGzZsiXue+hZGKpKuKd/QPbv3y8GBgYSFBQkO3bskNWrV4u5ubn06tUr1yWpK1eulOgJAZ/29Af+09MgHDt2TNzd3cXU1DTX8/pOnDgh/fv3l+rVqyuXAksi9fffvXv3lOf2hYaGytmzZyUxMVGmTJkienp6smnTJo32IiIzZ84UKysrZaB1SZffHr7FixdrvK8kj39RD7A+e/as3L9/X1nu5eUl5cuXzxWs1JOjlnT//Z7hnIT/w1BVQv3+++/K5T61UaNGiaenp8ay3377TcqWLSvdu3cv0WN9XuTq1avKXZObN2+WDz74QESejFFr06aNtGzZUg4fPqzxnqNHj8qQIUOUOb5Kqvj4eGnbtq3MmDFDfvjhB1GpVLJt2zYRefKhFxAQIPr6+vLzzz+LiOYv9JJ8ufRVevjUM8+XVE/flZuYmCgVKlSQ/v37a/Qoe3l5iY2NjezatUvS09Nl5syZ0rZtW8nKyirRQZSej6GqBFq7dq20a9dO4y8zkSdjBjw8PETkyS8d9V9oK1euFFNTU+nRowcfO5OH7Oxs6dOnjxgbG8tXX30lKpVKY/zK3r17pVOnTuLh4SFHjhzReO+jR48Ku9wiKTAwUN566y3R19eXZcuWicj/QkNSUpISrNTPmizp2MOnHdu3b5dLly5JSEiIODg4yPDhw5VHroiIvP/++2JlZSXNmzcXc3NzOXHihA6rpeKAoaoEUf/VmpycrNw2ff36daXHasOGDaJSqZQPfvWlrHXr1kmdOnXExcWlxN5unR+NGjUSAwMDmTp1qohoXhZUj7Hq3Lmz7N+/X1clFjnq77EzZ86IhYWF2Nvby5dffqkEhaeD1dixY0WlUsmuXbt0Vm9Rwh6+V3Pq1CnR09OTFStWiMiTHubKlSvnClYrV66UxYsXv/HzUZF2MFSVEOoP+KtXryofShcuXJCGDRvK/PnzlV6p/v37i7m5uTIdgIjIpEmTZP78+RpztNAT6vP6559/iqWlpdSrV08qVqwo4eHhIqL5QbZnzx5p1qyZdO3aVXkmFj0JB2lpaXLy5EmZMmWKuLq6yuTJk5Vgpfbw4UOZNGmSMj0AsYfvZV24cEFmzpwpM2bM0Fj+dLCKiorSUXVUnDFUlSC3b98Wa2trqVWrlmzatEkyMjKkV69e0rx5c1m+fLlkZ2dLbGysDBgwQPT09KR58+bStGlTKVWqlMacVKRpx44d4urqqoTVd999V2xtbfMMVhERERzg/5SzZ89K/fr15aefflKWjR8/XlxdXWXatGlKsFq6dKlG70FJxx6+gsnJyVHOSXR0tHh6ekr58uXl888/FxGRzMxMZf3mzZvFwcFBPvroI/ZOUYEZ6PoxOVR4Ll++jISEBDg6OmLt2rUwNTXFmjVrMGzYMKxatQp6enoYMmQIVq1ahS5duiAqKgoigjVr1qBGjRq6Lr9IERGoVCrExcVh8+bN8PHxgZeXFwBgy5Yt6NGjB7p27Ypt27bBzc0Ns2bNwunTp7Fx40bo6+vruPqiIzMzE9WrV8fixYuRk5ODPn36YM6cOVCpVNi9ezcuXryIcuXKISgoCOfOndN1uUWGvr4+7t27hxo1aiAsLAw7duzA1q1b8ejRI3z66acoX748AMDMzAzTpk2DoaEhqlWrpuOqdUf97Ll9+/YhNjYWbm5u+Oeff7Bjxw4MHz4c1tbWePz4MQwMDPDhhx8iMzMTs2bNgpmZmY4rp2JH16mOCtegQYPExcVFunXrJq1atZJdu3ZJRkaGDBw4UBo1aiTLli3LNTcL5e2PP/6QTp06SdOmTZVeKXUPQkZGhnTt2lUMDAykffv2YmpqqjHfF/3P6dOnpX///tKoUSNZv369snzu3LnSs2dPad26NXtK/4M9fAV38uRJZR6vrKwsWb58ubi6usqgQYOU8/X0HdEc7kAvgz1Vb6icnBzo6ekpX2dkZMDY2BjdunVDTk4OevfujW+++QZfffUVVCoVgoKCMHz4cAQHB+PRo0fw9/eHgQG/PZ7H1tYWMTExiI6OxpkzZ9CsWTPo6+sjJycHRkZG2LZtG5YuXYq0tDQsX76cvX3/79SpU0hKSkL79u0BAK6urhg1ahSWLFmC+fPnw8jICB9++CHGjRuHjIwMZGdno1SpUjquumhhD1/BnD17FteuXUNgYCDef/99AMAnn3yCjIwMbN26FZMmTcKsWbNQvnx5pceqTJkyOq6aiiVdpzrSPvXg6Rs3bih3/qjFx8dLzZo1ZdmyZRIfHy9du3aVd955R0JDQyUjI0M+/PBDjSfX0/Ndu3ZNXF1dpUWLFhp39T39Fy/ntPmff//9Vzp16iRubm5y8OBBjXWnTp2SunXrSu3atTV6rChv7OHLn5SUFKlYsaKoVCrp379/rvULFiyQVq1aSY8ePXJNM0NUUAxVb6gbN25IuXLlRKVSSefOnWXTpk3KoMsdO3ZIy5YtJT4+Xi5cuCBdu3aVNm3ayM8//yyZmZly584dHVdf9KiD0aVLlyQsLExOnjypPN7i8uXLUrduXfHw8NAICk9PqVDSPR0sd+7cKe+99564u7vnml5i4MCBUqlSJenQoQNnaf6PkydPym+//aaxLCIiQnx8fMTV1VU2b96sLE9PT+fs3085d+6c1K1bV+rUqSMxMTEiovk9+eWXX4qnpyd/99ErU4mI6Lq3jLTv+vXr6N69OwwNDZGRkYEGDRogLCwMn332GSwsLLBu3Tp88skn6NSpEy5cuIBRo0bB0NAQW7ZsQenSpXVdfpEi/z8ofdu2bcp5EhGYmJjg22+/RatWrXD58mV0794d9vb2GDVqFDw8PHRddpGgPncZGRnQ19dXLikfOHAA8+fPR1ZWFiZNmoS2bdsCAAICAlClShX069dPGWxNQEJCAvr164fExER89dVXaNOmjbIuIiICAwcORE5ODiZNmoS+ffvqrlAdkycdBdDT08OjR49gZGSEx48fw9jYGFFRUfDw8EC9evWwYcMGlCtXTvn+BJ6cYysrKx0fARV3DFVvsCtXrmDixInIyclB//79oVKpsHjxYlhYWOCXX35BkyZNcOTIERgZGSE6OhqlS5dG5cqVdV22zj09Hk09vuLEiRNwd3fHvHnz0KVLF1y9ehXff/89tm7din379qFly5a4evUq2rVrh8aNG2PdunUlfhyQ+gNrz549WLFiBZKTk2FpaYkZM2agfv36+P333zFv3jzcuHEDbdu2RXp6OrZv346TJ0/C3t5e1+UXCU9/6O/atQvfffcd0tLSMGnSJLRr105pN2jQIOzbtw+1atXC1q1bS9Rda+qf16ysLBgaGgIA9uzZg3Xr1uHKlSto0qQJOnfuDC8vL0RFRcHT0xP16tXD+vXrcwUrolemk/4xKjSXLl1SHpESHR0tKSkpEh4eLl26dJF169aJCMf85OXatWvKeXn8+LF8//330rZtW41Lenfv3pU+ffqIq6ur3L17V0REYmJi5O+//9ZJzUWJ+tzt3LlTjIyMJCAgQKZNmybNmjWTKlWqyMaNG0XkySWtwMBA5fIpny/5hPr8paena4zP279/v3Tu3Fk6dOggBw4cUJb7+/vLggULck2Y+qZT/zxGRUUpE3mGhISIiYmJzJw5U1auXCk9e/YUPT09ZdLYqKgoqVKliri5uXFmedI6hqoS4PLly+Lh4SEeHh7yxx9/6LqcIi89PV2aNWsmDg4OyofbggULxNLSUh48eCAi//vQ27Vrl9jb25f4Wb7VH25Ph87k5GRp06aNBAYGarTt3bu3xjnLycmRtLQ0SUlJKbyCizD199bu3bulS5cu0qpVK3n//feV524eOXJE3n33Xalfv774+/vLsGHDxMbGRm7cuKHLsgud+nstMjJSVCqVfPXVV5Kamiru7u6ycOFCEXlyY46dnZ2MGDFC472RkZHi7OzMiXhJ6/Re3JdFxZ2TkxOWLVsGPT09fPHFF/jjjz90XVKRZmRkhHnz5qFMmTJo0KABRATvv/8+KlasiFWrViExMVG5XODk5ARDQ0M8fPhQx1Xrjvryy7Vr1/D999/j1KlTAABDQ0MkJibCzs4OwJNpPQAgODgY5cuXx1dffQXgySUuU1NTjuXD/y737dq1C97e3nByckLbtm0RFxeH9957D5s2bULLli0xdepUdOnSBfv378c///yDvXv3lqhLpurvuQsXLsDNzQ1Tp07FpEmT8OjRI8TExKBVq1a4c+cOXF1d4eXlhWXLlgEAtm3bhitXrqB+/fqIjIxElSpVdHwk9MbRcaijQnT58mXp0qWLNGvWTJmskvK+Sy87O1vCw8OlRo0a0rhxYxER5TLV3LlzJTY2Vh4+fCgTJkyQ6tWrS1xcXGGXXSSoz93Zs2fl7bfflg8++EBCQ0OV9c2bN5f3339f+To9PV1ERPz8/MTb27tQay2K2MNXcOpzde7cObG2thZnZ2dl3f3796VLly7y3XffiYODgwwZMkSZkPfmzZsycOBACQkJ4ZAHem3YU1WCODk5Yd68eahcubLSe1DSqf/ijY2NxZ9//qks19PTQ8OGDbF27Vrcv38frVu3xsyZM+Ht7Y21a9fCwcEBHTp0wI8//ojNmzejQoUKOjwK3dHT08OlS5fQunVrdO3aFcuWLUPnzp2V9YGBgYiKikJAQAAAwNjYGMCTO63Kli2L7OxsSAm9V4Y9fAWnPmd//fUXmjZtijp16iApKQmjRo0CAJQrVw6VK1fG0KFD4erqim+++UZ5LNTy5ctx/PhxNGjQgAPT6bXh3X8lUGZmJoyMjHRdRpFx8+ZNuLq6IiEhAa1bt4abmxvc3d3RqFEjmJmZ4eTJk/D19YWZmRn++OMPxMbGYvfu3bC0tESDBg1QtWpVXR+CzqSnp6N///6oUKGCcokFALKyspCQkIBr167h2LFjWLNmDaytrdG6dWvExMRg8+bNOH78OGrXrq3D6nVHHQ7OnTuH7t27o3bt2hg8eLASSFu0aIHy5csjJCQEwP+eiDBy5EjcunUL27dv12H1unXq1Ck0b94cgYGBmDx5Mn744QcEBgaiZ8+eyvdg9+7dcfjwYYwePRoGBga4evUqfvrpJ/z++++oX7++jo+A3mTsqSqBGKg05eTkwN7eHm+//TZSUlJw584deHl5oXXr1ujfvz9iYmIwZcoUxMbGwsPDAzY2Nhg0aBA++OCDEh2oAMDAwACxsbGoWbOmsuzXX3/F+PHjUb16dfj6+iI4OBjz5s2Dubk5fvvtNzx48ADh4eElNlAB7OF7FWlpaRg+fDimTZsGfX199OzZE19++SU2bdoEPz8/AMDWrVvRs2dPhIWF4aeffsKjR49w7NgxBip67dhTRQTg6tWrGD9+vDKBYsWKFXHs2DEsW7YMWVlZiIqKwltvvYWoqCi8//772L59O+e3AZCcnIymTZuiZcuWGDNmDH7++WesWbMGderUQcuWLVGmTBl8/fXX6N69O7744guICLKyskp8sGcPn3aofwaTk5OxceNGBAYGolevXli6dCkAIDExESYmJtDT0yvx33NUOBiqiP5fdHQ0Ro0ahZycHHz55Zdo3LgxgCe/mHfu3IlLly5hz549+OGHH+Dq6qrjaouOAwcOwNPTE5UqVUJCQgLmzZuH9u3bo3r16sjKykKXLl1Qvnx5rF+/XtelFhmPHz9Gu3bt0KNHD6V35ddff8XevXvx/fffo2rVqjA1NcVXX32FoKAg3L9/H1ZWVvj8889Rt25dHVdfND0drPr06YPFixfruiQqgRiqiJ5y5coVjBw5EgAwadIktG7dWmO9eoZ10nTz5k3Ex8ejatWqsLa2Vpbn5OSgV69eqFGjBj7//HMAKPG9ewB7+F6X5ORkbN68GUOHDsWECRMwa9YsXZdEJQxDFdF/XLlyBZ9++ilEBFOnTkXz5s11XVKxlJmZiS+++AI//vgjDh06BCcnJ12XVKSwh+/1SEpKQkhICNzc3PD222/ruhwqYRiqiPJw5coVjB49Gvfv38fChQvRrFkzXZdUrKxfvx4nT57Epk2bsGfPHl4ufQb28L0eHO9IusK7/4jywDm9Xl50dDR++OEH3Lx5EwcPHmSgeg57e3s0bNhQI1BlZmZi2rRpOHr0qPIgdAaEguH5Il1hTxXRc3BOr5cTHx8PY2NjmJub67qUYoU9fETFG0fcEj0HA9XLKakzzL8KdQ+fpaUlDh48CGdnZ12XREQFxJ4qIqIigj18RMUbQxURERGRFnCgOhEREZEWMFQRERERaQFDFREREZEWMFQRERERaQFDFREREZEWMFQRERERaQFDFREREZEWMFQREb0Gq1evhoWFha7LIKJCxFBFRCXOgAEDlAcVGxoawtHREePHj0d6errW9tGzZ09cvnxZa9sjoqKPz/4johKpY8eOWLVqFbKyshAREQEfHx+oVCrMmTNHK9s3NTWFqampVrZFRMUDe6qIqEQyNjaGra0t7O3t4e3tDXd3d4SFhQEAcnJyMGvWLDg6OsLU1BT169fH1q1bNd6/Y8cOODk5wcTEBG3btsWaNWugUqmQmJgIIO/LfytXrsRbb70FIyMj1KhRA+vWrdNYr1Kp8P333+ODDz5AqVKl4OTkhB07dry2c0BE2sVQRUQlXlRUFI4dOwYjIyMAwKxZs7B27VoEBQXh/PnzCAgIQL9+/XD48GEAQExMDLp37w5vb2/89ddf+PjjjxEYGPjcfWzfvh2jRo3CmDFjEBUVhY8//hgDBw7EwYMHNdrNmDEDPXr0wNmzZ9G5c2f07dsXCQkJr+fAiUi7hIiohPHx8RF9fX0pXbq0GBsbCwDR09OTrVu3Snp6upQqVUqOHTum8R5fX1/p3bu3iIhMmDBB6tSpo7E+MDBQAMiDBw9ERGTVqlVibm6urG/evLkMGTJE4z0ffvihdO7cWfkagEyePFn5OiUlRQDInj17tHHYRPSacUwVEZVIbdu2xcqVK5GamoqFCxfCwMAA3bp1w/nz55GWloYOHTpotM/MzISrqysAIDo6Go0bN9ZY36RJk+fu7+LFixg6dKjGshYtWmDx4sUay+rVq6f8u3Tp0jAzM0N8fHyBj4+ICh9DFRGVSKVLl0b16tUBAD/++CPq16+PH374AXXq1AEAhIaGolKlShrvMTY2fu11GRoaanytUqmQk5Pz2vdLRK+OoYqISjw9PT189tlnGD16NC5fvgxjY2PcuHEDrVu3zrN9jRo1sHv3bo1lJ0+efO4+nJ2dcfToUfj4+CjLjh49ilq1ar36ARBRkcBQRUQE4MMPP8S4cePwzTffYOzYsQgICEBOTg7eeecdJCUl4ejRozAzM4OPjw8+/vhjLFiwABMmTICvry8iIyOxevVqAE96lvIybtw49OjRA66urnB3d8fOnTvx888/47fffivEoySi14mhiogIgIGBAfz8/DB37lzExMSgfPnymDVrFv755x9YWFigQYMG+OyzzwAAjo6O2Lp1K8aMGYPFixfDzc0NgYGBGD58+DMvEXp7e2Px4sX4+uuvMWrUKDg6OmLVqlVo06ZNIR4lEb1OKhERXRdBRFTcffnllwgKCsLNmzd1XQoR6Qh7qoiIXsKKFSvQuHFjlCtXDkePHsW8efPg5+en67KISIcYqoiIXsKVK1cwc+ZMJCQkoEqVKhgzZgwmTZqk67KISId4+Y+IiIhIC/iYGiIiIiItYKgiIiIi0gKGKiIiIiItYKgiIiIi0gKGKiIiIiItYKgiIiIi0gKGKiIiIiItYKgiIiIi0oL/AwD40UVp9m/5AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Between 10001 and 20k\n",
    "num_10k_to_20k = dataowner['femaInspectedDamageBetween10001And20000'].sum()\n",
    "print(\"Total Counts of Inspected Damage Between 10k and 20k:\", num_10k_to_20k)\n",
    "# By Region\n",
    "num_10k_to_20k_region = dataowner.groupby('Region')['femaInspectedDamageBetween10001And20000'].sum().reset_index()\n",
    "num_10k_to_20k_region = num_10k_to_20k_region.rename(columns={'femaInspectedDamageBetween10001And20000': 'Sum of Counts'})\n",
    "print(num_10k_to_20k_region)\n",
    "\n",
    "# Between 20k and 30k\n",
    "num_20k_to_30k = dataowner['femaInspectedDamageBetween20001And30000'].sum()\n",
    "print(\"Total Counts of Inspected Damage Between 20k and 30k:\", num_20k_to_30k)\n",
    "# By Region\n",
    "num_20k_to_30k_region = dataowner.groupby('Region')['femaInspectedDamageBetween20001And30000'].sum().reset_index()\n",
    "num_20k_to_30k_region = num_20k_to_30k_region.rename(columns={'femaInspectedDamageBetween20001And30000': 'Sum of Counts'})\n",
    "print(num_20k_to_30k_region)\n",
    "\n",
    "\n",
    "# Create the bar plot\n",
    "plt.bar(x, num_10k_to_20k_region['Sum of Counts'], width=0.35, label='10k to 20k')\n",
    "plt.bar(x + 0.35, num_20k_to_30k_region['Sum of Counts'], width=0.35, label='20k to 30k')\n",
    "\n",
    "plt.xlabel('Region')\n",
    "plt.ylabel('Sum of Counts')\n",
    "plt.title('10k_to_20k VS 20k_to_30k')\n",
    "plt.xticks(x + 0.35 / 2, num_10k_to_20k_region['Region'], rotation=45)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Geographical Map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60411, 26)\n",
      "(15454, 26)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>disasterNumber</th>\n",
       "      <th>state</th>\n",
       "      <th>county</th>\n",
       "      <th>city</th>\n",
       "      <th>zipCode</th>\n",
       "      <th>validRegistrations</th>\n",
       "      <th>averageFemaInspectedDamage</th>\n",
       "      <th>totalInspected</th>\n",
       "      <th>totalDamage</th>\n",
       "      <th>noFemaInspectedDamage</th>\n",
       "      <th>...</th>\n",
       "      <th>repairReplaceAmount</th>\n",
       "      <th>rentalAmount</th>\n",
       "      <th>otherNeedsAmount</th>\n",
       "      <th>approvedBetween1And10000</th>\n",
       "      <th>approvedBetween10001And25000</th>\n",
       "      <th>approvedBetween25001AndMax</th>\n",
       "      <th>totalMaxGrants</th>\n",
       "      <th>id</th>\n",
       "      <th>Region</th>\n",
       "      <th>incidentType</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15535</th>\n",
       "      <td>1539</td>\n",
       "      <td>FL</td>\n",
       "      <td>Brevard (County)</td>\n",
       "      <td>BAREFOOT BAY</td>\n",
       "      <td>32976</td>\n",
       "      <td>5</td>\n",
       "      <td>585.34</td>\n",
       "      <td>2</td>\n",
       "      <td>2926.72</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1094746c-26d2-4c39-b9f2-bd6b35c915ff</td>\n",
       "      <td>Southeast</td>\n",
       "      <td>Hurricane</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15536</th>\n",
       "      <td>1539</td>\n",
       "      <td>FL</td>\n",
       "      <td>Brevard (County)</td>\n",
       "      <td>CAPE CANAVERAL</td>\n",
       "      <td>32920</td>\n",
       "      <td>13</td>\n",
       "      <td>220.75</td>\n",
       "      <td>5</td>\n",
       "      <td>2869.77</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1935.92</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>eead5d6b-6684-4aac-b4bf-83af27f83ebc</td>\n",
       "      <td>Southeast</td>\n",
       "      <td>Hurricane</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15537</th>\n",
       "      <td>1539</td>\n",
       "      <td>FL</td>\n",
       "      <td>Brevard (County)</td>\n",
       "      <td>COCOA</td>\n",
       "      <td>32922</td>\n",
       "      <td>80</td>\n",
       "      <td>1070.47</td>\n",
       "      <td>49</td>\n",
       "      <td>85637.44</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>35072.43</td>\n",
       "      <td>8760.0</td>\n",
       "      <td>8234.76</td>\n",
       "      <td>21</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>e2feda04-9ac1-4f42-b3c4-b62c6dbe58ff</td>\n",
       "      <td>Southeast</td>\n",
       "      <td>Hurricane</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15538</th>\n",
       "      <td>1539</td>\n",
       "      <td>FL</td>\n",
       "      <td>Brevard (County)</td>\n",
       "      <td>COCOA</td>\n",
       "      <td>32926</td>\n",
       "      <td>158</td>\n",
       "      <td>1104.62</td>\n",
       "      <td>118</td>\n",
       "      <td>174530.73</td>\n",
       "      <td>25</td>\n",
       "      <td>...</td>\n",
       "      <td>63048.96</td>\n",
       "      <td>17924.0</td>\n",
       "      <td>44799.15</td>\n",
       "      <td>62</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>f177c73a-898b-4773-b270-2d468206ada1</td>\n",
       "      <td>Southeast</td>\n",
       "      <td>Hurricane</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15539</th>\n",
       "      <td>1539</td>\n",
       "      <td>FL</td>\n",
       "      <td>Brevard (County)</td>\n",
       "      <td>COCOA</td>\n",
       "      <td>32927</td>\n",
       "      <td>209</td>\n",
       "      <td>748.53</td>\n",
       "      <td>141</td>\n",
       "      <td>156442.37</td>\n",
       "      <td>40</td>\n",
       "      <td>...</td>\n",
       "      <td>37095.38</td>\n",
       "      <td>6380.0</td>\n",
       "      <td>56814.86</td>\n",
       "      <td>78</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>a6f6c0dc-c81e-473d-94ac-546b8966c9f9</td>\n",
       "      <td>Southeast</td>\n",
       "      <td>Hurricane</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       disasterNumber state            county            city zipCode  \\\n",
       "15535            1539    FL  Brevard (County)    BAREFOOT BAY   32976   \n",
       "15536            1539    FL  Brevard (County)  CAPE CANAVERAL   32920   \n",
       "15537            1539    FL  Brevard (County)           COCOA   32922   \n",
       "15538            1539    FL  Brevard (County)           COCOA   32926   \n",
       "15539            1539    FL  Brevard (County)           COCOA   32927   \n",
       "\n",
       "       validRegistrations  averageFemaInspectedDamage  totalInspected  \\\n",
       "15535                   5                      585.34               2   \n",
       "15536                  13                      220.75               5   \n",
       "15537                  80                     1070.47              49   \n",
       "15538                 158                     1104.62             118   \n",
       "15539                 209                      748.53             141   \n",
       "\n",
       "       totalDamage  noFemaInspectedDamage  ...  repairReplaceAmount  \\\n",
       "15535      2926.72                      1  ...                 0.00   \n",
       "15536      2869.77                      2  ...                 0.00   \n",
       "15537     85637.44                     11  ...             35072.43   \n",
       "15538    174530.73                     25  ...             63048.96   \n",
       "15539    156442.37                     40  ...             37095.38   \n",
       "\n",
       "       rentalAmount  otherNeedsAmount  approvedBetween1And10000  \\\n",
       "15535           0.0              0.00                         0   \n",
       "15536           0.0           1935.92                         2   \n",
       "15537        8760.0           8234.76                        21   \n",
       "15538       17924.0          44799.15                        62   \n",
       "15539        6380.0          56814.86                        78   \n",
       "\n",
       "       approvedBetween10001And25000  approvedBetween25001AndMax  \\\n",
       "15535                             0                           0   \n",
       "15536                             0                           0   \n",
       "15537                             4                           1   \n",
       "15538                            11                           1   \n",
       "15539                             5                           0   \n",
       "\n",
       "       totalMaxGrants                                    id     Region  \\\n",
       "15535               0  1094746c-26d2-4c39-b9f2-bd6b35c915ff  Southeast   \n",
       "15536               0  eead5d6b-6684-4aac-b4bf-83af27f83ebc  Southeast   \n",
       "15537               0  e2feda04-9ac1-4f42-b3c4-b62c6dbe58ff  Southeast   \n",
       "15538               0  f177c73a-898b-4773-b270-2d468206ada1  Southeast   \n",
       "15539               0  a6f6c0dc-c81e-473d-94ac-546b8966c9f9  Southeast   \n",
       "\n",
       "       incidentType  \n",
       "15535     Hurricane  \n",
       "15536     Hurricane  \n",
       "15537     Hurricane  \n",
       "15538     Hurricane  \n",
       "15539     Hurricane  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Only Hurricane\n",
    "Hurricane_data = merged_data[merged_data[\"incidentType\"] == 'Hurricane']\n",
    "print(Hurricane_data.shape)\n",
    "\n",
    "# Hurricane_data['state'].value_counts() \n",
    "# FL = 15454\n",
    "\n",
    "# Hurricane in florida\n",
    "hurricane_FL = Hurricane_data[Hurricane_data['state'] == \"FL\"]\n",
    "print(hurricane_FL.shape)\n",
    "hurricane_FL.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(15356, 26)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\simpson xu\\AppData\\Local\\Temp\\ipykernel_6296\\1121054502.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  hurricane_FL['zipCode'] = hurricane_FL['zipCode'].astype(str)\n"
     ]
    }
   ],
   "source": [
    "# Convert all values in 'zipCode' column to string\n",
    "hurricane_FL['zipCode'] = hurricane_FL['zipCode'].astype(str)\n",
    "\n",
    "# Check if values are five digits starting with '3'\n",
    "filter_1 = hurricane_FL['zipCode'].str.match('^3\\d{4}$')\n",
    "\n",
    "# Remove rows that do not match the criteria\n",
    "hurricane_FL = hurricane_FL[filter_1].reset_index(drop=True)\n",
    "\n",
    "print(hurricane_FL.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>disasterNumber</th>\n",
       "      <th>state</th>\n",
       "      <th>county</th>\n",
       "      <th>city</th>\n",
       "      <th>zipCode</th>\n",
       "      <th>validRegistrations</th>\n",
       "      <th>averageFemaInspectedDamage</th>\n",
       "      <th>totalInspected</th>\n",
       "      <th>totalDamage</th>\n",
       "      <th>noFemaInspectedDamage</th>\n",
       "      <th>...</th>\n",
       "      <th>repairReplaceAmount</th>\n",
       "      <th>rentalAmount</th>\n",
       "      <th>otherNeedsAmount</th>\n",
       "      <th>approvedBetween1And10000</th>\n",
       "      <th>approvedBetween10001And25000</th>\n",
       "      <th>approvedBetween25001AndMax</th>\n",
       "      <th>totalMaxGrants</th>\n",
       "      <th>id</th>\n",
       "      <th>Region</th>\n",
       "      <th>incidentType</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1539</td>\n",
       "      <td>FL</td>\n",
       "      <td>Brevard (County)</td>\n",
       "      <td>BAREFOOT BAY</td>\n",
       "      <td>32976</td>\n",
       "      <td>5</td>\n",
       "      <td>585.34</td>\n",
       "      <td>2</td>\n",
       "      <td>2926.72</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1094746c-26d2-4c39-b9f2-bd6b35c915ff</td>\n",
       "      <td>Southeast</td>\n",
       "      <td>Hurricane</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1539</td>\n",
       "      <td>FL</td>\n",
       "      <td>Brevard (County)</td>\n",
       "      <td>CAPE CANAVERAL</td>\n",
       "      <td>32920</td>\n",
       "      <td>13</td>\n",
       "      <td>220.75</td>\n",
       "      <td>5</td>\n",
       "      <td>2869.77</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1935.92</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>eead5d6b-6684-4aac-b4bf-83af27f83ebc</td>\n",
       "      <td>Southeast</td>\n",
       "      <td>Hurricane</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1539</td>\n",
       "      <td>FL</td>\n",
       "      <td>Brevard (County)</td>\n",
       "      <td>COCOA</td>\n",
       "      <td>32922</td>\n",
       "      <td>80</td>\n",
       "      <td>1070.47</td>\n",
       "      <td>49</td>\n",
       "      <td>85637.44</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>35072.43</td>\n",
       "      <td>8760.0</td>\n",
       "      <td>8234.76</td>\n",
       "      <td>21</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>e2feda04-9ac1-4f42-b3c4-b62c6dbe58ff</td>\n",
       "      <td>Southeast</td>\n",
       "      <td>Hurricane</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   disasterNumber state            county            city zipCode  \\\n",
       "0            1539    FL  Brevard (County)    BAREFOOT BAY   32976   \n",
       "1            1539    FL  Brevard (County)  CAPE CANAVERAL   32920   \n",
       "2            1539    FL  Brevard (County)           COCOA   32922   \n",
       "\n",
       "   validRegistrations  averageFemaInspectedDamage  totalInspected  \\\n",
       "0                   5                      585.34               2   \n",
       "1                  13                      220.75               5   \n",
       "2                  80                     1070.47              49   \n",
       "\n",
       "   totalDamage  noFemaInspectedDamage  ...  repairReplaceAmount  rentalAmount  \\\n",
       "0      2926.72                      1  ...                 0.00           0.0   \n",
       "1      2869.77                      2  ...                 0.00           0.0   \n",
       "2     85637.44                     11  ...             35072.43        8760.0   \n",
       "\n",
       "   otherNeedsAmount  approvedBetween1And10000  approvedBetween10001And25000  \\\n",
       "0              0.00                         0                             0   \n",
       "1           1935.92                         2                             0   \n",
       "2           8234.76                        21                             4   \n",
       "\n",
       "   approvedBetween25001AndMax  totalMaxGrants  \\\n",
       "0                           0               0   \n",
       "1                           0               0   \n",
       "2                           1               0   \n",
       "\n",
       "                                     id     Region  incidentType  \n",
       "0  1094746c-26d2-4c39-b9f2-bd6b35c915ff  Southeast     Hurricane  \n",
       "1  eead5d6b-6684-4aac-b4bf-83af27f83ebc  Southeast     Hurricane  \n",
       "2  e2feda04-9ac1-4f42-b3c4-b62c6dbe58ff  Southeast     Hurricane  \n",
       "\n",
       "[3 rows x 26 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hurricane_FL.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        zip  frequency\n",
      "0     33952      17468\n",
      "1     33023      17462\n",
      "2     33908      16836\n",
      "3     32738      16318\n",
      "4     33463      15377\n",
      "...     ...        ...\n",
      "1592  33388          1\n",
      "1593  33384          1\n",
      "1594  33366          1\n",
      "1595  33361          1\n",
      "1596  39993          1\n",
      "\n",
      "[1597 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "# Group by 'zipCode'\n",
    "# Calculate the sum of 'validRegistrations'\n",
    "aggregated = hurricane_FL.groupby('zipCode')['validRegistrations'].sum().reset_index()\n",
    "\n",
    "# Rename the columns to 'zip' and 'frequency'\n",
    "frequency_df = aggregated.rename(columns={'zipCode': 'zip', 'validRegistrations': 'frequency'})\n",
    "frequency_df = frequency_df.sort_values(by='frequency', ascending=False)\n",
    "\n",
    "# Reset the index\n",
    "frequency_df = frequency_df.reset_index(drop=True)\n",
    "\n",
    "print(frequency_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# geolocator = GoogleV3(api_key='YOUR_OWN')\n",
    "\n",
    "# def get_lat_lon(zip_code):\n",
    "#     location = geolocator.geocode(f\"{zip_code}, FL\", timeout=10)\n",
    "#     if location:\n",
    "#         return location.latitude, location.longitude\n",
    "#     else:\n",
    "#         return None, None\n",
    "\n",
    "# frequency_df['latitude'], frequency_df['longitude'] = zip(*frequency_df['zip'].apply(get_lat_lon))\n",
    "\n",
    "# print(frequency_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# frequency_df = frequency_df.dropna()\n",
    "# frequency_df.to_csv('frequency.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1590, 3)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# frequency_df.drop('zip', axis=1, inplace=True)\n",
    "frequency_df = pd.read_csv('frequency.csv')\n",
    "frequency_df = frequency_df[[\"latitude\", \"longitude\", \"frequency\"]]\n",
    "frequency_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       latitude  longitude  frequency\n",
      "0     26.986990 -82.095180   1.000000\n",
      "1     25.986874 -80.215400   0.999656\n",
      "2     26.510137 -81.942570   0.963817\n",
      "3     28.907532 -81.174750   0.934162\n",
      "4     26.606400 -80.131693   0.880289\n",
      "...         ...        ...        ...\n",
      "1585  26.117690 -80.254789   0.000000\n",
      "1586  27.664827 -81.515754   0.000000\n",
      "1587  27.664827 -81.515754   0.000000\n",
      "1588  27.664827 -81.515754   0.000000\n",
      "1589  27.664827 -81.515754   0.000000\n",
      "\n",
      "[1590 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "# Rescaling the 'frequency' column\n",
    "minColorVal = frequency_df['frequency'].min()\n",
    "maxColorVal = frequency_df['frequency'].max()\n",
    "\n",
    "frequency_df['frequency'] = (frequency_df['frequency'] - minColorVal) / (maxColorVal - minColorVal)\n",
    "\n",
    "print(frequency_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "colorGradient = {\n",
    "    0.0: 'blue',\n",
    "    0.1: 'cyan',\n",
    "    0.2: 'lightgreen',\n",
    "    0.3: 'green',\n",
    "    0.4: 'yellowgreen',\n",
    "    0.5: 'yellow',\n",
    "    0.6: 'orange',\n",
    "    0.7: 'lightcoral', \n",
    "    0.8: 'red',\n",
    "    0.9: 'darkred',\n",
    "    1.0: 'darkred' \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "import folium\n",
    "from folium.plugins import HeatMap\n",
    "\n",
    "florida_coordinates = [27.9944024, -81.7602544]\n",
    "mapObj = folium.Map(location=florida_coordinates, zoom_start=7)\n",
    "HeatMap(frequency_df, gradient=colorGradient).add_to(mapObj)\n",
    "mapObj.save('demo11.html')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing with URL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import json\n",
    "import pandas as pd\n",
    "\n",
    "data_list = []\n",
    "skip = 0\n",
    "top = 10000\n",
    "#while len(data_list) < 20720258:\n",
    "while len(data_list) < 20000:\n",
    "    url = f\"https://www.fema.gov/api/open/v1/IndividualsAndHouseholdsProgramValidRegistrations?$skip={skip}&$top={top}\"\n",
    "    response = requests.get(url)\n",
    "    data_list.extend(response.json()['IndividualsAndHouseholdsProgramValidRegistrations'])\n",
    "    skip += 10000\n",
    "\n",
    "data_df = pd.json_normalize(data_list)\n",
    "if data_df.iloc[0]['id'] == data_df.iloc[10000]['id']:\n",
    "    print(\"did not import successfully\")\n",
    "\n",
    "# data_df.head()\n",
    "\n",
    "# print(data_df.iloc[0])\n",
    "# print(data_df.iloc[10000])"
   ]
  }
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