result.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "500dc939-2c93-4cf5-9485-e5c7811bde6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import bz2\n",
    "import pickle \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "50e06b35-a0b3-42bf-a30a-0c4133a2616c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_result(fname):\n",
    "    if fname.endswith('.pbz2'):\n",
    "        with bz2.BZ2File(fname,'r') as h:\n",
    "            data = pickle.load(h)\n",
    "    else:\n",
    "        with open(fname,'rb') as h:\n",
    "            data = pickle.load(h)\n",
    "    return data\n",
    "\n",
    "def pass_result(data):\n",
    "    result_array = []\n",
    "\n",
    "    for run in data['runs']:\n",
    "        set_sparsity = run['set_sparsity']\n",
    "        set_metrics = run['run']['set_metrics']\n",
    "        accuracies = [metrics[-1] for metrics in set_metrics]\n",
    "        result_array.append((set_sparsity, accuracies))\n",
    "    \n",
    "    for item in result_array:\n",
    "        sparsity_level = item[0]\n",
    "        last_numbers = item[1]\n",
    "   \n",
    "    max_value = max(result_array, key=lambda x: x[1][-1])\n",
    "    max_sparsity_level = max_value[0]\n",
    "    max_last_number = max_value[1][-1]\n",
    "    return (max_sparsity_level,max_last_number,result_array)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a58cf1ff-343d-4f17-8798-85a09b31fd9a",
   "metadata": {},
   "source": [
    "## SET FMNIST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "451691d8-6cd8-497a-945c-2812bf1d636b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/ft/zv9hz6rx7q3b27t_fsw8h4300000gn/T/ipykernel_92975/537062134.py:4: DeprecationWarning: Please use `csr_matrix` from the `scipy.sparse` namespace, the `scipy.sparse.csr` namespace is deprecated.\n",
      "  data = pickle.load(h)\n"
     ]
    }
   ],
   "source": [
    "result_set_0 = load_result('/Users/arcade/Master/thesis_project/result/benchmarks_fmnist/set_mlp_density_run_0.pickle.pbz2')\n",
    "result_set_1 = load_result('/Users/arcade/Master/thesis_project/result/benchmarks_fmnist/set_mlp_density_run_1.pickle.pbz2')\n",
    "result_set_2 = load_result('/Users/arcade/Master/thesis_project/result/benchmarks_fmnist/set_mlp_density_run_2.pickle.pbz2')\n",
    "result_set_3 = load_result('/Users/arcade/Master/thesis_project/result/benchmarks_fmnist/set_mlp_density_run_3.pickle.pbz2')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8d08487c-505f-48b8-a843-535b94f3207c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Sparsity Level  Values                                         Accuracies\n",
      "0              21   0.814  [(1, [0.104, 0.088, 0.109, 0.102, 0.112, 0.088...\n",
      "1              31   0.821  [(1, [0.099, 0.095, 0.108, 0.108, 0.099, 0.095...\n",
      "2              27   0.827  [(1, [0.092, 0.097, 0.092, 0.097, 0.098, 0.099...\n",
      "3              27   0.826  [(1, [0.107, 0.085, 0.085, 0.085, 0.085, 0.106...\n"
     ]
    }
   ],
   "source": [
    "result = []\n",
    "\n",
    "for i in range(4):\n",
    "    result_set = globals()['result_set_{}'.format(i)]  \n",
    "    result_run = pass_result(result_set)\n",
    "    sparsity_level = result_run[0]\n",
    "    max_accuracy = result_run[1]\n",
    "    accuracies = result_run[2]\n",
    "    result.append((sparsity_level, max_accuracy,accuracies))\n",
    "\n",
    "df = pd.DataFrame(result, columns=['Sparsity Level', 'Values','Accuracies'])\n",
    "\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "0f30bcbd-cde8-4892-bc63-6596ceb6e086",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sparsity</th>\n",
       "      <th>average</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.11175</td>\n",
       "      <td>0.120</td>\n",
       "      <td>0.107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>0.11425</td>\n",
       "      <td>0.120</td>\n",
       "      <td>0.107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>0.12025</td>\n",
       "      <td>0.130</td>\n",
       "      <td>0.113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>0.54075</td>\n",
       "      <td>0.628</td>\n",
       "      <td>0.340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9</td>\n",
       "      <td>0.71400</td>\n",
       "      <td>0.734</td>\n",
       "      <td>0.682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11</td>\n",
       "      <td>0.77450</td>\n",
       "      <td>0.792</td>\n",
       "      <td>0.744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>13</td>\n",
       "      <td>0.78475</td>\n",
       "      <td>0.791</td>\n",
       "      <td>0.780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>15</td>\n",
       "      <td>0.79050</td>\n",
       "      <td>0.807</td>\n",
       "      <td>0.770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>17</td>\n",
       "      <td>0.79675</td>\n",
       "      <td>0.806</td>\n",
       "      <td>0.786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>19</td>\n",
       "      <td>0.81400</td>\n",
       "      <td>0.822</td>\n",
       "      <td>0.796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>21</td>\n",
       "      <td>0.80925</td>\n",
       "      <td>0.815</td>\n",
       "      <td>0.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>23</td>\n",
       "      <td>0.80150</td>\n",
       "      <td>0.810</td>\n",
       "      <td>0.798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>25</td>\n",
       "      <td>0.80850</td>\n",
       "      <td>0.820</td>\n",
       "      <td>0.792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>27</td>\n",
       "      <td>0.81700</td>\n",
       "      <td>0.827</td>\n",
       "      <td>0.803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>29</td>\n",
       "      <td>0.81600</td>\n",
       "      <td>0.826</td>\n",
       "      <td>0.803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>31</td>\n",
       "      <td>0.82025</td>\n",
       "      <td>0.825</td>\n",
       "      <td>0.814</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    sparsity  average    max    min\n",
       "0          1  0.11175  0.120  0.107\n",
       "1          3  0.11425  0.120  0.107\n",
       "2          5  0.12025  0.130  0.113\n",
       "3          7  0.54075  0.628  0.340\n",
       "4          9  0.71400  0.734  0.682\n",
       "5         11  0.77450  0.792  0.744\n",
       "6         13  0.78475  0.791  0.780\n",
       "7         15  0.79050  0.807  0.770\n",
       "8         17  0.79675  0.806  0.786\n",
       "9         19  0.81400  0.822  0.796\n",
       "10        21  0.80925  0.815  0.801\n",
       "11        23  0.80150  0.810  0.798\n",
       "12        25  0.80850  0.820  0.792\n",
       "13        27  0.81700  0.827  0.803\n",
       "14        29  0.81600  0.826  0.803\n",
       "15        31  0.82025  0.825  0.814"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fmnist_result_0 = df['Accuracies'][0]\n",
    "fmnist_result_1 = df['Accuracies'][1]\n",
    "fmnist_result_2 = df['Accuracies'][2]\n",
    "fmnist_result_3 = df['Accuracies'][3]\n",
    "fmnist_df_0 = pd.DataFrame(fmnist_result_0,columns = ['sparsity','accuracy_1'])\n",
    "fmnist_df_1 = pd.DataFrame(fmnist_result_1,columns = ['sparsity','accuracy_2'])\n",
    "fmnist_df_2 = pd.DataFrame(fmnist_result_2,columns = ['sparsity','accuracy_3'])\n",
    "fmnist_df_3 = pd.DataFrame(fmnist_result_3,columns = ['sparsity','accuracy_4'])\n",
    "fmnist_df_0['accuracy_1'] = fmnist_df_0['accuracy_1'].apply(max)\n",
    "fmnist_df_1['accuracy_2'] = fmnist_df_1['accuracy_2'].apply(max)\n",
    "fmnist_df_2['accuracy_3'] = fmnist_df_2['accuracy_3'].apply(max)\n",
    "fmnist_df_3['accuracy_4'] = fmnist_df_3['accuracy_4'].apply(max)\n",
    "fmnist_result = pd.merge(fmnist_df_0,fmnist_df_1)\n",
    "fmnist_result = pd.merge(fmnist_result,fmnist_df_2)\n",
    "fmnist_result = pd.merge(fmnist_result,fmnist_df_3)\n",
    "fmnist_result['average'] = fmnist_result[['accuracy_1','accuracy_2','accuracy_3','accuracy_4']].mean(axis = 1)\n",
    "fmnist_result['max'] = fmnist_result[['accuracy_1','accuracy_2','accuracy_3','accuracy_4']].max(axis = 1)\n",
    "fmnist_result['min'] = fmnist_result[['accuracy_1','accuracy_2','accuracy_3','accuracy_4']].min(axis = 1)\n",
    "fmnist_result = fmnist_result[['sparsity','average','max','min']]\n",
    "fmnist_result"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aabf274d-96ed-4165-a1d9-d15a85d8b263",
   "metadata": {},
   "source": [
    "## SET_MOTIF FMNIST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "143adf44-0cd5-440a-9609-6f2189b5ae9e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/ft/zv9hz6rx7q3b27t_fsw8h4300000gn/T/ipykernel_92975/537062134.py:4: DeprecationWarning: Please use `csr_matrix` from the `scipy.sparse` namespace, the `scipy.sparse.csr` namespace is deprecated.\n",
      "  data = pickle.load(h)\n"
     ]
    }
   ],
   "source": [
    "result_motif_set_0 = load_result('/Users/arcade/Master/thesis_project/result/benchmarks_motif_fmnist/set_mlp_density_run_0.pickle.pbz2')\n",
    "result_motif_set_1 = load_result('/Users/arcade/Master/thesis_project/result/benchmarks_motif_fmnist/set_mlp_density_run_1.pickle.pbz2')\n",
    "result_motif_set_2 = load_result('/Users/arcade/Master/thesis_project/result/benchmarks_motif_fmnist/set_mlp_density_run_2.pickle.pbz2')\n",
    "result_motif_set_3 = load_result('/Users/arcade/Master/thesis_project/result/benchmarks_motif_fmnist/set_mlp_density_run_3.pickle.pbz2')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "6816dbd0-5014-4680-bb48-4351f8be1507",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Sparsity Level  Values                                         Accuracies\n",
      "0              21   0.814  [(1, [0.104, 0.088, 0.109, 0.102, 0.112, 0.088...\n",
      "1              31   0.821  [(1, [0.099, 0.095, 0.108, 0.108, 0.099, 0.095...\n",
      "2              27   0.827  [(1, [0.092, 0.097, 0.092, 0.097, 0.098, 0.099...\n",
      "3              27   0.826  [(1, [0.107, 0.085, 0.085, 0.085, 0.085, 0.106...\n"
     ]
    }
   ],
   "source": [
    "result_motif = []\n",
    "\n",
    "for i in range(4):\n",
    "    result_set = globals()['result_motif_set_{}'.format(i)]  \n",
    "    result_run = pass_result(result_set)\n",
    "    sparsity_level = result_run[0]\n",
    "    max_accuracy = result_run[1]\n",
    "    accuracies = result_run[2]\n",
    "    result_motif.append((sparsity_level, max_accuracy,accuracies))\n",
    "\n",
    "df_motif = pd.DataFrame(result_motif, columns=['Sparsity Level', 'Values','Accuracies'])\n",
    "\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "42ed6df0-3869-453d-8b07-d2a7e8506b18",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sparsity</th>\n",
       "      <th>average</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.11175</td>\n",
       "      <td>0.120</td>\n",
       "      <td>0.107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>0.11425</td>\n",
       "      <td>0.120</td>\n",
       "      <td>0.107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>0.11425</td>\n",
       "      <td>0.120</td>\n",
       "      <td>0.107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>0.22425</td>\n",
       "      <td>0.337</td>\n",
       "      <td>0.115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9</td>\n",
       "      <td>0.67900</td>\n",
       "      <td>0.721</td>\n",
       "      <td>0.611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11</td>\n",
       "      <td>0.73500</td>\n",
       "      <td>0.767</td>\n",
       "      <td>0.711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>13</td>\n",
       "      <td>0.77150</td>\n",
       "      <td>0.790</td>\n",
       "      <td>0.755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>15</td>\n",
       "      <td>0.78250</td>\n",
       "      <td>0.798</td>\n",
       "      <td>0.773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>17</td>\n",
       "      <td>0.79050</td>\n",
       "      <td>0.803</td>\n",
       "      <td>0.780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>19</td>\n",
       "      <td>0.79100</td>\n",
       "      <td>0.811</td>\n",
       "      <td>0.775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>21</td>\n",
       "      <td>0.80250</td>\n",
       "      <td>0.812</td>\n",
       "      <td>0.787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>23</td>\n",
       "      <td>0.79650</td>\n",
       "      <td>0.801</td>\n",
       "      <td>0.787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>25</td>\n",
       "      <td>0.80225</td>\n",
       "      <td>0.823</td>\n",
       "      <td>0.783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>27</td>\n",
       "      <td>0.80375</td>\n",
       "      <td>0.816</td>\n",
       "      <td>0.792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>29</td>\n",
       "      <td>0.80800</td>\n",
       "      <td>0.815</td>\n",
       "      <td>0.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>31</td>\n",
       "      <td>0.81050</td>\n",
       "      <td>0.814</td>\n",
       "      <td>0.807</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    sparsity  average    max    min\n",
       "0          1  0.11175  0.120  0.107\n",
       "1          3  0.11425  0.120  0.107\n",
       "2          5  0.11425  0.120  0.107\n",
       "3          7  0.22425  0.337  0.115\n",
       "4          9  0.67900  0.721  0.611\n",
       "5         11  0.73500  0.767  0.711\n",
       "6         13  0.77150  0.790  0.755\n",
       "7         15  0.78250  0.798  0.773\n",
       "8         17  0.79050  0.803  0.780\n",
       "9         19  0.79100  0.811  0.775\n",
       "10        21  0.80250  0.812  0.787\n",
       "11        23  0.79650  0.801  0.787\n",
       "12        25  0.80225  0.823  0.783\n",
       "13        27  0.80375  0.816  0.792\n",
       "14        29  0.80800  0.815  0.801\n",
       "15        31  0.81050  0.814  0.807"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fmnist_motif_result_0 = df_motif['Accuracies'][0]\n",
    "fmnist_motif_result_1 = df_motif['Accuracies'][1]\n",
    "fmnist_motif_result_2 = df_motif['Accuracies'][2]\n",
    "fmnist_motif_result_3 = df_motif['Accuracies'][3]\n",
    "fmnist_motif_df_0 = pd.DataFrame(fmnist_motif_result_0,columns = ['sparsity','accuracy_1'])\n",
    "fmnist_motif_df_1 = pd.DataFrame(fmnist_motif_result_1,columns = ['sparsity','accuracy_2'])\n",
    "fmnist_motif_df_2 = pd.DataFrame(fmnist_motif_result_2,columns = ['sparsity','accuracy_3'])\n",
    "fmnist_motif_df_3 = pd.DataFrame(fmnist_motif_result_3,columns = ['sparsity','accuracy_4'])\n",
    "fmnist_motif_df_0['accuracy_1'] = fmnist_motif_df_0['accuracy_1'].apply(max)\n",
    "fmnist_motif_df_1['accuracy_2'] = fmnist_motif_df_1['accuracy_2'].apply(max)\n",
    "fmnist_motif_df_2['accuracy_3'] = fmnist_motif_df_2['accuracy_3'].apply(max)\n",
    "fmnist_motif_df_3['accuracy_4'] = fmnist_motif_df_3['accuracy_4'].apply(max)\n",
    "fmnist_motif_result = pd.merge(fmnist_motif_df_0,fmnist_motif_df_1)\n",
    "fmnist_motif_result = pd.merge(fmnist_motif_result,fmnist_motif_df_2)\n",
    "fmnist_motif_result = pd.merge(fmnist_motif_result,fmnist_motif_df_3)\n",
    "fmnist_motif_result['average'] = fmnist_motif_result[['accuracy_1','accuracy_2','accuracy_3','accuracy_4']].mean(axis = 1)\n",
    "fmnist_motif_result['max'] = fmnist_motif_result[['accuracy_1','accuracy_2','accuracy_3','accuracy_4']].max(axis = 1)\n",
    "fmnist_motif_result['min'] = fmnist_motif_result[['accuracy_1','accuracy_2','accuracy_3','accuracy_4']].min(axis = 1)\n",
    "fmnist_motif_result = fmnist_motif_result[['sparsity','average','max','min']]\n",
    "fmnist_motif_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "5fc3d6a2-4cca-48e1-8ff4-9f09d7596359",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f7ddb9aa4f0>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, figsize=(15, 6))\n",
    "axs[0].plot(fmnist_result['sparsity'], fmnist_result['max'], color='lightsteelblue')\n",
    "axs[0].plot(fmnist_result['sparsity'], fmnist_result['min'], color='lightsteelblue')\n",
    "axs[0].fill_between(fmnist_result['sparsity'], fmnist_result['min'], fmnist_result['max'], label='Error', color='lightsteelblue', alpha=0.4)\n",
    "axs[0].plot(fmnist_result['sparsity'], fmnist_result['average'], label = 'Average',color='blue')\n",
    "axs[0].set_xlabel('Sparsity')\n",
    "axs[0].set_ylabel('Accuracy')\n",
    "axs[0].set_title('Result SET FMNIST')\n",
    "axs[0].legend(loc = 'lower right',fontsize = 'medium')\n",
    "\n",
    "axs[1].plot(fmnist_motif_result['sparsity'], fmnist_motif_result['max'], color='rosybrown')\n",
    "axs[1].plot(fmnist_motif_result['sparsity'], fmnist_motif_result['min'], color='rosybrown')\n",
    "axs[1].fill_between(fmnist_motif_result['sparsity'], fmnist_motif_result['min'], fmnist_motif_result['max'], label='Error', color='rosybrown', alpha=0.4)\n",
    "axs[1].plot(fmnist_motif_result['sparsity'], fmnist_motif_result['average'], label = 'Average',color='brown')\n",
    "axs[1].set_xlabel('Sparsity')\n",
    "axs[1].set_ylabel('Accuracy')\n",
    "axs[1].set_title('Result SET-motif FMNIST')\n",
    "axs[1].legend(loc = 'lower right',fontsize = 'medium')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbac0f32-ae23-4d30-879a-bdc41a04dab4",
   "metadata": {},
   "source": [
    "## SET_FMNIST_STRESS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "87b8292a-9a56-4591-97f8-716a0169a340",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/ft/zv9hz6rx7q3b27t_fsw8h4300000gn/T/ipykernel_92975/537062134.py:4: DeprecationWarning: Please use `csr_matrix` from the `scipy.sparse` namespace, the `scipy.sparse.csr` namespace is deprecated.\n",
      "  data = pickle.load(h)\n"
     ]
    }
   ],
   "source": [
    "result_set_stress_0 = load_result('/Users/arcade/Master/thesis_project/result/benchmarks_fmnist_16/set_mlp_density_run_0.pickle.pbz2')\n",
    "result_set_stress_1 = load_result('/Users/arcade/Master/thesis_project/result/benchmarks_fmnist_16/set_mlp_density_run_1.pickle.pbz2')\n",
    "result_set_stress_2 = load_result('/Users/arcade/Master/thesis_project/result/benchmarks_fmnist_16/set_mlp_density_run_2.pickle.pbz2')\n",
    "result_set_stress_3 = load_result('/Users/arcade/Master/thesis_project/result/benchmarks_fmnist_16/set_mlp_density_run_3.pickle.pbz2')\n",
    "result_set_stress_4 = load_result('/Users/arcade/Master/thesis_project/result/benchmarks_fmnist_16/set_mlp_density_run_4.pickle.pbz2')\n",
    "result_set_stress_5 = load_result('/Users/arcade/Master/thesis_project/result/benchmarks_fmnist_16/set_mlp_density_run_5.pickle.pbz2')\n",
    "result_set_stress_6 = load_result('/Users/arcade/Master/thesis_project/result/benchmarks_fmnist_16/set_mlp_density_run_6.pickle.pbz2')\n",
    "result_set_stress_7 = load_result('/Users/arcade/Master/thesis_project/result/benchmarks_fmnist_16/set_mlp_density_run_7.pickle.pbz2')\n",
    "result_set_stress_8 = load_result('/Users/arcade/Master/thesis_project/result/benchmarks_fmnist_16/set_mlp_density_run_8.pickle.pbz2')\n",
    "result_set_stress_9 = load_result('/Users/arcade/Master/thesis_project/result/benchmarks_fmnist_16/set_mlp_density_run_9.pickle.pbz2')\n",
    "result_set_stress_10 = load_result('/Users/arcade/Master/thesis_project/result/benchmarks_fmnist_16/set_mlp_density_run_10.pickle.pbz2')\n",
    "result_set_stress_11 = load_result('/Users/arcade/Master/thesis_project/result/benchmarks_fmnist_16/set_mlp_density_run_11.pickle.pbz2')\n",
    "result_set_stress_12 = load_result('/Users/arcade/Master/thesis_project/result/benchmarks_fmnist_16/set_mlp_density_run_12.pickle.pbz2')\n",
    "result_set_stress_13 = load_result('/Users/arcade/Master/thesis_project/result/benchmarks_fmnist_16/set_mlp_density_run_13.pickle.pbz2')\n",
    "result_set_stress_14 = load_result('/Users/arcade/Master/thesis_project/result/benchmarks_fmnist_16/set_mlp_density_run_14.pickle.pbz2')\n",
    "result_set_stress_15 = load_result('/Users/arcade/Master/thesis_project/result/benchmarks_fmnist_16/set_mlp_density_run_15.pickle.pbz2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "6bb7cbea-cc97-4acf-81b4-2a72961e663b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Sparsity Level  Values                                         Accuracies\n",
      "0               21   0.814  [(1, [0.104, 0.088, 0.109, 0.102, 0.112, 0.088...\n",
      "1               31   0.821  [(1, [0.099, 0.095, 0.108, 0.108, 0.099, 0.095...\n",
      "2               27   0.827  [(1, [0.092, 0.097, 0.092, 0.097, 0.098, 0.099...\n",
      "3               27   0.826  [(1, [0.107, 0.085, 0.085, 0.085, 0.085, 0.106...\n",
      "4               21   0.816  [(1, [0.098, 0.091, 0.098, 0.091, 0.091, 0.097...\n",
      "5               31   0.825  [(1, [0.116, 0.098, 0.118, 0.116, 0.116, 0.1, ...\n",
      "6               29   0.822  [(1, [0.103, 0.088, 0.103, 0.106, 0.086, 0.088...\n",
      "7               27   0.807  [(1, [0.096, 0.091, 0.099, 0.099, 0.092, 0.11,...\n",
      "8               27   0.812  [(1, [0.106, 0.106, 0.106, 0.111, 0.106, 0.106...\n",
      "9               31   0.841  [(1, [0.109, 0.109, 0.104, 0.094, 0.094, 0.109...\n",
      "10              27   0.833  [(1, [0.093, 0.108, 0.112, 0.088, 0.088, 0.112...\n",
      "11              29   0.819  [(1, [0.1, 0.111, 0.104, 0.104, 0.1, 0.118, 0....\n",
      "12              29   0.816  [(1, [0.109, 0.119, 0.089, 0.098, 0.101, 0.089...\n",
      "13              23   0.817  [(1, [0.078, 0.104, 0.092, 0.109, 0.095, 0.095...\n",
      "14              31   0.812  [(1, [0.087, 0.104, 0.095, 0.104, 0.108, 0.088...\n",
      "15              19   0.812  [(1, [0.103, 0.1, 0.098, 0.1, 0.108, 0.097, 0....\n"
     ]
    }
   ],
   "source": [
    "result_stress = []\n",
    "\n",
    "for i in range(16):\n",
    "    result_set = globals()['result_set_stress_{}'.format(i)]  \n",
    "    result_run = pass_result(result_set)\n",
    "    sparsity_level = result_run[0]\n",
    "    max_accuracy = result_run[1]\n",
    "    accuracies = result_run[2]\n",
    "    result_stress.append((sparsity_level, max_accuracy,accuracies))\n",
    "\n",
    "df_stress = pd.DataFrame(result_stress, columns=['Sparsity Level', 'Values','Accuracies'])\n",
    "\n",
    "print(df_stress)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f2d81945-5f40-4ca6-9753-b8515413fd97",
   "metadata": {},
   "outputs": [],
   "source": [
    "result_stress = []\n",
    "\n",
    "for i in range(16):\n",
    "    result_set = globals()['result_set_stress_{}'.format(i)]  \n",
    "    result_run = pass_result(result_set)\n",
    "    sparsity_level = result_run[0]\n",
    "    max_accuracy = result_run[1]\n",
    "    accuracies = result_run[2]\n",
    "    result_stress.append((sparsity_level, max_accuracy,accuracies))\n",
    "\n",
    "df_stress = pd.DataFrame(result_stress, columns=['Sparsity Level', 'Values','Accuracies'])\n",
    "\n",
    "print(df_stress)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "aca34056-b040-4766-afa3-3dd6e839d8bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sparsity</th>\n",
       "      <th>average</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.112188</td>\n",
       "      <td>0.122</td>\n",
       "      <td>0.098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>0.113688</td>\n",
       "      <td>0.122</td>\n",
       "      <td>0.107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>0.112438</td>\n",
       "      <td>0.130</td>\n",
       "      <td>0.098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>0.555437</td>\n",
       "      <td>0.664</td>\n",
       "      <td>0.340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9</td>\n",
       "      <td>0.715875</td>\n",
       "      <td>0.768</td>\n",
       "      <td>0.660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11</td>\n",
       "      <td>0.766750</td>\n",
       "      <td>0.794</td>\n",
       "      <td>0.728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>13</td>\n",
       "      <td>0.783062</td>\n",
       "      <td>0.806</td>\n",
       "      <td>0.738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>15</td>\n",
       "      <td>0.790187</td>\n",
       "      <td>0.812</td>\n",
       "      <td>0.767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>17</td>\n",
       "      <td>0.799188</td>\n",
       "      <td>0.815</td>\n",
       "      <td>0.782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>19</td>\n",
       "      <td>0.804000</td>\n",
       "      <td>0.826</td>\n",
       "      <td>0.786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>21</td>\n",
       "      <td>0.807375</td>\n",
       "      <td>0.833</td>\n",
       "      <td>0.793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>23</td>\n",
       "      <td>0.809250</td>\n",
       "      <td>0.839</td>\n",
       "      <td>0.790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>25</td>\n",
       "      <td>0.809813</td>\n",
       "      <td>0.839</td>\n",
       "      <td>0.789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>27</td>\n",
       "      <td>0.812500</td>\n",
       "      <td>0.834</td>\n",
       "      <td>0.779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>29</td>\n",
       "      <td>0.811937</td>\n",
       "      <td>0.826</td>\n",
       "      <td>0.793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>31</td>\n",
       "      <td>0.817750</td>\n",
       "      <td>0.842</td>\n",
       "      <td>0.802</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    sparsity   average    max    min\n",
       "0          1  0.112188  0.122  0.098\n",
       "1          3  0.113688  0.122  0.107\n",
       "2          5  0.112438  0.130  0.098\n",
       "3          7  0.555437  0.664  0.340\n",
       "4          9  0.715875  0.768  0.660\n",
       "5         11  0.766750  0.794  0.728\n",
       "6         13  0.783062  0.806  0.738\n",
       "7         15  0.790187  0.812  0.767\n",
       "8         17  0.799188  0.815  0.782\n",
       "9         19  0.804000  0.826  0.786\n",
       "10        21  0.807375  0.833  0.793\n",
       "11        23  0.809250  0.839  0.790\n",
       "12        25  0.809813  0.839  0.789\n",
       "13        27  0.812500  0.834  0.779\n",
       "14        29  0.811937  0.826  0.793\n",
       "15        31  0.817750  0.842  0.802"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fmnist_stress_result_0 = df_stress['Accuracies'][0]\n",
    "fmnist_stress_result_1 = df_stress['Accuracies'][1]\n",
    "fmnist_stress_result_2 = df_stress['Accuracies'][2]\n",
    "fmnist_stress_result_3 = df_stress['Accuracies'][3]\n",
    "fmnist_stress_result_4 = df_stress['Accuracies'][4]\n",
    "fmnist_stress_result_5 = df_stress['Accuracies'][5]\n",
    "fmnist_stress_result_6 = df_stress['Accuracies'][6]\n",
    "fmnist_stress_result_7 = df_stress['Accuracies'][7]\n",
    "fmnist_stress_result_8 = df_stress['Accuracies'][8]\n",
    "fmnist_stress_result_9 = df_stress['Accuracies'][9]\n",
    "fmnist_stress_result_10 = df_stress['Accuracies'][10]\n",
    "fmnist_stress_result_11 = df_stress['Accuracies'][11]\n",
    "fmnist_stress_result_12 = df_stress['Accuracies'][12]\n",
    "fmnist_stress_result_13 = df_stress['Accuracies'][13]\n",
    "fmnist_stress_result_14 = df_stress['Accuracies'][14]\n",
    "fmnist_stress_result_15 = df_stress['Accuracies'][15]\n",
    "fmnist_stress_df_0 = pd.DataFrame(fmnist_stress_result_0,columns = ['sparsity','accuracy_1'])\n",
    "fmnist_stress_df_1 = pd.DataFrame(fmnist_stress_result_1,columns = ['sparsity','accuracy_2'])\n",
    "fmnist_stress_df_2 = pd.DataFrame(fmnist_stress_result_2,columns = ['sparsity','accuracy_3'])\n",
    "fmnist_stress_df_3 = pd.DataFrame(fmnist_stress_result_3,columns = ['sparsity','accuracy_4'])\n",
    "fmnist_stress_df_4 = pd.DataFrame(fmnist_stress_result_4,columns = ['sparsity','accuracy_5'])\n",
    "fmnist_stress_df_5 = pd.DataFrame(fmnist_stress_result_5,columns = ['sparsity','accuracy_6'])\n",
    "fmnist_stress_df_6 = pd.DataFrame(fmnist_stress_result_6,columns = ['sparsity','accuracy_7'])\n",
    "fmnist_stress_df_7 = pd.DataFrame(fmnist_stress_result_7,columns = ['sparsity','accuracy_8'])\n",
    "fmnist_stress_df_8 = pd.DataFrame(fmnist_stress_result_8,columns = ['sparsity','accuracy_9'])\n",
    "fmnist_stress_df_9 = pd.DataFrame(fmnist_stress_result_9,columns = ['sparsity','accuracy_10'])\n",
    "fmnist_stress_df_10 = pd.DataFrame(fmnist_stress_result_10,columns = ['sparsity','accuracy_11'])\n",
    "fmnist_stress_df_11 = pd.DataFrame(fmnist_stress_result_11,columns = ['sparsity','accuracy_12'])\n",
    "fmnist_stress_df_12 = pd.DataFrame(fmnist_stress_result_12,columns = ['sparsity','accuracy_13'])\n",
    "fmnist_stress_df_13 = pd.DataFrame(fmnist_stress_result_13,columns = ['sparsity','accuracy_14'])\n",
    "fmnist_stress_df_14 = pd.DataFrame(fmnist_stress_result_14,columns = ['sparsity','accuracy_15'])\n",
    "fmnist_stress_df_15 = pd.DataFrame(fmnist_stress_result_15,columns = ['sparsity','accuracy_16'])\n",
    "fmnist_stress_df_0['accuracy_1'] = fmnist_stress_df_0['accuracy_1'].apply(max)\n",
    "fmnist_stress_df_1['accuracy_2'] = fmnist_stress_df_1['accuracy_2'].apply(max)\n",
    "fmnist_stress_df_2['accuracy_3'] = fmnist_stress_df_2['accuracy_3'].apply(max)\n",
    "fmnist_stress_df_3['accuracy_4'] = fmnist_stress_df_3['accuracy_4'].apply(max)\n",
    "fmnist_stress_df_4['accuracy_5'] = fmnist_stress_df_4['accuracy_5'].apply(max) \n",
    "fmnist_stress_df_5['accuracy_6'] = fmnist_stress_df_5['accuracy_6'].apply(max)\n",
    "fmnist_stress_df_6['accuracy_7'] = fmnist_stress_df_6['accuracy_7'].apply(max)\n",
    "fmnist_stress_df_7['accuracy_8'] = fmnist_stress_df_7['accuracy_8'].apply(max)\n",
    "fmnist_stress_df_8['accuracy_9'] = fmnist_stress_df_8['accuracy_9'].apply(max)\n",
    "fmnist_stress_df_9['accuracy_10'] = fmnist_stress_df_9['accuracy_10'].apply(max)\n",
    "fmnist_stress_df_10['accuracy_11'] = fmnist_stress_df_10['accuracy_11'].apply(max)\n",
    "fmnist_stress_df_11['accuracy_12'] = fmnist_stress_df_11['accuracy_12'].apply(max)\n",
    "fmnist_stress_df_12['accuracy_13'] = fmnist_stress_df_12['accuracy_13'].apply(max)\n",
    "fmnist_stress_df_13['accuracy_14'] = fmnist_stress_df_13['accuracy_14'].apply(max)\n",
    "fmnist_stress_df_14['accuracy_15'] = fmnist_stress_df_14['accuracy_15'].apply(max)\n",
    "fmnist_stress_df_15['accuracy_16'] = fmnist_stress_df_15['accuracy_16'].apply(max)\n",
    "fmnist_stress_result = pd.merge(fmnist_stress_df_0,fmnist_stress_df_1)\n",
    "fmnist_stress_result = pd.merge(fmnist_stress_result,fmnist_stress_df_2)\n",
    "fmnist_stress_result = pd.merge(fmnist_stress_result,fmnist_stress_df_3)\n",
    "fmnist_stress_result = pd.merge(fmnist_stress_result,fmnist_stress_df_4)\n",
    "fmnist_stress_result = pd.merge(fmnist_stress_result,fmnist_stress_df_5)\n",
    "fmnist_stress_result = pd.merge(fmnist_stress_result,fmnist_stress_df_6)\n",
    "fmnist_stress_result = pd.merge(fmnist_stress_result,fmnist_stress_df_7)\n",
    "fmnist_stress_result = pd.merge(fmnist_stress_result,fmnist_stress_df_8)\n",
    "fmnist_stress_result = pd.merge(fmnist_stress_result,fmnist_stress_df_9)\n",
    "fmnist_stress_result = pd.merge(fmnist_stress_result,fmnist_stress_df_10)\n",
    "fmnist_stress_result = pd.merge(fmnist_stress_result,fmnist_stress_df_11)\n",
    "fmnist_stress_result = pd.merge(fmnist_stress_result,fmnist_stress_df_12)\n",
    "fmnist_stress_result = pd.merge(fmnist_stress_result,fmnist_stress_df_13)\n",
    "fmnist_stress_result = pd.merge(fmnist_stress_result,fmnist_stress_df_14)\n",
    "fmnist_stress_result = pd.merge(fmnist_stress_result,fmnist_stress_df_15)\n",
    "\n",
    "fmnist_stress_result['average'] = fmnist_stress_result[['accuracy_1','accuracy_2','accuracy_3','accuracy_4','accuracy_5','accuracy_6','accuracy_7','accuracy_8','accuracy_9','accuracy_10','accuracy_11','accuracy_12','accuracy_13','accuracy_14','accuracy_15','accuracy_16']].mean(axis = 1)\n",
    "fmnist_stress_result['max'] = fmnist_stress_result[['accuracy_1','accuracy_2','accuracy_3','accuracy_4','accuracy_5','accuracy_6','accuracy_7','accuracy_8','accuracy_9','accuracy_10','accuracy_11','accuracy_12','accuracy_13','accuracy_14','accuracy_15','accuracy_16']].max(axis = 1)\n",
    "fmnist_stress_result['min'] = fmnist_stress_result[['accuracy_1','accuracy_2','accuracy_3','accuracy_4','accuracy_5','accuracy_6','accuracy_7','accuracy_8','accuracy_9','accuracy_10','accuracy_11','accuracy_12','accuracy_13','accuracy_14','accuracy_15','accuracy_16']].min(axis = 1)\n",
    "fmnist_stress_result = fmnist_stress_result[['sparsity','average','max','min']]\n",
    "fmnist_stress_result"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12504c25-2554-4773-9dec-cbaa9e516e87",
   "metadata": {},
   "source": [
    "## SET_MOTIF_FMNIST_STRESS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "ac8d8642-cf79-42fb-b5d9-2748b7f86597",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/ft/zv9hz6rx7q3b27t_fsw8h4300000gn/T/ipykernel_92975/537062134.py:4: DeprecationWarning: Please use `csr_matrix` from the `scipy.sparse` namespace, the `scipy.sparse.csr` namespace is deprecated.\n",
      "  data = pickle.load(h)\n"
     ]
    }
   ],
   "source": [
    "result_motif_set_stress_0 = load_result('/Users/arcade/Master/thesis_project/result/benchmarks_motif_fmnist_16/set_mlp_density_run_0.pickle.pbz2')\n",
    "result_motif_set_stress_1 = load_result('/Users/arcade/Master/thesis_project/result/benchmarks_motif_fmnist_16/set_mlp_density_run_1.pickle.pbz2')\n",
    "result_motif_set_stress_2 = load_result('/Users/arcade/Master/thesis_project/result/benchmarks_motif_fmnist_16/set_mlp_density_run_2.pickle.pbz2')\n",
    "result_motif_set_stress_3 = load_result('/Users/arcade/Master/thesis_project/result/benchmarks_motif_fmnist_16/set_mlp_density_run_3.pickle.pbz2')\n",
    "result_motif_set_stress_4 = load_result('/Users/arcade/Master/thesis_project/result/benchmarks_motif_fmnist_16/set_mlp_density_run_4.pickle.pbz2')\n",
    "result_motif_set_stress_5 = load_result('/Users/arcade/Master/thesis_project/result/benchmarks_motif_fmnist_16/set_mlp_density_run_5.pickle.pbz2')\n",
    "result_motif_set_stress_6 = load_result('/Users/arcade/Master/thesis_project/result/benchmarks_motif_fmnist_16/set_mlp_density_run_6.pickle.pbz2')\n",
    "result_motif_set_stress_7 = load_result('/Users/arcade/Master/thesis_project/result/benchmarks_motif_fmnist_16/set_mlp_density_run_7.pickle.pbz2')\n",
    "result_motif_set_stress_8 = load_result('/Users/arcade/Master/thesis_project/result/benchmarks_motif_fmnist_16/set_mlp_density_run_8.pickle.pbz2')\n",
    "result_motif_set_stress_9 = load_result('/Users/arcade/Master/thesis_project/result/benchmarks_motif_fmnist_16/set_mlp_density_run_9.pickle.pbz2')\n",
    "result_motif_set_stress_10 = load_result('/Users/arcade/Master/thesis_project/result/benchmarks_motif_fmnist_16/set_mlp_density_run_10.pickle.pbz2')\n",
    "result_motif_set_stress_11 = load_result('/Users/arcade/Master/thesis_project/result/benchmarks_motif_fmnist_16/set_mlp_density_run_11.pickle.pbz2')\n",
    "result_motif_set_stress_12 = load_result('/Users/arcade/Master/thesis_project/result/benchmarks_motif_fmnist_16/set_mlp_density_run_12.pickle.pbz2')\n",
    "result_motif_set_stress_13 = load_result('/Users/arcade/Master/thesis_project/result/benchmarks_motif_fmnist_16/set_mlp_density_run_13.pickle.pbz2')\n",
    "result_motif_set_stress_14 = load_result('/Users/arcade/Master/thesis_project/result/benchmarks_motif_fmnist_16/set_mlp_density_run_14.pickle.pbz2')\n",
    "result_motif_set_stress_15 = load_result('/Users/arcade/Master/thesis_project/result/benchmarks_motif_fmnist_16/set_mlp_density_run_15.pickle.pbz2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "2f5c5d12-16a8-44be-9bfe-cf746c385c6c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Sparsity Level  Values                                         Accuracies\n",
      "0               25   0.804  [(1, [0.104, 0.104, 0.104, 0.109, 0.088, 0.102...\n",
      "1               31   0.806  [(1, [0.099, 0.099, 0.095, 0.095, 0.095, 0.108...\n",
      "2               25   0.823  [(1, [0.092, 0.097, 0.097, 0.098, 0.12, 0.097,...\n",
      "3               29   0.815  [(1, [0.107, 0.085, 0.093, 0.104, 0.085, 0.106...\n",
      "4               25   0.803  [(1, [0.098, 0.097, 0.097, 0.091, 0.107, 0.091...\n",
      "5               19   0.823  [(1, [0.116, 0.083, 0.098, 0.118, 0.118, 0.098...\n",
      "6               25   0.811  [(1, [0.103, 0.105, 0.105, 0.088, 0.103, 0.103...\n",
      "7               27   0.806  [(1, [0.096, 0.097, 0.091, 0.091, 0.091, 0.097...\n",
      "8               21   0.796  [(1, [0.106, 0.106, 0.106, 0.106, 0.107, 0.085...\n",
      "9               29   0.835  [(1, [0.109, 0.104, 0.098, 0.109, 0.094, 0.106...\n",
      "10              21   0.811  [(1, [0.093, 0.088, 0.088, 0.1, 0.108, 0.112, ...\n",
      "11              29   0.813  [(1, [0.1, 0.118, 0.1, 0.099, 0.099, 0.104, 0....\n",
      "12              25   0.813  [(1, [0.109, 0.109, 0.098, 0.119, 0.109, 0.089...\n",
      "13              25   0.810  [(1, [0.078, 0.095, 0.095, 0.112, 0.092, 0.095...\n",
      "14              31   0.800  [(1, [0.087, 0.085, 0.088, 0.085, 0.104, 0.124...\n",
      "15              25   0.819  [(1, [0.103, 0.105, 0.105, 0.105, 0.1, 0.097, ...\n"
     ]
    }
   ],
   "source": [
    "result_set_stress = []\n",
    "\n",
    "for i in range(16):\n",
    "    result_set = globals()['result_motif_set_stress_{}'.format(i)]  \n",
    "    result_run = pass_result(result_set)\n",
    "    sparsity_level = result_run[0]\n",
    "    max_accuracy = result_run[1]\n",
    "    accuracies = result_run[2]\n",
    "    result_set_stress.append((sparsity_level, max_accuracy,accuracies))\n",
    "\n",
    "df_motif_stress = pd.DataFrame(result_set_stress, columns=['Sparsity Level', 'Values','Accuracies'])\n",
    "\n",
    "print(df_motif_stress)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "38801291-520a-4f8d-b68d-14ff54c358ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sparsity</th>\n",
       "      <th>average</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.113000</td>\n",
       "      <td>0.124</td>\n",
       "      <td>0.107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>0.113000</td>\n",
       "      <td>0.124</td>\n",
       "      <td>0.106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>0.114375</td>\n",
       "      <td>0.122</td>\n",
       "      <td>0.107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>0.263813</td>\n",
       "      <td>0.592</td>\n",
       "      <td>0.108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9</td>\n",
       "      <td>0.657125</td>\n",
       "      <td>0.721</td>\n",
       "      <td>0.413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11</td>\n",
       "      <td>0.735625</td>\n",
       "      <td>0.788</td>\n",
       "      <td>0.684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>13</td>\n",
       "      <td>0.760938</td>\n",
       "      <td>0.807</td>\n",
       "      <td>0.707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>15</td>\n",
       "      <td>0.779125</td>\n",
       "      <td>0.800</td>\n",
       "      <td>0.759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>17</td>\n",
       "      <td>0.789437</td>\n",
       "      <td>0.816</td>\n",
       "      <td>0.761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>19</td>\n",
       "      <td>0.796750</td>\n",
       "      <td>0.826</td>\n",
       "      <td>0.775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>21</td>\n",
       "      <td>0.800438</td>\n",
       "      <td>0.818</td>\n",
       "      <td>0.782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>23</td>\n",
       "      <td>0.800312</td>\n",
       "      <td>0.818</td>\n",
       "      <td>0.787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>25</td>\n",
       "      <td>0.804750</td>\n",
       "      <td>0.824</td>\n",
       "      <td>0.782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>27</td>\n",
       "      <td>0.803438</td>\n",
       "      <td>0.825</td>\n",
       "      <td>0.785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>29</td>\n",
       "      <td>0.806875</td>\n",
       "      <td>0.835</td>\n",
       "      <td>0.790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>31</td>\n",
       "      <td>0.809063</td>\n",
       "      <td>0.832</td>\n",
       "      <td>0.794</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    sparsity   average    max    min\n",
       "0          1  0.113000  0.124  0.107\n",
       "1          3  0.113000  0.124  0.106\n",
       "2          5  0.114375  0.122  0.107\n",
       "3          7  0.263813  0.592  0.108\n",
       "4          9  0.657125  0.721  0.413\n",
       "5         11  0.735625  0.788  0.684\n",
       "6         13  0.760938  0.807  0.707\n",
       "7         15  0.779125  0.800  0.759\n",
       "8         17  0.789437  0.816  0.761\n",
       "9         19  0.796750  0.826  0.775\n",
       "10        21  0.800438  0.818  0.782\n",
       "11        23  0.800312  0.818  0.787\n",
       "12        25  0.804750  0.824  0.782\n",
       "13        27  0.803438  0.825  0.785\n",
       "14        29  0.806875  0.835  0.790\n",
       "15        31  0.809063  0.832  0.794"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fmnist_motif_stress_result_0 = df_motif_stress['Accuracies'][0]\n",
    "fmnist_motif_stress_result_1 = df_motif_stress['Accuracies'][1]\n",
    "fmnist_motif_stress_result_2 = df_motif_stress['Accuracies'][2]\n",
    "fmnist_motif_stress_result_3 = df_motif_stress['Accuracies'][3]\n",
    "fmnist_motif_stress_result_4 = df_motif_stress['Accuracies'][4]\n",
    "fmnist_motif_stress_result_5 = df_motif_stress['Accuracies'][5]\n",
    "fmnist_motif_stress_result_6 = df_motif_stress['Accuracies'][6]\n",
    "fmnist_motif_stress_result_7 = df_motif_stress['Accuracies'][7]\n",
    "fmnist_motif_stress_result_8 = df_motif_stress['Accuracies'][8]\n",
    "fmnist_motif_stress_result_9 = df_motif_stress['Accuracies'][9]\n",
    "fmnist_motif_stress_result_10 = df_motif_stress['Accuracies'][10]\n",
    "fmnist_motif_stress_result_11 = df_motif_stress['Accuracies'][11]\n",
    "fmnist_motif_stress_result_12 = df_motif_stress['Accuracies'][12]\n",
    "fmnist_motif_stress_result_13 = df_motif_stress['Accuracies'][13]\n",
    "fmnist_motif_stress_result_14 = df_motif_stress['Accuracies'][14]\n",
    "fmnist_motif_stress_result_15 = df_motif_stress['Accuracies'][15]\n",
    "fmnist_motif_stress_df_0 = pd.DataFrame(fmnist_motif_stress_result_0,columns = ['sparsity','accuracy_1'])\n",
    "fmnist_motif_stress_df_1 = pd.DataFrame(fmnist_motif_stress_result_1,columns = ['sparsity','accuracy_2'])\n",
    "fmnist_motif_stress_df_2 = pd.DataFrame(fmnist_motif_stress_result_2,columns = ['sparsity','accuracy_3'])\n",
    "fmnist_motif_stress_df_3 = pd.DataFrame(fmnist_motif_stress_result_3,columns = ['sparsity','accuracy_4'])\n",
    "fmnist_motif_stress_df_4 = pd.DataFrame(fmnist_motif_stress_result_4,columns = ['sparsity','accuracy_5'])\n",
    "fmnist_motif_stress_df_5 = pd.DataFrame(fmnist_motif_stress_result_5,columns = ['sparsity','accuracy_6'])\n",
    "fmnist_motif_stress_df_6 = pd.DataFrame(fmnist_motif_stress_result_6,columns = ['sparsity','accuracy_7'])\n",
    "fmnist_motif_stress_df_7 = pd.DataFrame(fmnist_motif_stress_result_7,columns = ['sparsity','accuracy_8'])\n",
    "fmnist_motif_stress_df_8 = pd.DataFrame(fmnist_motif_stress_result_8,columns = ['sparsity','accuracy_9'])\n",
    "fmnist_motif_stress_df_9 = pd.DataFrame(fmnist_motif_stress_result_9,columns = ['sparsity','accuracy_10'])\n",
    "fmnist_motif_stress_df_10 = pd.DataFrame(fmnist_motif_stress_result_10,columns = ['sparsity','accuracy_11'])\n",
    "fmnist_motif_stress_df_11 = pd.DataFrame(fmnist_motif_stress_result_11,columns = ['sparsity','accuracy_12'])\n",
    "fmnist_motif_stress_df_12 = pd.DataFrame(fmnist_motif_stress_result_12,columns = ['sparsity','accuracy_13'])\n",
    "fmnist_motif_stress_df_13 = pd.DataFrame(fmnist_motif_stress_result_13,columns = ['sparsity','accuracy_14'])\n",
    "fmnist_motif_stress_df_14 = pd.DataFrame(fmnist_motif_stress_result_14,columns = ['sparsity','accuracy_15'])\n",
    "fmnist_motif_stress_df_15 = pd.DataFrame(fmnist_motif_stress_result_15,columns = ['sparsity','accuracy_16'])\n",
    "fmnist_motif_stress_df_0['accuracy_1'] = fmnist_motif_stress_df_0['accuracy_1'].apply(max)\n",
    "fmnist_motif_stress_df_1['accuracy_2'] = fmnist_motif_stress_df_1['accuracy_2'].apply(max)\n",
    "fmnist_motif_stress_df_2['accuracy_3'] = fmnist_motif_stress_df_2['accuracy_3'].apply(max)\n",
    "fmnist_motif_stress_df_3['accuracy_4'] = fmnist_motif_stress_df_3['accuracy_4'].apply(max)\n",
    "fmnist_motif_stress_df_4['accuracy_5'] = fmnist_motif_stress_df_4['accuracy_5'].apply(max) \n",
    "fmnist_motif_stress_df_5['accuracy_6'] = fmnist_motif_stress_df_5['accuracy_6'].apply(max)\n",
    "fmnist_motif_stress_df_6['accuracy_7'] = fmnist_motif_stress_df_6['accuracy_7'].apply(max)\n",
    "fmnist_motif_stress_df_7['accuracy_8'] = fmnist_motif_stress_df_7['accuracy_8'].apply(max)\n",
    "fmnist_motif_stress_df_8['accuracy_9'] = fmnist_motif_stress_df_8['accuracy_9'].apply(max)\n",
    "fmnist_motif_stress_df_9['accuracy_10'] = fmnist_motif_stress_df_9['accuracy_10'].apply(max)\n",
    "fmnist_motif_stress_df_10['accuracy_11'] = fmnist_motif_stress_df_10['accuracy_11'].apply(max)\n",
    "fmnist_motif_stress_df_11['accuracy_12'] = fmnist_motif_stress_df_11['accuracy_12'].apply(max)\n",
    "fmnist_motif_stress_df_12['accuracy_13'] = fmnist_motif_stress_df_12['accuracy_13'].apply(max)\n",
    "fmnist_motif_stress_df_13['accuracy_14'] = fmnist_motif_stress_df_13['accuracy_14'].apply(max)\n",
    "fmnist_motif_stress_df_14['accuracy_15'] = fmnist_motif_stress_df_14['accuracy_15'].apply(max)\n",
    "fmnist_motif_stress_df_15['accuracy_16'] = fmnist_motif_stress_df_15['accuracy_16'].apply(max)\n",
    "fmnist_motif_stress_result = pd.merge(fmnist_motif_stress_df_0,fmnist_motif_stress_df_1)\n",
    "fmnist_motif_stress_result = pd.merge(fmnist_motif_stress_result,fmnist_motif_stress_df_2)\n",
    "fmnist_motif_stress_result = pd.merge(fmnist_motif_stress_result,fmnist_motif_stress_df_3)\n",
    "fmnist_motif_stress_result = pd.merge(fmnist_motif_stress_result,fmnist_motif_stress_df_4)\n",
    "fmnist_motif_stress_result = pd.merge(fmnist_motif_stress_result,fmnist_motif_stress_df_5)\n",
    "fmnist_motif_stress_result = pd.merge(fmnist_motif_stress_result,fmnist_motif_stress_df_6)\n",
    "fmnist_motif_stress_result = pd.merge(fmnist_motif_stress_result,fmnist_motif_stress_df_7)\n",
    "fmnist_motif_stress_result = pd.merge(fmnist_motif_stress_result,fmnist_motif_stress_df_8)\n",
    "fmnist_motif_stress_result = pd.merge(fmnist_motif_stress_result,fmnist_motif_stress_df_9)\n",
    "fmnist_motif_stress_result = pd.merge(fmnist_motif_stress_result,fmnist_motif_stress_df_10)\n",
    "fmnist_motif_stress_result = pd.merge(fmnist_motif_stress_result,fmnist_motif_stress_df_11)\n",
    "fmnist_motif_stress_result = pd.merge(fmnist_motif_stress_result,fmnist_motif_stress_df_12)\n",
    "fmnist_motif_stress_result = pd.merge(fmnist_motif_stress_result,fmnist_motif_stress_df_13)\n",
    "fmnist_motif_stress_result = pd.merge(fmnist_motif_stress_result,fmnist_motif_stress_df_14)\n",
    "fmnist_motif_stress_result = pd.merge(fmnist_motif_stress_result,fmnist_motif_stress_df_15)\n",
    "\n",
    "fmnist_motif_stress_result['average'] = fmnist_motif_stress_result[['accuracy_1','accuracy_2','accuracy_3','accuracy_4','accuracy_5','accuracy_6','accuracy_7','accuracy_8','accuracy_9','accuracy_10','accuracy_11','accuracy_12','accuracy_13','accuracy_14','accuracy_15','accuracy_16']].mean(axis = 1)\n",
    "fmnist_motif_stress_result['max'] = fmnist_motif_stress_result[['accuracy_1','accuracy_2','accuracy_3','accuracy_4','accuracy_5','accuracy_6','accuracy_7','accuracy_8','accuracy_9','accuracy_10','accuracy_11','accuracy_12','accuracy_13','accuracy_14','accuracy_15','accuracy_16']].max(axis = 1)\n",
    "fmnist_motif_stress_result['min'] = fmnist_motif_stress_result[['accuracy_1','accuracy_2','accuracy_3','accuracy_4','accuracy_5','accuracy_6','accuracy_7','accuracy_8','accuracy_9','accuracy_10','accuracy_11','accuracy_12','accuracy_13','accuracy_14','accuracy_15','accuracy_16']].min(axis = 1)\n",
    "fmnist_motif_stress_result = fmnist_motif_stress_result[['sparsity','average','max','min']]\n",
    "fmnist_motif_stress_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "e2382a93-432f-4a0d-98b9-d62dd866cba2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f75adeb69a0>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, figsize=(15, 6))\n",
    "axs[0].plot(fmnist_stress_result['sparsity'], fmnist_stress_result['max'], color='lightsteelblue')\n",
    "axs[0].plot(fmnist_stress_result['sparsity'], fmnist_stress_result['min'], color='lightsteelblue')\n",
    "axs[0].fill_between(fmnist_stress_result['sparsity'], fmnist_stress_result['min'], fmnist_stress_result['max'], label='Error', color='lightsteelblue', alpha=0.4)\n",
    "axs[0].plot(fmnist_stress_result['sparsity'], fmnist_stress_result['average'], label = 'Average',color='blue')\n",
    "axs[0].set_xlabel('Sparsity')\n",
    "axs[0].set_ylabel('Accuracy')\n",
    "axs[0].set_title('Result SET FMNIST stress test')\n",
    "axs[0].legend(loc = 'lower right',fontsize = 'medium')\n",
    "\n",
    "axs[1].plot(fmnist_motif_stress_result['sparsity'], fmnist_motif_stress_result['max'], color='rosybrown')\n",
    "axs[1].plot(fmnist_motif_stress_result['sparsity'], fmnist_motif_stress_result['min'], color='rosybrown')\n",
    "axs[1].fill_between(fmnist_motif_stress_result['sparsity'], fmnist_motif_stress_result['min'], fmnist_motif_stress_result['max'], label='Error', color='rosybrown', alpha=0.4)\n",
    "axs[1].plot(fmnist_motif_stress_result['sparsity'], fmnist_motif_stress_result['average'], label = 'Average',color='brown')\n",
    "axs[1].set_xlabel('Sparsity')\n",
    "axs[1].set_ylabel('Accuracy')\n",
    "axs[1].set_title('Result SET-motif FMNIST stress test')\n",
    "axs[1].legend(loc = 'lower right',fontsize = 'medium')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "929226a7-866c-42df-bef4-bea5fa2c5dc9",
   "metadata": {},
   "source": [
    "## SET Lung "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a58eae89-6e5e-401c-a1b1-2d52d45becb7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/ft/zv9hz6rx7q3b27t_fsw8h4300000gn/T/ipykernel_9736/418054726.py:7: DeprecationWarning: Please use `csr_matrix` from the `scipy.sparse` namespace, the `scipy.sparse.csr` namespace is deprecated.\n",
      "  data = pickle.load(h)\n"
     ]
    }
   ],
   "source": [
    "result_lung_0 = load_result('/Users/arcade/PycharmProjects/set_learn/robustness_set-main copy/set_motif/bench_mark_lung/one/set_mlp_density_run_0.pickle')\n",
    "result_lung_1 = load_result('/Users/arcade/PycharmProjects/set_learn/robustness_set-main copy/set_motif/bench_mark_lung/one/set_mlp_density_run_1.pickle')\n",
    "result_lung_2 = load_result('/Users/arcade/PycharmProjects/set_learn/robustness_set-main copy/set_motif/bench_mark_lung/one/set_mlp_density_run_2.pickle')\n",
    "result_lung_3 = load_result('/Users/arcade/PycharmProjects/set_learn/robustness_set-main copy/set_motif/bench_mark_lung/one/set_mlp_density_run_3.pickle')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "10d0fe4d-85a4-4d9d-8dd5-4e45b8b4985f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Sparsity Level    Values                                         Accuracies\n",
      "0               6  0.955882  [(1, [0.7205882352941176, 0.7205882352941176, ...\n",
      "1              13  0.941176  [(1, [0.6911764705882353, 0.6911764705882353, ...\n",
      "2               4  0.985294  [(1, [0.6911764705882353, 0.6911764705882353, ...\n",
      "3               6  0.926471  [(1, [0.7352941176470589, 0.7352941176470589, ...\n"
     ]
    }
   ],
   "source": [
    "result_lung = []\n",
    "\n",
    "for i in range(4):\n",
    "    result_set = globals()['result_lung_{}'.format(i)]  \n",
    "    result_run = pass_result(result_set)\n",
    "    sparsity_level = result_run[0]\n",
    "    max_accuracy = result_run[1]\n",
    "    accuracies = result_run[2]\n",
    "    result_lung.append((sparsity_level, max_accuracy,accuracies))\n",
    "\n",
    "df_lung = pd.DataFrame(result_lung, columns=['Sparsity Level', 'Values','Accuracies'])\n",
    "\n",
    "print(df_lung)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "56357477-488a-47a2-9b01-b16651f3afd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sparsity</th>\n",
       "      <th>average</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.900735</td>\n",
       "      <td>0.926471</td>\n",
       "      <td>0.867647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.926471</td>\n",
       "      <td>0.955882</td>\n",
       "      <td>0.897059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.944853</td>\n",
       "      <td>0.955882</td>\n",
       "      <td>0.926471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.948529</td>\n",
       "      <td>0.985294</td>\n",
       "      <td>0.911765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.970588</td>\n",
       "      <td>0.911765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0.959559</td>\n",
       "      <td>0.985294</td>\n",
       "      <td>0.926471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>13</td>\n",
       "      <td>0.955882</td>\n",
       "      <td>0.985294</td>\n",
       "      <td>0.926471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>32</td>\n",
       "      <td>0.959559</td>\n",
       "      <td>0.970588</td>\n",
       "      <td>0.941176</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sparsity   average       max       min\n",
       "0         1  0.900735  0.926471  0.867647\n",
       "1         2  0.926471  0.955882  0.897059\n",
       "2         3  0.944853  0.955882  0.926471\n",
       "3         4  0.948529  0.985294  0.911765\n",
       "4         5  0.941176  0.970588  0.911765\n",
       "5         6  0.959559  0.985294  0.926471\n",
       "6        13  0.955882  0.985294  0.926471\n",
       "7        32  0.959559  0.970588  0.941176"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lung_result_0 = df_lung['Accuracies'][0]\n",
    "lung_result_1 = df_lung['Accuracies'][1]\n",
    "lung_result_2 = df_lung['Accuracies'][2]\n",
    "lung_result_3 = df_lung['Accuracies'][3]\n",
    "lung_df_0 = pd.DataFrame(lung_result_0,columns = ['sparsity','accuracy_1'])\n",
    "lung_df_1 = pd.DataFrame(lung_result_1,columns = ['sparsity','accuracy_2'])\n",
    "lung_df_2 = pd.DataFrame(lung_result_2,columns = ['sparsity','accuracy_3'])\n",
    "lung_df_3 = pd.DataFrame(lung_result_3,columns = ['sparsity','accuracy_4'])\n",
    "lung_df_0['accuracy_1'] = lung_df_0['accuracy_1'].apply(max)\n",
    "lung_df_1['accuracy_2'] = lung_df_1['accuracy_2'].apply(max)\n",
    "lung_df_2['accuracy_3'] = lung_df_2['accuracy_3'].apply(max)\n",
    "lung_df_3['accuracy_4'] = lung_df_3['accuracy_4'].apply(max)\n",
    "lung_result = pd.merge(lung_df_0,lung_df_1)\n",
    "lung_result = pd.merge(lung_result,lung_df_2)\n",
    "lung_result = pd.merge(lung_result,lung_df_3)\n",
    "lung_result['average'] = lung_result[['accuracy_1','accuracy_2','accuracy_3','accuracy_4']].mean(axis = 1)\n",
    "lung_result['max'] = lung_result[['accuracy_1','accuracy_2','accuracy_3','accuracy_4']].max(axis = 1)\n",
    "lung_result['min'] = lung_result[['accuracy_1','accuracy_2','accuracy_3','accuracy_4']].min(axis = 1)\n",
    "lung_result = lung_result[['sparsity','average','max','min']]\n",
    "lung_result"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ace8ac33-a102-407f-882b-2436852feb03",
   "metadata": {},
   "source": [
    "## SET-motif Lung"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0543f445-b594-4115-a3df-d461f6c3379b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Sparsity Level    Values                                         Accuracies\n",
      "0              13  0.970588  [(1, [0.7205882352941176, 0.7205882352941176, ...\n",
      "1              13  0.955882  [(1, [0.6911764705882353, 0.6911764705882353, ...\n",
      "2               6  0.955882  [(1, [0.6911764705882353, 0.6911764705882353, ...\n",
      "3               2  0.941176  [(1, [0.7352941176470589, 0.7352941176470589, ...\n"
     ]
    }
   ],
   "source": [
    "result_motif_set_lung_0 = load_result('/Users/arcade/PycharmProjects/set_learn/robustness_set-main copy/set_motif/benchmarks_lung_motif_new/benchmark_lung_26_06_2023_15_25_04/set_mlp_density_run_0.pickle')\n",
    "result_motif_set_lung_1 = load_result('/Users/arcade/PycharmProjects/set_learn/robustness_set-main copy/set_motif/benchmarks_lung_motif_new/benchmark_lung_26_06_2023_15_25_04/set_mlp_density_run_1.pickle')\n",
    "result_motif_set_lung_2 = load_result('/Users/arcade/PycharmProjects/set_learn/robustness_set-main copy/set_motif/benchmarks_lung_motif_new/benchmark_lung_26_06_2023_15_25_04/set_mlp_density_run_2.pickle')\n",
    "result_motif_set_lung_3 = load_result('/Users/arcade/PycharmProjects/set_learn/robustness_set-main copy/set_motif/benchmarks_lung_motif_new/benchmark_lung_26_06_2023_15_25_04/set_mlp_density_run_3.pickle')\n",
    "result_motif_lung = []\n",
    "\n",
    "for i in range(4):\n",
    "    result_set = globals()['result_motif_set_lung_{}'.format(i)]  \n",
    "    result_run = pass_result(result_set)\n",
    "    sparsity_level = result_run[0]\n",
    "    max_accuracy = result_run[1]\n",
    "    accuracies = result_run[2]\n",
    "    result_motif_lung.append((sparsity_level, max_accuracy,accuracies))\n",
    "\n",
    "df_motif_lung = pd.DataFrame(result_motif_lung, columns=['Sparsity Level', 'Values','Accuracies'])\n",
    "\n",
    "print(df_motif_lung)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a62846e8-e283-4e19-9075-718e8903759f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sparsity</th>\n",
       "      <th>average</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.904412</td>\n",
       "      <td>0.926471</td>\n",
       "      <td>0.897059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.933824</td>\n",
       "      <td>0.955882</td>\n",
       "      <td>0.897059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.933824</td>\n",
       "      <td>0.955882</td>\n",
       "      <td>0.882353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.944853</td>\n",
       "      <td>0.985294</td>\n",
       "      <td>0.911765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.933824</td>\n",
       "      <td>0.970588</td>\n",
       "      <td>0.882353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.985294</td>\n",
       "      <td>0.897059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>13</td>\n",
       "      <td>0.959559</td>\n",
       "      <td>0.985294</td>\n",
       "      <td>0.926471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>32</td>\n",
       "      <td>0.966912</td>\n",
       "      <td>0.970588</td>\n",
       "      <td>0.955882</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sparsity   average       max       min\n",
       "0         1  0.904412  0.926471  0.897059\n",
       "1         2  0.933824  0.955882  0.897059\n",
       "2         3  0.933824  0.955882  0.882353\n",
       "3         4  0.944853  0.985294  0.911765\n",
       "4         5  0.933824  0.970588  0.882353\n",
       "5         6  0.941176  0.985294  0.897059\n",
       "6        13  0.959559  0.985294  0.926471\n",
       "7        32  0.966912  0.970588  0.955882"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lung_motif_result_0 = df_motif_lung['Accuracies'][0]\n",
    "lung_motif_result_1 = df_motif_lung['Accuracies'][1]\n",
    "lung_motif_result_2 = df_motif_lung['Accuracies'][2]\n",
    "lung_motif_result_3 = df_motif_lung['Accuracies'][3]\n",
    "lung_motif_df_0 = pd.DataFrame(lung_motif_result_0,columns = ['sparsity','accuracy_1'])\n",
    "lung_motif_df_1 = pd.DataFrame(lung_motif_result_1,columns = ['sparsity','accuracy_2'])\n",
    "lung_motif_df_2 = pd.DataFrame(lung_motif_result_2,columns = ['sparsity','accuracy_3'])\n",
    "lung_motif_df_3 = pd.DataFrame(lung_motif_result_3,columns = ['sparsity','accuracy_4'])\n",
    "lung_motif_df_0['accuracy_1'] = lung_motif_df_0['accuracy_1'].apply(max)\n",
    "lung_motif_df_1['accuracy_2'] = lung_motif_df_1['accuracy_2'].apply(max)\n",
    "lung_motif_df_2['accuracy_3'] = lung_motif_df_2['accuracy_3'].apply(max)\n",
    "lung_motif_df_3['accuracy_4'] = lung_motif_df_3['accuracy_4'].apply(max)\n",
    "lung_motif_result = pd.merge(lung_motif_df_0,lung_motif_df_1)\n",
    "lung_motif_result = pd.merge(lung_motif_result,lung_df_2)\n",
    "lung_motif_result = pd.merge(lung_motif_result,lung_df_3)\n",
    "lung_motif_result['average'] = lung_motif_result[['accuracy_1','accuracy_2','accuracy_3','accuracy_4']].mean(axis = 1)\n",
    "lung_motif_result['max'] = lung_motif_result[['accuracy_1','accuracy_2','accuracy_3','accuracy_4']].max(axis = 1)\n",
    "lung_motif_result['min'] = lung_motif_result[['accuracy_1','accuracy_2','accuracy_3','accuracy_4']].min(axis = 1)\n",
    "lung_motif_result = lung_motif_result[['sparsity','average','max','min']]\n",
    "lung_motif_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4ffb0bf5-7169-4cee-a13a-21be2ea50f99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.8676470588235294, 0.9952941176470589)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Pfwq/3z+q+1u+fDkuvPBC3HLLLVAUBaeffjrefPNN3HLLLfD7/Ufc+x3u+f/oRz/CSSedhJNPPhmf/exn0dLSgnA4jB07duB///d/833uzj77bKxYsQKrV69GfX099u7dix/+8IeYO3duvgz6Xe96FwDgRz/6ES699FJomobFixcPGngmKqtyT2MgoskpN8Vo06ZNAz4Xj8fFnDlzxKJFi4RpmkIIIV599VXx8Y9/XDQ0NAhN00RTU5M4/fTTxc9+9rP87a699lqxevVqUV1dLQzDEPPnzxdf/vKXRWdnZ9H933///WLp0qXC4XCIZcuWiUceeWRYk7OEEOKHP/yhmDdvnlAU5YgTNB955BFx0UUXiUWLFgmPxyM0TRNz5swRl1xyidi6dWvRuYebnAVgwNQwIUozdVQIIVpbW8X5558vampqhN/vFxdffLHYvHnzoFNH3W73kOsotG/fPnHeeecJj8cjvF6v+Id/+Afx+OOPCwDiscceG/Z6iYiIKIN7p4nfOw11/lB7olNPPVUsX7686NjevXvFRRddJGpra4WmaWLx4sXi5ptvzk9+zfnjH/8ojjnmGGEYRtGkzvGYOppMJsVXv/pVMXv2bOF0OsWpp54qXnnllSGnjvb/mRtsan0ikRBXXXWVaGhoEA6HQxx33HHihRdeEH6/X3z5y18+4jqHev5CCLF7925x+eWXi5kzZwpN00R9fb044YQTxHe+8538Obfccos44YQTRF1dndB1XcyZM0dcccUVYs+ePUWPs2HDBjFjxgwhy/KIJuISTSRJCCHGO5hHREST37//+7/juuuuw759+zBr1qxyL4eIiIiIxtHzzz+PE088Eb/85S9x0UUXlXs5RJMGS0eJiGiAn/70pwCAJUuWIJ1O4+mnn8aPf/xjXHzxxQyyEREREU0xTz31FF544QWsWrUKTqcTr776Kv7jP/4DixYtGnIoBxENjoE2IiIawOVy4Qc/+AH27NmDZDKJOXPm4JprrsF1111X7qURERERUYn5fD784Q9/wA9/+EOEw2HU1dXhzDPPxI033pifck9Ew8PSUSIiIiIiIiIiohLg6DgiIiIiIiIiIqISYKCNiIiIiIiIiIioBBhoIyIiIiIiIiIiKgEOQxiEbds4dOgQvF4vJEkq93KIiIhokhBCIBwOY8aMGZBlXs+sRNznERER0WgMd5/HQNsgDh06hNmzZ5d7GURERDRJ7d+/H7NmzSr3MmgQ3OcRERHRWBxpn8dA2yC8Xi+AzBfP5/OVeTVEREQ0WYRCIcyePTu/l6DKw30eERERjcZw93kMtA0iV0bg8/m4ASMiIqIRY0li5eI+j4iIiMbiSPs8Ng8hIiIiIiIiIiIqAQbaiIiIiIiIiIiISoCBNiIiIiIiIiIiohJgoI2IiIiIiIiIiKgEGGgjIiIiIiIiIiIqAQbaiIiIiIiIiIiISoCBNiIiIiIiIiIiohJgoI2IiIiIiIiIiKgEGGgjIiIiIiIiIiIqAQbaiIiIiIiIiIiISoCBNiIiIiIiIiIiohJgoI2IiIiIiIiIiKgEGGgjIiIiIiIiIiIqAQbaiIiIiIiIiIiISoCBNiIiIiIiIiIiohJgoI2IiIiIiIiIiKgEGGgjIiIiIiIiIiIqAQbaiIiIiIiIiIiISoCBNiIiIiIiIiIiohJgoI2IiIiIiIiIiKgEGGgjIiIiIiIiIiIqAQbaiIiIiIiIiIiISoCBNiIiIiIiIiIiohJgoI2IiIiIiIiIiKgEGGgjIiIiIiIiIiIqAQbaiIiIiIiIiIiISoCBNiIiIiIiIiIiohJgoI2IiIiIiIiIiKgEGGgjIiIiIiIiIiIqAQbaiIiIiIiIiIiISoCBNiIiIiIiIiIiohJQy70AmlyEEAAASZIm/LHTpo20aU3449LoKbIMXZPL8vNCREREIyNsGwKALE/8tXgrnUYqFpvwx6XRk1UVusvFfR4RUT8MtNGwxZMmnn75ABqrXVi9uGFCHzsST+PZVw7CssWEPi6Nna7K8Ll1+Fw6/G4dPrcOr0uDUoZNPBEREQ0unUjg7w88AHdtLVb+wz9M6GOnYjG8cP/9MJPJCX1cGjtV1+Gpr4enrq7v79paKJpW7qUREZUNA200bJ3BBExL4GBnFO+aZ8HQlQl77IOd0XyQjRfNJg8hgJRpozOYQGcwkT8uAXA7tXzgLReEc+gKr4oSERGVQbi9HalYDKlYDOHOTnjr6ibssTt27coH2SReiJs0hG3DTKXQe/Ageg8eLPqc0++Ht6EhE3jL/nH4fNznEdG0wEAbDVs0kc7/+2BXBPOb/RP22G09mVKCOr8DTTWuCXtcGhvbFognTcSSJhIpC8m0hVTahi0EIvE0IvE0DnZG8+drigyfW4PPbWSCcK5M9puqcNNNREQ0nmI9Pfl/t23bNqGBtq7duwEAvqYmNC1ZMmGPS2NjWxaS4TDioRCSkQhS8TjS8Ths00Q8GEQ8GET79u358xVNK8p889bVwV1XB1XXy/gsiIhKj4E2GrZIvC/Q1tYdn7BAWzJtoSecucrpc+lQZF4JmywUWYKmZrLWcmzbRiptI5o0EU9aSKZMJE0704PPstEVSqIrVFw64nao8LuNTBAum/3mNFReFSUiIiqRWG9v/t/de/cCJ544IY9rmya69+0DALhrayErE1cxQWMjKwrU2lq4a2vzx4QQSMXjiAeDSIbDSEYiSCcSSCcSsNJpBFtbEWxtLbofh88Hb7/yU6ffz30eEU1aDLTRsBVmtHWHExBCTMgvwPaeOIBMry+Xgz+yk50sy3AYMhxG8ffStG3EEyZiCRPxXPabacO2BaIJE9GEiUNdfeerigSfKxPEKyxBZfYbERHRyBUG2iKdnUgnk9AMY9wft/fQIVjpNBRNg6u6etwfj8aXJEkwXC4YLhfQ3Jw/bpsm4qEQEtnst2QshnQiATudRiJ7vGPnzvz5sqrmS07zQbi6OqgT8DNJRDRWjFrQsIhsqV+OaQl09MbRUD3+ZZzt2bJRp6Eym20KU2UZXpcOr6sv+00IgWTKQiyb/ZZIW0hlA3CmJdAdTqI7XJz95nKo+bLTXBDOxew3IiKiw4oXBNqEbaNjxw7MWL583B+3M1s2ang8ExLYo/KQVRXumhq4a2ryx4QQSCcSiAeDSIRCSEWjmfLTRAK2aSIUCCAUCBTdj+H1FgXevPX1mew39vYjogrCQBsNSyqdCWwAgMehIpIwcagrNu6BNiEE2nozGW1eJ6cXTTeSJMFhqENkv1mIJ9OZ8lMz0/vNsgVi2ay41q5Y/nxFlvoy31x92W+ayk0ZERGRbduIB4MAAFd1NWI9PejctWtCAm1de/ZkHreqatwfiyqLJEnQnU7oTif8TU3547ZpIhGJZMpPIxGkotF86WkyHEYyHEbnrl3582VFgTvb881TEITTHI5yPC0iIgbaaHgi2bJRVZHg9xiIJEx0BuPj/rg94STSpg1ZkuB1MdBGGZnsN7noZ0IIgZRpZQJtSQuJlIlU2kbKzATgesLJfK+/HKfRl/2WKz91O5j9RkRE00siFIKwbUiSBH9zM2I9Peg9eHDc24TEenszQxgkCZ76+nF7HJpcZFWFq6pqQPA1ne39lgiHkcxmv5nxOGzLQritDeG2tqLzDY+nKPDmra+Hs6oKMrPfiGicMdBGwxKN5wJtMvxuHQc7o4gmTMSSabiM8QuABfJlowqzj+iwJEmCoakwNBXV3r7jtm0jlrAQS6b7er9ls9/iSRPxpIlAd3H2m9elZYcv6PC5NPjdOjSVzZmJiGhqyk0cVQ0Dnro6SJKEdCKBYGsrqmbMGLfHzU0bNdxuGG73uD0OTQ2a0wnN6YSvMPvNspAIhzN93sJhpLK936xUKtMLLhLJ/5wBgKQocNfUDBi+oDud5XhKRDRFMdBGw5LLaNMUGZoqw6ErSKQsHOyIYtGsqnF73NwgBPbYotGSZRkelwxPv4zIZNpCLJEpPU2kMuWn6WwArjeSQm8kVXS+U1cygbeCElSPU+PPJRERTXq5/myqYeSHEkS7u9G+ffu4Bto6s2WjDp+PPbZoVGRFGTz7LZnM935LRiJIZ3u/CctCpKMDkY6OovN1tzuT9VYQfHNVV3MKLhGNCgNtNCy5jLZcVpnPrSORiqOtJzZugbZ40kQwmgl2+D36Ec4mGhlDU2BoysDst2Rm+EIiZSKZspDMZb+lLMRTcbT19JVM50qaC6ee+t06dI2bMiIimjxiBYE2APDU1SHa3Y3uffvG7TGtdBq9Bw4AANy1teP2ODQ9aYYBraEBvoaG/DHbtpEsyH5LxmJIx+OwUimkolF0R6Po3rs3f74ky3DX1BSVn3rq6ph9SURHxEAbDUskYQLIBCcAwOfS0N4TR08kBcu2oYzDVchcNpuhKXDq/FGl8SfLMjxOGZ5+gzdS6Wzvt5SJRNJCysxMPrWFQDCaygeEcxxav+w3tw6PQ4PMqblERFSBcqWjuebx7ro64J13EO3qQioWg+4q/fCr7v37YVsWFF3nIASaELIsw+n3w+n3Fx03c9lv4XBm+EI8jnQ8DmHbiHR2ItLZWXS+5nTCU19flP3mrq6GrPL9ChFl8NWAjkgIkc9oc+iZQJvLUKHIEixboK0nhhm1npI/bmF/NgYoqJx0TYGuKaiCkT9m2zbiqUwALp4ykUzZSJkWTEsgkbaQ6I2jvbcv+02SAK9Th9/TN/nU79bzwWsiIqJyyWW05QJqutMJzeVCOhZD2zvvYPZ73lPyx8z1zXJ4vVB1Vi5Q+aiGAW9DA7z9st9S0Wg+AJebfGomk0jH4+jZtw89BRmfkiTBVV0Nb0NDUfab7nazzQjRNMRAGx1RImXBsgWAvkCblC2Z642kEOiKlzzQZtkCHdkghdfFzRdVHlmW4XbIcDuKs9/Spo1oIp0ZtJCyspNPLQgBhGIphGLF2W+GpsDn1uB3GfksOK+T2W9ERDQxbNNEIhwGAOievv2cp64OPfv2oXP37pIH2oQQ+f5srurqkt43USnIsgyH1wuH11t03Eyl+nq/RaP54QvCshDt7ka0u7vofM3hKBq64Kmvh7umBgqz34imNP4PpyOKJnITRyWoSl+JqM+lozeSQkcwPtRNR60rlIBli8wESCd/TGny0FQZVR4DVZ7i7LdEPvvNQiJtIZXOZL8l0xY6ei109Cby52ey37Sivm++bPYbr4oSEVEpxUMhQAhIslw0edFTW4ueffsQPHQItm1DLmGbkGhXF5LhMCRZhqeurmT3SzTeVF2Ht74e3vr6/LF89lsohGQ4jGQ0inQ8nsl+SyTQc+AAerL9CAEAkgRXVdWAyaeGx8N9HtEUwQgGHVEk3jdxtDDLxpud4phIWQjHUiXNPGvPl42q0FSW1tHkJssyXA4ZrkGy32LJNGIJKzN8IW0jbVqwBRCKpRGKpQFE8+frqlzU983n0uF1aePSI5GIiKaHXH821TCKsmxcVVWQFAVWOo2eAwdQO2dOyR6zM1s2ang849L/jWgiHS77LREOIx4KIRUO53u/2ZaFWE8PYj09aHvnnfz5qmHkA2/ebOmpu7YWiqb1f0giqnBlf3d22223Yd68eXA4HFi1ahWee+65w55/6623YunSpXA6nVi8eDEeeOCBAef88Ic/xOLFi+F0OjF79mx8+ctfRiKRGOTeaDii2UEIuYmjOaoiw2VkNmQHO6MDbjcWge5MlpzbwVgwTV2aKsPvNtBc68K8Zh+WzKnC8pZqLJrlw8w6N2p8BtwOFVo2kzRl2ugMJrDzUAgvb+/En189hN+9sBdPv3QAm7e1450DvWjriSGeNCGEKPOzIyLK4F6vshVOHJUKLtzkJi4CQPv27SV9zK5s2ajD52MGD01Zqq7DU1uL+nnzMPPoozFv7VosPOUUzFu7Fk1LlqBq1iw4q6uhZoeQmMkkeg8exIFXXsFbf/wjNj38MJ699VY8f999eP13v8PuF19Ex86diIdC3OcRVbiyRjEeeeQRfOlLX8Jtt92GE088EXfccQfOPPNMbN26FXMGuWp2++23Y8OGDfj5z3+ONWvWYOPGjfj0pz+N6upqnH322QCAX/7yl7j22mtxzz334IQTTsA777yD9evXAwB+8IMfTOTTmzLyGW2DZJb53BpiSRNtPTEsmVOaHhuReDpfrup3sz8bTS+yLMNlyHAZxVcvzVz2W9JCImUhmS0/tQUQjqcRjqeLAt6aKheVnfpcOnwuDYpS9usrRDSNcK9X+Qoz2vrz1NUh0tFR1PR9rNKJBIKHDuXvn2g6kWUZhscDw1Pc39o0TSSDwUz5aSSS7/1mmybivb2I9/YWBbwVXc8PXMiVoLprazlYhKhCSKKM4fC1a9di5cqVuP322/PHli5dinPOOQc33njjgPNPOOEEnHjiibj55pvzx770pS9h8+bN+Otf/woA+Od//me89dZb+NOf/pQ/5+qrr8bGjRuPeAU1JxQKwe/3IxgMwufzjfbpTRlPv3QA4XgaM+pcqPc7iz4XT5p450AQkgR8aO0cqMrYyzx3Hgrijd3dcOgKjprl55VOoiHYto1k2kYsaSKeNJFIWUiZNtKmPeRtPE4VvuzghVwQzqmz9xtRqXAPUawS93r8HhV76Te/Qc+BA6hpaUHDggVFn0snk9iZ/bqfcPnlcJbg69W2bRveeOIJaA4H5q1dC5lN4YkGJYRAKh7PD19IZXu/pZNJYIi38E6/v2/wQjYIx8xRotIZ7h6ibL/ZUqkUtmzZgmuvvbbo+Ac+8AE8//zzg94mmUzCkU2tzXE6ndi4cSPS6TQ0TcNJJ52EBx98EBs3bsSxxx6LXbt24fHHH8ell1465FqSySSSyWT+41AoNIZnNrUIIfLZZbmJo4UcugJVkWBaAq1dMcxu8A44Z6TaejJloy5D5S8FosOQZRlOQ4bTKH4pN00bsZSJWCIbfEtbSJo2bFsgEjcRiZs41NWX/aYqUjbwZsDn6hvCoDL7jYjGoFL2etznHV6udLRwEEKOZhgwPB4kIxG0bduGljVrxvx4uWmjhtfLIBvRYUiSBMPlguFyAc3N+eO2aSIeCiERCiFR2PvNNBEPBhEPBtGxY0f+fEXT4K6tLR6+UFs7aBYrEZVG2X67dXZ2wrIsNDY2Fh1vbGxEIBAY9DZnnHEG7rrrLpxzzjlYuXIltmzZgnvuuQfpdBqdnZ1obm7GJz7xCXR0dOCkk06CEAKmaeKzn/3sgE1eoRtvvBE33HBDSZ/fVBFPmrCzF0wc2sBAmyRJ8Ll0dIeTCHSPPdBmWja6slNMc8MWiGhkVFWGT80Ey3Js20YqbSOaNJHIlZ+aFtKmDdMS6A4l0R1KFt2P26FmAnCubPmpW2cAnIiGrVL2etznDc1Kp5GMRABgQCP3HE9dHZKRCLr27BlzoE0Ike/P5q4uTcsRoulGVlW4a2ryPRSBvuy3RDCIRMHk03QiASudRigQQKjf667D5xswfMHp9xf1aiSi0Sn7ZaT+b9iEEEO+ibv++usRCARw3HHHQQiBxsZGrF+/HjfddBOUbMnis88+i+9+97u47bbbsHbtWuzYsQNf/OIX0dzcjOuvv37Q+92wYQOuuuqq/MehUAizZ88u0TOc3CK5QQiKPGRvJ587E2jrDCYO+/0bjo7eOGyRybDxcBACUcnIsgyHIcPRP/vNthFPmNny00z2W8q0YdkC0YSJaMJEa1csf74iS0V93/xuHV6XPmBYChFRTrn3etznDS2XzSYrypDZLZ66OnTt2YNQIADLNIsmk45UKBBAOh6HpCjsz0ZUQoXZb/5+2W+5yafJSCRTfpoNviWyWXGdu3blz5dVtTj7LftH65dpTESHV7ZIRl1dHRRFGXBFs729fcCVzxyn04l77rkHd9xxB9ra2tDc3Iw777wTXq8Xddlf1tdffz0uueQSXHnllQCAd73rXYhGo/jHf/xHfO1rX4M8SITeMAwYTJ0dVDQ/CEGCPMSm2OPUICEzEbE3kkK1d/Rfy/Zs2ajTUKEOMnyBiEpLlWV4XZlgWY4QAqm0hWjSRDxhIZE2kUrb+QBcdziJ7nBx9pvLUIv6vvlcOtwOZr8RTWeVstfjPm9o8YKJo4o2eCWBw+eDrKqwTRNde/agYeHCUT9eftqox5OftEhE40dWVbiqq+Hql0HaP/utcPhCuK0N4ba2ovMNj6c4+FZfD2dV1aDvrYmojIE2XdexatUqPPXUUzj33HPzx5966il89KMfPextNU3DrFmzAAAPP/wwzjrrrPx/8lgsNuA/vKIoEEJwDPIo5CaOqofJVlFkCW6nikjcxMHOyKgDbUIIBHoymTMeJ7PZiMpFkiQYugpDV4GCSiLLthFLWIgn04inLCRTfdlvsWQmKy7QXZz95nVpmd5vBSWozH4jmh6416t8sYJA21AXRiRJgqe2FqG2NnTs2DGmQFvn7t0AMg3beSGGqHx0pxO60wlfU1P+mG1Zfdlv4XAm+BaP50vMk5FI/v8wkMmEddfWFg9fqKuDNki/R6LppqzRjKuuugqXXHIJVq9ejeOPPx533nkn9u3bh3/6p38CkEn1P3jwIB544AEAwDvvvIONGzdi7dq16Onpwfe//3288cYbuP/++/P3efbZZ+P73/8+jjnmmHw5wfXXX4+PfOQj+ZIDGr7cIAT9CNllPpeOSNxER2981I8VjqWRSFmQJBT1liKiyqDIMrwuuah/ohACKdNCLGEilrSQSGWy39LZAFxvJIXeSKrofpy6kg2+afBlBzB4nBrfdBFNQdzrVbbCQNvheOrqEGprQ8+BA6N+rGQ0inB7e+b+6utHfT9END5kRYGrqgquqqqi4+lEIjP5NBxGMhLJ936zLQvh9vb8/+sc3e3O9HwrGL7gqqqCzNdnmkbKGmi74IIL0NXVhW9961tobW3FihUr8Pjjj2Pu3LkAgNbWVuzbty9/vmVZuOWWW7Bt2zZomoZ169bh+eefR0tLS/6c6667DpIk4brrrsPBgwdRX1+Ps88+G9/97ncn+ulNCbmMNmOQQQiFfC4dh7piCMXSSKUt6Ec4fzC5bDaHrh7x8YioMkiSBENTYWgqqguy32zbRixpIZZMI5G0kExbSKYzwbd4ykI8FUOgp+98WZYyE0+zWW/+bPnpaF5LiKhycK9X2WI9mRfiI2WguGtrAQDJSASRri54sh+PRK5sVHO5hhy8QESVR3M4oDkc8BWU/NuWhUQkkik/jUT6st9SKaSiUXRFo+jauzd/viTLcNfUZMpPCzLgdJerHE+JaNxJgjn2A4RCIfj9fgSDQfh8vnIvp2xsIfB/L+yBEMCCGT54nIefAvrW3h6kTBvvnl+LluaRf93++vohdIWSqPU5MKvePdplE1EFS6Wz2W+pzPTTlGkhlbYx1C8ih64U9X3zuXV4nNqQPSOJyo17iMrH71Gf5+68E6lYDDOWLy8qIRvMns2bkQgGMW/tWsw//vgRP9brv/sd2rdvh7exETNXrBjtkomogqWTSSSCQcQLs9/icQjbHvR83eXKZ73lgm/umhpmv1HFGu4ego2waEixhAkhAAmAoR25p5LPraMzmEBrT2zEgbaUaaE7lGmu7ncfPqBHRJOXrinQNQVV6CtTsm0b8VQmABdPmUimbKRMC6YlkEhZSKTiaOvpK0uXJcCby3zL/u1z68yEJSIaATOZRCqWqSbQPZ4jnu+tq0MiGETXnj0jDrTZlpXPbvHU1Ix8sUQ0KWiGAa2hAd6Ghvwx27aRikQQD4WQCIczk0/jcZipFFKxGLr37UN3QWazJMtwVVcPGL6gu1xsM0KTBgNtNKRcfzZNlaEqwwi0uTR0BhPoDiUghBjRC2FHTxwi+1guBwNtRNOJLMtwO2S4+/3fz/V+iydNxFNWdvKpBVsAwWgKwWgK+wvONzQFPnd2+IIrU37qcWqQZW7KiIj6iwWDADJTCbVhTAB119aiY+dOhDs6YKZSUPXh99MNHjoEK5XKTEAcRdkpEU1esizD4fPB0S/7x0wmM8G3UAjJSASpguy3aFcXol1dRedrDgc89fVFAThXTQ0UlSENqjz8qaQhFU4cHU7QzO3UIEmAaQl0BhOorxr+xJlANlvFZahQ+KaYiJAZwqJ7FFR5irPfEvnsNwuJlJXPfkumLXT0WujoTeTPlyTA68wMXSgsQXXozH4joukt159NNYxhlWkZHg9UXYeZSqFj5040L1067MfqzPZnc3i9IwrQEdHUpRoGvNnAWY5t20hFo4gHg0iGw0hme7+ZySTSiQR69u9Hz/6Cy6ySBHd1dVHpqae+Hobbzew3KisG2mhI0WygTRtGNhsAyJIEr1NDKJZGa1d02IE2IQTas4MQPE7+SBLR0GRZhssxMPM1bdqIJdOIJUwkUpnBCynTghBAKJZGKJbGgY6+83VV7gu8ZUtQPS6dgX4imjbiBRNHh/OGVJIkuOvqEDx0aMSBtq7duwEAzqoqvvkloiHJsgyH1ztgYIqZSg2YfJqKxyEsC9HubkS7u9G2bVv+fNUwMtlvBf3f3LW1zH6jCcOfNBpSJGECyLwhHS6fW0colkZ7b/zIJ2f1RJJImTZkKTO9lIhopDRVhl814HcPkv2WtBBPmkimLaTSFtKWQMq00RFMoCNYnP3mcWhFAbhc9hvfGBLRVBMrCLQNlycbaOs9eHDYbULiwSCi3d2Z2xdkrhARDZeq64Nnv8Vimcmn4TCS0SjSiQTMRAJmMoneAwfQe+BA351IElxVVUXDF7x1dTC8Xu7zqOQYaKMh5TLajBGUWHldmSyTaLavktM48o9YW3cmKOc0VGgjCOoRER3OUNlvZi77LZkpPU1my09tAYTjaYTjaaAzmj9fU+WioQt+tw6vS4Mi8/WKiCavXOmo7hx+qw93dTUgSUjH4wi3tR1xUinQVzZquN1wuDlVnohKQ5ZlODweOPoNczFTKSTC4b7eb9nyU9uyEOvpQaynB+3bt+fPV3Q9E3QrLD+tq4OisW84jR4DbTQoyxaIJTMZbc4RBNp0VYFDV5BIWTjYGcXCmf4j3qYtWzbqMlReTSCicaeqMnyqAV/B+z3btpFM24glMxcJEtnhC2nLRtq00RlKoDOUKLofjzOb/ebqC8Ax+42IJotcRpvucg37NrKqwlVVhVhPD9reeWdYgbZc2ajh80HiBQoiGmeqrsNTWwtPweAV27aRjsUGTD5NJxKwUikEDx1C8NChovtx+v19mW/Zvx0+H/d5NCwMtNGgYtmJo5KEEWeZ+VwaEikLbT2xIwbaEikTwWgKAOB3s2yUiMpDlmU4DXlAFm4m+81ELJnr/ZYJwNlCIBJPIxJP4yAKst8UGT53dvhCNgDndWnDmtxMRDRRcuVVQGbIwUh46uoQ6+lB1759WHSEc610Ot+43MNpo0RUJrIsw/B4Brze2aaJeCiUGb5QMPnUNk3Eg0HEg0F07NiRP1/RtKLS09wfDnmh/hhoo0FFCgYhjPQNotelo703gZ5wErZtQz7M1cu27LRRQxv4BpeIqNwy2W+ZgFmObdtIpW1EkybiSQvJlImkmcl8S1s2ukJJdIWSRffjdqhFU0/9bh1OZvESUZnkykYVTYM2gh5tQCbQ1r59O6JdXUjF44ctPe05cAC2ZUHRNLiqq8e0ZiKiUpNVFe6aGrhravLHhBBIxeN9k08jEaQTiUz2WzqNYGsrgq2tRffj8Pn6Sk+zfzv9fu7zpjFGNmhQ0ewgBE2VR/wC4XaokGUJli3Q1pNAc+3QJQm5slGnkbkNEVGlk2UZDkOGo3/2m20jnjARS5iI57LfTBu2LRBNmIgmTBzqiuXPVxWpqOw0929mvxHReCschCApw28RAmRKTTWnE+l4HO3vvINZ7373kOd2ZstGHT4fMz6IaFKQJAmGywXD5QKam/PHc9lvhb3fUtnst0T2eMfOnfnzZVXNlLDW1xcNXxjJABqavBhoo0HlM9pGMZxAkiR4nRqC0RRau6NDBtpsW6AjO500N0SBiGiyUmUZXpcOb8H0ZCEEUmkrOyDGQiJtIpW2kTJtmJZAdziJ7nBx9pvLUIsmn/pdOlwOZr8RUekUBdpG8driqatDz/796Ny9e8hAmxACXdlBCM6qqlGulIioMgyV/ZZOJDLZb6EQktFopvw0kYBtmgi1tSHU1lZ0P4bXO2Dwgquqij0spxgG2mhQ0WyPNn2UU0D9bh3BaAqdwfiQ53SFEjAtAUXOBOaIiKYaSZJg6CoMfbDsNwvxZLov+y1t5wfRxJImWrv7st8UWcoH3XwFJaic1ExEoxHPlo6ONrPCU1uLnv370XvoEIQQgwbrot3dSIRCmQuw9fVjWi8RUSWSJAm605kpoS8YDmObJhKRSF/vt+zkUyudzpSjhsPo3LUrf76sKHBnM97yAbj6emgORzmeFpUAA200qFxGm6GNrJwgJ5ehFk9aiMRT8DgHlgu0Z/uzOXWFpVJENK1kst/komxeIQRSpoVYwkQsaSGRymS/pc1MAK4nnERPv+w3p6HA7zbyfd98bh1uZr8R0RHkJ44epr/a4TirqyHJMqxUCr0HDqB69uwB5+Sy2XSPZ9SPQ0Q0GeUmNLv6ZfOms73fEpFIfviCGY/DtiyE29oQ7p/95nYXlZ566urgqq4+bA90qgwMtNEApmUjkbIAAA59dIE2VZHhNBTEkxYOdESxZM7AQFsg25/N7dT4ppCIpj1JkmBoKgxNRbW377ht24glLMRS6czwhYLst3jSQjwZQ6Bf9pvXpRX1ffO5dejq6F7PiWhqEULkhyHobveo7kOWZbhrahDp7ETb9u2DBtpy/dmcPh9LooiIAGhOJzSnE76CY7ZlIREOZ/q8RSJIRaOZwQupFJLRKJLRaP7CBQBIigJ3Tc2A4Qu8oFFZGGijAXKDEGRJGnVGGwD4XTriyTjae+NYMqd40lQ0kc5nzfncLBslIhqKLMvwuGR4+vWyTKYz2W/xpIlEykLStJDOBuB6Iyn0RlJF5zt0JZ/1litBdTs1yLzQQTStpGIxWOls5YLHM+r78dTVIdLZie59+wZ8zkwmETx0KH8eERENTlaUwbPfkslM9lt2+EI62/tNWBYiHR2IdHQUna+7XPDU12fKT7PBN1d1NeQRDryh0mCgjQaI5gchSGOaBOp16wj0xBGMJGFadlF5aFu2bNShK3Do/DEkIhopQ1NgaAqqvX09lmzbRixpIZ40EU+ZSKYy2W+mLZBIWUik4vnXXyBzQSWf/ZbNgPO7dehjuMhCRJUtni0bVXR9TJNA3bW1+fuLh8NwevtScbv27YOwbaiGwUEIRESjoBkGtIYG+Boa8sds20ayIPstGY1mer+lUkjFYujeuxfde/fmz5ey2ceefsMXjFFmM9PwMcJBA0QSfRNHx1LSmem9JsG0BALdMcyq77tq2p4tG3UaKrMpiIhKRJZleJwyPP0GzKSy2W+xVCb7LZW2kDJt2EIgGE0hGC3OfjO0guy3bADO49TGdPGFiCpD4cTRsWQ6aA4HdLcbqWgU7e+8g7mrVuU/15UtG3V4vVBUvt0gIioFWZbh9Pvh9PuLjpvJJOKhUD77LRWPIx2PQ9g2Ip2diHR2Fp2vOZ3w1NUVTT9119RA5ut1yfArSQPkM9rGmGYqSRJ8Lh3d4SRaCwJtpmWjI5gAAPhcLBslIhpvuqZA1xRUoTj7LZ7Klp+mTCRTNlKmBdMSSKYttPdmSv9zJAnwOvuGLuRKUI1R9vIkovKIjXHiaCFvXR26sv2DcoE2IUS+n5CrpmbMj0FERIenGga89fVFE55t20YqGs2Un4bD+d5vZjKJdDyOnv370bN/f/58SZLgqq7OZ7/lgnC6281+6qPAQBsNkMto07WxN671ujR0h5PoCva9WesMJmDbAqoiwe3gjyARUTnIsgy3Q4bbUXzBI23aiCbSfb3f0pkAnBBAKJZCKJYCCtqC6JoMv1uH32XA59bgc+vwOnVmvxFVqFxGm1aCQJu7rg5de/ci2NoKy7KgKArC7e1IxWKQZJn92YiIykSWZTi8XjgKyvoBwEyl+nq/RaNIxWL53m/R7m5Eu7vRtm1b/nzV4RiY/VZby2zlI+BXhwbIZbQ59NIE2gAgmbbRG0miymOgLVc2qqvQOAWPiKiiaKqMKo+BKk9x9lsiZeX7vyXSmfJT0xJIpW109CbQ0ZvIny9JgMfZN/k0lwVnaAqvihKVWa5Hm+Zyjfm+nD4fZFWFbZro3rMH9QsW5KeNGl4vNIdjzI9BRESlo+r6kNlviVAIiXA43/vNTCZhJhLoPXAAvQcO9N2JJMFVVTVg+ILh8XCfl8VAGxVJmzaSaRsAYJRgSIEiy3A7VEQTJg52RuF36/lG3G4nf/yIiCYDWZbhcshwDZL9FkumEUtYSKQz5adp04ItgHAsjXAsDSCaP19X5aK+bz63Dq9LgyKP/cIOER2ZECKf0eYoQTPsXKPtcHs7OnbsQP2CBfn+bE6fj2+4iIgmgcNlvyXCYcRDIaQikUz2WzwO27IQ6+lBrKcH7e+8kz9fNYxM1lvh8IXaWija9GsXxUgHFYlmy0YVWYKuluaNj8+tI5ow0dEbR7jBg3jShATA7xr9pCsiIio/TZXhVw34C96v27aNRNpGPGkiluybfJq2bKRMG53BBDqDBdlvANzOvsmn/mwAzqEz+42o1JKRCGzTBADoJchoAwBPXR3C7e3oOXAAqVgMoba2zPGCbAkiIpp8VF2Hp7YWnuyUaSCzz0vHYpny04LJp2a2/1vvwYPoPXiw6H6cVVVFpaeeujo4pvjFGAbaqEgkWzaqKnLJpoH6XDpau2IIRVM42BEBADgMhQ20iYimIFmW4TJkuAwVtQXHzVz2W9LK9n7LlJ/aIvO7JxJP42BnX/abpspFZac+lw6fS4OiMPuNaLQKJ44qemkueLqzb8AS4TAOvPYagMxEO6fPV5L7JyKiyiHLMgyPB4bHU3TcNE0kg0HEc5NPs73fbNNEvLcX8d5etG/fnj9f0fWiwJu3rg7uujqoJfrdVG4MtFGRXEabpkolizAbmgxNlZE2bew8FAIAuAxtSkewiYiomKrK8KkGfP2y35JpG7GkmR++kDJtpLN/ukIJdIUSRffjdqjwu42CElQNTkPl7xSiYcj1Z1N1HfIYp8vnqLoOh8+HRCiEvZs3AwAcXm/J7p+IiCqfqqpQa2vzF1+ATLuCVDyeH76Qyma/pZNJWKkUgocOIXjoUNH9OHy+TPZbQQac0++fdPs8BtqoSC6jTStR2SiQGRXsc+noCiVg2QIA4HNPvzptIiIqJssynIYMp1G8HTFNG7GUiVjC7Mt+M23YtkA0YSKaMHGoqy/7TVWkorLTXAacyuw3oiKxnh4AmYy2UvLU1SERCuXLUgvfaBER0fQkSRIMlwuGywU0N+eP26aJeCiUGb4QiSAdiyEVj8M2zcyxUAgdO3fmz1c0De7a2r7y0/p6eGprS/67rJQYaKMi0Xhmg2Ropb0K6XNp+awETZXhdjDQRkREg8tkv2WCZTm2bSOVthFNmkgkLSTSmQBc2rRhWgLdoSS6Q8mi+3E5VPj7BeBczH6jaSxfOlriaaCe2lp07toFAJBVlYE2IiIakqyqcNfUwF1Tkz+Wy37LBdpyvd/SiQSsdBqhQAChQKDofhxebybolg3AeXPZbxUwZIuBNioSyZaOljrQ5nFqkCRACMBlqFBkvskhIqLhk2UZDkOGo3/2m20jnjCz5aeZvm8p04ZlC8QSmay41q5Y/nxFltBU48LqxQ0T/RSIyi4XaDNKNAghx/B6oeg6rFQKDq93yvTYISKiiVGY/eZvasoft00zP/k0GYlkyk+zwbdEOIxEOJy/0ANkgnhVM2bgmPPOK8fTyGOgjfJS2cwAAHDqpf3RkGUJVR4DPeEkvC5msxERUWmosgyvS4e3IPtNCIGUaSGaMBFPWEikTaTSdl8ALmmWccVE5SFsO9+jTXe7D3/yCEmShKoZM9C1Zw/ctbXMGiUiopKQVRWu6mq4qquLjqficSSCQSTCYSSjUaTicZi58tNwuEyr7cNAG+XlstkUWYJawh5tObPr3WiockAvcbYcERFRIUmSYGgqDE0FvH3HLdtGMJKCy8HtD00/iXAYwrYBSSp5oA0A6ubPh3/GDGazERHRuNOdTuhOJ3yF2W+WhUhnJxSt/Ik93GlSXrRgEMJ4VHZKkgRHiTPliIiIhkuRZTh0ZUD5KdF0kO/PZhhQx+FNiCRJ0J3Okt8vERHRcMiKAsPthlIBF3zK3yWOKkYkOwhBU2Sm/BMRERFNIflAm65XRKNoIiKiqYq/ZSkvmujLaCMiIiKiqSPW0wMgk9FGRERE44cRFcqLxHMTR/ljQURERDSV5AYhaCzvJCIiGleMqBCAzIS2XEabwT5qRERERFNKrnRUd7nKuxAiIqIpjoE2AgAk0zZMSwAAHDqnghIRERFNFbZlIR4MAgCMcZg4SkRERH0YaCMAfRNHVUWCpvDHgoiIiGiqSIRCgBCQZJkZbUREROOMERUCAERygxAUGbLMiaNEREREU0XhxFFFZYsQIiKi8cRAGwEoyGjjxFEiIiKiKaVw4qgkc69HREQ0nviblgD0ZbTpDLQRERERTSn5jDbDKO9CiIiIpgHmjhOAvow2Q+MgBCIiIqKpJJ4NtGlOZ3kXQkREVAJWKoV0by9SwWDmT28v0sEg4p2dMGprMXfVqrKuj4E2ghACkYQJAHAw0EZEREQ0peRKRzkIgYiIKpWVSOQDZ+lcAC0YLAqo5Y5b8fiQ9+OeM2cCVz04BtoIiZQF2xYAAENnoI2IiIhoqrBME4lwGACgu91lXg0REU0nViKBVGGgbJCgWS4jzU4mR3TfkqZB9/szf6qqoPn9UJxOOJubx+nZDB8DbYRIvG/iqKqwRxsRERHRVBEPBgEAkixDZ+koERGNgRACVjw+MOssGyzrH0QbafBM1nXofj+0XACt8N/ZYFru34rTCUmSim6fjESg6Hopn/KoMNBGiCZyE0clyLJ0hLOJiIiIaLKIF0wcVTStzKshIqJKkw+eZQNlhUGzwYJpdjo9ovuXDaMvYFZVNTCAVvA5xeEYEDybjBhoo6KMNiIiIiKaOgonjk6FNy9ERHRkQgiY0ejAQFm/4QG5j8UIg2eKwzF41tlgmWcOxzg9y8rFQBshmh2EoKnsz0ZEREQ0lRQG2oiIaPLKBc+Kep31yzwrCp6Z5ojuX3E6hy7V7BdMm47Bs5FgoI3yGW2Gzow2IiIioqkkP3GU/dmIiCqOsO1M8GyoUs1+wTRhWSO6f8XpzJdrDlaqmf+4qqoieptNFQy0TXNCCMSyPdocnDhKRERENKXkMto4cZSIaGII20Y6Ehk4IGCQYQHpUGjEwTPV7T7ysIDsH5nBs7JgoG2aiyVN2AKQADg0BtqIiIiIpgornUYqGgUAGAy0ERGNmrBtpMPhI2aepYNBpEIhwLZHdP+q253PLBt0WEDBcZmDbSoeA23TXDSemzgqQ+EwBCIiIqIpI5fNJisKNJaOEhEVEbaNdCg0cDjAYMG0UAgQYkT3r3q9Rx4WkP2crDI0M5XwuznNRXKDEBQZMidREREREU0Zuf5sqmFAVli5QERTn7AspEKhgWWb/TPQenuRDodHFjyTJGgez+GHBeT6ofl8DJ5NY/zOT3O5jDZNZZCNiIiIaCqJF0wclXhBlYgmKds0B888G6TvWToSGXnwzOcbfFhAYeDM74fu80HiRQsaBgbaprlIdhCCrvIFg4iIiGgqiRUE2oiIKoltmocv1Sz4txkOj+zOC4Jng07YLAygeb0MnlHJMdA2zeUy2gwOQiAiIiKaUnKBNvZnI6KJYKfTQ0/Y7BdMM7ODWoZNlqH7fEXlmkOWbXq9kGT2H6fyYaBtGrNtgVi2R5uhM9BGRERENJXkerRx4igRjZadSuUDZoMG0Ar+PdLgmaQog5ZtFvU+y/5b83gYPKNJg4G2aSyWNCEASBJgaHzRIiIiIpoqzGQS6XgcAANtRFTMSiaLA2WHmbhpZV9HhktSlMH7mw3S+0x1uxk8oymJgbZpLJIbhKDIUBW+wBERERFNFbmyUVlVoToc5V0MEY07K5E4bKnmmIJnqjogUDYgmJb9W3W7OXyFpj0G2qaxvomjMl8MiYiIiKaQXNmoahiQ2eibaFKyEol8wOxwfc9SwSDsZHJE9y1p2uGHBRT8m8EzopFhoG0ay00cZTYbERER0dRSOHGUb5CJKoMQAlY8PiDTrKh0s+BzIw2eyZpW3N+sX+ZZ4b8Vp5OvDUTjhIG2aSyX0aazPxsRERHRlBIvCLQR0fjJB88KSzUHKdvMBdPsdHpE9y8bxuGHBRR8jsEzosrAQNs0FslOHHVoLCcgIiIimkpypaO6y1XmlRBNPkIImNHooFlmgwXRxCiDZ4MOCygo4dSrqqCwxyLRpMNA2zRl2TbiyWygTWegjYiIiGiqEELkS0c5cZQoIxc8KyrV7Bc0KwqemeaI7l9xOgefsDlIMI3BM6KpreyBtttuuw0333wzWltbsXz5cvzwhz/EySefPOT5t956K376059iz549mDNnDr72ta/hU5/6VNE5vb29+NrXvoZHH30UPT09mDdvHm655RZ86EMfGu+nM2lEs9lsspQZhkBEREQ0HrjXm3jpRAJmtreTzkAbTWHCtmFGIgOzzoboeyYsa0T3XxQ86z8soF8ATWGZNhFllTXQ9sgjj+BLX/oSbrvtNpx44om44447cOaZZ2Lr1q2YM2fOgPNvv/12bNiwAT//+c+xZs0abNy4EZ/+9KdRXV2Ns88+GwCQSqXw/ve/Hw0NDfjNb36DWbNmYf/+/fB6vRP99Cparj+bqsochkBERETjgnu98ohny0YVTYPGN/80yQjbRjoSGTBVc8DEzWAQ6VBoxMEz1e0efFhAv7JNze+Houvj9CyJaCqThBCiXA++du1arFy5Erfffnv+2NKlS3HOOefgxhtvHHD+CSecgBNPPBE333xz/tiXvvQlbN68GX/9618BAD/72c9w88034+2334amacNaRzKZRLJgoksoFMLs2bMRDAbh8/lG+/Qq2vYDvdi6twduh4qFM/3lXg4REdGEiCXScDk1tDSOz+/3UCgEv98/pfcQI1EJe73puM9r3boVW//wBxgeD1qOPZbN0anshG0jHQoNmnk2oIQzFAJse0T3r7rdQ0/YLAyg+XyQGTwjmrKSkQgUXcfcVavG5f6Hu88rW0ZbKpXCli1bcO211xYd/8AHPoDnn39+0Nskk0k4+tWzO51ObNy4Eel0Gpqm4X/+539w/PHH4/Of/zwee+wx1NfX46KLLsI111wDRRm8F9mNN96IG264oTRPbJKIJjIZbSwbJSKi6WaEyQ80SpWy15uO+7xYwcRRBtlovBQFzwrLNAcZFpAOhYAR5neoXu/gWWcFwbTcx7Ja9o5IRER5ZXtF6uzshGVZaGxsLDre2NiIQCAw6G3OOOMM3HXXXTjnnHOwcuVKbNmyBffccw/S6TQ6OzvR3NyMXbt24emnn8YnP/lJPP7449i+fTs+//nPwzRNfP3rXx/0fjds2ICrrroq/3HuSudUFolnerTpkyTQ1tEh4ePnuBCNSJg5y8as2SL7t42ZszL/njnLBtuQEBFNDUIAqRQQi0qIxTJ/R2NAPCYhGpUQjyH7t4RoNHtePHe+hFgU2b8zn4/HM7eLxYDT32fiycfL/QynvkrZ603HfV5u4qjKslEaB8meHux68EG0v/DCiDPPNK/38MMCcv/2+Rg8I6JJq+yvXv2vsgkhhrzydv311yMQCOC4446DEAKNjY1Yv349brrppvwVTNu20dDQgDvvvBOKomDVqlU4dOgQbr755iEDbYZhwJhmG5FcRpuhTY6Jo0/9XsWeXZm1drTLeOWlwc+rru4Lws0sCMLNmm1j1iwbPlbJEhGVlBBAMpENasUyf0ejhYGuvoBX4ef7AmUFAbNYcaDMssYnEycaZYbPRCr3Xm867vPi2Yw23eUq70JoShG2jUN/+AN2P/IIrHg8c1CSoGUzz/r3NxsQTPP5IA1RYURENJWULdBWV1cHRVEGXNFsb28fcOUzx+l04p577sEdd9yBtrY2NDc3484774TX60VdXR0AoLm5GZqmFZUOLF26FIFAAKlUCjpr8mFaNhKpTN2MU58cv+w2b8ys84KLUjjtdBMHD8g4sF/CwQMyDh6UcWC/jFBQQk+PjJ4e4PXXBn9eXl9f9lsuG25W9uOZswRqagVYYUFEU5EQQCIORGNDZIMVBrkGDZj1Hc+fmz1m2+P7wqkbAm6XgNMFuN2Ffwu43cj87RJwuQGXS8DlFnC50Pe3S8Cd/bekplFfrwCYmr25Kgn3euUhhMiXjnLiKJVKaOdObL/rLkR27wYAeOfPx8LLLoN3/nwGz4iI+ilboE3XdaxatQpPPfUUzj333Pzxp556Ch/96EcPe1tN0zBr1iwAwMMPP4yzzjoLspwpgTzxxBPx0EMPwbbt/LF33nkHzc3N037jlZObOCrLEvRJktG2ZVPmR/XMs9I4dd3gzXVCIWQCb4VBuAMyDu6XcfCAhK4uGeGQhLe3Knh76+DP2+kcWJKaD8jNtlFXLyBPjmpbIpqkbBuIx4fIBosWZIMNGTArCIgVBdAAIcY3IGY4hhfwGjxQljtv4O1LWT0US9hwOSfH777Jjnu98kjFYrDSmb2eg4E2GiMzGsXuhx/GoT/+ERACisuF+Z/4BJrf9z5I3BQTEQ2qrKWjV111FS655BKsXr0axx9/PO68807s27cP//RP/wQg01Pj4MGDeOCBBwBkNlEbN27E2rVr0dPTg+9///t44403cP/99+fv87Of/Sx+8pOf4Itf/CL+5V/+Bdu3b8e///u/4wtf+EJZnmMliiQy/dk0RYIsV376VltAwr69MmRZYOXqoTtY+3yAb5mNpcsG7xURiwGHDmSCbgf2ZwNy2Y8PHpDRFpARj0vYsV3Bju2DvwnTdYEZM23MzPWIm1UckGtsEiV9Q0hElcu2URTEygW5otG+PmJDBbwy/cYGBsriscx9jDens38QLBvc6nc8HwQrCJTlzu0fMHO5ACY1UH/c6028XH82RdehMPBIoySEQPtf/4qdDz6IdDAIAGg46SQsuPhi6FVV5V0cEVGFK2tI4IILLkBXVxe+9a1vobW1FStWrMDjjz+OuXPnAgBaW1uxb9++/PmWZeGWW27Btm3boGka1q1bh+effx4tLS35c2bPno0//OEP+PKXv4yjjz4aM2fOxBe/+EVcc801E/30KlYuo01T5EkxiWrzpsw7tyXLbHi9o78flwtYeJSNhUcBwMCAXTIJtB7qC8IVZsYd2C8j0CohlZKwZ7eCPbsHfwxFEWieMVRWnI3mGQLc8xJNLMvCwGywoqywftlgg/YUG9iEP5EY/9fPXFZXYcDL6SzOBhsQMHMNfrvcOU4nmJlLE4Z7vYmX68+m6jpkRr9pFGIHD2L7Pfeg9803AQDOGTOw6PLLUb1iRZlXRkQ0OUhCjHDO8jQQCoXg9/sRDAbh8029Hi4vbe/A/vYIqj0G5jR6yr2cI/rW9QbuvcvAJZel8K1/T5RtHel0JruuLxDXF4Q7eEDGoYMS0unDv/GWJIHGplw2XOHQhr6PHc4JekJEFSadBuKD9QaLDZINVhgwi/fLIitswh+XkBzngJgkFQa8CjPDCsof3f3KJl0Dyyf7l006HAyIjYdYIg2XU0NL4/j8fp/qe4ipYKp/j3Y89xz2btkCd10dZr/73eVeDk0iVjKJfb/9Lfb/7/9CWBZkTcOc887D7LPOgqxp5V4eEdERJSMRKLqOuatWjcv9D3cPwSK3aSiX0aZrk+Md3OaNmR/TNceaZV2HpgGzZgvMmm1hsIw42wba26QBPeIOZHvEHTggI5mQEGiVEGiVsWXT4I9TW1cwObVfj7iZs2x4Kj82SlNcKoWBvcFixdlghYGwXMBrQMP9XBZZPPN3KjW+ATFFKcj8cgu4nINkg+VKIp39ssiyQbGigFk2s8xwgENUiKhi5AYhaNNs0iqNTddLL2HHvfci0dEBAKg55hgsvOwyOBsayrwyIqLJh4G2aSiSDbQ5JsHE0WgU2PpmJiC4+tih+7NVAlkGmpoFmpotrFoz8PNCAF1d0qBBuIPZrLhIREJXp4yuTuDVlwf//vir+ial9g/CzZxlw1/FN/2U+XlLpfr1D4sPVj5ZkA3WPwg2oHwyc7sjZW6OlaoOJ+A1dKAsXzbpQl+2mEtAN/h/g4imvvzEUZervAuhSSHR2Ymd99+Pzk2ZK8BGTQ0Wrl+P2jVrJkWLGSKiSsRA2zSTNi2kzMywAGMSBNpeeUmBZUmYMTPT32wykySgrk6grk7gPccMHNggBBAKom9Qw/5+5akHJfT2yAj2Sgj2KnjzjcG/fx5PYUlqcY+4WbMFamsFgw0VRAggmQAKJ0UOyAbrF/DKBcIGBMxixeWTljW+32hd7yuDHDLgVRT8Gjh1Mt9TrOB27GNIRDQ6Qoh8jzaDE0fpMGzTxMEnnsCe3/wGdjIJyDJmfehDaDn/fCgOR7mXR0Q0qTHQNs1E4pnyS0WWoKuVH2jbvDGzxjUVns1WCpIE+KsAf5WN5e8afHJqJILBg3DZctWuzkxW3La3FWx7e/Dvr8ORmZyaK0/ty4bLZMY1NAr2hRqEEEAijkEnRRaVQQ4ZMCsMlGXOzd3etsc5IGaIbDAMA7K+Bp8kWZhFhmxW2cBAGdu1EBFVlmQ4DNuyAEmCzl4TNITgtm3YfvfdiGYHkfiOOgqLrrgCnuyQEiIiGhsG2qaZaKJv4qg8CbKacv3ZVpW5P1ul8HiAxUtsLF4yeCAuEQcOHizoEVcwuOHAARltgcykxF07FezaOfhjaJrAjJkDe8Tl/t3ULKBW8CuHbQOJxBDZYAVlkYMFzPLZYLHBbgcIMb7/aRyOwwS8XAXTI/sHygrKKAuzxZzZbLFK/n4REVHpxAomjiq8GkL9pEMh7HroIQSefRYAoHq9mH/RRWg69VRIvMpKRFQyfPs1zeT6s6mqXPF9F0wTeHlLJiur0vuzVQqHE1iw0MaChcBgAxtSKaD10CCDGrIBudZDmcmpe/dI2Ltn8A2XohRMTh3QIy6TLTec/su2DcTypY59QbDCgFc83i8brH/AbJBAWSw2/j/XTme2B1j/3mGugdlgrsLyyILgWL7PWDZQ5nQCSuUnmRIRUQUrDLTJDJxQlrBtBJ59Frt+9SuY4TAAoGndOsy/8EJoU3DyLhFRuTHQNsnYQmDrnm7EkqPL8ApGUgAATa3sIBsAvL1VRjQqwesTOGrx4BlcNDK6DsxtEZjbMvjkVNME2gLSgGmpuay4QwdkpFISDh2UcOigjE0vDv44DY2ZIFx1jUAiPkg2WTSTWTeeJCkTxDpSwKsoC8xVnA1WdLtsFpnTCZbWEhHRuBBCYPff/45IV9eobh/N3k5ljy3Kiuzbh+13343Qtm0AAPfs2Vh05ZXwL15c5pUREZWOsG1Eu7vRc+AAdJcLc1etKut6GGibZDp749h5KDTm+3Holf+t37wpk96zcpXFTJ8JoqrIlolaOPa4gYE42wY6O6RBesT1ZcXF4xLa22S0tw0vGiXLBeWPRT3DioNc7sMEygYLmDmcnDBJRESTS6SzE7tfHOIq1gjoTmcJVkOTmZVIYM+vf40DTzwB2DZkw0DL+edj5plnQmZPCSKaAoQQSITDCLW2ItTWBiudqd5LhMOwbbusmd18lZ1kwtnST12V4XePbjSfokio8VT+WL9cf7bV7M9WMWQZaGgUaGi0sHL1wM8LAXR3SfkgXG+vNGDqZP+AmeFgQIyIiAgAYt3dAADVMOBtbBzVfciKgqoZM0q5LJpEhBDo2rQJO+67D8nsz1PdmjVYcOmlcNTVlXl1RERjl4rHEQoEEAoEkIrF8sdlVYW/qQnNK1aUvU0WA22TTDSRCTo5DRUz6qbu2HYh+iaOsj/b5CFJQG2dQG2dwNHvYbkvERHRSOR6rOkuFxoXLSrvYmjSibe3Y8e996L75ZcBAI76eiy87DLUrlxZ5pUREY2NlU4j3N6OYCCAePZ3JQBIkgR3XR0ajzoKjUuWwOHxlD3IBjDQNulECzLaprKDByS0BWSoqsC738NAGxEREU19+WEGw5kqRJRlmyb2/+//Yt+jj8JOpyEpCmaffTbmnHsuFP4sEdEkJWwbka4uhFpbEenshBAi/zmn34+6+fPRvGwZPLW1FTc5mYG2SSaSyATaDH1qNy3b9GLmR3PF0RacrjIvhoiIiGgCxAsy2oiGo+fNN7Hj7rsRO3QIAOBftgyLrrgC7pkzy7wyIqKRE0IgHgxmSkPb2mCbfW2kNKcTNXPnonnZMlTNmAGlgvtNVu7KaADbFohlS0cdUzzQtiU7CGHVGmazERER0fQQ6+kBwEAbHVmqtxc7H3wQ7X/9KwBA8/ux4OKL0XDSSRVRNkVENBKpWAzBbN+1dDyeP65oGvwzZqB52TLUtbRMmoxvBtomkWg2m02Spn7paK4/2xr2ZyMiIqJpIJ1IIJ1IAAAMj6fMq6FKJWwbrX/8I3Y9/DCsWAyQJMx43/vQcsEF0PhzQ0STiJlKIdzWhmAggEQolD8uyTK8DQ1oXLwYjYsWTcrfiQy0TSK5QQiaIkNVpm6gLdgLbHs7m9HGQBsRERFNA7n+bIqmQZskV+xpYoV378b2u+5CeOdOAIBn3jwsuuIK+BYuLPPKiIiGx7YsRDo7EQoEEOnqykxBzHJVV6N+wQI0LV0Kd03NpM7OZaBtEolkByFoqjypf+iO5KUtmR/LlvkW6urEEc4mIiIimvxy/dkUXYekTO0WITQyZiyGPf/1Xzj45JOAEFCcTsy74ALM+MAHKq4BOBFRf0IIxHp7EQoEEG5rg231JdPobjdq585F8/Ll8Dc1QZ4iv/8YaJtEogWBtqls84ssGyUiIqLpJdefTTOMKX1BlYZPCIGOF17AzgceQCobiK0/4QQsuOQSGNXV5V0cEdERJCORfN81M5nMH1d0HVUzZ2LGsmWomTsXqq6XcZXjg4G2SSQ3cXTK92fLDkJYzUAbERERTRO50tHJ0uiZxlfs0CFsv/de9L7+OgDA2dSEhZdfjpqjjy7zyoiIhmYmkwhl+64lw+H8cUlR4GtoQNPixag/6igYU3zoDwNtk0guo83QpkY65WCSSeDVVzhxlIiIiKaXXKCNE0enNzuVwr7HHsO+xx6DME1ImoY555yDOWefDXkKZn0Q0eRnWxbCHR0ItbYi2t1d9Dl3TQ3qFy1C85IlcFZVTZuMbQbaJgnLshFPZQJPDn3qBtrefF1BMiGhpsbG/AV2uZdDRERENO6EEIhnS0d1t7vMq6Fy6X71VWy/5x4k2toAANXvfjcWXXYZnE1NZV4ZEVExIQRi3d0IBgIId3RAFPRdMzwe1M6bhxnLlsHb0DBl+q6NBANtk0Ru4qgsSVO6dLSwbHSaBLuJiIhomkvH4zBTKQCAwUDbtJPs7sbOBx5Ax9//DgDQq6ux8NJLUbd27bTJ/iCiyieEQDISQSjXdy37ewvItD2onjULzcuXo2b2bCiaVsaVlh8DbZNErj+bpkpQlCkcaMsOQljF/mxEREQ0TcQKJo6yR9v0ISwLB598Env+679gJRKAJGHmBz+Ilo99DCpLiImoQqQTiXxwLRmN5o/LigJfUxOali5F/YIF0J3OMq6ysjDQNknkJ44q8pS9siUEsGUTJ44SERHR9JKbOKrq+rQssZmOQtu3Y/vddyOyZw8AwLtoEY664gp4WlrKui4iIgCwTBPh9naEAoH87ygAgCTBU1uLhqOOQtPixXD4fFM2PjEWDLRNEpFcoE2dupuvXTtldHfLMBwCy9/FQBsRERFND5w4On2kIxHs/tWv0Pr004AQUN1uzLvwQjSffjokeepWrRBR5RO2jWh3N4KtrYh0dkLYfT3THT4f6ubNQ/OyZfDU10Pm69VhMdA2SUSzpaNTuj/bxkwQ8d3vscChSkRERDRdxLOBNs3hKO9CaNwIIdD2l79g1y9/iXQoBABoPOUUzP/kJ6H7/WVeHRFNV0IIJEKhTGloWxusdDr/Oc3hQPWcOZixbBmqZs2CojJ8NFz8Sk0SkXhmGIKhT/1A22qWjRIREdE0kivL0diXa0qK7t+P7ffcg+BbbwEAXLNmYdEVV6Bq6dIyr4yIpqtUPJ7vu5aKxfLHZVVFVXMzmpYtQ21LC/uujRIDbZNA2rSRTGeCTw5j6n7LCieOEhEREU0HQoh86Sgnjk4tViKBvY8+igO/+x2EZUHWdcz9h3/ArA9/GDIzQ4hoglnpNEJtbQgFAogHg/njkiTBU1+PhsWL0XTUUXB4vWVc5dTAV/hJIFc2qsjSlC0d7eiQsGeXAkkSWLnKLPdyiIiIiCZEMhqFbWYrF5jRNmV0bt6MHffdh2RnJwCgdtUqLFy/Ho76+jKvjIimE9u2Ee3sRDAQQLSzE0KI/Oecfj/qFizAjGXL4K6pYZ/IEmKgbRLITRxVFRnyFJ3o8VI2m+2oJTb8VeVdCxEREdFEiRdMHFXYpHbSS3R0YMd996FryxYAgFFXh4Xr16Nu9eoyr4yIpgshBOLBYL7vWu5iDgDoLhdq5szBjBUr4G9qYnbtOOFXdRKIJHITR6UpOzp3U64/2xqWjRIREdH0kSsbVQwDsjJ1p8tPdbZp4sDvfoe9jz4KO5mEpCiY9eEPY+5550HhkAsimgCpWAzB1laEAgGkE4n8cUXT4J8xA83Ll6Nu7lxOuJ4ADLRNAtHsIARNnbqbr80bMz+Kq49l2SgRERFNH7lAm8Y3PpNW71tvYfvddyN24AAAwL90KRZdfjncs2eXeWVENNWZqVS+71oiO9EYACRZhrehAY2LF6Nx0SIYHk8ZVzn9MNA2CeQy2gxtatZMx2PAm69nnhsHIRAREdF0kps4qjLradJJhULY9eCDaPvLXwAAmteL+RdfjMZTTpmyVShEVH62ZSHS2Ylgayui3d1AQd81V3U16hcuRPPSpXBVV/O1qEwYaJsEcj3aDK38GW22DUhS5k+pvPqKAtOU0NRsY+YsceQbEBEREU0R8dzEUQ5CmDSEbaP16aex+1e/ghmNAgCa3/tezLvwQmjMGiGicSCEQKynB6FAAOH2dthWX4KK4XajpqUFzcuWZfqusQ1B2THQVuFSpoWUaQMAnHp5/8M89aSKDV9xYN17Tdz8w8SRbzBMm17M9mc71ippAI+IiIiokuUaVgOA7naXeTU0HJG9e7H9rrsQ2r4dAOCeOxdHXXklfIsWlXllRDQVJSMRBAMBhAIBmMlk/rii66ieNQvNS5eiZu5cqBymU1EYaKtwuf5siixBLVOPNiGA++7S8e1vGBBCwm8e0XHmWSZOf19p+qlt2ZT5MVy1hv3ZiIiIaPpIhMOZrARJYqCtwpnxOPb8+tc4+PvfA7YNxeFAy8c/jplnnAGJ2SNEVEJmMpkPriUjkfxxWVHgbWxE05IlaFi4EDozoSsWA20VLhLPTRyVIZch28s0gW9/w4EH7slEyFvmW9izS8G3v2HgpFNMjDVwblnAS5szm5M17M9GRERE00i+P5uuQ9W0Mq+GBiOEQOeLL2LH/fcjlf1+1R93HBZ86lMwamrKvDoimips00S4owPBQACx7u6+T0gS3DU1aFi4EE1LlsBZVcW+a5MAA20VLpodhKAp8oT/h4pEgC/8kxPP/EmDJAlce30SF16cwuknerBnl4J779Lxmc+lxvQY72yTEQ5L8HgEFi+1S7RyIiIiosqX68+mGgYkeWoOvZrM4oEAtt97L3pefRUA4GhsxKLLLkPNe95T3oUR0ZQgbBvRgr5rwu57P2x4vaibNw/Ny5bBW1/PvmuTDANtFa4wo20itR6ScMWnXHjrTQUOh8APfhrHBz+cKe386r8l8dUvO/HTHxg47/w06htGP8Bg88bMC8Z7VlpQ+dNIRERE00isINBGlcNOp7Hvf/4H+/77vyHSaUiqijkf/Shmf/SjUNgHiYjGQAiBZDicKQ1ta4OV6ktcUQ0D1bNnY8by5aieNQsKM50nLYY2Klwuo03XJi7Q9ubrMq74lAttARm1dTbueiCG9xzTF13/h4+n8eD9Ol57RcFN/26MaTDC5o2ZH8E1a9mfjYiIiKaXXKBNczjKuxDK63n9dWy/5x7EW1sBAFUrVmDR5ZfDNWNGmVdGRJNZOpFAKBBAMBBAKjutGABkVYWvqQlNS5eifv586E5nGVdJpcJAWwUTQuQz2hz6xHyr/vSUii/8kxOxmIRFR1m458EYZs0uzliTZeCb30ngvLPc+M0jOj55aaooEDcSuYy21ezPRkRERNNMrkcbByGUX7KnB7t+8Qu0P/88AECvqsKCT30K9ccfz35IRDQqlmki3N6OUGtr/sIKAECS4KmrQ8OiRWhasgQOr5evM1MMA20VLJW2YVqZIJdDH/+a7Pvu1vDtrztg2xJOPMXEbXfG4PMPfu4xqyyc97EUHv21jhuuc+D//W8MI20tcvCAhEMHZSiKwHtWMtBGRERE04dt20iEQgAAg4G2shG2jUN/+AN2P/IIrHgckCTM+MAHMO+CC6Byoh8RjZCwbUS6uhAKBBDp7Czqu+bw+VC3YAFmLFsGd20tZPbmnLIYaKtgkWzZqKpI0JTx+09oWcB3vmHgvrsz/UE+flEK3/mPBI5UEn7N15J48nENr7yk4re/0fAPH0+P6HG3bMoED5evsMF9DBEREU0niVAIwrYhSRJ0boTKIrxzJ965+25Edu0CAHjnz8eiK6+Ed/78Mq+MiCYTIQQSoRCCgQDCbW2w0n3vizWnEzVz5qB5+XJUzZgBhY3JpwV+lytYfhCCIkOWxyeVNBoFvvg5J/70h0xU7ZqvJfCZz6cwnMzVhkaBf/5SEt/7rgPf+66BMz6Uhscz/MfevCnz47dqDfuzERER0fSSKxtVDYNvvCaYGY1i9yOP4NBTTwFCQHG5MO8Tn8CM972P01+JaNhSsVi+71o6Hs8flzUNVc3NaFq6FHXz5rEP5zTE3+oVLDcIQR2niaNtAQlXXOLCm28o0A2B7/8kjg+fPbKg12WfTuGRhzTs2a3g1h8ZuOZryWHfNt+fbS3LRomIiGh6KZw4yuDOxBBCoP1vf8POX/wC6WAQANBw0klYcPHF0Kuqyrs4IpoUrHQaobY2hAIBxLOvIwAgyTI8dXVoXLwYjUcdBYfXW8ZVUrkx0FbBotmMNn0cAm1b35Rx5adcaD0ko7bWxp33xbFy9cgDXoYBfO2bSXz6UhfuuVPHBRel0DJPHPF2oRDw9tbM81q9hoE2IiIiml7iBYE2Gn+xgwex/Z570PvmmwAA54wZWHT55ahesaLMKyOiSmfbNiKdnfm+axB973edVVWonz8fzdm+axxqQAADbRUtkshklxlaaQchPPMnFf/yGSeiUQkLFmYmi86Ze+Tg2FDe+34Tp5xm4i/PqvjONx246/74EW/z8hYFQkiYM9dGQ+PoH5uIiIhoMsqVjrKkaHxZqRT2/fa32P8//wNhWZA1DXPOOw+zzzoL8pEaEhPRtCWEQLy3N9N3rb0dttlX+aW7XKiZOxczli+Hv6kJMsv/qR/+RFQoIUQ+o81RwkDb//63ii993gnblnDCSSZu+3kM/qqx3ackAV//VgIfPN2NP/1Bw5+fSeHUdYfPUtu8MfOjt/pY9mcjIiKi6SdXOqpXyMRRYVnY+eCDcM2YgRnvf3+5l1MSXS+/jB333otEezsAoOaYY7Bw/Xo4GxvLvDIiqlTJaBShQAChQADpRCJ/XNF1VM2YgeZly1Db0gJV18u4Sqp0DLRVqETKgmVnMr0MvXSBtp/8wIBtSzjvYync+J8JlOr1YcEiG5densLddxr49tcdOOHp6GGnluYmjq4+lmWjRERENL3YpolEOAygcgJtnZs24eATTwCyjLo1ayZ1z7JkVxd23H8/OjduBAAYNTVYsH496tasYVkXEQ1gplKZvmutrfnXZiDTd83b0ICmJUvQsGgRjAp5vabKx0BbhcoNQtAUGapSmh5tPd0Str+TCXD929eTJQuy5XzhqiT++/9p2LlDwQP36LjiM6lBz0unM6WjAPuzERER0fQTD4UAISDJMnSns9zLAQC0PvNM5h+2jcCf/4w5H/1oeRc0CrZp4uDvf489v/417GQSkGXMOvNMtHzsY1BYoktEBWzLQqSjA8FAANHu7qK+a66aGtQvXIjmJUvgqq5mgJ5GjIG2ChXJlo2qigRZLs1/7C2bM8Gt+Qss1NaVvi+azw98ZUMSG77ixI9uMfCR89Korx/4OG++ISORkFBVbWPBIrvk6yAiIiKqZLn+bKphQKmA3j6Jzk70vPZa/uPAM89g9kc+MqneXAa3bcP2u+9GdN8+AIDvqKOw6Ior4Jk7t8wrI6JKIYRArKcn33dNWH1JH4bHg5qWFsxcvhzehgbISmn7pNP0Uv7f7DSoaDzTu0wr4cTRfLnm2vHLIvvYJ9L45f063nhdwX/+h4Hv3ZIYcM6WbH+2lastcJo9ERERTTexgomjUgVshgLPPgsIAe/ChYgdOIB4IIDg22+jaunSci/tiNLhMHY99BAC2Yw81ePB/IsuQtNpp1XE15aIyi8RiSDU2opQWxvMZDJ/XDUMVM2ciebly1E7Zw4UDkihEmGgrUJFcqWjauki6ZtfzNzXmnHsi6YowDe+k8DHPurGr3+l4eJPpfCudxdnrW3eOP7rICIiIqpUhRlt5SZsOxNoAzDzgx9E75tvIvDMMwg880xFB9pEtsR110MPwcz2VGo67TTMv+giaD5fmVdHROWWTiQyfdcCASQjkfxxWVHgbWzM912rlPJ9mloYaKtQuYmjhl6aK3HJBPDaq5kA16px7ou2+lgLHz03jcd+q+GG6x349WMx5CoPhAA2beQgBCIiIpq+4tmMNq0C3uD1vPEGkp2dUFwu1B17LBwNDQg88ww6/v53LFy/HqrLVe4lDhDZtw/b774boW3bAADu2bOx6Mor4V+8uMwrI6Jyskwz33ct1t3d9wlJgrumBo2LFqFx6VI4fb5JVRpPkw8DbRVICJEfhuAo0cTR119TkEpJqK2z0TJv/PuiXXt9Ak89qWLLJhWPParinH/IlMLu3SOhq1OGrgu862gG2oiIiGj6yZWO6hUQxAo8/TQAoPGkk6DoOnyLFsE1axZiBw6g/W9/w4z3v7/MK+xjJRLY85vf4MDjjwO2Ddkw0HL++Zh55pmQK6DXHRFNPGHbiHZ3IxQIINzRAWH3vdd1eL2omzcPTcuXw1tfD5nl5DRB+BupAsWTJmwBSAAcWmkCbYXlmhMRvG9qFvjcF5L4z/9w4D++48D7PxiB2w1szvZnO/rdFgwOfyIiIqJpxkqn82VMDo+nrGtJh8Po3LwZANC0bh0AQJIkNK1bh12/+AUCzzxTEYE2IQS6Nm3CjvvvR7KrCwBQt2YNFlx6KRx1dWVeHRFNNCEEkuEwgoEAQoEArHQ6/znN4UDV7NmYsWwZqmfProiBMzT98KeuAkUSmewvVZWhKKWJum/eNDFlo4Wu/EwK//UrHfv2yrjtxwb+dUMyH/Abz4EMRERERJUql80mKwo0R3mvOrY99xyEacLT0gLvvHn5440nn4zdDz2E8K5diOzdW9bJnfH2duy49150v/wyAMBRX4+Fl12G2pUry7YmIiqPdDyOYFsbQq2tSMVi+eOyqsLf1ISmpUtRN38++65R2THQVoFy/dk0RYZcgvQz2y6cOGqO+f6Gy3AAX/tGAp+53IW77tBxwUWpfKBtIgN+RERERJUiXjBxtJzljkKI/KTOptNPL/qc7vOhdvVqdL74IgLPPIOF69dP+Pps08T+//1f7Pvtb2GnUpAUBbPPPhtzzj0XSgUMkSCiiWGl0wi3tyMYCORfP4FM9q27rg6NRx2FxiVL4PB42HeNKgYDbRUokg20qWppXih2bpfR2yPD4RBYvmL8+7MVev8HTZx4iom//UXFNVc5sXNHNtC2moE2IiIimn5iBYG2cr4pDO/ciej+/ZA1DY0nnjjg883r1qHzxRfR9txzmH/RRZB1fcLW1vvmm9h+992IHToEAPAvW4ZFV1wB98yZE7YGIiofYduIdHUhFAgg0tlZ3HfN70f9/PloXrYMntpaSOy7RhWIgbYKlBuEYKgl6s+WzWZ7z0oLmlaSuxw2SQK+8a0EznyvG39/PvPjtnCRheoaMbELISIiIqoAsZ4eAJlAWznlhiDUrV0L1e0e8Pnqo4+GUVuLZFcXOjdvRsMJJ4z7mlLBIHY9+CDannsOAKD5/Vhw8cVoOOkkZqoQTXFCCCRCIQRbWxFqa4Nt9lViaU4naubORfOyZaiaMYN916ji8Se0AuUy2vQSD0JYfWx5ssgWLbZxyfoU7rvbKOs6iIho+jItG/GkhVAsiZRlA43lXhFNV7mMNq2MPYSsRALtL7wAIJO5NhhJltF02mnY+//+H1qffnrcA22tTz+NXb/8JcxoFJAkzHjf+9BywQXQyjwwgojGVyoWyw81SMfj+eOKpsE/Ywaaly5FbUtL2XtaEo0EA20VxrYFYslM9N6hlyrQlvk2r14zcf3Z+vvSV5J47FENPT0y1nAQAhERjRMhBBIpC4mUhXjKRCKZ+du0+jKpk+mJbaNAVCjXY8hwucq2ho6//x1WPA5HYyP8y5YNeV7Tqadi76OPoveNNxBvb4ezoWFc1tP18st45847AQCeefOw6Ior4Fu4cFwei4jKz0ylMn3XWluRCIXyxyVZhqe+Hk1HHYWGo46Cw+st4yqJRq/sBc233XYb5s2bB4fDgVWrVuG5bKr4UG699VYsXboUTqcTixcvxgMPPDDkuQ8//DAkScI555xT4lWPn1jShBCZkktDG/u3p71Nwr69MiRJ4Jgy9kXzVwF3PRDHP38pibPPSR/xfCIioiNJmzbCsRTae+PY1xbBtv29eH1XN945EMS+9gg6ehMIx9P5IJumyPC5NNT5eVV8InGv18dMJvOT8vQyZmq1ZocgNK9bd9iSTEdDA6pXrAAABJ59dlzWYpsmdma/x83vfS9Wfve7DLIRTUG2ZSHU1oYDr76KHX/9K9q2bcsH2VxVVZizejWO/eQnseYTn8CcVasYZKNJrawZbY888gi+9KUv4bbbbsOJJ56IO+64A2eeeSa2bt2KOXPmDDj/9ttvx4YNG/Dzn/8ca9aswcaNG/HpT38a1dXVOPvss4vO3bt3L77yla/g5JNPnqinUxKFE0dVZeyBtlx/tiXLbPh8Y767MVm52sJKDkEgIqIRsoVAMmUhnrKQSJqZv/tlqRWSJcDQFbgdGnwuHX6PjmqPAaehluR3Kw0f93rFcmWjsqqWrQwqdvAgQtu2AZKExlNOOeL5TevWoef119H27LNoOf/8kjceP/jEE4i3tkLz+zH/k59kY3OiKUQIgXhvL4KBAMLt7UV913S3G7Vz56J5+XL4m5ogK6Wp5iKqBGUNtH3/+9/HFVdcgSuvvBIA8MMf/hBPPvkkbr/9dtx4440Dzv/FL36Bz3zmM7jgggsAAPPnz8ff//53fO973yvafFmWhU9+8pO44YYb8Nxzz6G3YAxwpYskchNH5ZI0fd38YnbK5xoGuIiIqLIJIWBaoqjkM1cGOhRNleE0VPicGnxuHVUeAz6XBl1T2Dy9AnCvV6xw4mi53lS2ZjPTao45BkZNzRHPr1uzBqrHg2R3N7pffRW1xxxTsrWkenux99FHAQDzL7wQahnLaYmodJLRKEKtrQi2tcFMJPLHFV1H1cyZ+b5r6gROMyaaSGULtKVSKWzZsgXXXntt0fEPfOADeP755we9TTKZhKPf1T+n04mNGzcinU5Dy47U/Na3voX6+npcccUVRyxPyN1vMpnMfxwqqBOfaIUZbaWwZVPmW7zm2PL1ZyMiIurPtgUS6eIMtXjSgmUPnaXm0FW4HSp8bh1+t4Eqjw6XoUJhllpFqpS9XiXt8+IFgbZyBIJt00TbX/4CAGg+/fRh3UbWNDSefDIOPvEEAs88U9JA2+6HH4YVj8O7YMGwsuuIqHKZySRCbW0IBgJIhsP545KiwNfQgKbFi1F/1FFl7U9JNFHKFmjr7OyEZVlobCwe+9XY2IhAIDDobc444wzcddddOOecc7By5Ups2bIF99xzD9LpNDo7O9Hc3Iy//e1vuPvuu/HKK68Mey033ngjbrjhhrE8nZLJZbTpJejPFo0Cb76RuR9O+iQionIQQiBt2dkMtb6AWjI99O8lPZul5nVlstSqPQa8TmapTTaVsterpH1erKcHQCbQVg7dL72EdDAIze9HzXveM+zbNa1bh4NPPIGuLVuQCgah+/1jXkto585837eF69ezZJRoErItC+GODoQCAUS7uwHRd7HMVVODhoUL0bR0KVxVVfz9TdNK2aeO9v8PJ4QY8j/h9ddfj0AggOOOOw5CCDQ2NmL9+vW46aaboCgKwuEwLr74Yvz85z9HXV3dsNewYcMGXHXVVfmPQ6EQZs+ePbonNEbReHbiqDb2coJXX1ZgWRJmzLAxY+bgGQJERESlYtuib9pnykI8mfl7yCw1WYJDU+B2avC7NVS5Dfg9OpyGCoVvuqeMcu/1Kmmflysd1Z3Osjx+bghC0ymnQFaH/zbAM2cOvAsWILxzJ9qeew6zzzprTOsQto0d990HAGg8+WT4Fi0a0/0R0cQRQiDW3Z3pu9bRAWH1XTgzPB7UtrRgxvLl8DY0sO8aTVsjDrS1tLTg8ssvx/r16wdtYjtcdXV1UBRlwBXN9vb2AVc+c5xOJ+655x7ccccdaGtrQ3NzM+688054vV7U1dXhtddew549e4p6eNi2DQBQVRXbtm3DggULBtyvYRgwynRlsZBlC8SS2UCbPvYXpc0bM/exei2z2YiIqHSEEEibdlGGWiJlIpm2h7yNrslw6ZkstUzZpwGvS4WmMkutkpRqnwdUzl6vUvZ5QEGgrQylU8nubnRnswCb1q0b8e2b1q1DeOdOBJ5+GrM+/OEx/b9t/+tfEd6+HbJhYN6FF476foho4iTCYYQCAYQCAZipVP64ahionjULzcuWoWbOHCjZEn+i6WzEgbarr74a9913H771rW9h3bp1uOKKK3DuueeOeAOj6zpWrVqFp556Cueee27++FNPPYWPfvSjh72tpmmYNWsWgMxY97POOguyLGPJkiV4/fXXi8697rrrEA6H8aMf/ahsVy+HK5YtG5WkTHPnsdr0Yubbu2oN+7MREdHoWLZAol+GWjxpwRaDZ6kpsgSHns1Sc+nwu3VUew0YugpFZkCt0pVqnwdwr9dfOh7PNwU3vN4Jf/zAn/8MCAH/kiVwzZgx4ts3nHACdv7iF4gdOoTQO+/Av3jxqNZhxuPY9atfAQDmnnfesAYyEFF5pBMJhNraEAoEkIxE8sdlRYGvqQlNS5agfuHCsmXpElWqEQfa/uVf/gX/8i//gldffRX33HMPvvCFL+Bzn/scLrroIlx++eVYuXLlsO/rqquuwiWXXILVq1fj+OOPx5133ol9+/bhn/7pnwBkUv0PHjyIBx54AADwzjvvYOPGjVi7di16enrw/e9/H2+88Qbuv/9+AIDD4cCKFSuKHqOqqgoABhyvRJHcIARVhjrGxs6mCby8JZvRxv5sRER0BEIIpEy7OKCWMpE6TJaaoclwGSq8Lj3TS81rwONgltpkVsp9HsC9XqFcNpuiadAmeNKesG0EcmWjo8hmAwDV5UL92rVo+8tfEHjmmVEH2vb99rdI9fTA0diIWR/60Kjug4jGj2WaCLe3IxQI5PtKAgAkCZ7aWjQsWoTGJUvg9Pn4u55oCKPu0fbud78bP/rRj/Cf//mfuO2223DNNdfg9ttvx4oVK/DFL34Rl1122RH/411wwQXo6urCt771LbS2tmLFihV4/PHHMXfuXABAa2sr9u3blz/fsizccsst2LZtGzRNw7p16/D888+jpaVltE+jokQTmcwzTZHH/KK17S0Z0agEr1dg8ZKh3yQREdH0Y9kDhxMkUiaGaKUGRZbgzGWpuXX4PQaq3DocugqZWWpTUin2eQD3eoVihRNHJ7hvUe/WrUi0t0NxOlG/du2o76f59NPR9pe/oP2FF7Dg0kuhjjCLJR4I4MDjjwMAFlxyCWSWmBFVBGHbiGb7rkU6OiDsvvePDp8PdfPmoXnZMnjq6yGzhyrREUlCDFH7cQTpdBq//e1vce+99+Kpp57CcccdhyuuuAKHDh3CT3/6U6xbtw4PPfRQqdc7IUKhEPx+P4LBIHw+34Q97is7OrG3LYwqj465jWMrKbjvbg03XOfEKaeZuP9XsRKtkIiIJhMhBFJpe8BwgpQ5+AUYCYCuKXA5VPjyvdR0eJw6VEXilethKNceotS4zyu9nS+8gD0vvgh3bS1mj2DiZym89eMfo/3559H8vvfhqCuvHPX9CCGw6eqrET90CEf94z+i+fTTR3T7N26+GV1btqD66KPxrg0b+JpCVEZCiEzftdZWhNraYKXT+c9pDgeqZ8/GjOXLUTVrFpQRDE8hmsqGu4cY8f+Yl156Cffeey9+9atfQVEUXHLJJfjBD36AJUuW5M/5wAc+gFNOOWV0K5/GotkebXoJ+rNt2ZT51q4+lv3ZiIimA8vqP5wgU/o51OU0Vcn0UvM4M33UMn8MOHSFWWrTGPd54yeeLcFSJ3gwQzoSQcemTQCA5lGWjeZIkoTmdeuw65e/ROszz4wo0Nb96qvo2rIFkqJgwaWXMshGVCapeDw/1CAV60vIkFUV/uZmNC9ditp589h3jWgMRhxoW7NmDd7//vfj9ttvxznnnANtkJTvZcuW4ROf+ERJFjid5Hq0GdrYygmE6Js4uoYTR4mIphQhBJJpu6jkM56ykD5MlpqhK3AZKnwuHX6PjiqPDrdDL8ngHZpauM8bP/mJoxP85rX9r3+FSKfhnjMHnvnzx3x/jSefjN0PP4zw9u2I7t8P9zAGUNimiZ3ZPnszPvABuGfOHPM6iGj4rHQa4fZ2BFtbEQ8G88clSYKnvh4NRx2FxsWL4fB4GAQnKoERB9p27dqV76sxFLfbjXvvvXfUi5qOTCvTgBoAnPrYAm0HD0gItMpQVYF3v4eBNiKiySr3u6FwOEEiZR02S81pqPA4NPjcmYBalduAriuQuXGmYeA+b3wIIfJNxXW3e0Ift7VgCEIp3kDrVVWoWbkSXZs2ofWZZ7DwU5864m0OPfkkYocOQfP50HL++WNeAxEdmW3biHZ1IdjaimhnJwo7Rjn9ftQtWIDmpUvhqa2FxL5rRCU14kBbe3s7AoEA1vZrpPriiy9CURSsXr26ZIubTnKDEGRJgj7GjLZcNtvyd9lwusa8NCIiGmeZLDUL8WRfMC2RtJC2hshSkzLZz25HZuKn362j2uuA26GOeWo1TW/c542PVCyW739keDwT9riR3bsR3bsXkqah8eSTS3a/zevWoWvTJrQ99xzmX3jhYYcapIJB7PnNbwAA8z7xCagTGGgkmm6EEIgHg5nS0LY22GZfGyHN5ULtnDloXr4cVc3NkNl3jWjcjPh/1+c//3l89atfHbABO3jwIL73ve/hxRdfLNnippNotmxUU6Ux98bZvJH92YiIKpVp2QUZahYS2X8PNZlIU2Q4DQWe3MRPtwG/R4euMUuNSo/7vPERz5aNKroOVdcn7HED2Wy2ujVroJUwwFfz7ndDr65GqqcHXVu2oP6444Y8d/cjj8CKx+GZNw9Np51WsjUQUZ9ULIZgdqhBOh7PH1c0Df4ZM9C8bBnqWlomvEck0XQ14kDb1q1bsXLlygHHjznmGGzdurUki5qOIolcoE0ec1r/5k2ZjLbVa1g2SkRULrYQSKb6hhLk+qmZ1uAhNUkCHJoCt1PL9FJz66j2GnAazFKjicN93vjI9WdTDQOyMrbKheGykkm0/fWvAMY+BKE/SVHQdOqp2Pff/43WZ54ZMtAW3rUrH+xbuH49y9OISshMpRBua0MwEEAiFMofl2QZ3oYGNC5ejMZFiyY0i5aIMkYcaDMMA21tbZjfr5lqa2srVKafjlo+o22Mm69gL/DO25lNzKpjGWgjIhpvQgiYlhgwnCB5uCw1VYZTV+B16fC5dVR7DPhcGnRNYRNiKivu88ZHrAwTRztffBFWPA5HfT2qli8v+f03nXYa9v33f6PntdeQ6OiAo76+6PNCCOy4/35ACDSceCL8ixeXfA1E041tWYh0diIYCCDa1YXCpq2u6mrUZ/uuuWpquJ8gKqMR75je//73Y8OGDXjsscfg9/sBAL29vfi3f/s3vP/97y/5AqeLXEabro3tSt9LW1QIIaFlnoX6+qHe4hER0WjYQmT7p/WVfsaTJix78NdbWQIMXYUn10vNY6DaozNLjSoW93njI5fRpk1goC0/BOG008Ylk8zZ1ISq5cvR++abCPz5zwOGHLT/7W8IbdsG2TAw/6KLSv74RNNFbphKKBBAuL0dttWXTKG73aidOxfNy5fD39Q0YRmzRHR4Iw603XLLLTjllFMwd+5cHHPMMQCAV155BY2NjfjFL35R8gVOF5FsRptjjBNHc4MQVjObjYho1DJZanZ2OEEmSy2RtJBID/3aqqsynIYKrys78dPNLDWafLjPGx+5Hm26a2KmVMVaWxF86y1AktB46qnj9jhN69ZlAm3PPou5552XD+hZiQR2PfQQAGDOOefAqK0dtzUQTVXJSATBQAChQABmMpk/rug6qmfNQvPSpaiZO3dC+z4S0fCMONA2c+ZMvPbaa/jlL3+JV199FU6nE5dddhkuvPBCaIeZOERDS5s2UunMZDmDgTYiogll2yLfRy2XrRZPWYfJUpPg0DO91PwuDT6PgWp3JktNYZYaTXLc55WeECKf0WZM0MTNwLPPAsgMLXDU1Y3b49QdeyxUtxvJzk70vP46at79bgDAvsceQ6q7G46GBsz+8IfH7fGJphozmUSorQ3B1lYkI5H8cUlR4GtoQNOSJahftAjGBAXtiWh0RtVsw+124x//8R9LvZZpK5otG1VkCbo6+jdpqRTw6ischEBENBghBNKm3W84gYXk4bLUNBlOXYXPpeWnfXpdGnSVWWo0dXGfV1rJSAS2mZkEPxEZbcKy0PbnPwPIZJyNJ0XX0XDiiTj0hz8g8MwzqHn3uxFva8P+//s/AMCCiy+GzGwbosOyTRPhjg6EAgFEu7v7PiFJcNfUoH7hQjQvWQJnVRX3HkSTxKi72m7duhX79u1DKpUqOv6Rj3xkzIuabiL5QQgy5DG8eL75uoJkQkJ1tY35C+1SLY+IaNKx7MxwgkTKyg8oSBwmS02Rs1lqDg0+t4YqtwG/x4DTUKBwSh5NQ9znlU7hxFFlAoJOXS+/jFRvLzSfD7WrVo374zWffjoO/eEP6Ny8GelQCLsefBAinUbVihWoXbNm3B+faDISto1oru9aRwdEQd81w+tFXUsLmpctg7ehgX3XiCahEQfadu3ahXPPPRevv/46JEmCyE46yUXXLYuZVCOVmziqqvKYrlJsKigb5cUOIpoOhBBIZbPUciWf8ZSZL8cfjKFleqn5XBp8bgPVXgMehwqNWWpE3OeNg8KJoxPxhjmQHYLQePLJkCdgUqynpQWeefMQ2b0b237+c3Rt2gTIMhZeeilfU4kKCCGK+q5ZBRcyVMNA9ezZaF62DDWzZ0NhqT7RpDbi375f/OIXMW/ePPzxj3/E/PnzsXHjRnR1deHqq6/Gf/7nf47HGqe83MRRTR3bZmRLNtC2iv3ZiGgKymWp5TLU4tng2hBJavksNY9Tg8+lo8pjoMqjw6GrkGW++SMaDPd5pZcbhDARDcuTPT3oevllAEDT6aeP++PlNK1bhx27d2eCbABmfuADcM+ePWGPT1TJ0okEQoEAgoEAUtFo/risqvA1NqJp6VLUL1gA3eks4yqJqJRGHGh74YUX8PTTT6O+vh6yLEOWZZx00km48cYb8YUvfAEvZ3+50/BF45m+HYY2+qucQvQNQljDQBsRTWK5LLV40uwbUpC0kDIHz1KTAOiaApehwuvW4HcZqPLq8Dp1qIrEjAqiEeA+r/TypaMOxxHPFZaFt37yE0T37x/VY1nxOGDb8B11FNwzZ47qPkaj8cQTsesXv4CdTkP1ejH3/PMn7LGJKpFlmgi3tyPU2pp/DQAASBI8tbVoOOooNC1eDIfPx30K0RQ04kCbZVnweDwAgLq6Ohw6dAiLFy/G3LlzsW3btpIvcDrIZbSNJdC2a6eM7m4ZhkNg+bsYaCOiycGy7ExmWkFALZEaOktNlSU4DAUepw6fW4ffraPKzSw1olLhPq/08hNHhzEIIbxrFzr+/vcxP+bMM84Y832MhOp2o/HUU9H6xz9i/kUXQcv+DBFNJ8K2Ee3uRrC1FZHOTgi77wKhw+dD3bx5aF62DJ7shQwimrpGHGhbsWIFXnvtNcyfPx9r167FTTfdBF3Xceedd2L+/PnjscYpLZW2kM5maTj10ffR2LIpE6Q7+t0WDKMkSyMiKhkhBJJpu6jkM57qe/3rTwJg6NksNZcGv8dAtUeH28EsNaLxxH1eaQnbzpeO6m73Ec+Pt7YCADzz52PBxReP6jFVlwvuuXNHdduxWLh+PWZ96ENwzZgx4Y9NVC5CCCRCIQQDAYTb2mCl0/nPaQ4HaubMQfPy5aiaORPKBPRMJKLKMOL/7ddddx2i2dry73znOzjrrLNw8skno7a2Fo888kjJFzjV5bLZVEWCqo7+ygbLRomoUpiWnZ32WVD6mbIghspSUyQ4dTXTS82to8qjw+82YGgKs9SIJhj3eaWVCIczWS2SNKxAWywQAAB4W1pQtWzZeC+vpGRVZZCNpo1UPI5QayuCgQDS8Xj+uKxpqGpqQtOyZaibNw/aMErGiWjqGXGg7YyCVPT58+dj69at6O7uRnV1NTMMRiE/cVSRMZb3k5s3cRACEU2sTJaalR1O0Ff6mbaGyFKTMiXybocKrytT9lntMeB2alAVllAQVQLu80or35/NMKAOY4pgPBtoczY3j+eyiGgUrHQaobY2hAIBxIPB/HFJkuCpr0fD4sVoOuooOLzeMq6SiCrBiAJtpmnC4XDglVdewYoVK/LHa2pqSr6w6SKSHYSgqfKoN7CdnRJ278wG2labJVsbEVGOadkDpn0m0kNnqWmKDIehwJvLUnPr8HsM6JoCmW/WiSoS93mlFyuYOCoNoydTrnTU2dQ0nssiomGybRvRzk4EAwFEOjtRuPFxVlWhfv58NC9bBndNzbD+jxPR9DCiQJuqqpg7dy4si1lTpRLNlo5qY8jm2JItG128xIK/qhSrIqLpSgiBRL/hBPGUCdMaPKImSYBDU+By5AJqGvweB9wOlVlqRJMM93mlF+vpAZDJaDsSIQQz2ogqgBAC8WAQodZWhNrbYZt9iQy6y4WauXMxY/ly+JuaILPvGhENYlQ92jZs2IAHH3yQVzhLIBLPTRwdQ3+2XNnoGm6MiWj40ubA4QTJlIUhktSgqTKcugKvS4fPpaPKa8Dn0mBoCkvKiKYI7vNKKzcIQXM6j3huOhiEFY8DkgRnQ8M4r4yI+ktGowgFAggFAkgnEvnjiqahaubMTN+1lhaoul7GVRLRZDDiQNuPf/xj7NixAzNmzMDcuXPh7tfY9aWXXirZ4qY6IUQ+o80Yw8TR3CCE1ezPRkSDsIVAMmUVBdQSh8lSk6XMa5LbocLn0uH3ZHqpOQ1mqRFNddznlVaudFR3uY54bi6bzaithcw38kQTwkyl8n3XEqFQ/rgky/D+//buPEzOssz3+K/2tav3LWQlG1lIlARCAgFBCIQtOaJGRRZRR1wQlDkzcDAGHDWKgnpAcFTC4oBBETgOg4yMCoIgSxL2mAUCnYROOmvvtb/nj6q3kibdSVd1db9vdX8/19WXpLqWu6vK9JNf3c9z19WpfupU1U+eLF84bGGVAEpN3unO0qVLB6GMkSmWSOf+oev3ugq6j+4u6fVXsxNH53E+GzCSGYahZMroseXT3AbaF4/bqYDPrUhu4memS81LlxowIrHOK550KpU7MN3Xn4mj5vlsbBsFBlU6lVLHrl1q3bFDnXv39jh3LVhZqdpJk9Q4bZqCDIEBUKC8g7YVK1YMRh0j0oGJo46Cz2h75WWXkkmH6hvSOmp0Xxu+AAw36bShaKJnh1p3LKVUuu8uNb/ZpRbyqjzkU0XYq6DPLRddagCyWOcVT7StTTIMOZzOvDraggRtQNEZhqGuffvUtmOH2ltalD7oLEpfKKSq8eM1avp0RRoa5HQV1gABACZOb7RQx0GDEJzOwj4tOXjbKB+4AMOPYRhKpNLZDrUDgVos0XeXmjfbpVYW9Kg826VWFqBLDQCG0sETR139ODCdiaNA8UU7OnLnriVjsdzlbq9XFaNHq3H6dFWNHcu5awCKKu+gzel0HvYfakyq6r9cR5t7AIMQXsi8hHNPYNsoUOrSaePAtM94St2xzP/22aXmdMjvcSkc8CgS8qgi5FN52KuAzy0XI+YBFIB1XvEcPHHU0Y+/k3MTRwnagAFJxGK5cC3W0ZG73Olyqay+Xg3HHKO6SZP61WkKAIXIO2h7+OGHe/w5kUho3bp1uueee3TjjTcWrbCRwOxo8xYYtKVS0to12Y42Jo4CJcMwDCWS6R4datF4UrFEus/b+DzZLrWAR+UhnyrDPoWDbnncdKkBKB7WecWT62jz+Y54XSOdJmgDBiCdTKo9e+5a1969B77hcChUVaW6yZPVMHWqAhUVrJsADLq8g7YlS5YcctlHP/pRzZgxQw888IA++9nPFqWwkcDsaPN5CjsHYOMGp9rbHAqFDB0zve9/oAOwTiptKPq+4QTdsZTSRu9dai6nQ35vtkst6FV5yKvKMp98XrdcBW4xB4D+Yp1XPN3ZoM0TCBzxuvF9+5SOxyWnU/66ukGuDBgejHRanfv2qa25We27dslIH/j3kL+sTNUTJqhxxgyV1dbKSac/gCFUtDPa5s2bp89//vPFurthzzAMdUQz2z39BQZta17M3O4Dc1Lqx9EfAAaRYRiKJ9MHDSfIbP2MJw/fpRb0uVUW9CqSDdTKAl65XQ4+bQVgK6zz8mduHe3P9rQus5utrk5OFnVAnwzDUKy9Xa07dqht506l4vHc99x+vyrHjNGo6dNVOXq0XB6PhZUCGMmK8pu8u7tbt956q0aPHl2MuxsRovGU0tlzl3zewoK2l57PvHzHcz4bMKRS6UOHE0TjSfVxlJpcTocCXpfCQY8iwcw5ahUhr/xed8GDUABgqLDOy18qmVS0vV2S5A2Fjnh9BiEAh5fo7lbbzp1qbW5WvKsrd7nT7VakoUEN06ap9uij5e1HBykADLa8g7bKysoenRaGYai9vV3BYFD/8R//UdTihrOO7gMTR92uwlqZXzxo4iiA4jMMQ/FE+pDhBH11qTkkeT0uhfzmxE+fKsJehelSA1AiWOcVR3drqyTJ4XL16x/+uaCtsXFQ6wJKSSqRUHtLi1p37MhtxZYkh8OhUE2N6idPVv20afKHw6yxANhK3kHbj370ox5/kTmdTtXW1mrevHmqrKwsanHDWWfUnDjqKKij5b3tDr233SmXy9AHjiNoAwYqmUor2qNDLXOmWh9HqcntMs9Sy5yjVhHyKhL2ye9x0aUGoGSxziuObnPiqNfbr+1rDEIAMox0Wh179qhtxw517N7d89y18nLVHH20Rk2frlB1NeeuAbCtvIO2yy67bBDKGHlyHW0FThw1z2ebPiOtfuxIAJBlGIZiZpdadstndzylRF9dao7MwJKg331gOEHYp6DfU/D/fwHArljnFcfBE0f702lD0IaRzDAMRdva1NrcrPaWFqUSidz3PIGAqsaOVeOMGaoYNUouzjAEUALy/pvqrrvuUjgc1sc+9rEel//2t79VV1eXLr300qIVN5x1ZgcheN2Fnc/24guZl24u57MBfTK71LpzAwoyWz8P16UW8LkV9ntUHjY71Xzyel1ysiUBwAjAOq84zKDN4/Md8bpGOq3unTslsXUUI0u8q0ttO3aodccOJbq7c5e7PB6VNzaqYdo01UyYII/fb2GVAJC/vIO2733ve/rZz352yOV1dXX6p3/6JxZg/WR2tPkGOHF0DuezAdkutZS6Y6nceWrRWFKJVO+JmtmlFvJ7FAl6shM//Qr53QWfmQgAwwHrvOIwJ456+nE+W3T3bhnJpBxut/w1NYNdGmCpVCKhtp071bZjR+4sQ0lyOJ0K19aqYcoU1U2ZIn9ZmYVVAsDA5B20vfvuu5owYcIhl48bN05NTU1FKWq4MwxDXdkz2vwFTBxtb5f+8WYmDJh7PEEbRpZEMp3rTOvOdqvF4in10aQmj8upgM+lcMCj8pBX5WGfykNeeT10qQHA+7HOKw6zoy2viaP19XJw5hSGoXQqpY7duzPnru3Zo4O3FgQqKlQ7caIap01TqLqaoQYAhoW8g7a6ujq9+uqrGj9+fI/LX3nlFVVXVxerrmGtK5ZU2shMKPR58l9QrVvjUjrt0NhxadU39BUvAKUtbRiKZcO06EFbP5OH6VLze1wKBTwHzlIr8yngo0sNAPqLdd7ApRIJxTs7JUm+/gRtnM+GYcgwDHXv36/WHTvU3tKidPLAcTfeUEhV48Zp1PTpKm9slNNV2A4fALCrvIO2T3ziE/rqV7+qsrIynXLKKZKkp556SldddZU+8YlPFL3A4aiz25w46pSrgADgpez5bHOO53w2lD7DMJRMGbnhBLmtn/G+uzU97kyXWlnAm9n2GfYpEvTI63HxSSgADADrvIEzu9mcLle/to4StGE4iXV25s5dS0ajuctdXq8qjjpKjdOmqXr8eLm9XgurBIDBlXfQ9u1vf1vvvvuuPvzhD8udnfqSTqd1ySWX6Lvf/W7RCxyOOrKDEDwuZ0Fb1156IfOpz1zOZ0OJSacNRRMHd6hltn6m0r13qTkdks/rVtic+Bn2qSLspUsNAAYJ67yBM89nc/t8/erUyW0dZRACSlQyFsuduxZtb89d7nC5FKmrU/3UqaqbMkW+YNDCKgFg6OQdtHm9Xj3wwAP69re/rZdfflmBQEDHHnusxo0bNxj1DUtmR5vHnX/IlkhIL68tPGhLG4ZSKUMed+mFFPFESolU2uoykIdUysht+eyOpRRL9P2e9bqdCvjcKjOHE4R9KgvQpQYAQ4l13sB1Zzva3D5fv35/5TraCNpQQox0Wm0tLWrbsUOde/f2OHctWFWl2kmT1DhtmoIVFazjAIw4eQdtpsmTJ2vy5MnFrGXE6MgOQvC68z+PYP0bTnV3O1ReYWjS5PxDp+27OrW3PaaJoyIKBzx5394q3bGkNm5rPfIVYXtOh0N+b+YstfKQR+UhnypCXgX8brk4BBoAbIF1XuG6DgrajiSdTKq7pUWSFGTrKEpEKpnUtpdf7jE11BcOq2r8eB01Y4bK6uo4dw3AiJZ30PbRj35Uc+fO1bXXXtvj8h/84Ad64YUX9Nvf/rZoxQ1XXrdTbpdDXk/+v4D+sT5zm5nHplRIJtGR7abb3RotqaCttTMuKbOVsJBz7WANp0MKeDNdauUhn8rDPpUF3fK66VIDADtinTdwLq9Xbr9fHr//iNeNtrRI6bScXq+8lZVDUB0wMKlEQltfflnRtjY5XC5Vjx+vxunTVT12rFye0vm3BQAMpryDtqeeekorVqw45PKzzz5bP/zhD4tS1HA3Z0qdtjS3qTue/zCDTRszIdPkKYVtG40nM11wndGEDMMombCjLRu0TWiMaMb4KourQT5K5T0GAGCdVwxTP/QhRerr1bF79xGve/AgBAdd3bC5VCKhpnXrFGtvl9Pl0uQPfUhHzZzJWg8A3ifvoK2jo0PeXqbEeDwetbW1FaUo9G3ThkxH2+Sp+W8bjScO3CaZMtQdSyrot/8nT4lkWt3ZCZSjqkP8MgcAYJCwzhtaTBxFqUjG49q6bp1iHR1yut2actppGjV9OutyAOhF3h+dzZw5Uw888MAhl69evVrTp08vSlHo24GOtvyDtvcfRL+/I16UmgZbW1emTr/XpfIwo8ABABgsrPOGFoMQUAreH7Idc/rphGwAcBh5d7QtX75cF154od566y2dfvrpkqQ//elPuv/++/Xggw8WvUAc0NEhvbe98K2j7w/azPPa7K69K1NnVZmPw/IBABhErPOGVldzsyQ62mBfyVhMTevWKd7ZmQnZzjhDjcccY3VZAGBreQdtF1xwgR555BF997vf1YMPPqhAIKDZs2frz3/+syKRyGDUiKzNmzIhU21dWhUFnJcbzwZtVWU+7W2PqTueUjKVltvGwwXShqH2bEdbfVXQ4moAABjeWOcNre5s0Bakow02lIjFtHXtWsW7uuTyeDTtzDNVP2WK1WUBgO0VlLCce+65+tvf/qbOzk5t3rxZH/nIR3T11Vdrzpw5xa4PBzHPZ5tSwPls0oGOtupyv4K+TMa6vyNWnOIGSVc0qbQhuZwO1VUErC4HAIBhj3Xe0EjH44rt2SOJjjbYTyIaVdOaNZmQzevVtEWLCNkAoJ8KbmX685//rE9/+tMaNWqUbrvtNp1zzjl66aWXilkb3mfjhsK3jUpSLJ4J6MIBjxqy3WFtXfbePmpOG40EvfJ5XBZXAwDAyMA6b/B1t7RIhiFXICBPebnV5QA5ie5uNa1dq0R3t1xer2acdZbqJ0+2uiwAKBl5bR3dtm2b7r77bq1atUqdnZ36+Mc/rkQiod/97ncckDsENg9gEEI6bSiRytwuEvTI73Hp7eY2dUWTMgzDtoeZmkFgXWXAtjUCADAcsM4bWua20UBjI2sc2EY8G7Ilo1G5fT5NP/ts1U6YYHVZAFBS+t3Rds4552j69Ol68803deutt+q9997TrbfeOpi14X02ZreOTi5g66i5bdTpcCjoc6u6PCCn06FU2rDtUIRYIpWru4Hz2QAAGDSs84ZebuIo20ZhE/GuLjWtWZML2WYuXkzIBgAF6HdH2x//+Ed99atf1Re/+EVNpnV4yBVr4qjX45TH7ZLD4VBduV879nWrtTOusqC3qPUWgzkEIehzK2LD+gAAGC5Y5w29biaOwkbiXV2ZTrZYTG6/XzMXL1b1uHFWlwUAJanfHW1PP/202tvbNXfuXM2bN0+33Xabdu3aNZi14SADnTgaS2S64AJeV257gjnF064dbea20eqIT04nWyoAABgsrPOGXle2oy1I0AaLxTo7M51ssZg8gYCOPfdcQjYAGIB+B23z58/XL37xCzU3N+sLX/iCVq9eraOOOkrpdFpPPPGE2tvbB7POEW+gE0fj2Y62oN+Tu6y+MhO0xRJpxZOFDVgYLOmDtrQ2VIUsrgYAgOGNdd7QO/iMNsAqsY6OTMgWj+dCtqoxY6wuCwBKWt5TR4PBoC6//HI988wzeu2113TNNdfoe9/7nurq6nTBBRcMRo2QtCk7CGFSoRNHs0FbOHAgaAv43CoLZv68vyM+wAqLq6M7IcOQ3C6Hasp9VpcDAMCIwDpvaKSiUcX37ZNE0AbrRNvb1bR2rVKJhLzBoGadf74qR4+2uiwAKHl5B20Hmzp1qm666SZt27ZNv/71r4tVE3qxaUPmpZpSwMRRqfegTTowZMA8D80u2rL1lId88nryGo4LAACKgHXe4OneuVOS5A6H5QmHLa4GI1GPkC0U0qzzz1fFqFFWlwUAw8KAgjaTy+XS0qVL9fvf/74Yd4debNqY2To6qYCgLZVOK5kyJOmQoQLm9tGuaFLpdGEhXrEZhpE7n62+MmBxNQAAjGys84qPQQiwUndbm5rWrlU6mZQvHNbsCy5QOZ2VAFA0RQnaMLg6O6Xt28yJo/mHYeYgBJfToYCvZ3dYZZlPbpdTaUNq70oOvNgiiCVSSiTTckhqzHbcAQAADBdd2aAtSLiBIdbd2qqt69ZlQrayMs264AJF6uutLgsAhhWCthJgns9WU5tWZZWR9+3NbaNej1NuV8/pnU6HI9c11tppj+2jbZ2ZbrZQwK3g+7a6AgAAlLru7MRROtowlLr278+FbP5IRLMvuECRujqrywKAYYegrQRs3jjAiaPxTNAW8LrlcDgO+b65fbQzmiiwwuIyz2erifjl7KVeAACAUpYL2uhowxDp2rdPW19+WelUKheyldXWWl0WAAxLlgdtt99+uyZMmCC/3685c+bo6aefPuz1f/rTn2ratGkKBAKaOnWq7r333h7f/8UvfqGFCxeqsrJSlZWVOuOMM/TCCy8M5o8w6DZmByFMnjqwiaMhf+9DBeqyHW3xZFqxeGGPUSypVFqd0cwW1obqkKW1AACAgWOtdyg62jCUOvfu1daXX5aRSilQXq7ZS5YoXFNjdVkAMGxZGrQ98MADuvrqq3X99ddr3bp1WrhwoRYvXqympqZer3/HHXfouuuu0w033KA33nhDN954o7785S/rP//zP3PXefLJJ/XJT35Sf/nLX/Tcc89p7NixWrRokbZv3z5UP1bRbc5uHZ08udCJo5nbhfrYhunzuFQRzgxJ2NcRK+gxiqW9O9NV53U7VVXmt7QWAAAwMKz1DpXs6lKitVUSQRsGX+fevdr2yisy0mkFKioyIVt1tdVlAcCw5jAMI/9Dv4pk3rx5Ou6443THHXfkLps2bZqWLl2qlStXHnL9BQsW6KSTTtIPfvCD3GVXX321XnrpJT3zzDO9PkYqlVJlZaVuu+02XXLJJf2qq62tTeXl5WptbVUkEsnzp+qfLc1t6o4nFfT13mV2sJOPD2v7NqceeLhTJ5yYf8fZ61v2KpU2dPzUOo2q6b1LbMPWffpH036FfG5NGl2e92MUS1NLh/a1x1RXEdD8GSw+AQClZSjWEKXEjmu9oXiNmtevV8fu3QpWVBzyvfa33tLa66+Xp7xcC/793wfl8QFJ6tizR9tffVVGOq1gZaVmX3CBgpWVVpcFACWrv2sIyzra4vG41qxZo0WLFvW4fNGiRXr22Wd7vU0sFpPf37PLKRAI6IUXXlAi0fv5Yl1dXUokEqqqquqzllgspra2th5fdjHQiaPJVFqpdCZLLQv2PVjAPKetK55UKl1Y59xAGYah9uz5bPVVAUtqAAAAxWGXtZ7d1nlsG8VQ6Ni9W9uznWyh6mrNXrqUkA0AhohlQdvu3buVSqVU/75x0vX19dqRXYC831lnnaVf/vKXWrNmjQzD0EsvvaRVq1YpkUho9+7dvd7m2muv1VFHHaUzzjijz1pWrlyp8vLy3NeYMWMK/8GKbPOm4kwcdbscChyme6485JXP45RhHJj6OdS6YyklU4acDqm+ImhJDQAAoDjsstaz2zqvq7lZkhRkEAIGSfuuXdr26qsyDCMTsl1wgYLl1u1YAYCRxvJhCO+fgmkYRq+TMSVp+fLlWrx4sU488UR5PB4tWbJEl112mSTJ5XIdcv2bbrpJv/71r/XQQw8d8unowa677jq1trbmvrZu3Vr4D1RkmzYMcOJo9nw2r9sll7PvCZ4OhyPX1dbaGS/osQbKnDYaDngU7GNwAwAAKC1Wr/Xsts5j4igGU1tLi7a/9ppkGArX1mr20qUKELIBwJCyLGirqamRy+U65BPNlpaWQz75NAUCAa1atUpdXV1655131NTUpPHjx6usrEw175uc88Mf/lDf/e539cc//lGzZs06bC0+n0+RSKTHl11syg5CmFTAtlHpQEdbwOfqc1Frym0fjVrT0dbelXnc2vLAEWsFAAD2Zpe1nt3Wed3Zjja2jqLY2nbu1Huvvy4ZhspqazX7ggsUKCuzuiwAGHEsC9q8Xq/mzJmjJ554osflTzzxhBYsWHDY23o8Ho0ePVoul0urV6/WeeedJ6fzwI/ygx/8QP/2b/+mxx9/XHPnzh2U+ofKpg2Zn2vKlPyHIEgHgraQv+/z2Uy1FQE5HFIiZagrNrRhWyKZVlcsKUlq7GNgAwAAKB2s9Q5lGAZntGFQtO7YkQvZIvX1mr1kifyEbABgCUv3533961/XxRdfrLlz52r+/Pn6+c9/rqamJl1xxRWSMq3+27dv17333itJ2rhxo1544QXNmzdP+/bt0y233KLXX39d99xzT+4+b7rpJi1fvlz333+/xo8fn/sUNRwOKxwOD/0POUCbNma2SQy0oy0UOHLQ5nE7VV3m1+62qFo74gr6jnybYjGHIPg8LlWEvEP2uAAAYPCw1usp2d6uZGenJII2FE9rc7Oa33xTkhRpaNCs886Tz+b/XwCA4czSoG3ZsmXas2ePvvWtb6m5uVkzZ87UY489pnHjxkmSmpub1dTUlLt+KpXSzTffrA0bNsjj8ei0007Ts88+q/Hjx+euc/vttysej+ujH/1oj8dasWKFbrjhhqH4sYqms1PatjXb0VbAGW2GYSgWz9yurB9BmyTVVwW1uy2q9q6EGqvzfsiCtWW3jVaW+eRyWX50IAAAKALWej11ZUNBX1WVXD6fxdVgONj/3nvasX69JKl81Cgde9558gUZKgYAVnIYhpH/KMthrq2tTeXl5WptbR20czy2NLepO55U8DCTQF952amli8Oqrknrpdc68n6MRDKtN9/dJ0k6Y87ofm0fbe+K68/rtkuSZoyvlHsIQi/DMPT6O/uUThv64KQaja2nzR0AUJqGYg2BgRmK16h5/Xp17N6tYEVFj8t3/PWv2nD77aqYMUOzly8flMfGyLFv2zbt3LBBklRx1FE69rzz5A0ELK4KAIav/q4haB2ysc0bBzZx1Nw26nY55PMcOqmrN+GARwFf5rqtHUMzfbQzmlQ6bcjldKiugsUBAAAYnhiEgGLZt3VrLmSrHDOGkA0AbISgzcY2ZgchTC4waItngzafx9XvzjSHw6GGqswwgrauoQnazMcpC3rk8/YvEAQAACg1uaCtsdHiSlDK9jY1aefGjZKkyrFjNfOccwjZAMBGCNpsbPPGbNBW8MTRTEAXOMz21N7UV2Z+UXdGkxqKncXt2fPZ6ioCcjgcg/54AAAAVmDiKAZqz7vvqmXTJklS9bhxOpaQDQBsh6DNxjZuyHR3TR7gxNFwPwchmGoifjmdDqXShjqjiYIeu7/iiZSi8UydjdWhQX0sAAAAqxiGcSBoo6MNBdj9zjvatXmzJKl6wgTNWLxYHr/f4qoAAO9H0GZTB08cHWjQ1p8hCAdzuZyqLc/80h7sc9rMaaMBn0uRoHdQHwsAAMAqidZWpaJRyeFQoK7O6nJQYna//bZ2v/WWJKlm4kTNJGQDANsiaLOptzZlXprqmrSqqvPfvmkYRi5oiwTzC9okqb4yMxa8vTuZ923z0Z49n62qLNNFBwAAMBx1Zc9n89fWyunJf22GkckwDO166y3t3rJFklQ7aZJmnHWW3F4+oAYAuyJos6lNA5w4mkylZR6vFg7k/4vYPKctlkgpkSzsjLgjSacNtXdnOtoaq4KD8hgAAAB2wMRR5MsM2fa8844kqW7KFEI2ACgBBG02tSk7CGFSwdtGM7fzuJ3yefN/mYN+T+5st/2DtH20I5qQYUhul0M15bS+AwCA4YtBCMiHYRhq2bxZe999V5JUP3Wqpp95plx0QwKA7RG02dSmDZmXZsrUQieOZm7n87jkchb2Mjdku8zMc9SKzZw2Ggl55fPmNxkVAACglBC0ob8Mw1DLxo3a19QkSWqYNk3TzjiDkA0ASgRBm02ZW0cnTS6woy07yTPoKzzAMrePdsWSSqcLq6MvhmGorTOefRy2jQIAgOEtt3WUiaM4DMMwtHPDBu3btk2S1Dhjho758IcJ2QCghBC02VBXl7S1yexoG+jE0cKDtqoyv9wuh9JpQx3R4g5FiCXSiiczP1sD57MBAIBhzEincx1tQYI29MEwDO34xz+0f/t2SdKomTM19bTT5HKz8wMASglBmw1tzp7PVl1d2MRR6cAZbeECJo6anE6H6ioyXW2tRT6nzZw2GvK7c2fBAQAADEexvXuVTiTkcLnkr621uhzYkGEY2rF+vVrfe0+SdNSsWZr6oQ8RsgFACSJosyFz2+jkArvZDMNQPNvRVlbAxNGD1We7zTqixT2nzTz3rabcL6fDUdT7BgAAsBOzm81fWyuHy2VxNbAbwzDU/Oabas1uLx49e7amnHqqnIRsAFCSCNpsyJw4WmjQlkimZUhySCobQEebJNVVZIK2eCKdO/dtoFJpQ53dmaCtoSpUlPsEAACwK85nQ1+MdFrvvfGG2rJh7JjjjtPkU06Rk0AWAEoWQZsN5YK2KQObOOpxO+V1D+yXtN/rUnko0xW3vyM2oPsytXfFZShTX3XEX5T7BAAAsCsmjqI3ZsjWvnOnJGnsccdp0kknEbIBQIkjaLOhTRuyW0enFDoIIXM7n9clp3Pg2zLNYQXt3cXZPtqe3TZaEfbK4+YtCAAAhrdc0EZHG7KMdFrbX39d7S0tksOhcccfr4mEbAAwLJBy2MzBE0cLD9oyHW1BX3HOdaivzARtXbGkUunCajIZhqG27CAE834BAACGs67s1lEmjkKS0um0tr/2mjp27cqFbEfPn0/IBgDDBEGbzby16cDE0eqaQieOZoK2Yk3zrAh75XU7ZRgHutEK1R1PKZky5HBIDQRtAABgmDNSKUWzWwPZOop0KqXtr76qjt27JYdDE+bN09Enniink3+WAcBwwd/oNrNxw8AmjkoHgrZQkYI2h8Oh+sqAJKm1Mz6g+2rPdrOF/R4F/UxSAgAAw1t0924ZqZQcHo981dVWlwMLmSFb5549cjgcOvrEEzXhhBMI2QBgmOFvdZvZnO1om1TgtlHDMBTPntEWGeDE0YPVZ89p6+xODuh+2jozHXE1FX45HAM/Pw4AAMDOcuez1dfLQaAyYqVTKW175RV17t0rh9Opoxcs0Pjjj+c9AQDDEH+z28zGDZmXZMrUwiaOmiGbw5HpGiuWuoqAHJISqbS6Y4WFbclUWl3Z2zZWhYpWGwAAgF11Z89nYxDCyJVOJrX15ZfVtW+fHE6nJp50ksbNnUvIBgDDFH+728zmjQOdOJoJ6Lxupzzu4h2o6nG7VFnmkyTt74gVdB/m+W4+jzN3XwAAAMNZrqON89lGpFQ2ZOvev18Op1OTTj5ZY487jp0dADCMcUiWjXR3SVubMr90Bxq0+b1uOZ3F/QXeUBXU3vaYWvZH1bI/WvD9VJb55HaR8QIAgOEvN3HU5kFbOpXSuy++KF9ZmUbNmGF1OcNCKpnU1nXrFG1rk8Pl0qSFCzVm9mxCNgAY5kg7bGTzJqcMw1GUiaMBX/Ez1KNqQnINMLxzOKSjasJFqggAAMDeSqWjLdrWplhnp9p27FA6ObAzeSGlEolcyOZ0uTTl1FMJ2QBghKCjzUY2ZbeNFjoIQZJi2TPawoHiv7RBv0dnnzBGndGUpMKCQLfToWARz44DAACwq3QyqWhLiyT7n9EW7+rK/Xe0s1PB8nILqyltqURCTevWKdbeLqfLpckf+pCOmjmTkA0ARgiCNhvZtDHTYDh5auFBWzzb0RYODE6Y5Xa5VB4q3tlvAAAAw1W0pUUyDDl9PnkrK60u57AODtriHR0EbQVKxuPaum6dYh0dcrrdmnr66WqcNo2QDQBGEII2G8kFbVMKmziaThuKJzMhXSToLVpdAAAAyF9u4mhDg+2Dlnh3d+6/YweFbui/ZDyurWvXKtbZKafbrWM+/GE1TptmdVkAgCFG0GYjmzZkOsWmFNjRFk9mAjqnQwr6eWkBAACsVCrns0k9O9oSBG15S8Zialq7VvGuLrk8Hh3z4Q+r4ZhjrC4LAGAB0hibOHjiaKFntJnns3ndLnndbO8EAACwUm7iqM3PZzMMQ4mDOtoSsZiF1ZSeRDSqrevW5UK2aWeeqfopU6wuCwBgEYI2m3hrc2biaFVVWjWFThyNZzra/F6X7bcnAAAADHel0tGWjEZlpA980JskaOu3RDSqprVrlejulsvr1fRFi1Q3aZLVZQEALOS0ugBkbMxuGx3IIIRYdhBCiG2jAAAAlssFbTbvaDPPZ3O6M2vIVDyuZCJhZUklIdHdraY1a5To7pbb59OMs84iZAMAELTZxebcIIQiBG2DNHEUAAAA/ZOOxxXbs0eS/TvazPPZAuXlcnky68h4R4eVJdlevLtb765dq0Q0KrfPp+lnn63aiROtLgsAYAMEbTax0QzaphY2cVSS4tmgLRxg4igAAICVort2SYYhVzAoTyRidTmHZQZt/khEgYoKSVKUoK1P8a4uNa1Zo2Q2ZJu5eLFqJ0ywuiwAgE0QtNmEOXG00I62VNpQIpU52y0SoqMNAADASrGWFklSsKHB9mfnmkFbsKJCwWzQxuTR3sU6O9W0dq2SsZg8fr9mnnuuqsePt7osAICNcJiXDRw8cbTQM9rMbjan06Ggj6ANAADAStFs0Gb3baOSchNHg5WVB7aOHjSFFBmxjg41rVunVDwuTyCgY885R5VjxlhdFgDAZgjabKAoE0ezQZvX7ZTbZe9PTQEAAIa7XNBm80EIRjqdC9XC1dVyZQciMHm0p1hHh5rWrlUqkZA3GNTMc89V5VFHWV0WAMCGCNpsYNPGzLbRSQMahJC5bcDnsv32BAAAgOEuViJBWyIalQxDDodDgcrK3DoyGYvJyF4+0kXb27V13bpcyHbseeepYtQoq8sCANgUQZsNbNqQOSpvSoHbRqWDJo6ybRQAAMBypbJ11Dyfze33y+PzyVFZKUlKJRJKJRJye0f2kK1oW5ua1q1TOpmUNxTSrPPOU7nNw1MAgLUYhmADmzZlXoZJUwqfOJoL2gIEbQAAAFZKdncr0doqqQSCtuy2UV84LKfLJY/fL7fPJ4nJo90HhWy+cFizL7iAkA0AcEQEbTZgThwdSEebOQyhLEjQBgAAYKXu5mZJkqesTJ5w2OJqDs/saAtEIpIkh8ORmzwaH8FBW3drq7auXZsJ2crKNOuCCxSpr7e6LABACSBos1h3l9T0bubsi0LPaEul0kqmMkMUIsGR3d4PAABgte4dOyTZv5tNkhJm0JYN16TM9FHpQAg30nTt36+t69YpnUrJH4noA0uWKFJXZ3VZAIASwRltFnv7rczE0crKgUwczQR0LqdDfi8vKQAAgJW6sh1tdh+EIB0I08xwTToQupnbSkeSrn37tPWVV2SkUvKXl2v2+ecrXFNjdVkAgBJCKmOxjdlto5OnplXoUCfzfDavxyW3i8lQAAAAVjK3jtq9oy2dTmemjkoKV1fnLje3jiZjMSvKskzn3r3a9sorMtJpBSoqNOv883s8LwAA9AdBm8U2b8zs3p1c4LZR6UDQFvC6GMEOAABgsa733pNk/462RLZjzeF09rp1NBmLyTCMEbG+7NyzR9tefTUXsn3gggsUrKqyuiwAQAnijDaLbTSDtqlFmDjqZxACAACA1UrljDZz26gnEMhNGpUOdLSlk0klRkBXW8fu3bmQLVhZqQ8sXUrIBgAoGEGbxcyJowPpaItnz2gLB2lQBAAAsFK8rU2JtjZJpRO0+UIhOZ0H/lng9vnk8fsz1xnmk0fbd+3S9mzIFqqu1uylS3NBIwAAhSBos1A0emDi6OSpA986WhZg4igAAICV2t99V5LkiUTkDgQsrubwzK2j/vLyQ75nbh+NdXYOaU1Dqb2lRdtfe02GYWRCtiVLFOzluQAAIB8EbRbastk14ImjyVRaqXTmtmUBto4CAABYqS0btPnr6y2u5MjMjrZQLx1cZtBmXme4adu5U9tff10yDIVrazV76VIFIhGrywIADAPsNbTQW5sz20YnTRn4xFG3yyG/j5cTAADASmZHm6+uzuJKjswM0QLZUO1g5vZJs+ttOGnbsUPvvfmmZBgqq6vTrPPPl7+szOqyAADDBMmMhTZvzARtUwa0bTRzW5/HJZdz+E+EAgAAsDMzaPPbPGhLp1JKZgcdlNXUHPJ9cwppcpgNQ2htblbzm29KkiL19Zp1/vnyhcMWVwUAGE4I2ixkBm2Tpgx84mjA6x4Ro9cBAADsrGraNLVt3argmDFWl3JYZjeb0+WSr5duLnPraDIWk2EYw2Kduf+997Rj/XpJUqSxMROyBYMWVwUAGG4I2ixkbh0dSEdbPBu0hTifDQAAwHLTPvMZVZx4ojp277a6lMOKZ7eEegKB3ITRg5lbR9OplBLRqLw2H+xwJPu3b9eOf/xDklQxapSOPe88eQnZAACDgGEIFol2S1vfzTz9k6cMYOtoPBO0hf0EbQAAAOifRLajzRcO99qt5vJ45A2FJEmxjo4hra3Y9m3bdiBkO+ooHXv++YRsAIBBQ9BmkbffcsowHKqoTKumtrCJo4Zh5LaOhoM0JwIAAKB/coMQepk4ajK3j8Y6O4eipEGxd+tW7dywQZJUOWZMppOtxLvzAAD2RtBmkY0bD3SzFXrkRTJlKJ3N6CJBb5EqAwAAwHBnBm3BwwVt2e+Z1y01e5ua1LJxoySpauxYzTznHEI2AMCgow3KIuYghAFtG812s3lcTvk8rqLUBQAAgOHPPKPN7FrrjRm0JbPXLSV73nlHu956S5JUPX68Zpx9dq9n0QEAUGwEbRbZtCHb0TZ14BNHfR6nXC6aEwEAAHBkqWRSqXhckhSuqenzembQlojFhqKsotm9ZYt2v/22JKlmwgRNP+ssQjYAwJAhaLPIwVtHC2UGbQEfLyMAAAD6xxyE4HS75Q+H+7xeINvtlozFlE6n5XTa+4NdwzC0e8sW7dmyRZJUO3Gipp91ltxejlgBAAwde/+2HKa6D5o4OmVq4UFbPJG5bSjAxFEAAAD0j3nmmjcYlOswIVSgvFySZKTTSth8+6hhGNr99tu5kK1u8mRCNgCAJQjaLLBhg5ROO1ReUfjEUelAR1vYT9AGAACA/jHPZ/OFw3IcZiqXy+2Wr6xMkhTr6BiS2gphGIZ2vfWW9rzzjiSpfupUTV+0iJANAGAJgjYLvPlm5n8nTk4VPHHUMIxc0FbGxFEAAAD0k9nRZnasHU4ou3001tk5qDUVyjAMtWzapL3vvitJajjmGE074wy5PHwQDQCwBkGbBd54I/O/kyYXPgghkUrLyDbDlQVZSAAAAKB/zKDtcBNHTQFzIEL2NnZiGIZaNm7Uvq1bJUkN06frGEI2AIDFLA/abr/9dk2YMEF+v19z5szR008/fdjr//SnP9W0adMUCAQ0depU3XvvvYdc53e/+52mT58un8+n6dOn6+GHHx6s8gtiBm0TBxC0xeKZ89k8bqe8HlcxygIAACi6kbjWs7t8grbc5NFodDBLypthGNq5YYP2bdsmSWqcMUPHnH66XG6GhAEArGVp0PbAAw/o6quv1vXXX69169Zp4cKFWrx4sZqamnq9/h133KHrrrtON9xwg9544w3deOON+vKXv6z//M//zF3nueee07Jly3TxxRfrlVde0cUXX6yPf/zjev7554fqxzqiffsy/ztpygCCtuy2Ub/HJZezwP2nAAAAg2ikrvXsLJVIKJ1MSpLCNTVHvH7woMmjdmEYhnb84x/av327JGnUzJmaetpphGwAAFtwGIZR+Gn8AzRv3jwdd9xxuuOOO3KXTZs2TUuXLtXKlSsPuf6CBQt00kkn6Qc/+EHusquvvlovvfSSnnnmGUnSsmXL1NbWpj/84Q+565x99tmqrKzUr3/9637V1dbWpvLycrW2tioSiRT64x3WKxvaZDiSqogUtiB4b3endrVGVVcZ0PzpDUWuDgAAFGIo1hClxI5rvaF4jZrXr1fH7t25bjA76W5t1bsvvSSXx6OTP//5Iw4M6Nq3T8/dc48cTqcmn3KKnC5rd1IYhqEd69ertblZkjR61qxMXYRsAIBB1t81hGUdbfF4XGvWrNGiRYt6XL5o0SI9++yzvd4mFovJ7/f3uCwQCOiFF15QIpGQlPmU8/33edZZZ/V5n+b9trW19fgabJGI5PUVfnuzoy3ExFEAAGBDdlnrWbHOszNz26g3GOzXWWb+SERyOGSk07lppVYx0mk1v/nmgZDtAx/Q5FNPJWQDANiKZUHb7t27lUqlVF9f3+Py+vp67dixo9fbnHXWWfrlL3+pNWvWyDAMvfTSS1q1apUSiYR2794tSdqxY0de9ylJK1euVHl5ee5rzJgxA/zpBl8skTmjLRxgYQEAAOzHLmu9UlznDSYzaPOVlcnhOPLxI06XS4Hsp/axjo5Bre1wjHRa7735ptqyr/OY447T5IULLe+wAwDg/SwfhvD+X/CGYfT5S3/58uVavHixTjzxRHk8Hi1ZskSXXXaZJMl10C/ZfO5Tkq677jq1trbmvrZmJxfZlWEYimc72iKBw7f7AwAAWMnqtV6prfMGm9mVFiwv7/dtzHPa4p2dg1LTkRjptN57/XW179wpORwaO2eOJp10EiEbAMCWLAvaampq5HK5Dvn0saWl5ZBPKU2BQECrVq1SV1eX3nnnHTU1NWn8+PEqKytTTfYw14aGhrzuU5J8Pp8ikUiPLzuLJ9MyJDkkhYNsHQUAAPZjl7Veqa3zBlsi29EW6MfEUZN51pzZDTeUjHRa219/Xe27dkkOh8Ydf7wmErIBAGzMsqDN6/Vqzpw5euKJJ3pc/sQTT2jBggWHva3H49Ho0aPlcrm0evVqnXfeeXI6Mz/K/PnzD7nPP/7xj0e8z1Jins/m8TjldbPIAAAA9sNaz34Mw8iFZaGqqn7fzgzlEtHooNTVl3Q6rW2vvaaObMg2/vjjdfSJJ+beCwAA2JGlB3x9/etf18UXX6y5c+dq/vz5+vnPf66mpiZdccUVkjKt/tu3b9e9994rSdq4caNeeOEFzZs3T/v27dMtt9yi119/Xffcc0/uPq+66iqdcsop+v73v68lS5bo//2//6f/+Z//yU2qGg7MbaN+j0tO55HP1gAAALACaz17ScXjSqcy68hwdXW/b2d2tCVjscEoq1fpVErbX3tNnXv2SA6Hjp43T+NOOIGQDQBge5YGbcuWLdOePXv0rW99S83NzZo5c6Yee+wxjRs3TpLU3Nyspqam3PVTqZRuvvlmbdiwQR6PR6eddpqeffZZjR8/PnedBQsWaPXq1frGN76h5cuXa+LEiXrggQc0b968of7xBo05CCHoY9soAACwL9Z69mKez+b2euUNBvt9u1zQlg3qBnvbZjqV0rZXX1XX3r1yOJ2aMG+exh9/vByEbACAEuAwDMOwugi7aWtrU3l5uVpbWwftHI8tzW3qjicV9OWfdb79XpvauxOaOCqimRP6/2kkAAAYXEOxhsDADMVr1Lx+vTp2784FVHax/733tGP9egUrKjQ/O2SiP4x0Wn+57TYZ6bTGHX98bgrpYEinUtr2yivq2rdPDqdTRy9YoHHHHUfIBgCwXH/XEPzGKkHmGW3hAB1tAAAA6B/zfDZfnkGZw+lUIDulNNbRUfS6TKlkUltffjkXsk086SSNmzOHkA0AUFL4rVVi0oaheDKzdTQS9FpcDQAAAEqFOXG0kE67YHYgQryzs5gl5aSSSW17+WV1798vh9OpSQsXauxxx8nh4DxiAEBpsfSMNuQvnj2fzeGQQn5ePgAAADt567nntHPDBqUSCbncha3VyurqVDV2bJErO3BGW0FBW/Y25n0Uk5FOZ0K21lY5XC5NXrhQo2fPJmQDAJQkkpoSY04c9bqd8ngG9yBaAAAA5Kdr3z51798/oPvobm1VpKFBbm/xdi8YhpHbOhrKY+KoyexoG4zJo5179+ZCtimnnqqjjj2WkA0AULII2kqMeT6b3+uWkwUIAACArYw77jhVjxunZDRa0O2b1q1TrKNDbS0tqho9umh1JWMxGens5PqqqrxvHzAnjxb4cx1Ox549kqTyhgZCNgBAySNoKzFm0BZk2ygAAIDtRBoaFGloKPj20Y4ObV23Tp27dxc1aDO72dx+v7yBQN63N7eOJuPxzLZYT3GGchmGoc7duyVJ1ePHE7IBAEoewxBKTCx7RlvYz8RRAACA4aZmwgRJme2jhmEU7X4T2bPVvMFgQWfH+cJhObO3ixVxIEK8q0uJaFRyOFR79NFFu18AAKxC0FZizI62cICgDQAAYLipGDVKLo9H6WRSnXv3Fu1+zY42fyRS0O0dDkdu+2gxJ492ZLvZguXlCmTPgQMAoJQRtJWQdNpQIpnpaIsECdoAAACGG6fbrcrsxNGOXbuKdr9m0FbIxFGTedtidrR1Zs9nqxg9Wk4n/zQBAJQ+fpuVELObzengjDYAAIDhytw+2rVvX9Hus5hBm7kNdaBSyaS6shNaaydNKsp9AgBgNYK2EhLPBm1ej0set8viagAAADAYqsePl5QJx+JFCLUMw8iFY6Hq6oLvJ5jd2pmIxQZck6TM1ljDkMfvV0VjY1HuEwAAqxG0lRBzEILf62IiEwAAwDDlD4cVrqmRJLW3tAz4/hLRaGawgsOh0ADOQctNHo1GB1yTpNy00bL6erl9vqLcJwAAViNoKyHm1tEQ20YBAACGNXP7qBlGDUQiu23U4/fLEwgUfD/mMIRUIqFkPD6gmgzDUEf2fDbzZwUAYDggaCshB4I2r8WVAAAAYDBVZ8On7vZ2pVOpAd2XeT6bNxiU01X48SPeYFAuT2Yg10Anj8ba25WKx+VwOgnaAADDCkFbCTGDtrIAE0cBAACGs/KGBrl9PhmpVG4yZ6HMc94C5eUDuh+Hw5E7py3a0TGg+zK72YKVlfJHIgO6LwAA7ISgrUSk0oaSKUOSFAnR0QYAADCcOZzO3FCE9l27BnRfuYmjAwzapAPntJn3WaiO7JbYqjFjOHsYADCsELSVCHPiqMvpUMDHGW0AAADDnbmlsnv//gHdTy5oq6oaaEkKmJNHBzANNRmPK9rWJkmqnTRpwDUBAGAnBG0lwtw26vU45XbxqR8AAMBwVz1unORwKBGNKlbgmWhGOp0LxULV1QOuKTd5NBYr+D7MrbDeYFCR+voB1wQAgJ0QtJUIM2jze9201wMAAIwAnkAgF0S17dxZ0H0kolFJma2oZkg2EAcHbYZhFHQf5rbRSGNjbrgCAADDBUFbiYgl0pKkkJ9towAAACNFzdFHS5I69+4t6PbmtlGP3y+33z/gesxhCKlEQql4PO/bG+l07mdh2igAYDgiaCsRsXimoy3s51M/AACAkaImOxAh1t6uVDKZ9+3NoM0bCsnpHPjS3+P3y+3zSSps8mh3a6vSyaScLlfuZwMAYDghaCsR5tbRcJCgDQAAYKQI19bKGwzKSKfVUcD0UTNoC0QiRavJ7GqLFxC0dWTPZwtVV8sbChWtJgAA7IKgrQSkUmml0pkzMCJBr8XVAAAAYKg4HI7c9lHzbLN8xLODEMxwrBhyQVsBk0fNQQhV48Zx7jAAYFgiaCsB5vlsLqdDAR9ntAEAAIwk1dktlt379+d9W7OjrahBW3YgQr5BWyIaVSzbBVc7cWLR6gEAwE4I2kqAuW3U53HJ5eSTPwAAgJGkauxYOZxOJeNxdbe19ft26VRKyezU0VB1ddHqCRw0eTQfZkeev6xMZTU1RasHAAA7IWgrAWbQFvC5aLEHAAAYYdxerypGjZIkte/c2e/bJbIdZw6XS4Hy8qLVY3bHJWMxGYbR79uZ20bLR42S080uDQDA8ETQVgLMoC3IxFEAAIARyTynrXPv3n7fxtza6fH75clOCi0Gc+toOplUop9dbelUKle7+bMAADAcEbSVgNzE0QCf/AEAAIxE5jltsc5OpeLxft3GPJ/NFw7L4Szest/t9coTCGTqaW/v12269++XkU7L5fGoasyYotUCAIDdELTZnGEYuWEIZQEmjgIAAIxEwcpK+SMRyTDU3tLSr9uYQVsgEhmUeiQp3tnZr+ub57OFa2rkDQaLXg8AAHZB0GZzqbShdDpz9kVZkK2jAAAAI5HD4chtuWzPhlZHkhiEiaOmXNCWfYwj6ciez2Z25gEAMFwRtNmcuW3U7XLI72XrKAAAwEhVkw2pom1t/RpCYIZgoaqqotdintOWyE41PVIdie5uyeFQ7cSJRa8FAAA7IWizuVg8E7T5PC65XbxcAAAAI1XF6NFyut1KJRLq3r//sNdNp1JKZs9yC1VXF72WXNDWj2EI5rbRQCQyKN11AADYCcmNzZnnswV8dLMBAACMZC63OzdIoO0I57SZ3WxOt3tQzmgLZIO2VCx2xO46c9toxejRcrpcRa8FAAA7IWizOXPraMjP+WwAAAAjXfWECZKkrr17D3s9M2jzBAJyeYs/UMvsaEunUpltoX1IJZPq2rdPktg2CgAYEQjabM4M2sIBgjYAAICRzjynLd7Vddhtm/Fs+OUPheRwOIpeh8vjkS8cliTFOjr6vF7Xvn2SYcjt86li1Kii1wEAgN0QtNmYYRiKZ4M2Jo4CAADAH4nkzlxr37Gjz+uZE0fNLZ6DwbzvWGdnn9cxz2crq6uTx+8ftFoAALALgjYbS6YMpbNHXpTR0QYAAABJNdnto+bZZ72JD0HQZm4fNR/r/QzDUGe2RnPLKwAAwx1Bm42Z20Y9Lqd8Hg6OBQAAwIHQKtrWpnQ63et1zPArVFU1aHWYE0QT0Wiv3491dCgZi8nhdKqWoA0AMEIQtNmYGbT5vC65XLxUAAAAkMobG+XyepVOpdTZy1CEVDKpVCIhSbltpoPB7GhL9nFWnNlxF6ioUKC8fNDqAADATkhvbMwM2gI+t8WVAAAAwC6cTqeqs0MR2ltaDvm+2c3m8njkLysbtDrMjrZkLKZ0KnXI9zuz57NVjRkjh5N/dgAARgZ+49lYbuKon6ANAAAAB5jTR7v37z/ke+YgBE8gIJdn8M75DUQiksMhI50+ZPtoKpFQd2urJKl24sRBqwEAALshaLOxeDxz5kaIQQgAAAA4iNnRlujuPmQYgflnXzgsh8MxaDU43e5cx1y0o6PH98xto55gUOWNjYNWAwAAdkPQZlOGYSiWzHS0RYJei6sBAACAnXiDQZXV10uS2nbu7PG9eHe3pMGdOGrKTR7t7OxxuTltNFJfP6hddQAA2A1Bm00lkmkZRua/w3S0AQAA4H1qspM8zVDLlJs4OhRBmzl59KCuOsMwcjXVMG0UADDCELTZlHk+m9ftlNfjsrgaAAAA2I0ZYkXb23PDCAzDOBC0DeLEUZPZ0XbwGW3RtjalEgk5XS6CNgDAiEPQZlOxROZ8Np/HJZdz8M7WAAAAQGkqq6uTJxCQkU6rPTvhM5VIKJ1MShqaoM3cnpqIxXKXdWRrCVVVyRcOD3oNAADYCUGbTZkdbUEmjgIAAKAXDocj1zHWsWuXpMxwBElyeb3yhUKDXoO5dTQVi+W66sygrXLs2EEdxgAAgB0RtNmUGbQxcRQAAAB9qc4Gbd3790s6cD6bNxCQ0z34H9j6IxE5HI7MIK+uLiWiUcWyE0jrJk0a9McHAMBuCNpsKp4N2sJ+gjYAAAD0rjrbNZaMxdTd3p4L2vxlZUPSTeZ0OuUvL5eUmTxqDkHwhcMK19YO+uMDAGA3BG02ZBhG7oy2SIigDQAAAL1z+3wqHzVKktS+c2cuaAsMwcRRk7l9NNbZqY5s0Fbe2CjXEHTUAQBgNwRtNhRPZkI2h6QQHW0AAAA4jJqjj5Ykde3dq3j2jDYz/BoK5uTReGenOvfuzdQ0ceKQPT4AAHZC0GZDsXhm26jX45TX7bK4GgAAANhZ9fjxkqRoR4finZ2SpJAFQVvX3r0yUik5PR5VjR07ZI8PAICdELTZkDkIwedxy+lkUhMAAAD6Fqqqkq+sTDIMGenMzohQTc2QPb7ZPWdOHQ3X1MgXDA7Z4wMAYCcEbTZkns8W9HOuBQAAAA7P4XCoJjt9VMqc2+YdwqDr/efBVY8bN2SPDQCA3RC02VAsN3GUoA0AAABHdnDQ5g0Gh3QQgb+sTA7XgeNOajmfDQAwghG02VDcDNoCDEIAAADAkVWOHi1nNuzyl5UN6WM7HA4Fysszjx2JKFRVNaSPDwCAndAyZTNpw8hNHS0LeS2uBgAw1NLptOLxuNVloA8ej0cuF4OKYD8uj0eVY8dqz5YtClVXD/njl9XUqGvv3h6BHwCgJ9Z59lasdR5Bm83Es+ezORxSyMfLAwAjSTwe15YtW5TOHmYOe6qoqFBDQ4McDgYWwV6O+fCHte3VV3XUjBlD/tiTTj5ZgYoKjZo5c8gfGwBKAeu80lCMdR5Jjs2Y57N53S55PHwaCAAjhWEYam5ulsvl0pgxY+R0crqD3RiGoa6uLrW0tEiSGhsbLa4I6MkfDmvSggXWPHYkookWPTYA2B3rPPsr5jqPoM1mzKDN73XJySflADBiJJNJdXV1adSoUQoO4bRA5CcQCEiSWlpaVFdXxzZSAABwRKzzSkOx1nnEqDZjDkIIMXEUAEaUVCrb0ezlfE67MxfIiUTC4koAAEApYJ1XOoqxzrM8aLv99ts1YcIE+f1+zZkzR08//fRhr3/fffdp9uzZCgaDamxs1Gc+8xnt2bOnx3V+/OMfa+rUqQoEAhozZoy+9rWvKRqNDuaPUTRmR1uIiaMAMCJx7pf98Rrlh7UeAAAZrCHsrxivkaVB2wMPPKCrr75a119/vdatW6eFCxdq8eLFampq6vX6zzzzjC655BJ99rOf1RtvvKHf/va3evHFF/W5z30ud5377rtP1157rVasWKH169frzjvv1AMPPKDrrrtuqH6sAYnFMwcjhgnaAABAiWOtBwAARhpL9yfecsst+uxnP5tbPP34xz/Wf//3f+uOO+7QypUrD7n+3//+d40fP15f/epXJUkTJkzQF77wBd1000256zz33HM66aST9KlPfUqSNH78eH3yk5/UCy+8MAQ/0cCk04YSqUzQFgkStAEApGQqrVTaGLLHczkdcrssb3jHMMFaDwCAviXjcaWz20qHgtPlkpvtq4POsqAtHo9rzZo1uvbaa3tcvmjRIj377LO93mbBggW6/vrr9dhjj2nx4sVqaWnRgw8+qHPPPTd3nZNPPln/8R//oRdeeEEnnHCC3n77bT322GO69NJL+6wlFospFovl/tzW1jbAn64w5rZRp8OhoI8z2gBgpEum0npnR5viyaEbA+91OzW+IdLvsO2yyy7TPffcc8jlZ511lh5//PFil4cSYpe1nl3WeQAAHCwZj2v7a68pMYRHH3j8fh117LF5hW2s9fJnWZqze/dupVIp1dfX97i8vr5eO3bs6PU2CxYs0H333adly5YpGo0qmUzqggsu0K233pq7zic+8Qnt2rVLJ598sgzDUDKZ1Be/+MVDFnkHW7lypW688cbi/GADYAZtXo9THjdTzABgpEulDcWTabmcDrmcg3+mh/l4qbShfH4NnX322brrrrt6XObz+Xq9biKRkMfjOeJl/VHo7TA07LLWs8s6DwCAg6VTKSWiUbncbjndgx/NpJNJJaLRgjroWOvlx/K9Ie8/aM4wjD4Pn3vzzTf11a9+Vd/85je1Zs0aPf7449qyZYuuuOKK3HWefPJJfec739Htt9+utWvX6qGHHtKjjz6qf/u3f+uzhuuuu06tra25r61btxbnh8tTLJHpWAh4XRySCADIMbdzDvZXoWGez+dTQ0NDj6/KykpJmd/zP/vZz7RkyRKFQiF9+9vf1g033KAPfOADWrVqlY4++mj5fD4ZhqGmpiYtWbJE4XBYkUhEH//4x7Vz587c4/R1O9ib1Ws9u6zzAADojdPtltvrHfSvgYR5rPXyY1lHW01NjVwu1yGfaLa0tBzyyadp5cqVOumkk/S///f/liTNmjVLoVBICxcu1Le//W01NjZq+fLluvjii3NngRx77LHq7OzUP/3TP+n666+X03lotujz+fpMY4dSPNvRFvSXXmILAEBfVqxYoZUrV+pHP/qRXC6X7rrrLm3evFm/+c1v9Lvf/U4uV6Z9bunSpQqFQnrqqaeUTCb1pS99ScuWLdOTTz6Zu6/ebgd7sstazy7rPAAAhivWej1ZFrR5vV7NmTNHTzzxhP7X//pfucufeOIJLVmypNfbdHV1yf2+FNZ84s2Us6ur65AFlsvlkmEYtk9Cza2jTBwFAJSSRx99VOFwuMdl//qv/6rly5dLkj71qU/p8ssv7/H9eDyuX/3qV6qtrZWU+f3/6quvasuWLRozZowk6Ve/+pVmzJihF198Uccff3yvt4N9sdYDAGB4YK2XH0tP3P/617+uiy++WHPnztX8+fP185//XE1NTbntAdddd522b9+ue++9V5J0/vnn6/Of/7zuuOMOnXXWWWpubtbVV1+tE044QaNGjcpd55ZbbtEHP/hBzZs3T5s3b9by5ct1wQUX2D4NJWgDAJSi0047TXfccUePy6qqqnL/PXfu3ENuM27cuB4LqPXr12vMmDG5hZckTZ8+XRUVFVq/fn1u8fX+28HeWOsBAFD6WOvlx9KgbdmyZdqzZ4++9a1vqbm5WTNnztRjjz2mcePGSZKam5vV1NSUu/5ll12m9vZ23XbbbbrmmmtUUVGh008/Xd///vdz1/nGN74hh8Ohb3zjG9q+fbtqa2t1/vnn6zvf+c6Q/3z5SKXTSqYyn8JGgozbBQCUjlAopEmTJh32+0e6rK9zu95/eW/3BftirQcAQOljrZcfS4M2SfrSl76kL33pS71+7+677z7ksiuvvFJXXnlln/fndru1YsUKrVixolglDglzEILL6VDAZ/nLAgDAkJo+fbqampq0devW3Cedb775plpbWzVt2jSLq8NAsNYDAAAjaa1HomMT5rZRr8cpt4uJowCAA1JpQ1J6iB4nf7FY7JAD791ut2pqavp9H2eccYZmzZqliy66SD/+8Y9zB+SeeuqpvW5HAAAAGA7SyaSSQ/Q4hWKtlx+CNpuIxzNBW8Dr7nPkPQBgZHE5HfK6nYon0wWHYPnyup1yOfP7PfT444+rsbGxx2VTp07VP/7xj37fh8Ph0COPPKIrr7xSp5xyipxOp84++2zdeuutedUCAABQCpwulzx+vxLRqFIDCMHy4fH75SzgPFPWevlxGIxnOkRbW5vKy8vV2tqqSCQyKI+xpblN3fGkgtltok0727WvI64xtSEdN6VuUB4TAGBf0WhUW7Zs0YQJE+T3+3OXJ1NDF7JJmXDP7XIe+YojWF+vlTQ0awgMDK8RAGCo9bnOi8eVTqWGrA6nyyW3lzPhD6cY6zw62mzCPKMtxMRRAMBB3C6n3AxSBAAAGHYIvYYnPrK2CfOMtrIA/0cDAAAAAAAoRQRtNnDwtqCyIB1tAAAAAAAApYigzQbMbja3y6GAj928AAAAAAAApYigzQbi2fPZvG5X3pPeAAAAAAAAYA8EbTZgdrQFfC45HARtAAAAAAAApYigzQbMoC3k53w2AAAAAACAUkXQZgO5oC1A0AYAAAAAAFCqCNosZhiGYvHMGW0RgjYAAAAAAICSRdBmsWTKUNowJEllIa/F1QAAAAAAAKBQBG0WM7eNul0O+Twui6sBAKAwzz77rFwul84++2yrSwEAAEARsc7LD0GbxeLZoM3nccnt4uUAAJSmVatW6corr9QzzzyjpqamQXucVCqldDo9aPcPAACAnljn5Ydkx2KxROZNFPC5La4EAGAnhiF1dlrzlT3RoN86Ozv1m9/8Rl/84hd13nnn6e6775YkzZ8/X9dee22P6+7atUsej0d/+ctfJEnxeFz/8i//oqOOOkqhUEjz5s3Tk08+mbv+3XffrYqKCj366KOaPn26fD6f3n33Xb344os688wzVVNTo/Lycp166qlau3Ztj8f6xz/+oZNPPll+v1/Tp0/X//zP/8jhcOiRRx7JXWf79u1atmyZKisrVV1drSVLluidd97J7wkAAADIg2EYSnZ1WfJl5LnQY52XP9Idi5lbR8MMQgAAHKSrSwqHrXnsjg4pFOr/9R944AFNnTpVU6dO1ac//WldeeWVWr58uS666CL94Ac/0MqVK+VwOHLXra+v16mnnipJ+sxnPqN33nlHq1ev1qhRo/Twww/r7LPP1muvvabJkydLkrq6urRy5Ur98pe/VHV1terq6rRlyxZdeuml+r//9/9Kkm6++Wadc8452rRpk8rKypROp7V06VKNHTtWzz//vNrb23XNNdf0qLurq0unnXaaFi5cqL/+9a9yu9369re/rbPPPluvvvqqvF7OTgUAAMWX6u7Wb44/3pLH/viLL8odDPb7+qzz8kfQZjEzaAv5CdoAAKXpzjvv1Kc//WlJ0tlnn62Ojg796U9/0rJly/S1r31NzzzzjBYuXChJuv/++/WpT31KTqdTb731ln79619r27ZtGjVqlCTpn//5n/X444/rrrvu0ne/+11JUiKR0O23367Zs2fnHvP000/vUcO///u/q7KyUk899ZTOO+88/fGPf9Rbb72lJ598Ug0NDZKk73znOzrzzDNzt1m9erWcTqd++ctf5haId911lyoqKvTkk09q0aJFg/SMAQAAlAbWefkjaLOQYRi5oC0SJGgDABwQDGY6y6x67P7asGGDXnjhBT300EOSJLfbrWXLlmnVqlW6//77deaZZ+q+++7TwoULtWXLFj333HO64447JElr166VYRiaMmVKj/uMxWKqrq7O/dnr9WrWrFk9rtPS0qJvfvOb+vOf/6ydO3cqlUqpq6srd27Ihg0bNGbMmNziS5JOOOGEHvexZs0abd68WWVlZT0uj0ajeuutt/r/JAAAAOTBFQjo4y++aNlj9xfrvMIQtFkolTZy5+CEA2xPAQAc4HDkt33TKnfeeaeSyaSOOuqo3GWGYcjj8Wjfvn266KKLdNVVV+nWW2/V/fffrxkzZuQ+sUyn03K5XFqzZo1crp6Tt8MH7ZsNBAK5TyJNl112mXbt2qUf//jHGjdunHw+n+bPn694PJ6r4f23eb90Oq05c+bovvvuO+R7tbW1+T0RAAAA/eRwOPLavmkV1nmFIWizUDw7CMHjdsrnZS4FAKC0JJNJ3Xvvvbr55psPab+/8MILdd999+kzn/mMvvCFL+jxxx/X/fffr4svvjh3nQ9+8INKpVJqaWnJbTnor6efflq33367zjnnHEnS1q1btXv37tz3jznmGDU1NWnnzp2qr6+XJL34vk+OjzvuOD3wwAOqq6tTJBLJ6/EBAACGM9Z5hSPdsVAimQnafB6XXE5eCgBAaXn00Ue1b98+ffazn9XMmTN7fH30ox/VnXfeqVAopCVLlmj58uVav369PvWpT+VuP2XKFF100UW65JJL9NBDD2nLli168cUX9f3vf1+PPfbYYR970qRJ+tWvfqX169fr+eef10UXXaTAQVshzjzzTE2cOFGXXnqpXn31Vf3tb3/T9ddfL0m5T0Avuugi1dTUaMmSJXr66ae1ZcsWPfXUU7rqqqu0bdu2QXjGAAAASgPrvMKR7lgonsyczxb00VgIACg9d955p8444wyVl5cf8r0LL7xQL7/8stauXauLLrpIr7zyihYuXKixY8f2uN5dd92lSy65RNdcc42mTp2qCy64QM8//7zGjBlz2MdetWqV9u3bpw9+8IO6+OKL9dWvflV1dXW577tcLj3yyCPq6OjQ8ccfr8997nP6xje+IUny+/2SpGAwqL/+9a8aO3asPvKRj2jatGm6/PLL1d3dTYcbAAAY0VjnFc5hGOYpYTC1tbWpvLxcra2tg/YCbGlu01vvtaozmtSEhjLNmlgzKI8DACgN0WhUW7Zs0YQJE3ILBBTX3/72N5188snavHmzJk6cWPD9HO61Goo1BAaG1wgAMNRY5w0+O63zaKWykLl1NMzEUQAAiu7hhx9WOBzW5MmTtXnzZl111VU66aSTBrT4AgAAgPXsvM4jaLOIYRiKZ4O2MiaOAgBQdO3t7fqXf/kXbd26VTU1NTrjjDN08803W10WAAAABsjO6zyCNouY3WwOSWV0tAEAUHSXXHKJLrnkEqvLAAAAQJHZeZ3HMASLxBKZQQget1Net8viagAAAAAAADBQBG0WiSUyHW0+r0tOp8PiagAAdsGMIvvjNQIAAIVgDWF/xXiNCNosEs92tAV97N4FAGTGlEtSPB63uBIcSVdXlyTJ4+HoBwAAcGSs80pHMdZ5pDw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      "text/plain": [
       "<Figure size 1500x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "overall_min = min(lung_result['min'].min(), lung_result['average'].min())\n",
    "overall_max = max(lung_result['max'].max(), lung_result['average'].max())\n",
    "fig, axs = plt.subplots(1, 2, figsize=(15, 6))\n",
    "axs[0].plot(lung_result['sparsity'], lung_result['max'], color='lightsteelblue')\n",
    "axs[0].plot(lung_result['sparsity'], lung_result['min'], color='lightsteelblue')\n",
    "axs[0].fill_between(lung_result['sparsity'], lung_result['min'], lung_result['max'], label='Error', color='lightsteelblue', alpha=0.4)\n",
    "axs[0].plot(lung_result['sparsity'], lung_result['average'], label = 'Average',color='blue')\n",
    "axs[0].set_xlabel('Sparsity')\n",
    "axs[0].set_ylabel('Accuracy')\n",
    "axs[0].set_title('Result SET lung')\n",
    "axs[0].legend(loc = 'lower right',fontsize = 'medium')\n",
    "\n",
    "axs[1].plot(lung_motif_result['sparsity'], lung_motif_result['max'], color='rosybrown')\n",
    "axs[1].plot(lung_motif_result['sparsity'], lung_motif_result['min'], color='rosybrown')\n",
    "axs[1].fill_between(lung_motif_result['sparsity'], lung_motif_result['min'], lung_motif_result['max'], label='Error', color='rosybrown', alpha=0.4)\n",
    "axs[1].plot(lung_motif_result['sparsity'], lung_motif_result['average'], label = 'Average',color='brown')\n",
    "axs[1].set_xlabel('Sparsity')\n",
    "axs[1].set_ylabel('Accuracy')\n",
    "axs[1].set_title('Result SET-motif lung test')\n",
    "axs[1].legend(loc = 'lower right',fontsize = 'medium')\n",
    "\n",
    "axs[0].set_ylim(overall_min, overall_max+0.01)\n",
    "axs[1].set_ylim(overall_min, overall_max+0.01)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
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