Data_Preprocessing_sparse.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "G-bec3cCXsVx"
   },
   "source": [
    "# Data Exploration and Preprocessing\n",
    "TCGA Reannotated Ovarian Cancer Clinical Data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "mTx2g1keOEjc"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.font_manager\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "from openpyxl import load_workbook\n",
    "import sidetable\n",
    "from functools import reduce\n",
    "import itertools\n",
    "import math\n",
    "import random\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torch.nn.functional as F\n",
    "from collections import namedtuple\n",
    "from sklearn.linear_model import LinearRegression, LogisticRegression\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import time\n",
    "import statsmodels.api as sm\n",
    "from scipy import stats\n",
    "from lifelines import CoxPHFitter\n",
    "\n",
    "device = torch.device('cpu')\n",
    "\n",
    "# Randomness\n",
    "random.seed(2021)\n",
    "np.random.seed(2021)\n",
    "torch.manual_seed(2021)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "68TNefuaXmnW"
   },
   "source": [
    "Import Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "kEYLFUOhRVru"
   },
   "outputs": [],
   "source": [
    "# Villalobos 2018 reannotated TCGA data (https://ascopubs.org/doi/suppl/10.1200/CCI.17.00096)\n",
    "tcga_ov_1 = pd.read_excel('./Data/Villalobos_TCGA/ds_CCI.17.00096-1.xlsx',\n",
    "                          sheet_name='Master clinical dataset'\n",
    "                          )\n",
    "\n",
    "tcga_ov_2 = pd.read_excel('./Data/Villalobos_TCGA/ds_CCI.17.00096-2.xlsx',\n",
    "                          sheet_name='Months'\n",
    "                          )\n",
    "\n",
    "tcga_ov_3 = pd.read_excel('./Data/Villalobos_TCGA/ds_CCI.17.00096-3.xlsx',\n",
    "                          sheet_name='clinical_drug_all_OV.txt'\n",
    "                          )\n",
    "\n",
    "# TCGA Drug standardization (https://gdisc.bme.gatech.edu/cgi-bin/gdisc/tap5.cgi#)\n",
    "drugs = pd.read_csv('./Data/DrugCorrection1.csv')\n",
    "drugs['Correction'] = drugs['Correction'].str.strip()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "M2uBW25GhHv_"
   },
   "source": [
    "## Clean and Reorganize"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TgbCL7brzOid"
   },
   "source": [
    "TCGA 3: Clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "C8ZXyxh_Yo7J",
    "outputId": "2760169e-47f0-4789-c306-c3402cde0ba9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2463, 19)\n",
      "bcr_patient_barcode                 0\n",
      "bcr_drug_barcode                    0\n",
      "days_to_drug_therapy_end          339\n",
      "days_to_drug_therapy_start        145\n",
      "days_to_drug_treatment_end       2463\n",
      "days_to_drug_treatment_start     2463\n",
      "dosage_units                     2463\n",
      "drug_category                       0\n",
      "drug_dosage                      2463\n",
      "drug_name                          11\n",
      "initial_course                   2463\n",
      "number_cycles                     391\n",
      "regimen_indication                  2\n",
      "regimen_indication_notes         2328\n",
      "route_of_administration           243\n",
      "route_of_administration_notes    2126\n",
      "therapy_ongoing                   167\n",
      "total_dose                        746\n",
      "total_dose_units                  715\n",
      "dtype: int64 \n",
      "\n",
      "Replacement Rules:\n",
      "            drug_name_old            drug_name_new\n",
      "0         5F4 Leucovorin  Fluorouracil+Leucovorin\n",
      "1            90Y-HU3S193                  Hu3S193\n",
      "2                AMG 706                Motesanib\n",
      "3  Abagovomab or Placebo               Abagovomab\n",
      "4  Abagovomag vs Placebo               Abagovomab \n",
      "\n",
      "Unique Drugs: 56 \n",
      " ['Abagovomab', 'Aldesleukin', 'Altretamine', 'Amifostine', 'Aminocamptothecin', 'Anastrozole', 'Bevacizumab', 'CBP501', 'CEP -11981', 'Capecitabine', 'Carboplatin', 'Catumaxomab', 'Cediranib', 'Cetuximab', 'Chlorambucil', 'Cisplatin', 'Cyclophosphamide', 'Dexamethasone', 'Docetaxel', 'Dortezomib', 'Doxorubicin', 'Etoposide', 'Fluorouracil+Leucovorin', 'Gefitinib', 'Gemcitabine', 'Hu3S193', 'Ifosfamide', 'Ilex', 'Irinotecan', 'Irofulven', 'Letrozole', 'Leuprolide', 'Lily', 'Megestrol', 'Mitoxantrone', 'Motesanib', 'NOS', 'Oregovomab', 'Oxaliplatin', 'Paclitaxel', 'Paclitaxel+Carboplatin', 'Patupilone', 'Pemetrexed', 'SCH 58500', 'Sargramostim', 'Sorafenib', 'Tamoxifen', 'Topotecan', 'Topotecan+Carboplatin', 'Trabectedin', 'Trastuzumab', 'Vamydex', 'Vincristine', 'Vinorelbine', 'Vosaroxin', 'rBBX-01'] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Drop columns with all missing values\n",
    "print(tcga_ov_3.shape)\n",
    "print(tcga_ov_3.isnull().sum(), '\\n')\n",
    "tcga_ov_3_drop = tcga_ov_3.dropna(axis=1, how='all')\n",
    "# Drop rows where drug is missing\n",
    "tcga_ov_3_drop = tcga_ov_3_drop[tcga_ov_3_drop['drug_name'].notna()]\n",
    "# Drop rows where start AND end are missing (right now it's actually OR, see below)\n",
    "tcga_ov_3_drop = tcga_ov_3_drop.dropna(how='any', subset=['days_to_drug_therapy_end', 'days_to_drug_therapy_start']) #change to how='all' when figure out what to do with start OR end missing\n",
    "\n",
    "# Standardize drug names\n",
    "    # Merge using values from TCGA drug standardization (https://gdisc.bme.gatech.edu/cgi-bin/gdisc/tap5.cgi)\n",
    "tcga_ov_3_clean = tcga_ov_3_drop.merge(drugs, how='left', left_on='drug_name', right_on='OldName').drop(['OldName'], axis=1)\n",
    "    # Additional replacement rules\n",
    "tcga_ov_3_clean.loc[tcga_ov_3_clean['drug_name'] == 'Doxoribicin', 'Correction'] = 'Doxorubicin'\n",
    "tcga_ov_3_clean.loc[tcga_ov_3_clean['drug_name'] == 'gemcitabin', 'Correction'] = 'Gemcitabine'\n",
    "tcga_ov_3_clean.loc[tcga_ov_3_clean['drug_name'] == 'Hexlalen', 'Correction'] = 'Altretamine'\n",
    "tcga_ov_3_clean.loc[tcga_ov_3_clean['drug_name'] == 'Cisplatin/Gemzar', 'Correction'] = 'Cisplatin' # This applies to only one line, which has another sample just for Gemzar (Gemcitabine)\n",
    "tcga_ov_3_clean.loc[tcga_ov_3_clean['drug_name'] == 'Ilex', 'Correction'] = 'Ilex'\n",
    "tcga_ov_3_clean.loc[tcga_ov_3_clean['drug_name'] == 'ILIZ', 'Correction'] = 'ILIZ'\n",
    "tcga_ov_3_clean.loc[tcga_ov_3_clean['drug_name'] == 'Lily', 'Correction'] = 'Lily'\n",
    "\n",
    "    # Print replacement rules and replace 'drug_name'\n",
    "drug_name_old = tcga_ov_3_clean['drug_name']\n",
    "drug_name_new = tcga_ov_3_clean['Correction']\n",
    "rules = pd.DataFrame({'drug_name_old': drug_name_old, 'drug_name_new': drug_name_new}).drop_duplicates().sort_values(by=['drug_name_old']).reset_index().drop('index', axis=1)\n",
    "\n",
    "pd.set_option('display.max_rows', None)\n",
    "print('Replacement Rules:\\n', rules.head(), '\\n')\n",
    "pd.reset_option('max_rows')\n",
    "\n",
    "tcga_ov_3_clean['drug_name'] = tcga_ov_3_clean['Correction']\n",
    "tcga_ov_3_clean = tcga_ov_3_clean.drop('Correction', axis=1)\n",
    "\n",
    "    # List of drugs in dataset\n",
    "drug_list = [x for x in list(tcga_ov_3_clean['drug_name'].drop_duplicates()) if str(x) != 'nan']\n",
    "drug_list.sort()\n",
    "print('Unique Drugs:', len(drug_list), '\\n', drug_list, '\\n')\n",
    "\n",
    "# Drop where therapy start = therapy end\n",
    "tcga_ov_3_clean = tcga_ov_3_clean[tcga_ov_3_clean['days_to_drug_therapy_end'] != tcga_ov_3_clean['days_to_drug_therapy_start']] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RlpTIsomL8nT"
   },
   "source": [
    "TCGA 3: Fix/standardize time variables and fix order of therapy lines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "ycWmppEk6d5o"
   },
   "outputs": [],
   "source": [
    "# Fix values where start and end are switched\n",
    "tcga_ov_3_clean.loc[tcga_ov_3_clean['days_to_drug_therapy_start'] > tcga_ov_3_clean['days_to_drug_therapy_end'], ['days_to_drug_therapy_start', 'days_to_drug_therapy_end']] = tcga_ov_3_clean.loc[tcga_ov_3_clean['days_to_drug_therapy_start'] > tcga_ov_3_clean['days_to_drug_therapy_end'], ['days_to_drug_therapy_end', 'days_to_drug_therapy_start']].values\n",
    "\n",
    "# Set earliest drug therapy start to zero and subtract everything else by min days\n",
    "ther_start = tcga_ov_3_clean.groupby('bcr_patient_barcode')['days_to_drug_therapy_start']\n",
    "tcga_timefix = tcga_ov_3_clean.assign(start_day=ther_start.transform(min))\n",
    "      # keep start days for later use with tcga_ov_1\n",
    "tcga_start_days = tcga_timefix[['bcr_patient_barcode', 'start_day']].drop_duplicates()\n",
    "\n",
    "tcga_timefix['therapy_start'] = tcga_timefix['days_to_drug_therapy_start'] - tcga_timefix['start_day']\n",
    "tcga_timefix['therapy_end'] = tcga_timefix['days_to_drug_therapy_end'] - tcga_timefix['start_day']\n",
    "tcga_timefix = tcga_timefix.drop(['days_to_drug_therapy_end', 'days_to_drug_therapy_start', 'start_day'], axis=1)\n",
    "tcga_timefix = tcga_timefix.sort_values(by=['bcr_patient_barcode', 'therapy_start', 'therapy_end'])\n",
    "\n",
    "# Set up state list for each patient: barcode, timing, drug combo\n",
    "tcga_drug_lines = []\n",
    "for barcode in tcga_timefix['bcr_patient_barcode'].unique():\n",
    "    tcga_time = tcga_timefix[tcga_timefix['bcr_patient_barcode'] == barcode]\n",
    "    tcga_time = tcga_time[['therapy_start', 'therapy_end', 'drug_name']].drop_duplicates(keep='first').values.tolist() # Drop duplicate drugs that have different dosages or administration but same timing\n",
    "\n",
    "    points = [] # list of (offset, plus/minus, drug) tuples\n",
    "    for start,stop,drug in tcga_time:\n",
    "        points.append((start,'+',drug))\n",
    "        points.append((stop,'-',drug))\n",
    "    points.sort()\n",
    "\n",
    "    ranges = [] # output list of (start, stop, drug_set) tuples\n",
    "    current_set = []\n",
    "    last_start = None\n",
    "    for offset,pm,drug in points:\n",
    "        if pm == '+':\n",
    "            if last_start is not None:\n",
    "                ranges.append([last_start,offset,list(set(current_set.copy()))])\n",
    "            current_set.append(drug)\n",
    "            last_start = offset\n",
    "        elif pm == '-':\n",
    "            ranges.append([last_start,offset,list(set(current_set.copy()))])\n",
    "            current_set.remove(drug)\n",
    "            last_start = offset\n",
    "\n",
    "    # Finish off\n",
    "    if last_start is not None:\n",
    "        ranges.append([last_start,offset,list(set(current_set.copy()))])\n",
    "\n",
    "    # Remove the ranges where start = stop\n",
    "    range_drug = []\n",
    "    for i in range(len(ranges)):\n",
    "        if ranges[i][0] != ranges[i][1]: # add condition:  <& (ranges[i][2] != [])> to drop no-drug periods\n",
    "            range_drug.append(ranges[i])\n",
    "\n",
    "    # Sort drugs in each drug combo\n",
    "    for i in range(len(range_drug)):\n",
    "        range_drug[i][2].sort()\n",
    "\n",
    "    # Remove overlapping/back-to-back duplicate lines. Drop this section if decide to do something with dosages\n",
    "    ranges_final = []\n",
    "    for line in range(0, len(range_drug)-1):\n",
    "        if (range_drug[line+1][2] == range_drug[line][2]) & (range_drug[line+1][0] <= range_drug[line][1]):\n",
    "            range_drug[line][1] = range_drug[line+1][1]\n",
    "            range_drug[line+1][0] = range_drug[line][0]\n",
    "        if (range_drug[line][2] != range_drug[line+1][2]) | (range_drug[line][0] != range_drug[line+1][0]):\n",
    "            ranges_final.append(range_drug[line])\n",
    "    ranges_final.append(range_drug[len(range_drug)-1])\n",
    "\n",
    "    # Add the number of previous lines of therapy\n",
    "    for line in range(len(ranges_final)):\n",
    "        if line == 0:\n",
    "            ranges_final[line].append(0) \n",
    "        elif ranges_final[line-1][2] == []:\n",
    "            ranges_final[line].append(ranges_final[line-1][3])\n",
    "        else:\n",
    "            ranges_final[line].append(ranges_final[line-1][3] + 1)\n",
    "    \n",
    "    # Add treat transition var\n",
    "    for line in range(len(ranges_final)):\n",
    "        try:\n",
    "            if (ranges_final[line][2] != []) & (ranges_final[line+1][2] != []):\n",
    "                ranges_final[line].append(math.ceil((ranges_final[line][1] - ranges_final[line][0]) / 30))\n",
    "            elif (ranges_final[line][2] != []) & (ranges_final[line+1][2] == []):\n",
    "                ranges_final[line].append(math.ceil((ranges_final[line][1] - ranges_final[line][0]) / 30) - 1)\n",
    "            elif (ranges_final[line][2] == []) & (ranges_final[line+1][2] != []):\n",
    "                ranges_final[line].append(1)\n",
    "            elif (ranges_final[line][2] == []) & (ranges_final[line+1][2] == []):\n",
    "                ranges_final[line].append(0)\n",
    "        except IndexError:\n",
    "            ranges_final[line].append(0)\n",
    "    \n",
    "    # Add no treat transition var\n",
    "    for line in range(len(ranges_final)):\n",
    "        try:\n",
    "            if (ranges_final[line][2] != []) & (ranges_final[line+1][2] != []):\n",
    "                ranges_final[line].append(0)                \n",
    "            elif (ranges_final[line][2] != []) & (ranges_final[line+1][2] == []):\n",
    "                ranges_final[line].append(1)\n",
    "            elif (ranges_final[line][2] == []) & (ranges_final[line+1][2] != []):\n",
    "                ranges_final[line].append(math.ceil((ranges_final[line][1] - ranges_final[line][0]) / 30) - 1)\n",
    "            elif (ranges_final[line][2] == []) & (ranges_final[line+1][2] == []):\n",
    "                ranges_final[line].append(math.ceil((ranges_final[line][1] - ranges_final[line][0]) / 30))\n",
    "        except IndexError:\n",
    "            ranges_final[line].append(math.ceil((ranges_final[line][1] - ranges_final[line][0]) / 30)-1)\n",
    "\n",
    "    # Add patient barcodes\n",
    "    for line in range(len(ranges_final)):\n",
    "        ranges_final[line].insert(0, barcode)\n",
    "    \n",
    "\n",
    "    tcga_drug_lines.extend(ranges_final)\n",
    "\n",
    "tcga_drug_lines[3] = ['TCGA-04-1332', 0.0, 151.0, ['Carboplatin', 'Paclitaxel', 'Topotecan'], 0, 5, 1] # Special case to fix. Make sure to check this if make changes above\n",
    "\n",
    "# Back to df\n",
    "lines_df = pd.DataFrame(tcga_drug_lines, columns=['bcr_patient_barcode', 'start', 'end', 'therapy', 'previous_lines', 'treat', 'no_treat'])\n",
    "\n",
    "# List of patient barcodes\n",
    "tcga_barcodes = list(lines_df['bcr_patient_barcode'].unique())\n",
    "\n",
    "# Notes\n",
    "    # One thing to be aware of is that this code drops all values where therapy start and therapy end are equal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IX1joLnWxyzL"
   },
   "source": [
    "TCGA 1: Clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 603
    },
    "id": "eH71y_18xy4L",
    "outputId": "4f8952d2-ddea-497a-9769-0881141d9053"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NaNs:  bcr_patient_barcode                                 4\n",
      "total_days_overall_survival                        28\n",
      "outcome_overall_survival_censoring                 16\n",
      "vital_status                                       34\n",
      "days_to_tumor_progression                         570\n",
      "                                                 ... \n",
      "5th_chemo_regimen_days_outcome                    524\n",
      "6th_chemo_regimen_days_outcome                    552\n",
      "Days off platinum prior to recurrence 1st line     99\n",
      "Last day of platinum 1st line                     108\n",
      "Chemotherapy number of lines of therapy            27\n",
      "Length: 72, dtype: int64\n"
     ]
    },
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bcr_patient_barcode</th>\n",
       "      <th>total_days_overall_survival</th>\n",
       "      <th>outcome_overall_survival_censoring</th>\n",
       "      <th>age_at_initial_pathologic_diagnosis</th>\n",
       "      <th>race</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TCGA-04-1331</td>\n",
       "      <td>1300.0</td>\n",
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       "      <td>79.0</td>\n",
       "      <td>WHITE</td>\n",
       "      <td>G3</td>\n",
       "      <td>IIIC</td>\n",
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       "      <th>1</th>\n",
       "      <td>TCGA-04-1332</td>\n",
       "      <td>1217.0</td>\n",
       "      <td>1</td>\n",
       "      <td>70.0</td>\n",
       "      <td>WHITE</td>\n",
       "      <td>G3</td>\n",
       "      <td>IIIC</td>\n",
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       "      <td>TCGA-04-1336</td>\n",
       "      <td>1445.0</td>\n",
       "      <td>0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>WHITE</td>\n",
       "      <td>G3</td>\n",
       "      <td>IIIB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TCGA-04-1338</td>\n",
       "      <td>1418.0</td>\n",
       "      <td>0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>WHITE</td>\n",
       "      <td>G3</td>\n",
       "      <td>IIIC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TCGA-04-1342</td>\n",
       "      <td>531.0</td>\n",
       "      <td>1</td>\n",
       "      <td>80.0</td>\n",
       "      <td>WHITE</td>\n",
       "      <td>G2</td>\n",
       "      <td>IV</td>\n",
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       "    <tr>\n",
       "      <th>455</th>\n",
       "      <td>TCGA-61-2113</td>\n",
       "      <td>627.0</td>\n",
       "      <td>1</td>\n",
       "      <td>54.0</td>\n",
       "      <td>WHITE</td>\n",
       "      <td>G3</td>\n",
       "      <td>IIC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>456</th>\n",
       "      <td>TCGA-61-2610</td>\n",
       "      <td>1550.0</td>\n",
       "      <td>1</td>\n",
       "      <td>61.0</td>\n",
       "      <td>WHITE</td>\n",
       "      <td>G3</td>\n",
       "      <td>IIIC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>457</th>\n",
       "      <td>TCGA-61-2611</td>\n",
       "      <td>417.0</td>\n",
       "      <td>1</td>\n",
       "      <td>40.0</td>\n",
       "      <td>WHITE</td>\n",
       "      <td>G3</td>\n",
       "      <td>IIIC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458</th>\n",
       "      <td>TCGA-61-2612</td>\n",
       "      <td>173.0</td>\n",
       "      <td>1</td>\n",
       "      <td>63.0</td>\n",
       "      <td>WHITE</td>\n",
       "      <td>G3</td>\n",
       "      <td>IIIC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>459</th>\n",
       "      <td>TCGA-61-2613</td>\n",
       "      <td>561.0</td>\n",
       "      <td>1</td>\n",
       "      <td>73.0</td>\n",
       "      <td>WHITE</td>\n",
       "      <td>G3</td>\n",
       "      <td>IIIC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>460 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    bcr_patient_barcode  total_days_overall_survival  \\\n",
       "0          TCGA-04-1331                       1300.0   \n",
       "1          TCGA-04-1332                       1217.0   \n",
       "2          TCGA-04-1336                       1445.0   \n",
       "3          TCGA-04-1338                       1418.0   \n",
       "4          TCGA-04-1342                        531.0   \n",
       "..                  ...                          ...   \n",
       "455        TCGA-61-2113                        627.0   \n",
       "456        TCGA-61-2610                       1550.0   \n",
       "457        TCGA-61-2611                        417.0   \n",
       "458        TCGA-61-2612                        173.0   \n",
       "459        TCGA-61-2613                        561.0   \n",
       "\n",
       "     outcome_overall_survival_censoring  age_at_initial_pathologic_diagnosis  \\\n",
       "0                                     1                                 79.0   \n",
       "1                                     1                                 70.0   \n",
       "2                                     0                                 55.0   \n",
       "3                                     0                                 78.0   \n",
       "4                                     1                                 80.0   \n",
       "..                                  ...                                  ...   \n",
       "455                                   1                                 54.0   \n",
       "456                                   1                                 61.0   \n",
       "457                                   1                                 40.0   \n",
       "458                                   1                                 63.0   \n",
       "459                                   1                                 73.0   \n",
       "\n",
       "      race tumor_grade tumor_stage  \n",
       "0    WHITE          G3        IIIC  \n",
       "1    WHITE          G3        IIIC  \n",
       "2    WHITE          G3        IIIB  \n",
       "3    WHITE          G3        IIIC  \n",
       "4    WHITE          G2          IV  \n",
       "..     ...         ...         ...  \n",
       "455  WHITE          G3         IIC  \n",
       "456  WHITE          G3        IIIC  \n",
       "457  WHITE          G3        IIIC  \n",
       "458  WHITE          G3        IIIC  \n",
       "459  WHITE          G3        IIIC  \n",
       "\n",
       "[460 rows x 7 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Keep subset of variables \n",
    "print('NaNs: ', tcga_ov_1.isnull().sum())\n",
    "tcga_ov_1_keep = tcga_ov_1[['bcr_patient_barcode',\n",
    "                            'total_days_overall_survival',\n",
    "                            'outcome_overall_survival_censoring',\n",
    "                            # 'vital_status',\n",
    "                            # 'days_to_tumor_progression',\n",
    "                            # 'days_to_death',\n",
    "                            # 'days_to_last_followup',\n",
    "                            # 'days_to_tumor_recurrence',\n",
    "                            # 'time_to_failure',  \n",
    "                            # 'Cycles_of_adjuvant_therapy',\n",
    "                            # 'Adjuvant_chemotherapy_dose_intensity',\n",
    "                            'age_at_initial_pathologic_diagnosis',\n",
    "                            # 'anatomic_organ_subdivision',\n",
    "                            # 'days_to_birth',\n",
    "                            # 'initial_pathologic_diagnosis_method',\n",
    "                            # 'person_neoplasm_cancer_status',\n",
    "                            # 'pretreatment_history',\n",
    "                            # 'primary_therapy_outcome_success', # ask EKO about this\n",
    "                            'race',\n",
    "                            # 'residual_tumor',\n",
    "                            # 'site_of_tumor_first_recurrence',\n",
    "                            # 'tissue_source_site',\n",
    "                            'tumor_grade',\n",
    "                            # 'tumor_residual_disease',\n",
    "                            'tumor_stage',\n",
    "                            # 'tumor_tissue_site'\n",
    "                            # 'year_of_initial_pathologic_diagnosis',\n",
    "                            # 'Days off platinum prior to recurrence 1st line',\n",
    "                            # 'Last day of platinum 1st line',\n",
    "                            # 'Chemotherapy number of lines of therapy'\n",
    "                            ]]\n",
    "\n",
    "# Drop cases that don't have a survival metric\n",
    "tcga_ov_1_keep = tcga_ov_1_keep.dropna(subset=['total_days_overall_survival'])\n",
    "tcga_ov_1_keep.drop(tcga_ov_1_keep[tcga_ov_1_keep['total_days_overall_survival'] == 'cannot assess'].index, inplace=True)\n",
    "\n",
    "# Only keep samples that are in the cleaned 'lines' data\n",
    "tcga_ov_1_keep = tcga_ov_1_keep[tcga_ov_1_keep['bcr_patient_barcode'].isin(tcga_barcodes)].sort_values(by=['bcr_patient_barcode']).reset_index().drop('index', axis=1)\n",
    "\n",
    "# Adjust final survival by start of therapy day\n",
    "tcga_start_days = tcga_start_days[tcga_start_days['bcr_patient_barcode'].isin(tcga_barcodes)].sort_values(by=['bcr_patient_barcode']).reset_index().drop('index', axis=1)\n",
    "tcga_ov_1_keep['total_days_overall_survival'] = tcga_ov_1_keep['total_days_overall_survival'] - tcga_start_days['start_day'] # this needs work, getting some negative values\n",
    "\n",
    "# Fill blanks\n",
    "tcga_ov_1_keep = tcga_ov_1_keep.replace(r'^\\s*$', np.nan, regex=True)\n",
    "tcga_ov_1_keep = tcga_ov_1_keep.replace(np.nan, 'NOT SPECIFIED', regex=True)\n",
    "\n",
    "# Deceased only\n",
    "tcga_ov_1_keep_d = tcga_ov_1_keep.loc[tcga_ov_1_keep['outcome_overall_survival_censoring'] == 1].reset_index(drop=True)\n",
    "\n",
    "# Save CSVs\n",
    "# tcga_ov_1_keep.to_csv('tcga_ov_1_keep.csv', index=False)\n",
    "# tcga_ov_1_keep_d.to_csv('tcga_ov_1_keep_d.csv', index=False)\n",
    "\n",
    "# Show data\n",
    "tcga_ov_1_keep"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data summary table(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ############## Fix so that if a wb doesn't already exist it creates one\n",
    "\n",
    "# book = load_workbook('Table_1.xlsx')\n",
    "# book.template = False\n",
    "\n",
    "# writer = pd.ExcelWriter('Table_1.xlsx', engine='openpyxl')\n",
    "# writer.book = book\n",
    "\n",
    "# ##################### All Patients\n",
    "# # Race\n",
    "# try:\n",
    "#     del book['race']\n",
    "# except:\n",
    "#     pass\n",
    "# tcga_ov_1_keep.stb.freq(['race']).to_excel(writer, sheet_name='race')\n",
    "\n",
    "# # Tumor grade\n",
    "# try:\n",
    "#     del book['tumor grade']\n",
    "# except:\n",
    "#     pass\n",
    "# tcga_ov_1_keep.stb.freq(['tumor_grade']).to_excel(writer, sheet_name='tumor grade')\n",
    "\n",
    "# # Tumor stage\n",
    "# try:\n",
    "#     del book['tumor stage']\n",
    "# except:\n",
    "#     pass\n",
    "# tcga_ov_1_keep.stb.freq(['tumor_stage']).to_excel(writer, sheet_name='tumor stage')\n",
    "\n",
    "# # Death\n",
    "# try:\n",
    "#     del book['death']\n",
    "# except:\n",
    "#     pass\n",
    "# tcga_ov_1_keep.stb.freq(['outcome_overall_survival_censoring']).to_excel(writer, sheet_name='death')\n",
    "# # Age\n",
    "# try:\n",
    "#     del book['age']\n",
    "# except:\n",
    "#     pass\n",
    "# pd.DataFrame(tcga_ov_1_keep['age_at_initial_pathologic_diagnosis'].describe()).to_excel(writer, sheet_name='age')\n",
    "\n",
    "# # Overall survival\n",
    "# try:\n",
    "#     del book['days survival']\n",
    "# except:\n",
    "#     pass\n",
    "# pd.DataFrame(tcga_ov_1_keep['total_days_overall_survival'].describe()).to_excel(writer, sheet_name='days survival')\n",
    "\n",
    "# ##################### Deceased Patients\n",
    "# # Race\n",
    "# try:\n",
    "#     del book['race_d']\n",
    "# except:\n",
    "#     pass\n",
    "# tcga_ov_1_keep_d.stb.freq(['race']).to_excel(writer, sheet_name='race_d')\n",
    "\n",
    "# # Tumor grade\n",
    "# try:\n",
    "#     del book['tumor grade_d']\n",
    "# except:\n",
    "#     pass\n",
    "# tcga_ov_1_keep_d.stb.freq(['tumor_grade']).to_excel(writer, sheet_name='tumor grade_d')\n",
    "\n",
    "# # Tumor stage\n",
    "# try:\n",
    "#     del book['tumor stage_d']\n",
    "# except:\n",
    "#     pass\n",
    "# tcga_ov_1_keep_d.stb.freq(['tumor_stage']).to_excel(writer, sheet_name='tumor stage_d')\n",
    "\n",
    "# # Age\n",
    "# try:\n",
    "#     del book['age_d']\n",
    "# except:\n",
    "#     pass\n",
    "# pd.DataFrame(tcga_ov_1_keep_d['age_at_initial_pathologic_diagnosis'].describe()).to_excel(writer, sheet_name='age_d')\n",
    "\n",
    "# # Overall survival\n",
    "# try:\n",
    "#     del book['days survival_d']\n",
    "# except:\n",
    "#     pass\n",
    "# pd.DataFrame(tcga_ov_1_keep_d['total_days_overall_survival'].describe()).to_excel(writer, sheet_name='days survival_d')\n",
    "\n",
    "# writer.save()\n",
    "# writer.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vRX2hrEOPvMu"
   },
   "source": [
    "Add death dummy to drug lines data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "X95jfaknP5oS"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>therapy</th>\n",
       "      <th>previous_lines</th>\n",
       "      <th>treat</th>\n",
       "      <th>no_treat</th>\n",
       "      <th>death</th>\n",
       "      <th>race</th>\n",
       "      <th>age</th>\n",
       "      <th>tumor_stage</th>\n",
       "      <th>tumor_grade</th>\n",
       "      <th>total_ev</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TCGA-04-1331</td>\n",
       "      <td>0.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>[Carboplatin, Paclitaxel]</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>WHITE</td>\n",
       "      <td>79.0</td>\n",
       "      <td>IIIC</td>\n",
       "      <td>G3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TCGA-04-1331</td>\n",
       "      <td>133.0</td>\n",
       "      <td>445.0</td>\n",
       "      <td>[]</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>WHITE</td>\n",
       "      <td>79.0</td>\n",
       "      <td>IIIC</td>\n",
       "      <td>G3</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TCGA-04-1331</td>\n",
       "      <td>445.0</td>\n",
       "      <td>462.0</td>\n",
       "      <td>[Dortezomib]</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>WHITE</td>\n",
       "      <td>79.0</td>\n",
       "      <td>IIIC</td>\n",
       "      <td>G3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TCGA-04-1332</td>\n",
       "      <td>0.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>[Carboplatin, Paclitaxel, Topotecan]</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>WHITE</td>\n",
       "      <td>70.0</td>\n",
       "      <td>IIIC</td>\n",
       "      <td>G3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TCGA-04-1332</td>\n",
       "      <td>151.0</td>\n",
       "      <td>396.0</td>\n",
       "      <td>[]</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>WHITE</td>\n",
       "      <td>70.0</td>\n",
       "      <td>IIIC</td>\n",
       "      <td>G3</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1912</th>\n",
       "      <td>TCGA-61-2610</td>\n",
       "      <td>500.0</td>\n",
       "      <td>561.0</td>\n",
       "      <td>[]</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>WHITE</td>\n",
       "      <td>61.0</td>\n",
       "      <td>IIIC</td>\n",
       "      <td>G3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1913</th>\n",
       "      <td>TCGA-61-2610</td>\n",
       "      <td>561.0</td>\n",
       "      <td>1101.0</td>\n",
       "      <td>[Doxorubicin]</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>WHITE</td>\n",
       "      <td>61.0</td>\n",
       "      <td>IIIC</td>\n",
       "      <td>G3</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1914</th>\n",
       "      <td>TCGA-61-2611</td>\n",
       "      <td>0.0</td>\n",
       "      <td>238.0</td>\n",
       "      <td>[Carboplatin, Paclitaxel]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>WHITE</td>\n",
       "      <td>40.0</td>\n",
       "      <td>IIIC</td>\n",
       "      <td>G3</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1915</th>\n",
       "      <td>TCGA-61-2612</td>\n",
       "      <td>0.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>[Carboplatin, Paclitaxel]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>WHITE</td>\n",
       "      <td>63.0</td>\n",
       "      <td>IIIC</td>\n",
       "      <td>G3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1916</th>\n",
       "      <td>TCGA-61-2613</td>\n",
       "      <td>0.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>[Carboplatin, Paclitaxel]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>WHITE</td>\n",
       "      <td>73.0</td>\n",
       "      <td>IIIC</td>\n",
       "      <td>G3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1917 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           patient  start     end                               therapy  \\\n",
       "0     TCGA-04-1331    0.0   133.0             [Carboplatin, Paclitaxel]   \n",
       "1     TCGA-04-1331  133.0   445.0                                    []   \n",
       "2     TCGA-04-1331  445.0   462.0                          [Dortezomib]   \n",
       "3     TCGA-04-1332    0.0   151.0  [Carboplatin, Paclitaxel, Topotecan]   \n",
       "4     TCGA-04-1332  151.0   396.0                                    []   \n",
       "...            ...    ...     ...                                   ...   \n",
       "1912  TCGA-61-2610  500.0   561.0                                    []   \n",
       "1913  TCGA-61-2610  561.0  1101.0                         [Doxorubicin]   \n",
       "1914  TCGA-61-2611    0.0   238.0             [Carboplatin, Paclitaxel]   \n",
       "1915  TCGA-61-2612    0.0   131.0             [Carboplatin, Paclitaxel]   \n",
       "1916  TCGA-61-2613    0.0   105.0             [Carboplatin, Paclitaxel]   \n",
       "\n",
       "      previous_lines  treat  no_treat  death   race   age tumor_stage  \\\n",
       "0                  0      4         1      0  WHITE  79.0        IIIC   \n",
       "1                  1      1        10      0  WHITE  79.0        IIIC   \n",
       "2                  1      0         0      1  WHITE  79.0        IIIC   \n",
       "3                  0      5         1      0  WHITE  70.0        IIIC   \n",
       "4                  1      1         8      0  WHITE  70.0        IIIC   \n",
       "...              ...    ...       ...    ...    ...   ...         ...   \n",
       "1912               2      1         2      0  WHITE  61.0        IIIC   \n",
       "1913               2      0        17      1  WHITE  61.0        IIIC   \n",
       "1914               0      0         7      1  WHITE  40.0        IIIC   \n",
       "1915               0      0         4      1  WHITE  63.0        IIIC   \n",
       "1916               0      0         3      1  WHITE  73.0        IIIC   \n",
       "\n",
       "     tumor_grade  total_ev  \n",
       "0             G3         5  \n",
       "1             G3        11  \n",
       "2             G3         1  \n",
       "3             G3         6  \n",
       "4             G3         9  \n",
       "...          ...       ...  \n",
       "1912          G3         3  \n",
       "1913          G3        18  \n",
       "1914          G3         8  \n",
       "1915          G3         5  \n",
       "1916          G3         4  \n",
       "\n",
       "[1917 rows x 13 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge in final death event for each patient\n",
    "lines_df_2 = lines_df.merge(tcga_ov_1_keep[['bcr_patient_barcode', 'outcome_overall_survival_censoring', 'race', \n",
    "                                            'age_at_initial_pathologic_diagnosis', 'tumor_stage', 'tumor_grade']],\n",
    "                            on='bcr_patient_barcode').rename(columns={'bcr_patient_barcode': 'patient', 'outcome_overall_survival_censoring': 'death', 'age_at_initial_pathologic_diagnosis': 'age'})\n",
    "\n",
    "# Only keep death event on last line\n",
    "lines_df_2.loc[lines_df_2['patient'] == lines_df_2['patient'].shift(-1), 'death'] = 0\n",
    "\n",
    "# Fix no treat on last line where death = 0\n",
    "def fix_notreat(row):\n",
    "    if (row['treat'] == 0) & (row['no_treat'] == 0) & (row['death'] == 0):\n",
    "        return 1\n",
    "    else:\n",
    "        return row['no_treat']\n",
    "lines_df_2['no_treat'] = lines_df_2.apply(lambda row: fix_notreat(row), axis=1)\n",
    "\n",
    "# Total events\n",
    "lines_df_2['total_ev'] = lines_df_2.apply(lambda row: max(1, row['treat'] + row['no_treat'] + row['death']), axis=1)\n",
    "\n",
    "# Function to get indices of a therapy\n",
    "def get_index_pos(my_list, val):\n",
    "    return [i for i, x in enumerate(my_list) if x == val]\n",
    "\n",
    "lines_df_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 402
    },
    "id": "kId5QzCmZie1",
    "outputId": "35f4770e-50e1-4f61-f167-82d64a05b284"
   },
   "outputs": [
    {
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       "      <th></th>\n",
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      ],
      "text/plain": [
       "           patient  start     end                               therapy  \\\n",
       "0     TCGA-04-1331    0.0   133.0             [Carboplatin, Paclitaxel]   \n",
       "1     TCGA-04-1331  133.0   445.0                                    []   \n",
       "2     TCGA-04-1331  445.0   462.0                          [Dortezomib]   \n",
       "3     TCGA-04-1332    0.0   151.0  [Carboplatin, Paclitaxel, Topotecan]   \n",
       "4     TCGA-04-1332  151.0   396.0                                    []   \n",
       "...            ...    ...     ...                                   ...   \n",
       "1259  TCGA-61-2610  500.0   561.0                                    []   \n",
       "1260  TCGA-61-2610  561.0  1101.0                         [Doxorubicin]   \n",
       "1261  TCGA-61-2611    0.0   238.0             [Carboplatin, Paclitaxel]   \n",
       "1262  TCGA-61-2612    0.0   131.0             [Carboplatin, Paclitaxel]   \n",
       "1263  TCGA-61-2613    0.0   105.0             [Carboplatin, Paclitaxel]   \n",
       "\n",
       "      previous_lines  treat  no_treat  death   race   age tumor_stage  \\\n",
       "0                  0      4         1      0  WHITE  79.0        IIIC   \n",
       "1                  1      1        10      0  WHITE  79.0        IIIC   \n",
       "2                  1      0         0      1  WHITE  79.0        IIIC   \n",
       "3                  0      5         1      0  WHITE  70.0        IIIC   \n",
       "4                  1      1         8      0  WHITE  70.0        IIIC   \n",
       "...              ...    ...       ...    ...    ...   ...         ...   \n",
       "1259               2      1         2      0  WHITE  61.0        IIIC   \n",
       "1260               2      0        17      1  WHITE  61.0        IIIC   \n",
       "1261               0      0         7      1  WHITE  40.0        IIIC   \n",
       "1262               0      0         4      1  WHITE  63.0        IIIC   \n",
       "1263               0      0         3      1  WHITE  73.0        IIIC   \n",
       "\n",
       "     tumor_grade  total_ev  \n",
       "0             G3         5  \n",
       "1             G3        11  \n",
       "2             G3         1  \n",
       "3             G3         6  \n",
       "4             G3         9  \n",
       "...          ...       ...  \n",
       "1259          G3         3  \n",
       "1260          G3        18  \n",
       "1261          G3         8  \n",
       "1262          G3         5  \n",
       "1263          G3         4  \n",
       "\n",
       "[1264 rows x 13 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create version of final dataset that only includes patients who died\n",
    "d_pats = list(tcga_ov_1_keep['bcr_patient_barcode'].loc[tcga_ov_1_keep['outcome_overall_survival_censoring'] == 1])\n",
    "\n",
    "lines_df_d = lines_df_2[lines_df_2['patient'].isin(d_pats)].reset_index().drop(['index'], axis=1)\n",
    "lines_df_d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create version of data for regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>9292</th>\n",
       "      <td>TCGA-61-2613</td>\n",
       "      <td>[Carboplatin, Paclitaxel]</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>WHITE</td>\n",
       "      <td>IIIC</td>\n",
       "      <td>G3</td>\n",
       "      <td>Carboplatin,Paclitaxel</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9293</th>\n",
       "      <td>TCGA-61-2613</td>\n",
       "      <td>[Carboplatin, Paclitaxel]</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>WHITE</td>\n",
       "      <td>IIIC</td>\n",
       "      <td>G3</td>\n",
       "      <td>Carboplatin,Paclitaxel</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9294</th>\n",
       "      <td>TCGA-61-2613</td>\n",
       "      <td>[Carboplatin, Paclitaxel]</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>WHITE</td>\n",
       "      <td>IIIC</td>\n",
       "      <td>G3</td>\n",
       "      <td>Carboplatin,Paclitaxel</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9295</th>\n",
       "      <td>TCGA-61-2613</td>\n",
       "      <td>[Carboplatin, Paclitaxel]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>WHITE</td>\n",
       "      <td>IIIC</td>\n",
       "      <td>G3</td>\n",
       "      <td>Carboplatin,Paclitaxel</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9296 rows × 165 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           patient                    therapy  treat  no_treat  death   race  \\\n",
       "0     TCGA-04-1331  [Carboplatin, Paclitaxel]      1         0      0  WHITE   \n",
       "1     TCGA-04-1331  [Carboplatin, Paclitaxel]      1         0      0  WHITE   \n",
       "2     TCGA-04-1331  [Carboplatin, Paclitaxel]      1         0      0  WHITE   \n",
       "3     TCGA-04-1331  [Carboplatin, Paclitaxel]      1         0      0  WHITE   \n",
       "4     TCGA-04-1331  [Carboplatin, Paclitaxel]      0         1      0  WHITE   \n",
       "...            ...                        ...    ...       ...    ...    ...   \n",
       "9291  TCGA-61-2612  [Carboplatin, Paclitaxel]      0         0      1  WHITE   \n",
       "9292  TCGA-61-2613  [Carboplatin, Paclitaxel]      1         0      0  WHITE   \n",
       "9293  TCGA-61-2613  [Carboplatin, Paclitaxel]      1         0      0  WHITE   \n",
       "9294  TCGA-61-2613  [Carboplatin, Paclitaxel]      1         0      0  WHITE   \n",
       "9295  TCGA-61-2613  [Carboplatin, Paclitaxel]      0         0      1  WHITE   \n",
       "\n",
       "     tumor_stage tumor_grade                ther_str  months  ...  IIIB  IIIC  \\\n",
       "0           IIIC          G3  Carboplatin,Paclitaxel       0  ...     0     1   \n",
       "1           IIIC          G3  Carboplatin,Paclitaxel       1  ...     0     1   \n",
       "2           IIIC          G3  Carboplatin,Paclitaxel       2  ...     0     1   \n",
       "3           IIIC          G3  Carboplatin,Paclitaxel       3  ...     0     1   \n",
       "4           IIIC          G3  Carboplatin,Paclitaxel       4  ...     0     1   \n",
       "...          ...         ...                     ...     ...  ...   ...   ...   \n",
       "9291        IIIC          G3  Carboplatin,Paclitaxel       4  ...     0     1   \n",
       "9292        IIIC          G3  Carboplatin,Paclitaxel       0  ...     0     1   \n",
       "9293        IIIC          G3  Carboplatin,Paclitaxel       1  ...     0     1   \n",
       "9294        IIIC          G3  Carboplatin,Paclitaxel       2  ...     0     1   \n",
       "9295        IIIC          G3  Carboplatin,Paclitaxel       3  ...     0     1   \n",
       "\n",
       "      IV  NOT SPECIFIED_tumor_stage  G1  G2  G3  GB  GX  \\\n",
       "0      0                          0   0   0   1   0   0   \n",
       "1      0                          0   0   0   1   0   0   \n",
       "2      0                          0   0   0   1   0   0   \n",
       "3      0                          0   0   0   1   0   0   \n",
       "4      0                          0   0   0   1   0   0   \n",
       "...   ..                        ...  ..  ..  ..  ..  ..   \n",
       "9291   0                          0   0   0   1   0   0   \n",
       "9292   0                          0   0   0   1   0   0   \n",
       "9293   0                          0   0   0   1   0   0   \n",
       "9294   0                          0   0   0   1   0   0   \n",
       "9295   0                          0   0   0   1   0   0   \n",
       "\n",
       "      NOT SPECIFIED_tumor_grade  \n",
       "0                             0  \n",
       "1                             0  \n",
       "2                             0  \n",
       "3                             0  \n",
       "4                             0  \n",
       "...                         ...  \n",
       "9291                          0  \n",
       "9292                          0  \n",
       "9293                          0  \n",
       "9294                          0  \n",
       "9295                          0  \n",
       "\n",
       "[9296 rows x 165 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def fix_treat(row):\n",
    "    if (row['patient'] == row['pat_lag']) & (row['ther_lag'] != ''):\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "\n",
    "def fix_death(row):\n",
    "    if row['treat'] == 1:\n",
    "        return 0\n",
    "    else:\n",
    "        return row['death']\n",
    "\n",
    "def fix_notreat(row):\n",
    "    return (1 - row['treat'] - row['death'])\n",
    "\n",
    "def treat_state(row):\n",
    "    if row['ther_str'] == '':\n",
    "        return 0\n",
    "    else:\n",
    "        return 1\n",
    "    \n",
    "def make_reg_df(df): \n",
    "    df_reg = df.loc[df.index.repeat(df.total_ev)].reset_index(drop=True)\n",
    "    df_reg['ther_str'] = [','.join(map(str, l)) for l in df_reg['therapy']]\n",
    "    df_reg['ther_lag'] = df_reg['ther_str'].shift(-1)\n",
    "    df_reg['pat_lag'] = df_reg['patient'].shift(-1)\n",
    "\n",
    "    df_reg['treat'] = df_reg.apply(lambda row: fix_treat(row), axis=1) # prob that next state is treatment\n",
    "    df_reg['death'] = df_reg.apply(lambda row: fix_death(row), axis=1) # prob that next state is death\n",
    "    df_reg['no_treat'] = df_reg.apply(lambda row: fix_notreat(row), axis=1) # prob that next state no treatment\n",
    "    \n",
    "    df_reg = df_reg.drop(columns=['total_ev', 'ther_lag', 'pat_lag', 'start', 'end'])\n",
    "    \n",
    "    df_reg['months'] =  df_reg.groupby('patient').cumcount() # time trend\n",
    "    df_reg['treat_months'] = df_reg.groupby(['patient', 'ther_str', 'previous_lines']).cumcount()\n",
    "    \n",
    "    df_reg['treat_state'] = df_reg.apply(lambda row: treat_state(row), axis=1) # current state\n",
    "    \n",
    "    df_reg = df_reg.merge(pd.get_dummies(df_reg['ther_str']), left_index=True, right_index=True)\n",
    "    df_reg = df_reg.merge(pd.get_dummies(df_reg['race']), left_index=True, right_index=True)\n",
    "    df_reg = df_reg.merge(pd.get_dummies(df_reg['tumor_stage']), left_index=True, right_index=True)\n",
    "    df_reg = df_reg.merge(pd.get_dummies(df_reg['tumor_grade']), left_index=True, right_index=True)\n",
    "    df_reg = df_reg.rename(columns={'NOT SPECIFIED_x':'NOT SPECIFIED_race', 'NOT SPECIFIED_y':'NOT SPECIFIED_tumor_stage', 'NOT SPECIFIED':'NOT SPECIFIED_tumor_grade'})\n",
    "    \n",
    "    age = df_reg['age']\n",
    "    df_reg = df_reg.drop(['age'], axis=1)\n",
    "    df_reg.insert(12, 'age', age)\n",
    "    \n",
    "    prev_lines = df_reg['previous_lines']\n",
    "    df_reg = df_reg.drop(['previous_lines'], axis=1)\n",
    "    df_reg.insert(12, 'previous_lines', prev_lines)\n",
    "    \n",
    "    return df_reg\n",
    "\n",
    "df_d_reg = make_reg_df(lines_df_d)\n",
    "\n",
    "df_reg = make_reg_df(lines_df_2)\n",
    "\n",
    "# Save CSVs\n",
    "# df_reg.to_pickle('df_reg.pkl')\n",
    "# df_d_reg.to_pickle('df_d_reg.pkl')\n",
    "\n",
    "# Show data\n",
    "df_reg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Treatment summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def freq_heat(data, fig_title, file_name):\n",
    "    df_heat = data.stb.freq(['ther_str', 'months'])[['ther_str', 'months', 'count']].sort_values(by=['months', 'ther_str'])\n",
    "    df_heat = df_heat.pivot_table(index=['months'], columns='ther_str', values='count').reset_index().rename_axis(None, axis=1).set_index('months')\n",
    "    df_heat = df_heat.fillna(value=0).drop([''], axis=1)\n",
    "    s = df_heat.sum()\n",
    "    df_heat = df_heat[s.sort_values(ascending=False).index]\n",
    "    # convert to z-scores\n",
    "    for row in df_heat.index:\n",
    "        stats.zscore(df_heat.iloc[row])\n",
    "        df_heat.iloc[row] = stats.zscore(df_heat.iloc[row])\n",
    "    df_heat = df_heat.fillna(value=0)\n",
    "\n",
    "    # Make heatmap\n",
    "    sns.set(font_scale=1)\n",
    "\n",
    "    cmap = sns.color_palette(\"Blues\", as_cmap=True)\n",
    "    plt.figure(figsize=(16,16))\n",
    "    plt.rcParams[\"font.family\"] = \"Arial\"\n",
    "    ax = sns.heatmap(data=df_heat,\n",
    "                     cmap=cmap, \n",
    "                     square=1,\n",
    "                     vmin=0, vmax=3,\n",
    "                     cbar_kws={\"shrink\": .5},\n",
    "                     )\n",
    "    plt.title(fig_title, size=18, fontweight='bold')\n",
    "    plt.ylabel('Months from start of treatment', size=14)\n",
    "    plt.xticks(size=12, rotation='vertical')\n",
    "    plt.yticks(size=12)\n",
    "#     plt.savefig(file_name)\n",
    "\n",
    "# freq_heat(df_reg, 'Treatment Frequency and Timing - All Patients', 'heat_data_all_sparse.png')\n",
    "# freq_heat(df_d_reg, 'Treatment Frequency and Timing - Deceased Patients', 'heat_data_d_sparse.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WnwEXGPRAq7A"
   },
   "source": [
    "## Create MDP objects\n",
    "\n",
    "* This should probably be the start of the next notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZvulJ_dUAv2h"
   },
   "source": [
    "State and Action set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Ciq1VT6qIHUH",
    "outputId": "321fd508-7692-4145-8519-23ddd96ea4a9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "128\n",
      "[[], ['Abagovomab'], ['Aldesleukin'], ['Altretamine'], ['Amifostine', 'Carboplatin', 'Cisplatin', 'Paclitaxel'], ['Amifostine', 'Carboplatin', 'Paclitaxel'], ['Amifostine', 'Cisplatin', 'Paclitaxel'], ['Amifostine', 'Paclitaxel'], ['Aminocamptothecin'], ['Anastrozole']]\n",
      "[['Abagovomab'], ['Aldesleukin'], ['Altretamine'], ['Amifostine', 'Carboplatin', 'Cisplatin', 'Paclitaxel'], ['Amifostine', 'Carboplatin', 'Paclitaxel'], ['Amifostine', 'Cisplatin', 'Paclitaxel'], ['Amifostine', 'Paclitaxel'], ['Aminocamptothecin'], ['Anastrozole'], ['Anastrozole', 'Doxorubicin']]\n",
      "108\n",
      "[[], ['Aldesleukin'], ['Altretamine'], ['Amifostine', 'Carboplatin', 'Cisplatin', 'Paclitaxel'], ['Amifostine', 'Carboplatin', 'Paclitaxel'], ['Amifostine', 'Cisplatin', 'Paclitaxel'], ['Amifostine', 'Paclitaxel'], ['Aminocamptothecin'], ['Anastrozole'], ['Anastrozole', 'Doxorubicin']]\n",
      "[['Aldesleukin'], ['Altretamine'], ['Amifostine', 'Carboplatin', 'Cisplatin', 'Paclitaxel'], ['Amifostine', 'Carboplatin', 'Paclitaxel'], ['Amifostine', 'Cisplatin', 'Paclitaxel'], ['Amifostine', 'Paclitaxel'], ['Aminocamptothecin'], ['Anastrozole'], ['Anastrozole', 'Doxorubicin'], ['Anastrozole', 'Tamoxifen']]\n",
      "['AMERICAN INDIAN OR ALASKA NATIVE', 'ASIAN', 'BLACK OR AFRICAN AMERICAN', 'NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER', 'NOT SPECIFIED', 'WHITE']\n",
      "['AMERICAN INDIAN OR ALASKA NATIVE', 'ASIAN', 'BLACK OR AFRICAN AMERICAN', 'NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER', 'WHITE']\n",
      "['AMERICAN INDIAN OR ALASKA NATIVE', 'ASIAN', 'BLACK OR AFRICAN AMERICAN', 'NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER', 'NOT SPECIFIED', 'WHITE']\n",
      "['AMERICAN INDIAN OR ALASKA NATIVE', 'ASIAN', 'BLACK OR AFRICAN AMERICAN', 'NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER', 'WHITE']\n",
      "['IA', 'IB', 'IC', 'IIA', 'IIB', 'IIC', 'IIIA', 'IIIB', 'IIIC', 'IV', 'NOT SPECIFIED']\n",
      "['IA', 'IB', 'IC', 'IIA', 'IIB', 'IIC', 'IIIA', 'IIIB', 'IIIC', 'IV']\n",
      "['IIB', 'IIC', 'IIIA', 'IIIB', 'IIIC', 'IV', 'NOT SPECIFIED']\n",
      "['IIB', 'IIC', 'IIIA', 'IIIB', 'IIIC', 'IV']\n",
      "['G1', 'G2', 'G3', 'GB', 'GX', 'NOT SPECIFIED']\n",
      "['G1', 'G2', 'G3', 'GB', 'GX']\n",
      "['G1', 'G2', 'G3', 'GB', 'GX', 'NOT SPECIFIED']\n",
      "['G1', 'G2', 'G3', 'GB', 'GX']\n"
     ]
    }
   ],
   "source": [
    "# States\n",
    "state_set = ['N', 'T', 'D']\n",
    "\n",
    "# Combos/actions\n",
    "combos_all = list(lines_df_2['therapy'])\n",
    "combos_all.sort()\n",
    "combos_all = list(combos_all for combos_all,_ in itertools.groupby(combos_all))\n",
    "print(len(combos_all))\n",
    "print(combos_all[0:10])\n",
    "\n",
    "combos_all_drugs = combos_all.copy()\n",
    "combos_all_drugs.remove([])\n",
    "print(combos_all_drugs[0:10]) \n",
    "\n",
    "# Combos for deceased patients\n",
    "combos_d = list(lines_df_d['therapy'])\n",
    "\n",
    "combos_d.sort()\n",
    "combos_d = list(combos_d for combos_d,_ in itertools.groupby(combos_d))\n",
    "print(len(combos_d))\n",
    "print(combos_d[0:10])\n",
    "\n",
    "combos_drugs_d = combos_d.copy()\n",
    "combos_drugs_d.remove([])\n",
    "print(combos_drugs_d[0:10]) \n",
    "\n",
    "###### \"2\" versions are just dropping the 'NOT SPECIFIED' value\n",
    "# Race\n",
    "races_all = list(lines_df_2['race'].unique())\n",
    "races_all.sort()\n",
    "print(races_all)\n",
    "races_all2 = races_all.copy()\n",
    "races_all2.remove('NOT SPECIFIED')\n",
    "print(races_all2)\n",
    "    # Death only dataset\n",
    "races_d = list(lines_df_d['race'].unique())\n",
    "races_d.sort()\n",
    "print(races_d)\n",
    "races_d2 = races_d.copy()\n",
    "races_d2.remove('NOT SPECIFIED')\n",
    "print(races_d2)\n",
    "\n",
    "# Tumor stage\n",
    "tumor_stages_all = list(lines_df_2['tumor_stage'].unique())\n",
    "tumor_stages_all.sort()\n",
    "print(tumor_stages_all)\n",
    "tumor_stages_all2 = tumor_stages_all.copy()\n",
    "tumor_stages_all2.remove('NOT SPECIFIED')\n",
    "print(tumor_stages_all2)\n",
    "    # Death only dataset\n",
    "tumor_stages_d = list(lines_df_d['tumor_stage'].unique())\n",
    "tumor_stages_d.sort()\n",
    "print(tumor_stages_d)\n",
    "tumor_stages_d2 = tumor_stages_d.copy()\n",
    "tumor_stages_d2.remove('NOT SPECIFIED')\n",
    "print(tumor_stages_d2)\n",
    "\n",
    "# Tumor grade\n",
    "tumor_grades_all = list(lines_df_2['tumor_grade'].unique())\n",
    "tumor_grades_all.sort()\n",
    "print(tumor_grades_all)\n",
    "tumor_grades_all2 = tumor_grades_all.copy()\n",
    "tumor_grades_all2.remove('NOT SPECIFIED')\n",
    "print(tumor_grades_all2)\n",
    "    # Death only dataset\n",
    "tumor_grades_d = list(lines_df_d['tumor_grade'].unique())\n",
    "tumor_grades_d.sort()\n",
    "print(tumor_grades_d)\n",
    "tumor_grades_d2 = tumor_grades_d.copy()\n",
    "tumor_grades_d2.remove('NOT SPECIFIED')\n",
    "print(tumor_grades_d2)\n",
    "\n",
    "# Function to create random array for descriptive variables\n",
    "def rand_bin_array(K, N):\n",
    "    arr = np.zeros(N)\n",
    "    arr[:K]  = 1\n",
    "    np.random.shuffle(arr)\n",
    "    return arr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Restricted drug list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "77\n",
      "[['Abagovomab'], ['Altretamine'], ['Amifostine', 'Carboplatin', 'Cisplatin', 'Paclitaxel'], ['Amifostine', 'Carboplatin', 'Paclitaxel'], ['Amifostine', 'Paclitaxel'], ['Anastrozole'], ['Bevacizumab'], ['Bevacizumab', 'Carboplatin'], ['Bevacizumab', 'Carboplatin', 'Paclitaxel'], ['Bevacizumab', 'Cisplatin', 'Paclitaxel']]\n",
      "58\n",
      "[['Altretamine'], ['Amifostine', 'Carboplatin', 'Cisplatin', 'Paclitaxel'], ['Amifostine', 'Carboplatin', 'Paclitaxel'], ['Amifostine', 'Paclitaxel'], ['Anastrozole'], ['Bevacizumab'], ['Bevacizumab', 'Carboplatin'], ['Bevacizumab', 'Carboplatin', 'Paclitaxel'], ['Bevacizumab', 'Cyclophosphamide'], ['Bevacizumab', 'Doxorubicin']]\n"
     ]
    }
   ],
   "source": [
    "# Full dataset\n",
    "df_freq = df_reg.stb.freq(['ther_str'])[['ther_str', 'count']].sort_values(by=['count'], ascending=False)\n",
    "df_freq = df_freq.merge(df_reg[['therapy', 'ther_str']], on='ther_str').drop_duplicates(subset='ther_str')\n",
    "df_freq = df_freq[(df_freq['ther_str'] != '') & (df_freq['count'] >= 5)]\n",
    "combos_res_all = list(df_freq['therapy'])\n",
    "combos_res_all.sort()\n",
    "print(len(combos_res_all))\n",
    "print(combos_res_all[0:10])\n",
    "\n",
    "# Deceased dataset\n",
    "df_freq = df_d_reg.stb.freq(['ther_str'])[['ther_str', 'count']].sort_values(by=['count'], ascending=False)\n",
    "df_freq = df_freq.merge(df_reg[['therapy', 'ther_str']], on='ther_str').drop_duplicates(subset='ther_str')\n",
    "df_freq = df_freq[(df_freq['ther_str'] != '') & (df_freq['count'] >= 5)]\n",
    "combos_res_d = list(df_freq['therapy'])\n",
    "combos_res_d.sort()\n",
    "print(len(combos_res_d))\n",
    "print(combos_res_d[0:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Static transition matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DEeUFm3ZM8RU",
    "outputId": "0bcf7d3f-2c6a-410c-c70d-b3173b3f1b72"
   },
   "outputs": [],
   "source": [
    "# Treatment States = {No Treatment, Treatment, Death}\n",
    "# All or death-only action sets\n",
    "combos = combos_d # Switch to _all when use full dataset\n",
    "combos_drugs = combos_drugs_d\n",
    "combos_drugs_res = combos_res_d\n",
    "\n",
    "# Descriptive States (all or death-only)\n",
    "races = races_d\n",
    "races2 = races_d2\n",
    "tumor_stages = tumor_stages_d\n",
    "tumor_stages2 = tumor_stages_d2\n",
    "tumor_grades = tumor_grades_d\n",
    "tumor_grades2 = tumor_grades_d2\n",
    "\n",
    "# Dataset to use\n",
    "trans_data = df_d_reg # change to df_reg if using full dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fuspJEgVFPKy"
   },
   "source": [
    "Cox Proportional Hazard Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<lifelines.CoxPHFitter: fitted with 5931 total observations, 3312 right-censored observations>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit regressions\n",
    "    # Survival\n",
    "X_surv = trans_data.drop(['', 'no_treat', 'treat', 'patient', 'therapy', 'race', 'tumor_stage', \n",
    "                         'tumor_grade', 'ther_str', 'NOT SPECIFIED_tumor_stage', 'NOT SPECIFIED_tumor_grade', \n",
    "                         'NOT SPECIFIED_race', 'treat_months'], axis=1)\n",
    "cph_surv = CoxPHFitter(penalizer=0.1)\n",
    "cph_surv.fit(X_surv, duration_col='months', event_col='death', robust=True) # add this to do specific cols: , formula=\"fin + wexp + age * prio\"\n",
    "\n",
    "    # Treatment\n",
    "X_treat = trans_data.drop(['', 'treat', 'death', 'patient', 'therapy', 'race', 'tumor_stage',\n",
    "                         'tumor_grade', 'ther_str', 'NOT SPECIFIED_tumor_stage', 'NOT SPECIFIED_tumor_grade',\n",
    "                         'NOT SPECIFIED_race'], axis=1)\n",
    "cph_treat = CoxPHFitter(penalizer=0.1)\n",
    "cph_treat.fit(X_treat, duration_col='treat_months', event_col='no_treat', robust=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.7373231884465833, 0.8156244454562015)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def cph_probs(data, state, months, treat_months, prev_lines, action, age, race, tumor_stage, tumor_grade):\n",
    "    month = min(months, max(data['months']))\n",
    "    treat_month = min(treat_months, max(data['treat_months']))\n",
    "    if action == []:\n",
    "        pass\n",
    "    else:\n",
    "        act_ind = combos_drugs.index(action) + 2\n",
    "        \n",
    "    act_arr = np.zeros(len(combos_drugs)) # sim patient vectors = 0 if 'NOT SPECIFIED'\n",
    "    if action == []:\n",
    "        pass\n",
    "    else:\n",
    "        act_arr[combos_drugs.index(action)] = 1\n",
    "\n",
    "    x_surv = np.concatenate((np.array([age]), np.array([prev_lines]), np.array([state_set.index(state)]), act_arr, race, \n",
    "                             tumor_stage, tumor_grade))\n",
    "    x_surv = x_surv.reshape(1,-1)\n",
    "    \n",
    "    x_treat = np.concatenate((np.array([month]), np.array([age]), np.array([prev_lines]), np.array([state_set.index(state)]), \n",
    "                              act_arr, race, tumor_stage, tumor_grade))\n",
    "    x_treat = x_treat.reshape(1,-1)\n",
    "#     haz = (base_haz * part_haz.values[0]).iloc[months].values[0]\n",
    "    surv = cph_surv.predict_survival_function(x_surv).iloc[month].values[0]\n",
    "    treat_prob = cph_treat.predict_survival_function(x_treat).iloc[treat_month].values[0]\n",
    "    return surv, treat_prob\n",
    "\n",
    "race_arr = np.array([0, 0, 0, 0, 1])\n",
    "ts_arr = np.array([0, 0, 1, 0, 0, 0])\n",
    "tg_arr = np.array([0, 0, 1, 0, 0])\n",
    "\n",
    "# check that it works\n",
    "cph_probs(trans_data, 'T', 70, 15, 2, ['Carboplatin', 'Paclitaxel'], 80, race_arr, ts_arr, tg_arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cph_surv.baseline_survival_.to_csv('cph_surv_baseline.csv', index=False)\n",
    "# cph_treat.baseline_survival_.to_csv('cph_treat_baseline.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0. , 0.2, 0.4, 0.6, 0.8, 1. , 1.2]),\n",
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       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, '')])"
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     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family ['Arial'] not found. Falling back to DejaVu Sans.\n",
      "findfont: Font family ['Arial'] not found. Falling back to DejaVu Sans.\n"
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    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# CPH Death Regression Baseline Survival\n",
    "plt.style.use('default')\n",
    "plt.figure(figsize=(12,8))\n",
    "plt.rcParams['font.family'] = 'Arial'\n",
    "# cph.plot_partial_effects_on_outcome(covariates='age', values=[40, 50, 60, 70, 80], cmap='coolwarm')\n",
    "plt.plot(cph_surv.baseline_survival_)\n",
    "plt.title('Death Event Regression', fontsize=14)\n",
    "plt.xlabel('Months Since Start of Treatment', fontsize=14)\n",
    "plt.ylabel('Survival Probability', fontsize=14)\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "\n",
    "# plt.savefig('baseline_surv_death.png')\n",
    "\n",
    "# cph.print_summary()\n",
    "# cph_treat.print_summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([-0.2,  0. ,  0.2,  0.4,  0.6,  0.8,  1. ]),\n",
       " [Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, '')])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# CPH Remission/Recurrence Regression Baseline Survival\n",
    "plt.style.use('default')\n",
    "plt.figure(figsize=(12,8))\n",
    "plt.rcParams['font.family'] = 'Arial'\n",
    "plt.plot(cph_treat.baseline_survival_)\n",
    "plt.title('Remission Event Regression', fontsize=14)\n",
    "plt.xlabel('Months in Current State', fontsize=14)\n",
    "plt.ylabel('Continue Treatment Probability', fontsize=14)\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "\n",
    "# plt.savefig('baseline_surv_recurrence.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7Xbe4sqhFYNq"
   },
   "source": [
    "Q-Network: MLP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "NX_SB9BTFcA_"
   },
   "outputs": [],
   "source": [
    "# Define DQN, one hidden layer MLP for now\n",
    "class DQN(nn.Module):\n",
    "    def __init__(self, in_features, hidden_size, out_features):\n",
    "        super(DQN, self).__init__()\n",
    "        self.fc = nn.Linear(in_features, hidden_size)\n",
    "        self.relu = nn.ReLU()\n",
    "        self.fc_out = nn.Linear(hidden_size, out_features)\n",
    "\n",
    "    def forward(self, x):\n",
    "        out = self.fc(x)\n",
    "        out = self.relu(out)\n",
    "        out = self.fc_out(out)\n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a bigger NN\n",
    "class DQN_3(nn.Module):\n",
    "    def __init__(self, in_features, out_features):\n",
    "        super(DQN_3, self).__init__()\n",
    "        self.fc1 = nn.Linear(in_features, 128)\n",
    "        self.fc2 = nn.Linear(128, 256)\n",
    "        self.fc3 = nn.Linear(256, 256)\n",
    "        self.relu = nn.ReLU()\n",
    "        self.fc_out = nn.Linear(256, out_features)\n",
    "\n",
    "    def forward(self, x):\n",
    "        out = self.fc1(x) # in\n",
    "        out = self.relu(out)\n",
    "        out = self.fc2(out) # hidden 1\n",
    "        out = self.relu(out)\n",
    "        out = self.fc3(out) # hidden 2\n",
    "        out = self.relu(out)\n",
    "        out = self.fc3(out) # hidden 3\n",
    "        out = self.relu(out)\n",
    "        out = self.fc3(out) # hidden 4\n",
    "        out = self.relu(out)\n",
    "        out = self.fc3(out) # hidden 5\n",
    "        out = self.relu(out)\n",
    "        out = self.fc3(out) # hidden 6\n",
    "        out = self.relu(out)\n",
    "        out = self.fc_out(out) # out\n",
    "        return out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qwi3cx2gsa4M"
   },
   "source": [
    "Replay Memory\n",
    "\n",
    "https://pytorch.org/tutorials/intermediate/reinforcement_q_learning.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "rZ9jxVEmsefd"
   },
   "outputs": [],
   "source": [
    "Transition = namedtuple('Transition',\n",
    "                        ('state', 'action', 'next_state', 'reward'))\n",
    "\n",
    "class ReplayMemory(object):\n",
    "    def __init__(self, capacity):\n",
    "        self.capacity = capacity\n",
    "        self.memory = []\n",
    "        self.position = 0\n",
    "\n",
    "    def push(self, *args):\n",
    "        \"\"\"Saves a transition.\"\"\"\n",
    "        if len(self.memory) < self.capacity:\n",
    "            self.memory.append(None)\n",
    "        self.memory[self.position] = Transition(*args)\n",
    "        self.position = (self.position + 1) % self.capacity\n",
    "\n",
    "    def sample(self, batch_size):\n",
    "        return random.sample(self.memory, batch_size)\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.memory)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EB4ZbWbXCFzI"
   },
   "source": [
    "Model training loop\n",
    "\n",
    "https://pytorch.org/tutorials/intermediate/reinforcement_q_learning.html\n",
    "\n",
    "Use Q update formua: $Q^{\\pi}(s,a) \\leftarrow Q^{\\pi}(s,a) + \\alpha \\cdot [r + \\gamma \\cdot Q^{\\pi}(s', a') - Q^{\\pi}(s,a)]$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "G0b-nblgCDVR"
   },
   "outputs": [],
   "source": [
    "def optimize_model():\n",
    "    if len(memory) < BATCH_SIZE:\n",
    "        return\n",
    "    transitions = memory.sample(BATCH_SIZE)\n",
    "    # Transpose the batch (see https://stackoverflow.com/a/19343/3343043 for\n",
    "    # detailed explanation). This converts batch-array of Transitions\n",
    "    # to Transition of batch-arrays.\n",
    "    batch = Transition(*zip(*transitions))\n",
    "\n",
    "    # Compute a mask of non-final states and concatenate the batch elements\n",
    "    # (a final state would've been the one after which simulation ended)\n",
    "    non_final_mask = torch.tensor(tuple(map(lambda s: s is not None,\n",
    "                                          batch.next_state)), device=device, dtype=torch.bool)\n",
    "    non_final_next_states = torch.stack([s for s in batch.next_state\n",
    "                                                if s is not None])\n",
    "\n",
    "    state_batch = torch.stack(batch.state)\n",
    "    action_batch = torch.stack(batch.action)\n",
    "    reward_batch = torch.cat(batch.reward)\n",
    "\n",
    "    # Compute Q(s_t, a) - the model computes Q(s_t), then we select the\n",
    "    # columns of actions taken. These are the actions which would've been taken\n",
    "    # for each batch state according to policy_net\n",
    "    state_action_values = policy(state_batch).gather(1, action_batch).squeeze()\n",
    "\n",
    "    # Compute V(s_{t+1}) for all next states.\n",
    "    # Expected values of actions for non_final_next_states are computed based\n",
    "    # on the \"older\" target_net; selecting their best reward with max(1)[0].\n",
    "    # This is merged based on the mask, such that we'll have either the expected\n",
    "    # state value or 0 in case the state was final.\n",
    "    next_state_values = torch.zeros(BATCH_SIZE, device=device)\n",
    "    next_state_values[non_final_mask] = target_net(non_final_next_states).max(1)[0].detach()\n",
    "\n",
    "    # Compute the expected Q values\n",
    "    expected_state_action_values = state_action_values + ALPHA * (reward_batch + (GAMMA * next_state_values) - state_action_values)\n",
    "    expected_state_action_values = expected_state_action_values\n",
    "#     expected_state_action_values = (next_state_values * GAMMA) + reward_batch # old version\n",
    "\n",
    "    # Compute Huber loss\n",
    "    loss = F.smooth_l1_loss(state_action_values, expected_state_action_values)\n",
    "    # Compute MSE loss\n",
    "#     loss = F.mse_loss(state_action_values, expected_state_action_values)\n",
    "    \n",
    "    # Optimize the model\n",
    "    optimizer.zero_grad()\n",
    "    loss.backward()\n",
    "    for param in policy.parameters():\n",
    "        param.grad.data.clamp_(-1, 1)\n",
    "    optimizer.step()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QZLSsYJeheqC"
   },
   "source": [
    "## Environment\n",
    "\n",
    "Designed based on: https://github.com/MJeremy2017/reinforcement-learning-implementation/blob/master/GridWorld/gridWorld.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OZX71-HbqIf9"
   },
   "source": [
    "State Class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "vh4gKHOnAydB"
   },
   "outputs": [],
   "source": [
    "# Globals\n",
    "START_STATE = 'T'\n",
    "GAMMA = 0.99 # orig 0.99\n",
    "ALPHA = 0.01 # orig 0.01\n",
    "EPS_START = 0.90 # orig 0.9\n",
    "EPS_END = 0.01 # orig 0.05\n",
    "EPS_DECAY = 100 # Increase to slow decay. Orig 100\n",
    "BATCH_SIZE = 100 # orig 100\n",
    "TARGET_UPDATE = 10 # orig 10\n",
    "IN_FEATURES = 5 + len(races2)+len(tumor_stages2)+len(tumor_grades2) # health state, months, months on treatment, age, previous lines + dummy arrays\n",
    "OUT_FEATURES = len(combos_drugs) # Change for restricted vs. full action set ('_res' suffix)\n",
    "\n",
    "policy = DQN_3(IN_FEATURES, OUT_FEATURES).to(device)\n",
    "target_net = DQN_3(IN_FEATURES, OUT_FEATURES).to(device)\n",
    "target_net.load_state_dict(policy.state_dict())\n",
    "target_net.eval()\n",
    "\n",
    "optimizer = optim.RMSprop(policy.parameters())\n",
    "memory = ReplayMemory(10000)\n",
    "\n",
    "# State class initializes the simulated patient vector\n",
    "class State:\n",
    "    def __init__(self, state=START_STATE):\n",
    "        self.state = state\n",
    "        self.age = round(np.random.normal(trans_data['age'].mean(), trans_data['age'].std(), 1)[0])\n",
    "        self.race = random.choice(races) # choose from all including 'NOT SPECIFIED'\n",
    "        self.r_arr = np.zeros(len(races2)) # sim patient vectors = 0 if 'NOT SPECIFIED'\n",
    "        if self.race == 'NOT SPECIFIED':\n",
    "            pass\n",
    "        else:\n",
    "            self.r_arr[races2.index(self.race)] = 1\n",
    "        self.tumor_stage = random.choice(tumor_stages) \n",
    "        self.ts_arr = np.zeros(len(tumor_stages2))\n",
    "        if self.tumor_stage == 'NOT SPECIFIED':\n",
    "            pass\n",
    "        else:\n",
    "            self.ts_arr[tumor_stages2.index(self.tumor_stage)] = 1\n",
    "        self.tumor_grade = random.choice(tumor_grades)\n",
    "        self.tg_arr = np.zeros(len(tumor_grades2))\n",
    "        if self.tumor_grade == 'NOT SPECIFIED':\n",
    "            pass\n",
    "        else:\n",
    "            self.tg_arr[tumor_grades2.index(self.tumor_grade)] = 1\n",
    "        \n",
    "        self.isEnd = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cdgwb3ALqMS5"
   },
   "source": [
    "Environment Class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "9Rub4E2hlARd"
   },
   "outputs": [],
   "source": [
    "class CancerEnv:\n",
    "    def __init__(self):\n",
    "        self.State = State()\n",
    "        self.s_a = []\n",
    "        self.treat_mo = 0\n",
    "        self.prev_lines = 0\n",
    "        self.round = 0\n",
    "        self.actions = combos_drugs # Change for restricted vs. full action set ('_res' suffix)\n",
    "        # initial state and action reward\n",
    "        self.state_values = {'N':1, 'T':1, 'D':-1}       # not sure if need these, need action values instead. Can it be like: Combo: Value - xt?\n",
    "        self.statevals_list = [1, 1, -1]      # for [N, T, D]\n",
    "        self.s_a_values = {} \n",
    "        for i in state_set:\n",
    "            for j in combos:\n",
    "                self.s_a_values[repr([i, j])] = 0\n",
    "    \n",
    "    # Choose Action\n",
    "    def chooseAction(self):\n",
    "        # choose action with most expected value\n",
    "        mx_exp_reward = 0\n",
    "        exp_rate = EPS_END + (EPS_START - EPS_END) * math.exp(-1. * self.round / EPS_DECAY)\n",
    "        if self.State.state == 'N':\n",
    "            action = []\n",
    "        else:\n",
    "            if np.random.uniform(0, 1) <= exp_rate:\n",
    "                action = self.actions[np.random.choice(len(self.actions))]\n",
    "            else:\n",
    "                # greedy action\n",
    "                with torch.no_grad():\n",
    "                    state_t = torch.cat((torch.FloatTensor([state_set.index(self.State.state), len(self.s_a), self.treat_mo,\n",
    "                                                            self.prev_lines, self.State.age]), \n",
    "                                         torch.FloatTensor(self.State.r_arr),\n",
    "                                         torch.FloatTensor(self.State.ts_arr),\n",
    "                                         torch.FloatTensor(self.State.tg_arr))) #torch.FloatTensor([state_set.index(self.State.state), len(self.s_a)])\n",
    "\n",
    "                    act = policy(state_t).max(0)[1].item()\n",
    "                    action = self.actions[act]\n",
    "        return action\n",
    "\n",
    "        # Update step\n",
    "    def nxtPosition(self, action, months, treat_months, prev_lines):\n",
    "        # Choose probabilities from transition matrix\n",
    "#         probs = get_probs(action, self.state) # static probs\n",
    "        # Get regression probs\n",
    "            # OLS\n",
    "#         probs = reg_probs(trans_data, self.State.state, months, action, self.State.age, self.State.race, \n",
    "#                           self.State.tumor_stage, self.State.tumor_grade)\n",
    "\n",
    "#         nxtState = random.choices(state_set, weights=probs, k=1)[0]\n",
    "\n",
    "            # CPH\n",
    "        surv, treat_prob = cph_probs(trans_data, self.State.state, months, treat_months, prev_lines, action, self.State.age,\n",
    "                                     self.State.r_arr, self.State.ts_arr, self.State.tg_arr)\n",
    "        # Return next state\n",
    "        if np.random.uniform(0,1) > surv:\n",
    "            nxtState = 'D'\n",
    "        else:\n",
    "            nxtState = random.choices(['T', 'N'], weights=[treat_prob, 1-treat_prob], k=1)[0]\n",
    "        return nxtState\n",
    "\n",
    "    def takeAction(self, action):\n",
    "        position = self.nxtPosition(action, len(self.s_a)-1, self.treat_mo, self.prev_lines)\n",
    "        return position # State(state=position)\n",
    "      \n",
    "        # Give reward: for now, simple rewards\n",
    "    def giveReward(self):\n",
    "        if self.State.state == 'N':\n",
    "            return 0 \n",
    "        elif self.State.state == 'T':\n",
    "            return 0\n",
    "        elif self.State.state == 'D':\n",
    "            return len(self.s_a) \n",
    "    \n",
    "    def isEndFunc(self):\n",
    "        if self.State.state == 'D':\n",
    "            self.State.isEnd = True\n",
    "\n",
    "    def reset(self):\n",
    "        self.s_a = []\n",
    "        self.treat_mo = 0\n",
    "        self.prev_lines = 0\n",
    "        self.State = State()\n",
    "\n",
    "    def play(self, rounds=1000, verbose=True):\n",
    "        since = time.time()\n",
    "        total_reward_list = []\n",
    "        traj_list = []\n",
    "        \n",
    "        while self.round < rounds:\n",
    "            if self.State.isEnd:\n",
    "                # back propagate\n",
    "#                 reward = self.giveReward()\n",
    "                # explicitly assign end state to reward values\n",
    "#                 self.s_a_values[repr([self.State.state, action])] = reward  # this is optional\n",
    "#                 for s in reversed(self.s_a):\n",
    "#                     reward = self.s_a_values[repr([s[0], s[1]])] + self.lr * (reward - self.s_a_values[repr([s[0], s[1]])]) # I think i need to update this formula because using (s,a) now\n",
    "#                     self.s_a_values[repr([s[0], s[1]])] = reward\n",
    "                # Calculate total reward\n",
    "                total_reward = 0\n",
    "                for s in self.s_a:\n",
    "                    total_reward += self.state_values[s[0]]\n",
    "                if verbose:\n",
    "                    print(self.s_a)\n",
    "                    print('Total reward: ', total_reward)\n",
    "                    print('----------------------------------------------------')\n",
    "                total_reward_list.append(total_reward)\n",
    "                \n",
    "                if self.round % 1000 == 0:\n",
    "                    time_elapsed = time.time() - since\n",
    "                    print('Rounds complete: ', self.round,'/', rounds)\n",
    "                    print('Time elapsed: {:.0f}h {:.0f}m {:.0f}s'.format(time_elapsed // 3600, (time_elapsed % 3600) // 60, time_elapsed % 60))\n",
    "\n",
    "                self.reset()\n",
    "                self.round += 1\n",
    "            else:\n",
    "                action = self.chooseAction()\n",
    "                try:\n",
    "                    act_ind = torch.tensor([self.actions.index(action)])\n",
    "                except ValueError:\n",
    "                    pass\n",
    "                # append trace\n",
    "                self.s_a.append([self.State.state, action])\n",
    "                    \n",
    "                if verbose:\n",
    "                    print('current state {} action {}'.format(self.State.state, action))\n",
    "                \n",
    "                s_t = torch.cat((torch.FloatTensor([state_set.index(self.State.state), len(self.s_a)-1, self.treat_mo, \n",
    "                                                    self.prev_lines, self.State.age]),\n",
    "                                 torch.FloatTensor(self.State.r_arr),\n",
    "                                 torch.FloatTensor(self.State.ts_arr),\n",
    "                                 torch.FloatTensor(self.State.tg_arr))) \n",
    "                # by taking the action, it reaches the next state\n",
    "                self.State.state = self.takeAction(action)\n",
    "                \n",
    "                # Update previous lines\n",
    "                try:\n",
    "                    if (self.s_a[-2][1] != []) & (action != self.s_a[-2][1]):\n",
    "                        self.prev_lines += 1\n",
    "                    else:\n",
    "                        pass\n",
    "                except IndexError:\n",
    "                    pass\n",
    "                \n",
    "                # Fill simulation trajectory data\n",
    "                traj_list.append([self.round, action, self.State.race, self.State.tumor_stage, self.State.tumor_grade,\n",
    "                                  len(self.s_a)-1, self.treat_mo, self.prev_lines, self.State.age, state_set.index(self.State.state)])\n",
    "                \n",
    "                # Update months in current treat state\n",
    "                try: \n",
    "                    if self.State.state == self.s_a[-1][0]:\n",
    "                        self.treat_mo += 1\n",
    "                    else:\n",
    "                        self.treat_mo = 0\n",
    "                except IndexError:\n",
    "                    pass\n",
    "            \n",
    "                # save transition vals for memory\n",
    "                s_t1 = torch.cat((torch.FloatTensor([state_set.index(self.State.state), len(self.s_a), self.treat_mo,\n",
    "                                                     self.prev_lines, self.State.age]),\n",
    "                                  torch.FloatTensor(self.State.r_arr),\n",
    "                                  torch.FloatTensor(self.State.ts_arr),\n",
    "                                  torch.FloatTensor(self.State.tg_arr)))\n",
    "                r_t = torch.FloatTensor([self.giveReward()])\n",
    "                # store transition in memory\n",
    "                if (action == []) & (self.State.state != 'D'): ###########\n",
    "                    pass\n",
    "                else:\n",
    "                    memory.push(s_t, act_ind, s_t1, r_t)\n",
    "                \n",
    "                # mark is end\n",
    "                self.isEndFunc()\n",
    "                if verbose:\n",
    "                    print('nxt state', self.State.state)\n",
    "                    print('---------------------')\n",
    "            # perform an optimizaiton step\n",
    "            optimize_model()\n",
    "            # update target model\n",
    "            if self.round % TARGET_UPDATE == 0:\n",
    "                target_net.load_state_dict(policy.state_dict())\n",
    "        sim_trajectories = pd.DataFrame(traj_list, columns=['patient', 'therapy', 'race', 'tumor_stage','tumor_grade', 'months',\n",
    "                                                            'treat_months', 'previous_lines', 'age', 'treat_state'])\n",
    "        time_elapsed = time.time() - since\n",
    "        print('Rounds complete: ', rounds,'/', rounds)\n",
    "        print('Time elapsed: {:.0f}h {:.0f}m {:.0f}s'.format(time_elapsed // 3600, (time_elapsed % 3600) // 60, time_elapsed % 60))\n",
    "        return total_reward_list, sim_trajectories"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jpBhfZoBSw10"
   },
   "source": [
    "Treat patients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 757
    },
    "id": "7o5Wl8r3Sv-4",
    "outputId": "a75a8109-3699-47dd-d7f2-4cd3a2d3831c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rounds complete:  0 / 100\n",
      "Time elapsed: 0h 0m 0s\n",
      "Rounds complete:  100 / 100\n",
      "Time elapsed: 0h 0m 25s\n"
     ]
    }
   ],
   "source": [
    "if __name__ == '__main__':\n",
    "    env = CancerEnv()\n",
    "    reward_hist, sim_trajectories = env.play(rounds=200000, verbose=False)\n",
    "    # print(ag.s_a_values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results\n",
    "Copy below cells to other notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Total rewards"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([-20.,   0.,  20.,  40.,  60.,  80., 100., 120., 140.]),\n",
       " [Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, '')])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family ['Arial'] not found. Falling back to DejaVu Sans.\n"
     ]
    },
    {
     "data": {
      "image/png": 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DtULmt7/9LY499tim2evve9/7Gj6+4YYbAABvfetbMXv2bEydOlX2ZqvtEEBzgd+tc2n9vddffz2ef/55+fEPfvADPPvss1p+lxc++MEPNnx8zjnn4PXXX2963PPPP4+rrroKl112WbcOrYHddtsNJ5xwQsPnrBkO1vfhpZde2tBa8dWvfrXhvTVnzhzsvvvuDR+rfPe73wUAVKtVXHrppS037nRhfZ1cc801DY6BL33pS9KtQgghpDUs8AkhhGjh/e9/f0Pv8dq1a7H77rtj4cKF2HffffGe97xHjskDgBNPPLEhiGyzzTZrsI8/99xz2HXXXXH00UfjIx/5CO68804AwDvf+c6G33v00UfjoIMOwgEHHICddtqpZWHWLaZPn45zzjmn4XNXXHGF7DNOJpMNIXnVahWf+MQnsM022+D9738/Dj/8cOy2226YPn06Dj/8cPziF79oOGdArchTbc/i6+q5F/8vlF6gtoGy3377Nfysbp3Lo446qiFUb82aNdh1113x3ve+F3vuuSdOO+00Lb/HKyeeeGLDGMaHH34Y8+bNw//7f/8PRx55JN7znvdgyy23xFvf+lZccMEFPVWRv/CFLzRY5h999FHccccd8uP/+I//wKabbio/fvjhh7HddtvhkEMOwY477ojPfe5zDT/vyiuvbHAcWDeQLrnkEmyxxRaYPn06Lr30Ut1/TgOnnHIKNtlkE/nxs88+i+233x4HH3wwFixYgMsvvzzQ308IIf0AC3xCCCHauPXWW7H//vvLjycnJ3H//ffj4YcfbihUjz76aFx33XVN33/KKac0fPyXv/wFt912G2699Vap+J555pnYdttt5WOKxSL+8Ic/4L777sPIyAjOP/983X+WK84+++yG1PKVK1fi+9//vvz44x//OL7xjW80WKFfeukl3Hnnnfjtb3+LZcuWNQSPWXuWk8kkDjzwwKbfu3Dhwpb/L9hvv/2aes+7dS7T6TRuvvnmhg2c8fFx/N///R8ee+wxzJ8/v+Xf1C2y2SzuvPPOhqC5QqGAhx56CL/61a+wZMmSBtVbLbC7zbbbbtuk4l9yySXy/zfbbDPceeedDW0Ra9aswV133dWQXp9KpfDlL38ZixYtavhZZ5xxBrbeeuuGz7322msYGxvD7Nmz8ZGPfETjX9PIzJkz8bOf/QwDAwPyc+vXr8fdd9+Np556Crvvvjt22WWXwH4/IYT0AyzwCSGEaGPmzJlYsmQJbr75Zhx55JGYO3cucrkcBgYGMH/+fHz0ox/FnXfeiVtuuaVl0Nnll1+OL33pS9hhhx0aFvkqM2bMwMMPP4x//dd/xZw5c5DJZDBnzhyceOKJWLZsGd7+9rcH/We2Zdq0afj3f//3hs9deeWVMvkeqBXWTz/9NM4//3zstddemDFjBlKpFIaGhrDtttvigx/8IK6++mr885//xFZbbdX0O6wqK9Co4L/73e9u2hiw2p+B7p7LXXbZBY8//jhOPPFEbL755shms5g/fz7OPvtsPPbYY019+t1m7ty5eOSRR3DjjTfiqKOOwrx58zAwMIBMJoNNNtkEe++9N8444wz8+te/brk51U0+//nPN2wyPPbYYw1he7vvvjuefPJJXHvttTjwwAOxySabIJ1OY8qUKViwYAHOOOMM/OUvf2m5gTN9+nQ8+OCD8nnKZDLYeuutcdZZZ+Gvf/1rg9MhCN73vvfhz3/+Mz7ykY9g1qxZyOVyePvb346LL74YDzzwQMPmGSGEkGYSRpDxwoQQQgghhBBCCOkKVPAJIYQQQgghhJA+gAU+IYQQQgghhBDSB7DAJ4QQQgghhBBC+gAW+IQQQgghhBBCSB/AAp8QQgghhBBCCOkDWOATQgghhBBCCCF9QLrzQ4igWq1i1apVGBkZQSKR6PXhEEIIIYQQQgjpcwzDwNjYGObMmYNksr1GzwLfBatWrcJWW23V68MghBBCCCGEEBIzli9fjrlz57Z9DAt8F4yMjACondipU6f2+GgIIYQQQgghhPQ7o6Oj2GqrrWQ92g4W+C4QtvypU6eywCeEEEIIIYQQ0jWctIkzZI8QQgghhBBCCOkDWOATQgghhBBCCCF9AAt8QgghhBBCCCGkD2CBTwghhBBCCCGE9AEs8AkhhBBCCCGEkD6ABT4hhBBCCCGEENIHsMAnhBBCCCGEEEL6ABb4hBBCCCGEEEJIH8ACnxBCCCGEEEII6QNY4BNCCCGEEEIIIX0AC3xCCCGEEEIIIaQPYIFPCCGEEEIIIYT0ASzwCSGEEEIIIYSQPoAFPiGEEEIIIYQQ0gewwCeEEEIIIYQQQvoAFviEEEIIIYQQQkgfwAKfEEIIIYQQQgjpA1jgE0IIIYQQQgghfQALfEIIIYQQQgghpA9ggU8IIYQQQgghhPQBLPAJIYQQQgghhJA+gAU+IYQQQgghhBDSB7DAJ4QQQgghhBBC+gAW+IQQQgghhPQBT60axfqJYq8PgxDSQ1jgE0IIIYQQEnH++cZGHPaNB3DGzx7v9aEQQnoIC3xCCCGEEEIizvJ1kwCAVevzPT4SQkgvYYFPCCGEEEJIxMmXKgCAcrXa4yMhhPQSFviEEEIIIYREHFHgVypGj4+EENJLWOATQgghhBAScQrlmnJfrrLAJyTOsMAnhBBCCCEk4hSEgs8Cn5BYwwKfEEIIIYSQiJMvUcEnhLDAJ4QQQgghJPLkqeATQsACnxBCCCGEkMhj9uAzRZ+QOMMCnxBCCCGEkIhDBZ8QArDAJ4QQQgghJPLky7UCnz34hMQbFviEEEIIIYREHBGyZxhAlUU+IbGFBT4hhBBCCCERR/TgA1TxCYkzLPAJIYQQQgiJOKIHH2AfPiFxhgU+IYQQQgghEUct8JmkT0h8YYFPCCGEEEJIxCmUzKKeCj4h8YUFPiGEEEIIIRGnUFYVfBb4hMQVFviEEEIIIYREnDwVfEIIWOATQgghhBASefJU8AkhYIFPCCGEEEJI5GlI0a+wwCckrrDAJ4QQQgghJOKoFn2m6BMSX1jgE0IIIYQQEnHUkD324BMSX1jgE0IIIYQQEmEMw7Ao+CzwCYkrLPAJIYQQQgiJMIVyoyWfCj4h8YUFPiGEEEIIIRGmUGos8KngExJfWOATQgghhBASYdT+ewCoMGSPkNjCAp8QQgghhJAIk7cq+ByTR0hsYYFPCCGEEEJIhMk3Kfgs8AmJKyzwCSGEEEIIiTD5UmOBzx58QuILC3xCCCGEEEIijDVFv8wefEJiCwt8QgghkeWh59fg4l89iclipfODCSGkT2lS8NmDT0hsYYFPCCEksvzXH57Dfz/8Mh58fk2vD4UQQnqGNWSPPfiExBcW+IQQQiLLZF21mixRwSeExBf24BNCBCzwCSGERJZive+UahUhJM5YC3xeEwmJLyzwCSGERJZSpVbgU60ihMSZ5pA9XhMJiSuhK/A3btyIiy++GIceeihmzpyJRCKBxYsXNzymWq1i8eLF+OAHP4itttoKw8PD2HHHHfGlL30J+Xy+5c/94Q9/iHe84x0YGBjAW9/6Vnzzm9/swl9DCCEkSIqiwK8wMZoQEl+aFXxeEwmJK6Er8NesWYPLLrsMTz/9NHbZZZeWj5mYmMBJJ52EN954A//2b/+Gr3/969h7771x8cUX4/3vfz8Mo3HX8nvf+x5OPfVULFiwAN/85jfxrne9C2eddRauuuqqbvxJhBBCAqJUrl3vqVYRQuIMFXxCiCDd6wOwMnv2bKxevRpbbLEFli5dir322qvpMdlsFg8++CD23Xdf+bnTTjsN8+fPx8UXX4w//OEPOOiggwAAk5OT+M///E8cfvjhuOWWW+Rjq9UqvvjFL+L000/HjBkzuvPHEUII0Yqw6LPflBASZ9iDTwgRhE7Bz+Vy2GKLLdo+JpvNNhT3gqOOOgoA8PTTT8vPLVmyBG+++SY+9alPNTz2jDPOwPj4OH7zm99oOGpCCCG9QFj0S7ToE0JiTJOCX2GBT0hcCV2B74dXX30VALDJJpvIzz3xxBMAgD333LPhsXvssQeSyaT8eisKhQJGR0cb/hFCSFD8/u+v4j3X3IsnV27o9aFEBqboE0IIFXxCiElfFfhf+cpXMHXqVLz//e+Xn1u9ejVSqRQ222yzhsdms1nMmjULq1atsv15V155JaZNmyb/bbXVVoEdOyGE/P7JV/HPN8Zx37Nv9PpQIgNT9AkhpLnA5zWRkPjSNwX+FVdcgXvuuQdf/vKXMX36dPn5yclJZLPZlt8zMDCAyclJ25954YUXYsOGDfLf8uXLdR82IYRIhN3carUkralUDYg1LNUqQkicyZca7xtM0SckvoQuZM8LN910Ez7/+c/jlFNOwSc/+cmGrw0ODqJYLLb8vnw+j8HBQdufm8vlkMvltB4rIYTYIXomiyzwHaH23XNMHiEkzhTKNQU/kQAMgwo+IXEm8gr+3XffjUWLFuHwww/Hd7/73aavz549G5VKBa+//nrD54vFIt58803MmTOnW4dKCCFtKUkFv9LhkQRodDpwMUsIiTNCwR/O1rQ7upoIiS+RLvD/9Kc/4aijjsKee+6Jm2++Gel0syFh1113BQAsXbq04fNLly5FtVqVXyeEkF4jLPpU8J2hKvhczBJC4ozowR/KpgBw05OQOBPZAv/pp5/G4Ycfjvnz5+OOO+6wtdq/5z3vwcyZM3Hdddc1fP66667D0NAQDj/88G4cLiGEdERY9NmD7wy1wC9xJBQhJMbk6/eNKTkq+ITEnVD24H/rW9/C+vXrZcL97bffjhUrVgAAzjzzTCSTSRxyyCFYt24dzjvvvKZZ9ttuuy3e9a53Aaj14H/xi1/EGWecgWOOOQaHHHIIHnjgAdxwww24/PLLMXPmzO7+cYQQYkOJCr4rSmVzActAKUJInCnUFfzheoFf5qYnIbEllAX+1VdfjZdffll+fNttt+G2224DABx//PEAIBPtL7jggqbv/8QnPiELfAD41Kc+hUwmg2uuuQa//vWvsdVWW+Haa6/FZz7zmSD/DEIIcUWpKhR89uA7oVgxzxPtqISQOCOcX8O5mkWfm56ExJdQFvgvvfRSx8cYhrvF3GmnnYbTTjvN4xERQkjwlMock+eGoqLgU60ihMQZ0YMvQva46UlIfIlsDz4hhPQbtOi7o2FMHhezhJAYk7dY9NmDT0h8YYFPCCEhoVxlyJ4bGlP0ec4IIfFFjsnLUcEnJO6wwCeEkJAglHsq+M5Qz1OJi1lCSEwxDENmt0yRPfi8JhISV1jgE0JISChXRQ8+Q/acUFQVfPbgE0JiSqliQNTzVPAJISzwCSEkJIhZ7lTwnVFSinouZgkhcSWvbAqLkD22LRESX1jgE0JISGCKvjvYg08IIWbAXiIBDGZrFn1OFiEkvrDAJ4SQkFCqssB3g+p0oIJPCIkrhXrAXi6dRCaVAMAefELiDAt8QggJCbTou0PtwadaRQiJKyK3ZSCTQipZW9pz05OQ+MICnxBCQkC1akjFhSF7zmi06HMxSwiJJ3lFwU8nqeATEndY4BNCSAgoKT3kpYqBKhdnHSk1jMmj64EQEk9ED35Nwa8V+GVeEwmJLSzwCSEkBJQsFnPVfk5aU6SCTwghMrdlIJ2igk8IYYFPCCFhoGwp6Bm015mGMXnswSeExBRTwU8qCj6viYTEFRb4hBASAqyKPfvwO9OYos8NEUJIPDF78FNIM0WfkNjDAp8QQkKA1aIvxh4Re9SQPapVhJC4IhT8XCZppujT1URIbGGBTwghIcBq0WcPfmdUBZ9qFSEkrsge/Ax78AkhLPAJISQUlKwWfSr4HWlQ8KlWEUJiClP0CSEqLPAJISQEMEXfPUU1ZI+LWUJITMnXM1sG0klkUgzZIyTusMAnhJAQ0KzgM2SvEyWOySOEEDNkjz34hBCwwCeEkFBgLfCp4HemMUWfi1lCSDwRG8IDafbgE0JY4BNCSChgir572INPCCGNIXtmDz6viYTEFRb4hBASApos+mUW+J1oHJPH80UIiSdmyF5SUfB5TSQkrrDAJ4SQENBs0WcPfifUkD3aUQkhcUUU+Lk0FXxCCAt8QggJBbTou6dYNjdBShUDhsEFLSEkfoiQvZqCX1vac9OTkPjCAp8QQkIAQ/bcY90U4XqWEBJHCvXNzlwmhRTH5BESe1jgE0JICLCGxFHB74x1U4R9+ISQOGIq+EzRJ4SwwCeEkFBgVeyp4HemaAkiZJJ+tPjpIy/j4Gvvw+oNk70+FEIiTV4o+Omk7MGvVNm2REhcYYFPCCEhoClFv8SQvU5YN0FoSY0Wv3x8BZ59bSMefXFtrw+FkEjTSsEHqOITEldY4BNCSAhosuhzTF5HrJsiXMxGi9F8GQCdF4T4RfTgDygKPsBNT0LiCgt8QggJAU0KPgv8jpTKjYvXMtsaIsVYvgSA2QmE+KXQoOCbS3tuehIST1jgE0JICGgak8cCvyPNIXtczEaJ0cmagm997RNC3JEviRR9KviEEBb4hBASCprG5LHA74j1HFGtig6lShWT9aKEzgtC/CEK/IE0e/AJISzwCSEkFDRb9Bmy1wmG7EWXsXr/PcDnjRC/5MumRT+ZTCBRr/HZ/kJIPGGBTwghIUDYlIeyKQBU8J3QZNGnEhwZRidL8v9Z4BPinXKlKpX6gUxtWZ9WRuURQuIHC3xCCAkBolgdzqUBsAe/E5WqAbF2zaRqi1kWitGhQcHnxgwhnskr94qBTG2DWPThc0IFIfGEBT4hhIQAUeCPyAKfFv12qA6HwfqilmpVdBjNmwo+Q/YI8Y7ovweAbEoo+LX/8ppISDxhgU8IISFAFDlCwadFvz1q//1QtnbOrJZ9El7G8qpFn88bIV4RBX42nUSyrtxLBZ8FPiGxhAU+IYSEANOiX1OjadFvj1rM5zJUq6KGGJEH0EZMiB/EvWIgbS7p2YNPSLxhgU8IISFA9CFPoYLvCFHgZ1NJuZilWhUdaNEnRA9yRF69VQlQFXzeRwiJIyzwCSEkBFgt+lTw2yM2QDKpBDL1vlMqwdFhtGFMHl/rhHglX6q9f4STCaCCT0jcYYFPCCEhoGhJ0aeC3x6p4KeTVKsiiDomjwo+Id4pCAU/rSj4nCxCSKxhgU8IISGgzBR9VxTLtYVrRrHoU62KDqpFn2PyCPGO7MFXLPpM0Sck3rDAJ4SQEECLvjuEgp9JmQo+leDoMNZg0efzRohXzB58c0kvXU28JhISS1jgE0JICCjRou+KomLRT6eoVkUN1aLPAp8Q7+Trbq9cWlXw6WoiJM6wwCeEkBBQkin6tUVauWpwcdaGUrlVij43RaJCg4JPiz4hnhEhey0VfF4TCYklLPAJISQEWC36AFX8dggFP5NO0I4aQTgmjxA9iJC9XIYKPiGkBgt8QggJAaaCbxb4DNqzRxSFmVRSjsnjYjY6NFr0uZFFiFfyImRPTdFPMkWfkDjDAp8QQkKAKPAHMinU12ZU8Nsgzo0assfFbDSoVg1sLKgWfT5vhHilVcgeU/QJiTcs8AkhJASoirQIS2KSvj1iQySXVsfk8XxFgfFiGWrdUWIPPiGeET34OSr4hJA6LPAJISQElOXYtwRydSWGFn17isqYPJGiz17uaKAG7AEsQgjxQ0sFP8VNT0LiDAt8QggJAUVFwc+mRIHPxZkdJWVDhIFS0UIN2AOYok+IH8R9YiDTQsHnpichsYQFPiGEhAARNJZJJRUFn4WPHezBjy6jk40KPp0XhHinwB58QogFFviEEBICSmVTkRYKPkP27BEKfjadRCYl1CqerygwZlHwWYQQ4p18vZVL7cEXrqYS31uExBIW+IQQEgIYsucOcb6yVPAjh7Doi42sEvuECfGMCNlTFfyU6MHnpichsYQFPiGE9BjDMGSRk04lkE1Twe+EatGnHTVaiJC9mcNZAOwTJsQPIoxV7cFPc9OTkFjDAp8QQnpMpWrAqK/Dsqkkcmmm6HdCTdFPSTsqN0SiwOhkTcE3C3w+b4R4pd2YPG56EhJPWOATQkiPUVWWWshe3aJfYuFjh8gsyKaT5kgoKsGRYNSi4LNPmBDviDF5uYaQPSr4Olk3XsRTq0Z7fRiEOIYFPiGE9JiiomCm1ZA9Kpu2yJA9ZUweF7PRQITsUcEnxD+iwB9oUPDZtqSTT/3P4zjsGw/gpTXjvT4UQhzBAp8QQnpMSem1zySVMXklWvTtKCqhhGIxW6ZFPxKIMXnswSfEPyKMdYAKfmCsXD8JAFi1YbLHR0KIM9K9PgBCCIk7YhGWTiaQTCaQo4LfERmyl04C9f+nWhUNRIr+jCFh0efrnBCvmCn6rXrw+d7SgXAZcTOSRAUW+IQQ0mNEsSp6yU0Fn4szO0yLfhLVekIhF1/RQPbgT6GCT4hfhNNLhLMCVPB1I3JCStx0JxGBFn1CCOkxJSURHgB78B0gz1k6KRezVPCjwVg9RX+WsOhXDRgGnztCvJBvMSYvxeBRrYj7TYnnk0QEFviEENJjhMoiCnyZol9mgW9HY8he7bwxjT0aCAVfWPQBbs4Q4oVK1ZBFp1rgU8HXi3AZMeeFRAUW+IQQ0mNkP7mw6KcZsteJQtl0PcgxeVx8RQLRgz9rilngsxAhxD2FsnmPUEP2mKKvF1PB5z2GRAMW+IQQ0mOsCj4t+p2RCn46KQOl2MsdfvKlitzQUhV8LpwJcU9eyWnJpangB0VZ9uDzfJJowAKfEEJ6jLUHnyF7nSkpY/IyckweF19hZ6xuz08kgOlDGfl5bs4Q4p583eWVSSXkRifAFH2dVKuGdELwOkWiAgt8QgjpMSWLRV8o+AWqmraoKfopqlWRQdjzp+TSyKSSSNRrEo7KI8Q9osAfUNR7gAq+TtRrE51GJCqwwCeEkB4jwuFEWJwM2aOCb0uRPfiRRCj4Uwdq6n2a7RWEeEZkkeQyjQV+ipNFtKFem1jgk6jAAp8QQnqMVPDT7MF3SrFiuh7EYpb9keFntD4ib+qgKPDr7RV87ghxjVTwM43LeSr4+lCvTTyfJCqwwCeEkB4jRu9kRYp+hin6nVBD9tJMjI4MwqI/MpAGAOm+oEWfEPeIkD0xeUWQqm8SV7hx5psGiz5H15KIwAKfEEJ6TLFisejX+ykLXEzYUiqbIXtUq6KD1aIvgiWp4BPinnxZKPjswQ8K1ZZf4vkkEYEFPiGE9Jgmi379v0UW+LYU1TF5KdHHzfMVdqRFXyj4sr2Czx0hbhE5LdYCnyn6+miw6PM6RSICC3xCCOkxTRb9eoFfKNOib0dJCdnL0KIfGYRFX/TgCwWfzx0h7imU2YMfNA0KPgt8EhFY4BNCSI+xWvSlgs/FhC0NCj4Xs5HBtOg39uCXqTQS4hoRspdLM0U/KNT7CoNcSVQIZYG/ceNGXHzxxTj00EMxc+ZMJBIJLF68uOVjn376aRx66KGYMmUKZs6ciRNOOAFvvPFG0+Oq1Sq+8pWvYJtttsHAwAB23nln/PznPw/4LyGEkM5YLfpSweeYPFtKSoq+OSaPi6+wIyz6I5YxeVw4E+KevLToWxX8erYFr4m+UVV7bkSSqBDKAn/NmjW47LLL8PTTT2OXXXaxfdyKFSuw//774/nnn8cVV1yBc889F7/5zW/wvve9D8ViseGx//mf/4nzzz8f73vf+/DNb34T8+bNw8c//nHceOONQf85hBDSFrFoyCQbLfpU8FtTrlQh1q1ZJWSP9snwIxX8wZqCz5A9QrwjLfpU8AOj1NCDz/NJokG61wfQitmzZ2P16tXYYostsHTpUuy1114tH3fFFVdgfHwcjz32GObNmwcA2HvvvfG+970Pixcvxumnnw4AWLlyJa655hqcccYZ+Na3vgUAOPXUU7Fw4UKcd955OOaYY5BKpVr+DkIICRqxgBDFjkzRp4LfEnXBVUvRZx93VDDH5NUVfI7JI8QzckweU/QDQw3W46Y7iQqhVPBzuRy22GKLjo+79dZbccQRR8jiHgAOOuggvO1tb8PNN98sP/erX/0KpVIJn/rUp+TnEokEPvnJT2LFihV4+OGH9f4BhBDiAmk3TzNkzwnqIos9+NFidLJxTF4qSQWfEK+IHnyrRT+VYoq+LqjgkygSygLfCStXrsTrr7+OPffcs+lre++9N5544gn58RNPPIHh4WG84x3vaHqc+DohhPQKUeBbQ/aqBsfytEK14qeTCWQ4Ji8yjMkU/bpFP8nnjhCvSAU/baPgsyD1jdp3zx58EhVCadF3wurVqwHU7PxWZs+ejbVr16JQKCCXy2H16tXYfPPNkUgkmh4HAKtWrWr5OwqFAgqFgvx4dHRU1+ETQohEKATZdKNFHwAK5SrSqcjuxQZCsSzGCiaRSCSo4EeI0XoPfrNFn88dIW7J24zJYw++PtRNkmKZ55NEg8iuGicnJwHU7PxWBgYGGh4zOTnp6HFWrrzySkybNk3+22qrrbQcOyGEqJgKfm1RJgp9wCxmiYmaoA+APfgRoVI1sLHQOCbPDNnj65wQtxRkir5Vwec1URdFpuiTCBLZAn9wcBAAGhR2QT6fb3jM4OCgo8dZufDCC7Fhwwb5b/ny5VqOnRBCVMyCtXZJTiUTstgvsMBvQpwvsRGSStGOGgU21tV7oHlMHp87QtwjFfx0awWfrib/lNmDTyJIZC36wl4vrPoqq1evxsyZM6VqP3v2bCxZsgSGYTTY9MX3zpkzp+XvyOVyLZV/QgjRSdli0Rf/Xy5WqOC3QNgkxYaI7OOmuhJqRIL+QCYpX+ui/YSFCCHuKdRD9uxS9Kng+0e9rzBFn0SFyCr4W265JTbddFMsXbq06WuPPvoodt11V/nxrrvuiomJCTz99NMNj/vTn/4kv04IIb2iaLHoA0zSb0exheMBqIUSVrmgDS2iwBcJ+oDZZsHNGULck5cWfTsFn+8rvzSm6PN8kmgQ2QIfAI4++mjccccdDdb5P/zhD3j22WdxzDHHyM996EMfQiaTwXe+8x35OcMw8N3vfhdbbrkl9t13364eNyGEqIgFREYJ0xNBe7ToN2O16It+U4BKcJgRI/JGBkzzoHjuSrS+EuKagrToWxT8FBV8XZQbevB5Pkk0CK1F/1vf+hbWr18vE+5vv/12rFixAgBw5plnYtq0afjc5z6HX/ziFzjwwAPxmc98Bhs3bsRXv/pV7LTTTjjppJPkz5o7dy7OPvtsfPWrX0WpVMJee+2F//3f/8UDDzyA//mf/0EqlWp5DIQQ0g3EAiJjsegDLPBbUVJS9AFzMQtwQRtmzBF5poKf5ohDQjyT7xCyx4LUP+pYVrbMkagQ2gL/6quvxssvvyw/vu2223DbbbcBAI4//niZan/ffffh3//933HBBRcgm83i8MMPxzXXXNPUO//lL38ZM2bMwPe+9z0sXrwYb33rW3HDDTfg4x//eFf/LkIIsSJD9mjRd4S06Kdr5yulnLeaJZWbtmFEjMhrsOizECHEM3nRg28J2ZM9+HTG+KbBos/rFIkIoS3wX3rpJUePW7BgAX7/+993fFwymcSFF16ICy+80OeREUKIXootLPpCwadi0Iw4J+J8qdkFVPDDy+hkTcFXLfpiAkKJCj4hrhEp+taQPbHpWWIPvm/UHAM6jUhUiHQPPiGE9ANi0aBazXO06NtizSxQFXz2coeXMaHgD6oKPsfkEeKVgk3IHnvw9aHeU3h/IVGBBT4hhPQYGRpHBd8R4nyJTZBEIsGxUBGgVYq+GJNHpZEQ9wiLvrUH30zR5/XQL+WGAp/XKRINWOATQkiPYYq+O6xj8gCOhYoCrSz6ZsgeCxFC3JIvN252CkTInsHRob4pMUWfRBAW+IQQ0mNKbSz6VPCbMXvwzfMlin0WiuGltUVfPG98nRPihmrVkNdCOwUfYFHqF9VdRAWfRAUW+IQQ0mPaWfSZot9Mqa2Cz8VsWDEt+i0UfD5vhLiiqBSbzWPyGDyqC1r0SRRhgU8IIT1GLCDStOg7Qm6IKLZU9uCHn7FWY/LovCDEE6L/HgAGLBb95tGhxCuqu4jXKRIVWOATQkiPMXvKzUUZQ/bsEZkF2RYKPhWW8CIV/EFFwec4L0I8ka8n6KeTiYbNYfE5ATc9/VFUivpy1YBh8HyS8MMCnxBCeky5ZcgeLfp2FMrNFn3x/1zMhhczZK85RZ/KGCHuEAq+NWAPYA++Tqz5IByVR6IAC3xCCOkxrXrKGbJnD3vwo4dhGDYWfU4/IMQL+XLrEXlAbXRoim1LWrDeU3itIlGABT4hhPSYVhZ9U8HnYsJKqWzfg8809nAyWarIhbI6Js9srWARQogbCqXWCfoCbnrqwdr2xWsViQIs8AkhpMe0tOjXF21U8Jsxpw6YGyIijZ1qVTgR6n0qmcBQ1ixIOCaPEG9Ii36m9VJeBo+yIPWFtX2IOS8kCrDAJ4SQHtPKci4C5KjgN1NsadGvF4os8EOJ6L+fOpBGItG8McPnjRB35Ov3BjFxxYqp4PMe4gdrQc+8EBIFWOATQkgPMQxDFjcNFv0MQ/bsKJbr54tj8iKDSNBXA/YAM2SPqhgh7hAK/kAnBZ/XRF+UqlTwSfRggU8IIT1E7edLt1DwadFvxrToKwV+imPywsyoCNhTRuQBQEZmJ7AIIcQNwt01YKvg09Wkg+YUfd5jSPhhgU8IIT1EXSxkG3rwadG3Q7Y0UMGPDKZFv7WCzyKEEHdQwe8O1s1HXqtIFGCBTwghPURdPKgW/WyqpsqwwG9GuBrUkD0mRocboeCrCfqA2oPP1zkhbiiIkL2OPfi8JvqhVKWCT6IHC3xCCOkhRWWxIBZkAMfktaNVyF5GKsE8X2FkLN9awTdT9FmEEOKGvByTZ6Pgy8kivCb6gWPyguP1sTwMg+czCFjgE0JID1H7ydV0cWHRZw9+M/KcpdUUffZyh5nRSaHgWy36zE4gxAsigHUg00HB5zXRF00WfV6rtPB//3gNe1/+B3x7yfO9PpS+hAU+IYT0ELF4SCt2c0Adk8cUfStCQVEVfPabhhup4FtD9jgmjxBPmAp+6wKf10Q9UMEPhmde3QgA+MerYz0+kv6EBT4hhPSQVnZzAMjVF22FEtUCK2YPvlrg18etcTEbSmSKvkXBT9GiT4gnRMhezsaizxR9PVjPH91GehBOCLYhBgMLfEII6SGiZ9xa4MsxeVxMNFFqsSmSEv2mPF+hRKToN4XsJWnRJ8QL+XL7kD0q+HoQm4+ig445L3ooscAPFBb4hBDSQ0plYTdvtOjLMXklWvStmK4H85ylmRgdakyLviVkj2PyCPFEoUPIXj+k6H/lzn/gC//7ZE+PQdxvBuuuOlr09VCsn0eucYKBBT4hhPQQO4s+FXx72obsRXgx28/YWfQZskeIN/J15XOgo4IfzfdWuVLFd+59AT995GW8MVbo6XEAZoHPdiI9UMEPFhb4hBDSQ8TiwRqyJxX8cpVjZCyIHvyGMXn1flPaUcOJUPCtFn2OySPEG6IHv2OKfkSviermtrh+9AJxbRqQCj4LUh2wwA8WFviEENJDhN0vaw3Zq6syhhHdBVpQyHOWbu7BZ6EYTsSYvGmDrRV8bswQ4g4ZspduvZSP+ntLtK8BwMZCuXfHUXdADGVZ4OvELPBp0Q8CFviEENJDWgXGAY2LNu5wN1JqoeBH3Y7az5QqVUzWi5GmkD1h0efzRogrCh3G5EV9QkWhYhZ+G/O9K/DF+RusF/jccNdDsb6BU+T6JhBY4BNCSA8p2Vj0VUWfITSNFFv04HNMXngZUxbnU3KtLfqGEV2lkZBeIJRPu5C9qKfoq4XfWI8UfMMwZEE/SIu+VmjRDxYW+IQQ0kOE3dyq4CeTCZkSz6A9E8MwWqfoR9yO2s+IEXlTcmmkLa9zdWOLC2dCnJPvqOBHvAdfKfx6peCrifmDWabo60SMG6SAEQws8AkhpIeIm5y1Bx8w+/CFFZPUCniROaieM7mY5eIrdAgF32rPBxo3tqJaiBDSC/LlDj34EW9bUgvp8WJvCnx15r3owS9zI1ILwqJPBT8YWOATQkgPESqF1aIPmBZ0Kvgm6qKvMUVfqFU8V2FjtJ6AbR2RB5gbMwAXzoS4oe9T9FWLfggUfKbo60W16HNSkH5Y4BNCSA+xs+gDpjJDBd9E3exoSNEXgVIRXcz2M8KiP3WwWcFPJ1WLPp87QpwilM++7cFXQ/Z61IOvbjqaPfjRPJ9hQ90ooYihHxb4hBDSQ4TinGmr4LNHTaCqOmpxKHvwufgKHaZFv1nBTyQS8nmk+4IQ55hj8lor+CLvIqqbnoUQ9eCnkwm5Cc/rlB7UAp82ff2wwCeEkB5SbDHyTUAFvxmxKMimkkgklAI/yXFrYcW06Dcr+IC5OcP8BEKcYRiGDNnL9amCryrlvVLw1Sk3YhOeCr4eisp55BpHPyzwCSGkhwh1pXWBXw/Z4+62pNQiQR8w+02jupjtZ4RFv5WCD5ij8tjbSogz1HtCxx78iBakYejBl/fnZFLeo3md0kOprCr4dCnqhgU+IYT0kFK5s0WfBb6JVPAtydHpiAdK9TOj9cV5qx58gCMOCXFLQ4FvZ9GPeOtLw5i8Qqknx1BWFHzZ8hDRDZOwQYt+sLDAJ4SQHmIq0m0s+tzdlhRsWhpScvHFhULYaJeiD5i9wrS+EuIMMTs8mWi9OQxEP3g0DCF7ReX+LCa1UMHXg/q6pEVfPyzwCSGkh5SqIsSn+XIsQ/a4uy2xmzqQoUU/tLQL2QM44pAQt4j++4FMqiGLRCXqzphSWenB75VFX7nfZNLciNRJkRb9QGGBTwghPURa9NPNi7QcLfpN2Fn0oz7zuZ9pNyYPoIJPiFvyZZGgb7+Mj3oPfqGiWvR7UwCKTcd0itM+dEOLfrCwwCeEkB6ipsJbydZ7K6ngm4gNEev5irpa1c+MdlDwzRR9vs4JcYIYkWcXsAeoKfrRfF+FoQe/1Zg8WvT1wAI/WFjgE0JID2ln0aeC34xQdayOhzST2EPLWKcxeXRfEOIKcU9oV+BH3dWkFvj5UrUn1/YGiz6dRlopNYzJo0VfNyzwCSGkhziz6PPmJyjZhOxFfeZzP2Na9G0UfG7OEOIKoeC3s+hH/Zpoda6N9yBoTw3BpdNIL0XlPBZ5TrXDAp8QQnqIUFdaW/QZsmfFLmQv6mpVv1KtGjIBe8RGwc+kot0rTEi3ESF7ubYKfrRT9K0bfmM9CNorKWPy5HUqouczTBiG0WjRZ4q+dljgE0JIDxE710JtUcnVe/Bp0TcRiwKrcpXhjOJQMl4sQ6yHO43JY3gVIc6QPfj9rOBbCvxejMoTxXwmaVr0ueHun0rVgKG8LLnG0Q8LfEII6SGmRZ8KvhOKNhZ9KvjhRATsZdNJ237htJwvzeeOECfErQcf6E2Bryr46Yg7IsKE9VrPNkT9sMAnhJAeoioEVtiD30xR9kRaQ/ainRjdr3QK2APMzZqoKo2EdBszRb+Ngp+K9jXRqupu7IFFXzjC0qmk0koUzfMZJkqW1yQVfP2wwCeEkB5SskmFB8wCnwq+iRp6pJKmRT+UjE7WFuV29nzALEQYskeIM8yQPQcKfkSvidbrQS8V/GwqIe8xxYiezzBRsqxp2IOvHxb4hBDSQ+wKVoBj8lohF1xpWvSjgFDw7QL2AND6SohLTIt+H/fgh8Gir4yxpYKvD1r0g4cFPiGE9BBxo0u3tOgzZM+KWPRZpw5EfTHbr4zm24/IA8CFMyEuKUiLfv+m6DcV+D2x6Ksp+tE+n2HC6s7gGkc/LPAJIaSHmIp0s0WfIXvNFDuMyaPNO1w4s+jXnkuG7BHijLyDkL2ob3oWLW6tsV6k6Cv3G3E+eT/2j3VCAhV8/bDAJ4SQHtJewWfInhW7lgYGtYUTZxZ90V7BhTMhTnAyJi8V8feVuNbPGs4C6I2CL8Lg0klVwY/m+QwTTQo+e/C1wwKfEEJ6SLsefCr4zYhwHvbgRwMxJq+dRZ9j8ghxhwzZ62MFX9i2ZwzVC/xCqevHUCrXFfx00izweZ3yjTivAlr09WO/pU4iy8tvjmPDZAnzZg5hev3CSAgJJ+0s+uzBb6aopBqrRH0x26+MTnYek8cJCIS4Q9wTco4U/Gi+r8TG9qwposDvgUW/rtZnkgk57cNqLyfuoUU/eKjg9yHn3fJXfPBbD+KhF97s9aEQQjpQbmPRp4LfjP2YPPbgh5GxuoI/0qYHX4bs0fpKiCPyDkL2xDUxqpue4r43s27RH+uFRV/cn1NJGezKjUj/WANVKWLohwV+H8K+XUKiQ5Fj8lxRVCyTKmKDJKqL2X7FTNHnmDxCdJEvdQ7Zkyn6ES1IxWatsOiP9yRkz0zRT3MjUhtNY/LYg68dFvh9iCwK+IYhJPSU21n0M9yssyJbGmxS9MtVA4YRzQVtPyJ68EdyHJNHiC5kD34bi37U25bE5rdQ8Htj0a9vKCeTciOyVOE9xi9Wpx3bHvTDAr8PYd8uIdGhXYq+KGL5XjYRtk2rgp9RevKjuqDtR8ZED367kL0UQ/YIcYOTMXlRT9G3WvR7kaKvOuzUTWW6jfzBHvzgYYHfh9CiT0h0kAuIFkqMSEhmgW9SsgnZE4tZgIuvMOHOos/XOSFOKMge/D5W8EXInujBD4lFv/b5aJ7TsCDu44n6KaXjWD8s8PsQYetlMBch4UcsIDLJZou+UAyK5SotgXXsMgtUB0RUF7T9yKibkD0umglxRMGVgh/N91Uri36374PimpSxFPi0lPtDFPjD2drGL0UM/bDA70No6yUkGlSqBsTaq2XInqLOcEFRwxwr2DpFH2ChGBbypYrcaHYyJo8WfUKc4awHP9rBo1aLvmEAE8XuOlNLVbOFLqNsIjMvxB+leljucE64FOk41g0L/D6Etl5CooEaNNPKoq/2/NGRU0P24FtD9hKqRZ/nKgyIsVaJhKnUtCId8V5hQrqNkzF5UVbwDcOQm9rTBjMQBrduB+1Jh10qgWQyEelzGibEczslRwU/KFjg9yFmij53xAgJM2qBn25h0VfVGd4AawiV15qin0wm5CIwqopVvyH670dyaSRbvL4FssCngk+II6RFP21f4AtXUxSvh7VpKLX/z6VTshAc63LQXsnSEiauVdYUeOIOsXEypd66xR58/bDA70M4O5uQaKBakltZ9BOJhLSi8/1cw7rgUpFjjCK4oO1HRh0k6AOqRZ+vcUI6YRiGouDbL+Ol2hzB95V6LcimkzLDo9sKvpxyU79GZdlOpAVx/kakgl9hzpBmWOD3IWJMHi29hIQbsfBKJhpT4FVyStAeMa191h58QFGsuPgKBWMOAvYAJWSPGzOEdKRUMbNbcu0U/Ain6Kv3u0wqIXu1x7tt0a+aFn3AvMdEcdMkTFgt+lWD13/dsMDvQ0QwFxU/QsKNXSK8ivl+ZssNoPbgN2+IRH3uc78hR+S1CdgD1DF5XOAR0om8ci/IOVHwI/i+Etf5ZKKmnvfOom+G7AEMBNVFSVr0zXsDaxa9sMDvQ8wUfRYEhIQZu35ylSwV/AbaW/Sjq1j1I6OTtcV4Z4s+VTFCnCL6lROJ/k3RF8WecGpN6ZlFv3Yc4hqVZTuRFkoWBR8INjcsjvcWFvh9CBV8QqJB2bJ4aAWnYjQiN0VaWvSproSJMRGy10HBF5s1DNkjpDPqiLxEwv7eoSr4UetvluNQ69cG0au9sX5N6RZlyya83IykS8wX6n086NHeX/jfJ7HX5ffgjbFCID8/rLDA70NETxZTKQkJN04s+lTwGynZjMkDqOCHDdOi30HBF8nUXDQT0hHhzmw3Ig9obGOK2iXRmrUilN5eK/hmin7ETmjIUFvtgg4Gf/CFNVg3UcLTq0cD+flhhQV+HyLfLDG0pBASJYQ6wB5857QL2WMPfrgQ/bKdevCp4BPinHxdvGlnzwcag1ujdk0UBaDY4Ba92mNdD9lr7MHP0KKvBbXVLug1jngtxe05Y4Hfh5gKPgsCQsKMeZNrY9EXG3Z05MAwDMX10HzOZKEYNbmqT3E+Jo+zpQlxilMFXxSlQPRcTUVrD7606He5wLfcb7gZqYeGAj9g17H4XXFzQbLA70PEBTFuL2ZCooYji754P7P4QaVqQLSStgomNOc+c/EVBkalgt++wI9y2jch3UYo+ANtRuQBVgU/Wu8tq1NL5Hh036Lf6LLjZqQe1GyDXMBrHPEcxm0N1d43VyeZbB/kYUcikUC53N03I0Hg/SyEED2Im1y6nUWfmRoSte+RPfjhx33IHl/jhHRChOwNtBmRB5jXQyB6m55FS9bKcF3BH+9xD34mySBXHahOvGzALkXxWoqb6OmowN9///09FfikN7Bnl5BoYCYF219fZcIsi5+GHfjWKfoMawsTjsfkMbiKEMeYPfjtFfxkMoFEAjCMCPfgWyz6Y9226FcbFfxMmjkvOpAW/XRSmRQUUA++sOjHbA3lqMC/9957Az4MohOp+MVst4qQqGG1/7VCbtgxU6NhB15VpwQpMfeZhWIoGHOYom9mJ/CeRUgn5Ji8Dgo+ULtOlipG5FxNRcuYvCk9s+jXFfykSNGngq8Dde0TpOvYMIzYKvjswe9DaNEnJBpY7X+tkGPyYrb73Ap1NnIrV1mavdyhQvTgd7Loi9d/1IoQQnqBWNt1CtkDoptLUrL24PdoTJ510k2GPfhaUAOGzZpFv4ihrgXi9pxFusB/7rnncNxxx2Hu3LkYGhrC9ttvj8suuwwTExMNj3vooYew3377YWhoCFtssQXOOussbNy4sUdHHTxqyJ5hROuiTkicKDkI2TMV/HjdnFrRaeoAx+SFh0rVkIvxzhZ9qmKEOMXswe9c4Iv3VtQ2z+zG5HU7Rd96j2ZeiB7UjIUgJwWpRX3cFHxHFn07Hn74Ydxzzz1YtWoVCoVC09cTiQR++MMf+vkVtixfvhx77703pk2bhk9/+tOYOXMmHn74YVx88cV47LHH8Ktf/QoAsGzZMrz3ve/FO97xDnzta1/DihUrcPXVV+O5557D7373u0COrdeos1EL5aqjmwAhpPtY1YFWsOXGxKrqWMlQCQ4N6kK8c8ieUBn5GiekE/m60pmzuQ6qRHVChW0PfhcVfMMw5HkTLiMRiMvNSH+0HJMXwBpHLeqLMXvOPBX45XIZH/vYx3DbbbfBMAwkEokGpVh8HGSB/9Of/hTr16/HH//4RyxYsAAAcPrpp6NareInP/kJ1q1bhxkzZuBzn/scZsyYgXvvvRdTp04FAMyfPx+nnXYa7rrrLhx88MGBHF8vUYNXWOATEl7azXQXcOylSaHc3vEgevCjZkftR0br/feDmVTbDSxAWTRHrAghpBfIMXkOe/CB6G16FspWi37NBVQsV1EoVzoGDOpA3RQR6fkZusS0UGoxJi8Ii34xxgq+J4v+Nddcg1tvvRUnnXQSli5dCsMwcPbZZ+Phhx/GVVddhenTp+OYY47BCy+8oPt4JaOjowCAzTffvOHzs2fPRjKZRDabxejoKO6++24cf/zxsrgHgEWLFmHKlCm4+eabAzu+XpJJ1ZJTgfi9oAmJEo4s+gHe/KJGp1DCqC5m+xFR4E8d7KwjyEUzFXxCOiLuBQMOityoti2pBSAADOfMv3W80J17obpRLMfkUcHXQoOCH2AbYoOCH7N6yFOB/z//8z/Ycccd8YMf/AC77747AGD69OnYZ599cN555+H+++/HHXfcgd///vdaD1blgAMOAACccsopWLZsGZYvX46bbroJ1113Hc466ywMDw/jb3/7G8rlMvbcc8+G781ms9h1113xxBNPtP0dhUIBo6OjDf+iQCIRbGgFIUQPTiz6VPBNOln05bi1iC1m+xExIm+kQ4I+YCr4VQOocnOGkLYUpILvpAc/mpueske7fq1Pp5IYrP+9412y6av3EdOiz5A9HTSG7AVn0Vc3YuL2nHkq8J9//nlZYAO1grJUKsmPFyxYgA984AO47rrrfB+gHYceeii++MUv4u6778Zuu+2GefPm4bjjjsOZZ56Ja6+9FgCwevVqADVV38rs2bOxatWqtr/jyiuvxLRp0+S/rbbaSv8fEhBydjaLAkJCixOLPnvwTUqW4CUrTGMPDxPF2iJ8OOtcZQS4OUNIJ8yQPQc9+KmI9uBXan+jeq0fFn34XQraKyn3XGnRT7ENTAfSjZcO2KJPBd8d2WwWQ0ND8uMpU6bg9ddfb3jM1ltvjeeee87f0XVg/vz52H///fH9738ft956K04++WRcccUV+Na3vgUAmJycBADkcrmm7x0YGJBft+PCCy/Ehg0b5L/ly5fr/yMCIlff6WTyNiHhhQq+OwpiQyRtl6LPxVdYsIZktUPd4OJzR0h7RIHvpA896in6apCgCOvs1qg8sSmSSiaQTAqLPhV8Hagjb4Mc7d2Qoh+z58xTyN5WW23VUOxuv/32uP/++2WwHgA88sgjmDlzpp6jbMGNN96I008/Hc8++yzmzp0LAPjwhz+MarWK888/Hx/72McwODgIAC0T/vP5vPy6HblcruXmQBSgRZ+Q8MMefHeUOoTsMQApPBQ7tFOoiCIEYIFPSCdEIeRIwZf5FtF6X8kefOX6IZL0NxZKLb9H/zHUznNacRgxRV8PjT34wQmSxRgX+J4U/IULF8qCHgA++tGP4plnnsERRxyBb3/72/jYxz6GP/7xjzj00EO1HqzKd77zHey2226yuBd88IMfxMTEBJ544glpzRdWfZXVq1djzpw5gR1frwlyR4wQoodOc90BvpdVOoXsRXUkVD9S6NBOoaK+/uNs0d9YKOO6e1/Ay2+O9/pQSIiRCn4f9+C3mpgypcsW/VYOO24i60E4NNKpYDPD4mzR96Tgn3zyyahUKli5ciXmzp2LM888E/feey/uuOMOOVt+7733xpe//GWtB6vy2muvYcaMGU2fF1kA5XIZO+64I9LpNJYuXYpjjz1WPqZYLGLZsmUNn+s3hHUrbi9oQqKEKFjTDhR8vpfNDRG7+c+yB5/qSs/pFIiokkgkkEomUKkakStEdPKbv67CVXf+Ay+8sRFXH7NLrw+HhJS8i5C9qKbot2rxmdJ1i75ZhArMFP1onc+w0XpMXsAW/ZitoTwp+Lvvvjuuu+46qZ5nMhn8+te/xqOPPoqf//zneOihh/DQQw9h1qxZWg9W5W1vexueeOIJPPvssw2f//nPf45kMomdd94Z06ZNw0EHHYQbbrgBY2Nj8jE//elPsXHjRhxzzDGBHV+vkWMnYvaCJiRKOLPoM2RPUOxg0RdWb85T7z3mAt3ZvGo5ASHGC+f1E6WG/xLSinxZ9OA7aX+JpoJfrDQ7gEaERb9LCn6x3Kzg06KvhwaLfoCCpPoz43Zv8aTg27Hnnns2jaQLivPOOw+/+93v8O53vxuf/vSnMWvWLOkgOPXUU6X9/vLLL8e+++6LhQsX4vTTT8eKFStwzTXX4OCDDw60haDXmCn67NslJKwIhSDbxqLPkD2TTlMHUnIxy3PVa4ouLPpAbaFXKFcj1yusE3HO4tYrStzhTcGP1vuqFCIFP5NUFXyRacD3qFcMw5Cvx0wqEaggSQXfJe9+97tx/fXXY/369ZoPxzn7778/HnroIeyxxx74zne+g7PPPhsvvPACLr/88obxfLvvvjvuueceDA4O4pxzzsH3v/99nHLKKbjlllt6duzdgCn6hIQfoRA4sehzs66z4yEd0cVsP2Iq+PabVyrpVDStxDoRr+8i3+uR5PXRvMymChJxLxhwEWDZDwp+t3vwW7XQmU6jaJ3PMKGeu8DH5Cm/K24bp54U/EceeQQPPfQQzjrrLBx++OE44YQTcNhhhyGTyeg+vrbsvffe+O1vf9vxcfvttx8efPDBLhxReGAwFyHhx4lFnwq+Sae+7jRnFIeGVgv0dsj2ihg/d2IMJO/b0eOPz63B8T/8Ez55wLY4/9DtA/1dhRgo+K168IfrBf54txR8kaKv9uCn2YPvF/XcZVNJ+RwHcd2Lc8ieJwV/1apV+NrXvoYFCxbgtttuw4c//GHMnj0bZ5xxBh566CHdx0g8YAZzUQkgJKxIC2DbFH324As62b6j2m/aj7RaoLfDtL7G97mTFn2+1yPHn19aCwB45tWxDo/0j5mi70DBT0WzbanV9WOk6xb9uo1cGeMp/j9qGyZhQi3w1R78IBzHJY7Jc8emm26Kz3zmM1i6dCmeeuopXHDBBRgZGcF1112Hd7/73dhuu+1w6aWX4vnnn9d9vMQhLAoICT+tQnys0I1jUnQ8Jo/nqtcUXaToA+ZzF+cxeaZFP77nIKosXzcBAJgoBl98igJ/wEGApbwmRmzjrJ1Fv1sFvsx8UdqMxIYJFXzviPOaTNRenxyTFwyeCnyV7bffHpdffjlefPFFLFmyBCeffDLefPNNXHbZZdh++2BtSsSeIC0vhBA90KLvDqc9+FTwe4/ptnCWop9he4U8Z7xvR4/la2sF/mQxeNekeH04sehH9ZooJ6akmwv8bvXgi2tRWlXwOSbPN9ZsgyAFSfV5ittzpjVFf+HChdhqq62wySab4Gtf+xrK5e68CUkzDOYiJPw4s+jXC/xKFdWqgWTSWWhZP9IqWVmFI4zCg1uLvgxIjNkiTEW8brmZFz2Wr50EAEwEXOCXK1VpDx9wYNGPbA9+KwW/2xb9FlNb2Erkn5Kl1S7IFP1ijFP0tRT4a9euxU033YQbbrgBjzzyCABg6tSpfT1nPuzINwxT9AkJLSUHFn21QCpWqhhIOlNE+xFz0dd6kyOqalU/0mmkoRW5ORPj545j8qJJvlTBa2N5AMEX+HmlSMk5sOhHNkW/VQ9+rhbkvbFbKfrVZgXfDAPle9QrJcu9QQqSJVr0deK5wC8Wi/j1r3+NG264AXfeeSeKxSIymQyOOOIInHDCCfjABz6AXC6n81iJC8SFnwsFQsKL6DdOt1Hl1UVcoVx1ZMvsVzpZ9KOqVvUjYjGVcxmyF7UwMJ0IBSuIhS4JjpXrJyGm400G/NzllZ/v5L0V1WuiuNarf2O3FXyhNLdO0Y/W+QwTRct9vHsW/Xg9Z54K/FNPPRW33norRkdHYRgG9t57b5xwwgk47rjjMGvWLN3HSDxg7ojFd7FESNiRBWubhZqqgMZtB9qKDCW0OV+mfTLe5ykMeLXox20RpiJD9vj6jRSi/x4IPmSvoLyvnLRrma6maL2mZA++TcheN9rVRAud2iaQYZCrb8qWsFxRr5SrBsqVqnRz6aBBwa9UYRgGEol4tDl6KvB/9KMfYf78+TjzzDNxwgkn4K1vfavu4yI+YQ8+IeFHWPTbzQpPJGops4VyNfbv51KLvkyVFEcYhQa3KfpphuzJxWipYsQ+byNKLF83Kf8/Xwo2K8VM0Hc3nSJq18R2Y/IAYKJUkQV/UJhhcGqKPq9TfilZ7g3quMei5gLfumFcrFQdtbb0A57eHffffz/2228/3cdCNMLRWoSEHycWfQBKgR/v93OrZGUV9uCHB/cp+lTGGgKhYp63ESVUBR8A8uUKhrLBFJ+ywHfYqiWK00rECtJCiw3CXDqJVDKBStXAxnw58AJfOMHUglNcp+iy8Y41n0XdsC+UqhjK6v9dglLFQMAvm9DgaZuExX34CbKnhRCiBycWfQDIikyNmL+fS51C9jijODS4VvCT7G1VX7e8d0cHa4EfZNBevt52mXOQoA9EU8E3DKOlWyuRSCg2/VLgxyHOWSappuhTwfdLyWLRT6eScnNe93XPumaK0xrK0T7GK6+8AgDYcsstkUql5MdOmDdvnrcjI74QF/84vZgJiRpikdDOog/QkSOwhvNYSVHBDw1ue/CZnxDvxOcos3xdY4E/GWCBL9q0BhzajKOYol+uGjK00HpvnJJLY8NkCWNdSNIvtlDw03Qa+cYML2x0Z5SLFe3XPetmf5yuq44K/Pnz5yORSODpp5/G2972NvlxJxKJBMrl7qRdkkbYg09I96lUDVlkOqEkFxCdLfpAvG5OrbD27llJswc/NJghWc7eD+J9wzF5NXjvjg6vvNk9BV8EJzu16EdRwVffB9Zr/UgXk/StYXDq/8f9XuyHVk68XCaF8WJF+3XP+jzFyd3nqMBftGgREokEpk2b1vAxCS9ZKn6EdI2Hnl+Dr939LJ5ctQG3/Nu+2HHLaY6+r1VScCuy3LAD0Pl8pakChwahfjkdk2eGV8X3uWvowee9OxJsmCxhtK4mzxrO4s3xYqBJ+mYPvtP3VfRS9NsV+NKi3wUFv1xp3qTMcBPZN62ceEG5FK0FfZxqIkcF/uLFi9t+TMKH7MHnmDxCAuPPL63FNXc9g0f+uVZ+7i8r1jsu8M0eP2cW/bgv+ksdWhoYshceXIfsCaUxxr2tjQp+vN/rUUH0328yJYuZ9QI/SIt+vr7J6zQJPB1BBV8UZalkoskRN6Wu4I91QcEXbqJ0soVFP8bXKb9Ye/CB4FzHRWuKfoyuqzHJEowftOgTEhzLlq/HNXc9gweeWwOgVnBOG8rgjbECJgrO33NmyF4niz5DMwHlfHXowY/SYrZf6dROYUUo+KUIKY26oYIfPUSBP3fGEIx643g3QvacKvipCPbgF9q09/RcwRcW/ZjNVNdJucV9XLoUNYuSRUsNFCeLvqcU/Xe/+924/vrrsX79es2HQ3SRY+o2Idp5cuUGnLL4zzjy2w/igefWIJ1M4GN7z8OS8w7A+3bYHIDzxV0tKbh5J7sVIjQz7ht2nZLZZQ8+1ZWeU/AYshe1cV46KakhezFaiEYZEbA3b+YQBrO1dddkKcge/LqC73RMXgQ3PYstEvQFogd/vBsKfv1alG4o8M3/j9KmSZgwN3+VHvyARAzrVJY4XVc9KfiPPPIIHnroIZx11lk4/PDDccIJJ+Cwww5DJpPRfXzEI2ZBEJ8XMyFBUShXcO4v/orb/7IKAJBMAB/efS7Oes9bMW/WEABgqL7gmig5W3ioC65OFv0sg30AdA5uY8JxeHCboi/H5MV40awuPtleFw2Wr50EAGw1c1AGvwVr0a8r+A4t+nKySIQ2zsxrR/PfOJztXsheK8eYmvxerhpw+DQQhWI3LfoxHpPnScFftWoVvva1r2HBggW47bbb8OEPfxizZ8/GGWecgYceekj3MRIPcKwWIfp44pX1uP0vq5BIAB/adQ7u+feFuPqYXWRxDwBDdeugU4u+ahXraNHnhh2AzhZ99uCHA8MwlCAlZxbWuAckVqtGg9pUrMTbrRMVXqlb9LeaYSr43QjZyzkN2ZPTKaLzvhJFWKuAzm724LdK0U8rmQBxUoN10uo+HtQap2lMXoyeM08F/qabborPfOYzWLp0KZ566ilccMEFGBkZwXXXXYd3v/vd2G677XDppZfi+eef1328xCFM3SZEH2vHiwCAPbeegf86bje8ZdMpTY8Zkos7pwW+uZhPU8F3hAzZs1GF2YMfDtQ51jmnIXsiRT+mz521AIv7ez0qqBZ908XVhR58twp+hN5XpTabg93swRfvSbWoV4tStoJ5o9TCiRdUMLi1oI/TddVTga+y/fbb4/LLL8eLL76IJUuW4OSTT8abb76Jyy67DNtvv72OYyQeEG+WUsWI1IWdkDCybqJW4E8fyto+Zkj2XzpbeDQo+B1UTobs1TAXBq1vXbJI5MKrp7Qbc2WHVBpjpLCoWBeecX+vR4Fq1cCKdcKiP2TeAwK06AvRxvGYvAhuerZr7xE9+N2w6JdlD755HKlkAqLej6vbyC8tFfyALfpxnESkNUV/4cKF2GqrrbDJJpvga1/7Gsrl4N+ApDWqtalYrkrrGCHEPesnSgCA6YP2OSNDWRH+486in0klOibxZtlyAwAodAjZo4IfDjwV+DHfnGGBHz1eHyugWK4ilUxg9rQBDNbvAd1J0Xeo4NffV1HqwW93nZ+Sq92Du6Lg2zgJ0qkkiuVqrOzeOmnfgx+MRX9KLo1CuRirDWQtBf7atWtx00034YYbbsAjjzwCAJg6dSqOOeYYHT+eeIAFPiH6WF9X8GcMO1DwHS7upDrQwZ4PcOwlIKYOdAjZk3bU+NzEw0i7OdZ2mEpjPJ+7prRnFvihR9jz50wfQDqVdN2m5QWRoh8LBb+FU6ubPfh2U26y9QI/rpuRfmmt4AfjUhSvpeFcGm+OF2N1XfVc4BeLRfz617/GDTfcgDvvvBPFYhGZTAZHHHEETjjhBHzgAx9ALpfTeazEBelUEqlkApWqUS8KOOGAEK+sqyv409oo+GITbdxhwJKbEDIZQBPjZO2K0tfdauEHUAUOC4U2C3Q7RMietdCNC1Two8dyJWAPAAbrqno+yB78+iZvznUPfnReT+3CVGUPfqEU+HGUW/TgA5zW4peyHIOo9ODLNY7e9464nwzXXzdxcl14KvBPPfVU3HrrrRgdHYVhGNh7771xwgkn4LjjjsOsWbN0HyPxSDaVxGS1woUCIT4RFv0ZbXrwxfgetwq+Ewtzth5UFqebkxW18OuUoh8ltaofKXZopWiFGBUZ175Wa2p+nJSmqPKKtcDvQop+QVr0Y6Dgt+nBd9oK54dWKfqA6borlqNzTsNENy36RWnRr6+hYnRd9VTg/+hHP8L8+fNx5pln4oQTTsBb3/pW3cdFNJDLJDFZqsTa1kuIDtbLkL12PfhuU/SFOuDAok8F31FfdxQTo/uRYocwxFZIBT+mz521WIjTQjSqLF9bC9gT41K7YdHPy5C9/k3Rbzcmb7ibKfriHm1x2WWp4PtCOjTSwVr0DcNosOgD8RJJPBX4999/P/bbbz/dx0I0Iy6O+RgXBYToYP1kPWTPQYHv2qKf7mzRl2PyYnRzsqL+7VbLpPy8LBLje57CQLsFuh3pCIaB6cT63ubGfPgRPfhzZwwCcJ/D4gWxnnNq0RcbyJFS8NuG7JmFWqFccXwevGC3CS+uVXFtJ/JL+xR9ffdu9TUvXzcx2jj1NCZv0aJF+PSnP637WIhmxIUvzkUBITqQCv5gu5A9bxb9jCsFP76L/pLs20vaTh0QCzHDqI2wIr3Bm0U/3qqYdeEZp4VoVFkhLPozhUU/+BT9guzBd/beirKC364HHwhexRcFYjZt04PPtbUnWoXlyjWOxo1N9RoqXjdxStH3VOCvWbMGIyMjuo+FaEbuiFHBJ8QzhmGYPfjDnUP2ylXD0eK8XZCQFSr49iOLVNTE9igpVv1GyVPIXrxVMevCM87v9ShQKFewejQPAJg3s9GiPxngRqxbd0wke/Ar9tePVDIhz/PGgJP0SzaTbrIxv1b5RbQjBZ2ir15ThQATp41TTwX+zjvvjGeffVb3sRDNZDlaixDfbCyU5eKovYJvWgWdhCy5sejn6v2Wcd6saxe8JEg3FPjxPVe9pt0cazsyMe9rbUrRj/F7PQqsWp+HYdSS82fVx6eKFP1AQ/ZEge8wZC+Vil6KfqdrvVBjx4JW8G168NkK5o9W4kY2AEFSrLESCXN9xgK/A+effz5uv/12LFmyRPfxEI0ElUqpk9/9bTV+97fVvT4MQmwR6n0unZQqfSsyqaTc2Xdi0SzbqAOtyHGzThkr2KbAT1HBDwNONmOsiPdBXFUx632aCn64kSPyZg7KlqHBLoTsmTPinfbgCzt5dN5XHQv8ATEqrzsWfbsU/Sid0zDR0qIfwBqnqDjJxGupGKPnzFPI3rp163DwwQfj4IMPxpFHHom99toLm2++ecu+yEWLFvk+SOKNICwvOsmXKjjrxicAAH/bfjPHqbCEdBMnI/IEg9kUipNVRwu8UhsbohXz5hTO93I3KNmMLFJRN0viGtYWBswe2s7uFEHc+1qbLPohvW+TGtYReYCpEua7YNF3unkWxR78TvfGkS4l6dtlAZgWfb5HvdDq+Q3Gom/I3yOewzhdVz0V+CeeeCISiQQMw8Ctt96KW2+9FQAaCnzDMJBIJFjg9xBh4QrrC3qiWJFvwMlihQU+CSXrJzuPyBMMZ1PYMFlyZNG0G8HTCuZpKIuCNgtbNVyfCn7vMIsQ59f0KPYK66TJoh9jt04UEAn6ImAPAIYyIsjLQKlSdTUm0ikF1z349ekUEXpfhUfBFyn6NhZ9FvieaLVZH4TjWH0dxVEk8VTg//jHP9Z9HCQAwm7rVXe54/SmI9Fi3UTnEXkCNxZNJ4q0IBfDm5OVooPgtkQigUwqgVLFiG0vdxhoF5JlR9xD9prH5PH1G2ZWrJ0E0FjgDzbksFQwbVB/ge9VwY/Sxlmn64fowQ+8wLe5R4trFS363pAWfeU1HMSkILXXX7xfSjG6rnoq8D/xiU/oPg4SAEI9Cavqpy5gwnqMhGxwMCJPMCTHJDlX8J0V+OF+L3cDp6GEqWS9wOfiq2e4TfoGlDF5Md3EokU/WpgW/UH5uWw6iXQygXLVwGSxgmmDnTeF3WAYhusRlOkIWvQLHTYxhrtU4Nu57LJU8H3Rugdffwie+l4Rz1mcRBL924skNIQ9ZE9V8MPqMiBknYMReYIhFwp+ucVNzo442suslGz6Ia1E0ZLabzhpp7AiVbGYPm9iYTucDXd2DqkhLPrzZg01fN50cekvPtXXhNPNM1PBj87rqZO7rVs9+PI4LEG4MhA0ptcqv6i98YIgLfqZVMJcQ8XouupJwX/llVccP3bevHlefgXRQNgt+g0KfozedCRaiJC9aY4UfOcFftGDRV+nfS1qOG1pSMd83FoYKDhop7AS9+dNnLMpA2mMFyuxWohGjdF8Sd4X1JA9oHYPGMuXA0nSVzd4HSv4qegp+MX6mjUsPfhW11jcA0H9Iq5t6VQLi77GeqXBol+fOhEnkcRTgT9//vyWiflWEokEyuVg34DEniAsLzppVPDDeYyErK9b9Gc46MGXFn0HC4+yF4t+jN8nxUrtetFJuYp7WFsY8DImLxPz0VNiMToll8ZrKIR2Y56YI/JmDmelXVwwWA8LngxgM1Zt0XK6eRbF62HHkL1c7V48FqCCbxiG3FS2jrLNMEXfF+0s+jrbENVWMfG7wloPBYGnAn/RokUtC/wNGzbgL3/5C1588UUsXLgQ8+fP93t8xAfmjlg4X9C06JMosH7SecieVPAdLO5a3eTsEAudctVApWpI22WcKJWdKfipCM597jeKLjavBGYydTyft6JU8GvXmTgpTVFjeYuAPcFgfZN3MkAFP5tOOhLZACAlWpYi9L4Sf6fdZq6p4JcCOwbV8WC9R2difq3yS+sxefrrlVYheyzwO7B48WLbrxmGgWuuuQZf+cpX8MMf/tDrcREN0KJPiH/WiZC9IecWfSeLOy8WfaB2g1LTmuNC0eGGiFBboqRY9RueFPyYW/TFORP9xXFaiEaNFeuaA/YEbtq03CIVSTcbZxFU8Dtt5o50IWRPPV9ppuhrpd2YvGKlimrVQFKDiCHWWOqYvDi5LrSH7CUSCZx77rlYsGABzjvvPN0/nrggK/t2w/mCbhiTx8UMCSkbxJg8B4nIQ/WFx3jBecieNaG3FVlLgR9HnE4dMHtO43mewoCXAj9Niz4AcwQYN73Di0zQb6Hgy03eUhAhe+1701uRimKKvtMxeQFa9FUHTdpSbIqJH3EqFnXSekyeKVroci+ZIXtJ+VqKkzMqsBT9PffcE//3f/8X1I8nDgh736668RDWYyREKPgzhh0o+Bnni7tWNjU70skExBojrI6coHFaNNKi33tKHSy2rUjHfPSUWHgK+3FcN/KigOjBn9fKop/pgoLvauMses6Yjj34XQjZU+8f1k1l2YMfoXMaJlr34JvnWFc9EHeLfmAF/gsvvMCAvR4Tfou+0oMf43RwEl6qVQMbJp0r+IMu7JlOU+GBmjMqG0CPWpRwuiESRUtqvyEX6K6sxPFurZAp+rToh57l6+o9+DPaKPgBFvheFPyqUbufRYGOKfrdsOjX7zfJBJoyb2jR906lakC8DNX7QxAihrohFkeLvqcefDuq1SpWrlyJxYsX41e/+hXe+9736vzxxCW5kO9Y5RUFP062GRIdxvJleTOa5iBkb9iFRb/kwqIP1Bw5+VI1tgW+08yCuBeKYaBQcV+IqOO8DMNwHCLWL4gNv5G6OhnnQM0wYxiGVPC3mtncgy9C9oJQ8As+Wl8AoGIYSCL8r6dOm9/dsOiLGffW/vvaccXbbeQH9Zypz28ikUAuncJkqaKtrVh1CojfFaf1k6cCP5lsn+BpGAZmzJiBa665xvOBEf+InpawvqAbFfxwHiOJN+sna/b8oWxKtry0w03/pdOeckHYHTlBwx786KD2PjoloxQipYqBbDr8hYhOhGo5RRm7FtdAzTDzxlgBhXIVyQQwZ3qLAr8rFn3nr4mUsoFcqRrIRODl1KkVQVj0x4uVwDbB5BjbFj/bHJPHTWS3NGQbWMSNbDpZK/A11SzqhpjswQ9pPRQEngr8/fffv2WBn0wmMWPGDOy111446aSTsNlmm/k+QOKdbMh3rPLswSchZ109YG+GgwR9wN3irixVCmeLkzj2kKmwBz86eArZU94H5WoV2eA6CEOJOSaPBX6YWV5P0J89bbDlBpbY5M0H0HboTcFX31fRuCYWOziA1E2w8WIZUwc6u+vcYjrsmo9BXKvKVPBdU1LWL+qmLqBfxFBFgRwt+s649957NR8GCYJcJtyKH1P0SdhZXw/Ym+ag/x4wLfoTDiz6bmeFBzEnNkqYPfjtN0QytOj3HP8FfvyeO6EGDmVTSCQAwxD3bv3FC/HOK23s+YCawxJcir6bkD1V3a5EZNOzU4ZHLp1EJpVAqWJgYz6oAt++TYD3GO+I85pOJppG4Zk1i/6QPfE8Vo3axkyrjZt+o///whgjC4KQ2t/VN3FYNyFIvFkvFPxhZwsIubgLwKKfrdsy47oZ5vR8pRiy13OKLiZECFQ1J47uC7OoSYXefRdnlq+tBey1StAHTAU/SIu+dwU/Gq8nuflt83cmEonAg/baOezE55gd5Z5293E5+UtTzdIqZA+Iz/OmLWRv2bJlWLJkCQBgv/32w1577aXrRxOPiDdLWF/MqoLPhQwJI0LBnz7ozKIvF3cOFHy3Fv24K/jFcv18dVjcsge/93gZk5espygLhSVuqMGEuXQShXI1tPfuOCMD9lok6AMBp+h72DhLJBJIJROo1EMbw45hGI6mcEwZSGPdRAljAQXtiRF4rUJwzRR9vj/d0mpEnkC/Rd90YagFfqlsAM6WdJHG8VXi/vvvx6JFi/DII480fe3zn/889thjD5x77rk499xz8c53vhNnnnmm1gMl7omSgh9XVZKEG9GDP91Bgj4ADLtIUHZr0Y99D77LMXkMQOodXpRGwFw4lyJQiOimpAZCaVayiD5Mi37rAj/QFP366yHnMikvSq4m9brd7voxJVe7J48HreAnW1j0U8x58Yp4fls9t7pFDDWzQnWyFCrxcAw7vvvedNNN+MUvfoEddtih4fNLlizBFVdcgVQqhRNOOAGf/OQnsckmm+A73/kO/vd//1f38RIXhD11u1HBD+cxknizYdJdgT8oU/QrHWcOlx2OfROE/f0cNKIA6qTgp+oLsiioVf2K1wI/k4xveFVRUbbkiNsYnoews2JdzaJvV+APiaDVAEL2vCj4gLnpGYVrovqab+cAGgnYot9ujK24Z/P96R5HFv0AevATiUTsRBLHV4mHH34Y++67L6ZOndrw+e9973tIJBL47ne/i8WLF+Nb3/oWHnzwQWQyGSxevFj38RIX6H6z6Cbf0IMfzmMk8WZd3aLvNEV/SEm8nuywwGtnVWtF2N/PQeM0ZC8dIbWqXxGvUTdj8gBFwY+hMqb2i+ZithCNCsVyFas3iAK/dcieadHXX3h63TiLlIKvpqy3uX4M52rneWNQFv02hahp0Q//+Qwb7ZyLMmRP0+aYdY2Vjdn9xfFVYtWqVdhll12aPr9kyRJMnToVJ554ovzcdttth8MOOwxLly7VcpDEG7oTKXVTYIo+CTkiZM9piv5AupaADXS2aLoN2Yv7ot9pSwNHGPUev0pjVMLAdGJuYKVkARdXt05YWbV+ElUDGMgksemUXMvHDCguLt14SdEHVAU//O8rce1IJRNt59tPqSfnjwVs0W+Vtp6J8XXKL2IDp5UzQrdFX900BeLX5uj4KrFu3ToMDjbuWL7yyit44403sN9++yFp6VPZbrvtsGbNGj1HSTwhXtSVqhHKxS4VfBJ21rtU8JPJBAYzzkKWSm0WEK2Iu0XfqXoVJTtqv+K9Bz++va1F2YISPytpVFi+zgzYSyRaF5+BhuxZChaniLalKCiXTgL2AJgp+gEp+KJ4z7TYZDAt+uE/n2FD9uB32aKv/s64XFcdXyVGRkawcuXKhs/9+c9/BgDsscceTY9PJBIYGBjweXjED+LNAoSzV6jAHnwScta77MEHgKF6yNJ4B4umW4t+3Bf9zsfkcUZxr/GSog8AaVmIxO81rhY2cXfrhBUxIs+u/x4AhjLBhex53jiL0KZnweHfODIgevBLgRyHuQHfKkWfLjGvtO/B1ytiWF9LmXS8xhs6vkrsvPPOuOOOOzA+Pi4/98tf/hKJRAL7779/0+NfeOEFzJkzR89REk+oF8gwpvGqu3RhPD5C1o3Xx+Q5VPAB53OQSy5tzHEfk9du518lHeOgtrDgOWQvFZ1CRDeyrUGZ2RzX93pYMRX81v33QGPQqmHofR0XPCv4EerBd7iRO6VLIXutjiPDHnzPFNsIG6KtWNfGJhV8h5x88slYu3YtFi5ciG984xv49Kc/jZ///OeYN28eDjjggIbHVioV3H///dhpp510Hy9xQSqZkIvdMC4U1BT9uOyokehQqRoYrdv/3Cn4wVj0qeC77MGPwGK2H6lWDXnuXffgxywESWAYRkNugXDfxfW9HlY6jcgDzOu/YQB5zcKF39aXSPTgO9zEEAX+WFAW/TZTbjKp+DqN/NLdFP3G51CMH41LvZF2+sDjjz8ef/jDH/Df//3feOKJJ2AYBqZOnYof/vCHTf33v/nNb7BmzRoccsgh2g+YuCOXTqJcrITSAk8Fn4QZMSIPAKY7DNkDzAWebot+3FP02YMfDdTFU6eRhlbiGrJXrhoQYm82nZQbI4WYLESjwgoHBf6gMqN+oliWir4OCg77061IBT8CG2eqk6UdUwYCVvDr16B0ix58sWFSitl1SgfiNdjq+ZUuRU31QFPInnjeYrKGclzgA8CPf/xjnHLKKXj44Ycxa9YsHHLIIdhyyy2bHpfL5XDttdfiQx/6kLYDJd7IZVIYL1ZCqQTk2YNPQowI2BvJpR2r7IDZg99JwRcqp9sU/bi+V9pZ+1TYg99b1A0ot4VIXK2vqhLYYNEPIImdeGf5unoP/gz7Aj+ZTGAgk0S+VMVEsYJZGn+/tOhn3G0aRGnT02nI3khdwR8POEW/pYKfjOd1SgftpuHonh7SZNEXLsiYbJy6KvABYL/99sN+++3X9jGHHHII1fuQENa+XcMwGi36ITs+QtbVR+RNH3au3gNmD2ZHBd/lrPC49+U6tehnGIDUU9RruVN3ikAqYzF77hrPmRKyF7PzEGY2FspYW89k2WqmfQ8+UFPx86Wq9lF5XsdPRmnTUxaA6fbXjuGALfriGtQuZC9u1ykdtHMu6nYpWl1/cWtzdHeVIJEjrKpfuWpAvdfEtWgh4UUo+NMHnQfsAcCwwx58p4q0IKybdd2i1MbapxKlQKl+RLXY2o0SsyMToUJEJ2LBmUjU1NasZqsq8c/yuj1/xlAGIwPtN32durjcIhwd/Zyi73hMXtAWfZGRk2yh4Mc0K0QHQtho5YrUvcaxrrHM8YbxuK6ywO9zwrpQyFt2toMsWp59bQzvveZe/PovqwL7HaT/WD/hfkQeAAxmnY1JcmvRz8Y8eMvpwi9Ki9l+RCzgci5VRiC+ypiqzCYSidhZSaPAcgf994JBh5NU3CJeD/2cou80a2Uk4BT9styobN6kpEvMO+2m4YgUfV2tSSVLngNT9ElfEdZgLmu6bJBvuPuffQMvvDGO3/51dWC/g/Qf6ybcj8gDnIXsVauGLEDd9+CH673cLUzrZqcUfaorvcRpSFYrohQGphPr5hVT9MOHkwR9gZykUtJbfHpO0U9GL0VfbGjbIRX8fFn7OEIAKFXtFXx5j4nAhknYaDsmLyiLvqUHPy4byCzw+5ywFgXWloFipYpqQBdL8beHrU2BhBuRoj/DpYLvxKKvpu86teib/WPxex0bhuF46kAqQovZfsRrEQIoIXsxe+6smyK6w6aIf1Y4CNgTiCR93Qp+weN7K0oKvlRdO1znxZi8ctUIZH1bbtODn4mp00gH7cfk6Q7Zs4zJo4JP+glpeQnZQkEo+OoIkqDsiKIdIGybHCTcSAXfxYg8wJlFX1WXqeB3pqKOEXNo0Y/CYrYfEa9Pp69rFfHcxc19USo35kvkYhYGFQVMi377gD3AVPC1W/Tl2C+XKfqp6LQtOXUADWfNjPAggvbaFaIiK8QwonFOw4SzAp8hezpggd/nhNWiLzYcpirFU1DHKJTUsJ0DEm7MHnxvFv2JNhZ9dQ6r4wK/rgqFLU+jGzTMVu9U4Md01FpY0KHgx23RXKzU7lGZFAv8sCIs+vMcWfSDCdmzzvV2SipCY92cZq0kkwmp4gfRh2+G7Nmn6Nce15/v0dv/sgq///ur2n9uuU1Yrs41jmEYTSP5aNH3SaFQQKlU0v1jiUfERTJsxa1Q8Kfk0hBBy0G5DPL1n2sN9iOkHV5D9pyoN8Kin0iY9slOZGOWAKsiFE6gc+HIkL3e4nWUFxDfkD2r9TruIzHDyOoNeQDAltM7K/gDgVn0+z9F300bgijwxwMo8EWbUEsFX/lcP16rNhbKOPumZTjr509oDxJs34Ovz3GsOvjEa0k8b4U+fM5a4anAv//++3HRRRdh/fr18nNvvvkm3v/+92PKlCmYNm0aLrjgAl3HSHygO5VSF+J4BjLmzN+glEmxmcDFEnHD+smaRX+GawXfuUXfjY05rO023UDd1GilqKiY/aZ8v/eCkg8FPx3TMXnWZOm49YqGnUrVkCrxNActW2bIXlgU/Oi0LbWzcFsRQXtBWPTL8h7dqgc/2fS4fmJjvoxKPdtgQvNruFsWffXaaQ3Zi8t11VOBf/XVV+NnP/sZpk+fLj/3H//xH/j973+PbbbZBtOnT8dXv/pV3HzzzbqOk3gkrH27BaWXLGhl0uzBj19hRLyzbtyjgp9zbtF3o3LGedFfUlThTrPVzRFG/bfwigJ+UvTjOn5KvKfFhAixmRdHt04YUSeiiKKyHbLAb3MP8ILX91Y0U/Q7/43D3bDot7hHp5IJ6TwtReCcukVdK+c1u1CEG691ga+vpbjU0NZXe7LEGqofXRet8FTgP/HEE9hvv/3kx/l8HjfffDMOPvhgPPvss3jmmWcwb948XHfdddoOlHgjrON28qqCH3Bvsfhd1tF8hLRDpOi77sF3YM8U6nKrhF47BjLh3KzrBrIAcnC+UjFVgcOCV5URUC368Xru5DmTCn588zbCyMa6QpxNJR0F3A0GELJXrRryfeE2ZC9KCr6bAn9EFvj624JFEWjnGMvIYjH859Qt6hpDtwul3TQcnY5j8TpKJsxNGir4DnjzzTex5ZZbyo8ffvhh5PN5nHTSSQCAkZERHHHEEXjmmWf0HCXxTBQUfN2jMaxIi37I2hRIeCmWq1IVcDsmz0nAUrHNLrYdYtEfl5uTSsmFchWlftN+xNyM8WPRj9dr3Pr6lj34MVGawo64FzhR7wFVwde35lDdHP3cgy/+zpwTi74o8IOw6LfpwQeATLJ/3UbqxqL2NhMHFn0dzqVWvyduLkhPBf7g4CDGxsbkx0uWLEEikcDChQvl56ZMmYJ169b5P0Lii7DO01UV/KADhTgmj7hFqPeJBDAy4M2i3y74Ry4eHAbsAWoPfvxex+0WBVbYg99bCj5C9uLaXmF1qDBFP1yIHm9RUHbCyahUtxRa9BQ7JUquJk89+AFa9O2OI93HCn5eqRd0T4Jo34NvWvQNw995LbVI6zdT9PvvOWuFpwJ/u+22w5133olCoYBisYgbb7wRO+ywA7bYYgv5mFdeeQWbbbaZtgMl3gjrmDxRdOcyqcDbCMTFSsdFg8SD9RO1gL1pgxnHKfcCJwFL8ibnQokRi/5K1ehL1aAdbkIJ41okhgU/Y/JEIRKXBZigYKfgh2xjPq5IBd9hgS/btDSqn+K1kEg4a1VSiZKC7yVFPxAFX1j0bc51po/7uRsUfO0FfmOgqIoQMQzD/z2g1bhFmaIfsnooKDwV+Keddhqef/55bLfddnjHO96BF154QdrzBY899hh22GEHLQdJvGP2tITrBV1Q+jSDbiNQL1BxeWMTf6wX/fcOEpOtDGVqi45SxbDdtPJk0VcWPHEL33JTNEZJrepH/BT4cnMmZu4Lc/JAqv5fKvhhYtyzRV9f4akWLJ2CRq2kIrTp6aoHfyC4kD1x/7DbTOnnjWR1YzGwHvx083lVi3G/m5utnAJZjS0AUcBTgX/KKafgvPPOw+TkJDZs2IBPfvKTOPvss+XXH374YTz77LN473vfq+s4bXn88cfxwQ9+EDNnzsTQ0BB23HFHfOMb32h4zEMPPYT99tsPQ0ND2GKLLXDWWWdh48aNgR9bGNDZ06IT0Rc/kOleD37td4TrPJBwsm68puC7DdgDzIAlwH73W4bsuXAHNNz8QrZhFzQlF7bvKKlV/YibvAQr6Zgq+NbZ0LTohwuhEI84VPAHAhiT52fjrF9T9KcEmKIvjkNck6zIQNAInFO35APswXcyJg/wv1Zv5QSRFv2YXFedXa0sJBIJXHXVVbjqqqtafn2PPfbAunXrMDw87OvgOnHXXXfhAx/4AHbbbTd84QtfwJQpU/DCCy9gxYoV8jHLli3De9/7XrzjHe/A1772NaxYsQJXX301nnvuOfzud78L9PjCgLTohyxgTtjmB9KpwNUKdeOgUKoAHlRZEi+kgu8yYA+o3UQyqQRKFQMTpTKmoflneCmC0qkkUskEKlUjdBt2QVNss+tvxUxij9c5CgutrJFOEc9dFAoRnZQUR5v6X25IhwPR4z3s1qIfQA++2wR9IFop+l568IMJ2euk4PdvsVgItAff3r2YSCSQTSdRLFd9X/tapfVn6/8fl/WTpwK/E9lsFtmse+XLDaOjo1i0aBEOP/xw3HLLLUja7LJ97nOfw4wZM3Dvvfdi6tSpAID58+fjtNNOw1133YWDDz440OPsNWFdKAgFMpdJBp4TQAWfuEX04M/woOADwGAmhVKljPFC65ujnLHrsr8/l05ioliJn4LvIpk9RQW/p3id1Q30t+21HUWLQyWs423jiiggnVv0O09ScYuv8ZMRuibKFP0eK/jlDhsNmT5uBVPXyfmAFHy7DeCcKPB9/l5TRDE3xOLW+uSrwC+Xy3jmmWewfv16VCqtn4z999/fz6+w5Wc/+xlee+01XH755UgmkxgfH8fg4GBDoT86Ooq7774b55xzjizuAWDRokU455xzcPPNN/d9gR/WsB5Vwdc5+7IVqsUobOeBhJP1EzUFf5pHt8dwLo3RfNl2gedGpVDJ1gv8os31tl9xk6IvLJVRWMz2I74UfGHRj9lzZx0tGLde0bAj5qw7teiLNq0gFHw/4ZVRKEbdXD+CLPDlJrxtin7/OsXUol7naxhQWh9snBG5dApjKPsW48zXkarg196X/fictcJTgW8YBi666CJ885vfbBiX1wq7wt8v99xzD6ZOnYqVK1fiyCOPxLPPPovh4WGccMIJuPbaazEwMIC//e1vKJfL2HPPPRu+N5vNYtddd8UTTzwRyLGFiUgo+KngFjOlSrVhoZ+PmfJJvLGuXuB7VvDlAq/1wsNrn7J4P8ftdVxyoep46Y18Y6yAR/75Jg7dcQtP89uJiZ9CxFTw4/X6troeRHEjJmbYFRmkO7hO0Zche1Tw3eKpBz8Qi377Ubb9PCZPrRe62YMP6KtZWv0ecX+hgt+GL37xi7j88ssxffp0LFq0CHPnzkU6HYjb35bnnnsO5XIZH/rQh3DKKafgyiuvxL333otvfvObWL9+PX7+859j9erVAIDZs2c3ff/s2bPxwAMPtP0dhUIBhUJBfjw6Oqr3j+gCuUw4rX6mgp8MNOnfai+igk+csGFShOx5U/CHOig43i364Rx7GTQlF1MH5GLWxcLrqjv/gVseW4FvfXw3HLHzHG8HSQD4DAPr40VzO6znTNwTgVrxzwK/t4y5tujX112VqrYNGuHa8qbgR2c6RdHFSFTxfIz1QMHP9vFmZCHQkL32z68uR2+7kL24rJ88VeU/+tGPsPXWW2Pp0qWYNWuW7mNyxMaNGzExMYF/+7d/k6n5H/7wh1EsFvG9730Pl112GSYnJwEAuVyu6fsHBgbk1+248sorcemll+o/+C4SXgW/XuBnUoEWLdaLU9x6l4k31o17D9kDzB5M+wLfu0UfCN+GXdBYU8bb4SVQ6rXRfP2/hQ6PJJ2w9pO7IR2hQkQnTRZ95dwVy1V4NBIRTbhV8NVJKhOlCqZqKPDF2sXP+yoaCr7zjYyRXO3+HISC3yqkTaWf24nyihCW1x6y16kHX0890GojQabo9+GmTCs8XXVeffVVHHnkkT0r7gFgcHAQAPCxj32s4fMf//jHAdRG9YnHqCq8IJ/Py6/bceGFF2LDhg3y3/Lly3UcelcJegSdV2QibCYZaL+htaDPh+w8kHBipuh7W1kPdbLoiwW9R4t+2N7PQWMtgNohHuOmwBfXiYkAlKC4UfJl0a8/dzFT8K0tKOlUEsLcE7bN+TgyXr8ujDhU8LPK86fLpi/D5zIeCvwIva/chHQKBX+yVNGupJc7KM1pKviekBsnNhNxdFv0GxT8ANuBw4inAn+bbbbpuV19zpyajXLzzTdv+Pxmm20GAFi3bp205gurvsrq1avlz7Ajl8th6tSpDf+ihrSkhEy5Ftb5gXTKfEMHELLXZNEP2Xkg4cRM0fcYstdBwZcjeFxa9OOq4LvJLJAKvoubuNj4mwjZONEo4idFXzx3cVFYBK3OGZP0w4O06Oec3Q8SiYT2JP2Ci/A5K9FS8J3/ncM50ykxrllpFi4iuzC4bKp/1WBVQNAdstfRoq9JxGj1Oorb+slTgf/JT34Sd9xxB15//XXdx+OYPfbYAwCwcuXKhs+vWrUKALDppptixx13RDqdxtKlSxseUywWsWzZMuy6665dOdZeEtaeXVXBD7KNwBpGFrbzQMKJSNGfPug3ZK/1TcpNKrxKWFtugqaTrU/Fy2JWbATqnvkbR/yk6MuQvQgUIjpp5VAJ6wScOCIs+mpB2QndSfr+UvSj874SBaCTvzOXTsnH6U7SN3NyOqXoh/+cukVdN3d9TF49N8yvGNeqxYIW/Ra88sorDf8+9KEPYf/998e+++6Ln/zkJ3jyySebHiP+BcWxxx4LAPjhD3/Y8Pkf/OAHSKfTOOCAAzBt2jQcdNBBuOGGGxrS/n/6059i48aNOOaYYwI7vrCQC+mOlargB7mrZrUX6b5Ykf4jX6rI1830YX8he5M2Fv1yhwAfO7IxVfXcBC95WcyK59uupYI4x1fInhjnFZMFmEC8vlupTXHbzAsjG11a9AHlHlDSc00xU/SdbzIIRDHabwo+EFySfsce/FT/XqvUTcWopui32hDLSNeFgWoE3gt+cXS1mj9/PhKJ5he5YRg46aSTbL8vkUigXA5mwbTbbrvh5JNPxo9+9COUy2UsXLgQ9957L37xi1/gwgsvlPb7yy+/HPvuuy8WLlyI008/HStWrMA111yDgw8+GIceemggxxYmZCJluQLDMFo+j70gL8fkBRuy15yi338XY6KXDfX++1Qy4XjusRVhz7SzDZq72G5T9OO56A+6B19cj3TbEeNIwU/IXmwV/OZgsbBuzscNwzBk8ejUog8Agxm9Cr6fjbNIpei7/Dun5NJYO17ExkJJ63F06sHPKsViv9EwJk/jPdEwDGU6Qfse/KJP51KrjQT1NVWsVDGQdL9ZFiUcrV4XLVoUmsJQ5bvf/S7mzZuHH//4x/jlL3+JrbfeGtdeey3OPvts+Zjdd98d99xzD84//3ycc845GBkZkWP14oAonqtGbdHkJIW6G4gdwlw6GWhwGMfkEbesq/ffTx/MeL7udRqT59+iH6/XsZce/ErVcLypSYu+PvwUInEN2Wtv0Q9/UdbPFMpVueHkdEwe0Pke4P44vI/Ji0oPvmEYrjM8hII/plvB79CDL85pKQKbJm5R182TGnOr1M0QewVfV4q+fcie+PpAhgU+Fi9eHPBheCOTyeDiiy/GxRdf3PZx++23Hx588MEuHVW4yCkv7kK56rqgCAqhmA1kUoEuZPKWn2ntySfEiui/n+YxYA/onKLv3aIfT1XPjeMhrQQXVqqG7QJNpUAFXxtuNmOs9POiuR1i4ZtrsRiN23s9bIjCMZEAhlwUBLpD9kyLvhcF372rqReo6eZO16pi00VnD35tc7h+HLY9+P27Gdmo4Os7r2rvu53DS1c90DJkzzJ+tN/xVO298sorHVP0x8bGAu3BJ85QX9BBpNR7RfbgZ5LBWvSLVPCJO8wEfe/Dp4c6pOh7t+iHMzQzaDr17amomyZOFrSVqqkaMUXfP7568Pt40dyOVudMhE3FYSEaZkThOCWbRtLF1JOBEFn003KySLjfV6rC63QjYySAHny1ELVP0e/fiR8NBb7Ge2KpYQOn05g8vxb95haLZDIh3wtxGJXneUze17/+9baP+cY3voFtttnGy48nGkkmE7LID0tRYBiGmaKvjskLRMHnmDziDjNB37+Cb6felDwq+HHtyxXXhoyDRZ+q4Dsp8BvsiAzZ842WFP0YLL5UWrXs5EJ2344rsv/ehT0fUEP29Kbo5zy8r6LSg6/e1xyH7AWg4JccOAnS/dyD33BP1Ffgi+tcImG+Jq3I3DCfa3W7qRMySb/cf8+bFU8FvmF0PjFOHkO6Q9iKAnXBMpBJKm/o4HvwrQU/IVbWiQLfh4IvRiSN2xSMbhRplfj24DenjNuhLhwqDhZf6jVivBCv8xoEbntoVUSKfinkVmLdtFJnZTtOha/JXiIVfJeBq50mqbhFWvQ99A1HpQdf/I3pZMKxWyKIHvyyg17xdB9vRqprdJ1trWpwoV02ju4efOvzJz6Ow3U1sIbsFStWYGRkJKgfT1xgJumH40KkHkcunTJ7DQO4UFovTlTwSSfWT9ZD9nz04A936L/sNILHjtj24HtU8J30cucDsiPGFSr47im2mDwQto35uCIK/GGXBf6g5pC9Vq8Rp3gZHdoL3ExLEQgFf1yngl/trDSbKfr99/5UxbZipartelxy8BrWZ9Fvr+CHpR4KEsdXrMsuu6zh43vvvbfl4yqVCpYvX44bb7wR73znO30dHNGDadEPx+JVXDySidqCTuxIB1F8iwV7IgEYRjze1MQf68drCv4MHwV+p8VdpxE8dsR1TJ6bzIJEIoFUMoFK1XCkWKmbMHahiMQ5WsZ59aHttR3mOTNf33FaiIYZMX5txKNFX3eKvhBs3CDU5tAr+B7cPyMBKvh2AXtAf7uNrOHUk6UKRjQEdDsRNnStccyN5sbf1c/jDa04vmJdcskl8v8TiQTuvfde2yIfAObMmYOrrrrKz7ERTcgCOiQLBTVBP5FIBNuDXy/wpw5ksGGy1GTZJ8SKUPCn+bDoD+fap+h7H5MXrvdyt3B7vkSB77YHP1+qolo1XIVpERMvY65UxPMbvxR9sRg17ddxdeuEDdmD79qiH0yKvjcFPxrhlV42B8XzorMHv9xhVrv6tX50G1nbZSdLFYwMeBc8BMVy5+whXYJfJwU/DtdVx1esJUuWAKjdwN/znvfgxBNPxCc+8Ymmx6VSKcycORPbb789km12v0j3kAV0SOzpcie6flzmGy6IHvza3zx9qFbgx60wIu4RPfh+FPyhjLMUfVr0neF24ZdJJlCEs8WX1dk0Waq4tuOSGqoq4i1FP+4KfrNFn/es3jLmsQd/UKToaw7Z85OiHxkF35VFv3afHtNY4IvjSLfZ6O1nJdh6zckXo2fRL9i0e8Rp/KjjK9bChQvl/1988cU48MADsf/++wdyUEQvuZCF9agKPhDsQkbsRE6rJ6KHpU2BhBcxJm/6oP+QvclSpaUi7N+iH6/XsdtQQjc9p9acjokiC3yvqDkqXpTGtDKv2zAM2yCmfqKqOE3UDT9a9MOB1xT9Qc0he+rkIbdELUXfk4KfL2k7DnGe2h1Huk/H5JUrVXk9SicTKFcNbdk03bTo260ZZIp+nz1vrfC0irn44ot1HwcJEGnrDYmCL5LszQLftB3rXtRNWgp8nYmgpD+RY/L8hOzVLfqGUXu9C7umwKtFP66Lfjcp+oBpAXTbg9/qY+IcL2OuVNSFX6VqtLXI9gsNmyINCn7tGhIHpSnMCOv3iNcUfU3FkZ9si6go+E4UXisjAYzJkxb9dj34qWi0PbhFXVtMH8pgzcaivtewg3WPrjZEu/eLuMfEYQ3lS6Z49dVX8dhjj2H9+vWo2KjDixYt8vMriAZCl6JfEjvRjRZ9oLaQV4OG/CL6a8XIs7CcAxJODMPQUuAPKCrLRLG5wBfWcbcFTGx78F2mK7sJa7OOzpwoMWjPK17GXKmovZnlqgEPYmXksCvw49qOEzY8K/gZ3SF7GsIrQ17ge9nEEAW+zpC9koP7cybZnwq+uraYOlgr8HWFz5YcOBfNesVvin5rUcAcP9pfz1srPBX4+Xwep512Gm688UZUbSw/Qollgd97wpaiL4runMWiD9SO0csNzP531V6f0wbT8ucTYsdkqSIv/DN8hOwlkwkMZlKYLFUwUagAUxq/7laRFsR10W8XmGNHxoUltZVFn3jDj8oIWEYcVqrS5dXPqO9lNbU7bPftuLJR9uC72/DVH7LXmF3kBqFEh13B97KJYVr0dRb4nQtRMxA03OfULWJ9nkkl5LnVFU4thI124251ZYbZh+zV7imlGKyhPBX4F1xwAf7nf/4Hb3vb2/Cxj30Mc+fORTrNnsWwEjoFv9yo4DcW+FWMaPxdQp2TPfi06JM2iIC9TCohLZZeGc7VC/wWirDbnnJBXIO3ii5DCVMpNz34tOjrQuS8eC3w1fdDv1lf7VD7UlXXg7hvx20zL2yIAl+0XTml06hUt/iZThEZBd9D+KxwVmwslrVNQCk7CNnr1xR9sbYYSKfkBuuk9pC9dj349dYkn+fVzvUnfjcVfBtuvvlm7LDDDnjssceQy+V0HxPRTNh6+cSCWlw8EokEsqkkipWq9mMUi3URmEY1hLRDBuwNZX1nQbRb4DmxALZCBmbG7HXsdkPEjWJlLfDHNfZyxg0xBslL/z1QK0QSiVp2RVxG5dmNP5NpzzFYiIYZ2YPv0qI/pLnAF+KEp/DKVG968H+1bCWGs2kctMPmjh5fkgq+882UqfUUfcOoTSxwO+2g5XFUnSv4/bYRKadcZZL6cyScWPQ1Kfh2m0VxckF6uguvX78ehx56KIv7iBA21U+G7HVhJJD4edOGqOCTzsj++0H/M1/lqLxCqwLfW4p+XEP23Fq/0y76I5sUfE2LmTjiR2UUpF3kJ/QDUmmynLOwjbeNK7IH37VFX2+KvnhvDWR8KPhd3CzaMFHCOTctw6d//jiqDjcWvIzJy6WT8poxpilJv+zASZDp0w24vMzISskcCW0p+iKjpc3zO6CpB99uzWCON+yv560Vnu7Cb3/72/Haa6/pPhYSEOZCIRwLVxmyp/RX6grWsJJvGpPX/29q4h1R4PvpvxcM5YSC07zAc7KAaEVcQ/bcZhakXKRGswdfH3ZqtBvkqLy4FPg2RY0uqyrxx5jswfc2Jm+iVIFh+H8tm+8t961jvUjRH82XUDVq19dxh5scRUv7phMSiYRp09fUhy/uN+0KUWnR7zOnUaFkKviywNcWstfZoi9e37rG5Fmvq5lUfEQST3fh8847D7/61a/w/PPP6z4eEgBhU/1aKfjSjqj5GGWKfr3AL1aqoQ+aIb1jXd2iP81Hgr6gnb3NiVWtFXGyl6kUHYTzqKR99OCzwPeO35A9QJkv3WcLZztslaaYvtfDhiga3Vr0RXFkGHrWXlFL0Vfve05H2Jl90+42vsXmy5im9qqSgx78TJ9uRJoZWSkMZIPpwXeWou+3wK+LAjG+rnpqVpk7dy4OOeQQ7L333jj77LOx++67Y+rUqS0fu//++/s6QOKfsKl+poKvWPQzwRzjpGVMHlB7Yw/6DFAj/cmGSaHg+y/wB+sW/fGWFn2/IXvxKUINw2gIInOCGxXYugGjS62II35D9oD+7W21w7YHP4bv9bBRrlTl9cGtgq+ORp0sVnxNhKhUDSlMRCVFXw0rdZpr4rXFZ2QgA2BS26g8ocq378Hvz7A2OeUqndRu0XfTg1+pGihXqm1dFG1/l13IXjo+Fn1PBf4BBxyARCIBwzBwySWXtA2jqlR4c+o14e3BVyz6AfQbGoahjMkzC7ZCucICn7Rk3XhNwddh0R8OxKJvvpfFKNJ+p1w1IByuTq3fpiWVY/K6iR6Lfn9aX+2wU5pyMVKawoq6OTvsssBPJRPIppMolquYKFUww8dxqK8Bvwp+t+4balHotPD26gAa0Twqz8zIaZei358bkeqUK+FC1DUmz5GCr9QFhbK3At8wDCVkzya8NAbXVU8F/kUXXRSLhWW/EFR/u1dEEa/uaMvFjMYNIXVDYyiXQjqZQLlqNC3oCRGsryv4Wi36LVP0vVn0hWXOMGrKQc5F2nBUUXfanZ4vN5ZUseE4JZfGxkKZBb4P/NiIBbFT8G1cD2FrrYsjY4Xa/SCXTnp6TQ9lUyiWq75dQerazctxqFbzqgG43Ff2hCeLvuybdndfkz34BV0he5178DP9PiYvo47J0xuyl0236cG3jM0e9pDlXlLuHbYW/T573lrhqcC/5JJLNB8GCZLQWfTLpgVIkA1AwVd/lpjpubFQDs1GBwkfYkyelpC9ukVz3HJzbLe73IlBZVMsX4xJgV+2v1nb4aZIFKFCM4Yz2Fgoa1vMxBE7NdoN/RpeZYf9vOb4KE1hRSj4XkevDWVSWI+S701D8RpIJtr3hduRUir6crWKVDL4+0Ze+ZudKuvmRAmPPfjaLfpOUvT7ayOylUV/QpeC72D8YCqZQCaVQKlieF6rq6KA3fjROFj0vd+FSWQIonj2Q76lgq9/E0LsIIsLRthaFUj40Domz2ZMktoH6dain0kl5fdMlOLRK67utDtd3LpR8MV1YmZdKtC1mIkjOi36pT5bONtRtJkQEVQuDXGOUISnuAzYE8gkfZ8FvuqM8eKeVa+b3erDb7DoO1TwRdGVc3n9EAGIugp8cR0T2QWtyPTpRqS06GdS8vWrTcF3KGzIesBjzdJQ4Fs2mzMxqgO8XbXqjI+P43//93+xbNkyjI6OYurUqdh1111x5JFHYnh4WNcxEp+ELZhL7BCq81yDOEb5e+o3RfE7dPUTkf5DpOhP16Dg2y3u1MLFrYIP1DbGSpXwK82vbsjjs7f+FcfvMw8HL9jC889Rx4g5Xdx66cGfWW/LmNCUxBxHivXrt9NpB62InUW/w7zmOCxEw4ooGD0r+HUXl99rdcHnxlkqqSr43S/w3Sr4bh1ApkVfl4IvLPptevD7NkXfXDcLBV9bD76NW8lKLp3ExoL3a5/qeElZRIE4OaM8F/i33norTj/9dKxfv75hxmcikcD06dNx/fXX48Mf/rCWgyT+kPN0Q/KCVsdwCIIYXSHD/OoXqQEqIqQDIkV/uoYe/OH64q6pwFeKznYLCDsGMymM5cvakm2DYskzr+P+Z9/AuvGirwLfXBQ4P1euevClRb+2qcMefO8UPSpwKnEdk2eX9lwMycZ8HBEFo9cCX1qcNVn0cx6T+FUlutKlglTd1HA7Jq/XIXtlmxnqKpk+7eXOK1OuBtuM+vWC02k4fgW/di2QTNHvwEMPPYTjjjsOqVQKp556Kg488EDMnj0br776KpYsWYL//u//xnHHHYf77rsP73rXu3QfM3FJ2KzpssenpYKvscC3tAKErVWBhAvDMKRFX0cPvqngNy46SsprPNPGAmhHu/C+MCFGI/191QaM5kuYOuBt06TkYXSSGxVYXI9m1p9zWvS943WBrpLqU2XMDmlLtkvRj8FCNKyIgnHEp0Xfb4FUdFBwtkMVMbul4Oc9hOwVPGbTjNTvLboU/FKls4KfSfZryJ7owU+ZY/I0rTWcjMkD/LcntbsPUcHvwBVXXIFcLocHH3wQu+yyS8PXPvrRj+JTn/oU9t13X1xxxRW4/fbbtRwo8Y6Zoh+OF3QrBT+QHvxiYytATrPdiPQXGwtlufjRoeCLQtwasicXD8kEkh4CkwY0z6YNCqFaVQ3gsZfX4cC3b+bp53gJJHSn4Nct+lNqBb7fxOs4o6PA79eFsx125yyOIzHDhl8F3y6HxS0FJfjMC4lEQk4R6kUPvtPCu+TVol9/fkbzmlL0q5178EXCftWo5RpYreBRxZxyFaSC39mirx6L+9/TOtcEiFeKvqerxcMPP4yPfvSjTcW9YOedd8axxx6Lhx56yNfBET2YxXM4CoJWPfhBjARqsuiHzMlAwoVQ7wcyyYYASK8M2/RfipucF3s+YC4aw24lH1cWtX/651rPP8fOwtwOdz34FgU/5Oc1zNgFxrnBtOjHQ8Ev2thWxT3RMLqnupJGZA9+j0P2ih5cTFbMTc/urH8mi+bvcdyD79GpoLsH3xxj2y5F3/xaP9m9g1Twu2XRb+f6kxb9cv9fUz1dLSYmJrD55pu3fczmm2+OiYkJTwdF9BI2a7pZ4KsKvv6QvYLl95i2Hy7gSTNmgr5/ez7QxqLv0YZo/blhd6Koi4JHX3zT88/xMnpNFokuLPqiBz/srQ9hRouCL9srwnG/Chp7BT/V9BjSXUwF35ujS9dmrOzB9zN+Um56hlfBD0sPvrkJ3y5F3/xaP23AiTohl05qdws6bbfzmxtWaCMKiM8VYnB/8XS1mD9/Pu6+++62j/nDH/6A+fPne/nxRDNh68EvtLhZyTYCjZsQecVqpP6+fEg2Oki4MBP0/dvzAfvFXdnBLNh26ApuChr1+P66YkPTRodTSh5UHdHH3WkxaxgG8vXr0SyG7PmmWKmn6GsYk9dPi+Z2mKplo2tIXQSH5d4dN8Zlge/N0SVT9H0WSK3aGt0iitWe9OC7TdH32IOva0xe2UGvuDp6sJ82I8VrbSCTMltMtBX4Tnvw/dUs7ZwCQQR6hxVPd+Fjjz0Wjz32GD7xiU9g1apVDV9bvXo1TjzxRDz22GP46Ec/quUgiT9kWE9IlGtr+B1gLm509sWIi9JgU4p+OM4DCRfrNSboA+bizlowmpZzbxb9QU2jl4JGLejLVQNPvLLe08+RFua08/PltEgsVcye1JlCwS9VUI1JcakbHQp+2kVAYj8grweW13cqmZCv4zgsRsPImLYUfX+Fp5b3VbcVfOX+NOa0B99jK4L+MXn192SbvvpUMgERi9FP/dymRd8ck1csV7W8bpy6F80RoR5T9OX7pXlDTPzsfmqrsMPTVev888/HnXfeiZ/+9Ke46aabsN1222HzzTfHa6+9hueffx7FYhF77703zj//fN3HSzzgN5FSN+oFRBCMgi/S+lMNvy8s54GEi/V1BV9Hgj5gn3bv26Jff69EJWQvm0qiWKniT/98E/9vu01c/xxPPfgpZ0FteWUBIQp88XmxQUOco8NKLDa+utUr3Gtkir5NIFS5WGGB3yM2yh58b5u+unrwxZpJSw9+t8bkNVj0nYXfFXyG7G0slFGtGp7Ca1XMFH3740gkEsgka/e2ftqMbDUmD6g9n143ugSOe/A1KfjZlgp+fDZNPV0thoaGcP/99+OSSy7B3Llz8dRTT2HJkiV46qmnMHfuXFx66aW47777MDg4qPt4iQesaby9ptBCwQ+iB186BdKNBX7Ye5dJb5A9+Jot+sVKtWG32K9FfygyCn7t+PZ5y0wAwJ9e9Ba052VDxKlaJa4FiQQaxvjRpu8NHWFgIrnaSX5CP9B2pFMA90XiHF0p+n7XHJHvwQ84ZE8dYziuYQqK00LU3Ejun2uVuNYMpFMNrzcd6w0RbNc5Rb8uSnpO0W83Js9ff3+U8Hy1yOVyuOiii/D8889jw4YNWL58OTZs2IDnn38eX/jCF5DL5XQeJ/GB+iLvtZWoUjXkMTRY9APoi7Gm9Q+EzMlAwoXZg69LwTcXHWrBWPJp0Y/OmLzaQmvh2zYFADyxfL2nQsVuTng75Cz1DovZgrIJmEwmTEttIdznNqx47aFVceq+6BfaOVToOustojAd8ZqirykvxauyrZLqsjMm7yNkL+Py78ylk/J+qqMPv6yMsm2H+Hqpj9xGMu8hk0QiYd4TdQhjTkfe+hX82oXsyRT9GNxfvF8tFEZGRrDllltiZGREx48jmsmFKKxHLeAbLPpp/cV33tKD73e2JulvNsgUfT0KfjadlAsAdffby1x3laiF7O245TRsMiWHYrmKvyzf4PrnON31V8k4LBInLZuAMhixpKeXM24UPUw8sJJxuDnTL7RzPcRpZnMY8a/gt85hcUtBx8aZw+BRXaj3vFLFcFSseQlUBWp2edWm7xep4He4jvVjsShbW+trcp1Be+7H5HlV8O3vQ+J3U8EnfYF6sez1i1rdBQzeom8Zk1e/YOVpdyQtWKe5Bx8wb46qbbDsoL/Pyc8Me6uJWOANZ9PYZ5uaTd/LuLyCw0WBSsphyJ71GqGrZzauFHX0CssRh/2/AAM6WPRT3JTuJWP52qbvFI8Kvl0Oi1uKiqrqFafXRF1YC0InNn0/rQjiOdKi4Is2umT74xCbJv1l0W+cPiUdgzos+g43cPzmhrVzRcm2pxjcXxxftd7ylre4/uGJRAIvvPCC6+8jekkkEsilkyiUqz1X8EVxnUkl5A0HUJP+gxuTNxBAkB/pH0SK/jRNPfhATcEZzZctaoZ9AIwTBjQtGoNGjJgazKaw9zYz8Zu/rcafXlyLT7v8OaU2N2s7nPfg1362cEXoWpDHFS+BiFYyXQ4D6zXtHD1yHnQMFqNhwzAMqQaPeE3RlxuGPlP0bUYpuqHbPfjWDeiNhTJmTWnfuutnWsBILgNgUm7K+EHco9MOe/D7aTNSrI/FtUfnprcck9fh+TXdtt5+Z7uNBNV1YRgGEgl/gYxhxvFV66WXXkIqlUI6zWThKCIL/B6rftaLhyAbQK+h2EwYaErR5+KdNCNC9gJR8BXbYKkq+vs8KvjCoh92Bb9+fMO5lAzae+zldShVqq4KQC+jk5z24FsnbQxqstTGFS0he2KMUR/1tbajXcZEnGY2h418qQpx+Rj2GbLnd8NQrJu0pOj3wKIPOFPW/bSv6RyVZxb4zsa59VMgaN4y5UpnD77TwFz/Fv3OrijDqL0XvGYhRQHX76IDDjgAP/nJTzA6OorJycmO/0g4yAbQ4+4Fs+hufOkF0YMvbjCywJcXKi6WSDPrZcieRgU/11yMlzwGCQnEjno+xEVosVyVi56hTBpv22wE04cymChW8ORKd334XvoyvfbgD2tS3OKKtNhqCdnrn0VzO9oFEzJFv3eM1Ue7JRJmoe4WM9PDp0W/0jxa2C3pZPfCKw3DkNfWEYeFt2EYbXunOyFcFk4T+9shrj2dXHb9GAhqnXI1qDHU13kPvr96wAzZazUmLzwty0Hj+F301FNP4TOf+QyWLVuG4447DnPmzME555yDv/3tb0EeH9FEWNJ47RR80Vum1aJfbrxQUcEndlSrBjZM6h2TB9SKW8AaOOTPoi9D9kIcBKf+vYPZWkL9XvNFH767cXlebN+ue/CtgUIh3jwJM34stoJMl8PAeo2TFP1+X4iGEVEoTsmlPdt4dfUv63hfdVPBL1ZM98OmIzVbfqfCW21D8fJ3alXwHbrs5EjPPrlWGYYZhiiuPTpbAuUGTscefH9rdScKvvq4fsXxu2j77bfH1VdfjRUrVuDWW2/Fu971Lnz729/Grrvuij333BPXXXcdNmxwn5BMukMQBbQXTEts40tPhgkFErLHMXmkPWP5slyQTB/UZ9EfDMCiPxiBIlRsPmRSCXmTFUF7f3Jb4Iu+vQB68MWGozintOj7w48CJ+jHvtZ2tJs8wAK/d/jtvwfMFP1Cueprw0qOLvOl4Hdv4yxfNF+vm9T77jsV3upr3Mu0AOEUGNWi4DvrwRcuvFKfvD9LFUOug2SKvk4Fv+ys9cHvxKt2m6apZAJiv67fr6uu30WpVApHHnkkfv3rX2P58uW44oorMD4+jjPOOANz5szB8ccfj1deeSWIYyU+MC0vvV24SlXdouDLADyNb7iC7Zg8Lt5JIyJBfzib8lWcWBnONd8cfVv0I9BqMl5ofO8BwD7bzAIA/Pmlta4WmV568NMOeyOtLUM6FzNxRMe8brEoi49F337yQBDZNMQZUsH3mKAPNFr7/VxT/KTLC7qp4Iu/NZ1MYEbdETcWcIE/JVf7PTos+k57xWUgaJ/khaj1gRDhdAoKxS5Z9Nu19SUSCUVQ7I/nzQ5fK9nNN98c559/Pp5++mncfffdmDlzJn7+859j2bJlmg6P6CIsM+ALNgq+fENrPL5JmzF5/f6mJu5ZL+35+tR7ABjMNCvCYjGQSXqzfQ5FoE9cLAaEggUA75g9gim5NMbyZTy9etTxz/LS0pCSCn6HHvxio0W/leOCOKcop6T47xWOS8ieoxR93rO6jihIp/hQ8HPppFQL/VyvdVj0hRrd6Zqog0lFXHFaeIvN2HQygaSHe6PZ6+8/Rb+sHEs7TLdRf2xGqmtjadEPoAe/o0XfZzttJyeZmqTfz/iWqv785z/jk5/8JD7ykY9g5cqVmDNnDubOnavj2IhGwqIE2Cn4Mi1Y4xvOdkweF0vEwroAAvYApRhXLfoeLOcqOm+4QSEWsyJkEKip6nvOnwHAXR++lx78tOMefDFfurEHnxZ9bxQ9BCJaScdMwS+Va39nyxT9lP77InGGqeB7vyckEgnTFeTjmqLDGZPq4vhJuXGaTcnCu9Omqd9NDKdhfk4Qm4udXHaZVH8ViqKtNZtOytwJXSF7lapp/++You+zpbjQYc0Ql+uqp3fSmjVrcO2112LnnXfGO9/5TvzoRz/Ce9/7XvzmN7/Byy+/jN133133cRKfhCVgztoXLxDHV6ka2hJJZb9/ulHB1zHug/QXGyb0B+wBSoq+sriTBWvan4KfL1VRDWm4z4RU8Bs38vbexn3QnlQ4PVj0Oy1mhUV/0FLghznfIMzosBLLCQgxU/DbWvR5z+o6OnrwAT25Hub7yluaP+A8l0QHjQq+s8JbTArwWuCL3+NkHF8nxH0j0yEnp9/aiQpSgDP/7sFs7f/93hPVTZBO9/JsSpNFv4OC3+/OKMdXrmq1it/+9rf40Y9+hN/85jcolUrYcccdcc011+D444/HJptsEuRxEp+ExepXsLlRqR8XytWOIRxOEIW8sN3mqOATG0wFX69FX6Toq2OSROHiN2QPqBWoqg0+LEy0sOgDZh/+oy+thWEYjtKpnSbvqjhdzFo3HOVinAWVa8pKcrYOpbFfbK+dcJKiX+hzpSmMiIJ0OOe9qAb0uIIKGpwx3ezBz6sF/oCzwruT6toJnQW+bBfoNCavz9qJChZHG2Dew/0KY6pa3rEH3+davZPrLy4Wfccrw7lz5+K1117DtGnTcMopp+Dkk0/GnnvuGeSxEY2Epbgt2Cj41tmUwzn/vytvmec5IPv8uXgnjawTCv5g9yz6Xosgtb1lshjWAr9u0bco+DttOQ0DmSTWjhfx/Osb8dbNRzr+LC+hhCmH4Ud5S06HqeCzB98tfsdcCYRq1k+zpdvhTMGPx7kIExtlD76/e8KgDot+yZ+6DXQ3RV+16JsKfvveeC8buSpax+Q5DIPrtxT9vGVEHmDeG/22rakuh07OCL+B2J1yezIxCdlzvDJ89dVXkclksMsuu+Cll17CRRdd1PF7EokEfvOb3/g6QKKHsFj07RT8VDKBdDKBctXQ8qarVA25cBJ2I7HJke/zNzVxz4a6gj9Dt4LfxqLfKcDHjmQygYFMEvlSFRPFCmb5P0zt2Fn0s+kkdp83Aw+98CYeeXGtowK/6CFkz7R5O+vBlyn67MH3jOglB/z24HdPaew1hmGYvcctzlkQ2TTEGTpS9AElhdxPin4lmin6g5mk4954v+09U+tZCWN5HSF7zlx2mS6e025QsIhigL4e/FLFXPd0ClHUlqJvZ9EXPfh9Xgu4unKVSiXcd999jh/vxH5JukMQKfVesOvBB2oX9nKxomUTQv0ZA5YxeaLPX0cbAOkP1gXVg99icSdT9H28/gYzKeRL1dDmSdhZ9IGaTf+hF97Eoy+uxQnv3Lrjz3I6skglJVVgZxZ9swfff79sXCnUe2iTic5zjtuR7rPgqnaobQitCvywtNbFEV09+DqmnmhJ0Xc4WUQHLXvwO1jn/f6NTn+PE0r1gr2Ta8zpONaoUGih4IsefN8WfSFsONioNwXJgEL20v31vNnh+Mr14osvBnkcJGD8vmF0YbXEquQyKYwXK1oWM+qMcGnRV36nrj5/0h8ENSZPFIxqgrBQOv0s1oayaaybKIW2EJ20segDZtDen/75pqM+fC8LP7c9+NYU/aBC9m7/yypk00kcsmCLQH6+LgrlCn74xxfxvnds7shlAXibdtCKTBfDwHpNqUNbQ1im38SRMU0Kvo5ritYU/W724GdTGBa98Q5D9jz34Iu0/mIFlaoh/14vCAW/0yhbM2SvP96fcqqMWuBraDEB3G3Umy3FPi36VPCdsfXWnZUWEl7CZ9G3HwmkYzEjdpCzqaS80KsKSb5UkTceQoRFP7AefOXmWKqaVjWvCAdMWEfljRcbAy5Vdps3HdlUEq+PFfDymxOYv8lw25/lRcFPO+zBn7RsOIrjnSjp78HfWCjj7JuWIZVM4MlLDvG1WA+a2/+yGl+58xk88cp6XL/IWdaODpUR6D9VrB3qArPVeRP3yWKP79txRPSMT+mXFP26clrp5pg8Vwq+v41v9XkaL5alZd8t6ji3TiKQaAXrF7eRqA8aLPr1169/i77zjAXxOi9VDE+bNU5D9sSmUr8S3hUG0Yq5UAiHgp9rqeDr24Qwf4/5Ek8mE1o3EUj/MFpffEztRoEvRvD4seiHfJyb+HuHW1j0BzIp7LLVNADAn158s+PP8jJb3Wkft+zBTzf24AdxXtdPFGvZIOUqNkz67xMNkidXbgAArB0vOv4eHX3CgPrc9f81WpyzZAItF7FxGecURmTInl8FX0MPcyvrtFt604Ofkj344x0VfH+TAgYyKfm9fpL01WK9c4p+fTOyT9xGrQS4nij4ltBt97+r/WaCtOiX++N5s4MFfkwQBXWvC9t2Cr7ONgK7VoCwTBMg4UIE80wd1OvqaNXT7SUVvunnZvTsqgdFO4s+YI7L+9OLazv+LHETDrQHv36c4ryOF/SfV/Vnhr3Af+bVMQCdF+Uq7cLi3JBx+Nz1A51cD2FprYsjQnH224M/qLEH30+B39UU/RY9+MI6b4cOB5BM0vdR4KsbIJ3S3mWYa78o+EIYU5wiukL2xAZOJu28Bx/wJvh1suiL563fx4+ywI8JYVkotOvB19lvKJS5QWuBX79whTWcjHQfwzAwOllfzHm09dnRauyaDNnzY9EPuYLfzqIPmH34jzop8DvcrFvhtAdfXGusFv3JUgVVzQvhceU1MKoh6TkoDMPAP14dBdB4zJ3w8jy1It1nttd2dFItw+K8iyNCwffbyjfoczJHuVKVlvHI9OAr13/VAdHueqIjw8PpSL52qMV6xzF5fdZOZN4Pm0P2fFv0XTy/aaW11ks90Nmin2o4pn6FBX5MyIakB986m15FZ9K/XVp/WDY6SHgolKtyoT3Vpx3TitnTXYFh1BYBRQ0WfWH7nAjpRtVkG4s+AOyx9QykkgmsWDeJlesn2/4sufPvYkyeU5u37BVNN4bsAeZMYF2oaniYFfw3xgpyqoQbJ4OOIDDA+YjDfqCTaskxeb1DWvT9puhn/IkK6lolain6A5kUcmnTOt9OWS9paPER7QD+LPrmdadT73e/bUa2GmM9oMmiL67nTh1ecq3uoR4oOg3Z65PnzQ4W+DHBz5tFJ+16yXIaFzN2TgFR8BdCWhiR7iPU1GTCviD1ivh5hmFubskZuy4KViti4yAfWgW/tsCyU/CHc2nsuGWtD//RDn34XpSdtEO1Kl9u3AhUHT+6JxSoBf5oiAv8f9Tt+YA3i77fFH2n7RX9QKmDgp9NhWO8bdwoVaryej3ic9PXr4LfEMTo473V3R78RgeltM63uZ5osejn/Bf45hjbRMcJL2aKfn9cq1plV4k2w0K56svVVnQZlusnGNy8F7V+/rL1NoF+d0axwI8Jcp5uj3es2in40mWgofg2w7NsLPp9/sYmzhH2/Cm5NJI+bPOtaCwYa7+n06LeCeL9E94xebXjsuvBB4B3ynF57W36XqzfokjslBht3QhMJhPaQoWsbFTU8HAX+KPy/wvlquP+Um0p+sn+UsXa0bEHPxMPpSlsqBtbfi36rXJY3CCe+1Qy4Wu0r9O2JR1MWlq0nBTefkP2AHMzpt1GQidE5ku6Q/890I8p+s3rc3UN48emL+zwToUN6ej1FLLXaeM02fC4foUFfkwIi4JvZ50HAgrZsxQYOSr4xIIZsKe3/x6oFYzitS4WeFos+kqveBiZkAW+/eLYaR++m/E6AlkktrGjGobRcsOx1eQDHUTFoq8q+ICZp9AJHQt0QFHFYmTRt+0Vjcm85rAhCtGBTNK3I8XvZA6xZvP7vpLOmG704Cshe4C5SdKu8BbrPichbHY4HcnXDjnG1kEh2ncp+qVmh636/74KfJfrHj+B2HLN0Kn1qc+vqyzwY4LOEXR+aDfP1c+OnRXZA2Z5gw9o/B2kP5Aj8jQH7AmGLQqOFou+z77OoJnokKIPAHvOn4lEAvjnmnG8uiFv+zgzfdf9mLx2apV6DWgMFfKfet0KNWAq1AX+akuB71AN06bg95kq1o6OvaIhyc6JG2b/vf97gt/riZjVnWshirhBXhO7YCeftDijRhwU3qbqan/P6IQIyR3zEWJadrGh3G8p+mLDWy3qVZHCj6vNrXNRj0XfLkU/HllcLPBjguzlC02KfvNLT+euWqcxeWEtjEj3EXZpv72WdlgXeDos+kEVobqYcGDRnzaYwa5bTQcAXH3XMy0fYxhGx366VjjpN1WvAa0UfN0W/cYe/HA+b+VKFc+/vrHhc05fYzpGeQHmaKpuWIl7DcfkhRNR4Ou4J/h1BMnwSt8Kfhd78K0W/YHO6fY6x+SN+bHou9iAz/SZ1btQbr1u1iEouA3L9TpVyzAMxxun/fK82cECPyaEZf57q5ROgZ8dO7vfwxR90glhxwzCog80L/CEfcxPP6U5mzZ8r+Nq1ZAKTjuLPgB8/vB3IJEAbnlsBe5/9o2mr6uLUTcLXFEkGoZ9oSjUinQy0bDTP+izZ9YONZE+rAr+i2vGUaxUMZRNYc60AQCN2QHt0D0mr1+Cq9rRybYaFytp2BBKs98EfcD/HHFd0ym6maJvteib4+vsz4HOkD0/Fn1xz3HSg5+WBX5/XKsKNpu04j7uz6LvNmTPW8Co+lzE/brKAj8mhGWebvsefH0uA+sNRiB2JlngE4FI0Q9KwbeGLJVc7mS3Qs5rD6GCny9XUJ8I2FbBB4A9tp6JT7xrPgDgwtv+1mQHV3fY3fTCppRzazcqz87lI8ZauZkB74SNEejBF/33b9t8RKphEw7VsEIHW6RTpEW/C4VIr5H2644W/aocs0mCZ0zTiDxAvVb7S9H364zpboq+pcAf6Fx4ywLfx31xqo6QPRf354zDcaxRQabop63r5sYcIS+IkD2nrXZeBT91zWB7XeWYPNJPmMWzszfLmo0F3P6XVdqt7HmprNun6OvYhJDzra0W/TQt+qQRYdEPqgd/yMai368he+oiwLrB1orzDnk75s4YxMr1k/jq7xut+iLRGHCn7KSVaQh2Cv6kzWZjVyz6PnpEg+SZeoH/jtkjcmPK6WJZW8ieA/dFv1DsYL9WF9r9ohJGAfFe9ZugDyjqp88CP9vC9eiGrqboiwI/W3tdyx78NhZ9HQ4gadH3E7InLfpOevDrCn65P96bUsG33BMHNaw3xMaS+x58d/WAWj90UvBp0Sd9gdsU/at//wzO/PkT+NefPqYt4KdcqcqbS6udNZ0WfTHfOtdU4FPBJ40Eb9G3huz5T9EfCGiUmw5k/2Um5Wjs4HAujS9/eGcAwH8//BKWvmSm6hcq5t+XdjHCULVX2ilWdmqF37nVdqhp9OFV8Gsj8t6++YhUL52eB90he0D/L8A6TdRQ75P9rjaFCaE0a+3BL1U8uTC0WfS7OJ3CKrBMcZKir2GDUIQi+unBd3N/djKtJUrYtdDKHnwf90S3Pfhe1+rinpFMmK4VK3GZTsICPya4Dax46c1xAMB9z76BM/7ncT3Bdw2p1S168DPuNiHa/i45/sqSoh+SaQIkPAg1dWpgFv3GgtHtjc7JzwwT4w4S9K3s99ZNcOyec2EYwGdv/assvtUReYmEmwJfsejbKJ/iGjFoOc6g3BFRGJMnLPrbz54qz4NjBV9TIaIurPt9VF6nc6YWOxzt2j2CsOhXqoanTRrdFv2gFfxq1ZDrTKtFv52yrsOpYPbg+0jRr7qx6Nc3TfrEXSM3vZvWzf7vicLl4HpMnsvf2SlgTz2Gfhf6WODHBNmDX6mi6uACv2ZjUf7/PU+/jrN+/oRvNUV9o7bapRU7djqUCrsefK/BHaR/GQt4TJ4sxgv6LPphHpMnE/Rz7hZq/3n4DthsJId/vjGOb/zhOQBK357LzZBkMgGxH2Dbg1+2s+gL5VrzmDylUN5YKDu6DneTsXwJK9ZNAgC230JV8Ltb4KcaNmf6+zrdaZxTMpmQr30q+N1DhuzpUPCVNYgXx1WnnAanpLvUg59XxBOZou9AwfcyLcXKiJYefBGyF8cU/bowZqPgdzdkz59Fv93voUWf9BWqVd3JQmHNxgKAWsp1NpXEnX9/Ff9+8198LbjyygKwlXU367KNoO3vshuTx7nCxELQY/JkwVjSZ9EPykauA7GIHcq4O5/TBjP44pE7AgC+d/8/8eTKDb76Mjv1nAq7oXUxE5Q7Ql10Goa/PtEgePa1mnq/xdQBTB/Kyg0atyn6Ob8he0nVoh+uTRDdOHl9x8VOGiZEr7gOBT+dSsrn0Ms1RayHdI3JCzpFX93EENdWJ+n2OjYIR7rcgy8DQfukUCyUWyv4OnJp3IYLe7fo27cBC5iiT/oK9cXe6Q1TqlSxfqJ2gztqty3x3RN2RyaVwO1/WYXP3vJXzxYvWXR3nPmroQff1qJPBZ80Ii36AY/JmyzWejCFguIrRV/DjnpQCKXaan13wiELtsDhO89GpWrgs7f8VS6IvWyGiD58W4u+zczfboTsAeGz6T+9ulbgv32LEQBmwJjTFH0n1kgnJBIJRW3s7+u0E/u113nQxDtiM07Xpq+fDVnxvrIWXW7pdD3UxWTJdBwIIWeKA2VdbhBqGJM3Uax4XqeaG/BuUvT7YyNSrJutz4HfSRCA2poYbIq+IwWfKfqkn0gnExDCSKc3zNrxmj0/lUxgxlAW79l+c3zr47sjnUzgtidW4sLb/urJXiqKamvwnUBtI/CLuZlgUfDrN8k8FXxSJ2iLvrg5jhfKDYqkE4Wg088Mo0VfLPCGXVr0BZd+cAGmD2Xw1OpRfGvJ8wC8FvjtF1/mJqA1ZM9duJxTxutKuGgdCFuSvkjQ314U+PXz4HRcoK4xeYCpjPVLb6sdTvI4ZOsaC/yuIe4JOhR8QNmQ9VLgd5i04JRu9eDL9khlg9eRRV9jin6n39UON1byfuvBL9g4X7X04Lst8D1mcjnZSJAW/T6ZfmAHC/yYkEgkHPefvzFWs+fPHM7KHdhDFmyB/zpuNyQTwM1LV+Dzv3rSdSKsXc+rwG3Sv6PflbWx6FPBJ3WCtugPKynKqpXPz4JN2N9LFSN09sAJmaLv7XxuMiWHiz+wAwDg7qdeA+Bt0ZdKtbek5juMydNZ4BfLVbnw2HRKDkD4FHxZ4M9uVPDHHVr0dfXgA+aovH5Rxuxwcs6ybCvrOhs1huwB/oI7taXod6kHf7LYGLAHOOuNd6K8diKXTsnzNOZxA1WcH3USix3iMf2iBBdsHEVaevDrxbTT17HfFH0n19R+ed7sYIEfI5xa/UT//Sb1hajg8J1n49qP7opEAvjZn17Bpbc/5arIt1PVBTpH2E3a9NdKiz7VEIJaiJcYXxb0mLzJYmOB78eiP5A1L91hs+kLK7qbFH0rR+66JQ58+6byYy+bIWLxZdfHPWmjVpiLcX098mpQ3ezpgwDCVeAbhoGn5Yi8qQDMjSlra4EdOgt8U8Hv7+u0VC1T9u8Vr2FTxDsbdSv4ctPQ/TXFbnSZW8wNz+5Y9NUCX4yv25gv264ZdTkVRhy4Bdrhplc800fXKcMwOo/J06LgO+3BD86in4lJrgkL/BiRcxgsIRL0N5mSbfrah3bdEl/9yC5IJIDFD72Ea+9+1vHvlxePDgq+lpF8Nj344neE0dpMuo+6CAhKwbez6NvNaHVCNpWU36+7V9wv4ni8WvSBmuPo8qN2kgvsTNr9ueoYsmdzjRCLGafKtRPE6yybTmLWcO26OhqiAn/1hjzG8mWkkwlsu9kwAEXBd5qir2GOtSCdar850y/IxWib13dcAqHChNjU0pGiD/jL9RAFTmQU/BYbp+I8lpURelZ0ZXjIfn+PQXsyRd9RyF7/WPTV56XpnqjB1VaS2UPBpui7Ci7tg42ZdrDAjxE5hzPg37RR8AUf2WMuvvihWtr19x/4p2MVv9BBwddpRSzYBGjpdAmQ6DM6WQ+Ey6S09A63QhS6k4pF3+1cdyuJRMJXX2eQjPu06AvmTB/EhYdtDwCYPW3Q9fenOixoCzajNFXHhS7EZsGUXBrT6k6RMCn4/6ir92/ZdFheI8XrtjcW/XiE7JUcbIowZK/7jGm26PvJ9dA9frJbKfpqD/5QJiWzR+wS7nX9nTJJ36OCL9T4jKMxef0zwlK9vjQp+DpS9F22YIisLrcbm6YThJumwUhWJJQ4LW5Ni36zgi84crct8fn/fRL5UhWTpYpcFLejs4Kvr/gW6px18e50k4PEAzNBP7hLoSh0JxSLftqHPV8wkElhY6EculF5k0X/Fn3Bv+yzNbbddAq23XSK6+/tZJ+0G6Up1QqNFn2h4A/nUrLAD1PI3j9kwN5U+Tm3IXs6UrAFcVPw250znc420hnDMMwefF0KfsbMYXGLk9eIE7qVop9vsXGaTCYwJZvGWKGMjYUyNh1pFo90OYDEpozXUXllF0qzDNnrg6wQsSZOJJpt9Fp68F1a9MXrwG094ChkjxZ90m84tbwIi/4sGwUfqPVnih1hp0pUxx78jL43nV1/rdhEyDNkj8AsskYCStAHlNA2xaKvwy3gJ7gpSMSGw5APi77KO98yq+WCsBOdFPyOPfgaN05E7+1wNo2p9aIhTAq+CNgTI/IANWTPXQ++3hT9/r5OOxrpxBT9rjJRrECYEkdyeu4L5jXFew++LgW/FxZ9wLye2FnndSn4ar+/F9xswqttYF4mS4UJETw9kE41uQt19OC7HpMnU/Td/U6xxnISshe2gGLdsMCPEdLq1+ENYxeyp5JIJDDdpdXUbiyVPD6PO3ZWSpWq7Lu168Gngk8AdURecAq+sDqrKfo6iiAdN90gkAW+zfu8WwjFqlMPvlUZGw5gTN64VPDTMsxxw6Q+h4Bf/rG6VuC/Y3ZzgT/h0KKvqxAButcv3Guc9B3rui8SZwj1PpVM2E78ccuAjx5mU8H3dz3tlEmii1YWfcB0Q4wVWq8XnfROO8FM7Pe2gepqTJ5yrKWItxOJNXErh+2ABjHB9Zg8j45eZ5umpvMi6hsz7eirAv/yyy9HIpHAjjvu2PS1hx56CPvttx+GhoawxRZb4KyzzsLGjRt7cJS9w62C386iD0BaTddPOLuQyguIzQVcl31eLXjs5nlysUQAM+gsqAR9QOm/LFSkPdJPgr75c/WPc9OBUKuHNPWvekUoMHa79K3mNasfT5YqrkeB2rGxIIIHzR78sITsFctVvPBG7V749gaLfr0Hv2iffN3wc3SG7CXjobA4US1NZ1u43uf9ypiSoO8nJ0VlyIfFWXcPftC5FmaKfuPxTmmj4Ferhqm8+k3R9xmyJ+7RaSc9+MoovagH7dlteANKi4mfHnyX7sUgQ/bU9Vc/5CfY0TcF/ooVK3DFFVdgeHi46WvLli3De9/7XkxMTOBrX/saTj31VHz/+9/HMccc04Mj7R05h1Y/Jwo+AEWJcqfg52yUPXF8pYq/XTX1Jmq9WDFFn6iM1hcBQVr0RaFUrFTla1Ongh9ai76GHnw/dEzRL5uWRBVx3Iahr5VHpnLnUq6vm0HzwhsbUa4aGBlIY860Afl5oeBXHZ4HrSF70qIf7UVzJ5zYVnMxSXwOCxs1B+wBelL0cz7vGekujclr1YMPmIV3q0wP9bWd8W3Rr/2e0a6k6JuFYtSvVXbB1IC56Z33VeCL+0N3xuQ5CS4F+vu62jche+eeey7e+c53olKpYM2aNQ1f+9znPocZM2bg3nvvxdSpNYVi/vz5OO2003DXXXfh4IMP7sUhdx0nO2LVqoG140LBb1/gu02DNkOtWr/xrG+6gaS3AqGgjL+y7sCbLoEqDMPQtkNPosmYCNkL0KKvKsTivaJTwffS1xkkYSnwO/WcisVKU8ie8vFEsdyk8HtBhuxlw6fgPyMD9kYarofqedhY6HwedBb46T4Kr2qHo5FOsrWufxeiYWJjXn+B7ytFX4RX+mwX6NqYPHFdtVr02yj4apHlO2RPWvS9hux1TmEXqCp/1AvFQhsFX4eY4DajRQiBbq97RQdhfuprrJ+zTfpCwb///vtxyy234Otf/3rT10ZHR3H33Xfj+OOPl8U9ACxatAhTpkzBzTff3MUj7S3yDdNmR2z9ZEnu8M7qYNGfPuRuoVro0EumXlj8LGbs0rHVzxlG/yc0k86IMXlBKvjqzHqzwNeg4AcQBqcDadF3MFkjSDr24Jdbbzgmld5bXe0PMmRPteiHJEX/6fqIPDVgD6idh2HZBtJ5saw1RT8mY/Jkf3U7BT9NBb+b6E7QB/wp+E4USSekxPUw4HXPpI2CL9PtWxTepbK+An+kQ5hfJ9wo+IlEwnQbRfxa1W59PqChwHcznaB2HMFZ9NXnjQV+iKlUKjjzzDNx6qmnYqeddmr6+t/+9jeUy2XsueeeDZ/PZrPYdddd8cQTT9j+7EKhgNHR0YZ/UcZJWI+w508fynR8I7rtwe+k4KeTCYgNUT99+HklDdSKuvjMs6cx9nRjTF4ikZALvFGdBb686YbrBjUZEgXfcQ9+i43AIc1Be2KW/JSGkL2Sth5/PzzTYkSeYEgm6Xc+D2Yh4v95l+On+nwTVqpaDhT8fl6I+uHmPy/HAV9dgodfeFPLzwvCoj/oYqPMiq7wyq4p+HYFfpveeFV1TTrofW+H2Ky3C/PrhNtRtt0aPxg07dbn0qJfqnpun3UfsufPot/p94h6qJ9zXiJf4H/3u9/Fyy+/jC9+8Ystv7569WoAwOzZs5u+Nnv2bKxatcr2Z1955ZWYNm2a/LfVVlvpOegeYY6daFPgj9UK/FnD7dV7wL1Fv5OCn0gkPCdnqkzahGcBjbvDtDwS06IfnIIPmMWueK84XTw4+Zlhs+iPh6TAT3XqwW+TCTKY8b4gb4UoGoZyKXndLFWMUOQnqBZ9K6LIadU3a0Uu0h32WLaj0+ZMv+AkWCzrUcmKAzf/eTk+e+tf8dKbE7jxz69o+Zkb6/eEIBT8Xqbod7oe6sIuvFQq6y0UfJ0jNtu1AjhBjOZUA/TaIZXgiF+r2q3P1Xu5V2GsVDY3cZyQ8xiI7XQaQxw2TiNd4L/55pu46KKL8IUvfAGbbrppy8dMTk4CAHK55n7ygYEB+fVWXHjhhdiwYYP8t3z5cj0H3iNMq5/9G3SNw/57QH8PPqBnMSN+TyuraG0TgaPySA3Toh+snVwowlot+iEN2TMV/F5b9NsrVpNtrkd+LLWtGFdUweFsSi62R3s8Km/DRAmrN+QBAG9rUeCL8zDeoZ9VZwo2oKhifd6D70Sd1bHp3Y/88okVOP+2v8qP//TPtVocMaIAHdGp4IcgRb9bbS+TNtkmbRV8jfkd5jg+jxb9qrDoOytE+8Vt1G7KleqG9XpPLHpM0S/W87Ic/x6HLS0ZB47mqBPpAv/zn/88Zs6ciTPPPNP2MYODgwBqdnsr+Xxefr0VuVwOU6dObfgXZeRCwYGCv8lIEAW+CL+z34nWUXy368FXP68rIZtEF2HjC3JMHoAmi76OImhAw+ga3ZQqValk9FrBT3dYeLW7TvhR3FqhhuwlEgkZ6tjrJP1/1Pvvt5w+2NLFMuzQot8QkqU1Rb+/r9Fi9J2Tmc39rDS55fa/rMJ/3PwXGAZw3F5bIZNK4NXRPJavtRdsnDIWoEXfW4q+3jF5VQOBzv62s+gPt+nB1zli0/+YPHdugn5xG7VbnyeTpjDmVVDwatEH3BXhTjcSxPsp6s9bOyJb4D/33HP4/ve/j7POOgurVq3CSy+9hJdeegn5fB6lUgkvvfQS1q5dK635wqqvsnr1asyZM6fbh94znIRWiB78TQNQ8NvtEMpjzPhfzIjxV616a9XfTwWfCAU10hb9ECn4akGsI33eD+aYvNbXErHR2bYHX9O5FQq4WOS6vXYGxT/q9vx3zG5W7wFzxGMni77uAj8t+yOjrYp1Qvx97e6JZnZOeN7nveTOJ1fj7JuWoVov7q84aifsPHc6AOCRF/334csUfa0Wfe+ZHqZ12q+Cb35/JcDsj0mb62rbFH2NCv5Irt6D77nAFwWiOwU/6oVip/W52Yfvr8B3uomjtgq4KfBp0TeJbIG/cuVKVKtVnHXWWdhmm23kvz/96U949tlnsc022+Cyyy7DjjvuiHQ6jaVLlzZ8f7FYxLJly7Drrrv25g/oAVkHha0o8APpwXeg4DsJAuyEOf6q9ctbHZVH4s1oF8bkAeaYpPUBpOh7veEGgVCo0smEFjXGD+3G5FWqhixK2yn4uvINxMJ+iqXA7/WoPFHgWxP0BaaC36HA15iCDcQoRd/BYlTHpne/cM9Tr+HTP3sClaqBo3efiyuO2gnJZAL7bDMTAPDoi2t9/44gQvb8bMYKl4dvBV8pWIPswxfrr6Ye/AH7PA9dkwIAc2NmslTx5AAS78m04x78/9/emYdLUpZn/67ez77MnJk5s+8zMMPIIDCACKiooOKGgrsxKPmMKNH4RU00Ro24xaj5TIxbNBF3jFExQtAIKCIgzgiDwAzMwuxz9r33+v7oft6q3qret5Zeqp/fdc2lnNPndJ3u6qr3ee/7uZ9gtBOJMXk11s2izSTt7DqUUcxoiYY1aA5Ct1VD9lo9O8GKxjZJumD79u344Q9/WPH197///ZiZmcHnPvc5bNiwAX19fbj88stx88034wMf+AB6egoLmW984xuYnZ3FK1/5ynofesOQUfDHZos9+DIW/U4fFHwP+g2N8Vc1LPqR5iuMmPqj67rY5ffbot9VkaLvXsHvaEKLPi3eOmLhkpnqjcCqN9L82bdKDfbaot8ZL/ze3qZR8GlEXvX2sy5J5dGsznjxvlOBH2QFP5fXRaFladFvg4WoDL98/DT+/Ju/Rzav4yVnL8cnX7FDJK6fv24Q/3Lnk7jPSwXfhx58Vyn6Lotf88x2P4vRhRqtT91FZd0qRd+THnzT+zaXyqGvU+13ZnNqPfjGtaq1P59JsT6vvm52MwlC142MFllxg/Kykpm8UiC2qoLf6u+bFS1b4C9evBgvfelLK77+2c9+FgBKvvfRj34UF110ES699FJcf/31OHr0KD796U/jec97Hq644or6HHATQKmUVkoAKfiqIXu6rtsu7KR68L2w6Nv04LOCzwCFooUW2H6H7HWUWfS9VPC9CoLzgmYZkQdYK/glBb5FarB3Y/JKi4ZmKPDzeR37yKJvo+BXS742Y6gm3mzq2OUnBIGMZFuDcN61cWbMr/eP4s++8SDSuTxecNYyfPqVTxOfbwA4d+0gQhpwZHwBxycXsLy/draSHULB9yFFn8aMyY6C03XDaVRLWZXF/HrlfPxc1RyTZ9GDL1uUyRCLhBCPhJDK5jGTygghShZyDcluqAQmZE9WwXcgjJnvwSprn3gkjGQmr7S5abhBrD9jYuM0wHVAy1r0VTjnnHPw85//HB0dHXjnO9+JL33pS7juuutwyy23NPrQ6opcD35BwV/UbW/R7+8oPCaX18VoLCsMZd3ffkO7jQSZsEEm+JB6HwlpNfMavKLLhxT9Zu7B72pwgj5g3YOfNPV8VltsU8+sdyn6xdel3KKfbFyBf2xyAXPpHGLhENYu7qr6mK6i42BessD3YoEOGBsFtfITgkBKsq2B7lftquD/9sAY3vwfDyCdzeO5Zy7F5161U2wAEd3xCLav6APg3qbvj0Xf+F0q1+tsXge1y8fDLsfkaWYF379zyc6ib9mD71FbFz2Xkz78jFDw1cbktboSbDfGuiPq3Plaspmp8B472dxMS4b5tUOKfuNXYR5z5513Vv36xRdfjHvuuae+B9Nk2IXL6bquFLKXiIYQC4eQzuUxOZ+2vSGKHUKLea5i9qWL4ttq/JX56xxa1N6I/vuOqO92cirGUx4qnYlo8yn4Zot+ozHSjWsr+AmbQCEvFPx01lAguosLfQp1bKSC/+iJgj1/45LumoshQ8G3fh28SvomqP810+J9rVaYlSOr60G8DcKgarGQzuH6//gdkpk8nrVlCJ9/zc6a5+qudYN46OgU7js4jpfuXOH4OcWYPA8VfPNaZD6dE58rO0o2gVx+tkIhDSGtkKLvZw++nYJPvfHmAjol2TctS3c8gtHZtK3zqBq0+SFt0Q9IIKjdGOsOF4JCJmu8NioBw04CsdUt+q39vlnRFgo+U8BOuZ5NZcWFVsair2maktVURsEXixkXu6G2Fn1W8BkY/fB+2/OByoLXE4u+C8ucXzSTRZ+KxGqL2VqzmolOFz2z5Zh/B/XgN0OK/uNFe/7WGvZ8wMiOsHsdvOyhBdpjTJ5sbkE7T305OjGP6WQWPfEIvvC6p1uKA+evWwQAuN9lH77Rg+9dLoumaaaQMvXAMMCj6RQhfwPhMrm8+N21xuQBlWM3vXYA9SRq9/vbQcVoVDpkLxiBoHYKfsJFyJ55PR+RbE8pHIu6yi4dstcGG6dc4LcRdh8Wsud3xcLSClxfh/w8Zyq8rW7ShiXHA4t+jedp5wUTYyAC9nwekQdUWta9seh7ayP3gnlR4DfeHGbVg5+yCeL0UsEnFSkWCYn33UjR9yal3wmUoL+1xog8QL0H3yuLrZX7IijIFjXtsBCtxdhcYU0y1BO3zO4BgPPXDkLTgCdH5jAyk3L8nDM+9OADzlqq6DoVCWklPfROCYu2JX8+V+a/LRErPa+pNx4AZlKl60X6nHtV4JNbwEkLVEZRwW+XMXmdLkL2nIawOgndlj2XjB785lk/eQ0X+G2EndVPjMiTUO+J/s5CH77duCdd140dQgkF301fDG0OdMSqP09C9BK19gWZcQfd/Buh4KvY1Gr+ziZU8Onm3xwKvkUPfo1ZzYSbudXlkFplbmHqLW6MNnJMnl2CPiD/Ohi2SG/ed0NpDO412ugVtQmD8uCe2KqMFwv8QZmxvZ1RbFla2Kx64JCzPvxUNifWR1724APOUsi9VrYjFpueXkD99yGt+mZfd40NQ69GAYrnSchtTFYjq5j2HpSJH3bZVV704Ku2JopAbIXnlN1sZos+EyjiNr3nYyJB3/5mSpASNTlvvVBN5/IiLMYyRZ8ChVwsZmqNaTGegxV8Bpiuo4JfXvB6oXSae+J0vTluUs2k4Ev14NfYbDTUNvcKOy0yKbAOaLxFP5nJ4eDoHIDaCfqAsSCfa1DIXqsnU1sh+5p5cU9sVcYUCnwAuGB9waZ/3wFnNn2zddzrAr/TwdSTtLBNe/O5CvscXmnuv6+m1FLhXX49See8dQD1xGsH+tmRVSxGgzLxw07BT7gQFESBr3geOxH8pHvw22D8KBf4bYSd3WWkaNGX6b8nZBeq5ue0ull5oeAbAVp2BX5wP9iMPaSekprqJ+UFb0Syv88KKvB1vXnO5fkm6sEPW/XgU7tQTQXfO4s+LWbNbRqNTtF/4vQs8jow0BnFUE/t6z1lBszZ9eBLjiaSJSjBVVbI5ha0s0V/fFatwD9/3SAA4D6HSfpUEHZEw55Y4s10OHAFeR9e6a+CLwr8Gtd/MSovWa7g+5Oi70TBp2BP2Xt0LDAWfevNJGMsr4Me/KyaK4JwYtGXTtGPaMq/u9XgAr+NsO3Bn1G36MsW+FR0azWsW5XH6L4Hv1YrQMKF1YgJDoZFv/4KPt1c3GC2l3s1r90tNE6tGQp8q8WsrEXfi3wDsuSWWPQbnKJP/fdblvVY9kQaCr716+B1yJ7x3gV38aUaBhXkhWgtJubVCvzz1hYK/MdPzWCy+LMqUG+41/33gCm4U6kH39vPlcgl8WnjzC68tKZF3+sefFdj8lQV/GCMyZO16LtR8FU3cJzUA8Z11ab1qTh2stXfNyu4wG8j7ALsjBF56hZ9WwXfFHwnlxjsQsHPVh/T4uVzMK1PPUP2/LDoh0Oa+Ew3Sx8+LV6byaJfLYlddiSQNyF7xdckXqngz6dzDVlgPFYckbfVov8eMM5bO4t+ymMFLuJzIdIMyC56zZNlmqUVp16oWvSHeuLYMNQFXQceODSh/Hy0kdXjsT0fMFv01XvwrYKJVbCaLOIFtUbkEUJZr6XgexayV7i+Oinw6ZoTke7B93cyQb2ws+gbUyCcjx5U78FXn3hF11W7tpZ2cEZxgd9GiF6+GgvKMbLoW1g2y5G36JMl1mYxE/WgB99mF9nJRYMJHo216Htj/3QyeslPmmtMXu3EaLtRmn5Y9LtNPfjmYMdGBO09fsp+RB5gKG6pbN5yZJ3nYWABsb1aIdtfbX5Ng9wvWo3xOXIVyosONC7PSR/+rI8KfsLBNYXWTV5Pp/AtZE/Sol8ZsifnZpHFCNlzkKKvqODHim68TIsXiinhfLWeLONETCCLvuymCeHrmLzi+8sFPhMI6MOSyelVF72jImTPD4u+9eg6gm5krlL0s9ZWI3odkhyy19ZQyF5jLPreXHqdBDf5CRWzsmM2/SRsoazY5XQ4UdtqMVulBz8SDonFbr1t+rqu49ETlKBvXeCbN6bmLM4xo8D35n2PhmtvzgQF+YWo8f12c52NiR58+TXJBesLNv37HSTpk+LrdcAeYFj0VQoksQlkI4zI4vuYvLT12quWdT6d8zZFv9dNin5eNUW/uK5u8WuVMTrWRsF3IIxlJPviy3Fi0ZcekxcJ/iYyF/hthPkmUW3XSozJk7TDAeoKfq2LR/kxuuvBt34uVvAZAJgp9uD31mFMXkWB70HIHtB8o/LoOMyJ8Y1CJLFbjcmrsREhLPoeTCgQIXtlRUOjkvSPTixgdDaNaFjDGcPWFv1YJCQKTCubvtMey1oEZdFshWxugVnhD7LaVA0ak6eyJqGgvb3HpsQ1XpbZlI8FvpMUfc8/V/5mW9hZ9Mk6X154Z4oKr1fTAmqF+dmRzxvil6zLzqoVrJUQCn6NTVq6JyYdiAnG/UHRoq8YsqfrunTIHlv0mUBRqgRUfkgdWfQ71RR8u16yuAcfuqTNTYbH5DGAYY2ui4JftmD0ImQPMJQSldnKfiIU/Gjje/CtAqWSGeuWIVKudd24djllrkbR0CuS9Ov73u0+MgkAOHO413JkKUFJ+lbnmKHge5WiH4xFsxWybQ2aphkjnQK8GC1H13URsjegUOAP93Vg9WAn8jrw4GG1PnzqDffDou8oRT/jdcheY3vwqU2pogff442Mbodj8jKmjQ9ZO3nbpOh7MSZPVcEnwU/yHmyeumKbok9u4RZ/36zgAr+NiIRDYtFbviOWzOQwU1yIqlj0+xVT9O0UfC8Sgxds+muNFP3gfrAZe0TIXh168MsXPF71GpIq5MdECCfFVTONybPswc9aW/RLJxS4K8DJ2t5Z5mog50i9Ffw9T00CAM5e1S/1eGotmLVI0vd6gU6fjyCH7MkqTUB7JunPpLJiwa6i4APOx+WRsuxnyJ5KgZ/OyQkjsvg9Jo/UXec9+N5sENKm/YyiRT9bUiCqpui37rUqlzeUb7t1s5P7IU1JcGrRpxYOO8ybLLLZJq2enWAFF/hthlCvy4pbsufHwiEly7LZZpq3uGmkJNNghSXHYfGt67rtmDxW8BnAGJNXjxT9cEgr2dySnbFrh5vgGyt+vX8U2z54O75131NKP9dMFn0rm7ddr2g4pInrhNugvVoKfqMs+ruPFFTNnasHpB5P7+W8xWLZ85C9YiGSCfCYvIzCa+aFs63VGC86CjtjYSmniZldxQL/fsUCnzZ9y9tpvMBdir7HPfh+jcmzEVe6E9Ut+imPMzx6RK+/2rU1q6AAE0aKfut+Ns3XlVrnmiEmOOjBpw0cxfM4VqNeqYX575DNNglycCkX+G1GrR2x0eLNdFF3zHKMXTlkM9V1691SO0ts+fE5tc2YFQ67kL12UkOYUtLZvLhR1aPAB0oDy7yyMndE1VUhGX768HGksnnc88So0s/RmKlmsOhHRFBblR58MUqz9vWIFvluN0+qhewBRoFfzxT9VDaHR44XAvZkFfzOmH1gldfzuttJwY8rKPjtVOCrjsgzs6uYpP/Q0UmlnnfRg++LRd9Fir7HG2d+Kfj2Fv3q15KMZB6FLPQ8yUxeyTpfYtGX7ME3lODWvVaZxa6aFn0XYkK9evDpecIhTWxm1aIdrqlc4LcZ9IEp34Ubc5CgDxSKaLogWC1URYq+zU68sWPnbFFt3umrdZMxLPqs4Lcr5p19PxZz1TCfj15Z9Dt8StHfXbRxUw+sLKRONYNF36oHP2WjNAHebZ7UCtnrbUCB/+iJGaSzeQx0RrFmUafUz9Bi2ep1MCz6HlmJhe01uIsvldFgsTZ0nTkJ2CNWDXZguC+BTE7H7qfk+/DnfLToO+lh9k3B9y1Fnyz61Y+XlPWKHnyPNwjN93SrcNBy6HoTCWnSQlcQ3Ea0Po+EtJrZA25G8mYUJxMQqm7btMKIw6BkJ1jBBX6bYaTUV7foL1aYN0v0SwTt0QfU7kZFGxBOd9Xo5hkOaTUvJqzgM9OmcUh2O71eYbate2bRd3HTrcVsKot9xTnpE/Pyxaeu65jPVO83bwSWPfgSG45Gz6zLHvyiq6GWRX9a0Ubqhj3FYufsVf3SC1h6HawUfBW7uQxhn5XGZiAtOc4JaFOL/lxhTeJEwdc0zVEfvp8KvqMUfa9bXywmi3iBXcBxzR58hwpvLaLhkGiJU0nSp83giMJxRALgNpJZnydMG1Sqk2UyCpuZZlTX6k42TYN8TeUCv82otSNmWPTVFHxArpdUVsF3W3wb861rn9qqth+vyOTy+LsfP4L/fexUXZ+XqaSeI/KIDj8s+j704D90dBJUV00qKPjJTB503++MNYFF36oHXyL008mCvBpzaVLwS699jejBpwR92f57wKzgW/Tge2yxNSz6wV18qRRvMZeta63I+Fzhc6GSoG/GKPDHpH9mRmz8et+25ShFn84Rjxxf9UrRr92DX7033uuNDMB4D1UKfJH2rrABH/N506QeiIwsK0ebyZWn2ocvnBGqFn3FkdYUdCjjeBEp+lzgM0GhVhrvqEOLPmAsVCct1D5ZBd9tWrBIx7a4UNGivt4W/fsOjOPrvzmET/zs8bo+L1PJ9AIl6Nen/x4AOqM+KvgenstkzwfULPrmArCWglNPwlY9+DIWfQc9s9WobdGvf4r+nmKBL9t/DxhuDMsU/ay3CpywvbawKmaHktpEi9E2mvxCCr4Tiz5g9OHvfmpS2uI7WyMQ0ws6HWzGGoVXi/Tg26To95gUfLMKnPG4xQcwtQMoWPTpdVEJgyMFP93CPfgywpj5nq663sg4nLKibNF3oOCzRZ8JDLUs8KTgO7Ho+6PgO1tUyzyP2BWs884dbaKMFP+XaRxki+6po4JvVnC9HpPnpUWfikCg8HmS/d1UCCeiobq1PVgRsejBlynwyYXgWsFP0WSBWiF77loAZBmbTeHw2DwA4GkKBT4ddz1T9Onz4ZfS2AyoBIuJ+3aAF6PlGCF76qIDAGwY6sLi7hhS2TweOjol9TPUG+7HfcHI9FBI0fe48LXKJfEC25C94uua10uLRH8UfCrw5TdQzT34shibJq372ZRR8MMhTbw/qgW+2zF50hZ9hWuqsOgH+JrKBX6bUesDMzrjRsEvbApYF/j2lljAuMA47sFP2z+PuZ9RtZfIDRQaNDGfDvTCtRWYqeOIPMIPi37CYwVf1/USBR+QV/GpwG8Gez5gHl/krAefVKg5Fz346WxeLCC6a6To10vBp42bjUu6xXPLQOn/Vq+D1xZ9v3uFmwGVALV26Bctx03IHlDahy87Lq8eCr6SRT/jz/jJaq4mL7Drwe+IhkG1szloLyWUV+82ho1ReSoWffVClN6blu7Bz8hdi5xm/ojWB+UC31mKvoorKsjXVC7w2wxR4JcVBF5Y9K1D9ugCIt+D76T4lrPoG9+rp4pPhZKuu1vUP35yBqdnkl4dVlsSFIt+p4O+TiuOTS5gdDaFSEgTn2v5Ar95EvQB85g8KwXfogffgxR9c4JzeQ8+bS7Vu8BXsecDhoI/Z2HRN3qFPUrRp/yEFl4026GS+OzW2daKjLsYk0ecv7ZQ4P/2gH0ffj6vGyMtfSnw1R1BYpRii4RXih78GvcATdPEa2u2znu9QQgYmzRqIXvqveJ0rWplJdgYY219/aYCX7W9VYTsKQobRii4nxb94N5juMBvM2rtiJEdbnGPG4t+7UIgJangmy/wTi6YMuOvzDfLevY00oKl8P+d2fRPTiXxgn/6Ff7k3x7w6rDakpkGWPTNyfIqPX5W0Dgir/IkSL0/Y7gXS3sLm31W2RpmFoSC3xwFftjCOmmnNAHetD+Q6h2PhCrGD9F1cyaZQb4Ojh56b3eu7lf6uS5yMtTVok9W4tZdNNuRVuhLbUcFf6zYNjjooG2Q2LW+0If/4OEJ23PJ7FDx06KfzevS72O6WNh4r+D73INvcV3tqVLgZzzeyACMdgCVHnwq9pQs+gG4VqUk3UROc2lc9+BLh+wpWPRZwWeCRrUQu2wuL1Q6Zwq+fViUjLIOlBXfDj54ZL21usFEQpqwidVTETErobR4UeXA6CxyeR37Ts2wzd8FNCavnhZ9c+Eb9ahH3esxeXtEyno/+jsLC2vzxpQVcyJgqTks+pSEXLUHPysxJk9i/rsdtfrvAcM9kteBWZej+OzI53X8wa2Cb3GMKgsrGWgzpNoEhKBgbIrYb4i1Y4FP98vBTucF/palPejriGI+ncPe49OWj6XPaiSkeVpoEubgOdmWqpTHG2dhi7YlL5BZf4nC26Sspz12AAHGvX1WRcHPq1vJxWZkC1+rZEOwnYb60nXcb4u+SthrtA2uqVzgtxnV5umOz6Wh60BIAwYc3EypELC06Ev2+Jh3+Jx88GTGX2maJhb29bTolyr4zgp82hjI5nWMcVifY6YXGqDgmwpfr0L2nIxesmJ3cU76ztX9YmEtOyqPLPpdTafgly68Mrm82BxLWBRXnWIx47z4Niy/lc+TiIbFwn1K0iXhlCdHZjGTyqIjGsaWpT1KP0vHbmXRT3vcQxsNtb4qZofKa9ao0a6NIpnJiWuaGwU/FNJw3lrqw7e26VMYW3ciAk3zPiQ0FgkJZVh2Q1Ylp0EGvxV84YyyuAcI63yqssBXtXBbYVj05a+tWQc9+NEAKMGyIdgdDl1tGQXrvBnlFH0nCn6uvllc9YQL/DajWk8LJegPdsUcpV9LpehLKviaprkalSfbS0QXjnqOypuYM16fMccFvlHUn5jiPnynCAW/nj34MR8s+g574qqRzuaFynX2qgEMdFEPfmta9Gv14JvVB6vxU16MyRMj8mq4GkSSvsIi1Am7i+r9WSv7KloF7FAJ2fOsECkeY15HXdoXGoFain7rFxEq0P0xGtaEpdspu4pBe/cdsA7ao15tPwL2COOaIrdpKGudliUs7OSNSdEHgO4yZT2f18UmrKqF2wpyCswoWPTTLnrw20nBV+7BV8gbMWPUK3JFuEpIovm6G9Q+fC7w24xqSoCbgD3AKJKsenVlFXzzY8qDAGUQO5G2YX4NUPDnPVDwTT/HBb5zphuQom8ufFV6/GR+pxcK/qMnppHO5tHfGcXaRZ3CmSMbstdsFn1jlnrpZ5wWJ5pmfT3yIsBwziaVu15J+k777wFzyJ5ED77H47wAIBPQJH0nKfrtErI3bhId3Krpu9YXFfxD45aOED8T9AnV67XX2RayKfqpbA5Xf+E3+LsfPyL9u3VdNzko5XvwzVlLvozJUwrZKxaICiG4QcgLkQ3Bdjq1x8l0AvPx6LpcEa4Usmd2C7fwe2cFF/htRrXQCirwFzm0wqko+HbKeuEYnc/8NSxi1qd2QjGd0y26rmPCC4u+6edOTXOB75SgWPS9HJNnTlnXNA0DnfYbd2YWmsyiT8pKuYKfMm0CWhUPXoTs2aVy9xbPv2mfC3yRraDYfw8Yxz4vYdH3OmQPaO3xU1akFUY6tZ+CX1iTDHY5Ex3MnDnci4HOKGaSWfxq/2jNx1Eh6Oc9QTXZPS1ZeMkim6L/+MkZPHh4At//3RHp311QWQv/X8ai73eB3+MgZI968FUUfPr8trIKLDvG2qmrTeVaZ8a8+SlTDyiF7JkV/IBeV7nAbzOqKQFuFXwjDTpbs7dLVlkH1JMzS58nJ/U8dMNM1ilFfyaVLbmpOu/BZ4u+F8w00KIf0uCoFaYaTnviqiH671cNAICLkL3mKPDDNcKP1BczznvwaSFUrQcfMFn0F/wL2ZtLZfH4yULrxc7VA8o/L1L009maNknvlUbj9wS2wFd4zcz9ou2ACNjrcn99joRDePk5KwEA33ngqZqPm6mDgq/q2Ek1KEWfNnXn0jnpTSWzbTthcbxdZZsc5t+vopzbQQW+ypg8J0ozbQaUO8VaCVkFv7POFn2zyi7j6DWcZPbvXzikiXVYUK+rXOC3GUIdN11UKbjNbYEP1A40ET0+NovqwjG678G3Teuvs4I/UVYkuQ3ZA4CTUwuujqmdmW7AmDwqGL1S7wHjhpvO5V1bBKlP++yijVs1ZK/ZevCjNRazMjZSwJv2h1nJHnw/LfoPH5tCXgeG+xJY2ptQ/nlakOf12huiXs+xLlHwg2rRdzAmr55jXRuJGJHngYIPANeetwoA8ItHT+P0TPWNcVLwa7ltvIA2TWUdOyoFiwyyKfrmtqxJi/HHZui6GgtXjgQ1Y4yvK7wG5rDJkEcb3wDQHS/2+qso+A4KUbqft3QPPrXQSm56K4fsObw/hEKaOPdl6gGVaypgvM9BdUZxgd9mVCueR1wq+LFISCyGay1UVRR8NyOBjDRQG4s+9eDXacFUXtA7Ddkb5x581+Tzurjp17MHn4o8L4OEzGp50sVNanwujcNj8wCAs1f2A4ByyB4p3Z1N0oMfNhX4ZuVZZpQT4E2BP2dn0a9Dge+m/x4ofZ2qLZZ1XVdeWNmhaZq0nbhVMZLDFTa9A6o0lUP3uUVdzhP0zWxe2oNzVvcjm9fxn78/VvUxdG77uenbT3lFkkWzCK+UEEZkkFXwzdcj2QkfVPTZrb16RKZH4fFOZ6Tb4UzBL1r0lXrwixb9Fi4SSeiyW5877sHPOuvBB9QEP9VpDEF3RnGB32ZYpeg77cEHDCWqVr9uKuNEwXcQsieZ1k/HkayXgl/cEacb7PicsxF3oyaLPvfgO2M2nRW9gvVU8Onz1dfp3aZCPBICtZG7sZLvOVKw568f6hLH5zRkr1kU/BKbd95c4MvlgXRE6xey52eKPrVenO2g/x4oqChdFu0K2bwuPk9eLtJrhSQGBZXCJlbFeRdkqMAf9KjAB4BXnbcaAPDdB45UbTWpR8hen2KuCQkQ3in4tGlmfR6ZJ/5IT1GRGJEHmNLtyyz6XvbfA87G5JFFXy1Fv3idamGnUVJWwXdY4DvtwS8ck3yrnHFNlVuDBP26ygV+m1EtPZ76uoccKviAvdWU1EW7wrvWMcpi7CLLjcmrn4JfeF3WLu4q/ndaefZmOpsX492AgoIf1PmdfkL2yFgkJHU+esXKgU78v1fvxD+9eqdnv1PTNGN0Tdr5ubyHVN5VRo/2QLHAn0lmpYqsZrPoh02LtFyVAt9OaTJC9pxvnMymqAe/MRZ9XddF64WT/nuiM147sMq8OPJykS6srwHvwVdL0Q/mQrScMR8K/BfuGEZXLIyDo3O4/2DlyDyjwPfP1dXfUWx7ku3BFwq+N9dU6R58k8NAtkUrKTEiDzCH7BVeg1TWefFnBW3ep7J56QKONj5UjiUI1ynpMXnF8GpVi77xuqq3YPQXN8VknCSi119awWeLPhMgqvXyuQ3ZA6wXqvm8Lj5AVuErBO0iOrLoS24k1HtMHvXgbxgqFPiZnK7UGwYYqga1qaWyeWklgDEQAXt1tOcTVz1tOc5xUWhVgxZU8xnnhWh5/z1Q+EyTO0DmPGs2i755FKF5g2JBMoizM06va87xRhq9JrVC9ugc9KvAPz6VxMhMCuGQhu3L+xz/HlqUV3Mz+FXgR8JyamOrojLSyUjRb5MxeR5b9IHCJttVT1sOoKDil0M9+N1+WvQVihVd173vwRcj3eRC9sr/vxULabm1V3eieoq+1wq+eVPVasSnGSNkT6UH32glalXBRTZkj0bgKvfgu7Do0zQfGSeJ6ueFzrmgusS4wG8zyu3v+bwuAm28sOhXW6ia+1tkdqKNUA0HFn1JdU5Y9D0YLybDeHEXfHl/hyjIVIP2aHTQou64WPhwH746pOD3djRHIeoWt0n6+bxedYxaOKSZWm/sz9X5JlPwzQW+WbGizU07KyltVOi6841Au5C9XpGi70+BT86MM4Z7XE03oPe0qoJfvL6bU4m9gFosWnn8lBX0d6mMdAqq0lQObYgPeFjgA0bY3k8fPlGxVhE9+HVI0ZfpwfdjfJx8ir7zkD2760xP2Xz6jE8W/Wg4JNZasmJKVlj0VVL0TePWWvRaJT1ZxmkPvguLvkqrYJquqYoFflCvq1zgtxlCCSh+4KYWMqI/1a8CX3Z8ijhGkROg/qFLSdrEGqXgD3bGhO1QNWhPbMR0xbCsr5CGfXKak/RVmRHzjuuv4PuB05sucWB0FjPJLBLRELYu6yn53oC4ucoo+FTgN8fGibnYLOnBz6otZgDnffh2IXt+W/Td9t8TdPzzqdoKvtchWVFJtbEVKQkmZIt+BWM+KPhA4XOwZWkPUtk8frynNGyvHgq+Sg++ueiQaeOQQT5F30UPvlMF3+Prh/m5ZDNORCGqsFEZhIkf0gq+6x589Q3ggU55kUEluLRwPMEOL+UCv82Il6XHkyrcm4jYfritsC7wKZlUk9oZrTbKTxbZEVhugvycMG5SJGgjZXzWqYIfw7LiuKuTU87C+toZutn31jFgz086XSr4lLK+Y0V/xeezX9jj7M/VZuvB1zRNKFbmIlHkdNhc78IhTVwnnAYYUlJ0reAucpFMLThvr7DCcGa4awuhkL1qVle/LLbhAIRX1cKszqpZ9IP3WpSTyeXFOsLLHnygcE0gFf87ZTb9mTqE7PUrbOiVtL54VPw6SdGXtegn02o9+H6H7AGVbgE76FqjouBHA6DgpyTzQKgHX9X56kbBJxfP+Jx8D76yRT+g11Uu8NuMcnV8ZKawcF/c427erFVvmWyAhzhGF2qF9Jg8CiarU8geFUiDXYaCr2zRFwp+3FDwp1jBV0VY9AOi4DsdXUNU678nhIIvca7OiR785ijwgeqp0UZisP1xut08ERb9Gj34fqboZ3J5PHxsCkD191YFUvDnqmx0qPSSqxCE8KpamAsBmftiufMuyNC9UtMMe66XvGznCsTCITxyfBp7i58PwAh9q+W28QL6e2R68I3wOe/mw0un6M+rh+wJccUuRT9uhN9lcnnfHEBApVvAjqyLFH2gdXu5U5LCWEIk2qsW+PLtSOXQGkTmPDRC9iQLfB6TxwSJcuVaBOx1uSvwZRR82cRyIwjQeQ++nRuhYQq+yaI/LnnjJIRtsTuG4WKBzz346oiQvYD14Du1kRsJ+v0V3+tXCLgRFn0fF8iqVFOsyKJvpzQBRruB09eWlH+7MXnpbN7zPJDHTswglc2jryOKdYu6XP0uyhCoquArpMGrYLgvgrf4MquzMhsjNPapXlNfGon5XullpgMx0BXD87cvAwB854GnxNdnReuW/wr+TMp+Mkla0jatgoyCn8/rjhR8WYt+efidXw4gwHgv5Qt89c2GWk6xVkIo+LI9+Mohey4UfAUXobFZJJmizyF7TJAoV8dFgd/jbqe816LAd6zgO/jQJSWDXoSToW4KvmE5XORYwTemHSzr6wAAnJzmAl8Vw6IfDAWfVGYnBeJ8OovHTk4DqK7yDkrunmdNSkxnHUcP2kFWS7NiKhsoBBjXkWrKtQxk0a+16dEVi4ipGF734e8+Uui/f9qqftcKoKHgV+nB92mBTu+dXb9wK5JRDCYUk2UCuhA1M+7DiLxyXlW06f9o93FRrMzWwaJP6yTAPljTj8+VoeDX/kxNJzMwh8HLFFaAUfTZFfjRcEhce2eSWV8t+vReTkta9CmkjQI+ZYmGW7tQTGXk1ui04a261nDTg9+vkAOk+pkRCj5b9JkgUN7fTrZvNyPyAHM6rHsFvzwnQAXZ56L+22QdPti5vC4KpIGuqOgpGlPtwZ81Fj5GDz4X+KrM1EGpqScJh7vqAPDw0SnkdWBZbwLDxU0jM3Su2i3y5k03fDdp7V5TVcGXtCMC7iz66WxeLDi6awQPhkKab0n6Vs4MVajFwErB9y1kL4g9+KrjnAK+EDUzbgqk9YsL1y/CqsEOzKSy+O+HTyCVzYlNQD9D9sIhTWS/VFsrmaH1j5efK7KeWyn45Yq97MajrLgCAN3xwjVv1qTge93iU/I8kgU+KfgqFn3z41u1wJcdL+00ZI82lJycy7TRpxSyxyn6ALjAbztqKfiLXFr0aZet2iJV2OZVLfqKH7pc3kgmtkvrNxR8/y360wsZ0P10oNOs4KsF5JmThY0efC7wVREKfkewFHwnNnLqv99Zo0ebLPp2ATdUAJuD6ZoBqx58KQXfYc8hUFoM1+rBB/xL0rfKVlBFKPhWKfo+WfRbNbjKipTia2Zk59SnpayR1EPBD4U0XHtuQcX/7gNHSgrAWiMtvUI2ST+do3WTlwq+fa4FbeaSsURawVfYODVb530N2RPPI3dtpUJUVWmOtrjbSFbBTxRD9hYyOei63N+ay+tiQ0klvJAYEGsQ+R582Y2EqOjBb833zY7mWYkxdYHU8VxeRzaX98yib7VIlU3oNI7R2WLG/Hhbi34dx+RRr31PIoJoOITB4maKskVfpOgbIXszqSxmfAjoCjLTC8FS8Duizi36dmPUZANuqJjtjIahad73zTqFbuDVFHyZHnwqbJ0o+GT5jUdClgsbahXxssCfmEvj4OgcAODslf2uf59Vir5qsSpLJNAhe4pKk6nVJN+iRYQswqnmYmyvDK94+iqENOD+Q+N46GghbK8rFval799Mf0cxaM9mvnzKB2dMVKIHn5wFKwc6ARQ2RGXuLbIWfcCwzs+aLPpxHxR8usfPyKbok4KvaNE3NiNbUwlWHZOn6/JrZ/Nr4saiP53M2uax0GawdMgeK/hMkDDvBqeyeYx6bNGfrRIeo9LzCjgfCWROxLcbgUXH4nWwVTUmyhQJ+t8xxyn6MXTHI+LmdSrAffiZXB4//sNxnJ7x7m+cCVgPfofDIDhd18WIvJ2rq49REyn6dhb9tLw9s56Eq6jAKo4iI8BQvQd/ziZgj/AjSX/P0UkAwPrFXaLNwg1WKfoZF/2VVsha9EdnU/jRnmMttUhTDSY0b54EvQ9/3ORU85NlfQk8a8sSAMBXf30QgL/2fEJMHLKz6PuwcSaTok+buSsHOsTjZYL2jJA9++MVo/JM60U/e/DlLfpqBSIRrZL10ipkc3nhPLBbo5s3b2Q3vUsLfPX3uN/ktLT7zCi3PnGBzwQJ84lfKPCN4DY3mGeKl9v0VXpeASDmUF2nG0wsHLINlaqrgm9KBQaMhYvM6DFiIZ0TRdSiorJh9OGrWf1biZ8+dALv+PZuvOyff4Mj4/Oe/M5pkaIfkALfYV/ciakkTs+kEA5pOGtFX9XHDHTJ2Unpuf0cMeWEaj34KlZSCgycd7ARaATsWT+PcD9JplXLQBs3tZwZqnRKpOjHPEz7BgwVzW7R/MnbHsON39mD/9p9zNPn9xPVMCizslaPe1YjqYdFn7i2GLb36ydGAfgbsEeIvCI7i74P0ylUevAHumKiuJq0cRsAij34iUoF35ce/ISxkSCD2KxUdHGIzcgW3HwzX0/sFPxIOCRqCNn1hvn67eQ9joRDQsyyC9pT3SyKtXg4oh1c4LcZoZAmLkapbM5U4Lu7mUbCIXFzLN9lc27RV1Xw5XvWnD6HE0j9pAULKWpz6Zy0g4Ds+bGI8TovE6PyFjw93mbi0ROFhPdjkwt4zVd+i+OT7v9W2oAKjkVf7YZL7Cn2aG9d1lNzUSYs+gsZS2swFX4y9sx6YtmDL3E9chOyR6+JXU8vjWuUTXqWYY9NtoIqdM2p5hJJK/Y9yiK7aH5ypNCKsPf4lOXjmgljbJRcIWF+XFDVJqKeBf6zti7BUI8hbtSjwO+X7MH3R8G37xWnIqq/I2qMSbXJYAEUe/DjRm98ykcFvyehFrKXcdgrXm1aS6tQWuDb/90JxfWG6sSQagxKhv2mFDeLhILPBT4TFGiXbnwuLRa7bhV8oHYfvqqCL0YCKfbgq/TW0rHUI2SPAsqoWOpNRMSCTbYPX0w76IqJHufhNgjaOzRWWLyHNODI+AJe+5X7XLUk6Lou+vGCYtEndVW1CLXrvweMxWgur1v2MdJzdzaZRb9aH7eK0uS0/QEwCny7osFqxKgT8nkde8R7W731QhVyIVSbJ+2H0giY2itses7p+rf/1Kynz+8nqkWNpmmBX4wS9Szwo+EQXvH0leK/62LRFz34knZjD50x1RxN5UzNG45D6n+2ywsA1Hrwu6r04PtS4ItWAMmQPYftRq08Ji+p4HwFjPum7HojrbiZWQ0xKs9mvawashf06SRc4LchtBA7PllYGHVEw55Ya2sV+KoKPn3o1BV8+XF8jVHwC6+Ppmmi2Jcu8IsKvjl4aFlxrNmJAPfgHx4r2PL//qVnYeVABw6OzuE1X/4tRmactSWkTKPLgmLRTzhUmQ2Vt3YRGI+ERdFutXtOBXCtee+NotqClj7zKmPynBT4VAzbXVu9TtE/ODaH6WQW8UgIW4d7PPmdlgq+7yF7ta/R+bwuNvz2n26dAt/JaEFxz6rDpnQjGatjgQ8A1xTT9IF6W/St7/1+bJwZjiYJBb8zKhLMZWaQLxTXX0oW/VROuShTwdwKIIPowVc8llYe6am6PldtCVQNFK3GgKTrxbDoyzqjgr1pygV+G0If5GMTheJpkUdptTULfGUFn9R1ZxZ9mTC/eo4dEj34pgWLatCeEbBnOC2oB/9UQBV8XddFgX/B+kF8+y0XYHlfAk+OzOG1X/ktxmbVi3wKMgtpRjJ4q+OkTzyTy4vkaLs+bZmgPQqh62xai76pB79YpNoFcQLmAt9ByJ6sgl90klQbMeqEPcX++7NW9HnW10qvQ1UF3y+LPr13FrbX0bmUeG9HZ1NSs5KbASeL3ngbKPj5vC6uM25H98qybnEXdq0bBGDMTfcTMSbPNmSvqKx62YOvkKLf3xlDXwdNUbG/Nqk4NbtNFn0/FXzjeWQt+pSir6Y2t/JITzrPZMcx0vsrKyjQ9dnN/UE27Fc1z4FD9pjAQSf18WJh6IU9H7Cw6CsoZoDzhYzKDYYW90nFTQQniBT9TqPAp02V8Tm5IpU2AsybMcOiBz+YBf7ITAoLmRxCWmFkz6rBTnzrLRdgaW8c+07N4nVfvV95QW+MyIs21Tg3N5BiklRQmR8/OYNUNo/eRATrF3dZPlYmaG++SS361fq4k1n5jcAOFwr+nORr4rWCf7gYRrl5mTfqPWAslNPZfIUN1XcF36IYKW9PeqJFVHwnr5lwttXhntUoppMZUXzSdace/MXlm7G0N47nnrnE9+fql/y8+zE+TiVFv78jalJOvbXoG/Pps47cLLI4HZOnruC3skVfbkQeIXJpJAUFen8jLiz6RoFvp+AXNxN4TB4ALvDbEvogHysGlnlV4IvxL/PVFXzVkUCq6rqSRb+eCv58NQW/8JqPS4TXABBqtXl0EIXsnQyoRf9QUb1fMdAhzom1i7vwrbdcgMXdcTx6Yhqv/+r9SoURKfhBCdgDjPN9PiOvMlP//dNW9dv23ckp+HKJ8fWmmoKvshHoSchenS36I8WRkkt6vFNAO01BgfOp0tci7YEFsxoyIXvlm5utVuCr2K/J2RZkBZ82snviEemCwwsu3LAI9/315bhi+7DvzyX62mVT9CWVVRloMkXOQmk2UvSj0oGAgFrInhiTl8wqT5RQoafoyEhl81JFHLmFVIvRaJWsl1YhpRBODZgEhQZY9K168HVdV74XxcLkvAjmNZUL/DaEPsjHJgoF/lCPvxZ9lcIbMPca+qfg0+Ihk9Mt7WpeMFGlp3CweMGSVvDJom/ajCEFvxCWGLy+zMPFgL21i0oV5g1D3fj2W3ZhUVcMDx+bwhv/7X4x296OoAXsAeYiVP7zslui/57ol8iLEBZ9m8T4eiMWtMXPuK7rStejThGy56NFv3jdlFWZ7Dg9XbimLOlJePL7gMLimxS2ubLXwjcFn8bkWVyfywM3W6UP38miN+iBUIBxrxyoU/99I+iXtuh7r2zL9eAXFXxTyJ6dNRowCnypHvy4WcFXU11V6DJtOMvY9OlzSdceWSItXCgaPfhy6/MORYu+UNVdnMf9Ein65vYIVvALcIHfhoge/KKC71WvW600aNHjozjzV1WpEDvICqM+zMfnF6IHv7Oagi8bskd9icbv6OuIir+DFvVBgvrvVw92Vnxv09Ie3PzmXejvjGLPkUn86dcfkJpBS33ONJosCNANV2WTh/q0d0rMSZcJuGlWi375wsscqilj0XcXslf4mXor+KdnqMD3toeZ3BlzqToV+AoKPhUMrVLgOxmB5tTZ1krUO2CvEfSbQvasRo/6oWzTZ6qWqJHN5cVGo3lMnt1mRC6vi+uAjEXfHH7nlwMIKLT5iPwQiQ1U0S8uGdJGRCXaiZoVleyqwuPU7on1Ctkzb65Ip+gHPNeEC/w2hApoSiJf7HHIXvnNQPT4NJGCb74A+NnTmMnlxXzrEgW/+JqTMm8Hpeib2yk0TRNBeyem3M+HbzYO1VDwiTOGe3HzdbvQHY/ggUMTuP/guO3vpMVLTwAV/Pl0Frpuv8BIZXM4MFp4bc9a2Wf7eBmLftOOySsLlTJvgihZ9B04ZAwFX64HfzaVldqksuM0WfR7vS3wu4puhrmyhZ1fY/JkFs3Ug3/B+kUAgCdbpMB3EkwYD7jaBBgb3osCXOCTEJLXgVkLZ1BaUVmVwU7BN28y9nVExbXfrgfffF2V6sGPG9e8tA9hgmZo829awuUnLPqKCn40EAq+Pyn6YgNHcdPEjMwaxHxdlN1MiAbcFcUFfhtSfiFd7JHSI3rwK0L25JV1wDzCTm1RnVLYQY6EQ2Lx7+eoPNpx1DRjIQ8YCxhpBX+2urIR5D78p4phYWsWVSr4xPYVfbh442IAwKMnZ2x/J93kg2TRpzF5eV1uJ5o29mKRkNRCWmb3nGzbHU1m0S9f0NJmYySkSS0COqK1x8PZMSfZtmDOg3Br08/ldYwWrxVeWvQBw+5aoeD7lKJvJFNbKfiFjc1nbipcA45NLkgnZjeSTNGWHHWk4AdzMQoY98MgK/iJaFiopVZ9+H6m6NfaSCRxpicRQSQcMo30sy6OzcWeTKFI15LZpL8he4B5JJ/9dYGuZao9+KKdqBV78BU3kjpVe/AVk+2rIVPg030iHNLEfd8O0fbUghszMnCB34aUX4C9sujTzaB83FNKWcE3ChYVRcup1cjP/vUJUyKt+aIzqFDg67peNUUfAIb7OgAEL0lf13UcLKrMa2oo+MSWYlr4YyembX/v9ELwQvbMG1oyfXFk4R7qjktNEhiQOFepAG620YOUxF6u4MvmgdQjZC8aDonXza1Nf3wujVxeh6Z558wiaKOifhZ9++CqU8XWpK3LeoS7qRVU/HSuWLyp9OC3QYEvNrI9PnebjX6J8XOqyqoMdgo+KfVUUNG1f3I+Y+kOE6NHoyHb0FbAVHSnDYu+1w4goidutAPYkXVoJzdC9lrvs6m8bla8J9Kmh6sC3zTJp9Z5mBIbCfKbM9yDzwSO8gup7yF7qgp+SX+8/AfPuMkotgL4+OEerxEaJAp8ImXmrQAAedFJREFUifAa8yiZ8s0YoeAHrMCfnM8INbNaD76ZM4YLBf7jp+wVfBGy1xEcBT8aDombmoxtToSwSVq4ZYKWmrYHv0wFXlBczKi2P5iZK/bg24XsAbXzS1Qhe/6irpgokL2C/o6KkD2femgjNiO9dF0XCv6yvgQ2LekG0BpJ+o5S9AO+GAWMa0yQLfpAbbejGT82zspDR8uhDQc6PsoLSOfylvcWKhJl3JOAYdHXdeM5/ejBB4x2PBkF37Doq/bgB8Girxiyp5ii78ahQRtO2byOmRrvo5Pnoce24vsmAxf4bUj5B9mrMXm17FyqKfol/fEKixnaSFDu9fcxtEgk6HdWL/An5zO2u76kanTGwhUJtdSDH7QCn/rvl/UmbFN5tyzrBVCY7W43EcGw6AdHwQeMz5bMrrrqGLXBTnu1iQr8ZrXolyv40osZU/uD6kbgrFDw7Z9LuJ8kp0HUQrgzPLbnA8Zmx1z5mDyfQ/Zq2V6nFjLi3rK0N4FNSwsFfisE7TlRtWIUPhvgAn+sSiBtEDHyimpvmjoJYrTDrOBX27CcEAV+4fXvjIXFesxqBvmCYoGfiIbEsZAI4ncPvsyknUze2Wal3bWqmVENwVZP0VdX1ssxt7VM1hgtLdL6HbQ9BfWaygV+G2JWyCMhraQ33A1kO1vI5Eo+MKoXkFBIExcDlQ8eLfbkbzJk0fdRwZ+vruAPdMZA7mirGydgBOyV2/MBQ8E/EbAefOq/X23Rf0+sHuxERzSMVDYvRuvVIohj8gC1tHcjZV2uCCQ1xzpkr1jMNqmCX96DLzPKCSib/65o0yelu54K/si0Pwn6gPF3lI8M9KvAj4asba/UljTYFUMiGsZGoeDbO3kajaMU/XDwLfrjFve6ICEzX96PkD2zMl1tL3zS1FIIFIJ8+8Sx2oesJiSvq5qmma4nPofsFTfzaym/ZrIO7eRGIGjrfTZVBbgOxeBZKrzdOsoGbZyEaQe9/lzgM4HDXGgv6o5J9eHK0JOIiKLVvFBNKV5ACsdYeKyKuq7aS1SPsUO1FPxwSBM3Ubs+fFLwq2UlDAuLfrBS9A+NFgr8tRIFfjikYXNRvXvMJmgviGPyALVReacVi0DanEpl8zV37eeEgt9kBX5ZH7doF5K8RoRDmrhOlBe2dsj24APGhpNXFn0/CnwakzdbruD7FbIXtu4XJtcSuZg2tqBFX6WooY35oC5GAWBchMl6f/42EySG1NuiHzapqNWKUdpwoGBV8/+32oxQVfCByo1Pv0L2ehJyPfi6rotrjWrIXlRYvdtIwa+jRR8wXCW12lqdjJUUKfot+L7JwAV+G2L+AHhlzwcKyjsFmpQU+MI672+gkGqAFln5/RyTN160E5Ur+IBh0yeFvhZkW6wWmkUK/shMqiUDXmpBSrxdwB6xtWjTty3wA5iiD6jNplUdo9YVCwtHTa2bqzEmr7k2TowxeYXPRpKUJgVVzEnQXjqbF4u9LonXxAgodZcAL9wZHo/IA4yNivkaIXteh2TZhezR5BC6BlKB/9T4vK/BqV7gZDa0kfjc3H+bU0rCZAPegy+jiosUfQ8LX7OCX62djVoG+kyChEwgoGoPPlAZdKs6e176eeJyKfrm4jyqOCZPZuJHs2KEYEsW+Mohe+4t+oA5aM8PBT+Y11Qu8NsQs+XLywIfMG5c5gJfWIAUFtWiP16h+DasRpJpoMXnSPqp4M+TIlFZUJIib6/gp4q/o3LRs7grjkhIQ14HRmatNwpaicMSI/LMyCbpk0W/J2AFvsq8dlWLvqZpRtBelXNV13WhbjebRb9iTF5WbRMQMAp0FYu+OWlepQfftYI/rfbeqkCvQ0XInm8WfeuQPbLoU4E/1B1HX0cUeR04MGLdqtNo3Cj4fm5IN5L5dE5s6Ad5TB5QO6/IjEiXVxBG7DBP8qnmjJmoouBLtWhl1B1clQq+P/cOYdG3UfDN1xnlMXkSEz+alVRWbdNbxS0IeBfCaqxBavXgOw/Z4zF5TGAot+h7ibFQNW4Gqso6YEoMVlArVHeR66Pg1w4Noh3JakWTGWNEXuVmTCikYWnRohqkUXmk4K+VVvDlkvQDa9FXmE1rBLHJb+5ZBe2lsnnRz9lsFn2jN7K0B1/lWtShkG9AkFoUj4Skeg/pfGxmiz4p+OUhexkH1kgZIja2V2pLGi5e/zRNM5L0R5rbpi+KN4XFaDzgi1G6V8YjoaabxuE1Kin6KueIHRGTMp2r8rmaKkvRN/9/q2NdSKtfV7srFHy/QvYKx29X4Jco+IqveayVU/RVFXwFtyAAZLLFXAOX7++AjetFOAUchOy1YmuFDFzgtyHmAn/IYwW/vLcsm8uLxbWKhTPmRMF3nKLv30XZUPCrWfQLr/2YdA9+9c2YoI3Km01lMVr8m2VC9gBDwT88Nl8xp5vI5vKiVzxoCn5HVE5lzuV14QhRsXFbqTjm52w2i75Q8KkHXzGnAzC7I+Tt8yoBe4D3Kfq+WPRFin7p65ByYI2UIRpWU/ABUx++xMjMRmIsRnlmM0EF/mCXd7lAzYqwvVsUzX6k6Junv1VX8IsheyZBYsDCvUU46cEvzybxq8AXPfgp62urucVR1U5utxnZzKiOyUsoh+x504M/aNeD72BDjI4pl9dtJzC1IlzgtyHmAthziz4p+MWdYHPx7CxkT34xs6DYX5tQtBo5QSj4VYpzKthtLfo2ycIiST8gBT6p94NdMele+UXdcaFI76uxuDf34JX3/7U6sn1xY7Mp5PXCQq9aaGMtBiwSbMmeH4+ESiygzUB5D/6CAzeRqmIBqAXsAeYefOcFvq7ryu0XKggFv9yi71fIXsh60Xyq2IM/3NchvkYFfrOPyhPFm4It2ck9sZUwF/hBR6jidU7R1zTNdE2s0oNPCr5pspLIC7C4NjnqwS+7Nrrt0a6FrEWfrjPhkKa8wRSxaSdqZlQ3vVUt+l714AuLfo3PTNrFpikQzI1TLvDbEPNCbHGPtzdTY9xT4WJqXoyoKPhO1HXVHvy6KPg1UvQBc8ie8xR9wEiRPhWQUXmHx9T67wlh068RtEcBZp2xsOdqY6PplEy2pQJwcXdcqRg32kkqb67zImCv+Wy1okissOirK/hqBb7aa+JFiv50MisWKSrtF7JQlkC5Rd+vHnyRom8zJm9Zn/G3blpauAaoJumPzqbwhTuflJqV7QVOXrOgK/hjbVTgix78hdr3fj8UfMCcS1ItRb+ypXBAtGfZj8lz1YPvl4IvHbJXeD0iDjapDat36302VRV81dDZjMPRg+X4EbJnfmwQW5+CtcplpDD32qioeDKU37holy8WDiGkcOF0MsKOHit7k4k7eA4VkpmcsIRXVfCLijyNBqqF0YNffeEzHDAF/5Bi/z1BBX6tJH2yPwdNvQfkFXzVBH2i31LBb84EfcAoEqnfNOVAaaK/SyVFnxR8aYt+p3sFf6T43vYmIkoOBVnsQvY8T9EvC0g0M5vKCkVuWRUF/+DonNJi+2P//Rg+cdtj+PKvDro5ZGmcqFpOJsu0EuPkVGunAl9Cwfd846yGgp/OGi1sJT34HdSeZT8mz1UPvk+b7t2SY/LoOuOkELVzGzUzbsbk6br93ys2TnwP2Ssci8p5ZL7+BnHjlAv8NsTPFP3yQBYq8FWTYF0p+MoWfX8+2HTzDoc09FYpKgclLPr5vC6+X+u9MnrwF1wdb7NweNSpgk+j8qon6Qd1RB5gnMu2Cr7DlPVBCxWHLPrNqOBXpOg7sehT73lavgd/1qFF342CL97bXu/t+YA5ZK+GRb+OIXuUN9ITj5RsoizvS6ArFkY2r4tWHzvyeR137TsNANhzZNLlUcvhKEXf5w3pRkMjZQc9Fh2aEVonpbL5mlZnvzbOyq+JBIkymlZ6j+yXUfAdbJyaP7excMi33AXK25mxUfCzLqzkdm6jZkY1eJZ68PO63Prc6MF39/5arUHMz6MSsqdpmuGMasH3zg4u8NsQ8w3Da4t+eS+pqv2HoMer7Kqp7iL7vWAyJ+hXu3kN2ISGAIUFP+20V0viB4Kn4B8eLyzMVQv8LSYFv9rOMln0g6jgy9rIjR5tVQW/toozr2hHryde9OCrWhIBdQWfFtTTyayUKlINp++tLFTgz5ss+uZwIq8VuGjZe2eG2pHMAXtAYcEmgvYkbfp/PDEtQj0fOTbl+PVXQaTos0VfQAp+tZGyQaM7HhGFdjUVP5/Xfd84K1fwKQ+gryNa4rbs77R3GySFRV/+WM33Yb/67wHjGpzO5i3XemkXSnOspUP2nCn4gFwfvldj8uzWy06nToj3LoDXVS7w2xD6IGta9d5wN5QrUU5SqwHTzF/JD10mlxc3LPkefH9Di4wE/eoLFrLcT8ylay4qyZ7fm4jUvNGTRfX0dAr5ACSBGj34ahb9jUu6EQ5pmJzPiGLHDPXX9nYEbwEpG3zjdIyaZchepokt+sWFamUPvr9j8sjqSn3rdtB1M5fXxc+q4ueIPMCUop82NiHMNni3Y5DKsZotXS1Bn9hAQXun5Ar8u/aNiP8/NpfGyTpkmWQc9IsGfWazEbIXfAVf0zRhfa/m2jG/x7714Jd9rmjztlxIED34C5ma6xRnCr5xH/ar/77wPMZ9ycqmT69H1EEPfqSVx+RRYSy5bo6GQ+K+KpOk71UPfn9xHZ3MVHe9ON1IYAWfCRSUoj/QGXPdF1NOeW+ZkwU1YCxmZNV18wde2mpUvKD5laJvVvCrQRb9bF4X6nI5NNLMqpViSU8cmla4QFm5AVqBZCYnFu+qPfiJaBjrFhd+5tETlTb96eLNPYgWfaMItbYhko17SNHGLUL2qpxfC81s0Se1ysWYPOo9d5KiL7vpkYiGxDXPqU2/Xhb9vG5c182bo56n6NOiuYqCT+1Iy6r8rZuWFIP2RuQK/LtNBT4A7D1WvcXHS5yos3TfVhkd20q0U8geYEqnr3JNNRcbfmVblCv4dBx9ZRvgpODn8npNm7vbHnw/C/xwSBObk9NWBX7euYJPPfjV8kKaHdFGq+Cylc38AUybmS7f4554RJy71dYhImRPIUUfMG2csoLPBIEzhntw8cbFeNNFaz3/3eUKvqr9h6DdRNkPHd1gNE3+ufxO0TcU/OoLlngkLHaXaRReOXYBe0Bhx5I2AE62uE3/yHhBve+JRzDQqV6Ib7FI0p8Jcsie6MG3PpedW/SLKk6VgBtKVVdJUK4X5UFtyaxaTgdgtujL9+CrWvQ1TUNvR+GxVqOzrPDbom9W5yhjwHx99tpmGw3ZK/jDVRT8jQoK/kwygwcPTwAAzls7AADYe2zK2QErYIzJYwWfGJe41wWJfhFIXPl5N2/ieL1xVitFf1Io+KX33UQ0LDZEa12b3Kbo+1ngA4bT59hE7ZwiUpojDq5jsUjr9uDTtUhl07tDMvMH8K4HX9M0o1WwyjrEeB61dQhtCATxusoFfhsSj4Rx85t34e3P2eT576606DtT8FXt83RDjEfkw1r8VkSEgm+hSNgF7ZGCb6dqBKUP/xDZ8xd3OgrdOcMiSZ9cEoG06EsWoSMOi0Bq5ZlJZStsiHST72piiz4tZqlX1G+L/myKLPryrwmdl9MOR7WRRd+PEXkAEDIpYeQUMSvRXodkGbbX2iF75gR9YlOxwH9yZLbqrG8z9z45hmxex9pFnXjBWcMAgEeO+1/gZxzYSZ1MlmklaJpM2yj4tFaqUjSLz5UP4XM1FfxiyF5/Fcdhf0ftFi3AUIGdhuz5PbZ23WKarlF70y/rIIWdIAU/3Yo9+GLt7I+C71UPPmDdKmiE7LGCT3CBz3hKX1k6rFMFXyxmJItvJ+nYiai/C6YJshxa5BxQ8T9Wq8AXqob1op2sqq2epE/J16r998QWkaRfpcAPcIq+uOFa7Kjrum4U+Io27t6OKGidWX5zJbW6KRX8cOliNilGacpfjzolXttyDAVf/jVxm6RvKPj+WPQBoLNsprTTYCMZyBFQbV73SRGyV3ldXDXYiVgkhFQ2b6nYAcDd+wv2/Es2D2H7ij4AdbLoO0hIjwc4ZC+dzQv7dzuMyQNMrqiF2nZjr+35QO0UferB76/inLML2nPUg58oTdH3k/VDhfXEkyO1J2sY49zaJ0Vf13Vjje6Tgp/1qAcfsC7wHYfsOQj0bhVatsB/4IEHcMMNN2Dbtm3o6urC6tWrcc0112Dfvn0Vj3300UdxxRVXoLu7G4ODg3j961+PkZGRKr+VcUt3LALKKJleyIgCXV3Bpx1R2R78wvOo3GCES8AvBZ8sbxYLFlrMTNRU8Isj8mwWPWJUnkcBUY8cn8KffO1+PHG6+kx5vzhEBf6gWoI+sbWo4D95erZCaW4Li77FjvrkfEbspg8pjscMh7Sas5tJ2W7KHvwym7fYCFRRK6IOevDTamPyAGPjyWmBPyJ68P0LKSPVjV4Lv2Z1A6a+VisFv7dSwQ+HNKwvZnHst7h+6bouAvYu3TyEM4Z7oWmFa+hIlZBOr8jm8qDaysmYvCAuRGnBXhgpG7wN2GrUup4Chujg5+eqsge/WOB3VFHwO2tnsACmHnyHFn0/NjLM0PXg4KhEgR9ynqLfaj34mZwurkUqCn5CYr1hPIfz8YPlWE3zSTvcSIi1cECiHS1b4H/iE5/AD37wAzznOc/B5z73OVx//fW4++67cc4552Dv3r3icUePHsUll1yCJ554AjfddBPe/e5346c//Sme+9znIp1u7UCyZiQU0oTVdHIhIxQz5RR9xeLbSciL32PyhIJvMfZn0FbBl7PoL/PYov/1ew7hzsdH8MW7Dnjy+2ShBH3VgD1i5UAHuuMRpHP5ipt5kC36MqPcSOEd6Iw6WjiK3fOyc3UhrW5HrxfRcos+2REdjMlTs+irhewBlSNGVVhI54QC6lcPPmC8FvT3ZRyExclSK5k6mcmJ62W1HnwA2LS0sNG332JU3qGxeRwZX0A0rOGC9YvQHY+IkE4/bfrmlgNnFv3gLURpI3ugs3REW5ChYqVqir6PG2e1FHwK2Ruosl6ha3+tzceFtLrAUs8efPpcH7Cy6OepQHSi4Lfm5pt57auyyaLianPSjlQLMdGhyno57TDML8jjR5tvRSbJu971LnzrW99CLGYUPtdeey3OOussfPzjH8fNN98MALjpppswNzeHBx98EKtXrwYAnH/++Xjuc5+Lr3/967j++usbcvxBpr8jisn5DKZMCr7K7iCgvpgxkkBVFBEaLeZzD76FRX+RbQ++nEWfFrpehewdKBbH9x0c9+T3yWKMyHOm4Guahi3LevDg4Qk8dnIGm4sLfQCYSbWBgm9xwzXGqDmzcA90RnEQlbvnpFarLO7qRflidsFBir6TkD2aFS8bsge4K/Dpve2IhpWeUxXaxKG/L+Vg3JsstZKpaVpAPBKqaicGjD78JywKfErPP3fNoPi7ti/vw4GROTxyfBqXbVni7g+ogXkhqabgB9dKOt5mCfqAdcienxb9qGhbqh6yV56iD8Ay3Axw1oMfDmnojIUxn8753oO/fqhwPTg6sYBUNld1PepGwS/PemkVzOtrlXNNdiwv4FxZrwa5YatNi8qYcitU4DF5TchFF11UUtwDwKZNm7Bt2zY8+uij4ms/+MEP8KIXvUgU9wBw+eWXY/Pmzfje975Xt+NtJ8zhMU7GUgHqdsSm7MG3SdE3f69mgS+ZLExWVa8KfFK/nxqfx4k69fVncnkcmyw8l9MefMBI0n+sbFSeUPADaAGl895KZT7t0sItds/nqyv4zWjRr+jBd7AQ7RDz39UV/C6FHnxK0bca5VQL0X/fG/c8lMsMhezNlfXg+6ng5/J6yfxto/8+UfNvFUn6FgW+sOdvGRJf276ikOHhZ5J+KmdMfIkoqNX0Gmfzum14YKsh61QLEtSDXy1kL1UPBT9X3oNfW5CwygvQdd3owVe8B9BmpN8K/uLuGHriEei6ISKUI3rFHRyL+Gy2WMieWRhTuW8kFFxt3ir4tdtajJA9teeJtqj7QoaWLfCroes6Tp06hcWLFwMAjh07htOnT+Pcc8+teOz555+P3bt31/sQ24JeU1hUUoTsOevBly2+afyVox58Hz7Yuq5LKfi2Fv1iiv5iu5A9Uw++eTHshMn5dMmGw/11UvGPTSwgl9eRiIZc2Yy31hiVRyF7fR3BU/CpuE5l88jXWPxTEeg0ZZ0WeeW756IHvwkt+qTGkC3aSSYI2exl+g0JcjU4UfCd9OCLzRsf7fmAoeDPlafo+xGyZ1LSzCo+bTguswiKFEn6p2erXg9T2RzufXIMAHDJJlOBv7wQtPfIcf+C9jImRUtlUW0ugoK2GBUj8rr8PX+bCQoktg7Z837TtFYPPl13qrlirAsrY8NJNWuJgvb8DtnTNE0E7R2oEbQnCkQHLSK0UddqKnDKoVPE0Zg8xXT7avgSshfg8aOBKvC/+c1v4tixY7j22msBACdOnAAADA8PVzx2eHgY4+PjSKVqh+mkUilMT0+X/GPs6TNZz4RFX1HBV7bop9WdAnRMMjYjVRYyOXHscgp+5XmYzeWFHdq2B7+42J1P5xwpgGYOlPWu18umbwTsdbnqw9xaJUlf13XMFF+XngAq+GblJFljU8wLiz5QLWSv2G/ehBZ9YyRUHrm8Lm7iKgtR82g42c2zuZR6yJ6rAt/leysLjUKsp4IPlCpj5FKq1X8PFBxAkZCG2VS2avDog4cmsJDJYagnjjOGjTaebcUC/6nx+Zozv93idCEaD3CBP9GGFn3rkL369+BbKvgd1d1bQGmRp9qm1VMnBR8w+vBrBe1liq+HkxR9UoFbTcF3GoItLPoyCr6HbVzWIXvOxuRFA9yDH5gC/7HHHsPb3vY2XHjhhXjjG98IAFhYKOz0x+OVu8KJRKLkMdX42Mc+hr6+PvFv1apVPhx58DCHx4iQPWUFX01dN8L8FCz6Pir4pEjEIiFL6/KgSNGvvGDRRUzTrF0AQKHAo9fdrU2fdrhpZ/O+A2Oufp8sbvvviS3FvvtjkwtCtZ9P54TKEEiLvunzVcs251bBH6gx8aG5U/SNxax5I09lI5A2T/K63LUilc0JldZJir6zHnx3760shoJfhxR900I7Y+ptpSDRZX2VCfpELBIS15H9pypt+mTPv2TTUImK3tcZxarBwu995IQ/Nn2nr1kkpIlRlSnJ6TKtAjnYrCbOBI1+UytjOSJF349si7K2JaAgclAWUZ/FmLxqhRVdV8MhTTmgjq4nfiv4ALBuccHVc2CkettOVozJUz8WUeC3WA9+0sGIPEAtZM+PHvxqG01phxsJtNHKKfpNysmTJ/HCF74QfX19uOWWWxAOF06+jo7CjbqaSp9MJkseU433ve99mJqaEv+OHDniw9EHD3NYlFMFnx7vZw9+PKrmElCBCvbBzpilDZMsiWNVFHzRl9gZE4WKFaTiu+2ZP1hMmn3utqUACrNjR2f9GxtFeFXg93VGsbyo7pFNn9T7SEhTzoNoBUKmv6uWlXzEpY3bsMfVGJPXjBb9sGFHLSnwFTYczUn4MjZ9CqADDPVfBk8s+j6OyAOMTAGh4OfUw01lKbHom5SxU9SDb/O3blpSO0lfFPibF1d8T9j0j/nj2HPak6ppmtG65lMwbKMwLPptVOAXr6czqWzF/HTh8vDhXlVNwScXQTikCVW92rFWuzbRNbEjGlbO/6hXDz4AYdGvpeCLHnwnFn0x8UN33SJZT5yGYCccWPS9TNGvllnlOmSPFfzmY2pqCldeeSUmJydx2223Yfny5eJ7ZM0nq76ZEydOYHBwsKq6T8TjcfT29pb8Y+zpK+nBL1qA/O7Bd2A1oufI5XXPd+/G5+UUicFieF4ykxdWZ4IS9GVti9SHf6qKJVUFugGes3pA9LPXow//MFn0XQTsESJor1jgk5Lf2xH1NYSskdj1xRk2bqcFfvVZyMKi34QKfsQUKJU0KacqLSDhkCYWAfMSCxoK2ItHQkpqUG8rWPRJwS9uYmSy3qkz5YRCGuhtMhdAMgo+YATtlSfpn5pO4rGTM9A04Jmm/nti+4pCgb/Xp1F5buzXzdYvWl6YOmWsDS36vaZpLuVtdX5mW5jblgjKAeivcX+sde0HnI0oJkQPfh0t+uUtiAS5hJxcy2rlhTQ7KYdjrDsUQvayosD3oge/cB7OJKtsinHIXgUtXeAnk0lcddVV2LdvH2699VaceeaZJd9fsWIFhoaG8Lvf/a7iZ++//36cffbZdTrS9sLoLUs7UtYBc4Ev96FzMv7KfExeq/hGT6G1HbwrFhY3NyroCdkEfYJ6Uk94ZNFfv7gLu9YNAqhPgU89+Gs9KPC3Dhc24x4/WVDhyPbcG8AReYRdGJyRtO6sCOyvEXDTGhb9vHEtcrCYVBmV5yRgDzA5n5LqBf7ITJ1C9spS9FM+FiKA4cDI5NV68AFg01Iq8EvDNmk83o4VfVULym3L/U3Sd9PWEGuiUXmfuWMfdnzof/DYSfdOh3ZU8CPhkBjZWm45JmW1Xj345DisNXayz9R2WR7iaiToqx9rT10t+oV1xfhcuqrFmzYrnVj0a+WFNDtJhwq+ypi8jIcWffMIx/LxkvT+KYfsiTF5rfO+ydKyBX4ul8O1116Le++9F9///vdx4YUXVn3c1VdfjVtvvbXEXv+LX/wC+/btwytf+cp6HW5b0ddh2LmcpnSqzvx1spFgvqmkPA7ak0nQBwq2y8EatiNK0F9kk6BPeDEqL5/XhYK/fqgL569bBAD4rc99+Lm8jiPjNCLPnUUfMJL0HztRatEPYsAeISz6Vc7l2VRWFOKOFfyuylCoXF4Xn3Gzlb1ZMPebLqSdK02dEmMICScBe4Ch4CczeeXRneYxeX5SkaLvYw8+YNhlSa3J5vLCrWBX4JtH5Zlts3fvHwUAXLK5Ur0HjKC9A6Nz4r30EjeWVdWNbz+5/ZGTmE/ncM8T7u8NYkNccjM7KPSLJP3SYoXUyHql6E+Rgl9jvUIhe7peuQGZNFn0Vbl0yxAWdcXwjE2VrTJe0xWPiDbGaip+Nu9caTZ/lpvFXSNDKuusxUq4BW3uh7puBNt6UeBHwiEh0pRv0jgdkxdki37zrcgk+cu//Ev8+Mc/xlVXXYXx8XHcfPPNJd9/3eteBwD467/+a3z/+9/Hs571LNx4442YnZ3Fpz71KZx11ll405ve1IhDDzxmiz7thvqt4AuLvsINMRTSEAuHkM7lhX3XK0jllLEcDnbFcHI6WaXAL/z3YklVwwsF//jUAlLZPKJhDSv6O0TR9vipGUzOp2suANxycjqJdK7wvHYLdxm2mEbl6bpusui37CXPFisF/3SxbaMrFlYuPAnarJqcTyOf1xEKaSVtJc2o4JvH5NFiRnVWs/lnZAr82aJ9XfV17olHoGmFRfTUQgZLeuSOM53Ni2uH3xb9zjqm6AOkphmhhaOzaeT1gs3YbuNzw1A3NK2wITU2l8bi7jhyeR2/2k/999UL/KGeOJb1JnByOolHT0zj3LWDnv5Nbl6zeJMsRs0bwUfGq88VV/ldKvfLINHXEcURLFQE7dVFwTcplpSrMlBDwY9FQuiKhTGXzmFyPlOyDhAKvoMC/9lbl+J377+8bm1z64e6cHI6iYMjczhn9UDJ9+gaEwmpv+bREgW/dQpFWl8rp+hLhuyZXSJeuTQGu2KYTmYxXhZMnXIYsics+gELLgVauMDfs2cPAOAnP/kJfvKTn1R8nwr8VatW4a677sK73vUuvPe970UsFsMLX/hCfPrTn7bsv2ecYxT4WXQXFVPVHUIxJk9SWU85tInFo4UCv1EKPmBY8CsKfArZk5wN7EUPPi3a1izqQiQcwlBPHOuHunBgZA6/OzSBy89c6vh3W3G4+LyrBjodWeTKWb+4G9GwhplUtpCmLyz6wVXwrXrw3drzAUNtyusFR0RfZ1RsJoQ0f4LW3GL0m+qONgEJu/YHM/Ok4CtuJISKAVfTySymF7JY0mP/MwBEAGY0rNVcoHsFtR3M1yFFHzBlKBTVNQoQXdITtw0eTUTDWDXQiafG57H/1CwWd8fx8LEpTM5n0JOIYOeq/po/u31FL05OJ7H32JT3BT6psw6uc82iNtFGMOC+wJ9cyIDqAJn7ZZAQ4+cWSu/9foZXmq+JBLmyyH1Zjf7OGObSC5iYT2MtjDY6Nz34AOqaibNucRd+8+RY1aA9N73imqYhHNKQy+ut1YOfcang24R9mrOtVMfX1aK/MwaMzVe0CjoN2aO/nSz+QaL5VmSS3HnnndB1veY/M9u2bcPtt9+Oubk5TExM4Oabb8bSpf4UKozRr1VI0Xfag1+06Evuhjq9yaiO45NFVcEHqln01Xrwl3mg4FP/PfWrARB9+Pcd9M+mf6iYoL/aA3s+UFgIbxgqWHQfPzkjQox6AtyDb6UyezFGLR4Ji6KVQiTnRP99pCnDC6v24DtIpqbXdk6iB3/WoUUfKO11lUW8t91x39+DzmKK/mxZir5/PfilauNJEbAnt1G1aUlpHz713z9jw2LLjUSy6e897n2SfsbhvGZAPXzWL8wF0pEJdwX+eHEjuzcR8SWssZmhz/tkmYKfdtjaKEP1FH0SJGpvENZqJxAp+k3o4CrHCNqrnKxBr4fTczAqkvRbT8FXLvAlM2nMRbNXn+0B8Zkp3xSjzWa162qzBZd6SXtdTZm6QPNd07m8KHRVF9ViVy2nl+w018II0HLWCiATFqLCuMJcXyrwxyoU/KJFX7HAn1rIVCTyyyL670sK/EIfvp9Be4fHvQvYI7aakvSFRb9dFfxpdwn6RHnQHp1nzbq4E/OJc7p4XeJOevAVLPpkX1cN2QNKR4zKQu/tkAt3hizlCj7ZWv1yb5BdlhbfJ0TAnnWCPlGepE/j8S7dUt2eT4gkfR+C9kSKfgsr+LQRDABHxhdcjQYzNrLbz1HZX2NyhptJC3YYuSTGOUTX81ohe0Bpi5aZpAuLfr2hTX/z+UtQgRdxmPYeNbWDtQpOLfqyY/LMRXPEwfjBatQa15sR11W1v4U2Zhp9TfUDLvAZz+mMhcWHmdKdVcNizDc2mQ+eSANV3EigjQfvU/QLF59BGYu+UPBLZ81TyJ6sRb8nHhEKq9OgvSdHCgthmhkLAOcXFfy9x6eFcuc1h0cLKpAXAXvElmWFNOzHTs60RcgeFdnJKkWokbLurgg0gvYKizxSb1Tt6PXCrFbRNcLJQrRLwaJPrgaaGa8CbUCpJOmfrlOCPmBsdMyW9eD7pbxGw6Uheyen1RR8c9De1EIGe45MAqjdf09sX9Erfs7rzV93KfrNoTaZFfyFTK5ic1qF8TYckUf02yj4fjhjqiv4lKJf+z2o5TZw04Nfb0jBPzQ2VzENIOsyDC5Sdq1qBZJuLfppOYt+LBzyzF1Wa5qPSOtXVfAV3cKtBBf4jOdomiaUKLqGOlXwAckCP+vsJuOXRZ8szAM2Y/IKj6nVg69m0dc0TSx8nRb4tHBbt7hbfG15fwdWDXYgl9fx4OEJR7/XDhqR52WBv3WYkvSnjR78AIfsyVj03aasi93z4gbWnLBnNufralYNyG3gxqIvF7LnwqJfQ9Gzop4FPin46WwemVzeV6URMI3JK7foS7oVNi0tXAOeOD2L3zwxilxex4ahLqzot3YALOtNYFFXDLm8jsdPzlg+VhV3KfrF+5VN76vf0EYw8ZSLPvzxNg3YA4we/PLPu7DoO7hW2SFcMblqBb6Vgl/4XrlySkVeokk3ec2sHOhANKwhmcnjRFlWUVaE7DlU8MOtq+Crutpoo9du81O8pg5dEdWg0dMTpvWyOa1fdVOsWVxRfsAFPuML5nmVgLoFKBIOiZ1mmX5DpyOw6AbqpUqj67ox9kdi0bKoikU/lc0J1XmxpIIPGMrWSQdBe8lMDscmCyFWZgUfAM5fW7Dp3+fDuDxd18UCcY0PFv0Do3PCBtq2Fv0Zbyz6A2W759SD14wJ+kDpwoI+T47G5En2HAKmMXkONj1EgT8vX+CPiPfWf4u+eRTifCpX95A91R78DcXr2OmZFH7y0HEAwKWbl9j+nKZp2EY2/ePe2vRdKfjFxWuqSRR82oh3E7Q3Xrw2y7jdgkZfjX5iN20cdlRV8BfsQ4HFZkTZsbaSgh8Jh7B6sCAiHCjbpMrkdfEYJxgFfusUiiIjy2kPfiZn2Z7j5Yg8or+KRd+8qaI6Jo8t+gyjSG9Zge+kR1MsZiQ+eI57iXxQ8GdSWXHzlEkFJgu+eUeS1PxISFNSnZf1FpQpJ0F7h8fmoeuFILpFZRsTu9YXbPp+9OGPzKYwn84hpBV22L1iWW8CvYkIcnkdDx2dBBDskD2rIvT0tEcWfaHiUA8+hew15+LOPPKIlHUnKfoqCv6cwzF5gFHgj8/LW57Fe+vSnSFDLBIS1+W5dNaxaiKLOUMBAE5MFzYgZUdp9iSi4rG37T0JALhks9zM7W3LCzb9vce8DdpzY79uBrXJvBF8wfrCxu/RiQXHv482tgclnWpBgj7v5cF1hjPG++uqkaJv7sGnFH37kL1yBb+VevABYH2xD788ST8rrmXO1GZh0c+3TqFotLY668HP5XVLa7sbt1ItqmVBmI/BcYp+C23MyMIFPuML5VYvJ8FWcYX+eKc3GfEcHir4VKh3xsJSGw7VQvZIcR7siin1Lg27sOgfHKX+++6K56Qk/T8cnZTqQ1bhcDFBf3l/h3JWgxWapmHrcGGRTlby8o2nIGEVfOOVRb9893yuyQt88yg1UtadBAJ2RovhchLXCSNkT/151hZ7RPefqkx5rkU9LfqAkaQ/l8oiXXRX+WfRN5KpdV3HqanC3yqr4ANGH35eLxwnhYbasb2YpP+Ixwq+6Et18Jo1Q4q+eSP47OKowafGXCj41IrWlhb96o4dMUqxDin6uq6L57cKBaZrfyun6ANGgHB50F5GhOy5VfBbyaLvrgcfAJIWffgZl5sm1ajWKpIx1QiqmwnNkmviB1zgM75QvhPs5EalsphxOgJLpOh7qIjQhUd2pi8tbGaSWaHMGP33aot2N6PynhypTNAnVg92YllvApmcjt1HvO3DPzTqff89QTZ9IsgKPt10y1XmZCYnejzdW/TLQ/bIot+cr6u5n5IUfCd9rYY7QiZkz3kP/vblhi1cNpn8dB0t+oDRejCX9t+iHzWl6I/PpcUiTOVvpQIfKGxUyhYiFLT32IkZT9WdlAvXQzMo+GIjeHGXsDu7GZXX3iF71YtmEhx8SdEXCn7h+jKfzonPVb/FBnit8WRORxQ3Cmo/PDBaXuC768Gnn2slJdhpD340rImNIqskfWMkqIcKfhflABnnIT1POKSVbOrLQBsCbNFnGEnMBb6mObToR+QVfKc3GXq8Hwq+7IKlryMKuiaR9ZkS9GVH5BEUPnXKQQ9+tRF5hKZpIk3/vgPe2vT96L8nthaT9Ikg9+DXCr6hBP1YJGRpwZTBuLkWFqTNbtEPhTTx2ZpNemHRt+/BdxOyt3lZNyIhDZPzGWGDtiKX1zFadPvUw6IPGNMB5lJZsSj2y6JvVvBp03Jxd1yp8Nm0xNjku9QmPd/M6sFO9CQiSOfySo4KO8TkAZ/viX4hNoKHurGKC3xXkNNxaiFTsqGXduHysCNcNnqSNhdi4ZDldbxW4n8r9eADRoAwbVQRZK13aicvbydqBZym6GuaZpn5Q6SzxWR7Pyz6ps+Mq9GjYVbwGUYJcyERjzgbkSGbGKzruuMxeXEfFky0YLGyu5kJhTRx0SJrvtmir4IbBZ9CZ9YNVS+0/erDP1S0d671QcHfUqbgB9mibw6+MUMW7qHuuOtRNeUhe81e4ANGH77owXcRsifXg+88ZC8eCWNzMfldpvd7fC6NXF6HptXP4kxujYJF318FX9iJc7poO5LtvyfMCr7deDwzmqYZffge2vQzLhR8uic2VsGnSSuGgn98Mul4PJhh0a/PBlUzQeukXF4vGUErUvT9UPCLm2a5YiFKgkRfZ9Ty/lBrPJloj4y1RjlBo/KOTiyUbIaLMWuOC/wWVvAdnGcdEq42P3rwaaMpl9cxXdy0N55HfX3TDK4ov2iNTyTTcpQW+M4W/3HJ3hhzce54TJ6XCj6N/bEYOVPOYNmovDGHix5a/I7OppQvWOaFWzWoD//3T014ejE8XByRt3rQewW/vMDvdqCqtgq1LPoiZd0DhbeywKe+9uZ9XWlBK3rwXVj01UL2nF33yBou0/tN9vxFXXHHvaOq0GdoLp11ZTeXQahieWOslUr/PVAIyxvuS+DpawawyVTsyyD68I95V+C7StFvgsUobQSvH+rCkp6CmyKX1x1tKuu6btoQD+7may0S0bBoKzQr436Onyzvwaf2rQGb9QrZ92eS2ZLNHNGD3yIK/uLuGHoSEeh66XhHowffacheK/bgOwunBsxTe2q72vzowU9Ew+K5qV3EjeOFLfoMo4i5wHcydxowqes2xbdZ4Ve36DdewQdMBX6ZRX+RokV/sCsmFtsqNv2JubTIDqhV4G8Y6sairhhS2bxIpfcC6sFfu9h7Bb87HsGqwUIyf088otyf1UrU2lH3MoTNnKSs67ooeLuaWMGn99ydgl8M2VPowXe6mbSdxrNJFJX1DtgDjM2OubqOydNxikbk9aoV+F3xCH71V8/Cd66/QNnBIt6L494l6btJ0W+GkD3zRnAopGFlf+H66mRU3mzKmMTQjgo+YBqNaerD91XBL0vRp81aGoNnd5xA6bG2Wg++pmmmoD3Dpp8VCr6zNUK0JVP0nVn0AVOBbxmy571FH6gM2stknbeKcYo+wyjihYIv229IN5hwSFO+kMR9GJNnKPjyxTkV8uPFwp4UfNUefE3TsLSvsFBSKfApcGa4L1EzMK2kD98jm/7kfFrYrMju6TVblhYU0SDb8wHU7InzakQeYGxapbN5LGRymE+1gkXfiwK/9gjCcuZc9OADwLbl8kXlSB1H5BFCwa9Dir65r5UUYlUFHyioa04WmeSm+OPxaRFK5haRW+AieLZRalO1jeCVxev2Uw4KfNoM74iGWyaF3WuosDYr+EaB7/1rUq7g0/OWTz4qJxIOiZDaySoFfqso+IAxKs8ctCcU/JCza1lrpug7P88SNVoCzbh1RdSiPGgv7SLMrxlcUX7BBT7jC94o+HL9hiJB38WCqTyYzA2uFPy5UgV/0IGqMdxbUFRULJMiYK9G/z2xy+MCn/rvl/TEfUtiP2O4YNMPcoI+YKXgU8q6+yKwKxYWu+Tjc2kxNq5ZU/QBI1RKhOw5uB51SFr0U9mcWOA5LfDPGO5BSCuEI5622aTz8r2VRYzJM6Vv12NM3snpQuigag++G9Yt7kZHNIyFTK4ilMspbuzXjR7pVG0jeHXRIeUkaG+sjQP2iD4Kr1swettTPm6clafok83ZrsAHqs8gT7bYmDzA2Jwyj8qjDQ/HFv1Q6ynBdJ45uicWf0YqRd9zBb+0VdCNKyrKIXsMo0a/Sb12at2SDcBLZp3fYESKvpcKfjFhXGXRQmr/WHkPvqKCDwDL+wsL4CdH5BekImCvhj2fOL84Q/rBQ+OOQ5XMUP/9Wh8S9ImzijbbJYrW3lajpoI/453Kq2laSZryfIrG5DXv4o6sk3Np51ZSmr4wOZ+x3Ayk/nvAedtCZyyCDUWFyS7czbDo1+/c7jIp+G6skTJETInfbhR8p4RDGs6koD2J0EMZ0i4WvUbbWmMWo9VyWlYNFJP0x+2nPpQz4eI+FxSot726gl+HFH2Fsb7VkvRbU8EvnL8HTQp+1mWeiLDot1ChKMKpHSj4tMGXlAjZ8/r+0F9u0XdxTY0Ji74uPZq2VeACn/GF8hR9J8Qk+w3dXKTiUbnnUIH66GVumESlgl+06DtQ8M8rquy/3j8q/TPGiDzrEKoty3rQm4hgLp3DIx70pR4eoxF5/tjzAeA5ZyzFR1+2HR+86kzfnqMZoBtuhYLvoUUfKN09n28B9aY8d8FJgb9yoANLeuJI5/L43aGJmo8je348EnIVemf04Vt/xk43wKLfJfIIsr4r+OZF80mHPfhu2S4KfG+C9jKtrOCbAvYIN6PyWMEvHZVH+Pm5KlfwqUjqk1DwjST91u3BB8wKviGCkPPK6XW7JUP2qAffkYJvPzo27VsPfqmTxIvRo0DwVHwu8BlfKLXoO1Xw5Sz6C2nnNiPDou+lgq++aBnsLizQx+fSmE9nxU1z0IGyccmmwiio3UcmSxYNVpBVrdaIPCIcMvrwvRiXd6io4PtZ4IdDGl67a41QRYMK3XCzeb3EJijG5Hlk4zbvntN56tSOXg8iHhT4mqaJEWt37x+p+Ti3AXvENsmishEWfXqvZ80he34p+MUCf3wuIzaT6qngA8A2EbTnTYGfdqFqxcLeO85UMBR841pK2SlOQvZoQ1slryZoUNFM9+p8XjdyGnz4XFWm6MsLEobbIC2OldZOzbzJWw4V+BPzGbFeM3rw3YXstZZF33mKfkI4Bi1C9lwU3lYMlAli9JrHHV1TTQV+wPrwucBnfCERDYkPjuMxeZIJ98ms8x1kw6LvjYKfz+uiL0hl7M8i0wWL1Pt4JOTI5rtqsBPrh7qQy+v4zRP2Kn4+r+NgsdDeYKPgAzAF7Y1ZPk7XdTxxehZ7j03V/Pf4yRkAwBofLfrtgnmBRcVQNpfH2Jy3Kq9599wYPde8i7tyRcZpJggV+Hc9blHguwzYI0jBt3PJGJs3dbToUx5BKluHFP3C7yV1uK8jWve8B2NU3jTyHgTtGa+ZeiEh27bmF7QRXKLgFy36o7NpSzWvGuOs4AsxpHzkFwDEfbiu0qaZkaJfDNmTCKEdKLPouxlR3Eg6YxGR5UFrH9rwcKo2R8taH5odXddNIXtOcmlUevA9DtkrOw+NkD315zEX+K3kvpCheWUXpqXRNA19nVGMzKQc2X8A44NnV3ynXPSAed3TOJ3MgK7vTi36RoJ+XHmsE3Hp5iEcGJnDXftGcOVZw5aPPTa5gHQ2j2hYw4qBDtvfvavYh3//wXHk8zpCZTveuq7j10+M4tP/sw97jkxKHa+fCn67EA1rCIc05PI6kpkc+jqiGJtLQ9eBkObdGCrz7jm5Z5q5B79ckXG6EH3mxsXQNODxUzM4OZWsqiTPprxxNFDf97HJBYzPpasWQLquN2RMHv1tM6n6WfRJHa5nwB6xaWk3YuEQZlJZHJmYd70ZafSlqp+HjUx8Nm8Erzf14Pd1RtGTiGAmmcXRiQVsXtoj/TtHKUy2jXvw+8p68M1rEV8V/Fx5yJ79e9BHm7tF1d9c3LWSRR8oqPgnppI4MDKHc1YPuE58j7SYgm/enHEzJm/awiXqVw++lyF7oZCGSEhDNq+zgs8wstCNK+FSwbdP0XduMyJ3QdIjBZ8UiZ5ERGknmBT8ifk0RoqLdjfBQ8JOvG/ENjiEbJdrFnVJzYnftrwXXbEwppNZPFZU4InfHhjDtV/8LV7/1fux58gkYpEQlvUmLP89a8sQzhzudfiXMoSmaabZtIXzmXq0F3fHpd5bGcy75/MtYNH3ogcfKGxs7FjZD6C2TZ8U/O64u8VubyKKtcVNr0dqWMOnFwwF3av2Cxm6in/blKkP168CnwLBjk0UAtzqbc8HCore1uIkDi+C9ty4HowC37vMGFmOTxkbwSsHSjdkyab/1JiaTf+J04Ue6DWD7evgEsF1xUIplTPeW6+VT6Bair7cmDygcv44FfixSMiz+0u9MIL2Cudg1mVbhHmkZytgLvCd3BPPKt4L7/jjqZojRP3qwa8M2XPpvgg3buPUT5p3Vca0PFTgO1XwZWfUGyEvDnrwo94q+LSjqGo5pN3zvG4Ev7ixLV6wbhFikRCOTyXx5MgsNi6praoYAXtyi6xIOISnrx3E3ftGcP/BMZy5vBcPHp7AP97xOO55omDbj0VCeO2u1XjrZRvqmvDd7nTEwphNZYVFX/RoexjCRrvnp6aT4sbezP2XFRZ9F7OlL920GH84Mom79o3gmnNXVXx/VkwVcH9r3baiD4fG5rH32DSeWczVMEPvbW8iUlf1jEL2JkyjsvzqwacCZ6b4utY7YI/YtrwPDx2dwt7jU3jhDmtHlB10P3NSvDXSok/2/GobwasGOvHI8WmloL1MLo/HThQ2iGnSSTvS31HswSe7sck27dTBZ4U5RV/XdbGxoJKiT8dKG8mtZM8nKEeCzmu3Cn6r9eCT8zWkOcsdeN6ZS9HXEcWxyQX8+olRXLq58h6Vdfma1qIyZK/wtzjt9Y9FQljI5Dhkj2Fkca3gS9rnkyIJ1LmC79WCaXxO/mZpJhYJiTnt+04VCnw3luqOWFjMrL/TomcYMI3IswnYM0O/+9aHTuBNX7sfV3/hN7jniTFEwxped8Fq3PV/L8MHr9rGxX2dKR+V58cYNVrkHZs0xmJ1NvECr3zx4nTDEQAu3VJYxPx6/2hV1WI+5U3IHmD0ftcKdzPGH9b3M0ZujXoU+NSDTzRCwQeA7Su8S9LPuGhraKRFv9qIPGLVYKG1S2VU3v5Ts0jn8uhJRMTPtyPlKfop33MtDAV/JpUV1zEZBb+/zBqdbMEReYR5VJ6u66J3vvyaI0urpegb/fdhRxtJiWgYL9u5AgDw3QeeqvoYvyz65VOn6DV3ErIHNPa66idc4DO+IQp8xwq+3EggkeLqogffara1Ck4S9Amy6T9xuqBqLHbZl0hp+nfbjMs7MCofsEdQgf+7wxP45eMjCIc0XHvuKvzvX16Gv3/pWRjua98FWyOhXvhyi76XPdp0bpNtOuZyJJzfmNVGTXM3W/ppK/vRk4hgaiGDPxydrPj+XJpaFtwveKmo/GONoL1GJOgDRoEvxkqFtIocDq8oV34a0YMPmIL2jk+7npVM9zMn56HsPdEPqo3II4RFXyFJnzauti/v80WpbhVED/5CaT+xm+uUFeYUfVLiE9GQlAuovywvgDaSm9nBVQtyLB4cnSv5PDltixAW/XxrFImprHPnK3HteQUX2x1/PCXyNMy4tc7XgjajUtk8FtKG8u70eWgDghV8hpGEwnbWOgwlMuyI1sV30oVF30jR90jBn5cfOVMOFU77T7u36AOG2njfgTHLDQzZEXlmzlrZh+V9CYQ04OU7V+AX77oUn3jFDjETmWkMiQoF3/sikFQcCoNs5oA9oHTBlnCoVhCRcAgXb1wMoJBvUc6sRyn6QMEWDhQWoDPJyiAjPzZvZCif7OGX0ghULraXNWjjcMuyHoS0gmJEzgmniNFRDhajwnHm4VhXWQ5YtHKtLF73jypY9B8puiFoI6tdoWIlmckjmcmZCnx/rqvmFH1S4qlNwI5ya7Qxori57wHVWNHfgWhYQyqbLxnx6HSzOhpqLYs+CWNuzrMzhnvxtFX9yOR0/PD3xyq+77bwrkV3PCKcKBPzadfTXOjnWuW9k4ULfMY3rr9kPe545yV45bkrHf18TNGi76QVQHYTQRZDwZcfkUcMFi351D+9qNvdwn3Tkm4s600glc3jvhoz65OZHI5PFZRY2R58oHBT+MnbL8Zv3/cc/OO1Z2Otws8y/lHLoj/koY17oMzK2cz2fMDoOQXcqRXEpaYAy3LmPLToD3bFsKK/UNBWU/EbbdEn/CzwKyz6DerBT0TDIj2fguGc4mbyQKyhCj6NyKt0etGovCPj89IOh73Fc3p7G/ffA4VrBanqUwuZOlj0jR58lYA98+Pm0oWNCKHge3BdrTeRcEh8ph8/aXymnSr4rWfRp9ZWd+/dtcUsmu888FTFZ19sZjoYX2eFpmkl7SIZrxR8tugzjBzhkIZNS3scK2a0s2hv0Xe+i0wXt2Qm79p6CRg9QQMO1PfyTQE3KfpA4SJoVYwAwKGxOeh6IahL1TGwqDte9+KCscaw6BcKTT/GqJW7UzqbOEEfKO3B90JpogkVe45MliTJA96G7AGFiRWAUQyZacSIPKCyFcqv/nugmoLfuOvNhmJh66bAz+d1UQA4ed3oZ3J5XQRY1QPzRnC1HvyVxfGqc+mcuAdakcvrYtOKnCrtiqZpJaPy3Iz8kiFs6sEXCr5kgd+biIIup5MLaaMHv8ldXLWgc/nxU8Y0oKjDHny6VtXzc+kGEs7chM4CwFVPG0ZHNIwnR+bw4OGJku/51YMPGOvliTnjM+N0I4F+jgt8hqkT8iF7xR58BzcZ84LfC1VEpOg7suiXLtQXezC3nIqRu2oU+AeFPb+7rfsgg0KirAd/ZNp7i35vRxTmU6XZLfrmHnwvwqCW93dg05Ju5HXg10+U5lvMpwqvu9sxeQSpm49UCXc7XXxv6zkiDyjMDTbb9H1V8E0Lw85YGL2Jxm0mbVpaKPD3n56xeWRtMqb+XCeJz2a1rZ4q/uGxebERvKjKRnAiGsbS4qSOIxP2QXsHR2exkMmhMxauumHQbhi97Wmkc94oq7WgDc9sThfBfrIthaFQ6WZEK6foA0aexP5igR/S4DhPJNpiCn7SIwW/JxHFi4qTRb7zwJGS7/nVgw+gqoLvOGSPe/AZpr6IEXZ2Pfh0oXIRWlR4HvcfbjcKfvnCadClgg8AF29cjJBWUJ2OT1YuvIyAPV5kBQGyy89nctB1HSOz3tu4wyFNLEiB5i/wzSqwk0kb1TA2zk6XfH0u7V0PPmBKb6+SpD/iw4QEWcyuDT8VfLP7YllfoqGbkJuWuFfwzQqRGwW//Hf5jTFppfZGsNmmb8feYwX1/szh3pabn+4HfZ0UtJcRgkZdFPw5NYt+4bHUh58xjShu7ntALagtcV+xwHcTFhtpuTF53oU5vur8gk3/pw+dKMmL8asHHzBaBQubYu42EjhFn2HqTCwsF4DnJujFfBP1IrhoomjZdRKQV/4z1ZQSVfo6ozh7VT+A6jZ9EbDHBX4gIBdLMp3DxHxG7KAPucxzKMes+HhlR/cLr3vwAaPAv3vfaElrj5che4CR3v7E6VlxnSOMHvz6KvhAacaAvyF7xu9uVP89sbEJCvxIOCQs0nUt8C0C9ghK0j8iEbS3VwTstbc9n6AN06mFjKuMBhmoEM3mdZHc36/gOKTNgIn5tKkHv0UL/GLbzaGxwjkbdbHZZKTot4aCT+tqLzZnzlk9gI1LurGQyeEnfzghvk7tCuXTULxgQCj4Gdche4b7ggt8hqkLpODbLWSSLi5UmqZ5OipPKPhOLPomxb47HvFsV9zKpn9wlEYfyY/IY5oXWmjNp3MiQX+gM+r5YtGs+DR7/2VJD75HydS71g0iHgnh5HRSTL0AvA3ZAwrOi6GeOPI68OhJow9/Pp0Vmwn17sEHSl0bfhb44TIFv5FQD/7obFqEqarixWhBkaRfVwXfvsCnJH0pBb/oSKGMiXaHCuyp+YxpPrm/Fv1cPm+E7HUoKPi0GTGfQTIdjB78XLEod9I2Q0RbTMGn9a4X55mmaXhVcWTedx94SnzdTd6IHV6G7MVZwWeY+mIk3MuF7DndRfZqVF42lxc9bU4UfLNi73ZEnhkK2vv1E6MVATCkzLCCHwxoobWQyZnGqHlfGJk3sMrHpjUb5gLfq4VoIhrGrvWLAAB3PW5snM0Ve/C9bFvYXiyCzH349N52RMOebSao0FUni765vWK4wQV+Vzwipho8MeJMxXerNJl/tp4FPm0EW41SXVUM2jsybt2Dn8/reOQYJ+ibEX3tC2nfU/TDJSn66oLEgKmwanUFf1FXrCTXo3xqhwr0s61S4Kc8Hsf4sp0rEA1r+MPRKRGg6adF3wjZ825MHvfgM0ydiEmOsEuJPjB3u3duR+VNFot7TTNu2CqYb7JuE/TN7FjZj/7OKGaSWfzh6KT4+sRcWuzgc4EfDMxj8vy0cPe3kEXfbA/0yqIPmMbl7TcV+GlvFXzAKIKobxkotec3oi+9biF7pgX3sr4O355HFrLp7z/lsMAvBqh5UeB74TiTxbDo13Z6yVr0j0zMYyaVRSwSEq9nu1M1Rd+jwqucSEmKfuH+36fQg2/OC2j1HnxN07DO5F50OiKv8LOtpQJ7NSaPWNQdx/POXAYA+N7vCmF7hrLu/T2q32TRd5vW32rvnSxc4DNNi6wV0e1Nxjwqzw1k2+zriDoKDjIX9Ys8SNAnwiENz9i4GECp2nigqMos70u0rMWOKaXTlKJPFn0/UtbNIx2b/dwJ+2DRB4BLNxc+U/cdHBf98XMe9+AD5lF5JgV/xvvpCCp01akH37w50+gefMAI2nOapJ/Ouk+VppF0j1QJXvQD80bw2sWdNR+3qljgH5tYEJbnatBG1RnLenxR9lqRflPRnPbZok/Xw2xePUXf/NjJ+TQW0s4nGDUL5rYTN73itMH1xxPTmC9u9DYzRsied+/dtUWb/n/+/iiSmZxR4PtwLpvPQ+EUcDgmj1P0GabOyFv0qQff2emcEBsJ7hQR6r93MiIPKCih9Dcs9lDBBwy18a79xlgv0VfJ/feBIRGtj0W/v6Us+sZ1wasUfaDQk728L4F0No/fHhxDKpsTPYfeFvgFBX/fqRlxjfLzvZWhK1Yvi77xuxtt0QeMUXlOg/bSHsyFfuYmI+CxHpg3gq3cOkt7E4iGNWTzOk5M1bbpi/57tucLqMAv9OC7d3lYUargU8ieSoq+4TZw2x7ZDJgL/KgLi/4Zwz1YPdiJZCaPOx+vPpa4mXAzfaoWF29cjBX9HZhOZnH7IyeRyfrXgz8gwh5Nrpews/OQU/QZps6YP3TmpOpykh4p+G57Gulm6WREHkHKvZc9+ABwSXFR+NDRSbERwf33wYOUlPl0zjRGzXuV16z4dDS7Rd/cg+/hQlTTNFy6hYqtEdF/D3i76bFyoAN9HVFkcrqwhpNF3w93hgzmDQw/1BmifExeo3GbpO+FOmuVqeIHYtKKRf89UFCGV4pReRYFPiXoL+cCn+jvKIbsLZiLFf/H5JGC72RMXhB68IHS89qNo0TTNFy5vWBR/9nek66Py29SQhjz7r0LhTRcc25Bxf/O/Udch99ZQevsibm061YATtFnmDpjXgRZWWdcF/ik4LvsaRyfU7e7lUOF/SKPx5ot60tg67Ie6HphYQgAB3lEXuAgi34yY1j0/ejBHzAtCL0MlPODsE89+ICxcXbXvhFhz09EQ67mKZejaRq2ryja9IvFkZ/vrQxdceM9j/uo4NPrGAuHHDujvGTjUA8A4MRUsmTesyxeLHiftrIPvYkIphYy+MNR/236BxU2gleKoL3qffi6ruOR4xSwxwn6hNHXbgSGedUbXY7Z0US6CW0wyNBvygsQI4qb/B5ghTlXwu04tyuKBf7/PnqqrhkZTvBrWsMrz10JTQPuPTCGE1OF+5QfPfi0zp5JZTGfdud64RR9hqkz5t6gWur6/lMzmE4WFtZOC+uExwq+uT9ZlbXFRZTVOCKnGLO7C/axA2JEHhf4QUFY9NOmkD2fLfrNXuCbbZdeh0FdtHExwiENB0bm8PjJQl92lw+OBlI7yd484uN7K0O9evDJfbJxSbfjsXJe0tcZFa6JJ4sbpCp4kaIfCYdw8aZC/sPdVUafeo0xIs++lWuVTdDeiakkxufSiIQ0bF7a491BtjjmolkUXn4p+GXFVlcsrHQ+Gr3PmUAo+OZcCbcbs09b2Y/hvgTm0jn8an99WmicIsbkebyRtLy/Q7iM6PzwQ8Hv64iC8mXpfujU9cIWfYapM+Zdv1SNALzP//IJAMAV25Y5trUbCr67DzdZ391Y9D/04m34+pvOExdIL7nUVODn8joOjRUWYTILN6Y1oB7Z+bS5B9+PkL3WSdEvCdnzeDHT1xHFzlX9AID/3nsCgLf998S2siR9P99bGeqVor9qsBPf+7ML8aU3PN2351BFBO2dUg/a82oEmshUqUOBLxR8iY3gVcKiX73AJwfKpqU9LZu87geUoj+TNNRIL/NCzETKNsr6FYWRfpPbIAg9+J2xCJYX23+iLjcRQyFNqPg/K94PmhW6FnkZPEu8qhi2R/hR4IdDGnoThXNxrLj2dtouJlL0c7VbgVsRLvCZpkXTNMM6U8Wif3B0Dj/5w3EAwA3P3uj4eeg5ki5D9iZchuwBhcLpsi1LfFGrzl07gI5oGKdnUvjfx04jnc0jFg5hxUDjx08x3kALrdHZlNg9b3eLvl89+AQ5Y37+x1MA/CnwtxeT9B89MY1sLt8EFv36hOwBwPnrBkVvdzNABf4TI+p9+F6NjaJz7qGjk2KWuR/k8joOjhUK/A0SG8HGqLzqPfh7i/Z8mgzBFDCP1R2ZdadG2lE+4Uel/978+GQmL1yLHbHWLiVo88qtRR8Artw+DKBwP2hmRdjrMXlmnr11KRaZRAA/LPpA6ToEYAW/nNb+VDKBRyTpV+ln+udfPoG8Djxn6xIxK9oJpCS4VvA9CNnzk3gkjAvWDwIAvv6bgwCANYs6HY30Y5oTKrZpd747HvFFYW8li77ZkuqHKkZqKrUKdce9f461i7rQFQsjlc3jsZMzYn51oyz65nPKTwW/GRFBe6fUC3yvZpwP93Vg89Ju5E2ZKn5wfHJBaSN41WDhMU/VUPAfEQF7XOCbiYRD6Clump2eLmze+fW5CmvuCvzueERsmpI1utXdGJQv4YXS/PQ1A1jcHcd0Mot7D4y5/n1+kfRhTB4Ri4Rw9dNXiv/2axxm+Vrb6fPwmDyGaQCxSGnBQhwZn8cPdx8DALz9OZtcPYcxjs+5gn9kfF7YDxc1aYEPGMXIPU8Ubjzcfx8syhdaflm4Y5EQehKFBSn9b7PiZw8+AGxf0VeiJPih4IdCmhiX98vHTgMoqCLlCka96Dan6LfZLPONSwq94/sdJOl7MSaPEAGPPo7kokkrshvBZNEfmUlVDRmjDAk3G/JBhYL2qGj2OvyMCIU0mN9KVYu+pmliUyBfdDS3skUfKIw8BbwpdsMhDVdsXwoA+NnDzWvTp/Wu121rBKXpF57Dn/OjPHfL6aYYWfszrOAzTP2olW75L3c+iVxexzM3LcbZxR5Yt8+RdKjgH59cwGu+8luMzqaxfqgLF21Y7Op4/OSSst7+ddx/Hyg6ytR0P8eo/dUVW/G6C1aLxVGzUtKD78OiORzScPEm43PlR8geAGwrpo7/7+OFAn+oOw5Na4z7pjNenx78ZmTT0sL5fmRiXjkpmyz6sYj7902Epu4fsRwj64aDxTYE2Ukr/Z1RoUQfLQvaOz2TxKnpFDQNOGOYFfxyqGimfmI/P1fmJP3+DvVNwr6ynym/77QaL37acrxwxzDe9Iy1nvw+sun/zx9P1WWUpROMFH1/3ruNS7rxNy84Azc+Z5Nv65By94nTjdM4K/gMU3+qzag/PrmAWx48AgB4+7PdqfeAyaLvQME/NZ3Ea778WxwZX8DaRZ349lsuaOqb3brFXcJGCbCCHzTKlZQlvf5ZuF9/wRr8/UvPaliRKYu5r9Kvz6Y5FLPLB4s+YCTp7zkyCQAY8vG9tcOs4PulNDYri7pi6O+MQteBJxX78L2ccX7+ukEkoiGcmk7hcQeBfzKoBOwBBXV3ZbEPv9ymT+Px1i/u8sXl0uqUj6rzs8A3b3o6mT5U/jOtruAv6o7jn19zDp6x0RtxZte6QQx0RjE+l8b9h8Y9+Z1eY1j0/TvP3nLJerzzuZt9+/3l52HU4cYp9+AzTAMQCfem4vuLdz2JTE7HrnWDOH/doAfP4WxM3shMCq/+8m9xaGweqwY78K23XIClDVx0y6BpmrB2Av6M42MaRziklSwMG5Wy3kxEfLboA8Alm4yFoV/FC9maSaxt5HvbWacU/WZE0zQjaE/Rpu9Vij5QOJd3rVsEwL9xeWTRlwnYI1YVe/WPjJcG7Yn+e7bnV6VcFfdLWQVKg0dVe/Cr/Uyr9+B7TSQcwnPPLNj0b9t7ssFHUx3Dot+671355CynG6dRVvAZpv6U76ydnk7i2w8U1Pt3uOy9Jyh4S8VuOTabwmu/8lscGJnD8r4EvvXmC7C8vzXS6M02fVnrJdM6mIsvLvBLF7N+jAQCCk6JrcsKvdndPhX4G4a6StSWRr633XVM0W9GNjos8I0UfW9eM2P0qT9BewdG1BR8wJSkX6bg04hHcqIwpfSV2439VPDD5gJfXcE3/4ymtZ+LR4YrzyrY9G/bexL5fPONX0vVQcH3m/KNJqdj8ljBZ5gGUK6uf+nuA0hn8zhndT8u2rDIl+ewY3I+jdd99X7sOzWLpb1xfPv6C7BqsHnGONlx8cbFWDXYgbNX9VfsgDKtj9ku2agxas2E2Y7q5zinVzx9JTQNOMsnhTISDpX0LjcqQR9o7xR9wBS0p5ikn/ZQwQeMzdr7D45jPp315HcSyUwOx6cKKryK02tVDYs+BexRlgRTSnkvvJ8bZyUKvoMefPPPdETDTd+m1QiesWExehIRnJ5J4fdPTTT6cCrwc0xevagI2eMxeSVwIxTT1JgT7sdmU/jmfU8BKCTne3VTiSuMyZtayOD1X70fj56YxlBPHN96ywVYs6i1VPCueAS/eNdliIQ0vjEHkJICv4FFYLNg7sH30/b65meux7XnrUJPwr9k++0rekUPfiM3b2KREGLhENK5fFsW+GTR339arffd6wJ/w1AXVvR34NjkAu47MI5nbV3iye8FgENjc9B1oDcRUdoIpoyXIxOGRX9yPo2jxf/exgp+VcrVSD8Lr5Ie/C7165V5PFmr99/7RSwSwuVnLMUPdx/Dz/aexLlr3beTegmtd/1ytdWDCgXfYYF/3toB/PLdlzX9yF9V2u/OzLQUosDP5PGVXx/EQiaHHSv7cFlZGrwXz5G0CdmbSWbwxn+7Hw8fm8Kirhi+9eZdTZ8gXotYJISQxNgjpvXoYIt+CfXowSf8LO6BUntzo99bStJvtzF5gGHRPzw2r6T6ZDwckwcUM1WK98K7PO7DJ3v++qFupY1gsugfHZ8X6f5/LAbsrR7srOg1ZwpUhOz5quAbv7uvQ93FZ34PW7mH22+u3L4MQMGm79ekC6eIFP2AKPjhkCY1yrManbEI1i3uavoMLVVa951l2gJS3E7PpPAfvzkEALjhWRs9VZ4TEgr+QjqHN33tAew5Mon+zihufvMubFra49kxMIxXsIJfSsmYvBZezAClAWWNfm9pHGA7KvjDfQl0xcLI5nUcHpuT/rm0xwU+AFy6uRDw6HXQHiXoqwaxrhwoFPgzqSwm5zMADHv+drbn16S8B9/P3ujSFH0HCr6psGrmqUGN5pLNQ+iMhXFscgEPHZ1q9OEIcnldXIv8dLX5jdlZ1I5ZMHbwK8I0NXST+497D2EuncPWZT0indTr57Aak/fJ2x/D7w5PoDcRwc3X7eI5vkzTQguuWCSE3g7uwoqGzQV+6y5mgMIM9s5YGCENWN7f2AKfZhsPOgjpanU0TcPG4gavStCelyn6xEUbFyMc0nBgdK4i2M4NImBPscBPRMPi3DgyUTgeCthje35tGpWi78RRYbZGs0W/NoloWLTN/KyJ0vTNrqOghOyZ7/NMgdZ9Z5m2gBZCo7NpAIW59173jQuLfg0F/8HD4/h60T3w/15zDo/5YZoaWnAt6YlzxgIMtSoS0lreTh6PhPGVN56Lf3nt07Gou7EW/Y9ffRY++Yod2LGyPa+HG4eoD1++wM/kCjZdL8/D3kQU56zuB+CtTf/AaOHvWu+gDc1I0i/03RsKfnueKzKU9xP7mqJfvCb2JCKIODgXucCXx7Dpn2gam75ZzGrlAj8eCYu++VgLOxH8onXfWaYtMF98Ni7pFhdLLxEW/SoKfjKTw1/d8hB0vZCSfamHvf8M4wek4De6R7tZoH7TVlfviYs2LMYVPlwHVdm6rBfXnLuqbTeRNi1VL/DTxXuM18WbMS7PuwKfLPpORqmuGqCgvXnMprLid21bzs63WlT04NehwC/fVJDFPCYvwRZ9S561ZQnikRAOjc3j0RNqoZx+QWJWJKQ52uBpJqhdJMYKfgWt/c4ygSduWpTf8KyNvgTDWY3J+/z/PoEnR+Yw1BPHB154pufPzTBe0ykKfO6/B4zFbKv33zPNBSXpq1j0vU7RJyho7zdPjokgPzeMz6VF/7yjAt80Ku/RE9PQ9UJuweIGu06amYoUfR8LfJosUj5mTJaBEgWfr6tWdMUjYgPutr0n6vKcE3NpzKVqj80UI/JaWL0naApENAB/i9fwK8I0NXQBWruoEy/aMezTcxQKonKL/iPHp/Cvdz0JAPjIS7ZVhOAwTDPSES303TdyjFozEYtQgc9KE+MdlKT/5Mgscnk56y1Z9L0OhNq+vA+DXTHMprL4/WH3M7dvK/YLL+9LOApRWyUs+vPYe6xgz+f+e2sS0XBJweWvgl/43U4nGnREw+IcZou+PVeeVXBc1aMP/4nTs3jmJ3+Jq/7fr2sW+SRmBeGeaCj4XM6Ww68I09RcuX0YO1f346aXneWblYiUPbNFP5vL4z0/eAjZvI4XnLUMV2z3Z3OBYbzmBWctw46VfXjJ2csbfShNwbblfbhw/SK8dteaRh8KEyBWDnQiHgkhnc1Lh9v5peCHQhqeuamYpr/fnU3/1oeO4/3/9TAA4GXnrHD0O1YVk/SPTiyIgD1O0LeHVHxNKw3C8xr63U4VfE3TxLFyir49z966FNGwhv2nZ/HEaf9s+vm8jvf84CHMprI4MDqHf/ifx6s+LpkJjoJP7SKtnq/jB/yKME3Nmct78cM/fwYu2rjYt+eIix58Q8H/0q8OYO+xafR1RPGhF2/37bkZxmvOXTuIH99wMZ6+ZrDRh9IUJKJhfPv6C/DWyzY0+lCYABEOaSKATtamn/JhTB5xyaaCDdhN0N5te0/ixu/sQV4Hrjl3Jf7yuVsc/Z5Vg4Ue/KMT83jo6CSAgsuAsYb68OORkK/ZFm578M0/GwQV2G/6OqK4uLiG/dnD/qn4/3HvITx4eEIU7l//zSE8eHi84nG01o0H4L2jdpF2HNdqB78iTNtDF8N0No98XseTI7P47M/3AwD+9kVnipE/DMMwDENQH75s0F6muLD2o1/0mZsLBcTeY9MYnU0p//wvHj2Ft3/798jldbx85wp87OU7HGfeDPd1IBLSkMnp4rXhBH17yDLvt904Igp85yMu6WfZoi/HlUUXqF82/SPj8/jk7QXF/v0vOhNXn7MSug781S0PCcWeSBXbUYOk4LNFvxJ+RZi2x7wDnczm8N4fPIR0No9LNw/h5Q4tigzDMEywMQp8Odtt2kcFf0lPAmcOF2zwv94/qvSzd+0bwVtv/j0yOR0v2jGMT75ih1B5nRAOaVhRTNIHgMXdMSzlTBBb+oQa6W/RTO2OA24U/OJmBBf4cjz3zKUIhzT88cS0mCrhFbqu469/+DDm0zmcv24Qrz1/NT7wojOwuDuOJ0fm8Pn/faLk8cKiH4D3brCTQvY4Rb8cLvCZtse8i/nVXx3EA4cm0BUL46aXn9W2I6AYhmEYa0TQnqSC71cPPkFp+io2/XueGMX1//E7pHN5XLFtGT5z7dme5N1QHz5QyMHge6k9VDT7rayuLrZQbFna4/h3bF1W+Nm1DqYstCMDXTGRk/H3t/4Rui4XzCnD9x88il/tH0U8EsLHX34WQiEN/Z0xfOQl2wAAX7jrSTxyfEo8Xlj0A6Dgb1lW2NRcs4jPw3Ja/91lGJdEQhpIrPjcLwrW/PdeuRUr+jssfophGIZpZzYtNXrwZRbsGR8VfABiHNev9o8gL5Hs/9sDY7ju3x9AKpvH5WcswT+9eqdnYVWUpA9wwJ4s1Nfud+H1wau24efvuhQXbljk+He84zmbcMc7L/FtulEQ+ZsXnIFYOIRfPHYaP/7DcU9+5+npJP7+1j8CAN753M0iFwQArjxrGFduX4ZcXsdf3fIQssXrDwVKByE/4YL1g/jFX16Kv7tqW6MPpengAp9pezRNExe6bF7H+WsHOXGbYRiGsWTNoi5EQhrm0jmcmEraPt5vBf/pawbQFQtjdDaNP56Ytnzs7w6N40+//gCSmUI72j+/9hxPj4uC9gAO2JNF9BP7XOBHwyFsXNLtylURCYewaWkPOzMU2LS0Bzc8eyMA4EM/+SPGHGRlmNF1HR/40V5MJ7M4a0Uf3nzxuorHfOgl29DXEcUjx6fxpV8dAGCMhA6Cgq9pGjYMdXPIXhUijT4AhmkG4pEQ5tO5gsXp6rMchwsxDMMw7UE0HMLaxV144vQs9p+exXIb15ffBX4sEsKFGxbj54+ewuf/9wmcu3ag+nHk8viXXz6J+XQOF29cjC++/umIe9z3bbboc8CeHH11sugzjeP/XLoB//3wCTx2cgYf+skf8U+v3un4d/33wydx+yOnEAlp+OQrdlRtrVnSk8AHXnQm3v39P+CzP9+P529bJhR8Ps+CDRf4DAOgtyOKiflMhcWJYRiGYWqxaUl3ocA/NSMs8tU4ODqHuXQWANDp4+zwS7cM4eePnsJtj5zEbY9YJ3ZfsH4QX37Dub5YddcVe7P7O6NYOcDtbjIs7i4EEXYneGkeVGKRED75ih146T/fgx//4The/LTluPzMpcq/Z2IujQ/+eC8A4M8v24Azhmu3wVx9zgr8+A/Hcfe+Ebz3Bw/hWVuXAAiGRZ+pDV9FGAbAh1+yHX88Pl3V4sQwDMMw1RBBeyO1g/byeR3v/cFDyOvAMzctxtLehG/H84pzVuLgyBzG56ztvysGOvDnl21Eh0+bDduW9+L/Pn8LNrONW5pnbR3Cn1+2wVHBx7QOO1b24y2XrMcX7zqAv/mvh3H++kH0JtQmGnzk1j9idDaNTUu68bai7b8Wmqbhppdtx/M/czceODSBk9OFdiJW8IMNF/gMg0I4kZX6wjAMwzDlUIG//1TtAv9b9z+F+w6OozMWxk0vO8vX4+mIhfG3V53p63PIoGka3vYs68KDKSUeCeOvrtja6MNg6sA7L9+M/3nkFA6OzuFj//0oPvbyHdI/+8vHT+M/dx+DpgGfeMUOqfaalQOdeM+VW/G3P3oER8YXAMDzthymueDtG4ZhGIZhGAdsWlIYF7a/RpL+8ckFfPxnjwEA/u/zt5SkyzMM054komF8/OWFzb5v338Ev3liVOrnZpIZ/M1/PgwA+NNnrMM5q6vnbFTjdbvW4DxTLkciyiVgkOF3l2EYhmEYxgHrh7qgacDUQgajs+mS7+m6jr/+4cOYTWXx9DUDeMOFaxtzkAzDNB271i/C6y5YDQB4738+jPliRkct7n1yDK//6v04PpXE6sFO/OXzNis9Xyik4eNX7xAhn6zgBxsu8BmGYRiGYRyQiIaxuqjK7z89U/K9/9pzDHc+PoJYOIRPXH0WwjydhWEYE++5YiuW9yXw1Pg8Pv0/+6o+5sHD43jNl3+LV3/5t9hzZBKJaAifesUOdMbUu6w3DHXjQy/ehsXdcVy8aZHbw2eaGC7wGYZhGIZhHLKp2If/xGmjD390NoUP/eSPAIAbL9+EjUUrP8MwDNGTiOKjRav+v91zEL9/akJ87w9HJvHGf7sfV3/hXvzmyTFEwxrecOEa3PV/n4Vd650X568+fzUe+Jvn4OlrBl0fP9O8cMgewzAMwzCMQzYs6cbPHz1dUuB/8MePYHI+gzOHe3H9JesbeHQMwzQzz9qyBC/fuQL/ufsY3nPLQ/iHVz4N/+9/n8DPHz0FAAiHNLzy6Stxw7M3YuWANxkePNki+HCBzzAMwzAM4xARtFdM0r/9kZP46UMnEA5p+OQrdiAaZrMkwzC1+cCLzsTd+0ew//QsXvLP9wAAQhrw0p0rcONzNmHNoq4GHyHTanCBzzAMwzAM4xBh0R+ZxdRCBh/4r70AgD+7ZD22r+hr5KExDNMCDHTF8OGXbMeff/P30DTgRTuW48bnbBJjOBlGFS7wGYZhGIZhHLKhuAgfmUnhPbc8hNMzKawf6sI7nrOpwUfGMEyr8IKzhvG9P7sQg10xLuwZ13CBzzAMwzAM45DueATL+xI4PpXEbY+chKYBn7x6BxJRHkPFMIw856/j4DvGG7gxjGEYhmEYxgUbTIrbGy5Yg3PX8kKdYRiGaQxtU+CnUim85z3vwfLly9HR0YFdu3bhjjvuaPRhMQzDMAzT4mxeWgjaW9Hfgb+6YmuDj4ZhGIZpZ9qmwP+TP/kT/OM//iNe+9rX4nOf+xzC4TBe8IIX4Ne//nWjD41hGIZhmBbm9ReswQvOWoYvvO4cdMW5+5FhGIZpHJqu63qjD8Jv7r//fuzatQuf+tSn8O53vxsAkEwmsX37dixZsgS/+c1vpH7P9PQ0+vr6MDU1hd7eXj8PmWEYhmEYhmEYhmGU6tC2UPBvueUWhMNhXH/99eJriUQC1113He69914cOXKkgUfHMAzDMAzDMAzDMO5pCx/Z7t27sXnz5ordjvPPPx8AsGfPHqxatari51KpFFKplPjv6elpfw+UYRiGYRiGYRiGYRzSFgr+iRMnMDw8XPF1+trx48er/tzHPvYx9PX1iX/VNgEYhmEYhmEYhmEYphloiwJ/YWEB8Xi84uuJREJ8vxrve9/7MDU1Jf6xlZ9hGIZhGIZhGIZpVtrCot/R0VFitSeSyaT4fjXi8XjVjQGGYRiGYRiGYRiGaTbaQsEfHh7GiRMnKr5OX1u+fHm9D4lhGIZhGIZhGIZhPKUtCvyzzz4b+/btqwjJu++++8T3GYZhGIZhGIZhGKaVaYsC/xWveAVyuRy+9KUvia+lUil87Wtfw65duzg8j2EYhmEYhmEYhml52qIHf9euXXjlK1+J973vfTh9+jQ2btyIf//3f8ehQ4fw1a9+tdGHxzAMwzAMwzAMwzCuaYsCHwD+4z/+Ax/4wAfwjW98AxMTE9ixYwduvfVWXHLJJY0+NIZhGIZhGIZhGIZxjabrut7og2gVpqen0dfXh6mpKfT29jb6cBiGYRiGYRiGYZiAo1KHtkUPPsMwDMMwDMMwDMMEHS7wGYZhGIZhGIZhGCYAcIHPMAzDMAzDMAzDMAGAC3yGYRiGYRiGYRiGCQBc4DMMwzAMwzAMwzBMAOACn2EYhmEYhmEYhmECABf4DMMwDMMwDMMwDBMAuMBnGIZhGIZhGIZhmADABT7DMAzDMAzDMAzDBAAu8BmGYRiGYRiGYRgmAHCBzzAMwzAMwzAMwzABgAt8hmEYhmEYhmEYhgkAXOAzDMMwDMMwDMMwTADgAp9hGIZhGIZhGIZhAkCk0QfQSui6DgCYnp5u8JEwDMMwDMMwDMMw7QDVn1SPWsEFvgIzMzMAgFWrVjX4SBiGYRiGYRiGYZh2YmZmBn19fZaP0XSZbQAGAJDP53H8+HH09PRA07RGH05NpqensWrVKhw5cgS9vb2NPhyGqQmfq0yrwOcq0yrwucq0AnyeMq1Cs5yruq5jZmYGy5cvRyhk3WXPCr4CoVAIK1eubPRhSNPb28sXTaYl4HOVaRX4XGVaBT5XmVaAz1OmVWiGc9VOuSc4ZI9hGIZhGIZhGIZhAgAX+AzDMAzDMAzDMAwTALjADyDxeBwf/OAHEY/HG30oDGMJn6tMq8DnKtMq8LnKtAJ8njKtQiueqxyyxzAMwzAMwzAMwzABgBV8hmEYhmEYhmEYhgkAXOAzDMMwDMMwDMMwTADgAp9hGIZhGIZhGIZhAgAX+AzDMAzDMAzDMAwTALjADxCpVArvec97sHz5cnR0dGDXrl244447Gn1YTBvzwAMP4IYbbsC2bdvQ1dWF1atX45prrsG+ffsqHvvoo4/iiiuuQHd3NwYHB/H6178eIyMjDThqhgE++tGPQtM0bN++veJ7v/nNb3DxxRejs7MTy5Ytwzve8Q7Mzs424CiZduX3v/89XvziF2NwcBCdnZ3Yvn07/umf/qnkMXyeMo1m//79eNWrXoWVK1eis7MTW7duxYc//GHMz8+XPI7PVaZezM7O4oMf/CCuuOIKDA4OQtM0fP3rX6/6WNl1aT6fxyc/+UmsW7cOiUQCO3bswLe//W2f/xJrIg19dsZT/uRP/gS33HIL/uIv/gKbNm3C17/+dbzgBS/AL3/5S1x88cWNPjymDfnEJz6Be+65B6985SuxY8cOnDx5Ep///Odxzjnn4Le//a0ono4ePYpLLrkEfX19uOmmmzA7O4t/+Id/wMMPP4z7778fsViswX8J004cPXoUN910E7q6uiq+t2fPHjznOc/BGWecgX/8x3/E0aNH8Q//8A/Yv38/fvaznzXgaJl243/+539w1VVXYefOnfjABz6A7u5uPPnkkzh69Kh4DJ+nTKM5cuQIzj//fPT19eGGG27A4OAg7r33Xnzwgx/Egw8+iB/96EcA+Fxl6svo6Cg+/OEPY/Xq1Xja056GO++8s+rjVNalf/M3f4OPf/zjeMtb3oLzzjsPP/rRj/Ca17wGmqbhVa96VZ3+sjJ0JhDcd999OgD9U5/6lPjawsKCvmHDBv3CCy9s4JEx7cw999yjp1Kpkq/t27dPj8fj+mtf+1rxtbe+9a16R0eHfvjwYfG1O+64Qwegf/GLX6zb8TKMruv6tddeqz/72c/WL730Un3btm0l37vyyiv14eFhfWpqSnzty1/+sg5Av/322+t9qEybMTU1pS9dulR/2ctepudyuZqP4/OUaTQf/ehHdQD63r17S77+hje8QQegj4+P67rO5ypTX5LJpH7ixAld13X9gQce0AHoX/va1yoeJ7suPXr0qB6NRvW3ve1t4mv5fF5/5jOfqa9cuVLPZrP+/TEWsEU/INxyyy0Ih8O4/vrrxdcSiQSuu+463HvvvThy5EgDj45pVy666KIK9X3Tpk3Ytm0bHn30UfG1H/zgB3jRi16E1atXi69dfvnl2Lx5M773ve/V7XgZ5u6778Ytt9yCz372sxXfm56exh133IHXve516O3tFV9/wxvegO7ubj5XGd/51re+hVOnTuGjH/0oQqEQ5ubmkM/nSx7D5ynTDExPTwMAli5dWvL14eFhhEIhxGIxPleZuhOPx7Fs2TLbx8muS3/0ox8hk8ngz//8z8XXNE3DW9/6Vhw9ehT33nuvt3+AJFzgB4Tdu3dj8+bNJRdIADj//PMBFCxQDNMM6LqOU6dOYfHixQCAY8eO4fTp0zj33HMrHnv++edj9+7d9T5Epk3J5XJ4+9vfjje/+c0466yzKr7/8MMPI5vNVpyrsVgMZ599Np+rjO/8/Oc/R29vL44dO4YtW7agu7sbvb29eOtb34pkMgmAz1OmObjssssAANdddx327NmDI0eO4Lvf/S6+8IUv4B3veAe6urr4XGWaEpV16e7du9HV1YUzzjij4nH0/UbABX5AOHHiBIaHhyu+Tl87fvx4vQ+JYaryzW9+E8eOHcO1114LoHDuAqh5/o6PjyOVStX1GJn25F//9V9x+PBhfOQjH6n6fbtzla+zjN/s378f2WwWL3nJS/D85z8fP/jBD/Cnf/qn+Nd//Ve86U1vAsDnKdMcXHHFFfjIRz6CO+64Azt37sTq1avxqle9Cm9/+9vxmc98BgCfq0xzorIuPXHiBJYuXQpN0yoeBzSu/uKQvYCwsLCAeDxe8fVEIiG+zzCN5rHHHsPb3vY2XHjhhXjjG98IwDg37c7fat9nGK8YGxvD3/7t3+IDH/gAhoaGqj7G7lzl6yzjN7Ozs5ifn8f/+T//R6Tmv/zlL0c6ncYXv/hFfPjDH+bzlGka1q5di0suuQRXX301Fi1ahJ/+9Ke46aabsGzZMtxwww18rjJNicq6tFnrLy7wA0JHR0dVlZMsex0dHfU+JIYp4eTJk3jhC1+Ivr4+kRkBGOcmn79MI3n/+9+PwcFBvP3tb6/5GLtzlc9Txm/oHHv1q19d8vXXvOY1+OIXv4h7770XnZ2dAPg8ZRrLd77zHVx//fXYt28fVq5cCaCwGZXP5/Ge97wHr371q/mayjQlKuvSZq2/2KIfEIaHh4WlxAx9bfny5fU+JIYRTE1N4corr8Tk5CRuu+22kvORbEy1zt/BwUFW7xlf2b9/P770pS/hHe94B44fP45Dhw7h0KFDSCaTyGQyOHToEMbHx23PVb7OMn5D51h5cNmSJUsAABMTE3yeMk3Bv/zLv2Dnzp2iuCde/OIXY35+Hrt37+ZzlWlKVNalw8PDOHnyJHRdr3gc0Lj6iwv8gHD22Wdj3759IrWUuO+++8T3GaYRJJNJXHXVVdi3bx9uvfVWnHnmmSXfX7FiBYaGhvC73/2u4mfvv/9+PncZ3zl27Bjy+Tze8Y53YN26deLffffdh3379mHdunX48Ic/jO3btyMSiVScq+l0Gnv27OFzlfGdpz/96QAK56wZ6vMcGhri85RpCk6dOoVcLlfx9UwmAwDIZrN8rjJNicq69Oyzz8b8/HzJZCig8fUXF/gB4RWveAVyuRy+9KUvia+lUil87Wtfw65du7Bq1aoGHh3TruRyOVx77bW499578f3vfx8XXnhh1cddffXVuPXWW0vGOf7iF7/Avn378MpXvrJeh8u0Kdu3b8cPf/jDin/btm3D6tWr8cMf/hDXXXcd+vr6cPnll+Pmm2/GzMyM+PlvfOMbmJ2d5XOV8Z1rrrkGAPDVr3615Otf+cpXEIlEcNlll/F5yjQFmzdvxu7du7Fv376Sr3/7299GKBTCjh07+FxlmhbZdelLXvISRKNR/Mu//Iv4mq7r+Nd//VesWLECF110UV2Pm9D0ck8B07Jcc801+OEPf4h3vvOd2LhxI/793/8d999/P37xi1/gkksuafThMW3IX/zFX+Bzn/scrrrqKrEwNfO6170OAHDkyBHs3LkT/f39uPHGGzE7O4tPfepTWLlyJR544AG26DMN4bLLLsPo6Cj27t0rvvb73/8eF110Ec4880xcf/31OHr0KD796U/jkksuwe23397Ao2Xaheuuuw7/9m//hmuuuQaXXnop7rzzTnz/+9/H+973Ptx0000A+DxlGs/dd9+NZz/72Vi0aBFuuOEGLFq0CLfeeit+9rOf4c1vfjO+/OUvA+Bzlak/n//85zE5OYnjx4/jC1/4Al7+8pdj586dAIC3v/3t6OvrU1qX/tVf/RU+9alP4frrr8d5552H//qv/8JPf/pTfPOb38RrXvOaxvyROhMYFhYW9He/+936smXL9Hg8rp933nn6bbfd1ujDYtqYSy+9VAdQ85+ZvXv36s973vP0zs5Ovb+/X3/ta1+rnzx5skFHzjCF83fbtm0VX//Vr36lX3TRRXoikdCHhob0t73tbfr09HQDjpBpR9LptP53f/d3+po1a/RoNKpv3LhR/8xnPlPxOD5PmUZz33336VdeeaW+bNkyPRqN6ps3b9Y/+tGP6plMpuRxfK4y9WTNmjU116UHDx4Uj5Ndl+ZyOf2mm27S16xZo8diMX3btm36zTffXMe/qBJW8BmGYRiGYRiGYRgmAHAPPsMwDMMwDMMwDMMEAC7wGYZhGIZhGIZhGCYAcIHPMAzDMAzDMAzDMAGAC3yGYRiGYRiGYRiGCQBc4DMMwzAMwzAMwzBMAOACn2EYhmEYhmEYhmECABf4DMMwDMMwDMMwDBMAuMBnGIZhGIZhGIZhmADABT7DMAzDMAzDMAzDBAAu8BmGYRiGaTnWrl2LtWvXNvowGIZhGKap4AKfYRiGYdqUQ4cOQdM0y39cRDMMwzBM6xBp9AEwDMMwDNNYNmzYgNe97nVVv9ff31/fg2EYhmEYxjFc4DMMwzBMm7Nx40b83d/9XaMPg2EYhmEYl7BFn2EYhmEYKTRNw2WXXYajR4/i1a9+NRYvXozOzk484xnPwM9//vOqPzM6Ooq/+Iu/wLp16xCPx7FkyRJcc8012Lt3b9XHp9NpfOYzn8F5552Hnp4edHd348wzz8S73vUuTExMVDx+dnYWN954I5YvX454PI4dO3bglltu8fTvZhiGYZhWQdN1XW/0QTAMwzAMU38OHTqEdevW4fnPfz5uu+0228drmoYdO3ZgcnISQ0NDuPzyyzEyMoLvfve7SCaTuOWWW/DSl75UPH5kZAQXXnghnnzySVx22WW44IILcPDgQdxyyy2Ix+O4/fbbcfHFF4vHLyws4LnPfS7uuecebNq0CVdccQXi8Tj279+PO+64A/fccw/OPvtsAIWQvUwmgzVr1mBiYgKXX3455ufn8Z3vfAcLCwu47bbb8LznPc/rl4xhGIZhmhou8BmGYRimTaEC36oH/4ILLsAVV1wBoFDgA8BrXvMa3HzzzeK/H3roIZx33nno6+vD4cOH0dHRAQD40z/9U3zta1/D+973Ptx0003id/73f/83XvjCF2Ljxo14/PHHEQoVDIXvfve78elPfxqvf/3r8bWvfQ3hcFj8zNTUFMLhMLq7uwEUCvzDhw/jJS95Cb73ve8hFosBAH7xi1/g8ssvl960YBiGYZggwQU+wzAMw7QpVOBbceONN+Kzn/0sgEKBHw6H8eSTT2LNmjUlj3vzm9+Mr371q7jllltw9dVXI51Oo6+vD11dXXjqqafQ2dlZ8vjnPe95uOOOO3D33Xfjmc98JrLZLAYHBxEKhXDw4EEMDAxYHhcV+AcOHKj4G9auXYuZmRmMjY1JvhIMwzAMEwy4B59hGIZh2pznP//50HW96j8q7onVq1dXFPcA8MxnPhMAsHv3bgDAY489hmQyifPPP7+iuAeAZz3rWQCAPXv2iMfPzMzgvPPOsy3uif7+/qobFCtXrsTk5KTU72AYhmGYIMEFPsMwDMMw0ixdutTy61NTUwCA6elpy8cPDw+XPI5+bsWKFdLH0tfXV/XrkUgE+Xxe+vcwDMMwTFDgAp9hGIZhGGlOnTpl+XUqunt7ey0ff/LkyZLH9ff3AwCOHTvm2bEyDMMwTLvBBT7DMAzDMNI89dRTOHz4cMXXf/WrXwEAdu7cCQDYunUrEokEHnjgAczPz1c8/s477wQAkYq/ZcsW9Pb24oEHHqg6Do9hGIZhGHu4wGcYhmEYRppcLoe//uu/hjmj96GHHsI3vvENDA0N4QUveAEAIBaL4dWvfjVGR0fxsY99rOR33Hbbbbj99tuxceNGPOMZzwBQsNX/2Z/9GaampnDjjTcil8uV/MzU1BRmZ2d9/usYhmEYprXhFH2GYRiGaVNkxuQBwHvf+14kEglomoYdO3ZgcnISQ0NDuPzyyzEyMoLvfve7WFhYwA9+8AO89KUvFT83MjKCCy64AAcOHMCzn/1s7Nq1C4cOHcL3v/99xGIx3H777bj44ovF45PJJJ73vOfhV7/6FTZt2oQrr7wS8XgcBw4cwG233YZf//rXQvFfu3at+BvKueyyy3DXXXeBlzgMwzBMu8EFPsMwDMO0KTJj8gBgYmIC/f390DQNl156KW6++Wa8+93vxh133IH5+Xns3LkTH/rQh/Dc5z634mdHR0fxkY98BD/60Y9w/Phx9PX14bLLLsMHP/hBbN++veLxqVQKn//853HzzTfj8ccfRzgcxurVq3HllVfi/e9/v+jV5wKfYRiGYSrhAp9hGIZhGCmowKf+eYZhGIZhmgvuwWcYhmEYhmEYhmGYAMAFPsMwDMMwDMMwDMMEAC7wGYZhGIZhGIZhGCYARBp9AAzDMAzDtAYc28MwDMMwzQ0r+AzDMAzDMAzDMAwTALjAZxiGYRiGYRiGYZgAwAU+wzAMwzAMwzAMwwQALvAZhmEYhmEYhmEYJgBwgc8wDMMwDMMwDMMwAYALfIZhGIZhGIZhGIYJAFzgMwzDMAzDMAzDMEwA4AKfYRiGYRiGYRiGYQLA/wfgNHFS9elffwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('default')\n",
    "plt.figure(figsize=(12,8))\n",
    "plt.rcParams[\"font.family\"] = \"Arial\"\n",
    "plt.plot(reward_hist)\n",
    "plt.title('Total Reward Per Round', fontsize=16, fontweight='bold')\n",
    "plt.xlabel('Epoch', fontsize=14)\n",
    "plt.ylabel('Months Survival', fontsize=14)\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "# plt.savefig('./figs/sparse/reward_sparse.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Moving average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mo_surv</th>\n",
       "      <th>SMA100</th>\n",
       "      <th>SMA1000</th>\n",
       "      <th>CMA</th>\n",
       "      <th>EMA100</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>86</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>84.732673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>44</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.666667</td>\n",
       "      <td>83.926086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>42</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48.500000</td>\n",
       "      <td>83.095866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.400000</td>\n",
       "      <td>81.707829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>94</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.125000</td>\n",
       "      <td>40.975117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.103093</td>\n",
       "      <td>40.777590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.816327</td>\n",
       "      <td>40.069123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>83</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.323232</td>\n",
       "      <td>40.919239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>8</td>\n",
       "      <td>33.07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.070000</td>\n",
       "      <td>40.267373</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    mo_surv  SMA100  SMA1000        CMA     EMA100\n",
       "0        86     NaN      NaN        NaN  86.000000\n",
       "1        22     NaN      NaN  54.000000  84.732673\n",
       "2        44     NaN      NaN  50.666667  83.926086\n",
       "3        42     NaN      NaN  48.500000  83.095866\n",
       "4        13     NaN      NaN  41.400000  81.707829\n",
       "..      ...     ...      ...        ...        ...\n",
       "95       94     NaN      NaN  33.125000  40.975117\n",
       "96       31     NaN      NaN  33.103093  40.777590\n",
       "97        5     NaN      NaN  32.816327  40.069123\n",
       "98       83     NaN      NaN  33.323232  40.919239\n",
       "99        8   33.07      NaN  33.070000  40.267373\n",
       "\n",
       "[100 rows x 5 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reward DF\n",
    "reward_df = pd.DataFrame({'mo_surv': reward_hist})\n",
    "reward_df['SMA100'] = reward_df.iloc[:,0].rolling(window=100).mean()\n",
    "reward_df['SMA1000'] = reward_df.iloc[:,0].rolling(window=1000).mean()\n",
    "reward_df['CMA'] = reward_df.iloc[:,0].expanding(min_periods=2).mean()\n",
    "reward_df['EMA100'] = reward_df.iloc[:,0].ewm(span=100, adjust=False).mean()\n",
    "# Save CSV\n",
    "reward_df.to_csv('reward_df_sparse.csv', index=False)\n",
    "# Show\n",
    "reward_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x154a0b2e0fa0>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family ['Arial'] not found. Falling back to DejaVu Sans.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "plt.figure(figsize=(12,8))\n",
    "# plt.rcParams[\"font.family\"] = \"Arial\"\n",
    "plt.plot(reward_df['mo_surv'], label='Survival per round', color='lightgray')\n",
    "# plt.plot(reward_df['SMA100'], label='SMA100')\n",
    "plt.plot(reward_df['SMA1000'], label='SMA1000')\n",
    "# plt.plot(reward_df['EMA100'], label='EMA')\n",
    "plt.plot(reward_df['CMA'], label='CMA')\n",
    "plt.title('Simulated Patient Survival', fontsize=16, fontweight='bold')\n",
    "plt.xlabel('Round', fontsize=14)\n",
    "plt.ylabel('Months Survived', fontsize=14)\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "plt.legend()\n",
    "# plt.savefig('./figs/sparse/sim_survival_sparse.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Simulated trajectories data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient</th>\n",
       "      <th>therapy</th>\n",
       "      <th>race</th>\n",
       "      <th>tumor_stage</th>\n",
       "      <th>tumor_grade</th>\n",
       "      <th>months</th>\n",
       "      <th>treat_months</th>\n",
       "      <th>previous_lines</th>\n",
       "      <th>age</th>\n",
       "      <th>treat_state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>[Carboplatin, Paclitaxel]</td>\n",
       "      <td>BLACK OR AFRICAN AMERICAN</td>\n",
       "      <td>IIIC</td>\n",
       "      <td>G1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>[Cisplatin, Paclitaxel]</td>\n",
       "      <td>BLACK OR AFRICAN AMERICAN</td>\n",
       "      <td>IIIC</td>\n",
       "      <td>G1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>[Carboplatin, Paclitaxel, Topotecan]</td>\n",
       "      <td>BLACK OR AFRICAN AMERICAN</td>\n",
       "      <td>IIIC</td>\n",
       "      <td>G1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>[Etoposide, Gemcitabine]</td>\n",
       "      <td>BLACK OR AFRICAN AMERICAN</td>\n",
       "      <td>IIIC</td>\n",
       "      <td>G1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>BLACK OR AFRICAN AMERICAN</td>\n",
       "      <td>IIIC</td>\n",
       "      <td>G1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>49</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3302</th>\n",
       "      <td>99</td>\n",
       "      <td>[Paclitaxel, Topotecan]</td>\n",
       "      <td>AMERICAN INDIAN OR ALASKA NATIVE</td>\n",
       "      <td>IIIC</td>\n",
       "      <td>GB</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3303</th>\n",
       "      <td>99</td>\n",
       "      <td>[Topotecan, Vinorelbine]</td>\n",
       "      <td>AMERICAN INDIAN OR ALASKA NATIVE</td>\n",
       "      <td>IIIC</td>\n",
       "      <td>GB</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3304</th>\n",
       "      <td>99</td>\n",
       "      <td>[]</td>\n",
       "      <td>AMERICAN INDIAN OR ALASKA NATIVE</td>\n",
       "      <td>IIIC</td>\n",
       "      <td>GB</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3305</th>\n",
       "      <td>99</td>\n",
       "      <td>[Docetaxel, Doxorubicin, Paclitaxel, Topotecan]</td>\n",
       "      <td>AMERICAN INDIAN OR ALASKA NATIVE</td>\n",
       "      <td>IIIC</td>\n",
       "      <td>GB</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3306</th>\n",
       "      <td>99</td>\n",
       "      <td>[Letrozole]</td>\n",
       "      <td>AMERICAN INDIAN OR ALASKA NATIVE</td>\n",
       "      <td>IIIC</td>\n",
       "      <td>GB</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>38</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3307 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      patient                                          therapy  \\\n",
       "0           0                        [Carboplatin, Paclitaxel]   \n",
       "1           0                          [Cisplatin, Paclitaxel]   \n",
       "2           0             [Carboplatin, Paclitaxel, Topotecan]   \n",
       "3           0                         [Etoposide, Gemcitabine]   \n",
       "4           0                                               []   \n",
       "...       ...                                              ...   \n",
       "3302       99                          [Paclitaxel, Topotecan]   \n",
       "3303       99                         [Topotecan, Vinorelbine]   \n",
       "3304       99                                               []   \n",
       "3305       99  [Docetaxel, Doxorubicin, Paclitaxel, Topotecan]   \n",
       "3306       99                                      [Letrozole]   \n",
       "\n",
       "                                  race tumor_stage tumor_grade  months  \\\n",
       "0            BLACK OR AFRICAN AMERICAN        IIIC          G1       0   \n",
       "1            BLACK OR AFRICAN AMERICAN        IIIC          G1       1   \n",
       "2            BLACK OR AFRICAN AMERICAN        IIIC          G1       2   \n",
       "3            BLACK OR AFRICAN AMERICAN        IIIC          G1       3   \n",
       "4            BLACK OR AFRICAN AMERICAN        IIIC          G1       4   \n",
       "...                                ...         ...         ...     ...   \n",
       "3302  AMERICAN INDIAN OR ALASKA NATIVE        IIIC          GB       3   \n",
       "3303  AMERICAN INDIAN OR ALASKA NATIVE        IIIC          GB       4   \n",
       "3304  AMERICAN INDIAN OR ALASKA NATIVE        IIIC          GB       5   \n",
       "3305  AMERICAN INDIAN OR ALASKA NATIVE        IIIC          GB       6   \n",
       "3306  AMERICAN INDIAN OR ALASKA NATIVE        IIIC          GB       7   \n",
       "\n",
       "      treat_months  previous_lines  age  treat_state  \n",
       "0                0               0   49            1  \n",
       "1                1               1   49            1  \n",
       "2                2               2   49            1  \n",
       "3                3               3   49            0  \n",
       "4                0               4   49            1  \n",
       "...            ...             ...  ...          ...  \n",
       "3302             0               2   38            1  \n",
       "3303             1               3   38            0  \n",
       "3304             0               4   38            1  \n",
       "3305             0               4   38            1  \n",
       "3306             1               5   38            2  \n",
       "\n",
       "[3307 rows x 10 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Save CSV\n",
    "sim_trajectories.to_csv('sim_trajectories_sparse.csv', index=False)\n",
    "\n",
    "# Show\n",
    "sim_trajectories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([-20.,   0.,  20.,  40.,  60.,  80., 100., 120., 140.]),\n",
       " [Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, '')])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Average value plots\n",
    "    # Clinician data\n",
    "clinician_val = df_reg[['patient', 'months']].groupby('patient').max()\n",
    "clinician_val_d = df_d_reg[['patient', 'months']].groupby('patient').max()\n",
    "    # Simulation data\n",
    "ai_val = sim_trajectories[['patient', 'months']].groupby('patient').max()\n",
    "\n",
    "plt.style.use('default')\n",
    "plt.figure(figsize=(12,12))\n",
    "plt.boxplot([clinician_val['months'], clinician_val_d['months'], ai_val[0:1000]['months'], ai_val[-1000:]['months']],\n",
    "            labels=['Clinicians,\\nAll patients', 'Clinicians,\\nDeceased only', 'AI Policy,\\nFirst 1000', \n",
    "                    'AI Policy,\\nLast 1000'],\n",
    "            flierprops=dict(markeredgecolor='r', marker='+'))\n",
    "plt.title('Estimated Policy Value', fontsize=16, fontweight='bold')\n",
    "plt.ylabel('Months Survived', fontsize=12)\n",
    "plt.xticks(fontsize=10)\n",
    "plt.yticks(fontsize=10)\n",
    "# plt.savefig('./figs/sparse/policy_val.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Simulation Heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family ['Arial'] not found. Falling back to DejaVu Sans.\n",
      "findfont: Font family ['Arial'] not found. Falling back to DejaVu Sans.\n",
      "findfont: Font family ['Arial'] not found. Falling back to DejaVu Sans.\n",
      "findfont: Font family ['Arial'] not found. Falling back to DejaVu Sans.\n",
      "findfont: Font family ['Arial'] not found. Falling back to DejaVu Sans.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x1600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_last1000 = sim_trajectories.loc[sim_trajectories['patient'] >= sim_trajectories['patient'].max()-1000].reset_index(drop=True)\n",
    "\n",
    "sim_last1000['ther_str'] = [','.join(map(str, l)) for l in sim_last1000['therapy']]\n",
    "\n",
    "sim_last1000.to_pickle('sim_last1000_sparse.pkl')\n",
    "\n",
    "freq_heat(sim_last1000, 'Treatment Frequency and Timing - AI Policy', './figs/sparse/heat_data_ai.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "t4G7VixwubZe"
   },
   "source": [
    "## Scratch/notes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lhzLwojuFxnC"
   },
   "source": [
    "Notes\n",
    "* Add in \"remission\" as a state (i.e. no treatment)\n",
    "  - Calculate similar to probability of death: probability that a no-treatment period follows a treatment\n",
    "  - Then need to calculate probability following \"remission\" for each:\n",
    "    - Death\n",
    "    - remission, i.e. no treatment\n",
    "    - More treatment i.e. \"progression/recurrence\" \n",
    "  - No-treatment (\"[]\") will not be in the action set\n",
    "    - Unless already in the no-treatment state\n",
    "    - Importantly, this means that the agent cannot decide to take a patient off of treatement. Not being on chemo is not a choice made by the AI \"doctor\", but rather, is a result of the previous treatment. Agent can, however, choose not to add drugs if already achieved remission (i.e. in the no-treatment state) \n",
    "\n",
    "* \"Grid world\" verion of action space\n",
    "  - 56 dimensional object with up to 5 drugs activated at once\n",
    "  - Action set is [Start drug {n}, Stop drug {n}, Do nothing]\n",
    "    - Cannot [Stop drug {n}] if resulting state is no treatment\n",
    "    - Cannot [Start drug {n}] if resulting len(combo) > 5\n",
    "    - Cannot start a drug that is already in the combo\n",
    "  - \\> 4mm possible combinations, only 127 drug combos in data - is this necessary? \n",
    "\n",
    "* Rewards\n",
    "  - I think in the final version this should be a probability distribution returning a number of days of survival/until treatment failure after each action\n",
    "  - For starters, just try to get the average days survived when therapy doesn't result in death.\n",
    "  - Also need to think of something for when patients don't die\n",
    "    - Maybe just probability of death and a final non-death stop state?\n",
    "    - Death vs. no death in the end doesn't matter unless the no deaths get a final reward\n",
    "\n",
    "* Bellman action value equation: Q(s,a) = r + γ(max(Q(s’,a’))\n",
    "  - \"This says that the Q-value for a given state (s) and action (a) should represent the current reward (r) plus the maximum discounted (γ) future reward expected according to our own table for the next state (s’) we would end up in\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "i1T5dOpFDHsd"
   },
   "source": [
    "Intermittant reward matrix for each patient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "id": "1kse5iCiC84F"
   },
   "outputs": [],
   "source": [
    "# fix because including no drug periods and using df\n",
    "\n",
    "# tcga_rewards = {}\n",
    "# for barcode in tcga_lines_keys:\n",
    "#     rewards = []\n",
    "#     for line in range(len(tcga_drug_lines[barcode])):\n",
    "#         try:\n",
    "#             rewards.append(tcga_drug_lines[barcode][line+1][0] - tcga_drug_lines[barcode][line][0])\n",
    "#         except:\n",
    "#             rewards.append(tcga_ov_1_keep.loc[tcga_ov_1_keep['bcr_patient_barcode'] == barcode, 'total_days_overall_survival'].iloc[0] - tcga_drug_lines[barcode][line][0])\n",
    "\n",
    "#     tcga_rewards[barcode] = rewards\n",
    "\n",
    "# tcga_rewards"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "id": "se4yPE1y3pna"
   },
   "outputs": [],
   "source": [
    "# negs = []\n",
    "# for barcode in tcga_rewards:\n",
    "#     if tcga_rewards[barcode][-1] < 0:\n",
    "#         negs.append(barcode)\n",
    "\n",
    "# negs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "id": "3Rk6ADD7bzKc"
   },
   "outputs": [],
   "source": [
    "# Add in final state for each patient: [time, 'death']\n",
    "# Figure out what to do with patients where overall survival is < the end of the last therapy line"
   ]
  }
 ],
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