infection_rate.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 484,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from google.cloud import bigquery\n",
    "from pandas import ExcelWriter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 485,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %load_ext google.cloud.bigquery"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 486,
   "metadata": {},
   "outputs": [],
   "source": [
    "# client = bigquery.Client()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 487,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sql = \"\"\"SELECT * from covid19_open_data.infectionrate_vs_population where location_key=country_code\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 488,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df = client.query(sql).to_dataframe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 531,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>location_key</th>\n",
       "      <th>country_code</th>\n",
       "      <th>country_name</th>\n",
       "      <th>subregion1_code</th>\n",
       "      <th>subregion1_name</th>\n",
       "      <th>subregion2_code</th>\n",
       "      <th>subregion2_name</th>\n",
       "      <th>aggregation_level</th>\n",
       "      <th>new_confirmed</th>\n",
       "      <th>cumulative_confirmed</th>\n",
       "      <th>population</th>\n",
       "      <th>new_tested</th>\n",
       "      <th>cumulative_tested</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-10-14</td>\n",
       "      <td>CM</td>\n",
       "      <td>CM</td>\n",
       "      <td>Cameroon</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>21441.0</td>\n",
       "      <td>25876380.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-10-26</td>\n",
       "      <td>JO</td>\n",
       "      <td>JO</td>\n",
       "      <td>Jordan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>55055.0</td>\n",
       "      <td>10101694.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-10-05</td>\n",
       "      <td>TH</td>\n",
       "      <td>TH</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>69625582.0</td>\n",
       "      <td>3697.0</td>\n",
       "      <td>1076332.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-09-15</td>\n",
       "      <td>SK</td>\n",
       "      <td>SK</td>\n",
       "      <td>Slovakia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>188.0</td>\n",
       "      <td>5768.0</td>\n",
       "      <td>5450421.0</td>\n",
       "      <td>4027.0</td>\n",
       "      <td>397513.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-04-18</td>\n",
       "      <td>LK</td>\n",
       "      <td>LK</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>21803000.0</td>\n",
       "      <td>702.0</td>\n",
       "      <td>10494.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77059</th>\n",
       "      <td>2020-05-10</td>\n",
       "      <td>LU</td>\n",
       "      <td>LU</td>\n",
       "      <td>Luxembourg</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3886.0</td>\n",
       "      <td>613894.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3124.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77060</th>\n",
       "      <td>2020-07-30</td>\n",
       "      <td>TD</td>\n",
       "      <td>TD</td>\n",
       "      <td>Chad</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>935.0</td>\n",
       "      <td>15946876.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77061</th>\n",
       "      <td>2020-05-31</td>\n",
       "      <td>SL</td>\n",
       "      <td>SL</td>\n",
       "      <td>Sierra Leone</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>861.0</td>\n",
       "      <td>7813215.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77062</th>\n",
       "      <td>2020-03-24</td>\n",
       "      <td>KE</td>\n",
       "      <td>KE</td>\n",
       "      <td>Kenya</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>52573973.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77063</th>\n",
       "      <td>2020-03-26</td>\n",
       "      <td>XK</td>\n",
       "      <td>XK</td>\n",
       "      <td>Kosovo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>1883018.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>77064 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            date location_key country_code  country_name  subregion1_code  \\\n",
       "0     2020-10-14           CM           CM      Cameroon              NaN   \n",
       "1     2020-10-26           JO           JO        Jordan              NaN   \n",
       "2     2020-10-05           TH           TH      Thailand              NaN   \n",
       "3     2020-09-15           SK           SK      Slovakia              NaN   \n",
       "4     2020-04-18           LK           LK     Sri Lanka              NaN   \n",
       "...          ...          ...          ...           ...              ...   \n",
       "77059 2020-05-10           LU           LU    Luxembourg              NaN   \n",
       "77060 2020-07-30           TD           TD          Chad              NaN   \n",
       "77061 2020-05-31           SL           SL  Sierra Leone              NaN   \n",
       "77062 2020-03-24           KE           KE         Kenya              NaN   \n",
       "77063 2020-03-26           XK           XK        Kosovo              NaN   \n",
       "\n",
       "       subregion1_name  subregion2_code  subregion2_name  aggregation_level  \\\n",
       "0                  NaN              NaN              NaN                  0   \n",
       "1                  NaN              NaN              NaN                  0   \n",
       "2                  NaN              NaN              NaN                  0   \n",
       "3                  NaN              NaN              NaN                  0   \n",
       "4                  NaN              NaN              NaN                  0   \n",
       "...                ...              ...              ...                ...   \n",
       "77059              NaN              NaN              NaN                  0   \n",
       "77060              NaN              NaN              NaN                  0   \n",
       "77061              NaN              NaN              NaN                  0   \n",
       "77062              NaN              NaN              NaN                  0   \n",
       "77063              NaN              NaN              NaN                  0   \n",
       "\n",
       "       new_confirmed  cumulative_confirmed  population  new_tested  \\\n",
       "0              108.0               21441.0  25876380.0         NaN   \n",
       "1             1968.0               55055.0  10101694.0         NaN   \n",
       "2               10.0                3600.0  69625582.0      3697.0   \n",
       "3              188.0                5768.0   5450421.0      4027.0   \n",
       "4               10.0                 254.0  21803000.0       702.0   \n",
       "...              ...                   ...         ...         ...   \n",
       "77059            9.0                3886.0    613894.0         1.0   \n",
       "77060            9.0                 935.0  15946876.0         NaN   \n",
       "77061            9.0                 861.0   7813215.0         NaN   \n",
       "77062            9.0                  25.0  52573973.0         NaN   \n",
       "77063            9.0                  88.0   1883018.0         NaN   \n",
       "\n",
       "       cumulative_tested  \n",
       "0                    NaN  \n",
       "1                    NaN  \n",
       "2              1076332.0  \n",
       "3               397513.0  \n",
       "4                10494.0  \n",
       "...                  ...  \n",
       "77059             3124.0  \n",
       "77060                NaN  \n",
       "77061                NaN  \n",
       "77062                NaN  \n",
       "77063                NaN  \n",
       "\n",
       "[77064 rows x 14 columns]"
      ]
     },
     "execution_count": 531,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_1 = pd.read_excel('infectionrate_vs_population.xlsx')\n",
    "df = df_1.drop(columns=['Unnamed: 0'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 490,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cur_dir = %pwd\n",
    "# data_path = cur_dir + '/data'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 491,
   "metadata": {},
   "outputs": [],
   "source": [
    "# writer = ExcelWriter(data_path + \"/\" + \"infectionrate_vs_population.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 492,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.to_excel(writer, sheet_name='Sheet1')\n",
    "# writer.save()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 493,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 77064 entries, 0 to 77063\n",
      "Data columns (total 14 columns):\n",
      " #   Column                Non-Null Count  Dtype         \n",
      "---  ------                --------------  -----         \n",
      " 0   date                  77064 non-null  datetime64[ns]\n",
      " 1   location_key          76752 non-null  object        \n",
      " 2   country_code          76752 non-null  object        \n",
      " 3   country_name          77064 non-null  object        \n",
      " 4   subregion1_code       0 non-null      float64       \n",
      " 5   subregion1_name       0 non-null      float64       \n",
      " 6   subregion2_code       0 non-null      float64       \n",
      " 7   subregion2_name       0 non-null      float64       \n",
      " 8   aggregation_level     77064 non-null  int64         \n",
      " 9   new_confirmed         72059 non-null  float64       \n",
      " 10  cumulative_confirmed  72061 non-null  float64       \n",
      " 11  population            76440 non-null  float64       \n",
      " 12  new_tested            21569 non-null  float64       \n",
      " 13  cumulative_tested     21836 non-null  float64       \n",
      "dtypes: datetime64[ns](1), float64(9), int64(1), object(3)\n",
      "memory usage: 8.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 494,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sml = df.drop(columns=['subregion1_code', 'subregion2_code', 'subregion2_name','subregion1_name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 495,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(77064, 10)"
      ]
     },
     "execution_count": 495,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sml.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 496,
   "metadata": {},
   "outputs": [],
   "source": [
    "kys = df_sml['location_key'].unique()\n",
    "vals = df_sml['country_name'].unique()\n",
    "# print(kys,vals,type(kys),type(vals))\n",
    "\n",
    "countries = dict(zip(kys, vals))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 497,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of counties:  247\n",
      "Countries for which population data is not available:  ['Namibia' 'Svalbard and Jan Mayen']\n"
     ]
    }
   ],
   "source": [
    "# df_sml['population'].isnull()\n",
    "# Counties with population is not available\n",
    "check = df_sml[df_sml['population'].isna()]\n",
    "print(\"Total number of counties: \",df_sml['country_name'].nunique())\n",
    "print(\"Countries for which population data is not available: \",check['country_name'].unique())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "new_confirmed column has to be numerical and also zero is possible. Hence replacing nan with zero.\n",
    "\n",
    "2 counties has nan values in population. Hence dropping. Could not be found in external source. Namibia, Svalbard and Jan Mayen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 498,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sml_nn = df_sml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 499,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>location_key</th>\n",
       "      <th>country_code</th>\n",
       "      <th>country_name</th>\n",
       "      <th>aggregation_level</th>\n",
       "      <th>new_confirmed</th>\n",
       "      <th>cumulative_confirmed</th>\n",
       "      <th>population</th>\n",
       "      <th>new_tested</th>\n",
       "      <th>cumulative_tested</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-10-14</td>\n",
       "      <td>CM</td>\n",
       "      <td>CM</td>\n",
       "      <td>Cameroon</td>\n",
       "      <td>0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>21441.0</td>\n",
       "      <td>25876380</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-10-26</td>\n",
       "      <td>JO</td>\n",
       "      <td>JO</td>\n",
       "      <td>Jordan</td>\n",
       "      <td>0</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>55055.0</td>\n",
       "      <td>10101694</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-10-05</td>\n",
       "      <td>TH</td>\n",
       "      <td>TH</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>69625582</td>\n",
       "      <td>3697</td>\n",
       "      <td>1076332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-09-15</td>\n",
       "      <td>SK</td>\n",
       "      <td>SK</td>\n",
       "      <td>Slovakia</td>\n",
       "      <td>0</td>\n",
       "      <td>188.0</td>\n",
       "      <td>5768.0</td>\n",
       "      <td>5450421</td>\n",
       "      <td>4027</td>\n",
       "      <td>397513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-04-18</td>\n",
       "      <td>LK</td>\n",
       "      <td>LK</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>21803000</td>\n",
       "      <td>702</td>\n",
       "      <td>10494</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date location_key country_code country_name  aggregation_level  \\\n",
       "0 2020-10-14           CM           CM     Cameroon                  0   \n",
       "1 2020-10-26           JO           JO       Jordan                  0   \n",
       "2 2020-10-05           TH           TH     Thailand                  0   \n",
       "3 2020-09-15           SK           SK     Slovakia                  0   \n",
       "4 2020-04-18           LK           LK    Sri Lanka                  0   \n",
       "\n",
       "   new_confirmed  cumulative_confirmed  population  new_tested  \\\n",
       "0          108.0               21441.0    25876380           0   \n",
       "1         1968.0               55055.0    10101694           0   \n",
       "2           10.0                3600.0    69625582        3697   \n",
       "3          188.0                5768.0     5450421        4027   \n",
       "4           10.0                 254.0    21803000         702   \n",
       "\n",
       "   cumulative_tested  \n",
       "0                  0  \n",
       "1                  0  \n",
       "2            1076332  \n",
       "3             397513  \n",
       "4              10494  "
      ]
     },
     "execution_count": 499,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sml_nn[['new_confirmed']] = df_sml_nn['new_confirmed'].fillna(0)\n",
    "# filling na values in new_tested with zero\n",
    "df_sml_nn['new_tested'] = df_sml_nn['new_tested'].fillna(0)\n",
    "df_sml_nn['cumulative_tested'] = df_sml_nn['cumulative_tested'].fillna(0)\n",
    "df_sml_nn= df_sml_nn[df_sml_nn['population'].notna()]\n",
    "df_sml_nn = df_sml_nn.astype({\"population\":int, \"new_tested\":int, \"cumulative_tested\":int})\n",
    "df_sml_nn.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 500,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 76440 entries, 0 to 77063\n",
      "Data columns (total 10 columns):\n",
      " #   Column                Non-Null Count  Dtype         \n",
      "---  ------                --------------  -----         \n",
      " 0   date                  76440 non-null  datetime64[ns]\n",
      " 1   location_key          76440 non-null  object        \n",
      " 2   country_code          76440 non-null  object        \n",
      " 3   country_name          76440 non-null  object        \n",
      " 4   aggregation_level     76440 non-null  int64         \n",
      " 5   new_confirmed         76440 non-null  float64       \n",
      " 6   cumulative_confirmed  71751 non-null  float64       \n",
      " 7   population            76440 non-null  int64         \n",
      " 8   new_tested            76440 non-null  int64         \n",
      " 9   cumulative_tested     76440 non-null  int64         \n",
      "dtypes: datetime64[ns](1), float64(2), int64(4), object(3)\n",
      "memory usage: 6.4+ MB\n"
     ]
    }
   ],
   "source": [
    "df_sml_nn.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 501,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pivot = df.pivot(index='date', columns=[\"location_key\"],values='new_confirmed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 502,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pivot = df_pivot[:-2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 503,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "countries having all NaN values in new_confirmed column: ['AN', 'AQ', 'BV', 'CC', 'CX', 'GS', 'GZ', 'HM', 'IO', 'NF', 'SJ', 'TF', 'UM']\n"
     ]
    }
   ],
   "source": [
    "df_pivot.shape\n",
    "all_nan_countries = []\n",
    "all_loc_keys = df_pivot.columns\n",
    "for key in all_loc_keys:\n",
    "    if df_pivot[key].isna().sum() == df_pivot.shape[0]:\n",
    "        all_nan_countries.append(key)\n",
    "print(f\"countries having all NaN values in new_confirmed column: {all_nan_countries}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "all new_confirmed values in 'AN', 'AQ', 'BV', 'CC', 'CX', 'GS', 'GZ', 'HM', 'IO', 'NF', 'SJ', 'TF', 'UM' are NaN. Hence dropping these columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 504,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_loc_keys = df_pivot.columns\n",
    "len_keys = len(all_loc_keys)\n",
    "df_clean = df_pivot\n",
    "for key in all_loc_keys:\n",
    "    if df_clean[key].isna().sum() == df_clean.shape[0]:\n",
    "        df_clean = df_clean.drop(columns=key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 505,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cleaned = df_clean.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 506,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pivot_tested = df.pivot(index='date', columns=[\"location_key\"],values='new_tested')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 507,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "countries having all NaN values in new_tested column: ['AD', 'AF', 'AG', 'AI', 'AL', 'AM', 'AN', 'AO', 'AQ', 'AS', 'AW', 'AZ', 'BA', 'BB', 'BF', 'BI', 'BJ', 'BM', 'BN', 'BQ', 'BR', 'BS', 'BT', 'BV', 'BW', 'BZ', 'CC', 'CF', 'CG', 'CK', 'CM', 'CN', 'CV', 'CW', 'CX', 'DE', 'DJ', 'DM', 'DZ', 'EG', 'EH', 'ER', 'ES', 'FK', 'FM', 'FO', 'GA', 'GD', 'GE', 'GF', 'GG', 'GI', 'GL', 'GM', 'GN', 'GQ', 'GS', 'GU', 'GW', 'GY', 'GZ', 'HK', 'HM', 'HN', 'HT', 'IM', 'IO', 'JE', 'KG', 'KH', 'KI', 'KM', 'KN', 'KP', 'KY', 'LA', 'LB', 'LC', 'LR', 'LS', 'MC', 'MD', 'ME', 'MH', 'ML', 'MN', 'MO', 'MP', 'MQ', 'MS', 'MU', 'NC', 'NE', 'NF', 'NI', 'NL', 'NR', 'NU', 'PF', 'PG', 'PN', 'PR', 'PW', 'RE', 'SB', 'SC', 'SD', 'SG', 'SH', 'SJ', 'SL', 'SM', 'SO', 'SR', 'SS', 'ST', 'SX', 'SY', 'SZ', 'TC', 'TD', 'TF', 'TJ', 'TK', 'TL', 'TM', 'TO', 'TT', 'TV', 'TZ', 'UM', 'UZ', 'VA', 'VC', 'VE', 'VG', 'VI', 'VU', 'WF', 'WS', 'XK', 'YE', 'YT'] count:143\n"
     ]
    }
   ],
   "source": [
    "df_pivot_tested.shape\n",
    "all_nan_countries_tested = []\n",
    "all_loc_keys = df_pivot_tested.columns\n",
    "for key in all_loc_keys:\n",
    "    if df_pivot_tested[key].isna().sum() == df_pivot_tested.shape[0]:\n",
    "        all_nan_countries_tested.append(key)\n",
    "print(f\"countries having all NaN values in new_tested column: {all_nan_countries_tested} count:{len(all_nan_countries_tested)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "countries having all NaN values in new_confirmed column: ['AD', 'AF', 'AG', 'AI', 'AL', 'AM', 'AN', 'AO', 'AQ', 'AS', 'AW', 'AZ', 'BA', 'BB', 'BF', 'BI', 'BJ', 'BM', 'BN', 'BQ', 'BR', 'BS', 'BT', 'BV', 'BW', 'BZ', 'CC', 'CF', 'CG', 'CK', 'CM', 'CN', 'CV', 'CW', 'CX', 'DE', 'DJ', 'DM', 'DZ', 'EG', 'EH', 'ER', 'ES', 'FK', 'FM', 'FO', 'GA', 'GD', 'GE', 'GF', 'GG', 'GI', 'GL', 'GM', 'GN', 'GQ', 'GS', 'GU', 'GW', 'GY', 'GZ', 'HK', 'HM', 'HN', 'HT', 'IM', 'IO', 'JE', 'KG', 'KH', 'KI', 'KM', 'KN', 'KP', 'KY', 'LA', 'LB', 'LC', 'LR', 'LS', 'MC', 'MD', 'ME', 'MH', 'ML', 'MN', 'MO', 'MP', 'MQ', 'MS', 'MU', 'NC', 'NE', 'NF', 'NI', 'NL', 'NR', 'NU', 'PF', 'PG', 'PN', 'PR', 'PW', 'RE', 'SB', 'SC', 'SD', 'SG', 'SH', 'SJ', 'SL', 'SM', 'SO', 'SR', 'SS', 'ST', 'SX', 'SY', 'SZ', 'TC', 'TD', 'TF', 'TJ', 'TK', 'TL', 'TM', 'TO', 'TT', 'TV', 'TZ', 'UM', 'UZ', 'VA', 'VC', 'VE', 'VG', 'VI', 'VU', 'WF', 'WS', 'XK', 'YE', 'YT'] count:143"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 508,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_loc_keys_tested = df_pivot_tested.columns\n",
    "len_keys = len(all_loc_keys_tested)\n",
    "df_clean_tested = df_pivot_tested\n",
    "for key in all_loc_keys_tested:\n",
    "    if df_clean_tested[key].isna().sum() == df_clean_tested.shape[0]:\n",
    "        df_clean_tested = df_clean_tested.drop(columns=key)\n",
    "df_cleaned_tested = df_clean_tested.fillna(0)\n",
    "# df_cleaned_tested"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 509,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculating Infection rates for all location keys\n",
    "infection_rate = df_cleaned.sum(axis = 0) / df_cleaned.shape[0]\n",
    "infection_rate = infection_rate.to_frame(name = \"infection rate\")\n",
    "\n",
    "infection_rate_test = pd.DataFrame()\n",
    "infection_rate_test = infection_rate\n",
    "\n",
    "infection_rate_test['location_key'] = infection_rate.index\n",
    "infection_rate_test.reset_index(drop=True, inplace=True)\n",
    "\n",
    "# infection_rate_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 510,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  import sys\n"
     ]
    }
   ],
   "source": [
    "# Infection_rate vs Population. preparation for ploting\n",
    "\n",
    "merged_inner = pd.merge(left=infection_rate_test, right=df_pop, left_on='location_key', right_on='key')\n",
    "\n",
    "new_final = merged_inner[['infection rate','location_key','population']]\n",
    "\n",
    "new_final['country'] = new_final['location_key'].map(countries) \n",
    "\n",
    "new_final = new_final.astype({\"population\":int}) \n",
    "\n",
    "zoomed = new_final.loc[(new_final['population']<400000000) & (new_final['infection rate']<10000)]\n",
    "zoomed = zoomed.sort_values(\"infection rate\", ascending=False)\n",
    "\n",
    "new_final = new_final.rename(columns={\"infection rate\": \"infection_rate\"})\n",
    "new_final['infection_rate'] = round(new_final['infection_rate'], 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 511,
   "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>infection_rate</th>\n",
       "      <th>location_key</th>\n",
       "      <th>population</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>42.10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2494530</td>\n",
       "      <td>Namibia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16.57</td>\n",
       "      <td>AD</td>\n",
       "      <td>77142</td>\n",
       "      <td>Andorra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>447.24</td>\n",
       "      <td>AE</td>\n",
       "      <td>9770529</td>\n",
       "      <td>United Arab Emirates</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>135.45</td>\n",
       "      <td>AF</td>\n",
       "      <td>38041754</td>\n",
       "      <td>Afghanistan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.44</td>\n",
       "      <td>AG</td>\n",
       "      <td>97118</td>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>6.67</td>\n",
       "      <td>YE</td>\n",
       "      <td>29161922</td>\n",
       "      <td>Yemen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>14.68</td>\n",
       "      <td>YT</td>\n",
       "      <td>256518</td>\n",
       "      <td>Mayotte</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>2362.68</td>\n",
       "      <td>ZA</td>\n",
       "      <td>58558270</td>\n",
       "      <td>South Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>54.12</td>\n",
       "      <td>ZM</td>\n",
       "      <td>17861030</td>\n",
       "      <td>Zambia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>27.25</td>\n",
       "      <td>ZW</td>\n",
       "      <td>14645468</td>\n",
       "      <td>Zimbabwe</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>234 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     infection_rate location_key  population               country\n",
       "0             42.10          NaN     2494530               Namibia\n",
       "1             16.57           AD       77142               Andorra\n",
       "2            447.24           AE     9770529  United Arab Emirates\n",
       "3            135.45           AF    38041754           Afghanistan\n",
       "4              0.44           AG       97118   Antigua and Barbuda\n",
       "..              ...          ...         ...                   ...\n",
       "229            6.67           YE    29161922                 Yemen\n",
       "230           14.68           YT      256518               Mayotte\n",
       "231         2362.68           ZA    58558270          South Africa\n",
       "232           54.12           ZM    17861030                Zambia\n",
       "233           27.25           ZW    14645468              Zimbabwe\n",
       "\n",
       "[234 rows x 4 columns]"
      ]
     },
     "execution_count": 511,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 512,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "1it [00:05,  5.54s/it]\n"
     ]
    }
   ],
   "source": [
    "from datalab.context import Context\n",
    "new_final.to_gbq('covid19_open_data.infection_rate', \n",
    "                           'seraphic-being-294617',\n",
    "#                  Context.default().project_id,\n",
    "                         chunksize=10000, \n",
    "                         if_exists='replace'\n",
    "                         )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 513,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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uAICJM3PmzMycOTNJL4xad91186xnPWvM173lllty7LHH5vLLL8/666+fBQsW5Pbbb89NN92U2bNnZ9ttt80mm2yywmsMetyf61e/+lW+//3v56abbhp1/49+9KM88MADueCCC/KHP/wh66yzzrjXsGjRonz6058e9+sCAMBDmZFRAENg1qxZOfLII7Pzzjtnyy23zPe+970kvQBqv/32y7x58/LJT34yH/7whzNjxox873vfy+23356XvvSlecYznpFnPOMZueiii5Ikv/nNb7L33ntnhx12yN/93d+ltfag75s/f34e+9jHZt11102SrLvuupk2bVq++93vZu7cuXnlK1+ZGTNm5O6778773//+POMZz8i2226bN77xjWmt5cwzz3zQcZdddlme+9znZqeddso+++yT2267LUly4oknZuutt8706dNz0EEHPaiWhQsX5jWveU2222677LDDDjn//POTJHvvvXfmz5+/5H6X9e///u/527/92+y9994566yzlvotjzjiiDznOc/J05/+9Fx66aV5yUteki222CLvec97lhz3hS98ITvvvHNmzJiRv/u7v8uiRYuW/Bbvfe9788xnPjMXX3xxZs2alblz5yZJvvWtb2XHHXfM9ttvnz333DNJcskll+RZz3pWdthhhzzrWc/K9ddfv4p/fQAAeGgRRgEMifvvvz+XXHJJ/u3f/i3ve9/7lto3derUvOlNb8oRRxyRK6+8Ms9+9rPz9re/PUcccUQuvfTSfPnLX87rX//6JMn73ve+7L777rniiiuy//775xe/+MWDvmv77bfPxhtvnGnTpuU1r3lNvv71rydJnvvc52bmzJn54he/mCuvvDJrr712Dj/88Fx66aW55pprcvfdd+fss8/Oy172sqWOW3PNNfPWt741Z555Zi677LK89rWvzbvf/e4kyXHHHZcrrrgiV111VT75yU8+qJaPfexjSZKrr746p556ag455JAsXLgwZ511Vv7yL/9yyf0u6/TTT88rXvGKHHzwwTn11FOX2veoRz0qF1xwQd70pjflgAMOyMc+9rFcc801mT17dn7zm9/kRz/6UU4//fRcdNFFufLKK7PGGmvki1/8YpLkD3/4Q7bddtv88Ic/zO67777kmrfffnve8IY35Mtf/nL++7//O1/60peSJFtttVUuuOCCXHHFFXn/+9+ff/zHfxzsDw4AAA9RpukBPARU1UrbX/KSlyRJdtppp8ybN2+l1zz33HOXWsvod7/7XX7/+9/nggsuyFe+8pUkyQtf+MJssMEGDzp3jTXWyLe+9a1ceumlOe+883LEEUfksssuy6xZsx507Pnnn58TTjghf/zjH/Pb3/4222yzTV70ohctdcz111+fa665JnvttVeS3vS2Jz7xiUmS6dOn55WvfGUOPPDAHHjggQ+6/oUXXpi3vvWtSXrBzuabb56f/OQnWW+99ZZ775deemme8IQnZPPNN89mm22W1772tbnzzjuX3Ov++++fJNluu+2yzTbbLKnlKU95Sm6++eZceOGFueyyy/KMZzwjSXL33XdnypQpS36bl770pQ/6zh/84Ad5znOek2nTpiVJNtxwwyTJXXfdlUMOOSQ//elPU1W57777lls3AAAMA2EUwEPA4x//+Nx5551Ltf32t79dEmwkyVprrZWkF4bcf//9K73mAw88kIsvvjhrr732g/YtL/xa9pidd945O++8c/baa6+85jWveVAYtXDhwhx22GGZO3dunvSkJ+WYY47JwoULH3St1lq22WabXHzxxQ/a941vfCMXXHBBzjrrrHzgAx/ItddemzXXXHOpc1fVqaeemh//+MeZOnVqkl4QN3J02OLf8hGPeMSS7cWf77///rTWcsghh+Sf//mfH3TtRz/60VljjTVGvcfRftf/83/+T/bYY4989atfzbx580YN9AAAYJiYpgfwELDuuuvmiU98Ys4777wkvSDqW9/61lLTwFbmsY99bH7/+98v+bz33nvnox/96JLPV155ZZLkOc95zpIpZ+ecc86DQrAkufXWW3P55Zcvde7mm2/+oO9ZHDxttNFGWbBgQc4888xR63na056W22+/fUkYdd999+Xaa6/NAw88kJtvvjl77LFHTjjhhPzP//xPFixYsFQtI+v9yU9+kl/84hd52tOettzf4YEHHsiXvvSlXHXVVZk3b17mzZuXr33taw+aqrcie+65Z84888zMnz8/Se/vsbyF0hfbdddd893vfjc33njjknOS3sioTTfdNElvUXcAABh2wiiAh4jPfe5z+eAHP5gZM2bkec97Xo4++uj85V/+5cDnv+hFL8pXv/rVJQt6n3jiiZk7d26mT5+erbfeesl6TEcffXQuuOCC7Ljjjvmv//qvPPnJT37Qte6777684x3vyFZbbZUZM2bk9NNPz0c+8pEkyaGHHpo3velNmTFjRtZaa6284Q1vyHbbbZcDDzxwybS2ZY9btGhRzjzzzBx55JHZfvvtM2PGjHz/+9/PokWL8qpXvWrJ4uRHHHFEHve4xy1Vy2GHHZZFixZlu+22yyte8YrMnj17qdFMy7rggguy6aabLgmAkl6gdd111y1ZNH1ltt5663zwgx/M3nvvnenTp2evvfZa6blPeMITctJJJ+UlL3lJtt9++7ziFa9Ikrzzne/MUUcdld12223JIugAADDM6s+Z3jBMZs6c2RY/5eihbs6cOaZ3MCb6EGOlDzFW+hDjQT9irPQhxkofYqyGoQ9V1WWttZmj7TMyCgAAAIDOCKMAAAAA6IwwCgAAAIDOCKMAAAAA6IwwCmAyTJmy4s8AAABDShgF0LWqtNtvz25Vmfaub2S3/udUTXZlAAAAE04YBdClKVPSklSSC5O04/fLhf3Prb8fAABgmAmjALo0f352T5YEUvPypyBq9/5+AACAYSaMAujYrUeenWnLtE3rtwMAAAw7YRRAxzY5fr/cuEzbjf12AACAYSeMAujSlClLrRE1NVlqDSlrRgEAAMNOGAXQpfnzl1ojqo48e6k1pKwZBQAADLs1J7sAgIed1lJTpuSixcHTca03IkoQBQAAPAwYGQUwGZYNngRRAADAw4QwCgAAAIDOCKMAAAAA6IwwCgAAAIDOCKMAAAAA6IwwCgAAAIDOCKMAAAAA6IwwCgAAAIDOCKMAAAAA6IwwCgAAAIDOCKMAAAAA6IwwCgAAAIDOCKMAAAAA6IwwCgAAAIDOCKMAAAAA6IwwCgAAAIDOCKMAAAAA6IwwCgAAAIDOCKMAAAAA6IwwCgAAAIDOCKMAAAAA6IwwCgAAAIDOCKMAAAAA6ExnYVRVPbqqLqmq/66qa6vqff32Davq21X10/77BiPOOaqqbqiq66tqnxHtO1XV1f19J1ZV9dvXqqrT++0/rKqpXd0fAAAAACvX5cioe5I8r7W2fZIZSfatql2SvCvJea21LZKc1/+cqto6yUFJtkmyb5KPV9Ua/Wt9Iskbk2zRf+3bb39dkjtba09N8uEkx3dwXwAAAAAMqLMwqvUs6H98ZP/VkhyQ5JR++ylJDuxvH5DktNbaPa21G5PckGTnqnpikvVaaxe31lqSzy1zzuJrnZlkz8WjpgAAAACYfJ2uGVVVa1TVlUnmJ/l2a+2HSTZurd2WJP33Kf3DN01y84jTb+m3bdrfXrZ9qXNaa/cnuSvJ4yfkZgAAAABYZWt2+WWttUVJZlTV45J8taq2XcHho41oaitoX9E5S1+46o3pTfPLxhtvnDlz5qygjIeOBQsWDM29MDn0IcZKH2Ks9CHGg37EWOlDjJU+xFgNex/qNIxarLX2P1U1J721nn5dVU9srd3Wn4I3v3/YLUmeNOK0zZLc2m/fbJT2kefcUlVrJlk/yW9H+f6TkpyUJDNnzmyzZs0apzubXHPmzMmw3AuTQx9irPQhxkofYjzoR4yVPsRY6UOM1bD3oS6fpveE/oioVNXaSZ6f5MdJzkpySP+wQ5J8rb99VpKD+k/Im5beQuWX9Kfy/b6qdumvB/XqZc5ZfK2XJflOf10pAAAAAFYDXY6MemKSU/pPxHtEkjNaa2dX1cVJzqiq1yX5RZKXJ0lr7dqqOiPJdUnuT/KW/jS/JHlzktlJ1k5yTv+VJCcn+XxV3ZDeiKiDOrkzAAAAAAbSWRjVWrsqyQ6jtP8myZ7LOefYJMeO0j43yYPWm2qtLUw/zAIAAABg9dPp0/QAAAAAeHgTRgEAAADQGWEUAAAAAJ0RRgEAAADQGWEUAAAAAJ0RRgEAAADQGWEUAAAAAJ0RRgEAAADQGWEUAAAAAJ0RRgEAAADQGWEUAAAAAJ0RRgEAAADQGWEUAAAAAJ0RRgEAAADQGWEUAAAAAJ0RRgEAAADQGWEUAAAAAJ0RRgEAAADQGWEUAAAAAJ0RRgEAAADQGWEUAAAAAJ0RRgEAAADQGWEUAAAAAJ0RRgEAAADQGWEUAAAAAJ0RRgEAAADQGWEUAAAAAJ0RRgEAAADQGWEUAAAAAJ0RRgEAAADQGWEUAAAAAJ0RRgEAAADQGWEUAAAAAJ0RRgEAAADQGWEUAAAAAJ0RRgEAAADQGWEUAAAAAJ0RRgEAAADQGWEUAAAAAJ0RRgEAAADQGWEUAAAAAJ0RRgEAAADQGWEUAAAAAJ0RRgEAAADQGWEUAAAAAJ0RRgEAAADQGWEUAAAAAJ0RRgEAAADQGWEUAAAAAJ0RRgEAAADQGWEUAAAAAJ0RRgEAAADQGWEUAAAAAJ0RRgEAAADQGWEUAAAAAJ0RRgEAAADQGWEUAAAAAJ0RRgEAAADQGWEUAAAAAJ0RRgEAAADQGWEUAAAAAJ0RRgEAAADQGWEUAAAAAJ0RRgEAAADQGWEUAAAAAJ0RRgEAAADQGWEUAAAAAJ0RRgEAAADQGWEUAAAAAJ0RRgEAAABMkHXXXXeVjp8zZ06OOuqoJMlZZ52V4447biLKmlRrTnYBAAAAADzY/vvvn/3333+yyxh3RkYBAAAATLA5c+Zk1qxZednLXpatttoqr3zlK9NaS5J861vfylZbbZXdd989X/nKV5acM3v27Bx++OFJkq9//et55jOfmR122CHPf/7z8+tf/3pS7mM8CKMAAAAAOnDFFVfk3/7t33Ldddfl5z//eS666KIsXLgwb3jDG/L1r3893/ve9/KrX/1q1HN33333/OAHP8gVV1yRgw46KCeccELH1Y8f0/QAAAAAOrDzzjtns802S5LMmDEj8+bNy7rrrptp06Zliy22SJK86lWvyj//8z8/6Nxbbrklr3jFK3Lbbbfl3nvvzbRp0zqtfTwZGQUAAADQgbXWWmvJ9hprrJH7778/SVJVKz33rW99aw4//PBcffXV+dSnPpWFCxdOWJ0TTRgFAAAAMEm22mqr3HjjjfnZz36WJDn11FNHPe6uu+7KpptumiQ55ZRTOqtvIgijAAAAACbJox/96Jx00kl54QtfmN133z2bb775qMcdc8wxefnLX55nP/vZ2WijjTqucnxZMwoAAABggixYsCBJMmvWrMyaNWtJ+0c/+tEl2/vuu29+/OMfL/k8Z86cJMmhhx6aQw89NElywAEH5IADDpjwertgZBQAAAAAnRFGAQAAANAZYRQAAAAAnRFGAQAAANAZYRQAAADAIKZMWfFnBiKMAgAAAFiZqrTbb89uVZn2rm9kt/7nVE12ZQ85wigAAACAFZkyJS1JJbkwSTt+v1zY/9z6+xmcMAoAAABgRebPz+7JkkBqXv4URO3e38/ghFEAAAAAK3HrkWdn2jJt0/rtrBphFAAAAMBKbHL8frlxmbYb++2sGmEUAAAAwIpMmbLUGlFTk6XWkLJm1KoRRgEAAACsyPz5S60RVUeevdQaUtaMWjVrTnYBAAAAAKu91lJTpuSixcHTca03IkoQtcqMjAIAAAAYxLLBkyDqzyKMAgAAAKAzwigAAAAAOiOMAgAAAKAzwigAAAAAOiOMAgAAAKAzwigAAAAAOiOMAgAAAKAzwigAAAAAOiOMAgAAAKAzwigAAAAAOiOMAgAAAKAzwigAAAAAOiOMAgAAAKAzwigAAAAAOiOMAgAAAKAzwigAAAAAOiOMAgAAAKAzwigAAAAAOiOMAgAAAKAzwigAAAAAOiOMAgAAAKAzwigAAAAAOiOMAgAAAKAzwigAAAAAOiOMAgAAAKAzwigAAAAAOiOMAgAAAKAzwigAAAAAOiOMAgAAAKAzwigAAAAAOiOMAgAAAKAzwigAAAAAOiOMAgAAAKAzwigAAAAAOiOMAgAAAKAzwigAAAAAOiOMAgAAAKAzwigAAAAAOiOMAgAAAKAzwigAAAAAOiOMAgAAAKAzwigAAAAAOiOMAgAAAKAzwigAAAAAOiOMAgAAAKAzwigAAAAAOiOMAgAAAKAzwigAAAAAOiOMAgAAAKAzwigAAAAAOiOMAgAAAKAzwigAAAAAOiOMAgAAAKAzwigAAAAAOiOMAgAAAKAzwigAAAAAOiOMAgAAAKAzwigAAAAAOiOMAgAAAKAznYVRVfWkqjq/qn5UVddW1dv77cdU1S+r6sr+6wUjzjmqqm6oquurap8R7TtV1dX9fSdWVfXb16qq0/vtP6yqqV3dHwAAAAAr1+XIqPuT/O/W2tOT7JLkLVW1dX/fh1trM/qvbyZJf99BSbZJsm+Sj1fVGv3jP5HkjUm26L/27be/LsmdrbWnJvlwkuM7uC8AAAAABtRZGNVau621dnl/+/dJfpRk0xWcckCS01pr97TWbkxyQ5Kdq+qJSdZrrV3cWmtJPpfkwBHnnNLfPjPJnotHTQEAAAAw+aqX53T8pb3pcxck2TbJ/0pyaJLfJZmb3uipO6vqo0l+0Fr7Qv+ck5Ock2RekuNaa8/vtz87yZGttf2q6pok+7bWbunv+1mSZ7bW7ljm+9+Y3siqbLzxxjuddtppE3vDHVmwYEHWXXfdyS6DhzB9iLHShxgrfYjxoB8xVvoQY6UPMVbD0If22GOPy1prM0fbt2bXxVTVukm+nOTvW2u/q6pPJPlAktZ//5ckr00y2oimtoL2rGTfnxpaOynJSUkyc+bMNmvWrFW8i9XTnDlzMiz3wuTQhxgrfYix0ocYD/oRY6UPMVb6EGM17H2o06fpVdUj0wuivtha+0qStNZ+3Vpb1Fp7IMn/S7Jz//BbkjxpxOmbJbm1377ZKO1LnVNVayZZP8lvJ+ZuAAAAAFhVXT5Nr5KcnORHrbV/HdH+xBGHvTjJNf3ts5Ic1H9C3rT0Fiq/pLV2W5LfV9Uu/Wu+OsnXRpxzSH/7ZUm+0yZjHiIAAAAAo+pymt5uSf42ydVVdWW/7R+THFxVM9KbTjcvyd8lSWvt2qo6I8l16T2J7y2ttUX9896cZHaStdNbR+qcfvvJST5fVTekNyLqoAm9IwAAAABWSWdhVGvtwoy+ptM3V3DOsUmOHaV9bnqLny/bvjDJy8dQJgAAAAATqNM1owAAAAB4eBNGAQAAANAZYRQAAAAAnRFGAQAAANAZYRQAAAAAnRFGAQAAANAZYRQAAAAAnRFGAQAAANAZYRQAAAAAnRFGAQAAANAZYRQAAAAAnRFGAQAAANAZYRQAAAAAnRFGAQAAANAZYRQAAAAAnRFGAQAAANAZYRQAAAAAnRFGAQAAANAZYRQAAAAAnRFGAQAAANAZYRQAAAAAnRFGAQAAANAZYRQAAAAAnRFGAQAAANAZYRQAAAAAnRFGAQAAANAZYRQAAAAAnRFGAQAAANAZYRQAAAAAnRFGAQAAANAZYRQAAAAAnRFGAQAAANAZYRQAAAAAnRFGAQAAANAZYRQAAAAAnRFGAQAAANAZYRQAAAAAnRFGAQAAANAZYRQAAAAAnRFGAQAAANAZYRQAAAAAnRFGAQAAANAZYRQAAAAAnRFGAQAAANAZYRQAAAAAnRFGAQAAANAZYRQAAAAAnRFGAQAAANAZYRQAAAAAnRFGAQAAANAZYRQAAAAAnRFGAQAAANAZYRQAAAAAnRFGAQAAANAZYRQAAAAAnRFGAQAAANAZYRQAAAAAnRFGAQAAANAZYRQAAAAAnRFGAQAAANAZYRQAAAAAnRFGAQAAANAZYRQAAAAAnRFGAQAAANAZYRQAAAAAnRFGAQAAANAZYRQAAAAAnRFGAQAAANAZYRQAAAAAnRFGAQAAANAZYRQAAAAAnRFGAQAAANAZYRQAAAAAnRFGAQAAANAZYRQAAAAAnRFGAQAAANAZYRQAAAAAnRFGAQAAANAZYRQAAAAAnRFGAQAAANAZYRQAAAAAnRFGAQAAANAZYRQAAAAAnRFGAQAAANAZYRQAAAAAnRFGAQAAANAZYRQAAAAAnRFGAQAAANAZYRQAAAAAnRFGAQAAANAZYRQAAAAAnRFGAQAAANAZYRQAAAAAnRFGAQAAANAZYRQAAAAAnRFGAQAAANAZYRQAAAAAnRFGAQAAANAZYRQAAAAAnRFGAQAAANAZYRQAAAAAnRFGAQAAANAZYRQAAAAAnRFGAQAAANAZYRQAAAAAnRFGAQAAANAZYRQAAAAAnRFGAQAAANAZYRQAAAAAnRFGAQAAANAZYRQAAAAAnRFGAQAAANAZYRQAAAAAnRFGAQAAANAZYRQAAAAAnRFGAQAAANAZYRQAAAAAnRFGAQAAANAZYRQAAAAAnRFGAQAAANAZYRQAAAAAnRFGAQAAANAZYRQAAAAAnRFGAQAAANAZYRQAAAAAnRFGAQAAANAZYRQAAAAAnRFGAQAAANAZYRQAAAAAnRFGAQAAANAZYRQAAAAAnRFGAQAAANAZYRQAAAAAnRFGAQAAANAZYRQAAAAAnRFGAQAAANAZYRQAAAAAnRFGAQAAANAZYRQAAAAAnRFGAQAAANAZYRQAAAAAnRFGAQAAANAZYRQAAAAAnRFGAQAAANAZYRQAAAAAnRFGAQAAANAZYRQAAAAAnRFGAQAAANAZYRQAAAAAnRFGAQAAANCZzsKoqnpSVZ1fVT+qqmur6u399g2r6ttV9dP++wYjzjmqqm6oquurap8R7TtV1dX9fSdWVfXb16qq0/vtP6yqqV3dHwAAAAAr1+XIqPuT/O/W2tOT7JLkLVW1dZJ3JTmvtbZFkvP6n9Pfd1CSbZLsm+TjVbVG/1qfSPLGJFv0X/v221+X5M7W2lOTfDjJ8V3cGAAAAACD6SyMaq3d1lq7vL/9+yQ/SrJpkgOSnNI/7JQkB/a3D0hyWmvtntbajUluSLJzVT0xyXqttYtbay3J55Y5Z/G1zkyy5+JRUwAAAABMvklZM6o/fW6HJD9MsnFr7bakF1glmdI/bNMkN4847ZZ+26b97WXblzqntXZ/kruSPH5CbgIAAACAVbZm119YVesm+XKSv2+t/W4FA5dG29FW0L6ic5at4Y3pTfPLxhtvnDlz5qyk6oeGBQsWDM29MDn0IcZKH2Ks9CHGg37EWOlDjJU+xFgNex/qNIyqqkemF0R9sbX2lX7zr6vqia212/pT8Ob3229J8qQRp2+W5NZ++2ajtI8855aqWjPJ+kl+u2wdrbWTkpyUJDNnzmyzZs0ah7ubfHPmzMmw3AuTQx9irPQhxkofYjzoR4yVPsRY6UOM1bD3oS6fpldJTk7yo9bav47YdVaSQ/rbhyT52oj2g/pPyJuW3kLll/Sn8v2+qnbpX/PVy5yz+FovS/Kd/rpSAAAAAKwGuhwZtVuSv01ydVVd2W/7xyTHJTmjql6X5BdJXp4krbVrq+qMJNel9yS+t7TWFvXPe3OS2UnWTnJO/5X0wq7PV9UN6Y2IOmiC7wkAAACAVdBZGNVauzCjr+mUJHsu55xjkxw7SvvcJNuO0r4w/TALAAAAgNXPpDxNDwAAAICHJ2EUAAAAAJ0RRgEAAADQGWEUAAAAAJ0RRgEAAADQGWEUAAAAAJ0RRgEAAADQGWEUAAAAAJ0RRgEAAADQGWEUAAAAAJ0RRgEAAADQGWEUAAAAAJ0RRgEAAADQGWEUAAAAAJ0RRgEAAADQGWEUAAAAAJ0RRgEAAADQGWEUAAAAAJ0RRgEAAADQGWEUAAAAAJ0RRgEAAADQGWEUAAAAAJ0RRgEAAADQGWEUAAAAAJ1Zc5CDquopSZ6bZGqStZPcnuTyJBe11hZOWHUAAAAADJUVhlFV9cokb08yM8mvk9ya5O4kGyb5YJKFVfXFJMe31m6a4FoBAAAAeIhbbhhVVVckuT/J7CQvba3dvMz+tZLsmuSgJHOr6rDW2pcmsFYAAAAAHuJWNDLqPa21byxvZ2vtniRzksypqvckmTbOtQEAAAAwZJYbRq0oiBrl2DuS3DEuFQEAAAAwtAZ6ml5VXVlVh1fVBhNdEAAAAADDa6AwKsk3krwzya1VdWpV7TmBNQEAAAAwpAYKo1pr706yeZKXJFkjyTeqal5VvbeqnjyRBQIAAAAwPAYdGZXWc05r7a+TbJLkU0n+McnPq+o/q2rfiSoSAAAAgOEwcBi1WFXtkuS4JO9KcmuS9yX5WZIzq+rfxrU6AAAAAIbKcp+mN1JVTUny6iSvSfKXSc5K8rLW2rdHHHNmv/3vx79MAAAAAIbBQGFUkluS3JDk5CSntNbuGOWYuUkuHa/CAAAAABg+g4ZRe7bWvreiA1prv0uyx9hLAgAAAGBYDfo0vRUGUQAAAAAwiEFHRqWqXpPk4CRPTvKokftaa08Z57oAAAAAGEIDjYyqqn9I8i9JLksyNcl/JLkmyYZJPjNBtQEAAAAwZAYKo5K8IckbW2tHJbkvyUdba/unF1BtPlHFAQ9da6yxRmbMmJHtt98+O+64Y77//e+Py3Vf//rX57rrrkuSTJ06NXfcMdrzFAAAAFhdDTpNb7Mkl/S3706yXn/71H77G8a5LuAhbu21186VV16ZJPnP//zPHHXUUfnud7+71DGLFi3KGmussUrX/fSnPz1eJQIAADAJBh0Z9askG/W3b0qya3/7qUnaeBcFDJff/e532WCDDZIkc+bMyR577JG/+Zu/yXbbbZckOfDAA7PTTjtlm222yUknnZQkOeusszJjxozMmDEjT3va0zJt2rQkyaxZszJ37tzJuREAAADGbNCRUd9Jsn+Sy5OcnOTDVfXXSXZMcsYE1QY8hN19992ZMWNGFi5cmNtuuy3f+c53luy75JJLcs011ywJmD7zmc9kww03zN13351nPOMZeelLX5r9998/+++/f5Lkr//6r/Pc5z53Uu4DAACA8TVoGPXG9EdRtdY+WVV3JtktyZeTfGqCagMewkZO07v44ovz6le/Otdcc02SZOedd14SRCXJiSeemK9+9atJkptvvjk//elP8/jHPz5JcsIJJ2TttdfOW97ylm5vAAAAgAkxUBjVWnsgyQMjPp+e5PSJKgoYLrvuumvuuOOO3H777UmSddZZZ8m+OXPm5Nxzz83FF1+cxzzmMZk1a1YWLlyYJDnvvPPypS99KRdccMGk1A0AAMD4W24YVVXPGfQirTX/UwSW68c//nEWLVq0ZLTTSHfddVc22GCDPOYxj8mPf/zj/OAHP0iS3HTTTTnssMPyrW99K2uvvXbXJQMAADBBVjQyak56i5NX//PihcqX/Zwkq/Y4LGDoLV4zKklaaznllFNGfXLevvvum09+8pOZPn16nva0p2WXXXZJksyePTu/+c1v8uIXvzhJsskmm+Sb3/xmZ/UDAAAwMVYURj1hxPYzk3woybFJLu637ZrkH5O8c2JKAx7KFi1aNGr7rFmzMmvWrCWf11prrZxzzjmjHnf00Uc/qH3OnDlLtufNmzfWMgEAAOjYcsOo1tpvFm9X1QeSvL219u0Rh/y8quYnOSHJNyauRAAAAACGxSMGPG7rJLeM0v7LJFuNXzkAAAAADLNBw6hrkxxdVUtWEe5vv7e/DwAAAABWakVrRo305iRnJ/llVV3Vb9suyaIkL5yIwgAAAAAYPgOFUa21S6tqWpJXpTctr5J8Mcm/t9b+MIH1AauDKVOS+fOX/xkAAAAGtMIwqqremuRrrbVftNb+mOSkbsoCVhtVaUl2r8qtR56dTY7fLxcmqaqktcmuDgAAgIeYla0Z9YIkP6mqK6rqmKravouigNXElClp6Q2FvDBJWxxEJWn9/QAAALAqVhhGtdb+KslGSf4pyRZJzq+qm6rqxKp6XlWt0UWRwCSZPz+7J0sCqXn5UxC1e38/AAAArIqVPk2vtbagtfal1tork0xJ8rr0/j/62SS3V9UXquplVbXOBNcKTIJbjzw705Zpm9ZvBwAAgFW10jBqpNba/a21c1trb22tbZ7k+Ul+nuQ9Sf7XRBQITK5Njt8vNy7TdmO/HQAAAFbVQE/TW57W2uVJLk/y3qp65PiUBKw2pkxZao2oaekFUYvXkPJUPQAAAFbVwGFUVb0iyZ7pTdUbOaKqtdYOaK3dN97FAZNs/vzU4qfpJakjz87uIxYxF0QBAACwqgYKo6rq/yb5+yTnJ7k1/QdpAQ8DraWmTMlFi4On45oRUQAAAPzZBh0Z9eokB7fWzpzIYoDV1LLBkyAKAACAP9OgC5g/IsmVE1gHAAAAAA8Dg4ZRJyV51UQWAgAAAMDwG3Sa3uOS/E1V7ZXkqiRLLVbeWnvbONcFAAAAwBAaNIzaOn+aprfVMvssZg4AAADAQAYKo1pre0x0IQAAAAAMv0FHRiVJqurRSZ6a3mion7XWFk5IVQAAAAAMpYEWMK+qR1bV/01yZ5L/TnJ1kjur6oSqeuREFggAAADA8Bh0ZNTxSQ5O8qYkF/bbnp3kn9MLtN4x/qUBAAAAMGwGDaP+JslrW2vfHNH2s6q6PcmnI4wCAAAAYAADTdNLsn6Sn43S/rMkjxu3agAAAAAYaoOGUf+d5G2jtL89yZXjVg0AAAAAQ23QaXrvTPLNqtorycXpPU1v1ySbJPmrCaoNAAAAgCEz0Mio1toFSbZM8qUk6yZZr7/9tNbahSs6FwAAAAAWG3RkVFprtyZ59wTWAgAAAMCQW24YVVU7JrmytfZAf3u5WmuXj3tlAAAAAAydFY2MmpvkL5LM72+3JDXKcS3JGuNfGgAAAADDZkVh1LQkt4/YBgAAAIAxWW4Y1Vq7aeTHJDe31tqyx1XVkyeiMAAAAACGz0BP00tyY5InLNtYVY/v7wMAAACAlRo0jKr0Rkcta90kC8evHAAAAACG2YrWjEpVndjfbEn+uar+OGL3Gkl2TnLlxJQGAAAAwLBZYRiVZLv+eyV5epJ7R+y7N8nlST40AXUBAAAAMIRWGEa11vZIkqr6bJK3t9Z+10lVAAAAAAylQdeM+sck6y3bWFWbVdXG41sSAAAAAMNq0DDqc0n+apT2fZJ8fvzKAQAAAGCYDRpGPSPJBaO0fy/JzPErBwAAAIBhNmgYtWaStUZpf/Ry2gEAAADgQQYNo36Y5M2jtL8lyaXjVw4AAAAAw2yFT9Mb4d1JvlNV2yc5r9/2vCQ7JHn+RBQGAAAAwPAZaGRUa+0HSXZNcmOSlyR5aX9719ba9yeuPAAAAACGyaAjo9Ja++8kr5zAWgAAAAAYcoOuGZWq2riq3lFVH6+qjfptu1XVtIkrDwAAAIBhMlAYVVU7Jbk+vZFRr0+yXn/XXkmOnZjSAAAAABg2g46M+lCSj7TWdkhyz4j2/0yy27hXBQAAAMBQGjSM2inJKaO035Zk4/ErBwAAAIBhNmgYdXeSDUZp3yrJ/PErBwAAAIBhNmgY9bUkR1fVWv3PraqmJjk+yZcnojAAAAAAhs+gYdQ7kmyY5PYkj0lyYZIbkvxPkvdMSGUAAAAADJ01Bzmotfa7JLtX1fOS7JheiHV5a+3ciSwOAAAAgOGy3DCqqhYleWJrbX5VfSbJ21tr30nync6qAwAAAGCorGia3t1J1u1vH5Lk0RNfDgAAAADDbEXT9L6f5D+q6rIkleTEqrp7tANba6+diOIAAAAAGC4rCqP+Nr2Fy5+apCV5fJJ7uigKAAAAgOG03DCqtfbrJP+QJFV1Y5KDW2u/6aowAAAAAIbPoE/TmzbRhQAAAAAw/AYKo5Kkqp6ZZM8kU7LMwuettbeNc10AAAAADKGBwqiqekeSE5LckOTW9NaQWqyNehIAAAAALGPQkVFvT/K21tpHJ7IYAAAAAIbbI1Z+SJJkvSTfnMhCAAAAABh+g4ZRpybZdyILAQAAAGD4DTpN7+Yk76uq3ZJcleS+kTtba/863oUBAAAAMHwGDaNen2RBkmf1XyO1JMIoAAAAAFZqoDCqtTZtogsBAAAAYPgNumYUAAAAAIzZckdGVdWJSY5qrf2hv71crbW3jXtlAAAAAAydFU3T2y7JI0dsL08bv3IAAAAAGGbLDaNaa3uMtg0AAAAAfy5rRgEAAADQGWEUAAAAAJ0RRgEAAADQGWEUAAAAAJ0RRgEAAADQmeU+TW+kqnrycna1JAtba7ePX0kAAAAADKuBwqgk89ILnkZVVb9L8tkk72yt3T8OdQEAAAAwhAYNow5OckKSTyb5Yb/tmUnemOSYJI9L8p4kv09y9LhWCAAAAMDQGDSMenOSI1prXxnR9p2quj7J21trz62q+UneF2EUAAAAAMsx6ALmz0xy9Sjt1yR5Rn/74iSbjUdRAAAAAAynQcOom9KbkresNyT5RX/7CUl+Ox5FAQAAADCcBp2m97+TfLmqXpDk0vQWM39Gkr9M8tL+Mc9Icsa4VwgAAADA0BgojGqtfaOqtkxv7ainJakkZyX5ZGvtF/1jPj5hVQIAAAAwFAYKo6pqjX7odNQE1wMAAADAEBt0zahfVdWJVbXzhFYDAAAAwFAbNIx6d5LpSS6uqp9U1Xur6i8nsC4AAAAAhtBAYVRr7aTW2qwkmyc5Ob1Fy39aVRdX1WETWB8AAAAAQ2TQkVFJktbaLa2141tr2yeZmeTRSf6/CakMAAAAgKEz0ALmI1XV7klemeTlSR6V5AvjXRQAAAAAw2nQp+ltk14AdXCSTZOcm+RtSb7aWrt74soDAAAAYJgMOjLq6iSXJvlwklNba7dPXEkAAAAADKtBw6intdZ+OqGVAAAAADD0Bn2aniAKAAAAgDEbKIyqqkdV1fuq6idVtbCqFo18TXSRAAAAAAyHgcKoJB9IckiSf0nyQJJ/SPKxJL9JctjElAYAAADAsBk0jPrrJG9qrX0qyaIkX2utvS3J0Un2mqjiAAAAABgug4ZRGye5rr+9IMnj+tvfSrL3ONcEAAAAwJAaNIz6RZJN+ts3JNmnv71rkrvHuygAAAAAhtOgYdRXk+zZ3/5IkvdV1Y1JZif59ATUBQAAAMAQWnOQg1prR43YPrOqbk6yW5KftNbOnqjiAAAAABguA4VRy2qt/TDJD8e5FgAAAACG3HKn6VXV7oNepKrWrartVnLMZ6pqflVdM6LtmKr6ZVVd2X+9YMS+o6rqhqq6vqr2GdG+U1Vd3d93YlVVv32tqjq93/7Dqpo6aP0AAAAAdGNFa0adXFXnVdXBVbXeaAdU1fSqOiG9Rc23X8l3zU6y7yjtH26tzei/vtm/7tZJDkqyTf+cj1fVGv3jP5HkjUm26L8WX/N1Se5srT01yYeTHL+SegAAAADo2IrCqG2SfCXJe5P8tqp+XFXnV9U5VfWDqrozval6myZ5XmvtCyv6otbaBUl+O2BdByQ5rbV2T2vtxvTCrp2r6olJ1mutXdxaa0k+l+TAEeec0t8+M8mei0dNAQAAALB6qF6ms5KDqmYm2T3J5knWTnJHkiuSnN9aGzRgSn/q3NmttW37n49JcmiS3yWZm+R/t9burKqPJvnB4oCrqk5Ock6SeUmOa609v9/+7CRHttb260//27e1dkt/38+SPLO1dscodbwxvdFV2XjjjXc67bTTBr2F1dqCBQuy7rrrTnYZPITpQ4yVPsRY6UOMB/2IsdKHGCt9iLEahj60xx57XNZamznavkGfpjc3vbBovH0iyQeStP77vyR5bZLRRjS1FbRnJfuWbmztpCQnJcnMmTPbrFmzVqno1dWcOXMyLPfC5NCHGCt9iLHShxgP+hFjpQ8xVvoQYzXsfWhF0/QmXGvt1621Ra21B5L8vyQ793fdkuRJIw7dLMmt/fbNRmlf6pyqWjPJ+hl8WiAAAAAAHZjUMKq/BtRiL06y+El7ZyU5qP+EvGnpLVR+SWvttiS/r6pd+utBvTrJ10acc0h/+2VJvtMGmYMIAAAAQGcGmqY3Hqrq1CSzkmxUVbckOTrJrKqakd50unlJ/i5JWmvXVtUZSa5Lcn+St7TWFvUv9eb0nsy3dnrrSJ3Tbz85yeer6ob0RkQdNOE3BQAAAMAq6SyMaq0dPErzySs4/tgkx47SPjfJtqO0L0zy8rHUCAAAAMDEmtRpegAAAAA8vAwcRlXVX1XV2VV1XVUtXij89VW158SVBwAAAMAwGSiMqqpXJjkjyU+TTEvyyP6uNZK8c2JKAwAAAGDYDDoy6p1J3tBaOyK9BcUX+0GSGeNdFAAAAADDadAwaoskF4/SviDJeuNXDgAAAADDbNAw6tYkW47S/pwkPxu/cgAAAAAYZoOGUSclObGqdut/flJVHZLkhCSfmJDKAAAAABg6aw5yUGvthKpaP8m3kzw6yflJ7knyodbaxyawPgAAAACGyEBhVJK01t5dVccm2Tq9EVXXtdYWTFhlAAAAAAydgabpVdVnquqxrbU/ttbmttYuaa0tqKp1quozE10kAAAAAMNh0DWjDkmy9ijtayd59fiVAwAAAMAwW+E0varaMEn1XxtU1f0jdq+R5IVJfj1x5QEAAAAwTFa2ZtQdSVr/dd0o+1uSo8e7KAAAAACG08rCqD3SGxX1nSQvTfLbEfvuTXJTa+3WCaoNAAAAgCGzwjCqtfbdJKmqaUlubq090ElVAAAAAAyllY2MSpK01m5KkqraJMmTkzxqmf0XjH9pAAAAAAybgcKofgj170mek946UdV/X2yN8S8NAAAAgGHziAGP+7cki5JsneSPSZ6d5OVJfpRk3wmpDAAAAIChM9DIqCTPTfLC1tqPq6olub21dlFV3ZPkA0m+PWEVAgAAADA0Bh0ZtXaSO/rbv00ypb99XZLp410UAAAAAMNp0DDqx0m26m9fmeRNVbV5krck+eUE1AUAAADAEBp0mt5HkvxFf/v9Sb6V5OAk9yQ5ZALqAgAAAGAIDRRGtda+OGL78qqamt5IqV+01u5Y7okAAAAAMMJKp+lV1SOr6ldVtc3ittbaH1trlwuiAAAAAFgVKw2jWmv3JbkvSZv4cgAAAAAYZoMuYP7/JTmqqgZdYwoAAAAAHmTQcOnZSZ6b5JdVdU2SP4zc2Vrbf7wLAwAAAGD4DBpG3ZHkyxNZCAAAAADDb9Cn6b1mkOOqarckc1tr94ypKgAAAACG0qBrRg3qnCSbjvM1AQAAABgS4x1G1ThfDwAAAIAhMt5hFAAAAAAslzAKAAAAgM4IowAAAADozHiHUW2crwcAAADAELGAOQAAAACdWXM8L9Zae+x4Xg8AAACA4TJQGFVVGyY5NsmeSaZkmRFVrbX1xr80AAAAAIbNoCOjTk6yQ5KTktwaa0MBAAAA8GcYNIzaM8lerbUfTmQxAAAAAAy3QRcwn59kwUQWAgAAAMDwGzSMeneS91fVuhNZDAAAAADDbdBpeu9JMjXJ/Kq6Kcl9I3e21qaPc10AAAAADKFBw6gzJ7QKAAAAAB4WBgqjWmvvm+hCAAAAABh+g46MSpJU1fOSbJ2kJbm2tTZnIooCAAAAYDgNFEZV1aZJvppkpyS39ps3qaq5SV7cWrt1uScDAAAAQN+gT9M7McmiJE9trT2ptfakJFv0206cqOIAAAAAGC6DTtPbK8ms1tqNixtaaz+vqrclOW9CKgMAAABg6Aw6Mmp5HhiXKgAAAAB4WBg0jDovyYlV9aTFDVX15CQfiZFRAAAAAAxo0DDqbUkek+TnVXVTVc1L8rN+29smqDYAAAAAhsxAa0a11m5OsmNV7ZVkqySV5LrW2rkTWRwAAAAAw2XQBcyTJK21byf59gTVAgAAAMCQW24YVVX/K8nHW2sL+9vL1Vr713GvDAAAAIChs6KRUW9NckqShf3t5WlJhFEAAAAArNRyw6jW2rTRtgEAAADgzzXQ0/Sq6tVVtdYo7Y+qqlePf1kAAAAADKOBwqgkn02y/ijtj+3vAwAAAICVGjSMqvTWhlrWk5PcNX7lAAAAADDMVrSAearq6vRCqJbku1V1/4jdayTZPMk3J648AAAAAIbJCsOoJGf237dN8o0kC0bsuzfJvCRfHv+yAAAAABhGKwyjWmvvS5KqmpfktNbaPV0UBQAAAMBwGnTNqHlJdlm2saqeW1XPGdeKAAAAABhag4ZRH06ywSjt6/X3AQAAAMBKDRpGPS3Jf4/SfnV/HwAAAACs1KBh1N1JNhmlfbP0FjIHAAAAgJUaNIz6zyTHVdWSqXpVtWGSf+rvAwAAAICVWuHT9EZ4R5ILksyrqqv6bdOTzE9y0EQUBgAAAMDwGSiMaq3dVlXbJ3llkhlJKskpSf69tfbHiSsPAAAAgGEy6Mio9EOn/zeBtQAAAAAw5AZdMypV9VdVdXZVXVdVT+q3vb6q9py48gAAAAAYJgOFUVX1yiRnJPlpkmlJHtnftUaSd05MaQAAAAAMm0FHRr0zyRtaa0ckuX9E+w/SW0MKAAAAAFZq0DBqiyQXj9K+IMl641cOAAAAAMNs0DDq1iRbjtL+nCQ/G79yAAAAABhmg4ZRJyU5sap2639+UlUdkuSEJJ+YkMoAAAAAGDprDnJQa+2Eqlo/ybeTPDrJ+UnuSfKh1trHJrA+AAAAAIbIckdGVdVzqmpJWNVae3eSjZLsnGSXJE9orf2fiS8RAAAAgGGxopFR5yd5YpL5VfXzJM9orf0mydxOKgMAAABg6Kxozag7k0zrb09dybEAAAAAsFIrGhn15STfrarbkrQkc6tq0WgHttaeMhHFAQAAADBcVhRGvSnJWUm2SPKvST6b5PddFAUAAADAcFpuGNVaa0m+kSRVtX2Sf2mtCaMAAAAA+LOtaGTUEq2110x0IQAAAAAMv4HCqKp6dJK3J9kzyZQss5h5a236+JcGAAAAwLAZKIxK8vEkL07ypSTfT29BcwAAAABYJYOGUQcmeXlr7dwJrAUAAACAIfeIlR+SJPljkpsnshAAAAAAht+gYdQJSf5XVQ16PAAAAAA8yKDT9PZK8uwk+1bVdUnuG7mztbb/eBcGAAAAwPAZNIy6I8lXJ7IQAAAAAIbfQGFUa+01E10IAAAAAMPPGlAAAAAAdGaFI6Oq6qxBLmLNKAAAAAAGsbJper/ppAoAAAAAHhZWGEZZKwoAAACA8WTNKAAAAAA6I4wCAAAAoDPCKAAAAAA6I4wCAAAAoDPCKAAAAAA6I4wCAAAAoDPCKAAAAAA6I4wCAAAAoDPCKAAAAAA6I4wCAAAAoDPCKAAAAAA6I4wCAAAAoDPCKAAAAAA6I4wCAAAAoDPCKAAAAAA6I4wCAAAAoDPCKAAAAAA6I4wCAAAAoDPCKAAAAAA6I4wCAAAAoDPCKAAAAAA6I4wCAAAAoDPCKAAAAAA6I4wCAAAAoDPCKAAAAAA6I4wCAAAAoDPCKAAAAAA6I4wCAAAAoDPCKAAAAAA6I4wCAAAAoDPCKAAAAAA6I4wCAAAAoDPCKAAAAAA6I4wCAAAAoDPCKAAAAAA6I4wCAAAAoDPCKAAAAAA6I4wCAAAAoDPCKAAAAAA6I4wCAAAAoDPCKAAAAAA6I4wCAAAAoDPCKAAAAAA6I4wCAAAAoDPCKAAAAAA6I4wCAAAAoDPCKAAAAAA6I4wCAAAAoDPCKAAAAAA6I4wCAAAAoDPCKAAAAAA6I4wCAAAAoDPCKAAAAAA601kYVVWfqar5VXXNiLYNq+rbVfXT/vsGI/YdVVU3VNX1VbXPiPadqurq/r4Tq6r67WtV1en99h9W1dSu7g0AAACAwXQ5Mmp2kn2XaXtXkvNaa1skOa//OVW1dZKDkmzTP+fjVbVG/5xPJHljki36r8XXfF2SO1trT03y4STHT9idAAAAAPBn6SyMaq1dkOS3yzQfkOSU/vYpSQ4c0X5aa+2e1tqNSW5IsnNVPTHJeq21i1trLcnnljln8bXOTLLn4lFTAAAAAKweJnvNqI1ba7clSf99Sr990yQ3jzjuln7bpv3tZduXOqe1dn+Su5I8fsIqBwAAAGCVrTnZBSzHaCOa2graV3TOgy9e9cb0pvpl4403zpw5c/6MElc/CxYsGJp7YXLoQ4yVPsRY6UOMB/2IsdKHGCt9iLEa9j402WHUr6vqia212/pT8Ob3229J8qQRx22W5NZ++2ajtI8855aqWjPJ+nnwtMAkSWvtpCQnJcnMmTPbrFmzxuduJtmcOXMyLPfC5NCHGCt9iLHShxgP+hFjpQ8xVvoQYzXsfWiyp+mdleSQ/vYhSb42ov2g/hPypqW3UPkl/al8v6+qXfrrQb16mXMWX+tlSb7TX1cKAAAAgNVEZyOjqurUJLOSbFRVtyQ5OslxSc6oqtcl+UWSlydJa+3aqjojyXVJ7k/yltbaov6l3pzek/nWTnJO/5UkJyf5fFXdkN6IqIM6uC0AAAAAVkFnYVRr7eDl7NpzOccfm+TYUdrnJtl2lPaF6YdZAAAAAKyeJnuaHgAAAAAPI8IoAAAAADojjAIAAACgM8IoAAAAADojjAIAAACgM8IoAAAAADojjAIAAACgM8IoAAAAADojjAIAAACgM8IoAAAAADojjAIAAACgM8IoAAAAADojjAIAAACgM8IoAAAAADojjAIAAACgM8IoAAAAADojjAIAAACgM8IoAAAAADojjAIAAACgM8IoAAAAADojjAIAAACgM8IoAAAAADojjAIAAACgM8IoAAAAADojjAIAAACgM8IoAAAAADojjAIAAACgM8IoAAAAADojjAIAAACgM8IoAAAAADojjAIAAACgM8IoAAAAADojjAIAAACgM8IoAAAAADojjAIAAACgM8IoAAAAADojjAIAAACgM8IoAAAAADojjAIAAACgM8IoAAAAADojjAIAAACgM8IoAAAAADojjAIAAACgM8IoAAAAADojjAIAAACgM8IoAAAAADojjAIAAACgM8IoAAAAADojjAIAAACgM8IoAAAAADojjAIAAACgM8IoAAAAADojjAIAAACgM8IoAAAAADojjAIAAACgM8IoAAAAADojjAIAAACgM8IoAAAAADojjAIAAACgM8IoAAAAADojjAIAAACgM8IoAAAAADojjAIAAACgM8IoAAAAADojjAIAAACgM8IoAAAAADojjAIAAACgM8IoAAAAADojjAIAAACgM8IoAAAAADojjAIAAACgM8IoAAAAADojjAIAAACgM8IoAAAAADojjAIAAACgM8IoAAAAADojjAIAAACgM8IoAAAAADojjAIAAACgM8IoAAAAADojjAIAAACgM8IoAAAAADojjAIAAACgM8IoAAAAADojjAIAAACgM8IoAAAAADojjAIAAACgM8IoAAAAADojjAIAAACgM8IoAAAAADojjAIAAACgM8IoAAAAADojjAIAAACgM8IoAAAAADojjAIAAACgM8IoAAAAADojjAIAAACgM8IoAAAAADojjAIAAACgM8IoAAAAADojjAIAAACgM8IoAAAAADojjAIAAACgM8IoAAAAADojjAIAAACgM8IoAAAAADojjAIAAACgM8IoAAAAADojjAIAAACgM8IoAAAAADojjAIAAACgM8IoAAAAADojjAIAAACgM8IoAAAAADojjAIAAACgM8IoAAAAADojjAIAAACgM8IoAAAAADojjAIAAACgM8IoAAAAADojjAIAAACgM8IoAAAAADojjILV1BprrJEZM2Zk2223zYte9KL8z//8z7hd+/Wvf32uu+66cbseAAAADEoYBauptddeO1deeWWuueaabLjhhvnYxz42btf+9Kc/na233nrcrgcAAACDEkbBQ8Cuu+6aX/7yl0mSWbNmZe7cuUmSO+64I1OnTk2SXHvttdl5550zY8aMTJ8+PT/96U/zhz/8IS984Quz/fbbZ9ttt83pp5/+oGu8+c1vzsyZM7PNNtvks5/9bPc3BwAAwMPKmpNdALBiixYtynnnnZfXve51Kzzuk5/8ZN7+9rfnla98Ze69994sWrQo3/zmN7PJJpvkG9/4RpLkrrvuetB5xx57bDbccMMsWrQoO+20U6666qpMnz59Qu4FAAAAjIyC1dTdd9+dGTNm5PGPf3x++9vfZq+99lrh8bvuumv+6Z/+Kccff3xuuummrL322tluu+1y7rnn5sgjj8z3vve9rL/++g8674wzzsiOO+6YHXbYIfPmzbOWFAAAABNKGAWrqcVrRt1000259957l6wZteaaa+aBBx5IkixcuHDJ8X/zN3+Ts846K2uvvXb22WeffOc738mWW26Zyy67LNttt12OOuqovP/971/qO2688cZ86EMfynnnnZerrroqu+yyy1LXBAAAgPEmjILV3Prrr58TTzwxH/rQh3Lfffdl6tSpueyyy5IkZ5555pLjfv7zn+cpT3lK3va2t2X//ffPVVddlVtvvTWPecxj8qpXvSrveMc7cvnlly917d/97ndZZ511sv766+fXv/51Lrnkkk7vDQAAgIcfa0bBQ8AOO+yQ7bffPqeddlre8Y535K//+q/z+c9/Ps973vOWHHP66afnC1/4Qh75yEfmL/7iL/Le9743l156af7hH/4hj3jEI/LIRz4yn/jEJ5a67vbbb58ddtgh22yzTZ7ylKdk22237frWAAAAeJgRRsFqasGCBUt9/vrXv75k+6qrrlqy/cEPfjBJctRRR+Woo45a6px99tkn++yzz4OuPWfOnCXbs2fPXqp91qxZY6gaAAAAVkwYxUPWGmuske22227J5//4j//I1KlTJ68gAAAAYKWEUTxkLV7gezSttbTW8ohHWBYNAAAAVif+p87QmDdvXp7+9KfnsMMOy4477pibb745b37zmzNz5sxss802Ofroo5ccO3Xq1Bx99NHZcccds9122+XHP/5xkt7UuNe85jXZbrvtMn369Hz5y19OkvzXf/1Xdt111+y44455+ctf/qApdAAAAMBghFE8ZN19992ZMWNGZsyYkRe/+MVJkuuvvz6vfvWrc8UVV2TzzTfPsccem7lz5+aqq67Kd7/73aXWWtpoo41y+eWX581vfnM+9KEPJUk+8IEPZP3118/VV1+dq666Ks973vNyxx135IMf/GDOPffcXH755Zk5c2b+9V//dVLuGQAAAB7qTNPjIWvZaXrz5s3L5ptvnl122WVJ2xlnnJGTTjop999/f2677bZcd911mT59epLkJS95SZJkp512yle+8pUkybnnnpvTTjttyfkbbLBBzj777Fx33XXZbbfdkiT33ntvdt1111UrdsqUZP785X8GAACAhwlhFENlnXXWWbJ944035kMf+lAuvfTSbLDBBjn00EOzcOHCJfvXWmutJL2F0O+///4kvbWmqmqpa7bWstdee+XUU0/984qqSkuye1VuPfLsbHL8frkw6X1Pa3/eNQEAAOAhyjQ9htbvfve7rLPOOll//fXz61//Ouecc85Kz9l7773z0Y9+dMnnO++8M7vssksuuuii3HDDDUmSP/7xj/nJT34yWBFTpqQlqSQXJmmLg6gkrb8fAAAAHk6EUQyt7bffPjvssEO22WabvPa1r10yzW5F3vOe9+TOO+/Mtttum+233z7nn39+nvCEJ2T27Nk5+OCDM3369Oyyyy5LFjxfqfnzs3uyJJCalz8FUbv39wMAAMDDiWl6PGQt+0S7qVOn5pprrlmqbfbs2aOeO2/evCXbM2fOzJw5c5Ik6667bk455ZQHHf+85z0vl1566Z9V561Hnp1px++XeSPapiWpI8/+s64HAAAAD2VGRsEE2+T4/XLjMm039tsBAADg4UYYBRNpypSl1oiamiy1hpQ1owAAAHi4EUbBRJo/f6k1ourIs5daQ8qaUQAAADzcWDMKJlprqSlTctHi4Om41hsRJYgCAADgYcjIKOjCssGTIAoAAICHKWEUq4dl106ylhIAAAAMJWEUk68q7fbbs1tVpr3rG9mt/zlVk10ZAAAAMM6EUUyuKVOWerpcO36/pZ4+Z4QUAAAADBdhFJNr/vylni43L1nq6XPWVgIAAIDhIoxi0t165NmZtkzbtH47AAAAMFyEUUy6TY7fLzcu03Zjvx0AAAAYLqtNGFVV86rq6qq6sqrm9ts2rKpvV9VP++8bjDj+qKq6oaqur6p9RrTv1L/ODVV1YpVVsFdrU6YstUbU1GSpNaSsGQUAAADDZbUJo/r2aK3NaK3N7H9+V5LzWmtbJDmv/zlVtXWSg5Jsk2TfJB+vqjX653wiyRuTbNF/7dth/ayq+fOXWiOqjjx7qTWkrBkFAAAAw2V1C6OWdUCSU/rbpyQ5cET7aa21e1prNya5IcnOVfXEJOu11i5urbUknxtxDqur1lJPeEIuai03HvfCXNT/nNYmuzIAAABgnFVbTf7DX1U3JrkzvUExn2qtnVRV/9Nae9yIY+5srW1QVR9N8oPW2hf67ScnOSe9h7Ed11p7fr/92UmObK3tt8x3vTG90VPZeOONdzrttNMm/P66sGDBgqy77rqTXQYPYfoQY6UPMVb6EONBP2Ks9CHGSh9irIahD+2xxx6XjZj5tpQ1uy5mBXZrrd1aVVOSfLuqfryCY0dbB6qtoH3phtZOSnJSksycObPNmjXrzyh39TNnzpwMy70wOfQhxkofYqz0IcaDfsRY6UOMlT7EWA17H1ptpum11m7tv89P8tUkOyf5dX/qXfrvixcQuiXJk0acvlmSW/vtm43SDgAAAMBqYLUIo6pqnap67OLtJHsnuSbJWUkO6R92SJKv9bfPSnJQVa1VVdPSW6j8ktbabUl+X1W79J+i9+oR5wAAAAAwyVaXaXobJ/lqLz/Kmkn+vbX2raq6NMkZVfW6JL9I8vIkaa1dW1VnJLkuyf1J3tJaW9S/1puTzE6ydnrrSJ3T5Y0AAAAAsHyrRRjVWvt5ku1Haf9Nkj2Xc86xSY4dpX1ukm3Hu0YAAAAAxm61mKYHAAAAwMODMAoAAACAzgijAAAAAOiMMAoAAACAzgijmHTHHntsttlmm0yfPj0zZszID3/4w1W+xllnnZXjjjtuAqoDAAAAxtNq8TQ9Hr4uvvjinH322bn88suz1lpr5Y477si99967ytfZf//9s//++09AhQAAAMB4MjKKSXXbbbdlo402ylprrZUk2WijjbLJJptk6tSpOfLII7Pzzjtn5513zg033JAk+frXv55nPvOZ2WGHHfL85z8/v/71r5Mks2fPzuGHH54kOfTQQ/O2t70tz3rWs/KUpzwlZ5555uTcHAAAAPAgwigm1d57752bb745W265ZQ477LB897vfXbJvvfXWyyWXXJLDDz88f//3f58k2X333fODH/wgV1xxRQ466KCccMIJo173tttuy4UXXpizzz4773rXu7q4FQAAAGAApukxqdZdd91cdtll+d73vpfzzz8/r3jFK5as/XTwwQcveT/iiCOSJLfcckte8YpX5Lbbbsu9996badOmjXrdAw88MI94xCOy9dZbLxk9BQAAAEw+I6OYdGussUZmzZqV973vffnoRz+aL3/5y0mSqlpyzOLtt771rTn88MNz9dVX51Of+lQWLlw46jUXT/tLktbaBFYPAAAArAphFJPq+uuvz09/+tMln6+88spsvvnmSZLTTz99yfuuu+6aJLnrrruy6aabJklOOeWUjqsFAAAAxkoYxaT6+te/ni233DJPfepTM3369Fx33XU55phjkiT33HNPnvnMZ+YjH/lIPvzhDydJjjnmmLz85S/Ps5/97Gy00Uar9F1XXnllvvnNby75fNZZZy2ZEggAAAB0w5pRTKpLLrkku+++e/bcc88lIdRib3rTm3L00Ucv1XbAAQfkgAMOeNB1Dj300Bx66KFJek/WG2nBggVJemHU3Llz84IXvCBJsv/++2f//fcfnxsBAAAABmJkFJNmwYIFueiii3LyySfntNNOS5LMmTMne+yxR+644448+9nPzgMPPJDDDjss22yzTfbbb7+84AUvyJlnnpkkueyyy/Lc5z43O+20U/bZZ5/cdtttSZJZs2blyCOPzM4775wtt9wy3/ve93Lvvffmve99b04//fTMmDEjp59+embPnp3DDz88SS/Metvb3pZnPetZecpTnrLkOxYsWJA999wzO+64Y7bbbrt87Wtfm4RfCgAAAIaHMIpJ8x//8R/Zd999s+WWW2bDDTfM5ZdfnqQ3Wurqq6/OT37yk3zlK1/JvHnzcvXVV///7d17fM71/8fxx/vaZjNz3kYOMSKnzeZ8GIZKsRxClBxDkg76EuWn1smX7FuivoSKJJRj0dnXHCvHOZ+ZMxsyNtvs8Pn9cW1XO2Eyw3rebzc31/X+nN7X5/rQPHu/X2+mT5/Ob7/9BkBiYiLPP/888+fPZ9OmTfTr149Ro0Y5zp2UlMT69euZMGECb775JgUKFOCtt96iW7duhIeH061btyz9OXXqFGvWrGHp0qWMHDkSADc3NxYtWsTmzZtZsWIF//rXv1QQXUREREREROQmaJqe3DZz5szhpZdeAqB79+7MmTOHdu3a0aBBA3x8fABYs2YNXbt2xWazUbp0aVq2bAnYC5/v2LGDBx98EIDk5GTuuecex7kfe+wxAOrWrUtERESO+tOxY0dsNhs1atTgzJkzgH0lvtdee41Vq1Zhs9k4ceIEZ86coXTp0rlxC0RERERERET+cRRGyW1x7tw5/ve//7Fjxw6MMSQnJ2OMoW3bthQqVMix39VGIVmWRc2aNR0jpTJzdXUFwMnJiaSkpBz1Ke2Y9NedPXs2UVFRbNq0CRcXFypWrEh8fHyOziciIiIiIiIiWWmanuQeb+9rv09n/vz59OrViyNHjhAREcGxY8fw8fFhzZo1GfYLDAxkwYIFpKSkcObMGcLCwgC4//77iYqKyjBtb+fOndfsXuHChbl06dINfaTo6Gi8vb1xcXFhxYoVHDly5IaOFxEREREREZGMFEZJ7jAGKyqKpsbgM3IZTVPfY0y2u8+ZM4dOnTplaOvcuTNfffVVlrZy5cpRq1YtnnnmGRo2bEhcXBx16tRh/vz5jBgxgtq1a1OmTJksq/FlVqxYMX788Uf8/f15/fXX2b9//3U/Vo8ePdi4cSP16tVj9uzZODs7c/78+Sz7VaxYkbNnzwL2wuo+Pj5s2bKFb7/9lrFjx173OjkRFhZGcHBwrpxLRERERERE5HbRND25ed7eWIAB1gA+44JZk/reAoy3N0RGZjgkbYRTei+88AIvvPBChjabzUZoaCgeHh6cO3eOBg0aUK1aNQD8/f1ZtWoVACEhIXh4eGQ5t6enp6NmVOvWrR21oEJCQihSpAjvvvsuADNmzMhw3ZiYGMfx6acC/vrrr9x7771XvRXbtm2jS5cuzJs3j4CAAAICAmjfvv1V9xcRERERERH5p9HIKLl5kZEEgiOQiuCvICowdfvNCA4Oxt/fn2bNmjF69Gi8vLyuuX9QUBAjRoygQYMGVK1aldWrVwN/jSyKiIhgypQpfPDBB/j7+7N69WqioqLo3Lkz9evXp379+qxduxaw17Z66KGHCAgI4JlnnrnmSnq7d++mY8eOzJo1iwYNGgD2kGvIkCEA9OnThxdeeIEmTZpQqVIl5s+fD0BKSgqDBw+mZs2aBAcH07ZtW8e2H3/8kWrVqhEYGMjChQsd1zp//jwdO3bEz8+PRo0asW3bNsAesvXu3ZuHHnqIihUrsnDhQl555RV8fX15+OGHSUxM/DtfgYiIiIiIiEiuURglueLkiKX4ZGrzSW3PqdOnT9O9e3cqV65MjRo1aNu2Lfv27SMsLIzw8HB27dpFnz59AHuNqFq1al31XElJSaxfv54JEybw5ptvZthWsWJFBg0axNChQwkPD2f06NH07NmToUOHsmHDBhYsWED//v359ttvefTRRwkMDGTLli20b9+eo0ePXvWaHTp04KOPPiIwMPCq+5w6dYo1a9awdOlSRo4cCcDChQuJiIhg+/btTJ8+3TESKz4+ngEDBvDdd9+xevVqTp8+7TjPG2+8QUBAANu2bWPMmDH06tXLse3gwYMsW7aMJUuW8NRTT9GyZUu2b99OwYIFWbZs2dW/ABEREREREZE8oDBKckWZccEcztR2OLU9JyzLolOnTgQFBXHw4EF27drFmDFjHNPq0jNXqUOVvv2xxx4DoG7duo5peteyfv16hgwZgr+/P+3bt+fixYu0bNmSy5cv89RTTwHQrl07ihcvftVzPPDAA0yfPp3k5OSr7tOxY0dsNhs1atRwfLY1a9bQtWtXbDYbpUuXpmXLlgDs2bMHHx8fqlSpgjHG0Y+0Y3r27AlAq1atOHfuHNHR0QA88sgjuLi44OvrS3JyMg8//DAAvr6+OboXIiIiIiIiIreSwii5ed7eGWpEVYQMNaSutapemhUrVuDi4sKgQYMcbf7+/gQGBjJ8+HBq1aqFr68v8+bNo2TJko7gBewjiL7++ms++OADAgICuHDhAq6ursyYMYM+ffpw8uRJfHx8WLRoEYcOHSIgIIDp06dz+fJlxzni4uJwdXUlKSmJqVOncuLECRYsWMDx48cxxvDdd9/RsGFDLl68yGOPPZZtSPbRRx8BMHjw4Kt+TldXV8frtCl/15r6d7XgLbtj0vZNu4bNZsPFxcXRbrPZSEpKuuq1RERERERERPKCwii5eZGRGWpEmRFLM9SQyknNqB07dlC3bt0s7QsXLiQ8PJytW7fy66+/Mnz4cC5duoS3t7ejyPj48eM5ceIEK1euZM6cOezZs4eEhATAPrrIy8uL9evXM336dJycnNiyZQuVKlVy1IUCKFu2LF26dOG///0v/fr1Izw8HIAyZcowe/ZsAgMDeeONN0hOTqZTp0689957Wfpqs9mYM2cOe/fu5fXXX8/x7QsMDGTBggWkpKRw5swZRwH2atWqcfjwYQ4ePAjYVyBM07x5c2bPng3Ya2F5enpSpEiRHF9TRERERERE5HbRanpy84xxBFEnRyylTLrV9LjGqJ+cWLNmDU888QROTk6UKlWKFi1asGHDBv7zn//QsWNH/P39OXz4MM899xyVK1cG7COD0mo7BQYGsnr1ary8vChUqBClSpUC4OGHH+b999/H398fsAdac+fOZdasWezdu5cPP/yQFi1a0KBBA1atWsXs2bO5fPkyLi4ufPzxx9x3333Z9tfV1ZUlS5bQokULSpUqRaFCha77GTt37szy5cupVasWVatWpWHDhhQtWhQ3NzemTp1Ku3bt8PT0JDAwkB07dgD2QuV9+/bFz88Pd3d3Zs6ceVP3WURERERERCSvKIySm+PtnWFKnk+6IMoCjLd3jkZG1axZ07GCXHpXm8JWpUoVfHx8CA8Pp2PHjjzwwAOObX5+ftSsWZPNmzdTtGhRR50kd3d35s6dC9hHPHXv3p2PPvqIoKAgihUrxrx58wC49957mTBhAosWLcLNzY2ff/6ZoKAgXn75Zdq3b09YWBghISEZ+pO+FlPRokUdI6sAR9H1GTNmZDgmbWSXzWYjNDQUDw8Pzp07R4MGDfD19QXsodmePXuyfP4SJUqwZMmSLO2Z+5V2jey2iYiIiIiIiNwOmqYnNycyMsOUvAjIMGUvJ0EU2ItwJyQkMG3aNEfbhg0bKF68OPPmzSM5OZmoqChWrVpFgwYNMhybfsravn37OHr0KPfff/8NfYy0IGrNmjUULVqUokWLZtgeHR1N2bJlAW7JKKTg4GD8/f1p1qwZo0ePpnTp0rl+DREREREREZE7gUZGyU07OWIpPuOCiUjX5oO9dlROGWNYtGgRL730EmPHjsXNzY2KFSsyYcIEYmJiqF27NsYY3nvvPUqXLp1hJNLgwYMZNGgQvr6+ODs7M2PGjAyFwnOiePHiNGnShIsXL/LZZ59l2R4SEkLXrl0pW7YsjRo14vDhzGsH3py0OlEiIiIiIiIi+Z251kpe/wT16tWzNm7ceLu7kSvCwsIICgrK8+s2NeavGlGp0kZGrf2HP193m9v1DEn+oWdIbpaeIckNeo7kZukZkpulZ0huVn54howxmyzLqpfdNk3Tk5vj7Z2hRlRFyFBDCm/v29Y1EREREREREbnzKIySmxMZmaFGlBmxNEMNqRzVjMocWCnAEhEREREREcm3FEbJzbMsjJcXay2Lw2PbsTb1PTmZomcMVlQUTY3BZ+Qymqa+x5jrHysiIiIiIiIidx2FUZI7Mo+AyuGIqPRT+qxxwRmm/GmElIiIiIiIiEj+ozBKcl9Op91FRmaY0hcBGab85SjQEhEREREREZG7isIoyV03OO3u5Iil+GRq80ltFxEREREREZH8R2GU5J6/Me2uzLhgDmdqO5zaLiIiIiIiIiL5j8IoyT03Ou3O2ztDWFUx3bFrUreLiIiIiIiISP6iMEpy1Q1Nu4uMzBBWmRFLM4RZd3rNKGMMPXv2dLxPSkrCy8uL4OC/N6prypQpfPHFF7nVPREREREREZE7kvPt7oDkL2VSp+aldxgIHBcMY62sB1gWxtubtWnB01jLPiLqDg+iAAoVKsSOHTuIi4ujYMGC/PLLL5QtW/Zvn2/QoEG52DsRERERERGRO5NGRknu+bvT7jIHT3dBEJXmkUceYdmyZQDMmTOHJ554wrEtNjaWfv36Ub9+fQICAliyZAkAL7zwAm+99RYAP/30E82bNyclJYWQkBBCQ0MBOHDgAA888AC1a9emTp06HDx4EMuyGD58OLVq1cLX15d58+bl8acVERERERERuXkKoyT33OXT7v6O7t27M3fuXOLj49m2bRsNGzZ0bHv33Xdp1aoVGzZsYMWKFQwfPpzY2FjGjh3LvHnzWLFiBS+88AKff/45NlvGP4o9evTgueeeY+vWraxbt4577rmHhQsXEh4eztatW/n1118ZPnw4p06dyuuPLCIiIiIiInJTFEZJrnFycsK/dm18nZwo06ULO19vyVrLwnh5gZXNFL18wM/Pj4iICObMmUPbtm0zbPv5558ZO3Ys/v7+BAUFER8fz9GjR3F3d2fatGk8+OCDDBkyhMqVK2c47tKlS5w4cYJOnToB4Obmhru7O2vWrOGJJ57AycmJUqVK0aJFCzZs2JBnn1VEREREREQkNyiMklxTsGBBwsPD2ZGURIECBZgyZYp9w3VGRCUlJeVB726d9u3bM2zYsAxT9AAsy2LBggWEh4cTHh7O0aNHqV69OgDbt2+nZMmSnDx5Msv5rKsEd1drFxEREREREbmbKIySW6JZs2YcOHDgqnWTZsyYQdeuXXn00Ud56KGHCAsLy7AK3ZAhQ5gxY8Zt6v2N6devH6+//jq+vr4Z2tu0acOkSZMcIdKWLVsAOHLkCP/5z3/YsmULP/zwA3/88UeG44oUKUK5cuVYvHgxAAkJCVy+fJnmzZszb948kpOTiYqKYtWqVTRo0ODWf0ARERERERGRXKQwSnJdUlISP/zwA76+vletmwTw22+/MXPmTP73v//d5h7fnHLlyvHiiy9maR89ejSJiYn4+flRq1YtRo8ejWVZPP3004SGhlKmTBk+/fRT+vfvT3x8fIZjZ82axcSJE/Hz86NJkyacPn2aTp064efnR+3atWnVqhXvvfcepUuXzquPKSIiIiIiIpIrnG93ByT/iIuLw9/fH7CPjHr66adp0qQJ3377rWOVuLS6SQAPPvggJUqUuF3dvWkxMTFZ2oKCgggKCgLs0xY/+eSTLPv8+uuvjtd169Zl+/btAISEhDjaq1Spkm1IN378eMaPH3+TPRcRERERERG5fRRGSa5JqxmVXlrdpPvvvz9D+x9//EGhQoUc752dnUlJSXG8zzxSSERERERERETyB03Tk1vqanWTMqtQoQK7du0iISGB6Oholi9fnpfdFBEREREREZE8opFRckuNHj2al156CT8/PyzLomLFiixdujTLfuXLl+fxxx/Hz8+PKlWqEBAQcBt6KyIiIiIiIiK3msIoyTXZ1VC6Wt2kPn360KdPnwxt7733Hu+9996t6p6IiIiIiIiI3AE0TU/uCh4eHgBERETw1VdfXXf/iIgIatWqdau7JSIiIiIiIiI3SGGU3FVyGkbdMG/va78XERERERERkVyhMEruKiNHjmT16tX4+/vzwQcfEBERQbNmzahTpw516tRh3bp1WY5p1qxZhlX+mjZtyrZt2/7awRisqCiaGoPPyGU0TX2PMXnwiURERERERET+WRRGyV1l7NixjnBp6NCheHt788svv7B582bmzZvHCy+8kOWY/v37M2PGDAD27dtHQkICfn5+9o3e3liAAdYA1rhg1qS+t1K3i4iIiIiIiEjuURgl2btLpq0lJiYyYMAAfH196dq1K7t27cqyT9euXVm6dCmJiYl89tlnGQunR0YSCI5AKoK/gqjA1O0iIiIiIiIiknsURklWd9G0tQ8++IBSpUqxdetWNm7cyJUrV7Ls4+7uzoMPPsiSJUv4+uuvefLJJzNsPzliKT6ZjvFJbRcRERERERGR3KUwSjK6w6etFS5cmEuXLjneR0dHc88992Cz2Zg1axbJycnZHte/f39eeOEF6tevT4kSJTJsKzMumMOZ9j+c2i4iIiIiIiIiuUthlGR0h09b8/Pzw9nZmdq1a/PBBx8wePBgZs6cSaNGjdi3bx+FChXK9ri6detSpEgR+vbtm3GDt3eGsK0iZAjjbnf4JiIiIiIiIpLfON/uDsid5+SIpfiMCyYiXZsPYG7jtLWYmBgAXFxcWL58eYZt6VfG+/e//w1AxYoV2bFjh6P95MmTpKSk8NBDD2U8cWQkxhhH2GZGLCUw3Wiw2x2+iYiIiIiIiOQ3GhklWeS3aWtffPEFDRs25N1338Vmy+aRtyyMlxdrLYvDY9uxNvU9lpX3nRURERERERHJ5xRGSUZ32LS1d999l5o1a+Ln54e/vz9//PHHDZ+jV69ezJo1i7Jlyzra+vTpw/z58//aKfMIqHTvFy1ahDGGPXv2ONqioqJo2LAhAQEBrF69Oss1+/fvn+3KfiIiIiIiIiL/dAqjJKPIyAw1osyIpRlqSOXltLXffvuNpUuXsnnzZrZt28avv/5K+fLl/9a5wsLCWLdu3d86ds6cOQQGBjJ37lxH2/Lly6lWrRpbtmyhWbNmGfZPTk5m+vTp1KhR429dT0RERERERCQ/UxglWd0h09ZOnTqFp6cnrq6uAHh6elKmTBnAHgYFBATg6+tLv379SEhIAOy1os6ePQvAxo0bCQoKIiIigilTpvDBBx/g7+/vGMm0atUqmjRpQqVKlTKOkkonJiaGtWvX8umnnzrCqPDwcF555RW+//57/P39iYuLw8PDg9dff52GDRvy22+/ERQUxMaNGwH48ccfqVOnDrVr16Z169YArF+/niZNmhAQEECTJk3Yu3fvLbqLIiIiIiIiIncWhVGSvWtMW8srDz30EMeOHaNq1aoMHjyYlStXAhAfH0+fPn2YN28e27dvJykpicmTJ1/1PBUrVmTQoEEMHTqU8PBwx0imU6dOsWbNGpYuXcrIkSOzPXbx4sU8/PDDVK1alRIlSrB582b8/f1566236NatG+Hh4RQsWJDY2Fhq1arFH3/8QWBgoOP4qKgoBgwYwIIFC9i6dSvffPMNANWqVWPVqlVs2bKFt956i9deey23bpuIiIiIiIjIHU1hlOS9zHWnrlKHysPDg02bNjF16lS8vLzo1q0bM2bMYO/evfj4+FC1alUAevfuzapVq264Gx07dsRms1GjRg3OnDmT7T5z5syhe/fuAHTv3p05c+Zku5+TkxOdO3fO0v7777/TvHlzfHx8AChRogQA0dHRdO3alVq1ajF06FB27tx5w/0XERERERERuRs53+4OyD+MMfZ6VMZwcsRSyowLthdMNybbaYBOTk4EBQURFBSEr68vM2fOxN/f/6qnd3Z2JiUlBbCPoLqWtOl/AFY21z537hz/+9//2LFjB8YYkpOTMcbw3nvvZdnXzc0NJyenLO2WZdk/WyajR4+mZcuWLFq0iIiICIKCgq7ZVxEREREREZH8QiOjJO94e2dYmc9KC6KwF0jPPEJq79697N+/3/E+PDycChUqUK1aNSIiIjhw4AAAs2bNokWLFoB9St6mTZsAWLBggePYwoULc+nSpRvq7vz58+nVqxdHjhwhIiKCY8eO4ePjw5o1a3J8jsaNG7Ny5UoOHz4MwPnz5wH7yKi01f1mzJhxQ/0SERERERERuZspjJK8ExmZYWW+CMiwcl/mulQxMTH07t2bGjVq4Ofnx65duwgJCcHNzY3PP/+crl274uvri81mY9CgQQC88cYbvPjiizRr1izDSKVHH32URYsWZShgfj1z5syhU6dOGdo6d+7MV199leOP7OXlxdSpU3nssceoXbs23bp1A+CVV17h1VdfpWnTpiQnJ+f4fCIiIiIiIiJ3O03Tkzx1csRSfMYFE5GuzQcwI5Zm2bdu3bqsW7cu2/O0bt2aLVu2ZGlv1qwZ+/bty9Jep04dYmJiMuyXXvptacLCwrK0vfDCC47Xffr0uerx6Y995JFHeOSRRzJsb9y4cYZ+vv3221muJSIiIiIiIpIfaWSU5Kky44I5nKntcGp7XtOIJBEREREREZG8pzBK8o63d4YaURUhQw2pq62ql5vCwsJo2bIlTz75JL6+voB9Vb26detSs2ZNpk6d6tjXw8ODUaNGUbt2bRo1anTVFfdEREREREREJOcURkneiYzMUCPKjFiaoYZU5ppRue3MmTO8/fbbrFy5km3btlG0aFEWLVrEZ599xqZNm9i4cSMTJ07k3LlzAMTGxtKoUSO2bt1K8+bNmTZt2i3tn4iIiIiIiMg/gWpGSd6yLIy3N2vTgqexln1E1C0OosA+Aqpx48akpKSwYsUKjhw5wrfffsvEiRNZtGgRAMeOHWP//v2ULFmSAgUKEBxsnz4YEBDA8uXLb3kfRURERERERPI7jYySvJM2DS8teMr8/hZKTk6mQIECtG/fnkKFCgFQoUIFfH19+eWXX2jZsiUFChQgOTmZr7/+GgCbzUarVq148sknGT58OMePH6dFixY8/vjjVK1alZEjRzJ79mwaNGiAr68vBw8eBOC7776jYcOGBAQE8MADDzim94WEhNCvXz+CgoKoVKkSEydOBGD06NF8+OGHjr6OGjXKsU1EREREREQkv1EYJXnDGKyoKJoag8/IZTRNfY8xeXL5lJQU6tSpk6U9OjqaS5cu4enpyaxZs0hKSmLZsmUcPmwvs75+/XreffddJkyYAMDWrVv58MMP2b59O7NmzWLfvn2sX7+e/v37M2nSJAACAwP5/fff2bJlC927d+e9995zXG/Pnj389NNPrF+/njfffJPExESefvppZs6c6ejn3Llz6dGjxy2+IyIiIiIiIiK3h6bpya3n7Z2hULnPuOAMhcxNHk3TS++5555jzZo1ODs7ExkZydtvv01oaCjOzs5ER0ezf/9+ABo0aICPjw+bNm0CoH79+txzzz0AVK5cmYceeggAX19fVqxYAcDx48fp1q0bp06d4sqVK/j4+Diu265dO1xdXXF1dcXb25szZ85QsWJFSpYsyZYtWzhz5gwBAQGULFkyL2+HiIiIiIiISJ7RyCi59SIjMxQqj4AMhczzIoj6/vvv2bx5M0FBQSxdupSPP/6Y5cuXc+7cOZo2bcq3337LhQsXiImJ4fTp0zz00EP88MMPjil9Xbp0YeTIkbi6ujrOabPZHO9tNhtJSUkAPP/88wwZMoTt27fzySefEB8f7zgm/fFOTk6OY/r378+MGTP4/PPP6dev3y2/HyIiIiIiIiK3i8IoyRMnRyzFJ1ObT2p7XmjVqhXx8fFMnjzZ0Xb58mUA2rRpw+TJk0lMTARg3759xMbG/u1rRUdHU7ZsWQDH9Lvr6dSpEz/++CMbNmygTZs2f/vaIiIiIiIiInc6TdOTPFEmdWpeeoeBwHHB9hX1bjFjDIsXL2bo0KG89957eHl5UahQIcaNG0fXrl2JiIigTp06WJaFl5cXixcv/tvXCgkJoWvXrpQtW5ZGjRo56k9dS4ECBWjZsiXFihXDycnpb19bRERERERE5E6nMEpuPW/vDDWifLAHUWk1pMijmlH33HMPc+fOzXbbmDFjGDNmTIa2oKAggoKCrvo+LCws220dOnSgQ4cOWa4REhKS4f2OHTscr1NSUvj999/55ptvcvZhRERERERERO5SmqYnt15kZIYaUWbE0gw1pPK6ePmdZteuXdx33320bt2aKlWq3O7uiIiIiIiIiNxSGhklecOyMN7erE0LnsZaf39EVObjbsNqfLmpRo0aHDp06HZ3Q0RERERERCRPaGSU5J3MgdHfCZCMwYqKoqkx+IxcRtPU9xiTO30UERERERERkVtKYZTcPby9HVP71gBWalH0tCmAeHvfxs6JiIiIiIiISE4ojJK7R2RkhlpTEZChFtXdPFVPRERERERE5J9CYZTk2OnTp+nevTuVK1emRo0atG3blqlTpxIcHJzt/v3792fXrl0A2Gw2zp49e9N9ODliKT6Z2nxS20VERERERETkzqcwSrLl5OSEv78/tWvXpk6dOqxdu5ZOnToRFBTEwYMH2bVrF2PGjOHMmTOOYzw8PDKcY/r06dSoUSNDW/qAKjk5+Yb7dWhcMIcztR1ObRcRERERERGRO59W05NsFSxYkPDwcAB++uknnn32WYoVK8agQYMc+/j7+3PhwgWWL19Oly5duHz5Mj169ODLL7/EGENQUBChoaHUq1cPy7IICgoiNjaWr7/+GmdnZy5fvkzv3r05ffo0x48fJz4+nhdffJECBQqwceNG6taty7///W/uueceqlatiuv06Uzir6l5PtiDKEfNKGPAsjJ8jpCQEDw8PBg2bFhe3DYRERERERERuQ6FUXJdFy9exLIs6tatC8D48eP5+uuvSUhIICAggC1btrBz505++OEHDh06xOrVq5k7dy4bNmzgpZdeokiRIgCEhYXRtGlTLl26xMiRI5k0aRJOTk60adMGb29vFi9ezMSJEylUqBDR0dFMnz6dUqVKMXr0aHr06MGVYsWIuXCBz/mrZhSkBlGQJYgSERERERERkTuPwijJVlxcHP7+/sTHx3Pq1CkGDhxIUlISP//8M/v372f9+vVYlkXTpk2pUqUK5cqVwxiDv78/8+fPJyIigvr16zNy5EieeOIJx3ljYmKw2WxMmzaNy5cv88UXXwCQmJiIq6srxhgqVqxIdHQ0jz/+OOXLl6dNmzZ4enri4eHBV85F+fnsEQYCR4FDwFqgSOuBnEu9xrvvvssXX3xB+fLl8fLycoRoIiIiIiIiInL7qWaUZCttmt6ePXv48ccf+frrr9m0aRM///wzP//8MwEBAdSpU4ejR4+SmJjoOM7JyYk9e/bQtWtXADw9PWnZsqVju2VZFChQgLfffhsnJye6du2Kv78///rXv3B2diYgIICUlBRKlSqFMYYuXbrg4+PDqFGjqFKlCs5nj/Bx6rn2AD8BJ4GLy6eSmJjIpk2bmDt3Llu2bGHhwoVs2LAhz+6ZiIiIiIiIiFyfwii5rsaNGxMXF0dMTAxbt27l1VdfJTw8nGnTpjFw4EDKly+fYX/rKtPlgoKCOH/+PImJifj7+5OUlISvry9ubm7UqFGDS5cusWbNGg4dOoSTkxNhYWHs37+f06dP8+qrr7IlLIziQDL2qXm7gQKAF3AfcKZ0aVavXk3x4sVZt24dRYoUoX379rf03kj+kLn4/vWEhYVddRXJ3Pb666/z66+/5sm1RERERERE8oLCKLmuPXv2kJKSwpIlS0hISODFF1+kevXqhISEULBgQRISEjLsX7VqVRYsWADAuXPnCAsLA+z/gK9WrRr33XcfTz/9NAAff/wxsbGxPPfccyQnJzuCrQIFChASEkKvXr1ITEykf//+BHXo4AiiPgVMUD8CU987AUmbNgHQsmVLHnjggVt9W+5ImVdBXLduXa5f4+8EMUFBQWzcuPGGr9WnTx/mz59/w8flJ2+99dY/9nkWEREREZH8SWGUZCutZpS/vz/dunVj5syZlC9fnlWrVjFu3DicnZ05evQoixYtYsqUKY7jPvroIyZNmkS5cuWIjIxk8uTJNGzYEDc3Nzw9PSlWrBhffvkl33//PR4eHvz555+4urrSqlUrXF1dee6553B2tpcy6927N25ubri7uxMQEMCSJUuIKVSI12w2Hurbl/oFjnOlXj18nZw4Wbw4lmXRvHlzJk6cyFdffcWlS5eYNGkSP/30E3Xq1MHX15c9e/YAEBsbS79+/ahfv77j3PlB2vTKrVu38u9//5tXX331dnfprhIWFkZQUBBdunShWrVq9OjRwzHS78cff6RatWoEBgaycOFCxzHnz5+nY8eO+Pn50ahRI7Zt2wbYV3Ls168fQUFBVKpUiYkTJzqO+fLLL2nQoAH+/v4888wzJCcnk5ycTJ8+fahVqxa+vr588MEHQMZA7q233qJ+/frUqlWLgQMHXnUUooiIiIiIyJ1MYZRkKzk5mfDwcEew0a5dO8e2F198ke3bt7N9+3Z+++03KleuDNiLkwPYbDZCQ0PZtWsXn376Kfv27cNmsz9qYWFh1KtXD4By5crRrFkzzp8/z6lTp+jTpw8A99xzD2XLlqVHjx4EBwdTvHhxxowZg2VZ3HvvvexPTubee++ladOmbNiwgR1JSaSkpLB8+XLq1KmDj48Pw4YNo3Pnzri5uVGoUCE2b97Ms88+S2hoKGAvct6qVSs2bNjAihUrGD58OLGxsXl1e/PExYsXKV68OGD/blq3bu0I5dLCt4iICKpXr86AAQOoWbMmw4cPJy4uDoANGzbg5+dH48aNGT58OLVq1cpyjfXr19OkSRMCAgJo0qQJe/fuBexhZvfu3fHz86Nbt26OcwL8/PPPNG7cmDp16tC1a1fHczNy5Ehq1KiBn58fw4YNc+y/atUqmjRpQqVKlfJklNSWLVuYMGECu3bt4tChQ6xdu5b4+HgGDBjAd999x+rVqzl9+rRj/zfeeIOAgAC2bdvGmDFj6NWrl2Pbnj17+Omnn1i/fj1vvvkmiYmJ7N69m3nz5rF27VrCw8NxcnJi9uzZhIeHc+LECXbs2MH27dvp27dvlr4NGTLE/szv2EFcXBxLly695fdDREREREQktymMkmydPn2a7t27U7lyZWrUqIExhn79+jm2h4aGEhISctXjg4ODue+++6hbty6jR48mNjbWMcIjJCSEadOm4eTkxJIlS9i2bRu///67o2h56dKl2b9/Pz/++CNbt27Fw8ODe+65BycnJ2JjYzl79iwhISHUrFmThg0b4uvrS6FChTh79iwAfn5+TJw4kZ9//pnixYuzf/9+/P39GT16NLNmzcLf358JEybQq1cvChQoQGBgIPHx8Rw9etTR/5CQEF577TVq1aqVoWZP2jS4WrVq0bVrVy5fvkxERES2QQ1krPeTfqpa27ZtuXDhwt/6bqZMmeJYhTCztBFt1apVo3///owePRoANzc3Fi1axObNm1mxYgX/+te/HKNq9u/fz3PPPcfOnTspVKiQY4pl3759mTJlCr/99htOTk7ZXq9atWqsWrWKLVu28NZbb/Haa68BMHnyZNzd3dm2bRujRo1iU+oUyrNnz/LOO+/w66+/snnzZurVq8f777/P+fPnWbRoETt37mTbtm383//9n+Map06dYs2aNSxdupSRI0f+rXt2Ixo0aEC5cuWw2Wz4+/sTERHBnj178PHxoUqVKhhjeOqppxz7r1mzhp49ewLQqlUrzp07R3R0NADt2rXD1dUVT09PvL29OXPmDMuXL2fTpk3Ur18ff39/li9fzqFDh6hUqRKHDh3i+eef58cff6RIkSJZ+rZixQrHM/+///2PnTt33vL7ISIiIiIiktucb3cH5M5jWRadOnWid+/ezJ07FwBXV1d++OEHzp49i6en53XPERYWRkhICB4eHo4RT+m5u7vz559/Zmg7f/48Pj4+ADRp0oR9+/bxww8/UK5cOV5//XXWrVuHMQaA+Ph4Bg8ezMaNGylfvjwhISHEx8dnuY7NZmPlypV4enoycOBAli9fTnh4OHXr1mX37t14e3sTFhaW5TOlpKQ4Xr/11luO12nT4AB69OjBlClTeOyxx656H9Ifm973339/1WOuZ9CgQVfdlr5/v/32G7169WLHjh1YlsVrr73GqlWrsNlsnDhxgjNnzgDg4+ODv78/APfffz8RERFcuHCBS5cu0aRJEwCefPLJbEfhREdH07t3b/bv348xxrGy4qpVq3jhhRcAezjo5+cHwO+//86uXbto2rQpAFeuXKFx48YUKVIENzc3+vfvT7t27TLUpOrYsSM2m40aNWo4+nwrubq6Ol47OTmRlJQE4Hj2Mstuqlzavtmdy7Isevfuzb///e8sx23dupWffvqJjz/+mK+//prPPvvMsS2nz7yIiIiIiMidTmGUZLFixQpcXFwyhB4uLi688MILdOzYkZiYGCIjI7l06RJHjhzhvffeo23btuzatQvLsnjwwQd5//33mTJlCklJSbz++uuUKVOG2NhYihQpwqJFi7hw4QJJSUncc889vPrqq/z5559MnTqVChUqkJCQQGhoKF999RXVqlXjqaee4pdffnEERGfPnqVv375ERUVx//334+XlRVJSElWqVKFnz57Mnj2b+fPnM3ToUCIjI1m6dCnz58/Hw8ODEydOULBgQYoVK0ZiYiKWZXHPPffg4uJCcnIyTk5ONG3alPDwcAYMGADYa/bUr1+f6dOnk5SURJ06ddi/fz9Xrlxh+fLl1K5dmytXrlC4cGHKly9PuXLlmDVrFg0bNqR58+a0b9+eLl26ZLjHFStWZOPGjcTExPDwww/TsGFDtmzZQtWqVfniiy9wd3enYsWKdOvWjRUrVgDw1Vdfcd999zlCvmHDhhEUFETDhg1ZsWIFFy5cIDk5GbBPs1y4cCH79u2jZs2a1K9fn/j4eJYuXcpTTz1FcnIyLVq04O23384QmNhsNkdgkhOjR4+mZcuWLFq0iIiICIKCghzbsgtv0p6POXPmZNm2fv16li9fzty5c/noo4/43//+B2QMdG5XjaRq1apx+PBhDh48SOXKlTP0v3nz5syePZvRo0c7gs3sRjWlad26NR06dGDo0KF4e3tz/vx5Ll26RKFChShQoACdO3emcuXKWULctODJ09OTmJgY5s+fn+W5EhERERERuRtomp5ksWPHDurWrZul/bnnnuP48eOsPH6cwYMH4+7uzpAhQ+hbtiwFChTgwoUL7Nmzh1WrVrF27Vp69uxJUlISO3fu5MCBAwQFBXHq1Cn69etH5cqV8fT0pHLlygwdOpSJEycyffp0du/ezaVLl9i8eTOFCxcmNjaWQ4cOERwcjIuLCwCjRo1ixIgRjn/Mnz17lsuXL5OYmMi2bduwLMuxWp+rqyu//PILYB8pVKRIESpWrEilSpUA+xSwpKQk7r33Xl5++WWuXLnC+++/z3PPPef43NHR0YSGhjJ16lSuXLmCk5MT69ev54EHHuCpp55i9OjRHDp0iFatWjFmzBiKFSvGq6++SufOnR21sq5l7969DBw4kG3btlGkSBH++9//OrYVKVKE9evXM2TIEF566aVsj09KSmL9+vVMmDCBK1euAPDpp59y5coVihYtysaNG1mxYgVubm588803VKlShcTERL7//ntq1KiR7TmLFy9O4cKF+f333wEcI+Qyi46OpmzZsgDMmDHD0Z4W0ID9eUor6t2oUSPWrl3LgQMHALh8+TL79u0jJiaG6Oho2rZty4QJExyju+4Ubm5uTJ06lXbt2hEYGEiFChUc20JCQti4cSN+fn6MHDmSmTNnXvNcNWrU4J133uGhhx7Cz8+PBx98kFOnTnHixAmCgoLw9/enT58+WUZOFStWjAEDBuDr60vHjh2pX7/+LfmsIiIiIiIit5pGRv0TeHtDZGTG95C1Lf37bBQpUoReR47wITB29Gi4ryEv1KvHNiB+3TrHaJC0Is0//fQTCQkJdOjQAZvNRtmyZXnyySfZv38/Xbt2ZezYsRw7doySJUvi7u5OaGgooaGhFC9e3FGjyMXFhfDwcE6ePMnZs2epVasW3377Ldu3b+fixYtERUVhjMHZ2Znq1atTpkwZ9uzZw4QJEzh48CDLli1j//79eHt7ExkZSUhICDNmzKBGjRps3boVT09PTp8+TVJSEsePHwfA19eXBQsW8PHHH+Pq6sqxY8cICQnBzc0Ny7LYuHEjtWvXpmjRohw/fpzY2Fh8fHx45ZVXeO+992jSpAkff/wx33//vaNg+rWUL1/eMW3tqaeeYuLEiY4C3k888YTj96FDh2Z7fNo0wbp165KSkuKocxQXF0fZsmVp0qQJxhjWr1/P77//zqlTpyhZsiR79+69ahgF9kBrwIABFCpUiKCgIIoWLZpln1deeYXevXvz/vvv06pVK0f7s88+S9++ffHz88Pf358GDRoA4OXlxYwZM3jiiSdISEgA4J133qFw4cJ06NCB+Ph4LMtyrCSXV9KKqAcFBWUY3fXRRx85Xj/88MOO1RjTK1GiRLarMWauqbZjxw7H627dutGtW7csx2zevDlLW/qQ75133uGdd9656ucQERERERG5GyiMyu+MwQICjeHkiKWUGRfMmtRNmduMMWBZ1KxZM/tVy7y9eRGoDNQADhz4gzVAYeAF4D+FCzsCrd9++41p06Zx//33Ex4eztmzZxk8eLBjypWrq6tjypUxhmeffdZR/DrNG2+8QeXKldm8eTN9+vTh9ddfd2x7/vnnmTdvHocOHcLd3Z2goCCSkpIwxuDi4oIxhsOHD1OuXDlOnDjhqAlljCElJQXLsrAsi5SUFFJSUli1ahWhoaEkJydnKA5euHBhnJ2d2bNnD61bt8YYQ6lSpTh16pRjn4iICIKDg2natCkRERGUKVOG5OTkqxY1z/oVmau+v9rr9NLuqZOTExUqVCA8PJzOnTszcOBA2rRpk2X/kydPsmzZMkaMGMHw4cOzhCRpYUzNmjUdI5rGjh3rWAUxfWDTuHFj9u3b5zj+7bffBuy1qzKPpnJycsLX15ekpCSqV6/OzJkzcXd3d2xfv36943VISAihoaEZghiAl19+mV9//ZUHHngg23uxePFiqlates2QTURERERERG4vTdPLz7y9sQADrAGstNDpKm1W6jGtWrUiISGBadOmOU6VkpLCym++YR1QCIgEXko9rgUwE4hMDTVWrlzJtm3bKFmyJIcOHeLw4cN4eno6RjutXLmSDz/8kMuXLzNw4EAKFSrE119/TbNmzdi4cSMnTpxg9+7dnD592nH9+Ph4Jk6cSGRkJIULF+ann36iePHi1KhRg5kzZzqmk6WNUDl79ix79+5l0KBBnD9/np07d1K+fHlWrFjBgQMHWLhwIfHx8cTGxpKYmMijjz7KrFmzcHFxyTA65eLFiyQkJPDFF1/w448/Ou5Fz549uXTpEpMnT+ahhx7iwIEDdO7cme7du/PVV185CoLnxNGjR/ntt98AmDNnDoGBgY5t8+bNc/zeuHHjHJ+zTZs2TJ482VFQfN++fcTGxnLkyBG8vb0ZMGAATz/9dLYjcdIsW7bMsXLg6tWrM6xw93ekFVffsWMHBQoUYMqUKTd8jrfeeuuqQRTYw6hdu3bdTDdFRERERETkFlMYlZ9FRhIIjkAqgr9Cp+zaAlOPKVy4MIsWLeKXX36hcuXK1KxZkytXrlCmUyfex/7QHAemAK9jD6KqABVKlcLNzY0uXbpQpUoVbDYbcXFxVKlShbJly7Jy5UrAvoJa7dq1cXd3Jy4uDoAOHTqwefNmunfvTpcuXRzTpgAmT55MyZIlefbZZ3F2dubixYvExcURFhbGkSNHeOONN2jUqBEADRo0ID4+Hn9/f6pUqcL999/PPffcQ0BAAFFRUaxYsYKUlBRHoe+kpCScnJzYvXs30dHRpKSk8NBDDzmubbPZePTRR+nZsydjxoxxjLr65ptvKF26NO+//z7BwcHcd999VK9enZSUFC5fvoyvr2+Ov6a0UUJ+fn6cP3+eZ5991rEtISGBhg0b8uGHH97Q1LX+/ftTo0YN6tSpQ61atXjmmWdISkoiLCwMf39/AgICWLBgAS+++OJVz9GtWzdHeLRs2TK8vLxyfP3radasGQcOHOC7776jYcOGBAQE8MADD2S7Wt60adN45JFHiIuLo0+fPo5ReyNHjqRGjRr4+fkxbNgw1q1bx7fffsvw4cPx9/fn4MGDTJs2jfr161O7dm06d+7M5cuXAXtR+hdeeIEmTZpQqVKl7EcCioiIiIiIyK2RNl3pn/qrbt26Vn6xYsWKLG0VRyy1KoBlpftVIfVX5raKI5ZalmVZhQoVuuo1KoGVku64pNT3lSDLvklJSdaKFSus119/3SpVqpT1+eefW5ZlWV27drXKly9v1apVyypTpoz173//27Isy2rRooW1YcMGy7IsKyoqyqpQoYJlWZbVoUMHa/ny5Y7zBgQEOParUKGCFRUVZVmWZb3xxhvW+PHjLcuyrMGDB1tTp061LMuylixZYnXp0sWyLMs6fPiwVbFixQz9nDx5shUQEGD5+vpanp6e1pw5cxznPnjwoGVZlnXlyhWrRIkSWa4TFhZmBQYGWrVq1bIqVqxoPfjgg9ZTTz111fuX2eHDh62aNWtmuy39Z8sr2T1DuSXtuUpMTLTat29v/fe//7XOnz9vpaSkWJZlWdOmTbNefvlly7L+useTJk2yHn30USs+Pt6yLMvq3bu39c0331jnzp2zqlat6jj2zz//zLA9zdmzZx2vR40aZU2cONGxX5cuXazk5GRr586dVuXKlW/Z5/6nuZXPkPwz6BmS3KDnSG6WniG5WXqG5Gblh2cI2GhdJYtRzah8Ln2NqDSHs9nvMBA4LhjGWo62U6dO0a1bNy5evEhSUhKTjx/nAPYaUS8DPwH/Af4HlABqOTvTpF8/Plm0CBMVRUREBGPHjiUqKorixYszc+ZMunfvzvfff89LL73EO++8Q0hIiGPJemdnZ1JSUoC/lrFPc7V6SdlJTk5mwYIFfPvtt7z77rtYlsW5c+e4dOkSAIUKFfrrcx8+TGhoKBs2bKB48eL06dMnw7WvV7OpT58+LF68mNq1a/PAAw/w+++/s3Hjxhz39Z8kLi7OMX2xWbNmPP300+zdu5du3bpx6tQprly5go+Pj2P/WbNmUa5cORYvXuxYSTFNkSJFcHNzo3///rRr147g4OBsr7ljxw7+7//+jwsXLhATE5OhhlbHjh2x2WzUqFEj2xFZIiIiIiIicmtoml5+5u2doR5URf6anpdd25rUY9J89dVXtGnThvDwcLZu3Yr/sWMAxAK1gD+wT+17DlgPxA5bwuJp0/ju7Fn2GsNTTz3FpEmT2LRpE40aNWL//v2OoKdQoULExMRkmB5VsWJFNm3aBJChvXnz5syePRuwhwtpRbUzCwkJYdiwYfz666/Url2bY8eOERERwZEjR+jcuTOLFy/OcszFixcpVKgQRYsW5cyZM/zwww8Ztl+vZtOlS5e45557SExMxBjDY489RtWqVbPtX3YqVqyYoYB4ehEREY7C6/lBWs2o8PBwJk2aRIECBXj++ecZMmQI27dv55NPPskQBNaqVYuIiAjHKofpOTs7s379esf3+vDDD/+18emnHS/79OnDRzt3sn37dt54440M508r/A44iumLiIiIiIjIracwKj+LjMxQD8qMWOqoIZVdm0k9Jk39+vX5/PPPCQkJYfv27RSuXBkAJ+Ax/gqywoBGwIlxwbgAu4AzwB+//46vry8FCxZk0aJFFCxYkGLFilGnTh3+85//0LFjR+rXr++43rBhw5g8eTJNmjTh7NmzjvZnn32WmJgY/Pz8eO+992jQoME1P/acOXPo1KlThrbOnTvz1VdfZdm3du3aBAQEULNmTfr160fTpk0zbL9ezaa3336bhg0b8uCDD1KtWrVr9uuGpQsGs32fD0RHR1O2bFkAZs6cmWFbQEAAn3zyCe3bt+fkyZMZtsXExBAdHU3btm2ZMGEC4eHhABSeOZOLFy/S1Bh8Ri7jeEQEpf/8k0RjHIGmiIiIiIiI3F6appffWRbG25u1aSHTWMsRamRpSxdEgX1E0qpVq1i2bBk9e/ZkeGgofXv3BqAIQOX6VDi4gQRgI1AeCAHigOFAqdKlOXXqVJYutWrVivbt2zNs2LAM7dWqVcsw6umdd94B7CNq5s6dm2HfKVOmsGvXLiIiIrKcf8aMGVna2rdvT/v27QGyjETKbn/Ace433ngjQ3tISIjj9bPPPpuh4HiuMcYeGBrDyRFLHdMtjTH2al35REhICF27dqVs2bI0atSIw4czTiINDAwkNDSUdu3a8csvvzjaL126RIcOHYiPj8eyLHtQ6O1NN2Ag4ApcGRfMh9iD0gqA75o1XOrWLe8+nIiIiIiIiGRLYdQ/UVRUxkAjmyAK4MiRI5QtW5YBAwYQGxvL5s2bKVioEMTGEmNZ9O7dmyXelbj02zw8gRhgPvaV9oqPWIrPqnf55ptv6Nq1K5ZlsW3bNmrXrp0rH2HQoEE3f5LMn/sq9yHPeXtnmDrpkxZEkTqC7U7p5w1Kv0Jimg4dOtChQ4cs7ekDvzZt2jhqPaUPDtevX5/xoN69GWEMO0kd5ZfqWeyjACelrtyY+TxX65uIiIiIiIjcGpqml98ZgxUV5Zi2ZKWOuLFS3zdN3U42xbnDwsLw9/cnICCABQsW8OKLL9o3pBYAb9y4MW6/zWMkUBUoC5wESgPe44KZPXs2zzzzDF5eXri7u9OwYUPKli3Lv//9b1577bUMI4o6duxI3bp1qVmzJlOnTnW0e3h4MGrUKGrXrk2jRo04k1pDKSQkhNDQUPD2Ztq0adSvX5/atWvTuXNnLl++jIeHxw3dl2vdhzwXGZlh6mQEZJhueTcGUXnl5Iil+GRq80ltFxERERERkTuDwqj8LNMIG2ucfcWxtLjFyjTiJm36Xtookd69e7Njxw62bNnC6tWrHSudxcTEkJyczPIXX+Rz4F2gOFAQOAc8jn1qlE/Dhvz55594e3vj7+9P+/btsSwLDw8PGjRowIIFCzh37hwAn332GZs2bWLjxo1MnDjR0R4bG0ujRo3YunUrzf/4g6nnztHUGCb8uo+Phg/HiorisYED2bBhA1u3bqV69ep8+umnN3xfsrsPt5NClb+nzLjgLKtFHk5tFxERERERkTuDwqj87BojbP7uiJu4uDj8/f0pWbIk5wMDeQi4AOwFCqYWQ+8FrE49n81mIzo6GpvNxqBBgyhVqhTly5enWLFiVKlShUceeYQ6depQrVo1fHx8aNSoEUePHqVu3boMGDAAYwwTJkwgzsuLOsDPwBXg0to5FMZen2o7UN/FBXd3d8aPH59hClZMTAytW7emTp06+Pr6smTJkrti5JFClb/hGqtHZl4pUkRERERERG4fhVH53NVG2GR+f90RN6n/kC9YsCDh4eEccXbmypUrfPzhh5iSJSlVvjyHx7ZjrWVhSpSAgADHoWXLlqV48eLYbDaioqJ44oknAHB2dub//u//cHd3JyYmhpMnT/LFF19Qo0YNjhw5wvnz57HZbOzfv59PRo3iaezBUxHgBaAU8D7QGtjt6oqnpyfDhg3DxcWFxMRE/Pz8aNKkCV5eXmzevJkvvviCJ554Aj8/PzZX8KP837kPeUGhyt9zjdUjM68UKSIiIiIiIrePwqh87mojbDK/v+aIm3T1lS5fSaapMRQ5d46Ja9YQGhqK+6lTFC9enNWrVwMw6/nnadGihePw+++/n71793LlyhUuXrxIw4YNHdtefvllduzYgYuLC5Zl0aVLF7Zs2YKHhwcRERG4ubnx3HPPMWXKFJwC2pEAHAX+C2wG3gQsF1dcXFzYuXMn69ato2TJkiQmJvK///2PTZs24eHhgZ+fH0FBQSQlJfHzzz9T9sg26t3ofcgrd0OokjkQu1MCMsvCeHmx1rL+Cke9vPLVCoQiIiIiIiJ3O4VR+dk1Rthca8TNu+++S82aNfHz88Pf2Znf0++TGO84pz9Q+8wZ5s6dS8OGDRk4cCB+fn4sWLCAYcOGObrRqFEjTp8+zfLly/FOF1qcOXOGM2fOsHr1apo0aQLA8ePH8fPzw8nJifbt23PlyhVWr17N0aNHKb1lGU7AHMAJeDD19wqJCVy8eJHg4GBq1arFmTNncHZ2xtPTk9mzZ3Pp0iU2bdpEQkICRYoUIb5mTXalfp47duTRnRyq3MnF3yFrWHcnhHciIiIiIiLioDAqP8tmhA2p7yH7ETe//fYbS5cuZfPmzWzbto1fT5/m+XT7pJCxvtJ3CQn07NmTqVOnsnv3brZt28alS5dwdXUF7NP6nn32WYoWLcrHH3/MokWLALhw4QJPPPEEbm5uFCpUiFdeeYXExESKFy/OZ599RoECBShYsCCvvfaavbNxcewA3LCv1pcAnE/9HLuBds7ODBgwgEmTJlGpUiXHLYiOjsbb2xsXFxdSUlLshdE3bcKkfo47duQR3Jmhyl1Q/F1ERERERETubAqj8jsvrwwjbIyXlz2IucqIm1OnTuHp6ekIkzw9PTnR8z8USj3dEuyr5lUEjv9roSP46dOnD8YYWrZsycmTJ2nZsiWVK1cmPj6egIAA3N3dSUxMpE6dOvTp04ddu3axZMkSLly4QI0aNZg2bRrly5cnOjqaKVOm8Oeffzqm/QGUr1qVQUAhoDyQYnNmO3AP0Bwo8cQTDBo0iAIFChAbG0tSUhJnz55l27ZtTJ8+naJFi+Lk5ESxYsUAmP355wQWKHBnjjy6k90Fxd9FRERERETkzqYwKj9Lm04VFZVxOlVm6QKEhx56iGPHjlG1alUGDx7MypUrqTjrX5TCPiVuAPbwoQAQ/5/HMtR/cnFxISIiglKlSrFixQqeffZZbDYb4eHh7Nu3jw4dOjBhwgSmT5+OZVn89NNPnDlzhlq1avHjjz9SpkwZqlevjmVZ9OjRgwMHDgAQFBTETz/9xP7AQIo6OeHk5MSQwYMYNXEiv5csyeny5Vm0aBH33XcfM2bMYO/evXz++efUqVOHr7/+mscff5zdu3fj4uJC2bJlad++PbNmzeLD/fuveh/k6q5WFP+OKP4uIiIiIiIidzzn290BuUUyTafyyTSdynh7Zxu+eHh4sH37dnx8fJg/fz6fTZ7MBGABcAa4CHwGHAGSgJJL/wogbDYbwcHBfPrpp462tGl2r7/+On/88QeHDx8mLi6OhIQEHnzwQU6fPk18fDw2m41z585hs9no1q0bly9fZunSpXzyyScAVKlShcqVK7Py+HGso0dZunQpbdq0odTZs7Tq04dWrVrRq1cvAJ555hl69+7Nli1b8PPzo1+/fgA88MADPPnkk3Tp0iUXb/Q/T5nUZym9w0DguGAYq5FlIiIiIiIicm0aGZVf3cR0qoIFC3LgwAEiIyOpHxjIpNRj4gBTwY8JwEJgLFBl4UI2bNjA999/T0JCAt999x3x8fF89tlnjvpQy5cvZ+zYsbzwwgts2LCBihUrEh8fT2JiIvfffz8VKlRg7NixVKpUif379zN48GCWLVtG3bp1KVWqFA0bNqRHjx588cUXxMfH4+rqyhtvvMHvv//OwIED+fnnn5k6dSoAY8aMwUo31c7cKUW184trFMW/Y4q/i4iIiIiIyB1NYVQ+dt3pVJmDA29v9u7di2VZ2Gw23NzcWLduHbuNITK1hlTi8Z1sc3VlI+BavDgrV66kdevWJCUlAZCcnAzA//3f/3H27Fl+/PFHunfvDsDkyZMpWLAg33//PcYYihcvzrp16zh79iyTJk0iLCyMEiVKUL16db766itWrFjB9u3b+eGHH0hISMDV1ZUHH3yQhIQEZs+eDcCmTZto1aoVL730EmAPo9I0b96cuXPnkpyczKlTp1ixYkWu3dt/rGyK4t+Rxd9FRERERETkjqUwKh8rMy6Yw5naDqe2p9WTqmcMxhhsxlAgKoqgatW4fPkylmVRrlw5vL29qVmzJjOd7TM6R4wYgZubG0WLFiUwMJBz586RkJBAYmIiAKVKlaJMmTIkJSVx4MABNm/ezLlz50hMTOTy5csYY7AsC2MMa9euJSkpiYsXL3L48GESExOx2Wy4urri6upKiRIl8PT0dPQ9Pj6eb775hpSUFEqVKsWBAwc4ceIEy5YtY/fu3YwcOZK4uDji4uLo0aMHnTp1Yv/+/Xh4eFC2bFnKli3rOJeHhwejRo2idu3aNGrUiDNnzjBjxgy8vLzw9/d3/Nq1a1eO7/eUKVP44osvbug7CgsLY926dTd0zG2XWuxdxd9FRERERETk71DNqPwq03QqH+xBVNp0qrTYIC2NTAZSsK9Ol+bIkSMULlyYo0ePEh8fD8DEiRNxdXXl3Llz7N+/n5iYGJKTk9m+fTuVKlXi6NGjnD17FgA3NzeSk5Np164dy5YtIzY2lri4OMAeWhUrVoyDBw8SFxdHSkoKlmVx4cIFfv/9dyIjI0lJScFms2FZFpZl4eXlxcWLFzHGUL16dU6ePEnbtm0ZOnQosbGxeHh48NFHHxETE+P4DBs3bqREiRLce++9JCcn07JlSwBiY2OZNGkSlSpVYt++ffTt25fHH3+cbt268dFHH/2tWz5o0KAM72fMmMHGjRuZMGECzs4Z/6h5eHgQExPDd999xy+//MK2bdv+1jVvm8wjoDQiSkRERERERHJIYVR+FRlpH4VEuulU6YqYNwXeBNxSd98CPAScS3eKpKQk/vzzzwynjY2JcYQ9e/bsYc+ePQD4+NgnBEalW63v8uXLODs7szS1yPnFixcd206ePMnJkyezdDsxMZHDhw9jWRYeHh7ExcVhjCElJYW2bdsyc+ZMbDYbzZo1Y/z48fzxxx+sX7+e/fv3s3jxYhISEqhXrx5xcXF06dIFYwyLFi3i9OnTGGNo1qwZHh4eODk50bNnTz7++GOmT5/OkCFDaN26dba3MiwsjDfeeINSpUoRHh7OY489hq+vLx9++CFxcXEsXryYypUrExISgoeHB8OGDSMoKAgPDw+2bNmCj48PVatW5Z133uHKlSuULFkSy7KIiIhg9uzZODk54e/vz6RJk6hWrRqDBg3i6NGjAEyYMIGmTZuycuVKXnzxRcBeB2vVqlUULlz4Og+BiIiIiIiIyJ1H0/Tys2tMpzo5YimPA3VTd22KPYhyLnZPhlOULFkSm+2vxyS7iVjXeojSakkZY6hatWoOu22fxleiRAm8vb0pUaIENpuNL7/8EoDnnnuO5s2bU7RoUS5fvszixYvx9/enQoUKuLq6smfPHtq3b89bb73F+PHjWbx4MWXKlKFMmTL897//ZciQIY5rABQrVozChQsTHR3NvHnzqFChAu7u7hhjKFmyJP3792fNmjUsXryYtm3b8v777/N///d/LFu2jP79+9OiRQs2btwI2EdcVaxYEbCHcfXq1eOXX36hQ4cOPPLII2zZsoXu3buTmJjIhQsXaNiwIcnJyYSHh9OkSROaN29OeHg4CQkJBAcH079/fwBCQ0P5+OOPCQ8PZ/Xq1RQsWDBH91JERERERETkTqMwKr+7ynSqMuOCeZ3UotNAPPaV0cyFUxl2P3/+PCkpKde8xLW2pgU+lmWxb98+R3uRIkUy7Ofi4gLgCL4syyIpKYmoqCjOnz/P/fffz6OPPgrAzJkz6dSpEwsWLMDb25vSpUsza9YsWrRoQUpKCrGxscyZMwewT4ebPXs2iYmJnDx5krFjx1KgQIEMnykqKork5GTKly/Pgw8+iJ+fH9HR0RQqVIhu3brRtWtXWrVqRWJiIvXr16dRo0a0bt2aUqVKMWnSJE6fPs2TTz7JmjVrAPsIsOTkZBo0aMD69etZs2YNbm5uhIaGct999zF+/HhSUlL44IMP2LJli6MfU6dO5dChQ3h4eGCz2fjmm2+4ePEily5domnTprz88stMnDiRCxcu8P333zN27Nhrfi8iIiIiIiKSv50+fZru3btTuXJlatSoQdu2bZk6dSrBwcHZ7t+/f/8bqo18q2ia3j9Raj0pJ8A9XfMRoECmXZ2dnR3FydOzce0QKo2VWtQ6rXB5mvRT9uCvEVQpKSkUL16cP//8k1OnTjmO2b17N3v37gXg0qVL2Gw2Hn74Yc6fP+84944dOxx9bdq0KcePH8fV1ZX//Oc/XLx4ETc3N9asWUOtWrUAmDdvHitWrGDPnj00btwYFxcXjh8/zoEDB6hfvz5xcXEsX76cxo0b4+rqis1mo1u3bowdO5Yff/wRy7Lw8/Pj6NGjfPnllzzyyCPUqVPHEUZNnTqVBx54gEWLFpGcnEyJEiU4evQoXl5eJCcn8/333xMbG8uVK1coW7Yszs7OJCUlUalSJWbPno27uztvvfUWrVq1Ii4ujmqpxeUbNWpEmTJlaNmyJQ0aNODChQt8+umnNGvWLAffiIiIiIiIiOQHlmXRqVMnevfuzdy5cwEIDw/nu+++u+ox06dPz6vuXZNGRv0TRUZigIJAbGpTAexT8DJXIcouiIKcBVHpWddZaS399rQ6VZmPSU5Oxs3NXuVqyZIlFC1aFGMM7u72SC0mJoakpCTc3d3p3bs3ycnJxMfHU7BgQUqXLs3o0aMJDg5m2rRp+Pn5UalSJdzd3alQoQIbNmzgwoULAPTu3Zvw8HAAChYsyJo1a1i9ejWWZREfH8/+/fsZPXo0NpuN6OhoR/98fX359ttvSU5OZvPmzSQkJDim01mWRUpKCiVLlnR8vri4OIoXL07RokU5efIkf/75JwULFiQsLIzQ0FAAFi5cSNOmTSlQoAC//vorLi4u1KtXj5MnT/LTTz+xfv16nnjiCYKDgwkICOCBBx7gzJkzN/jtiIiIiIiIyN1mxYoVuLi4ZFhMy9/fn2bNmhETE0OXLl2oVq0aPXr0cPz7OigoyFFmJrtV5gG+++47GjZseEv/jakwKj/z9r76e8tyTNEDuJL6+9lb3ae/wcXFBcuyKF++PPPmzXMEO8ePH8eyLOrUqYNlWY4V6y5fvky3bt2w2Wx07dqV++67j8jISD744APH1LdHH30UX19fNmzYwKFDh/Dx8eHLL79k+/btvP/++9SsWROAsWPH8s4779CsWTMsy+Kjjz7Czc2N8PBwLMti165dJCUl0blzZ9zd3Tl9+jROTk54eHjQvn17fvnlF5KSknBycuLSpUtcuHCBhIQEwB5Gpa2qB/DII4/QsmVLihQpwjvvvMP999/PkSNH+Oqrrzhy5AgxMTGMGDGCggULUqhQISpXrgzAk08+iZeXl6Me1XvvvZdn340xhn/961+O96GhoYSEhAAwZcoUvvjii1y7VpMmTXLtXCIiIiIiIne7HTt2ULdu3Wy3bdmyhQkTJrBr1y4OHTrE2rVrs+wTGxtLo0aN2Lp1K82bN2fatGkABAYG8vvvv9/Sf2MqjMqvjMGKiqKpMfiMXEbT1PeYvyKoONvf+/pdc6uPOZRW+PzYsWN06NABm81GyZIlcXZ2xrIsNm7cSEBAgKPYus1m4/Lly1iWxbJly9i0aRPu7u489thjNG7cGDc3N4oVK8bChQupVasWvr6+REdHs3fvXo4ePcoXX3zhqGE1atQoKlasyNKlSylUqBAHDx4kOTmZvXv3UrBgQT755BOaN29O8eLFuXDhgmOlPHd3dx588EECAwMB+/TDdu3a4e/vj6enJ25ubri6uuLi4kK5cuVwcnJi9uzZVKlShcTERJKTkxk0aBCxsbGEhITQsmVL/u///o9ChQoxefJkjDE4OTkBcObMGU6cOIGvry/jx49n586defbduLq6snDhQs6ezRpjDho0iF69et30NZKTkwFYt27dTZ9LRERERETkn6BBgwaUK1cOm82Gv78/ERERWfYpUKCAo7ZU3bp1HfscP36cNm3a3NJ/YyqMyo+8vbGwFydfA1jjglmT+t5K3Q787RXZEnKlkzmX/sE3xpCSksK5c+eIi4tz1LQKDw/nzJkzpKSk4OTkxH333UeBAgXw8PCgWLFijsLomzZtIj4+nunTp3Px4kX69etH4cKFsSyLYsWKcfLkSbp168ZLL72EMQYvLy969uzJK6+8AsDEiRMpVqwYn332GcYY5s2bh4eHB5s3b2bdunW89957GGPo0KED/fr14+uvv8bZ2RmbzUZQUBAFChQgJiaGxYsXExcXhzHGUVNqw4YNvP/++zRr1ozSpUvTt29fACpVqsSMGTOYP3++4x6k9+qrr1KkSBG2b9/OJ598Qnx8fF58LYC9ptjAgQP54IMPsmwLCQlxTDfcsGEDfn5+NG7cmOHDhzvqdiUnJzN8+HDq16+Pn58fn3zyCQBhYWG0bNmSJ598El9fX8A+hBTs0zFbt25NnTp18PX1ZcmSJXnxUUVERERERO4oNWvWZNOmTdluc3X9axiJk5OTo05zei4uLo5/X6bf5/nnn2fIkCG39N+YKmCeH0VGEmiMI4CKSG22gEBgbWQkeHsTGxt7tTPcsdLmuab9nv4P1JUr9smGiYmJjtUBKlSogLu7Oxs2bGDKlCmOfVNSUkhJSeH999+nZcuWbNy4EWMMtWvXpmDBgri4uJCcnMzy5cspXLgw48ePB8DT05PixYtTrVo1EhISWLduHfHx8ZQpU4YKFSo4ztuhQweioqJ48sknHdP0RowYQUpKCgULFuT111/HycmJ3bt38+CDD+Ls7EyvXr24fPkyRYsWpVOnTkyYMIFChQrRtWtXLMuiefPmHD16lL59+3Ls2DGOHz/OK6+8wsWLF3FycuLZZ5/lm2++IS4ujjfeeIM333wzT76T5557Dj8/P0dgl52+ffsydepUmjRpwsiRIx3tn376KUWLFmXDhg0kJCTQtGlTHnroIQDWr1/Pjh078PHxyXAuNzc3Fi1aRJEiRTh79iyNGjWiffv2WUI6ERERERGR/KxVq1a89tprTJs2jQEDBgD2gQArV668qfNGR0dTtmxZwL6a/a2gkVH51MkRS/HJ1OYDnATHFL78yMXFxfGHxhjDqlWr2LRpEy4uLo5pfPDXqLATJ07w3XffkZKSwpUrV0hJSaFt27YUL14cZ2dnSpUqxUsvvUSbNm1YvXo1S5YsoW7duvznP/8B4OjRo1y4cIGiRYvSunVr3nzzTZydnRkyZAgBAQG0atUKJycnvL29uXLlCp988gktWrTg8uXLvPLKK7z55psUKVKE0qVL4+bmRnR0NFWrVuXtt9/m66+/plq1avTs2ZOUlBSOHj3KSy+9RHh4OOPHj+epp55i3rx5vPTSS446Vn379qVevXqsXLmSbdu25ck9L1KkCL169WLixInZbr9w4QKXLl1y1Hx68sknHdt+/vlnvvjiC/z9/WnYsCHnzp1j//79gH1YaeYgCuxB5GuvvYafnx8PPPAAJ06cUNF2ERERERH5x0mbafPLL79QuXJlatasSUhICGXKlLmp84aEhNC1a1eaNWuGp6dnLvU2I42MyqfKpE7NS+9w6u8W+TeFTExM5MSJE4A9tIiNjXUUPL9w4QIpKfZ1AC9cuIAxBsuysNlsjpFQiYmJzJkzB8uyKFKkCPHx8Xz11VccPHiQ1atXU7FiRU6ePMmyZcsoWLAgKSkpxMfHc+XKFWbOnMn7779PkSJF2LBhA82aNSM5ORkPDw+efvppPv74Y9q3b8+lS5c4d+4cb731FgCnT59m/vz5bN++nerVq/Pf//6Xo0ePUrx4cVxcXHj44YdZtWoVO3bsYMaMGbRu3ZrBgwcD9qLn999/P4cOHWLKlClMnTqVpKQkTp06xa5du/Dz88uT+/7SSy9Rp04dx9TC9K61kqJlWUyaNIk2bdpkaA8LC6NQoULZHjN79myioqIcIWPFihXzdGqiiIiIiIjInaJMmTJ8/fXXWdrTRkoBfPTRR47XYWFhjtdpi2kBdOnShS5dugDQoUMHOnTocAt6+5f8mkn8c3l7g7e3Y4oe2MOntBpS+X0iU+apWumDEHd3d8fr48ePO4Kp6Ohox0p8aQHV8OHDcXZ2JiUlhUuXLpGcnIyLiwteXl7ExMTg7+9PXFyc43xHjx4lJSUFd3d3SpYsSWhoKJZl0aNHD4KCgvj++++Jjo7miy++yBLOFCtWjFdffRWw/wUwf/585s2bR926ddm+fTsA586d4/Lly4B97q+7uzsHDx7EycmJQYMGcfjwYUJDQ1m+fDnbtm2jXbt2joBmxowZeHl54e/vT7Vq1bKt73Szvv32W0qUKMGnn36aZVv58uUpXLgw3333HV26dGHu3LmObW3atGHy5MkkJiYCsG/fvutOH923bx/Lly+nQYMGfPnllxw5csSxbcyYMY7XERERjtpUf9cPP/xAvXr1qF69OtWqVWPYsGE3dT4RERERERFRGJW/bNpkX0EvdQpediGUBTjdnt7liWuNwrl06ZLjdVoQlfY6LRFOO378+PGcP3+eCxcucPLkSSzLIjo6mpUrV2JZliMYcnd3p3DhwoA9JOrcuTMxMTF89tlnuLq60qhRI7777jsGDRpESkoK48ePZ+jQoXz88cfce++9fPDBB4wbN46aNWuSkpJC9+7dGTp0KPPnz6dFixY0btyYMWPGcPbsWcc10+bstmjRgh9++AHLsli8eDGFChWifPnynDlzhh9++CHDZ+/WrRvh4eGsXbuWd999l2PHjgF/FQW/UWFhYY5VF7799luWLVuGv79/tqvqgb021KhRozhx4gSWZVG0aFEA+vfvT40aNahTpw61atXimWeeybawXnpOTk64ubnh5OREWFgY1apVc2xLH0bdrB07djBkyBC+/PJLdu/ezY4dO6hUqVKunV9EREREROSfSmFUfpE6IihtBT1Hc+bdsmmTq8su3EpOTmb37t0A/Pnnn44RUseOHeOzzz7j5MmTHD9+nA0bNhAZGUlKSgrPPPMMYJ+SB3D+/HmOHTvGyy+/THJyMn379uWLL74gMDCQlJQUDh8+zIsvvsjFixfZunUrSUlJlC5dmkGDBnH58mXi4uKYOXMm7u7u7Nu3j/Hjx3Ps2DFiY2O57777iI+PZ968eVn6XrJkSe677z5OnToF2AvAN2jQAH9/f5555hmSk5MBe0j1r3/9izp16tC6dWuiUgPOoKAgNm7cCNgLxnt6etK+fXvatWvHuXPnaN68OXPmzOHNN98kJCTEMZKoZs2afPvtt1y6dImiRYtSt25dhg0bRu3atVm6dCkDBw5kx44drFixgqJFixIUFMQ777xDo0aN8PPzo1OnThw7dozvv/+eadOmYVkWhQsXZvr06ezevZuKFSsycuRI4uLi8Pf3p0ePHo7vasCAAdSsWZOHHnrI8V0dPHiQhx9+mLp169KsWTP27NmT5V699957jBo1yhF2OTs7O6ZHHjlyhNatW+Pn50fr1q05evQoAH369OGFF16gSZMmVKpUybECYkpKCoMHD6ZmzZoEBwfTtm1bx7bly5cTEBCAr68v/fr1IyEhr9erFBERERERyVsKo/IDb2/SRyaGa4dOKVdpl7/neiN54NojtlJSUjh27Bh9+/blwoULjnOeOHGC1atXZ6iHlJiY6BjV9dprr5GcnExSUhLnzp1zhBg2m42YmBj+97//UbBgQZ5++mlmzZpFQEAA//73v9m+fTuPP/44lSpVIiEhgT///JMmTZrw3Xff8eSTT1KiRAliY2OZN28ejz76KC1atODNN9/kxx9/ZP369fTv35+FCxc6+jRjxgxmzZrF+vXrKV68OK1bt2bcuHGULVvWEbgsW7aMhg0bsmvXLsaMGcOuXbs4fPgwa9asoXz58nzyySfUqlUrQ4DWq1cvxo0bx7Zt2/D19eXNN9+kbdu2DBo0iKFDh7JixYoM93Hs2LEULFiQ8PBwZs+eDcD+/ft57rnn2LlzJ8WKFWPBggUADBw4kEmTJrFp0yZCQ0MdIVN6O3bsoG7dutl+Z0OGDKFXr15s27aNHj168MILLzi2nTp1ijVr1rB06VLHyoELFy4kIiKC7du3M336dH777TcA4uPj6dOnD/PmzWP79u0kJSUxefLkqz4rIiIiIiIi+YHCqPwgMpLA290HuSHlypXL8L5AgQIANG7cOEO7k9NfkypbtGiRYXph1apVSU5OxsnJiX79+jleFy5cmJSUFB5//HGGDh1KzZo1SUxMJC4ujtdee42uXbsSEhJCZGQkxhgKFizIJ598Qnx8PI888gh79+7FGMPBgwdZuXIl9erVY9WqVQwYMIBatWoxbdo0xwiv9B588EFcXV2JioriX//6Fz179nSEMcWLF6dVq1ZUr16dCxcusHPnTpo0acKvv/5KmTJl2LFjBzt27ODhhx8G7HW8Lly4QIsWLQDo3bs3q1atuuH77OPjg7+/PwB169YlIiKCmJgY1q1bR9euXR0jwtJGiuXUb7/95lgVsGfPnqxZ89d4xI4dO2Kz2ahRo4Zjlb81a9bQtWtXbDYbpUuXpmXLlgDs3bsXHx8fqlatelOfU0RERERE5G6iMCqfyLxy3rVoCcXbL23FvzRXrlwBcIyYSZM2bQ7sI3XS69y5M1euXCE5OZnPPvuMhIQEkpOTadiwIS4uLqxbt44ZM2Zw5MgRUlJScHNzwxjDwoULiY6OpkaNGjg7O7Nt2zbq1avnKPA+YcIELMuiXr167Ny5k/3795OQkICPjw9FihTBsiyeeuqpDMEY/FU8vmPHjhhjuOeeexxhzM8//8zq1as5dOgQderU4dKlS5w8eRJfX19+/fVXRowYwerVqx21pHKLq6ur47WTkxNJSUmkpKRQrFgxwsPDHb/Spl2mV7NmTTZt2pSj66QvnJ/+mmkj4q42Mu5aI+ZEREREROQfxNs74/utW29PP/KIwqj8IPNDex2FblE3JOfSRkJlZrNl/SOZttJfkSJFMqwI+PbbbztelypVivvuuw+AX375BScnJ7799ltcXFwAKFGiBOHh4RQvXpzevXvz888/U6FCBZKSkoiMjMTLy4u3336b+Ph4QkNDARg1ahTt2rVj5cqVBAQEYIyhYsWKjoAm82iiX375hYSEBIwxLF68mKZNm2YIYwYPHkzlypUJDw8nNDSUI0eOUKlSJTZt2oSPjw+vvvoqb731FgBFixalePHirF69GoBZs2Y5Rkldi4uLi2NlvqspUqQIPj4+fPPNN46+bc3mL/rhw4czZswY9u3bB9inU77//vsANGnSxLEq4OzZswkMvPbYxMDAQBYsWEBKSgpnzpxxLKdarVo1IiIiOHDgwA19ThERERERyUeMsS9GZgw+I5fR1BhISnLUhs6PFEblB6nFpXMq+hZ1Q3LuakWqM482KlCggKMm1YEDB7h8+bIjsNq3bx9OTk44OTlx8uRJR6Bx5coVEhISmDVrFj169MDd3d1RuLtWrVpUrVqVNWvWcOrUKQoUKMBDDz1EWFgYY8aMoVKlSpQsWRJnZ2f++OMPZs+eze7duxk/fjyHDx/m8ccfZ/LkyfTr188xmitNYGAgq1evZtiwYXTu3Jl69eo5trVp04avv/7aMdLrkUcewdPTk+rVq9OiRQuSkpIYNmwYmzdvdhwzc+ZMhg8fjp+fH+Hh4bz++uvXva8DBw7Ez8/PUcD8ambPns2nn35K7dq1qVmzJkuWLMmyj5+fHxMmTOCJJ56gevXq1KpVyxHATZw4kc8//xw/Pz9mzZrFhx9+eM3rde7cmXLlyjlWDGzYsCFFixbFzc2Nzz//nK5du+Lr64vNZmPQoEHX/ZwiIiIiIpJPpNaATluMzBoX7Jj5ZKVuz4/MP32aSL169ay01cHuSt7eWFFRGCAsNJSg1NXLrsZCCeSdzBiTYeqWu7s7ly9fBqB8+fJcvHiR6Ohox7a4uDiMMY7aUomJiZQvX54zZ85QpUoVnJycOHr0KCkpKZQvXx5vb2/i4uI4ffo0pUuXZvv27cTExODv70/09u04V6rExYsXiYyMpG2BAri2a0f79u3p06cPP/74Iy+99BKenp4EBgayY8cOli5dyowZM9i4cSMfffQRffr0ITg4mC5dugD2VfliYmIA+PDDD5k+fbqj/csvv+TAgQMMHz4cm82Gi4sLkydPzhBi5ScxMTF4eHhw7tw5GjRowNq1aylduvTt7lauCwsLIygo6HZ3Q+5ieoYkN+g5kpulZ0hulp4huRFNjWENGRchCwsNZdSwYay9izMbY8wmy7Ky/Qeeygfd7SIjCUx9cHPKAsKBekBVYALwIGC8vPj1q69o074zVtxFagMfAKFAI2A04I39D0gUUBgomPo6xb0YjS9fYA5QBfgOGAAcT92/WOp+Y4HTwIfp/kB98MEHzJw5k1OnTnHhwgUKFizIpUuXqFq1KidOnOCXX36hYcOGWT5HTEwMv//+O7179yY+Pp5y5cqRmJjIwYMHWbhwIe3atcsQ1lmAD3A4tU9W6mduGhXl+INfEdgIlAQCIcsf/Oz+krCAxsDvloVlWTz33HPExMSwbds2kpKSqFChAjNmzKB+/fps3LgRT0/PLJ+lYsWKPP300yxYsIArV64QEBDAtGnTMkzLu5XCt27FAqYOGsTYqGrYxgWz9MoVzKJFkLpy3sMPP8yePXuyHNunTx/69OkD2FfWSy8tiAJ48cUXefHFFzNsr1y5Mm3atMnVz3KnCg4O5sKFC1y5coXRo0fnyyBKRERERERu3MkRS/EZF0xEurbtqe35lcKofODkDexrgCnARMATKE5qEAUQGckDQK24i8RhD5+CUn9VTT0+EnACigAxQCxQFPjz8gU2Yw+ivIEuwCWgDdAHewjlC1QAPgf7UMPISACGDh3K0KFDAWjWrBm7d+8mNDSUSZMmMWDAgGyDKPjrH/dFixbl3XffxWazMWrUKL788kt7EJX6mYwxWNjDJTNiKYGpwx7TAqW012mxU0n+GiKZvp94e2fYN32w9Tjg7+zMlapVrxokRUREZPs50jz77LOMHj36mvvcEumGhd6PfVhoEukCu/T3QP62tDpRIiIiIiIi6ZVJNzUvjW9qO2Pv3pFR16IwKh84dIP7DwKewT4qiMjILIFLOH8FNWmP/b50x6cFMUX5K4xJvy2tlHP6wKd7pu1rrxJupBWsBhwB1dVk94/7Xr16Zd3RsjDe3n9dc6yV4TNfM6xK389rBFsvAy+n1nb6O64XVN1SmUbXpfXket+ViIiIiIiI3KSrDHqAbAZI5CMqH3S3S62ufyM19tMecMcDnS6IIt2UtrTRMpnPbfjrD0dmPtiHEp5Mu8ZVtue5zH9407+3LIyXF2sti8Nj27E29T3Zzc29kX3vIidHLM3yfd2270pEREREROSfIjLS8W9wx6CH1E1ZBkjkIwqjxB5opS4laWGvm5RdkJQ+bjHYR9BkDqoOA4fGBXOI7AOrw6QONcwtmVcW+LsrDVwrrLqZfe8SZcYFZ/m+cv27EhERERERkayyGfSAs/NdP+jhWhRG3c1Sp4zdyKgo+Gtkk2WM/RcZR0EdJuv0u7Tj0vZNLy3ASt+X9COqModYjqGG1wuSrhcspQZoTY3BZ+Qymqa+TxstlmtBVX6XblgoZPwuHd+ViIiIiIiI3DqZBznUrn17+pFH8l0YZYx52Biz1xhzwBgz8nb3506SORRK/zp9Ae/spuZld1z6tsOZ3me+lkXGIKtpVFTG4Oh6wVJm6Ypur8FedDtDIfIbPd8/WbphoXv5a1ioI1zMByO/RERERERE5M6Rr8IoY4wT8DHwCFADeMIYU+P29urOkd1Ip8yh1PWkBRTpR0NlPpejJtVVru8DWYMjrhEsZTcyJzIyQ2ASkW7/9O05Pt8/Xeqw0Pvr1s1XtbBERERERETkzpOvwiigAXDAsqxDlmVdAeYCHW5zn24dy8KHrNPmbqX0gQ/ZFL2GjNX/07tacIRlXTNYutrInGsV3f475/vHy4e1sEREREREROTOY6x8NPLBGNMFeNiyrP6p73sCDS3LGpJpv4HAQIBSpUrVnTt3bp73NVds2pThbUy5cngcP543165bl72bNnH/dXbbDvheYxul78O3bFG2n4iG0wcy7Jt+e3audv29wJXS993w+QRiYmLw8PC43d2Qu5ieIblZeoYkN+g5kpulZ0hulp4huVn54Rlq2bLlJsuy6mW3Lb+FUV2BNpnCqAaWZT1/tWPq1atnbdy4Ma+6mLsyFTAPCw0laNiwW3Kp9NdJP1Uvc5Hy9O8DsY9SKpM6TS799vT7rLUsmhqToYh25u1ZeHtjRUU5+pE2Giv9CKgbOp8AEBYWRlBQ0O3uhtzF9AzJzdIzJLlBz5HcLD1DcrP0DMnNyg/PkDHmqmFUfpumdxwon+59OeDkberLXcvK5vf0RcgN2GsMpWvzSf2Vfh/HspReXpDuHBXT7bMGIF0Qle32q9SMSh88ZS66fcPnExEREREREZE8kd/CqA1AFWOMjzGmANAd+PY29+nWyRQIke71zfziKr8byFjUOrXItfHyIsKyiEj3PkPh6+sER+k/R7bbr1a7KPV6jtArXdHtv3U+EREREREREbnlnG93B3KTZVlJxpghwE+AE/CZZVk7b3O3bi3Lwpi/JqPlZEU8By8viIrK+t6ywBj7797e9vAm7ffMclr02rIw3t6sTds+1sp4zuttv5qrXf/vnk9EREREREREbql8FUYBWJb1PfD97e5HnkobhRQWlnFEUm6cMy28yY0Q53rBVW6v5qbV4URERERERETuOPltmp6IiIiIiIiIiNzBFEaJiIiIiIiIiEieURglIiIiIiIiIiJ5RmGUiIiIiIiIiIjkGYVRIiIiIiIiIiKSZxRGiYiIiIiIiIhInlEYJSIiIiIiIiIieUZhlIiIiIiIiIiI5BmFUSIiIiIiIiIikmcURomIiIiIiIiISJ5RGCUiIiIiIiIiInlGYZSIiIiIiIiIiOQZhVEiIiIiIiIiIpJnFEaJiIiIiIiIiEieURglIiIiIiIiIiJ5RmGUiIiIiIiIiIjkGYVRIiIiIiIiIiKSZxRGiYiIiIiIiIhInlEYJSIiIiIiIiIieUZhlIiIiIiIiIiI5BmFUSIiIiIiIiIikmcURomIiIiIiIiISJ5RGCUiIiIiIiIiInlGYZSIiIiIiIiIiOQZhVEiIiIiIiIiIpJnFEaJiIiIiIiIiEieURglIiIiIiIiIiJ5RmGUiIiIiIiIiIjkGYVRIiIiIiIiIiKSZxRGiYiIiIiIiIhInlEYJSIiIiIiIiIieUZhlIiIiIiIiIiI5BmFUSIiIiIiIiIikmcURomIiIiIiIiISJ5RGCUiIiIiIiIiInlGYZSIiIiIiIiIiOQZhVEiIiIiIiIiIpJnFEaJiIiIiIiIiEieURglIiIiIiIiIiJ5RmGUiIiIiIiIiIjkGYVRIiIiIiIiIiKSZxRGiYiIiIiIiIhInlEYJSIiIiIiIiIieUZhlIiIiIiIiIiI5BljWdbt7sNtZYyJAo7c7n7kEk/g7O3uhNzV9AzJzdIzJDdLz5DkBj1HcrP0DMnN0jMkNys/PEMVLMvyym7DPz6Myk+MMRsty6p3u/shdy89Q3Kz9AzJzdIzJLlBz5HcLD1DcrP0DMnNyu/PkKbpiYiIiIiIiIhInlEYJSIiIiIiIiIieUZhVP4y9XZ3QO56eobkZukZkpulZ0hyg54juVl6huRm6RmSm5WvnyHVjBIRERERERERkTyjkVEiIiIiIiIiIpJnFEbdhYwxDxtj9hpjDhhjRmaz3RhjJqZu32aMqXM7+il3rhw8Qz1Sn51txph1xpjat6Ofcue63jOUbr/6xphkY0yXvOyf3Ply8gwZY4KMMeHGmJ3GmJV53Ue5s+Xgv2VFjTHfGWO2pj5DfW9HP+XOZYz5zBgTaYzZcZXt+plarikHz5B+ppZrut4zlG6/fPcztcKou4wxxgn4GHgEqAE8YYypkWm3R4Aqqb8GApPztJNyR8vhM3QYaGFZlh/wNvl8vrLcmBw+Q2n7jQN+ytseyp0uJ8+QMaYY8F+gvWVZNYGued1PuXPl8O+h54BdlmXVBoKA/xhjCuRpR+VONwN4+Brb9TO1XM8Mrv0M6WdquZ4ZXPsZyrc/UyuMuvs0AA5YlnXIsqwrwFygQ6Z9OgBfWHa/A8WMMffkdUfljnXdZ8iyrHWWZf2Z+vZ3oFwe91HubDn5ewjgeWABEJmXnZO7Qk6eoSeBhZZlHQWwLEvPkaSXk2fIAgobYwzgAZwHkvK2m3InsyxrFfbn4mr0M7Vc0/WeIf1MLdeTg7+HIJ/+TK0w6u5TFjiW7v3x1LYb3Uf+uW70+Xga+OGW9kjuNtd9howxZYFOwJQ87JfcPXLy91BVoLgxJswYs8kY0yvPeid3g5w8Qx8B1YGTwHbgRcuyUvKme5JP6GdqyU36mVpuWH7+mdr5dndAbpjJpi3zkog52Uf+uXL8fBhjWmL/D2fgLe2R3G1y8gxNAEZYlpVsH5QgkkFOniFnoC7QGigI/GaM+d2yrH23unNyV8jJM9QGCAdaAZWBX4wxqy3LuniL+yb5h36mllyhn6nlJkwgn/5MrTDq7nMcKJ/ufTns/8fvRveRf64cPR/GGD9gOvCIZVnn8qhvcnfIyTNUD5ib+h9NT6CtMSbJsqzFedJDudPl9L9lZy3LigVijTGrgNqAwiiBnD1DfYGxlmVZwAFjzGGgGrA+b7oo+YB+ppabpp+p5Sbl25+pNU3v7rMBqGKM8Uktwtkd+DbTPt8CvVJXAGkERFuWdSqvOyp3rOs+Q8aYe4GFQE+NQpBsXPcZsizLx7KsipZlVQTmA4Pzw380Jdfk5L9lS4BmxhhnY4w70BDYncf9lDtXTp6ho9hH1mGMKQXcDxzK017K3U4/U8tN0c/UcrPy88/UGhl1l7EsK8kYMwR7JX0n4DPLsnYaYwalbp8CfA+0BQ4Al7H/n0ERIMfP0OtASeC/qSl8kmVZ9W5Xn+XOksNnSOSqcvIMWZa12xjzI7ANSAGmW5Z1zWWP5Z8jh38PvQ3MMMZsxz7daoRlWWdvW6fljmOMmYN9pUVPY8xx4A3ABfQzteRMDp4h/Uwt15SDZyjfMvaRyyIiIiIiIiIiIreepumJiIiIiIiIiEieURglIiIiIiIiIiJ5RmGUiIiIiIiIiIjkGYVRIiIiIiIiIiKSZxRGiYiIiIiIiIgIAMaYz4wxkcaY665kbIypYIxZbozZZowJM8aUy8k1FEaJiIiIZMMYE2SMsYwxnjd5noqp58n15byNMVWMMWeMMUVT3+dKn3N47QzXutH3udQHV2PM0Vtxb0VERP7BZgAP53DfUOALy7L8gLeAf+fkIIVRIiIickcxxsxIDS0sY0yiMeaQMSbUGFPodvftelL/j+BHmZqPAfcA4bfgkmOA/1qWFZ36fl3qtc7dzEmNMRHpvoNkY8wJY8wUY0zhdLvd6LVypW/pWZaVAIwHxuXWOUVERP7pLMtaBZxP32aMqWyM+dEYs8kYs9oYUy11Uw1geerrFUCHnFxDYZSIiIjciX7FHlxUAv4PGIz9/7zddSzLSrYs67RlWUm5eV5jTHmgI/B5umtdSb2WlQuXeAv7d3Av0BtoC7z3d6+Vy31LbzYQaIypmcvnFRERkb9MBZ63LKsuMAz4b2r7VqBz6utOQGFjTMnrnUxhlIiIiNyJElKDi2OWZX2FPXDoCI6pWRNSp6fFG2N+N8YEph2YbjpYsDEmPHWfTcaYuun26WOMiUl/wetNIzPGlDTGzDHGHDfGxBljdhpj+qbbPgNoATyXblRRxeym6Rljmhtj/kjt2xljzAfGmALptocZY/5rjBljjDmbWrch1BiT/me3bsB2y7KOXu0zpH1OY0xrY8wOY0ysMWaFMcYnB9/BpdTv4IRlWb8CXwN1cnq/srl/WfY3xjxmjNlujEkwxhwzxowyxph02yOMMf9njPnEGHMx9d4PT39ey7LOA2uBJ3LSDxEREbkxxhgPoAnwjTEmHPgE+/+wAnsw1cIYswX7z0EngOv+DziFUSIiInI3iANcUl+/hz2I6QcEANuBH40x92Q6JhQYAdQDDgHLjDHuN9EHN2AzEAzUBD4EPjHGtE7d/iLwG/aRSvek/jqW+STGmLLAD8CW1P4/jT1IyVxjoQf2H+aaAEOAl7B/7jTNgI056Lcr8Cr2+9UYKAZMycFx6ft8L9AG+ONGjrvOOesC3wALAV9gZGo/h2TadSj277gO9ul47xljGmfaZz32H4BFREQk99mAC5Zl+af7VR3AsqyTlmU9ZllWADAqtS36WidLO6GIiIjIHcsY0wB4ElieWjfqWWCEZVnLLMvaDQwCzgDPZTr0bcuyfrIsawfQF3uY9OTf7UfqCKHxlmWFW5Z1yLKsqdiDlCdSt0cDV4DLqSOKTluWlZzNqQYDp4DBlmXttixrKfYgZkimsGyXZVmvW5a1z7Ksr7HXYWidbnuF1PNcjzPwnGVZ6y3L2oY9pGuZaZRVdt5NHVUVBxwB/gRey8H1cuplYKVlWW+kfsbZ/BUgpvezZVkfWZZ1wLKsScABMt4HgJNAxVzsm4iIiKSyLOsicNgY0xXA2NVOfe2Z7meKV4HPcnJOhVEiIiJyJ3o4NQiJxz7aaBXwPFAZ+wiptWk7pgY+v2EvoJneb+n2icE+uibzPjlmjHFKnUa2zRhzLnWa32PYayrdiOrAb5ZlpaRrWwMUAO5L17Yt03EnAe907wsC8Tm4XoJlWXsznccF+wipa3kf8Af8sIc/BbCPLsutnx+rk+57TLUGKGuMKZKu7Xr3Aewj5wrmUr9ERET+0Ywxc7D/HHV/6hT5p7GP2H7aGLMV2MlfhcqDgL3GmH1AKeDdnFzDOdd7LSIiInLzVgEDgUTgpGVZiQDppuJlVwT7RgpjpwAmU5tLdjumMwz4F/bpeNuBGOyr2WUORq7HcPW+pm9PzGZb+iDoLFA8B9fLXLch7RrXC5XOWZZ1IPX1fmPMS9h/MG3JX6vm3Izcug8AJYCoXOiTiIjIP55lWVerw/hwNvvOB+bf6DU0MkpERETuRJdTp2UdSQuiUh3APhUufcFyJ+y1kHZlOkejdPsUAmoBu1ObogD3TCNw/K/Tp0DgO8uyZlmWFQ4cBKpm2ucK4HSd8+wCGmcaYRSYeuzB6xyb3hZuYqTX35A25fBm6m6lt4t032OqQOC4ZVmXbvBctbDX8xIREZG7gMIoERERuWtYlhULTAbGGmPaGmOqp74vxV9LDKf5P2PMg8aYmtjrF1wBvkrd9gcQC/zbGHOfMaYz9lpO17IPaG2MCTTGVAM+AjKvShcBNEhdQc/zKlPa/guUAf5rjKlujGkHjAU+sizr8nVvwl9+AhoZY27VSPfCxpjSxph7Uut2jcc+GmtdLp3/P9hX3wkxxlQ1xvTAPvLsvb9xrmbAj7nULxEREbnFFEaJiIjI3WYE8DX2VevCsdc0etiyrMzFvEdiDzw2A1WA4NQwC8uyzmOvffAg9il3A4HR17nuO9hXbfsB+zTCWGB2pn1CsYdeu7CPvspST8qyrBPAI9hX0gvHHpTN4caLg3+PvVZSmxs8Lqdex14g/SSwFPvnfdCyrHO5cXLLsjYDXYHOwA7sgdxY7CFfjqWurFeUvzFFQERERG4PY1k3Ul5BRERE5M5mjAnCvvKcl2VZZ29vb24tY8wgoKtlWZlXl/vHMMZ8A2yxLGvM7e6LiIiI5IwKmIuIiIjcvaYBJYwxRS3Lir7dnclrxhhXYCvwwe3ui4iIiOScRkaJiIhIvvJPGhklIiIicjdSGCUiIiIiIiIiInlGBcxFRERERERERCTPKIwSEREREREREZE8ozBKRERERERERETyjMIoERERERERERHJMwqjREREREREREQkzyiMEhERERERERGRPPP/P4AZ6CIWtB4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# x = np.log(new_final['population'])\n",
    "# y = np.log(new_final['infection rate'])\n",
    "x = new_final['population']\n",
    "y = new_final['infection_rate']\n",
    "lab = new_final['country']\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(20,20))\n",
    "ax.scatter(x, y)\n",
    "\n",
    "# ax = plt.subplot(212)\n",
    "ax.set_xlabel('Population(in Billion)', fontsize=14)\n",
    "ax.set_ylabel('Infection_rate (avg infection/day)', fontsize=14)\n",
    "ax.set_title('Infection_rate VS Population', fontsize=18)\n",
    "\n",
    "\n",
    "for i, txt in enumerate(lab):\n",
    "    ax.annotate(txt, (x[i], y[i]), xytext=(10,10), textcoords='offset points')\n",
    "    plt.scatter(x, y, marker='x', color='red')\n",
    "\n",
    "plt.grid()\n",
    "plt.show()\n",
    "# plt.figsize(20,20)\n",
    "# new_final.plot.scatter(x='population', y='infection rate', label='country', figsize=(16, 16));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 514,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "x1 = zoomed['population']\n",
    "y1 = zoomed['infection rate']\n",
    "lab1 = zoomed['country']\n",
    "\n",
    "fig1, ax1 = plt.subplots(figsize=(20,20))\n",
    "ax1.scatter(x1, y1)\n",
    "\n",
    "# ax1 = plt.subplot(212)\n",
    "ax1.set_xlabel('Population(in Millions)', fontsize=14)\n",
    "ax1.set_ylabel('Infection_rate (avg infection/day)', fontsize=14)\n",
    "ax1.set_title('Infection_rate VS Population', fontsize=18)\n",
    "\n",
    "\n",
    "for i, txt1 in enumerate(lab1):\n",
    "#     print(txt1)\n",
    "    ax.annotate(txt1, (x[i], y[i]), xytext=(10,10),textcoords='offset points')\n",
    "    plt.scatter(x1, y1, marker='x', color='green')\n",
    "\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 515,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculating total testing for all location keys\n",
    "total_testing = df_cleaned_tested.sum(axis = 0)\n",
    "total_testing = total_testing.to_frame(name = \"Total_testings\")\n",
    "\n",
    "total_testing_test = pd.DataFrame()\n",
    "total_testing_test = total_testing\n",
    "\n",
    "total_testing_test['location_key'] = total_testing_test.index\n",
    "total_testing_test.reset_index(drop=True, inplace=True)\n",
    "\n",
    "# total_testing_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 516,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pivot_tested_cumu = df.pivot(index='date', columns=[\"location_key\"],values='cumulative_tested')\n",
    "\n",
    "all_loc_keys_tested_cumu = df_pivot_tested_cumu.columns\n",
    "len_keys_cumu = len(all_loc_keys_tested_cumu)\n",
    "df_clean_tested_cumu = df_pivot_tested_cumu\n",
    "for key in all_loc_keys_tested_cumu:\n",
    "    if df_clean_tested_cumu[key].isna().sum() == df_clean_tested_cumu.shape[0]:\n",
    "        df_clean_tested_cumu = df_clean_tested_cumu.drop(columns=key)\n",
    "df_cleaned_tested_cumu = df_clean_tested_cumu.fillna(0)\n",
    "# df_cleaned_tested_cumu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 517,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14137717"
      ]
     },
     "execution_count": 517,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loc_list = df_cleaned_tested_cumu.columns\n",
    "max_list=[]\n",
    "for items in loc_list:\n",
    "    max_list.append(max(df_cleaned_tested_cumu[items]))\n",
    "loc_cumu_max = dict(zip(loc_list, max_list))\n",
    "for keys in loc_cumu_max:\n",
    "    loc_cumu_max[keys] = int(loc_cumu_max[keys])\n",
    "loc_cumu_max['AE']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 518,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{nan: 131715, 'AE': 14137717, 'AR': 2512406, 'AT': 2452692, 'AU': 9097814, 'BD': 2429010, 'BE': 5281944, 'BG': 793277, 'BH': 1823595, 'BO': 335456, 'BR': 6421441, 'BY': 2573132, 'CA': 9884582, 'CH': 2157721, 'CI': 197091, 'CL': 0, 'CN': 160000000, 'CO': 4240181, 'CR': 242306, 'CU': 909632, 'CY': 503927, 'CZ': 2584114, 'DE': 23393311, 'DK': 5569081, 'DO': 616366, 'EC': 512577, 'EE': 280693, 'ET': 1526221, 'FI': 1619634, 'FJ': 13535, 'FR': 17891276, 'GB': 35600586, 'GH': 543316, 'GR': 1941656, 'GT': 451070, 'HK': 3753969, 'HR': 559016, 'HU': 1209151, 'ID': 3080718, 'IE': 1715684, 'IL': 4918243, 'IN': 117736791, 'IQ': 2970814, 'IR': 5224252, 'IS': 194748, 'IT': 17183569, 'JM': 99548, 'JO': 2003989, 'JP': 3239618, 'KE': 730363, 'KR': 2677232, 'KW': 955447, 'KZ': 3754917, 'LI': 900, 'LK': 590435, 'LT': 1080306, 'LU': 1131669, 'LV': 497949, 'MA': 3451451, 'MG': 87659, 'MK': 271599, 'MM': 317765, 'MR': 88310, 'MT': 251746, 'MV': 208105, 'MW': 65901, 'MX': 2116581, 'MY': 2238559, 'MZ': 196793, 'NG': 686395, 'NL': 2692871, 'NO': 1782611, 'NP': 1507190, 'NZ': 1132820, 'PA': 690678, 'PE': 630521, 'PH': 4751099, 'PK': 4676263, 'PL': 4919545, 'PT': 3482414, 'PY': 377309, 'QA': 1015828, 'RO': 3341867, 'RS': 1421154, 'RU': 64682511, 'RW': 568868, 'SA': 8494327, 'SG': 3879052, 'SI': 402703, 'SK': 901327, 'SN': 221850, 'SV': 478223, 'TG': 126869, 'TH': 1090380, 'TN': 225033, 'TR': 15127811, 'TW': 104017, 'UA': 3613899, 'UG': 563835, 'US': 157290087, 'UY': 344350, 'VN': 1260799, 'ZA': 4963174, 'ZM': 297999, 'ZW': 145800}\n"
     ]
    }
   ],
   "source": [
    "print(loc_cumu_max)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 519,
   "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>Total_testings</th>\n",
       "      <th>location_key</th>\n",
       "      <th>infection_rate</th>\n",
       "      <th>population</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14137717.0</td>\n",
       "      <td>AE</td>\n",
       "      <td>447.24</td>\n",
       "      <td>9770529</td>\n",
       "      <td>United Arab Emirates</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2512406.0</td>\n",
       "      <td>AR</td>\n",
       "      <td>3925.87</td>\n",
       "      <td>44938712</td>\n",
       "      <td>Argentina</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2452692.0</td>\n",
       "      <td>AT</td>\n",
       "      <td>426.71</td>\n",
       "      <td>8858775</td>\n",
       "      <td>Austria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9097814.0</td>\n",
       "      <td>AU</td>\n",
       "      <td>89.14</td>\n",
       "      <td>25364307</td>\n",
       "      <td>Australia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2429010.0</td>\n",
       "      <td>BD</td>\n",
       "      <td>1341.98</td>\n",
       "      <td>163046161</td>\n",
       "      <td>Bangladesh</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total_testings location_key  infection_rate  population  \\\n",
       "0      14137717.0           AE          447.24     9770529   \n",
       "1       2512406.0           AR         3925.87    44938712   \n",
       "2       2452692.0           AT          426.71     8858775   \n",
       "3       9097814.0           AU           89.14    25364307   \n",
       "4       2429010.0           BD         1341.98   163046161   \n",
       "\n",
       "                country  \n",
       "0  United Arab Emirates  \n",
       "1             Argentina  \n",
       "2               Austria  \n",
       "3             Australia  \n",
       "4            Bangladesh  "
      ]
     },
     "execution_count": 519,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_inner_tested.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 520,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "# Infection_rate vs rating. preparation for ploting\n",
    "# merged_inner_tested = merged_inner_tested.astype({\"Total_testings\":int}) \n",
    "merged_inner_tested = pd.merge(left=total_testing_test, right=new_final, left_on='location_key', right_on='location_key',how=\"outer\")\n",
    "# merged_inner_tested['Total_testings'] = \n",
    "\n",
    "for ind, loc in enumerate(merged_inner_tested.location_key):\n",
    "    try:\n",
    "        merged_inner_tested['Total_testings'][ind] = max(merged_inner_tested['Total_testings'][ind], loc_cumu_max[loc])\n",
    "    except KeyError:\n",
    "        continue\n",
    "# new_final = merged_inner[['infection rate','location_key','population']]\n",
    "\n",
    "# new_final['country'] = new_final['location_key'].map(countries) \n",
    "\n",
    "\n",
    "\n",
    "# zoomed = new_final.loc[(new_final['population']<400000000) & (new_final['infection rate']<10000)]\n",
    "# zoomed = zoomed.sort_values(\"infection rate\", ascending=False)\n",
    "\n",
    "# new_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 521,
   "metadata": {},
   "outputs": [],
   "source": [
    "# merged_inner_tested[20:40]\n",
    "merged_inner_tested = merged_inner_tested.rename(columns={\"infection rate\": \"infection_rate\"})\n",
    "merged_inner_tested['infection_rate'] = round(merged_inner_tested['infection_rate'],2)\n",
    "merged_inner_tested['population'] = merged_inner_tested['population'].astype(float)\n",
    "# merged_inner_tested['population'] = merged_inner_tested['population'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 522,
   "metadata": {},
   "outputs": [],
   "source": [
    "# merged_inner_tested[merged_inner_tested['location_key'].str.contains('CN')]\n",
    "merged_inner_tested['population'] = merged_inner_tested['population'].fillna(0)\n",
    "merged_inner_tested['population'] = merged_inner_tested['population'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 523,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 523,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_inner_tested['population'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 524,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 234 entries, 0 to 233\n",
      "Data columns (total 5 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   Total_testings  104 non-null    float64\n",
      " 1   location_key    233 non-null    object \n",
      " 2   infection_rate  234 non-null    float64\n",
      " 3   population      234 non-null    int64  \n",
      " 4   country         234 non-null    object \n",
      "dtypes: float64(2), int64(1), object(2)\n",
      "memory usage: 21.0+ KB\n"
     ]
    }
   ],
   "source": [
    "merged_inner_tested.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 525,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "1it [00:05,  5.83s/it]\n"
     ]
    }
   ],
   "source": [
    "# pip install datalab\n",
    "from datalab.context import Context\n",
    "merged_inner_tested.to_gbq('covid19_open_data.infection_rate_VS_testing', \n",
    "                           'seraphic-being-294617',\n",
    "#                  Context.default().project_id,\n",
    "                         chunksize=10000, \n",
    "                         if_exists='replace'\n",
    "                         )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 526,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 234 entries, 0 to 233\n",
      "Data columns (total 5 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   Total_testings  104 non-null    float64\n",
      " 1   location_key    233 non-null    object \n",
      " 2   infection_rate  234 non-null    float64\n",
      " 3   population      234 non-null    int64  \n",
      " 4   country         234 non-null    object \n",
      "dtypes: float64(2), int64(1), object(2)\n",
      "memory usage: 21.0+ KB\n"
     ]
    }
   ],
   "source": [
    "# pip install pandas-gbq\n",
    "merged_inner_tested.info()\n",
    "# merged_inner_tested['location_key'][:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 527,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from datalab.context import Context\n",
    "# merged_inner_tested.to_gbq('covid19_open_data.infection_rate', \n",
    "#                            'seraphic-being-294617',\n",
    "# #                  Context.default().project_id,\n",
    "#                          chunksize=10000, \n",
    "#                          if_exists='replace'\n",
    "#                          )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 528,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/22Uwi/QB9AH0AfQB9AH0AfQBVoc+cN55513XWnvwrBZxL3GvD/fmzZuXJUuWzHYZU2Lx4sVZtGjRbJfBLNIH0AfQB9AH0AfQB9AHWB36QFVdMasF3IvcZ7YLAAAAAADuHuEeAAAAAPSUcA8AAAAAekq4BwAAAAA9JdwDAAAAgJ4S7gEAAABATwn3AAAAAKCnhHsAAAAA0FPCPQAAAADoKeEeAAAAAPSUcA8AAAAAekq4BwAAAAA9JdwDAAAAgJ4S7gEAAABATwn3AAAAAKCnhHsAAAAA0FPCPQAAAADoKeEeAAAAAPSUcA8AAAAAekq4BwAAAAA9JdwDAAAAgJ4S7gEAAABATwn3AAAAAKCnhHsAAAAA0FPCPQAAAADoKeEeAAAAAPSUcA8AAAAAekq4BwAAAAA9JdwDAAAAgJ4S7gEAAABATwn3AAAAAHpk6dKl2XrrrVdqe/Ob35yjjjpqwvOWLFmSV7ziFUmSxYsX5xvf+MYq33vevHm57rrr7tL+kY98JNtss0223XbbFbU9MEmq6uCqeuhk1x32uHuiqk6oqgur6tXj7P9eVZ0wzTV8uKq2nMprrj2VFwMAAABg9bRw4cIsXLgwySDcmzNnTv7kT/7kHl/3yiuvzBFHHJHzzz8/G264YZYvX54HPOABv+t2H5zk4iRXTXKZYY+7W6rqIUn+pLX2qHH2Py6DQXBPqqr1W2u/nYYa1mqtvWiqr2vkHgAAAMAa5FWvelUOPfTQ7LTTTnnsYx+br33ta0kGgd5+++2XpUuX5oMf/GDe8573ZMGCBfna176Wa6+9Ns985jPz+Mc/Po9//ONzzjnnJEl++ctf5qlPfWq23377/N3f/V1aa3e537Jly/KABzwgc+bMSZIV37dW1bOSLEzyyaq6oKrWq6o3VdW5VXVxVR1TA2Mdt2NV/W9VnVdV/1NVmyVJVb2iqi7tRuCdOLqWqrpfVX20qi6qqu9W1e7dri8lmdtd/4lj/Nmem+Tj3XH7j7je4qp6T1WdXVXfr6rHV9WpVfWjqnr7iOOeV1Xf6a7/oapaq2tfXlVvrapvJ9mlu97Cbt8+VXV+N2LwrK5tp6r6Rlf7N6rqjyf79xbuAQAAAKxhbrvttnznO9/Jv/3bv+Utb3nLSvvmzZuXl7zkJXn1q1+dCy64IE984hPzyle+Mq9+9atz7rnn5tOf/nRe9KLBALO3vOUt2W233fLd7343+++/f37605/e5V7bbbddNt1008yfPz8veMEL8rnPfS5J0lo7JcmSJAe21ha01m5K8t7W2uNba1snWS/JfqOPS3Jbkv9I8qzW2o5JPpLkiO52r0+yfWtt2yQvGePRX9bde5skz0lyfFXdL4PA7sddHV8b47y/SnJSkhO680a6tbX2pCQfTPLZ7h5bJzm4qh7Ujfr7qyS7dvXfnuTA7tz1k1zcWntCa+3rKy5YVQ9O8p9Jntla2y7Js7tdP0jypNba9knelOQdY9S6Eq/lAgAAAPRIVU3a/oxnPCNJsuOOO2bp0qWTXvPMM8/MpZdeeufvG264ITfeeGPOPvvsnHrqqUmSfffdNxtttNFdzl1rrbXyxS9+Meeee27OOuusvPrVr06S8ebP272qXpfk/kk2TnJJks+NOuaPMwjPvtw901pJru72XZjBCL//TvLfY1x/twyCwbTWflBVVyR5bJIbxnv2qnp8kmtba1dU1ZVJPlJVG7XWru8OOa37vijJJa21q7vzfpLkEd09d0xyblfvekmWdefcnuTTY9x25yRnt9Yu72r9Vde+YQaB5OZJWpJ1xqt7BeEeAAAAQI886EEPyvXXX79S269+9avMnz//zt/rrrtukkHwdtttt016zTvuuCPf/OY3s956691l33hh4uhjdtppp+y0007Za6+98vjHP/6BYxxzvyTvT7KwtfazqnpzkvuNdbkMQrRdxti3b5InZTAS75+qaqvW2m2jzl1Vz0myRVUt7X5vkOSZST7c/b6l+75jxPaK32t39zy+tXbYGNe+ubV2+xjtlUF4N9rbkny1tfb0qpqXZPFkxXstFwAAAKBH5syZk8022yxnnXVWkkGw98UvfjG77bbb0Nd4wAMekBtvvPHO30996lPz3ve+987fF1xwQZLkSU96Uj75yU8mSc4444y7hIpJctVVV+X8888ffe6t3c8bkzyg214R5F1XVXOSPGvEZUYe98MkD66qXZKkqtapqq2q6j5JHtFa+2qS12WwIu+cUeWcne6V2Kp6bJJHdtcbU3fNZyfZtrU2r7U2L8nTctdXcydyVpJnVdXc7pobV9WYC3eM8M0kT66q+SvO6do3TPLzbvvgYW4u3AMAAADomY997GN5+9vfngULFmSPPfbI4Ycfnj/6oz8a+vw///M/z2c+85k7F9Q4+uijs2TJkmy77bbZcsst88EPfjBJcvjhh+fss8/ODjvskC996Ut55CMfeZdr/f73v89rXvOabLHFFlmwYEFOOumkJPlZt/u4JB+sqgsyGPX2nxm83vrfSc4dcZmRx62VQfB3ZFV9L8kFSf6ka/9EVV2U5LtJ3tNa+/Woct6fZK3umJOSHNxauyXje1KSn7fWfj6i7ewkW65YxGMyrbVLk7wxyZeq6sIkX04y4bmttWuTvDjJqd0zntTteleSf66qczJ43knVWKuc3JssXLiwLVmyZLbLmBKLFy/OokWLZrsMZpE+gD6APoA+gD6APoA+wOrQB6rqvNbawlkt4l7CyD0AAAAA6CnhHgAAAAD0lHAPAAAAAHpKuAcAAAAAPSXcAwAAAJhNc+dO/BsmINwDAAAAmC1Vaddem12rMv/1n8+u3e9UzXZl9IRwDwAAAGA2zJ2blqSSfD1JO3K/fL373br9MBnhHgAAAMBsWLYsuyV3BnxL84dgb7duP0xGuAcAAAAwS6469PTMH9U2v2uHYQj3AAAAAGbJQ4/cL5eParu8a4dhCPcAAAAAZsPcuSvNsTcvWWkOPnPuMQzhHgAAAMBsWLZspTn26tDTV5qDz5x7DGPt2S4AAAAA4F6rtdTcuTlnRZD3zjYYsSfYY0hG7gEAAADMptFBnmCPVSDcAwAAAICeEu4BAAAAQE8J9wAAAACgp4R7AAAAANBTwj0AAAAA6CnhHgAAAAD0lHAPAAAAAHpKuAcAAAAAPSXcAwAAAICeEu4BAAAAQE8J9wAAAACgp4R7AAAAANBTwj0AAAAA6CnhHgAAAAD0lHAPAAAAAHpKuAcAAAAAPSXcAwAAAICeEu4BAAAAQE8J9wAAAACgp4R7AAAAANBTwj0AAAAA6CnhHgAAAAD01IyFe1V1v6r6TlV9r6ouqaq3dO0bV9WXq+pH3fdGI845rKouq6ofVtXeI9p3rKqLun1HV1V17etW1Uld+7erat5MPR8AAAAAzLSZHLl3S5I9WmvbJVmQZJ+q2jnJ65Oc1VrbPMlZ3e9U1ZZJDkiyVZJ9kry/qtbqrvWBJC9Osnn32adrf2GS61trj0nyniRHzsBzAQAAAMCsmLFwrw0s736u031akqclOb5rPz7JX3TbT0tyYmvtltba5UkuS7JTVW2WZIPW2jdbay3Jx0ads+JapyTZc8WoPgAAAABY08zonHtVtVZVXZBkWZIvt9a+nWTT1trVSdJ9z+0Of1iSn404/cqu7WHd9uj2lc5prd2W5DdJHjQtDwMAAAAAs2ztmbxZa+32JAuq6oFJPlNVW09w+Fgj7toE7ROds/KFq16cwWu92XTTTbN48eIJyuiP5cuXrzHPwt2jD6APoA+gD6APoA+gD6AP3LvMaLi3Qmvt11W1OIO58q6pqs1aa1d3r9wu6w67MskjRpz28CRXde0PH6N95DlXVtXaSTZM8qsx7n9MkmOSZOHChW3RokVT9GSza/HixVlTnoW7Rx9AH0AfQB9AH0AfQB9AH7h3mcnVch/cjdhLVa2X5ClJfpDktCQHdYcdlOSz3fZpSQ7oVsCdn8HCGd/pXt29sap27ubTe/6oc1Zc61lJvtLNywcAAAAAa5yZHLm3WZLjuxVv75Pk5Nba6VX1zSQnV9ULk/w0ybOTpLV2SVWdnOTSJLcleVn3Wm+SHJLkuCTrJTmj+yTJsUk+XlWXZTBi74AZeTIAAAAAmAUzFu611i5Msv0Y7b9Msuc45xyR5Igx2pckuct8fa21m9OFgwAAAACwppvR1XIBAAAAgKkj3AMAAACAnhLuAQAAAEBPCfcAAAAAoKeEewAAAADQU8I9AAAAAOgp4R4AAAAA9JRwDwAAAAB6SrgHAAAAAD0l3AMAAACAnhLuAQAAAEBPCfcAAAAAoKeEewAAAADQU8I9AAAAAOgp4R4AAAAA9JRwDwAAAAB6SrgHAAAAAD0l3AMAAACAnhLuAQAAAEBPCfcAAAAAoKeEewAAAADQU8I9AAAAAOgp4R4AAAAA9JRwDwAAAAB6SrgHAAAAAD0l3AMAAACAnhLuAQAAAEBPCfcAAAAAoKeEewAAAADQU8I9AAAAAOgp4R4AAAAA9JRwDwAAAAB6SrgHAAAAAD0l3AMAAACAnhLuAQAAAEBPCfcAAAAAoKeEewAAAADQU8I9AAAAAOgp4R4AAAAA9JRwDwAAAAB6SrgHAAAAAD0l3AMAAACAnhLuAQAAAEBPCfcAAAAAoKeEewAAAADQU8I9AAAAAOgp4R4AAAAA9JRwDwAAAAB6SrgHAAAAAD0l3AMAAACAnhLuAQAAAEBPCfcAAAAAoKeEewAAAADQU8I9AAAAAOgp4R4AAAAA9JRwDwAAAAB6SrgHAAAAAD0l3AMAAACAnhLuAQAAAEBPCfcAAAAAoKeEewAAAADQU8I9AAAAAOgp4R4AAAAA9JRwDwAAAAB6SrgHAAAAAD0l3AMAAACAnhLuAQAAAEBPCfcAAAAAoKeEewAAAADQU8I9AACAHpszZ84qHb948eLst99+SZLTTjst73znO6ejLABmyNqzXQAAAACzY//998/+++8/22UAcA8YuQcAALAGWLx4cRYtWpTDDz88W2yxRQ488MC01pIkX/ziF7PFFltkt912y6mnnnrnOccdd1xe/vKXJ0k+97nP5QlPeEK23377POUpT8k111wzK88BwKoR7gEAAKwhvvvd7+blL395Lr300vzkJz/JOeeck5tvvjl/+7d/m8997nP52te+ll/84hdjnrvbbrvlW9/6Vr773e/mgAMOyLve9a4Zrh6Au8NruQAAAGuInXbaKQ9+8INzn/vcJwsWLMjSpUszZ86czJ8/P5tvvnmS5HnPe16OOeaYu5x75ZVX5q/+6q9y9dVX59Zbb838+fNnunwA7gYj9wAAANYQ66677p3ba621Vm677bYkSVVNeu7f//3f5+Uvf3kuuuiifOhDH8rNN988bXUCMHWEewAAAGuwLbbYIpdffnl+/OMfJ0lOOOGEMY/7zW9+k4c97GFJkuOPP37G6gPgnhHuAQAArMHud7/75Zhjjsm+++6b3XbbLY961KPGPO7Nb35znv3sZ+eJT3xiNtlkkxmuEoC7y5x7AAAAPbZ8+fIkyaJFi7Jo0aIsXrw4SfLe9773zmP22Wef/OAHP7jLuQcffHAOPvjgJMnTnva0PO1pT5v2egGYWkbuAQAAAEBPCfcAAAAAoKeEewAAAADQU8I9AAAAAOgp4R4AAMDqYu7ciX8DwCjCPQAAgNVBVdq112bXqsx//eeza/c7VbNdGQCrMeEeAADAbJs7Ny1JJfl6knbkfvl697t1+wFgLMI9AACA2bZsWXZL7gz4luYPwd5u3X4AGItwDwAAYDVw1aGnZ/6otvldOwCMR7gHAACwGnjokfvl8lFtl3ftADAe4R4AAMBsmzt3pTn25iUrzcFnzj0AxiPcAwAAmG3Llq00x14devpKc/CZcw+A8aw92wUAAACQpLXU3Lk5Z0WQ9842GLEn2ANgAkbuAQAArC5GB3mCPQAmIdwDAAAAgJ4S7gEAAABATwn3AAAAAKCnhHsAAAAA0FPCPQAAAADoKeEeAAAAAPSUcA8AAAAAekq4BwAAAAA9JdwDAAAAgJ4S7gEAAABATwn3AAAAAKCnhHsAAAAA0FPCPQAAAADoKeEeAAAAAPSUcA8AAAAAekq4BwAAAAA9JdwDAAAAgJ4S7gEAAABATwn3AAAAAKCnhHsAAAAA0FPCPQAAAADoKeEeAAAAAPSUcA8AAAAAekq4BwAAAAA9JdwDAAAAgJ4S7gEAAABATwn3AAAAAKCnhHsAAAAA0FPCPQAAAADoKeEeAAAAAPSUcA8AAAAAekq4BwAAAAA9JdwDAAAAgJ4S7gEAAABATwn3AAAAAKCnhHsAAAAA0FPCPQAAAADoKeEeAAAAAPSUcA8AAAAAekq4BwAAAAA9JdwDAAAAgJ4S7gEAAABATwn3AAAAAKCnhHsAAAAA0FPCPQAAAADoKeEeAAAAAPSUcA8AAAAAekq4BwAAAAA9JdwDAAAAgJ4S7gEAAABATwn3AAAAAKCnhHsAAAAA0FPCPQAAAADoKeEeAAAAAPSUcA8AAAAAekq4BwAAAAA9JdwDAAAAgJ4S7gEAAABATwn3AAAAAKCnZizcq6pHVNVXq+r7VXVJVb2ya39zVf28qi7oPn824pzDquqyqvphVe09on3Hqrqo23d0VVXXvm5VndS1f7uq5s3U8wEAAADATJvJkXu3JfnH1trjkuyc5GVVtWW37z2ttQXd5wtJ0u07IMlWSfZJ8v6qWqs7/gNJXpxk8+6zT9f+wiTXt9Yek+Q9SY6cgecCAAAAgFkxY+Fea+3q1tr53faNSb6f5GETnPK0JCe21m5prV2e5LIkO1XVZkk2aK19s7XWknwsyV+MOOf4bvuUJHuuGNUHAAAAAGuaGuRjM3zTweuyZyfZOsk/JDk4yQ1JlmQwuu/6qnpvkm+11j7RnXNskjOSLE3yztbaU7r2JyY5tLW2X1VdnGSf1tqV3b4fJ3lCa+26Ufd/cQYj/7LpppvueOKJJ07vA8+Q5cuXZ86cObNdBrNIH0AfQB9AH0AfQB9AH2B16AO77777ea21hbNaxL3E2jN9w6qak+TTSV7VWruhqj6Q5G1JWvf9r0n+JslYI+7aBO2ZZN8fGlo7JskxSbJw4cK2aNGiVXyK1dPixYuzpjwLd48+gD6APoA+gD6APoA+gD5w7zKjq+VW1ToZBHufbK2dmiSttWtaa7e31u5I8p9JduoOvzLJI0ac/vAkV3XtDx+jfaVzqmrtJBsm+dX0PA0AAAAAzK6ZXC23khyb5PuttXePaN9sxGFPT3Jxt31akgO6FXDnZ7Bwxndaa1cnubGqdu6u+fwknx1xzkHd9rOSfKXNxnvHAAAAADADZvK13F2T/HWSi6rqgq7t/yV5TlUtyOD12aVJ/i5JWmuXVNXJSS7NYKXdl7XWbu/OOyTJcUnWy2AevjO69mOTfLyqLstgxN4B0/pEAAAAADCLZizca619PWPPifeFCc45IskRY7QvyWAxjtHtNyd59j0oEwAAAAB6Y0bn3AMAAAAApo5wDwAAAAB6SrgHAAAAAD0l3AMAAACAnhLuAQAAAEBPCfcAAAAAoKeEewAAAADQU8I9AAAAAOgp4R4AAAAA9JRwDwAAAAB6SrgHAAAAAD0l3AMAAACAnhLuAQAAAEBPCfcAAAAAoKeEewAAAADQU8I9AAAAAOgp4R4AAAAA9JRwDwAAAAB6SrgHAAAAAD0l3AMAAACAnhLuAQAAAEBPCfcAAAAAoKeEewAAAADQU8I9AAAAAOgp4R4AAAAA9JRwDwAAAAB6SrgHAAAAAD0l3AMAAACAnhLuAQAAAEBPCfcAAAAAoKeEewAAAADQU8I9AAAAAOgp4R4AAAAA9JRwDwAAAAB6SrgHAAAAAD0l3AMAAACAnhLuAQAAAEBPCfcAAAAAoKeEewAAAADQU8I9AAAAAOgp4R4AAAAA9JRwDwAAAAB6SrgHAAAAAD0l3AMAAACAnhLuAQAAAEBPCfcAAAAAoKeEewAAAADQU8I9AAAAAOgp4R4AAAAA9JRwDwAAAAB6SrgHAAAAAD0l3AMAAACAnhLuAQAAAEBPCfcAAAAAoKeEewAAAADQU8I9AAAAAOgp4R4AAAAA9JRwDwAAAAB6SrgHAAAAAD0l3AMAAACAnhLuAQAAAEBPCfcAAAAAoKeEewAAAADQU8I9AAAAAOgp4R4AAAAA9JRwDwAAAAB6SrgHAAAAAD0l3AMAAACAnhLuAQAAAEBPCfcAAAAAoKeEewAAAADQU8I9AAAAAOgp4R4AAAAA9JRwDwAAAAB6SrgHAAAAAD0l3AMAAACAnhLuAQAAAEBPCfcAAAAAoKeEewAAAADQU8I9AAAAAOgp4R4AAAAA9JRwDwAAAAB6SrgHAAAAAD0l3AMAAACAnhLuAQAAAEBPCfcAAAAAoKeEewAAAADQU8I9AAAAAOgp4R4AAAAA9JRwDwAAAAB6SrgHAAAAAD0l3AMAAACAnhLuAQAAAEBPCfcAAAAAoKeEewAAAADQU8I9AAAAAOgp4R4AAAAA9JRwDwAAAAB6SrgHAAAAAD0l3AMAAACAnhLuAQAAAEBPCfcAAAAAoKeEewAAAADQU8I9AAAAAOgp4R4AAAAA9JRwDwAAAAB6SrgHAAAAAD0l3AMAAACAnhLuAQAAAEBPCfcAAAAAoKeEewAAAADQU8I9AAAAAOgp4R4AAAAA9JRwDwAAAAB6SrgHAAAAAD0l3AMAAACAnhLuAQAAAEBPCfcAAAAAoKeEewAAAADQU8I9AAAAAOgp4R4AAAAA9JRwDwAAAAB6SrgHAAAAAD0l3AMAAACAnhLuAQAAAEBPCfcAAAAAoKeEewAAAADQU8I9AAAAAOgp4R4AAAAA9JRwDwAAAAB6SrgHAAAAAD0l3AMAAACAnhLuAQAAAEBPCfcAAAAAoKeEewAAAADQU8I9AAAAAOgp4R4AAAAA9JRwDwAAAAB6SrgHAAAAAD0l3AMAAACAnhLuAQAAAEBPCfcAAAAAoKeEewAAAADQU8I9AAAAAOgp4R4AAAAA9JRwDwAAAAB6SrgHAAAAAD0l3AMAAACAnhLuAQAAAEBPCfcAAAAAoKdmLNyrqkdU1Ver6vtVdUlVvbJr37iqvlxVP+q+NxpxzmFVdVlV/bCq9h7RvmNVXdTtO7qqqmtft6pO6tq/XVXzZur5AAAAAGCmzeTIvduS/GNr7XFJdk7ysqraMsnrk5zVWts8yVnd73T7DkiyVZJ9kry/qtbqrvWBJC9Osnn32adrf2GS61trj0nyniRHzsSDAQAAAMBsmLFwr7V2dWvt/G77xiTfT/KwJE9Lcnx32PFJ/qLbflqSE1trt7TWLk9yWZKdqmqzJBu01r7ZWmtJPjbqnBXXOiXJnitG9QEAAADAmmZW5tzrXpfdPsm3k2zaWrs6GQSASeZ2hz0syc9GnHZl1/awbnt0+0rntNZuS/KbJA+alocAAAAAgFm29kzfsKrmJPl0kle11m6YYGDdWDvaBO0TnTO6hhdn8FpvNt100yxevHiSqvth+fLla8yzcPfoA+gD6APoA+gD6APoA+gD9y4zGu5V1ToZBHufbK2d2jVfU1Wbtdau7l65Xda1X5nkESNOf3iSq7r2h4/RPvKcK6tq7SQbJvnV6Dpaa8ckOSZJFi5c2BYtWjQFTzf7Fi9enDXlWbh79AH0AfQB9AH0AfQB9AH0gXuXmVwtt5Icm+T7rbV3j9h1WpKDuu2Dknx2RPsB3Qq48zNYOOM73au7N1bVzt01nz/qnBXXelaSr3Tz8gEAAADAGmcmR+7tmuSvk1xUVRd0bf8vyTuTnFxVL0zy0yTPTpLW2iVVdXKSSzNYafdlrbXbu/MOSXJckvWSnNF9kkF4+PGquiyDEXsHTPMzAQAAAMCsmbFwr7X29Yw9J16S7DnOOUckOWKM9iVJth6j/eZ04SAAAAAArOlmZbVcAAAAAOCeE+4BAAAAQE8J9wAAAACgp4R7AAAAANBTwj0AAAAA6CnhHgAAAAD0lHAPAAAAAHpKuAcAAAAAPSXcAwAAAICeEu4BAAAAQE8J9wAAAACgp4R7AAAAANBTwj0AAAAA6CnhHgAAAAD0lHAPAAAAAHpKuAcAAAAAPSXcAwAAAICeEu4BAAAAQE8J9wAAAACgp4R7AAAAANBTwj0AAAAA6CnhHgAAAAD0lHAPAAAAAHpKuAcAAAAAPbX2MAdV1aOTPDnJvCTrJbk2yflJzmmt3Txt1QEAAAAA45ow3KuqA5O8MsnCJNckuSrJTUk2TvL2JDdX1SeTHNlau2KaawUAAAAARhg33Kuq7ya5LclxSZ7ZWvvZqP3rJtklyQFJllTVS1tr/zWNtQIAAAAAI0w0cu+NrbXPj7eztXZLksVJFlfVG5PMn+LaAAAAAIAJjBvuTRTsjXHsdUmum5KKAAAAAIChDLVablVdUFUvr6qNprsgAAAAAGA4Q4V7ST6f5HVJrqqqE6pqz2msCQAAAAAYwlDhXmvtDUkeleQZSdZK8vmqWlpVb6qqR05ngQAAAADA2IYduZc2cEZr7S+TPDTJh5L8vyQ/qar/qap9pqtIAAAAAOCuhg73VqiqnZO8M8nrk1yV5C1JfpzklKr6tymtDgAAAAAY17ir5Y5UVXOTPD/JC5L8UZLTkjyrtfblEcec0rW/aurLBAAAAABGGyrcS3JlksuSHJvk+NbadWMcsyTJuVNVGAAAAAAwsWHDvT1ba1+b6IDW2g1Jdr/nJQEAAAAAwxh2tdwJgz0AAAAAYOYNO3IvVfWCJM9J8sgk9x25r7X26CmuCwAAAACYxFAj96rqtUn+Ncl5SeYl+e8kFyfZOMlHpqk2AAAAAGACQ4V7Sf42yYtba4cl+X2S97bW9s8g8HvUdBUHAAAAAIxv2HDv4Um+023flGSDbvuEJM+c6qIAAAAAgMkNG+79Iskm3fYVSXbpth+TpE11UQAAAADA5IYN976SZP9u+9gk766qryY5Kcmp01EYAAAAADCxYVfLfXG6ILC19sGquj7Jrkk+neRD01QbAAAAADCBocK91todSe4Y8fukDEbtAQAAAACzZNxwr6qeNOxFWmtnT005AAAAAMCwJhq5tziDxTKq+71i4YzRv5NkraktCwAAAACYzEQLajw4ydzue78kP0zy/AxWyH1Mt/2D/GGhDQAAAABgBo07cq+19ssV21X1tiSvbK19ecQhP6mqZUneleTz01ciAAAAADCWiUbujbRlkivHaP95ki2mrhwAAAAAYFjDhnuXJDm8qtZb0dBtv6nbBwAAAADMsIkW1BjpkCSnJ/l5VV3YtW2T5PYk+05HYQAAAADAxIYK91pr51bV/CTPy+A13EryySSfaq39dhrrAwAAAADGMWG4V1V/n+SzrbWfttZ+l+SYmSkLAAAAAJjMZHPu/VmS/6uq71bVm6tqu5koCgAAAACY3IThXmvtT5NskuQdSTZP8tWquqKqjq6qPapqrZkoEgAAAAC4q0lXy22tLW+t/Vdr7cAkc5O8MIM59z6a5Nqq+kRVPauq1p/mWgEAAACAESYN90Zqrd3WWjuztfb3rbVHJXlKkp8keWOSf5iOAgEAAACAsQ21Wu54WmvnJzk/yZuqap2pKQkAAAAAGMbQ4V5V/VWSPTN4NXfkiL/WWntaa+33U10cAAAAADC+ocK9qvqXJK9K8tUkVyVp01gTAAAAADCEYUfuPT/Jc1prp0xnMQAAAADA8IZdUOM+SS6YxjoAAAAAgFU0bLh3TJLnTWchAAAAAMCqGfa13AcmeW5V7ZXkwiQrLZ7RWnvFFNcFAAAAAExi2HBvy/zhtdwtRu2zuAYAAAAAzIKhwr3W2u7TXQgAAAAAsGqGHbmXJKmq+yV5TAaj9X7cWrt5WqoCAAAAACY11IIaVbVOVf1LkuuTfC/JRUmur6p3VdU601kgAAAAADC2YUfuHZnkOUlekuTrXdsTk/xzBgHha6a+NAAAAABgIsOGe89N8jettS+MaPtxVV2b5MMR7gEAAADAjBvqtdwkGyb58RjtP07ywCmrBgAAAAAY2rDh3veSvGKM9lcmuWDKqgEAAAAAhjbsa7mvS/KFqtoryTczWC13lyQPTfKn01QbAAAAADCBoUbutdbOTvLYJP+VZE6SDbrtP26tfX2icwEAAACA6THsyL201q5K8oZprAUAAAAAWAXjhntVtUOSC1prd3Tb42qtnT/llQEAAAAAE5po5N6SJA9JsqzbbklqjONakrWmvjQAAAAAYCIThXvzk1w7YhsAAAAAWI2MG+611q4Y+TPJz1prbfRxVfXI6SgMAAAAAJjYUKvlJrk8yYNHN1bVg7p9AAAAAMAMGzbcqwxG7402J8nNU1cOAAAAADCsiebcS1Ud3W22JP9cVb8bsXutJDsluWB6SgMAAAAAJjJhuJdkm+67kjwuya0j9t2a5PwkR01DXQAAAADAJCYM91pruydJVX00yStbazfMSFUAAAAAwKSGnXPv/yXZYHRjVT28qjad2pIAAAAAgGEMG+59LMmfjtG+d5KPT105AAAAAMCwhg33Hp/k7DHav5Zk4dSVAwAAAAAMa9hwb+0k647Rfr9x2gEAAACAaTZsuPftJIeM0f6yJOdOXTkAAAAAwLAmXC13hDck+UpVbZfkrK5tjyTbJ3nKdBQGAAAAAExsqJF7rbVvJdklyeVJnpHkmd32Lq21b0xfeQAAAADAeIYduZfW2veSHDiNtQAAAAAAq2DYOfdSVZtW1Wuq6v1VtUnXtmtVzZ++8gAAAACA8QwV7lXVjkl+mMHIvRcl2aDbtVeSI6anNAAAAABgIsOO3Dsqyb+31rZPcsuI9v9JsuuUVwUAAAAATGrYcG/HJMeP0X51kk2nrhwAAAAAYFjDhns3JdlojPYtkiybunIAAAAAgGENG+59NsnhVbVu97tV1bwkRyb59HQUBgAAAABMbNhw7zVJNk5ybZL7J/l6ksuS/DrJG6elMgAAAABgQmsPc1Br7YYku1XVHkl2yCAUPL+1duZ0FgcAAAAAjG/ccK+qbk+yWWttWVV9JMkrW2tfSfKVGasOAAAAABjXRK/l3pRkTrd9UJL7TX85AAAAAMCwJnot9xtJ/ruqzktSSY6uqpvGOrC19jfTURwAAAAAML6Jwr2/zmAhjcckaUkelOSWmSgKAAAAAJjcuOFea+2aJK9Nkqq6PMlzWmu/nKnCAAAAAICJDbta7vzpLgQAAAAAWDVDhXtJUlVPSLJnkrkZtRBHa+0VU1wXAAAAADCJocK9qnpNkncluSzJVRnMwbdCG/MkAAAAAGBaDTty75VJXtFae+90FgMAAAAADO8+kx+SJNkgyRemsxAAAAAAYNUMG+6dkGSf6SwEAAAAAFg1w76W+7Mkb6mqXZNcmOT3I3e21t491YUBAAAAABMbNtx7UZLlSf6k+4zUkgj3AAAAAGCGDRXutdbmT3chAAAAAMCqGXbOPQAAAABgNTPuyL2qOjrJYa2133bb42qtvWLKKwMAAAAAJjTRa7nbJFlnxPZ42tSVAwAAAAAMa9xwr7W2+1jbAAAAAMDqwZx7AAAAANBTwj0AAAAA6CnhHgAAAAD0lHAPAAAAAHpKuAcAAAAAPTXuarkjVdUjx9nVktzcWrt26koCAAAAAIYxVLiXZGkGQd6YquqGJB9N8rrW2m1TUBcAAAAAMIlhw73nJHlXkg8m+XbX9oQkL07y5iQPTPLGJDcmOXxKKwQAAAAAxjRsuHdIkle31k4d0faVqvphkle21p5cVcuSvCXCPQAAAACYEcMuqPGEJBeN0X5xksd3299M8vCpKAoAAAAAmNyw4d4VGbyCO9rfJvlpt/3gJL+aiqIAAAAAgMkN+1ruPyb5dFX9WZJzM1hc4/FJ/ijJM7tjHp/k5CmvEAAAAAAY01DhXmvt81X12Azm3vvjJJXktCQfbK39tDvm/dNWJQAAAABwF0OFe1W1VhfiHTbN9QAAAAAAQxp2zr1fVNXRVbXTtFYDAAAAAAxt2HDvDUm2TfLNqvq/qnpTVf3RNNYFAAAAAExiqHCvtXZMa21RkkclOTaDRTR+VFXfrKqXTmN9AAAAAMA4hh25lyRprV3ZWjuytbZdkoVJ7pfkP6alMgAAAABgQkMtqDFSVe2W5MAkz05y3ySfmOqiAAAAAIDJDbta7lYZBHrPSfKwJGcmeUWSz7TWbpq+8gAAAACA8Qw7cu+iJOcmeU+SE1pr105fSQAAAADAMIYN9/64tfajaa0EAAAAAFglw66WK9gDAAAAgNXMUOFeVd23qt5SVf9XVTdX1e0jP9NdJAAAAABwV0OFe0neluSgJP+a5I4kr03yviS/TPLS6SkNAAAAAJjIsOHeXyZ5SWvtQ0luT/LZ1torkhyeZK/pKg4AAAAAGN+w4d6mSS7ttpcneWC3/cUkT53imgAAAACAIQwb7v00yUO77cuS7N1t75LkpqkuCgAAAACY3LDh3meS7Nlt/3uSt1TV5UmOS/LhaagLAAAAAJjE2sMc1Fo7bMT2KVX1syS7Jvm/1trp01UcAAAAADC+ocK90Vpr307y7SmuBQAAAABYBeO+lltVuw17kaqaU1XbTHLMR6pqWVVdPKLtzVX186q6oPv82Yh9h1XVZVX1w6rae0T7jlV1Ubfv6Kqqrn3dqjqpa/92Vc0btn4AAAAA6KOJ5tw7tqrOqqrnVNUGYx1QVdtW1bsyWGRju0nudVySfcZof09rbUH3+UJ33S2THJBkq+6c91fVWt3xH0jy4iSbd58V13xhkutba49J8p4kR05SDwAAAAD02kTh3lZJTk3ypiS/qqofVNVXq+qMqvpWVV2fwau5D0uyR2vtExPdqLV2dpJfDVnX05Kc2Fq7pbV2eQbh4U5VtVmSDVpr32yttSQfS/IXI845vts+JcmeK0b1AQAAAMCaqAYZ2SQHVS1MsluSRyVZL8l1Sb6b5KuttWEDu3Svyp7eWtu6+/3mJAcnuSHJkiT/2Fq7vqrem+RbKwLDqjo2yRlJliZ5Z2vtKV37E5Mc2lrbr3vdd5/W2pXdvh8neUJr7box6nhxBqP/summm+544oknDvsIq7Xly5dnzpw5s10Gs0gfQB9AH0AfQB9AH0AfYHXoA7vvvvt5rbWFs1rEvcSwq+UuySB8m2ofSPK2JK37/tckf5NkrBF3bYL2TLJv5cbWjklyTJIsXLiwLVq0aJWKXl0tXrw4a8qzcPfoA+gD6APoA+gD6APoA+gD9y4TvZY77Vpr17TWbm+t3ZHkP5Ps1O26MskjRhz68CRXde0PH6N9pXOqau0kG2b414ABAAAAoHdmNdzr5tBb4elJVqyke1qSA7oVcOdnsHDGd1prVye5sap27ubTe36Sz44456Bu+1lJvtKGeecYAAAAAHpqqNdyp0JVnZBkUZJNqurKJIcnWVRVCzJ4fXZpkr9LktbaJVV1cpJLk9yW5GWttdu7Sx2Swcq762UwD98ZXfuxST5eVZdlMGLvgGl/KAAAAACYRTMW7rXWnjNG87ETHH9EkiPGaF+SZOsx2m9O8ux7UiMAAAAA9MmsvpYLAAAAANx9Q4d7VfWnVXV6VV1aVSsWrnhRVe05feUBAAAAAOMZKtyrqgOTnJzkR0nmJ1mn27VWktdNT2kAAAAAwESGHbn3uiR/21p7dQYLXKzwrSQLprooAAAAAGByw4Z7myf55hjty5NsMHXlAAAAAADDGjbcuyrJY8dof1KSH09dOQAAAADAsIYN945JcnRV7dr9fkRVHZTkXUk+MC2VAQAAAAATWnuYg1pr76qqDZN8Ocn9knw1yS1JjmqtvW8a6wMAAAAAxjFUuJckrbU3VNURSbbMYMTfpa215dNWGQAAAAAwoaFey62qj1TVA1prv2utLWmtfae1tryq1q+qj0x3kQAAAADAXQ07595BSdYbo329JM+funIAAAAAgGFN+FpuVW2cpLrPRlV124jdayXZN8k101ceAAAAADCeyebcuy5J6z6XjrG/JTl8qosCAAAAACY3Wbi3ewaj9r6S5JlJfjVi361JrmitXTVNtQEAAAAAE5gw3Gut/W+SVNX8JD9rrd0xI1UBAAAAAJOabORekqS1dkWSVNVDkzwyyX1H7T976ksDAAAAACYyVLjXhXqfSvKkDObZq+57hbWmvjQAAAAAYCL3GfK4f0tye5Itk/wuyROTPDvJ95PsMy2VAQAAAAATGmrkXpInJ9m3tfaDqmpJrm2tnVNVtyR5W5IvT1uFAAAAAMCYhh25t16S67rtXyWZ221fmmTbqS4KAAAAAJjcsOHeD5Js0W1fkOQlVfWoJC9L8vNpqAsAAAAAmMSwr+X+e5KHdNtvTfLFJM9JckuSg6ahLgAAAABgEkOFe621T47YPr+q5mUwku+nrbXrxj0RAAAAAJg2k76WW1XrVNUvqmqrFW2ttd+11s4X7AEAAADA7Jk03Gut/T7J75O06S8HAAAAABjWsAtq/EeSw6pq2Dn6AAAAAIBpNmxY98QkT07y86q6OMlvR+5sre0/1YUBAAAAABMbNty7Lsmnp7MQAAAAAGDVDLta7guGOa6qdk2ypLV2yz2qCgAAAACY1LBz7g3rjCQPm+JrAgAAAABjmOpwr6b4egAAAADAOKY63AMAAAAAZohwDwAAAAB6SrgHAAAAAD011eFem+LrAQAAAADjsKAGAAAAAPTU2lN5sdbaA6byegAAAADA+IYK96pq4yRHJNkzydyMGvHXWttg6ksDAAAAACYy7Mi9Y5Nsn+SYJFfF3HoAAAAAMOuGDff2TLJXa+3b01kMAAAAADC8YRfUWJZk+XQWAgAAAACsmmHDvTckeWtVzZnOYgAAAACA4Q37Wu4bk8xLsqyqrkjy+5E7W2vbTnFdAAAAAMAkhg33TpnWKgAAAACAVTZUuNdae8t0FwIAAAAArJphR+4lSapqjyRbJmlJLmmtLZ6OogAAAACAyQ0V7lXVw5J8JsmOSa7qmh9aVUuSPL21dtW4JwMAAAAA02LY1XKPTnJ7kse01h7RWntEks27tqOnqzgAAAAAYHzDvpa7V5JFrbXLVzS01n5SVa9Icta0VAYAAAAATGjYkXvjuWNKqgAAAAAAVtmw4d5ZSY6uqkesaKiqRyb59xi5BwAAAACzYthw7xVJ7p/kJ1V1RVUtTfLjru0V01QbAAAAADCBoebca639LMkOVbVXki2SVJJLW2tnTmdxAAAAAMD4hl1QI0nSWvtyki9PUy0AAAAAwCoYN9yrqn9I8v7W2s3d9rhaa++e8soAAAAAgAlNNHLv75Mcn+Tmbns8LYlwDwAAAABm2LjhXmtt/ljbAAAAAMDqYajVcqvq+VW17hjt962q5099WQAAAADAZIYK95J8NMmGY7Q/oNsHAAAAAMywYcO9ymBuvdEemeQ3U1cOAAAAADCsiRbUSFVdlEGo15L8b1XdNmL3WkkeleQL01ceAAAAADCeCcO9JKd031sn+XyS5SP23ZpkaZJPT31ZAAAAAMBkJgz3WmtvSZKqWprkxNbaLTNRFAAAAAAwuWHn3FuaZOfRjVX15Kp60pRWBAAAAAAMZdhw7z1JNhqjfYNuHwAAAAAww4YN9/44yffGaL+o2wcAAAAAzLBhw72bkjx0jPaHZ7CwBgAAAAAww4YN9/4nyTur6s5Xc6tq4yTv6PYBAAAAADNswtVyR3hNkrOTLK2qC7u2bZMsS3LAdBQGAAAAAExsqHCvtXZ1VW2X5MAkC5JUkuOTfKq19rvpKw8AAAAAGM+wI/fShXj/OY21AAAAAACrYNg591JVf1pVp1fVpVX1iK7tRVW15/SVBwAAAACMZ6hwr6oOTHJykh8lmZ9knW7XWkleNz2lAQAAAAATGXbk3uuS/G1r7dVJbhvR/q0M5uADAAAAAGbYsOHe5km+OUb78iQbTF05AAAAAMCwhg33rkry2DHan5Tkx1NXDgAAAAAwrGHDvWOSHF1Vu3a/H1FVByV5V5IPTEtlAAAAAMCE1h7moNbau6pqwyRfTnK/JF9NckuSo1pr75vG+gAAAACAcYw7cq+qnlRVd4Z/rbU3JNkkyU5Jdk7y4NbaP01/iQAAAADAWCYauffVJJslWVZVP0ny+NbaL5MsmZHKAAAAAIAJTTTn3vVJ5nfb8yY5FgAAAACYYRON3Pt0kv+tqquTtCRLqur2sQ5srT16OooDAAAAAMY3Ubj3kiSnJdk8ybuTfDTJjTNRFAAAAAAwuXHDvdZaS/L5JKmq7ZL8a2tNuAcAAAAAq4mJRu7dqbX2gukuBAAAAABYNUOFe1V1vySvTLJnkrkZtbhGa23bqS8NAAAAAJjIUOFekvcneXqS/0ryjQwW2AAAAAAAZtGw4d5fJHl2a+3MaawFAAAAAFgF95n8kCTJ75L8bDoLAQAAAABWzbDh3ruS/ENVDXs8AAAAADDNhn0td68kT0yyT1VdmuT3I3e21vaf6sIAAAAAgIkNG+5dl+Qz01kIAAAAALBqhgr3WmsvmO5CAAAAAIBVYw49AAAAAOipCUfuVdVpw1zEnHsAAAAAMPMmey33lzNSBQAAAACwyiYM98y1BwAAAACrL3PuAQAAAEBPCfcAAAAAoKeEewAAAADQU8I9AAAAAOgp4R4AAAAA9JRwDwAAAAB6SrgHAAAAAD0l3AMAAACAnhLuAQAAAEBPCfcAAAAAoKeEewAAAADQU8I9AAAAAOgp4R4AAAAA9JRwDwAAAAB6SrgHAAAAAD0l3AMAAACAnhLuAQAAAEBPCfcAAAAAoKeEewAAAADQU8I9AAAAAOgp4R4AAAAA9JRwDwAAAAB6SrgHAAAAAD0l3AMAAACAnhLuAQAAAEBPCfcAAAAAoKeEewAAAADQU8I9AAAAAOgp4R4AAAAA9JRwDwAAAAB6SrgHAAAAAD0l3AMAAACAnhLuAQAAAEBPCfcAAAAAoKeEewAAAADQU8I9AAAAAOgp4R4AAAAA9JRwDwAAAAB6SrgHAAAAAD0l3AMAAACAnhLuAQAAAEBPCfcAAAAAoKeEewAAAADQU8I9AAAAAOgp4R4AAAAA9JRwDwAAAAB6SrgHAAAAAD0l3AMAAACAnhLuAQAAAEBPCfcAAAAAoKeEewAAAADQU8I9AAAAAOgp4R4AAAAA9NSMhXtV9ZGqWlZVF49o27iqvlxVP+q+Nxqx77CquqyqflhVe49o37GqLur2HV1V1bWvW1Unde3frqp5M/VsAAAAADAbZnLk3nFJ9hnV9vokZ7XWNk9yVvc7VbVlkgOSbNWd8/6qWqs75wNJXpxk8+6z4povTHJ9a+0xSd6T5MhpexIAAAAAWA3MWLjXWjs7ya9GNT8tyfHd9vFJ/mJE+4mttVtaa5cnuSzJTlW1WZINWmvfbK21JB8bdc6Ka52SZM8Vo/oAAAAAYE0023PubdpauzpJuu+5XfvDkvxsxHFXdm0P67ZHt690TmvttiS/SfKgaascAAAAAGbZ2rNdwDjGGnHXJmif6Jy7XrzqxRm82ptNN900ixcvvhslrn6WL1++xjwLd48+gD6APoA+gD6APoA+gD5w7zLb4d41VbVZa+3q7pXbZV37lUkeMeK4hye5qmt/+BjtI8+5sqrWTrJh7voacJKktXZMkmOSZOHChW3RokVT8zSzbPHixVlTnoW7Rx9AH0AfQB9AH0AfQB9AH7h3me3Xck9LclC3fVCSz45oP6BbAXd+BgtnfKd7dffGqtq5m0/v+aPOWXGtZyX5SjcvHwAAAACskWZs5F5VnZBkUZJNqurKJIcneWeSk6vqhUl+muTZSdJau6SqTk5yaZLbkrystXZ7d6lDMlh5d70kZ3SfJDk2ycer6rIMRuwdMAOPBQAAAACzZsbCvdbac8bZtec4xx+R5Igx2pck2XqM9pvThYMAAAAAcG8w26/lAgAAAAB3k3APAAAAAHpKuAcAAAAAPSXcAwAAAICeEu4BAAAAQE8J9wAAAACgp4R7AAAAANBTwj0AAAAA6CnhHgAAAAD0lHAPAAAAAHpKuAcAAAAAPSXcAwAAAICeEu4BAAAAQE8J9wAAAACgp4R7AAAAANBTwj0AAAAA6CnhHgAAAAD0lHAPAAAAAHpKuAcAAAAAPSXcAwAAAICeEu4BAAAAQE8J9wAAAACgp4R7AAAAANBTwj0AAAAA6CnhHgAAAAD0lHAPAAAAAHpKuAcAAAAAPSXcAwAAAICeEu4BAAAAQE8J9wAAAACgp4R7AAAAANBTwj0AAAAA6CnhHgAAAAD0lHAPAAAAAHpKuAcAAAAAPSXcAwAAAICeEu4BAAAAQE8J9wAAAACgp4R7AAAAANBTwj0AAAAA6CnhHgAAAAD0lHAPAAAAAHpKuAcAAAAAPSXcAwAAAICeEu4BAAAAQE8J9wAAAACgp4R7AAAAANBTwj0AAAAA6CnhHgAAAAD0lHAPAAAAAHpKuAcAAAAAPSXcAwAAAICeEu4BAAAAQE8J9wAAAACgp4R7AAAAANBTwj0AAAAA6CnhHgAAAAD0lHAPAAAAAHpKuAcAAAAAPSXcAwAAAICeEu4BAAAAQE8J9wAAAACgp4R7AAAAANBTwj0AAAAA6CnhHgAAAAD0lHAPAAAAAHpKuAcAAAAAPSXcAwAAAICeEu4BAAAAQE8J9wAAAACgp4R7AAAAANBTwj0AAAAA6CnhHgAAAAD0lHAPAAAAAHpKuAcAAAAAPSXcAwAAAICeEu4BAAAAQE8J9wAAAACgp4R7AAAAANBTwj0AAAAA6CnhHgAAAAD0lHAPAAAAAHpKuAcAAAAAPSXcAwAAAICeEu4BAAAAQE8J9wAAAACgp4R7AAAAANBTwj0AAAAA6CnhHgAAAAD0lHAPAAAAAHpKuAcAAAAAPSXcAwAAAICeEu4BAAAAQE8J9wAAAACgp4R7AAAAANBTwj0AAAAA6CnhHgAAAAD0lHAPAAAAAHpKuAcAAAAAPSXcAwAAAICeEu4BAAAAQE8J9wAAAACgp4R7AAAAANBTwj0AAAAA6CnhHgAAAAD0lHAPAAAAAHpKuAcAAAAAPSXcAwAAAICeEu4BAAAAQE8J9wAAAACgp4R7AAAAANBTwj0AAAAA6CnhHgAAAAD0lHAPAAAAAHpKuAcAAAAAPSXcAwAAAICeEu4BAAAAQE8J9wAAAACgp4R7AAAAANBTwj0AAAAA6CnhHgAAAAD0lHAPAAAAAHpKuAcAzKi11lorCxYsyNZbb50///M/z69//espu/aLXvSiXHrppVN2PQAAWN0J9wCAGbXeeuvlggsuyMUXX5yNN94473vf+6bs2h/+8Iez5ZZbTtn1AABgdSfcAwBmzS677JKf//znSZJFixZlyZIlSZLrrrsu8+bNS5Jccskl2WmnnbJgwYJsu+22+dGPfpTf/va32XfffbPddttl6623zkknnXSXaxxyyCFZuHBhttpqqxx++OEz/3AAADAD1p7tAgCAe6fbb789Z511Vl74whdOeNwHP/jBvPKVr8yBBx6YW2+9Nbfffnu+8IUv5KEPfWg+//nPJ0l+85vf3OW8I444IhtvvHFuv/327Lnnnrnwwguz7bbbTsuzAADAbDFyDwCYUTfddFMWLFiQBz3oQfnVr36Vvfbaa8Ljd9lll7zjHe/IkUcemSuuuCLrrbdettlmm5x55pk59NBD87WvfS0bbrjhXc47+eSTs8MOO2T77bfPJZdcYi4+AADWSMI9AGBGrZhz74orrsitt95655x7a6+9du64444kyc0333zn8c997nNz2mmnZb311svee++dr3zlK3nsYx+b8847L9tss00OO+ywvPWtb13pHpdffnmOOuqonHXWWbnwwguz7777rnRNAABYUwj3AIBZseGGG+boo4/OUUcdld///veZN29ezjvvvCTJKaeccudxP/nJT/LoRz86r3jFK7L//vvnwgsvzFVXXZX73//+ed7znpfXvOY1Of/881e69g033JD1118/G264Ya655pqcccYZM/psAAAwU8y5BwDMmu233z7bbbddTjzxxLzmNa/JX/7lX+bjH/949thjjzuPOemkk/KJT3wi66yzTh7ykIfkTW96U84999y89rWvzX3uc5+ss846+cAHPrDSdbfbbrtsv/322WqrrfLoRz86u+6660w/GgAAzAjhHgAwo5YvX77S78997nN3bl944YV3br/97W9Pkhx22GE57LDDVjpn7733zt57732Xay9evPjO7eOOO24KqgUAgNWbcA96bq211so222yTZPC/MJ955pmZN2/e7BYFAAAAzAjhHvTcionpk8GIlZHBXmstrbXc5z6m1wQAAIA1kf+NH9YwS5cuzeMe97i89KUvzQ477JCf/exnOeSQQ7Jw4cJstdVWOfzww+88dt68eTn88MOzww47ZJtttskPfvCDJIMRgC94wQuyzTbbZNttt82nP/3pJMmXvvSl7LLLLtlhhx3y7Gc/+y6v1gEAAAAzS7gHPXfTTTdlwYIFWbBgQf7pn/4pSfLDH/4wz3/+8/Pd7343j3rUo3LEEUdkyZIlufDCC/O///u/K81ptckmm+T888/PIYcckqOOOipJ8ra3vS0bbrhhLrroolx44YXZY489ct111+Xtb397zjzzzJx//vlZuHBh3v3ud8/KMwMAAAADXsuFnhv9Wm6SPOpRj8rOO+985zEnn3xyjjnmmNx22225+uqrc+mll2bbbbdNkjzjGc9Ikuy444459dRTkyRnnnlmTjzxxDvP32ijjXL66afn0ksvvXPFyVtvvTW77LLLdD8esLqbOzdZtmz83wAAwLQS7sEaaP31179z+/LLL89RRx2Vc889NxtttFEOPvjg3HzzzXfuX3fddZMMFua47bbbkgzm6quqla7ZWstee+2VE044YQaeAOiFqrQku1XlqkNPz0OP3C9fTwb/70drs10dAADcK3gtF9ZwN9xwQ9Zff/1suOGGueaaa3LGGWdMes5Tn/rUvPe9773z9/XXX5+dd94555xzTi677LIkye9+97v83//937TVDazm5s5NS1JJvp6krQj2krRuPwAAMP2Ee7CG22677bL99ttnq622yt/8zd/c+VrtRN74xjfm+uuvz9Zbb53tttsuX/3qV/PgBz84xx13XJ7znOdk2223zc4773znAhzAvdCyZdktuTPgW5o/BHu7dfsBAIDp57Vc6LnRK9bOmzcvF1988Uptxx133JjnLl269M7thQsX3jln35w5c3L88cff5fg99tgj55577j2qF1hzXHXo6Zl/5H5ZOqJtfpI69PRZqggAAO59jNwDAO6Whx65Xy4f1XZ51w4AAMwM4R4AsOrmzl1pjr15yUpz8JlzDwAAZoZwDwBYdcuWrTTHXh16+kpz8JlzDwAAZoY59wCAu6e11Ny5OWdFkPfONhixJ9gDAIAZY+QeAHD3jQ7yBHsAADCjhHuwOhk9R5U5qwAAAIAJCPdgdVGVdu212bUq81//+eza/U7VbFcGAAAArKaEe7A6mDt3pVUm25H7rbQKpRF8AAAAwFiEe7A6WLZspVUmlyYrrUJpDisAAABgLMI9WE1cdejpmT+qbX7XDgAAADAW4R6sJh565H65fFTb5V07AAAAwFhWm3CvqpZW1UVVdUFVLenaNq6qL1fVj7rvjUYcf1hVXVZVP6yqvUe079hd57KqOrrKagT0wNy5K82xNy9ZaQ4+c+4BAAAAY1ltwr3O7q21Ba21hd3v1yc5q7W2eZKzut+pqi2THJBkqyT7JHl/Va3VnfOBJC9Osnn32WcG64e7Z9mylebYq0NPX2kOPnPuAQAAAGNZ3cK90Z6W5Phu+/gkfzGi/cTW2i2ttcuTXJZkp6raLMkGrbVvttZako+NOAdWb62lHvzgnNNaLn/nvjmn+53WZrsyAAAAYDVVbTUJDqrq8iTXZzBY6UOttWOq6tettQeOOOb61tpGVfXeJN9qrX2iaz82yRkZLDL6ztbaU7r2JyY5tLW236h7vTiD0X3ZdNNNdzzxxBOn/flmwvLlyzNnzpzZLoNZpA+gD6APoA+gD6APoA+wOvSB3Xff/bwRb2Yyjdae7QJG2LW1dlVVzU3y5ar6wQTHjjWPXpugfeWG1o5JckySLFy4sC1atOhulLv6Wbx4cdaUZ+Hu0QfQB9AH0AfQB9AH0AfQB+5dVpvXcltrV3Xfy5J8JslOSa7pXrVN971i4rErkzxixOkPT3JV1/7wMdoBAAAAYI2zWoR7VbV+VT1gxXaSpya5OMlpSQ7qDjsoyWe77dOSHFBV61bV/AwWzvhOa+3qJDdW1c7dKrnPH3EOAAAAAKxRVpfXcjdN8plBHpe1k3yqtfbFqjo3yclV9cIkP03y7CRprV1SVScnuTTJbUle1lq7vbvWIUmOS7JeBvPwnTGTDwIAAAAAM2W1CPdaaz9Jst0Y7b9Msuc45xyR5Igx2pck2XqqawQAAACA1c1q8VouAAAAALDqhHsAAAAA0FPCPQAAAADoKeEeAAAAAPSUcA8AAAAAekq4BwAAAAA9JdwDAAAAgJ4S7gEAAABATwn3AAAAAKCnhHsAAAAA0FPCPQAAAADoKeEed9tnPvOZVFV+8IMfTPu9LrjggnzhC1+48/dpp52Wd77zndN+XwAAAIDVmXCPu+2EE07IbrvtlhNPPPEu+26//fYpvdfocG///ffP61//+im9BwAAAEDfCPe4W5YvX55zzjknxx577J3h3uLFi7P77rvnuc99brbZZpvccccdeelLX5qtttoq++23X/7sz/4sp5xySpLkvPPOy5Of/OTsuOOO2XvvvXP11VcnSRYtWpRDDz00O+20Ux772Mfma1/7Wm699da86U1vykknnZQFCxbkpJNOynHHHZeXv/zlSZKDDz74/7d352FVVW0fx7/rMImgOOJUClqmIpMiTqiYY2U+mppaj2ZlZWWWZWn1WDZYab4NVk++TaKlZmlpkQ2vJc6lqOCUY6I4JDgDynj2+8c5nABRcRb9fa6Li7PXXnvvtfdZ2u72XmsxbNgwWrVqRd26dV3XSE9Pp0OHDjRp0oTg4GDmzp17GZ6UiIiIiIiIiMjF4365GyCl05w5c+jatSv169enUqVKrF69GoAVK1awfv16AgMDmTVrFklJSaxbt46UlBQaNmzIfffdR05ODo899hhz586latWqzJw5k+eff57PPvsMgNzcXFasWMG8efN46aWXmD9/Pi+//DLx8fG8//77AMTExBRqz759+1iyZAmbNm2ie/fu9O7dmzJlyvDtt99Svnx5Dhw4QIsWLejevTvGmEv6rERERERERERELhYF9+SczJgxgyeeeAKAfv36MWPGDG677TYiIyMJDAwEYMmSJfTp0webzUb16tVp3749AJs3b2b9+vV06tQJcAzhrVGjhuvcd9xxBwBNmzYlKSmpRO3p0aMHNpuNRo0asX//fgAsy+K5555j0aJF2Gw29uzZw/79+6levfqFeAQiIiIiIiIiIpedgnty1g4ePMhvv/3G+vXrMcaQl5eHMYZbb70VHx8fVz3Lsoo93rIsgoKCWL58ebH7vby8AHBzcyM3N7dEbco/puB1p02bRmpqKqtWrcLDw4OAgAAyMzNLdD4RERERERERkdJAc+7JWZs1axYDBw5k586dJCUlkZycTGBgIEuWLClULyoqitmzZ2O329m/fz9xcXEA3HTTTaSmprqCezk5OWzYsOG01yxXrhxpaWln1c6jR4/i7++Ph4cHCxYsYOfOnWd1vIiIiIiIiIjIlU7BPTlrM2bMoGfPnoXKevXqxfTp008qu+6662jcuDEPPfQQzZs3x8/PD09PT2bNmsXIkSMJDQ0lLCyMZcuWnfaa7du3Z+PGja4FNUri7rvvJj4+noiICKZNm0aDBg3O7kZFLqGkpCQaN25cqGzMmDFMmDDhtMfFx8czbNgwwLGozfr168/62gEBARw4cOC05atWrSIwMJA1a9bw3Xff8cYbb5z1dYoTFxdHt27dLsi5RERERERErkUalitnLT8Dr6Bhw4a5Agz5bDYbEyZMwNfXl4MHDxIZGUlwcDAAYWFhLFq06LTnrlKlimvOvUqVKrFy5cpCdQcNGgScvLhGenq66/hTDf0VuVpEREQQEREBOP785M85eSGtXbuW3r17M3PmTMLDwwkPD6d79+4X/DoiIiIiIiJy9pS5JxdVt27dCAsLo02bNowePVqLWYico+joaEaOHElkZCT169dn8eLFwD+Zb0lJSUyaNIlZs2YRFhbG4sWLSU1NpVevXjRr1oxmzZqxdOlSwDFvZufOnQkPD+ehhx465fyYAH/++Sc9evTg888/JzIyEnAE1IcOHQo4guzDhg2jVatW1K1bl1mzZgFgt9t55JFHCAoKolu3btx6662ufT/99BMNGjQgKiqKb775xnWtQ4cO0aNHD0JCQmjRogVr164FHBmM99xzD507dyYgIIBvvvmGZ555huDgYLp27UpOTs4FftoiIiIiIiKlh4J7clHFxcWRkJDAb7/9xk8//US9evVo1KgRt956K1u2bCn2mOKGJ56r6Oho4uPjTyq/kMMKRS6V3NxcVqxYwTvvvMNLL71UaF9AQABDhgyhd+/eJCQk0KZNGx5//HGGDx/OypUrmT17NoMHDwbgpZdeIioqijVr1tC9e3d27dp1ymv+61//4v333ycqKuqUdfbt28eSJUuIjY1l1KhRAHzzzTckJSWxbt06PvnkE1cWbWZmJg888ADff/89ixcv5u+//3ad58UXXyQ8PJy1a9fy2muvMXDgQNe+7du388MPPzB37lz+/e9/0759e9atW4e3tzc//PDD2T9MERERERGRq4SG5cpFZ1kWPXv25J577uHLL78EICEhgf3791O/fv3L0qbu3btrWKFcUYwxZyy/4447AGjatKlryPrpzJ8/n40bN7q2jx07RlpaGosWLXJlzN12221UrFjxlOfo2LEjn3zyCV26dMHNza3YOj169MBms9GoUSPXsOAlS5bQp08fbDYb1atXp3379gBs2rSJwMBAbrzxRgD+/e9/89FHH7mOmT17NgA333wzBw8e5OjRowDccssteHh4EBwcTF5eHl27dgUgODi4RM9CRERERETkaqXMPbnoFixYgIeHB0OGDHGVhYWFERUVxdNPP03jxo0JDg4udqGMzMxM7r33XoKDgwkPD2fBggWAY1hgjx49uP322wkMDOT999/nrbfeIjw8nBYtWnDo0CHXOb744gtatWpF48aNWbFihev4/GGF33//Pc2bNyc8PJyOHTtelDnLRM6kcuXKHD58uFDZoUOHqFKlimvby8sLADc3N3Jzc894TrvdzvLly0lISCAhIYE9e/ZQrlw54NTBxKLef/99AB555JFT1slvF+Aa4nu6ob6nunZxx+TXzb+GzWbDw8PDVW6z2Ur0LERERERERK5WCu4J+Puffvs8rV+/nqZNm55U/s0335CQkEBiYiLz58/n6aefZt++fYXqfPDBBwCsW7eOGTNmcM8995CZmek67/Tp01mxYgXPP/88ZcuWZc2aNbRs2ZKpU6e6zpGRkcGyZcv473//y3333XdSO6Kiovj9999Zs2YN/fr1Y/z48Rfy9kVKxNfXlxo1avDrr78CjsDeTz/9dNrhsEWVK1eO48ePu7Y7d+7sCs6BI2MWoG3btkybNg2AH3/88aSgYkE2m40ZM2awefNmXnjhhRK3JSoqitmzZ2O329m/f79rsZwGDRqwY8cOtm/fDjhW385XsF1xcXFUqVKF8uXLl/iaIiIiIiIi1yIF9651xmClptLaGAJH/UBr5zYlzOo5H0uWLKF///64ublRrVo12rVrd9KKuEuWLGHAgAGAIyhQp04d11x97du3p1y5clStWhU/Pz9uv/124ORhev379wccgYNjx45x5MiRQtfYvXs3Xbp0ITg4mDfffJMNGzZcpDsWOb2pU6fy6quvEhYWxs0338yLL75IvXr1Snz87bffzpIlS1wLakycOJH4+HhCQkJo1KgRkyZNAhxz2y1atIgmTZrwyy+/ULt27dOe18vLi7lz5/Ldd9+5Au5n0qtXL6677joaN27MQw89RPPmzfHz86NMmTJ89NFH3HbbbURFRVGnTh3XMWPGjHG1d9SoUUyZMqXE9y4iIiIiInKt0px71zJ/fyzAAEuAwHHdWOLctgDj7w8pKed9maCgINcqmQWdbtheSeoUHApos9kKDdsrOEyv6BDAotuPPfYYTz75JN27dycuLo4xY8acsV0iF0OjRo1cQ8+Lys98A6hSpYorgB0dHU10dDQA9evX59NPP3VtA8UOd69cuTK//PKLa/vtt98u9poFg+R+fn6uzD9wrJILjiHuBaWnpwOOP4cTJkzA19eXgwcPEhkZSXBwMABdu3Zl06ZNJ12vUqVKzJ0796Tyon8m869R3D4REREREZFrjTL3rmUpKUSBK8CXxD+BvSjn/gvh5ptvJisri48//thVtnLlSipWrMjMmTPJy8sjNTWVRYsWERkZWejYgsP0tmzZwq5du7jpppvO6vr5wY0lS5bg5+eHn59fof1Hjx6lVq1aAMoUErmAunXrRlhYGG3atGH06NFUr179cjdJRERERETkqqPMvWvc3pGxBI7rRlKBskDAjIy9YNcwxvDtt9/yxBNP8MYbb1CmTBkCAgJ45513SE9PJzQ0FGMM48ePp3r16oWyhR555BGGDBlCcHAw7u7uxMTEFMrYK4mKFSvSqlUrjh07xmeffXbS/jFjxtCnTx9q1apFixYt2LFjx/nesohQONtQRERERERELg5TkqGRV7OIiAgrPj7+cjfjgoiLiys0HK8kWhvjGoqbLz9zb+k13jdKo3PpA3J1UR8Q9QFRHxD1AVEfEPUBuRL6gDFmlWVZEZe1EdcIDcu9lvn7F5pjLwAKzcF3oVfNFRERERERERGRC0vBvWtZSkqhOfbMyNhCc/BdqDn3RK5JRYPjCpaLiIiIiIjIRaDg3rXOsjBVq7LUstjxxm0sdW6jIbki584YrNRUWhtD4KgfaO3cpshKzSIiIiIiIiLnS8E9OTlDTxl7IufO37/Q8HZrXLdCw9+VwSciIiIiIiIXkoJ7cnY01FDk9FJSCg1vT4JCw98VPBcREREREZELScE9KTkNNRQpkb0jYwksUhboLBcRERERERG5kBTck5LRUEOREqs5rhs7ipTtcJaLiIiIiIiIXEgK7knJaKihSMn4+xcKfAdAocC4AuEiIiIiIiJyISm4JyW2d2QsNmBAgbIA4Hfv8nTrdm4ZSZMmTWLq1KkXoHUiV4iUlEKBbzMytlBgXIFwERERERERuZDcL3cDpPSoOa4bqcB64ATgDXwI9Dlx7JzPOWTIkAvTOJEriWVh/P1Zmh/Ie8NyZOwpsCciIiIiIiIXmDL3pGScQw0BugK1cWQifQmMBvi//wMgIyOD++67j2bNmhEeHs7cuXMBGDZsGC+//DIAP//8M23btsVutzNmzBgmTJgAwLZt2+jYsSOhoaE0adKE7du3Y1kWTz/9NI0bNyY4OJiZM2dewpsWOQ9FA3kK7ImIiIiIiMhFoMw9KZmUFIxzVdwfgeP1W9FqyzJOAPcBSzp1AmDs2LHcfPPNfPbZZxw5coTIyEg6duzIG2+8QbNmzWjTpg3Dhg1j3rx52GyFY8t33303o0aNomfPnmRmZmK32/nmm29ISEggMTGRAwcO0KxZM9q2bUuNGjUu7f2LiIiIiIiIiFyBlLknALi5uREWFkbjxo3p06cPx48fP7mSZQGQYFk0LJfFg599xq3e3rBggavKL7/8whtvvEFYWBjR0dFkZmaya9cuypYty8cff0ynTp0YOnQo9erVK3TqtLQ09uzZQ8+ePQEoU6YMZcuWZcmSJfTv3x83NzeqVatGu3btWLly5cV7ECIiIiIiIiIipYiCewKAt7c3CQkJrF+/Hk9PTyZNmlR8RR8fALp3786IESPos3Rpod2WZTF79mwSEhJISEhg165dNGzYEIB169ZRuXJl9u7de9JpLWfgsKTlIiIiIiIiIiKi4J4Uo02bNmzbtu2U8+fFxMSwYsUKatSowVNPPUVCQoIrm65Lly706dOHyZMnA7BmzRoAdu7cyf/8z/+wZs0afvzxR/74449C1yxfvjzXXXcdc+bMASArK4vjx4/Ttm1bZs6cSV5eHqmpqSxatIjIyMhL9CRERERERERERK5sCu5JIbm5ufz4448EBwe75s9buXIlCxYs4Omnn3Zl0iUkJLBo0SJ+++23QsePHj2avLw8Ro8eTePGjRk9ejSWZXH//fczYcIEatasyaeffsrgwYPJzMwsdOznn3/OxIkTCQkJoVWrVvz999/07NmTkJAQQkNDufnmmxk/fjzVq1e/ZM9DRERERERERORKpgU1BIATJ04QFhYGODL37r//flq1asV3333nWs02MzOT+Ph4/vjjDzp16kSlSpUACAsLo1mzZoBjeG/79u2JiIhg0KBBrvPPnz/f9blp06asW7cOgDFjxrjKb7zxxpOChQBvvvkmb7755oW8XRERERERERGRq4KCewL8M+deQfnz5910002Fyv/44w98nHPvAbi7u2O3213bRTPyRERERERERETk4tCwXDmlLl268N5777mG4ubPn1dUnTp12LhxI1lZWRw9epRff/31UjZTREREREREROSapcw9OaXRo0fzxBNPEBISgmVZBAQEEBsbe1K966+/njvvvJOQkBBuvPFGwsPDL0NrRURERERERESuPQruCQDp6eknlXl7e/O///u/J5UPGjSo0Hx6AOPHj2f8+PEXq3kiIiIiIiIiIlIMDcsVKWV8fX0BSEpKYvr06Wesn5SUROPGjS92s0RERERERETkMlBw71rm73/6bbmilTS4JyIiIiIiIiJXLwX3rlXGYKWm0toYAkf9QGvnNsZc7pZJCY0aNYrFixcTFhbG22+/TVJSEsOGDaNJkyY0adKEZcuWnXRMmzZtCq2K3Lp1a9auXXsJWy0iIiIiIiIiF5KCe9cif38swABLAGtcN5Y4ty3nfrnyvfHGG65g3fDhw/H392fChAmsXr2amTNnMmzYsJOOGTx4MDExMQBs2bKFrKwsQkJCLnHLRURERERERORCUXDvanSm4bYpKUSBK8CXxD+BvSjnfil9cnJymDBhAsHBwfTp04eNGzeeVKdPnz7ExsaSk5PDZ599dtLCKCIiIiIiIiJSuii4d7Up4XDbvSNjCSxyaKCzXEqnt99+m4oVK5KYmEh8fDzZ2dkn1SlbtiydOnVi7ty5fPXVV9x1112XoaUiIiIiIiIicqEouHc1SUws8XDbmuO6saPI4Tuc5VI6lCtXjrS0NNf20aNHqVy5Mjabjc8//5y8vLxijxs8eDDDhg2jWbNmVKpU6VI1V0REREREREQuAgX3riahoSUbbuvvXyjoF1DgmCXO/XLlCwkJwd3dndDQUN5++20eeeQRfv75Z1q0aMGWLVvw8fEp9rimTZtSvnx57r333kvcYhERERERERG50NwvdwPkwto7MpbAcd1IKlAWCJiCw21TUjDGuIJ+ZmQsUQWy/DTn3pUtPT0dAA8PD3799ddC+z799FOio6MBeP311wEICAhg/fr1rjp79+7FbrfTuXPnS9NgEREREREREblolLl3lSnxcFvLwlStylLLYscbt7HUuY1lXaqmymUwdepUmjdvztixY7HZ9MdfREREREREpLTT/91fTRITz264bdEMvUuYsTd27FiCgoIICQkhLCyMP/7445zOExcXx7Jly1zbgwYNYtasWSU69ttvv8UYw6ZNm1xlqampNG/enPDwcBYvXnzSMYMHDy52FdrSYuDAgSQnJ9OnT5/L3RQRERERERERuQAU3LuahIYWmmPPjIwtNAfflTLcdvny5cTGxrJ69WrWrl3L/Pnzuf7668/pXEWDe2djxowZREVF8eWXX7rKfv31Vxo0aMCaNWto06ZNofp5eXl88sknNGrU6JyuJyIiIiIiIiJyoSm4d7UpBcNt9+3bR5UqVfDy8gKgSpUq1KxZE3AE18LDwwkODua+++4jKysLcMwbd+DAAQDi4+OJjo4mKSmJSZMm8fbbbxMWFubKtFu0aBGtWrWibt26p8ziS09PZ+nSpXz66aeu4F5CQgLPPPMM8+bNIywsjBMnTuDr68sLL7xA8+bNWb58OdHR0cTHxwPw008/0aRJE0JDQ+nQoQMAK1asoFWrVoSHh9OqVSs2b958kZ6iiIiIiIiIiIiCe1enyzjctiQ6d+5McnIy9evX55FHHmHhwoUAZGZmMmjQIGbOnMm6devIzc3lww8/POV5AgICGDJkCMOHDychIcGVabdv3z6WLFlCbGwso0aNKvbYOXPm0LVrV+rXr0+lSpVYvXo1YWFhvPzyy/Tt25eEhAS8vb3JyMigcePG/PHHH0RFRbmOT01N5YEHHmD27NkkJiby9ddfA9CgQQMWLVrEmjVrePnll3nuuecu1GMTERERERERETmJgntyyfn6+rJq1So++ugjqlatSt++fYmJiWHz5s0EBgZSv359AO655x4WLVp01ufv0aMHNpuNRo0asX///mLrzJgxg379+gHQr18/ZsyYUWw9Nzc3evXqdVL577//Ttu2bQkMDASgUqVKABw9epQ+ffrQuHFjhg8fzoYNG07f2KLzIBbdFhERERERERE5DffL3QC5Nrm5uREdHU10dDTBwcFMmTKFsLCwU9Z3d3fHbrcDjgy/08kf7gtgFTMc+eDBg/z222+sX78eYwx5eXkYYxg/fvxJdcuUKYObm9tJ5ZZlYYw5qXz06NG0b9+eb7/9lqSkJKKjo0/dUGMc8yMaw96RsdQc182xIIoxV9QwahERERERERG5cilzTy65zZs3s3XrVtd2QkICderUoUGDBiQlJbFt2zYAPv/8c9q1awc4huCuWrUKgNmzZ7uOLVeuHGlpaWd1/VmzZjFw4EB27txJUlISycnJBAYGsmTJkhKfo2XLlixcuJAdO3YAcOjQIcCRuVerVi0AYmJiTn0Cf/9CKxlb+YE9HAugKINPREREREREREpCwT255NLT07nnnnto1KgRISEhbNy4kTFjxlCmTBkmT55Mnz59CA4OxmazMWTIEABefPFFHn/8cdq0aVMok+7222/n22+/LbSgxpnMmDGDnj17Firr1asX06dPL/E9VK1alY8++og77riD0NBQ+vbtC8AzzzzDs88+S+vWrcnLyzv1CVJSCq1knASFVjq+0uZJFBEREREREZErk4blyiXXtGlTli1bBjjm31u7dq1rX4cOHVizZs1Jx7Rp04YtW7acVF6/fv1Cx+cvqpEvPT39pGPi4uJOKhs2bJjr86BBg055fMFjb7nlFm655ZZC+1u2bFmona+88spJ18q3d2QsgeO6kVSgLBAwI2NPeYyIiIiIiIiISEHK3JMrzmkz3q4iNcd1Y0eRsh3OchERERERERGRklBwT64IcXFxtG/fnrvuuovg4GDAsept06ZNCQoK4qOPPnLV9fX15fnnnyc0NJQWLVqcckXcK5q/f6E59gKg0Bx8mnNPREREREREREpCwT25YqxYsYKxY8eyceNGAD777DNWrVpFfHw8EydO5ODBgwBkZGTQokULEhMTadu2LR9//PHlbPa5SUkpNMeeGRlbaA4+zbknIiIiIiIiIiWh4J5cMSIjIwkMDHRtT5w40ZWdl5yc7Fph19PTk27dHENXmzZtSlJS0uVo7vmzLEzVqiy1LHa8cRtLndtY1uVumYiIiIiIiIiUEgruyfkrOoT0HIeU+vj4uD7HxcUxf/58li9fTmJiIuHh4WRmZgLg4eGBMQYANzc3cnNzz63dV4KiGXrK2BMRERERERGRs6DgnpwfY7BSU2ltDIGjfqC1cxtn8O1cHT16lIoVK1K2bFk2bdrE77//foEaLCIiIiIiIiJy9VBwT86dv3+hRSCscd0KLRJxPotCdO3aldzcXEJCQhg9ejQtWrS4EC0WEREREREREbmquF/uBkgplpJClDGugF6Sszh/kYilJRhimp6eDkB0dDTR0dGuci8vL3788cfTHgPQu3dvevfufS6tFxEREREREREp9ZS5J+dl78hYAouUBTrLRURERERERETk4lJwT85LzXHd2FGkbIezXERERERERERELi4F9+Tc+fsXmmMvwPk7fw6+85lzT0REREREREREzkzBPTl3KSmuwF4UYEbGEsU/AT5KMOeeiIiIiIiIiIicOy2oIefHsjD+/o7FM/z9wbIcv/O3FeATEREREREREbloFNyT85eSAsY4MviMYe/IWGrmr6JrjCPgJyIiIiIiIiIiF5yG5cr58/cvNNeeNa5bobn4NPeeiIiIiIiIiMjFoeCenL+UlEJz7SVBobn4NDRXREREREREROTiUHBPzpuvry97R8YSWKQ8ENg7MrZQ2d9//02/fv2oV68ejRo14tZbb+Wjjz6iW7duxZ578ODBbNy4EYCAgAAOHDhwEe5ARERERERERKR0UnBPLoia47qxo8B2HrDDWZ7Psix69uxJdHQ027dvZ+PGjfz000+8+OKLLFq0iCZNmrBs2bJC5/3kk09o1KhRoTJfX98ztqdgUFBERERERERE5GqlBTXk/GVksARYCLwErADqABuBqkBTDw8y69enS5cueHh4MGTIEGw2Gx4eHljOxTZ8fHzw8fGhY8eO9OzZky+++AJjDNHR0bRr146DBw8WuuQXX3zBxIkTyc7Opnnz5vz3v//Fzc3Ntf+TTz4ptqljxozB19eXESNGXIQHISIiIiIiIiJyaSlzT86fj49rjr1FQMWHPqGic/szYFVODvHx8cyYMYNGjRqxb98+AHbv3k3ZsmU5ceIEKSkp3H333XTq1Im//vqLpUuX8uabb7Jq1So+/PBDVqxY4bqc3W5n5syZLF68mFatWvH111/TtGlTbr31VmbNmgVAdHQ08fHxQOFMv40bN/Lll18CMGjQIB5++GHat29P3bp1WbhwIffddx8NGzZk0KBBF/2xiYiIiIiIiIicL2XuyYVhWZgKFWgbHs6CSffDpPvB35+JjzzCt6GhABw5coQjR47wxx9/4ObmRtWqVcnMzMQYg2VZPPPMMzRp0oQtW7Zw55130rJlS5o2bUrbtm2ZOnUqWVlZ/Pzzz2RmZvLTTz9RoUIF3NzcqFWrFmXLluXXX39l586dPPPMM9hs/8Sts7Ozuemmm7j++uvJzMws1OzDhw/z22+/8d1333H77bezdOlSPvnkE5o1a0ZCQgJhYWGX8imKiIiIiIiIiJwVZe7JhTNnDj4+Pq7NuK++Yv78+SxfvpzExETq169/0jx4ZcqUwd/fn4oVK9KlSxc2btxIv379qFWrFt9//z3x8fF89NFHHD58mJycHJo3b467uzsjRoygdevWtG7dms2bN9O5c2d8fX35z3/+w4oVK9i5cye5ubmsWrWK3Nxc1qxZwzfffMO2bdsKXf/222/HGENwcDDVqlUjODgYm81GUFAQSUlJl+KpiYiIiIiIiIicMwX35IIoOPQ1KSmJxo0bc/ToUSpWrEjZsmUZOnQoGzZsYNu2bfz222/k5uZSrlw5Tpw4QWpqKr6+vmzbtg273U5mZiaVK1fG19eXsLAwHnroIQYMGICvry+vvPIKubm5vPXWW8THx7N3714OHTrEkSNHqF69Oh4eHlSpUgVPT08OHjzI4sWLMcawbNkyypcvT3h4eKF2e3l5AWCz2Vyf87dzc3MvzcMTERERERERETlHCu7JOXFzcyMsLIzQ0FBOnDiB3W4/qU7Xrl3Jzc0lJCSE3377jTp16hAYGOjK3svIyMCyLCIjIzHGkJmZSV5eHp6enjRu3Ji0tDQ2bNgAQHp6Ona7nW3btuHp6cnUqVMpU6YMf/75J506dSI1NZUdO3bw4osvuubay8vLA8DPz49atWq5ji/OiBEjOHbs2MV4VCIiIiIiIiIiF43m3JNz4u3tTUJCAgA///wzt956K9HR0QD069eP5ORkQkNDadq0KfPmzeOll15i9+7dTJ8+HXd3d9zd3alWrRp79uxh06ZN7N27F2MMbm5uNG/enIULF2K32zly5AivvvoqlmXxn//8h/fffx+73U5OTg6pqalYlkViYiLr16+nZs2a7N27lz59+nD8+HHuv/9+KleuzPHjx2nXrh3169cnOTmZvXv30rhxY7Kzs7ntttsu30MUERERERERETlPytyT83bs2DGMMQCcOHGClStXkpWVhbu7O6tWrWLp0qUcOHCAyZMn4+vry7Fjx8jNzaVs2bJYlkWtWrUACAwMxN/fn/vuu49169Zx2223UaFCBZo1a0ZERASvv/462dnZWJbF0KFD8fX1JSAggICAAIwxrnn2oqOjqVWrFq+88grlypUjOzubY8eO4eXlRa9evYiMjMRut5Oamsr06dMBx7DiHj160KpVK+rWrUu3bt3o3bv3ZXumIiIiIiIiIiIloeCenJMTJ04QFBRE+fLl6du3L3a7nVtvvZX9+/cTGRnJDTfcQFxcHPv27WPHjh0cPnwYu91O7dq1SUpKwsPDg+TkZPr06cPatWux2WyUKVOGAQMG4ObmxvXXX8+vv/7K8ePH2bhxIw0aNOCGG27A29ubZs2akZWVRVpaGjt27GD79u3k5OTg6elJeno6c+fOxRjDiBEjyMrKomzZsnTq1Il27drRqVMnfv/9d44ePQpASEiI65727dvHkiVLiI2NZdSoUa7ygIAADhw4cNIzGDNmDBMmTADghRdeYP78+SV+fgcPHiQsLIywsDCqV69OrVq1XNvZ2dmnPTZ/TkMREREREREREQ3LlXNSpkwZypcvz/jx4wkNDaV169aMHTuWuLg49uzZw759++jYsSMZGRkcOnSItLQ0PDw88PX1xd3d3bVgBkBOTg5eXl4YY7jrrruYMGECv/zyC/Xq1SMnJ4e+ffty9OhR9uzZw/Hjx1m3bh1ly5alWrVq7N+/n+nTp3P99dfTu3dv0tLS8Pb2pkKFCqSmpmKz2fD09GTNmjVUqlSJDz74gICAAFq1auWaxy9fjx49sNlsNGrUiP3795/V83j55ZfPqn7lypVdw5rHjBmDr68vI0aMOONx+fMIioiIiIiIiIiAMvfkHNntdjw8PBgyZAgtW7YEoFatWuzfv5/Dhw/j4eFBWloadrudmJgY1q5dS15eHvHx8eTl5XHs2DH27dvnWkzjxIkTbNu2jbZt25KXl0dWVhbHjh0jKSmJV155hSlTpuDj40PFihXp27cvx44dY/fu3ViWxY4dO+jRowdHjhzhyJEjuLu74+3tTV5eHn/++ScZGRkArF+/nrS0NIKDg/n+++/5+uuv+fDDD4mOjmbhwoU8+OCD1K9fn8WLF5OZmclnn33G008/zb59+2jUqBEBAQFERkaybNky2rZty6RJk5gwYQKLFy9m0KBBzJo1C4CVK1fSqlUrQkNDiYyMJC0trUTPtOA54J8ViOPi4mjfvj133XUXwcHBhY7566+/CA8PZ+XKlWzfvp1nnnmGpk2b0qZNGzZt2kRaWhqBgYHk5OQAjiHUAQEBrm0RERERERERKd0U3JPC/P1Pv+1kt9tp2rQpAJs2bcKyLCpXrkx6ejqVK1cmPj4eHx8fLMti9+7d9O/fH09PT1emXIUKFbAsi7feeou8vDxsNptrnr569epRsWJFdu3ahc1mo3bt2q6gHTiGpVaoUIG8vDyys7N5+eWX2bNnD5ZlYbfbOXToEKtXr8Zut2O32/H09MSyLPr27UuZMmWYM2cOPj4+BAUFcfDgQdc8fg0aNOCdd97hpZdeAmDRokX4+flRo0YNHn30UapUqULfvn0ZPHgwXbp0YciQITz55JOEhYW5nkt2djZ9+/bl3XffJTExkfnz5+Pt7X3eX8uKFSsYO3asa6VhgM2bN9OrVy8mT55Ms2bNePDBBxk2bBirVq1iwoQJPPLII5QrV47o6Gh++OEHAL788kt69eqFh4fHebdJRERERERERC4/DcuVfxiDBUQZw96RsdQc140l4Fgsw7IKVc3JzuaLL75gwYIFWJZFbPnyuNWowZiNG1m1ahUDBgzgyIYNVK9eneXLlzNlyhRyc3MJCgoiMTGR/fv3U6ZMGZo2bUpWVhbJycns3LmTBx54gEWLFhEQEMDhw4epUaMGVapU4YcffsDd3Z3s7Gzq1KnDtm3b8PHxITc3l2PHjjmbb4iMjKRatWrExsbi4eFBdnY2mZmZZGZmUrNmTU6cOEHVqlU5fvw4mZmZtGrViqNHj1KtWjVyc3Np2rQpSUlJAGzYsIFly5axd+9eZs6cyV9//YW7uzu7d+/mtddeIzw8nGbNmlGuXDnXc9m8eTPlypUjMjKSn376iS5dupz11xAQEEB8fHyhssjISH7++WfKli1L27ZtSU1N5V//+hezZ88mKCiI9PR0li1bxs6dO13zAGZlZQEwePBgxo8fT48ePZg8eTIff/zxWbdJRERERERERK5MytwTB39/LMAASwArP7AHWM79LsbwC5B94ABHu47Fd+1abj12DCs1lX9Vrcrfv/9OyurV3GK3s+/vvwkICKBSpUqMGDGCdevWYbPZaNCgAbfccgv33HMPlStX5qeffqJevXr4+fkRERGBzWbjyJEjREdHA445/hYsWEB4eDjGGD755BNnUwxxcXFcf/31dOnShcDAQNasWeM6/rrrrmPx4sWuufuMMQwePJiHH36Yp556inr16gHwwAMPUK1aNdzc3MjNzeWee+7Bsizee+89atasyQ8//ECLFi1o3rw5np6ebN26lYoVK/Lll18ydepU16OxLIuDBw8SFRXFjBkzin3U+RmGRbm7u7vKLcsqtLCGj48PQ4YMYeDAgQD4+flx/fXXs3TpUsCRSVmhQgU++eQTEhISSEhI4M8//wSgdevWJCUlsXDhQvLy8rQYh4iIiIiIiMhVRME9cUhJIQpcAb4k/gnsRTn3A64gYAegAfBsgSBgPDAWmAhcD7zHP4HBDh06MGvWLMqUKYO3tzeLFi0iJyeHP//8k//7v/+jQ4cObNu2jV9//ZX//Oc/p23qkCFD6NixY6Gy5ORk/v3vf/PDDz9w6NAhrr/+et5//33c3NyIi4sjISGBfv36YbfbGT9+PHPnziU9PZ24uDiSk5NJS0tj48aN7Nu3j927d/Prr7/SoEED7r//fvbu3cu//vUvDh8+TGxsLKGhobRq1YqGDRsSGRnJ6tWrXe246aab2L9/P08++SS//PILqamp5ObmkpSURMOGDXnkkUdo0qQJycnJPPzww0RERPDBBx/w888/ExAQwKpVqwB46KGHyMnJITIykj179gCFV+fNyMjg4MGDDB8+nMjISNzd3QkMDCQuLg5wBAcTExNd7Ro4cCD9+/fn3nvvLWmPEBEREREREZFSQME9cdk7MpbAImWBznIXZxAQ4FtgPnADEASMAWoBbwF7gUjAF3hhyBAaNWrEq6++SmZmpmtobP369fn777957LHH2Lt3L9nZ2a658opKT09n0KBB5OXlMWbMGNfCE0ePHqV37954eXnxn//8B2MMx48fZ9euXTz77LPs3r2bsWPH0qxZM+bNm8d1111H//792bp1K2PHjuXGG28kIyOD1157jdDQULp3747dbic8PJycnBwqV64MwO7du1m7di1ff/01Xbp0Ye/evUyZMoV169bx+OOPu9q5cuVKwsLCGDduHCdOnKB169ZkZmYCjiG7AwcOZM2aNdSpU4exY8cSHx/Pww8/zF9//UXr1q1ZuHAh+/bt4+DBg/j4+DB06FDef//9k55H+fLlWb16NXv27GH37t08/vjjTJs2jXnz5hEaGkpQUBBz58511b/77rs5fPgw/fv3L2FvEBEREREREZHSQME9cak5rhs7ipTtcJYXlB8ErAl8BWwHNgA/ADcCC3Bk/h0B/EfG8vLLLwPQt29f1yq2ZcqUYfPmzQQHB/P4449z/Phx7HY748aNY/z48fzyyy8MGDAAgCZNmtCmTRt+/vlnFi9eTJUqVQBYvXo1NWrU4Ndff+WGG25g4cKFHDt2jAEDBtCmTRvat2+PZVk8//zz5OTk8MUXX5CSksKsWbPw8/MjJCSESpUqERUVxQcffMCGDRtYuXIltWvX5ptvviErK4u3336b7OxsDh06RHh4ONOmTeOZZ56hZs2abNiwgb/++ovAwEBiYmLo3bs3M2bM4MEHH+T3339nypQphIaGula9rVOnDi1atHA9x6+++oomTZowZ84cjh07xv79+/n999+pUaMGkydPJj09nf79+7NlyxZiY/8JsAYEBDBp0iTatGlDmzZt8PLyws3NjcDAQMaPH09iYiIbN27khRdecB2zZMkSevfuTYUKFc6jh4iIiIiIiIjIlUYLaoiDv3+hOfYCcQT28ufgw9/fMTTX35+aqamOsjPYAUSN6wZv/LMYx4kTJwgLCyMzM5PatWtz66230rFjR7Zs2UJOTg7+/v40atQIX19fmjZtysqVKwkNDaVz585ERUVx4sQJsrOzue222yhfvjw2m42WLVty6NAhbrvtNj799FNsNhv+/v5ERkaSkJDA7Nmz+f777/njjz/w8fGhUaNGdOnShTlz5rgWmQgICGD//v2uLLt8xpgSP8K8vDxmz57Nd999x9ixY13z76WlpQGOefNcz2bHDiZMmMDKlSupWLEigwYNKnTtgtctrg2DBg1izpw5hIaGEhMT4xqOW5zHHnuMH3/8kXnz5pX4XkRERERERESkdFDmnjikpBSaY884h+Lmh+UC75tMa2OwnIG9/LoBBerks6DQ4hwFF+Pw9vYmISEBb29v3nvvPWbNmsWhQ4eYNm0aGRkZTJo0iczMTDIyMvD396dnz54cO3aMHj16sHLlStavX09eXh6rVq3C3d2d6tWr88orr9CvXz+ysrIYNGiQa/VccMxdt3nzZt544w0qV67Mm2++Sd26dV3Der28vFxz3VmW5SoHaNu2LdOmTQNg/fr1rF279rSPcP78+YSGhpKcnExSUhI7d+6kV69ezJkz56S6x44dw8fHBz8/P/bv38+PP/5YaP/MmTNdv1u2bHnS8WlpadSoUYOcnBxXG0/lvffeY9u2bdSvX/+09URERERERESk9FHmnvyTlWdZGGNYgjPjjn+CeH8V2c4PAu4oUJavYB0D/yzGUYxmzZqxd+9eYmNjqV27Nl9//TUAbm5uNGzYkPLly/Pwww/To0cPKlWqRG5uLvv27SM5OZm///6b5cuXc/jwYXJzc9m0aRPXX389O3fuJD09ncWLF9OyZUvc3d256aabuO+++3jiiSeoUKECXbp0Yc2aNQCMGDGCO++8kxMnTnDgwAFX2x5++GHuvfdeQkJCCAsLIzIy8rSPccaMGfTs2bNQWa9evfjwww9p06ZNofLQ0FDCw8MJCgqibt26tG7dutD+rKwsmjdvjt1uL3bV3VdeeYXmzZtTp04dgoODXdmBIiIiIiIiInJtUXDvWmeMI1BnDHuBv/gn467g0Nx8+UG9pVWrsjQl5Z/jgWVAWedxgcDnlStToUCwrDht27Zl0qRJPPPMM0ybNo127doBUKZMGWw2R2JpREQER48epVy5csTFxdGxY0dycnI4duwYdrsdu92OMYZPP/2Ue+65h6effppJkyZhs9l48803+eWXX1yBsPvvv5+0tDQmT57MoEGDAGjQoAFr167F19eXV199lVdffRVwZBl++eWXxbY7KSnppLKYmJiTyrp370737t0BR/bfmeoXPPeLL75YqHzMmDGuzw8//DAPP/xwsceLiIiIiIiIyLVDwb1rmb9/oeGzBVfKNTgWxSgqEOeQ3TducxRUrYoBlqak4OvrS3rZsgDcc8stfFC/Ps8XOT49Pb3Q9s6dOxkwYAD33nsv77zzDklJSbzzzjv4+vq6gllHjhzBy8uLlStXkpeXR25uLo0aNaJ+/fpEREQwfPhw+vTpg2VZJCYm8uabb+Lj44Ovry/16tU7ZSCsaHCtaNtERERERERERK50mnPvWpaSQhT/DJ9N4uQhtkUVWj3XOQdf69RUAkf9wImMDKzUVEhNpWXLluzZsweAhIQEWrRoQUhICD179uTw4cMAREdH8+CDD+Ln50eZMmWIiYnhzz//5MYbbyQ7O9t1zUGDBmGz2fDz86NZs2Y0a9YMAF9fX8LDw7nvvvvw8fHhpptuYu7cuYwZM4Zly5YB8PHHH9OsWTNCQ0Pp1asXx48fB+Dvv/+mX79+1KtXj0aNGnHrrbeyZcuW83ueBeYWLLqdv2KuiIiIiIiIiMiFpODeNW5vCeoUXDjDtUiGczhu/rY1rhveODrU3cCvjz9O9+7dyc3NJSIiAmMMa9euJTg4mJdeesmVJRcWFkZGRgbjxo0jJSWFmJgY1q9fj7+/PwcPHgTgs88+Y9++fRw9ehQ3NzcmTJjAmDFjyMjIoGvXrqSlpfHoo48ycOBAXnjhBQA6d+7MiBEjuOOOO1i5ciWJiYk0bNiQTz/9FMuy6NmzJ9HR0Wzfvp2NGzfy2muvsX///nN/kPmBTmMIHPWDa/ERzmK1XRERERERERGRs6Xg3jWuZjFlBRfEyA/g7YBCWX5Y1klZfydwdKjpwIFWrejUqRNz5szBZrNRuXJlAO655x4WLVrkulb+fHTBwcEEBQVRo0YN3NzcqFu3LsnJyQBMnDiR0NBQWrRoQXJyMlu3bgXA09OTbt0cWYRNmzYtdh689evX06ZNG4KDg5k2bRobNmxgwYIFeHh4MGTIEFe9sLAwwsPD6dChA02aNCE4OJi5c+cCjjnwGjZsyAMPPEBQUBCdO3fmxIkTgDMz0MODUKA38AuOQOfnQCugGTDax8d1nfT09GKvISIiIiIiIiJyLhTcu5b5+7OEUw/FDaRwQG+pZWGqVgXLUXvvyNhC8/R5O+uWj+jB33//zQcffMCsWbMo65yHDxzz5/3111+EhISwevVqdu3aBcDUqVPZvHkznTt3ZuDAgWRnZ/PAAw9Q182Nd955h6+++orExESCjh9nxIgRhIaGkpuby/LlywFYsmQJs2fPJjQ0lG+++QZwzOfXtWtX/v77b/z9/Rk6dCiZmZmsX7+epk2bnnS/ZcqU4dtvv2X16tUsWLCAp556Cst5r1u3buXRRx9lw4YNVKhQgdmzZwM4MgNzcvAFGgCf4Qh0PgEMATyB6uPHl+gaIiIiIiIiIiJnS8G9a1lKymnn2KvFyQE9UlJc+2uO68aOIseUBQLj51CrVi3efPNN/vzzTypXrsyhQ4cAxyqvDRs2ZO3atdStW7fQirBHjx5l7ty5TJ8+na1bt3JbfDxv2e3kHD1K+O2DCDeGP3JzCVu6lMTERLy9vQkKCmLDhg3Mnj2bLl26kJiYyC233ALA0KFDcXd3Z+nSpfTt25dx48ad9nFYlsVzzz1HSEgIHTt2ZM+ePa6huoGBgYSFhQGFswTzMwNXVqnDOGCD81xLgRdwBEAHDBhQomuIiIiIiIiIiJwtrZZ7rataFVJTXUG+QBxDcF1z6/n7FwrojR07lunTp+O2cSM2YAUQSeGVdtcAzRYupHJQENdffz2WZREXF0dISAhbt27l119/BaBChQquufQAqlWrhre3NwDHDh2iC9AER/bgL1t/5wZgPdDd2a7s7Gw+/vhjvLy8aNGiBWXKlAFwnWP58uW8/vrrNG/enNq1a5OWlgZAUFAQs2bNKvQYBg0ahI+PDwcOHGDVqlV4eHgQEBBAZmYmAF5eXq66bm5urmG5gwYNYs6cOTwSFsZgYGGBc24Dosd1g+eOusqmTZtGampqsdcQERERERERETlbyty71hXI3osCzMjYwnPrFQjsLV++nNjYWFanpJAIzAeu45+AYLqzngG6jx5NcnIyr732GiNGjCA6Opq1a9eSl5eHp6cnAHFxcXh6emKMISAggAcffNB1Lb/KlXkSx7DWWKAK8DVQAXgRyN27l+eeew5wZMM1atSImJgYAMaMGcOIESMAeOihh9ixYwfz58/H19eXmJgYbr75ZrKysvj4449d1ztw4ACpqan4+/vj4eHBggUL2Llz5xkfX1paGjU6dGABjrkGwbH4SGtgJo4A6bRatVz1jx49etbXEBERERERERE5FQX3BJxDb5daFjveuK3QUFxjDE899RSAK8vu9UcfJQqoDOzBkV1ngDlABpBdpQp33XUX4Fgo44033mDfvn1MnDiRvLw8brvtNtq3b8/YsWM5cOAAbdu25aWXXuK1114DYNWqVViWxcry/pQFPsAReIwGjgArK1Tn3XffxW63k5mZSYcOHfj4448JDw8nNDSU22+/nePHj9OqVStuvvlmhg0bRsOGDTl+/DizZs3CGMM333zDuHHj8PT0pFy5cqxYsYImTZoQHx9PREQE06ZNo0GDBmd8dK+88grNy5WjM3ATMA9HgHSPs92RwNHnn3fVv/vuu8/6GiIiIiIiIiIip6JhuYKbmxvBwcHgnFOuX79+jHJm7Hl5efHNN9/w7LPP0rlzZ4YOHcp7771HTvit1Fgzj904huEC3OP8veLAAfJuvJHO/foVus6wYcN49dVXsSyLgwcP8v333/PHH38QEhKCzWajXbt25OTk8Nhjj/HLL79wc0QEfsB44HdgIHAX8P2Rv4mJiSE1NZXevXsTFBTECy+8wIcffoibmxu7du3i008/ZeLEibRs2ZJ169bRtGlTJk2axJAhQ+jduzd//PEHgYGBbN68mf3799OoUSNuuOEG1wIdRa1fv971OT8rEBxzCD788MOODX9/3s/PdHzDKjSkedSoUQBUqVLllNcQERERERERETlbCu4J3t7eJCQkFLvP3d2dBx98kLfffpuxY8fyxBNPsHHjRn6dMoUsoAVwCAjGMRx1HXALkAu4f/kl9VasoFq1ajz77LOkp6dz9OhRvLy8sCyLZ599lpCQEMaPH4+bmxsvvvgiH3/8MevXr+f+5s0JdJ6nJtDWef77gBjA7N/PmEcewdfXF4DGjRtTsWJFjhw5Qnp6Ohs2bOCxxx6jS5cudOrUia1bt5KQkOBavGLRokX0798fNzc3atasyc0333z+D7LAEOZit0VERERERERELjANy5VTWrlyJSdOnGDatGlMmDCB3bt3s2rVKjZ+8QW7gPrAdqAn0APYiGN4bhaQCbTu0oVBgwYRHx9P3759iY6OxtPTkxYtWrBgwQKGDx9Oq1atGDNmDHl5eXz//fcsXryY8uXLE3nfffgDITiG+pqRsazBsRrvFGDonXcWauugQYOoWLEiL774Ii+++GKhRSoKLoZhWf+sDfzDDz9c+IcmIiIiIiIiInIJKXPvGufm5obdbsfb2xtjDLVq1eLVV1+l59Ch9C1XDi8vL9avX09bDw9CQ0PJysriuN3OCmAVjiDeFziixPkhtBPO3xEREXz11Vfk5ORQs2ZNsrKyyMjIYMmSJXTs2JGkpCT27NlDbm4uxhheeeUV/Pz8OHz4MLGxsdz73HOUf+cdnl+xgqCgIHzeK8s+NzeYOJGVH37I9u3bqVSpEqtWrSIlJYWyZcvy888/89VXX+Hm5kavXr1cK+cW1bZtW+68807y8vJISUlhwYIFrnkCRURERERERERKC2XuXeO8vb3x8fEhOzubGjVqsHfvXqb260fHAwdI2rGDEycyqWwMGbm5eB46hLu7O5ZlMahBA3Kd53gFsOMYMnsCxwq3ALNmzSI5ORmbzUZgYCBPPfUU5cqV48SJE2zbtg3LssjJyQHAbreTk5OD3W4HIC8vj3feeYfnMjO55ZZbaNWqlSPr7osv2LFjB9u3b+ehhx6iRYsWzJkzh/bt2xMbG0tsbCxly5Z1nXfr1q0cPnyYyZMn89Zbb3H8+HEWL17MH3/8gd1ux8fHh8jISNq1a8f48eNp2rQpQUFBfPTRR65n5Ovry/PPP09oaCgtWrRwDe0tiaSkJBo3blyobMyYMUyYMOG0x8XHxzNs2DDAsarwsmXLSnzNfAEBARw4cKDYfWvWrMEYw88//3zW5z3TufPFxMRQtWpVwsLCXD8bN24s8TUmTZrE1KlTz6pd5/qsREREREREREorBfcEcATX8vLyOH78OD8DKUBFACxOAMnAvwFz7BhlypRhypQpuOHoQF8BeThWh7WAHOc5t27ejKenJ3a7nX379vHVV1+5rpeXlwdAhQoV8PT0xNPTE29vbypXroybmxs2m43w8HBef/11Fi1axO7du8nNzWXEiBF89NFH1KlTh4iICHbs2MG9995LdHQ0vXv35qabbuKGG26gRo0afPfdd4SHh3Ps2DHKli3L0aNHCQ4O5v7776d69eqULVuWzMxMkpOTmTNnDj/99BOrVq0iPj6eiRMncvDgQQAyMjJo0aIFiYmJtG3blo8//rjYZxgTE8PQoUOL3Zc/N+DevXsLPYdTiYiIYOLEicDFCVjNmDGDqKgoZsyYUex+y7Jcgdbz0bdvXxISElw/jRo1KvGxQ4YMYeDAgSeV5+bmFlPbQcE9ERERERERudYouHeNy8jIICMjA4CdO3cCjk6xB8dCGQaoDaQC7wBHLIvMzEy6dOlCFo6MvQTnuew4gnumwHZqairgyGD7+eefOXbsGIBrTrzDhw+TmZlJTk4OWVlZbN26FbvdTmpqKomJibzxxht07tyZ5ORkcnJycHNz4+jRo2zZsoVx48bxww8/UCYmxnU/Gzdu5P0NG9iyZQtubm5kZmbSrFkzdu3aRaVKlZgyZQrLly9n7NixhebfA5g4caIrOy85OZmtW7cC4OnpSbdu3QBo2rQpSUlJ5/y8a9asyZ0F5guMjo5m5MiRREZGUr9+fRYvXgw4glTdunUjKSmJSZMm8fbbbxMWFsbixYtJTU2lV69eNGvWjGbNmrF06VIADh48yNNPP014eDgPPfTQSfeXz7IsZs2aRUxMDL/88ovru0hKSqJhw4Y88sgjNGnShOTkZB5++GEiIiIICgrixRdfLHSeN998k8jISCIjI9m2bVuJn0FcXBzt2rXjzjvvpH79+owaNYpp06YRGRlJcHAw27dvBwpnOEZHR/Pcc8/Rrl073n33Xb7//nuaN29OeHg4HTt2ZP/+/Wf1rBYuXOjKJgwPDyctLa3E7RcRERERERG5kii4dw0rmpnl5uYGODLv0gEPoFmB/blA5cqVAThy5Mgpz1swr8oY47pWcZlg+dfMr2Oz2ahYsSJubm6u1XVff/11qlWr5qqfk5Pj2pfg58c7J07wytNPM/XLWaSmpuJ2+DA5xriG5rZt25aoqCg8PT0ZMGAAP/zwAzfccIMr+PXFF1/QoEED3nzzTSIiIli9ejXh4eG0b9+ep556itzcXDp27Og4t5sbsbGxxMfHA3DgwAECAgJc95CcnEzXrl256aabeOmll06636SkJP773/8CjuzF7du38+mnn5KZmcnNN9980jEBAQEMGTKE4cOHk5CQQJs2bXj88ccZPnw4K1euZPbs2QwePBiAl156icaNG7NmzRq6d+/Orl27iv1+li5dSmBgIPXq1SM6Opp58+a59m3evJmBAweyZs0a6tSpw9ixY4mPj2ft2rUsXLiQtWvXuuqWL1+eFStWMHToUJ544olirzVz5sxCw3JPnHDMyJiYmMi7777LunXr+Pzzz9myZQsrVqxg8ODBvPfee8We68iRIyxcuJCnnnqKqKgofv/9d9asWUO/fv0YP378WT2rCRMm8MEHH5CQkMDixYtPOTejiIiIiIiIyJVOC2pcw9566y0AWrRowe+//07jxo1JTEx07c8BVhQ55phzqGpJFZc95ubmRkhICGvWrMHDw4O8vDyuu+46kpOT8fT05ODBg67jypYty9ChQ0lJSQFg165deHh4kJuby1+bNhGNI1uwF/BVVgZ1gTZAKGBzBhN37tyJl5cX1atXp0+fPixcuJDMzEy8vLxYu3YtM2fOZOzYscTExODl5cWbb77J77//TlZWFk2aNMHb25t27drx0ksvER0dfdr7XbFiBevXr6ds2bI0a9aMJk2aFFvPGMNHH31EZmYms2fPpl27dmzatMmVIXg68+fPLzR33bFjx0hLS2PRokU888wzANx2221UrFix2ONnzJhBv379AOjXrx+ff/45d9xxBwB16tShRYsWrrpfffUVH330Ebm5uezbt4+NGzcSEhICQP/+/V2/hw8fXuy1+vbty/vvv39SebNmzahRowYA9erVo3PnzgAEBwezYMGCU54r3+7du+nbty/79u0jOzubwMDAYo851bNq3bo1Tz75JHfffTd33HEH1113XbHHi4iIiIiIiFzpFNy7hi1fvhxwDDsF+PLLL2nYsCFu/DPE1gbcBPzpPMacfJqzlpeXx5o1a4B/hucmJycX2i5TpgyZmZmkp6eTnp7uaqefnx/79u0DYPQrr/Dy6NHkAFOBckAAjmzDTYCXjw8xMTFMmTKF1atX8/fff7NmzRqysrL45JNP2LRpE507d+bo0aPs3LmTpKQksrKyuOGGG2jRogWLFi2ib9++PPTQQ/z73//mjjvuOGNwr1OnTq7sxjvuuIONGzdy+PDhk+6/SpUqfPfdd9SoUQMfHx8AqlSpctr55PLZ7XaWL19ebLZZfqbkqeTl5TF79my+++4719DkgwcPuoal5rcFYMeOHUyYMIGVK1dSsWJFBg0a5Pp+il7rTNctysvLy/XZZrO5tm022ymfQcG2PfbYYzz55JN0796duLg4xowZU+wxp3pWo0aN4rbbbmPevHm0aNGC+fPn06BBg7O6BxEREREREZErgYblXsPys+PyM5v69u2Ll5cXnjgCe+AI8m0pcMyZQ08XRn4QqeCw3ezs7EJBpNGjR5Njc8Sno4GtODINvwT2jox1BQXvueceXn31VYYMGcLx48dZsmQJTz31FMOHD+f555/niSeeYO3atRw7doysrCw2bNhAXFyc61r55zHG0Lt3bxo3buwaYlww2JVfp6AyZcpQo0YN1wIiR44cIT09naioKCzLKlFQrFy5coXmhOvcuXOhbLiEhATAMfx4/vz5APz4448nBRXBkckWGhpKcnIySUlJ7Ny5k169ejFnzpyT6h47dgwfHx/8/PzYv38/P/74Y6H9M2fOdP1u2bLlGe/jQjp69Ci1atUCYMqUKa7ykj6r7du3ExwczMiRI4mIiGDTpk2XpuEiIiIiIiIiF5iCe9eogwcP8vvcuYBj3jiAtWvXkpWVxYkidfMKfLbb3LgUbDZH18wPouUH+VJTU11lERERlLfnYgOWAx1xLALSDag57tTDW1u2bMmAAQN499136dChA7NmzXIN+z106JBrYRG73c6sWbMAmD59OlFRUYBjHrxVq1YBuPbn+7//+z8OHTrEiRMnmDNnDq1bt2bq1KlkZ2cTFhbGXXfdhb+/v2so6t69e12ZasUF4wBuv/12vv32W9ciERMnTiQ+Pp6QkBAaNWrEpEmTAHjxxRdZu3YtTZo04ZdffqF27donnWvGjBn07NmzUFmvXr2YPn36SXVDQ0MJDw8nKCiI++67j9atWxfan5WVRfPmzXn33Xd5++23i2170Tn3LtRKtmPGjKFPnz60adOGKlWquMpL+qzeeecdGjduTGhoKN7e3txyyy0XpF0iIiIiIiIil5o51Yqa14qIiAgrf3GE0i4uLu70w0b9/cEZxMIY8nCOyzY2sE5e7OJMjDHFzqlns9mKXTwjX5UqVVwBRTc3N1dWG4CHh4drVdyC5e3atWPhwoXYbDYsy8KyLCb5+LA7I4NXcSz+UQ3IAtoCXwOmalXX/cbExBAfH+/K4tq7dy9NmjRh69atzJs3j9dffx273Y6HhwcffPABLVq0wNfXl+HDhzNv3jz8/PyYOXMmVatWZdOmTdx55534+vpy880388UXX5CUlERMTAzz5s0jIyODbdu2cdddd7lWmPX19SU9PZ2kpCS6devG+vXryc3N5ZlnnuGnn37Cw8ODBx54gKFDh57191DQGfuAXPXUB0R9QNQHRH1A1AdEfUCuhD5gjFllWVbEZW3ENULBvWsluGcMFhAF7AX+wjF/3oWYQ+9ScnNzo0yZMmRkZHD99dfjnpyMH7AWqNBlKId+fp+/gECA8+zb+QG5op+vZFfCX+ByeakPiPqAqA+I+oCoD4j6gFwJfUDBvUtHw3KvNv7+J2/7+2PhCOQt4Z/59EqL/CG64FgQIiMjw/XZq0EDNgNV/P3pUes4DRo0wFSqdN6BvXyWZZ02C1FERERERERE5HJScO9qsmoVVmoqrY0hcNQPtDYGKzUVUlOJAleAL8n5+1Thr4LBtMvFw8PD9blocK1BgwbYbDYOHTpEtWrVuLVXLzw8PPjqq6/46aefCDh4kICAAEaNGkXLli2JiIhgwoQJGGOoXbu2a9619PR0OnToQJMmTQgODmaucw7CpKQkGjZsyMCBA2nSpAnJycnk5eWxceNGDhw4QMuWLfnhhx9KdB8xMTGuobaTJk1i6tSpF+LxiIiIiIiIiIgAzinX5Crg7w8jR7qy8wLHdWMJ/wTx9uIYqppU4JBThfAuRqbaqebnO5WcnJxT7tu2bRtubm5kZmaycOFC3N3dyc3NxWaz0bp1a/z9/UlNTWXKlCns27eP4cOH8+qrr9KiRQuioqIYPXo0Dz74IGXKlOHbb7+lfPnyHDhwgBYtWtC9e3cANm/ezOTJk/nvf/8LQG5uLsuWLePjjz/m1VdfpVOnTq725Obm4u5+5j9KQ4YMKfH9i4iIiIiIiIiUxOVP0ZILIyWFzRSfnRcF1AR2FKh+qefau5BzO9rtdgYOHAhAUFAQ1atXp1q1atjtdjp27EhCQgLZ2dn4+fnxzjvvcOONN5KRkcEff/zBW2+9xYEDB+jfvz+NGzfmxhtvxNvbm9q1a7Nr1y72799P+/btsSyL6OhoOnfuzLJly8jNzeWRRx7hYHw8devWJTo6mueee452np68++67fP/999StW5eqVavSsWNH9u/fX6jNvr6+jBkzhtGjR9O7d28+/vhjmjVrRmhoKL169eL48eNn9Qw2bdpEWFgY4eHhbN++vdC+1157zfU5KSmJxo0bn+OTdvjxxx+JiIigYcOGNGjQgBEjRpzX+URERERERETkwlFw72phDDedYtcS509+sC/gUrXpIjDGYLfb+fTTTwHYsGEDu3fvdgXTpkyZ4lpNt2vXrjz55JM8+eST2O12LMuiQYMGeHl58fXXX/PXX3+Rm5vLr7/+ir+/P15eXkRFRWG32wkKCuLw4cMkJydz00034e7uTqucHPrY7Qy84QZ+/+sgn73+OnE5OTw1YoQrI7Bv377069eP8ePHF9t+Pz8/Zs2axR133MHKlStJTEykYcOGrvspqTlz5vCvf/2LNWvWUK9evUL7Cgb3ztf69esZOnQoX3zxBX/++Sfr16+nbt26F+z8IiIiIiIiInJ+NCz3auBcMONU8oN6+Vl9Oyi9Ud0zZQC6ubmRl5eH3W7H09MTHx8f0tPT8fLyIjc3l+rVq7N161b8/PxIS0vj0KFD3HHHHaSmpmK327HZbHh4eLB79258fHzw8PCgVatW5OXmcjfwI7APsCev50vgIBABfF+vHv9Tsya7du1i8uTJAJQvX546deoUat+hQ4do3LgxH3zwAc8//zybNm3i2LFjlCtXDoDHHnusUP2EhASGDBnC8ePHqVevHp999hnLly/nnXfewc3NjUWLFrFgwQJX/Y8++ogTJ04QFhZGUFAQY8eOJS8vjwceeIBly5ZRq1Yt5s6di7e3N9u3b+fRRx8lNTWVsmXL8vHHH9OgQYNC1x8/fjzPP/+8q9zd3Z1HHnkEgJ07d3LfffeRmppK1apVmTx5MrVr12bQoEGUL1+e+Ph4/v77b8aPH0/v3r2x2+0MHTqUhQsXEhgYiN1u57777qN37978+uuvjBgxgtzcXJo1a8aHH36Il5fXWfUNERERERERkWtRaY3xSEGpqa6PRYfbmiKfrWLqXE3y8vIARxBw69atpKenA5CVlUVeXh6//fYbOTk5pKWlUbduXby8vEhNTXUFAvPy8vD19aVWrVpYloW7uzv/+7//i7HZeBqYARwBqgO+OJ7n38BjISF06NABHx8fpk+fTpMmTfj666/ZsWNHcc1k0KBBtGvXjnbt2vHhhx9y++23c/fdd59Ub+DAgYwbN461a9cSHBzMSy+9xK233sqQIUMYPnx4ocAewIMPPoi3tzcJCQlMmzYNgK1bt/Loo4+yYcMGKlSowOzZs11133vvPVatWsWECRNcQbuC1q9fT9OmTYu9h6FDhzJw4EDWrl3L3XffzbBhw1z79u3bx5IlS4iNjWXUqFEAfPPNNyQlJbFu3To++eQTli9fDkBmZiaDBg1i5syZrFu3jtzcXD788MNirykiIiIiIiIihSm4V9o5s/bOFLDLD+wFXvwWXVY33ngjAGXLliUuLg4ovPKuMY4n5e3tzY4dO1zBvNzcXMARBHRzc2PXrl0YY1xz4d19992kl6vCTUAIUNV5viZAbnl/jh49SsWKFenUqRNz5szBzc2NO+64g61btxbbzrS0NBITExk8eDBffvklAJUqVSpU5+jRoxw5coR27doBcM8997Bo0aKzfiaBgYGEhYUB0LRpU5KSkkhPT2fZsmX06dOHsLAwHnroIfbt23dW512+fDl33XUXAAMGDGDJkiWufT169MBms9GoUSPXkOklS5bQp08fbDYb1atXp3379oBj8ZLAwEDq169/XvcpIiIiIiIici1ScK+0S0khCk47LLeg4vPISq/84az5kpKSADh+/DhHjhwBoG3btpQvXx53d3fGjRtH9erVycjIoHr16uTm5nL8+HHKlSvHAw88QN26dTl48CDe3t5YlsVNNzlmMuzWrRv2tANUBHL453kvA9yPpTBmzBg++OADfv75Z6pUqXLGdr/yyissWLCAUaNGnTQU9kIrOLzVzc2N3Nxc7HY7FSpUICEhwfXz559/nnRsUFAQq1atKtF18gOnRa+ZP5T6VEOqL+RiKyIiIiIiIiLXGgX3rgJ7S1jPcPUNybXZ/unCxhhXBp6fnx9+fn4A5OTk4OnpSc2aNWnTpg1Vq1bFZrORl5eHh4cHNWvWZNOmTdx7773YbDYSExM5ceIEAGPGjCE6Opot997LI8AaoAGwE2gKzMYxRPdfDzzAm2++iZubG88++yw//vgjc+bM4cknn+T99993nevBBx8E4OGHH+Z//ud/uPHGG3n77beJiYnh0KFDhe7Nz8+PihUrsnjxYgA+//xzVxbf6Xh4eJCTk3PaOuXLlycwMJCvv/4acATYEhMTT6r39NNP89prr7FlyxbAsVLxW2+9BUCrVq1cWYfTpk0jKirqtNeMiopi9uzZ2O129u/f78qsbNCgAUlJSWzbtu2s7lNEREREREREFNy7KtQ8zb6rISfKZrMVygorqGrVqri5ueHp6YkxBnd3d+rUqUOXLl04evSoK4Ps8OHD7Nu3j59++on+/fvTuHFj0tLSAMcw0M6dO9OhQweSkpJcw1N9fHzYsGEDTZs25bfmzXkRx/NcDaRVDcAPcM12mJICOAJYAwYMICwsjF69ehEREXHK+xo8eDC1a9cmJCSE0NBQpk+fflKdKVOm8PTTTxMSEkJCQgIvvPDCGZ/Xgw8+SEhISLFz+BU0bdo0Pv30U0JDQwkKCmLu3Lkn1QkJCeGdd96hf//+NGzYkMaNG7uez8SJE5k8eTIhISF8/vnnvPvuu6e9Xq9evbjuuuto3LgxDz30EM2bN8fPz48yZcowefJk+vTpQ3BwMDabjSFDhpzxPkVEREREREQEzLU+JC4iIsKKj4+/3M04d/7+WKmpGCBuwgSiR4wotDv/2y1uoQ0PwB9IcX4+XmC/B5BL0eCgwc3N5lq0AhyBN7vd7tjrDK7l5OS4yo0xrmGX7u7u5Obmula0zZdfHhgYyFtvvcXgwYM5ePAgbm5ueHh4kJWVhaenJ9nZ2RhjqFKlChkZGWRlZfHggw8yZcoUxo4dy6RJk9i5cyc2m41bb72VdevWsXPnTjZu3Ej79u0JCgpixowZNGzYkEOHDlG5cmWefvppPv30U8qWLUuXLl2YNWsW69evB8DX19e1IEfB550fyCt2+zKLi4sjOjr6cjfjlNLT0/H19eXgwYNERkaydOlSqlevfrmbdVW50vuAXHzqA6I+IOoDoj4g6gNyJfQBY8wqy7JOnfEiF4z75W6AnKeUFEcArUBRwQU28hfSKGgS0BB4B+hczDGrgCeB0UA/wBOoBOzBoseAAWRmZlK5cmXXcFMoErTx92fpnj3/BG3OMgAWGRlJdHQ0mzZtKjTstqiAgABeeuklPvjgAwAef/zxU9YtuGrt7t27C+0ruMrrmDFjTt+4ovdxBQX2SoNu3bpx5MgRsrOzGT16tAJ7IiIiIiIiIudJwb3SrpjVcovL0itoiPOnaB0LxxDfcsAbQAfAFxgGTAVqANkzZvDpoUOULVu20PGuoE1iIv8BetWowd6RsdQc140lOBdbKEGW6NSpU3n++ed56623ThvYg38Wz7hYTsrak/OWP8+eiIiIiIiIiFwYCu6VdikpRBnDkhJUPdUQ3YLKAJsLbJuRscwe1401+cdlZhZ7XFxcXKFA4yAgMD+w57y2KUEG38CBAxk4cOAZ70VERERERERERLSgxlVh78jYEtU73Wq5+YG/HYCxLEddy2LHG7ex1LIwVaueOfMuJYUo/hnim8Q/gb0o5/7z5u9/+m0RERERERERkWuIgntXgb/GdTuv4wtm9FlA6/w5/AquUFvCwNzekbEEFikLpOQByNMyBis1ldbGEDjqB0c7U1MLt1NERERERERE5Bqi4F5p5wxsnU94Kz+jz8IRiCs4lPZsM+NqjuvGjiJlO5zl56XAkN8lgFVkyK8y+ERERERERETkWnTVBfeMMV2NMZuNMduMMaMud3suqmIW0ziT0w2sPe+htP7+hQJuAVAoIHdeAbhLMeRXRERERERERKSUuaqCe8YYN+AD4BagEdDfGNPo8rbqIkpNPetDShoIPKehtCkphQJuZmRsoYDc+QbgLuqQXxERERERERGRUuiqCu4BkcA2y7L+siwrG/gS+NdlbtPFY1kEcvpsvDOegn8y7Ao656G0zsU3lp7tYhwlcNGG/IqIiIiIiIiIlFLGugBBlyuFMaY30NWyrMHO7QFAc8uyhhap9yDwIEC1atWafvnll5e8rRfKulWrCHZ+Tr/uOnx3776wF3B3h9DQC3vOc5GYCLm5rs114Lpv4Mpp52WWnp6Or6/v5W6GXEbqA6I+IOoDoj4g6gOiPiBXQh9o3779KsuyIi5rI64R7pe7ARdYcaNOT4peWpb1EfARQEREhBUdHX2Rm3WR+PvTLjXVddNxEyYQPWLEGQ8rujpuwaG0e0fGUrPAYhUXIuPugoiOdqyWyxXezsssLi6OUtuf5YJQHxD1AVEfEPUBUR8Q9QFRH7i2XG3Bvd3A9QW2rwP2Xqa2XHxnMedewYU38oN5rjLLwvj7szR/Trw3LMfiF1faIhWlpZ0iIiIiIiIiIpfI1Tbn3krgRmNMoDHGE+gHfHeZ23TxWFahQB0FPhf9KbrPQOG58IoGyK7UgFlpaaeIiIiIiIiIyCVwVQX3LMvKBYYCPwN/Al9ZlrXh8rbqInMG+Apm5RX741zYolBQT4ExEREREREREZFS7WoblotlWfOAeZe7HZdUfvZdXNzp555TME9ERERERERE5KpyVWXuiYiIiIiIiIiIXEsU3BMRERERERERESmlFNwTEREREREREREppRTcExERERERERERKaUU3BMRERERERERESmlFNwTEREREREREREppRTcExERERERERERKaUU3BMRERERERERESmlFNwTEREREREREREppRTcExERERERERERKaUU3BMRERERERERESmlFNwTEREREREREREppRTcExERERERERERKaUU3BMRERERERERESmlFNwTEREREREREREppRTcExERERERERERKaUU3BMRERERERERESmlFNwTEREREREREREppRTcExERERERERERKaUU3BMRERERERERESmlFNwTEREREREREREppRTcExERERERERERKaUU3BMRERERERERESmlFNwTEREREREREREppRTcExERERERERERKaUU3BMRERERERERESmlFNwTEREREREREREppRTcExERERERERERKaUU3BMRERERERERESmlFNwTEREREREREREppRTcExERERERERERKaUU3BMRERERERERESmlFNwTEREREREREREppRTcExERERERERERKaUU3BMRERERERERESmlFNwTEREREREREREppRTcExERERERERERKaUU3BMRERERERERESmlFNwTEREREREREREppRTcExERERERERERKaUU3BMRERERERERESmlFNwTEREREREREREppRTcExERERERERERKaUU3BMRERERERERESmljGVZl7sNl5UxJhXYebnbcYFUAQ5c7kbIZaU+IOoDoj4g6gOiPiDqA6I+IFdCH6hjWVbVy9yGa8I1H9y7mhhj4i3Lirjc7ZDLR31A1AdEfUDUB0R9QNQHRH1A1AeuLRqWKyIiIiIiIiIiUkopuCciIiIiIiIiIlJKKbh3dfnocjdALjv1AVEfEPUBUR8Q9QFRHxD1AVEfuIZozj0REREREREREZFSSpl7IiIiIiIiIiIipZSCe6WQMaarMWazMWabMWZUMfuNMWaic/9aY0yTy9FOuXhK0Afudn73a40xy4wxoZejnXLxnKkPFKjXzBiTZ4zpfSnbJxdfSfqAMSbaGJNgjNlgjFl4qdsoF1cJ/lvgZ4z53hiT6OwD916OdsrFYYz5zBiTYoxZf4r9eh+8ypWgD+h98Cp3pj5QoJ7eB69SJekDeh+8Nii4V8oYY9yAD4BbgEZAf2NMoyLVbgFudP48CHx4SRspF1UJ+8AOoJ1lWSHAK2i+hatKCftAfr1xwM+XtoVysZWkDxhjKgD/BbpblhUE9LnU7ZSLp4R/DzwKbLQsKxSIBv7HGON5SRsqF1MM0PU0+/U+ePWL4fR9QO+DV78YTt8H9D549YvhNH1A74PXDgX3Sp9IYJtlWX9ZlpUNfAn8q0idfwFTLYffgQrGmBqXuqFy0ZyxD1iWtcyyrMPOzd+B6y5xG+XiKsnfAwCPAbOBlEvZOLkkStIH7gK+sSxrF4BlWeoHV5eS9AELKGeMMYAvcAjIvbTNlIvFsqxFOL7TU9H74FXuTH1A74NXvxL8PQB6H7yqlaAP6H3wGqHgXulTC0gusL3bWXa2daT0Otvv937gx4vaIrnUztgHjDG1gJ7ApEvYLrl0SvL3QH2gojEmzhizyhgz8JK1Ti6FkvSB94GGwF5gHfC4ZVn2S9M8uQLofVAK0vvgNUjvg4LeB68Z7pe7AXLWTDFlRZc8LkkdKb1K/P0aY9rjeJmLuqgtkkutJH3gHWCkZVl5jqQducqUpA+4A02BDoA3sNwY87tlWVsuduPkkihJH+gCJAA3A/WA/zPGLLYs69hFbptcGfQ+KIDeB69x76D3wWud3gevEQrulT67gesLbF+H41/kz7aOlF4l+n6NMSHAJ8AtlmUdvERtk0ujJH0gAvjS+SJXBbjVGJNrWdacS9JCudhK+t+CA5ZlZQAZxphFQCigl7mrQ0n6wL3AG5ZlWcA2Y8wOoAGw4tI0US4zvQ+K3gdF74Oi98FrhIbllj4rgRuNMYHOSbH7Ad8VqfMdMNC5SloL4KhlWfsudUPlojljHzDG1Aa+AQboX2WuSmfsA5ZlBVqWFWBZVgAwC3hEL3JXlZL8t2Au0MYY426MKQs0B/68xO2Ui6ckfWAXjn+pxxhTDbgJ+OuStlIuJ70PXuP0Pih6HxT0PnjNUOZeKWNZVq4xZiiO1Y7cgM8sy9pgjBni3D8JmAfcCmwDjuP4l3u5SpSwD7wAVAb+6/yXulzLsiIuV5vlwiphH5CrWEn6gGVZfxpjfgLWAnbgE8uy1l++VsuFVMK/B14BYowx63AM0RxpWdaBy9ZouaCMMTNwrIJcxRizG3gR8AC9D14rStAH9D54lStBH5Cr3Jn6gN4Hrx3GMVJDREREREREREREShsNyxURERERERERESmlFNwTEREREREREREppRTcExERERERERERKaUU3BMRERERERERESmlFNwTEREREREREREAjDGfGWNSjDFnXFnXGFPbGLPAGLPGGLPWGHPrpWijFKbgnoiIiFw2xpg4Y8z7l7sdAMaYaGOMZYypcomuN8gY81uB7RhjTOyluPYp2rPeGDOmNJ27aP852+0L1IahxpjvLuQ5RURELrMYoGsJ6/4H+MqyrHCgH/Dfi9UoOTUF90RERARnUOt0PzElOL73RWjXIGNM+kU4b5IxZkSR4mVADeDghb5eMdf3BF4FXrrY17rK3QE8exHrl8THQIQxps0FPq+IiMhlYVnWIuBQwTJjTD1jzE/GmFXGmMXGmAb51YHyzs9+wN5L2FRxcr/cDRAREZErQo0Cn7vhCFgULDtxaZtz6VmWlQ38fYku1xvItCxr4SW63nkzxng6n9EVw7KsQ2eude71S3jOLGPMdGAYsPhCn19EROQK8REwxLKsrcaY5jgy9G4GxgC/GGMeA3yAjpevidcuZe6JiIgIlmX9nf8DHCmmrJ8xZpsxJtv5+4H8Y40xSc6PXzsz+JKc5fWMMXONMX8bYzKMMauNMd1K2iZjTDQwGfApkEE4xrnP0xgzzhiz23nulcaYLgWO9TDGTDTG7DXGZBljko0xbzj3xQF1gDfzz5t/vYLDcvOzBo0xHZzDSjOcc8oEFmnns8aY/c66U40xLxZ4JqdyF1DsUE5jzOPGmD3GmMPGmMnGmLIF9p00rLTocF5nnf8aY14zxhxwzpkzwRhjK1DH3/ndnDDG7DTG3FdMOyxjzKPGmG+MMRnAa87y253/ap9pjNlhjBnrzEQs8bmLudYY5zO+x5lVme68d09jzCPO7++gMeatIvdxVsNsi9Y3xlQ0xkxxPusTxpj5xpigAvtL1AdwfJfdC35XIiIiVwtjjC/QCse7XgLwv/zzj8D9gRjLsq4DbgU+L/jfark09MBFRETktIwxPYH3gXeAxsC7wH+NMbc7qzRz/n4Ax4te/rYv8CPQCQgFZgPfmH+GcZzJMuAJ4LjzvDWACc59k4F2OIJkwcAU4HtjTKhz/zCgJ465X24E+gKbnfvuAHYDLxc476l44RjGeR/QEqgATMrfaYzpB7wIPA80Af4EnizBvUUB8cWUt8HxjDs629wTeLwE5yvqbiAXx4v4UBzPsW+B/THADc7r9AAGAgHFnOdFYB6OZ/yBM4A6DUd/CMLxXHrjDPyd5bmLCgD+hSNztBfQB5iLoz91BgYDj+F4JhdKDNDced1IHH3tJ2OMd4E6p+0DTvE4RsS0vIBtExERuVLYgCOWZYUV+Gno3Hc/8BWAZVnLgTLAJZm/WP6hYbkiIiJyJiOAzy3Lys942mKMaQqMBL63LCvVGAOOlz7XsFbLshKBxALnGesMCPbGMd/caVmWlW2MOer4+M95jTH1cPwrcYBlWbucxe8bYzoCDwGP4MjM2wIstizLAnbhCBZiWdYhY0wekFbwvKfgDjxqWdZm57UnAJONMTbLsuw4Am8xlmV94qz/ujGmPVD/VCc0xlTAMSfNvmJ2HwMetiwrF/jTGPM10AF4/QztLGqjZVkvOD9vMY5Myw7ADGNMfeAWIMqyrKXONt0D/FXMeWYWuDeMMVOANy3Lmuws2m6MGQl8YYx5GkcgtaTnLsoNuNeyrKPAemPMTzgCuLWcw4H/NMYsBdrjCBSfF2PMjUB3oJ1zbiGMMQNw9JW7gfz7PlMfwLKs486+GnC+7RIREbnSWJZ1zJmt38eyrK+N48UvxPmutwvHO0aMMaYhjuBe6uVs77VImXsiIiJyJg2BpUXKlgCNTneQMcbHGDPeGLPROewxHYgAap9ne5oABtjoHDKZ7jz3bUA9Z50YIAxHYOsDY8xt5zhEJCs/qOO0F/DAkb0F0ABYUeSYP85wzvyssMxi9m10BvYKXs+/ZE0tZG2R7YLnaQjYKdBuy7J2UvwE2EWzC5sCzxd57tNxzLFT/SzPXdQuZ2Av335gS5F5/vZzbs+jOPltXZ5f4Lz+Ogr37TP1gXwn+Oe7FRERKbWMMTNw/PfxJuOYAuV+HP/wdb8xJhHYgCPrHeAp4AFn+QxgkPMfVuUSUuaeiIiIlERxL2lnenGbAHTFkfm3FceQx6mA5+kOKgGb89rNgJwi+04AWJa12hgT4Lz+zTiG7SYaYzrlZ1uVUG6R7fx7thVTVlIHncdULGZf0fuxilzLjiOwWZDHWZ6n6PGnk1Fk24Zjhd+vi6mbepbnLqq4NhdX5nYe1yjodG0t+J2WpA8AVEKZCiIichWwLKv/KXZ1LabuRqD1xW2RnIky90RERORM/sQxR1xBUcDGAts5nBx0iQKmWpY127KstTjmuavH2cku5rxrcARmqluWta3Iz578SpZlpVmW9bVlWQ/jyOq7GcdccKc677nYhGOutoKKbhfizETbyBkyH08hlZPnCAwtruJp/InjHTB/bkSMMbWBmiU4djXQoJjnvi1/KPF5nPtS24ijra558owx5XHML7jxVAcVxzlUvAyO5yMiIiJySSm4JyIiImfyJjDAuXLqjcaYx3AMzRhfoE4S0MEYU90Yk5+RtgXoaYxpYowJBr7AEQA5G0lAGWNMJ2NMFWNMWcuytuBY1CHGGNPbGFPXGBNhjBlhjLkDwBjzpDGmvzGmoTHmBhwLbxzDEWDMP28bY0wt41wd9xy9CwwyxtznfDbP4Fig4UzZfD9zcsC0JH4DbjHGdDfG3GSMeQu4/mxO4Bxi+hPwv8aYlsaYMBzDmE+U4PCXgbuMMS8bYxobYxo4v4PxF+Dcl5RlWVtxLNjxv8aYNgX66DEcQ43PRhvgL+c5RURERC4pBfdERETktCzLmoNjldLhODKaHgcesSzr+wLVnsKx0EEyjsw6cKwamwIsxrFq7u/Oz2dz7WU4ViadgSNr7RnnrntxrJg7Hkf2XCzQFtjp3J8GPI1j7rfVOObfu8WyrOPO/S/gCIpt5zyGUlqW9SXwCvAGjvtu7GxvcfPpFfQx0NUYU+ksL/lZgZ+lQDrw7VmeA2AQsANHsPB7HMGspDMdZFnWzziyINvjeLYrgFE4JtM+r3NfJvfiuIfvnL/LAl0tyzrbYGR/HN+piIiIyCVnNM+hiIiIyIVjjPkWcLcs6/Yz1PsS2GBZ1iuXpmVyMRhjGgO/AvWLLAgiIiIickloQQ0RERGRc2SMKQs8jGMoai7QC8fqcb1KcPgzQM+L1zq5RGoCAxXYExERkctFmXsiIiIi58gY441j6Gk44I1jVeDxlmVNu6wNExEREZFrhoJ7IiIiIiIiIiIipZQW1BARERERERERESmlFNwTEREREREREREppRTcExERERERERERKaUU3BMRERERERERESmlFNwTEREREREREREppRTcExERERERERERKaX+H4QbcQL9xkiFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# x = np.log(new_final['population'])\n",
    "# y = np.log(new_final['infection rate'])\n",
    "x = merged_inner_tested['Total_testings']\n",
    "y= merged_inner_tested['infection_rate']\n",
    "lab = merged_inner_tested['country']\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(20,20))\n",
    "ax.scatter(x, y)\n",
    "\n",
    "# ax = plt.subplot(212)\n",
    "ax.set_xlabel('Total testing (hundred million)', fontsize=14)\n",
    "ax.set_ylabel('Infection_rate (avg infection/day)', fontsize=14)\n",
    "ax.set_title('Infection_rate VS Total testing', fontsize=18)\n",
    "\n",
    "\n",
    "for i, txt in enumerate(lab):\n",
    "    ax.annotate(txt, (x[i], y[i]), xytext=(10,10), textcoords='offset points')\n",
    "    plt.scatter(x, y, marker='x', color='red')\n",
    "\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 529,
   "metadata": {},
   "outputs": [],
   "source": [
    "# client = bigquery.Client()\n",
    "# sql1 = \"\"\"SELECT * from covid19_open_data.Demographics order by key\"\"\"\n",
    "# df_pop = client.query(sql1).to_dataframe()\n",
    "# writer = ExcelWriter(data_path + \"/\" + \"Demographics.xlsx\")\n",
    "# df_pop.to_excel(writer, sheet_name='Sheet1')\n",
    "# writer.save()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 530,
   "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>int64_field_0</th>\n",
       "      <th>key</th>\n",
       "      <th>population</th>\n",
       "      <th>population_male</th>\n",
       "      <th>population_female</th>\n",
       "      <th>rural_population</th>\n",
       "      <th>urban_population</th>\n",
       "      <th>largest_city_population</th>\n",
       "      <th>clustered_population</th>\n",
       "      <th>population_density</th>\n",
       "      <th>...</th>\n",
       "      <th>population_age_10_19</th>\n",
       "      <th>population_age_20_29</th>\n",
       "      <th>population_age_30_39</th>\n",
       "      <th>population_age_40_49</th>\n",
       "      <th>population_age_50_59</th>\n",
       "      <th>population_age_60_69</th>\n",
       "      <th>population_age_70_79</th>\n",
       "      <th>population_age_80_89</th>\n",
       "      <th>population_age_90_99</th>\n",
       "      <th>population_age_80_and_older</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11802</td>\n",
       "      <td>None</td>\n",
       "      <td>2494530.0</td>\n",
       "      <td>1208865.0</td>\n",
       "      <td>1285665.0</td>\n",
       "      <td>1221272.0</td>\n",
       "      <td>1273258.0</td>\n",
       "      <td>417380.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.973746</td>\n",
       "      <td>...</td>\n",
       "      <td>489521.0</td>\n",
       "      <td>480544.0</td>\n",
       "      <td>357165.0</td>\n",
       "      <td>234870.0</td>\n",
       "      <td>146762.0</td>\n",
       "      <td>84248.0</td>\n",
       "      <td>39007.0</td>\n",
       "      <td>9049.0</td>\n",
       "      <td>2262.0</td>\n",
       "      <td>11312.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>AD</td>\n",
       "      <td>77142.0</td>\n",
       "      <td>58625.0</td>\n",
       "      <td>55581.0</td>\n",
       "      <td>9269.0</td>\n",
       "      <td>67873.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>163.842553</td>\n",
       "      <td>...</td>\n",
       "      <td>12022.0</td>\n",
       "      <td>10727.0</td>\n",
       "      <td>12394.0</td>\n",
       "      <td>21001.0</td>\n",
       "      <td>20720.0</td>\n",
       "      <td>14433.0</td>\n",
       "      <td>8657.0</td>\n",
       "      <td>3904.0</td>\n",
       "      <td>976.0</td>\n",
       "      <td>4881.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>AE</td>\n",
       "      <td>9770529.0</td>\n",
       "      <td>6766808.0</td>\n",
       "      <td>3003721.0</td>\n",
       "      <td>1290785.0</td>\n",
       "      <td>8479744.0</td>\n",
       "      <td>2833079.0</td>\n",
       "      <td>5914068.0</td>\n",
       "      <td>135.609110</td>\n",
       "      <td>...</td>\n",
       "      <td>786876.0</td>\n",
       "      <td>1841983.0</td>\n",
       "      <td>2722996.0</td>\n",
       "      <td>1907265.0</td>\n",
       "      <td>889205.0</td>\n",
       "      <td>225625.0</td>\n",
       "      <td>58028.0</td>\n",
       "      <td>9915.0</td>\n",
       "      <td>2478.0</td>\n",
       "      <td>12394.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>AF</td>\n",
       "      <td>38041754.0</td>\n",
       "      <td>19529725.0</td>\n",
       "      <td>18512029.0</td>\n",
       "      <td>28244481.0</td>\n",
       "      <td>9797273.0</td>\n",
       "      <td>4114030.0</td>\n",
       "      <td>4114030.0</td>\n",
       "      <td>56.937760</td>\n",
       "      <td>...</td>\n",
       "      <td>7706599.0</td>\n",
       "      <td>5461114.0</td>\n",
       "      <td>3607808.0</td>\n",
       "      <td>2379338.0</td>\n",
       "      <td>1464078.0</td>\n",
       "      <td>834370.0</td>\n",
       "      <td>376098.0</td>\n",
       "      <td>66176.0</td>\n",
       "      <td>16544.0</td>\n",
       "      <td>82721.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>AF_BAL</td>\n",
       "      <td>1325700.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   int64_field_0     key  population  population_male  population_female  \\\n",
       "0          11802    None   2494530.0        1208865.0          1285665.0   \n",
       "1              0      AD     77142.0          58625.0            55581.0   \n",
       "2              1      AE   9770529.0        6766808.0          3003721.0   \n",
       "3              2      AF  38041754.0       19529725.0         18512029.0   \n",
       "4              3  AF_BAL   1325700.0              NaN                NaN   \n",
       "\n",
       "   rural_population  urban_population  largest_city_population  \\\n",
       "0         1221272.0         1273258.0                 417380.0   \n",
       "1            9269.0           67873.0                      NaN   \n",
       "2         1290785.0         8479744.0                2833079.0   \n",
       "3        28244481.0         9797273.0                4114030.0   \n",
       "4               NaN               NaN                      NaN   \n",
       "\n",
       "   clustered_population  population_density  ...  population_age_10_19  \\\n",
       "0                   NaN            2.973746  ...              489521.0   \n",
       "1                   NaN          163.842553  ...               12022.0   \n",
       "2             5914068.0          135.609110  ...              786876.0   \n",
       "3             4114030.0           56.937760  ...             7706599.0   \n",
       "4                   NaN                 NaN  ...                   NaN   \n",
       "\n",
       "   population_age_20_29  population_age_30_39  population_age_40_49  \\\n",
       "0              480544.0              357165.0              234870.0   \n",
       "1               10727.0               12394.0               21001.0   \n",
       "2             1841983.0             2722996.0             1907265.0   \n",
       "3             5461114.0             3607808.0             2379338.0   \n",
       "4                   NaN                   NaN                   NaN   \n",
       "\n",
       "   population_age_50_59  population_age_60_69  population_age_70_79  \\\n",
       "0              146762.0               84248.0               39007.0   \n",
       "1               20720.0               14433.0                8657.0   \n",
       "2              889205.0              225625.0               58028.0   \n",
       "3             1464078.0              834370.0              376098.0   \n",
       "4                   NaN                   NaN                   NaN   \n",
       "\n",
       "   population_age_80_89  population_age_90_99  population_age_80_and_older  \n",
       "0                9049.0                2262.0                      11312.0  \n",
       "1                3904.0                 976.0                       4881.0  \n",
       "2                9915.0                2478.0                      12394.0  \n",
       "3               66176.0               16544.0                      82721.0  \n",
       "4                   NaN                   NaN                          NaN  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 530,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pop.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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