{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4f580f07",
   "metadata": {},
   "source": [
    "# Constraint Satisfaction Matcher\n",
    "\n",
    "The ConstraintSatisfactionMatcher can be used to optimize any linear function of the baseline covariates. We support constraints on the size of the subset populations and the allowed mismatch.\n",
    "\n",
    "Here, we demonstrate the optimization of balance subject to size constraints only. Namely, we solve:\n",
    "\n",
    "\\begin{equation}\n",
    "\\begin{aligned}\n",
    "& \\underset{\\hat{P}}{\\text{minimize}}\n",
    "& & \\sum_k |\\mu_{\\hat{P}k} - \\mu_{Tk}| \\\\\n",
    "& \\text{subject to}\n",
    "& & |\\hat{P}| = P^* \\\\\n",
    "& & & |\\hat{T}| = T^* \\\\\n",
    "\\end{aligned}\n",
    "\\end{equation}\n",
    "\n",
    "where $P$ and $T$ refer to two populations we are trying to match, $\\hat{P}$ and $\\hat{T}$ are the subsets of $P$ and $T$ we are seeking, $P^*$ and $T^*$ are fixed integers, and  $k$ indexes the covariates of $P$ and $T$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0f723264-db60-46d9-846d-b8dc17998db1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging \n",
    "logging.basicConfig(\n",
    "    format=\"%(levelname)-4s [%(filename)s:%(lineno)d] %(message)s\",\n",
    "    level='INFO',\n",
    ")\n",
    "from pybalance.utils import (\n",
    "    BetaBalance, \n",
    "    BetaXBalance, \n",
    "    GammaBalance, \n",
    "    GammaXBalance,\n",
    "    GammaXTreeBalance\n",
    ")\n",
    "from pybalance.sim import generate_toy_dataset\n",
    "from pybalance.lp import ConstraintSatisfactionMatcher\n",
    "from pybalance.visualization import (\n",
    "    plot_numeric_features, \n",
    "    plot_categoric_features, \n",
    "    plot_binary_features,\n",
    "    plot_per_feature_loss,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6bd8a3d5-3c19-466f-8994-91b394935bb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "time_limit = 360"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2d42b61e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <b>Headers Numeric: </b><br>\n",
       "        ['age', 'height', 'weight']<br><br>\n",
       "        <b>Headers Categoric: </b><br>\n",
       "        ['gender', 'haircolor', 'country', 'binary_0', 'binary_1', 'binary_2', 'binary_3'] <br><br>\n",
       "        <b>Populations</b> <br>\n",
       "        ['pool', 'target'] <br>\n",
       "        <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>age</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>gender</th>\n",
       "      <th>haircolor</th>\n",
       "      <th>country</th>\n",
       "      <th>population</th>\n",
       "      <th>binary_0</th>\n",
       "      <th>binary_1</th>\n",
       "      <th>binary_2</th>\n",
       "      <th>binary_3</th>\n",
       "      <th>patient_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>62.511573</td>\n",
       "      <td>190.229250</td>\n",
       "      <td>105.165097</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>pool</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>68.505065</td>\n",
       "      <td>161.121236</td>\n",
       "      <td>95.001474</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>pool</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>50.071384</td>\n",
       "      <td>162.325356</td>\n",
       "      <td>84.290576</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>pool</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>44.423692</td>\n",
       "      <td>150.948096</td>\n",
       "      <td>82.031381</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>pool</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41.695052</td>\n",
       "      <td>132.952651</td>\n",
       "      <td>54.857540</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>pool</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>21.474205</td>\n",
       "      <td>168.602546</td>\n",
       "      <td>70.342128</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>target</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>40.643320</td>\n",
       "      <td>188.188724</td>\n",
       "      <td>61.611744</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>target</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>29.472765</td>\n",
       "      <td>161.408162</td>\n",
       "      <td>57.214095</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>target</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>41.291949</td>\n",
       "      <td>150.968833</td>\n",
       "      <td>91.270798</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>target</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>67.530294</td>\n",
       "      <td>155.124741</td>\n",
       "      <td>56.196505</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>target</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11000 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "<pybalance.utils.matching_data.MatchingData at 0x7f90003178b0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = generate_toy_dataset()\n",
    "m"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbd5ea80-904c-42b9-8611-0553dec48b05",
   "metadata": {},
   "source": [
    "## Optimize Beta (Mean Absolute SMD)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6172737a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO [matcher.py:65] Scaling features by factor 240.00 in order to use integer solver with <= 0.2841% loss.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'objective': 'beta',\n",
       " 'pool_size': 1000,\n",
       " 'target_size': 1000,\n",
       " 'max_mismatch': None,\n",
       " 'time_limit': 360,\n",
       " 'num_workers': 4,\n",
       " 'ps_hinting': False,\n",
       " 'verbose': True}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "objective = beta = BetaBalance(m)\n",
    "matcher = matcher_beta = ConstraintSatisfactionMatcher(\n",
    "    m, \n",
    "    time_limit=time_limit,\n",
    "    objective=objective,\n",
    "    ps_hinting=False,\n",
    "    num_workers=4)\n",
    "matcher.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "271c5183",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO [matcher.py:418] Solving for match population with pool size = 1000 and target size = 1000 subject to None balance constraint.\n",
      "INFO [matcher.py:421] Matching on 15 dimensions ...\n",
      "INFO [matcher.py:428] Building model variables and constraints ...\n",
      "INFO [matcher.py:437] Calculating bounds on feature variables ...\n",
      "INFO [matcher.py:527] Applying size constraints on pool and target ...\n",
      "INFO [matcher.py:611] Solving with 4 workers ...\n",
      "INFO [matcher.py:90] Initial balance score: 0.2328\n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 1, time = 0.02 m\n",
      "INFO [matcher.py:101] Objective:\t452948000.0\n",
      "INFO [matcher.py:120] Balance (beta):\t0.2298\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 2, time = 0.03 m\n",
      "INFO [matcher.py:101] Objective:\t452876000.0\n",
      "INFO [matcher.py:120] Balance (beta):\t0.2297\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 3, time = 0.04 m\n",
      "INFO [matcher.py:101] Objective:\t452777000.0\n",
      "INFO [matcher.py:120] Balance (beta):\t0.2297\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 4, time = 0.04 m\n",
      "INFO [matcher.py:101] Objective:\t452736000.0\n",
      "INFO [matcher.py:120] Balance (beta):\t0.2296\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 5, time = 0.04 m\n",
      "INFO [matcher.py:101] Objective:\t452730000.0\n",
      "INFO [matcher.py:120] Balance (beta):\t0.2296\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 6, time = 0.05 m\n",
      "INFO [matcher.py:101] Objective:\t452596000.0\n",
      "INFO [matcher.py:120] Balance (beta):\t0.2295\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 7, time = 0.05 m\n",
      "INFO [matcher.py:101] Objective:\t452537000.0\n",
      "INFO [matcher.py:120] Balance (beta):\t0.2294\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 8, time = 0.06 m\n",
      "INFO [matcher.py:101] Objective:\t452040000.0\n",
      "INFO [matcher.py:120] Balance (beta):\t0.2291\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 9, time = 0.08 m\n",
      "INFO [matcher.py:101] Objective:\t451825000.0\n",
      "INFO [matcher.py:120] Balance (beta):\t0.2289\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 10, time = 0.08 m\n",
      "INFO [matcher.py:101] Objective:\t451808000.0\n",
      "INFO [matcher.py:120] Balance (beta):\t0.2289\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 11, time = 0.08 m\n",
      "INFO [matcher.py:101] Objective:\t451755000.0\n",
      "INFO [matcher.py:120] Balance (beta):\t0.2289\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 12, time = 0.08 m\n",
      "INFO [matcher.py:101] Objective:\t22699000.0\n",
      "INFO [matcher.py:120] Balance (beta):\t0.0104\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 13, time = 0.10 m\n",
      "INFO [matcher.py:101] Objective:\t22422000.0\n",
      "INFO [matcher.py:120] Balance (beta):\t0.0124\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 14, time = 0.12 m\n",
      "INFO [matcher.py:101] Objective:\t22306000.0\n",
      "INFO [matcher.py:120] Balance (beta):\t0.0124\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 15, time = 0.13 m\n",
      "INFO [matcher.py:101] Objective:\t22291000.0\n",
      "INFO [matcher.py:120] Balance (beta):\t0.0124\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 16, time = 0.14 m\n",
      "INFO [matcher.py:101] Objective:\t22121000.0\n",
      "INFO [matcher.py:120] Balance (beta):\t0.0122\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 17, time = 0.16 m\n",
      "INFO [matcher.py:101] Objective:\t22119000.0\n",
      "INFO [matcher.py:120] Balance (beta):\t0.0102\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 18, time = 0.21 m\n",
      "INFO [matcher.py:101] Objective:\t22103000.0\n",
      "INFO [matcher.py:120] Balance (beta):\t0.0121\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 19, time = 0.23 m\n",
      "INFO [matcher.py:101] Objective:\t22101000.0\n",
      "INFO [matcher.py:120] Balance (beta):\t0.0122\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 20, time = 0.25 m\n",
      "INFO [matcher.py:101] Objective:\t22099000.0\n",
      "INFO [matcher.py:120] Balance (beta):\t0.0122\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 21, time = 0.27 m\n",
      "INFO [matcher.py:101] Objective:\t22088000.0\n",
      "INFO [matcher.py:120] Balance (beta):\t0.0122\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 22, time = 0.56 m\n",
      "INFO [matcher.py:101] Objective:\t22084000.0\n",
      "INFO [matcher.py:120] Balance (beta):\t0.0122\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 23, time = 3.14 m\n",
      "INFO [matcher.py:101] Objective:\t22083000.0\n",
      "INFO [matcher.py:120] Balance (beta):\t0.0101\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 24, time = 5.71 m\n",
      "INFO [matcher.py:101] Objective:\t22082000.0\n",
      "INFO [matcher.py:120] Balance (beta):\t0.0102\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:618] Status = FEASIBLE\n",
      "INFO [matcher.py:619] Number of solutions found: 24\n"
     ]
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       "<pybalance.utils.matching_data.MatchingData at 0x7f8fc073e970>"
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   "id": "87a0dbdf",
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   "source": [
    "Note that it is possible for the \"balance\" value to go up (hopefully only slightly) as the solver progresses. This behavior is due to rounding errors incurred when casting a continuous problem into the discrete space understood by the solver. The \"balance\" reported here is the actual balance computed to full accuracy on the original dataset; the \"objective\" value here is the actual quantity being optimized and should never increase."
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  {
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   "id": "c2e79558",
   "metadata": {},
   "outputs": [
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       "<pybalance.utils.matching_data.MatchingData at 0x7f8fc073e970>"
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  {
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    {
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",
      "text/plain": [
       "<Figure size 1200x900 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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S1KlTJ8c6nJGZmanAwMBC78sLX0ny9fVVmzZtHGPnadOmjeP3Q4cOKSMjQ927d1dwcLDjZ9myZfl2V5OkZs2aOX6vWbOmgoKCHKGdN+306dOO2wcPHtTAgQNVv359hYSEKDIyUtLVEwddy+nTp3X8+HHdfvvt19kC+WupUKGCQkJC8o0tSRaLJd8ueQAAOKOkWZ+cnOxodPOQ9b8pa1nv8SZ94cKFioyMVGBgoNq3b+/YPeFaEhMT1ahRI1ksFoWHh+uJJ57QlStX3FQtAHin0NBQnT9/vtjL//5SJZcuXZIkffTRR9qzZ4/jZ//+/fmOVZMkPz8/x+8mkynf7bxpebu3SVKfPn107tw5vfbaa9q+fbu2b98u6eolZa7FYrE49RhuNLYknTt3zvEJPvIj7wHA2Mh642S9R5v01atXKyEhQZMnT9bu3bvVvHlz9ezZs8CnFXlWrFihcePGafLkyUpOTtYbb7yh1atX6+mnn3Zz5QDgPnnhk+frr79WVFSUfHx8FB0drZycnHzznD17VikpKYqJiZEkNW7cuMA6kpKS1LBhQ/n4+DhVQ8uWLXXgwIF8J7H5fT15cnJytGvXLkVHR19zXTExMQoICFBqaqoaNGiQ7yc8PNypegqT97gnTJig22+/XdHR0QX+2PD395ekfN8qVKxYUZGRkdqwYUOxx87z/fffq2XLliVeT3lD3gPA9ZU066Ojo5WUlJRvHWR92c16jx6TPnv2bA0fPlzDhg2TdPW6fB999JEWL16scePGFZh/69at6tSpkwYNGiRJioyM1MCBAws8qQGgPElNTVVCQoL+/ve/a/fu3Zo/f77jjK1RUVHq27evhg8frn/961+qWLGixo0bpzp16qhv376SpISEBLVv317PPPOM7rvvPm3btk0LFizQyy+/7HQNt956qy5duqQffvhBTZo0yXffwoULFRUVpejoaM2ZM0fnz5/XQw89dM11VaxYUWPGjNETTzwhm82mm2++WRcvXlRSUpJCQkI0ZMiQYmwlqUqVKqpWrZpeffVVhYWFKTU1tUCW1KhRQxaLRZ9++qnq1q2rwMBAVapUSVOmTNE//vEP1ahRQ3fccYd+/fVXJSUl6bHHHnN6/CNHjujYsWPq1q1bseovz8h7ALi+kmb96NGj1bZtW02fPl0DBgwg68t41nusSc/OztauXbs0fvx4xzSz2axu3bpp27ZthS7TsWNHvf3229qxY4fatWunn376SR9//LEefPDBa46TlZWlrKwsx+309HRJktVqdZwNEcaS9//C/w9cxWq1ym63y2az5dtlKe/39ONHpIIfGrtc+okjjnH/uOvU9Tz44IPKyMhQu3bt5OPjo8cff1x/+9vfHOt44403NGrUKN15553Kzs5W586d9eGHH8rHx0c2m00tW7bUkiVL9Pzzz+uZZ55RWFiYpk6dqsGDB+erI28bFaZKlSrq16+f3n77bT333HOOxyFdPbHKzJkztWfPHjVo0EBr1qxR1apV8z3OPz7mqVOnKjQ0VDNmzNBPP/2kypUrq2XLlho/fvw1l/vjv3k1/37aihUrNGrUKDVp0kSNGjVSYmKibrvtNsd6zGazEhMT9cwzz2jSpEnq3LmzvvjiC8c2njt3rsaMGaPQ0FDdc889BZ4vf9w+v5+2YsUKde/eXeHh4YVuR5vNJrvdLqvVWuBbjfL8fkfeGxuZixspK88Rb8/6Fi1aaNWqVZoyZYqmT5/u1qzPy+I/rpusL37Wm+yF7c/gBsePH1edOnW0devWfCciGDt2rDZt2nTNT8vnzZunMWPGyG63KycnR//4xz/0yiuvXHOcKVOmaOrUqQWmr1ixQkFBQSV/IAAMz9fXV7Vq1VJ4eLhjFyhJOnr0qNq1b68rmZluqyXQYtGO7dud3tXrzjvvVNOmTTVjxoxSruzGvv/+e919993avXu3goODlZqaqubNm2vz5s1q2rSpp8vzqOzsbLVu3VqvvfaaOnTocM15jh49qpMnTyonJyfffRkZGRo0aJAuXryokJAQd5TsNuQ9AHcg612DrL82d2Z9mboE28aNG/Xcc8/p5ZdfVvv27XXo0CHFx8dr+vTpjssN/NH48eOVkJDguJ2enq7w8HD16NGj3P0hVF5YrVatX79e3bt3L3DyBqA4rly5oqNHjyo4ODjfWUv//Oc/K3n/fqWlpbmtltDQUEVERDg9v6+vr/z9/Uv0fmW32/Xrr7+qYsWK+c4cW1QdO3bUzJkzdfbsWdWuXVvBwcGSfjv7qTc7dOiQnn76afXo0eOa81y5ckUWi0VdunQpcPbcvG99cRV57z5kLm6krDxHvD3rXaU4We+qvzOMzp1Z77EmPTQ0VD4+Pjp16lS+6adOncp3Pbvfmzhxoh588EH97W9/kyQ1bdpUly9f1iOPPKJ//vOfMpsLngcvICBAAQEBBab7+fkZ+o0G/B/BdXJzc2UymWQ2mwu8T0RGRjou3WFUebUXV97uWCVdj6R8x5/lrauw7eptGjZsqIYNG153HrPZ7Dhr7R/f28rzex15XzawnXAjRn+OeHvWu1JRs96Vf2cYmTuz3mNNur+/v1q3bq0NGzaoX79+kq7+B2/YsEFxcXGFLpORkVHgPz5vX38P7bUPAKVq48aNni7hmiIjI3nvxQ2R9wBwfWQ9/siju7snJCRoyJAhatOmjdq1a6fExERdvnzZcfbXwYMHq06dOo7jM/r06aPZs2erZcuWjt3fJk6cqD59+jh9aQEAAOBe5D0AAM7zaJM+YMAAnTlzRpMmTdLJkyfVokULffrpp6pZs6akq5ci+P0n6RMmTJDJZNKECRN07NgxVa9eXX369NGzzz7rqYcAAABugLwHAMB5Hj9xXFxc3DV3d/vjrh++vr6aPHmyJk+e7IbKAACAq5D3AAA4p/we2Q8AAAAAQBlDkw4AAAAAgEHQpAMAAAAAYBAePyYdADwpNTVVaWlpbhsvNDRUERERbhvPGZGRkRo1apRGjRrl6VIAAHA5sp6sL2to0gF4rdTUVEU3bqSMzCtuGzPIEqjkAymGC28AAMojsh5lEU06AK+VlpamjMwrevuRFooOCy718ZJPXNIDr+5RWloawQ0AgBuQ9SiLaNIBeL3osGC1iqzk6TIK1bVrVzVp0kSS9NZbb8nPz0+PPvqopk2bJpPJpPPnzys+Pl4ffPCBsrKydMstt2jevHmKiopyrOO9997TpEmT9NNPPyksLEyPPfaYRo8e7amHBACA23lL1h86dIisLwc4cRwAGNybb74pX19f7dixQ3PnztXs2bP1+uuvS5KGDh2qnTt3au3atdq2bZvsdrt69+4tq9UqSdq1a5fuu+8+3X333dq7d6+mTJmiiRMnaunSpR58RAAA4PdKmvX9+/fXfffdp3379pH15QDfpAOAwYWHh2vOnDkymUxq1KiR9u3bpzlz5qhr165au3atkpKS1LFjR0nS8uXLFR4erjVr1ujee+/V7Nmzddttt+nJJ59USEiIGjdurP3792vWrFkaOnSoZx8YAACQVPKsv/322zVx4kRJUsOGDcn6Mo5v0gHA4Dp06CCTyeS4HRsbq4MHD2r//v3y9fVV+/btHfdVq1ZNjRo1UnJysiQpOTlZnTp1yre+Tp066eDBg8rNzXXPAwAAANdF1uP3aNIBAAAAADAImnQAMLjt27fnu/31118rKipKMTExysnJyXf/2bNnlZKSopiYGElSdHS0kpKS8i2flJSkhg0bysfHp/SLBwAAN0TW4/do0gHA4FJTU5WQkKCUlBStXLlS8+fPV3x8vKKiotS3b18NHz5cW7Zs0d69e/XAAw+oTp066tu3ryRp9OjR+uKLLzRr1iz9+OOPevPNN7VgwQKNGTPGw48KAADkKWnWb9iwQdOnTyfrywlOHAfA6yWfuGTocQYPHqzMzEy1a9dOPj4+io+P1yOPPCJJWrJkieLj43XnnXcqOztbXbp00ccffyw/Pz9JUqtWrbRq1SpNmjRJs2bNUlhYmKZNm8aJZAAAXqW8Z/2///1vTZo0SdOnTyfrywGadABeKzQ0VEGWQD3w6h63jRlkCVRoaGiRlvHz81NiYqJeeeWVAvdVqVJFy5Ytu+7y99xzj7p3766QkBCZzQV3oDpy5EiR6gEAoKzwpqy/5557rnk/WV+20KQD8FoRERFKPpCitLQ0t40ZGhqqiIgIt40HAIA3I+tRFtGkA/BqERERBCkAAOUYWY+yhiYdAAxs48aNni4BAACUIrIef8TZ3QEAAAAAMAiadAAAAAAADIImHQAAAAAAg6BJBwAAAADAIGjSAQAAAAAwCJp0AAAAAAAMgkuwAfBqqampSktLc9t4oaGhXKsVAAA3IutR1tCkA/BaqampahzdWJkZmW4b0xJk0YHkA06Hd9euXdWiRQslJiaWbmFOMlo9AABcD1lfdEarxxvRpAPwWmlpacrMyFSXsV1UKbxSqY938ehFbX5hs9LS0tz6CXt2drbbxgIAwEi8Kev9/f3dNh5KF8ekA/B6lcIrKTQqtNR/ivrHwdChQ7Vp0ybNnTtXJpNJJpNJhw8f1sMPP6x69erJYrGoUaNGmjt3boHl+vXrp2effVa1a9dWdHS0JGnr1q1q0aKFAgMD1aZNG61Zs0Ymk0l79uxxLPv999/rjjvuUHBwsGrWrKkHH3zQsYtgYfUcOXKkRNseAAB3KO9Z36hRI0lkfXnBN+kAYFBz587Vjz/+qCZNmmjatGmSpCpVqqhu3bp65513VK1aNW3dulWPPPKIwsLC1L9/f8eyGzZsUEhIiNavXy+bzab09HT17dtXvXv31ooVK/TLL79o1KhR+ca7cOGCbrvtNv3tb3/TnDlzlJmZqaeeekr9+/fXF198UWg91atXd9v2AACgvHFV1ktSenq6+vTpQ9aXAzTpAGBQlSpVkr+/v4KCglSrVi3H9KlTpzp+r1evnrZt26Z///vf+YK7QoUKev311+Xv7y+bzabExESZTCa99tprCgwMVExMjI4dO6bhw4c7llmwYIFatmyp5557zjFt8eLFCg8P148//qiGDRsWWg8AACgeV2W9JC1atIisLydo0gGgjFm4cKEWL16s1NRUZWZmKjs7Wy1atMg3T9OmTfMdm3bo0CE1a9ZMgYGBjmnt2rXLt8zevXv15ZdfKjg4uMCYhw8fVsOGDV37QAAAQKGKk/UpKSlkfTlBkw4AZciqVas0ZswYvfTSS4qNjVXFihU1a9Ysbd++Pd98FSpUKPK6L126pD59+uj5558vcF9YWFixawYAAM4j60GTDgAG5u/vr9zcXMftpKQkdezYUSNGjHBMO3z48A3X06BBA73zzjvKyspSQECAJOmbb77JN0+rVq303nvvKTIyUr6+hcfDH+sBAAAl46qsb9Sokd5++22yvhzg7O4AYGCRkZHavn27jhw5orS0NEVFRWnnzp1at26dfvzxR02cOLFAABfmr3/9q2w2mx555BElJydr3bp1evHFFyVJJpNJkjRy5EidO3dOAwcO1DfffKPDhw9r3bp1GjZsmCOs/1iPzWYrvQcPAIAXcFXWDxo0iKwvJ/gmHYDXu3j0omHHGTNmjIYMGaKYmBhlZmbqwIED+vbbbzVgwACZTCYNHDhQI0aM0CeffHLd9YSEhOi///2vRo4cqRYtWqhp06aaNGmSBg0a5Dh2rXbt2kpKStJTTz2lHj16KCsrSzfddJN69eols9lcaD0///yzIiMji/y4AABwJ2/J+g8++ECPPvooWV/Gmex2u93TRbhTenq6KlWqpIsXLyokJMTT5aAQVqtVH3/8sXr37i0/Pz9Pl4Ny4MqVK/r5559Vr169fCdTSU1NVePoxsrMyHRbLZYgiw4kH1BERITbxsy7BFtISIgjgCVp+fLlGjZsmC5evCiLxeK2erzVtZ6HEtlUGtimziFzcSNl5Tni7Vl/Le7I+mv9neGNXJX1fJMOwGtFREToQPIBpaWluW3M0NBQj4X2smXL1KBBA9WpU0d79+51XBeVBh0AUF55Y9bXr1+frC/jaNIBeLWIiAhDfNLtDqdOndKUKVN08uRJhYWF6d5779Wzzz7r6bIAAChV3pT1J0+e1KRJk8j6Mo4mHQC8xJNPPqmnnnrK02UAAIBSMnbsWI0dO9bTZaCEvPugAQAAAAAADIQmHQAAAAAAg6BJB+A1uM4nPInnHwCUPt5r4UmuunAax6QDKPf8/f1lNpt1/PhxVa9eXf7+/jKZTJ4uy21sNpuys7N15coVr780iifY7XZlZ2frzJkzMpvN8vf393RJAFDueHvWexJ/Z1xlt9t15swZmUymEl+ukCYdQLlnNptVr149nThxQsePH/d0OW5nt9uVmZkpi8XCHyweFBQUpIiICK/+AwYASou3Z70n8XfGb0wmk+rWrSsfH58SrYcmHYBX8Pf3V0REhHJycpSbm+vpctzKarVq8+bN6tKlS4k/2UXx+Pj4yNfX1+v/eAGA0uTNWe9J/J3xGz8/vxI36BJNOgAvkrf7kbcFiI+Pj3JychQYGOh1jx0A4F28Nes9ib8zXI997gAAAAAAMAiadAAAAAAADIImHQAAAAAAg6BJBwAAAADAIGjSAQAAAAAwCJp0AAAAAAAMgiYdAAAAAACD8HiTvnDhQkVGRiowMFDt27fXjh07rjv/hQsXNHLkSIWFhSkgIEANGzbUxx9/7KZqAQBAcZD3AAA4x9eTg69evVoJCQlatGiR2rdvr8TERPXs2VMpKSmqUaNGgfmzs7PVvXt31ahRQ++++67q1KmjX375RZUrV3Z/8QAAwCnkPQAAzvNokz579mwNHz5cw4YNkyQtWrRIH330kRYvXqxx48YVmH/x4sU6d+6ctm7dKj8/P0lSZGSkO0sGAABFRN4DAOA8jzXp2dnZ2rVrl8aPH++YZjab1a1bN23btq3QZdauXavY2FiNHDlS//3vf1W9enUNGjRITz31lHx8fApdJisrS1lZWY7b6enpkiSr1Sqr1erCRwRXyft/4f8HcA1eU8ZXnv9vyHtj4/0BN8JzBDfCc8Q5Rdk+HmvS09LSlJubq5o1a+abXrNmTR04cKDQZX766Sd98cUXuv/++/Xxxx/r0KFDGjFihKxWqyZPnlzoMjNmzNDUqVMLTP/ss88UFBRU8geCUrN+/XpPlwCUK7ymjCsjI8PTJZQa8r5s4P0BN8JzBDfCc+T6ipL1Ht3dvahsNptq1KihV199VT4+PmrdurWOHTumWbNmXTO0x48fr4SEBMft9PR0hYeHq0ePHgoJCXFX6SgCq9Wq9evXq3v37o7dHAEUH68p48v71hdXkffuw/sDboTnCG6E54hzipL1HmvSQ0ND5ePjo1OnTuWbfurUKdWqVavQZcLCwuTn55dvV7fo6GidPHlS2dnZ8vf3L7BMQECAAgICCkz38/PjSWRw/B8BrsVryrjK8/8LeV82sJ1wIzxHcCM8R66vKNvGY5dg8/f3V+vWrbVhwwbHNJvNpg0bNig2NrbQZTp16qRDhw7JZrM5pv34448KCwsrNLABAIBnkfcAABSNR6+TnpCQoNdee01vvvmmkpOT9eijj+ry5cuOs78OHjw434lmHn30UZ07d07x8fH68ccf9dFHH+m5557TyJEjPfUQAADADZD3AAA4z6PHpA8YMEBnzpzRpEmTdPLkSbVo0UKffvqp4+QyqampMpt/+xwhPDxc69at0xNPPKFmzZqpTp06io+P11NPPeWphwAAAG6AvAcAwHkeP3FcXFyc4uLiCr1v48aNBabFxsbq66+/LuWqAACAK5H3AAA4x6O7uwMAAAAAgN/QpAMAAAAAYBDFatK//PJLV9cBAAAMhrwHAMD9itWk9+rVS3/605/0zDPP6OjRo66uCQAAGAB5DwCA+xWrST927Jji4uL07rvvqn79+urZs6f+/e9/Kzs729X1AQAADyHvAQBwv2I16aGhoXriiSe0Z88ebd++XQ0bNtSIESNUu3ZtPf7449q7d6+r6wQAAG5G3gMA4H4lPnFcq1atNH78eMXFxenSpUtavHixWrdurc6dO+uHH35wRY0AAMDDyHsAANyj2E261WrVu+++q969e+umm27SunXrtGDBAp06dUqHDh3STTfdpHvvvdeVtQIAADcj7wEAcC/f4iz02GOPaeXKlbLb7XrwwQf1wgsvqEmTJo77K1SooBdffFG1a9d2WaEAAMC9yHsAANyvWE36/v37NX/+fN19990KCAgodJ7Q0FAu3QIAQBlG3gMA4H7F2t198uTJuvfeewsEdk5OjjZv3ixJ8vX11S233FLyCgEAgEeQ9wAAuF+xmvRbb71V586dKzD94sWLuvXWW0tcFAAA8DzyHgAA9ytWk26322UymQpMP3v2rCpUqFDiogAAgOeR9wAAuF+Rjkm/++67JUkmk0lDhw7Nt/tbbm6uvvvuO3Xs2NG1FQIAALci7wEA8JwiNemVKlWSdPWT9YoVK8pisTju8/f3V4cOHTR8+HDXVggAANyKvAcAwHOK1KQvWbJEkhQZGakxY8awqxsAAOUQeQ8AgOcU6xJskydPdnUdAADAYMh7AADcz+kmvVWrVtqwYYOqVKmili1bFnoimTy7d+92SXEAAMC9yHsAADzL6Sa9b9++jhPH9OvXr7TqAQAAHkTeAwDgWU436b/f5Y3d3wAAKJ/IewAAPKtYx6TDu6SmpiotLc1t49lsNknS3r17ZTab3TJmVlZWvksMlUehoaGKiIjwdBlOcfdzzhPK0v8HAAAA3MfpJr1KlSrXPS7t986dO1fsgmAsqampim7cSBmZV9w2psVi0cqVK9WlSxdlZma6ZUyzSbLZ3TKUx1iCLDqQfMDwjWFqaqoaN45WZmaGp0spVRZLkA4cSDb8/we8D3kPAIBnOd2kJyYmlmIZMKq0tDRlZF7R24+0UHRYsFvGtJn9dUzS5nGxMtuyS3283T+d0fC3UtRycEvVbVu31MfzhItHL2rzC5uVlpZm+KYwLS1NmZkZav/QZIWERXq6nFKRfuKIti+eWib+P+B9yHsAADzL6SZ9yJAhpVkHDC46LFitIiu5ZSyr/HRMUvOIEPnJWurjXc64LEkKrhWs0KjQUh8PzgkJi1TViEaeLgPwOuQ9AACe5XSTnp6erpCQEMfv15M3HwAAKFvIewAAPKtIx6SfOHFCNWrUUOXKlQs9Xs1ut8tkMik3N9elRQIAAPcg7wEA8Cynm/QvvvhCVatWlSR9+eWXpVYQAADwHPIeAADPcrpJv+WWWwr9HQAAlB/kPQAAnlXs66SfP39eb7zxhpKTkyVJMTExGjZsmOPTdwAAUPaR9wAAuFexmvTNmzerT58+qlSpktq0aSNJmjdvnqZNm6YPPvhAXbp0cWmRRpaamqq0tDRPl1Fq8v4oA+B67np92Ww2SdLevXtlNpvdMqYkZWVlKSAgwG3jeUJoaGi5voweeQ8AgPsVq0kfOXKkBgwYoFdeeUU+Pj6SpNzcXI0YMUIjR47Uvn37XFqkUaWmpqpx42hlZmZ4upRSZtKJi1ckuecSbEB5Z83KliQ98MADbhnPYrFo5cqV6tKlizIzM90ypiSZTZLN7rbhPMISZNGB5APltlEn7wEAcL9iNemHDh3Su+++6whsSfLx8VFCQoKWLVvmsuKMLi0tTZmZGWr/0GSFhEV6upxScebnA9qz4nldyMjxdClAuZGTc/X1NP3uhurdtEapj2cz++uYpM3jYmW2ZZf6eJK0+6czGv5WiloObqm6beu6ZUx3u3j0oja/sFlpaWnltkkn7wEAcL9iNemtWrVScnKyGjVqlG96cnKymjdv7pLCypKQsEhVjWh04xnLoMzL5X0vAcBz6oUGqVVk6e+hYpWfjklqHhEiP1lLfTxJupxxWZIUXCtYoVGhbhkTrkfeAwDgfk436d99953j98cff1zx8fE6dOiQOnToIEn6+uuvtXDhQs2cOdP1VQIAALcg7wEA8Cynm/QWLVrIZDLJbv/tAMOxY8cWmG/QoEEaMGCAa6oDAABuRd4DAOBZTjfpP//8c2nWAQAADIC8BwDAs5xu0m+66abSrAMAABgAeQ8AgGcV68Rxefbv36/U1FRlZ+c/W/D//d//lagoAABgHOQ9AADuU6wm/aefftJdd92lffv25TtuzWQySbp6DVUAAFC2kfcAALifuTgLxcfHq169ejp9+rSCgoL0ww8/aPPmzWrTpo02btzo4hIBAIAnkPcAALhfsb5J37Ztm7744guFhobKbDbLbDbr5ptv1owZM/T444/r22+/dXWdAADAzch7AADcr1jfpOfm5qpixYqSpNDQUB0/flzS1ZPNpKSkuK46AADgMeQ9AADuV6xv0ps0aaK9e/eqXr16at++vV544QX5+/vr1VdfVf369V1dIwAA8ADyHgAA9ytWkz5hwgRdvnxZkjRt2jTdeeed6ty5s6pVq6bVq1e7tEAAAOAZ5D0AAO5XrCa9Z8+ejt8bNGigAwcO6Ny5c6pSpYrjjK8AAKBsI+8BAHC/El0nXZKOHj0qSQoPDy9xMQAAwJjIewAA3KNYJ47LycnRxIkTValSJUVGRioyMlKVKlXShAkTZLVaXV0jAADwAPIeAAD3K9Y36Y899pjef/99vfDCC4qNjZV09TItU6ZM0dmzZ/XKK6+4tEgAAOB+5D0AAO5XrCZ9xYoVWrVqle644w7HtGbNmik8PFwDBw4ktAEAKAfIewAA3K9Yu7sHBAQoMjKywPR69erJ39+/pDUBAAADIO8BAHC/YjXpcXFxmj59urKyshzTsrKy9OyzzyouLs5lxQEAAM8h7wEAcD+nd3e/++67893+/PPPVbduXTVv3lyStHfvXmVnZ+v22293bYUAAMBtyHsAADzL6Sa9UqVK+W7fc889+W5zSRYAAMo+8h4AAM9yuklfsmRJqRWxcOFCzZo1SydPnlTz5s01f/58tWvX7obLrVq1SgMHDlTfvn21Zs2aUqsPAABvUVp5T9YDAOCcYh2TnufMmTPasmWLtmzZojNnzhRrHatXr1ZCQoImT56s3bt3q3nz5urZs6dOnz593eWOHDmiMWPGqHPnzsUaFwAAOKekeU/WAwDgvGI16ZcvX9ZDDz2ksLAwdenSRV26dFHt2rX18MMPKyMjo0jrmj17toYPH65hw4YpJiZGixYtUlBQkBYvXnzNZXJzc3X//fdr6tSpql+/fnEeAgAAuAFX5T1ZDwCA84p1nfSEhARt2rRJH3zwgTp16iRJ2rJlix5//HGNHj3a6eumZmdna9euXRo/frxjmtlsVrdu3bRt27ZrLjdt2jTVqFFDDz/8sL766qvrjpGVlZXvrLTp6emSJKvVKqvV6lSd12Kz2WSxWORnlnxNthKty6j8fUyyWCySb4Cs8nPLmHnjuGs8u4+/LBaL/H385Wsv1kvC8PxMfrJYLLLZbCV+3pc2Xleu5+7XlMTrqqiM+rp0Rd67I+ul0s378ixv27CNyqf//e9/Onv2bInWYbNdzeJvv/1WZnOJdsJVVlaWAgICSrQOV6tWrZrq1q3r6TLKNN5HnFOU7WOy2+32og4QGhqqd999V127ds03/csvv1T//v2d3hXu+PHjqlOnjrZu3arY2FjH9LFjx2rTpk3avn17gWW2bNmi++67T3v27FFoaKiGDh2qCxcuXPM4tSlTpmjq1KkFpq9YsUJBQUFO1QkAQGnKyMjQoEGDdPHiRYWEhHi6HAdX5L07sl4i7wEAxlaUrC/W1xsZGRmqWbNmgek1atQo8u7uRfHrr7/qwQcf1GuvvabQ0FCnlhk/frwSEhIct9PT0xUeHq4ePXqU+A+hvXv3qkuXLrp1zMuqEh5VonUZ1YmU77Tt5dF6bVgz9W8b5pYxrfLT+ir3q/v55fJT6X8ity3lhHrN/U4dR3VU/S7lc5fKs4fP6pMnP9HmzZsdl1EyKl5Xrufu15TE66qo8r71NRpP5H1xsl4q3byXXPONpCu56ts/q9Wq9evXq3v37vLzc9/eNih9eXn62rBmalSzQrHXYzP760TTfyhs3yKZbdnFr+dImh5bdVDNBjZTnVZ1ir0eV0o/lq6kxKQy8feRkfE+4pyiZH2xmvTY2FhNnjxZy5YtU2BgoCQpMzNTU6dOzfcp+Y2EhobKx8dHp06dyjf91KlTqlWrVoH5Dx8+rCNHjqhPnz6OaXm74Pj6+iolJUV/+tOf8i0TEBBQ6G41fn5+JX4Smc1mZWZmymqTcuwl2/3HqLJz7crMzJRystz2x30eP1ndMqYpN1uZmZnKzs1Wjimn1MfzBKvdqszMTJnNZsO/efK6Kj3uek1JvK6KyqivS1fkvTuyXirdvE9NTdWf/9xEmZml90VEUVksQTpwIFkREREuWZ8rthOMJS9Po6v7qVWEpdjrscpPJyS1rBsoP/kUez2Zl3yUmZkp/1B/VWpQ6cYLuEFZ+vuoLOB95PqKsm2K1aQnJiaqV69eqlu3ruNTp7179yowMFDr1q1zej3+/v5q3bq1NmzYoH79+km6GsQbNmxQXFxcgfkbN26sffv25Zs2YcIE/frrr5o7dy7XbgUAwIVckfflIevT0tKUmZmh9g9NVkhYpNvH/6P0E0e0ffFUpaWluaxJB+CdUlNTlZaWVqJ15H2Qunfv3nJ53oLQ0FC3v9cWq0lv2rSpDh48qOXLl+vAgQOSpIEDB+r++++/ejKkIkhISNCQIUPUpk0btWvXTomJibp8+bKGDRsmSRo8eLDq1KmjGTNmKDAwUE2aNMm3fOXKlSWpwHQAAFAyrsr78pL1IWGRqhrRyKM1AICrpKamKrpxI2VkXinReiwWi1auXKkuXbpc3VOwBMwmyVbkM6aVLkuQRQeSD7i1US9yk261WtW4cWN9+OGHGj58eIkLGDBggM6cOaNJkybp5MmTatGihT799FPHMXCpqakl/kQGAAAUjSvznqwHAONJS0tTRuYVvf1IC0WHBRd7PTazv45J2jwutkTnLdj90xkNfytFLQe3VN22xjjj/sWjF7X5hc1u33OpyE26n5+frlwp2actfxQXF1foLm+StHHjxusuu3TpUpfWAgAAXJ/3ZD0AGFN0WLBaRRb/PAFW+emYpOYRISU6983ljMuSpOBawQqNcv7EoeVRsT62HjlypJ5//nnl5JTPkwEBAADyHgAATyjWMenffPONNmzYoM8++0xNmzZVhQr5L+vw/vvvu6Q4AADgOeQ9AADuV6wmvXLlyrrnnntcXQsAADAQ8h4AAPcrUpNus9k0a9Ys/fjjj8rOztZtt92mKVOmFPmM7gAAwLjIewAAPKdIx6Q/++yzevrppxUcHKw6depo3rx5GjlyZGnVBgAAPIC8BwDAc4rUpC9btkwvv/yy1q1bpzVr1uiDDz7Q8uXLHRewBwAAZR95DwCA5xSpSU9NTVXv3r0dt7t16yaTyaTjx4+7vDAAAOAZ5D0AAJ5TpCY9JydHgYGB+ab5+fnJai3+9fAAAICxkPcAAHhOkU4cZ7fbNXToUAUEBDimXblyRf/4xz/yXZaFS7IAAFB2kfcAAHhOkZr0IUOGFJj2wAMPuKwYAADgeeQ9AACeU6QmfcmSJaVVBwAAMAjyHgAAzynSMekAAAAAAKD00KQDAAAAAGAQNOkAAAAAABgETToAAAAAAAZBkw4AAAAAgEHQpAMAAAAAYBA06QAAAAAAGARNOgAAAAAABkGTDgAAAACAQdCkAwAAAABgEDTpAAAAAAAYhK+nCwAAAADKutTUVKWlpXm6DIfk5GRPlwCgmGjSAQAAgBJITU1V48bRyszM8HQpf2DSiYtXJFXydCEAioAmHQAAACiBtLQ0ZWZmqP1DkxUSFunpciRJZ34+oD0rnteFjBxPlwKgiGjSAQAAABcICYtU1YhGni5DkpR52Wjf6gNwFk06AAAAAJQyzlsAZ9GkAwAAAEAp4rwFKAqadAAAAAAoRZy3AEVBkw4AAAAAbsB5C+AMs6cLAAAAAAAAV9GkAwAAAABgEDTpAAAAAAAYBE06AAAAAAAGQZMOAAAAAIBB0KQDAAAAAGAQNOkAAAAAABgETToAAAAAAAZBkw4AAAAAgEHQpAMAAAAAYBA06QAAAAAAGARNOgAAAAAABkGTDgAAAACAQdCkAwAAAABgEDTpAAAAAAAYBE06AAAAAAAGQZMOAAAAAIBB0KQDAAAAAGAQNOkAAAAAABgETToAAAAAAAZBkw4AAAAAgEHQpAMAAAAAYBA06QAAAAAAGARNOgAAAAAABuHr6QIkaeHChZo1a5ZOnjyp5s2ba/78+WrXrl2h87722mtatmyZvv/+e0lS69at9dxzz11zfgAA4HlkveslJyeXeB02m02StHfvXpnNJfvuJisrSwEBASWuyZVCQ0MVERHh6TIAoEg83qSvXr1aCQkJWrRokdq3b6/ExET17NlTKSkpqlGjRoH5N27cqIEDB6pjx44KDAzU888/rx49euiHH35QnTp1PPAIAADA9ZD1rmXNypYkPfDAAyVel8Vi0cqVK9WlSxdlZmaWaF1mk2Szl7gkl7IEWXQg+QCNOoAyxeNN+uzZszV8+HANGzZMkrRo0SJ99NFHWrx4scaNG1dg/uXLl+e7/frrr+u9997Thg0bNHjwYLfUDAAAnEfWu1ZOTo4kafrdDdW7acEPOYrCZvbXMUmbx8XKbMsu9np2/3RGw99KUcvBLVW3bd0S1eQqF49e1OYXNistLY0mHUCZ4tEmPTs7W7t27dL48eMd08xms7p166Zt27Y5tY6MjAxZrVZVrVq10PuzsrKUlZXluJ2eni5JslqtslqtJaj+6i5iFotFfmbJ12Qr0bqMyt/HJIvFIvkGyCo/t4yZN467xrP7+Mtiscjfx1++do9/blUq/Ex+slgsstlsJX7elzZeV67n7teUxOuqqIz+uiwJd2S95F15n/ceUq9WFTWNDC3Ruqzy0zFJMRHV5Kfib6dLWVZZLBZVrVNVtRrUKlFNruLO7DPac0RyXda4KkOMmAs8R3iO3Iinst5kt9s9tmPS8ePHVadOHW3dulWxsbGO6WPHjtWmTZu0ffv2G65jxIgRWrdunX744QcFBgYWuH/KlCmaOnVqgekrVqxQUFBQyR4AAAAukJGRoUGDBunixYsKCQnxdDku5Y6sl8h7AICxFSXrjfERRTHNnDlTq1at0saNG68Z2uPHj1dCQoLjdnp6usLDw9WjR48S/yG0d+9edenSRbeOeVlVwqNKtC6jOpHynba9PFqvDWum/m3D3DKmVX5aX+V+dT+/vESf6jtrW8oJ9Zr7nTqO6qj6XeqX+niecPbwWX3y5CfavHmzmjdv7ulyrovXleu5+zUl8boqqrxvfVGQM1kveVfeu/I9xFXvD0Z8zbsz+4z2HJFc9zzhOeIaPEdurLw/R4qS9R5t0kNDQ+Xj46NTp07lm37q1CnVqnX9XaVefPFFzZw5U59//rmaNWt2zfkCAgIKPdOon5+f/PxKtkuG2WxWZmamrDYpx14+r2aXnWu/eiKZnCy3/XGfx09Wt4xpys1WZmamsnOzlWPKKfXxPMFqtyozM1Nms7nEz/vSxuuq9LjrNSXxuioqo78uS8IdWS95V96XxntISd8fjPiad2f2Ge05Irn+ecJzpGR4jtxYeX+OFGV5jz5D/P391bp1a23YsMExzWazacOGDfl2ifujF154QdOnT9enn36qNm3auKNUAABQDGQ9AABF4/Hd3RMSEjRkyBC1adNG7dq1U2Jioi5fvuw4A+zgwYNVp04dzZgxQ5L0/PPPa9KkSVqxYoUiIyN18uRJSVJwcLCCg4M99jgAAEDhyHoAAJzn8SZ9wIABOnPmjCZNmqSTJ0+qRYsW+vTTT1WzZk1JUmpqqszm377wf+WVV5Sdna2//vWv+dYzefJkTZkyxZ2lAwAAJ5D1AAA4z+NNuiTFxcUpLi6u0Ps2btyY7/aRI0dKvyAAAOBSZD0AAM4xxlkLAAAAAAAATToAAAAAAEZBkw4AAAAAgEHQpAMAAAAAYBA06QAAAAAAGARNOgAAAAAABkGTDgAAAACAQdCkAwAAAABgEDTpAAAAAAAYBE06AAAAAAAGQZMOAAAAAIBB0KQDAAAAAGAQNOkAAAAAABgETToAAAAAAAZBkw4AAAAAgEHQpAMAAAAAYBA06QAAAAAAGARNOgAAAAAABkGTDgAAAACAQdCkAwAAAABgEDTpAAAAAAAYBE06AAAAAAAGQZMOAAAAAIBB0KQDAAAAAGAQNOkAAAAAABgETToAAAAAAAZBkw4AAAAAgEHQpAMAAAAAYBA06QAAAAAAGARNOgAAAAAABkGTDgAAAACAQdCkAwAAAABgEDTpAAAAAAAYBE06AAAAAAAGQZMOAAAAAIBB0KQDAAAAAGAQNOkAAAAAABgETToAAAAAAAZBkw4AAAAAgEHQpAMAAAAAYBA06QAAAAAAGARNOgAAAAAABkGTDgAAAACAQdCkAwAAAABgEDTpAAAAAAAYBE06AAAAAAAGQZMOAAAAAIBB0KQDAAAAAGAQNOkAAAAAABgETToAAAAAAAZBkw4AAAAAgEHQpAMAAAAAYBA06QAAAAAAGIQhmvSFCxcqMjJSgYGBat++vXbs2HHd+d955x01btxYgYGBatq0qT7++GM3VQoAAIqDrAcAwDkeb9JXr16thIQETZ48Wbt371bz5s3Vs2dPnT59utD5t27dqoEDB+rhhx/Wt99+q379+qlfv376/vvv3Vw5AABwBlkPAIDzPN6kz549W8OHD9ewYcMUExOjRYsWKSgoSIsXLy50/rlz56pXr1568sknFR0drenTp6tVq1ZasGCBmysHAADOIOsBAHCerycHz87O1q5duzR+/HjHNLPZrG7dumnbtm2FLrNt2zYlJCTkm9azZ0+tWbOm0PmzsrKUlZXluH3x4kVJ0rlz52S1WktUf3p6ugIDA3Xp2I+SNbNE6zKqzJM/KTAwUClp2dr4469uGdNm9lfGnzP01aFLMtuyS328749nKzAwUBnHM5S2L63Ux/OEX0/8qsDAQKWnp+vs2bOeLue6eF25nrtfUxKvqyKv69erzwO73e6K0gzFHVkveVfeu/I9xFXvD0Z8zbsz+4z2HJFc9zzhOeIaPEdurLw/R4qU9XYPOnbsmF2SfevWrfmmP/nkk/Z27doVuoyfn599xYoV+aYtXLjQXqNGjULnnzx5sl0SP/zwww8//Bj+5+jRo64JWANxR9bb7eQ9P/zwww8/ZePHmaz36Dfp7jB+/Ph8n8bbbDadO3dO1apVk8lk8mBluJb09HSFh4fr6NGjCgkJ8XQ5QJnHa8r47Ha7fv31V9WuXdvTpZRZ5H3x8P6AG+E5ghvhOeKcomS9R5v00NBQ+fj46NSpU/mmnzp1SrVq1Sp0mVq1ahVp/oCAAAUEBOSbVrly5eIXDbcJCQnhhQ64EK8pY6tUqZKnSygV7sh6ibwvKd4fcCM8R3AjPEduzNms9+iJ4/z9/dW6dWtt2LDBMc1ms2nDhg2KjY0tdJnY2Nh880vS+vXrrzk/AADwHLIeAICi8fju7gkJCRoyZIjatGmjdu3aKTExUZcvX9awYcMkSYMHD1adOnU0Y8YMSVJ8fLxuueUWvfTSS/rLX/6iVatWaefOnXr11Vc9+TAAAMA1kPUAADjP4036gAEDdObMGU2aNEknT55UixYt9Omnn6pmzZqSpNTUVJnNv33h37FjR61YsUITJkzQ008/raioKK1Zs0ZNmjTx1EOAiwUEBGjy5MkFdlsEUDy8puBpZL1x8f6AG+E5ghvhOeJ6Jru9HF7vBQAAAACAMsijx6QDAAAAAIDf0KQDAAAAAGAQNOkAAAAAABgETToAAAAAAAZBkw5DWbhwoSIjIxUYGKj27dtrx44dni4JKNM2b96sPn36qHbt2jKZTFqzZo2nSwJgEGQurof8wI3MmDFDbdu2VcWKFVWjRg3169dPKSkpni6rXKBJh2GsXr1aCQkJmjx5snbv3q3mzZurZ8+eOn36tKdLA8qsy5cvq3nz5lq4cKGnSwFgIGQuboT8wI1s2rRJI0eO1Ndff63169fLarWqR48eunz5sqdLK/O4BBsMo3379mrbtq0WLFggSbLZbAoPD9djjz2mcePGebg6oOwzmUz6z3/+o379+nm6FAAeRuaiKMgPOOPMmTOqUaOGNm3apC5duni6nDKNb9JhCNnZ2dq1a5e6devmmGY2m9WtWzdt27bNg5UBAFC+kLkASsPFixclSVWrVvVwJWUfTToMIS0tTbm5uapZs2a+6TVr1tTJkyc9VBUAAOUPmQvA1Ww2m0aNGqVOnTqpSZMmni6nzPP1dAEAAAAAgLJr5MiR+v7777VlyxZPl1Iu0KTDEEJDQ+Xj46NTp07lm37q1CnVqlXLQ1UBAFD+kLkAXCkuLk4ffvihNm/erLp163q6nHKB3d1hCP7+/mrdurU2bNjgmGaz2bRhwwbFxsZ6sDIAAMoXMheAK9jtdsXFxek///mPvvjiC9WrV8/TJZUbfJMOw0hISNCQIUPUpk0btWvXTomJibp8+bKGDRvm6dKAMuvSpUs6dOiQ4/bPP/+sPXv2qGrVqoqIiPBgZQA8iczFjZAfuJGRI0dqxYoV+u9//6uKFSs6zmlRqVIlWSwWD1dXtnEJNhjKggULNGvWLJ08eVItWrTQvHnz1L59e0+XBZRZGzdu1K233lpg+pAhQ7R06VL3FwTAMMhcXA/5gRsxmUyFTl+yZImGDh3q3mLKGZp0AAAAAAAMgmPSAQAAAAAwCJp0AAAAAAAMgiYdAAAAAACDoEkHAAAAAMAgaNIBAAAAADAImnQAAAAAAAyCJh0AAAAAAIOgSQcAAAAAwCBo0gGUmqFDh6pfv36eLgMAgHKja9euGjVqVLGXX7p0qSpXruyyeq4lMjJSiYmJpT4OUB7RpAMAAABeYsCAAfrxxx89XQaA66BJB2BYdrtdOTk5ni4DAIByw2KxqEaNGte8Pzs7243VXJtR6gA8gSYd8AK//vqr7r//flWoUEFhYWGaM2dOvt3lsrKyNGbMGNWpU0cVKlRQ+/bttXHjRsfyebvGrVu3TtHR0QoODlavXr104sQJxzy5ublKSEhQ5cqVVa1aNY0dO1Z2uz1fHTabTTNmzFC9evVksVjUvHlzvfvuu477N27cKJPJpE8++UStW7dWQECAtmzZUqrbBgCAssZms2ns2LGqWrWqatWqpSlTpjjumz17tpo2baoKFSooPDxcI0aM0KVLlxz3/3F39ylTpqhFixZ6/fXXVa9ePQUGBkqSLly4oL///e+qWbOmAgMD1aRJE3344YeO5d577z39+c9/VkBAgCIjI/XSSy9dt+bU1FT17dtXwcHBCgkJUf/+/XXq1Kkb1gF4I5p0wAskJCQoKSlJa9eu1fr16/XVV19p9+7djvvj4uK0bds2rVq1St99953uvfde9erVSwcPHnTMk5GRoRdffFFvvfWWNm/erNTUVI0ZM8Zx/0svvaSlS5dq8eLF2rJli86dO6f//Oc/+eqYMWOGli1bpkWLFumHH37QE088oQceeECbNm3KN9+4ceM0c+ZMJScnq1mzZqW0VQAAKJvefPNNVahQQdu3b9cLL7ygadOmaf369ZIks9msefPm6YcfftCbb76pL774QmPHjr3u+g4dOqT33ntP77//vvbs2SObzaY77rhDSUlJevvtt7V//37NnDlTPj4+kqRdu3apf//+uu+++7Rv3z5NmTJFEydO1NKlSwtdv81mU9++fXXu3Dlt2rRJ69ev108//aQBAwZctw7Aa9kBlGvp6el2Pz8/+zvvvOOYduHCBXtQUJA9Pj7e/ssvv9h9fHzsx44dy7fc7bffbh8/frzdbrfblyxZYpdkP3TokOP+hQsX2mvWrOm4HRYWZn/hhRcct61Wq71u3br2vn372u12u/3KlSv2oKAg+9atW/ON8/DDD9sHDhxot9vt9i+//NIuyb5mzRrXPHgAAMqZW265xX7zzTfnm9a2bVv7U089Vej877zzjr1atWqO20uWLLFXqlTJcXvy5Ml2Pz8/++nTpx3T1q1bZzebzfaUlJRC1zlo0CB79+7d80178skn7TExMY7bN910k33OnDl2u91u/+yzz+w+Pj721NRUx/0//PCDXZJ9x44d16wD8Fa+Hv6MAEAp++mnn2S1WtWuXTvHtEqVKqlRo0aSpH379ik3N1cNGzbMt1xWVpaqVavmuB0UFKQ//elPjtthYWE6ffq0JOnixYs6ceKE2rdv77jf19dXbdq0cezyfujQIWVkZKh79+75xsnOzlbLli3zTWvTpk1JHjIAAOXaH/cy+30mf/7555oxY4YOHDig9PR05eTk6MqVK8rIyFBQUFCh67vppptUvXp1x+09e/aobt26Bf42yJOcnKy+ffvmm9apUyclJiYqNzfX8Y377+cPDw9XeHi4Y1pMTIwqV66s5ORktW3bttA6AG9Fkw54uUuXLsnHx0e7du0qEKrBwcGO3/38/PLdZzKZChxzfqNxJOmjjz5SnTp18t0XEBCQ73aFChWcXi8AAN6msEy22Ww6cuSI7rzzTj366KN69tlnVbVqVW3ZskUPP/ywsrOzr9mk/zF3LRZLqdV+PeQ/cBXHpAPlXP369eXn56dvvvnGMe3ixYuOy6+0bNlSubm5On36tBo0aJDvp1atWk6NUalSJYWFhWn79u2OaTk5Odq1a5fjdkxMjAICApSamlpgnN9/sg4AAIpn165dstlseumll9ShQwc1bNhQx48fL/J6mjVrpv/973/XvFRbdHS0kpKS8k1LSkpSw4YNC3zgnzf/0aNHdfToUce0/fv368KFC4qJiSlyfUB5xzfpQDlXsWJFDRkyRE8++aSqVq2qGjVqaPLkyTKbzTKZTGrYsKHuv/9+DR48WC+99JJatmypM2fOaMOGDWrWrJn+8pe/ODVOfHy8Zs6cqaioKDVu3FizZ8/WhQsX8tUxZswYPfHEE7LZbLr55pt18eJFJSUlKSQkREOGDCmlLQAAgHdo0KCBrFar5s+frz59+igpKUmLFi0q8npuueUWdenSRffcc49mz56tBg0a6MCBAzKZTOrVq5dGjx6ttm3bavr06RowYIC2bdumBQsW6OWXXy50fd26dVPTpk11//33KzExUTk5ORoxYoRuueUWDnEDCsE36YAXmD17tmJjY3XnnXeqW7du6tSpk6Kjox2XN1myZIkGDx6s0aNHq1GjRurXr5+++eYbRUREOD3G6NGj9eCDD2rIkCGKjY1VxYoVddddd+WbZ/r06Zo4caJmzJih6Oho9erVSx999JHq1avn0scLAIA3at68uWbPnq3nn39eTZo00fLlyzVjxoxireu9995T27ZtNXDgQMXExGjs2LHKzc2VJLVq1Ur//ve/tWrVKjVp0kSTJk3StGnTNHTo0ELXZTKZ9N///ldVqlRRly5d1K1bN9WvX1+rV68u7kMFyjWTvSgHlQIoFy5fvqw6deropZde0sMPP+zpcgAAAAD8f+zuDniBb7/9VgcOHFC7du108eJFTZs2TZIKnJkVAAAAgGfRpANe4sUXX1RKSor8/f3VunVrffXVVwoNDfV0WQAAAAB+h93dAQAAAAAwCE4cBwAAAACAQdCkAwAAAABgEDTpAAAAAAAYBE06AAAAAAAGQZMOAAAAAIBB0KQDAAAAAGAQNOkAAAAAABgETToAAAAAAAZBkw4AAAAAgEHQpAMAAAAAYBA06QAAAAAAGARNOgAAAAAABkGTDgAAAACAQdCkAwAAAABgEL6eLsDdbDabjh8/rooVK8pkMnm6HAAAZLfb9euvv6p27doym/n83BXIewCAkRQl672uST9+/LjCw8M9XQYAAAUcPXpUdevW9XQZ5QJ5DwAwImey3uua9IoVK0q6unFCQkI8XI3zrFarPvvsM/Xo0UN+fn6eLsdj2A6/YVtcxXa4iu1wVVndDunp6QoPD3dkFEqurOa9tyirr1XAyHhdGVtRst7rmvS8Xd5CQkLKVGhbrVYFBQUpJCTEq190bIffsC2uYjtcxXa4qqxvB3bLdp2ymvfeoqy/VgEj4nVVNjiT9Rz4BgAAAACAQdCkAwAAAABgEDTpAAAAAAAYhNcdkw7Au+Xm5spqtXq6DJezWq3y9fXVlStXlJub6+lyPMao28HPz08+Pj6eLgMAvEJ5zfobMWoGehN/f3+XXEqVJh2AV7Db7Tp58qQuXLjg6VJKhd1uV61atXT06FGvPvmYkbdD5cqVVatWLcPVBQDlRXnP+hsxcgZ6C7PZrHr16snf379E66FJB+AV8kK7Ro0aCgoKKnfhZbPZdOnSJQUHB7vkE9yyyojbwW63KyMjQ6dPn5YkhYWFebgiACifynvW34gRM9Cb2Gw2HT9+XCdOnFBERESJnn806QDKvdzcXEdoV6tWzdPllAqbzabs7GwFBgZ6dTAbdTtYLBZJ0unTp1WjRg12fQcAF/OGrL8Ro2agN6levbqOHz+unJycEl0Gj/89AOVe3nFpQUFBHq4E3izv+eeNx0kCQGkj62EEebu5l/ScADTpALyGt+32BmPh+QcApY/3WniSq55/NOkAAAAAABgEx6QDAK6ra9euatGihRITEw2xHsATUlNTlZaW5ukySlVoaKgiIiI8XQYADyHvjYMmHQDgUhs3btStt96q8+fPq3Llyo7p77//folOogJ4Smpqqho3jlZmZoanSylVFkuQDhxIplEH4BTyvvTQpAMA3KJq1aqeLgEolrS0NGVmZqj9Q5MVEhbp6XJKRfqJI9q+eKrS0tJo0gGUCHlfchyTDgAG1bVrV8XFxSkuLk6VKlVSaGioJk6cKLvdLkk6f/68Bg8erCpVqig4OFh//etfdfDgQcfyS5cuVeXKlbVmzRpFRUUpMDBQPXv21NGjRx3zDB06VP369cs37qhRo9S1a9dr1vXWW2+pTZs2qlixomrVqqVBgwY5rgF+5MgR3XrrrZKkKlWqyGQyaejQoY7HM2rUKMd6fl9/UFCQ7rjjjkLrX7dunaKjoxUcHKxevXrpxIkTxdmcQImFhEWqakSjcvlTXj98AMqCouT99fJyzZo1at26tYKCgsj7Mo4mHQAM7M0335Svr6927NihuXPnavbs2Xr99dclXQ3cnTt3au3atUpKSpIk3Xnnnfku8ZWRkaFnn31Wy5YtU1JSki5cuKD77ruvRDVZrVZNnz5de/fu1Zo1a3TkyBFHMIeHh+u9996TJKWkpOjEiROaO3duoev5ff3btm2T3W5X7969C9T/4osv6q233tLmzZuVmpqqMWPGlKh+AACMxtm8v15ezpgxQ6+88oq++uor8r6MY3d3ADCw8PBwzZkzRyaTSY0aNdK+ffs0Z84cde3a1dGcd+zYUTabTa+++qqaNGmiNWvW6N5775V0NWAXLFig9u3bS7r6R0B0dLR27Nihdu3aFaumhx56yPF7/fr1NW/ePLVt21aXLl1ScHCwYze3GjVq5DtG7fcOHjyYr35JWr58ucLDwwvUv2jRIv3pT3+SJMXFxWnatGnFqhsAAKNyNu+la+flvHnzFB0drZCQEPK+jOObdAAwsA4dOuS75mZsbKwOHjyo/fv3y9fX19F8S1ePAWvUqJGSk5Md03x9fdW2bVvH7caNG6ty5cr55imqXbt2qU+fPoqIiFDFihV1yy23SLp6ci1nJScnF6i/WrVqBeoPCgpyBLYkhYWFOXa1AwCgvChK3heWl+R9+UKTDgBezGw2O455y/P73c/+6PLly+rZs6dCQkK0fPlyffPNN/rPf/4jScrOznZ5fX88O6zJZCpQLwAAuD7yvmyhSQcAA9u+fXu+219//bWioqIUExOjnJycfPefO3dOKSkpiomJcUzLycnRzp07HbdTUlJ04cIFRUdHS5KqV69e4MQse/bsuWY9Bw4c0NmzZzVz5kx17txZjRs3LvBJt7+/vyQpNzf3muuJjo4uUP/Zs2cL1A8AgDcoSt4XlpfkfflCkw4ABpaamqqEhASlpKRo5cqVmj9/vuLj4xUVFaW+fftq+PDh2rJli/bu3atHHnlEderUUd++fR3L+/n56bHHHtP27du1a9cuDR06VB06dHAcn3bbbbdp586dWrZsmQ4ePKjJkyfr+++/v2Y9ERER8vf31/z58/XTTz9p7dq1mj59er55brrpJplMJn344Yc6c+aMLl26VGA9hdX/wAMPFKgfAABvUJS8Lywv/fz8FB8fr507d5L35QBNOgAY2ODBg5WZmal27dpp5MiRio+P1yOPPCJJWrJkiVq3bq0777xTnTp1kiR9+OGH+XYZCwoK0lNPPaVBgwapU6dOCg4O1urVqx339+zZUxMnTtTYsWPVtm1b/frrrxo8ePA166levbqWLl2qd955RzExMZo5c6ZefPHFfPPUqVNHU6dO1bhx41SzZk3FxcUVuq7f1x8bGyu73a6PP/64wC5vAACUd87m/bXyMigoSE8++aSGDx+uzp07k/dlnMnuZTv7p6enq1KlSrp48aJCQkI8XY7TrFarPv74Y/Xu3durn9Bsh9+wLa5yZjtcuXJFP//8s+rVq6fAwEA3V1h8Xbt2VYsWLZSYmHjDeW02m9LT0xUSEiKz+ernr0uXLtWoUaN04cKF0i3UQArbDkZxvedhWc0mI3PlNt29e7dat26t7v9coqoRjVxUobGcS03R+meHadeuXWrVqlWpj0eGwdXKatZLRcv7wuTl/blz5wybgd7CVVnP/x4AAAAAAAZBkw4AAAAAgEH4eroAAEDhNm7cWKLlhw4dqqFDh7qkFgAAUDpclfc2m801BcHj+CYdAAAAAACDoEkHAAAAAMAgaNIBAAAAADAImnQAAAAAAAyCJh0AAAAAAIOgSQcAAAAAwCC4BBsAr5aamqq0tDS3jRcaGqqIiAi3jeesyMhIjRo1SqNGjbrmPNnZ2YqJidGyZcvUsWNH9xVnACaTSf/5z3/Ur1+/Qu/fv3+/evTooZSUFFWoUMG9xQEArousv4qsvz4jZT1NOgCvlZqaqsaNo5WZmeG2MS2WIB04kGzI8L6RRYsWqV69emU6tI8cOaJ69erp22+/VYsWLVy23piYGHXo0EGzZ8/WxIkTXbZeAEDJkPVFQ9ZfmzuzniYdgNdKS0tTZmaG2j80WSFhkaU+XvqJI9q+eKrS0tLKXHDb7XYtWLBA06ZNK/F6cnNz5etb/uJn2LBhGj58uMaPH18uHx8AlEVkvfPI+htzV9ZzTDoArxcSFqmqEY1K/ac4fxx07dpVcXFxiouLU6VKlRQaGqqJEyfKbrc75jl//ryGDBmiyMhIBQcH64477tDBgwfzree9997Tn//8ZwUEBCgyMlIvvfRSkerYtWuXDh8+rL/85S+OaUeOHJHJZNKqVavUsWNHBQYGqkmTJtq0aZNjno0bN8pkMumTTz5R69atFRAQoC1btshms2nGjBmqV6+eLBaLmjdvrnfffbfAcuvWrVPLli1lsVh022236fTp0/rkk08UHR2tkJAQDRo0SBkZv3078umnn6pXr16qWrWqqlWrpjvvvFOHDx923F+vXj1JUsuWLWUymdS1a1fHfYsXL3Zso7CwMMXFxeXbBmlpabrrrrsUFBSkqKgorV27Nt/93bt317lz5/I9fgCAMZSHrB88eLCqVKmioKCga2Z9bGysLBZLuc/6m2++WZUrVy63WU+TDgAG9+abb8rX11c7duzQ3LlzNXv2bL3++uuO+4cOHapdu3ZpxYoVSkpKkt1uV+/evWW1WiVdDd3+/fvrvvvu0759+zRlyhRNnDhRS5cudbqGr776Sg0bNlTFihUL3Pfkk09q9OjR+vbbbxUbG6s+ffro7Nmz+eYZN26cZs6cqeTkZDVr1kwzZszQsmXLtGjRIv3www964okn9MADDxQIvSlTpmjBggXaunWrjh49qv79+ysxMVErVqzQRx99pM8++0zz5893zH/58mWNHDlSO3bs0IYNG2Q2m3XXXXfJZrNJknbs2CFJ+vzzz3XixAm9//77kqRXXnlFI0eO1COPPKJ9+/Zp7dq1atCgQb5apk6dqv79++u7775T7969df/99+vcuXOO+/39/dWiRQt99dVXTm9XAAAk57J+586dWrt2rbZt21Zo1t933326++67tXfv3nKf9QkJCdq5c2e5zfrytw8CAJQz4eHhmjNnjkwmkxo1aqR9+/Zpzpw5Gj58uA4ePKi1a9fqq6++UpMmTRQSEqLly5crPDxca9as0b333qvZs2fr9ttvdxw/1bBhQ+3fv1+zZs3S0KFDnarhl19+Ue3atQu9Ly4uTvfcc4+kqwH46aef6o033tDYsWMd80ybNk3du3eXJGVlZem5557T559/rtjYWElS/fr1tWXLFv3rX//SLbfc4ljumWeeUadOnSRJDz/8sMaPH6/Dhw+rfv36kqS//vWv+vLLL/XUU09Jku655x6lp6crJCREZrNZixcvVvXq1bV//341adJE1atXlyRVq1ZNtWrVyjfO6NGjFR8f75jWtm3bfI9z6NChGjhwoCTpueee07x587Rjxw716tXLMU/t2rX1yy+/OLVNAQDI40zWJyUlOY4VLyzrb7vtNj355JMKCQlR48aNy3XW/155zHq+SQcAg+vQoYNMJpPjdmxsrA4ePKjc3FwlJyfL19dX7du3d9xfrVo1NWrUSMnJyZKk5ORkR/jl6dSpk2MdzsjMzFRgYGCh9+WFryT5+vqqTZs2jrHztGnTxvH7oUOHlJGRoe7duys4ONjxs2zZsny7q0lSs2bNHL/XrFlTQUFBjtDOm3b69GnH7YMHD+rhhx9WgwYNFBISosjISElXTxx0LadPn9bx48d1++23X2cL5K+lQoUKCgkJyTe2JFkslny75AEA4Ayy/ipns37gwIGqX79+uc16jzfpCxcuVGRkpAIDA9W+fXvH7gnXkpiYqEaNGslisSg8PFxPPPGErly54qZqAcA7hYaG6vz588Ve/veXKrl06ZIk6aOPPtKePXscP/v37893rJok+fn5OX43mUz5budNy9u9TZL69u2rCxcu6F//+pe2b9+u7du3S7p6SZlrsVgsTj2GG40tSefOnXN8go/8yHsAMLaykvV9+vTRuXPn9Nprr5XbrPdok7569WolJCRo8uTJ2r17t5o3b66ePXsW+LQiz4oVKzRu3DhNnjxZycnJeuONN7R69Wo9/fTTbq4cANwnL3zyfP3114qKipKPj4+io6OVk5OTb56zZ88qJSVFMTExkqTo6GglJSXlW0dSUpIaNmwoHx8fp2po2bKlDhw4kO8kNr+vJ09OTo527dql6Ojoa64rJiZGAQEBSk1NVYMGDfL9hIeHO1VPYfIe9+jRo3X77bcrOjq6wB8b/v7+kpTvW4WKFSsqMjJSGzZsKPbYeb7//nu1bNmyxOspb8h7ALg+st45eY97woQJ5TrrPXpM+uzZszV8+HANGzZM0tXr8n300UdavHixxo0bV2D+rVu3qlOnTho0aJAkKTIyUgMHDizwpAaA8iQ1NVUJCQn6+9//rt27d2v+/PmOM7ZGRUWpb9+++vvf/64XX3xRtWrV0tNPP606deqob9++kqTRo0erbdu2mj59ugYMGKBt27ZpwYIFevnll52u4dZbb9WlS5f0ww8/qEmTJvnuW7hwoaKiohQdHa05c+bo/Pnzeuihh665rooVK2rMmDF64oknZLPZdPPNN+vixYtKSkpSSEiIhgwZUoytJFWpUkXVqlXTm2++qQYNGuh///tfgSypUaOGLBaLPv30U9WtW1eBgYGqVKmSpkyZon/84x+qUaOG7rjjDv36669KSkrSY4895vT4R44c0bFjx9StW7di1V+ekfcAcH3OZP3w4cP1r3/9SxUrVtS4ceMKzfpZs2bpwQcf1Pbt28t11r/66qsKCwtTampqucx6jzXp2dnZ2rVrl8aPH++YZjab1a1bN23btq3QZTp27Ki3335bO3bsULt27fTTTz/p448/1oMPPnjNcbKyspSVleW4nZ6eLkmyWq2OsyGWBXm1lqWaSwPb4Tdsi6uc2Q5Wq1V2u102my3fLkt5v6cfPyIV/NDY5dJPHHGM+8ddp67nwQcfVEZGhtq1aycfHx89/vjj+tvf/uZYxxtvvKFRo0bpvvvuk9VqVefOnfXhhx/Kx8dHNptNLVq00KpVqzRlyhRNnz5dYWFhmjp1qgYPHpyvjrxtVJgqVaqoX79+evvtt/Xcc885Hod09cQqM2fO1J49e9SgQQOtWbNGVatWzfc4//iYp06dqtDQUM2YMUM//fSTKleurJYtW2r8+PHXXO6P/+bV/Ptpy5cvV3x8vJo1a6ZGjRopMTFRt912m2M9ZrNZiYmJeuaZZzRp0iR17txZX3zxhWMbz507V2PGjFFoaKjuueeeAs+XP26f309bsWKFunfvrvDw8EK3o81mk91ul9VqLfCtRnl+HZeHvLfZbLJYLPIzS74m51+7ZYmf+eruoDabzS3PRzIMruYtWX/nnXcqOzv7mlk/adIkzZo1q1xn/YoVKzRq1Cg1adKk3Ga9yV7Y/gxucPz4cdWpU0dbt27NdyKCsWPHatOmTdf8tHzevHkaM2aM7Ha7cnJy9I9//EOvvPLKNceZMmWKpk6dWmD6ihUrFBQUVPIHAsDwfH19VatWLYWHhzt2gZKko0ePql379rqSmem2WgItFu3Yvt3pXb3uvPNONW3aVDNmzCjlym7s+++/1913363du3crODhYqampat68uTZv3qymTZt6ujyPys7OVuvWrfXaa6+pQ4cO15zn6NGjOnnypHJycvLdl5GRoUGDBunixYsKCQlxR8luQ94DcAey3jXI+mtzZ9aXqUuwbdy4Uc8995xefvlltW/fXocOHVJ8fLymT5/uuLTQH40fP14JCQmO2+np6QoPD1ePHj3K1B9CVqtV69evV/fu3Quc0MCbsB1+w7a4ypntcOXKFR09elTBwcH5zlr65z//Wcn79ystLc1d5So0NFQRERFOz+/r6yt/f/8bvl/Z7Xb9+uuvqlixYr6zw7pSx44dNXPmTJ09e1a1a9dWcHCwpN/OfmoE7tgOhTl06JCefvpp9ejR45rzXLlyRRaLRV26dClw9ty8b31xldHyfu/everSpYtuHfOyqoRHlWhdRnX+6EF9+eIIbd68Wc2bNy/18cgwuJo3ZP2NuCIDy0LWe4o7s95jTXpoaKh8fHx06tSpfNNPnTqV73p2vzdx4kQ9+OCD+tvf/iZJatq0qS5fvqxHHnlE//znP2U2FzwPXkBAgAICAgpM9/PzK5OhUFbrdjW2w2/YFlddbzvk5ubKZDLJbDYXeJ+IjIx0XLrDqPJqv568Xa6cmbckfn/8Wd44hW1XT3HXdvijhg0bqmHDhtedx2w2O85a+8fnanl+DZeHvDebzcrMzJTVJuXYjfFcdzWr7erll8xms1ufj2QYXMUbsv5GXJWBRs96T3Fn1nusSff391fr1q21YcMG9evXT9LVJ9aGDRsUFxdX6DIZGRkFnhx5+/p7aK99AChVGzdu9HQJ1xQZGcl7L26IvAeA6yPr8Uce3d09ISFBQ4YMUZs2bdSuXTslJibq8uXLjrO/Dh48WHXq1HEcn9GnTx/Nnj1bLVu2dOz+NnHiRPXp08fpSwsAAAD3Iu8BAHCeR5v0AQMG6MyZM5o0aZJOnjypFi1a6NNPP1XNmjUlXb0Uwe8/SZ8wYYJMJpMmTJigY8eOqXr16urTp4+effZZTz0EAABwA+Q9AADO8/iJ4+Li4q65u9sfd/3w9fXV5MmTNXnyZDdUBgAAXIW8BwDAOd599D8AAAAAAAZCkw4AAAAAgEHQpAMAAAAAYBAePyYdADwpNTVVaWlpbhsvNDRUERERbhvPGZGRkRo1apRGjRrl6VIAAHA5sp6sL2to0gF4rdTUVEU3bqSMzCtuGzPIEqjkAymGC28AAMojsh5lEU06AK+VlpamjMwrevuRFooOCy718ZJPXNIDr+5RWloawQ0AgBuQ9SiLaNIBeL3osGC1iqzk6TIK1bVrVzVp0kSS9NZbb8nPz0+PPvqopk2bJpPJpPPnzys+Pl4ffPCBsrKy1KVLF82fP19RUVGOdbz33nuaNGmSDh06pLCwMD322GMaPXq0px4SAABuV56y/pZbbtG8efPI+nKME8cBgMG9+eab8vX11Y4dOzR37lzNnj1br7/+uiRp6NCh2rlzp9asWaN169bJbrerd+/eslqtkqRdu3apf//+uu+++7Rv3z5NmTJFEydO1NKlSz34iAAAwO85k/Vr167Vtm3brpn1AwYMUFJSkiZNmkTWl3F8kw4ABhceHq45c+bIZDKpUaNG2rdvn+bMmaOuXbtq7dq1SkpKUocOHZSenq63335bN910k9asWaN7771Xs2fP1u23366JEydKkho2bKj9+/dr1qxZGjp0qGcfGAAAkORc1nfs2FGStHz5coWHhxfI+gkTJig9PV2tWrXSgQMHyPoyjG/SAcDgOnToIJPJ5LgdGxurgwcPav/+/fL19VX79u0d91WrVk2NGjVScnKyJCk5OVmdOnXKt75OnTrp4MGDys3Ndc8DAAAA10XW4/do0gEAAAAAMAiadAAwuO3bt+e7/fXXXysqKkoxMTHKycnJd//Zs2eVkpKimJgYSVJ0dLSSkpLyLZ+UlKSGDRvKx8en9IsHAAA3RNbj92jSAcDgUlNTlZCQoJSUFK1cuVLz589XfHy8oqKi1LdvXw0fPlxbtmzRvn379OCDD6pOnTrq27evJGn06NHasGGDpk+frh9//FFvvvmmFixYoDFjxnj4UQEAgDzOZv3evXv1wAMPFJr1zzzzjA4dOkTWlwOcOA6A10s+ccnQ4wwePFiZmZlq166dfHx8FB8fr0ceeUSStGTJEsXHx+v//u//lJ2drc6dO+vjjz+Wn5+fJKlVq1b697//rUmTJmn69OkKCwvTtGnTOJEMAMCrlIesv/POO5Wdna0uXbpcM+ufeeYZsr4coEkH4LVCQ0MVZAnUA6/ucduYQZZAhYaGFmkZPz8/JSYm6pVXXilwX5UqVbRs2TLZbDalp6crJCREZnP+naTuuece3XPPPddc/5EjR4pUDwAAZUV5yvrrueeee3TXXXdd828Bsr5soUkH4LUiIiKUfCBFaWlpbhszNDRUERERbhsPAABvRtajLKJJB+DVIiIiCFIAAMoxsh5lDU06ABjYxo0bPV0CAAAoRWQ9/oizuwMAAAAAYBA06QAAAAAAGARNOgAAAAAABkGTDgAAAACAQdCkAwAAAABgEDTpAAAAAAAYBJdgA+DVUlNTlZaW5rbxQkNDuVYrAABuRNajrKFJB+C1UlNT1Ti6sTIzMt02piXIogPJB5wO765du6pFixZKTEws3cKcZLR6ALhWcnKyW8ax2WySpL1798psdt+OnVlZWQoICHDbeJ5Ag5gfWV90RqvHG9GkA/BaaWlpyszIVJexXVQpvFKpj3fx6EVtfmGz0tLS3PoHVHZ2tvz9/d02HoCyx5qVLUl64IEH3DKexWLRypUr1aVLF2Vmuq95Mpskm91tw3lEURvE8s6bst7Xl9auvOB/EoDXqxReSaFRoZ4uo4ChQ4dq06ZN2rRpk+bOnStJOnTokJ577jl98cUXOnnypCIiIjRixAg99thj+Za7cOGC2rZtq4ULFyogIEA///yztm7dqhEjRujAgQNq0qSJJkyYoLvuukvffvutWrRoIUn6/vvv9eSTT+qrr75ShQoV1KNHD82ZM0ehoaGF1vPzzz8rMjLS3ZsGgIvl5ORIkqbf3VC9m9Yo9fFsZn8dk7R5XKzMtuxSH0+Sdv90RsPfSlHLwS1Vt21dt4zpbp5qEMuC8pD18fHx+Zb7Y9YfPnxY27dv11NPPUXWl3E06QBgUHPnztWPP/6oJk2aaNq0aZKkKlWqqG7dunrnnXdUrVo1bd26VY888ohq1qypXr16OZbdsGGDQkJCtH79eklSenq6+vTpo969e2vFihX65ZdfNGrUqHzjXbhwQbfddpv+9re/ac6cOcrMzNRTTz2l/v3764svvii0nurVq7tnYwBwi3qhQWoVWfrfNlrlp2OSmkeEyE/WUh9Pki5nXJYkBdcKNmSzBu9UlKwPCwtT//79HcsWlvUDBw4k68sBmnQAMKhKlSrJ399fQUFBqlWrlmP61KlTHb/Xq1dP27Zt0zvvvJOvSa9QoYJef/11x27uixYtkslk0muvvabAwEDFxMTo2LFjGj58uGOZBQsWqGXLlnruuecc0xYvXqzw8HD9+OOPatiwYaH1AACA4ilK1v/73//O16T/MetffvllmUwmvfrqqwoKCiLryzCadAAoYxYuXKjFixcrNTVVmZmZys7OduzClqdp06b5jkNPSUlRs2bNFBgY6JjWrl27fMvs3btXX375pYKDgwuMefjwYTVs2NC1DwQAABSqOFn/448/6s9//jNZXw7QpANAGbJq1SqNGTNGL730kmJjY1WxYkXNmjVL27dvzzdfhQoVirzuS5cuqU+fPnr++ecL3BcWFlbsmgEAgPPIetCkA4CB+fv7Kzc313E7KSlJHTt21IgRIxzTDh8+fMP1NGrUSG+//Xa+yw998803+eZp1aqV3nvvPUVGRl7zDLF/rAcAAJSMq7K+YcOGjqy3WCySyPqyiiYdgNe7ePSiYceJjIzU9u3bdeTIEQUHBysqKkrLli3TunXrVK9ePb311lv65ptvVK9eveuuZ9CgQfrnP/+pRx55ROPGjVNqaqpefPFFSZLJZJIkjRw5Uq+99poGDhyosWPHqmrVqjp06JBWrVql119/XT4+PgXqqVq1qluvcQwAQHF4S9ZPmDBBf//73zV+/HiyvgyjSQfgtUJDQ2UJsmjzC5vdNqYlyKLQUOfPKjxmzBgNGTJEMTExyszM1IEDB/Ttt99qwIABMplMGjhwoEaMGKFPPvnkuusJCQnRBx98oEcffVQtWrRQ06ZNNWnSJA0aNMhx7Frt2rWVlJSkp556Sj169FBWVpZuuukm9erVyxHOf6yHy7IAAIzM27J+5cqVGjt2LFlfxtGkA/BaEREROpB8QGlpaW4bMzQ0tEjXrm3YsKG2bduWb9qSJUu0ZMmSfNOeffZZpaenS5KWLl1a6Lo6duyovXv3Om4vX75cfn5++eqJiorS+++/X6R6AAAwqvKU9TNmzHD8fq2sb9++vb799ltHw03Wl0006QC8WkRERJGCtCxbtmyZ6tevrzp16mjv3r2O66LmHbcGAEB55E1Zv2rVKsXExCg8PJysL8No0gHAS5w8eVKTJk3SyZMnFRYWpnvvvVfPPvusp8sCAAAucurUKc2cOZOsL+No0gHAS4wdO1Zjx471dBkAAKCUxMfHa+LEiZzorYzjfw8AAAAAAIOgSQfgNex2u6dLgBfj+QcApY/3WniSq55/NOkAyj0/Pz9JUkZGhocrgTfLe/7lPR8BAK5D1sMIsrOzJUk+Pj4lWg/HpAMo93x8fFS5cmWdPn1akhQUFCSTyeThqlzLZrMpOztbV65c8erj0Iy4Hex2uzIyMnT69GlVrly5xMENACjIG7L+RoyYgd7EZrPpzJkzCgoKkq9vydpsmnQAXqFWrVqS5Ajv8sZutyszM1MWi8Xr/ij5PSNvh8qVKzuehwAA1yvvWX8jRs5Ab2E2mxUREVHi7U+TDsArmEwmhYWFqUaNGrJarZ4ux+WsVqs2b96sLl26ePXu1EbdDn5+fnyDDgClrLxn/Y0YNQO9ib+/v0v2YqBJB+BVfHx8ymWz5OPjo5ycHAUGBnp1MLMdAADlNetvhAwsPzhYAQAAAAAAg6BJBwAAAADAIGjSAQAAAAAwCJp0AAAAAAAMgiYdAAAAAACDoEkHAAAAAMAgaNIBAAAAADAIjzfpCxcuVGRkpAIDA9W+fXvt2LHjuvNfuHBBI0eOVFhYmAICAtSwYUN9/PHHbqoWAAAUB3kPAIBzfD05+OrVq5WQkKBFixapffv2SkxMVM+ePZWSkqIaNWoUmD87O1vdu3dXjRo19O6776pOnTr65ZdfVLlyZfcXDwAAnELeAwDgPI826bNnz9bw4cM1bNgwSdKiRYv00UcfafHixRo3blyB+RcvXqxz585p69at8vPzkyRFRka6s2QAAFBE5D0AAM7zWJOenZ2tXbt2afz48Y5pZrNZ3bp107Zt2wpdZu3atYqNjdXIkSP13//+V9WrV9egQYP01FNPycfHp9BlsrKylJWV5bidnp4uSbJarbJarS58RKUrr9ayVHNpYDv8hm1xFdvhKrbDVWV1O5S1eouiPOS9zWaTxWKRn1nyNdlKtC6j8vcxyWKxSL4Bssqv1MfLG8MdY+Wx+/jLYrHI38dfvnaPfk9VavxMfrJYLLLZbOX6fQWFK6sZ6C2K8v/isXeotLQ05ebmqmbNmvmm16xZUwcOHCh0mZ9++klffPGF7r//fn388cc6dOiQRowYIavVqsmTJxe6zIwZMzR16tQC0z/77DMFBQWV/IG42fr16z1dgiGwHX7DtriK7XAV2+GqsrYdMjIyPF1CqSkveb9y5cr//1taiddlSDVq6/HOVx+jO4/8X1/lfvcN1kFa2eH//5513TnLrrrS4JWDdezYMR07dszT1cBDyloGeouiZH2Z+hjRZrOpRo0aevXVV+Xj46PWrVvr2LFjmjVr1jVDe/z48UpISHDcTk9PV3h4uHr06KGQkBB3lV5iVqtV69evV/fu3R27/nkjtsNv2BZXsR2uYjtcVVa3Q963vrjKaHm/d+9edenSRbeOeVlVwqNKtC6jOpHynba9PFqvDWum/m3DSn08q/y0vsr96n5+ufzknm/9tqWcUK+536njqI6q36W+W8Z0t7OHz+qTJz/R5s2b1bx5c0+XAzcrqxnoLYqS9R5r0kNDQ+Xj46NTp07lm37q1CnVqlWr0GXCwsLk5+eXb1e36OhonTx5UtnZ2fL39y+wTEBAgAICAgpM9/PzK5NP3rJat6uxHX7DtriK7XAV2+GqsrYdylKtRVUe8t5sNiszM1NWm5Rj9/iFcUpFdq5dmZmZUk6W25pmSfKT1W3jmXKzlZmZqezcbOWYctwyprtZ7VZlZmbKbDaX6/cVXF9Zy0BvUZT/E48ljb+/v1q3bq0NGzY4ptlsNm3YsEGxsbGFLtOpUycdOnRINttvx4P9+OOPCgsLKzSwAQCAZ5H3AAAUjUc/Dk5ISNBrr72mN998U8nJyXr00Ud1+fJlx9lfBw8enO9EM48++qjOnTun+Ph4/fjjj/roo4/03HPPaeTIkZ56CAAA4AbIewAAnOfRY9IHDBigM2fOaNKkSTp58qRatGihTz/91HFymdTUVJnNv32OEB4ernXr1umJJ55Qs2bNVKdOHcXHx+upp57y1EMAAAA3QN4DAOA8j584Li4uTnFxcYXet3HjxgLTYmNj9fXXX5dyVQAAwJXIewAAnFM+z34CAAAAAEAZVKwm/csvv3R1HQAAwGDIewAA3K9YTXqvXr30pz/9Sc8884yOHj3q6poAAIABkPcAALhfsZr0Y8eOKS4uTu+++67q16+vnj176t///reys7NdXR8AAPAQ8h4AAPcrVpMeGhqqJ554Qnv27NH27dvVsGFDjRgxQrVr19bjjz+uvXv3urpOAADgZuQ9AADuV+ITx7Vq1Urjx49XXFycLl26pMWLF6t169bq3LmzfvjhB1fUCAAAPIy8BwDAPYrdpFutVr377rvq3bu3brrpJq1bt04LFizQqVOndOjQId1000269957XVkrAABwM/IeAAD3KtZ10h977DGtXLlSdrtdDz74oF544QU1adLEcX+FChX04osvqnbt2i4rFAAAuBd5DwCA+xWrSd+/f7/mz5+vu+++WwEBAYXOExoayqVbAAAow8h7AADcr1i7u0+ePFn33ntvgcDOycnR5s2bJUm+vr665ZZbSl4hAADwCPIeAAD3K1aTfuutt+rcuXMFpl+8eFG33npriYsCAACeR94DAOB+xWrS7Xa7TCZTgelnz55VhQoVSlwUAADwPPIeAAD3K9Ix6XfffbckyWQyaejQofl2f8vNzdV3332njh07urZCAADgVuQ9AACeU6QmvVKlSpKufrJesWJFWSwWx33+/v7q0KGDhg8f7toKAQCAW5H3AAB4TpGa9CVLlkiSIiMjNWbMGHZ1AwCgHCLvAQDwnGJdgm3y5MmurgMAABgMeQ8AgPs53aS3atVKGzZsUJUqVdSyZctCTySTZ/fu3S4pDgAAuBd5DwCAZzndpPft29dx4ph+/fqVVj0AAMCDyHsAADzL6Sb997u8sfsbAADlE3kPAIBnFes66QAAAAAAwPWc/ia9SpUq1z0u7ffOnTtX7IIAAIDnkPcAAHiW0016YmJiKZYBAACMgLwHAMCznG7ShwwZUpp1AAAAAyDvAQDwLKeb9PT0dIWEhDh+v568+QAAQNlC3gMA4FlFOib9xIkTqlGjhipXrlzo8Wp2u10mk0m5ubkuLRIAALgHeQ8AgGc53aR/8cUXqlq1qiTpyy+/LLWCAACA55D3AAB4ltNN+i233FLo7wAAoPwg7wEA8Cynm/Q/On/+vN544w0lJydLkmJiYjRs2DDHp+8AAKDsI+8BAHAvc3EW2rx5syIjIzVv3jydP39e58+f17x581SvXj1t3rzZ1TUCAAAPIO8BAHC/Yn2TPnLkSA0YMECvvPKKfHx8JEm5ubkaMWKERo4cqX379rm0SAAA4H7kPQAA7lesb9IPHTqk0aNHOwJbknx8fJSQkKBDhw65rDgAAOA55D0AAO5XrCa9VatWjmPTfi85OVnNmzcvcVEAAMDzyHsAANzP6d3dv/vuO8fvjz/+uOLj43Xo0CF16NBBkvT1119r4cKFmjlzpuurBAAAbkHeAwDgWU436S1atJDJZJLdbndMGzt2bIH5Bg0apAEDBrimOgAA4FbkPQAAnuV0k/7zzz+XZh0AAMAAyHsAADzL6Sb9pptuKs06AACAAZD3AAB4VrEuwZZn//79Sk1NVXZ2dr7p//d//1eiogAAgHGQ9wAAuE+xmvSffvpJd911l/bt25fvuDWTySTp6jVUAQBA2UbeAwDgfsW6BFt8fLzq1aun06dPKygoSD/88IM2b96sNm3aaOPGjS4uEQAAeAJ5DwCA+xXrm/Rt27bpiy++UGhoqMxms8xms26++WbNmDFDjz/+uL799ltX1wkAANyMvAcAwP2K9U16bm6uKlasKEkKDQ3V8ePHJV092UxKSorrqgMAAB5D3gMA4H7F+ia9SZMm2rt3r+rVq6f27dvrhRdekL+/v1599VXVr1/f1TUCAAAPIO8BAHC/YjXpEyZM0OXLlyVJ06ZN05133qnOnTurWrVqWr16tUsLBAAAnkHeAwDgfsVq0nv27On4vUGDBjpw4IDOnTunKlWqOM74CgAAyjbyHgAA9yvRddIl6ejRo5Kk8PDwEhcDAACMibwHAMA9inXiuJycHE2cOFGVKlVSZGSkIiMjValSJU2YMEFWq9XVNQIAAA8g7wEAcL9ifZP+2GOP6f3339cLL7yg2NhYSVcv0zJlyhSdPXtWr7zyikuLBAAA7kfeAwDgfsVq0lesWKFVq1bpjjvucExr1qyZwsPDNXDgQEIbAIBygLwHAMD9irW7e0BAgCIjIwtMr1evnvz9/UtaEwAAMADyHgAA9ytWkx4XF6fp06crKyvLMS0rK0vPPvus4uLiXFYcAADwHPIeAAD3c3p397vvvjvf7c8//1x169ZV8+bNJUl79+5Vdna2br/9dtdWCAAA3Ia8BwDAs5xu0itVqpTv9j333JPvNpdkAQCg7CPvAQDwLKeb9CVLlpRmHQAAwADIewAAPKtYx6TnOXPmjLZs2aItW7bozJkzxV7PwoULFRkZqcDAQLVv3147duxwarlVq1bJZDKpX79+xR4bAABcnyvynqwHAMA5xWrSL1++rIceekhhYWHq0qWLunTpotq1a+vhhx9WRkZGkda1evVqJSQkaPLkydq9e7eaN2+unj176vTp09dd7siRIxozZow6d+5cnIcAAABuwFV5T9YDAOC8YjXpCQkJ2rRpkz744ANduHBBFy5c0H//+19t2rRJo0ePLtK6Zs+ereHDh2vYsGGKiYnRokWLFBQUpMWLF19zmdzcXN1///2aOnWq6tevX5yHAAAAbsBVeU/WAwDgPKePSf+99957T++++666du3qmNa7d29ZLBb1799fr7zyilPryc7O1q5duzR+/HjHNLPZrG7dumnbtm3XXG7atGmqUaOGHn74YX311VfXHSMrKyvfpWPS09MlSVarVVar1ak6jSCv1rJUc2lgO/yGbXEV2+EqtsNVZXU7GLVeV+S9O7JeKt28t9lsslgs8jNLviZbidZlVP4+JlksFsk3QFb5lfp4eWO4Y6w8dh9/WSwW+fv4y9derD+BDc/P5CeLxSKbzWbY9xWUnrKagd6iKP8vxXqHysjIUM2aNQtMr1GjRpF2f0tLS1Nubm6BddWsWVMHDhwodJktW7bojTfe0J49e5waY8aMGZo6dWqB6Z999pmCgoKcrtUo1q9f7+kSDIHt8Bu2xVVsh6vYDleVte1Q1EPF3MUVee+OrJdKP+9Xrlz5/39LK/G6DKlGbT3e+epj/NiNw66vcr/7Busgrezw/3/Puu6cZVddafDKwTp27JiOHTvm6WrgIWUtA71FUbK+WE16bGysJk+erGXLlikwMFCSlJmZqalTpyo2NrY4q3TKr7/+qgcffFCvvfaaQkNDnVpm/PjxSkhIcNxOT09XeHi4evTooZCQkNIq1eWsVqvWr1+v7t27y8/PfZ86Gw3b4Tdsi6vYDlexHa4qq9sh71tfo/FE3hcn66XSzfu9e/eqS5cuunXMy6oSHlWidRnViZTvtO3l0XptWDP1bxtW6uNZ5af1Ve5X9/PL5Sf3fOu3LeWEes39Th1HdVT9LuXzEIqzh8/qk//H3p2HN1Xn/f9/JV1T2gJSdloLUqAMqyylqIjIJiO3uCKoUHSKc0NnwLqBP9ncQFEoCorKAOKwuDOMC4gdEYXKDqIUBBTLzV62Ag1t2uT3B99mjG2hS5pz2j4f19ULcnLO+bxz2pN3Xsk5J49/obVr16p9+/ZGlwMfq6w9sLooTa8vU0hPSUlR//791aRJE/cTwI4dOxQcHKxVq1aVeD0RERHy8/PTsWPHPKYfO3ZMDRo0KDT//v37deDAAQ0cONA9zem8dNiZv7+/9uzZo2uuucZjmaCgIAUFBRVaV0BAQKX8462sdXsb2+G/2BaXsB0uYTtcUtm2g1lr9Ua/90Wvlyq231utVtntdjmcUp6rXF+MY1q5+S7Z7XYpL8dnoVmSAuTw2XiW/FzZ7Xbl5ucqz5LnkzF9zeFyyG63y2q1mvZ5BRWvsvXA6qI0v5MyhfS2bdtq7969Wrx4sftQtSFDhui+++67dD5TCQUGBqpTp05KTU11f7WK0+lUamqqkpKSCs3fqlUr7dy502Pa008/rXPnzmnWrFmKjIwsy8MBAABF8Ea/p9cDAFA6pQ7pDodDrVq10qeffqrExMRyF5CcnKzhw4erc+fO6tq1q1JSUnThwgWNGDFCkjRs2DA1btxYU6dOVXBwsNq0aeOxfK1atSSp0HQAAFB23uz39HoAAEqu1CE9ICBAFy9e9FoBgwcP1okTJzRx4kQdPXpUHTp00MqVK90XmMnIyJDVWjUPLQMAwKy82e/p9QAAlFyZDncfPXq0XnzxRc2bN0/+/uX/CoukpKQiD3mTpDVr1lx22YULF5Z7fAAAUJg3+z29HgCAkilTx920aZNSU1P15Zdfqm3btqpRo4bH/R9//LFXigMAAMah3wMA4HtlCum1atXSnXfe6e1aAACAidDvAQDwvVKFdKfTqenTp+vnn39Wbm6uevXqpcmTJ5fqiu4AAMDc6PcAABinVFdpef755/XUU08pNDRUjRs31quvvqrRo0dXVG0AAMAA9HsAAIxTqpC+aNEivf7661q1apWWL1+uf//731q8eLGcTmdF1QcAAHyMfg8AgHFKFdIzMjI0YMAA9+3evXvLYrHo8OHDXi8MAAAYg34PAIBxShXS8/LyFBwc7DEtICBADofDq0UBAADj0O8BADBOqS4c53K5lJCQoKCgIPe0ixcv6q9//avH17LwlSwAAFRe9HsAAIxTqpA+fPjwQtPuv/9+rxUDAACMR78HAMA4pQrpCxYsqKg6AACASdDvAQAwTqnOSQcAAAAAABWHkA4AAAAAgEkQ0gEAAAAAMAlCOgAAAAAAJkFIBwAAAADAJAjpAAAAAACYBCEdAAAAAACTIKQDAAAAAGAShHQAAAAAAEyCkA4AAAAAgEkQ0gEAAAAAMAlCOgAAAAAAJkFIBwAAAADAJPyNLgAAAAAAqpqMjAxlZmb6bDyn0ylJ2rFjh6xW33wWm5OTo6CgIJ+MZZSIiAhFRUX5dExCOgBUMG80aSMaBAAAKJuMjAzFtmqpbPtFn41ps9m0dOlS9ejRQ3a73SdjWi2S0+WToQxjC7Fpd/pun74OI6QDQAXKyMhQq1axstuzy7Uemy1Eu3enE9QBAKgEMjMzlW2/qH+O7KDYhqE+GdNpDdQhSWvHxcvqzK3w8bb+ckKJ7+5Rx2Ed1aRLkwofzwhnD57V2pfWKjMzk5AO4MqfvpbkkCY+fTVeZmam7PZsxT04SeENo8u0jqwjB7Rh/hSfNwgAAFA+sQ1DdW10TZ+M5VCADklqHxWuADkqfLwL2RckSaENQhURE1Hh41UnhHTAhEpyiFRJDmkKsQUrffceQ4Kdt87DqipvNIQ3jNZVUS2NLgMAAAAmR0gHTKgkh0hd6ZCm9CPndf9b2w359NVbh3hLHOYNAACA6oWQDpjY5Q6R8vUhTaXhjUO8JQ7zBgAAQPVDSAdQYTjEGwAAACgd33yBHgAAAAAAuCJCOgAAAAAAJkFIBwAAAADAJAjpAAAAAACYBCEdAAAAAACTIKQDAAAAAGAShHQAAAAAAEyCkA4AAAAAgEkQ0gEAAAAAMAlCOgAAAAAAJkFIBwAAAADAJAjpAAAAAACYBCEdAAAAAACTIKQDAAAAAGAShHQAAAAAAEyCkA4AAAAAgEkQ0gEAAAAAMAlCOgAAAAAAJkFIBwAAAADAJAjpAAAAAACYhL/RBQC/l5GRoczMzGLvdzqdkqQdO3bIai3+PaacnBwFBQWVu56IiAhFRUWVez0AAAAAUBKEdJhGRkaGYlu1VLb9YrHz2Gw2LV26VD169JDdbi92PqtFcrrKX5MtxKbd6bsJ6gAAAAB8whQhfc6cOZo+fbqOHj2q9u3b67XXXlPXrl2LnPftt9/WokWL9OOPP0qSOnXqpBdeeKHY+VF5ZGZmKtt+Uf8c2UGxDUOLnMdpDdQhSWvHxcvqzC1ynq2/nFDiu3vUcVhHNenSpMz1nD14VmtfWqvMzExCOgCUE70eAICSMTykv/fee0pOTtbcuXMVFxenlJQU9evXT3v27FG9evUKzb9mzRoNGTJE3bt3V3BwsF588UX17dtXP/30kxo3bmzAI4C3xTYM1bXRNYu8z6EAHZLUPipcAXIUOc+F7AuSpNAGoYqIiaioMot1pUP2SyI9Pd1L1QCA8ej1AACUnOEhfcaMGUpMTNSIESMkSXPnztVnn32m+fPna9y4cYXmX7x4scftefPm6aOPPlJqaqqGDRvmk5qB4mRkZKhVq1jZ7dleWJtFR85elFT0GxYl5Y3Az7n5AMqDXg8AQMkZGtJzc3O1ZcsWjR8/3j3NarWqd+/eSktLK9E6srOz5XA4dNVVVxV5f05OjnJycty3s7KyJEkOh0MOR9GfxJpRQa2VqebScjqdstlscloD5VBAkfMUTC/ufkly+QXKZrMp0C9Q/q6y/4kHWAIu1eN0lni7Hz9+XJJLN4ycorD6ZQ+1J3/bqx8+mKkzuX5l3hb2fJdsNpsSExPLXEcBW4hNmzdtVpMmJTt9oOB3GWCV/C3OMo8bYNUVfwdm3ze8sS0KtkN6err74olFjSNJ27Ztu+xFFevUqVPi32NlZPa/h+JUtnpLwxe9XqrYfu+t5zQzC/SzyGazSf5Bl+2x3lKSfu5t3np9YGZlee2CilGS17Xe5uv9in2qdEqzvMXlcnnh8lplc/jwYTVu3Fjr169XfHy8e/oTTzyhb775Rhs2bLjiOkaNGqVVq1bpp59+UnBwcKH7J0+erClTphSavmTJEoWEhJTvAQAA4AXZ2dkaOnSozp49q/DwcKPL8Spf9HqJfg8AMLfS9PpK/ZbHtGnTtGzZMq1Zs6bYpj1+/HglJye7b2dlZSkyMlJ9+/atVC+EHA6HVq9erT59+iggwHfvOvvSjh071KNHD60dF6/2UUX/bhwK0Ora96nP6cXFnpOetueI+s/6Qd3HdlezHs3KXM/J/Sf1xeNfaO3atWrfvn2pHsNNj72u2pExZR77yJ4flPb6o3p7RDvd06VhkfNcaVtUhe1w+uBeff3yqMuObfZ9wxvbouDv4en/aa6+resWOY/TGqgjbf+qhjvnFntRxZ3/d0aj/+md6x2U9ugKXzH730NxCj71RWEl6fVSxfZ7bz2nmVlJ+o43laSfe5u3+qKZlaVno2KU5HWtt/l6v2KfKp3S9HpDQ3pERIT8/Px07Ngxj+nHjh1TgwYNLrvsyy+/rGnTpumrr75Su3btip0vKCioyO/LDggIqFQv4ApU1rpLwmq1ym63y+rMveITS4Acxc5jyc+V3W5Xbn6u8ix5Za7H4XJcqsdqLfE2L3gMDqeU5yr+kOMryc13XfqKubycMm+LqrAdHE6VeGyz7hve2BYFfw9X1/RT5yhbkfM4FKAjkjo2CVaA/Iqcx37+tOx2u9e++eD06dNq2rRpmddTkcz691CcylRrafmi10sV2++99ZxmZqXpO950uX7ubd7qi2ZWlp6NilGa17Xe5qv9in2qdEqzvKEhPTAwUJ06dVJqaqoGDRok6dL5G6mpqUpKSip2uZdeeknPP/+8Vq1apc6dO/uoWgCoGoz65gNUT/R6AABKx/DD3ZOTkzV8+HB17txZXbt2VUpKii5cuOC+AuywYcPUuHFjTZ06VZL04osvauLEiVqyZImio6N19OhRSVJoaKhCQ4v+bm0AQNVwpa84LLiA3o4dOy57Ab2cnJwiP3UtLb75oGTo9QAAlJzhIX3w4ME6ceKEJk6cqKNHj6pDhw5auXKl6tevL+nSC7Lfv9B64403lJubq7vuustjPZMmTdLkyZN9WToAwIcyMjIU26qlsu0Xi53HZrNp6dKl6tGjx6VDd4thtUhOL1w21RZi0+703QT1K6DXAwBQcoaHdElKSkoq9pC3NWvWeNw+cOBAxRcEADCdzMxMZdsv6p8jOyi2YdGfpjqtgTokae24+GIvoLf1lxNKfHeP187Nz8zMJKSXAL0eAICSMUVIBwCgpGIbhura6JpF3udQgA5Jah8VXuxFcy5kX5DEufkAAMCcquYlSgEAAAAAqIQI6QAAAAAAmAQhHQAAAAAAkyCkAwAAAABgEoR0AAAAAABMgpAOAAAAAIBJENIBAAAAADAJQjoAAAAAACZBSAcAAAAAwCQI6QAAAAAAmAQhHQAAAAAAkyCkAwAAAABgEoR0AAAAAABMgpAOAAAAAIBJENIBAAAAADAJQjoAAAAAACZBSAcAAAAAwCQI6QAAAAAAmAQhHQAAAAAAkyCkAwAAAABgEoR0AAAAAABMwt/oAgDgStLT04u9z+l0SpJ27Nghq7X49x0jIiIUFRXl9doAAEDpZWRkKDMz0+gyKszlXrsAV0JIB2BajpxcSdL9999f7Dw2m01Lly5Vjx49ZLfbi58vOEgffPiRGjZsWOZ6CPoAAJRfRkaGWrWKld2ebXQpFcyiI2cvSqppdCGoZAjp8JryviPKO474o7y8PEnSs3e00IC29Yqcx2kN1CFJa8fFy+rMLXKeHRmn9eCCn3TrrbeWqx5biE2703cT1MuI5wgAgCRlZmbKbs9W3IOTFN4w2uhyKsSJX3dr+5IXdSY7z+hSUAkR0uEV3ntHlHccUVjTiBBdG13034RDATokqX1UuALkKHKeC9kXJEkdh3VUky5NylTD2YNntfaltcrMzCSklwHPEQCAPwpvGK2roloaXUaFsF+o6kcJoCIR0uEV3nhHlHccUdFCG4QqIibC6DKqJZ4jAAAASoaQXsV44yIc5TnvtjzviPKOI1D18RwBAABweYT0KiQjI0OtYlvJnl38xbNKgvNuAQAAAMAYhPQqJDMzU/Zsu3o80UM1I8t2vibn3QIAAACAcQjpVVDNyJqcdwsAAAAAlZDV6AIAAAAAAMAlhHQAAAAAAEyCkA4AAAAAgEkQ0gEAAAAAMAlCOgAAAAAAJkFIBwAAAADAJAjpAAAAAACYBCEdAAAAAACTIKQDAAAAAGAShHQAAAAAAEyCkA4AAAAAgEkQ0gEAAAAAMAlCOgAAAAAAJkFIBwAAAADAJAjpAAAAAACYBCEdAAAAAACTIKQDAAAAAGAShHQAAAAAAEyCkA4AAAAAgEkQ0gEAAAAAMAlCOgAAAAAAJkFIBwAAAADAJEwR0ufMmaPo6GgFBwcrLi5OGzduvOz8H3zwgVq1aqXg4GC1bdtWn3/+uY8qBQAAZUGvBwCgZAwP6e+9956Sk5M1adIkbd26Ve3bt1e/fv10/PjxIudfv369hgwZooceekjbtm3ToEGDNGjQIP34448+rhwAAJQEvR4AgJIzPKTPmDFDiYmJGjFihFq3bq25c+cqJCRE8+fPL3L+WbNmqX///nr88ccVGxurZ599Vtdee61mz57t48oBAEBJ0OsBACg5fyMHz83N1ZYtWzR+/Hj3NKvVqt69eystLa3IZdLS0pScnOwxrV+/flq+fHmR8+fk5CgnJ8d9++zZs5KkU6dOyeFwlPMRSMePH9exY8fKvR6r1Sqn01ns/U6nU9nZ2fr2229ltRb93srevXsVHBysc7+eky6WrY5zR84pODhYWVlZOnnyZImXy8rKUnBwsM4f+lly2Ms0tv3oLwoODtaezFyt+flckfM4rYHK/lO2vt13XlZnbpHz/Hg4V8HBwco+nK3MnZllqkUq27bwxnaQvLMt2A7/5Y1twb7xX0b9TVSF7VDsus5dejwul6tc6zEjX/R6qWL7vbee08ysJPuXN5VkX/U2b+37ZubN56WKxD5VMXy9X7FPlXJdpen1LgMdOnTIJcm1fv16j+mPP/64q2vXrkUuExAQ4FqyZInHtDlz5rjq1atX5PyTJk1ySeKHH3744Ycf0/8cPHjQOw3WRHzR610u+j0//PDDDz+V46ckvd7QT9J9Yfz48R7vxjudTp06dUp16tSRxWIxsLLSycrKUmRkpA4ePKjw8HCjyzEM2+G/2BaXsB0uYTtcUlm3g8vl0rlz59SoUSOjS6m0qkq/ry4q674KmBn7lbmVptcbGtIjIiLk5+dX6HDxY8eOqUGDBkUu06BBg1LNHxQUpKCgII9ptWrVKnvRBgsPD2enE9vh99gWl7AdLmE7XFIZt0PNmjWNLqFC+KLXS1Wv31cXlXFfBcyO/cq8StrrDb1wXGBgoDp16qTU1FT3NKfTqdTUVMXHxxe5THx8vMf8krR69epi5wcAAMah1wMAUDqGH+6enJys4cOHq3PnzuratatSUlJ04cIFjRgxQpI0bNgwNW7cWFOnTpUkjRkzRjfeeKNeeeUV/fnPf9ayZcu0efNmvfXWW0Y+DAAAUAx6PQAAJWd4SB88eLBOnDihiRMn6ujRo+rQoYNWrlyp+vXrS5IyMjI8rmbevXt3LVmyRE8//bSeeuopxcTEaPny5WrTpo1RD8EngoKCNGnSpEKH8lU3bIf/Yltcwna4hO1wCdvBnOj1+CP2VcD72K+qDovLVQW/7wUAAAAAgErI0HPSAQAAAADAfxHSAQAAAAAwCUI6AAAAAAAmQUgHAAAAAMAkCOkAAAAAAJgEIb0SmDNnjqKjoxUcHKy4uDht3LjR6JJ8bu3atRo4cKAaNWoki8Wi5cuXG12SIaZOnaouXbooLCxM9erV06BBg7Rnzx6jy/K5N954Q+3atVN4eLjCw8MVHx+vL774wuiyDDdt2jRZLBaNHTvW6FJ8bvLkybJYLB4/rVq1MrosAMXgtQ3gPbxOrnoI6Sb33nvvKTk5WZMmTdLWrVvVvn179evXT8ePHze6NJ+6cOGC2rdvrzlz5hhdiqG++eYbjR49Wt9//71Wr14th8Ohvn376sKFC0aX5lNNmjTRtGnTtGXLFm3evFm9evXSbbfdpp9++sno0gyzadMmvfnmm2rXrp3RpRjmT3/6k44cOeL++e6774wuCUAReG0DeBevk6sevifd5OLi4tSlSxfNnj1bkuR0OhUZGam//e1vGjdunMHVGcNiseiTTz7RoEGDjC7FcCdOnFC9evX0zTffqEePHkaXY6irrrpK06dP10MPPWR0KT53/vx5XXvttXr99df13HPPqUOHDkpJSTG6LJ+aPHmyli9fru3btxtdCoAr4LUNUHF4nVw18Em6ieXm5mrLli3q3bu3e5rValXv3r2VlpZmYGUwi7Nnz0q6FFCrq/z8fC1btkwXLlxQfHy80eUYYvTo0frzn//s8VxRHe3du1eNGjVSs2bNdN999ykjI8PokgD8Aa9tAODK/I0uAMXLzMxUfn6+6tev7zG9fv362r17t0FVwSycTqfGjh2r6667Tm3atDG6HJ/buXOn4uPjdfHiRYWGhuqTTz5R69atjS7L55YtW6atW7dq06ZNRpdiqLi4OC1cuFAtW7bUkSNHNGXKFN1www368ccfFRYWZnR5AP4fXtsAwJUR0oFKavTo0frxxx+r7Xm3LVu21Pbt23X27Fl9+OGHGj58uL755ptqFdQPHjyoMWPGaPXq1QoODja6HEPdcsst7v+3a9dOcXFxuvrqq/X+++9Xy1MgAABA5UVIN7GIiAj5+fnp2LFjHtOPHTumBg0aGFQVzCApKUmffvqp1q5dqyZNmhhdjiECAwPVvHlzSVKnTp20adMmzZo1S2+++abBlfnOli1bdPz4cV177bXuafn5+Vq7dq1mz56tnJwc+fn5GVihcWrVqqUWLVpo3759RpcC4Hd4bQMAV8Y56SYWGBioTp06KTU11T3N6XQqNTW12p57W925XC4lJSXpk08+0X/+8x81bdrU6JJMw+l0Kicnx+gyfOrmm2/Wzp07tX37dvdP586ddd9992n79u3VNqBLly6mt3//fjVs2NDoUgD8Dq9tAODK+CTd5JKTkzV8+HB17txZXbt2VUpKii5cuKARI0YYXZpPnT9/3uMTsV9//VXbt2/XVVddpaioKAMr863Ro0dryZIl+te//qWwsDAdPXpUklSzZk3ZbDaDq/Od8ePH65ZbblFUVJTOnTunJUuWaM2aNVq1apXRpflUWFhYoesR1KhRQ3Xq1Kl21yl47LHHNHDgQF199dU6fPiwJk2aJD8/Pw0ZMsTo0gD8Aa9tAO/idXLVQ0g3ucGDB+vEiROaOHGijh49qg4dOmjlypWFLrhS1W3evFk33XST+3ZycrIkafjw4Vq4cKFBVfneG2+8IUnq2bOnx/QFCxYoISHB9wUZ5Pjx4xo2bJiOHDmimjVrql27dlq1apX69OljdGkwyP/93/9pyJAhOnnypOrWravrr79e33//verWrWt0aQD+gNc2gHfxOrnq4XvSAQAAAAAwCc5JBwAAAADAJAjpAAAAAACYBCEdAAAAAACTIKQDAAAAAGAShHQAAAAAAEyCkA4AAAAAgEkQ0gEAAAAAMAlCOgAAAFBJ9OzZU2PHji32/ujoaKWkpPisHgDeR0gHUGEOHDggi8Wi7du3G10KAADVwqZNmzRy5EijyyiRDz74QK1atVJwcLDatm2rzz//3OiSAFMgpAMwXG5urtElAABQJdStW1chISEVOoY3+vb69es1ZMgQPfTQQ9q2bZsGDRqkQYMG6ccff/RChUDlRkgHqjCn06mXXnpJzZs3V1BQkKKiovT8889Lknbu3KlevXrJZrOpTp06GjlypM6fP+9etqjD6QYNGqSEhAT37ejoaL3wwgt68MEHFRYWpqioKL311lvu+5s2bSpJ6tixoywWi3r27ClJSkhI0KBBg/T888+rUaNGatmypZ555hm1adOm0GPo0KGDJkyY4KUtAgBA5ZeXl6ekpCTVrFlTERERmjBhglwul6TCh7tbLBbNmzdPt99+u0JCQhQTE6MVK1a478/Pz9dDDz2kpk2bymazqWXLlpo1a5bHeBXRt2fNmqX+/fvr8ccfV2xsrJ599llde+21mj17dhm3ClB1ENKBKmz8+PGaNm2aJkyYoF27dmnJkiWqX7++Lly4oH79+ql27dratGmTPvjgA3311VdKSkoq9RivvPKKOnfurG3btmnUqFH63//9X+3Zs0eStHHjRknSV199pSNHjujjjz92L5eamqo9e/Zo9erV+vTTT/Xggw8qPT1dmzZtcs+zbds2/fDDDxoxYkQ5twQAAFXHO++8I39/f23cuFGzZs3SjBkzNG/evGLnnzJliu655x798MMPGjBggO677z6dOnVK0qU39Js0aaIPPvhAu3bt0sSJE/XUU0/p/fff91iHt/t2Wlqaevfu7TGtX79+SktLK82mAKokf6MLAFAxzp07p1mzZmn27NkaPny4JOmaa67R9ddfr7ffflsXL17UokWLVKNGDUnS7NmzNXDgQL344ouqX79+iccZMGCARo0aJUl68sknNXPmTH399ddq2bKl6tatK0mqU6eOGjRo4LFcjRo1NG/ePAUGBrqn9evXTwsWLFCXLl0kSQsWLNCNN96oZs2alX1DAABQxURGRmrmzJmyWCxq2bKldu7cqZkzZyoxMbHI+RMSEjRkyBBJ0gsvvKBXX31VGzduVP/+/RUQEKApU6a4523atKnS0tL0/vvv65577nFP93bfPnr0aKHXG/Xr19fRo0dLviGAKopP0oEqKj09XTk5Obr55puLvK99+/bugC5J1113nZxOp/tT8JJq166d+/8Wi0UNGjTQ8ePHr7hc27ZtPRq9JCUmJmrp0qW6ePGicnNztWTJEj344IOlqgcAgKquW7duslgs7tvx8fHau3ev8vPzi5z/9726Ro0aCg8P9+jVc+bMUadOnVS3bl2FhobqrbfeUkZGhsc66NuA7/BJOlBF2Wy2ci1vtVrd57cVcDgcheYLCAjwuG2xWOR0Oq+4/t+/QVBg4MCBCgoK0ieffKLAwEA5HA7dddddpawcAAD83uV69bJly/TYY4/plVdeUXx8vMLCwjR9+nRt2LDBYxlv9+0GDRro2LFjHtOOHTtW6Mg7oDrik3SgioqJiZHNZlNqamqh+2JjY7Vjxw5duHDBPW3dunWyWq1q2bKlpEtXhz1y5Ij7/vz8/FJfcbXgHffi3tn/I39/fw0fPlwLFizQggULdO+995b7zQYAAKqaPwbo77//XjExMfLz8yv1utatW6fu3btr1KhR6tixo5o3b679+/eXaNny9O34+PhCr1FWr16t+Pj4Uj8GoKrhk3SgigoODtaTTz6pJ554QoGBgbruuut04sQJ/fTTT7rvvvs0adIkDR8+XJMnT9aJEyf0t7/9TQ888ID7/LBevXopOTlZn332ma655hrNmDFDZ86cKVUN9erVk81m08qVK9WkSRMFBwerZs2al13mL3/5i2JjYyVdeuEAAAA8ZWRkKDk5WQ8//LC2bt2q1157Ta+88kqZ1hUTE6NFixZp1apVatq0qd59911t2rTJ/Q0tV1LWvj1mzBjdeOONeuWVV/TnP/9Zy5Yt0+bNmz2+JQaorvgkHajCJkyYoEcffVQTJ05UbGysBg8erOPHjyskJESrVq3SqVOn1KVLF9111126+eabPb725MEHH9Tw4cM1bNgw90VgbrrpplKN7+/vr1dffVVvvvmmGjVqpNtuu+2Ky8TExKh79+5q1aqV4uLiSv2YAQCo6oYNGya73a6uXbtq9OjRGjNmjEaOHFmmdT388MO64447NHjwYMXFxenkyZPuC8KWRFn7dvfu3bVkyRK99dZbat++vT788EMtX768yK91A6obi+uPJ50CgIFcLpdiYmI0atQoJScnG10OAAC4DPo24H0c7g7ANE6cOKFly5bp6NGjfDc6AAAmR98GKgYhHYBp1KtXTxEREXrrrbdUu3Zto8sBAACXcbm+HRoaWuxyX3zxhW644YaKLg+otDjcHQAAAIBX7du3r9j7GjduzLe3AJdBSAcAAAAAwCS4ujsAAAAAACZBSAcAAAAAwCQI6QAAAAAAmAQhHQAAAAAAkyCkAwAAAABgEoR0AAAAAABMgpAOAAAAAIBJENIBAAAAADAJQjoAAAAAACZBSAcAAAAAwCQI6QAAAAAAmAQhHQAAAAAAkyCkAwAAAABgEoR0AAAAAABMwt/oAnzN6XTq8OHDCgsLk8ViMbocAADkcrl07tw5NWrUSFYr7597A/0eAGAmpen11S6kHz58WJGRkUaXAQBAIQcPHlSTJk2MLqNKoN8DAMyoJL2+2oX0sLAwSZc2Tnh4uMHVoCgOh0Nffvml+vbtq4CAAKPLASo99inzy8rKUmRkpLtHofzo9+bG8xLgfexX5laaXl/tQnrBIW/h4eE0bZNyOBwKCQlReHg4TzCAF7BPVR4clu099Htz43kJ8D72q8qhJL2eE98AAAAAADAJQjoAAAAAACZBSAcAAAAAwCSq3TnpAKovl8ulvLw85efnG12KTzkcDvn7++vixYvV7rGbhZ+fn/z9/TnnHAAqWHXt9RL93gwCAgLk5+dX7vUQ0gFUC7m5uTpy5Iiys7ONLsXnXC6XGjRooIMHDxISDRQSEqKGDRsqMDDQ6FIAoEqqzr1eot+bgcViUZMmTRQaGlqu9RDSAVR5TqdTv/76q/z8/NSoUSMFBgZWq+bldDp1/vx5hYaGymrlLCdfc7lcys3N1YkTJ/Trr78qJiaG3wMAeFl17/US/d5oLpdLJ06c0P/93/8pJiamXJ+oE9IBVHm5ublyOp2KjIxUSEiI0eX4nNPpVG5uroKDg2naBrHZbAoICNBvv/3m/l0AALynuvd6iX5vBnXr1tWBAwfkcDjKFdL57QGoNmhYMBJ/fwBQ8XiuhZG8dfQGf8UAAAAAAJgEIR0AAAAAAJMgpAMALqtnz54aO3asadYDAAC8j35vHlw4DgDgVWvWrNFNN92k06dPq1atWu7pH3/8sQICAowrDAAAeA39vuIQ0gEAPnHVVVcZXQIAAKhg9Pvy43B3ADCpnj17KikpSUlJSapZs6YiIiI0YcIEuVwuSdLp06c1bNgw1a5dWyEhIbrlllu0d+9e9/ILFy5UrVq1tHz5cnXq1EkhISHq16+fDh486J4nISFBgwYN8hh37Nix6tmzZ7F1vfvuu+rcubPCwsLUoEEDDR06VMePH5ckHThwQDfddJMkqXbt2rJYLEpISHA/nt8f/lbS+letWqXY2FiFhoaqf//+OnLkSFk2JwAApkS/p9//ESEdAEzsnXfekb+/vzZu3KhZs2ZpxowZmjdvnqRLDXfz5s1asWKF0tLS5HK5NGDAADkcDvfy2dnZmjp1qt544w19++23OnPmjO69995y1eRwOPTss89qx44dWr58uQ4cOOBuzJGRkfroo48kSXv27NGRI0c0a9asItdT0vpffvllvfvuu1q7dq0yMjL02GOPlat+AADMhn5Pv/89DncHABOLjIzUzJkzZbFY1LJlS+3cuVMzZ85Uz549tWLFCq1bt07du3eXJC1evFiRkZFavny57r77bkmXGuyrr76q2NhYhYeH65133lFsbKw2btyorl27lqmmBx980P3/Zs2a6dVXX1WXLl10/vx5hYaGug9zq1evnsc5ar+3d+/eEtc/d+5cXXPNNZKkpKQkPfPMM2WqGwAAs6Lf0+9/j0/SAcDEunXrJovF4r4dHx+vvXv3ateuXfL391dcXJz7vjp16qhly5ZKT093T/P391eXLl3ct1u1aqVatWp5zFNaW7Zs0cCBAxUVFaWwsDDdeOONkqSMjIwSryM9Pb1E9YeEhLgbtiQ1bNjQfagdAABVBf2efv97hHQAqMasVqv7nLcCvz/87I8uXLigfv36KTw8XIsXL9amTZv0ySefSJJyc3O9Xt8frw5rsVgK1QsAAC6Pfl+5ENIBwMQ2bNjgcfv7779XTEyMWrdurby8PI/7T548qT179qh169buaXl5edq8ebP79p49e3TmzBnFxsZKkurWrVvowizbt28vtp7du3fr5MmTmjZtmm644Qa1atWq0DvdgYGBkqT8/Pxi1xMbG1ui+gEAqA7o9/g9QjoAmFhGRoaSk5O1Z88eLV26VK+99prGjBmjmJgY3XbbbUpMTNR3332nHTt26P7771fjxo112223uZcPCAjQmDFjtHnzZm3ZskUJCQnq1q2b+/y0Xr16afPmzVq0aJH27t2rSZMm6ccffyy2nqioKAUGBuq1117TL7/8ohUrVujZZ5/1mOfqq6+WxWLRp59+qhMnTuj8+fOF1lPS+gEAqA7o9/g9QjoAmNiwYcNkt9vVtWtXjR49WmPGjNHIkSMlSQsWLFCnTp106623Kj4+Xi6XS59//rnHIWMhISF6/PHHlZiYqBtuuEGhoaF677333Pf369dPEyZM0BNPPKEuXbro3LlzGjZsWLH11K1bVwsXLtQHH3yg1q1ba9q0aXr55Zc95mncuLGmTJmicePGqX79+kpKSipyXSWpHwCA6oB+j9+zuKrZwf5ZWVmqWbOmzp49q/DwcKPLQREcDoc+//xzDRgwgJ0XXnHx4kX9+uuvatq0qYKDg40up8R69uypDh06KCUlpUzLL1y4UGPHjtWpU6eUlZWl8PBwWa28N2uUy/0d0pu8j21qbvR6eFtl7fUS/b4q8Vav57cHAAAAAIBJENIBAAAAADAJf6MLAAAUbc2aNeVaPiEhQQkJCXI6nd4pCAAAeB39Hn/EJ+kAAAAAAJgEIR0AAAAAAJMgpAMAAAAAYBKEdAAAAAAATIKQDgAAAACASXB1dwDVWkZGhjIzM302XkREhKKionw2HgAA1R29HpUNIR1AtZWRkaFWrWJlt2f7bEybLUS7d6ebrnlHR0dr7NixGjt2bLHz5ObmqnXr1lq0aJG6d+/uu+JMwGKx6JNPPtGgQYOKvH/Xrl3q27ev9uzZoxo1avi2OABAsej1/0Wvvzwz9XpCOoBqKzMzU3Z7tuIenKTwhtEVPl7WkQPaMH+KMjMzTde4S2Lu3Llq2rRppW7aBw4cUNOmTbVt2zZ16NDBa+tt3bq1unXrphkzZmjChAleWy8AoHzo9aVDry+eL3s9IR1AtRfeMFpXRbU0ugxTc7lcmj17tp555plyryc/P1/+/lWv/YwYMUKJiYkaP358lXx8AFCZ0euvjF5/Zb7q9Vw4DgBMrGfPnkpKSlJSUpJq1qypiIgITZgwQS6Xyz3P6dOnNWzYMNWuXVshISG65ZZbtHfvXo/1rFixQm3btlVQUJCio6P1yiuvlKqOLVu2aP/+/frzn//snnbgwAFZLBYtW7ZM3bt3V3BwsNq0aaNvvvnGPc+aNWtksVj0xRdfqFOnTgoKCtJ3330np9OpqVOnqmnTprLZbGrfvr0+/PDDQsutWrVKHTt2lM1mU69evXT8+HF98cUXio2NVXh4uIYOHars7P8ewrhy5Updf/31qlWrlurUqaNbb71V+/fvd9/ftGlTSVLHjh1lsVjUs2dP933z58/Xn/70JwUFBalhw4ZKSkry2AaZmZm6/fbbFRISopiYGK1YscLj/j59+ujUqVMejx8AgCvxVq//6KOPFB8fL5vNRq9X5e71hHQAMLl33nlH/v7+2rhxo2bNmqUZM2Zo3rx57vsTEhK0efNmrVixQmlpaXK5XBowYIAcDoekS013xIgRGjx4sHbu3KnJkydrwoQJWrhwYYlr+Pbbb9WiRQuFhYUVuu/xxx/Xo48+qm3btik+Pl4DBw7UyZMnPeYZN26cpk2bpvT0dLVr105Tp07VokWLNHfuXP3000965JFHdP/99xdqepMnT9bs2bO1fv16HTx4UPfcc49SUlK0ZMkSffbZZ/ryyy/12muvuee/cOGCkpOTtXnzZqWmpspqter222+X0+mUJG3cuFGS9NVXX+nIkSP6+OOPJUlvvPGGRo8erZEjR2rnzp1asWKFmjdv7lHLlClTdM899+iHH37QgAEDdN999+nUqVPu+wMDA9WhQwd9++23Jd6uAABI3un19957r+644w7t2LGDXq/K3eur3jEIAFDFREZGaubMmbJYLGrZsqV27typmTNnKjExUXv37tWKFSu0bt069/ljixcvVmRkpJYvX667775bM2fO1I033qinn35aVqtVLVq00K5duzR9+nQlJCSUqIbffvtNjRo1KvK+pKQk3XnnnZIuNcCVK1fqH//4h5544gn3PM8884z69OkjScrJydELL7ygr776SvHx8ZKkZs2a6bvvvtObb76pG2+80b3cc889p+uuu06S9NBDD2n8+PHav3+/mjVrJkm666679PXXX+vJJ5+UJHcdBebPn6+6detq165datOmjerWrStJqlOnjho0aOAxzqOPPqoxY8a4p3Xp0sVjXQkJCRoyZIgk6YUXXtCrr76qjRs3qn///u55GjVqpN9+++3yGxMAgD8ob6+fMWOGevXqpccff1zh4eFq1aoVvb4S93rDP0mfM2eOoqOjFRwcrLi4OPc7H8VJSUlRy5YtZbPZFBkZqUceeUQXL170UbUA4HvdunWTxWJx346Pj9fevXuVn5+v9PR0+fv7Ky4uzn1/nTp11LJlS6Wnp0uSdu/e7XG/JF133XXudZSE3W5XcHBwkfcVNF9J8vf3V+fOnd1jF+jcubP7//v27VN2drb69Omj0NBQ98+iRYs8DleTpHbt2rn/X79+fYWEhLibdsG048ePu2/v3btXQ4YMUbNmzRQeHq7o6GhJl67uW5zjx4/r8OHDuvnmmy+zBTxrqVGjhsLDwz3GliSbzeZxSB7+i34PAMUrb69PT093B90C9Pr/qmy93tBP0t977z0lJydr7ty5iouLU0pKivr166c9e/aoXr16heZfsmSJxo0bp/nz56t79+76+eeflZCQIIvFohkzZhjwCACgeoiIiNDOnTvLvPzvv6rk/PnzkqTPPvtMjRs39pgvKCjI43ZAQID7/xaLxeN2wbSCw9skaeDAgbr66qv19ttvq1GjRnI6nWrTpo1yc3OLrc1ms5XoMVxpbEk6deqUrrnmmhKtrzqh3wOA+dHrzdPrDf0kfcaMGUpMTNSIESPUunVrzZ07VyEhIZo/f36R869fv17XXXedhg4dqujoaPXt21dDhgy54rvxAFCZbdiwweP2999/r5iYGPn5+Sk2NlZ5eXke85w8eVJ79uxR69atJUmtWrUqtI5169apRYsW8vPzK1ENHTt21O7duz0uYvP7egrk5eVpy5Ytio2NLXZdrVu3VlBQkDIyMtS8eXOPn8jIyBLVU5SCx/3000/r5ptvVmxsrE6fPu0xT2BgoCR5fKoQFham6OhopaamlnnsAj/++KM6duxY7vVUNfR7ALi88vb62NhYrVu3zmMd9PrK2+sN+yQ9NzdXW7Zs0fjx493TrFarevfurbS0tCKX6d69u/75z39q48aN6tq1q3755Rd9/vnneuCBB4odJycnRzk5Oe7bWVlZkiSHw+G+0ALMpeD3wu8H3uJwOORyueR0Oj3eDS34f9bhA1LhfuR1WUcOuMf947uyl5ORkaFHHnlEI0eO1NatW/Xaa69p+vTpcjqduuaaa/Q///M/SkxM1BtvvKGwsDCNHz9ejRs31sCBA+V0OvXII4+oW7duevbZZzV48GClpaVp9uzZmj17tkcdBduoKDfeeKPOnz+vnTt3qk2bNu7HIV06jPmaa65RbGysUlJSdPr0aSUkJHg8zt//v0aNGnr00Uf1yCOPKC8vT9dff73Onj2r9evXKywsTMOHDy9yuT/+W1BzwbSaNWuqTp06evPNN1W/fn1lZGToqaee8lhPRESEbDabvvjiCzVq1EjBwcGqWbOmJk6cqFGjRqlu3brq37+/zp07p/Xr13tc9bWo39vvpx04cECHDh1Sr169ityOTqdTLpdLDoej0Aumqvx8R79HUej18DZ6/SOKi4vT9OnTdf/99+v777+n11fiXm9YSM/MzFR+fr7q16/vMb1+/fravXt3kcsMHTpUmZmZuv766+VyuZSXl6e//vWv7l9MUaZOnaopU6YUmv7ll18qJCSkfA8CFWr16tVGl4Aqwt/fXw0aNND58+c9DoUKCgpSsM2mDQsKP0dUlGCbTUFBQe4AcSV5eXkaPHiwzp49q7i4OPn5+enhhx/Wvffe617HrFmzNG7cOA0cOFAOh0Pdu3fXsmXLZLfbZbfbFRMTowULFmjq1Kl6/vnnVb9+fY0fP1533HGHex1Op1MXL14stq6AgADdeuutWrBggSZNmiTpv4eyTZgwQVOnTtXOnTvVrFkzLVmyRIGBgcrKynKfs3Xu3DlZrf89eOuxxx5TWFiYpk6dqgMHDqhmzZpq3769HnnkkWKXu3jxolwul0eNOTk5ys/Pd0+bN2+exo0bp3bt2ql58+Z68cUXdeutt8put7vnmTZtml566SVNmjRJ8fHx+vTTT3X77bfrzJkzmjNnjh5//HHVqVNH//M//+Mx1u/XIV160fD7bbZw4UL16tVLtWvXLnI75ubmym63a+3atcrLy/O4ryqfx06/x+XQ6+Et1b3XN2/e3N3rp0+fTq+v5L3e4irqeAYfOHz4sBo3bqz169d7XIjgiSee0DfffFPokA/p0nfp3XvvvXruuecUFxenffv2acyYMUpMTNSECROKHKeod9YjIyOVmZmp8PBw7z8wlJvD4dDq1avVp0+fQueFAGVx8eJFHTx40H3Rqt/LyMhQZmamz2qJiIhQVFRUiefv1auX2rdvr5kzZ5Z5TJfLpXPnziksLMzjojSl9cMPP6hfv37au3evQkNDdeDAAV1zzTXasmWLOnToUOb1VgW5ublq2bKl/vnPfxa6cE+Bixcv6sCBA4qMjCz0d5iVlaWIiAidPXu2yvUm+j2KQq+Ht1X3Xi95p9/T64vny15v2CfpERER8vPz07FjxzymHzt2zONS+b83YcIEPfDAA/rLX/4iSWrbtq0uXLigkSNH6v/7//4/j3duCgQFBRW6OIF06Z0imoK58TuCt+Tn58tischqtRZ6noiOjnZfFdSsCmovq4LDscq7ng4dOujFF1/Ub7/9prZt27rXVdR2rW7+7//+T0899ZRuuOGGYuexWq3uC+L88bmtKj/X0e9xOfx+4C3VvddL3un39Pri+bLXG7alAwMD1alTJ4+T951Op1JTUz3eaf+97OzsQn8cBcf6G3RAAABUKwkJCWrbtq3RZZhO8+bN9fDDDxtdhinR7wGgcqHXF82Xvd7Qr2BLTk7W8OHD1blzZ3Xt2lUpKSm6cOGCRowYIUkaNmyYGjdurKlTp0q6dLn9GTNmqGPHju7D3yZMmKCBAweW+KqFAFCZrFmzxugSihUdHU1gQonQ7wGgePR6/JGhIX3w4ME6ceKEJk6cqKNHj6pDhw5auXKl++IyGRkZHu+kP/3007JYLHr66ad16NAh1a1bVwMHDtTzzz9v1EMAAABXQL8HAKDkDLtwnFGysrJUs2bNKnlxnqrC4XDo888/14ABAzhPDV5x8eJF/frrr2ratGmhi3hUB06nU1lZWQoPD6/255MZ6XJ/h/Qm72Obmhu9Ht5W3Xu9RL83A2/1en57AAAAAACYBCEdAAAAAACTIKQDAAAAAGAShl44DgCMlpGRoczMTJ+NFxERoaioKJ+NBwBAdUevR2VDSAdQbWVkZCi2VUtl2y/6bMwQW7DSd+8xVfOOjo7W2LFjNXbsWKNLAQDAq+j1l9DrKxdCOoBqKzMzU9n2i/rnyA6KbRha4eOlHzmv+9/arszMTFM1bgAAqip6PSojQjqAai+2Yaiuja5pdBkAAKCC0OtRmXDhOAAwsZ49eyopKUlJSUmqWbOmIiIiNGHCBLlcLknS6dOnNWzYMNWuXVshISG65ZZbtHfvXo91fPTRR4qPj5fNZlN0dLReeeUVIx4KAAAogrd6fdu2bVW/fn01a9aMXl/JEdIBwOTeeecd+fv7a+PGjZo1a5ZmzJihefPmSZISEhK0efNmrVixQmlpaXK5XBowYIAcDockacuWLbr33nt1xx13aMeOHZo8ebImTJighQsXGviIAADA75W3199zzz0aPHiw1q1bp4kTJ9LrKzkOdwcAk4uMjNTMmTNlsVjUsmVL7dy5UzNnzlTPnj21YsUKrVu3Tt27d5ckLV68WJGRkVq+fLnuvvtuzZgxQ7169dLjjz+u8PBwtWrVSrt27dL06dOVkJBg7AMDAACSyt/rb775Zj399NPKysrStddeq927d9PrKzE+SQcAk+vWrZssFov7dnx8vPbu3atdu3bJ399fcXFx7vvq1Kmjli1bKj09XZKUnp6u6667zmN91113nfbu3av8/HzfPAAAAHBZ9Hr8HiEdAAAAAACTIKQDgMlt2LDB4/b333+vmJgYtW7dWnl5eR73nzx5Unv27FHr1q0lSbGxsVq3bp3H8uvWrVOLFi3k5+dX8cUDAIArotfj9zgnHUC1l37kvKnHycjIUHJysh5++GFt3bpVr732ml555RXFxMTotttuU2Jiot58802FhYVp3Lhxaty4sW677TZJ0qOPPqouXbpo+vTpeuCBB7RhwwbNnj1br7/+ujcfGgAAplYdev1zzz2nAQMGaOfOnfT6So6QDqDaioiIUIgtWPe/td1nY4bYghUREVGqZYYNGya73a6uXbvKz89PY8aM0ciRIyVJCxYs0JgxY3TrrbcqNzdXPXr00Oeff66AgABJ0rXXXqtly5Zp4sSJmj59uho2bKhnnnmGC8kAAKqF6tLr33//fU2cOFHPPfccvb4KIKQDqLaioqKUvnuPMjMzfTZmRESEoqKiSrVMQECAUlJS9MYbbxS6r3bt2lq0aNFll7/zzjvVp08fhYeHy2otfJbTgQMHSlUPAACVRXXq9bfffruysrKK7Pf0+sqFkA6gWouKiip1IwUAAJUHvR6VDReOAwAAAADAJPgkHQBMbM2aNUaXAAAAKhC9Hn/EJ+kAAAAAAJgEIR0AAAAAAJMgpAMAAAAAYBKEdAAAAAAATIKQDgAAAACASXB1dwDVWkZGhjIzM302XkREBN/VCgCAD9HrUdkQ0gFUWxkZGWoV20r2bLvPxrSF2LQ7fXeJm3fPnj3VoUMHpaSkVGxhJWS2egB4jy+DjNPplCTt2LFDVqvvDuzMyclRUFCQz8YzAgHRE72+9MxWT3VESAdQbWVmZsqebVePJ3qoZmTNCh/v7MGzWvvSWmVmZvr0BVRubq7PxgJQOWVkZCi2VUtl2y/6ZDybzaalS5eqR48estt9F56sFsnp8tlwhihtQKzqqlOv9/cn2lUV/CbLydeHzxiBd2RR1dWMrKmImAijyygkISFB33zzjb755hvNmjVLkrRv3z698MIL+s9//qOjR48qKipKo0aN0pgxYzyWO3PmjLp06aI5c+YoKChI27Zt0/r165WUlKTdu3erTZs2evrpp3X77bdr27Zt6tChgyTpxx9/1OOPP65vv/1WNWrUUN++fTVz5kxFREQUWc+vv/6q6OhoX28aAF6WmZmpbPtF/XNkB8U2DK3w8ZzWQB2StHZcvKxO37yRuPWXE0p8d486DuuoJl2a+GRMXzMqIFYG1aHX79+/Xxs2bNCTTz5Jr6/kCOnlkJGRoVatYmW3ZxtdSoWy2UK0e3c6T/aAj82aNUs///yz2rRpo2eeeUaSVLt2bTVp0kQffPCB6tSpo/Xr12vkyJFq2LCh7rnnHveyqampCg8P1+rVq+V0OpWVlaXbbrtNAwYM0JIlS/Tbb79p7NixHuOdOXNGvXr10l/+8hfNnDlTdrtdTz75pO655x795z//KbKeunXr+mx7AKh4sQ1DdW10xX/a6FCADklqHxWuADkqfDxJupB9QZIU2iDUlGEN1ZO3er0kZWVlaciQIfT6KoCQXg6ZmZmy27MV9+AkhTeMNrqcCpF15IA2zJ/CO7KAAWrWrKnAwECFhISoQYMG7ulTpkxx/79p06ZKS0vT+++/79G4a9SooXnz5ikwMFBOp1MpKSmyWCx6++23FRwcrNatW+vQoUNKTEx0LzN79mx17NhRL7zwgnva/PnzFRkZqZ9//lktWrQosh4AAFA23ur1kvT666/LYrHorbfeUkhICL2+EiOke0F4w2hdFdXS6DIAVBNz5szR/PnzlZGRIbvdrtzcXPchbAXatm3rbtrSpUPn2rVrp+DgYPe0rl27eiyzY8cOff311woNLXyo6/79+9WiRQvvPhAAAFCksvT6n3/+WX/605/o9VUAIR0AKpFly5bpscce0yuvvKL4+HiFhYVp+vTp2rBhg8d8NWrUKPW6z58/r4EDB+rFF18sdF/Dhg3LXDMAACg5ej0I6QBgYoGBgcrPz3ffXrdunbp3765Ro0a5p+3fv/+K62nevLk++OADj68f2rRpk8c81157rT766CNFR0cXe4XYP9YDAADKx1u9vkWLFvrnP/+pnJwc2Ww2SfT6ysp3X0wJACi16OhobdiwQQcOHFBmZqZiYmK0efNmrVq1Sj///LMmTJhQqAEX5a677pLT6dTIkSOVnp6uVatW6eWXX5YkWSwWSdLo0aN16tQpDRkyRJs2bdL+/fu1atUqjRgxwt2s/1hPwXcdAwCAsvFWrx86dKicTqcefvhhen0lxyfpAKq9swfPmnacxx57TMOHD1fr1q1lt9u1e/dubdu2TYMHD5bFYtGQIUM0atQoffHFF5ddT3h4uP71r39p9OjR6tChg9q2bauJEydq6NCh7nPXGjVqpHXr1unJJ59U3759lZOTo6uvvlr9+/eX1Wotsh6+lgUAUBlUl16/dOlSPfHEE/T6So6QDqDaioiIkC3EprUvrfXZmLYQmyIiSv7VPy1atFBaWprHtAULFmjBggUe06ZOner+/8KFC4tcV/fu3bVjxw737cWLFysgIMDjmxtiYmL08ccfl6oeAADMqrr1+ri4OG3bts0duOn1lRMhHUC1FRUVpd3pu5WZmemzMSMiIgz7OsNFixapefPmaty4sXbs2OH+XtSC89YAAKhqqluvX7ZsmVq3bq3IyEh6fSVGSAdQrUVFRRnWSH3t2LFjmjx5so4ePaqGDRvq7rvv1vPPP290WQAAVKjq1uunTZtGr6/kCOkAUE08/vjjevLJJ40uAwAAVJAxY8ZowoQJ7sPdUTnx2wMAAAAAwCQI6QAAAAAAmAQhHUC14XK5jC4B1Rh/fwBQ8XiuhZG89fdHSAdQ5QUEBEiSsrOzDa4E1VnB31/B3yMAwHvo9TCD3NxcSZKfn1+51sOF4wBUeX5+fqpVq5aOHz8uSQoJCZHFYjG4Kt9xOp3Kzc3VxYsXuZCMAVwul7Kzs3X8+HHVqlWr3I0bAFBYde/1Ev3eaE6nUydOnFBISIj8/csXswnpAKqFBg0aSJK7eVcnLpdLdrtdNput2r1gMZNatWq5/w4BAN5XnXu9RL83A6vVqqioqHJvf0I6gGrBYrGoYcOGqlevnhwOh9Hl+JTD4dDatWvVo0cPDrU2SEBAAJ+gA0AFq869XqLfm0FgYKBXjmIgpAOoVvz8/KpdWPLz81NeXp6Cg4Np2gCAKq869nqJfl+VcLICAAAAAAAmQUgHAAAAAMAkCOkAAAAAAJgEIR0AAAAAAJMgpAMAAAAAYBKEdAAAAAAATMLwkD5nzhxFR0crODhYcXFx2rhx42XnP3PmjEaPHq2GDRsqKChILVq00Oeff+6jagEAQFnQ7wEAKBlDvyf9vffeU3JysubOnau4uDilpKSoX79+2rNnj+rVq1do/tzcXPXp00f16tXThx9+qMaNG+u3335TrVq1fF88AAAoEfo9AAAlZ2hInzFjhhITEzVixAhJ0ty5c/XZZ59p/vz5GjduXKH558+fr1OnTmn9+vUKCAiQJEVHR/uyZAAAUEr0ewAASs6wkJ6bm6stW7Zo/Pjx7mlWq1W9e/dWWlpakcusWLFC8fHxGj16tP71r3+pbt26Gjp0qJ588kn5+fkVuUxOTo5ycnLct7OysiRJDodDDoejXI/B6XTKZrMpwCr5W5zlWpdZBVglm80mp9NZ7u1VUgXj+Go8oKpjnzK/qvy7qQr9vjooeE3jtAbKoYAKH69gDF+MVcDlFyibzaZAv0D5uwz9nKrCBFgCfP66DeZBvze30vxeDHuGyszMVH5+vurXr+8xvX79+tq9e3eRy/zyyy/6z3/+o/vuu0+ff/659u3bp1GjRsnhcGjSpElFLjN16lRNmTKl0PQvv/xSISEh5X4cS5cu/X//yyz3ukypXm2NXLpUhw4d0qFDh3w69OrVq306HlDVsU+ZV3Z2ttElVJiq0u+rg6VLl+qQJF92+9W17/PdYN2kpd3+3/9zLjtn5dVEGrZ0mCGv22Ae9HtzKk2vr1RvIzqdTtWrV09vvfWW/Pz81KlTJx06dEjTp08vtmmPHz9eycnJ7ttZWVmKjIxU3759FR4eXq56duzYoR49euimx15X7ciYcq3LrE4f3KuvXx6ltWvXqn379j4Z0+FwaPXq1erTp4/7MEcAZcc+ZX4Fn/riErP1++qg4DXN2nHxah9V8dvLoQCtrn2f+pxerAD55lO/tD1H1H/WD+o+trua9WjmkzF97eT+k/ri8S98+roN5kG/N7fS9HrDQnpERIT8/Px07Ngxj+nHjh1TgwYNilymYcOGCggI8DjULTY2VkePHlVubq4CAwMLLRMUFKSgoKBC0wMCAsr9x2u1WmW32+VwSnkuwy+UXyEcTslut8tqtfp8Z/fG7wjAf7FPmVdV/r1UhX5fHRS8prE6c30WmiUpQA6fjWfJz5Xdbldufq7yLHk+GdPXHC6HYa/bYB4875lTaX4nhiXLwMBAderUSampqe5pTqdTqampio+PL3KZ6667Tvv27ZPT+d/zv3/++Wc1bNiwyIYNAACMRb8HAKB0DP34Nzk5WW+//bbeeecdpaen63//93914cIF99Vfhw0b5nGhmf/93//VqVOnNGbMGP3888/67LPP9MILL2j06NFGPQQAAHAF9HsAAErO0HPSBw8erBMnTmjixIk6evSoOnTooJUrV7ovLpORkSGr9b/vI0RGRmrVqlV65JFH1K5dOzVu3FhjxozRk08+adRDAAAAV0C/BwCg5Ay/cFxSUpKSkpKKvG/NmjWFpsXHx+v777+v4KoAAIA30e8BACiZqnm1MwAAAAAAKiFCOgAAAAAAJlGmkP711197uw4AAGAy9HsAAHyvTCG9f//+uuaaa/Tcc8/p4MGD3q4JAACYAP0eAADfK1NIP3TokJKSkvThhx+qWbNm6tevn95//33l5uZ6uz4AAGAQ+j0AAL5XppAeERGhRx55RNu3b9eGDRvUokULjRo1So0aNdLf//537dixw9t1AgAAH6PfAwDge+W+cNy1116r8ePHKykpSefPn9f8+fPVqVMn3XDDDfrpp5+8USMAADAY/R4AAN8oc0h3OBz68MMPNWDAAF199dVatWqVZs+erWPHjmnfvn26+uqrdffdd3uzVgAA4GP0ewAAfMu/LAv97W9/09KlS+VyufTAAw/opZdeUps2bdz316hRQy+//LIaNWrktUIBAIBv0e8BAPC9MoX0Xbt26bXXXtMdd9yhoKCgIueJiIjgq1sAAKjE6PcAAPhemQ53nzRpku6+++5CDTsvL09r166VJPn7++vGG28sf4UAAMAQ9HsAAHyvTCH9pptu0qlTpwpNP3v2rG666aZyFwUAAIxHvwcAwPfKFNJdLpcsFkuh6SdPnlSNGjXKXRQAADAe/R4AAN8r1Tnpd9xxhyTJYrEoISHB4/C3/Px8/fDDD+revbt3KwQAAD5FvwcAwDilCuk1a9aUdOmd9bCwMNlsNvd9gYGB6tatmxITE71bIQAA8Cn6PQAAxilVSF+wYIEkKTo6Wo899hiHugEAUAXR7wEAME6ZvoJt0qRJ3q4DAACYDP0eAADfK3FIv/baa5WamqratWurY8eORV5IpsDWrVu9UhwAVFUZGRnKzMz0yVhOp1OStGPHDlmtZbpeaJnk5OQU+93aVUVERISioqKMLsOr6PcAABirxCH9tttuc7/YGjRoUEXVAwBVXkZGhmJbtVS2/aJPxrPZbFq6dKl69Oghu93ukzElyWqRnC6fDWcIW4hNu9N3V6mgTr8HAMBYJQ7pvz/kjcPfAKDsMjMzlW2/qH+O7KDYhqEVPp7TGqhDktaOi5fVmVvh40nS1l9OKPHdPeo4rKOadGnikzF97ezBs1r70lplZmZWqZBOvwcAwFhlOicdAFB+sQ1DdW10zQofx6EAHZLUPipcAXJU+HiSdCH7giQptEGoImIifDImAABAVVDikF67du3Lnpf2e6dOnSpzQQAAwDj0ewAAjFXikJ6SklKBZQAAADOg3wMAYKwSh/Thw4dXZB0AAMAE6PcAfMGX33JilKr4DSDwjRKH9KysLIWHh7v/fzkF8wEAgMqFfg+gomVkZKhVq1jZ7dlGl1KhbLYQ7d6dTlBHqZXqnPQjR46oXr16qlWrVpHnq7lcLlksFuXn53u1SAAA4Bv0ewAVLTMzU3Z7tuIenKTwhtFGl1Mhso4c0Ib5U6rcN4DAN0oc0v/zn//oqquukiR9/fXXFVYQAAAwDv2+aFX90Nz09HSjS0A1FN4wWldFtTS6DMB0ShzSb7zxxiL/DwAAqg76fWHV5dBcyaIjZy9KqvivhgQAFK/M35N++vRp/eMf/3C/89q6dWuNGDHC/e47AACo/Oj31ePQ3BO/7tb2JS/qTHae0aUAQLVXppC+du1aDRw4UDVr1lTnzp0lSa+++qqeeeYZ/fvf/1aPHj28WiQAAPA9+r2nqnxorv1CVT9KAAAqjzKF9NGjR2vw4MF644035OfnJ0nKz8/XqFGjNHr0aO3cudOrRQIAAN+j3wMA4HvWsiy0b98+Pfroo+6GLUl+fn5KTk7Wvn37vFYcAAAwDv0eAADfK9Mn6ddee63S09PVsqXnIV/p6elq3769VwqDufjyqq9Op1OStGPHDlmtZXofqdRycnIUFBTkk7GMEhERwVeAACgV+j0AAL5X4pD+ww8/uP//97//XWPGjNG+ffvUrVs3SdL333+vOXPmaNq0ad6vEoZx5ORKku6//36fjWmz2bR06VL16NFDdrvdJ2NaLZLT5ZOhDGMLsWl3+m6COoDLot8DAGCsEof0Dh06yGKxyOX6b5J54oknCs03dOhQDR482DvVwXB5eZeu8vrsHS00oG09n4zptAbqkKS14+JldeZW+HhbfzmhxHf3qOOwjmrSpUmFj2eEswfPau1La5WZmUlIB3BZ9HsAAIxV4pD+66+/VmQdMLmmESG6Nto335vqUIAOSWofFa4AOSp8vAvZFyRJoQ1CFRETUeHjAYCZ0e8BADBWiUP61VdfXZF1AAAAE6DfAwBgrDJdOK7Arl27lJGRodxcz0OS/+d//qdcRQEAAPOg3wMA4DtlCum//PKLbr/9du3cudPjvDWLxSLp0neoAgCAyo1+DwCA75Xp+63GjBmjpk2b6vjx4woJCdFPP/2ktWvXqnPnzlqzZo2XSwQAAEag3wMA4Htl+iQ9LS1N//nPfxQRESGr1Sqr1arrr79eU6dO1d///ndt27bN23UCAAAfo98DAOB7ZfokPT8/X2FhYZKkiIgIHT58WNKli83s2bPHe9UBAADD0O8BAPC9Mn2S3qZNG+3YsUNNmzZVXFycXnrpJQUGBuqtt95Ss2bNvF0jAAAwAP0eAADfK1NIf/rpp3XhwqXvln7mmWd066236oYbblCdOnX03nvvebVAAABgDPo9AAC+V6aQ3q9fP/f/mzdvrt27d+vUqVOqXbu2+4qvAACgcqPfAwDge+X6nnRJOnjwoCQpMjKy3MUAAABzot8DAOAbZbpwXF5eniZMmKCaNWsqOjpa0dHRqlmzpp5++mk5HA5v1wgAAAxAvwcAwPfK9En63/72N3388cd66aWXFB8fL+nS17RMnjxZJ0+e1BtvvOHVIgEAgO/R7wEA8L0yhfQlS5Zo2bJluuWWW9zT2rVrp8jISA0ZMoSmDQBAFUC/BwDA98p0uHtQUJCio6MLTW/atKkCAwPLWxMAADAB+j0AAL5XppCelJSkZ599Vjk5Oe5pOTk5ev7555WUlOS14gAAgHHo9wAA+F6JD3e/4447PG5/9dVXatKkidq3by9J2rFjh3Jzc3XzzTd7t0IAAOAz9HsAAIxV4pBes2ZNj9t33nmnx22+kgUAgMqPfg8AgLFKHNIXLFhQkXUAAAAToN8DAGCsMp2TXuDEiRP67rvv9N133+nEiRNlXs+cOXMUHR2t4OBgxcXFaePGjSVabtmyZbJYLBo0aFCZxwYAAJfnjX5PrwcAoGTKFNIvXLigBx98UA0bNlSPHj3Uo0cPNWrUSA899JCys7NLta733ntPycnJmjRpkrZu3ar27durX79+On78+GWXO3DggB577DHdcMMNZXkIAADgCrzV7+n1AACUXJlCenJysr755hv9+9//1pkzZ3TmzBn961//0jfffKNHH320VOuaMWOGEhMTNWLECLVu3Vpz585VSEiI5s+fX+wy+fn5uu+++zRlyhQ1a9asLA8BAABcgbf6Pb0eAICSK/E56b/30Ucf6cMPP1TPnj3d0wYMGCCbzaZ77rlHb7zxRonWk5ubqy1btmj8+PHuaVarVb1791ZaWlqxyz3zzDOqV6+eHnroIX377beXHSMnJ8fjq2OysrIkSQ6HQw6Ho0R1FsfpdMpmsynAKvlbnOVal1kF+llks9kk/yA5FOCTMQvG8dV4Lr9A2Ww2BfoFyt9Vpl3C9AIsAbLZbHI6neX+u0f5FTx3OK2BPvk79/U+JbFflZZZ90tv9Htf9HqJfl9evu73PC9VjMrS76vDPhVglc9/FwXjmPl3X52V5vdicblcrtIOEBISoi1btig2NtZj+k8//aSuXbvqwoULJVrP4cOH1bhxY61fv17x8fHu6U888YS++eYbbdiwodAy3333ne69915t375dERERSkhI0JkzZ7R8+fIix5g8ebKmTJlSaPqSJUsUEhJSojoBAKhI2dnZGjp0qM6ePavw8HCjy3HzRr/3Ra+X6PcAAHMrTa8v09uI8fHxmjRpkhYtWqTg4GBJkt1u15QpUzwasLedO3dODzzwgN5++21FRESUaJnx48crOTnZfTsrK0uRkZHq27dvuV8I7dixQz169NBNj72u2pEx5VqXWR3Z84PSXn9Ub49op3u6NPTJmA4FaHXt+9Tn9GIFqOLfCUzbc0T9Z/2g7mO7q1mPqnlI5cn9J/XF419o7dq17u86hnEKnjvWjotX+6iKD2S+3qck9qvSKvjU12yM6Pdl6fUS/b68fN3veV6qGJWl31eHfer0wb36+uVRPv1dOBwOrV69Wn369FFAgO+OUkHJlKbXlymkp6SkqH///mrSpIn7j27Hjh0KDg7WqlWrSryeiIgI+fn56dixYx7Tjx07pgYNGhSaf//+/Tpw4IAGDhzonuZ0XjpExt/fX3v27NE111zjsUxQUJCCgoIKrSsgIKDcf7xWq1V2u10Op5TnKteF8k0rN98lu90u5eX4rIkWCJDDJ2Na8nNlt9uVm5+rPEtehY9nBIfLIbvdLqvVypO2CRQ8d1iduT7dr3y1T0nsV6Vl1v3SG/3eF71eot+Xl1H9nucl76os/b467FMOpwz7XXjjeQ/eV5rfSZlCetu2bbV3714tXrxYu3fvliQNGTJE991336XzmUooMDBQnTp1UmpqqvurVZxOp1JTU5WUlFRo/latWmnnzp0e055++mmdO3dOs2bNUmRkZFkeDgAAKII3+j29HgCA0il1SHc4HGrVqpU+/fRTJSYmlruA5ORkDR8+XJ07d1bXrl2VkpKiCxcuaMSIEZKkYcOGqXHjxpo6daqCg4PVpk0bj+Vr1aolSYWmAwCAsvNmv6fXAwBQcqUO6QEBAbp48aLXChg8eLBOnDihiRMn6ujRo+rQoYNWrlyp+vXrS5IyMjJktVbNw2AAADArb/Z7ej0AACVXpsPdR48erRdffFHz5s2Tv3/5v8IiKSmpyEPeJGnNmjWXXXbhwoXlHh8AABTmzX5PrwcAoGTK1HE3bdqk1NRUffnll2rbtq1q1Kjhcf/HH3/sleIAAIBx6PcAAPhemUJ6rVq1dOedd3q7FgAAYCL0ewAAfK9UId3pdGr69On6+eeflZubq169emny5MmluqI7AAAwN/o9AADGKdVVWp5//nk99dRTCg0NVePGjfXqq69q9OjRFVUbAAAwAP0eAADjlCqkL1q0SK+//rpWrVql5cuX69///rcWL14sp9NZUfUBAAAfo98DAGCcUoX0jIwMDRgwwH27d+/eslgsOnz4sNcLAwAAxqDfAwBgnFKF9Ly8PAUHB3tMCwgIkMPh8GpRAADAOPR7AACMU6oLx7lcLiUkJCgoKMg97eLFi/rrX//q8bUsfCULAACVF/0eAADjlCqkDx8+vNC0+++/32vFAAAA49HvAQAwTqlC+oIFCyqqDgAAYBL0ewAAjFOqc9IBAAAAAEDFIaQDAAAAAGAShHQAAAAAAEyCkA4AAAAAgEkQ0gEAAAAAMAlCOgAAAAAAJkFIBwAAAADAJAjpAAAAAACYBCEdAAAAAACTIKQDAAAAAGAShHQAAAAAAEzC3+gCAOCPMjIylJmZaXQZFSY9Pd3oEgAAAGBShHQAppKRkaFWrWJlt2cbXUoFs+jI2YuSahpdCAAAAEyEkA7AVDIzM2W3ZyvuwUkKbxhtdDkV4sSvu7V9yYs6k51ndCkAAKAC+fLoOafTKUnasWOHrFbfnNWck5OjoKAgn4xllIiICEVFRfl0TEI6AFMKbxitq6JaGl1GhbBfqOpHCQAAUL05cnIlSffff7/PxrTZbFq6dKl69Oghu93ukzGtFsnp8slQhrGF2LQ7fbdPgzohHQAAAAC8KC/v0tFyz97RQgPa1vPJmE5roA5JWjsuXlZnboWPt/WXE0p8d486DuuoJl2aVPh4Rjh78KzWvrRWmZmZhHQAAAAAqOyaRoTo2mjfXH/GoQAdktQ+KlwBclT4eBeyL0iSQhuEKiImosLHq074CjYAAAAAAEyCkA4AAAAAgEkQ0gEAAAAAMAlCOgAAAAAAJkFIBwAAAADAJAjpAAAAAACYBCEdAAAAAACTIKQDAAAAAGAShHQAAAAAAEyCkA4AAAAAgEkQ0gEAAAAAMAlCOgAAAAAAJkFIBwAAAADAJAjpAAAAAACYBCEdAAAAAACTIKQDAAAAAGAShHQAAAAAAEyCkA4AAAAAgEkQ0gEAAAAAMAlCOgAAAAAAJkFIBwAAAADAJAjpAAAAAACYBCEdAAAAAACTIKQDAAAAAGAShHQAAAAAAEyCkA4AAAAAgEmYIqTPmTNH0dHRCg4OVlxcnDZu3FjsvG+//bZuuOEG1a5dW7Vr11bv3r0vOz8AADAevR4AgJIxPKS/9957Sk5O1qRJk7R161a1b99e/fr10/Hjx4ucf82aNRoyZIi+/vprpaWlKTIyUn379tWhQ4d8XDkAACgJej0AACVneEifMWOGEhMTNWLECLVu3Vpz585VSEiI5s+fX+T8ixcv1qhRo9ShQwe1atVK8+bNk9PpVGpqqo8rBwAAJUGvBwCg5PyNHDw3N1dbtmzR+PHj3dOsVqt69+6ttLS0Eq0jOztbDodDV111VZH35+TkKCcnx307KytLkuRwOORwOMpRveR0OmWz2RRglfwtznKty6wC/Syy2WySf5AcCvDJmAXj+Go8l1+gbDabAv0C5e8ydJeoMAGWANlsNjmdznL/3Vc09ivv8/U+JbFflZbZ98vy8EWvl+j35cXzUtVQWfo9+1TF4DW09xnV6y0ul8tVrtHK4fDhw2rcuLHWr1+v+Ph49/QnnnhC33zzjTZs2HDFdYwaNUqrVq3STz/9pODg4EL3T548WVOmTCk0fcmSJQoJCSnfAwAAwAuys7M1dOhQnT17VuHh4UaX41W+6PUS/R4AYG6l6fWV+i2PadOmadmyZVqzZk2xTXv8+PFKTk52387KynKf21beF0I7duxQjx49dNNjr6t2ZEy51mVWR/b8oLTXH9XbI9rpni4NfTKmQwFaXfs+9Tm9WAGq+HeB0/YcUf9ZP6j72O5q1qNZhY9nhJP7T+qLx7/Q2rVr1b59e6PLuSz2K+/z9T4lsV+VVsGnviisJL1eot+XF89LVUNl6ffsUxWD19DeZ1SvNzSkR0REyM/PT8eOHfOYfuzYMTVo0OCyy7788suaNm2avvrqK7Vr167Y+YKCghQUFFRoekBAgAICyncoiNVqld1ul8Mp5bkMP72/QuTmu2S326W8HJ810QIBcvhkTEt+rux2u3Lzc5Vnyavw8YzgcDlkt9tltVrL/Xdf0divKo6v9imJ/aq0zL5flocver1Evy8vnpeqhsrS79mnKhavob3HqF5v6F4RGBioTp06eVwIpuDCML8/JO6PXnrpJT377LNauXKlOnfu7ItSAQBAGdDrAQAoHcMPd09OTtbw4cPVuXNnde3aVSkpKbpw4YJGjBghSRo2bJgaN26sqVOnSpJefPFFTZw4UUuWLFF0dLSOHj0qSQoNDVVoaKhhjwMAABSNXg8AQMkZHtIHDx6sEydOaOLEiTp69Kg6dOiglStXqn79+pKkjIwMWa3//cD/jTfeUG5uru666y6P9UyaNEmTJ0/2ZekAAKAE6PUAAJSc4SFdkpKSkpSUlFTkfWvWrPG4feDAgYovCAAAeBW9HgCAkqmaV2oAAAAAAKASIqQDAAAAAGAShHQAAAAAAEyCkA4AAAAAgEkQ0gEAAAAAMAlCOgAAAAAAJkFIBwAAAADAJAjpAAAAAACYBCEdAAAAAACTIKQDAAAAAGAShHQAAAAAAEyCkA4AAAAAgEkQ0gEAAAAAMAlCOgAAAAAAJkFIBwAAAADAJAjpAAAAAACYBCEdAAAAAACTIKQDAAAAAGAShHQAAAAAAEyCkA4AAAAAgEkQ0gEAAAAAMAlCOgAAAAAAJkFIBwAAAADAJAjpAAAAAACYBCEdAAAAAACTIKQDAAAAAGAShHQAAAAAAEyCkA4AAAAAgEkQ0gEAAAAAMAlCOgAAAAAAJkFIBwAAAADAJAjpAAAAAACYBCEdAAAAAACTIKQDAAAAAGAShHQAAAAAAEyCkA4AAAAAgEkQ0gEAAAAAMAlCOgAAAAAAJkFIBwAAAADAJAjpAAAAAACYBCEdAAAAAACTIKQDAAAAAGAShHQAAAAAAEyCkA4AAAAAgEkQ0gEAAAAAMAlCOgAAAAAAJkFIBwAAAADAJAjpAAAAAACYBCEdAAAAAACTIKQDAAAAAGAShHQAAAAAAEyCkA4AAAAAgEkQ0gEAAAAAMAlThPQ5c+YoOjpawcHBiouL08aNGy87/wcffKBWrVopODhYbdu21eeff+6jSgEAQFnQ6wEAKBnDQ/p7772n5ORkTZo0SVu3blX79u3Vr18/HT9+vMj5169fryFDhuihhx7Stm3bNGjQIA0aNEg//vijjysHAAAlQa8HAKDkDA/pM2bMUGJiokaMGKHWrVtr7ty5CgkJ0fz584ucf9asWerfv78ef/xxxcbG6tlnn9W1116r2bNn+7hyAABQEvR6AABKzt/IwXNzc7VlyxaNHz/ePc1qtap3795KS0srcpm0tDQlJyd7TOvXr5+WL19e5Pw5OTnKyclx3z579qwk6dSpU3I4HOWqPysrS8HBwTp/6GfJYS/XuszKfvQXBQcHa09mrtb8fM4nYzqtgcr+U7a+3XdeVmduhY/34+FcBQcHK/twtjJ3Zlb4eEY4d+ScgoODlZWVpZMnTxpdzmWxX3mfr/cpif2q1Os6d+nvwOVyeaM0U/FFr5fo9+XF81LVUFn6PftUxeA1tPcZ1utdBjp06JBLkmv9+vUe0x9//HFX165di1wmICDAtWTJEo9pc+bMcdWrV6/I+SdNmuSSxA8//PDDDz+m/zl48KB3GqyJ+KLXu1z0e3744YcffirHT0l6vaGfpPvC+PHjPd6NdzqdOnXqlOrUqSOLxWJgZShOVlaWIiMjdfDgQYWHhxtdDlDpsU+Zn8vl0rlz59SoUSOjS6m06PeVC89LgPexX5lbaXq9oSE9IiJCfn5+OnbsmMf0Y8eOqUGDBkUu06BBg1LNHxQUpKCgII9ptWrVKnvR8Jnw8HCeYAAvYp8yt5o1axpdQoXwRa+X6PeVFc9LgPexX5lXSXu9oReOCwwMVKdOnZSamuqe5nQ6lZqaqvj4+CKXiY+P95hfklavXl3s/AAAwDj0egAASsfww92Tk5M1fPhwde7cWV27dlVKSoouXLigESNGSJKGDRumxo0ba+rUqZKkMWPG6MYbb9Qrr7yiP//5z1q2bJk2b96st956y8iHAQAAikGvBwCg5AwP6YMHD9aJEyc0ceJEHT16VB06dNDKlStVv359SVJGRoas1v9+4N+9e3ctWbJETz/9tJ566inFxMRo+fLlatOmjVEPAV4WFBSkSZMmFTpsEUDZsE/BaPR6/BHPS4D3sV9VHRaXqwp+3wsAAAAAAJWQoeekAwAAAACA/yKkAwAAAABgEoR0AAAAAABMgpAOAAAAAIBJENJhKnPmzFF0dLSCg4MVFxenjRs3Gl0SUKmtXbtWAwcOVKNGjWSxWLR8+XKjSwIA+j3gRfT6qoeQDtN47733lJycrEmTJmnr1q1q3769+vXrp+PHjxtdGlBpXbhwQe3bt9ecOXOMLgUAJNHvAW+j11c9fAUbTCMuLk5dunTR7NmzJUlOp1ORkZH629/+pnHjxhlcHVD5WSwWffLJJxo0aJDRpQCoxuj3QMWh11cNfJIOU8jNzdWWLVvUu3dv9zSr1arevXsrLS3NwMoAAIC30O8B4MoI6TCFzMxM5efnq379+h7T69evr6NHjxpUFQAA8Cb6PQBcGSEdAAAAAACTIKTDFCIiIuTn56djx455TD927JgaNGhgUFUAAMCb6PcAcGWEdJhCYGCgOnXqpNTUVPc0p9Op1NRUxcfHG1gZAADwFvo9AFyZv9EFAAWSk5M1fPhwde7cWV27dlVKSoouXLigESNGGF0aUGmdP39e+/btc9/+9ddftX37dl111VWKiooysDIA1RX9HvAuen3Vw1ewwVRmz56t6dOn6+jRo+rQoYNeffVVxcXFGV0WUGmtWbNGN910U6Hpw4cP18KFC31fEACIfg94E72+6iGkAwAAAABgEpyTDgAAAACASRDSAQAAAAAwCUI6AAAAAAAmQUgHAAAAAMAkCOkAAAAAAJgEIR0AAAAAAJMgpAMAAAAAYBKEdAAAAAAATIKQDlQhPXv21NixY4u9Pzo6WikpKT6rBwAAeBe9Hqj6COlANbJp0yaNHDnS6DKu6KefftKdd96p6OhoWSwWXmwAAFBClaXXv/3227rhhhtUu3Zt1a5dW71799bGjRuNLgswBUI6UI3UrVtXISEhFTpGbm5uudeRnZ2tZs2aadq0aWrQoIEXqgIAoHqoLL1+zZo1GjJkiL7++mulpaUpMjJSffv21aFDh7xQIVC5EdKBKiYvL09JSUmqWbOmIiIiNGHCBLlcLkmFD4GzWCyaN2+ebr/9doWEhCgmJkYrVqxw35+fn6+HHnpITZs2lc1mU8uWLTVr1iyP8RISEjRo0CA9//zzatSokVq2bKlnnnlGbdq0KVRbhw4dNGHChCs+hi5dumj69Om69957FRQUVMYtAQBA1VQVev3ixYs1atQodejQQa1atdK8efPkdDqVmppaxq0CVB2EdKCKeeedd+Tv76+NGzdq1qxZmjFjhubNm1fs/FOmTNE999yjH374QQMGDNB9992nU6dOSZKcTqeaNGmiDz74QLt27dLEiRP11FNP6f333/dYR2pqqvbs2aPVq1fr008/1YMPPqj09HRt2rTJPc+2bdv0ww8/aMSIERXzwAEAqCaqYq/Pzs6Ww+HQVVddVeplgarG3+gCAHhXZGSkZs6cKYvFopYtW2rnzp2aOXOmEhMTi5w/ISFBQ4YMkSS98MILevXVV7Vx40b1799fAQEBmjJlinvepk2bKi0tTe+//77uuece9/QaNWpo3rx5CgwMdE/r16+fFixYoC5dukiSFixYoBtvvFHNmjWriIcNAEC1URV7/ZNPPqlGjRqpd+/epV4WqGr4JB2oYrp16yaLxeK+HR8fr7179yo/P7/I+du1a+f+f40aNRQeHq7jx4+7p82ZM0edOnVS3bp1FRoaqrfeeksZGRke62jbtq1H05akxMRELV26VBcvXlRubq6WLFmiBx980BsPEQCAaq2q9fpp06Zp2bJl+uSTTxQcHFzq5YGqhk/SgWouICDA47bFYpHT6ZQkLVu2TI899pheeeUVxcfHKywsTNOnT9eGDRs8lqlRo0ah9Q4cOFBBQUH65JNPFBgYKIfDobvuuqviHggAACiSmXv9yy+/rGnTpumrr77yeDMBqM4I6UAV88em+v333ysmJkZ+fn6lXte6devUvXt3jRo1yj1t//79JVrW399fw4cP14IFCxQYGKh7771XNput1DUAAABPVaXXv/TSS3r++ee1atUqde7cudS1A1UVIR2oYjIyMpScnKyHH35YW7du1WuvvaZXXnmlTOuKiYnRokWLtGrVKjVt2lTvvvuuNm3apKZNm5Zo+b/85S+KjY2VdOlFQEnl5uZq165d7v8fOnRI27dvV2hoqJo3b176BwIAQBVSFXr9iy++qIkTJ2rJkiWKjo7W0aNHJUmhoaEKDQ0t/QMBqhBCOlDFDBs2THa7XV27dpWfn5/GjBmjkSNHlmldDz/8sLZt26bBgwfLYrFoyJAhGjVqlL744osSLR8TE6Pu3bvr1KlTiouLK/G4hw8fVseOHd23X375Zb388su68cYbtWbNmtI+DAAAqpSq0OvfeOMN5ebmFjo8ftKkSZo8eXJpHgJQ5VhcBV+qCABe5nK5FBMTo1GjRik5OdnocgAAgJfR6wHv45N0ABXixIkTWrZsmY4ePcp3owMAUAXR64GKQUgHUCHq1auniIgIvfXWW6pdu7bHfZc71+yLL77QDTfcUNHlAQCAcqLXAxWDw90B+Ny+ffuKva9x48ZcBR4AgEqOXg+UHSEdAAAAAACTsBpdAAAAAAAAuISQDgAAAACASRDSAQAAAAAwCUI6AAAAAAAmQUgHAAAAAMAkCOkAAAAAAJgEIR0AAAAAAJMgpAMAAAAAYBKEdAAAAAAATIKQDgAAAACASRDSAQAAAAAwCUI6AAAAAAAmQUgHAAAAAMAk/I0uwNecTqcOHz6ssLAwWSwWo8sBAEAul0vnzp1To0aNZLXy/jkAANVZtQvphw8fVmRkpNFlAABQyMGDB9WkSROjywAAAAaqdiE9LCxM0qUXQuHh4QZXg6I4HA59+eWX6tu3rwICAowuB6j02KfMLysrS5GRke4eBQAAqq9qF9ILDnEPDw8npJuUw+FQSEiIwsPDCRSAF7BPVR6chgUAADjxDQAAAAAAkyCkAwAAAABgEoR0AAAAAABMotqdk15S+fn5cjgcRpdRLTkcDvn7++vixYvKz883uhyfCggIkJ+fn9FlAAAAADAIIf0PXC6Xjh49qjNnzhhdSrXlcrnUoEEDHTx4sFpeRKlWrVpq0KBBtXzsAAAAQHVHSP+DgoBer149hYSEEJQM4HQ6df78eYWGhspqrT5nZLhcLmVnZ+v48eOSpIYNGxpcEQAAAABfI6T/Tn5+vjug16lTx+hyqi2n06nc3FwFBwdXq5AuSTabTZJ0/Phx1atXj0PfAQAAgGqmeiWgKyg4Bz0kJMTgSlCdFfz9cU0EAAAAoPohpBeBQ9xhJP7+AAAAgOqLkA4AAAAAgEkQ0uE1PXv21NixY8u9nl69emn8+PHlLwgAAAAAKhkuHAfDrFmzRjfddJNOnz6tWrVquad/+OGHstvtxhUGAAAAAAbhk3SYzlVXXaWwsDCjywAAAAAAnyOkVwE9e/ZUUlKSkpKSVLNmTUVERGjChAlyuVySpNOnT2vYsGGqXbu2QkJCdMstt2jv3r3u5RcuXKhatWpp+fLliomJUXBwsPr166eDBw+650lISNCgQYM8xh07dqx69uxZbF3vvvuuOnfurLCwMDVo0EBDhw51fwf4gQMHdNNNN0mSateuLYvFooSEBEmFD3cvaf2rVq1SbGysQkND1b9/fx05cqRM2xMAAAAAjEJIryLeeecd+fv7a+PGjZo1a5ZmzJihefPmSboUsDdv3qwVK1YoLS1NLpdLAwYM8PiKr+zsbD3//PNatGiR1q1bpzNnzujee+8tV00Oh0PPPvusduzYoeXLl+vAgQPuIB4ZGamPPvpIkrRnzx4dOXJEs2bNKnI9Ja3/5Zdf1rvvvqu1a9cqIyNDjz32WLnqBwAAAABf45z0KiIyMlIzZ86UxWJRy5YttXPnTs2cOVM9e/bUihUrtG7dOnXv3l2StHjxYkVGRmr58uW6++67JV0K1LNnz1ZcXJykS6E/NjZWGzduVNeuXctU04MPPuj+f7NmzfTqq6+qS5cuOn/+vEJDQ3XVVVdJkurVq+dxTvrv7d27t8T1z507V9dcc40kKSkpSc8880yZ6gYAAAAAo/BJehXRrVs3j+/Xjo+P1969e7Vr1y75+/u7w7ck1alTRy1btlR6erp7mr+/v7p06eK+3apVK9WqVctjntLasmWLBg4cqKioKIWFhenGG2+UJGVkZJR4Henp6SWqPyQkxB3QJalhw4buQ+sBAAAAoLIgpKNErFar+xz3Ar8/3PyPLly4oH79+ik8PFyLFy/Wpk2b9Mknn0iScnNzvV5fQECAx22LxVKoXgAAAAAwO0J6FbFhwwaP299//71iYmLUunVr5eXledx/8uRJ7dmzR61bt3ZPy8vL0+bNm9239+zZozNnzig2NlaSVLdu3UIXYtu+fXux9ezevVsnT57UtGnTdMMNN6hVq1aFPtkODAyUJOXn5xe7ntjY2BLVDwAAAABVASG9isjIyFBycrL27NmjpUuX6rXXXtOYMWMUExOj2267TYmJifruu++0Y8cO3X///WrcuLFuu+029/IBAQH629/+pg0bNmjLli1KSEhQt27d3Oej9+rVS5s3b9aiRYu0d+9eTZo0ST/++GOx9URFRSkwMFCvvfaafvnlF61YsULPPvusxzxXX321LBaLPv30U504cULnz58vtJ6S1g8AAAAAVQEhvYoYNmyY7Ha7unbtqtGjR2vMmDEaOXKkJGnBggXq1KmTbr31VsXHx8vlcunzzz/3OEQ8JCRETz75pIYOHarrrrtOoaGheu+999z39+vXTxMmTNATTzyhLl266Ny5cxo2bFix9dStW1cLFy7UBx98oNatW2vatGl6+eWXPeZp3LixpkyZonHjxql+/fpKSkoqcl0lqR8AAAAAqgKLq5qduJuVlaWaNWvq7NmzCg8P97jv4sWL+vXXX9W0aVMFBwcbVGHp9ezZUx06dFBKSkqZll+4cKHGjh2rM2fOeLWusnI6ncrKylJ4eLis1ur3PlJl/TuEeTkcDn3++ecaMGAAb26Z1OV6EwAAqF6qXwICAAAAAMCkCOkAAAAAAJiEv9EFoPzWrFlTruUTEhKUkJDglVoAAAAAAGXHJ+kAAAAAAJgEIR0AAAAAAJMgpAMAAAAAYBKEdAAAAAAATIKQDgAAAACASRDSAQAAAAAwCb6CrYQyMjKUmZnps/EiIiIUFRXls/FKKjo6WmPHjtXYsWOLnSc3N1etW7fWokWL1L17d98VZwIWi0WffPKJBg0aVOT9u3btUt++fbVnzx7VqFHDt8UBAAAAMD1CeglkZGSoVatY2e3ZPhvTZgvR7t3ppgzqVzJ37lw1bdq0Ugf0AwcOqGnTptq2bZs6dOjgtfW2bt1a3bp104wZMzRhwgSvrRcAAABA1UBIL4HMzEzZ7dmKe3CSwhtGV/h4WUcOaMP8KcrMzKx0Id3lcmn27Nl65plnyr2evLw8BQYGeqky8xgxYoQSExM1fvx4+fuzCwIAAAD4L85JL4XwhtG6Kqplhf+U5Y2Anj17KikpSUlJSapZs6YiIiI0YcIEuVwu9zynT5/WsGHDVLt2bYWEhOiWW27R3r17Pdbz0Ucf6U9/+pOCgoIUHR2tV155pVR1bNmyRfv379ef//xn97QDBw7IYrFo2bJl6t69u4KDg9WmTRt988037nnWrFkji8WiL774Ql26dFH9+vX13Xffyel0aurUqWratKlsNpvat2+vDz/8sNByq1atUseOHWWz2dSrVy8dP35cX3zxhWJjYxUeHq6hQ4cqO/u/R0KsXLlS119/vWrVqqU6dero1ltv1f79+933N23aVJLUsWNHWSwW9ezZ033f/Pnz3duoYcOGSkpK8tgGmZmZuv322xUSEqKYmBitWLHC4/4+ffro1KlTHo8fAAAAACRCepXyzjvvyN/fXxs3btSsWbM0Y8YMzZs3z31/QkKCNm/erBUrVigtLU0ul0sDBgyQw+GQdClg33PPPbr33nu1c+dOTZ48WRMmTNDChQtLXMO3336rFi1aKCwsrNB9jz/+uB599FFt27ZN8fHxGjhwoE6ePOkxz7hx4/TCCy9ow4YNateunaZOnapFixZp7ty5+umnn/TII4/o/vvvLxRwJ0+erNmzZ2v9+vU6ePCg7rnnHqWkpGjJkiX67LPP9OWXX+q1115zz3/hwgUlJydr8+bNSk1NldVq1e233y6n0ylJ2rhxoyTpq6++0pEjR/Txxx9Lkt544w2NHj1aI0eO1M6dO7VixQo1b97co5YpU6bonnvu0Q8//KABAwbovvvu06lTp9z3BwYGqkOHDvr2229LvF0BAAAAVA8ca1uFREZGaubMmbJYLGrZsqV27typmTNnKjExUXv37tWKFSu0bt0697niixcvVmRkpJYvX667775bM2bM0M033+w+V7pFixbatWuXpk+froSEhBLV8Ntvv6lRo0ZF3peUlKQ777xT0qWwu3LlSv3jH//QE0884Z7nmWeeUZ8+fZSVlaWgoCC98MIL+uqrrxQfHy9Jatasmb777ju9+eabuvHGG93LPffcc7ruuuskSQ899JDGjx+v/fv3q1mzZpKku+66S19//bWefPJJSXLXUWD+/PmqW7eudu3apTZt2qhu3bqSpDp16qhBgwYe4zz66KMaM2aMe1qXLl081pWQkKAhQ4ZIkl544QW9+uqr2rhxo/r37++ep1GjRvrtt9+uuD0BAAAAVC98kl6FdOvWTRaLxX07Pj5ee/fuVX5+vtLT0+Xv76+4uDj3/XXq1FHLli2Vnp4uSUpPT3cH3QLXXXedex0lYbfbFRwcXOR9BUFbkvz9/dW5c2f32AU6d+7s/v++ffuUnZ2tPn36KDQ01P2zaNEij0PTJaldu3bu/9evX18hISHugF4w7fjx4+7be/fu1ZAhQ9SsWTOFh4crOjpa0qWLBBbn+PHjOnz4sG6++ebLbAHPWmrUqKHw8HCPsSXJZrN5HH4PAAAAAJIJQvqcOXMUHR2t4OBgxcXFuQ8zLk5KSopatmwpm82myMhIPfLII7p48aKPqsWVRERE6PTp02Ve/vdfS3b+/HlJ0meffabt27e7f3bt2uVxXrokBQQEuP9vsVg8bhdMKziUXZIGDhyoU6dO6e2339aGDRu0YcMGSZe+Pq44NputRI/hSmNL0qlTp9yf1gMAAABAAUND+nvvvafk5GRNmjRJW7duVfv27dWvX79CnzoWWLJkicaNG6dJkyYpPT1d//jHP/Tee+/pqaee8nHl5lQQNAt8//33iomJkZ+fn2JjY5WXl+cxz8mTJ7Vnzx61bt1akhQbG6t169Z5rGPdunVq0aKF/Pz8SlRDx44dtXv3bo8L1v2+ngJ5eXnasmWLYmNji11X69atFRQUpIyMDDVv3tzjJzIyskT1FKXgcT/99NO6+eabFRsbW+iNhYKryv/+CIKwsDBFR0crNTW1zGMX+PHHH9WxY8dyrwcAAABA1WLoOekzZsxQYmKiRowYIenS92t/9tlnmj9/vsaNG1do/vXr1+u6667T0KFDJUnR0dEaMmRIoXBaXWVkZCg5OVkPP/ywtm7dqtdee819dfaYmBjddtttSkxM1JtvvqmwsDCNGzdOjRs31m233SZJevTRR9WlSxc9++yzGjx4sNLS0jR79my9/vrrJa7hpptu0vnz5/XTTz+pTZs2HvfNmTNHMTExio2N1cyZM3X69Gk9+OCDxa4rLCxMjz32mB555BE5nU5df/31Onv2rNatW6fw8HANHz68DFtJql27turUqaO33npLDRs2VEZGRqG/t3r16slms2nlypVq0qSJgoODVbNmTU2ePFl//etfVa9ePd1yyy06d+6c1q1bp7/97W8lHv/AgQM6dOiQevfuXab6AQAAAFRdhoX03NxcbdmyRePHj3dPs1qt6t27t9LS0opcpnv37vrnP/+pjRs3qmvXrvrll1/0+eef64EHHih2nJycHOXk5LhvZ2VlSZIcDof7quYFHA6HXC6XnE6nx+HJBf/POnxAKvwBsddlHTngHvePh0lfzgMPPKDs7Gx17dpVfn5++vvf/66//OUv7nX84x//0NixY3XrrbcqNzdXN9xwgz799FP5+fnJ6XSqQ4cOWrZsmSZPnqxnn31WDRs21JQpUzRs2DCPOgq2UVFq166tQYMG6Z///KdeeOEF9+OQLl1Ebdq0adq+fbuaN2+u5cuX66qrrvJ4nE6n0/0pvMvl0pQpUxQREaGpU6fql19+Ua1atdSxY0eNHz++0HK////v/y1Y1++nLVmyRGPHjlWbNm3UsmVLpaSkqFevXu71WK1WpaSk6LnnntPEiRN1ww036D//+Y97G8+aNUuPPfaYIiIidOeddxb6e/nj9vn9tCVLlqhPnz6KjIwscjsWbAOHw1HiIxiAyyl4rvvjcx7Mg98NAAAoYHEVdVyyDxw+fFiNGzfW+vXrPS4o9sQTT+ibb74p9tPxV199VY899phcLpfy8vL017/+VW+88Uax40yePFlTpkwpNH3JkiUKCQnxmObv768GDRooMjLSfbizJB08eFBd4+J00W4v7cMss2CbTRs3bCjxYd233nqr2rZtq6lTp1ZwZVf2448/6o477tDWrVsVGhqqjIwMtW/fXmvXrlXbtm2NLs9Qubm56tSpk95++21169at2HkOHjyoo0ePKi8vz8cVAjBCdna2hg4dqrNnzyo8PNzocgAAgIEq1VewrVmzRi+88IJef/11xcXFad++fRozZoyeffZZ99eG/dH48eOVnJzsvp2VlaXIyEj17du30Auhixcv6uDBgwoNDfW4Qvmf/vQnpe/apczMzIp5YEWIiIhQVFRUief39/dXYGCgKV7cde/eXdOmTdPJkyfVqFEjhYaGSvrvlc6vxPX/t3f3cVHVef/H3zMwA4P3NamIGNri3U+FlCTsimzX0vVar6w2TSuVSvdKKdrJtWwVvLnMyjIsTR/mpd1519263eq6lLIpqWmiW6jp5mIaKFJiMDIjM78/vJyNQBcQZg7M6/l4+HhwvnO+5/s5cM7jMW/POd/j9er06dNq0aJFpdnqm4KDBw/q8ccf180333zBdc6cOSObzabk5OQLzpTflH377bdKSLhGTmfTnv3eZovQ55/vUMeOHRt8LLfbrY0bN+qmm26qMrEhjOH8XV4AAAABC+l2u10hISEqLCys1F5YWFjpvdQ/NX36dN1zzz26//77JUm9e/dWaWmpJkyYoD/+8Y8ym6vOgxcWFqawsLAq7RaLpcqX1YqKCplMJpnN5irbiomJ8b2my6jO124EP33W/HxN1f1eq3P+FnAj7U996dq1q7p27XrRdcxms2+G+mAMVN9//72Ki08q8d4MtYyMCXQ5DaLku8Patnymvv/+e3Xu3Nlv4wbrMdUY8HcBAADnBSykW61W9evXT1lZWRo+fLikc+EsKytLqamp1fYpKyurEtrOP7MboLv2DWPTpk2BLuGCYmJigv7vg9prGRmjyzp1C3QZAAAAgF8F9HZ3h8OhsWPHKiEhQf3791dmZqZKS0t9s72PGTNGUVFRvueshw0bpvnz5+vqq6/23e4+ffp0Vjcd3gAALB9JREFUDRs2jAm2AAAAAACNXkBD+siRI3XixAmlp6eroKBA8fHxWr9+vdq1ayfp3CvFfnrlfNq0aTKZTJo2bZqOHj2qK664QsOGDdOcOXMCtQsAAAAAANSbgE8cl5qaesHb239+C3doaKgyMjKUkZHhh8oAAAAAAPCvpjUrFwAAAAAAjRghHQAAAAAAgyCkAwAAAABgEAF/Jr2xyM/PV1FRkd/Gs9vt6tSpk9/Gq4mYmBg9/PDDevjhhwNdCgAAAAA0SYT0GsjPz1eP7t1U5jzjtzEjbOHK27ffcEEdAAAAANBwCOk1UFRUpDLnGb0+IV49Ips3+Hh53/2ou5fuVlFRESEdAAAAAIIIIb0WekQ2V9+YVoEuo1oDBw5Ur169JEmvvfaaLBaLHnjgAc2aNUsmk0nff/+90tLS9N5776m8vFw33HCDnn/+ecXGxvq28fbbbys9PV0HDx5UZGSkHnzwQT3yyCOB2iUAAAAACDpMHNeEvPLKKwoNDdX27du1YMECzZ8/X8uWLZMkjRs3Tp9//rneffdd5eTkyOv1aujQoXK73ZKknTt3asSIEbrzzju1d+9ezZgxQ9OnT9fLL78cwD0CAAAAgODClfQmJDo6Ws8995xMJpO6deumvXv36rnnntPAgQP17rvvasuWLRowYIAkaeXKlYqOjta6det0xx13aP78+frVr36l6dOnS5K6du2qr776SvPmzdO4ceMCuFcAAAAAEDy4kt6EXHvttTKZTL7lpKQkff311/rqq68UGhqqxMRE32eXX365unXrpry8PElSXl6errvuukrbu+666/T111+roqLCPzsAAAAAAEGOkA4AAAAAgEEQ0puQbdu2VVr+7LPPFBsbq549e+rs2bOVPj958qT279+vnj17SpJ69OihLVu2VOq/ZcsWde3aVSEhIQ1fPAAAAACAkN6U5Ofny+FwaP/+/Vq9erVeeOEFpaWlKTY2VrfccovGjx+vTz/9VLm5ubr77rsVFRWlW265RZL0yCOPKCsrS7Nnz9aBAwf0yiuvaOHChZo8eXKA9woAAAAAggcTx9VC3nc/GnqcMWPGyOl0qn///goJCVFaWpomTJggSVqxYoXS0tL0m9/8Ri6XS8nJyfrwww9lsVgkSX379tUbb7yh9PR0zZ49W5GRkZo1axaTxgEAAACAHxHSa8ButyvCFq67l+7225gRtnDZ7fZa9bFYLMrMzNTixYurfNamTRu9+uqrF+1/++236/bbb7/g54cPH65VPQAAAACA2iGk10CnTp2Ut2+/ioqK/Dam3W5Xp06d/DYeAAAAACDwCOk11KlTJ0IzAAAAAKBBEdKbiE2bNgW6BAAAAADAJWJ2dwAAAAAADIKQDgAAAACAQRDSAQAAAAAwCEI6AAAAAAAGQUgHAAAAAMAgCOkAAAAAABgEr2Crofz8fBUVFfltPLvdznvZAQAAACDIENJrID8/X917dJezzOm3MW0RNu3L21fjoD5w4EDFx8crMzOzYQurIaPVAwAAAACNASG9BoqKiuQscyp5SrJaRbdq8PFOHTml7KezVVRU5Ner6S6XS1ar1W/jAQAAAAAq45n0WmgV3Ur2WHuD/6vtfwSMGzdOmzdv1oIFC2QymWQymXTo0CHdd9996ty5s2w2m7p166YFCxZU6Td8+HDNmTNHHTp0ULdu3SRJW7duVXx8vMLDw5WQkKB169bJZDJp9+7dvr5///vf9etf/1rNmzdXu3btdM899/geB6iunsOHD1/S7x4AAAAAggFX0puABQsW6MCBA+rVq5dmzZolSWrTpo06duyoN998U5dffrm2bt2qCRMmKDIyUiNGjPD1zcrKUsuWLbVx40ZJUklJiYYNG6ahQ4dq1apV+uc//6mHH3640ng//PCDfvnLX+r+++/Xc889J6fTqUcffVQjRozQxx9/XG09V1xxhX9+GQAAAADQiBHSm4BWrVrJarUqIiJC7du397XPnDnT93Pnzp2Vk5OjN954o1JIb9asmZYtW+a7zX3JkiUymUx66aWXFB4erp49e+ro0aMaP368r8/ChQt19dVX64knnvC1LV++XNHR0Tpw4IC6du1abT0AAAAAgIsjpDdhixYt0vLly5Wfny+n0ymXy6X4+PhK6/Tu3bvSc+j79+9Xnz59FB4e7mvr379/pT65ubn65JNP1Lx58ypjHjp0SF27dq3fHQEAAACAIEFIb6LWrFmjyZMn69lnn1VSUpJatGihefPmadu2bZXWa9asWa23/eOPP2rYsGF66qmnqnwWGRlZ55oBAAAAINgR0psIq9WqiooK3/KWLVs0YMAATZw40dd26NChf7udbt266fXXX1d5ebnCwsIkSTt27Ki0Tt++ffX2228rJiZGoaHVH0I/rwcAAAAA8O8xu3sTERMTo23btunw4cMqKipSbGysPv/8c23YsEEHDhzQ9OnTq4Tt6owePVoej0cTJkxQXl6eNmzYoGeeeUaSZDKZJEmTJk1ScXGxRo0apR07dujQoUPasGGDUlJSfMH85/V4PJ6G23kAAAAAaCK4kl4Lp46cMuw4kydP1tixY9WzZ085nU7t27dPX3zxhUaOHCmTyaRRo0Zp4sSJ+uijjy66nZYtW+q9997TAw88oPj4ePXu3Vvp6ekaPXq07zn1Dh06aMuWLXr00Ud18803q7y8XFdeeaWGDBkis9lcbT3ffPONYmJiar1fAAAAABBMCOk1YLfbZYuwKfvpbL+NaYuwyW6313j9rl27Kicnp1LbihUrtGLFikptc+fO9f388ssvV7utAQMGKDc317e8cuVKWSwWderUydcWGxurd955p1b1AAAAAAAujpBeA506ddK+vH0qKiry25h2u71SKPanV199VV26dFFUVJRyc3N970C32WwBqQcAAAAAggUhvYY6deoUsNDsbwUFBUpPT1dBQYEiIyN1xx13aM6cOYEuCwAAAACaPEI6qpgyZYqmTJkS6DIAAAAAIOgwuzsAAAAAAAZBSK+G1+sNdAkIYhx/AAAAQPAipP+ExWKRJJWVlQW4EgSz88ff+eMRAAAAQPDgmfSfCAkJUevWrXX8+HFJUkREhEwmU4CrCj4ej0cul0tnzpzxvXc9GHi9XpWVlen48eNq3bq1QkJCAl0SAAAAAD8jpP9M+/btJckX1OF/Xq9XTqdTNpstKP+TpHXr1r7jEAAAAEBwIaT/jMlkUmRkpNq2bSu32x3ocoKS2+1Wdna2kpOTg+6Wb4vFwhV0AAAAIIgR0i8gJCSEsBQgISEhOnv2rMLDw4MupAMAAAAIbsHzwC8AAAAAAAZHSAcAAAAAwCAI6QAAAAAAGAQhHQAAAAAAgyCkAwAAAABgEIR0AAAAAAAMgpAOAAAAAIBBBDykL1q0SDExMQoPD1diYqK2b99+0fV/+OEHTZo0SZGRkQoLC1PXrl314Ycf+qlaAAAAAAAaTmggB1+7dq0cDoeWLFmixMREZWZmavDgwdq/f7/atm1bZX2Xy6WbbrpJbdu21VtvvaWoqCj985//VOvWrf1fPAAAAAAA9SygIX3+/PkaP368UlJSJElLlizRBx98oOXLl+uxxx6rsv7y5ctVXFysrVu3ymKxSJJiYmL8WTIAAAAAAA0mYCHd5XJp586dmjp1qq/NbDZr0KBBysnJqbbPu+++q6SkJE2aNEl//vOfdcUVV2j06NF69NFHFRISUm2f8vJylZeX+5ZLSkokSW63W263ux73CPXl/N+Fv09w8ng8stlsspilUJMn0OU0CItZstls8ng8fjnOOaeMj78NAAA4L2AhvaioSBUVFWrXrl2l9nbt2mnfvn3V9vnHP/6hjz/+WHfddZc+/PBDHTx4UBMnTpTb7VZGRka1febOnauZM2dWaf/LX/6iiIiIS98RNJiNGzcGugQEyOrVq//vp6KA1tFg2rbRhNWrdfToUR09etRvw3JOGVdZWVmgSwAAAAYR0Nvda8vj8aht27ZaunSpQkJC1K9fPx09elTz5s27YEifOnWqHA6Hb7mkpETR0dG6+eab1bJlS3+Vjlpwu93auHGjbrrpJt9jDQgeubm5Sk5O1o2TX1Sb6NhAl9Mgvj/ytT55ZqKys7MVFxfX4ONxThnf+bu8AAAAAhbS7Xa7QkJCVFhYWKm9sLBQ7du3r7ZPZGSkLBZLpVvbe/TooYKCArlcLlmt1ip9wsLCFBYWVqXdYrHwZdXg+BsFJ7PZLKfTKbdHOusN+AsoGoTbIzmdTpnNZr8e45xTxsXfBQAAnBewb8BWq1X9+vVTVlaWr83j8SgrK0tJSUnV9rnuuut08OBBeTz/ek71wIEDioyMrDagAwAAAADQmAT0MpXD4dBLL72kV155RXl5eXrggQdUWlrqm+19zJgxlSaWe+CBB1RcXKy0tDQdOHBAH3zwgZ544glNmjQpULsAAAAAAEC9Cegz6SNHjtSJEyeUnp6ugoICxcfHa/369b7J5PLz82U2/+v/EaKjo7Vhwwb9/ve/V58+fRQVFaW0tDQ9+uijgdoFAAAAAADqTcAnjktNTVVqamq1n23atKlKW1JSkj777LMGrgoAAAAAAP9rmrMyAQAAAADQCNUppH/yySf1XQcAAAAAAEGvTiF9yJAhuuqqq/Q///M/OnLkSH3XBAAAAABAUKpTSD969KhSU1P11ltvqUuXLho8eLDeeOMNuVyu+q4PAAAAAICgUaeQbrfb9fvf/167d+/Wtm3b1LVrV02cOFEdOnTQQw89pNzc3PquEwAAAACAJu+SJ47r27evpk6dqtTUVP34449avny5+vXrp+uvv15ffvllfdQIAAAAAEBQqHNId7vdeuuttzR06FBdeeWV2rBhgxYuXKjCwkIdPHhQV155pe644476rBUAAAAAgCatTu9Jf/DBB7V69Wp5vV7dc889evrpp9WrVy/f582aNdMzzzyjDh061FuhAAAAAAA0dXUK6V999ZVeeOEF3XbbbQoLC6t2HbvdzqvaAAAAAACohTrd7p6RkaE77rijSkA/e/assrOzJUmhoaG64YYbLr1CAAAAAACCRJ1C+o033qji4uIq7adOndKNN954yUUBAAAAABCM6hTSvV6vTCZTlfaTJ0+qWbNml1wUAAAAAADBqFbPpN92222SJJPJpHHjxlW63b2iokJ79uzRgAED6rdCAAAAAACCRK1CeqtWrSSdu5LeokUL2Ww232dWq1XXXnutxo8fX78VAgAAAAAQJGoV0lesWCFJiomJ0eTJk7m1HQAAAACAelSnV7BlZGTUdx0AAAAAAAS9Gof0vn37KisrS23atNHVV19d7cRx5+3atateigMAAAAAIJjUOKTfcsstvonihg8f3lD1AAAAAAAQtGoc0n96izu3uwMAAAAAUP/q9J50AAAAAABQ/2p8Jb1NmzYXfQ79p4qLi+tcEAAAAAAAwarGIT0zM7MBywAAAAAAADUO6WPHjm3IOmBg+fn5Kioq8tt4Ho9HkpSbmyuz2T9PZJSXl/smRmyq7Ha7OnXqFOgyAAAAAFxEjUN6SUmJWrZs6fv5Ys6vh8YvPz9fPbp3U5nzjN/GtNlsWr16tZKTk+V0Ov0yptkkebx+GSpgbBE27cvbR1AHAAAADKxWz6R/9913atu2rVq3bl3t8+ler1cmk0kVFRX1WiQCp6ioSGXOM3p9Qrx6RDb3y5ges1VHJWU/liSzx9Xg4+36xwmNf22/rh5ztTpe07HBxwuEU0dOKfvpbBUVFRHSAQAAAAOrcUj/+OOPddlll0mSPvnkkwYrCMbUI7K5+sa08stYbll0VFJcp5ayyN3g45WWlUqSmrdvLnusvcHHAwAAAIALqXFIv+GGG6r9GQBQN3l5eX4ZJxDzPEjM9QAAAFAXNQ7pP/f999/rf//3f31fMnv27KmUlBTf1XYAQPXc5ece47j77rv9Ml4g5nmQmOsBAACgLuoU0rOzszVs2DC1atVKCQkJkqTnn39es2bN0nvvvafk5OR6LRIAmpKzZ89Kkmbf1lVDe7dt8PH8Pc+DxFwPAAAAdVWnkD5p0iSNHDlSixcvVkhIiCSpoqJCEydO1KRJk7R37956LRIAmqLO9gi/zPXg73keJOZ6AAAAqKs6PZx48OBBPfLII76ALkkhISFyOBw6ePBgvRUHAAAAAEAwqVNI79u3b7UTHuXl5SkuLu6SiwIAAAAAIBjV+Hb3PXv2+H5+6KGHlJaWpoMHD+raa6+VJH322WdatGiRnnzyyfqvEgAAAACAIFDjkB4fHy+TySSv919T9U6ZMqXKeqNHj9bIkSPrpzoAAAAAAIJIjUP6N99805B1AAAAAAAQ9Goc0q+88sqGrAMAAAAAgKBXp1ewnffVV18pPz9fLlfl9+7+13/91yUVBQAAAABAMKpTSP/HP/6hW2+9VXv37q30nLrJZJJ07p3pAAAAAACgdur0Cra0tDR17txZx48fV0REhL788ktlZ2crISFBmzZtqucSAQAAAAAIDnW6kp6Tk6OPP/5YdrtdZrNZZrNZ//Ef/6G5c+fqoYce0hdffFHfdQIAAAAA0OTV6Up6RUWFWrRoIUmy2+06duyYpHOTy+3fv7/+qgMAAAAAIIjU6Up6r169lJubq86dOysxMVFPP/20rFarli5dqi5dutR3jQAAAAAABIU6hfRp06aptLRUkjRr1iz95je/0fXXX6/LL79ca9eurdcCAQAAAAAIFnUK6YMHD/b9/Itf/EL79u1TcXGx2rRp45vhHQAAAAAA1M4lvSddko4cOSJJio6OvuRiAAAAAAAIZnWaOO7s2bOaPn26WrVqpZiYGMXExKhVq1aaNm2a3G53fdcIAAAAAEBQqNOV9AcffFDvvPOOnn76aSUlJUk691q2GTNm6OTJk1q8eHG9FgkAAAAAQDCoU0hftWqV1qxZo1//+te+tj59+ig6OlqjRo0ipAMAAAAAUAd1ut09LCxMMTExVdo7d+4sq9V6qTUBAAAAABCU6hTSU1NTNXv2bJWXl/vaysvLNWfOHKWmptZbcQAAAAAABJMa3+5+2223VVr+61//qo4dOyouLk6SlJubK5fLpV/96lf1WyEAAAAAAEGixiG9VatWlZZvv/32Ssu8gg0AAAAAgEtT45C+YsWKhqwDAAAAAICgV6dn0s87ceKEPv30U3366ac6ceJEnbezaNEixcTEKDw8XImJidq+fXuN+q1Zs0Ymk0nDhw+v89gAAAAAABhFnUJ6aWmp7r33XkVGRio5OVnJycnq0KGD7rvvPpWVldVqW2vXrpXD4VBGRoZ27dqluLg4DR48WMePH79ov8OHD2vy5Mm6/vrr67ILAAAAAAAYTp1CusPh0ObNm/Xee+/phx9+0A8//KA///nP2rx5sx555JFabWv+/PkaP368UlJS1LNnTy1ZskQRERFavnz5BftUVFTorrvu0syZM9WlS5e67AIAAAAAAIZT42fSf+rtt9/WW2+9pYEDB/rahg4dKpvNphEjRmjx4sU12o7L5dLOnTs1depUX5vZbNagQYOUk5NzwX6zZs1S27Ztdd999+lvf/vbRccoLy+v9Kq4kpISSZLb7Zbb7a5RncHM4/HIZrPJY7bKLYtfxjw/jr/G84ZYZbPZZA2xKtRbp1PC8Cwmy7m/o8dj+OP+/DFnMUuhJk+gy2kQ1hCTbDabFBrml+Pc3+eUxHlVW0Y/LwEAgP+YvF6vt7adIiIitHPnTvXo0aNS+5dffqn+/furtLS0Rts5duyYoqKitHXrViUlJfnap0yZos2bN2vbtm1V+nz66ae68847tXv3btntdo0bN04//PCD1q1bV+0YM2bM0MyZM6u0r1q1ShERETWqEwCAhlRWVqbRo0fr1KlTatmyZaDLAQAAAVSnyxtJSUnKyMjQq6++qvDwcEmS0+nUzJkzK4Xt+nb69Gndc889eumll2S322vUZ+rUqXI4HL7lkpISRUdH6+abb+aLUA3k5uYqOTlZ2Y8lKa6Tf35fblm0sc1duun7lbKo4a8u5ez/TkMW7NGAhweoS3LTfHzi5KGT+ugPHyk7O1txcXGBLueizh9zN05+UW2iYwNdToP4bv8e5bz4iF5K6aMR10Q2+Hj+PqckzqvaOn+XFwAAQJ1CemZmpoYMGaKOHTv6vpjk5uYqPDxcGzZsqPF27Ha7QkJCVFhYWKm9sLBQ7du3r7L+oUOHdPjwYQ0bNszX5vGcux02NDRU+/fv11VXXVWpT1hYmMLCwqpsy2KxyGLx362fjZXZbJbT6ZTZ4/Lbl/vzLHL7ZUxThUtOp1OuCpfOms42+HiB4Pa6z/0dzWbDH/fnjzm3RzrrvaQXUBiWq8Irp9MpnS3363nlr3NK4ryqLaOflwAAwH/qFNJ79+6tr7/+WitXrtS+ffskSaNGjdJdd9117jnLGrJarerXr5+ysrJ8r1HzeDzKyspSampqlfW7d++uvXv3VmqbNm2aTp8+rQULFig6OrouuwMAAAAAgCHUOqS73W51795d77//vsaPH3/JBTgcDo0dO1YJCQnq37+/MjMzVVpaqpSUFEnSmDFjFBUVpblz5yo8PFy9evWq1L9169aSVKUdAAAAAIDGptYh3WKx6MyZM/VWwMiRI3XixAmlp6eroKBA8fHxWr9+vdq1aydJys/Pl9ncNG95BQAAAADgp+p0u/ukSZP01FNPadmyZQoNvfRX66SmplZ7e7skbdq06aJ9X3755UseHwAAAAAAI6hTwt6xY4eysrL0l7/8Rb1791azZs0qff7OO+/US3EAAAAAAASTOoX01q1b6/bbb6/vWgAAAAAACGq1Cukej0fz5s3TgQMH5HK59Mtf/lIzZsyo1YzuAAAAAACgerWakW3OnDl6/PHH1bx5c0VFRen555/XpEmTGqo2AAAAAACCSq1C+quvvqoXX3xRGzZs0Lp16/Tee+9p5cqV8ng8DVUfAAAAAABBo1YhPT8/X0OHDvUtDxo0SCaTSceOHav3wgAAAAAACDa1Culnz55VeHh4pTaLxSK3212vRQEAAAAAEIxqNXGc1+vVuHHjFBYW5ms7c+aM/vu//7vSa9h4BRsAAAAAALVXq5A+duzYKm133313vRUDAAAAAEAwq1VIX7FiRUPVAQAAAABA0KvVM+kAAAAAAKDhENIBAAAAADAIQjoAAAAAAAZBSAcAAAAAwCAI6QAAAAAAGAQhHQAAAAAAgyCkAwAAAABgEIR0AAAAAAAMgpAOAAAAAIBBENIBAAAAADAIQjoAAAAAAAZBSAcAAAAAwCAI6QAAAAAAGAQhHQAAAAAAgyCkAwAAAABgEIR0AAAAAAAMgpAOAAAAAIBBENIBAAAAADAIQjoAAAAAAAZBSAcAAAAAwCAI6QAAAAAAGAQhHQAAAAAAgyCkAwAAAABgEIR0AAAAAAAMgpAOAAAAAIBBENIBAAAAADAIQjoAAAAAAAZBSAcAAAAAwCAI6QAAAAAAGAQhHQAAAAAAgyCkAwAAAABgEIR0AAAAAAAMgpAOAAAAAIBBENIBAAAAADAIQjoAAAAAAAZBSAcAAAAAwCAI6QAAAAAAGAQhHQAAAAAAgyCkAwAAAABgEIR0AAAAAAAMgpAOAAAAAIBBENIBAAAAADAIQjoAAAAAAAZhiJC+aNEixcTEKDw8XImJidq+ffsF133ppZd0/fXXq02bNmrTpo0GDRp00fUBAAAAAGgsAh7S165dK4fDoYyMDO3atUtxcXEaPHiwjh8/Xu36mzZt0qhRo/TJJ58oJydH0dHRuvnmm3X06FE/Vw4AAAAAQP0KDXQB8+fP1/jx45WSkiJJWrJkiT744AMtX75cjz32WJX1V65cWWl52bJlevvtt5WVlaUxY8b4peafys/PV1FRkd/H9Ze8vLxAlwAAAAAAQSOgId3lcmnnzp2aOnWqr81sNmvQoEHKycmp0TbKysrkdrt12WWXVft5eXm5ysvLfcslJSWSJLfbLbfbfQnVS99++60SEq6R01l2SdsxOpstQsd+9Ki3LH4Zz/1/47j9NJ43xCqbzSZriFWh3oD/v1WDsJgsstls8ng8l3zcNzSPxyObzSaLWQo1eQJdToOwhphks9mk0DC/HOf+PqckzqvaMvp5CQAA/Mfk9Xq9gRr82LFjioqK0tatW5WUlORrnzJlijZv3qxt27b9221MnDhRGzZs0Jdffqnw8PAqn8+YMUMzZ86s0r5q1SpFRERc2g4AAFAPysrKNHr0aJ06dUotW7YMdDkAACCAGvXljSeffFJr1qzRpk2bqg3okjR16lQ5HA7fcklJie859kv9IpSbm6vk5GTdOPlFtYmOvaRtGdV3+/co58VH9FJKH424JtIvY7pl0cY2d+mm71fKooa/upSz/zsNWbBHAx4eoC7JXRp8vEA4eeikPvrDR8rOzlZcXFygy7kozqv65+9zSuK8qq3zd3kBAAAENKTb7XaFhISosLCwUnthYaHat29/0b7PPPOMnnzySf31r39Vnz59LrheWFiYwsLCqrRbLBZZLJd266fZbJbT6ZTbI531BnwOvgbhqvDK6XRKZ8v99uX+PIvcfhnTVOGS0+mUq8Kls6azDT5eILi9bjmdTpnN5ks+7hsa51XD8dc5JXFe1ZbRz0sAAOA/Af0GbLVa1a9fP2VlZfnaPB6PsrKyKt3+/nNPP/20Zs+erfXr1yshIcEfpQIAAAAA0OACfru7w+HQ2LFjlZCQoP79+yszM1OlpaW+2d7HjBmjqKgozZ07V5L01FNPKT09XatWrVJMTIwKCgokSc2bN1fz5s0Dth8AAAAAAFyqgIf0kSNH6sSJE0pPT1dBQYHi4+O1fv16tWvXTtK5V5yZzf+64L948WK5XC799re/rbSdjIwMzZgxw5+lAwAAAABQrwIe0iUpNTVVqamp1X62adOmSsuHDx9u+IIAAAAAAAiApjkrEwAAAAAAjRAhHQAAAAAAgyCkAwAAAABgEIR0AAAAAAAMgpAOAAAAAIBBENIBAAAAADAIQjoAAAAAAAZBSAcAAAAAwCAI6QAAAAAAGAQhHQAAAAAAgyCkAwAAAABgEIR0AAAAAAAMgpAOAAAAAIBBENIBAAAAADAIQjoAAAAAAAZBSAcAAAAAwCAI6QAAAAAAGAQhHQAAAAAAgyCkAwAAAABgEIR0AAAAAAAMgpAOAAAAAIBBENIBAAAAADAIQjoAAAAAAAZBSAcAAAAAwCAI6QAAAAAAGAQhHQAAAAAAgyCkAwAAAABgEIR0AAAAAAAMgpAOAAAAAIBBENIBAAAAADAIQjoAAAAAAAZBSAcAAAAAwCAI6QAAAAAAGAQhHQAAAAAAgyCkAwAAAABgEIR0AAAAAAAMgpAOAAAAAIBBENIBAAAAADAIQjoAAAAAAAZBSAcAAAAAwCAI6QAAAAAAGAQhHQAAAAAAgyCkAwAAAABgEIR0AAAAAAAMgpAOAAAAAIBBENIBAAAAADAIQjoAAAAAAAZBSAcAAAAAwCAI6QAAAAAAGAQhHQAAAAAAgyCkAwAAAABgEIR0AAAAAAAMgpAOAAAAAIBBENIBAAAAADAIQ4T0RYsWKSYmRuHh4UpMTNT27dsvuv6bb76p7t27Kzw8XL1799aHH37op0oBAAAAAGg4AQ/pa9eulcPhUEZGhnbt2qW4uDgNHjxYx48fr3b9rVu3atSoUbrvvvv0xRdfaPjw4Ro+fLj+/ve/+7lyAAAAAADqV8BD+vz58zV+/HilpKSoZ8+eWrJkiSIiIrR8+fJq11+wYIGGDBmiP/zhD+rRo4dmz56tvn37auHChX6uHAAAAACA+hUayMFdLpd27typqVOn+trMZrMGDRqknJycavvk5OTI4XBUahs8eLDWrVtX7frl5eUqLy/3LZ86dUqSVFxcLLfbfUn1l5SUKDw8XD8ePSC5nZe0LaNyFvxD4eHh2l/k0qYDp/0ypsdsVdn/K9PfDv4os8fV4OP9/ZhL4eHhKjtWpqK9RQ0+XiCc/u60wsPDVVJSopMnTwa6nIvivKp//j6nJM6rWm/r9LnjwOv11kdpAACgETN5A/iN4NixY4qKitLWrVuVlJTka58yZYo2b96sbdu2VeljtVr1yiuvaNSoUb62F198UTNnzlRhYWGV9WfMmKGZM2c2zA4AAFCPjhw5oo4dOwa6DAAAEEABvZLuD1OnTq105d3j8ai4uFiXX365TCZTACvDhZSUlCg6OlpHjhxRy5YtA10O0OhxThmf1+vV6dOn1aFDh0CXAgAAAiygId1utyskJKTKFfDCwkK1b9++2j7t27ev1fphYWEKCwur1Na6deu6Fw2/admyJYECqEecU8bWqlWrQJcAAAAMIKATx1mtVvXr109ZWVm+No/Ho6ysrEq3v/9UUlJSpfUlaePGjRdcHwAAAACAxiLgt7s7HA6NHTtWCQkJ6t+/vzIzM1VaWqqUlBRJ0pgxYxQVFaW5c+dKktLS0nTDDTfo2Wef1X/+539qzZo1+vzzz7V06dJA7gYAAAAAAJcs4CF95MiROnHihNLT01VQUKD4+HitX79e7dq1kyTl5+fLbP7XBf8BAwZo1apVmjZtmh5//HHFxsZq3bp16tWrV6B2AfUsLCxMGRkZVR5TAFA3nFMAAACNR0BndwcAAAAAAP8S0GfSAQAAAADAvxDSAQAAAAAwCEI6AAAAAAAGQUgHAAAAAMAgCOkwlEWLFikmJkbh4eFKTEzU9u3bA10S0KhlZ2dr2LBh6tChg0wmk9atWxfokgAAAHARhHQYxtq1a+VwOJSRkaFdu3YpLi5OgwcP1vHjxwNdGtBolZaWKi4uTosWLQp0KQAAAKgBXsEGw0hMTNQ111yjhQsXSpI8Ho+io6P14IMP6rHHHgtwdUDjZzKZ9Kc//UnDhw8PdCkAAAC4AK6kwxBcLpd27typQYMG+drMZrMGDRqknJycAFYGAAAAAP5DSIchFBUVqaKiQu3atavU3q5dOxUUFASoKgAAAADwL0I6AAAAAAAGQUiHIdjtdoWEhKiwsLBSe2Fhodq3bx+gqgAAAADAvwjpMASr1ap+/fopKyvL1+bxeJSVlaWkpKQAVgYAAAAA/hMa6AKA8xwOh8aOHauEhAT1799fmZmZKi0tVUpKSqBLAxqtH3/8UQcPHvQtf/PNN9q9e7cuu+wyderUKYCVAQAAoDq8gg2GsnDhQs2bN08FBQWKj4/X888/r8TExECXBTRamzZt0o033lilfezYsXr55Zf9XxAAAAAuipAOAAAAAIBB8Ew6AAAAAAAGQUgHAAAAAMAgCOkAAAAAABgEIR0AAAAAAIMgpAMAAAAAYBCEdAAAAAAADIKQDgAAAACAQRDSgSZk4MCBevjhhy/4eUxMjDIzM/1WDwAAAIDaIaQDQWTHjh2aMGFCoMv4t9555x0lJCSodevWatasmeLj4/Xaa68FuiwAAACgwYUGugAA/nPFFVc0+Bgul0tWq/WStnHZZZfpj3/8o7p37y6r1ar3339fKSkpatu2rQYPHlxPlQIAAADGw5V0oIk5e/asUlNT1apVK9ntdk2fPl1er1dS1dvdTSaTli1bpltvvVURERGKjY3Vu+++6/u8oqJC9913nzp37iybzaZu3bppwYIFlcYbN26chg8frjlz5qhDhw7q1q2bZs2apV69elWpLT4+XtOnT/+3+zBw4EDdeuut6tGjh6666iqlpaWpT58++vTTT+v4WwEAAAAaB0I60MS88sorCg0N1fbt27VgwQLNnz9fy5Ytu+D6M2fO1IgRI7Rnzx4NHTpUd911l4qLiyVJHo9HHTt21JtvvqmvvvpK6enpevzxx/XGG29U2kZWVpb279+vjRs36v3339e9996rvLw87dixw7fOF198oT179iglJaVW++P1en3bT05OrlVfAAAAoLHhdnegiYmOjtZzzz0nk8mkbt26ae/evXruuec0fvz4atcfN26cRo0aJUl64okn9Pzzz2v79u0aMmSILBaLZs6c6Vu3c+fOysnJ0RtvvKERI0b42ps1a6Zly5ZVus198ODBWrFiha655hpJ0ooVK3TDDTeoS5cuNdqPU6dOKSoqSuXl5QoJCdGLL76om266qda/DwAAAKAx4Uo60MRce+21MplMvuWkpCR9/fXXqqioqHb9Pn36+H5u1qyZWrZsqePHj/vaFi1apH79+umKK65Q8+bNtXTpUuXn51faRu/evas8hz5+/HitXr1aZ86ckcvl0qpVq3TvvffWeD9atGih3bt3a8eOHZozZ44cDoc2bdpU4/4AAABAY8SVdCDIWSyWSssmk0kej0eStGbNGk2ePFnPPvuskpKS1KJFC82bN0/btm2r1KdZs2ZVtjts2DCFhYXpT3/6k6xWq9xut37729/WuC6z2axf/OIXks49y56Xl6e5c+dq4MCBtdxDAAAAoPEgpANNzM8D9GeffabY2FiFhITUeltbtmzRgAEDNHHiRF/boUOHatQ3NDRUY8eO1YoVK2S1WnXnnXfKZrPVuobzPB6PysvL69wfAAAAaAwI6UATk5+fL4fDod/97nfatWuXXnjhBT377LN12lZsbKxeffVVbdiwQZ07d9Zrr72mHTt2qHPnzjXqf//996tHjx6SzgX+mpo7d64SEhJ01VVXqby8XB9++KFee+01LV68uE77AQAAADQWhHSgiRkzZoycTqf69++vkJAQpaWlacKECXXa1u9+9zt98cUXGjlypEwmk0aNGqWJEyfqo48+qlH/2NhYDRgwQMXFxUpMTKzxuKWlpZo4caK+/fZb2Ww2de/eXa+//rpGjhxZp/0AAAAAGguT9/wLlAGgnnm9XsXGxmrixIlyOByBLgcAAAAwPK6kA2gQJ06c0Jo1a1RQUFDrd6MDAAAAwYqQDqBBtG3bVna7XUuXLlWbNm0qfda8efML9vvoo490/fXXN3R5AAAAgCFxuzsAvzt48OAFP4uKirqkWeABAACAxoyQDgAAAACAQZgDXQAAAAAAADiHkA4AAAAAgEEQ0gEAAAAAMAhCOgAAAAAABkFIBwAAAADAIAjpAAAAAAAYBCEdAAAAAACDIKQDAAAAAGAQ/x+RVsDoL+C3XwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1200x1800 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "match = matcher.get_best_match()\n",
    "m_data = m.copy().get_population('pool')\n",
    "m_data.loc[:, 'population'] = m_data['population'] + ' (prematch)'\n",
    "match.append(m_data)\n",
    "# fig = plot_per_feature_loss(match, beta, 'target', debin=False)\n",
    "fig = plot_numeric_features(match, hue_order=['pool (prematch)', 'pool', 'target', ])\n",
    "fig = plot_categoric_features(match,  hue_order=['pool (prematch)', 'pool', 'target'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "310a104c-9639-41bf-b5f6-929dca8436b5",
   "metadata": {},
   "source": [
    "## Optimize Beta With Cross Terms Added"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46f0112e",
   "metadata": {},
   "source": [
    "Sometimes it helps to add a known (non-optimal) solution as a hint to the solver. A natural choice for hinting the solver is to take a solution from PS matching. We can choose to use the PS as a hint to the solver by passing ps_hinting=True."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b928facc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO [preprocess.py:442] Added cross term height * weight to matching features.\n",
      "INFO [preprocess.py:442] Added cross term gender * age to matching features.\n",
      "INFO [preprocess.py:442] Added cross term binary_0 * binary_3 to matching features.\n",
      "INFO [preprocess.py:442] Added cross term binary_0 * age to matching features.\n",
      "INFO [preprocess.py:442] Added cross term binary_3 * weight to matching features.\n",
      "INFO [preprocess.py:442] Added cross term binary_2 * weight to matching features.\n",
      "INFO [preprocess.py:442] Added cross term binary_1 * binary_3 to matching features.\n",
      "INFO [preprocess.py:442] Added cross term binary_2 * binary_3 to matching features.\n",
      "INFO [preprocess.py:442] Added cross term binary_2 * height to matching features.\n",
      "INFO [preprocess.py:442] Added cross term binary_0 * binary_2 to matching features.\n",
      "INFO [matcher.py:65] Scaling features by factor 240.00 in order to use integer solver with <= 0.3751% loss.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'objective': 'beta_x',\n",
       " 'pool_size': 1000,\n",
       " 'target_size': 1000,\n",
       " 'max_mismatch': None,\n",
       " 'time_limit': 360,\n",
       " 'num_workers': 4,\n",
       " 'ps_hinting': True,\n",
       " 'verbose': True}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "objective = beta_x = BetaXBalance(m)\n",
    "matcher = matcher_betax = ConstraintSatisfactionMatcher(\n",
    "    m, \n",
    "    time_limit=time_limit,\n",
    "    objective=objective,\n",
    "    ps_hinting=True,\n",
    "    num_workers=4)\n",
    "matcher.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c4bc59e4-2153-4a3c-aac6-386eee1de375",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO [matcher.py:418] Solving for match population with pool size = 1000 and target size = 1000 subject to None balance constraint.\n",
      "INFO [matcher.py:421] Matching on 25 dimensions ...\n",
      "INFO [matcher.py:428] Building model variables and constraints ...\n",
      "INFO [matcher.py:437] Calculating bounds on feature variables ...\n",
      "INFO [matcher.py:527] Applying size constraints on pool and target ...\n",
      "INFO [matcher.py:533] Applying hint ...\n",
      "INFO [matcher.py:540] Training PS model as guide for solver ...\n",
      "/opt/miniconda3/envs/pybalance/lib/python3.9/site-packages/pybalance/lp/matcher.py:542: 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",
      "  target.loc[:, \"ix\"] = list(range(len(target)))\n",
      "/opt/miniconda3/envs/pybalance/lib/python3.9/site-packages/pybalance/lp/matcher.py:543: 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",
      "  pool.loc[:, \"ix\"] = list(range(len(pool)))\n",
      "INFO [preprocess.py:442] Added cross term height * weight to matching features.\n",
      "INFO [preprocess.py:442] Added cross term gender * age to matching features.\n",
      "INFO [preprocess.py:442] Added cross term binary_0 * binary_3 to matching features.\n",
      "INFO [preprocess.py:442] Added cross term binary_0 * age to matching features.\n",
      "INFO [preprocess.py:442] Added cross term binary_3 * weight to matching features.\n",
      "INFO [preprocess.py:442] Added cross term binary_2 * weight to matching features.\n",
      "INFO [preprocess.py:442] Added cross term binary_1 * binary_3 to matching features.\n",
      "INFO [preprocess.py:442] Added cross term binary_2 * binary_3 to matching features.\n",
      "INFO [preprocess.py:442] Added cross term binary_2 * height to matching features.\n",
      "INFO [preprocess.py:442] Added cross term binary_0 * binary_2 to matching features.\n",
      "INFO [matcher.py:180] Training model SGDClassifier (iter 1/50, 0.001 min) ...\n",
      "INFO [matcher.py:136] Best propensity score match found:\n",
      "INFO [matcher.py:137] \tModel: SGDClassifier\n",
      "INFO [matcher.py:139] \t* alpha: 0.1045355473186929\n",
      "INFO [matcher.py:139] \t* class_weight: None\n",
      "INFO [matcher.py:139] \t* early_stopping: False\n",
      "INFO [matcher.py:139] \t* fit_intercept: False\n",
      "INFO [matcher.py:139] \t* loss: modified_huber\n",
      "INFO [matcher.py:139] \t* max_iter: 1500\n",
      "INFO [matcher.py:139] \t* penalty: l1\n",
      "INFO [matcher.py:140] \tScore (beta_x): 0.1650\n",
      "INFO [matcher.py:141] \tSolution time: 0.002 min\n",
      "INFO [matcher.py:180] Training model SGDClassifier (iter 2/50, 0.002 min) ...\n",
      "INFO [matcher.py:136] Best propensity score match found:\n",
      "INFO [matcher.py:137] \tModel: SGDClassifier\n",
      "INFO [matcher.py:139] \t* alpha: 0.0354658577371722\n",
      "INFO [matcher.py:139] \t* class_weight: None\n",
      "INFO [matcher.py:139] \t* early_stopping: False\n",
      "INFO [matcher.py:139] \t* fit_intercept: False\n",
      "INFO [matcher.py:139] \t* loss: modified_huber\n",
      "INFO [matcher.py:139] \t* max_iter: 1500\n",
      "INFO [matcher.py:139] \t* penalty: l2\n",
      "INFO [matcher.py:140] \tScore (beta_x): 0.0887\n",
      "INFO [matcher.py:141] \tSolution time: 0.003 min\n",
      "INFO [matcher.py:180] Training model LogisticRegression (iter 3/50, 0.003 min) ...\n",
      "INFO [matcher.py:136] Best propensity score match found:\n",
      "INFO [matcher.py:137] \tModel: LogisticRegression\n",
      "INFO [matcher.py:139] \t* C: 0.7627616953429366\n",
      "INFO [matcher.py:139] \t* fit_intercept: True\n",
      "INFO [matcher.py:139] \t* max_iter: 500\n",
      "INFO [matcher.py:139] \t* penalty: l2\n",
      "INFO [matcher.py:139] \t* solver: saga\n",
      "INFO [matcher.py:140] \tScore (beta_x): 0.0670\n",
      "INFO [matcher.py:141] \tSolution time: 0.006 min\n",
      "INFO [matcher.py:180] Training model LogisticRegression (iter 4/50, 0.006 min) ...\n",
      "INFO [matcher.py:136] Best propensity score match found:\n",
      "INFO [matcher.py:137] \tModel: LogisticRegression\n",
      "INFO [matcher.py:139] \t* C: 0.015804928600429955\n",
      "INFO [matcher.py:139] \t* fit_intercept: True\n",
      "INFO [matcher.py:139] \t* max_iter: 500\n",
      "INFO [matcher.py:139] \t* penalty: l2\n",
      "INFO [matcher.py:139] \t* solver: saga\n",
      "INFO [matcher.py:140] \tScore (beta_x): 0.0615\n",
      "INFO [matcher.py:141] \tSolution time: 0.007 min\n",
      "INFO [matcher.py:180] Training model LogisticRegression (iter 5/50, 0.007 min) ...\n",
      "/opt/miniconda3/envs/pybalance/lib/python3.9/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:180] Training model LogisticRegression (iter 6/50, 0.024 min) ...\n",
      "/opt/miniconda3/envs/pybalance/lib/python3.9/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:180] Training model LogisticRegression (iter 7/50, 0.046 min) ...\n",
      "/opt/miniconda3/envs/pybalance/lib/python3.9/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:180] Training model LogisticRegression (iter 8/50, 0.067 min) ...\n",
      "INFO [matcher.py:180] Training model LogisticRegression (iter 9/50, 0.068 min) ...\n",
      "/opt/miniconda3/envs/pybalance/lib/python3.9/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:180] Training model LogisticRegression (iter 10/50, 0.085 min) ...\n",
      "INFO [matcher.py:180] Training model SGDClassifier (iter 11/50, 0.104 min) ...\n",
      "INFO [matcher.py:180] Training model LogisticRegression (iter 12/50, 0.105 min) ...\n",
      "INFO [matcher.py:180] Training model LogisticRegression (iter 13/50, 0.118 min) ...\n",
      "/opt/miniconda3/envs/pybalance/lib/python3.9/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:180] Training model SGDClassifier (iter 14/50, 0.139 min) ...\n",
      "INFO [matcher.py:180] Training model LogisticRegression (iter 15/50, 0.140 min) ...\n",
      "INFO [matcher.py:180] Training model LogisticRegression (iter 16/50, 0.141 min) ...\n",
      "/opt/miniconda3/envs/pybalance/lib/python3.9/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:180] Training model SGDClassifier (iter 17/50, 0.163 min) ...\n",
      "INFO [matcher.py:180] Training model SGDClassifier (iter 18/50, 0.164 min) ...\n",
      "INFO [matcher.py:180] Training model SGDClassifier (iter 19/50, 0.165 min) ...\n",
      "INFO [matcher.py:180] Training model SGDClassifier (iter 20/50, 0.166 min) ...\n",
      "INFO [matcher.py:180] Training model LogisticRegression (iter 21/50, 0.167 min) ...\n",
      "/opt/miniconda3/envs/pybalance/lib/python3.9/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:180] Training model SGDClassifier (iter 22/50, 0.188 min) ...\n",
      "INFO [matcher.py:180] Training model SGDClassifier (iter 23/50, 0.189 min) ...\n",
      "INFO [matcher.py:180] Training model SGDClassifier (iter 24/50, 0.190 min) ...\n",
      "INFO [matcher.py:180] Training model SGDClassifier (iter 25/50, 0.191 min) ...\n",
      "INFO [matcher.py:180] Training model SGDClassifier (iter 26/50, 0.192 min) ...\n",
      "INFO [matcher.py:180] Training model SGDClassifier (iter 27/50, 0.196 min) ...\n",
      "INFO [matcher.py:180] Training model LogisticRegression (iter 28/50, 0.198 min) ...\n",
      "/opt/miniconda3/envs/pybalance/lib/python3.9/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:180] Training model SGDClassifier (iter 29/50, 0.214 min) ...\n",
      "INFO [matcher.py:180] Training model LogisticRegression (iter 30/50, 0.215 min) ...\n",
      "INFO [matcher.py:180] Training model LogisticRegression (iter 31/50, 0.230 min) ...\n",
      "/opt/miniconda3/envs/pybalance/lib/python3.9/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:180] Training model SGDClassifier (iter 32/50, 0.251 min) ...\n",
      "INFO [matcher.py:180] Training model LogisticRegression (iter 33/50, 0.252 min) ...\n",
      "INFO [matcher.py:180] Training model SGDClassifier (iter 34/50, 0.254 min) ...\n",
      "INFO [matcher.py:136] Best propensity score match found:\n",
      "INFO [matcher.py:137] \tModel: SGDClassifier\n",
      "INFO [matcher.py:139] \t* alpha: 0.02606111348517078\n",
      "INFO [matcher.py:139] \t* class_weight: balanced\n",
      "INFO [matcher.py:139] \t* early_stopping: True\n",
      "INFO [matcher.py:139] \t* fit_intercept: False\n",
      "INFO [matcher.py:139] \t* loss: log_loss\n",
      "INFO [matcher.py:139] \t* max_iter: 1500\n",
      "INFO [matcher.py:139] \t* penalty: l2\n",
      "INFO [matcher.py:140] \tScore (beta_x): 0.0603\n",
      "INFO [matcher.py:141] \tSolution time: 0.255 min\n",
      "INFO [matcher.py:180] Training model SGDClassifier (iter 35/50, 0.255 min) ...\n",
      "INFO [matcher.py:180] Training model LogisticRegression (iter 36/50, 0.256 min) ...\n",
      "/opt/miniconda3/envs/pybalance/lib/python3.9/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:180] Training model LogisticRegression (iter 37/50, 0.278 min) ...\n",
      "INFO [matcher.py:180] Training model SGDClassifier (iter 38/50, 0.280 min) ...\n",
      "INFO [matcher.py:180] Training model LogisticRegression (iter 39/50, 0.281 min) ...\n",
      "/opt/miniconda3/envs/pybalance/lib/python3.9/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:180] Training model LogisticRegression (iter 40/50, 0.302 min) ...\n",
      "INFO [matcher.py:180] Training model LogisticRegression (iter 41/50, 0.319 min) ...\n",
      "INFO [matcher.py:180] Training model LogisticRegression (iter 42/50, 0.328 min) ...\n",
      "INFO [matcher.py:180] Training model SGDClassifier (iter 43/50, 0.330 min) ...\n",
      "INFO [matcher.py:180] Training model LogisticRegression (iter 44/50, 0.331 min) ...\n",
      "INFO [matcher.py:180] Training model LogisticRegression (iter 45/50, 0.344 min) ...\n",
      "INFO [matcher.py:180] Training model SGDClassifier (iter 46/50, 0.351 min) ...\n",
      "INFO [matcher.py:180] Training model SGDClassifier (iter 47/50, 0.352 min) ...\n",
      "INFO [matcher.py:180] Training model SGDClassifier (iter 48/50, 0.354 min) ...\n",
      "INFO [matcher.py:180] Training model SGDClassifier (iter 49/50, 0.355 min) ...\n",
      "INFO [matcher.py:180] Training model SGDClassifier (iter 50/50, 0.356 min) ...\n",
      "INFO [matcher.py:136] Best propensity score match found:\n",
      "INFO [matcher.py:137] \tModel: SGDClassifier\n",
      "INFO [matcher.py:139] \t* alpha: 0.02606111348517078\n",
      "INFO [matcher.py:139] \t* class_weight: balanced\n",
      "INFO [matcher.py:139] \t* early_stopping: True\n",
      "INFO [matcher.py:139] \t* fit_intercept: False\n",
      "INFO [matcher.py:139] \t* loss: log_loss\n",
      "INFO [matcher.py:139] \t* max_iter: 1500\n",
      "INFO [matcher.py:139] \t* penalty: l2\n",
      "INFO [matcher.py:140] \tScore (beta_x): 0.0603\n",
      "INFO [matcher.py:141] \tSolution time: 0.255 min\n",
      "INFO [matcher.py:577] Hint achieves objective value = 147332.\n",
      "INFO [matcher.py:579] Applying hints ...\n",
      "INFO [matcher.py:611] Solving with 4 workers ...\n",
      "INFO [matcher.py:90] Initial balance score: 0.2324\n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 1, time = 0.08 m\n",
      "INFO [matcher.py:101] Objective:\t147332000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0603\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 2, time = 0.08 m\n",
      "INFO [matcher.py:101] Objective:\t147176000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0601\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 3, time = 0.09 m\n",
      "INFO [matcher.py:101] Objective:\t147050000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0601\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 4, time = 0.09 m\n",
      "INFO [matcher.py:101] Objective:\t147002000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0601\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 5, time = 0.09 m\n",
      "INFO [matcher.py:101] Objective:\t146721000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0601\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 6, time = 0.10 m\n",
      "INFO [matcher.py:101] Objective:\t145561000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0596\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 7, time = 0.10 m\n",
      "INFO [matcher.py:101] Objective:\t145369000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0596\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 8, time = 0.13 m\n",
      "INFO [matcher.py:101] Objective:\t145229000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0596\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 9, time = 0.13 m\n",
      "INFO [matcher.py:101] Objective:\t145192000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0595\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 10, time = 0.13 m\n",
      "INFO [matcher.py:101] Objective:\t145144000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0596\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 11, time = 0.14 m\n",
      "INFO [matcher.py:101] Objective:\t145129000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0595\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 12, time = 0.14 m\n",
      "INFO [matcher.py:101] Objective:\t144996000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0595\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 13, time = 0.16 m\n",
      "INFO [matcher.py:101] Objective:\t144843000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0594\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 14, time = 0.16 m\n",
      "INFO [matcher.py:101] Objective:\t144588000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0594\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 15, time = 0.20 m\n",
      "INFO [matcher.py:101] Objective:\t144406000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0594\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 16, time = 0.21 m\n",
      "INFO [matcher.py:101] Objective:\t144195000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0593\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 17, time = 0.23 m\n",
      "INFO [matcher.py:101] Objective:\t143827000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0593\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 18, time = 0.23 m\n",
      "INFO [matcher.py:101] Objective:\t143679000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0592\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 19, time = 0.25 m\n",
      "INFO [matcher.py:101] Objective:\t143649000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0591\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 20, time = 0.26 m\n",
      "INFO [matcher.py:101] Objective:\t143574000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0591\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 21, time = 0.27 m\n",
      "INFO [matcher.py:101] Objective:\t143526000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0592\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 22, time = 0.27 m\n",
      "INFO [matcher.py:101] Objective:\t143245000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0591\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 23, time = 0.29 m\n",
      "INFO [matcher.py:101] Objective:\t142588000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0589\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 24, time = 0.31 m\n",
      "INFO [matcher.py:101] Objective:\t142455000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0589\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 25, time = 0.34 m\n",
      "INFO [matcher.py:101] Objective:\t141967000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0588\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 26, time = 0.35 m\n",
      "INFO [matcher.py:101] Objective:\t141924000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0588\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 27, time = 0.35 m\n",
      "INFO [matcher.py:101] Objective:\t141829000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0587\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 28, time = 0.35 m\n",
      "INFO [matcher.py:101] Objective:\t141681000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0587\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 29, time = 0.38 m\n",
      "INFO [matcher.py:101] Objective:\t141606000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0587\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 30, time = 0.38 m\n",
      "INFO [matcher.py:101] Objective:\t141322000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0585\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 31, time = 0.39 m\n",
      "INFO [matcher.py:101] Objective:\t141285000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0585\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 32, time = 0.39 m\n",
      "INFO [matcher.py:101] Objective:\t141229000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0586\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 33, time = 0.43 m\n",
      "INFO [matcher.py:101] Objective:\t141161000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0586\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 34, time = 0.43 m\n",
      "INFO [matcher.py:101] Objective:\t140938000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0584\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 35, time = 0.44 m\n",
      "INFO [matcher.py:101] Objective:\t140793000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0584\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 36, time = 0.44 m\n",
      "INFO [matcher.py:101] Objective:\t140697000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0584\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 37, time = 0.45 m\n",
      "INFO [matcher.py:101] Objective:\t140642000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0584\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 38, time = 0.45 m\n",
      "INFO [matcher.py:101] Objective:\t140592000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0584\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 39, time = 0.47 m\n",
      "INFO [matcher.py:101] Objective:\t140562000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0583\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 40, time = 0.47 m\n",
      "INFO [matcher.py:101] Objective:\t140520000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0583\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 41, time = 0.62 m\n",
      "INFO [matcher.py:101] Objective:\t140167000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0582\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 42, time = 0.65 m\n",
      "INFO [matcher.py:101] Objective:\t140140000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0583\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 43, time = 0.66 m\n",
      "INFO [matcher.py:101] Objective:\t140132000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0583\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 44, time = 0.67 m\n",
      "INFO [matcher.py:101] Objective:\t139980000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0583\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 45, time = 0.68 m\n",
      "INFO [matcher.py:101] Objective:\t139917000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0583\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 46, time = 0.68 m\n",
      "INFO [matcher.py:101] Objective:\t139651000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0582\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 47, time = 0.70 m\n",
      "INFO [matcher.py:101] Objective:\t139638000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0583\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 48, time = 0.72 m\n",
      "INFO [matcher.py:101] Objective:\t139610000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0583\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 49, time = 0.74 m\n",
      "INFO [matcher.py:101] Objective:\t139189000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0581\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 50, time = 0.76 m\n",
      "INFO [matcher.py:101] Objective:\t139131000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0580\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 51, time = 0.76 m\n",
      "INFO [matcher.py:101] Objective:\t138919000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0580\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 52, time = 0.76 m\n",
      "INFO [matcher.py:101] Objective:\t138885000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0580\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 53, time = 0.78 m\n",
      "INFO [matcher.py:101] Objective:\t138879000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0580\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 54, time = 0.80 m\n",
      "INFO [matcher.py:101] Objective:\t138853000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0579\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 55, time = 0.83 m\n",
      "INFO [matcher.py:101] Objective:\t138782000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0579\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 56, time = 0.83 m\n",
      "INFO [matcher.py:101] Objective:\t138716000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0579\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 57, time = 0.85 m\n",
      "INFO [matcher.py:101] Objective:\t138683000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0579\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 58, time = 0.85 m\n",
      "INFO [matcher.py:101] Objective:\t138483000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0579\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 59, time = 0.88 m\n",
      "INFO [matcher.py:101] Objective:\t137815000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0576\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 60, time = 0.88 m\n",
      "INFO [matcher.py:101] Objective:\t137752000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0576\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 61, time = 0.88 m\n",
      "INFO [matcher.py:101] Objective:\t137486000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0575\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 62, time = 0.89 m\n",
      "INFO [matcher.py:101] Objective:\t137401000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0575\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 63, time = 0.90 m\n",
      "INFO [matcher.py:101] Objective:\t137333000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0575\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 64, time = 0.93 m\n",
      "INFO [matcher.py:101] Objective:\t137132000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0574\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 65, time = 0.93 m\n",
      "INFO [matcher.py:101] Objective:\t137105000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0574\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 66, time = 0.93 m\n",
      "INFO [matcher.py:101] Objective:\t137099000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0574\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 67, time = 0.98 m\n",
      "INFO [matcher.py:101] Objective:\t137080000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0575\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 68, time = 0.98 m\n",
      "INFO [matcher.py:101] Objective:\t137005000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0574\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 69, time = 0.99 m\n",
      "INFO [matcher.py:101] Objective:\t136985000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0574\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 70, time = 1.04 m\n",
      "INFO [matcher.py:101] Objective:\t136974000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0574\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 71, time = 1.04 m\n",
      "INFO [matcher.py:101] Objective:\t136947000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0574\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 72, time = 1.10 m\n",
      "INFO [matcher.py:101] Objective:\t136838000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0574\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 73, time = 1.21 m\n",
      "INFO [matcher.py:101] Objective:\t136729000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0573\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 74, time = 1.24 m\n",
      "INFO [matcher.py:101] Objective:\t136654000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0574\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 75, time = 1.24 m\n",
      "INFO [matcher.py:101] Objective:\t136559000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0574\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 76, time = 1.25 m\n",
      "INFO [matcher.py:101] Objective:\t136503000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0573\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 77, time = 1.25 m\n",
      "INFO [matcher.py:101] Objective:\t136457000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0573\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 78, time = 1.26 m\n",
      "INFO [matcher.py:101] Objective:\t136435000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0573\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 79, time = 1.36 m\n",
      "INFO [matcher.py:101] Objective:\t136386000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0574\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 80, time = 1.37 m\n",
      "INFO [matcher.py:101] Objective:\t136349000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0573\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 81, time = 1.38 m\n",
      "INFO [matcher.py:101] Objective:\t136313000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0573\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 82, time = 1.38 m\n",
      "INFO [matcher.py:101] Objective:\t136286000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0573\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 83, time = 1.38 m\n",
      "INFO [matcher.py:101] Objective:\t136020000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0573\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 84, time = 1.41 m\n",
      "INFO [matcher.py:101] Objective:\t135535000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0570\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 85, time = 1.47 m\n",
      "INFO [matcher.py:101] Objective:\t135513000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0570\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 86, time = 1.48 m\n",
      "INFO [matcher.py:101] Objective:\t135473000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0570\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 87, time = 1.48 m\n",
      "INFO [matcher.py:101] Objective:\t135452000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0570\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 88, time = 1.51 m\n",
      "INFO [matcher.py:101] Objective:\t135375000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0570\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 89, time = 1.53 m\n",
      "INFO [matcher.py:101] Objective:\t135312000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0569\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 90, time = 1.54 m\n",
      "INFO [matcher.py:101] Objective:\t134988000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0568\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 91, time = 1.58 m\n",
      "INFO [matcher.py:101] Objective:\t134977000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0568\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 92, time = 1.59 m\n",
      "INFO [matcher.py:101] Objective:\t134937000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0568\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 93, time = 1.59 m\n",
      "INFO [matcher.py:101] Objective:\t134931000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0568\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 94, time = 1.61 m\n",
      "INFO [matcher.py:101] Objective:\t134909000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0567\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 95, time = 1.66 m\n",
      "INFO [matcher.py:101] Objective:\t134887000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0566\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 96, time = 1.67 m\n",
      "INFO [matcher.py:101] Objective:\t134614000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0566\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 97, time = 1.68 m\n",
      "INFO [matcher.py:101] Objective:\t134579000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0566\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 98, time = 1.68 m\n",
      "INFO [matcher.py:101] Objective:\t134522000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0566\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 99, time = 1.68 m\n",
      "INFO [matcher.py:101] Objective:\t134400000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0565\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 100, time = 1.69 m\n",
      "INFO [matcher.py:101] Objective:\t134234000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0565\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 101, time = 1.69 m\n",
      "INFO [matcher.py:101] Objective:\t134107000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0564\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 102, time = 1.73 m\n",
      "INFO [matcher.py:101] Objective:\t133877000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0564\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 103, time = 1.74 m\n",
      "INFO [matcher.py:101] Objective:\t133872000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0564\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 104, time = 1.77 m\n",
      "INFO [matcher.py:101] Objective:\t133834000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0562\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 105, time = 1.79 m\n",
      "INFO [matcher.py:101] Objective:\t133706000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0561\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 106, time = 1.84 m\n",
      "INFO [matcher.py:101] Objective:\t133469000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0560\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 107, time = 1.87 m\n",
      "INFO [matcher.py:101] Objective:\t133214000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0559\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 108, time = 1.87 m\n",
      "INFO [matcher.py:101] Objective:\t133090000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0559\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 109, time = 1.87 m\n",
      "INFO [matcher.py:101] Objective:\t133063000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0559\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 110, time = 1.88 m\n",
      "INFO [matcher.py:101] Objective:\t133033000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0559\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 111, time = 1.89 m\n",
      "INFO [matcher.py:101] Objective:\t133028000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0559\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 112, time = 1.90 m\n",
      "INFO [matcher.py:101] Objective:\t133008000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0558\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 113, time = 1.91 m\n",
      "INFO [matcher.py:101] Objective:\t132983000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0559\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 114, time = 1.91 m\n",
      "INFO [matcher.py:101] Objective:\t132978000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0559\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 115, time = 1.95 m\n",
      "INFO [matcher.py:101] Objective:\t132841000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0558\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 116, time = 1.96 m\n",
      "INFO [matcher.py:101] Objective:\t132785000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0558\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 117, time = 1.96 m\n",
      "INFO [matcher.py:101] Objective:\t132779000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0558\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 118, time = 2.02 m\n",
      "INFO [matcher.py:101] Objective:\t132600000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0557\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 119, time = 2.02 m\n",
      "INFO [matcher.py:101] Objective:\t132480000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0557\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 120, time = 2.03 m\n",
      "INFO [matcher.py:101] Objective:\t132453000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0557\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 121, time = 2.04 m\n",
      "INFO [matcher.py:101] Objective:\t132264000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0556\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 122, time = 2.16 m\n",
      "INFO [matcher.py:101] Objective:\t132136000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0557\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 123, time = 2.19 m\n",
      "INFO [matcher.py:101] Objective:\t132122000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0557\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 124, time = 2.21 m\n",
      "INFO [matcher.py:101] Objective:\t132117000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0557\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 125, time = 2.21 m\n",
      "INFO [matcher.py:101] Objective:\t132079000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0557\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 126, time = 2.31 m\n",
      "INFO [matcher.py:101] Objective:\t132025000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0556\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 127, time = 2.33 m\n",
      "INFO [matcher.py:101] Objective:\t132007000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0556\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 128, time = 2.33 m\n",
      "INFO [matcher.py:101] Objective:\t131985000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0556\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 129, time = 2.33 m\n",
      "INFO [matcher.py:101] Objective:\t131979000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0556\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 130, time = 2.35 m\n",
      "INFO [matcher.py:101] Objective:\t131957000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0555\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 131, time = 2.39 m\n",
      "INFO [matcher.py:101] Objective:\t131715000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0555\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 132, time = 2.47 m\n",
      "INFO [matcher.py:101] Objective:\t131695000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0555\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 133, time = 2.49 m\n",
      "INFO [matcher.py:101] Objective:\t131689000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0554\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 134, time = 2.51 m\n",
      "INFO [matcher.py:101] Objective:\t131687000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0556\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 135, time = 2.51 m\n",
      "INFO [matcher.py:101] Objective:\t131585000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0556\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 136, time = 2.52 m\n",
      "INFO [matcher.py:101] Objective:\t131572000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0556\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 137, time = 2.52 m\n",
      "INFO [matcher.py:101] Objective:\t131526000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0555\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 138, time = 2.52 m\n",
      "INFO [matcher.py:101] Objective:\t131488000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0556\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 139, time = 2.58 m\n",
      "INFO [matcher.py:101] Objective:\t131421000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0555\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 140, time = 2.58 m\n",
      "INFO [matcher.py:101] Objective:\t131418000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0555\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 141, time = 2.60 m\n",
      "INFO [matcher.py:101] Objective:\t131344000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0555\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 142, time = 2.61 m\n",
      "INFO [matcher.py:101] Objective:\t131338000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0555\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 143, time = 2.61 m\n",
      "INFO [matcher.py:101] Objective:\t131174000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0555\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 144, time = 2.62 m\n",
      "INFO [matcher.py:101] Objective:\t131116000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0554\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 145, time = 2.73 m\n",
      "INFO [matcher.py:101] Objective:\t131101000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0554\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 146, time = 2.73 m\n",
      "INFO [matcher.py:101] Objective:\t131083000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0554\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 147, time = 2.74 m\n",
      "INFO [matcher.py:101] Objective:\t131069000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0554\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 148, time = 2.74 m\n",
      "INFO [matcher.py:101] Objective:\t131061000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0554\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 149, time = 2.74 m\n",
      "INFO [matcher.py:101] Objective:\t131055000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0554\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 150, time = 2.74 m\n",
      "INFO [matcher.py:101] Objective:\t131053000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0554\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 151, time = 2.75 m\n",
      "INFO [matcher.py:101] Objective:\t131017000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0554\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 152, time = 2.77 m\n",
      "INFO [matcher.py:101] Objective:\t130827000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0553\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 153, time = 2.77 m\n",
      "INFO [matcher.py:101] Objective:\t130822000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0553\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 154, time = 2.79 m\n",
      "INFO [matcher.py:101] Objective:\t130586000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0553\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 155, time = 2.81 m\n",
      "INFO [matcher.py:101] Objective:\t130560000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0552\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 156, time = 2.82 m\n",
      "INFO [matcher.py:101] Objective:\t130554000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0553\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 157, time = 2.83 m\n",
      "INFO [matcher.py:101] Objective:\t130462000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0553\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 158, time = 2.84 m\n",
      "INFO [matcher.py:101] Objective:\t130390000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0552\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 159, time = 2.87 m\n",
      "INFO [matcher.py:101] Objective:\t130376000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0552\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 160, time = 2.93 m\n",
      "INFO [matcher.py:101] Objective:\t130053000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0552\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 161, time = 2.93 m\n",
      "INFO [matcher.py:101] Objective:\t130016000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0551\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 162, time = 2.93 m\n",
      "INFO [matcher.py:101] Objective:\t129852000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0551\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 163, time = 2.94 m\n",
      "INFO [matcher.py:101] Objective:\t129796000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0551\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 164, time = 2.94 m\n",
      "INFO [matcher.py:101] Objective:\t129752000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0551\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 165, time = 2.94 m\n",
      "INFO [matcher.py:101] Objective:\t129638000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0550\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 166, time = 2.98 m\n",
      "INFO [matcher.py:101] Objective:\t129415000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0550\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 167, time = 3.04 m\n",
      "INFO [matcher.py:101] Objective:\t129413000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0549\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 168, time = 3.05 m\n",
      "INFO [matcher.py:101] Objective:\t129338000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0549\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 169, time = 3.07 m\n",
      "INFO [matcher.py:101] Objective:\t129216000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0548\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 170, time = 3.11 m\n",
      "INFO [matcher.py:101] Objective:\t129055000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0539\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 171, time = 3.17 m\n",
      "INFO [matcher.py:101] Objective:\t128661000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0546\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 172, time = 3.27 m\n",
      "INFO [matcher.py:101] Objective:\t128657000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0545\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 173, time = 3.28 m\n",
      "INFO [matcher.py:101] Objective:\t128319000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0544\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 174, time = 3.46 m\n",
      "INFO [matcher.py:101] Objective:\t128266000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0543\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 175, time = 3.67 m\n",
      "INFO [matcher.py:101] Objective:\t127794000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0539\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 176, time = 3.72 m\n",
      "INFO [matcher.py:101] Objective:\t127703000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0542\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 177, time = 3.73 m\n",
      "INFO [matcher.py:101] Objective:\t127624000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0542\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 178, time = 3.77 m\n",
      "INFO [matcher.py:101] Objective:\t127604000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0542\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 179, time = 3.78 m\n",
      "INFO [matcher.py:101] Objective:\t127576000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0542\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 180, time = 3.78 m\n",
      "INFO [matcher.py:101] Objective:\t127565000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0541\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 181, time = 3.78 m\n",
      "INFO [matcher.py:101] Objective:\t127533000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0541\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 182, time = 3.78 m\n",
      "INFO [matcher.py:101] Objective:\t127526000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0541\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 183, time = 3.79 m\n",
      "INFO [matcher.py:101] Objective:\t127520000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0541\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 184, time = 3.79 m\n",
      "INFO [matcher.py:101] Objective:\t127464000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0541\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 185, time = 3.79 m\n",
      "INFO [matcher.py:101] Objective:\t127463000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0541\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 186, time = 3.82 m\n",
      "INFO [matcher.py:101] Objective:\t127450000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0541\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 187, time = 3.82 m\n",
      "INFO [matcher.py:101] Objective:\t127394000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0541\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 188, time = 3.83 m\n",
      "INFO [matcher.py:101] Objective:\t127388000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0541\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 189, time = 3.83 m\n",
      "INFO [matcher.py:101] Objective:\t127253000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0540\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 190, time = 3.84 m\n",
      "INFO [matcher.py:101] Objective:\t127241000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0540\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 191, time = 3.84 m\n",
      "INFO [matcher.py:101] Objective:\t127209000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0539\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 192, time = 3.84 m\n",
      "INFO [matcher.py:101] Objective:\t127189000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0539\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 193, time = 3.84 m\n",
      "INFO [matcher.py:101] Objective:\t126998000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0540\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 194, time = 3.86 m\n",
      "INFO [matcher.py:101] Objective:\t126837000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0539\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 195, time = 3.86 m\n",
      "INFO [matcher.py:101] Objective:\t126768000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0539\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 196, time = 3.88 m\n",
      "INFO [matcher.py:101] Objective:\t126686000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0538\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 197, time = 3.88 m\n",
      "INFO [matcher.py:101] Objective:\t126679000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0538\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 198, time = 3.89 m\n",
      "INFO [matcher.py:101] Objective:\t126673000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0538\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 199, time = 3.93 m\n",
      "INFO [matcher.py:101] Objective:\t126450000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0536\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 200, time = 4.02 m\n",
      "INFO [matcher.py:101] Objective:\t126359000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0536\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 201, time = 4.02 m\n",
      "INFO [matcher.py:101] Objective:\t126346000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0537\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 202, time = 4.02 m\n",
      "INFO [matcher.py:101] Objective:\t126318000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0536\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 203, time = 4.04 m\n",
      "INFO [matcher.py:101] Objective:\t125810000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0535\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 204, time = 4.04 m\n",
      "INFO [matcher.py:101] Objective:\t125799000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0535\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 205, time = 4.05 m\n",
      "INFO [matcher.py:101] Objective:\t125767000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0535\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 206, time = 4.05 m\n",
      "INFO [matcher.py:101] Objective:\t125760000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0534\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 207, time = 4.08 m\n",
      "INFO [matcher.py:101] Objective:\t125507000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0534\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 208, time = 4.10 m\n",
      "INFO [matcher.py:101] Objective:\t125496000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0533\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 209, time = 4.10 m\n",
      "INFO [matcher.py:101] Objective:\t125464000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0533\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 210, time = 4.15 m\n",
      "INFO [matcher.py:101] Objective:\t125016000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0532\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 211, time = 4.15 m\n",
      "INFO [matcher.py:101] Objective:\t124988000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0532\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 212, time = 4.16 m\n",
      "INFO [matcher.py:101] Objective:\t124956000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0532\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 213, time = 4.39 m\n",
      "INFO [matcher.py:101] Objective:\t124605000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0529\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 214, time = 4.57 m\n",
      "INFO [matcher.py:101] Objective:\t124569000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0530\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 215, time = 4.60 m\n",
      "INFO [matcher.py:101] Objective:\t124537000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0532\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 216, time = 4.63 m\n",
      "INFO [matcher.py:101] Objective:\t124416000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0532\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 217, time = 4.64 m\n",
      "INFO [matcher.py:101] Objective:\t124370000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0531\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 218, time = 4.66 m\n",
      "INFO [matcher.py:101] Objective:\t123956000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0530\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 219, time = 4.67 m\n",
      "INFO [matcher.py:101] Objective:\t123910000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0530\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 220, time = 4.71 m\n",
      "INFO [matcher.py:101] Objective:\t123851000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0530\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 221, time = 4.77 m\n",
      "INFO [matcher.py:101] Objective:\t123830000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0530\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 222, time = 4.77 m\n",
      "INFO [matcher.py:101] Objective:\t123810000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0530\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 223, time = 4.80 m\n",
      "INFO [matcher.py:101] Objective:\t123045000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0527\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 224, time = 4.98 m\n",
      "INFO [matcher.py:101] Objective:\t122999000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0527\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 225, time = 5.20 m\n",
      "INFO [matcher.py:101] Objective:\t122997000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0528\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 226, time = 5.20 m\n",
      "INFO [matcher.py:101] Objective:\t122958000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0528\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 227, time = 5.25 m\n",
      "INFO [matcher.py:101] Objective:\t122833000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0528\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 228, time = 5.25 m\n",
      "INFO [matcher.py:101] Objective:\t122802000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0528\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 229, time = 5.26 m\n",
      "INFO [matcher.py:101] Objective:\t122708000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0527\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 230, time = 5.30 m\n",
      "INFO [matcher.py:101] Objective:\t122646000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0527\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 231, time = 5.30 m\n",
      "INFO [matcher.py:101] Objective:\t122600000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0526\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 232, time = 5.31 m\n",
      "INFO [matcher.py:101] Objective:\t122464000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0526\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 233, time = 5.33 m\n",
      "INFO [matcher.py:101] Objective:\t122463000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0526\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 234, time = 5.34 m\n",
      "INFO [matcher.py:101] Objective:\t122453000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0526\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 235, time = 5.34 m\n",
      "INFO [matcher.py:101] Objective:\t122421000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0525\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 236, time = 5.35 m\n",
      "INFO [matcher.py:101] Objective:\t122333000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0526\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 237, time = 5.35 m\n",
      "INFO [matcher.py:101] Objective:\t122319000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0526\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 238, time = 5.35 m\n",
      "INFO [matcher.py:101] Objective:\t122280000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0526\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 239, time = 5.35 m\n",
      "INFO [matcher.py:101] Objective:\t122118000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0526\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 240, time = 5.44 m\n",
      "INFO [matcher.py:101] Objective:\t122093000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0526\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 241, time = 5.44 m\n",
      "INFO [matcher.py:101] Objective:\t121931000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0526\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 242, time = 5.44 m\n",
      "INFO [matcher.py:101] Objective:\t121912000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0526\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 243, time = 5.54 m\n",
      "INFO [matcher.py:101] Objective:\t121870000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0526\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 244, time = 5.55 m\n",
      "INFO [matcher.py:101] Objective:\t121820000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0525\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 245, time = 5.56 m\n",
      "INFO [matcher.py:101] Objective:\t121793000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0525\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 246, time = 5.58 m\n",
      "INFO [matcher.py:101] Objective:\t120770000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0521\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 247, time = 5.79 m\n",
      "INFO [matcher.py:101] Objective:\t120522000.0\n",
      "INFO [matcher.py:120] Balance (beta_x):\t0.0521\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:618] Status = FEASIBLE\n",
      "INFO [matcher.py:619] Number of solutions found: 247\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "        <b>Headers Numeric: </b><br>\n",
       "        ['age', 'height', 'weight']<br><br>\n",
       "        <b>Headers Categoric: </b><br>\n",
       "        ['gender', 'haircolor', 'country', 'binary_0', 'binary_1', 'binary_2', 'binary_3'] <br><br>\n",
       "        <b>Populations</b> <br>\n",
       "        ['pool', 'target'] <br>\n",
       "        <div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>gender</th>\n",
       "      <th>haircolor</th>\n",
       "      <th>country</th>\n",
       "      <th>population</th>\n",
       "      <th>binary_0</th>\n",
       "      <th>binary_1</th>\n",
       "      <th>binary_2</th>\n",
       "      <th>binary_3</th>\n",
       "      <th>patient_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>55.261578</td>\n",
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       "      <td>94.438359</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
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       "      <td>1</td>\n",
       "      <td>10000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>63.113091</td>\n",
       "      <td>165.563337</td>\n",
       "      <td>67.433016</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>10001</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>58.232216</td>\n",
       "      <td>160.859857</td>\n",
       "      <td>71.915385</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>10002</td>\n",
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       "      <th>3</th>\n",
       "      <td>58.996941</td>\n",
       "      <td>140.357415</td>\n",
       "      <td>115.606615</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>target</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>36.850195</td>\n",
       "      <td>189.983706</td>\n",
       "      <td>53.000581</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
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       "      <td>10004</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>9933</th>\n",
       "      <td>68.194783</td>\n",
       "      <td>127.495418</td>\n",
       "      <td>69.177329</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>9933</td>\n",
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       "    <tr>\n",
       "      <th>9947</th>\n",
       "      <td>64.290077</td>\n",
       "      <td>168.091011</td>\n",
       "      <td>63.511962</td>\n",
       "      <td>1.0</td>\n",
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       "      <th>9958</th>\n",
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       "      <td>80.695438</td>\n",
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       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>pool</td>\n",
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       "      <td>9958</td>\n",
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       "      <th>9982</th>\n",
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       "      <td>139.401060</td>\n",
       "      <td>89.848616</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>pool</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>9983</th>\n",
       "      <td>68.616093</td>\n",
       "      <td>167.546870</td>\n",
       "      <td>58.683367</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9983</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2000 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "<pybalance.utils.matching_data.MatchingData at 0x7f8f5079c550>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matcher.match()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c104c51f-a3be-4244-90df-843d1975327a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1250x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "match = matcher.get_best_match()\n",
    "m_data = m.copy().get_population('pool')\n",
    "m_data.loc[:, 'population'] = m_data['population'] + ' (prematch)'\n",
    "match.append(m_data)\n",
    "fig = plot_per_feature_loss(match, objective, 'target', debin=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0ca2b67-933c-4cfd-b433-315ed781edf4",
   "metadata": {},
   "source": [
    "## Optimize Gamma (Area Between CDFs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "12f7223f-06bf-4550-8952-3da49698130a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO [preprocess.py:335] Discretized age with bins [18.05, 27.54, 37.04, 46.53, 56.02, 65.51, 75.0].\n",
      "INFO [preprocess.py:335] Discretized height with bins [125.01, 136.68, 148.34, 160.01, 171.67, 183.34, 195.0].\n",
      "INFO [preprocess.py:335] Discretized weight with bins [50.0, 61.67, 73.33, 85.0, 96.66, 108.33, 120.0].\n",
      "INFO [matcher.py:65] Scaling features by factor 200.00 in order to use integer solver with <= 0.0000% loss.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'objective': 'gamma',\n",
       " 'pool_size': 1000,\n",
       " 'target_size': 1000,\n",
       " 'max_mismatch': None,\n",
       " 'time_limit': 360,\n",
       " 'num_workers': 4,\n",
       " 'ps_hinting': True,\n",
       " 'verbose': True}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "objective = gamma = GammaBalance(m)\n",
    "matcher = matcher_gamma = ConstraintSatisfactionMatcher(\n",
    "    m, \n",
    "    time_limit=time_limit,\n",
    "    objective=objective,\n",
    "    ps_hinting=True,\n",
    "    num_workers=4)\n",
    "matcher.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5895e06a-2aff-4afd-b771-71d2dd25e7a8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO [matcher.py:418] Solving for match population with pool size = 1000 and target size = 1000 subject to None balance constraint.\n",
      "INFO [matcher.py:421] Matching on 27 dimensions ...\n",
      "INFO [matcher.py:428] Building model variables and constraints ...\n",
      "INFO [matcher.py:437] Calculating bounds on feature variables ...\n",
      "INFO [matcher.py:527] Applying size constraints on pool and target ...\n",
      "INFO [matcher.py:533] Applying hint ...\n",
      "INFO [matcher.py:540] Training PS model as guide for solver ...\n",
      "/opt/miniconda3/envs/pybalance/lib/python3.9/site-packages/pybalance/lp/matcher.py:542: 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",
      "  target.loc[:, \"ix\"] = list(range(len(target)))\n",
      "/opt/miniconda3/envs/pybalance/lib/python3.9/site-packages/pybalance/lp/matcher.py:543: 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",
      "  pool.loc[:, \"ix\"] = list(range(len(pool)))\n",
      "INFO [preprocess.py:335] Discretized age with bins [18.05, 27.54, 37.04, 46.53, 56.02, 65.51, 75.0].\n",
      "INFO [preprocess.py:335] Discretized height with bins [125.01, 136.68, 148.34, 160.01, 171.67, 183.34, 195.0].\n",
      "INFO [preprocess.py:335] Discretized weight with bins [50.0, 61.67, 73.33, 85.0, 96.66, 108.33, 120.0].\n",
      "INFO [matcher.py:180] Training model SGDClassifier (iter 1/50, 0.001 min) ...\n",
      "INFO [matcher.py:136] Best propensity score match found:\n",
      "INFO [matcher.py:137] \tModel: SGDClassifier\n",
      "INFO [matcher.py:139] \t* alpha: 4.774501445415405\n",
      "INFO [matcher.py:139] \t* class_weight: None\n",
      "INFO [matcher.py:139] \t* early_stopping: True\n",
      "INFO [matcher.py:139] \t* fit_intercept: False\n",
      "INFO [matcher.py:139] \t* loss: modified_huber\n",
      "INFO [matcher.py:139] \t* max_iter: 1500\n",
      "INFO [matcher.py:139] \t* penalty: elasticnet\n",
      "INFO [matcher.py:140] \tScore (gamma): 0.2142\n",
      "INFO [matcher.py:141] \tSolution time: 0.002 min\n",
      "INFO [matcher.py:180] Training model LogisticRegression (iter 2/50, 0.002 min) ...\n",
      "INFO [matcher.py:136] Best propensity score match found:\n",
      "INFO [matcher.py:137] \tModel: LogisticRegression\n",
      "INFO [matcher.py:139] \t* C: 0.058541995189524146\n",
      "INFO [matcher.py:139] \t* fit_intercept: False\n",
      "INFO [matcher.py:139] \t* max_iter: 500\n",
      "INFO [matcher.py:139] \t* penalty: l2\n",
      "INFO [matcher.py:139] \t* solver: saga\n",
      "INFO [matcher.py:140] \tScore (gamma): 0.0472\n",
      "INFO [matcher.py:141] \tSolution time: 0.004 min\n",
      "INFO [matcher.py:180] Training model LogisticRegression (iter 3/50, 0.004 min) ...\n",
      "INFO [matcher.py:136] Best propensity score match found:\n",
      "INFO [matcher.py:137] \tModel: LogisticRegression\n",
      "INFO [matcher.py:139] \t* C: 0.0909955270741388\n",
      "INFO [matcher.py:139] \t* fit_intercept: True\n",
      "INFO [matcher.py:139] \t* max_iter: 500\n",
      "INFO [matcher.py:139] \t* penalty: l1\n",
      "INFO [matcher.py:139] \t* solver: saga\n",
      "INFO [matcher.py:140] \tScore (gamma): 0.0448\n",
      "INFO [matcher.py:141] \tSolution time: 0.005 min\n",
      "INFO [matcher.py:180] Training model SGDClassifier (iter 4/50, 0.005 min) ...\n",
      "INFO [matcher.py:180] Training model SGDClassifier (iter 5/50, 0.007 min) ...\n",
      "INFO [matcher.py:180] Training model LogisticRegression (iter 6/50, 0.008 min) ...\n",
      "/opt/miniconda3/envs/pybalance/lib/python3.9/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:136] Best propensity score match found:\n",
      "INFO [matcher.py:137] \tModel: LogisticRegression\n",
      "INFO [matcher.py:139] \t* C: 0.15085738749804528\n",
      "INFO [matcher.py:139] \t* fit_intercept: False\n",
      "INFO [matcher.py:139] \t* max_iter: 500\n",
      "INFO [matcher.py:139] \t* penalty: l1\n",
      "INFO [matcher.py:139] \t* solver: saga\n",
      "INFO [matcher.py:140] \tScore (gamma): 0.0347\n",
      "INFO [matcher.py:141] \tSolution time: 0.031 min\n",
      "INFO [matcher.py:180] Training model SGDClassifier (iter 7/50, 0.031 min) ...\n",
      "INFO [matcher.py:180] Training model LogisticRegression (iter 8/50, 0.032 min) ...\n",
      "INFO [matcher.py:180] Training model LogisticRegression (iter 9/50, 0.033 min) ...\n",
      "INFO [matcher.py:180] Training model LogisticRegression (iter 10/50, 0.037 min) ...\n",
      "/opt/miniconda3/envs/pybalance/lib/python3.9/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:136] Best propensity score match found:\n",
      "INFO [matcher.py:137] \tModel: LogisticRegression\n",
      "INFO [matcher.py:139] \t* C: 22.70303412073022\n",
      "INFO [matcher.py:139] \t* fit_intercept: False\n",
      "INFO [matcher.py:139] \t* max_iter: 500\n",
      "INFO [matcher.py:139] \t* penalty: l1\n",
      "INFO [matcher.py:139] \t* solver: saga\n",
      "INFO [matcher.py:140] \tScore (gamma): 0.0331\n",
      "INFO [matcher.py:141] \tSolution time: 0.061 min\n",
      "INFO [matcher.py:180] Training model LogisticRegression (iter 11/50, 0.061 min) ...\n",
      "INFO [matcher.py:180] Training model LogisticRegression (iter 12/50, 0.071 min) ...\n",
      "INFO [matcher.py:180] Training model LogisticRegression (iter 13/50, 0.072 min) ...\n",
      "/opt/miniconda3/envs/pybalance/lib/python3.9/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:180] Training model LogisticRegression (iter 14/50, 0.094 min) ...\n",
      "INFO [matcher.py:180] Training model SGDClassifier (iter 15/50, 0.097 min) ...\n",
      "INFO [matcher.py:180] Training model SGDClassifier (iter 16/50, 0.099 min) ...\n",
      "INFO [matcher.py:180] Training model LogisticRegression (iter 17/50, 0.100 min) ...\n",
      "INFO [matcher.py:180] Training model SGDClassifier (iter 18/50, 0.103 min) ...\n",
      "INFO [matcher.py:180] Training model LogisticRegression (iter 19/50, 0.104 min) ...\n",
      "/opt/miniconda3/envs/pybalance/lib/python3.9/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:136] Best propensity score match found:\n",
      "INFO [matcher.py:137] \tModel: LogisticRegression\n",
      "INFO [matcher.py:139] \t* C: 0.7964686611607528\n",
      "INFO [matcher.py:139] \t* fit_intercept: False\n",
      "INFO [matcher.py:139] \t* max_iter: 500\n",
      "INFO [matcher.py:139] \t* penalty: l1\n",
      "INFO [matcher.py:139] \t* solver: saga\n",
      "INFO [matcher.py:140] \tScore (gamma): 0.0308\n",
      "INFO [matcher.py:141] \tSolution time: 0.130 min\n",
      "INFO [matcher.py:180] Training model LogisticRegression (iter 20/50, 0.130 min) ...\n",
      "/opt/miniconda3/envs/pybalance/lib/python3.9/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:180] Training model LogisticRegression (iter 21/50, 0.151 min) ...\n",
      "INFO [matcher.py:180] Training model SGDClassifier (iter 22/50, 0.153 min) ...\n",
      "INFO [matcher.py:180] Training model LogisticRegression (iter 23/50, 0.154 min) ...\n",
      "/opt/miniconda3/envs/pybalance/lib/python3.9/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:180] Training model SGDClassifier (iter 24/50, 0.177 min) ...\n",
      "INFO [matcher.py:180] Training model SGDClassifier (iter 25/50, 0.178 min) ...\n",
      "INFO [matcher.py:180] Training model LogisticRegression (iter 26/50, 0.179 min) ...\n",
      "INFO [matcher.py:180] Training model SGDClassifier (iter 27/50, 0.194 min) ...\n",
      "INFO [matcher.py:180] Training model SGDClassifier (iter 28/50, 0.195 min) ...\n",
      "INFO [matcher.py:180] Training model LogisticRegression (iter 29/50, 0.196 min) ...\n",
      "INFO [matcher.py:180] Training model SGDClassifier (iter 30/50, 0.202 min) ...\n",
      "INFO [matcher.py:180] Training model SGDClassifier (iter 31/50, 0.203 min) ...\n",
      "INFO [matcher.py:180] Training model LogisticRegression (iter 32/50, 0.204 min) ...\n",
      "INFO [matcher.py:180] Training model LogisticRegression (iter 33/50, 0.208 min) ...\n",
      "/opt/miniconda3/envs/pybalance/lib/python3.9/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:180] Training model LogisticRegression (iter 34/50, 0.231 min) ...\n",
      "INFO [matcher.py:180] Training model SGDClassifier (iter 35/50, 0.241 min) ...\n",
      "INFO [matcher.py:180] Training model SGDClassifier (iter 36/50, 0.242 min) ...\n",
      "INFO [matcher.py:180] Training model SGDClassifier (iter 37/50, 0.243 min) ...\n",
      "INFO [matcher.py:180] Training model LogisticRegression (iter 38/50, 0.244 min) ...\n",
      "INFO [matcher.py:180] Training model SGDClassifier (iter 39/50, 0.256 min) ...\n",
      "INFO [matcher.py:180] Training model SGDClassifier (iter 40/50, 0.257 min) ...\n",
      "INFO [matcher.py:180] Training model LogisticRegression (iter 41/50, 0.258 min) ...\n",
      "INFO [matcher.py:180] Training model LogisticRegression (iter 42/50, 0.265 min) ...\n",
      "INFO [matcher.py:180] Training model SGDClassifier (iter 43/50, 0.266 min) ...\n",
      "INFO [matcher.py:180] Training model SGDClassifier (iter 44/50, 0.267 min) ...\n",
      "INFO [matcher.py:180] Training model SGDClassifier (iter 45/50, 0.269 min) ...\n",
      "INFO [matcher.py:180] Training model SGDClassifier (iter 46/50, 0.270 min) ...\n",
      "INFO [matcher.py:180] Training model LogisticRegression (iter 47/50, 0.271 min) ...\n",
      "INFO [matcher.py:180] Training model LogisticRegression (iter 48/50, 0.273 min) ...\n",
      "/opt/miniconda3/envs/pybalance/lib/python3.9/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:180] Training model SGDClassifier (iter 49/50, 0.295 min) ...\n",
      "INFO [matcher.py:180] Training model SGDClassifier (iter 50/50, 0.297 min) ...\n",
      "INFO [matcher.py:136] Best propensity score match found:\n",
      "INFO [matcher.py:137] \tModel: LogisticRegression\n",
      "INFO [matcher.py:139] \t* C: 0.7964686611607528\n",
      "INFO [matcher.py:139] \t* fit_intercept: False\n",
      "INFO [matcher.py:139] \t* max_iter: 500\n",
      "INFO [matcher.py:139] \t* penalty: l1\n",
      "INFO [matcher.py:139] \t* solver: saga\n",
      "INFO [matcher.py:140] \tScore (gamma): 0.0308\n",
      "INFO [matcher.py:141] \tSolution time: 0.130 min\n",
      "INFO [matcher.py:577] Hint achieves objective value = 100400.\n",
      "INFO [matcher.py:579] Applying hints ...\n",
      "INFO [matcher.py:611] Solving with 4 workers ...\n",
      "INFO [matcher.py:90] Initial balance score: 0.2110\n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 1, time = 0.05 m\n",
      "INFO [matcher.py:101] Objective:\t115600000.0\n",
      "INFO [matcher.py:120] Balance (gamma):\t0.0349\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 2, time = 0.13 m\n",
      "INFO [matcher.py:101] Objective:\t115400000.0\n",
      "INFO [matcher.py:120] Balance (gamma):\t0.0349\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 3, time = 0.14 m\n",
      "INFO [matcher.py:101] Objective:\t114600000.0\n",
      "INFO [matcher.py:120] Balance (gamma):\t0.0346\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 4, time = 0.14 m\n",
      "INFO [matcher.py:101] Objective:\t114400000.0\n",
      "INFO [matcher.py:120] Balance (gamma):\t0.0346\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 5, time = 0.16 m\n",
      "INFO [matcher.py:101] Objective:\t113800000.0\n",
      "INFO [matcher.py:120] Balance (gamma):\t0.0344\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 6, time = 0.17 m\n",
      "INFO [matcher.py:101] Objective:\t102000000.0\n",
      "INFO [matcher.py:120] Balance (gamma):\t0.0310\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 7, time = 0.17 m\n",
      "INFO [matcher.py:101] Objective:\t101400000.0\n",
      "INFO [matcher.py:120] Balance (gamma):\t0.0308\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 8, time = 0.19 m\n",
      "INFO [matcher.py:101] Objective:\t100800000.0\n",
      "INFO [matcher.py:120] Balance (gamma):\t0.0306\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 9, time = 0.20 m\n",
      "INFO [matcher.py:101] Objective:\t100400000.0\n",
      "INFO [matcher.py:120] Balance (gamma):\t0.0305\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 10, time = 0.26 m\n",
      "INFO [matcher.py:101] Objective:\t97800000.0\n",
      "INFO [matcher.py:120] Balance (gamma):\t0.0300\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 11, time = 0.26 m\n",
      "INFO [matcher.py:101] Objective:\t97200000.0\n",
      "INFO [matcher.py:120] Balance (gamma):\t0.0298\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 12, time = 0.31 m\n",
      "INFO [matcher.py:101] Objective:\t97000000.0\n",
      "INFO [matcher.py:120] Balance (gamma):\t0.0298\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 13, time = 0.32 m\n",
      "INFO [matcher.py:101] Objective:\t96600000.0\n",
      "INFO [matcher.py:120] Balance (gamma):\t0.0297\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 14, time = 0.37 m\n",
      "INFO [matcher.py:101] Objective:\t91000000.0\n",
      "INFO [matcher.py:120] Balance (gamma):\t0.0282\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 15, time = 0.37 m\n",
      "INFO [matcher.py:101] Objective:\t90800000.0\n",
      "INFO [matcher.py:120] Balance (gamma):\t0.0282\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 16, time = 0.38 m\n",
      "INFO [matcher.py:101] Objective:\t90600000.0\n",
      "INFO [matcher.py:120] Balance (gamma):\t0.0281\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 17, time = 0.42 m\n",
      "INFO [matcher.py:101] Objective:\t80800000.0\n",
      "INFO [matcher.py:120] Balance (gamma):\t0.0249\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 18, time = 0.43 m\n",
      "INFO [matcher.py:101] Objective:\t80600000.0\n",
      "INFO [matcher.py:120] Balance (gamma):\t0.0248\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 19, time = 0.44 m\n",
      "INFO [matcher.py:101] Objective:\t80400000.0\n",
      "INFO [matcher.py:120] Balance (gamma):\t0.0247\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 20, time = 0.48 m\n",
      "INFO [matcher.py:101] Objective:\t19000000.0\n",
      "INFO [matcher.py:120] Balance (gamma):\t0.0070\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 21, time = 0.64 m\n",
      "INFO [matcher.py:101] Objective:\t18800000.0\n",
      "INFO [matcher.py:120] Balance (gamma):\t0.0069\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:96] =========================================\n",
      "INFO [matcher.py:97] Solution 22, time = 0.66 m\n",
      "INFO [matcher.py:101] Objective:\t18400000.0\n",
      "INFO [matcher.py:120] Balance (gamma):\t0.0068\n",
      "INFO [matcher.py:125] Patients (pool):\t1000\n",
      "INFO [matcher.py:126] Patients (target):\t1000\n",
      "INFO [matcher.py:140]  \n",
      "INFO [matcher.py:618] Status = OPTIMAL\n",
      "INFO [matcher.py:619] Number of solutions found: 22\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "        <b>Headers Numeric: </b><br>\n",
       "        ['age', 'height', 'weight']<br><br>\n",
       "        <b>Headers Categoric: </b><br>\n",
       "        ['gender', 'haircolor', 'country', 'binary_0', 'binary_1', 'binary_2', 'binary_3'] <br><br>\n",
       "        <b>Populations</b> <br>\n",
       "        ['pool', 'target'] <br>\n",
       "        <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>gender</th>\n",
       "      <th>haircolor</th>\n",
       "      <th>country</th>\n",
       "      <th>population</th>\n",
       "      <th>binary_0</th>\n",
       "      <th>binary_1</th>\n",
       "      <th>binary_2</th>\n",
       "      <th>binary_3</th>\n",
       "      <th>patient_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>55.261578</td>\n",
       "      <td>139.396134</td>\n",
       "      <td>94.438359</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>target</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>63.113091</td>\n",
       "      <td>165.563337</td>\n",
       "      <td>67.433016</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>target</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>10001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>58.232216</td>\n",
       "      <td>160.859857</td>\n",
       "      <td>71.915385</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>target</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>58.996941</td>\n",
       "      <td>140.357415</td>\n",
       "      <td>115.606615</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>target</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>36.850195</td>\n",
       "      <td>189.983706</td>\n",
       "      <td>53.000581</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>target</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10004</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9932</th>\n",
       "      <td>72.514956</td>\n",
       "      <td>159.248205</td>\n",
       "      <td>118.505187</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>pool</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9933</th>\n",
       "      <td>68.194783</td>\n",
       "      <td>127.495418</td>\n",
       "      <td>69.177329</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>pool</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9965</th>\n",
       "      <td>41.035792</td>\n",
       "      <td>130.021437</td>\n",
       "      <td>80.495109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>pool</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9966</th>\n",
       "      <td>40.121009</td>\n",
       "      <td>168.339212</td>\n",
       "      <td>100.428001</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>pool</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>9966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9984</th>\n",
       "      <td>57.366166</td>\n",
       "      <td>151.483411</td>\n",
       "      <td>82.271539</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>pool</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9984</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2000 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "<pybalance.utils.matching_data.MatchingData at 0x7f9011ec1dc0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matcher.match()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "84afee09-642c-40d0-8929-0afa6544660b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1350x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x900 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1800 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "match = matcher.get_best_match()\n",
    "m_data = m.copy().get_population('pool')\n",
    "m_data.loc[:, 'population'] = m_data['population'] + ' (prematch)'\n",
    "match.append(m_data)\n",
    "fig = plot_per_feature_loss(match, objective, 'target', debin=False)\n",
    "fig = plot_numeric_features(match, hue_order=['pool (prematch)', 'pool', 'target', ])\n",
    "fig = plot_categoric_features(match,  hue_order=['pool (prematch)', 'pool', 'target'])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pybalance",
   "language": "python",
   "name": "pybalance"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.19"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}