DHT Portfolio Analysis - use NIH Reporter.ipynb

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   "source": [
    "# Digital Health Portfolio Analysis Scripts\n",
    "## For any questions, please contact the corresponding author of the publication\n",
    "==============================================================================================\n",
    "\n",
    "### Files needed to be placed in the datapath folder specified below include:\n",
    "- Grant portfolio downloaded from publicly available NIH Reporter Grants Module: ttps://reporter.nih.gov\n",
    "    - filename labeled here as: 'Reporter_SearchResult_Export_06Aug2024_060442.xlsx' \n",
    "    - -link: https://reporter.nih.gov/search/7mdx8PkeIk2QW5JBbwGDgA/projects\n",
    "- NIH Budgets per IC: https://report.nih.gov/funding/nih-budget-and-spending-data-past-fiscal-years/budget-and-spending\n",
    "    - Grants, Contracts, and Other Mechanisms: Awards, average funding, and total funding by Institute/Center (~160KB)\n",
    "    - filename 'T106 2023 RES GR_OTHER MECH and CNTRCTS_Num and Tot and Avg Fund by IC and Mech.xlsx'\n",
    "- Excel of IC Names and Acronyms needed to translate between different dataframes here.  Ths is in the github folder.\n",
    "    - filename: IC_Names.xlsx"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b4329be-5053-4244-ad05-9ce6c0a32dd0",
   "metadata": {},
   "source": [
    "### Search Terms: \"decentralized trial\",  \"remote trial\", \"virtual trial\", \"digital health\", \"digital health technology\", \"digital health technologies\", \"mobile app\", \"mobile application\", \"mobile health\", \"mobile health technology\", \"mobile health technologies\", mhealth, \"smart connected health\", \"remote patient monitoring\", \"connected home health\", \"wearable device\", wearable, accelerometer, accelerometry, actigraph, actigraphy, \"remote healthcare delivery\", \"virtual care\", \"digital patient engagement\", telehealth, telemedicine. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3c63f813-5df8-476a-98e9-8f71168bc19e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[WinError 183] Cannot create a file when that file already exists: 'C:\\\\Users\\\\radmantc\\\\\\\\OneDrive - National Institutes of Health\\\\\\\\Digital Health Portfolio Analysis\\\\Outputs Reporter\\\\'\n"
     ]
    }
   ],
   "source": [
    "datapath='C:\\\\Users\\\\radmantc\\\\Documents\\\\Digital Health Portfolio Analysis\\\\MasterData\\\\'#source data downloaded from iSearch\n",
    "\n",
    "outpath='C:\\\\Users\\\\radmantc\\\\\\OneDrive - National Institutes of Health\\\\\\Digital Health Portfolio Analysis\\\\Outputs Reporter\\\\'\n",
    "\n",
    "import os\n",
    "try:\n",
    "    os.mkdir(outpath)\n",
    "    #os.mkdir(outpath + '\\\\blah blah subfolder\\\\')\n",
    "except OSError as error:\n",
    "    print(error)  \n",
    "    \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8f6ff921-ed6b-4b6d-811a-fc61cae75377",
   "metadata": {},
   "outputs": [],
   "source": [
    "#load iSearch results\n",
    "inputname='Reporter_SearchResult_Export_06Aug2024_060442.xlsx' \n",
    "dfi = pd.read_excel(datapath + inputname) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "58e4e365-0e14-4a85-b485-4fafeb867a51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['NIH Spending Categorization', 'Project Abstract', 'Project Terms',\n",
       "       'Project Title', 'Public Health Relevance', 'Administering IC',\n",
       "       'Application ID', 'Award Notice Date', 'Opportunity Number',\n",
       "       'Project Number', 'Type', 'Activity', 'IC', 'Serial Number',\n",
       "       'Support Year', 'Suffix', 'Program Official Information',\n",
       "       'Project Start Date', 'Project End Date', 'Study Section',\n",
       "       'Subproject Number', 'Contact PI Person ID',\n",
       "       'Contact PI / Project Leader', 'Other PI or Project Leader(s)',\n",
       "       'Congressional District', 'Department', 'Primary DUNS', 'Primary UEI',\n",
       "       'DUNS Number', 'UEI', 'FIPS', 'Latitude', 'Longitude',\n",
       "       'Organization ID (IPF)', 'Organization Name', 'Organization City',\n",
       "       'Organization State', 'Organization Type', 'Organization Zip',\n",
       "       'Organization Country', 'ARRA Indicator', 'Budget Start Date',\n",
       "       'Budget End Date', 'CFDA Code', 'Funding Mechanism', 'Fiscal Year',\n",
       "       'Total Cost', 'Total Cost (Sub Projects)', 'Funding IC(s)',\n",
       "       'Direct Cost IC', 'InDirect Cost IC', 'NIH COVID-19 Response',\n",
       "       'Total Cost IC'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfi.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "dbbd3f61-ee4c-44f2-a177-c2169f566985",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['C06', 'D43', 'DP1', 'DP2', 'DP3', 'DP5', 'DP7', 'F30', 'F31',\n",
       "       'F32', 'F99', 'FI2', 'G08', 'G11', 'G12', 'G13', 'G20', 'K00',\n",
       "       'K01', 'K02', 'K07', 'K08', 'K12', 'K18', 'K22', 'K23', 'K24',\n",
       "       'K25', 'K26', 'K43', 'K76', 'K99', 'KL2', 'N01', 'N02', 'N43',\n",
       "       'N44', 'OT2', 'P01', 'P20', 'P2C', 'P30', 'P41', 'P50', 'P51',\n",
       "       'P60', 'R00', 'R01', 'R03', 'R13', 'R15', 'R16', 'R18', 'R21',\n",
       "       'R24', 'R25', 'R33', 'R34', 'R35', 'R36', 'R37', 'R41', 'R42',\n",
       "       'R43', 'R44', 'R56', 'R61', 'RC2', 'RF1', 'RM1', 'S10', 'SB1',\n",
       "       'T15', 'T32', 'T35', 'TL1', 'U01', 'U10', 'U13', 'U18', 'U19',\n",
       "       'U24', 'U2C', 'U2R', 'U34', 'U41', 'U43', 'U44', 'U45', 'U54',\n",
       "       'UB1', 'UC4', 'UE5', 'UF1', 'UG1', 'UG3', 'UH2', 'UH3', 'UH4',\n",
       "       'UL1', 'UM1', 'Y01', 'ZIA', 'ZIC', 'ZID', 'ZIE', 'ZIH', 'ZIJ'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#REMOVE ACTIVITY CODES ETC....REPORTER DOES NOT HAVE THIS FUNC\n",
    "np.sort(dfi['Activity'].unique())\n",
    "##NOTE: this is the list from iSearch: \n",
    "#['C06', 'D43', 'DP1', 'DP2', 'DP3', 'DP5', 'F30', 'F31', 'F32',\n",
    "#       'F99', 'G08', 'G12', 'G13', 'G20', 'K00', 'K01', 'K02', 'K07',\n",
    "#       'K08', 'K12', 'K18', 'K22', 'K23', 'K24', 'K25', 'K26', 'K43',\n",
    "#       'K76', 'K99', 'KL2', 'OT2', 'P01', 'P20', 'P2C', 'P30', 'P41',\n",
    "#       'P42', 'P50', 'R00', 'R01', 'R03', 'R13', 'R15', 'R16', 'R18',\n",
    "#       'R21', 'R24', 'R25', 'R33', 'R34', 'R35', 'R36', 'R37', 'R41',\n",
    "#       'R42', 'R43', 'R44', 'R56', 'R61', 'RC2', 'RF1', 'RM1', 'S10',\n",
    "#       'SB1', 'T15', 'T32', 'T35', 'TL1', 'U01', 'U10', 'U18', 'U19',\n",
    "#       'U24', 'U2C', 'U2R', 'U34', 'U41', 'U43', 'U44', 'U54', 'UE5',\n",
    "#       'UF1', 'UG1', 'UG3', 'UH2', 'UH3', 'UH4', 'UL1', 'UM1'],"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "768e07bb-252e-4102-af20-31d371c15358",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2015 2016 2017 2018 2019 2020 2021 2022 2023]\n"
     ]
    }
   ],
   "source": [
    "#make sure you have year range correct:\n",
    "dfi['Fiscal Year']=dfi['Fiscal Year'].astype(int)\n",
    "endyear=2023\n",
    "dfi=dfi[dfi['Fiscal Year']<=endyear]\n",
    "endyear=str(endyear) #needed to label plots later\n",
    "print( np.sort(dfi['Fiscal Year'].unique()) ) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f07628c6-3f30-4284-a8f4-9169cd32e833",
   "metadata": {},
   "outputs": [],
   "source": [
    "#remove L mechanisms\n",
    "dfi=dfi[~dfi[\"Activity\"].str.get(0).isin(['L'])]\n",
    "dfi=dfi[~dfi[\"Activity\"].str.get(0).isin(['S'])]\n",
    "dfi=dfi[~dfi[\"Activity\"].str.get(0).isin(['Y'])]\n",
    "dfi=dfi[~dfi[\"Activity\"].str.get(0).isin(['Z'])]\n",
    "\n",
    "dfi = dfi.loc[dfi['Type'].isin([1, 2, 5])]\n",
    "dfi['Type']=dfi['Type'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f29ae149-1f62-4eb1-a5dd-49cf67b4525a",
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    {
     "data": {
      "text/plain": [
       "array(['C06', 'D43', 'DP1', 'DP2', 'DP3', 'DP5', 'DP7', 'F30', 'F31',\n",
       "       'F32', 'F99', 'FI2', 'G08', 'G11', 'G12', 'G13', 'G20', 'K00',\n",
       "       'K01', 'K02', 'K07', 'K08', 'K12', 'K18', 'K22', 'K23', 'K24',\n",
       "       'K25', 'K26', 'K43', 'K76', 'K99', 'KL2', 'OT2', 'P01', 'P20',\n",
       "       'P2C', 'P30', 'P41', 'P50', 'P60', 'R00', 'R01', 'R03', 'R13',\n",
       "       'R15', 'R16', 'R18', 'R21', 'R24', 'R25', 'R33', 'R34', 'R35',\n",
       "       'R36', 'R37', 'R41', 'R42', 'R43', 'R44', 'R56', 'R61', 'RC2',\n",
       "       'RF1', 'RM1', 'T15', 'T32', 'T35', 'TL1', 'U01', 'U10', 'U13',\n",
       "       'U18', 'U19', 'U24', 'U2C', 'U2R', 'U34', 'U41', 'U43', 'U44',\n",
       "       'U54', 'UB1', 'UE5', 'UF1', 'UG1', 'UG3', 'UH2', 'UH3', 'UH4',\n",
       "       'UL1', 'UM1'], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sort(dfi['Activity'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a102f3ba-866b-40b1-9c8a-22ad8872a1ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 5])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sort(dfi['Type'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27c80477-1ba0-4e16-945c-3456afc97537",
   "metadata": {},
   "source": [
    "## Supplemental Table 9: Total DHT and yearly funding prior to deduplication:\r",
    "### Columns: \n",
    "Total DHT Cost,s\t$ change from previous F,Y\ty-o-y % chang\n",
    "\n",
    "(note: Supplemental Table 10 is created direct from iSearch Facets view)n:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "75d097a6-eaff-4bbf-a848-7009d1a05807",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Yearly DHT funding per year =  Fiscal Year\n",
      "2015    3.290493e+08\n",
      "2016    4.458756e+08\n",
      "2017    6.202341e+08\n",
      "2018    7.802221e+08\n",
      "2019    9.554427e+08\n",
      "2020    1.018878e+09\n",
      "2021    1.370943e+09\n",
      "2022    1.551505e+09\n",
      "2023    1.733144e+09\n",
      "dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\radmantc\\AppData\\Local\\Temp\\ipykernel_16992\\3627710275.py:2: FutureWarning: The provided callable <function sum at 0x0000019E683FD440> is currently using DataFrameGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"sum\" instead.\n",
      "  fundsIC_all=pd.pivot_table(dfi,index=[\"Administering IC\"],columns=[\"Fiscal Year\"],values=\"Total Cost\",aggfunc=np.sum)\n"
     ]
    }
   ],
   "source": [
    "#yearly funding per year for DHTs prior to dedup\n",
    "fundsIC_all=pd.pivot_table(dfi,index=[\"Administering IC\"],columns=[\"Fiscal Year\"],values=\"Total Cost\",aggfunc=np.sum)\n",
    "print('Yearly DHT funding per year = ', fundsIC_all.sum(axis=0)) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4468d4be-e688-4fbf-9998-57f42a846605",
   "metadata": {},
   "source": [
    "# Figure 2.  Total Funding for DHT Grants by ICs Tier from FY2015 to FY 2023"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3198937c-08d0-4710-bd95-a19a508c6eda",
   "metadata": {},
   "source": [
    "## Fig. 2, step a) Read in total IC budgets from file to derive the 3 budget tiers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f9f88948-ec23-4982-a137-44259518c352",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['FIC' 'FY Total' 'NCATS' 'NCCAM**' 'NCCIH' 'NCI' 'NEI' 'NHGRI' 'NHLBI'\n",
      " 'NIA' 'NIAAA' 'NIAID' 'NIAMS' 'NIBIB' 'NICHD' 'NIDA' 'NIDCD' 'NIDCR'\n",
      " 'NIDDK' 'NIEHS' 'NIGMS' 'NIMH' 'NIMHD' 'NINDS' 'NINR' 'NLM' 'OD' 'OD '\n",
      " 'ORS' 'ORWH' 'TOTAL']\n",
      "                         Budget\n",
      "Fiscal Year Acronym            \n",
      "2014        FIC        68700283\n",
      "            NCATS     526957260\n",
      "            NCCAM**   103732556\n",
      "            NCI      3501043439\n",
      "            NEI       564640085\n",
      "...                         ...\n",
      "2023        NIMHD     467468005\n",
      "            NINDS    2582049640\n",
      "            NINR      163524421\n",
      "            NLM        84624714\n",
      "            OD       1389381330\n",
      "\n",
      "[253 rows x 1 columns]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\radmantc\\AppData\\Local\\anaconda3\\Lib\\site-packages\\openpyxl\\worksheet\\header_footer.py:48: UserWarning: Cannot parse header or footer so it will be ignored\n",
      "  warn(\"\"\"Cannot parse header or footer so it will be ignored\"\"\")\n"
     ]
    }
   ],
   "source": [
    "#https://report.nih.gov/funding/nih-budget-and-spending-data-past-fiscal-years/budget-and-spending\n",
    "#Grants, Contracts, and Other Mechanisms: Awards, average funding, and total funding by Institute/Center (~160KB)\n",
    "budgets = pd.read_excel(datapath + 'T106 2023 RES GR_OTHER MECH and CNTRCTS_Num and Tot and Avg Fund by IC and Mech.xlsx', header=1) #note older versions of this database have more header lines \n",
    "\n",
    "budgets.rename(columns={'Institute/Center':'Acronym', 'Total Funding':'Budget'}, inplace=True) #needed to eventually do a join to grant data\n",
    "print(np.unique(budgets.Acronym))  #note Total is in some rows\n",
    "\n",
    "budgetsTmp=budgets.set_index(['Fiscal Year', 'Acronym'])\n",
    "\n",
    "#budgetsTmp = budgetsTmp[ ~budgetsTmp.index.get_level_values(1).str.contains('TOTAL') ]\n",
    "budgetsTmp = budgetsTmp[ (~budgetsTmp.index.get_level_values(1).str.contains('TOTAL') ) & (~budgetsTmp.index.get_level_values(1).str.contains('FY Total')) ]\n",
    "\n",
    "budgetsTmp=budgetsTmp.iloc[:, 0].to_frame()\n",
    "print(budgetsTmp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "01ee5f23-df63-4128-bc17-28798ceeb9fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\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>Fiscal Year</th>\n",
       "      <th>Acronym</th>\n",
       "      <th>Budget</th>\n",
       "      <th>Research Grants - Total Number</th>\n",
       "      <th>Research Grants - \\nTotal Funding</th>\n",
       "      <th>Research Grants  - \\nAverage Funding</th>\n",
       "      <th>R&amp;D Contracts - \\nTotal Number</th>\n",
       "      <th>R&amp;D Contracts - \\nTotal Funding</th>\n",
       "      <th>R&amp;D Contracts - \\nAverage Funding</th>\n",
       "      <th>Fellowships - \\nTotal Number</th>\n",
       "      <th>...</th>\n",
       "      <th>Fellowships - \\nAverage Funding</th>\n",
       "      <th>Training Grants - \\nTotal Number</th>\n",
       "      <th>Training Grants -\\nTotal Funding</th>\n",
       "      <th>Training Grants -\\nAverage Funding</th>\n",
       "      <th>Construction Grants - Total Number</th>\n",
       "      <th>Construction Grants -\\nTotal Funding</th>\n",
       "      <th>Construction Grants -Average Funding</th>\n",
       "      <th>Other Awards - \\nTotal Number</th>\n",
       "      <th>Other Awards - \\nTotal Funding</th>\n",
       "      <th>Other Awards - \\nAverage Funding</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2014</td>\n",
       "      <td>FY Total</td>\n",
       "      <td>23522203229</td>\n",
       "      <td>42167</td>\n",
       "      <td>20986917504</td>\n",
       "      <td>497709.524130</td>\n",
       "      <td>1142</td>\n",
       "      <td>1691706935</td>\n",
       "      <td>1.481355e+06</td>\n",
       "      <td>3060</td>\n",
       "      <td>...</td>\n",
       "      <td>43771.649020</td>\n",
       "      <td>1929</td>\n",
       "      <td>598836973</td>\n",
       "      <td>310439.073613</td>\n",
       "      <td>16</td>\n",
       "      <td>67190904</td>\n",
       "      <td>4.199432e+06</td>\n",
       "      <td>187</td>\n",
       "      <td>43609667</td>\n",
       "      <td>2.332068e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>2015</td>\n",
       "      <td>FY Total</td>\n",
       "      <td>23488164224</td>\n",
       "      <td>42166</td>\n",
       "      <td>21174344957</td>\n",
       "      <td>502166.317815</td>\n",
       "      <td>1003</td>\n",
       "      <td>1497257096</td>\n",
       "      <td>1.492779e+06</td>\n",
       "      <td>3172</td>\n",
       "      <td>...</td>\n",
       "      <td>43195.195776</td>\n",
       "      <td>1899</td>\n",
       "      <td>623122581</td>\n",
       "      <td>328131.954186</td>\n",
       "      <td>18</td>\n",
       "      <td>8009492</td>\n",
       "      <td>4.449718e+05</td>\n",
       "      <td>194</td>\n",
       "      <td>48414937</td>\n",
       "      <td>2.495615e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>2016</td>\n",
       "      <td>FY Total</td>\n",
       "      <td>25225846739</td>\n",
       "      <td>43431</td>\n",
       "      <td>22649752290</td>\n",
       "      <td>521511.185328</td>\n",
       "      <td>1231</td>\n",
       "      <td>1684760524</td>\n",
       "      <td>1.368611e+06</td>\n",
       "      <td>3330</td>\n",
       "      <td>...</td>\n",
       "      <td>44255.776577</td>\n",
       "      <td>1923</td>\n",
       "      <td>656496704</td>\n",
       "      <td>341391.941758</td>\n",
       "      <td>13</td>\n",
       "      <td>5894192</td>\n",
       "      <td>4.533994e+05</td>\n",
       "      <td>209</td>\n",
       "      <td>81571293</td>\n",
       "      <td>3.902933e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>2017</td>\n",
       "      <td>FY Total</td>\n",
       "      <td>26972060072</td>\n",
       "      <td>44445</td>\n",
       "      <td>24031670764</td>\n",
       "      <td>540705.833367</td>\n",
       "      <td>1234</td>\n",
       "      <td>1958815068</td>\n",
       "      <td>1.587370e+06</td>\n",
       "      <td>3511</td>\n",
       "      <td>...</td>\n",
       "      <td>45078.143549</td>\n",
       "      <td>1913</td>\n",
       "      <td>663623617</td>\n",
       "      <td>346902.047569</td>\n",
       "      <td>1</td>\n",
       "      <td>4900000</td>\n",
       "      <td>4.900000e+06</td>\n",
       "      <td>233</td>\n",
       "      <td>154781261</td>\n",
       "      <td>6.642973e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>2018</td>\n",
       "      <td>FY Total</td>\n",
       "      <td>28975943278</td>\n",
       "      <td>46930</td>\n",
       "      <td>25906788735</td>\n",
       "      <td>552030.443959</td>\n",
       "      <td>1316</td>\n",
       "      <td>1863662206</td>\n",
       "      <td>1.416157e+06</td>\n",
       "      <td>3571</td>\n",
       "      <td>...</td>\n",
       "      <td>46069.942593</td>\n",
       "      <td>1940</td>\n",
       "      <td>691323660</td>\n",
       "      <td>356352.402062</td>\n",
       "      <td>2</td>\n",
       "      <td>7800000</td>\n",
       "      <td>3.900000e+06</td>\n",
       "      <td>200</td>\n",
       "      <td>341852912</td>\n",
       "      <td>1.709265e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>2019</td>\n",
       "      <td>FY Total</td>\n",
       "      <td>31192343463</td>\n",
       "      <td>49092</td>\n",
       "      <td>28143252479</td>\n",
       "      <td>573275.736963</td>\n",
       "      <td>1188</td>\n",
       "      <td>1726073825</td>\n",
       "      <td>1.452924e+06</td>\n",
       "      <td>3732</td>\n",
       "      <td>...</td>\n",
       "      <td>46439.693998</td>\n",
       "      <td>1913</td>\n",
       "      <td>688301270</td>\n",
       "      <td>359802.023001</td>\n",
       "      <td>14</td>\n",
       "      <td>69467663</td>\n",
       "      <td>4.961976e+06</td>\n",
       "      <td>261</td>\n",
       "      <td>391935288</td>\n",
       "      <td>1.501668e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>2020</td>\n",
       "      <td>FY Total</td>\n",
       "      <td>31888383542</td>\n",
       "      <td>50046</td>\n",
       "      <td>29256433921</td>\n",
       "      <td>584590.854834</td>\n",
       "      <td>943</td>\n",
       "      <td>1127714222</td>\n",
       "      <td>1.195879e+06</td>\n",
       "      <td>3980</td>\n",
       "      <td>...</td>\n",
       "      <td>47594.987437</td>\n",
       "      <td>1944</td>\n",
       "      <td>716845013</td>\n",
       "      <td>368747.434671</td>\n",
       "      <td>10</td>\n",
       "      <td>55043091</td>\n",
       "      <td>5.504309e+06</td>\n",
       "      <td>189</td>\n",
       "      <td>542919245</td>\n",
       "      <td>2.872589e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>2021</td>\n",
       "      <td>FY Total</td>\n",
       "      <td>33340390184</td>\n",
       "      <td>50410</td>\n",
       "      <td>30172387202</td>\n",
       "      <td>598539.718350</td>\n",
       "      <td>930</td>\n",
       "      <td>1016713978</td>\n",
       "      <td>1.093241e+06</td>\n",
       "      <td>4224</td>\n",
       "      <td>...</td>\n",
       "      <td>48767.641098</td>\n",
       "      <td>1931</td>\n",
       "      <td>722141870</td>\n",
       "      <td>373973.003625</td>\n",
       "      <td>12</td>\n",
       "      <td>57500000</td>\n",
       "      <td>4.791667e+06</td>\n",
       "      <td>215</td>\n",
       "      <td>1165652618</td>\n",
       "      <td>5.421640e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Fiscal Year   Acronym       Budget  Research Grants - Total Number  \\\n",
       "27          2014  FY Total  23522203229                           42167   \n",
       "54          2015  FY Total  23488164224                           42166   \n",
       "80          2016  FY Total  25225846739                           43431   \n",
       "106         2017  FY Total  26972060072                           44445   \n",
       "132         2018  FY Total  28975943278                           46930   \n",
       "158         2019  FY Total  31192343463                           49092   \n",
       "184         2020  FY Total  31888383542                           50046   \n",
       "210         2021  FY Total  33340390184                           50410   \n",
       "\n",
       "      Research Grants - \\nTotal Funding  \\\n",
       "27                          20986917504   \n",
       "54                          21174344957   \n",
       "80                          22649752290   \n",
       "106                         24031670764   \n",
       "132                         25906788735   \n",
       "158                         28143252479   \n",
       "184                         29256433921   \n",
       "210                         30172387202   \n",
       "\n",
       "     Research Grants  - \\nAverage Funding   R&D Contracts - \\nTotal Number  \\\n",
       "27                           497709.524130                            1142   \n",
       "54                           502166.317815                            1003   \n",
       "80                           521511.185328                            1231   \n",
       "106                          540705.833367                            1234   \n",
       "132                          552030.443959                            1316   \n",
       "158                          573275.736963                            1188   \n",
       "184                          584590.854834                             943   \n",
       "210                          598539.718350                             930   \n",
       "\n",
       "     R&D Contracts - \\nTotal Funding   R&D Contracts - \\nAverage Funding  \\\n",
       "27                         1691706935                       1.481355e+06   \n",
       "54                         1497257096                       1.492779e+06   \n",
       "80                         1684760524                       1.368611e+06   \n",
       "106                        1958815068                       1.587370e+06   \n",
       "132                        1863662206                       1.416157e+06   \n",
       "158                        1726073825                       1.452924e+06   \n",
       "184                        1127714222                       1.195879e+06   \n",
       "210                        1016713978                       1.093241e+06   \n",
       "\n",
       "     Fellowships - \\nTotal Number  ...  Fellowships - \\nAverage Funding  \\\n",
       "27                           3060  ...                     43771.649020   \n",
       "54                           3172  ...                     43195.195776   \n",
       "80                           3330  ...                     44255.776577   \n",
       "106                          3511  ...                     45078.143549   \n",
       "132                          3571  ...                     46069.942593   \n",
       "158                          3732  ...                     46439.693998   \n",
       "184                          3980  ...                     47594.987437   \n",
       "210                          4224  ...                     48767.641098   \n",
       "\n",
       "     Training Grants - \\nTotal Number  Training Grants -\\nTotal Funding  \\\n",
       "27                               1929                         598836973   \n",
       "54                               1899                         623122581   \n",
       "80                               1923                         656496704   \n",
       "106                              1913                         663623617   \n",
       "132                              1940                         691323660   \n",
       "158                              1913                         688301270   \n",
       "184                              1944                         716845013   \n",
       "210                              1931                         722141870   \n",
       "\n",
       "     Training Grants -\\nAverage Funding  Construction Grants - Total Number   \\\n",
       "27                        310439.073613                                   16   \n",
       "54                        328131.954186                                   18   \n",
       "80                        341391.941758                                   13   \n",
       "106                       346902.047569                                    1   \n",
       "132                       356352.402062                                    2   \n",
       "158                       359802.023001                                   14   \n",
       "184                       368747.434671                                   10   \n",
       "210                       373973.003625                                   12   \n",
       "\n",
       "     Construction Grants -\\nTotal Funding  \\\n",
       "27                               67190904   \n",
       "54                                8009492   \n",
       "80                                5894192   \n",
       "106                               4900000   \n",
       "132                               7800000   \n",
       "158                              69467663   \n",
       "184                              55043091   \n",
       "210                              57500000   \n",
       "\n",
       "     Construction Grants -Average Funding  Other Awards - \\nTotal Number   \\\n",
       "27                           4.199432e+06                             187   \n",
       "54                           4.449718e+05                             194   \n",
       "80                           4.533994e+05                             209   \n",
       "106                          4.900000e+06                             233   \n",
       "132                          3.900000e+06                             200   \n",
       "158                          4.961976e+06                             261   \n",
       "184                          5.504309e+06                             189   \n",
       "210                          4.791667e+06                             215   \n",
       "\n",
       "     Other Awards - \\nTotal Funding  Other Awards - \\nAverage Funding  \n",
       "27                         43609667                      2.332068e+05  \n",
       "54                         48414937                      2.495615e+05  \n",
       "80                         81571293                      3.902933e+05  \n",
       "106                       154781261                      6.642973e+05  \n",
       "132                       341852912                      1.709265e+06  \n",
       "158                       391935288                      1.501668e+06  \n",
       "184                       542919245                      2.872589e+06  \n",
       "210                      1165652618                      5.421640e+06  \n",
       "\n",
       "[8 rows x 21 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#note this dataframe has \"TOTAL\" and FY Total entries that have been removed in budgetsTmp dataframe\n",
    "budgets[budgets.Acronym=='TOTAL']  #\n",
    "budgets[budgets.Acronym=='FY Total']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b2bee911-1f70-49d6-a9d9-39ab32790f5d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\radmantc\\AppData\\Local\\Temp\\ipykernel_16992\\2949806020.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  budgetMean['Budget Tier'][budgetMean['Budget']<3.76e+08]='< 376M'\n",
      "C:\\Users\\radmantc\\AppData\\Local\\Temp\\ipykernel_16992\\2949806020.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  budgetMean['Budget Tier'][(budgetMean['Budget']>3.76e+08) & (budgetMean['Budget']<1.13e+09)]='376M to 1.13B'  #NIDA=11535629938\n",
      "C:\\Users\\radmantc\\AppData\\Local\\Temp\\ipykernel_16992\\2949806020.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  budgetMean['Budget Tier'][budgetMean['Budget']>1.13e+09]='> 1.13B'\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Index(['ORS', 'ORWH', 'NLM', 'FIC', 'NCCAM**', 'NINR', 'NCCIH', 'NIMHD',\n",
       "       'NIDCR', 'NIBIB', 'NIDCD', 'NIAAA', 'OD ', 'NHGRI', 'NIAMS', 'NIEHS',\n",
       "       'NCATS', 'NEI', 'OD', 'NICHD', 'NIDA', 'NIMH', 'NIDDK', 'NINDS', 'NIA',\n",
       "       'NIGMS', 'NHLBI', 'NIAID', 'NCI'],\n",
       "      dtype='object', name='Acronym')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calculate budget tiers:\n",
    "budgetMean = pd.pivot_table(budgetsTmp, values='Budget', aggfunc=\"mean\", index=['Acronym']) #\n",
    "budgetMean.sort_values(by='Budget',inplace=True)\n",
    "budgetMean['Budget Tier']='none'\n",
    "budgetMean['Budget Tier'][budgetMean['Budget']<3.76e+08]='< 376M'\n",
    "budgetMean['Budget Tier'][(budgetMean['Budget']>3.76e+08) & (budgetMean['Budget']<1.13e+09)]='376M to 1.13B'  #NIDA=11535629938 \t\n",
    "budgetMean['Budget Tier'][budgetMean['Budget']>1.13e+09]='> 1.13B'\n",
    "budgetMean.columns=['budgetMean','Budget Tier']\n",
    "budgetMean.index"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28602652-12ec-420d-a3fa-fdfee801bef7",
   "metadata": {},
   "source": [
    "## Fig. 2, step b): read in codes of IC names to acronyms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "598da3d9-f959-4ba1-b1cf-45e9fe341866",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Admin IC Long Admin IC Short\n",
      "0            FIC             TW\n",
      "1          NCATS             TR\n",
      "2          NCCIH             AT\n",
      "3            NCI             CA\n",
      "4            NEI             EY\n",
      "5          NHGRI             HG\n",
      "6          NHLBI             HL\n",
      "7            NIA             AG\n",
      "8          NIAAA             AA\n",
      "9          NIAID             AI\n",
      "10         NIAMS             AR\n",
      "11         NIBIB             EB\n",
      "12         NICHD             HD\n",
      "13          NIDA             DA\n",
      "14         NIDCD             DC\n",
      "15         NIDCR             DE\n",
      "16         NIDDK             DK\n",
      "17         NIEHS             ES\n",
      "18         NIGMS             GM\n",
      "19          NIMH             MH\n",
      "20         NIMHD             MD\n",
      "21         NINDS             NS\n",
      "22          NINR             NR\n",
      "23           NLM             LM\n",
      "24            OD             OD \n",
      "========================================\n",
      "\n",
      "   Admin IC Long Admin IC Short  Fiscal Year        DHT$\n",
      "0            FIC             TW         2015   3142955.0\n",
      "0            FIC             TW         2016   4004387.0\n",
      "0            FIC             TW         2017   6647302.0\n",
      "0            FIC             TW         2018   6633309.0\n",
      "0            FIC             TW         2019   8061701.0\n",
      "..           ...            ...          ...         ...\n",
      "24            OD             OD         2019  54892673.0\n",
      "24            OD             OD         2020  10251048.0\n",
      "24            OD             OD         2021  51188384.0\n",
      "24            OD             OD         2022  33919651.0\n",
      "24            OD             OD         2023  72279909.0\n",
      "\n",
      "[224 rows x 4 columns]\n"
     ]
    }
   ],
   "source": [
    "IC = pd.read_excel(datapath + 'IC_Names.xlsx') #qvr database\n",
    "IC.rename(columns={'Acronym':'Admin IC Long','Code': 'Admin IC Short'}, inplace=True)\n",
    "#IC.rename(columns={'Code': 'Admin IC'}, inplace=True)\n",
    "#IC.drop(['Admin IC'], axis=1,inplace=True)\n",
    "\n",
    "print(IC,'\\n========================================\\n')\n",
    "\n",
    "tmp=fundsIC_all.stack(0).reset_index(level=1)\n",
    "tmp.rename(columns={0:'DHT$'}, inplace=True)\n",
    "tmp=IC.join(tmp, on='Admin IC Long')\n",
    "print(tmp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f32739cd-7f1f-4048-a946-a40d885962cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#join IC names to budgets and budget Means\n",
    "tmp.rename(columns={'Admin IC Long':'Acronym'}, inplace=True)\n",
    "tmp=tmp.set_index(['Acronym', 'Fiscal Year'])\n",
    "df=tmp.join(budgetsTmp)\n",
    "df=df.join(budgetMean,on='Acronym')\n",
    "df.index.names = ['Admin IC','Fiscal Year']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "570f9362-577e-4e78-888f-3f3d055a42db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                     Admin IC Short        DHT$     Budget   budgetMean  \\\n",
      "Admin IC Fiscal Year                                                      \n",
      "NCATS    2016                    TR  23097598.0  570085566  601007809.1   \n",
      "         2017                    TR  30050160.0  578421698  601007809.1   \n",
      "         2018                    TR  34238819.0  596068934  601007809.1   \n",
      "         2019                    TR  33587784.0  638972236  601007809.1   \n",
      "         2020                    TR  44777064.0  637599365  601007809.1   \n",
      "         2021                    TR  52136466.0  648946868  601007809.1   \n",
      "         2022                    TR  87685436.0  643154976  601007809.1   \n",
      "         2023                    TR  83715510.0  645946539  601007809.1   \n",
      "\n",
      "                        Budget Tier  \n",
      "Admin IC Fiscal Year                 \n",
      "NCATS    2016         376M to 1.13B  \n",
      "         2017         376M to 1.13B  \n",
      "         2018         376M to 1.13B  \n",
      "         2019         376M to 1.13B  \n",
      "         2020         376M to 1.13B  \n",
      "         2021         376M to 1.13B  \n",
      "         2022         376M to 1.13B  \n",
      "         2023         376M to 1.13B  \n"
     ]
    }
   ],
   "source": [
    "print(df[df['Admin IC Short']=='TR'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "daae34eb-1148-46ac-bce0-220f1e32ded9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "% DHT Funding out of NIH Budget =  3.2327064246824766\n",
      "Total NIH Funding 2015 - 2023 272381483384\n"
     ]
    }
   ],
   "source": [
    "#% DHT funding out of all of NIH funding:\n",
    "print('% DHT Funding out of NIH Budget = ',df['DHT$'].sum() / df.Budget.sum() * 100)\n",
    "print('Total NIH Funding 2015 -',endyear,df.Budget.sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b4eceb7-7e70-4f1f-96d3-e53905c201f4",
   "metadata": {},
   "source": [
    "## plot of total funds for 3 budget tiers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1a4490e5-3f3e-485a-ba0f-333cb7971c5e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\radmantc\\AppData\\Local\\anaconda3\\Lib\\site-packages\\numpy\\core\\fromnumeric.py:3643: FutureWarning: The behavior of DataFrame.std with axis=None is deprecated, in a future version this will reduce over both axes and return a scalar. To retain the old behavior, pass axis=0 (or do not pass axis)\n",
      "  return std(axis=axis, dtype=dtype, out=out, ddof=ddof, **kwargs)\n",
      "C:\\Users\\radmantc\\AppData\\Local\\Temp\\ipykernel_16992\\4148394128.py:66: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  [ax[i].set_yticklabels(labels) for i in range(2)]\n",
      "C:\\Users\\radmantc\\AppData\\Local\\Temp\\ipykernel_16992\\4148394128.py:66: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  [ax[i].set_yticklabels(labels) for i in range(2)]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Budget Tier\n",
      "< 376M           1.629508e+07\n",
      "376M to 1.13B    1.892174e+07\n",
      "> 1.13B          7.134018e+07\n",
      "dtype: float64\n",
      "['−0.2', '0.0', '0.2', '0.4', '0.6', '0.8', '1.0', '1.2', '1.4', '1.6']\n",
      "['$-250M', '$0M', '$20M', '$40M', '$60M', '$80M', '$100M', '$120M', '$140M', '$160M']\n",
      "/n==========FIGURE 2===========/n\n"
     ]
    },
    {
     "data": {
      "image/png": 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t0sZisVz08fLLLxdpt2PHDt1+++3y9fVVQECAevbsqZycnGLb2blzZ5H18vLyFBQUJIvFoujo6Etm7dmzp5544olrPubc3FyNGTNGbdu2lZubm4YOHVqi9ebOnatu3brJx8dHAQEBxV5PSkpSv379VK9ePXl6eio0NFSPPfaY0tPTHW2io6OLvIfe3t664YYb9O9///uajwsAAABAxUOBjhJr0KCB5s+frx9++EE//PCDbr/9dg0ZMkS//PKLo01sbGyRR2RkpCwWi4YPH+5os2PHDvXr1099+vTRrl27tHv3bj322GNycSn66xgaGqqoqKgiyz755BP5+fmV7YH+ic1mk7e3tx5//HH17t27xOvl5+frnnvu0aOPPnrR111cXDRkyBB99tlnOnLkiJYvX65NmzbpkUceKdb28OHDio2N1cGDB/Xwww/r0Ucf1ebNm6/6mAAAAABUTBToKLFBgwZpwIABatGihVq0aKG5c+fKz8+vSC93SEhIkcenn36qXr16qWnTpo42kydP1uOPP67p06frhhtuUPPmzXX33XfL09OzyP7Cw8O1Zs2aIj3rkZGRCg8Pv2zOMWPGaOvWrXrttdccvc/Hjx+XJG3dulWdOnWSp6en6tatq+nTp6uwsPCS2/L19dVbb72lCRMmKCQkpMTv1fPPP6/Jkyerbdu2F309MDBQjz76qG666SY1atRId9xxhyZOnKht27YVaxscHKyQkBA1adJEjz/+uBo3bqy9e/eWOAsAAACAyoECvSLJz7r0oyC3FG1zStb2GthsNq1Zs0ZZWVnq2rXrRdvEx8fryy+/VEREhGNZQkKCYmJiFBwcrG7duqlOnTrq0aOHtm/fXmz9jh07qkmTJlq3bp0k6dSpU/r22281atSoy2Z77bXX1LVrV02YMMHRkx8aGqozZ85owIABuvnmm/Xjjz/qrbfe0rJlyzRnzpxreCec4+zZs/r444/Vo0ePS7YxDEMbNmzQqVOn1Llz53JMBwAAAKA8uJkdAH/yr3qXfq15H+nBtf/3/OVmUkH2xds2ukUa++X/PX+1rZSdVLzd7LRSRzxw4IC6du2q3Nxc+fn56ZNPPlHr1q0v2nbFihWqUaOGhg0b5lh29OjR87uePVuvvPKKwsLCtHLlSt1xxx36+eef1bx58yLbGDt2rCIjIzVy5EhFRUVpwIABql279mUzWq1WeXh4yMfHp0iv9+LFixUaGqo333xTFotF119/vc6ePaunn35as2bNKnaJfXm4//779emnnyonJ0eDBg3S0qVLi7Vp0KCBpPP339vtdv3zn//UbbfdVt5RAQAAAJQxetBRKi1bttT+/fu1c+dOPfroowoPD9fBgwcv2jYyMlIPPvigvLy8HMvsdrsk6eGHH9bYsWN14403atGiRWrZsqUiIyOLbWPkyJHasWOHjh49quXLl2vcuHFXnf3QoUPq2rWrLBaLY1n37t2VmZmp06dPX/V2r8WiRYu0d+9erV+/Xn/88YemTJlSrM22bdu0f/9+7d+/X0uXLtW//vUvvfXWWyakBQAAAFCW6EGvSP5x9tKvWVyLPn/y98u0/cv3Lk8cuPpMf+Hh4aFmzZpJkm666Sbt3r1br732mt55550i7bZt26bDhw/rgw8+KLK8bt26klSs171Vq1Y6efJksf0FBQVp4MCBioiIUG5urvr376+MjIyrym4YRpHi/MIyScWWl5cL9+pff/31CgoK0q233qpnn33W8T5JUpMmTRwjwd9www2KiYnR3LlzLzkAHQAAAIDKiQK9IvHwNb9tKRmGoby8vGLLly1bpo4dO6p9+/ZFljdu3Fj16tXT4cOHiyw/cuSI+vfvf9F9jBs3TgMGDNDTTz8tV1fXi7b5Kw8PD9lstiLLWrdurXXr1hUp1L///nvVqFFD9evXL9F2y9KFLwsu9n7+maura5GB8wAAAABUDRToKLF//OMf6t+/v0JDQ5WRkaE1a9YoOjpaGzZsKNIuPT1da9eu1YIFC4ptw2Kx6Mknn9Rzzz2n9u3bKywsTCtWrNCvv/6qjz766KL77devn86dOyd/f/8SZ23cuLFiYmJ0/Phx+fn5qWbNmpo4caJeffVVTZo0SY899pgOHz6s5557TlOmTLns/ecHDx5Ufn6+kpOTlZGRof3790uSwsLCJEm7du3S6NGjtXnzZkehf/LkSSUnJ+vkyZOy2WyOdZo1ayY/Pz/95z//UXx8vG6++Wb5+fnp4MGDeuqpp9S9e3c1bty4yP4TEhKUm5urvLw87dq1S6tWrdLdd99d4vcCAAAAQOVAgY4Si4+P16hRoxQbGyur1ap27dppw4YNuvPOO4u0W7NmjQzD0P3333/R7TzxxBPKzc3V5MmTlZycrPbt2+vrr7/Wddddd9H2FotFtWrVKlXWadOmKTw8XK1bt1ZOTo6OHTumxo0b6z//+Y+efPJJtW/fXjVr1lRERIRmzpx52W0NGDBAJ06ccDy/8cYbJf1fj3d2drYOHz6sgoICR5tZs2ZpxYoVxdbZsmWLevbsKW9vby1ZskSTJ09WXl6eQkNDNWzYME2fPr3Y/lu2bClJcnNzU2hoqB5++GHNnj27VO8HAAAAgIrPYlyoMuBU6enpslqtSktLK9bzm5ubq2PHjqlJkyZFBlADnIHfLwBAZZNdkK3O752fQjTmgRj5uPuUy34vd75WnfG+AFfHGf93GMUdAAAAAIAKgAIdAAAAAIAKgAIdAAAAAIAKgAIdAAAAAIAKgAIdAAAAAIAKgAIdAAAAAIAKgAIdAAAAAIAKgAIdAAAAAIAKgAIdAAAAAIAKgAIdAAAAAIAKgAId5ebbb7/VoEGDVK9ePVksFq1fv/6K68TGxuqBBx5Qy5Yt5eLioieeeKJYm48//lg33XSTAgIC5Ovrq7CwMK1atapImzFjxshisTgeQUFB6tevn3766ScnHR0AAAAAXBsKdJTY2bNnVVhYeNXrZ2VlqX379nrzzTdLvE5eXp5q166tZ555Ru3bt79om5o1a+qZZ57Rjh079NNPP2ns2LEaO3asvvrqqyLt+vXrp9jYWMXGxmrz5s1yc3PTwIEDr/p4AAAAAMCZKkyBPmbMGLMj4AqWLFmiBg0aaOrUqTpw4ECp1+/fv7/mzJmjYcOGlXidxo0b67XXXtPo0aNltVov2qZnz56666671KpVK1133XX6+9//rnbt2mn79u1F2nl6eiokJEQhISEKCwvT008/rVOnTuncuXOlPhYAAIALOI8F4CwVpkC/mI8//lh9+/ZVrVq1ZLFYtH///ku2NQxD/fv3v+il0ykpKRo1apSsVqusVqtGjRql1NRUx+vHjx+XxWKRm5ubzpw5U2Td2NhYubm5yWKx6Pjx4847uIvILsgu9aPQ/n892oX2QmUXZCu3MLdE2y2tp59+Wq+//roOHz6sDh06qEOHDnrttdcqVIFrGIY2b96sw4cP67bbbrtku8zMTK1evVrNmjVTUFBQOSYEAADVQXU7jwXgHG5m7jwxMVFTp07Vli1bFB8fr+3bt6tDhw5699135eHhoaysLHXv3l333HOPJkyYcNltvfrqq7JYLBd97YEHHtDp06e1YcMGSdJDDz2kUaNG6fPPPy/Srl69elq5cqVmzJjhWLZixQrVr19fJ0+evMajvbLO73Uu9Tqv9HhFfRv3lSRtPrlZ07ZO0011blJUvyhHm37r+iklL6XYugfCS9cL7uXlpXvvvVf33nuvEhIS9N5772nFihV68sknNWDAAIWHh2vQoEFycyv/X6u0tDTVr19feXl5cnV11eLFi3XnnXcWafPFF1/Iz89P0vnL7evWrasvvvhCLi4V+nsqAABQAXEeC6AsmFqZTJ48WTExMVq1apUGDBigJUuWqEmTJrLb7ZKkUaNGadasWerdu/dlt/Pjjz9q4cKFioyMLPbaoUOHtGHDBi1dulRdu3ZV165dtWTJEn3xxRc6fPhwkbbh4eGKiooqsmz58uUKDw+/xiOteoKDg/XEE09o7969+vTTT7Vjxw4NGzZMP//8syl5atSoof3792v37t2aO3eupkyZoujo6CJtevXqpf3792v//v2KiYlRnz591L9/f504ccKUzAAAoPLiPBZAWTC1B33fvn0aNWqUevTooaioKPXq1Uu9evUq1Tays7N1//33680331RISEix13fs2CGr1arOnf+vd7pLly6yWq36/vvv1bJlS8fywYMH6+2339b27dt1yy23aPv27UpOTtagQYP0wgsvXDZHXl6e8vLyHM/T09NLdRySFPNATKnX8XD1cPx8R8M7FPNAjFwsRb932TB8Q6m3eyUZGRn66KOPtGrVKn377bfq0aOHwsPD1bp1a6fvqyRcXFzUrFkzSVJYWJgOHTqkefPmqWfPno42vr6+jjaS1LFjR1mtVi1ZskRz5swp78gAAKAS4zwWQFkwtUDv3r27oqKiLjk6d0lMnjxZ3bp105AhQy76elxcnIKDg4stDw4OVlxcXJFl7u7uGjlypCIjI3XLLbcoMjJSI0eOlLu7+xVzzJs3T88///zVHcT/5+Puc03ru7m4yc2l+D/ptW73ApvNpo0bN2rVqlVav369GjRooNGjR2v58uVq2LChU/bhLIZhFPmguRiLxSIXFxfl5OSUUyoAAFBVcB4LoCyYeon7woULNWLECE2ePFkrV65UWFiY3n777RKv/9lnn+mbb77Rq6++etl2F7unxzCMiy6PiIjQ2rVrFRcXp7Vr12rcuHElyjJjxgylpaU5HqdOnSrRepXJv/71L91///3y8/PTpk2bdOTIEc2cObPExXlmZqbjEnNJOnbsmPbv31/kvqgZM2Zo9OjRRda7sE5mZqbOnTun/fv36+DBg47X582bp6+//lpHjx7Vr7/+qoULF2rlypUaOXJkke3k5eUpLi5OcXFxOnTokCZNmqTMzEwNGjToKt8RAABKJ7sgW21XtFXbFW2vasBWVBycxwIoC6b2oPv6+mru3LmaO3euhg4dqv79+2vy5MlycXHRQw89dMX1v/nmG/3xxx8KCAgosnz48OG69dZbFR0drZCQEMXHxxdb99y5c6pTp06x5W3atNH111+v+++/X61atVKbNm0uO+rmBZ6envL09Lxiu8ps1KhRevLJJ+Xl5XVV6//www9FLv2aMmWKpPP3TC1fvlzS+dFG/zqQyY033uj4ec+ePXrvvffUqFEjx2ikWVlZmjhxok6fPi1vb29df/31evfddzVixIgi29mwYYPq1q0r6fw969dff73Wrl1b5DJ4AACAkuA8FkBZMLVA/7OAgAA9/PDD2rhxo7Zt21aiP2zTp0/X+PHjiyxr27atFi1a5OgV7dq1q9LS0rRr1y516tRJkhQTE6O0tDR169btotsdN26cJk6cqLfeeusaj6pqady48TWt37NnTxmGcdk2Fwr1P7vSOnPmzLniPeTLly+/6LYBAACuFeexAJzF1AJ98uTJGjp0qMLCwmSz2bRlyxZt3bpVM2fOlCQlJyfr5MmTOnv2rCQ5RqsMCQkp8virhg0bqkmTJpKkVq1aqV+/fpowYYLeeecdSeenpxg4cGCRgTX+bMKECbrnnnuKfaMJAAAASJzHAigbpt6D3rBhQ02ZMkWhoaF67733NHr0aI0bN06TJk2SdP7enBtvvFF/+9vfJEn33XefbrzxxlLd3yNJq1evVtu2bdWnTx/16dNH7dq106pVqy7Z3s3NTbVq1TJlPm8AAABUfJzHAigLFuNK1w+XkzFjxlSpS5DT09NltVqVlpYmf3//Iq/l5ubq2LFjatKkyVXfzw1cCr9fAFBxZRdkq/N756fMinkgxmkzrVR2Zr0vlztfK43qdB4L4NKc8X/H1B50AAAAAABwXoUp0KvSt44AAACoPjiPBeAsFaZABwAAAACgOqNABwAAAACgAqBABwAAAACgAqBABwAAAACgAqBAh1NFR0fLYrEoNTVV0vlBUwICAkzNBAAAAACVAQU6Su3777+Xq6ur+vXr55TtWSwWrV+/vsiyLVu2aMCAAQoKCpKPj49at26tqVOn6syZM1e1j549e8pisWj+/PnFXhswYIAsFotmz559VdsGAAAAAGegQEepRUZGatKkSdq+fbtOnjzp9O2/88476t27t0JCQrRu3TodPHhQb7/9ttLS0rRgwYKr3m5oaKiioqKKLDt79qy++eYb1a1b91pjAwAAAMA1cTM7QHVnt9uVlJRkaoagoCC5uJTsu5qsrCx9+OGH2r17t+Li4rR8+XLNmjXLaVlOnz6txx9/XI8//rgWLVrkWN64cWPddtttjkvnT5w4occee0zbt29Xfn6+GjdurJdfflkDBgy45LYHDhyoDz/8UN999526d+8u6fwl+H369Cn2RUN+fr5mzpyp1atXKzU1VW3atNGLL76onj17SpKSkpL02GOPadu2bUpOTtZ1112nf/zjH7r//vsd2+jZs6fatWsnLy8vLV26VB4eHnrkkUfoqQcAAABwURToJktKSlJwcLCpGRISElS7du0Stf3ggw/UsmVLtWzZUiNHjtSkSZP07LPPymKxOCXL2rVrlZ+fr6eeeuqir1+4n/1//ud/lJ+fr2+//Va+vr46ePCg/Pz8LrttDw8PPfjgg4qKiipSoL/00kvFiuaxY8fq+PHjWrNmjerVq6dPPvlE/fr104EDB9S8eXPl5uaqY8eOevrpp+Xv768vv/xSo0aNUtOmTdW5c2fHdlasWKEpU6YoJiZGO3bs0JgxY9S9e3fdeeedV/8mAQAAAKiSuMQdpbJs2TKNHDlSktSvXz9lZmZq8+bNTtv+b7/9Jn9//ytecn7y5El1795dbdu2VdOmTTVw4EDddtttV9x+RESEPvzwQ2VlZenbb79VWlqa/va3vxVp88cff+j999/X2rVrdeutt+q6667TtGnTdMsttzguka9fv76mTZumsLAwNW3aVJMmTVLfvn21du3aIttq166dnnvuOTVv3lyjR4/WTTfd5NT3CwAAAEDVQQ86Suzw4cPatWuXPv74Y0mSm5ubRowYocjISPXu3dsp+zAMo0S98Y8//rgeffRRbdy4Ub1799bw4cPVrl27K67Xrl07NW/eXB999JG2bNmiUaNGyd3dvUibvXv3yjAMtWjRosjyvLw8BQUFSZJsNpvmz5+vDz74QGfOnFFeXp7y8vLk6+tbbH9/VrduXSUkJFwxJwAAAIDqhwIdJbZs2TIVFhaqfv36jmWGYcjd3V0pKSkKDAy85n20aNFCaWlpio2NvWwv+vjx49W3b199+eWX2rhxo+bNm6cFCxZo0qRJV9zHuHHj9L//+786ePCgdu3aVex1u90uV1dX7dmzR66urkVeu3AZ/YIFC7Ro0SK9+uqratu2rXx9ffXEE08oPz+/SPu/Fv8Wi0V2u/2KGQEAAABUPxToJgsKCjK9R/VCr/DlFBYWauXKlVqwYIH69OlT5LXhw4dr9erVeuyxx645y913363p06frpZdeKjJI3AWpqamO+9BDQ0P1yCOP6JFHHtGMGTO0ZMmSEhXoDzzwgKZNm6b27durdevWxV6/8cYbZbPZlJCQoFtvvfWi29i2bZuGDBniuNzfbrfrt99+U6tWrUpxtAAAAADwfyjQTebi4lLiAdrM9MUXXyglJUURERGyWq1FXrv77ru1bNkypxTooaGhWrRokR577DGlp6dr9OjRaty4sU6fPq2VK1fKz89PCxYs0BNPPKH+/furRYsWSklJ0TfffFPi4jgwMFCxsbHFercvaNGihR588EGNHj1aCxYs0I033qjExER98803atu2rQYMGKBmzZpp3bp1+v777xUYGKiFCxcqLi6OAh0AAADAVWOQOJTIsmXL1Lt372LFuXS+B33//v3au3evU/Y1ceJEbdy4UWfOnNFdd92l66+/XuPHj5e/v7+mTZsm6fw94P/zP/+jVq1aqV+/fmrZsqUWL15c4n0EBAQUu1/8z6KiojR69GhNnTpVLVu21ODBgxUTE6PQ0FBJ0rPPPqsOHTqob9++6tmzp0JCQjR06NBrOm4AAAAA1ZvFMAzD7BBVUXp6uqxWq9LS0uTv71/ktdzcXB07dkxNmjSRl5eXSQlRVfH7BQAVV3ZBtjq/d346zpgHYuTj7mNyoorBrPflcudr1RnvC3B1nPF/hx50AAAAAAAqAAp0AAAAAAAqAAp0AAAAAAAqAAp0AAAAAAAqAAp0EzE+H8oCv1cAAABA5USBboIL829nZ2ebnARV0YXfq0vN8w4AAACgYnIzO0B15OrqqoCAACUkJEiSfHx8ZLFYTE6Fys4wDGVnZyshIUEBAQFydXU1OxIAAACAUqBAN0lISIgkOYp0wFkCAgIcv18AAAAAKg8KdJNYLBbVrVtXwcHBKigoMDsOqgh3d3d6zgEAAIBKigLdZK6urhRUAAAAAAAGiQMAAAAAoCKgQAcAAAAAoAKgQAcAAAAAoAKgQAcAAAAAoAKgQAcAAAAAoAKgQAcAAAAAoAKgQAcAAAAAoAKgQAcAAAAAoAKgQAcAAAAAoAKgQAcAAE6XXZCttivaqu2KtsouyDY7DgAAlQIFOgAAAExls9scP++J31PkOQBUJxToAAAAMM2mE5s09LOhjucTN09U33V9tenEJvNCAYBJKNABAABgik0nNmlK9BQlZCcUWZ6QnaAp0VMo0gFUOxToAAAAKHc2u03zd82XIaPYaxeWvbjrRS53B1CtUKADAACg3O1N2Kv47PhLvm7IUFx2nPYm7C3HVABgLgp0AAAAlLtz2eec2g4AqgIKdAAAAJS7Gh41StSutk/tMk4CABUHBToAAADKXUP/hrLIcsnXLbIoxCdEHYI7lGMqADAXBToAAADKXSP/RhrXZtxFX7tQuD/d6Wm5uriWZywAMBUFOgAAAMpcen66pkZP1fdnvncse6LjE1rUc5GCfYKLtK3jU0cLey5U70a9yzsmAJjKzewAAAAAqPqW/7xcG09s1I/nftR/hv1HHq4ekqTejXqrc0hndVvTTZK0+I7F6lavGz3nAKolCnQAAACUuYfaPaTfU3/XhLYTHMX5BX8uxjvW6UhxDqDa4hJ3AAAAOF1Kbor+d///yma3SZK83Lz0+u2vq23ttiYnA4CKix50AAAAONUPcT/o6W1PKyE7Qe4u7nqo3UNmRwKASoECHQAAAE5hs9u05MASvfXjW7IbdjX2b6weDXqYHQsAKg0KdAAAAFyzc9nnNGPbDMXExUiSBl83WM90fkY+7j4mJwOAyoMCHQAAANfk+zPfa8b2GUrOTZa3m7dmdpmpwdcNNjsWAFQ6FOgAAAC4KgX2Av3vvv/Vsp+XSZJaBLbQyz1eVlNrU5OTAUDlRIEOAACAUovNjNVT3z6l/ef2S5JGtByhaTdNk5ebl7nBAKASo0AHAABAqXxz8hs9+92zSs9Pl5+7n57v9rz6NO5jdiwAqPQo0AEAAFBi+bZ8vbz7ZaXnp6ttrbZ66baX1KBGA7NjAUCVQIEOAACAEvNw9dDLPV7WxuMbNenGSXJ3dTc7EgBUGRToAAAAuKz/HP2Pcm25GtZ8mCSpTa02alOrjcmpAKDqoUAHAADAJX1/5ns9ve1pebh4KKx2mJoGMEI7AJQVCnQAAABcUtd6XdUrtJda1myphv4NzY4DAFUaBToAAAAcDMPQf4/9Vz1Ce8jX3VcWi0Wv9npVLhYXs6MBQJXHX1oAAABIkrIKsjR923Q9ve1pzdk5R4ZhSBLFOQCUE3rQAQAAoINJB/Xk1id1MuOkXC2uui7gOrMjAUC1Q4EOAABQjRmGofd+fU8LfligAnuB6vrW1Uu3vaSw4DCzowFAtUOBDgAAUE2l5aVp1nez9M2pbyRJvUJ76YXuL8jqaTU5GQBUTxToAAAA1dD+hP166tunFJsVK3cXd029aaoeuP4BWSwWs6MBQLVFgQ4AAFCN2A27on6O0hv73pDNsCm0Rqhe7vGybgi6wexoAFDtUaADAABUE0k5SXpm+zP67ux3kqT+jftrVtdZ8vPwMzkZAECiQAcAAKg25sbM1Xdnv5OXq5emd5quYc2HcUk7AFQgFOgAAADVxFM3P6WknCTN7DJTzQObmx0HAPAXLmYHuGDMmDFmRwAAAKhS4rPi9eHhDx3PQ3xDtKL/CopzJ+M8FoCzVJgC/WI+/vhj9e3bV7Vq1ZLFYtH+/fuLvJ6cnKxJkyapZcuW8vHxUcOGDfX4448rLS2tSLuUlBSNGjVKVqtVVqtVo0aNUmpqquP148ePy2KxyM3NTWfOnCmybmxsrNzc3GSxWHT8+PEyOlIAAADnSs1N1T2f36MXdr6gLSe3mB2n2uE8FsDVMLVAT0xMVHh4uBo2bKj3339fzZo107333qv8/HxJUlZWlrp376758+dfdP2zZ8/q7NmzeuWVV3TgwAEtX75cGzZsUERERJF2DzzwgPbv368NGzZow4YN2r9/v0aNGlVse/Xq1dPKlSuLLFuxYoXq16/vpCMGAAAoHwFeARp43UC1qtlKTQOamh2nyuE8FkCZMEw0cuRIo2XLlkZ0dLQxdOhQ45tvvjGeeuopIycnp0i7Y8eOGZKMffv2XXGbH374oeHh4WEUFBQYhmEYBw8eNCQZO3fudLTZsWOHIcn49ddfi2x/5syZRvPmzYtsr2XLlsazzz5rSDKOHTtW4mNLS0szJBlpaWklXgcAgKoiKz/LaLO8jdFmeRsjKz/L7DgVRlm/L6fSTxmxmbGO5/mF+UZuYa7T9+NsZv2+XMv5GuexAP7KGf93TO1B37dvn0aNGqUePXrIarWqV69eevHFF+Xl5XXV20xLS5O/v7/c3M6Pf7djxw5ZrVZ17tzZ0aZLly6yWq36/vvvi6w7ePBgpaSkaPv27ZKk7du3Kzk5WYMGDbrifvPy8pSenl7kAQAAUF6+PvG17v38Xj317VMqsBdIktxd3eXp6mlysqqJ81gAZcHUAr179+6KiorSF1984ZTtJSUl6YUXXtDDDz/sWBYXF6fg4OBibYODgxUXF1dkmbu7u0aOHKnIyEhJUmRkpEaOHCl3d/cr7nvevHmOe4OsVqtCQ0Ov8WgAAACuLM+Wpzk752hK9BRlFGTIbtiVmZ9pdqwqj/NYAGXB1AJ94cKFGjFihCZPnqyVK1cqLCxMb7/99lVtKz09XX/729/UunVrPffcc0Veu9j8noZhXHR5RESE1q5dq7i4OK1du1bjxo0r0f5nzJihtLQ0x+PUqVNXdRwAAAAldSztmB788kF9cPgDSdK4NuMU1S9KgV6BJier+jiPBVAWTC3QfX19NXfuXP32228aPHiwHn30UU2ZMkX//ve/S7WdjIwM9evXT35+fvrkk0+KfFMYEhKi+Pj4YuucO3dOderUKba8TZs2uv7663X//ferVatWatOmTYkyeHp6yt/fv8gDAACgrHz+x+ca8cUIHU45rJpeNfVW77c0ueNkubtcuccU147zWABlocJMsxYQEKCHH35Y/fv317Zt20q8Xnp6uvr06SMPDw999tlnxe776dq1q9LS0rRr1y7HspiYGKWlpalbt24X3ea4ceMUHR1d4m8dAQAAykt2Qbae2f6M/rH9H8opzFGnkE5aO2itbql/i9nRqi3OYwE4i5uZO588ebKGDh2qsLAw2Ww2bdmyRVu3btXMmTMlnZ8f8uTJkzp79qwk6fDhw5LOf5sYEhKijIwM9enTR9nZ2Xr33XeLDGpRu3Ztubq6qlWrVurXr58mTJigd955R5L00EMPaeDAgWrZsuVFc02YMEH33HOPAgICyvgdAAAAKLnDyYf15LdP6ljaMblYXPRI+0f0UNuH5Oriana0aofzWABlwdQCvWHDhpoyZYp+++03ZWVlOb7tmzRpkiTps88+09ixYx3t77vvPknSc889p9mzZ2vPnj2KiYmRJDVr1qzIto8dO6bGjRtLklavXq3HH39cffr0kXR+lMs333zzkrnc3NxUq1Ytpx0nAADAtTAMQ2uPrNVLu19Sni1Pwd7Bmn/bfN0ccrPZ0aotzmMBlAWLYRiG2SEkacyYMVq+fLnZMZwmPT1dVqvVMV0GAADVSXZBtjq/d35qqJgHYuTj7mNyoorhat+XD379QHNi5kiSbq1/q+bcMkc1vWqWWc7yZtbvi7PO1ziPBSA55/+OqT3oAAAAuLJB1w3SmsNrNLTZUI1qPUoulgozjBAAwIkqTIFelb51BAAAuBaGYWjzyc26o+Edslgs8nH30YeDPmSE9gqK81gAzsLXrwAAABWIYRiatnWaJkdP1sqDKx3LKc4BoOqjQAcAAKhALBaLOtftLA8XD3m7eZsdBwBQjirMJe4AAADVlc1u07mccwrxDZEk3dPiHnWt11WhNUJNTgYAKE/0oAMAAJQTm93m+HlP/B7Z7DYl5iTq4U0Pa8yGMUrPPz8PtsVioTgHgGqIHnQAAIBysOnEJs3bNc/xfOLmiQr0DFSBvUCZBZnydvPWoaRD6ly3s4kpAQBmokAHAAAoY5tObNKU6CkyZBRZnpKXIkkK8QnRO3e+o6YBTc2IBwCoILjEHQAAoAzZ7DbN3zW/WHH+Z4YMNfJvVI6pAAAVEQU6AABAGdqbsFfx2fGXbROfHa+9CXvLKREAoKKiQAcAAChD57LPObUdAKDqokAHAAAoI6m5qfry6Jclalvbp3YZpwEAVHQMEgcAAFBGtpzaom/PfHvZNhZZVMenjjoEdyinVACAiooedAAAACfKKcxx/Dyk2RDd1ewuPRb2mCyyFGt7YdnTnZ6Wq4truWUEAFRM9KADAAA4QXZBtl7f97q2nNyiT4Z8Ih93H7lYXPTP7v+UJF0XcJ3m7ZqnhOwExzp1fOro6U5Pq3ej3mbFBgBUIPSgAwAAOIGLxUXRp6J1NuusNp/cXOz13o16a/3g9Y7ni+9YrA3DN1CcAwAc6EEHAAC4SsfTjiu0RqhcXVzl5eal57s9L7thV9d6XS/a/s+XsXes05HL2gEARdCDDgAAUErZBdla+MNC3fXpXfrwyIeO5Z3rdr5kcQ4AwJXQgw4AAFBChmFo08lNenHXi4rPjpck/Zz4s8mpAABVBQU6AABACZxIP6F5MfP03dnvJEn1/errH53/odsa3GZyMgBAVUGBDgAAcBm5hblaemCpIn+OVIG9QO4u7opoG6GINhHycvMyOx4AoAqhQAcAALiErae2at6ueTqTeUaS1L1ed83oPEON/BuZnAwAUBVRoAMAAPzF2cyzmr9rvrac2iLpT/OVN+wti8VicjoAQFVFgQ4AAPAnMbExemzzY8q15crN4qZRrUfpkfaPyMfdx+xoAIAqjgIdAADgT9rWait/T3+1qdFGz3R+Rs0Cm5kdCQBQTVCgAwCAai0+K14f/faRHm3/qFwsLvJx99HqAatVx6cOl7MDAMoVBToAAKi28m35uv/L+3Uu55zq+dbTXc3vkiSF+IaYnAwAUB25mB0AAADALB6uHgq/IVztardTq6BWZscBAFRzFOgAAKDaSMpJ0jPbn9HO2J2OZQ+2elCr+q/S9TWvNzEZAABc4g4AAKoBm92mtUfW6vV9rysjP0M/J/6sT4Z8IheLi9xcOB0CAFQMfCIBAIAq7cC5A5oTM0cHkw5KklrVbKWZXWbKxcKFhACAioUCHQAAVElpeWl6de+rWndknQwZquFeQ5M6TNK9Le6Vq4ur2fEAACiGAh0AAFQpdsOuT3//VIv2LFJKXookaVDTQZpy0xTV8q5lcjoAAC6NAh0AAFQZvyb/qjk75+jHcz9KkpoFNNMznZ/RTSE3mZwMAIAro0AHAACVXmZ+pv53///qvV/fk92wy8fNRxPDJuqBVg/I3cXd7HgAAJQIBToAAKj0UvNStfbIWtkNu/o27qsnb3pSdXzrmB0LAIBSoUAHAACVUmJOouOe8gY1Gmh6p+mq51dP3ep1MzkZAABXh/lFAABApWIYhl7b+5r6fNRH+xL2OZbf3eJuinMAQKVGgQ4AACoVi8WilNwUFdgL9M3Jb8yOAwCA03CJOwAAqPBOpp+Uq4ur6vvVlyT9vcPf1Su0l3qE9jA5GQAAzkMPOgAAqLByC3P1v/v/V3d9epfm7JwjwzAkSYFegRTnAIAqhx50AABQIX17+lv9K+ZfOpN5RpJks9uUU5gjH3cfk5MBAFA2KNABAECFcjbzrObvmq8tp7ZIkoJ9gvXUzU+pT6M+slgsJqcDAKDsUKADAIAKocBWoBUHV+idH99Rri1XrhZXjWo9So+0f0S+7r5mxwMAoMxRoAMAANPtjN2puTvn6nj6cUlSh+AOmtllppoHNjc3GAAA5YgCHQAAmCYhO0Gv7H5F/z3+X0lSTa+amnbTNA1sOpDL2QEA1Q4FOgAAMEViTqIGrx+srIIsuVhcNKLlCD1242Py9/A3OxoAAKagQAcAAKao5V1LPUN76lT6KT3T5Rm1DmptdiQAAExFgQ4AAJzOZrc5ft4Tv0fd6nVTal6q3tj3hh5u97Dq+tWVJM3qMktebl5ysbiYFRUAgAqDAh0AADjVphObNG/XPMfziZsnqo5PHQV5B+lg0kGl5aVpUa9FksSc5gAA/AkFOgAAcJpNJzZpSvQUGTKKLE/ITlB8drxC/UI1ps0Yc8IBAFDBcT0ZAABwCpvdpvm75hcrziXJkCGLLCqwF6hNUBsT0gEAUPFRoAMAAKfYm7BX8dnxl3zdkKG47DjtTdhbjqkAAKg8KNABAIBTnMs+59R2AABUNxToAADAKWr71HZqOwAAqhsKdAAA4BQdgjvI1933kq9bZFGIT4g6BHcox1QAAFQeFOgAAMApfk/9XTkFORd9zSKLJOnpTk/L1cW1PGMBAFBpUKADAIBrZrPb9M8d/5RddrWr1U7BPsFFXq/jU0cLey5U70a9TUoIAEDFxzzoAADgmn1w+AP9lPiT/Nz9tLDnQvm4+ajbmm6SpMV3LFa3et3oOQcA4Aoo0AEAwDWJy4rTa3tfkyQ90eEJ1fGto+yCbMfrHet0pDjHZfm4++hA+AGzYwCA6bjEHQAAXDXDMDQ3Zq6yC7MVVjtM97S8x+xIAABUWhToAADgqm06uUnRp6Ll5uKm57o+JxcLpxYAAFwtLnEHAABXrbZ3bTW1NlXvRr3VLLCZ2XEAAKjUKNABALhK2QXZ6vxeZ0lSzAMx8nH3MTlR+QsLDtPaQWvNjgEAQJVAgQ4AAErNZrc5Bn7zcPUwOQ0AAFUDN4oBAIBSybfl64H/PKDInyNVYC8wOw4AAFUGBToAACiV/xz7jw4mHdSKX1YUmU4NAABcGy5xBwAApTLkuiGyyCI/dz9ZPa1mxwEAoMqgQAcAAKVisVg0pNkQs2MAAFDlcIk7AAAokb3xe5WRn2F2DAAAqiwKdAAAcEWJOYl67JvHNHT9UJ1IP2F2HAAAqiQKdAAAcEXzd81XRn6GavnUUn2/+mbHAQCgSqowBfqYMWPMjgAAAC5i66mt+ur4V3K1uGp219lyc2EIG+DPOI8F4CwVpkC/mI8//lh9+/ZVrVq1ZLFYtH///mJt8vLyNGnSJNWqVUu+vr4aPHiwTp8+XaRNSkqKRo0aJavVKqvVqlGjRik1NdXx+vHjx2WxWOTm5qYzZ84UWTc2NlZubm6yWCw6fvx4GRwlAAAVV3ZBtubEzJEkjW49Wq2CWpmcCKgcOI8FcDVMLdATExMVHh6uhg0b6v3331ezZs107733Kj8/X5KUlZWl7t27a/78+ZfcxhNPPKFPPvlEa9as0fbt25WZmamBAwfKZrM52jzwwAPav3+/NmzYoA0bNmj//v0aNWpUsW3Vq1dPK1euLLJsxYoVql+fS/kAANXTG/veUFxWnOr71dcj7R8xOw5QYXAeC6AsmFqgT548WTExMVq1apUGDBigJUuWqEmTJrLb7ZKkUaNGadasWerdu/dF109LS9OyZcu0YMEC9e7dWzfeeKPeffddHThwQJs2bZIkHTp0SBs2bNDSpUvVtWtXde3aVUuWLNEXX3yhw4cPF9leeHi4oqKiiixbvny5wsPDy+DoAQCo2H5O/Fnv/fqeJOnZLs/Kx93H5ERAxcF5LICyYGqBvm/fPo0aNUo9evSQ1WpVr1699OKLL8rLy6tE6+/Zs0cFBQXq06ePY1m9evXUpk0bff/995KkHTt2yGq1qnPnzo42Xbp0kdVqdbS5YPDgwUpJSdH27dslSdu3b1dycrIGDRp0xSx5eXlKT08v8gAAoLIqsBdo9vezZTfs+lvTv6l7/e5mRwIqFM5jAZQFUwv07t27KyoqSl988cVVrR8XFycPDw8FBgYWWV6nTh3FxcU52gQHBxdbNzg42NHmAnd3d40cOVKRkZGSpMjISI0cOVLu7u5XzDJv3jzHvUFWq1WhoaFXdUwAAFQEqw6u0uGUw7J6WvXkTU+aHQeocDiPBVAWTC3QFy5cqBEjRmjy5MlauXKlwsLC9Pbbb1/zdg3DkMVicTz/88+XanNBRESE1q5dq7i4OK1du1bjxo0r0T5nzJihtLQ0x+PUqVNXfwAAAJjoVPopvbX/LUnStJumKcg7yOREQMXDeSyAsmBqge7r66u5c+fqt99+0+DBg/Xoo49qypQp+ve//12i9UNCQpSfn6+UlJQiyxMSElSnTh1Hm/j4+GLrnjt3ztHmz9q0aaPrr79e999/v1q1aqU2bdqUKIunp6f8/f2LPAAAqGwMw9ALO19Qri1XnUM6a8h1Q8yOBFRInMcCKAsVZpq1gIAAPfzww+rfv7+2bdtWonU6duwod3d3ff31145lsbGx+vnnn9WtWzdJUteuXZWWlqZdu3Y52sTExCgtLc3R5q/GjRun6OjoEn/rCABAVbHtzDbtiN0hT1dPzeo666K9dACK4jwWgLO4mbnzyZMna+jQoQoLC5PNZtOWLVu0detWzZw5U5KUnJyskydP6uzZs5LkGK0yJCREISEhslqtioiI0NSpUxUUFKSaNWtq2rRpatu2rWPEzFatWqlfv36aMGGC3nnnHUnSQw89pIEDB6ply5YXzTVhwgTdc889CggIKON3AACAiuWW+rdoVtdZyrflq6F/Q7PjABUW57EAyoKpBXrDhg01ZcoU/fbbb8rKynJ82zdp0iRJ0meffaaxY8c62t93332SpOeee06zZ8+WJC1atEhubm669957lZOTozvuuEPLly+Xq6urY73Vq1fr8ccfd4ySOXjwYL355puXzOXm5qZatWo5+3ABAKjwXCwuuqfFPWbHACo8zmMBlAWLYRiG2SEkacyYMVq+fLnZMZwmPT1dVqtVaWlp3McDAFVUdkG2Or93fvqjmAdiKvU84X+k/qG6vnWddgxV6b1xJt6XisVZ52ucxwKQnPN/p8Lcgw4AAMyRU5ijxzY/pqGfDtXh5MNmxwEAoNqqMAV6VfrWEQCAyiQ2M1aGDNkMm+r71Tc7DlDpcB4LwFlMvQcdAACYr2lAU308+GOdyjglPw8/s+MAAFBtVZgedAAAYB4fdx+1rHnxUaEBAED5oEAHAKCa+ujIR1rz6xrZDbvZUQAAgLjEHQCAaik2M1Yv7X5JOYU5CvQKVN/Gfc2OBABAtUcPOgAA1YxhGJobM1c5hTm6MfhG3dnoTrMjAQAAUaADAFDtbDyxUVtPb5Wbi5ue6/qcXCycDgAAUBHwiQwAQDWSlpemeTHzJEnj247XdQHXmZwIAABcQIEOAEA18ureV5WUm6TG/o01vu14s+MAAIA/oUAHAKCa2BO/Rx8d+UiS9FzX5+Tp6mlyIqAom80mu51ZBQBUX6Uu0HNycpSdne14fuLECb366qvauHGjU4MBAADnybfl6/kdz0uShjcfrptCbjI5EfB/7Ha70tLSdOLECWVkZJgdBwBMU+oCfciQIVq5cqUkKTU1VZ07d9aCBQs0ZMgQvfXWW04PCAAArt2yA8t0LO2YgryCNLnjZLPjAA45OTmKjY3V6dOnlZmZKcMwzI4EAKYpdYG+d+9e3XrrrZKkjz76SHXq1NGJEye0cuVKvf76604PCAAArs3R1KNacmCJJGl65+myelpNTgRIhYWFSkxM1MmTJ5WWliZ/f3+5urqaHQsAisguyFbbFW3VdkVbZRdkX3mFa+RW2hWys7NVo0YNSdLGjRs1bNgwubi4qEuXLjpx4oTTAwIAgKtnN+x6fsfzKrAX6LYGt6lvo75mR0I1ZxiGMjMzlZiYqKysLPn4+DjOLQGguit1D3qzZs20fv16nTp1Sl999ZX69OkjSUpISJC/v7/TAwIAgKsXlxWnM5ln5O3mrZmdZ8pisZgdCdVYXl6e4uLidOrUKRUUFCgwMFBeXl5mxwKACqPUPeizZs3SAw88oMmTJ+uOO+5Q165dJZ3vTb/xxhudHhAAAFy9en71tH7Ieh1KPqS6fnXNjoNqymazKS0tTUlJScrPz1eNGjXk5lbq01AAqPJK/Zfx7rvv1i233KLY2Fi1b9/esfyOO+7QXXfd5dRwAADg2vl5+OnmkJvNjoFqKjMzU0lJScrIyJC3t7cCAwPNjgQAFdZVfXUZEhKikJCQIss6derklEAAAODabT+zXQnZCbqr2V1c1g5T5OfnKyUlRcnJybJYLAoICJCLS6nvrgSAaqVEBfqwYcNKvMGPP/74qsMAAIBrl1WQped3PK+4rDgV2Ao04voRZkdCNWK325Wenq6kpCTl5OSoRo0acnd3NzsWAFQKJfoa02q1Oh7+/v7avHmzfvjhB8fre/bs0ebNm2W1Mm0LAABm83T11MhWI9U8sLkGNxtsdhxUI9nZ2Tpz5ozOnDkjwzBUs2ZNinMAKIUS9aBHRUU5fn766ad177336u2333bMVWmz2TRx4kRGcQcAoAJwc3FT+A3hGtlqpFxdmFcaZa+wsFApKSlKSkqS3W6X1WrlcnYAuAqlvgc9MjJS27dvdxTnkuTq6qopU6aoW7duevnll50aEAAAlEyBvUB2wy5PV09JojhHmTMMQxkZGTp37pxycnLk6+srT09Ps2MBQKVV6q82CwsLdejQoWLLDx06JLvd7pRQAACg9Fb+slJ3f3a39sTvMTsKqoHc3FydPXtWp06dks1mU2BgIMU5AFyjUvegjx07VuPGjdPvv/+uLl26SJJ27typ+fPna+zYsU4PCAAAruxU+im99eNbyrPl6XTGaXWs09HsSKiibDabUlNTlZSUpMLCQvn7+xe5shIAcPVKXaC/8sorCgkJ0aJFixQbGytJqlu3rp566ilNnTrV6QEBAMDlGYah53c+rzxbnjrX7azB1zEwHJzPMAxlZWUpMTFRmZmZ8vHxkZ+fn9mxAKBKKXWB7uLioqeeekpPPfWU0tPTJYnB4QAAMNHnRz9XTGyMPF09NavLLOY9h9Pl5eUpOTlZKSkpcnV1VWBgIL9nAFAGSl2g/xmFOQAA5krOTdbLu88P0PpI+0fU0L+hyYlwOT7uPjoQfsDsGCVmt9uVlpampKQk5ebmMqc5AJSxUg8SFx8fr1GjRqlevXpyc3OTq6trkQcAACg/r+x+Ral5qWoe2FzhN4SbHQdVSFZWlk6fPq0zZ87IYrEwpzkAlINS96CPGTNGJ0+e1LPPPqu6detyeRMAACb5/uz3+vzo57LIotldZ8vdheIJ166goMBxObthGAoICGBOcwAoJ6Uu0Ldv365t27YpLCysDOIAACqi7IJsdX6vsyQp5oEY+bj7mJwIOYU5emHHC5Kk+6+/X+1qtzM5ESo7u92ujIwMJSYmKicnR35+fvLw8DA7FgBUK6Uu0ENDQ2UYRllkAQAAJfTWj2/pdOZp1fGpo8c7PG52HFRyOTk5SkpKUmpqqjw9PVWzZk2zIwFAtVTq65VeffVVTZ8+XcePHy+DOAAA4Ep+Tf5VK39ZKUma2WWmfN19TU6EyqqwsFCJiYk6efKkMjIyZLVa5evL7xMAmKXUPegjRoxQdna2rrvuOvn4+BQbLCQ5Odlp4QAAQFE2u02zv58tm2HTnY3uVM/QnmZHQiVkGIYyMzOVmJiorKws+fr6ytPT0+xYAFDtlbpAf/XVV8sgBgAAKAm77Lqj4R2Ky4rTjE4zzI6DSigvL89xObubmxtzmgNABVLqAj08nClcAAAwi7uLuya0m6CRrUfK283b7DioRGw2m1JTU5WcnKz8/HzVqFFDbm6lPhUEAJShUv9VPnny5GVfb9iw4VWHAQAAF2cYhgqNQsdUahTnKI3MzEwlJSUpIyND3t7eCgwMNDsSAOAiSl2gN27c+LKXQdlstmsKBAAAitt4YqPe/vFtPdf1OYUFh5kdB5VEfn6+UlJSlJycLIvFwpzmAFDBlbpA37dvX5HnBQUF2rdvnxYuXKi5c+c6LRgAADjPMAy989M7+j31d+04u4MCHVdkt9uVnp6uxMRE5ebmqkaNGsUG9gUAVDylLtDbt29fbNlNN92kevXq6eWXX9awYcOcEgwAAJxnsVi0rM8yrfhlhSLaRpgdBxVcdna2kpKSlJ6ezpzmAFDJOG1kkBYtWmj37t3O2hwAAPiTQK9APdHxCbNjoAIrKChQamqqkpKSZLfbZbVauZwdACqZUhfo6enpRZ4bhqHY2FjNnj1bzZs3d1owAACqu3xbvr478516hvZkGixckmEYysjI0Llz55STk8Oc5gBQiZW6QA8ICCh2kmAYhkJDQ7VmzRqnBQMAoLpbemCp3vrxLQ1rPkzPd3ve7DiogHJzcx1zmnt4eDCnOQBUcqUu0Lds2VLkuYuLi2rXrq1mzZoxlyYAAE7yR+ofWnJgiSSpW71uJqdBRXNhTvOkpCQVFhbK399frq6uZscCAFyjElfUs2bN0vTp09WjRw9JUkpKCnNoAgBQBuyGXc/veF6F9kL1aNBDfRr1MTsSKgjDMBxzmmdmZsrHx0d+fn5mxwIAOEmJRw6ZO3euMjMzHc8bNWqko0ePlkkoAACqs4+OfKR9Cfvk4+ajmV1mcskyJEl5eXmKi4vTqVOnlJeXp8DAQHl5eZkdCwDgRCXuQTcM47LPAQDAtUvITtCiPYskSY93eFwhviEmJ4LZ7Ha70tLSlJSUpNzcXPn7+3NbIQBUUfx1BwCgApm/a74yCzLVJqiN7mt5n9lxYLKsrCzHnObe3t7MaQ4AVVyJC3SLxaKMjAx5eXnJMAxZLBZlZmYWm3bN39/f6SEBAKgOtpzcoq9PfC1Xi6tmd5stVxcG/aquCgoKlJycrOTkZEnnZ9FhTnMAqPpKdYl7ixYtijy/8cYbizy3WCyy2WzOTQgAQDWQmZ+puTFzJUljbhijljVbmpwIZrDb7crIyFBiYqJycnLk5+cnDw8Ps2MBAMpJiQv0v06vBgAAnOeNfW8oPjteoTVC9Uj7R8yOAxPk5OQ45jT39PTkcnYAqIZKXKBfmF4NAAA410/nftL7v74vSXq2y7PycmNk7uqksLDQMae53W6X1WplTnMAqKYYJA4AAJMt2rNIhgwNvm6wutbranYclJMLc5onJiYqKytLvr6+8vT0NDsWAMBEFOgAAJjs5R4v6819b+rvHf5udhSUk9zcXCUnJys1NVVubm4KDAxkvnsAAAU6AABmq+VdS7O7zTY7BspBfn6+0tPTlZycrIKCAtWoUYM5zQEADszXAQCACQzD0J74PWbHQDnJz89XYmKiTpw4obi4OEevOcU5AODPSlygu7q6KiEhoSyzAABQbXz2x2cas2GMZmybIcMwzI6DMvLXwtzV1VU1a9aUlxcDAQIAiivVPOgAAMA5zuWck6vFVc0Dm3PvcRV04VL2lJQU5eXlycfHh2nTAABXxHVVAACYYHzb8erRoIcaWxubHQVOVFBQoLS0NKWmpio3N1fe3t4U5gCAEitVgf7VV1/JarVets3gwYOvKRAAANVF88DmZkeAkxQUFDh6zCnMAQBXq1QFenh4+GVft1gsstls1xQIAICqKrsgW89+96weaveQWtZsaXYcOAGFOQDAmUpVoMfFxSk4OLissgAAUKW9/ePb2nhiow4mHdTnd30uNxfuNKusKMwBAGWhxGcGDGADAMDVO5R0SCsPrpQkPd3paYrzSupCYZ6amqqcnBwKcwCAUzGKOwAAZcxmt2n2jtmyGTb1adRHPUN7mh0JpVRYWKiMjAwlJyc7CvPAwEA6MAAATlXiAj08PFze3t5lmQUAgCrpvV/f08Gkg6rhXkPTO003Ow5KgcIcAFCeSlygv/baazIMQ+np6Zdt5+/vf82hAACoKs5mntUb+96QJE25aYpq+9Q2ORFKgsIcAGCGEhfoAQEBl/1QMgyDUdwBAPgTwzA0N2aucgpz1CG4g4Y1H2Z2JFzBXwtzLy8vCnMAQLkpcYG+ZcsWx8+GYWjAgAFaunSp6tevXybBAACo7L468ZW+Pf2t3F3c9VzX5+RicTE7Ei6hsLBQmZmZSkpKojAHAJimxAV6jx49ijx3dXVVly5d1LRpU6eHAgCgskvLS9P8mPmSpAltJ6hpAJ+XFZHNZlNGRgaFOQCgQmCOFwAAysCiPYuUlJukptamimgbYXaccufj7qMD4QfMjnFJFOYAgIqIAh0AACfbHbdb635bJ0l6rutz8nD1MDkRLrhQmCcnJys7O1uenp4U5gCACuOaCnQ+zAAAKO5g0kG5WFw0vPlwdajTwew40PnCPDMzU8nJycrKyqIwBwBUSCUu0IcNKzrybG5urh555BH5+voWWf7xxx87J9lFjBkzRsuXLy+z7QMA4AzhN4SrY52Oaujf0Owo1R6FOSoKzmMBlESJh5O1Wq1FHiNHjlS9evWKLS9vmZmZeuyxx9SgQQN5e3urVatWeuutt4q0ycvL06RJk1SrVi35+vpq8ODBOn36dJE2FotFFotFO3fuLLZuUFCQLBaLoqOjy/pwAABVRJtabeTv4W92jGrLZrMpLS1NJ0+e1KlTp1RYWKjAwED5+vpSnKPC4DwWwF+VuAc9KiqqLHNcUmJioqZOnaotW7YoPj5e27dvV4cOHfTuu+/Kw8NDkydP1pYtW/Tuu++qcePG2rhxoyZOnKh69eppyJAhkqQnnnhCn3/+udasWaOgoCBNnTpVAwcO1J49e+Tq6urYV2hoqKKiotSlSxfHsk8++UR+fn5KTk4u92MHAFQedsOu+bvm694W9zJiu4nsdrsyMjKUkpKirKwsubu702MO03AeC6C0rmpCVsMwlJiYqKSkJGfnKWby5MmKiYnRqlWrNGDAAC1ZskRNmjSR3W6XJO3YsUPh4eHq2bOnGjdurIceekjt27fXDz/8IElKS0vTsmXLtGDBAvXu3Vs33nij3n33XR04cECbNm0qsq/w8HCtWbNGOTk5jmWRkZEKDw8v8+MEAFQ+NrvN8fPre1/X6kOrFb4hXDmFOZdZC2XBbrcrPT1dJ0+e1OnTp5Wfny+r1So/Pz+Kc5iG81gApVWqAj0uLk6jR49WYGCg6tSpo+DgYAUGBmrcuHGKj48vk4D79u3TqFGj1KNHD1mtVvXq1UsvvviivLy8JEm33HKLPvvsM505c0aGYWjLli06cuSI+vbtK0nas2ePCgoK1KdPH8c269WrpzZt2uj7778vsq+OHTuqSZMmWrfu/Mi7p06d0rfffqtRo0ZdMWdeXp7S09OLPAAAVdemE5s09LOhjufvH35fHq4e6hXaS95u3uYFq2b+XJifPHmySGHu4nJV/RCA03AeC6C0SnyJe3p6urp166bMzEyNHTtW119/vQzD0MGDB/X+++9r+/bt2rt3r/z8/JwasHv37oqKilL79u0v+vrrr7+uCRMmqEGDBnJzc5OLi4uWLl2qW265RdL5LxU8PDwUGBhYZL06deooLi6u2PbGjh2ryMhIjRw5UlFRURowYIBq1659xZzz5s3T888/fxVHCACobDad2KQp0VNkyCiyvMBWoPW/r9dtDW5T70a9TUpXPdjtdsfgb5mZmfLw8FBAQABFOSoUzmMBlFaJP8Vee+01ubq66pdfftGiRYv08MMP65FHHtHrr7+uX375RYZh6PXXX3d6wIULF2rEiBGaPHmyVq5cqbCwML399tuO119//XXt3LlTn332mfbs2aMFCxZo4sSJxS77+SvDMC56ydvIkSO1Y8cOHT16VMuXL9e4ceNKlHPGjBlKS0tzPE6dOlW6AwUAVAo2u03zd80vVpxLcix7cdeLRS5/h/Nc6DE/deqUTp48qby8PAUEBNBjjgqJ81gApVXiT7Ivv/xS//jHPy76LVxwcLBmzJihzz//3KnhJMnX11dz587Vb7/9psGDB+vRRx/VlClT9O9//1s5OTn6xz/+oYULF2rQoEFq166dHnvsMY0YMUKvvPKKJCkkJET5+flKSUkpst2EhATVqVOn2P6CgoI0cOBARUREKDc3V/379y9RTk9PT/n7+xd5AACqnr0JexWffenbugwZisuO096EveWYquq7MPjbqVOndOrUKeXm5iogIEA1atSgMEeFxXksgNIq8SfakSNH1K1bt0u+3q1bNx0+fNgpoS4lICBADz/8sPr3769t27apoKBABQUFxT6YXV1dHYNvdOzYUe7u7vr6668dr8fGxurnn3++5PGMGzdO0dHRGj16dJHRMQEAOJd9zqntcHl/LsxPnjyp3NxcWa1WCnNUOpzHAiiJUt2DHhAQcMnXAwICymRAicmTJ2vo0KEKCwuTzWbTli1btHXrVs2cOVP+/v7q0aOHnnzySXl7e6tRo0baunWrVq5cqYULF0o6P397RESEpk6dqqCgINWsWVPTpk1T27Zt1bv3xe8P7Nevn86dO8e3hwCAYrIKskrUrrbPle/7xKXZ7XZlZWUpJSVFGRkZcnNzk9VqpShHpcJ5LIDSKnGBbhjGZT8ULRaLDKP4/XjXqmHDhpoyZYp+++03ZWVlKTo6WuPGjdOkSZMkSWvWrNGMGTP04IMPKjk5WY0aNdLcuXP1yCOPOLaxaNEiubm56d5771VOTo7uuOMOLV++/JLfKlosFtWqVcvpxwIAqLzshl2rDq7Soj2LLtvOIovq+NRRh+AO5ZSsajEMQ5mZmUpNTVV6erpcXV0pzFFpcR4LoLQsRgmrahcXF1mt1kvOJWoYhtLT02Wzld2gOGPGjNHy5cvLbPvOlJ6eLqvVqrS0NL7BBCqJ7IJsdX6vsyQp5oEY+bj7mJyo4qju701iTqJmbp+p785+J0lqV6udfkr8qVg7i85/Ri7suZBR3EvJMIwiPeYuLi7y9fXlEt1qJiUlRfXr17/sVZvOVJ7na5zHApVTac6BnPF/p8Q96FFRUVe1AwAAKrNtp7dp5nczlZybLC9XLz1585O6p8U92nxys+btmqeE7ARH2zo+dfR0p6cpzkvhYoV5jRo1KMwBANVSiQv08PDwssxRIpXlW0cAQOWXb8vXoj2L9O6hdyVJzQOb6+XbXtZ1AddJkno36q3OIZ3Vbc35gZoW37FY3ep1k6sLhWVJUJijuuE8FkBJlLhABwCgOnlh5wta//t6SdKDrR7U5I6T5enqWaTNn4vxjnU6UpyXwJ8L8/T0dApzAAD+hAIdAICLmNB2gnbH7daMTjPUI7SH2XEqPcMwlJ2d7SjMLRaL/P39KcwBAPgTCnQAACSl5aVpx9kd6teknySpoX9DfXHXF3Jz4aPyWlwozFNTU5WWliaLxUKPOQAAl8BZBwCg2kvLS9M9n9+juKw4BXoFqnPd86O1Upxfm9zcXCUlJTkKcz8/P7m58Z4CAHApTCoKAKj2rJ5WdavXTQ1qNJCfu5/ZcaqEvLw8nT17VqmpqfLz85PVaqU4BwDgCkr0STllypQSb3DhwoVXHQYAgPISmxkrd1d31fKuJUl66uanZMiQr7uvyckqv8LCQsXHxysnJ0cBAQGyWCxmRwIAoFIoUYG+b9++Is/37Nkjm82mli1bSpKOHDkiV1dXdezY0fkJAQBwso3HN2r2jtlqE9RGb9/5tlwsLvJx9zE7VpVgt9uVkJCg9PR0BQYGUpwDAFAKJSrQt2zZ4vh54cKFqlGjhlasWKHAwEBJUkpKisaOHatbb721bFICAOAE2QXZemn3S1r32zpJUmZBpjLyM2T1tJqcrGowDEPnzp1TcnKyrFYrxTkAAKVU6pvBFixYoI0bNzqKc0kKDAzUnDlz1KdPH02dOtWpAQEAcIZDSYf01LdP6Xj6cVlkUUTbCE0Mmyh3F3ezo1UZycnJOnfuHKO0AwBwlUpdoKenpys+Pl433HBDkeUJCQnKyMhwWjAAAJzBMAy9e+hdLdqzSAX2AgV7B+tft/7LMVI7nCMtLU3x8fHy9fWVuztfegAAcDVKXaDfddddGjt2rBYsWKAuXbpIknbu3Kknn3xSw4YNc3pAAACuVlJOkmZ+N1Pbz2yXJPUM7al/dvunAr0Cr7AmSiMzM1NxcXHy9PSUp6en2XEAAKi0Sl2gv/3225o2bZpGjhypgoKC8xtxc1NERIRefvllpwcEAOBqfH/me/1j+z+UlJskDxcPPXnzkxrRcgT3RTtZbm6u4uPjJUne3t4mpwEAoHIrdYHu4+OjxYsX6+WXX9Yff/whwzDUrFkz+foyLQ0AwHwFtgK9tvc1rTi4QpLULKCZXrrtJTUPbG5ysqonPz9fcXFxysvLU0BAgNlxAACo9FyudsXY2FjFxsaqRYsW8vX1lWEYzswFAMBVWfzjYkdxPqLlCL3/t/cpzsvAhbnOMzMzZbUyCj4AAM5Q6h70pKQk3XvvvdqyZYssFot+++03NW3aVOPHj1dAQIAWLFhQFjkBACiRsW3G6rsz3+mR9o/o9oa3mx2nSrLb7Tp37pzS0tIUEBDAbQMAADhJqXvQJ0+eLHd3d508eVI+Pj6O5SNGjNCGDRucGg4AgCvJyM/Q6kOrHVdy+Xv464OBH1CclxHDMJSUlKSkpCT5+/vLxeWqL8YDAAB/Ueoe9I0bN+qrr75SgwYNiixv3ry5Tpw44bRgAABcSYGtQA98+YCOpx+Xp6un7m5xtyTRo1uGUlNTlZCQID8/P7m5lfo0AgAAXEapv/bOysoq0nN+QWJiIlOrAADKlburu4Y3H676fvW5z7wcpKenKy4uTt7e3vLw8DA7DgAAVU6pC/TbbrtNK1eudDy3WCyy2+16+eWX1atXL6eGAwDgr+Ky4vRH6h+O56NvGK11g9epfe32Jqaq+rKzsxUfHy83Nzd5eXmZHQcAgCqp1Nemvfzyy+rZs6d++OEH5efn66mnntIvv/yi5ORkfffdd2WREQAASdLmE5s16/tZCvIO0pq/rZGPu49cLC7ydWeqz7KUl5en2NhY2Ww2+fv7mx0HAIAqq9Q96K1bt9ZPP/2kTp066c4771RWVpaGDRumffv26brrriuLjACAai6nMEf/3PFPPRH9hNLz0+Xr5quM/AyzY1ULBQUFjrnOKc4BAChbVzW6S0hIiJ5//nlnZwEAoJjDyYf19LdP64+0P2SRRWPbjNVjYY/J3dXd7GhVns1mU0JCgjIyMhQYGGh2HAAAqrwSFeg//fRTiTfYrl27qw4DAMAFhmHovV/f08IfFirfnq/a3rX1r1v/pS51u5gdrVowDEOJiYlKSUmR1WplZHwAAMpBiQr0sLAwWSwWGYZR5AP6wpyzf15ms9mcHBEAUN0k5yZr1neztPX0VklSjwY99M/u/1RNr5omJ6seLsx1npiYKH9/f7m6upodCQAAU9js/1ff7onfo271usnVpew+F0t0D/qxY8d09OhRHTt2TOvWrVOTJk20ePFi7d+/X/v379fixYt13XXXad26dWUWFABQPew4u0N3f3a3tp7eKg8XD83oNENv3P4GxXk5SktLU3x8vHx8fJjrHABQbW06sUlDPxvqeD5x80T1XddXm05sKrN9luhTt1GjRo6f77nnHr3++usaMGCAY1m7du0UGhqqZ599VkOHDnV6SABA1VdgK9Ab+9/Q8p+Xy5Chptameum2l9SyZkuzo1UrmZmZio+Pl5eXlzw9Pc2OAwCAKTad2KQp0VNkyCiyPCE7QVOip2hhz4Xq3ai30/db6lHcDxw4oCZNmhRb3qRJEx08eNApoQAA1c9Hv32kqJ+jZMjQPS3u0ZqBayjOy1lOTo7i4uJksVjk7e1tdhwAAExhs9s0f9f8YsW5JMeyF3e9WOTyd2cpdYHeqlUrzZkzR7m5uY5leXl5mjNnjlq1auXUcACA6uPuFnfrtga3aVHPRZrVdZa83SgQy1N+fr7i4uJUUFAgPz8/s+MAAGCavQl7FZ8df8nXDRmKy47T3oS9Tt93qW8se/vttzVo0CCFhoaqffv2kqQff/xRFotFX3zxhdMDAgCqpsz8TC3/Zbkebvew3F3d5e7irv+943/NjlUtFRYWKj4+XtnZ2QoICDA7DgAApjqXfc6p7Uqj1AV6p06ddOzYMb377rv69ddfZRiGRowYoQceeEC+vr5ODwgAqHoMw9CEjRP0c9LPyrPlaepNU82OVG3Z7XYlJCQoLS1NAQEBTKcGAKj2avvUdmq70ihVgV5QUKCWLVvqiy++0EMPPeT0MACA6sFisWhCuwl6afdLuqPhHWbHqbYuzHWenJwsq9UqF5dS3/kGAECV0yG4g+r41FFCdsJF70O3yKI6PnXUIbiD0/ddqk9id3d35eXl8e06AKDU4rPi9UPcD47ntze8XZ8O/VRhwWHmharmkpOTde7cOdWoUYO5zgEA+P9cXVw1vdP0i75m0fla+OlOT5fJfOil/qp80qRJevHFF1VYWOj0MACAqumbk99o+OfD9UT0E4rP+r9BVzxdmcbLLOnp6UpISJC3t7fc3d3NjgMAQIXSu1FvLey5UME+wUWW1/GpU2ZTrElXcQ96TEyMNm/erI0bN6pt27bF7jv/+OOPnRYOAFC55Rbm6pUfXtEHhz+QJLWq2Ur59nyTUyErK0txcXFyd3eXl5eX2XEAh7S0NJ09e1Z33nmn2VEAQL0b9VbnkM7qtqabJGnxHYvVrV63Muk5v6DUBXpAQICGDx9eFlkAAFXIbym/6alvn9Lvqb9LksJbh+vvHf4ud1d6a82Um5uruLg4GYYhHx8fs+MAkqTffvtNy5Yt00cffaTAwEAdP36cKzsAVAh/LsY71ulYpsW5dBUFelRUVFnkAABUEYZhaM3hNXpl9yvKt+cryCtI/7rlX+pWv5vZ0aq9goICxcXFKS8vj+nUYDq73a7o6GgtW7ZM0dHRjuU5OTn6+OOPNWLECPPCAYBJSl2gAwBwKSm5KZr1/SxFn4qWJN1a/1a90P0FBXkHmZoLks1mU3x8vLKysijOYaqsrCytXbtWkZGR+uOPPy7a5rXXXqNAB1AtXVWB/tFHH+nDDz/UyZMnlZ9f9F7CvXv3OiUYAKBy2RW7SzO2zVBCToLcXdw1peMUPdjqQWb+qADsdrvOnTun1NRU5jqHaU6dOqWoqCi9//77Sk9Pv2Q7Nzc3NW7cWDk5OfL29i7HhABgvlKP4v76669r7NixCg4O1r59+9SpUycFBQXp6NGj6t+/f1lkBABUYAX2Ar229zWN3zheCTkJauzfWO/97T2NbD2SQrACMAxDycnJSkxMlL+/P3Odo1wZhqGYmBhNmDBB3bp10zvvvHPJ4jwwMFATJkzQjz/+qPfee4/iHEC1VOoe9MWLF+vf//637r//fq1YsUJPPfWUmjZtqlmzZik5ObksMgIAKrDtp7dr6YGlkqThzYfrqZufko87g49VFGlpaUpISJCfn5/c3LizDeUjLy9Pn332mZYuXaqff/75sm2vv/56RURE6K677lJubq7q1atXTikBoOIp9Sf1yZMn1a3b+YF+vL29lZGRIUkaNWqUunTpojfffNO5CQEAprPZbY6f98TvKTLFSM/QnhrRcoQ6hXRSn8Z9zIqIi8jIyFBcXJw8PT3l4eFhdhxUAwkJCVq1apVWrVqlc+fOXbKdxWJR7969FRERoVtuucVxtU1ubm55RQWACqnUBXpISIiSkpLUqFEjNWrUSDt37lT79u117NgxGYZRFhkBACbadGKT5u2a53g+cfNE+bj5aEbnGRrabKgsFotmdplpYkJcTHZ2tuLi4uTq6sqlwihzBw4c0NKlS/XZZ58VG5/oz3x9fXXfffdp7NixatKkSTkmBIDKodQF+u23367PP/9cHTp0UEREhCZPnqyPPvpIP/zwg4YNG1YWGQEAJtl0YpOmRE+RoaJfwGYXZuvZ756Vn7ufejfqbVI6XEpeXp7i4uJUWFgoq9VqdhxUUYWFhfrqq6+0bNkyxcTEXLZto0aNNHbsWI0YMUL+/v7llBAAKp9SF+j//ve/ZbfbJUmPPPKIatasqe3bt2vQoEF65JFHnB4QAGAOm92m+bvmFyvO/+zFXS+qV2gvx+XuMF9hYaHi4+OVk5PDdGooE6mpqVqzZo2ioqJ0+vTpy7bt1q2bxo8fr969e8vVlb8TAHAlpS7QXVxciowAe++99+ree+91aigAgPn2JuxVfHb8ZdvEZcdpb8Je3RxyczmlwuVcmOs8PT1dgYGBjKIPp/r9998VGRmpDz/8UDk5OZds5+npqbvuuksRERFq3bp1OSYEgMqv1AV69+7d1aNHD/Xs2VPdu3eXr69vWeQCAJjsXPalB3i6mnYoW4ZhKDExUSkpKbJarRTncArDMLR161YtXbpUW7ZsuWzbOnXqKDw8XCNHjlRQUFA5JQSAqqXUBfrAgQO1detWvfnmm8rNzVXHjh0dBfstt9wiPz+/ssgJAChHdsOuH+J/KFHb2j61yzgNSiI5OVnnzp1TjRo1uJQY1yw7O1sfffSRli1bpt9///2ybcPCwjR+/Hj97W9/Y7YAALhGpS7QZ8yYoRkzZshms2n37t2Kjo5WdHS0Fi5cKIvFory8vLLICQAoJ2l5aXpm+zPaenrrZdtZZFEdnzrqENyhnJLhUtLS0hQfHy9fX1+5u7ubHQeV2JkzZ7R8+XKtXr1aaWlpl2zn6uqqAQMGaPz48erYsSNXbACAk5S6QL/gt99+048//qgff/xRP/30k/z9/XXrrbc6MxsAoJz9dO4nTds6TbFZsfJw8dDgZoO17si6YgPFWXT+ZPzpTk8zQJzJMjMzHXOde3p6mh0HlZBhGNq9e7eWLl2qDRs2yGazXbJtQECARo4cqdGjR6t+/frlmBIAqodSF+gjRozQt99+K7vdrttuu0233XabZsyYoXbt2pVFPgBAOTAMQ+8eelcL9yxUob1QDWs01Cs9XlGroFbqXq+75u2ap4TsBEf7Oj519HSnp5lizWS5ubmKjz8/kB9znaO08vPz9dlnn2nZsmX66aefLtu2RYsWioiI0PDhw/ldA4AyVOoCfe3atapVq5bGjBmjXr166dZbb+W+cwCoxNLz0zXru1nafHKzJKlPoz56vtvz8vM4/7e9d6Pe6hzSWd3WdJMkLb5jsbrV60bPucny8/MVFxenvLw8plNDqSQmJmrVqlVauXKlEhISLtv2jjvu0Pjx43XrrbdyGTsAlINSF+jJycn69ttvFR0drZkzZ+qXX35R+/bt1bNnT/Xs2VP9+/cvi5wAnCi7IFud3+ssSYp5IEY+7j4mJ4JZfkn6RVOjp+pM5hm5u7jryZuf1H0t7yt2Iv7nYrxjnY4U5ya7MNd5ZmamAgMDzY6DSuLnn3/WsmXLtH79euXn51+ynY+Pj0aMGKGxY8fquuuuK8eEAIBSF+gBAQEaPHiwBg8eLEn6448/NGfOHC1cuFCvvPLKZe9bAgBUHIk5iRrz3zHKteWqvl99LeixQDfUusHsWLgCu92uc+fOKS0tTQEBAfRq4rJsNps2btyoZcuWaceOHZdtGxoaqrFjx+q+++6T1Wotp4QAgD+7qh70rVu3OkZv/+WXX1SzZk0NGTJEvXr1KouMAIAyUMu7lsa3Ha+DSQf1wi0vyN/D3+xIuALDMJSUlKSkpCT5+/vLxcXF7EiooNLS0rRmzRpFRUXp1KlTl23btWtXRUREqE+fPkzRBwAmK3WBXrt2bdWqVUu33nqrJkyYoJ49e6pNmzZlkQ0A4GS/Jv8qL1cvNbY2liRNaDdBFlnoha0kUlNTlZCQID8/P7m5XfVELKjC/vjjD0VFRemDDz5Qdnb2Jdt5eHho6NChioiI4DwOACqQUn+6//jjjxf9Q26z2fT5559r6NChzsgFAHCyrae2akr0FDW2NtbqAavl5eYlFws9sJVFenq64uLi5O3tLQ8PD7PjoAIxDEPbtm3TkiVL9M0331y2bXBwsEaPHq2RI0eqdu3a5ZQQAFBSpS7Q/1qc//rrr4qMjNSKFSuUkpJy2UFHAADmaR3UWn4efgrxDVG+PV9e8jI7EkooOztb8fHxcnNzk5cX/244LycnRx999JEiIyN15MiRy7Zt166dxo8fr0GDBvEFDwBUYFd1fVxWVpY++OADLVu2TDt37lSvXr00d+5ces8BoIJJzElULe9akqTaPrW1esBq1fOrR895JZKXl6fY2FjZbDb5+zNOAKQzZ85oxYoVWr16tVJTUy/ZzsXFRQMGDND48eN10003cSsLAFQCpSrQd+zYoaVLl+rDDz9U8+bN9eCDDyomJkavv/66WrduXVYZAQBXYf3v6/WvmH/phe4vqG/jvpKkBjUamJwKpVFQUMBc55B0/jL2PXv2aOnSpfrPf/5z2VlzrFarHnzwQY0ZM0b169cvx5QAgGtV4gK9devWys7O1gMPPKCYmBhHQT59+vQyCwcAKL2cwhzN3TlXn/7xqSTpq+NfOQp0VB42m00JCQnKyMhgrvNqLD8/X1988YWWLVum/fv3X7Zts2bNFBERobvvvls+Pj7lExAA4FQlLtB///133XffferVq5datWpVlpkAAFfpaOpRTd06Vb+n/i4Xi4smtp+oCe0mmB0LpWQYhhITE5WSkiKr1cqlydVQUlKSVq1apZUrVyo+Pv6ybW+//XZFRETotttuY+o9AKjkSlygHzt2TMuXL9ejjz6qnJwc3X///XrwwQc5aQCACuLzPz7XCztfUE5hjmp519KLt76oTnU7mR0LpXRhrvPExET5+/szL3U1c/DgQS1btkyffPKJ8vLyLtnO29tb9957r8aNG6dmzZqVY0IAQFkqcYFev359PfPMM3rmmWf0zTffKDIyUt27d1dhYaGWL1+u8ePHq0WLFmWZFQBwEbmFuZq/a77W/bZOktQppJNevO1Fx+BwqFzS0tIUHx8vHx8f5jqvJmw2mzZt2qQlS5Zox44dl23boEEDjR07Vvfddx/jEgBAFXRVn/y33367br/9dqWlpWn16tWKjIzUK6+8ojZt2uinn35ydkYAwCUcTzuuaVun6XDKYVlk0cPtH9Yj7R6Rqwu9rpVRZmam4uPj5eXlJU9PT7PjoIxlZGRozZo1ioqK0okTJy7btnPnzoqIiFDfvn354gYAqrBr+gtvtVo1ceJETZw4Ufv371dkZKSzcgEArmDD8Q2a/f1sZRVkqaZXTc27dZ661etmdixcpZycHMXFxcliscjb29vsOChDR48eVVRUlD744ANlZWVdsp2Hh4cGDx6s8ePHq23btuWYEABgFqd9BRsWFqbXX3/dWZsDAFzGwj0LFfVzlCSpQ3AHvdzjZQX7BJucClcrPz9fcXFxKigokNVqNTsOyoBhGNq2bZuWLVumzZs3yzCMS7atVauWRo8erVGjRik4mP/XAFCdcI0UAFRCrWuen+pyfNvx+p+w/5GbC3/OK6vCwkLFx8crOzube4qroJycHH300UeKjIzUkSNHLtu2TZs2ioiI0JAhQ7jFAQCqKc7oAKCSSMtLk9XzfO9qvyb91CygmZoFMnpzZWa325WQkKC0tDQFBAQwM0oVcvr0aa1YsULvvfeeUlNTL9nOxcVF/fr10/jx49WpUyd+BwCgmqNAB4AKrsBWoIV7FmrD8Q1aO2itY3R2ivPK7cJc58nJybJarcxfXQUYhqFdu3Zp6dKl2rBhg+x2+yXb+vv76/7779fYsWMVGhpajikBABUZBToAVHCFRqF2xu5UYk6itp7aquEthpsdCU6QnJysc+fOqUaNGsx1Xsnl5ubq008/VWRkpH7++efLtm3WrJnGjRunu+++W76+vuWUEABQWZSoQC/N4G+PP/74VYcBABTn7eatBT0X6ETaCfVq2MvsOHCC9PR0JSQkyNvbW+7u7mbHwVWKi4vTypUr9e677yopKemybW+//XZFRETotttu42oJAMAllahAX7RoUYk2ZrFYyrRAHzNmjJYvX15m2weAiqDAXqA39r4hf09/jW87XpLU1NpUTa1NTU4GZ8jKylJcXJzc3d3l5eVldhxchX379mnZsmX6/PPPVVhYeMl2Pj4+GjFihMaMGaNmzbglpbrjPBZASZToK9xjx46V6HH06NGyzntRhw4d0uDBg2W1WlWjRg116dJFJ0+edLyel5enSZMmqVatWvL19dXgwYN1+vTpItuwWCyyWCzauXNnkeV5eXkKCgqSxWJRdHR0eRwOgGosLitO4zaMU9QvUXpz35s6lX7K7EhwotzcXMXFxckwDPn4+JgdB6VQUFCg9evXa9CgQRo4cKA++eSTSxbnDRs21HPPPac9e/Zozpw5FOe4LM5jAfxZhb/GKjExUeHh4WrYsKHef/99NWvWTPfee6/y8/MlSX/88YduueUWXX/99YqOjtaPP/6oZ599tkivxBNPPKFPPvlEa9as0fbt25WZmamBAwfKZrMV2VdoaKiioqKKLPvkk0/k5+dX9gcKoNrbfma77vn8Hu0/t19+7n56ucfLCvVn8KiqoqCgQHFxccrLy1ONGjXMjoMSSkpK0muvvaYuXbrof/7nf7R3795Ltu3evbuioqK0fft2PfTQQ/L39y/HpKiIOI8FUFpXNUjc6dOn9dlnn+nkyZOOPzAXLFy40CnBLpg8ebJ2796tVatW6dVXX9Xjjz9eZGTUZ555RgMGDNBLL73kWKdp0/+7DDQtLU3Lli3TqlWr1Lt3b0nSu+++q9DQUG3atEl9+/Z1tA0PD9frr7+uV199Vd7e3pKkyMhIhYeH64UXXnDqcQHABYX2Qi3ev1hLDiyRJLWq2UoLeiygOK9CbDab4uPjlZWVxVznlcQvv/yiZcuWaf369crLy7tkOy8vLw0fPlxjx45Vq1atyjEhKgPOYwGUVql70Ddv3qyWLVtq8eLFWrBggbZs2aKoqChFRkZq//79Tg+4b98+jRo1Sj169JDValWvXr304osvysvLS3a7XV9++aVatGihvn37Kjg4WJ07d9b69esd6+/Zs0cFBQXq06ePY1m9evXUpk0bff/990X21bFjRzVp0kTr1q2TJJ06dUrffvutRo0adcWceXl5Sk9PL/IAgCtJyE7QhI0THMX5iJYjtGrAKorzKuTCXOepqamyWq3Mc12B2Ww2/ec//9Hdd9+tPn366IMPPrhkcV63bl3NmDFDu3fv1ksvvURxjoviPBZAaZW6QJ8xY4amTp2qn3/+WV5eXlq3bp1OnTqlHj166J577nF6wAuXi33xxRfFXktISFBmZqbmz5+vfv36aePGjbrrrrs0bNgwbd26VdL5EVY9PDwUGBhYZN06deooLi6u2DbHjh2ryMhISVJUVJQGDBig2rVrXzHnvHnzZLVaHQ/mNAVwJTtjd+qez+/RD/E/yMfNRy/f9rJmdpkpT1dPs6PBSQzDUHJyspKSkuTv78/o3RVUamqq3n77bXXr1k0TJkzQjh07Ltn25ptv1ttvv62dO3fqscceU82aNcsxKSobzmMBlFapzxQOHTqk8PBwSZKbm5tycnLk5+enf/7zn3rxxRedHnDhwoUaMWKEJk+erJUrVyosLExvv/22JDkuDxoyZIgmT56ssLAwTZ8+XQMHDnS0uRTDMC7aizFy5Ejt2LFDR48e1fLlyzVu3LgS5ZwxY4bS0tIcj1OnGNgJwMXZ7DYt3r9YD218SMm5yWoR2EIfDPxA/Zr0MzsanCw1NVUJCQny8/OTm9tV3VWGMvTbb79p+vTpuummm/TCCy8UG3jrAnd3dw0fPlz//e9/HQPF8e+JkuA8FkBplfrTxdfX13G5V7169fTHH3/ohhtukHR+IAxn8/X11dy5czV37lwNHTpU/fv31+TJk+Xi4qIxY8bIzc1NrVu3LrJOq1attH37dklSSEiI8vPzlZKSUuTbx4SEBHXr1q3Y/oKCgjRw4EBFREQoNzdX/fv3V0ZGxhVzenp6ytOTXi8Al5eYk6jp26YrJjZGkjS8+XBN7zRdXm5Mt1XVZGRkKD4+Xp6envLw8DA7Dv4/u92uLVu2aNmyZY5eykupXbu2Ro8erZEjRyo4OLicEqIq4TwWQGmVuge9S5cu+u677yRJf/vb3zR16lTNnTtX48aNU5cuXZwe8M8CAgL08MMPq3///tq2bZs8PDx088036/Dhw0XaHTlyRI0aNZJ0/n4cd3d3ff31147XY2Nj9fPPP1/0D5skjRs3TtHR0Ro9erRcXV3L7oAAVDtnM89qT9weebt561+3/Euzu82mOK+CsrOzFRcXJ1dXV8dgTTBXZmamIiMjddttt2n06NGXLc7bt2+v119/XTExMZoyZQrFOZyC81gAJVHqHvSFCxcqMzNTkjR79mxlZmbqgw8+ULNmzbRo0SKnB5w8ebKGDh2qsLAw2Ww2bdmyRVu3btXMmTMlSU8++aRGjBih2267Tb169dKGDRv0+eefO+Z6tFqtioiI0NSpUxUUFKSaNWtq2rRpatu2rWM0zL/q16+fzp07x/QoAJyuXe12+mf3f6p1UGtdF3Cd2XFQBvLy8hQXF6fCwkJZrVaz41R7x48fV2RkpD744APH+cvFuLq6asCAAYqIiNBNN93EYH5wCs5jAZRWqQv0P0/94OPjo8WLFzs10F81bNhQU6ZM0W+//aasrCxFR0dr3LhxmjRpkiTprrvu0ttvv6158+bp8ccfV8uWLbVu3Trdcsstjm0sWrRIbm5uuvfee5WTk6M77rhDy5cvv+S3ihaLRbVq1SrT4wJQPaTkpmj297M1MWyiWtZsKUkadN0gk1OhrBQWFiouLk45OTlMp2YiwzC0fft2LVu2TJs2bZJhGJdsGxgYqAcffFDh4eGqV69eOaZEdcB5LIDSshiX+9S6iKZNm2r37t0KCgoqsjw1NVUdOnTQ0aNHnRrwz8aMGaPly5eX2fadKT09XVarVWlpaXyDiQonuyBbnd/rLEmKeSBGPu4+JieqGMrifXn2u2e1/vf1ur7m9fpw4IeVtleO35mL+/P7suO+HUpLTHPcK1pZ/60rs5ycHK1bt06RkZHFLhv+q1atWikiIkJDhw7lNoQKJCUlRfXr1y+3L7jK83yN81igcirNOZAz/u+Uugf9+PHjstlsxZbn5eXpzJkzVxUCAKqqKR2nKC4rTk/e/CQFWxWXmJiozJRM5jo3wZkzZ7R8+XK99957Sk1NvWQ7i8WiPn36KCIiQt26dePfCQBQ4ZS4QP/ss88cP3/11VdF7quz2WzavHmzGjdu7NRwf1VZvnUEUH2l5aXpP8f+o/uvv1+SFOgVqCV9lpicCuUhMTFRtQNqMyhTOTEMQ7t379bSpUu1YcOGi3YeXODv76/77rtPY8aMcQy+BZQ3zmMBlESJC/ShQ4dKOv/t84V50C9wd3dX48aNtWDBAqeGA4DK5KdzP2na1mmKzYqVt5u3hjYbanYklJHCwkLl5OToXOo5xzJfX1+5u7ubmKp6yMvL06effqrIyEgdOHDgsm2bNm2qiIgI3XPPPfL19S2nhAAAXL0SF+h2u12S1KRJE+3evZvBJwDg/zMMQ+8eelcL9yxUob1QDWs0VMvAlmbHgpPZbDbl5OQoKytLGRkZys3NVb6R73idOYTLVnx8vFatWqVVq1YpMTHxsm179eqliIgI9ejRQy4upZ5RFgAA05T6HvRjx46VRQ4A+H/s3Xd8FGXiBvBne0uyaaRBgAAqBBAEpARpCgKK2AFpCXin4qkncCec+jssp2L37FeUgKiA7QQVFJQi0kFQwEILLYSQbLLZbN+d+f0x2U2WdEi2JM/388mH3Zl3Z98dNtl59m0RqcxVhr//8Hd8e+JbAMCoDqPweNbjiFZHh7hm1BR8odxms/lDuUwmg1arRVxcHJyCM9RVbPH27t2Ld955B6tWrYLb7a61nF6vx4QJEzBjxgx06dIliDUkIiJqOo0O6ACwceNGvPDCC/jll18gk8nQrVs3/PWvf8WQIUOaun5ERGHrQPEBzN0wF6fLT0MlV+Ev/f6CO7rewYmnIpwgCP5QXlZWBqdTCuEajQaxsbH8/w0Ct9uNr776Cu+88w52795dZ9n27dsjJycHkyZN4rrzREQU8Rod0JcuXYoZM2bglltuwQMPPABRFLFlyxb/moyTJ09ujnoSEYUNURSx7LdleH7n83ALbrSNaosXh72I7ondQ101ukCCIMDhcMBms8FsNsPhcAAAtFotYmJi2E06SEwmE5YuXYrFixejoKCgzrJZWVn4wx/+gJEjR3JiPiIiajEaHdCfeuopPPfcc5g9e7Z/25///Ge89NJLePLJJxnQiahFK3eV47Gtj+HrvK8BAFenX40nr3oSMWquExtpqobysrIyOBwOiKIIrVYLo9HIUB5EBw8exDvvvIPPPvvM32OhJlqtFrfccgtmzJiBzMzMINaQiIgoOBod0I8ePYobbrih2vbx48fj4YcfbpJKERGFo99Mv2Huxrk4XnYcSpkSc/rNwdRuU9nlOYKIohgQyu12OwRBYEt5CHi9Xqxduxb//e9/sXXr1jrLpqSkICcnB1OmTEF8fHyQakhERBR8jQ7o6enp+Pbbb6tNwPLtt98iPT29ySpGRBROXF4X7l13LwrthUg1pOL5Yc+jV5teoa4WNYAvlDscDpjNZn8o12g0iI6OZvfoIDObzfjwww+Rm5uLkydP1lm2X79+uPPOOzF27FguYUdERK1CgwP6zJkz8c9//hNz587FAw88gL179yIrKwsymQybN29Gbm4u/vnPfzZnXYmImpVX8Ppv7z67G1lpWVDIpfCmVqjxf4P+D5/8/gn+cdU/YNRwMqpwJooinE4n7Ha7f0y5x+OBRqNBVFQUQ3kIHD58GO+88w4++ugj2O32WsupVCqMHz8ed955J3r14pdgRETUujQ4oC9evBgLFy7ErFmzkJKSghdffBErVqwAAHTr1g3Lly/HjTfe2GwVJSJqTuuOr8MzO57x37/323uRoE3AhMsm4N7e9wIAhqcPx7B2w9ilPUyJogiXywW73Y6ysjLYbDZ4PB6o1Wro9XoolRe0cAldBEEQsGHDBrzzzjvYsGFDnWUTExMxffp0TJs2DUlJScGpIBERUZhp8NWKKIr+2zfffDNuvvnmZqkQEVGwrTu+DnM2zIEIMWB7saMYb+17C210bXD7ZbcDAMN5GPK1lFssFthsNrjd7qCFcq1Ci69Hfd2szxGJRFHExx9/jFdffRVHjx6ts+zll1+OO++8EzfccAM0Gk2QakhERBSeGnXlwgtTImppvIIXC3csrBbOq3p739u45ZJb/N3dKfTObyl3u91QqVTQarWIjo4OdfVaNbfbjUceeQTvv/9+rWUUCgWuu+463HnnnejXrx+vL4iIiCo0KqBfeuml9X6Imkymi6oQEVEw7Sncg7O2s3WWKbQXYk/hHlyZcmWQakU1cblccDgcsFgssFqtcLlcUKlU/sneKPRKSkpw991344cffqhxf2xsLKZOnYrp06ejbdu2Qa4dERFR+GtUQH/88cdhNHJiJCJqOc7ZzjVpOWpabrcbdrsd5eXlsFqtcDqdUCqV0Gq1iIqKCnX1qIqjR48iOzu7xi7tXbt2xcyZM3HLLbdAp9OFoHZERESRoVEBfdKkSZy4hYhalFhNbIPKtdG3ad6KkJ/H4/GH8vLycjidTigUCmi1WhgMhlBXj2qwZcsW/PGPf0RpaWnAdq1WixdffBE33ngju7ETERE1QIMDOj9YiailOWU5hX/uqXt5SBlkSNYno09SnyDVqnWqGsp9LeVyuRxarRZ6vZ6fQWHsww8/xPz58+HxeAK2JycnY9GiRVwqjYiIqBEuaBZ3IqJIt+HkBjy8+WFYXBbolXrYPLZqZWSQQuG8/vM4QVwz8Hq9sNvtsFqtsFgscDqdkMlk0Gq1iI2NZSgPc16vF08//TTefvvtavu6d++O3NxcpKWlhaBmREREkavBAV0QhOasBxFRUHgED1798VUs2r8IAHB54uV4YdgLOFB8AM/seAaFtkJ/2WR9Mub1n4eRHUaGqrotjtfrhcPh8Idyh8MBmUwGjUbDUB5BrFYr7rvvPnzzzTfV9o0ePRqvvfYahyMQERFdgOZdIJaIKIyctZ7FQ5sewp7CPQCAqd2mYk7fOVApVEiNSsWAlAHIWpYFAHjzmjeRlZbFlvMmIAgC7HY7bDYbysrK4HQ6AYChPEKdPn0aOTk5OHjwYLV99957L/72t79BLpeHoGZERESRjwGdiFqFrflbMf/7+TA5TIhSReGJwU9gVIdRAWWqhvG+yX0Zzi+CIAhwOByw2Wwwm81wOBwApEnDYmJiGOAi1N69ezFjxgwUFhYGbFcqlXj22WcxadKkENWMiIioZWBAJ6IWb8VvK/CPbf+ACBGXxV2Gl4a/hPYx7UNdrRanaigvKyuDw+GAKIrQarUwGo0M5RFu1apVePDBB/1ftvjExsbiP//5D7KyskJUMyIiopaDAZ2IWrzL21wOtUKNcZ3GYX7/+dAqtaGuUoshimJAKLfb7RAEgS3lLYgoivjnP/+J559/vtq+Tp06YfHixejUqVMIakZERNTyMKATUYtU4ihBnDYOANA1vis+Hf8pW82biC+U2+12mM1mfyhXq9WIjo6GQsGhAS2F0+nEX/7yF3z66afV9g0ePBj//ve/ERsbG/yKERERtVBs2iCiFkUURSw+sBijPxmNn8/97N/OcN50Tp06hby8PJw+fRputxtRUVGIi4uDwWBgOG9BioqKMGHChBrD+ZQpU/D+++8znBMRETUxtqATUYuz79w+2D12rMlbg55teoa6OhFPEASYzWb/fafTiVhDLJRKfoS0VL/99huys7Nx8uTJgO0ymQx///vf8cc//pGz7xMRETUDXl0RUYsik8nweNbjGNpuKG7sfGOoqxPxrFYriouLUVhSOWt3VFQUlAp+fLRU69evx6xZs2CxWAK2GwwGvPHGGxg1alQtjyQiIqKLxS7uRBTRRFHEit9W4OHvH4YoigCAaHU0bupyE1v4LoLL5cLZs2dx4sQJWK1WdmVuJRYtWoTp06dXC+dt27bF//73P4ZzIiKiZsYmECKKWDa3DU9sewJfHv0SADCqwyiMaD8ixLWKbF6vF2VlZSguLobT6URUVBRUKhUcXkf9D6aI5fF4sGDBAuTm5lbbd8UVV+Ddd99FUlJS8CtGRETUyjCgE1FEOlJ6BHM2zMFR81EoZAr8uc+fMTx9eKirFbFEUfR3Z7dYLNDpdIiLiwt1tSgIysrKcM8992Djxo3V9o0fPx4vvfQSdDpdCGpGRETU+jCgE1HEWXVkFZ7c9iTsHjva6Nrg+WHPo29y31BXK2I5nU6YTCaUlJRALpcjNjaW65e3EsePH0d2djYOHTpUbd+cOXMwZ84cDhUhIiIKIgZ0arFsbhsGfDAAALB98nboVfoQ14gultPrxMIdC/Hx7x8DAAakDsCzQ55Fgi4hxDWLTF6vF6WlpTCZTHA6nYiOjoZKpQp1tShIduzYgTvvvBMmkylgu0ajwYsvvoibb745RDUjIiJqvRjQiSginCw7ibkb5+IX0y+QQYa7e92Ney6/Bwo5191uLFEUUV5ejuLiYpSXl0On0yE+Pj7U1aIg+uijj/DQQw/B5XIFbE9MTMQ777yDfv36hahmRERErRsDOhGFvW+Pf4v/++H/YHFbEKeJw8IhC5HVNivU1YpIDocDJpMJpaWlUCgUiIuLYxfmVkQQBDz33HN47bXXqu3r2rUrFi9ejHbt2oWgZkRERAQwoBNRGHMLbryy+xUsObgEANC7TW88P+x5pBhSQlyzyOPxeGA2m1FcXAy3243o6GgolfwIaE3sdjseeOABfPXVV9X2XX311XjzzTcRHR0dgpoRERGRD6/OiChsmZ1mfHH0CwBAdmY2/tz3z1DJOUa6MURRhMViQXFxMaxWK/R6PaKiokJdLQqygoICzJw5E/v27au27w9/+AP+/ve/Q6HgcBEiIqJQY0AnorCVqEvE80Ofh8VtwTXtrwl1dSKO3W73d2dXqVTszt5K7d+/H9nZ2SgoKAjYrlAo8I9//APTp08PUc2IiIjofAzoRBQ2vIIXb//0Ni6JvQTXdrwWANA/tX+IaxV5PB4PSktLUVxcDK/Xi5iYGLaOtlJr1qzBfffdB7vdHrA9JiYG//rXvzB06NAQ1YyIiIhqwoBORGHjs8Of4e19b8OgMqBvcl8un9ZIgiCgvLwc586dg91uh8FggEajCXW1KAREUcRbb72Fp59+GqIoBuzr2LEjFi9ejC5duoSodkRERFQbBnQiChs3dbkJ3534Dtd1uo7hvJFsNhtMJhPMZjPUajW7s7diLpcL8+fPx/Lly6vtGzhwIP7zn/9wWT0iIqIwxYBORCEjiiJWHlmJ6zKug0qhglKuxBvXvMFg2Qhut9vfnV0QBHZnb+VMJhPuuusubN26tdq+CRMm4Nlnn4VarQ5BzYiIiKghGNCJKCTMTjMe/eFRbDi5Ab+YfsH8/vMBgOG8gQRBgMViQVFREbuzEwDg8OHDyM7ORl5eXsB2mUyGhx9+GLNmzeLvFxERUZhjQCeioDtQfABzN8zF6fLTUMvV6BzbOdRViihWq9XfnV2r1bI7O+H777/H3XffDbPZHLBdp9Phtddew9ixY0NUMyIiImoMBnQiChpRFLH8t+V4budzcAtutItqhxeHv4jMhMxQVy0iuFwulJSUoKSkBKIoIjY2FnK5PNTVohB777338Mgjj8Dr9QZsT0lJQW5uLnr27BmimhEREVFjMaATUVBY3VY8vuVxrM5bDQC4Ov1qPHnVk4hRx4S4ZuFPEASUlZWhqKgIDocDUVFRHEdM8Hq9eOKJJ/Df//632r6ePXti0aJFSE1NDUHNiIiI6EIxoBNRsztUcghzNsxBXlkelDIlHuz7IKZnTme37AawWq0oKiqCxWKBVqvl7NsEACgvL8e9996Lb7/9ttq+6667Dv/85z+h1+tDUDMiIqKWRa/S4+fsn4P2fAzoRNSsVh5ZiSe3PgmH14EkfRJeGPYCrki6ItTVCntOp9PfnV0mk7E7O/mdOnUKOTk5+OWXX6rtu++++zBv3jy+V4iIiCIUAzoRNQuHx4GFOxbik0OfAACy0rLwzJBnEK9lC3BdvF4vysrKUFxcDKfTiaioKKhUqlBXi8LE7t27MXPmTBQVFQVsV6lUeP7553H77beHqGZERETUFBjQiahZ/H3L37H62GrIIMOs3rNwV8+7oJBzfe7aiKLo785eXl4OnU6HuLi4UFeLwsjnn3+O2bNnw+l0BmyPi4vDO++8gwEDBoSoZkRERNRUGNCJqFnc0+se/HzuZ/x90N8xKG1QqKsT1pxOJ0wmE0pKSqBQKNidnQKIooiXX34ZL774YrV9Xbp0weLFi9GxY8fgV4yIiIiaHAM6ETUJt9eN3YW7MTB1IACgk7ETVt28Cko5/8zUxuv1orS0FCaTCS6XC9HR0VAqeb6oksPhwNy5c/G///2v2r6hQ4fi7bffhtFoDH7FiIiIqFmwiaYFsLlt6Lm4J3ou7gmb2xbq6lArZHPbkPN1Du5eezd2Fuz0b2c4r5koirBYLDh58iTOnDkDuVyOuLg4hnMKcO7cOdx+++01hvPp06fjvffeYzgnIiJqYXg1SEQXTafUoWNMRxwzH4PD4wh1dcKaw+GAyWRCaWkpFAoF4uLiuNwcVfPLL78gOzsbp0+fDtgul8vx+OOPY8aMGXzfEBERtUAM6ER0QbyCFw6vAwaVATKZDI8MeAT39r4XbaPahrpqYcnj8fi7s7vdbnZnp1qtW7cO9957L6xWa8D2qKgovPXWW7j66qtDVDMiIiJqbrw6JKJGK7IXYd6medAqtXjt6tcgl8mhV+mhV+lDXbWw4+vOXlxcDKvVCr1ej6ioqFBXi8KQKIr473//iyeeeAKCIATsS09PR25uLrp27Rqi2hEREVEwMKATUaPsLNiJhzY9hCJ7EXRKHY6WHkWXuC6hrlZYstvt/u7sKpWK3dmpVm63G48++iiWLl1abV+/fv3wzjvvIDExMQQ1IyIiomBiQCeiBhFEAe/ufxev/fgaBFFAl9gueHH4i+hk7BTqqoUdt9vt787u9XoRExMDhYJrwFPNSktLcffdd2Pz5s3V9t1yyy14/vnnodVqQ1AzIiIiCjYGdCKql9lpxsObH8amU5sAAOM7j8cjAx5hl/bzCIIAi8WCoqIi2O12GAwGaDSaUFeLwtixY8eQnZ2NI0eOVNv317/+FX/+85/Z64KIiKgVYUAnojr9fO5nzN04F2esZ6CWq/HwgIdxyyW3MDScx2azobi4GGazGRqNht3ZqV5bt27FH/7wB5SWlgZs12q1ePnllzF+/PjQVIyIiIhChgGdiGokiiI++PUDvLDrBXgED9Kj0/HS8JfQNZ6TVFXldrtRUlICk8kEQRBgNBrZnZ3qtXz5csybNw9utztge5s2bbBo0SJcccUVIaoZERERhRIDOhFVU+4qx4ItC/DN8W8AACPbj8QTg59AtDo6xDULH4IgoKysDMXFxbDb7YiKioJarQ51tSjMCYKAZ555Bm+++Wa1fZmZmcjNzUXbtlyqkIiIqLViQCeiAMfMx3D/d/fjeNlxKGVKzOk3B1O7TWV37SqsVitMJhPKysqg0WgQHx8f6ipRBLDZbLj//vuxZs2aavtGjRqFN954AwaDIQQ1IyIionDBgE7UCnkFr//27rO7kZWWBYVc6pYdq4mFw+NAsj4ZLwx7Ab2TeoeoluHH5XL5u7MDgNFohFwuD3GtgkOr0OLrUV+HuhoRKz8/HzNmzMD+/fur7bv77rvxyCOPcGgEERERMaATtTbrjq/DMzue8d+/99t7kaRPwt/6/w0jO4xEnDYOb1zzBpL0SYjTxoWwpuHD6/X6u7M7HA52Z6dG2bdvH2bMmIGzZ88GbFcqlXjmmWcwefLkENWMKLx4PB6IohjqahARhVTraPohIgBSOJ+zYQ4KbYUB2wtthZi9YTbWHV8HALgs/jKGc0gT5ZWXl+PUqVM4ffo0ZDIZ4uPjGc6pwb788kvccsst1cJ5bGwsPvjgA4ZzatW8Xi/sdjtKS0thMpn883moVKpQV42IKGTYgk7USngFLxbuWAgRtbdOLNyxECPSR/i7u7dmLpcL5iIzSkpKIJfLERsb22q6s9PFE0URr7/+OhYuXFhtX0ZGBhYvXozOnTuHoGZEoeP1euFyueByueD1eqFUKqFWq5GQkACdTgeNRgO1Ws05T4ioVWNAJ2ol9hTuwVnb2TrLnLWdxZ7CPbgy5cog1Sp8nTx5EjKPDNHR0WzNoUZxOp146KGH8PHHH1fbl5WVhX//+9+Ii2MPFWr5BEGA0+mE2+2Gx+OBQqGAWq1GXFxcQCDnl59ERJUY0IlaiXO2c01arqXxeDwoLS3135fL5YiLZ4iixjGZTLjzzjuxY8eOavsmT56Mp556ikMkqMUSBCGghVwmk0Gj0cBoNEKv10OtVkOj0TCQExHVgQGdqJVoo2/TpOVaCo/Hg/LychQXF6OkvMS/XafThbBWFIl+//13ZGdn48SJEwHbZTIZHn30Udx9993sukstiiiK/kDu8XgAABqNBtHR0TAYDP4Wcq5QQETUcAzoRK1E26i2UMgU8IreGvfLIEOyPhl9kvoEuWah4fV6YbFYUFxcDLvdDq1Wy27HdME2btyIu+++GxaLJWC7Xq/HG2+8gWuvvTZENSNqOqIowu12+0O5XC6HSqWCXq9HVFSUP5Arlby8JCK6UPwLStQKHDMfwz1r76kznAPAvP7zWvwEcV6vF+Xl5TCZTLBardBoNIiLi4NMJoPD6wh19SjClJaW4pNPPsHjjz8Orzfw9ys1NRWLFy9G9+7dQ1Q7oovncrn8oRwA1Go1tFotEhMTodFooNFoGMiJiJpQRP1FzcnJQW5ubqirQRRR9hftx73r7kWJswQdYjpgWuY0/PunfwcstZasT8a8/vMwssPIENa0edUVzInq4na7cfz4cRw5cgRHjx7FkSNH/D/FxcU1PqZ379549913kZycHOTaEl0ct9vtD+SCIPjHjcfFxUGr1UKj0XDizAvE61giaogWNUuHb3zfK6+8ErDd6XTi/vvvR2JiIgwGA8aPH49Tp04FlJHJZJDJZNi2bVu1xyYkJEAmk2HDhg3N/AqImtaW/C2Y+fVMlDhLkJmQicVjFmPiZRPxv/H/85d585o3sebWNS02nAuCALPZjJMnT+LkyZPweDyIi4uDwWBgOCc/URRx7tw5bNu2De+//z6eeOIJZGdn46qrrkLnzp0xbNgwzJw5E//4xz/w4YcfYseOHbWG83HjxuHjjz9mOKeI4PF4YLPZ/GuROxwOKJVKJCUloUOHDujYsSPat2+PxMRErlHezHgdS0RABLSgFxUVYe7cuVi/fj3Onj2LzZs3o0+fPli6dGnATLj/+9//sH37dqSlpVU7xoMPPohVq1Zh2bJlSEhIwNy5czFu3Djs3r07YOKS9PR0LFq0CAMHDvRv++yzzxAVFQWTydS8L5Soia0+thoPb34YHsGDgakD8cqIV2BQGQAgoBt73+S+LbJbuyAI/hbz8vJyqNVqrmVOsNvtOHbsWEAr+NGjR3H06FGUlZVd9PEffPBBzJ07l+8zClu+tcidTicEQfCvRZ6YmAidTge1Ws21yJsQr2OJqLHCPqDPnj0bO3fuxHvvvYdXXnkFDzzwANasWQNBEPxlTp8+jfvuuw9ff/01rr/++oDHm81mvPPOO3jvvfcwcqTUQrh06VKkp6dj3bp1GD16tL9sdnY2Xn31Vbzyyiv+GZzfffddZGdn48knnwzCqyVqGu//8j6e3fEsRIgY03EMnrrqKagVrWNpJ18wLykpgcViYTBvhQRBwJkzZwICuO/26dOnIYpikz6fUqlEt27d8Oc//xljx45t0mMTXSzfWuS+pc+USiVUKhUSEhIC1iJnIG8evI4losYK+4D+448/Ytq0aRg2bBgWLVqEESNGYMSIEf79giBg2rRp+Otf/1rjRDy7d++G2+0OmEE3LS0NPXr0wJYtWwL+sPXt2xcZGRn45JNPMHXqVJw8eRKbNm3CG2+8Ue8fNqfTCafT6b/fFC0xRI0liiJe3/s6/v3TvwEAd3S9A/P7z4dc1vLDqSAIsFqt/hZzpVLJYN7CWSyWamPCfYHc4Wj6Cf8SExPRuXNndOrUCZ07d/bf7tChA7v9Utiouha5x+OBQqHwf1Gp1+v9gZx/G4OD17FE1FhhH9AHDx6MRYsWoVevXjXuf/bZZ6FUKvHAAw/UuL+goABqtbra8knJyckoKCioVn7GjBl49913MXXqVCxatAjXXXcd2rSpf13oZ555Bo8//ngDXhFR8ymyF+Gj3z4CAPyp959w9+Utf91lURQDWsyVSiWMRiMvPlsIj8eDkydPVgvgR44cQWFhYf0HaCSNRoOMjIyAEO4L4rGxsU3+fEQXSxRFOJ1O/+RucrkcGo0GMTEx/kCu0Wj4NzFEeB1LRI0V9gH9pZdewtNPP43Zs2fjyJEj2Lt3L+655x7cc8892L17N/75z39iz549jQ4hoijW+JipU6di/vz5OHr0KHJzc/Hqq6826Hh/+9vfMGfOHP/9srIypKenN6pORBerjb4N3hr5Fn4x/YLbLr0t1NVpVqIowmq1+oO5XC5HTExMwHg8ihwmk6nGLul5eXlwu91N/nypqakBAdz3k5aWxvcQhTVRFP1Ln/l+NzQaDaKiogICOd/H4YHXsUTUWGEf0A0GA5566ik89dRTuOmmmzB27FjMnj0bcrkcNpsNhYWFaN++vb+81+vF3Llz8corryAvLw8pKSlwuVwoKSkJ+PaxsLAQWVlZ1Z4vISEB48aNw5133gmHw4GxY8fCYrHUW0/fByJRsFlcFhwpPYLeSb0BAN0Tu6N7Ystdd7mmYB4dHc2L0QjgdDr9y5Wd/1NaWtrkz2cwGKp1Sffd1+v1Tf58RM1BFEX/sme+QK5Wq6HT6dCmTRv/Mmhcizw88TqWiBorov6ax8bG4u6778Y333yD77//Hq+88op/wgyf0aNHY9q0aZgxYwYAaTyOSqXC2rVrMWHCBADAmTNnsH//fjz33HM1Ps/MmTNx3XXXYd68ebzop7BW4ijBXWvvwvGy43jn2nfQs03PUFep2YiiCJvNhpKSEpjNZgbzMCWKIs6ePVtjl/STJ08GTIzUFORyOdLT02sM4snJyS1+iAe1TL5A7nK5AAAqlQoajQYJCQnQarVQq9Wc9yAC8TqWiBoi7AP67NmzcdNNN6F3797wer1Yv349Nm7ciEcffRQJCQlISEgIKK9SqZCSkoLLLrsMAGA0GnHnnXdi7ty5SEhIQHx8PP7yl7+gZ8+e1f4o+owZMwbnzp1DTExMs78+oosRpYpCnCYOhcrCFrlUGlAZzEtLS2E2myGTydiVPQzYbLaArui+20ePHkV5eXmTP19sbGy17uidOnVCx44d2epDEc/j8fiXPgOklQE0Gg3i4uKg1Wqh0WgYyCMUr2OJqLHCPqC3b98ec+bMwaFDh2C1WrFhwwbMnDkT999/f4OP8fLLL0OpVGLChAmw2+245pprkJubW+sFvkwmQ2JiYlO9BKJmo1Ko8MqIV1DsKEZ6dMsaK3Z+MAeAqKgoduMMAVEUsXnzZnz99dc4dOgQjhw5gjNnzjT586hUKnTs2LFaS3jnzp0RHx/f5M9HFCq+pc98a5GrVCqo1WokJSUFBHL2AIl8vI4losaSiU29IGwzysnJQW5ubqir0SBlZWUwGo0wm83N/g2mzW3DgA8GAAC2T94OvYpjK4GWe152FuzElvwteOCKBy744i2cz40oirDb7f5gLopi0IK5w+vAjd/dCAD4/OrPoVVom/05w5nFYsFHH32ExYsX4/Dhw0123KSkpBq7pKenp/MLGGqxRFGEw+GA0+mEKIrQarWIjo72B3KuRR4awbxe43UsUcvXFL87vBIiiiDrjq/DvE3z4BJcyDBmYHzn8aGuUpOy2Wwwm83+CcMMBgMDWwj89ttvyM3NxSeffAKr1XpBx9BqtdXWC/f9y4s9ak2cTiccDge8Xi90Oh0SExNhMBig0+m49BkREVUTUVe+kfKtI1Fz+Pj3j/HktichiAKuTr8aozuODnWVmkzVFnNBEGAwGDjeMsg8Hg++/vprLFq0CFu3bm3QY2QyGdq2bVtjl/TU1FSGD2q13G43HA4H3G431Go1YmNjERUVBZ1Oxy8dWzFexxJRQ/BTgijMiaKI//z8H7z242sAgFsvuRWPDnwUSnnk//ra7XZ/izmDeWgUFhbi/fffx9KlS1FQUFBn2UsuuQQ33ngjunTpgs6dOyMjIwM6nS5INSUKb16v19+FXalUwmAwICYmBjqdDmq1OtTVC38uK/B0mnT74XxAbQhtfYiIQiTyr/CJWjBBFPDsjmfxwa8fAAD+2POPuP+K+yN+nKLD4fAHc4/HA4PBwAvYIBJFEbt27UJubi6+/PJL/9rKNZHL5RgzZgyys7MxePDgiH/vETUl37hyh8MBmUwGvV6PxMRE6HQ6aDQa/r4QEVGjMaAThSm3141HNj+C1XmrAQDz+8/HlG5TQlyri1NTMI+Ojg51tVoNu92O//3vf1i0aBEOHDhQZ9nExERMnjwZU6dORdu2bYNUQ6LwJ4qif1y5b7K35ORk6PV6aLVaDu0gIqKLwoBOFIZsbhtmb5iNLflboJQp8Y+r/oHrO10f6mpdMKfT6R9j7nK5EBUVxWAeRHl5eVi8eDFWrFjhn4CvNn379kVOTg6uv/56ri9OVIXL5YLD4YDH4/GvUR4dHQ2dTlfrcldERESNxYBOFGZKHCX407d/ws9FP0On1OHl4S9jcNvBoa7WBXE6nf4Wc7fbDb1ej6ioqFBXq1UQBAHr169Hbm4u1q9fj7pW1NRqtbjpppuQk5ODnj17BrGWROHN6/XCbrfD5XJBpVIhOjraH8o5XwYRETUHBnSiMJJfno+7196NvLI8xGpi8eY1b6Jnm8gLTC6XC2azGSUlJXC5XDAYDAzmQVJSUoLly5djyZIlOH78eJ1l27dvj+zsbEyYMAHx8fFBqiFReBMEwT/Zm1wuh16vR1JSkn9cORERUXNiQCcKI8fMx3DSchIphhT8a9S/0MnYKdRVahSXy4WysjKUlJTA6XSyxTyI9u/fj9zcXHz22WdwOBx1lr366quRnZ2NESNGsGsuESone3M6nQAAjUaDlJQU/7hyTvZGRETBwoBOFEYGtx2MF4e9iO6J3ZFiSAl1dRqspmDOFtnm53K58OWXX2LRokXYvXt3nWWNRiMmTpyI6dOnIyMjI0g1JApvLpcLdrsdgiBAo9EgISHBv145J3sjIqJQYEAnCrHvT32PjjEdkR6TDgC4psM1Ia5Rw7ndbn8wdzgc0Ol0DOZBkJ+fj6VLl+L9999HUVFRnWUzMzMxY8YM3HzzzVyznAiAx+OB3W6H2+2GWq2G0Wj0jytXKnlZREREocVPIqIQ+u7Ed5izYQ5SDal4//r3Ea+NjHDLYB58oihiy5YtyM3Nxddffw2v11trWaVSiXHjxiEnJwf9+vVj91xq9bxer78Lu1KphF6vR0pKCnQ6HdRqdairR0RE5MeAThRCl7e5HKmGVPRJ7oNodfgvO+bxePzB3G63M5gHQXl5OT7++GMsXrwYv//+e51lU1JSMHXqVEyZMgVJSUlBqiFRePKNK3c4HJDJZNDpdEhISIBer4dGo+EXV0REFJYY0IlCKFGXiKXXLUWcNg5yWfiOd/R4PLBYLDCZTP5gHhcXxwvcZnTo0CEsXrwYH330EcrLy+ssO2jQIOTk5GD06NFc+olaPafTCbvdDlEUodVqkZSUBIPBAK1Wy3HlREQU9hjQiYLILbjx+JbH0T+1P8Z3Hg8ASNAlhLhWtWMwDy6Px4O1a9ciNzcXmzdvrrOsXq/HbbfdhuzsbHTt2jVINSQKT263G3a7HR6PBxqNBnFxcf5x5VypgIiIIgkDOlGQ2D12/GXjX7Dp1CasyVuDQamD0EbfJtTVqpHX64XFYkFxcTHsdju0Wi2DeTMqKirCBx98gPfeew/5+fl1lu3cuTNycnJw2223ISYmJkg1JAo/Xq8XdrsdLpcLKpUKBoMBRqMROp2OPUmIiChiMaATBYHZacZ9396Hvef2QqPQ4IVhL4RlOGcwDx5RFLFnzx7k5ubiiy++gMvlqrWsXC7HqFGjkJOTgyFDhvD/g1otQRD8XdgVCgV0Oh3atGnjH1dOREQU6RjQiZrZWetZ3LPuHhwuPYxodTRev/p19EnuE+pqBfB6vSgvL4fJZILVavV3EWUQbHp2ux0rV65Ebm4ufvrppzrLxsfHY/LkyZg2bRratWsXpBoShRdRFOF0OuFwOAAAGo0GKSkp/nHl/DtFREQtCQM6UTM6Zj6Gu9fejTPWM0jSJeGtUW/h0rhLQ12tAGVlZbCX2RnMm9mJEyewZMkSfPjhhygtLa2z7BVXXIGcnByMGzcOWq02OBUkCjMulwt2ux2CIECj0SAhIQFRUVHQ6XSc7I2IiFosBnSiZrK/aD/uXXcvSpwl6BjTEW+Pehtto9qGuloApBYpn9OnTyNaG81g3gwEQcDGjRuRm5uLb7/9NuC8n0+j0WD8+PHIyclB7969g1dJojDi8XjgcDjgcrmgVqsRExODmJgY6HQ6KJW8ZCEiopaPn3ZEzWBL/hY8uP5B2D12dE/ojjdHvol4bXisF26z2XDm3Bn/faPRCL1KH8IatTylpaVYsWIFFi9ejLy8vDrLtmvXDtOnT8cdd9zBNeWpVRIEwb9euVKphF6vR1JSEvR6PdRqdairR8EieCtvH98CdL4akHMGfiJqfRjQiZrY6mOr8fDmh+ERPBiYOhCvjHgFBpUh1NWC0+lEaWmptGSax+7fzq6iTefAgQNYvHgxPv30U9jt9jrLDhs2DDk5Objmmmu4DBS1OqIo+kO5TCaDTqdDamoqDAYDNBpNy+7N47ICT6dJtx/OB9Sh/3wIuYMrgdUPVd5//zYgJg0Y8yyQOT509SIiCgEGdKIm9MEvH2DhjoUQIWJMxzF4+qqnoVKEdrkfj8cDs9kMk8kEp9OJ6OhoaOSc7bipuFwurF69Grm5udixY0edZWNiYnD77bcjOzsbnTt3DlINicKHbwZ2URSh1WqRlJTkn+yNXxa2UgdXAiumAzhvCFDZGWn7hCUM6UTUqjCgEzUhi8sCESLu6HoH5vefD7ksdBecgiDAYrGgqKgIdrsder3e34Xa6/XW82iqT0FBAZYuXYr3338fhYWFdZbt1q0bsrOzccstt8BgYGsZtS5utxsOhwNut9s/EWVUVBT0ej17j7R2ghdYMw/VwjlQsU0GrJkPdL2e3d2JqNVgQCdqQnddfhe6J3bH4LTBIeuiKYoirFYriouLUV5eDrVazQngmogoiti+fTsWLVqENWvWwOPx1FpWqVRi7NixmDFjBvr378/zT62O0+mEzWbzjyuPiYmBXq+HShXaXkUURo7/AJTl11FABMpOS2PSM4YErVpERKHEgE50EZxeJ/6171/4Q88/QK/SQyaT4aq2V4WsPna7HSUlJSgtLYVcLofRaGS30SZgtVrxySefYPHixfj111/rLJuUlISpU6diypQpSElJCVINicKH2+1GeXk5lEolEhMTYTQauVwgVSrLB45uBI5tBH5b3bDHlJ9t3joREYURBnSii/C37/+GtcfX4lDJIbx2zWshq4fL5fKPM/d6vYiKiuKSRE3g8OHDWLJkCVasWAGLxVJn2QEDBiA7Oxtjx47lzNPUKnm9XlgsFshkMsTHxyMuLo7BnCS/fgkc/hY4tgkoPtT4x0clN32diIjCFK/giS7CjO4zsLdwL6ZlTgvJ83u9XpSVlaG4uBgOh8M/AzJdOFEQYdlnQfY72dj8/eY6y+p0Otxyyy3IyclBZmZmkGpIFF4EQUB5eTkEQYDRaERcXBz0ei7d2Gq5rMDZA0B6/8ptm18GTu2UbsvkQGpvoNMwoMNVwMr7AEsBah6HLpNmc++QFYSKExGFBwZ0okbyCB4o5dKvTs82PbH61tXQKIIbikVRhMVigclkQnl5OXQ6HdfQvkgnT57EB8s+wO/v/Q53sRsncKLWshkZGcjOzsaECRNgNBqDWEui8OGb78LtdiM6Ohrx8fEwGAycb6G18bqBU7uk1vFjG4GTOwDRC8zLA7QVfx8vnwikXQFkDAM6XgXoYisfP/a5ilncZQgM6RXvozELOUEcEbUqDOhEjfCr6VfM3TAXzw19Dt0TuwNA0MO5zWaDyWSC2WyGSqXiBHAXwW63Y82aNVi2bBk2b667tVwmk2HkyJHIycnB0KFDObafWi1RFGG32+FwOBAVFYWUlBRERUXxd6K1+W0NsPO/0gRubmvgPmM6UHIcSL1cut//j7UfJ3O8tJTa6ocAy5nK7TFpUjjnEmtE1MowoBM10M6CnXjguwdQ7i7HP/f8E/++9t9BfX6n04nS0lKYTCaIooiYmBguUXQBRFHETz/9hGXLluF///sfysrK6iwfGxuLyZMnY9q0aWjfvn2QakkUnux2O+x2O3Q6Hdq1a4fo6Gj+HWrpRBEwHZVax7uMAmLTpe1lp4HDa6Xb+gSg4xCp23rGMCC+E9CYL44zxwOdhgMLK4495WOg89VsOSeiVokBnagB1h1fh3mb5sEluNA3uS9eHP5i0J7b4/H4J4BzOp2Ijo7mMkUXwGQy4dNPP8WyZcvwyy+/1Fu+R88emDljJsaPHw+dTheEGhKFL4fDAZvNBq1Wi9TUVBiNRk5E2ZJZCipnWj+6ESg7JW2/7oXK1vBLrgWufUoK5UndgYvtQVE1jHfIYjgnolaLn65E9fj494/x5LYnIYgCrk6/Gs8Ney4o3doFQYDFYkFRURHsdjv0ej3HmTeS1+vFxo0bsWzZMnzzzTdwu911ljcajbj55psxadIk9OjRg0MHqNVzuVwoLy+HWq1GcnIyjEYjVyloyQp/AVZkA0W/BW6Xq4D0AYChTeW22HQg674me2qby4tMxwcAgIMuL/R8mxFRK8WATlQLURTx75/+jdf3vg4AuPWSW/HowEf9E8Q15/NarVYUFxf7L4w5zrxxjh07huXLl+Ojjz5CQUFBnWVlMhmGDBmCSZMmYfTo0VwWighSz53y8nLI5XK0adMGsbGxXCGiJXHZgJPbpNbx2PbAlXdK22PaAsWHAciA1F6VXdbbDwLUnJmfiCgYGNCJaiCIAp7d8Sw++FX6Nv+PPf+I+6+4v9lDst1uR0lJCUpLSyGXy2E0GjnxUgPZbDZ8+eWXWLZsGbZt21Zv+fT0dEycOBG333472rVrF4QaEoU/r9eL8vJyiKKI2NhYxMXFcYhHS+B1A6f3VHZZP7UD8LqkfWl9KgO6NgaY9hmQ0hPQs8cWEVEoMKATncftdeORzY9gdd5qAMD8/vMxpduUZn1Ol8vlH2fu9XoRFRXF8Z0NIIoi9uzZg+XLl+Pzzz9HeXl5neU1Gg2uu+46TJw4EYMHD+aXH0QVBEGA1WqF1+v1L5mm1+vZc6clEEXg9X5ASV7g9pi2Uut456sDt3caFrSqERFRdUwARFXY3DbM3jAbW/K3QClT4h9X/QPXd7q+2Z7P6/WirKwMxcXFcDgcMBgM7EbaAOfOncMnn3yCZcuW4dChQ/WW79WrFyZOnIgbb7wRsbGxzV9BogghiiJsNhtcLheioqIQHx+PqKgoBvNII4pAybHKid2KDgP3fC/NpC6TAam9AYcZyBgqhfKMYUBC58bNtE5EREHBgE5UocxVhru/uRv7i/dDp9Th5eEvY3Dbwc3yXKIowmKxwGQyoby8HDqdjhPA1cPj8eC7777DsmXL8O2338Lj8dRZPi4uDrfeeismTpyIzMzMINWSKDJUXcvcYDAgKSkJ0dHR7FUSScoLKwL5BuDoJsB8InB/0SGgzaXS7RteATTGi59pnYiImh0DOlEFg9KAZEMyTpWfwhvXvIHL21zeLM9js9lgMplgNpuhUqk4AVw9Dh8+jOXLl+Pjjz9GYWFhnWXlcjmGDx+OiRMnYtSoUeyNQFSDqmuZt23bFjExMVzLPJgEb+Xt41savt63wwwodYCyYnrzH/4JbH29cr9cBbS7Umol7zQMiOtYuU8X1yRVJyKi5seATlRBIVfg2aHP4qz1LNrHtG/y4zudTpSWlsJkMkEURV4U16G8vByrVq3CsmXLsGvXrnrLd+zYERMnTsRtt92GtLS0INSQKPI4nU5YrVZoNBqkpqYiJiYGKpUq1NVqXQ6uBFY/VHn//duAmDRgzLNA5vjAsm47cHJ7Zbf1/B+BqZ9UjhnvNAI4tqlipvXhQIdBgNoQrFdCRETNhAGdWrW9hXuxJm8N5l05DzKZDBqFpsnDucfj8U8A53Q6ER0dzYviGoiiiB07dmDZsmVYtWoV7HZ7neW1Wi3GjRuHO+64AwMGDGAvBKJauN1ulJeXQ6lUIikpCbGxsVzLPBQOrgRWTAcgBm4vOyNtn7AEaNcP2Pu+FMpP7gC8zsCyZ/ZVBvRLRko/RETUojCgU6tV6ijFPevugdVtRYeYDrij6x1NenxBEGCxWFBUVAS73Q69Xs9x5jUoKCjARx99hOXLl+PYsWP1lu/Tpw8mTZqE8ePHIzo6Ogg1JACQuW1IXdQXAHBmxm6IKq6JHO6qrmUeHx+PuLg4aLXa4FXAZQWerujR8nB+627dFbzAmnmoFs6Bim0yYM18YNKHwHf/qNwVnVbRQj5U+jG23CUhvULludmRV4ohmdFQyPnFKxG1Pgzo1GrFamMxv/98rD2+Fjd1uanJjiuKIqxWK4qLi1FeXg61Ws1x5udxuVxYt24dli1bhvXr10MQhDrLJyYm4rbbbsPEiRNx6aWXBqmWRJFJEASUl5dDEAQYjUbExcVBr+cXKiF1fAtQll9HAREoOy2NM+91B9C2L9BpOJDQpVXMtL5m/xks+Hy//37O0p+RajyEBTdkYkyP1BDWjIgo+BjQqdWxuCyIVkstrzd1uQk3dr6xycKz3W5HSUkJSktLIZfLYTQaOStyFb/++iuWLVuGTz75BCaTqc6yCoUC11xzDSZNmoSrr76awwKI6iEIAmw2G9xuN2JiYhAXFweDwcAvB0NNEICd/21YWWshcPPbzVufMLNm/xnMWrqnWt+CArMDs5buwVtT+zCkE1GrwoBOLZa3yky5u8/uxqDUQXj1x1fx7YlvsWTsEiToEgCgSS5eXS6Xf5y51+tFVFQUlEr+egGA2WzG559/juXLl2Pv3r31lu/cuTMmTZqE2267DUlJSc1fQaII51vL3Ol0IioqCikpKYiKiuKXg6HitABnDwDtB0r35XJp7HhDRCU3X73CkFcQ8fiqg3V1/Mfjqw5iVGYKu7sTUavBBEEt0rrj6/DMjmf89+/99l5oFVo4vA4AwA/5P2B85/G1PbzBvF4vysrKUFxc7F9PmEt7SS15W7duxbJly/DVV1/B4XDUWd5gMGD8+PGYOHEi+vXrxxY/ogaqumRaeno6oqKiuDpEsIkiUHwY+P1r4NA3Und2mRyYd6xy3P3w+cDqeVIX9hrjqEyazb1DVjBrHnI7jplwxlz754MI4IzZgR3HTBjUOSF4FSMiCiEGdGpx1h1fhzkb5kA87yLIF84ndZ100eFcFEVYLBaYTCaUl5dDp9NxAjgAp0+fxooVK7BixQqcOHGi3vIDBgzAxIkTMW7cOBgMrXgCKaJGcjgcsNls0Gq1aNu2LaKjo9lrJ9hO7wb2LQcOfQ2U5AXui+8ElJ4AkrpJ93tNAlR6YMV0iJBBFvD5VPGF5JiFDVsPPcJZnR4cPFOGn0+ZsXr/mQY9ptBS95e8REQtCT/NqUXxCl4s3LGwWjivasOJDZh/5XwoLvBCyGazwWQywWw2Q6VStfoJ4BwOB77++mssX74cmzZtgijWfu4BIDk5GbfffjsmTJiAzp07B6mWRC2Dy+XyTz6ZkpICo9HI+RmCpfQEoDVKPwBwYjuw41/SbYUa6DAYuHQ0cMm1QEINf9syxwMTlkD86iHIyqsE05g0KZyfvw56C2BxuHEwvww/nzZj/2kzfj5txtEiK+r5mKgmKTqIqw8QEYUYAzq1KDsLduKs7WydZQpsBdhTuAdXplzZqGM7nU6UlpbCZDJBFEXExMS06q6k+/fvx7Jly/DZZ5+htLS0zrJKpRLXXnstJk6ciOHDh7Olj6iR3G43rFYrFAoF2rRpg9jYWA6naW5eN3Bye0XX9bXAuV+A8a8DfaZJ+y8dDZz7Vfo3Yxigiar/mJnj4Wg3BDMX/hdJKMVzU4dC23Vki2g5L3O4sb8iiO8/XYb9FWG8JikxWvRoG4PMtBi8t/U4SmzuGsvJAKQYteifwR5qRNR68CqZWoRNpzZh1ZFV2HByQ4PKn7Oda/CxPR6PfwI4p9OJ6OjoVttiVVJSgv/9739YtmwZ9u/fX2/5yy67DJMmTcKtt96KhASOHyRqLK/Xi/LycgBAXFwcYmNjodPpQlyrFsxZDhz8XBpLfmQ94DRX7pPJA7uyJ3QGxr/a+OeQK7BNyAQALGw/KCLDeanNhQMVLeO+1vHjxbYay6YZtejR1oiebY3oUfHTJrryy6XM1BjMWroHAGrq+I8FN2RygjgialUY0CniCKKAX0y/oJOxE3RK6UL1x8IfsSZvTYOP0Ubfpv7nEQRYLBYUFRXBbrdDr9e3ynHmXq8XmzdvxrJly7BmzRq4XK46y0dHR+PGG2/EHXfcgV69erXq7v9EF6rqWua+JdP0ej1/n5qaIAC2YiCq4jPB6wI+/xP8UVGfAHQZKXVb73w1oG99nwElVpc/iB/Il/49abLXWLZtrA492xrRs11FGE+LQUJU3T09xvRIxVtT+2DBygM4W+b0b08xarkOOhG1SgzoFHGmr56Ofef24bWrX8Pw9OEAgNEdR0MGGa5qexUe2vQQCm2FNY5Dl0GGZH0y+iT1qfX4oijCarXCZDLBYrFArVa3ynHmx48f90/4lp+fX2/5rKwsTJo0Cddddx1b+IgukO/vj8vlQnR0NOLj4xEVFdXq/v40K3spcOQ7qZX80FqgzWXAjK+kffp44IopQHQqcMlooG2fiGzhvlBF5U5/N/WfK7qqny6tOYy3j9ejZ1sjureNkVrH04yIM6gv6HnH9EjF4C6J6PnYNwCA3BlXYsglbdhyTkStEgM6hSVRFJFXlodNpzZhV8EuvDziZSjl0tu1a3xX/F7yO85aK8ead43viq7xXQEA8/vPx5wNc6odU1bRYW5e/3m1ThBnt9tRUlKC0tJSyOVyGI3GVrWWsN1ux+rVq7Fs2TL88MMP9ZZPTU3FxIkTMWHCBHTo0CEINSRqmURRhN1u9y/XyLXMm1jhr8Dvq6VAfmIbIHqr7PMCbgegqpiI7MY3QlPHICu0OKQgfqoM+/OlUF7bkmcdE/T+buo92xrRPc0Io75ph3pVDeP9M+IZzomo1WJAp7Dh9Dqxq2AXNp3ahO9Pf4+TlpP+fXsL96JfSj8AwH2978NDVz4EtaLmb+pHdhiJl4a/hGd2PINCW6F/e7I+GfP6z8PIDiOrPcblcvnHmXu9XkRFRbWaicxEUcTevXuxbNkyfP755ygrK6uzvFqtxujRo3HHHXfgqquuatUT5RE1haprmbdr1w7R0dH8vbpYbjugqtKTZ91jUkD3SbwMuPRaqet6+kBAeWEtv5FAFEWcLXNWaRWX/i20OKuVlcmAjEQDeqRVjhnv3jYGMdrWOe8KEVEotI4EQmGrwFqA709/j02nNmH7me2weyq70inlSvRL7oeh7YaiQ0xl62ysNrbe447sMBIDUgYga1kWAODNa95EVlpWtZZzr9eLsrIyFBcX+1uuWsvMyJ4yD0q3luK6Z67D77/9Xm/5zMxM3HHHHbjpppta5Vh8oqbmdDphtVqh1WqRmpoKo9HYar4YbBamo8Dv30hd1/M2A3/aDsRnSPu63SC1ml9yLXDJKCCuY0ir2lxEUcQZs8MfxKUwXoai8prDeOc2UZWTt6XFoHtbI6I0fA8SEYUS/wpT0O0t3IuNpzZi06lN+L0kMBgm6ZIwpN0QDGk3BANTB8KgMlzw81QN432T+wbcF0URFosFJpMJ5eXl0Ol0rSJ0iqKIHTt24J1F7+C3r36D6BVRgIJay8fGxuLmm2/GpEmT0KNHjyDWlKjlcrlcsFqtUKlUSE5OhtFohFrdcltwm43HBZzYUhnKiw8F7s/7vjKgXzFF+gkDXqFyfpQdeaUYkhl9Qd25RVHEqRK7f+K2n0+X4cBpM4qt1SfylMuAS5Ki/ePFe7Y1oltqDAwM40REYYd/manZWd3WgKD9wq4XsO/cPgDSuPDL21yOoe2GYmi7obgs7rJmnwzJZrPBZDLBbDZDpVK1igngrFYrPv30UyxevBi//PJLnWVlMhmGDh2KiRMnYvTo0dBqtUGqJVHL5vF4UF5eDrlcjoSEBMTGxvL3q7EEAfCNy/99DbBiWuU+uRJoP0hqJb90NJB4aWjqWIc1+89gweeVS1TmLP0ZqcZD9c5WLooiTprsUst4fmXreE3rhyvkMlySFOWfTb17mhGZqTHQqTlsgogoEjCgU7Oxe+y465u7cKD4ANZPWA+jxggAGJsxFmlRaRjSdgiuansV4rRxQamP0+lEaWkpSkpK/EsXtfRxnocPH8aSJUuwYsUKWCyWOsump6dj4sSJuP3229GuXbsg1ZCo5fOtZS6KIoxGo3/JNGoAwQuc2gUc+lpqJb/semDE36R9nYYDMe2kfy8ZBXQeAWiNoaxtndbsP4NZS/dUW1+kwOzArKV78NbUPhjTIxWCIOK4yXbebOpmlDk81Y6plMtwaXK01E29ndQy3jUlGlpVy/5sIyJqyRjQqUnY3DZsO7MNBdYCTO42GQCgU+pgcVngFtzYd24fhrYbCgCY0m0KpnQLbldDk8kEe5kdTqcT0dHRUKla7oQ3Ho8Ha9euRW5uLjZv3lxnWZlKhvHjxmPypMnIysrijNFETUgQBFitVng8Hv9a5gaDocX32LloNhNweJ0UyA+vA+wllfvkysqAro0BZu+XBlOHOa8g4vFVB2tY/NO/4jrmfrQPi344hoP5Flic1cO4WiHHZSnR0njxiq7ql6VEQ6NkGCciakkY0OmCnSg7gU2nNklLoZ3dBbfghlahxa2X3gqNQppo7fHBjyNZn4wUQ0rQ6iWKIpxOJ8qslbORFxQUID46vkWPMz937hw++OADLF26tN51y9t3aA/XIBdir4rFS+NfglbBbrZUM1EU4XK54Kny++Q8tAHWlP6ArDIYyGSygJ+GbqvpJ9KJogibzQaXywWDwYC0tDQYDIbW9wWYUGUps+NbgM5X17+muCAAr/UF7KbKbVoj0GWk1HW9y3mrcETI+2XHMVOtS5j5WJ1ebD8mfRmhVsrRrSKM+yZxuzQ5GmplK3sPEVF1LivwdJp0++F8QH3h8zVReGJApwZzeV3YfXa3fxm042XHA/a3i2qHoe2Gwu62+wN6rza9glI3j8cDp9MJu92O8vJyOBwOlDvL/fvj4uKgVba8ECqKInbt2oXFixfjiy++gNtdfTyij0wmwzXXXIOcnBwMuGoAbt5wcxBrSpHCF8hdLhc8HqkVL65gM9J3v+Av03HzXAhRqbAPXwBX5zEQRRGCINT54zu2r6zvdk0/9akt1IfDlwG+JdP0er1/ybRWF8wB4OBKYPVDlfffvw2ISQPGPAtkjgecFuDIeqmV/NyvwJ1rpbAtl0td1c/9VjHj+rVAuysBRWRfrhzINzeo3B390zFtYEdckhwFlaIVvm+IiIgBnepWaCvE96ekZdC2ndkGm8fm36eUKdE3ua9/1vWMmIygtX75QoTD4YDNZoPVaoXL5YIoitBoNNDr9VAbKmdFbgmtclXZ7XZ89tlnyM3NxYEDB+osGxcXhzvuuAPTpk1D+/btAQAOb90tOdR6CIIAl8sFt9sNt9sNmUzm/x2KioqCLm8dNN/Pg+y8zrny8gIYvpgFw4QlUuCqR00h/Pzg3tCf+r4MaMzx61NfuK/6t8W3lnnbtm0RHR3depdMO7gSWDEdOL9Dd1m+NKlbUiZQdAgQqnyheO5XIKmbdPvmfwGKyB+GdMZsxxf7zmDlvnz8fLphAX18r7bITItp5pqFJ71aibyF14e6GkREIddKrx6oIWxuG8Z8MgbuKhdRibpEDGkrBfJBqYMQpY4KWn28Xq+/ldxiscDpdMLtdkOhUECj0cBoNAZcLHu81cfwRbqjR49i8eLF+Oijj2A2133B17t3b2RnZ2P8+PGcKZr8fIHc10Iul8uhVqthMBhgMBigVquh0WikcCl4gfV/R7WgBVRskwFr5gNdr6+363Iwu6+H4ssAAEhJSYHRaGzRc1zUS/ACa+ah5vdMhcKD0r/xnYBLRksTvMV3qtwfweG8uNyJr/YXYNXefOzIq+ymL5cBSoUcLo9Q4+NkAFKMWvTPaLnDsIiIqGEY0AkAcMpyCm/sfQMlzhK8PfJtAIBepUff5L6wuq0Y0m4IhrYbim7x3SCXBa/bna+VvGrXdV8ruVarRXR0dNDqEiperxfffvstcnNzsXHjxjrLajQa3HjjjcjOzkbv3r2DU0EKa1UDudfrhUwmg1qtRnR0NPR6PTQaDdRqdc2tvcd/kFo9ayUCZael8cUZQ5rtNTRWKL4MANA6u7Kf7/iWet4zFW7+N9BrYvPXJwjKHG58c+AsVu7Lxw+HiwLWOe/fMR439ErF2J6p2JVnwqylewAEfn3he6cuuCHzgtZDJyKiloUBvQXwVpmIZ/fZ3chKy4KijtYsURRxqPQQXF4XeiT2AABolVp8cfQLAECRvQiJukQAwJsj34RKHrzWDEEQ4HQ64XA4YLFY4HA44HK5IJfL/a3kreUiuLi4GB9++CHee+89nDp1qs6y7du3x/Tp0zFx4sQWPREe1c/3O+R2u/0t5BqNBtHR0QEt5HUuMVj4C/Dzx8CexQ170vKzTVP5CNRSJrZrEqUngC2vNqxsfZPFhTm7y4vvfi3Eyn2nsf63cwEt4z3bGnFDr1SMuzwNabE6//YxPVLx1tQ+WPD5fpy1uPzbU4zaetdBJyKi1oMBPcKtO74Oz+x4xn//3m/vRbI+GfP7z8fIDpWz3drcNuwo2OGf4K3AWoABqQPw32v/C0Dquv7QlQ+ha3xXxGpi/Y8LRjh3u93+VnJf13VBEKBWq6FWqxEVFbxu9KEmiiJ+/PFH5ObmYtWqVXC5XLWWlclkGDFiBLKzszFixIgWv6Y71czr9Qa0kCsUCqjVahiNRuh0On8Leb3vD8tZYO9S4OdPgMK65zWoZtMLgELdoK7u1AKd3AlsewM4+Dkg1tyFu5qo5OatUzNweQRsPnwOK/fmY+3Bs7C6Kr8c75IUhfG90jDu8lR0alP7Z9aYHqkY3F6Pnk9LS2DmTu2JIZnpbDknIiI/BvQItu74OszZMAfieWP9Cm2FmLNhDh4e8DC8ohffn/oeOwt2wiVUhj2NQoMoVRREUfS3/kzLnBaUevta+JxOp7+V3Ol0BrT0tbawabfbsXLlSuTm5uKnn36qs2xsbCwmTpyI6dOno2PHjsGpYCshc9uQuqgvAODMjN0QVfoQ16i62gJ5bGysNDlixRdbDfodEsXKZapKjwPfPiHdlqukccHdbwbW/h2wFKDOMcXnfpEm/4rtAFzzd6DnbRf9OilCfDAJ+H115f2MYcDZ/dJa5jW+Z2TSbO4dsoJVw4viFURsP1aMVfvysXp/AUptlXOytIvT4YZeabjh8jR0S41ucE8KhbZySaT+l6YxnBMRUQAG9AjlFbxYuGNhtXAOwL/tqe1PBWxPM6T5x5L3T+kf1GXHPB4PHA4HHA4HysrK4HQ64fV6oVKpoFarodfrW2U30by8PCxZsgTLly9HaWlpnWV79uyJnJwc3HjjjdDpdHWWpZbD4/HA7Xb7e5YolUqoVCrExcUFtJA3eOiHvQT4ZZXUhT2hMzDuZWl7uyuBnhOkseTdbgB0cdJ2pbZiRm4Zahw5O/5VoOQ4sOsdKeR7nE30yiksOcoAdZS0HBoAJHcHjnwrvXcGzgJSelSZxb2W98yYhWHd00IURfx4shSr9uXjy5/OoNBS+Z5OjNJg3OWpuKFXGvq0j22Vn1tERNS8GNAj1J7CPThrq3/c56Vxl2Jcp3EY2m4oOhk7BXXiJF8reXl5OWw2G1wul3+CqqioqFbXSu7j9Xqxfv16LF68GOvXr69zqSe1Wo1x48YhJycHffr04cVgK+DxePwt5KIoQqFQQKVSISEhATqdzt9C3qi5GFxW4LfVwP5PgENrK5e3KvgJGPu8tMa0TAbc+p/qj80cD0xYIq1pbTlTuT0mTQpaviXWhswFfl4R2Hq+8x1pormBfwLa9W38yaDwUXoC2P4vYPdi4PZFUg8LABj0J6D/XUB0lS7rDX3PhBFRFPFrgQWr9uVj1U/5OGmy+/cZdSqM7ZGCG3qlYWCnBLZ4ExFRs2JAj1DnbOcaVO7OHnfiuk7XNXNtJF6vt9qM6x6PB0ql0r+ucmsOmCaTCcuXL8eSJUtw4sSJOsu2bdsW06ZNwx133IHExMQg1ZBCoWog97WQq9VqJCYmQqvVXlggr+qb/wN2/hdw2yq3JXUHet4KdL9FCuf1yRwPdBoOLEyX7k/5GOh8dWArqFoP9M2pvC8IwJbXgJJj0hcD6QOlMMdx6pGlpvHlBz+vDOj6WialbMh7JgzkFVmxal8+Vu7Lx6HCcv92vVqBUZnJGN8rDUMuaQO1snVMTkpERKHHgB6h2ujbNGm5CyGKIlwuF5xOJ6xWK6xWK5xOqSugr9t6jUs3tTL79u1Dbm4uVq5cCYfDUWfZYcOGIScnB9dcc02r7WHQ0rndbn+XdVEU/cM8fIHc12X9gr7MErxA3mag/SBAqZa2yZVSOI/rCPS4TWrhTurW+GNXDVYdsuoPWnI5MPE9YOubwM8fASe3ST+xHYCB9wJXTAE0LX+ZxIgkCMAvK4GtbwCndlRu7zQcGHQf0PmaBh3G5gEyHR8AAA6mDoA+TML5GbMdX/50Biv35eOnU2b/drVCjuGXtcH43mm4umsS9Gp+fhERUfDx0ydC9Unqg2R9MgpthTWOQ5dBhmR9Mvok9WnS5/V6vdWWQXO73VAoFNBoNIiN5Zg8AHA4HFi5ciWWLFmCH3/8sc6yMTExmDBhAqZPn47OnTsHqYYULG63Gy6XC263G6Io+nuUGI3GgBbyC/69EUXg9G5pTPmBz4DyAuCOZcBlY6X9/WYCXccBbftUTggXLCk9gZvfAkYuAHb8p3Kc+pp5wKmdwG3vBLc+1DAyGbDxOWk2f4U6cHx5hCoud2L1/gKs3JePnXkm+EYWKeQyZHVOwPheabi2ewqMuuAtK0pERFSTiAroOTk5yM3NDXU1woJCrsD8/vMxZ8OcavtkFRPxzOs/r8710BvK10pus9lQXl4e0PKn1WoRHc1WMJ+TJ09iyZIl+PDDD1FSUlJn2czMTOTk5ODmm2+GXh9+s4VT44mi6G8h940h9wXwuLg4aDQaaDQaqFSqi/8i6+xBYP/HUjAvPV65XRsLlBdW3o9Nl35CKToFuOb/pHHq+z4Etr0JXPmHyv3m09JM8RynHhqlJ4Bd7wJD/gJooqSAPuyv0nvsyj8Eji+PIBaHG18fOItV+/Kx+XARvELll9lXdozD+F5pGNszFYlRmhDWkloTXscSUUNEVEA/n9vtxqOPPoqvvvoKR48ehdFoxMiRI7Fw4UKkpaX5yzmdTvzlL3/Bhx9+CLvdjmuuuQZvvvkm2rVr5y/ju1jeunUrBg4cGPDYtLQ0mEwmrF+/HsOHDw/a66vPyA4j8dLwl/DMjmdQaKu8IE/WJ2Ne/3kB66A3hm8ZNIfDgfLyctjtdrhcLv8yaDExMRc+HrYFEgQBGzduRG5uLr799ts6J31TqVS4/vrrkZOTg379+rG3QYTzBXJfCzkAf5f1uLg4fwt5kwTyqooOA28NqryvMgBdr5O6sHe+urJ7e7hR64Er7wT6zghszd/yGrD9rcgcp+6yAk9XfN48nA+oDXWXDyendgFbX5dmXRe9QHQaMOAuaV/3m6WfCONwe/HtL4VYtS8f3/1WCJencl32Hm1jKtYqT0NaLFfCoNBr7dexRFSzsA/oRUVFmDt3LtavX4+zZ89i8+bN6NOnD5YuXQq73Y49e/bg//7v/9CrVy+UlJTgwQcfxPjx47Fr1y7/MR588EGsWrUKy5YtQ0JCAubOnYtx48Zh9+7dAeN809PTsWjRooA/bJ999hmioqJgMpmC+robamSHkRiQMgBZy6Q1Zd+85k1kpWU1uuXcNybWZrPBYrH411j2tf5FRUU1R/UjWmlpqX/St7y8vDrLpqamYurUqZgyZQratGm+eQGoefnmXfC1kgNSINdoNEhISPCPH2/SQG4pkLquO8zA8PnStsQuQNoVUqDqeStw6ZjICobnf8EneKS11zlOvfl5PcCvX9Q8vjw5M2TVuhguj4DNh89h1b4z+OZAAawur39f5zYGjO/VFjf0SkWnNvwco+DjdSwRNVbYB/TZs2dj586deO+99/DKK6/ggQcewJo1ayAIAoxGI9auXRtQ/rXXXkP//v1x4sQJtG/fHmazGe+88w7ee+89jBwptSgvXboU6enpWLduHUaPHu1/bHZ2Nl599VW88sor/nWm3333XWRnZ+PJJ58M3otupKphvG9y3waFc98yaFVbyZ1OJ2QyGbRabateBq0++/fvR25uLj777LN6J3276qqrkJOTg1GjRnHCvAhUNZBXXSZQq9X6A7mvy3qTspmkSbp+/lia9A0ioNIDWfdXBvE/fBs5rcz1uf4Fqfv7zv9IXa1949TXPw1k3QcMeyjUNWwZXDap50VJnnQ/gseXewUR248VY9W+fKzeX4BSm9u/r22sDjf0SsP4Xmnolhoddj2V9Gol8hZeH+pqUJDwOpaIGivsE8OPP/6IadOmYdiwYVi0aBFGjBiBESNG1FrebDZDJpMhNjYWALB792643W5ce+21/jJpaWno0aMHtmzZEvCHrW/fvsjIyMAnn3yCqVOn4uTJk9i0aRPeeOONev+w+db89ikrK7vAV9x8PB4PHA6Hf4I3p9MJj8fj75Lb2pdBq4vT6cSXX36J3Nxc7N69u86yUVFRmDBhArKzs9GlS5cg1ZCaQtXhCSWlpRCVTqjVauh0OiQmJga0kDeLw+ukydQOf1u5VjkAtOsvzb5edfhESwnnPjGpwDV/l8ZB+8apFx8GnJZQ1yyy2UyVS6Gp9UByD8BRJo0tj7Dx5aIoYu/JUqzcl48vfzqDQkvlZ25ilAbjLk/FDb3S0Kc9Jyul8MHrWCJqrLAP6IMHD8aiRYvQq1evess6HA7Mnz8fkydPRkxMDACgoKDAPx60quTkZBQUFFQ7xowZM/Duu+9i6tSpWLRoEa677roGdUl+5pln8PjjjzfwVQWHr/Xv/FZyANBoNDAYDGwlr8fp06fx3nvv4YMPPkBxcXGdZbt27Yrs7GzceuutMBgiqLtxK1Z1DLnL5YLc64Bv1F9qairUhlhoNJrm6/3gqbgYUlZMUnXmJ+D3NdLt5B5Aj1uln7gOzfP84ajqOPVD30gzwfsc2yS1qg/6E3DZdS3vS4qm5Btf/uuXwH07pWX2AOC6FwBdLKCKnDHYvxaUYeXefKz6KR8nTXb/9hitEmN7pGJ87zQM7JQAhZyhnMIPr2OpyQmVw3hwfIs09ww/D1uUsA/oL730Ep5++mnMnj0bR44cwd69e3HPPffgnnvuCSjndrsxadIkCIKAN998s97jiqJY4zfsU6dOxfz583H06FHk5ubi1VdfbVA9//a3v2HOnMoZ1cvKypCeHvyZkwVBgNVqDVgGzePxQKlUQq1Wcxm0BhBFEd9//z1yc3Oxdu1aCIJQa1mlUomxY8ciJycHAwYM4LmNAFUDOQD/agSJiYnQyCo/9OJiY5tnXLfglYLm/o+BX1ZJgenyCdK+nrdJa5b3uA1I6tr0zx1J5HLgsjGB27a9DZzYKv3EdQQGzOI49ap848u3vQmc3F65/dBaoP8fpdsxqaGpWyPlFVmxap8Uyn8/W+7frlcrMCozGTdcnoahl7aBWskJSym8teTrWJvLg8y/fw0AOPjEaOjVYR8rIt/BlcDqKsO+3r8NiEkDxjwLZI4PXb2oSYX9b5LBYMBTTz2Fp556CjfddBPGjh2L2bNnQy6X4667pNlm3W43JkyYgGPHjuG7777zf+sIACkpKXC5XCgpKQn49rGwsBBZWVnVni8hIQHjxo3DnXfeCYfDgbFjx8Jiqb+LpW8saqgdP34cqOgZ6+u2zrHPDWM2m/HRRx9h8eLFOHr0aJ1lk5OTMXXqVEyePBkpKSlBqiFdCI/H418qEKg+y3rAGHKXtXkqIYrSut++tcqtVZZBO7yuMqDHtgeufrR56tASXP+i9MXFrnelcdS+cep9s4EBdwPGdvUeokVy2YDdudJM+KUnpG0KNdDzdmmyvQgZX15gduCLn/Kxal8+9p0y+7erFXIMv6wNbuiVhmu6JTEEUEThdSw1mYMrgRXTAZy3WlDZGWn7hCUM6S1ERH3KxcbG4u6778Y333yD77//HnfddZf/j9qhQ4ewfv16JCQkBDymb9++UKlUWLt2LSZMkC6Cz5w5g/379+O5556r8XlmzpyJ6667DvPmzYu4LuCiKCLWGMtl0Brh4MGDyM3Nxaeffgq73V5n2UGDBiE7OxtjxoxpvnHIdFE8Ho9/VQJBEPyBPDk5uWnXIW8olxV4c1DgWuW6OCDzRqmlvEP1CyyqhX+cesV66lvfBExHgC2vSl90zNoSuHxbayF6gQ3PAM4yQBcfNuPLq647viOvFEMyo6t1QzdZXfjq5zNYtS8fO/JM/mkW5DJgcJdE3NArDaO7p8Co499biny8jqULJnilL6XPD+dAxTYZsGZ+ZC1TSrUK+4A+e/Zs3HTTTejduze8Xi/Wr1+PjRs34tFHH4XH48Ftt92GPXv24IsvvoDX6/WPx4mPj4darYbRaMSdd96JuXPnIiEhAfHx8fjLX/6Cnj17+mfDPN+YMWNw7ty5gG8wI4VarWY4bwCXy4XVq1cjNzcXO3bsqLOswWDAbbfdhunTp6Nr11be7TgMeb1ef5d1r9frH86RmJjobyFXq9XBC+Smo8DpPVJ3dUDqJh+dAliLpA/OnrcBnUaE71rlkUBtkAJo35nAoa+lJcN63FIZzt12aaK9y8a2zAuVU7uAg58Do56QXrMmGhj+N2lc+eUTpXH8IbZm/xks+Hy//37O0p+RajyEBTdkYnCXRHxz4CxW7svH5sNFAUH+yo5xuKFXGq7rmYrEKLbmUeTjdSw1ieNbgLL8OgqIQNlpqVzGkKBVi5pH2Af09u3bY86cOTh06BCsVis2bNiAmTNn4v7778fJkyexcuVKAEDv3r0DHrd+/XoMHz4cAPDyyy9DqVRiwoQJsNvtuOaaa5Cbm1vrt4oymQyJiYnN+bIoRPLz8/H+++/j/fffx7lz5+ose8kllyAnJwe33noroqM5xjVcCIIAp9MJt9sNj8cDhUIBtVqN+Ph46HQ6qNVqaDSa4M4HUHYGOPCp1IU9fw8gV0qTtvhmz775bSAqJSyCU4sil0sh/LKxgTPc71sGfPGgNE594L1A7ymAJsLXwBa8leuX+8aXXzIKyBgq3R50b+jqdp41+89g1tI91dp5zpgduGfpHijlMniqhPIebWNww+VpGNcrDW1jI2fyOqKG4HUsNYnys01bjsJa2Af02bNnY/bs2QCAnJwc5Obm+vd17NgxYFmk2mi1Wrz22mt47bXXai1T13FiY2Mb9DwUnkRRxJYftuDD9z7E119/Da/XW2tZhUKB0aNHIycnB1lZWS120jeZ246fj0ljVY+57YBCG+Ia1U4QhIAWcplMBo1GA6PRCL1e728hb5KeI42ZGdVmklox939SuVY5AMjkQMchgK24MqDHd7r4ulHdqv6uCh5pGEFJnjSZzndPAf1ygP53Rd44dacF+HEpsO2tymESvvHl0Wl1PzYEvIKIx1cdrLETpo9HENEpUY8be7fDuF6p6Nwmwr88IaoDr2OpSUQ1cMhSQ8tRWAv7gE50ITweD/bt24dzX55D6Q+lmJY/rc7ybdq0wZQpUzBlyhSkpYXfRW9r4lse0OVywe12+wN5dHQ0DAaDP5A3+bi6xs6Muv8T4Ku/VN5PHyCNKe9+ExCV1LR1o8bp/0eg9+SK9dTfktZT/+GfwJbXpa7wN70NKCLg46/oEPCfq6Wx5UBYjS+vic3lwdJtx3HG7Ki37FM398SgzmzhIyJqkA5Z0jVJ2RnUPA5dJu3nvDYtQgRcoVSq+q0jUVVutxv79u3D1q1bsW3bNuzcuRNWa/0zcvfv3x85OTkYO3Ys1OrWMyZYq9AE3A7l9+rV1iKXy6FSqaDX6xEVFeWf2K1ZJ7qpb2bUwX+WWi+7jASumCrt634z8ON70r89bpVmYKfwETBO/RtpTfC87wGHOTCci2J4TSxnOVsZvuM7S60h0SlSV/1ek8Jq/XKr04Pdx0uw7Wgxth8zYd/J0oCu63UptDibuXZE4YfXsXTB5AqpwWDFdAAyBF6vVHyGjVnYMuddaYUiKqAT+TidTuzbtw9btmzBtm3bsGvXrnpnYPfR6XS45ZZbkJOTg8zMzGauKdXE1zruW4tcrVZXrkVeEciDtjxgvTOjAvjhFenf8sLKgG5IBO7eFIQKhgm1AXjMXH+5cONbT/2yMcCZfYCsysWL+RSw5KaKFvcQjlOvOr68+DAw+4AUxOVyYPrnQHSqdDvErE4PdvkC+dFi/HTKXC2QJxhUKLa66z1WUnT4DqshIgpLmeOlpdRWPwRYzlRuj0mTwjmXWGsxGNApIjgcDuzZswfbtm3D1q1bsWfPHjgc9XejrKpTp07Izs7G7bffDqPR2Ew1pZq43W5/IBcEAWq1uva1yIOt3plRK2TeBFz1YHPXhppTaq/A+7sWAcWHLmqcus3lRabjAwDAQZcX+sZ0xKltfPmpXZWz8BrbNuKATavc6cGuPBO2HTVh29Fi/HzaHDDjOgC0jdVhYKcEDOgUj0GdEpBq1GLIc+tRYHbU1gkTKUYt+mfEB+U1EBG1KJnjgU7DgYXp0v0pH9c9Xw5FJAZ0Ckt2ux27du3Ctm3bsG3bNvz4449wOhvfJbJbohzDOigw6IE3cPXVY7kEXZB4PB5/l3XfWuQajQaxsbGhWYv8fKIoBaK8zdJEbw3R7QYg7YrmrRcF15A50trqvvXUfePUu98MDPoT0LZP8zyv5Syw9TVg9+KwGl9ucbj9LeTbjpqwv4ZA3i6uIpBnxGNgpwSkx1dfmWDBDZmYtXRPbZ0wseCGzGrroRNR5Kn692HHMROGXNKGv9vBUDWMd8hiOG+BGNApLFitVuzatcs/hnzv3r1wu+vvJnm+rl27YuDAgRg4cCCuuKIr+n8ldfc5dlUWw3kz8q1F7nQ6IQhCwFrkvqXPgroW+flEUZrRO2+z9HP8B8B8snHHaOUzo9pcHmT+/WsAwMEnRkOvbgEfHzWtp573PbD/Y+C31cBffpPWGG9qjlJgS8VszImXhmx8ucXhxq48XyCXWsjPH0KeHq/DwIwEDKgI5TUF8vON6ZGKt6b2wYKVB3C2rPKL1RSjFgtuyMSYHqlN/VKIKMjW7D+DBSsP+O/nLNqJVP6OEzWJFnCFRZHIYrFg586d/i7rP/30EzweT6OOIZPJ0K1bNwwaNMgfyuPjK7tNOh0lTV3tFsPuFtCxolvud24B2kb2LvetRe5rIZfL5VCr1UhISAjdWuRV+ZaT8T3/ugVS62hVciWQ1kf69nnfsoq1QzkzaqtUdT31M/ukFnVDYmU4F0VpjfvLxlYfpy54MVB+EEkohfyEEug6MrA1wze+vOh3YOhfpW1tLpNut+svTTwYpC8Pyxxu7DxmwvZjUpf1/TUE8vbxegzsFF/RbT3hgtclH9MjFYO7JKLnY98AAHJnXMnWNaIWYs3+M5i1dE+1T8wCswOzlu7BW1P7MKQTXQQGdAoKs9mMHTt2+Lus//TTTxAEoVHHkMvl6N69OwYOHIhBgwahf//+iIuLa6YaU1VV1yL3eDyQy+X+LutNvhb5hRBFoPgIcLyihTzvB+D2XKD9AGl/ai9ArgLa9gU6XgV0HCwti6Y2SPvb9gVWTIcIGWScGbV1S+0F3PKvyi95AODkduDTPwBaI9A3B+h/tzQ2/OBKaL96CMvUFZP1rHi9cmm+ziMCx5fLlUCvyZVjyq9+tNlfitnuC+RSl/UD+dUDeYcEPQZmJGBg53gMyEhA2gUG8ppUDeP9M+IZzolaAK8g4vFVB2udVlUG4PFVBzEqM4W/80QXiAGdmkVJSQl27Njh77J+4MCBRgdyhUKBnj17BgTymJiYZqoxVXX+WuS+FvJmX4u8McrPAb+uqgzk5QWB+/O+rwzol10PzD8BqGvpnlsxM6r41UOQlXNmVELg0muucmnJM9849a1vAO2uBE5sRbXLz7IzwIppgFIHeCpWltDFA1fe2exd2M02N3bkmbD9aDG2HSvGgfyygO8ZAKBjgh4DOyX4J3ZLNYbPsm1EFP52HDPhjLn2SXpFAGfMDuw4ZsKgzgnBqxhRC8KATk3CZDL5W8e3bt2KX375BeL5V4b1UCqVuPzyy/1d1q+88kpERzfD+E+qxhfIfbOtA4BGo4HBYPAH8mZfi7zuCkpdhOVKIKGztK0kD/hidmUZhVoKTR2vkn7aXVm5T9WAJZ0yx8PRbghmLvwvklCK56YOhfb87srUOnUZCdy3K3Cc+omtAFA9oPvalTx2IOESYNC9wOWTav9y6CKU2lzYcUyaZX37sWIcPFM9kGckGiq7rGckIMXI5c2I6MIVWhq2gk5DyxFRdQzodEGKior8rePbtm3Dr7/+2uhjqFQq9O7d299C3q9fPxgMhmaoLdWl1GyGxukO7Vrk5xNF4NxvUhDyTepmPQf0nQHc8IpUJq030PmaylDert/Ft1DKFdgmZAIAFrYfxHBOlaqOU9+1CPjiwfofc/1LQKehTVaFUpvLP358+1ETfimoHsg7JRowoFOCP5QnxzCQE1HTSYpu2N+UhpYjouoY0KlBzp49628d37ZtGw4dOtToY6jVavTp08c/oVu/fv2g07F7ZbBUnWndYbf6tyclJSE2LiG0a5H7eFzSWN+8HwBbUeA+pRbwVpnZX6ECpn0a3PoRAQ2f2d1aeFFPU2KtEsiPmfBrDYG8cxtfIE/AwIx4JDGQE1Ez6p8Rj1SjFgVmR23TqiLFqEX/jPga9hJRQzCgU43y8/MDuqwfPXq00cfQarXo06cPBg0ahEGDBuGKK66AVsuLx2ARRdE/07rH44FCofAvfSYKsQCk8BAfFwd9VFSdx2pyggCc+0VqHXeWVc5urVQDBT9L4VypA9L7V3ZZb9sXUGqCW0+imjR0yb1GLs1nsrqwo2JCt21Hi/FrgaVamS5JUf41yAd0imcrFREFlUIuw4IbMjFr6R7IgJqmVcWCGzI5QRzRRWBAJwDAqVOn/K3jW7duxfHjxxt9DJ1Oh379+mHgwIHIyspCr169oNEwUAWT2+32h3KZTAaNRuOf2E2r1fpnWreVlwW3YoIAFB6QWsbzvgeObwHsJmmfOgoYPBtQVPw5GrMQ0MQAbfswkFN46pAlTSBYdgZeEdghdEUhYpGEUvSX/wqFDA1amq+43FkxhlwK5b+drR7IL0mK8ofxARkJaBPN3wkiCq0xPVLx1tQ+WLDyAM6WOf3bU7gOOlGTYEBvhURRxIkTJ/xhfOvWrTh16lSjj2MwGHDllVf6x5BffvnlUKvVzVBjqk3VbuuiKEKlUkGj0SAhIQFarTZ048gFIXBt5+VTgd++DCyjMgDtB0pLnnmdlQH90tHBqyfRhZArgDHPYs2Hr+Ix93QUoHKm4lQUY4FqCcaMeaDaHAZFAYG8GL+fLa926EuTo/wTuvXPiGcgJ6KwNKZHKgZ3SUTPx74BAOTOuBJDLmnDlnOiJsCA3gLYXF5YflkIALAPEhB13vWcKIo4duxYQCA/c+ZMDUeqW3R0NK688kpkZWVh4MCB6NmzZ+gmEWul6uq2rtPp/OPIZbIgf0AKXqDgp4oW8s3AiS3AfbuBqDbS/rQrgGMbKwL5VUCHq6RJ3hQhHvNODeatsoD2jmOmVn8htka4ErPcD1Ybg1mAeMxyP4i3hL7oawkM5IcKqwfyy5Kj/RO69c+IR8L5f8CJiMJU1c+A/hnxrfozgagpMV21QKIo4siRIwFd1s+ePdvo4xiNRvTv39/fZb179+6hXfe6kexuAR0dHwAAvnIL0EToUM2q3dZrWo9co9FAXrW1ugECwlZeKYZkRjf+g9V0FPjlC2mG9eNbAac5cP/xH4DuN0m3B84CrqrSjT1MNcl5aYHW7D+DBSsP+O/nLNqJ1FbcldEriHh81UGINS6yJm2774Mf4RGqT6HUNSW6Yh3yePTPSEC8oeX2OtKrlchbeH2oq0FERBRRwvtqmRpEFEW4zh2H8+R+PPTTMfy4azfOnTvX6OPExsb6Z1gfNGgQunXrFlGBvKWo2m1dEASo1eom7ba+Zv8ZLPh8v/9+ztKfkRrzOxaM71572PJ6gIJ9QHQaEFNR5sR2YO3/VZbRxADtB0ld1jteBaT0qrIvyJPQXYAaz4vxUKsNoT5r9p/BrKV7qrcUmx2YtXQP3praJyjnRxRFCCLg9grwCCK8XhFuQYDHK8JT9V9BrLgtwuMV4PaK8AqVZb2CtK3yMZVlAx4nSGU9XrHiGBXbvCLyzXacMde9xq8vnFcGcqmFvCUHciIiIrp4DOgR7r333sPs2bNRXFwMAPimEY9NSEjAgAED/LOsX3bZZY1uiaWLF8xu61LY2l0RtiqPV1DmwKylu/HW1L5S2PK6gTP7pO7qeZuBE9sAlwW49h9A1v3SgzpeBVw6pnKW9ZTLI3bd8HAJoc1BFCsCqleEyyvA7fvxSPc9QuVtd5Ufl0eE0+3F31ceqHEpHd+2v3y0D7uOl8ArVD6PP9gKVQKxt2p4rh6kpaAcGIg9FSHcd9xI8/TNPTB5QIdQV4OIiIgiCAN6hEtISPCH8/q0adPG3zo+aNAgXHLJJcEfqxxMghcD5QeRhFLozxYBhqvDJkC63W5/K3lTdVuvj1cQ8fin1cM5IHXLlUHA45/sxKgfl0BxcivgOm+8rNYIuKu0GsamA5OXN2kdQ6Gyu3J1IqQz9fiqgxiVmeLv7i6KUrCsGnTPD7f+297KgCrdF+H2VCnr9R3nvPsVj6vtuDUdy33eY331On/t7KZU7vTiv98fa74nqIdSLoNCLoNKIYdSIYNSLoNSLq/YJoNSIZe2KaTtvtsqhVRGKZdDpahyjKplfcdTyKGSy6Co2JZfasP720/WW7eMxPDvOUJEREThhQE9wl111VWQy+UQBKHavpSUFH8gHzhwIDp37tyyA3kV2mPf4JLN/8AydUVX/+8A7/YUmLP+BkfGtUGvT23d1uPj45t1tnVRFHHO4sSJ0/nYcPAUzthq//8XIccZO/DD72cwVFGOUtGAnWI3bBO6YafYDb+5O0D4Rg7ZN6sBWWXEl8kAGWTwvbVkAGQyWeVXALLKbZXlq9yv2OYrLGvAsVF1f0WZ2o6NWurle5zN5amzu7II4IzZgcsf+xoi4A+/kUylqAymaqUcKoXvRwqpvm1muwuHC631Hm/EZW3QNTXGH44Dgq0v/FZsrz0IS/+qGhWuZSH5m+YVRHz36zkUmB01frEjg7TcUP+M+GBXjYiIWjq1AXjMXH85ilgM6BEuJiYGl/fuhb17foQiug3GjBiIEUOHYeDAgejYsWOrCeRVaY99g7i1DwLnXTrLrWcRt/ZBlIx6pdlDep3d1rVaaFRyqOCRwqLOUPnAvB8At63ixyH966n4NzoN6H1HZdnPZklribvt8DhtcDms8Dqlx+apOmG+5lHkFVlhdXmxXXMvLhG6Abi/3rp/4LkGz3im4FcxHSLOb8UXcf55bS2sLm+d+9VKOdQVrbgqhXTbF3hVCjlUSjnUVe+ft1+tDNznL6uUgun5QVpd5bhV7yvr2Od7bEP/Lmw9Uow7/rOt3nJ3De2MQZ0T6i3XUijkMiy4IROzlu6BDIG/Eb4zu+CGTE4wSERERI3GgN4CPP/Ky8h+/zCUxmQ8dWcXtImKCXWVQkfwwrjlGaCG+ZVlECFChpgtz8DR4Zrq3d0FL2ReB2SeKj8V9wWNEZ64LlI5rwv6Xz6C3GMHvE7IPHbIPE6ILivgsqHceAlKu06Ruq1HGZDy6U2QeZ3S8dx2wG0HxIqwd8loYMqKyjosvRXw2Gt8ad70LPzS5jocK7LiWJEVM/Z/hWhvKQDpFzngl9mhx4HiMgCAXAZ45BokCmUNOoW3XdUdlw+5EUBFHBcBsSKCSLdRcVsM6DrtK+fbJlaUqXoc1Hic8x533n34H1u5PaAOVfaj2vNXr4PvtaDiOAfzy/DUV7/Ue15euL0X+neMh6pKkFZXaSFuiV+G9c+IR6pRy5biGozpkYq3pvbBgs/346zF5d+e0opntyciIqKLx4DeAvTu0weqL6uvr9saqQt2Q2EtqHW/DCKU1gIkLR8De5dxsFz5Z2m7oxSpSwbV+jhbl3Eovfp5//3YLf+otawmw4uYDh0qu62bTwBeV82FzwvjQnJ3uJxO2EUVygU1yjxKlLoVKHIqcOBoMv792mZ/2ZOKCZBBhENUwwE1dPooxBmNSIiLRVJiG/ynfTdkJBqQHq+DRj4GKcd+QOp/8lCAuBpaxgEZBKTAhBFdM6CIidA16S7AwE4JePeHY/WG0JuvaNvqWkTZUly3MT1SMbi9Hj2fln4vc6f2xJDM9FZ7PoiIiOjiMaC3AFXXbv75jB3DOrfetZsVtoYtL6e0nIKiPN9/X1TqAvaLCg1Epdb/I2jjK7utO50wtLsaolILmVoPlS4GCm2U/0ed0AVqQ5Vu69M/BxRqQKUDlFq4FVqcLgeOmQUcNblx7H8/I6/IhmNFVuSb59Q5oVeCQY2OiQZkJBrQMfFu6d8EAzom6qFX1/3rrMgYjAUxr2FWWTZkEAJCugwCABkWxHwJRcaHDTqHLQVDaN38LcUrD+BsmdO/nS3Fkqrvi/4dY1vt+4SIWh+9Wom8hdeHuhpELQ4DeoRbs/8M/l5l7eZHV+cjKeocHhzaDsO7xIauYqEgioDHWX85AOYBf4Wzw/DKDQo1CqZvkQK5QgPIpPAaMNu62Sx1W4+JgXjbojpnWxcEaa3kY0VW5BWl4WiRFXlFVhwrKsTJEnvAlyrni9YqkeEL4QkGdGrjC+EGGHWqRp2SAHIFxtw0DW99+Aoec09HASrHDKfAhAWq9zDmpgfCZqb7YGJ35bqN6ZGKwV0S0fMxaSHH3BlXYsglbRhGiYiIiJoYA3oEq23t5sJyNx7+6hievi6jdYR0UYTm+HpE734dqpIj8OrbQG4rgiACO4SuKEQsklCK/vJfIZcBXkMyrD2zA4OoTAZBGyfNtu5wNmi2dd8M6f7wXWzFsXNW5BVbkVdsg8tTfWZ9H61KHhC+/YE80YAEg7r5xjNnjseYO4BrvpyHXWXGynMTa4Fi7DNA5vjmed4IwO7KdQtoKc6I53khIiIiagYM6BGqrrWbfV7ZdApDOhlb7oV0lWCuLpYm+RJUetguvRmbd/+Ix6u1EhdjgWoJBmRN9YfzOmdb1+mg0WhQ7hKRZ7LhWF4J8oqt/kDumyG9NiqFDO3j9QHh23c7OVoLeaj+XzLHw91uCP658L9IQimemzoUiq4jW2XL+fkU2sqhCf0vTWu5vztEREREFJYY0CPUjmOmOtduBqSW9Nc3n0bnRB3UFesSVy79JKuylFPg/arlwjKg1BLMrd2nwnp5Dr47JcMj7mHVvrw4i3jc634QT7jaY6DVCpfLBZlMBrVaDblGDwvkOG3x4LTZhbzifKl7erEVpTZ3rVWRy4B2cXp0TDSgU6IBHRP0yGgThYwEA9JitVAqqnd/DwtyBbYJmQCAhe0HMZwTEREREYUBBvQIVWipO5z7LN/bsEnTaqOQocq6yjKo5LIq6zDLatynVvpCfpXQX61c9S8LfPsq14OucswqXyRonCbEfzsHMq8zIJgL2jh4BRGvbDpQwyJrlQuvPb/hNCb2TkShTcQpswsnTHacK69llvUKqUat1BW9jQEZCZUt4unxOmiUDLdERERERHTxGNAjVFJ0w5bB6pVmgF6tgNsrwO0V/T+u8+67BaFie2C7s1cEvB4RTk/tXbmbn4g+skPYI17q3/IX5VgoZSKWCuNg3WeEav9pqBT58HhFFJbX3uINAGVOAf/ZXlhte4JBXa0rekaiAR0S6p8hnYiIiIiI6GIxdUSo/hnxSDVqa127GQCSolR4/ZZLGtVNXRRFeISqQV6ERxDgqhLsPRXbq4Z+/32hli8A6tgXeMyK+4IIl0fAYGEXZsk+Rg/ZMdzkfAJ7xS4AgBc8E6rU2tPo89e3QyyGXNImYHx4jPYiZkgnIiIiIiK6SAzoEarq2s21eXBou0aPIZfJfF3JL7aGF0EUoTmxQRpjXnQQACAodXg7CzjbtjPsThfcXhFeyAC5EqJcCYVKDciVOHDWime/PlzvU/zl2q4Y1Dmh3nJERERERETBwoAewXxrN//98/0orLJ2c1KUKjLXQfcF812vQ11cGcyLO98KU9cpUEQnIU6tRtvkRKjVaqjVaqhUKigUld8mDO4qYsm2U7X2LJBBWtu6f0Z8cF4TEbVoerUCedrJ0h11fmgrQ0RERBGPAT3CjemRiis6GDDgqe8BAP8Ym4ZhnZPCc/b1GoiiCLfbDbfbDY/Dis6bHofafhaCUgdLt8lwX3k3NLGpaK9SQaVS+dcgr01dPQt8Z2TBDZkRc36IKMypDcBj5lDXgoiIiFoIBvQWoGrY7JmqC9vw6QvjHo8HLpcLgteL6HM74W47CCqNDkZjGjxDHoKsLA+yrPthjEm+oOep7FnwMwotlRPGpRi1WHBDJsb0SG2ql0TUaujVSuQtvD7U1SAiIiJq0RjQqVlUDeNutxuCIAAAVCoV1CoVEst/QtSu16Es/Bne8W9AcdlU6YFJf2iS5x/TIxVXpMow4PndAIC377gUo3p2CdsvL4KN3XJrxhBKRERERKHEgN4SuGz+mzK3HUBMUJ9eFEV4PB5/y7jX65Umm6vokh4fHw+NRgO1SgX18fVQrH8esjN7pQerDFC4LM1Sr6phvF/7GIZzIiIiIiIKawzo1GhVW8Y9Hk9AGI+Li4NWq5VaytVqKJVKaez3718DG54BqgRz9P8jkHU/YEgM4ashIiIiIiIKDwzoVCdfEHe73fB6vQAApVIJpVIJo9EInU4HVcUEbiqVCjJZLa3UW16TwjmDORERERERUY0Y0Mmvaqu42y1NruZrGY+JifGHcd/yZrWGcVEEDn0DtLsS0FcsZzbiYWkbgzkREREREVGNGNBbKa/XC5fL5R87DgAKhQIqlQpRUVHQ6/UBLeNyubz+g/qC+YZngPwfgavmACMXSPs6DpZ+iIiIiIiIqEYM6K2A1+ut1jLuC+MGgwF6vd7fKt7gMF7V+cEckLqyq3RN/EqIiIiIiIhaLgb0FkYUBDgcDn/LuCAIUCgUUKvV0Ol0SEhI8IdxtVrd+DB+vkNrgfVPBQZzjjEnIiIiIiJqNAb0FqDqmtb7vKshCAK0Wi30ej00Go2/ZVyhUDT9k//6hRTOVfqKYP4Ag3kkUBuAx8yhrgUREREREVXBgN7CtG/fATHGlOYJ476u7LHtgaRu0rYhcwFNDDD4zwzmREREREREF4EBvYXRaDRNH87PH2PedRww6X1pX2x74Nonm/b5mkjVngU29ZEQ14aIiIiIiKhuDOhUuxonf9MDCV0AQQAudvx6c1Pr0TOjPQBgu1of4soQERERERHVjQGdanZkPfDt44HBnGPMiYiIiIiImg0DOtWs8CAnfyMiIiIiIgoiBnSq6Mq+FlCqgU7DpW19ZwAOM9D/LgZzIiIiIiKiIGBAb818wXzDM0D+HqBNN2DWFmlsuVoPjHg41DUkIiIiIiJqNRjQW6PzgzkgdWW/ZBTgdQJyXWjrR0RERERE1AoxoLc2J7YBa/4WGMyv/IM0xjyqTWjrRkRERERE1IoxoLc2TosUzpU6oP8fgKw/M5gTERERERGFAQb0lkDw+m/KT2wDLhsLyBVSV/bD6wDrOaD3ZKlAl5HA6KeBnhMYzImIiIiIiMIIA3qkO7gS2q/+6r+rXT4FiEkDek0Gjq4HTu8GtEag6/XSvzIZMOhPIawwERERERER1YQBPZIdXAmsmA4ZxMDtZfnA9y9It5U6oM90qTWdiIiIiIiIwhYDeqQSvMCaeQBEyGoro44C7tsFxKQGsWJERERERER0IeShrgBdoONbpJbyurjKgeLDwakPERERERERXRQG9EhVfrZpyxEREREREVFIMaBHqqjkpi1HREREREREIcWAHqk6ZEmztdc6Al0GxLSVyhEREREREVHYY0CPVHIFMOZZADVNE1dxf8xCqRwRERERERGFPQb0SJY5HpiwBOL53dhj0oAJS6T9REREREREFBG4zFqkyxwPR/oA6F+8FADgmPg+tJeNZcs5ERERERFRhGFAbwmqhHGh/UCG8wp6lR4/Z/8c6moQERERERE1CLu4ExEREREREYUBBnQiIiIiIiKiMMCATkRERERERBQGGNCJiIiIiIiIwgADOhEREREREVEYYEAnIiIiIiIiCgMtKqDn5OSEugpERERERBeE17JE1KICek1EUcRjjz2GtLQ06HQ6DB8+HAcOHAgo07FjR8hkMixbtqza47t37w6ZTIbc3Nwg1ZiIiIiISMJrWaLWJeIDelFREbKzs9G+fXt8+OGH6NKlCyZMmACXywUAeO655/DSSy/h9ddfx86dO5GSkoJRo0bBYrEEHCc9PR2LFi0K2LZt2zYUFBTAYDAE7fUQERERUevBa1kiqiriA/rs2bOxfft2vPfee7juuuvwn//8BxkZGRAEAaIo4pVXXsEjjzyCW265BT169MDixYths9nwwQcfBBxnypQp2LhxI06ePOnf9u6772LKlClQKpXBfllERERE1ArwWpaIqor4gP7jjz9i2rRpGDZsGIxGI0aMGIFnn30WWq0Wx44dQ0FBAa699lp/eY1Gg2HDhmHLli0Bx0lOTsbo0aOxePFiAIDNZsPy5csxc+bMBtXD6XSirKws4IeIiIiIqC7hcC3L61ii8BHxAX3w4MFYtGgRvvjii2r7CgoKAEh/sKpKTk7276tq5syZyM3NhSiK+Pjjj9G5c2f07t27QfV45plnYDQa/T/p6emNfzFERERE1KqEw7Usr2OJwkfEB/SXXnoJEydOxOzZs7FkyRL07t0bb7/9dkAZmUwWcF8UxWrbAOD6669HeXk5Nm3ahHfffbfBrecA8Le//Q1ms9n/U7V7ERERERFRTcLhWpbXsUThI+IHpBgMBjz11FN46qmncNNNN2Hs2LGYPXs25HI5Ro4cCUD69jE1NdX/mMLCwmrfRAKAUqnEtGnTsGDBAmzfvh2fffZZg+uh0Wig0Wgu/gURERERUasRDteyvI4lCh8R34JeVWxsLO6++26MHTsW33//PTIyMpCSkoK1a9f6y7hcLmzcuBFZWVk1HmPmzJnYuHEjbrzxRsTFxQWr6kRERETUyvFalogiPqDPnj0bGzduhNlshtfrxfr167Fx40b07dsXMpkMDz74IJ5++ml89tln2L9/P3JycqDX6zF58uQaj9etWzcUFRVVW6YirKn16JnRHj0z2gNqfahrQ0REREQNxGtZIqoq4ru4t2/fHnPmzMGhQ4dgtVqxYcMGzJw5E/fffz8A4KGHHoLdbse9996LkpISDBgwAN988w2io6NrPWZCQkKwqk9ERERErRivZYmoKpkoimKoK9FUcnJykJubG+pqAADKyspgNBphNpsRExPTrM9lc9sw4IMBAIDtk7dDr2IrOhEREVF9gnm91hDhci0bbueFKFI0xe9OxHdxJyIiIiIiImoJWlQLejjhN49ERERE4Y3XazXjeSG6MGxBJyIiIiIiImohGNCJiIiIiIiIwgADOhEREREREVEYYEAnIiIiIiIiCgMM6ERERERERERhgAGdiIiIiIiIKAwwoBMRERERERGFAQZ0IiIiIiIiojDAgE5EREREREQUBhjQiYiIiIiIiMIAAzoRERERERFRGGBAJyIiIiIiIgoDDOhEREREREREYYABnYiIiIiIiCgMMKATERERERERhQEGdCIiIiIiIqIwwIBOREREREREFAYY0ImIiIiIiIjCAAM6ERERERERURhgQCciIiIiIiIKAwzoRERERERERGGAAZ2IiIiIiIgoDDCgExEREREREYUBBnQiIiIiIiKiMMCATkRERERERBQGGNCJiIiIiIiIwgADOhEREREREVEYYEAnIiIiIiIiCgMM6ERERERERERhgAGdiIiIiIiIKAwwoBMRERERERGFAWWoK9BSiaIIACgrKwtxTYiIiIioJr7rNN91G0l4HUt0YZribwoDejOxWCwAgPT09BDXhIiIiIjqYrFYYDQaQ12NsMHrWKKLczF/U2QivzJsFoIgID8/H9HR0ZDJZM3+fGVlZUhPT8fJkycRExPT7M8XKXheasdzUzOel9rx3NSM56V2PDc143mpWSjOiyiKsFgsSEtLg1zOkZ8+Db2O5Xs5NHjeg6+h57wp/qawBb2ZyOVytGvXLujPGxMTw1/UGvC81I7npmY8L7XjuakZz0vteG5qxvNSs2CfF7acV9fY61i+l0OD5z34GnLOL/ZvCr8qJCIiIiIiIgoDDOhEREREREREYYABvYXQaDRYsGABNBpNqKsSVnheasdzUzOel9rx3NSM56V2PDc143mpGc9L5OH/WWjwvAdfMM85J4kjIiIiIiIiCgNsQSciIiIiIiIKAwzoRERERERERGGAAZ2IiIiIiIgoDDCgExEREREREYUBBvQw8cwzz+DKK69EdHQ0kpKScNNNN+G3334LKCOKIh577DGkpaVBp9Nh+PDhOHDgQECZf//73xg+fDhiYmIgk8lQWlpa7bk6duwImUwW8DN//vzmfHkXJZjnBgC+/PJLDBgwADqdDomJibjlllua66VdlGCdlw0bNlR7v/h+du7c2dwvs9GC+X75/fffXCkG2QAALAZJREFUceONNyIxMRExMTEYPHgw1q9f35wv76IE89zs2bMHo0aNQmxsLBISEnDXXXehvLy8OV/eBWuK82IymXD//ffjsssug16vR/v27fHAAw/AbDYHHKekpATTpk2D0WiE0WjEtGnTav1bFA6CeW6eeuopZGVlQa/XIzY2Nhgv74IF67zk5eXhzjvvREZGBnQ6HTp37owFCxbA5XIF7bU2VjDfM+PHj0f79u2h1WqRmpqKadOmIT8/Pyivk6pzu92hrgLVg3ODNz1BEJr8mAzoYWLjxo3405/+hG3btmHt2rXweDy49tprYbVa/WWee+45vPTSS3j99dexc+dOpKSkYNSoUbBYLP4yNpsNY8aMwcMPP1zn8z3xxBM4c+aM/+fRRx9tttd2sYJ5bj755BNMmzYNM2bMwL59+/DDDz9g8uTJzfr6LlSwzktWVlbAe+XMmTP4wx/+gI4dO6Jfv37N/jobK5jvl+uvvx4ejwffffcddu/ejd69e2PcuHEoKCho1td4oYJ1bvLz8zFy5Eh06dIF27dvx5o1a3DgwAHk5OQ090u8IE1xXvLz85Gfn48XXngBP//8M3Jzc7FmzRrceeedAc81efJk7N27F2vWrMGaNWuwd+9eTJs2LaivtzGCeW5cLhduv/12zJo1K6iv8UIE67z8+uuvEAQB//rXv3DgwAG8/PLLePvtt+v9jA+lYL5nRowYgRUrVuC3337DJ598giNHjuC2224L6uslycGDB/HYY49h69atKCkpYRAMUzKZzH+b/0cXz+v1Qi5vhjgtUlgqLCwUAYgbN24URVEUBUEQU1JSxIULF/rLOBwO0Wg0im+//Xa1x69fv14EIJaUlFTb16FDB/Hll19urqo3u+Y6N263W2zbtq343//+t1nr31ya8z1TlcvlEpOSksQnnniiSevfXJrrvJw7d04EIG7atMm/raysTAQgrlu3rnleTBNrrnPzr3/9S0xKShK9Xq9/248//igCEA8dOtQ8L6YJXex58VmxYoWoVqtFt9stiqIoHjx4UAQgbtu2zV9m69atIgDx119/baZX07Sa69xUtWjRItFoNDZ53ZtTMM6Lz3PPPSdmZGQ0XeWbWTDPzeeffy7KZDLR5XI13QugBrn11ltFmUwmGo1GccCAAeKzzz4r7t+/X7TZbKGuWqvmcDjETZs2ia+//rq4YsUK0el0VitT9bOaGm716tViVlaW2KtXL3HRokX+7Xa7/aKPzRb0MOXrxhUfHw8AOHbsGAoKCnDttdf6y2g0GgwbNgxbtmxp9PGfffZZJCQkoHfv3njqqafCurvc+Zrr3OzZswenT5+GXC7HFVdcgdTUVIwdO7Za995w1dzvGZ+VK1eiqKgobFtDz9dc5yUhIQHdunXDkiVLYLVa4fF48K9//QvJycno27dv076IZtJc58bpdEKtVgd8q6zT6QAAmzdvboqqN6umOi9msxkxMTFQKpUAgK1bt8JoNGLAgAH+MgMHDoTRaLyo38lgaq5zE+mCeV7MZrP/eSJBsM6NyWTC+++/j6ysLKhUqiZ8BdQQY8aMQa9evXDjjTdi4MCBePXVV9GnTx/cfPPNWLx4MY4fPx5R15otxWuvvYZZs2bh//7v/zBr1iy8++67MJlM+PDDD/H+++/D5XI1TwtwC7d9+3bcd9996NChAwYNGoRHHnnk/9u776iorv1t4M/QEUeKwIBYAEESDRrACoiYIIIF9CaKxH7Vq0GJJbbEqxBL7F0x0RiMRmOJxhglYo0ixSAZMMYSvQSQIiKEYqHv9w9f5hdSLcwckOezFss15xxmP2czC/mec/beuHbtGqZOnYpRo0bh/fffR0ZGxjO/P38i9ZAQAjNmzICnpydeeeUVAFA9MqtQKGodq1Aonvpx2qlTp2Lv3r04e/YspkyZgnXr1iEkJKRuwquZOvsmNTUVABAeHo7//ve/OHr0KExNTdGrVy8UFBTU0Rmoh7o/M7+1fft29O3bF61atXr2wBqizn6RyWQ4efIklEol5HI5DAwMsHbtWhw/frzej58F1Ns3r732Gu7cuYOVK1eivLwcv/76q+qR3JycnDo6A/Woq37Jz8/HokWLMHHiRNW2O3fuwNLS8g/HWlpa1tthEb+lzr5pyDTZL//73/+wceNGTJo0qY7Sq5cm+mbOnDkwMjJC8+bNkZGRga+//rqOz4KexPjx4zFz5kz88ssvaNGiBa5fv45jx45BV1cXEydORNeuXfHmm28iLy9P6qiNyrJlyzB79mzcu3cP4eHhOHToEPr164d3330XEyZMgJubG5KTk6WO2eAsX74cr732Gj799FNs2bIFnTt3xr/+9S+cO3cO6enpWLFiBWbOnImysrJnen8W6PXQlClTcPnyZXzxxRd/2PfbsSPA4//8fr/tn0yfPh29evVCx44dMX78eHz00UfYvn078vPznyu3Jqizb2omeZg3bx7eeOMNuLm5ITIyEjKZDAcOHHi+4Gqm7s9MjczMTERHR/9hHGB9pc5+EUIgJCQElpaWiImJwffff4/AwEAMGDCg3hehgHr7pkOHDvjss8+wevVqNGnSBFZWVrC3t4dCoYC2tvZzZ1enuuiX4uJi9O/fH+3bt0dYWNjfvsffvU99o+6+aag01S/Z2dnw8/PDkCFDMH78+LoJr2aa6JtZs2ZBqVTixIkT0NbWxqhRozi2VsNq+nv48OGYN28eDh48iLCwMPj4+OCbb75BQUEBFi9ejEePHsHCwkLitI3Hnj17YGRkhJEjR0JLSwuDBw/GqVOn8NZbb+Hw4cP49NNPkZWVhW+++UbqqA3OsWPHEBoaCgMDAwCPfz97enoiLi4OFy9exKZNm/DDDz/g6tWrz/T+LNDrmdDQUBw5cgRnz55Fy5YtVdutrKwA4A9Xl+/evfuHq9BPq3v37gCAW7duPdf7qJu6+8ba2hoA0L59e9U2fX192NvbP9djKuqmyc9MZGQkmjdvjoCAgGcPrCHq7pczZ87g6NGj2Lt3Lzw8PODq6oqIiAgYGhris88+q5uTUBNNfGbeeust3LlzB1lZWcjPz0d4eDjy8vJgZ2f3/CegJnXRLyUlJfDz80PTpk3x1Vdf1XrU1srKCrm5uX9oNy8v77l/j6ubuvumodJUv2RnZ6N3797o0aMHtm7dqoYzqXua6htzc3O0a9cOffr0wd69exEVFYWEhAQ1nBH9ld9eWOnbty8iIyNx4cIF+Pv74+rVq2jSpAkmTJiAkydPSpiy8dm0aRNCQkJUP589e/bA1dUVISEh6Nq1K4YNG4apU6ciPj5e4qQNy759+6CtrQ1bW1sAjyc4/fnnn/Huu+9CT08PQggMHDgQlZWVtSbYfRos0OsJIQSmTJmCQ4cO4cyZM3/4I9bOzg5WVla1frmVl5fj3LlzcHd3f662lUolgP8rUOsbTfWNm5sb9PX1ay0HU1FRgbS0NLRp0+b5T6SOafozI4RAZGQkRo0aVa//sNZUvzx8+BAA/jB2S0tLSy1LbtQFKX7PKBQKNG3aFPv27YOBgQH69OnzXOegDnXVL8XFxfD19YWenh6OHDmiurJeo0ePHigqKsL333+v2nbx4kUUFRU99+9xddFU3zQ0muyXrKwseHt7w9XVFZGRkfV+vKiUn5maO7nP+lgpPb2SkhLcvHkTKSkpKC4uRnx8PHR0dLBjxw5cu3YNoaGh9XaJzRfZo0ePkJCQgAsXLqjmfvniiy/wn//8Bzo6OigtLQXweClHU1NTKaM2OElJSTA2NsaIESOwYsUKTJo0CR06dMBLL70EHR0dyGQypKen49dff4WXl9ezNfLc08xRnXj77beFsbGx+O6770ROTo7q67ezXy5btkwYGxuLQ4cOiR9//FEEBwcLa2trUVxcrDomJydHKJVKsW3bNtUM00qlUuTn5wshhIiLixNr1qwRSqVSpKamin379okWLVqIgIAAjZ/zk9JU3wghxNSpU4WNjY2Ijo4W169fF+PGjROWlpaioKBAo+f8JDTZL0IIcerUKQFAXL16VWPn+Cw01S95eXmiefPm4l//+pdITk4WN27cEDNnzhS6uroiOTlZ4+f9JDT5mdm4caNISkoSN27cEJs2bRKGhoZi/fr1Gj3fJ1UX/VJcXCy6desmnJ2dxa1bt2q9T2Vlpep9/Pz8RMeOHUV8fLyIj48Xzs7OYsCAARo/5yelyb5JT08XSqVSfPDBB6Jp06ZCqVQKpVIpSkpKNH7e/0RT/ZKVlSUcHBzEa6+9JjIzM2sdU19pqm8uXrwoNm7cKJRKpUhLSxNnzpwRnp6eom3btqK0tFSSc2+MvLy8hJubm2jbtq1o0aKFGDBggLC1tRW2traiW7duwtfXVwjxePZ+0pzCwkKxYcMG0bt3b6GtrS3Mzc2FTCartfKMEEIYGxuL06dPS5SyYbp586bYunWrGDdunPD29hZWVlbCxcVFrFu3TiiVSiHE49+DgwYNeuY2WKDXEwD+9Ou30/ZXV1eLsLAwYWVlJfT19YWXl5f48ccfa71PWFjY375PUlKS6NatmzA2NhYGBgbCyclJhIWFiQcPHmjwbJ+OpvpGiMdLiL377rvC0tJSyOVy4ePjI65cuaKhM306muwXIYQIDg4W7u7uGjiz56PJfklMTBS+vr7CzMxMyOVy0b17dxEVFaWhM316muybkSNHCjMzM6Gnpyc6duwodu7cqaGzfHp10S81S8792dcvv/yiOi4/P18MHz5cyOVyIZfLxfDhw/9xaUMpabJvRo8e/afHnD17VnMn/IQ01S+RkZF/eUx9pam+uXz5sujdu7cwMzMT+vr6wtbWVkyaNElkZmZq+Iwbr8zMTCGTyYRMJhPvvPOOOHDggIiOjhb3798XGRkZIj09XXVhhgW65lVWVorCwkKRlJQkli1bJjw9PYVMJhOvvPKKiIiIEJ9//rkwMzOTOmaDVVFRIS5evCjWrl0rgoKChLu7u/Dw8BCjRo3604shT0MmBGfSICIiIiKiJyP+/6R+P/74Iz799FNs374durq68PT0REBAAHx8fGBlZQV9fX2poxKAyspKFBQU4KeffsL+/ftx7NgxZGZmIjQ0FOvXr5c6XoNSWlqKBw8eoHnz5qptRUVFiIuLQ3x8POLi4lBeXo7z588/cxss0ImIiIiI6LkcPXoUmzZtwqlTp2BsbIyAgAD0798fPXv2rPcTYb5ISktLcf78eZw5cwZ+fn7w9vautb+iogK5ublISEjAa6+9BjMzM2mCNjAPHz7EwYMHsXPnTrRq1QqhoaFwcXH5w3GZmZl4+PAh2rVr98xtsUAnIiIiIqInVlRUBB0dHRgZGaGyshI6OjqqfRUVFdi+fTsiIyORmJiIffv2YciQIRKmbVzefvttnDhxApaWlsjNzUV0dDTMzc2RlpYGJycnNGnSROqIDdLSpUtx+PBh2NjYIC0tDVZWVti4cSPOnTuHxMREDBgwAP7+/nUykScLdCIiIiIiemKTJ09GeXk5Fi9erLo7XlBQgJKSklor3+Tk5MDCwqJWAU/qc+HCBQQHB2PHjh2ws7PD+++/D3Nzc1y+fFm1atOiRYswderUWsvj0T+ztrbG6tWr8dZbb6G0tBR+fn4oKytDUVER2rRpg9zcXKxfvx49e/Z87rbq91odRERERERUr+zZswdBQUFQKBRITk6Gj48PfH19MWnSJMydOxe3b98G8Lio0dbWljht47Flyxb4+vri9ddfh729Pby8vLB37174+Pjg559/xvTp07Fq1Srk5eVJHbVBuXDhArS1tREYGAgA0NXVxYULFzB27FhcvHgRH3/8MSoqKrB///46aY8FOhERERERPZGvvvoKZmZm8PHxwa1btzB8+HA0b94cQ4cOhb29PaKiohAWFqZaa5t3ajXnhx9+QL9+/VSv9+7di9GjR2PBggWwtrZGcHAwWrVq9VwTmDVGly5dgre3NyoqKgAAkZGRMDExwciRIyGXy9G6dWvMnDkT169fR2Vl5XO3x+dNiIiIiIjoichkMjg5OQEA9u/fDzs7O0RGRqrGNn/77bcYP348fvrpJ7i5uUkZtVEpKCjAkCFDYGBgAODxZHEtW7ZESEiI6hgHBweUlJTAyMhIqpgNkpeXFxwcHGBoaAgA8PX1haOjIwwNDVUrGly/fh0GBgZ1MpyDY9CJiIiIiOiJ3LlzBx07dsSyZcuQmZmJR48eYenSpbWO8ff3h7e3N+bMmSNRysarrKxMtbzd7yfwi42Nhb+/P4qLi6WK12D9vi9/q7i4GJ06dcKKFSvqZEJEPuJORERERERPxMrKCsuWLcPq1auhVCqxY8cOnD9/Ho8ePQIA3Lt3D4mJifDy8pI4aeNUU5wLIaCjo4Oae7Hp6elYvXo1Bg8eLGW8Bqe6uhoA/rI4LywsxMaNG2FsbFxnqxXwEXciogbM29sbr776KtatW6f2tsLDw3H48GEkJyervS0iIqq/xowZg6KiInz88cfIzc3F2LFjERgYiIcPHyIxMREuLi7o0aOH1DEbld/f4a0Z+1/z75UrV2BlZYUpU6ZIkq+hqlk2reZCx+/nVKisrISZmRkWLVpUZ23yEXcionpuzJgx+Oyzz/6w/ebNmzAzM4Ouri7kcrnac/xdgX7u3Dn4+Pjg7Nmz8PT0VG1/8OABnJ2dERgYiLVr16o9IxERqUdhYSFSU1Ph6uqq2paTk4Nz584hKioKFy5cQNu2bdGnTx8MHToUtra20oVtxKqqqgDgD7PnFxcXo6KiAs2bN5ciVoN069YtVFZW4qWXXqq1vbq6uk7WO/8rLNCJiOq5MWPGIDc3F5GRkbW2W1hYaHT5mn+6gz5jxgwcOXIEKSkpqgloJk+ejDNnzkCpVKomrqkr5eXl0NPTq9P3JCKiP7dkyRIcPHgQu3fvxssvv/ynx5SUlGjkgjH9n/T0dIwYMQJjxoxBcHCwarI+4PHdXS0tLWhpafFn8wx8fX1RVlaGnj17omvXrujcuTNatGhR65jExESYmZmhbdu2ddYux6ATETUA+vr6sLKyqvWlra0Nb29vTJs2TXVcREQEHB0dYWBgAIVCgTfffFO1r7q6GsuXL4eDgwP09fXRunVrLFmyRLV/zpw5aNeuHZo0aQJ7e3vMnz9ftaTIk/jwww+hp6enmhTo7Nmz2LZtG3bt2gV9fX2sWLEC9vb2MDQ0RKdOnfDll1+qvreqqgrjxo2DnZ0dDA0N4eTkhPXr19d6/zFjxmDQoEFYunQpWrRogXbt2j1tNxIR0TNatWoVsrOzMXjwYHz11Ve19tWM05XL5eC9P83atm0bYmNjsWTJErRs2RKDBg3C0aNHATweN62lpYUbN25g8ODBKCwslDZsA5KamoqYmBg0a9YMJ0+exOLFizFt2jQsW7YMZ8+eVU20984772Dbtm112jbHoBMRvSAuXbqEd955B7t27YK7uzsKCgoQExOj2v/ee+9h27ZtWLt2LTw9PZGTk4Pr16+r9svlcuzYsQMtWrTAjz/+iAkTJkAul2P27NlP1L6BgQF27twJd3d3+Pj4YPr06Xj//ffRuXNnzJs3D4cOHcKWLVvg6OiI8+fPY8SIEbCwsECvXr1QXV2Nli1bYv/+/TA3N0dcXBz+85//wNraGkOHDlW1cfr0adV/lvwjkIhIM06ePAktLS3ExsZi+vTpmDhxIkpKSjBq1CgAtcflct1zzUpJScHUqVMxZMgQpKSk4OjRoxg7diz09PQQEBCAiRMn4uTJk0hOToaJiYnUcRuM2NhYdOnSBevWrYNcLsdXX32FkydP4sCBAzh69CjatWsHGxsbXLx4Ebt3767bxgUREdVro0ePFtra2sLIyEj19eabbwohhOjVq5eYOnWqEEKIgwcPimbNmoni4uI/vEdxcbHQ19cX27Zte+J2V6xYIdzc3FSvw8LCRKdOnf7x+xYsWCC0tLSEm5ubqKioEPfv3xcGBgYiLi6u1nHjxo0TwcHBf/k+ISEh4o033lC9Hj16tFAoFKKsrOyJz4GIiJ5f//79xcSJE4UQQpSWloqZM2eKFi1aiLVr10obrJH79ddfxeTJk8XKlSuFEEJUVlaKnJwc8d1334mFCxcKd3d30bx5cyGTycTGjRslTtuw/PTTT2L58uUiPT291vbLly+LDz/8UPTr108YGRmJLl261HnbvINORNQA9O7dG1u2bFG9rhnj/Vt9+vRBmzZtYG9vDz8/P/j5+WHw4MFo0qQJrl27hrKyMrz++ut/2caXX36JdevW4datW7h//z4qKyvRrFmzp8763//+FwsXLsTcuXOho6MDpVKJ0tJS9OnTp9Zx5eXlcHFxUb3+6KOP8MknnyA9PR2PHj1CeXk5Xn311Vrf4+zszHHnREQadvLkSSQkJAB4PORq3rx5qKqqwty5c/Hzzz/jgw8+gIWFhdonz6LaTExMsHjxYuTn5wN4PDFczTC47t27Y9SoUdi/fz/mzp2LMWPGSBu2gWnfvj0cHR2hq6sLIQSqq6uhra0NZ2dnODs74+HDh3B0dMS///3vOm+bBToRUQNgZGQEBweHvz1GLpfjhx9+wHfffYcTJ05gwYIFCA8PR2JiIgwNDf/2exMSEjBs2DB88MEH6Nu3L4yNjbF3716sXr36qbPq6uoC+L81Q2vGJh47dgw2Nja1jq1Zr3X//v2YPn06Vq9ejR49ekAul2PlypW4ePFireP/7MIEERGpz5EjR2BsbFzrgqqJiQnWrFmDzp07Y9GiRaiurkZERASLcwmYmJjUenRdCAGZTAZ9fX20adMGt2/fhpubG5o2bSpdyAaq5u8ZmUymmpS3uroaMpkMP/zwA3JycjB8+PA6b5cFOhHRC0RHRwc+Pj7w8fFBWFgYTExMcObMGfTr1w+GhoY4ffo0xo8f/4fvi42NRZs2bTBv3jzVtvT09DrJ1L59e+jr6yMjIwO9evX602NiYmLg7u6OkJAQ1bb//e9/ddI+ERE9GyEEAgICcOPGDdVrmUymKlKGDBmCsrIyvP3227h+/Tq+++47aQNTrTkAysrKIIRAeHi4dIFeMDUXoZydnfH555+rZWZ8FuhERC+Io0ePIjU1FV5eXjA1NUVUVBSqq6vh5OQEAwMDzJkzB7Nnz4aenh48PDyQl5eHn376CePGjYODgwMyMjKwd+9edOnSBceOHfvDLL3PSi6XY+bMmZg+fTqqq6vh6emJ4uJixMXFoWnTphg9ejQcHBywc+dOREdHw87ODrt27UJiYiLs7OzqJAMRET29mmLP1NS01uuaIkVXVxdjx46Fg4MDrly5Ik1I+kv6+vpYs2aN6k4w1R1jY2O89dZbanlvFuhERC8IExMTHDp0COHh4SgtLYWjoyO++OILdOjQAQAwf/586OjoYMGCBcjOzoa1tTUmTZoEAAgMDMT06dMxZcoUlJWVoX///pg/f36dXXVftGgRLC0tsXTpUqSmpsLExASurq54//33AQCTJk1CcnIygoKCIJPJEBwcjJCQEHz77bd10j4RET2b+/fv4/79+zA0NISxsXGtfZWVlQCAnj17okePHlLEo3/A4rxu1Dw98ujRIwghaq03X9dkQnCdGiIiIiIiqu3rr79GWFgYCgsLERgYiJUrV0JPTw8PHz5Ua4FCz2/58uVwcXGBr6+v1FFeCDUF+u7du7FkyRIsXboUgYGBammLd9CJiIiIiKiWmzdvYtq0aQgKCoKtrS3mzp2LwMBAnD59GhkZGTAyMsKMGTPQrl07qaM2OjXF4l+5d+8eDh06hK1bt3I+lzpS099GRkZwdHTEyy+/rL62eAediIiIiIh+a8qUKcjJycHBgwcBAB9++CG2b98OCwsLvPLKK0hKSoK9vT327NmjWpGDNKtmlZQ/mz3/3r17KC0tRcuWLTUdq8EqLS2FgYFBnR33rLgWAhERERER1RIdHY2goCDV65iYGLi6uuLUqVP45JNPMGfOHFy5cgW//PKLhCkbn/Pnz+Ps2bOorKyElpaWqjivrq7Gb++7mpubszh/CtnZ2QgLC8PZs2eRm5urml/ht6qqqlBQUAADAwOo8x43C3QiIiIiIlJJT0/HK6+8gtatWwN4XJgolUq88847qvW0X3/9dTRr1gypqalSRm10JkyYgMmTJyM4OBhr1qzB5cuXATy+i17zGPbXX3+N5ORkCVM2PKtWrcLKlSvRr18/9OvXDytXrsSlS5dQWFioKsYzMjIwZswYFBUV/e0Qg+fFR9yJiIiIiEjlwYMHuHTpElq3bg07OztUVlYiNTUVjo6OqsLk6tWr8PDwQFZWFieM0xClUglfX1+MGDECOTk5uH37NmQyGRwcHNCrVy/4+PigVatWsLKywqRJk7j++VPw9vZGQEAAvL298fHHH2Pfvn0oKyuDl5cXhg4dCi8vL2zduhVHjhzBjRs31JqFBToREREREf2jmsnJqqqqMGfOHCQnJ+PUqVNSx2o0tm7dis8//xyffvopWrVqhXPnziEmJgYpKSm4e/cujIyMYGpqim+++Qb37t2DXC6XOnKDUFBQgIULF8LW1hbTpk1TbT9+/Dg2b96M48ePw8zMDHl5ediwYQOmTJmi1jws0ImIiIiI6IlFRERg27ZtWL58OZfx0qA7d+7g+PHj6N+/PywsLFTbs7OzERsbi4SEBHzyySfw9PTEsWPHJEzasFRWVuLmzZvQ09ND27ZtUV5eDj09PdV+IQTee+89rF69GsXFxTA0NFRrHhboRERERET0xLKysnD58mX4+/tLHaXREkJACFFrBveSkhK0bt0an3zyCd544w0J070YqqqqAADa2tqYOHEilEolvv/+e7W3y3XQiYiIiIjoidnY2MDGxkbqGI2aTCZTzQdQM/QgISEBJSUlLM7riLa2NgCgvLwcWlpamD9/vkba5R10IiIiIiJ6YjUFIdUvpaWlSEpKgoeHh9RRXjjFxcVo1qyZRtpigU5ERERERAAej8fV0eFDtkRS4TroREREREQEALh79y4iIiKkjkHUaLFAJyIiIiJq5KqrqwEAUVFRWLp0KR48eCBxIvq9mgefayYvoxcTC3QiIiIiokauZjZwKysrODo6Iisrq9b+6OhoxMXFoaKiQop4hMcTw1VUVKgmLwMeF+scsfxiYYFOREREREQAAIVCAaVSCXt7e9W2qKgoDB8+HN9//z10dXUlTNf41BTfly5dwqxZs9C5c2d07doVq1atwv3796Gtrc0J+55DTf9WVlbixIkTOHTokMSJWKATERERETV6NYVKamoqWrVqhUePHgEArl27hvfeew/jxo3DtGnTJEzY+NTMlp+Tk4OxY8fizJkzGDZsGLp06YL169fDysoKu3fvljpmg1bzuT9w4ACGDx+O+fPnIy8vT9JMLNCJiIiIiBq5mruwJiYmMDExQVFREW7fvo0pU6bAyclJtQY0H6fWnJp5ATZs2AALCwvEx8fjvffew+bNm6FUKvHvf/8ba9asQX5+vsRJGy4tLS2kp6dj8uTJeP311wFI/xlngU5ERERE1MjVFCXm5uZISUlBy5YtMW3aNMjlcqxatQpNmzbl+ucaVjPWPDExEd27d4eenh6EEKisrIS5uTkmTZqE8vJyREVFSZy04amZaO/ixYuYNm0a/P39ERoaitzcXFhaWkqajQU6EREREREBALKzs9GyZUuEhoZCqVRi+fLlaN26NQCwOJeIr68vDh48iKKiIshkMtU69S+//DJKSkpgbGwsccKGp+bix9SpU9GsWTOsWLECv/76K1xcXJCTkyNpNhboRERERESN3G8fcU9LS8O3336LiIgIODk5SZyMBg0aBAMDAwwcOBAfffQRUlJScOfOHYSHh+PBgwcICAiQOmKDUTNsIC8vDx988AEyMjKwbt062NjYwNTUFJmZmZJfiGKBTkREREREAAB7e3sEBARgw4YN6Nu3r9RxGq2aIQdpaWlo164dIiIiYG1tjV27dmHkyJGwsbFBdHQ01q5dK3HShqVmOcGFCxciOjoaGzduhKmpKQDgwYMHKCgogJWVlZQRoSNp60REREREVG/Y2Nhg8+bNMDMzkzpKo1VdXQ0tLS0cOHAAs2bNQlpaGjw8PODm5obY2FhkZGSgffv2sLa2Vg0/oCeXl5eHqqoqLFmyBL169VJtv3v3LlxcXJCRkSFpv7JAJyIiIiIiFRbn0tqyZQtcXFywYcMGBAUFqbYbGBioZhqnZ2dhYYFly5ahWbNmtbbb29sjKSlJNT5dKnzEnYiIiIiIqB7Iy8vDli1bMG3aNMTGxgIA4uPjUVJSUuu48ePH49ixY1JEfCH8vjgHHl+Y8vPzg42NjaRLrcmE1Au9ERERERERNXI1y9gVFBRg1qxZOHz4MOzs7GBoaAgnJyd07doVXbp0gYmJCdq2bYtr165xEr86UlMSZ2dnQ6FQqGbKlwILdCIiIiIionrk+PHjaNKkCSwsLHDgwAHExMSgsLAQ2traKCgogIODA9c/f0GxQCciIiIiIqrHqqqqcO7cOSQkJKBdu3bo1q0bWrVqJXUsUgMW6ERERERERPVIzePuNet21ywPRi8+FuhERERERET1XM3ya/Ri40+YiIiIiIhIQlVVVf84cziL88aB66ATERERERFJKCIiAubm5nB3d0eLFi2gq6srdSSSCB9xJyIiIiIikkhRURFMTU0BAHZ2dujduzeGDh0KV1dXmJmZ8c55I8M76ERERERERBIpLS1Fnz59cP/+fbi6uuLq1avw8/ODg4MDhgwZgkGDBuGll16CXC6XOippAC/HEBERERERSUShUGDHjh1o3749MjMzsWHDBty+fRtBQUHYtWsXPD090bNnT+zYsUPqqKQBLNCJiIiIiIgkUlVVBWtra6xYsQIKhQITJkzAzZs3sWjRImRkZCAmJgb29vbIy8uTOippAMegExERERER1ROLFy/G9u3bMXfuXIwePRoGBgZSRyIN4hh0IiIiIiIiCeTn5yM7Oxs2NjZ48OAB7ty5gxkzZqC8vByhoaEwNTXF0KFDuQZ6I8ICnYiIiIiISMMuXLiAkSNHonXr1lAqlXB2doaBgQHi4+PRtWtXNG/eHDo6j8s1mUwmcVrSFBboREREREREGvbZZ58hPT0dxsbGWLt2LeRyOV5++WW0adMGGRkZsLCwgEKhAMACvTHhGHQiIiIiIiINKysrw+7du7Fp0yZcvnwZLi4u6NOnDwIDA9GhQwc0adKEj7U3QizQiYiIiIiIJJSWloaIiAjs2rULubm56NatG4YNGwYPDw+4uLhAW1tb6oikISzQiYiIiIiINCg3NxcKhQJVVVWQyWS17pTHxcVhy5Yt+OKLL6BQKJCVlSVhUtI0PjNBRERERESkIYWFhfD398exY8egpaWlKs7T0tJQXl4Od3d37Nq1C5WVlYiKipI4LWkaJ4kjIiIiIiLSkC1btsDAwAB9+/ZFaWkp9uzZg3Xr1kGhUMDa2hqBgYF48803AQCdOnWSOC1pGgt0IiIiIiIiDfnyyy8RHBwMHR0dzJo1CwkJCXB3d4eFhQVSUlIQHh4Oc3NzeHt7Sx2VJMACnYiIiIiISAMePXoEBwcHmJmZAXhcrEdERGDgwIEAgPLycgwePBhbt26Fl5cXZ3FvhPgTJyIiIiIi0gBDQ0O0b98e69evR0JCAjp06ABbW1vVfj09PcyePRvJyckoKiqSLihJhgU6ERERERGRhoSGhkKhUODDDz9EZmYmNmzYgPz8fNX+pKQk6OrqwtTUVMKUJBU+4k5ERERERKQhZmZmWL58OebNm4dffvkF165dQ15eHjw9PXH69GlkZmbi3XfflTomSYTroBMREREREalZUlISXnrpJRgZGam2xcbGIjo6GlFRUSgsLISrqytGjhyJ/v37c/x5I8UCnYiIiIiISM06duyIESNGYPbs2X+6v6ysDEIIGBgYaDgZ1Se8LENERERERKRGO3fuxJUrV7BmzRqMGzcOd+/erbW/uroa+vr6LM6Jd9CJiIiIiIjUyc3NDQMHDoSzszNCQkLg4eGBiIgIWFlZQQgBmUwmdUSqJ1igExERERERqcm9e/egUCiQnZ0NhUKB8+fPIyQkBFZWVti8eTOcnJykjkj1CB9xJyIiIiIiUpMNGzagR48eUCgUqK6uhpeXF9atW4fMzEwMHDgQJ06cAADwvikBLNCJiIiIiIjUZvfu3Rg2bBgAqGZm9/HxQWxsLDp16oRZs2bh1KlTfMydAPARdyIiIiIiIrWpqqqCEAI6Ojq1tmlra+PGjRuYO3cuvv76axw/fhy+vr4SJqX6gAU6ERERERGRGjzpBHBz587FjBkzYGlpqYFUVJ+xQCciIiIiIlKTBw8eoLCwEDY2NrW2V1dXo6qqCrq6uigtLeUSawSAY9CJiIiIiIjq3J07d7Bw4ULY2tpi+PDhiImJUe2rqKiAlpYWdHV1AYDFOanwDjoREREREVEdmzBhAm7evInAwECcOHECOjo6mDVrFg4ePIi7d+/C19cXo0aNgra2ttRRqR5hgU5ERERERFSHhBAwMTFBVFQUPDw8AADt27eHkZERTE1NYWRkhJSUFGzevBn+/v4Sp6X6ROefDyEiIiIiIqIndeDAAdjY2KBz584AgLt37+LmzZs4fPgw+vfvDyEEevfujVOnTsHPz49LrJEKx6ATERERERHVofT0dPj7+6OiogIAsHfvXrz66qvo378/qqurIZPJEBQUhOTkZBbnVAvvoBMREREREdWhAQMGICcnB02aNAEABAUFITAwEACgpfX4Hml8fDzs7e0ly0j1E8egExERERERaVBWVhZcXFxw5MgRdO/eXeo4VI+wQCciIiIiItKQ9PR0LFiwAFlZWTh16pTUcaieYYFORERERESkIVVVVTh//jzkcrlqEjmiGizQiYiIiIiIiOoBzuJOREREREREVA+wQCciIiIiIiKqB1igExEREREREdUDLNCJiIiIiIiI6gEW6ERERERERET1AAt0IiIiIiIionqABToRERERERFRPcACnYiIiIiIiKgeYIFOREREREREVA/8P1ILGs3gHy3aAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfTmp=df.reset_index([0,1])\n",
    "df_piv_m=pd.pivot_table(dfTmp, index=['Budget Tier'], columns='Fiscal Year', values='DHT$',aggfunc=['mean'])\n",
    "#df_piv_m=pd.pivot_table(dfTmp, index=['Fiscal Year'], columns='Budget Tier', values='DHT$',aggfunc=['mean'])\n",
    "\n",
    "df_piv_m.columns = df_piv_m.columns.get_level_values(1)\n",
    "lst = [\"< 376M\", \"376M to 1.13B\", \"> 1.13B\"]\n",
    "df_piv_m = df_piv_m.loc[lst]\n",
    "df_piv_sd=pd.pivot_table(dfTmp, index=['Budget Tier'], columns='Fiscal Year', values='DHT$',aggfunc=['std'])\n",
    "df_piv_sd.columns = df_piv_sd.columns.get_level_values(1)\n",
    "df_piv_sd = df_piv_sd.loc[lst]\n",
    "\n",
    "#main plot, lines of each tier:\n",
    "fig, ax = plt.subplots(1,2, gridspec_kw={'width_ratios': [3, 1]}, figsize=(10, 8))\n",
    "fig.tight_layout(pad=2)\n",
    "linestyle = [\"-\"]*1 + [\"--\"]*1 + [\"-.\"]*1\n",
    "fig=df_piv_m.T.plot(ax=ax[0] , style=linestyle, fontsize=10) #plot.area(...., stacked=False)#lw=linewidth\n",
    "\n",
    "#conf. int. for tiers:\n",
    "lines=fig.get_lines()# [0].get_color() \n",
    "colors = [line.get_color() for line in lines]\n",
    "ci = 1.96 * np.std(df_piv_m.T['> 1.13B'])/np.sqrt(len(df_piv_m.columns)) #11.5B+  3.6 to 11.5B     less 3.6B\n",
    "ax[0].errorbar(x=df_piv_m.columns,y=df_piv_m.T['> 1.13B'],yerr=ci, fmt ='o', color=colors[2])\n",
    "#ax[0].fill_between(df_piv_m.columns, (df_piv_m.T['11.5B+']-ci), (df_piv_m.T['11.5B+']+ci), color=colors[0], alpha=.1)\n",
    "ci = 1.96 * np.std(df_piv_m.T['376M to 1.13B'])/np.sqrt(len(df_piv_m.columns)) #11.5B+  3.6 to 11.5B     less 3.6B\n",
    "#ax[0].fill_between(df_piv_m.columns, (df_piv_m.T['3.6 to 11.5B']-ci), (df_piv_m.T['3.6 to 11.5B']+ci), color=colors[1], alpha=.1)\n",
    "ax[0].errorbar(x=df_piv_m.columns,y=df_piv_m.T['376M to 1.13B'],yerr=ci, fmt ='o', color=colors[1])\n",
    "ci = 1.96 * np.std(df_piv_m.T['< 376M'])/np.sqrt(len(df_piv_m.columns)) #11.5B+  3.6 to 11.5B     less 3.6B\n",
    "#ax[0].fill_between(df_piv_m.columns, (df_piv_m.T['less 3.6B']-ci), (df_piv_m.T['less 3.6B']+ci), color=colors[2], alpha=.1)\n",
    "ax[0].errorbar(x=df_piv_m.columns,y=df_piv_m.T['< 376M'],yerr=ci, fmt ='o', color=colors[0])\n",
    "\n",
    "#grand mean plot and error:\n",
    "df_mean=np.mean(fundsIC_all, axis=0)\n",
    "ci = 1.96 * np.std(fundsIC_all)/np.sqrt(len(fundsIC_all.index))\n",
    "ax[0].plot(df_mean.T,linewidth=3, color='k',linestyle='-')\n",
    "ax[0].fill_between(df_mean.index, (df_mean-ci), (df_mean+ci), color='k', alpha=.1)\n",
    "\n",
    "#nicify left plot\n",
    "ylims=ax[0].get_ylim()\n",
    "fig.set_ylabel('Total Awarded DHT Funds')\n",
    "leg = fig.legend(loc=\"upper left\")\n",
    "legendText=list(df_piv_m.index)\n",
    "legendText.append('All ICs Mean')\n",
    "leg = fig.legend(legendText,loc=\"upper left\", title=\"Budget Tier\")#, loc=0, frameon=True)\n",
    "#leg.set_title(\")\n",
    "\n",
    "##sideplot with IC means\n",
    "#vals=fundsSubset.loc[list(fundsSubset.index)[:], list(fundsSubset.columns)].mean(axis=1)\n",
    "vals = df_piv_m.mean(axis=1)\n",
    "print(vals)\n",
    "barerr=1.96*np.std(df_piv_m,axis=1)/np.sqrt(len(list(df_piv_m.columns)))\n",
    "x=list(df_piv_m.index)[:]\n",
    "marks=vals\n",
    "for pos, y, err, ccolor in zip(x, marks,\n",
    "                               barerr, colors):\n",
    "    plt.errorbar(x=pos,y=y,yerr=err, fmt ='o', color=ccolor)\n",
    "ax[1].set_ylim(ylims)\n",
    "plt.xticks(rotation=70)\n",
    "\n",
    "#change ytick labels to remove 1e8 notation:\n",
    "plt.draw()\n",
    "labels = [item.get_text() for item in ax[1].get_yticklabels()]\n",
    "print(labels)\n",
    "labels = ['$' + str(round(float(item)*100)) + 'M' for item in labels[1:]]\n",
    "labels.insert(0,'$-250M')\n",
    "print(labels)\n",
    "[ax[i].set_yticklabels(labels) for i in range(2)]\n",
    "\n",
    "print('/n==========FIGURE 2===========/n')\n",
    "plt.show()\n",
    "fig.figure.savefig(outpath + 'Figure2_mHealthFundingByBudgetTier_sideplotAvgBar_totalGrants.tiff', dpi=600,bbox_inches='tight') #"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e50cdb1-a078-45aa-ac14-5626d26912d4",
   "metadata": {},
   "source": [
    "# Figure 3. Total Funding for DHT Grants as a Percentage of Total IC Budget according to ICs Tier from FY2015 to FY 2023"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b4f2a92d-d536-43c2-b975-8d08dcc3f3e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                     Admin IC Short        DHT$      Budget    budgetMean  \\\n",
      "Admin IC Fiscal Year                                                        \n",
      "FIC      2015                    TW   3142955.0    77474918  8.243364e+07   \n",
      "         2016                    TW   4004387.0    68595980  8.243364e+07   \n",
      "         2017                    TW   6647302.0    71382502  8.243364e+07   \n",
      "         2018                    TW   6633309.0    80666953  8.243364e+07   \n",
      "         2019                    TW   8061701.0    81922565  8.243364e+07   \n",
      "...                             ...         ...         ...           ...   \n",
      "OD       2019                    OD  54892673.0  1004091774  9.355206e+08   \n",
      "         2020                    OD  10251048.0  1132605378  9.355206e+08   \n",
      "         2021                    OD  51188384.0  1174679362  9.355206e+08   \n",
      "         2022                    OD  33919651.0  1248671608  9.355206e+08   \n",
      "         2023                    OD  72279909.0  1389381330  9.355206e+08   \n",
      "\n",
      "                        Budget Tier  percentBudg  \n",
      "Admin IC Fiscal Year                              \n",
      "FIC      2015                < 376M     4.056739  \n",
      "         2016                < 376M     5.837641  \n",
      "         2017                < 376M     9.312229  \n",
      "         2018                < 376M     8.223081  \n",
      "         2019                < 376M     9.840635  \n",
      "...                             ...          ...  \n",
      "OD       2019         376M to 1.13B     5.466898  \n",
      "         2020         376M to 1.13B     0.905086  \n",
      "         2021         376M to 1.13B     4.357647  \n",
      "         2022         376M to 1.13B     2.716459  \n",
      "         2023         376M to 1.13B     5.202309  \n",
      "\n",
      "[224 rows x 6 columns]\n"
     ]
    }
   ],
   "source": [
    "#convert percentage\n",
    "df['percentBudg']=(df['DHT$']/df.Budget)*100\n",
    "print(df)\n",
    "#output spreadsheet\n",
    "df.to_csv(outpath + 'mHealthFundingByIC_percentOverall_totalGrants.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "223b3b16-e09a-4e98-882a-9f1d354b00e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25\n",
      "totals mean ci =  Fiscal Year\n",
      "2015    5.799052e+06\n",
      "2016    6.527579e+06\n",
      "2017    8.946518e+06\n",
      "2018    1.104038e+07\n",
      "2019    1.393949e+07\n",
      "2020    1.709633e+07\n",
      "2021    2.224145e+07\n",
      "2022    2.344596e+07\n",
      "2023    2.641188e+07\n",
      "dtype: float64\n",
      "legendText =  ['< 376M', '376M to 1.13B', '> 1.13B', 'All ICs %DHT Mean', 'NIH Total (right-axis)']\n",
      "['−1.0', '−0.5', '0.0', '0.5', '1.0', '1.5', '2.0']\n",
      "2 − 0.0\n",
      "3 − 0.5\n",
      "4 − 1.0\n",
      "5 − 1.5\n",
      "6 − 2.0\n",
      "['', '', '$0M', '$500M', '$1.0B', '$1.5B', '$2.0B']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\radmantc\\AppData\\Local\\anaconda3\\Lib\\site-packages\\numpy\\core\\fromnumeric.py:3643: FutureWarning: The behavior of DataFrame.std with axis=None is deprecated, in a future version this will reduce over both axes and return a scalar. To retain the old behavior, pass axis=0 (or do not pass axis)\n",
      "  return std(axis=axis, dtype=dtype, out=out, ddof=ddof, **kwargs)\n",
      "C:\\Users\\radmantc\\AppData\\Local\\Temp\\ipykernel_16992\\946656910.py:108: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax2.set_yticklabels(labels)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Budget Tier\n",
      "< 376M           8.692528\n",
      "376M to 1.13B    3.207866\n",
      "> 1.13B          3.316118\n",
      "dtype: float64\n",
      "9\n",
      "/n==========FIGURE 3===========/n\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#percent tiers plot, with 2nd axis with nih total spending:\n",
    "df=df.reset_index([0,1])\n",
    "\n",
    "df_piv_m=pd.pivot_table(df, index=['Budget Tier'], columns='Fiscal Year', values='percentBudg',aggfunc=['mean'])\n",
    "df_piv_m.columns = df_piv_m.columns.get_level_values(1)\n",
    "lst = [\"< 376M\", \"376M to 1.13B\", \"> 1.13B\"]\n",
    "df_piv_m = df_piv_m.loc[lst]\n",
    "df_piv_sd=pd.pivot_table(df, index=['Budget Tier'], columns='Fiscal Year', values='percentBudg',aggfunc=['std'])\n",
    "df_piv_sd.columns = df_piv_sd.columns.get_level_values(1)\n",
    "df_piv_sd = df_piv_sd.loc[lst]\n",
    "\n",
    "#redo above percent tiers plot, but add 2nd axis with nih total spending:\n",
    "fig, ax = plt.subplots(1,2, gridspec_kw={'width_ratios': [6, 3]}, figsize=(10, 8))\n",
    "fig.tight_layout() #pad=2.5)\n",
    "\n",
    "# set the spacing between subplots\n",
    "plt.subplots_adjust(left = 0.125,  # the left side of the subplots of the figure\n",
    "    right = 0.9,   # the right side of the subplots of the figure\n",
    "    bottom = 0.1,  # the bottom of the subplots of the figure\n",
    "    top = 0.9,     # the top of the subplots of the figure\n",
    "    wspace = .5,  # the amount of width reserved for space between subplots,\n",
    "                  # expressed as a fraction of the average axis width\n",
    "    hspace = 0.2,  # the amount of height reserved for space between subplots,\n",
    "                  # expressed as a fraction of the average axis height\n",
    ")\n",
    "ax2 = ax[0].twinx()\n",
    "\n",
    "\n",
    "#main plot, lines of each tier:\n",
    "linestyle = [\"-\"]*1 + [\"--\"]*1 + [\"-.\"]*1\n",
    "fig=df_piv_m.T.plot(ax=ax[0] , style=linestyle, fontsize=10) \n",
    "\n",
    "#conf. int. for tiers:\n",
    "lines=fig.get_lines()# [0].get_color() \n",
    "colors = [line.get_color() for line in lines]\n",
    "\n",
    "#grand mean plot and error:\n",
    "df_pivot=pd.pivot(df, index='Admin IC', columns='Fiscal Year', values='percentBudg')\n",
    "\n",
    "df_percmean=np.mean(df_pivot, axis=0)\n",
    "print(len(fundsIC_all.index))\n",
    "ax[0].plot(df_percmean,linewidth=3, color='k',linestyle='-')\n",
    "#leg = fig.legend(loc=\"upper left\")\n",
    "#leg.set_title(\"Budget Tier\")\n",
    "#legendText=list(df_piv_m.index)\n",
    "#legendText.append('All ICs Mean')\n",
    "#fig.legend(legendText, loc='lower left', frameon=True,bbox_to_anchor=(.1, .85))\n",
    "#leg2=fig.legend(legendText, loc='lower left',bbox_to_anchor=(.92, .5)) #1.0, 0.5))\n",
    "\n",
    "#nicify left plot\n",
    "ylims=ax[0].get_ylim()\n",
    "fig.set_ylabel('Total Awarded DHT Funds as % of IC Budget')\n",
    "\n",
    "#2nd axis with NIH total spending (not %):\n",
    "df_total_all=pd.pivot(df, index='Admin IC', columns='Fiscal Year', values='DHT$')\n",
    "totals=df_total_all.sum(axis=0)\n",
    "ax2.plot(totals.index,totals,linewidth=3, color='navy',linestyle='--')\n",
    "ax[0].plot(np.NaN, np.NaN, color='navy',linestyle='--', label='') #this is needed so there is an extra index in the legend\n",
    "ci = 1.96 * np.std(df_total_all)/np.sqrt(len(df_total_all.index))\n",
    "print('totals mean ci = ',ci)\n",
    "ax2.fill_between(totals.index, (totals-ci), (totals+ci), color='k', alpha=.1)\n",
    "ax2.set_ylim(bottom=-1000000000, top=ax2.get_ylim()[1])\n",
    "\n",
    "#leg = fig.legend(frameon=False)#loc=\"upper left\")\n",
    "#leg.remove()\n",
    "#fig.legend(loc='center left',bbox_to_anchor=(1.0, 0.5)).get_figure()\n",
    "legendText=list(lst)\n",
    "legendText.append('All ICs %DHT Mean'); \n",
    "#legendText.append(''); \n",
    "legendText.append('NIH Total (right-axis)');\n",
    "print('legendText = ',legendText)\n",
    "#fig.legend(legendText, loc=4,frameon=True)\n",
    "leg2=fig.legend(legendText, loc='lower left',bbox_to_anchor=(1.92, .5), title=\"Budget Tier\") #1.0, 0.5))\n",
    "leg2.get_figure()\n",
    "#spaces added below to center text on 2nd axis:\n",
    "ax2.set_ylabel('                                                                                                ' +\n",
    "             'Total Awarded DHT Funds (Total $)')\n",
    "#change last item text color:\n",
    "texts = leg2.get_texts()\n",
    "texts[-1].set_color(\"navy\")\n",
    "\n",
    "#left plot, percent mean errorbars (has to be after legend to preserve order of lines):\n",
    "ci = 1.96 * np.std(df_pivot)/np.sqrt(len(fundsIC_all.index))\n",
    "ax[0].fill_between(df_percmean.index, (df_percmean-ci), (df_percmean+ci), color='k', alpha=.1)\n",
    "ci = 1.96 * np.std(df_piv_m.T['> 1.13B'])/np.sqrt(len(df_piv_m.columns)) #11.5B+  3.6 to 11.5B     less 3.6B\n",
    "ax[0].errorbar(x=df_piv_m.columns,y=df_piv_m.T['> 1.13B'],yerr=ci, fmt ='o', color=colors[2])\n",
    "ci = 1.96 * np.std(df_piv_m.T['376M to 1.13B'])/np.sqrt(len(df_piv_m.columns)) #11.5B+  3.6 to 11.5B     less 3.6B\n",
    "ax[0].errorbar(x=df_piv_m.columns,y=df_piv_m.T['376M to 1.13B'],yerr=ci, fmt ='o', color=colors[1])\n",
    "\n",
    "\n",
    "#change 2nd ytick labels to remove 1e8 notation:\n",
    "plt.draw()\n",
    "labels = [item.get_text() for item in ax2.get_yticklabels()]\n",
    "print(labels)\n",
    "for i,clab in enumerate(labels):\n",
    "    if clab[0]!='\\N{MINUS SIGN}':\n",
    "        print(i,'\\N{MINUS SIGN}',clab)\n",
    "        labels[i]=float(clab)\n",
    "        if labels[i]>=1.0:\n",
    "            #labels[i]='$' + str(round(labels[i])) + 'B'\n",
    "            labels[i]='$' + str((labels[i])) + 'B'\n",
    "\n",
    "        else:\n",
    "            labels[i]='$' + str(round(labels[i]*1000)) + 'M'\n",
    "    else:\n",
    "        labels[i]='' \n",
    "print(labels)\n",
    "ax2.set_yticklabels(labels)\n",
    "ax2.spines['right'].set_color('navy')\n",
    "ax2.tick_params(axis='y', colors='navy')\n",
    "ax2.yaxis.label.set_color('navy')\n",
    "ci = 1.96 * np.std(df_piv_m.T['< 376M'])/np.sqrt(len(df_piv_m.columns)) #11.5B+  3.6 to 11.5B     less 3.6B\n",
    "ax[0].errorbar(x=df_piv_m.columns,y=df_piv_m.T['< 376M'],yerr=ci, fmt ='o', color=colors[0])\n",
    "\n",
    "\n",
    "##sideplot with IC means\n",
    "#vals=fundsSubset.loc[list(fundsSubset.index)[:], list(fundsSubset.columns)].mean(axis=1)\n",
    "vals = df_piv_m.mean(axis=1)\n",
    "print(vals)\n",
    "print(len(list(df_piv_m.columns)))\n",
    "barerr=1.96*np.std(df_piv_m,axis=1)/np.sqrt(len(list(df_piv_m.columns)))\n",
    "x=list(df_piv_m.index)[:]\n",
    "marks=vals\n",
    "for pos, y, err, ccolor in zip(x, marks,\n",
    "                               barerr, colors):\n",
    "    ax[1].errorbar(x=pos,y=y,yerr=err, fmt ='o', color=ccolor)\n",
    "ax[1].set_ylim(ylims)\n",
    "ax[1].tick_params(axis='x', rotation=70)\n",
    "ax[1].set_ylabel('Total Awarded DHT Funds as % IC Budget')\n",
    "\n",
    "print('/n==========FIGURE 3===========/n')\n",
    "\n",
    "plt.show()\n",
    "fig.figure.savefig(outpath + 'Figure3_mHealthFundingByBudgetTier_percent_sideplotAvgBar_wNIHtotals_totalGrants.tiff', dpi=600,bbox_inches='tight') #\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99f32f67-a731-4668-a83e-526c60788277",
   "metadata": {},
   "source": [
    "# Supplemental Figure 1a and b. Overall DHT awards distribution by grant type from 2015-2023 (pie chart and bar chart)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b700fe84-b225-4085-b383-4f3be7f44df6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\radmantc\\AppData\\Local\\Temp\\ipykernel_16992\\941254976.py:2: FutureWarning: The provided callable <function sum at 0x0000019E683FD440> is currently using DataFrameGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"sum\" instead.\n",
      "  mechYrByIC=pd.pivot_table(dfi,index=[\"Administering IC\", \"Fiscal Year\"],columns=[\"Activity\"],values=\"Total Cost\",aggfunc=np.sum)\n"
     ]
    },
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Activity</th>\n",
       "      <th>C06</th>\n",
       "      <th>D43</th>\n",
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       "      <th>F32</th>\n",
       "      <th>...</th>\n",
       "      <th>UB1</th>\n",
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       "      <th>UH4</th>\n",
       "      <th>UL1</th>\n",
       "      <th>UM1</th>\n",
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       "    <tr>\n",
       "      <th>Fiscal Year</th>\n",
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       "      <th>2016</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>21299562.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2018</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27443730.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>693765.0</td>\n",
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       "      <td>71247391.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2023</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>688274.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65328777.0</td>\n",
       "      <td>4023408.0</td>\n",
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       "  </tbody>\n",
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       "<p>8 rows × 92 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Activity     C06  D43  DP1  DP2  DP3  DP5  DP7  F30  F31  F32  ...  UB1  UE5  \\\n",
       "Fiscal Year                                                    ...             \n",
       "2016         NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  NaN  NaN   \n",
       "2017         NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  NaN  NaN   \n",
       "2018         NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  NaN  NaN   \n",
       "2019         NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  NaN  NaN   \n",
       "2020         NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  NaN  NaN   \n",
       "2021         NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  NaN  NaN   \n",
       "2022         NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  NaN  NaN   \n",
       "2023         NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  NaN  NaN   \n",
       "\n",
       "Activity     UF1  UG1       UG3  UH2  UH3  UH4         UL1        UM1  \n",
       "Fiscal Year                                                            \n",
       "2016         NaN  NaN       NaN  NaN  NaN  NaN  23097598.0        NaN  \n",
       "2017         NaN  NaN       NaN  NaN  NaN  NaN  21299562.0        NaN  \n",
       "2018         NaN  NaN       NaN  NaN  NaN  NaN  27443730.0        NaN  \n",
       "2019         NaN  NaN       NaN  NaN  NaN  NaN  25141320.0        NaN  \n",
       "2020         NaN  NaN       NaN  NaN  NaN  NaN  34614228.0        NaN  \n",
       "2021         NaN  NaN       NaN  NaN  NaN  NaN  43817455.0        NaN  \n",
       "2022         NaN  NaN  693765.0  NaN  NaN  NaN  71247391.0        NaN  \n",
       "2023         NaN  NaN  688274.0  NaN  NaN  NaN  65328777.0  4023408.0  \n",
       "\n",
       "[8 rows x 92 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#mean of mhealth funding by IC/Year and grant Mechanism through pivot table\n",
    "mechYrByIC=pd.pivot_table(dfi,index=[\"Administering IC\", \"Fiscal Year\"],columns=[\"Activity\"],values=\"Total Cost\",aggfunc=np.sum)\n",
    "mechYrByIC.to_csv(outpath + 'mHealthFundingByMechYearIC.csv')\n",
    "\n",
    "mechYrByIC.xs('NCATS') #.index['Admin IC']\n",
    "#mechYrByIC.iloc[df.columns.get_level_values('Admin IC') == 'TR']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "fb0e856c-8c80-48eb-9b58-bab29357dbc1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Admin IC Long Admin IC Short Admin IC Activity  Fiscal Year        DHT$\n",
      "0            FIC             TW      FIC      D43         2015    491290.0\n",
      "0            FIC             TW      FIC      R21         2015   1140149.0\n",
      "0            FIC             TW      FIC      R25         2015    751742.0\n",
      "0            FIC             TW      FIC      U01         2015    759774.0\n",
      "0            FIC             TW      FIC      D43         2016    228824.0\n",
      "..           ...            ...      ...      ...          ...         ...\n",
      "24            OD             OD       OD      OT2         2023  45563172.0\n",
      "24            OD             OD       OD      R43         2023    395558.0\n",
      "24            OD             OD       OD      U24         2023   3499985.0\n",
      "24            OD             OD       OD      UG1         2023   1991327.0\n",
      "24            OD             OD       OD      UG3         2023  12176871.0\n",
      "\n",
      "[2579 rows x 6 columns]\n"
     ]
    }
   ],
   "source": [
    "#we will need IC acronym on mechYrByIC:\n",
    "tmp=mechYrByIC.stack(0).reset_index(level=1) \n",
    "tmp.rename(columns={0:'DHT$'}, inplace=True)\n",
    "tmp=tmp.reset_index(level=1) \n",
    "IC['Admin IC'] = IC['Admin IC Long']\n",
    "tmp=IC.join(tmp, on='Admin IC')\n",
    "print(tmp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "266693fa-8a45-4b10-b1fe-fb7ce84e5732",
   "metadata": {},
   "outputs": [],
   "source": [
    "#join mechYrByIC with budget\n",
    "tmp.rename(columns={'Admin IC Long':'Acronym'}, inplace=True)\n",
    "tmp.set_index(['Acronym','Fiscal Year'], inplace=True) #bring back year column into index for join\n",
    "df=tmp.join(budgetsTmp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "08894092-7769-472d-bbc0-84570d1a4190",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                    Admin IC Short Admin IC Activity        DHT$      Budget  \\\n",
      "Acronym Fiscal Year                                                            \n",
      "FIC     2015                    TW      FIC      D43    491290.0    77474918   \n",
      "        2015                    TW      FIC      R21   1140149.0    77474918   \n",
      "        2015                    TW      FIC      R25    751742.0    77474918   \n",
      "        2015                    TW      FIC      U01    759774.0    77474918   \n",
      "        2016                    TW      FIC      D43    228824.0    68595980   \n",
      "...                            ...      ...      ...         ...         ...   \n",
      "OD      2023                    OD       OD      OT2  45563172.0  1389381330   \n",
      "        2023                    OD       OD      R43    395558.0  1389381330   \n",
      "        2023                    OD       OD      U24   3499985.0  1389381330   \n",
      "        2023                    OD       OD      UG1   1991327.0  1389381330   \n",
      "        2023                    OD       OD      UG3  12176871.0  1389381330   \n",
      "\n",
      "                     percentBudg  \n",
      "Acronym Fiscal Year               \n",
      "FIC     2015            0.634128  \n",
      "        2015            1.471636  \n",
      "        2015            0.970304  \n",
      "        2015            0.980671  \n",
      "        2016            0.333582  \n",
      "...                          ...  \n",
      "OD      2023            3.279386  \n",
      "        2023            0.028470  \n",
      "        2023            0.251910  \n",
      "        2023            0.143325  \n",
      "        2023            0.876424  \n",
      "\n",
      "[2579 rows x 6 columns]\n"
     ]
    }
   ],
   "source": [
    "#convert percentage\n",
    "df['percentBudg']=(df['DHT$']/df.Budget)*100\n",
    "print(df)\n",
    "#output spreadsheet\n",
    "df.to_csv(outpath + 'mHealthFundingByIC_percentOverall_totalGrants.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87b9b4de-701c-4410-83d3-452c1918cf3c",
   "metadata": {},
   "source": [
    "## Supplemental Table 5 (needed for Supp. Fig. 1)  : Listing of grant codes to group into grant categories:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b5f2ce62-47f2-4b4d-9e47-18224bfc5ff4",
   "metadata": {},
   "outputs": [],
   "source": [
    "codes=[]\n",
    "label = ['Research Project Grants']\n",
    "codes.append(['UH4','UE5','RM1','DP1','U2R', 'DP2', 'DP3', 'DP5', 'P01', 'R00', 'R01', 'R03', 'R15', 'R18', 'R21', 'R24', 'R25', 'R33', 'R34', 'R35', 'R36', 'R37', 'R56', 'R61', 'RC2', 'RF1', 'U2C', 'UF1', 'UG1', 'UG3', 'UH2', 'UH3', 'UM1', 'U01', 'U10', 'U18', 'U19', 'U24', 'U34', 'U45'])\n",
    "\n",
    "label.append('Research Center Grants')\n",
    "codes.append(['U41','R16','P42','P20', 'P2C', 'P30', 'P41', 'P50', 'U54', 'UL1'])\n",
    "\n",
    "label.append('Training and Career Development')\n",
    "#codes.append(['KL2', 'TL1', 'K00', 'T15' 'D43', 'DP2', 'DP3', 'DP5', 'F30', 'F31', 'F32', 'F99','K01', 'K02', 'K07', 'K08', 'K12', 'K18', 'K22', 'K23', 'K24', 'K25', 'K43', 'K76', 'K99', 'T32', 'T35'])\n",
    "codes.append(['T15', 'K26','KL2', 'TL1', 'K00','D43', 'F30', 'F31', 'F32', 'F99', 'K01', 'K02', 'K07', 'K08', 'K12', 'K18', 'K22', 'K23', 'K24', 'K25', 'K43', 'K76', 'K99', 'T32', 'T35'])\n",
    "\n",
    "label.append('SBIR/STTR')\n",
    "codes.append(['R43', 'R44', 'SB1', 'U43', 'U44', 'R41', 'R42'])\n",
    "\n",
    "label.append('Other')\n",
    "codes.append(['C06','G08', 'G12', 'G13', 'G20', 'S10', 'R13', 'OT2'])\n",
    "\n",
    "df['codetype'] = 'Other'\n",
    "df.loc[df['Activity'].str.contains('|'.join(codes[0])), 'codetype'] = label[0] #Conditional that returns a boolean Series with column labels specified\n",
    "df.loc[df['Activity'].str.contains('|'.join(codes[1])), 'codetype'] = label[1] #Conditional that returns a boolean Series with column labels specified\n",
    "df.loc[df['Activity'].str.contains('|'.join(codes[2])), 'codetype'] = label[2] #Conditional that returns a boolean Series with column labels specified\n",
    "df.loc[df['Activity'].str.contains('|'.join(codes[3])), 'codetype'] = label[3] #Conditional that returns a boolean Series with column labels specified"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5d8569bd-0692-4f93-9fde-605506cbaea0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['G11', 'R13', 'OT2', 'U13', 'FI2', 'G12', 'P60', 'UB1', 'G08',\n",
       "       'G13', 'G20', 'DP7', 'C06'], dtype=object)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.codetype=='Other']['Activity'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a15a7623-1ffc-488f-b306-86509a541d0e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\radmantc\\AppData\\Local\\Temp\\ipykernel_16992\\1117385150.py:2: FutureWarning: The provided callable <function sum at 0x0000019E683FD440> is currently using DataFrameGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"sum\" instead.\n",
      "  mHealthFundsMech=pd.pivot_table(df,index=[\"Admin IC\"],columns=[\"codetype\"],values=\"DHT$\",aggfunc=np.sum)\n"
     ]
    }
   ],
   "source": [
    "#b. mean of mhealth funding %budget by IC/Year/Code Type through pivot table\n",
    "mHealthFundsMech=pd.pivot_table(df,index=[\"Admin IC\"],columns=[\"codetype\"],values=\"DHT$\",aggfunc=np.sum)\n",
    "mHealthFundsMech.to_csv(outpath +'mHealthFundingByCodeType_Avg_totalGrants.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "defd839c-63fa-4e57-b2ae-e329c58a1732",
   "metadata": {},
   "source": [
    "## Now we are ready to plot Supplemental Figure 1a and b:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f6021925-3114-46fd-89b3-ac5511173a7a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "processing:  84327307.0 Other\n",
      "0 1.0%\n",
      "processing:  979026311.0 Research Center Grants\n",
      "1 11.1%\n",
      "processing:  6750719398.0 Research Project Grants\n",
      "processing special $B case:  6750719398.0 Research Project Grants\n",
      "2 76.7%\n",
      "processing:  632229712.0 SBIR/STTR\n",
      "3 7.2%\n",
      "processing:  358990985.0 Training and Career Development\n",
      "4 4.1%\n",
      "0 Text(-0.01804928315575562, 0.5997284580354374, '        $84M : 1.0%') 37.0\n",
      "1 Text(-0.23887655868925856, 0.5503980284383087, '$979M : 11.1%         ') 37.0\n",
      "2 Text(0.015439800747623352, -0.5998013108962614, '        $6.75B : 76.7%') 37.0\n",
      "3 Text(0.27799033285697156, 0.5317155017846199, '        $632M : 7.2%') 37.0\n",
      "4 Text(0.07663960232734884, 0.595085179915536, '        $359M : 4.1%') 37.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\radmantc\\AppData\\Local\\Temp\\ipykernel_16992\\2939499351.py:3: FutureWarning: The provided callable <function sum at 0x0000019E683FD440> is currently using DataFrameGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"sum\" instead.\n",
      "  mHealthFundsCode=pd.pivot_table(df,index=[\"Admin IC\"],columns=[\"codetype\"],values=\"DHT$\",aggfunc=np.sum).sum()\n",
      "C:\\Users\\radmantc\\AppData\\Local\\Temp\\ipykernel_16992\\2939499351.py:22: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  print('processing: ', mHealthFundsCode[i], pie_labels[i])\n",
      "C:\\Users\\radmantc\\AppData\\Local\\Temp\\ipykernel_16992\\2939499351.py:27: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  if mHealthFundsCode[i] > 1E+09:\n",
      "C:\\Users\\radmantc\\AppData\\Local\\Temp\\ipykernel_16992\\2939499351.py:31: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  csum= '$' + str(round(mHealthFundsCode[i].round()/1E+08*100))+ 'M' #XXE8, E7 etc\n",
      "C:\\Users\\radmantc\\AppData\\Local\\Temp\\ipykernel_16992\\2939499351.py:28: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  print('processing special $B case: ', mHealthFundsCode[i], pie_labels[i])\n",
      "C:\\Users\\radmantc\\AppData\\Local\\Temp\\ipykernel_16992\\2939499351.py:29: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  csum= '$' + str(round(mHealthFundsCode[i]/1E+09, 2))+ 'B' #XXE8, E7 etc\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Supp Fig 1a\n",
    "#pie chart for sum of funding by category\n",
    "mHealthFundsCode=pd.pivot_table(df,index=[\"Admin IC\"],columns=[\"codetype\"],values=\"DHT$\",aggfunc=np.sum).sum()\n",
    "#mHealthFundsCode.plot.pie(ylabel='',title='Sum of DHT Funding',autopct='%.1f%%')\n",
    "\n",
    "###side by side pie chart\n",
    "\n",
    "#fix labels per 4/25/24 request:\n",
    "pie_labels = list(mHealthFundsCode.index)\n",
    "#pie_labels[ pie_labels.index('Research Projects') ] = 'Research Projects/\\nResearch Center'\n",
    "\n",
    "fig, axs = plt.subplots(1, 1, figsize=(10, 8))\n",
    "patches, texts, pcts = axs.pie(\n",
    "    mHealthFundsCode, labels=pie_labels, autopct='%.1f%%',\n",
    "    #wedgeprops={'linewidth': 3.0, 'edgecolor': 'white'},\n",
    "    textprops={'size': 'x-large'},\n",
    "    startangle=90)\n",
    "# For each wedge, set the corresponding text label color to the wedge's\n",
    "# face color.\n",
    "from decimal import Decimal\n",
    "for i, patch in enumerate(patches):\n",
    "    print('processing: ', mHealthFundsCode[i], pie_labels[i])\n",
    "\n",
    "    texts[i].set_color(patch.get_facecolor())\n",
    "    #csum='$%.2E' % Decimal(mHealthFundsCode[i].round() ) #XXE8, E7 etc\n",
    "    #csum= '$' + str(round(mHealthFundsCode[i]))\n",
    "    if mHealthFundsCode[i] > 1E+09:\n",
    "        print('processing special $B case: ', mHealthFundsCode[i], pie_labels[i])\n",
    "        csum= '$' + str(round(mHealthFundsCode[i]/1E+09, 2))+ 'B' #XXE8, E7 etc\n",
    "    else:\n",
    "        csum= '$' + str(round(mHealthFundsCode[i].round()/1E+08*100))+ 'M' #XXE8, E7 etc\n",
    "    print(i, pcts[i].get_text())\n",
    "    cpct=pcts[i].get_text()\n",
    "    if i==1:\n",
    "        pcts[i].set_text('' + csum  + ' : ' + cpct + '         ')\n",
    "    else:\n",
    "        pcts[i].set_text('        ' + csum  + ' : ' + cpct)\n",
    "#plt.setp(pcts, color='white',rotation='vertical')\n",
    "plt.setp(pcts, color='white',rotation=37) #'37')\n",
    "for ipct, tx in enumerate(pcts):\n",
    "    rot = tx.get_rotation()\n",
    "    print(ipct, tx, rot)\n",
    "    if ipct==0:\n",
    "        tx.set_rotation(90)\n",
    "    if ipct==1:\n",
    "        tx.set_rotation(120)\n",
    "    if ipct==2:\n",
    "        tx.set_rotation(0)\n",
    "    if ipct==3:\n",
    "        tx.set_rotation(47)\n",
    "    if ipct==4:\n",
    "        tx.set_rotation(85)\n",
    "plt.setp(texts, fontweight=600)\n",
    "axs.set_title('Sum of DHT Funding', fontsize=18)\n",
    "#plt.tight_layout()\n",
    "fig.figure.savefig(outpath + 'SuppFig1a_mHealthFunds_grantCts_pie1_funding_totalGrants.tiff', dpi=600) #\n",
    "\n",
    "plt.show()\n",
    "\n",
    "#plt.setp(pcts) #this lists all settable properties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "2fcc52c6-43a0-4cc0-8a6e-fb999ebe2de3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\radmantc\\AppData\\Local\\Temp\\ipykernel_16992\\2021852697.py:5: FutureWarning: The provided callable <function sum at 0x0000019E683FD440> is currently using DataFrameGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"sum\" instead.\n",
      "  mHealthFundsRPG=pd.pivot_table(df2,columns=[\"Activity\"],values=\"DHT$\",aggfunc=np.sum).sum()\n",
      "C:\\Users\\radmantc\\AppData\\Local\\Temp\\ipykernel_16992\\2021852697.py:14: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax[0].set_xticklabels(labels)\n",
      "C:\\Users\\radmantc\\AppData\\Local\\Temp\\ipykernel_16992\\2021852697.py:19: FutureWarning: The provided callable <function sum at 0x0000019E683FD440> is currently using DataFrameGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"sum\" instead.\n",
      "  mHealthFundsRPG=pd.pivot_table(df2,columns=[\"Activity\"],values=\"DHT$\",aggfunc=np.sum).sum()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['0.0', '0.5', '1.0', '1.5', '2.0', '2.5', '3.0', '3.5']\n",
      "['$0.0B', '$0.5B', '$1.0B', '$1.5B', '$2.0B', '$2.5B', '$3.0B', '$3.5B']\n",
      "['0.0', '0.5', '1.0', '1.5', '2.0', '2.5', '3.0', '3.5']\n",
      "['$0M', '$50M', '$100M', '$150M', '$200M', '$250M', '$300M', '$350M']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\radmantc\\AppData\\Local\\Temp\\ipykernel_16992\\2021852697.py:30: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax[1].set_xticklabels(labels)\n"
     ]
    },
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",
      "text/plain": [
       "<Figure size 2000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Supplemental Figure 1b\n",
    "#bar plot with IC x Infra/RPG grouping\n",
    "fig, ax = plt.subplots(1,2,figsize=(20, 8))\n",
    "df2=df[ df[\"codetype\"]=='Research Project Grants' ]\n",
    "mHealthFundsRPG=pd.pivot_table(df2,columns=[\"Activity\"],values=\"DHT$\",aggfunc=np.sum).sum()\n",
    "plt.subplot(1, 2, 1)\n",
    "mHealthFundsRPG.sort_values().plot.barh(xlabel='Sum of DHT Funding ($): Research Project Grants')#,autopct='%.1f%%',)\n",
    "#change xtick labels to remove 1e8 notation:\n",
    "plt.draw()\n",
    "labels = [item.get_text() for item in ax[0].get_xticklabels()]\n",
    "print(labels)\n",
    "labels = ['$' + str((float(item)*1)) + 'B' for item in labels[:]] #1E8 = 1 * 10^8 = 100,000,000 \n",
    "print(labels)\n",
    "ax[0].set_xticklabels(labels)\n",
    "#mHealthFundsRPG.sort_values().to_csv(outpath + '\\\\Outputs\\\\perActCode\\\\' + 'mHealthFundsbyActCode_RPG.csv')\n",
    "\n",
    "\n",
    "df2=df[ df[\"codetype\"]=='Research Center Grants' ]\n",
    "mHealthFundsRPG=pd.pivot_table(df2,columns=[\"Activity\"],values=\"DHT$\",aggfunc=np.sum).sum()\n",
    "plt.subplot(1, 2, 2)\n",
    "mHealthFundsRPG.sort_values().plot.barh(xlabel='Sum of DHT Funding ($): Research Center Grants')\n",
    "#change xtick labels to remove 1e8 notation:\n",
    "plt.draw()\n",
    "labels = [item.get_text() for item in ax[1].get_xticklabels()]\n",
    "print(labels)\n",
    "labels = ['$' + str(round(float(item)*100)) + 'M' for item in labels[:]] #1E7 = 1 * 10^7 = 10,000,000\n",
    "#labels = ['$' + str((float(item)*1)) + 'B' for item in labels[:]] #1E7 = 1 * 10^7 = 10,000,000\n",
    "\n",
    "print(labels)\n",
    "ax[1].set_xticklabels(labels)\n",
    "mHealthFundsRPG.sort_values().to_csv(outpath + 'SuppFig1b_mHealthFundsbyActCode_ResCenters.csv')\n",
    "\n",
    "\n",
    "fig.figure.savefig(outpath + 'SuppFig1b_mHealthFundsbyActCode_ProjAndCenter_totalGrants.tiff', dpi=600) #\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d506a79b-0f16-4bf7-b9f7-b1003eda4a9f",
   "metadata": {},
   "source": [
    "# Supplemental Figure 2. DHT according to DHT Category (pie chart)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aee67609-b9e8-475a-85ec-b5e5156b298f",
   "metadata": {},
   "source": [
    "## Supp. Fig. 2, Step a) Deduplicate Grants:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d804f29a-1839-4671-b056-afc969f16e49",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "\n",
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       "        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>Project Number</th>\n",
       "      <th>Fiscal Year</th>\n",
       "      <th>Application ID</th>\n",
       "      <th>Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>354</th>\n",
       "      <td>1C06OD036020-01</td>\n",
       "      <td>2023</td>\n",
       "      <td>10795328</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1396</th>\n",
       "      <td>1D43TW012481-01</td>\n",
       "      <td>2023</td>\n",
       "      <td>10676557</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1397</th>\n",
       "      <td>1D43TW012481-01</td>\n",
       "      <td>2023</td>\n",
       "      <td>10676557</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3607</th>\n",
       "      <td>1DP1AG082343-01</td>\n",
       "      <td>2022</td>\n",
       "      <td>10487819</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5949</th>\n",
       "      <td>1DP2AI164315-01</td>\n",
       "      <td>2021</td>\n",
       "      <td>10295403</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13305</th>\n",
       "      <td>5UM1CA182913-04</td>\n",
       "      <td>2017</td>\n",
       "      <td>9338194</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11890</th>\n",
       "      <td>5UM1CA182913-05</td>\n",
       "      <td>2018</td>\n",
       "      <td>9552089</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9372</th>\n",
       "      <td>5UM1DA049406-02</td>\n",
       "      <td>2020</td>\n",
       "      <td>9917748</td>\n",
       "      <td>5</td>\n",
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       "      <th>7548</th>\n",
       "      <td>5UM1DA049406-03</td>\n",
       "      <td>2021</td>\n",
       "      <td>10142414</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>5067</th>\n",
       "      <td>5UM1DA049406-04</td>\n",
       "      <td>2022</td>\n",
       "      <td>10388180</td>\n",
       "      <td>5</td>\n",
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       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "        Project Number  Fiscal Year  Application ID  Type\n",
       "354    1C06OD036020-01         2023        10795328     1\n",
       "1396   1D43TW012481-01         2023        10676557     1\n",
       "1397   1D43TW012481-01         2023        10676557     1\n",
       "3607   1DP1AG082343-01         2022        10487819     1\n",
       "5949   1DP2AI164315-01         2021        10295403     1\n",
       "...                ...          ...             ...   ...\n",
       "13305  5UM1CA182913-04         2017         9338194     5\n",
       "11890  5UM1CA182913-05         2018         9552089     5\n",
       "9372   5UM1DA049406-02         2020         9917748     5\n",
       "7548   5UM1DA049406-03         2021        10142414     5\n",
       "5067   5UM1DA049406-04         2022        10388180     5\n",
       "\n",
       "[13171 rows x 4 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#sort by grant number, fy, appl id and type\n",
    "dfi_st=dfi.loc[dfi['Type'].isin([1, 2, 5])]\n",
    "#df.loc[df['col1'].isin([value1, value2, value3, ...])]\n",
    "dfi_st=dfi_st.sort_values(['Project Number', 'Fiscal Year', 'Application ID', 'Type'])\n",
    "\n",
    "dfi_st[['Project Number', 'Fiscal Year', 'Application ID', 'Type']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b2cf074a-6fb2-469d-b759-9e36df57821c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "354      1C06OD036020\n",
      "1396     1D43TW012481\n",
      "1397     1D43TW012481\n",
      "3607     1DP1AG082343\n",
      "5949     1DP2AI164315\n",
      "             ...     \n",
      "13305    5UM1CA182913\n",
      "11890    5UM1CA182913\n",
      "9372     5UM1DA049406\n",
      "7548     5UM1DA049406\n",
      "5067     5UM1DA049406\n",
      "Name: Grant Number, Length: 13171, dtype: object\n"
     ]
    }
   ],
   "source": [
    "dfi_st['Grant Number']=[proj.split('-')[0] for proj in dfi_st['Project Number']]\n",
    "print(dfi_st['Grant Number'])\n",
    "grant_nums=dfi_st['Grant Number'].unique()\n",
    "dfi_dedup = pd.DataFrame(index=range(len(grant_nums)),columns=dfi_st.columns) \n",
    "\n",
    "#ignore warnings:\n",
    "from warnings import simplefilter\n",
    "simplefilter(action=\"ignore\", category=pd.errors.PerformanceWarning)\n",
    "\n",
    "i=0\n",
    "for grant in grant_nums:\n",
    "    cgrants=dfi_st[dfi_st['Grant Number']==grant]\n",
    "    dfi_dedup.iloc[i] = cgrants.iloc[0]\n",
    "    i+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1beecbe4-9aaa-4982-bd6d-5df997fe9a51",
   "metadata": {},
   "outputs": [],
   "source": [
    "#the following block is needed only if you plan on assigning manual verification to co-authors:\n",
    "#add column of random numbers and assign curators\n",
    "import random\n",
    "dfi_dedup['rand_ix'] = random.sample(range(len(dfi_dedup)), len(dfi_dedup))\n",
    "dfi_dedup=dfi_dedup.set_index('rand_ix')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a8a4aa0c-7bec-4f76-b993-a246ee725d70",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfi_dedup.to_csv(outpath + 'dedup_' + inputname + '.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e53b4860-1647-4377-8ce0-fbe43a141b80",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfi_dedup[['Grant Number', 'Fiscal Year', 'Application ID', 'Type']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc37aeb2-90be-4f1f-864c-97d969125f98",
   "metadata": {},
   "source": [
    "## Supplemental Table 2 (needed for Supp. Fig. 2): \r",
    "### \tSearch Terms per Category Name\r\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e15cd7e8-79c4-4280-864d-1587fd717c21",
   "metadata": {},
   "outputs": [],
   "source": [
    "#append column to db that indicates count of terms (putting some into same category)\n",
    "terms=[\n",
    "    [\"decentralized trial\", \"remote trial\", \"virtual trial\"],\n",
    "    ['digital health' , 'digital health technology' , 'digital health technologies'],\n",
    "    ['mobile app' , 'mobile application' , 'mobile health' , 'mobile health technology', 'mobile health technologies' , 'mhealth'],\n",
    "    ['smart connected health' , 'remote patient monitoring' , 'connected home health'],\n",
    "    ['wearable device' , 'wearable'],\n",
    "    ['remote healthcare delivery' , 'virtual care' , 'digital patient engagement' , 'telehealth' , 'telemedicine']\n",
    "]\n",
    "categories=['Trials','Digital Health','Mobile', 'Connected', 'Wearables' , 'Telehealth']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e177555b-1b93-47e2-958c-db253e096c29",
   "metadata": {},
   "outputs": [],
   "source": [
    "#counts of categories\n",
    "ngrants=dfi_dedup.shape[0]\n",
    "for ix,ccat in enumerate(categories):\n",
    "    catcounts=[0]*ngrants #placeholder for counts per category\n",
    "    for term in terms[ix]:\n",
    "        catcounts=catcounts+dfi_dedup['Project Title'].fillna('').apply(str.lower).str.count(term)+dfi_dedup['Project Abstract'].fillna('').apply(str.lower).str.count(term)\n",
    "    dfi_dedup['cts_' + ccat]=catcounts \n",
    "    \n",
    "dfi_dedup.to_csv(outpath + 'iSearchTermCounts.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ffa70746-8055-4bbc-a8e9-61011d243669",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfi_mask=dfi_dedup.iloc[: , -6:]>0\n",
    "dfi_mask.columns=dfi_mask.columns.str.replace('cts_','')\n",
    "dfi_mask=dfi_mask.add_prefix('mask_')\n",
    "dfi_mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7f913997-7582-4d62-a33c-a02263b9aab2",
   "metadata": {},
   "outputs": [],
   "source": [
    "grantCts=dfi_mask.sum()\n",
    "grantPct=round((grantCts/ngrants)*100,1)\n",
    "grantPct.index=['Trials', 'Digital Health     ','Mobile','Connected','Wearables','Telehealth']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1fe0039-b20e-458d-a8c6-bba9514c279a",
   "metadata": {},
   "source": [
    "## Plot Supplemental Figure 2:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a07b432d-a1c2-48ab-ac5f-acf91f4a4f88",
   "metadata": {},
   "outputs": [],
   "source": [
    "#pie chart of grant categories (from data derived at top of notebook)\n",
    "fig, axs = plt.subplots(1, 1, figsize=(10, 8))\n",
    "patches, texts, pcts = axs.pie(\n",
    "    grantPct, labels=grantPct.index, autopct='%.1f%%',\n",
    "    #wedgeprops={'linewidth': 3.0, 'edgecolor': 'white'},\n",
    "    textprops={'size': 'x-large'},\n",
    "    startangle=90)\n",
    "# For each wedge, set the corresponding text label color to the wedge's\n",
    "# face color.\n",
    "from decimal import Decimal\n",
    "for i, patch in enumerate(patches):\n",
    "    texts[i].set_color(patch.get_facecolor())\n",
    "    csum=str(grantCts[i])\n",
    "    cpct=pcts[i].get_text()\n",
    "    pcts[i].set_text('        ' + csum  + ' : ' + cpct)\n",
    "    print(pcts[i].get_text())\n",
    "plt.setp(pcts, color='white',rotation='vertical')\n",
    "plt.setp(texts, fontweight=600)\n",
    "axs.set_title('Grant Counts', fontsize=18)\n",
    "#plt.tight_layout()\n",
    "\n",
    "fig.figure.savefig(outpath + 'SuppFig2_mHealthFunds_grantCts_pie2_cats.tiff', dpi=600) #"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7864f727-4cfa-4c62-907a-8e69396a2cd7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "6f687407-8297-4075-9095-71bcc4b6cbc3",
   "metadata": {},
   "source": [
    "# Supplemental Figure 4.  Most frequent words by DHT Area of Interest in the NIH DHT portfolio from 2015 – 2023"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf7ca286-844d-48e4-a327-a9e9b3a3f137",
   "metadata": {},
   "source": [
    "## Supp. Fig. 4, step a) read in and process dataframe of manual curations downloaded from iSearch curations system and individual curations combined into final votes (aka \"derived\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88173562-aeef-44dd-8bcb-719c0ec7849d",
   "metadata": {},
   "source": [
    "### Curation was not conducted for NIH Reporter data.  Please refer to the iSearch script in Github."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "54ecf125-a692-4779-92a1-d9f4cc7da6c5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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