nfip_pennsylvania.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e431d059-244c-44a7-b7f7-de51df6fab96",
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "from datetime import datetime\n",
    "import duckdb\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import geopandas as gpd\n",
    "import folium\n",
    "import re\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler, OneHotEncoder, MinMaxScaler\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import classification_report, accuracy_score\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15a87930-3386-40ac-965a-951010200e9a",
   "metadata": {},
   "source": [
    "## FIMA NFIP Redacted Claims - v2 dataset, FEMA\n",
    "“FIMA NFIP Redacted Claims - v2 | FEMA.gov.” 2023b. Www.fema.gov. \n",
    "    July 5, 2023. https://www.fema.gov/openfema-data-page/fima-nfip-redacted-claims-v2.\n",
    "\n",
    "Congress passed the National Flood Insurance Act (NFIA), 42 U.S.C. 4001 in 1968, creating the National Flood Insurance Program (NFIP) in order to reduce future flood losses through flood hazard identification, floodplain management, and providing insurance protection. The Department of Housing and Urban Development (HUD) originally administered the NFIP, and Congress subsequently transferred the NFIP to FEMA upon its creation in 1979. FEMA and insurance companies participating in FEMA's Write Your Own (WYO) program offer NFIP insurance coverage for building structures as well as for contents and personal property within the building structures to eligible and insurable properties. The WYO program began in 1983 with NFIP operating under Part B of the NFIA and allows FEMA to authorize private insurance companies to issue the Standard Flood Insurance Policy (SFIP) as FEMA's fiduciary and fiscal agent. FEMA administers NFIP by ensuring insurance applications are processed properly; determining correct premiums; renewing, reforming, and cancelling insurance policies; transferring policies from the seller of the property to the purchaser of the property in certain circumstances; and processing insurance claims.\r\n",
    "\r\n",
    "The paid premiums of SFIPs and claims payments for damaged property are processed through the National Flood Insurance Fund (NFIF). NFIF was established by the National Flood Insurance Act of 1968 (42 U.S.C. 4001, et seq.), and is a centralized premium revenue and fee-generated fund that supports NFIP, which holds these U.S. Treasury funds.\r\n",
    "\r\n",
    "This dataset provides details on NFIP claims transactions. It is derived from the NFIP system of record, staged in the NFIP reporting platform and redacted to protect policy holder personally identifiable information.\r\n",
    "\r\n",
    "The Flood Insurance Claims Manual (https://nfipservices.floodsmart.gov/insurance-manuals) provides claims guidance to WYOs, vendors, adjusters, and examiners so that policyholders experience consistent and reliable service. The Manual provides processes for handling claims from the notice of loss to final payment. The NFIP has provided answers to Frequently Asked Questions (FAQs) to assist the public in understanding and navigating the data our program makes available: https://www.fema.gov/sites/default/files/documents/fema_nfip-data-faq record.\r\n",
    "\r\n",
    "This dataset is not intended to be an official federal report, and should not be considered an official federal report.\r\n",
    "\r\n",
    "FEMA's terms and conditions and citation requirements for datasets (API usage or file downloads) can be found on the OpenFEMA Terms and Conditions page: https://www.fema.gov/about/openfema/terms-conditions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "583a682d-7dde-4b50-b667-70ce1b6918b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File downloaded successfully.\n"
     ]
    }
   ],
   "source": [
    "# URL of the CSV file to download\n",
    "url = \"https://www.fema.gov/about/reports-and-data/openfema/FimaNfipClaims.csv\"\n",
    "\n",
    "# Send an HTTP GET request to the URL\n",
    "response = requests.get(url)\n",
    "\n",
    "# Check if the request was successful (status code 200)\n",
    "if response.status_code == 200:\n",
    "    \n",
    "    # Specify the file path where you want to save the downloaded file\n",
    "    file_path = \"FimaNfipClaims_all.csv\"\n",
    "    \n",
    "    # Write the content of the response to a file\n",
    "    with open(file_path, 'wb') as file:\n",
    "        file.write(response.content)\n",
    "    print(\"File downloaded successfully.\")\n",
    "    \n",
    "else:\n",
    "    print(\"Failed to download file.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "647a8f9e-5732-4dcb-b1d7-f487bff80811",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read the full file, create a variable.\n",
    "# I've been dividing my dataframes by state, but just in case I want the full thing, it's accessible.\n",
    "# Given the size of these files, I usually read them with low_memory set to False.\n",
    "\n",
    "all_nfip = pd.read_csv('FimaNfipClaims_all.csv', low_memory = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "949c59ac-a7ba-4809-98d3-131f8d72eb24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['PA'], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter for Penn. data only, create a variable for that dataframe.\n",
    "# If I were working with multiple states, I'd specify PA in the variable name.\n",
    "# But this notebook is exclusive to PA, and it's easier to call the dataframe something simple like nfip.\n",
    "# And just to be sure I didn't overlook something in my filter coding, I check the unique values in the state column.\n",
    "\n",
    "nfip = all_nfip[all_nfip['state'] == 'PA']\n",
    "nfip.state.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46f137e7-d425-4792-ba26-5ed2cfb9ae1f",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fde6ac5c-8644-47d7-9637-41755ec9677a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(76671, 73)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nfip.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3dea2598-ec84-411e-b8be-2f8320a0a0d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 76671 entries, 35 to 2285241\n",
      "Data columns (total 73 columns):\n",
      " #   Column                                      Non-Null Count  Dtype  \n",
      "---  ------                                      --------------  -----  \n",
      " 0   agricultureStructureIndicator               76671 non-null  int64  \n",
      " 1   asOfDate                                    76671 non-null  object \n",
      " 2   basementEnclosureCrawlspaceType             50153 non-null  float64\n",
      " 3   policyCount                                 76671 non-null  int64  \n",
      " 4   crsClassificationCode                       1132 non-null   float64\n",
      " 5   dateOfLoss                                  76671 non-null  object \n",
      " 6   elevatedBuildingIndicator                   76671 non-null  int64  \n",
      " 7   elevationCertificateIndicator               17162 non-null  object \n",
      " 8   elevationDifference                         3825 non-null   float64\n",
      " 9   baseFloodElevation                          4265 non-null   float64\n",
      " 10  ratedFloodZone                              70240 non-null  object \n",
      " 11  houseWorship                                76671 non-null  int64  \n",
      " 12  locationOfContents                          47483 non-null  float64\n",
      " 13  lowestAdjacentGrade                         3290 non-null   float64\n",
      " 14  lowestFloorElevation                        3617 non-null   float64\n",
      " 15  numberOfFloorsInTheInsuredBuilding          76424 non-null  float64\n",
      " 16  nonProfitIndicator                          76671 non-null  int64  \n",
      " 17  obstructionType                             36462 non-null  float64\n",
      " 18  occupancyType                               76638 non-null  float64\n",
      " 19  originalConstructionDate                    76669 non-null  object \n",
      " 20  originalNBDate                              76667 non-null  object \n",
      " 21  amountPaidOnBuildingClaim                   63235 non-null  float64\n",
      " 22  amountPaidOnContentsClaim                   63235 non-null  float64\n",
      " 23  amountPaidOnIncreasedCostOfComplianceClaim  63235 non-null  float64\n",
      " 24  postFIRMConstructionIndicator               76671 non-null  int64  \n",
      " 25  rateMethod                                  73061 non-null  object \n",
      " 26  smallBusinessIndicatorBuilding              76671 non-null  int64  \n",
      " 27  totalBuildingInsuranceCoverage              76671 non-null  float64\n",
      " 28  totalContentsInsuranceCoverage              76671 non-null  float64\n",
      " 29  yearOfLoss                                  76671 non-null  int64  \n",
      " 30  primaryResidenceIndicator                   76671 non-null  int64  \n",
      " 31  buildingDamageAmount                        62323 non-null  float64\n",
      " 32  buildingDeductibleCode                      65514 non-null  object \n",
      " 33  netBuildingPaymentAmount                    76671 non-null  float64\n",
      " 34  buildingPropertyValue                       62323 non-null  float64\n",
      " 35  causeOfDamage                               76135 non-null  object \n",
      " 36  condominiumCoverageTypeCode                 73985 non-null  object \n",
      " 37  contentsDamageAmount                        30692 non-null  float64\n",
      " 38  contentsDeductibleCode                      59251 non-null  object \n",
      " 39  netContentsPaymentAmount                    76671 non-null  float64\n",
      " 40  contentsPropertyValue                       30692 non-null  float64\n",
      " 41  disasterAssistanceCoverageRequired          68473 non-null  float64\n",
      " 42  eventDesignationNumber                      2921 non-null   object \n",
      " 43  ficoNumber                                  35395 non-null  float64\n",
      " 44  floodCharacteristicsIndicator               2563 non-null   float64\n",
      " 45  floodWaterDuration                          72045 non-null  float64\n",
      " 46  floodproofedIndicator                       76671 non-null  int64  \n",
      " 47  floodEvent                                  38547 non-null  object \n",
      " 48  iccCoverage                                 44108 non-null  float64\n",
      " 49  netIccPaymentAmount                         76671 non-null  float64\n",
      " 50  nfipRatedCommunityNumber                    76663 non-null  float64\n",
      " 51  nfipCommunityNumberCurrent                  14705 non-null  object \n",
      " 52  nfipCommunityName                           14970 non-null  object \n",
      " 53  nonPaymentReasonContents                    23287 non-null  float64\n",
      " 54  nonPaymentReasonBuilding                    14539 non-null  float64\n",
      " 55  numberOfUnits                               76580 non-null  float64\n",
      " 56  buildingReplacementCost                     62323 non-null  float64\n",
      " 57  contentsReplacementCost                     30646 non-null  float64\n",
      " 58  replacementCostBasis                        71574 non-null  object \n",
      " 59  stateOwnedIndicator                         76671 non-null  int64  \n",
      " 60  waterDepth                                  67471 non-null  float64\n",
      " 61  floodZoneCurrent                            14695 non-null  object \n",
      " 62  buildingDescriptionCode                     25363 non-null  float64\n",
      " 63  rentalPropertyIndicator                     76671 non-null  int64  \n",
      " 64  state                                       76671 non-null  object \n",
      " 65  reportedCity                                76671 non-null  object \n",
      " 66  reportedZipCode                             75270 non-null  float64\n",
      " 67  countyCode                                  75414 non-null  float64\n",
      " 68  censusTract                                 69536 non-null  float64\n",
      " 69  censusBlockGroupFips                        69536 non-null  float64\n",
      " 70  latitude                                    75541 non-null  float64\n",
      " 71  longitude                                   75541 non-null  float64\n",
      " 72  id                                          76671 non-null  object \n",
      "dtypes: float64(41), int64(12), object(20)\n",
      "memory usage: 43.3+ MB\n"
     ]
    }
   ],
   "source": [
    "nfip.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f5f17304-79b9-4478-bd10-4c57ea09bc4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
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       "    .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>agricultureStructureIndicator</th>\n",
       "      <th>basementEnclosureCrawlspaceType</th>\n",
       "      <th>policyCount</th>\n",
       "      <th>crsClassificationCode</th>\n",
       "      <th>elevatedBuildingIndicator</th>\n",
       "      <th>elevationDifference</th>\n",
       "      <th>baseFloodElevation</th>\n",
       "      <th>houseWorship</th>\n",
       "      <th>locationOfContents</th>\n",
       "      <th>lowestAdjacentGrade</th>\n",
       "      <th>...</th>\n",
       "      <th>stateOwnedIndicator</th>\n",
       "      <th>waterDepth</th>\n",
       "      <th>buildingDescriptionCode</th>\n",
       "      <th>rentalPropertyIndicator</th>\n",
       "      <th>reportedZipCode</th>\n",
       "      <th>countyCode</th>\n",
       "      <th>censusTract</th>\n",
       "      <th>censusBlockGroupFips</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>76671.000000</td>\n",
       "      <td>50153.000000</td>\n",
       "      <td>76671.000000</td>\n",
       "      <td>1132.000000</td>\n",
       "      <td>76671.000000</td>\n",
       "      <td>3825.000000</td>\n",
       "      <td>4265.000000</td>\n",
       "      <td>76671.000000</td>\n",
       "      <td>47483.000000</td>\n",
       "      <td>3290.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>76671.000000</td>\n",
       "      <td>67471.000000</td>\n",
       "      <td>25363.000000</td>\n",
       "      <td>76671.000000</td>\n",
       "      <td>75270.000000</td>\n",
       "      <td>75414.000000</td>\n",
       "      <td>6.953600e+04</td>\n",
       "      <td>6.953600e+04</td>\n",
       "      <td>75541.000000</td>\n",
       "      <td>75541.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.000143</td>\n",
       "      <td>1.670927</td>\n",
       "      <td>1.049145</td>\n",
       "      <td>7.696996</td>\n",
       "      <td>0.098029</td>\n",
       "      <td>-1.457516</td>\n",
       "      <td>425.773388</td>\n",
       "      <td>0.000196</td>\n",
       "      <td>2.708633</td>\n",
       "      <td>342.176109</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000639</td>\n",
       "      <td>5.077144</td>\n",
       "      <td>1.515712</td>\n",
       "      <td>0.006795</td>\n",
       "      <td>17611.710376</td>\n",
       "      <td>42061.528788</td>\n",
       "      <td>4.205554e+10</td>\n",
       "      <td>4.205554e+11</td>\n",
       "      <td>40.579704</td>\n",
       "      <td>-76.916145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.011977</td>\n",
       "      <td>0.693655</td>\n",
       "      <td>1.901201</td>\n",
       "      <td>1.171829</td>\n",
       "      <td>0.297356</td>\n",
       "      <td>27.411532</td>\n",
       "      <td>707.417783</td>\n",
       "      <td>0.013986</td>\n",
       "      <td>1.110734</td>\n",
       "      <td>676.016119</td>\n",
       "      <td>...</td>\n",
       "      <td>0.025272</td>\n",
       "      <td>17.355559</td>\n",
       "      <td>1.828617</td>\n",
       "      <td>0.082153</td>\n",
       "      <td>1436.894686</td>\n",
       "      <td>38.664339</td>\n",
       "      <td>4.401506e+08</td>\n",
       "      <td>4.401506e+09</td>\n",
       "      <td>0.561647</td>\n",
       "      <td>1.682336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-859.000000</td>\n",
       "      <td>-15.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-7.300000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-144.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2568.000000</td>\n",
       "      <td>42001.000000</td>\n",
       "      <td>5.029950e+09</td>\n",
       "      <td>5.029950e+10</td>\n",
       "      <td>18.300000</td>\n",
       "      <td>-120.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-5.000000</td>\n",
       "      <td>79.600000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>22.625000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>16925.000000</td>\n",
       "      <td>42029.000000</td>\n",
       "      <td>4.202930e+10</td>\n",
       "      <td>4.202930e+11</td>\n",
       "      <td>40.200000</td>\n",
       "      <td>-77.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>322.300000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>198.200000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17801.000000</td>\n",
       "      <td>42067.000000</td>\n",
       "      <td>4.206396e+10</td>\n",
       "      <td>4.206396e+11</td>\n",
       "      <td>40.400000</td>\n",
       "      <td>-76.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>565.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>494.075000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>18930.000000</td>\n",
       "      <td>42091.000000</td>\n",
       "      <td>4.209120e+10</td>\n",
       "      <td>4.209120e+11</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>-75.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>236.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>792.000000</td>\n",
       "      <td>9990.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>9992.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>770.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>73160.000000</td>\n",
       "      <td>42133.000000</td>\n",
       "      <td>7.203716e+10</td>\n",
       "      <td>7.203716e+11</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>-65.600000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 53 columns</p>\n",
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      ],
      "text/plain": [
       "       agricultureStructureIndicator  basementEnclosureCrawlspaceType  \\\n",
       "count                   76671.000000                     50153.000000   \n",
       "mean                        0.000143                         1.670927   \n",
       "std                         0.011977                         0.693655   \n",
       "min                         0.000000                         0.000000   \n",
       "25%                         0.000000                         1.000000   \n",
       "50%                         0.000000                         2.000000   \n",
       "75%                         0.000000                         2.000000   \n",
       "max                         1.000000                         4.000000   \n",
       "\n",
       "        policyCount  crsClassificationCode  elevatedBuildingIndicator  \\\n",
       "count  76671.000000            1132.000000               76671.000000   \n",
       "mean       1.049145               7.696996                   0.098029   \n",
       "std        1.901201               1.171829                   0.297356   \n",
       "min        1.000000               6.000000                   0.000000   \n",
       "25%        1.000000               7.000000                   0.000000   \n",
       "50%        1.000000               8.000000                   0.000000   \n",
       "75%        1.000000               8.000000                   0.000000   \n",
       "max      236.000000              10.000000                   1.000000   \n",
       "\n",
       "       elevationDifference  baseFloodElevation  houseWorship  \\\n",
       "count          3825.000000         4265.000000  76671.000000   \n",
       "mean             -1.457516          425.773388      0.000196   \n",
       "std              27.411532          707.417783      0.013986   \n",
       "min            -859.000000          -15.000000      0.000000   \n",
       "25%              -5.000000           79.600000      0.000000   \n",
       "50%               0.000000          322.300000      0.000000   \n",
       "75%               3.000000          565.000000      0.000000   \n",
       "max             792.000000         9990.000000      1.000000   \n",
       "\n",
       "       locationOfContents  lowestAdjacentGrade  ...  stateOwnedIndicator  \\\n",
       "count        47483.000000          3290.000000  ...         76671.000000   \n",
       "mean             2.708633           342.176109  ...             0.000639   \n",
       "std              1.110734           676.016119  ...             0.025272   \n",
       "min              1.000000            -7.300000  ...             0.000000   \n",
       "25%              2.000000            22.625000  ...             0.000000   \n",
       "50%              2.000000           198.200000  ...             0.000000   \n",
       "75%              3.000000           494.075000  ...             0.000000   \n",
       "max              7.000000          9992.000000  ...             1.000000   \n",
       "\n",
       "         waterDepth  buildingDescriptionCode  rentalPropertyIndicator  \\\n",
       "count  67471.000000             25363.000000             76671.000000   \n",
       "mean       5.077144                 1.515712                 0.006795   \n",
       "std       17.355559                 1.828617                 0.082153   \n",
       "min     -144.000000                 1.000000                 0.000000   \n",
       "25%        0.000000                 1.000000                 0.000000   \n",
       "50%        0.000000                 1.000000                 0.000000   \n",
       "75%        3.000000                 1.000000                 0.000000   \n",
       "max      770.000000                12.000000                 1.000000   \n",
       "\n",
       "       reportedZipCode    countyCode   censusTract  censusBlockGroupFips  \\\n",
       "count     75270.000000  75414.000000  6.953600e+04          6.953600e+04   \n",
       "mean      17611.710376  42061.528788  4.205554e+10          4.205554e+11   \n",
       "std        1436.894686     38.664339  4.401506e+08          4.401506e+09   \n",
       "min        2568.000000  42001.000000  5.029950e+09          5.029950e+10   \n",
       "25%       16925.000000  42029.000000  4.202930e+10          4.202930e+11   \n",
       "50%       17801.000000  42067.000000  4.206396e+10          4.206396e+11   \n",
       "75%       18930.000000  42091.000000  4.209120e+10          4.209120e+11   \n",
       "max       73160.000000  42133.000000  7.203716e+10          7.203716e+11   \n",
       "\n",
       "           latitude     longitude  \n",
       "count  75541.000000  75541.000000  \n",
       "mean      40.579704    -76.916145  \n",
       "std        0.561647      1.682336  \n",
       "min       18.300000   -120.500000  \n",
       "25%       40.200000    -77.700000  \n",
       "50%       40.400000    -76.600000  \n",
       "75%       41.000000    -75.500000  \n",
       "max       45.000000    -65.600000  \n",
       "\n",
       "[8 rows x 53 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nfip.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f289fd11-169f-47fc-a678-914520c2b18c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Currently Unavailable'], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reportedCity jumps out as a column I may not want\n",
    "# Let's check it out\n",
    "\n",
    "nfip.reportedCity.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a991896c-ddef-48bc-b18a-ad2eb6435a1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# First dropping of columns. These are just the ones I can see right away I won't need.\n",
    "\n",
    "nfip = nfip.drop(['reportedCity', 'censusTract', 'censusBlockGroupFips', 'state'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "52fcd89c-f32b-4bef-9719-5c84ba817b88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988,\n",
       "       1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999,\n",
       "       2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010,\n",
       "       2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021,\n",
       "       2022, 2023, 2024])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# More EDA, checking out the years this data covers\n",
    "\n",
    "years = nfip.yearOfLoss.unique()\n",
    "years_list = years.tolist()\n",
    "np.sort(years_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1cd05df3-badc-4228-9c10-0e543f022305",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['agriculture_structure_indicator', 'as_of_date',\n",
       "       'basement_enclosure_crawlspace_type', 'policy_count',\n",
       "       'crs_classification_code', 'date_of_loss',\n",
       "       'elevated_building_indicator', 'elevation_certificate_indicator',\n",
       "       'elevation_difference', 'base_flood_elevation', 'rated_flood_zone',\n",
       "       'house_worship', 'location_of_contents', 'lowest_adjacent_grade',\n",
       "       'lowest_floor_elevation', 'number_of_floors_in_the_insured_building',\n",
       "       'non_profit_indicator', 'obstruction_type', 'occupancy_type',\n",
       "       'original_construction_date', 'original_n_b_date',\n",
       "       'amount_paid_on_building_claim', 'amount_paid_on_contents_claim',\n",
       "       'amount_paid_on_increased_cost_of_compliance_claim',\n",
       "       'post_f_i_r_m_construction_indicator', 'rate_method',\n",
       "       'small_business_indicator_building',\n",
       "       'total_building_insurance_coverage',\n",
       "       'total_contents_insurance_coverage', 'year_of_loss',\n",
       "       'primary_residence_indicator', 'building_damage_amount',\n",
       "       'building_deductible_code', 'net_building_payment_amount',\n",
       "       'building_property_value', 'cause_of_damage',\n",
       "       'condominium_coverage_type_code', 'contents_damage_amount',\n",
       "       'contents_deductible_code', 'net_contents_payment_amount',\n",
       "       'contents_property_value', 'disaster_assistance_coverage_required',\n",
       "       'event_designation_number', 'fico_number',\n",
       "       'flood_characteristics_indicator', 'flood_water_duration',\n",
       "       'floodproofed_indicator', 'flood_event', 'icc_coverage',\n",
       "       'net_icc_payment_amount', 'nfip_rated_community_number',\n",
       "       'nfip_community_number_current', 'nfip_community_name',\n",
       "       'non_payment_reason_contents', 'non_payment_reason_building',\n",
       "       'number_of_units', 'building_replacement_cost',\n",
       "       'contents_replacement_cost', 'replacement_cost_basis',\n",
       "       'state_owned_indicator', 'water_depth', 'flood_zone_current',\n",
       "       'building_description_code', 'rental_property_indicator',\n",
       "       'reported_zip_code', 'county_code', 'latitude', 'longitude', 'id'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# CamelCase in column names drives me nuts in Tableau, so I like to change NFIP column names to snake case.\n",
    "# This function has come in super handy.\n",
    "\n",
    "def camel_to_snake(name):\n",
    "    return re.sub(r'(?<!^)(?=[A-Z])', '_', name).lower()\n",
    "\n",
    "nfip.columns = [camel_to_snake(col) for col in nfip.columns]\n",
    "\n",
    "nfip.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a78ddb4e-6054-452f-9187-aac356c06b13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['agriculture_structure_indicator', 'as_of_date',\n",
       "       'basement_enclosure_crawlspace_type', 'policy_count',\n",
       "       'crs_classification_code', 'date_of_loss',\n",
       "       'elevated_building_indicator', 'elevation_certificate_indicator',\n",
       "       'elevation_difference', 'base_flood_elevation', 'rated_flood_zone',\n",
       "       'house_worship', 'location_of_contents', 'lowest_adjacent_grade',\n",
       "       'lowest_floor_elevation', 'number_of_floors_in_the_insured_building',\n",
       "       'non_profit_indicator', 'obstruction_type', 'occupancy_type',\n",
       "       'original_construction_date', 'original_nb_date',\n",
       "       'amount_paid_on_building_claim', 'amount_paid_on_contents_claim',\n",
       "       'amount_paid_on_increased_cost_of_compliance_claim',\n",
       "       'post_firm_construction_indicator', 'rate_method',\n",
       "       'small_business_indicator_building',\n",
       "       'total_building_insurance_coverage',\n",
       "       'total_contents_insurance_coverage', 'year_of_loss',\n",
       "       'primary_residence_indicator', 'building_damage_amount',\n",
       "       'building_deductible_code', 'net_building_payment_amount',\n",
       "       'building_property_value', 'cause_of_damage',\n",
       "       'condominium_coverage_type_code', 'contents_damage_amount',\n",
       "       'contents_deductible_code', 'net_contents_payment_amount',\n",
       "       'contents_property_value', 'disaster_assistance_coverage_required',\n",
       "       'event_designation_number', 'fico_number',\n",
       "       'flood_characteristics_indicator', 'flood_water_duration',\n",
       "       'floodproofed_indicator', 'flood_event', 'icc_coverage',\n",
       "       'net_icc_payment_amount', 'nfip_rated_community_number',\n",
       "       'nfip_community_number_current', 'nfip_community_name',\n",
       "       'non_payment_reason_contents', 'non_payment_reason_building',\n",
       "       'number_of_units', 'building_replacement_cost',\n",
       "       'contents_replacement_cost', 'replacement_cost_basis',\n",
       "       'state_owned_indicator', 'water_depth', 'flood_zone_current',\n",
       "       'building_description_code', 'rental_property_indicator',\n",
       "       'reported_zip_code', 'county_code', 'latitude', 'longitude', 'id'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A couple columns names need tidying\n",
    "\n",
    "nfip = nfip.rename(columns = {'post_f_i_r_m_construction_indicator': 'post_firm_construction_indicator', 'original_n_b_date': 'original_nb_date'})\n",
    "nfip.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "fd60295c-e39a-4270-9f29-ddd56fbff3cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([42109., 42017., 42013., 42071., 42003., 42015., 42129., 42001.,\n",
       "          nan, 42005., 42007., 42009., 42011., 42095., 42019., 42021.,\n",
       "       42023., 42025., 42027., 42029., 42031., 42033., 42035., 42081.,\n",
       "       42037., 42097., 42039., 42041., 42079., 42043., 42133., 42045.,\n",
       "       42047., 42049., 42051., 42055., 42059., 42061., 42063., 42065.,\n",
       "       42067., 42069., 42073., 42075., 42077., 42113., 42083., 42085.,\n",
       "       42087., 42089., 42091., 42093., 42099., 42101., 42105., 42107.,\n",
       "       42111., 42115., 42117., 42119., 42121., 42123., 42125., 42127.,\n",
       "       42131., 42053., 42057., 42103.])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A lot of my analysis will depend on geo-locators. FIPS and zip codes are always important to clean.\n",
    "\n",
    "nfip.county_code.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "15af685b-839e-4f60-bdfc-221988df14c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([17870., 19007., 16602., ..., 15547., 15633., 18464.])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nfip.reported_zip_code.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "af545c06-bd72-4eb0-98a8-8eced88f4d7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "nfip['county_code'] = nfip['county_code'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1fc8bbd2-7d63-4cd0-952a-869e0d611f1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "nfip['reported_zip_code'] = nfip['reported_zip_code'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f70d1a94-02a5-4a09-a517-0e2944bf259e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now they're strings and not floats, I gotta get rid of that \".0\" at the end\n",
    "\n",
    "nfip['reported_zip_code'] = nfip['reported_zip_code'].str[:-2] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c0f60fa3-87e8-4271-9893-ce026a0eb647",
   "metadata": {},
   "outputs": [],
   "source": [
    "nfip['county_code'] = nfip['county_code'].str[:-2] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d394b982-b890-43d8-987d-ff97a8f72a22",
   "metadata": {},
   "outputs": [],
   "source": [
    "# NFIP data is split into building and contents claims. For birds-eye-level analysis, I find it helpful to combine them.\n",
    "# So I set up four new columns for that, plus one to add the county name, which is helpful during EDA.\n",
    "\n",
    "nfip['damage'] = ''\n",
    "nfip['net_payment'] = ''\n",
    "nfip['total_payment'] = ''\n",
    "nfip['coverage'] = ''\n",
    "nfip['county_name'] = ''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "39a83ae3-1c52-441b-bce0-71481dd27371",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Concatenate building and contents damage\n",
    "\n",
    "nfip['damage'] = nfip['building_damage_amount'].fillna(0) + nfip['contents_damage_amount'].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ac65c742-2386-45c9-923d-301c93dda908",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Concatenate building and contents net payment\n",
    "\n",
    "nfip['net_payment'] = nfip['net_building_payment_amount'].fillna(0) + nfip['net_contents_payment_amount'].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "7615aa0b-de7b-4a5f-9175-146d145cf65a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Concatenate building and contents total payment\n",
    "\n",
    "nfip['total_payment'] = nfip['amount_paid_on_building_claim'].fillna(0) + nfip['amount_paid_on_contents_claim'].fillna(0) + nfip['amount_paid_on_increased_cost_of_compliance_claim'].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "309e0bcb-0087-40c0-9e1f-85f37a4bc4f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Concatenate building and contents total policy coverage\n",
    "\n",
    "nfip['coverage'] = nfip['total_building_insurance_coverage'].fillna(0) + nfip['total_contents_insurance_coverage'].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "68f9dd8c-21c5-4837-b7ac-cec082360e65",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a dictionary of PA county names, in anticipating of filling the new county_name column\n",
    "\n",
    "pa_counties = {\n",
    "    '42001': 'Adams',\n",
    "    '42003': 'Allegheny',\n",
    "    '42005': 'Armstrong',\n",
    "    '42007': 'Beaver',\n",
    "    '42009': 'Bedford',\n",
    "    '42011': 'Berks',\n",
    "    '42013': 'Blair',\n",
    "    '42015': 'Bradford',\n",
    "    '42017': 'Bucks',\n",
    "    '42019': 'Butler',\n",
    "    '42021': 'Cambria',\n",
    "    '42023': 'Cameron',\n",
    "    '42025': 'Carbon',\n",
    "    '42027': 'Centre',\n",
    "    '42029': 'Chester',\n",
    "    '42031': 'Clarion',\n",
    "    '42033': 'Clearfield',\n",
    "    '42035': 'Clinton',\n",
    "    '42037': 'Columbia',\n",
    "    '42039': 'Crawford',\n",
    "    '42041': 'Cumberland',\n",
    "    '42043': 'Dauphin',\n",
    "    '42045': 'Delaware',\n",
    "    '42047': 'Elk',\n",
    "    '42049': 'Erie',\n",
    "    '42051': 'Fayette',\n",
    "    '42053': 'Forest',\n",
    "    '42055': 'Franklin',\n",
    "    '42057': 'Fulton',\n",
    "    '42059': 'Greene',\n",
    "    '42061': 'Huntingdon',\n",
    "    '42063': 'Indiana',\n",
    "    '42065': 'Jefferson',\n",
    "    '42067': 'Juniata',\n",
    "    '42069': 'Lackawanna',\n",
    "    '42071': 'Lancaster',\n",
    "    '42073': 'Lawrence',\n",
    "    '42075': 'Lebanon',\n",
    "    '42077': 'Lehigh',\n",
    "    '42079': 'Luzerne',\n",
    "    '42081': 'Lycoming',\n",
    "    '42083': 'McKean',\n",
    "    '42085': 'Mercer',\n",
    "    '42087': 'Mifflin',\n",
    "    '42089': 'Monroe',\n",
    "    '42091': 'Montgomery',\n",
    "    '42093': 'Montour',\n",
    "    '42095': 'Northampton',\n",
    "    '42097': 'Northumberland',\n",
    "    '42099': 'Perry',\n",
    "    '42101': 'Philadelphia',\n",
    "    '42103': 'Pike',\n",
    "    '42105': 'Potter',\n",
    "    '42107': 'Schuylkill',\n",
    "    '42109': 'Snyder',\n",
    "    '42111': 'Somerset',\n",
    "    '42113': 'Sullivan',\n",
    "    '42115': 'Susquehanna',\n",
    "    '42117': 'Tioga',\n",
    "    '42119': 'Union',\n",
    "    '42121': 'Venango',\n",
    "    '42123': 'Warren',\n",
    "    '42125': 'Washington',\n",
    "    '42127': 'Wayne',\n",
    "    '42129': 'Westmoreland',\n",
    "    '42131': 'Wyoming',\n",
    "    '42133': 'York'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "7b525a4a-bc0a-4bc4-b912-c7305cc7c4e5",
   "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>agriculture_structure_indicator</th>\n",
       "      <th>as_of_date</th>\n",
       "      <th>basement_enclosure_crawlspace_type</th>\n",
       "      <th>policy_count</th>\n",
       "      <th>crs_classification_code</th>\n",
       "      <th>date_of_loss</th>\n",
       "      <th>elevated_building_indicator</th>\n",
       "      <th>elevation_certificate_indicator</th>\n",
       "      <th>elevation_difference</th>\n",
       "      <th>base_flood_elevation</th>\n",
       "      <th>...</th>\n",
       "      <th>reported_zip_code</th>\n",
       "      <th>county_code</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>id</th>\n",
       "      <th>damage</th>\n",
       "      <th>net_payment</th>\n",
       "      <th>total_payment</th>\n",
       "      <th>coverage</th>\n",
       "      <th>county_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0</td>\n",
       "      <td>2021-07-24T13:15:12.062Z</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2004-09-17T00:00:00.000Z</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>17870</td>\n",
       "      <td>42109</td>\n",
       "      <td>40.8</td>\n",
       "      <td>-76.8</td>\n",
       "      <td>8d3cd611-ca6b-4fd0-ae71-c5e852556767</td>\n",
       "      <td>12233.0</td>\n",
       "      <td>11233.28</td>\n",
       "      <td>11233.28</td>\n",
       "      <td>31700.0</td>\n",
       "      <td>Snyder</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>0</td>\n",
       "      <td>2020-01-22T16:55:53.194Z</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2007-04-15T00:00:00.000Z</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>19007</td>\n",
       "      <td>42017</td>\n",
       "      <td>40.1</td>\n",
       "      <td>-74.9</td>\n",
       "      <td>f115927f-a8f4-4d7a-af49-ed93000ef678</td>\n",
       "      <td>2842.0</td>\n",
       "      <td>2341.81</td>\n",
       "      <td>2341.81</td>\n",
       "      <td>350000.0</td>\n",
       "      <td>Bucks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>0</td>\n",
       "      <td>2020-01-22T16:55:53.194Z</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2014-06-11T00:00:00.000Z</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>16602</td>\n",
       "      <td>42013</td>\n",
       "      <td>40.5</td>\n",
       "      <td>-78.4</td>\n",
       "      <td>2d054cf7-9513-41ec-b27b-b4973c530284</td>\n",
       "      <td>1126.0</td>\n",
       "      <td>126.27</td>\n",
       "      <td>126.27</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>Blair</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>0</td>\n",
       "      <td>2020-01-22T16:55:53.194Z</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-07-14T00:00:00.000Z</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>16602</td>\n",
       "      <td>42013</td>\n",
       "      <td>40.5</td>\n",
       "      <td>-78.4</td>\n",
       "      <td>bb486acb-ac2f-4bc8-9ae0-9cb6fc9bdf20</td>\n",
       "      <td>2024.0</td>\n",
       "      <td>1024.41</td>\n",
       "      <td>1024.41</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>Blair</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>0</td>\n",
       "      <td>2020-01-22T16:55:53.194Z</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-05-01T00:00:00.000Z</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>16602</td>\n",
       "      <td>42013</td>\n",
       "      <td>40.5</td>\n",
       "      <td>-78.4</td>\n",
       "      <td>3b990a40-135e-442c-87db-b7551eb9b443</td>\n",
       "      <td>2283.0</td>\n",
       "      <td>3244.14</td>\n",
       "      <td>3244.14</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>Blair</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 74 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    agriculture_structure_indicator                as_of_date  \\\n",
       "35                                0  2021-07-24T13:15:12.062Z   \n",
       "95                                0  2020-01-22T16:55:53.194Z   \n",
       "96                                0  2020-01-22T16:55:53.194Z   \n",
       "97                                0  2020-01-22T16:55:53.194Z   \n",
       "98                                0  2020-01-22T16:55:53.194Z   \n",
       "\n",
       "    basement_enclosure_crawlspace_type  policy_count  crs_classification_code  \\\n",
       "35                                 0.0             1                      NaN   \n",
       "95                                 1.0             1                      NaN   \n",
       "96                                 2.0             1                      NaN   \n",
       "97                                 2.0             1                      NaN   \n",
       "98                                 2.0             1                      NaN   \n",
       "\n",
       "                date_of_loss  elevated_building_indicator  \\\n",
       "35  2004-09-17T00:00:00.000Z                            0   \n",
       "95  2007-04-15T00:00:00.000Z                            0   \n",
       "96  2014-06-11T00:00:00.000Z                            0   \n",
       "97  2015-07-14T00:00:00.000Z                            0   \n",
       "98  2017-05-01T00:00:00.000Z                            0   \n",
       "\n",
       "   elevation_certificate_indicator  elevation_difference  \\\n",
       "35                             NaN                   NaN   \n",
       "95                             NaN                   NaN   \n",
       "96                             NaN                   NaN   \n",
       "97                             NaN                   NaN   \n",
       "98                             NaN                   NaN   \n",
       "\n",
       "    base_flood_elevation  ... reported_zip_code  county_code  latitude  \\\n",
       "35                   NaN  ...             17870        42109      40.8   \n",
       "95                   NaN  ...             19007        42017      40.1   \n",
       "96                   NaN  ...             16602        42013      40.5   \n",
       "97                   NaN  ...             16602        42013      40.5   \n",
       "98                   NaN  ...             16602        42013      40.5   \n",
       "\n",
       "    longitude                                    id   damage  net_payment  \\\n",
       "35      -76.8  8d3cd611-ca6b-4fd0-ae71-c5e852556767  12233.0     11233.28   \n",
       "95      -74.9  f115927f-a8f4-4d7a-af49-ed93000ef678   2842.0      2341.81   \n",
       "96      -78.4  2d054cf7-9513-41ec-b27b-b4973c530284   1126.0       126.27   \n",
       "97      -78.4  bb486acb-ac2f-4bc8-9ae0-9cb6fc9bdf20   2024.0      1024.41   \n",
       "98      -78.4  3b990a40-135e-442c-87db-b7551eb9b443   2283.0      3244.14   \n",
       "\n",
       "    total_payment  coverage county_name  \n",
       "35       11233.28   31700.0      Snyder  \n",
       "95        2341.81  350000.0       Bucks  \n",
       "96         126.27   70000.0       Blair  \n",
       "97        1024.41   70000.0       Blair  \n",
       "98        3244.14   70000.0       Blair  \n",
       "\n",
       "[5 rows x 74 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fill the county_name column and check what the dataframe looks like now\n",
    "\n",
    "nfip['county_name'] = nfip.apply(\n",
    "    lambda row: pa_counties.get(row['county_code'], row['county_name']),\n",
    "    axis = 1\n",
    ")\n",
    "nfip.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "49fcd64f-514e-4a7f-997f-8d16629543e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Not bad. Right away I see I want to clean up that date_of_loss column though.\n",
    "\n",
    "nfip.date_of_loss.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "321f7bb6-8549-403d-a5e5-e5da7c9a3bb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35         2004-09-17T00:00:00.000Z\n",
       "95         2007-04-15T00:00:00.000Z\n",
       "96         2014-06-11T00:00:00.000Z\n",
       "97         2015-07-14T00:00:00.000Z\n",
       "98         2017-05-01T00:00:00.000Z\n",
       "                     ...           \n",
       "2285237    2010-07-10T00:00:00.000Z\n",
       "2285238    2006-06-28T00:00:00.000Z\n",
       "2285239    2005-04-02T00:00:00.000Z\n",
       "2285240    2001-06-17T00:00:00.000Z\n",
       "2285241    2008-12-12T00:00:00.000Z\n",
       "Name: date_of_loss, Length: 76671, dtype: object"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nfip.date_of_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "007612e7-10d3-4ce9-aa14-cefd25a56e04",
   "metadata": {},
   "outputs": [],
   "source": [
    "nfip['date_of_loss'] = nfip['date_of_loss'].str[:10] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "1bdcaf10-189d-438a-9b69-f42866509b19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35         2004-09-17\n",
       "95         2007-04-15\n",
       "96         2014-06-11\n",
       "97         2015-07-14\n",
       "98         2017-05-01\n",
       "              ...    \n",
       "2285237    2010-07-10\n",
       "2285238    2006-06-28\n",
       "2285239    2005-04-02\n",
       "2285240    2001-06-17\n",
       "2285241    2008-12-12\n",
       "Name: date_of_loss, Length: 76671, dtype: object"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nfip.date_of_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "60ad26e1-07ac-4588-9b73-4485f8e0b1f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "nfip['date_of_loss'] = pd.to_datetime(nfip['date_of_loss'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "af0ebcb6-89b5-4c51-944a-6e9efefd1077",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35        2004-09-17\n",
       "95        2007-04-15\n",
       "96        2014-06-11\n",
       "97        2015-07-14\n",
       "98        2017-05-01\n",
       "             ...    \n",
       "2285237   2010-07-10\n",
       "2285238   2006-06-28\n",
       "2285239   2005-04-02\n",
       "2285240   2001-06-17\n",
       "2285241   2008-12-12\n",
       "Name: date_of_loss, Length: 76671, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nfip.date_of_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "054baa6b-79fc-458a-ac83-06e88662744b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35         1950-05-13T00:00:00.000Z\n",
       "95         1970-01-01T00:00:00.000Z\n",
       "96         1974-07-01T00:00:00.000Z\n",
       "97         1974-07-01T00:00:00.000Z\n",
       "98         1974-07-01T00:00:00.000Z\n",
       "                     ...           \n",
       "2285237    1986-01-01T00:00:00.000Z\n",
       "2285238    1953-01-01T00:00:00.000Z\n",
       "2285239    1953-01-01T00:00:00.000Z\n",
       "2285240    1950-07-01T00:00:00.000Z\n",
       "2285241    2002-01-01T00:00:00.000Z\n",
       "Name: original_construction_date, Length: 76671, dtype: object"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Another date column to clean\n",
    "\n",
    "nfip.original_construction_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "35bc0487-9bf5-4237-981d-4b9e48270c0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "nfip['original_construction_date'] = nfip['original_construction_date'].str[:10] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "c37e6db9-ed57-4896-9502-17975a9a8c87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35         1950-05-13\n",
       "95         1970-01-01\n",
       "96         1974-07-01\n",
       "97         1974-07-01\n",
       "98         1974-07-01\n",
       "              ...    \n",
       "2285237    1986-01-01\n",
       "2285238    1953-01-01\n",
       "2285239    1953-01-01\n",
       "2285240    1950-07-01\n",
       "2285241    2002-01-01\n",
       "Name: original_construction_date, Length: 76671, dtype: object"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nfip.original_construction_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "f0ece211-d735-4ca8-ba06-81c29630b864",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 'agriculture_structure_indicator',\n",
       " 1: 'as_of_date',\n",
       " 2: 'basement_enclosure_crawlspace_type',\n",
       " 3: 'policy_count',\n",
       " 4: 'crs_classification_code',\n",
       " 5: 'date_of_loss',\n",
       " 6: 'elevated_building_indicator',\n",
       " 7: 'elevation_certificate_indicator',\n",
       " 8: 'elevation_difference',\n",
       " 9: 'base_flood_elevation',\n",
       " 10: 'rated_flood_zone',\n",
       " 11: 'house_worship',\n",
       " 12: 'location_of_contents',\n",
       " 13: 'lowest_adjacent_grade',\n",
       " 14: 'lowest_floor_elevation',\n",
       " 15: 'number_of_floors_in_the_insured_building',\n",
       " 16: 'non_profit_indicator',\n",
       " 17: 'obstruction_type',\n",
       " 18: 'occupancy_type',\n",
       " 19: 'original_construction_date',\n",
       " 20: 'original_nb_date',\n",
       " 21: 'amount_paid_on_building_claim',\n",
       " 22: 'amount_paid_on_contents_claim',\n",
       " 23: 'amount_paid_on_increased_cost_of_compliance_claim',\n",
       " 24: 'post_firm_construction_indicator',\n",
       " 25: 'rate_method',\n",
       " 26: 'small_business_indicator_building',\n",
       " 27: 'total_building_insurance_coverage',\n",
       " 28: 'total_contents_insurance_coverage',\n",
       " 29: 'year_of_loss',\n",
       " 30: 'primary_residence_indicator',\n",
       " 31: 'building_damage_amount',\n",
       " 32: 'building_deductible_code',\n",
       " 33: 'net_building_payment_amount',\n",
       " 34: 'building_property_value',\n",
       " 35: 'cause_of_damage',\n",
       " 36: 'condominium_coverage_type_code',\n",
       " 37: 'contents_damage_amount',\n",
       " 38: 'contents_deductible_code',\n",
       " 39: 'net_contents_payment_amount',\n",
       " 40: 'contents_property_value',\n",
       " 41: 'disaster_assistance_coverage_required',\n",
       " 42: 'event_designation_number',\n",
       " 43: 'fico_number',\n",
       " 44: 'flood_characteristics_indicator',\n",
       " 45: 'flood_water_duration',\n",
       " 46: 'floodproofed_indicator',\n",
       " 47: 'flood_event',\n",
       " 48: 'icc_coverage',\n",
       " 49: 'net_icc_payment_amount',\n",
       " 50: 'nfip_rated_community_number',\n",
       " 51: 'nfip_community_number_current',\n",
       " 52: 'nfip_community_name',\n",
       " 53: 'non_payment_reason_contents',\n",
       " 54: 'non_payment_reason_building',\n",
       " 55: 'number_of_units',\n",
       " 56: 'building_replacement_cost',\n",
       " 57: 'contents_replacement_cost',\n",
       " 58: 'replacement_cost_basis',\n",
       " 59: 'state_owned_indicator',\n",
       " 60: 'water_depth',\n",
       " 61: 'flood_zone_current',\n",
       " 62: 'building_description_code',\n",
       " 63: 'rental_property_indicator',\n",
       " 64: 'reported_zip_code',\n",
       " 65: 'county_code',\n",
       " 66: 'latitude',\n",
       " 67: 'longitude',\n",
       " 68: 'id',\n",
       " 69: 'damage',\n",
       " 70: 'net_payment',\n",
       " 71: 'total_payment',\n",
       " 72: 'coverage',\n",
       " 73: 'county_name'}"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get ready to reorder my columns, making them easier for me to read and analyze.\n",
    "# First step is creating an enumerated dictionary of column names. (I love enumerating.)\n",
    "\n",
    "cols = list(nfip.columns)\n",
    "col_dict = {i: col for i, col in enumerate(cols)}\n",
    "col_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "13b5d8f3-272f-4e0b-9188-8f83b20777df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "74"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# After some tedious decisions, I think I have the order I want.\n",
    "# I check the length of my list of columns to make sure I have 74.\n",
    "\n",
    "my_list = [68, 5, 29, 19, 47, 64, 73, 65, 69, 72, 71, 70, 56, 57, 58, 54, 53, 34, 40, 35, 45, 60, 46,  2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 26, 27, 28, 30, 31, 37, 32, 33, 39, 36, 38, 41, 42, 43, 44, 48, 49, 50, 51, 52, 55, 59, 61, 62, 63, 66, 67, 0, 1]\n",
    "len(my_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "419131aa-bdd9-47fa-8ec8-c68fbd7e6033",
   "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>id</th>\n",
       "      <th>date_of_loss</th>\n",
       "      <th>year_of_loss</th>\n",
       "      <th>original_construction_date</th>\n",
       "      <th>flood_event</th>\n",
       "      <th>reported_zip_code</th>\n",
       "      <th>county_name</th>\n",
       "      <th>county_code</th>\n",
       "      <th>damage</th>\n",
       "      <th>coverage</th>\n",
       "      <th>...</th>\n",
       "      <th>nfip_community_name</th>\n",
       "      <th>number_of_units</th>\n",
       "      <th>state_owned_indicator</th>\n",
       "      <th>flood_zone_current</th>\n",
       "      <th>building_description_code</th>\n",
       "      <th>rental_property_indicator</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>agriculture_structure_indicator</th>\n",
       "      <th>as_of_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>8d3cd611-ca6b-4fd0-ae71-c5e852556767</td>\n",
       "      <td>2004-09-17</td>\n",
       "      <td>2004</td>\n",
       "      <td>1950-05-13</td>\n",
       "      <td>Tropical Storm Ivan</td>\n",
       "      <td>17870</td>\n",
       "      <td>Snyder</td>\n",
       "      <td>42109</td>\n",
       "      <td>12233.0</td>\n",
       "      <td>31700.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>A16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>40.8</td>\n",
       "      <td>-76.8</td>\n",
       "      <td>0</td>\n",
       "      <td>2021-07-24T13:15:12.062Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>f115927f-a8f4-4d7a-af49-ed93000ef678</td>\n",
       "      <td>2007-04-15</td>\n",
       "      <td>2007</td>\n",
       "      <td>1970-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19007</td>\n",
       "      <td>Bucks</td>\n",
       "      <td>42017</td>\n",
       "      <td>2842.0</td>\n",
       "      <td>350000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>40.1</td>\n",
       "      <td>-74.9</td>\n",
       "      <td>0</td>\n",
       "      <td>2020-01-22T16:55:53.194Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>2d054cf7-9513-41ec-b27b-b4973c530284</td>\n",
       "      <td>2014-06-11</td>\n",
       "      <td>2014</td>\n",
       "      <td>1974-07-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16602</td>\n",
       "      <td>Blair</td>\n",
       "      <td>42013</td>\n",
       "      <td>1126.0</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>40.5</td>\n",
       "      <td>-78.4</td>\n",
       "      <td>0</td>\n",
       "      <td>2020-01-22T16:55:53.194Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>bb486acb-ac2f-4bc8-9ae0-9cb6fc9bdf20</td>\n",
       "      <td>2015-07-14</td>\n",
       "      <td>2015</td>\n",
       "      <td>1974-07-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16602</td>\n",
       "      <td>Blair</td>\n",
       "      <td>42013</td>\n",
       "      <td>2024.0</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>40.5</td>\n",
       "      <td>-78.4</td>\n",
       "      <td>0</td>\n",
       "      <td>2020-01-22T16:55:53.194Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>3b990a40-135e-442c-87db-b7551eb9b443</td>\n",
       "      <td>2017-05-01</td>\n",
       "      <td>2017</td>\n",
       "      <td>1974-07-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16602</td>\n",
       "      <td>Blair</td>\n",
       "      <td>42013</td>\n",
       "      <td>2283.0</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>40.5</td>\n",
       "      <td>-78.4</td>\n",
       "      <td>0</td>\n",
       "      <td>2020-01-22T16:55:53.194Z</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 74 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      id date_of_loss  year_of_loss  \\\n",
       "35  8d3cd611-ca6b-4fd0-ae71-c5e852556767   2004-09-17          2004   \n",
       "95  f115927f-a8f4-4d7a-af49-ed93000ef678   2007-04-15          2007   \n",
       "96  2d054cf7-9513-41ec-b27b-b4973c530284   2014-06-11          2014   \n",
       "97  bb486acb-ac2f-4bc8-9ae0-9cb6fc9bdf20   2015-07-14          2015   \n",
       "98  3b990a40-135e-442c-87db-b7551eb9b443   2017-05-01          2017   \n",
       "\n",
       "   original_construction_date          flood_event reported_zip_code  \\\n",
       "35                 1950-05-13  Tropical Storm Ivan             17870   \n",
       "95                 1970-01-01                  NaN             19007   \n",
       "96                 1974-07-01                  NaN             16602   \n",
       "97                 1974-07-01                  NaN             16602   \n",
       "98                 1974-07-01                  NaN             16602   \n",
       "\n",
       "   county_name county_code   damage  coverage  ...  nfip_community_name  \\\n",
       "35      Snyder       42109  12233.0   31700.0  ...                  NaN   \n",
       "95       Bucks       42017   2842.0  350000.0  ...                  NaN   \n",
       "96       Blair       42013   1126.0   70000.0  ...                  NaN   \n",
       "97       Blair       42013   2024.0   70000.0  ...                  NaN   \n",
       "98       Blair       42013   2283.0   70000.0  ...                  NaN   \n",
       "\n",
       "    number_of_units  state_owned_indicator  flood_zone_current  \\\n",
       "35              0.0                      0                 A16   \n",
       "95              1.0                      0                 NaN   \n",
       "96              1.0                      0                 NaN   \n",
       "97              1.0                      0                 NaN   \n",
       "98              1.0                      0                 NaN   \n",
       "\n",
       "   building_description_code  rental_property_indicator  latitude  longitude  \\\n",
       "35                       NaN                          0      40.8      -76.8   \n",
       "95                       1.0                          0      40.1      -74.9   \n",
       "96                       1.0                          0      40.5      -78.4   \n",
       "97                       1.0                          0      40.5      -78.4   \n",
       "98                       1.0                          0      40.5      -78.4   \n",
       "\n",
       "    agriculture_structure_indicator                as_of_date  \n",
       "35                                0  2021-07-24T13:15:12.062Z  \n",
       "95                                0  2020-01-22T16:55:53.194Z  \n",
       "96                                0  2020-01-22T16:55:53.194Z  \n",
       "97                                0  2020-01-22T16:55:53.194Z  \n",
       "98                                0  2020-01-22T16:55:53.194Z  \n",
       "\n",
       "[5 rows x 74 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Alright, let's scramble!\n",
    "# And check what it looks like\n",
    "\n",
    "nfip = nfip.iloc[:, [68, 5, 29, 19, 47, 64, 73, 65, 69, 72, 71, 70, 56, 57, 58, 54, 53, 34, 40, 35, 45, 60, 46,  2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 26, 27, 28, 30, 31, 37, 32, 33, 39, 36, 38, 41, 42, 43, 44, 48, 49, 50, 51, 52, 55, 59, 61, 62, 63, 66, 67, 0, 1]]\n",
    "nfip.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "8943d33f-3fdd-4454-9216-182a401a2813",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(76671, 74)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nfip.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "3bced314-9a83-4e11-8d26-3534170ac557",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The 'non_payment_reason_building' column looks like it could be helpful information. But it's numeric;\n",
    "# I want the language each numeric code represents, so I create a dictionary.\n",
    "\n",
    "npr = {\n",
    "1 : 'Claim denied that was less than deductible', \n",
    "2 : 'Seepage', \n",
    "3 : 'Backup drains', \n",
    "4 : 'Shrubs not covered', \n",
    "5 : 'Sea wall', \n",
    "6 : 'Not actual flood', \n",
    "7 : 'Loss in progress', \n",
    "8 : 'Failure to pursue claim', \n",
    "9 : 'Debris removal only', \n",
    "10 : 'Fire', \n",
    "11 : 'Fence damage', \n",
    "12 : 'Hydrostatic pressure', \n",
    "13 : 'Drainage clogged', \n",
    "14 : 'Boat piers', \n",
    "15 : 'Not insured, damage before inception of policy', \n",
    "16 : 'Not insured, wind damage', \n",
    "17 : 'Type of erosion not included in definition of flood or flooding', \n",
    "18 : 'Landslide', \n",
    "19 : 'Type of mudflow not included in definition of flood or flooding', \n",
    "20 : 'No demonstrable damage', \n",
    "97 : 'Other', \n",
    "98 : 'Error-delete claim (no assignment)',\n",
    "99 : 'Erroneous assignment'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "0e407c1e-7bc8-4991-a9f9-b5a602f002f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# I'm always nervous to change a column directly, so even though I'll eventually drop the column with numeric codes, I create a second nonpayment reason column, for building and contents each.\n",
    "\n",
    "nfip['non_payment_reason_contents_2'] = ''\n",
    "nfip['non_payment_reason_building_2'] = ''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "06b78888-959c-4a9b-b5c7-8b3d2297dd41",
   "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>id</th>\n",
       "      <th>date_of_loss</th>\n",
       "      <th>year_of_loss</th>\n",
       "      <th>original_construction_date</th>\n",
       "      <th>flood_event</th>\n",
       "      <th>reported_zip_code</th>\n",
       "      <th>county_name</th>\n",
       "      <th>county_code</th>\n",
       "      <th>damage</th>\n",
       "      <th>coverage</th>\n",
       "      <th>...</th>\n",
       "      <th>state_owned_indicator</th>\n",
       "      <th>flood_zone_current</th>\n",
       "      <th>building_description_code</th>\n",
       "      <th>rental_property_indicator</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>agriculture_structure_indicator</th>\n",
       "      <th>as_of_date</th>\n",
       "      <th>non_payment_reason_contents_2</th>\n",
       "      <th>non_payment_reason_building_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>8d3cd611-ca6b-4fd0-ae71-c5e852556767</td>\n",
       "      <td>2004-09-17</td>\n",
       "      <td>2004</td>\n",
       "      <td>1950-05-13</td>\n",
       "      <td>Tropical Storm Ivan</td>\n",
       "      <td>17870</td>\n",
       "      <td>Snyder</td>\n",
       "      <td>42109</td>\n",
       "      <td>12233.0</td>\n",
       "      <td>31700.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>A16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>40.8</td>\n",
       "      <td>-76.8</td>\n",
       "      <td>0</td>\n",
       "      <td>2021-07-24T13:15:12.062Z</td>\n",
       "      <td>No demonstrable damage</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>f115927f-a8f4-4d7a-af49-ed93000ef678</td>\n",
       "      <td>2007-04-15</td>\n",
       "      <td>2007</td>\n",
       "      <td>1970-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19007</td>\n",
       "      <td>Bucks</td>\n",
       "      <td>42017</td>\n",
       "      <td>2842.0</td>\n",
       "      <td>350000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>40.1</td>\n",
       "      <td>-74.9</td>\n",
       "      <td>0</td>\n",
       "      <td>2020-01-22T16:55:53.194Z</td>\n",
       "      <td>No demonstrable damage</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>2d054cf7-9513-41ec-b27b-b4973c530284</td>\n",
       "      <td>2014-06-11</td>\n",
       "      <td>2014</td>\n",
       "      <td>1974-07-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16602</td>\n",
       "      <td>Blair</td>\n",
       "      <td>42013</td>\n",
       "      <td>1126.0</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>40.5</td>\n",
       "      <td>-78.4</td>\n",
       "      <td>0</td>\n",
       "      <td>2020-01-22T16:55:53.194Z</td>\n",
       "      <td>No demonstrable damage</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>bb486acb-ac2f-4bc8-9ae0-9cb6fc9bdf20</td>\n",
       "      <td>2015-07-14</td>\n",
       "      <td>2015</td>\n",
       "      <td>1974-07-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16602</td>\n",
       "      <td>Blair</td>\n",
       "      <td>42013</td>\n",
       "      <td>2024.0</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>40.5</td>\n",
       "      <td>-78.4</td>\n",
       "      <td>0</td>\n",
       "      <td>2020-01-22T16:55:53.194Z</td>\n",
       "      <td>No demonstrable damage</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>3b990a40-135e-442c-87db-b7551eb9b443</td>\n",
       "      <td>2017-05-01</td>\n",
       "      <td>2017</td>\n",
       "      <td>1974-07-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16602</td>\n",
       "      <td>Blair</td>\n",
       "      <td>42013</td>\n",
       "      <td>2283.0</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>40.5</td>\n",
       "      <td>-78.4</td>\n",
       "      <td>0</td>\n",
       "      <td>2020-01-22T16:55:53.194Z</td>\n",
       "      <td>Other</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 76 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      id date_of_loss  year_of_loss  \\\n",
       "35  8d3cd611-ca6b-4fd0-ae71-c5e852556767   2004-09-17          2004   \n",
       "95  f115927f-a8f4-4d7a-af49-ed93000ef678   2007-04-15          2007   \n",
       "96  2d054cf7-9513-41ec-b27b-b4973c530284   2014-06-11          2014   \n",
       "97  bb486acb-ac2f-4bc8-9ae0-9cb6fc9bdf20   2015-07-14          2015   \n",
       "98  3b990a40-135e-442c-87db-b7551eb9b443   2017-05-01          2017   \n",
       "\n",
       "   original_construction_date          flood_event reported_zip_code  \\\n",
       "35                 1950-05-13  Tropical Storm Ivan             17870   \n",
       "95                 1970-01-01                  NaN             19007   \n",
       "96                 1974-07-01                  NaN             16602   \n",
       "97                 1974-07-01                  NaN             16602   \n",
       "98                 1974-07-01                  NaN             16602   \n",
       "\n",
       "   county_name county_code   damage  coverage  ...  state_owned_indicator  \\\n",
       "35      Snyder       42109  12233.0   31700.0  ...                      0   \n",
       "95       Bucks       42017   2842.0  350000.0  ...                      0   \n",
       "96       Blair       42013   1126.0   70000.0  ...                      0   \n",
       "97       Blair       42013   2024.0   70000.0  ...                      0   \n",
       "98       Blair       42013   2283.0   70000.0  ...                      0   \n",
       "\n",
       "    flood_zone_current  building_description_code  rental_property_indicator  \\\n",
       "35                 A16                        NaN                          0   \n",
       "95                 NaN                        1.0                          0   \n",
       "96                 NaN                        1.0                          0   \n",
       "97                 NaN                        1.0                          0   \n",
       "98                 NaN                        1.0                          0   \n",
       "\n",
       "   latitude  longitude  agriculture_structure_indicator  \\\n",
       "35     40.8      -76.8                                0   \n",
       "95     40.1      -74.9                                0   \n",
       "96     40.5      -78.4                                0   \n",
       "97     40.5      -78.4                                0   \n",
       "98     40.5      -78.4                                0   \n",
       "\n",
       "                  as_of_date  non_payment_reason_contents_2  \\\n",
       "35  2021-07-24T13:15:12.062Z         No demonstrable damage   \n",
       "95  2020-01-22T16:55:53.194Z         No demonstrable damage   \n",
       "96  2020-01-22T16:55:53.194Z         No demonstrable damage   \n",
       "97  2020-01-22T16:55:53.194Z         No demonstrable damage   \n",
       "98  2020-01-22T16:55:53.194Z                          Other   \n",
       "\n",
       "   non_payment_reason_building_2  \n",
       "35                                \n",
       "95                                \n",
       "96                                \n",
       "97                                \n",
       "98                                \n",
       "\n",
       "[5 rows x 76 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# And now I fill those columns\n",
    "\n",
    "nfip['non_payment_reason_contents_2'] = nfip.apply(\n",
    "    lambda row: npr.get(row['non_payment_reason_contents'], row['non_payment_reason_contents_2']),\n",
    "    axis = 1\n",
    ")\n",
    "nfip.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "8213752b-61a1-48d2-991f-07e986b02f1b",
   "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>id</th>\n",
       "      <th>date_of_loss</th>\n",
       "      <th>year_of_loss</th>\n",
       "      <th>original_construction_date</th>\n",
       "      <th>flood_event</th>\n",
       "      <th>reported_zip_code</th>\n",
       "      <th>county_name</th>\n",
       "      <th>county_code</th>\n",
       "      <th>damage</th>\n",
       "      <th>coverage</th>\n",
       "      <th>...</th>\n",
       "      <th>state_owned_indicator</th>\n",
       "      <th>flood_zone_current</th>\n",
       "      <th>building_description_code</th>\n",
       "      <th>rental_property_indicator</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>agriculture_structure_indicator</th>\n",
       "      <th>as_of_date</th>\n",
       "      <th>non_payment_reason_contents_2</th>\n",
       "      <th>non_payment_reason_building_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>8d3cd611-ca6b-4fd0-ae71-c5e852556767</td>\n",
       "      <td>2004-09-17</td>\n",
       "      <td>2004</td>\n",
       "      <td>1950-05-13</td>\n",
       "      <td>Tropical Storm Ivan</td>\n",
       "      <td>17870</td>\n",
       "      <td>Snyder</td>\n",
       "      <td>42109</td>\n",
       "      <td>12233.0</td>\n",
       "      <td>31700.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>A16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>40.8</td>\n",
       "      <td>-76.8</td>\n",
       "      <td>0</td>\n",
       "      <td>2021-07-24T13:15:12.062Z</td>\n",
       "      <td>No demonstrable damage</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>f115927f-a8f4-4d7a-af49-ed93000ef678</td>\n",
       "      <td>2007-04-15</td>\n",
       "      <td>2007</td>\n",
       "      <td>1970-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19007</td>\n",
       "      <td>Bucks</td>\n",
       "      <td>42017</td>\n",
       "      <td>2842.0</td>\n",
       "      <td>350000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>40.1</td>\n",
       "      <td>-74.9</td>\n",
       "      <td>0</td>\n",
       "      <td>2020-01-22T16:55:53.194Z</td>\n",
       "      <td>No demonstrable damage</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>2d054cf7-9513-41ec-b27b-b4973c530284</td>\n",
       "      <td>2014-06-11</td>\n",
       "      <td>2014</td>\n",
       "      <td>1974-07-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16602</td>\n",
       "      <td>Blair</td>\n",
       "      <td>42013</td>\n",
       "      <td>1126.0</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>40.5</td>\n",
       "      <td>-78.4</td>\n",
       "      <td>0</td>\n",
       "      <td>2020-01-22T16:55:53.194Z</td>\n",
       "      <td>No demonstrable damage</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>bb486acb-ac2f-4bc8-9ae0-9cb6fc9bdf20</td>\n",
       "      <td>2015-07-14</td>\n",
       "      <td>2015</td>\n",
       "      <td>1974-07-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16602</td>\n",
       "      <td>Blair</td>\n",
       "      <td>42013</td>\n",
       "      <td>2024.0</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>40.5</td>\n",
       "      <td>-78.4</td>\n",
       "      <td>0</td>\n",
       "      <td>2020-01-22T16:55:53.194Z</td>\n",
       "      <td>No demonstrable damage</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>3b990a40-135e-442c-87db-b7551eb9b443</td>\n",
       "      <td>2017-05-01</td>\n",
       "      <td>2017</td>\n",
       "      <td>1974-07-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16602</td>\n",
       "      <td>Blair</td>\n",
       "      <td>42013</td>\n",
       "      <td>2283.0</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>40.5</td>\n",
       "      <td>-78.4</td>\n",
       "      <td>0</td>\n",
       "      <td>2020-01-22T16:55:53.194Z</td>\n",
       "      <td>Other</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 76 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      id date_of_loss  year_of_loss  \\\n",
       "35  8d3cd611-ca6b-4fd0-ae71-c5e852556767   2004-09-17          2004   \n",
       "95  f115927f-a8f4-4d7a-af49-ed93000ef678   2007-04-15          2007   \n",
       "96  2d054cf7-9513-41ec-b27b-b4973c530284   2014-06-11          2014   \n",
       "97  bb486acb-ac2f-4bc8-9ae0-9cb6fc9bdf20   2015-07-14          2015   \n",
       "98  3b990a40-135e-442c-87db-b7551eb9b443   2017-05-01          2017   \n",
       "\n",
       "   original_construction_date          flood_event reported_zip_code  \\\n",
       "35                 1950-05-13  Tropical Storm Ivan             17870   \n",
       "95                 1970-01-01                  NaN             19007   \n",
       "96                 1974-07-01                  NaN             16602   \n",
       "97                 1974-07-01                  NaN             16602   \n",
       "98                 1974-07-01                  NaN             16602   \n",
       "\n",
       "   county_name county_code   damage  coverage  ...  state_owned_indicator  \\\n",
       "35      Snyder       42109  12233.0   31700.0  ...                      0   \n",
       "95       Bucks       42017   2842.0  350000.0  ...                      0   \n",
       "96       Blair       42013   1126.0   70000.0  ...                      0   \n",
       "97       Blair       42013   2024.0   70000.0  ...                      0   \n",
       "98       Blair       42013   2283.0   70000.0  ...                      0   \n",
       "\n",
       "    flood_zone_current  building_description_code  rental_property_indicator  \\\n",
       "35                 A16                        NaN                          0   \n",
       "95                 NaN                        1.0                          0   \n",
       "96                 NaN                        1.0                          0   \n",
       "97                 NaN                        1.0                          0   \n",
       "98                 NaN                        1.0                          0   \n",
       "\n",
       "   latitude  longitude  agriculture_structure_indicator  \\\n",
       "35     40.8      -76.8                                0   \n",
       "95     40.1      -74.9                                0   \n",
       "96     40.5      -78.4                                0   \n",
       "97     40.5      -78.4                                0   \n",
       "98     40.5      -78.4                                0   \n",
       "\n",
       "                  as_of_date  non_payment_reason_contents_2  \\\n",
       "35  2021-07-24T13:15:12.062Z         No demonstrable damage   \n",
       "95  2020-01-22T16:55:53.194Z         No demonstrable damage   \n",
       "96  2020-01-22T16:55:53.194Z         No demonstrable damage   \n",
       "97  2020-01-22T16:55:53.194Z         No demonstrable damage   \n",
       "98  2020-01-22T16:55:53.194Z                          Other   \n",
       "\n",
       "   non_payment_reason_building_2  \n",
       "35                                \n",
       "95                                \n",
       "96                                \n",
       "97                                \n",
       "98                                \n",
       "\n",
       "[5 rows x 76 columns]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nfip['non_payment_reason_building_2'] = nfip.apply(\n",
    "    lambda row: npr.get(row['non_payment_reason_building'], row['non_payment_reason_building_2']),\n",
    "    axis = 1\n",
    ")\n",
    "nfip.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "cab39eb7-cf7a-49ae-9c51-6e74e54c8c27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['', 'Error-delete claim (no assignment)', 'No demonstrable damage',\n",
       "       'Not actual flood', 'Erroneous assignment',\n",
       "       'Claim denied that was less than deductible',\n",
       "       'Failure to pursue claim', 'Other', 'Boat piers', 'Seepage',\n",
       "       'Backup drains', 'Sea wall', 'Not insured, wind damage',\n",
       "       'Not insured, damage before inception of policy',\n",
       "       'Hydrostatic pressure', 'Shrubs not covered', 'Fence damage',\n",
       "       'Drainage clogged',\n",
       "       'Type of mudflow not included in definition of flood or flooding',\n",
       "       'Type of erosion not included in definition of flood or flooding',\n",
       "       'Landslide', 'Fire', 'Debris removal only', 'Loss in progress'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# I get nervous seeing the first five rows of a new column blank.\n",
    "# Let's take a look at that column's unique values.\n",
    "\n",
    "nfip['non_payment_reason_building_2'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "dbe9e19b-3245-442d-9708-19d7e1ac2f08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['No demonstrable damage', 'Other', '',\n",
       "       'Error-delete claim (no assignment)',\n",
       "       'Claim denied that was less than deductible', 'Not actual flood',\n",
       "       'Erroneous assignment', 'Failure to pursue claim', 'Boat piers',\n",
       "       'Loss in progress', 'Seepage',\n",
       "       'Not insured, damage before inception of policy', 'Backup drains',\n",
       "       'Hydrostatic pressure', 'Sea wall', 'Fence damage',\n",
       "       'Drainage clogged',\n",
       "       'Type of mudflow not included in definition of flood or flooding',\n",
       "       'Not insured, wind damage',\n",
       "       'Type of erosion not included in definition of flood or flooding',\n",
       "       'Landslide', 'Fire', 'Shrubs not covered', 'Debris removal only'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Phew. non_payment_reason_contents_2 looks OK from the .head(), but for good measure I'll run .unique() on it too.\n",
    "\n",
    "nfip['non_payment_reason_contents_2'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "211fcdf1-928b-4af3-b3aa-fec10694e088",
   "metadata": {},
   "outputs": [],
   "source": [
    "# OK I feel ready to drop the originals\n",
    "\n",
    "nfip = nfip.drop(['non_payment_reason_building', 'non_payment_reason_contents'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "37afc2eb-cdcf-4a9a-8af4-30fe0f111757",
   "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>id</th>\n",
       "      <th>date_of_loss</th>\n",
       "      <th>year_of_loss</th>\n",
       "      <th>original_construction_date</th>\n",
       "      <th>flood_event</th>\n",
       "      <th>reported_zip_code</th>\n",
       "      <th>county_name</th>\n",
       "      <th>county_code</th>\n",
       "      <th>damage</th>\n",
       "      <th>coverage</th>\n",
       "      <th>...</th>\n",
       "      <th>state_owned_indicator</th>\n",
       "      <th>flood_zone_current</th>\n",
       "      <th>building_description_code</th>\n",
       "      <th>rental_property_indicator</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>agriculture_structure_indicator</th>\n",
       "      <th>as_of_date</th>\n",
       "      <th>nonpayment_reason_contents</th>\n",
       "      <th>nonpayment_reason_building</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>8d3cd611-ca6b-4fd0-ae71-c5e852556767</td>\n",
       "      <td>2004-09-17</td>\n",
       "      <td>2004</td>\n",
       "      <td>1950-05-13</td>\n",
       "      <td>Tropical Storm Ivan</td>\n",
       "      <td>17870</td>\n",
       "      <td>Snyder</td>\n",
       "      <td>42109</td>\n",
       "      <td>12233.0</td>\n",
       "      <td>31700.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>A16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>40.8</td>\n",
       "      <td>-76.8</td>\n",
       "      <td>0</td>\n",
       "      <td>2021-07-24T13:15:12.062Z</td>\n",
       "      <td>No demonstrable damage</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>f115927f-a8f4-4d7a-af49-ed93000ef678</td>\n",
       "      <td>2007-04-15</td>\n",
       "      <td>2007</td>\n",
       "      <td>1970-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19007</td>\n",
       "      <td>Bucks</td>\n",
       "      <td>42017</td>\n",
       "      <td>2842.0</td>\n",
       "      <td>350000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>40.1</td>\n",
       "      <td>-74.9</td>\n",
       "      <td>0</td>\n",
       "      <td>2020-01-22T16:55:53.194Z</td>\n",
       "      <td>No demonstrable damage</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>2d054cf7-9513-41ec-b27b-b4973c530284</td>\n",
       "      <td>2014-06-11</td>\n",
       "      <td>2014</td>\n",
       "      <td>1974-07-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16602</td>\n",
       "      <td>Blair</td>\n",
       "      <td>42013</td>\n",
       "      <td>1126.0</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>40.5</td>\n",
       "      <td>-78.4</td>\n",
       "      <td>0</td>\n",
       "      <td>2020-01-22T16:55:53.194Z</td>\n",
       "      <td>No demonstrable damage</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>bb486acb-ac2f-4bc8-9ae0-9cb6fc9bdf20</td>\n",
       "      <td>2015-07-14</td>\n",
       "      <td>2015</td>\n",
       "      <td>1974-07-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16602</td>\n",
       "      <td>Blair</td>\n",
       "      <td>42013</td>\n",
       "      <td>2024.0</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>40.5</td>\n",
       "      <td>-78.4</td>\n",
       "      <td>0</td>\n",
       "      <td>2020-01-22T16:55:53.194Z</td>\n",
       "      <td>No demonstrable damage</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>3b990a40-135e-442c-87db-b7551eb9b443</td>\n",
       "      <td>2017-05-01</td>\n",
       "      <td>2017</td>\n",
       "      <td>1974-07-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16602</td>\n",
       "      <td>Blair</td>\n",
       "      <td>42013</td>\n",
       "      <td>2283.0</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>40.5</td>\n",
       "      <td>-78.4</td>\n",
       "      <td>0</td>\n",
       "      <td>2020-01-22T16:55:53.194Z</td>\n",
       "      <td>Other</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 74 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      id date_of_loss  year_of_loss  \\\n",
       "35  8d3cd611-ca6b-4fd0-ae71-c5e852556767   2004-09-17          2004   \n",
       "95  f115927f-a8f4-4d7a-af49-ed93000ef678   2007-04-15          2007   \n",
       "96  2d054cf7-9513-41ec-b27b-b4973c530284   2014-06-11          2014   \n",
       "97  bb486acb-ac2f-4bc8-9ae0-9cb6fc9bdf20   2015-07-14          2015   \n",
       "98  3b990a40-135e-442c-87db-b7551eb9b443   2017-05-01          2017   \n",
       "\n",
       "   original_construction_date          flood_event reported_zip_code  \\\n",
       "35                 1950-05-13  Tropical Storm Ivan             17870   \n",
       "95                 1970-01-01                  NaN             19007   \n",
       "96                 1974-07-01                  NaN             16602   \n",
       "97                 1974-07-01                  NaN             16602   \n",
       "98                 1974-07-01                  NaN             16602   \n",
       "\n",
       "   county_name county_code   damage  coverage  ...  state_owned_indicator  \\\n",
       "35      Snyder       42109  12233.0   31700.0  ...                      0   \n",
       "95       Bucks       42017   2842.0  350000.0  ...                      0   \n",
       "96       Blair       42013   1126.0   70000.0  ...                      0   \n",
       "97       Blair       42013   2024.0   70000.0  ...                      0   \n",
       "98       Blair       42013   2283.0   70000.0  ...                      0   \n",
       "\n",
       "    flood_zone_current  building_description_code  rental_property_indicator  \\\n",
       "35                 A16                        NaN                          0   \n",
       "95                 NaN                        1.0                          0   \n",
       "96                 NaN                        1.0                          0   \n",
       "97                 NaN                        1.0                          0   \n",
       "98                 NaN                        1.0                          0   \n",
       "\n",
       "   latitude  longitude  agriculture_structure_indicator  \\\n",
       "35     40.8      -76.8                                0   \n",
       "95     40.1      -74.9                                0   \n",
       "96     40.5      -78.4                                0   \n",
       "97     40.5      -78.4                                0   \n",
       "98     40.5      -78.4                                0   \n",
       "\n",
       "                  as_of_date  nonpayment_reason_contents  \\\n",
       "35  2021-07-24T13:15:12.062Z      No demonstrable damage   \n",
       "95  2020-01-22T16:55:53.194Z      No demonstrable damage   \n",
       "96  2020-01-22T16:55:53.194Z      No demonstrable damage   \n",
       "97  2020-01-22T16:55:53.194Z      No demonstrable damage   \n",
       "98  2020-01-22T16:55:53.194Z                       Other   \n",
       "\n",
       "    nonpayment_reason_building  \n",
       "35                              \n",
       "95                              \n",
       "96                              \n",
       "97                              \n",
       "98                              \n",
       "\n",
       "[5 rows x 74 columns]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Rename my new columns so they don't look like second bananas\n",
    "\n",
    "nfip = nfip.rename(columns = {'non_payment_reason_contents_2': 'nonpayment_reason_contents', 'non_payment_reason_building_2': 'nonpayment_reason_building'})\n",
    "nfip.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "7d13ef5f-9a89-4f70-a06a-6d8e3417556b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: 'id', 1: 'date_of_loss', 2: 'year_of_loss', 3: 'original_construction_date', 4: 'flood_event', 5: 'reported_zip_code', 6: 'county_name', 7: 'county_code', 8: 'damage', 9: 'coverage', 10: 'total_payment', 11: 'net_payment', 12: 'building_replacement_cost', 13: 'contents_replacement_cost', 14: 'replacement_cost_basis', 15: 'building_property_value', 16: 'contents_property_value', 17: 'cause_of_damage', 18: 'flood_water_duration', 19: 'water_depth', 20: 'floodproofed_indicator', 21: 'basement_enclosure_crawlspace_type', 22: 'policy_count', 23: 'crs_classification_code', 24: 'elevated_building_indicator', 25: 'elevation_certificate_indicator', 26: 'elevation_difference', 27: 'base_flood_elevation', 28: 'rated_flood_zone', 29: 'house_worship', 30: 'location_of_contents', 31: 'lowest_adjacent_grade', 32: 'lowest_floor_elevation', 33: 'number_of_floors_in_the_insured_building', 34: 'non_profit_indicator', 35: 'obstruction_type', 36: 'occupancy_type', 37: 'original_nb_date', 38: 'amount_paid_on_building_claim', 39: 'amount_paid_on_contents_claim', 40: 'amount_paid_on_increased_cost_of_compliance_claim', 41: 'post_firm_construction_indicator', 42: 'rate_method', 43: 'small_business_indicator_building', 44: 'total_building_insurance_coverage', 45: 'total_contents_insurance_coverage', 46: 'primary_residence_indicator', 47: 'building_damage_amount', 48: 'contents_damage_amount', 49: 'building_deductible_code', 50: 'net_building_payment_amount', 51: 'net_contents_payment_amount', 52: 'condominium_coverage_type_code', 53: 'contents_deductible_code', 54: 'disaster_assistance_coverage_required', 55: 'event_designation_number', 56: 'fico_number', 57: 'flood_characteristics_indicator', 58: 'icc_coverage', 59: 'net_icc_payment_amount', 60: 'nfip_rated_community_number', 61: 'nfip_community_number_current', 62: 'nfip_community_name', 63: 'number_of_units', 64: 'state_owned_indicator', 65: 'flood_zone_current', 66: 'building_description_code', 67: 'rental_property_indicator', 68: 'latitude', 69: 'longitude', 70: 'agriculture_structure_indicator', 71: 'as_of_date', 72: 'nonpayment_reason_contents', 73: 'nonpayment_reason_building'}\n"
     ]
    }
   ],
   "source": [
    "# Do this reordering thing again so my new columns are in a more sensible place\n",
    "# More enumerating, yay! This time I'll print the dictionary so it doesn't take up so much space.\n",
    "\n",
    "cols = list(nfip.columns)\n",
    "col_dict = {i: col for i, col in enumerate(cols)}\n",
    "print(col_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "b11e224d-a26e-4c5f-9f3a-25c3d37e902d",
   "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>id</th>\n",
       "      <th>date_of_loss</th>\n",
       "      <th>year_of_loss</th>\n",
       "      <th>original_construction_date</th>\n",
       "      <th>flood_event</th>\n",
       "      <th>reported_zip_code</th>\n",
       "      <th>county_name</th>\n",
       "      <th>county_code</th>\n",
       "      <th>damage</th>\n",
       "      <th>coverage</th>\n",
       "      <th>...</th>\n",
       "      <th>nfip_community_name</th>\n",
       "      <th>number_of_units</th>\n",
       "      <th>state_owned_indicator</th>\n",
       "      <th>flood_zone_current</th>\n",
       "      <th>building_description_code</th>\n",
       "      <th>rental_property_indicator</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>agriculture_structure_indicator</th>\n",
       "      <th>as_of_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>8d3cd611-ca6b-4fd0-ae71-c5e852556767</td>\n",
       "      <td>2004-09-17</td>\n",
       "      <td>2004</td>\n",
       "      <td>1950-05-13</td>\n",
       "      <td>Tropical Storm Ivan</td>\n",
       "      <td>17870</td>\n",
       "      <td>Snyder</td>\n",
       "      <td>42109</td>\n",
       "      <td>12233.0</td>\n",
       "      <td>31700.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>A16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>40.8</td>\n",
       "      <td>-76.8</td>\n",
       "      <td>0</td>\n",
       "      <td>2021-07-24T13:15:12.062Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>f115927f-a8f4-4d7a-af49-ed93000ef678</td>\n",
       "      <td>2007-04-15</td>\n",
       "      <td>2007</td>\n",
       "      <td>1970-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19007</td>\n",
       "      <td>Bucks</td>\n",
       "      <td>42017</td>\n",
       "      <td>2842.0</td>\n",
       "      <td>350000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>40.1</td>\n",
       "      <td>-74.9</td>\n",
       "      <td>0</td>\n",
       "      <td>2020-01-22T16:55:53.194Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>2d054cf7-9513-41ec-b27b-b4973c530284</td>\n",
       "      <td>2014-06-11</td>\n",
       "      <td>2014</td>\n",
       "      <td>1974-07-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16602</td>\n",
       "      <td>Blair</td>\n",
       "      <td>42013</td>\n",
       "      <td>1126.0</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>40.5</td>\n",
       "      <td>-78.4</td>\n",
       "      <td>0</td>\n",
       "      <td>2020-01-22T16:55:53.194Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>bb486acb-ac2f-4bc8-9ae0-9cb6fc9bdf20</td>\n",
       "      <td>2015-07-14</td>\n",
       "      <td>2015</td>\n",
       "      <td>1974-07-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16602</td>\n",
       "      <td>Blair</td>\n",
       "      <td>42013</td>\n",
       "      <td>2024.0</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>40.5</td>\n",
       "      <td>-78.4</td>\n",
       "      <td>0</td>\n",
       "      <td>2020-01-22T16:55:53.194Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>3b990a40-135e-442c-87db-b7551eb9b443</td>\n",
       "      <td>2017-05-01</td>\n",
       "      <td>2017</td>\n",
       "      <td>1974-07-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16602</td>\n",
       "      <td>Blair</td>\n",
       "      <td>42013</td>\n",
       "      <td>2283.0</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>40.5</td>\n",
       "      <td>-78.4</td>\n",
       "      <td>0</td>\n",
       "      <td>2020-01-22T16:55:53.194Z</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 74 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      id date_of_loss  year_of_loss  \\\n",
       "35  8d3cd611-ca6b-4fd0-ae71-c5e852556767   2004-09-17          2004   \n",
       "95  f115927f-a8f4-4d7a-af49-ed93000ef678   2007-04-15          2007   \n",
       "96  2d054cf7-9513-41ec-b27b-b4973c530284   2014-06-11          2014   \n",
       "97  bb486acb-ac2f-4bc8-9ae0-9cb6fc9bdf20   2015-07-14          2015   \n",
       "98  3b990a40-135e-442c-87db-b7551eb9b443   2017-05-01          2017   \n",
       "\n",
       "   original_construction_date          flood_event reported_zip_code  \\\n",
       "35                 1950-05-13  Tropical Storm Ivan             17870   \n",
       "95                 1970-01-01                  NaN             19007   \n",
       "96                 1974-07-01                  NaN             16602   \n",
       "97                 1974-07-01                  NaN             16602   \n",
       "98                 1974-07-01                  NaN             16602   \n",
       "\n",
       "   county_name county_code   damage  coverage  ...  nfip_community_name  \\\n",
       "35      Snyder       42109  12233.0   31700.0  ...                  NaN   \n",
       "95       Bucks       42017   2842.0  350000.0  ...                  NaN   \n",
       "96       Blair       42013   1126.0   70000.0  ...                  NaN   \n",
       "97       Blair       42013   2024.0   70000.0  ...                  NaN   \n",
       "98       Blair       42013   2283.0   70000.0  ...                  NaN   \n",
       "\n",
       "    number_of_units state_owned_indicator flood_zone_current  \\\n",
       "35              0.0                     0                A16   \n",
       "95              1.0                     0                NaN   \n",
       "96              1.0                     0                NaN   \n",
       "97              1.0                     0                NaN   \n",
       "98              1.0                     0                NaN   \n",
       "\n",
       "    building_description_code  rental_property_indicator latitude  longitude  \\\n",
       "35                        NaN                          0     40.8      -76.8   \n",
       "95                        1.0                          0     40.1      -74.9   \n",
       "96                        1.0                          0     40.5      -78.4   \n",
       "97                        1.0                          0     40.5      -78.4   \n",
       "98                        1.0                          0     40.5      -78.4   \n",
       "\n",
       "    agriculture_structure_indicator                as_of_date  \n",
       "35                                0  2021-07-24T13:15:12.062Z  \n",
       "95                                0  2020-01-22T16:55:53.194Z  \n",
       "96                                0  2020-01-22T16:55:53.194Z  \n",
       "97                                0  2020-01-22T16:55:53.194Z  \n",
       "98                                0  2020-01-22T16:55:53.194Z  \n",
       "\n",
       "[5 rows x 74 columns]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nfip = nfip.iloc[:, [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 72, 73, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71]]\n",
    "\n",
    "nfip.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "5af5348f-6b16-431b-a976-d50beac3f8d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# And let's download this as a .csv!\n",
    "# I tend to save these by state, so I'll specify PA in the file name.\n",
    "\n",
    "nfip.to_csv('nfip_pa.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6e2ee59-a16b-4df5-acc5-17235f6c6509",
   "metadata": {},
   "source": [
    "## Visualizations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "54a6eff9-90bb-463d-8b0a-87cc13ed0436",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What does the spread of total payment look like?\n",
    "\n",
    "plt.figure(figsize = (10, 5))\n",
    "sns.histplot(nfip['total_payment'], bins = 50, kde = True)\n",
    "plt.xlabel('Total Payment')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Distribution of Total Flood Insurance Payments')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "d85cf332-37ed-4f0b-9965-14b503e35ed4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Hm. That's extraordinarily unhelpful. Histograms can be a real pain to figure out sometimes.\n",
    "# But I'll try another permutation.\n",
    "\n",
    "plt.figure(figsize = (10, 5))\n",
    "sns.histplot(nfip['total_payment'], bins = 500, kde = True)\n",
    "plt.xlabel('Total Payment')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Distribution of Total Flood Insurance Payments')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "d6c3928d-e4db-420b-b547-25de93bb8630",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Maybe we try a different variable!\n",
    "\n",
    "plt.figure(figsize = (12, 6))\n",
    "top_counties = nfip['county_name'].value_counts().head(10)\n",
    "sns.barplot(x = top_counties.index, y=top_counties.values)\n",
    "plt.xticks(rotation = 45)\n",
    "plt.xlabel('County')\n",
    "plt.ylabel('Number of Claims')\n",
    "plt.title('Top 10 Penn. Counties with the Most Flood Insurance Claims')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "40200bd8-205d-4b9c-895c-77070f25dc3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6LKigCxcuQFdXF7169ZLYn1e/BeuzJB4/fgx1dXWoq6vDxsYGK1euxIABA8RzLIWFheHixYvo0aOHeALt3r17Q19fv0RDxSZNmoTbt29LbA0aNCgyvbyfIXnvrRs3buD9+/eF5lJq1KiRuN0/VqdOncRRNEDuZwKA+PNrYmKCihUr4ueff8Yvv/wCPz8/qau1lVTPnj0L7cvKysKSJUvg7u4ODQ0NqKmpQUNDA0+fPpX6uf5wKKu0vPv6+iIpKQljx44tMorrU59BpeFT72dra2vUqVNH/NrExAQWFhbw8PCQiACqUqUKAJTo2VQUQRAA4KPmeSqt51dJlaSt69evj5MnT2LWrFm4dOkS0tLSyixfRPRl4jAxIiL64piZmUFHRwdhYWFypc8Li5c2FMjGxqbQHxbFDRkq+F50dDQAYNq0aUWu8FTcPBJff/01zp8/j3nz5qFevXowMDCASCRCx44dP/rLe3x8PNTU1ApN1CoSiWBlZVVomICpqWmhc2hqapb4+sOGDcPChQuxZMkS3Lt3D+vWrZOaLicnB23btsXr168xb948VK9eHbq6usjJyUHDhg1LdN2SrkrUqVMnWFpaIjo6Gt7e3hKdCEWJj4+HlZVVoT8aLSwsoKam9knDLipWrIh9+/ZBJBJBS0sLTk5OEnOzbN++HYIgoFevXhJDmrp27Yo9e/YgKCgIbm5uMq9ja2srsZS8LPJ+huS9t/LSWVlZFUonbV9JFPz85g1fy/sciUQinD9/HosWLcLy5csxdepUmJiYYMCAAfjxxx8lVsArCWll9vb2xoYNGzBz5kw0b94cxsbGUFFRwciRI6V+rmXlPW/eKFtb2yLz8anPoLyO4LCwMLk+SwWVxv0sbdU9DQ2NQvs1NDQA5A7dLI69vT2ePn2K1NTUIoeO5s0NJO9Q4zyl+fwqqZK09dq1a2Fra4v9+/dj2bJl0NLSQrt27fDzzz+jUqVKZZZHIvpysDOIiIi+OKqqqmjVqhVOnjyJly9fFvuHEpD/B1dkZGShtK9fv5aYLwgo/pfigu/lHTt79mz06NFD6jFFzYfy9u1b/PPPP5g/fz5mzZol3p+eno43b94UmQdZTE1NkZWVhdjYWIk/5gVBQFRUVInnx5CXnZ0dWrdujYULF8LV1bXI1a4ePXqE+/fvY8eOHRgyZIh4f0hISImvWdJf9ceMGYPk5GRUrVoVEydORNOmTcVz8xTF1NQUt27dgiAIEteLiYlBVlZWoc9PSWhpaRXZSZOTkyOeJLyoz9b27duxfPnyj75+UeT9DMl7b+Wli4qKKnStqKioQhOglzYHBwds27YNAPDkyRP4+PhgwYIFyMjIwKZNmwDkdsTkTQj+oaI6+6R99v744w8MHjwYS5YskdgfFxcnjuwqiby6LzgZ94c+5RkEAO3atcPmzZtx5MgRieeQvErzfi4tbdu2xZkzZ/D333+jX79+hd4XBAHHjh2DqakpatasCSD3XgRQ6DNQsCOtPMtbkrbW1dUVz9MVHR0tjhLq0qULgoKCyjyvRPTfx2FiRET0RZo9ezYEQcCoUaOQkZFR6P3MzEz8/fffACAe8vXHH39IpLl9+zYCAwPRqlWrj86Hq6srKlWqhPv376Nu3bpSt6IiD0QiEQRBKLQa2tatW5GdnS2xr2DEQHHyylOwvH/99RdSU1M/qbyyTJ06FV26dBFPhCpN3h/RBcv922+/FUpbknLLsnXrVvzxxx9Yv349jh07hsTERAwbNkzmca1atUJKSgqOHDkisX/Xrl3i98vC6dOn8fLlS4wbNw4XL14stFWtWhW7du0qNDF0aZD3MyTvvdWwYUNoaWmJJ9bO4+vrWypDfkqicuXKmDt3LqpXr4579+6J9zs6OuLBgwcSaS9cuICUlBS5zy0SiQp9ro8fP45Xr159VF4bNWoEQ0NDbNq0STysqaBPeQYBuUutV69eHUuXLpWYlPpDp0+fxrt376S+V5L7+XMZMWIELC0tMXv2bMTExBR6f/ny5QgKCsKYMWPE+c7rkCz4GTh27JjE67Ior7yRmB/b1paWlhg6dCj69++P4ODgItuSiJQLI4OIiOiL5OnpiY0bN2Ls2LGoU6cOvv32W1StWhWZmZnw8/PD5s2bUa1aNXTp0gWurq4YPXo01q1bBxUVFXTo0EG84pGdnR2mTJnySXn57bff0KFDB7Rr1w5Dhw5FhQoV8ObNGwQGBuLevXs4cOCA1OMMDAzQrFkz/PzzzzAzM4OjoyMuX76Mbdu2FYoiyFvyefPmzdDX1xcPKZI2xKtNmzZo164dZs6ciaSkJDRu3Fi8ElStWrUwaNCgTypvcdq2bYu2bdsWm8bNzQ0VK1bErFmzIAgCTExM8Pfff+Ps2bOF0lavXh0AsGbNGgwZMgTq6upwdXUt8dCehw8fYuLEiRgyZIi4A2jbtm3o1asXVq9ejcmTJxd57ODBg7FhwwYMGTIE4eHhqF69Oq5du4YlS5agY8eOaN26dYnyIq9t27ZBTU0Nc+bMkbpy0jfffIOJEyfi+PHj6NatW6leW97PkLz3lrGxMaZNm4bFixdj5MiR6N27N168eIEFCxZ88jAxWR48eIDx48ejd+/eqFSpEjQ0NHDhwgU8ePBAIhJm0KBBmDdvHr7//ns0b94cAQEBWL9+vXhFMnl07twZO3bsgJubG2rUqIG7d+/i559/lhm9WBQ9PT2sXLkSI0eOROvWrTFq1ChYWloiJCQE9+/fx/r16wF8/DMIyI20PHz4MNq2bQtPT098++23+Oqrr6Crq4vnz5/j4MGD+Pvvv5GQkCD1+JLcz5+LkZER/vrrL3Tu3Bl16tTB9OnTUbNmTSQlJWH//v3Ys2cP2rRpIzFnWL169eDq6opp06YhKysLxsbGOHz4MK5duyZx7rIob/Xq1XHp0iX8/fffsLa2hr6+fpHRXPK2dYMGDdC5c2fUqFEDxsbGCAwMxO7du+Hp6SkxFJWIlFj5zFtNRERUOvz9/YUhQ4YI9vb2goaGhqCrqyvUqlVL+P7774WYmBhxuuzsbGHZsmVC5cqVBXV1dcHMzEwYOHCg8OLFC4nzFbUaVN7KRT///LPUfNy/f1/o06ePYGFhIairqwtWVlZCy5YthU2bNonTSFsZ5uXLl0LPnj0FY2NjQV9fX2jfvr3w6NEjqathrV69WnBychJUVVUFAMLvv/8uCIL01WnS0tKEmTNnCg4ODoK6urpgbW0tfPvtt+IVqfIUXKHpw3qQZ4Un/H81seJIWxEsICBAaNOmjaCvry8YGxsLvXv3FiIiIqSu6DR79mzBxsZGUFFRkai/ovKe915e/aWkpAhubm6Cu7u7kJqaKpFu3Lhxgrq6unDr1q1iyxAfHy+MGTNGsLa2FtTU1AQHBwdh9uzZhVZg+pjVxKSJjY0VNDQ0il0NKCEhQdDW1ha6dOlSZBpZn9s8n/IZkvfeysnJEZYuXSrY2dkJGhoaQo0aNYS///5b7s9aUauJSSvbh5+j6OhoYejQoYKbm5ugq6sr6OnpCTVq1BBWrVolsQpZenq6MGPGDMHOzk7Q1tYWmjdvLvj7+xe5mtjt27cLXTchIUEYMWKEYGFhIejo6AhNmjQRrl69WqiMec+CvJWsCpYp797Oc+LECaF58+aCrq6uoKOjI7i7uwvLli2TSCPPM6g4iYmJwg8//CDUrl1b0NPTE9TV1QV7e3th4MCBwvXr1wuV/2Pu56JWE5N2HxR1f8vzzMnz/PlzYezYsYKTk5Ogrq4uXklv0aJFEm2f58mTJ0Lbtm0FAwMDwdzcXJgwYYJw/PjxQs/tTymvtHvN399faNy4saCjoyOxul5RK5nJ09azZs0S6tatKxgbGwuampqCs7OzMGXKFCEuLk6uuiMixScShCJiTomIiIiIiBTEw4cP0bRpU3h4eODkyZPQ1tYu7ywREZUbzhlEREREREQKr3r16jh69Chu3ryJHj16SJ1vjohIWTAyiIiIiIiIiIhIiTAyiIiIiIiIiIhIibAziIiIiIiIiIioHFy5cgVdunSBjY0NRCIRjhw5IvOYy5cvo06dOtDS0oKzszM2bdpU4uuyM4iIiIiIiIiIqBykpqaiZs2aWL9+vVzpw8LC0LFjRzRt2hR+fn6YM2cOJk6ciL/++qtE1+WcQURERERERERE5UwkEuHw4cPw8vIqMs3MmTNx7NgxBAYGiveNGTMG9+/fx40bN+S+FiODiIiIiIiIiIhKQXp6OpKSkiS29PT0Ujv/jRs30LZtW4l97dq1w507d5CZmSn3edRKLUdERJ/ouLpreWeB5PD6ZHB5Z4HkdOxAoOxERCSX7Mys8s4CySEp9k15Z4HkZGxjUd5ZIDn8/VuV8s7CRynPvytuf9cfCxculNg3f/58LFiwoFTOHxUVBUtLS4l9lpaWyMrKQlxcHKytreU6DzuDiIiIiIiIiIhKwezZs+Ht7S2xT1NTs1SvIRKJJF7nzf5TcH9x2BlERERERERERApDpC5/p0hp09TULPXOnw9ZWVkhKipKYl9MTAzU1NRgamoq93k4ZxARERERERER0RfA09MTZ8+eldh35swZ1K1bF+rq6nKfh51BRERERERERETlICUlBf7+/vD39weQu3S8v78/IiIiAOQOOxs8eLA4/ZgxY/D8+XN4e3sjMDAQ27dvx7Zt2zBt2rQSXZfDxIiIiIiIiIhIYaiold8wsZK6c+cOvvrqK/HrvPmGhgwZgh07diAyMlLcMQQATk5OOHHiBKZMmYINGzbAxsYGa9euRc+ePUt0XXYGERERERERERGVgxYtWogngJZmx44dhfY1b94c9+7d+6TrsjOIiIiIiIiIiBSGSJ0z4sjCGiIiIiIiIiIiUiLsDCIiIiIiIiIiUiIcJkZERERERERECuNLmkC6vDAyiIiIiIiIiIhIiTAyiIiIiIiIiIgUhkidkUGyMDKIiIiIiIiIiEiJMDKIiIiIiIiIiBQG5wySjZFBRERERERERERKhJ1BRERERERERERKhMPEiIiIiIiIiEhhcAJp2RgZRERERERERESkRBgZREREREREREQKgxNIy8bIICIiIiIiIiIiJcLOICIiIiIiIiIiJcJhYkRERERERESkMESqHCYmCyODiIiIiIiIiIiUCCODiIiIiIiIiEhhqDAySCZGBhERERERERERKRFGBhERERERERGRwhCpMDJIFkYGEREREREREREpEXYGEREREREREREpEQ4TIyIiIiIiIiKFIVJl3IssrCEiIiIiIiIiIiXCyCAiIiIiIiIiUhhcWl42RgYRERERERERESkRdgYRERERERERESkRDhMjIiIiIiIiIoUhUuEwMVkYGUREREREREREpEQYGURERERERERECoMTSMvGyCCS29ChQ+Hl5VVsmhYtWmDy5MmfJT+yyJNfeYhEIhw5cgQAEB4eDpFIBH9//yLTX7p0CSKRCImJiQCAHTt2wMjI6JPzUVoK5k8e/6V2JSIiIiIiok/DyKAv0NChQ5GYmCjuoPhc1qxZA0EQPus1/wsiIyNhbGz80cf37dsXHTt2LMUcfX6HDh2Curp6eWejXJk0qQvnqSNgWLsatGwscKfnWEQfO1/8MU3rwX3FLOi5V0L66xiErtyKiM37JNJYdW+LygsmQaeiPd6FRiD4+1WIPnquLIuiFPyu7MHtc9uQ+jYWZtaV8FWvObB1qSs17RP/M7h/dS9iXgYiOysDptaV0KjjeDi5NxWneXTjEE79MbvQsZNXP4CaumaZlUPRdWxujB5tTWBsqIaI1+nY4hONgJC0ItNXq6SDEb0tYG+jiTeJWfjrTDxOXUkUv29vrYEBXc1R0V4LlmYa2OIThWPnEz5DSRRbabcTADSqpY8BXc1hba6OyNhM7D4ai5v+yWVcEuUwoJsF2jc3gZ6OKoKfvcOvf7xGxOv0Yo9pXMcAg7pbwtpcA5GxGdh5KBo37iWJ369WWQc925vDxVEbpkbq+GHdc9zwSyrmjCTL8P4O6NrOGvp6agh4koxfNj1FWMS7Yo9p3sgMIwc4ooK1Nl5FpmHL7jBcuRkvkcbMRAPfDnVGwzom0NRUwYtXafhpbTCCQ1PKsjgKic8+UnSMDCK5GRoa/qciXD4XKysraGp+/B972trasLCwKMUcfX4mJibQ19cv72yUK1VdHSQ9CMbjSYvkSq/taIt6f2/Gm2t3ca2eF0KWbULVVd/BqntbcRqjhh6o9ecqvNpzFFfrdMOrPUdRe+9qGNWvUVbFUApBd0/g4sGlaNjuWwyefQQVXOrgrw2jkPTmtdT0L0Nuw8GtEXqM3YxBMw/BvlIDHN70LaJfBEik09DSw7dLrkls7Aj6eE3q6mNkH0v4nIjHpMVheByShgUT7GFuLP13KktTdcyfYIfHIWmYtDgMB07GY3RfKzSqlf9s0tRQQVRcJnYejsWbt1mfqygKrSzaydVZGzNGVcDFW28x8YcwXLz1FjNHV0BlR63PVSyF1auDGbq3NcPGP15j8g8hSHibhR+nOUFbq+iv/G4VdTBrjD0u+CZi3PwQXPBNxOwx9nB11han0dJUQdiL99j4h/TnKJXMgJ526Otli19+C8FI73uIT8jAqkU1oK2tWuQxVV0NsHCGO05fjMbQiXdw+mI0Fs10h3vl/HtLX1cNG5fXQla2gGkLHmLg2NtYvy0Uyal8HpYUn31fPpGqqNy2L4XSdgadOnUKTZo0gZGREUxNTdG5c2eEhoYCyB8K5OPjg6ZNm0JbWxv16tXDkydPcPv2bdStWxd6enpo3749YmNjxefMycnBokWLYGtrC01NTXh4eODUqVPi96UNz/H394dIJEJ4eDiA/CFFp0+fRpUqVcTXiYyMBAAsWLAAO3fuxNGjRyESiSASiXDp0qViy5pXnn379qFRo0bQ0tJC1apVJY7Lzs7GiBEj4OTkBG1tbbi6umLNmjUS5yk47Co1NRWDBw+Gnp4erK2tsXLlyhK0AJCQkIDBgwfD2NgYOjo66NChA54+fSp+X1ZdyGvhwoWwsLCAgYEBvvnmG2RkZIjfc3R0xOrVqyXSe3h4YMGCBeLXHw4Tk+bEiROoXLkytLW18dVXX4nbsmA58ixYsAAeHh7YvXs3HB0dYWhoiH79+iE5Of9XgeTkZAwYMAC6urqwtrbGqlWrSjRUKz09HTNmzICdnR00NTVRqVIlbNu2TWra+Ph49O/fH7a2ttDR0UH16tWxd+9eiTQFr+3o6IjFixeL29/BwQFHjx5FbGwsunXrBj09PVSvXh137tyRK79fgtjTV/Bk/mpEHTkrV3qH0f3wPiISAVOXICXoGV5sP4gXOw7B2Xu4OI3ThCGIO+eL0OWbkRr8DKHLNyPuwk04ThhSVsVQCnfO/47qnj1Ro3FvmFpVRMte30Hf2Ar+V/dKTd+y13eo32YUrB1qwNjCEU27ecPY3AGhDy9IpBOJRNA1NJfY6ON5tTbF2euJOHM9ES+jMrDVJxpxCZno0Fx6JGb75kaIfZOJrT7ReBmVgTPXE3HueiK6tzURp3n6/D1+/ysGV+8kITMz53MVRaGVRTt1a2UC/8BUHDwVj5fRGTh4Kh73g1LRtZWJ1HOS/LzamGHfPzHwvZeE56/SsXLbS2hqqKBFA6NijjGFX0AKfE7E4mVUOnxOxMI/MAXd2piJ09x5mIJdh6Phe4/RQKWhd9cK2OUTgSs34hAW8Q4/rgqCpqYq2jYv+sfDPt0q4I5/Av44+AIRL9Pwx8EXuHs/EX262orTDOhlh5i4dCxdE4zAp8mIiknH3QeJeB31/nMUS6Hw2UfKQGk7g1JTU+Ht7Y3bt2/j/PnzUFFRQffu3ZGTk//lcf78+Zg7dy7u3bsHNTU19O/fHzNmzMCaNWtw9epVhIaG4vvvvxenX7NmDVauXIkVK1bgwYMHaNeuHbp27SrRwSGPd+/eYcWKFdi9ezeuXLmCiIgITJs2DQAwbdo09OnTR9wpEhkZiUaNGsl13unTp2Pq1Knw8/NDo0aN0LVrV8TH54aW5uTkwNbWFj4+PggICMD333+POXPmwMfHp9jzXbx4EYcPH8aZM2dw6dIl3L17V+5yDh06FHfu3MGxY8dw48YNCIKAjh07IjMzU666kMf58+cRGBiIixcvYu/evTh8+DAWLlwo9/GyvHjxAj169EDHjh3h7++PkSNHYtasWTKPCw0NxZEjR/DPP//gn3/+weXLl/HTTz+J3/f29sb169dx7NgxnD17FlevXsW9e/fkztfgwYOxb98+rF27FoGBgdi0aRP09PSkpn3//j3q1KmDf/75B48ePcLo0aMxaNAg3Lp1q9hrrFq1Co0bN4afnx86deqEQYMGYfDgwRg4cCDu3bsHFxcXDB48WCmHFgK5UT+x565L7Is9cxWGdapBpJb7q5JxQw/EnbsmkSbu7FUYe9b6bPlUNNlZGYh+8RiOVZpI7Hes0hivn/nJdQ4hJwcZ6anQ0jGS2J+R/g6/zf0Km75rhkMbvykUOUTyU1MFXOy14BeQKrHfLyAVVSpqSz3GzVmnUPp7AalwcdCGqtJ+mylbZdVObs7ahc/5OBVVKuqUXuaVkJW5OkyM1HHvcf5woKwsAQ+DU1HFpei6dauog3uPJIep3HuUDHe2R5mwsdSCmYkm/vXLH8KamSXA/1EiqrkZFHlcNTcD/Ov3RmLfLb83qFYl/5jG9U0RFJKMH2a64+/dnti+uja6tLUq/UIoOD77FINIRaXcti+F0s4Z1LNnT4nX27Ztg4WFBQICAsR/NE+bNg3t2rUDAEyaNAn9+/fH+fPn0bhxYwDAiBEjsGPHDvE5VqxYgZkzZ6Jfv34AgGXLluHixYtYvXo1NmzYIHfeMjMzsWnTJlSsWBEAMH78eCxalDs0RU9PD9ra2khPT4eVVcke7uPHjxeXe+PGjTh16hS2bduGGTNmQF1dXaKTxMnJCb6+vvDx8UGfPn0KnSslJQXbtm3Drl270KZNGwDAzp07YWtrWyitNE+fPsWxY8dw/fp1cWfWnj17YGdnhyNHjqB3794y60IeGhoa2L59O3R0dFC1alUsWrQI06dPxw8//ACVUrhRN27cCGdnZ6xatQoikQiurq54+PAhli1bVuxxOTk52LFjh3jo1aBBg3D+/Hn8+OOPSE5Oxs6dO/Hnn3+iVatWAIDff/8dNjY2cuXpyZMn8PHxwdmzZ9G6dWsAgLOzc5HpK1SoINHBNmHCBJw6dQoHDhxAgwYNijyuY8eO+OabbwAA33//PTZu3Ih69eqJ227mzJnw9PREdHR0iT+rikDT0gzp0XES+zJi4qGirg4NM2OkR8VC08oM6dGSY/3To+OhacWIk4+VlpIAIScbOgamEvt19M2QmhRbxFGSbp/fjsyMNLjW6SDeZ2LljA6DlsLMxhUZ71Nw9+Iu7F3ZH0PmHIWxhWNpFkEpGOipQVVVhMQkyaELiclZMDLQlXqMsYEqEpMLpE/KgpqqCAZ6akhI4jCI0lZW7WRkoFb4nElZMDYoeogMyWZskDu3n7S6tTAtet4/Y8Mi2sNQaf9MKFMmxhoAgDeJGRL7ExIzYGlR9HAhEyMNJCRmSuxLSMwUnw8AbKy04dVBG/uPvMSuAxFwr6yPyaNdkJkp4NTF6FIshWLjs4+UhdI+5UNDQzFv3jzcvHkTcXFx4oigiIgIuLu7AwBq1Mift8PS0hIAUL16dYl9MTExAICkpCS8fv1a3FGUp3Hjxrh//36J8qajoyPu/AAAa2tr8XU+haenp/jfampqqFu3LgIDA8X7Nm3ahK1bt+L58+dIS0tDRkYGPDw8pJ4rNDQUGRkZEuc0MTGBq6urXHkJDAyEmpqaRGeDqakpXF1dJfL0qXVRs2ZN6Ojk97Z7enoiJSUFL168gIODg9znKUpgYCAaNmwIkSh/bOiHdVIUR0dHiTl4PizXs2fPkJmZifr164vfNzQ0lLtu/f39oaqqiubNm8uVPjs7Gz/99BP279+PV69eIT09Henp6dDVlf6fXR557g8AiImJkdoZlHedD2UKOVAXfTm96TIVjIrK+5x8uF9aGiWNpipNIhQcry1I3KdFCbzzD3xPrEf3b36Frn5+h5KNkwdsnDzErys418aun7rj3qU/0KrP3FLKtfIp+EmX1UJF3lKFzkSlqSzaqVCLiaTso2K1aGiECYPzfyiav/o5ACn1L2VfQYXbQ8T2KCVtmltg+rjK4tczFj3M/UehG0v2TSAt2vrDXSoiICgkGZt3hwEAnj5LgaO9Drw62rAz6CPw2fdlE6l8OXP3lBel7Qzq0qUL7OzssGXLFtjY2CAnJwfVqlWTmE/mw9WT8v6IKLjvw2FlH6bLIwj5f4DkRaJ8+CD/cEiUtOvmnbOshtrk5c3HxwdTpkzBypUr4enpCX19ffz8889FDhX61PwUdfyH9QWUXV182CYFzyetTYrysXmRVq68z1LeOaV9luShrS09fLUoK1euxKpVq7B69WpUr14durq6mDx5ssS9II089weAQvdInqVLlxYastdfZIIBqmZS039p0qPjCkX4aJibICczExnxiblpouKgaSVZXk0Lk0IRRSQ/bT1jiFRUkZokWYfvkuOho1/8Zyvo7gmc/uM7dBm5Bg5uxQ+/FamowMqhOhJiwz81y0opKSUL2dkCjA0kv4YY6hf+1TRPQlK21PRZ2QKSU7LLLK/KrKzaKfeXcMk0RvpqSExiO5bELf8kBD/LX31KXS33/11jQzUkfDCBuqGUaIQPJbyV1h6qSOQk7KXi2r/xCHiSP4eihnru3wMmxhqIT8j/rmVsqF4oWuhDbxIzJKKAAMDYSB0JHxwTn5CB8BeSK5I9f/EOLRox4rgk+OwjZaFAP8HLLz4+HoGBgZg7dy5atWqFKlWqICHh05aeNTAwgI2NDa5dk5z/w9fXF1WqVAEAmJvnPog/nADZ39+/xNfS0NBAdnbJHxo3b94U/zsrKwt3796Fm5sbAODq1ato1KgRxo4di1q1asHFxUU8obY0Li4uUFdXlzhnQkICnjx5Ilde3N3dkZWVJdHZFB8fjydPnojrqzTcv38faWn5S0DevHkTenp64uFs5ubmEu2RlJSEsLAwuc/v7u4uUQd51/gUFStWhLq6Ov7991+JfMk791T16tWRk5ODy5cvy5X+6tWr6NatGwYOHIiaNWvC2dm5xPNcfYzZs2fj7du3ElsfFcWZQC/xpj/MWkl2KJi3aYK3dx9ByMr9IpFw0x9mrSSjCc1aN0HCDfnmtqHCVNU0YGlXFeFBkvM1hQf5wsa56LmYAu/8g1O7Z6HTsJWoWK2FzOsIgoCYl4HQM+AX7I+RlQ2ERLxHrSqSEYgeVXQRGCp92d6gZ+/gUSB9LXddhDxPQzbnii4TZdVOQc/SpKYJDC1+WW2SlPY+B5ExGeIt4nU63iRmorZ7/hyBaqoiVHfVRWBI0XUbFPoOtapKrhhau5o+AtgepSItLRuvIt+Lt7CId4h7k456HvkTEaupieBRzQiPgoqeoPtRUJLEMQBQv5YJHgXmH/Mw8C3sK0jOP2NXQQdRMZxAuiT47CNloZSdQcbGxjA1NcXmzZsREhKCCxcuwNvb+5PPO336dCxbtgz79+9HcHAwZs2aBX9/f0yaNAlAbgeKnZ0dFixYgCdPnuD48eMlXoELyB1i9ODBAwQHByMuLk7uSJYNGzbg8OHDCAoKwrhx45CQkIDhw4eL83bnzh2cPn0aT548wbx583D79u0iz6Wnp4cRI0Zg+vTpOH/+PB49eoShQ4fKPQ9PpUqV0K1bN4waNQrXrl3D/fv3MXDgQFSoUAHdunWT6xzyyMjIwIgRIxAQEICTJ09i/vz5GD9+vDifLVu2xO7du3H16lU8evQIQ4YMgaqq/ON2x4wZg9DQUHh7eyM4OBh//vmnxDxSH0NfXx9DhgwRT9D9+PFjDB8+HCoqKnINc3F0dMSQIUMwfPhwHDlyBGFhYbh06VKRk4G7uLjg7Nmz8PX1RWBgIL755htERUV9UhnkoampCQMDA4ntvzxETFVXBwY13WBQM7cDVcfJFgY13aBlZw0AcF3sjZq/588V9XzzPmg72KDKz7Og5+YM26E9YTesJ579sl2cJnz9Lpi1aQznaaOg6+oM52mjYNbKE+Hrdn7ewimYuq2G4aHvQTz0PYj4qFBcPLgEyW8iUbNJ7nxuV46uxImdM8TpA+/8g5M7Z6J5j5mwcayJ1LexSH0bi/S0/AlVfY+vR1jAVSTGvUDMi0Cc/mMOYl8GoWbT/p+9fIriyLl4tGlihNaNDGFrpYGRvS1gbqKOk1dyf5wZ7GWOKUOtxelPXU6Ehak6RvS2gK2VBlo3MkSbxkY4fCZ/QlU1VcDJVhNOtppQUxPB1EgdTraasDYver4UKl5ZtNOx829Qy10XPduZwtZSAz3bmaJmFV0cO/+m0PWpZI6cjUOfzhbwrG0Ahwqa8B5hi/SMHFy6lShOM3WkLYb2tBS/Pno2HrWr6qFXBzPYWmmiVwczeFTRw9Gz+RGWWpoqcLbTgrNd7pw2lmbqcLbTgrkJ762PceDYKwzqbY9mDU3hZK+D7ya7Ij09G2cu50+FMHeKK74Z7CRxTL1aJhjQ0w72ttoY0NMOdWsawefYS3Ga/UdfoaqrPgb1tkcFay20aW6Bru2scej4689aPkXAZ9+XT0VVVG7bl0Iph4mpqKhg3759mDhxIqpVqwZXV1esXbsWLVq0+KTzTpw4EUlJSZg6dSpiYmLg7u6OY8eOoVKlSgByh9Ds3bsX3377LWrWrIl69eph8eLF4gl35TVq1ChcunQJdevWRUpKCi5evChX3n/66ScsW7YMfn5+qFixIo4ePQozs9xhE2PGjIG/vz/69u0LkUiE/v37Y+zYsTh58mSR5/v555+RkpKCrl27Ql9fH1OnTsXbt2/lLsfvv/+OSZMmoXPnzsjIyECzZs1w4sSJQkOoPkWrVq1QqVIlNGvWDOnp6ejXr5/EsvGzZ8/Gs2fP0LlzZxgaGuKHH34oUWSQvb09/vrrL0yZMgW//vor6tevjyVLlog72T7WL7/8gjFjxqBz584wMDDAjBkz8OLFC2hpFT2x4Ic2btyIOXPmYOzYsYiPj4e9vT3mzJkjNe28efMQFhaGdu3aQUdHB6NHj4aXl1eJ2lIZGNapBs/zu8Wv3Vfk1ueLXYfwYMRsaFqbQ9su/0tBWvhL3O4yGu4rZ8Ph2wFIfx2Dx1N+RNThM+I0CTf84DfAG64LJ8N14US8C30Bv6+nIPHfB5+vYArIrU5HpKUm4MbJX5GaFAMz68roMXYzDE0rAABS38YiKSE/IvD+tf3IycnC+f2LcH5//gT1VRt0R4fBuav8pacl4cyf3+Ndciw0tPRhaeeOflP+gLVjDdDHuXYnGQa60ejXyQwmhmp4/jodC9dHIPZNbuSciaGaxB+a0fGZWLjuBUb2sUSn5sZ48zYLm/dHwdcvv9POxEgda+flT5jfo60perQ1xcPgVMz5JeLzFU6BlEU7BT1Lw/KtrzComzkGdDVHVGwGlm95hSfhjF74VAdPxkFTQwXjBtpAT1cVwc/eYe7KMKS9zw+fMzdRx4cjuAND3+GnTREY3MMSg7pbIjImAz9tikDws/wIiEqO2lg2M//eGt0/d66is9cSsGp7fmcEyWfPXy+gqaEC728rQV9PHQFPkjDl+wdIS8uP/Lc010LOBzMEPApKwoLlARg1yAkjBzjiVVQavl8eiIAnH9xbT5MxZ8ljfDPYCUP7OSAyOg1rt4Tg7OVPn3tU2fDZR8pAJCjrus9KJDw8HE5OTvDz8ytyQmj6b0tNTUWFChWwcuVKjBgxoryzU2aOq8s3STaVr9cng8s7CySnYwcCZSciIrlkZ3IOnS9BUiyjLL4UxjYW5Z0FksPfv5XeFB6f0/32zcrt2jVPXSm3a5eEUkYGEf3X+fn5ISgoCPXr18fbt2+xaFFutEJpDqEjIiIiIiIi5fTfnaCD5LZkyRLo6elJ3Tp06FAuebp69WqRedLT05N9AjkUd/6rV6+WyjXK04oVK1CzZk20bt0aqampuHr1KszMzD5L3RIREREREZHiYmSQAhgzZgz69Okj9T1tbW1UqFChzJamL0rdunU/aqW0kiju/BUqVCjTa5e1WrVq4e7du1Lf+xx1S0RERERE9KUSybmwkTJjZ5ACMDExgYnJf2tJbm1tbbi4uJTpNcr6/P9Vn6NuiYiIiIiISHGxM4iIiIiIiIiIFIZI5ctZ4r28MHaKiIiIiIiIiEiJMDKIiIiIiIiIiBSGiiojg2RhZBARERERERERkRJhZxARERERERERkRLhMDEiIiIiIiIiUhicQFo2RgYRERERERERESkRRgYRERERERERkcIQqTDuRRbWEBERERERERGREmFnEBERERERERGREuEwMSIiIiIiIiJSGJxAWjZGBhERERERERERKRFGBhERERERERGRwmBkkGyMDCIiIiIiIiIiUiKMDCIiIiIiIiIihcHIINkYGUREREREREREpETYGUREREREREREpEQ4TIyIiIiIiIiIFIZIhXEvsrCGiIiIiIiIiIiUCCODiIiIiIiIiEhhqKhyAmlZGBlERERERERERKRE2BlERERERERERKREOEyMiIiIiIiIiBSGSIXDxGRhZBARERERERERkRJhZBARERERERERKQwuLS8ba4iIiIiIiIiISIkwMoiIiIiIiIiIFAbnDJKNkUFEREREREREREqEnUFEREREREREREqEw8SIiIiIiIiISGFwmJhsjAwiIiIiIiIiIlIijAwiIiIiIiIiIoXBpeVlYw0RERERERERESkRdgYRERERERERESkRDhMjIiIiIiIiIoXBCaRlY2cQEf1nvD4ZXN5ZIDnYdHAt7yyQvIYfKe8ckByEHKG8s0ByYDt9GVTUVMs7CySnnOzs8s4CkVJjZxARERERERERKQxOIC0ba4iIiIiIiIiISIkwMoiIiIiIiIiIFIeIcwbJwsggIiIiIiIiIiIlws4gIiIiIiIiIiIlwmFiRERERERERKQwuLS8bIwMIiIiIiIiIiJSIowMIiIiIiIiIiKFwaXlZWMNEREREREREREpEXYGEREREREREREpEQ4TIyIiIiIiIiKFwQmkZWNkEBERERERERGREmFkEBEREREREREpDE4gLRtriIiIiIiIiIhIiTAyiIiIiIiIiIgUBucMko2RQURERERERERESoSdQURERERERERESoTDxIiIiIiIiIhIYXCYmGyMDCIiIiIiIiIiUiKMDCIiIiIiIiIixcGl5WViDRERERERERERKRF2BhERERERERERKREOEyMiIiIiIiIihSEScQJpWRgZRERERERERERUTn799Vc4OTlBS0sLderUwdWrV4tNv2fPHtSsWRM6OjqwtrbGsGHDEB8fX6JrsjOIiIiIiIiIiBSGSEWl3LaS2r9/PyZPnozvvvsOfn5+aNq0KTp06ICIiAip6a9du4bBgwdjxIgRePz4MQ4cOIDbt29j5MiRJbouO4OIiIiIiIiIiMrBL7/8ghEjRmDkyJGoUqUKVq9eDTs7O2zcuFFq+ps3b8LR0RETJ06Ek5MTmjRpgm+++QZ37twp0XXZGUREREREREREVArS09ORlJQksaWnp0tNm5GRgbt376Jt27YS+9u2bQtfX1+pxzRq1AgvX77EiRMnIAgCoqOjcfDgQXTq1KlE+WRnEBEREREREREpDJGKqNy2pUuXwtDQUGJbunSp1HzGxcUhOzsblpaWEvstLS0RFRUl9ZhGjRphz5496Nu3LzQ0NGBlZQUjIyOsW7euRHXEziAiIiIiIiIiolIwe/ZsvH37VmKbPXt2sccUXP1MEIQiV0QLCAjAxIkT8f333+Pu3bs4deoUwsLCMGbMmBLlk0vLExEREREREZHi+IiJnEuLpqYmNDU15UprZmYGVVXVQlFAMTExhaKF8ixduhSNGzfG9OnTAQA1atSArq4umjZtisWLF8Pa2lquazMyiIiIiIiIiIjoM9PQ0ECdOnVw9uxZif1nz55Fo0aNpB7z7t07qBTo7FJVVQWQG1EkL0YGEREREREREZHCEKlIH2L1X+Tt7Y1Bgwahbt268PT0xObNmxERESEe9jV79my8evUKu3btAgB06dIFo0aNwsaNG9GuXTtERkZi8uTJqF+/PmxsbOS+LjuDiIiIiIiIiIjKQd++fREfH49FixYhMjIS1apVw4kTJ+Dg4AAAiIyMREREhDj90KFDkZycjPXr12Pq1KkwMjJCy5YtsWzZshJdVySUJI6IiKgMbTlX3jkgedh0cC3vLJCcNg0/Ut5ZIDkIOfwq9iXIzswq7yyQHFLfJpV3FkhO+qZG5Z0FksPxrdXKOwsf5c3ib8rt2iZzfyu3a5cEI4OIiIiIiIiISGGIRJweWRbWEBERERERERGREmFnUBGGDh0KLy8vudK2aNECkydP/qTrhYeHQyQSwd/fHwBw6dIliEQiJCYmFnnMjh07YGRkJH69YMECeHh4fFI+SkokEuHIkSOf9ZpERERERERERVIRld/2hfiihom1aNECHh4eWL16dZkeUx7s7OwQGRkJMzOzjz7HtGnTMGHChFLMlWyRkZEwNjb+rNckKi9+V/bg9rltSH0bCzPrSviq1xzYutSVmvaJ/xncv7oXMS8DkZ2VAVPrSmjUcTyc3JuK0zy6cQin/phd6NjJqx9ATV2zzMqhyEya1IXz1BEwrF0NWjYWuNNzLKKPnS/+mKb14L5iFvTcKyH9dQxCV25FxOZ9EmmsurdF5QWToFPRHu9CIxD8/SpEH+UkV5+qY3Nj9GhrAmNDNUS8TscWn2gEhKQVmb5aJR2M6G0BextNvEnMwl9n4nHqSqJEmka19DGgqzmszdURGZuJ3UdjcdM/uYxLotg6tjBGj3amMMlrp/3RePz0XZHpq1XWwcg+lvntdDoeJy8nSKRpVFsfA7tZ5LfTkRjc8GM7lYYBXpbo0NwEerqqCH72Dht2vULE6/Rij2lc1wCDu1vB2kIDkTEZ2PlXFHzv5c+9U62yLnp1NIeLgzZMjdWxaG04btzj3DyfYlhfe3Rpawl9XTUEPE3Bqs2hCH9R9H0FAM0bmmLE1w6wsdLC66j32LLnOa7eipc457B+9hLHxCdkoPvwf8ukDMri664WaN/MGHo6qggOS8PGPa9l3lONahtgkJcFrM01EBmbgV2Hows94zq1MEGPdmYwMcp9tm7eF1nss5WotDEy6D9CVVUVVlZWUFP7+P45PT09mJqalmKuZLOysoKmZvn90ZqZmVlu1/7SZWdnIycnp7yz8cUIunsCFw8uRcN232Lw7COo4FIHf20YhaQ3r6WmfxlyGw5ujdBj7GYMmnkI9pUa4PCmbxH9IkAinYaWHr5dck1iY0fQx1PV1UHSg2A8nrRIrvTajrao9/dmvLl2F9fqeSFk2SZUXfUdrLq3FacxauiBWn+uwqs9R3G1Tje82nMUtfeuhlH9GmVVDKXQpK4+RvaxhM+JeExaHIbHIWlYMMEe5sbS/x+0NFXH/Al2eByShkmLw3DgZDxG97VCo1r64jSuztqYMaoCLt56i4k/hOHirbeYOboCKjtqfa5iKZymdQ0wqq8VfI7HYeKiZ3j89B0WTLSHuUkR7WSmjgUT7fH46TtMXPQMPifiMLqfFRrVzm8nN2dtzBxti4s3EzFh0TNcvJmImaNtUdlJ+3MVS2H17miOHu3M8OsfrzBp4VMkvM3CkunO0NYq+iu/W0UdzP7WAed9EzD2+6c475uA2WMd4Oqc3x5amip4FpGGX/949TmKofC+7l4BfbraYPWWZxg94z7eJGTglwVVoa2lWuQxVV31MX+aG05fisHwKX44fSkGC6e5okolPYl0zyJS4TXslngbOvleWRdHofVqb4bubUyx6c9ITFkcioS3mVjs7QhtzWLuKWdtzPrGDhduJGL8whBcuJGIWd/Yw/WDZ1zTegYY1c8K+0/EYuKiUDx6koqFkxxgbqL+OYpFBOAL6gwaOnQoLl++jDVr1kAkEkEkEiE8PByXL19G/fr1oampCWtra8yaNQtZWVnFHpOdnY0RI0bAyckJ2tracHV1xZo1az4pf1lZWRg/fjyMjIxgamqKuXPn4sOF2qQNpzIyMsKOHTsAFB4mJs2OHTtgb28PHR0ddO/eHfHx8RLvFxwmljfUbcWKFbC2toapqSnGjRsn0YESGRmJTp06QVtbG05OTvjzzz/h6OgodyTVh+XKK8OhQ4fw1VdfQUdHBzVr1sSNGzfE6Z8/f44uXbrA2NgYurq6qFq1Kk6cOCEu34fD3gDgyJEjEInyQ+3yyrh9+3Y4OztDU1MTgiDg1KlTaNKkibj+O3fujNDQUPFx8uQNAK5fv47mzZtDR0cHxsbGaNeuHRIScn/NFAQBy5cvh7OzM7S1tVGzZk0cPHhQrnoCgMePH6NTp04wMDCAvr4+mjZtKs5jTk4OFi1aBFtbW2hqasLDwwOnTp0SH+vp6YlZs2ZJnC82Nhbq6uq4ePEiACAjIwMzZsxAhQoVoKuriwYNGuDSpUvi9Hn1+88//8Dd3R2ampp4/vw5bt++jTZt2sDMzAyGhoZo3rw57t2T/OIQFBSEJk2aQEtLC+7u7jh37lyhz/SrV6/Qt29fGBsbw9TUFN26dUN4eLjc9fNfd+f876ju2RM1GveGqVVFtOz1HfSNreB/da/U9C17fYf6bUbB2qEGjC0c0bSbN4zNHRD68IJEOpFIBF1Dc4mNPl7s6St4Mn81oo6clSu9w+h+eB8RiYCpS5AS9Awvth/Eix2H4Ow9XJzGacIQxJ3zRejyzUgNfobQ5ZsRd+EmHCcMKatiKAWv1qY4ez0RZ64n4mVUBrb6RCMuIRMdmkuPNm3f3AixbzKx1ScaL6MycOZ6Is5dT0T3tibiNN1amcA/MBUHT8XjZXQGDp6Kx/2gVHRtZSL1nCSbVxtTnL2WgDPXcttpy/7cdurYXHqddmhujNg3mdiy///tdC0R564noEfb/B+rurY2gV9AKg6cjMfLqAwcOJnbTt1as50+lVdbM+z7Owa+d5Pw/FU6Vm55AU1NFbRoaFTsMfceJ8PneCxeRqbD53gs/ANT4NU2//+jOw+TsetQNHzvMhqoNPTuXAG7D77AlZvxCIt4hyVrn0BTUxVtmhX9HaB3ZxvcuZ+APYdeIuJVGvYceom7D96id5cKEumyswW8ScwUb2+TuArep+jW2hT7j8fC914Snr9Oxy/bX0FTQwXNGxgWfUwbM/gFpODAybj/P+PicD8oBd1a5z8Hu7cxw5lrCThzNQEvItOxZX9U7rO1BZ+DpUWkolJu25fii8npmjVr4OnpiVGjRiEyMhKRkZFQV1dHx44dUa9ePdy/fx8bN27Etm3bsHjx4iKPsbOzQ05ODmxtbeHj44OAgAB8//33mDNnDnx8fD46fzt37oSamhpu3bqFtWvXYtWqVdi6dWtpFR+3bt3C8OHDMXbsWPj7++Orr74Sl7M4Fy9eRGhoKC5evIidO3dix44d4g4oABg8eDBev36NS5cu4a+//sLmzZsRExPzSXn97rvvMG3aNPj7+6Ny5cro37+/uINu3LhxSE9Px5UrV/Dw4UMsW7YMenp6Ms4oKSQkBD4+Pvjrr7/EnWepqanw9vbG7du3cf78eaioqKB79+6FIl+Ky5u/vz9atWqFqlWr4saNG7h27Rq6dOmC7OxsAMDcuXPx+++/Y+PGjXj8+DGmTJmCgQMH4vLlyzLz/OrVKzRr1gxaWlq4cOEC7t69i+HDh4uvvWbNGqxcuRIrVqzAgwcP0K5dO3Tt2hVPnz4FAAwYMAB79+6V6GDcv38/LC0t0bx5cwDAsGHDcP36dezbtw8PHjxA79690b59e/E5AODdu3dYunQptm7disePH8PCwgLJyckYMmQIrl69ips3b6JSpUro2LEjkpNzQ1lzcnLg5eUFHR0d3Lp1C5s3b8Z3330nUb53797hq6++gp6eHq5cuYJr165BT08P7du3R0ZGhtxt+1+VnZWB6BeP4VilicR+xyqN8fqZn1znEHJykJGeCi0dI4n9Genv8Nvcr7Dpu2Y4tPGbQpFDVLaMGnog9tx1iX2xZ67CsE41iP4fqWnc0ANx565JpIk7exXGnrU+Wz4VjZoq4GKvBb+AVIn9fgGpqFJRenSIm7NOofT3AlLh4qANVZW8NNqFz/k4FVUq6pRe5pWImirg4iClnR6nwK3IdtKG3+MUiX33Hv+/nVTz0ujAL6BgmpQi257kY2WuARMjddx7lD8UJTNLwMOgFLi7FH0PVHHRwb1Hku1x92EyqhRzDH08a0tNmJpo4LZ/onhfZpaA+4/fopqbfpHHVXXVlzgGAP71T0A1V8ljbK21cWhbPezfVBfzvV1hbclo449lZaaee0998EzLyhLwKDi12Psj9/8iKc+4/x+jpiqCi4O0Z2UK/7+iz+qLmTPI0NAQGhoa0NHRgZWVFYDcP+zt7Oywfv16iEQiuLm54fXr15g5cya+//57qccAuUOyFi5cKH7t5OQEX19f+Pj4oE+fPh+VPzs7O6xatQoikQiurq54+PAhVq1ahVGjRn1awf9vzZo1aNeunTg6pHLlyvD19ZWIHpHG2NgY69evh6qqKtzc3NCpUyecP38eo0aNQlBQEM6dO4fbt2+jbt3ceU+2bt2KSpUqfVJep02bhk6dOgEAFi5ciKpVqyIkJARubm6IiIhAz549Ub16dQCAs7Nzic+fkZGB3bt3w9w8/9eTnj17SqTZtm0bLCwsEBAQgGrVqsmVt+XLl6Nu3br49ddfxemrVq0KILez6ZdffsGFCxfg6ekpzvu1a9fw22+/iTtkirJhwwYYGhpi3759UFfPDf+sXLmy+P0VK1Zg5syZ6NevHwBg2bJluHjxIlavXo0NGzagb9++mDJlCq5du4amTXPnnPnzzz/x9ddfQ0VFBaGhodi7dy9evnwJGxsbcVlPnTqF33//HUuWLAGQO6zu119/Rc2aNcXXbtmypURef/vtNxgbG+Py5cvo3Lkzzpw5g9DQUFy6dEl8H/34449o06aN+Jh9+/ZBRUUFW7duFUdy/f777zAyMsKlS5fQtm1bfMnSUhIg5GRDx0ByGKaOvhlSk2LlOsft89uRmZEG1zodxPtMrJzRYdBSmNm4IuN9Cu5e3IW9K/tjyJyjMLZwLM0iUBE0Lc2QHh0nsS8jJh4q6urQMDNGelQsNK3MkB4tGYmZHh0PTStGcX0sAz01qKqKkFjgF+vE5CwYGehKPcbYQBWJyQXSJ2VBTVUEAz01JCRlwchArfA5k7JgbFD00AsqWl47JRSo04TkbNQ2lP4V0thQDQnJ2ZLpk7Kgpvb/dnqbBWPDotrpi/la+p9k/P82KdheiUlZsDDVKPY4ae1hUkQb06cxNcptizeJklMdvEnMhJV50R03JkYaSChwTEJiJkyM89s24Gkylqx5ghev02BspI7Bve3x69KaGDLpHpKSGSFUUnn3lLT7w9y06OFcxoa5z7oPJbzNf8YZ6KlK/z8wKVt8Tfp0oi9oIufy8sVEBkkTGBgIT09PiWFEjRs3RkpKCl6+fFnssZs2bULdunVhbm4OPT09bNmyBRERER+dl4YNG0rkw9PTE0+fPhVHlXyqvLJ+qOBraapWrQpV1fwvwdbW1uLIn+DgYKipqaF27dri911cXD55QugaNfLn0bC2tgYA8TUnTpyIxYsXo3Hjxpg/fz4ePHhQ4vM7ODhIdAQBQGhoKL7++ms4OzvDwMAATk5OAFCoTYvLW15kkDQBAQF4//492rRpAz09PfG2a9cuieFoRfH390fTpk3FHUEfSkpKwuvXr9G4cWOJ/Y0bN0ZgYCAAwNzcHG3atMGePXsAAGFhYbhx4wYGDBgAALh37x4EQUDlypUl8nf58mWJ/GloaEjUQV75x4wZg8qVK8PQ0BCGhoZISUkR111wcDDs7OwkOlTr168vcY67d+8iJCQE+vr64mubmJjg/fv3RdZPeno6kpKSJLbMjOIn4ytvIhT8T0WQuO+LEnjnH/ieWI8uw1dBVz+/Q8nGyQPu9bvBwtYNti510XXEahhbOOLepT9KOedUrA8i7gAAeW364X5paQruoxIrWIOy7qYim+qDMxVqFZGUfVQyBesdMj7+Bd4UiXcX3U4itlOJfeVphEObqoo3NdXcmi7UNnLUrdRHXKnlVLm1aWaOU396ijdVNfEdIZFOnv9WhIL3VoFjbt1LwOWb8XgW8Q53H7zFzMWPAQDtv7L41GIohRYNDHFwfRXxppp3TxVM+BF9DNLuKX61oPL2RXc9CkLhP8TyHpLF/YHm4+ODKVOmYOXKlfD09IS+vj5+/vln3Lp1q8zyKhKJCj3ASzL5ccFj5VWw80EkEomHThV1zo+9lrRr5rVD3jVHjhyJdu3a4fjx4zhz5gyWLl2KlStXYsKECVBRUZGrjnR1C/9q3KVLF9jZ2WHLli2wsbFBTk4OqlWrVmiIUnF509YuOjw9L83x48dRoYLk2Gx5JtAu7twF85On4Od7wIABmDRpEtatW4c///wTVatWFUf45OTkQFVVFXfv3pXo/AMgMQxPW1u70HWGDh2K2NhYrF69Gg4ODtDU1ISnp6e47qTdZwXl5OSgTp064s6qDxXsuMuzdOlSiQg9AOg8aD66Dl5Q7LXKg7aeMUQqqkhNkowgeZccDx394lcADLp7Aqf/+A5dRq6Bg1ujYtOKVFRg5VAdCbHhn5plklN6dFyhCB8NcxPkZGYiIz4xN01UHDStJNtZ08KkUEQRyS8pJQvZ2UKhSBBD/cIRCnkSkrKlps/KFpCckvvDi7ToEiN9NSQmlc4PM8pG3E6GBetUteh2eiulDQzUkJUlIDk1u8g0xbU9SXfTLwlBofkrD6n/v5PBpEBUgpG+GhLfFv2dMy9a60OG+oUjG+jjXPv3DQKe5A8pV1fP/S3exEgD8Qn57WJsqI6Et0UPrX+TmCERBQQARobqSEgs+pj36Tl49jwVttYcgimPW/7JCA7L/xEz754yNih8TxWMwPuQtHvqw8jVpJRsqc9Ww2KerfQRRF903Mtn8UXVkIaGhkSkjbu7O3x9fSU6EHx9faGvry/+g73gMQBw9epVNGrUCGPHjkWtWrXg4uIiV3RHcW7evFnodaVKlcR/mJubmyMyMlL8/tOnT/HunfxLB7q7u0u9xqdwc3NDVlYW/Pzy/4MKCQlBYmLiJ51XFjs7O4wZMwaHDh3C1KlTsWXLFgC5dZScnIzU1Py5CYqbUDtPfHw8AgMDMXfuXLRq1QpVqlQRT/pcEjVq1MD589KXoM6bcDkiIgIuLi4Sm52dnVznvnr1qtTOLQMDA9jY2ODaNck5SXx9fVGlShXxay8vL7x//x6nTp3Cn3/+iYEDB4rfq1WrFrKzsxETE1Mofx9G9Ehz9epVTJw4ER07dkTVqlWhqamJuLj8P3LzhvdFR0eL992+fVviHLVr18bTp09hYWFR6PqGhtIn2Js9ezbevn0rsXXoV3iZ9f8CVTUNWNpVRXiQ5Nwy4UG+sHEuet6YwDv/4NTuWeg0bCUqVmsh8zqCICDmZSD0DDj86HNJvOkPs1aSnXTmbZrg7d1HEP4/p1fCTX+YtZKM3DNr3QQJN+SbL4oKy8oGQiLeo1YVyc59jyq6CAyVvrR80LN38CiQvpa7LkKepyE7Jy9NmtQ0gaFcqvdjZGUDIc/fF6pTD3c9BBXZTmnwcJecC1DcTtl5ad6hlnvBdtIrsu1JurT3OYiMyRBvEa/T8SYxE7Wq5s8ho6YqQnU3PQSEFH0PBIa8Q+2qkm1Wu5oeAos5huSX9j4br6Lei7fwF+8Q/yYDdWsaidOoqYlQs6ohHgUlF3mex8HJqPfBMQBQz8MIj4KLPkZdTQQHWx3EJ3z58zd+DmnpRd1T+feHmqoI1Vx1i70/pD8H8++prGwBIc/TUEtaGv5/RZ/RF9UZ5OjoiFu3biE8PBxxcXEYO3YsXrx4gQkTJiAoKAhHjx7F/Pnz4e3tDZX/z+Jd8JicnBy4uLjgzp07OH36NJ48eYJ58+YV+uO2pF68eAFvb28EBwdj7969WLduHSZNmiR+v2XLlli/fj3u3buHO3fuYMyYMVKHDBVl4sSJOHXqFJYvX44nT55g/fr1MucLksXNzQ2tW7fG6NGj8e+//8LPzw+jR4+WGj1SWiZPnozTp08jLCwM9+7dw4ULF8QdHg0aNICOjg7mzJmDkJAQ/PnnnxKTXRclb/WqzZs3IyQkBBcuXIC3t3eJ8zZ79mzcvn0bY8eOxYMHDxAUFISNGzciLi4O+vr6mDZtGqZMmYKdO3ciNDQUfn5+2LBhA3bu3Cnz3OPHj0dSUhL69euHO3fu4OnTp9i9ezeCg4MBANOnT8eyZcuwf/9+BAcHY9asWfD395f4DOnq6qJbt26YN28eAgMD8fXXX4vfq1y5MgYMGIDBgwfj0KFDCAsLw+3bt7Fs2TLxam1FcXFxwe7duxEYGIhbt25hwIABEpFMbdq0QcWKFTFkyBA8ePAA169fF08gnfc5GTBgAMzMzNCtWzdcvXoVYWFhuHz5MiZNmlTkkE1NTU0YGBhIbOoa/91JDuu2GoaHvgfx0Pcg4qNCcfHgEiS/iUTNJrnzPF05uhInds4Qpw+88w9O7pyJ5j1mwsaxJlLfxiL1bSzS0/K/tPkeX4+wgKtIjHuBmBeBOP3HHMS+DELNpv0/e/kUhaquDgxqusGgphsAQMfJFgY13aBllzss1HWxN2r+vkyc/vnmfdB2sEGVn2dBz80ZtkN7wm5YTzz7Zbs4Tfj6XTBr0xjO00ZB19UZztNGwayVJ8LXyb73qWhHzsWjTRMjtG5kCFsrDYzsbQFzE3WcvJLbmT/YyxxThlqL05+6nAgLU3WM6G0BWysNtG5kiDaNjXD4zBtxmmPn36CWuy56tjOFraUGerYzRc0qujh2/k2h65N8jpyNR9umxmjT2Ci3nfpYwtxEHScu57bTkO4W8B5uI05/8nICLEzVMbKPJWytNNCmsRHaNDHGoTP5827ltpMeerY3ha2VBnq2N4VHFV0cPcd2+lRHzsShbxcLNKptAIcKmvAeaYv09BxcupkoTjN1lB2G9sr/oejo2TjUrqaP3h3NYWutid4dzVHLXR9HzuTPiaelqQJney0422sBACzNNOBsr8VlsD/SgX9eYWAvOzRtYAonex3MnlAJ6enZOHslv87nTKyM0QMdxK8P/vMadT2M8XX3CrCvoI2vu1dA3RpGOPD3K3GasUMcUbOqAawtNFGlkh4WzagCXR1VnLr4aYvDKLOj5+LRp6M5PGvpw8FGE1OGV0B6Rg4u33orTuM9vAKG9LAUvz52Lg613fXQq70ZbK000Ku9GTyq6OHoufzn4OGzceJnq521Jkb1tcp9tl7ic5A+ny9qmNi0adMwZMgQuLu7Iy0tDWFhYThx4gSmT5+OmjVrwsTEBCNGjMDcuXOLPWbMmDHw9/dH3759IRKJ0L9/f4wdOxYnT5786LwNHjwYaWlpqF+/PlRVVTFhwgSMHj1a/P7KlSsxbNgwNGvWDDY2NlizZg3u3r0r9/kbNmyIrVu3Yv78+ViwYAFat26NuXPn4ocffvjoPAPArl27MGLECDRr1gxWVlZYunQpHj9+DC0trU86b1Gys7Mxbtw4vHz5EgYGBmjfvj1WrVoFADAxMcEff/yB6dOnY/PmzWjdujUWLFggUY/SqKioYN++fZg4cSKqVasGV1dXrF27Fi1atChR3ipXrowzZ85gzpw5qF+/PrS1tdGgQQP075/7h/kPP/wACwsLLF26FM+ePYORkRFq166NOXPmyDy3qakpLly4gOnTp6N58+ZQVVWFh4eHeJ6giRMnIikpCVOnTkVMTAzc3d1x7NixQpN5DxgwAJ06dUKzZs1gb28v8d7vv/+OxYsXY+rUqXj16hVMTU3h6emJjh07Fpu37du3Y/To0ahVqxbs7e2xZMkSTJs2Tfy+qqoqjhw5gpEjR6JevXpwdnbGzz//jC5duog/Jzo6Orhy5QpmzpyJHj16IDk5GRUqVECrVq1gYGAgu/K/AG51OiItNQE3Tv6K1KQYmFlXRo+xm2FomhuFmPo2FkkJ+dF/96/tR05OFs7vX4Tz+xeJ91dt0B0dBv8EAEhPS8KZP7/Hu+RYaGjpw9LOHf2m/AFrR8l5nUh+hnWqwfP8bvFr9xW59+eLXYfwYMRsaFqbQ9suv4MhLfwlbncZDfeVs+Hw7QCkv47B4yk/IurwGXGahBt+8BvgDdeFk+G6cCLehb6A39dTkPhvyec8o3zX7iTDQDca/TqZwcRQDc9fp2Ph+gjEvsmNyDIxVJP4QzM6PhML173AyD6W6NTcGG/eZmHz/ij4+uV3sAY9S8Pyra8wqJs5BnQ1R1RsBpZveYUn4e8/e/kUxdU7SdDXU0W/zvnttGBtBGLf5Ea6GhsVaKe4TCxYG5HbTi2MEf82C5v3RcH33gftFJqG5ZtfYqCXBQZ2s0BUbAaWbX6JJ2GMDPpUB07EQkNDBeMGV4CeriqCQ9/huxXPkPY+f3VVC1N1iaj6wJB3+GljBAb3tMSgHpaIjMnA0o3PEfwsvz0qOWlj+ayK4tfffJ3bAXj22hv8srX4eTqpsD8Pv4Kmhiq8R1eEnp4aAp8mY+rCx0h7nz+awdJcU6KdHgUnY+HKIIz82gEj+jvgdfR7LFgZjMCn+StSmZtqYr63Kwz11ZGYlImAJ8kYM/M+omP/23My/pcdPBUHDQ0VjB1gk3tPPUvDvF/CkZaef0+Zm2pIzPUTGJqGZZtfYJCXJQZ65T3jXiD4g2fc1dtJMNCNQv8uFuJn6/w1z8XPVvp0nEBaNpHwqRPEkEJ5+fIl7OzscO7cuSInUya6fv06mjRpgpCQEFSsWFH2AXLacq7UTkVlyKaDa3lngeS0afiR8s4CyUHI4VexL0F2Jufy+BKkvk0q7yyQnPRNjco7CySH41uryU70H5T0y+Ryu7aB9+pyu3ZJfFGRQVT6Lly4gJSUFFSvXh2RkZGYMWMGHB0d0axZs/LOGv2HHD58GHp6eqhUqRJCQkIwadIkNG7cuFQ7goiIiIiIiEqFyhc1I065YA3JEBERIbFUd8HtU5aj/y/IzMzEnDlzULVqVXTv3h3m5ua4dOkS1NXVsWfPniLLXbVq1fLO+n/KmDFjiqyrMWPGlHf2PllycjLGjh0LNzc3DB06FPXq1cPRo0fLO1tERERERET0EThMTIasrCyEh4cX+b6joyPU1BQzwCo5OVliBakPqaurw8HBQep7yigmJgZJSdLDkg0MDGBhYfGZc/Rl4jCxLwOHiX05OEzsy8BhYl8GDhP7MnCY2JeDw8S+DF/sMLHVJV9QqLQYTP6l3K5dEorZi1GK1NTU4OLiUt7ZKBf6+vrQ19eXnZBgYWHBDh8iIiIiIqL/gLJaHVuRcJgYEREREREREZESYWQQERERERERESkOTiAtE2uIiIiIiIiIiEiJMDKIiIiIiIiIiBSGSIVzBsnCyCAiIiIiIiIiIiXCziAiIiIiIiIiIiXCYWJEREREREREpDhEjHuRhTVERERERERERKREGBlERERERERERIqDE0jLxMggIiIiIiIiIiIlws4gIiIiIiIiIiIlwmFiRERERERERKQwRJxAWibWEBERERERERGREmFkEBEREREREREpDk4gLRMjg4iIiIiIiIiIlAgjg4iIiIiIiIhIYYhUGPciC2uIiIiIiIiIiEiJsDOIiIiIiIiIiEiJcJgYERERERERESkOESeQloWRQURERERERERESoSRQURERERERESkODiBtEysISIiIiIiIiIiJcLOICIiIiIiIiIiJcJhYkRERERERESkODiBtEyMDCIiIiIiIiIiUiKMDCIiIiIiIiIihSHiBNIysYaIiIiIiIiIiJQII4OIiIiIiIiISHGIGPciC2uIiIiIiIiIiEiJsDOIiIiIiIiIiEiJcJgYERERERERESkOFS4tLwsjg4iIiIiIiIiIlAgjg4iIiIiIiIhIYYg4gbRMrCEiIiIiIiIiIiXCziAiIiIiIiIiIiXCYWJE9J9x7EBgeWeB5DH8SHnngOQ0ZrtXeWeB5NBy+6DyzgLJoddlr/LOAslBz9iwvLNAcuIwHipTnEBaJt6BRERERERERERKhJFBRERERERERKQ4GHkmE2uIiIiIiIiIiEiJMDKIiIiIiIiIiBSHiHMGycLIICIiIiIiIiIiJcLOICIiIiIiIiIiJcJhYkRERERERESkOFQY9yILa4iIiIiIiIiISIkwMoiIiIiIiIiIFAeXlpeJNUREREREREREpETYGUREREREREREpEQ4TIyIiIiIiIiIFIeKqLxz8J/HyCAiIiIiIiIiIiXCyCAiIiIiIiIiUhycQFom1hARERERERERkRJhZxARERERERERkRLhMDEiIiIiIiIiUhwiTiAtCyODiIiIiIiIiIiUCCODiIiIiIiIiEhxqDDuRRbWEBERERERERGREmFkEBEREREREREpDs4ZJBMjg4iIiIiIiIiIlAg7g4iIiIiIiIiIlAiHiRERERERERGR4hAx7kUW1hARERERERERkRJhZBARERERERERKQ4uLS8Ta4iIiIiIiIiISImwM4iIiIiIiIiISIlwmBgRERERERERKQ6RqLxz8J/HyCAiIiIiIiIiIiXCyCAiIiIiIiIiUhxcWl4m1hARERERERERkRJhZBARERERERERKQ7OGSQTI4OIiIiIiIiIiJQIO4OIiIiIiIiIiJQIh4kRERERERERkeJQYdyLLKyh/4AWLVpg8uTJH318eHg4RCIR/P39AQCXLl2CSCRCYmJikcfs2LEDRkZG4tcLFiyAh4fHR+fhv6RgfRARERERERFRPkYGKQA7OztERkbCzMzso88xbdo0TJgwoRRzRZ9LixYt4OHhgdWrV5d3VgAAIpEIhw8fhpeXV3lnpdR1bG6MHm1NYGyohojX6djiE42AkLQi01erpIMRvS1gb6OJN4lZ+OtMPE5dSRS/b2+tgQFdzVHRXguWZhrY4hOFY+cTPkNJFF9ptxUANKqljwFdzWFtro7I2EzsPhqLm/7JZVwSxWXSpC6cp46AYe1q0LKxwJ2eYxF97HzxxzStB/cVs6DnXgnpr2MQunIrIjbvk0hj1b0tKi+YBJ2K9ngXGoHg71ch+ui5siyKwtt/Jxg7bzxGXEoaKpobYXrbuqhtbyk17bxj1/H3g2eF9jubGeLQmK4AgJDYRGy8fB8BkfGIfJuKaW3qYmCDKmVaBmXydVcLtG9mDD0dVQSHpWHjnteIeJ1e7DGNahtgkJcFrM01EBmbgV2Ho3HDT/L51qmFCXq0M4OJUe5zdfO+SDx++q4si6LQBnSzQPvmJrnt9Owdfv1Ddjs1rmOAQd0txe2081A0btxLEr9frbIOerY3h4ujNkyN1PHDuue44ZdUzBlJHl93MUe7ZkbQ01HFk7A0bPwzSo57Sh8Du1nkf2c4ElPonurYwhg92pnCJO+7yv5o3lOlSOAE0jIxMkgBqKqqwsrKCmpqH9+3p6enB1NT01LMVcllZmaW6/WJitOkrj5G9rGEz4l4TFochschaVgwwR7mxtLvO0tTdcyfYIfHIWmYtDgMB07GY3RfKzSqpS9Oo6mhgqi4TOw8HIs3b7M+V1EUXlm0lauzNmaMqoCLt95i4g9huHjrLWaOroDKjlqfq1gKR1VXB0kPgvF40iK50ms72qLe35vx5tpdXKvnhZBlm1B11Xew6t5WnMaooQdq/bkKr/YcxdU63fBqz1HU3rsaRvVrlFUxFN7px+H4+cwdjGxSHftGdUYtewuM23sBkW9Tpaaf0bYezk3uJd5OT+wBQ20NtKniIE7zPjMLFYz0MKllLZjpaX+uoiiFXu3N0L2NKTb9GYkpi0OR8DYTi70doa1Z9Fd+N2dtzPrGDhduJGL8whBcuJGIWd/Yw9Upv22a1jPAqH5W2H8iFhMXheLRk1QsnOQAcxP1z1EshdOrgxm6tzXDxj9eY/IPIUh4m4UfpzlBW6uYdqqog1lj7HHBNxHj5ofggm8iZo+xh6tzfjtpaaog7MV7bPzj9ecohlLo2d4UXm1MsOnPKHj/GIaEt1n4YYq9zHtq5mhbXLyZiAmLnuHizUTMHG2Lyh/eU3UNMKqvFXyOx2Hiomd4/PQdFky0h7kJYzXo82Fn0H9EVlYWxo8fDyMjI5iammLu3LkQBAFAbqTFkSNHJNIbGRlhx44dAOQbFrVjxw7Y29tDR0cH3bt3R3x8vMT7BYeJDR06FF5eXlixYgWsra1hamqKcePGSXTYREZGolOnTtDW1oaTkxP+/PNPODo6yh2hIhKJsGnTJnTr1g26urpYvHgxAODvv/9GnTp1oKWlBWdnZyxcuBBZWVkSx23cuBEdOnQQX/vAgQNFXic7OxsjRoyAk5MTtLW14erqijVr1hRKt337dlStWhWampqwtrbG+PHjxe+9ffsWo0ePhoWFBQwMDNCyZUvcv3+/UP1t374d9vb20NPTw7fffovs7GwsX74cVlZWsLCwwI8//ihxTXnPu3v3bjg6OsLQ0BD9+vVDcnKyuJ0uX76MNWvWQCQSQSQSITw8XGbdP378GJ06dYKBgQH09fXRtGlThIaGAgBycnKwaNEi2NraQlNTEx4eHjh16pT42IyMDIwfPx7W1tbQ0tKCo6Mjli5dCgBwdHQEAHTv3h0ikUj8WhF4tTbF2euJOHM9ES+jMrDVJxpxCZno0NxYavr2zY0Q+yYTW32i8TIqA2euJ+Lc9UR0b2siTvP0+Xv8/lcMrt5JQmZmzucqisIri7bq1soE/oGpOHgqHi+jM3DwVDzuB6WiaysTqeck2WJPX8GT+asRdeSsXOkdRvfD+4hIBExdgpSgZ3ix/SBe7DgEZ+/h4jROE4Yg7pwvQpdvRmrwM4Qu34y4CzfhOGFIWRVD4e2+FYDuHi7oUasSnM0MMaNtPVgZ6ODA3WCp6fW1NGCmpy3eHkfGIyktA91qVhSnqWZjBu/WddC+qhPUVflVtDR1a22K/cdj4XsvCc9fp+OX7a+gqaGC5g0Miz6mjRn8AlJw4GQcXkZl4MDJONwPSkG31vk/EnZvY4Yz1xJw5moCXkSmY8v+KMQlZKJjCz4DP4ZXGzPs+ycmt51epWPltpfQ1FBBiwZGxRxjCr+AFPiciMXLqHT4nIiFf2AKurXJHxlw52EKdh2Ohu89RgOVlm6tTLD/RBxu+CXn3lO/v/7/PWVQ5DFdW5vALyAVB07G//+eyv3O0K11/v3i1cYUZ68l4My13O8qW/bnflfp2Jz3FH0+/B/4P2Lnzp1QU1PDrVu3sHbtWqxatQpbt24tlXPfunULw4cPx9ixY+Hv74+vvvpK3PFSnIsXLyI0NBQXL17Ezp07sWPHDnEHFAAMHjwYr1+/xqVLl/DXX39h8+bNiImJKVHe5s+fj27duuHhw4cYPnw4Tp8+jYEDB2LixIkICAjAb7/9hh07dhTqRJk3bx569uyJ+/fvY+DAgejfvz8CAwOlXiMnJwe2trbw8fFBQEAAvv/+e8yZMwc+Pj7iNBs3bsS4ceMwevRoPHz4EMeOHYOLiwsAQBAEdOrUCVFRUThx4gTu3r2L2rVro1WrVnjz5o34HKGhoTh58iROnTqFvXv3Yvv27ejUqRNevnyJy5cvY9myZZg7dy5u3rxZ4vMeOXIE//zzD/755x9cvnwZP/30EwBgzZo18PT0xKhRoxAZGYnIyEjY2dkVW+evXr1Cs2bNoKWlhQsXLuDu3bsYPny4uMNtzZo1WLlyJVasWIEHDx6gXbt26Nq1K54+fQoAWLt2LY4dOwYfHx8EBwfjjz/+EHf63L59GwDw+++/IzIyUvz6S6emCrjYa8EvQPKXcL+AVFSpKP1XbTdnnULp7wWkwsVBG/zbp+yUVVu5OWsXPufjVFSpqFN6madiGTX0QOy56xL7Ys9chWGdahD9PzLWuKEH4s5dk0gTd/YqjD1rfbZ8KpLM7GwERr6Bp7O1xP6Gzja4/zJWrnMc8Q9BAydr2BjplUUW6QNWZuowMVLHvccp4n1ZWQIeBaeiikvRz6rc51uKxL57j1PEx6ipiuDioA2/x1LS8BlYYlbm0tvpoax2qqiDe48khxnde5QMd7ZBmbH8/z3l9zj///+sLAGPnrwr9rOf+71C2v2S+z1ETRVwcZDyXeVxCtyK+K5CH0GkUn7bF4JxaP8RdnZ2WLVqFUQiEVxdXfHw4UOsWrUKo0aN+uRzr1mzBu3atcOsWbMAAJUrV4avr69EtIc0xsbGWL9+PVRVVeHm5oZOnTrh/PnzGDVqFIKCgnDu3Dncvn0bdevWBQBs3boVlSpVKlHevv76awwfnv+r7qBBgzBr1iwMGZL7K66zszN++OEHzJgxA/Pnzxen6927N0aOHAkA+OGHH3D27FmsW7cOv/76a6FrqKurY+HCheLXTk5O8PX1hY+PD/r06QMAWLx4MaZOnYpJkyaJ09WrVw9AbqfYw4cPERMTA01NTQDAihUrcOTIERw8eBCjR48GkNvptH37dujr68Pd3R1fffUVgoODceLECaioqMDV1RXLli3DpUuX0LBhwxKdd8eOHdDX1xfX0fnz5/Hjjz/C0NAQGhoa0NHRgZWVlVx1vmHDBhgaGmLfvn1QV88N765cubL4/RUrVmDmzJno168fAGDZsmW4ePEiVq9ejQ0bNiAiIgKVKlVCkyZNIBKJ4OCQH/pvbm4OIDdyTd78fAkM9NSgqipCYpLkUK7E5CwYGehKPcbYQBWJyQXSJ2VBTVUEAz01JCRxWFhZKKu2MjJQK3zOpCwYG6iWbgGoSJqWZkiPjpPYlxETDxV1dWiYGSM9KhaaVmZIj5aMfE2PjoemlfnnzKrCSHiXjmxBgImu5HBIU10txKW8l3l8bPI7XA95jSXdm5RVFukDxoa5X+ulPavMTYsezmVsqIaEAkOVE95mwdgg93wGeqrSn6tJ2eJrkvyMDXLbQlo7WchoJ6n/D7ENykxx99RHtZX4nsr9rlLwu2BCcjZqsz3pM+Kn7T+iYcOGEH0wyZWnpydWrlyJ7OzsTz53YGAgunfvLrHP09NTZmdQ1apVoaqa/4eOtbU1Hj58CAAIDg6GmpoaateuLX7fxcUFxsbSh2EUJa8jKc/du3dx+/ZtiUig7OxsvH//Hu/evYOOjo44/wXLU9wwuU2bNmHr1q14/vw50tLSkJGRIR4WFxMTg9evX6NVq1ZSj7179y5SUlIKzamUlpYmHloF5A6RyuuwAQBLS0uoqqpC5YNlDS0tLcXRUx97Xmtr6xJHYH3I398fTZs2FXcEfSgpKQmvX79G48aNJfY3btxYPHxt6NChaNOmDVxdXdG+fXt07twZbdu2LXQuWdLT05GeLjn5XnZ2BlRVNUp8rs9FKPBa1rR0QoED8m5xodCZqLSVRVsVajWRlH1UtopsKKH4NAX3UYmICkzCKSC/6otz7EEo9LU00NK1+IhV+jgtGhhi/CAb8esFa58DkP6sKimRlOcbb62P06KhESYMzm+n+av/304F61PKvoIKt62I/w+VohYNDDBuYH5bLVwXAUD6d4qStpW0e6rgDnnOSyXwBUXolBd2Bn0BRCKReP6gPCWZbLngsfIq2FkgEomQk5NT7DlLei1dXclf6nNycrBw4UL06NGjUFotreInai34ZTWPj48PpkyZgpUrV8LT0xP6+vr4+eefcevWLQCAtnbx4Zg5OTmwtrbGpUuXCr1nZGQk/re0+iquDj/lvHnn+Biyypt3jQ8JgiDeV7t2bYSFheHkyZM4d+4c+vTpg9atW+PgwYMlysfSpUslIrYAoFLtsXCtO76II8pPUkoWsrMF8S86eQz1C//ykychKVtq+qxsAckpn97JS9KVVVt9+IteHiN9NSQmsS0/l/TouEIRPhrmJsjJzERGfGJumqg4aFpJrqypaWFSKKKI5GOsowlVkQjxKZIr8b1JfQ9T3eL/TxYEAUf8Q9GpuhPUVRlBVxZu+ScjOCz/xyN1tdz/p40NJCN9jPSLj0ZNeFs4uuTDaMiklOzc56phweekapHPVcp3yz8Jwc/yV4gSt1OBiCxDKRGoH/owWiuPkb4qErkARam55Z+C4Gcf3FPquZ0JBe+pj2mrD7+HiL+rFLzveE/RZ8busv+IvHlkPnxdqVIlqKqqwtzcHJGRkeL3nj59infv5F920N3dXer5P4WbmxuysrLg5+cn3hcSEoLExMRPOm/t2rURHBwMFxeXQtuHETbSyuPm5ib1nFevXkWjRo0wduxY1KpVCy4uLhKRN/r6+nB0dMT589KXNK5duzaioqKgpqZWKE9mZmZSj5G3rKVxXg0NjRJFkNWoUQNXr16V2qFoYGAAGxsbXLsmOeeGr68vqlSpIpGub9++2LJlC/bv34+//vpLPM+Rurq6XPmZPXs23r59K7G51Botdzk+p6xsICTiPWpVkey89Kiii8BQ6cuVBz17B48C6Wu56yLkeRqyOVd0mSmrtgp6liY1TWAol4D9XBJv+sOsVSOJfeZtmuDt3UcQ/j/nWcJNf5i1koxsNGvdBAk3/EAlp66qiirWJrgRFimx/1ZYJGraFj/07s7zaLxISEZ3D5eyzKJSS0vPQWRMhniLeJ2ON4mZqFU1f34mNVURqrnqIjCk6GdV0LM0eLhLzulUy11PfExWtoCQ52moJS0Nn4Eypb2X3k613SXbqbqsdgp9h1pV9SX21a6mjwC2QalJS89BZGymeBPfU+75//+rqQLVKusU+9kPevZO4hgg737J/R6SlQ2EPH9f6HuFh7segor4rkIlJ4hE5bZ9KdgZ9B/x4sULeHt7Izg4GHv37sW6devE89e0bNkS69evx71793Dnzh2MGTNG6hCfokycOBGnTp3C8uXL8eTJE6xfv17mEDFZ3Nzc0Lp1a4wePRr//vsv/Pz8MHr0aGhraxcZoSOP77//Hrt27cKCBQvw+PFjBAYGYv/+/Zg7d65EugMHDmD79u148uQJ5s+fj3///Vdi9a8Pubi44M6dOzh9+jSePHmCefPmFZrYeMGCBVi5ciXWrl2Lp0+f4t69e1i3bh0AoHXr1vD09ISXlxdOnz6N8PBw+Pr6Yu7cubhz585Hl7W0zuvo6Ihbt24hPDwccXFxMqOGxo8fj6SkJPTr1w937tzB06dPsXv3bgQH564MM336dCxbtgz79+9HcHAwZs2aBX9/f/HncdWqVdi3bx+CgoLw5MkTHDhwAFZWVuJopryOtaioKCQkJBSZD01NTRgYGEhs/+UhYkfOxaNNEyO0bmQIWysNjOxtAXMTdZy8klvGwV7mmDI0f5LVU5cTYWGqjhG9LWBrpYHWjQzRprERDp/JnxxcTRVwstWEk60m1NREMDVSh5OtJqzNuVTvpyiLtjp2/g1queuiZztT2FpqoGc7U9Ssootj598Uuj7JR1VXBwY13WBQM7cjX8fJFgY13aBll9s2rou9UfP3ZeL0zzfvg7aDDar8PAt6bs6wHdoTdsN64tkv28VpwtfvglmbxnCeNgq6rs5wnjYKZq08Eb5u5+ctnAIZ1MAdh/1CcMQ/BM/i3uLnM7cR+TYVvWrnzjW39sI9zD16vdBxR/xDUL2CGVwsCg8fz8zORlDUGwRFvUFWdg5ikt8hKOoNIt5wBaRPdfRcPPp0NIdnLX042GhiyvAKSM/IweVbb8VpvIdXwJAeluLXx87Foba7Hnq1N4OtlQZ6tTeDRxU9HD2XP//W4bNxaNvUGG0aG8HOWhOj+lrB3EQdJy7xGfgxjpyNQ5/OFvCsbQCHCprwHmGL9IwcXLqVKE4zdaQthvbMb6ejZ+NRu6oeenUwg62VJnp1+H87nc2PfNTSVIGznRac7XIj9yzN1OFspwVzE36v+FhHz79B745m4ntq8rC8eyr/eeU93AZDuluIX+d+Z9BDz/amsLXSQM/2pvCooouj5/LvlyNn48X3lK2VBkb2scy9py4X/d2ZqLRxmNh/xODBg5GWlob69etDVVUVEyZMEE8gvHLlSgwbNgzNmjWDjY0N1qxZg7t378p97oYNG2Lr1q2YP38+FixYgNatW2Pu3Ln44YcfPinPu3btwogRI9CsWTNYWVlh6dKlePz4sczhXMVp164d/vnnHyxatAjLly+Huro63NzcxJNF51m4cCH27duHsWPHwsrKCnv27IG7u7vUc44ZMwb+/v7o27cvRCIR+vfvj7Fjx+LkyZPiNEOGDMH79++xatUqTJs2DWZmZujVqxeA3CFTJ06cwHfffYfhw4cjNjYWVlZWaNasGSwtLaVeUx6ldd5p06ZhyJAhcHd3R1paGsLCwopd0t3U1BQXLlzA9OnT0bx5c6iqqsLDw0M8T9DEiRORlJSEqVOnIiYmBu7u7jh27Jh4cnA9PT0sW7YMT58+haqqKurVqyeeJBvI/bx6e3tjy5YtqFChglxL3X8Jrt1JhoFuNPp1MoOJoRqev07HwvURiH2TG5FgYqgm8WUrOj4TC9e9wMg+lujU3Bhv3mZh8/4o+PrlrwRiYqSOtfOcxa97tDVFj7ameBicijm/RHy+wimYsmiroGdpWL71FQZ1M8eAruaIis3A8i2v8CRc9iS6JJ1hnWrwPL9b/Np9xRwAwItdh/BgxGxoWptD2y6/0y4t/CVudxkN95Wz4fDtAKS/jsHjKT8i6vAZcZqEG37wG+AN14WT4bpwIt6FvoDf11OQ+O+Dz1cwBdOuqiMS09Lx29UHiEtJg4u5Edb3ayleHSw2JQ2RbyVXxEl+n4HzQRGY3rae1HPGJKeh39bj4te7bgZg180A1LG3xLbBJZ+DjvIdPBUHDQ0VjB1gAz1dVQQ/S8O8X8KRlp7/Q5G5qYbEvCSBoWlYtvkFBnlZYqCXBaJiM7Bs8wsEh+VHKFy9nQQD3Sj072Ihfq7OX/McsW/kn7aA8h08GQdNDRWMG5jXTu8wd2UY0t5/0E4m6vjw973A0Hf4aVMEBvewxKDuloiMycBPmyIQ/Cy/nSo5amPZzPzvFaP7585/c/ZaAlZtf1n2BVNAf52Kh6a6Cr792kp8T32/KkLynjJRR84H91RQaBqWb36JgV4WGNgt7556iScf3lN3kqCvp4p+nfO/qyxYG8F7ij4rkfCxE8oQFfDy5UvY2dnh3LlzRU7GXBpEIhEOHz4MLy+vMrsGlY8u3wSWdxaIFMqY7V7lnQWSQ8vtg8o7CySHXpe9yjsLJAchh3/afClUOJ/YF+GfLdJ/cP+ve3fFp9yurdOsT7lduyQYGUQf7cKFC0hJSUH16tURGRmJGTNmwNHREc2aNSvvrBERERERERFREThnEH20zMxMzJkzB1WrVkX37t1hbm6OS5cuQV1dHXv27IGenp7UrWrVquWddYU2ZsyYIut+zJgx5Z09IiIiIiKisiUSld/2hWBkEH20du3aoV27dlLf69q1Kxo0aCD1vZJMfi0NRzYWb9GiRZg2bZrU9wwMDD5zboiIiIiIiOi/hp1BVCb09fWhr68vOyGVOgsLC1hYWMhOSEREREREREqJw8SIiIiIiIiISHGoqJTf9hF+/fVXODk5QUtLC3Xq1MHVq1eLTZ+eno7vvvsODg4O0NTURMWKFbF9+/YSXZORQURERERERERE5WD//v2YPHkyfv31VzRu3Bi//fYbOnTogICAANjb20s9pk+fPoiOjsa2bdvg4uKCmJgYZGVllei67AwiIiIiIiIiIoUhfEETOf/yyy8YMWIERo4cCQBYvXo1Tp8+jY0bN2Lp0qWF0p86dQqXL1/Gs2fPYGJiAgBwdHQs8XU5TIyIiIiIiIiIqBSkp6cjKSlJYktPT5eaNiMjA3fv3kXbtm0l9rdt2xa+vr5Sjzl27Bjq1q2L5cuXo0KFCqhcuTKmTZuGtLS0EuWTnUFEREREREREpDhEKuW2LV26FIaGhhKbtAgfAIiLi0N2djYsLS0l9ltaWiIqKkrqMc+ePcO1a9fw6NEjHD58GKtXr8bBgwcxbty4ElURh4kREREREREREZWC2bNnw9vbW2KfpqZmsceICgxrEwSh0L48OTk5EIlE2LNnDwwNDQHkDjXr1asXNmzYAG1tbbnyyc4gIiIiIiIiIqJSoKmpKbPzJ4+ZmRlUVVULRQHFxMQUihbKY21tjQoVKog7ggCgSpUqEAQBL1++RKVKleS6NoeJEREREREREZHCEEQq5baVhIaGBurUqYOzZ89K7D979iwaNWok9ZjGjRvj9evXSElJEe978uQJVFRUYGtrK/e12RlERERERERERFQOvL29sXXrVmzfvh2BgYGYMmUKIiIiMGbMGAC5w84GDx4sTv/111/D1NQUw4YNQ0BAAK5cuYLp06dj+PDhcg8RAzhMjIiIiIiIiIgUyRe0tHzfvn0RHx+PRYsWITIyEtWqVcOJEyfg4OAAAIiMjERERIQ4vZ6eHs6ePYsJEyagbt26MDU1RZ8+fbB48eISXZedQURERERERERE5WTs2LEYO3as1Pd27NhRaJ+bm1uhoWUlxWFiRERERERERERKhJFBRERERERERKQwSjqRszJiDRERERERERERKRFGBhERERERERGR4viCJpAuL4wMIiIiIiIiIiJSIowMIiIiIiIiIiLFwTmDZGINEREREREREREpEXYGEREREREREREpEQ4TIyIiIiIiIiKFIXACaZkYGUREREREREREpEQYGUREREREREREioMTSMvEGiIiIiIiIiIiUiLsDCIiIiIiIiIiUiIcJkZERERERERECkMAJ5CWhZFBRERERERERERKhJFBRERERERERKQwBE4gLRNriIiIiIiIiIhIiTAyiIiIiIiIiIgUByODZGINEREREREREREpEXYGEREREREREREpEQ4TIyIiIiIiIiKFIYi4tLwsjAwiIiIiIiIiIlIijAwiIiIiIiIiIoXBpeVlYw0RERERERERESkRRgYREVGJCDlCeWeB5NRy+6DyzgLJ4cLw3eWdBZLHEK/yzgHJQV1To7yzQHLKzsou7ywQKTV2BhERERERERGR4uAE0jJxmBgRERERERERkRJhZBARERERERERKQxOIC0ba4iIiIiIiIiISImwM4iIiIiIiIiISIlwmBgRERERERERKQwBnEBaFkYGEREREREREREpEUYGEREREREREZHC4ATSsrGGiIiIiIiIiIiUCCODiIiIiIiIiEhxiDhnkCyMDCIiIiIiIiIiUiLsDCIiIiIiIiIiUiIcJkZERERERERECkNg3ItMrCEiIiIiIiIiIiXCyCAiIiIiIiIiUhgCJ5CWiZFBRERERERERERKhJ1BRERERERERERKhMPEiIiIiIiIiEhhCCLGvcjCGiIiIiIiIiIiUiKMDCIiIiIiIiIihSGAE0jLwsggIiIiIiIiIiIlwsggIiIiIiIiIlIYnDNINtYQEREREREREZESYWcQEREREREREZES4TAxIiIiIiIiIlIYgogTSMvCyCAiIiIiIiIiIiXCyCAiIiIiIiIiUhhcWl42RgYRERERERERESkRdgYRERERERERESkRDhMjIiIiIiIiIoUhiBj3IgtriIiIiIiIiIhIiTAyiIiIiIiIiIgUBieQlo2RQURERERERERESoSRQURERERERESkMDhnkGysISIiIiIiIiIiJcLOICIiIiIiIiIiJcLOIAXTokULTJ48+aOPDw8Ph0gkgr+/PwDg0qVLEIlESExMLPKYHTt2wMjISPx6wYIF8PDw+Og8UPkq2J5ERERERERfEgGictu+FJwziCTY2dkhMjISZmZmH32OadOmYcKECaWYK8UlEolw+PBheHl5lXdWSA4dmxujR1sTGBuqIeJ1Orb4RCMgJK3I9NUq6WBEbwvY22jiTWIW/joTj1NXEiXSNKqljwFdzWFtro7I2EzsPhqLm/7JZVwSxdexhTF6tDOFSV5b7Y/G46fvikxfrbIORvaxzG+r0/E4eTlBIk2j2voY2M0iv62OxOCGH9vqU+y/E4ydNx4jLiUNFc2N/sfefUdFcbVhAH/WXVh6r0oXpaixYWKJLYpYomCJGivBEmOPGksSFXs0dvOpUYMt9hI1xoa9G0WwAIIgRaV3kF6+PwgLCwsLNhSe3zl7Djtz586duTuFO++9gx+6OaCFmaHMtHNP3MDfD5+VmW6lp4mj4/oAAIJik7DpygP4RcYjMvkVZjg6YNhndu90G2o6nc8dYDV9FDRbNIZSXQPc6z8e0ScuVLxM+1awXzkbavYNkBURg+BV2xC+Zb9UGqO+3dDQfQpU6pshPTgcAfPWIPr4+Xe5KbXGkD4G6N5BG2oqQgSEZGDTngiER2RVuEzbFhoY7mIAY31FRMZmY9df0WXOb7066aCfkx50tArPq1v2R1Z4XqXy9eighb6O2oX3E5HZ+ONQTIX3E40aKMNtgAHMjBWRkJyLv84l4My1ZMl8U2NFDOmth/pmSjDUVcC2QzH4+2JiuflR1QzprQ+nDlpQUxEiMCQDm/ZGVeKYkn/PUNV7FaK3jZFBJEUoFMLIyAgi0eu3E6qpqUFXV/ctlurdyMnJqbZ1Z2dnV9u66fV87qCO0QMNcfBUPKYsDoFvUAbcJ5lBX1v2sWKoq4D5k0zhG5SBKYtDcOh0PMYOMkLb5uqSNDZWypg5ph4u3UnG5EUhuHQnGbPG1kNDC6X3tVk1UnsHDYwZZISD/8Rh8sJn8H2aDvfJZtDXKaeu9BTgPtkMvk/TMXnhMxw8FYexg43QtkVxXdlaKWPWWBNcup2ESQuf4dLtJMwaa4KGlsrva7NqnLO+ofj13D2M/rwJ9o/5Es3NDDBh30VEJr+SmX5mt1Y4P3WA5HN2cj9oKivC0c5ckiYzJxf1tNQw5Yvm0FNj3bwNQlUVpDwMgO+UhZVKr2xhglZ/b0HCdS9cb+WCoOWb0WjNTzDq202SRqt1MzTfuwYv9xzHtZbOeLnnOFrsWwutTz95V5tRawzoroe+jrrYvDcS3y8ORmJyDhZPs4CyuPxbflsrZcz+1hQXbyVh4oIgXLyVhNnfmsGmxPmtfSsNjBlshAOnYjF5YTAeB77Cginm0NdReB+bVaN83lIdo74ywKEzCfh+aRj8gtIxb4IJ9Mq5nzDQVcC8CSbwC0rH90vDcPhMAkYPNESb5mqSNGLFOoiOy8HuY7FISM59X5tSK/TvrgsXRx1s3huFaUtCkJici0Xfm8k9puTdM1T1XoWqrkBQp9o+H4uPp6RUabm5uZg4cSK0tLSgq6uLn3/+GQUFBQAKI1GOHTsmlV5LSws7duwAULabmCw7duyAmZkZVFRU0LdvX8THx0vNL91NzNXVFS4uLli5ciWMjY2hq6uLCRMmSDXGREZGolevXlBWVoalpSX27t0LCwsLrF27tlLbLBAIsGnTJvTo0UOSx6FDhyTzi7br4MGD6NSpE5SUlPDnn38iPz8fCxcuhImJCcRiMZo1a4YzZ86UWW7//v1o27YtlJSU0KhRI1y+fFlq/X5+fujZsyfU1NRgaGiI4cOHIy4uTjK/U6dOmDhxIqZNmwY9PT04OjrCwsICANC3b18IBAJYWFggNDQUderUwb1796Ty37BhA8zNzSX1KEt+fj5MTEywefNmqen379+HQCDAs2eFT9RXr16NJk2aQFVVFaamphg/fjzS0tLKzbeo/kqaOnUqOnXqJPleUFCAFStWwMrKCsrKymjatCkOHz5cbp4fI5euuvC8kYRzN5LwIiob2w5GIy4xBz06astM372jFmITcrDtYDReRGXj3I0knL+RhL7ddCRpnLvowMf/FQ6ficeL6GwcPhOPB09eoU8XHZl5UuW4OOrC83oizl0vrKutBwrrqmdH2fu1R0dtxCbkYOuB/+rqehLO30hEv27Fjdp9uurA2+8VDp2Ox4uobBw6XVhXzl1ZV69r9x0/9G1mjX7NG8BKTxMzu7WCkYYKDnkFyEyvrqQIPTVlycc3Mh4pGdlwblpfkqZxXT1M69oS3RtZQkHIW5y3IfbsVQTOX4uoY56VSm8+djAywyPhN30p0p48w3OPw3i+4yisprlJ0lhOGom48zcRvGILXgU8Q/CKLYi7eBsWk0a+q82oNZy76uLAP7G4eT8FYRFZWO3xEmLFOuj4mWb5yzjqwdsvDYdOx/13fovDgydpcO5afA7s66iHc9cTce5aIp5HZmHrgajC82onngOryrmLNs7fTIbnjWS8iMrGH4diC+8nOmjJTN+9vSZiE3Lwx6FYvIjKhueNZFy4mQyXEtefoLBM7Dgai2v3UpGTW/69IlWdcxcdHDgVh1veqYXH1PaI/44pjXKXqcw9Q1XvVYjeBd4p1UA7d+6ESCTCnTt3sH79eqxZswbbtm17K3nfuXMHbm5uGD9+PHx8fNC5c2csXrxY7nKXLl1CcHAwLl26hJ07d2LHjh2SBigAGDFiBCIiInD58mUcOXIEW7ZsQUxMTJXKNnfuXPTv3x8PHjzAsGHD8PXXX8Pf318qzaxZszB58mT4+/vDyckJ69atw6pVq7By5Uo8fPgQTk5O6NOnD54+fSq13A8//IDp06fD29sbbdu2RZ8+fSSNYJGRkejYsSOaNWuGe/fu4cyZM4iOjsbAgQOl8iiqlxs3buD333/H3bt3AQDbt29HZGQk7t69CwsLC3Tt2hXbt2+XWnb79u1wdXWFQFB+H9Q6depg8ODB2LNnj9T0vXv3ok2bNrCyspKkW79+PR4/foydO3fi4sWLmDlzZhX2dFk///wztm/fjk2bNsHX1xfff/89hg0bhitXrrxRvh8KkRCwNlOCt590xIK33yvY1ZcdfWBrpVIm/X2/V7A2V0bR/6i2Vspl8/R9Bbv6Km+v8LWMSAhYm8uoK9802JZbV8rw9pVuEL3v+19dCYvSqMDbr3SatHLrnyqWk5cH/8gEtLEylpre2qouHryIrVQex3yC8JmlMepqqclPTO+NVutmiD1/Q2pa7Llr0GzZGIL/oo61WzdD3PnrUmniPK9Bu03z91bOmshITwE6Wgq4X+J8lptbgMcBr2BnXf51pfBaJOP89t8yIqEA1uayzpNpvF5VkUgI1DdTgk+pa5SPfzpsrcq/Rvn4S3cd8vZ7BWtzJbDN+90y/O+Y8vYtrq/c3AI8Dkyv8Lcv757hde5ViN4FnkJqIFNTU6xZswY2NjYYOnQoJk2ahDVr1ryVvNetWwcnJyfMnj0bDRs2xOTJk+Hk5CR3OW1tbfz222+wtbXFl19+iV69euHChcIxB548eYLz589j69at+Oyzz9CiRQts27YNGRnl952W5auvvsLo0aPRsGFDLFq0CA4ODtiwYYNUmqlTp6Jfv36wtLRE3bp1sXLlSsyaNQuDBw+GjY0Nli9fjmbNmpWJSJo4cSL69+8POzs7bNq0CZqamvjjjz8AAJs2bUKLFi2wdOlS2Nraonnz5vDw8MClS5cQGBgoycPa2horVqyAjY0NbG1toa+vD6AwMsvIyEjyffTo0di3bx+ysgr7Ij948AA+Pj745ptv5O6DoUOH4saNGwgLCwNQGC20f/9+DBs2TGofdO7cGZaWlvjiiy+waNEiHDx4sEr7uqRXr15h9erV8PDwgJOTE6ysrODq6ophw4bh999/f+18PyQaaiIIhQIkpUiHXiel5kJLQ3Y4r7aGEEmppdKn5EIkFEBDrXAZLQ1R2TxTcqGtIXyLpa9diuoqsdR+TUzNg7ZmOXWlKUJiap50+pRciETFdaWtWV5dMZz7dSSmZyGvoAA6qtJdInVVlRCXlil3+djUdNwIikDf5tbvqoj0msSGesiKjpOalh0TjzoKClDUK4ykFBvpIStaOqo4KzoeYiP991bOmqjoHFfVc5W2pgiJpboWJSYXL6OhJpR9DUwp/7xKskn2Zen7g9TccvelloZIZvrC+wneL7xLFR5TFfz25d0zvM69ClUdB5CWj41BNVDr1q2lIkjatGmDp0+fIi8vr4KlKsff3x9t2rSRmlb6uyyNGjWCUFh8wTI2NpZE/gQEBEAkEqFFixaS+dbW1tDWlt39pjyyylU6MsjBwUHyd0pKCiIiItCuXTupNO3atSuzXMm8RSIRHBwcJGm8vLxw6dIlqKmpST62trYAgODgYJnrroiLiwtEIhH++usvAICHhwc6d+4s6VZWkebNm8PW1hb79u0DAFy5cgUxMTFSUUqXLl2Co6Mj6tWrB3V1dYwYMQLx8fF49Ur2OB3y+Pn5ITMzE46OjlL7YNeuXVLbX1pWVhZSUlKkPnl5H/ZYSqUDr+Wd6kv36is6LAtK5FQmmFsgYxpVXel9j7L1IZ1eeqZAMrn8uhKwrt5Y6WjHAhQfJxU58TAY6kqK+MLG9N0UjN5MuSe/gorTVHiQUmmdPtPE4d/sJB+hsHA/y7quVJWs8xur7O0psy+BCocCKDurnLqmN9LpMw0c2mAr+YjKOabk3lPIWkbWPUNV71WI3jI2PdYyAoGgzMWmKgMpV3ShqoiCgvQAgwKBAPn5+RXm+brrKr2eklRVVeWmKSgoqLA7Vunl8vPz0bt3byxfvrxMGmPj4i4QstYti6KiIoYPH47t27ejX79+2Lt3b6XHTgIKo4P27t2L2bNnY+/evXBycpK8HS4sLAw9e/bEuHHjsGjRIujo6OD69esYNWpUub+DOnXqVPibKarHf/75B/Xq1ZNKJxaLyy3nsmXLsGDBAqlpDVqMh43DxEpv6/uSkpaLvLyCMk9WNdXLPvkpkpiSJzN9bl4BUtMKG2ZlPa3VUhchKeXNG25rK0ldaZber8Ly6ypZRj1oiJCbW4DUV3nlpqmo/qli2ipiCAUCxKdJR4AmvMqErmrFA6gXFBTgmE8wejWxhIKQT8U/NFnRcWUifBT1dZCfk4Ps+KTCNFFxEBtJv7VUbKBTJqKIKnbHJxUBIcUPXRREhfcl2hrSkT5a6qIyEQglJSaXjXIoGbmakpYn87yqWcF5lWST7EuZ1xPZ137Z9wpCqfsJejvu+KQh4FmJY0qhMG6i9DGlKSOyuyR59wyvc69CVVdQmadLtRwjg2qg27dvl/neoEEDCIVC6OvrIzIyUjLv6dOnSE+v/CsM7e3tZeb/JmxtbZGbmwtvb2/JtKCgICQlJVUpH1nlKorQkUVDQwN169bF9evS4xbcvHkTdnbSryIumXdubi68vLwkebdo0QK+vr6wsLCAtbW11EdeA5CCgoLMiK3Ro0fj/Pnz2LhxI3JyctCvX78K8ylpyJAhePToEby8vHD48GEMHTpUMu/evXvIzc3FqlWr0Lp1azRs2BAREREV5lf6NwNAaoBxe3t7iMVihIeHl9l+U9Pyn9rPmTMHycnJUh/r5mMrvZ3vU24eEBSeieZ20vXZzE4V/sGyuzM+eZaOZqXSN7dXRVBYBvLyi9JkyEzjH8zXir6u3LzCgTRL79dm9mp4Um5dZaCZvfS4M5K6yitKk47m9qXrSq3c+qeKKQiFsDPWwa0Q6XPLnZBINDWpuKvQvbBoPE9MRd9m7CL2IUq67QO9Lm2lpuk7fo5kr8coyC38Jyfxtg/0ukhH5ep1/RyJt7xBlZeRlY/ImGzJJzwiCwlJOWjeqPh8JhIK0NhGFf5B5V9XZJ8D1STL5OYVICgsA81lpeH1qkpy84Dg8Ew0tZMeb6aZnQqePKvgGlU6vb0qgsIyJfcT9HZkZOUjMjZH8pEcUyWu/yIh0LihSoW/fXn3DK9zr0L0LrAxqAZ6/vw5pk2bhoCAAOzbtw8bNmzAlClTAABffPEFfvvtN9y/fx/37t3DuHHjykTtVGTy5Mk4c+YMVqxYgcDAQPz2229Sb996Hba2tujatSvGjh2Lf//9F97e3hg7diyUlZUrFaFT5NChQ/Dw8EBgYCDmz5+Pf//9FxMnVhxl8sMPP2D58uU4cOAAAgICMHv2bPj4+Ej2V5H//e9/+Ouvv/DkyRNMmDABiYmJcHMrfDPKhAkTkJCQgK+//hr//vsvnj17hnPnzsHNzU1u1zwLCwtcuHABUVFRSExMlEy3s7ND69atMWvWLHz99ddQVq78YHKWlpZo27YtRo0ahdzcXDg7O0vm1a9fH7m5udiwYQOePXuG3bt3l3n7WGlffPEF7t27h127duHp06eYP38+Hj9+LJmvrq6OGTNm4Pvvv8fOnTsRHBwMb29v/O9//8POnTvLzVcsFkNDQ0PqIxQqVno737dj5+Ph+LkWurbVhImRIkZ/ZQB9HQWcvlpYbyNc9PG9a3Ek2JkrSTDQVcCorwxgYqSIrm014dhOC3+dS5CkOXEhAc3tVdHfSRcmhoro76SLpnaqOHEhocz6qfKOecajW3ttOLbTKqyrgYbQ11HAqSuFdTWyrwGmudWVpD99JREGugoYPdAQJkaKcGynBcfPtXH0XPGYJoV1pYb+3XVhYqSI/t110cxOFcfPs65e1/DP7PGXdxCO+QThWVwyfj13F5HJrzCgRUMAwPqL9/Hz8RtlljvmE4Qm9fRgbVC2K3FOXh6eRCXgSVQCcvPyEZOajidRCQhPSHnn21NTCVVVoNHUFhpNCx+AqFiaQKOpLZRMC893Nounoen24sjYsC37oWxeF3a/zoaarRVMXPvD9Jv+eLbaQ5Im9Ldd0HNsB6sZY6BqYwWrGWOg16UNQjeUf82gyjl+Ph4De+qjTXN1mNcV43u3esjKzseVO8mSNNPc6mFkP0PJ9xPn49DCXg0DuuvBxEgRA7rroZmdGo6fLz4H/uUZJzmvmhqLMWaQUeF59TLPgVV1/EIiHNtpoUsbDZgYKWLUAH3oaSvgzLUkAMBwZz1MHWkkSX/mWjL0dRTg1l8fJkaK6NJGA13bauJYieuPSAhYmohhaSKGglAAXS0RLE3EMNKv/D0+yXb8QgK+6qknOaamflN0TBVfV6a51cXIvgaS75W5Z5B3r0JvrqBAUG2fjwW7idVAI0aMQEZGBj799FMIhUJMmjQJY8cWRlysWrUK33zzDTp06IC6deti3bp18PLyqnTerVu3xrZt2zB//ny4u7uja9eu+Pnnn7Fo0aI3KvOuXbswatQodOjQAUZGRli2bBl8fX2hpFRxd4GSFixYgP3792P8+PEwMjLCnj17YG9vX+EykydPRkpKCqZPn46YmBjY29vjxIkTaNCggVS6X375BcuXL4e3tzfq16+P48ePS7pe1a1bFzdu3MCsWbPg5OSErKwsmJubo3v37qhTp+L21lWrVmHatGnYunUr6tWrh9DQUMm8UaNG4ebNm5JGp6oYOnQoJkyYgBEjRkg1JDVr1gyrV6/G8uXLMWfOHHTo0AHLli3DiBEjys3LyckJc+fOxcyZM5GZmQk3NzeMGDECjx49kqRZtGgRDAwMsGzZMjx79gxaWlpo0aIFfvzxxyqX/UN1/V4qNFSjMbiXHnQ0RQiLyMKC38IRm1D4pFtHUwR9neKbruj4HCzY8ByjBxqiV0dtJCTnYsuBKNz0TpWkefIsAyu2vcRwZ30M7aOPqNhsrNj6EoGh8gfQpfJdu5cCdTUhBn9ZXFfu68MRm1DYvVFbq1RdxeXAfX14YV110kZ8ci627I/Czfsl6io4Ayu2vMAwFwMMczZAVGw2lm95gcAQPsF7XU6NLJCUkYXfrz1EXFoGrPW18NvgLyRvB4tNy0BksvRYZqmZ2bjwJBw/dGslM8+Y1AwM3vaP5Puu237YddsPLc0M8ceIbu9uY2owzZaN0ebCbsl3+5WF5/Xnu47i4ag5EBvrQ9m0uCE8I/QF7vYeC/tVc2D+3VBkRcTA9/sliPrrnCRN4i1veA+dBpsFU2GzYDLSg5/De8j3SPr34fvbsBrq8Jk4KCrWwfihdaGmKkTAswzMXR2KjKziEBJ9XUWpcUn8gzOwfMtzDHcxxDCXovPbcwSUOL9du5sCDdUofN3bQHJenb8uTHJepcq77pUKdVUhBvXSg46GEGGR2Vj4vxeS+wltTRH0SlyjYuJzsPB/LzBqgAF6dtRCQnIuth2Mxi3v4rdV6WiKsPYnC8n3vo466Ouog0eB6fh5zfP3tm010ZEz8RAr1MF3Q4wkx9S8NeHSx5SOAvJLHFOVuWeQd69C9D4ICt7GwCxEb9mLFy9gamqK8+fPo0uXLnLTCwQC/PXXX3BxcXmr5QgNDYWlpSW8vb3RrFmzt5q3PEuWLMH+/fulGl1qut7f+stPRNWuIJ+XjY/FoQ5Hq7sIVAkX3XbLT0TVbuPII9VdBKoEURUi3ql65eVyzKOPwcmtFT9c/1AFBYdU27qt61tW27qrgpFB9EG4ePEi0tLS0KRJE0RGRmLmzJmwsLBAhw4dqrto711aWhr8/f2xYcOGN464IiIiIiIiqm0KOCKOXNxD9EHIycnBjz/+iEaNGqFv377Q19fH5cuXoaCggD179ki9srzkp1GjRtVd9Ldu4sSJ+Pzzz9GxY8cyXcTGjRtX7r4YN25cNZWYiIiIiIiIPibsJkYfvNTUVERHR8ucp6CgAHNz8/dcouoTExODlBTZA6FqaGjAwMBA5ryPBbuJfRzYTezjwW5iHwd2E/s4sJvYx4HdxD4e7Cb2cfhYu4kFBodX27ob1jertnVXBbuJ0QdPXV0d6urq1V2MD4KBgcFH3+BDRERERERE1YvdxIiIiIiIiIiIahFGBhERERERERFRjVEAQXUX4YPHyCAiIiIiIiIiolqEkUFEREREREREVGMwMkg+RgYREREREREREdUijAwiIiIiIiIiohqDkUHyMTKIiIiIiIiIiKgWYWMQEREREREREVEtwm5iRERERERERFRjFBSwm5g8jAwiIiIiIiIiIqpFGBlERERERERERDUGB5CWj5FBRERERERERES1CBuDiIiIiIiIiIhqEXYTIyIiIiIiIqIag93E5GNkEBERERERERFRLcLIICIiIiIiIiKqMRgZJB8jg4iIiIiIiIiIahFGBhERERERERFRjVFQwMggeRgZRERERERERERUi7AxiIiIiIiIiIioFmE3MSIiIiIiIiKqMfI5gLRcjAwiIiIiIiIiIqpFGBlERERERERERDUGXy0vHyODiIiIiIiIiIhqETYGERERERERERHVIuwmRkREREREREQ1RkEBu4nJw8ggIiIiIiIiIqJahJFBRERERERERFRjcABp+RgZRERERERERERUTTZu3AhLS0soKSmhZcuWuHbtWqWWu3HjBkQiEZo1a1bldbIxiIiIiIiIiIioGhw4cABTp07FTz/9BG9vb7Rv3x49evRAeHh4hcslJydjxIgR6NKly2utl41BRERERERERFRjFBQIqu1TVatXr8aoUaMwevRo2NnZYe3atTA1NcWmTZsqXO7bb7/FkCFD0KZNm9faR2wMIiIiIiIiIiJ6C7KyspCSkiL1ycrKkpk2OzsbXl5e6Natm9T0bt264ebNm+WuY/v27QgODsb8+fNfu5xsDCIiIiIiIiKiGqMAgmr7LFu2DJqamlKfZcuWySxnXFwc8vLyYGhoKDXd0NAQUVFRMpd5+vQpZs+ejT179kAkev13gvFtYkREREREREREb8GcOXMwbdo0qWlisbjCZQQC6e5lBQUFZaYBQF5eHoYMGYIFCxagYcOGb1RONgYRERERERERUY3xOmP3vC1isVhu408RPT09CIXCMlFAMTExZaKFACA1NRX37t2Dt7c3Jk6cCADIz89HQUEBRCIRzp07hy+++KJS62ZjEBF9MPJycqu7CFQJBfkF1V0EqqQBV1yquwhUGSNdqrsEVAnjd/av7iJQJazuv7u6i0CVpKyuVt1FIKp2ioqKaNmyJTw9PdG3b1/JdE9PTzg7O5dJr6GhgUePHklN27hxIy5evIjDhw/D0tKy0utmYxARERERERERUTWYNm0ahg8fDgcHB7Rp0wZbtmxBeHg4xo0bB6Cw29nLly+xa9cu1KlTB40bN5Za3sDAAEpKSmWmy8PGICIiIiIiIiKqMfKruwBVMGjQIMTHx2PhwoWIjIxE48aNcerUKZibmwMAIiMjER4e/tbXKygoKGC8PxF9EHq6PZKfiKodu4l9POqI+NJQoreF3cQ+Duwm9vFgN7GPw8mt9tVdhNdy+0lyta27ta1mta27KhgZREREREREREQ1RnUOIP2x4CNDIiIiIiIiIqJahI1BRERERERERES1CLuJEREREREREVGNUQB2E5OHkUFERERERERERLUII4OIiIiIiIiIqMbgANLyMTKIiIiIiIiIiKgWYWQQEREREREREdUYHDNIPkYGERERERERERHVImwMIiIiIiIiIiKqRdhNjIiIiIiIiIhqjPyC6i7Bh4+RQUREREREREREtQgjg4iIiIiIiIioxuAA0vIxMoiIiIiIiIiIqBZhYxARERERERERUS3CbmJEREREREREVGMUFLCbmDyMDCIiIiIiIiIiqkUYGURERERERERENUYBXy0vFyODiIiIiIiIiIhqEUYGEREREREREVGNkc9Xy8vFyCAiIiIiIiIiolqEjUFERERERERERLUIu4kRERERERERUY3BV8vLx8ggIiIiIiIiIqJahJFBRERERERERFRj8NXy8jEyiIiIiIiIiIioFmFjEBERERERERFRLcJuYkRERERERERUYxSAA0jLw8ggIiIiIiIiIqJahJFBRERERERERFRj5HMAabkYGUREREREREREVIswMoiIiIiIiIiIaoyCAo4ZJA8jg4iIiIiIiIiIapG30hjUqVMnTJ06FQBgYWGBtWvXvo1sawV3d3c0a9asuotBtdTly5chEAiQlJT01vPesWMHtLS03nq+RERERERE9Gbeejexu3fvQlVV9W1nW0ZoaCgsLS3h7e39UTemzJgxA5MmTapUWnd3dxw7dgw+Pj7vtlBUa7Rt2xaRkZHQ1NSs7qJ8VIY6G6B7Rx2oqQgR8CwdG/+MQHhEVoXLtGupgeF9DWGsr4jI2GzsPBqNW/dTJPMbN1RB/+76sLZQhq6WAhZtCMMt75QKciR5hroYokdHHaipFtbT/3a9lF9PDhoY0dcIxgaKiIzJxs4jUbgpVU+qGNBTH9bmytDVVsDC9aFS9UhVN6SPAbp30C48nkIysGmP/OOpbQsNDHcxkBxPu/6Kxi3vVKk0vTrpoJ+THnS0RAiPyMKW/ZHwfZr+LjelxmNdfbh0PneA1fRR0GzRGEp1DXCv/3hEn7hQ8TLtW8F+5Wyo2TdAVkQMgldtQ/iW/VJpjPp2Q0P3KVCpb4b04HAEzFuD6OPn3+Wm1BojBtRDry/0oa4mgn9QGtZ7hCHsRUaFy7T/VBvfDDSBsaEYkdFZ+OPAC9y4myiZ39vRAH26GsBQXwwACHuRgd1HX+Jfn+R3ui01Vc9O2ujnpAsdzcJz09YD0RWemxo3VMHogYYwqytGQlIujpyNx+kriVJp2rZQxzBnAxjrKyAyNge7j8WUOSfS21HAAaTleuvdxPT19aGiovK2s32ncnJyqm3dampq0NXVrbb111bZ2dnVXYRyvc+yKSoqwsjICAKB7D61eXl5yM/Pf2/l+RgM6KGHvt30sOnPCExdFITE5FwsmWEJZaXyT6e29VUwe5wZLt5MwoT5Qbh4MwlzxpnBxkpZkkZJXAchzzOx6c+I97EZNd5XPfXRz0kPG/98iSkLniIxORdLf7CSW09zvjPHhZuJGD/vKS7cTMSc8eZl6ulZeAY2/vnyfWxGjTegux76Oupi895IfL84GInJOVg8zQLK4grqyUoZs781xcVbSZi4IAgXbyVh9rdmsLEsrqf2rTQwZrARDpyKxeSFwXgc+AoLpphDX0fhfWxWjcS6+rAJVVWQ8jAAvlMWViq9soUJWv29BQnXvXC9lQuClm9GozU/wahvN0kardbN0HzvGrzccxzXWjrj5Z7jaLFvLbQ+/eRdbUatMbiPMQb0NMKG7WEY/6MvEpNysOJHmwqvUfYN1DB3ijU8r8Vh7KzH8LwWh3lT6sPWuvghfFx8Nrbue47xP/li/E++8PZNwcIZDWBuolxuviRbewcNjBlkhIP/xGHywmfwfZoO98lm0NeRHUthqKcA98lm8H2ajskLn+HgqTiMHWyEti3UJWlsrZQxa6wJLt1OwqSFz3DpdhJmjTVBQ0vWD1WPKjcGvXr1CiNGjICamhqMjY2xatUqqfmlu4m5u7vDzMwMYrEYdevWxeTJkyXz/vzzTzg4OEBdXR1GRkYYMmQIYmJiJPMTExMxdOhQ6OvrQ1lZGQ0aNMD27dsBAJaWlgCA5s2bQyAQoFOnTpLltm/fDjs7OygpKcHW1hYbN26UzAsNDYVAIMDBgwfRqVMnKCkp4c8//6xwm+Pj4/H111/DxMQEKioqaNKkCfbt2yeV5vDhw2jSpAmUlZWhq6uLrl274tWrVwAKu+J8+umnUFVVhZaWFtq1a4ewsDDJ/ikZ2VRe2h07dmDBggV48OABBAIBBAIBduzYAQBYvXo1mjRpAlVVVZiammL8+PFIS0uT5FnUXefs2bOws7ODmpoaunfvjsjISKlt8PDwQKNGjSAWi2FsbIyJEydK5iUnJ2Ps2LEwMDCAhoYGvvjiCzx48KDC/VbSiRMn4ODgACUlJejp6aFfv36SeYmJiRgxYgS0tbWhoqKCHj164OnTp5L1Kisr48yZM1L5HT16FKqqqpLtfPnyJQYNGgRtbW3o6urC2dkZoaGhkvSurq5wcXHBsmXLULduXTRs2BCA/N9gUdkbNGgAZWVldO7cGTt37izTtermzZvo0KEDlJWVYWpqismTJ0vqXx4LCwssXrwYrq6u0NTUxJgxYyqVZ0xMDHr37g1lZWVYWlpiz549Usdf0W+9ZCRZUlISBAIBLl++DKBsN7Gi38rJkydhb28PsViMsLAwZGdnY+bMmahXrx5UVVXx2WefSfIosmPHDpiZmUFFRQV9+/ZFfHx8pbb/Y+PiqIf9J2Nw834Kwl5mYdUfLyBWrINOn2lVsIwuvP3ScPBULF5EZeHgqVj4+KfB2VFPkubeozTs+itaKgqFXp9LNz3s/zsGN73+q6etzyEW10Gn1loVLnPfNxUH/4nFi8gsHPynsJ5cuulL0tx7lIpdR6Nx04v19DY4d9XFgX9iC4+niCys9ngJsWIddPys/GhFZ0c9ePul4dDpOLyIysah03F48CQNzl2LH6z0ddTDueuJOHctEc8js7D1QBTiEnPQs5PO+9isGol19WGLPXsVgfPXIuqYZ6XSm48djMzwSPhNX4q0J8/w3OMwnu84CqtpbpI0lpNGIu78TQSv2IJXAc8QvGIL4i7ehsWkke9qM2qNfj0MsfdYBK7fTUToiwws3/gMSuI66NKu/AfE/XoawutRMvYdj8TziEzsOx6J+49T0L+HkSTNrftJ+NcnGS8iM/EiMhMeB14gIzMf9g3efa+NmsbFURee1xNx7noSXkRlY+uB6MJzU0fZ56YeHbURm5CDrQei8SIqG+euJ+H8jUT061Zcp3266sDb7xUOnY7/75wYjwdPXsG5K89370I+BNX2+VhUuTHohx9+wKVLl/DXX3/h3LlzuHz5Mry8vGSmPXz4MNasWYPff/8dT58+xbFjx9CkSRPJ/OzsbCxatAgPHjzAsWPHEBISAldXV8n8uXPnws/PD6dPn4a/vz82bdoEPb3Cf57+/fdfAMD58+cRGRmJo0ePAgC2bt2Kn376CUuWLIG/vz+WLl2KuXPnYufOnVJlmzVrFiZPngx/f384OTlVuM2ZmZlo2bIlTp48icePH2Ps2LEYPnw47ty5AwCIjIzE119/DTc3N/j7++Py5cvo168fCgoKkJubCxcXF3Ts2BEPHz7ErVu3MHbsWJmRGBWlHTRoEKZPn45GjRohMjISkZGRGDRoEACgTp06WL9+PR4/foydO3fi4sWLmDlzplTe6enpWLlyJXbv3o2rV68iPDwcM2bMkMzftGkTJkyYgLFjx+LRo0c4ceIErK2tAQAFBQXo1asXoqKicOrUKXh5eaFFixbo0qULEhISKtx3APDPP/+gX79+6NWrF7y9vXHhwgU4ODhI5ru6uuLevXs4ceIEbt26hYKCAvTs2RM5OTnQ1NREr169sGfPHqk89+7dC2dnZ6ipqSE9PR2dO3eGmpoarl69iuvXr0savEpG2Vy4cAH+/v7w9PTEyZMnAcj/DYaGhmLAgAFwcXGBj48Pvv32W/z0009SZXn06BGcnJzQr18/PHz4EAcOHMD169elGtPk+fXXX9G4cWN4eXlh7ty5lcrT1dUVoaGhuHjxIg4fPoyNGzeWach6Henp6Vi2bBm2bdsGX19fGBgY4JtvvsGNGzewf/9+PHz4EF999RW6d+8uabS7c+cO3NzcMH78ePj4+KBz585YvHjxG5flQ2OkrwAdLQXc9y1ubM3NLcCjgFewsy4/ItK2vgruP5YOAb7/OBX29T+uKMqPhZG+YmE9ldjnObkFePQkDfYV1JOdtQruP06Tmub1KLXCuqXXZ6Qn+3h6LO94slKGt590Pd33TZMsIxIKYG2uDG9fGWl4zL0W1lXNo9W6GWLP35CaFnvuGjRbNoZAVBj5oN26GeLOX5dKE+d5Ddptmr+3ctZExgZi6Gor4t7D4q5bObkFeOCfikYN1ctdzr6BmtQyAHDvYTIaNVSTmb6OAOjcRgdK4jrwC0yTmYZkEwkBa3MlePtJP9j19k2DbX3ZUTy2VrLOZa9gba4MobAojYrsc2I5eRK9a1UaMygtLQ1//PEHdu3aBUdHRwDAzp07YWJiIjN9eHg4jIyM0LVrVygoKMDMzAyffvqpZL6bW/HTBysrK6xfvx6ffvop0tLSoKamhvDwcDRv3lzScGBhYSFJr69f+KRWV1cXRkbFLeKLFi3CqlWrJJEnlpaW8PPzw++//46RI4ufZEydOlUqOqUi9erVk2o4mTRpEs6cOYNDhw7hs88+Q2RkJHJzc9GvXz+Ym5sDgKTRKyEhAcnJyfjyyy9Rv359AICdnZ3M9aSkpFSYVk1NDSKRSGp7i7aliKWlJRYtWoTvvvtOKiIqJycHmzdvluQ7ceJELFxYHEq8ePFiTJ8+HVOmTJFMa9WqFQDg0qVLePToEWJiYiAWF/ZBXrlyJY4dO4bDhw9j7NixFe6/JUuWYPDgwViwYIFkWtOmTQEAT58+xYkTJ3Djxg20bdsWALBnzx6Ympri2LFj+OqrrzB06FCMGDEC6enpUFFRQUpKCv755x8cOXIEALB//37UqVMH27ZtkzSybd++HVpaWrh8+TK6dSsMeVZVVcW2bdugqKgoKYe83+DmzZthY2ODX3/9FQBgY2ODx48fY8mSJZLlfv31VwwZMkRSDw0aNMD69evRsWNHbNq0CUpKShXuHwD44osvpH5jI0aMqDDP8PBwnD59Grdv38Znn30GAPjjjz/K/W1VRU5ODjZu3Cipo+DgYOzbtw8vXrxA3bp1ARSOdXXmzBls374dS5cuxbp16+Dk5ITZs2cDABo2bIibN2+Wiej62GlrFHZbSErJlZqelJILA93yuzRoa4pkLqOt+daHbSNAsl8TZdaToqxFJMvJqicd1tM7UVRPsva5vpzjKTFZepnE5FxoaxTmp6EmhFAokJFvHo+518S6qnnEhnrIio6TmpYdE486CgpQ1NNGVlQsxEZ6yIqWjvLNio6H2Egf9Pq0tQqPmcRk6WEqEpNzYKgnLnc5HS0F2ceTlvQxaGmqjA2L7KGoUAcZmXmYv+opwl5mvqXS1w4aaiIIhYIy9xGJqXloUc65SVtThMTUPOn0KbkQiQTQUCs8F5Z7P6jB8x1Vjyr98oKDg5GdnY02bdpIpuno6MDGxkZm+q+++gpr166FlZUVunfvjp49e6J3794Q/ffEwdvbG+7u7vDx8UFCQoJkbJLw8HDY29vju+++Q//+/XH//n1069YNLi4ukgYDWWJjY/H8+XOMGjVK0tUGKIy4KT1AbsnIFHny8vLwyy+/4MCBA3j58iWysrKQlZUlGSi7adOm6NKlC5o0aQInJyd069YNAwYMgLa2NnR0dODq6gonJyc4Ojqia9euGDhwIIyNjcuspyppS7p06RKWLl0KPz8/pKSkIDc3F5mZmXj16pWkjCoqKpKGIAAwNjaWRJHExMQgIiICXbp0kZm/l5cX0tLSyoxtlJGRgeDgYLn7z8fHR6o+SvL394dIJJI0aACFDXw2Njbw9/cHAPTq1QsikQgnTpzA4MGDceTIEairq0saeby8vBAUFAR1demnKZmZmVLla9KkiVRDECD/NxgQECBpFCtSskGz5PpLRi8VFBQgPz8fISEhlWqgKf17lJdnYGAgRCKR1HK2trZv5e1dioqK+OST4vEA7t+/j4KCAknXuiJZWVmS34S/vz/69u0rNb9NmzYVNgYVHUcl5eVlQygs/5/1961Tay1MGlFX8n3+2sLunaUHpBPImFZamdkCQdlp9Fo6t9HCpJH1JN/nrwkFIKNOBDLqoZQydVuJZahyOn2miYnDi48n9/X/HU+lE75GdLWsepJZl6zMSmFd1RKydnzp6aycN9alnS6+H2Mh+f7j8kAA5d1LVLxvS88XAGUOqOcRmRg76zHUVEVo/6k2Zo23wrQF/mwQeh1Vvd+TVT+Qrrcytya8z3hneKqSr0qNQfJOUKWZmpoiICAAnp6eOH/+PMaPH49ff/0VV65cQXZ2Nrp164Zu3brhzz//hL6+PsLDw+Hk5CTp2tOjRw+EhYXhn3/+wfnz59GlSxdMmDABK1eulLm+on/kt27dKtW4AADCovi8/1TljWerVq3CmjVrsHbtWsnYPFOnTpWUUygUwtPTEzdv3sS5c+ewYcMG/PTTT7hz5w4sLS2xfft2TJ48GWfOnMGBAwfw888/w9PTE61bty6zrqqkBYCwsDD07NkT48aNw6JFi6Cjo4Pr169j1KhRUgNjKyhIPzUQCASS+lRWrjg0MT8/H8bGxmXGiAFQqcaHivIv7zdVUFAgifJRVFTEgAEDsHfvXgwePBh79+7FoEGDJI2K+fn5aNmyZZmuZEBxBBlQts5fvXol9zdYshzllTk/Px/ffvut1HhYRczMzMrd9pJKl01engEBAQBQ7sDPQGH3wdLlrcxg6crKylL55ufnQygUwsvLq8xxpKamVmYdlbVs2TKpaDEAsG42Dg2aj69yXu/KHZ8UBDwrfmuEgqhwv5R+2q2pUfZJT0kln4QX0VIXIim5/GWo8m57p+BJcNl60ilVT1rqIiQll38MFD21K0lTvWxkA72eOz6pCAgpbqCXHE8aZeup9NPYkmTVk1aJYzAlLQ95eQUy6lJY4XFKxVhXNV9WdFyZCB9FfR3k5+QgOz6pME1UHMRGelJpxAY6ZSKKqGI3vRLhH1TcNUhBofD+TEdLAQlJxdckLU2FCu8LEpJyoFMqCkhLU1Qmwig3rwAR0VkAshD47BVs6quiXw8jrNkW+uYbU0ukpOXKPDdpVXBuknmvpyFCbm4BUl/llZtGU73ie0iid6lKYwZZW1tDQUEBt2/flkxLTExEYGBgucsoKyujT58+WL9+PS5fvoxbt27h0aNHePLkCeLi4vDLL7+gffv2sLW1lTneib6+PlxdXfHnn39i7dq12LJlCwBIIjzy8orD8QwNDVGvXj08e/YM1tbWUp+iAadfx7Vr1+Ds7Ixhw4ahadOmsLKykoyVUkQgEKBdu3ZYsGABvL29oaioiL/++ksyv3nz5pgzZw5u3ryJxo0bY+/eveWur7y0ioqKUtsLAPfu3UNubi5WrVqF1q1bo2HDhoiIqNrbiNTV1WFhYYELF2S/grRFixaIioqCSCQqs1+LxnCqyCeffFJu3vb29sjNzZWMvwQUDtgdGBgoFVEzdOhQnDlzBr6+vrh06RKGDh0qVb6nT5/CwMCgTPkqemV6ZX6Dtra2uHv3rtS0e/fuldk/vr6+ZdZtbW1dJhKpsuTlaWdnh9zcXKmyBAQESA1qXdQQVnKg8JKDSVdW8+bNkZeXh5iYmDJlKeqyaG9vL3VeAFDme2lz5sxBcnKy1Mfqk9FVLt+7lJGZj8iYbMknPCILCUk5aGFf3D9fJBSgiY0q/IPKf9Xok+B0NG8kHbnWorE6/IL56uS3obx6KrnPRUIBmtiqwa+CevIPSkeLRtJjL7RorFZh3VLlZWSVV0/Sx1NjecfTsww0s5eup+b2xfWUm1eAoLAMNJeVhsdcpbCuar6k2z7Q6yIdba/v+DmSvR6jILfwH9PE2z7Q69JOKo1e18+ReMv7vZWzJsjIzEdEdJbkE/YiA/GJ2WjZREOSRiQUoKmdOnwDy3/FuN/TNLRsUqqnwyea8JUzHpBAACgofDwD2n4IcvOAoLBMNLOTfljbzF4NT4IzZC4j+3yniqCwDBT9+/bkWTqa26uWSqMG/3LypDdTUCCots/HokqNQWpqahg1ahR++OEHXLhwAY8fP4arq6skAqG0HTt24I8//sDjx4/x7Nkz7N69G8rKyjA3N4eZmRkUFRWxYcMGPHv2DCdOnMCiRYuklp83bx6OHz+OoKAg+Pr64uTJk5IGAgMDA8lbpqKjo5GcXDigmru7O5YtW4Z169YhMDAQjx49wvbt27F69erX2T8AChvBiiJ//P398e233yIqKkoy/86dO1i6dCnu3buH8PBwHD16FLGxsbCzs0NISAjmzJmDW7duISwsDOfOnSvT0FFEXloLCwuEhITAx8cHcXFxyMrKQv369ZGbmyvZj7t378bmzZurvI3u7u5YtWoV1q9fj6dPn+L+/fvYsGEDAKBr165o06YNXFxccPbsWYSGhuLmzZv4+eefyzSMyDJ//nzs27cP8+fPh7+/Px49eoQVK1YAKBwLx9nZGWPGjMH169fx4MEDDBs2DPXq1YOzs7Mkj44dO8LQ0BBDhw6FhYWFVKTU0KFDoaenB2dnZ1y7dg0hISG4cuUKpkyZghcvXpRbrsr8Br/99ls8efIEs2bNQmBgIA4ePCh5i1tR9MysWbNw69YtTJgwAT4+PpJxkCZNmlS5nS+DvDxtbGzQvXt3jBkzBnfu3IGXlxdGjx4tFYWlrKyM1q1b45dffoGfnx+uXr2Kn3/+ucpladiwoWTcpqNHjyIkJAR3797F8uXLcerUKQCQRLOtWLECgYGB+O233+SOFyQWi6GhoSH1+ZC6iJXnmGccBn5pgDYtNGBeT4xpo0yQlZ2Py3eSJGmmjzaBa39DyffjnvFo0UgNA3rowcRIjAE99NDMTg3HPYufriqJ68DKVAlWpoVjTBnqKcDKVImvV35Nx87FYVBvA7QtqqfRJsjKysfl20mSNNPHmMJ1QPEYbMc949CisTq+6qkPE2Mxvuqpj+b26jh2LlaSRklcB1ZmSrAyK6onRViZsZ5e1/Hz8RjYUx9tmqvDvK4Y37vVQ1Z2Pq7cKR4kdZpbPYzsV3w8nTgfhxb2ahjQXQ8mRooY0P2/4+l88dgmf3nGoVt7bTi204KpsRhjBhlBX0cBpy7Lf+kByca6+rAJVVWg0dQWGk1tAQAqlibQaGoLJdPCoQZsFk9D0+3LJenDtuyHsnld2P06G2q2VjBx7Q/Tb/rj2WoPSZrQ33ZBz7EdrGaMgaqNFaxmjIFelzYI3SD9UhaquqOnozHEpS7atdKGhYkyZo63QmZWPi7cKD42Zo23wqjBJlLLOHyiicF9jGFaVwmD+xijRWMNHDld/D/JqMEmaGKrBkN9RViaKsNtkAma2mvgwvWa+YbXd+mYZ7zk3GRipIjRAw0Lz01XEgEAI/saYJpbcXfa01cSYaCrgNEDDWFipAjHdlpw/FwbR88V7/sTFxLQ3F4N/bvrwsRIEf2766KZnSqOn+f5jqpHlUer+vXXX5GWloY+ffpAXV0d06dPlzTElKalpYVffvkF06ZNQ15eHpo0aYK///5bMs7Ijh078OOPP2L9+vVo0aIFVq5ciT59+kiWV1RUxJw5cxAaGgplZWW0b98e+/fvLyy4SIT169dj4cKFmDdvHtq3b4/Lly9j9OjRUFFRwa+//oqZM2dCVVUVTZo0kRpkuarmzp2LkJAQODk5QUVFBWPHjoWLi4tkuzU0NHD16lWsXbsWKSkpMDc3x6pVq9CjRw9ER0fjyZMn2LlzJ+Lj4yWvbP/222/LrEdFRaXCtP3798fRo0fRuXNnJCUlYfv27XB1dcXq1auxfPlyzJkzBx06dMCyZcswYsSIKm3jyJEjkZmZiTVr1mDGjBnQ09PDgAEDABQ2epw6dQo//fQT3NzcEBsbCyMjI3To0AGGhoZycgY6deqEQ4cOYdGiRfjll1+goaGBDh06SOZv374dU6ZMwZdffons7Gx06NABp06dkuraJhAI8PXXX+PXX3/FvHnzyuy3q1evYtasWejXrx9SU1NRr149dOnSBRoaGiiPvr6+3N+gpaUlDh8+jOnTp2PdunVo06YNfvrpJ3z33XeSwbQ/+eQTXLlyBT/99BPat2+PgoIC1K9fX/K2t9dRmTy3b9+O0aNHSxrKFi9ejLlz50rl4+HhATc3Nzg4OMDGxgYrVqyQjLVUFdu3b5cMMv7y5Uvo6uqiTZs26NmzJwCgdevW2LZtG+bPnw93d3d07doVP//8c5nGtZrg8Ok4iBXrYMKwulBTFSLgWTp+XhWCjMx8SRp9HQXkF3+Ff3A6ftkcjhH9DDG8ryEiY7Lxy+ZwBDwrfhLUwEIZy2dZSb6P/brwBsPzeiLWeJTfqEmyHToVC0XFOpgwol5hPQWn46eVz6TqyUBXQaqLo39QOn7ZFI4R/Q0xvF9hPS3bFCZdT5bKWDG7ePy1b4cU1VMCVm9jPVXV4TNxUFSsg/FDi46nDMxdHYqMrBLHk66iVL9//+AMLN/yHMNdDDHMxQBRsdlYvuU5AkKK6+na3RRoqEbh694G0NEUISwiC/PXhSE2QX5XWZKNdfVh02zZGG0u7JZ8t1/5IwDg+a6jeDhqDsTG+lA2LR6DMiP0Be72Hgv7VXNg/t1QZEXEwPf7JYj665wkTeItb3gPnQabBVNhs2Ay0oOfw3vI90j69+H727Aaav+JSCgq1sEUN3Ooq4rgH5SGWUsDpK9ReopS1yi/wDQsXh+EbwaawHVgPUREZ2HRumA8CSp+45W2pgJmT6gPHS0FvErPw7PwdMxZFgCvRynvdftqgmv3UqCuJsTgL/Uk5yb39eGSc5O2lkjqQVB0XA7c14dj9EBD9OqkjfjkXGzZH4Wb94ujvZ4EZ2DFlhcY5mKAYc5F58QXCAxhZNC7kM8xg+QSFLzOYB9EtdiSJUuwefNmPH/+vLqLUoaFhQWmTp36Ro2f1amn26PqLgJVQgGvrh+NOqIqBQATUQXG7+xf3UWgSljdf7f8RPRBUFZXk5+Iqt3JrfbVXYTXcuxunvxE74hLK6H8RB8AvseOSI6NGzeiVatW0NXVxY0bN/Drr79i4sSJ1V0sIiIiIiIiotfCR4YofGuZmpqazM/SpUuru3gfvEaNGpW7/2S94etj8/TpUzg7O8Pe3h6LFi3C9OnT4e7uXqllr127Vu6+KXoTFxEREREREb09BQXV9/lYMDIIwLZt25CRIbuvpo6Oznsuzcfn1KlT5b6yvDJjCn3o1qxZgzVr1rzWsg4ODq/1Bq/XFRoa+t7WRURERERERB8nNgYBqFevXnUX4aNmbm5e3UX4YCkrK8Pa2rq6i0FERERERFRrFODjecV7dWE3MSIiIiIiIiKiWoSNQUREREREREREtQi7iRERERERERFRjZH/EQ3kXF0YGUREREREREREVIswMoiIiIiIiIiIaoyP6RXv1YWRQUREREREREREtQgjg4iIiIiIiIioxmBkkHyMDCIiIiIiIiIiqkXYGEREREREREREVIuwmxgRERERERER1Rj5BYLqLsIHj5FBRERERERERES1CCODiIiIiIiIiKjG4ADS8jEyiIiIiIiIiIioFmFjEBERERERERFRLcJuYkRERERERERUY7CbmHyMDCIiIiIiIiIiqkUYGURERERERERENUY+I4PkYmQQEREREREREVEtwsYgIiIiIiIiIqJahN3EiIiIiIiIiKjGKCgQVHcRPniMDCIiIiIiIiIiqkUYGURERERERERENQZfLS8fI4OIiIiIiIiIiGoRRgYRERERERERUY3BV8vLx8ggIiIiIiIiIqJahI1BRERERERERES1CLuJEREREREREVGNwQGk5WNkEBERERERERFRLcLIICIiIiIiIiKqMRgZJB8jg4iIiIiIiIiIahE2BhERERERERER1SLsJkZERERERERENUY+u4nJxcggIiIiIiIiIqJahJFBRERERERERFRjcABp+RgZRERERERERERUizAyiIg+GCmxCdVdBKqEOiJhdReBKklNW7O6i0CVoCBWrO4iUCWs7r+7uotAlTDtyPDqLgJV0h/f/VPdRaAaLD+/ukvw4WNkEBERERERERFRLcLGICIiIiIiIiKiWoTdxIiIiIiIiIioxuAA0vIxMoiIiIiIiIiIqBZhZBARERERERER1RiMDJKPkUFERERERERERLUIG4OIiIiIiIiIiGoRdhMjIiIiIiIiohojn93E5GJkEBERERERERFRLcLIICIiIiIiIiKqMQqqdQRpQTWuu/IYGUREREREREREVIswMoiIiIiIiIiIagy+Wl4+RgYREREREREREdUibAwiIiIiIiIiIqpF2E2MiIiIiIiIiGqM/PzqLsGHj5FBRERERERERES1CCODiIiIiIiIiKjG4ADS8jEyiIiIiIiIiIioFmFjEBERERERERFRLcJuYkRERERERERUY+Szm5hcjAwiIiIiIiIiIqomGzduhKWlJZSUlNCyZUtcu3at3LRHjx6Fo6Mj9PX1oaGhgTZt2uDs2bNVXicbg4iIiIiIiIioxigoqL5PVR04cABTp07FTz/9BG9vb7Rv3x49evRAeHi4zPRXr16Fo6MjTp06BS8vL3Tu3Bm9e/eGt7d3ldbLxiAiIiIiIiIiomqwevVqjBo1CqNHj4adnR3Wrl0LU1NTbNq0SWb6tWvXYubMmWjVqhUaNGiApUuXokGDBvj777+rtF6OGURERERERERENUZBNQ4alJWVjaysLKlpYrEYYrG4TNrs7Gx4eXlh9uzZUtO7deuGmzdvVmp9+fn5SE1NhY6OTpXKycggIiIiIiIiIqK3YNmyZdDU1JT6LFu2TGbauLg45OXlwdDQUGq6oaEhoqKiKrW+VatW4dWrVxg4cGCVysnIICIiIiIiIiKit2DOnDmYNm2a1DRZUUElCQQCqe8FBQVlpsmyb98+uLu74/jx4zAwMKhSOdkYREREREREREQ1RnW+Wr68LmGy6OnpQSgUlokCiomJKRMtVNqBAwcwatQoHDp0CF27dq1yOdlNjIiIiIiIiIjoPVNUVETLli3h6ekpNd3T0xNt27Ytd7l9+/bB1dUVe/fuRa9evV5r3YwMIiIiIiIiIqIa43Ve8V5dpk2bhuHDh8PBwQFt2rTBli1bEB4ejnHjxgEo7Hb28uVL7Nq1C0BhQ9CIESOwbt06tG7dWhJVpKysDE1NzUqvl41BRERERERERETVYNCgQYiPj8fChQsRGRmJxo0b49SpUzA3NwcAREZGIjw8XJL+999/R25uLiZMmIAJEyZIpo8cORI7duyo9HrZGEREREREREREVE3Gjx+P8ePHy5xXuoHn8uXLb2WdbAwiIiIiIiIiohojvzpHkP5IcABpIiIiIiIiIqJahJFBRERERERERFRjfEwDSFcXRgYREREREREREdUijAwiIiIiIiIiohqDkUHyMTJIjoKCAowdOxY6OjoQCATQ0tLC1KlT3/l6LSwssHbt2jfOZ8eOHdDS0nrjfD4Gly9fhkAgQFJS0mvn0alTp3dev6GhoRAIBPDx8XmjfNzd3dGsWTPJd1dXV7i4uLxRnkRERERERFTzMTJIjjNnzmDHjh24fPkyrKysMGDAgOouUo0iEAjw119/vZVGjLZt2yIyMhKamppy016+fBmdO3dGYmKiVGPZ0aNHoaCg8MZlqYipqSkiIyOhp6f3VvNdt24dCt5iE7i7uzuOHTv2xo1WNZHb1+bo42QMdTUR/AJTsXrzU4SEp1e4TMe2ehg91AL1jJXxMjIDW3eH4OrteKk0ejqK+M7VCq1b6kAsroPnLzPwy/oABASnvcvNqbG+GWSG3t0Moa4qgt/TNKzZEozQ53LqqbUuRg0xR10jJUREZWLrnjBcu1NcT98MMsM3g82klolPzEZft3/fyTbUBkOdDdC9ow7UVIQIeJaOjX9GIDwiq8Jl2rXUwPC+hjDWV0RkbDZ2Ho3GrfspkvmNG6qgf3d9WFsoQ1dLAYs2hOGWd0oFOVJFenTQQl9HbWhrihAemY0/DsXALyij3PSNGijDbYABzIwVkZCci7/OJeDMtWTJfFNjRQzprYf6Zkow1FXAtkMx+Pti4vvYlFphxIB66PWFPtTVRPAPSsN6jzCEvSi/vgCg/afa+GagCYwNxYiMzsIfB17gxt3iOuntaIA+XQ1gqC8GAIS9yMDuoy/xr09yeVlSOXQ+d4DV9FHQbNEYSnUNcK//eESfuFDxMu1bwX7lbKjZN0BWRAyCV21D+Jb9UmmM+nZDQ/cpUKlvhvTgcATMW4Po4+ff5abUeN0/14BzF21oawjxPCobHkfi4P8ss9z09tZK+KavHkyNFJGQnIdjFxJx7kbxtadrGw10+lQdZsaKAIDg51nY83c8gsIrvuYRvSuMDJIjODgYxsbGaNu2LYyMjCASsf0MAHJycqq7CFJycnKgqKgIIyMjCASC185HR0cH6urqb7FkZQmFwnfyW9LU1Pwgo8A+tN/Kmxra3xSDXEyw+vcgjJ52H/GJ2Viz8BMoKwvLXaaRjQYWzLTH2UvRcJ18D2cvRWPhLHvYNyz+ramrirBpRXPk5hVghvsjDBt/F7/9EYzUV7nvY7NqnCF962Fgn7pYu/UZxs58gITEbKx2bwRlpYrqSR3zZ9ji7OUYuH3vjbOXY7Bghg3sGqhJpXsW/gou39yRfFyn3n/Xm1NjDeihh77d9LDpzwhMXRSExORcLJlhCWWl8m9PbOurYPY4M1y8mYQJ84Nw8WYS5owzg42VsiSNkrgOQp5nYtOfEe9jM2q0z1uqY9RXBjh0JgHfLw2DX1A65k0wgZ627GuYga4C5k0wgV9QOr5fGobDZxIweqAh2jQvPo7EinUQHZeD3cdikZDMc9zbNLiPMQb0NMKG7WEY/6MvEpNysOJHmwqPKfsGapg7xRqe1+IwdtZjeF6Lw7wp9WFrrSpJExefja37nmP8T74Y/5MvvH1TsHBGA5ibKJebL8kmVFVBysMA+E5ZWKn0yhYmaPX3FiRc98L1Vi4IWr4Zjdb8BKO+3SRptFo3Q/O9a/Byz3Fca+mMl3uOo8W+tdD69JN3tRk1Xrvmavimnz6OnEvE9BXP4R+ciZ+/q1v+uU9HhJ+/rQv/4ExMX/EcRz0TMaq/Plo3LT6OGjdQxnWvVMzb8BJzVr9AXGIu5o+vCx3N8u9N6PXlFxRU2+djwcagCri6umLSpEkIDw+HQCCAhYVFmTSJiYkYMWIEtLW1oaKigh49euDp06dSaY4cOYJGjRpBLBbDwsICq1atkpofExOD3r17Q1lZGZaWltizZ0+VypmUlISxY8fC0NAQSkpKaNy4MU6ePCmV5uzZs7Czs4Oamhq6d++OyMhIyby7d+/C0dERenp60NTURMeOHXH/vvQ/NwKBAJs3b4azszNUVVWxePFi5OXlYdSoUbC0tISysjJsbGywbt26MuXz8PCQbL+xsTEmTpwIAJL92bdv3zL79++//0bLli2hpKQEKysrLFiwALm5xTeMsspTuptYWFgYevfuDW1tbaiqqqJRo0Y4deoUQkND0blzZwCAtrY2BAIBXF1dAZTtJpaVlYWZM2fC1NQUYrEYDRo0wB9//AGgsO6HDh0KfX19KCsro0GDBti+fbvc+irdTayo3BcuXICDgwNUVFTQtm1bBAQESC33yy+/wNDQEOrq6hg1ahQyM6WfTJTuJpafn4/ly5fD2toaYrEYZmZmWLJkiWT+rFmz0LBhQ6ioqMDKygpz586VNNzs2LEDCxYswIMHDyAQCCAQCLBjxw4AQHh4OJydnaGmpgYNDQ0MHDgQ0dHRknyLuq95eHjAysoKYrH4rUYsVbev+tTDroPhuHorDiHh6Viy5gnEYiG6dTQod5mBzvVwzycRfx5+jvAXGfjz8HN4PUjCwD4mkjRDB5giJi4Ly9YFwP9pKqJisuD1MAkRUeU/gaLyffVlPew+/BxXb8cjJDwdS9cHQiwWwrGDfgXL1MW9B4nYc/QFwl9mYM/RF/B6mIyveteTSpeXV4CEpBzJJzmF/8y+LhdHPew/GYOb91MQ9jILq/54AbFiHXT6TKuCZXTh7ZeGg6di8SIqCwdPxcLHPw3OjsXRlvcepWHXX9G4eZ/RQG/KuYs2zt9MhueNZLyIysYfh2IRl5iDHh20ZKbv3l4TsQk5+ONQLF5EZcPzRjIu3EyGS1cdSZqgsEzsOBqLa/dSkZNbc64PH4J+PQyx91gErt9NROiLDCzf+AxK4jro0k63/GV6GsLrUTL2HY/E84hM7DseifuPU9C/h5Ekza37SfjXJxkvIjPxIjITHgdeICMzH/YNVMvNl2SLPXsVgfPXIuqYZ6XSm48djMzwSPhNX4q0J8/w3OMwnu84CqtpbpI0lpNGIu78TQSv2IJXAc8QvGIL4i7ehsWkke9qM2q83p21cOF2Cs7fSsHL6Bx4HI1DfGIunD6X3QPB6XNNxCXmwuNoHF5G5+D8rRRcvJ0C5y+0JWnW7orGmespCH2ZjZcxOdi0LwaCOgJ80lDlfW0WkRQ2BlVg3bp1WLhwIUxMTBAZGYm7d++WSePq6op79+7hxIkTuHXrFgoKCtCzZ0/JP9VeXl4YOHAgBg8ejEePHsHd3R1z586V/GNdlEdoaCguXryIw4cPY+PGjYiJialUGfPz89GjRw/cvHkTf/75J/z8/PDLL79AKCxuYU5PT8fKlSuxe/duXL16FeHh4ZgxY4ZkfmpqKkaOHIlr167h9u3baNCgAXr27InU1FSpdc2fPx/Ozs549OgR3NzckJ+fDxMTExw8eBB+fn6YN28efvzxRxw8eFCyzKZNmzBhwgSMHTsWjx49wokTJ2BtbQ0Akv25fft2qf179uxZDBs2DJMnT4afnx9+//137NixQ6ohQ1Z5SpswYQKysrJw9epVPHr0CMuXL4eamhpMTU1x5MgRAEBAQAAiIyNlNmIBwIgRI7B//36sX78e/v7+2Lx5M9TUCp9uzp07F35+fjh9+jT8/f2xadOmN+r69dNPP2HVqlW4d+8eRCKR1DYdPHgQ8+fPx5IlS3Dv3j0YGxtj48aNFeY3Z84cLF++XFLOvXv3wtDQUDJfXV0dO3bsgJ+fH9atW4etW7dizZo1AIBBgwZh+vTpaNSoESIjIxEZGYlBgwahoKAALi4uSEhIwJUrV+Dp6Yng4GAMGjRIat1BQUE4ePAgjhw5UqO6mdU1VIKejhj/eheHzufkFsDncRIa22qUu1xjWw38650gNe2OdwIa2xUv0+5TXTwJSsWiWfb4e3cbeKxtgd7djEpnRZVgbCiGro4i7vokSabl5BbggW8yGtuWH/nXyEZdahkA+NcnEY1tpJcxMVbG0T9a4cBmB8yfZgNjQ/HbLH6tYaSvAB0tBdz3Le4GmZtbgEcBr2BnXf6NsW19Fdx/LH19uv84Ffb1eTP9tomEQH0zJfj4vZKa7uOfDlsr2REhtlbK8PGX7o7p7fcK1uZKEPKu850yNhBDV1sR9x4Wd93KyS3AA/9UNGpY/rnPvoGa1DIAcO9hMho1VJOZvo4A6NxGB0riOvALZDfmd02rdTPEnr8hNS323DVotmwMwX9R5tqtmyHu/HWpNHGe16Ddpvl7K2dNIhIC9U3FePBE+lzm8yQdtpZKMpdpaKEEHxnp65uJyz33KSoKIKwDpKbnvZVyk7SC/Or7fCzY56kCmpqaUFdXl3TrKe3p06c4ceIEbty4gbZt2wIA9uzZA1NTUxw7dgxfffUVVq9ejS5dumDu3LkAgIYNG8LPzw+//vorXF1dERgYiNOnT+P27dv47LPPAAB//PEH7OzsKlXG8+fP499//4W/vz8aNmwIALCyspJKk5OTg82bN6N+/foAgIkTJ2LhwuLQ1C+++EIq/e+//w5tbW1cuXIFX375pWT6kCFDyjS6LFiwQPK3paUlbt68iYMHD2LgwIEAgMWLF2P69OmYMmWKJF2rVq0AAPr6hU/otbS0pPbvkiVLMHv2bIwcOVKyPYsWLcLMmTMxf/78cssTEhIiVbbw8HD0798fTZo0KbNfdHQKn1AaGBiU27UqMDAQBw8ehKenJ7p27Vomj/DwcDRv3hwODg4AIDNyrCqWLFmCjh07AgBmz56NXr16ITMzE0pKSli7di3c3NwwevRoAIX79fz582Wig4qkpqZi3bp1+O233yT7sX79+vj8888laX7++WfJ3xYWFpg+fToOHDiAmTNnQllZGWpqahCJRFJ14+npiYcPHyIkJASmpqYAgN27d6NRo0a4e/eupG6zs7Oxe/duSR3XFDrahX28E5KypaYnJmXD0ED2zQEA6GgpIjFJurtcYlKOJD8AqGukDJceyjhw7AV2HQqHfUN1TB1rjZycApy5FF06S6qArlZRPUnv84SkHBjpl99wU5l68nuaiqXrAvE8IgPaWgoY8ZUZNi5ripFT7iMllRFCVaGtUTg+W1KpyKqklFwY6JY/dpu2pkjmMtqavKV52zTUhBAKBUgq9dtOSs2FtqbsiBAtDRGSUl+VSS8SCqChJkRiCv/peVe0tQqPm8TkUuex5BwY6lV07lNAYqnueonJuZL8iliaKmPDInsoKtRBRmYe5q96irCXjF5918SGesiKjpOalh0TjzoKClDU00ZWVCzERnrIipYehzArOh5io5p1H/a+qKsWnfukz1fJqXnQUpfdpUtbQwifUumTUvMqPPcN76OLhORcPAyoeEwvoneFd05vwN/fHyKRSNKIAwC6urqwsbGBv7+/JI2zs7PUcu3atcPatWuRl5cnyaOoQQEAbG1tKz32i4+PD0xMTCQNQbKoqKhIGoIAwNjYWCryKCYmBvPmzcPFixcRHR2NvLw8pKenIzw8XCqfkmUssnnzZmzbtg1hYWHIyMhAdna25A1XMTExiIiIQJcuXSq1LUW8vLxw9+5dqUigvLw8ZGZmIj09HSoqKuWWp6TJkyfju+++w7lz59C1a1f0798fn3xS+b7TPj4+EAqFkgaa0r777jv0798f9+/fR7du3eDi4iJpFHwdJctmbGwMoHAfmpmZwd/fH+PGjZNK36ZNG1y6dElmXv7+/sjKyqpw3x8+fBhr165FUFAQ0tLSkJubCw2N8qNbivI1NTWVNAQBgL29PbS0tODv7y9pDDI3N5fbEJSVlYWsLOkB8/LzslFHqFjOEu+fY0cD/DCh+NiaufBR4R+lezUIBGWnlSKrq1zJSXUEwJOgVGzZXdio+fRZGizMVODSsy4bg+Rw7KCP6eOsJd9nLfH97y/pfS4QyH/NaOl6Kr3MnfslBrkNB3wDfLFvkwO6dzbAwRMcn6YinVprYdKIupLv89eGAShbJwIZ00orM1sgkHcI0huQXUfl7/GyswrH8mMdvV1d2uni+zEWku8/Lg8EUPX6goz5AqBMhT2PyMTYWY+hpipC+0+1MWu8FaYt8GeD0PtQplIFZafLSlODuulXhzK7T87tnozksvMB4NJFC5+3UMe8DS/ZXZaqDRuD3kB5F9aCggLJIMYl/5a1XNHfrzvosbKy/IH7Sr8dSyAQSJXB1dUVsbGxWLt2LczNzSEWi9GmTRtkZ0tHP6iqSj8FPHjwIL7//nusWrUKbdq0gbq6On799VfcuXOn0mWTJT8/HwsWLEC/fv3KzFNSKo6+KF2e0kaPHg0nJyf8888/OHfuHJYtW4ZVq1Zh0qRJlSqHvPL36NEDYWFh+Oeff3D+/Hl06dIFEyZMwMqVKyuVf2kl66no95Cf/3pxhvLKfvv2bQwePBgLFiyAk5MTNDU1sX///jLjWZUm6/csa7q8ugGAZcuWSUWWAYBpg5Ews/lG7rLvy/V/4+EXeE/yXVGhMM5XR1sR8YnFx4e2pkKZaKGSEpKypaJLgMInuIkllolPzC7zpquw5+no1JZP9eS5/m8C/AK9Jd8ViupJSxHxicVPyLU1FZCYXLV60tKUrqfSMrPy8SzsFUyMOYiqPHd8UhDwrPg3riAqPGdoa4qkohI0NcpG/pSUmJwLbQ3p2xctdSGSOBDxW5eSloe8vIIy+1tTXYSkciJ8klJk109uXgFS0xgV9Dbd9EqEf1BxN63ic5+CVGSklqZChcdHQlIOdEpFAWlpispEGOXmFSAiOgtAFgKfvYJNfVX062GENdtC33xjqFxZ0XFlInwU9XWQn5OD7PikwjRRcRAbSQ9VIDbQKRNRRJWT+qro3CcdBaSpJkRyquzzWGJK2aghzaJz3yvpZZy/0EJ/R224/y8CYRHl32PQm6lJY5a+K+y9/Qbs7e2Rm5srafwAgPj4eAQGBkq6ednb2+P6dek+vDdv3kTDhg0hFAphZ2eH3Nxc3LtX/A9nQECAZBBkeT755BO8ePECgYGBr70d165dw+TJk9GzZ0/JQM9xcfIvHteuXUPbtm0xfvx4NG/eHNbW1ggODpbMV1dXh4WFBS5cKP91mQoKCsjLkz5BtmjRAgEBAbC2ti7zqVOnaj9ZU1NTjBs3DkePHsX06dOxdetWAICiYuE/fKXXXVKTJk2Qn5+PK1eulJtGX18frq6u+PPPP7F27Vps2bKlSuWrLDs7O9y+fVtqWunvJTVo0ADKysrl7vsbN27A3NwcP/30ExwcHNCgQQOEhYVJpVFUVCyzf+zt7REeHo7nz59Lpvn5+SE5ObnSXRuLzJkzB8nJyVIfE+uhVcrjXcvIyMPLyEzJJyQ8HXEJWWjVrHgwQJFIgGaNtfD4SfkD1T5+kiK1DAB82lwHj/2Ll3nknwyzetJjnpjWU0FUDJ+4ypORmYeXUZmST+jzdMQnZMOhqZYkjUgkQNNGmnj8JLXcfHwDUtGqxDIA0KqZFh4HlL+MgkgAcxMVqcZBki0jMx+RMdmST3hEFhKSctDCvnhcEpFQgCY2qvAPSi83nyfB6WjeSHr8kxaN1eEXXP4y9Hpy84Dg8Ew0tZM+NzWzU8GTZ7K7NTx5loFmpdPbqyIoLBN5H9E4Ch+DjMx8RERnST5hLzIQn5iNlk2Ko3xFQgGa2qnDN7D885jf0zS0bCI9KK7DJ5rwlTMekEAAKCi8/htcqXKSbvtAr4t05Lm+4+dI9nqMgv9erpJ42wd6XdpJpdHr+jkSb3mDqi43r/C1701tpM9lTW1V8CRE9n1ZYGgmmtqWTR8cniV17nP+QgsDnLSxaHMEgp/zlfJUvdgY9AYaNGgAZ2dnjBkzBtevX8eDBw8wbNgw1KtXT9I1bPr06bhw4QIWLVqEwMBA7Ny5E7/99ptkAGcbGxt0794dY8aMwZ07d+Dl5YXRo0dXOqqmY8eO6NChA/r37w9PT0+EhITg9OnTOHPmTKW3w9raGrt374a/vz/u3LmDoUOHVmr91tbWuHfvHs6ePYvAwEDMnTu3zCDb7u7uWLVqFdavX4+nT5/i/v372LBhg2R+UWNRVFQUEhMLu1/MmzcPu3btgru7O3x9feHv748DBw5IjXFTGVOnTsXZs2cREhKC+/fv4+LFi5IGC3NzcwgEApw8eRKxsbFISyt7w2NhYYGRI0fCzc0Nx44dQ0hICC5fviwZIHvevHk4fvw4goKC4Ovri5MnT1a5QaSypkyZAg8PD3h4eCAwMBDz58+Hr69vuemVlJQwa9YszJw5E7t27UJwcDBu374teROatbU1wsPDsX//fgQHB2P9+vX466+/ymx/SEgIfHx8EBcXh6ysLHTt2hWffPIJhg4divv37+Pff//FiBEj0LFjR7nd9koTi8XQ0NCQ+nxIXcTKc+jESwz/ygwdWuvC0kwFP021QVZWHs5dKe56+fP3Nvh2hKXUMq2a62Bof1OYmShjaH9TODTVwsETLyRpDhx/iUY26hj+lRnqGSvBsaMB+jgZ4+g/7Hr0Og6dfIlhA0zR/rPCepozqQGysvLgeTVWkubHyQ0xdpi55PvhkxFwaKaNIX3rwayeMob0rQeHT7Rw6O+XkjTjR1qgaSMNGBuIYddADQtn2kFVRYgzlyo36D9JO+YZh4FfGqBNCw2Y1xNj2igTZGXn4/KdJEma6aNN4Nq/ePD7457xaNFIDQN66MHESIwBPfTQzE4Nxz2LH2IoievAylQJVqaF0aSGegqwMlWCvk75YxGRbMcvJMKxnRa6tNGAiZEiRg3Qh562As5cSwIADHfWw9SRxWPLnbmWDH0dBbj114eJkSK6tNFA17aaOHa+eBB9kRCwNBHD0kQMBaEAuloiWJqIYaTP+nlTR09HY4hLXbRrpQ0LE2XMHG+FzKx8XLhRPJ7MrPFWGDXYRGoZh080MbiPMUzrKmFwH2O0aKyBI6ejJGlGDTZBE1s1GOorwtJUGW6DTNDUXgMXrkuPU0PyCVVVoNHUFhpNbQEAKpYm0GhqCyXTwiECbBZPQ9PtyyXpw7bsh7J5Xdj9OhtqtlYwce0P02/649lqD0ma0N92Qc+xHaxmjIGqjRWsZoyBXpc2CN2w8/1uXA3y96UkdGmjgS9aq6OeoQK+6asHPW0Rzl0vHGx9aG9dTB5W/CbZs9eToa8tgmtfPdQzVMAXrdXRpbUGjl8s7l7u0kULQ77Uxf/2xiAmPhda6kJoqQuhpMhG1XchP7/6Ph8LdhN7Q9u3b8eUKVPw5ZdfIjs7Gx06dMCpU6ckXX5atGiBgwcPYt68eVi0aBGMjY2xcOFCyavMi/IYPXo0OnbsCENDQyxevFgy4HRlHDlyBDNmzMDXX3+NV69ewdraGr/88kull/fw8MDYsWPRvHlzmJmZYenSpVJvGyvPuHHj4OPjg0GDBkEgEODrr7/G+PHjcfr0aUmakSNHIjMzE2vWrMGMGTOgp6eHAQMGSOavWrUK06ZNw9atW1GvXj2EhobCyckJJ0+exMKFC7FixQooKCjA1tZWMnhyZeXl5WHChAl48eIFNDQ00L17d8nbsurVq4cFCxZg9uzZ+OabbzBixAipN7wV2bRpE3788UeMHz8e8fHxMDMzw48//gigMHJmzpw5CA0NhbKyMtq3b4/9+/dXqYyVNWjQIAQHB2PWrFnIzMxE//798d133+Hs2bPlLjN37lyIRCLMmzcPERERMDY2low75OzsjO+//x4TJ05EVlYWevXqhblz58Ld3V2yfP/+/XH06FF07twZSUlJ2L59O1xdXXHs2DFMmjQJHTp0QJ06ddC9e3epBr6abs+R5xAr1sG07xpAXU0BfoEp+H7eQ2RkFEdRGeorIb9EZOrjJylwX+GHMcMtMXqoBV5GZWDeCn/4lXhS++RpKn5c6otvR1jCdbA5IqMzsH5rEDyvsJHhdez96yXEikJMG1sfamoi+D9NxfQFvsjILFlPYqkQ4scBqViw6glGDzHHqK/NERGdCfdVAfB/WtxYrK8rxvxpNtBUV0BSSg78AlMxbtYDRMfy6d7rOHw6DmLFOpgwrC7UVIUIeJaOn1eFICOz+E5KX0dB6sbKPzgdv2wOx4h+hhje1xCRMdn4ZXM4AkpEqjSwUMbyWcUD/o/9unCsIs/riVjjUdwIS/Jd90qFuqoQg3rpQUdDiLDIbCz83wvEJhRGJGhriqBXopEtJj4HC//3AqMGGKBnRy0kJOdi28Fo3PIuPo50NEVY+5OF5HtfRx30ddTBo8B0/LymOPKUqm7/iUgoKtbBFDdzqKuK4B+UhllLA6SOKQM9Ralzn19gGhavD8I3A03gOrAeIqKzsGhdMJ4EFQ8Erq2pgNkT6kNHSwGv0vPwLDwdc5YFwOtR+VGxJJtmy8Zoc2G35Lv9ysL7yue7juLhqDkQG+tD+b+GIQDICH2Bu73Hwn7VHJh/NxRZETHw/X4Jov46J0mTeMsb3kOnwWbBVNgsmIz04OfwHvI9kv59+P42rIa54Z0GddU6GOikA21NEcIjs7BkcwRiE/8792kIoadd4tyXkIvFv0fAra8eerTXREJyLv44EovbD4qPo+6fa0JBJMDMUcZS6zpwOgEHTku/dZbofRAUsDMdEX0gPu9dfpc8+nDUEcl+kwZ9eNS0NeUnomqnIP7woyIJSEtMlp+Iqt20I8OruwhUSX989091F4Eq4eh6a/mJPkDzdlZfF/6FIz+O6zq7iRERERERERER1SJsDPrA7dmzB2pqajI/jRo1qu7ikQxLly4tt8569OhR3cUjIiIiIiKiWo5jBn3g+vTpg88++0zmvNKvjKcPw7hx4zBw4ECZ8yo7MDgRERERERG9nnwOhiMXG4M+cOrq6lBXV5efkD4YOjo60NHRqe5iEBEREREREcnExiAiIiIiIiIiqjEKGBokF8cMIiIiIiIiIiKqRdgYRERERERERERUi7CbGBERERERERHVGAXsJSYXI4OIiIiIiIiIiGoRRgYRERERERERUY2RzwGk5WJkEBERERERERFRLcLGICIiIiIiIiKiWoTdxIiIiIiIiIioxijgCNJyMTKIiIiIiIiIiKgWYWQQEREREREREdUYBfnVXYIPHyODiIiIiIiIiIhqEUYGEREREREREVGNkc8xg+RiZBARERERERERUS3CxiAiIiIiIiIiolqE3cSIiIiIiIiIqMbgq+XlY2QQEREREREREVEtwsggIiIiIiIiIqox8vMZGSQPI4OIiIiIiIiIiGoRNgYREREREREREdUi7CZGRERERERERDUGx4+Wj5FBRERERERERES1CCODiIiIiIiIiKjGKOAA0nIxMoiIiIiIiIiIqBZhZBARERERERER1Rj5HDRILkYGERERERERERHVImwMIiIiIiIiIiKqRdhNjIiIiIiIiIhqDA4gLR8jg4iIiIiIiIiIahFGBhERERERERFRjcHIIPkYGUREREREREREVIuwMYiIiIiIiIiIqBZhNzEiIiIiIiIiqjHYS0w+RgYREREREREREdUijAwiIiIiIiIiohqDA0jLx8YgIvpgaNc1qO4iUCXk5+VVdxGokgQCBgB/DPJyeUx9DJTV1aq7CFQJf3z3T3UXgSpp1KZe1V0Eqoz1AdVdAnpH2BhERERERERERDVGQQEjg+ThI0MiIiIiIiIiolqEjUFERERERERERLUIu4kRERERERERUY2RzwGk5WJkEBERERERERFRLcLIICIiIiIiIiKqMTiAtHyMDCIiIiIiIiIiqkXYGEREREREREREVIuwmxgRERERERER1RgFHEBaLkYGERERERERERHVIowMIiIiIiIiIqIag5FB8jEyiIiIiIiIiIioFmFkEBERERERERHVGPl8tbxcjAwiIiIiIiIiIqpF2BhERERERERERFSLsJsYEREREREREdUYHEBaPkYGERERERERERHVIowMIiIiIiIiIqIao4ADSMvFyCAiIiIiIiIiolqEjUFERERERERERLUIu4kRERERERERUY2RzwGk5WJkEBERERERERFRLcLIICIiIiIiIiKqMfhqefkYGUREREREREREVIswMoiIiIiIiIiIagy+Wl4+RgYREREREREREdUibAwiIiIiIiIiIqpF2E2MiIiIiIiIiGqMgvz86i7CB4+RQUREREREREREtQgjg4iIiIiIiIioxsjnq+XlYmQQEREREREREVEtwsYgIiIiIiIiIqJahN3EiIiIiIiIiKjGKChgNzF5GBlERERERERERFSLMDKIiIiIiIiIiGqMAg4gLRcjg4iIiIiIiIiIapFa2xgUFRUFR0dHqKqqQktLCwAgEAhw7Nixai3Xx8zV1RUuLi7VXYx3hr8PIiIiIiKiD19BfkG1fT4Wtbab2Jo1axAZGQkfHx9oamoCACIjI6GtrV3NJft4rVu3Tmqgrk6dOqFZs2ZYu3Zt9RWK3rvLly+jc+fOSExMlDS01hQ9O2qjXzcdaGuKEB6Rha0Ho+EXlFFu+sYNVDDqKwOY1RUjISkXR87F48zVJKk0bZurY2gffRjrKyAyNge7j8fitk/qO96S2mFIHwN076ANNRUhAkIysGlPBMIjsipcpm0LDQx3MYCxviIiY7Ox669o3PKWro9enXTQz0kPOlqFv4Mt+yPh+zT9XW5KjTaktz6cOmhBTUWIwJAMbNobVYl6UscwZ4Pi4+ZYTJl66tlJG/2cdKFTdLweiGY9vSHW1YevqvuycUMVjB5oWHydOhuP01cSpdJUpg6parp/rgHnLtrQ1hDieVQ2PI7Ewf9ZZrnp7a2V8E1fPZgaKSIhOQ/HLiTi3I0UyfyubTTQ6VN1mBkrAgCCn2dhz9/xCAqv+Pik8ul87gCr6aOg2aIxlOoa4F7/8Yg+caHiZdq3gv3K2VCzb4CsiBgEr9qG8C37pdIY9e2Ghu5ToFLfDOnB4QiYtwbRx8+/y00hKletjQwKDg5Gy5Yt0aBBAxgYGAAAjIyMIBaLq7lkHy9NTc0a98//u5adnf3e15mTk1Ol6VTocwd1jB5oiIOn4jFlcQh8gzLgPskM+tqy29QNdRUwf5IpfIMyMGVxCA6djsfYQUZo21xdksbGShkzx9TDpTvJmLwoBJfuJGPW2HpoaKH0vjarxhrQXQ99HXWxeW8kvl8cjMTkHCyeZgFlcfmXPVsrZcz+1hQXbyVh4oIgXLyVhNnfmsHGUlmSpn0rDYwZbIQDp2IxeWEwHge+woIp5tDXUXgfm1Xj9O+uCxdHHWzeG4VpS0KQmJyLRd+bya2nWWNNcOl2EiYtfIZLt5Mwa6wJGpasJwcNjBlkhIP/xGHywmfwfZoO98lm0Neptc/A3hjr6sNX1X1pqKcA98lm8H2ajskLn+HgqTiMHWyEti2Kr1OVqUOqmnbN1fBNP30cOZeI6Suewz84Ez9/Vxd65dxPGOiI8PO3deEfnInpK57jqGciRvXXR+umqpI0jRso47pXKuZteIk5q18gLjEX88fXhY6m8H1tVo0jVFVBysMA+E5ZWKn0yhYmaPX3FiRc98L1Vi4IWr4Zjdb8BKO+3SRptFo3Q/O9a/Byz3Fca+mMl3uOo8W+tdD69JN3tRlEFaqxjUGdOnXC5MmTMXPmTOjo6MDIyAju7u4AAAsLCxw5cgS7du2CQCCAq6srAOluQKGhoRAIBNi/fz/atm0LJSUlNGrUCJcvX650GXx9fdGrVy9oaGhAXV0d7du3R3BwMAAgPz8fCxcuhImJCcRiMZo1a4YzZ85Ili1a/8GDB9G+fXsoKyujVatWCAwMxN27d+Hg4AA1NTV0794dsbGxkuWKumotXboUhoaG0NLSwoIFC5Cbm4sffvgBOjo6MDExgYeHh2SZy5cvQyAQICkpSTLNx8cHAoEAoaGhAIAdO3ZAS0sLZ8+ehZ2dnWTdkZGRZdZd9PeVK1ewbt06CAQCCAQChISEwNraGitXrpTaT48fP0adOnUk+6YiAoEA27ZtQ9++faGiooIGDRrgxIkTkvlF5Szp2LFjEAgEku/u7u5o1qwZPDw8YGZmBjU1NXz33XfIy8vDihUrYGRkBAMDAyxZsqTM+iMjI9GjRw8oKyvD0tIShw4dkpr/8uVLDBo0CNra2tDV1YWzs7NkH5bcR8uWLUPdunXRsGFDuduclZWFmTNnwtTUFGKxGA0aNMAff/zxWttrZWUFsViMgoICCAQCbN68Gc7OzlBVVcXixYsBAH///TdatmwJJSUlWFlZSX4/lamD0NBQdO7cGQCgra0tdXx97Fy66sLzRhLO3UjCi6hsbDsYjbjEHPToKDuasHtHLcQm5GDbwWi8iMrGuRtJOH8jCX276UjSOHfRgY//Kxw+E48X0dk4fCYeD568Qp8uOjLzpMpz7qqLA//E4ub9FIRFZGG1x0uIFeug42ea5S/jqAdvvzQcOh2HF1HZOHQ6Dg+epMG5q64kTV9HPZy7nohz1xLxPDILWw9EIS4xBz07sc5eh3MXHRw4FYdb3qmF9bQ94r960ih3mT5ddeDt9wqHTsf/V0+Fx41z1+I6cHHUhef1RJy7Xni8bj1QeLz27Mh6el2sqw9fVfdlj47aiE3IwdYD/12nrifh/I1E9OtWfM6rTB1S1fTurIULt1Nw/lYKXkbnwONoHOITc+H0uezrk9PnmohLzIXH0Ti8jM7B+VspuHg7Bc5fFN9/rN0VjTPXUxD6MhsvY3KwaV8MBHUE+KShyvvarBon9uxVBM5fi6hjnpVKbz52MDLDI+E3fSnSnjzDc4/DeL7jKKymuUnSWE4aibjzNxG8YgteBTxD8IotiLt4GxaTRr6rzajV8gvyq+3zsaixjUEAsHPnTqiqquLOnTtYsWIFFi5cCE9PT9y9exfdu3fHwIEDERkZiXXr1pWbxw8//IDp06fD29sbbdu2RZ8+fRAfHy933S9fvkSHDh2gpKSEixcvwsvLC25ubpJ/qtetW4dVq1Zh5cqVePjwIZycnNCnTx88ffpUKp/58+fj559/xv379yESifD1119j5syZWLduHa5du4bg4GDMmzdPapmLFy8iIiICV69exerVq+Hu7o4vv/wS2trauHPnDsaNG4dx48bh+fPnVdqf6enpWLlyJXbv3o2rV68iPDwcM2bMkJl23bp1aNOmDcaMGYPIyEhERkbCzMwMbm5u2L59u1RaDw8PtG/fHvXr169UORYsWICBAwfi4cOH6NmzJ4YOHYqEhIQqbUtwcDBOnz6NM2fOYN++ffDw8ECvXr3w4sULXLlyBcuXL8fPP/+M27dvSy03d+5c9O/fHw8ePMCwYcPw9ddfw9/fX7J/OnfuDDU1NVy9ehXXr1+XNJqVjAC6cOEC/P394enpiZMnT8ot64gRI7B//36sX78e/v7+2Lx5M9TU1Kq0vUFBQTh48CCOHDkCHx8fyfT58+fD2dkZjx49gpubG86ePYthw4Zh8uTJ8PPzw++//44dO3aUaRgrrw5MTU1x5MgRAEBAQIDc4+tjIRIC1mZK8PZ7JTXd2+8V7OrLfjpqa6VSJv19v1ewNleGsE5RGuWyefq+gl193ry9CSM9BehoKeC+b5pkWm5uAR4HvIKddfn7trA+0qSm3fdNkywjEgpgba4Mb18ZaVhnVWb4Xz15+xYfA7m5BXgcmF7h/iw8tmTVQeGxKBIC1uYyjlffNNiWc7xSxVhXH77X2Ze2VrLOZ/9dp4RFaSquQ6oakRCobyrGgyfSXfd8nqTD1lJ2VHBDCyX4yEhf30wsuZ8oTVFRAGEdIDU9762Um+TTat0MsedvSE2LPXcNmi0bQyAqjPrSbt0MceevS6WJ87wG7TbN31s5iUqq0Y1Bn3zyCebPn48GDRpgxIgRcHBwwIULF6Cvrw+xWAxlZWUYGRlJxgySZeLEiejfvz/s7OywadMmaGpqSqIyKvK///0Pmpqa2L9/PxwcHNCwYUN88803sLGxAQCsXLkSs2bNwuDBg2FjY4Ply5fLHF9nxowZcHJygp2dHaZMmYL79+9j7ty5aNeuHZo3b45Ro0bh0qVLUsvo6Ohg/fr1sLGxgZubG2xsbJCeno4ff/wRDRo0wJw5c6CoqIgbN6RPWPLk5ORg8+bNcHBwQIsWLTBx4kRcuCC776ympiYUFRWhoqICIyMjGBkZQSgU4ptvvkFAQAD+/fdfSZ5//vkn3NzcZOYji6urK77++mtYW1tj6dKlePXqlSS/ysrPz4eHhwfs7e3Ru3dvdO7cGQEBAVi7di1sbGwkdVU6Euyrr77C6NGj0bBhQyxatAgODg7YsGEDAGD//v2oU6cOtm3bhiZNmsDOzg7bt29HeHi4VD6qqqrYtm0bGjVqhMaNG1dYzsDAQBw8eBAeHh7o27cvrKys0KVLFwwaNKhK25udnY3du3ejefPm+OSTTySRQ0OGDIGbmxusrKxgbm6OJUuWYPbs2Rg5ciSsrKzg6OiIRYsW4ffff5fKr7w6EAqF0NEpfFpoYGAg9/j6WGioiSAUCpCUkis1PSk1F1oassO6tTWESEotlT4lFyKhABpqhctoaYjK5pmSC20NhnW/CW3Nwv0re9+W3/VEW1OExGTpZRKTi5fRUBPK/h2k5EnWSZVXYT1VsD+1Ncs7borqqfB4TSyVJjGV9fS6WFcfvtfZl9qaIiSmSjcWJKbkQiQqvk7Jq0OqGnXV/64jpfZ7cmoetNRlX/u1NYRILpU+KTXvv/sJ2csM76OLhORcPAwof1xDervEhnrIio6TmpYdE486CgpQ1CuM4hIb6SErWjqoICs6HmIj/fdWztqEA0jLV6PP5J98It3/0tjYGDExMVXKo02bNpK/RSIRHBwcJJEgFfHx8UH79u2hoFB2HImUlBRERESgXbt2UtPbtWuHBw8eSE0ruQ2GhoYAgCZNmkhNK71NjRo1Qp06daTSlGx0EAqF0NXVrfK+UFFRkYreeZ39aWxsjF69esHDwwOffvopTp48iczMTHz11VeVzqPkPlFVVYW6unqVy2FhYQF19eI+8YaGhhAKhWX2W+l8S/4eir4XRdp4eXkhKChIKl8AyMzMlOoC16RJEygqKlaqnD4+PhAKhejYsWOl0pfH3Nwc+vplLzQODg5S3728vHD37l2pSKC8vDxkZmYiPT0dKiqFT4DfRh1kZWUhK0t6YMO8vGwIhZXbN9Wh9KldIDNVifSlFijqvVdQIqcylwuBjGlUoU6faWLi8LqS7+7rwwDI3rdVJZBRH7LqtfQ0KqvTZxqYMKy4nhZsCAcg+7iStz/LLCPruCldT5XIlwqxrj5iVd2XpWYKJJPLv07JrEOqkjJ1Imeflnf/IatuXbpo4fMW6pi34SVycllT71W5N34FFafhCY+qSY1uDCrdECMQCJCf/+Z9+EqOx1IeZWX54bOl8ykax6WkkttQNK/0tNLbJGu7K9oXRQ0gJS/8sgYTlpVHwWucvEaPHo3hw4djzZo12L59OwYNGiRpZKgMedtSukyV3ZbX/b0U1Ut+fj5atmyJPXv2lElTsiFGVVW1zPzyyPsdVXZ7y1tn6en5+flYsGAB+vXrVyatklJx+PLbOLaWLVuGBQsWSE1r0GI8bBwmVimf9yElLRd5eQVlnoRqqpd9YlokMSVPZvrcvAKkphU+4ZP1dFVLXYSkFIZ1V8Udn1QEhBQ3uCqICo9JbQ3pSB8tdVGZp+YlJSaXjXIoGb2VkpZX+DvQLF2vwnJ/B1Tsjk8aAp6VqCeFwmtP6XrSlBExV1LJaC3JMuol6ylXZj1psZ4qjXX18XmdfSmrfrQ0RMjNLUDqq7xy01R07aOKpb767zpSKgJYU61s9E+RxJSyUUOa6sLC+4lX0ss4f6GF/o7acP9fBMIi3v9LSmqzrOi4MhE+ivo6yM/JQXZ8UmGaqDiIjfSk0ogNdMpEFBG9LzW6m9jbUHLMmNzcXHh5ecHW1lbucp988gmuXbsm8x9zDQ0N1K1bF9evS/cZvXnzJuzs7N680FVU1FBRcjDokuPKvC5FRUXk5ZW9sPXs2ROqqqrYtGkTTp8+XaUuYvLo6+sjNTUVr14V95l/G9tSpPQYQrdv35b8Hlq0aIGnT5/CwMAA1tbWUp/X7SrVpEkT5Ofn48qVKzLnv+3tbdGiBQICAsqU39raWipqqiJFUU+y6r6kOXPmIDk5Wepj3Xzsa5f9XcrNA4LCM9HcTrrxrJmdKvyDZYdgP3mWjmal0je3V0VQWAby8ovSZMhM4x/MVypXRUZWPiJjsiWf8IgsJCTloHmj4rG1REIBGtuowj+o/H375FkGmtlLj8fV3F5NskxuXgGCwjLQXFYa1plcGVn5iIzNkXwk9WRffAyIhIWvuq5ofz55li61DFBUB4XHYm4eEBSWWebYamavhiflHK8kjXX18XmdfSn7nPffdSqvKE3FdUhVk5tX+Nr3pjbSD0Gb2qrgSYjsV8sHhmaiqW3Z9MHhWZL7CaCwIWiAkzYWbY5A8HO+Uv59S7rtA70ubaWm6Tt+jmSvxyj4b8zYxNs+0Osi3TNEr+vnSLzl/d7KWZuwm5h8bAyS43//+x/++usvPHnyBBMmTEBiYmKlGi8mTpyIlJQUDB48GPfu3cPTp0+xe/duBAQEACgcmHr58uU4cOAAAgICMHv2bPj4+GDKlCnvepPKsLa2hqmpKdzd3REYGIh//vkHq1ateuN8LSwscOfOHYSGhiIuLk4SOSIUCuHq6oo5c+bA2tq6TNerN/HZZ59BRUUFP/74I4KCgrB3717s2LHjreV/6NAheHh4IDAwEPPnz8e///6LiRMLI1mGDh0KPT09ODs749q1awgJCcGVK1cwZcoUvHjx4rXWM5I67gAAiIdJREFUZ2FhgZEjR8LNzQ3Hjh1DSEgILl++jIMHDwJ4+9s7b9487Nq1C+7u7vD19YW/vz8OHDiAn3/+udJ5mJubQyAQ4OTJk4iNjUVaWprMdGKxGBoaGlKfD7mL2LHz8XD8XAtd22rCxEgRo78ygL6OAk5fTQQAjHDRx/euxpL0Z64kwUBXAaO+MoCJkSK6ttWEYzst/HWueLDzExcS0NxeFf2ddGFiqIj+TrpoaqeKExeqNiA6lXX8fDwG9tRHm+bqMK8rxvdu9ZCVnY8rd5Ilaaa51cPIfoaS7yfOx6GFvRoGdNeDiZEiBnTXQzM7NRw/X9y//y/POHRrrw3HdlowNRZjzCAj6Oso4NRl1tnrOH4hAV/11JPU09RviuopRZJmmltdjOxrIPleeNyooX93XZgYKaJ/d100s1PF8fPFdXDMM15STyZGihg90LCwnq4kvtftq0lYVx8+eftyZF8DTHMr7v53+koiDHQVMHqgIUyMFOHYTguOn2vj6Lnic15l6pCq5u9LSejSRgNftFZHPUMFfNNXD3raIpy7Xnh9GtpbF5OHFR9HZ68nQ19bBNe+eqhnqIAvWqujS2sNHL9YfIy4dNHCkC918b+9MYiJz4WWuhBa6kIoKb5G/2gCUPhqeY2mttBoWvjQV8XSBBpNbaFkWnivZ7N4GppuXy5JH7ZlP5TN68Lu19lQs7WCiWt/mH7TH89WF7/BOfS3XdBzbAerGWOgamMFqxljoNelDUI37Hy/G0f0nxrdText+OWXX7B8+XJ4e3ujfv36OH78OPT09OQup6uri4sXL+KHH35Ax44dIRQK0axZM8k4QZMnT0ZKSgqmT5+OmJgY2Nvb48SJE2jQoMG73qQyFBQUsG/fPnz33Xdo2rQpWrVqhcWLF1dpHB9ZZsyYgZEjR8Le3h4ZGRkICQmBhYUFAGDUqFFYunTpW40KAgoHz/7zzz/xww8/YMuWLejatSvc3d0xduzbiThZsGAB9u/fj/Hjx8PIyAh79uyBvb09gMIxla5evYpZs2ahX79+SE1NRb169dClSxdoaJT/6l15Nm3ahB9//BHjx49HfHw8zMzM8OOPP76T7XVycsLJkyexcOFCrFixAgoKCrC1tcXo0aMrnUe9evWwYMECzJ49G9988w1GjBjxVhvkqsv1e6nQUI3G4F560NEUISwiCwt+C0dsQuHTHh1NEfR1irvPRcfnYMGG5xg90BC9OmojITkXWw5E4aZ3qiTNk2cZWLHtJYY762NoH31ExWZjxdaXCAyV/XSQKu/wmTgoKtbB+KF1oaYqRMCzDMxdHYqMrOLHqPq6ilLd9P2DM7B8y3MMdzHEMBcDRMVmY/mW5wgIKX4Cfu1uCjRUo/B1bwPJ72D+ujDEJpSNAiX5jpyJh1ihDr4bYiSpp3lrwqXrSUcBJR+yPQnOwIotLzDMxQDDnIvq6QUCS9bTvRSoqwkx+Mvi49V9fTjr6Q2wrj588valtlap61RcDtzXhxdepzppIz45F1v2R+Hm/RLXqUrUIVXNDe80qKvWwUAnHWhrihAemYUlmyMQm1h4P6GtIYSednE9xSTkYvHvEXDrq4ce7TWRkJyLP47E4vaD4qjw7p9rQkEkwMxRxlLrOnA6AQdOs+HudWi2bIw2F3ZLvtuvLLz3fr7rKB6OmgOxsT6UTYv3d0boC9ztPRb2q+bA/LuhyIqIge/3SxD11zlJmsRb3vAeOg02C6bCZsFkpAc/h/eQ75H078P3t2G1yOsMZ1LbCAq4l2QKDQ2FpaUlvL290axZs+ouTo1z48YNdOrUCS9evJAMjE3U+1v5g7NT9cuX0wWQPhwCAQOAiah2UVT+cKOMSdqoTb2quwhUCb1yAqq7CK/F+bvqK/fxTTbVtu6qYGQQvVdZWVl4/vw55s6di4EDB7IhiIiIiIiIiOg94yPD1zRu3DioqanJ/IwbN666i/fB2rdvH2xsbJCcnIwVK1ZIzduzZ0+5+7RRo0bVVOJ369q1a+Vus5qamvwMiIiIiIiISEp+fn61fV7Hxo0bYWlpCSUlJbRs2RLXrl2rMP2VK1fQsmVLKCkpwcrKCps3b67yOtlN7DXFxMQgJSVF5jwNDQ0YGBjInEflS01NRXR0tMx5CgoKMDc3f88levcyMjLw8uXLcudbW1u/x9JUP3YT+ziwm9jHg93EiKi2YTexjwe7iX0cPtZuYtX5f8Xfv1ftDeEHDhzA8OHDsXHjRrRr1w6///47tm3bBj8/P5iZmZVJHxISgsaNG2PMmDH49ttvcePGDYwfPx779u1D//79K71eNgYR0QeDjUEfBzYGfTzYGEREtQ0bgz4ebAz6OHysjUFfjvGrtnWf3GpfpfSfffYZWrRogU2bNkmm2dnZwcXFBcuWLSuTftasWThx4gT8/Yv/dxo3bhwePHiAW7duVXq9vEskIiIiIiIiInoLsrKykJKSIvXJysqSmTY7OxteXl7o1q2b1PRu3brh5s2bMpe5detWmfROTk64d+8ecnIq/zZONgYRERERERERUY1RUJBfbZ9ly5ZBU1NT6iMrwgcA4uLikJeXV+bFSoaGhoiKipK5TFRUlMz0ubm5iIuLq/Q+4tvEiIiIiIiIiIjegjlz5mDatGlS08RicYXLCAQCqe8FBQVlpslLL2t6RdgYRERERERERET0FojFYrmNP0X09PQgFArLRAHFxMSUif4pYmRkJDO9SCSCrq5upcvJbmJEREREREREVGMU5BdU26cqFBUV0bJlS3h6ekpN9/T0RNu2bWUu06ZNmzLpz507BwcHBygoKFR63WwMIiIiIiIiIiKqBtOmTcO2bdvg4eEBf39/fP/99wgPD8e4ceMAFHY7GzFihCT9uHHjEBYWhmnTpsHf3x8eHh74448/MGPGjCqtl93EiP7f3p1H1Zz/fwB/3iKVSkUhk1ZKKgpjZ+zbTMIMM5aSbGMp+zAzshuMJclYBtm+Gsk+Y0+MENFmiajIUpaaRFla7u8P3F/XTWUmvW/u83FO59z7/txz5nnmnnLv6/N6v95ERERERET0yfjQDh2R+vXrh7S0NMyePRspKSmwt7fHgQMHYGZmBgBISUlBcnKy7PUWFhY4cOAAxo8fj5UrV8LExAR+fn7o06fPB/13JdK3k4aIiAT7akSc6AhUAvl5eaIjUAlJJGwAJiLVoqGlIToClZDnqh6iI1AJ9Mi5LjrCv9JtcKyw//bBjY7C/tsfgp8SiYiIiIiIiIhUCLeJEREREREREdEnI1+aLzqC0mNnEBERERERERGRCmFnEBERERERERF9MsrTAGlR2BlERERERERERKRC2BlERERERERERJ8MaT5nBhWHnUFERERERERERCqExSAiIiIiIiIiIhXCbWJERERERERE9MngAOnisTOIiIiIiIiIiEiFsDOIiIiIiIiIiD4ZUikHSBeHnUFERERERERERCqExSAiIiIiIiIiIhXCbWJERERERERE9MnI5wDpYrEziIiIiIiIiIhIhbAziIiIiIiIiIg+GdJ8DpAuDjuDiIiIiIiIiIhUCDuDiIiIiIiIiOiTIeXMoGKxM4iIiIiIiIiISIWwGEREREREREREpEK4TYyIiIiIiIiIPhlSKQdIF4edQUREREREREREKoSdQURERERERET0yeAA6eKxM4iIiIiIiIiISIWwGEREREREREREpEK4TYyIiIiIiIiIPhnSfA6QLg47g4iIiIiIiIiIVIhEKpVyshIR0Ufw8uVL/PLLL5g2bRoqVaokOg69B9+n8oPvVfnA96l84PtUfvC9Kh/4PlF5w2IQEdFHkpmZiSpVquDJkyfQ09MTHYfeg+9T+cH3qnzg+1Q+8H0qP/helQ98n6i84TYxIiIiIiIiIiIVwmIQEREREREREZEKYTGIiIiIiIiIiEiFsBhERPSRVKpUCTNmzOAQQSXH96n84HtVPvB9Kh/4PpUffK/KB75PVN5wgDQRERERERERkQphZxARERERERERkQphMYiIiIiIiIiISIWwGEREREREREREpEJYDCIiIiIiIiIiUiEsBhERERERERERqZAKogMQEX2KHj58iIcPHyI/P19u3dHRUVAieldubi5OnDiBhIQE9O/fH7q6urh//z709PSgo6MjOh4REamg3NxcaGpqIjo6Gvb29qLjENEnjMUgIqJSdPHiRbi7uyMuLg5SqRQAIJFIIJVKIZFIkJeXJzghAcDt27fRtWtXJCcn4+XLl+jUqRN0dXWxaNEivHjxAqtXrxYdkd6wtLREREQEqlatKreekZEBZ2dnJCYmCkpGVD4YGBhAIpGU6LXp6ekfOQ0Vp0KFCjAzM+PnhXJiyJAhWL58OXR1deXWs7KyMHbsWGzYsEFQMqLiSaRvv60QEdF/5ujoCGtra/zwww+oXr26wgdwMzMzQcmoIFdXV+jq6mL9+vWoWrUqYmJiYGlpiZMnT2Lo0KG4ceOG6Ij0hpqaGlJTU2FsbCy3/uDBA9SuXRsvX74UlIzederUKaxZswYJCQkIDg5GrVq1sGXLFlhYWKBVq1ai46msTZs2yR6npaVh7ty56NKlC5o3bw4AOHv2LA4fPozp06dj/PjxomJSAQEBAdixYwe2bt0KQ0ND0XGoCOrq6khJSVH4N+rx48eoUaMGcnNzBSUjKh47g4iISlFSUhJ27doFa2tr0VGoCGFhYTh9+jQ0NDTk1s3MzHDv3j1Bqaigffv2yR4fPnwYVapUkT3Py8tDSEgIzM3NBSSjwuzcuRODBg3CgAEDEBUVJSvSPX36FPPnz8eBAwcEJ1Rd7u7ussd9+vTB7NmzMWbMGNmal5cX/P39cezYMRaDlISfnx9u3rwJExMTmJmZoXLlynLXIyMjBSWjtzIzMyGVSiGVSvH06VNoamrKruXl5eHAgQMKBSIiZcNiEBFRKerQoQNiYmJYDFJy+fn5hbbg3717V6HVm8RwdXUF8HqbZcEvswBQsWJFmJubY8mSJQKSUWHmzp2L1atXw83NDX/88YdsvUWLFpg9e7bAZFTQ4cOHsXDhQoX1Ll26YOrUqQISUWHe/v0j5aWvrw+JRAKJRIK6desqXJdIJJg1a5aAZEQlx2IQEVEpWrduHdzd3XH58mXY29ujYsWKctddXFwEJaOCOnXqBF9fX6xduxbA6w9tz549w4wZM9C9e3fB6QiAbPi6hYUFIiIiUK1aNcGJqCjXr19HmzZtFNb19PSQkZFR9oGoUFWrVsXu3bsxefJkufU9e/YozOUicWbMmCE6AhUjNDQUUqkU7du3x86dO+W282loaMDMzAwmJiYCExIVj8UgIqJSdObMGYSFheHgwYMK1zhAWnksW7YM7dq1g52dHV68eIH+/fvjxo0bqFatGgIDA0XHowKSkpIU1jIyMqCvr1/2Yei9atasiZs3byps3QsLC4OlpaWYUKRg1qxZ8PT0xIkTJ2Qzg8LDw3Ho0CGsW7dOcDp618WLFxEXFweJRAI7Ozs4OTmJjkRvtG3bFsDrf6NMTU2hpqYmOBHRh+MAaSKiUmRubo4vv/wS06dPR/Xq1UXHoSI8f/4cgYGBiIyMRH5+PpydnTFgwABoaWmJjkYFLFy4EObm5ujXrx8A4JtvvsHOnTtRs2ZNHDhwAA0aNBCckABg0aJF2LRpEzZs2IBOnTrhwIEDuH37NsaPHw8fHx+5GTUk1rlz5+Dn5yc79dLOzg5eXl5o2rSp6Gj0xsOHD/Htt9/ixIkT0NfXh1QqxZMnT9CuXTv88ccfMDIyEh2RCsjIyMD58+fx8OFDWVfrW25uboJSERWPxSAiolKkq6uL6OhoWFlZiY5C9EmwtLTE1q1b0aJFCxw9ehR9+/bF9u3bERQUhOTkZBw5ckR0RHrjp59+wrJly/DixQsAQKVKlTBp0iTMmTNHcDKi8qVfv35ISEjAli1bUK9ePQDA1atX4e7uDmtra3awKpH9+/djwIAByMrKgq6urtwpshKJBOnp6QLTERWNxSAiolLk7u6O1q1bY+jQoaKjUBEKnlRVkEQigaamJqytrWFhYVHGqagwWlpaiI+Ph6mpKby9vfHixQusWbMG8fHxaNq0Kf755x/REamA7OxsXL16Ffn5+bCzs4OOjo7oSPSOhIQEBAQEIDExEb6+vjA2NsahQ4dgamqK+vXri45HAKpUqYJjx46hSZMmcuvnz59H586dOYdLidStWxfdu3fH/Pnzoa2tLToO0QfhzCAiolJUt25dTJs2DWFhYXBwcFAYIO3l5SUoGRXk6uoKiUSCd++HvF2TSCRo1aoV9uzZAwMDA0EpCQAMDAxw584dmJqa4tChQ5g7dy4AQCqVcgaXEtLW1kbjxo1Fx6D3OHnyJLp164aWLVvi77//xty5c2FsbIzY2FisW7cOwcHBoiMSXg/Qf/fzA/D6JMV3tyGRWPfu3YOXlxcLQVQusTOIiKgUFdVNIpFIkJiYWIZp6H1CQkLw008/Yd68efj8888BvL7j+vPPP2P69OmoUqUKRowYgaZNm2L9+vWC06q2MWPG4M8//0SdOnUQFRWFW7duQUdHB9u3b8fChQsRGRkpOiIBaNeundz2iHcdP368DNPQ+zRv3hzffPMNJkyYAF1dXcTExMDS0hIRERFwdXXFvXv3REckAD179kRGRgYCAwNlJ1Ldu3cPAwYMgIGBAXbv3i04Ib3Vu3dvfPvtt+jbt6/oKEQfjJ1BRESlqLCTj0j5eHt7Y+3atWjRooVsrUOHDtDU1MTw4cNx5coV+Pr6YsiQIQJTEvD65Ddzc3PcuXMHixYtkm07SklJwahRowSno7caNmwo9zwnJwfR0dG4fPky3N3dxYQiBZcuXcK2bdsU1o2MjJCWliYgERXG398fPXv2hLm5OUxNTSGRSJCcnAwHBwds3bpVdDwqoEePHpg8eTKuXr1aaEe4i4uLoGRExWNnEBERqRwtLS1ERETA3t5ebv3SpUv4/PPP8fz5c9y+fRv16tVDdna2oJRE5d/MmTPx7NkzLF68WHQUAvDZZ58hKCgILVq0kOsM2r17NyZNmoSEhATREamAo0eP4tq1a7JT3zp27Cg6Er2jqCPlJRIJtzOTUmMxiIjoP5owYQLmzJmDypUrY8KECUW+dunSpWWUiorSqlUr6OrqYvPmzbIjeh89egQ3NzdkZWXh77//xrFjxzBq1CjEx8cLTksJCQnw9fVFXFwcJBIJ6tWrh3HjxsHS0lJ0NCrGzZs38fnnn/NEHSUxZcoUnD17Fjt27EDdunURGRmJBw8ewM3NDW5ubpgxY4boiARg8+bN6NevHypVqiS3/urVK/zxxx88rpyISgWLQURE/1G7du2we/du6Ovro127du99nUQi4dwMJXH9+nX07NkTSUlJci34lpaW2Lt3L+rWrYs9e/bg6dOnGDRokOi4Ku3w4cNwcXFBw4YN0bJlS0ilUpw5cwYxMTHYv38/OnXqJDoiFWHLli344YcfcP/+fdFRCK+37w0ePBh//PEHpFIpKlSogLy8PPTv3x8bN26Eurq66IgEQF1dHSkpKTA2NpZbT0tLg7GxMbtNlNSLFy+gqakpOgZRibEYREREKkkqleLw4cOIj4+HVCqFra0tOnXqVGTLN5U9JycndOnSBQsWLJBbnzp1Ko4cOcIB0kqid+/ecs+lUilSUlJw4cIFTJ8+nR0nSkAqlSI5ORlGRkZITU1FZGQk8vPz4eTkhDp16oiORwWoqanhwYMHss7Vt2JiYtCuXTt22imRvLw8zJ8/H6tXr8aDBw8QHx8PS0tLTJ8+Hebm5vD09BQdkei9WAwiIiIipaWpqYlLly4pfFmNj4+Ho6MjXrx4ISgZFeTh4SH3XE1NDUZGRmjfvj06d+4sKBUVlJ+fD01NTVy5coXFHyXl5OQEiUSCmJgY1K9fHxUq/P9ZP3l5eUhKSkLXrl0RFBQkMCUVNHv2bGzatAmzZ8/GsGHDcPnyZVhaWiIoKAjLli3D2bNnRUckei+eJkZEVMoiIiKwY8cOJCcn49WrV3LXdu3aJSgVvSsrKwsnT54s9H3y8vISlIreZWRkhOjoaIUvr9HR0QpbKEicgIAA0RGoGGpqaqhTpw7S0tJYDFJSrq6uAF7/fevSpYvs9EQA0NDQgLm5Ofr06SMoHRVm8+bNWLt2LTp06ICRI0fK1h0dHXHt2jWByYiKx2IQEVEpejvYsXPnzjh69Cg6d+6MGzduIDU1Fb169RIdj96IiopC9+7dkZ2djaysLBgaGuLx48fQ1taGsbExi0FKZNiwYRg+fDgSExPRokULSCQShIWFYeHChZg4caLoeETlyqJFizB58mSsWrVK4TRFEu/tdkpzc3P069eP82fKgXv37sHa2lphPT8/Hzk5OQISEZUct4kREZUiR0dHjBgxAqNHj5Yd22thYYERI0agZs2amDVrluiIBOCLL75A3bp1sWrVKujr6yMmJgYVK1bEwIED4e3trTD/hMSRSqXw9fXFkiVLZEOITUxMMHnyZHh5eUEikQhOqLoMDAxK/P+fM06Ug4GBAbKzs5GbmwsNDQ1oaWnJXef7RPRhGjdujHHjxmHgwIGyz32WlpaYNWsWjh07hlOnTomOSPRe7AwiIipFCQkJ6NGjBwCgUqVKyMrKgkQiwfjx49G+fXsWg5REdHQ01qxZA3V1dairq+Ply5ewtLTEokWL4O7uzmKQEnn7+zN+/Hg8ffoUAKCrqys4FQGAr6+v6Aj0gfielQ95eXlYtmwZgoKCCt3KzKKd8pgxYwYGDRqEe/fuIT8/H7t27cL169exefNm/Pnnn6LjERWJxSAiolJkaGgo+8Jaq1YtXL58GQ4ODsjIyEB2drbgdPRWxYoVZR0N1atXR3JyMurVq4cqVaogOTlZcDoqKCkpCbm5uahTp45cEejGjRuoWLEizM3NxYVTce7u7qIj0Afie1Y+zJo1C+vWrcOECRMwffp0/PTTT7h16xb27NkDHx8f0fGogK+++grbt2/H/PnzIZFI4OPjA2dnZ+zfvx+dOnUSHY+oSCwGERGVotatW+Po0aNwcHBA37594e3tjePHj+Po0aPo0KGD6Hj0hpOTEy5cuIC6deuiXbt28PHxwePHj7FlyxY4ODiIjkcFDB48GEOGDFEYeHvu3DmsW7cOJ06cEBOM3uv58+cKszL09PQEpaHMzEzZ///MzMwiX8v3STn873//w++//44ePXpg1qxZ+O6772BlZQVHR0eEh4dzrp2SyM3Nxbx58zBkyBCcPHlSdByiD8aZQUREpSg9PR0vXryAiYkJ8vPzsXjxYoSFhcHa2hrTp0+HgYGB6IgE4MKFC3j69CnatWuHR48ewd3dXfY+BQQEoEGDBqIj0ht6enqIjIxUGNB58+ZNNG7cGBkZGWKCkZysrCz88MMPCAoKQlpamsL1vLw8AakIANTV1ZGSkgJjY2OoqakVOudJKpVCIpHwfVISlStXRlxcHGrXro2aNWvir7/+grOzMxITE+Hk5IQnT56Ijkhv6Ojo4PLly+xSpXKJnUFERKXI0NBQ9lhNTQ1TpkzBlClTBCaid0mlUhgZGaF+/foAXh9dfuDAAcGp6H0kEols62VBT5484RdXJTJlyhSEhobit99+g5ubG1auXIl79+5hzZo1WLBggeh4Ku348eOyf5tCQ0MFp6GS+Oyzz5CSkoLatWvD2toaR44cgbOzMyIiIlCpUiXR8aiAjh074sSJExg8eLDoKEQfjJ1BRET/UXFt9wWxBV+8/Px8aGpq4sqVKwpbj0j5fPnll9DW1kZgYCDU1dUBvO4y6devH7KysnDw4EHBCQkAateujc2bN+OLL76Q6+basmULAgMDWXAl+gBTp06Fnp4efvzxRwQHB+O7776Dubk5kpOTMX78eBZYlciaNWswc+ZMDBgwAI0aNULlypXlrru4uAhKRlQ8FoOIiP6j97XdF8QWfOVSv359rF+/Hs2aNRMdhYpx9epVtGnTBvr6+mjdujUA4NSpU8jMzMTx48dhb28vOCEBr7dKXLlyBWZmZvjss8+wa9cufP7550hKSoKDgwOePXsmOiIB+Pvvv4u83qZNmzJKQh/i3LlzOH36NKytrVlcUDJqamrvvcbPfaTsuE2MiOg/Ytt9+bNo0SJMnjwZq1atYjFBydnZ2SE2Nhb+/v6IiYmBlpYW3NzcMGbMGLltmSSWpaUlbt26BTMzM9jZ2SEoKAiff/459u/fD319fdHx6I0vvvhCYa3gzQx+cVVOTZs2RdOmTUXHoELk5+eLjkD0r7EziIiIVI6BgQGys7ORm5sLDQ0NaGlpyV1PT08XlIz+rVGjRmH27NmoVq2a6CgqadmyZVBXV4eXlxdCQ0PRo0cP5OXlITc3F0uXLoW3t7foiAQoDB7OyclBVFQUpk+fjnnz5vHUSyXxyy+/oHr16hgyZIjc+oYNG/Do0SP88MMPgpJRQbm5udDU1ER0dDRvLFG5xGIQEVEpCggIgI6ODr755hu59R07diA7Oxvu7u6CklFBmzZtKvI636fyR09PD9HR0bC0tBQdhQAkJyfjwoULsLKy4ul85cDff/+N8ePH4+LFi6KjEABzc3Ns27YNLVq0kFs/d+4cvv32WyQlJQlKRu+ysrLCrl27+HeOyiUWg4iISpGNjQ1Wr16Ndu3aya2fPHkSw4cPx/Xr1wUlI/q06erqIiYmhsUgQW7dusWjlcuxuLg4NGnShLOdlISmpibi4uJgYWEht56YmAg7Ozu8ePFCUDJ6V0BAAHbs2IGtW7dy6zKVO5wZRERUim7fvq3w4Q0AzMzMkJycLCARvU9CQgICAgKQkJCA5cuXw9jYGIcOHYKpqans2HkiKhlLS0u0aNECgwYNwjfffMMvRUoqNjZW7rlUKkVKSgoWLFjAzgYlYmpqitOnTyt8njh9+jRMTEwEpaLC+Pn54ebNmzAxMYGZmZnCaWKRkZGCkhEVj8UgIqJSZGxsjNjYWIU75DExMahataqYUKTg5MmT6NatG1q2bIm///4b8+bNk71369atQ3BwsOiIROXKhQsXEBgYiLlz58Lb2xtdunTBwIED4eLigkqVKomOR280bNgQEokE724MaNasGTZs2CAoFb1r6NChGDduHHJyctC+fXsAQEhICKZMmYKJEycKTkcFubq6io5A9K9xmxgRUSmaMmUKgoKCEBAQIDui9+TJkxgyZAi+/vprLF68WHBCAoDmzZvjm2++wYQJE+S2F0VERMDV1RX37t0THZE+ELeJKQepVIoTJ05g27Zt2LlzJ/Ly8tCnTx8WGpTE7du35Z6rqanByMgImpqaghJRYaRSKaZOnQo/Pz+8evUKwOutYz/88AN8fHwEpyOiTwWLQUREpejVq1cYNGgQduzYgQoVXjdf5ufnw83NDatXr4aGhobghAQAOjo6uHTpEiwsLOSKCLdu3YKtrS3nMZRDLAYpn8jISHh6eiI2NpZHlhP9C8+ePUNcXBy0tLRQp04ddtkRUaniNjEiolKkoaGB7du3Y+7cuYiOjoaWlhYcHBxgZmYmOhoVoK+vj5SUFIV5DFFRUahVq5agVFSY5ORkmJqaQiKRyK1LpVLcuXMHtWvXBgAMHDgQenp6IiJSAXfu3EFgYCC2bduGS5cuoXnz5vD39xcdi97w8/Mr8Wu9vLw+YhIqCR0dHTRp0kR0DCqCmpqawr9PBbEQTsqMnUFERALwGGyxpkyZgrNnz2LHjh2oW7cuIiMj8eDBA7i5ucHNzQ0zZswQHZHeUFdXR0pKCoyNjeXW09LSYGxszA/aSmLt2rX43//+h9OnT8PGxgYDBgxA//79ecKYkrGwsMCjR4+QnZ0NfX19AEBGRga0tbVhZGQke51EIkFiYqKglKqpd+/e2LhxI/T09NC7d+8iX7tr164ySkXF2bt3r9zznJwcREVFYdOmTZg1axY8PT0FJSMqHjuDiIgEYB1erHnz5mHw4MGoVasWpFIp7OzskJeXh/79++Pnn38WHY8KkEqlhd51ffbsGeecKJE5c+bg22+/xfLly9GwYUPRceg95s2bh99++w3r16+HjY0NAOD69esYNmwYRowYgQEDBghOqLqqVKki+1tXpUoVwWmopHr27Kmw9vXXX6N+/frYvn07i0Gk1NgZREQkAOebKIeEhARERUUhPz8fTk5OqFOnjuhI9MaECRMAAMuXL8ewYcOgra0tu5aXl4dz585BXV0dp0+fFhWRCnhf0e5do0aNwuzZs1GtWrUySEXvsrKyQnBwMJycnOTWL168iK+//hpJSUmCkhF9WhISEuDo6IisrCzRUYjei51BRESkck6ePIm2bdvCysoKVlZWouNQIaKiogC8LjJcunRJbvi6hoYGGjRogEmTJomKR+8oSSEIALZu3YpJkyaxGCRISkoKcnJyFNbz8vLw4MEDAYmIPj3Pnz/HihUr8Nlnn4mOQlQkFoOIiEjldOrUCTVq1ED//v0xcOBA2Nvbi45E7wgNDQUAeHh4YPny5RwO/YlgQ7pYHTp0wLBhw7B+/Xo0atQIEokEFy5cwIgRI9CxY0fR8VSak5NTiYuqkZGRHzkNlZSBgYHc+yaVSvH06VNoa2tj69atApMRFY/FICIiAUr6gY8+jvv37+OPP/5AYGAgFi1aBHt7ewwcOBD9+/fnnTwlExAQIDoC0Sdjw4YNcHd3x+eff46KFSsCAHJzc9GlSxesW7dOcDrV5urqKnv84sUL/Pbbb7Czs0Pz5s0BAOHh4bhy5QpGjRolKCEVZtmyZXKf6dTU1GBkZISmTZvCwMBAYDKi4nFmEBGRAJwZpDySkpKwbds2BAYG4tq1a2jTpg2OHz8uOha9kZWVhQULFiAkJAQPHz5Efn6+3HWeeFS+8G+fcoiPj8e1a9cglUpRr1491K1bV3QkKmDo0KGoWbMm5syZI7c+Y8YM3LlzBxs2bBCUjIg+JSwGEREJEBYWhiZNmqBSpUqioxBez8s4ePAgpk+fjtjYWB5XrkS+++47nDx5EoMGDULNmjUVuuq8vb0FJaN/g8Ug5fDq1SskJSXBysoKFSpwo4CyqVKlCi5cuKBwqMGNGzfQuHFjPHnyRFAyeldAQAB0dHTwzTffyK3v2LED2dnZcHd3F5SMqHj8609EVIrenoD0LolEAk1NTVhbW6Nnz55o1apVGSejwpw+fRr/+9//EBwcjBcvXsDFxQXz588XHYsKOHjwIP766y+0bNlSdBSici87Oxtjx47Fpk2bALzuELK0tISXlxdMTEwwdepUwQkJALS0tBAWFqZQDAoLC4OmpqagVFSYBQsWYPXq1QrrxsbGGD58OItBpNRYDCIiKkVRUVGIjIxEXl4ebGxsIJVKcePGDairq8PW1ha//fYbJk6ciLCwMNjZ2YmOq7J+/PFHBAYG4v79++jYsSN8fX3h6uoqd3w5KQcDAwMYGhqKjkGlZODAgRwGLtC0adMQExODEydOoGvXrrL1jh07YsaMGSwGKYlx48bh+++/x8WLF9GsWTMAr2cGbdiwAT4+PoLTUUG3b9+GhYWFwrqZmRmSk5MFJCIqOW4TIyIqRb6+vjh16hQCAgJkX3gyMzPh6emJVq1aYdiwYejfvz+eP3+Ow4cPC06rulq0aIEBAwagX79+POJayW3duhV79+7Fpk2bWKxTYubm5hgyZAgGDx6M2rVri45D72FmZobt27ejWbNmclv2bt68CWdnZ2RmZoqOSG8EBQVh+fLliIuLAwDUq1cP3t7e6Nu3r+BkVFDt2rXh7+8PFxcXufW9e/di9OjRuHv3rqBkRMVjMYiIqBTVqlULR48eVej6uXLlCjp37ox79+4hMjISnTt3xuPHjwWlJCo/nJyckJCQAKlUCnNzc9kJSG/xiGXlsGLFCmzcuBExMTFo164dPD090atXL85FUzLa2tq4fPkyLC0t5YpBMTExaNOmDWfREH2gKVOmICgoCAEBAWjTpg0A4OTJkxgyZAi+/vprLF68WHBCovfjNjEiolL05MkTPHz4UKEY9OjRI9kdV319fbx69UpEPCogISEBvr6+iIuLg0Qikd11tbKyEh2NCih43DIpr7Fjx2Ls2LGIiYnBhg0b4OXlhVGjRqF///4YMmQInJ2dRUckAE2aNMFff/2FsWPHAoBsIPvvv/8uO8KclMerV68KPUWR3XfKY+7cubh9+zY6dOggG8aen58PNzc3ziAkpcfOICKiUjRgwACcPXsWS5YsQZMmTSCRSHD+/HlMmjQJLVq0wJYtW/DHH39g8eLFuHDhgui4Kuvw4cNwcXFBw4YN0bJlS0ilUpw5cwYxMTHYv38/OnXqJDoiUbmWk5OD3377DT/88ANycnJgb28Pb29veHh4KJwIR2XnzJkz6Nq1KwYMGICNGzdixIgRuHLlCs6ePYuTJ0+iUaNGoiMSXp8aNmTIEJw5c0ZuXSqVQiKR8MRLJXTjxg1ER0dDS0sLDg4OMDMzEx2JqFgsBhERlaJnz55h/Pjx2Lx5M3JzcwEAFSpUgLu7O5YtW4bKlSsjOjoaANCwYUNxQVWck5MTunTpggULFsitT506FUeOHOHWIyWTkZGB4OBgJCQkYPLkyTA0NERkZCSqV6+OWrVqiY5HBeTk5GD37t0ICAjA0aNH0axZM3h6euL+/fvw9/dHu3btsG3bNtExVdrly5fx66+/4uLFi8jPz4ezszN++OEHODg4iI5Gb7Rs2RIVKlTA1KlTUbNmTYUCaoMGDQQlo39LT08P0dHRsLS0FB2FSIbFICKij+DZs2dITEyEVCqFlZUVdHR0REeiAjQ1NXHp0iWFY3vj4+Ph6OiIFy9eCEpG74qNjUXHjh1RpUoV3Lp1C9evX4elpSWmT5+O27dvY/PmzaIjEl7PbgoICEBgYCDU1dUxaNAgDB06FLa2trLXREREoE2bNnj+/LnApKorJycHw4cPx/Tp0/mFVMlVrlwZFy9elPv9ofKt4IwuImWhJjoAEdGnSEdHB46OjmjQoAELQUrIyMhI1qFVUHR0NIyNjcs+EL3XhAkTMHjwYNy4cQOampqy9W7duuHvv/8WmIwKatKkCW7cuIFVq1bh7t27WLx4scIXWTs7O3z77beCElLFihWxe/du0TGoBOzs7HjIBBF9dBwgTURUirKysrBgwQKEhIQUOvQxMTFRUDIqaNiwYRg+fDgSExPRokULSCQShIWFYeHChZg4caLoeFRAREQE1qxZo7Beq1YtpKamCkhEhUlMTCx2RkblypUREBBQRomoML169cKePXswYcIE0VGoCAsXLsSUKVMwf/58ODg4KJyiqKenJygZEX1KWAwiIipFQ4cOxcmTJzFo0KBC9/mTcpg+fTp0dXWxZMkSTJs2DQBgYmKCmTNnwsvLS3A6KkhTU1N2El9B169fh5GRkYBEVJh27dohIiICVatWlVvPyMiAs7MzC+FKwtraGnPmzMGZM2fQqFEjVK5cWe46//4ph44dOwIAOnToILfOAdJEVJo4M4iIqBTp6+vjr7/+QsuWLUVHoRJ6+vQpgNf7+Un5DB8+HI8ePUJQUBAMDQ0RGxsLdXV1uLq6ok2bNvD19RUdkQCoqakhNTVVYZvlgwcPULt2bbx8+VJQMirIwsLivdckEgmLdkri5MmTRV5v27ZtGSWh0sIB0qSM2BlERFSKDAwMYGhoKDoGFSMpKQm5ubmoU6eOXBHoxo0bqFixIszNzcWFIzmLFy9G9+7dYWxsjOfPn6Nt27ZITU1F8+bNMW/ePNHxVN6+fftkjw8fPowqVarInufl5SEkJIS/T0okKSlJdAQqARZ7Pj3svyBlxM4gIqJStHXrVuzduxebNm2Ctra26Dj0Hm3btsWQIUPg7u4ut75161asW7cOJ06cEBOM3uv48eOIjIyUHYX9dhsFiaWm9v6zSN4WVpcsWYIvv/yyDFPRf8UuhrIXGxsLe3t7qKmpITY2tsjXOjo6llEqKkpOTg5sbGzw559/ws7OrsjXhoWFoUmTJqhUqVIZpSMqHotBRESlyMnJCQkJCZBKpTA3N1cY+hgZGSkoGRWkp6eHyMhIWFtby63fvHkTjRs3RkZGhphgROWUhYUFLly4oDAziMonHoNd9gputVRTU4NEIim0m4Qzg5RLrVq1cOzYMdSrV090FKIPxm1iRESlyNXVVXQEKgGJRCKbFVTQkydP+CFbCZ0/fx4nTpwo9IS+pUuXCkpFb+Xk5MDc3BxpaWksBhH9S0lJSbKh+NzOV36MHTsWCxcuxLp161ChAr9aU/nCziAiIlI5X375JbS1tREYGAh1dXUAr+eb9OvXD1lZWTh48KDghPTW/Pnz8fPPP8PGxgbVq1eXO6FPIpHg+PHjAtPRW0ZGRjhz5gzq1KkjOgqVAnYGlQ89evTAunXrULNmTdFRVFavXr0QEhICHR0dODg4KJzQt2vXLkHJiIrH8iUREamcRYsWoU2bNrCxsUHr1q0BAKdOnUJmZiaLC0pm+fLl2LBhAwYPHiw6ChXBzc0N69evx4IFC0RHIVIZf//9N54/fy46hkrT19dHnz59RMcg+ldYDCIi+o8MDQ0RHx+PatWqwcDAQK5z4V3p6ellmIzex87ODrGxsfD390dMTAy0tLTg5uaGMWPG8DQ4JaOmpoaWLVuKjkHFePXqFdatW4ejR4+icePGCnfHuZ2vfCnq3zEi+n8BAQGiIxD9aywGERH9R8uWLZMdT+7r6ys2DJWYiYkJ5s+fX+RrRo0ahdmzZ6NatWpllIreNX78eKxcuZK/W0ru8uXLcHZ2BgDEx8fLXWNhofzhFAmiksvNzcWJEyeQkJCA/v37Q1dXF/fv34eenh50dHRExyN6L84MIiIieg8eryxefn4+evTogfj4eNjZ2Smc0Md5DEQf7tWrV0hKSoKVlVWhQ295DHb5wNlO4t2+fRtdu3ZFcnIyXr58ifj4eFhaWmLcuHF48eIFVq9eLToi0XupiQ5ARFTeZWZmlviHyhfeLxFv7NixCA0NRd26dVG1alVUqVJF7oeUy82bN3H48GHZHBP+DimX7OxseHp6QltbG/Xr10dycjIAwMvLS27eU6tWrVgIIioBb29vNG7cGP/88w+0tLRk628HSxMpM24TIyL6j/T19Uu8DYLHlhN9mM2bN2Pnzp3o0aOH6ChUhLS0NPTt2xehoaGQSCS4ceMGLC0tMXToUOjr62PJkiWiIxKAadOmISYmBidOnEDXrl1l6x07dsSMGTMwdepUgemIyp+wsDCcPn0aGhoacutmZma4d++eoFREJcNiEBHRfxQaGip7fOvWLUydOhWDBw9G8+bNAQBnz57Fpk2b8Msvv4iKSFRuGRoawsrKSnQMKsb48eNRsWJFJCcno169erL1fv36Yfz48SwGKYk9e/Zg+/btaNasmdxNDDs7OyQkJAhMRv/Gjz/+yEMPBMvPzy/0Rt/du3dl8ySJlBW3iRER/Udt27aV/WzevBlLly7FL7/8AhcXF7i4uOCXX37B4sWLeeIE0b8wc+ZMzJgxA9nZ2aKjUBGOHDmChQsX4rPPPpNbr1OnDm7fvi0oFb3r0aNHMDY2VljPysrioG8lsmnTJvz111+y51OmTIG+vj5atGgh9/s0bdo06OvrC0hIb3Xq1EnugAOJRIJnz55hxowZ6N69u7hgRCXAYhARUSk6e/YsGjdurLDeuHFjnD9/XkAiovLNz88PBw8eRPXq1eHg4ABnZ2e5H1IOWVlZ0NbWVlh//PgxZ88okSZNmsgVGd4WgH7//XdZNyuJN3/+fNn8mbNnz8Lf3x+LFi1CtWrVMH78eMHpqKBly5bh5MmTsLOzw4sXL9C/f3+Ym5vj3r17WLhwoeh4REXiNjEiolJkamqK1atXK2yJWLNmDUxNTQWlon9r4MCB0NPTEx1Dpbm6uoqOQCXQpk0bbN68GXPmzAHwusiQn5+PX3/9Fe3atROcjt765Zdf0LVrV1y9ehW5ublYvnw5rly5grNnz+LkyZOi49Ebd+7cgbW1NYDXW/u+/vprDB8+HC1btsQXX3whNhzJMTExQXR0NAIDAxEZGYn8/Hx4enpiwIABcgOliZQRj5YnIipFBw4cQJ8+fWBlZYVmzZoBAMLDw5GQkICdO3eyZViJnDp1CmvWrEFCQgKCg4NRq1YtbNmyBRYWFmjVqpXoeETlytWrV/HFF1+gUaNGOH78OFxcXHDlyhWkp6fj9OnTnPukRC5duoTFixfj4sWLyM/Ph7OzM3744Qc4ODiIjkZvGBsb4/Dhw3BycoKTkxPGjx8PNzc3JCQkoEGDBnj27JnoiET0CWBnEBFRKerevTvi4+OxatUqXLt2DVKpFD179sTIkSPZGaREdu7ciUGDBmHAgAGIiorCy5cvAQBPnz7F/PnzceDAAcEJqaCMjAwEBwcjISEBkydPhqGhISIjI1G9enXUqlVLdDzC6wHEsbGxWLVqFdTV1ZGVlYXevXtj9OjRqFmzpuh4VICDgwM2bdokOgYVoVOnThg6dCicnJwQHx8vO03xypUrMDc3FxuOFFy/fh0rVqxAXFwcJBIJbG1tMWbMGNja2oqORlQkdgYREZHKKXinVVdXFzExMbC0tER0dDS6du2K1NRU0RHpjdjYWHTs2BFVqlTBrVu3cP36dVhaWmL69Om4ffs2Nm/eLDoiUbmRmZlZ6LpEIkGlSpUUjscmMTIyMvDzzz/jzp07+P7779G1a1cAwIwZM6ChoYGffvpJcEJ6Kzg4GN999x0aN24sm7sVHh6OiIgIbNu2Dd98843ghETvx2IQEdF/FBsbW+LXOjo6fsQkVFLa2tq4evUqzM3N5YpBiYmJsiGQpBw6duwIZ2dnLFq0SO69OnPmDPr3749bt26Jjkhv/PPPP1i/fr3s7ni9evXg4eHBo6+ViJqaWpGnhn322WcYPHgwZsyYATU1njNDVBxLS0sMHDgQs2fPllufMWMGtmzZgsTEREHJiIrHbWJERP9Rw4YNIZFIUFxtXSKRIC8vr4xSUVFq1qyJmzdvKrTbh4WFwdLSUkwoKlRERATWrFmjsF6rVi12cCmRkydPomfPntDT05OdqOjn54fZs2dj3759aNu2reCEBAAbN27ETz/9hMGDB+Pzzz+HVCpFREQENm3ahJ9//hmPHj3C4sWLUalSJfz444+i46qsgIAA6OjoKHSV7NixA9nZ2XB3dxeUjN6VmpoKNzc3hfWBAwfi119/FZCIqORYDCIi+o+SkpJER6APNGLECHh7e2PDhg2QSCS4f/8+zp49i0mTJsHHx0d0PCpAU1Oz0K0t169fh5GRkYBEVJjRo0ejb9++splBAJCXl4dRo0Zh9OjRuHz5suCEBACbNm3CkiVL0LdvX9mai4sLHBwcsGbNGoSEhKB27dqYN28ei0ECLViwAKtXr1ZYNzY2xvDhw1kMUiJffPEFTp06JTv97a2wsDC0bt1aUCqikuE2MSIiUkk//fQTli1bJtsSVqlSJUyaNEl2NDYph+HDh+PRo0cICgqCoaEhYmNjoa6uDldXV7Rp0wa+vr6iIxIALS0tREdHw8bGRm79+vXraNiwIZ4/fy4oGRWkra2NmJgY1KlTR279xo0baNCgAbKzs5GUlIT69esjOztbUErS1NTEtWvXFLpXb926hXr16vH3SbB9+/bJHt+/fx8+Pj7o27ev3CmyO3bswKxZszBy5EhRMYmKxWIQEdF/tG/fPnTr1g0VK1aU+4BQGBcXlzJKRSWRnZ2Nq1evIj8/H3Z2dtDR0REdid6RmZmJ7t2748qVK3j69ClMTEyQmpqK5s2b48CBA6hcubLoiASgZcuWmDx5MlxdXeXW9+zZg4ULF+Ls2bNigpGcunXronfv3liwYIHc+tSpU7F7925cv34dFy5cQM+ePXHv3j1BKal27drw9/dX+Mywd+9ejB49Gnfv3hWUjACUeJ4WxwOQsuM2MSKi/8jV1RWpqakwNjZW+CJUED8UKI8nT54gLy8PhoaGsvkmAJCeno4KFSpAT09PYDoqSE9PD2FhYTh+/DgiIyORn58PZ2dndOzYUXQ0KsDLywve3t64efOm3N3xlStXYsGCBXKD9jlIX5zFixfjm2++wcGDB9GkSRNIJBJERETg2rVrCA4OBvB6Tle/fv0EJ1Vt3377Lby8vKCrq4s2bdoAeD2Xy9vbG99++63gdJSfny86AlGpYGcQERGpnG7duuGrr77CqFGj5NZXr16Nffv24cCBA4KSEZVPxd0pfztkn0Vx8W7fvo3Vq1fj+vXrkEqlsLW1xYgRIxS2JJE4r169wqBBg7Bjxw5UqPD63n1+fj7c3NywevVqaGhoCE5IRJ8CFoOIiEjlGBoa4vTp06hXr57c+rVr19CyZUukpaUJSkbv8vLygrW1Nby8vOTW/f39cfPmTc4MUhK3b98u8WvNzMw+YhKiT0d8fDxiYmKgpaUFBwcH/u4oqfPnz+PEiRN4+PChQtfQ0qVLBaUiKh63iRERlaLZs2cXeZ0nVSmHly9fIjc3V2E9JyeHgzmVzM6dOwudxdWiRQssWLCAxSAlwS+p5Ut2djaSk5Px6tUruXVu4VMudevWRd26dUXHoCLMnz8fP//8M2xsbFC9enVIJBLZtYKPiZQRO4OIiEqRk5OT3POcnBwkJSWhQoUKsLKyQmRkpKBkVNAXX3wBBwcHrFixQm599OjRiI2NxalTpwQlo3dpamri8uXLCsf23rx5E/b29rLT4Ei8hIQE+Pr6Ii4uDhKJBPXq1YO3tzesrKxER6M3Hj16BA8PDxw8eLDQ69zCJ86ECRMwZ84cVK5cGRMmTCjytew2UR7Vq1fHwoULMXjwYNFRiD4YO4OIiEpRVFSUwlpmZiYGDx6MXr16CUhEhZk3bx46duyImJgYdOjQAQAQEhKCiIgIHDlyRHA6Ksja2hqHDh3CmDFj5NYPHjwIS0tLQanoXYcPH4aLiwsaNmyIli1bQiqV4syZM6hfvz7279+PTp06iY5IAMaNG4d//vkH4eHhaNeuHXbv3o0HDx5g7ty5WLJkieh4Ki0qKgo5OTmyx1Q+qKmpoWXLlqJjEP0r7AwiIioDly9fxpdffolbt26JjkJvREdH49dff0V0dDS0tLTg6OiIadOmoU6dOqKjUQEbNmzAmDFjMHnyZLRv3x7A68LdkiVL4Ovri2HDhglOSMDrrsguXboUemT5kSNH2BWpJGrWrIm9e/fi888/h56eHi5cuIC6deti3759WLRoEcLCwkRHJCpXFi1ahPv373PLMpVLLAYREZWBsLAwfPXVV/jnn39ERyEqd1atWoV58+bh/v37AABzc3PMnDkTbm5ugpPRW5qamrh06ZJCMTU+Ph6Ojo7czqck9PT0EBsbC3Nzc5ibm+N///sfWrZsiaSkJNSvXx/Z2dmiIxKAIUOGYPny5dDV1ZVbz8rKwtixY7FhwwZByehd+fn56NGjB+Lj42FnZ4eKFSvKXd+1a5egZETF4zYxIqJS5OfnJ/dcKpUiJSUFW7ZsQdeuXQWlosLk5+fj5s2bhZ7+0aZNG0GpqDDff/89vv/+ezx69AhaWlrQ0dERHYneYWRkhOjoaIViUHR0NIyNjQWlonfZ2Njg+vXrMDc3R8OGDbFmzRqYm5tj9erVqFmzpuh49MamTZuwYMEChWLQ8+fPsXnzZhaDlMjYsWMRGhqKdu3aoWrVqhwaTeUKi0FERKVo2bJlcs/V1NRgZGQEd3d3TJs2TVAqeld4eDj69++P27dv490GWYlEwiGqSsrIyEh0BHqPYcOGYfjw4UhMTESLFi0gkUgQFhaGhQsXYuLEiaLj0Rvjxo1DSkoKAGDGjBno0qUL/ve//0FDQwMbN24UG46QmZkJqVQKqVSKp0+fQlNTU3YtLy8PBw4cYHFVyWzevBk7d+5Ejx49REch+mDcJkZERCqnYcOGqFu3LmbNmoWaNWsq3MmrUqWKoGRUmODgYAQFBRV6FDZn0SgHqVQKX19fLFmyRLadz8TEBJMnT4aXlxfvliup7OxsXLt2DbVr10a1atVEx1F5ampqRf6uSCQSzJo1Cz/99FMZpqKimJmZ4fDhw7C1tRUdheiDsRhERPSR3LlzBxKJBJ999pnoKPSOypUrIyYmRuG4clI+fn5++Omnn+Du7o7ff/8dHh4eSEhIQEREBEaPHo158+aJjqjycnNz8b///Q9dunRBjRo18PTpUwBQ2OJCYuXk5MDGxgZ//vkn7OzsRMehQpw8eRJSqRTt27fHzp07YWhoKLumoaEBMzMzmJiYCExI7woICMChQ4cQEBAAbW1t0XGIPgiLQUREpSg3NxezZs2Cn58fnj17BgDQ0dHB2LFjMWPGDIXBgiRG+/btMWXKFM5xKgdsbW0xY8YMfPfdd9DV1UVMTAwsLS3h4+OD9PR0+Pv7i45IALS1tREXFwczMzPRUagItWrVwrFjx1CvXj3RUagIt2/fRu3atdlRVw44OTkhISEBUqkU5ubmCp/z2L1Kyowzg4iIStGYMWOwe/duLFq0CM2bNwcAnD17FjNnzsTjx4+xevVqwQkJeD3wceLEiUhNTYWDg4PChzdHR0dByehdycnJaNGiBQBAS0tL1nUyaNAgNGvWjMUgJdG0aVNERUWxGKTkxo4di4ULF2LdunWoUIFfA5RVXFwc7ty5g1atWgEAVq5cid9//x12dnZYuXIlDAwMBCekt1xdXUVHIPrX2BlERFSKqlSpgj/++APdunWTWz948CC+/fZbPHnyRFAyKkhNTU1hTSKRQCqVcoC0krG0tERwcDCcnZ3RpEkTDB06FCNGjMCRI0fw7bffIj09XXREArBjxw5MnToV48ePR6NGjVC5cmW56yywKodevXohJCQEOjo6cHBwUHifeAy2cnBwcMDChQvRvXt3XLp0CY0bN8bEiRNx/Phx1KtXDwEBAaIjEtEngLcEiIhKkaamJszNzRXWzc3NoaGhUfaBqFBJSUmiI1AJtW/fHvv374ezszM8PT0xfvx4BAcH48KFC+jdu7foePRGv379AABeXl6yNRZYlY++vj769OkjOgYVIykpSTbXaefOnfjqq68wf/58REZGonv37oLTEdGngp1BRESlaPbs2bh27RoCAgJQqVIlAMDLly/h6emJOnXqYMaMGYITEpUv+fn5yM/Pl21pCQoKQlhYGKytrTFy5EgWWZXE7du3i7zO7WNEJWdoaIiwsDDY2dmhVatWcHNzw/Dhw3Hr1i3Y2dkhOztbdER6o7gT4FgIJ2XGziAiov/o3e6EY8eO4bPPPkODBg0AADExMXj16hU6dOggIh4V4erVq4UeV+7i4iIoEb1LTU1Nbltf37590bdvX4GJqDAs9pQfubm5OHHiBBISEtC/f3/o6uri/v370NPTg46Ojuh4BKBVq1aYMGECWrZsifPnz2P79u0AgPj4eJ5QqmR2794t9zwnJwdRUVHYtGkTZs2aJSgVUcmwM4iI6D/y8PAo8Wu5z185JCYmolevXrh06ZJsKwsA2d093skTKzY2tsSv5Swa5XH9+nWsWLECcXFxkEgksLW1xdixY2FjYyM6Gr1x+/ZtdO3aFcnJyXj58iXi4+NhaWmJcePG4cWLFzzkQEkkJydj1KhRuHPnDry8vODp6QkAGD9+PPLy8uDn5yc4IRVn27Zt2L59O/bu3Ss6CtF7sRhERCTA6dOn0bhxY9lWMipbX331FdTV1fH777/D0tIS58+fR1paGiZOnIjFixejdevWoiOqtLdt98V9ROEsGuURHByM7777Do0bN5adpBgeHo6IiAhs27YN33zzjeCEBLw++UhXVxfr169H1apVERMTA0tLS5w8eRJDhw7FjRs3REck+iQkJCTA0dERWVlZoqMQvReLQUREAujp6SE6OhqWlpaio6ikatWq4fjx43B0dESVKlVw/vx52NjY4Pjx45g4cSKioqJER1Rpxc2fKYjbk5SDpaUlBg4ciNmzZ8utz5gxA1u2bEFiYqKgZFRQtWrVcPr0adjY2EBXV1dWDOIsGvEyMzOhp6cne1yUt68j5fT8+XNMmzYNBw8exPXr10XHIXovzgwiIhKAdXix8vLyZLMxqlWrhvv378PGxgZmZmb84KYEWOApf1JTU+Hm5qawPnDgQPz6668CElFh8vPzC+2mu3v3LnR1dQUkorcMDAyQkpICY2Nj6OvrFzqUmKfzKR8DAwO590oqleLp06fQ1tbG1q1bBSYjKh6LQUREpHLs7e0RGxsLS0tLNG3aFIsWLYKGhgbWrl3Lbi0lxFk0yu+LL77AqVOnYG1tLbceFhbGbZdKpFOnTvD19cXatWsBvN5q+ezZM8yYMYNHlgt2/PhxGBoaAgBCQ0MFp6GS8vX1lXuupqYGIyMjNG3aFAYGBmJCEZUQt4kREQlQsD2fyt7hw4eRlZWF3r17IzExEV9++SWuXbuGqlWrYvv27Wjfvr3oiPQGZ9GUD6tXr4aPjw/69u2LZs2aAXj9Pu3YsQOzZs2CiYmJ7LU8rU+c+/fvo127dlBXV8eNGzfQuHFj3LhxA9WqVcPff/8NY2Nj0RFVXm5uLubNm4chQ4bA1NRUdBwi+oSxGEREJACLQconPT1dod2bxOMsmvJBTU2tRK/jFhfxnj9/jsDAQERGRiI/Px/Ozs4YMGAAtLS0REejN3R1dXHp0iWYm5uLjkIlkJGRgfPnz+Phw4fIz8+Xu1bY9lkiZcFiEBGRABwgLU5ubi40NTURHR0Ne3t70XGoGNra2oiNjVXYfnTjxg00aNCAA2+JPkB2dja0tbVFx6BiuLq6wtXVFYMHDxYdhYqxf/9+DBgwAFlZWdDV1ZW7oSSRSJCeni4wHVHRODOIiEgA1uHFqVChAszMzNidUE5wFs2nxcHBAQcOHOD2F0GMjY3h6uqKQYMGoVOnTiXu6KKy1a1bN0ybNg2XL19Go0aNULlyZbnr3GqpPCZOnIghQ4Zg/vz5LLRSucPOICIiUjkBAQHYsWMHtm7dKhvYScqJs2g+LdwiK9auXbsQGBiIv/76C3p6eujXrx8GDhyIJk2aiI5GBRRVpONWS+VSuXJlXLp0iX/TqFxiMYiIqBQ9ePAAkyZNQkhICB4+fKjQAcQPcMrByckJN2/eRE5ODszMzBTuukZGRgpKRu/iLJpPC4tByuHp06cIDg5GYGAgQkNDYWFhgYEDB8LHx0d0NKJypXfv3vj222/Rt29f0VGIPhiLQUREpahbt25ITk7GmDFjULNmTYVhxD179hSUjAqaNWtWkddnzJhRRkmIVAuLQcrn6tWrGDBgAGJjY1lQVUIvXryApqam6Bj0HuvXr8fs2bPh4eEBBwcHVKxYUe46O1ZJmbEYRERUinR1dXHq1Ck0bNhQdBSiTw6/FJV/LAYphxcvXmDfvn3Ytm0bDh06BGNjY3z33XdYuHCh6GiE113E8+fPx+rVq/HgwQPEx8fD0tIS06dPh7m5OTw9PUVHpDe4pY/KM06NIyIqRaamphwOTVSK8vLyMGfOHNSqVQs6Ojqyo+SnT5+O9evXC05HVL4cOXIE7u7uqF69OkaOHAljY2McPnwYycnJLAQpkXnz5mHjxo1YtGgRNDQ0ZOsODg5Yt26dwGT0rvz8/Pf+sBBEyo7FICKiUuTr64upU6fi1q1boqNQEdTU1KCurv7eH1Ie/FJEVHpcXV2RnZ2NTZs24cGDB1i7di3atm0rOha9Y/PmzVi7di0GDBgg92+So6Mjrl27JjAZ/VsODg64c+eO6BhEcni0PBFRKerXrx+ys7NhZWUFbW1thb3j6enpgpJRQbt375Z7npOTg6ioKGzatKnYeUJUtt5+KerQoQNGjhwpW+eXovJpzZo1qF69uugYKis1NRV6enqiY1Ax7t27B2tra4X1/Px85OTkCEhE/9WtW7f43pHSYTGIiKgU+fr6io5AJVDYIO+vv/4a9evXx/bt2zmPQYnwS1H54OfnV+i6RCKBpqYmrK2t0aZNG/Tv37+Mk1FBenp6yMvLw549exAXFweJRIJ69eqhZ8+e7IpUIvXr18epU6dgZmYmt75jxw44OTkJSkVEnxoWg4iISpG7u7voCPQfNG3aFMOGDRMdgwrgl6LyYdmyZXj06BGys7NhYGAAqVSKjIwMaGtrQ0dHBw8fPoSlpSVCQ0NhamoqOq7KunnzJrp374579+7BxsYGUqkU8fHxMDU1xV9//QUrKyvREQmvT7QcNGgQ7t27h/z8fOzatQvXr1/H5s2b8eeff4qOR0SfCM4MIiIqZXl5edi5cyfmzp2LefPmYffu3RwiWA48f/4cK1aswGeffSY6ChUwY8YMjBkzBgsXLpR9KRo2bBjmz58PHx8f0fHojfnz56NJkya4ceMG0tLSkJ6ejvj4eDRt2hTLly9HcnIyatSogfHjx4uOqtK8vLxgZWWFO3fuIDIyElFRUUhOToaFhQW8vLxEx6M3vvrqK2zfvh0HDhyARCKBj48P4uLisH//fnTq1El0PCL6RPBoeSKiUsS7ruWDgYEBJBKJ7LlUKsXTp0+hra2NrVu3wsXFRWA6etfhw4cxf/58XLx4Efn5+XB2doaPjw86d+4sOhq9YWVlhZ07d6Jhw4Zy61FRUejTpw8SExNx5swZ9OnTBykpKWJCEipXrozw8HA4ODjIrcfExKBly5Z49uyZoGREnzZdXV3ExMTA0tJSdBQiGW4TIyIqRW/vuoaHh8PQ0BAAkJaWhoEDB8LLywt//fWX4IQEvN7SUrAYpKamBiMjIzRt2hQGBgYCk1FBubm5mDdvHoYMGYKTJ0+KjkNFSElJQW5ursJ6bm4uUlNTAQAmJiZ4+vRpWUejAipVqlToe/Ds2TO50/pILEtLS0RERKBq1apy6xkZGXB2dkZiYqKgZET0KWFnEBFRKeJdV6LSpaOjg8uXL8Pc3Fx0FCpCjx49kJqainXr1slmOUVFRWHYsGGoUaMG/vzzT+zfvx8//vgjLl26JDit6nJzc0NkZCTWr1+Pzz//HABw7tw5DBs2DI0aNcLGjRvFBiQAr29QpKamwtjYWG79wYMHqF27Nl6+fCkoGb0rKSkJFhYWxb5u27Zt6NmzJypXrlwGqYhKhp1BRESliHddy4dDhw5BR0cHrVq1AgCsXLkSv//+O+zs7LBy5Up2BymRjh074sSJExg8eLDoKFSE9evXY9CgQWjUqBEqVqwI4HVXUIcOHbB+/XoArwt7S5YsERlT5fn5+cHd3R3NmzeXe59cXFywfPlywelo3759sseHDx9GlSpVZM/z8vIQEhLCwriSeXtSoqenJ77++mtoamoW+jqepEjKiJ1BRESliHddywcHBwcsXLgQ3bt3x6VLl9C4cWNMnDgRx48fR7169RAQECA6Ir2xZs0azJw5EwMGDECjRo0U7qpyvpNyuXbtGuLj4yGVSmFrawsbGxvRkagQN2/eRFxcHKRSKezs7GBtbS06EuF1RxAASCQSvPsVrWLFijA3N8eSJUvw5ZdfiohHhbh8+TI2bNiA//3vf3j58iX69esHT09P2WdAImXGYhARUSnKyMiAu7s79u/fr3DXdePGjXJ3+UicgluPZs6cicuXLyM4OBiRkZHo3r27bMYJiff2y1FhJBIJT+ojok+OhYUFIiIiUK1aNdFRqIRyc3Oxf/9+bNy4EQcPHkSdOnXg6emJQYMGwcjISHQ8okKxGERE9BHwrqtyMzQ0RFhYGOzs7NCqVSu4ublh+PDhuHXrFuzs7JCdnS06IlG5kpeXh40bNyIkJAQPHz5Efn6+3PXjx48LSkYFff3112jcuDGmTp0qt/7rr7/i/Pnz2LFjh6BkRJ+Gly9f4rfffsO0adPw6tUrVKxYEf369cPChQtRs2ZN0fGI5Lz/dhsREf1r1tbW+Oqrr+Di4lJoIUhPT4+ngQjUqlUrTJgwAXPmzMH58+fRo0cPAEB8fDw+++wzweno33BwcMCdO3dEx1BZ3t7e8Pb2Rl5eHuzt7dGgQQO5H1IOJ0+elP29K6hr1674+++/BSSiwnh5ecHPz09h3d/fH+PGjSv7QFSsCxcuYNSoUahZsyaWLl2KSZMmISEhAcePH8e9e/fQs2dP0RGJFHCANBGRAGzKFMvf3x+jRo1CcHAwVq1ahVq1agEADh48iK5duwpOR//GrVu3kJOTIzqGyvrjjz8QFBSE7t27i45CRXjfYQYVK1ZEZmamgERUmJ07d8oNk36rRYsWWLBgAXx9fcs+FBVq6dKlCAgIwPXr19G9e3ds3rwZ3bt3l21xtrCwwJo1a2Brays4KZEiFoOIiEjl1K5dG3/++afC+rJlywSkISr/NDQ0uB22HLC3t8f27dvh4+Mjt/7HH3/Azs5OUCp6V1paWqEzBvX09PD48WMBieh9Vq1ahSFDhsDDwwM1atQo9DW1a9eWnapIpExYDCIiIpWUn5+PmzdvFjrfpE2bNoJSEZVPEydOxPLly+Hv7w+JRCI6Dr3H9OnT0adPHyQkJKB9+/YAgJCQEAQGBnJekBKxtrbGoUOHMGbMGLn1gwcPwtLSUlAqKszRo0dRu3ZthcMOpFIp7ty5g9q1a0NDQwPu7u6CEhK9H4tBRESkcsLDw9G/f3/cvn1bYcseT6gi+nBhYWEIDQ3FwYMHUb9+fdlpim/t2rVLUDIqyMXFBXv27MH8+fMRHBwMLS0tODo64tixY2jbtq3oePTGhAkTMGbMGDx69EiuaLdkyRJuEVMyVlZWSElJgbGxsdx6eno6LCws+HmClBqLQUREAvDOuVgjR45E48aN8ddff6FmzZp8P4j+I319ffTq1Ut0DCqBHj16FDpEuqDAwEC4uLigcuXKZZSKChoyZAhevnyJefPmYc6cOQAAc3NzrFq1Cm5uboLTUUHvmwH57NkzaGpqlnEaog/Do+WJiATQ1dVFTEwM270FqVy5MmJiYjjj5BPC3ymi0qOnp4fo6Gj+PimBR48eQUtLCzo6OqKjUAETJkwAACxfvhzDhg2Dtra27FpeXh7OnTsHdXV1nD59WlREomKxM4iISICDBw/KTrCiste0aVPcvHmTxaBPyJo1a1C9enXRMYg+CbxXrDyMjIxER6BCREVFAXj9u3Lp0iW5U/o0NDTQoEEDTJo0SVQ8ohJhZxAR0X/09u5QSSxduvQjJqGS2r17N37++WdMnjwZDg4OCvNNHB0dBSWjd/n5+RW6LpFIoKmpCWtra7Rp0wbq6uplnIycnZ0REhICAwMDODk5FbndMjIysgyT0X/FTjvxgoODERQUhOTkZLx69UruGn+flIeHhweWL18OPT090VGIPhg7g4iI/qO3d4feunjxIvLy8mBjYwMAiI+Ph7q6Oho1aiQiHhWiT58+AF7PZXhLIpFAKpVygLSSWbZsGR49eoTs7GwYGBhAKpUiIyMD2tra0NHRwcOHD2FpaYnQ0FCYmpqKjqtSevbsiUqVKgEAXF1dxYYh+oT4+fnhp59+gru7O/bu3QsPDw8kJCQgIiICo0ePFh2PCggICBAdgehfY2cQEVEpWrp0KU6cOIFNmzbBwMAAAPDPP//Aw8MDrVu3xsSJEwUnJAC4fft2kdfNzMzKKAkVJzAwEGvXrsW6detgZWUFALh58yZGjBiB4cOHo2XLlvj2229Ro0YNBAcHC05L9GlgZ5BYtra2mDFjBr777ju598LHxwfp6enw9/cXHVGl9e7dGxs3boSenh569+5d5Gt5kiIpMxaDiIhKUa1atXDkyBHUr19fbv3y5cvo3Lkz7t+/LygZUflkZWWFnTt3omHDhnLrUVFR6NOnDxITE3HmzBn06dMHKSkpYkISfWJYDBJLW1sbcXFxMDMzg7GxMY4ePYoGDRrgxo0baNasGdLS0kRHVGkeHh7w8/ODrq4uPDw8inwtO4dImXGbGBFRKcrMzMSDBw8UikEPHz7E06dPBaUiANi3bx+6deuGihUrYt++fUW+1sXFpYxSUXFSUlKQm5ursJ6bm4vU1FQAgImJCX+/BDAwMChyTlBB6enpHzkNlSYzMzOFWWpUdmrUqIG0tDSYmZnBzMwM4eHhaNCgAZKSkjjcWwkULPCw2EPlGYtBRESlqFevXvDw8MCSJUvQrFkzAEB4eDgmT55cbCsxfVyurq5ITU2FsbFxkfNNODNIubRr1w4jRozAunXr4OTkBOB1V9D333+P9u3bAwAuXboECwsLkTFVkq+vr+xxWloa5s6diy5duqB58+YAgLNnz+Lw4cOYPn26oIRUmIyMDAQHByMhIQGTJ0+GoaEhIiMjUb16ddkpl5cvXxacUrW1b98e+/fvh7OzMzw9PTF+/HgEBwfjwoUL/CxBRKWG28SIiEpRdnY2Jk2ahA0bNiAnJwcAUKFCBXh6euLXX39F5cqVBSckKl9SU1MxaNAghISEyDoVcnNz0aFDB2zZsgXVq1dHaGgocnJy0LlzZ8FpVVefPn3Qrl07jBkzRm7d398fx44dw549e8QEIzmxsbHo2LEjqlSpglu3buH69euwtLTE9OnTcfv2bWzevFl0RAKQn5+P/Px8VKjw+r59UFAQwsLCYG1tjZEjR8odY05iPXjwAJMmTUJISAgePnyo0LnFm0ukzFgMIiL6CLKyspCQkACpVApra2sWgcopBwcHHDhwgKdUKYFr164hPj4eUqkUtra2stP6SDno6OggOjoa1tbWcus3btyAk5MTnj17JigZFdSxY0c4Oztj0aJFcnOBzpw5g/79++PWrVuiI6q83NxczJs3D0OGDOG/PeVAt27dkJycjDFjxqBmzZoKW2d79uwpKBlR8bhNjIjoI6hcuTIcHR1Fx6D/6NatW7IOLxLL1tYWtra2omPQe1StWhW7d+/G5MmT5db37NmDqlWrCkpF74qIiMCaNWsU1mvVqiWbwUViVahQAb/++ivc3d1FR6ESCAsLw6lTpxQOOSAqD1gMIiL6j3jEKNHHk5eXh40bN8pa8PPz8+WuHz9+XFAyKmjWrFnw9PTEiRMnZDODwsPDcejQIaxbt05wOnpLU1MTmZmZCuvXr1+HkZGRgERUmI4dO+LEiRMYPHiw6ChUDFNTUw71pnKLxSAiov+oSpUqsrbgKlWqCE5D9Gnx9vbGxo0b0aNHD9jb25f49CoqW4MHD0a9evXg5+eHXbt2QSqVws7ODqdPn0bTpk1Fx6M3evbsidmzZyMoKAjA64H5ycnJmDp1Kvr06SM4Hb3VrVs3TJs2DZcvX0ajRo0UtprzxEvl4evri6lTp2LNmjUwNzcXHYfog3BmEBER0XsUnKlBYlSrVg2bN29G9+7dRUchKvcyMzPRvXt3XLlyBU+fPoWJiQlSU1PRvHlzHDhwgPPtlISamtp7r/HES+ViYGCA7Oxs5ObmQltbW3bQwVvp6emCkhEVj51BREREpLQ0NDQUhhKTcihsu9H76OnpfcQkVFJ6enoICwvD8ePHERkZifz8fDg7O6Njx46io1EB726HJeXl6+srOgLRv8bOICKiUhYcHIygoCAkJyfj1atXctciIyMFpaJ/g51B4i1ZsgSJiYnw9/fnFjElo6amVux7IpVK2clARESkhNgZRERUivz8/PDTTz/B3d0de/fuhYeHBxISEhAREYHRo0eLjkcfaM2aNahevbroGCotLCwMoaGhOHjwIOrXr6/Qgs+h7OKEhoaKjkAfyMvLC9bW1vDy8pJb9/f3x82bN9nloESysrJw8uTJQm8svfv+kTjJyclFXq9du3YZJSH6cOwMIiIqRba2tpgxYwa+++47ua4SHx8fpKenw9/fX3REwuuiXWEkEgk0NTVhbW2NNm3aQF1dvYyT0bs8PDyKvB4QEFBGSYjKv1q1amHfvn1o1KiR3HpkZCRcXFxw9+5dQcmooKioKHTv3h3Z2dnIysqCoaEhHj9+DG1tbRgbGyMxMVF0RHqjuA5JdkWSMmMxiIioFGlrayMuLg5mZmYwNjbG0aNH0aBBA9y4cQPNmjVDWlqa6IgEwMLCAo8ePUJ2djYMDAwglUqRkZEBbW1t6Ojo4OHDh7C0tERoaChMTU1FxyVSSrGxsbC3t4eamhpiY2OLfK2jo2MZpaKiaGpq4vLlywpzuG7evAl7e3u8ePFCUDIq6IsvvkDdunWxatUq6OvrIyYmBhUrVsTAgQPh7e2N3r17i45Ib8TExMg9z8nJQVRUFJYuXYp58+bxvSKl9v5R9URE9MFq1KghK/iYmZkhPDwcAJCUlATW3pXH/Pnz0aRJE9y4cQNpaWlIT09HfHw8mjZtiuXLlyM5ORk1atTA+PHjRUclUloNGzbE48ePZY+dnJzQsGFDhR8nJyfBSekta2trHDp0SGH94MGDnI2mRKKjozFx4kSoq6tDXV0dL1++hKmpKRYtWoQff/xRdDwqoEGDBnI/jRs3xrBhw7B48eL3diETKQvODCIiKkXt27fH/v374ezsDE9PT4wfPx7BwcG4cOEC7w4pkZ9//hk7d+6ElZWVbM3a2hqLFy9Gnz59kJiYiEWLFqFPnz4CU6ouZ2dnhISEwMDAAE5OTkW24HMouzhJSUkwMjKSPSblN2HCBIwZMwaPHj1C+/btAQAhISFYsmQJ5wUpkYoVK8r+7lWvXh3JycmoV68eqlSpUuyMGlIOdevWRUREhOgYREViMYiIqBStXbtWdiTsyJEjYWhoiLCwMHz11VcYOXKk4HT0VkpKCnJzcxXWc3NzkZqaCgAwMTHB06dPyzoaAejZsycqVaoEAHB1dRUbht7LzMys0MekvIYMGYKXL19i3rx5mDNnDgDA3Nwcq1atgpubm+B09JaTkxMuXLiAunXrol27dvDx8cHjx4+xZcsWODg4iI5HBWRmZso9l0qlSElJwcyZM1GnTh1BqYhKhjODiIhI5fTo0QOpqalYt26dbAtLVFQUhg0bhho1auDPP//E/v378eOPP+LSpUuC0xIpv82bNxd5nYUG5fPo0SNoaWlBR0dHdBR6x4ULF/D06VO0a9cOjx49gru7O8LCwmBtbY2AgAA0aNBAdER6o7AB0lKpFKampvjjjz/QvHlzQcmIisdiEBFRKfvnn3+wfv16xMXFQSKRoF69evDw8IChoaHoaPRGamoqBg0ahJCQENlR5bm5uejQoQO2bNmC6tWrIzQ0FDk5OejcubPgtETKz8DAQO55Tk4OsrOzoaGhAW1tbaSnpwtKRkT08Zw8eVLuuZqaGoyMjGBtbY0KFbgJh5Qbi0FERKXo5MmT6NmzJ/T09NC4cWMAwMWLF5GRkYF9+/ahbdu2ghNSQdeuXUN8fDykUilsbW1hY2MjOhLhdWGhqDlBBbHIoLxu3LiB77//HpMnT0aXLl1ExyEADx48wKRJkxASEoKHDx8qHGzAY7CJiFQHi0FERKXI3t4eLVq0wKpVq6Curg7g9YfrUaNG4fTp07h8+bLghETKb9OmTbLHaWlpmDt3Lrp06SJrtz979iwOHz6M6dOn88Q3JXfhwgUMHDgQ165dEx2FAHTr1g3JyckYM2YMatasqVB07dmzp6BkVBCLduXLvXv3cPr0aTx8+FA2N/ItLy8vQamIisdiEBFRKdLS0kJ0dLRCh8n169fRsGFDPH/+XFAyKigvLw8bN26UfdB+98Pb8ePHBSWjd/Xp0wft2rXDmDFj5Nb9/f1x7Ngx7NmzR0wwKpGoqCi0bdtWYcgqiaGrq4tTp06hYcOGoqNQEVi0Kz8CAgIwcuRIaGhooGrVqnLvlUQiQWJiosB0REXjRkYiolLk7OyMuLg4hWJQXFwcP3wrEW9vb2zcuBE9evSAvb19ibckUdk7fPgwFi5cqLDepUsXTJ06VUAiKsy+ffvknr89Ucff3x8tW7YUlIreZWpqqtBlQsonLCyMRbtywsfHBz4+Ppg2bRrU1NRExyH6ICwGERGVIi8vL3h7e+PmzZto1qwZACA8PBwrV67EggULEBsbK3uto6OjqJgq748//kBQUBC6d+8uOgoVo2rVqti9ezcmT54st75nzx5UrVpVUCp6l6urq9xziUQCIyMjtG/fHkuWLBETihT4+vpi6tSpWLNmDczNzUXHofdg0a78yM7OxrfffstCEJVL3CZGRFSKivswIJFIIJVKIZFIuOdfIBMTE5w4cQJ169YVHYWKsXHjRnh6eqJr166ymUHh4eE4dOgQ1q1bh8GDB4sNSArebrvklyPlY2BggOzsbOTm5kJbW1t2muJbHMiuHI4cOYIlS5awaFcOTJkyBYaGhuxUpXKJxSAiolJ0+/btEr/WzMzsIyahoixZsgSJiYnw9/fnFrFy4Ny5c/Dz80NcXBykUins7Ozg5eWFpk2bio5GBaxfvx7Lli3DjRs3AAB16tTBuHHjMHToUMHJ6K2Cw9kL4+7uXkZJ6F3vnqKYlZXFol05kJeXhy+//BLPnz+Hg4ODwnu1dOlSQcmIisdiEBGRAD169MC6detQs2ZN0VFUUq9evRAaGgpDQ0PUr19f4cPbrl27BCUjKp+mT5+OZcuWYezYsXKnvvn7+8Pb2xtz584VnJBIuRVXqCuIRTvlMWfOHMyYMQM2NjaoXr26wgBpHkhByozFICIiAXR1dRETEwNLS0vRUVSSh4dHkdcDAgLKKAkV5kNOntLT0/uISaikqlWrhhUrVuC7776TWw8MDMTYsWPx+PFjQcnofZ4/f46cnBy5Nf4+EX0YAwMDLFu2jFuWqVziAGkiIlI5LPYoN319/WK373H2lnLJy8tD48aNFdYbNWqE3NxcAYmoMFlZWfjhhx8QFBSEtLQ0hev8fVIOBw4cgLq6Orp06SK3fuTIEeTl5aFbt26CktG7KlWqxBMTqdxiMYiIiIiUSmhoqOgI9IEGDhyIVatWKczHWLt2LQYMGCAoFb1rypQpCA0NxW+//QY3NzesXLkS9+7dw5o1a7BgwQLR8eiNqVOnFvp+5OfnY+rUqSwGKRFvb2+sWLECfn5+oqMQfTBuEyMiEoDbxMqes7MzQkJCYGBgACcnpyI7TyIjI8swGVH5NGHCBNnj3NxcbNy4EbVr10azZs0AvD717c6dO3Bzc8OKFStExaQCateujc2bN+OLL76Anp4eIiMjYW1tjS1btiAwMBAHDhwQHZEAaGlpIS4uTuEksVu3bqF+/frIysoSE4wU9OrVC8ePH0fVqlU5g5DKHXYGERGRSujZsycqVaoEAHB1dRUbhooUGxsLe3t7qKmpITY2tsjXOjo6llEqeldUVJTc80aNGgEAEhISAABGRkYwMjLClStXyjwbFS49PR0WFhYAXs8HensqVatWrfD999+LjEYFVKlSBYmJiQrFoJs3b6Jy5cpiQlGh9PX10bt3b9ExiP4VdgYREQnAziCi91NTU0NqaiqMjY2hpqYGiUSCwj6ucGYQ0YdxdHTEihUr0LZtW3Tu3BmOjo5YvHgx/Pz8sGjRIty9e1d0RAIwfPhwhIeHY/fu3bCysgLwuhDUp08fNGnSBOvWrROckIg+BewMIiIqRX///TdatGiBChXk/7zm5ubizJkzaNOmDQDgxx9/hKGhoYiIREovKSkJRkZGssdEVDo8PDwQExODtm3bYtq0aejRowdWrFiB3NxchXlPJM6vv/6Krl27wtbWFp999hkA4O7du2jdujUWL14sOB0RfSrYGUREVIrU1dWRkpICY2NjufW0tDQYGxuzi0EgAwODYk+oeuvt1gkiok9ZcnIyLly4ACsrKzRo0EB0HCpAKpXi6NGjiImJgZaWFhwdHWU3lEh5pKWlwcfHB6GhoXj48CHy8/PlrvPzBCkzdgYREZWit8ddvystLY37/AXz9fWVPU5LS8PcuXPRpUsXNG/eHABw9uxZHD58GNOnTxeUkAqzefPmIq+7ubmVURKi8m/z5s3o16+fbH5a7dq1Ubt2bbx69QqbN2/m75MSkUgk6Ny5Mzp37vze1zg4OODAgQMwNTUtw2RU0MCBA5GQkABPT09Ur169xDediJQBO4OIiErB2+GBe/fuRdeuXWUftAEgLy8PsbGxsLGxwaFDh0RFpAL69OmDdu3aYcyYMXLr/v7+OHbsGPbs2SMmGCkwMDCQe56Tk4Ps7GxoaGhAW1ubd12JPgC7Vz8tnD8onq6uLsLCwthZR+WSmugARESfgipVqqBKlSqQSqXQ1dWVPa9SpQpq1KiB4cOHY+vWraJj0huHDx9G165dFda7dOmCY8eOCUhE7/PPP//I/Tx79gzXr19Hq1atEBgYKDoeUbnyvu7Vu3fvokqVKgISEZVvtra2eP78uegYRP8Kt4kREZWCgIAAAIC5uTkmTZrELWFKrmrVqti9ezcmT54st75nzx5UrVpVUCoqqTp16mDBggUYOHAgrl27JjoOkdJzcnKCRCKBRCJBhw4d5A45yMvLQ1JSUqEFciIq2m+//YapU6fCx8cH9vb2qFixotx1PT09QcmIisdiEBFRKZoxY4boCFQCs2bNgqenJ06cOCGbGRQeHo5Dhw7xyN5yQl1dHffv3xcdg6hccHV1BQBER0ejS5cu0NHRkV3T0NCAubk5+vTpIygdUfmlr6+PJ0+eoH379nLrb7vwuPWSlBmLQUREpejBgweYNGkSQkJC8PDhQ7w7lo0fCpTD4MGDUa9ePfj5+WHXrl2QSqWws7PD6dOn0bRpU9HxqIB9+/bJPZdKpUhJSYG/vz9atmwpKBVR+fL2RoW5uTm+/fZbubl2RPTvDRgwABoaGti2bRsHSFO5wwHSRESlqFu3bkhOTsaYMWNQs2ZNhQ8FPXv2FJSMqHxSU5MfbyiRSGBkZIT27dtjyZIlqFmzpqBkROXPnTt3IJFI8NlnnwEAzp8/j23btsHOzg7Dhw8XnI4+FAdIi6etrY2oqCjY2NiIjkL0wdgZRERUisLCwnDq1Ck0bNhQdBR6R2ZmZolfyz3+yiM/P1/h8bsFIiIqmf79+2P48OEYNGgQUlNT0bFjR9jb22Pr1q1ITU2Fj4+P6Ij0AdasWYPq1auLjqHSGjdujDt37rAYROUSi0FERKXI1NRUYWsYKQd9ff1i27e5x185rV+/HsuWLcONGzcAvB4gPW7cOAwdOlRwMqLy5fLly/j8888BAEFBQXBwcMDp06dx5MgRjBw5ksUggfz8/Er8Wi8vLwCvi3sk1tixY+Ht7Y3JkyfDwcFBYYC0o6OjoGRExeM2MSKiUnTkyBEsWbIEa9asgbm5ueg4VMDJkydL/Nq2bdt+xCT0IaZPn45ly5Zh7NixsmHfZ8+ehb+/P7y9vTF37lzBCYnKDx0dHVy+fBnm5uZwcXFBy5Yt8cMPPyA5ORk2NjY8IlsgCwsLueePHj1CdnY29PX1AQAZGRnQ1taGsbExEhMTBSSkwhTWqSqRSHhzicoFFoOIiEqRgYEBsrOzkZubC21tbYU7ROnp6YKSEZVP1apVw4oVK/Ddd9/JrQcGBmLs2LF4/PixoGRE5U/Tpk3Rrl079OjRA507d0Z4eDgaNGiA8PBwfP3117h7967oiARg27Zt+O2337B+/XrZ9qPr169j2LBhGDFiBAYMGCA4Ib11+/btIq+bmZmVURKiD8diEBFRKdq0aVOR193d3csoCb0rNjYW9vb2UFNTQ2xsbJGvZVu38jAwMMD58+dRp04dufX4+Hh8/vnnyMjIEBOMqBw6ceIEevXqhczMTLi7u2PDhg0AgB9//BHXrl3Drl27BCckALCyskJwcDCcnJzk1i9evIivv/4aSUlJgpIR0aeExSAiIlIJampqSE1NhbGxMdTU1GRt3O9iW7dyGTt2LCpWrIilS5fKrU+aNAnPnz/HypUrBSUjKp/y8vKQmZkJAwMD2dqtW7dkW5BIPG1tbZw4cUI23+mt8+fP44svvkB2dragZFSYhIQE+Pr6Ii4uDhKJBPXq1YO3tzesrKxERyMqEgdIExGVouTk5CKv165du4yS0LuSkpJgZGQke0zKa8KECbLHEokE69atw5EjR9CsWTMAQHh4OO7cuQM3NzdREYnKLXV1dblCEADOuFMyHTp0wLBhw7B+/Xo0atQIEokEFy5cwIgRI9CxY0fR8aiAw4cPw8XFBQ0bNkTLli0hlUpx5swZ1K9fH/v370enTp1ERyR6L3YGERGVorcdJ+/DjhOi4rVr165Er5NIJDh+/PhHTkNUvjk7OyMkJAQGBgZwcnIq8t+oyMjIMkxG7/Po0SO4u7vj0KFDstmDubm56NKlCzZu3MgOLiXi5OSELl26YMGCBXLrU6dOxZEjR/g7RUqNnUFERKUoKipK7nlOTg6ioqKwdOlSzJs3T1AqetfmzZuLvM6OE7FCQ0NFRyD6ZPTs2ROVKlUCALi6uooNQyViZGSEAwcOID4+HnFxcQCAevXqoW7duoKT0bvi4uIQFBSksD5kyBD4+vqWfSCiD8DOICKiMvDXX3/h119/xYkTJ0RHIUBhi0ROTg6ys7OhoaEBbW1tnvpGRERK4e1XtaI6ukgcU1NTLF26FN98843celBQECZNmlTs+AAikdREByAiUgV169ZFRESE6Bj0xj///CP38+zZM1y/fh2tWrVCYGCg6HhERB/FnTt35I6PP3/+PMaNG4e1a9cKTEWF2bx5MxwcHKClpQUtLS04Ojpiy5YtomPRO4YNG4bhw4dj4cKFOHXqFMLCwrBgwQKMGDECw4cPFx2PqEjsDCIiKkWZmZlyz6VSKVJSUjBz5kxcu3YN0dHRYoJRiVy4cAEDBw7EtWvXREchIip1rVu3xvDhwzFo0CCkpqaibt26sLe3R3x8PLy8vODj4yM6IgFYunQppk+fjjFjxsiGEp8+fRorV67E3LlzMX78eNER6Q2pVApfX18sWbIE9+/fBwCYmJhg8uTJ8PLyYkcXKTUWg4iISlFhA6SlUilMTU3xxx9/oHnz5oKSUUlERUWhbdu2CkU9IqJPgYGBAcLDw2FjYwM/Pz9s374dp0+fxpEjRzBy5EgkJiaKjkgALCwsMGvWLIX5dZs2bcLMmTN5IqaSevr0KQBAV1dXcBKikuEAaSKiUvTu4Fs1NTUYGRnB2toaFSrwT66y2Ldvn9zztx1c/v7+aNmypaBUREQfV05OjmyY9LFjx+Di4gIAsLW1RUpKishoVEBKSgpatGihsN6iRQu+T0omKSkJubm5qFOnjlwR6MaNG6hYsSLMzc3FhSMqBr+ZEBGVorZt24qOQCXw7ok6EokERkZGaN++PZYsWSImFBHRR1a/fn2sXr0aPXr0wNGjRzFnzhwAwP3791G1alXB6egta2trBAUF4ccff5Rb3759O+rUqSMoFRVm8ODBGDJkiML7cu7cOaxbt44Hh5BS4zYxIqJSlpCQAF9fX8TFxUEikaBevXrw9vaGlZWV6GhERKTCTpw4gV69eiEzMxPu7u7YsGEDAODHH3/EtWvXsGvXLsEJCQB27tyJfv36oWPHjmjZsiUkEgnCwsIQEhKCoKAg9OrVS3REekNPTw+RkZGwtraWW7958yYaN26MjIwMMcGISoCdQUREpejw4cNwcXFBw4YNZUMfz5w5g/r162P//v3o1KmT6Ij0Dh7bS0Sq4osvvsDjx4+RmZkJAwMD2frw4cOhra0tMBkV1KdPH5w7dw7Lli3Dnj17IJVKYWdnh/Pnz8PJyUl0PCpAIpHIZgUV9OTJE+Tl5QlIRFRy7AwiIipFTk5O6NKlCxYsWCC3PnXqVBw5cgSRkZGCktG71q9fj2XLluHGjRsAgDp16mDcuHEYOnSo4GRERB/H8+fPIZVKZYWf27dvY/fu3ahXrx66dOkiOB1R+fPll19CW1sbgYGBUFdXBwDk5eWhX79+yMrKwsGDBwUnJHo/FoOIiEqRpqYmLl26pLB3PD4+Ho6Ojnjx4oWgZFTQ9OnTsWzZMowdO1Z2wtvZs2fh7+8Pb29vzJ07V3BCIqLS17lzZ/Tu3RsjR45ERkYGbG1tUbFiRTx+/BhLly7F999/LzoivZGXl4c9e/bItpzb2dnBxcVFVnAg5XD16lW0adMG+vr6aN26NQDg1KlTyMzMxPHjx2Fvby84IdH7sRhERFSKTE1NsXTpUnzzzTdy60FBQZg0aRKSk5MFJaOCqlWrhhUrVuC7776TWw8MDMTYsWPx+PFjQcmIiD6eatWq4eTJk6hfvz7WrVuHFStWICoqCjt37oSPjw/i4uJERyS8njfTo0cP3L17FzY2NpBKpYiPj4epqSn++usvziBUMvfv34e/vz9iYmKgpaUFR0dHjBkzBoaGhqKjERWJM4OIiErRsGHDMHz4cCQmJqJFixayoY8LFy7ExIkTRcejN/Ly8tC4cWOF9UaNGiE3N1dAIiKijy87O1t2/PWRI0fQu3dvqKmpoVmzZrh9+7bgdPSWl5cXLC0tcfbsWVlBIS0tDQMHDoSXlxf++usvwQmpIBMTE8yfP7/I14waNQqzZ89GtWrVyigVUfHYGUREVIqkUil8fX2xZMkS3L9/H8DrDwmTJ0+Gl5cXhxQribFjx6JixYpYunSp3PqkSZPw/PlzrFy5UlAyIqKPx9HREUOHDkWvXr1gb2+PQ4cOoXnz5rh48SJ69OiB1NRU0REJQOXKlREeHg4HBwe59ZiYGLRs2RLPnj0TlIz+LT09PURHR8PS0lJ0FCIZdgYREZUiiUSC8ePHY/z48bLTJd7ehSWxJkyYIHsskUiwbt06HDlyBM2aNQMAhIeH486dO3BzcxMVkYjoo/Lx8UH//v0xfvx4dOjQQTYz7ciRIzylSolUqlSp0BOqnj17Bg0NDQGJ6L9i/wUpI3YGERGRSmjXrl2JXieRSHD8+PGPnIaISIzU1FSkpKSgQYMGUFNTAwCcP38eenp6sLW1FZyOAMDNzQ2RkZFYv349Pv/8cwDAuXPnMGzYMDRq1AgbN24UG5A+mK6uLmJiYtgZREqFxSAiolKUlpYGHx8fhIaG4uHDh8jPz5e7np6eLigZ/Rt3796FiYmJ7AsTERHRx5aRkQF3d3fs378fFStWBADk5ubCxcUFGzduRJUqVQQnpA/FYhApI24TIyIqRQMHDkRCQgI8PT1RvXp1zggq5+zs7LjHn4g+KREREdixYweSk5Px6tUruWu7du0SlIoK0tfXx969e3Hjxg1cu3YNUqkUdnZ2sLa2Fh2NiD4hLAYREZWisLAwhIWFoUGDBqKjUClg8ywRfUr++OMPuLm5oXPnzjh69Cg6d+6MGzduIDU1Fb169RIdj95Rp04d1KlTR3QMIvpEsRhERFSKbG1t8fz5c9ExiIiIFMyfPx/Lli3D6NGjoauri+XLl8PCwgIjRoxAzZo1RcdTaQUPOSjOuydhkvIbOHAg9PT0RMcgksOZQUREpSgiIgJTp06Fj48P7O3tZXv93+IHgfKFe/yJ6FNSuXJlXLlyBebm5qhWrRpCQ0Ph4OCAuLg4tG/fHikpKaIjqiwDAwPY29ujQoUKkEgk7+1M5SEHyueff/7B+vXrERcXB4lEAltbWwwZMgSGhoaioxEViZ1BRESlSF9fH0+ePEH79u3l1qVSKSQSCfLy8gQlIyIiVWdoaCg7srxWrVq4fPkyHBwckJGRgezsbMHpVNuTJ0+wc+dOGBsbw9LSEhEREahataroWFSMkydPomfPntDT00Pjxo0BACtWrMCcOXOwb98+tG3bVnBCovdjMYiIqBQNGDAAGhoa2LZtGwdIfwL4/hHRp6R169Y4evQoHBwc0LdvX3h7e+P48eM4evQoOnToIDqeSjMwMEBSUhKMjY1x69YthdNISTmNHj0affv2xapVq6Curg4AyMvLw6hRozB69GhcvnxZcEKi9+M2MSKiUqStrY2oqCjY2NiIjkKlgNvEiOhTkp6ejhcvXsDExAT5+flYvHgxwsLCYG1tjenTp8PAwEB0RJU1fPhwbNq0CSYmJkhOTsZnn30mKy68KzExsYzT0ftoaWkhOjpa4XPf9evX0bBhQ86RJKXGziAiolLUuHFj3Llzh8WgT8TVq1dhYmIiOgYRUakoOMNETU0NU6ZMwZQpUxRet2DBAowcORL6+vplmE61rV27Fr1798bNmzfh5eWFYcOGQVdXV3QsKoazszPi4uIUPvfFxcWhYcOGYkIRlRA7g4iIStGOHTswc+ZMTJ48GQ4ODgoDpB0dHQUlo969e5f4tbt27fqISYiIlJuenh6io6PZFSmIh4cH/Pz8WAwqB7Zv344pU6Zg7NixaNasGQAgPDwcK1euxIIFC1CvXj3Za/kZkJQNi0FERKVITU3tvdc4QFosDw+PEr82ICDgIyYhIlJu3CJLVDJFfe4DIDsZjp8BSRlxmxgRUSlKSkoSHYHegwUeIiIiKk383EflGYtBRESlyMzMDMDrWTPJycl49eqV7JpEIpFdJyIiIqLyjZ/rqDxjMYiIqBQlJiaiV69euHTpkqw1GPj/I8rZIqw8goODERQUpFC0A4DIyEhBqYiIiEiZ7du3D926dUPFihWxb9++Il/r4uJSRqmIPhyLQUREpcjb2xsWFhY4duwYLC0tce7cOaSnp2PixIlYvHix6Hj0hp+fH3766Se4u7tj79698PDwQEJCAiIiIjB69GjR8YiIiEhJubq6IjU1FcbGxnB1dX3v6zgniJRd0ROviIjog5w9exazZ8+GkZER1NTUoK6ujlatWuGXX36Bl5eX6Hj0xm+//Ya1a9fC398fGhoamDJlCo4ePQovLy88efJEdDwiolKXm5uLTZs2ITU1tdjXtm7dGlpaWmWQiqj8yc/Ph7Gxsezx+35YCCJlx2IQEVEpysvLg46ODgCgWrVquH//PoDXe8qvX78uMhoVkJycjBYtWgAAtLS08PTpUwDAoEGDEBgYKDIaEdFHUaFCBXz//fd4+fJlsa89cOAAatasWQapiMofQ0NDPH78GAAwZMgQ2WcIovKGxSAiolJkb2+P2NhYAEDTpk2xaNEinD59GrNnz+YRvUqkRo0aSEtLA/C6UBceHg7g9akgb+c8ERF9apo2bYro6GjRMYjKtVevXiEzMxMAsGnTJrx48UJwIqJ/hzODiIhK0c8//4ysrCwAwNy5c/Hll1+idevWqFq1KrZv3y44Hb3Vvn177N+/H87OzvD09MT48eMRHByMCxcuoHfv3qLjERF9FKNGjcKECRNw584dNGrUCJUrV5a77ujoKCgZUfnRvHlzuLq6olGjRpBKpfDy8nrvtsoNGzaUcTqikpNIeQuUiOijSk9Ph4GBgexEMRLv7X7+ChVe3xMJCgpCWFgYrK2tMXLkSGhoaAhOSERU+tTUFDcFvD35ksNuiUrmwYMHWLZsGRISErBr1y506dIFlSpVKvS1u3fvLuN0RCXHYhAREamc5ORkmJqaKhTopFIp7ty5g9q1awtKRkT08dy+fbvI62ZmZmWUhOjTYGFhgQsXLqBq1aqioxB9MBaDiIhI5airqyMlJUV2GshbaWlpMDY25t1xIiIiKpGQkBCEhITg4cOHyM/Pl61LJBKsX79eYDKionGANBERqZy3WyLe9ezZM2hqagpIRERUNrZs2YKWLVvCxMRE1ink6+uLvXv3Ck5GVP7Mnj0bnTt3RkhICB4/fox//vlH9pOeni46HlGROECaiIhUxoQJEwC8vls3ffp0aGtry67l5eXh3LlzaNiwoaB0REQf16pVq+Dj44Nx48Zh3rx5si5IfX19+Pr6omfPnoITEpUvq1atwsaNGzFo0CDRUYg+GItBRESkMqKiogC87gy6dOmS3KBoDQ0NNGjQAJMmTRIVj4joo1qxYgV+//13uLq6YsGCBbL1xo0b828f0b/w6tUrtGjRQnQMon+FxSAiIlIZoaGhAAAPDw8sX74cenp6ghMREZWdpKQkODk5KaxXqlQJWVlZAhIRlW9Dhw7Ftm3bMH36dNFRiD4Yi0FERKRyAgICZI/v3r0LiUSCWrVqCUxERPTxWVhYIDo6WuHUsIMHD8LOzk5QKqLy68WLF1i7di2OHTsGR0dHVKxYUe760qVLBSUjKh6LQUREpHLy8/Mxd+5cLFmyBM+ePQMA6OrqYuLEifjpp5+gpsbzFYjo0zN58mSMHj0aL168gFQqxfnz5xEYGIhffvkF69atEx2PqNyJjY2VzRq8fPmy3LXCDqogUiY8Wp6IiFTOtGnTsH79esyaNQstW7aEVCrF6dOnMXPmTAwbNgzz5s0THZGI6KP4/fffMXfuXNy5cwcAUKtWLcycOROenp6CkxERUVliMYiIiFSOiYkJVq9eDRcXF7n1vXv3YtSoUbh3756gZEREZePx48fIz8+HsbGx6ChERCQA++CJiEjlpKenw9bWVmHd1tYW6enpAhIREX187du3R0ZGBgCgWrVqskJQZmYm2rdvLzAZERGVNRaDiIhI5TRo0AD+/v4K6/7+/mjQoIGAREREH9+JEyfw6tUrhfUXL17g1KlTAhIREZEoHCBNREQqZ9GiRejRoweOHTuG5s2bQyKR4MyZM7hz5w4OHDggOh4RUamKjY2VPb569SpSU1Nlz/Py8nDo0CGeqEhEpGI4M4iIiFROcnIyKlSogJUrV+LatWuQSqWws7PDqFGjkJubi9q1a4uOSERUatTU1GQnGxX20V9LSwsrVqzAkCFDyjoaEREJwmIQERGpHHV1daSkpCgMTk1LS4OxsTHy8vIEJSMiKn23b9+GVCqFpaUlzp8/DyMjI9k1DQ0NGBsbQ11dXWBCIiIqa9wmRkREKud990GePXsGTU3NMk5DRPRxmZmZAQDy8/MFJyEiImXBYhAREamMCRMmAAAkEgl8fHygra0tu5aXl4dz586hYcOGgtIREX1cmzZtQrVq1dCjRw8AwJQpU7B27VrY2dkhMDBQVjQiIqJPH7eJERGRymjXrh0A4OTJk2jevDk0NDRk1zQ0NGBubo5JkyahTp06oiISEX00NjY2WLVqFdq3b4+zZ8+iQ4cO8PX1xZ9//okKFSpg165doiMSEVEZYTGIiIhUjoeHB5YvXw49PT3RUYiIyoy2tjauXbuG2rVr44cffkBKSgo2b96MK1eu4IsvvsCjR49ERyQiojKiJjoAERFRWQsICGAhiIhUjo6ODtLS0gAAR44cQceOHQEAmpqaeP78uchoRERUxjgziIiIiIhIBXTq1AlDhw6Fk5MT4uPjZbODrly5AnNzc7HhiIioTLEziIiIiIhIBaxcuRLNmzfHo0ePsHPnTlStWhUAcPHiRXz33XeC0xERUVnizCAiIiIiIiIiIhXCbWJERERERCokOzsbycnJePXqldy6o6OjoERERFTWWAwiIiIiIlIBjx49wuDBg3Ho0KFCr+fl5ZVxIiIiEoUzg4iIiIiIVMC4ceOQkZGB8PBwaGlp4dChQ9i0aRPq1KmDffv2iY5HRERliJ1BREREREQq4Pjx49i7dy+aNGkCNTU1mJmZoVOnTtDT08Mvv/wiO12MiIg+fewMIiIiIiJSAVlZWTA2NgYAGBoa4tGjRwAABwcHREZGioxGRERljMUgIiIiIiIVYGNjg+vXrwMAGjZsiDVr1uDevXtYvXo1atasKTgdERGVJR4tT0RERESkAv73v/8hJycHgwcPRlRUFLp06YK0tDRoaGhg48aN6Nevn+iIRERURlgMIiIiIiJSQdnZ2bh27Rpq166NatWqiY5DRERliNvEiIiIiIhUUKVKlaCmpgZ1dXXRUYiIqIyxGEREREREpALGjRuH9evXAwDy8vLQpk0bODs7w9TUFCdOnBAbjoiIyhSLQUREREREKiA4OBgNGjQAAOzfvx+3bt3CtWvXMG7cOPz000+C0xERUVliMYiIiIiISAU8fvwYNWrUAAAcOHAA33zzDerWrQtPT09cunRJcDoiIipLLAYREREREamA6tWr4+rVq8jLy8OhQ4fQsWNHAK8HSXNuEBGRaqkgOgAREREREX18Hh4e6Nu3L2rWrAmJRIJOnToBAM6dOwdbW1vB6YiIqCyxGEREREREpAJmzpwJe3t73LlzB9988w0qVaoEAFBXV8fUqVMFpyMiorIkkUqlUtEhiIiIiIiIiIiobLAziIiIiIjoE+Xn54fhw4dDU1MTfn5+Rb7Wy8urjFIREZFo7AwiIiIiIvpEWVhY4MKFC6hatSosLCze+zqJRILExMQyTEZERCKxGEREREREREREpEK4TYyIiIiI6BM1YcKEEr1OIpFgyZIlHzkNEREpCxaDiIiIiIg+UVFRUXLPL168iLy8PNjY2AAA4uPjoa6ujkaNGomIR0REgrAYRERERET0iQoNDZU9Xrp0KXR1dbFp0yYYGBgAAP755x94eHigdevWoiISEZEAnBlERERERKQCatWqhSNHjqB+/fpy65cvX0bnzp1x//59QcmIiKisqYkOQEREREREH19mZiYePHigsP7w4UM8ffpUQCIiIhKFxSAiIiIiIhXQq1cveHh4IDg4GHfv3sXdu3cRHBwMT09P9O7dW3Q8IiIqQ9wmRkRERESkArKzszFp0iRs2LABOTk5AIAKFSrA09MTv/76KypXriw4IRERlRUWg4iIiIiIVEhWVhYSEhIglUphbW3NIhARkQpiMYiIiIiIiIiISIVwZhARERERERERkQphMYiIiIiIiIiISIWwGEREREREREREpEJYDCIiIiIiIiIiUiEsBhERERERERERqRAWg4iIiIiIiIiIVAiLQUREREREREREKoTFICIiIiIiIiIiFfJ/ziaFdCA/5W0AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Good old bar charts! Do I dare try a correlation matrix?\n",
    "\n",
    "plt.figure(figsize = (12, 8)) # Let's make this guy a lil bigger than the last one\n",
    "numerical_cols = ['amount_paid_on_building_claim', 'total_building_insurance_coverage', 'building_replacement_cost', 'building_property_value', 'disaster_assistance_coverage_required', 'flood_characteristics_indicator', 'nfip_community_number_current']\n",
    "corr = nfip[numerical_cols].corr()\n",
    "sns.heatmap(corr, annot = True, cmap = 'coolwarm', fmt = \".2f\")\n",
    "plt.title('Correlation Matrix of PA Flood Insurance Claim Qualities')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "43412922-3044-4c50-a560-6076310af781",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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t2jT+93//l+3bt3PVVVdlXjMMA5vNhtVqzWyLRCL88Y9/7HEOp9O531moz3zmM/zkJz+hsLDwuF7QH3roIV544QWuuuoqhg8fnhnLT3/6U2pra/nCF75wyGN0dnbyzDPPZP3K+ZFHHsFisWQC9bGegbvuuuv4f//v//H888/zpz/9icsvv/yAIdFqtXL66aczYcIE/vznP/P+++/3afidN28eCxcu5P33389aBOXhhx/GMIxMuczBvhPDMHr8I+O5556jtraWMWPG9NlY9z3n3uPq9tvf/na/+8+cOZM5c+Zw55138tFHH3HDDTfg9Xr7bDwejyfTg/ikk07KzAwfrQP9HRQZ6hR+j8KWLVt49NFH2bVrV+bXfN/85jd58cUXeeihh/jJT37C1q1b2bFjB4899hgPP/wwqVSKW2+9lc9//vOHVQ8ncix0tyh76qmnsFgsmXrfbmeddVamOf7e9b4XX3wxd999N1dddRU33HADLS0t/OIXv9jvrOnUqVP5y1/+wuLFixk1ahQul4upU6fyjW98g8cff5wzzzyTW2+9lZNOOol0Ok1NTQ0vv/wyt99+O6effvoRf7ZIJJKpj4xEImzdupWnnnqKv/3tb5x11lk88MADmX3nzp3LDTfcwHXXXcd7773HmWeeidfrpa6ujjfeeIOpU6dmaj6ha6bzq1/9KjU1NYwbN47nn3+e3//+93z1q1/NBOrc3Fyqq6t5+umnmTdvHgUFBRQVFWW1ejsa559/PsOGDeNrX/sa9fX1WSUPAA888ACvvvoqF198McOHDycajWZm2c8999zMft3BcvPmzUc8lltvvZWHH36Yiy++mB/84AdUV1fz3HPPcf/99/PVr36VcePGAQf/Tj7zmc+waNEiJkyYwEknncTKlSv5+c9/zrBhw454XIcyZ84c8vPzufHGG/ne976H3W7nz3/+M2vWrDnge2655RauvPJKDMPga1/7Wp+P6d577+VTn/oUZ5xxBl/96lcZMWIEnZ2dbN68mWefffaIrhdTp07liSee4De/+Q0zZ87EYrEccDZcZEjpz7vtTjSA+eSTT2aed99d6/V6sx42m838whe+YJpm153gQOauZ9M0zZUrV5pAVhsfkePtP/7jP0zAPOWUU3q89tRTT5mA6XA4zFAolPXaH/7wB3P8+PGm0+k0R40aZS5cuNB88MEHTcDctm1bZr/t27eb559/vpmbm2sCWXedB4NB89vf/rY5fvx40+FwZFo83XrrrVntxdirjdjh6L7jvvvh9XrNUaNGmZ///OfNxx57zEylUvt93x/+8Afz9NNPN71er+l2u83Ro0ebX/rSl7Lu3O/udLB06VLzlFNOMZ1Op1leXm7+13/9l5lIJLKO9/e//92cPn266XQ6TcC85pprTNP8pNtDU1NT1v4PPfRQj+/vYP7rv/7LBMyqqqoen2nFihXm5ZdfblZXV5tOp9MsLCw0zzrrLPOZZ57J2q+6urrXnQD2101jx44d5lVXXWUWFhaadrvdHD9+vPnzn/+8x7gO9J20tbWZ119/vVlSUmJ6PB7zU5/6lPn666+bZ511Vla3kKNtdbavt956y5w9e7bp8XjM4uJi8ytf+Yr5/vvvH/AcsVjMdDqd5oUXXnjY341pdn3PF198cY/t+/vZ3rZtm/nlL3/ZrKysNO12u1lcXGzOmTPH/NGPfpTZp7vbw2OPPbbfz7332FtbW83Pf/7zZl5enmkYhqlLvkgXwzT3uf1ZDsgwDJ588kkuu+wyoKtJ+he/+EXWrl2b9WtggJycHMrKyvje977HT37yk0zzeeiajfJ4PLz88sucd955x/MjiMgROvvss2lubuajjz7q76FIP3j22Wf57Gc/y3PPPZdVIy8iJx6VPRyF6dOnk0qlaGxszKx+ta+5c+eSTCbZsmULo0ePBmDjxo0AVFdXH7exiohI761bt44dO3Zw++23M23atIPeAC0iJwaF30MIBoNZNXHbtm1j9erVFBQUMG7cOL74xS/ypS99ibvuuovp06fT3NzMq6++ytSpU5k/fz7nnnsuM2bM4Mtf/jL33HMP6XSam266ifPOOy9TDyciIgPT1772Nd58801mzJjB//7v/2qJYJFBQGUPh7B06dKsG366XXPNNSxatIhEIsGPfvQjHn74YWprayksLGT27NnccccdTJ06Fehq+/Pv//7vvPzyy3i9Xi666CLuuusudXsQEREROc4UfkVERERkyNAKbyIiIiIyZCj8ioiIiMiQoRve9iOdTrN7925yc3N1c4OIiIjIAGSaJp2dnVRUVGCxHP58rsLvfuzevZuqqqr+HoaIiIiIHMLOnTt7tSqkwu9+5ObmAl1fps/n6+fRiIiIiMi+AoEAVVVVmdx2uBR+96O71MHn8yn8ioiIiAxgvS1R1Q1vIiIiIjJk9Gv4XbhwIaeeeiq5ubmUlJRw2WWXsWHDhkO+b9myZcycOROXy8WoUaN44IEHeuzz+OOPM2nSJJxOJ5MmTeLJJ588Fh9BRERERE4g/Rp+ly1bxk033cTbb7/NkiVLSCaTnH/++YRCoQO+Z9u2bcyfP58zzjiDVatW8V//9V/cfPPNPP7445l9VqxYwZVXXsnVV1/NmjVruPrqq/nCF77AO++8czw+loiIiIgMUANqhbempiZKSkpYtmwZZ5555n73+c///E+eeeYZPv7448y2G2+8kTVr1rBixQoArrzySgKBAC+88EJmnwsvvJD8/HweffTRQ44jEAjg9/vp6OhQza+IiIjIAHSkeW1A1fx2dHQAUFBQcMB9VqxYwfnnn5+17YILLuC9994jkUgcdJ+33nprv8eMxWIEAoGsh4iIiIgMPgMm/JqmyW233canPvUppkyZcsD96uvrKS0tzdpWWlpKMpmkubn5oPvU19fv95gLFy7E7/dnHurxKyIiIjI4DZjw+/Wvf50PPvjgsMoS9m1p0V25sff2/e1zoFYYCxYsoKOjI/PYuXNnb4cvIiIiIieAAdHn99///d955plnWL58+SFX6CgrK+sxg9vY2IjNZqOwsPCg++w7G9zN6XTidDqP4hOIiIiIyImgX2d+TdPk61//Ok888QSvvvoqI0eOPOR7Zs+ezZIlS7K2vfzyy5xyyinY7faD7jNnzpy+G7yIiIiInHD6NfzedNNN/OlPf+KRRx4hNzeX+vp66uvriUQimX0WLFjAl770pczzG2+8kR07dnDbbbfx8ccf84c//IEHH3yQb37zm5l9brnlFl5++WXuvPNO1q9fz5133snf//53vvGNbxzPjyciIiIiA0y/tjo7UA3uQw89xLXXXgvAtddey/bt21m6dGnm9WXLlnHrrbeydu1aKioq+M///E9uvPHGrGP89a9/5dvf/jZbt25l9OjR/PjHP+Zzn/vcYY1Lrc5EREREBrYjzWsDqs/vQKHwKyIiIjKwDYo+vyIiIiIix5LCr4iIiIgMGQOi1ZnIsRaMJqltjxCKJ8lx2KjIc5Pj0o+/iIjIUKOrvwx6u9rCLFnXQHs4kdmW57Fz3qRShuV7+nFkIiIicryp7EEGtWA02SP4ArSHEyxZ10AwmuynkYmIiEh/UPiVQa22PdIj+HZrDyeobY/s9zUREREZnBR+ZVALxQ8+sxs+xOsiIiIyuCj8yqDmdRy8rN1ziNdFRERkcFH4lUGtMs9Nnse+39fyPHYq89zHeUQiIiLSnxR+ZVDLcdk4b1JpjwDc3e1B7c5ERESGFl35ZdAblu/hiplV1LZHCMeTeBw2KtXnV0REZEjS1V+GhByXjfFluf09DBEREelnKnsQERERkSFD4VdEREREhgyFXxEREREZMhR+RURERGTIUPgVERERkSFD4VdEREREhgyFXxEREREZMhR+RURERGTIUPgVERERkSFD4VdEREREhgyFXxEREREZMhR+RURERGTIUPgVERERkSFD4VdEREREhgyFXxEREREZMhR+RURERGTIUPgVERERkSFD4VdEREREhgyFXxEREREZMhR+RURERGTIUPgVERERkSFD4VdEREREhgyFXxEREREZMhR+RURERGTIUPgVERERkSFD4VdEREREhgyFXxEREREZMhR+RURERGTIUPgVERERkSFD4VdEREREhgyFXxEREREZMvo1/C5fvpxLLrmEiooKDMPgqaeeOuj+1157LYZh9HhMnjw5s8+iRYv2u080Gj3Gn0ZEREREBrp+Db+hUIiTTz6ZX/3qV4e1/7333ktdXV3msXPnTgoKCrjiiiuy9vP5fFn71dXV4XK5jsVHEBEREZETiK0/T37RRRdx0UUXHfb+fr8fv9+fef7UU0/R1tbGddddl7WfYRiUlZX12ThFREREZHA4oWt+H3zwQc4991yqq6uztgeDQaqrqxk2bBif+cxnWLVq1UGPE4vFCAQCWQ8RERERGXxO2PBbV1fHCy+8wFe+8pWs7RMmTGDRokU888wzPProo7hcLubOncumTZsOeKyFCxdmZpX9fj9VVVXHevgiIiIi0g8M0zTN/h4EdJUqPPnkk1x22WWHtf/ChQu566672L17Nw6H44D7pdNpZsyYwZlnnsl99923331isRixWCzzPBAIUFVVRUdHBz6fr1efQ0RERESOvUAggN/v73Ve69ea3yNlmiZ/+MMfuPrqqw8afAEsFgunnnrqQWd+nU4nTqezr4cpIiIiIgPMCVn2sGzZMjZv3sz1119/yH1N02T16tWUl5cfh5GJiIiIyEDWrzO/wWCQzZs3Z55v27aN1atXU1BQwPDhw1mwYAG1tbU8/PDDWe978MEHOf3005kyZUqPY95xxx3MmjWLsWPHEggEuO+++1i9ejW//vWvj/nnEREREZGBrV/D73vvvcc555yTeX7bbbcBcM0117Bo0SLq6uqoqanJek9HRwePP/449957736P2d7ezg033EB9fT1+v5/p06ezfPlyTjvttGP3QURERETkhDBgbngbSI60gFpEREREjo8jzWsnZM2viIiIiMiRUPgVERERkSFD4VdEREREhgyFXxEREREZMhR+RURERGTIUPgVERERkSFD4VdEREREhgyFXxEREREZMhR+RURERGTIUPgVERERkSFD4VdEREREhgyFXxEREREZMhR+RURERGTIUPgVERERkSFD4VdEREREhgyFXxEREREZMhR+RURERGTIUPgVERERkSFD4VdEREREhgyFXxEREREZMhR+RURERGTIUPgVERERkSFD4VdEREREhgyFXxEREREZMhR+RURERGTIUPgVERERkSFD4VdEREREhgyFXxEREREZMhR+RURERGTIUPgVERERkSFD4VdEREREhgyFXxEREREZMhR+RURERGTIUPgVERERkSFD4VdEREREhgyFXxEREREZMhR+RURERGTIUPgVERERkSFD4VdEREREhgyFXxEREREZMhR+RURERGTIUPgVERERkSFD4VdEREREhox+Db/Lly/nkksuoaKiAsMweOqppw66/9KlSzEMo8dj/fr1Wfs9/vjjTJo0CafTyaRJk3jyySeP4acQERERkRNFv4bfUCjEySefzK9+9atevW/Dhg3U1dVlHmPHjs28tmLFCq688kquvvpq1qxZw9VXX80XvvAF3nnnnb4evoiIiIicYAzTNM3+HgSAYRg8+eSTXHbZZQfcZ+nSpZxzzjm0tbWRl5e3332uvPJKAoEAL7zwQmbbhRdeSH5+Po8++uhhjSUQCOD3++no6MDn8/XmY4iIiIjIcXCkee2ErPmdPn065eXlzJs3j9deey3rtRUrVnD++ednbbvgggt46623Dni8WCxGIBDIeoiIiIjI4HNChd/y8nJ+97vf8fjjj/PEE08wfvx45s2bx/LlyzP71NfXU1pamvW+0tJS6uvrD3jchQsX4vf7M4+qqqpj9hlEREREpP/Y+nsAvTF+/HjGjx+feT579mx27tzJL37xC84888zMdsMwst5nmmaPbXtbsGABt912W+Z5IBBQABYREREZhE6omd/9mTVrFps2bco8Lysr6zHL29jY2GM2eG9OpxOfz5f1EBEREZHB54QPv6tWraK8vDzzfPbs2SxZsiRrn5dffpk5c+Yc76GJiIiIyADTr2UPwWCQzZs3Z55v27aN1atXU1BQwPDhw1mwYAG1tbU8/PDDANxzzz2MGDGCyZMnE4/H+dOf/sTjjz/O448/njnGLbfcwplnnsmdd97JpZdeytNPP83f//533njjjeP++URERERkYOnX8Pvee+9xzjnnZJ53191ec801LFq0iLq6OmpqajKvx+NxvvnNb1JbW4vb7Wby5Mk899xzzJ8/P7PPnDlz+Mtf/sK3v/1tvvOd7zB69GgWL17M6aeffvw+mIiIiIgMSAOmz+9Aoj6/IiIiIgPbkOrzKyIiIiJyJBR+RURERGTIUPgVERERkSFD4VdEREREhgyFXxEREREZMhR+RURERGTIUPgVERERkSFD4VdEREREhgyFXxEREREZMhR+RURERGTIUPgVERERkSFD4VdEREREhgyFXxEREREZMhR+RURERGTIUPgVERERkSFD4VdEREREhgyFXxEREREZMhR+RURERGTIUPgVERERkSFD4VdEREREhgyFXxEREREZMhR+RURERGTIUPgVERERkSFD4VdEREREhgyFXxEREREZMhR+RURERGTIUPgVERERkSFD4VdEREREhgyFXxEREREZMhR+RURERGTIUPgVERERkSFD4VdEREREhgyFXxEREREZMhR+RURERGTIUPgVERERkSFD4VdEREREhgyFXxEREREZMhR+RURERGTIUPgVERERkSFD4VdEREREhgyFXxEREREZMhR+RURERGTIUPgVERERkSFD4VdEREREhox+Db/Lly/nkksuoaKiAsMweOqppw66/xNPPMF5551HcXExPp+P2bNn89JLL2Xts2jRIgzD6PGIRqPH8JOIiIiIyImgX8NvKBTi5JNP5le/+tVh7b98+XLOO+88nn/+eVauXMk555zDJZdcwqpVq7L28/l81NXVZT1cLtex+AgiIiIicgKx9efJL7roIi666KLD3v+ee+7Jev6Tn/yEp59+mmeffZbp06dnthuGQVlZ2WEfNxaLEYvFMs8DgcBhv1dEREREThwndM1vOp2ms7OTgoKCrO3BYJDq6mqGDRvGZz7zmR4zw/tauHAhfr8/86iqqjqWwxYRERGRfnJCh9+77rqLUCjEF77whcy2CRMmsGjRIp555hkeffRRXC4Xc+fOZdOmTQc8zoIFC+jo6Mg8du7ceTyGLyIiIiLHWb+WPRyNRx99lO9///s8/fTTlJSUZLbPmjWLWbNmZZ7PnTuXGTNm8Mtf/pL77rtvv8dyOp04nc5jPmYRERER6V8nZPhdvHgx119/PY899hjnnnvuQfe1WCyceuqpB535FREREZGh4YQre3j00Ue59tpreeSRR7j44osPub9pmqxevZry8vLjMDoRERERGcj6deY3GAyyefPmzPNt27axevVqCgoKGD58OAsWLKC2tpaHH34Y6Aq+X/rSl7j33nuZNWsW9fX1ALjdbvx+PwB33HEHs2bNYuzYsQQCAe677z5Wr17Nr3/96+P/AUVERERkQOnXmd/33nuP6dOnZ9qU3XbbbUyfPp3vfve7ANTV1VFTU5PZ/7e//S3JZJKbbrqJ8vLyzOOWW27J7NPe3s4NN9zAxIkTOf/886mtrWX58uWcdtppx/fDiYiIiMiAY5imafb3IAaaQCCA3++no6MDn8/X38MRERERkX0caV474Wp+RURERESOlMKviIiIiAwZCr8iIiIiMmQo/IqIiIjIkKHwKyIiIiJDRq/D76c//Wna29t7bA8EAnz605/uizGJiIiIiBwTvQ6/S5cuJR6P99gejUZ5/fXX+2RQIiIiIiLHwmGv8PbBBx9k/rxu3brM6moAqVSKF198kcrKyr4dnYiIiIhIHzrs8Dtt2jQMw8AwjP2WN7jdbn75y1/26eBERERERPrSYYffbdu2YZomo0aN4h//+AfFxcWZ1xwOByUlJVit1mMySBERERGRvnDY4be6uhqAdDp9zAYjIiIiInIsHXb43dvGjRtZunQpjY2NPcLwd7/73T4ZmIiIiIhIX+t1+P3973/PV7/6VYqKiigrK8MwjMxrhmEo/IqIiIjIgNXr8PujH/2IH//4x/znf/7nsRiPiIiIiMgx0+s+v21tbVxxxRXHYiwiIiIiIsdUr8PvFVdcwcsvv3wsxiIiIiIickz1uuxhzJgxfOc73+Htt99m6tSp2O32rNdvvvnmPhuciIiIiEhfMkzTNHvzhpEjRx74YIbB1q1bj3pQ/S0QCOD3++no6MDn8/X3cERERERkH0ea13o987tt27bevkVEREREZEDodc1vt3g8zoYNG0gmk305HhERERGRY6bX4TccDnP99dfj8XiYPHkyNTU1QFet709/+tM+H6CIiIiISF/pdfhdsGABa9asYenSpbhcrsz2c889l8WLF/fp4ERERERE+lKva36feuopFi9ezKxZs7JWd5s0aRJbtmzp08GJiIiIiPSlXs/8NjU1UVJS0mN7KBTKCsMiIiIiIgNNr8PvqaeeynPPPZd53h14f//73zN79uy+G5mIiIiISB/rddnDwoULufDCC1m3bh3JZJJ7772XtWvXsmLFCpYtW3YsxigiIiIi0id6PfM7Z84c3nzzTcLhMKNHj+bll1+mtLSUFStWMHPmzGMxRhERERGRPtHrFd6GAq3wJiIiIjKwHbcV3ro1NjbS2NhIOp3O2n7SSScd6SFFRERERI6pXofflStXcs011/Dxxx+z76SxYRikUqk+G5yIiIiISF/qdfi97rrrGDduHA8++CClpaVqbyYiIiIiJ4xeh99t27bxxBNPMGbMmGMxHhERERGRY6bX3R7mzZvHmjVrjsVYRERERESOqV7P/P7P//wP11xzDR999BFTpkzBbrdnvf7Zz362zwYnIiIiItKXeh1+33rrLd544w1eeOGFHq/phjcRERERGch6XfZw8803c/XVV1NXV0c6nc56KPiKiIiIyEDW6/Db0tLCrbfeSmlp6bEYj4iIiIjIMdPr8Pu5z32O11577ViMRURERETkmOp1ze+4ceNYsGABb7zxBlOnTu1xw9vNN9/cZ4MTEREREelLhrnvMm2HMHLkyAMfzDDYunXrUQ+qvx3pWtEiIiIicnwcaV47okUuRERERERORL2u+V26dOkxGIaIiIiIyLHX65nfCy+8kMrKSq677jquueYaqqqqjsW4RI5aMJqktj1CMpGipiOMBYOUaWKaYLMYWCyQSKYp9DoZVuAhGEsRiifJcdioyHOT47JlHWd/rx3O+XtzzCM9l4iIiByeXl9Vd+/ezZ/+9CcWLVrE97//febNm8f111/PZZddhsPh6NWxli9fzs9//nNWrlxJXV0dTz75JJdddtlB37Ns2TJuu+021q5dS0VFBf/xH//BjTfemLXP448/zne+8x22bNnC6NGj+fGPf8zll1/e248qJ7BdbWFWbG6gLC+H+1/dxMQKP69vasJiGITjKdKmiddhZfaoQnLdNmrborgdVopzXQDkeeycN6mrnd+SdQ20hxOZY3e/Nizfc9DzH+h9BzrmaSMLeG97K62h3p1LREREDl+vyx4KCgq4+eabef/993nvvfcYP348N910E+Xl5dx8882sWbPmsI8VCoU4+eST+dWvfnVY+2/bto358+dzxhlnsGrVKv7rv/6Lm2++mccffzyzz4oVK7jyyiu5+uqrWbNmDVdffTVf+MIXeOedd3r7UeUEFYwmWbKugVHFPu79+0b8HgfLNjYRiqdoCcWpbY/QHIzREoqzsTFIbVuUFVtb2NwYJBxPAtAeTvDutlae/6AuK6R2v7ZkXQPBaPKg59/f+w50zMZAjEVvbsdqGL06l4iIiPROr8Pv3qZNm8a3vvUtbrrpJkKhEH/4wx+YOXMmZ5xxBmvXrj3k+y+66CJ+9KMf8bnPfe6wzvfAAw8wfPhw7rnnHiZOnMhXvvIVvvzlL/OLX/wis88999zDeeedx4IFC5gwYQILFixg3rx53HPPPQc8biwWIxAIZD3kxFXbHqE9nKA+EGPljnbK/S62NIXIcdoIxpKkTYglTWwWC+3hBFaLQWc0STyZpjUUzxwnGEtS0xre7znawwlq2yMHPf/+HOiYndEENa1h9td65WDnEhERkd45ovCbSCT461//yvz586muruall17iV7/6FQ0NDWzbto2qqiquuOKKvh4rK1as4Pzzz8/adsEFF/Dee++RSCQOus9bb711wOMuXLgQv9+feaiO+cQW2jN72xGOYwLxVFekNE3YO12mTZO0aZJIpwFImSaxRDrzeiyRJpH65Pm+umeJD3T+/TnQMeN7tsWS+z/fgc4lIiIivdPr8Pvv//7vlJeXc+ONNzJu3DhWrVrFihUr+MpXvoLX66Wqqoqf/vSnrF+/vs8HW19f32NZ5dLSUpLJJM3NzQfdp76+/oDHXbBgAR0dHZnHzp07+3zscvx4HV2l7H6PAwNwWLtKCQwD2KuqwGIYWAwDu6Xrr4HVMHDaP/kr4bRbsFsP/FfE49h/ybz3ANsPdkzHnm1O2/7Pd6BziYiISO/0+oq6bt06fvnLX/JP//RPB7zBraKi4pgtgWzsUxPZvUbH3tv3t8++2/bmdDpxOp19OErpT5V5bvI8dsp8TmZW51HXEWV0sZdgLEmO00Y8GcduNUim0+R57KTSJrkuGw6bhQLvJz/TOU4bwws8hOKpHufI89ipzHMf9Pz7K3040DH9bjtzRxficVjJddpwOqwYpklLKI7PfeBziYiISO/0Ovy+8sorhz6ozcZZZ511RAM6mLKysh4zuI2NjdhsNgoLCw+6z76zwTJ45bhsnDeplBWbG7jl3HH85rXNXDC5lLe3tpI2TXKcNlLpNG6HjXElOeS6bcweVYjbYc3MsOZ57Jw6sgA4cLeHA7Ug6z7//t63v2NaDBhXlsumhk6eXbObyJ7Si9JcJ/MmlTKzOl/tzkRERPrIEV9R161bR01NDfF4PGv7Zz/72aMe1IHMnj2bZ599Nmvbyy+/zCmnnILdbs/ss2TJEm699dasfebMmXPMxiUDz7B8DxdNraK2PcJ/XDCe93e2cem0CmyWrtoHr8NKjttKPGFS6HVk+vyG40k8DhuVe/XXvWJm13H299rBzn+w9+39mttuZfnGJqwWCxPL/XRGEyRSaexWCw0dUfLcvWshKCIiIgfW6/C7detWLr/8cj788EMMw+hRdpBK9fwV8YEEg0E2b96ceb5t2zZWr15NQUEBw4cPZ8GCBdTW1vLwww8DcOONN/KrX/2K2267jX/7t39jxYoVPPjggzz66KOZY9xyyy2ceeaZ3HnnnVx66aU8/fTT/P3vf+eNN97o7UeVE1yOqytwPraymY5ICsj+2czz2LliZtUhg2yOy8b4stwjOv+B3rf3axvqOzNlEA6bhcKcT0pwQvEUte2RIzq/iIiI9NTrG95uueUWRo4cSUNDAx6Ph7Vr17J8+XJOOeWUXi99/N577zF9+nSmT58OwG233cb06dP57ne/C0BdXR01NTWZ/UeOHMnzzz/P0qVLmTZtGj/84Q+57777+Kd/+qfMPnPmzOEvf/kLDz30ECeddBKLFi1i8eLFnH766b39qDIIHKzt2PFsIRaMJtlQ38n7NW1srO/M6tt7sO4QoE4PIiIifckwu6duD1NRURGvvvoqJ510En6/n3/84x+MHz+eV199ldtvv51Vq1Ydq7EeN4FAAL/fT0dHBz6fr7+HI0fh/Zo2lm1oOuDrZ48vZvrw/GM6hoOt9jYs38OG+k6e/7DugO+fP7VcM78iIiL7ONK81uuZ31QqRU5ODtAVhHfv3g1AdXU1GzZs6O3hRI6pg7Udg2PfQuxgq711r9zW3R1ifw7WVUJERER6r9fhd8qUKXzwwQcAnH766fzsZz/jzTff5Ac/+AGjRo3q8wGKHI3+DpaHU3bR3R1i33EeqquEiIiI9F6vr6rf/va3CYVCAPzoRz/iM5/5DGeccQaFhYUsXry4zwcocjQO1nbseATLw63nPVR3CBEREekbvb6ynn322SSTXRfsUaNGsW7dOlpbW8nPzz/oQhIi/aU/g2Vvyi6OtKuEiIiIHL7DLntobm7m4osvJicnB5/Px5w5c9i6dSsABQUFCr4yoHUHy+nD8xlflnvcZlT7u+xCREREsh12+F2wYAErV67kjjvu4Oc//znNzc38f//f/3csxybSJw7WZuxYUz2viIjIwHLYrc6GDx/OAw88wPz58wFYv349U6ZMIRKJZFZXGyzU6mzwOFSbseMlGE2qnldERKQPHfNWZ7t3784sRgEwYcIEHA5HptWZyEBzOG3Gjpfu1eY8DhuheJLd7ZHjen4RERHpcthTT6ZpYrNl726z2Uin030+KJG+cDhtxo7XDWYDZQZaRERkqOtV+J03b15WAA6Hw1xyySU4HI7Mtvfff79vRyhyhAbKssGHmoG+YmaVSiBERESOk8O+4n7ve9/rse3SSy/t08GI9KX+Xt2t20CagRYRERnqjir8igxk3W3G9hc8j2ebsYEyAy0iIiJHsLyxyIlioLQZGygz0CIiInIEK7yJnEgGwrLBA2UGWkRERBR+ZQjo72WDu2egD9TtQTe7iYiIHD+66oocBwNhBlpEREQUfkWOm/6egRYREZHDDL/33XffYR/w5ptvPuLBiMiBdS+RHIonyXHYqNDMsYiISK8Zpmmah9pp5MiRh3cww2Dr1q1HPaj+dqRrRYscK1ohTkREJNuR5rXDmjbatm3bEQ9MpL+d6DOmWiFORESk7+iKKYPaYJgx1QpxIiIifeeIwu+uXbt45plnqKmpIR6PZ712991398nARI7WYJkx1QpxIiIifafXV/5XXnmFz372s4wcOZINGzYwZcoUtm/fjmmazJgx41iMUeSIDJYZU60QJyIi0nd6vbzxggULuP322/noo49wuVw8/vjj7Ny5k7POOosrrrjiWIxR5IgMlhnT7hXi9kcrxImIiPROr8Pvxx9/zDXXXAOAzWYjEomQk5PDD37wA+68884+H6DIkfI6bFgMKM5xUJTjINdpoyjXSXGOA4tx4syYdq8Qt28A1gpxIiIivdfrq6bX6yUWiwFQUVHBli1bmDx5MgDNzc19OzqRo1CZ52ZEkZdX1jXQ0BnLbC/NdTJvUukJNWOqFeJERET6Rq+vnLNmzeLNN99k0qRJXHzxxdx+++18+OGHPPHEE8yaNetYjFHkiO1qC1GU66Ai300iZeKwWoglk+xqC/X30HpNK8SJiIgcvV6H37vvvptgMAjA97//fYLBIIsXL2bMmDH893//d58PUORI1bZHKPK6WFfbSU1bmHTaxGIxGJ7vYXyp74S54U1ERET6Tq/D76hRozJ/9ng83H///X06IJG+Ek2keHNzM82heFZ9b3Mozpubm5lQrtX7REREhppe3/A2atQoWlpaemxvb2/PCsYi/S0cT2bV+u6toTO2324PwWiSDfWdvF/Txsb6ToLRE6MjhIiIiByeXs/8bt++nVQq1WN7LBajtra2TwYl0hcshoHLbiGaSPd4zWW3YDWMrG2DYTU4ERERObjDDr/PPPNM5s8vvfQSfr8/8zyVSvHKK68wYsSIPh2cyNHI8zgYVZzD7rYI8VSaZMrEZjVwWC1U5LvxexyZfQfLanAiIiJycId9Nb/ssssAMAwj0+e3m91uZ8SIEdx11119OjiRo1GZ56Yox45pmtgsBvFkGqfNSiKdpigne3GIwbIanIiIiBzcYYffdLrrV8cjR47k3Xffpaio6JgNSqSvjC3x8Y9tO9jcGMxsG1OSw5zR2T+/g2U1OBERETm4Xv8ed9u2bcdiHCJ9bldbmOc+2I3VMJhc4SOZMrEYBjabhVc+bmB4gSfT8cF7iNXeTpTV4EREROTget3tAWDZsmVccskljBkzhrFjx/LZz36W119/va/HJnJUGjqjNAS6uj1sbgzyQW0HH9S2s2ZnO6tq2qnriGT2rcxz91g+uFuex35CrQYnIiIiB9br8PunP/2Jc889F4/Hw80338zXv/513G438+bN45FHHjkWYxQ5It03uG1o6KQpGMdigNVigGkSTabY0hjMtDLLcdk4b1JpjwDc3e1BN7uJiIgMDoZpmmZv3jBx4kRuuOEGbr311qztd999N7///e/5+OOP+3SA/SEQCOD3++no6MDn00IIJ6pVNW384qUNfFDbQa7TRiCaIJ7sql23WgyumzuST08o5pQRhZn3BKNJatsjhONJPA4blXluBV8REZEB6EjzWq9nfrdu3coll1zSY/tnP/tZ1QPLgOK2W6kq8PQIvk6bhYunVpDrtLGhvpOPdnVkzQCPL8tl+vB8xpflKviKiIgMMr0Ov1VVVbzyyis9tr/yyitUVVX1yaBE+kIkkeJTYwoZVezFajFw2a3kumxceepwAtE4i97axt8/buQv79bw2Mqd7GoL9/eQRURE5Bg77GmtL3/5y9x7773cfvvt3HzzzaxevZo5c+ZgGAZvvPEGixYt4t577z2WYxXpFY/DRjCa4KLJZZwxtphIPEVFnovXNjTyztZWCnO6FrlIpNJazEJERGSIOOyaX6vVSl1dHSUlJTz55JPcddddmfreiRMn8n/+z//h0ksvPaaDPV5U8zs4BKNJXl3fwDvbWnj+w3rawgk+e3IFSzc0MrrYS67LRjxpMqYkh8IcJwDzp5ZrMQsREZETwJHmtcOe4to7I19++eVcfvnlvRuhSD/Y2NDJ2toApblOyvwufC4bTruF9kiCcDxFgddBruuTDg9azEJERGRw61XNr2EYfT6A+++/n5EjR+JyuZg5c+ZB+wVfe+21GIbR4zF58uTMPosWLdrvPtFotM/HLgNbbXuEna1h0qZJic9FOJYimTYJhBPsaAmT47RR6nPhsH3y10CLWYiIiAxuvbrSjxs37pABuLW19bCPt3jxYr7xjW9w//33M3fuXH77299y0UUXsW7dOoYPH95j/3vvvZef/vSnmefJZJKTTz6ZK664Ims/n8/Hhg0bsra5XK7DHpcMDqF4kngqTSieAuLMGVPA+NIcPj2hhPpAdE/ZQyqzvxazEBERGfx6FX7vuOMO/H5/n5387rvv5vrrr+crX/kKAPfccw8vvfQSv/nNb1i4cGGP/f1+f9b5n3rqKdra2rjuuuuy9jMMg7Kysj4bp5yYvA4bDquFQDjOlIpi3tzcwl9X1nLJSeWE4inawwlOGtb185TnsXPWuGJq2yOE4klyHDYq1ONXRERk0OnVlf2f//mfKSkp6ZMTx+NxVq5cybe+9a2s7eeffz5vvfXWYR3jwQcf5Nxzz6W6ujprezAYpLq6mlQqxbRp0/jhD3/I9OnTD3icWCxGLBbLPA8EAr34JDJQFXgdJFImE8p9rNrZRk1r13LGz35Qx1njihlXmoPfbWfO6GJy3VaWbWiiNZTIvL97dbdh+Z7++ggiIiLSxw675rev632bm5tJpVKUlpZmbS8tLaW+vv6Q76+rq+OFF17IzBp3mzBhAosWLeKZZ57h0UcfxeVyMXfuXDZt2nTAYy1cuDAzq+z3+9WveJBoDcU5ZUQ+Uyr91LV/UvPtsVup74iyqTHIzj2B+LX12cEXyLQ/614AQ0RERE58R9TtoS/tG6pN0zysoL1o0SLy8vK47LLLsrbPmjWLWbNmZZ7PnTuXGTNm8Mtf/pL77rtvv8dasGABt912W+Z5IBBQAB4EQvEkLcE4PpeNacPzSKVNLAa47FbiyTSplEkqbVLfEaE9nNjvMdrDCWrbI2p/JiIiMkgcdvhNp9N9euKioiKsVmuPWd7GxsYes8H7Mk2TP/zhD1x99dU4HI6D7muxWDj11FMPOvPrdDpxOp2HP3g5IXgdNmxWC3ablcZAlJQJ5T4XW5tChGJJKvPdhOIpTokmCEQT+PZqebY3tT8TEREZPHq9vHFfcTgczJw5kyVLlmRtX7JkCXPmzDnoe5ctW8bmzZu5/vrrD3ke0zRZvXo15eXlRzVeOfFU5rkp8NopzXVy6sgCRhV6wQC/247fY8fjsFGa68RutbC1KUg8uf9/4Kn9mYiIyODRr1f12267jauvvppTTjmF2bNn87vf/Y6amhpuvPFGoKscoba2locffjjrfQ8++CCnn346U6ZM6XHMO+64g1mzZjF27FgCgQD33Xcfq1ev5te//vVx+UwycOS4bJwyooA/vLGVHIeNUCLJ2toORhXnMKY4B7/HzqcnlmKYJgUeB53RRGalt25qfyYiIjK49Gv4vfLKK2lpaeEHP/gBdXV1TJkyheeffz7TvaGuro6ampqs93R0dPD4449z77337veY7e3t3HDDDdTX1+P3+5k+fTrLly/ntNNOO+afRwaWYDTJm5uaCUZTdESSzBiez0VTyqhpiZAy0yTTJn9esYNSn5MLJpexri67y0d3twe1OxMRERk8DPNY3cl2AjvStaJlYNlQ38kfV2xnU2MQgCmVPt7a0kJ9R4STKv2ML/cRS6TxOGzYLDD/pHIshoVwPInHYaNSfX5FREQGrCPNa7qyy6AViidJpNIUeOykTJMcp41oPMX8qeW8vbWVDQ1BUmkTh81CVYGHs8aXcMqIvlvERURERAYehV8ZtLwOGynTZF1dgNZQHI/TRlWBm7e3tNDYGSPHZccwTGwWg2gixavrG5lQ5tNsr4iIyCDWb90eRI61HKeVls447ZEEbocVj8NKSa6LXW0RrBYDn8tGSa6L4lwXTpuVQKSrp6+IiIgMXgq/MmjtbA0zucLHpHIfkXiK7U0h7FYLhgEOW9ePvtdpw2oxcNkt5Lrs6ukrIiIyyCn8yqDVHkmwvqGT0lwnF59UQWW+h2nD8yjOddEeThCOp0ilTVx2C6OKc3DYLOrpKyIiMsjpSi+Dlt9tJ5ZM887WFuLJNCZQ3xGm3O8i12WjOMdJUa6TklwXDptFPX1FRESGAM38yqA1vMBDvtueCb4A79e0U5nvxmHtamlmQCb4njepa1ntDfWdvF/Txsb6ToJRlUGIiIgMJpr5lUErGEtx0dQyNjUG2dLU1es3kTLZ3BDkn2ZW0hZOMK40lzEluVTmuWmPxHls5U7aw4nMMbpD8bB8T399DBEREelDCr8yaIXiSbY3hzhrXBHzJpYQT6Zx2izkuGzs7ojicdjwuT4pdViyriEr+AK0hxMsWdfAFTOr1AJNRERkENDVXAYtr8NGUyDOmNIcnli1iy1NQc6dWMr7H7YRTqQ4pbqA5s4Ym5uCTK/KIxDJDr7xZJrOaIK6jggf1rYztTJPAVhEROQEpyu5DFqVeW4mVvp4anUtZT4XV5wyjNq2KJ8aa8NiQCiWwuu00h5O8PK6Bir8LpqCcQAC0QRbm4JEE2kA1u4OsL6+UyUQIiIiJziFXxmUgtEkte0RcpxWplfl4XZYeeXjRpZuaMJqMXA7rAzP93DhlHLq2iPEU2nK/S6ga8Z37+AL4LRZaA7GVQIhIiJygtMVXAadXW3hTP1uXUcEm8VgR00Ym9XA57JjGBBLptnSFOS1DY2MK8mhNRQnkeoKu53RRFbwLc11Yuz5c3u4axW48WW5RzXG7nAeiifJcdioyHMrUIuIiBwHutrKoBKMJrNuXHNYu25w294SYniBJ1PmYJpdzc8aAlGmD8+jozFIca6LZCpCXSo7+M4dU0R9IJrZdrSrwO0dzrupq4SIiMjxofArg0pteyQrVPrddvLcNi6fXonTZqEpGMdiQHs4zjtbW4Gu9mcluU4CkThnjitm6jA/a3cHcNosGEB9IEra/OQcR7MK3L7hvJu6SoiIiBwfusrKoBLaa1bWYsDoEg9+t4Ofv7SB9fVBCr12gtEkw/LdzJ9azqvrG8hz2xlXkkN9IEokkWJqZR7r6ztp3nPz296OdhW4fcP53vqqpEJEREQOTCu8yaDi3WtWttDrwOe08+g/drK7vatsoT2cwGm30BFJsnpnO1fPqmZyRS6JdBqvw0Y0kQLgvEml5HnsWcfuLk04mpnZ0CFKJo62pEJEREQOTjO/MqhU5rnJ89i76nqBYDzFa+sbGVOSg8WI0h5JEoimgBQmJiOKcvj7xw1sbQrhtFtoCcUzLc2umFlFbXuEcDyJx2Gjsg9uSvMeomTiaEoqRERE5NB0pZVBJcdl47xJpSxZ10AskQYzScqEbc0hSnKdlPhcmCYYBowp8vLS2npW1bQzsshLRb4bh82SVX/b1yUIe4fzfR1tSYWIiIgcmsoeZNAZlu/hiplVTK704ffaGZbnYnRxDm6HDYOu0oPNjUHsNisdka4yiAKvA5/rkzKH7vrbvtYdzo9FSYWIiIgcmq60MijluGxMrczjw9o2Sv0u3t/RTnfDBp/LxtiSHIpy7DQHu0oN0ntan3UvaRxPpaltC/dJqcO+usP5kZZUqEewiIjIkdMVUwa1NzY1M29CKbFEms1NQTAhkUrjd9uZPaaIX/99Mw6HFbvV0mNJ49r2CI+t3HlM+u/muGxHVFKhHsEiIiJHR+FXBq3a9ggdkSTbW8OcMiKfT40pIpZK4bRaCUYTRGNJSvxOosk0boeVjQ2dmeDbvarbQOq/qx7BIiIiR081vzJoheJJHDYLdW1RVu5o470drYSiKULxJIFYksbOOP9y2nBOG5lPJJ7KCr5zxxTREurq83us6n9763B6BIuIiMjBaZpIBi2vw0Z7JE5TMEZnLMG5E0tZuaOVxs4YPredxs4YJwX8/POpw9nVHmZEkfeAq7oNhP676hEsIiJy9DTzK4NWgddBKJokGEsytcLHe9tb2d4SJhxPkUqbOKwGu9ujLNvUREmui85okuZgnKZgPCv4wsDov6sewSIiIkdP4VcGrdZQnBnDCyjwOijMcbGzNYLXaaOqwMOYkhwMwO3o6uubSJk92o91Gyj9d7t7BO/PQBmjiIjIQKfwK4NWezjOR7s7GFvipbrQy8zqfEYUeoin0qzY0sKGhiDbm8MEogmS6fSA77+rHsEiIiJHT1dLGbTSpkk4nqQlGGdsqcn6+k4C0a66WKsBTpuFUCzJ1qYgNovlqPvvHo3D7d3bn2MUEREZDHTFlEHL47AxosDL7FEFVBd5+fSEEtpCcWLJNO3hOC67FafdQpnPjc3a9Z59++8Go0k21HdmQmm+10FrKN6nC0z0tnfvkfYIFhEREYVfGcRcditnTyjhvlc2srMtwmkjC9nREqIwx4nVamF9fYAxJbnke+w890EduS57VtjcO5RaDCjzuXhvRxs5LltmKeSjXWBCvXtFRESOL9X8yqDltBv8zxtbcTusnDOhBDD54qxqzp1UytQKH9d/ahQjCr0sWVdPSzDOknUNBPeURewbSgu9Dt7c3ExNa5itTUHiya6ewN0htft9vaXevSIiIseXppRk0NrSGKQk18l721upaY3gslup74jicdiYUuGjpi1MY2eUwhwnsWSaxkCM2vYI48tye4RSE2jojAEQTaTpjCYozHECn4TUIylFUO9eERGR40vhVwatRMpk1c523A4bdR1ROsJxglHbnj6/aT47rZJ7lmzAbrPSHklQ0xpmWlUelXnuHqE0tmf1t0+Onf38SEOqeveKiIgcXyp7kEHLNE2SqTS72yNYDAjFU7SE4nREEryxuYWNDZ18ac4IOqMJ4sk0dR0RNjZ0smRdPel0drh12rP/qtit2c+PNKSqd6+IiMjxpfArg5bdaqHQ6ySZNumMJomnTJJpE5vFwAQaA1EaOmKcPCwPgAq/m8ZAlGAs2bUghsOaOZYBlOZ2lTm47BZyXZ8E1qMJqerdKyIicnzpyiqDlsNqdK3u5nUQjKUwMbEYBjaLQVs4jtVi0BqOMyzfTTCa5NSRBaza2U51kZfWUJizxhWzamc77eEELaE4c8cUZbo9OGxd/27si5Cq3r0iIiLHj66uMmilgVK/k9U72wnGEpgmGAY4bVamVPqJxlNUFXoo8DowTVi1s51U2sRps9AZ7TrGvqH00xNLaQ3F+zykHqx37+EugCEiIiKHpiuoDFouq5WJZT7W1XXy/o72zPYKv4NxJbk0dkapbYtiGAYbG4NAV2mDsWc/t8O231BavKf84Xjo7QIYIiIicnAKvzJoBRNJfrN0C1+eO4ILJ5USS5ok0mmSSZOmYJR5E0p4/qN68qNd9baluU7mjimiPhDts5vNjmbWVgtgiIiI9D1dOWXQCkQSWC0G6xuCNAZiNHZGSaRMLAYUeB3MHlXI188Zw5amIOPKcjGA+kAUn7tvbjY72lnbw1kAQ8sci4iI9I7CrwxaTpuV4QUe3t7awq7WCF6nFYfNggm0huJ8WNvB2JJcLp02rM9vNuuLWVstgCEiItL3FH5l0Mp12RhV7OWdba047BaSaRMHYLMYeOw2WkNx1jcEGFmc0+c3m/XFrK0WwBAREel7/X71vP/++/n5z39OXV0dkydP5p577uGMM87Y775Lly7lnHPO6bH9448/ZsKECZnnjz/+ON/5znfYsmULo0eP5sc//jGXX375MfsMMvAEo0miiRSji3OYPboQqwE+t4NYMkUskcZlt2KzGCSTaV7f1IjXacPrsJJOg9/joCLPTXskftCyhYMF471nbePJruWQk2mTRCqNAdS0hDBNk0gihXev9+59TK/ditdhJRRP9fh83TXJfdkJoqkzxubGTjoiCfLcdkaX5B72zX3qSCEiIieKfr06LV68mG984xvcf//9zJ07l9/+9rdcdNFFrFu3juHDhx/wfRs2bMDn82WeFxcXZ/68YsUKrrzySn74wx9y+eWX8+STT/KFL3yBN954g9NPP/2Yfh4ZGLprbccXuqnId1PTGmZcaS5vb22hPhDDaoDNapDncXDW+BL+/PYOkqbJycPyGVnoZktzmEnlPna1h0nukzu7yxbOmVDMa+ubDhiMu2dtA9EE25qC2CwWalrDhOJJXHYrJT4nz67ZnbnBzue2c9rIAt7b3kprqOuYFgNGFHmpaQ1ljaP7PIcK572xdncHi97cTk1rOLNteIGHa+eOYHKF/7C+b3WkEBGRE4FhmqbZXyc//fTTmTFjBr/5zW8y2yZOnMhll13GwoULe+zfPfPb1tZGXl7efo955ZVXEggEeOGFFzLbLrzwQvLz83n00UcPa1yBQAC/309HR0dWyJaBLxhN8tjKnbSHE5w/oYQ/v7uDaCLNW1u6gm83AxhZ5OGUEQWMK/Hy8rpG6gJRZo8qZHSxF6fNymsbGplU7s8saNEtnkwzqtjDrrZoj/PneexcMbMKgEfe2cGbW5qxGAa1bV2zonarBZ/LTlWBm4ZAjAq/i9NGFlDbHqU5GOPkYX6agvHM8SxGVwgdU5JLMp3O1CQDmc95oDEc7sxrU2eMn724Piv4dhte4OE/LpxwwBngvb/vox2HiIhIbxxpXuu35Y3j8TgrV67k/PPPz9p+/vnn89Zbbx30vdOnT6e8vJx58+bx2muvZb22YsWKHse84IILDnrMWCxGIBDIesiJae9a2+1tYR5fWcu5E0t7BNhhBW5OqS7gyVW1uB12rBaDna0R4qk0NouFzmiSaKKrXGFfndEEHZGeN5tZDLBbDD6sbWdTYydThvko87kw+KQMotzv4uQqPx/VdhBLpmjojGHuOWZNa5h9/yWaNmF7SxiHzcL04fmML8slx2U7rJriw7W5sXO/wRegpjXM5sbOA763L8chIiJyPPTblExzczOpVIrS0tKs7aWlpdTX1+/3PeXl5fzud79j5syZxGIx/vjHPzJv3jyWLl3KmWeeCUB9fX2vjgmwcOFC7rjjjqP8RDIQ7F1rG4gmKchxUtcRoczvZlpVPqm0idViUB+I8vLH9SRSJuF4kvSeX4CkUibhRAq7tWupi0Qq3eMc8VS6axW4vbbZLDC6OJctTUH+sa2VghwnHeEYZT4Xc8cU8WFtB1aLQTSR4rX1jSTTJul01zljyTTxPeeJJXueD3p2dujLThAdkf2H126B/QT9YzEOERGR46Hffx9pGEbWc9M0e2zrNn78eMaPH595Pnv2bHbu3MkvfvGLTPjt7TEBFixYwG233ZZ5HggEqKqq6tXnkIFh7w4J+W474VgSt93Gu9taMekqd+ieXfW5bFgM8DptdBf/WK0GHruVWDJNaa4Tu7XnL0e6Z3O7WQwYWeRl8Xs1bG4MUuB14HHY8LlsXbXGW1qwWrpWkfM4rCT3hF6LpesoTpsFx57z7Buqu+3b2aEvO0H43faDvu5zH/hY6kghIiInmn4reygqKsJqtfaYkW1sbOwxc3sws2bNYtOmTZnnZWVlvT6m0+nE5/NlPeTEVJnnJs/TFeZKcp2MK82hI5JgWlVeVmC1WSCVNjm5Ko902qQ1FKeqwI3DaiGZThOKJZk3qZThBdk3bOV57Fw2vZLUXqXyhV4Hr29qZnNjEJvFwGmzAtAWTrCypg2300qJz5XZ3+uwZfbrXk4512VneIGH/f0TbX+rze39OQ9n/4MZU5Lb43N26643PpC+HIeIiMjx0G/h1+FwMHPmTJYsWZK1fcmSJcyZM+ewj7Nq1SrKy8szz2fPnt3jmC+//HKvjiknrhyXjfMmlZLnsbOzI8g/nzacd3e0cvXsak6uysMEHFYDr8PG+LJcvjx3JC98WEd5nouLp5Qzo8pPczBOUa6TmdX5XHV6NfOnlnP2+GLmTy3niplVjCrOYd7E0kzoM4Hd7VFsFoN8rwPrnhnd7jIHj8NKca6T0lwnkXiKUcVeKvLcVPi7SiJaQnFKfE6unTsiK1TDJ10T9r1pbO/PeTj7H0xxbte59w3A3d0eDtburC/HISIicjz0a7eHxYsXc/XVV/PAAw8we/Zsfve73/H73/+etWvXUl1dzYIFC6itreXhhx8G4J577mHEiBFMnjyZeDzOn/70J37605/y+OOP87nPfQ6At956izPPPJMf//jHXHrppTz99NN8+9vf7lWrM3V7OPEFo0leWV/PRzsD5LhseJwGo4pzCMfTBCNJPC4rRV4HreE4GxqCzByej9ViEEkmGVGQm5mxPFjv2u7ettubg7y8roEdLeFM8N3bpyeUcNIwPx6HjXA8idUwcDts2K1d4di916py3cc83NXmerv/wXT3+Q1EkvjcNsYcQZ/fvlwlT0RE5GCONK/169XpyiuvpKWlhR/84AfU1dUxZcoUnn/+eaqrqwGoq6ujpqYms388Hueb3/wmtbW1uN1uJk+ezHPPPcf8+fMz+8yZM4e//OUvfPvb3+Y73/kOo0ePZvHixerxO8TkuGzkexx4nFYefns70USaULzrRrbpw/MZXeQlZ09N7q6WMKl0mmC0a8GJuaNKDtpDN8/t2CcUeyjJddEcjBFN9LxhrcBrZ2pl3mGFwRxX14x0d5jc2Nh50EUjuvfvC8W5zsMOu8dyHCIiIsdSv878DlSa+T2xdQfHtlCMTQ1BfvXaZlKmicNqcOa4Ej7Y1UF7OE57OM7kSh8um5VzJpRQ2xYhlkzzr7OqWbG1Zb8tvGxWGJbnYXvLJ63BRhR62NUepjWUYGtTMCsADy/wcNOnRzOiMOewx76+voOP6zrBMDBMk5ZQHJ9bi0aIiIjs7YSc+RXpa3uvNua0QWWehwnlucQSKSrzPayrC7CrLUwqbZLjshGOp9jZGqHM72LqMD917TEaO6P7Db7xZJrVOzvIm+jI2l7TGmZEkRenNYrLZqUzmiCRSlPqc3HZ9MrDDr672sI8/0Edb25pzgTo0lxnZhW4JesatGiEiIjIUdJVVAaNYDSZVarQ0BGnusBDkddBmd9Fqd+F1YApFT7qO6LUd0TwuezkOG20RxL43HbaQgkSqf3/MqQzmiCaSPfoxZs2YXtziAsml2EYxhHVvXaPvaY1nDVz3NAZ483NzZw2soCmYJza9ojKC0RERI6Cwq8MGvuuNuZx2qhpDTFrdCEvfljPqxua2N4cwmoxmFTmY86YIp5ZvZvWcJyqfA9bG4OMLsmlJNcFdPQ4fvdCFPvrxZs2IZJIMX14/lGNPb6fRTW6V4EDLRohIiJytPqt1ZlIX9t3tTGHzUK+18mTq2qx2SyU+1wUeh0Uep2sqw/w0roGRhXnYLUYGAakgZrWEGV+13571zqslkxf3v05mgUdusfu2M+iGvDJym9aNEJEROToKPzKoLHvamPJVBqnzcZHtQHW7g6woyVEIJokkkiSTJtdtb4+F06bldHFORR5nCRT0BqK77d37fACD/MmldISivc499Eu6NA99lyXHZe9519Lp82iRSNERET6gKaRZNDoXm2su/TBZbewsaETm8Xo+leeYTCqyEtbOEFzMI7DaoABJw/zM2d0IR3RrveF40nGl+VyxcyqHr1r2yNxWkPx/bZAO5ob0fYe+6jinKyuEaW5TnKcNk4dWaCb3URERI6SrqQyaHSvNvbJTW9dBQqVeW48TivJtInNYmFShYsCrx2rxWDG8Dw21neyqSFI8Z4liLtLCw7Uu/aU6gIaO6PYrQYluS6G5XuOOpTuPXaASeV+OqNdN+GdP6mUMSW5Cr4iIiJ9QFdTGVSG5XsyM7br6zvojCRx2S2s3NFGd5OGCr+LMr8Lt8PK6p3trN0dYEqFj3gyTYnPecDSgr3bqHXrmvW19Ukw3XvsWilNRETk2FDNrww63TO2RV4Hf/tgN1WFXsr3CrS7O6I4bRbmTy2nriNKZzRJTWuEYCzBaQcoLdi3jVq39nCCJesaCEb7pgtD99inD89nfJlme0VERPqarqwyaEWSadrDCQwjwqUnV+CwWbBbDFKmyYa6TgzT5J9mVNIajtPcGccE1tS0UZLr6rHM775t1PbWHk6o/66IiMgJQuFXBq1wLMmFU8tZsraB3yzdQvfaFaOLvZw7oYTQntKCpvYYpfkukmlIAe9tb2FihS9rZbZ926j1OJf674qIiJwQFH5l0CrJdfGXf+ykriOC32MHEzCgMRDlpXX1XDN7BHkeJ5OrbCx+dydrdwfwOKzkex2cUp3PdXNGMqqkKwDv20ZtX+q/KyIicmLQFVsGrZRp0h5JEIgm2HtFYgOwWhIEYyk6InGeXr2beCrNxHIfpmmSSKV5d1sr+R47N5w5hhyXrUcbtb2p/66IiMiJQze8yaDVGIgyLN9NSa4L617LsvndNqoLvYTiSUxg7e4A63YHWLOrnZrWME2dMXJddna2RqhtjwCftCLbd+GLvujxKyIiIsePrtgyaFktFurao3gdVmZU55M2TayGQSyRYnd7BK/Dxo6WMIFoErfdQiplYrUYdEQSpE0YVuDOquVVKzIREZETn67aMmj5XDZcdis7WsNsagoBUOZz0h5JUJXvxue20RqKYbcaWC0G8WSatNl1V1wo1hV6bRYLG+o7CcWT5DhsVOS51dVBRETkBKbwK4OWBZg+3I/XaWP1zjasFoO0aVKc62TO6CKi8STbmkOMLvZS29ZV3mBgYDEMct02Cj0ONjd2sr0lnDlmd5nDsHzPUY0tGE1S2x7JCtWaQRYRETn2dLWVQSucSlHodVKcm+BrZ48hnkrjcVhJpNJ8VBsATFbvbOf8yWVYDIPd7REsFgOf286kilxOGZHPhoZg1jG7F7W4YmbVIcPqgQLugVeKO/pQLSIiIgen8CuDlt9l5x/bWxlR5MHrtLJtd5B8jwO/y06Jz4XXYaEyz80LH9Zx0ZRyLj6pHNOEHKcNq8WgMRCjqTNGPJXGYbWQ67LjsFkOa1GLAwXcs8YW8/rmpgOuFHc4oVpERESOnK6yMmhF4ynmjiniyVW1FHhdvLG5hcZAlFkjC3E7rBR4HHzzgvG8tqGRxkCUd7dFmVjeVSYxssjLH97cRiiWyhzPZbcwqjgHn8tORzjeoxa4O7QebCnkp1bXUpLrxGJAodeBCcQSaZwOK4ZpaqU4ERGRY0zhVwatlGnyxPu7mDE8n031nZgpkwsnl7G+vpO2cByLYfBxfYDzJpZwxphidrWFmVDuY0xJLss2NmQFX4BoIs2mhk4q89zsbAuzoTGIYZq0hOL43J+ULRxsKeT6QJRSn5Myn4s3NzfT0BnLvFaa62Scgq+IiMgxpfArg9KutjDRhMnu9gifm15JRzRJdZGHQCTB52ZUkjZNwvEUXoeNijwXL6+t57LpFbjtVlpDcaLxNKW5zqxwGkum2N0eJ89jZ3VNOxsbg5TmOpk7poj6QDRTtnCwpZAdVgsuh5Vl65uyjg3Q0BnjjU3NTCr3q/RBRETkGNEVVgad7rKD4hw7X5ozkvX1QZZtbMJuNagu9PLBrg5mjy5id3uEWNIkEE0yd2wRGxs6mT68gHA8SUsozhlji9jREiZpmsSSaZKpNPFUmqmVfp7/sB7oCqxvbm7mtJEFNAXj1O7pH3wguS47VsPoEXyhq6wikVLpg4iIyLGk8CuDTnfZwYTSHJasa6Q+EMVqMRiW72FLU5DTRhbyyvoGOiIJdrdHcdktTK7wccMZo3h9cyPnTSwHIA00BWM0dsaIJlLEU2n8LjuWyuzzNXTGMPf8ORxPMrYk94BLIZf4nOR5HLjsFqKJT9Zc7q4ndtgsWQtriIiISN9S+JVBp7vsIBhLEk+laQzEcNgsOGwWhhd6WbWzjZ2tEcaV5mA1IJU2WbOzneWbmnDZLEQSKYYXeHhtfWNmhrYzmqAjkqAtFGfphiZGF3nZ2PhJG7RYsivIehy2zFLIB2pnFoqlmFTupzOaIJFKY9+rk0T3MUREROTY0FVWBp3usoNoIk17OEGJz8UpI/KJJ9P43XaWb2qmviMKgNNmIZxI47JZqG2LMKo4h9q2CIU5DlrD8cwxrRYDr8PGsHw3GxuDjCrJyTqn02bB6rFTmecGDr4UcjCapMTnzITdveXtdQwRERHpewq/MuhU5rnJ89ix2wxOHpbH3z+u591trXidNpIpE0yTL88dwTtbW4km0tgtBi67hWTaJJU2aQnFaA3FcFgtlPkcWC1dq761huJ0RBLkeeyYppk5X2mukxynjVNHFmTdqJbjsjG+LDez2MXGxs5MW7SDzQzrZjcREZFjR1dZGXS6yw62NAZZVxegPM/Dx3UBOqMJRhR62dAQJBBNcObYYna0hEgDLruVRMrEbrXgslvY1hSmORijPhDl1JEF5LkdeJw2Ks00DpuVyjw3hTlOHFYL06vyGF2Sm9Xnt3tlt3Q6zc7WCDWtYdJ78nJ3yD3QzLCIiIgcO7rSyqA0LN/DR7XtOGwW1u0O0BKMY7MajC3J4eRhfsKJNIU5Ti45qZy2cIJRJTkEowkmV+QSTaTY1RZmWL6HNbva2d0WIcdhY1xJDss3NtEUjNEeTmCzGAwv8FCY68yE1r1Xdosn06yr68DvsmfaoaXN7NXcjrarw4GWUBYREZH901VSBq1kyiSeTNMSipHvtXPuhFJW7WyjM5okEEmwpTHI+NJcqgo9/G3NbkYUeVlbFyDHaafS72JsWS5NnTGiyTRep403NzcTjCeZXOnH57IDEIqnMkEWyCpl6IwmiCbSRBPZ7dCAw1oi+VAOtIRy92IbIiIi0lPPO25EBgmbxcBigNNmZXK5j/d2tFLTEqYtHMdps1CR52JDYydvbGphUoWf1TvbwTQIRhP8Y0cbyVSaCWW5XDatgpFFXnxuO5PKPwm+3bqD7L4ru8VTn7Qy27sdWrejaWl2sCWUl6xrIBhVuzQREZH9UfiVQSUYTbKhvpP3a9rwOK1UFXiIJlIU5TrZ1hwmnjKJJ00sFoNQLEUskeKj3R3YrRbyPA78Hjtuu42S3K5+vJFEikg8hcNm6arx3U+HBugKsvuu7OawZu/b3Q6t29G0NDvYEsrdYVxERER6UtmDDBr71tvOm1jMqSPy+bguQCJlYrFAjsNGvtfBsHw3FsPYs/iFm8IcBx2RBGt2ttMZTWIYMLYkl4umlhGOJ3HaLNR1RHDs05O32/6CbK7LnrWYhdNmoXPPa0fb0uxgSyjD0c0qi4iIDGYKvzIo7F0GEIgm2NoUZEplLq9vauarZ4+mtj1CSzBGIpUmFE+xpTFIdaGHps4YfredcDyJ1TAAcNgspE2TWDLF4nd3cum0CrY3h4jGU+zoDGdWY+suf9g7yO69spvD1rXf1qYgfpcdY89Y+6Kl2cGWUIZjv1CGbrQTEZETla5WMih0lwHEk2m2NgWJJtLEkibDCzw89OZ2rp5VTY7LxtraAAAm0NgZw2IYlPpc1LVHCcWT5HscpE2TCr+HWCLNx3WdXDA5TVs4zvmTS3l9UzO726PsagtTle8hx2ljcoWP3e2R/fbv9bnszB1dxGkjCwBw91FLs+5exvsrfTjWC2XoRjsRETmRKfzKoNBdBtDdYQGgwGPn8ZWtVBd5WLOznRynDavVYGdrBJfNgs3StQjGtOF5PLlqFwZgGFBd4OW0kQW8taWZtGkSjqcoyXXxyseN5DhtTB3mJ99jJ5pI47RbWLahiY5IAp/bzvmTSvnMSRW0huLHtH/voZZQ7s35ejOLe6gb7a6YWaUZYBERGdB0lZJBobsMYO8OC9FkimH5btw2KyOLvcRTaT4/YxgehzVTHtEeimM14DMnVdDUGaM410lHOMGqne2ZG9TyPQ5eXltPQ2cMgHElOby7rZVwPInPbcfnstMe6QqDu1rDnDOhhFNHFhx1D99DOdgSyoert7O4h3Oj3bH+3CIiIkdD4VcGhe4ygJZgLLMtFEty5rhiVtW0sXxjM29uaQYT7FYL1YUeqgs9bGsJ0R5JUO5388JHdcwaVUhnNIkJWC0GY0pysFuNTPAF8Lps7O6IkO9xsLs9gqvYmnmtoTNGMJY8brOg3UsoQ+/rcI9kFlc32omIyIlO4VcGhe4ygEQyzc62MNFEmqp8D29sbqYxEKMlGKPC78Zpt2IxuoJxOJ7i3IllvLq+gStOrWJDQyeNnTHy3HaiyTSji3M4f1Ipmxo793R8sGK1GCRSJjaLgWFAMm2STH/SwTeVNumIJKhpDfNhbTtTK/MADhpK++LmsSOpwz2SWdz+vtFORETkaOlKJYPGsHwPV51ezYzqPD6u66QjmqQyz8Mj/9hJWyhORZ6b5qYgdquFMr+LV9c3Mqo4hy+cUsXOliCXT6ugORRndHEO+V47yVSa9nAC0zRoDcVx2SycMrKA6kI350wowWox2N0WIZHuWkkulkzRFooTjCXZ0RLm47oAjZ0xalpDJFOfjHPvULpvaLUYMLzAQ0Wem2Asid1qUJrrojLf0+d1uEcyi9ufN9qJiIj0BYVfGVRyXDZOGVHIhDI/yzc2UdMWpiUYJ8dppakzStckbZpgNMkp1fn43XbawnEmlPmoaQ2zpSnI+DIfu9oimBhYMBlZ5OGsscUU+Zys3N5GXXuE+o4Y7ZE4ZT43n55YwttbWmgLxSnJdRGKJrFaDKoLPazb3UkwlqQiz43VMGgJxTOh9DMnVfQIvmU+Fy+trWdXW4Ryv4vOaJKKfDfnTSxl6jB/n9bhHsksbl/eaCciItIfdKWSQSnHZcPEJJpIYbMYOO1WOmNd069eqwXD6Lo5rq4jwjtbW/lgVwcFXgczq/N5dk0tiVRXl4fSXCdzxxZx5rhiHn23hu0tIYq8ToYXeLC0G4TiSd7a1MyIIi+ptMlpIwv4oLaDM8YU8er6RpZuaAKgwOtgZKGXuWOKqA9EaQ8n2NzYmRUgC70Olm9sYmNDJ02dMQwDtjeHWLu7g12tYb78qZHkuR19Vod7pLO4fXGjnYiISH/R1UoGLavFIBxPUpHnIhRPYTW6Ql2uy05kT/uyeCLNpsZOQrEk+V4HdpvB6OIc1td3rcXW0BnjzU3NnD2hGIthMK40F6/DRp7HzshiL+3hOM3BONOr83DaLKza2c6MqjxqWsNZSwyn02bXsTY3c9rIApqCcQKR7NBpAjtawzR1xogl06T3qiX+uC7AB7s6qMrzYLNZsuqDj7QO92hmcfe+0U5EROREYjn0LsfW/fffz8iRI3G5XMycOZPXX3/9gPs+8cQTnHfeeRQXF+Pz+Zg9ezYvvfRS1j6LFi3CMIwej2g0eqw/igwwFkzSSZOrTqtmYrmP4QUe8j0O7FYDv9vG5Ao/b25p5oLJ5UQTKba3hKjriDKq2MvYkhxmDs9nSqWPcCJFLPlJEM3z2CnMceJx2KjI8zCmJAePw8bY0lz+aUYlU4f5qWkNY7UYn4xlz58bOmN0H8nvtlOc46Aox0Gu04bFMHDbrST2tGuz7P1+w8DjsPLY+zt5/sM6lm1o4rkP63hs5U7sFoMCr32/30GB106B18GG+k7er2ljY30nwegnM8Hds7jzp5Zz9vhi5k8t54qZVVqsQkRkgApGkwf8f7ocnn6d+V28eDHf+MY3uP/++5k7dy6//e1vueiii1i3bh3Dhw/vsf/y5cs577zz+MlPfkJeXh4PPfQQl1xyCe+88w7Tp0/P7Ofz+diwYUPWe10u1zH/PDKw+J12cj12/vzODsry3EwdlkcqbeJz2xhZ6GF3a4jtLWFMEyaU+1i2sYlkyqS2LcprGxpJpU1ynHZOGuYnEk+ysaETv9tO1V7BsHsp5co8N69taCSaSDMs3820qjw2NHTiddiIJVM4bZ+0Q4sl0xR47eR7nazZVUtNaxiA6cPzaAhEGVWcQ3MwRiT+yV1yxblOWkNxGgJR/G5HZnt7OMGyTU2cM6GY19Y3Zc3gFnjtnDKigL99sPugXSA0iysicmLQCpt9o1/D7913383111/PV77yFQDuueceXnrpJX7zm9+wcOHCHvvfc889Wc9/8pOf8PTTT/Pss89mhV/DMCgrKzumY5eBLRhNYrVbcNi66nvf296KuWfKtarAjd9dzskj8qnc2ERDZ5Tpw/Mw6GpVZrcZJFJpIvE0TZ0xEuk0M6vzqCrwEE2k2NEawuvs+quztSmI32XHZjEYVZzD1qYgraE4NS1hJpTlYrMYdEQSpM2uMozRRV6KchyU+Vw8/8Fu3HYrdmvXPk2dMfI8DlpDMcaV5PJhbUfm84wq9tIWimO3WqjriOCwWsh12XHYLLSHE8QSZo863AKvo0fwhRN7Nba+aAsnInIi0gqbfaffvqV4PM7KlSv51re+lbX9/PPP56233jqsY6TTaTo7OykoKMjaHgwGqa6uJpVKMW3aNH74wx9mheN9xWIxYrFPFjEIBAK9+CQyENW2R2gIxHh4xXYmVeQxrSqfZNrEajFoCET544rt/OiyqVTmeXA7bFTmu/ncjGFU5LlwWA1iSZNkOo3bYSUaTxFPprnkpAre2dpCXUeU5mA0s4zyyVV51HVE8bnsTCr3E4gm2FDfSVWBhxxXVzlCOJakqtDNmp0drN0doDOa5O1trVgtUJjjxLZnXGeNL+LtLa2ZfsRpE6oKPFwwqYzfLt9CjsuO3dpVreSyWxhVnIPPZSccT/aYwd1Q3zmoVmMbKDMeCuAi0h+0wmbf6bf/Yzc3N5NKpSgtLc3aXlpaSn19/WEd46677iIUCvGFL3whs23ChAksWrSIqVOnEggEuPfee5k7dy5r1qxh7Nix+z3OwoULueOOO478w8iAE4onCUSTxJMmtW1hOsJx0mZXO7HOWJJYMk00nqKxM8rW5jCTyny8v7ONT40posibS1W+m45IAoOuuthgLEUwluSkKj+njiygJRTH57Lx7rZWHnlnByU+FxPKffhcdopynDiGWXA7rIT2lC5U5uWwZlcHRbldtcKxRJpU2qS+I0pDIEauy0ZTZ4ydrWHGluYyZ3Qh8yaUUBeI4nfb2NUexmGzZoIvQDSRZmtTkEnl/v3e1LZ3F4h4Mk1nNEE8lc7MGp9Iq7ENlBmPgRLARWTo0QqbfaffpysMw8h6bppmj2378+ijj/L973+fp59+mpKSksz2WbNmMWvWrMzzuXPnMmPGDH75y19y33337fdYCxYs4Lbbbss8DwQCVFVV9fajyABit1jwu22MKvayuz1KbfsnNzz6XDZGFeeQ47YxsdyHz+3A7bQwujiHllCMv30Y7OrXG0ty5vhiDLpmWW1WO/Fkiuc/qMPlsDK+LJcP9pQm7G6PYLUYTK3Mw2Gz4HPZmVaVj8NmIRxPEk2kqG2P4rBZsBhQ4nMyrcpPIOoFE6xWaAvFSaZNPq4LMKrYS7nfxSnV+aTSJtuag4wq8mYtswxdAdhuNfbblsxusdASjBFNptjZEia1p/SCPZ/n7PHFbKjvPCFmMAfCjMdACeAiMjRphc2+02/fVFFREVartccsb2NjY4/Z4H0tXryY66+/nscee4xzzz33oPtaLBZOPfVUNm3adMB9nE4nTqfz8AcvA9qutjCbGzupLvSQ47QRjCWx7dXXJBhLkuuyke+xU13oZUxJDu/vaCOaSBFLpmnds+hFniefNze1kEqnKfG5aA7GGFHoZXKlj9r2CIlUmuEFHmpawyTTJqE9x83dE4AMyPS/fb+mLRN8y3wu3tzczD+2tdLQGSWZMplS6eeMscW8vqmJklwXkViKVBrK/G5C8STB2hRzxxTx5ubmrABcmuvkU2OLeoSu7u8gkUrzwc4OwokURTkO3A47mF030G1p7G7nFgcG9gzmQJjxGAgBXESGLq2w2Xf6Lfw6HA5mzpzJkiVLuPzyyzPblyxZwqWXXnrA9z366KN8+ctf5tFHH+Xiiy8+5HlM02T16tVMnTq1T8YtA1v37FxzZwyP08K8CaUk09AQiGBgYDFgWL6bcyaUsrM1hMWA1zc2sa0lRKHXSUVeV7mDy25hxZZWmoMxTq7Ky8zK7m6PUNcRoSTXxd/XNTBvQikWw2BXW5iZ1fms3NFGMp3e07EhTp7Hzllji4knUtR1RBhR6GX5xiZaQnGqCjyE4ynawnHqOiJEEik+NaaIPI+DVTvbGVeWSziexOuwkTahPhDltJEFmHR1jHDaLBiQ1f1h7+8gEEnwqbFFbGsO4Tft1LZH2NYUYkxpDl67h2fX1HHVrOE0BbtKQgbyDOZAmPHobQBXbbCI9CWtsNl3+vWbuu2227j66qs55ZRTmD17Nr/73e+oqanhxhtvBLrKEWpra3n44YeBruD7pS99iXvvvZdZs2ZlZo3dbjd+vx+AO+64g1mzZjF27FgCgQD33Xcfq1ev5te//nX/fEg5rmrbI9S0htnaFOSkYX7e3tbCvAnFFHidxJMpHDYrYLKlMUiBx4fdYhBNprFaLFgtBvFkmonlPkYUevmwtoPJlUVU+D2k0iZp0yQQTfD0mt2ML8slmTZ5ZX0D04fnc8GUMt7f0Uaex05JrguHzUI8mWZ9XYD3trdy5rhiQtEkgWiCj3Z3kO91kEqblOQ6GZbv7to/laa60MvSjU0UeR0YkFk9rftf+03BeOazdrL/f+3Xtkf29P510BFOcP7kUlpDcUYFvdisBtF4mqUbm0imTZZubGL6sLzMcQfqDOZAmPHoTQBXbbCIHAtaYbNv9Ou3deWVV9LS0sIPfvAD6urqmDJlCs8//zzV1dUA1NXVUVNTk9n/t7/9Lclkkptuuombbrops/2aa65h0aJFALS3t3PDDTdQX1+P3+9n+vTpLF++nNNOO+24fjbpH+3hOFubgnREElgtBmOKc1hV0857O9oI7GkEPrzAzaUnV+D3OAhE4lw0pYyWUJzd7RFaQ3EsBsQTScaW5PKP7a2srunAZjVoDsYp97u4eEp51uprsUQKY8+5y/1dISwQTbCrNcyWpiCBaJLKPDejS3JIpU2SaZO2UJziXBdeZ9dNbNtawphm1ypwRV4Hc8cUEU2mMv9T682/9jsi8T0lFTGmD8+jM5rghQ/r8bvtJFJpvE4bhmFgNaAzksTt+KSzBAzMmyYGwozH4QZw1QaLyLGk3uxHzzBN0zz0bkNLIBDA7/fT0dGBz+fr7+FIL6zY0sxdL2+kqTPKjy6bwsMrdrBqZzuhWBJzT7eHHJeNPI+D00cW4LAavLK+iaIcB5PKfaza2c7c0UVMG+7ngaVbqWkNU57nxjRNOiIJUmmTqnw386eW8+ya3Zw+upB/bGtlaoWf93e2Z/r25jhtFOU4iCbTJFMmE8tyWbs7wMlVfpZvaiZtQkmuk5JcF+FEkt1tEaLJNJdNqyAST5EyTeZNzJ4l7P41+sH+tR+MJvnd8i28s60VgHElOdhtFh57bycOm4V8j4NgLLmnPtlOUY6Ds8YXMzy/qwQjlkxT7ndRnOtkdEkuxbl9Wwt/tKUAh/MdHEuHM6O7ob6T5z+sO+Ax5k8t14VLRKQPHGle0/SDDCoeR9eNbHUdEdojCQwDQrEkkT09eXOcVkKxFNFElGTaJN/jYHxpLqFYkmTa5J9PqeLeVzfRHklQle8hGEuSTpukTBO71YLdCh2RBOV+F5fPGMYbm5rABI/TRkc4jsXrIJaAEYVetjQF2d0eweO00RCIkuO04bZb8TpstITi2CwGDpsFh82Bx27DbjUYW5KLw2YhkTKpbYuwszWMx9H1voo89yFDU217hFTaZGqlj8o8N4YBBV4nK7a0EIx1lV1YDAOf206F301dIEoqDQ2BGB/tbmdrU5jKfDed0STlfhdfPH04BXtqoY82ZPZFKUB/z3gczq8cB8LNeSIicmAKvzKouOxWThtVQHs4gQWDps44DquFHKcN0wS3w0pbKMbEcj/VhV7SaRO/p6sc4IWP6sj3OLh4ajn1gSiTyv18tLuDULwrrIVjSfI8dsaX+VlfHySaTPHR7gA2i0F7OE5JrovGzigzq/N5e1sLRV4noXiKjkiCcCxJNJEmkkjxlU+NZFVNG16nHYth4HRYyXFYOXVk12ItS9Y1ZOqWo4k0pblO5o4pYsXW5h6zwfsKx5OMKPTQEIjx2Mpd7GwNc8qIfIpyHBTkOLBbDAyjawW7XW0Ryn0uchxW/vZhHX63neEFHlpCcVpDMeoDUUzT5JKTK3hnWwsTy3OZUOY/ohA8mEoBDhXAB8LNeSIicmCWQ+8icuKozHMTiiaZUJaL1WJgtXQFYqeta6ljp83CZ06upL4jwnMf7ObltfX8deUuatsjnDW2hGgixZpdHYws8tLUGaMy382EslzGlORwUlUe+V4H25pDeBxWEikTm8Ug3+tga1OI6cPzyPc48LntmKZJTWuYVNrEabOyszVCUzDGP7a18lFtgByXjW3NITY1drKhLkBTZ4x4Is0rHzfQGIhlgi9AQ2eMNzc3YzUMlqxrIBg98Myh225lR0uYZRubaAsncDusrK/rZFiBh2A0SV1HV7/jRMpkcrmP+VPLMIFwPIXbbqUjkqA1FCNlQrnPxfs17Xy0O8DfPqjjl69u5pF3drCrLdzr/y6H0yZssOiuDd4ftSMSEel/moKQQSXHZeOzJ1fym2VbcNhhcoWfXW0Rdu1Z5OJTYwp4Z2sL8VQai2HQGU2QNqGmNUwkkeJLs6p5Y3MSt92G12ljU2MnnZE4dltXNwibxYLfbSXfayfXZWNzkZdEyiSZSvPe9lYq8txU5XuozPPgc9nJddsIRpNYLAZ1HVG2NHZS0xaiKMdJjstGJJ4inkpT0xrm8fd3UZzjpLEzSjoNHoeV7uVeOmNJ7FYLpmGyqy3MhPL91zYlUiZYYHdHBIsBlj0rwr21uZmThuVRXeSh3OcmbZoYQEsovud9aUwgmkyRTJsUeh3UtkfojCaIJbpWqYsmusZ5JDO1Q6kUYCDcnCciIgem/wvLoDOqJIebPj2ahvYoOU4rJw/zY+7qIBBJUOZ383FdJ5Mr/LjtFnam0owvzSGeTNMejpNImyRTJhgmE8p8vL6xGYfNQlMw1jVr53czvToPDNjWHCIYS7K5IUjaNMl12bFaLITjCSaV+1i2qYmP6zqxGmAYMLzAw4VTysA0CMdT1LZFMh0oDCDXZcPEZEN9JxYDHDYLNqsFr8NKSzDO5qYgH+zqoK49Qo7Ltt/yh3A8id1iyVoGOZ5Mk0iZbGkK0h5OkDvWTnjPsssAXoc186v61J6WD3abhc5oV3Bz2q2ZfROp9BG1QxtqpQBqRyQiMnDp/8QyKI0ozGF9XYA3N7dQkOPg8umV2K0WCjx2OiNJ1td3kkqnaQ3FiSbSFOc6mTLMz+72CGnTxGIY/OGNbdxw5siuLg42K15n11+XSCJJfXuMmtYQU8r9RONpalpDRBJJSnxOxpX5eOy9XdS1R/DtCbQGBo2dMTbWd3LqiAJSe5bxdtutOGwWXHYLzcEYbaEEBV4HmxuDhOJJYok0NquR6Qe8dneAQGT/dbK72sI0BLpu5OsOsYYBXmdXiUZgz0186XRXv2Kfq+tX8/leB6ePKmBTQzCz/HG6q+KCkUU5WbOy3aG6tzO1A6FP7/HW3zfniYjI/in8yqAUjCaJxNPUB6J8WNvBq+ubALjxrFG8vafsodzvItdtp8RnpTOaYFtTiDPGFDNnTCFlPidnjC1iZ1uESeU+Esk025rDrN7ZRiSeoj4QxeOwkedxUp7n4tQR+Qwv9LC6pp3NDUHiyTQuu5W2cBybxUI4niTPbcMEkqZJZzTB2t1ds9EmUOF3U5nvpiTXydtbWwjFkmB0rVBot1op8bmoD0SZP6WUHKedYDzJO9taqMr3ULEnOK7c0cbG+gDNoThuh5VdbWGshkGex4HVYuwZr51h+W6Kch1UF3jxexxU5rmZVOFj0Zvb2dYcwmYxsFi6gu+5E0sybdNcdgu5ewJzb2dqVQogIiIDha44Muh0t9Qq8zlpjyQYlu8mx2UnnTYpynFQlOtkS1MIwzAIRBJE4ik8DituhxWfy8Yp1QV8tKuDghwHz39YR1s4TiJpUuJzYrUYzBpdyB/f3kEkkWJLYyfVRTk4bBZe/Kiebc0hhhV4+Ki2g8mVPjojSaLJFA6rhdZQnJZgnOIcB29samZiuY9IPEkq3bUoRkckwaQKH3//uJFgLEkilcbvsZPncTC62Mtf393J1z49lidX12K3WCjMcVDm75pRnVaVx8tr61lfF+BTY4o5c2wRr21oYntziLQZZ3Sxl8kVfqYPz2Nrc4i0CScNy8/MTE6u8HPTp0ezsT5IWyiOz23DZbOwemcbE8p9eBxdwTcST+FzH9lM7YFKAaCrN240kSIcT2IxDPI9Di0HLCIix4SuLDKo7N1Sa0SBm3PGFfPO9lZq2yOYJnxYG+DkYXm47VaiiRROmwXDMLq6OQzzE46nePz9XV0roFkMJpTmsmJrCzVtYQLRBCOLvLxf086nJ5SysaGTQDSJw2qAARsaOgGwWrpaiW1tDGG3WXDZrXgcNlJpM3PTXDKVJtdpw+OwYbXAnKJCyv1udreHGFnkZUZ1Hi6blWA8ya7WEG9tbqHU72Zna5jNjUG8DhulfhfQ1S3hb2t2AxBNplm6sZGplX4+P2MYabNrRbkxJTkEInGSqTQFXgexRJqGQCRTh7p2dweL3txOTWtXJ4dIPEWex86Fk0t5a1UtJgYVfhfzJpUyszr/iEPpvqUAu9rCvPJxAy6blTc3N9PQGcNltzCqOIfhBR4tBywiIn1O4VcGlb1bajmsFsaW5rK5KUgolqTS72JKuY/a9gj/NKMSh81CTUuYYDyJ12Fj6YYmZo8u5L0d7Ywvy2V9fSeNnTHmjCpgZ1uEjkiCaCLNrrZOyv0uPq4L4N8zC9oeSWDQdXNYazDGsHwPLaEY9a1RLBaDcaW5tITiFHgdrNzRxgWTy2gIxKhtCWGxGF1t1fLczJtYyotrG6kPRMn32HlveyvGnkUpXHYLiVQau9VCPJUmHE8ST9px2CzUdUQpzHFgsxik0iZrdnWwdneApmAMA/g/F4zPtEFLmiaJlEkak5ZgnJOq/FnBN5U26YjEaQnFeHEtfP6UKt7f0YbNamFjfScj/v/27jtKrrNM8P/3prqVQ8fq3K2cbVmyjXGQBQ7YZBgyDAwMO8wMYMxvSLvsGWZYBpbZ2fHsLOCF3xz/YD2ABzDGZGxjCctJtmRZOXerc6wcb917398ft7tQW5KDwIrv5xwdnb51q+qturdvP/XW8z5PY4hS1anPzJ6sa9vcsXi+Tm5zH1QMVakHvuBVlTg6VcCva+ddDWBJkiTp3Cf/okgXlONLalUdl4cPTLKgKcTb13ehqQo7BjM8O5yhPRagpymEX1fpiAcYS5dZ0xFjplBF4AWACnBkqsgrFjTQGjEZSpep2l6JNE1R0FWFmYJFqmixrC3KGy/t8NInDI2bV7Wy+cA0U3kLRQhUBToTXoe2ctVh31iegZkiM0ULVfFmi2eKFoam0NcU4uc7x2iPB1jUGiFTsihbDq0RPyPpMumihd+nMT373Auaw/WucOPZCnP9yuMBA5/m1TeOBnTGMlWe7E8xmi2jqwrNET/tMT+GrtI/XcQVgrLlUHNcXAG6qnBkqoCuKgRNnaNTBQ5PuiRjfqYLFg0hg/W9DWztTzGZq5Kv1Kg5Lo1hk8UtYQZTJWbX3Z20k9vcB5WmsK8e+M6p1FzylRo+XX3JlSUkSZIk6fnI4Fe6oBxfUqtY9Ro3RP0GlZrDjqEsU/kKb7y0g80HprjvmWFsV9AQMrm0M8brLmln24C3uEvxMhlQFe9xIgEDJ1VGU8EVAoEXHF7em6AzEWDHUJqH90+RKVkkY34ChsbrL2lnRbsXtDWEfOQrNoMzRa5f2sJv9k7QkQgQCxpULIdMucbgTJFcucZ/ee1yRtJlxnNVMkWLVy5sJB7ykYz52T+Wx29oHJzIEzBUSpZDyFCJN3l1hd95RTeDqRLPDKYp17zZ2VWdMfyGzm/3D9UD30TIWwQ3ka8ynqlQsmwm81WqNQdFUeopIQ0hH8WqPa/pRtX2/tcUhW8/OoDf0DiWKlKpuQR9Gs8MZtg+mOZVy1ooVr2Saifr5Db3QaU6+7jPVXO87RdSDWBJkiTp7JPBr3RBOb6klqYquK5gRXuUgekiv90/yeV9Ddy3Y4RMqUZbLOAFj6ZOtlzj/h2jvG5NG7mqTW9jCGW1N/O7LBkm4FPJl2u4LnQlAhQqNqs7Y7x2dRv3PTOCaWh0xAOULBvHFUzkq/z02TGuW9pE2XL4xc4xblyRZDhVpmQ57B/PM1PwZnpjAQNXCBY2hxmYKTGerXDLqiSuEJiGF0w+1Z/ymkykS8T8OlctbGJgpshVCxp5YN8kU/kKNyxrwRaC3qYgG5c2s3skx2i2zLULGylbNrmKV0bN1DW02fSIqu0Q9utM5quULQeBQFcUXCGo2F4puIjfqAe+AKaukgcEMFWoUqzaKIpSv71o2RRT3vM5jlevGDihPvDcBxXTOHmjybmyahdaDWBJkiTp7JJ/VaQLylxJrR8/M0zUr7OoNczDB6eImAYCSEb9bO1PoeLN7Bqal0erqQrj2TLjuQoHJwo8vH+SlqifdT0J/uOpYeIhH7euaWM4VeLGlW0MTOfZM+qy7Via7UMZLu2MU6jYrO1OoCkKVdshETLoTgQZzZS5/calPLB3nNdd0o5pqDSGfDSGfBSqNqmiha4qDKZKJIIGIZ/O9sE0Jcvh6FSRfLWGEN7scU9DkNFsmf3jOV7R18jPd43huC6vX9POE0dnyFa8fZ/0pbi8t4HXrEzSP10gEfJTqTmzjTi88aWLFrYrMHWF3sYgO4YyKCiguyiK98GhbzalYk57zE884JU7E8J7/4qWQ3i2BrLtiPq+ZculWnNoDJv1bcfP4s59UFGA1og5L/VhrqzahVoDWJIkSTp7ZPArXXDiAR+d8SBl26WvyZtNDcc0/LqKgqh3XKvaLkFTQ1HAb2iUZ9v4zhSq9fzZo1MFrlvSxKHJIocmCqzrSXDPU4M0hU0GUyUu722gpyFINGAwlCpxLFWiuyFAqlgjGfOztstlJFshGTPpSgQ5li6RH/Vq+w5MF/EbGl0NQTIli6m8xTWLmjg8VWD/eJ613XF+s3ecZNTPkmSEnsYgfkMDAemSRW9zEO2QwlULm3nqWIpMyeLGla00hk3KlktjyJtRvrK3kUzFJmjqFKs2AZ9WD3wBDk7kefPaDio1l31jOWzHm91d2Bzm9WvaODyZA7zA98YVrfx6zzjjuSpru+NM5aukZ3OVTV1D134/A2xoCoXK/JSG42dx5z6oPLRvgqsXNZ1Q7aElarJhSfMLLpyTXtjJFiXK91GSpIuVvPpJF5yRTJmBmRJLk2EQAr+uUq059DYFMXS13sVMCIEQEDC8JheJoA/T8G5vCvtQgMFUmVcubGIoPUVLxCTg01jSGmFJa4SIqaOrCr2NIY7NeIGs39AABZ+uUKh6X/13xv20RPz8+JlRsmWbkXSRm1a2UbNdpgtVsqUarVE/bbEAN61M8r8ePIiuq9iuwKcpXLmgkaf7U2w+MEVPY5Bi1aanMcTVi5q4aUUrPU1BHMdlSTLCD7eNsGskS80RBAyVxS0RPv7qRQxMFVjSEmbncJZsuYapq4AgbOo0hE2+v3WIK/sSvHZ1kqojCM6WgvvtgUnec2UP7fEgfl2rB74AxYpNR8JrvpEuWjRHvNJrIZ9OLGBQrNjz2iyfbBa3MxHk1tXtHJnMk4x14rgCv6HRGDYxDYUH90wwMFPCclx8mkp3Q5Bb17TJ8mcvwVzd65M1F5HvoyRJFyMZ/EoXnLmFVCFDJRow2DOaoyVisqYjTsGyScb8jGe9EmSJoI/WmJ/pfJWIH4QryJZqVGwHXVUImTrposXqjhj900WOzRS5f8co3Y0hmsI+Pnh1Hz/aPsL2wTRBnz5b8UCQCBmYusrWgRk2LG5m10iOZ4eyLGuLUKoJHtw7zisWNrG2O4Htuqxoj/HIoUmGUyXW9zXy+JFpTF3lygVNPHF0hqFUGVUBRwhUVWHncIb7nvE6xv1kxyjrehL0T5fYNZLFcb3AVwF2jmT59mMDfPDqXjIVm0u6vLJqNcf1ur3Fg6xIRtnekGbL4ZnZlAi33mxiUUuYnkSQeNjH40em64EvwEi2zOvXtJOv2ByeLFC1HXRLYV1Pgu6GIE8dS7G0NQqcupPbqQKzjQk/P312lKcH0vPyjYfSJaqOw5+9coGcuXwRjq97fbyTLUCUJEm6WMirnnTBmVtI5dM1nhnKcHlfAwFdo6cxSHPExy0rk/zu0DTZsldCbNdQho3LW2iJmEzOljpT8HJaHVfgCsHO4SymrlK2XFa0RxECRjIV/r/HBnj7eq+ZxAN7xwG4amEDK9pj9DWGUFWImDo7R7OsaI8SMjX6GoPYruCp/hSFqk3I1AmbOpWay0imzMGJPMvaIpQtm7aYnwf3Tc6+HhUhvMDF1FWOzZRY0xUjV6nRP10kX/Vyjrf2p3BcQWcigK6pZCs1CpZDc9hHQ9DHirYoE/kql3RFKZYd/uPpIboSQY5MFdk/nicRNOiMB4iHfLxjfReGodGZCNLdEGJxS7heazjiNxhOl3jH+m4OT+apuS5Bn44KNEZM/ur6hWiqWu/kdqo6vycLzJ44kmL3cG5e4AteCbSnB9Jcv6TI6s7Yy3L+XEiOr3v9XM9dgChJknSxkMGvdMGZW0g1U7QAQbXmEvbp3LdjlKFUieaIj1cva2VxsoFEwOA1q5KEfBrf2zrEsrYoEb9Oriwo11y6IyZjuQpj2TIbljRjOS6VmkvFdkgVqkzmKixvi9AR9/Ofb12BZTtUbJeZQpXvPnmMouWwtDXC1YuamMhVsGyX1qifsuVQqNqz6QcQ8etc0hFjNFthKFXkhuUtFC2HppDJZd1xZgpVClV7Ni1ApTXqZyxbAcAVXv7yRK7C4pYImgILm8NM5CtkSzV0TWHXcJZtg2neeXkXgzMlhHDx6xq/PDxOoWIzlCpxaWec65c2U7O9LnALmsPMFKtcF28BIB70zVu8BmC78MxQmpaIj55okGzJJuTXiZoayVjgeb9Wf77AbCpfxXJOXgKtUnOZzFcAGfy+kOILlImTZeQkSboYyeBXuuDMLaTaPZLl8aMpuhIBjqVq2K5Lc8QL3n53aKreke2tl3XQHDEpWQ75So3OeICW7gStUZOGkMlYtszNK5Nc0Ztg08EpKjWH8VyFkuWgqRDxG/xo2wiW45AI+jg0WSAeMFjdGedXu8fQVYWFLWEu723g6FQBRYFrlzR5bY5d18slNlT+46lhLMflva/oZc9ojgf3TvDGtR0Mpko0hHys7ohTsW2GUmXGshUUvFnugKHiN1R0VUEIQVvMz0imTKXmzDa/cDENjYPjeZ44muLVy1q5/9kR9ozm2HJoGkVR6G4IMp4rs2c0iz3bhvlP1nWytiten7E9vozcHNvxavtuOTyDqiiULG/RYGvEpFRziQd8J53xHcmU6Z8u0BQxUYRgpmjVG2IA6JpXii3o02afR9QX05UtB+O4hXXSqYVeoEycLCMnSdLFSF75pAtKoWIzlC6SKlggBKlClVtXtXHv9uH6zCl4JbrW9SR49PA0AzMlipbDjStaifg1liaj7BvLcXCiQLacJmBoGLqKK0BTVY5MFbFsh9aonyv6Gtk5nMXQFBzhdX2zbJf+mRKOEGxc2uxVjsiU2bismbLlLbzbNZLl6YE0sYDBktYIRcvmVctbGcmUefLoDI4QXN7XgBDQHgswMFOo37dkOfg0lbaYN4McDxgUqzbxgI+GoEFT2GS6aGHqXhOMpckIxWoNVVHIlWscmSpQrjloqkLQpxM0vQV/lZpDe9zPVN7rHDdTtNg2mKGzIURzxCTs19m4rJmn+lNkSzamT8OnKfx6zziGqlKo/n4WcSJf5aG9EyxtjbCsLVrffnyO70yhyqHJAq0Rk6sXNTGeq9QDYBXoaQry5JHUvNnLkE9nXU+CltnFddLzO9kHljmyjJwkSRerk1eXl6Tz0HC6xDODKfqni/xm7wQAVy1sJF2qUqk5VGouliOwHYFlu6RLFkuTEXLlGiXLq0d7bLrEr/eMs/ngFEPpEmPZCmO5MlP5CtuOpelMBKjaDgtbwuQqNiFT4+mBFHtGs8wULEKmjqaCT1PIlGpc1tPASLbMvTtG+OG2EZIxP0OpEn5d47KeBImQwXCmzKGJPNsH0yxPRpjIVwj5dCZyFR49PM2azhgdiSCpYhUhmG0h7KO3KcSPd4wwnKkQ9Oms701gGhqxoEFvY5CexhCvXNTELauSHJosEPbr6JpKybK9yha6t2+xajOWrVCuOfVZ30UtYQK6xkN7JxiYKQBwdLLAvdtGePJoin3jOfaMZDkwluOSzjiKAgGfhk9TCfo0gj6NyXx1Nj3B89wc34jfwG+oTOSrPHp4msaQr75vY8SkrzFEbLam8JxYwGBBc+iE9Avp5Oa+BYkH57+Pp1qAKEmSdDGQVz7pglCo2ByayLP5wBS9TUE2HZjkVUuasWyBoij1GV8FrzOZK7yfNVWhIeTDsl2eGUqzuCXCliMzBH0aZcshU7JQ8NERD7BrJMuHrumjMx5gIluhIxGgPRagarvEgj4cVzCULtEaDXB0qkAsoNM/XeTguJfqYOoqFdth04Epwn4vWM2WarRGdOIBg6rt0hw1WdEWYyxbZnCmBAr8ZMcIV/Q1sr4nQTLq5+qFjUwXLL7zxDFsR1Cq2kT9BtctaWZopkRLxI+LF+SPZkqMZir4NJX1vV6Oc2PYa7Vs6iqr2qOMZMr4DY2hVImB6RLjuQqLWsJMF6ssbAmTKloMzBT4xuYjDKZKgLcQUFWgJern0FSR7kSAzYemEbPva8in090QxHZEPc1hPFfmyGSBiN/Ap6v4dK+e79GpAhN5b6EheIFZb2OQX+6eYHVnlHW9CWqOwNAULNthPFclVbTqKSzS8+tMBHnbui5GMmVKln3KBYiSJEkXC3n1ky4Iw+kSD+6boCXi53cHpxGAz1AJmxpTuQo3r0zizFZv0DWFbMliNFOmrylEzXFJhHzkyjV2j2bpagiSKlRRVYXepiCO6+WcNkVMBPDZW5azczjDsVSZwVSJQtXGdcVsfq0gYHiNM5KxAAFDY0lrmIDhpQj4NBUB1ByBqqgoCswUq4BCybIZz1Voj/kZy5ZxBfh1lWTMT6pokS5ZdCWCPDucYW13nPde2T37OLCgOcRPnhlhz2iWlqifqu3SFvOzqiPGk0dnWNoSQdVV9o/leHD/BJbtsmskwOW9CRxH8LtDU2TKNg0hgzWdcRY0BXl2KEPFdrl1dZKnB1L4dZW13XEqNQfHFUznqxybKVKo2nQ3BGiL+hnLVRBCULRsJvMVwn6dH2wbIlOqETF1Dk0W6k0son6DqN9gRVuMfKVGS8RLI+mIBzg4mSds6li2O1s+7vcVJny6KhdqvURhvy6rOkiSJM2Swa90QZjIVxhJV+hKBNE1hWrNYSxf4brFzRydLnJkqsi2YykyZRu/rrCqI8brLmn3clxzVRpCPhQUBtMlqrbjlfwK+byqCcUK+YqNonhd2Q5P5nFc+M3ecdZ2J2iKmIxnKuiaOhvEmSxvixLzG+wZy9E/XaRhtpXxyo4YzRGTWMDg6FSBwVSJcs3rxqarXimzpwfT9DWF6J8ucuOKVlwBxapDS8THUKrEwfE8r1jQwC92jTGRt7iir4H943kUBXyGRnm2PNhIpoztCDYubSHg0/jFrvH6orGgodE/XWTvWI5P3riEaNAgW64RMDRy5RrPDmXwGxpT+Sply0FTVPaP5xnOlClVbVwh6GwIsr4nwcGJPFG/wTWLG/HpGirg4tVZ/t2BKTLlGpbjEmoO47iCSs3l0ESenkbvg4dvNqjtbQrVA7Tfl6tTT5riIBdqSZIkSadL/gWRLgi2I/DpCg1hH6qi0NcUQkWhYtlYtouuKbxiQSOK4tXvnchVeKo/RTLmJ1exaYuaxII+uuMBXEd4uaVNIQ5PFkiVLITw6uYOZ8roqsrBiRwbljRzbLrIu6/oZipfJVOyMDSVRMjH3tEcG5Y287XfHibi1/HpKsPpMvtGczSETGqOQ7ZcI+TTqdYsao6gMWxQqNSwbcGq9iiXdsX57b5JpgpVdFUhV/Ye/9Y1bTy0b5JrFjfzo+0j9DYE2D6YoacxxMB0idFqGQSoikKqaPHq5S20Rk0KVZuZQpVVHTHGsxUGU2UEsHskx2CqyFimQqpo0RD20RwxMXSVvqYQmqrwxNEZRrNlHNetp4yMZ8sUKhFuWdWGIwSHJvKMZys8M5SmOxHiA1f3MnAszXTeQgCmpmJoCvmKTb5Sw9DUenWI7oYgpvH7Cg4vdqGWbNsrSZIkvVTyr4R0QWiJ+DF1lWhAZzJfpTMRoCXkI2BofOvRAcayFYQAU1dIhHysaIsxlCqyejYQHJhxyAznuP2GRXQ1BhnLeqXMpgpVDFWhLRFgTWeCRw5N0h4LEDQ0msMmEVPnnq2DWK4g6tcJGDqXdEbZuLSZwZkSi5NhgoZGyPRyeiOmzmU9CZ4aSDGSnvG2+XXaEwGWJ6P0T5ewhYuqKPx6zwT9UwVqjrcIrRJ0OTpVIFOu8eplLVRqDpd2xmgMmySCPvaN5SjXbAKGhqYqqIpCoWKzaySH35dgPFvh6kVNREydzkSQZW0RJvNV4gGDEVUhHjRoipiYukpT2EfQp6GrKpYjGM96i/BSJQvwSpHdsLyVZ4ez9Xq9o9ky3Q0h3nRpJ1sOTfGbPROsaIvS1yQoWQ6mrvK6NW38ePsIM47Ans3Dbo2YrO9JsPnAFE2X+Qn79fpCrVO15Q37ddm2V5IkSTotMviVLgh9TSGWJCMgFJJRE0NTiQV8bDo0SnciyOU9DbgIEkEvt3cwVaK7MUTQ9Ba2NYR9TGTL9E+XqDkON6xowXYEXY1BfJrKRK7Mr/eMETA0yjWHjniArkSA7z09xFCqDEApoLOiPeo1qkhXuLIvwerOLu55aoh8xWurXKrZZMs13nNlT32RW3djiHShypHpIs0RH5qqYLuC4XSJiu1iz+a7ViyHy3sbGEqVmMxVaI155b6EgIaQwbFUEcsRFKo15trU+XWViKljaAp/tXEhjx2eZjBVpGa77BrN0ZEIsLI9yvLWKA8fnCRfsfHpXtkyBbikO066aDFVqNIe92O7LlM1hzUdMbYPZihbDp3xALlKje6GIDXH5amBFIuTEXoaQuwZy3JwokBttmFFb2OIjUtbyFZq9DSGcIVAgXqZs+M7jj3fQi3ZtleSJEk6XfKvg3ReeKGvt8N+nTev7eSJo9Nc1tPAYKrEYLZEU9hkNFtm60CKRNBH0bJpipis7UqwZyTDopYwVy1qZO9oDtPQEEIghJcbMZot85s944R8XtpC1G8QDRioitdVrSXmn21QoaEqCmFT58hkkaCpkS5aXLOogZ/sGOUVCxp54ug02wczVCyHZwYzPH0szauWtvDzXWPsHMkyminTFDJRFNBVlVLVwbJd/IZK3naZKlhMFyzM2ZzcxohJS9Tkbes7qdkuuUoNy3ZRZ6tKzAWbHYkAg2mvFfD/ffwYh6eKmLpCS8TPsmSEgxN5nhnM8MZLO3j06DTlmoNAkC5a9DaFWNAYomI7RPxe6bXORIDGsMnStihD6TK2K5guVDk04ZVDawj5SMb8rOmI89D+CdzZ9tBzhlIlflayuGZRE0IIpgvWvOP83IVsp1qo9WLa9nbEAy9LSoRMtZAkSTq/ySu2dM57sV9vdyaCtEUCTGRTrGqP4roKDx+YpFC1Wdgcxm945bX8hgZC8P5X9jJVqLJzOEtT2CQWsAiaOpmS1+BBVxWu7Gtkz2iWSs1BCG8shqaytivBVL5KzXaJBwwUBcazXqUD4QoaQj7KNZd81eYnO0aYLlTrAXTNcRlKlTiWKrK0NcJv909i6hqJsI+wqdMc1hlMF7Fsl5LleDV0DZVyzUUIQbpskypaNIZ8FC2HX+wc4/2v7OXxIzP0z5TQFG+hWFPEZE1nnEcOTnHj8lbSJQtztnPaTLGKEIIVbVFUReHJ/hnaYgGuXdyEqnh5udmyxS93jfGha/u8lBFDozFsIgT4NLVeLq1cc9FUr8FHsWozma8SDegcmihwWXccv6FhOS6uC4amULYclraGGclUTjjWL3Yh2wu17Z3IlXnsyPQfPSVCplpIkiSd/2TwK53TXsrX28PpEnnLoq85wu8OTnHdkmb2juZY0xVjOFUmXbLIV2yqtksiZHBZT4Jtx9IsaY1gqCo9DUFK1RrNET+GqtA/XWJBc4iJXIWxrBeo2Y5geVuENZ0x8lWbQ5MFDE1lcWuYVMnCp6lomsJMwaJSc0lG/WwbSNEeDzCRq5Au1jANlWrN5chUkTde2k48ZBD168wULBJBH9985Agr2uN0NwTZNphBVSDk0zA0L4+3rzHIWKbMwuYwjx+e4eh0gYlsmbXdcdZ0xrFdQcCn0hb1U3Nc/nrjQgxN5XVr2qnUvNrFu0dzGJpKT2OIXLlGT0OQhpCPiuWQr9pMZCvsn8ixPBmlWHV406UdPDWQouZ4LYcDPo2IX6cjEaR/qoB/dtYcoFS1EQKiAR1DU0kVLXIVr0KErioYmkrNFjzXyTqOnWqW9fna9lq2y0Su8kdPiZCpFpIkSRcGeaWWzmlzX2/P1Xu1jiuNNff19tJkhELF5smjM1RrDpsOTrF3NMerl7bS0xgkU7R47eo2QqZGqeYQNDTGcxWGZkr4NJVdI1netq6DNV0xxrMlVEBRFBa2hPj248dojvhZ0xmnPe6nPRZAVSFdrNHTGOTSzjhPHUtRs13yFYeg4c36JoI+HNcFBEHTS1WwnNkGEKqCoynYjst4tkK+ZNMe9TNYLeEzNN56WRdHpvKs7m5gqlBlMFVGmU2raI2a9DWFKFoOUb/OKxc1znZ2U1FVhWeH08wULN62votNB6e4pCPO3nKeiKmTLdeI+HX8hsYrFzby9ECafKWGaWhkyzb37RjBcQU1x6UjHmDj0lb2jeUYTJfYM5pjcXOYpckI0wWv3vCC5jDjmQqxgIEjvGYWAuhqCBLwqazpiDOSKTNT/H1qgwIEfSqZco3GkI+p2bSHk3Uce75Z1uerBmFoCn5doynso1pzMX0aihDMFK1558zpnosn84c8riRJknRmyeBXOqcVLZtcpcbRqQKV2fq1QL1RwlyOqFfT1kVVVQ6M58lVbCzhsKA5zKqOGD99dpRnh7P1+y9vi/DhaxfguIItR6bRVZVf7x7nnZd3k61U+cXucRY1hbl6YRMdiQANIR+uK5gqVBlJlTmWKhI2Dd55ZRets00oYgHdywWO+GkMm+wezdLTEMLQVMqWgz7bTNwVEA0Y5Cs2uVKNy3rjPLx/kp3DWaIBg3jQhxBg6KoXYHclaAr7aAr7GJgu4riC/WN5Do7n2TGcpS1mcvWiJhJBgxXJKNcsaWLTgSlWtMXw+1Qe2TtFtlzz8nmF1xDjFQsaWd0ZI1OqkSlZWLbDdMHCpykkgt7zVGoONyxvpWa7TOWr7B7JsjwZJRnzM5mvEA8aswGhRcinEzA0OhNBXremjbaon9aoScmyCZk6uXJttsudRnvMT8DQWNgSZmWHdtKOYy9mlvVU1SCWt0W4Z+sQo9nfp1W0Rrz3aDxXOe0GGS+UaiEbb0iSJJ0fZPArndMMVT0h8AWo1LyyX7rqRZRFy6tSMJqt4LgCy3bRFJX1PQl+8PQwmbLF0tYwrgBVgXLV5vtPDvKmy9pnF3p5+bn37hjmNSuTvH5NG41hH0cmihQtm2MzXvOLmu0S8OlYjqAlavKNTUe4fkkzy9siLG6NYKgKrhD8x9ND6KrCuu4GliejPDOYxnG9IgxBn0YsYADQFPHx0L4JQKFQtRnJVPDpCq9e1oKpqdy0PEmpZmOoCg/snWR1Z4wdQxkm8xWawyaNIYOS5bC1P8V1S5pYnIwghIKmqKztjvHtx48xNvuexINedYnxbJXHj8zwriu6ODZToiMRYChV5u3r/Axlyvh1heawiQus6oixdyzLTKFK0KdxcCJPX3OI4XSZ4VSJpojJtYuayFVsGkMGsYCPX+4aZ2FLmPZEgN0jObJlrxJEoWrTGjW5cUWSbNnyFu6FzHoqw/EpDtWaw2TOy5N+ruNnWZ9bDaIh5OPfnxiYF/gCTOSrPHp4miv6Gk67QcbzpVqAbLwhSZJ0vpBXa+mcZmgKMb9BpVY94baY38CY7VgW8um4QuDTVBBw08pW/JrX9MJFkKvYHJstSaYp0BQ2yVVr6KrKgqYwQ+kSzWGTgZkie0Zz7B/L0ZEIsnskS9V2OTCRx3ZcuhNBXrW8hcawj8ePztAZ9zOZt9g5kqUtFmDfWI7ORICwqdPdEKRYtfmzq3tJhAxMXfMC0JqLpkIs4GMqb1GoOlRqLrXZRh23rGzj2eEMv9w9wbWLm5jIVQiaOut6Evh0lVy5xvqeBEOpMu2xAOWagyMEo+kKTSGT3+wZZ+tAiuXtEUbSZVqjfso1x6v/qzjomkqmVCPg09FUhclclV3DGXobQ9y0opXpQpV8xWuI8R/bhlAE3Lgiydb+GbKlGtWaw67hLNctaWL7sTSW7TKcLnPL6iQ7R6ZRgcFUkUKlxrJkhJDfK7XW1xTCr+s8sHec0WyFxS1h9o/n6W4I0hI16Z8uIlBQhPfhZe9Ytt4G+bnmZlmfWw3iwHieqi3wG+oJH5gm8lVMQz0hr/jFerGNNyRJkqRzmwx+pXNaueZw9aImHj08zUT+9wHw3NfYlZrXIawjHmD/WJZqzeHtl3exdzRLQ8gkM1MkVbToawqxwqdhO96iq0K15lVnAGIBHeEKLu2Os6wtQkPIR0BXeeTwDEPpEo4jaI/5GU6XOTpTwtk7wZvWegvALumM4dO9ygdHp6ZZ3RnjppWtvPPyLnaNZBnPVjkwnuPqBU080T/DwfECjuvWGzzcujrJcKZMvmLTHDFZ1BJm10iGwXQJTYVU0WJpMsKx6SL9U0VuWd3KopYwYb/Opd1xcpUaFctBVRWGUiUiAYONy1pY2R6lOWzyyoWNs+XZFFxXMJIpcWiigCMEE7kK331ykGTMz62r2nhmKMOjm4+QL9fqi8puXZ3kgb0TzBQtFreEGUyVELPH5Ze7x3jFgkZes6qN0UyZ7oYgB8fzZKs2bfEAmXKNQtWmUnOp2g7JqJ9f7honXaoRMr3Z72TUz4N7J+ifKdIa9VOyHFojJjeubKXmCI5OFVjRFjthBvhUs6xz3wAsaA6fNFWmNeo/7UVpL6bxhiRJknTuk1dr6Q/2ctY9Dfp0xnMVruhrQABV28XUVRxHsHc0RzLu5+B4npCpoSpem9ynB1OomlLvRrao2ZthnC5Umasx0Bw2WZqM4tdVbliR5AdPD5Eq1fjlrjEWt0Z41bIW2uJ+DoznQFEI+jTWdifoiAewbNerk9sawdQ1HNfr0ra4OUwy5senady7fYTh2QVSl3bFKVSzpIsWu0ezqIpCzXFpjpgcnCjQGvExka2gKV4+7u6RLO0xP4qi0BDymnIUqjYFq4gQXupEyXKp1FyCPg3H8VovW7bLaLrMd7cOEvPrfPaW5Yykyzx+ZAZFgaLl0B7zc/3SFp4aSFGtueiaSjLqZ89ojpmixUyhiq6qaIrCWLbMo4dnWN0Z59d7xrmsJ0F3IshEtuLVI1a8ahGb9k/RFPbRGDKxXUE86MPQFKo1h9aon31jXupDoWqzdyxHLGCwpDVMW8zP7w5OcXS6SLFq05lQCfpgMl9ly6Fp1nXH2T2aYzJfQVOV+kLHlqh5ylnWudSEqN9gRVuMfKVGbbZJSMRv0Br9w2Znn6/xhiSdDlk3WpLOPPkbJv1BTqfu6Uu52HfEA0QDRr0qAMDI7AK4mN9gaKbEjsEMjSEfe8dyrO6IEjR0OuI6uWqNlogPXVVY3BpmWVukPvObKVtUag7NYZMth6cp1Rxef0k7y5IRxrNlHj00TWvcz+svaSdftVncEmY4XebgRIFdwxnylRpBU+cVCxpQNfBrGnc9NsB0ocqu0RxP9c/wigWNrO2K0xYL8JNnRwHBouYw2XINxxUsS0bwGxq9jUFiAZO9Y1nGsxWyZS9gawyZ+HQvRSEe9NEeD6CrKmPZCoMzJWqOiyMETWEfPU1BklGTXaM5FAXefWUP9+0YoWjZ2K7Adr0GGKmixc6RDK+/pJ1UoUoy5qezIciOoQxCgO2C31BQVYV40HtP13bHifoNIqbOdUub+fXuccKmzoalzUzkqqzrTfCT2efaOpBGBdrifi7vbSBXtuhuDOG63utZ0hrBr6tMF6rkKjV2DGWwHJdKzWEqX6VYteluCDKUKrFhSTM7R7LsH8sRnk196G4I8rpL2p73fJlLTfDpKo1hc955+cdITThV4w1Jeqlk3WhJOjtk8CudttOpe/pSL/bP/arZst164Du3er8x5OPB/RP0NYZ45NA0+8fzqIrCK/oSVCyXd17Zxdb+NGOZCpqmMJ6tEDQ03nFFNzmrRtVxyZQs7n7iGIamkCnV+JN1ndhCsHMow0Suyo7BDPmKTWvUz9sv7+LIVIGDE3l+8LTD0mSYQtXh7eu6yFdr7BvL84oFi9k7muOH24d57ep29ozmiPl11nTGmClYXLGgkT0jGZ7qT3P1oiYOTuRRVYU/WdDIr3aPE/UbLE1G2DeWw3W9ALcx7OOJ/hTxgIE/GWa6YNW7u205PMO7r+xmumDxoWsW4Dc0suUaLRE/lZpbL2Hm01VURaEp4uNnO0dRFYXGsI/O2aCwMeSrz5iXqjbRgIFPU7m0K05fU4hs2WLj0hYqtkNnPEgl5HD/jlGOThUJ+XR6GgKMZb26yM8Mpr1Sc7kqC5rCxAI6pq4yXbC8POFwmVylhgB0VUFToVRzyJZrNEVMRjNlNi5pwTRUSpaDT1dRgKcHUvQ0hE4aAMvUBOl8UKjY9E8X2TGURtdUmsM+ZooWrpB1oyXpTJC/WdJpe6l1T19qsHz8DPHlPQ3omsJopkx73I8CDKZKZMs1TF2lMxZg75hXi/Y1q5IAREyDTLnGvz3aTyLooy3mx3IEV/Q10Bz28ZMdo3zg6l6WJyMUKjX2jebpbovwZH+KfWN5dM1rdOEKwVC6hO0IipaNX1dZ3BymMeSjMWzS2xgibHr1c2uOQ09jkO2DGY5M5Nm4tBVDU/BpCkvbovQ2hbhmcRPbjqVxhMAR3qysoasEDI1Cpcatq9s4NlNk90iWkmVz5YJGJvNVNFVha/8M49kKYdP7ul0YWr0Gcs12UFSFQqWGqkBvUwghIGTqlGs2CLBdgamrZEs1KjUX23VxHEGuUmNgpkTE1OluDKKrCsOpMqrqYBoaIVNj53CGo9NFehpDtET9+DSFsN/HRK7MW9Z20Bbzkwj7ePJoiiNTBYZSJfyGjk93iAR0Htw3yXShymimwrJkhOO6HmPqGq6Atqh/tjZwlYaQwZZD06xqj3HdkmbGcxVmU6Wft6auTE24OJyv6QJzEwBHJgscmvTagh9fim8uAJZ1oyXp5XPuXymkc9ZLrXv6UoLlU80Qr2yPoilQmg3cYgGDgKGhqgrtsQAHJ/NkSjZXL2qk4rg8dniGy3sbyZQtJvMWuqZwaLJA1XLYuLyZsmWjqRA1fXQ0BGiN+okHfKiqQqpo0RbzKiU4bghV9fJYx7Jl3nZ5J7/aNc5U3qJ/qkjRckhGTa5b0sxvdo+zsiNGPGCwdzTH9UubeO8renh4/yR7R7O88dJ2Nh2YIhowWN4WxdRV8pUagzMlJnIV/mrjQsqW1z3usp4GhlMlFNVrcjGWKXPVwkba4wGE8KpWVG2HHccyBAyNqVyVkXSZsKlxbKZEIuRjSWuEnQczpEoWYZ/ujS1oUKjadCQC+HWNlojJ7pEc+UqNbLlGIuSjbDksb4tSc1y6G4JeibagQaXmoikKPl2lWLX50DUL+PGOEfaN5fFpCpd2J7i8t4G3r+/CpylsOjDF7w5OYWgqN65sZdOBKQZT3tg6G4JUaw49jSFqjkvZcgj6NFqifrIlm66GIKWaw9aBFBuWtpCbbT39QjV1ZWrCmXG2AtDzNV3g+AkAy/n9YszjS/HNpXjJutHS+eZ8+kB6bo5KOqueewInQj5SRctbSa+q6JpCueZQrTnzvq57rueuyD9VsKwq3tftEznvOUOGxkP7JxnNlE/o6Pbw/kkcV/DTnWM4rsvarjiOECSjfh49MkLM1Lnt1YvQNIWiZbM4GebHz4xwYCIPAhQFVnfEueGSNvKVGkKAIwTXLG7kf/82xVvWdjIwXaIrESAx28QhGfPTEPJyh7PlGu1RP0/3pynVHI5OFSnXHCzbZZcCqZLFW9d3su1Yhg2Lm1jbHUdVFKqOS76SYPOBKRAKVy1ooC0exNRVmsI+fLrGgfEcDSEfhqpy66o2rlzQiCtcwGuX7DdUPvDKPrYcnmbzgWlcvPetPebnLZd18OxwlkOTeaq2S1ciSNFymMznUVBY2x3n4QNTLGuLsrY7Ttg0+H9uWsJIpsxv9o7xlnVd7B3NMTx73LsSARpDPtZ0xuhI+Pn17gmm8l6t36ePpdE1hdevaec1K5P80wMH2TeWB6DmCLb2p3i6P8Xqzhh/tXEhO0e85iJCuDzdn6a3IUhPY4jGsMGGJc389NlRtvaniAR0UkWLZckob72sgwf2TjCRrZCt1HAFJIIGY5kK1y1prp9b59PF9kJztgLQM91m+o95jh0/AeDT5lcwmcj/fkEuyLrR0vnlfPtAKn+7pHmOP4F1FRY2Rzg2U0QApqEylCoxnq2wvieB39Dq1QIMXcXUFHKVGvbshMZcZ7C5wLlac7ikM0q2UkNDRSh4C6HCJocn8wzMeHmjmuK16TV1jWLVRlMV4gEdv66QLTksag3zmpWt9E8XWdYWZd9Yjo1LWzAVhY/duIitR1MI4eW17hjMYNkuy5PR2UYPPgZmCvzw6SH6mkIcmSzSFPHxutUhPrShl4ChYsxWiogFDNZ1x7n/2VEqtkABIn6dBS1hHjs6w97RHLmKjaJ4bXt9usr2YxmuXdTMuq4oAZ+GC+TLNrGAzq2rkixtDRELmGwfSrNtMIMrBC0RP4mQj9euaec/nhpix1CGVR0xAj4N0BlOlTgyVWBxS5hHj0xzdLpIS9T0Uj4MlbFchaePpWkO+4gGDMazFXKVGs0RE8cVHBjPcXlvD7esaqN/usBjh6fZOZRhquCVjlvX28DIdInrljSzoDmM4woSIQMFuO+ZUSL+JkYyZY5MFXjjJe3sGcvh01QeOzLDtYubGEyVUGbPH0UBBKgqHJ0qkC/X0FUF2xWoqoKLYO9YjnzV5qaVrXzvyUHiQYPXXdJGwPDSMzKlGr/dP0nA0JjMV3GFQNcUYgGvScdItsRSK8KRqTzPHEuTKdn1FsaOELx6+R9+sT1fguq5cWZKFq4QBGc77b3c4z3TAejxzmSb6eF0iaf6UxSqdr1VdtincXlfw2mdY8dPAET8xgn1qKuzF09ZN1o6n5zN68HpOuuj+frXv84//uM/MjY2xsqVK7njjju49tprT7n/5s2b+eQnP8mePXtob2/n05/+NB/5yEfm7fOjH/2I//pf/ytHjhxh4cKFfOlLX+LNb37zy/1STsu59Ef2+BNYVaCvKcQ9Tw9yeLKAX9eo2A6JoI9XLmhkSTLCruEsU/kqfkOjULEo1wRL2yI8dniavuYwR6eLFCo263sSjOe8RVDDqRI3rGhh72geVFiZjHJosohfV3n8yAQHJvP8xXULifkNHAFLWiO0xf38avc4k7kqo5kyfU0haq7g6oWNzOSrxEM+SpbDZ1+3lCePpAib+mzHMR+aprCyI0rIpyNQvBbIChyayNMeD3BkskCxavOr3ePctDLJlkOT/HL3GJ2JAOlijfZ4gOuWNPP/PdpPqebSEDRY15NAxSs51hT24bgCRQHb8RaVZSsWPU1xvvG7o2w/lqm/v5d1x/nYqxaxZzjFRLZCoerU3/dU0UJV4MblLSxJRvnR9mGKlo3jQv9UgZaYn/U9DSDApymkSxY+TaVSc/BpKqWqgx7TiAYMMiULVVEYmC6SjPkJmwGWJCNMHJrCp6kMpUus6oh5KR2Wy1Suyo0rkvxs1xj37RilULFpinj5zFf0JGgKm0wXqqzrTtDT5DXCMHWVmYLFcLpEMuYHvLbBquJ1uPPpKi1RPxP5Ks0RP1XbwdQ1In4dBQXbESQC3rcGg+kSuqrQ2xRiYLpIIuTDdgTrehJoqoIK3LC8lZ3DWbYdS9McMYn5fTx22CvhVqjaCH6fN/nQvgneetnpX2zPlxmMuXEOpkr1msZz78HjR6f/KB8CTuVMBqDPdabaTBcqNtuOpXl4/+QJNcZVVSEe8L3kc+z4LoEnq0dt6iqaXJwpnWfO5vXgdJ3V36577rmHT3ziE3z961/n6quv5v/8n//DLbfcwt69e+nu7j5h//7+fm699VY+/OEPc/fdd/Poo4/yV3/1VzQ3N/PWt74VgMcff5x3vOMdfPGLX+TNb34zP/7xj3n729/Oli1buPLKK8/0S3xe59of2eNP4MaQj0cOTXN4dkGG7bqULIeaXWFxMsydm4/y+JGZ+td0l3bFWNed4Dd7JnhFX4K85TAwXfQaHNQcLumKsXM4g6EpjGWrDKaKTOarbD2awnZcWqJ+FrWEWdIaYevRFL89MIWqwMLmMImQwYKmMAPTRUo1l5orODJVIOLXedOlHfy/jxzldatbEK6C36ezfTDLvvEcTWEfHfEAD+yd4Oh0kVjAIFWs0dsY4JrFzeiqiqIqTOerxII6mw5McVlPgo74BAoKJcthz2iOxrDJ6s44T/anqNouxaqNK8CyXXKV3/+h9WkKjSEfYZ/OE0dn5gW+ANsHM3zt4cN87FWLaYpMYruifv+q7XBspsRHNy7iXx48xP7xHFf0NbJ7JIs6u/hsKF0iW7EImjotUT9NYR/W7Ox6pmx5dZBrLrmKg9/Q0FSF4XQZRQEh4Fe7x/EbGq0RP+PZCgMzRUxd47LuOJP5CruHM+Qq3oI+x4WxTJkttleP+P2v7OXH20f45e5xjk0XAehrCnPrmjZqtkPY1IkFjHqXPctxyZdrRP0GmurVK17QHAaoV+vIV2p0JALoqkLI1AmZ+mxVCkHJ8krRRQMGq9pjDEyXsF2XaMBgQVOIzYemODJZIOTT6UgEKFnOvLzJ073Yni8zGHPjnMxV5wVPx78HL+d4z1QAejJnqs30cLrEQ3sn5gW+4L3HD+2dYGlrhGVt0Zf0mM/tEnh8PepowODyXm9G+Vw4xyTpxTqb14PTpb7wLi+f//k//ycf+tCH+PM//3OWL1/OHXfcQVdXF9/4xjdOuv+dd95Jd3c3d9xxB8uXL+fP//zP+eAHP8j/+B//o77PHXfcwY033sjnPvc5li1bxuc+9zle/epXc8cdd5yhV/XivNAf2ULlzJ8sx5/AAhjNVOo/O65ACMHGZS08uHeC/eO5efcdTpV5+MAkjSEDRVUZTZcpzs5sTuSrVG2XbLnGivYoD+4dp1B1CJk6+UqNSMBg/3iOQtVmMFVm30TemyFUvK5cu0dyPH0sxYr2GFG/TqHqzYgem/EqMaRLNQKaj4rt8ps93kxY2NTpbQzxwN4JDk0WcVzqX80PzJTZcniKnsYgIVNnLFvBdmAsW6ZsOcSDPmqOS8X22hBP5iosaArREPK+ppwpWDSEvOc7nuUIYrMLyWYKJ/8UvP1YhlylxuHJAmG/zuqOKN2NQZa2Ruo1fXcMZ3FccIUgU64R9GkEfRqVmoPf0BnNVBiY9hpe7BvLMZgqM1O0aAgZ+DQFVQFV8V6tMvsBwnFdVEWhbDkULJvGsK8+czxdsJguVgmaGqamEA14QWil5h0zn66xYzBDpeZgqAqJ2SoXuUqNh/ZNcvPKJDXHK7mWr9ikSxYKcGl3nFUdUa7sa2BFW4yo3yDqN7h6YRMfvKaPzoYgHfEAi1oi9DSGaAqbLG6N4De8kma65jUD6Ux4X/9OFSxqjktD2MdounzCOTt3rnlNQE7v9+fFzGCcC+bGma/UTtrGWfDyjvdMBaAnMxdAnswfM11gIl85IfD9/W1VJvOVk972fOZK8R0/fp+usrAlzLuv7GZZW1QGvtJ552xeD07XWRuRZVls27aNz372s/O233TTTTz22GMnvc/jjz/OTTfdNG/bzTffzL/9279Rq9UwDIPHH3+c22+//YR9ni/4rVarVKu/v8jlcrlT7vvHci5+TXD8Cex1/1LqPyuKgoJXE3bncBaes8BNIOifLnHVwkaqNafe0nbupC9ZXiAcMQ2G0mUWt4TxsmgVEN7MpK6qpEtlxrMVFrWEmc5XcYUXeA+ly1zR10BvY5Ddo97xEULUKzKM5ssoKPTPFOuVECq2y0Suymwaaj0gBEgVa9iuoGLZ1Byv3bCXvmFTrP4+cHJng9CSZdfLqg2nS7z/6l52jWTZO7vQC6CrIcDargSZks147tR/GPNlb+a4ULGJmDoHJ/IEDI1M2cZ6TkCtKJAu1gj7NUqWQ29jkFTBwnK8GehKzUVRvHJmi5rDdDZ4bYUNTcHQTMJ+nSt6G5jIVbAdQdjvzdAKAdmyjTEbYJqaRmvU781ml210TUHXFEI+DUcI0iWLtliAsVwFIQTO7AlwZLLAm9a2c2SqyKHJAq4QqIrC4pYwH9mwkLXdDSxuiZ607FihYnNkqjjv92BuJmxpa4TOhgARv0HYp9efF0DBGxuzd7Ofs9qyarunfbE9X2Yw5sZ5fMWA483ljr5c433uDObxXu581TNVy9l2TrKK9zi1F7j9VGQpPulCczavB6frrP22TU9P4zgOra2t87a3trYyPj5+0vuMj4+fdH/btpmenqatre2U+5zqMQG+/OUv83d/93en+UpOz7n4R/b4E9g0vC8FQj6douUtOjN0hWrNRVVnFzadRM12MUMauqbgHheUBH2ad7vr/VFWVQUFqDkOxmyQ7bjegiVXwOHJAj2NQZrCPlJFH6rqBciWI+o5vdZskDOZr1CdLb8VNnXKlk086CNdrNXzUXMVr9OZX1fxGyrJmJ/JQhXH9YLi1phJ/1SBcKAJdXaBVtCnYeoqJctGoOC4oCkKrTE/e0dyvHZ1G+t7EqRLNXRNIV20GM9XeM8V3fxg29BJ3x9FgUhAZ2lrmELVZmCmWK+UsTwZIWR6v5Ji9qNByKdTrNqULZdnBtP8+bULODJZYLpg1dMvFreGuX5JMw6CoKFxZV8jiZCPdLFKyKexuCVEvuKwcWkzBybyHJksEA8YCLzAvi0W8NImBLgC4iEDQ1PrgXjY1OluCHJgvFAPQAFiAYPVnTFiAR9feMNKDoznyZZrxAJeg46O2dSdU5UdO1UQ0xI12bCkmd2jWZ7uT9OeCNSfN+TTaY74CPp0ypaD7Xod+6zjHjcW0E/7Ynu+zGDMjfO5FQPmmLpKnpdvvGe7mciZCCBbIv4TFqTN8RsqLRH/aT+2LMUnXUjO9vXgdJz1ESnPiaKEECdse6H9n7v9pT7m5z73OT75yU/Wf87lcnR1db3w4P8A5+If2eNPYAWI+nW0hiCj2TK6puLTVPyGhut645speie5gpf/auoqDSGzHrjPBXKtERNTV4kFDLTZjmJly0FRFAzN6zgW9RsYqneMfJqCIwQT2QqtUT+KArmyV+7q6FSBpohJMuanPeZnYXOQy7obSAR9FC2btpifiZxXkivg86pTJEI+kjH/bNUIg6lClSNTBW5d3YaiwIr2KBsWNXFksoCpa9RsF8t26Yj7SQR95Co1itUaTbOd0G5Y1sr9O0cZy5RZ2R6jMexDCGiPB6jWbOJ+nUs74zx1LD1vglxVYF1PgmTMj6rA1GzjioCh0tsY4k1r2ylWa6xsi7BvPM9UocrC5hADMyXSpRqJkMnTx1Jc1tPAqo4oE7kqqgoVyyFfsTk8UaA9HsBnaDSHTHZUakwWqmzbmsayBZd1J9CmCrgCDF0laKi0xwOsaIswMFOspxloqsZ4tkLA0OhrChELGLRGAzSGTVJFy1v1bqg0hLwgNOjT6UgE68HuS/F8QYxPV3EcgaYqTBWqpAreIr+AT6czHsAV4oSZ8u6GIJf3NZz2xfZ8mcGYG6dluycEaK0RE4WXf7xnewbz5Q4g+5pCrO9N8PRAet776zdU1vcm6GsKvWzPLUnnm7N9PXipztqompqa0DTthBnZycnJE2Zu5ySTyZPur+s6jY2Nz7vPqR4TwDRNTNM8nZdx2s7VP7LHn8BLkhG2HJqmrzlE2XLIV20SQYOlrWGGZ0/wVLE2m2PpcM3iJla0R7nnqSEMTWFZMkq55tSrPVzaFUdRFDYsaWHLoSnyFZuFLWEKlRqdiQBhUyNi6jSGfVRnv86PzM46FqsOritoCPmYyFbQEwHa4wF+sXuMt17WTkfUzzPDGZojJlXbpWYLXNerGPBUf4rxbAVNVXBcQXdDgNeuamd5MkLUbxDyqRyezPNX1y9iy8EpgoZXNaEl4kNTFG5Y3sr+iRwblzYT8eskYyaLW0LoisJM0fIqNagKJcsmETDYeizFbTcs5l8eOsQzg5n6e3tpV4yPv3oxIxkv8BZ4qQ+aqnipCz6VSMDgb25eyt1PDDKYKhIyddpiftb3JNiwpBnLcRhMldk+mGEkVaLquCxsCvEn6zv5ty0DPDPotUt962UdDEwXyZZrswGtwmNHpultCnLDilYagj6aQybTxQpPDaR5y2Ud7BzOMJapMJGroCoKfbOVHVa0R5kpWmRKtRM+lP0xztVTBTGdiSDxgK9ea3nLoWlqjqBsOVy9qAnzWNorJzfburk16udNazvobQz/QWM5H2Ywjh9nxQ6fUO2hYjtnZLwX8gxm2K/z5rWdmJrGYKpEzXExNJXuhiC3rmk7Z84FSTpXnE/XA0Uc/z3mGXbllVeybt06vv71r9e3rVixgje+8Y18+ctfPmH/z3zmM/z0pz9l79699W1/+Zd/yY4dO3j88ccBeMc73kE+n+cXv/hFfZ9bbrmFeDzO9773vRc1rlwuRywWI5vNEo2+tNW8L8W5Vu3hZOZKsc19ktMUOJYq8s3f9ZMtWQR9Oo4Q9DWFePu6TkazRQQqrVE/rdEADbMNMubuHzY1BlNFHto3yWS+iqGqmLpK0NS4vDcBKGw6OEW6aGFoCrqq0hrz09sYYmC6gN/QiQV0dM1beKUKmClWWN0UoawojOYq/GLXOEPpEsmwjzev6+JH24cZTJVmZ5thaTLCn1/TR75qMZyusCIZpSHoYzBbYPdwAZ/mjWlpMlLPN/UW6GnUbAfbcehujPCTHaMcmMijKgpBU6M5bHL90maCPo18ucyCljijGS/lIurX6Yj7mSpUqDnerHqxalObndk0dAXLdokHfTxyYIqAqRPy6bjCpT0eJOL30jviAR/NYR9DmTLFikMsYBALGhway5Cruhi6hu16Jdlaon6+++QQ246lAG+2vq8pxK2rk9z9xDEqNZfexhAblzdTsWz2jeXpbQphaN4sfVsswIKWEL2N4XPiXH3uufjcc+uPOcvw3Oc6V2cw5saZLVk4s3V+/YZ2zo73fHS+nAuSdDE63XjtrAa/99xzD+973/u48847ueqqq/jmN7/Jt771Lfbs2UNPTw+f+9znGBkZ4Tvf+Q7glTpbtWoVf/EXf8GHP/xhHn/8cT7ykY/wve99r17q7LHHHuO6667jS1/6Em984xv5yU9+wuc///mXVOrsTAW/cH5eWAsVm6F0kcGZErmKNxvckQhQq7mYL/I1nOx1g7cQsFJzKFk2mqIQC3rlyso1h8OTeXJlm5CpkQj4mMxX0DWFpohJumARFy4ETLLVGtOzX8+3BzXikSADmRLZsheEdsYDlEsWecehKWJSrTgYPo2RbBnbgXhAJ+LTqdheakap5pKv1PDpKg1Bg6rtoAuFxoif0VyFTNnC1DXaIn6EcBnNV1EUaIv4KVo2k3mLSECnJx5kJl9iumLTFgvWWwmHTJ14QMequTgCNB1UVKYLFipekBkLGozlyrgONIS8Sg0T+Qq6phIwNGxHoKneQqeK5eDTVZqCPlRNYSxXpVi1aQj56EgESOUtMhWvikRbzEtnmClUGcuWKVkOUb9Bc8Q8oeTS+XiuSpIkSReu8zL4Ba/JxVe/+lXGxsZYtWoV//zP/8x1110HwAc+8AEGBgbYtGlTff/Nmzdz++2315tcfOYznzmhycUPf/hDPv/5z3P06NF6k4u3vOUtL3pMZzL4lSRJkiRJkl668zb4PRfJ4FeSJEmSJOncdrrx2lltciFJkiRJkiRJZ5IMfiVJkiRJkqSLhgx+JUmSJEmSpIuGDH4lSZIkSZKki4YMfiVJkiRJkqSLhgx+JUmSJEmSpIuGDH4lSZIkSZKki4YMfiVJkiRJkqSLhgx+JUmSJEmSpIuGDH4lSZIkSZKki4YMfiVJkiRJkqSLhgx+JUmSJEmSpIuGDH4lSZIkSZKki4Z+tgdwLhJCAJDL5c7ySCRJkiRJkqSTmYvT5uK2F0sGvyeRz+cB6OrqOssjkSRJkiRJkp5PPp8nFou96P0V8VLD5YuA67qMjo4SiURQFOVsD0f6I8jlcnR1dTE0NEQ0Gj3bw5FeZvJ4Xzzksb54yGN9cXkxx1sIQT6fp729HVV98Zm8cub3JFRVpbOz82wPQ3oZRKNRedG8iMjjffGQx/riIY/1xeWFjvdLmfGdIxe8SZIkSZIkSRcNGfxKkiRJkiRJFw0Z/EoXBdM0+du//VtM0zzbQ5HOAHm8Lx7yWF885LG+uLycx1sueJMkSZIkSZIuGnLmV5IkSZIkSbpoyOBXkiRJkiRJumjI4FeSJEmSJEm6aMjgV5IkSZIkSbpoyOBXuih8/etfp6+vD7/fz7p163jkkUfO9pCkl+DLX/4yl19+OZFIhJaWFt70pjdx4MCBefsIIfjCF75Ae3s7gUCA66+/nj179szbp1qt8rGPfYympiZCoRBveMMbGB4ePpMvRXqJvvzlL6MoCp/4xCfq2+SxvrCMjIzw3ve+l8bGRoLBIJdeeinbtm2r3y6P94XBtm0+//nP09fXRyAQYMGCBfz93/89ruvW9zljx1pI0gXu+9//vjAMQ3zrW98Se/fuFbfddpsIhULi2LFjZ3to0ot08803i7vuukvs3r1b7NixQ7z2ta8V3d3dolAo1Pf5yle+IiKRiPjRj34kdu3aJd7xjneItrY2kcvl6vt85CMfER0dHeKBBx4Q27dvFxs3bhSXXHKJsG37bLws6QVs3bpV9Pb2ijVr1ojbbrutvl0e6wtHKpUSPT094gMf+IB48sknRX9/v3jwwQfF4cOH6/vI431h+G//7b+JxsZG8bOf/Uz09/eLH/zgByIcDos77rijvs+ZOtYy+JUueFdccYX4yEc+Mm/bsmXLxGc/+9mzNCLpDzU5OSkAsXnzZiGEEK7rimQyKb7yla/U96lUKiIWi4k777xTCCFEJpMRhmGI73//+/V9RkZGhKqq4le/+tWZfQHSC8rn82Lx4sXigQceEBs2bKgHv/JYX1g+85nPiGuuueaUt8vjfeF47WtfKz74wQ/O2/aWt7xFvPe97xVCnNljLdMepAuaZVls27aNm266ad72m266iccee+wsjUr6Q2WzWQAaGhoA6O/vZ3x8fN5xNk2TDRs21I/ztm3bqNVq8/Zpb29n1apV8lw4B/31X/81r33ta7nhhhvmbZfH+sJy//33s379et72trfR0tLC2rVr+da3vlW/XR7vC8c111zDQw89xMGDBwF49tln2bJlC7feeitwZo+1/sd4QZJ0rpqensZxHFpbW+dtb21tZXx8/CyNSvpDCCH45Cc/yTXXXMOqVasA6sfyZMf52LFj9X18Ph+JROKEfeS5cG75/ve/z/bt23nqqadOuE0e6wvL0aNH+cY3vsEnP/lJ/vN//s9s3bqVj3/845imyZ/+6Z/K430B+cxnPkM2m2XZsmVomobjOHzpS1/iXe96F3Bmf7dl8CtdFBRFmfezEOKEbdL54aMf/Sg7d+5ky5YtJ9x2OsdZngvnlqGhIW677TZ+85vf4Pf7T7mfPNYXBtd1Wb9+Pf/wD/8AwNq1a9mzZw/f+MY3+NM//dP6fvJ4n//uuece7r77br773e+ycuVKduzYwSc+8Qna29t5//vfX9/vTBxrmfYgXdCamprQNO2ET4STk5MnfLqUzn0f+9jHuP/++3n44Yfp7Oysb08mkwDPe5yTySSWZZFOp0+5j3T2bdu2jcnJSdatW4eu6+i6zubNm/lf/+t/oet6/VjJY31haGtrY8WKFfO2LV++nMHBQUD+bl9IPvWpT/HZz36Wd77znaxevZr3ve993H777Xz5y18GzuyxlsGvdEHz+XysW7eOBx54YN72Bx54gFe+8pVnaVTSSyWE4KMf/Sj33nsvv/3tb+nr65t3e19fH8lkct5xtiyLzZs314/zunXrMAxj3j5jY2Ps3r1bngvnkFe/+tXs2rWLHTt21P+tX7+e97znPezYsYMFCxbIY30Bufrqq08oW3jw4EF6enoA+bt9ISmVSqjq/LBT07R6qbMzeqxf8nI9STrPzJU6+7d/+zexd+9e8YlPfEKEQiExMDBwtocmvUh/+Zd/KWKxmNi0aZMYGxur/yuVSvV9vvKVr4hYLCbuvfdesWvXLvGud73rpCVyOjs7xYMPPii2b98uXvWqV8lySOeB46s9CCGP9YVk69atQtd18aUvfUkcOnRI/Pu//7sIBoPi7rvvru8jj/eF4f3vf7/o6Oiolzq79957RVNTk/j0pz9d3+dMHWsZ/EoXha997Wuip6dH+Hw+cdlll9VLZEnnB+Ck/+666676Pq7rir/9278VyWRSmKYprrvuOrFr1655j1Mul8VHP/pR0dDQIAKBgHjd614nBgcHz/CrkV6q5wa/8lhfWH7605+KVatWCdM0xbJly8Q3v/nNebfL431hyOVy4rbbbhPd3d3C7/eLBQsWiP/yX/6LqFar9X3O1LFWhBDitOavJUmSJEmSJOk8I3N+JUmSJEmSpIuGDH4lSZIkSZKki4YMfiVJkiRJkqSLhgx+JUmSJEmSpIuGDH4lSZIkSZKki4YMfiVJkiRJkqSLhgx+JUmSJEmSpIuGDH4lSZIkSZKki4YMfiVJkqQ/ii984Qtceumlp3Xf973vffzDP/xD/edSqcRb3/pWotEoiqKQyWROer+/+Zu/4eMf//hpPackSRcnGfxKkiTNuvPOO4lEIti2Xd9WKBQwDINrr7123r6PPPIIiqJw8ODBF3zcTZs2PW8A98egKEr9XygUYvHixXzgAx9g27ZtL9vz3XfffX+Ux9q5cyc///nP+djHPlbf9u1vf5tHHnmExx57jLGxMdLpNIqisGPHjnn3/fSnP81dd91Ff3//H2UskiRd+GTwK0mSNGvjxo0UCgWefvrp+rZHHnmEZDLJU089RalUqm/ftGkT7e3tLFmy5IyNTwgxLzB/rrvuuouxsTH27NnD1772NQqFAldeeSXf+c53ztgYT8f//t//m7e97W1EIpH6tiNHjrB8+XJWrVpFMplEUZST3relpYWbbrqJO++880wNV5Kk85wMfiVJkmYtXbqU9vZ2Nm3aVN+2adMm3vjGN7Jw4UIee+yxeds3btwIwN1338369euJRCIkk0ne/e53Mzk5CcDAwEB9v0QigaIofOADHwC8YParX/0qCxYsIBAIcMkll/DDH/5w3nMoisKvf/1r1q9fj2maPPLII6ccfzweJ5lM0tvby0033cQPf/hD3vOe9/DRj36UdDpd3++xxx7juuuuIxAI0NXVxcc//nGKxWL99t7eXr74xS/y7ne/m3A4THt7O//6r/8673aAN7/5zSiKUv95zv/9v/+X3t5eYrEY73znO8nn86ccs+u6/OAHP+ANb3hDfdv111/PP/3TP/G73/0ORVG4/vrr6evrA2Dt2rX1bXPe8IY38L3vfe+UzyFJknQ8GfxKkiQd5/rrr+fhhx+u//zwww9z/fXXs2HDhvp2y7J4/PHH60GtZVl88Ytf5Nlnn+W+++6jv7+/HuB2dXXxox/9CIADBw4wNjbGv/zLvwDw+c9/nrvuuotvfOMb7Nmzh9tvv533vve9bN68ed6YPv3pT/PlL3+Zffv2sWbNmpf0em6//Xby+TwPPPAAALt27eLmm2/mLW95Czt37uSee+5hy5YtfPSjH513v3/8x39kzZo1bN++nc997nPcfvvt9cd46qmngN/PNM/9DN6M7X333cfPfvYzfvazn7F582a+8pWvnHJ8O3fuJJPJsH79+vq2e++9lw9/+MNcddVVjI2Nce+997J161YAHnzwwfq2OVdccQVDQ0McO3bsJb03kiRdpIQkSZJU981vflOEQiFRq9VELpcTuq6LiYkJ8f3vf1+88pWvFEIIsXnzZgGII0eOnPQxtm7dKgCRz+eFEEI8/PDDAhDpdLq+T6FQEH6/Xzz22GPz7vuhD31IvOtd75p3v/vuu+8Fxw2IH//4xydsL5fLAhD//b//dyGEEO973/vEf/pP/2nePo888ohQVVWUy2UhhBA9PT3iNa95zbx93vGOd4hbbrnleZ/vb//2b0UwGBS5XK6+7VOf+pS48sorTznuH//4x0LTNOG67rztt912m9iwYUP95/7+fgGIZ5555oTHyGazAhCbNm065fNIkiTN0c9a1C1JknQO2rhxI8Vikaeeeop0Os2SJUtoaWlhw4YNvO9976NYLLJp0ya6u7tZsGABAM888wxf+MIX2LFjB6lUCtd1ARgcHGTFihUnfZ69e/dSqVS48cYb5223LIu1a9fO23b8rOhLJYQAqOfMbtu2jcOHD/Pv//7v8/ZxXZf+/n6WL18OwFVXXTXvca666iruuOOOF3y+3t7eebm7bW1t9RSQkymXy5imecqc3hcjEAgAzMvJliRJOhUZ/EqSJB1n0aJFdHZ28vDDD5NOp9mwYQMAyWSSvr4+Hn30UR5++GFe9apXAVAsFrnpppu46aabuPvuu2lubmZwcJCbb74Zy7JO+TxzAfLPf/5zOjo65t1mmua8n0Oh0Gm/nn379gHUc2Zd1+Uv/uIvTloerLu7+3kf68UEqIZhnHCfudd6Mk1NTZRKJSzLwufzveDjn0wqlQKgubn5tO4vSdLFRQa/kiRJz7Fx40Y2bdpEOp3mU5/6VH37hg0b+PWvf80TTzzBn/3ZnwGwf/9+pqen+cpXvkJXVxfAvGoRQD2ocxynvm3FihWYpsng4GA9wH453HHHHUSjUW644QYALrvsMvbs2cOiRYue935PPPHECT8vW7as/rNhGPNez+maqwu8d+/e560RfLL3cM7u3bsxDIOVK1f+weORJOnCJxe8SZIkPcfGjRvZsmULO3bsmBeYbtiwgW9961tUKpX6Yrfu7m58Ph//+q//ytGjR7n//vv54he/OO/xenp6UBSFn/3sZ0xNTVEoFIhEIvzN3/wNt99+O9/+9rc5cuQIzzzzDF/72tf49re/fVrjzmQyjI+Pc+zYMR544AH+5E/+hO9+97t84xvfIB6PA/CZz3yGxx9/nL/+679mx44dHDp0iPvvv39ejV2ARx99lK9+9ascPHiQr33ta/zgBz/gtttuq9/e29vLQw89xPj4+LxKEi9Vc3Mzl112GVu2bHne/VpaWggEAvzqV79iYmKCbDZbv+2RRx7h2muvrac/SJIkPa+znXQsSZJ0rplbXLVs2bJ524eGhgQgFi5cOG/7d7/7XdHb2ytM0xRXXXWVuP/++09YnPX3f//3IplMCkVRxPvf/34hhBCu64p/+Zd/EUuXLhWGYYjm5mZx8803i82bNwshTr5Q7lSA+j+/3y8WLlwo3v/+94tt27adsO/WrVvFjTfeKMLhsAiFQmLNmjXiS1/6Uv32np4e8Xd/93fi7W9/uwgGg6K1tVXccccd8x7j/vvvF4sWLRK6rouenh4hhLfg7ZJLLpm33z//8z/Xbz+VO++8U7ziFa+Yt+25C96EEOJb3/qW6OrqEqqqzrttyZIl4nvf+97zPockSdIcRYjZ1RCSJEmShDer+4lPfIJPfOITZ+T5KpUKS5cu5fvf//4JC+1eyM9//nM+9alPsXPnTnRdZvJJkvTCZNqDJEmSdFb5/X6+853vMD09/ZLvWywWueuuu2TgK0nSiyavFpIkSdJZd7qL/t7+9rf/kUciSdKFTqY9SJIkSZIkSRcNmfYgSZIkSZIkXTRk8CtJkiRJkiRdNGTwK0mSJEmSJF00ZPArSZIkSZIkXTRk8CtJkiRJkiRdNGTwK0mSJEmSJF00ZPArSZIkSZIkXTRk8CtJkiRJkiRdNP5/o7g1dKZwi2wAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Wow, that tells us almost nothing at all. There's only correlation where you'd expect it, like between property value and replacement cost.\n",
    "# A scatterplot can be helpful when done right. Let's give it a go:\n",
    "\n",
    "plt.figure(figsize = (8, 6))\n",
    "sns.scatterplot(x = nfip['water_depth'], y = nfip['total_payment'], alpha = 0.5) # In theory we should see a positive correlation\n",
    "plt.xlabel('Water Depth (ft)')\n",
    "plt.ylabel('Total Payment')\n",
    "plt.title('Water Depth vs. Total Payment')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "37278b26-a29c-43db-a993-8d09afbcfdf8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Wow, okay, not really! But we have all those unhelpful data points of 0 water depth.\n",
    "# It'll probably look a little more sensical when stuff like that is factored out.\n",
    "# But I like to do that in Tableau; I find it more user-friendly to check.\n",
    "# So let's move onto a geo-viz, maybe this will give us a taste of something:\n",
    "\n",
    "plt.figure(figsize = (10, 6))\n",
    "sns.scatterplot(x = nfip['longitude'], y=nfip['latitude'], hue=nfip['total_payment'], palette = 'viridis', alpha = 0.5) # I actually don't know how many claims have the lat/long, but since we're just fooling around with visualization tools, limited data may be our friend.\n",
    "plt.xlabel('Longitude')\n",
    "plt.ylabel('Latitude')\n",
    "plt.title('Flood Insurance Claims in Pennsylvania')\n",
    "plt.legend(title = \"Total Payment\")\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "9724d14b-c539-41cc-ba30-c5165cbab975",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\natal\\AppData\\Local\\Temp\\ipykernel_6536\\2113666532.py:6: FutureWarning: \n",
      "\n",
      "The `ci` parameter is deprecated. Use `errorbar=None` for the same effect.\n",
      "\n",
      "  sns.lineplot(x = nfip['year_of_loss'], y = nfip['total_payment'], estimator = 'sum', ci = None)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Jeez, we've got some odd coordinates! For NFIP data I like to work with FIPS codes anyway, those tend to be more available than lat/long.\n",
    "\n",
    "# OK this next one I'm very interested in; claims by year.\n",
    "\n",
    "plt.figure(figsize = (10, 5))\n",
    "sns.lineplot(x = nfip['year_of_loss'], y = nfip['total_payment'], estimator = 'sum', ci = None)\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Total Payments')\n",
    "plt.title('Pennsylvania Flood Insurance Payments Over Time')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "280989c8-3fd3-4cf4-91eb-7acd1d4b8abd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Very interesting! I see a peak around Sandy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2daccbed-a876-4751-aa02-c9ebf6f954bb",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}