Remove Low ET data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "230798\n"
     ]
    }
   ],
   "source": [
    "\n",
    "Test_Data = pd.read_csv(r\"C:\\Users\\oscar\\OneDrive\\Documents\\Year4_Project\\FULL_ele_and_Mu_data_BACKUP_1.csv\") \n",
    "print(len(Test_Data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "label_0 = Test_Data[Test_Data['label'] == 0]\n",
    "label_1 = Test_Data[Test_Data['label'] == 1]\n",
    "\n",
    "# Plot distributions of energies for both labels\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.hist(label_0['TOB_ET'], bins=100, alpha=0.5, color='blue', label='Jets', range=(0, 100))\n",
    "plt.xlabel('TOB_ET GeV' , fontsize=17)\n",
    "plt.ylabel('Frequency', fontsize=17)\n",
    "plt.title('Distribution of Energies for Jets', fontsize=19)\n",
    "plt.axvline(x=16, color='red', linestyle='--', label='Threshold at 16 GeV')\n",
    "plt.legend(fontsize=12)\n",
    "plt.tick_params(axis='both', which='major', labelsize=13) \n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "# Plot histogram for label 1\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.hist(label_1['offline_ele_pt'], bins=100, alpha=0.5, color='orange', label='Electrons',range=(0, 100))\n",
    "plt.xlabel('Offline Electron pT GeV', fontsize=17)\n",
    "plt.ylabel('Frequency', fontsize=17)\n",
    "plt.title('Distribution of Energies for Electrons', fontsize=19)\n",
    "plt.legend(fontsize=13)\n",
    "plt.tick_params(axis='both', which='major', labelsize=13)  # Adjusts the font size of the numbers on the axes\n",
    "\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "47516\n",
      "89057\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(len(label_0[label_0['TOB_ET'] > 16]))\n",
    "print(len(label_1[label_1['offline_ele_pt'] > 16]))\n",
    "reduced_0 = label_0[label_0['TOB_ET'] > 16]\n",
    "reduced_0.head()\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.hist(reduced_0['TOB_ET'], bins=100, alpha=0.5, color='blue', label='Jets', range=(0, 100))\n",
    "plt.xlabel('TOB_ET GeV')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Distribution of Energies for Jets')\n",
    "plt.axvline(x=16, color='red', linestyle='--', label='Threshold at 16 GeV')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "47516\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Assuming label_1 has more than 10,000 instances\n",
    "downsampled_label_1 = label_1.sample(n= len(reduced_0), random_state=69)\n",
    "print(len(downsampled_label_1))\n",
    "\n",
    "# Now you can check the length to confirm it's been downsampled\n",
    "\n",
    "# Plot histogram for label 1\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.hist(downsampled_label_1['offline_ele_pt'], bins=100, alpha=0.5, color='orange', label='Label 1',range=(15, 100))\n",
    "plt.xlabel('Offline Electron pT')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Distribution of Energies for Label 1')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>L0_1</th>\n",
       "      <th>L0_2</th>\n",
       "      <th>L0_3</th>\n",
       "      <th>L0_4</th>\n",
       "      <th>L0_5</th>\n",
       "      <th>L0_6</th>\n",
       "      <th>L0_7</th>\n",
       "      <th>L0_8</th>\n",
       "      <th>L0_9</th>\n",
       "      <th>L1_1</th>\n",
       "      <th>...</th>\n",
       "      <th>Had_5</th>\n",
       "      <th>Had_6</th>\n",
       "      <th>Had_7</th>\n",
       "      <th>Had_8</th>\n",
       "      <th>Had_9</th>\n",
       "      <th>label</th>\n",
       "      <th>TOB_ET</th>\n",
       "      <th>offline_ele_pt</th>\n",
       "      <th>TOB_eta</th>\n",
       "      <th>ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>19.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.2375</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.40</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>50.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.8375</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.35</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>35.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0125</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.50</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0</td>\n",
       "      <td>16.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6125</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.45</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.6625</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 104 columns</p>\n",
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      ],
      "text/plain": [
       "    L0_1  L0_2  L0_3  L0_4  L0_5  L0_6  L0_7  L0_8  L0_9  L1_1  ...  Had_5  \\\n",
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       "6    0.0  0.00   0.0   0.0  2.40   0.0   0.0  0.00   0.0   0.0  ...    4.0   \n",
       "10   0.0  1.35   0.0   0.0  1.75   0.0   0.0  0.95   0.0   0.0  ...    3.0   \n",
       "16   0.0  0.00   0.0   0.0  0.00   0.0   0.0  0.00   0.0   0.0  ...    6.0   \n",
       "18   0.0  0.00   0.0   0.0  3.75   0.0   0.0  0.00   0.0   0.0  ...    0.0   \n",
       "\n",
       "    Had_6  Had_7  Had_8  Had_9  label  TOB_ET  offline_ele_pt  TOB_eta  ID  \n",
       "3     0.0    0.0   0.00    0.0      0    19.2             0.0   2.2375   0  \n",
       "6     4.0    0.0   0.00    5.0      0    50.4             0.0  -0.8375   0  \n",
       "10    0.0    0.0   0.00    0.0      0    35.2             0.0  -1.0125   0  \n",
       "16    0.0    2.0   5.50    1.5      0    16.9             0.0   0.6125   0  \n",
       "18    0.0    0.0   2.45    0.0      0    23.5             0.0   1.6625   0  \n",
       "\n",
       "[5 rows x 104 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "downsampled_label_1.head()\n",
    "reduced_0.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Concatenate the two DataFrames\n",
    "combined_df = pd.concat([downsampled_label_1, reduced_0])\n",
    "\n",
    "\n",
    "combined_df = combined_df.sample(frac=1).reset_index(drop=True)\n",
    "\n",
    "# Save the combined DataFrame to a CSV file\n",
    "combined_df.to_csv(r'\\Users\\oscar\\OneDrive\\Documents\\Year4_Project\\QNN_Ideas\\Train_data_Low_ET_gone_1.csv', index=False)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20000\n",
      "75032\n"
     ]
    }
   ],
   "source": [
    "\n",
    "full_df = pd.read_csv('Train_data_Low_ET_gone_1.csv')\n",
    "\n",
    "# Separate the data with label 1 and label 0\n",
    "label_1 = full_df[full_df['label'] == 1]\n",
    "label_0 = full_df[full_df['label'] == 0]\n",
    "\n",
    "# Sample 5000 rows from each label\n",
    "test_data_label_1 = label_1.sample(n=10000, random_state=1)\n",
    "test_data_label_0 = label_0.sample(n=10000, random_state=1)\n",
    "\n",
    "# Concatenate the two samples to create the test set\n",
    "test_data = pd.concat([test_data_label_1, test_data_label_0])\n",
    "\n",
    "# Remove the test data samples from the full dataset to create the training set\n",
    "train_data = full_df.drop(test_data.index)\n",
    "\n",
    "# Save the test set to a new CSV file\n",
    "test_data.to_csv('Above_16GeV_TEST_DATA.csv_1', index=False)\n",
    "\n",
    "# Save the training set to a new CSV file\n",
    "train_data.to_csv('Above_16GeV_Train_DATA.csv_1', index=False)\n",
    "\n",
    "print(len(test_data))\n",
    "print(len(train_data))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "      <th>L1_1</th>\n",
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       "      <th>label</th>\n",
       "      <th>TOB_ET</th>\n",
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       "      <th>TOB_eta</th>\n",
       "      <th>ID</th>\n",
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       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
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       "   L0_1  L0_2  L0_3  L0_4   L0_5  L0_6  L0_7   L0_8  L0_9  L1_1  ...   Had_5  \\\n",
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       "\n",
       "   Had_6  Had_7  Had_8  Had_9  label  TOB_ET  offline_ele_pt  TOB_eta  ID  \n",
       "0    0.0   0.00    0.0    0.0      1    10.7        33.42019  -1.5125   3  \n",
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       "\n",
       "[5 rows x 104 columns]"
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     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "file_path = r'C:\\Users\\oscar\\OneDrive\\Documents\\Year4_Project\\QNN_Ideas\\Above_16GeV_Train_DATA.csv_1'\n",
    "Test_Data = pd.read_csv(file_path)\n",
    "Test_Data.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lpenDDz/Mt/2fPn06y1kS14/R3MgMxDNnznRonz59uiQpIiIi19vMDXd39yzH884772R5FGGmDz74wOHeArNnz9aVK1fs4zU8PFz+/v567bXXbngPgpMnT+a6xocfflhbt251CPf79+/XmjVr9M9//jPX2wOAoogZewBArixbtkz79u3TlStXlJycrDVr1iguLk6VKlXSd999Z7/h1Y1MnjxZGzZsUEREhCpVqqSUlBS99957Kl++vFq1aiXpasgOCAjQnDlzVKJECRUrVkzNmzfP87XHpUuXVqtWrTRo0CAlJydr5syZqlq1qsMN14YOHaqvvvpKXbp00SOPPKLff/9dn3/+ucPN7HJbW/fu3dW+fXu9+OKLOnz4sBo0aKCVK1fq22+/1ahRo7JsO6+GDx+u999/XwMHDtT27dtVuXJlffXVV9q0aZNmzpx5W7Odx48f1+eff56lvXjx4urRo0euttW2bVs98cQTiomJUUJCgjp37ixPT08dOHBAixYt0ltvvaWHH374ptt44okn9O6776pPnz4aOXKkypUrp/nz59vH3M3OqMjp/ufNm6f33ntPDz30kKpUqaJz587pww8/lL+/vz2k50aDBg00YMAAffDBBzpz5ozatm2rLVu2aN68eerRo4fD7HheWK1WvfLKK1naS5curaefflr333+/PvvsM5UsWVK1a9dWfHy8Vq1ale0jFC9fvqwOHTrokUce0f79+/Xee++pVatW9hsW+vv7a/bs2Xr88cfVuHFj9e7dW3fddZeOHj2qpUuXqmXLllm+zLqVp59+Wh9++KEiIiI0duxYeXp6avr06QoKCrLfZBAAcAtOvCM/AMBEMh+Hlfnj5eVlBAcHG506dTLeeusth8eqZbr+cVerV682HnzwQSMkJMTw8vIyQkJCjD59+hi//fabw+u+/fZbo3bt2oaHh4fD4+Xatm1r1KlT54b1Zfe4u//85z/GhAkTjMDAQMPX19eIiIgwjhw5kuX106ZNM+6++27D29vbaNmypbFt27Ys27xZbdc/Xs0wrj4abPTo0UZISIjh6elpVKtWzXjzzTcNm83m0E+SERkZmaWm7B7Dd73k5GRj0KBBRtmyZQ0vLy+jXr16N3wkX3497u7a48zukWvZPersgw8+MJo0aWL4+voaJUqUMOrVq2eMHz/eOHHiRI7q/OOPP4yIiAjD19fXuOuuu4wxY8YY//d//2dIMjZv3uxQ1/WfR072//PPPxt9+vQxKlasaHh7exuBgYHG/fffb2zbtu2W71l274XVajUmTZpkhIaGGp6enkaFChWMCRMmGGlpaQ79cvP5ZO4vu8+oSpUqhmEYxunTp+1jo3jx4kZ4eLixb9++LGMr8/d7/fr1xvDhw41SpUoZxYsXN/r27Wv8/fffWfa9du1aIzw83ChZsqTh4+NjVKlSxRg4cKDD+5STx91lOnbsmPHwww8b/v7+RvHixY3777/fOHDgQI7fCwAo6iyGYYK78gAAAGRj5syZGj16tP7880/dfffdzi4HAIBCR7AHAACmcenSJYeb36WlpalRo0bKyMjQb7/95sTKAABwHq6xBwAAptGzZ09VrFhRDRs21NmzZ/X5559r37592T7GDgCAooBgDwAATCM8PFwfffSR5s+fr4yMDNWuXVtffPGFHn30UWeXBgCA03AqPgAAAAAAJsZz7AEAAAAAMDGCPQAAAAAAJsY19jlgs9l04sQJlShRQhaLxdnlAAAAAADucIZh6Ny5cwoJCZGb283n5An2OXDixAlVqFDB2WUAAAAAAIqYY8eOqXz58jftQ7DPgRIlSki6+ob6+/s7uZqbs1qtWrlypTp37ixPT09nlwNkwRiFq2OMwtUxRuHqGKNwdWYZo6mpqapQoYI9j94MwT4HMk+/9/f3N0Ww9/Pzk7+/v0sPUhRdjFG4OsYoXB1jFK6OMQpXZ7YxmpPLwbl5HgAAAAAAJkawBwAAAADAxJwa7GfPnq369evbT3EPCwvTsmXL7OvT0tIUGRmpMmXKqHjx4urVq5eSk5MdtnH06FFFRETIz89PgYGBGjdunK5cueLQZ926dWrcuLG8vb1VtWpVxcbGFsbhAQAAAABQ4Jx6jX358uX1+uuvq1q1ajIMQ/PmzdODDz6oHTt2qE6dOho9erSWLl2qRYsWqWTJkhoxYoR69uypTZs2SZIyMjIUERGh4OBg/fjjj0pMTFT//v3l6emp1157TZJ06NAhRURE6Mknn9T8+fO1evVqDR06VOXKlVN4eLgzDx8AAAAAnCIjI0NWq9XZZTiF1WqVh4eH0tLSlJGR4dRaPD095e7uftvbcWqw7969u8Pyq6++qtmzZ2vz5s0qX768Pv74Yy1YsED33XefJGnu3LmqVauWNm/erBYtWmjlypXas2ePVq1apaCgIDVs2FBTpkzRc889p+joaHl5eWnOnDkKDQ3VtGnTJEm1atXSxo0bNWPGDII9AAAAgCLn/Pnz+vPPP2UYhrNLcQrDMBQcHKxjx47l6MZ0Bclisah8+fIqXrz4bW3HZe6Kn5GRoUWLFunChQsKCwvT9u3bZbVa1bFjR3ufmjVrqmLFioqPj1eLFi0UHx+vevXqKSgoyN4nPDxcTz31lHbv3q1GjRopPj7eYRuZfUaNGpVtLenp6UpPT7cvp6amSrr6zY6rf6uVWZ+r14miizEKV8cYhatjjMLVMUZdW0ZGho4dO6ZixYqpTJkyTg+2zmAYhi5cuKBixYo59fgNw9Dff/+tY8eOKTQ0NMvMfW5+h5we7Hft2qWwsDClpaWpePHi+vrrr1W7dm0lJCTIy8tLAQEBDv2DgoKUlJQkSUpKSnII9ZnrM9fdrE9qaqouXbokX1/fLDXFxMRo0qRJWdpXrlwpPz+/PB9rYYqLi3N2CcBNMUbh6hijcHWMUbg6xqhr8vDwUHBwsEqXLl2kv3zx8vJyieP39vbWyZMntXr16iz3irt48WKOt+P0YF+jRg0lJCTo7Nmz+uqrrzRgwACtX7/eqTVNmDBBUVFR9uXU1FRVqFBBnTt3NsVz7OPi4tSpUydTPJMRRQ9jFK6OMQpXxxiFq2OMura0tDQdO3ZMJUqUkI+Pj7PLcQrDMHTu3DmVKFHC6WcspKWlydfXV23atMnyeWSeOZ4TTg/2Xl5eqlq1qiSpSZMm2rp1q9566y09+uijunz5ss6cOeMwa5+cnKzg4GBJUnBwsLZs2eKwvcy75l/b5/o76ScnJ8vf3/+Gs/XS1W9NvL29s7R7enqa5o+TmWpF0cQYhatjjMLVMUbh6hijrikjI0MWi0Vubm5ycyuaTz+32WySZH8fnMnNzU0Wi+WGvy+5+f1xuU/SZrMpPT1dTZo0kaenp1avXm1ft3//fh09elRhYWGSpLCwMO3atUspKSn2PnFxcfL391ft2rXtfa7dRmafzG0AAAAAAGBmTp2xnzBhgrp27aqKFSvq3LlzWrBggdatW6cVK1aoZMmSGjJkiKKiolS6dGn5+/vrmWeeUVhYmFq0aCFJ6ty5s2rXrq3HH39cU6dOVVJSkl566SVFRkbaZ9yffPJJvfvuuxo/frwGDx6sNWvW6Msvv9TSpUudeegAAAAA4DKio+/s/cXGxmrUqFE6c+bMbW3HYrHo66+/Vo8ePfKlrvzi1Bn7lJQU9e/fXzVq1FCHDh20detWrVixQp06dZIkzZgxQ/fff7969eqlNm3aKDg4WIsXL7a/3t3dXUuWLJG7u7vCwsLUr18/9e/fX5MnT7b3CQ0N1dKlSxUXF6cGDRpo2rRp+uijj3jUHQAAAACYxMCBA10uTGdn1qxZqly5snx8fNS8efMsl48XBKfO2H/88cc3Xe/j46NZs2Zp1qxZ2fapVKmSvv/++5tup127dtqxY0eeagQAAAAAICcWLlyoqKgozZkzR82bN9fMmTMVHh6u/fv3KzAwsMD263LX2AMAAAAAkBvTp09XvXr1VKxYMVWoUEFPP/20zp8/n6XfN998oxo1aig4OFhdunTRsWPHHNZ/++23aty4sXx8fHTPPfdo0qRJWR5Dd6s6hg0bpkGDBql27dqaM2eO/Pz89Mknn9z2Md4MwR4AAAAAYGpubm56++23tXv3bs2bN09r1qzR+PHjHfpcvHhRr776qmJjY7V8+XKdPXtWvXv3tq//4Ycf1L9/f40cOVJ79uzR+++/r9jYWL366qs5quHy5cvavn27Onbs6FBXx44dFR8fnz8Hmg2CPQAAAADA1EaNGqX27durcuXKuu+++/TKK6/oyy+/dOhjtVr17rvvKiwsTA0bNtTcuXP1448/2q+BnzRpkp5//nkNGDBA99xzjzp16qQpU6bo/fffz1ENf/31lzIyMhQUFOTQHhQUpKSkpPw50Gw4/Tn2AAAAAADcjlWrVikmJkb79u1Tamqqrly5orS0NF28eFF+fn6SJA8PD917773219SsWVMBAQHau3evmjVrpl9++UWbNm1ymKHPyMjIsh1XRLAHAAAAAJjW4cOHdf/99+upp57Sq6++qtKlS2vjxo0aMmSILl++nONAfv78eU2aNEk9e/bMss7Hx+eWry9btqzc3d2VnJzs0J6cnKzg4OCcHUwecSo+AAAAAMC0tm/fLpvNpmnTpqlFixaqXr26Tpw4kaXflStXtG3bNvvy/v37debMGdWqVUuS1LhxY+3fv19Vq1bN8uPmduvo7OXlpSZNmmj16tX2NpvNptWrVyssLCwfjjR7zNgDRUh09O2tBwAAAJzl7NmzSkhIcGgrU6aMqlatKqvVqnfeeUfdu3fXpk2bNGfOnCyv9/T01DPPPKOZM2cqPT1dEyZMUIsWLdSsWTNJ0sSJE3X//ferYsWKevjhh+Xm5qZffvlFv/76q1555ZUc1RgVFaUBAwaoadOmatasmWbOnKkLFy5o0KBBt338N0OwBwAAAIAizgwTPOvWrVOjRo0c2oYMGaKPPvpI06dP1xtvvKEJEyaoTZs2iomJUf/+/R36+vn56bnnnlO/fv10/PhxtWrVyuExdOHh4VqyZIkmT56sN954Q56enqpZs6aGDh2a4xofffRRnTx5UhMnTlRSUpIaNmyo5cuXZ7mhXn4j2AMAAAAAXFpsbKxiY2OzXT969GiNHj3aoe3xxx+3/3vgwIEaOHCgJKlHjx5KTU2Vv79/llPsw8PDFR4enu1+DMO4Za0jRozQiBEjbtkvP3GNPQAAAAAAJkawBwAAAADAxAj2AAAAAACYGNfYA7DLyU1TzHBjFQAAAKAoYcYeAAAAAIqYnNwEDgUvvz4Hgj0AAAAAFBHu7u6SpMuXLzu5Ekj/+xwyP5e84lR8AAAAACgiPDw85Ofnp5MnT8rT0zPL496KApvNpsuXLystLc2px2+z2XTy5En5+fnJw+P2ojnBHgAAAACKCIvFonLlyunQoUM6cuSIs8txCsMwdOnSJfn6+spisTi1Fjc3N1WsWPG26yDYAwAAAEAR4uXlpWrVqhXZ0/GtVqs2bNigNm3ayNPT06m1eHl55ctZAwR7AAAAAChi3Nzc5OPj4+wynMLd3V1XrlyRj4+P04N9fil6F1QAAAAAAHAHIdgDAAAAAGBiBHsAAAAAAEyMYA8AAAAAgIkR7AEAAAAAMDGCPQAAAAAAJkawBwAAAADAxAj2AAAAAACYGMEeAAAAAAATI9gDAAAAAGBiBHsAAAAAAEyMYA8AAAAAgIkR7AEAAAAAMDGCPQAAAAAAJkawBwAAAADAxAj2AAAAAACYGMEeAAAAAAATI9gDAAAAAGBiBHsAAAAAAEyMYA8AAAAAgIkR7AEAAAAAMDGCPQAAAAAAJkawBwAAAADAxAj2AAAAAACYGMEeAAAAAAATI9gDAAAAAGBiBHsAAAAAAEyMYA8AAAAAgIkR7AEAAAAAMDGCPQAAAAAAJkawBwAAAADAxAj2AAAAAACYGMEeAAAAAAATI9gDAAAAAGBiHs4uAIC5REff3noAAAAA+YsZewAAAAAATIxgDwAAAACAiRHsAQAAAAAwMYI9AAAAAAAmRrAHAAAAAMDECPYAAAAAAJgYwR4AAAAAABMj2AMAAAAAYGIEewAAAAAATIxgDwAAAACAiRHsAQAAAAAwMacG+5iYGN17770qUaKEAgMD1aNHD+3fv9+hT7t27WSxWBx+nnzySYc+R48eVUREhPz8/BQYGKhx48bpypUrDn3WrVunxo0by9vbW1WrVlVsbGxBHx4AAAAAAAXOqcF+/fr1ioyM1ObNmxUXFyer1arOnTvrwoULDv2GDRumxMRE+8/UqVPt6zIyMhQREaHLly/rxx9/1Lx58xQbG6uJEyfa+xw6dEgRERFq3769EhISNGrUKA0dOlQrVqwotGMFAAAAAKAgeDhz58uXL3dYjo2NVWBgoLZv3642bdrY2/38/BQcHHzDbaxcuVJ79uzRqlWrFBQUpIYNG2rKlCl67rnnFB0dLS8vL82ZM0ehoaGaNm2aJKlWrVrauHGjZsyYofDw8CzbTE9PV3p6un05NTVVkmS1WmW1Wm/7uAtSZn2uXiecw60Qvsq71dBjjMLVMUbh6hijcHWMUbg6s4zR3NRnMQzDKMBacuXgwYOqVq2adu3apbp160q6eir+7t27ZRiGgoOD1b17d/3rX/+Sn5+fJGnixIn67rvvlJCQYN/OoUOHdM899+jnn39Wo0aN1KZNGzVu3FgzZ86095k7d65GjRqls2fPZqkjOjpakyZNytK+YMEC+34BAAAAACgoFy9e1GOPPaazZ8/K39//pn2dOmN/LZvNplGjRqlly5b2UC9Jjz32mCpVqqSQkBDt3LlTzz33nPbv36/FixdLkpKSkhQUFOSwrczlpKSkm/ZJTU3VpUuX5Ovr67BuwoQJioqKsi+npqaqQoUK6ty58y3fUGezWq2Ki4tTp06d5Onp6exy4GJiYgp+HxMm3Hw9YxSujjEKV8cYhatjjMLVmWWMZp45nhMuE+wjIyP166+/auPGjQ7tw4cPt/+7Xr16KleunDp06KDff/9dVapUKZBavL295e3tnaXd09PTpT/4a5mpVhQem63g95HTYccYhatjjMLVMUbh6hijcHWuPkZzU5tLPO5uxIgRWrJkidauXavy5cvftG/z5s0lXT1tX5KCg4OVnJzs0CdzOfO6/Oz6+Pv7Z5mtBwAAAADATJwa7A3D0IgRI/T1119rzZo1Cg0NveVrMq+lL1eunCQpLCxMu3btUkpKir1PXFyc/P39Vbt2bXuf1atXO2wnLi5OYWFh+XQkAAAAAAA4h1ODfWRkpD7//HMtWLBAJUqUUFJSkpKSknTp0iVJ0u+//64pU6Zo+/btOnz4sL777jv1799fbdq0Uf369SVJnTt3Vu3atfX444/rl19+0YoVK/TSSy8pMjLSfjr9k08+qT/++EPjx4/Xvn379N577+nLL7/U6NGjnXbsAAAAAADkB6cG+9mzZ+vs2bNq166dypUrZ/9ZuHChJMnLy0urVq1S586dVbNmTY0ZM0a9evXSf//7X/s23N3dtWTJErm7uyssLEz9+vVT//79NXnyZHuf0NBQLV26VHFxcWrQoIGmTZumjz766IaPugMAAAAAwEycevO8Wz1pr0KFClq/fv0tt1OpUiV9//33N+3Trl077dixI1f1AQAAAADg6lzi5nkAAAAAACBvCPYAAAAAAJgYwR4AAAAAABMj2AMAAAAAYGIEewAAAAAATIxgDwAAAACAiRHsAQAAAAAwMYI9AAAAAAAmRrAHAAAAAMDECPYAAAAAAJgYwR4AAAAAABMj2AMAAAAAYGIEewAAAAAATIxgDwAAAACAiRHsAQAAAAAwMYI9AAAAAAAmRrAHAAAAAMDECPYAAAAAAJgYwR4AAAAAABMj2AMAAAAAYGIEewAAAAAATIxgDwAAAACAiRHsAQAAAAAwMQ9nFwDgzhIdffP1bm5SgwaFUgoAAABQJDBjDwAAAACAiRHsAQAAAAAwMYI9AAAAAAAmRrAHAAAAAMDECPYAAAAAAJgYwR4AAAAAABMj2AMAAAAAYGIEewAAAAAATIxgDwAAAACAiRHsAQAAAAAwMYI9AAAAAAAmRrAHAAAAAMDECPYAAAAAAJgYwR4AAAAAABMj2AMAAAAAYGIEewAAAAAATIxgDwAAAACAiRHsAQAAAAAwMYI9AAAAAAAmRrAHAAAAAMDECPYAAAAAAJgYwR4AAAAAABMj2AMAAAAAYGIEewAAAAAATIxgDwAAAACAiRHsAQAAAAAwMYI9AAAAAAAmRrAHAAAAAMDECPYAAAAAAJgYwR4AAAAAABMj2AMAAAAAYGIEewAAAAAATIxgDwAAAACAiRHsAQAAAAAwMYI9AAAAAAAmRrAHAAAAAMDECPYAAAAAAJgYwR4AAAAAABMj2AMAAAAAYGIEewAAAAAATMypwT4mJkb33nuvSpQoocDAQPXo0UP79+936JOWlqbIyEiVKVNGxYsXV69evZScnOzQ5+jRo4qIiJCfn58CAwM1btw4XblyxaHPunXr1LhxY3l7e6tq1aqKjY0t6MMDAAAAAKDAOTXYr1+/XpGRkdq8ebPi4uJktVrVuXNnXbhwwd5n9OjR+u9//6tFixZp/fr1OnHihHr27Glfn5GRoYiICF2+fFk//vij5s2bp9jYWE2cONHe59ChQ4qIiFD79u2VkJCgUaNGaejQoVqxYkWhHi8AAAAAAPnNw5k7X758ucNybGysAgMDtX37drVp00Znz57Vxx9/rAULFui+++6TJM2dO1e1atXS5s2b1aJFC61cuVJ79uzRqlWrFBQUpIYNG2rKlCl67rnnFB0dLS8vL82ZM0ehoaGaNm2aJKlWrVrauHGjZsyYofDw8EI/bgAAAAAA8otTg/31zp49K0kqXbq0JGn79u2yWq3q2LGjvU/NmjVVsWJFxcfHq0WLFoqPj1e9evUUFBRk7xMeHq6nnnpKu3fvVqNGjRQfH++wjcw+o0aNumEd6enpSk9Pty+npqZKkqxWq6xWa74ca0HJrM/V64RzuLnAXTXc3BijcG38HYWrY4zC1TFG4erMMkZzU5/LBHubzaZRo0apZcuWqlu3riQpKSlJXl5eCggIcOgbFBSkpKQke59rQ33m+sx1N+uTmpqqS5cuydfX12FdTEyMJk2alKXGlStXys/PL+8HWYji4uKcXQJcUIMGzq7gfxijcHWMUbg6xihcHWMUrs7Vx+jFixdz3Ndlgn1kZKR+/fVXbdy40dmlaMKECYqKirIvp6amqkKFCurcubP8/f2dWNmtWa1WxcXFqVOnTvL09HR2OXAxMTHOruDqjH29eoxRuC7+jsLVMUbh6hijcHVmGaOZZ47nhEsE+xEjRmjJkiXasGGDypcvb28PDg7W5cuXdebMGYdZ++TkZAUHB9v7bNmyxWF7mXfNv7bP9XfST05Olr+/f5bZekny9vaWt7d3lnZPT0+X/uCvZaZaUXhsNmdX8D+MUbg6xihcHWMUro4xClfn6mM0N7U59YpbwzA0YsQIff3111qzZo1CQ0Md1jdp0kSenp5avXq1vW3//v06evSowsLCJElhYWHatWuXUlJS7H3i4uLk7++v2rVr2/tcu43MPpnbAAAAAADArJw6Yx8ZGakFCxbo22+/VYkSJezXxJcsWVK+vr4qWbKkhgwZoqioKJUuXVr+/v565plnFBYWphYtWkiSOnfurNq1a+vxxx/X1KlTlZSUpJdeekmRkZH2Wfcnn3xS7777rsaPH6/BgwdrzZo1+vLLL7V06VKnHTsAAAAAAPnBqTP2s2fP1tmzZ9WuXTuVK1fO/rNw4UJ7nxkzZuj+++9Xr1691KZNGwUHB2vx4sX29e7u7lqyZInc3d0VFhamfv36qX///po8ebK9T2hoqJYuXaq4uDg1aNBA06ZN00cffcSj7gAAAAAApufUGXvDMG7Zx8fHR7NmzdKsWbOy7VOpUiV9//33N91Ou3bttGPHjlzXCAAAAACAK3OBp1oDAAAAAIC8ItgDAAAAAGBiLvG4OwBFT0xM9o/fi44u1FIAAAAAU2PGHgAAAAAAEyPYAwAAAABgYgR7AAAAAABMjGAPAAAAAICJEewBAAAAADAxgj0AAAAAACZGsAcAAAAAwMQI9gAAAAAAmBjBHgAAAAAAEyPYAwAAAABgYgR7AAAAAABMjGAPAAAAAICJEewBAAAAADAxgj0AAAAAACZGsAcAAAAAwMQI9gAAAAAAmBjBHgAAAAAAE/NwdgEAcL3o6PzpAwAAABQFzNgDAAAAAGBiBHsAAAAAAEyMYA8AAAAAgIkR7AEAAAAAMDGCPQAAAAAAJkawBwAAAADAxAj2AAAAAACYGMEeAAAAAAATI9gDAAAAAGBiBHsAAAAAAEwsT8H+jz/+yO86AAAAAABAHuQp2FetWlXt27fX559/rrS0tPyuCQAAAAAA5FCegv3PP/+s+vXrKyoqSsHBwXriiSe0ZcuW/K4NAAAAAADcQp6CfcOGDfXWW2/pxIkT+uSTT5SYmKhWrVqpbt26mj59uk6ePJnfdQIAAAAAgBu4rZvneXh4qGfPnlq0aJHeeOMNHTx4UGPHjlWFChXUv39/JSYm5ledAAAAAADgBm4r2G/btk1PP/20ypUrp+nTp2vs2LH6/fffFRcXpxMnTujBBx/MrzoBAAAAAMANeOTlRdOnT9fcuXO1f/9+devWTZ9++qm6desmN7er3xOEhoYqNjZWlStXzs9aAQAAAADAdfIU7GfPnq3Bgwdr4MCBKleu3A37BAYG6uOPP76t4gAAAAAAwM3lKdgfOHDgln28vLw0YMCAvGweAAAAAADkUJ6usZ87d64WLVqUpX3RokWaN2/ebRcFAAAAAAByJk/BPiYmRmXLls3SHhgYqNdee+22iwIAAAAAADmTp2B/9OhRhYaGZmmvVKmSjh49ettFAQAAAACAnMlTsA8MDNTOnTuztP/yyy8qU6bMbRcFAAAAAAByJk/Bvk+fPnr22We1du1aZWRkKCMjQ2vWrNHIkSPVu3fv/K4RAAAAAABkI093xZ8yZYoOHz6sDh06yMPj6iZsNpv69+/PNfYACkV09O2tBwAAAO4UeQr2Xl5eWrhwoaZMmaJffvlFvr6+qlevnipVqpTf9QEAAAAAgJvIU7DPVL16dVWvXj2/agEAAAAAALmUp2CfkZGh2NhYrV69WikpKbLZbA7r16xZky/FAQAAAACAm8tTsB85cqRiY2MVERGhunXrymKx5HddAAAAAAAgB/IU7L/44gt9+eWX6tatW37XAwAAAAAAciFPj7vz8vJS1apV87sWAAAAAACQS3kK9mPGjNFbb70lwzDyux4AAAAAAJALeToVf+PGjVq7dq2WLVumOnXqyNPT02H94sWL86U4AAAAAABwc3kK9gEBAXrooYfyuxYAAAAAAJBLeQr2c+fOze86AAAAAABAHuTpGntJunLlilatWqX3339f586dkySdOHFC58+fz7fiAAAAAADAzeVpxv7IkSPq0qWLjh49qvT0dHXq1EklSpTQG2+8ofT0dM2ZMye/6wQAAAAAADeQpxn7kSNHqmnTpjp9+rR8fX3t7Q899JBWr16db8UBAAAAAICby9OM/Q8//KAff/xRXl5eDu2VK1fW8ePH86UwAAAAAABwa3masbfZbMrIyMjS/ueff6pEiRK3XRQAAAAAAMiZPAX7zp07a+bMmfZli8Wi8+fP6+WXX1a3bt3yqzYAAAAAAHALeToVf9q0aQoPD1ft2rWVlpamxx57TAcOHFDZsmX1n//8J79rBAAAAAAA2chTsC9fvrx++eUXffHFF9q5c6fOnz+vIUOGqG/fvg430wMAAAAAAAUrz8+x9/DwUL9+/TR16lS99957Gjp0aK5D/YYNG9S9e3eFhITIYrHom2++cVg/cOBAWSwWh58uXbo49Dl16pT69u0rf39/BQQEaMiQITp//rxDn507d6p169by8fFRhQoVNHXq1DwdMwAAAAAAriZPM/affvrpTdf3798/R9u5cOGCGjRooMGDB6tnz5437NOlSxfNnTvXvuzt7e2wvm/fvkpMTFRcXJysVqsGDRqk4cOHa8GCBZKk1NRUde7cWR07dtScOXO0a9cuDR48WAEBARo+fHiO6gQAAAAAwFXlKdiPHDnSYdlqterixYvy8vKSn59fjoN9165d1bVr15v28fb2VnBw8A3X7d27V8uXL9fWrVvVtGlTSdI777yjbt266d///rdCQkI0f/58Xb58WZ988om8vLxUp04dJSQkaPr06QR7AAAAAIDp5SnYnz59OkvbgQMH9NRTT2ncuHG3XdS11q1bp8DAQJUqVUr33XefXnnlFZUpU0aSFB8fr4CAAHuol6SOHTvKzc1NP/30kx566CHFx8erTZs28vLysvcJDw/XG2+8odOnT6tUqVJZ9pmenq709HT7cmpqqqSrX2BYrdZ8Pb78llmfq9cJ53DL88U3+VmD1eF/Cwq/Asgr/o7C1TFG4eoYo3B1ZhmjuakvT8H+RqpVq6bXX39d/fr10759+/Jlm126dFHPnj0VGhqq33//XS+88IK6du2q+Ph4ubu7KykpSYGBgQ6v8fDwUOnSpZWUlCRJSkpKUmhoqEOfoKAg+7obBfuYmBhNmjQpS/vKlSvl5+eXL8dW0OLi4pxdAlxQgwbOruB/6tUr2DH6/fcFunkUAfwdhatjjMLVMUbh6lx9jF68eDHHffMt2EtXQ/WJEyfybXu9e/e2/7tevXqqX7++qlSponXr1qlDhw75tp/rTZgwQVFRUfbl1NRUVahQQZ07d5a/v3+B7Tc/WK1WxcXFqVOnTvL09HR2OXAxMTHOruDqTH29enHatauTbLaCG6MTJhTYpnGH4+8oXB1jFK6OMQpXZ5YxmnnmeE7kKdh/9913DsuGYSgxMVHvvvuuWrZsmZdN5sg999yjsmXL6uDBg+rQoYOCg4OVkpLi0OfKlSs6deqU/br84OBgJScnO/TJXM7u2n1vb+8sN+mTJE9PT5f+4K9lplpReGw2Z1fwPzabZ4EGe4Y/bhd/R+HqGKNwdYxRuDpXH6O5qS1Pwb5Hjx4OyxaLRXfddZfuu+8+TZs2LS+bzJE///xTf//9t8qVKydJCgsL05kzZ7R9+3Y1adJEkrRmzRrZbDY1b97c3ufFF1+U1Wq1vzFxcXGqUaPGDU/DBwAAAADATPIU7G35NO13/vx5HTx40L586NAhJSQkqHTp0ipdurQmTZqkXr16KTg4WL///rvGjx+vqlWrKjw8XJJUq1YtdenSRcOGDdOcOXNktVo1YsQI9e7dWyEhIZKkxx57TJMmTdKQIUP03HPP6ddff9Vbb72lGTNm5MsxAAAAAADgTE69R/a2bdvUqFEjNWrUSJIUFRWlRo0aaeLEiXJ3d9fOnTv1wAMPqHr16hoyZIiaNGmiH374weE0+fnz56tmzZrq0KGDunXrplatWumDDz6wry9ZsqRWrlypQ4cOqUmTJhozZowmTpzIo+4AAAAAAHeEPM3YX3tjuVuZPn16tuvatWsnwzCyXb9ixYpbbr906dJasGDBTfvUr19fP/zwwy23BQAAAACA2eQp2O/YsUM7duyQ1WpVjRo1JEm//fab3N3d1bhxY3s/i8WSP1UCAAAAAIAbylOw7969u0qUKKF58+bZb0B3+vRpDRo0SK1bt9aYMWPytUgAAAAAAHBjebrGftq0aYqJiXG4q3ypUqX0yiuvFOhd8QEAAAAAgKM8BfvU1FSdPHkyS/vJkyd17ty52y4KAAAAAADkTJ6C/UMPPaRBgwZp8eLF+vPPP/Xnn3/q//7v/zRkyBD17Nkzv2sEAAAAAADZyNM19nPmzNHYsWP12GOPyWq1Xt2Qh4eGDBmiN998M18LBAAAAAAA2ctTsPfz89N7772nN998U7///rskqUqVKipWrFi+FgcAAAAAAG4uT6fiZ0pMTFRiYqKqVaumYsWK3fSZ9AAAAAAAIP/lKdj//fff6tChg6pXr65u3bopMTFRkjRkyBAedQcAAAAAQCHK06n4o0ePlqenp44ePapatWrZ2x999FFFRUXxyDsAThcdnT99AAAAAFeXp2C/cuVKrVixQuXLl3dor1atmo4cOZIvhQEAAAAAgFvL06n4Fy5ckJ+fX5b2U6dOydvb+7aLAgAAAAAAOZOnYN+6dWt9+umn9mWLxSKbzaapU6eqffv2+VYcAAAAAAC4uTydij916lR16NBB27Zt0+XLlzV+/Hjt3r1bp06d0qZNm/K7RgAAAAAAkI08zdjXrVtXv/32m1q1aqUHH3xQFy5cUM+ePbVjxw5VqVIlv2sEAAAAAADZyPWMvdVqVZcuXTRnzhy9+OKLBVETAAAAAADIoVzP2Ht6emrnzp0FUQsAAAAAAMilPJ2K369fP3388cf5XQsAAAAAAMilPN0878qVK/rkk0+0atUqNWnSRMWKFXNYP3369HwpDgAAAAAA3Fyugv0ff/yhypUr69dff1Xjxo0lSb/99ptDH4vFkn/VAQAAAACAm8pVsK9WrZoSExO1du1aSdKjjz6qt99+W0FBQQVSHAAAAAAAuLlcXWNvGIbD8rJly3ThwoV8LQgAAAAAAORcnm6el+n6oA8AAAAAAApXroK9xWLJcg0919QDAAAAAOA8ubrG3jAMDRw4UN7e3pKktLQ0Pfnkk1nuir948eL8qxAAAAAAAGQrV8F+wIABDsv9+vXL12IAAAAAAEDu5CrYz507t6DqAIBCFx19e+sBAAAAV3BbN88DAAAAAADORbAHAAAAAMDECPYAAAAAAJgYwR4AAAAAABMj2AMAAAAAYGIEewAAAAAATIxgDwAAAACAiRHsAQAAAAAwMYI9AAAAAAAmRrAHAAAAAMDECPYAAAAAAJgYwR4AAAAAABMj2AMAAAAAYGIEewAAAAAATIxgDwAAAACAiRHsAQAAAAAwMYI9AAAAAAAmRrAHAAAAAMDEPJxdAAC4qujo21sPAAAAFAZm7AEAAAAAMDGCPQAAAAAAJkawBwAAAADAxAj2AAAAAACYGMEeAAAAAAATI9gDAAAAAGBiBHsAAAAAAEyMYA8AAAAAgIkR7AEAAAAAMDGCPQAAAAAAJkawBwAAAADAxAj2AAAAAACYGMEeAAAAAAAT83B2AQBgVtHR+dMHAAAAuB3M2AMAAAAAYGIEewAAAAAATIxgDwAAAACAiRHsAQAAAAAwMYI9AAAAAAAm5tRgv2HDBnXv3l0hISGyWCz65ptvHNYbhqGJEyeqXLly8vX1VceOHXXgwAGHPqdOnVLfvn3l7++vgIAADRkyROfPn3fos3PnTrVu3Vo+Pj6qUKGCpk6dWtCHBgAAAABAoXBqsL9w4YIaNGigWbNm3XD91KlT9fbbb2vOnDn66aefVKxYMYWHhystLc3ep2/fvtq9e7fi4uK0ZMkSbdiwQcOHD7evT01NVefOnVWpUiVt375db775pqKjo/XBBx8U+PEBAAAAAFDQnPoc+65du6pr1643XGcYhmbOnKmXXnpJDz74oCTp008/VVBQkL755hv17t1be/fu1fLly7V161Y1bdpUkvTOO++oW7du+ve//62QkBDNnz9fly9f1ieffCIvLy/VqVNHCQkJmj59usMXAAAAAAAAmJFTg/3NHDp0SElJSerYsaO9rWTJkmrevLni4+PVu3dvxcfHKyAgwB7qJaljx45yc3PTTz/9pIceekjx8fFq06aNvLy87H3Cw8P1xhtv6PTp0ypVqlSWfaenpys9Pd2+nJqaKkmyWq2yWq0Fcbj5JrM+V68TzuHmAnfVcHOzOvzvnY5fRfPh7yhcHWMUro4xCldnljGam/pcNtgnJSVJkoKCghzag4KC7OuSkpIUGBjosN7Dw0OlS5d26BMaGpplG5nrbhTsY2JiNGnSpCztK1eulJ+fXx6PqHDFxcU5uwS4oAYNnF3B/9SrVzTG6PffO7sC5BV/R+HqGKNwdYxRuDpXH6MXL17McV+XDfbONGHCBEVFRdmXU1NTVaFCBXXu3Fn+/v5OrOzWrFar4uLi1KlTJ3l6ejq7HLiYmBhnV3B1pr5evTjt2tVJNtudP0YnTHB2Bcgt/o7C1TFG4eoYo3B1ZhmjmWeO54TLBvvg4GBJUnJyssqVK2dvT05OVsOGDe19UlJSHF535coVnTp1yv764OBgJScnO/TJXM7scz1vb295e3tnaff09HTpD/5aZqoVhcdmc3YF/2OzeRaJYM+voXnxdxSujjEKV8cYhatz9TGam9pc4IrbGwsNDVVwcLBWr15tb0tNTdVPP/2ksLAwSVJYWJjOnDmj7du32/usWbNGNptNzZs3t/fZsGGDw/UJcXFxqlGjxg1PwwcAAAAAwEycGuzPnz+vhIQEJSQkSLp6w7yEhAQdPXpUFotFo0aN0iuvvKLvvvtOu3btUv/+/RUSEqIePXpIkmrVqqUuXbpo2LBh2rJlizZt2qQRI0aod+/eCgkJkSQ99thj8vLy0pAhQ7R7924tXLhQb731lsOp9gAAAAAAmJVTT8Xftm2b2rdvb1/ODNsDBgxQbGysxo8frwsXLmj48OE6c+aMWrVqpeXLl8vHx8f+mvnz52vEiBHq0KGD3Nzc1KtXL7399tv29SVLltTKlSsVGRmpJk2aqGzZspo4cSKPugMAAAAA3BGcGuzbtWsnwzCyXW+xWDR58mRNnjw52z6lS5fWggULbrqf+vXr64cffshznQAAAAAAuCqXvXkeANwJoqNvbz0AAABwKy578zwAAAAAAHBrBHsAAAAAAEyMYA8AAAAAgIkR7AEAAAAAMDGCPQAAAAAAJkawBwAAAADAxAj2AAAAAACYGM+xBwAnyslz7HnWPQAAAG6GGXsAAAAAAEyMYA8AAAAAgIkR7AEAAAAAMDGCPQAAAAAAJkawBwAAAADAxLgrPgC4uFvdFZ+75gMAABRtzNgDAAAAAGBiBHsAAAAAAEyMYA8AAAAAgIkR7AEAAAAAMDGCPQAAAAAAJkawBwAAAADAxAj2AAAAAACYGMEeAAAAAAATI9gDAAAAAGBiBHsAAAAAAEyMYA8AAAAAgIkR7AEAAAAAMDGCPQAAAAAAJkawBwAAAADAxAj2AAAAAACYGMEeAAAAAAATI9gDAAAAAGBiBHsAAAAAAEyMYA8AAAAAgIkR7AEAAAAAMDEPZxcAALg90dH50wcAAADmxIw9AAAAAAAmxow9ABQBt5qxZ0YfAADAvAj2AABO5wcAADAxTsUHAAAAAMDECPYAAAAAAJgYwR4AAAAAABMj2AMAAAAAYGIEewAAAAAATIxgDwAAAACAiRHsAQAAAAAwMYI9AAAAAAAm5uHsAgAA5hAdfXvrAQAAUDCYsQcAAAAAwMQI9gAAAAAAmBjBHgAAAAAAEyPYAwAAAABgYgR7AAAAAABMjGAPAAAAAICJEewBAAAAADAxgj0AAAAAACZGsAcAAAAAwMQI9gAAAAAAmBjBHgAAAAAAEyPYAwAAAABgYh7OLgAAcGeIjs6fPgAAAMgdZuwBAAAAADAxgj0AAAAAACZGsAcAAAAAwMRcOthHR0fLYrE4/NSsWdO+Pi0tTZGRkSpTpoyKFy+uXr16KTk52WEbR48eVUREhPz8/BQYGKhx48bpypUrhX0oAAAAAAAUCJe/eV6dOnW0atUq+7KHx/9KHj16tJYuXapFixapZMmSGjFihHr27KlNmzZJkjIyMhQREaHg4GD9+OOPSkxMVP/+/eXp6anXXnut0I8FAAAAAID85vLB3sPDQ8HBwVnaz549q48//lgLFizQfffdJ0maO3euatWqpc2bN6tFixZauXKl9uzZo1WrVikoKEgNGzbUlClT9Nxzzyk6OlpeXl6FfTgAUKTd6q743DUfAAAg91w+2B84cEAhISHy8fFRWFiYYmJiVLFiRW3fvl1Wq1UdO3a0961Zs6YqVqyo+Ph4tWjRQvHx8apXr56CgoLsfcLDw/XUU09p9+7datSo0Q33mZ6ervT0dPtyamqqJMlqtcpqtRbQkeaPzPpcvU44h5sLXHzj5mZ1+F/gWq7wp4u/o3B1jFG4OsYoXJ1Zxmhu6nPpYN+8eXPFxsaqRo0aSkxM1KRJk9S6dWv9+uuvSkpKkpeXlwICAhxeExQUpKSkJElSUlKSQ6jPXJ+5LjsxMTGaNGlSlvaVK1fKz8/vNo+qcMTFxTm7BLigBg2cXcH/1KvHGEVW33/v7Ar+h7+jcHWMUbg6xihcnauP0YsXL+a4r0sH+65du9r/Xb9+fTVv3lyVKlXSl19+KV9f3wLb74QJExQVFWVfTk1NVYUKFdS5c2f5+/sX2H7zg9VqVVxcnDp16iRPT09nlwMXExPj7AquztTXqxenXbs6yWZjjMLRhAnOroC/o3B9jFG4OsYoXJ1ZxmjmmeM54dLB/noBAQGqXr26Dh48qE6dOuny5cs6c+aMw6x9cnKy/Zr84OBgbdmyxWEbmXfNv9F1+5m8vb3l7e2dpd3T09OlP/hrmalWFB6bzdkV/I/N5kmwRxau9GeLv6NwdYxRuDrGKFydq4/R3NTmAlfc5tz58+f1+++/q1y5cmrSpIk8PT21evVq+/r9+/fr6NGjCgsLkySFhYVp165dSklJsfeJi4uTv7+/ateuXej1AwAAAACQ31x6xn7s2LHq3r27KlWqpBMnTujll1+Wu7u7+vTpo5IlS2rIkCGKiopS6dKl5e/vr2eeeUZhYWFq0aKFJKlz586qXbu2Hn/8cU2dOlVJSUl66aWXFBkZecMZeQAAAAAAzMalg/2ff/6pPn366O+//9Zdd92lVq1aafPmzbrrrrskSTNmzJCbm5t69eql9PR0hYeH67333rO/3t3dXUuWLNFTTz2lsLAwFStWTAMGDNDkyZOddUgAgALGI/UAAEBR49LB/osvvrjpeh8fH82aNUuzZs3Ktk+lSpX0vSvdZhkAAAAAgHzk0sEeAFC0MNsOAACQe6a6eR4AAAAAAHBEsAcAAAAAwMQI9gAAAAAAmBjBHgAAAAAAEyPYAwAAAABgYgR7AAAAAABMjGAPAAAAAICJEewBAAAAADAxgj0AAAAAACZGsAcAAAAAwMQ8nF0AAACFKTr65uvd3KQGDQqlFAAAgHxBsAcAmMatQnl+iomRbDbn1wEAAHArBHsAAHIpJ8Ge8A8AAAoLwR4AgAJwq2BP8AcAAPmFm+cBAAAAAGBiBHsAAAAAAEyMYA8AAAAAgIkR7AEAAAAAMDGCPQAAAAAAJkawBwAAAADAxHjcHQAATpAfj7vjkXkAAEBixh4AAAAAAFMj2AMAAAAAYGIEewAAAAAATIxr7AEAMKmcXGPPdfgAANz5mLEHAAAAAMDECPYAAAAAAJgYp+IDAFCE3epUfU7lBwDA9TFjDwAAAACAiRHsAQAAAAAwMU7FBwDgDsap9AAA3PmYsQcAAAAAwMSYsQcAALeFG/ABAOBcBHsAAOB0fDkAAEDecSo+AAAAAAAmRrAHAAAAAMDEOBUfAABkKz9Ogec0egAAChYz9gAAAAAAmBjBHgAAAAAAE+NUfAAAUCTk5JIALhsAAJgRM/YAAAAAAJgYM/YAAMDlMdsOAED2CPYAAOCOQLAHABRVnIoPAAAAAICJMWMPAADw/91q1p+zAgAArogZewAAAAAATIwZewAAgBzKjxn72z0rwM1NatBAiomRbLa8bQMAcGdhxh4AAAAAABNjxh4AAKAQMZsOAMhvBHsAAIA7TE6+PHCFywoAAPmDU/EBAAAAADAxZuwBAACKIGbTAeDOQbAHAACAyyqMywoK69IFACgoBHsAAAAUCMIwABQOgj0AAABMjZv4ASjqCPYAAADAbeJ0fgDORLAHAADAHc1VAnVhPGIQQNFEsAcAAABugUANwJUR7AEAAADkys2+6HBzkxo0yPvrc9MHwFUEewAAAMAkCiPsukqg5qaIQM4R7AEAAADY3UmB+U76IoQvOnAzBHsAAAAA+S4mRrLZCm77rhJkXeWyAoJ/0UawBwAAAIACRKj+H1f5IuROQ7AHAAAAgDucmcIyZx/kXpEK9rNmzdKbb76ppKQkNWjQQO+8846aNWvm7LIAAAAAADl0u8E+J09uMBs3ZxdQWBYuXKioqCi9/PLL+vnnn9WgQQOFh4crJSXF2aUBAAAAAJBnRSbYT58+XcOGDdOgQYNUu3ZtzZkzR35+fvrkk0+cXRoAAAAAAHlWJE7Fv3z5srZv364JEybY29zc3NSxY0fFx8dn6Z+enq709HT78tmzZyVJp06dktVqLfiCb4PVatXFixf1999/y9PT09nlwMVcvuzsCiQ3t6tj9PLlv2WzMUbhehijcHWMUbg6xihcXeYYdfXMdO7cOUmSYRi37Fskgv1ff/2ljIwMBQUFObQHBQVp3759WfrHxMRo0qRJWdpDQ0MLrEYAAAAAAK537tw5lSxZ8qZ9ikSwz60JEyYoKirKvmyz2XTq1CmVKVNGFovFiZXdWmpqqipUqKBjx47J39/f2eUAWTBG4eoYo3B1jFG4OsYoXJ1ZxqhhGDp37pxCQkJu2bdIBPuyZcvK3d1dycnJDu3JyckKDg7O0t/b21ve3t4ObQEBAQVZYr7z9/d36UEKMEbh6hijcHWMUbg6xihcnRnG6K1m6jMViZvneXl5qUmTJlq9erW9zWazafXq1QoLC3NiZQAAAAAA3J4iMWMvSVFRURowYICaNm2qZs2aaebMmbpw4YIGDRrk7NIAAAAAAMizIhPsH330UZ08eVITJ05UUlKSGjZsqOXLl2e5oZ7ZeXt76+WXX85yKQHgKhijcHWMUbg6xihcHWMUru5OHKMWIyf3zgcAAAAAAC6pSFxjDwAAAADAnYpgDwAAAACAiRHsAQAAAAAwMYI9AAAAAAAmRrC/g8yaNUuVK1eWj4+Pmjdvri1btji7JBRRMTExuvfee1WiRAkFBgaqR48e2r9/v0OftLQ0RUZGqkyZMipevLh69eql5ORkJ1WMou7111+XxWLRqFGj7G2MUTjb8ePH1a9fP5UpU0a+vr6qV6+etm3bZl9vGIYmTpyocuXKydfXVx07dtSBAwecWDGKkoyMDP3rX/9SaGiofH19VaVKFU2ZMkXX3pebMYrCtmHDBnXv3l0hISGyWCz65ptvHNbnZEyeOnVKffv2lb+/vwICAjRkyBCdP3++EI8ibwj2d4iFCxcqKipKL7/8sn7++Wc1aNBA4eHhSklJcXZpKILWr1+vyMhIbd68WXFxcbJarercubMuXLhg7zN69Gj997//1aJFi7R+/XqdOHFCPXv2dGLVKKq2bt2q999/X/Xr13doZ4zCmU6fPq2WLVvK09NTy5Yt0549ezRt2jSVKlXK3mfq1Kl6++23NWfOHP30008qVqyYwsPDlZaW5sTKUVS88cYbmj17tt59913t3btXb7zxhqZOnap33nnH3ocxisJ24cIFNWjQQLNmzbrh+pyMyb59+2r37t2Ki4vTkiVLtGHDBg0fPrywDiHvDNwRmjVrZkRGRtqXMzIyjJCQECMmJsaJVQFXpaSkGJKM9evXG4ZhGGfOnDE8PT2NRYsW2fvs3bvXkGTEx8c7q0wUQefOnTOqVatmxMXFGW3btjVGjhxpGAZjFM733HPPGa1atcp2vc1mM4KDg40333zT3nbmzBnD29vb+M9//lMYJaKIi4iIMAYPHuzQ1rNnT6Nv376GYTBG4XySjK+//tq+nJMxuWfPHkOSsXXrVnufZcuWGRaLxTh+/Hih1Z4XzNjfAS5fvqzt27erY8eO9jY3Nzd17NhR8fHxTqwMuOrs2bOSpNKlS0uStm/fLqvV6jBma9asqYoVKzJmUagiIyMVERHhMBYlxiic77vvvlPTpk31z3/+U4GBgWrUqJE+/PBD+/pDhw4pKSnJYYyWLFlSzZs3Z4yiUPzjH//Q6tWr9dtvv0mSfvnlF23cuFFdu3aVxBiF68nJmIyPj1dAQICaNm1q79OxY0e5ubnpp59+KvSac8PD2QXg9v3111/KyMhQUFCQQ3tQUJD27dvnpKqAq2w2m0aNGqWWLVuqbt26kqSkpCR5eXkpICDAoW9QUJCSkpKcUCWKoi+++EI///yztm7dmmUdYxTO9scff2j27NmKiorSCy+8oK1bt+rZZ5+Vl5eXBgwYYB+HN/r/fsYoCsPzzz+v1NRU1axZU+7u7srIyNCrr76qvn37ShJjFC4nJ2MyKSlJgYGBDus9PDxUunRplx+3BHsABSoyMlK//vqrNm7c6OxSALtjx45p5MiRiouLk4+Pj7PLAbKw2Wxq2rSpXnvtNUlSo0aN9Ouvv2rOnDkaMGCAk6sDpC+//FLz58/XggULVKdOHSUkJGjUqFEKCQlhjAJOwKn4d4CyZcvK3d09y92ak5OTFRwc7KSqAGnEiBFasmSJ1q5dq/Lly9vbg4ODdfnyZZ05c8ahP2MWhWX79u1KSUlR48aN5eHhIQ8PD61fv15vv/22PDw8FBQUxBiFU5UrV061a9d2aKtVq5aOHj0qSfZxyP/3w1nGjRun559/Xr1791a9evX0+OOPa/To0YqJiZHEGIXrycmYDA4OznLz8StXrujUqVMuP24J9ncALy8vNWnSRKtXr7a32Ww2rV69WmFhYU6sDEWVYRgaMWKEvv76a61Zs0ahoaEO65s0aSJPT0+HMbt//34dPXqUMYtC0aFDB+3atUsJCQn2n6ZNm6pv3772fzNG4UwtW7bM8pjQ3377TZUqVZIkhYaGKjg42GGMpqam6qeffmKMolBcvHhRbm6OUcLd3V02m00SYxSuJydjMiwsTGfOnNH27dvtfdasWSObzabmzZsXes25wan4d4ioqCgNGDBATZs2VbNmzTRz5kxduHBBgwYNcnZpKIIiIyO1YMECffvttypRooT9mqSSJUvK19dXJUuW1JAhQxQVFaXSpUvL399fzzzzjMLCwtSiRQsnV4+ioESJEvZ7PmQqVqyYypQpY29njMKZRo8erX/84x967bXX9Mgjj2jLli364IMP9MEHH0iSLBaLRo0apVdeeUXVqlVTaGio/vWvfykkJEQ9evRwbvEoErp3765XX31VFStWVJ06dbRjxw5Nnz5dgwcPlsQYhXOcP39eBw8etC8fOnRICQkJKl26tCpWrHjLMVmrVi116dJFw4YN05w5c2S1WjVixAj17t1bISEhTjqqHHL2bfmRf9555x2jYsWKhpeXl9GsWTNj8+bNzi4JRZSkG/7MnTvX3ufSpUvG008/bZQqVcrw8/MzHnroISMxMdF5RaPIu/Zxd4bBGIXz/fe//zXq1q1reHt7GzVr1jQ++OADh/U2m83417/+ZQQFBRne3t5Ghw4djP379zupWhQ1qampxsiRI42KFSsaPj4+xj333GO8+OKLRnp6ur0PYxSFbe3atTf8b9ABAwYYhpGzMfn3338bffr0MYoXL274+/sbgwYNMs6dO+eEo8kdi2EYhpO+UwAAAAAAALeJa+wBAAAAADAxgj0AAAAAACZGsAcAAAAAwMQI9gAAAAAAmBjBHgAAAAAAEyPYAwAAAABgYgR7AAAAAABMjGAPAAAAAICJEewBAHAxFy9eVK9eveTv7y+LxaIzZ87csK1y5cqaOXOm/XUWi0XffPON0+rO1K5dO40aNcrZZQAAUGQQ7AEAKCTHjh3T4MGDFRISIi8vL1WqVEkjR47U33//7dBv3rx5+uGHH/Tjjz8qMTFRJUuWvGHb9RITE9W1a9cCPYbY2FhZLJYsPz4+PgW2T1f/oiA6OvqG78m1PwAAFCQPZxcAAEBR8McffygsLEzVq1fXf/7zH4WGhmr37t0aN26cli1bps2bN6t06dKSpN9//121atVS3bp17a+/Udv1goODC/w4JMnf31/79+93aHN2eDUMQxkZGfLwKPz/tBk7dqyefPJJ+/K9996r4cOHa9iwYYVeCwCgaGLGHgCAQhAZGSkvLy+tXLlSbdu2VcWKFdW1a1etWrVKx48f14svvijp6uz0tGnTtGHDBlksFrVr1+6GbTdy7an4hw8flsVi0eLFi9W+fXv5+fmpQYMGio+Pd3jNxo0b1bp1a/n6+qpChQp69tlndeHChZsei8ViUXBwsMNPUFBQtv3T09M1duxY3X333SpWrJiaN2+udevWOfTZtGmT2rVrJz8/P5UqVUrh4eE6ffq0Bg4cqPXr1+utt96yz34fPnxY69atk8Vi0bJly9SkSRN5e3tr48aNSk9P17PPPqvAwED5+PioVatW2rp1q30/ma9bvXq1mjZtKj8/P/3jH//I8kXFtTLfyy+++EL/+Mc/5OPjo7p162r9+vWSpOLFizu8F+7u7ipRooRDGwAABYlgDwBAATt16pRWrFihp59+Wr6+vg7rgoOD1bdvXy1cuFCGYWjx4sUaNmyYwsLClJiYqMWLF9+wLadefPFFjR07VgkJCapevbr69OmjK1euSLp6FkCXLl3Uq1cv7dy5UwsXLtTGjRs1YsSIfD3+ESNGKD4+Xl988YV27typf/7zn+rSpYsOHDggSUpISFCHDh1Uu3ZtxcfHa+PGjerevbsyMjL01ltvKSwsTMOGDVNiYqISExNVoUIF+7aff/55vf7669q7d6/q16+v8ePH6//+7/80b948/fzzz6patarCw8N16tSpLO/LtGnTtG3bNnl4eGjw4MG3PI5x48ZpzJgx2rFjh8LCwtS9e/csl1EAAOAMnIoPAEABO3DggAzDUK1atW64vlatWjp9+rROnjypwMBA+fn5ycvLy2Gm90ZtOTF27FhFRERIkiZNmqQ6dero4MGDqlmzpmJiYtS3b1/79evVqlXT22+/rbZt22r27NnZXjd/9uxZFS9e3KGtdevWWrZsWZa+R48e1dy5c3X06FGFhITYa1q+fLnmzp2r1157TVOnTlXTpk313nvv2V9Xp04d+7+9vLzk5+d3w2OfPHmyOnXqJEm6cOGCZs+erdjYWPu9Bj788EPFxcXp448/1rhx4+yve/XVV9W2bVtJV78ciIiIUFpa2k3vFTBixAj16tVLkjR79mwtX75cH3/8scaPH5/tawAAKAwEewAAColhGIW+z/r169v/Xa5cOUlSSkqKatasqV9++UU7d+7U/PnzHWq02Ww6dOhQtl9ElChRQj///LND2/VnImTatWuXMjIyVL16dYf29PR0lSlTRtLVGft//vOfuT84SU2bNrX/+/fff5fValXLli3tbZ6enmrWrJn27t3r8Lrs3peKFStmu6+wsDD7vz08PNS0adMs2wUAwBkI9gAAFLCqVavKYrFo7969euihh7Ks37t3r0qVKqW77ror3/ft6elp/3fmDe5sNpsk6fz583riiSf07LPPZnndzQKum5ubqlatmqP9nz9/Xu7u7tq+fbvc3d0d1mXO+mf3pUBOFCtWLE+vu9n7AgCA2XCNPQAABaxMmTLq1KmT3nvvPV26dMlhXVJSkubPn69HH3200O8s37hxY+3Zs0dVq1bN8uPl5ZUv+2jUqJEyMjKUkpKSZR+Zp9bXr19fq1evznYbXl5eysjIuOW+qlSpIi8vL23atMneZrVatXXrVtWuXfu2j2Xz5s32f1+5ckXbt2/P9qwGAAAKE8EeAIBC8O677yo9PV3h4eHasGGDjh07puXLl6tTp066++679eqrrxZ6Tc8995x+/PFHjRgxQgkJCTpw4IC+/fbbW948zzAMJSUlZfm50Yx39erV1bdvX/Xv31+LFy/WoUOHtGXLFsXExGjp0qWSpAkTJmjr1q16+umntXPnTu3bt0+zZ8/WX3/9JUmqXLmyfvrpJx0+fFh//fVXtjPrxYoV01NPPaVx48Zp+fLl2rNnj4YNG6aLFy9qyJAht/luSbNmzdLXX3+tffv2KTIyUqdPn87RTfcAAChoBHsAAApBtWrVtG3bNt1zzz165JFHVKVKFQ0fPlzt27dXfHy8/Rn2hal+/fpav369fvvtN7Vu3VqNGjXSxIkT7Te5y05qaqrKlSuX5SclJeWG/efOnav+/ftrzJgxqlGjhnr06KGtW7faT/evXr26Vq5cqV9++UXNmjVTWFiYvv32W/sz6ceOHSt3d3fVrl1bd911l44ePZptba+//rp69eqlxx9/XI0bN9bBgwe1YsUKlSpVKo/vkuO2X3/9dTVo0EAbN27Ud999p7Jly972dgEAuF0Wwxl38gEAADCJw4cPKzQ0VDt27FDDhg2dXQ4AAFkwYw8AAAAAgIkR7AEAAAAAMDFOxQcAAAAAwMSYsQcAAAAAwMQI9gAAAAAAmBjBHgAAAAAAEyPYAwAAAABgYgR7AAAAAABMjGAPAAAAAICJEewBAAAAADAxgj0AAAAAACb2/wDgB1PIkPkaagAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "37516\n",
      "37516\n"
     ]
    }
   ],
   "source": [
    "# Split the DataFrame into two based on the 'label' column\n",
    "electrons = Test_Data[Test_Data['label'] == 1]\n",
    "muons = Test_Data[Test_Data['label'] == 0]\n",
    "# Plot distributions of energies for both labels\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.hist(muons['TOB_ET'], bins=100, alpha=0.5, color='blue', label='Label 0', range=(0, 100))\n",
    "plt.xlabel('Offline Electron pT')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Distribution of Energies for Label 0')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "# Plot histogram for label 1\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.hist(electrons['offline_ele_pt'], bins=100, alpha=0.5, color='orange', label='Label 1',range=(0, 100))\n",
    "plt.xlabel('Offline Electron pT')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Distribution of Energies for Label 1')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "print(len(electrons))\n",
    "print(len(muons))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>L0_1</th>\n",
       "      <th>L0_2</th>\n",
       "      <th>L0_3</th>\n",
       "      <th>L0_4</th>\n",
       "      <th>L0_5</th>\n",
       "      <th>L0_6</th>\n",
       "      <th>L0_7</th>\n",
       "      <th>L0_8</th>\n",
       "      <th>L0_9</th>\n",
       "      <th>L1_1</th>\n",
       "      <th>...</th>\n",
       "      <th>L1_wide_centre_sum</th>\n",
       "      <th>L1_wider_centre_sum</th>\n",
       "      <th>L2_centre_sum</th>\n",
       "      <th>L2_wide_centre_sum</th>\n",
       "      <th>L2_spread_sum</th>\n",
       "      <th>L2_vertical_sum</th>\n",
       "      <th>L3_centre_sum</th>\n",
       "      <th>Had_centre_sum</th>\n",
       "      <th>Had_peripheral_sum</th>\n",
       "      <th>Had_out_sum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.050</td>\n",
       "      <td>1.850</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.725</td>\n",
       "      <td>2.300</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.875</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.250</td>\n",
       "      <td>1.575</td>\n",
       "      <td>2.350</td>\n",
       "      <td>16.850</td>\n",
       "      <td>3.650</td>\n",
       "      <td>7.925</td>\n",
       "      <td>0.40</td>\n",
       "      <td>15.100</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.525</td>\n",
       "      <td>1.175</td>\n",
       "      <td>0.825</td>\n",
       "      <td>5.425</td>\n",
       "      <td>2.675</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.75</td>\n",
       "      <td>3.175</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.825</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6.500</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>17.300</td>\n",
       "      <td>1.325</td>\n",
       "      <td>2.825</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.950</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.750</td>\n",
       "      <td>0.950</td>\n",
       "      <td>9.700</td>\n",
       "      <td>1.600</td>\n",
       "      <td>0.000</td>\n",
       "      <td>4.325</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 116 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   L0_1  L0_2  L0_3  L0_4   L0_5  L0_6  L0_7   L0_8  L0_9  L1_1  ...  \\\n",
       "0   0.0   0.0   0.0   0.0  0.000   0.0   0.0  0.000   0.0   0.0  ...   \n",
       "1   0.0   0.0   0.0   0.0  0.000   0.0   0.0  0.875   0.0   0.0  ...   \n",
       "2   0.0   0.0   0.0   0.0  0.000   0.0   0.0  0.000   0.0   0.0  ...   \n",
       "3   0.0   0.0   0.0   0.0  2.825   0.0   0.0  0.000   0.0   0.0  ...   \n",
       "4   0.0   0.0   0.0   0.0  0.950   0.0   0.0  0.000   0.0   0.0  ...   \n",
       "\n",
       "   L1_wide_centre_sum  L1_wider_centre_sum  L2_centre_sum  L2_wide_centre_sum  \\\n",
       "0               3.050                1.850          0.000               2.725   \n",
       "1               5.250                1.575          2.350              16.850   \n",
       "2               3.525                1.175          0.825               5.425   \n",
       "3               6.500                0.000          0.000              17.300   \n",
       "4               0.750                0.950          9.700               1.600   \n",
       "\n",
       "   L2_spread_sum  L2_vertical_sum  L3_centre_sum  Had_centre_sum  \\\n",
       "0          2.300            0.000           0.00           0.000   \n",
       "1          3.650            7.925           0.40          15.100   \n",
       "2          2.675            0.000           0.75           3.175   \n",
       "3          1.325            2.825           0.00           0.000   \n",
       "4          0.000            4.325           0.00           2.500   \n",
       "\n",
       "   Had_peripheral_sum  Had_out_sum  \n",
       "0                 0.0         0.00  \n",
       "1                 0.0         2.45  \n",
       "2                 0.0         0.00  \n",
       "3                 0.0         0.00  \n",
       "4                 0.0         2.00  \n",
       "\n",
       "[5 rows x 116 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### new prams L0_5 \n",
    "Test_Data['L0_centre_sum'] = Test_Data['L0_5']\n",
    "#Test_Data['L0_peripheral_sum'] = Test_Data['L0_2'] + Test_Data['L0_4'] + Test_Data['L0_6'] + Test_Data['L0_8']\n",
    "Test_Data['L1_centre_sum'] = Test_Data['L1_18'] + Test_Data['L1_19'] \n",
    "Test_Data['L1_wide_centre_sum'] = Test_Data['L1_17'] + + Test_Data['L1_20'] \n",
    "Test_Data['L1_wider_centre_sum'] = Test_Data['L1_16'] + + Test_Data['L1_15'] + Test_Data['L1_21'] + Test_Data['L1_22']\n",
    "Test_Data['L2_centre_sum'] = Test_Data['L2_18'] + Test_Data['L2_19'] \n",
    "Test_Data['L2_wide_centre_sum'] = Test_Data['L2_17'] + Test_Data['L2_20']\n",
    "Test_Data['L2_spread_sum'] = Test_Data['L2_16'] + Test_Data['L2_21'] \n",
    "Test_Data['L2_vertical_sum'] = Test_Data['L2_30'] + Test_Data['L2_31'] + Test_Data['L2_32'] + Test_Data['L2_29'] + Test_Data['L2_5'] + Test_Data['L2_6'] + Test_Data['L2_7'] + Test_Data['L2_8'] \n",
    "#Test_Data['L2_spread_sum'] = Test_Data['L2_21'] + Test_Data['L2_16']\n",
    "# Test_Data['L3_peripheral_sum'] = Test_Data['L3_2'] + Test_Data['L3_4'] + Test_Data['L3_6'] + Test_Data['L3_8']\n",
    "### new prams L3_5\n",
    "Test_Data['L3_centre_sum'] = Test_Data['L3_5']\n",
    "### new prams Had_5\n",
    "Test_Data['Had_centre_sum'] = Test_Data['Had_5']\n",
    "#Test_Data['Had_Vertical'] = Test_Data['Had_2'] + Test_Data['Had_8']\n",
    "#Test_Data['Had_Horizontal'] = Test_Data['Had_4'] + Test_Data['Had_6']\n",
    "Test_Data['Had_peripheral_sum'] = Test_Data['Had_2'] + Test_Data['Had_4'] + Test_Data['Had_6'] + Test_Data['Had_8']\n",
    "Test_Data['Had_out_sum'] = Test_Data['Had_1'] + Test_Data['Had_3'] + Test_Data['Had_7'] + Test_Data['Had_9']\n",
    "\n",
    "#test with more L2 data \n",
    "#Test_Data['rest_of_L2']= Test_Data['L2_1'] + Test_Data['L2_2'] + Test_Data['L2_3'] + Test_Data['L2_4'] + Test_Data['L2_5'] + Test_Data['L2_6'] + Test_Data['L2_7'] + Test_Data['L2_8'] + Test_Data['L2_9'] + Test_Data['L2_10'] + Test_Data['L2_11'] + Test_Data['L2_12'] + Test_Data['L2_13'] + Test_Data['L2_14'] + Test_Data['L2_15'] + Test_Data['L2_22'] + Test_Data['L2_23'] + Test_Data['L2_24'] + Test_Data['L2_25'] + Test_Data['L2_26'] + Test_Data['L2_27'] + Test_Data['L2_28'] + Test_Data['L2_29'] + Test_Data['L2_30'] + Test_Data['L2_31'] + Test_Data['L2_32'] + Test_Data['L2_33'] + Test_Data['L2_34'] + Test_Data['L2_35'] + Test_Data['L2_36']\n",
    "Test_Data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Specify your new file path or name\n",
    "new_file_path = r'\\Users\\oscar\\OneDrive\\Documents\\Year4_Project\\QNN_Ideas\\New_prams_2.csv'\n",
    "\n",
    "# Save the DataFrame to a new CSV file\n",
    "Test_Data.to_csv(new_file_path, index=False)\n"
   ]
  }
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