HCA 203 PUF Aggregator - Part 2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning Notebook for Combined PUF File - PART II\n",
    "\n",
    "Greatly revised to solve some data problems. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import random\n",
    "import math\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sb\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.rcsetup as mrc\n",
    "\n",
    "#For standardizing variables\n",
    "import sklearn.preprocessing as pre\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "cc_summary_all = pd.read_csv(\"/home/kylier/python/DS/data/PUF/PUF_BENE_SUMMARY_ALL.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 116339 entries, 0 to 116338\n",
      "Data columns (total 52 columns):\n",
      " #   Column               Non-Null Count   Dtype  \n",
      "---  ------               --------------   -----  \n",
      " 0   Unnamed: 0           116339 non-null  int64  \n",
      " 1   DESYNPUF_ID          116339 non-null  object \n",
      " 2   YEAR_CNT             116339 non-null  int64  \n",
      " 3   MEAN_AGE             86464 non-null   float64\n",
      " 4   B_SEX                116339 non-null  int64  \n",
      " 5   NH_WHITE             116339 non-null  int64  \n",
      " 6   AA_BLACK             116339 non-null  int64  \n",
      " 7   OTHER                116339 non-null  int64  \n",
      " 8   NW_HISPANIC          116339 non-null  int64  \n",
      " 9   B_ALZHDMTA           116339 non-null  float64\n",
      " 10  B_CHF                116339 non-null  float64\n",
      " 11  B_CNCR               116339 non-null  float64\n",
      " 12  B_COPD               116339 non-null  float64\n",
      " 13  B_DEPRESSN           116339 non-null  float64\n",
      " 14  B_DIABETES           116339 non-null  float64\n",
      " 15  B_ISCHMCHT           116339 non-null  float64\n",
      " 16  B_OSTEOPRS           116339 non-null  float64\n",
      " 17  B_CHRNKIDN           116339 non-null  float64\n",
      " 18  B_RA_OA              116339 non-null  float64\n",
      " 19  B_STRKETIA           116339 non-null  float64\n",
      " 20  B_ESRD               116339 non-null  float64\n",
      " 21  MEAN_CVRG_MOS_PY     116339 non-null  float64\n",
      " 22  MEAN_RX_CVRG_MOS_PY  116339 non-null  float64\n",
      " 23  TOTAL_VISITS         116339 non-null  int64  \n",
      " 24  IP_VISITS            116339 non-null  int64  \n",
      " 25  OP_VISITS            116339 non-null  int64  \n",
      " 26  MEAN_CODES_CT        116339 non-null  float64\n",
      " 27  MEAN_PROCEDURE_CT    116339 non-null  float64\n",
      " 28  MEAN_NUM_PRVDRS      116339 non-null  float64\n",
      " 29  TOTAL_INP_DAYS       116339 non-null  float64\n",
      " 30  MEAN_INP_STAY        37566 non-null   float64\n",
      " 31  TOT_MEDREIMB_IP      116339 non-null  float64\n",
      " 32  TOT_BENRES_IP        116339 non-null  float64\n",
      " 33  TOT_PPPYMT_IP        116339 non-null  float64\n",
      " 34  TOT_MEDREIMB_OP      116339 non-null  float64\n",
      " 35  TOT_BENRES_OP        116339 non-null  float64\n",
      " 36  TOT_PPPYMT_OP        116339 non-null  float64\n",
      " 37  CBSA                 89478 non-null   float64\n",
      " 38  FIPS_STATE_CODE_x    114530 non-null  float64\n",
      " 39  FIPS_COUNTY_CODE_x   114530 non-null  float64\n",
      " 40  TOTIPCOST            116339 non-null  float64\n",
      " 41  TOTOPCOST            116339 non-null  float64\n",
      " 42  ALLCOSTS             116339 non-null  float64\n",
      " 43  NEW_CHRONIC          116339 non-null  float64\n",
      " 44  TOTCHRONIC           116339 non-null  float64\n",
      " 45  VISITS_FLAG          116339 non-null  int64  \n",
      " 46  PERCENT_COSTS        116339 non-null  float64\n",
      " 47  MEAN_ELIX_GRP_TOT    116258 non-null  float64\n",
      " 48  MEAN_ELIX_SCORE      116258 non-null  float64\n",
      " 49  FIPS_COUNTY_CODE_y   114449 non-null  float64\n",
      " 50  FIPS_STATE_CODE_y    114449 non-null  float64\n",
      " 51  IP_VISIT_PROP        116339 non-null  float64\n",
      "dtypes: float64(40), int64(11), object(1)\n",
      "memory usage: 46.2+ MB\n"
     ]
    }
   ],
   "source": [
    "cc_summary_all.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DESYNPUF_ID                0\n",
       "YEAR_CNT                   0\n",
       "MEAN_AGE               29875\n",
       "B_SEX                      0\n",
       "NH_WHITE                   0\n",
       "AA_BLACK                   0\n",
       "OTHER                      0\n",
       "NW_HISPANIC                0\n",
       "B_ALZHDMTA                 0\n",
       "B_CHF                      0\n",
       "B_CNCR                     0\n",
       "B_COPD                     0\n",
       "B_DEPRESSN                 0\n",
       "B_DIABETES                 0\n",
       "B_ISCHMCHT                 0\n",
       "B_OSTEOPRS                 0\n",
       "B_CHRNKIDN                 0\n",
       "B_RA_OA                    0\n",
       "B_STRKETIA                 0\n",
       "B_ESRD                     0\n",
       "MEAN_CVRG_MOS_PY           0\n",
       "MEAN_RX_CVRG_MOS_PY        0\n",
       "TOTAL_VISITS               0\n",
       "IP_VISITS                  0\n",
       "OP_VISITS                  0\n",
       "MEAN_CODES_CT              0\n",
       "MEAN_PROCEDURE_CT          0\n",
       "MEAN_NUM_PRVDRS            0\n",
       "TOTAL_INP_DAYS             0\n",
       "MEAN_INP_STAY          78773\n",
       "TOT_MEDREIMB_IP            0\n",
       "TOT_BENRES_IP              0\n",
       "TOT_PPPYMT_IP              0\n",
       "TOT_MEDREIMB_OP            0\n",
       "TOT_BENRES_OP              0\n",
       "TOT_PPPYMT_OP              0\n",
       "CBSA                   26861\n",
       "TOTIPCOST                  0\n",
       "TOTOPCOST                  0\n",
       "ALLCOSTS                   0\n",
       "NEW_CHRONIC                0\n",
       "TOTCHRONIC                 0\n",
       "VISITS_FLAG                0\n",
       "PERCENT_COSTS              0\n",
       "MEAN_ELIX_GRP_TOT         81\n",
       "MEAN_ELIX_SCORE           81\n",
       "IP_VISIT_PROP              0\n",
       "FIPS_STATE_CODE         1809\n",
       "FIPS_COUNTY_CODE        1809\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc_summary_all.drop(columns=cc_summary_all.columns[0], axis=1, inplace=True)\n",
    "cc_summary_all[\"FIPS_STATE_CODE\"] = cc_summary_all[\"FIPS_STATE_CODE_x\"].copy()\n",
    "cc_summary_all[\"FIPS_COUNTY_CODE\"] = cc_summary_all[\"FIPS_COUNTY_CODE_x\"].copy()\n",
    "cc_summary_all.drop([\"FIPS_COUNTY_CODE_x\", \"FIPS_STATE_CODE_x\", \"FIPS_COUNTY_CODE_y\", \"FIPS_STATE_CODE_y\"], axis=1, inplace=True)\n",
    "cc_summary_all.isnull().sum() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checkpoint\n",
    "\n",
    "**TO DOs:**\n",
    "\n",
    "    Remove rows where FIPS codes are NULL\n",
    "    Impute NaN ELIX TOTALS and SCORES to 0\n",
    "    Impute NaN MEAN_INP_STAY to 0\n",
    "    Leave NaN values alone for CBSA and MEAN_AGE\n",
    "        \n",
    "    REDO County Aggregation:\n",
    "\n",
    "        Mean Age and CIs\n",
    "        Prevalence of TOT_CHRONIC/BENE_COUNT\n",
    "        Incidence of NEW_CHRONIC/BENE_COUNT\n",
    "        Prop. Elix Sums SUM(Elix Scores)/BENE_USERS\n",
    "        Costs / Beneficiary\n",
    "        Covered Days Per Beneficiary\n",
    "        Case Rates / Beneficiary\n",
    "    \n",
    "    5/29/21: ADD:\n",
    "    \n",
    "         # of Procedures / User\n",
    "         # of Diagnoses per Claim / User\n",
    "         Prop. IP Visits (County Level)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "cc_summary_all = cc_summary_all.dropna(subset=['FIPS_STATE_CODE'])\n",
    "cc_summary_all[['MEAN_ELIX_GRP_TOT', 'MEAN_ELIX_SCORE', 'MEAN_INP_STAY']] = cc_summary_all[['MEAN_ELIX_GRP_TOT', 'MEAN_ELIX_SCORE', 'MEAN_INP_STAY']].fillna(value=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Beneficiary Count & User Count\n",
    "\n",
    "There are a total of 114,530 beneficiaries represented for the years 2008-2010.\n",
    "\n",
    "There are a total of 85,524 with 1 or more visits (Users)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(114530, 49)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc_summary_all.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(85524, 49)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc_summary_all[cc_summary_all[\"TOTAL_VISITS\"]>0].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Beneficiary Distribution by State & County"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='DESYNPUF_ID', ylabel='Count'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.set(rc={\"figure.figsize\":(15, 5)})\n",
    "sb.set_theme(style=\"whitegrid\", palette=\"mako\")\n",
    "sb.histplot(x=cc_summary_all.groupby([\"FIPS_STATE_CODE\",\"FIPS_COUNTY_CODE\"])[\"DESYNPUF_ID\"].count(), binwidth=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## User Distribution by State and County\n",
    "\n",
    "Beneficiaries with Visits > 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='DESYNPUF_ID', ylabel='Count'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "c = cc_summary_all[cc_summary_all[\"TOTAL_VISITS\"]>0]\n",
    "\n",
    "sb.set(rc={\"figure.figsize\":(15, 5)})\n",
    "sb.set_theme(style=\"whitegrid\", palette=\"mako\")\n",
    "sb.histplot(x=c.groupby([\"FIPS_STATE_CODE\",\"FIPS_COUNTY_CODE\"])[\"DESYNPUF_ID\"].count(), binwidth=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Users with > 3 Visits By State and County\n",
    "\n",
    "61,000 out of 85,524 have > 3 visits.\n",
    "\n",
    "5,419 users have > 3 but < 5 visits.\n",
    "\n",
    "4,198 users have > 5 but < 7 visits.\n",
    "\n",
    "3,708 users have > 7 but < 9 visits.\n",
    "\n",
    "6,528 users have > 9 but < 12 visits.\n",
    "\n",
    "5,288 users have > 12 but < 15 visits.\n",
    "\n",
    "6,933 users have > 15 but < 20 visits.\n",
    "\n",
    "4,074 users have > 20 but < 25 visits.\n",
    "\n",
    "5,079 users have > 25 visits over the three year period."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='DESYNPUF_ID', ylabel='Count'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cc = cc_summary_all[cc_summary_all[\"TOTAL_VISITS\"]>25]\n",
    "\n",
    "sb.set(rc={\"figure.figsize\":(15, 5)})\n",
    "sb.set_theme(style=\"whitegrid\", palette=\"mako\")\n",
    "sb.histplot(x=cc.groupby([\"FIPS_STATE_CODE\",\"FIPS_COUNTY_CODE\"])[\"DESYNPUF_ID\"].count(), binwidth=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Once you get past 40 visits, there are fewer and fewer \"super utilizers\" in that category.\n",
    "\n",
    "The max visits is 60, but very very few beneficiaries had > 50 visits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    114530.00000\n",
       "mean          7.29043\n",
       "std           8.37808\n",
       "min           0.00000\n",
       "25%           0.00000\n",
       "50%           4.00000\n",
       "75%          12.00000\n",
       "max          60.00000\n",
       "Name: TOTAL_VISITS, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc_summary_all[\"TOTAL_VISITS\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    114182.000000\n",
       "mean          7.177804\n",
       "std           8.136110\n",
       "min           0.000000\n",
       "25%           0.000000\n",
       "50%           4.000000\n",
       "75%          12.000000\n",
       "max          40.000000\n",
       "Name: TOTAL_VISITS, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Removing those beneficiaries with > 40 visits (about 350 rows)\n",
    "\n",
    "cc_summary_all = cc_summary_all[cc_summary_all[\"TOTAL_VISITS\"]<41]\n",
    "cc_summary_all[\"TOTAL_VISITS\"].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Beneficiary Count & User Counts After Outlier Removal\n",
    "\n",
    "There are now a total of 114,182 beneficiaries represented for the years 2008-2010.\n",
    "\n",
    "And there are a total of 85,176 with 1 or more visits (Users).\n",
    "\n",
    "The maximum number of visits is 40."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(114182, 49)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc_summary_all.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(85176, 49)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc_summary_all[cc_summary_all[\"TOTAL_VISITS\"]>0].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Power of the Elixhauser Score\n",
    "\n",
    "The below correlation matrix shows that the Elixhauser Score has some ability to represent patients' chronic conditions burdens, but it isn't perfect.  The TOTCHRONIC variable is a bit better in this regard, so we should use both of these measures to help us to represent severity of disease."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cost_ed_conds = cc_summary_all[[\"ALLCOSTS\", \"TOTAL_VISITS\", \"TOTAL_INP_DAYS\", \"MEAN_ELIX_SCORE\",\n",
    "                                \"TOTCHRONIC\", \"B_ALZHDMTA\", \"B_CHF\", \"B_CNCR\", \"B_DEPRESSN\", \"B_DIABETES\", \n",
    "                                \"B_ISCHMCHT\", \"B_OSTEOPRS\", \"B_COPD\", \"B_CHRNKIDN\", \"B_ESRD\",\n",
    "                                \"B_RA_OA\", \"B_STRKETIA\"]]\n",
    "cost_ed_corr = cost_ed_conds.corr(method=\"spearman\")\n",
    "lr_cost_ed_corr = cost_ed_corr.where(np.tril(np.ones(cost_ed_corr.shape)).astype(np.bool))\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = (10.0, 10.0)\n",
    "cost_ed_hmap=sb.heatmap(lr_cost_ed_corr, annot=True, cmap=\"mako\", fmt=\".2f\")\n",
    "fig = cost_ed_hmap.get_figure()\n",
    "fig.tight_layout()\n",
    "fig.savefig(\"cost_desynpuf_conditions_heatmap.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Keeping Some Variables\n",
    "\n",
    "In light of this, I will keep the following disease-specific variables, and aggregate them to their county-level prevalence rates: B_CNCR, B_ESRD, and B_STRKETIA.\n",
    "\n",
    "I will drop the other disease-specific variables, since I believe that Elixhauser plus TOTCHRONIC do a good-enough job of representing their impact."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 114182 entries, 0 to 116338\n",
      "Data columns (total 34 columns):\n",
      " #   Column               Non-Null Count   Dtype  \n",
      "---  ------               --------------   -----  \n",
      " 0   DESYNPUF_ID          114182 non-null  object \n",
      " 1   YEAR_CNT             114182 non-null  int64  \n",
      " 2   MEAN_AGE             85176 non-null   float64\n",
      " 3   B_SEX                114182 non-null  int64  \n",
      " 4   NH_WHITE             114182 non-null  int64  \n",
      " 5   AA_BLACK             114182 non-null  int64  \n",
      " 6   OTHER                114182 non-null  int64  \n",
      " 7   NW_HISPANIC          114182 non-null  int64  \n",
      " 8   B_CNCR               114182 non-null  float64\n",
      " 9   B_STRKETIA           114182 non-null  float64\n",
      " 10  B_ESRD               114182 non-null  float64\n",
      " 11  MEAN_CVRG_MOS_PY     114182 non-null  float64\n",
      " 12  MEAN_RX_CVRG_MOS_PY  114182 non-null  float64\n",
      " 13  TOTAL_VISITS         114182 non-null  int64  \n",
      " 14  IP_VISITS            114182 non-null  int64  \n",
      " 15  OP_VISITS            114182 non-null  int64  \n",
      " 16  MEAN_CODES_CT        114182 non-null  float64\n",
      " 17  MEAN_PROCEDURE_CT    114182 non-null  float64\n",
      " 18  MEAN_NUM_PRVDRS      114182 non-null  float64\n",
      " 19  TOTAL_INP_DAYS       114182 non-null  float64\n",
      " 20  MEAN_INP_STAY        114182 non-null  float64\n",
      " 21  CBSA                 89202 non-null   float64\n",
      " 22  TOTIPCOST            114182 non-null  float64\n",
      " 23  TOTOPCOST            114182 non-null  float64\n",
      " 24  ALLCOSTS             114182 non-null  float64\n",
      " 25  NEW_CHRONIC          114182 non-null  float64\n",
      " 26  TOTCHRONIC           114182 non-null  float64\n",
      " 27  VISITS_FLAG          114182 non-null  int64  \n",
      " 28  PERCENT_COSTS        114182 non-null  float64\n",
      " 29  MEAN_ELIX_GRP_TOT    114182 non-null  float64\n",
      " 30  MEAN_ELIX_SCORE      114182 non-null  float64\n",
      " 31  IP_VISIT_PROP        114182 non-null  float64\n",
      " 32  FIPS_STATE_CODE      114182 non-null  float64\n",
      " 33  FIPS_COUNTY_CODE     114182 non-null  float64\n",
      "dtypes: float64(23), int64(10), object(1)\n",
      "memory usage: 30.5+ MB\n"
     ]
    }
   ],
   "source": [
    "cc_summary_all.drop([\"B_ALZHDMTA\", \"B_CHF\", \"B_DEPRESSN\", \"B_DIABETES\", \"B_ISCHMCHT\", \"B_CHRNKIDN\", \n",
    "                     \"B_OSTEOPRS\", \"B_COPD\", \"B_RA_OA\"], axis=1, inplace=True, errors=\"ignore\")\n",
    "\n",
    "# Also drop columns used to compute Inpatient and Outpatient costs, since we already have sums.\n",
    "cc_summary_all.drop([\"TOT_MEDREIMB_IP\",  \"TOT_BENRES_IP\", \"TOT_PPPYMT_IP\", \"TOT_MEDREIMB_OP\", \"TOT_BENRES_OP\", \n",
    "                     \"TOT_PPPYMT_OP\"], axis=1, inplace=True, errors=\"ignore\")\n",
    "cc_summary_all.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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C8e7hC4CFpSXtPDuwetkK0tPSuHDmPGF/HMHT3/uZx0UIIYQQL5ZMXkuIWq3m+PHjuLq66j0eHx/P6dOnNdu3b98mISEBFxeXJ9Y9Z84cBgwYQL169Zg1axYhISFaTx94GuFnb/DrtjMs/rAHm74dwu17Kfy88YTm+JfT/RjWq6lme9Gs7hwIHEOjulWY9nonDgSOoYmHU5HqKujXn9bQs7UPv634lQM79tKztQ+//rSGO3Hx9Grrx524eACat2tJvxGDmDbuLUZ0G0Qlp8oMHT9SU8+kmW+SmZHBQM8+fD7jUybPfBNX95rPNCZCCCGEePGMcnJkRfCL4unpiZmZGebm5mRlZVG7dm0+/vhjypcvr1P25s2bfPDBB9y8eRMLCwvUajVDhgxh0KBBOnU98umnn3Ljxg2WL1/Opk2bMDXNXUt64MABFi1aRFBQUJGXD7QfuPw59Lj41qzoUSJxAWpaOZdYbCGEEKKgtdf2vdB4Q2oU74d7oqOjmT59OomJidjZ2TF//nydhNz9+/eZMWMGcXFxZGVl0bp1a95//30UimdbtSqTV6GXTF6FEEKIklPaJ6/Dhw+nb9++BAQEEBwcTFBQEKtXr9YqM3fuXBQKBdOmTSMrK4vBgwczatSop7qRvCC5YUsIIYQQopR50T8ckJycTHJyss5+W1tbncd63r9/n4sXL7JixQoA/P39+eSTT0hISMDBwUFTzsjIiNTU1Nx7VjIzycrKonLlys/cVpm8lrBvv/2WvXt1f+npl19+eeZnswohhBBCFMWqVav49ttvdfZPmjRJ51c64+LiqFy5MiYmJkDu4zorVapEXFyc1uR1woQJTJ48mfbt25OWlsaQIUN4+eWXn7mtMnktYZMmTWLSpEkl3QwhhBBClCIv+lFZI0aMoHfv3jr7Df2YUlGEhoZSt25dVq1aRWpqKmPHjiU0NBRfX99naapMXoUQQggh/r/TtzzAECcnJ+Lj41GpVJiYmKBSqbhz5w5OTk5a5QIDA/nss88wNjbGxsYGT09PwsPDn3nyKo/KEkIIIYQoZdQ5OS/0VRyOjo54eHgQEhICQEhICB4eHlpLBgBcXFw4dOgQAJmZmYSFhVG7du1nHhuZvAohhBBCiGKZPXs2gYGB+Pj4EBgYyJw5cwAYO3Ys58+fB2DmzJmcPHmSHj160KtXL1xdXRkwYMAzx5ZHZQm95FFZQgghRMn5IWrXC403zs3vhcZ7FpJ5FUIIIYQQZYZMXoUQQgghRJkhTxsQQgghhChlXvSjssoSybwKIYQQQogyQzKvQgghhBCljFoSrwbJ5FXolVnXukTiDnpnT4nEXf9lV6KVt0oktjzlQAghhCg6mbwKIYQQQpQyKnmSqUGy5lUIIYQQQpQZknkVQgghhChl5GkDhknmVQghhBBClBmSeRVCCCGEKGVkzathknkVQgghhBBlhkxehRBCCCFEmSHLBoQQQgghShlZNmCYZF6FEEIIIUSZIZlXIYQQQohSRlXSDSjFJPMqhBBCCCHKDMm8CiGEEEKUMrLm1TDJvAohhBBCiDJDMq9CCCGEEKWMZF4Nk8mrKBJbSzPeD2hNq1rOJCrTWbr3DLvPx+iUc6tUnjd9X6aekwN21ha0/DBQ6/icvu1o4VYFC1MTElLSWXP4IsGnIgzHtTJj1tB2tPJwJjElg6XBJ9lzIlpv2UGe9Rnu3RBzMxMOnr7G/PVhZGWrAejXqR7+rWvh7mzPnhPRfLLmcKH93bZ+C3u3hRITEU0nX0/e+Xi6wbKbAzeyceU6MjIyae/VgUkzp2JmZgbAw6RkFs35gpNhJyhvV55RU8bQ2a9LobGFEEIIYZhMXkWRvNu9JVkqNb4LNlGnij2Lhnbm6u0HRN1N0iqXrcph39/X2HTsCl8OfkWnnlWH/ubTrWFkqdTUqGDL96O8uRyXwKW4BP1xB7YmS6XGb/oG6rg48NWELly9+YDouEStcq08nBnRtSETvtnNvUQl81/3ZGz3piwNPgnAvSQlv4Seo7WHM+amT/6zd6joyKtjh3HyyHEyMjIMljtx5Bi/rVjH5z8sxLFiBT5+6wMCl61k9P/GAfDtvG9QmCpYv38zkZcj+HDKDGrWccfVveYT2yCEEOL/L5UkXg2SNa8FeHp64u/vj1qt1tp35coVpk+fTmCgdhZx/vz5LFmyxGB9GzduZMyYMZrtK1euULduXX7//XfNvo8//pivv/5aK1ZBffr0ITw8HIAlS5Ywf/58/vzzTwICAggICKBdu3a0adNGs713716WLFmitS8gIIDVq1c/7bBgYWqCZ/1qLD9wlrTMbM5ev8uhS7H4NdGdgF2/n8y2U5FE3UnUW1fU3SSyVLnjm5MDOYCLQzn9cc0UdG5ag+XbT5OWkc3ZyDv8ee4Gfi3ddcp2b12LbUeuEh2XyMO0TH7ZdRb/1rU0x38/c51DZ6+TlGp4IlpQe6+OtO3cHhs720LL7du+G59efri618TG1obBY4exd3soAOlpafy1/xDDJ4zG0sqSBk0b0rpTWw6E7C1SG4QQQgihSzKvj1EqlQQHB9O7d+9nrqtly5Z89tlnZGdno1AoOH78OI0bNyY8PJxXXnkFgGPHjjFr1qxi1duhQwc6dOgA5E5olUol06ZN0xy/dOkSvXr10tr3LKo72qLKyeH6/YeafVfjH9DMtfJT1feefwv8m7hjYabg0q0E/rp6S3/cSrao1DncuJOcH/dmAk1rV9Ep6+Zkx6Fz1/PLxSbgWN4SW2tzkos4YX0a1yJjaPNKu/x21KnFg/sPSE5M4s7tOxibGONSo1qB4+6cP3n2X2uPEEII8V8nmdfHTJo0iSVLlpCZmfnMddWoUYPy5ctz4cIFIHeiOn78eE6cOAFAQkIC169fp2nTps8c699kZa4gNT1La19KehZWZqZPVd+CkOO8MncDY3/aze//XCczW/+jmK3MFaSmPRY3LRMrc924luYKUgqUTUnLPX/Weso+T+lp6ViXy88cW5ezBkCpTCNdmabZLnhcmar8V9skhBCi7FOR80JfZYlkXh/ToEEDGjRowLp16xgxYoTWsR9++IGNGzdqtu/cucPgwYMLra9FixaEh4fTuHFjLly4wMKFC1m0aBEpKSkcO3aMRo0aYWFhoSk/ZcoUzM3NNdsxMTFP1Y+tW7dy5MgRzfZbb71Fp06dnqouZUa2ziTQ2twUZWaWgU88mTonh7PX7+LXuCZ9W9Tht/DL+uNaPhbXwgxlhm7ctIxsrC3yy1pb5t4wlaqn7PNkYWmBMjVVs/3ovZWVJRZWljoTVWWKEitrq3+1TUIIIcR/mUxe9XjzzTcZPnw4/fr109o/btw4hg4dqtmeP3/+E+tq1aoVoaGheHl5UaNGDRQKBc2aNePEiRMcO3aMli1bapVfvHgxderU0Wz36dPnqfrwPJcNXL+fjImxEdUcbLiRkLt0oE4Ve4PrWovDxNgYFwcb/XHv5MWtaMONu7lxa7vYE3VLN25UXCK1XezZfyomt1xVe+4npf2rSwYAari7EnUlko5dO+e240ok9o722NqVx8zcDFW2ipvXYqlawyXveAQ13Fz/1TYJIYQo++SGLcNk2YAebm5udOrUiRUrVjxzXa1ateLUqVOEhYXRokULID8be+zYMVq1avXMMf5t6VkqDv5zg3GejbEwNaFR9Yp0rOfCrjP6H1llpjDG1MSkwPvcPzN7a3O8G9TA0kyBsZERrWs50bWhKyeib+uPm5nN72euM86/KRZmChq5VaJjo+rsOhapU3ZneCQ929ShZpXy2FiaMdqvMSFH8x/BZWJshJnCBBNjI633hqiyVWRmZKJWqVGr1WRmZKLSs7yhi78Pu7fu5FpkDA+TH7Lup0C8e/gCYGFpSTvPDqxetoL0tDQunDlP2B9H8PT3NhhXCCGEEIWTzKsBkydPpk+fPqhU+tdjFlW1atWws7NjzZo1zJs3D8idvH7zzTfEx8fTpEmT59Daf9+CkGN80KsNu6f1J0mZwfztx4i6m0Tl8lZsmNSDgd9uJz5JiZOdNcFv5d/sdvjDwdx6kEKvRVvJyYG+LeswvUcrjIzgdlIqX+06waFLsYbjrg/j/WHtCZ0/kKTUDOavCyM6LpHK9tas/6AXgz7ZSvyDVI5evMmavedZ+qYv5qYmHDxzjR93nNbUM8qvMWO7N9Fs+7Vy58cdZ/hpxxm9cX/9aQ1rl6/SbB/YsZchr4/AJ8CPcX1H8kPQSio5VaZ5u5b0GzGIaePeIjMjg3ZeHRk6fqTmc5NmvslXsxcw0LMPtna2TJ75pjwmSwghxBPJjxQYJpNXA6pUqUJAQAC//PLLM9fVsmVLQkNDadiwIQCVKlUCoFGjRlrrW5+nx9e8enp68r///e+p60tOy+TddX/o7I9PUvLK3A2a7bjEVJ0fJngkUZnBG78U7zFRycpM3lt+QDfug1Q6v7VWa9+6AxdZd+Ci3np+KmSiqs+wN0Yy7I2Reo9tPbJLa7vvsAH0HTZAb1mb8rZ8tOjTIscVQgghROGMcnJkai90GZqA/tuM7mWXSNz1X3YtkbgANa2cSyy2EEKI0mnqhc0vNN6il57uHpuSIGtehRBCCCFEmSHLBp6DjRs36vz6FsDnn3+Oh4dHCbRICCGEEGWZPG3AMJm8Pgf9+/enf//+Jd0MIYQQQoj/PFk2IIQQQgghygzJvAohhBBClDKybMAwybwKIYQQQogyQzKvQgghhBCljFoyrwZJ5lUIIYQQQpQZknkVQgghhChlZM2rYZJ5FUIIIYQQZYZkXoUQQgghShlVjqReDZHMqxBCCCGEKDMk8yr0sjyfXCJxMypYlkjchuVdSyTu+aQYopW3SiQ2QE0r5xKLLYQQwjB52oBhknkVQgghhBBlhkxehRBCCCFEmSHLBoQQQgghShl1jlFJN6HUksyrEEIIIYQoMyTzKoQQQghRyqhKugGlmGRehRBCCCFEmSGZVyGEEEKIUkYelWWYZF6FEEIIIUSZIZlXIYQQQohSRjKvhknmVQghhBBClBmSeRVCCCGEKGUk82qYZF6FEEIIIUSxREdHM3DgQHx8fBg4cCAxMTF6y+3cuZMePXrg7+9Pjx49uHfv3jPHlsyrEEIIIYQolo8++ojBgwcTEBBAcHAwH374IatXr9Yqc/78eb799ltWrVpFxYoVefjwIWZmZs8cWyavosj692jEq72bYW5mwqGjUXz1/R9kZav1ln1nfCcav+SMi5Md8789QOjBy3rLLfq4J80auuDZdxmqQq6RDOpSn2G+DTE3VXDwVAwL1oYZjF27mgOzRrTDtYodMbcTmbvqL67eSACgW5taDPDyoFolW1LTs9gTHsWyLScNxp34vym89c47WFhaErxlC29OnERmZqZOubbt2rE5ZLvWvnLlyjFkwECCt2zhm+++ZeDgwZpjpqamZGZmco+HWp/Ztn4Le7eFEhMRTSdfT975eLrBtm0O3MjGlevIyMikvVcHJs2cqvlH4WFSMovmfMHJsBOUtyvPqClj6OzXxWBdQgghSpcXvWwgOTmZ5ORknf22trbY2tpq7bt//z4XL15kxYoVAPj7+/PJJ5+QkJCAg4ODptzKlSsZPXo0FStWBMDGxua5tFWWDYgiadGkGoP7NOOtj4IZ+HogTpVtGfVqS4PlI2Lus2j5Ia5E3TVYpkvH2pgYP/lPsNVLzgz3a8Skr3bTe8ZGqla0YWzPpnrLKkyMWTDRi9CjkXi/uZadRyJYMNELhUluHAszExZtOIbP1HW89lkIzT2cGNy1gd66vLy9efvdd/H38eWlWrWpWbMmsz76SG/ZI3/9RRV7B82rf0AvHj58yN7duwH438RJWsc3btjAlqAgnXocKjry6thhdA3wK3RMThw5xm8r1jFv+UJW7VhHXGwcgctWao5/O+8bFKYK1u/fzHufzWLJZ18TExldaJ1CCCH+/1q1ahVeXl46r1WrVumUjYuLo3LlypiYmABgYmJCpUqViIuL0yoXGRnJjRs3GDJkCL1792bp0qXk5Dz7rFwmrwYkJSXRsGFD5s6dq3Psjz/+oG7duuzbt++J9cTGxlK/fn0CAgIICAjAz89PK60+ffp0AgMDDX5+7dq11K1bl3/++Udrf0pKCp988gne3t74+/vj7+/P999/D8DmzZuZMmWKpuzBgwfp2LEj586de2J7DfHtXJed+/4h5sYDUlIzWP3bCXw71zNYfuuuvzl1/iaZWfp/4M7ayoyRA1vw/eqwJ8bu1qYW2w5fIfpWIg+VmfwScpbubWvpLdusbhVMjI1Yv+8iWdlqfjvwD0ZA83pOAGz+4zJnr8aTrVJzN1HJ7vAoGteqpLeuIcOGsWrFSv65eJHExETmz/2MocOHPbG9AIOHD2Pr5s0olUqdY1ZWVgT07s2vq9foHGvv1ZG2ndtjY2erc6ygfdt349PLD1f3mtjY2jB47DD2bg8FID0tjb/2H2L4hNFYWlnSoGlDWndqy4GQvUVquxBCiJKXo36xrxEjRrB//36d14gRI566DyqVisuXL7NixQrWrFnDoUOHCA4OfuaxkcmrAdu3b6dJkybs2LFD5zJxUFAQrVu3ZtOmTUWqy8bGhuDgYIKDg1m7di2LFi3i7l3DGUl9sYIKZOlycnJ4/fXXycnJYceOHYSEhLBp0yasra11Ph8SEsLHH3/Mzz//TKNGjYoUUx/Xag5ExNzXbEfG3MfR3gpbG/Onqm/s0FYEh/5NwgPdyd3j3JztibjxQLN9NTYBx/JW2FrrxnZztiMy9oHWvoibD3BzttNbd5PalYm6laj3mMdL9fm7wIT//LlzVK5SReuSiD6Wlpb06tNH7+QUIKBPH+7dvcvhP/8stJ7CXIuMwa2Ou2bbrU4tHtx/QHJiErHXYjE2McalRrUCx925FhXz1PGEEEL8t9na2uLi4qLzenzJAICTkxPx8fGoVLkJKpVKxZ07d3ByctIq5+zsjK+vL2ZmZpQrVw4vL69nSqQ9IpNXA4KCgpgwYQJ16tThwIEDmv0PHjwgLCyMhQsXcvr06SJPQh9JTU3F1NQUCwuLJ5a9fPkyDx484LPPPiMkJEQziQ4LC+PmzZvMmDFDs8bRwsKCYcO0s4Lr16/nm2++YfXq1dSuXbtY7XycpaUpqcoMzXaKMrctVhbFX3hd170iDeo5sXnH+aLFNleQkpb/BeLRe2sLUz1lTbXKAqSmZWKlp2z3trXwcK3A2j1/641rbW1NUlKSZvvR+3JPWLMT0KcP9+/d489Dh/QeHzJsKOsC1xZax5Okp6VjXa5cflvL5X5xUSrTSFemabYLHlemPvmLghBCiNJBnfNiX8Xh6OiIh4cHISEhQG6izMPDQye54+/vz+HDh8nJySErK4ujR49Sr57hq7ZFJTds6XHp0iWSkpJo3bo1d+/eJSgoCF9fXwCCg4Pp3LkzFSpUwNvbm61btzJ27NhC63v48CEBAQGoVCpiYmJ45513irRoedOmTfTq1YuqVavi4eHBvn376NatGxcuXKB+/fqYmupOyB4JDw/n1KlTBAUFUbly5eINALnrUd9+4xUAzv9zi7S0LKwt8yeq1la5sZXpujcvFcbICKa+3pElPx82eIOWTys3pg1tC8DZq/GkZWRjbZnfV+u8CXNqepbOZ9MytNuZW94U5WNlOzapzsS+zZn81W6SUnIn5QNefZXFS78D4Mjhw6Smpmp943z0PuWh9k1WjytsclrVxYX2HTsy6Y3xhdbxJBaWFihTUzXbj95bWVliYWWpM1FVpiixsrZ6pphCCCHEI7Nnz2b69OksXboUW1tb5s+fD8DYsWOZMmUKDRs2pHv37vz9999069YNY2Nj2rdvT79+/Z45tkxe9di0aRMBAQEYGRnRtWtXPv30U+Lj46lcuTKbN29mxowZAPTu3ZtZs2Y9cfL6aNkAQHx8PIMGDeLll1+mYcOGBj+TmZlJSEgIGzZs0MQKCgqiW7duRVrsXLNmTe7fv8+OHTsYPXp0Ubuuse/QVfYduqrZ/mBqF9xrVuDgkUgA3F0rcP+BkuSHGYaq0Mva0oy67pX46O2uAJgYGwGw8acRzP5iN8fvJrI7PIrd4VGaz8wZ05HaLg7sPxED5D5N4H6SkuRU3dhRtxJ1bsByd3Fg08FLmu3WL1VlxvB2vL14L5E385cY/LZuHb+tW6fZ/mX1aho2asTmvOUhDRs1Iv72bRISEgz2r6qLCx06dWLKhIl6jw8eOpTwsDBiop/t5qka7q5EXYmkY9fOAERdicTe0R5bu/KYmZuhylZx81osVWu45B2PoIab6zPFFEII8eI8h/ua/lXu7u5s3LhRZ/+PP/6oeW9sbMyMGTM086bnRSavj8nMzGT79u2Ym5trJpxZWVls2bKFdu3aERkZyaxZszTl79y5w6lTp2jWrFmR6q9cuTKNGzcmPDy80MnrgQMHSElJYeTIkQCo1Wru3btHXFwcDRo04NdffyU7OxuFQv8prFSpEl9//bVmKcHTTGAL2v37ZaZP9mLvH1e4/0DJ8P4vE1pgQvg4hcIYYyMjjPLem5makJWtIkWZSd/XVua3s4INy7/ox7h3NpKYnAZ2uutYd4VF8sGo9oSGR3I/KY1R3Ruz40iE3rinLt9Gpc5hgFd9tvxxiYAOdQE4cSn3DsiX6zkxZ0xHpi09wMWYwh+U/GtgIMt//okN69ZxOy6O92bOINDAOtZHXh0yhPCwMKKjovQfHzqURV9+YfDzqmwVKpUKtUqNWq0mMyMTExMTTBQmWuW6+Puw8KPP6ezXBYeKjqz7KRDvHrlXBywsLWnn2YHVy1Yw9aN3iLwcQdgfR/hq5ZJC2y6EEEKUBTJ5fcy+fftwc3NjXYEM3OnTp5k2bRq3b99mzJgxTJ06VXNs+fLlBAUFFXnympKSwoULF/D39y+0XFBQEB9++CH9+/fX7Pvwww/ZsmUL48ePp0qVKnz++ee89957mJmZkZGRwapVqxg3bpymfJUqVVi9evVzmcAeO32D9VtP8/UnAZibKTgUFsmKdcc0xxd80J1zF+MIDDoFwJcf9aBpg6oANPRw4t0Jnfnf+1s5c+EWCYlpms+Zmeb+CT5IVBpcRnD0wk0Cd//N0nf8MDc14eCpa/y47bTm+KIp3pyJiGfVznNkq9RM+24/M0e0Y0Kfl7kWl8S07/aTrcp9Juzo7o2xtjTjqynems+fvRqP90rduPv27GHRwoXs3LtH85zXuXPmaI5v3r6NI4f/4su8SyWQm1n9+quFevvRsnUrqrpUZcsm3UdkPfLrT2tYuzz/sSQHduxlyOsj8AnwY1zfkfwQtJJKTpVp3q4l/UYMYtq4t8jMyKCdV0eGjh+p+dykmW/y1ewFDPTsg62dLZNnvomre02DcYUQQpQu8vOwhhnlPI8Hbv2HjBkzBk9PTwYXeKA8QJcuXYiNjWXHjh24u+ff5X3z5k169uzJn3/+iZWV7prC2NhYunbtqrlhKiMjAx8fH80EePr06ezfvx9LS0vNZ959910++OADDh06pLXm8vjx48yYMYO9e/eSmprKwoUL+fPPPzWf7dGjB+PGjWPz5s38/vvvLF68GMh9HtuwYcMYPHhwkSewnXovLVK55y2jguWTC/0L/l75eonEPZ8UUyJxH6lp5Vyi8YUQQujX86/NLzTetnZ9Xmi8ZyGTV6GXTF5fDJm8CiGE0Ecmr4bJsgEhhBBCiFJGUouGyeT1OXnjjTd0fhbNyclJ86tXQgghhBDi2cnk9TmRSaoQQgghnhfJvBomv7AlhBBCCCHKDMm8CiGEEEKUMvKoLMMk8yqEEEIIIcoMybwKIYQQQpQysubVMMm8CiGEEEKIMkMyr0IIIYQQpYxkXg2TzKsQQgghhCgzZPIqhBBCCCHKDFk2IIQQQghRysiyAcNk8ir0yqhoWTKBTYxKJKxHTa8SiTt03M4SiQsQ+EM3opW3SiR2TSvnEokrhBCi7JPJqxBCCCFEKSOZV8NkzasQQgghhCgzJPMqhBBCCFHKSObVMMm8CiGEEEKIMkMyr0IIIYQQpUyOuqRbUHpJ5lUIIYQQQpQZknkVQgghhChlZM2rYZJ5FUIIIYQQZYZMXoUQQgghRJkhywaEEEIIIUoZWTZgmGRehRBCCCFEmSGZVyGEEEKIUkYyr4ZJ5lUIIYQQQpQZknkVQgghhChlJPNqmExeRZEN6lKfYT4NMTdTcPBUDAvWhpGVrf8nQGq7ODBrRDtcneyIiUtk7qq/uBqbAICbsx1T+rekXg1H7MpZ0HrcCoMxba3MmDmsHa08nElMyWBZ8En2HI/W3z7P+gzr2hBzMxMOnr7GgnX57StOPQW9OrIXI8b2x9zCnAO7/+Lzj74lKyur0M90792F2fPf5tNZXxO8cbdmf9VqVXj7/Tdo1qIhWVlZbNu0h+V/JOmtY6BvA4b4N8LcTMHvx6P5csVfhse6ugPTx3bE1dmOmFuJfP7jIa5ezx3rd0e1o2u7WpqyChNjsrNVeI9drVXHtvVb2LstlJiIaDr5evLOx9MN9m9z4EY2rlxHRkYm7b06MGnmVMzMzAB4mJTMojlfcDLsBOXtyjNqyhg6+3UpdLyEEEKI4pDJ678sMzOTr776in379qFQKLCwsGDSpEmYm5vz5ZdfAnDv3j3UajWVKlUCYNKkSVy6dAmlUsm0adM0dQUGBvL333/z+eefExsbS9euXaldu7bmuL29PStXrtQ6plarMTU15f3336dZs2ZP3Y9W9Z0Z7tuIiQtDuZekZP54T8b2aMrSLSd1yipMjFkw0YsN+y8Q9Pslenesy4KJXvR/P4hslZpslZr9J6IJ+v0SX0z0KjTuO4Nak52tptu0DdRxcWDhxC5cjX1AdFyidvs8nBnu05CJX+/Obd/rnoz1b8rSrSeLVU9Brds3Y8S4AUwYPp27dxL44rsPeP1/Q/n2S8OTbRvbcox8fQCRV2K0x8RUwbcrPmPj2u3MfHMeapWa6jWrQvmXdepo2bAqQ3s0ZspnO7j3QMlnU715re/LfL/huE5ZhYkxn7/lzW+hF9i87yIBnh58/pY3A9/eSLZKzRcr/uKLFX9pys8a1xG1nq/zDhUdeXXsME4eOU5GRobB/p04cozfVqzj8x8W4lixAh+/9QGBy1Yy+n/jAPh23jcoTBWs37+ZyMsRfDhlBjXruOPqXtNgnUIIIfSQzKtBsub1XzZ79mxu377Njh07CA0NZcGCBXz88cdYWFgQHBxMcHAwgwYNolevXpptb2/vItVtY2Oj+UxwcDArV67UObZ9+3b69evHrFmznqkf3drUYtvhK0THJfJQmckvO87SvW0tvWWb1a2CibER6/ddJCtbzW8H/sHICJrXcwLgenwy2/+6SvStB4XGtDBT0LlpDZZvP01aRjZnI+/w57kb+LVy19++I1fz27fzLN1b1yp2PQV1792FbZt2ExVxnYfJKfy8dB3+vQvPIk58eyQbVgeT+CBZa3+PPt7cvXOfX1dsIT0tg8zMLCIux+itw69DbUL+uEz0zdy+rNx6mm4daust28zDCRNjYzaE/k1WtppNey4ARrz8krNOWQtzBa+0cGXXn1d1jrX36kjbzu2xsbMttH/7tu/Gp5cfru41sbG1YfDYYezdHgpAeloaf+0/xPAJo7G0sqRB04a07tSWAyF7C61TCCGEKA6ZvP6Lbt68ya5du5g9ezbm5uYA1KlThzfeeINvv/32hbWjVatWxMXFPVMdbs72RMTmTzavxibgWN4KW2tzPWXtiLypPTGNiH2Am7NdsWJWr2SLSp3DjTv5E8GrsQm4OenW4+ZkR0TesoT89llia21erHq06qxdg6uX8pcWXLkUhWNFB8rb2egtX79RHTwa1CZo3U6dYw0a1yPuZjzf/PQxe8PX8/2a+bjXcdVbT82q9kRcy+9LxLX7ONpZYVtOd6xrutgTcSNBa1/kjQRqVrXXKftKC1cePEznzKXbeuMWxbXIGNzq5E/63erU4sH9ByQnJhF7LRZjE2NcalQrcNyda1ExTx1PCCH+v8rJebGvskSWDfyLrly5QvXq1bGzs9Pa36RJE7755psnfn7r1q0cOXJEs/3gwQPatm2r2X748CEBAQGa7caNG/Pxxx/r1LN37166dev2FD3IZ2mhICUtU7P96L21hSnJqdqXmS3NTbXKAqSmZWJlYVrsmKlp2utLDdVjaa4gpUBZTfvMTYtVT0FWVpakPEzNrzPvvZW1JUmJD7XKGhsbM+2jiXz5yTJy9PwrUKlKBZq3asTb4+dwLOwMg4YHsHDZhwx+fxfZKu21rFYWpnrH2srClOSUx8baQkGq8rGxVmZiZanbN78OtQk9HFFon58kPS0d63LlNNvW5awBUCrTSFemabYLHlemKp8pphBCCFGQTF7/RfomMcXRq1cvvWteH3m0NECfRxPbhIQEVCoVGzZsKFZsn5ZuTBuaO1E+GxFPWno21gUme9YWuTfopKbr3ryUlpGlOZ5f3hSlnrKFSUvPxvqxSZi1hZneetIyHmufZV77MrKKXI9vj87M+HgyAGdO/o1SmYZ1OSvN8XJ575WpaTrx+w3xJ+JyDOfPXNLbl4z0DM6cvMCRQycACPw5iNcmvIprVTvcXOx5d3R7AM5evo0yPUurvY/6orff6dma449YWZqhfGyyXsnBmib1nJj/02G97SsqC0sLlKn5E/pH762sLLGwstSZqCpTlFhZWyGEEEI8L7Js4F9Up04drl+/TmJiotb+M2fOULdu3X819qOJ7e+//063bt146623ivX53cei8JwSiOeUQKYu3kvUrQfUruagOV7bxYH7SUqdrCtA1K1EarloX7Z2d3Eg6lZisdpw/U4yJsZGVKuYf5m+los9UXpusoqKS6R2gZi1Xey5n5RGcmpGkesJ3X6QTk370KlpH/435kOirl6jdj23/DrruXH/boJO1hWgRZvGvOLdhtC/1hL611oaNfXgzeljeffD8QBEXI42+GVmz5FIvMeswnvMKt75YjfRNx9Qq7pjflurO3A/UamTdQWIjn2Ae3UHrX21qtsT/diyDd/2tfn7ajy37uq2vThquLsSdSVSsx11JRJ7R3ts7crjUsMFVbaKm9diCxyPoIab6zPFFEKI/49k2YBhMnn9F7m4uODr68vs2bM1d3BfuXKF77//nkmTJr2QNpiYmPDuu+9y9+5d9u3b99T17DoaSY92tXF1Ko+NlRmjujdmxxH9l6BPXb6NSp3DAM/6mCqM6dfZA4ATl/LX3ZopTFAojDXvTRW6f4rpmdn8fuY6Y3s0xcJMQSO3SnRsXJ1d4ZE6ZXcdjaRH2zq4Vslrn19jdhyNKHY9Be3cup+Afl2p6V4dG9tyjB4/iJAt+sdwzrSvGOD3OkMCJjEkYBL//H2VH79dy9KvVuW2b9sBGjapR8u2TTA2NubVkb1IfJBMzM1EnbpCD0fg36kOrs522FiZMTKgKTv13GQFcOqfONRqNf19XsJUYUxf7/oAnLxwS6ucX4da7Dykvw4AVbaKzIxM1Co1arWazIxMVNkqnXJd/H3YvXUn1yJjeJj8kHU/BeLdwxcAC0tL2nl2YPWyFaSnpXHhzHnC/jiCp3/RbkAUQgghikKWDfzLZs+ezcKFC+nWrRumpqaYm5sza9YsWrZs+cx1P77mFdC7jMDc3Jw333yT7777ji5dnu6Zm0cv3CRw998sfdsPc1MTDp66xo/bT2uOL5rizZmr8azadY5slZppS/czc3g7JvR5mWu3k5i2dL9mbaeTYzm2zOuv+eyhpcOJu/eQ3jM36cT9Yl0Ys4a1Z9eCgSSlZrBgXRjRcYlUtrdm3Ye9ePXjrcQ/SOXoxZsE7j3P0qm+ue07fY0fQ04/sZ7ChP15ktU/bWLZms8xtzDn4O7DLP8mUHP8m58+5vSJC6z8fgMpD1O11sdmZWWTmqIkNSX3Mvq16Jt8+O4XTJ8zGXtHOy5fiODtN2aT7dBCJ274uVjW7jjHklndMTcz4ffjMfwclP9Isi/f9eHc5dus3naWbJWa6Yv2MX1MB8YPbEHMrUSmL9qntY72pVqVqGhvzcFjhp9r++tPa1i7fJVm+8COvQx5fQQ+AX6M6zuSH4JWUsmpMs3btaTfiEFMG/cWmRkZtPPqyNDxIzWfmzTzTb6avYCBnn2wtbNl8sw35TFZQgjxFMpaNvRFMsp51oWZ4j+psB8O+FeZGJVIWNX+4q0Jfl7MWvYtkbgAgT882018z6Kmle6jvIQQQuTrsH3zC433Z48+LzTes5DMqxBCCCFEaSOpRYNkzasQQgghhCgzJPMqhBBCCFHK5KifXOb/K8m8CiGEEEKIMkMyr0IIIYQQpYzcTm+YZF6FEEIIIUSZIZNXIYQQQghRZsiyASGEEEKI0kbWDRgkmVchhBBCCFFmSOZVCCGEEKKUkcSrYZJ5FUIIIYQQZYZkXoUQQgghShvJvBokmVchhBBCCFFmSOZV6GWUXTJf+XJMjEokrlX5miUSN6uE+gsweHJoicT9dYkv0cpbJRK7ppVzicQVQojiKu1rXqOjo5k+fTqJiYnY2dkxf/58XF1d9ZaNioqid+/eDB48mGnTpj1zbMm8CiGEEEKIYvnoo48YPHgwu3fvZvDgwXz44Yd6y6lUKj766CO6dOny3GLL5FUIIYQQorTJecGvYrh//z4XL17E398fAH9/fy5evEhCQoJO2R9++IFXXnnFYFb2acjkVQghhBDi/7nk5GRiY2N1XsnJyTpl4+LiqFy5MiYmJgCYmJhQqVIl4uLitMpdunSJw4cPM3LkyOfaVlnzKoQQQgjx/9yqVav49ttvdfZPmjSJyZMnF7u+rKwsPvjgA+bNm6eZ5D4vMnkVQgghhChlXvQNWyNGjKB37946+21tbXX2OTk5ER8fj0qlwsTEBJVKxZ07d3ByctKUuXv3LtevX2fcuHFAbmY3JyeHlJQUPvnkk2dqq0xehRBCCCH+n7O1tdU7UdXH0dERDw8PQkJCCAgIICQkBA8PDxwcHDRlnJ2dCQ8P12wvWbIEpVIpTxsQQgghhPhPUr/gVzHNnj2bwMBAfHx8CAwMZM6cOQCMHTuW8+fPP02Pi0wyr0IIIYQQoljc3d3ZuHGjzv4ff/xRb/mnWTdriExehRBCCCFKm1L+IwUlSZYNCCGEEEKIMkMyr0IIIYQQpUxOaf992BIkmVchhBBCCFFmSOZVCCGEEKK0kcSrQTJ5FUU2yPslhnZriLmpgt9PxrBgzRGysvU/X6N2NQdmjmqPq5MdMXGJfLbiMFdv6P7m8ZJ3fWnu4Uz7MStQqXX/S7W1MmPmsHa08nAmMSWDZcEn2XM8Wn/7POszrGtDzM1MOHj6GgvWhWnaV5x6Cur/qievjvDG3NyMQwdO89Xn68nKytYpV768NXMXvkH1GpUxNjHmevRtln6zmb/PRQHg6f0yo173x8HRlqzMbMKPXOCbL38jyUDcgT4vMbRbI8zNFPx+IoYvVv1leKyrOzDjtQ6asZ73859cvZ471t3a12bGa+3JyFRpyr+7aA+nL93WP4beLzHUL+8cnyrCOR5Z4ByvzD/HXVrWZExAMxxtLcnMVnH0fCwLfz2KMj1Lp55t67ewd1soMRHRdPL15J2PpxsYFdgcuJGNK9eRkZFJe68OTJo5FTMzMwAeJiWzaM4XnAw7QXm78oyaMobOfl0M1iWEEKJskmUDokhavVSVYd0aMfmLUPq89xvOFW0Y06uZ3rIKE2PmT+7C7rBIuk4OZOeRCOZP7oLCRPvPrWtrN0yMC/8TfGdQa7Kz1XSbtoHZKw7x3qttqOlkp9s+D2eG+zRk0je76f3+JqpWsGGsf9Ni11NQi9YeDB7RlbcmLGZgz/dxqlqBUa9311s2LS2D+Z+sIaDrNPw93+HX1XuZ99V4TPL6fP5cFJNe+5Lund/m1V4fYqIwZswbPfTW1apBVYZ1b8yUBbvo+/aG3LHuXchY/8+b3Uci8Jmwhl2HrzL/f95aY/13xB26vL5a8zI0cW31UlWG+TVi8peh9Jn2G84VbBgT8IRzfDSSrlN0z/G5q3d4fV4I3pMD6Td9IyYmxrxuoA8OFR15dewwugb46T3+yIkjx/htxTrmLV/Iqh3riIuNI3DZSs3xb+d9g8JUwfr9m3nvs1ks+exrYiKf/AVFCCFE2SKTV1Ek3drVYvufV4i+lchDZSYrtp+he7taess2q1cFhYkR6/deICtbzcZ9FzEyguYe+T8bZ21pyms9m/LdxuMGY1qYKejctAbLt58mLSObs5F3+PPcDfxaueu2r00tth25SnRcbvt+2XmW7q1rFbuegny7t2bntiPERMWR8jCN1T/vwte/td6ymZnZ3Lh2h5ycHIyMjFCr1diWt8bG1gqAu/EPSEpK1ZRXq3KoWq2i3rr82tdm+6HLRN/MG+ttp+nWvrbess08nDAxMWLD7ryx3nsRjODl+k56yxemW9tabD9c4ByHPOEcGxc4x/svYkT+Ob7zIJWklIz8/qpzcKmk/5db2nt1pG3n9tjYFf7LLvu278anlx+u7jWxsbVh8Nhh7N0eCkB6Whp/7T/E8AmjsbSypEHThrTu1JYDIXuLPQ5CCFEq5LzgVxnyn5m8enp64u/vj1qt1toXGBiIn19+Rufhw4fUr1+ftWvXavb9/PPPvPvuuwbrDg8Pp0+fPlr7rly5gqenp1asK1euAHDp0iWGDRtGQEAA3bp1Y9CgQdy7dw+A6dOn07FjRwICAvDx8eHLL7/U1KFSqejQoQMTJkzQiV+3bl1++OEHg22qW7cuqam5k6OcnBxWrVpF9+7d6datG76+vrz//vskJycXMoKFq1nVXuuy/9UbCTiWt8LW2lynrJuzPRGxD7T2RcYmULOqvWZ7fN/mbDl4iftJaQZjVq9ki0qdw407+e2+GpuAm56MqZuTHRGxCVrlHMtbYmttXqx6CnJ1cyLi6s38PlyJxdGxPLblrQ1+5pdfZ7H3r2+Y99V4QrYeJvFBiuZYw8bu7Di4kNBDi+jo2YSN6w7qraNmVTsiruf3JeJ6Ao52+se6ZlU7Ih5bjhF5IwG3AmNdp4YjO78dwvr5/RjZswkmxkYG4j6Hc+ycH7dRrcrsXTKUA0uH88rLNdiw74LeuEV1LTIGtzr5Xzjc6tTiwf0HJCcmEXstFmMTY1xqVCtw3J1rUTHPFFMIIUTp859a86pUKgkODqZ3796afS1atODzzz/n3r17VKhQgZMnT9KgQQPCw8MZMmQIAMeOHcPb2/u5tePtt9/mnXfeoXPnzgDExMRgaWmpOT5u3DiGDh3Kw4cPCQgIoGnTpnh5eXHo0CEqVarEyZMnNe19pGLFiqxatYpBgwY98beHv/76a44fP86qVauoUKECarWaffv2kZSUVOTfLX6cpbmC1LRMzXZK3nsrC1OSUzO0y1ooSFFmau1LUWZhZWEKQD1XRxrWqsSiX49S0d7wRNDSQkFqmvYaydS0TE09j7cvpUDZR+2zNjctVj1adVqZk5qSP7lOyXtvZWVOcoEsakGjB8/FzExBh1eaoDA10Tp2/mwk3Tu/TYWK5fHv1Z7bcfehnJVOHVYWppr2F+yLlaWesTY3JVWp3bcUZX7fzly+zdCZm7l9P4WaVe35ZEJnVGo1a0LO6fa3OOfYXKHVxtzyWVpjei4iHu/JgVS0s6Jnx7rE3UvhWaSnpWNdrpxm27pc7t+OUplGujJNs13wuDJV+UwxhRCipMiTsgz7z2ReASZNmsSSJUvIzMz/n6qRkRGNGjXi2LFjQO5EdejQoVy+fBnIzXaePHmSVq1aPbd23L59m8qVK2u2XV1dsbbWnaTZ2NjQsGFDoqNz1+UFBQUxaNAgunTpQnBwsFbZSpUq4evra/Bn1x5JTU1lxYoVfPrpp5rJr7GxMV27dqVatWqFfragrq3d2L90GPuXDuOrqV1Jy8jG2tJMc9zaIve9vhtw0tKzsbbUnhhaW5qiTM/CyAjeHdqWr38N13uD1hPrsTDTHzMjG+sCE6dHbU3NyCpyPV18W7Drj6/Y9cdXLPhmImnKDKytLfI/Uy73C4hSqT2Re1xmZjb795xgyAgf3GtX1Tl+724Sx8Iu8NHc0QB0bePOvuXD2bd8OAvf7ooyPUv/WKfp63cWVjpjnd+3W3cfEncvhZwciIp9wIrgM3RuUTM3bis39n83jP3fDeOrN/POsUURz/Fj451b3lRv2buJSo7+Hcsnr7+iO1jFYGFpgTI1/0vDo/dWVpZYWFnqTFSVKUqsrHW/HAghhCjb/lOT1wYNGtCgQQPWrVuntb9ly5aEh4cDcPz4cVq3bk2NGjW4evUqFy9exMbG5okTu8jISAICAjSvKVOmGCz7xhtvMGTIEEaPHs3ixYuJjIzUWy4+Pp5Tp05Rv359EhISCA8Px8/Pjz59+hAUFKRTfsKECQQFBXHnzp1C22lmZoabm1uh/XmSPUej8JqwBq8Ja3hr0R6ibz6gVjUHzfHa1Ry4n6TUycgBRN16QC0XB6197i4ORN98gLWFGfVcK/DJ+FcIWTSIXz7MvWkpeOFAGteurPWZ63eSMTE2olpFG82+Wi72RMUl6saMS6S2S/4l69ou9txPSiM5NaPI9ewLPY5fp7fw6/QW7/3vO2Ki4nCv7ZLfh9pVuX8/yWDW9XEmCmOcq1bQf8zEBGeX3DWve8IiNTdTvb1wD9E3E6ldcKyrO3A/Uf9YR99M1DovALWqORB184FOWchdUvJo0cCe8Ci8Jq7Ba+Ia3vr6OZzjag5E39If18TYmKoVny7r/0gNd1eiruT/txR1JRJ7R3ts7crjUsMFVbaKm9diCxyPoIab6zPFFEKIEiNrXg36T01eAd58801+/PFHzfpPgFatWnHs2DFSUlJQKpVUqlSJFi1acOzYMY4dO0bLli2fWK+7uzvBwcGa1+LFiw2WHTt2LKGhoQQEBHDr1i369u3L8eP5Nyb98MMPBAQEMH78eMaMGUPbtm0JDg6mc+fOlCtXjpdffhmVSsWZM2e06nV0dKR///4sXbrUYOx/6xc5dh2JoEeH2rg622FjZcbIHo3Z8VeE3rKnLt1Gpc5hQJf6mCqM6efpAcCJf+JIScukx1vrGf5RMMM/CubtRbk31Iyas40LUXe16knPzOb3M9cZ26MpFmYKGrlVomPj6uwK1/0ysOtoJD3a1sG1SnlsrMwY5deYHUcjil1PQbt3htMtoA01alahnI0lw0f7ERpyVG/Z+g1cadjYHYXCBDNzU14d7o2Dgy0X/44BcrO6lSrnTq4rV3FgzISenDp+Wf9Y/3UV/4518se6ZxN2Hr6qf6z/iUOtzmGA90uYKozp2yV3rE9ejAOgdSMX7G1zs8c1nMozKqApf56+rj9uWN45dsqL6/+Ec5yj/xxDbla3skPu1YYqjta80acZJ/65pbcuVbaKzIxM1Co1arWazIxMVNkqnXJd/H3YvXUn1yJjeJj8kHU/BeLdwxcAC0tL2nl2YPWyFaSnpXHhzHnC/jiCp//zWw4khBCidPhPrXkFcHNzo1OnTqxYsUKzr1mzZsTGxrJnzx6aNct9XE+LFi345ZdfyMjIwMfH57m3o3Llyposrbm5Obt376ZFixZA/prXgjZv3kxCQoLmJrCHDx8SFBREkyZNtMqNGTMGPz8/XnrpJb1xa9WqRUZGBtHR0dSsWfO59efo3zcJ3HWe7971y32O6skYftp6SnP8q6ldOXvlNqt2nCNbpWb6t/uYMbI9E/o1JyYukenf7iNblXszXUJy/jpSs7x1oQnJabnLCBTa36e+WBfGrGHt2bVgIEmpGSxYF0Z0XCKV7a1Z92EvXv14K/EPUjl68SaBe8+zdKov5qa5z3n9MeT0E+spzLGwi6xfvZevl72Jubkphw6eYcXyHZrjC76ZyLnTEQSu3I2pmSlT3u6Pc9UKZGeriIq8xbSpS7l/L/dJrq41nXh9Ui9sbK14mKwk/MgFfvguGKrW04kbfv4ma3ee59vp3TA3M+H3EzH8tCV/rBe+3ZWzl+NZHXI2d6y/2cf019ozfkBzYm4lMv2b/LFuXt+Z98d0xNJCQUJSGruPRLJq+xnD5zj0sXMcXOAcv5l3jncWOMcj2jOhr+45rulsz8R+LbCxNuNhaiZHzseyLOiE3ri//rSGtctXabYP7NjLkNdH4BPgx7i+I/khaCWVnCrTvF1L+o0YxLRxb5GZkUE7r44MHT9S87lJM9/kq9kLGOjZB1s7WybPfBNX9+f334AQQrxQZSwb+iIZ5fxHfjzX09OT77//njp16nD79m369OmDSqVizZo11KlTh6FDh3L37l0mTZpEjx49yM7OpmvXriQnJ7N161ZcXFwM1h0eHs78+fPZvHmzZt+VK1d44403OHDggE78ffv28corr6BQKMjIyGDMmDF4eXkxcuRIpk+fToMGDbQmr+fOnWPKlCkcPHgQI6Pci7rx8fH4+/tz6NAhzp07pxX/559/Zu3atdjZ2Wn21a1bl1OnTmFtbc3ChQs5c+YMX3/9NY6OjuTk5LB9+3aaNGlC9erVizSebUb/UrwT8JzkmJfMxQDzE8dKJG5W/ZdLJC5AjkL/Uwf+bb8u8S2RuAA1rZxLLLYQQhRHy5WbXmi8YyP7vdB4z+I/t2wAoEqVKgQEBJCYmKjZ16pVK2JiYjRLBBQKBTVq1MDW1rbQievTCA0NpXv37vTs2ZPevXtTt25dzZMN9AkKCqJ79+6aiSvkZm49PDzYvXu3TvmhQ4eiUuleVn3krbfewtPTk2HDhtGtWze6devG8ePHsbOze6Z+CSGEEOIFycl5sa8y5D+TeRXPl2ReXwzJvL5YknkVQpQVLVdsfKHxjo3q/0LjPYv/ZOZVCCGEEEL8N/3nbth6Fo/WyRbUuHFjPv744xJqkRBCCCH+X1I/ucj/VzJ5LaDgDVlCCCGEEKL0kcmrEEIIIURpI3ckGSRrXoUQQgghRJkhmVchhBBCiFJGngVlmGRehRBCCCFEmSGZVyGEEEKI0kYyrwZJ5lUIIYQQQpQZknkVQgghhChtZNGrQZJ5FUIIIYQQZYZkXoVeasuS+V5jlFUy3zTTW7QskbgARiX15Tq7ZAK/OnV3icRdt8iHaOWtEold08q5ROIKIcR/kUxehShBJTZxFUIIUbrJ/x8MkmUDQgghhBCizJDMqxBCCCFEaSOZV4Mk8yqEEEIIIcoMybwKIYQQQpQ2knk1SDKvQgghhBCizJDMqxBCCCFEaaOW1KshknkVQgghhBBlhmRehRBCCCFKG0m8GiSZVyGEEEIIUWbI5FUIIYQQQpQZsmxACCGEEKK0kWUDBknmVQghhBBClBmSeRVCCCGEKG0k82qQTF5FkdhamTFrSDtaeTiTmJrB0uCT7DkRrbfsoM71Gd61IeamJhw8c43568PIylZjqjDmvYGtaVHPGVsrc2LvJrNs2ynCLt58YvxBXeozzKch5mYKDp6KYcHa3Dr1qe3iwKwR7XB1siMmLpG5q/7iamwCAG7Odkzp35J6NRyxK2dB63ErDPd3aF5/U57QX8/6DPduiLmZCQdP5/cXoF+nevi3roW7sz17TkTzyZrDhfbT1sqMmcPy4y4LPsme44bjDuuaH3fBOu243dvkxt17IppPVhceV1Nnl/oM822IuWkRxrla3jhXsSPmdt4438gd525tajHAy4NqlWxJTc9iT3gUy7acRFXIcwsHeT12jn99wjkeXuAcr37sHPcrcI5f13+Ot63fwt5tocRERNPJ15N3Pp5usG2bAzeyceU6MjIyae/VgUkzp2JmZgbAw6RkFs35gpNhJyhvV55RU8bQ2a+LwbqEEEI8G1k2YEBSUhINGzZk7ty5mn2bN29mypQpOmXDw8Pp06eP3nru3r3Le++9h5eXF/7+/vTq1YvffvtNc3zfvn307t0bX19funTpwueff05mZqbm+K5du+jVqxcBAQH4+vry9ttvA9C/f38CAgLo1q0b9evXJyAggICAAGbMmEFOTg6LFy+me/fu9OzZk27durFihf7/gRfVuwNbk6VS4zdjAx+tPMS0QW2o6WSnU66VhzMjujZk4uLd9PpgE86ONozt3hQAE2Nj4h8oGb8oFK931rI85DRzX3sFJ4dyhcZuVd+Z4b6NmPTVbnrP2EjVCjaM7dFUb1mFiTELJnoRGh6J95tr2RkWwYKJXihMcv/Us1Vq9p+IZu6qv4rW3+l5/X21CP19fxPOFfL7C3AvSckvoefYHna10HiPvDOoNdnZarpN28DsFYd4r5C4w30aMumb3fR+f1PumPhrx12x6xwhRYwL0OolZ4b7FRjnijaM7fmEcT6aN85HtMfZwsyERRuO4TN1Ha99FkJzDycGd21gOPajc7xoN71nFuEcT8g7x1PzzvGEx87xyWjmri78HDtUdOTVscPoGuBXaLkTR47x24p1zFu+kFU71hEXG0fgspWa49/O+waFqYL1+zfz3mezWPLZ18RE6v/CIYQQRZaT82JfZYhMXg3Yvn07TZo0YceOHVqTyeJIS0tj6NChuLu7s3fvXkJCQli9ejVqdW426fjx43z88cd8/vnnhIaGsmPHDm7fvs2cOXMAuHPnDnPmzGHZsmUEBweza9cuxowZA8DGjRsJDg7mhx9+wMbGhuDgYIKDg5k3bx6hoaGEhYWxefNmtm3bxtatW+nQocNTj4WFmYLOTWqwPOQ0aRnZnI28w5/nb+DX0l2nbPdWtdgWdpXouEQepmXyS+hZ/FvXAiA9M5ufdp4hLiGFnBz46+9Ybt1/SL3qjoXG79amFtsOX8mtU5nJLzvO0r1tLb1lm9WtgomxEev3XSQrW81vB/7ByAia13MC4Hp8Mtv/ukr0rQeF97dpDZZvL9Dfcwb627oW244U6O+u/P4C/H7mOofOXicpNaPQPhYat5Vu3G5tCsRVZvLLzrN0f8q4WnUevkL0rbw6Q4o5zuSP8+Y/LnP2ajzZKjV3E5XsDo+ica1Khcf+64p2f9oYiF0nL/b+vNgHi3+OAdp7daRt5/bY2NkWWm7f9t349PLD1b0mNrY2DB47jL3bQwFIT0vjr/2HGD5hNJZWljRo2pDWndpyIGRvoXUKIYR4ejJ5NSAoKIgJEyZQp04dDhw48FR1hISEYGdnx+uvv46xce5Q29raMmjQIACWLFnC+PHjqVu3LgDm5ubMnj2bnTt3cvPmTe7du4dCocDOzg4AIyMjPDw8nhg3Pj4ee3t7zWVNMzMzatXSPxEoiuqVbFGpc7hxJ1mz72psAm56MoJuTnaay7ePyjnaWmJrba5T1sHGguqVyhMVl1hofDdneyJi8yciV2MTcCxvpbdON2c7Im9qT1oiYh/g5qzbVkP09vdmgt463JzsuHrzsf6W19/fp4pbyDhHPD7OTxlXU6ezPRE3ijHOsY+N803D49ykdmWibiUaju302Dm+8RTnWM84PQ/XImNwq5P/BcKtTi0e3H9AcmISsddiMTYxxqVGtQLH3bkWFfOvtEUI8f9Izgt+lSEyedXj0qVLJCUl0bp1a/r06UNQUNBT1XPhwgUaNWpk8Pjly5dp0qSJ1j47OzuqVavGlStXqFevHo0aNeKVV15hypQprFy5kgcPCs8mAXTr1o3IyEi6du3KjBkzCA4OJjs7+6n6AGBlriA1PUtrX0paJlbmpjplLc0VpBQom5KWm7W2fqysibERc0Z2ZGd4BNfikwqNb2mh0NSjVaeFvvimWmUBUtMysdJT1hArcwWpacXob9qT+1sUlha6cQ21/XnG1a7z+Y9z97a18HCtwNo9fxuO/YLPcXGkp6VjXS5/aYt1OWsAlMo00pVpmu2Cx5Wpyn+lLUIIIeSGLb02bdpEQEAARkZGdO3alU8//ZT4+Phi15PzjGtIjI2NWbp0KVeuXOH48ePs27ePn3/+me3bt2uysfpUqlSJHTt2cObMGU6ePMn333/Ptm3b+Pnnn5+qHcqMbJ1JhLWlGcqMLJ2yaY+VtbbMzf6mFihrZARzRnQkW6Xmiw1HderwaenGtKFtATgbEU9a+mN1WuTVma4vfpbmeH55U5R6yhqizMjG2vKx/lo8fX+LKi3dQFy9/Xz2uD6tCozz1fjcOi2LMc6WTx7njk2qM7FvcyZ/tZuklPwlDD4t3Zg2pJBzbFnMc2xZvHNcHBaWFihTUzXbj95bWVliYWWpM1FVpiixsrb6V9oihPh/pIxlQ18kybw+JjMzk+3btxMUFISnpyfdunUjKyuLLVu2FLuuBg0acP78eYPH69aty5kzZ7T2JSYmcuPGDWrXrq3ZV6dOHYYMGcKKFSuwsbHh2LFjT4ytUCho3rw5r7/+OmvWrOHw4cMkJiYWuw8A1+8kY2JsRLWKNpp9tava673cHxWXSO2q9lrl7ienkVxg7eX7Q9rhYGvB9B8P6r37fPexKDynBOI5JZCpi/cSdesBtas55Nfp4sD9JKVWnZr4txKp5WKvtc/dxaHQS9ZF6q+Lvd46ouISqe3yWH+T0vS27Wni1nIpZJwLxnUpftzd4VF4Tg7Ec3KBcXYpMM7Vnm2cW79UlRnD2/HOkn06l/l3H4vC83+BeP4vkKlL9hIV91js4p7jqg5PXH7ytGq4uxJ1JTI//pVI7B3tsbUrj0sNF1TZKm5eiy1wPIIabq7/SluEEELI5FXHvn37cHNz49ChQxw4cIADBw7wyy+/sHnz5mLX1b17dxISEvjpp580Wdjk5GRWrVoFwMSJE1m2bBmXL18GICMjg9mzZ+Pr64uLiwvx8fGcPn1aU9/t27dJSEjAxcWl0Lh///03sbH5/zO9cOEC5cuXx9a28BtTDEnPzOb3M9cZ598UCzMFjdwq0bFRdXYdi9Qpu/NYJD3b1qFmlfLYWJox2rcxIUcjNMenDWqDaxU73v5+PxlZqiLF33U0kh7tauPqVB4bKzNGdW/MjiMResueunwblTqHAZ71MVUY069z7hrhE5fiNGXMFCYoFMaa96YK7f8MitXf8Eh6tinQXz/t/poYG2GmMMHE2EjrvT6P4o7tUSBu4+rsCteNu+toJD3a1sG1St6Y+DVmh564xkZGGD8hrqbOsEh6tC/mOHvpH+eX6zkxZ0xHZiw7wMWYe4XG1fSn4Dnu1pgdYQZiX3nsHL9S/HMMoMpWkZmRiVqlRq1Wk5mRiSpb92+yi78Pu7fu5FpkDA+TH7Lup0C8e/gCYGFpSTvPDqxetoL0tDQunDlP2B9H8PT3fmKfhRBCPB2jnGe9tv0fM2bMGDw9PRk8eLDW/i5dutCrVy9+/PFHypcvr9nfp08f2rRpw2uvvYaDQ37mqG3btnz++efEx8fzxRdfcOrUKaytrVEoFAwePJj+/fsDsGfPHpYuXUp6ejpZWVl4eXnx1ltvYWFhwc2bN/nggw+4efMmFhYWqNVqhgwZornhCyA2Npa+ffsSHh6u2ffnn3+yePFiUlJSMDMzw9LSkrfffpsWLVoUeRxaTVyptW1rZcb7Q9vTsp4TSakZfJf33NPK9tas/6AXgz7ZSvyD3Muprz567mnec14/z3vuaRUHa4I/6U9GlgqVOv/5nZ+vC2P38SgAjLL0/zm+2uWlvOePmnDw1DXmrz2ieQbooinenLkaz6pd5wCoU82BmXnPAL12O4m5qw5zJe/5o06O5dgyr79W3XH3HtLrQ+11zbZWZrw/rEB/tz6hvwWea/t5geetjunehLHdm2jV/eOOM/y040xufx/rrq2VGbOGtaelR27cpVtzn/Na2d6adR/24tWPC8T1ynvOq2ne82UfizvGXzvuTyH5cQHI1h3rV70fG+fAx8Y5Ip5VOwuMc97zdK/FaY/zd2/70rh2ZTILfEE5ezWeqYv3GvzK/GqXl3Kf8/qoPwXP8eS82AXP8bAC53j1Y+f4M91znG71UGvfmu9Xsnb5Kq19Q14fgU+AH+P6juSHoJVUcqoMQNCa39i4cj2ZGRm08+rI5Fnaz3n9avYCTh09ia2dLaOnjNV5zmtNK2f9nRZCCANaLFz/QuMdf3vQkwuVEjJ5FXo9Pnl9UQxNXv9tOYrCs5L/lscnry+UnsnrC1FC13vWLfIpmcDI5FUIUXwyeTVMbtgSQgghhChtJLdokKx5FUIIIYQQZYZkXoUQQgghShv1k4uUpOjoaKZPn05iYiJ2dnbMnz8fV1dXrTLfffcdO3fuxMTEBIVCwdSpU5/pFz8fkcmrEEIIIYQolo8++ojBgwcTEBBAcHAwH374IatXr9Yq06hRI0aPHo2lpSWXLl1i6NChHD58GAsLi2eKLcsGhBBCCCFKm1L887D379/n4sWL+Pv7A+Dv78/FixdJSEjQKtehQwcsLS2B3Gfb5+TkPPUz5wuSzKsQQgghxP9zycnJJCcn6+y3tbXVeU58XFwclStXxsTEBAATExMqVapEXFyc1mNDC9q6dSvVq1enSpUqz9xWmbwKIYQQQpQ2L/hhA6tWreLbb7/V2T9p0iQmT578THUfO3aMb775hl9++eWZ6nlEJq9CCCGEEP/PjRgxgt69e+vs1/frnE5OTsTHx6NSqTAxMUGlUnHnzh2cnJx0yp4+fZp3332XpUuX4ubm9lzaKpNXIYQQQoj/5/QtDzDE0dERDw8PQkJCCAgIICQkBA8PD50lA+fOnWPq1KksXryYl1566bm1VW7YEkIIIYQobXJyXuyrmGbPnk1gYCA+Pj4EBgYyZ84cAMaOHcv58+cBmDNnDunp6Xz44YcEBAQQEBDA5cuXn3loJPMqhBBCCCGKxd3dnY0bN+rs//HHHzXvg4KC/pXYMnkVQgghhCht5NdhDZJlA0IIIYQQosyQzKsoVYwyS+b38HIsSug/hbSS+/0/I1XJfK3PMTIqkbjvnDtaInG/bNSaaOWtEokNUNPKucRiCyGegWReDZLMqxBCCCGEKDMk8yqEEEIIUdpI5tUgybwKIYQQQogyQzKvQgghhBClzVM8e/X/C8m8CiGEEEKIMkMmr0IIIYQQosyQZQNCCCGEEKVNyT1JsdSTzKsQQgghhCgzJPMqhBBCCFHayP1aBknmVQghhBBClBmSeRVCCCGEKGWMJPNqkGRehRBCCCFEmSGZVyGEEEKI0kZ+pMAgmbyKIrG1MmPWkHa08nAmMTWDpcEn2XMiWm/ZQZ3rM7xrQ8xNTTh45hrz14eRla3GVGHMewNb06KeM7ZW5sTeTWbZtlOEXbz5xPiDur7E0O6NMDdV8PuJGBas/ousbP3PEald3YGZozvg6mRHTFwin/3yJ1evJ+iUW/KeH83rO9N+9C86TySxtTJj1qvtaFXPKbe/20+x56SB/r5Sn+FdGuT29+w15v92VKdt1SrasHZ6AAfOxDB7zeEn97dLfYb5NsTcVMHBUzEsWBtmuL/VHJg1oh2uVeyIuZ3I3FV/cfVGbn+7tanFAC8PqlWyJTU9iz3hUSzbchKVWv8/ioO8X2Jot9y4v5+MYcGaI4XGnTmqff44rzisiVvQknd9ae7hTPsxKwzG1fTZpyHmZkXos0ten/Niz131F1djc2O7OdsxpX9L6tVwxK6cBa3HrdBbR3aKkps/B5Hy91UUNtZU7u+DXZsmestm3kkgLnAbqZejMVIosO/YnCoD/YpdD8C29VvYuy2UmIhoOvl68s7H0w2W3Ry4kY0r15GRkUl7rw5MmjkVMzMzAB4mJbNozhecDDtBebvyjJoyhs5+XQzWJYQQ/xWybEAUybsDW5OlUuM3YwMfrTzEtEFtqOlkp1OulYczI7o2ZOLi3fT6YBPOjjaM7d4UABNjY+IfKBm/KBSvd9ayPOQ0c197BSeHcoXGbtWgKsO6N2by/F30eWcDzpVsGNO7md6yChNj5k/xZveRCLpOXMPOw1eZP8UbhYn2n3rXNu6YmBj+83+3f2uyVCr8Zv3GR6v/ZNqA1tSsoqe/9ZwZ0aUBE7/dQ6/ZQbn99Wuit75/rt8rtJ+aOl9yZrhfIyZ9tZveMzZStaINY3s2NdjfBRO9CD0aifeba9l5JIIFE700/bUwM2HRhmP4TF3Ha5+F0NzDicFdGxiIW5Vh3Rox+YtQ+rz3G84VbRjTq5BxntyF3WGRdJ0cyM4jEcyf3EV3nFu7YWL85H9mWtV3ZrhvgT5XsGFsjyf0OTyvz2Hafc5Wqdl/Ipq5q/4qNGbc6mCMFCbUWzILlzcGcmvVVtJj43XKqbOziV7wM9b13am3eBZ1F03XmpwWtZ5HHCo68urYYXQN8Cu0fSeOHOO3FeuYt3whq3asIy42jsBlKzXHv533DQpTBev3b+a9z2ax5LOviYnU/wVLCFEG5bzgVxkik1fxRBZmCjo3qcHykNOkZWRzNvIOf56/gV9Ld52y3VvVYlvYVaLjEnmYlskvoWfxb10LgPTMbH7aeYa4hBRycuCvv2O5df8h9ao7Fhq/W7vabD90mehbiTxUZrIi+DTd29fWW7ZZPScUJkas33OBrGw1G/ddxMgImtd30pSxtjTltYCmfPfbMcP9bVyd5TvOkJaZzdmoO/z59w38Wrjp9relO9uOXiX6dl5/d5/Fv1UtrTLezVx5mJbJ8StxhfZT0982tdh2+Iqmv7+EnKV721p6yzarWwUTYyPW77tIVraa3w78gxHQvF5ufzf/cZmzV+PJVqm5m6hkd3gUjWtV0h+3XS22/5kfd8X2M3RvZyBuvSq547z3sXH2eGycezblu43Hi97nuLw+7yhmn43y+3w9Ppntf10l+tYDg/HUGZkkn7hA5b7emFiYY13HFZumHiQeOa1TNvHPU5ja21LBtwPG5mYYm5liUd2p2PU80t6rI207t8fGzrbQMdm3fTc+vfxwda+Jja0Ng8cOY+/2UADS09L4a/8hhk8YjaWVJQ2aNqR1p7YcCNlbaJ1CCPFf8J9ZNtC/f38yMzPJysoiJiaG2rVzJzf169dnypQpzJs3jwsXLmBsbEz16tWZNm0aderUYdmyZYSG5v4P4fr16zg4OFCuXG4m8Pvvv8fJyYl+/fqRlZVFcHCwJl5sbCx9+/YlPDz8iW3bsWMHq1evZsOGDVr7Fy9eTFxcHPPmzaNu3bqcOnUKa2trjh49ysKFC8nMzCQzM5OKFSuycuVKjI2NGTZsGKNHj+bSpUuFtvvatWsG6yiu6pVsUalzuHEnWbPvamwCTWtX0Snr5mTHoXPXtco52lpia21OcmqGVlkHGwuqVypPVFxiofFrVrXj0Olr+XXeSMCxvJXeOt2q2hHx2KXryBsJ1Kxqz9HzucsTxvdrzpYD/3A/Ka3w/t4t0N+bD2haq7L+/p6/oVXO0dYSWytzkpUZWFuYMq5bEyZ+u4eebfRPuHXqdLbnzzMF6owtpL/OdkTGak/SIm4+wM3ZjqMXdJdjNKldmahbiXrj1qxqz6HTBc5dYePsbE/EY3EjY/PG+e+8ce7bnC0HLxkcZ50+ny1Gn28+1udYw33WJ+P2PTA2wrxKRc0+y2pOpF7WzVwqI69jWsGOmC9XkBYdi7lLZZyH9sSiWpVi1VNc1yJjaPNKO822W51aPLj/gOTEJO7cvoOxiTEuNaoVOO7O+ZNnnzmuEEKUdv+ZyevGjRuB/Enlo4lmVlYWPXv2ZMCAASxevBiA0NBQRo0axc6dOxk/fjzjx48H0EwMO3furKn36tWr3L9/H1NTUy5cuMBLL71U7LZ5e3vz8ccfExkZibt7brYyJyeHrVu3smDBAq2y2dnZTJkyhdWrV1OvXj0ALl68iJGRkVa5wtqdnZ1NQEDAE+soKitzBanpWVr7UtIysTI31Slraa4gpUDZlLRMAKzNTbUmISbGRswZ2ZGd4RFci08qNL6lhSmpefUUrNPK0lRnYmNpYUpKmp62WuS2tZ5rBRrWrsyitUep6GCtv79mxemvKSnpum2ztlCQrMzg9W5N2HY0gjuJykL7qF2nQlOPdp16+mtuqlUWILVAfwvq3rYWHq4V+Gy1/svpluYK/eOsL66FghSldtwUZVaBcXakYa1KLPr1KBXt9Y+zTn3/Qp8NUadnYGJlobXP2MoCVXqGTtmshCRSL0VR43/DsX7Jnft7jnDtm9XU/vytYtVTXOlp6ViXy19SY10udxyVyjTSlWma7YLHlalF/zsTQpRyZexS/ov0n5m8GrJjxw5sbGwYNWqUZp+vry+hoaEEBgYyceLEQj+/adMmAgICMDMzIygo6Kkmr2ZmZnTv3p3Nmzfz7rvvAnD06FFMTU1p3ry5VtnU1FSUSiUVKlTQ7Ktfv36x4j2POgpSZmRj/djEwNrSDGVGlk7ZtMfKWlvm3lySWqCskRHMGdGRbJWaLzYc1amjaxt3po3IzTidvXKbtPQsrC3M8uvMe69M0xM/PUt/W9OzMDKCd4e35eu1Rwu9cUiZqae/FqYG+qsd71HbUtOzqV3VnhZ1nRm2YLvBWAA+rdyYNrRtbn+vxueOoaW+Og3EtzTT2mdtYYrysbIdm1RnYt/mTP5qN0kpuROrrq3dmDa83WNx9Yyzvrjp2m2E3GUCmnEe2pavfw03OM4+LQv0OSI+tz6942hozJ/c58IYW5ijStOeYKrTMjCxMNcta2aKdW1XbBrXBaCCXwfubjtAxq27xaqnuCwsLVCmpmq2H723srLEwspSZ6KqTFFiZW31zHGFEKK0+89PXi9fvkzjxo119jdp0oRTp04V+tmsrCy2b9/O+vXrMTU1pVevXkyfPl1zt29x9OvXj3HjxvHWW29hYmLC5s2b6du3r0658uXLM2DAALp27UrLli1p1qwZPXr0wMnJSU+t+j2POgq6ficZE2MjqlW04cbdhwDUrmqv93J/VFwitavas/9UjKbc/eQ0rezZ+0Pa4WBrwdSl+/RObvaERbInLFKzPef1V6hV3YH9x3Mvxdau7sD9JKVORg4g6mYir/o21Nrn7uJA0P6LWFuaUc+1Ap9MyM1QG+dlooMXDWLmij84E3WnkP46GO6vswP785Y1aPqrzKBbSzecHKzZNqcfkJvZNDYyomYVO0Z8EaKpY3d4FLvDo/L7O6YjtV0c2H8ibwyrFdLfW4k6N2C5uziw6eAlzXbrl6oyY3g73l68V+ty+56jUew5WiDuuE7UqlZgnAuN+4BXfXTjBh34B2uLvHEe/woAxsZ547xwILOWHuRM9B12H4ti97ECsV/rSO1qDuw/mddnlyf02VtPn3+/pFPWEPMqFUClJuP2vdz3QPr1OMyr6i4NsahWBeXVazr7i1tPcdVwdyXqSiQdu+b+vUZdicTe0R5bu/KYmZuhylZx81osVWu45B2PoIab6zPHFUKUEvKoLIP+8zds5TzDyT948CA1a9akevXqODk5Ub9+ffbufbobIurXr0+FChX4888/SUlJYf/+/QQEBOgt++GHHxIcHIyXlxfnz5/H39+fmJiYYsV7HnU8kp6Zze9nrjPOvykWZgoauVWiY6Pq7DoWqVN257FIeratQ80q5bGxNGO0b2NCjkZojk8b1AbXKna8/f1+MrJURYq/68hVenSsg6uzHTZWZozs0YQdh6/qLXvqUhwqdQ4DvF/CVGFMPy8PAE5cjCNFmUmPqesY/uFWhn+4lbcX7QFg1EfB/H0t/0kA6ZnZ/H72OuO65fW3ZkU6NqzGruNROvF2Ho+kZ5va+f31aURIeG5/t/x1hT4fb2bo/G0Mnb+NLYevcORiLP9bVvjf0K6wSHq0r42rU3lsrMwY1b0xO45E6C176vLt3P561c/tb+e8/l7KvTns5XpOzBnTkRnLDnAxpvCnHew6EkGPDrULjHNjdvxlIO6lvLhd8uJ65sX9J46UtEx6vLWe4R8FM/yjYN5elNvfUXO2cSHqrv7YRyPp0a6YffbU32cAM4UJCoWx5r2pQvufOmNzM2ybv8SdzXtRZ2SSeiWG5NMXsWur+4QDu7ZNUUbeIOVCBDlqNfd3/4VJOWvMnSsWq55HVNkqMjMyUavUqNVqMjMyUWXr/rfQxd+H3Vt3ci0yhofJD1n3UyDePXwBsLC0pJ1nB1YvW0F6WhoXzpwn7I8jePp7G4wrhBD/Ff/5zGu9evX49ddfdfafOXOGOnXqFPrZoKAgIiIi8PT0BECpVBIUFET37t2fqi19+vRh8+bN3LlzhxYtWlC5suHsTLVq1ahWrRr9+/dnzJgxHDx4UGvpQ1E8jzoeWbAhjPeHtif084EkpWYwf30Y0XGJVLa3Zv0HvRj0yVbiH6Ry9OJN1uw9z9L/+Wqe8/rjjtw7r6s4WNOnQ10yslTsnDdQU/fn68LYrWdi+MjR8zcJ3Hme76Z1w9zMhIMnYvhpS37W/Ku3unL2SjyrQs6SrVIzffE+Zoxuz4T+zYm5lcj0xfvIVuU+LzShwM1DZqYmufuS08guZ6Ld341HeX9wO0LnDsjt729Hib6d19+ZAQz6LDi3v//cYs3+v1k6ySe3bWeu8+OuMwBkZKm0JujKzCwyslQkphS+HvLohZsE7v6bpe/45Y7hqWv8uC3/7vVFU7w5ExHPqp3nyFapmfbdfmaOaMeEPi9zLS6Jad/t1/R3dPfGWFua8dWU/EnN2avxTF2sO4E++vdNAned57t3/XL7cjKGn7YWGOepXTl75TarduTGnf7tPmaMbM+Efs2JiUtk+rcFxjlZ/zir1Dmg0F17renz2wX6vP2xPl+NZ9WuvD4v3c/M4Xl9vp3EtKX5fXZyLMeWef01nz20dDhx9x4yKUK7z07DA7j5cxD/TPoURTkrnEf0wsKlMpn3E4mYsYha86Zi5miHuVNFXF4fwK2VW8hOTsXC1Zkabw7HWKEotB5Dfv1pDWuXr9JsH9ixlyGvj8AnwI9xfUfyQ9BKKjlVpnm7lvQbMYhp494iMyODdl4dGTp+pOZzk2a+yVezFzDQsw+2drZMnvkmru41DcYVQpQxkng1yCjnWVKTpdDjTwHIzMykR48eDBo0SDNxCw0N5eOPP2bnzp3Y2dlpPlvwxqc7d+7g6+vLoUOHNHfxZ2Rk0KFDB7Zu3YparS7y0wYeSUxMxNPTk6pVqzJ58mS6du2qOfboaQMAp0+fpl27dhgZGZGcnMyAAQN4++238fb21ntT2eP7UlNTC62jKFpNXFnkfj1PxqlFy8Y+b2rbkvkeZ5Sm/yH8LyS2qmT+08/RM3l9EaqOLl8icb9s1LpE4j5S08q5ROMLIZ5Oy5lrXmi8Y58Ne6HxnsV/PvNqZmbGL7/8wueff86aNWswNjamWrVq/PLLL1oT18dt3bqVjh07aiauAObm5nh5ebF582Z69epFcnIyHTt21Bx3c3Nj5cqVBuu0s7OjY8eOHD16VGvyWVBOTg5r167lk08+wdzcHJVKRY8ePYo86XxedQghhBCiBP2nUovP138u8yqeD8m8vhiSeX1xJPMqhChLWs54wZnXeZJ5FUIIIYQQT6vkchulnkxen6N//vmH6dOn6+wfOnQo/fv31/MJIYQQQghRHDJ5fY48PDy0fkJWCCGEEEI8XzJ5FUIIIYQobeSWJIP+8z9SIIQQQggh/jsk8yqEEEIIUdpI4tUgybwKIYQQQogyQzKvQgghhBCljWReDZLMqxBCCCGEKDMk8yqEEEIIUdpI5tUgybwKIYQQQogyQzKvQgghhBCljJE859UgmbwK/YxKJmyOWQldDMguoX8kFCU00JTgFakS6nI5s5KJO2jLkZIJDKzv3ZZo5a0SiV3TyrlE4goh/vtk2YAQQgghhCgzJPMqhBBCCFHayKoBgyTzKoQQQgghygzJvAohhBBClDaSeTVIMq9CCCGEEKLMkMyrEEIIIURpoy7pBpReknkVQgghhBBlhmRehRBCCCFKG1nzapBkXoUQQgghRJkhmVchhBBCiNJGfh7WIMm8CiGEEEKIMkMmr0IIIYQQosyQZQNCCCGEEKWNrBowSCavokhsrcyYNaQdreo5k5iawdJtJ9lzIlpv2UGd6zPcuyHmpiYcPHON+RvCyMrOfWDd7OEdaFHXCUszBfcfprFm799sC7v6xPiDutRnmE9DzM0UHDwVw4K1+XU+rraLA7NGtMPVyY6YuETmrvqLq7EJALg52zGlf0vq1XDErpwFrcetMNzfoe1o5eFMYkoGS4ML6a9nXn/NTDh4+hrz1+e3rV+nevi3roW7sz17TkTzyZrDhfbT1sqMmcPy4y4LPsme44bjDuuaH3fBuvy4xalHq84u9Rnm2xBz0yKMc7W8ca5iR8ztvHG+kTvO3drUYoCXB9Uq2ZKansWe8CiWbTmJSm34X+NBXo+d41+fcI6HFzjHqx87x/0KnOPX9Z/j7BQlkcuDSDp/FYWNNdUH+VChXRO9ZdPjE4hZtY3kf6IxViio+Epzagzx0yqTFnePc9O+wbFlA2pNGmiwn7Zmpsxq3ZxWTpVJzMhg6Zm/2RNzQ6ecdw0XxjZ6CUcLCzLVKsJu3Wbh8TOkZmcD4GRtxXstmtKggiNZajUHrsey6ORZVAbWyW1bv4W920KJiYimk68n73w83WAbNwduZOPKdWRkZNLeqwOTZk7FzMwMgIdJySya8wUnw05Q3q48o6aMobNfF4N1CSHE8ybLBkSRvDugNVnZavxmbOCjlYeYNrANNavY6ZRr5eHMCO+GTFy8m14fbsK5gg1juzXVHF+15zy9PtqE57u/8s7yA7zRoxn1qjkWGrtVfWeG+zZi0le76T1jI1Ur2DC2R1O9ZRUmxiyY6EVoeCTeb65lZ1gECyZ6oTDJ/VPPVqnZfyKauav+Kry/A1uTpVLjNz2vv6+2oaaTgf52zevv+3n97Z7ftntJSn4JPcf2IkzQAd4Z1JrsbDXdpm1g9opDvFdI3OE+DZn0zW56v78pd0z8mxa7Hq06X3JmuF+Bca5ow9ieTxjno3njfER7nC3MTFi04Rg+U9fx2mchNPdwYnDXBoZjPzrHi3bTe2YRzvGEvHM8Ne8cT3jsHJ+MZu7qws9x9C/BGClMePn7WdSaOJDon7eivBGvU06dnc0/n/1M+ZfceXnZLJp9N50K7ZvolItZEUw5N5dCYwK826IpWWo1fkHb+eivY0xr0Yya5W11yp29e5+xew7itTGYPsG7MDEy5vXGL2mOv9eiKQnpGXTfHMLQnXtpWrkifeu4G4zrUNGRV8cOo2uAn8EyACeOHOO3FeuYt3whq3asIy42jsBlKzXHv533DQpTBev3b+a9z2ax5LOviYl88hcjIUQx5bzgVzFFR0czcOBAfHx8GDhwIDExMTplVCoVc+bMoUuXLnh7e7Nx48biB9KjxCev/fv3JyAggG7dulG/fn0CAgIICAhgxowZxMXFMWXKFLy8vPD29ua1117jypUrACxbtkxTtmnTpnh5eWm24+LiAOjXrx8BAQFa8WJjY2nVqlWR27dkyRLmz58PQHh4OHXr1uWHH37QHA8PD6dPnz6a7bp169KjRw969uxJr169CAsLK7R+T09PfH196dmzJ97e3owfP55Tp07plCvYl5ycHEaOHMmvv/6qOZ6ZmUn37t35448/yMzMZPbs2XTv3p0ePXrg7+/P9u3bi9znx1mYKejcpAbLd5wmLTObs1F3+PP8Dfxa6v6PsnurWmwLu0r07UQepmXyy66z+LeupTkefTtRk03LyckhJyeHqhVsCo3frU0tth2+QnRcIg+Vmfyy4yzd29bSW7ZZ3SqYGBuxft9FsrLV/HbgH4yMoHk9JwCuxyez/a+rRN96UHh/m9Zg+fbTpGVkczbyDn+eM9Df1rXYduRqbtv09Pf3M9c5dPY6SakZhfax0LitdON2a1MgrjKTX3aepXte3OLUo1Pn4StE38qrM6SY40z+OG/+4zJnr8aTrVJzN1HJ7vAoGteqVHjsv65o96eNgdh18mLvz4t9sPjnWJWeScKxC1Qb4I2JhTm29Vyxf9mDe4dP65S9+8cpzOxtcereARMLM4zNTLGu4aRV5t6Rs5hYWWLboPAxtjAxoXM1F5afvUBatoqzd+/z581b+NWsrlP2jjKNpIxMzbY6J4dqNuU0207lrNl/PZZMtZqE9AyO3rqNm55J8CPtvTrStnN7bOwMlwHYt303Pr38cHWviY2tDYPHDmPv9lAA0tPS+Gv/IYZPGI2llSUNmjakdae2HAjZW2idQoj/no8++ojBgweze/duBg8ezIcffqhTZvv27Vy/fp09e/awYcMGlixZQmxs7DPHLvFlA49m4bGxsfTt25fg4GAAsrKy6NmzJwMGDGDx4sUAhIaGMmrUKHbu3Mn48eMZP348AMOGDWP06NF07txZU+/Vq1e5f/8+pqamXLhwgZdeeonnoWLFiqxatYpBgwZha6v/fwLr16/H2tqaffv28eabbxIWFoaxseHvCYsXL6ZOnToA7Nmzh3HjxvHzzz/TuHFjg32ZO3cugwYNomPHjri4uLBkyRKaNGlCp06d+Omnn0hMTGTbtm2YmJiQmprK3bt3n7rP1SvZolLncONOsmbf1ZsJNK1VRaesWxU7Dp27rlXO0dYSW2tzkvMmcO8OaI1/61pYmCm4dOM+Ry4U/ofs5mzPn2fzL6tejU3AsbyVVp35Ze2IvKk9aYmIfYCbsx1HL9x8tv7W1tNfp8f6G5uAY3lLvW17qrixCTQzEPfPs/rjVrG3LnI9WnU62/PnmWKMc+xj43zT8Dg3qV2ZqFuJhmM7PXaObzzFOXYq+jlOj7sHxkZYOlXU7LOq4UTyP7oZxJSr1zGvaMc/n68gNTIWy2qVqTmyJ1bVc8czW5lO7Ma9eLw/hjsHTxQat7qtDaqcHG48TMnv64MkmlaqoLd844qOfPVKe8qZmZKWnc20P/K/DG+4HIF3jWqcjL+LrZkpbZyrsPzshSL1vzDXImNo80o7zbZbnVo8uP+A5MQk7ty+g7GJMS41qhU47s75k2efOa4QQpvRC17zmpycTHJyss5+W1tbnfnO/fv3uXjxIitW5C7L8vf355NPPiEhIQEHBwdNuZ07d9K/f3+MjY1xcHCgS5cuhIaGMmbMmGdqa4lnXg3ZsWMHNjY2jBo1SrPP19eXFi1aEBgY+MTPb9q0iYCAAHr16kVQUNBza1elSpXw9fXlxx9/fGLZdu3akZiYSGJiYpHr79q1K4MGDeLnn3/W7NPXl6pVqzJlyhRmzpzJuXPn2LVrFzNmzADg9u3bVKhQARMTEwCsra1xdXUteicfY2WuIDU9S2tfSlomVhamOmUtzRWkpGVplQOwNs8v+8VvR+n89lrGfbWT389cIzNbVWh8SwuFph6tOvXGN9UqC5BqoK2GWJkrSE3T01/zp+tvUVla6MY11PbC4hanHt06n/84d29bCw/XCqzd87fh2C/4HKsyMlBYWWjtM7G0QJWm+4UjMyGJ+2HnqOLblmbLZmDftB6XF65Gnbf2NHbjXip2boG5o90T41opFKRmPfa3lZmFlan+tp+9ex+vjcH4bw4h8OIVbqWmao6djr+LW3lbDgwIIKSPP//cf8Afsbee2IYnSU9Lx7pcfobXupw1AEplGunKNM12wePKVOUzxxVClKxVq1bh5eWl81q1apVO2bi4OCpXrqyZZ5iYmFCpUiXNle+C5ZydnTXbTk5O3L59+5nbWuKZV0MuX76syTwW1KRJE72X1QvKyspi+/btrF+/HlNTU3r16sX06dM1Nxw8qwkTJtCjRw+GDRtWaLldu3ZRpUoVrW8hRdG4cWMOHDgAFN6X/v37s2fPHl577TW++eYbyuX9D6d///689tprhIeH07RpUzp27EiXLk9/Q4UyI1tnEmFtYYbysQktQNpjZa0tc8c8NUO7rDonh7NRd/Bt6U7fDvX47Y9/NMd8WroxbWhbAM5GxJOW/lidFnl16o2fpTmeX95Ub1sNUWZkY22pp78ZT9/fokhLNxC3mONc1Hp8WhUY56vxuXVaFmOcLZ88zh2bVGdi3+ZM/mo3SSn5E0Oflm5MG1LIObYs5jm2LN45NjE315moqtIyMLE01ylrbGaKTV1X7JvUBcDJvwM3txwg7eZdyMkh6XwEDT+fXKS4yuxsrE21/9m1NlWgzCq87XfT0jl66zaftm/FiF37MQK+8ezAlqtRjNlzEEuFgg9aN2dS04Z8e/p8kdpiiIWlBcoCk+RH762sLLGwstSZqCpTlFhZWz1TTCGEHoXc4PpvGDFiBL1799bZb+gqc0kqtZnXnGf4ZYmDBw9Ss2ZNqlevjpOTE/Xr12fv3ue3JsvR0ZH+/fuzdOlSvccHDRpEQEAAO3bs4Lvvvit2/QX7/qS+jB49mqpVq9K2bVvNvrp167J//36mTZuGo6Mjn3zyid61KEV1/U4yJsZGVKuYvza1dlV7ouISdcpG3U6ktou9Vrn7yWkGL6GbGBvh8tia193HovCcEojnlECmLt5L1K0H1K6W/wWgtosD95OUeuuMupVIrQLxAdxdHAq9ZP04vf11sddbR1Scnv4mGe5vcePWcjEwzo/HdcmPW9R6dodH4Tk5EM/JBcbZpcA4V3u2cW79UlVmDG/HO0v26Vzm330sCs//BeL5v0CmLtlLVNxjsYt7jqs66B0nQyycKpCjUpMWd0+zT3k9DiuXyjplHy0P0Cf5YhQZ9x5wetJ8Tr4xl7iQP7l/7G/OzViit/z15IeYGBlrrV2tbW9HVJLupbrHmRgb4ZL3BdXW3Iwq1lZsvBJBllpNcmYm26NiaOtc+NKQoqjh7krUlUjNdtSVSOwd7bG1K49LDRdU2SpuXostcDyCGm6uzxxXCFGybG1tcXFx0Xnpm7w6OTkRHx+PSpV75VSlUnHnzh2cnJx0yt26lX9FKC4ujipVnv3fqVI7ea1Xrx5nz+quozpz5oxmfaghQUFBRERE4OnpiaenJ//8889zXToAMGbMGPbt28f169d1jq1fv57g4GB+/vlnGjQwfIe1IefPn6d27drAk/tibGysdz2tubk57du353//+x+LFy8mJCSk2O14JD0zm9/PXmdc96ZYmClo5FaJjo2qs+tYpE7ZneGR9GxTh5pVymNjacZon8aEHI0AwL6cBd4v18TSTIGxkRGtPJzp+nJNTlyJ06mnoF1HI+nRrjauTuWxsTJjVPfG7DgSobfsqcu3UalzGOBZH1OFMf06ewBw4lJ+DDOFCQqFsea9qUJ7/NIzs/n9zHXG+T9Ff/3y+wu5Ew4zhQkmxkZa7/V5FHdsjwJxG1dnV7hu3F1HI+nRtg6uVfLGxK8xO/LiFqcerTrDIunRvpjj7KV/nF+u58ScMR2ZsewAF2Pu6a1Dpz8Fz3G3xuwIMxD7ymPn+JXin2MTCzMcWr5E7Ma9qNIzeXg5hgcnLlKhve4TDiq0b0pKxA2SzkeQo1Zze9dfKGyssaxakUpeLWny9bs0/HwKDT+fQuUurbBvWg+PGaN06gFIV6n4/cZNxjWqj4WJCY0qOtLRxZld0br/jvi4VqOylSUAVayteKNxA47fvgNAUkYmNx+m0Le2OyZGRpQzNaW7Ww2uPkgyOMaqbBWZGZmoVWrUajWZGZmo9CzZ6eLvw+6tO7kWGcPD5Ies+ykQ7x6+AFhYWtLOswOrl60gPS2NC2fOE/bHETz9vQ3GFUI8pVL8tAFHR0c8PDw0c4uQkBA8PDx0rjT7+vqyceNG1Go1CQkJ7Nu3Dx8fn+IF06PULhvo1q0by5YtY8WKFZp1r6GhoRw7dozZs2cb/NydO3c4fvw4hw4d0lxGz8jIoEOHDlqz/2f1aD3usmXLsLOze2717tu3j3Xr1vHTTz89sS8F15EUdOLECVxdXalQIfcmkAsXLuDi8uRH+BRmwYYw3h/SntB5A0lKzWD+hjCibydS2d6a9e/3YtCnW4l/kMrRf26yZt95lk7xzX3O69lr/Lgz9w7uHHLo074u0wa2wdgI4h6ksijoGIfO6z7jsqCjF24SuPtvlr7tl1vnqWv8uD3/rvBFU7w5czWeVbvOka1SM23pfmYOb8eEPi9z7XYS05buJ1uV+4QDJ8dybJnXX/PZQ0uHE3fvIb0+1P5ys2B9GO8Pa0/o/Lz+rgsjOi6vvx/0YtAnef29eJM1e8+z9E1fzXNtf9yR37ZRfo0Z272JZtuvlTs/7jjDTzvO6O3rF+vCmDWsPbsW5MZdUCDuug978erH+XED955n6dS8uKev8WPI6SfWU6RxfqfAOG97bJwj4lm1M2+cv9vPzBF54xyXxLTv8sd5dPfGWFua8dWU/EnN2avxTF2s/wrI0Qs3CdzzN0vf8svvT8FzPDkv9qNzvGw/M4e1Y0LvvHO87LFz/FmBc/xd7jmefl07ds3RAUQuD+LkG5+iKGdFzdd6YVWtMhn3Ejn7ziIafzkV8wp2WDpXpNbEAUT9vIXs5FSsXJ2p+85wjBUKUICJef4Shv9j77zjmrreP/7OIOwtIksRRUFxo7gHiIoLR7XDbdW2au2uo/bX2q392ulq+23VamvVintvq7gV3IuhTDc7JJDk90cwEJIgtira73n35ask97nncz7nZjx57rnnSm0USK3kWDk5YIlZR08wvXUoW57pQ7ZKzcyjJ0jKzsHTzpY/enfnuQ1buV6gpLazExObNcJRoSBXrSY2LZO5caXzhif/dZA3WjRlWIP6aHU6jl+/yTcVXDj1+3+X8NsPpXPXdm3czpCXRtA9OopxA0fy46pFVPfyJLRdK54Z8RyTx72JWqWiXURHhr4y0rDfxGmv89WHs3g2fABOLk68Ou11/OvUtqgrEAj+nXz44YdMmTKFefPm4eTkZFiZaezYsUyaNIlGjRoRHR1NfHw83bp1A2DChAn4+flV1GylkOj+yfn5h8i91QYOHz5seC4tLY0vvviCs2fPIpVK8fPzY/LkyQQFBRntW3a1gR9//JFz587xzTffGMVMnToVHx8f+vXrR2RkJB4epVcZBwQEsGjRIrP9+v777ykoKGDy5MkcPnyYmTNnEhMTA+gTyW7duuHu7m54rn79+pw4cQJ7e3uz7ZUnPDwchUKBQqFAqVRSp04dxo0bR/Pmze/rZeLEiSZ9AlizZg2LFi2iqKgIqVSKu7s7U6dOpX79+pXqE0DYRPPj8aiRqKvm5aiTm6+GPmoe99WkRhRXkXgVne+p/5JzleheOG/+RguPgz/6t71/0COitp35H9cCgaByWLqJzqPi0I/mzxg9iTwxyavgyUIkr48Hkbw+PkTy+ngRyatA8M9oPfYxJ68/PT3J6xM751UgEAgEAoFAICjPEzvn9XFy/vx5pkwxvc/30KFDGTRokJk9How5c+aYXe3gl19+wd294lujCgQCgUAg+N9DIk6MW0Qkr0BwcLDhzl6PgokTJzJx4sRH1r5AIBAIBALB/woieRUIBAKBQCB40hCFV4uIOa8CgUAgEAgEgqcGUXkVCAQCgUAgeNIQlVeLiMqrQCAQCAQCgeCpQVReBQKBQCAQCJ40tKL0aglReRUIBAKBQCAQPDWI5FUgEAgEAoFA8NQgpg0IBAKBQCAQPGFU6e3Dn3BE5VUgEAgEAoFA8NQgKq8C80gkVSOr0VaJrs6min7HKavGL4Ckii4G0FXRa+v8maoZa2efqqsRDNt2qEp0l3RrTVJBepVo17bzrhJdgeChIyqvFhGVV4FAIBAIBALBU4OovAoEAoFAIBA8YUh0ovRqCVF5FQgEAoFAIBA8NYjKq0AgEAgEAsGThii8WkRUXgUCgUAgEAgETw2i8ioQCAQCgUDwpCFuD2sRUXkVCAQCgUAgEDw1iORVIBAIBAKBQPDUIKYNCAQCgUAgEDxhiNvDWkZUXgUCgUAgEAgETw2i8ioQCAQCgUDwpCFuUmARUXkVCAQCgUAgEDw1iMqrQCAQCAQCwZOGqLxaRCSvgkrhZKfgvRfaERbkRVa+innrTrDteJLZ2Oe6NGB41xCsrWTsjrvKzBWHKCrWAvDh8Pa0rOeFrULO7VwlS3acZd3By/fVfy6yIUOjGmFtJWfPiWRmLYk1tFmeQD83po1sj7+XC8kZWXy2aD+XU+4A0LVVbcZEN8fdyRZ1sYZDp1OZ/fsh8jBuy8lOwXvPl/G7vgK/ncv4jTf2ew8/D0d+mxLNrrhkPlyy//5+uzZgWA+9390nkpn128EK/b43oh3+NVxIzszi08UHDH57tqnL4Ihg/Ko7kV9YxLbDicxffRyNhfUDH/U4FxQWVey5eyOsFZXw7FviuUT708UHuJyq1w7wdmHSoFYE1XLHxcGG1uMWmm3DSWHF9LahhHl5kqVSMe/kGbYmpZjERfr7Mq5JQ9xtbVBrNBxMz+Q/R+LILyo2ihnTuAE17O24XVjIRweOEXfjllldR7kVbwa3oIV7dXLUan5OOMPu66kmcd28avJmcAvUGo3huenxsZzKuoWVRMqrQU1p7lodRysr0pX5/JJwlqO3r1scXwBHKyumNmtBy+rVyVar+eHcGbanmmqX5dt27WnhUZ1Oa1ejKfdl6mtvz+LwruxJT+Pj48cstrHuj9VsX7eF5CtJdOoRztsfTbEYG7N0JSsXLUOlUtM+ogMTp72BQqEAIDc7h69nfMnxg8dwdnFm1KQxdInqWmH/BQLBvw8xbUBQKd4Z3JoijYaoaSv4YPFfTH62NbVruJjEhQV5M6JrCBO+30a/D1bhXc2RsT2bGrYv3naGfh+uIvzdZbz94y5e7t2MID+3CrXDGvowLKoxr/5nCwMmr8C7miNjopubjZXLpMx8tStbDyXQbdJSNsVeYearXZHL9C/1U5dv8NLnG4h8dSnPTFmJTCblpf6mbb0zqMTveyv44Ne/mDz4Pn7nbKPfh6vwdndkbFRTs+2dv2Y+mTH1683wqMZM/Gor/aeuxMfDkbF9m1n0O2tCBFsOJRD5+m9sir3CrAkRBr82ChlfLz9C9zeW8eJnGwgN9uKFbiEWdB//OBu0G3gzvEcZz9UcGdvnPp4Pl3g+aOy5WKNl57EkPl18wKIewDthzSjSaumxcj3/t/8Ik8OaE+DsZBIXf+M2Y7bsJvyPtfRfvRmZRMrLTRsatrfyqs7E5o34OPYYnZet4aUte0jLzbOo+2r9phTrtAz+ayOfnz3Ka0HNqGXvaDb2fPZt+u5dZ/h3Kkv/GpJJJNwsVPLWiX3027ueRQnnmB7SCk8buwo9v9WkKUVaLX03b+SjY0d5q0kzajua1waI9PVDJrH8NfFmk6ZcuHu3Qk0ANw93nh87jG7RURXGHYs9woqFy/j8h9ks3riMjNQMls5fZNg+5/NvkVvJ+WNnDO9+9h7ff/YNyQnmf1QKBE87Et3j/fc0IZJXwX2xUcjp0qQmP2yIQ6kuJj7xBn+dTiGqVYBJbK+wOqw7dJmkzCxylWp+2RJP77C6hu1JmVmGappOBzqdDp9qlr88AXq2rcv6/ZdISs8it0DNwg1x9GpX12xs86AayKUS/th+lqJiLSt3nkMChAZ7AXDjbj7ZeSpDvFarw7e6ccJi8LuxjN8zKUS1NOO3VTm/W439AkQ29ydXqebopYwKfRr8tqnLujJ+f9kQT6+2FvzWr4FMKuGPHecoKtayYtd5vd8gvd+YvReJv3ydYo2Wm1kFbD2cSJO61c3rPuZxNus5o8Tzxgf0LCn1fO16DusPXCYp3XJSZSOXEV7Tlx9OnkVZrCH+xm32paQTVaemSeyNAiXZKnWpF50OX0cHw+NxTRry86nznLl1Bx1wU1nITWWheV2pjPbVfViUcI5CjYaz2bc5eDODrjVMdSuiUKthSdJ5rhcWoAMO384kU1lAoKOLZc8yGZ28ffjv+XMoNRpO3bnN/swMuvuZ17aXyxkdFMT8s6fNbo/w8SWvqIjjt27et7/tIzrStkt7HF0svwYAdqzfSvd+UfjXqY2jkyMvjB3G9vVb9J6VSg7s3Mfw8aOxtbMlpFkjWndqy64N2++rLxAI/l3cd9pAeHg4arWavXv3IpPJAFi1ahXTpk3j/fffx87Ojs8++wwfHx/DPh06dODtt982PJ41axa//vor+/btw83NzahtOzs71q1bh1QqNTy3YMEC6tWrZ7Y/qampdOvWjcDAQMNzrq6uLFq0iNTUVAYOHMjhw4dN9qtfvz4nTpzAysqKgQMH8uqrr9KtWzcA1q1bx6+//sry5csNHsuj0+lYunQpy5cvp7i4GBsbG9zd3ZkwYQLNmzfn8OHDjBs3Dn9/fzQaDS4uLsyYMYM6deoAMGXKFGJjY3F1daWwsJDIyEijMSrL/Pnz2bJF/4F97do13NzccHDQf1kuWLAAT09PfvzxR1avXo1UKkUikTBy5EgGDx7MxYsXeffddwHIzs4mLy/PcGwGDx7MkCFDzGpWRM3qTmi0OlJu5hieu5x2l2Z1PU1iA7xc2Hc6xSjO3ckWJztrcgr0ycw7g8PoHVYXG4WcCym3iT2bVqF+bR9X9sVdK20z5Q7uznY42VuTk68yig3wduVKqnHSkpB6h9rerhw6o9dpXNeT2a9F4mCnQKkqYsrcnY/Mr72NFeN6NmXCnG30bRNosr85Arxd+SuuTJupFfl1IaGc3ytpdwnwduGQmXFtGuhJYnqWWd3HPc4mnuMfwHNaOc+plj2bo6aTIxqdjmtlKqSX72bT3LOa2fgm1d35Orw9DgorlEXFvLvnIABSCQS7u7IvNZ1V/XqgkEnZm5LO98dPodKYTnnwsXNAq9ORpizVTcjLprGLed06ji782aEXucVqdmSksOzqRbRm5sG5KKzxtXPgan6OmVb0+DnotVPyy2hnZ9O0mnntlxo0ZHVSErdVKpNtdnI5Y4KDee3AfnrX8reo+aBcTUimTed2hscB9epy9/ZdcrKyuZF5A6lMim8tvzLb63D6ePxD0xcInijE7WEtUqk5rx4eHuzfv59OnToBsGbNGho2LD1t1rZtW7777juz+xYXF7Nu3TqaN2/O2rVrGTVqlNH2goIC1q5dS//+/SvdaUdHR9auXVvp+LIoFAo+//xzXnnlFUJDQ9FoNHz55ZcsXLjQYuIK8M0333D06FF+/vlnPD31SczBgwe5dOkSzZvrT4fWqVOHmJgYAL788ks+//xz/vvf/xraGDduHEOHDiU3N5fo6GiaNWtGRESEidYrr7zCK6+8AsCwYcMYPXo0Xbp0MWyfO3cuBw8eZPny5bi4uJCRkcGYMWOwtrYmOjraMDYxMTHs2bPH4rGpLHbWcvLLzVXMK1RjZ2NlEmursCJPWVqluve3vY3ckLx+ueIws1ceoVFtD5oH1kBdrDFpx6hNazn5Ztq0s7EySWxsreVG+vr4IqO+nrpynchXl+LhYkffjvXJuGV8itdOYcavUo2dtRm/1lbkFVr2+1LPpqw7dIUbWQUVeqzIQ2mb5vxamfjNV5o/Nr3a1iXYvxqf/Wr+dPrjHmej9mwejWdL2Mnl5BeVO8ZFRdhZmW8j/sZtwv9Yi4etDf3qBZCRlw+Am40NVjIpETV9GLd1D8VaLf/p0pbRjYKZH3fW1KdcTn6xsW5+cRF2ctOP4lN3bzHu0A6uFxZQy96J6Y1aodFp+ePqJaM4mUTC1IYt2ZZ5jZSCCsZYLifPnGcz2vVdXGjk7s63p0/hYWtrsn1scAM2XL3KDaXSot7foVBZiL1DaVXb3sEegIICJYUFSsPjstsL8iv/3hIIBP8OKjVtoH///oakLCUlBaVSabEyWp69e/dSs2ZNJk2axKpVq0y2T5w4ke+//x61Wm1m70dDSEgIAwYMYMaMGbz//vsMHz6cunXNn6IEyM/P55dffuGTTz4xJK4Abdq04bnnnjO7T6tWrcjIMH+a2NHRkUaNGpGU9OBztdRqNT/++CMzZszAxcUFAC8vL959912+//77B26vMhSoirEvlxjY21iZvfhGqS4yirW30V9okV9YbBSn1emIT7xBdRc7Bnaob7StW1gAO+cOY+fcYXz1ejeUqmJDO2XbNKv/AH29mVXAoTOpfPxSZ2O/agttqMzpWfYb6ONKy/reLNt9zmS/snQPC2DX90PZ9f1Qvp4Uqfdga65NC/q2CqPnzPnt2LQmEwaG8sa32w2n86tynLu3CmDXd0PZ9V2J58JiC+Noaczv77kiCoqLsbcyTtrsreQUFFXcxk1lIQfTMvmkYxgAqpKLqVZcSOC2spBslZrfz12mrW8Ns/sri4tNkkV7uZyC4mKT2MzCAjJLpgUk5+ewNOkCHar7GMVIgMkNQynWaplzMa7CviuLi7Evr21lqi1BPzf221OnTC7QAqjr7EyoR3WWX7n/hZYPio2tDQX5+YbH9/62s7PFxs7WJFEtyCvAzr7ieb4CgeDfR6Uqr2FhYfz+++9kZ2ezevVq+vXrx5kzZwzbY2NjiY6ONjweOnQogwYNAvRTDAYMGEBoaChFRUWcOnWKxo0bG2JDQkIICQlh2bJljBgxolKdvle5vEeTJk346KOPKrXvPSZMmEBUVBTOzs6MHj26wtiEhASsra0JCDCd82gOrVbLzp076dmzp9nt169f58SJEzz77LMP1GeA5ORkrKysDNMR7tG0aVNSUlLIy8szTDF4WFy7kYNMKsHPw5GUm7kABPq4kZiZZRKbmJFFoI8bO09e1cf5unI7R2moupZHJpXgW27O67bDiWw7nGh4PGNsJ+r6ubHzmD7ZD/Rz43Z2gUlFDiAx/S7Pl7sgqY6fG6t2n7egL8XHw3genkW/GRb8epfx61Pqt2erALzc7Fk34xlAX62USiTUruHCiC83GNrYejiRrWX9julIoK8bO48lV8JvlskFWHV83fhz9wXD49YNfZg6vB1vfbfd6HR7VY7z1iOJbD1SRvvFjgT6ubHzeIln3/t4jjTjec8Fk1hLXMvJRSaR4ufoQErJ1IF6ri4kZls+7V7qRWKY85qrLuJ6fgE6Knd6L60gD5lEio+tPWlKfWIW4OBc4en+e+h0OiRIjJ57K7gFrgob3os7YDbRLEtKXh4yqRRfe3tSS5LCuk7OJOUYa9tbWRHk4sqMlq0AkEr0mjHdo3j/6GHqu7hQw86OVd31F1/ZyuXIJBL8HZ14cc+uSoyCZWrV8SfxUgIdu+nPNCVeSsDV3RUnF2cU1go0xRrSrqbiU8u3ZPsVagX4/yNNgeBJRSKWyrJIpSqvEomEqKgoNm7cyKZNm+jVq5fR9rZt27J27VrDv3uJ6+3btzly5Ag9evQAoF+/fvz5558m7b/++uv89NNP5Jf5xV0R96YN3Pv3oIkrwIULF1Cr1dy8eZPs7OwH2jcnJ4fo6GiioqKYOHGi4fmEhASio6Np27YtR44cMZlf+uOPPxIdHc0rr7zCmDFjaNu27QP3W2fhxSwp+YK59/+HSaG6mD3x1xjXqxk2CjmNa3vQsZEfm8skH/fYdCSBvm0CqV3DGUdbBaO7N2bD4SsAuDrYENncH1uFPokLC/KmW4vaHLuUWaH+5oNX6NMhEH8vFxztFIzs3YSNB66YjT1xIRONTsfgrg2wkkt5JjwYgGPn9VXwbmEBeLrpTz3WcLfn5QHNOXY+3bzfnuX8HjXj96hlv6sPXGLARzEMnbmOoTPXsXr/JWLPpfLa/IovMNl8MIE+7QPx93LG0U7BqF5N2Bhrwe/FTDRaHYMjSvx2KfF7Qe+3RZAXM8Z0ZOr8XZxLrni1g8c9zkbahxLo0+4BPYeb9wygkMuQy6WGv63kxh91hcUadl9LY1zTBtjIZTT2cKejnzebE65Rnu61/fC01586r2FvxytNQziaccOwff2VZAYH1cXVxhpHhRXPBweyP9X8a7pQq2H/zTRGBDTARiqjobMbbT282ZFpqtvS3RMXhTUAfnYODKkdROyt0jF8rX5Tato78n58LGqt+SXFjLQ1GvampzEmuAE2MhmN3Nxo7+XN1hRj7byiIvpt2cSo3TsZtXsn7xyMBeDFPbs4d+cO65KTeXb7VsP2tUmJxGZm8las5SXgNMUa1Co1Wo0WrVaLWqVGY2a6UNfe3dm6ZhNXE5LJzcll2X+XEtlH//1hY2tLu/AO/Dp/IYVKJWfjTnNwbyzhvSPv610gEPy7qPQ6rwMGDGDQoEG0atUKV1fXSu2zZs0aNBoNffv2BfTzX5VKJdOmTcPGxsYQFxAQQKdOnVi40Px6jA8blUrFlClT+OSTT4iLi+Pjjz/m66+/thhfp04dVCoVycnJ+Pv74+TkxNq1a9m9eze//PKLUVxMTAxqtZo333yTDz/8kG+++caw/d6c13+Cv78/RUVFJCQkGFVfT548ia+vL/b29hXs/feZteIQ04e0Y8tng8nOVzFz+SGSMrPwdLXnj/eiee7TtVy/m8+h8+ks2XGGea92L1n39Bo/bYoDQIeOAe3rM/nZNkglkHE3n69jjhpd8GSOQ2fSWLrlNHPficJaIWP38WT+u/aEYftXr3cj/lImizedolijZcqcHUwd0Z7xA0NJzshiypwdFJdcPFPb25UJz7TE0V5Bbr6a2NOpzF9luj7lrJWHmP5CO7Z8WuJ3RRm/06J57rMyfneeYd7E7vq+xV3jp816v6oiDaqi0i/oAnURqiINWXnmq9AGv2fTWLr1DPPejtKP4Ymr/LTupGH715Miibty3eB38tydTBvRjvEDWnA1I5vJc3ca/I7u1QR7WwVfTSr9go+/fJ03vjNNoKtinE08v1XG8/pyni9fZ/HmEs/zdjJteInnzGwmzyv17OXuwOrPBxn23TdvOBm3conetNVIc9bhE7zfNpStg/qQrVYz8/AJErNz8LS3ZXnf7jy7bivX85UEODsxsXkjnBQKctRqYtMymXey9MzTz6fO42JjzZ/9uqPWaNmRnMLCU+Yr0ADfX4jjrQYtWNGxF7lFar69cJKr+bl4WNvyc+tIXjy0nZsqJc1cPXinQQtsZHKy1Cp2Zl5jWfJFAKrb2NLbNwC1RsOK9qXFhG8unGTXdcvvp9nxcUxt3oL1Ub3IUauZHX+SpNxcPG1tWRIRybCd27muVHKnzEVaipJrAe6qVGh0Ooo1GsN0CQClRoNaqyGrgqlfv/93Cb/9sNjweNfG7Qx5aQTdo6MYN3AkP65aRHUvT0LbteKZEc8xedybqFUq2kV0ZOgrIw37TZz2Ol99OItnwwfg5OLEq9Nex79ObYu6AsFTjai8WkSis1TKK6Hs1f/Lly+nSZMmBAUFMWXKFEJCQrCzs7N4UVCvXr147733jCqML774In379iU6Otqo7czMTAYMGIBGo2HJkiUVrjZgaUWByqw2YG9vz8yZM8nKyuLzzz+nqKjIsPpAZKTlX/BfffUVJ06cYPbs2YZ5r5s2bWLZsmUsWbKEw4cPM3PmTMPc4Fu3btGtWzd+++03goODDeP1oMmruQu25syZw+HDh5kzZw7Ozs5kZGQwduxYxowZQ79+/Qxx/+SCrbBXF98/6BEgLaj44q1Hhdbe8sV6jxKJ8v4Vs0emXUVXsupkD//sQGXQtq94SbZHhbNP1a1IqMytGt0l3VpXjTBQ2867yrQFgodJxwHzHqvevpjxj1Xvn/BAd9iyNEez/JzXkJAQBg8eTHZ2Nq1bG3+I9enTh1WrVhnFA9SoUYPo6GijSqYlys95BQxX2Ofk5NCxY0fD8wEBASxatMjwOC4ujs2bN7Nu3ToArKys+Pzzzxk/fjwtW7Y0XARVnjfeeIPFixczevRoNBoNrq6uODk5MWHCBLPx1apVY/To0cyZM4e5c+fe19ODMH78eKRSKYMGDTKskDB8+HCjxFUgEAgEAsHTy9N244DHyX0rr4L/TUTl9fEgKq+PD1F5fXyIyqtA8M/p1P/xVl73rv6XVl4FAoFAIBAIBI8BUVu0yBObvL788ssm66R6eXmxYMGCR6a5cuVKli5davL8F198QXBw8EPXO3/+PFOmTDF5vuxSYwKBQCAQCASCUp7Y5PVRJqmWGDRo0GNNGoODg//2ncIEAoFAIBD8ixG3h7VI1U3GEggEAoFAIBAIHhCRvAoEAoFAIBAInhqe2GkDAoFAIBAIBP+riNvDWkZUXgUCgUAgEAgETw2i8ioQCAQCgUDwpKGrunXAn3RE5VUgEAgEAoFA8NQgKq8CgUAgEAgETxpizqtFROVVIBAIBAKBQPDUICqvAoFAIBAIBE8YYrUBy4jkVfBkIaki3aq6k0lV+a1K7SrSlVSRrjK3anSh6s46nrmbUCW6Ia51SCpIrxLt2nbeVaIrEPwvIpJXgUAgEAgEgicNUXm1iJjzKhAIBAKBQCB4ahDJq0AgEAgEAoHgqUFMGxAIBAKBQCB40niKb1KgVCqZOnUqZ8+eRSaTMXnyZLp06WISt2PHDubNm4darUan0zFw4EBGjx593/ZF8ioQCAQCgUAgeGj8/PPP2Nvbs337dpKTkxkyZAjbtm3D3t7eKM7Dw4P58+fj6elJbm4uAwYMoHHjxoSGhlbYvpg2IBAIBAKBQPCkodM91n85OTmkpqaa/MvJyXngrm/evJnnnnsOAH9/f0JCQti3b59JXJMmTfD09ATA0dGROnXqkJaWdt/2ReVVIBAIBAKB4H+cxYsXM2fOHJPnJ06cyKuvvvpAbaWnp+Pj42N47OXlRWZmZoX7JCQkEBcXx4wZM+7bvkheBQKBQCAQCJ4wJNrHO+d1xIgR9O/f3+R5Jycnk+f69+9Perr5NZVjY2MfWPvGjRuMHz+e//u//zNUYitCJK8CgUAgEAgE/+M4OTmZTVTNsXr16gq3e3t7k5aWhpubGwAZGRmEhYWZjb19+zajRo1izJgx9OzZs1L6Ys6rQCAQCAQCwZPGY57z+jDp0aMHy5cvByA5OZnTp0/ToUMHk7i7d+8yatQohgwZwqBBgyrdvkheBQKBQCAQCAQPjRdffJGcnBwiIyN56aWX+Oijj3BwcADg22+/ZdmyZQD8+OOPJCcns3z5cqKjo4mOjmbVqlX3bV+i04n7jwlMCXt1cZXoSpWaKtHV2lTN7ziJuurefhJt1WjrZJKq0W3vWCW6Vo5VVyOoqk/3Ka08qkQ3xLVOlegC1LbzrjJtwb+T8IjPHqverp3THqveP0FUXgUCgUAgEAgETw3igi1BpXCyU/DeC+0IC/IiK1/FvHUn2HY8yWzsc10aMLxrCNZWMnbHXWXmikMUFeuvmvxweHta1vPCViHndq6SJTvOsu7g5fvqPxfZkKE9GmFtJWfPiWRmLY01tFmeQD83po1sj38NF5Izs/hs0X4up9wBoGfbugyKaIBfdSfylUVsO5LAgpjjlG/JyU7Be0PaERbkXeL3ONuOVeA3slGp3+UHy/jtQMv6ZfxuP1M5v10bMKx7I6wVcnafSGbWbwct+/V1470R7fD3ciE5I4tPFx/gcqreb4C3C5MGtSKoljsuDja0HrewYt2HNM5dW9ZmTHRz3J1sURdrOHQmldm/H6KgsMiydkQ5z7/fx/PwMp5/Lef5mTKeXzLv2UlhxXttQgnz9iSrUMW8k2fYlpxiEhfp78vYxg1xt7VBrdVwMC2T2UfjyC8qNop5sXEDatjbcVtZyMexx4i7ccusrqOVFVObtaBl9epkq9X8cO4M21NTLY4LwLft2tPCozqd1q5GU66c6mtvz+LwruxJT+Pj48cqbMfRyoqpzVvQqkR7wdn7a3/XXq/dcY157V8jurInLY2PKtAuyMljxZeLuXj8LPZODvQcO5DmEaYXb+h0Orb8soajWw6gLlThXdePAZOGUKO2fsmdeW/M4tq5RKQyGQDO1VyY/OunFnXX/bGa7eu2kHwliU49wnn7oykWY2OWrmTlomWoVGraR3Rg4rQ3UCgUAORm5/D1jC85fvAYzi7OjJo0hi5RXS0PmkAgeKSIyqugUrwzuDVFGg1R01bwweK/mPxsa2rXcDGJCwvyZkTXECZ8v41+H6zCu5ojY3s2NWxfvO0M/T5cRfi7y3j7x1283LsZQX5uFWqHNfRhWFRjXp29hQFTVuDt4ciY6OZmY+UyKTMndmXroQS6vbaUTbFXmDmxK3KZ/qVurZDzzR+H6fH674z5bD2hwd680L2Reb/FWqKmLueDRfuY/Gwb836DvRkR2YgJ322l3//9WeK3WRm/p+n3wZ+Ev/M7b/+wi5f7NCfIz71ivw28Gd6jMRO/2kr/qSvxqebI2D7NzMbKZVJmTYhgy+EEIl//jU0HrzBrQoTBb7FGy85jSXy6+ECFmvBwx/nUlRu89MUGIict5ZmpK5FJpbzU33xbRp6/3kr/aZXwPL7E8xslnseX83w8iU9/rdjzO62aUaTVErVyPR/sP8LksObUdja90jb+xm3Gbt1NxPK1DFi9We+laUPD9lZe1ZnQrBGfxB6jy7I1vLx1D2m5eRZ132rSlCKtlr6bN/LRsaO81aQZtR0tT2mI9PVDJrH8Uf1mk6ZcuHu3Qq9ltYu1Wvps2siMY0d5u2nF2t3uo/1WJbVjvv0dmZWMD1d9xZD3xhLzzVIyk0wXIo/fe4yjW/Yz4dvJfLTmW/wb1GHZ5z8bxfSf9AKfbZrLZ5vmVpi4Arh5uPP82GF0i46qMO5Y7BFWLFzG5z/MZvHGZWSkZrB0/iLD9jmff4vcSs4fO2N497P3+P6zb0hOMP9jViB4aDzFF2w9ah5J8hoeHk779u3RaErnL65atYr69euzdOlSYmJiCA0NNUzOjY6O5j//+Y9RG7NmzSIkJIQ7d+6YtN27d2+0ZdY/Cw8P59KlSxb7k5qaSoMGDYz0Ro4cadhmafmG+vXrk5+fj1qtpk+fPmzbts2wbd26dTzzzDNGHstz6NAhBg0aRHR0NFFRUQwfPtzQb51Ox+LFi+nVqxc9e/akR48eTJ8+3XAni4yMDCZNmkRERASRkZG8+OKLRh6///572rRpQ3R0ND169OCtt96ioKDAaEx69Ohh5Dn1PhUWS9go5HRpUpMfNsShVBcTn3iDv06nENUqwCS2V1gd1h26TFJmFrlKNb9siad3WF3D9qTMLEM1Tf9+0eFTreK5iD3b1mX9X5dISs8it0DNwvVx9Gpb12xs8/o1kEsl/LH9LEXFWlbuPIcECA3yAmD1ngvEX75OsUbLzawCth5KoHHd6qZ+m9bih40ny/k1nU/XK6wu6w6W8bs5nt6tLfnVVc5vm7qs23+JpAy93182xlfoVyaV8MeOcxQVa1mx6zwSSanfa9dzWH/gMknp908wHuY437ibT3aeyhCv1erwrW55CZaebeqy7kAZz5vi6dXGgna9Es87SzzvfnDPNnIZXWr68kPcWZTFGuJv3uav1HSiAmqaxN4oUJKtUht58XN0MDwe27ghP58+z5lbd9ABN5WF3FQWmteVyejk7cN/z59DqdFw6s5t9mdm0N3PVBfAXi5ndFAQ88+eNrs9wseXvKIijt+6adFrWe3OPj78dE/79m32Z2TQvaZl7VHBQcw7Y1k7t6iIYzcr1lYpVZz+6zg9RvXD2taG2o0CadCmCce3HzSJvZNxC/+QQNy9PZDKpDTv2prrV82vJVkZ2kd0pG2X9ji6VLz8z471W+neLwr/OrVxdHLkhbHD2L5+CwCFSiUHdu5j+PjR2NrZEtKsEa07tWXXhu1/u18CgeCf8cimDXh4eLB//346deoEwJo1a2jYsLRa0bZtW7777juz+xYXF7Nu3TqaN2/O2rVrGTVqlNH2goIC1q5da3YxXUs4Ojqydu3av+EEFAoFn3/+Oa+88gqhoaFoNBq+/PJLFi5ciKzk9JU5D5MmTeLXX38lKCgIgHPnziGR6C9W+eabbzh69CiLFy+mWrVqaLVaduzYQXZ2Nra2towePZrBgwcbxmjLli2MGjWKTZs24ezsDEC/fv2YPHkyarWakSNHsnTpUsaNG2fow3fffUe9evX+luey1KzuhEarI+Vm6S3iLqfdpVld04WEA7xc2Hc6xSjO3ckWJztrcgr0ycw7g8PoHVYXG4WcCym3iT1b8a3ganu7si/uWmmbqXdwd7bDyd6anHyVUWyAjytXUo2TloTUO9T2ceWQGZ2m9WqQmJ5l3u+Nsn7v0KxuDVO/NVzYd+qaUZy7k61R394Z3Jrercv6rfhHRIC3K3/FlxnDivx6u5CQZuz3SupdArxdzPqtiIc9zo3rejJ7UiQOdgqUqiKmzN1p2bNXOc8pf8OzV+U913R0RKPTkVKmQnr5bjbNPKuZjW/i4c5X4e1xUFihLC5m8h594iWVQLC7K3+lpvNndA+sZVL2pqTz/YlTqDSmUx78HBzQ6nSk5JfqJmRn07Saed2XGjRkdVISt1Uqk212cjljgoN57cB+etfyv69ng3ZeqfaVirQbNmRNYgXaDYJ5bf/9tW+lXkcileLhV/r+8a7jR0L8RZPYpl1aEb/7KDdTMnHzqsaxbbHUbxViFLPpvzFs/CmG6n6e9HixP3WbBlWoXxmuJiTTpnM7w+OAenW5e/suOVnZ3Mi8gVQmxbeWX5ntdTh9PP4f6woEFaJ7vDcpeJp4ZMlr//79iYmJoVOnTqSkpKBUKiudSO3du5eaNWsyadIkPvzwQ5PkdeLEiXz//ff06tXLMCfpURMSEsKAAQOYMWMGKpWK4cOHU7eu+coQQH5+PgUFBVQr88XQoEEDw7aFCxeyZs0aw3apVEq3bt0AfaLv6Oho5LtHjx5s2bKFpUuXMmHCBCMthUJBs2bNyMjIeGh+y2JnLSe/3FzFvEI1djZWJrG2CivylKVVqnt/29vIDcnrlysOM3vlERrV9qB5YA3UxRWvMGBrLSe/wLRNOxsrk8TG1lpupK+PLzLb117tAgmuVY3PF++/v1+lBb/WcvKURUZxAPbWpX37csUhZq88XHm/NnILY2jOr5WJ33wLfb0fD3ucT125TuSkpXi42NG3Y30ybls+lf64PdtZyckvKneM1UXYyc23EX/zNhHL1+Jha0N0YADpefkAuNnYYCWTEl7Lh5e27aFYq+XLzm0Z1SiYBXFnTX3K5eSV1y0qwk5u+lFc38WFRu7ufHv6FB62tibbxwY3YMPVq9xQKivn2Zx2sXntIBcXGru58+2pCrSTK6etUhZia2/cho29LSoz1Wknd2dqNw5k5ojpSKVSnKu78fLstwzbe417Bs9a3sjlMk7uPsLC977njR8/oJpPdZO2HoRCZSH2DqXVdHsHewAKCpQUFigNj8tuL8gvQCAQVA2PLHkNCwvj999/Jzs7m9WrV9OvXz/OnDlj2B4bG0t0dLTh8dChQw0L1K5atYoBAwYQGhpKUVERp06donHjxobYkJAQQkJCWLZsGSNGjKhUf3Jzc430mjRpwkcfffRAniZMmEBUVBTOzs6MHj26wlhnZ2cGDx5Mt27daNWqFc2bN6dPnz54eXmRkJCAQqEgIMD0tDvAxYsXadKkicnzTZs25cSJEybP5+XlcfToUd544w2j5ydNmoS1tTUAMpmMmJiYylo1okBVjH25xMDexsrsxTdKdZFRrL2N/sdFfmGxUZxWpyM+8QY9WgYwsEN9Vuy9YNjWLSyAycP0VZD4y9dRqoqxt1WYtGlWX1WMvW25vtqa9rVj05qMHxjKpNlb9Ke3yyyVZd6vwrJeWb8l/cxXGcca/Laqw8AOQazYe96wrXurACYPbav3e+U6ysJiC2NoTr/IsL003vyxKc/jGGeAm1kFHDqTysfjOjPy43WlnodU4Nn2AT1b0LZEQVEx9lbGH3/2VnIKiitu46aykEPpmXzSIYwRm3aiKpk2tOJCArdLkrFl5y8zqlGQ2eRVWVyMvdycrvH7Q4J+Pum3p06ZXCQFUNfZmVCP6ozabbmaXZ4Cc9pyy9rfWNAOdHamZfXqjNxVOW1rWxsKC4wT1cICJda2Niax239dR8qFZKYvn4WjmzMnth9iwVuzeeeXGShsrKkVXPqZ2bJ7O+J2HeHC4dO0HxBRqb5YwsbWhoL8fMPje3/b2dliY2drkqgW5BVgZ2/3jzQFgvsiKq8WeWTJq0QiISoqio0bN7Jp0yaWLVtmlLxamjZw+/Ztjhw5wqxZswD9qfE///zTKHkFeP311xk+fDjPPPNMpfrzT6YN3OPChQuo1Wpu3rxJdna24bZnlvi///s/Ro0axaFDh9i3bx8//PADq1at4n5L61Z26d01a9Zw4MABrl69SpcuXWjdurXR9oc1beDajRxkUgl+Ho6k3MwFINDHjcTMLJPYxIwsAn3c2Hnyqj7O15XbOUpD1bU8MqkE33JzQLcdTmTb4UTD4xljO1HXz42dJVf7B/q5cTu7wKQiB5CYdpfnI41PM9bxdWPVrtJksXVDH6aMaMfb3203Of1s2a8riRlm/GZmEejrys6TyYa42zlKs32z5HfrkUS2Hinj98WOBPq5sfN4SZu+FfhNz+IFM37/3HPBJLY8j3qcyyKTSvHxKJ13aNaz7z/w7FM5z/e4lpuLTCLFz9HBMHUg0NWFxKyc++wJMokE35I5r7nqIq7nF6Cjcu/ZlLw8ZFIpvvb2pJYkSHWdnEnKMda1t7IiyMWVGS1bASAtmW4U0z2K948epr6LCzXs7FjVXX8hkq1cjkwiwd/RiRf37Kq8trMzSblmtF1d+aiVsfbqHlG8f6RUO6ZHOW0nJ0bvNtWu5uuJVqPhZup1PHz1U40yElKp4W+6Lmp6QipNu7TExUP/2dqyRzvWzv2D61cz8Kvvb2pKIqn02FdErTr+JF5KoGO3LgAkXkrA1d0VJxdnFNYKNMUa0q6m4lPLt2T7FWoFmOmPQCB4LDzS1QYGDBhgSKBcXV0rtc+aNWvQaDT07duX8PBwli1bxubNmyksNP7lHhAQQKdOnVi4sOKlfx4WKpWKKVOm8Mknn/DMM8/w8ccfV2o/Pz8/Bg0axPfff0+zZs3YvXs3devWRaVSkZRk/mrVoKAg4uNN51PFxcUZJaP9+vVj3bp17Nixg7NnzxruWPGwKVQXsyf+GuN6NcNGIadxbQ86NvJjc5nk4x6bjiTQt00gtWs442irYHT3xmw4fAUAVwcbIpv7Y6uQI5VICAvypluL2hy7lFmh/ubYK/RpH4i/lwuOdgpG9mrCxtgrZmNPXMxEo9MxOKIBVnIpz3QJBuDYBf2UihZBXnw4thPT5u3iXJL5pYxM/AZUp2Pjmmw+kmDq93ACfdvUK+O3CRsOlfHbonap3+B7fiue3rH5UAJ92gXi7+WMo52CUffzq9UxONy8XwCFXIZcLjX8bSU3/7Z/mOPcLSwATzf9qdYabva83L85xy5YvvDGxHPPJmw8aEH7UjnPnR/cc2Gxhj0paYxr0gAbuYzGHu509PNmc+I1ytO9th+edvrT3jXs7Xi5WQhHM28Ytm9ISGZw/bq42ljjqLDiuaBADqSaf00XajTsTU9jTHADbGQyGrm50d7Lm60pxrp5RUX027KJUbt3Mmr3Tt45GAvAi3t2ce7OHdYlJ/Ps9q2G7WuTEonNzOSt2P3mZC1qd/DyZus1U+3ozZsYuWsnI3ft5O1Yvfbo3bs4e+cOa5OTGbxtq2H7mhLtNw+Y17a2taZRh+ZsXbgWlVJF0pnLnI2No0VkG5NYv/r+xO89Ru6dbLRaLce3HUSj0VDNpzrKvAIuHj1DkboIjUbDiR2HSDx1ifqhIWZU9WiKNahVarQaLVqtFrVKjcbMtJ2uvbuzdc0mriYkk5uTy7L/LiWyTw8AbGxtaRfegV/nL6RQqeRs3GkO7o0lvHekRV2B4KEgVhuwyCNd59XPz4833njD7ClwS8TExDB37lzatm1reO7FF19k69atRqf9AV599VUGDBhQ4RX/D4tvvvmGJk2a0KlTJ9q2bcvAgQPZvn07kZHmP8Dy8/M5efIk7dq1QyKRkJOTQ2pqKr6+vtjb2zNy5Ej+7//+j2+++QZ3d3d0Oh3r16+nadOm9OzZk/nz57Nw4ULDvNctW7Zw5MgRPvzwQxMtDw8P3nvvPaZPn87AgQMNUwUeJrNWHGL6kHZs+Www2fkqZi4/RFJmFp6u9vzxXjTPfbqW63fzOXQ+nSU7zjDv1e76dU/jr/HTpjgAdOgY0L4+k59tg1QCGXfz+TrmqNEFXuY4dDaNpVtOM/edKH2bJ5L579rS6RNfvdaN+MuZLN50imKNlilzdjB1ZHvGDwwlOSOLKXN2UFxy8cyo3k2xt1Uw+7Vuhv3jL1/n9R92GPtdfpDpQ9qz5fNnS/weLPU7vR/PfbKmxG8aS3acZt6kHiV+r/LTppOW/a46Ujm/W88w7617fq/y0/qThu1fT4ok7vJ1Fm/W+508byfThrdj/IAWXM3MZvK8nQa/Xu4OrP689H7R++YNJ+NWLv2n/flIx7m2tysTBrbE0V5Bbr6a2NOpzI+xvA7oobNpLN12hnlvlmifLOf51UjirpTxPH8n04a1Y3z/Es/zy3n+rIznuXrP/TZvNT7Gh08wvW0oWwb1IVulZubhEyRl5+BpZ8sffbvz3LqtXC9QUtvZiYnNGuForSBXpSY2PZO5J0rPIv186jzO1tasjO6OWqNlx9UUFp42X4EGmB0fx9TmLVgf1YsctZrZ8SdJys3F09aWJRGRDNu5netKJXfKXCilKLkw9K5KhUano1ijMUxZAFBqNKi1GrLUahO9svwnLo5pzVuwoWcvstVq/hNXqr20ayRDd/wN7WINak3F2gNeG8LyLxfx4cA3sHdyYMDrQ6lR24e712/z5aj/452FH+Hq6U6X56PIy8rlq3EfoS5UUc27OiM+HI+tgx15Wbls/mUNN69lIJFKqV7Ti1EfTaB6TdMLKe/x+3+X8NsPpXcL3LVxO0NeGkH36CjGDRzJj6sWUd3Lk9B2rXhmxHNMHvcmapWKdhEdGfrKSMN+E6e9zlcfzuLZ8AE4uTjx6rTX8a9Tu8KxFggEj45HcnvY8PBwFixYYHLKesqUKYSEhGBnZ8dnn32Gj4+PYVtISAiDBw9mwoQJ7Nu3D6m0tFKyZs0aYmJi+PXXX03anjlzJr/88gvr16+3eIo8NTWVbt26ERgYaPT82rVrSU1NJTIyEg+P0tsZBgQEsGjRIurXr8+JEye4fPkyr7/+OuvWrcPJSX/q8+zZs4wfP561a9fi4uJiopmXl8c777xDYmIi1tbWaDQaevbsabjYSqfTsWjRIlauXGl4HBoayjvvvIOTkxNpaWl88cUXnD17FqlUip+fH5MnTzasXPD9999TUFDA5MmTDZrDhw8nIiKCESNGEB4ejkKhMEpkP/nkExo1Ml3T1Bzi9rCPB3F72MeoK24P+9gQt4cVCP454Z3+77Hq7dr7YNcBVSWPJHkVPP2I5PXxIJLXx6grktfHhkheBYJ/jkheLSPusCUQCAQCgUAgeGp4pHNeHzcvv/yyyVqnXl5eLFiw4JFprly5kqVLl5o8/8UXXxAcHPzIdAUCgUAgEPyLEUtlWeRflbw+yiTVEoMGDTKsTysQCAQCgUAgeLT8q5JXgUAgEAgEgn8DOlF5tYiY8yoQCAQCgUAgeGoQlVeBQCAQCASCJw2xGJRFROVVIBAIBAKBQPDUICqvAoFAIBAIBE8aYs6rRUTlVSAQCAQCgUDw1CAqrwKBQCAQCARPGqLyahFReRUIBAKBQCAQPDWI5FUgEAgEAoFA8NQgpg0IzKKzllSJriSnipYGcaoiv8oqPC2kqaKxllbNWMtsq0a3ZWCVyAJw+ELVHOPl129Wie68K1Wk27I1SQXpVaINUNvOu8q0BY8QMW3AIqLyKhAIBAKBQCB4ahCVV4FAIBAIBIInDHF7WMuIyqtAIBAIBAKB4KlBVF4FAoFAIBAInjTE7WEtIiqvAoFAIBAIBIKnBlF5FQgEAoFAIHjiEHNeLSEqrwKBQCAQCASCpwZReRUIBAKBQCB4whCrDVhGVF4FAoFAIBAIBE8NInkVCAQCgUAgEDw1iGkDAoFAIBAIBE8aYtqARUTlVSAQCAQCgUDw1CAqrwKBQCAQCARPGqLyahGRvAoqhZOtgumD2xJW34usfBXzNp1k68kks7HPdwhmeJcQrK1k7Dp9jZmrDlGk0b8J93z6vFGstZWMVbGX+M+aIxXqP9u9IUN7NsZaIWfPsWS+XHyAomLzb+zAmm5MfbED/l4uJGdk8fnPf3H52h0AerYPZOqL7VGpNYb4d77exrGbt4z92il479l2hNW75/cE206Y9/tcxwYMD9f73X3qKjP/LPULENnUnxe7N6GGiz23cwv5eNl+4pJuVOj3uciGDI1qhLWVnD0nkpm1JNayXz83po1sb/D72aL9XE7R++3aqjZjopvj7mSLuljDodOpzP79EAWFReZ1uzVkaK/Get1jycz6teJxnja6dJw/+8V4nKeNNh7nt7/ZxskLmZY9d23AsO6NsFbI2X0imVm/HbSs7evGeyPaGbQ/XXyAy6l67QBvFyYNakVQLXdcHGxoPW6h2TYcrayYFtqCVp6eZKtUzD9zlu0pKRb7B/B9xw60qF6dDqti0JTc/WZgnTr0rFWLOs5ObE9J4dNjxytsQ51bwOm5q7gVfxkrR3vqD+2OT8emZmMLMu9w9ud13DmbhNRKjl9EKEHDo/Tbbtzl7I9ruHvxGlIrOV5tQgge3RupTGZR29HKimktyng+WwnPHUo8x5R6/qBlS1p4eGArl3O7sJDfLl1ifXKyxTaK8wpI+mEVOacvI3e0x/e57ri3M++58Podri1eR+75JKRyOdU6h+I3RO85Yc5ycs8moFGpsXJ2wKtPJzzCW1rU1eQXcHPxKpTnLiNzsMetf3ccwkx1c2OPc3PxKiQKK8NzNSaOwLZ+AADp//kRVWIKyPQnK+UuTvh9/JZF3XV/rGb7ui0kX0miU49w3v5oisXYmKUrWbloGSqVmvYRHZg47Q0UCoW+X9k5fD3jS44fPIazizOjJo2hS1RXi20JBP8riORVUCneGRBGkUZLjw9XUs/bja9fDOdy+h0Sr2cbxbWu583w8BAmLNjGzRwls0Z2Zlz3pszddAKAzu8tM8TaKORs+WAQO08lV6gdFuLDsF5NeHXmJm7dLeDzSV0Z078581ceM4mVy6TMfC2S5dvOELPzPP26BDHztUgGv7uS4pKE8syVG7zy6UbjHd2N3wrvDGhNUbGGqA9WUM/Hja/GRHA57S5J17OM+1bfmxERIYyft41bOQXMHNWFsT2aMm+j3m+rel5M6N2C6Uv2cvbaLao52VXoFSCsoQ/Dohoz8T+buZVVwBcTIhgT3Zz5qyz4fbUrK7afZdXu8/TrFMTMV7syeOqfFGu0nLp8g5c+30B2ngpbazmTh7fjpf7N+XrZYYvjPHHmJr3u/cZ5UiQrtp1h1a7z9OscxMxJkQyebDzOL3+20WRfs54beDO8R2MmzN7CrewCZr4Sztg+zZi32jQRlMukzJoQwfKdZ1m15wL9O9Zn1oQIBk1fRbFGS7FGy85jSazac4EvJ0RY1Hy7WTOKtFp6r99AoIsL/2nfjivZWSTl5JqN7+bnh0wiMXn+llLJ4gsXaOXpibXs/jOxzv60FqlcRsQv75GTnMGxTxfh5O+FY01PozhtUTFHZvxMzajWNHvrBSRSCfnppT+yzv64BoWzAxE/T6M4v5DDM37m2pZD+Pdqd3/PG0o8t2vHlawsknIfzPOvFy7w2fHjFGm11HJ0ZE7HjlzKyuJiVpbZdq7+shaJXEbTBe9RkJzB5VmLsKvpha1fOc/FxVz67Geqd2tNnUl6z4UZpZ69oztj/dJApFZylGk3uPDxT9j5e2Mf4GNW99bvet1a/3kPdUoGGd8vQuHnhcLb0yTWOqAmPpNftjR0uD/fF6cOlhPlsrh5uPP82GEcjz2KSqWyGHcs9ggrFi7jix9n4+5RjY/efJ+l8xcx+rVxAMz5/FvkVnL+2BlDwsUr/N+kqdSuVwf/OrUr1Q/B041O3B7WIv+6Oa+DBg0iOjqanj170qBBA6Kjo4mOjmbq1KlkZGQwadIkIiIiiIyM5MUXX+TSpUsAzJ8/3xDbrFkzIiIiDI8zMjI4deoUo0aNIiIigt69ezNs2DCOHj0KwLBhw9i9e7dRPyZNmkRMTAwAMTExhIaGGvo1btw4bt68aYjVarUsWLCA7t27ExUVRc+ePVmxYoVhe2pqKvXr1+eDDz4wei4sLMzwODw83OAFYN26dfTr148ePXoQFRXFG2+8QXp6+t8aUxuFnPBGNflhy0mU6mLik2+w71wKUS3qmMT2Cq3DuiNXSLyeTa5SzS/bT9E71DQOIKJxTe7mFXIyseIqZFT7QNbvu0hSWha5BWoWrjtJz/aBZmObB3shk0lYvvUsRcVaVm4/BxJo0cDrgfx2aVyTH7bE6f0m3eCvsylEhQaY+m1Zh3WHL5N0PavEbzy9W9Y1bB/bvSk/b4/nzNVb6HRwM7uAm9kFFer3bFuX9fsvkZRe4ndDHL3a1TUb2zyoBnKphD+2l/jdeQ4JEBqs93vjbj7ZeaVfnlqtDt/qTuZ125WM8z3dtSfpZWmcg7yQyyT8sa1Ed8c5JBIIfYBxNtJuU5d1+y+RlKHX/mVjPL3aWvBcvwYyqYQ/dpyjqFjLil3n9dpBeu1r13NYf+AySel3LerZyGR09vXhp7PnUGo0nLp9m/3p6fSoWctsvL1czugGwcw9fcZk2970dPalp5Ojtpyk3KO4UE3mobMEvhCJ3NYat2B/qrcMJm3vSZPY1N0nsHZzIqBvB+Q2CmQKK5z8S8e34PpdvNo2QqawwtrVEY9m9ci9Zvm9ZCOT0dnHh5/OlfNcqwLPwcHMPWPqOSk3lyKt/keKTqdDp9PhY29vth1NoZq7R87iOzgSmY01jkH+uLQI5tZ+U8+39p7AytWJGr06ILNRIFVYYVer1LOtnydSq5IfmhIJEokE1fXbZnW1KjX5J87iGh2J1MYam0B/7JsEk3vIVPdh0z6iI227tMfRxfx77R471m+le78o/OvUxtHJkRfGDmP7+i0AFCqVHNi5j+HjR2NrZ0tIs0a07tSWXRu2P/L+CwRPOv+6yuvKlSsBfXI3cOBA1q5dC0BRURF9+/Zl8ODBfPfddwBs2bKFUaNGsWnTJl555RVeeeUVQJ+Mjh49mi5dugBw8eJFXnrpJWbNmkWHDh0AuHr1KhcuXKh0v9q2bct3332HTqfjzTffZM6cOcyYMQPQJ84HDx5k+fLluLi4kJGRwZgxY7C2tiY6OhoAOzs7du7cyejRo6ll4cum7BgsXLiQefPm4e/vD8Dhw4e5desW3t7ele7zPWpWc0Kj03HtVml15nL6XZrXMa1eBNRwZu/Z0tOQl9Lv4u5ki7OdNdkFxl/uvULrsOl44n31a/u48NeJq4bHV67dwd3FDid7a3LyVSaxV0pOmd8jIeUOAT6uHD6dBkC9Wu5smjOEnHwVWw5cYcmGeDRl4mt66P2m3Mwx8tvMrF8X9p1JMYpzd7LFyc6aPKWaYD93/jqbwp/T+mMtl7H3TArfrz+Gqkhj0lapB1f2xV0rbTPlDu7O5v0GeLtyJdU4SUtIvUNtb1cOndH7bVzXk9mvReJgp0CpKmLK3J0WdF3Yd7J0nCvUtTDOtX1cOVRmnDd/XzLOsVf4dUM8Gq35SkKAtyt/xZcZx9SKPLuQkGbs+UrqXQK8XTh0Ns1s++Wp6eiAVqcjJS+vVDM7m2bVPMzGvxwSwurERG4XFlaqfUvkp99CIpXg4F2q41TLizvnTKekZF26hm11F45+vJCsK6k41vSkwZi+ONWqAYB/73Zk7D+Fe0gARXlKbp64RL3nIy1q13Sw4Nnj73l+u2lTetaqhY1czsW7dzmYaX5KSGHGLZBKsPEq1bGt5UXueVPP+ZevYe3hwqUvFpKfkIqtnyc1R/bFrmYNQ0zyz2u4ve8EWnURdv7eODerb1a36Lp+rBWepboKPy8KL5mf/qNOSSf5jY+R2dvi0LoZLlGdkZSZgnFn9VbuxGzBqoYHbv26GaYU/BOuJiTTpnNppTygXl3u3r5LTlY2NzJvIJVJ8a3lV2Z7HU4fj//HuoKnBDHn1SL/uuTVEhs3bsTR0ZFRo0YZnuvRowdbtmxh6dKlTJgwweK+P/30E88884whcQWoVavWfZNIc0gkElq2bMmePXsAUKvV/Pjjj8TExODi4gKAl5cX7777Lh9//LEheVUoFIwePZpvv/2Wr776qkKNOXPm8MknnxgSV8CoSvug2FnLyVcaz5HMKyzCztrKJNZWYUVeobpMnNrQRtnk1dPFnmYBnnyy4uD99W2syFOWabPkbztbK5PExtbaivyCcn0tUGNno+9r3MVMhk6LIfN2HrV9XPl4fBc0Wi2LDp4r1VOY86uunN+Svtlby1HIpVjJZYQ3qcVL32+hWKvly9HhjOramAWbLVd/bK3l5Jvza2POr9xobPTxRQa/AKeuXCfy1aV4uNjRt2N9Mm7lYQ5bGyvzuubG2caKvPJjpDQe5yHv6cc5wMeVj1/pgkaj5deNpyxoy80eY3uznq1MPOeX0a4MtnI5eUXG/c8vKsLOyvQjMcjVhUbV3PkmPh4PW9tKa5hDU6hCbmdj9Jzc3oZipWnVtvB2NrfPJNJi6nBaNKpD8sZYjn/xK52+exOplRz3hrVJ2X6EbUNmoNNq8enSHM+wBha1LXqWm/Hs4kIj94o9/ycujq/i4ghxd6e5hwdqrfkvWq1Khay8Z1sbtGY8q+9kk3sukbpvD8cppA7XN8dyZfavhMx+E2lJP/1f7EetUX3Ju3SN3HOJSMz0/56u1NZYV2prg7bQVNcmsDa+H76O3M2FovQbXP9pGchkuEZ1BsBtQA8U3p5IZDLyjsaTOWcxvu9Pwqq6u1ntylKoLMTewcHw2N5BX70uKFBSWKA0PC67vSC/4jM3AsH/Av+6aQOWuHjxIk2aNDF5vmnTply8eLHCfc+dO2d237J88sknhmkG0dHRxMbGmo1Tq9Xs27ePnj17ApCcnIyVlRV16hifWm/atCkpKSnklamSDBkyhJMnT3L+/HmL/bh9+zaZmZn37e+DUKAqxr5cYmBvY0WByvSiH6W6CIcysQ42CkMbZenVIoD4pBuk3zFNpLq1qcOOH4az44fhzH6rGwWFRdjbKspol7SpNKOvKsLOtlxfbRWGC5TSb+aScSsPnQ4SU++ycG0cXVoazx8rUJvxa23Zr32ZpPZe3/JVxYbq6oq/LnA7V0l2vople8/SNth4fl63sAB2zh3GzrnD+Or1bihVxYZ2jPyauchKaenYmIm9mVXAoTOpfPxSZ71umzrsXDCcnQuG89Wb3VAWFpnXNTfOhUWmuhbGOSH1Lr+sMx7n7q0C2PXdUHZ9N5SvJ0WiLDT2YRhHs56N+1mRZ0soi4uxL5f02MutKCgyfp1K0M8T/SYu3nCx0j9BZmNNcbkzEMUFKuS21iaxUoUVrsH+VG9eH6mVnNrRHSjKLSAv9SY6rZYjH/1CjdYhdFs2g66Lp1OUp+Tiki0Wtc16trKioNiC5/j7e9YCp27fxsPWlgEB5iuRUmtrk0RVo1QhteDZob4/Lk3rI5XLqdG7A8W5BRSm3TSKk0ilOAb5o76Tzc0dhyqtq1WqkNqY6lp5uGFVzQ2JVIrCtwauvcLJP37asN0moCZSG2skVnIc27bApm4tCs5U/L1RGWxsbSjIzzc8vve3nZ0tNna2JolqQV4Bdvb3nzcv+HegQ/tY/z1N/M8kr/9k4nNl9p0+fTpr1641/Gvbtq3R9tjYWKKjo2nTpg25ublERUVV2Lak5CIJSZmLJaytrRk/fnyFlddHMcH72q0cZFIJftUcDc/V83IlMTPbJDYxM5tAL1fD40BvV27nKE2mDPQMrcPGY+anDGw7mEDXl36l60u/8tbsbSSlZRHo51baZk03bmcVmFTkAJLSsqhbJhagrp8biWnm5z/qdDrKX45y7aap30BvNxIzs8z4zSLQu0zfSvzmFKjIVaq5fjcfHRUfk22HE4mYsISICUt485ttJKXdNfIQ6OfG7WzzfhPT71LX19hvHT83i/M9ZVIpPh76eXjbDiYQ8fKvRLz8K29+pR/nujXLjbMlXTPjXMfXjaRKjvPWI4mET1pK+KSlvPHddhLT7xofY9+KPGdR19fV6Lk6vm4kpmeZ1TbHtdw8ZFIpvmWqXnVdnEnKyTGKs7eyIsjVlY9bh7G+dy9+jggHYE2vnjSp9uBVN3vvaui0WqMLr3KTM3DwM52S4lSrhslr8x5FeUoKb2VTK6oNMis5Ckd7fMNbcOO45YTqWp4Zz84VeA4LY32vXvwcXuK5Z0+auJv3LJNI8CnTbllsvKqh02iNLrwquJaBra+pZ9sy0wMqg06rpfD6HbPbrDz1Y110vVRXnZqBlZmLtUyQSKj4bSuBh/BZW6uOP4mXEgyPEy8l4OruipOLM761fNEUa0i7mlpm+xVqBfj/Y12B4GnnfyZ5DQoKIj7edK5QXFwc9erVq3Dfhg0bcuqU+dOdlaVt27asXbuWvXv3UlxcbJh36+/vT1FREQkJCUbxJ0+exNfXF/tyF0EMGDCAlJQUjh0zvQIcoFq1anh6ev7j/palUF3M7tPXGNe9KTYKOY39PejY0I/NxxNMYjceT6Bvq0BqezrjaKtgdNdGbDhmHNeolgcezrb3XWXgHpsPXKZ3x3r4e7vgaKdgZN+mbNp/2WzsifMZaLU6Bkc2xEouZWDXYACOn8sAoHVjX1yd9KcSa3k5Myq6GX+dvGbURqG6mD2nrzGuR7NSvyF+bDaTbG86lkDfsDJ+Ixuz4egVw/YNR68wuH0wrg42ONoqeK5jAw6cSzVpx8jvwSv06RCIv1eJ395N2HjgitnYExcy0eh0DO7aACu5lGfC9X6Pndf77RYWgKeb/jVUw92elwc059h58xfubY69TJ+y49ynKRstjfOFDDRlxvmZiBLde+PcqNw4923GvnLjbKR9KIE+7QLx93LG0U7BqF5N2BhrwfPFTL12eInnLiXaFzIMMQq5DLlcavjbSm78UVeo0bA3LY2xDRpgI5PRyN2dDt7ebLl21Sgur6iIvhs2MmL7DkZs38Hb+w8AMGrHTs7e1idNMokEhVSKVCIx/G3uCn0AuY2CGmENufTHdooL1dw5n8z1o+fw6dTMJNa7UzPuXkrhVvwVdBotyRsOoHC0x8HXA4WTPbaerlzbegitRkNRvpK03Sdw8rec/Fn0fNWM540bGbFjByN27ODtAyWed+7k7J07uFpb09XXF1uZDCkQ5ulJpJ8fx2+Yv1hMZqPAtVVD0lZuR1OoJvdiMlnHzlGtvaln9/bNyL+SQvbpK+i0Wq5vPoDc0R4bHw+KsvO4HRuPplCFTqslO/4Sd2LjcWpo/oJQqbUC+2YNubNuO1qVmsIryeTHncOxtaluwemLFJesMqHOuMHdjbuwb6p/XWkKlBScvYS2qAidRkPu4ZMUXk7CtqHl7w1NsQa1So1Wo0Wr1aJWqdEUm85z79q7O1vXbOJqQjK5Obks++9SIvv0AMDG1pZ24R34df5CCpVKzsad5uDeWMJ7W57XLBD8r/A/M+e1Z8+ezJ8/n4ULFxrmvW7ZsoUjR47w4YcfVrjvmDFjGDlyJK1btzZUVBMTEzl//jy9evV6oH44ODgwY8YMnn/+eUaOHImHhwdjx47lww8/ZM6cOTg7O5ORkcGXX37Jq6++arK/TCbj9ddfZ9asWRY1xo8fzxdffMG8efOoWbMmAH/99RdOTk5/ezrBrJjDvP9sW7Z+OIjsfDUzYw6TeD0bTxd7lr/Tl2e/XMf1rHwOXUxnyZ4zzHu5m37d09PX+HFrnFFbvULrsPv0NZOpBJY4fDqN3zadZs6UnlgrZOw5lsx/V58wbJ/9VjfiL17n1w3xFGu0TPl2B1NebM8rg0NJTs9iyrc7DMs3hTbwZvqYjtjayLmTrWRrbAKL18eBi/HamLNWHWL6s+3YMmMw2QUqZq46RNL1LDxd7PljcjTPzVyr93shnSW7zzBvfPeSdV6v8dOWUr8/b4vH2d6alVP7oy7SsCM+mYU7Kv5hcehMGku3nGbuO1FYK2TsPp7Mf9eW+v3q9W7EX8pk8aZTer9zdjB1RHvGDwwlOSOLKXNK/db2dmXCMy1xtFeQm68m9nSq2SW3AA6dTmPpptPMnawf593lxvmrN7sRf+k6i++N83c7mDq6PeMHlYzzd6W6LRt4837ZcT6YwOINcWZ1AQ6dTWPp1jPMeytKP44nrvLT+tJ5wV9PiiTu8nUWb9Z7njxvJ9OGt2P8gBZczcxm8rydBm0vdwdWfz7IsO++ecPJuJXLwD3bjDS/PHGS90JD2dinN9lqNV+eOElSTi6etrb81r0bQ7Zu47pSyZ0ySx0pSi7guatSGU6pjwwO4sUGpXNNe9Sqxc/nzvHzOfPTexqOi+b03FXsHPUJVo52NBzXD8eanihvZrHvta/p+O0b2Hq44ODjQdPXB3Pmh9Wos/NxCvCmxdThhqvtW7w7lHO/bCBh9V4kUinuIQEEj+ptcYwBvjxZ4rl3ieeTJ0nKLfHcrRtDtt3fs06no39AAO80a4ZUIiGzoIBv4+P5KyPDkiy1RkeT9MMq4l7+BLmDHbVe7IetnyeqW1mceftrQv7zBtbVXLD19qD2hMFc/Xk1RTn52Pt7E/j2cKRyORoJ3Nx+mKs/r0Gn02FdzYWaw3rjGmp5nm+1IdHcXLSKq299gtTeDo8h/VB4e1J8O4uUD7/G78M3kLu7oLyQwM1Ff+rn5zo54BDWDJco/QW7aDTcXbMNdeZNJFIpVjU88Bw/DEUN8xe6Afz+3yX89sNiw+NdG7cz5KURdI+OYtzAkfy4ahHVvTwJbdeKZ0Y8x+Rxb6JWqWgX0ZGhr4w07Ddx2ut89eEsng0fgJOLE69Oe10sk/W/hLhgyyIS3b90IbF7qw0cPly6nmVaWhpffPEFZ8+eRSqV4ufnx+TJkwkKCjLat/xqA6Cv0H799dekpaVha2uLq6srkyZNIjQ01Gz8pEmT6Ny5MwMGDCAmJoY9e/YYqq0AU6dOxdHRkWnTphmWylqzZg2yki+K4cOH8/zzz1v0MnDgQFJTUw3PhYeHs2DBAkMVec2aNSxatIjCwkKkUilBQUG88847eHlVbimjVm//Wqm4h438ZuXnLT5MNO5V8ztOmm151YFHjqZq3vo6RdWc8JF2q3jZokdFWLClk/+PnsMXquYYB9SuGs+3zS9X+8iZ17J11QiXUNvuwVeRETz5dGg26v5BD5G/Tpq/ocuTyL82eRX8M0Ty+ngQyevjQySvjw+RvD5eRPL676R905GPVW9/3KLHqvdP+J+Z8yoQCAQCgUAgePr5n5nzKhAIBAKBQPDUIOa8WkRUXgUCgUAgEAgETw2i8ioQCAQCgUDwpCEqrxYRlVeBQCAQCAQCwVODSF4FAoFAIBAInjB0Ou1j/fcwUSqVvP7660RGRtKjRw92795dYbxKpaJnz54MGDCgUu2L5FUgEAgEAoFA8ND4+eefsbe3Z/v27SxYsIDp06eTn59vMf7rr7+madOmlW5fJK8CgUAgEAgEgofG5s2bee655wDw9/cnJCSEffv2mY09duwYycnJREdHV7p9ccGWQCAQCAQCwRPH473JSE5ODjk5OSbPOzk54eT0YDd5SU9Px8fHx/DYy8uLzMxMk7iCggI+++wz5s+fT3JycqXbF8mrQCAQCAQCwf84ixcvZs6cOSbPT5w4kVdffdXouf79+5Oenm62ndjY2Eprzpo1ixdeeAFPT0+RvAoEAoFAIBA8zTzsi6jux4gRI+jfv7/J8+aqrqtXr66wLW9vb9LS0nBzcwMgIyODsLAwk7jjx4+zb98+5s2bh0qlIjs7mz59+rB+/foK2xfJq0AgEAgEAsH/OH9neoAlevTowfLly2nUqBHJycmcPn2a2bNnm8SVTVIPHz7MzJkziYmJuW/7InkVCAQCgUAgeMJ43JXXh8mLL77IlClTiIyMRCqV8tFHH+Hg4ADAt99+S/Xq1Xn++ef/dvsSnU73eGcEC54Kwl5dXCW6EnXVvFl1iqpZeENS+PR+OP1tJFUjq6ljUzXCdlW4qEt+Fb2+rKvoIEurRldWo+rqQMv6tKky7dp23lWm/b9Am4aDHqvewbMrH6veP0FUXgUCgUAgEAieNERt0SJinVeBQCAQCAQCwVODqLwKBAKBQCAQPGE8zXNeHzWi8ioQCAQCgUAgeGoQyatAIBAIBAKB4KlBTBsQCAQCgUAgeOIQ0wYsISqvAoFAIBAIBIKnBlF5FQgEAoFAIHjCEBdsWUZUXgUCgUAgEAgETw2i8ioQCAQCgUDwhCFugGoZUXkVCAQCgUAgEDw1iMqrQCAQCAQCwZOGmPNqEVF5FQgEAoFAIBA8NYjKq6BSONkpeO+FdoQFeZGVr2LeuhNsO55kNva5Lg0Y3jUEaysZu+OuMnPFIYqK9b8gPxzenpb1vLBVyLmdq2TJjrOsO3j5vvrPRTRgWPdGWCvk7D6RzKzfDxraLE+grxvvDW+Hv5cLyRlZfPrrAS6n3qmwLfWT5rdrA4b1aIS1VUkff6vAr58b741oh38NF5Izs/h08QEup+j99mxTl8ERwfhVdyK/sIhthxOZv/o4Gq35uVRVpWvQLntcKtL2LdG+d4wXlx7jAG8XJg1qRVAtd1wcbGg9bqHZNpxsFEyPakVrfy+ylCrm7o1n6/mrJnG9QmrzbIt6+Lk6kq8uYuu5q8zbG4+mZD5aZdsx6FormB7RktY1a+jjD55i66VrprpB/jzbJBA/lxLdi1eZd/C0QXfvSwOM4q3lMv48ncB/9p2wrG2jYHrPcn09Z8FzaIlnlQXPlWjHyHO3VrSuVeJ5/ym2XjSj28CfZ5vWM/a8/5RB9x5+Lg4sGxbFrssp/N+WQxXrdm1Zqht7iq0XzYx1sJmxji0d6wUDuxBSwx2NVv96vJmv5JlfN1vWVVgxrWULwmp4kqVSMf/UWbZdS7EYDzCncwdCPavTbkVM6Tg/YDvr/ljN9nVbSL6SRKce4bz90RSLsTFLV7Jy0TJUKjXtIzowcdobKBQKAHKzc/h6xpccP3gMZxdnRk0aQ5eorhX2X/Dw0Yl1Xi0ikldBpXhncGuKNBqipq2gnq8bX70cweW0uyRlZhnFhQV5M6JrCOO/38at7AJmju3C2J5NmbdO/4W6eNsZPv09lqJiLbU8nZg/qQeXUm9zIeWOGdWSNht4M7xHYyZ8tUXf5svhjO3TjHmrj5vEymVSZo2PYPnOs6zae4H+Heoza3wEg95fRbFGa7GtuRtPPjl+G3ozPKoxE2Zv4VZWATPHhzO2bzPmxVjwOyGC5TvOsmrPBfp3rM+sCREMek/v10Yh4+vlRzibeBNXRxu+nBjBC/khLNly+onRNTrGs0uOyyv3OcYTSo5xWe3peu1ijZadx5JYtecCX06IsDjO70aGUqzR0n3OaupVd+GbQZ24fPMuibdyjOJs5DK+2nmCM+m3cbWzZvbAjgxtFcTiw+cfqB2DbufmFGu1dP95LfWqufBNnw5cvpVF4h0zun+d5EzmHVxtrZnduz1DVWoWH78AQKcfYoxit74Yzc4rFSdI73Yr6ev3q6nn6cI3z3Ti8g0znq1kfLWjjOdnOjK0MIjFh84/UDsG3fAW+vgf1lDPw4Vv+nXk8q27JN4u71nOV3tPcibjtt5zdAeGhgax+Oj5cu2Fcu665feQIa5LyVj/tFav27cDl29aGOt9Zca6T3uGtlCz+NgFQ8yXe06w9mzifTUB3m7ejGKtlp5rN1DPxYXZHdpxOSuLpJxcs/Hda/khk0j+cTtuHu48P3YYx2OPolKpLPbvWOwRVixcxhc/zsbdoxofvfk+S+cvYvRr4wCY8/m3yK3k/LEzhoSLV/i/SVOpXa8O/nVqV8q/QPCoEdMGHhLh4eH06NGD6OhoevTowfTp0ykqKrIYX1RUxLfffkv37t3p1asXUVFRfPHFFxQVFXH48GHq16/Pjz/+aIg/fPgwAwaUVlry8vL4+OOPiYyMpHfv3vTu3ZsFCxYAEBMTQ2hoKNHR0fTs2ZNx48Zx8+bNv+3NRiGnS5Oa/LAhDqW6mPjEG/x1OoWoVgEmsb3C6rDu0GWSMrPIVar5ZUs8vcPqGrYnZWYZqmk6nf5qSp9qjhXq92xTl3UHLpGUkUVugZpfNsXTq01ds7HN69VAJpXwx85zFBVrWbH7PBIJhAZ5VbqtJ8Lv/kskpZf0cUM8vdpa8Fu/xO+OEr+7ziOh1G/M3ovEX75OsUbLzawCth5OpEnd6k+UrpH2veOy8QG1yxzja9dzWH/gMknpdy3q2VjJCK/vy4K/TqMsKiY+7Rb7LqfRs6Hpl/OquCvEpd6kWKvlZp6SLeeSaeLr8cDtgD5JCq/jy4JDJfEZt9iXlE7PIH9T3TMJxKXf0uvmK9ly8SpNvKqZbTeirh93lSpOplt+nxv6uq9EO/UW+65Y8HyynOezZjxXoh2D50BfFsSWxKffYl9iOj2DzXg+dYW4tJulni9cpYm3sefIejXJVak5eu26Ra8G3bq+LDhYCd3TlR/r+2Ejk9HF14cfTp9DWawh/tZt/kpPJ8q/ltl4eys5LzYMZs6pM/+oHYD2ER1p26U9ji5OFfZxx/qtdO8XhX+d2jg6OfLC2GFsX78FgEKlkgM79zF8/Ghs7WwJadaI1p3asmvD9gccCYHg0SGS14fId999x9q1a9m4cSNXrlxh+3bLb/apU6dy5coVVq1axcaNG1m3bh21a9dGrdafwPbw8GDx4sXk5JhWMnQ6HS+99BI6nY6NGzeyYcMG/vzzT+zt7Q0xbdu2NfTF3t6eOXPm/G1fNas7odHqSLlZ2pfLaXcJqOFiEhvg5cLltLtGce5OtjjZWRuee2dwGHtnD2Hl+/25laMk9mxahfoBXq5cSS3TZsod3J3tcLK3No31diEhzThpuZJ6lwAvl4rbKtO/Kvfr7cqVlDJtpt7Hb2o5v2l3CfA27StA00BPEtOznihdg3bqA2ibO8YWtM1R01V/jK/dLa1gXb6ZRUA15/vu28y3Oom3sv9WOzVdHNHodFzLyiuNv5VFgFvFyQZAMx8Pk4rhPXoF+bPxQnKF+9d0M9PXG1kEeFTCs191Em9m/612arre81x2jO4S4F4JXR8PEm9nGx7bK+S81DaEb/fG3XffUt2HM9YT2jZi+7h+/HdQBM19PCzrOjqg0elIySujm5VNgJN53VcahRBzJZE7ysJ/1M6DcDUhmYB6dQyPA+rV5e7tu+RkZZN6NRWpTIpvLb8y2+twNTH5H+sKHhB9xePx/XuKEMnrI0ClUqFSqXCy8CGTnJzMjh07+OSTT3BwcADAysqKZ5991pCAVq9enR49evDTTz+Z7H/w4EHS0tKYOnWqYY6SjY0Nw4YNM4mVSCS0bNmSjIyMv+3HzlpOfqFxFTmvUI2djZVJrK3Cijxl6QzSe3/b25TOUPlyxWG6vP07477ezJ74a6iLNRXq29rILbRpRt/aWB8gX1naV8ttlfavyv1aPzy/ZenVti7B/tX4bdsZk21VqQsP9xhXBjuFnHxVuWOsKsJOUfFMqj6NahPs5caSI+f/Vjtm49VF2FlV3Pc+wbUJru7GkhMXTLZ5OtjR3MfjvsmrndU/8FyjjOcHbMdOYWU+/n6eG9Qm2NONJWVO3b/cthHrziRyPa+gwn0r7mcldKu7seR4qe73++Ppt2gjPX9ex+ozCXzVtwM+zvZm97eVy8kvd9Ytv6gIOyvT8QlydaFxNXdWXk74R+08KIXKQuxLvnsA7B30XgoKlBQWKA2Py24vyL//mAsEjwsx5/UhMmnSJKytrbl27Rrt27enffv2ZuPOnTtHrVq1cHauuPIwfvx4+vTpY5KUnj17lgYNGmB1nw9/ALVazb59++jZs2fljZSjQFVskkTY21hRUGg6LUKpLjKKtbfRJ9f5hcVGcVqdjvjEG/RoGcDADvVZsbf0i6J7qwAmD2kLQPyV6ygLjfXtbe+1aUZfVWTQLI0v7avltkr799j9hgUweWiJ38vXUaqKsbc116YFv7bl/Jrpa8emNZkwMJRXv9pKdp6qSnWh5BgPreAY30+7/DG2cHwsUaAuxt663DFWWFGgLrawB3QK9GFCp6ZM+GMX2SXJ84O2U6Auxl5hJr6CKUadAnyY0LYxE9bsIbuw/KWF+qprfMYt0nPyLbYBUFBkpq/WlfDcuZznB2ynQF304J7r+DChfRMmrNpt8FzPw4VWNWswZOlWyybL6hZZGGv1fca6XWMmxBiP9dky82s3nk+me72atPP3ZkW86cWXyuJi7MslmPZWVhQUGY+PBHinRTO+PhlvckHag7Tzd7CxtaEgv/T1cu9vOztbbOxsTRLVgrwC7Ozt/rGu4MEQt4e1jKi8PkTuTRs4dOgQKpWKRYsW/aP23N3dGTRoEPPmzTN6vjJ33YiNjSU6Opo2bdqQm5tLVFTU3+7HtRs5yKQS/DxK52oG+riRWO7iJYDEjCwCfdxK43xduZ2jJKfA/MUDMqkE33JzQLceSST8taWEv7aUN77fTmLGXQJ9y7bpxu3sAnLyTdtMTM+irq+r0XN1fNxIzMgq6Z+Ftsr077H7PZxI+KtLCX91KW98t53E9HJ99HtAv75uRqfoWzf0Yerwdrz9/Q6j0+1VpQslx3jSUsInldH2+wfHuJz2/bh2t+QYu5ZWnwKruximA5SnTW0v3uvRijdX7SWhTMyDtnMtK1cf71wmvpqLxekAbWrW4L3wUN7c8BcJt8232TPInw3nky16NWjfsdDXmxV4jmrFm3/uJaFMzIO2c+1uiWeXMvEeLkbTAYx0a9Xgva4teXPtPiPPLXyr4+Vkz4YxfdgyLpqhLerTJdCXJS90ezBdS2NdqwbvRYTy5jrLY30PHfrk06xubh4yiRS/MpXNui7OJJabAmZvZUWwmyuftAljY99e/BIZDsC6Pj1pUs290u38HWrV8SfxUmm1N/FSAq7urji5OONbyxdNsYa0q6lltl+hVoD/P9YVCB4WInl9BFhbW9O5c2diY2PNbm/QoAFXr14lO7viD0iAMWPGsGPHDq5dK13eJSQkhHPnzlFcbPkX+L05r3v37qW4uJjvvvvuwY2UUKguZk/8Ncb1aoaNQk7j2h50bOTH5iOmV95uOpJA3zaB1K7hjKOtgtHdG7Ph8BUAXB1siGzuj61CjlQiISzIm24tanPsUmaF+psPJdCnXSD+Xs442ikY1bMJGw9eMRt74lImGq2OweENsJJLeaZzMADHLmRUuq0q93swgT7ty/SxVxM2xlrwe7HEb0SJ3y7GflsEeTFjTEemzt/FueRbT6QumDkuldEON68NoJDLkMulhr+t5MYfdYVFGnZfSuWl9o2xsZLR2KcanQJ92HTWdDm00JqefNSnDZNX7+dcxp2/3Q5AYbGG3QlpvNQ6BBu5jMZe1ehU25tNZk75h/pW56NurZm8KdbilfWNa7hT3cH2vqsMGPp6MZWXOpTpa10Lnmt58lHfCjxXsh2D5yupvNSmkd6zdzU61fFhk5mEO9SvOh9FtWHyhgMmnmNOJ9D/lw0MWbqVIUu3EnMqgQNJGby6em8FuuXGOsDbvK5vdT7q3prJG03H2kFhReuaNVDIpMgkEnrUr0UzHw8OXTX/Pi7UaNiTlsbYkAbYyGQ0ruZOR29vNicbLw2WV1RE73UbGbZtB8O27eDNvw4AMHL7Ts7euVPpdsqiKdagVqnRarRotVrUKjUaM9OUuvbuztY1m7iakExuTi7L/ruUyD49ALCxtaVdeAd+nb+QQqWSs3GnObg3lvDekRZ1BY8GnU77WP89TUh04ua5D4Xw8HAWLFhAvXr10Gq1vPXWW3h4eDBt2jSz8W+++SYajYZPP/0UBwcHNBoNS5cu5ZlnnuHMmTPMnDmTmBj9Ujg///wzv/32Gy4uLsTExKDT6Xj++ecJCQnh3XffRaFQoFKpWLx4MePGjSMmJoY9e/YYEtYLFy7w/PPPs23bNjw8LF9oUJawVxcbPXayUzB9SDta1fciO1/F3JJ1Tz1d7fnjvWie+3Qt1+/qTz09X3bd0/hrfLFcv16ni4M1n4/uTKCPG1IJZNzNZ8Xe86yNLT31JlGbfwM937Whfg1QKxm7T15l5m+xhqv4v341krgr11m8+RQA9fzcmDZMvwbo1cxsPv11P5fKLE1lri11uZ9xj81voQW/kQ1L1luVsfvEVWYuLeN3UonfTWX8lqx5ejUjm08Xl/qd+1YPmgR6oi4q/QKLv3ydN74zfzHhY9G1ULJ6vms57d/KaV8ud4yHlznGZbS93B1Y/fkgo7YzbuXS9+cNRs852Sh4PyqMMP8aZBeqmLNHvz6rp6MdK8b0ZPB/N3E9t4D5z4XT1M/DaK5yXOpNXlu5t8J2DNgZv7icrBW837UlYX4l8bH6dV49HexYMaQHg3/bwvW8Aub370xT73K6Gbd4bd0+w+OpXUKxkcv4YPth84Oab/z6crJR8H7Pkr4qVcwpWZ/V06mM55wC5j9vxnNKOc9m2jFgbXyQnawVvN+tFWG1SuJL1nn1dLRjxfAoBv+6WT/Wz3ShqU853bSbvLZmH+UZ2zoEPxcH43VepWZ0I1sSVrNkrA/o13n1dLRjxdAeDF66Ra87oLOpbvotXlu7Dxdba76N7kgtV0e0Oh3Jd3JZcOg0R8qsdiCrYXx630lhxXstQ2lVozrZKjXzTp1h27UUPO1sWdajG89v2cb1AqXRPl52dqzuE2Wyzqu5dsqyrE8bw99LFizitx+MP7uHvDSC7tFRjBs4kh9XLaK6lycAq5asYOWiP1CrVLSL6Mir7xmv8/rVh7M4ceg4Ti5OjJ401uw6r7XtvE2eEzw8WtR5vGvrHk/Y8Vj1/gkieX1IhIeHo1AosLa2pqioiMDAQD766COL81rVajVz585ly5YtWFlZodVq6dSpE2+++SYnTpwwSl5VKhXdunXD3d3d8FxeXh6zZ8/mr7/+wtbWFoA+ffqYTV5Bv7qBo6OjxWS6POWT18eFpeT1UaNTVM1JCEvJ678aS+dbHzGaOjZVI2xXhSe48qvo9WVdRQdZWjW65ZPXx0nZ5PVxI5LXR0vzAMvrVD8KTiTufKx6/wSRvArMIpLXx4NIXh8fInl9jIjk9bEhktd/LyJ5tYxYbUAgEAgEAoHgieN/sLhRSUTy+giZM2eO2RsV/PLLL7i7u1dBjwQCgUAgEAiebkTy+giZOHEiEydOrOpuCAQCgUAgEPxrEMmrQCAQCAQCwRPG07Z81eNErPMqEAgEAoFAIHhqEJVXgUAgEAgEgicMsRiUZUTlVSAQCAQCgUDw1CAqrwKBQCAQCARPGGLOq2VE5VUgEAgEAoFA8NQgKq8CgUAgEAgETxyi8moJUXkVCAQCgUAgEDw1iMqrQCAQCAQCwROGWG3AMiJ5FZhHUkW6VXWWRFtFHxJVeO5Doqkazzp51ZiWuFfNx53EuqreTKC1qhptSRXpUkVvY5mi6o7x4C93V4nuine6kFSQXiXate28q0RX8OQgpg0IBAKBQCAQCJ4aROVVIBAIBAKB4AlDLJVlGVF5FQgEAoFAIBA8NYjKq0AgEAgEAsEThq6qJnE/BYjKq0AgEAgEAoHgqUFUXgUCgUAgEAieMMScV8uIyqtAIBAIBAKB4KlBVF4FAoFAIBAInjBE5dUyovIqEAgEAoFAIHhqEJVXgUAgEAgEgicNcXtYi4jKq0AgEAgEAoHgqUEkrwKBQCAQCASCpwYxbUAgEAgEAoHgCUOHuGDLEiJ5FVQKJzsF7z3fjrAgL7LyVcxbf4Jtx5PMxj7XuQHDu4ZgbSVjd/xVZq44RFGx/k344bD2tKznha21nNs5SpbsPMu6g5fvq/9c1wYM69EIays5u08kM+u3g4Y2yxPo58Z7I9rhX8OF5MwsPl18gMspdwDo2aYugyOC8avuRH5hEdsOJzJ/9XGKzfkd0o6wYG+937XH2XbMgt8uDRjerZHeb9xVZv6h75uVXMq7z7amZZA3TnbWpN7MYf66Exw8l3Z/vxENGNa9EdaKEr+/V+DX1433hrfD38uF5IwsPv31AJdT9X4DvF2Y9Ewrgmq54+JgQ+uXFlasG9mQoSXjvOdEMrOWxlY4ztNGtjeM82eL9peOc9u6DIpooB9nZRHbjiSwIOY4Gq3lOVzPhTdgWLdGWCtk7D55lVnL7uN5WNvSY7wk1uDZUluqcm04WVsxvUNLwnw8ySpUMe/YabYmpJhoRQb4Ma55Q9ztbFBrNBxMyeQ/B0+SX1SMlVTK5HbNaeldHSdrBak5ecw7doaDqZkWfToprHivTShh3iW6J8+wLdmMrr8vYxs3xN3WBrVWw8G0TGYfjSO/qNgo5sXGDahhb8dtZSEfxx4j7sYty9rWCqZ3DqW1bw2yClXMPXyarVeumcT1qleLZxsF4ufsSL66iK1XrjHv8Gk0JXPw/F0cebdDc4KruXK3UMV3B0+xJ9ny69pJUWasVSrmHT3N1kQLY92szFinlo41wIxOrWjpXR0buZw7ykKWnLrI2kvm35d6v1VzjB2trJjavAWtqlcnW61mwdkzbE9NtRgP8F379rTwqE7HNasN43wPX3t7fo3oyp60ND46fqzCdpxsFEzv25rWdbzIKlAxd2ccW88km8TV8XDmtW7NCfZ2w8XOhpYzfjPavmBEV0J8q6HR6t+DN3OUPDN3vUXddX+sZvu6LSRfSaJTj3De/miKxdiYpStZuWgZKpWa9hEdmDjtDRQKBQC52Tl8PeNLjh88hrOLM6MmjaFLVNcKPQv+9xDTBgSV4p1BrSnSaIh6bwUf/PoXkwe3pnYNF5O4sCBvRnQNYcKcbfT7cBXe7o6MjWpq2L54+xn6zVhF+LvLePvHXbzcqxlBfm4Vaoc19GZ4VGMmfrWV/lNX4uPhyNi+zczGymVSZk2IYMuhBCJf/41NsVeYNSECuUz/UrdRyPh6+RG6v7GMFz/bQGiwFy90CzH1+2xrijRaoqYu54NF+5j8XBtqe5nxG+zNiG6NmPDdVvq9/6feby9932RSKdfvFvDK11uIePs3fthwkk9f7IyXm0PFfht4M7xHYyZ+vZX+01biU82RsX0q8Ds+gi2HE4h84zc2HbzCrPGlfos1WnYeT+LTXw9UqAkQ1tCHYVGNeXX2FgZMWYG3hyNjoptb1J05sStbDyXQ7bWlbIq9wsyJXQ261go53/xxmB6v/86Yz9YTGuzNC90bWdYO9mZ490ZM/HYr/af/qffcuwLPL4ez5XAikW/9zqZDCcx6OdygXdm23mnbnCKtlh6/reP/9hxmcrsWBLg4mcTFX7/FmPW7CP91Df2Xb0ImlfByqP41I5NKuJ5fwMsb9xD+6xp+OH6Wz8Jb4+VgZ9HrO62aUaTVErVyPR/sP8LksObUdjaje+M2Y7fuJmL5Wgas3oxMKuWlpg0N21t5VWdCs0Z8EnuMLsvW8PLWPaTl5lnUBXi3fXOKNVq6L17H+zsPM6VDcwJcTbVt5HK+OhBH5KK1jFq9k5Y+1RnapL7es0TCf3q0Z//VDCIWreWzvcf5KCKMms6WX9eGsf69EmO9YRfhS9bQf8UmZBIJL7cofX8ujr9A9PJNhC9Zw1vbD/ByixCC3F3ur/uYj/FbTZpSrNXSZ9NGZhw7yttNm1Hb0dFifDdfP2QSy1/HbzVpyoW7dy1uL8u7PVvqj/F/VvF+zAGm9GpJgIezSVyxVsuOc9f4eN1hi219uekonT5fQafPV1SYuAK4ebjz/NhhdIuOqjDuWOwRVixcxuc/zGbxxmVkpGawdP4iw/Y5n3+L3ErOHztjePez9/j+s29ITrD8A+XfjE6ne6z/niZE8iq4LzYKOV2a1OSHjXEo1cXEJ97grzMpRLUMMInt1aoO6w5dJikzi1ylml+2xtM7rK5he1JmlqGapkP/5vSpZvlDHfTV0nX7L5GUnkVugZpfNsTTq21ds7HN69dAJpXwx45zFBVrWbHrPBIgNMgLgJi9F4m/fJ1ijZabWQVsPZxIk7rVTf02rcUPG06iVBUTn3CDv06nENWqjqnfsLqsO3iZpIwSv1vi6d1a37dCdTH/3RRHxp08dDo4cCaV9Nu5BNV0v7/fA5f0bRao+WVTPL3aWPBbr8TvzhK/u88jkZT6vXY9h/UHLpOUfv8vvp5t67L+r9JxXrg+rsJxlksl/LH9LEXFWlbuPGc0zqv3XDAe50MJNC43ziaeYy8be25dgWeZhD92lfNc36vSbdnIZYT7+/LDsTMoizXEX7/NvqvpRAXWMtG7ka8kW6U2PNbqdPg66RO1wmINP504R0ZeATpgf0oG6Xn5BFVzNdt3G7mMLjV9+SHurF735m3+Sk0nKqCmqW5BOV2tDj/H0gRxbOOG/Hz6PGdu3UEH3FQWclNZaH6A73kO8GHB0TMoi4uJz7zFvqvp9Kxn6nnVuQTiMm9RrNVyM1/JlsvXaFKjGqCvunrY2/D7qUtodTqOpd8gPvMWPev5W9b19+WH42XG+lo6UXUfbKwBErNyKNLe+/zQ/1d2u1ndx32MZTI6+/jw0/lzKDUaTt2+zf6MDLrXND3GAPZyOaOCg5h35rTZ7RE+vuQWFXHs5k2z2420rWSEN/Bjwe54lEXFxKfcZN/FNHo2rm0Se/V2LutOJpB4I+u+7VaG9hEdadulPY5mfhyUZcf6rXTvF4V/ndo4OjnywthhbF+/BYBCpZIDO/cxfPxobO1sCWnWiNad2rJrw/aH0kfBvwcxbeAhEh4ejkKhwNraGpVKRWhoKB988AFWVlb3jb/H3Llz8fX1ZfPmzfzwww/odDpUKhUNGzZk9uzZRvspFAqKiooYPXo0gwYNAuDw4cOMGzeO2rVrU1RUBED79u0ZP348zs6mv74rQ83qTmi0OlJu5hieu5x2l2Z1PU1iA7xc2Hc6xSjO3ckWJztrcgr0J27fGRRG77C62CjkXEi5TezZik+jB3i78ldcmTZT7+DubIeTvTU5+apysS4kpBonalfS7hLg7cIhMzpNAz1JTM8y7/dGGb+pd2gWWMO831PXjOLcnWzN9s3N0Yaa1Z1JzMiiIgK8XPkrvozflPv4TSvnN/UuAV7m/VZEbW9X9sWV82JJ18eVK+XGOSH1DrV9XM2Pc70aJuNs1J6XC3/Fl9c2P44BXmY8p5V4PpdmsS1na4UhQanp7IhGp+NaTmml8vKdLJp7eZjtXxNPd77u3gEHhRXKomLe3RFrNs7N1pqaTo4k3s0xu72mo143pUyF9PLdbJp5VjOv6+HOV+Ht9brFxUzecxAAqQSC3V35KzWdP6N7YC2Tsjclne9PnEKlMT/VwuA5u4z27WyLnsvSzMuDxLvZ+gcSicl2iQQC3MwnLWbH+vZ9xrpbmbHeaTzW77ZtRu9Af2zkci7cusuBlIzK6z6GY+zn4IBWpyMlr1T3SnY2TauZP8YvNWzImsQkbqvKT2wBO7mcMQ2CeW3/fnrX8je7f1lquus/u67dyTU8d/n6XZrXsvzDsSImRDRlYtemXL2Vy7xdcZy4euNvtVOWqwnJtOnczvA4oF5d7t6+S05WNjcybyCVSfGt5Vdmex1OH4//x7pPI+ImBZYRyetD5rvvvqNevXpoNBqGDBnC9u3b6dmz533jy3Ljxg1mzJjB6tWr8fLyQqfTceHCBbP7Xbp0iQEDBtCxY0c8PfXJZJ06dYiJiQEgLy+PL774gpEjR/Lnn38ik8ke2JOdQk5+YZHRc3lKNXbWpkm5rbUVeYVqozgAexu5IXn9cuVhZv95hEa1PWhetwbqYk2F+rbWckM7xm1amSQ2ttZWRrEA+Uo1djamfe3Vti7B/tX4rNwpdTvrB/ErJ69MrKFv1sZ9k0klzBjZkU2Hr3D1enbFfm0ejd/7YWstJ7/AVNfOrK7cRDdPWWR+nNsFElyrGp8v3l+hdp7y/uNoLhaMPVtqy85Kbkhe7eRy8tXljrG6CDsr8x+J8ddvE/7rGjzsbOgXFEBGbr5JjEwi4aPOYWy8nMzV7Fwzrej7kF9kRldu/njF37xNxPK1eNjaEB0YQHqeXtfNxgYrmZTwWj68tG0PxVotX3Zuy6hGwSyIO2tZ25xnRcVfA33q+xPs4cone48CkJyVw12limFN6/P7qUuEelenuZcHx9LNVwbNjnXRfcZ6SclY1zcd61mxJ/nPwZM0qu5OC6/qqC0k61V2jOVy8sof4+Ii7OSmukEuLjR2c+fbU6fwsLU12T42uAEbkq9yQ6k0q2WirZCTryqnrTL/2XU/vt9xkqSb2RRptHQLqcVXz3dmyA+bSLtb8dSU+1GoLMTeobRabu9gD0BBgZLCAqXhcdntBfkF/0hT8O9DTBt4RKhUKlQqFU5OFZ9CMcetW7eQy+W4uLgAIJFICA4ONhtbr149nJycuH79utntDg4OfPDBB9y9e5e//vrrgfsCUKAuxr5cUmJvY0VBuQ9JAKWqyCjW3kY/CT+/0PiSKK1OR3ziDaq72DGwfX2jbd3DAtj1/VB2fT+UrydFolQVY29rrk0L+rYK076Wi+3YtCYTBobyxrfbyc4zTo4KVGb82ios+DWOvadd9gtEIoEZIzpSrNHy5fJDJm10bxXArm+HsuvboXz9aiTKQgttWvJrU86vralfc3QLC2DnnGHsnDOMr17rVjLOpW3da9dcW+WPiSXdjk1rMn5gKG9+u81onLu3DGDX10PY9fUQvp7YtVLjaKRt8npUGLQttVVQ5kKnguJi7MslbfYKK6MYc9wsKORgSiafhLc2el4CzOjciiKtli9jT1rcv6CoGPtyyZO9lZyC4oqP101lIYfSM/mkQxgAKo3+wqi42AAAZalJREFUB9+KCwncVhaSrVKz7Pxl2vqYnh0w1i43blZyCtSWPXfy92ZCWGNe2/QX2SU/SjVaHW9vOUD7ml5sGd6XIU3qsyMhlRt55hMMs2NtVcmxTs3kky6tTbZpdfpks7qdLQODTafzWNR9HMe4uBj7comqvVxOQbGxrgT9XNZvTp0yuUALINDZmZbVq7P8yv0vaDVoq4uxL5eo2lub/6y+H2fTblOgLqZIo2VjfBKnUm7SLtD7gdspj42tDQX5pT8M7v1tZ2eLjZ2tSaJakFeAnb3l+cX/ZnRoH+u/h4lSqeT1118nMjKSHj16sHv3boux58+fZ8iQIfTs2ZOePXuyd+/e+7YvKq8PmUmTJmFtbc21a9do37497du3r1Q8gEwmIyYmhqCgIBo3bkznzp0JCwujefPmREdH4+pqOsfq+PHjuLq6EhQUZFHDysqK4OBgLl++TOfOnR/Y07UbOcikEvw8HEm5qa82BPq4mT39nZiRRaC3GztPXi2Jc+V2jtJQdS2PTCbBt9yc162HE9l6ONHweMaYjgT6urHzWLK+TT83bmcXmFTkABLTs0wuwKrj68afu0sr160b+jB1eDve+m67yelny35dLfv1cWXniWRjv2X6Nn1IO9ycbHhj3g6zV9tvPZLI1iNl/L5Y4vd4SZu+9/EbWc6vjxt/7rlgEluebYcT2VZ2nMd2oq6fGztLVlWocJzT7vJ8eV1fN1btOm943LqhD1NGtONtM+O89WgiW4+W0R7dkUDfMuPo68rtbKV57YwsXuja0Oi5Oj6u/Ln3gmG7ubbKzmm8lp2LTCLFz8mBlJLTyvXcnC2eCi6LTCoxmWc5vWMobrY2vLH1L7OJiEE3t0TX0cEwdSDQ1YXErEroSiT4lsx5zVUXcT2/AB2Vv8jiWnau/nXt7EBKydSBQHeX0ukA5WjjV4P3OoXy+ub9JNwxjrlyJ5uX1u0xPP65XzgbLiZb1n2IY13Z7VV1jFPy8pBJpfja25NakpjVdXYmKddY197KiiBXVz5q1QoAacl0jNU9onj/yGHqu7hQw86OmB76C6Bs5XJkEgn+Tk6M3r3LvOfbJZ9dbo6klEwdCPR0JfFmxWd7KoNOBxJMp4w8KLXq+JN4KYGO3boAkHgpAVd3V5xcnFFYK9AUa0i7mopPLd+S7VeoFeD/j3UFj5eff/4Ze3t7tm/fTnJyMkOGDGHbtm3Y2xtX1gsKCpg4cSKzZ8+madOmFBcXk5tr/qxGWUTl9SHz3XffsXbtWg4dOoRKpWLRokWVil+7dq3hVL9UKmXevHksWbKEsLAw9u7dS9++fcnKyjLsN2nSJLp3786wYcN46623DMuMWOKfXElYqC5mT/w1xvVsho1CTuPaHnRs5MfmMsnHPTYdTaBvm0Bq13DG0VbB6O6N2XD4CgCuDjZENvfHViFHKpEQFuRNt+a1OXbZ8pIzAJsPJtCnfSD+Xs442ikY1asJG2OvmI09cTETjVbH4IgGWMmlPNNFX7E+dkE/L65FkBczxnRk6vxdnEs2v6RQobqYPXHXGNe7xG9AdTo2rsnmIwmmfo8k0LdtvVK/PZqw4VBp3yY/1wb/Gi68tWAnqqKKp0cY/B5KoE+7Mn57NmHjQQt+L5X4DS/x29nYL4BCLkMulxr+tpKbf9tvjr1SMs4uONopGHm/cdZVPM4fju3EtHm7OJdkeekmI89t6+Ffo8RzVBM2HrqP5y7Beu1O+h9uxy5mVLqtwmINu5NTGdeiITZyGY093elYy4fNl6+a6HWvUxNPe/0p3RoOdrwS2oij6aVnOqa0a46/ixNvbdtvcb5pWd09KWmMa9JAr+vhTkc/bzYnmi5X1b22H552Jbr2drzcLISjmaVzDjckJDO4fl1cbaxxVFjxXFAgBypYvqmwWMPupDReCg3Ra9dwp5O/N5sumXoO9a7ORxFhTN4Wy7kbd0y213VzRiGTYi2XMbRJfdztbCwmr4XFGnZfTWVc85Kxrl4y1lcqMdYtSsfa1caayAA/bOUypBJo7eNJt4CaHEs3Pw+zyo6xRsPe9DTGBDfARiajkZsbHby82XrN+BjnFRURvXkTI3ftZOSunbwdq59jO3r3Ls7eucPa5GQGb9tq2L4mKZHYzEzePGB5+k1hkYbd51N4qXNjbKxkNPbzoFN9XzadMn+1vkImxapklY6yfztYW9G6jhcKmRSZREKPRv40q1WdQwnpFrU1xRrUKjVajRatVotapUZjZkpY197d2bpmE1cTksnNyWXZf5cS2acHADa2trQL78Cv8xdSqFRyNu40B/fGEt47soIR//fyNK82sHnzZp577jkA/P39CQkJYd++fSZxGzZsoEWLFjRt2hQAuVxutlBXHonuaVsf4QkmPDycBQsWGOaw/v777+zZs4cff/yxUvEV0bNnT15//XW6detmtN/mzZuZPn06W7dupVq1ahw+fJiZM2caEmGAoqIiIiMjmTFjBp06daqUl7BJi40eO9kpmP5CO1rV9yI7X8XcknVePV3t+WNaNM99tpbrd/VVhue7NGB4RIh+jc24a3yxQr9ep4uDNZ+P7kygtxtSKWTcyWfF3vOsLbPOq0Rp/ovh+ciGJeu8yth94iozy6w/+vWkSOKuXGfxplMA1PNzY9oI/bqnVzOy+XTxfi6VrD86960eNAn0RF0mkYy/fJ3XF+ww9Tu0Pa2CSvyWrPPq6WrPH+/347mP15T6DW/A8MjSdV6/KFnntYabPWs/HoSqSGNYKxHgi2UHDVVHSbH5t9/zXRvq13m10q9TOvO3Mn5fLfG7uYzfYSV+M7P59NdSv17uDqz+bJBR2xm3cun/3p9INKbaz0U2ZFhU45JxTmbWklLdr17rRvzlTKNxnjqyPbVL1pf9bFGp7py3o8yO85vfbkNnZT55fj6iZG3We57LrPP69cSues9b9Fdk1/N1Y1rJOq9XM7P5dMkBLpVZ59VcW6omxnMKnayteL9DS1r5eJKtUjP36Cm2JqTgaW/L8md68OyfW7ier+SV0BB6BtbCSaEgR60mNiWTeUdPk61SU8PBjnXP9UJVrDGqxn2+/zhbE/TJisTauFrlpLBiettQWnmV6J44zbbkFDztbPmjb3eeW7eV6wVKXm7akF4BtXC0VpCrUhObnsncE2fIUesryDKJhDdbNqV7bT/UGi07rqYw5/hp1GVea9p84/eTk7WC9zu3JMzXk+xCFXNK1nn1dLBjxbPdGbx8K9fzCpjfpzNNvaqh1pQev7iMW7y2ST/1aFLrxkQHByCXSojLuMWX+0+SWubCKImVqef3O7aklXeZsU4sGeuBPXh2VclYtzAz1sf0Y+1io+CL8LYEujkjkUjIzCtg+bnLrL1YJjEr95J+XMfYyt34WgJHKyumNW9By3LrvHra2rK0ayRDd2znerl5rDXs7FjVvYfZdV4BRgcF42tvb7LOq/qUcTtONgrej25NWIAX2UoVc3bo13n1dLJjxYTeDJ67ges5BXg527Pu9X5G+6Zn5RH97Vpc7Kz59oUu1KrmhFanI/lWDgt2x3MksfTH0Yr/b+/O42rK/z+Av267JGuEspeIiEgGEUlZutnnmyW7keXHmLFmLGMdw0zWsSQztlG3IiSJkcHIlj1UBqFQKq33Vvf3Rzru7d4bpvu5p/J+zmMej3PvOc77nNOt+z6f8/m8P9/1kvu3f2z3x/7f5L83PKeMhYu7KyYP8cIOkT/q1i8alyH64zAC/A9BnJeHr3r3wIxF8nVeNyxdh+v/XINxDWOMnzlJoc5rU8Oyd1+oCCzqKe8uyMq1R5eRkaH4ZMLY2Pizu0Da2toiMjIStWoVlcJcunQpGjdujHHjxsltt2rVKmRmZuL169d49eoVrK2tMW/evI8OMKfkVY1kk8rCwkJ8++23MDExwcKFCz+6vazk5GS8ePECtrZFtSmTkpIgFArh5+eH1q1bK/y7mTNnwtTUFAsXLlRIXrOysrB27Vrcvn37swZslUxeNUVV8sqaVK/sj8P+C1XJq0ZiK0leNUFV8spaYceqH9+IgZLJqyaVTF41pWTyqjE8/TqVTF41qWTyqiklk1dNouSVjZmLp2Hz5s0K70+fPh0zZsyQe8/DwwMvXihvib948SLs7Ow+KXldsWIFzp49i0OHDqFOnTpYvXo1MjMzsXr16lKPlfq8qllxH1aJRAILCwt4e3t/0vbFfvzxR9SqVQubNm3C8+fPYWBggMLCQvzf//0fWrdurXQf3377LQYPHoxJkyYBAOLj4+Hu7o78/HxIpVJ069YN/v7+/6nSACGEEEI0T9OlssaOHQsPDw+F95W1ugYHB5e6rwYNGuD58+dc8vry5UvY29sr3c7e3h516xaVcxs4cKDKBj9ZlLyq0ZkzyjvR/5ft/fz8PvnfNW7cGNeuXQMAmJiY4ObNL7MmHiGEEEL+m//SPUCVfv364c8//0Tbtm3x77//4vbt21ytelmurq6YPHkyMjMzYWRkhKioKLRs2VLJHuVR8koIIYQQUs5U5F6dEyZMwPz58+Hs7AwtLS0sX74cRu/r+/7666+oW7cuvv76azRo0AATJ07EyJEjIRAIYGZmhhUrVnx0/9TnlbHNmzcjIkJxajs/Pz/Url36NKF8oj6vmkF9XjWH+rxqDvV51Rzq81p5NTf5+GBudYp//VCj8cqCWl4Zmz59OqZPn873YRBCCCGkAlH3xAGVCdV5JYQQQgghFQa1vBJCCCGElDPUq1M1anklhBBCCCEVBrW8EkIIIYSUM5qu81qRUMsrIYQQQgipMCh5JYQQQgghFQZ1GyCEEEIIKWdowJZq1PJKCCGEEEIqDGp5JYQQQggpZ2iSAtUoeSXlC18zaX6BT2ekAp4udiFP09Jm8zT1MD+zdxbR5udnLOVpWlrwNeVxDf4eYvI1xfTwTed4iXt4hiMeZ7/gJfaXMi1tRUDJKyGEEEJIOUN9XlWjPq+EEEIIIaTCoJZXQgghhJByhiYpUI1aXgkhhBBCSIVBLa+EEEIIIeUM9XlVjVpeCSGEEEJIhUHJKyGEEEIIqTCo2wAhhBBCSDkj/RILkH8ianklhBBCCCEVBrW8EkIIIYSUM1QqSzVqeSWEEEIIIRUGtbwSQgghhJQzVCpLNWp5JYQQQgghFQa1vJJPYmyoh0VffwV7q/pIy8rD1tDrOHXtsdJtR/ZsjTF92kBfVxtnbz7B2sP/QJIv33fH3KQa9s93x5mYf7H0j78/Gn9kn9YY7dIW+no6OHv9X6zbf0lhn8UszGph0div0KR+Dfz7Mg0r917Ao8RUAECzBjUwc1hnWDWujRpGBugyeY/q8x31FexbNUBaZh62HrmGU1dVnK9Ta4xxbgt9PW2cvfEEaw99OLahjlYY0KUFmjeoiVNXH2PFJ5wrAIzsXeJ8D3zkfMfInO/vH873c/c1sk9rjO7XFvq6n3Cdzd9fZ9Ma+Dfp/XV+VhTXzaEFhvduBfO6xsjKleDU5QRsC76GgkLVLQmsz7mgxD6M9fWwuE8ndGlkirScPGy5eAvhD58qxOpv1QQj2lvAvEY1ZIklCH/wBFsv3kaBTKuIs4U5Jtm3gWk1Q6Rk52JZxGXEvHij9NiN9fWwuLdM3EulxG1XIu6lD3HPTRkst72+jjYCb8djfdR1pXG52L3s0MXcFGm5edjyz22EP1ISu2VjjGgrE/vRU2z950Ps+tUMMa9HR7StVxuSwgJExidiw98xctdEIS5f17pvZ3Rp/D7u37cQ/uCJYtzWTTCivaV83L9vKZyPeQ0jHBztijOPnmHJyX9UXudqurpYaNcRnevVQ3peHrbduYuIZ89Ubg8Am3p0R8e6ddFdFIQCqRS6WlqYa9senerWRTU9PTzPzML2u3fwT1JyqfsxrqKHxcIusG/RAGnZudgaEYPwW/8qbNesbnX8X7+OsGpQCzWqGqCzzz659cuGfoVOzUxhoKuN1Mxc/PH3PRy5Fqc6roEeFrt2Rpcm9Yuu9bmbCL+v5Fq3aYoRHS1hXvP9tb73BFvP3eSu9bAOFhjQpilamNTAqftPsOzE5VLP9+ihYEQcPYl/4x7DsZ8T5i6fr3LboH0BCPA/iLw8Mbr17o7pC2dDT08PAPAuPQMbl/2Ea5euonqN6hg3cyJ6ufYpNTZr1OdVNWp5JZ/ku2FdICkogOuiw/jh9/OYN7wLmprWUNjO3qoBxvZpA+/NpyBcKkKD2tUwybW90v3df6r8C0dhn60bYEw/G0zfEA6PBQFoWKcaJg20VbqtjrYW1nn3xsnL8XD+v/04cSkO67x7Q0e76KOeX1CIyKuPsXLvhdLPd0QXSAoK4Tr/T/zgH4V5XzugaX0l59uqAcb2bQtv33AIFweiQZ1qmNT/w7G9Sc+G38lbCL306JPOVe58N4bDY+EnnO+09+c7+/35Tvtwvp+zL3vrBhjjKnOdTaph0qCPXOd/3l/ni/LX2UBPGxv/jIbL7IOYsOoY7FrVx//6tilX5/x9zw7ILyiEy64j8An/B/N7dUSzWsYK2xnoamND1A047wjBuD9Po5N5PYzq0JJb39m8HmZ81Q7LT0fDcZsIkwPP4Hl6lspz/b5nB+QXFsJl9xH4nPoH83uqiKujjQ3nb8B5ZwjGHVaM6/hbEPe/y+4jyMsvQGRc6QnS9z3ex95zFD4RlzG/Rwc0q6kstg42XIiBs98RjBNFolPDuhjV/kPseT064m1OLlz3HoXnnxHo0MAEQ9u0KP2c+bjWTh2L4v4WAp+wS5jfuyOa1VZxvuduwHl7MMYdjCiKa2elZH92uJecqvB+SXNtbSEpLMSA0GNYGn0F33WwRVPjaiq372tuDm2BQO49bYEAr3JyMO1cFPoeOYod9+5ihb09TA0NS4393YDOkBQUot/aQCwJuIB5AzujWd3qCtvlF0px+s4T/BiiPAnfG3UH7j8Hw2nlYXy7/y9M7d0OVg1qqYz7vbNd0bXeHAyf0IuY72KHZnVUfK4jr8PZNwjjfj+FTo3rYVTnD9f6TWYO/C7dxdHbCaWeZ7FaJrXx9aTR6OvuWup2Vy9G4/Ceg1j928/Ye/wgXia+xL5t/tz6zat/hY6uDg5FBuH7VYuwadUv+DdeeYMF4d8Xl7w6OTmhX79+cHd3R79+/bB48WJIJBKV24eFhUEoFHLbf/vttwCAYcOGwd3dHW5ubmjdujXc3d3h7u6OBQsWIDExkXtv4MCBGDx4MK5f/9AasmnTJqxdu5Z7/eeff6JPnz54+vQpLl++jHbt2nH7c3d3x/fff4/z589zr7/66is4ODhwryMiIhT2CQD79+9Hy5Ytcf/+/TJdMwM9HfRq1wi/HY9BjjgfNxNe4fydZ3Dt1Exh2/6dm+PoP4/wOCkN73LE8Au/iQH28l9qzh2a4F2OGFcevvyk+G4OLXD074d4/DIN77LF8Dt+E/27Kv+i7NDSFNpaAhw6fQ+S/EIcPnMfAgFgZ1UfAPA0OQOhFx7h8Yu3pZ+vbWP8FnoDOXn5uBn/CudvPYNr5+aK59ulBY5efFR0bDli+IXdxIAuH47tr5iniLr5FOlZeZ90rtz5XpA53xM30d9Bxflavj/fyPfne1b+fD9nX9x1fvF+22OfeZ3xIW7QuQe4+SgZ+QWFeJ2WjfDLCWjXom65OWcDHW04tTDD9n9uI0eSj5sv3yDq8Qu4WTVRiCe6HY+YF2+QX1iI11k5OPngCdo1qMOtn9KlDXZF38WdpBRIAbzOysHrrBylx26gow2n5p8Y946SuPXrKO4UQO8W5nibk4cbL14rXc/FbtYQ2y/fQU5+Pm4mvUHUvy/g1rKxYuy78Yh5KRP70VO52A2MqyIi7hnEBYVIycnFpadJSpNRLi5f19rCDNsvvo/74g2iEl7ArZWSuLfiEPP89Ye4sfJxAcDZshHe5Ylx5WnpLZ8G2troadYQO+/eQ05BAW6lpODvFy/Qr5HidQaAqjo6GN+6FbbcviP3fm5BAXbfu4+k7GxIAVx8mYSXWdmwqllDdWxdbTi1NsdvkTeL/lY/fY2o2ES4tmuqsO3TNxk4ej0eCa/SlO4r4VU6JAVFLX/S9/+b1TJSHbelGbaff3+tn79B1KPncLNWjCuKiUNM4vtrnZmDk/f+RTszE2792YeJOPfoOdJzPu1vZrfePdC1VzdUq6H881fsdGg4XISuaNK8KaoZV8P/Jo1GROhJAEBuTg4uREZhzLTxqGJYBW1s26KLY1ecORbxScfAilQq1ej/FckX2W3A19cXlpaWKCgogKenJyIiIuDm5qaw3atXr7Bs2TIEBwejfv36kEqliI2NBQAEBAQAABITEzFkyBAcOXKE+3eJiYmoVq0a996BAwewaNEihIWFKcTYtWsXgoKCsH//ftSrVw8vX75E8+bNERQUpLBt9+7dARQlv9nZ2Zg3bx63rvi4ZIlEInTp0gUikQiLFy/+nEskp1FdYxQUSvHsdQb33qPnb2Hbop7Cts3q10DU7Wdy29U2rgJjQ31kZOehqoEuJru1h/fmUxjkYPFJ8Zs1qInzN2X2mZiK2tUNYVxVHxklksJmDWog/rl8YhqX+BbNGtTAP3eff975vpI931TYWpgqP99bHx5/Fh1bFaXH9qma1S9xvs/+w/nWLzrfUvf1ruS+auJ8zGdc58QScZ+rvs7tLeoh4UUar+dc3UAP6bliAECjmtVQIJXiaVrmh+1ep6GDzJeoKrYNTJCQUvTZ0BII0KpuTUQl6CNojBv0dLRxLv45fP++ibyCkh0VgEY1lMR9k4YODT8hbkMTJKRmKF3X36oJjsf+W+q/52Kny8ROSUeHBp8Qu74JElLTudeHbj1CX4tGuPbiNYz19dC1UX1sj76j9N/ydq25uO9k4r5FBzPVN1Fc3IYmSEj5cL5V9XQwpWsbeAf+hUFtFG/a5eJWM0KhVIpnmTLnm54O2zrKz3dqmzYITkhASm5uqfutqa8P82pGeJzxTuU2jeoYF51zisw5J71FhyaKf6s/xfcDOmGAbXMY6Okg9kUqLjx8oTxuzaK/mU/fyl7rNHQw/4RrbVYXCW/SP7pdWT2J/xcOPb/iXjezbIG3KW+RkZaOV0mvoKWtBbPG5jLrm+P2tZvMj4v8N19cy6usvLw85OXlwdhY+R3bmzdvoKOjgxo1agAABAIBWrVq9dlx7O3t8fKlYivjxo0bceLECezbtw/16v23Py6qPHjwAG/fvsWqVatw7NgxiMXi/7wvQz0dZOXKt05n5ohhqK+rsG0VfV1k5orltgOAqgZF90lT3Nrj6D9xeJWW/cnxqxjocPuR36eK+Dny55qVI4ahkm1VMdTXQVbOp56vDjJltuWOTcm2n0qd5/t5+2Jznft3bYFWTepg/ynlyc3nH+d/O2dD3Q/36oa6OsjKK/EzFktgqFv6z21gq6ZoVa8W/rhedLNYy1AfutracGphjkmBZ+B5IBwtTWpifOfWSv+9oV4Z4tb9EFdWPSNDdGho8tHk1VBXB1niErHzJHLXRWlsqyZoVbcm/oh5wL13/cUrNKtpjL8meuDE2IG4/zoVfz1WfnPI37XWVYyb9wlxW7+Pe/XDtZ7atS2O3klAcubH/25V0dFBZomneVkS5dfZqmYNtK1TG4Fx8aXuU1sgwNLOnRH25AmevFOdvCr9W50rUfq361OsO3YFPX/8E5N2huOve08hzle8SeDiKrvWeh/5bLVtilb1a+GP6LI9HfwUuTm5qGr0oeW4qlFVAEB2dg5ys3O417Lrs7M+/XuKaNYX2fI6c+ZM6Ovr4+nTp+jWrRu6deumdDsrKyvY2NigZ8+esLe3R4cOHeDu7o6aNWt+VjxlLbtBQUEwMTHBoUOHYGQk/ygmPj4e7u7u3GtnZ2dMnz79s2IGBgZCKBSiYcOGaNWqFU6fPq20dflTZIvzFZKIqga6yM5T7G6RkyeR27aqQVFn+KzcfFg0rIlOLRtg9LrQUuO5dG6GeaO6AgBuxiUjJzdfxT5VxddTPFYl26qSnZePqlVKnq+eivMtcWxV3h+bkm1VcencDPM8SznfKp95vlU+nG9p+3Kxl7nOj5KLzqXKZ1znKh+/zj3aN4L3EDvM2BCO9MwPLah8nHO2JJ97L1uSj6p6JX7GerrILqULkWOzhvD+ygbewX9xLbh577/MD998hJTsopaz/TceYELn1th26bbCPrLF/zFuVxt4h3yIK6u/VRPcfPkGLzJU9/0E3p+zbsnYOnLXRSF20wbw7mID76PnuNgCAJsG9EDQvQRMCDoDQ10d+Dh1wgwHG2y6dEt5XF6uteTz4zZvCO9u7eAtOsvFtTSpgc6NTOG5L1zlv5OVk5+PqjryX61VdXQVrrMARX1jf4m5qXKgW/F2P3TuBElhIX6+EVNq7GxxvsKNc1V95X+rP1WhVIqbT1/DtV1TDOlsicP/PFDYRmlcPV1ki0v5bFk0hLdje3gfOoP0HMXPtboZVDFAdtaH35HiZUPDKjAwrKKQqGZnZsOwaun9i1mjAVuqfZEtr76+vjhy5Aj++ecf5OXlwd/fX+l2Wlpa2Lp1K/744w/Y29vj3LlzGDRoENLS0j4a4927d3B3d0f37t3x+++/45tvvpFbb2Njg7S0NERFRSn82+bNm+PIkSPc/5+buIrFYhw7dgweHh4AAA8PD4hEos/ah6ynrzKgrSWAucmHAQcWDWsh4WWawrYJL9NgIdOp36JhTaRk5CAjOw8dLUxRv1ZVHF02FCd+HA5PJ2v0atcYe78bILeP8OgEOM3cB6eZ+zDbNwIJL97Cwlxmn2a1kJKerfSxfMKLNLQwk7+5aG5Wq9RH1p90vmY1le4j4WUaLGTiWTSsiZT0nM/qMhAenQCnWfvgNGsfZm+KQMLLt7AwK8P5yvxsSttX+OUEOM3YB6cZMtdZdlvzsl3nLtYNsWDMV5i76bTCY34+zlk28Xv69l3Rz7j6hxtHizo1uEfUJTk0NsWi3naYE3oe8TKPk9/lSZD8LvuTZyB/mqYiroruAA6NTLHIyQ5zjsnHleVm1QTH7v9bhtjK9+tgbopFPe0w58TfiJfZxthAD6bVquLw7ThICguRnidGaOxjfNWovvK4fF3r4rg1ZOKa1JDrDqAQt08nzDkSJRe3o1ld1DeuimMTB+LkZHeM6tgSvSzM8Mf/+iqP+y4T2lpaMJNplGhRozoeZ8ifb1VdXVjVrIkVXewROqA/dvd2AgCE9HdDuzq1ue0W2nVETX19LLx0qdQkFyjqx6qtJYB5rQ9/uyzr11TZr/VzaGtpwayW8kFnT9++j1tT5lrXraGyO4BD0/pY1K8z5ojOIV4DXQYAoHHzJkh4+KGFO+FhPGrWrgnjGtVh1tgMBfkFeP4kUWZ9HBo3a6KRYyOf74tMXovp6+ujZ8+euHjxYqnbWVpawtPTE3v27EG1atUQHR390X0X93n966+/4Obmhjlz5sitb9GiBXbt2oVVq1bhxIkTZTqPks6cOYPMzEx4eXnByckJGzZswOXLl5V2XfgUueJ8/HXzKSa72cJATwc2TU3Qo605wq4ojgY9cSUegxws0NS0OqpV0cN4Fxscu1xUXiX4wkMMXh6EUWuPYtTaowj++yEu3kvErG2ld4oP+yceA7+yQJP61VHNUA/j+rfD8YvKS7Zcf5CEgkIphju1hq6OFob2KurmcTX2w7nr6WhDR0eLW9bVkf81yBXn46+Yp5g84P35NquLHjaNEBat+GjvxOV4DHKw/HC+ru1w7J8Px6atJYCejja0tQRyy591vm7tcPySivN9WOJ8e8qf7+fsK+xSPAZ2+8zr3Fv5de5oVR/LJvbAgm1ncO/fj1eV0PQ55+YX4Gz8c0zp0gYGOtqwqV8Hjs0a4ISSR+92ZnWxvG8XzDt+UelI89B7jzGinQVqVtFHNX1dfN3eEn8/Vt43UGncph+Je0J5XACwMa2NukZVPlplgIud8BxTOr+PbVobjk0a4ISS0lF2DetiubM95p28iHuv5GOn54rxPD0TQ62bQ1sggJGeLvq3bIJHKWmffs6autZxiZji0LYoboM6cGzeECeUJPp25nWx3NUB845dUIgbdDseHn7H4LkvHJ77whF0Kx4XHr/EjOBzyuMWFODc8+eY1Lo1DLS10bZ2bXRv0AAnn8pf50yJBIOOHcfYiNMYG3Eac/8uqoAy7nQk7qYUHcN3trZoXK0avr9wEeLCj7fC5UoKcPb+M0zu3Q4GutqwaWSCHlZmCLupfNS8no4WdLW1ZZaL/hbWrKoP57aNUUVPB1oCAbq0qI++Nk1wNSFJddyHiZjSzaYobsM6cLRoiBN3FePaNaqH5QMdMC/4b9x7qfgz1hYIoKetBW2BFrS4ZdV/MwvyCyDOE6OwoBCFhYUQ54lRoKR7Q58BLggPOYEn8f/iXcY7HNy1D84D+wEADKpUwVdO3fH7tj3IzcnB3ZjbuHTuIpwGOKuMqwlSDf9XkQikFW2IWRk5OTlh+/btsLS0RGFhIb799luYmJhg4cKFCtsmJyfjxYsXsLUtKrOTlJQEoVAIPz8/tG5d1M+qeMDW5csfatGVfC8vLw8uLi5YvHgx+vTpIzfgKjY2FhMmTMCiRYvg5uaGy5cvY+3atUoHbBVTNmBL9r1Jkyahb9++GDZsGLd+yZIlMDU1xbRp0z7pOtnP3Cv32thQD4v/9xU6t6yP9Kw8bHlf57Vezao4tNAdI1cdQfLboscwX/dqjTG92xTVPY15ijWHldfrnOjaDmZ1qsnVeRXkKv8D/XUf6/f1R7Vx9voTrN1/kdvnxpnOiHmUjL1hRY8sLc1rYeH7GqBPktKxcu/fePi+/mj92kYIXj1Mbt8v37yDcIl8y7SxoR4Wj+6GzlbvzzekqM5rvZpVcchHiJErQj6cr1NrjOn7/thinmDNwQ/nO7F/e0zq315u3zuPx2DX8Zii8y1Q/uv3dR/rojqluu9rx8qe7wxnxMSVON/RMuf7+4fzLXVfSi71184lrvO+Etc5Lhl7T8jEfV9P98lL+eu85dt+aGdRD2LJhy+Rm4+SMds3QuUtM+tzzm1pIBfPWF8PPn06wb6RKdJz87D5QlHt0XpGhjg8qh+G7zuJ5MxsbBvcE+0bmMj194t58QazjhY9NdHWEmBujw5wadkI4vwCRDx6hk0XbkL8fqQ2SnzvcnHN38e9KBPXsx+G738f10NJ3Jcf4gLAgl52MNDRxg8RKmphassHN9bXg0+vTrA3r1cU+32d13pGhjj8tQuGHwwviu3eE+3r14G4oMQ5Hz9fdP1r18Ccbu1hUbsGCqVSXH3+CuvOX8fb4hHi+VLFuJq41gVK4vbtDPvGpkjPycPm93Ve61UzxOExrhj+exiS32Vj29BeaN+wRNznrzErRPHJ2KQubWBew0iuzquuufwj82q6ulhkZ4dO9eoiXSzGttt3EPHsGepVqYL9Ln3hGX4KyTnyVRJMDQ0R5ObK1Xktfp1XUCDX4rru2nWckqkZm39Tfj/GVfTg4+GAzs3rIz07D1sibiD81r+oV90Qf84YiBGbQpGcno36NariyLcecv/2xdtMCDeEoIahPtaM7AEL05oQCICk9Cz8eemBXJ1XqZG2fFwDPfi42sO+yfuf8V9FdV7rVTPE4YluGL7rRNG1HumE9uYlrnXia8wKKLoZmPRVG0zu1lZu3zv+vo2dF4r6zB+e4Si37o/t/tj/m/z3leeUsXBxd8XkIV7YIfJH3fpFY0pEfxxGgP8hiPPy8FXvHpixSL7O64al63D9n2swrmGM8TMnKdR5bWrYAJpUx7CGRuO9yU7TaLyy+CKTVz09Pejr60MikcDCwgLLly9H9eqKdfCeP38OHx8fPH/+HAYGBigsLISnpydGjhzJbfMpySsAhISEYO/evQgODlZIPmUT2Nq1a2Py5Mlo0qQJ92/r1q2LnTt3cq9LS169vLzg4uKCqKgouYFoV65cwYIFCxAREQFBKXexxUomr5qiKnllTarz8WvCgqrkVSP46k7F0/OeAmue+q/x89Eqos1T8HyePtc8/T6VTF41qWTyqiklk1dNKZm8apKmk9faVUov/6VuKTnKu/KUR19c8ko+DSWvmkHJq+ZQ8qpBlLxqDCWvmkPJa/nxRVYbIIQQQggpz6htUTVKXgFs3rwZERGKg4b8/PxQu3ZtJf+CEEIIIYTwgZJXANOnT//sclSEEEIIIaxQnVfVvuhSWYQQQgghpGKhlldCCCGEkHKmotVe1SRqeSWEEEIIIRUGJa+EEEIIIaTCoG4DhBBCCCHlDJXKUo1aXgkhhBBCSIVBLa+EEEIIIeUMlcpSjVpeCSGEEEJIhUEtr4QQQggh5UwhlcpSiVpeCSGEEEJIhSGQ0nA2QgghhBBSQVDLKyGEEEIIqTAoeSWEEEIIIRUGJa+EEEIIIaTCoOSVEEIIIYRUGJS8EkIIIYSQCoOSV0IIIYQQUmFQ8koIIYQQQioMSl4JIYQQQkiFQckrIYQQQgipMCh5JYQQQgghFQYlr4QQQgghpMKg5JVUSvn5+Xwfgtrdvn1b5Tp/f3/NHch7Z86cweDBgzUelw8ZGRm8xH369Cn8/f1x+vRppnGOHz+Op0+fcq+XLl0KOzs7CIVCPHr0iGnsAwcOMN1/eZKUlKT09/jWrVtITk7m4YgIqZgoeSVltmHDBm45MDBQbp2Pjw+zuDNnzsTbt28V3r99+zbTpGrfvn3cclRUlNy6TZs2MYs7ffp0xMfHK7y/Z88epgnAjRs34ObmBltbW/j4+OD58+cYPnw4fvzxR4wZM4ZZXADIzMxEYmKiwvvPnj1DZmYmk5j+/v7cdS4oKMDEiRPRuXNn2Nvb49q1a0xiFvPy8kJsbCyAokRnyJAhuHDhAn7++Wds376dWdzt27fDxMQEAHD69GmcO3cOfn5+GD58OFavXs0sLgAEBAQw3b8qL168ULnu7t27TGL+9NNPyM3NVXg/IyMD69atYxLzU6SkpGDNmjVMY0ilUpw7dw67d+/G7t27ERUVBalUyjSmMsnJydi2bRv69u2r8dhEfSh5JWV2/vx5bnn//v1y6+7cucMsbqtWrSAUChEeHg4AkEgk+PnnnzFr1izMnj2bWVyRSMQtb9y4UW7dmTNnmMVdunQpJk2ahOfPn3Pv7dmzBwcPHsTvv//OLO7KlSsxevRoBAYGomHDhhgxYgRsbGxw8uRJCIVCZnEBYN26dVwyJysmJobZl31gYCAaNWoEoKhF8uXLl7hw4QLWrVuH9evXM4lZ7NWrV7CysgIAHD16FA4ODti5cyf+/PNPHD9+nFlcgUCAKlWqACj6fR48eDBsbGzwv//9DykpKczi8snb25tbHjp0qNy6xYsXM4kZGxuLTp06KbzfrVs3PHjwgElMWSkpKVi2bBmmTp2Ko0ePQiwWY/369XB2dsa7d++Yxc3IyMCQIUOwdOlSXLt2DVevXsUPP/yAIUOGMI1bTCKR4OTJk5g4cSKcnZ2RlJSEVatWMY9L2NHh+wBIxSd791zyTprlnfU333wDJycnLFiwAMeOHcPjx4/Rpk0bhISEwNjYmFlcvs63V69eyMrKwvjx47Fv3z4cO3aMS1xNTU2ZxRWLxfj6668BAM2bN8eBAwewaNEiCAQCZjGLXb16FcuXL1d4f+DAgcxaIrW1taGrqwsAuHTpEtzd3VG7dm04Ojoq3Kyom76+Prd8/fp19OnTBwBgbGwMbW1tZnELCwu55Rs3bmD+/PlK17GQmJiIWbNmqVz/66+/Mokr+7taspsRq9/j0vbL+joDwKJFi2BoaAhHR0ecOHECAQEBkEgkOHjwIFq2bMks7tatW2FtbY0ffvgBOjpFaYdEIsGKFSuwZcsWuc+bOsXGxkIkEuHYsWNo3bo1hEIh4uPjsWzZMibxiOZQ8krKTDaJKZnQsE5wmjVrhk6dOiEwMBDVqlXDuHHjmCauAL/nO2DAAGRlZWHw4MEwNDTEH3/8gXr16jGNWTJpql27tkYSVwDQ0lL9cIjVMRQUFEAikUBXVxfXr1+Xa5WTSCRMYhbT1dXFo0ePULt2bVy5ckWuBTAvL49Z3E6dOmHOnDmoU6cO3r59Czs7OwBFLXXFyQYrRkZG6NmzJ9MYyvDxe6yvr49nz57B3Nxc7v1nz57J3biw8uzZM64Ff8iQIejatSuioqJgaGjINO6FCxfw559/yn2WdHV1MX/+fAwfPpxZXKFQCAcHBwQFBaF+/foAgF9++YVZPKI5lLySMpNtOZFdlkqlco+41e3OnTuYN28erK2tcfbsWVy6dAmTJ0/GiBEjMGXKFGYtVcnJydwja9llqVSKV69eMYkJFPXxFQgEkEqlEIvFMDMzk3v0xaqF6uHDh3BwcOBeZ2RkwMHBAVKpFAKBAJcuXWISt1hqaipq1aql8B6r1rG+ffvCy8sLNWvWhEAggK2tLQDg+fPnqFq1KpOYxebMmYNRo0YhJycHw4YNg5mZGYCiL/+mTZsyi7tw4UL8/vvvSEpKwu7du6GnpwcAePz4Mby8vJjFBYAaNWrAw8ODaQxl8vLyEB8fD6lUKrdcvI6FcePG4ZtvvsHChQthY2MDoGiw1urVqzFx4kQmMWUV/1yLl83NzZknrkDR30ZlcVjH9vHxQVBQEDw9PTF48GC4u7szjUc0RyDlo8c0qVSCg4NLXc/qi6lnz57w8fFB7969ufdSU1OxfPlyPHny5KPH9V9t3ry51PXTp09nEpev6/yxG5CGDRsyiQsAe/fuRUREBFauXInGjRsDAJ48eQIfHx84OTkxS6xOnjyJpKQk9O/fnxvIdPfuXbx9+xbdunVjEhMouhmqU6cOsrKy5J4gZGdnQyqVMkuez58/j+7duzPZ98cIhUKEhIRoPK6Tk1Op61n1Xw8ICMCWLVu46gL16tXDN998gxEjRjCJJ6tTp07o2rUr9/rixYtyr1ndAA8ePBhBQUFK13l4eDD7W13swYMHXPeBrKwsLFmyBC4uLjAyMmIal7BDySupsNLT01G9enWl68LCwuDq6sok7unTp7m+iIS9jRs3wt/fn3usmpeXBy8vL2aD8hYuXMjbYA5NfJErM2DAAHTo0AHz58/XSEucrIcPH8LS0hJAUZIuO3isMit+elC7dm2NxeTrBrh9+/Zo0aKFwvtSqRQJCQm4ceMGk7glSSQSREZGIigoCFeuXNFYXKJ+lLySMouPj0dCQgKcnZ0BAKtWreJGkI4ZMwatWrViEvfFixdo0KCB0nV3796FtbU1k7h8JRg7duxA1apV4enpKff+zp07AQCTJk1iEnf8+PHw8/MDUFTxYOnSpdw6TV2L7OxsxMXFAQBatGjBNMHi6+cL8NcKKRaLsWnTJoSHh2P58uXo0qWLRuOHhobC19eXK4tmZmaGGTNmYNCgQRo9DqDoWvz5558YPXq02vdd/BlWRVmCVxlER0eXur5z584aOpIPXr16hbp162o8LlEP6vNKyszX1xdubm7c63PnzmHMmDHIzs7Gjh07mI3Q9vb25pKMoUOHytWYXbx4MbMEhK/7vdDQULkyXcXGjBmDYcOGMUteU1NTueWbN2/KrWN9LXJycgAUDaCxsLBQeL+ytdBlZmbi3LlzKtc7Ojoyiaunp4dvv/0Wffv2xYQJE2BmZiY3WK5k/WZ1CggIwN69e+Hj44P27dtDIBDg+vXrWL9+PXJzc5kN6MnNzcW+ffvw8uVL9O3bF/b29jh06BC2bNmC5s2bM0leJ0+erHKdQCBAZGSk2mPK4usGuLTklGUpto+V0/v++++ZxSZsUfJKyuzp06dwcXHhXlepUoX741jyj6Q68VHqBii6Yy/tjyLLP4iyAy6KaWKUcrGS15V11QFbW1tukJpsrOLX9+/fV3vMkgPUSsZkOUAtJSUFu3fvVvr5FQgEzJJXoOhJxvr169G+fXuMGzeOaWkuWXv37sXu3bvlqmY4OjrC0tISEydOZJa8Llq0CElJSbC1tcWGDRvQsGFD3LlzBz/++COz68yyDvSn4OsGGABev36N5ORkWFlZQUdHB6mpqfjtt9+4R/gsaLoLDNEcSl5JmZVMHH/++WdumeW0mnyVrNLS0uLlj2JeXh7y8/MVShdJJBKuJZKF0q4za8omKGCtSZMm2LFjh8bjAkDjxo2ZTjihysGDB7F9+3bMmDFDoWA/a1KpVGm5t+LSRqzcu3cPoaGh0NHRQWZmJrp164bIyEiN9kHlAx83wAEBAVi2bBmqV6+OWrVq4dtvv8XcuXPRrVs3pcm0ulhZWdH4hEqKkldSZhKJBJmZmdzIzebNmwMoegQqFouZxeWj1A0AmJiYMKsoUJoePXpgzZo1WLBgAdcqVlhYiJ9++onpSHHZlsjiMllAUdLBaopWPunp6TGtoFAenTp1CgcPHlToQ56UlISgoCBMmzaNWez8/Hy8e/cO1apVk3s/IyND4cZYnQwMDLgbQSMjIzRp0oR54tqlSxeVN4ACgQAXL15kGp+vG2B/f38EBwfDwsIC165dw9ixY7F+/Xr069ePWUwA2LJlCyWvlRQlr6TM+vfvz43QLk5gMzMzsXjxYrm+sOqWm5sr95hLdpllCyFffV5nz56NKVOmwNnZGa1btwZQ1HpkamrK9Vlj4dSpU8z2/TFDhgwp9cs+ICBA7TGLZ9fiA+svc1X27NnDLUskEkREREAkEuHOnTvMv/w9PDwwbdo0rFixAk2aNAFQVF/2hx9+YFr/VbZGM6DYHYhF9x9lrYxXrlzBpk2bULNmTbXHK4mvG2AdHR2uz3rHjh1hZmamkc86jUevvKjaACmz/Px8zJ8/H5GRkdyXz7///ovevXtjzZo1zGfo0bS///6baa3Pj7l06RLu3r0LqVSKNm3aKO2fWVkoG6X89OlTbNu2DYWFhTh79izzY8jIyEB0dDTMzMxgZWXFNFZpg7UAdgO2AOD+/fsIDAzEiRMn0KpVK8TGxuKvv/5S+phZnaRSKbZu3YqdO3dysSQSCSZOnIhp06YxuxHlq15zsdjYWGzYsAGJiYn4v//7P/Tt25dpPADIysrClClT8OLFC6U3wKzqCLu5uWHTpk1cMjlz5ky516yqLHTt2hVCoVDlehqwVXFR8krU5smTJ7h37x4AoHXr1mjcuDFyc3NhYGCgkfiaSjLWrl1b6heqJv8gJicnIygoCMHBwcxaSBcsWFDq+tWrVzOJW9KbN2+wZcsWnD59GuPHj4enpyeTxGru3LmYOHEirKyskJaWBnd3dxgZGeHt27eYPXs2hg0bpvaYxUob4S4QCJj1h/Xw8EB2djY8PDwgFAphamoKJycnjQwwSk5ORr169SAWi/HkyRNIpVI0adKEedLMl2fPnuGXX37BjRs38M0332DIkCGlToPMwsWLF3Hv3j2N3QCXNiEEyyoL3bp1w8iRI1Wu56P7F1GPytUkRnjVuHFjbhakmJgY7Ny5E+Hh4cxGkvKVZLCeIvRjigttBwYGIjo6Gh4eHkyL6rdp04Zb3rlzJ9MRycpkZmZix44dEIlEGD58OMLCwpjOjHP37l3u5ufIkSNo3rw5/Pz8kJSUhClTpjBNXv/44w9m+y6NtrY2JBIJxGIx189UU4Pzpk6diuDgYOjp6cmVQ9OEd+/eYd++fdzAwJYtW2LUqFFys5up07Jly3D69GlMmDABq1ev5i1B79q1q9zMWqzxVWWBr/EJhD1KXonapKSkICQkBIGBgUhMTMSMGTNw5MgRZvHu3bvHS5LB1x/D2NhYborD1q1bQygUIj4+HsuWLWMaV7bcWUBAANPyZyXt2rUL/v7+6NevH0JDQ1GrVi3mMWWfFFy7do3r82lqaqrxagtPnz7FmTNnYG5uLjcNsroFBgYiLi4OgYGBGDFiBJo1a4bs7Gzk5OQwr6XL18O/R48eYdy4cejUqRNsbW0BFNUxdnNzw549e5gk0gcPHoShoSG2b9+O3377jXtfE2XYAH76kCujqadk9GC58qLklZRZZGQkRCIRrl27BhcXF/z444/47rvvSi3IrQ6y5V00mWR8rCW5U6dOTOIKhUI4ODggKCiIKyP0yy+/MImliqaTt/Xr18PExAQ3btxQ+nliVTw/OTkZ1atXR3R0NGbOnMm9z7KKBQB4eXlh/vz5sLKyQlJSEoYMGYL27dsjMTERjx49wtSpU5nFbtGiBebPn4+5c+fizJkzEIlE6N69O3r06IENGzYwi8vXxAxr166Fj4+PXI1qADh58iTWrFmD3bt3qz0m60kIPmbevHkK78n2IWeFr6dkc+fOZbJfwj9KXkmZeXt7w8HBAWFhYVzLmKaSHD6SjDVr1nDLCQkJaNasGfdaIBAwS6h8fHwQFBQET09PDB48GO7u7kzilCd81DydPHkyhEIhdHV10bFjR24wSUxMjMrpiNXl1atXXEvU0aNH4eDgAF9fX2RkZMDT05Np8lpMR0cHffv2Rd++ffHq1SumT08A/iZmePbsmULiChRVfGCVrMuWYMvOzoZAINDoLHGyM10p60POCl9PyY4dO8YNrt26datcybcpU6bItX6TioWSV1Jmv/32G0QiEVxdXeHk5FTq6E514ivJkC13IxQKmRbZluXp6QlPT088ePAAIpEII0aMQFZWFkQiEVxcXJj1A5UdoFayvBDAdoCaqmkl8/LycPLkSSYxXV1dYWdnhzdv3sg90qxfvz5WrFjBJGYx2acJ169f554mGBsbM5/x6tatW/Dz80NcXBwAwMLCAuPGjWPex5mviRkKCgr+07qyCg0Nha+vLxITEwEAZmZmmDFjBgYNGsQspixN9yEH+HtK9uDBA245IiJCLnlNTk5mFpewR8krKTNHR0c4OjoiLS0NR44cwapVq5CcnIxff/0VAwcOlGuZVKdPTTJSU1OZ9ZXU9GN0oGhQycKFC/Hdd99xj3d//PFH3Lhxg0k82QFq//vf/5jE+FS3bt1CYGAgTp48idatWzNrfTYxMUF+fj6ioqIAFCVyrFtdgaIas48ePULt2rVx5coVLF68mFvH8mlCcbeMkSNHYsCAAZBKpbh16xYmTpyInTt3ol27dsxi88Xa2hp79+7F2LFj5d739/eHtbU1k5gBAQHYu3cvfHx80L59ewgEAly/fh3r169Hbm4us6lwi/HRh7wYH0/JZFvzNT21NWGLkleiNjVq1MDYsWMxduxY3LlzByKRCF9//TUuX77MLKaJiQlMTEzk3is51eSECRMQHBzM7Bj4oqurCxcXF7i4uODYsWPM4vA9xWJqaipCQkIQFBQEiUSCtLQ0hIaGKp1SVB3EYjGWL1+O48ePo1GjRgCKHjG7ublhyZIlTEeIz5kzB6NGjUJOTg6GDRsGMzMzAMCFCxfQtGlTZnF37dqFVatWwdnZmXvP2dkZ7dq1w2+//YatW7cyi/2pxeoDAwPVOnXtggULMH78eISFhXHJeUxMDDIyMuDn56e2OLL27t2L3bt3y312HR0dYWlpiYkTJzJPXvnqQ87XUzI+p7YmbFGdV8KUWCzmvuynTZvG9EtQFaFQiJCQELXtr/ixKqBYbBtgV3C7ND179sRff/3FZN8eHh68Jf/Tp0/H1atX4ezsDA8PD3To0IF5/dG1a9ciKSkJS5cuRfXq1QEAaWlpWLZsGUxNTZUOelGngoICZGVlyZVrys7OhlQqZVamzcXFBeHh4Z+9TpNYfA7z8/Nx7Ngx7vFyy5Yt0b9/f2azrPXv3x/Hjx//7HXqomzSD1mquumow+vXr7mnZLLdkAoKCpglsO3bt+f+HsfFxXHLUqkUCQkJzJ5WEfao5ZUwJdtK9eLFC16OQd133CVbLEpOS8vHiGKW96B83t/evHkTZmZmaN++PVq1agWAfQtKZGQkjh07JvfZrVGjBtasWYMBAwYwTV6L55fX1dWVm2teIBAwPe/SJhLR1CQjH6Puz2FmZibS0tIU+ug/e/YMNWvWZNIPND8/H+/evUO1atXk3s/IyODq67LUsWNHiMVihUFiOTk5TJ8ovHjxAg0aNFD6lOzu3bvMktcdO3Yw2S/hHyWvRGMqy2Mbvgpul4bltS0553tJLAdsnTt3DlFRURCJRFi3bh169erFvFyVjo6O0i9yfX19Zi1yxWxtbSEQCOQSteLXAoEA9+/fZxJXIpEgPj5eaYIokUiYxPxc6v6Mr1u3Dj169OC6ZhSLiYnBlStXsHz5crXGA4paj6dNm4YVK1ZwU2k/fvwYP/zwAzw8PNQer6T169ejWbNmCqP7AwICkJSUxOx32dvbm2s1Hzp0qFz3hMWLFzN7slO/fn2Ym5srXRcWFsYkJtEMSl5JpafuFhtfX1906dIF7du31+gMOfv371e5TraVTt20tLRgaGjIbP8fi92zZ0/07NkTqampOHr0KO7evQsnJycMGDAAc+bMUXtMAwMDuUeMxR4+fCg3apqF4pmeNC03N1dlVYHKctNZ0tWrV5UmqAMHDsT27duZxJwyZQoKCgogFAq5vx0SiQQTJ07ElClTmMSUFRUVpbT2qaenJ9zd3Zklr7J/g0u2MLN8sjNhwgTs379focU3LCwMK1euhKurK7PYhC1KXkmFVVBQgFOnTqF69ero2rUrfv/9d1y8eBFNmjSBt7c392hOdhCKOqSnp2PFihV49uwZ2rZtC3t7e9jb26Ndu3ZMk9k7d+6oXFfa3OFlxfcUi2fPnsXjx4/RunVreHl5wcvLC7du3UJQUBCTeNOmTcPEiRMxbdo02NjYACjqvrBt2za50f8sLFq0CEOHDuVmfNKUT32awLJyx8eoO8nR0tJSuY5Vwi4QCODt7Y1JkybhyZMnkEqlaNKkicZugrW0tJSWXNPW1mZ6k1LawCmWcSdMmIBx48Zh//79XP/14sR1165dzOIS9ih5JRpjamqq1v0tW7YMDx8+hFgshpmZGfLy8tCzZ09cuXIFS5YswcaNGwEUPbJSJx8fHwBFX+RXrlxBdHQ0li9fjsTERNjY2GDv3r1qjVds9erVTPb7MXz2ef35559x4sQJtG3bFv7+/pg8eTJGjRoFGxsbLrFUtz59+qB69erYvHkzNmzYAKlUilatWmHdunVMB7QARX0A586dCz09PQwePBhCoVCh1YhPrCp3FN+gtGrVCg4ODkq3kZ0cRF2UJeOpqanMP/N6enpy08+mpKRg586dmD9/PtO4YrFY6ZS/WVlZEIvFzOLm5eVx3VJkl4vXsTJixAi8e/cOEyZMwN69exEVFYWVK1di9+7daNmyJbO4hD2qNkDKTHb0vTKsRt+7ubnh+PHjyMnJQffu3XHp0iXo6emhsLAQgwYNYlo+qlhWVhauXr2Ky5cvcwO1WI7MTklJgb6+PoyMjHD37l1cvnwZTZs2Ra9evZjF/Pvvv2FgYIDNmzfLjcqePn067OzsmMUFimr5BgQEwMjICMnJyfD29mZWzqc8uXTpEoKDgxEZGYlOnTph6NCh6NWrF/OJCj5G3ZU7APkblOvXr3M3KKzt3bsXERERWLlyJRo3bgwAePLkCXx8fODk5AQvLy+1x0xJScHmzZvx8uVLuLm5oV+/fvD19cX+/fvh5uaGlStXqj2mrF9//RXx8fFYtWoVNyDt3bt3WLJkCRo1aoTZs2cziVvakyFNDHL95ZdfEBkZibS0NOzevRuWlpZM4xH2qOWVlJmyeoECgQBZWVlIT09nNshET08PAoEAhoaGMDc35x69aWlpMR1Yc+7cOURHR+PKlSsQi8Xo0KED7OzsMG7cOKatZH/88Qc2bNgAPT09eHt7w9/fH+3atcOhQ4dw+/ZtucLf6pSbm4tFixZh6tSp3Ej7Gzdu4Ntvv4WPjw/TGrAGBgbcl2y9evWYznxULCAgAFKpVKHm5u+//46qVatiyJAhzI/BwcEBDg4OyMzMRFhYGDZt2oQffvgBFy5cYB67NCwe8Z4+fRpHjhyRu0HRRPI6duxYpKamYtCgQVxf5ry8PIwdO5ZJ4goUdQsxNDSEo6MjTpw4gYCAAEgkEhw6dEgjLYHe3t6YP38+unfvzg0Y+/fff+Hk5IQZM2Ywi8tXt5TigaZSqRSvX79Gu3bt5G6+WA42JWxR8krKrOQfpuzsbOzZswcHDhxg9iUAFD0CK378JLsMsH0UNWXKFLRv3x7e3t7o3r17qX3n1OnQoUOIjIxEVlYW+vfvj8jISJiYmCAzMxPDhw9nlrxu3boVu3btknvM2apVK9jZ2WHevHlMk9fU1FS5gWolX7OYj/3gwYNKpysVCoXw8vLSSPIKFH3hXrt2DefPn0diYiJ69OihkbiaxscNSrHZs2djypQp3NOjFi1aMB2c+OzZM66W65AhQ9C1a1dERUVpbECkjo4O1q9fjydPnuDevXuQSqWwtrbmWp6L8dW3Wd3dUmSvqyZuiIjmUPJK1CY/Px8HDx7Ezp074ejoiKCgIGazIAGKI6RL1ltlJTQ0FNHR0QgODsbKlSvRqFEjdO7cGZ07d0bbtm2ZPdrV1dVFrVq1UKtWLTRu3Jhr5TUyMmI64CM3N1cucS1maWnJvGxV165d5QaqlXzNgkQiUVrj09jYWCO1OBMSEhAUFIQjR47AxMQEHh4eWLFiBTfghE8sepnxcYMiy9DQEDY2Nnj69CkOHz4Mc3Nz9O7dm0ks2d9TPT09mJub81LJo3HjxgoJqyy+ZiVU9+eLz4GmhC1KXolahISEYNOmTWjbti327t3LdCrLYnzVW7WwsICFhQX3pRofH49//vkH8+fPx6tXr3D9+nUmcWUTch0dHZXr1E0ikUAikSh0xRCLxUwHeQD8DFIrrexYVlYW09gjR47Ev//+iwEDBmDHjh3cxAya8vbtW9SsWVPlenVX7gD4uUEBAC8vL8yfPx9WVlZISkrCkCFD0L59eyQmJuLRo0eYOnWq2mMmJiZi1qxZKl//+uuvao/5X/A1FEZTZdnOnDmDzZs3M6tYQtij5JWU2cCBA5GdnY0ZM2agTZs2KCgokBvExcd0qawVFhbizp07iI6ORnR0NK5du4bq1avDxcWFWcyHDx9yI7EzMjK4ZalUiszMTGZxe/fujXnz5mHZsmVc+bGMjAwsXbqUWQtVsdTUVFSpUoUbHX316lWEh4fD3Nwcnp6eTFq5O3bsiJ07dyrUPfXz80OHDh3UHk/WmDFj0KdPn4+2pAcGBmLo0KFqi3vp0iXMnj0baWlpqF+/PrZs2YLWrVsrbKfuyh0Af1U0Xr16BSsrKwDA0aNH4eDgAF9fX2RkZMDT05NJ8rpw4UK51z179lR7DHWoLLV9b9y4gUWLFuHly5fo378/vvnmG8yePRtv3rxh1s2KaAYlr6TMilujfH19lc4OxMd0qSxNmjQJ169fR/Xq1WFnZwcXFxf4+PionMlFXU6dOsV0/6rMmTMHS5cuhaOjo9yo7H79+uHbb79lGtvb2xtr165Fo0aNkJCQgEmTJsHd3R0RERFITExUSAbU4bvvvsOoUaMQGRmJdu3aAQBu3bqF1NRU7Nu3T+3xZLm5uX3Sdvv371dr8vrTTz9h5cqV+Oqrr3DixAls2LCB9zqYsbGx2LJlCzZt2sRk/7ITTly/fp3ru21sbMys648mZtGqyNTd4rty5UqMHj0anTt3RkREBEaMGIF+/frh+++/1+gEM0T9KHklZVYep0tlqW/fvvDx8UGjRo1K3S42NpZr2VGHhg0bftJ206ZNw9atW9UWV09PD6tWrcL06dPx8OFDSKVSWFpafvLxlEVGRgZ3nY8fP45+/fph6dKlyMvLYzZwqk6dOggJCUFoaCg3qGXw4MEYMGCAQn1Mvqj7S76goIBrRR88eLDSAWusPH78GKtWrUJSUhL69++P//3vf/jhhx9w/vx5jB8/nllcXV1dPHr0CLVr18aVK1fkJqBg1Zf7Y2XGhEIhk7ifi8Xni48JZcRiMb7++msAQPPmzXHgwAEsWrSo0rQsf8koeSVMiMVihIeHQyQSwd/fn+/DUauS84KrsmDBAl4GPbx48YLJfhs0aIAGDRow2bcqsq0jMTEx3Je7vr6+Qr9fdTIwMFD6c16wYAFvj7llsfjyzc3N5ZIWqVQq95pl0r5kyRK0a9cOo0ePRmRkJEaOHInmzZsjPDwctWvXZhZ3zpw5GDVqFHJycjBs2DCYmZkBAC5cuMCsz77sjf4///yDLl26cK8FAgHz5JWvJJKvCWVKtqDXrl2bEtdKgpJXola3b99GYGAgTp48ibZt25ablgQ+VPZBD5pgZGSEc+fOoV69erh+/To3y1JBQQHzSgfKXLp0SeMxNeHBgwewtbWV+8wWvxYIBMxqNQNAWloa5s6dCwDo1q0bvvrqK2zcuJHpzQkAdOnSBRcvXkRWVhaMjY25921tbdG+fXvutTqfoPj6+nLLQqFQ7rUm8JVEXr16VemEMiNGjMCgQYPUGkvWgwcP5GZsKx4rUPy5rqy/z18CSl5Jmb19+xZHjhyBSCSCRCKBUChElSpVeO8zx7fKlETyZdGiRZg7dy5XvL64PNjZs2fRpk0bjR9PeZmQUN3HERsbq9b9fQ7ZJFVLSwumpqbME9di2tracokrAIXSVayeoPDx94GvJJKvCWUiIiKY7Zvwi5JXUmbdu3eHnZ0dli1bxo3GDggI4PmoSGVgZWWldJrfPn36MJ0cQRU+b0jGjx8PPz8/AOBaoCuDx48fyw0+K/ma7+mAy8sNizrwlUTyNaHMli1bsGrVKmb7J/yh5JWU2ZgxYxAaGooNGzZgyJAhTMtFVSSa/NLLzc2FgYEBAMDU1FRjcVmTLbmmDIsybEOGDFGapEqlUqSkpKg93qdKSEjgltU5EBBQfc5AUcLO8mZ0x44dzPatDuq8YTl37hy3nJmZKfcaABwdHdUWSxm+kki+JpRh2d2F8EsgrUy3lYQ3BQUFOHfuHEQiEaKjo1FQUICtW7fKDUj40gQEBHzy4K7/KiYmBoGBgQgPD8eVK1eYxuKDk5OTynWsyrBFR0eXur5z585qj/kpevbsib/++ovJvpWd89OnT7Ft2zYUFhbi7NmzTOJWBB4eHmrrNjB69GiV6wQCAfMqD3z8PvFJnT87Ur5Q8krULjU1FcHBwQgODkZGRgaioqL4PiS12rx5c6nrWU9JmJKSgpCQEAQGBiIxMREzZszAgAEDNF4J4Evk6+vLW3FzlsmrrDdv3mDLli04ffo0xo8fD09PT6Y1MWNiYvDw4UMMHz4cADBz5kykpaUBKPpd4utmoZhQKPxoiStSPllbWyv0aQZAA7YqAeo2QNSuVq1amDBhAiZMmIBbt27xfThqJztF6NGjR5kOdJAVGRkJkUiEa9euwcXFBT/++CO+++47TJ48WSPxCRAUFMQ0ee3SpYvKLgvv3r1jFhcoeoy9Y8cOiEQiDB8+HGFhYTAyMmIaEyjqlzhhwgTudVxcHL7//ntkZ2fDz8+PWfJanMAok5GRwSU9xdNAq8PVq1fx9u1bhVJUJ06cgKmpKfMZ3L40TZo0KffdUsh/Q8krKbMNGzZgzpw5ABSnrQwICICNjQ1fh8bEvHnzuOVLly7JvWbJ29sbDg4OCAsLQ61atQBU/ooGTk5OKpM5Ph5zsn5QJRKJmO5flV27dsHf3x/9+vVDaGgo9/nShJSUFLnuRXp6ety0qfv372cWd8SIEVizZg2aNWsm9/7p06excuVKrquEOrv+bNq0CT/++KPC+61atcKyZcsqXU1svunp6WlkMhWieZS8kjI7f/48l7yWnLbyzp07fB2WRmgyefztt98gEong6uoKJyenL6KG7m+//QagKGmcNWuWxutilsT6563qizYpKQlBQUGYNm0ak7jr16+HiYkJbty4obQln+WI/9zcXLnXf/zxB7f89u1bZnFHjRoFLy8veHl5Ydy4cUhPT8fy5cuRkJCALVu2MImZmpqqdBrppk2bIjU1lUnMLxnLCgqEX5S8kjKTbY0q2TJFXarVx9HREY6OjkhLS8ORI0ewatUqJCcn49dff8XAgQMVWpAqAwsLC27ZwMBA7jUrM2fOVNnam56ezjx+MYlEgoiICIhEIty5c4dpaTBNTgdbkra2Nl6/fs3V8C2e5enVq1fQ0tJiFnfQoEFwcHDAkiVLcOLECbx+/RpDhw7FunXrmNWZLZmof+o68t8cPnxY7nVGRgaio6NhZmam9oodRLMoeSVlJvtFX/JLvzI+1pZ9lJmamqrwaFOdfeSUqVGjBsaOHYuxY8fizp07EIlEGDBgAO7du8c07peiV69eKtdp4gvv/v37CAwMxIkTJ9CqVSvExsbi/PnzTAdN8Tkoavjw4Zg9ezZWrlyJxo0bAyiqdLBo0SJuEBcrurq60NfXR1paGqRSKdq3b890ggRzc3NcvHgRXbt2lXv/0qVLNOCSgblz52LixImwsrJCWloa3N3dYWRkhLdv32L27NnMq8EQdih5JWWWmJiIWbNmQSqVcstAUUvV8+fPeT469ZPtCtG1a1feuka8evUKFy9exMWLF6lflxp5eHjIvX716hVCQkIgEokglUqZPbovjp2dnc2V+DE1NYWTkxPTxBUA1q1bV+r677//nlns0aNH482bNxg4cCBXqzgvLw9eXl4YM2YMs7gRERFYsWIFhgwZgp9++gn379/HggULEBYWhoULFzIZrPZ///d/mDp1KoYNG8aNBbh16xYCAgKwfft2tcf70t29e5e74Txy5AiaN28OPz8/JCUlYcqUKZS8VmCUvJIyW7hwIbdcstWqtFasikp2ZLSm5efn48yZMwgMDMTNmzeRn58PPz8/tGvXjrdjYkm2eH7JmZcAdn0xi6+zSCRCTEwM8vPzsXv3brk571nQ1taGRCKBWCxGfn4+AM08vSg5JaqmzZ49G1OmTOEmpWjRooXCMcXGxqq15dvX1xfbtm2DtbU1AMDGxgbBwcHYuHEjBg0ahDNnzqgtVrF69erB398fO3fuxOnTpyGVSmFtbQ0/Pz96jM1A8c0QAFy7do3remNqalopnwp+SajOKymzj40IZv0YXdOKR8DL/uoIBAJkZWUhIyOD2eP71atX4/jx47C0tISHhwecnZ3h5ubG5Eu2vOBjwgC+r3NcXBwCAwMRGhqKZs2aIS4uDmfOnEGVKlU0Er80JauJaJK6C85LJBKVA3pu3rzJ5IaQiuZrloeHB7Zv347q1aujZ8+e2LdvHzcrn6urK8LCwng+QvJfUcsrKbMVK1agTZs2GhlMUx6UTGKys7OxZ88eHDhwAGPHjmUW9+DBg7C1tcXkyZO50kKVvfXgU5PTpUuXYunSpWqJyed1Tk5ORosWLTB//nzMnTsXZ8+ehUgkQvfu3dGjRw9s2LBBI8ehSslqIpqk7naW/Px8rnW7JEtLS7XGKkZtRZo1efJkCIVC6OrqomPHjlziGhMTQ32MKzhKXkmZrVy5EiEhIYiLi4NQKMSAAQNQvXp1vg+Lufz8fBw8eBA7d+6Eo6MjgoKCUK9ePWbx/v77b4SGhmLdunVIT0+HUChEQUEBs3gVyc2bN9W2Lz6v89SpU7mWOR0dHTg7O8PZ2RmvXr3CkSNHNHIMpeEz+VL3DYStra3SJyjFNYTv37+v1nhA0UQQ586dU7ne0dFR7TG/ZK6urrCzs8ObN2/kumXUr18fK1as4F6npqZqtLYxKTvqNkDUJjExEcHBwQgLC4OlpSW++eYbtGzZku/DYiIkJASbNm1C27ZtMWvWLDRt2lSj8WNjYxEYGIhjx46hefPmGDhwIEaOHKnRYyhPWD2O1fR1Lu9TkfL52LsyPHK3tbVF27Ztld4ECAQCXkuWfckqw2frS0Mtr0RtzMzM4OXlhTp16sDX1xdfffVVpUxeBw4ciOzsbMyYMQNt2rRBQUEBN9AEAPdoiiUrKyssXrwY8+bNQ0REBIKDg7/o5JUVTV9naplTTZPtLOPHj4efn5/a99u4cWNKUMshasOreCh5JWUmlUpx/vx5BAUF4eHDh3B1dcXhw4eVziRTGWRlZQEoGq2s7LGjJqcs1dXVhZubG9zc3DQW80ukqeuckpKC3bt3q2yZ4zt5ZfElX/yYXpmMjAwYGxsD0OzAz/j4eI3FIvyr7GMHKiNKXkmZ9ejRAyYmJhg8eDC8vb0hEAiQl5cnV/amMqnMo/srKpYzMWlSeWyZk22FXLNmjdr3P2LECKxZs0ZhhrjTp09j5cqVOHv2LABotCYnq2SmX79+TPZLyJeGkldSZrq6ukhLS4Ofnx/27NnDa0skqVzmzZsHe3t7dO7cGWZmZiq3E4lEGjyqL0tCQgK3zKIW6ahRo+Dl5QUvLy+MGzcO6enpWL58ORISErBlyxa1x+PT1KlT8e7dO+zbtw+xsbEAgJYtW2LUqFFcCzPRPOo2UPFQ8krKjFoiCSstW7bEqVOnsHr1alSrVg329vZcMlsZS918iS1zgwYNgoODA5YsWYITJ07g9evXGDp0KNatW8d0qtYuXboobWGVSqV49+4dk5iPHj3CuHHj0KlTJ9ja2gIoqpTh5uaGPXv2fDHlBjWloKAAp06dQvXq1dG1a1f8/vvvuHjxIpo0aQJvb29Uq1YNAODs7MzzkZLPRdUGCCHlXmFhIe7du4fo6GhER0fj+vXrMDY2xunTp/k+NLUrby1zPXv2xF9//cU0RlpaGpYuXYo7d+5ALBZj5cqV6N69O9OYH5u6msWUyxMnTsSwYcPg4uIi9/7JkycREBCA3bt3qz3ml2zJkiV4+PAhxGIxzMzMkJeXh549e+LKlSuQSqXYuHEj34dI/iNqeSWElHtaWlqoUqUK9PX1oa+vj2rVqqFRo0Z8H5ba8dUyx0crZLGIiAisWLECQ4YMwU8//YT79+9jwYIFCAsLw8KFC2FkZMQkLovk9GOePXumkLgCRS3ufE9AURldvXoVx48fR05ODrp3745Lly5BT08PI0aMwKBBg/g+PFIGlLwSQsqtAwcOIDo6GrGxsWjSpAns7Owwfvx4tGnTBtra2nwfntqtXbsWPj4+Slvm1qxZw6xljs8+w76+vti2bRusra0BADY2NggODsbGjRsxaNAgZt2SZAeilZyhjVXdz9Imu6AJR9RPT08PAoEAhoaGMDc3h56eHoCim2FVUwOTioGSV0JIubVy5Uq0bdsW06dPR5cuXVCnTh2+D4kpvlrmVLVCJiUlISgoCNOmTWMWOygoSCGR0NPTw7x585j2AU5NTeWWS87Qxqo3nbW1Nfbu3aswjbS/vz+XvBP1EYvFiI+Ph1QqlVsGgLy8PJ6PjpQFJa+EkHLr8uXLuHbtGqKjo/H7778jJycHtra2sLe3R6dOnVC3bl2+D1GtykPLnEQiQUREBEQiEe7cuYM+ffowjZefn4/8/Hyl6ywtLZnGLlYyWWVVKmvBggUYP348wsLC0K5dOwBATEwMMjIymEyK8KXLzc3FpEmTuNeyy1TbtWKj5JUQUm4ZGRnB0dGRK86flZWFU6dOwdfXF0+fPmUy/zyf+GyZu3//PgIDA3HixAm0atUKsbGxOH/+PPeolRVbW1ulk30UT17A6mcsm7xoKpHx9fXF0aNHcezYMTx48AAA8PXXX6N///70GJsBqoRTeVG1AUJIuZaamorLly9zlQaePXuGdu3awd7eHtOnT+f78NQqKSkJ48ePh7GxsdKWufr16zOJ6+HhgezsbHh4eEAoFMLU1BROTk6V+svf2tqaq+AgO5OXVCpFZmYm7ty5o/aYrPrSEvKloZZXQki55ebmhsTERNjY2KBTp07w8fFBhw4dmLcG8oWvljltbW1IJBKIxWLuEX55eKwqO6hK3U6dOsVkv4QQ9ih5JYSUW56enujcubNcIvX06VNuubJNPXz//n3o6OhAKBRqNG5gYCDi4uIQGBiIESNGoFmzZsjOzkZOTg6qVKmi0WORFR8fz2zfn1oqa9q0adi6dataYj58+BAODg4K7xd3kbh06ZJa4hBS2VHySggpt3bv3s21vBX3cBIIBMjKykJ6enql6/PKl+TkZLRo0QLz58/H3LlzcfbsWYhEInTv3h09evTgrQZpeWj9ffHihdr21aRJE+zYsUNt+yPkS0XJKyGk3CrZ5zI7Oxt79uzBgQMH4OXlxc9BMcRXy9zUqVO5vpg6OjpwdnaGs7MzXr16hSNHjjCJWVGoM4HW09PjZXIEQiobSl4JIeVefn4+Dh48iJ07d8LR0RFBQUGoV68e34eldny1zKkat1u3bl258kIs8Dm7l6ZRRQFC1IOSV0JIuRYSEoJNmzahbdu22Lt3L5o2bcr3ITHDV8tcZmYmzp07p3J9cakyFvic3UvTDh8+zPchEFIpUPJKCCm3Bg4ciOzsbMyYMQNt2rRBQUEB4uLiuPWVbcAWXy1zKSkp2L17t9IWWIFAwDR5Le+P0U1NTfk+BEJICVTnlRBSbjk5OXHLygrZR0ZG8nFYlY5QKERISAgvsWXLYS1duhRLly7l1rGsiyp7E6RMZbsxIqQyoZZXQki5VZmL5JMiqamp3PLNmzfl1rFsW5k8ebLCe1TJgpCKgZJXQgj5wvXr14/vQwCgmKyyLJX1pVWyIKQyoeSVEEK+cFOnTsW7d++wb98+xMbGAgBatmyJUaNGcdOmsiKboPJR1/VLqWRBSGVCfV4JIeQL9+jRI4wbNw6dOnVCu3btABQ9wr9y5Qr27NkDCwsLZrGtra25BDkjI4NblkqlyMzMxJ07d5jFlq1kMWvWrEpdyYKQyoSSV0II+cJNnDgRw4YNg4uLi9z7J0+eREBAAHbv3s0s9vPnz0tdz6oaQclKFiXRgC1Cyi9KXgkh5Avn4uKC8PBwpev69u2LU6dOafiIFE2bNg1bt25V2/6okgUhFRf1eSWEkC9cQUHBf1qnSS9evFDr/qiSBSEVlxbfB0AIIYRf1tbW2Lt3r8L7/v7+sLa25uGIFGliMJdYLEZoaChVGyCknKOWV0II+cItWLAA48ePR1hYGDdgKyYmBhkZGdwEApXZ7du3ERgYiJMnT6Jt27YQCoV8HxIhpBSUvBJCyBfO19cXR48exbFjx/DgwQMAwNdff43+/fvzNmUta2/fvsWRI0cgEokgkUggFApRpUoV7Nq1i+9DI4R8BCWvhBDyhbt//z50dHTKdYujqampWvfXvXt32NnZYdmyZejQoQMAICAgQK0xCCFsUPJKCCGEN3FxcaWuLy5ZtW3bNrXGHTNmDEJDQ7FhwwYMGTJEoUwYIaT8olJZhBDyhZOdKECWVCqFQCDApUuXmMWWLVlVTCAQICsrC+np6bh//z6z2AUFBTh37hxEIhGio6NRUFCArVu3okuXLsxiEkLKjpJXQgj5wvXv3x87duxQuZ7VRAHKZGdnY8+ePThw4AAGDRqEefPmaSRuamoqgoODERwcjIyMDERFRWkkLiHk81HySgghXzgPDw8EBwfzegz5+fk4ePAgdu7cCUdHR0yfPh316tXj5Vhu3boFGxsbXmITQj6O6rwSQsgXju+KAiEhIXBxccG1a9ewd+9erFixgnniumHDBm45MDBQbh0N3CKkfKOWV0IIIbwZOHAgsrOzMWPGDLRp00ZhffGALXWTbW0u2fJcHlqiCSGqUbUBQgghvMnKygJQVGtWIBBAtj1FIBAgMjKSSVzZOCXbcKhNh5DyjZJXQgghvDlz5gwvcWWnmy059awmpqIlhPx3lLwSQggpV8RiMcLDwyESieDv788kRmJiImbNmgWpVMotA0Wtrs+fP2cSkxCiHtTnlRBCSLlw+/ZtBAYG4uTJk2jbti0GDBjAbNavj/Vp9fDwYBKXEFJ21PJKCCGEN2/fvsWRI0cgEokgkUggFApRpUoV7Nq1i2nc7OxspvsnhLBDySshhBDedO/eHXZ2dli2bBk6dOgAQDOlqlasWIE2bdrAwsKCeSxCiHpR8koIIYQ3Y8aMQWhoKDZs2IAhQ4bAxcVFI3FXrlyJkJAQxMXFQSgUYsCAAahevbpGYhNCyob6vBJCCOFVQUEBzp07B5FIhOjoaBQUFGDr1q3o0qUL89iJiYkIDg5GWFgYLC0t8c0336Bly5bM4xJC/jtKXgkhhJQbqampCA4ORnBwMDIyMhAVFcU85rt373Ds2DH4+vpizpw5GDZsGPOYhJD/jpJXQggh5dKtW7dgY2PDZN9SqRTnz59HUFAQHj58CFdXVwiFQpibmzOJRwhRHy2+D4AQQsiXa8OGDdxyYGCg3DqWA7d69OiBX375BXZ2dvj111/h6uqKvLw8xMXFIS4ujllcQkjZUcsrIYQQ3nh4eHA1V2WXlb1WJycnJ25Zk9PSEkLKjqoNEEII4Y1s0liyLYVl2wpf09ISQsqOug0QQgjhjUAgULqs7DUhhADU8koIIYRHiYmJmDVrFqRSKbcMFLW6Pn/+nOejI4SUR9TnlRBCCG8+1qfVw8NDQ0dCCKkoqOWVEEIIb7Kzs/k+BEJIBUPJKyGEEN6sWLECbdq0gYWFBd+HQgipIKjbACGEEN6IRCKEhIQgNzcXQqEQAwYMQPXq1fk+LEJIOUbJKyGEEN4lJiYiODgYYWFhsLS0xDfffIOWLVvyfViEkHKISmURQgjhnZmZGby8vDB69GhcvnwZt27d4vuQCCHlFLW8EkII4Y1UKsX58+cRFBSEhw8fwtXVFUKhEObm5nwfGiGknKLklRBCCG+6d+8OExMTDB48GPb29goTE7Ro0YKnIyOElFeUvBJCCOGNk5MTtywQCOSmhBUIBIiMjOTjsAgh5Rglr4QQQgghpMKgAVuEEEIIIaTCoOSVEEIIIYRUGJS8EkIIIYSQCoOSV0IIIYQQUmFQ8koIIYQQQiqM/wdcGdX9ilYbHgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "final_corr = cc_summary_all[[\"MEAN_AGE\", \"B_SEX\", \"NH_WHITE\", \"AA_BLACK\", \"OTHER\", \"NW_HISPANIC\", \n",
    "                             \"ALLCOSTS\", \"TOTAL_VISITS\", \"TOTAL_INP_DAYS\", \"MEAN_ELIX_GRP_TOT\", \"MEAN_ELIX_SCORE\",\n",
    "                             \"TOTCHRONIC\", \"B_CNCR\", \"B_ESRD\", \"B_STRKETIA\"]]\n",
    "f_corr = final_corr.corr(method=\"pearson\")\n",
    "lr_f_corr = f_corr.where(np.tril(np.ones(f_corr.shape)).astype(np.bool))\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = (10.0, 10.0)\n",
    "f_corr_hmap=sb.heatmap(lr_f_corr, annot=True, cmap=\"mako\", fmt=\".2f\")\n",
    "fig = f_corr_hmap.get_figure()\n",
    "fig.tight_layout()\n",
    "fig.savefig(\"f_corr_heatmap.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## All Things Considered,\n",
    "\n",
    "These variables show a decent amount of independence.  The model coefficients should be *very* interesting!!\n",
    "\n",
    "NOTE: Because MEAN_ELIX_GRP_TOT and MEAN_ELIX_SCORE are 85% correlated, I will keep MEAN_ELIX_SCORE only."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>index</th>\n",
       "      <th>DESYNPUF_ID</th>\n",
       "      <th>YEAR_CNT</th>\n",
       "      <th>MEAN_AGE</th>\n",
       "      <th>B_SEX</th>\n",
       "      <th>NH_WHITE</th>\n",
       "      <th>AA_BLACK</th>\n",
       "      <th>OTHER</th>\n",
       "      <th>NW_HISPANIC</th>\n",
       "      <th>B_CNCR</th>\n",
       "      <th>...</th>\n",
       "      <th>ALLCOSTS</th>\n",
       "      <th>NEW_CHRONIC</th>\n",
       "      <th>TOTCHRONIC</th>\n",
       "      <th>VISITS_FLAG</th>\n",
       "      <th>PERCENT_COSTS</th>\n",
       "      <th>MEAN_ELIX_GRP_TOT</th>\n",
       "      <th>MEAN_ELIX_SCORE</th>\n",
       "      <th>IP_VISIT_PROP</th>\n",
       "      <th>FIPS_STATE_CODE</th>\n",
       "      <th>FIPS_COUNTY_CODE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0000438E79D01BEA</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>12.0</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>00010E7BEA69142C</td>\n",
       "      <td>2</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1620.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>00015BF6509E1DF7</td>\n",
       "      <td>3</td>\n",
       "      <td>75.1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>25860.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000356</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.00</td>\n",
       "      <td>37.0</td>\n",
       "      <td>119.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>00018DD17736A661</td>\n",
       "      <td>1</td>\n",
       "      <td>73.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6930.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000095</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>1.571429</td>\n",
       "      <td>0.00</td>\n",
       "      <td>26.0</td>\n",
       "      <td>101.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>000240D599ED789C</td>\n",
       "      <td>3</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>509056.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.007014</td>\n",
       "      <td>0.766667</td>\n",
       "      <td>2.016667</td>\n",
       "      <td>0.15</td>\n",
       "      <td>55.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114177</th>\n",
       "      <td>116334</td>\n",
       "      <td>FFFDFD377BF9EC59</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>41.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114178</th>\n",
       "      <td>116335</td>\n",
       "      <td>FFFF1E240FFD97D8</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>27.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114179</th>\n",
       "      <td>116336</td>\n",
       "      <td>FFFF58406F9882DD</td>\n",
       "      <td>3</td>\n",
       "      <td>69.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1230.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000017</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>0.00</td>\n",
       "      <td>48.0</td>\n",
       "      <td>469.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114180</th>\n",
       "      <td>116337</td>\n",
       "      <td>FFFFCA5AFABA1CF6</td>\n",
       "      <td>1</td>\n",
       "      <td>69.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>51.0</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114181</th>\n",
       "      <td>116338</td>\n",
       "      <td>FFFFF4859B6D7402</td>\n",
       "      <td>3</td>\n",
       "      <td>69.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8068.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000111</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.00</td>\n",
       "      <td>25.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>114182 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         index       DESYNPUF_ID  YEAR_CNT  MEAN_AGE  B_SEX  NH_WHITE  \\\n",
       "0            0  0000438E79D01BEA         3       NaN      1         1   \n",
       "1            1  00010E7BEA69142C         2      71.0      0         1   \n",
       "2            2  00015BF6509E1DF7         3      75.1      0         1   \n",
       "3            3  00018DD17736A661         1      73.0      0         1   \n",
       "4            4  000240D599ED789C         3      67.0      0         0   \n",
       "...        ...               ...       ...       ...    ...       ...   \n",
       "114177  116334  FFFDFD377BF9EC59         3       NaN      0         0   \n",
       "114178  116335  FFFF1E240FFD97D8         3       NaN      0         1   \n",
       "114179  116336  FFFF58406F9882DD         3      69.0      1         1   \n",
       "114180  116337  FFFFCA5AFABA1CF6         1      69.0      1         0   \n",
       "114181  116338  FFFFF4859B6D7402         3      69.0      1         1   \n",
       "\n",
       "        AA_BLACK  OTHER  NW_HISPANIC  B_CNCR  ...  ALLCOSTS  NEW_CHRONIC  \\\n",
       "0              0      0            0     0.0  ...       0.0          0.0   \n",
       "1              0      0            0     0.0  ...    1620.0          0.0   \n",
       "2              0      0            0     0.0  ...   25860.0          3.0   \n",
       "3              0      0            0     0.0  ...    6930.0          0.0   \n",
       "4              1      0            0     0.0  ...  509056.0          5.0   \n",
       "...          ...    ...          ...     ...  ...       ...          ...   \n",
       "114177         0      1            0     0.0  ...       0.0          0.0   \n",
       "114178         0      0            0     0.0  ...       0.0          0.0   \n",
       "114179         0      0            0     0.0  ...    1230.0          4.0   \n",
       "114180         1      0            0     0.0  ...    1100.0          0.0   \n",
       "114181         0      0            0     0.0  ...    8068.0          0.0   \n",
       "\n",
       "        TOTCHRONIC  VISITS_FLAG  PERCENT_COSTS  MEAN_ELIX_GRP_TOT  \\\n",
       "0              0.0            0       0.000000           0.000000   \n",
       "1              2.0            1       0.000022           0.333333   \n",
       "2              7.0            1       0.000356           0.555556   \n",
       "3              2.0            1       0.000095           0.428571   \n",
       "4              6.0            1       0.007014           0.766667   \n",
       "...            ...          ...            ...                ...   \n",
       "114177         0.0            0       0.000000           0.000000   \n",
       "114178         0.0            0       0.000000           0.000000   \n",
       "114179         7.0            1       0.000017           0.555556   \n",
       "114180         6.0            1       0.000015           1.500000   \n",
       "114181         3.0            1       0.000111           0.333333   \n",
       "\n",
       "        MEAN_ELIX_SCORE  IP_VISIT_PROP  FIPS_STATE_CODE  FIPS_COUNTY_CODE  \n",
       "0              0.000000           0.00             12.0              57.0  \n",
       "1              0.500000           0.00             10.0               3.0  \n",
       "2              0.555556           0.00             37.0             119.0  \n",
       "3              1.571429           0.00             26.0             101.0  \n",
       "4              2.016667           0.15             55.0              35.0  \n",
       "...                 ...            ...              ...               ...  \n",
       "114177         0.000000           0.00             41.0              11.0  \n",
       "114178         0.000000           0.00             27.0              19.0  \n",
       "114179         3.333333           0.00             48.0             469.0  \n",
       "114180         2.500000           0.00             51.0              59.0  \n",
       "114181         0.666667           1.00             25.0               5.0  \n",
       "\n",
       "[114182 rows x 35 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Setting up for aggregation.\n",
    "\n",
    "cc_summary3 = cc_summary_all.copy()\n",
    "cc_summary3.reset_index(inplace=True)\n",
    "cc_summary3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "cc_county = cc_summary3.groupby([\"FIPS_STATE_CODE\", \"FIPS_COUNTY_CODE\"]).aggregate({\"DESYNPUF_ID\":\"count\",\n",
    "                                                                                    \"VISITS_FLAG\":\"sum\",\n",
    "                                                                                    \"MEAN_AGE\": [\"mean\",\"std\",\"count\"],\n",
    "                                                                                    \"B_SEX\":\"mean\",\n",
    "                                                                                    \"NH_WHITE\":\"sum\",\n",
    "                                                                                    \"AA_BLACK\":\"sum\",\n",
    "                                                                                    \"OTHER\":\"sum\",\n",
    "                                                                                    \"NW_HISPANIC\":\"sum\",\n",
    "                                                                                    \"B_CNCR\":\"sum\",\n",
    "                                                                                    \"B_STRKETIA\":\"sum\",\n",
    "                                                                                    \"B_ESRD\":\"sum\",\n",
    "                                                                                    \"MEAN_CVRG_MOS_PY\":\"sum\",\n",
    "                                                                                    \"MEAN_RX_CVRG_MOS_PY\":\"sum\",\n",
    "                                                                                    \"TOTAL_VISITS\":\"sum\",\n",
    "                                                                                    \"IP_VISITS\":\"sum\",\n",
    "                                                                                    \"OP_VISITS\":\"sum\",\n",
    "                                                                                    \"MEAN_CODES_CT\":\"sum\",\n",
    "                                                                                    \"MEAN_PROCEDURE_CT\":\"sum\",\n",
    "                                                                                    \"MEAN_NUM_PRVDRS\":\"sum\",\n",
    "                                                                                    \"TOTAL_INP_DAYS\":\"sum\",\n",
    "                                                                                    \"MEAN_INP_STAY\":\"mean\",\n",
    "                                                                                    \"CBSA\":\"mean\",\n",
    "                                                                                    \"TOTCHRONIC\":[\"sum\",\"mean\"],\n",
    "                                                                                    \"NEW_CHRONIC\":[\"sum\",\"mean\"],\n",
    "                                                                                    \"MEAN_ELIX_SCORE\":\"sum\",\n",
    "                                                                                    \"TOTIPCOST\":\"sum\",\n",
    "                                                                                    \"TOTOPCOST\":\"sum\",\n",
    "                                                                                    \"ALLCOSTS\":\"sum\"})\n",
    "\n",
    "\n",
    "cc_county.columns = [\"CNTY_BENE_CT\", \"CNTY_BENE_USRS\", \"CNTY_MEAN_AGE\", \"AGE_STD\", \"AGE_N\", \n",
    "                     \"CNTY_SEX_PROP\", \"NH_WHITE_N\", \"AA_BLACK_N\", \"OTHER_N\", \"NW_HISPANIC_N\",\n",
    "                     \"TOT_CNCR_CASES\", \"TOT_STRKETIA_CASES\", \"TOT_ESRD_CASES\", \n",
    "                     \"TOT_CVRG_MOS_PY\", \"TOT_RX_CVRG_MOS_PY\",\"CNTY_TOT_VSTS\", \"CNTY_TOT_IP_VSTS\", \n",
    "                     \"CNTY_TOT_OP_VSTS\", \"CNTY_TOT_CODES\", \"CNTY_TOT_PROCS\", \"CNTY_TOT_PRVDRS\", \n",
    "                     \"CNTY_TOT_IP_DAYS\", \"CNTY_MEAN_IP_STAY\", \"CBSA\", \"CNTY_TOT_CCS\", \"CNTY_MEAN_CCS\", \n",
    "                     \"CNTY_TOT_NEW_CCS\", \"CNTY_MEAN_NEW_CCS\", \"CNTY_SUM_ELIX_SCORES\", \"CNTY_TOT_IP_COST\",\n",
    "                     \"CNTY_TOT_OP_COST\", \"CNTY_ALL_COSTS\"]\n",
    "\n",
    "ALL_TOTALS = cc_county[\"CNTY_ALL_COSTS\"].sum()\n",
    "cc_county[\"CNTY_PERCENT_COSTS\"] = cc_county[\"CNTY_ALL_COSTS\"] / ALL_TOTALS * 100\n",
    "cc_county[\"CNTY_USR_PROP\"] = cc_county[\"CNTY_BENE_USRS\"]/cc_county[\"CNTY_BENE_CT\"]\n",
    "cc_county[\"CNTY_IP_PROP\"] = cc_county[\"CNTY_TOT_IP_VSTS\"]/cc_county[\"CNTY_TOT_VSTS\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "cc_county[\"CNTY_MEAN_AGE\"] = cc_county[\"CNTY_MEAN_AGE\"].round(decimals=1)\n",
    "cc_county[\"CNTY_AGE_95CIL\"] = cc_county[\"CNTY_MEAN_AGE\"]-(1.96*(cc_county[\"AGE_STD\"]/np.sqrt(cc_county[\"AGE_N\"])))\n",
    "cc_county[\"CNTY_AGE_95CIU\"] = cc_county[\"CNTY_MEAN_AGE\"]+(1.96*(cc_county[\"AGE_STD\"]/np.sqrt(cc_county[\"AGE_N\"])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "cc_county[\"CNTY_PROP_NH_WHITE\"] = cc_county[\"NH_WHITE_N\"]/cc_county[\"CNTY_BENE_CT\"]\n",
    "cc_county[\"CNTY_PROP_AA_BLACK\"] = cc_county[\"AA_BLACK_N\"]/cc_county[\"CNTY_BENE_CT\"]\n",
    "cc_county[\"CNTY_PROP_OTHER\"] = cc_county[\"OTHER_N\"]/cc_county[\"CNTY_BENE_CT\"]\n",
    "cc_county[\"CNTY_PROP_NW_HISPANIC\"] = cc_county[\"NW_HISPANIC_N\"]/cc_county[\"CNTY_BENE_CT\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "cc_county[\"TOT_CNCR_PREV\"] = cc_county[\"TOT_CNCR_CASES\"]/cc_county[\"CNTY_BENE_CT\"]\n",
    "cc_county[\"TOT_STRK_PREV\"] = cc_county[\"TOT_STRKETIA_CASES\"]/cc_county[\"CNTY_BENE_CT\"]\n",
    "cc_county[\"TOT_ESRD_PREV\"] = cc_county[\"TOT_ESRD_CASES\"]/cc_county[\"CNTY_BENE_CT\"]\n",
    "cc_county[\"TOT_CVRG_MOS_PB_PY\"] = cc_county[\"TOT_CVRG_MOS_PY\"]/cc_county[\"CNTY_BENE_CT\"]\n",
    "cc_county[\"TOT_RXCVRG_MOS_PB_PY\"] = cc_county[\"TOT_RX_CVRG_MOS_PY\"]/cc_county[\"CNTY_BENE_CT\"]\n",
    "cc_county[\"TOT_VSTS_PB\"] = cc_county[\"CNTY_TOT_VSTS\"]/cc_county[\"CNTY_BENE_CT\"]\n",
    "cc_county[\"TOT_VSTS_PU\"] = cc_county[\"CNTY_TOT_VSTS\"]/cc_county[\"CNTY_BENE_USRS\"]\n",
    "cc_county[\"TOT_IP_VSTS_PU\"] = cc_county[\"CNTY_TOT_IP_VSTS\"]/cc_county[\"CNTY_BENE_USRS\"]\n",
    "cc_county[\"TOT_OP_VSTS_PU\"] = cc_county[\"CNTY_TOT_OP_VSTS\"]/cc_county[\"CNTY_BENE_USRS\"]\n",
    "cc_county[\"CODES_PU\"] = cc_county[\"CNTY_TOT_CODES\"]/cc_county[\"CNTY_BENE_USRS\"]\n",
    "cc_county[\"PROCS_PU\"] = cc_county[\"CNTY_TOT_PROCS\"]/cc_county[\"CNTY_BENE_USRS\"]\n",
    "cc_county[\"PRVDRS_PU\"] = cc_county[\"CNTY_TOT_PRVDRS\"]/cc_county[\"CNTY_BENE_USRS\"]\n",
    "cc_county[\"TOT_IPDAYS_PU\"] = cc_county[\"CNTY_TOT_IP_DAYS\"]/cc_county[\"CNTY_BENE_USRS\"]\n",
    "cc_county[\"TOT_CC_PREV\"] = cc_county[\"CNTY_TOT_CCS\"]/cc_county[\"CNTY_BENE_CT\"]\n",
    "cc_county[\"TOT_CCS_PU\"] = cc_county[\"CNTY_TOT_CCS\"]/cc_county[\"CNTY_BENE_USRS\"]\n",
    "cc_county[\"TOT_CC_INCD\"] = cc_county[\"CNTY_TOT_NEW_CCS\"]/cc_county[\"CNTY_BENE_CT\"]\n",
    "cc_county[\"TOT_NEW_CC_PU\"] = cc_county[\"CNTY_TOT_NEW_CCS\"]/cc_county[\"CNTY_BENE_USRS\"]\n",
    "cc_county[\"TOT_ELIX_PU\"] = cc_county[\"CNTY_SUM_ELIX_SCORES\"]/cc_county[\"CNTY_BENE_USRS\"]\n",
    "cc_county[\"TOT_IP_COST_PB\"] = cc_county[\"CNTY_TOT_IP_COST\"]/cc_county[\"CNTY_BENE_CT\"]\n",
    "cc_county[\"TOT_OP_COST_PB\"] = cc_county[\"CNTY_TOT_OP_COST\"]/cc_county[\"CNTY_BENE_CT\"]\n",
    "cc_county[\"TOT_COST_PB\"] = cc_county[\"CNTY_ALL_COSTS\"]/cc_county[\"CNTY_BENE_CT\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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></th>\n",
       "      <th>CNTY_BENE_CT</th>\n",
       "      <th>CNTY_BENE_USRS</th>\n",
       "      <th>CNTY_MEAN_AGE</th>\n",
       "      <th>AGE_STD</th>\n",
       "      <th>AGE_N</th>\n",
       "      <th>CNTY_SEX_PROP</th>\n",
       "      <th>NH_WHITE_N</th>\n",
       "      <th>AA_BLACK_N</th>\n",
       "      <th>OTHER_N</th>\n",
       "      <th>NW_HISPANIC_N</th>\n",
       "      <th>...</th>\n",
       "      <th>PRVDRS_PU</th>\n",
       "      <th>TOT_IPDAYS_PU</th>\n",
       "      <th>TOT_CC_PREV</th>\n",
       "      <th>TOT_CCS_PU</th>\n",
       "      <th>TOT_CC_INCD</th>\n",
       "      <th>TOT_NEW_CC_PU</th>\n",
       "      <th>TOT_ELIX_PU</th>\n",
       "      <th>TOT_IP_COST_PB</th>\n",
       "      <th>TOT_OP_COST_PB</th>\n",
       "      <th>TOT_COST_PB</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FIPS_STATE_CODE</th>\n",
       "      <th>FIPS_COUNTY_CODE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1.0</th>\n",
       "      <th>1.0</th>\n",
       "      <td>16</td>\n",
       "      <td>13</td>\n",
       "      <td>65.4</td>\n",
       "      <td>19.365814</td>\n",
       "      <td>13</td>\n",
       "      <td>0.187500</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.307692</td>\n",
       "      <td>3.692308</td>\n",
       "      <td>4.937500</td>\n",
       "      <td>6.076923</td>\n",
       "      <td>2.562500</td>\n",
       "      <td>3.153846</td>\n",
       "      <td>1.492308</td>\n",
       "      <td>33386.750000</td>\n",
       "      <td>11793.125000</td>\n",
       "      <td>45179.875000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>95</td>\n",
       "      <td>65</td>\n",
       "      <td>70.3</td>\n",
       "      <td>12.226355</td>\n",
       "      <td>65</td>\n",
       "      <td>0.463158</td>\n",
       "      <td>60</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.297436</td>\n",
       "      <td>5.615385</td>\n",
       "      <td>3.747368</td>\n",
       "      <td>5.476923</td>\n",
       "      <td>1.705263</td>\n",
       "      <td>2.492308</td>\n",
       "      <td>1.651029</td>\n",
       "      <td>53552.105263</td>\n",
       "      <td>27044.000000</td>\n",
       "      <td>80596.105263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>72.4</td>\n",
       "      <td>14.223165</td>\n",
       "      <td>12</td>\n",
       "      <td>0.642857</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.888889</td>\n",
       "      <td>4.833333</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>4.083333</td>\n",
       "      <td>1.785714</td>\n",
       "      <td>2.083333</td>\n",
       "      <td>1.791204</td>\n",
       "      <td>54292.571429</td>\n",
       "      <td>12438.571429</td>\n",
       "      <td>66731.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.0</th>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>69.0</td>\n",
       "      <td>15.493427</td>\n",
       "      <td>12</td>\n",
       "      <td>0.411765</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.833333</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>4.470588</td>\n",
       "      <td>6.333333</td>\n",
       "      <td>2.588235</td>\n",
       "      <td>3.666667</td>\n",
       "      <td>1.985728</td>\n",
       "      <td>27511.411765</td>\n",
       "      <td>18995.294118</td>\n",
       "      <td>46506.705882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.0</th>\n",
       "      <td>22</td>\n",
       "      <td>12</td>\n",
       "      <td>68.8</td>\n",
       "      <td>18.347496</td>\n",
       "      <td>12</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.555556</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>2.181818</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.227273</td>\n",
       "      <td>2.250000</td>\n",
       "      <td>0.707341</td>\n",
       "      <td>43042.818182</td>\n",
       "      <td>6786.818182</td>\n",
       "      <td>49829.636364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">56.0</th>\n",
       "      <th>37.0</th>\n",
       "      <td>34</td>\n",
       "      <td>17</td>\n",
       "      <td>72.9</td>\n",
       "      <td>6.450376</td>\n",
       "      <td>17</td>\n",
       "      <td>0.529412</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.254902</td>\n",
       "      <td>2.647059</td>\n",
       "      <td>2.558824</td>\n",
       "      <td>5.117647</td>\n",
       "      <td>1.147059</td>\n",
       "      <td>2.294118</td>\n",
       "      <td>1.255637</td>\n",
       "      <td>27809.176471</td>\n",
       "      <td>22610.588235</td>\n",
       "      <td>50419.764706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39.0</th>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>70.5</td>\n",
       "      <td>7.194303</td>\n",
       "      <td>5</td>\n",
       "      <td>0.538462</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.266667</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>1.769231</td>\n",
       "      <td>4.600000</td>\n",
       "      <td>0.692308</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>0.912540</td>\n",
       "      <td>2339.076923</td>\n",
       "      <td>6016.923077</td>\n",
       "      <td>8356.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41.0</th>\n",
       "      <td>18</td>\n",
       "      <td>6</td>\n",
       "      <td>72.0</td>\n",
       "      <td>11.516206</td>\n",
       "      <td>6</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.444444</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.611111</td>\n",
       "      <td>4.833333</td>\n",
       "      <td>0.611111</td>\n",
       "      <td>1.833333</td>\n",
       "      <td>1.323545</td>\n",
       "      <td>2518.888889</td>\n",
       "      <td>4288.333333</td>\n",
       "      <td>6807.222222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43.0</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45.0</th>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>75.5</td>\n",
       "      <td>6.363961</td>\n",
       "      <td>2</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>133.333333</td>\n",
       "      <td>133.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3010 rows × 62 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  CNTY_BENE_CT  CNTY_BENE_USRS  CNTY_MEAN_AGE  \\\n",
       "FIPS_STATE_CODE FIPS_COUNTY_CODE                                                \n",
       "1.0             1.0                         16              13           65.4   \n",
       "                3.0                         95              65           70.3   \n",
       "                5.0                         14              12           72.4   \n",
       "                7.0                         17              12           69.0   \n",
       "                9.0                         22              12           68.8   \n",
       "...                                        ...             ...            ...   \n",
       "56.0            37.0                        34              17           72.9   \n",
       "                39.0                        13               5           70.5   \n",
       "                41.0                        18               6           72.0   \n",
       "                43.0                         8               0            NaN   \n",
       "                45.0                        15               2           75.5   \n",
       "\n",
       "                                    AGE_STD  AGE_N  CNTY_SEX_PROP  NH_WHITE_N  \\\n",
       "FIPS_STATE_CODE FIPS_COUNTY_CODE                                                \n",
       "1.0             1.0               19.365814     13       0.187500          11   \n",
       "                3.0               12.226355     65       0.463158          60   \n",
       "                5.0               14.223165     12       0.642857           7   \n",
       "                7.0               15.493427     12       0.411765          12   \n",
       "                9.0               18.347496     12       0.636364          13   \n",
       "...                                     ...    ...            ...         ...   \n",
       "56.0            37.0               6.450376     17       0.529412          34   \n",
       "                39.0               7.194303      5       0.538462          13   \n",
       "                41.0              11.516206      6       0.500000          17   \n",
       "                43.0                    NaN      0       0.625000           8   \n",
       "                45.0               6.363961      2       0.600000          15   \n",
       "\n",
       "                                  AA_BLACK_N  OTHER_N  NW_HISPANIC_N  ...  \\\n",
       "FIPS_STATE_CODE FIPS_COUNTY_CODE                                      ...   \n",
       "1.0             1.0                        5        0              0  ...   \n",
       "                3.0                       34        1              0  ...   \n",
       "                5.0                        7        0              0  ...   \n",
       "                7.0                        5        0              0  ...   \n",
       "                9.0                        9        0              0  ...   \n",
       "...                                      ...      ...            ...  ...   \n",
       "56.0            37.0                       0        0              0  ...   \n",
       "                39.0                       0        0              0  ...   \n",
       "                41.0                       0        1              0  ...   \n",
       "                43.0                       0        0              0  ...   \n",
       "                45.0                       0        0              0  ...   \n",
       "\n",
       "                                  PRVDRS_PU  TOT_IPDAYS_PU  TOT_CC_PREV  \\\n",
       "FIPS_STATE_CODE FIPS_COUNTY_CODE                                          \n",
       "1.0             1.0                2.307692       3.692308     4.937500   \n",
       "                3.0                3.297436       5.615385     3.747368   \n",
       "                5.0                1.888889       4.833333     3.500000   \n",
       "                7.0                2.833333       4.666667     4.470588   \n",
       "                9.0                1.555556       5.500000     2.181818   \n",
       "...                                     ...            ...          ...   \n",
       "56.0            37.0               1.254902       2.647059     2.558824   \n",
       "                39.0               1.266667       0.600000     1.769231   \n",
       "                41.0               1.444444       0.000000     1.611111   \n",
       "                43.0                    NaN            NaN     0.000000   \n",
       "                45.0               0.333333       0.000000     0.466667   \n",
       "\n",
       "                                  TOT_CCS_PU  TOT_CC_INCD  TOT_NEW_CC_PU  \\\n",
       "FIPS_STATE_CODE FIPS_COUNTY_CODE                                           \n",
       "1.0             1.0                 6.076923     2.562500       3.153846   \n",
       "                3.0                 5.476923     1.705263       2.492308   \n",
       "                5.0                 4.083333     1.785714       2.083333   \n",
       "                7.0                 6.333333     2.588235       3.666667   \n",
       "                9.0                 4.000000     1.227273       2.250000   \n",
       "...                                      ...          ...            ...   \n",
       "56.0            37.0                5.117647     1.147059       2.294118   \n",
       "                39.0                4.600000     0.692308       1.800000   \n",
       "                41.0                4.833333     0.611111       1.833333   \n",
       "                43.0                     NaN     0.000000            NaN   \n",
       "                45.0                3.500000     0.333333       2.500000   \n",
       "\n",
       "                                  TOT_ELIX_PU  TOT_IP_COST_PB  TOT_OP_COST_PB  \\\n",
       "FIPS_STATE_CODE FIPS_COUNTY_CODE                                                \n",
       "1.0             1.0                  1.492308    33386.750000    11793.125000   \n",
       "                3.0                  1.651029    53552.105263    27044.000000   \n",
       "                5.0                  1.791204    54292.571429    12438.571429   \n",
       "                7.0                  1.985728    27511.411765    18995.294118   \n",
       "                9.0                  0.707341    43042.818182     6786.818182   \n",
       "...                                       ...             ...             ...   \n",
       "56.0            37.0                 1.255637    27809.176471    22610.588235   \n",
       "                39.0                 0.912540     2339.076923     6016.923077   \n",
       "                41.0                 1.323545     2518.888889     4288.333333   \n",
       "                43.0                      NaN        0.000000        0.000000   \n",
       "                45.0                 1.000000        0.000000      133.333333   \n",
       "\n",
       "                                   TOT_COST_PB  \n",
       "FIPS_STATE_CODE FIPS_COUNTY_CODE                \n",
       "1.0             1.0               45179.875000  \n",
       "                3.0               80596.105263  \n",
       "                5.0               66731.142857  \n",
       "                7.0               46506.705882  \n",
       "                9.0               49829.636364  \n",
       "...                                        ...  \n",
       "56.0            37.0              50419.764706  \n",
       "                39.0               8356.000000  \n",
       "                41.0               6807.222222  \n",
       "                43.0                  0.000000  \n",
       "                45.0                133.333333  \n",
       "\n",
       "[3010 rows x 62 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc_county"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    3010.000000\n",
       "mean       37.934219\n",
       "std        94.595933\n",
       "min         1.000000\n",
       "25%         6.000000\n",
       "50%        13.000000\n",
       "75%        32.000000\n",
       "max      2488.000000\n",
       "Name: CNTY_BENE_CT, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc_county[\"CNTY_BENE_CT\"].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Representative Counties\n",
    "\n",
    "So, if we remove the counties with fewer than 50 beneficiaries, we have 490 counties.\n",
    "\n",
    "If we remove those with fewer than 30, we have 811 counties.\n",
    "\n",
    "There are 121 counties with ONE beneficiary.\n",
    "\n",
    "There are 693 counties with 5 OR FEWER beneficiaries, and 1267 counties with 10 OR FEWER.\n",
    "\n",
    "As we discussed I am going to leave all the counties here.  But we may choose to exclude some later, as we analyze."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "MultiIndex: 3010 entries, (1.0, 1.0) to (56.0, 45.0)\n",
      "Data columns (total 62 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   CNTY_BENE_CT           3010 non-null   int64  \n",
      " 1   CNTY_BENE_USRS         3010 non-null   int64  \n",
      " 2   CNTY_MEAN_AGE          2957 non-null   float64\n",
      " 3   AGE_STD                2766 non-null   float64\n",
      " 4   AGE_N                  3010 non-null   int64  \n",
      " 5   CNTY_SEX_PROP          3010 non-null   float64\n",
      " 6   NH_WHITE_N             3010 non-null   int64  \n",
      " 7   AA_BLACK_N             3010 non-null   int64  \n",
      " 8   OTHER_N                3010 non-null   int64  \n",
      " 9   NW_HISPANIC_N          3010 non-null   int64  \n",
      " 10  TOT_CNCR_CASES         3010 non-null   float64\n",
      " 11  TOT_STRKETIA_CASES     3010 non-null   float64\n",
      " 12  TOT_ESRD_CASES         3010 non-null   float64\n",
      " 13  TOT_CVRG_MOS_PY        3010 non-null   float64\n",
      " 14  TOT_RX_CVRG_MOS_PY     3010 non-null   float64\n",
      " 15  CNTY_TOT_VSTS          3010 non-null   int64  \n",
      " 16  CNTY_TOT_IP_VSTS       3010 non-null   int64  \n",
      " 17  CNTY_TOT_OP_VSTS       3010 non-null   int64  \n",
      " 18  CNTY_TOT_CODES         3010 non-null   float64\n",
      " 19  CNTY_TOT_PROCS         3010 non-null   float64\n",
      " 20  CNTY_TOT_PRVDRS        3010 non-null   float64\n",
      " 21  CNTY_TOT_IP_DAYS       3010 non-null   float64\n",
      " 22  CNTY_MEAN_IP_STAY      3010 non-null   float64\n",
      " 23  CBSA                   1091 non-null   float64\n",
      " 24  CNTY_TOT_CCS           3010 non-null   float64\n",
      " 25  CNTY_MEAN_CCS          3010 non-null   float64\n",
      " 26  CNTY_TOT_NEW_CCS       3010 non-null   float64\n",
      " 27  CNTY_MEAN_NEW_CCS      3010 non-null   float64\n",
      " 28  CNTY_SUM_ELIX_SCORES   3010 non-null   float64\n",
      " 29  CNTY_TOT_IP_COST       3010 non-null   float64\n",
      " 30  CNTY_TOT_OP_COST       3010 non-null   float64\n",
      " 31  CNTY_ALL_COSTS         3010 non-null   float64\n",
      " 32  CNTY_PERCENT_COSTS     3010 non-null   float64\n",
      " 33  CNTY_USR_PROP          3010 non-null   float64\n",
      " 34  CNTY_IP_PROP           2957 non-null   float64\n",
      " 35  CNTY_AGE_95CIL         2766 non-null   float64\n",
      " 36  CNTY_AGE_95CIU         2766 non-null   float64\n",
      " 37  CNTY_PROP_NH_WHITE     3010 non-null   float64\n",
      " 38  CNTY_PROP_AA_BLACK     3010 non-null   float64\n",
      " 39  CNTY_PROP_OTHER        3010 non-null   float64\n",
      " 40  CNTY_PROP_NW_HISPANIC  3010 non-null   float64\n",
      " 41  TOT_CNCR_PREV          3010 non-null   float64\n",
      " 42  TOT_STRK_PREV          3010 non-null   float64\n",
      " 43  TOT_ESRD_PREV          3010 non-null   float64\n",
      " 44  TOT_CVRG_MOS_PB_PY     3010 non-null   float64\n",
      " 45  TOT_RXCVRG_MOS_PB_PY   3010 non-null   float64\n",
      " 46  TOT_VSTS_PB            3010 non-null   float64\n",
      " 47  TOT_VSTS_PU            2957 non-null   float64\n",
      " 48  TOT_IP_VSTS_PU         2957 non-null   float64\n",
      " 49  TOT_OP_VSTS_PU         2957 non-null   float64\n",
      " 50  CODES_PU               2957 non-null   float64\n",
      " 51  PROCS_PU               2957 non-null   float64\n",
      " 52  PRVDRS_PU              2957 non-null   float64\n",
      " 53  TOT_IPDAYS_PU          2957 non-null   float64\n",
      " 54  TOT_CC_PREV            3010 non-null   float64\n",
      " 55  TOT_CCS_PU             2971 non-null   float64\n",
      " 56  TOT_CC_INCD            3010 non-null   float64\n",
      " 57  TOT_NEW_CC_PU          2966 non-null   float64\n",
      " 58  TOT_ELIX_PU            2957 non-null   float64\n",
      " 59  TOT_IP_COST_PB         3010 non-null   float64\n",
      " 60  TOT_OP_COST_PB         3010 non-null   float64\n",
      " 61  TOT_COST_PB            3010 non-null   float64\n",
      "dtypes: float64(52), int64(10)\n",
      "memory usage: 1.4 MB\n"
     ]
    }
   ],
   "source": [
    "cc_county.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking Calculated Fields"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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></th>\n",
       "      <th>CNTY_MEAN_AGE</th>\n",
       "      <th>CNTY_AGE_95CIL</th>\n",
       "      <th>CNTY_AGE_95CIU</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FIPS_STATE_CODE</th>\n",
       "      <th>FIPS_COUNTY_CODE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1.0</th>\n",
       "      <th>1.0</th>\n",
       "      <td>65.4</td>\n",
       "      <td>54.872624</td>\n",
       "      <td>75.927376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>70.3</td>\n",
       "      <td>67.327674</td>\n",
       "      <td>73.272326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>72.4</td>\n",
       "      <td>64.352487</td>\n",
       "      <td>80.447513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.0</th>\n",
       "      <td>69.0</td>\n",
       "      <td>60.233768</td>\n",
       "      <td>77.766232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.0</th>\n",
       "      <td>68.8</td>\n",
       "      <td>58.418927</td>\n",
       "      <td>79.181073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">56.0</th>\n",
       "      <th>37.0</th>\n",
       "      <td>72.9</td>\n",
       "      <td>69.833686</td>\n",
       "      <td>75.966314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39.0</th>\n",
       "      <td>70.5</td>\n",
       "      <td>64.193915</td>\n",
       "      <td>76.806085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41.0</th>\n",
       "      <td>72.0</td>\n",
       "      <td>62.785116</td>\n",
       "      <td>81.214884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45.0</th>\n",
       "      <td>75.5</td>\n",
       "      <td>66.680000</td>\n",
       "      <td>84.320000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3010 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  CNTY_MEAN_AGE  CNTY_AGE_95CIL  \\\n",
       "FIPS_STATE_CODE FIPS_COUNTY_CODE                                  \n",
       "1.0             1.0                        65.4       54.872624   \n",
       "                3.0                        70.3       67.327674   \n",
       "                5.0                        72.4       64.352487   \n",
       "                7.0                        69.0       60.233768   \n",
       "                9.0                        68.8       58.418927   \n",
       "...                                         ...             ...   \n",
       "56.0            37.0                       72.9       69.833686   \n",
       "                39.0                       70.5       64.193915   \n",
       "                41.0                       72.0       62.785116   \n",
       "                43.0                        NaN             NaN   \n",
       "                45.0                       75.5       66.680000   \n",
       "\n",
       "                                  CNTY_AGE_95CIU  \n",
       "FIPS_STATE_CODE FIPS_COUNTY_CODE                  \n",
       "1.0             1.0                    75.927376  \n",
       "                3.0                    73.272326  \n",
       "                5.0                    80.447513  \n",
       "                7.0                    77.766232  \n",
       "                9.0                    79.181073  \n",
       "...                                          ...  \n",
       "56.0            37.0                   75.966314  \n",
       "                39.0                   76.806085  \n",
       "                41.0                   81.214884  \n",
       "                43.0                         NaN  \n",
       "                45.0                   84.320000  \n",
       "\n",
       "[3010 rows x 3 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc_county[[\"CNTY_MEAN_AGE\", \"CNTY_AGE_95CIL\", \"CNTY_AGE_95CIU\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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></th>\n",
       "      <th>CNTY_PROP_NH_WHITE</th>\n",
       "      <th>CNTY_PROP_AA_BLACK</th>\n",
       "      <th>CNTY_PROP_OTHER</th>\n",
       "      <th>CNTY_PROP_NW_HISPANIC</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FIPS_STATE_CODE</th>\n",
       "      <th>FIPS_COUNTY_CODE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1.0</th>\n",
       "      <th>1.0</th>\n",
       "      <td>0.687500</td>\n",
       "      <td>0.312500</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>0.631579</td>\n",
       "      <td>0.357895</td>\n",
       "      <td>0.010526</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.0</th>\n",
       "      <td>0.705882</td>\n",
       "      <td>0.294118</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.0</th>\n",
       "      <td>0.590909</td>\n",
       "      <td>0.409091</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">56.0</th>\n",
       "      <th>37.0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39.0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41.0</th>\n",
       "      <td>0.944444</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43.0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45.0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3010 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  CNTY_PROP_NH_WHITE  CNTY_PROP_AA_BLACK  \\\n",
       "FIPS_STATE_CODE FIPS_COUNTY_CODE                                           \n",
       "1.0             1.0                         0.687500            0.312500   \n",
       "                3.0                         0.631579            0.357895   \n",
       "                5.0                         0.500000            0.500000   \n",
       "                7.0                         0.705882            0.294118   \n",
       "                9.0                         0.590909            0.409091   \n",
       "...                                              ...                 ...   \n",
       "56.0            37.0                        1.000000            0.000000   \n",
       "                39.0                        1.000000            0.000000   \n",
       "                41.0                        0.944444            0.000000   \n",
       "                43.0                        1.000000            0.000000   \n",
       "                45.0                        1.000000            0.000000   \n",
       "\n",
       "                                  CNTY_PROP_OTHER  CNTY_PROP_NW_HISPANIC  \n",
       "FIPS_STATE_CODE FIPS_COUNTY_CODE                                          \n",
       "1.0             1.0                      0.000000                    0.0  \n",
       "                3.0                      0.010526                    0.0  \n",
       "                5.0                      0.000000                    0.0  \n",
       "                7.0                      0.000000                    0.0  \n",
       "                9.0                      0.000000                    0.0  \n",
       "...                                           ...                    ...  \n",
       "56.0            37.0                     0.000000                    0.0  \n",
       "                39.0                     0.000000                    0.0  \n",
       "                41.0                     0.055556                    0.0  \n",
       "                43.0                     0.000000                    0.0  \n",
       "                45.0                     0.000000                    0.0  \n",
       "\n",
       "[3010 rows x 4 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc_county[[\"CNTY_PROP_NH_WHITE\", \"CNTY_PROP_AA_BLACK\", \"CNTY_PROP_OTHER\", \"CNTY_PROP_NW_HISPANIC\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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></th>\n",
       "      <th>CNTY_TOT_VSTS</th>\n",
       "      <th>TOT_VSTS_PB</th>\n",
       "      <th>TOT_VSTS_PU</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FIPS_STATE_CODE</th>\n",
       "      <th>FIPS_COUNTY_CODE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1.0</th>\n",
       "      <th>1.0</th>\n",
       "      <td>112</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>8.615385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>720</td>\n",
       "      <td>7.578947</td>\n",
       "      <td>11.076923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>92</td>\n",
       "      <td>6.571429</td>\n",
       "      <td>7.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.0</th>\n",
       "      <td>152</td>\n",
       "      <td>8.941176</td>\n",
       "      <td>12.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.0</th>\n",
       "      <td>86</td>\n",
       "      <td>3.909091</td>\n",
       "      <td>7.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">56.0</th>\n",
       "      <th>37.0</th>\n",
       "      <td>138</td>\n",
       "      <td>4.058824</td>\n",
       "      <td>8.117647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39.0</th>\n",
       "      <td>38</td>\n",
       "      <td>2.923077</td>\n",
       "      <td>7.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41.0</th>\n",
       "      <td>61</td>\n",
       "      <td>3.388889</td>\n",
       "      <td>10.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43.0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45.0</th>\n",
       "      <td>2</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3010 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  CNTY_TOT_VSTS  TOT_VSTS_PB  TOT_VSTS_PU\n",
       "FIPS_STATE_CODE FIPS_COUNTY_CODE                                         \n",
       "1.0             1.0                         112     7.000000     8.615385\n",
       "                3.0                         720     7.578947    11.076923\n",
       "                5.0                          92     6.571429     7.666667\n",
       "                7.0                         152     8.941176    12.666667\n",
       "                9.0                          86     3.909091     7.166667\n",
       "...                                         ...          ...          ...\n",
       "56.0            37.0                        138     4.058824     8.117647\n",
       "                39.0                         38     2.923077     7.600000\n",
       "                41.0                         61     3.388889    10.166667\n",
       "                43.0                          0     0.000000          NaN\n",
       "                45.0                          2     0.133333     1.000000\n",
       "\n",
       "[3010 rows x 3 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc_county[[\"CNTY_TOT_VSTS\", \"TOT_VSTS_PB\", \"TOT_VSTS_PU\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping columns that were used for calculations that are no longer needed.\n",
    "\n",
    "cc_county.drop([\"AGE_STD\", \"AGE_N\", \"NH_WHITE_N\", \"AA_BLACK_N\", \"OTHER_N\", \"NW_HISPANIC_N\", \n",
    "               \"TOT_CVRG_MOS_PY\", \"TOT_RX_CVRG_MOS_PY\", \"CNTY_TOT_PRVDRS\", \"CNTY_TOT_IP_DAYS\"], axis=1, inplace=True, errors=\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3010, 52)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc_county.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>FIPS_STATE_CODE</th>\n",
       "      <th>FIPS_COUNTY_CODE</th>\n",
       "      <th>CNTY_BENE_CT</th>\n",
       "      <th>CNTY_BENE_USRS</th>\n",
       "      <th>CNTY_MEAN_AGE</th>\n",
       "      <th>CNTY_SEX_PROP</th>\n",
       "      <th>TOT_CNCR_CASES</th>\n",
       "      <th>TOT_STRKETIA_CASES</th>\n",
       "      <th>TOT_ESRD_CASES</th>\n",
       "      <th>CNTY_TOT_VSTS</th>\n",
       "      <th>...</th>\n",
       "      <th>PRVDRS_PU</th>\n",
       "      <th>TOT_IPDAYS_PU</th>\n",
       "      <th>TOT_CC_PREV</th>\n",
       "      <th>TOT_CCS_PU</th>\n",
       "      <th>TOT_CC_INCD</th>\n",
       "      <th>TOT_NEW_CC_PU</th>\n",
       "      <th>TOT_ELIX_PU</th>\n",
       "      <th>TOT_IP_COST_PB</th>\n",
       "      <th>TOT_OP_COST_PB</th>\n",
       "      <th>TOT_COST_PB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16</td>\n",
       "      <td>13</td>\n",
       "      <td>65.4</td>\n",
       "      <td>0.187500</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>112</td>\n",
       "      <td>...</td>\n",
       "      <td>2.307692</td>\n",
       "      <td>3.692308</td>\n",
       "      <td>4.937500</td>\n",
       "      <td>6.076923</td>\n",
       "      <td>2.562500</td>\n",
       "      <td>3.153846</td>\n",
       "      <td>1.492308</td>\n",
       "      <td>33386.750000</td>\n",
       "      <td>11793.125000</td>\n",
       "      <td>45179.875000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>95</td>\n",
       "      <td>65</td>\n",
       "      <td>70.3</td>\n",
       "      <td>0.463158</td>\n",
       "      <td>18.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>720</td>\n",
       "      <td>...</td>\n",
       "      <td>3.297436</td>\n",
       "      <td>5.615385</td>\n",
       "      <td>3.747368</td>\n",
       "      <td>5.476923</td>\n",
       "      <td>1.705263</td>\n",
       "      <td>2.492308</td>\n",
       "      <td>1.651029</td>\n",
       "      <td>53552.105263</td>\n",
       "      <td>27044.000000</td>\n",
       "      <td>80596.105263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>72.4</td>\n",
       "      <td>0.642857</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>92</td>\n",
       "      <td>...</td>\n",
       "      <td>1.888889</td>\n",
       "      <td>4.833333</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>4.083333</td>\n",
       "      <td>1.785714</td>\n",
       "      <td>2.083333</td>\n",
       "      <td>1.791204</td>\n",
       "      <td>54292.571429</td>\n",
       "      <td>12438.571429</td>\n",
       "      <td>66731.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>69.0</td>\n",
       "      <td>0.411765</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>152</td>\n",
       "      <td>...</td>\n",
       "      <td>2.833333</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>4.470588</td>\n",
       "      <td>6.333333</td>\n",
       "      <td>2.588235</td>\n",
       "      <td>3.666667</td>\n",
       "      <td>1.985728</td>\n",
       "      <td>27511.411765</td>\n",
       "      <td>18995.294118</td>\n",
       "      <td>46506.705882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>22</td>\n",
       "      <td>12</td>\n",
       "      <td>68.8</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>86</td>\n",
       "      <td>...</td>\n",
       "      <td>1.555556</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>2.181818</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.227273</td>\n",
       "      <td>2.250000</td>\n",
       "      <td>0.707341</td>\n",
       "      <td>43042.818182</td>\n",
       "      <td>6786.818182</td>\n",
       "      <td>49829.636364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3005</th>\n",
       "      <td>56.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>34</td>\n",
       "      <td>17</td>\n",
       "      <td>72.9</td>\n",
       "      <td>0.529412</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>138</td>\n",
       "      <td>...</td>\n",
       "      <td>1.254902</td>\n",
       "      <td>2.647059</td>\n",
       "      <td>2.558824</td>\n",
       "      <td>5.117647</td>\n",
       "      <td>1.147059</td>\n",
       "      <td>2.294118</td>\n",
       "      <td>1.255637</td>\n",
       "      <td>27809.176471</td>\n",
       "      <td>22610.588235</td>\n",
       "      <td>50419.764706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3006</th>\n",
       "      <td>56.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>70.5</td>\n",
       "      <td>0.538462</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>38</td>\n",
       "      <td>...</td>\n",
       "      <td>1.266667</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>1.769231</td>\n",
       "      <td>4.600000</td>\n",
       "      <td>0.692308</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>0.912540</td>\n",
       "      <td>2339.076923</td>\n",
       "      <td>6016.923077</td>\n",
       "      <td>8356.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3007</th>\n",
       "      <td>56.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>18</td>\n",
       "      <td>6</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>61</td>\n",
       "      <td>...</td>\n",
       "      <td>1.444444</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.611111</td>\n",
       "      <td>4.833333</td>\n",
       "      <td>0.611111</td>\n",
       "      <td>1.833333</td>\n",
       "      <td>1.323545</td>\n",
       "      <td>2518.888889</td>\n",
       "      <td>4288.333333</td>\n",
       "      <td>6807.222222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3008</th>\n",
       "      <td>56.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3009</th>\n",
       "      <td>56.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>75.5</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>133.333333</td>\n",
       "      <td>133.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3010 rows × 54 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      FIPS_STATE_CODE  FIPS_COUNTY_CODE  CNTY_BENE_CT  CNTY_BENE_USRS  \\\n",
       "0                 1.0               1.0            16              13   \n",
       "1                 1.0               3.0            95              65   \n",
       "2                 1.0               5.0            14              12   \n",
       "3                 1.0               7.0            17              12   \n",
       "4                 1.0               9.0            22              12   \n",
       "...               ...               ...           ...             ...   \n",
       "3005             56.0              37.0            34              17   \n",
       "3006             56.0              39.0            13               5   \n",
       "3007             56.0              41.0            18               6   \n",
       "3008             56.0              43.0             8               0   \n",
       "3009             56.0              45.0            15               2   \n",
       "\n",
       "      CNTY_MEAN_AGE  CNTY_SEX_PROP  TOT_CNCR_CASES  TOT_STRKETIA_CASES  \\\n",
       "0              65.4       0.187500             3.0                 3.0   \n",
       "1              70.3       0.463158            18.0                 8.0   \n",
       "2              72.4       0.642857             0.0                 0.0   \n",
       "3              69.0       0.411765             2.0                 3.0   \n",
       "4              68.8       0.636364             4.0                 1.0   \n",
       "...             ...            ...             ...                 ...   \n",
       "3005           72.9       0.529412             8.0                 3.0   \n",
       "3006           70.5       0.538462             3.0                 2.0   \n",
       "3007           72.0       0.500000             2.0                 1.0   \n",
       "3008            NaN       0.625000             0.0                 0.0   \n",
       "3009           75.5       0.600000             0.0                 0.0   \n",
       "\n",
       "      TOT_ESRD_CASES  CNTY_TOT_VSTS  ...  PRVDRS_PU  TOT_IPDAYS_PU  \\\n",
       "0                5.0            112  ...   2.307692       3.692308   \n",
       "1               18.0            720  ...   3.297436       5.615385   \n",
       "2                4.0             92  ...   1.888889       4.833333   \n",
       "3                4.0            152  ...   2.833333       4.666667   \n",
       "4                4.0             86  ...   1.555556       5.500000   \n",
       "...              ...            ...  ...        ...            ...   \n",
       "3005             4.0            138  ...   1.254902       2.647059   \n",
       "3006             1.0             38  ...   1.266667       0.600000   \n",
       "3007             0.0             61  ...   1.444444       0.000000   \n",
       "3008             0.0              0  ...        NaN            NaN   \n",
       "3009             1.0              2  ...   0.333333       0.000000   \n",
       "\n",
       "      TOT_CC_PREV  TOT_CCS_PU  TOT_CC_INCD  TOT_NEW_CC_PU  TOT_ELIX_PU  \\\n",
       "0        4.937500    6.076923     2.562500       3.153846     1.492308   \n",
       "1        3.747368    5.476923     1.705263       2.492308     1.651029   \n",
       "2        3.500000    4.083333     1.785714       2.083333     1.791204   \n",
       "3        4.470588    6.333333     2.588235       3.666667     1.985728   \n",
       "4        2.181818    4.000000     1.227273       2.250000     0.707341   \n",
       "...           ...         ...          ...            ...          ...   \n",
       "3005     2.558824    5.117647     1.147059       2.294118     1.255637   \n",
       "3006     1.769231    4.600000     0.692308       1.800000     0.912540   \n",
       "3007     1.611111    4.833333     0.611111       1.833333     1.323545   \n",
       "3008     0.000000         NaN     0.000000            NaN          NaN   \n",
       "3009     0.466667    3.500000     0.333333       2.500000     1.000000   \n",
       "\n",
       "      TOT_IP_COST_PB  TOT_OP_COST_PB   TOT_COST_PB  \n",
       "0       33386.750000    11793.125000  45179.875000  \n",
       "1       53552.105263    27044.000000  80596.105263  \n",
       "2       54292.571429    12438.571429  66731.142857  \n",
       "3       27511.411765    18995.294118  46506.705882  \n",
       "4       43042.818182     6786.818182  49829.636364  \n",
       "...              ...             ...           ...  \n",
       "3005    27809.176471    22610.588235  50419.764706  \n",
       "3006     2339.076923     6016.923077   8356.000000  \n",
       "3007     2518.888889     4288.333333   6807.222222  \n",
       "3008        0.000000        0.000000      0.000000  \n",
       "3009        0.000000      133.333333    133.333333  \n",
       "\n",
       "[3010 rows x 54 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc_county = cc_county.reset_index()\n",
    "cc_county"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "cc_county.to_csv(\"/home/kylier/python/DS/data/PUF/PUF_COUNTY_SUMMARY.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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