070_Final_Multi-Trial_Attack.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f9009d01",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "pd.set_option('display.max_rows', None)\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['figure.figsize'] = [12, 8]\n",
    "plt.rcParams.update({'font.size': 18})\n",
    "plt.rc('legend', fontsize=12) \n",
    "\n",
    "import random\n",
    "from scipy.special import rel_entr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d51d0153",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>FairlyActiveMinutes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1503960366</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "           Id  FairlyActiveMinutes\n",
       "0  1503960366                   13\n",
       "1  1503960366                   19\n",
       "2  1503960366                   11\n",
       "3  1503960366                   34\n",
       "4  1503960366                   10"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "################## DATA FRAME ##################\n",
    "datafile = 'Data/Fitbit_Kaggle/dailyActivity_merged.csv'\n",
    "all_fields = ['Id', 'ActivityDate', 'TotalSteps', 'TotalDistance', 'TrackerDistance', \n",
    "          'VeryActiveDistance', 'ModeratelyActiveDistance', 'LightActiveDistance', 'SedentaryActiveDistance', \n",
    "          'VeryActiveMinutes', 'FairlyActiveMinutes', 'LightlyActiveMinutes', 'SedentaryMinutes', 'Calories']\n",
    "\n",
    "#pd.set_option('display.max_rows', 100)\n",
    "df = pd.read_csv(datafile, skipinitialspace=True, usecols=all_fields)\n",
    "fields = 'FairlyActiveMinutes'\n",
    "selected_data = df[['Id',fields]]\n",
    "n_rows, n_cols = df.shape\n",
    "\n",
    "selected_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7945d815",
   "metadata": {},
   "outputs": [],
   "source": [
    "def isMatch(v1,v2,k,th):\n",
    "    '''\n",
    "    Are a match is there is a common k-subsequence that is matched with L1 dist <= th\n",
    "    '''\n",
    "    \n",
    "    # If len(v1)=31 and k=31, then this is range(0,1) ---- k <= len(v1)\n",
    "    for i in range(0,len(v1)-k+1):\n",
    "        a = np.array([v1[i:i+k]])\n",
    "        for j in range(0,len(v2)-k+1):\n",
    "            b = np.array([v2[j:j+k]])\n",
    "            \n",
    "            # Should I zero pad b, truncate a, or just return false?\n",
    "            while (a.shape[1] > b.shape[1]):\n",
    "                b = np.append(b, 0)\n",
    "                b = np.reshape(b, (1, len(b)))\n",
    "            \n",
    "            if (np.linalg.norm((a - b), ord=1) <= th):\n",
    "                return True\n",
    "            \n",
    "    return False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "12f3c912",
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_n_attacks(df, num_attacks, ths=[0,2,5], k_vec=[1,5,10,20,30], sample_size=2):\n",
    "    print('This version is only configured to compare against the original source data')\n",
    "    \n",
    "    fields = 'FairlyActiveMinutes'\n",
    "    my_cols = ['Trial', 'Test_IDs', 'Threshold', 'K', 'Precision', 'Recall', 'Accuracy']\n",
    "    res_df = pd.DataFrame(columns=my_cols)\n",
    "\n",
    "    all_IDs = df.Id.unique()\n",
    "    num_users = len(all_IDs)\n",
    "\n",
    "    user_dict = dict()\n",
    "    for idx, user in enumerate(all_IDs):\n",
    "        temp_df = df.loc[df['Id'] == user]\n",
    "        user_dict[user] = temp_df[fields].values\n",
    "    ##########################################################\n",
    "\n",
    "    # LOOPING THROUGH THE NUMBER OF ATTACKS DESIRED\n",
    "    for trial_num in range(num_attacks):\n",
    "        #test_IDs = all_IDs[[0, 1]]\n",
    "        test_IDs = random.sample(list(all_IDs), 2)\n",
    "        n_attack_training = len(test_IDs)\n",
    "\n",
    "        training_IDs = [ID for ID in all_IDs if ID not in test_IDs]\n",
    "        shuffled_training_IDs = list(random.sample(training_IDs, len(training_IDs)))\n",
    "        attack_IDs = list(test_IDs) + shuffled_training_IDs[0:n_attack_training]\n",
    "        #attack_IDs = list(test_IDs) + list(training_IDs[0:2])\n",
    "        print(\"SANITY CHECK:\")\n",
    "        for i in attack_IDs:\n",
    "            print(f\"{i} in training_IDs: {i in training_IDs}\")\n",
    "\n",
    "        print(\"TEST IDs:\")\n",
    "        print()\n",
    "        print(test_IDs)\n",
    "        for i in range(len(test_IDs)):\n",
    "            print(\"TEST VALS \" + str(i+1))\n",
    "            print(user_dict[test_IDs[i]])\n",
    "            print()\n",
    "        ##########################################################\n",
    "        # Max number of elements for any field\n",
    "        max_elements = 31\n",
    "\n",
    "        # Storage Initialization\n",
    "        data_storage = np.zeros((len(attack_IDs), len(training_IDs), len(ths), max_elements, len(k_vec)))\n",
    "\n",
    "        n_attack_training = len(attack_IDs) - len(test_IDs)\n",
    "        true_positives = np.zeros((len(ths), len(k_vec)))\n",
    "        false_negatives = np.zeros((len(ths), len(k_vec)))\n",
    "        ##########################################################\n",
    "        matching_matrix = np.zeros((len(attack_IDs), len(training_IDs), len(ths), len(k_vec))) + 9\n",
    "        for i, attack_ID in enumerate(attack_IDs):\n",
    "            v1 = user_dict[attack_ID]\n",
    "            for k_idx, k in enumerate(k_vec):\n",
    "                # POSSIBLE ISSUE\n",
    "                # We shuffled the training IDs... so matching_matrix is thrown off... need to compensate for this somehow...\n",
    "                for j, training_ID in enumerate(training_IDs): #shuffled_training_IDs[0:n_attack_training] #(training_IDs[:2]):\n",
    "                    v2 = user_dict[training_ID]\n",
    "\n",
    "                    for l, th in enumerate(ths):\n",
    "                        if (isMatch(v1,v2,k,th)):\n",
    "                            matching_matrix[i,j,l,k_idx] = 1\n",
    "                        else:\n",
    "                            matching_matrix[i,j,l,k_idx] = 0\n",
    "        ##########################################################\n",
    "        for attack_idx, attack_ID in enumerate(attack_IDs):\n",
    "            if attack_ID in training_IDs:\n",
    "                label_idx = training_IDs.index(attack_ID)\n",
    "                for j in range(len(ths)):\n",
    "                    for k_idx in range(len(k_vec)):\n",
    "                        if matching_matrix[attack_idx, label_idx, j, k_idx] == 1:\n",
    "                            true_positives[j, k_idx] += 1\n",
    "                        else:\n",
    "                            false_negatives[j, k_idx] += 1                    \n",
    "        ##########################################################                 \n",
    "        for j in range(len(ths)):\n",
    "            for k_idx in range(len(k_vec)):\n",
    "                if true_positives[j, k_idx] == n_attack_training:\n",
    "                    # Note that this is not necessarily mean all of them were true positives\n",
    "                    print(f\"Th={ths[j]}, k={k_vec[k_idx]}: Correct number of positives found ({int(true_positives[j, k_idx])})!\")\n",
    "                else:\n",
    "                    print(f\"Th={ths[j]}, k={k_vec[k_idx]}: Incorrect number of positives... found ({int(true_positives[j, k_idx])}) vs expected ({n_attack_training})\")\n",
    "        ##########################################################            \n",
    "        for j in range(len(ths)):\n",
    "            for k_idx in range(len(k_vec)):\n",
    "                all_positives = np.count_nonzero(matching_matrix[:,:,j,k_idx] == 1) \n",
    "                false_positives = all_positives - true_positives[j, k_idx]\n",
    "                true_negatives = np.product(matching_matrix.shape) - all_positives - false_negatives[j, k_idx]\n",
    "\n",
    "                precision = true_positives[j, k_idx] / (true_positives[j, k_idx] + false_positives)\n",
    "                recall = true_positives[j, k_idx] / (true_positives[j, k_idx] + false_negatives[j, k_idx])\n",
    "                accuracy = (true_positives[j, k_idx] + true_negatives) / np.product(matching_matrix.shape)\n",
    "\n",
    "                new_row = [trial_num, test_IDs, ths[j], k_vec[k_idx], precision, recall, accuracy]\n",
    "                new_df = pd.DataFrame([new_row], columns=my_cols)\n",
    "                res_df = pd.concat([res_df, new_df])\n",
    "\n",
    "        print(\"--------------------------------------------\")\n",
    "        print(\"END OF ATTACK NUMBER \" + str(trial_num+1))\n",
    "        print(\"--------------------------------------------\")\n",
    "        print()\n",
    "\n",
    "    return res_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "03433a56",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This version is only configured to compare against the original source data\n",
      "SANITY CHECK:\n",
      "8583815059 in training_IDs: False\n",
      "5553957443 in training_IDs: False\n",
      "3372868164 in training_IDs: True\n",
      "4319703577 in training_IDs: True\n",
      "TEST IDs:\n",
      "\n",
      "[8583815059, 5553957443]\n",
      "TEST VALS 1\n",
      "[ 7 23  0  0  0  0 22 10 27 18 54 44 44  6 91  5 28 67 28  2  0  0  0  0\n",
      " 22  0 72  4 43 71  0]\n",
      "\n",
      "TEST VALS 2\n",
      "[13  0 41 38  0  0  0 28  0 42 16  0  0 11 18 16 13 15  0  0  9 29  0 29\n",
      "  9  0  8 22 40  0  6]\n",
      "\n",
      "Th=0, k=1: Correct number of positives found (2)!\n",
      "Th=0, k=5: Correct number of positives found (2)!\n",
      "Th=0, k=10: Correct number of positives found (2)!\n",
      "Th=0, k=20: Correct number of positives found (2)!\n",
      "Th=0, k=30: Incorrect number of positives... found (1) vs expected (2)\n",
      "Th=2, k=1: Correct number of positives found (2)!\n",
      "Th=2, k=5: Correct number of positives found (2)!\n",
      "Th=2, k=10: Correct number of positives found (2)!\n",
      "Th=2, k=20: Correct number of positives found (2)!\n",
      "Th=2, k=30: Incorrect number of positives... found (1) vs expected (2)\n",
      "Th=5, k=1: Correct number of positives found (2)!\n",
      "Th=5, k=5: Correct number of positives found (2)!\n",
      "Th=5, k=10: Correct number of positives found (2)!\n",
      "Th=5, k=20: Correct number of positives found (2)!\n",
      "Th=5, k=30: Incorrect number of positives... found (1) vs expected (2)\n",
      "--------------------------------------------\n",
      "END OF ATTACK NUMBER 1\n",
      "--------------------------------------------\n",
      "\n",
      "SANITY CHECK:\n",
      "8253242879 in training_IDs: False\n",
      "5553957443 in training_IDs: False\n",
      "1644430081 in training_IDs: True\n",
      "4702921684 in training_IDs: True\n",
      "TEST IDs:\n",
      "\n",
      "[8253242879, 5553957443]\n",
      "TEST VALS 1\n",
      "[ 2 11 16  0 51  5 16  0 18 20  0 26  4 54 14  5 16 14  0]\n",
      "\n",
      "TEST VALS 2\n",
      "[13  0 41 38  0  0  0 28  0 42 16  0  0 11 18 16 13 15  0  0  9 29  0 29\n",
      "  9  0  8 22 40  0  6]\n",
      "\n",
      "Th=0, k=1: Correct number of positives found (2)!\n",
      "Th=0, k=5: Correct number of positives found (2)!\n",
      "Th=0, k=10: Correct number of positives found (2)!\n",
      "Th=0, k=20: Correct number of positives found (2)!\n",
      "Th=0, k=30: Correct number of positives found (2)!\n",
      "Th=2, k=1: Correct number of positives found (2)!\n",
      "Th=2, k=5: Correct number of positives found (2)!\n",
      "Th=2, k=10: Correct number of positives found (2)!\n",
      "Th=2, k=20: Correct number of positives found (2)!\n",
      "Th=2, k=30: Correct number of positives found (2)!\n",
      "Th=5, k=1: Correct number of positives found (2)!\n",
      "Th=5, k=5: Correct number of positives found (2)!\n",
      "Th=5, k=10: Correct number of positives found (2)!\n",
      "Th=5, k=20: Correct number of positives found (2)!\n",
      "Th=5, k=30: Correct number of positives found (2)!\n",
      "--------------------------------------------\n",
      "END OF ATTACK NUMBER 2\n",
      "--------------------------------------------\n",
      "\n",
      "SANITY CHECK:\n",
      "2347167796 in training_IDs: False\n",
      "3372868164 in training_IDs: False\n",
      "8053475328 in training_IDs: True\n",
      "8792009665 in training_IDs: True\n",
      "TEST IDs:\n",
      "\n",
      "[2347167796, 3372868164]\n",
      "TEST VALS 1\n",
      "[13 32 48 24 72  7 16  7 43 26 27 35  0 11  0  9  0  0]\n",
      "\n",
      "TEST VALS 2\n",
      "[ 0  8 25  0  0  2  7  0  3  9  0  2  7  0  2 13  0  0  4  0]\n",
      "\n",
      "Th=0, k=1: Correct number of positives found (2)!\n",
      "Th=0, k=5: Correct number of positives found (2)!\n",
      "Th=0, k=10: Correct number of positives found (2)!\n",
      "Th=0, k=20: Correct number of positives found (2)!\n",
      "Th=0, k=30: Incorrect number of positives... found (1) vs expected (2)\n",
      "Th=2, k=1: Correct number of positives found (2)!\n",
      "Th=2, k=5: Correct number of positives found (2)!\n",
      "Th=2, k=10: Correct number of positives found (2)!\n",
      "Th=2, k=20: Correct number of positives found (2)!\n",
      "Th=2, k=30: Incorrect number of positives... found (1) vs expected (2)\n",
      "Th=5, k=1: Correct number of positives found (2)!\n",
      "Th=5, k=5: Correct number of positives found (2)!\n",
      "Th=5, k=10: Correct number of positives found (2)!\n",
      "Th=5, k=20: Correct number of positives found (2)!\n",
      "Th=5, k=30: Incorrect number of positives... found (1) vs expected (2)\n",
      "--------------------------------------------\n",
      "END OF ATTACK NUMBER 3\n",
      "--------------------------------------------\n",
      "\n",
      "SANITY CHECK:\n",
      "8253242879 in training_IDs: False\n",
      "1844505072 in training_IDs: False\n",
      "3977333714 in training_IDs: True\n",
      "4558609924 in training_IDs: True\n",
      "TEST IDs:\n",
      "\n",
      "[8253242879, 1844505072]\n",
      "TEST VALS 1\n",
      "[ 2 11 16  0 51  5 16  0 18 20  0 26  4 54 14  5 16 14  0]\n",
      "\n",
      "TEST VALS 2\n",
      "[ 0  0  0  0  0  8 12  0  0 13  0  0  0  0  0  0  0  0  0  7  0  0  0  0\n",
      "  0  0  0  0  0  0  0]\n",
      "\n",
      "Th=0, k=1: Correct number of positives found (2)!\n",
      "Th=0, k=5: Correct number of positives found (2)!\n",
      "Th=0, k=10: Correct number of positives found (2)!\n",
      "Th=0, k=20: Correct number of positives found (2)!\n",
      "Th=0, k=30: Correct number of positives found (2)!\n",
      "Th=2, k=1: Correct number of positives found (2)!\n",
      "Th=2, k=5: Correct number of positives found (2)!\n",
      "Th=2, k=10: Correct number of positives found (2)!\n",
      "Th=2, k=20: Correct number of positives found (2)!\n",
      "Th=2, k=30: Correct number of positives found (2)!\n",
      "Th=5, k=1: Correct number of positives found (2)!\n",
      "Th=5, k=5: Correct number of positives found (2)!\n",
      "Th=5, k=10: Correct number of positives found (2)!\n",
      "Th=5, k=20: Correct number of positives found (2)!\n",
      "Th=5, k=30: Correct number of positives found (2)!\n",
      "--------------------------------------------\n",
      "END OF ATTACK NUMBER 4\n",
      "--------------------------------------------\n",
      "\n",
      "SANITY CHECK:\n",
      "4558609924 in training_IDs: False\n",
      "1644430081 in training_IDs: False\n",
      "5577150313 in training_IDs: True\n",
      "7086361926 in training_IDs: True\n",
      "TEST IDs:\n",
      "\n",
      "[4558609924, 1644430081]\n",
      "TEST VALS 1\n",
      "[ 0  7  0 30 12 19 15  9  0 21 25  5  0 10 41 14 11 29 29  0  0 10  0  8\n",
      "  0 16 35 30 18 31  0]\n",
      "\n",
      "TEST VALS 2\n",
      "[51 16 58  4 42 13 33 58  0  0  0 15  0  1 41  0 53  0 71 24  7 94  0 12\n",
      "  6 17  0  6 19  0]\n",
      "\n",
      "Th=0, k=1: Correct number of positives found (2)!\n",
      "Th=0, k=5: Correct number of positives found (2)!\n",
      "Th=0, k=10: Correct number of positives found (2)!\n",
      "Th=0, k=20: Correct number of positives found (2)!\n",
      "Th=0, k=30: Correct number of positives found (2)!\n",
      "Th=2, k=1: Correct number of positives found (2)!\n",
      "Th=2, k=5: Correct number of positives found (2)!\n",
      "Th=2, k=10: Correct number of positives found (2)!\n",
      "Th=2, k=20: Correct number of positives found (2)!\n",
      "Th=2, k=30: Correct number of positives found (2)!\n",
      "Th=5, k=1: Correct number of positives found (2)!\n",
      "Th=5, k=5: Correct number of positives found (2)!\n",
      "Th=5, k=10: Correct number of positives found (2)!\n",
      "Th=5, k=20: Correct number of positives found (2)!\n",
      "Th=5, k=30: Correct number of positives found (2)!\n",
      "--------------------------------------------\n",
      "END OF ATTACK NUMBER 5\n",
      "--------------------------------------------\n",
      "\n",
      "SANITY CHECK:\n",
      "1644430081 in training_IDs: False\n",
      "6290855005 in training_IDs: False\n",
      "1624580081 in training_IDs: True\n",
      "8877689391 in training_IDs: True\n",
      "TEST IDs:\n",
      "\n",
      "[1644430081, 6290855005]\n",
      "TEST VALS 1\n",
      "[51 16 58  4 42 13 33 58  0  0  0 15  0  1 41  0 53  0 71 24  7 94  0 12\n",
      "  6 17  0  6 19  0]\n",
      "\n",
      "TEST VALS 2\n",
      "[ 0  0  0  0  0 12  0  0  0  0  0  0  0  3  0  0  0  0  0 95  0  0  0  0\n",
      "  0  0  0  0  0]\n",
      "\n",
      "Th=0, k=1: Correct number of positives found (2)!\n",
      "Th=0, k=5: Correct number of positives found (2)!\n",
      "Th=0, k=10: Correct number of positives found (2)!\n",
      "Th=0, k=20: Correct number of positives found (2)!\n",
      "Th=0, k=30: Correct number of positives found (2)!\n",
      "Th=2, k=1: Correct number of positives found (2)!\n",
      "Th=2, k=5: Correct number of positives found (2)!\n",
      "Th=2, k=10: Correct number of positives found (2)!\n",
      "Th=2, k=20: Correct number of positives found (2)!\n",
      "Th=2, k=30: Correct number of positives found (2)!\n",
      "Th=5, k=1: Correct number of positives found (2)!\n",
      "Th=5, k=5: Correct number of positives found (2)!\n",
      "Th=5, k=10: Correct number of positives found (2)!\n",
      "Th=5, k=20: Correct number of positives found (2)!\n",
      "Th=5, k=30: Correct number of positives found (2)!\n",
      "--------------------------------------------\n",
      "END OF ATTACK NUMBER 6\n",
      "--------------------------------------------\n",
      "\n",
      "SANITY CHECK:\n",
      "2022484408 in training_IDs: False\n",
      "4702921684 in training_IDs: False\n",
      "2873212765 in training_IDs: True\n",
      "4445114986 in training_IDs: True\n",
      "TEST IDs:\n",
      "\n",
      "[2022484408, 4702921684]\n",
      "TEST VALS 1\n",
      "[14  5  3  9 11 29  3  7 63 53 10  0 26  8 24 20 20 40 23 28  8 14 27 20\n",
      " 17  2  0 47 28 25 16]\n",
      "\n",
      "TEST VALS 2\n",
      "[  0   0   0   8  24  66  30   8  29  41   0  66  95  15   8  16   9  19\n",
      "  36   0  40   0  15   5  16  46 125  12  37  41   0]\n",
      "\n",
      "Th=0, k=1: Correct number of positives found (2)!\n",
      "Th=0, k=5: Correct number of positives found (2)!\n",
      "Th=0, k=10: Correct number of positives found (2)!\n",
      "Th=0, k=20: Correct number of positives found (2)!\n",
      "Th=0, k=30: Correct number of positives found (2)!\n",
      "Th=2, k=1: Correct number of positives found (2)!\n",
      "Th=2, k=5: Correct number of positives found (2)!\n",
      "Th=2, k=10: Correct number of positives found (2)!\n",
      "Th=2, k=20: Correct number of positives found (2)!\n",
      "Th=2, k=30: Correct number of positives found (2)!\n",
      "Th=5, k=1: Correct number of positives found (2)!\n",
      "Th=5, k=5: Correct number of positives found (2)!\n",
      "Th=5, k=10: Correct number of positives found (2)!\n",
      "Th=5, k=20: Correct number of positives found (2)!\n",
      "Th=5, k=30: Correct number of positives found (2)!\n",
      "--------------------------------------------\n",
      "END OF ATTACK NUMBER 7\n",
      "--------------------------------------------\n",
      "\n",
      "SANITY CHECK:\n",
      "6290855005 in training_IDs: False\n",
      "3977333714 in training_IDs: False\n",
      "2026352035 in training_IDs: True\n",
      "5577150313 in training_IDs: True\n",
      "TEST IDs:\n",
      "\n",
      "[6290855005, 3977333714]\n",
      "TEST VALS 1\n",
      "[ 0  0  0  0  0 12  0  0  0  0  0  0  0  3  0  0  0  0  0 95  0  0  0  0\n",
      "  0  0  0  0  0]\n",
      "\n",
      "TEST VALS 2\n",
      "[ 19  46  23  42  83  58  95  67  98   0  12  92  95   9  95  10   8  32\n",
      "  52  40 143  41  96  88  55  86 116 122 115   0]\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Th=0, k=1: Correct number of positives found (2)!\n",
      "Th=0, k=5: Correct number of positives found (2)!\n",
      "Th=0, k=10: Correct number of positives found (2)!\n",
      "Th=0, k=20: Correct number of positives found (2)!\n",
      "Th=0, k=30: Correct number of positives found (2)!\n",
      "Th=2, k=1: Correct number of positives found (2)!\n",
      "Th=2, k=5: Correct number of positives found (2)!\n",
      "Th=2, k=10: Correct number of positives found (2)!\n",
      "Th=2, k=20: Correct number of positives found (2)!\n",
      "Th=2, k=30: Correct number of positives found (2)!\n",
      "Th=5, k=1: Correct number of positives found (2)!\n",
      "Th=5, k=5: Correct number of positives found (2)!\n",
      "Th=5, k=10: Correct number of positives found (2)!\n",
      "Th=5, k=20: Correct number of positives found (2)!\n",
      "Th=5, k=30: Correct number of positives found (2)!\n",
      "--------------------------------------------\n",
      "END OF ATTACK NUMBER 8\n",
      "--------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "res_df = run_n_attacks(df, 8, sample_size=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f6622caa",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>[2347167796, 3372868164]</td>\n",
       "      <td>5</td>\n",
       "      <td>30</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.999462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>[8253242879, 1844505072]</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.016129</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.934409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>[8253242879, 1844505072]</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.994624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>[8253242879, 1844505072]</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.996774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>[8253242879, 1844505072]</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>[8253242879, 1844505072]</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>[8253242879, 1844505072]</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.016129</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.934409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>[8253242879, 1844505072]</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0.117647</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.991935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>[8253242879, 1844505072]</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.995161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>[8253242879, 1844505072]</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>[8253242879, 1844505072]</td>\n",
       "      <td>2</td>\n",
       "      <td>30</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>[8253242879, 1844505072]</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.016129</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.934409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>[8253242879, 1844505072]</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.984946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>[8253242879, 1844505072]</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.995161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>[8253242879, 1844505072]</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.999462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>[8253242879, 1844505072]</td>\n",
       "      <td>5</td>\n",
       "      <td>30</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>[4558609924, 1644430081]</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.016129</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.934409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>[4558609924, 1644430081]</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>[4558609924, 1644430081]</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>[4558609924, 1644430081]</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>[4558609924, 1644430081]</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>[4558609924, 1644430081]</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.016129</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.934409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>[4558609924, 1644430081]</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.998387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>[4558609924, 1644430081]</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>[4558609924, 1644430081]</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>[4558609924, 1644430081]</td>\n",
       "      <td>2</td>\n",
       "      <td>30</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>[4558609924, 1644430081]</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.016129</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.934409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>[4558609924, 1644430081]</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.989247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>[4558609924, 1644430081]</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>[4558609924, 1644430081]</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>[4558609924, 1644430081]</td>\n",
       "      <td>5</td>\n",
       "      <td>30</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>[1644430081, 6290855005]</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.016129</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.934409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>[1644430081, 6290855005]</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.989247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>[1644430081, 6290855005]</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.996774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>[1644430081, 6290855005]</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>[1644430081, 6290855005]</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>[1644430081, 6290855005]</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.016129</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.934409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>[1644430081, 6290855005]</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0.071429</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.986022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>[1644430081, 6290855005]</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.995161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>[1644430081, 6290855005]</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>[1644430081, 6290855005]</td>\n",
       "      <td>2</td>\n",
       "      <td>30</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>[1644430081, 6290855005]</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.016129</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.934409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>[1644430081, 6290855005]</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0.037037</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.972043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>[1644430081, 6290855005]</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.990323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>[1644430081, 6290855005]</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>[1644430081, 6290855005]</td>\n",
       "      <td>5</td>\n",
       "      <td>30</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>[2022484408, 4702921684]</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.016129</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.934409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>[2022484408, 4702921684]</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.989247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>[2022484408, 4702921684]</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.999462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>[2022484408, 4702921684]</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>[2022484408, 4702921684]</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>[2022484408, 4702921684]</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.016129</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.934409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>[2022484408, 4702921684]</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0.071429</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.986022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>[2022484408, 4702921684]</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.995161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>[2022484408, 4702921684]</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>[2022484408, 4702921684]</td>\n",
       "      <td>2</td>\n",
       "      <td>30</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>[2022484408, 4702921684]</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.016129</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.934409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>[2022484408, 4702921684]</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.976344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>[2022484408, 4702921684]</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.153846</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.994086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>[2022484408, 4702921684]</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>[2022484408, 4702921684]</td>\n",
       "      <td>5</td>\n",
       "      <td>30</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>[6290855005, 3977333714]</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.016129</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.934409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>[6290855005, 3977333714]</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0.095238</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.989785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>[6290855005, 3977333714]</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.996774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>[6290855005, 3977333714]</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>[6290855005, 3977333714]</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>[6290855005, 3977333714]</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.016129</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.934409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>[6290855005, 3977333714]</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.988172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>[6290855005, 3977333714]</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.995161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>[6290855005, 3977333714]</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>[6290855005, 3977333714]</td>\n",
       "      <td>2</td>\n",
       "      <td>30</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>[6290855005, 3977333714]</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.016129</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.934409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>[6290855005, 3977333714]</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0.060606</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.983333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>[6290855005, 3977333714]</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.105263</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.990860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>[6290855005, 3977333714]</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>[6290855005, 3977333714]</td>\n",
       "      <td>5</td>\n",
       "      <td>30</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Trial                  Test_IDs Threshold   K  Precision  Recall  Accuracy\n",
       "0     0  [8583815059, 5553957443]         0   1   0.016129     1.0  0.934409\n",
       "0     0  [8583815059, 5553957443]         0   5   0.666667     1.0  0.999462\n",
       "0     0  [8583815059, 5553957443]         0  10   1.000000     1.0  1.000000\n",
       "0     0  [8583815059, 5553957443]         0  20   1.000000     1.0  1.000000\n",
       "0     0  [8583815059, 5553957443]         0  30   1.000000     0.5  0.999462\n",
       "0     0  [8583815059, 5553957443]         2   1   0.016129     1.0  0.934409\n",
       "0     0  [8583815059, 5553957443]         2   5   0.060606     1.0  0.983333\n",
       "0     0  [8583815059, 5553957443]         2  10   1.000000     1.0  1.000000\n",
       "0     0  [8583815059, 5553957443]         2  20   1.000000     1.0  1.000000\n",
       "0     0  [8583815059, 5553957443]         2  30   1.000000     0.5  0.999462\n",
       "0     0  [8583815059, 5553957443]         5   1   0.016129     1.0  0.934409\n",
       "0     0  [8583815059, 5553957443]         5   5   0.037037     1.0  0.972043\n",
       "0     0  [8583815059, 5553957443]         5  10   0.500000     1.0  0.998925\n",
       "0     0  [8583815059, 5553957443]         5  20   1.000000     1.0  1.000000\n",
       "0     0  [8583815059, 5553957443]         5  30   1.000000     0.5  0.999462\n",
       "0     1  [8253242879, 5553957443]         0   1   0.016129     1.0  0.934409\n",
       "0     1  [8253242879, 5553957443]         0   5   1.000000     1.0  1.000000\n",
       "0     1  [8253242879, 5553957443]         0  10   1.000000     1.0  1.000000\n",
       "0     1  [8253242879, 5553957443]         0  20   1.000000     1.0  1.000000\n",
       "0     1  [8253242879, 5553957443]         0  30   1.000000     1.0  1.000000\n",
       "0     1  [8253242879, 5553957443]         2   1   0.016129     1.0  0.934409\n",
       "0     1  [8253242879, 5553957443]         2   5   0.500000     1.0  0.998925\n",
       "0     1  [8253242879, 5553957443]         2  10   1.000000     1.0  1.000000\n",
       "0     1  [8253242879, 5553957443]         2  20   1.000000     1.0  1.000000\n",
       "0     1  [8253242879, 5553957443]         2  30   1.000000     1.0  1.000000\n",
       "0     1  [8253242879, 5553957443]         5   1   0.016129     1.0  0.934409\n",
       "0     1  [8253242879, 5553957443]         5   5   0.090909     1.0  0.989247\n",
       "0     1  [8253242879, 5553957443]         5  10   1.000000     1.0  1.000000\n",
       "0     1  [8253242879, 5553957443]         5  20   1.000000     1.0  1.000000\n",
       "0     1  [8253242879, 5553957443]         5  30   1.000000     1.0  1.000000\n",
       "0     2  [2347167796, 3372868164]         0   1   0.016129     1.0  0.934409\n",
       "0     2  [2347167796, 3372868164]         0   5   0.166667     1.0  0.994624\n",
       "0     2  [2347167796, 3372868164]         0  10   0.250000     1.0  0.996774\n",
       "0     2  [2347167796, 3372868164]         0  20   1.000000     1.0  1.000000\n",
       "0     2  [2347167796, 3372868164]         0  30   1.000000     0.5  0.999462\n",
       "0     2  [2347167796, 3372868164]         2   1   0.016129     1.0  0.934409\n",
       "0     2  [2347167796, 3372868164]         2   5   0.062500     1.0  0.983871\n",
       "0     2  [2347167796, 3372868164]         2  10   0.222222     1.0  0.996237\n",
       "0     2  [2347167796, 3372868164]         2  20   1.000000     1.0  1.000000\n",
       "0     2  [2347167796, 3372868164]         2  30   1.000000     0.5  0.999462\n",
       "0     2  [2347167796, 3372868164]         5   1   0.016129     1.0  0.934409\n",
       "0     2  [2347167796, 3372868164]         5   5   0.037736     1.0  0.972581\n",
       "0     2  [2347167796, 3372868164]         5  10   0.153846     1.0  0.994086\n",
       "0     2  [2347167796, 3372868164]         5  20   1.000000     1.0  1.000000\n",
       "0     2  [2347167796, 3372868164]         5  30   1.000000     0.5  0.999462\n",
       "0     3  [8253242879, 1844505072]         0   1   0.016129     1.0  0.934409\n",
       "0     3  [8253242879, 1844505072]         0   5   0.166667     1.0  0.994624\n",
       "0     3  [8253242879, 1844505072]         0  10   0.250000     1.0  0.996774\n",
       "0     3  [8253242879, 1844505072]         0  20   1.000000     1.0  1.000000\n",
       "0     3  [8253242879, 1844505072]         0  30   1.000000     1.0  1.000000\n",
       "0     3  [8253242879, 1844505072]         2   1   0.016129     1.0  0.934409\n",
       "0     3  [8253242879, 1844505072]         2   5   0.117647     1.0  0.991935\n",
       "0     3  [8253242879, 1844505072]         2  10   0.181818     1.0  0.995161\n",
       "0     3  [8253242879, 1844505072]         2  20   1.000000     1.0  1.000000\n",
       "0     3  [8253242879, 1844505072]         2  30   1.000000     1.0  1.000000\n",
       "0     3  [8253242879, 1844505072]         5   1   0.016129     1.0  0.934409\n",
       "0     3  [8253242879, 1844505072]         5   5   0.066667     1.0  0.984946\n",
       "0     3  [8253242879, 1844505072]         5  10   0.181818     1.0  0.995161\n",
       "0     3  [8253242879, 1844505072]         5  20   0.666667     1.0  0.999462\n",
       "0     3  [8253242879, 1844505072]         5  30   1.000000     1.0  1.000000\n",
       "0     4  [4558609924, 1644430081]         0   1   0.016129     1.0  0.934409\n",
       "0     4  [4558609924, 1644430081]         0   5   1.000000     1.0  1.000000\n",
       "0     4  [4558609924, 1644430081]         0  10   1.000000     1.0  1.000000\n",
       "0     4  [4558609924, 1644430081]         0  20   1.000000     1.0  1.000000\n",
       "0     4  [4558609924, 1644430081]         0  30   1.000000     1.0  1.000000\n",
       "0     4  [4558609924, 1644430081]         2   1   0.016129     1.0  0.934409\n",
       "0     4  [4558609924, 1644430081]         2   5   0.400000     1.0  0.998387\n",
       "0     4  [4558609924, 1644430081]         2  10   1.000000     1.0  1.000000\n",
       "0     4  [4558609924, 1644430081]         2  20   1.000000     1.0  1.000000\n",
       "0     4  [4558609924, 1644430081]         2  30   1.000000     1.0  1.000000\n",
       "0     4  [4558609924, 1644430081]         5   1   0.016129     1.0  0.934409\n",
       "0     4  [4558609924, 1644430081]         5   5   0.090909     1.0  0.989247\n",
       "0     4  [4558609924, 1644430081]         5  10   1.000000     1.0  1.000000\n",
       "0     4  [4558609924, 1644430081]         5  20   1.000000     1.0  1.000000\n",
       "0     4  [4558609924, 1644430081]         5  30   1.000000     1.0  1.000000\n",
       "0     5  [1644430081, 6290855005]         0   1   0.016129     1.0  0.934409\n",
       "0     5  [1644430081, 6290855005]         0   5   0.090909     1.0  0.989247\n",
       "0     5  [1644430081, 6290855005]         0  10   0.250000     1.0  0.996774\n",
       "0     5  [1644430081, 6290855005]         0  20   1.000000     1.0  1.000000\n",
       "0     5  [1644430081, 6290855005]         0  30   1.000000     1.0  1.000000\n",
       "0     5  [1644430081, 6290855005]         2   1   0.016129     1.0  0.934409\n",
       "0     5  [1644430081, 6290855005]         2   5   0.071429     1.0  0.986022\n",
       "0     5  [1644430081, 6290855005]         2  10   0.181818     1.0  0.995161\n",
       "0     5  [1644430081, 6290855005]         2  20   1.000000     1.0  1.000000\n",
       "0     5  [1644430081, 6290855005]         2  30   1.000000     1.0  1.000000\n",
       "0     5  [1644430081, 6290855005]         5   1   0.016129     1.0  0.934409\n",
       "0     5  [1644430081, 6290855005]         5   5   0.037037     1.0  0.972043\n",
       "0     5  [1644430081, 6290855005]         5  10   0.100000     1.0  0.990323\n",
       "0     5  [1644430081, 6290855005]         5  20   1.000000     1.0  1.000000\n",
       "0     5  [1644430081, 6290855005]         5  30   1.000000     1.0  1.000000\n",
       "0     6  [2022484408, 4702921684]         0   1   0.016129     1.0  0.934409\n",
       "0     6  [2022484408, 4702921684]         0   5   0.090909     1.0  0.989247\n",
       "0     6  [2022484408, 4702921684]         0  10   0.666667     1.0  0.999462\n",
       "0     6  [2022484408, 4702921684]         0  20   1.000000     1.0  1.000000\n",
       "0     6  [2022484408, 4702921684]         0  30   1.000000     1.0  1.000000\n",
       "0     6  [2022484408, 4702921684]         2   1   0.016129     1.0  0.934409\n",
       "0     6  [2022484408, 4702921684]         2   5   0.071429     1.0  0.986022\n",
       "0     6  [2022484408, 4702921684]         2  10   0.181818     1.0  0.995161\n",
       "0     6  [2022484408, 4702921684]         2  20   1.000000     1.0  1.000000\n",
       "0     6  [2022484408, 4702921684]         2  30   1.000000     1.0  1.000000\n",
       "0     6  [2022484408, 4702921684]         5   1   0.016129     1.0  0.934409\n",
       "0     6  [2022484408, 4702921684]         5   5   0.043478     1.0  0.976344\n",
       "0     6  [2022484408, 4702921684]         5  10   0.153846     1.0  0.994086\n",
       "0     6  [2022484408, 4702921684]         5  20   1.000000     1.0  1.000000\n",
       "0     6  [2022484408, 4702921684]         5  30   1.000000     1.0  1.000000\n",
       "0     7  [6290855005, 3977333714]         0   1   0.016129     1.0  0.934409\n",
       "0     7  [6290855005, 3977333714]         0   5   0.095238     1.0  0.989785\n",
       "0     7  [6290855005, 3977333714]         0  10   0.250000     1.0  0.996774\n",
       "0     7  [6290855005, 3977333714]         0  20   1.000000     1.0  1.000000\n",
       "0     7  [6290855005, 3977333714]         0  30   1.000000     1.0  1.000000\n",
       "0     7  [6290855005, 3977333714]         2   1   0.016129     1.0  0.934409\n",
       "0     7  [6290855005, 3977333714]         2   5   0.083333     1.0  0.988172\n",
       "0     7  [6290855005, 3977333714]         2  10   0.181818     1.0  0.995161\n",
       "0     7  [6290855005, 3977333714]         2  20   1.000000     1.0  1.000000\n",
       "0     7  [6290855005, 3977333714]         2  30   1.000000     1.0  1.000000\n",
       "0     7  [6290855005, 3977333714]         5   1   0.016129     1.0  0.934409\n",
       "0     7  [6290855005, 3977333714]         5   5   0.060606     1.0  0.983333\n",
       "0     7  [6290855005, 3977333714]         5  10   0.105263     1.0  0.990860\n",
       "0     7  [6290855005, 3977333714]         5  20   1.000000     1.0  1.000000\n",
       "0     7  [6290855005, 3977333714]         5  30   1.000000     1.0  1.000000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_df.head(10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9e3de229",
   "metadata": {},
   "outputs": [],
   "source": [
    "res_df.fillna(0)\n",
    "width = 0.8\n",
    "num_trials = 8\n",
    "my_xs = [1,5,10,20,30]\n",
    "ths = [0,2,5]\n",
    "my_metrics = ['Precision', 'Recall', 'Accuracy']\n",
    "my_color_scheme =  ['red', 'orange','yellow', 'gold']\n",
    "\n",
    "results0_list = [[] for ele in range(num_trials)]\n",
    "results2_list = [[] for ele in range(num_trials)]\n",
    "results5_list = [[] for ele in range(num_trials)]\n",
    "\n",
    "for i in range(num_trials):\n",
    "    temp_df = res_df.loc[res_df['Trial'] == i]\n",
    "    \n",
    "    results0_list[i] = [[], [], []] \n",
    "    results2_list[i] = [[], [], []] \n",
    "    results5_list[i] = [[], [], []]\n",
    "    \n",
    "    for m, metric in enumerate(my_metrics):\n",
    "        for j, th in enumerate(ths):\n",
    "            given_thresh_df = temp_df.loc[temp_df['Threshold'] == th]\n",
    "            if th==0:\n",
    "                results0_list[i][m].extend(list(given_thresh_df[metric]))\n",
    "            elif th==2:\n",
    "                results2_list[i][m].extend(list(given_thresh_df[metric]))\n",
    "            elif th==5: \n",
    "                results5_list[i][m].extend(list(given_thresh_df[metric]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2aa297ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "my_color_vec = ['blue', 'orange', 'green', 'red', 'purple', 'brown', 'pink', 'gray']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "658253e0",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 864x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "plt.figure(figsize=(12,8))\n",
    "for i in range(num_trials):\n",
    "    ax.plot(my_xs, results2_list[i][0], linewidth=3, color=my_color_vec[i], label=\"Trial \" + str(i+1))\n",
    "    \n",
    "    ax.fill_between(my_xs, results0_list[i][0], results5_list[i][0], alpha=0.5)\n",
    "\n",
    "ax.set_title('Precision As a Function of Vector Size and Trial')\n",
    "ax.set_ylabel('Precision')\n",
    "ax.set_xlabel('K [Values in Vector]')\n",
    "ax.legend([\"Trial 1\", \"Trial 2\", \"Trial 3\", \"Trial 4\", \"Trial 5\", \"Trial 6\", \"Trial 7\", \"Trial 8\"], loc='lower right')\n",
    "ax.grid(visible=True, which='both')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ca68864e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 864x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "plt.figure(figsize=(12,8))\n",
    "for i in range(num_trials):\n",
    "    ax.plot(my_xs, results2_list[i][1], linewidth=3, color=my_color_vec[i], label=\"Trial \" + str(i+1))\n",
    "    \n",
    "    ax.fill_between(my_xs, results5_list[i][1], results0_list[i][1])\n",
    "\n",
    "ax.set_title('Recall As a Function of Vector Size and Trial')\n",
    "ax.set_ylabel('Recall')\n",
    "ax.set_xlabel('K [Values in Vector]')\n",
    "ax.legend([\"Trial 1\", \"Trial 2\", \"Trial 3\", \"Trial 4\", \"Trial 5\", \"Trial 6\", \"Trial 7\", \"Trial 8\"], loc='lower left')\n",
    "ax.grid(visible=True, which='both')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "20ebe4e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Fz+aVXGpjs7pJv87Hd3kxVnPOz+ozUs4fKuKVdaVMHHaxtKXtZ0XkD4If0RNB2Z4qd7XS9uD7prsikjDGlLtVbKm0QZW8Nlj+sjHmS8UVIhLDt0qVs5rxg2/9/hV84f0h/JuHLPCBFfZzW9H7g1W0LbnZMsYMAP8avBCR5+OLoVuCMS0542tghd+PH1T2WmNM+aQyi6Z3WyNSwTKxSP3ZXA/l5M71DhFxz7E1/jT+NbUdP8tKOduDZd85HAP4N7Q7g/31nsdxfBw/wPDa4LfoEP5TwfLc8CP4YiiEH59TrYhZ7fd2MXJW4O1LtDlf56yYb+GL+Kqs4QH/iO9u9B3gLYu0ORUsZ9fwaVnu9/f1xphHy+rW+nekF9/daBuVz8e2Nd7fWnE+PyPlLNA88cp6c1uw/IQxRhZ74T8+Ls4Zfz/+H9bdInLdwm4X8B18UX4ocCmphtyPbqU/wC+pUFYtjcHyVIW6N1DZ2vbfwfLVwSPoqjDGPI4fXHYlfiBfG/AlY0ylfVdESnPDv3yJc3QoaLPsBF3GmLsJBD1wWRXDyH1m/RUEPMDrq+jjbDiDf/00SeU8/mdzPZQQ3PA8gu+D+lNVbpa7yVipYSbnZ/zGRepzT1y+u0j9WrHcOHL53Nd0HGZhzvjc8d5e1i6DnwrUxk8FWS257+0bA8vzcix3Hu/Hj+HYKSLXlFcGOdMvC/pZlR90hT6rsf7nhN1ibmPlff42/lPBJ4FXL3ajaozpw/8udAc3/mvBcr+/a0nuev2Z8orAov3a8vINwvn8jJSzQEW8sm6UicMFgT9lfCRY3gYQ/Oj/dW5bEbmkrG9HRF6eWzfGDALvxw9u/UwQTFXcPioi5ZNKfSdY/kHxH+DAdeO3lhnvUuQe+/5K8R/I4A/wX1XawBjzAL7vaBPwCfGnhC8ef72ILBZ8+I/B8reD5UoDWm/BtzQPEwT6LcL38H/08zdbInKziLysXMAEf8Bywrcan81n8F03LhGR64v6EfEnjLp+0S3XgMDVKWepfkdxnYj8LGv/h+1Pg+V7ROSG4orgmF8iIsX+tznr9d4V7ucD+P68LxWRny+uEJE34LtHzLPya2al/AP+E6KfF5GXlo3jd/FTUA6z/O/Earg9WP4y8AL8460UUPxn+NlZ3isiryqvDCbIeaWIXFtU/Hn8FJN7gX8tnyRHRNrLxOkwvgBvE5GG8n0Yf0bU3FOrfyy+oQza54J+P1gUqHu21Ik/sdHrpfJEUtdT+E4sG4gt/qR6f4N/rK+oYpx/FCw/Wuk3TvzJ5N4QJAqohtzv76+W9fMi/AxDa8k/4f9u/aKUTnhl4f/9WrMYjzXmfH5Gytmw3ulx9PXcfVHIDT8ChJZpuzdom09jiG+xvj0oz1CYIOZb+Dmlx8v6iOD7TedSrX0raP9dKk/2tIdCOrdn8P0378X/Uf4rlk4xeXyJY7kO/w+1wf+x/Bh+Ks1MMJ7jFKU3LNqumUK6tWngq/guKT+kbLKnsu0cfHGdSwe3YGKhZT77XPrP91TR9m+Cth8N1n8zWB/Dt2R+GF/Y5Ca2eooKcwAs0vc/FZ3rbwbH/lSw/v9Yeaq0Q8udq7L2N1KY6OdHwfXwo6Cs4v5ZJoUbhdR/t1eo+1MKKeLuC66Nr1E22VPQ9nJ8EZwNrvF/w3/Scd1y1yV+tpHc9Xh/cI5y+cYzVEhzudg1WlS/4lRz+BmUcpM9fT8YRy7V5gxwS4VtbmeVKSaL+ijOGZ+/dhdp+z/wRb7Bn2jqi/ixGndRyPn/y2Xb7MQPNjX4vt1fwJ+s6O7gc393WfvP5M5V8Bn8K/DXRfXlkz19Fj9IfDQoW2qypwXXWRWfT33RZzOL/zv7sWC/jxTVfYey/PNUnuwpN/HVvSw+6dMvlfXzO/jXtsH30/5s8LqPwm/0rVUez08XjfkB/N+RO4L13O/6gmt3qWuapb9fxRNVfQf/e/w0hZvjVZ2XCt/FQ2Xlh1jmN3GxNufiM9LXuXmt+wD09dx94aeTM8D7qmz/QNC+PGf8q/HFzRl8cX4aX8j8fIU+bHwf7O/iC8t5fL/Vz1Jhpkf89GZfw58QZyb4IXvlYj/aS/2Yl7W7El+EDwX9PowveC2WEEj4NyK/hS8AJvFvao7h/7E/VMVn/TsrPEfFueGvraL9ZRTdbOE/Zv+T4I/XqeDzHsL/A/7/UTZ5zzJ9W/iWoYeDz2wE+BL+RDvL/sGq0F9umyXPVdk2t+ALqJng8/8mvvW24v6XGxfLiCv89G6fxZ8NMoWfVei7wbVSPhHLa/HF5BSFP8C3VXNd4t8EfCzYTxo/9uBTFE1qU9Z+0Ws0qF/VH/Pg8/oi/nc5hf9dvp2iCZTK2t9efJyrfeGnSMx9ZgtmHi5ruxv/hvKp4LsxjX+T/wX8+QoWTDoE1OFblHPX7nSw/QeAg2Vtm/CF+ykKN43Hy9qE8S2iDwT9zQZ9/x6VZ9Jc8jpb5ngFeD6+GP0G/s3LDP5vbS/+E8KfBawK2x5moYjPXTtLvRaME/+3+HZ818p5/BuYJ/FviP4HK5jACj9N7ffw/wZM4X9vfm6pa3epa5rlv18/jf+bPRvs8yv4aZJXfV4qfJ6HysoPsUoRfy4+I32dm1duZkxFUS5QgnRhffhBeVuMMcPrPCRFURRFUc4S9YlXlAuf/4mf4/qjKuAVRVEU5cJALfGKcgEiInvwA1m78QNI54BLzPmbrVZRFEVRlHOI5olXlAuTDuAX8X1H7wN+VwW8oiiKolw4qCVeURRFURRFUTYZ6hOvKIqiKIqiKJsMdadZIc3NzWbbtm3LtpuZmSEejy/bTlk/9BxtfPQcbXz0HG189BxtfPQcbXzW6xzdf//9Z4wxLZXqVMSvkG3btnHfffct2+7w4cMcOnTo3A9IWTV6jjY+eo42PnqONj56jjY+eo42Put1jkTkxGJ16k6jKIqiKIqiKJsMFfGKoiiKoiiKsslQEa8oiqIoiqIomwwV8YqiKIqiKIqyyVARryiKoiiKoiibDBXxiqIoiqIoirLJUBGvKIqiKIqiKJsMFfGKoiiKoiiKsslQEa8oiqIoiqIomwwV8YqiKIqiKIqyyVARryiKoiiKoiibDBXxiqIoiqIoirLJWFcRLyL/W0Q+KSLHRMSIyPFV9vNyEblDRGZEZDToc/sibetE5D0i0isi8yLymIj8iojIWR2MoiiKoiiKopwnnHXe/18Co8ADQP1qOhCRVwOfAh4GfhuoA34T+KGIXGWM6StqGwK+ARwE3gM8AbwMeC/QBvzx6g5DURRFURRFUc4f6y3idxpjjgGIyKNAYiUbi4iLL8ZPAdcbY6aD8q8C9+OL8rcUbfJLwNXArxtj3hOUfUBEPg38voh80Bhz4iyOR1EURVEURVHOOevqTpMT8GfBjUAn8K85AR/0+xBwGHhdIPRz/AwwC3ygrJ93Ay7wurMcj6IoiqIoiqKcc9bbEn+2XB0s76xQdxdwM3AR8JiIWMAVwAPGmPmytvcAXlF/iqKsJcaD9BSkxyE1gUmNkRobZbp3lOmBcWYGppkemmN6KMX0mQwTAzAxZJHJCA/z+fUevbIEBvQcrTMekMYj1m4R74kQagtDQ4h03GHOBg/DY3fevd7DVJZBz9HGZ8fMLD2vePl6DyPPZhfxncGyt0JdrqwLeAxoAKKV2hpjkiIyErRVngNkZmfJzs8z9vhj6z2UzYHJItlpJDuJZKfK3k8F76fJzE4zO5JkdiTNzIjHzLgwM2ozMxZiZiLO9ESC6fEEM5Nx0slQ2U6iwUtRlIo4hoaLoGaXjdtpQ6NNKirM2IbZTIoRz2Mk3zgN2TRk13G8inKBkZmdXe8hlLDZRXwsWCYr1M2XtVmqba59rFKFiLyFwLe+ra2Nw4cPLzuw6enpqtop60N2fo6ZuSR3Pvrkeg/lvCAmjcssDrM4zOCYWVxm8i/H+HUuM7hmBocZ3GBdMvOkJq2CAJ9I5N9PTySKxHk3ybnIeh+qomxq7Kih4WIhvtPCaXcwDTbJsDAjWSYzKcaMYQyPnP2dLCrUFeU8ceLEcU5uIG232UV87pYoXKEuUtZmqba59hVvsYwx7wfeD3DVVVeZQ4cOLTuww4cPU007ZX049a1v88jkLM2DM+d1vyb4z2Awxl83wTpl67l6jEFIY8sstszgWLPYMocjszjWbLCcC97P4Vp+uWvl3s/hWKmScXieMDsV8wV5IManJ+KBSG8O1v26uemK97Znje1midYlidSkCddkSTkhjo21M2kSTHh1zIdruellA6Rra8/J/jccxl9IpTJj8utSVCkLyvy2lfsoalPWR76ucIGWbCNF25T3bbmTeKnakrFIUX352MqRXPnCqiWxBBAJ9ieIBH0FOxTx2+SyBwuSr5egsSWSLydon+tbKHRm5frPbbfggyglbdKMZScYNWOMZicY9cYY9cb9V3acaCrJAWllR7SLRKwJIjXMuy5TxmMqneIMcAYPCL63meo+k4jtUGc7xLMe4fkk9uwsMpdi1nIIp9NkjcEAngcenv8+eBlj/CWGrP+L5NcbL/9b5AXlXlDrYTAm19bfHgxGCn3nagxgJNhOcltQ1Jv/DzFFezcYywvaGbC8/FaK4iOIETB+iKcFYGww+W88uW9/8T+wsPJfYhuL3FfaL/d/UwTEX7cQslmPlrf/Jpcc3H/ej3IxNruIz6WP7MJPF1lMzjUm5z4zBsxRwWVGRMJAE/DdczBGZQMyNTSGsSHjmVLRHIiYgsA2RcLaXye/XirEF93OGCxJ4sosju0La19gB++DspA1h2PNESpp4y9ta/m/4sbA/EyU6bE4E+MJpieamJ7YWiLUc1bzmYk4xqx9XLtYhlCNRyhhcGosnLiNlXCxYiGcGgcnYeHELZyEhRWWvMC644kd/P0nbyGb9cdU35zml//oFJ3NNoPuVr/vnCA0RUI1JxqLxO7CclNUX9THisqLxG6Fvkv3aSr0uUx5mdjdDFiBCJ5tGKBmogNLJF8mApYl+TIpqsvXFy2toqWUtcn3aRW9pyDOzzfT2Rn6kv30pQYKr2Q/falB+lL99CX7OZMa5YpUB7dk9rLD2crWWAsm3Mms7TLpZZnNpAEYCl6YLKSqM6fHHZc6yyaeyRBLJalNp2gUi4ZEgljxDW9dDQDiOjzc3Mzlk1NgFYQJluWvW1awvoL3luXfQOXfW4gly75ffPvF+0JY0M5DSGUNyUyW+YxHMuuRzGRJBu9TGY9kOkMqnSGZyZBMZ0hn/Pepslc6kyGVLVpWeGW8DJlsloyXIe3569lgmfEyZE1hudjLw38V3mcxQZknGbJeCrE9PMlgyl5IBmP5S6zcy1vbC1upipvC/2e9h1DCZhfx9wbLa4FvltVdA0wCTwMYYzwReQA4KCJhY0yxW83z8G/g7jvH41U2ACaTZWRoinRrgv6J8hjnSng4kiwR2649h2uXCu3ceqhCmSWr+8E1BlLzIcYnGkut5uO+5Tzv2hKUeVl7VftZEiEvvPPLhB0sS9ftiC+2VtA13zh6Ge/7xDUYz9+uqSPF//fHJ9hvZjCpLBf1bywfxI2AkBPJ5WK4VDTLIgK54nZWaXup0K5YWOduO+6bH+ZgT8N6fhxrgjGGscw4val++lOD9CX76U0N0Jfqpz81QO98H72pAaY9/+mdZOHm2R28MLuHa0NbiEauJhNKMGvZTLhpkpYvygeDF9ms/1oGAWrcELVYxDNpovNJ3OQcES9LQ20trbEoIaeQdE1CIezaGqyaWn+ZqClZl0gE+7vfpem1r137D22dsPHTycXXeyBryEqf3meyHql0lvlUhvl0hmQqQzKdJZn2b1pSwc1L+Xr5TUz+ZiZbWJbcwGSz+RuX/A1NhVfWy5DJ3bQU39DkblyC957J5m9oci8jC5cLbmSswhIrA3b63J2MJQi7G0s2b6zRLIGIdOBP5HTSGJP7q/5doB/4JRF5V1Ge+APAIeCDxpjiM/1R4AX4/u3vKSr/TfwHlp84l8egbAzmhgYIm2foMePYdcMLxfgCa/gcUuyLsAakU06RKI+X+ZgnlgkAXRvsqFQQ46UC3U1Y2DFrRcK8WkzU4TNP38BHPrQ7X9bWneRP/9cRtpokZBYPYFlvViyQhRJLcrkgrtZSXSzOlerJmixD6TOlFvRkP73JPk7N9dKXGmAoc4YkpW5n0YzDy2f38CJ20e5eRijSQMqNMWMJE5k0mRr/5jyfLSGTBpYXF7YItW6IWgOxdJrw3Dx2ao5UNstUyMV1hHrboSUcpbWjhZrmVuzaGuzaWuyaGqzcsqYGCYf1engO4tgWjm0Ri7jLN75AyWQ95lMZkqms/8Qld0OTv6kJXsU3MOllnsjkblwqPJEZGOqnu7luvQ+7hHUV8SLys8DWYLUFCInIHwbrJ4wx/1nU/K+ANwE34eeAxxiTFpHfAD4OfF9EPgDUAr8FDAN/VLbLDwA/D/ydiGzDd8F5OfCTwJ8bY55d0wNUNiTZJ/6R57X+tb/Ssob9ZixmJuMVA0CnJmqYGq9lZiLBzESM1Ny5EeZWWApiPL7QYu7mLOZxC8tZpz/8Fsx2tvOpu67hC//ali/u2TrH3/zaMzREl3YdElgomq1qrdGVhHGF7aylRHluFMpGIOkl6U8N5V1ZepP9nJw5wam5XvrTgwxmhjljxvGo/DSsJRXjFXN7uUReQEu4EytcT8qNMCUwKSm8WkMKOJnbIJOq2E85rmVT57jUeIZYKklofh6ZT5KULJPhEMa2yALTlkU6bFPb0ER3axudW7fR2N1DqLERq7YWK3RufisUZbPj2BaJaIjEeUpqdvjwYVobNtbzn/W2xP8i/oRNxfxZsPwu8J8sgzHmkyIyB/wh8Lf4xrtvAb9rjOkta5sSkRcBfw68Ad8P/ijwduCfzuI4lE2Ee+aLVbf1PGFuKsb4WAPjY01MjtUzNVHnW8zH48xORpmdjDA/GSI1c26+TuJQ0WLulpfFbazQxhaXmfoYQ1v385XP9PC1DzXnyy/aPsNfvO0INfGCu0FrTZiRkMNlPfULxLSK6OcGU9lp+pMDnE72cXz6OKdmTnJqPifORxg2o0wwtWw/O+cauDW5lz32durD7Ui4ljk3xJQxTJGCEEzjvwBIV/cMKBdImsh6xJJJ3Pl5THqeOTymImHEskhbNpOOjSTCUBdDbIdoyCUWj9G+ZQtdO3bR1NVNNFGz6s9JUZTnJusq4o0xh1bQ9jbgtkXqvgR8qcp+xoG3BS/luYaXJpR5hnTKZfxMHU/3viCwkMeYm4wyNxliftIlOWWTmrJIz7DirBlVYVEixu2477riJCycGrvI97w0AHTTErIY37qdiaYevvzvzXz74435qkt3T/HHv3qUeLRgKQ07FlubYoymIOycAz9/ZV0xxnAmdYaT0yc4OnWMk7On6Jvvoz8zzGD2DMNmlBEzzqxUE7MCeHDFXBBIaveQiLRiQjXMOi5TXpaZaBqiMIr/8oNNqhPqhUDSLNH5eZz5ebKZJLO2xVwkDLZFyrZJh2yIJhCnDrFtXNtBbBsswbUsoiGXxqZmOrdupaVrCzXNzViWXtuKoqye9bbEK8p5JTt0Pyce6+Lj73o9qfnFso2uklwAaLyyf3lx2UoDQDcrIjDf2shwzz4ylsvn3tvCDz5fCIK8Yt8k7/jlo0RCpXdK25vj2NbaZ89Rzj2z89McnzjGienjnJw9TW+qn4H0MIPeGYbNGCOMM8oEaVkm41L518ODW2b8QNLuUDfRSDOZUJxZy2Eym2E+yOCUDyT1qsv4UhpImiE6P489P086k2batUjFIojtkLRtkokQUhdFbAccG9e2Czkti7AtIeq61MTjdHT30LplC/XtHYQiOpmZoihrh4p45TlF6tn/5q6vXrsiAe8HgJYJ8nhgMS/O2BKzEPvCF+bV4sVDnNmxl/lEI14WPvGuVu75en2+/prLxvn9Nz9LyC0V8C2JEPUx9QPeSKQzSWamJxifPsOz0yfpne+jLz3AQGaYobp5hkdPB+J8knGZwlssG9MyX49oxuHW2Yu4mt20u12EIo2kQzGmRZjMpEkvCCTNUE0SdUuEunwgacYPJE3Ok8xmmYyFMSaLODZzrs18JI44tWDbvkW9iqdglggR1yEWDtPa1k5bTw8N7Z3E6xs2/1M0RVE2LCrilecU3uBdnD5yaX49usXFrakcAGoHAn3dAkA3K7YwtaWLsbadYFlkM/CJv2rj3u8XovpvvGqU3/7545R7yri2sLVpYwUOXahksxlmZyaZmZ1gdnaaM3Nn6J3voz89xEB2mCFvlDOMc4ZxRmWCMZlkSipMjjZOMMNKdTSlorxibi8HZTfN4Q6scD3zbphpgYlKgaTpagNJLeqcUBBImiI8Nw+pNEkMk7GoP5mRazMbc5irqQO7CbEt7FWIbAEiru/bXl9fT/uWrTR2dFLb0objPnezhSiKcn5REa88p5g8doK56ecD4CRgxy82qqVsDUk31TC8dT+ZcOA2MGv4xJ+2c+8DBQH/kuvO8BtvPIldQfhta4rjVKpQqsLzsszNTTMzPc7M7BRz8zPMzc0xnB5hIDvEUHaEITPKMGOMMpEX52MyyZwU+YgLfjLuVXLJfAevmLuE3U4P8XAzXjjBbDAj6WQQSDqB/wJWFUgaTaUIzSUhlWbWspiKx8CkSTsWUzURZhprfJ90WZs/dGHHJhpySUSjtHZ109zRRV17hwakKoqybqiIV547pMYZeqZwycd2xlTArxEm4jC2fRfT9R0AiGeoGczwz3/Tzf2PFwT8K28c4lded5pK7u4NMZemhLrRlON5WVLJOaamx5mdnWZ+fpr5+TnmU/PMpecYzo4w6I0ybEZLrOZjMhm8nyKT8z8/yzhKMUK9qaHB1PC8mW6en91GR7QTy46RCseYcWwmvSwzkTRE4HRuwxXOSFpr2SQyWaLJJO5cCi+TZcYJMZtwMVmPeVdI1sawm2qxAqG+1leOa/vBqFHXpbmllebOburbOzQgVVGUDYOKeOU5Q/rUd+g92p1fj+5Wt42zxoK5jlbOdO/BWA4YQ+1Mlpq+NH/+9zt45JmClfKnXjLAL/xkX6U4QGxL2N4c57mUOjKZnGNmZoLZ2UlfnCdnmU/Ok0onSWfTzGRmGTZjDDGWF+ajMsl4XpxPMsE0nu2dtTgHcIxDo6mlIffK1nD1XDs7TRMJtw4vGmcuHGLasvxAUjtTak330pBafqKjfCCpWMTTGaLJJPZ8imzGMBWOkozaGAtmHIfZWhdpsLAcC9u2qvJPXy22ZRF1HWIhl9pEDS1dvmiva2vXgFRFUTYkKuKV5wyZk9/m9JGCiI/virFx5wTd+GRrIwzv2E8qWgtAOJmlfSSFNwL/5x938eSzhZukn31lHz/z8oGKAh5ga2OM0AWQSjIXAOr7mk+TTPnCPJn2s52kM2nSXoYpb44zMlZmMZ8sWZ8OzS6/wyqJmjANpo4GU0ODqaPR1FJvamnO1LB3roYWK4aEI6QiEWYiLtMiTGRSpMMeJTPgVTnRUS6QtMZAPJ0hPJ/ETqZIecJULE7GOBiBKTvEVCKE1AliC5Zj4dpWxYwva40lQjTwa4+FwzS1tdPQ3kG9BqQqirJJUBGvPGdI9z/A4Mlb8uuxnVHoUxG/UoxrMdmzlYnWbQBYWUPrWIrGyTTjUw5/8A+7OHoqlm//5tec5jUvHlq0v9qIQ2vtGqf7XEP8ANApZmZ9d5a5+RmSyTmS6WRgNQ+m6jZZ0ibLNLMVhHnw3p5k1JlkXtbuuqsx8bz1vNHUFSzpwXpTKkpbKovjCJlIiGQkzIzrMiWGiXSK3pgpZHvBq9o/PR9IagyxZIpwMgXJNEnLZjKWwBibrAiT4mDiYagBbLBsP1jccuzzKpT9YFSXaMi3ttfVN9DQ3kl9e4cGpCqKsilREa88NzCGkadG8bK+tTfSAnbcpjovXSVHsqWe4W378ZwQGEPDVIbWsRR21jAy7vK/372LkwMF14O3veEkP3bjmUX7swR2tCQ43240lQJAk8l5kql5UplU3mKe8bJkPA8jhixZJplZIM5HZZJxp4L/+VliGYs6k6CRYmHuW9NzYr3e1OLiEErOE8/OYzsWmWiY+XCYaddhynj0p1P0l/SchXR1V/6CQNJkEpPKMueEmYxFEGMT272F8WdOQSyGiRvftScQ6/Y6iPUcAoQch1jIt7bHozHq29r9V3unBqQqirLpURGvPCcwk0foP1KYZCjSFVnH0Ww+vJjLyI6LmatpBiA6n6XjTJJIys/bPTgS4vfevYv+Yf9ztcTwWz93ghdfO7pkv1saYkTctXGjyQWATs9MMDs7xdxcIQA0lUn6wjyb8cW58TBFU/GmSTMmUwVRLpOMWpOM2QXBPs4URtZm+t4F/udFLi45gV5HAqsod2MkOUs8m0Jch3Q0xFw4zLRjc8bLMhN1gERhByYDqepuJsoDSZ1kCpMxTLsRZhNh8GAem3nHBScOcfzPwQJj+0urxsJ2LGSdJ+gK2XYQjOoL99rGZurbOjQgVVGUCxIV8cpzgvSJb9Jb5A8f69YsKFVhC9NdHYx27AbLwsl4tI2mqJsuCMTewTC/9+7dDI/5n6ltGX73F57lhqvGl+w6EXZor1v+ZmqxANB0JpW3mmc835XFY6HInmO+1Pfcnlzofy7nwv8859JSW7ZeR5woUvb0wXhZEsk5ImSQkE0y6jEXcpiyg0DSaIiSHCxVBpIC1BYFkkaSvn961oPJSIyUhCALs9jMhkKlaV7EYCzAJu+zbjt2yWzDYglOaH3+lDhWIYNMNOQQi8Wpb/ODUXWGVEVRLnRUxCvPCbJ93y8Jao12q//rcqQb4gxvv4RMOIZ4hqaxFM0TaSyvIJSP90X43+/ezdik/3m6jscfvOVZrrlsYrFuAd/VYXuLH/iamp8hOTdNem6G9PwsydlpzkyNciY5jb1vC1955N6KfRgMU8wWsrVYZS4uQTaXc+F/Xu57niurN7VEWdy/X7IZalKzhGQCEw6RjESYCblMW8JExiMdL7upyaaoxudrQSBpMomVTJNGmIgmyIqLAaaxmQ6HWThEgwncYHJi3bL910bCFiESiPZYyCHkuNS2tOZdZDQgVVGU5xIq4pXnBDPPPsHk6B4ALNcQadVLfzFM2GZs2w6mG7vBGBIzGdpHU4TSXkm7Z05G+YO/383kjP9ZhkNZ/uhXjnHF3qmizgwmMwepWUjPQXoeycyTkAyDAxm8ZBKMf1OQNFlGyDBhMmTEMM0sU/NJeq3HGZWJvCjPB4musf95vamhgVJBXuzykvM/Xw47nSaRniFkW0WBpA5TAhPpLJ6JFbX2IFPdTYZjWdQ7IRKeIZ5OE5pPIukM82IzGU9gcMgCk+JAJAIVH3KUiXXbT99oWdaGzO5piRB2HWKuSzTk+hMuJWqob++krq2dutZ2DUhVFOU5iyoZ5cInm2TwqYLbQazLRuwNqFjWG4G59mbObLkYY7uEUh7tI0kScwtNwY8fi/N/3rOTmTn/JyQazvCnP38nlzT3Qt88VjqJZFJYmRRiSl1cLBFCIRtPBA/DJFlGTZpZshggLRk+7XyDbzh34p32KliNV4ZrnAXpFRvKXFzK/c+X7TOZpCY7j+UI2UiYuUCoTxqPybRQ4p++wkDSWtuhJggkdZMpSGeYdUJMxRKIsUkD45YDsaVcRQKxbvliXRzBduwNK9ZzCBB2fH/2aMgl4jq4boja1jYNSFUURSlDRbxyweMN3Uffkc78eqRL/WTL8RJhhnbsIxWvx/IMLSNJmibTkM1iMvOBJX0WSc/z8DP1/NHHXsR82v/5qAnP8Tc/+WkujgzAyPL7irgWKTzGTIZxyZAuEvm9MsT7Qp/glDVQ1bijJlLme14Q5zmXl0r+59UQSc4Sy6awygJJJ70sM1EbKJosbJWBpJFUCnc+hZf1mHEjzMYrBJIGLHYExjJ5y3reb/085VpfC3LBqLFAtNuWRby+gfq2Turb26lpbtGAVEVRlAqoiFcueNInvlHiDx/boo/fc3i2Yby5loloGBl4mPrJJB0zYdxUBiuTwsqUBk7e/ex2/uhLLyKV9T/DhtgM//cnP8nOlsXTSOYwGFKuYdTKMG18q3suDtVg+I59Dx9zv0q6yEWmy+2kZX6h73nOvWUp//NlxxMEkkbJQMglGQ0zFwqtWSBpjVgk0hkiyVQQSGoWBpK6IajyctzsYj2HY1nEAkt71HVxbAs3HNGAVEVRlBWiIl654En33Unfs1fn16Ndzx0R73ke2WyGbCZNNpvFy2bxMhnIZvG8M2TNENLr0SJxWpytRK0aYL5iX987sps//8qPkfF8q2hTfIp3vuaT9DQunUYyLYYpK8u0lfUznZQlkJlgmg+GPsvD9lP5Msc4vC59K6/a+TNMP3ps1cdfCCT1gkDSMDOhUBBIatYgkFSIp9N+IGkqTdoUAklhqUDSxTGWn7ZxPSdGWktskbx7TNR1cW0Ly7KpaWqmvr2D+rYO4g2Nm/b4FEVR1gsV8coFz+hTvWRS1wEQrje4tRfOo3nP88hk0mSzGbxMFi9Yks29KuU1n8GzB/GsFI44NDlbqbValhRR33xyL3/99ZfhGd9vvL12gr99zSforKuchcZgmBGPaTvLrPgBsZYsdGp52HqKfw99hkmZyZdt8dp5a+qn6DJtVQk7O5MikZrFtS2yywaSmpUHkhpDPLVUIKm9RCDp4hgpnRhps4v1HJYIkSBPe9T1g1FFhEg8oQGpiqIoa4iKeOXCZn6YwWcKj+YjXWcZJXme8bwsmUwmsKZnMNmsL9IzWUw2i3grmXwoi7EGyFhTiAj1djuNdhe2LP0z8KVHLuVd33oJJpDg3Q2jvPPVn6ClZnpB2zSGKdu3umeKTO5CqddHijQfd7/Gt527S7Z/SeY6Xpt+MW6Zj8lSgaRTaQuzykDSsG1TZ7skPI9Y8mwCSRcnNzESNogjwUym6z8x0loh4It21/drD7sOlgh2Lv1jYG2PJGo2/Q2KoijKRkJFvHJBk+39bskkT9HuDSTijSGbd3fJBNZ0X6iT8ZYV6SuTQ2Nk7GGwPGJSS4uzlbAVW3arTz94Bf/03Zvz69ubhvl/r/4kjfHC5EgGw6xlmLIyzFkepsKQraLJgU5KP+8LfZI+ayhfVm9q+KXUazg420XD0Cks12Js5zZGTp5gskbOWSDpTCyMeEISm2SVgaSLUsXESBcKIcfOp31MOg7tDXUApQGpTS1Y9oXz1EtRFGWjoSJeuaBJn/x2aVDr+ZzkKS/S02QyvkD3sllMzoqe9WDNRPpiJPHsfjxrHocQzc5WauzGqrb88D3P59/uuD6/vqdtgL9+1aeoi/o+8wWru0eGIId8JQEfWF89PL7h3MmnnP8ma7LsGIXLB+DW/mZu6a+nZvKrPH7llTz0/OczjlmRUM8FksbTGaK5QFJjmAwvHUi6us94c0yMtJa4thXMiloIRgVwQmHGnDC7Dl6uAamKoijnGRXxygXN3ImHGOl/KQBiGyIdayjijSEbuLt4gTU9L9IzWch6C3Kk5zj3tlkPI8NkrHEsS2i0u2iwO7FkeaFpDPz7HS/kw/deky+7pPM0f/kTnyEeTjJj+YGqvtV9aXceAZxMCnv4WZ4e/RovHBrmbQNw2SDUpHzNf7Inxj0vfB5P7b6IrEClO4HiQNJYOk0kmcJKpUh7FhOxeD6QdAabmRUGki7xSWyqiZHWEtuSvHtMTrSLCCLWgoDU7373u7Ru27HeQ1YURXnOoSJeuXAxHsNPTOZXY+2C5a5AfeVFeppsUdCoCYS6ZLMVLc+w3hpviqw9iLEy1FiNNDs9uFKdqjUG/um7N/GZh67Ml12x5QR/9OOfJR2Z57TtW92NYeGxG0NsZorGoQEah/ppGh6gcWiQ2rEzWGVify4a5a5rDnDf1Vcz0tS0YBwh22GnG6ZmZobpmVRJIOmU2EytIpB0iaPelBMjrSWWCNGiYNRQUYBtJJ6grq2D+vYODUhVFEXZQKiIVy5YzPgT9B1pya+HyyZ5MphAoGdKsrv47i7eBhbpi5EOAldnCFtRWpxdxKy6qrf2DLz7Wy/mS48eyJddvf0ob3vVpxlx03jG5D8PyWapHx2mcWiApqEBGof9V3R2ZpHe/U1PbdnC/VddxeP79pGpIAY7a+rYHYoycWaMYQ+svRcz8cypqo9hOS6UXOtnSz4YNRDtEdfJi3YNSFUURdkcqIhXLlgyp75N75Gu/HpsS2HinuT8HOFUmpn+wYrbbj7JMkLGPoNlWbQ6W6mzqkvPmCPrCX/z37fyzSf358uu3vMEv/Rjn0NS07T2DQSWdd/K3jAyhJ2tLgPMdDTCt649wBMHriRZ17qgPmTbXNraxU4nxIlTpzlCGqyz+2lSsb6QsGPnRXvUdUuCjeP1DdS1ddDQ3qEBqYqiKJsEFfHKBUvm9Pc5fbTgqxstCmpNJ5Nr4za97szi2QN4Vopaq4VmZwu2rMzdIZ21+POvvoIfHNnNDo5ygIf5sZavc3PmezR/oJ/EZOVc8JVIuSGOtkX5dvsEj+/qJrnlKtpj+3ErjKkjUcuVnVvpEZtHnn6aHxpAViYeL7SJkdYS17aJhhxirksk5OIUpbR0QuG8pV0DUhVFUTYnKuKVC5axp48yP+Nblt24R6ihIBCzqdR6DWuN8DAyQMaeJGolaHF2E7ESy28WYKVTxEcGCA0O8OR9If5y6jMc4GFqCHK/DwevJZiurWOktYPRlnZGWts51hrnPW3fI+Y0cCVX0kbbgm1cy+bStk6u7OihNpvlwcce4QsZgxFZ8vHHhTox0lriWBbRkJPPIuMWWdPzAamBaNcZUhVFUTY/KuKVC5PMLENPePnVaFeoIFqMwaTS6zSwNUAmyFhD2JZFu7ODWrtl8bbGEJqdIjHUR+JMH4nhPhLD/cTGh/OZcy5bZndZ22asqZXR1nZGWzsYaWlntLWdVGC9NRgeco4wEBrjJ+SnF0zUBNCeqOXKjh4ubevEm5vj4Uce5vFkGs9a3MXFczzEBrfBuWAmRlpLLJHAPcb3bQ/ZpTc1GpCqKIpyYaMiXrkgMcP30Hus4A8f6S6kMkmlUyuc6XSjkMKz+jH2PA3BbKtWkfuJZLPExoZJDPeSONMfCPY+QnOLB5uWMxeNMVpkXR9tbWe8sQVTwUfawzDhpBkITdMiOyi/lXAtm0taO7mys4fOmjpSc7M8et+9PDI3R8ayYQlhnrEzRGuiIKICPsAPRvWt7LGQQ9hxSkS7ZTvUtbblre0akKooinJhoyJeuSBJnfjmopM8pVPJ9RjSWWCCnO9jxJ1aWuzdxFKQGDiet6wnzvQRHxnAqjLY1EN4mot4iMt5mAMkLoEd148xG69ZMvjTYEhbMO0YZpwsIjY1lGbAaYvXcGVnD5e2dRFxXDLJJI/cfw8PTU2TsmywlvZ7T9kporWRC3Km05UgQNhxfBeZkEvEdfMTZ+XQgFRFUZTnLirilQuS5Mm7GTx5nb8ihmhXQcRnk5vIH95MEZs5QuvYJN2jHg0jj5EY/gzRqbGqu0iHQr5lvaWd3rqtvPuhX+A7EzcwRwyAN9zyNW654j5mqV20Dw/DrGOYcQzpQCcKBQu5DVza3s2VnT101dQjImQzGZ548H4eGBtjznaWFe8ASStJKBHCPsvsNJuVkB1kkAncZOyypxAlAalt7YSisXUaqaIoirLePDf/UioXPGceHcJ4vgCKtoAdKYghL5nakCkk7UyahrFhGkcGaBwdoHHkNI2jo4TSmar7mKupZ7S1neG2Dt8dpqWdybp6EIvRyVre+Yn/weCEP7mSYHjTrV/ihZc+vGh/KcsX7rOOwVT40BK28MLteznQ1k0k8Ln2PI+jjz7M/WfOMGU5YFf3M5OyklhxIexcGHmDqsEPRnWD2VEdnDJLugakKoqiKIuhIl658Jjto/9IIVNLpGiSp2w2g2S9SludP4whOjdN48gQTaMDNI4M0jgySO3k6IKZTRfDs21mGtuYbulksrmDofZ2+tpamY1F/fmYjKG4q6GxBt75if/ByGQ9AJZ4/OIrPs/z9z62sO8KVvdiMiZDe8jixy6+hp7G5hJReeKJx7ivf4Ax215RrveUlSITzVDvNlS9zWbEFiEScom5LtGQg2svzLATjiV8a7sGpCqKoihLoCJeueDI9n+P3iJ/+Gh3YZKnVHL+vI5FvCx14yM0jg7SNDJI4+ggjSNDROerDzZNReNMN3cw3dKZf800tDDnWkzZWWYtg8GfTdVUCNjtG2ninR9/IxMzNQA4doa3vvIzHNz9dOl+lrG6j5oz7A8neP2Bl1Abryndx5GnuffkKYZtG1bol52yUsyH52kKN23GWbaWxBLxZ0YNAlLDFdJiakCqoiiKshpUxCsXHOkT3+bUM1vy67GySZ7OFaHkfCDSB/PL+vFhnCqDTQ0wXd/IbEs30y1dTLf4wj0Vr80Hm2YxTNke03aWjGQDqzuYRSz4JwfbeNenfoap2TgArpPm1171SS7ZfgxY3upujMdR8zQvCLfwOwdeTTRRKt6HTh7nvqNH6bNWLt4B0laKGXeG5kjzBSFcBQgXifaI6ywIRgWI1TVQ364BqYqiKMrqURGvXHBMPfMo0+MvBcAOeYRbC5e5Sa5BfnhjqJkaL4j1QLDXTFc/s2nacRltbGO0qYEzzWHmW7dit12GhBYGKhoM85aXt7r7JSxwmSnnWF8n7/7UG5hN+u5EYTfJr7/m4+zZcnJZq7vtGY57R7g07PIvB36BeG2pm8tofy/3Pvkkp8SqKmC14mcgaSadKZqiTdir7GMjkAtGjQWivTwYFTQgVVEURVl7VMQrFxZelsHHZ/OrsS47n6rQ8zxYQZAoFAebDpZY2UPp6jPcTMdrA8Huv0Ya25iqjWPsQVzbptXdRthaKOqyGKZtX7xnxCxrdS/m6VM9/P2nX0cy7QeJxsJz/MZPfYT2Lb0MLmJ1x0AsKyTTE8Sjw7zzwBuoayqddXXqzDD3PvoIT2YMIcdetftLWtJMuBPUR+oJ2aHlN9hAuLlg1CAg1bEXinYRi0RTEw1tnRqQqiiKopwTVMQrFxYTj9F3pCA8w90FcZxKLe0PH52dzov0ppEBGkaHqJsYqTrYNGtZjDe0MNrYxkhTmy/cG9vyM5v6GJAzYPXREtpCjd1U0ofBMC++eJ+1PQwrE+8Ajz67g/d+7qdIZXw3ou09z/Jzr/kUVu0sYxV0pONBPC1E04Zw9Aw/dsVNNLdtKWkzMz7GQz96mB8lM3iWhWtbqxbwGUkz6U4SD8WJuRvfIm3nZkYNRLtrWxUFeT4gta2DutY2nNDmujlRFEVRNhcq4pULiszpby8+yVORP3xiaowtd/yQ3Xc/GGSIWVmw6Xw46lvWiwT7RH0z3pK+zTMYe5g6t4kGe3vJbKvlVnegKpeZch585iLe98VXI5bHwYMP8ryr76Gjc6DQXw4D0ayQSAshD1rcDM+7fB8d3TtLj3N6ih899AAPzqXys6zaAtYqJ2LKSJoJdxLXCVEbWjwv/XqSD0YNssiEKgSjQhCQ2tJKfXunBqQqiqIo5x0V8coFRerE9+h/dl9+vXiSJy/l+8NvP/oYNx7+XFUWdgNM1jUy0tjOaFMro03tjDa2MhtbembTUrIYa4CI49DiXowr4aDvhVZ3ALMCq3sx9zyxjy/cey0veel/c9llPyISWRjEm7O6xzKCjdBqw8G92+nesaekXWp+jgcevItHp9IkiyZqEvzc5qshIxkm3Eksy6Ix0rBhBG8uGDUWWNrDiwSjQiEgtb6tg9pmDUhVFEVR1g8V8coFxZlHT5JJHwAg0uDh1vgiyxgDgYi/+MkHKgr4fLBpY2vef32soZWMezZuEWNYzhTNoR7iVh1QsLpP21nSJVb31Yl3D8OPBraQbRvll3/l/QsbBFb3eFoIeyAIjWK4ckcPPRfvK2maTaf44cPf4amRDCknsmCiJseSVbnR+AJ+AgQaI41YsrobgbUi7Nh595io62AtcmPihMLUt7X71nYNSFUURVE2ECrilQuH9CQDTxYso5Huwsyf6XQKPIOVzdAy1Jsvf/jAdYw0dzDa2MZkbcMKrOvLMQ/2MPVuE/X2VhCYF4+pcqs7rNhlJkdaDDOuYQKL5p0nFtSXW90BavG4sqeT7fsvRYqEq5fN8sNHv8XhgWdpsndhnIUTDFnCohbqpcgGAt6IoSHcgGuf/8mLXNvKp32MhtxFnyYUB6TWtbeTqG8s+ZwURVEUZaOgIl65YDDDd9N7pCu/HumK5N/n/OGbh/txsn6Gmum2Nu6/+uY1HoWHkWFirkOzuwcRh8kyqzv44t0YU+aovjymKK97KrhfsSnkoc9mLSJpizrj5a3uADE8ruhsY/dlB7CKrOvG87jvie/zrv4vcLm5gUZnd8Uh5d1oVqjhs5JhPBDwCTdB1I0uv9EaYFsW0ZyLTMjFXcLtxQ9Ibae+rVMDUhVFUZRNg4p45YIhc2rxoNZMyhfx7QMn82XDF128xiOYwnWmaQp1I3aMMdtj1k6XWd1X5zKTs7rPLDab6mgDzz6zk1t2P0YinCKntsN4XN7ayMUHrlggTh9/+l7+v1P/TJ3ZwQvkZcgSLi62JSt+SJGVbF7Ah+3IOQ1ktUSIBsGo0ZBLyK4cjAqlAal1be1Ea2o3jH++oiiKolSLinjlgmHmmXsZHbweAMs2RDoKIt4k0wilIv7MnrUS8Wks6wy1oSbscAejdpa0FCaVWq3LjMEwa/viPVXBkJzNWjz55MXcf/+VuGnh7a/+BJGQv18Xj0sb67jk4BW4kVLr97PPPsbvHf8n7jSnuc28gTZpW9h5EZb4aRZXQlayeRcaRxwaIvWrTklZCYF8Bpmo60+ytJQQj9U15H3bNSBVURRFuRBQEa9cGBjDwI9G8quxDrAcX9RlMhkk6yGeR+vgqXybM3suhqdGFnS1ot0yQiQSwonsYNIGQ6aojlWJ9+Ws7nMzMX5457U8+ODlzMwkuGT7EX71NZ8i5GawjWFfXYzLDl5BJFFTsl3f6SP88TP/zL+a73ML1/M/zdtwZemfAN+NZmXBrF4g4D3x/CDa6NkHsgoQchyiIYeY6xJx3SXTXGpAqqIoinKhs64iXvzn978BvBXYBgwDnwDeYYxZNmm3iLjAbwM/C+wApoHDwB8YY56s0P4K4I+AFwJx4Ajwr8B7jDHZ8vbKJmLmBP1HGvKr4a6iSZ6S/iRPxTOtTsdrmG1uWbWI95hFoimcSBspyyF1li4zBsOcbZhexOqOgUhG+MEd1/Llwy/CBOr+4O4necuPfZaQnWFPPMzlBy4n3tBYsumZwdP83yc/wP/zvkI9tbzN/BJ72FXVuHw3muoVvBe40HjiAdAQacCxVvcz41gWjmXRXpsgGnKxlwgw1YBURVEU5bnGelvi3wX8OvBZ4J3A3mD9oIi8yBjjLbah+Mri88DLguV7gBbgV4G7ROQ6Y8zjRe1vAP4bmAD+Af+G4cXBGPYBb1nzo1POG97gD0uCWqNbCv7feX/4/oIrzWB7z4oz0Rgga2fJhGaxIwlCViNeUd1qxHvO6j7rGLwKw7E9iGeEcNLmg1/6Se57qpAS8vl7H+EXXvZ59sQdDl52JbUtrSXbTo4O84+P/TvvSH+GrOVxubmEN5jXEKc6q7TFytxoygV8baiGiBNZZquFRFyHumiERDjEuGWRiIQrttOAVEVRFOW5zLqJeBHZD7wd+Iwx5jVF5c/ii+zXAx9ZooufwBfw7zfGvLVo+/8EHg36eFFR+38APOBaY8yxoOy9IvI+4C0i8iFjzA/O/siU9SB94jucPlo5qNWr4A8/0N5Tdd8GSLmQDiVxQjYRqzmf9WU1LjM5q/uMa0guZnXPQiJjEc5CJuPwz194DT86elG+yfWX3s8fvvLrXHXJJTR2dpdsPjs5zr8/8l/8bvKjzFopQhLi9d6Pc425Kj/uanDs6t1oPLwSAR91IiTcmmW2KiBAIhKmLhoh4lb+WdKAVEVRFEUpsJ6W+Dfg/+1+d1n5B4C/Bt7I0iL+pmD5weJCY8wxEfk+8BIR6THGnBSRBuAA8PUiAZ/jdnwr/M8DKuI3KSOPPE5y9qUAuHEPt95Xx56XRdIZMIa2EhG/dcn+DJC1IBWCtOPh2BniTgILO0gPuUqru2OYdZe2usczgh24yyRTLv/4uZ/iiRM78u3+9NWf4q0/bmjd9pKS7ZOzM3zyR5/i12f/jTFrDizYarZwm/d6Wmhe0VhX4kbj4eV94AFcy6U+3FDVDYBtWdRFw9RGIjj2QvcXy3bo3LFXA1IVRVEUpYz1FPFX41vG7ykuNMbMi8hDQf1S5J6xz1aoy5U9HzhZZdtrltmfslHJphh4PJVfjXW7eQGaClxp6sfPEEnOATAXiTFR30Rdha5yVvdUSMhaYEuSuBPBldiqxHu1Vvd4xiKSpcRSPpcM8Q+ffj3P9PpPDX7skkf4h199gu37dpR0kUkm+c7D3+C26XfTZ02ABWKEl5qbeZm5BZuVCV8BnBUK+Kzlh5RYYtEYaVj2BiDiONTFIsTDoUUmkBJ2P/86Hjt2nG0HrljR+BVFURTlucB6ivhO4IwxJlmhrhe4TkRCxphUhXqAx4LlzcCPcoUiEsMX7wBbguUgcAa4RkSixpi5on5uKmurbDbGf0TfkY78ari7kFIxN8nTUv7wpVZ3wQgIGaKORdjyc5ubFbrMZMQwvUKrezHTcxHe/amf4fhAJzfsfJp3v+URDj5/K378tk82k+HRh+/mLWP/yD3OEd+JHWg0DbzJex072V79gItwq8xGY8oEPPiBrPYigawCxMMh6mMRws7SKSF3Xvk8Wnq2wbHjKxu8oiiKojxHWE8RHwMqCXiA+aI2i4n4/wL+EPhTEZkBvgk0A38SLHPbY4wxIvIu4C+Az4jIO/BF/YuC9plc20qIyFsIAl/b2to4fPjwsgc3PT1dVTvl7Omc+kzJJE+Ja5qwdycAqMnUgmfY8si38vUT111N3Qv3YiUi1Fy/FyOUpHK0MIhYCJKfqKkaPGOYmplmbGKCmblKD32gJh6nobaORCy+qIgdn4jwd399E432JD/4409z7Q1bsKyC+4/neRx/+nHeO/Vt3jXzYTynEP/9wpoX8kstv0DMXn1KxWrdzLOSpYZCMLFt2RVTSQq+24xtCYIwB8wtaFUgFIvxxMnTPHHytH6PNgF6jjY+eo42PnqONj4b8Rytp4ifBVoXqYsUtamIMWZMRF4EfAh4f1HV94C/wRf4k0Xlf40v1P8XBReeaeB/4ov7RT8LY8z7c/u46qqrzKFDhxZrmufw4cNU0045e6Y/+26GTh3wV8QQ9ubIPpPEGMNEbx/ieTQ+8mi+/bOzYUbveIK66/dy+qFCJlJHMkTtELa4K3KZyQS+7jPLWN1jGcGZmccbmi+5MIsZnarh0994Ke9/w7d50Ys7sN1S3/0Tjz/Kt44/wV+GP8ZRu/B0IWLCvM78JFePH2R6fJjpqkdfQICQbS1rhTdBEGuxBT7mRBf4wYccm/pohEQkjOUJLJprqkDP/gN077skv67fo42PnqONj56jjY+eo43PRjxH6yni+4B9IhKu4FLThe9qs5gVHgBjzCP46Sh34bvn9BljjojI/w2aPFnU1gP+UET+CrgUX248HCzfB9y1FgelnH8GHuzDmIMAxFoNdsS3BqfTKcQYaqbGiM9OAZByw4w1tpJ2yNvYLfGI2BYhiVXt8+77usOM663Y130xpiYSXBzv5PvvHcENl2bP6X3mKe47cowvysN8OPol5qXwldlptvFz3utoorG8yxVRzaROvgvNZImAd60QdeF6CDaPhUPURSNEl5lFtZyuPfvp2rt/dYNXFEVRlOcY6yni7wVeAjwP+H6uUEQiwOX4FvWqMMYcwZ+4KcfL8K3wP6zQdoYiwS4ir8XXHl9Z0eiVjUFyhIFnCvnBIyX+8L5XVnFqycH2LRjLImv74jJieYStMCBV+bznre6Owaswl5DtQSzwdXcqTbdaAccYdicauPLQZYTjpW4wQyee5b4nn+ZoZpb/Cn+Re5xH8nWWsXi5eTEvMYewqDCYFWALS86ACv6Ny4Q7ScYqzEpriU1jpAHHsqiJ+iki3VVkkGnfdRE9lx7QlJGKoiiKUiXrKeI/Dvw+8JsUiXjgzfhuLx/OFYhIB1AHnDTGLOpiE7R9O3AJ8CfLzfoqIk3AX+L7x//Lyg9BWW/M8F30FvnDR7oKEwNlU/6DnLb+0vzwBvDsLDZC2Fp+MiKDYd6GaSewupfrzFVY3QEs47G3JsaBgweJ1pbmyhnp7eOBxx7lZCrL0/YJ/jX6KUatiXx9s2niNu8NbFuDeGzBnx11KXwBP1Ei4AHa4k201tRSEw4vexOwGK3bdrL98qtUwCuKoijKClg3EW+MeURE/gl4m4h8Bt8Snpux9buU5oj/K+BN+JlkDucKReQrwDHgcXzviJcArwK+jO/nTlHblwO/DXwDGAC2Ar8ENAA/bow5s9bHqJx7MqcPlwS1VjPJU9YG22JZ15HlrO5WUYaZaq3uAGI8dsfDHLzsAImm0vztY4NnOHznMUadETLi8YXQt/mq+32MFB4TXOtdzWvNKwlTeSbTlbKcG43BMFlugbezbGvooKum9azEd/OWrey88nkq4BVFURRlhaynJR58K/xx/Mwvr8C3iL8HeEfgw74cdwKvA24L1p8Afg14nzEmW9b2OH42nF8HGoN9fQv4c2PMU2dxDMo6Mv7og0yPXw+AHfYIt/iXdCaTRrIesZlJaqfG/TLbYaS5g4wNdoUsKlCd1T0czKa6Equ7v61he8ThiksPUN/WUVI1PTbOv348ybA9wI7OUQZllA9EPslxqzffJmaivMF7DQe5tPp9LoMlLJKnPRhyIODTVhowOE4W28nSkWilu7btrPbd0NHFruddiyzzFEBRFEVRlIWsq4gPhPY7g9dS7W6jINSLy/8M+LMq9/U4cOuKB6lsXIxh4JHx/Gq820ICl45ULj98kRV+qK0bz7bJ2BAVt6Src2V1z42z27W4av8+mrpLA1bnpqb50CenecfnruUtP/FZtrf38z33AT7mfpmUpPPtLjI7+Vnvp2mgfmX7XoK8G82ihxNY4O0kbiDeRaAmVMuWmrNz46lrbWfPtddjWToDq6IoiqKshvW2xCvK6pl6hr5nCu4o4a54/n0mmKm12B9+sG2LP+uq7WGJhTHBbKqOx/xaW90D2my46qLdtO/YVVKempvj058f4Tf/6ybmPZv/9dMfprb1JP8U+TwPWY/l29nG5sfNrdxkXnjWwavl2JYskRPeMBOeQMKzhC0v3y5kh9lZv/Os3F9qmlq4+AU3YK0iAFZRFEVRFB8V8cqmxRu6o8QfPlrkD2+SvhW7xB++w/eHtwQmXY+B48+SiSz02jorq3tAo2W4asc2tuzZW1KeSaX4+tf6+bUP3siJsWYaaib5nTd8iOG2+/k791NMSCGDfLtp5U3e69lSNKHSWmEJ2BWEuAAxcZiKnYHQNHbJNha763fhLjIjazXE6xvZ+8JD2I67fGNFURRFURZFRbyyaUk9e5j+4wW3jliXLww9LwvpDJG5GRrG/XhlTyyGWrvJ2DAfcZgNGciWZloJZyGetoiu0uoOUCseV/Z0sX3fpSW+3l42w4/ufZI3vvtFPDZwHQAtdWO87Q3/wXdaP8O3rO+X9HO9dy0/aV5OiBBrje9GUxrM6iAkJERMHPpjvcy5C6eL2l63g5i7+plgo7V17LvhJpzQ2h+ToiiKojzXUBGvbFoGHzhCNr0dgEhjFifh241z/vBtg6fybYdbOsk6LhkbUm5BXFsG4umzs7oDxPC4srONXZddXuImYjyPZx9/hH//doy/+Mxt+fL2xjO87ufexfvrPkiv9OfLEybO//B+ikspteCvJb4bjX+sYbGpEZcoDgj0RnuZriDgO+KdNEYaVr3PSKKG/TfcjBtePqWnoiiKoijLoyJe2Zxk5hh4vJCAKNpdSLeYzvnDl0zy5OeHzzpZMoE7iIjQPiNYq7S6A4QxHGxtZM+BgwsszKeefJz7nj3Bx+6+hk8cfnG+vKt1gCt//u28N/I50lJ4GrDP7OGN3k9RS82qx7MclvhW+Ji41IhLSAo3HKejp5lypxZsUxeupyuxepeecCzOvhtuJhRdvRVfURRFUZRSVMQrm5OxB+k90plfjXQVZmr1kv4kT+39C/3hPbcgWmORCNZ0alW7d43hsqZa9h+8AjcSLakbOHaE+54+wkAGvnTn9Xz+h4fydd07Hybxhp/hC87j+TLHOPykeQU3mGtX7cZTDbYRGpwwtZa7IMVmb7S3ooCP2BF21O1YIgB2adxIlH033EwknlhdB4qiKIqiVERFvLIp8QZ+UDGo1RiDSWUIpeZpHB30y4DBNt8fPuMUxGs8GgNWJuJt47G/Ls6lB68gkii1mJ85dYL7nniS3pSHQfj0927ia/e8IF/fft1/MP7it3FaCu4qXaaD27zX00H7isaxElxjETMutY5LpEJGmL5oH5Pu5IJyW2x2N+zGWWUaSCcUZt/1NxGtqV3V9oqiKIqiLI6KeGVTMvnYDxgbuhIAy/GItPsiPp1OIsbQNngay/iznI40tZMORUjbhnSZiE8zXtX+xBj2JMIcvPwgsfpS3/DxgX7uf/RRjs9nQAQP4WPfeinffvBqv4EzR/1r38rAxf9Zst1N3gv5cXMrLmufqUUMhHGIGgcXC1uEsFNBwEf6mHAnKvXAjvqdRJzV+bDbjsu+628iXr96P3pFURRFURZHRbyyKel/YDD/Pt4JllM6yVMlf3jP9cgGGl4MRCMRCtMpLYIx7Iq6HLzsMmpbWkuqpkfO8ODDD/PMbBIjli/gPeFD//0KfvDI5X6j9ocIv/6nGK8/kt+u1tTws95Ps5eLVnPoS2IZIRqId7sor3zUtRe4xPRH+pkIVRLw0J3ooj5ct7ox2A57rz9EorFpVdsriqIoirI8KuKVzcfcIP1PF3ysQ52FSZ6ylfzh231/+GyRP3w4y9ITFhlDT9jmqv37aegsDeqcm5zg4Qcf5ImpWTyxIPAvz2Qt/u2rP8G9T+wH8eCadyMv+j2SduFW4TKzn5/xXkOCOGuJYyxixiGCs8CvPmQLtl1aNhAZYDw0XrGvxkgj7fGOVY1DLIuLr7uB2ubW5RsriqIoirJqVMQrm4+Ru+k9WhDWsS1BVhhjMKk0TiZN85m+fP1g+5YF/vDh7OICvsMRrrp4D61bt5eUJ2dnePTBB3h0bJqMSF68A6SyDu/70qt5+OmLoKYXXnUb7PwmJqgPGZfXmFdynXnemgWv+i4zNlHj4mJV7NcSCLulbjSDkUHGQmMV+4w5MbbVbV9VIKuIxZ5rr6e+fXU3AIqiKIqiVI+KeGXTkT55uCyo1RfxmWwGyXq0DPVie/5MrGP1zcxH474/vF0k4r2FKrXZMly9eyedu0rdXDLJJI8/eD8Pj4yTwnebyWGAORPmnz/3Gp44tgMu/iz8+C9BbDTfZovp4jbvDbTRsibHbxkhgkOszGWmEpEyN5rB8CCjodGKbR3LYVfDrgWZa6pD2P28a2ns7F6+qaIoiqIoZ42KeGXTMfzAj0jNXw9AqCaLW+eLzlRyHoD2Sv7wIYMXaFPLgOsV+qsXj6u29bB13yUl+8mm0zz98IM8MHiGeSwoE8xZy2Lai/LPn3gtRwYa4JVvhiv/NV8vRnixOcTLzYtw1uCr5hiLaOAyU01ue9cWnCI3mqHwEKPhygIehJ31uwjb4UXql2bnlc+juWfbqrZVFEVRFGXlqIhXNhdelv4fFVI0xrqdvG97JhX4ww8s9IfPuMWuNCAI0ZlZLt/Szo5LLsOyCvWe53HskYe5//QA0wjl4t0TIePaTCejvPejP8Vx0wtvvQWansm3aTB1/Jz3enaz46wPOWx8l5nQIi4zlRDxrfA5hsPDjIRHFm3fU9NDbWh1k0xtO3AlbTt2rWpbRVEURVFWh4p4ZXMx+SR9RwpBk+GuQoCol0xhZ7O0Dp7Ol+X84bNF+dGjGeG6tkYu2rkf2yn9Cpx4/FHuO36KcSNQJpgNkHFssrbF1GyMf/zIa+jdfTsc+iOwCzOvXuFdxuvNTxJj9TOUiiHIMuPiLOMyU4mIU3CjORM+w5nwmUXbNkdbaI2tLhC155IDdF508aq2VRRFURRl9aiIVzYVZuiOipM8eV4WSWdpOtOPk/UF9VRNPTOJOtK2IeUUBPnVDXXsvep5Jf32PvMU9x05xhlvoXgHyNoWGcfGiDA+leDvP38twy97PWz9fr5NxIT5afMqrjYHVx286hghatyqXWYq9mEJbuBGMxIaYTg0vGjbhJtga23PqgJZuy7eT/feS5ZvqCiKoijKmqMiXtlUzD7zPYZO7/RXxBDt9EV8JX/4gcAfPhs2mECk2h7s27E732boxLPc98RT9Gehknj3LCHj2HiBu83oeC3/7x6HqdfdBJFCjvXtpoc3ea+nmdXlRg8Zm5hxCGGfVfYaASIhG8QX8EPhoUqHBYBrhdhZvwtrFYGsHbv20HPJgVWPU1EURVGUs0NFvLKp6L/3OBjf/zre7mGHfQGaXsIfPluUWjKRJT9pUyaZ5MuPPIlXwQxtgIzrkLULlvmTo2HePXyY1Ms+nm8nxuJl5mZeam7GZuGMqEshhiDLzOpcZioRdi0sgdHQ6JICXrDYVb+LkL3y2WJbt+1k2+VXLp1nX1EURVGUc4qKeGXzkJ6m/4mCcIx0RfPvvWQK8TzaBk7lywbae4L88AVx3e2G8u+Hek9WFPA5v3dTVHfP2Cj/GfoYZt+JfFlNqpU3269lB1tXdBh24DITPQuXmUo4lhCyLcZCYwyGBxcV8ADb6raSCK18wqnmLVvZeeXzVMAriqIoyjqjIl7ZPIzeR++RwiRPke4IACaY5KlxdIhQOgnAbCzBVG1DkB++IDj3NhVytQ9OF9xhADzLIu3YGKvQPkuWj8/czR2NXwSrkJfy4skb+MX4LUSJVD38kLGJGofwWbrMVELws9GMh8YZCA8sKeDbYm00R5tXvI+Gzm52Pe9axFqbpwaKoiiKoqweFfHKpiE78IPSoNYu36qeTicRU8EfXoRM2Mv7wzse7Cryhx8MLPRGhLRb8HvPMcwI78t8moHao4XCuXpeNvVGXtG0paox51xmosbBXaG7zUoIuxaT4XH6I/1LCvjaUC1baqobezF1re3sueaFWNa5OwZFURRFUapHRbyyaRh98B5mJq8EwIlkCTf7gjKV9K3vFf3hi3Kl13kQjvsuJLMT40zVxMk4Nhm7fBZWw91yPx/ni6TD8/ly6+T1vNn5SS5tSy87Vt9lxiGKu6YuMxX3ZcFsZGpZAR+2w+yo37liV5ia5hYufsENWLYKeEVRFEXZKKiIVzYHxtD/YCFVYrzbQgK3l2wyBcbQVibiMza+QA/YGi740PefOA5NiRJ/eYAZZvmYfIYHrUcKhVmH0A/+D7+1u4stbYtPmATgGouYcc+Jy8xiJKPTDEaXFvCW+IGsrrWyr3y8vpG9LziE7aw8AFZRFEVRlHOHinhlczB7mr6n6/OrodwkT4E/fP34CNH5WQDmw1HGG1oW+MNf0t6Zf39qZBSaSmcofZqjfMj6OONS5Ct/5iISX/0Av/mSp2lvrizgxUAYh9g5dpmpxGx4irH40j7wANvrdhBzVzb5VKy2nn033IQTCi3fWFEURVGU84qKeGVzMHI3p58p+MPHugN/+GwGyXq0DxSyxgy2b/H94SNeXty6WejZvivf5kQ6k59PNUOGL8s3+KZ8FyOmsM/73kLjXe/g11//JZobSoNgASwjwayqDvYapYhcCRP2JJO1g1jLCPiOeCeNkYYV9R1J1LDvhptww9UH7iqKoiiKcv5QEa9sCpInvsfAifb8ej6odZFJnvz88AWreBMWtuNf7mP9vcxYNjFggCH+w/oYp6S3sLPZJvj8v9E6fD1vf+PHaaibKhmLayyixiGCc95cZsoZk0nGa/sJLZMppj5cT1eia8k25YRjcfbfeAuh6Mos94qiKIqinD9UxCubgoG7HiebuR6AaHMGJ+6L10zgD9/ev9AfPl00ydPOeCEnel/vaTyBb0x8k9ut/yAtRYGqR14Cn/8gHRGXt7/pI9QmfBcd32XGJmrcIFR1/fKkj8sk/fHT1IWW9lOPOFF21O1gJXGsbiTKvhtuJhxbeQ55RVEURVHOHyrilY2Pl6bvkUKWmGhXOP/epFIkpseJz/rW8rQbYrSpnZRjyOT8TAxcumV7fptnx8d4v/1RHhl6vOBLngnBN/8G7v51trQP8Ws/81ESsfl1d5kpZ0KmOOqepDW2tJ+6LTa763dhryAlpBMKs/+Gm4nW1J7tMBVFURRFOceoiFc2PuOP0nek4EoT7vLdPDwvC+lsiRV+sK0bz7LIhE1eoEc8aOvyc6Nn02neb77NI/J4of+h/fDpj8DgZezYcppfef2nqAlniHkhwms8q+rZMCFTPG0fpzHhYi1pXhd21u8k4lTvz247Lvuuv4lYXf1Zj1NRFEVRlHOPinhlw2OG7yiZ5CkX1JpcxB8+Y0PWLVjN24qs0cOnTvAD58FC5/f+Cnz9nZCJsmfbcd7+05+n3rUImci6usyUMynTPG0fJxKxiLhLW9e7E13Uheuq7tuyHfZef4hEY9PZDlNRFEVRlPOEinhlwzP56B2MD18EgOV6hNv8yzaTSgGU5IcfbO8h65Tmh99VW8jMcmzgJGMy7q/M18JX/x48l8t2HeN/veZrxJyN95WYkhmeso+DbaiLLu0H3xhppD3eUXXfYllc/IIbqW1uPctRKoqiKIpyPll/J19FWYbee0/n3ye6DJbjW8i9ZIrozBR1k2MAZGyb4ZZOUrYhYxf84S/buTu//bdni9xojr0YPJfnX3yE33vt14k5ReklNwjTMsNT9rN44lEfC2EvkU8y5sTYVre96kBWEYs9115PfVv78o0VRVEURdlQbDyzo6IUkxqn/4mC9TnU6WdNMcEkT8WuNMMtXWRth0ykIMbjHtTWNwKQnJnm43Jnoe8jt3LDpU/yqz/2bWxrIwr4WZ60nyUrHpGQRXQJNxrHctjVsAtbqr0vF3Y/71oaO7uXb6ooiqIoyoZDLfHKxmbkHnqPFvKcR7v9YM1UKomYMn/4jsAfvig/fKdTyOLSd+JZnrSO5dftZ1/ML730extSwM/ILE/ax8iKhwjUx0JLWNiFnfW7CNvhxRosYOdVz6e5Z9taDFVRFEVRlHVARbyyocn031km4n2rfDqZBCr7w6edgtrd09SSf//omWNkJOOvDO1nX2eEaLgoR/wGYVbmeCKwwAPUx1ycJdxottb2UBuqqbr/7ZdfRdv2nWc9TkVRFEVR1g8V8cqGZuju+0jN+xbmcG0Gt9a/ZLOpJOH5WRrHhgHwxGKotZuk45ENBK8YuGTXnnxfn03eV+j4yK0cPDBwno6ienwBf4ysZAEIuxax0OJeb83RFlqi1Qel9lxygI7de5ZvqCiKoijKhkZFvLJxMYa+B8byq7FuBxEBYyCVpm3wVL7uTHMHaTdENlywWNd6QjgSBWByeIgvOaUi/ooNJuLnZJ4n7GNkAgEvAg1LuNEk3ARba3uqDmTtung/3XsvWaPRKoqiKIqynqiIVzYu08foO1LIXR7qSgCQzqQha0omecrlh88UBX9uCRcmOzp56lmG5Yy/korROHaALV2T5/gAqqdcwAPURl0cu7JCd60QO+t3YVUZyNqxaw89lxxYk7EqiqIoirL+qIhXNi4jd1ec5CmdquAP39FD2oF0kejd31HY9u7xJwv9Hr+JK7YNVW3BPtfMS5In7GOkc/76QMixSIQru9EIFrvqdxGyl84Zn6N1+y62XX6l/xRDURRFUZQLAhXxyoZl9sgPGe71A1PFMkQ7g0mekimcVJKmEd8dxgCDbd1kXIMX+MNbBi7a5gdvetks/2nuKHR85FYO7jxx/g5kCZIVBDwCDTF30ZuMbXXbSITiVfXfvGUrO6+4WgW8oiiKolxgqIhXNiy9dzwFxhef8fYsVsi/XL1kirah01jGTw052thGMhwlXZRhsRELy/Zda86cPsmd1qP5Ouvoi7l0W2ECqfUiKSket4+RktIMObURB9ep/NVsi7XRHG2qWFdOQ2c3u553LWLp11xRFEVRLjT0r7uyMckm6X+04B8e6fL927PZLJLJVvaHL8oPvy2WyL8/MnCMlKT8lZFd7E3E1z21ZEpSPG4fXSDgHVuoiVR2k6kN1bKlZktV/de1trPnmhdiWYtPEKUoiqIoyuZFRbyyMRl7iN4jHfnVcLfvPpJKzQOL+MMX5Ye/ZMv2/PtvTj9S6PfIrRzcWdh2PUiRrmiBR6AhXjkbTdgOs6N+Z1VuMTXNLVz8ghvyTyIURVEURbnwUBGvbEi84TtLg1q7fOt0JpnCzqRpGe7L1w20bSEdMphA4Doe9HT4E0Sl5+f4IIcLHR+5lcvX0R8+RZonnKMkc08GikiEHcIV3GgssdlVvwvXWjxffL6Phib2vuAQtlNd0KuiKIqiKJsTFfHKhmTk/nuYnfKt7240S6jJtyp7qRQtw33Ynu9qM17XxFwsQTpUsFC3WHbeYt134lmO271+RSZEw5mr6GkZPY9HUiBNmiecY8xXEPC2JdRGKwvv7XXbibmxZfuP1daz9/pDOKHQWY9VURRFUZSNjYp4ZUPSd2/B0p7YAmIJJjfJU7ErTXsPGQeybuFS3lnbkH//o6GnC52euIErtp5Zl9SSaTKBgE9WrG+Iu1gVxtUZ76Qx0rCwooxIooZ9N96MW5QbX1EURVGUCxcV8crGY36YvicLlme30w9STaWSYFgQ1Jq2IW0XLuXLduzOv/9U6q5Cv+vkD58hw5POMeYWEfCxsE3EXei/Xh+upzPRtWz/4Vic/TfeQiiYnVZRFEVRlAufdRXxImKJyG+JyJMiMi8ip0TknSJSVRJsEXFF5PdF5AkRSYrIiIh8WkQuXqR9j4i8T0SOiMiciPSKyBdF5Ia1PTLlrCib5Cna7VuX08l5xMvSOlRIDznQ0UMqbHKZKAl70FLfCMDM2CifkEJ+eOvYi7l0+6nzcAAFsmR5wnmWWZmvWG9ZUFfBjSbiRNlRt2PZpwZuJMq+G24mHKsub7yiKIqiKBcGy0fKnVveBfw68FngncDeYP2giLzIGOMttqH4Ts+fB14WLN8DtAC/CtwlItcZYx4vat8J3I9/zO8DngE6gTcD3xGRHzfGfHntD1FZKelTdzJwoj2/Hg2CWrOpFC1nBnAzflaXqUQd04k6MkUu4O1OYeX4ySPMW3P+ykQ3e8KNxM5jaklfwB9jVuYWbdMQC2GX+dHYYrO7fhf2MukhnVCY/TfcTLSmdk3GqyiKoijK5mHdRLyI7AfeDnzGGPOaovJngX8AXg98ZIkufgJfwL/fGPPWou3/E3g06ONFRe3fBDQDrzLGfL6o/UfxBf2bARXxG4C+O36El70KgFhLGidmgTGYZGV/+OL88LsbW/Lv7xp/vNDpkVu54jy60mTJ8qTzLDNLCPhoqJIbjbCzficRZ2nfdttx2Xf9TcTq6s9+sIqiKIqibDrW053mDYAA7y4r/wAwC7xxme1vCpYfLC40xhwDvg/cIiI9RVU5c2UfpQwAHjBT1aiVc4vx6H1oKr8a7fYt6+lMGvHMIv7wBUv2pTsvyr//98zhQr/n0R/eI8uT9rNMy+yibSwL6mPuAneZ7ppu6sJ1S/Zv2Q57rz9EorG6mVsVRVEURbnwWE8RfzW+eL6nuNAYMw88FNQvRThYVlJKubLnF5V9PVi+V0RuFJEuEbka+Cgwje/Oo6w3k0/R93TBmh4KglrTySQYQ9tgwac95w9PIIRjBmqjfkDsSO8p7rAf8ys8m/rBa+hpHTnnw/fweNI+zrS1uIAH3w++3I2mMdJEe6x9kS18LMvm4hfcSG1z61mPVVEURVGUzct6+sR3AmeMMZVSdvQC14lIyBizMKm2T6DQuBn4Ua5QRGIUxHt+jnpjzGER+TXgT6F49h+eAa4xxjyx2EBF5C3AWwDa2to4fPjwYk3zTE9PV9VOKaV99mslQa2J61qwt0eJZeqoPZEhHMzYOl9bBy9/AaGJMzA+DkB3LJHf7uiZ44VOT13HlZemcPaXCl+JONj71lIMG9Kk2CHLWNIFnDIBb4lFxIkyvsweIokaHnriSXjiybMb6iZBv0cbHz1HGx89RxsfPUcbn414jtZTxMeAyjn3YL6ozWIi/r+APwT+VERmgG/i+7z/SbDMbV/MMHBf0PZp4CLgt4Evi8iNxpiKqUuMMe8H3g9w1VVXmUOHDi15YACHDx+mmnZKKRNf/BgTIx0A2K5HKDlD9plZxvv76Xy4kC6yv6mTkbufZKLFhsCdZk9zW77+qyN3F54zHbmVAy1PkX18qGRf9r7WBWWrxcPjKfs4k9b0ku1EoK02glPkAuRYLvua9uUfLS2yJRc9/zqae7atxXA3Dfo92vjoOdr46Dna+Og52vhsxHO0nu40s7CobokUtamIMWYMP3D1KL7APobvmhMH/iZoNplrLyJvxg+U/f+MMX9rjPmCMeZvgz62AH+1+kNR1oreO47m3ye6s4gtZLNZJJOlfaDUHz5pQyZn0Tawr2cnAJlUivea/863lSMv4bJzmFrSYHi6CgEPUBt1SwQ8CLvqdxK2l55ldedVz3/OCXhFURRFURZnPUV8H9AsIpWEfBe+q81iVngAjDGPGGMOAruBG4HdxpgbKdwcFPsc/G/gSWPMo+V9BO1uXN1hKGtGZpbexwoCN9Tp5z5PpeZ9f/gyEZ+OFPzhaxEirp+KcuDkMYbsMb9iupU9TjvxyJKX0qrJCfiJKgR8yLFIhEsffm2t7aEmVLPkdtsvv4q27TvPapyKoiiKolxYrKeIvzfY//OKC0UkAlyO7/ZSFcaYI8aY7xljjgRFL8O3wv+wqFkXsFjibYf1z5mvjN5P35HO/Gqk2/eGyiRT1E6OEpvzEwglQxFGGlvJhAqCv6fIH/6hoaLwhqMv5Yqd58YKbzA8Y59g3Jpatq0INMRLs9G0RFtoiS7tk99z6eV07N5ztkNVFEVRFOUCYz1F/McBA/xmWfmb8X3ZP5wrEJEOEbk4CFpdEhF5O3AJ8C5jTHHayMeBPSJyTVn7a/F94+9dzUEoa4c3eBe9x7ry67Fu37LuJZMlqSUH27eQdYV0UX74fd3b8u8/OVeYpZUjt3L5OUgtaTAcsU8yZk0u3xioiTi4duHrlnAT9NRuXXJG1u69l9B98f6zHaqiKIqiKBcg62Z9NsY8IiL/BLxNRD4DfIXCjK3fpXSip7/Cn6zpJooyy4jIV/B94R/HvyF4CfAq/Emb/qJsl3+EPzPsN0TkX/Cz0uwGfgU/ePZP1vQAlRUzcNf9pJN7AYjUp3FrbYzxIJ1Z6A/vQDbwLRcDu9p98T83NcmH5Xt+QyPUDVzHtra1nsPLcNQ+yag1UVVr1xZqIm5h3Qqxq34X1hIKvmP3xWzZf9lZj1RRFEVRlAuT9XYh+U3gOH76xlcAZ4D3AO8wxnhVbH8n8DrgtmD9CeDXgPcZY7LFDY0xXxCRF+Nno/kFoA4Yw88f/2fGmIfO7lCUs6X33iH8+ziId/tW9lQqCYYF/vCpiMmvN1g2ru23P37iabJWcOn0XcUVHXNLWrtXjuGofYqRKgU8Ag3xUH4MgsXuhl24trvoJq3bd7HtwBXI2g5cURRFUZQLiHUV8YHQfifLTLRkjLmNglAvLv8z4M9WsL9vA99e0SCV88NsH31P1+ZX3U4/2DOdTBKfGqdm2hfNacdluKWdTKjgmrK9pj7//s7RRwp9HrmVg7tOrOEgDUft05yxxqveIhF2CDmFsW6r20bcjS/avrlnGzuvuFoFvKIoiqIoS7KePvGKUmDkbk4/U5jkKdrtJxjKJlO0DxQCU4fausm4NukiYbx/6478+39OFVJLcuSlXLrt9JoN8Zh9mjPWWNXtbVuojRYs7m2xdpqjTYu2b+jsZtfV1yCWfi0VRVEURVkaVQvKhmDu2J2c6WsBQCyPaIcLxmBS6QX+8PMueEF+eMtAT4M/t9f4YD/3OUGCorkGLmILiehi84mtjGft0wyvQMADNMZC5NLY14Zq2VLTvWjb+rYO9lzzQixrsQRKiqIoiqIoBVTEKxuC0z8opPRPdGSxQkI6k0a8CvnhwwV/+BbHxQ4s10/1Fk0LcPTFXLGjb03GdtzuZcgaXdE28bBN2PXHFbbD7KzfuaiLTE1zC3uuux7LVgGvKIqiKEp1qIhX1h8vS99Dhcl5I4ErTSqZJDI7Tf3ECABZy2aotYt0kT/8rsAKD/D1iaKpBY7cysGdZ+8Pf8LuY9AaWdE2tgV1gRuNJTa76nfjWJXDTxINTex9wSFsZ/FAV0VRFEVRlHJUxCvrz8Rj9B5py6+Guvyg1mwqSftgwR9+uLWTZNghU+IP789k6mUzvNf7er68pu8GtrWfOathnbT6GLBW3kd9LIQV+NHsqNtOzI1WbBerrWfv9YdwQqGzGqeiKIqiKM89VMQr6445cyenjxT8xQuTPKVp6y/zhw+ZvD+8Y6At4We0GTh1nEE7SPs4cBlXtKfy/uir4ZTVT7+9cgEfDdlEQ75bTGe8k4ZIQ8V2kUQt+268GTccWf0gFUVRFEV5zqIiXll3Rh64j7lpfzLeUCxDqNEmm80gmeyCoNZMkebtCEXyEyY9OPBooeLIrRw8i1laT1sD9NnDK97OsqA+5t+A1Icb6Ex0VWwXjsXZf+PNhCKVLfSKoiiKoijLoSJeWXdO31kQ3IktICKkkklCyTkaRwcB8EQYbOsm7RaCPy9q7ci///jM9wodHn0Jl20vuOGshF5rkF57aFXb1kVdbEuIOFF21G2vOMmUG4my78ZbCMcWzxWvKIqiKIqyHCrilfUlPUnf44WgT7czAUAmlaRt8DQ5HTzS3MFsNEzaKSjjPZ1bAEjOzvAR+aFfmIpzUXYXNatILdlnDXHaGlzVYURci1jIwRab3fW7sCukinRCYfbfcDPRRM2q9qEoiqIoipJDRbyyvozcW+IPH+n2XUy8ZGqBP3wybDCBeTuM0BzzBf+JE0+TtTy/4bFbOLhtYMXD6LeGOWUNwCr86EX8YFYRYWf9TiLOQj9323HZd8PNxOrqV74DRVEURVGUMlTEK+tKuvcuBk8WMtPEulyM8SCdWeAPnw4XFHZ3NJ7Pu/7DkYcKHa4iteSANcxJq39VAh58NxrHFrpruqkL1y2ot2yHvdffRKKhcXU7UBRFURRFKUNFvLKu9P3wR3hZ3/Uk3prCjlqkUkmcVIrmM/35dgPtW0ryw18cuNIAfGD+v/Pva/puZHtH9UGpg9YIJ85CwIcci3jYoTHSRHusfUG9Zdlc/IIbqW1uWd0OFEVRFEVRKqAiXlk/jOH0vYWZUHOpJdPJJK1Dp7GM7yIz2tDKdDxKxi4o7V3NvmCeGhnmTueIX3hmDwebrKpTS2bIcNzqXbWAF4GGuEvcjbGtbtuCQFYRi4uuvZ76toXiXlEURVEU5WxQEa+sHzMn6Hu6kEfd7fRzvmeTKdqKXWk6epgPk/eHj4lFfdRPSfnUqScK/a0gteS0zJKU9KoFPEBNxCHqhtnVsBtbyr9Kwu7nX0djZ+U0k4qiKIqiKGeDinhl/Ri5uySoNdodAmMwqTTtRUGtg+09pCMmv741UfA7/+r43YX+jryEA9urE/Ej1vjqxw24tlAbDbGrfidhe+GMqzuvej7NW7ae1T4URVEURVEWQ0W8sm5MPH43k6O+ILdDWSKtDulMGjudpmW4N9+ur2NLSX74i7t6ADCex/u9wB8+HWFnai81sepSS47KxOoHLtAQD7G1dis1oYXpIrcfvIq27TtX37+iKIqiKMoyqIhX1o3TPziSf1/TnUVsf5KnluE+nGwWgInaRiZrakr84Xc0+kGiQ70nOJ2zqJ+4kSu3VTdJ07TMkJL0qsddE3boqmmjNbYwWLXn0svp2LVn1X0riqIoiqJUg4p4ZX3Ipuj9USq/Gu6MB8XJUn/49h7mIpCLGq2zbBKhMAAP9j9S6G8F/vAjZ2GFd2yho7aBntqFrjLdey+h++L9q+5bURRFURSlWlTEK+vD+I/oO9KRXw13+yLeS6ZL8sMPdvSQDhc2217fnH//8env5t/HTh9iR0d1lvhRa/UiviUR56KGXVhlqWg6dl/Mlv2XrbpfRVEURVGUlaAiXlkXvIE76T1WyNwS7XLJZjNYqTRtg6fz5X0dPWSK8sPv6fQDYdPJeT7GHX7h+FauqI9UlVpyWmZX7UqTiLhc2roH13ZLylu372LbgSvyk08piqIoiqKca1TEK+vCwD0PkUn5YjjakMKttUkl52kcGcBN+2420/FaRuvryBSp8211TQCcPPE081bGLzxyK1fsPFXVfkdkfFXjtS3hsrbdxN14SXlzzzZ2XnG1CnhFURRFUc4rKuKVdeH0XX359/Fu/zLMpFIlrjQD7T3MRyXvD9/khIi4vvD/wfADhc6euZUDO6rzh1+tK83Oxi7a4s0lZY2d3ey6+hrE0q+RoiiKoijnF1UfyvknOULfE5H8am6SJy+ZWtIffndzW/79fyS/5b/JOuyYv5Ta2Pyyu12tK01TrJ59LdtLyurbOrjomhdiWfYiWymKoiiKopw7VMQr55+Re8omeYrgeR6k0rQNFNxi+jp6SvLD72r1A2FnJsb4jjzpF558IVf2jFS129FVuNK4doirOi8ucZepbW5lz3U3YNkq4BVFURRFWR9UxCvnnbmjdzPS77umWLZHpMMlnUpSPzpMJDnnt4nEGGpuIhvkhxdgS10DAE+ffMwvgJWlllyhK42IxYG2vcRChRlZEw1NXPzCQ9iOs6K+FEVRFEVR1hIV8cp55/QPnsi/r+lMY7lCOpWs7A8f0B6OErJ94fyV0bvy5bFT1aWWXI0rzZbEVrrr6gr7qmtg7w034bjuElspiqIoiqKce1TEK+cXY+h9oGARj3T5vvHZcn/49h7S4YKI393WmX//H5lv+2+m2jlYU49tmWV3u1JXmlq3lf3tXbmYWiKJWvbdcBNuKLz0hoqiKIqiKOcBFfHK+WXqGXqfLmR5cbtqwBhMmYjv6+wh7RYuzx2NrQCc6T/FM/awX3iOXGkidg37WrcRCfYfjiXYf+PNhCLRqvtQFEVRFEU5l6iIV84rZvguTh8tDmoNkUqnqB0fJTY7DUAyFGagrRUvyA9vA9219QA82PtwobMjt3L5juXzw6/ElcaxwnTHt9Fe6z8hCEWi7LvxZsKx+DJbKoqiKIqinD9UxCvnlZEH72d+xrdoh+JpQg32An/4wbYtzEYLl2Z3vBY7yMX+manv+YWexbaZy6iLzy27z1GpzgovYtHo9rCrtRYRcEJh9t14C9FETbWHpyiKoiiKcl6oWsSLyB+ISOfyLRVlcU7d8Wz+fc0Wg4hU7Q+fTaf5qLnTL+x9Hld2T1e1z6omeBJocLawrbGeaMjGdkPsu+FmYrV1y2+rKIqiKIpynlmJJf7PgBMi8kUReZWIaJJsZWVk5uh7pBCEGuryLdxeKl0i4ns7esgU+cNvb/B96E+deoYJK5jU6cjLOLjzxLK7nJFZkpJatl2N3UZDtJ6O+ii247L3hYdINDRWdViKoiiKoijnm5WI+GuAfwOuBz4NnBaRvxaRi87JyJQLj7EHOX2kK78a7oqRzWZIjI9SMzUOQNpx6evuzPvDh0RoT/gzuv5g8L78ttFTN7Grc/nUkiNVuNJE7TridjM7WuI4ts3FL7iB2uaWlRyZoiiKoijKeaVqEW+MuccY88tAB/DzwNPA7wBPiMj3RORnRUTTdyiLkj59F4Mn2/wVMUS7XJLJ+ZJZWodbu0r84bfWNmAFeR4/Pvddv3C2iYPR5upSSy7jSuNaEeqcTjrro9REQlx07fXUtbav8MgURVEURVHOLysObDXGzBljPmSMuRG4CPi/wE7gdqBfRN4rIpev6SiVC4LeH/4I4/mXXE1rCjtikU2lFkzyVOwPv6ulA4D56Sm+Io/4hUdfwhU7epfd33KuNAI0OD1EXJctjTF2P/86Gju7Fm2vKIqiKIqyUTjb7DTHgfuBJ/A1UQJ4M3C/iHxZRDrOsn/lAuL0PQX3l9gWf/ZVL5mirTg/fEdpfvjtDU0APH3yMTwJLO9HbuXAjuXzwy+ZlUbAlhCWuOxoiXPR866lecvWlRyOoiiKoijKurEqES8i+0Xk74A+4OPAHuDPgR3AFuAvgJuAf1+jcSqbnblB+p4spGp0OurwPI/IxAQN42cAyFoWJ7d2YwL3mahl0xxLAPDfI3fmt90yeQX1ieVTSy41wVPUqkOwaKuNcPl119K6bceqDktRFEVRFGU9cKptKCIJ4A3ALwJXAx7wNeD9wJeNMV5R83eIyDTwR2s4VmUzM3I3p48UT/IUJpVK0l7kD3+mpZOZWOGS3NHYggSC/sPp7/u3nP0Hubpzftndzcjckq40UaseS4QX3HgdHbv2rOKAFEVRFEVR1o+qRTwwAESB08CfAv9mjDm9RPsTQXtFYfKxu5ga87PMOKEs4VaHmamZJf3hdzT5GWLGhvt5yArE/pFbq0otOSrji9ZZ4hCy4sQSMbbvv2QVR6MoiqIoirK+rMSd5lvAjwPbjTF/soyAxxjzcWOMzgirAHDqB0fy72u3ZBBLyKZK/eF7O3tIO0X+8PV+fviHTj+cL4ucvJndXYPL7m85V5qm9g7i8fiKjkFRFEVRFGWjULUl3hjzE+dyIMoFjJfl9IOz+dVQVxSMwZmeomlkwG8iwomtPRC4z9Q6IRqiMQC+NPkDf8P5Wg64ndjWU0vubllXGruei/fvZX5m5GyOSlEURVEUZd2o2lIuIreIyF8tUf9XInLT2gxLuaCYfIK+I6351VBXDal0ivb+U+ScZ0Yb25iuCeXb7Gz223vZLB/O3uEXHnsRV+7oX3Z3S7nSOFaYsJvgwKU6R5miKIqiKJuXlbi7/C6wa4n67UEbRSkhO3AXfcc68+vRrhDpVJL2/iJ/+I4e0qHi1JK+K03f6aMMWlN+4ZFbuXzn8qkll3alqadr+3aikfBKD0NRFEVRFGXDsBIRfwC4a4n6u4M2ilLC4N0Pkkm7AMQak7g1Ntmy/PCnO7eSdgpBrdvr/fzwdwzcly/bMnEVDYmCW04llnSlEd8f/pJL9672UBRFURRFUTYEKxHxdcDMEvVzQMPZDUe5EDl9V2F21cQWX6hb0zO0DPfly5/dsTXvD98UjpIIRwD47GzgDz+0j6vassvuaylXmrDEidU0sGdXz0oPQVEURVEUZUOxEhHfC1y5RP2V+GkoFaVAepreRwuXmdNRSzaboXXgJFYwtcBYfTNTtZF8mx2BP3xqfo7P8oBfeORWDp6lK03ErmfH7l1YliZNUhRFURRlc7MSNfNl4E0i8qLyChG5BXgT8JWV7FxELBH5LRF5UkTmReSUiLxTRKrK/Sciroj8vog8ISJJERkRkU+LyMUV2h4WEbPE6xsrGbtSJaP3lUzyFOmOkUrOl/rDt5f6w+8IUkseOfEYSfGt7+ETN3NR99L3iLNLuNKIWESsWi6/fP+qD0VRFEVRFGWjsJLJnv4CeA3wdRH5KvAQYICDwMvwrfB/tsL9vwv4deCzwDuBvcH6QRF5UdkssCWIP5Xn54N9fx54D9AC/Cpwl4hcZ4x5vGz8/1qhq9cBPwZ8cYVjV6pg9uhdjA74/u2W4xHpcJmemirxhz+1ZRsZu+APvzXwh//2mSAEIx3lgL0N2zq+5L5GlnCliVi1NLa20dHWtLoDURRFURRF2UCsJE/8oIhcB/wzvnB+ea4K+CrwNmPM8vn/AkRkP/B24DPGmNcUlT8L/APweuAjS3TxE8E43m+MeWvR9v8JPBr0kX9qYIypaGkXkT8EksB/VTt2pXpO/+BxYCcAtZ0pLEeQ2Tlahwp+8sd2bM/7w7fHa4i6fhDsx+d/CDbw7E1cuX35CZ5Gl5ngaffePas/EEVRFEVRlA3EipyDjTEnjDEvB5qB5wPXAM3GmB8zxhxf4b7fAAjw7rLyDwCzwBuX2T6Xk/6DZWM8BnwfuEVEloxgFJHrgT3AZ40xo9UNW6kaY+i9byy/Gu0O4Xkezf2ncLIZACZr6pmsj+bb7GhsAWBq9Aw/sIJZXo/cyuU7Tyy5q1mZY34RVxpLHCJOLQcvW+BlpSiKoiiKsilZVYSfMWbMGHOvMeYeY8zY8ltU5GrAA+4p63se31Xn6mW2zyX6rpRzMFf2/GX6+MVgWcnNRjlbZk/T+2R9ftXurCeVmqd9oNQfPlUhP/zDpx8kNxNU59jzaKxZOrXkiCydG75961YS8dgqDkJRFEVRFGXjsRKf+DwikgDqqXATYIxZPoWITydwxhiTrFDXC1wnIiFjzCJJv3ksWN4M/KhobDEK4n3LYjsXkVrgp4BngW9XOWZlBZjhuzh9tBDUGusOkUxO0VYU1Hpi6w6ytn8ZWUBPnZ+l9GvjgT/86E6e17L8ZTpqjS9aF7Xr2bdfXWkURVEURblwWJGIF5HXA3+IH4C6GHaV3cXwfdErMV/UZjER/1/BWP5URGaAb+K7+fxJsMxtvxhvCOr//f9v78/D4zjLvO/7e7b2vWVJtizJtmTFm6zYcnbHceIkJgECBOYmhCUQG0huhgADk3nueRh4SAJ4SJibZYbAvISAjcGsAQ8EQmYgoESJt+yxE8fxEslueZVlWZu1X+8f1WptLVmyW2otv89x9NFVV1VddbZLlZxdfdZVzjk3VKBmdidwJ8CMGTMoLy8fanUAGhsbh7XeZJa16/e0NnsP+U1Ibyfx0unEt/vJ/V5PPfzR65ZD2xkACtIziY+JxXV18dOOp72sft9bufi6emIWTR90P110scDC/3BjGPExSZyqOUx5+eE+y3SMxj8do/FPx2j80zEa/3SMxr/xeIyGncSb2bvxbjR9A/g+8IngfCzwbmAn8IcR7LsZGCwzS+y1TljOuVPB4S43Ag/1WvQU8ABegl8/xP4/BnTSr6Z+kH091L2PSy65xK1atepsm1BeXs5w1pvMXvzDd0PTaQWddO49SdwrzxPX4n1Ha0pO4/DpBkjy/gy7S2mOHamiyufdohBfdT0XLHmdztcGHaiIQ76jHI45HnZZemwuS8uuCXssdIzGPx2j8U/HaPzTMRr/dIzGv/F4jEZSE/9PwG6gDPhSsO1Hzrn3A5cA8/Fq2YfrMJBtZglhluXjldoMdhUeAOfcTufcMmAecA0wzzl3DT318q+H287MLsSruX/cOVcdbh05T13tVL/UHpqNz0ulvb2NGYd7blA9mjurXz28N/zj9iPPeQ0d8SyhmNiYwRN4GGJUGoNEXwZLlw71w5GIiIjIxDOSJH4J8OPgjafdWVUMgHNuF96V6s+PoL9ng/u/rHejmSXifVF4brgdOef2Oeeecs4FhzPhbXhX4Z8ZZJOPB991Q+toqdtFYO/M0Gx8fhrtra19HvL0ZtE8uoL18LHmoyDdq4f/fcNWb4WDK7lkTs2Qu/FGpQlflZVgqWRMm86cgplhl4uIiIhMVCNJ4mOAk8HpM8H3jF7L9wClI+jvl3hjzH+2X/sdeLXqm7obzGymmS0M3rQ6JDP7dDCObznnmsIsTwA+BBxjZOU/MgJtB7dw/FCwWsocSXlxdLT2HZlm39zC0PQc/zRifD462tr4VfeARfveSlnx0PdJ1w41Kk2MnwsWzT/nzyAiIiIyXo3kxtYAMAfAOXfGzI7jldE8Ely+ABiQNA/GObfTzL4LfMrMfgs8Rs8TW5+k74Oevgbcjjc2fHl3o5k9BhwAXsP7QnADXn3+H/Ge0BrOu4Es4OvOuY7hxisjc3jLTpzzroCn57YSk+gjY/8RElu9738tCUnUZaWF1i8M1sMfOPgajcEr67m1l5OV/gpDOTlIKY2Zj6SYDC5SKY2IiIhMQiNJ4rfgPQG1ux7+98A/mFkz3hX9u4BHR7j/zwKVeCO/3ATUAN8BvuScG7oQ2rMVuBVYE5zfHYzj+865zkG26R4b/ocjjFVGILD9GOAl8ckFMXR0dFBQXRlafiR3Nu3xPQMZzfV79fAVJ571GurzuSxz6B9ezljLoKU0ib50phfMwp+RFna5iIiIyEQ2kiT+e8B7zCzJOXcG+AJePfu9weWv4t38OmzBRPsbwddQ662hJ1Hv3f4V4Csj3OcNI1lfzkFbHdWv9dyvHJvnp61fKc3+CxbQ5fOe5pQQE0NumleZ9eszz4SGlrzoLKU0J61u0GVJPj+LNDa8iIiITFLDTuKdc8/i3YzaPX8CKDOzJXhDNe4e5tVzmeRczXYC+3oe8pSYn0hb6+k+N7Xu71UPX+jPxmdGc30df7bXAIivXM2Ctx8dcj+DjUoTY3EkJ/q5sGTeeXwKERERkfFrWDe2mlmKmX3JzG7sv8w594pz7lUl8NKtftd2Guu8Mpa4xA4ScmJJqTlOSnMDAG1x8dRm9dwT3T205K6DL9FlDrpiKO1aMOTQkmeshTODlNIkxWQwa24x8fFxkfpIIiIiIuPKsJL44Cgv/wLMGt1wZDIIPLM3NJ0+qx1HF7mH3gy1HZ0xi/Y+48N7N7X+5dR2r+HQci6dXTfkPk4OMSpNos/PhUt0Q6uIiIhMXiMZYnI/kDtagcgk4RyHnm8IzSbmJ9HW1tqnHv6NBYtwwXr4lLh4cpJTAdjUFhzWfzhDS/rqwrbH+RJJz8ihuDD/PD6EiIiIyPg2kiT+e8AdZpY1WsHIJNB4gMNv9PyJxOT5vYc89b6pdW5RaLooMxszo+ZYgNdijgAw4+QVZKc3DrqLIUtpfH6KF8zD5xvJn7aIiIjIxDKS0WkagFpgj5n9GNgLNPdfyTm3MUKxyQTUeWQbhyvzQvPJ+XG4EwHS608B0BETS212r3r44NCSz1U/7zU05XBpun/IfQxaSmOQ6MvgomWLz/0DiIiIiEwAI0niN/Sa/twg6zhASfwUdnTr83S2e0l6SnYLMSk+sp7f37M8t6DP+PDd9fB/rN/mNey7kYuKDw25j8FKaRIsjZy8WWRP85/7BxARERGZAEaSxF87alHIpBHYFgC8JD61ANrb2/oMLbl7YSnOvHr4jIQk/IlJdHZ08LOuLeCDuMrVLLzxyKD9n21UmgUl8yP3YURERETGqZGME//kaAYik0BnK9U7XWg2Li+d9n4PeTrQa3z4oswszIyDgTeo9TWDMxZ3lBAXs2PQXQxWSmPmIzl+GmUXLjz/zyEiIiIyzunuP4mcUy8R2NdTDx+fn0rs6VNknjoBQJf5qM32h5Z3l9JsORZ8htjhi7msoGnIXQxWSpPkyyC/sIikxISwy0VEREQmk2FfiTezLw1jNeec+8p5xCMTWNPerZw6Ng0AX2wniblxZG4/EFp+NLeAtt718MGbWn/b5JXSsO+tlC2uGrT/oUelyaD0Qo0NLyIiIlPDSGri7x1imQMs+K4kfoqqrngVKAAgI7+VLjrIPdyTlL+2+EII1sNnJ6eSlpBIa3MTv+MlAHJOrCAnY3//bkNqBymlibE4UlNzWDhvTmQ+iIiIiMg4N5IkvihMWyxQjDdaTQZweySCkokp8NxJupP4pIJ42gaMD18Ymu6+Cr/74Mt0+rrgjJ9LU7PxnikW3snBSmli/MxdMF9jw4uIiMiUMZIbWwerc9hvZn8GngLWAv8SicBkgmk5QfXu1NBsTF4m1lDHtJNHAe8nmpPZmaHlRZleEv+3muBNrAfewsVzqwft/oy1cIbBH/BUtrTkPD+AiIiIyMQRkUuXzjkHPAJ8JBL9ycTTdXwbgX0FofmkgkRyDh7A57zRao7MnEV7XE89fGHwSvzP2p4GIPbNt7Bo1uFB+6+1017BVj9xviSycvLJy82OxMcQERERmRAiWX8QD2RFsD+ZQGqe3UFbizcyTGJaGzFpjtzAm6Hlr5YuCdXDz0xNJykunrqTx3jO5/3As6itlLjYrkH7r/WFr4dP8vmZt0hjw4uIiMjUEpEk3swuAf4B2B2J/mTiqd7Wk7Cnz+6gva2t7/jwRT23VBQGh5Z86dALXsOxC7k8L3ypDHilNM20DGg3M5LjMrmoTKPSiIiIyNQykiEmDwyyaBqQBnQAH49EUDLBuC4CL/Yk2fH5qXQ1NZJ9oqc85mSv8eHnBktp/nR6m9ew760sm9+T8Pc3WClNgi+VmbOLSE1JPr/4RURERCaYkYxOcxDv/sTeHPAC8AbwkHOuMkJxyURSv4fAG9NDs7F5GWQdepGYLq885nBuPu3x3p+az4zZGdPo6urip53PgA+yj1/J9MsGv6l1qFKaklI9oVVERESmnpGMTrNqFOOQCay1aivHA14Sb+ZInBnLjJ095TW7LiwLTeen+YmPjaX60F4O+05DWwqXJOUB4ZP4FmsNW0rjsxhSk7JZvHBuRD+LiIiIyESggbXlvB1++mVwXr1L+swzdPk6mHmkZ0TSA3N76uGLgvXw244+7zW8eR2XFB0ZtO+TVhe2lCbRl86cefOIix3Jj0kiIiIik8Owk3gzu9XMNg6x/Mdm9t7IhCUTSWDH0dB0yqwYOpobmX4sEGrrXQ/fPT787xq3ABB74AYWzR48iR+0lCbGT1mZxoYXERGRqWkkV+I/BQw+BiB0Ap8+v3BkwuloJrCrZ/z3mJl+/IEqYjs7ADg8M5+OOO9qeazPR0G6n/bWFn7tngVgQesS4mM7w3Y9WClNjMWTmZnPrLwZkf40IiIiIhPCSJL4RcCLQyx/EdCl0SnGnXyO6r35ofmEvGRmHNwfmn+lVz387IxpxPpi2Fu1ixZfB9TM54oZ/e+V7jHYqDRJMRnMWzQfn0/VYCIiIjI1jSQLSsG72j4YhzfUpEwhp3duo6k+FYC4xHZipjlyD/fUw7/Zux4+OLTkkzXeVXj2vZVlxYMPLXnSVxe2PSk2k4tUSiMiIiJT2EiS+DeBq4ZYfhXeMJQyhQSe3hOazpjdRkdLCzOOHQK8b3W12Zmh5d03tf6q5WkAMo+tYEZmfdh+ByulifclMzO/CH+Gvi+KiIjI1DWSJH4zcIuZfaz/AjP7KHAL8NtIBSYTQ+C5niQ8sSCJtMNVJLR5T1+tzptFR6xXL58QE8vM1HQaT9dSbm9ARwKXxc8etN9BS2l8GRobXkRERKa8kYzPdz9wM/CQmX0OeAnvYmsZXi38HuBfIxyfjGfNh6nekxGajZnpZ3rljtD8ziVloelCfxY+n4+dB1/0kvPKa7ik6PigXZ8MMyqNmZGSmM2FJfMiEr6IiIjIRDXsK/HOuQZgBfB9YCbwQeBDQB7wn8CVzrnwtREyKXUc3sqRypmh+YS8OHKrK0PzbxbNCU0XBoeW/J9T2wGIOfAWSuYcDtuvV0pzZkB7gi+N2XPnER8fF4nwRURERCasET0pxzl3Gvikmd0FZONdUz3hnBt8iBGZtI5ueY7OjkQAUnPO0OVrJfeod1uEM6M2q6cefq7fq4f/ScfT4IP5Z8qIj90zsFOGKqXxs2SpbmgVEREROacx+pznhHPuuBL4qSuw7VBoOm2WI/lYNUktzQAcKphFZ7AePjkunpyUVI4dqWK/7wTUzeaKnMGvpocrpfFZDP6MXIoL88NsISIiIjK1jOSJrXeZ2V+GWP4/Zva/IxOWjHtdnVS/3B6ajctLZ3rlvtD8ztKloekifxZmxnOHn/ca9r2VZcU9XwB6ax2klCbRl0HxgoUaG15ERESEkV2JXwPsHWL5G8BHzysamThOv0pgb089fOzMdGYE3gzNVxYVhqa7h5Z8tGErAP4jVzNz2sCr7QAnB33Ak59lZYsjELiIiIjIxDeSJH4esHOI5a8G15EpoHHPM9Sd8GreY+I6iZnWQe4R7yFPnT5fn3r4oswsOtvb+EXXNuiM5ZK4orB9QvhSmliLZ0ZuIdOz/ZH9ECIiIiIT1EiS+DggcYjliWdZLpNIdcWroemMghbia4+R2tQAwKFZs+iK8f60MhKSyExM5s1Dr3Pa1wKHVnDZnJNh+xyslCYpxs9CjQ0vIiIiEjKSJP4N4C1DLL8B2H9+4chEEXiuJjSdXBDH9Dd7Kq12XlgWmi7K9Orhnz72LAC+AzdQMqc6bJ+DldKkxGextHRBZAIXERERmQRGksT/HLjBzL5iZvHdjWYWZ2b34SXxP4t0gDIOtdcTeDUhNBsz09+3Hr6wZ3z4Ir83PvwjZ7YAcEFTGQlxnWG7rQ1TShPvS2ZW4TySk/Qjj4iIiEi3kYwT/y3gbcAXgL83s9fxnti6CJgGVADfiHiEMu50Hd/B4f09Qz3G5MaT+7hXD98RG8upaf7QsqLMbM401vMn2wkNuVyZlRq2z1ZrpSlcKY3PT+kSjQ0vIiIi0ttIntjajne1/f8FAsAy4CLgEPB/gOsJWwwhk82JHdtpa/GuxCdltJLYfpKM+loAKufMwQWHgcxOTiEtIZHXDr5MlznYfyMXFR8M22e4BzyZGRmpuSwonj16H0ZERERkAhrRoNvOuXbn3Nedc2XOuZTgaxnwN+A/gMOjEqWMK9Vbem59SJ/dQXZlTz38q4svDE0XBp/S+sTJHQBkHL6a3MxBhpYMU0qT4Etn7vyFxAQfGiUiIiIinpGU0/RhZtOA24CPAaV411HfiFBcMl45R+DFptBsQn4yMw71JPWVhYWh6aJMrx5+U1sF+IyLfBdgNnCU0lZrG6SUJoNlyzQ2vIiIiEh/I378pZndaGa/BKrx6uTjgfuAC51zGgdwsmuqIrAnOzRruRnkVnv18K0JCdRlZoSWFfqzqD1xmFdiqqH6Mi6fXR+2y1qrG1BK47MYcnLmkJebHXYbERERkalsWFfizawIWAvcDhQAJ4BHgA8CX3DO/XbUIpRxpbVyCyeqcwAwXxeJyY1knjoOePXwmJeN56amkxwXz5bqFwDw7b+R0ksHGVoyTClNks/PgpJFo/ERRERERCa8Ia/Em9kHzewJYC/ezavPAe8B8vGuvutG1immuuJFcN5hz8g7Q3b1vtAfwe6S0tB63UNL/un0NgDmNl1EQlzHgP4GLaWJy6RsiZJ4ERERkXDOdiX+p8AB4LPAz5xztd0LzMyNYlwyTgW2HwHmAZBSYMyo2hda9mafevhsujo7+WnnFmiZxpUZGXiDGvUVblSaWF8CBbPmkZ6WPAqfQERERGTiO1tNfBtQCNwMvM3MkkY9Ihm/OtsI9LovNSbPz4zqSgCak5Op96cD4DNjdsY0DgX2ctzXAPtv4KLiwUpp6ga0Jfn8LNbY8CIiIiKDOlsSn4t3FT4L+AlwzMx+aGZXE4FSGjPzmdnnzOx1M2sxs0Nm9g0zSxnm9nFm9i9mttvMWs3spJn9xswGvcHWzErM7GdmdiS4TcDMNpvZjPP9PJOdO/Uy1XvzQvOxGUZ2zRGg71X4/DQ/CbGxbD32LABph68mb1rdgP7CltIYpCVNZ/HCuRGPX0RERGSyGDKJd87VOecedM5dBFyCl8i/G29c+KfxntiaMXgPZ/Ut4JvAa8CngV8DnwEeNbOz1esb8DtgHbAH+BzeWPVXAdvMbMClXDO7EXgeWBJc9++BB/H+HdLP43NMCXUvbaG5wft+FZ/czvTT+/A5r6rq9YU9/9zdQ0tubtoKwMW2oPt+1z7CldLEWwpF8xYSF3vOo5+KiIiITHrDzpSccy8AL5jZPwL/C298+FXAw2b2D3ij1Wx2zr06nP7MbDFe4v5b59z/6tX+Jl6C/X7gZ0N0cTPwNuAh59z/7rX9T4BdwT5W92qfHuyvHHhX8Am0MgKBp1/H+3EG/LNbmHGopx6+sqgwNF3kz6btTDObeQGOlHF5/sAbVwFqw41KE+OnbKnGhhcREREZyojHiXfOtTrnfuacux4oxrsSngl8GXh5BF19AO867Lf7tf8AaMZ7kNRQrg2+r+8X3wGgArjezGb3WvQJYBrwf5xz7WaWbGZxI4h3ygs8XxeaTsxPYEbgTQDq09NpTEsFINbnoyDdz56DO2m3TuzADZQWDryhtc3aaKS5T5uZkZU5i9kFqmwSERERGcqIk/jenHOVzrkv4d38+nZgJOPFXwp0ATv69dkCvBRcPpSE4HtzmGXdbZf3ans7UA/4zewloAloMbMKMzvbvqT1JNW7U3vms1LIOe7drPpmUVGoeXbGNGJjYiiv8Q5rYcMlJMYPHFryZJhSmkRfOgtKSvD5zuvPUkRERGTSi0i25DyPO+feN4LN8oAa51xrmGXVQLaZxQ+xfXfZznW9G80smZ7kfVavRQvwyocex/uS8F68se9LgfJgeY8MoqN6G0cqZ4bmczqqie3sBGDP/AWh9sLg+PC/aH0GWtNYkZYTtr/BS2k0Ko2IiIjI2UTz7sFkIFwCD9DSa522Qdb5KfBF4Mtm1gT8BcjGewhVdq/tu6UBMcAm59ya7kYzex7vRt0vAbeG25GZ3QncCTBjxgzKy8uH+FiexsbGYa03USTv+D1dnd7INGkzz1DoqwG8O5urintGkinKzKL+1Am22H448B4ue0sLMfnT+/TlcBRbWp82AxIT0njlpedH9XP0NtmO0WSkYzT+6RiNfzpG45+O0fg3Ho9RNJP4ZmD6IMsSe60TlnPulJmtBjYCD/Va9BTwAF6CX9+r/QyQCmzo10+5mR3Eu0l3sH091L2PSy65xK1aNeiqIeXl5QxnvYli669+HJpOz+/Ev81LtmunTaM50TtcCTGx5KVm8Myuv4BBavU15Bbvp7PfRfcjvhMcjDnSpy0lJoub3n4bl140dlfiJ9sxmox0jMY/HaPxT8do/NMxGv/G4zGKZvHxYbySmYQwy/LxSm0GuwoPgHNup3NuGd4jRK8B5jnnrqGnXv71Xqt33115NExXR/BuzpVwnKP65Z4fTWKnJzPj6EGgbz38HP80fD4f/123HYAyt2jwoSX7SUnM5sLFF0Q4cBEREZHJKZpJ/LPB/V/Wu9HMEoEy4LnhduSc2+ece8o51z3m4dvwrsI/02u17htoC8J0UQAcH+7+ppyGvQTe6BkxJtXXQFyHN0Ln3nnzQ+1F/mxcVxc/7XgaTiziyryBo3i20U6j9f2BJdaXQFHxYhIThroFQkRERES6RTOJ/yVeSfVn+7XfgVfLvqm7wcxmmtnC4E2rQzKzT+PdrPot51xTr0U/Cb5/ot/678S78v/YSD/AVNGw+xlO1/gBiInrYFbdGwA4M6p6Pam1KDOLI0fepMpXi+2/gdLC6gF91frqBoxKk+Tzs2TpolGKXkRERGTyiVpNvHNup5l9F/iUmf0WL4lehPfE1ifp+6CnrwG3440NX97daGaPAQfwnvjqgBvwnij7R7zx63vv7y9m9nPgA8Ht/gDMwXvg1BHg3kh/xsmi+qmddD+YN3P2GXID3g8ex6dPpzXRq1xKjotnekoav3v9cQBm119KUnzdgL5O9i+lMcjMKOCCwnA/kIiIiIhIONF+tv1ngUq8kV9uAmqA7wBfcs51DWP7rXgjyqwJzu8G7gK+75zrDLP+R/AeSPVRvIdM1eE9afYLzrnD5/YRJr/Ac8fpTuKT82PI3V4F9K2HL/JnYWb8vmELdCSxInkm3j9vj3ClNAmWwvyFizU2vIiIiMgIRDWJDyba3wi+hlpvDT2Jeu/2rwBfGcH+OvBGrnlgJHFOaR1nCOzqebBtQnwnCa3eCKD7Lui5EbUoM5uOtlZ+6Z6Fyuu5ZO6xAV2FLaWJ8bNMY8OLiIiIjIguf8qQuk48z+H9eaH5vDPeVfgun4+Ds+eE2ov8Wew/+BrNvjaSA6soyK4d0Ff/UhozH7m5FzA9RwMDiYiIiIyEkngZ0vHt22hv9UaNSc48w+xj3qidh2fOpD3eu0KfnpBIZlIyFSeeBWBp5+IBQ0uGK6VJ9KWzaLEelCsiIiIyUkriZUjVW/aGpjNmtZNbXQnAm3N7ntI6NzMbM+NXZ56B2rmsyB3YT7hSmpT4aZQtWTAKUYuIiIhMbkriZUiBFxpD0ykZXSSf8eb3zy0OtRf6s2iur+PP9hq2/0aWhBtasl8pTYzFMruwhOSkxAHrioiIiMjQoj06jYxnZ44S2N1Tr57ZehKAjthYArN6hoQsysxm195tYDDr9KUkJfQtm2mjnYYBpTR+lizRDa0iIiKTRX19PcePH6e9feDDHie6jIwMdu/ePSp9p6SkUFBQMOKR+pTEy6BaDjxDzeEcAMzXxdzTrwFwqKCAzljvTycrKYX0hET+cmo7dMaxPHEWsKdPP7W+0wNKaTLScll4QeFofwQREREZA/X19Rw7doz8/HySkpKw/jfHTXANDQ2kpaVFvN+uri6qq6upqalh+vTpI9pW5TQyqOqKF0LT/vxm8g/vB/qND5+ZBcBP2p+GqpVcWnRiQD+1VtdnPs6XyPz5S4iJjRmFqEVERGSsHT9+nPz8fJKTkyddAj+afD4fM2bM4PTp02dfuf+2oxCPTBKB7T3Pv0rPaSOtoQ7oe1NrkT+bE0cP8brvKEmHrmV2Tt+hJcOV0iT5/JSVLRq9wEVERGRMtbe3k5SUFO0wJqS4uDg6OjpGvJ2SeAmvq5PAyz0PzfVTD0BrfDzV+fmh9sLMLJ4//DwAS8IMLTmglMYgJ6eI/Jkj+8lIRERExjddgT835/rvpiRewnKnX6N6b89YkfmNlQAcnDMHF7zxIjc1neS4eP5Qvw3q87gqJ2FAP/1HpUmwVBYtvnD0AhcRERE5RxUVFSxYMLzhrzds2MBVV101yhENTkm8hHXqhS2caUwGICG5jQuO7QL61sMX+rPo7OjgZ11bvKEliwJ9+vBKaZr6tCXHTWPZEpXSiIiIyNhITU0NvXw+H0lJSaH5TZs29Vl35cqV7NmzZ5CeRubOO+9kwYIF+Hw+NmzYEJE+e1MSL2EFKl4LTWcWNJF5ugaAA0V9H/J08NAeTvnOkH/qcpIT+g4pdapfKY2Zj9mzF5Geljy6wYuIiIgENTY2hl6zZ8/m0UcfDc1/6EMfCq13LnXpQ1m6dCnf+973uOiiiyLabzcl8RJW4PlToenMBO8BT81JSRyb6ZXYGMbsjGk8c/w56IpheXzhgD5O9iulSfSls7h08egFLSIiIjJM5eXlFBQU8MADD5Cbm8vatWtDbd3uv/9+iouLycvLo6SkhM2bNw+7/7vuuovrr7+exMTRebClxomXgdobqX6t5w8u58xRACoLC0Nt+ekZJMTG8pumZyBwBZcXnurbRZhSmrSk6ZSWFCMiIiIyHhw9epTa2lqqqqro6upi+/btfZYXFxdTUVFBSkoKjz/+OLfddhv79u1j5syZUYq4h67EywDt1ds5WtVzU+v82p1Av6ElM7NpaWrkUV4m8eB1zJ5+sk8f/UelibE4Lpi/lLhYfW8UERGZ7MzG7nU+fD4f9913HwkJCWGHyLzlllvIy8vD5/Nx6623Mm/ePHbs2HF+O40QJfEywJGnd9DV6T2IKWN6I3m1B4F+D3nyZ/H6oVfo9HWxuL10wEnUv5QmKSaDsqUloxu4iIiIyAjk5OQMWe6yceNGysrKmDVrFn6/n127dlFTUzOGEQ5OSbwMENj6Zmh6WkYDBtSnpXEyOxuAWJ+PWemZ/K1mBzRlc3VWap/tw5XSTMuczZyCXERERETGi6HGaK+qquKOO+7gwQcfpLKykrq6OkpLS3HOjWGEg1Ntg/TlHNUvtYRmszu8b5u9r8LPSs8kNiaGTa0VsP8GlhZV9+mi1lffp5QmzpdESclSfD59ZxQREZkKxkmee16ampowM3JycgBYv349u3btGvb2bW1tdHV14Zyjvb2dlpYW4uPjI5YPKauSvpoDBPZkh2YL6/cCUNm7lCYzm7qaozwfc5C82itISWzr08VJq+sznxTjZ1mZRqURERGRiaOkpIS7776b5cuXU1xczM6dO1mxYsWwt7/hhhtISkpiy5Yt3HnnnSQlJfHUU09FLD5diZc+6l99mvqTGQDExncwv/ZVHAPr4V98cysAV8TNBSpDywaU0hjkFyxkmj9tDKIXERERGVxlZWVoetWqVQQCfR9U2b9t3bp1rFu3joaGBtLS+uYya9asYc2aNYPuq7y8PBIhD0pX4qWP6qdeDk1nZdcR6zo5NW0ap/1+AOJjYslLy+BPp7fD4Yu5YnZjn+37l9IkWBqlpaVjEbqIiIjIlKEkXvoIPHssNJ0TWwv0vQpf6J8GwE87nyax6joKZ/S9Q7u2XylNSkI2SxbPH6VoRURERKYmJfHSo6ud6p09fxL5TYeA/kl8Foer93HEV8+i1qV9hpZsp4P6XqU0Zj6KL1hCYkL86McuIiIiMoUoiZeQrppXqN7f8wSyBad24sz6JPFzM7PZduR5aMngmmn+Ptuf6veApyRfBkuX6IZWERERkUhTEi8hx7Y+Q0dbHACpaU1kdtVxPCeH5pQUAJLj4pmeksbvmrbA/tUsLTzSZ/v+o9JkpOUzb+6sMYldREREZCpREi8h1c/sDU3npJwEBpbSdLS18oh7jhknl5Oa1Bpa1r+UJsbiWLSoTGPDi4iIiIwCZVgSEni+ITQ9s927yv7m3LmhtqLMLPZW7aLF18HlsfP6bDuglCbGz8XLNCqNiIiIyGhQEi+etlMEdveMf1pc9zpdPh9Vc+aE2or82TxZ8ywcK2VFwZk+m/cvpZmRewHTczJHNWQRERGRqUpJvABwZv9WTh7xntTq83Uyq/MQR2bOpDUxEYD0hESmJSXzi5YK4quupzC3Z2jJ/qU0cb4klpSWjWn8IiIiIueroqKCBQsWDGvdDRs2cNVVV41yRINTEi8AVD/5XGg6K/0UcXRwoM9TWrNpOl3LU7aXha1L8PUqnelfSpMcN40lpcM7AURERERGU2pqaujl8/lISkoKzW/atKnPuitXrmTPnj3nvc833niDm2++mZycHKZNm8aNN94YkX57i41obzJhBbYHgHwAcs174FNl7yQ+M4tXDr0E7clcnZEDHAwtO2mnQ9NmRlHRhaSmJI1F2CIiIjJOfevPb4xq/597y/AeJtnY2PN0+cLCQh5++GFWr149YL2Ojg5iYyOTGtfV1fGud72L9evXk5aWxpe//GVuvvlmXn/99Yj0D7oSLwDOUf1SR2i2sHE/HTExHJw9O9RW5M/if05tgzev5aLCnqe6dtBBvfWcHAm+NJZeuGRs4hYRERE5R+Xl5RQUFPDAAw+Qm5vL2rVrQ23d7r//foqLi8nLy6OkpITNmzcPq+/LLruMj33sY0ybNo24uDg+97nPsWfPHk6ePBmx+HUlXnAN+wnsnR6aL2yvJFA4i444b8z4rKQU0hOT+EnH00yv+UfS5vYMLVnbr5QmLXkGi+YXjlXoIiIiIufs6NGj1NbWUlVVRVdXF9u3b++zvLi4mIqKClJSUnj88ce57bbb2LdvHzNnzhykx/CeeuopcnNzycrKiljsuhIv1D73NC1NXvlLYsIZMqntOz58ZhbHDldywFfDpda31r13KY3PYli4cBkxsTFjE7iIiIjIefD5fNx3330kJCSQlDSwFPiWW24hLy8Pn8/Hrbfeyrx589ixY8eI9hEIBLjrrrv45je/GamwASXxAgQqXg1N5yYcw+j7kKcifzbPHnkeTs5jZX5P2U3/UppEXwbLyi4ck5hFREREzldOTg6JwZH4wtm4cSNlZWXMmjULv9/Prl27qKmpGXT9/k6cOMENN9zAJz/5ST7wgQ9EIuQQJfFC4Lme+qxZLQdpi4+nOj8/1Fbkz+LRhq3EV66maOaJUHv/Uprs7EJm5fWU5YiIiIiMZ2Y26LKqqiruuOMOHnzwQSorK6mrq6O0tBTn3LD6PnXqFDfccAPvete7+MIXvhCpkEOUxE91na1Uvxofmp3TVknV7Nl0xXglMTNS0kkw+HnXdua3LO0ztGRtr1KaGIundPFFYxa2iIiIyGhqamrCzMjJyQFg/fr17Nq1a1jb1tfXc+ONN7JixQruv//+UYlPSfwU1179PEerZgTnHPlU9y2lyczizUOv09DluDqt5yaODjo43auUJjk2k4uWLhqrsEVERERGVUlJCXfffTfLly+nuLiYnTt3smLFimFtu3nzZp599lnWr1/fZ5z6gwcPnn3jYdLoNFPc4ae24rq873JZSbUknmnlzblzQ8uLMrN4+o0/QNVKLplzPNRe66vvU0oza3YJGempYxa3iIiIjG/DHcd9LFVWVoamV61aRSAQ6LO8f9u6detYt24dDQ0NpKWl9Vl3zZo1rFmzJux+br/9dm6//faIxR2OrsRPcYGtB0LTeZ3VNCclcTQ3FwDDmJMxjV+feYbsEytIS+41tKTVhabjfcmULSkbq5BFREREpjwl8VNc9YtnQtNz2qqoKiyE4E0e+ekZdJ1p5nHbxcUsDK3njUrTFJpPScxh8cLiMYtZREREZKpTEj+VtZwg8HpmaLaAAAf6DS356qGX6aov4JqZPX8qtb56nHl3ZpsZ8+cvIz4+buziFhEREZnilMRPYfU7K2g4lQ5AnK+NHI5T2e+m1idO7iCucjXFvYeW7FVKk+BL46KlS8YsZhERERFREj+lBZ58KTQ9M+YITWmp1ASHUYoxHwXpmfy0vYILmi4KDS3Zv5Qm019A4eyRPXpYRERERM6PkvgpLPDs0dD0rPaDfYaWnJWRSf3Jo+xyx7k6pSDUfqpXKY3PYihdfAk+n/6MRERERMaSsq+pynVR/UrPbAGBAePDv1D9Ahy6kssKe57oerJXKU1ijJ+Ly0rHIloRERER6SWqSbyZ+czsc2b2upm1mNkhM/uGmaUMc/s4M/sXM9ttZq1mdtLMfmNmC8Osu8rM3CCvP0T+041vnSd3c3j/9ND8gIc8+bN57PQ2ph2/ivTkFmBgKU1e3jyyMjPGLmgRERERAaJ/Jf5bwDeB14BPA78GPgM8amZDxmZmBvwOWAfsAT4H/AdwFbDNzEoG2fQh4MP9Xv/3vD/JBHPsmafpaPdGlMmIqaM9M47Tfj8A8TEx5Can8rOurVzker4P9S6lifXFs/TCS8Y8bhEREZHRUlFRwYIFC4a17oYNG7jqqqtGOaLBRS2JN7PFeIn7b51zf+ec+4Fz7h+BfwSuBd5/li5uBt4GPOSce7dz7nvOufuA5UA8XkIfzlbn3E/7vcoj8qEmkOpn9oSmCzr7ltLMycji8OH9HG9OZtWMhFB7rZ0OTafEZ7O0dPw9iU1ERESkt9TU1NDL5/ORlJQUmt+0aVOfdVeuXMmePXsG6Wn4ampqWLFiBVlZWfj9fpYvX84zzzxz3v32Fs0r8R8ADPh2v/YfAM3AbWfZ/trg+/rejc65A0AFcL2ZzQ63oZmlmFniSAOeTALP9yTkBQQGDC259dizxL65mgtm1gDQSSenrbFnnblLSErsSfBFRERExqPGxsbQa/bs2Tz66KOh+Q996EOh9To6OiK2z9TUVH70ox9x4sQJTp06xT//8z/zzne+M6L7iGYSfynQBezo3eicawFeCi4fSncG2RxmWXfb5WGW/TvQCJwxszfM7B+CpTlTR0czgdd6bjvI739Tqz+L3zRuobjpImJ8XvlMre90qJQm3pfMxWXLxjZmERERkQgqLy+noKCABx54gNzcXNauXRtq63b//fdTXFxMXl4eJSUlbN68eVh9JyYmsmDBAnw+H845YmJiOHXqFLW1tRGLPzZiPY1cHlDjnGsNs6wauNLM4p1zbYNs/2rw/TogNM6KmSXTk7zP6rV+O/B74DHgcHD/H8P7JaAMWHtOn2ICat63jdqjWQDE0EHM9A6aUlMBSIqNw++L4Xe8xEeS/hk4BvQtpUlPm8n84rA/coiIiIhMGEePHqW2tpaqqiq6urrYvn17n+XFxcVUVFSQkpLC448/zm233ca+ffuYOXN4z8hZsmQJr7/+Ou3t7Xz84x9n+vTpZ99omKKZxCcD4RJ4gJZe6wyWxP8U+CLwZTNrAv4CZAP3Bd+7twfAOfcMXh19iJn9AC+pX2NmP3TOPR1uR2Z2J3AnwIwZMygvLx/yg4H3081w1osGX/nvgGkAzOAoh4rmhJYVZWbxRmAX7YcvYsVb4onJmA44Ci0Nh1f/lJqSyVNPPRWN0CNqPB8j8egYjX86RuOfjtH4NxmOUUZGBg0NDaH5tEfTx2zfDe+sH/a6zjmam5tpaGigubkZn8/HP/3TP9HW5qWbzc3NOOdCn+Wtb30rAJ2dnbz97W+nuLiYJ598kptuuomWlhY6Ozv7fO7+nnnmGVpaWnj00Udpa2sbdN2WlpYR/w1EM4lvBgb7OpLYa52wnHOnzGw1sBFvxJluTwEP4CX4Qx5V51yXmX0NuBF4OxA2iXfOPdS9j0suucStWrVqqG4B7yea4awXDX/78a9C0/3Hhy/0Z1Ne+Xsyj64krfoQndVwwlfLgZgAAIkxGXzqzn9mxvSsMY870sbzMRKPjtH4p2M0/ukYjX+T4Rjt3r2btLS0qOx7JPs1M5KTk0lLSyM5OZmcnBxygk+rB0hOTsbMQn1u3LiRb37zm1RWVgLeF66mpibS0tJITEwkJibmrPtPS0vjox/9KIsWLWL58uUsXbp0wDqJiYksWzayUuVo1sQfBrLNLNzdkfl4pTaDXYUHwDm30zm3DJgHXAPMc85dQ0+9/OvDiKMy+J491EqTSfVL7aHpPF81lYWFofm5mVn8vPVplnb1jNDZu5Rm+vS5kyKBFxERERnqtsiqqiruuOMOHnzwQSorK6mrq6O0tBTn3Dntq729nQMHDpxrqANE80r8s8ANwGV4o8kAEBw1pgzvivqwOOf2Aft6Nb0N7yr8cMbymRd8Pzbc/U1krrGa6jd6kvCE3DZaE70fPtLiE4k908y2llN8OScFONNnVBqfxVB24WXRCFtEREQmkg+eW6I7njQ1NWFmoSv169evZ9euXcPadtu2bXR0dHDZZZfR2dnJf/zHf3Ds2DEuvzzcmCvnJppX4n8JOOCz/drvwKtlDw3caWYzzWxh8KbVIZnZp4FS4FvOuaZe7QMuHwd/Bbg3OPvoCOOfkE4++xQtzUkAJNPEibk9PyEVZWbxcuBFYquuZ37eSaDvqDTJcZksW7Jo7IMWERERGWMlJSXcfffdLF++nOLiYnbu3MmKFSuGtW1rayt33XUXWVlZ5Ofn89hjj/HHP/6RvLy8iMUXtSvxzrmdZvZd4FNm9lu8G0wX4T2x9UngZ71W/xpwO97Y8OXdjWb2GHAA74mvDu/K/ruBP+I9ybW3x83sMPA8PaPT3IZ3Jf47zrkdTAGBp3bSXW00cHz4bH79xu8pbLiyZ2jJXqU0s+eUkpqSNKbxioiIiERKd207wKpVqwgEAn2W929bt24d69ato6GhYUDt+5o1a1izZk3Y/VxzzTW8/PLLEYs7nGiW04B3Fb4Sb+SXm4Aa4DvAl5xzXcPYfitwK7AmOL8buAv4vnOus9+6j+Al+J8G/EAT8CJwj3Pu5+f+ESaWwLM1eLccQF5MNU/PXhlaVpieyU87nub6hA8BNX1KaWJ9CVy89JIoRCwiIiIi/UU1iQ8m2t8IvoZabw09iXrv9q8AXxnmvh7AG7Vm6urqoPrVnkMeX9BGR1wcANOSkmk+dZSDx2dz5WyvCumUrz5USpOaPJ3FC+aOfcwiIiIiMkA0a+JljLUdfpljVd018I6motTQsiJ/NjuOPE/GkZX4U88AcNLqvIUGC+ZfTExszNgGLCIiIiJhKYmfQg4/uQXnvEM+neMcKup5oG1RZha/a9jCks5SgD6lNPGWwmXLLhr7gEVEREQkLCXxU0hgy/7QdG78YQKzepL4/OQ0ftX6GtdNywD6ltJMy5rN7IIZYxusiIiIiAxKSfwUUv1iaMRNkma30OXzDv+MlDSOHd1L68GrWJRfC8DJ4Kg0ZsbSxZEb01REREREzp+S+CnCtZ0msDsjNN9alBiaLszM5qnjzzKn/lJifC5YStMAQGKMn0uXXTjm8YqIiIjI4JTETxH1rzxNY503vmk8rRwt6imPKfJn8auWZ7gy3ht9pncpTV7BfDLSUwd2KCIiIiJRoyR+igg8+WJoOjfpCEdnzgTAMHJifPylxnH1rDagp5TGZ7FcslSlNCIiIjI1VFRUsGDBgmGtu2HDBq666qpRjmhwSuKniMCOw6Hp5DlNYAZAXloGe6t3kX7kGjJTm/uU0qQkZrOkZH5U4hURERGJhNTU1NDL5/ORlJQUmt+0aVOfdVeuXMmePXsiuv8f//jHmBkPP/xwRPuN9hNbZSw4R/XLPQ/A7SzqOexFmVn8ufp3lHZcDfQtpSm+YCnx8XFjG6uIiIhMDn/72uj2f+3nh7VaY2NjaLqwsJCHH36Y1atXD1ivo6OD2NjIpsanTp3ia1/7GosXL45ov6Ar8VNC56kDHD6QE5o/WdwzXZSZzabmHVyfmQ1AbbCUJs6XyBUXXTa2gYqIiIiMkfLycgoKCnjggQfIzc1l7dq1obZu999/P8XFxeTl5VFSUsLmzZtHtI/Pf/7zfOYznyE7OzvS4SuJnwqOPl1BZ7v3zTIj9RS12dMAiDEfSWeaeCOwkMX5p+ikk7pgKU16eh5Fs/OiFrOIiIjIaDt69Ci1tbVUVVXx0EMPDVheXFxMRUUFgUCAe+65h9tuu40jR44Mq+8dO3bw3HPP8YlPfCLSYQNK4qeE6qd3h6bTihpC07My/Lx89EVmnfaGlqzrLqUxKC25DJ9Pfx4iIiIyefl8Pu677z4SEhJISkoasPyWW24hLy8Pn8/Hrbfeyrx589ixY8dZ++3s7OSTn/wk3/nOd0Ytn1KWNgUEnq8LTbsiC00X+bN5tH4ry+PmAT2j0iT4Urni4mVjGqOIiIjIWMvJySExMXHQ5Rs3bqSsrIxZs2bh9/vZtWsXNTU1Z+33e9/7HkuWLGH58uWRDLcP3dg62XW2EXi155vl6QumhaZnp/n5+anDfC2/k65eo9JMz72A7Gn+sY5UREREZEyZ2aDLqqqquOOOO3jiiScoLS3F7/dTVlaGc+6s/T7xxBM8+eSTPPbYYwDU1tby4osv8tJLL/Hggw9GJHYl8ZNc075nOXUsEwCfv4PG9BQA4mNi6Dh9lPbq5WQtOMNJXz1d5jDzcfGSK6IZsoiIiEjUNTU1YWbk5HgDgqxfv55du3YNa9sNGzbQ0tISmv+7v/s73vve9/Kxj30sYvEpiZ/kqsu3h6Yz5tZzCu9K/JyMaWw9/jyL20uBnlKapPhMLlpSMvaBioiIiIwjJSUl3H333Sxfvhwz4/bbb2fFihXD2tbv9/eZj4+PJz09nYyMjIjFpyR+kgtsrYJg4m5FnaH2wsxsvn58G9dnLKeLk6FSmjmFi0lKTIhGqCIiIjKZDHMc97FUWVkZml61ahWBQKDP8v5t69atY926dTQ0NJCWltZn3TVr1rBmzZph7be8vPxcQx6Ubmyd5Kpfag1NN13Q8+0vPyGJxwKpXJhXzylfA13miLFYli+7MhphioiIiMgIKImfxNyZGqrf8OrhyYHWpHgAkmLjOFlzgNy6y4mN6eKk1QGQlprLgnlzohStiIiIiAyXkvhJrGb7U7Se8YZNii/quSJf6M/ib7U7uCLmgj6j0ixYcLHGhhcRERGZAJSxTWKBJ18JTccXt4WmizKz+Hndbq7Liw2V0sT5EllxyeXRCFNERERERkhJ/CQWePa4N2HQOjc51J4NvH5oIdnpzdQGR6XJyi5k5oysKEQpIiIiIiOlJH6yco7qXcHDOxPa42IASItP4M3jr7KodQlddFJn9WBQdqHGhhcRERGZKJTET1Kth1/j+MHglfWirlB7UWY2fzq9nevT80OlNImx6Vy+rCw6gYqIiIjIiCmJn6QOP/kMznmHN3F+e6i9MGMaP69uoSyvMVRKkzdrIakpSVGJU0RERERGTkn8JBV4Zq83EQNtBfGh9sQzDXByKb6YDuqsHjMfl5dpbHgRERGRiooKFixYMKx1N2zYwFVXXTXKEQ1OSfwkVf1iozeRD10xBkBmYjK7Tr7MZTHzOeWrp8scKUnZLF00P4qRioiIiIye1NTU0Mvn85GUlBSa37RpU591V65cyZ49eyKyXzMjJSUltK+Pf/zjEem3W2xEe5NxwbU3E3gt1ZuZ6wAviS/KzOYXlf/N6twLQ6U0xReUERMbE6VIRUREREZXY2NjaLqwsJCHH36Y1atXD1ivo6OD2NjIpsYvv/wyF1xwQUT77KYr8ZPQ6Ve20nTaS+LjF7pQ+6yUNP50MJPs9EbqrJ4Yi2PlpSuiFaaIiIhI1JSXl1NQUMADDzxAbm4ua9euDbV1u//++ykuLiYvL4+SkhI2b94cxYj70pX4SShQ/rw3EQftMyzU3tVwjDlnloRKabIyC5hTkBulKEVERESi6+jRo9TW1lJVVUVXVxfbt2/vs7y4uJiKigpSUlJ4/PHHue2229i3bx8zZ84cVv9XX301XV1dXHnllXzzm9+ksLAwYrEriZ+EAtsDQBbMBmdeEj89JY3ttc9zXerV1NprAJSWXBbFKEVERGSysvvs7CtFiLvHnX2lQfh8Pu677z4SEhLCLr/lllsAaGho4NZbb+VrX/saO3bs4Oabbz5r308++SRXXHEFzc3NfPGLX+Qd73gHL730UsRKdlROMwlVv9LpTRT3/FEX+bP45ZEjXJzXRJ01EBeTzFWXXBqlCEVERESiLycnh8TExEGXb9y4kbKyMmbNmoXf72fXrl3U1NQMq++rr76a+Ph4/H4///7v/86bb77J7t27IxW6rsRPNh111RzZPw2AhEVGa7A9Nz6eN44U0rioji7rIj/vAvwZadELVERERCTKzAb/xaCqqoo77riDJ554gtLSUvx+P2VlZTh3blf+zeyctw1HSfwkc7TiSTo7YiERWv1emwGn66q41FfCSd8hMLh0qW5oFRERkdFxPiUu40VTUxNmRk5ODgDr169n165dw9r21Vdfpb29nQsvvJAzZ87wxS9+kfz8fBYtWhSx+FROM8lUVwR/pimke2RJ8tL8/PXki6yekUSdNZAUn8nFFy6OVogiIiIi415JSQl33303y5cvp7i4mJ07d7JixfAugh47doxbb72V9PR05s6dS2VlJX/4wx+Ii4uLWHy6Ej/JBJ6rBaYTM8/RGcziCzOz+NbOLv6h4Bhd1sWcuaXEx0fuj0hERERkIqisrAxNr1q1ikAg0Gd5/7Z169axbt06GhoaSEvrW4a8Zs0a1qxZE3Y/1113XcQeGjUYXYmfTLo6CbwaD0Dswp4aL39nO7ENi6j1ncbMx8pLoveIYBERERE5f0riJ5Gm/S9Sd9wPqdCa4rXFmI/quj1ckzKbU9ZAWlouFxTOjmqcIiIiInJ+lMRPIoG/bfMmCnvaCtL9/PHYARbknaTLuli08GJ8Ph12ERERkYlM2dwkEthSCUD84p47wgszMvnboQwa4k8S44vnmss0Ko2IiIjIRKckfhKpfukMAFbcUw8f31LPBW4Bp6yB7OlF5GRlRis8EREREYkQJfGTRFdLPdVvZIAfWr17W4nzxfD6qVe5YnosXdbFRUuujGqMIiIiIhIZSuIniZptFbSdSYC5PaU0c/zT+N2hOhKmHSE+PpXly5ZFMUIRERERiRQl8ZNE4KmXAEhe2lNKU5CUwoGaAuqsnvxZC0lKTIhSdCIiIiISSUriJ4nAjmMAdBT0JPEdzce5JCWXLp9jxcVXRys0ERERkQmhoqKCBQsWDGvdDRs2cNVV0Xv2jpL4ycA5qncCOdAW45XTJMbGsaPmDebm15CcnMWFC+ZFN0YRERGRKEhNTQ29fD4fSUlJoflNmzb1WXflypURe9JqZ2cnX/ziF8nLyyMtLY1ly5ZRV1cXkb4BYiPWk0RN69EDHD80Dd/yLrqC38uK/Fl8Z8ebrCiqpXTedRobXkRERMbU91763qj2/8myTw5rvcbGxtB0YWEhDz/8MKtXrx6wXkdHB7GxkUuN77nnHrZs2cLWrVuZPXs2r776KomJiRHrP6qZnZn5zOxzZva6mbWY2SEz+4aZpQxz+zgz+xcz221mrWZ20sx+Y2YLh7HtEjNrNzNnZu89/08TPdV/qwBnpCztOZw5sTH42mbhfMY1l6mURkRERKS38vJyCgoKeOCBB8jNzWXt2rWhtm73338/xcXF5OXlUVJSwubNm4fV96lTp/j2t7/ND37wA+bMmYOZUVpaOnmSeOBbwDeB14BPA78GPgM8amZDxmZmBvwOWAfsAT4H/AdwFbDNzEqG2NYH/ABoicBniLrAM2+AQcv0nnr4+oYAy3KMjKwCZs2cEcXoRERERMano0ePUltbS1VVFQ899NCA5cXFxVRUVBAIBLjnnnu47bbbOHLkyFn73blzJ7GxsTzyyCPk5uYyf/58vvvd70Y09qgl8Wa2GC9x/61z7u+ccz9wzv0j8I/AtcD7z9LFzcDbgIecc+92zn3POXcfsByIx0voB/NpYDHwb+f7OcaD6hfqIRfazauHT41P4Mkj1SROO8ySxZdHOToRERGR8cnn83HfffeRkJBAUlLSgOW33HILeXl5+Hw+br31VubNm8eOHTvO2m8gEOD06dO88cYbvPnmmzzyyCPce++9/PnPf45c7BHraeQ+ABjw7X7tPwCagdvOsv21wff1vRudcweACuB6M5vdfyMzmwV8FbgXODjSoMcb19lG4NVkEkp7xocvzJhGxZFkLDaOay65IorRiYiIiIxfOTk5Q5a4bNy4kbKyMmbNmoXf72fXrl3U1NSctd/uLwRf+tKXSEpKYsmSJbz//e/nsccei1js0UziLwW6gD5fZ5xzLcBLweVD6R70vDnMsu62cJehvwccYOCXhwmp7uXtNDekkFDacygzXAezErKYkT+PtNRh3V4gIiIiMuV41dnhVVVVcccdd/Dggw9SWVlJXV0dpaWlOOcG3abbkiVLztr/+Yrm6DR5QI1zrjXMsmrgSjOLd861DbL9q8H364BXuhvNLJme5H1W7w3M7FbgJmCFc65juP+wZnYncCfAjBkzKC8vP+s2jY2Nw1rvfLX98TGISaEpo6ft8Okq3nFNKTNmZI9JDBPVWB0jOXc6RuOfjtH4p2M0/k2GY5SRkUFDQ0OfttbWcCle5PTf33A452hubqahoYHm5macc3366d12/PhxzIzk5GQ6Ozv5z//8T3bt2kVLSwsNDQ20tLTQ2dkZNo7p06dz5ZVXcu+99/L1r3+dyspKfvGLX/CjH/0o7PotLS0j/huIZhKfDAx2dFt6rTNYEv9T4IvAl82sCfgLkA3cF3zv3h4AM/PjXX3/gXNu60gCdc49BDwEcMkll7hVq1addZvy8nKGs975+tP3fg/5js7gfGZiMn+tOkpBzE4+fNu/EhfBoZImm7E6RnLudIzGPx2j8U/HaPybDMdo9+7dpKWl9WlLSBjdJ8X3399wdCflaWlpJCcnY2Z9+unddumll3L33XezevVqzIzbb7+dFStWkJiYSFpaGomJicTExAwax69+9Ss+9rGPUVhYyPTp0/nqV7/KO9/5zrDrJiYmsmzZshF9lmhmeM3A9EGWJfZaJyzn3CkzWw1sJJhgBz0FPICX4Nf3av+/eOVD/++5BjweVb/YTurFRvcIqLPTMvh5XRpXXVeqBF5ERESiZrjjuI+lysrK0PSqVasIBAJ9lvdvW7duHevWraOhoWFAsr5mzRrWrFkz6L7y8/N5/PHHIxJ3ONGsiT8MZJtZuK9p+XilNoNdhQfAObfTObcMmAdcA8xzzl1DT7386wBmdhHwUeBBIMvMLjCzC+j5EpEbbBvdr4wR1lF/giMHMomZ33MY49saWZhjXHPpNVGMTERERERGUzQv1T4L3ABchjeaDABmlgiU4V1RHxbn3D5gX6+mt+FdhX8mOD8bbyScLwdf/X0n+H4p8Nxw9xttR558iq6YGBqSukJte2sCFM1NZ96cOVGMTERERERGUzSvxP8ScMBn+7XfgVfLvqm7wcxmmtnC4E2rQzKzTwOlwLecc03B5h3ALWFe3aPufyM4v/9cP0w0VFe8ihV10Z3CT09J46+H2ylZdElU4xIRERGR0RW1K/HOuZ1m9l3gU2b2W+AxYBHeE1ufBH7Wa/WvAbfjjQ1f3t1oZo/hDRf5Gt4XghuAdwN/xHuSa/e+DgOP9I/BzFKDk9uccwOWj3eBHSfIuDybuuB8XmISZyyFay+9KpphiYiIiMgoi/adj58FKvGGb7wJqMErbfmSc65r8M1CtgK3AmuC87uBu4DvO+c6B9toUnCOwMtxcFUMBMem6ThTy9XLppOV6Y9qaCIiIiIyuqKaxAcT7W8EX0Ott4aeRL13+1eAr5zH/jcAG851+2hq3P8qp1sysDgvgTfglaM1XPO+90Y3MBEREREZddGsiZfzEPjrFuIXObqfGTYzLYMttcaKsrM96FZEREREJjol8RNU4On9pF8aE5rPiYlh6eKFJCTERzEqERERERkLSuInqOqtjbTNtNB8Q+NJPvKuq6MYkYiIiMjEVlFRwYIFC4a17oYNG7jqqugNJqIkfgLqajtD4ISfevPq4X1mPHe0ntIL5kc5MhEREZHxJTU1NfTy+XwkJSWF5jdt2tRn3ZUrV7Jnz57z3mdFRUWf/aampmJm/OY3vznvvrtFe3QaOQcntj5DYlkcjcH5gtQM9rVPw+fTdzIRERGR3hobG0PThYWFPPzww6xevXrAeh0dHcTGRiY1XrlyZZ/9lpeX8853vpO3vvWtEekfdCV+Qgr87XlSynr+yPyuk8+v/bsoRiQiIiIysZSXl1NQUMADDzxAbm4ua9euDbV1u//++ykuLiYvL4+SkhI2b958Tvv68Y9/zHvf+15SUlIiFb6uxE9Egb8epGl1Ht1D0xw5Xct75uZFNygRERGRCebo0aPU1tZSVVVFV1cX27dv77O8uLiYiooKUlJSePzxx7ntttvYt28fM2fOHPY+mpubeeSRR3j00UcjGruuxE9Ahw7F0eg6AIjzxbC7JSHKEYmIiIj0YjZ2r/Pg8/m47777SEhIICkpacDyW265hby8PHw+H7feeivz5s1jx44dI9rHb37zG7Kzs7nmmmvOK9b+lMRPMC1HA3BhZmi+ICWNm25+fxQjEhEREZmYcnJySExMHHT5xo0bKSsrY9asWfj9fnbt2kVNTc2I9vHjH/+Yj3zkI9h5fuHoT+U0E0z1X58kblEc0A5AUtsZbrxyXnSDEhEREZmAhkqsq6qquOOOO3jiiScoLS3F7/dTVlaGc27Qbfo7dOgQ5eXlfP/7349EuH0oiZ9gDv3xJeoX+qHLmw80njnfX5JEREREImsEie541dTUhJmRk5MDwPr169m1a9eI+vjJT37ClVdeSXFxccTjUznNBHNo3xmau7x6+MTYOHxzyqIbkIiIiMgkVFJSwt13383y5cspLi5m586drFixYkR9bNy4kdtvv31U4tOV+AnEdXXSOi8rNJ+XmMQ7br0pihGJiIiITByVlZWh6VWrVhEIBPos79+2bt061q1bR0NDA2lpaX3WXbNmDWvWrBlyf6+//vp5xzwYXYmfQE69/DxdRT03X8Q2N5GZHh/FiEREREQkGpTETyCHfvsEtfGdofmaLg0tKSIiIjIVKYmfQPa/fITWLi+JT42LZ9G1b49yRCIiIiISDUriJ5DG/J7x4WfExnP9ysVRjEZEREREokVJ/ATR3nCaltyeJ4n5Gho1tKSIiIjIFKUkfoI49NtHqfG1h+bPZOZFMRoRERERiSYl8RPEzvIXae/ynvCUEZ/IOz/8/ihHJCIiIiLRoiR+gmiclh6azukypuekDbG2iIiIiExmSuIniMZpPePDx9Q3RDESERERkcmpoqKCBQsWDGvdDRs2cNVVV41yRINTEj8BHHrySU50tobm0y+8KIrRiIiIiEwcqampoZfP5yMpKSk0v2nTpj7rrly5kj179kRkv3/961+56KKLSE9PZ+7cuTz00EMR6bdbbER7k1Gx45E/0ZntjUyTlZDIWz/47ugGJCIiInIWJ77z4Kj2n/PpTw1rvcbGxtB0YWEhDz/8MKtXrx6wXkdHB7GxkUmN29vbec973sPXv/517rzzTp577jmuvfZaLr/8cpYuXRqRfehK/ATQlp4ams5s6cLn09iSIiIiIuejvLycgoICHnjgAXJzc1m7dm2ordv9999PcXExeXl5lJSUsHnz5mH1XVtbS319PR/+8IcxMy699FIWLVrEa6+9FrH4lcRPAHWp8aHp+FP1UYxEREREZPI4evQotbW1VFVVhS13KS4upqKigkAgwD333MNtt93GkSNHztrvjBkz+MAHPsD69evp7Oxk69atVFVVRbSGXkn8BDCzpp4FZyAvPomSVSuiHY6IiIjIpODz+bjvvvtISEggKSlpwPJbbrmFvLw8fD4ft956K/PmzWPHjh3D6vsDH/gAX/7yl0lISGDlypWsW7eOWbNmRSx21cRPAO/+xlejHYKIiIjIpJOTk0NiYuKgyzdu3Mg3v/lNKisrAa++vqam5qz9vv7669x6661s3ryZt7zlLezdu5d3vOMd5OXlcdNNN0Ukdl2JFxEREZEpyWzw+wyrqqq44447ePDBB6msrKSuro7S0lKcc2ftd9euXSxYsIAbb7wRn8/HggULuOmmm/jTn/4UsdiVxIuIiIiI9NPU1ISZkZOTA8D69evZtWvXsLZdtmwZe/fu5a9//SvOOfbv388f/vCHiI1MAyqnEREREREZoKSkhLvvvpvly5djZtx+++2sWDG8exOLi4v50Y9+xGc+8xmqqqrIyMjgQx/6EB/72MciFp+SeBERERGJuOGO4z6WumvbAVatWkUgEOizvH/bunXrWLduHQ0NDaSlpfVZd82aNaxZs2bQfb3vfe/jfe97X0TiDkflNCIiIiIiE4ySeBERERGRCUZJvIiIiIjIBKMkXkRERERkglESLyIiIiLnbTjjp8tA5/rvpiReRERERM5LXFwcZ86ciXYYE1J7ezuxsSMfMFJJvIiIiIicl+nTp1NdXU1zc7OuyI9AV1cXx44dIyMjY8Tbapx4ERERETkv6enpABw+fJj29vYoRxN5LS0tJCYmjkrfKSkpZGdnj3g7JfEiIiIict7S09NDyfxkU15ezrJly6IdRh8qpxERERERmWCUxIuIiIiITDBK4kVEREREJhgl8SIiIiIiE4xpGKCRMbMTQNUwVs0GakY5HDk/Okbjn47R+KdjNP7pGI1/OkbjX7SO0RznXE64BUriR4mZPeecuyTaccjgdIzGPx2j8U/HaPzTMRr/dIzGv/F4jFROIyIiIiIywSiJFxERERGZYJTEj56Hoh2AnJWO0finYzT+6RiNfzpG45+O0fg37o6RauJFRERERCYYXYkXEREREZlglMSLiIiIiEwwSuIjxMx8ZvY5M3vdzFrM7JCZfcPMUqIdm/QwMzfIqzHasU0lZvZ5M/u1mR0I/vtXnmX9BWb2X2Z2ysyazKzCzK4bo3CnpJEcIzO7d4hz65/GMOwpxczmm9mXzWybmZ0wswYze8nMvhDu/z06j8beSI6RzqOxFzwnNpnZbjM7bWbNwTzum2Y2c5D1x805FButHU9C3wI+A2wGvgEsCs4vM7PVzrmuaAYnfVQw8AaV9mgEMoX9K1ALvAD4h1rRzIqBLUAH8HXgNHAH8N9m9jbn3F9GN9Qpa9jHqJfPMfBhKM9HMCbp66PAXcDvgU14/x27Fvgq8D4zu8I5dwZ0HkXRsI9RLzqPxk4BMBMvdwvgnR8XAncC7zezMufccRin55BzTq/zfAGLgS7gN/3aPw044IPRjlGv0DFxwIZoxzHVX8DcXtO7gMoh1v0V0AmU9WpLxXty8h6CN+jrFdVjdG/w3CqMdtxT6QVcAmSEaf9q8Hh8qlebzqPxf4x0Ho2TF3BL8Fj8n15t4+4cUjlNZHwAMODb/dp/ADQDt411QDI0M4s3s9RoxzFVOecODGe94M/N7wLKnXMv9dq+EXgYmA9cOhoxTnXDPUb9mVm6melX3jHgnHvOOXc6zKJfBt9LQedRNA33GPWn8yjqqoLvmTB+zyEl8ZFxKd6V+B29G51zLcBL6D+O48178b5cNZjZcTP7jpllRDsoCWsJkABsDbNsW/Bd59f48QreT8wtZrbFzN4W7YCmqILg+7Hgu86j8af/MepN59EYM7NEM8s2swIzuwH4fnDRY8H3cXkO6VteZOQBNc651jDLqoErzSzeOdc2xnHJQDuAXwP7gHTg7cCngGvM7Mrgt2oZP/KC79VhlnW35Y9RLDK4Orz7TLYAp4AFwGeBP5rZR51zG6IW2RRjZjHAl/Dqdn8WbNZ5NI4McoxA51E0fRz4Tq/5SuA251xFcH5cnkNK4iMjGQiXwAO09FpHSXyUOecu79e00cxeAdYB/xB8l/EjOfge7vxq6beORIlz7tv928zsR3i19N8ys0f0BXnMfBu4AvgX59yeYJvOo/Hl2ww8RjqPouu/gNfxatyX4ZXO5PRaPi7PIZXTREYz3s8s4ST2WkfGp3/D+4J1U7QDkQG6z5tw55fOrXHMOXcS+P/hjWxzZXSjmRrM7Ct4vyw+5Jz7Wq9FOo/GiSGOUVg6j8aGcy7gnPuLc+6/nHP3ALcDD5jZ54OrjMtzSEl8ZBwGss0s3MHNxyu10VX4cco5107wGEY7FhngcPA93M+U3W3hft6U8aEy+K5za5SZ2b3AF4H1wCf6LdZ5NA6c5RgNpTL4rvNojDjnXgFeBD4ZbBqX55CS+Mh4Fu/f8rLejWaWCJQBz0UhJhmm4HEqIPwNRhJdO/F+vlweZtkVwXedX+PXvOC7zq1RZGb3APcAG4GPu+DYd73oPIqyYRyjoeg8io4kYFpwelyeQ0riI+OXeOOJfrZf+x14NVKbxjogGcjMsgZZ9BW8+0MeHcNwZBiC9Z+PAqvMbGl3e3B40I8De+k3KpSMLTOLDTe6k5nNAv4eOIl3o56MAjP7Et744j8B1rowDxbUeRRdwzlGOo+iw8xyB2m/Fm/4z20wfs8hG9mXQRmMmX0Hr85tM96QRN1PbH0GuC7cSStjy8y+hfeN+W/AQbwbWN6O9/S87cC1buCT82QUmNmHgTnB2U8D8XhPOgaocs79pNe6F+D9x7Ed78nI9XhfkC8EbnLO/fdYxT2VDPcYmZkfeBPvxrDd9Iyq8XG8c+wDzrlfj1ngU4iZ3QU8iPffs/8Pb6jj3o455/4cXFfnURQM9xjpPIoOM9uM98TWv+KNDZ8IXAy8H6/GfVX3uPDj8RxSEh8hwSGjPov3qN5CvEcm/xL4ku4mHx/M7Ga8+rZSIAvvyWt78Z7C9s3guP4yBsysHLhmkMVPOudW9Vt/EXB/cJt44AXgXqdHxY+a4R6j4L1A3wUuxytLS8X7798zwNedc7rCO0rMbAPeDXiD6XMu6Twae8M9RjqPosPM3od3fJbgjUbj8JL5PwP/5pw72G/9cXUOKYkXEREREZlgVBMvIiIiIjLBKIkXEREREZlglMSLiIiIiEwwSuJFRERERCYYJfEiIiIiIhOMkngRERERkQlGSbyIiIiIyASjJF5EJMLMzPV6jclDQMxsg5lNiAd/mFll8GFSMkJmltvv72tDtGMSkehQEi8iEmRmq4KJ0T+FWXaNmZ02syNmtmQY3VUAHwa+Ftx+cbDv/zpLDGuD6/3LuXyGqcjMpplZi5m9dJb1rg3+2z40SnGsMbPPjkbfvdTh/V19eJT3IyLjnJJ4EZGzMLN3AI8DtcBK59wrw9jsgHPup865JwCcc68C24GbzGz6ENutBTqBH59n2OPZAuCGSHXmnKsF/gtYambLhlh1bfD9R5Hadz9rgM+OUt8AOOdagn9XPx3N/YjI+KckXkRkCGb2QWAzcABY4Zzbdx7d/RCIBW4bZF8XACuB/3bOVZ/HfsY151yrc64twt3+MPi+NtxCM0sD/hfwmnNuW4T3PeqC8YuIhCiJFxEZhJn9PfAT4AXgaufc4fPs8hdAE4MkmvS7UmxmN5jZL83sgJmdMbM6M/sfM7tmmPGXm1llmPbCYFnJvf3azcz+3syeN7NmM2sws7+Z2bVh+viIme0IxtQUjHGTmeUMI64BNfHdbWa20Mz+GNz3aTN7xMxyh/FxnwCqgA+ZWUKY5e8Hkul1Fd7M5pnZT4IlUm3BGP7NzFLCxJxrZv8R/JytZnbczP5sZm/pjh+4BpjTr2Z9Va8+rg5uczp4PF8ws4+F2Vd5MJa5wc9fC9QP499ARKaQ2GgHICIyHpnZ54F/Bf4K3OycazzfPp1zDWb2CHC7mV3qnHu21/58wEeAE8Dvg81rgGnARiAA5AMfB54ws2udcxXnG1M/PwE+ADwCrAcSgA8Bfzazv3PO/T4Y62145T4VwJeAM8Bs4G3A9OBnOBf5QDneLx//D7AU+N9AOmcpv3HOdQVv8rwHeBfw636rrAXag58RM7sY79jWAd8HqoP7+wywwsyucc61B9ctBJ4BZuAdi+eAFOAKYDXwZ7wymq8B2cDneu13d7CPdwY/11HgG0AD3heLh81srnPuC/3iTQWeDO73C3j/riIiPZxzeumll156OQewCnDA/uD7ZiDhHPpxwIZBlq0MLv9ev/Ybg+3f7NWWEmb7GUAN8Fi/9g3ef9L7tJUDlWH6KAzu695ebe8Jtt3Zb91YvKT1TcCCbb/FuzIce47/zpVAeZg2B7yvX/t3g+0Lh9HvHLz7Cfr/2ywI9vHbXm0vA68Daf3W7f53WNOr7bFg241h9ukbxr93DN6vBHVAXq/2eLwkvROY168fB3z1XP/O9NJLr8n/UjmNiMhAM4PvB5xzrZHs2HlXz98APmBmib0WDbjp0jnX1D1tZqlmloWX8G0HLo9kXHh1+g3Af5lZdvcL8AOP4iX+84LrnsYrTbnJzCyCMRx2zv2qX9tfg+8XnG1j51wVXlnNDWaW12tR97/tDwHM7EJgCfAzIKHf530ar+TphuC604C3Ao875/47zD67hvG5Lsb7peJHrldJlvPuC/g3vNLWm8Ns93+H0beITFFK4kVEBrofL3n8RzP7xij0/yO85Pg9AGaWCbwb2OGc29W9kpkVm9kvzOwUXoJdg1eq8nYgM8IxLQLSgGPBffR+3RtcZ0bw/V/xriz/F3DCzH5jZh+PwM2XB8K0nQy+Zw2zjx/iXfm+HcDMYvDKlA7jjTAE3mcFuI+Bn/U4XqlM92e9ADDgxeF+iDCKgu+vhlnWfbzn9ms/4ZyrO499isgkp5p4EZGBmoF34F2B/kcz8znnPneWbUbix8BX8a4Q/xz4IF79efcIK5hZKvAUXkL5bWAnXiLfBXweuG4Y+xns4U/h/ttveEnsB4fobxeAc26vmZUA1wdf1wA/AO4zs6udc/uHEVs4nUMsG+4V///CGwp0LV6N+lvxfln5mnOuu//uvr5BT2Lf36l+657Pg7TO5deK5vPYn4hMAUriRUTCcM6dCd6M+Hvgs2ZmzrnPRqjvo2b2J7xylFl4CWcz3ug13a4H8oCPOufW997ezL46zF3V4pVy9Nf/qi/AXmA+sM0N4ybeYJnRY8EXZvZ24I/APwJ3DTO+iHPOtZrZJuDTZraCnlKa3v+Ge4Pvnc65sz1Rdy9eAj/U+POh3Q/S3v2lZnGYZSXB93C/QoiIDErlNCIig3DOncEb6eTPwD+Y2X9EsPsf4v03+Bt4ifYjzrnewwj2v2rszZjdwPDr4d8A0szssl7b++g7ekq3jcF4vhauIzOb0Ws6O8wqLwTfpw0zttHU/YvG/wO8E3jKObe31/IX8X5V+ISZDfhCY2axwVp4nPcgqT8BbzOz1WHW7X18GoHMMPcJvAAcBNb2Hi7TzOKCMTrgdyP7iCIy1elKvIjIEIJX5N+Fl2R9Olha86kIdP1HvOEGbwnO93+K6NPB5d8IDnEYAMqAD+OV1lw4jH08BNwNbDazfwfagPcS5r/9zrlHzGw98Ckzuwj4A14NfgGwHK82vDvh/R8zO41X7nMIr75/DV4y+pNhxDWqnHMvm9nz9Nws+qN+y52ZfRjvvodXzOxHePXqyXif8+/wSpY2BDf5FLAF+JOZ/Rh4HkjC+zJVCfxzcL1teGVYD5rZFrwvYn91zh03s0/hjXb0rJk9hFcadSveMJX/2u9LhojIWSmJFxE5C+dci5ndjFdvfVfwSuunnHPnXCftnOsws43A/8Ert3iq3/I6M7sR+Drwabz/Xj+Pd1PrxxhGEu+ce9PM3o13I+pX8G4S/QleUvt6mPU/amZ/A+7ES2Lj8b5IvBCc7/afwPvwxnCfFuz3ReDTzrm/De9fYNT9EO8XjgYGjhmPc+4lM1uG97neBXwiuG4lXvL+RK913zSzS4D/D+/f/yN4NfMv431R6vZtvC867w325wOuBY475x41s+uBL+JdfY/HG0P+DufcwxH6zCIyhdh5/D9IRETCMDOHV9/+aaCtX5mMyDkLfoHsHqnnBPBj59ya6EUkItGimngRkdHxfrwk67fRDkQmlRn0DIcpIlOYymlERCLvLb2mTw66lsjI1dL37+vwYCuKyOSmchoRERERkQlG5TQiIiIiIhOMkngRERERkQlGSbyIiIiIyASjJF5EREREZIJREi8iIiIiMsEoiRcRERERmWCUxIuIiIiITDD/fwnmZZu1ESiSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 864x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "plt.figure(figsize=(12,8))\n",
    "for i in range(num_trials):\n",
    "    ax.plot(my_xs, results2_list[i][2], linewidth=3, color=my_color_vec[i], label=\"Trial \" + str(i+1))\n",
    "    \n",
    "    ax.fill_between(my_xs, results0_list[i][2], results5_list[i][2], alpha=0.5)\n",
    "\n",
    "ax.set_title('Accuracy As a Function of Vector Size and Trial')\n",
    "ax.set_ylabel('Accuracy')\n",
    "ax.set_xlabel('K [Values in Vector]')\n",
    "ax.legend([\"Trial 1\", \"Trial 2\", \"Trial 3\", \"Trial 4\", \"Trial 5\", \"Trial 6\", \"Trial 7\", \"Trial 8\"], loc='lower right')\n",
    "ax.grid(visible=True, which='both')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7e03292f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}